analysis/Candidate/CandidateGenes_TT-OtherSolidTumor_Oncoplot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Suggesting for Other Solid Tumor\n",
    "----- \n",
    "- Date : 19th May 2019\n",
    "- using R3.5 kernal\n",
    "- on 89"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "source(\"~/bin/da.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "suppressPackageStartupMessages({\n",
    "    library(vcd)\n",
    "    library(readr)\n",
    "    library(maftools)\n",
    "    library(corrplot)\n",
    "    library(knitr)\n",
    "    library(wordcloud)\n",
    "    library(RColorBrewer)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] ‘1.8.0’"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "packageVersion(\"maftools\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Muataional Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_name=\"../KCPG_Variants/data/Mutation_data_cleaned_051019.xls\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df= readxl::read_excel(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>3016</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 3016\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 3016\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 3016   84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Var1     Freq\n",
       "------  -----\n",
       "CNS       613\n",
       "WLM       405\n",
       "NBL       343\n",
       "RHB       259\n",
       "OS        241\n",
       "HGG       229\n",
       "RB        221\n",
       "EWS       192\n",
       "STS       177\n",
       "GCT       139\n",
       "ACT        79\n",
       "LGG        52\n",
       "CA         35\n",
       "OST        18\n",
       "RCC         5\n",
       "GICT        2\n",
       "LMPRT       2\n",
       "PGL         2\n",
       "BCC         1\n",
       "NM          1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( sort( table(df$Tumor_types ),decreasing = T), format=\"pandoc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>known</th><td> 290</td></tr>\n",
       "\t<tr><th scope=row>suggesting</th><td>2726</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       "\tknown &  290\\\\\n",
       "\tsuggesting & 2726\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| known |  290 |\n",
       "| suggesting | 2726 |\n",
       "\n"
      ],
      "text/plain": [
       "           [,1]\n",
       "known       290\n",
       "suggesting 2726"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbind( df$Known_Cancer_Predisposition_Genes %>% table )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df$Known_Cancer_Predisposition_Genes == 'suggesting',]\n",
    "df =droplevels(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>2726</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 2726\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 2726\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 2726   84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df%>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "    Het Het-Alt     Hom \n",
       "   2710       8       8 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df$genotype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "< table of extent 0 >"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sanity check\n",
    "df$Mode_of_Inheritance_abb %>% table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "1273"
      ],
      "text/latex": [
       "1273"
      ],
      "text/markdown": [
       "1273"
      ],
      "text/plain": [
       "[1] 1273"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len( unique(df$Tumor_Sample_Barcode) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "1175"
      ],
      "text/latex": [
       "1175"
      ],
      "text/markdown": [
       "1175"
      ],
      "text/plain": [
       "[1] 1175"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len( unique(df$Hugo_Symbol) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA Clinical_Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file_name_clinical = \"../KCPG_Variants/data/Combined_Clinical_Data_03.xlsx\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = readxl::read_excel( file_name_clinical )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Tumor_Sample_Barcode'</li>\n",
       "\t<li>'Tumor_type'</li>\n",
       "\t<li>'Tumor_type2'</li>\n",
       "\t<li>'Age_of_Onset'</li>\n",
       "\t<li>'Sex'</li>\n",
       "\t<li>'Age_group'</li>\n",
       "\t<li>'sample_type'</li>\n",
       "\t<li>'sequencing_type'</li>\n",
       "\t<li>'Access_unit'</li>\n",
       "\t<li>'Status'</li>\n",
       "\t<li>'Ethnicity'</li>\n",
       "\t<li>'Race'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Tumor\\_Sample\\_Barcode'\n",
       "\\item 'Tumor\\_type'\n",
       "\\item 'Tumor\\_type2'\n",
       "\\item 'Age\\_of\\_Onset'\n",
       "\\item 'Sex'\n",
       "\\item 'Age\\_group'\n",
       "\\item 'sample\\_type'\n",
       "\\item 'sequencing\\_type'\n",
       "\\item 'Access\\_unit'\n",
       "\\item 'Status'\n",
       "\\item 'Ethnicity'\n",
       "\\item 'Race'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Tumor_Sample_Barcode'\n",
       "2. 'Tumor_type'\n",
       "3. 'Tumor_type2'\n",
       "4. 'Age_of_Onset'\n",
       "5. 'Sex'\n",
       "6. 'Age_group'\n",
       "7. 'sample_type'\n",
       "8. 'sequencing_type'\n",
       "9. 'Access_unit'\n",
       "10. 'Status'\n",
       "11. 'Ethnicity'\n",
       "12. 'Race'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Tumor_Sample_Barcode\" \"Tumor_type\"           \"Tumor_type2\"         \n",
       " [4] \"Age_of_Onset\"         \"Sex\"                  \"Age_group\"           \n",
       " [7] \"sample_type\"          \"sequencing_type\"      \"Access_unit\"         \n",
       "[10] \"Status\"               \"Ethnicity\"            \"Race\"                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                      \n",
       "                                       CCF PCGP SJLIFE\n",
       "  Adrenocortical Carcinoma               1   19      2\n",
       "  Basal Cell Carcinoma                   0    0      1\n",
       "  Carcinoma                              0    0     14\n",
       "  Central Nervous System                 1    0    322\n",
       "  Ewing's Sarcoma                        6    0     89\n",
       "  Germ Cell Tumor                        1    0     73\n",
       "  Giant Cell Tumor                       0    0      3\n",
       "  High Grade Glioma                      5   75      0\n",
       "  Low Grade Glioma                       2   15      7\n",
       "  Low Malignant Potential Renal Tumors   2    0      0\n",
       "  Neuroblastoma                          3   48    139\n",
       "  Non-Malignant Tumor                    1    0      3\n",
       "  Osteosarcoma                          14    2    113\n",
       "  Other Solid Tumor                      0    0     10\n",
       "  Paraganglioma                          0    0      1\n",
       "  Renal Cell Carcinoma                   1    0      2\n",
       "  Retinoblastoma                         0    0     98\n",
       "  Rhabdomyosarcoma                       3   29    102\n",
       "  Soft Tissue Sarcoma                    8    0     85\n",
       "  Wilms' tumor                           2    0    205"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2$Tumor_type , df2$Access_unit )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>f</th><th scope=col>n</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Central Nervous System              </td><td>323                                 </td></tr>\n",
       "\t<tr><td>Wilms' tumor                        </td><td>207                                 </td></tr>\n",
       "\t<tr><td>Neuroblastoma                       </td><td>190                                 </td></tr>\n",
       "\t<tr><td>Rhabdomyosarcoma                    </td><td>134                                 </td></tr>\n",
       "\t<tr><td>Osteosarcoma                        </td><td>129                                 </td></tr>\n",
       "\t<tr><td>Retinoblastoma                      </td><td> 98                                 </td></tr>\n",
       "\t<tr><td>Ewing's Sarcoma                     </td><td> 95                                 </td></tr>\n",
       "\t<tr><td>Soft Tissue Sarcoma                 </td><td> 93                                 </td></tr>\n",
       "\t<tr><td>High Grade Glioma                   </td><td> 80                                 </td></tr>\n",
       "\t<tr><td>Germ Cell Tumor                     </td><td> 74                                 </td></tr>\n",
       "\t<tr><td>Low Grade Glioma                    </td><td> 24                                 </td></tr>\n",
       "\t<tr><td>Adrenocortical Carcinoma            </td><td> 22                                 </td></tr>\n",
       "\t<tr><td>Carcinoma                           </td><td> 14                                 </td></tr>\n",
       "\t<tr><td>Other Solid Tumor                   </td><td> 10                                 </td></tr>\n",
       "\t<tr><td>Non-Malignant Tumor                 </td><td>  4                                 </td></tr>\n",
       "\t<tr><td>Giant Cell Tumor                    </td><td>  3                                 </td></tr>\n",
       "\t<tr><td>Renal Cell Carcinoma                </td><td>  3                                 </td></tr>\n",
       "\t<tr><td>Low Malignant Potential Renal Tumors</td><td>  2                                 </td></tr>\n",
       "\t<tr><td>Basal Cell Carcinoma                </td><td>  1                                 </td></tr>\n",
       "\t<tr><td>Paraganglioma                       </td><td>  1                                 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " f & n\\\\\n",
       "\\hline\n",
       "\t Central Nervous System               & 323                                 \\\\\n",
       "\t Wilms' tumor                         & 207                                 \\\\\n",
       "\t Neuroblastoma                        & 190                                 \\\\\n",
       "\t Rhabdomyosarcoma                     & 134                                 \\\\\n",
       "\t Osteosarcoma                         & 129                                 \\\\\n",
       "\t Retinoblastoma                       &  98                                 \\\\\n",
       "\t Ewing's Sarcoma                      &  95                                 \\\\\n",
       "\t Soft Tissue Sarcoma                  &  93                                 \\\\\n",
       "\t High Grade Glioma                    &  80                                 \\\\\n",
       "\t Germ Cell Tumor                      &  74                                 \\\\\n",
       "\t Low Grade Glioma                     &  24                                 \\\\\n",
       "\t Adrenocortical Carcinoma             &  22                                 \\\\\n",
       "\t Carcinoma                            &  14                                 \\\\\n",
       "\t Other Solid Tumor                    &  10                                 \\\\\n",
       "\t Non-Malignant Tumor                  &   4                                 \\\\\n",
       "\t Giant Cell Tumor                     &   3                                 \\\\\n",
       "\t Renal Cell Carcinoma                 &   3                                 \\\\\n",
       "\t Low Malignant Potential Renal Tumors &   2                                 \\\\\n",
       "\t Basal Cell Carcinoma                 &   1                                 \\\\\n",
       "\t Paraganglioma                        &   1                                 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| f | n |\n",
       "|---|---|\n",
       "| Central Nervous System               | 323                                  |\n",
       "| Wilms' tumor                         | 207                                  |\n",
       "| Neuroblastoma                        | 190                                  |\n",
       "| Rhabdomyosarcoma                     | 134                                  |\n",
       "| Osteosarcoma                         | 129                                  |\n",
       "| Retinoblastoma                       |  98                                  |\n",
       "| Ewing's Sarcoma                      |  95                                  |\n",
       "| Soft Tissue Sarcoma                  |  93                                  |\n",
       "| High Grade Glioma                    |  80                                  |\n",
       "| Germ Cell Tumor                      |  74                                  |\n",
       "| Low Grade Glioma                     |  24                                  |\n",
       "| Adrenocortical Carcinoma             |  22                                  |\n",
       "| Carcinoma                            |  14                                  |\n",
       "| Other Solid Tumor                    |  10                                  |\n",
       "| Non-Malignant Tumor                  |   4                                  |\n",
       "| Giant Cell Tumor                     |   3                                  |\n",
       "| Renal Cell Carcinoma                 |   3                                  |\n",
       "| Low Malignant Potential Renal Tumors |   2                                  |\n",
       "| Basal Cell Carcinoma                 |   1                                  |\n",
       "| Paraganglioma                        |   1                                  |\n",
       "\n"
      ],
      "text/plain": [
       "   f                                    n  \n",
       "1  Central Nervous System               323\n",
       "2  Wilms' tumor                         207\n",
       "3  Neuroblastoma                        190\n",
       "4  Rhabdomyosarcoma                     134\n",
       "5  Osteosarcoma                         129\n",
       "6  Retinoblastoma                        98\n",
       "7  Ewing's Sarcoma                       95\n",
       "8  Soft Tissue Sarcoma                   93\n",
       "9  High Grade Glioma                     80\n",
       "10 Germ Cell Tumor                       74\n",
       "11 Low Grade Glioma                      24\n",
       "12 Adrenocortical Carcinoma              22\n",
       "13 Carcinoma                             14\n",
       "14 Other Solid Tumor                     10\n",
       "15 Non-Malignant Tumor                    4\n",
       "16 Giant Cell Tumor                       3\n",
       "17 Renal Cell Carcinoma                   3\n",
       "18 Low Malignant Potential Renal Tumors   2\n",
       "19 Basal Cell Carcinoma                   1\n",
       "20 Paraganglioma                          1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fct_count( df2$Tumor_type , sort = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                      \n",
       "                                       ACT BCC  CA CNS EWS GCT GICT HGG LGG\n",
       "  Adrenocortical Carcinoma              22   0   0   0   0   0    0   0   0\n",
       "  Basal Cell Carcinoma                   0   1   0   0   0   0    0   0   0\n",
       "  Carcinoma                              0   0  14   0   0   0    0   0   0\n",
       "  Central Nervous System                 0   0   0 323   0   0    0   0   0\n",
       "  Ewing's Sarcoma                        0   0   0   0  95   0    0   0   0\n",
       "  Germ Cell Tumor                        0   0   0   0   0  74    0   0   0\n",
       "  Giant Cell Tumor                       0   0   0   0   0   0    3   0   0\n",
       "  High Grade Glioma                      0   0   0   0   0   0    0  80   0\n",
       "  Low Grade Glioma                       0   0   0   0   0   0    0   0  24\n",
       "  Low Malignant Potential Renal Tumors   0   0   0   0   0   0    0   0   0\n",
       "  Neuroblastoma                          0   0   0   0   0   0    0   0   0\n",
       "  Non-Malignant Tumor                    0   0   0   0   0   0    0   0   0\n",
       "  Osteosarcoma                           0   0   0   0   0   0    0   0   0\n",
       "  Other Solid Tumor                      0   0   0   0   0   0    0   0   0\n",
       "  Paraganglioma                          0   0   0   0   0   0    0   0   0\n",
       "  Renal Cell Carcinoma                   0   0   0   0   0   0    0   0   0\n",
       "  Retinoblastoma                         0   0   0   0   0   0    0   0   0\n",
       "  Rhabdomyosarcoma                       0   0   0   0   0   0    0   0   0\n",
       "  Soft Tissue Sarcoma                    0   0   0   0   0   0    0   0   0\n",
       "  Wilms' tumor                           0   0   0   0   0   0    0   0   0\n",
       "                                      \n",
       "                                       LMPRT NBL  NM  OS OST PGL  RB RCC RHB\n",
       "  Adrenocortical Carcinoma                 0   0   0   0   0   0   0   0   0\n",
       "  Basal Cell Carcinoma                     0   0   0   0   0   0   0   0   0\n",
       "  Carcinoma                                0   0   0   0   0   0   0   0   0\n",
       "  Central Nervous System                   0   0   0   0   0   0   0   0   0\n",
       "  Ewing's Sarcoma                          0   0   0   0   0   0   0   0   0\n",
       "  Germ Cell Tumor                          0   0   0   0   0   0   0   0   0\n",
       "  Giant Cell Tumor                         0   0   0   0   0   0   0   0   0\n",
       "  High Grade Glioma                        0   0   0   0   0   0   0   0   0\n",
       "  Low Grade Glioma                         0   0   0   0   0   0   0   0   0\n",
       "  Low Malignant Potential Renal Tumors     2   0   0   0   0   0   0   0   0\n",
       "  Neuroblastoma                            0 190   0   0   0   0   0   0   0\n",
       "  Non-Malignant Tumor                      0   0   4   0   0   0   0   0   0\n",
       "  Osteosarcoma                             0   0   0 129   0   0   0   0   0\n",
       "  Other Solid Tumor                        0   0   0   0  10   0   0   0   0\n",
       "  Paraganglioma                            0   0   0   0   0   1   0   0   0\n",
       "  Renal Cell Carcinoma                     0   0   0   0   0   0   0   3   0\n",
       "  Retinoblastoma                           0   0   0   0   0   0  98   0   0\n",
       "  Rhabdomyosarcoma                         0   0   0   0   0   0   0   0 134\n",
       "  Soft Tissue Sarcoma                      0   0   0   0   0   0   0   0   0\n",
       "  Wilms' tumor                             0   0   0   0   0   0   0   0   0\n",
       "                                      \n",
       "                                       STS WLM\n",
       "  Adrenocortical Carcinoma               0   0\n",
       "  Basal Cell Carcinoma                   0   0\n",
       "  Carcinoma                              0   0\n",
       "  Central Nervous System                 0   0\n",
       "  Ewing's Sarcoma                        0   0\n",
       "  Germ Cell Tumor                        0   0\n",
       "  Giant Cell Tumor                       0   0\n",
       "  High Grade Glioma                      0   0\n",
       "  Low Grade Glioma                       0   0\n",
       "  Low Malignant Potential Renal Tumors   0   0\n",
       "  Neuroblastoma                          0   0\n",
       "  Non-Malignant Tumor                    0   0\n",
       "  Osteosarcoma                           0   0\n",
       "  Other Solid Tumor                      0   0\n",
       "  Paraganglioma                          0   0\n",
       "  Renal Cell Carcinoma                   0   0\n",
       "  Retinoblastoma                         0   0\n",
       "  Rhabdomyosarcoma                       0   0\n",
       "  Soft Tissue Sarcoma                   93   0\n",
       "  Wilms' tumor                           0 207"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "table(df2$Tumor_type , df2$Tumor_type2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>2726</li>\n",
       "\t<li>84</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 2726\n",
       "\\item 84\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 2726\n",
       "2. 84\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 2726   84"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1507</li>\n",
       "\t<li>12</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1507\n",
       "\\item 12\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1507\n",
       "2. 12\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1507   12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## sanity check\n",
    "df %>% dim\n",
    "df2 %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ttype =\"Other Solid Tumor\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_ss = df2[df2$Tumor_type == ttype,]\n",
    "ss =df$Tumor_Sample_Barcode %in% intersect( df$Tumor_Sample_Barcode, unique( df2_ss$Tumor_Sample_Barcode ))\n",
    "df_ss = df[ ss, ]\n",
    "df_ss = droplevels(df_ss)\n",
    "df2_ss = droplevels(df2_ss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ss$Hugo_Symbol=as.character(df_ss$Hugo_Symbol) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                   \n",
       "                    OST\n",
       "  Other Solid Tumor  10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(df2_ss$Tumor_type , df2_ss$Tumor_type2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "character(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "setdiff( df_ss$Tumor_Sample_Barcode, df2_ss$Tumor_Sample_Barcode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><td>16</td><td>10</td></tr>\n",
       "\t<tr><td>84</td><td>12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t 16 & 10\\\\\n",
       "\t 84 & 12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 16 | 10 |\n",
       "| 84 | 12 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2]\n",
       "[1,] 16   10  \n",
       "[2,] 84   12  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbind(dim(df_ss), dim(df2_ss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a MAF object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Summarizing..\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   ID summary  Mean Median\n",
      " 1:        NCBI_Build      38    NA     NA\n",
      " 2:            Center      NA    NA     NA\n",
      " 3:           Samples       9    NA     NA\n",
      " 4:            nGenes      15    NA     NA\n",
      " 5:   Frame_Shift_Del       1 0.111      0\n",
      " 6:   Frame_Shift_Ins       1 0.111      0\n",
      " 7: Missense_Mutation       7 0.778      1\n",
      " 8: Nonsense_Mutation       6 0.667      1\n",
      " 9:       Splice_Site       1 0.111      0\n",
      "10:             total      16 1.778      2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Gene Summary..\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins Missense_Mutation\n",
      " 1:         TTN               0               0                 2\n",
      " 2:      CEP290               0               0                 0\n",
      " 3:       CHRNE               0               0                 0\n",
      " 4:       CLCN1               0               0                 1\n",
      " 5:      DNAH11               0               0                 0\n",
      " 6:       HSPG2               0               0                 0\n",
      " 7:       KCNV2               0               0                 1\n",
      " 8:         LXN               0               1                 0\n",
      " 9:       MYO3A               0               0                 0\n",
      "10:      NOTCH1               0               0                 0\n",
      "11:       RDH12               0               0                 1\n",
      "12:      SEC23B               0               0                 1\n",
      "13:       SUMF1               0               0                 1\n",
      "14:          TG               0               0                 0\n",
      "15:      UNC13D               1               0                 0\n",
      "    Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n",
      " 1:                 0           0     2              1              1\n",
      " 2:                 1           0     1              1              1\n",
      " 3:                 0           1     1              1              1\n",
      " 4:                 0           0     1              1              1\n",
      " 5:                 1           0     1              1              1\n",
      " 6:                 1           0     1              1              1\n",
      " 7:                 0           0     1              1              1\n",
      " 8:                 0           0     1              1              1\n",
      " 9:                 1           0     1              1              1\n",
      "10:                 1           0     1              1              1\n",
      "11:                 0           0     1              1              1\n",
      "12:                 0           0     1              1              1\n",
      "13:                 0           0     1              1              1\n",
      "14:                 1           0     1              1              1\n",
      "15:                 0           0     1              1              1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NOTE: Possible FLAGS among top ten genes:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"TTN\"    \"DNAH11\" \"HSPG2\" \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Checking clinical data..\n",
      "Done !\n"
     ]
    }
   ],
   "source": [
    "dfx = read.maf(maf=df_ss,clinicalData = df2_ss , isTCGA = FALSE, verbose = T, removeDuplicatedVariants = FALSE, useAll = TRUE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>ID</th><th scope=col>summary</th><th scope=col>Mean</th><th scope=col>Median</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>NCBI_Build       </td><td>38               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Center           </td><td>NA               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Samples          </td><td> 9               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>nGenes           </td><td>15               </td><td>   NA            </td><td>NA               </td></tr>\n",
       "\t<tr><td>Frame_Shift_Del  </td><td> 1               </td><td>0.111            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Frame_Shift_Ins  </td><td> 1               </td><td>0.111            </td><td> 0               </td></tr>\n",
       "\t<tr><td>Missense_Mutation</td><td> 7               </td><td>0.778            </td><td> 1               </td></tr>\n",
       "\t<tr><td>Nonsense_Mutation</td><td> 6               </td><td>0.667            </td><td> 1               </td></tr>\n",
       "\t<tr><td>Splice_Site      </td><td> 1               </td><td>0.111            </td><td> 0               </td></tr>\n",
       "\t<tr><td>total            </td><td>16               </td><td>1.778            </td><td> 2               </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " ID & summary & Mean & Median\\\\\n",
       "\\hline\n",
       "\t NCBI\\_Build        & 38                  &    NA               & NA                 \\\\\n",
       "\t Center            & NA                &    NA             & NA               \\\\\n",
       "\t Samples           &  9                &    NA             & NA               \\\\\n",
       "\t nGenes            & 15                &    NA             & NA               \\\\\n",
       "\t Frame\\_Shift\\_Del   &  1                    & 0.111                 &  0                   \\\\\n",
       "\t Frame\\_Shift\\_Ins   &  1                    & 0.111                 &  0                   \\\\\n",
       "\t Missense\\_Mutation &  7                  & 0.778               &  1                 \\\\\n",
       "\t Nonsense\\_Mutation &  6                  & 0.667               &  1                 \\\\\n",
       "\t Splice\\_Site       &  1                  & 0.111               &  0                 \\\\\n",
       "\t total             & 16                & 1.778             &  2               \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| ID | summary | Mean | Median |\n",
       "|---|---|---|---|\n",
       "| NCBI_Build        | 38                |    NA             | NA                |\n",
       "| Center            | NA                |    NA             | NA                |\n",
       "| Samples           |  9                |    NA             | NA                |\n",
       "| nGenes            | 15                |    NA             | NA                |\n",
       "| Frame_Shift_Del   |  1                | 0.111             |  0                |\n",
       "| Frame_Shift_Ins   |  1                | 0.111             |  0                |\n",
       "| Missense_Mutation |  7                | 0.778             |  1                |\n",
       "| Nonsense_Mutation |  6                | 0.667             |  1                |\n",
       "| Splice_Site       |  1                | 0.111             |  0                |\n",
       "| total             | 16                | 1.778             |  2                |\n",
       "\n"
      ],
      "text/plain": [
       "   ID                summary Mean  Median\n",
       "1  NCBI_Build        38         NA NA    \n",
       "2  Center            NA         NA NA    \n",
       "3  Samples            9         NA NA    \n",
       "4  nGenes            15         NA NA    \n",
       "5  Frame_Shift_Del    1      0.111  0    \n",
       "6  Frame_Shift_Ins    1      0.111  0    \n",
       "7  Missense_Mutation  7      0.778  1    \n",
       "8  Nonsense_Mutation  6      0.667  1    \n",
       "9  Splice_Site        1      0.111  0    \n",
       "10 total             16      1.778  2    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Hugo_Symbol</th><th scope=col>Frame_Shift_Del</th><th scope=col>Frame_Shift_Ins</th><th scope=col>Missense_Mutation</th><th scope=col>Nonsense_Mutation</th><th scope=col>Splice_Site</th><th scope=col>total</th><th scope=col>MutatedSamples</th><th scope=col>AlteredSamples</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>TTN   </td><td>0     </td><td>0     </td><td>2     </td><td>0     </td><td>0     </td><td>2     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>CEP290</td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>CHRNE </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>CLCN1 </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>DNAH11</td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>HSPG2 </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Del & Frame\\_Shift\\_Ins & Missense\\_Mutation & Nonsense\\_Mutation & Splice\\_Site & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t TTN    & 0      & 0      & 2      & 0      & 0      & 2      & 1      & 1     \\\\\n",
       "\t CEP290 & 0      & 0      & 0      & 1      & 0      & 1      & 1      & 1     \\\\\n",
       "\t CHRNE  & 0      & 0      & 0      & 0      & 1      & 1      & 1      & 1     \\\\\n",
       "\t CLCN1  & 0      & 0      & 1      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\t DNAH11 & 0      & 0      & 0      & 1      & 0      & 1      & 1      & 1     \\\\\n",
       "\t HSPG2  & 0      & 0      & 0      & 1      & 0      & 1      & 1      & 1     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Frame_Shift_Ins | Missense_Mutation | Nonsense_Mutation | Splice_Site | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| TTN    | 0      | 0      | 2      | 0      | 0      | 2      | 1      | 1      |\n",
       "| CEP290 | 0      | 0      | 0      | 1      | 0      | 1      | 1      | 1      |\n",
       "| CHRNE  | 0      | 0      | 0      | 0      | 1      | 1      | 1      | 1      |\n",
       "| CLCN1  | 0      | 0      | 1      | 0      | 0      | 1      | 1      | 1      |\n",
       "| DNAH11 | 0      | 0      | 0      | 1      | 0      | 1      | 1      | 1      |\n",
       "| HSPG2  | 0      | 0      | 0      | 1      | 0      | 1      | 1      | 1      |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins Missense_Mutation\n",
       "1 TTN         0               0               2                \n",
       "2 CEP290      0               0               0                \n",
       "3 CHRNE       0               0               0                \n",
       "4 CLCN1       0               0               1                \n",
       "5 DNAH11      0               0               0                \n",
       "6 HSPG2       0               0               0                \n",
       "  Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n",
       "1 0                 0           2     1              1             \n",
       "2 1                 0           1     1              1             \n",
       "3 0                 1           1     1              1             \n",
       "4 0                 0           1     1              1             \n",
       "5 1                 0           1     1              1             \n",
       "6 1                 0           1     1              1             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@gene.summary %>% head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of unique genes after making MAF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>15</li>\n",
       "\t<li>9</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 15\n",
       "\\item 9\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 15\n",
       "2. 9\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 15  9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx@gene.summary %>% dim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Samples with top variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Tumor_Sample_Barcode    DEL   INS   SNP   total\n",
       "---------------------  ----  ----  ----  ------\n",
       "SJST019550                0     1     2       3\n",
       "SJST043961                1     0     2       3\n",
       "SJST019552                0     0     2       2\n",
       "SJST042112                0     0     2       2\n",
       "SJST043460                0     0     2       2\n",
       "SJST019553                0     0     1       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( dfx@variant.type.summary %>% head,format = \"pandoc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Tumor_Sample_Barcode    Variants\n",
       "---------------------  ---------\n",
       "SJST019550                     3\n",
       "SJST043961                     3\n",
       "SJST019552                     2\n",
       "SJST042112                     2\n",
       "SJST043460                     2\n",
       "SJST019553                     1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knitr::kable( dfx@variants.per.sample %>% head,format = \"pandoc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Genes with 2 or more mutations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0"
      ],
      "text/latex": [
       "0"
      ],
      "text/markdown": [
       "0"
      ],
      "text/plain": [
       "[1] 0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs = as.data.frame( dfx@gene.summary )  \n",
    "gf_sx = nrow( gs[ gs$MutatedSamples >= 2,] )\n",
    "gf_sx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_genes=length(gs$Hugo_Symbol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "15"
      ],
      "text/latex": [
       "15"
      ],
      "text/markdown": [
       "15"
      ],
      "text/plain": [
       "[1] 15"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Hugo_Symbol</th><th scope=col>Frame_Shift_Del</th><th scope=col>Frame_Shift_Ins</th><th scope=col>Missense_Mutation</th><th scope=col>Nonsense_Mutation</th><th scope=col>Splice_Site</th><th scope=col>total</th><th scope=col>MutatedSamples</th><th scope=col>AlteredSamples</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>TTN   </td><td>0     </td><td>0     </td><td>2     </td><td>0     </td><td>0     </td><td>2     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>CEP290</td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>CHRNE </td><td>0     </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>CLCN1 </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>DNAH11</td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "\t<tr><td>HSPG2 </td><td>0     </td><td>0     </td><td>0     </td><td>1     </td><td>0     </td><td>1     </td><td>1     </td><td>1     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllll}\n",
       " Hugo\\_Symbol & Frame\\_Shift\\_Del & Frame\\_Shift\\_Ins & Missense\\_Mutation & Nonsense\\_Mutation & Splice\\_Site & total & MutatedSamples & AlteredSamples\\\\\n",
       "\\hline\n",
       "\t TTN    & 0      & 0      & 2      & 0      & 0      & 2      & 1      & 1     \\\\\n",
       "\t CEP290 & 0      & 0      & 0      & 1      & 0      & 1      & 1      & 1     \\\\\n",
       "\t CHRNE  & 0      & 0      & 0      & 0      & 1      & 1      & 1      & 1     \\\\\n",
       "\t CLCN1  & 0      & 0      & 1      & 0      & 0      & 1      & 1      & 1     \\\\\n",
       "\t DNAH11 & 0      & 0      & 0      & 1      & 0      & 1      & 1      & 1     \\\\\n",
       "\t HSPG2  & 0      & 0      & 0      & 1      & 0      & 1      & 1      & 1     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Hugo_Symbol | Frame_Shift_Del | Frame_Shift_Ins | Missense_Mutation | Nonsense_Mutation | Splice_Site | total | MutatedSamples | AlteredSamples |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| TTN    | 0      | 0      | 2      | 0      | 0      | 2      | 1      | 1      |\n",
       "| CEP290 | 0      | 0      | 0      | 1      | 0      | 1      | 1      | 1      |\n",
       "| CHRNE  | 0      | 0      | 0      | 0      | 1      | 1      | 1      | 1      |\n",
       "| CLCN1  | 0      | 0      | 1      | 0      | 0      | 1      | 1      | 1      |\n",
       "| DNAH11 | 0      | 0      | 0      | 1      | 0      | 1      | 1      | 1      |\n",
       "| HSPG2  | 0      | 0      | 0      | 1      | 0      | 1      | 1      | 1      |\n",
       "\n"
      ],
      "text/plain": [
       "  Hugo_Symbol Frame_Shift_Del Frame_Shift_Ins Missense_Mutation\n",
       "1 TTN         0               0               2                \n",
       "2 CEP290      0               0               0                \n",
       "3 CHRNE       0               0               0                \n",
       "4 CLCN1       0               0               1                \n",
       "5 DNAH11      0               0               0                \n",
       "6 HSPG2       0               0               0                \n",
       "  Nonsense_Mutation Splice_Site total MutatedSamples AlteredSamples\n",
       "1 0                 0           2     1              1             \n",
       "2 1                 0           1     1              1             \n",
       "3 0                 1           1     1              1             \n",
       "4 0                 0           1     1              1             \n",
       "5 1                 0           1     1              1             \n",
       "6 1                 0           1     1              1             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs %>% head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0+Enz9/Xrx4sZmZmYyMjIaGhq2t7f79+5ubm1nrJCQkYBjm7e3d7hXIfZHS0tLI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SIcTXGPYpKSkeHh6c5bz8NXZ0dJSVle3d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\naJAIAQAADGiQCAEAAAxo/w9koBNtQ9atuAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=5, repr.plot.height=4)\n",
    "gs[ gs$MutatedSamples >= 1,] %>% ggdotchart(\"Hugo_Symbol\",\"MutatedSamples\", \n",
    "                  rotate = TRUE,\n",
    "                  sorting = \"descending\",\n",
    "                  show.label = F,size = \"MutatedSamples\", \n",
    "                  ggtheme = theme_pubr() )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plotmafSummary\n",
    "to plot the summary of the maf file, which displays number of variants in each sample as a stacked barplot and variant types as a boxplot summarized by Variant_Classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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exw8vLyij5w2rRpiZ4TJkwomVIBCiSVdgEAAMDJ5YorrkhPTw8h5OTk7Nixo2XL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\nAAAQeQIOAAAAIPIEHAAAAEDkCTgAAACAyBNwAAAAAJEn4AAAAAA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      "text/plain": [
       "Plot with title “Top 15\n",
       "mutated genes”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=10)\n",
    "plotmafSummary(maf=dfx, rmOutlier=F ,dashboard=TRUE,addStat='median', textSize=2,top=total_genes, showBarcodes=F )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_plotmafsummary_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../Suggesting\\_Variants/plots/Suggesting\\_TT-Other\\_Solid\\_Tumor\\_plotmafsummary\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_plotmafsummary_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_plotmafsummary_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=12, repr.plot.height=15)\n",
    "out_svg = paste0('../Suggesting_Variants/plots/Suggesting_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_plotmafsummary_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=10, width=12)\n",
    "plotmafSummary(maf=dfx, rmOutlier=F ,dashboard=TRUE,addStat='median', textSize=2,top=total_genes, showBarcodes=F )\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DkihBg8eLDBYND+MXPmzKNHj2qvWt9d9m1Fjx49XFxcXFxcvLy8SkpK7rvvPiFE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\nhCaZAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “Variant Classification summary”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=9, repr.plot.height=5)\n",
    "plotmafSummary(maf = dfx, rmOutlier = FALSE ,dashboard = F,addStat = 'median', textSize = 2, top = gf_sx, showBarcodes=F )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Oncoplots or waterfall plots\n",
    "- Adding Clinical Data on oncoplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make Oncoplots with the total genes from the gene summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SoxMTFDhgwpvdnU1FRDRwoAAACgpimTqOh0utmzZ7u4uPTp00cqGTp0aOfOnUND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fT0rBPcT609Z37dqVn5+/cuVKNzc35U9X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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=9, repr.plot.height=7)\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        sortByAnnotation = TRUE,\n",
    "        writeMatrix = FALSE,\n",
    "        clinicalFeatures = c(\"Age_group\",\"Sex\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_oncoplot_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../Suggesting\\_Variants/plots/Suggesting\\_TT-Other\\_Solid\\_Tumor\\_oncoplot\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_oncoplot_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_oncoplot_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../Suggesting_Variants/plots/Suggesting_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_oncoplot_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=7, width=9)\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        sortByAnnotation = TRUE,\n",
    "        writeMatrix = FALSE,\n",
    "        clinicalFeatures = c(\"Age_group\",\"Sex\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3/9Vco0lZiYmCFDhpTebGpqqqEjBQAAAFDTlElUdDrd7NmzXV1d+/TpI5UMHTq0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\ny3RdOdh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u7DUKQkh5eXl5eXlqauqePXuSk5O1tbUJIWVlZZcuXaLt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\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=9, repr.plot.height=7)\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        writeMatrix = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_oncoplot_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../Suggesting\\_Variants/plots/Suggesting\\_TT-Other\\_Solid\\_Tumor\\_oncoplot\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_oncoplot_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_oncoplot_02.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../Suggesting_Variants/plots/Suggesting_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_oncoplot_02.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=7, width=9)\n",
    "\n",
    "oncoplot(maf = dfx,bgCol = \"white\",\n",
    "        top = total_genes,\n",
    "        \n",
    "        fontSize = 10,\n",
    "        SampleNamefontSize = 8,\n",
    "        titleFontSize = 12,\n",
    "        legendFontSize = 14,\n",
    "        annotationTitleFontSize = 10,\n",
    "        annotationFontSize = 9,\n",
    "        \n",
    "        drawColBar = TRUE,\n",
    "        drawRowBar = TRUE,\n",
    "        showTumorSampleBarcodes = F,\n",
    "        writeMatrix = FALSE)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=6, repr.plot.height=4)\n",
    "# oncoplot(maf = dfx,bgCol = \"white\",\n",
    "#         top = gf_sx,\n",
    "        \n",
    "#         fontSize = 10,\n",
    "#         SampleNamefontSize = 8,\n",
    "#         titleFontSize = 12,\n",
    "#         legendFontSize = 14,\n",
    "#         annotationTitleFontSize = 10,\n",
    "#         annotationFontSize = 9,\n",
    "        \n",
    "#         drawColBar = TRUE,\n",
    "#         drawRowBar = TRUE,\n",
    "#         showTumorSampleBarcodes = F,\n",
    "#         writeMatrix = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# out_svg = paste0('../Suggesting_Variants/plots/Suggesting_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_oncoplot_03.svg')\n",
    "# paste0(\"Saving : \", out_svg)\n",
    "# svg(out_svg, height=4, width=6)\n",
    "\n",
    "# oncoplot(maf = dfx,bgCol = \"white\",\n",
    "#         top = gf_sx,\n",
    "        \n",
    "#         fontSize = 10,\n",
    "#         SampleNamefontSize = 8,\n",
    "#         titleFontSize = 12,\n",
    "#         legendFontSize = 14,\n",
    "#         annotationTitleFontSize = 10,\n",
    "#         annotationFontSize = 9,\n",
    "        \n",
    "#         drawColBar = TRUE,\n",
    "#         drawRowBar = TRUE,\n",
    "#         showTumorSampleBarcodes = F,\n",
    "#         writeMatrix = FALSE)\n",
    "# dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Transition and Transversions\n",
    "returns a list of summarized tables in various ways. Summarized data can also be visualized as a boxplot showing overall distribution of six different conversions and as a stacked barplot showing fraction of conversions in each sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "An object of class  MAF \n",
       "                   ID summary  Mean Median\n",
       " 1:        NCBI_Build      38    NA     NA\n",
       " 2:            Center      NA    NA     NA\n",
       " 3:           Samples       9    NA     NA\n",
       " 4:            nGenes      15    NA     NA\n",
       " 5:   Frame_Shift_Del       1 0.111      0\n",
       " 6:   Frame_Shift_Ins       1 0.111      0\n",
       " 7: Missense_Mutation       7 0.778      1\n",
       " 8: Nonsense_Mutation       6 0.667      1\n",
       " 9:       Splice_Site       1 0.111      0\n",
       "10:             total      16 1.778      2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NULL"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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969h36kT58+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\n+R8wRrd2bEpDrwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=13,repr.plot.height=6)\n",
    "dfx.titv = titv(maf = dfx, plot = F, useSyn = T  )\n",
    "plotTiTv(res = dfx.titv ,showBarcodes = F, textSize=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "15"
      ],
      "text/latex": [
       "15"
      ],
      "text/markdown": [
       "15"
      ],
      "text/plain": [
       "[1] 15"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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19vMgrvPF746HuX+4eM0+QCALgpl/sFBla6IPGi3PBZrxx5cVPN96MsiR4FT/mS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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=8, repr.plot.height=8)\n",
    "set.seed(42)\n",
    "geneCloud(input = dfx, top=total_genes, minMut = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in wordcloud::wordcloud(words = gs[, Hugo_Symbol], gs[, MutatedSamples], :\n",
      "“UNC13D could not be fit on page. It will not be plotted.”"
     ]
    },
    {
     "data": {
      "image/png": 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aFWDuvv779s2TLhpY7dZSwY8+uvvxYev+7v77948WLzx7l69aqxxaILFy40vTuo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\nk9TUVPlVSZL++te//v7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IOCgjZs2DBq//Ti4uLvv/++o6ODfmnLli2zZs0aNUkA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u9KutrT19+rRKUhyjcePGbdiwgfGvQEpK\nSmdnp/pTUgOJZd4EQQiFwqamJlaSoSstLaWfTKMJvYgAQN+gIATQevSDDTgczuLFi2UfwdnZ\nmXEf0eTJk+XeXzR//nx6Rw0pp1kojqIoensPgiBWr17t7+8/1tF4PN727dsllpxlZWU9evRo\n1M9aWFi8/vrrjO06SktLi4qKxpoMqFNJSUlaWhrjpXXr1jEuwLaysoqNjWX8SFpaWklJiTLz\nk5erq2t4eDg93tnZeezYMfXnowaOjo704J07d9SfCSPG/lsoCAFA/VAQAmi9J0+eSEQ8PDwk\ndsGNasqUKfTg5MmT5c7KyMjI1dVVIlheXi73gKOqra1taGiQCLq4uNDnKmVkY2MTGRkpEUxJ\nSZFlio/D4axatWrGjBn0SyMVG6AJpJwsHxgYyPgbKjZ//nzGvy8URe3fv1/Gc+1VbcWKFZaW\nlvR4VlYWvbewDmD8sZadna0Js/QURTEWhBKL1QEA1AAFIYDWo+9CYfwaJB29eCMIQsGmoPTv\nxwKBQJEBpWP8dhUbG6tIY88FCxZITBI2NDTIuPCVJMnNmzfTF63l5+fj7GmNdfz4ccamRCYm\nJmvXrpXyQfHWWR6PR7/07Nmz48ePKy1FBRgaGiYmJjJeSk5O7u/vV3M+qmZpaUn/C9jQ0EDv\nJKx+JSUlra2tEkFDQ0MXFxdW8gEAfYaCEEDr0Vve078DjYqxk4GC7Q3os5Qq3apUX18vEbGy\nspKjNh7OyMhIooMIQRA5OTkyfpzP5y9dulQiKBKJHj58qEhWoCKPHj2iH7siFh8fT++TJMHe\n3j46Oprx0rVr12RZbKwG06ZNY2wC/OzZs9TUVPXno2p+fn704M8//8zuJKFIJGJcpuvl5aUJ\nZ1cCgL7Bzx0ArUefbhrpnAkpGGu/Ub8BS0dPQ6UFIX2m1M/PT/Fz/7y9vSUijFORIwkMDKQf\n2kFf5Qus6+rqOnDgAOOladOmhYSEyDJIRETESDM8SUlJGjIzHB8fb2xsTI+npaVpyDkZSjR3\n7lx6sKioaKTKXz2uXbtWVVVFj8+aNUv9yQAAoCAE0Hr0hV4GBgZjHWT8+PH0oByF5XD0A7VU\n+oWYXhBKtAmVD/37fVNTk+znZ3C5XHpLG8YOlsCun376if5HiPjPMksZ3yxwOJyNGzcyTvK0\nt7f/9NNPimapDObm5qtWraLHRSLRwYMHVX02jJrZ2dnR3+kQBHH8+PHm5mb150MQREdHx6lT\np+hxU1PTadOmqT8fAAAUhABaj374Mn0R6agY9z4pOL3W09MjEZGjUpUd/Yhnxip3rOjzexRF\njWmqk96pVXMa34PYnTt3RloJHBcXx9iIZSROTk70dcJiOTk5GtLics6cOYwtcCorKy9fvqz+\nfFSK8bejr6/vX//6F/2Hhqp1dXV98803jD+iw8LCuFyumvMBACBQEALoAPo8nhwTcar4IqKU\ntayy6+vrk4jQS2U5MObc1tYm+wj0glBDek6CWFtb20hzd15eXnPmzBnrgMuWLRtpH+9PP/00\npj88KiJugcP4t/7ChQvqz0elpkyZQt8JTBBEbW3t559/zthDSEV6enq++eabmpoa+iUTE5NF\nixapLRMAgOFQEAJoPVNTU4kI4xcO6Tgczrhx45SU0f+iz4Mx7lxSFvpUnlK2LDIWb2Oag6XP\ni9JrV2ALRVFJSUmMv6EGBgYjHeYuHZfL3bhxI+MHu7u7k5KSNOHYA3t7+6ioKHpcx5aMiq1e\nvZqx+n327Nnnn3+unqajra2t33zzzUjPioqKUsoLLAAAOWBxAoDWoy+MlO8g7Lffflu53wVL\nS0slIiqdITQzM5OYe1HKVAzjURn0InxMI+Cbn+a4du3a48ePGS/FxsbSZ3dl5ObmtnDhwvT0\ndPqlx48fX7t2LTQ0VL6RlSgyMvLOnTv6sKPVwcFh9erVR44coV9qb2//7LPPFixYEB0draI3\nVhRFXb9+/fjx4yMtDXB3d1+wYIEqHg0AIAvMEAJoPfpBxhUVFfQzGEbl6urq9n8pkpVAIKC3\n0XN2dlZkTOnoPVHlK4wlPH36VJZnSUH/dVCwfSsoS1NT00gnBLq7u4eFhSky+IoVK0aqJ48f\nP64J+0i5XO769esV78SrFcLCwujtncQoirp8+fJHH31069YtpU+Q1tbWfvPNN8nJySNVg3w+\nf/PmzThtAgBYhBlCAK3n6ekpEaEo6vz581u2bGElH7GMjAz6Vyt67apE9vb2ElM9jx49Ghwc\nVHApbH5+vkSEJEnZZwgpiqJPQNFXtwJBEH19fYcOHVL6sIaGhiM11fzxxx8Zl+9yudxNmzYp\nWCnx+fyNGzd+9dVX9Et9fX0//vjjm2++yXoZ4O7uPn/+/MzMTHbTUAOSJF944YWvv/56pENf\nOjo69u/f//PPP4eEhDz//PNOTk6KPG5gYCA3N/fKlSvSX0uRJPniiy/a2dkp8iwAAAWhIATQ\nes7OzoaGhhKvn7OysqZOnRocHMxKSu3t7RkZGfS4grOO0k2dOlWiQWJPT8/NmzfnzZsn95h9\nfX0PHjyQCNrb28v+PT43N5fetUKlvw7aa2Bg4OrVq0ofdqRTFtLS0srKyhg/smLFCnt7e8Uf\nPWXKlHnz5jEeeVdWVpaWlrZ48WLFn6KglStX5uXlaUKrG1Xj8/k7duz48ssvGaf9xTo6OtLT\n09PT052cnKZOneri4uLi4mJlZSXL2wGKourr68vLy8vLy/Py8mTZw7xu3TrGhjcAAOqEghBA\n63E4nJkzZ165ckUivn//fpIkg4KC1JzPwMDA3r176V06HB0dlXIOxEg8PT15PJ5QKBweTE1N\nDQkJ4fP58o2ZmZlJ3wHo6+sr48e7u7sZVyR6eXnJlw8oy9OnT0+fPs14ycXFJSIiQlkPWrVq\n1cOHDxnLrdOnT0+dOnXixInKepZ8DA0NExISdu/ezW4a6mFiYvLmm2/u2bOnqKhI+p01NTVD\n3blMTEwmTZpkYWFhbGxsZGRkZGRkbGxMkmR3d3d3d3dPT09XV1dbW1tFRYXsDYTFjV7l6GEL\nAKB0KAgBdEFkZOS1a9cklmgODAx8//33Dx8+jIqKUtuSpM7Ozn379jFOvKi6qTqPx/Py8nr4\n8OHwYFtb2/79+1988UU5lv/V1dWdO3eOHpexIOzv7//uu+/oh1/z+XwPD4+xJgNKNDAw8MMP\nPwwMDNAvcTicTZs2KXElp5GRUWJi4q5du0ZK47333mP99Lnp06cHBATk5uaym4Z6GBsbv/rq\nq0ePHpV9Orqrq6ugoECJORgaGm7dunWkPY0AAGqGTcwAusDGxmak1aFZWVkffvjhN998k56e\nXlZW1tvbq6KW94ODg9nZ2Z988gnjNyczM7OQkBBVPHc4xprz3r17R48eHWuviPb29l27dtHf\n9xsbG9M3bdI1NTV99dVXjL8UYWFhPB5vTMmAcqWmpo50NEtUVJTSp+z8/PxG+utZU1OTmpqq\n3MfJZ+3atYaGhmxnoSZcLjcxMfF3v/udSvsej8TFxeWDDz5ANQgAmgMzhAA6IjY2tqCgoKOj\ng/Fqfn7+UHOUcePGmZiYfPDBB0pZwCkUCsvKygoLC7Ozs1taWka6LTIyUg1VkJeX15QpU+iL\nwTIyMurr61944QUZu7kUFxd///33jL+YERER0v9DWltbr1y5kp6eLrF4VYzH4+H4aXaVl5f/\n+uuvjJcmTpy4ZMkSVTw0Pj6+oKCAcVPZr7/+6ufnx/q2UgsLi1WrVqmiqY/Gmj59uru7e2pq\n6tWrV9Vz+iKfz4+Ojg4PD1f6oa8AAIpAQQigIywsLF566aUvv/xycHBQ+p2Dg4MdHR0KfgF6\n9OjR+fPnW1tbW1tbRx3Kx8dHiZuypIuJifn888/p8YKCgvfffz8sLCw8PNzCwoLxsxRFlZeX\nX7x4MS8vj/EGExOT8PDwkR594cKF/Pz84uJiKb8gUVFROHOCReL2noyT5CRJbtq0SUWrN83M\nzNauXbtv3z76JYqifvzxxw8++EDuna7KMm/evKysrJEa7egkMzOzhISEsLCwtLS0nJwcxpaz\nSmFsbDx79uyIiIiRfvgAALCIVNHiMQBgRVZW1oEDB2Qp9v76179aWlrK/aCMjAzGU57pxo8f\n/+GHH6rzoIVjx44xHgg+xNnZecqUKVZWVmZmZlwut6urq7Ozs6ampri4eKQpVrG1a9dKOT/6\nlVdeYdyWNsTX13fHjh16cuwbgHbp7e29ffv29evXKysrlTjspEmTwsLCgoODDQwMlDgsAIAS\nYYYQQKfMmjXL1tb2u+++05Am8ra2tq+88oqaj91btWrV06dPpTSBqK6urq6uHuuwwcHBipxU\n7ujouGXLFlSDAJrJ0NAwNDQ0NDS0qqoqJyfnyZMnFRUV/f39cgzF5XJdXFzc3d0DAgKee+45\n/K0HAA2HghBA17i7u7///vspKSk5OTnsLgHw8vJ68cUX1d+2gcPhbNu27fPPP6+vr1fWmM7O\nzhs3bpT7i93kyZNffvllY2NjZeUDACoyadKkSZMmEQQhEolqa2vLy8ufPHnS2NgoPmSiq6tr\n+PZgDodj8h/jx493c3Nzd3efNGkS651jAQBkhyWjADqrqanp4sWLt27dYuxuotIloxYWFkuX\nLg0NDVVi+/6x6u7u3rdv3+PHjxUfavLkydu3bx91npNxySiHw1mwYMHKlSvRWRRANwiFwu7u\n7oGBARMTEz6fjwlAANB2eIMFoLNsbW0TExPXrFlTXl5eXFxcVFRUX1/f2dmp0tdAdnZ24eHh\nc+fOZb3+MTY2/v3vf3/ixAnp+wlHFRoaGh8fL9/7/smTJ69du9bJyUmRBABAo/B4PKW0aAYA\n0BAoCAF0HI/HmzJlypQpU6KjowmCEIlEXV1dAoFAiV0Lq1sAACAASURBVPv6TExMXF1dp06d\nOnXqVDs7O2UNqzgOhxMXFxcSEvLLL7/IMVXo5OQUExPj5+c31g8aGxuHhITMnTvX2dl5rJ8F\nAAAAUCcsGQUAeTx9+rSxsdHGxsbGxsbIyIjtdEZXUlJy+fLl/Px8+lnzEjgcjru7e3h4eEBA\nwJgWgyUlJTk7O7u5uU2cOBE7iAAAAEAroCAEAD0yODhYVlaWn5/f0NAgEAg6OjoEAgGfzzc3\nNzczM7OysvLy8vL29kb3FwAAANATKAgBAAAAAAD0FBY1AQAAAKjbiRMnsrKyJIJffPEFK8kA\ngD5DQQgAAACgbr29vR0dHWxnAQBAsHZEGAAAAAAAALALBSGAfhGJRO3t7d3d3dg/DAAAAABY\nMgqg40QiUVlZ2f3794uKitra2gQCgbgU5HA4FhYW7u7uHh4egYGBSjyWUOzrr7+mB19//XXl\nPgUAAAAAFIGCEEBnCYXC9PT09PT0zs5O+lWRSNTS0tLS0nLnzp2UlJQZM2YsXrxYiQepFxYW\nKmsoVRCJRAKBgMfjGRkZjemwQQAAAABdgoIQQAdRFJWTk3PixInW1lZZ7h8cHLxz505OTs78\n+fNXrFhhYmKi6gzVDzOlAAAAAHQoCAF0jUgkSklJycjIGOsHKYrKzMx88ODByy+/rMSpQtZh\nphQAAABgJCgIAXRKf3//vn377t+/L/cILS0tf/vb31544YWAgAAlJsYKzJQCAAAASIcuowC6\ng6KopKQkRapBMaFQ+N133yk+DrtEItHRo0f37dsnYzU4RDxT+sknn1RXV6soNwAAAAANgYIQ\nQHdcu3btzp07ShlKJBJ999132rvcsb+/f8+ePXKsmx0ininNzc1VYlYAAAAAmgZLRgF0RG1t\n7dGjR0e6yuVyvb29/f397e3tx48f39PT09DQUF9fn5+fX1FRwfiRgYGB77777sMPP7S0tFRV\n0qqh3JnSl156yd/fXymJAQAAAGgaFIQAOuLMmTMDAwP0OEmSs2fPjomJGT9+/PC4q6srQRAr\nVqzIz89PTU0tLy+nf7arq2vfvn07d+7kcLRpNYHSZ0p37Njh5eWllAEBAAAANAoKQgBdUF9f\nz7i40dDQ8MUXX/T19R3pgyRJ+vr6+vj4ZGZmHj16VHwSw3ClpaUXL15csmSJkjNWGcyUAgAA\nAMgOBSGALrhw4QK9ljMwMNi5c+ekSZNG/ThJkgsWLDAzM/vhhx8GBwclrp49e3bmzJnaUg5h\nphQAAABAdvhyA6D1RCJRXl4ePb5+/XpZqsEhQUFBv//970mSlIj39/enpKQolKK6SJkp3bFj\nx8aNGyWqwSHimdK333577dq19F8B4j8zpUpOFwAAAIBtKAgBtF5VVVV3d7dE0NfXd+bMmWMd\nysfHZ9myZfT4vXv3SktL5cxPjaTMlEpZNztEPFP629/+dty4cfSrZ8+eHesJFgAAAAAaDgUh\ngNYrKCigB5cvXy7faFFRUeJVlBJ+/vlneq2lUTBTCgAAADBWKAgBtB5925u9vb2bm5t8o40b\nN27r1q08Hk8iXlZW9uDBA/nGVA/MlAIAAACMFQpCAK3X0dEhEZFleaQU9vb2jG1FT548KRKJ\nFBlZpTBTCgAAADBWKAgBtF5nZ6dEZEwrJBlFRkbS24rW1dXdunVLwZFVBzOlAAAAAGOFghBA\n6wkEAomIubm5gmMaGBisXLmSHj9z5oxQKFRwcBXBTCkAAADAWKEgBNB69GP3jIyMFB82JCSE\nvmaytbU1IyND8cFVATOlAAAAAGOFghBA6xkbG0tE6L1V5ECSZFxcHD1+/vx5pYyvdJgpBQAA\nABgrFIQAWo9eENLnyuTj4eERGBgoEezu7j558qRSxlcuzJQCAAAAjBUKQgCtZ2JiIhGpqqpS\n1uCxsbFcLlciePXq1eLiYmU9QlkwUwoAAAAwVigIAbSehYWFRITxAAb52NjYRERE0ONJSUn9\n/f3KeopSYKYUAAAAYKxQEAJoPQ8PD4lIbW1tRUWFssZfunSplZWVRLCpqen06dPKeoRSYKYU\nAAAAYKxQEAJoPXpBSBDEqVOnlHV+Op/Pj4+Pp8fT09M1qhzCTCkAAADAWEm+8AYArePs7Mzn\n8/v6+oYHCwoK0tLSIiMjlfIIf3//adOmPXz4cHiQoqhvv/32rbfemjhxolKeoiAPD4979+4N\nj4hnSuktYeSzdOnS7OzslpaW4UHxTOnq1auV8ggAUK433niD7RRGJPFDGwCALSgIAbQeh8MJ\nDg6+fv26RPzEiRMGBgbz588nSVLBR5AkuXbt2pKSkt7e3uHxnp6ef/7zn2+//ba1tbWCj1Dc\nSDOlr732muK/AsR/Zkp3794tEU9PT/fz8/P09FT8EQCgXF1dXWynAACg6bBkFEAXREZGMtY8\nhw8f3rNnT0dHh+KPsLGxWb9+PT3e1tb2zTffNDQ0KP4IBYlnSiWC4plSZT1CPFMqERTPlD59\n+lRZTwEAAABQGxSEALrA3t5++vTpjJfy8vLee++9H3/8sby8XCQSKfKU4ODgOXPm0OMNDQ0f\nf/zxhQsXFBxfQeKZUnr8xIkTmZmZStlRKZ4pNTQ0lIiLZ0qbm5sVfwQAAACAOo3705/+xHYO\nAKAEzs7Ot27doh/OThCESCSqqam5cePG5cuXS0tLnz171tLSIj5Dj35Ug3ReXl73798XCAT0\nRxQUFDx48GBwcNDU1NTExCQ1NZX+8ejo6DE9bqwmTJiQmZlJjz969KimpmbKlCn0KcSxMjY2\ntrGxkdisSBBEb2/vo0ePfHx8TE1Nh8dZ+XUAADHGv4CaDD8cAED9SGX1IQQA1uXm5u7Zs0f2\n+5977rl33313rE9pbW394osvpM+G2dvbMy4i3bt371gfN1Z79uzJzc1lvMTlcoOCgubPn+/q\n6srhKLQ+Iikp6caNG4yPWLFiRURExND427dvp9+mhl8HACBG+AuoyfDDQbmUdRotHY/HU/wN\nI4CGQFMZAN0REBAQFRV19uxZlT7F0tLyD3/4wxdffCFlayKLWwpXrlxZUFAg0fxGbGBgICsr\nKysry8jIyMPDw83Nzdra2tLS0traeqxNceLj4588eVJbW0t/xM8//3znzp05c+ZMnTrV1tZW\n/v8SAACQqqOjg8/nSynMdu7cqaJHL1y4cM2aNSoaHEDNUBAC6JTo6GgLC4sjR44MDg6q7il2\ndnavv/76v/71Lw3cNWdvb79582bpM6U9PT0PHz4cOkVDjplSPp//6quvjjRTWl1dfeTIEXEy\nYxoWAJRr4cKFbKcAStbf319YWFhYWFhQUFBbW/vRRx85OjqynRSAdkNBCKBTSJIMDQ11dnbe\nu3dva2ur6h7k6Oj4/vvv//vf/378+LHqniIfzJQCgBjmcHRJZ2dnZmZmRkaG6haCAugndBkF\n0EHPPffcxx9/nJCQoNLjAU1MTHbs2LFy5UoN3EcRHR2dmJg4btw4lT5FPFOqCWcwAgDosK6u\nrsOHD7/77rtnzpxBNQigdJghBNBNBgYGYWFhoaGhd+/evXv3bnFxsSoOaCZJcsmSJXPmzDl/\n/vyVK1cYe5yyAjOlAAC6obCw8Mcff2xra2M7kf/PxMTEzs6O7SwAlAYFIYAuEx/NFxwcTFHU\n06dPi4uL6+rq2tvbBQJBe3u7gYGBUp5iZma2Zs2aJUuW3Lt37+7duyUlJRrSvlg8U3rz5s2L\nFy+qbrujeKb0woUL586d6+vrU9FTAAD0zcDAwC+//HLx4kW2EyEIgjA3N/f09Jw8ebKnp6eD\ngwNJkmxnBKA0KAgB9AJJkk5OTk5OTqp7hLm5eVhYWFhYmEAgKC8vr66urqmpqampaWtrEwqF\nqnuudHo+UwoAoKUGBwf37ds30jFCcps4ceLUqVOFQmFHR0d7e3t7e3tjY6P0j6xatWr69Om2\ntrYoAkFXoSAEACUzMzPz9/f39/cX/78URfX19QkEAinNV1RNz2dKAQC0i0gk2r9/v9KrQYIg\nXF1dY2Njh0c6Ojru379///79goICxtd54pOEUA2CDsPB9AAAKiFlphRnTwMAjISiqOTk5OvX\nr0u/zcLCwt/fPyYmxsTEZKR7tm/fLhGZM2fOxo0bGW9ubm4+derU7du36ZecnZ3feOMNY2Pj\n0XIH0EooCAEA1GH4TKm7uzvb6QAAaKicnJzvv/9eyg1Tp05dtmyZm5vbqLN2YyoIxSoqKg4c\nOFBbWysR9/b23rFjh6qbVwOwAsdOAACoA0mShoaGtra2qAYBAEbS2dl55MiRka46Ojq+8847\nO3bscHd3V9EaTldX17ffftvLy0siXlBQcPToUVU8EYB1KAgBAAAAlODRo0ePHj1qa2vD8iu5\npaSkCAQCxkszZ85899133dzcVJ2DkZHRjh07Zs6cKRG/cuXK3bt3Vf10APVDUxkAAACA/9Xb\n29vT02NpaSnHZ2/dupWTk0MQhImJiZOTk7Ozs7+/v4eHB4eD9+8yqayszM7OZrwUGRkZGxur\nts4uXC5348aNDQ0NFRUVw+M//fSTp6enmZmZetIAUA8UhAAgq0ePHhEE4eTkNH78ePRbAwCd\nIRQKi4qKiouLi4qKqqqqli1bFh0drciAXV1dRUVFRUVF6enp5ubmgYGBgYGBHh4e+MkpXWZm\nJmN81qxZ6qwGxbhc7osvvvjJJ590d3cPBTs7O48dO7Z161Z1ZgKgaigIAfQLXn4DAAypr6+/\ndu3azZs3h3/pV66Ojo6MjIyMjAwHB4fY2Nhp06ahLGTU3d19584denzixIkbNmxg5RfN2tp6\n3bp1+/btGx68fft2ZGSkSs/1BVAzFIQAug8vvwEAJJSVlZ06daq4uFhtT6yrq9u1a5enp+eq\nVatcXV3V9lxtcevWraGzeYZbv349l8va99WgoKDU1NT6+vqhCEVRp0+ffvnll9lKCUDpUBAC\n6DK8/B7S3NxcXl7e0dHR2dnZ2dnZ399vampqbm5uZmbm6urq4OCgmWkDgNJ1d3efPHny6tWr\nrDy9uLj4s88+i4yMXLlyJZZXDMfYr2X27Nlq6CIjBUmSS5cu/fHHH4cHHzx40NLSYmVlxVZW\nAMqFghBAN+Hlt1hxcfHt27cLCwubmpqk3GZpaenj4zN79mwPDw+15QYA6vfw4cMDBw6M1MdS\nbS5evNjY2Lh161Y+n89uJhpiYGCgsrKSHg8PD1d/MhKCg4NPnjzZ1tY2FKEo6tatW1FRUSxm\nBaBEKAgBdA1efhMEQVFUcXFxamqqjCVxa2vrjRs3bty44ePjEx0dze4LaQBQkezs7P3794tE\nIrYTIQiCyMvL+/LLL1955ZXx48eznQv7qqqqBgYGJIKurq7Ozs6s5DPcuHHjfH19b9y4MTx4\n584dFISgM1AQAugUvPwmCGJgYOCnn36S+MdbRvn5+fn5+awXtACgdBkZGVJOPFeKyMjIiRMn\nVlRUVFRUtLe3j3p/ZWXl7t2733zzTRb3yGmIsrIyejA4OFj9mTDy9vaW+Delrq6ura3NwsKC\nrZQAlEjffwAB6BK8/CYIQiAQ7Nmzp7S0VJFBLl68WF1dvW3bNhMTE2UlBgAsunv3rizVoLm5\nuY+Pz9SpU+V7iouLi4uLC0EQFEU1NjbevXs3Jyfn6dOnUj7y5MmTU6dOrV69Wr4n6gzG9aKa\ns4bfy8uLHiwqKqIfXg+gjVAQAugIvPwmCEIoFP7jH/+oqalRfKiCgoJ//vOfb775Jo/HU3w0\nAGBRa2trcnKylBsMDAxmzZoVGhrq5OSklP5SJEna29svW7Zs2bJlNTU1qampubm5I92clpbm\n6enp5+en+HO1F31hC4/H05yjHczMzCwtLVtbW4cHq6qqUBCCbkBBCKAL8PJb7MiRI0qpBsUq\nKiqSk5M3b96MBqQA2ouiqP3794/UaZnD4SxZsiQiIsLY2FhFCTg5Ob300kuVlZVHjhwpLy9n\nvCcpKemzzz7T59dPXV1dEhF7e3uNWklramoqURA2NjaylQyAcmnQ3zQAkA9efovl5ORcv35d\nuWNmZWVNnjx57ty5yh0WANTmzp07hYWFjJecnZ03b96snmkoFxeXnTt3Hjt2LDMzk35VIBDc\nunUrNDRUDZloJnpBaGpqykomIzE3N5eISG9eDaBFUBACaDe8/BYTnxQs5QY+n+/t7e3j42Nj\nYyP+d10gEDx79kzcRaavr2+kD545c2bmzJn6/OYeQHtRFHXp0iXGS5MnT/79739vaGiotmS4\nXG5CQoK9vf3Ro0fpV9PS0ubOnau3vazoBaFS/tlKTEyUiEyYMEG+oegFKj1nAC2FghBAu+Hl\nt9iDBw8aGhoYL3G53PDw8CVLljB2iAkNDe3u7v71118vXbpEb3pOEERbW1tmZmZERISSMwYA\n1SsvL6+oqKDHXV1dd+zYwUob5PDw8KdPn9KXMzQ2Nj548GD69OnqT0kT9Pf3S0SU8hpOif/u\nUBQlEZHyJhFAu+jpiygA3SD95febb76pzh354pff8fHxjFfT0tJU2v40PT2dMW5iYvLGG2+s\nWrVKSr9QY2Pj2NjYnTt3jrRC6dKlS/SvAgCg+S5fvkwPcrlcdk+ET0hImDhxIj2elZWl/mQ0\nBP23g/XzkyR0dHRIROhFLICWQkEIoMWkv/xW51KoIeHh4Yw77sQvv1X00N7e3pKSEnqcy+W+\n8cYb7u7usgzi5ua2c+dOxnfSra2tdXV1imYJAOpFUVR+fj49HhUVZW9vr/58hnC53OXLl9Pj\npaWlevvuiV4QtrW1sZLJSOi9tQ0MDFjJBEDpUBACaDG8/BYrKytj/BYVFxc3pjlSR0fHuLg4\nxkuPHj2SMzkAYEldXR19fzWPxwsLC2Mjnf8jICCAvplNIBDobeNKIyMjiYgshxupE32GkMV/\nZwGUCwUhgLbCy+/hI9OD1tbWcuwemTdvno2NDT3OOAMJAJqsrKyMHgwMDFRdky3ZkST5/PPP\n0+OMP830Af0Hb1dXl1AoZCUZOqFQSG8hQy9iAbQUCkIAbYWX30MqKyvpwfDwcDn69XE4nPDw\ncHpc095VA8CoGAvCGTNmqD8TRpMnT6YHR+rVrPMYm38WFxerPxNGjJnY2tqqPxMAVUBBCKCt\n8PJ7SGdnJz04bdo0+UZj/CB9sRAAaLiWlhZ60MXFRf2ZMHJxcaFvWtbbd0+MBeHdu3fVnwkj\nxj3w7C7GAVAiFIQA2govv4fQV/KYmZnZ2dnJN5qtrS39AGIUhABah76GYvz48RYWFqwkQ8fl\ncukVhd4ebcf4T0ZeXt7g4KD6k5FAURRjQci4Wx5AG+EcQgBtpRUvvyV2gKjo5Tf9K9SECRNI\nkpRvNJIkJ0yYIFEBasKXEvXLy8vbvXs321kQBEF4eXm9/vrrhEamJOGdd95h7I64aNGikVoW\nKaizs3Pnzp30+N69e1XxOC1C/8nAuEOYRfTjcPS2ILS3t7ewsJD4u9PV1VVYWOjr68tWVmI1\nNTWM/+B6enqqPxkAVcAMIYC2wsvvIT09PRIRKacOykLBjwPQXbp0iXGzK6gO/YekpnUBQUE4\nhCRJH5//x969x0VZ5v8fn8GBgRk5ykEMAUENxRMigmc8Vp7aVDKPW/atbW2rX9tWu31ry9oO\nW98y23Y3s21bxUwwddUOJp4t5SAHFRFUjgqKIIgMp4GZ3x98t4ffuW8Q5zxzv55/7X7umev6\npKW87/u6r2uosL5nzx6bH8Xx/fffC4u+vr72dn8BMBqBEHBU3PzuhokvUtrDe5hwMnq9ftOm\nTdJ81Gwrra2tBhV7C4TC02KFIVY6EhIShMXi4uKffvrJ+s3c2kBWVpawPnr0aKPXoQD2hkAI\nOCpufnfDxL+ne/XqZa5OgJ9VVFSkpaXZugsJER4TZ28/wQv/SFQopPsuz+DBg0Vva3799de2\nenCq1+u3bt0qeik+Pt7KzQCWQyAEHBU3vwGHs3v37mvXrtm6C6kQ/hEk+nqnDQlfq5byWedy\nuXzq1KnCukaj+fLLL3U6nfVbOnr0aGlpqbAeEhISGhpq9XYASyEQAo6Km9+Aw9FqtZs3b7b5\nO1ESIbxHZv+BUBhiJWXKlCne3t7CelZW1pdffmnl/3Dy8vK2bNkieum+++6zt79wAVPwwxng\nqNzd3VtaWm6t2P/POlK++e2I+vTpM2HCBOO+++OPPwqLoaGh/fv3N2K04OBgu23pThUUFKSn\np4u+LgXzCggIqKqqurVSV1en1+vt5Ed5nU4nPM/Gz8/PJs3YCVdX19mzZ4vGsKNHj7q5uSUl\nJVnnt+/8+fMbNmwQfSwZHBwcGxtrhR4AqyEQAo7Kw8PDIAHafyCU+M1vh9O/f/+VK1ca913R\n9BUTEzN79mwna8kIKSkp0dHRnp6eVp5XaoKDgw2Oj9NqtdeuXTP6kFLzunDhgnCTIc46nzx5\ncnp6uuihtfv3729ra0tKSrL0vcVz58598sknBscm/eyhhx6yk3sKgLmwZBRwVAEBAQaVzpvf\nNmlGiJvfQFc0Gk1qaqqtu3B+/fr1ExYzMzOt34monJwcYZFA6OLi8vDDD7u6uopePXr06J/+\n9KeLFy9aaHatVpuamrp27VrhaUadxo8fHxUVZaHZAVshEAKOSrhirfPmt02aEeLmN9CN9PT0\n/Px8W3fh5O6++25hMT093R5unOn1etFAGBkZaf1m7E1QUNDSpUu7ulpdXf3ee+9t377d4KUJ\n05WXl7/11lvdbAUcGBiYlJRk3kkBe0AgBBwVN78Bh7Z582bhXsEwI19fX+GNs6tXr5aVldmk\nn1udP3++rq7OoOju7h4WFmaTfuzN+PHj77333q6u6vX6vXv3vvDCC1999dXVq1dNnEuv1586\nderDDz988803Kysru/qYUqlcvXo1p9TCKREIAUfFzW/AUYSHhwuLtbW1u3fvtnov0jJixAhh\ncfv27bb9c1Kn04muGY6KinJx4Qez//WLX/yi+x2kWltbDx48+Mc//vGjjz766aefqqur7+i3\nVafTlZWV7d2795VXXvnrX/9aUFDQzYcVCsUTTzxh9FZSgJ1jUxnAUXXe/DbYQ6/z5rfoT5/W\nxM1v4FYLFiz47LPPhG/VpqWlxcXF8d+F5UycOHHv3r0GxcLCwqNHj06ePNkmLclksqNHj5aX\nlwvr7D17K7lcvmLFCnd39/3793f/yfz8/M4F2F5eXgMHDoyMjPTz81OpVL1791ar1SqVSqfT\naTQajUbT1NSk0WiqqqouXLhQUlLSw0f0CoVi9erVQ4cONcM/FWCXCISAAxsxYoRBIJTJZNu3\nb3/22WdtuAcaN78BAyqVavHixRs2bDCo6/X6TZs2vfTSS/ynYSGBgYFDhgwRPvzZtm1bdHR0\nnz59rN9SQ0PDzp07hfXevXsPHz7c+v3YM7lcnpSU5O/vn5qa2pOD6RsaGrKzs7Ozs83Yg5eX\n1xNPPMHyFjg3/gYCHNjEiROFxc6b39Zv5mfc/AaEYmNjRX/cr6io6GYTC5juvvvuExZbW1s/\n/vhj4dE4lqbRaNatW9fU1CS8lJiYqFBwm96QXC6fNm3a888/7+vra/3Zw8PDX3rpJdIgnB6B\nEHBgnTe/hfVt27bV1tZavx8ZN7+BLsjl8qVLl4qen7Zr166amhrrtyQRd99997Bhw4T1ysrK\nd99915q/8s3NzevWrbt06ZLwklqtnjFjhtU6cTgRERGvvfbajBkzrLb4RaFQLFiw4MUXX7RJ\nEAWsjEAIODZufgOOws/P7/777xfWtVptcnKyPWwH5awWLVok+udPTU3Nu+++a51NR+vq6tat\nW9fVXHPmzPHw8LBCG47L3d09KSnp5ZdfHjlypKXniouLe/XVV++55x7WckMi+BcdcGzc/AYc\nyNSpU0X3fCooKEhPT7d6O1IRHBy8aNEi0Us3btx4++23t27dKnonyyz0ev3Ro0dfe+21kpIS\n0Q9ERkZOnTrVQrM7mZCQkNWrV7/yyivx8fFdHV5vNIVCER8f/8orr/zXf/1XYGCgeQcH7Bk3\n7AGHt2jRonPnzrW3txvUO29+P/nkk1bYw7Curm79+vX2c/M7JyfHlLv+wi1SZTLZG2+8YUJH\n/8crr7xirqHgWFxcXFasWPHmm28Kd8hISUmJjo729PS0SWNOLzExsaCgIC8vT3hJr9cfOHAg\nKytrwYIF8fHx5n0oVFlZmZKS0s2RBkql8uGHH+ZJ1B0JCQlZtWrVkiVLMjMzc3NzL168aMoh\n9XK5PCIiYvTo0QkJCb179zZjn4CjIBACDq/z5vdXX30lvNR583vq1Knz5s2z0HG6er3+2LFj\n27Zt6+rvY5vc/G5qajL7/X7Rh5/AnQoJCZk1a9b3339vUNdoNKmpqatWrbJJV05PLpc/+uij\na9eu7eoxXUNDwxdffLF9+/axY8eOGzcuJCTElOna29tzcnIOHz58/vz57rt6/PHHeRhlHA8P\nj8mTJ0+ePFmn012+fPn8+fPnz5+/cOGC8HwXIYVCERISEh4ePmDAAG7EAARCwBlw8xtwIHPn\nzj158uS1a9cM6unp6QkJCRx3ZiFKpfKpp556//33L1++3NVnGhoa0tLS0tLSQkJChg0bFhYW\nFhYW5ufn15O9TPR6/ZUrV4qLi4uLi3NzcxsbG2/7laVLl4qu+ccdcXFx6d+/f//+/adNm6bX\n69va2hobGzsPHuz8H21tba6urm5ubkql0tfXt0+fPt7e3jY8nAmwNwRCwBlw8xtwIK6ursuX\nL1+7dq3wUnJy8quvviq6GSlMp1arf/e7333yySeFhYXdf/LSpUs/LwpQq9WhoaE+Pj4qlcrD\nw8PDw0OlUsnl8s6VCM3NzRqNpr6+vrS0tOcLFztPXZ8wYYJJ/zwQkMvlSqVSqVTa5JBJwEER\nCAEnwc1vwIFERUWNGzfu+PHjBvXa2to9e/YsXLjQJl1JgUqlevrpp7du3XrkyJEefkWj0XSz\nDsII7u7uq1atssJumQDQEwRCwHlw8xtwIElJLP5hGQAAIABJREFUSadPnxbeW9m3b19cXFxo\naKhNupIChUKxbNmy6OjojRs3ajQaK88eFhb22GOPBQQEWHleAOgKgRBwKtz8BhyFWq1+8MEH\nP//8c4O6Xq/fuHHjSy+9xMu3FjVq1KjIyMg9e/YcOXJEuOmrJSiVynnz5k2bNq1Xr15WmA4A\neohACDgbCd78JnzCQY0dOzY9PT0/P9+gXlFRkZaWNmvWLJt0JR2enp5LlixJTEzct29fVlZW\na2urhSZSqVTjx4+fOXOmj4+PhaZwAsK3aocNGzZz5kybNANICoEQcE6Suvm9evVqK88ImIVc\nLl+6dOmaNWva2toMLu3atWv06NH+/v42aUxSgoODV65c+eCDD2ZkZBw7dsyUI0yFQkNDExMT\n4+Li3NzczDisUzp37pxBhY1hAOsgEAJOi5vfgP3z9/efP3/+tm3bDOparTY5OfmZZ55hc3zr\ncHd37zzUrry8PCsrq6SkpLS0VBjUe0KhUISFhUVGRsbExAwYMIDfQQB2jkAIODlufgN2bvr0\n6RkZGeXl5Qb1goKCjIyM+Ph4m3QlWaGhoZ07+uh0usrKyuLi4pKSkurq6s59tjQajVar/fnD\nLi4u6v/w9vaOiIiIjIwMDQ1VKPj5CoDD4A8sQBK4+Q3YLRcXlxUrVrz11lt6vd7gUkpKSnR0\ndO/evW3SmMS5uLiEhISEhIRMnjz51rpWq21qampvb1er1Uqlkj8DATg6AiEgLdz8BuxQaGjo\njBkz9u3bZ1BvbGxMTU195JFHbNIVRLm6unp7e9u6CwAwG36qAySKm9+AXZk3b152dnZtba1B\n/cSJE/Hx8UOHDrVJVwAAp8cZRwD+j86b33369HF3dycNAlajVCqXLVsmemnz5s3GLfAGAOC2\nCIQAANiF6Oho0S1kampqdu/ebf1+AABSQCAEAMBeJCUlqdVqYT0tLa2iosL6/QAAnB6BEAAA\ne+Hp6ZmUlCSs63S6jRs36nQ667cEAHBuBEIAAOxIQkJCVFSUsF5eXr5//37r9wMAcG7sMgoA\nFlRaWnrt2jWDYlxcnE2agUOQy+XLli17/fXXbz0DptOuXbtiYmL8/f1t0hgAwCkRCAHAgn78\n8ccjR44YFAmE6F5gYODcuXN37NhhUG9ra/vyyy+feuopdgAGAJgLgRCARVjzydiLL75oUBk7\nduzChQstMRdgHTNnzszMzLx06ZJBPT8/PyMjQ3QzUgAAjEAgBGAR1nwyVl9fb1DRaDTGDXX4\n8OH8/HyD4urVq40bDTBar169VqxY8c477+j1eoNLKSkp0dHRvXv3tkljAAAnQyAEHBhPxszu\n0qVLeXl5tu4CkMlksvDw8GnTpgk3kmlsbExNTX3kkUds0hUAwMkQCAEHxpMxwLnNnz8/Ozu7\nrq7OoH7ixImEhIQhQ4bYpCsAgDPh2AkAMtl/nowZsHVTgNS5u7svW7ZM9FJycnJbW5uV+wEA\nOB+eEAIAYL+GDx8+ZsyYrKwsg3pNTc2ePXsWLFhgk64AK6ioqPj2229t3UWXZs+ebesWAPMg\nEAIAYNcWL1589uzZpqYmg/q+ffvi4uJ8fX1t0hVgaeXl5eXl5bbuoksEQjgNlowCAGDXvLy8\nRDeL0ul0mzZt0ul01m8JAOA0CIQAANi7CRMmDBo0SFgvKys7cOCA9fsBADgNAiEAAPZOLpev\nWLFCoRB50WPv3r3W7wcA4DQIhAAAOICgoKA5c+YI6ywZBQCYgkAIAIBjmDVrVr9+/WzdBQDA\nqRAIAQBwDAqFYvny5XK53NaNAACcB8dOAADgMCIjI6dMmXLo0CFbNwJY3IQJE1auXGnrLgDn\nxxNCAAAcyQMPPODj42PrLgAAToJACACAI3F3d1+yZImtuwAAOAkCIQAADmbUqFExMTG27gIA\n4AwIhAAAOJ6HHnrI3d3d1l0AABwegRAAAMfj4+OzcOFCW3cBAHB4BEIAABzSpEmTIiMjbd0F\nAMCxcewEAMD81q9fb+sWDFmnpT//+c9WmKWTXC5/4YUXrDYdAMAp8YQQAAAAACSKQAgAAAAA\nEkUgBAAAAACJIhACAAAAgEQRCAEAAABAogiEAAAAACBRBEIAAAAAkCgCIQAAAABIFIEQAAAA\nACSKQAgAAAAAEkUgBAAAAACJUti6AQAAAEjdr3/9a4NKnz59bNIJIDUEQgBOKD8//6OPPjLi\ni5WVlcKicUN1MyAAwMCoUaNs3QIgUQRCAE6ovr6+vr7eXKPl5+ebaygAAAC7QiAEnApPxgAA\nANBzBELAqfBkDAAAAD3HLqMAAAAAIFEEQgAAAACQKAIhAAAAAEgU7xACcHiRkZG2bgEAAMAh\nEQgBOLwXXnjB1i0AAAA4JAIh4MB4MgYAAABTyPV6va17AAAAAADYAJvKAAAAAIBEEQgBAAAA\nQKIIhAAAAAAgUQRCAAAAAJAoAiEAAAAASBSBEAAAAAAkikAIAAAAABLFwfQAcGfOnDkjk8lC\nQkK8vb3lcrmt2wEAADAegRCAFLW0tDQ3N/v6+hrx3ePHj2dlZclkMrVaHRIS0r9//5EjRw4c\nONDFhTUXAADAwcj1er2tewAAa9BqtYWFhUVFRYWFheXl5bNnz543b54R42zYsKEzEN7Ky8sr\nNjY2NjZ24MCBPDYEAACOgieEAHqqM1AVFhZevHixoaGhqalJqVT6+Ph4e3vfddddMTExd911\nl31moStXrhw9evSnn35qamqy0BQNDQ0HDx48ePBgcHDwggULhg8fbp+/FAAAALciEAK4vebm\n5kOHDh04cKChoeHWukajuX79ukwmy8nJ2bNnT2BgYFxc3KxZs9zd3W3UqaGLFy/u3LmzqKjI\najNWVVX99a9/HTx48MKFC8PDw602LwAAgBEIhIAzu379+qlTp0pKShr+46mnngoNDb2jQc6d\nO/fFF1/U1dXd9pPV1dXffPPNjz/+uHjx4piYGNs+ImtqatqxY8eRI0dsMntRUdHbb789a9as\nBx54gHcLAQCA3SIQAk6otbX1wIEDJ0+erKioMLh0p68N//DDD9u3b7+jb9XX169fvz4mJubR\nRx91dXW9o+nM5fTp0//6179u3rxpk9l/9sMPP1RXV69atUqpVNq2EwAAAFHctwacil6vP3Hi\nxCuvvLJz505hGrxT+/bt+/rrr43beionJ+fvf/+7Vqs1sQcjpKen/+1vf7N5GuyUm5v7/vvv\n37hxw9aNAAAAiCAQAs5Do9G89957//znP80SP/Ly8rZt22bKCPn5+dbPhAcPHvz88891Op3l\nppg1a9b9998/cuRIb2/vnny+rKzs73//e3t7u+VaAgAAMA5LRgEnUV9f/+GHH1ZVVZlltIaG\nho0bN5o+Tn5+/p49ex544AHTh+qJkydPfvXVV7f9mJeX19ChQ4cNG2bcLGFhYWFhYTKZTK/X\nV1dXnzx5Misr6/Lly918paSkZOfOnYsWLTJuRgAAAAshEALO4Pr16++//35NTY25BkxJSWls\nbOzqqqura1RUlL+/v5ub240bN65du1ZcXNzVytIffvghLi4uJCTEXL11pa6uLjk5uZsPuLm5\nJSQkTJ48OSQkxCwb3sjl8qCgoNmzZ8+ePfvSpUt79uzJycnp6sP79u0bPHjwiBEjTJ8XAADA\nXAiEgMPT6/X//Oc/zZgGq6qqhAevd3J1dZ0zZ8706dPd3NxurTc0NBw/fvybb75pbW01+IpO\np9u0adOLL75o0c029Xr9F1980dUxgy4uLvfee+/MmTNVKpWFGggJCXniiSfKysq++uqr4uJi\n0c9s3Ljx7bffttVGOwAAAEJy47aLAGA/Dh482M06SXd39+jo6OjoaD8/Py8vr6CgIIXiNneC\nPv/88/T0dGFdpVI988wz3ZytV1NTs2HDhtLSUuGlxx57bMyYMd3Pa4qMjIx//OMfopf69+//\n8MMPW+ERZaf29vbU1NRDhw6JXl22bNnkyZOt0wkAAMBt8YQQcGw1NTXbt28XveTu7j5v3rzE\nxMTbJsBbtbS0ZGdnC+tyufyJJ57o/qR1f3//p59++t13371y5YrBpWPHjlkuEOr1+v3794te\nGjRo0G9+8xt3d3cLTS2kUCiWLFkSFBS0detW4dV9+/ZNnDiRkwkBAICd4IcSwLGlpaW1tbUJ\n6+Hh4W+88caMGTPuKA3KZLJTp06J7gs6bdq0u++++7ZfV6vVq1evFgaec+fO1dbW3lEnPVdc\nXCz6WDI8PPypp56yZhr82bRp0yZOnCisV1dXnzp1yvr9AAAAiCIQAg6stbX1+PHjwnpkZOSz\nzz7r5eVlxJiijwddXV3vu+++Ho4QFBSUmJhoUNTr9T/99JMR/fTEgQMHhEWFQmHbE+GXLFly\n1113CesnTpywfjMAAACiCISAA8vKymppaTEoqtXqX//618Y9Fuvo6CgoKBDWExISPD09ez7O\nfffdJ3xImJeXZ0RLt6XX68+ePSusz5kzJygoyBIz9pBCoZg7d66wfuHCBV7eBgAAdoJACDiw\nH3/8UVh88MEH7yi83aq0tFSYMGUy2Z0eluDl5TV48GCD4uXLly1xSH1VVZVwc1FXV1fhU0rr\ni4mJ6du3r0Hx5s2b1dXVNukHAADAAIEQcFRarVb44lxQUFB8fLzRY4o+HnRxcRGmu9saPXq0\nQUWn01VUVBjZWdcuXrwoLMbGxlruhImek8vl48aNE9YvXLhg/WYAAACECISAo6qoqOjo6DAo\nTpgwwZQj10XXXg4cONCIBaiRkZHCYllZmTFtdUs0EArjqK0MGjRIWOzqoEIAAAArIxACjqqk\npERYNOXxYEtLi+iYQ4cONWK0oKAgYTS1RCC8fv26sBgWFmb2iYwTFhYmPIn+xo0bNmkGAADA\nAIEQcFTC9aJ9+vTx8fExesCioiKdTiesDxkyxIjRXF1dAwMDDYoNDQ3GdNYt4QuE3t7epvw6\nmJdCoRDubaPRaGzSDAAAgAECIeCo6uvrDSomPhYTXS+qUqlCQ0ONG9Db29ugIgxvphOGK39/\nf7PPYgq1Wm1QIRACAAA7QSAEHJUwXIWEhJgyoOiOMnfffbfwAIkeEr55aIlAKBzTw8PD7LOY\ngkAIAADsFoEQcFTNzc0GFWHw6Lm6urorV64I68atF+0kPBRe2LPpWltbDSr2FgitE4wBAACM\nQCAEHJV5n4yJPh6UyWRRUVFGj+nm5mZQsUQQEsZOU/ZZtQTh80CFQmGTTgAAAAwQCAFHZd4n\nY6KB0NfXV7gxTM8Jz7i3RFQTPn8Tvl1pW8I9RYUhFgAAwCYIhICjEgYhrVZr3FB6vV40EA4Z\nMsSUCCd8HmiJw+KFMdj+A6ER5zoCAABYAoEQcFTCcGX0ViWXL1++efOmsG7KC4QyawXCgIAA\ng0pdXZ1erzf7RMbR6XTCwzb8/Pxs0gwAAIABAiHgqIRbyIiGup6wxAuEMrEnY5bY7iU4ONig\notVqr127ZvaJjHPhwoWOjg6DovBkQgAAAJsgEAKOSvi0rayszLihzpw5Iyz269fPy8vLuAFl\nMll9fb1w6aYlnhD269dPWMzMzDT7RMbJyckRFgmEAADAThAIAUclXCp58eJFI5ZK3rx5s7Cw\nUFg3cb1oSUmJsGiJIHT33XcLi+np6fawalSv14sGwsjISOs3AwAAIMTW54CjGjhw4JEjR26t\nNDY2lpaWDhgw4I7GycnJEc1OJgbCixcvCovh4eGmjCnK19c3ODi4qqrq1uLVq1fLysosMd0d\nOX/+fF1dnUHR3d09LCzMJv1IUH19fWFhYVVVVVVVVXV1dXNzc0tLS2trq5ubm/o/fH19Bw4c\nGBkZGRQUZG9nlgAAYGkEQsBRiT5lOnz48J0GwpMnTwqLLi4ugwYNMrIzmayjo0N00aaFEtqI\nESMMAqFMJtu+ffuzzz5rw5/vdTpdamqqsB4VFeXiwuoMy7p27dqxY8dOnTpVWVkp+oGWlpaW\nlpba2trO//vTTz/JZDK1Wh0dHT1t2rQ7/Y9IVHZ29vr164X1NWvW9O3b1/TxhTZs2JCVlWVQ\nDAoKev3113vy9X//+9/ffvuteVvy8PBQqVQqlUqtVvfv3z8iImLAgAE+Pj4m/of54osvim4m\nPGPGjKSkJFNG7kpjY+Nzzz0nrIv+Fgu9++67ovfILM3Hx+fPf/6z9ecF4FgIhICj6tOnj7e3\nt8HGLZmZmfPmzevTp08PB2loaBBdLzpgwABTjkbIzc0VfYFQuMzVLCZOnLh3716DYmFh4dGj\nRydPnmyJGXvi6NGj5eXlwnpCQoL1m5GOoqKi77777uzZs0Z8V6PRZGRkZGRkRERETJ8+PTY2\nlgeGJmpubm5ubu4M3ufOnessBgcHJyYmJiQkmP38lf37948dO5Yn8ABwR7hLDTgquVw+fPhw\ng2J7e3tycnLPX5/bu3ev2deL6vX6tLQ0YX3QoEEW+vE6MDBQtOFt27b9/AjIyhoaGnbu3Cms\n9+7dW/i7BrO4evXq3/72t/fff9+4NHir4uLiDRs2rFu3zt7OtHQOVVVVW7ZseeGFF3bt2tXe\n3m7GkfV6/aZNm4T7+gIAukEgBBzY1KlThcWzZ8/+8MMPPfl6dXX14cOHRS+NGDHC6K4OHTpU\nXFwsrE+YMMHoMW/rvvvuExZbW1s//vhj4ekXlqbRaNatWyc8hlEmkyUmJioULM0wM71ef/jw\n4TfeeCMvL8+MwxYUFLz++uvZ2dlmHBM/a21t/eabb955552ulvUap6KiQvSGFACgKwRCwIGF\nhISIvum3ffv2H374ofvnhK2trZ9++qlWqxVe6tOnT2hoqHEtVVVVff3118K6j4+PRZ+M3X33\n3cOGDRPWKysr33333ZqaGstNbaC5uXndunWXLl0SXlKr1TNmzLBaJxLR1ta2fv36L7/8UvRf\nZhNpNJr169cfOnTI7COjU0VFxVtvvSW6KbHRdu/ebT8nkQKA/SMQAo5t+vTpovWvv/76448/\n7mrBZF1d3QcffFBRUSF61ehXpyorK9etWyf6c/mECRMsvZPKokWLRB++1dTUvPvuu0Yf0nhH\n6urq1q1b19Vcc+bM8fDwsEIb0qHRaNauXSt6tocBhULh7+8/aNCguLi4MWPGREdHR0RE9PCk\nzS1btqSnp5vcLMRptdqPP/64urrajANu3rzZHg6eAQCHwMolwLGNGjVq2LBhoifLnzlz5uWX\nX46Ojh49enRgYKCXl1d7e/u1a9fOnDlz/Pjxrh6nuLi4iK5Eva1z586tX79edJ2kUqmcNGmS\nEWPekeDg4EWLFn311VfCSzdu3Hj77benTp06b948lUplidn1ev2xY8e2bdvW0tIi+oHIyEjj\nfmHRlebm5g8++ED0YezPAgMD4+Pj4+LiAgMDhbc59Hp9fX19WVlZdnb2yZMnu3mf7YsvvlCr\n1aJPoZ2bXC4fP368EV9sb2/v3FHm2rVrt30Vs7Gx8eOPP/7jH/9orgXVBQUF6enpdr6Bk4+P\nT3R0tEWnUKvVFh0fgHMgEAKOTS6Xr1y5cs2aNRqNRnhVp9OdPn369OnTPR8wISHBz8/vjnq4\ncuXKzp07u3lKs2DBAl9f3zsa0ziJiYkFBQWiL5Lp9foDBw5kZWUtWLAgPj7evI8rKysrU1JS\nCgoKuvqAUql8+OGHOW3CjNrb2z/55JNu0uDgwYMXLlwYFhbWzeNuuVzu6+vr6+s7atSoxYsX\nHzx48LvvvhONhTqd7l//+teaNWssdEPBbvXq1WvlypUmDtLQ0FBUVHTs2LFu/hu5evXqsWPH\nEhMTTZzrZykpKdHR0Z6enuYa0Ozuuusu039tAcB0BELA4Xl7e69YseKTTz4xfSiVSnX//fff\n9mPt7e2NjY3Xr18/d+7cmTNniouLu1mdNWjQoClTppjeW0/I5fJHH3107dq1Xb2S1NDQ8MUX\nX2zfvn3s2LHjxo0LCQkxZbr29vacnJzDhw+fP3+++64ef/zxwMBAU+aCga1bt/58jIEBpVK5\nYMGCKVOm3NHKZ7VaPXfu3NjY2C+++KK0tFT4gYaGhh07dixbtsy4hqXMy8trzJgxY8aMKSsr\n+8c//nH16lXRj+3Zs2fcuHFKpdIsk2o0mtTU1FWrVpllNABwYgRCwBnExMT88pe/3Lhxo4mv\nzSxZssTHx6f7z+zZs2f37t09HFCpVK5cudKah7kplcqnnnrq/fffv3z5clefaWhoSEtLS0tL\nCwkJGTZsWFhYWFhYmJ+fX0/61Ov1V65cKS4uLi4uzs3NbWxsvO1Xli5dKsGlhhaVl5d35MgR\n0Uv9+vV78skn/f39jRs5ODj4t7/97bp160SPET969Oi4ceMiIiKMGxxhYWH//d//3dUtm5s3\nb+bk5JhxnWd6enp8fLyll2UCgKMjEAJOYvz48R4eHp999pnR53rNmzdv7Nixt/1Yz8/4UqlU\nzzzzjPWfjKnV6t/97neffPJJYWFh95+8dOnSz2sO1Wp1aGioj4+PSqXy8PDw8PBQqVRyubyp\nqampqam5uVmj0dTX15eWlnb1lqCQXC5fsWKFRc/bkKDGxsaNGzeKXvL393/mmWdue1Oje0ql\n8umnn37vvfeE61H1ev2OHTuee+45U8aXOKVS+cQTT7zxxhuiN1POnj1r3hf/Nm/e/Oqrr5rr\nqSMAOCUCIeA8YmJinn/++S1btogueOuGXC6///777733XjM24+Xl9f/+3/+76667zDhmz6lU\nqqeffnrr1q1dPUcS0mg03bzgZAR3d/dVq1aNHDnSjGNCJpPt3r1bNEt4e3s/++yzJqbBTu7u\n7o888sibb76p0+kMLhUVFV29ejUoKMj0WSTLx8dnxowZO3fuFF46e/asXq83bk1BeHi48I++\n2tra3bt3L1q0yIgBAUAi2OEAcCrh4eG///3vf/nLX3p7e/fwK8HBwS+88MJ9991nxoWdERER\nzz//vK3SYCeFQrFs2bJf//rXNtlnLyws7OWXXyYNmt2VK1dEQ75cLjdlpahQSEjIPffcI3qp\n53cZ0JUpU6aI7rF08+bNrg7Lua0FCxaIniOSlpZmnVNnAMBB8YQQcDad28THx8efP38+Nzc3\nNze3rq5O+DG1Wj148OBJkyYNHTrUjFHQ19d3wYIFcXFx1nxvsBujRo2KjIzcs2fPkSNHhE97\nLEGpVM6bN2/atGm9evWywnRSk5aWJvr7mJiYGBYWZt65Zs2alZaWJjyg5fjx47/4xS9cXV3N\nO52kqFSq/v37i+a0mzdvGhfsVSrV4sWLN2zYYFDX6/WbNm166aWX2OYXAEQRCAHn1KtXr6io\nqKioqMWLFzc1Nd24caOhoaGhoUGhUKjVal9f34CAADNmNrlcPmDAgNGjR0+ZMsXNzc1cw5qF\np6fnkiVLEhMT9+3bl5WV1draaqGJVCrV+PHjZ86caZZVixBqaWnJyMgQ1j09PefPn2/26VQq\nVUJCwtGjRw3qGo2msLCQjYJMFB4e3lUgNHrM2NjYEydOCA/aqaioSEtLmzVrltEjA4ATIxAC\nTk4ul6vVarVa3a9fPzMO6+rq6unp6enpGRAQMGzYsGHDhtnzeV8ymSw4OHjlypUPPvhgRkbG\nsWPHzLuELDQ0NDExMS4uzt7CsJPJzMwUzfMLFiyw0PGAkydPFgZCmUxWVFREIDRRV39i9GTn\n3q7I5fKlS5e+9tprwn9Pdu3aNXr0aDMuKgYAp0EgBHBnZs6cec899yiVSjtZFHpH3N3dJ0+e\nPHny5PLy8qysrJKSktLS0ra2NiOGUigUYWFhkZGRMTExAwYMcMRfDYcjms08PT3j4+MtNGP/\n/v29vLwaGhoM6kVFRRaaUTq6yvAmLuz08/O7//77U1JSDOparTY5OfmZZ57hP1UAMEAgBHBn\nLPQoxspCQ0NDQ0NlMplOp6usrCwuLi4pKamuru48ZEKj0dz65piLi4v6P7y9vSMiIiIjI0ND\nQxUK/gi1noqKCtHnuvHx8ZZ7XVMulw8ZMiQ9Pd2gXlZW1tLS4u7ubqF5paCrA2xM/1WdOnVq\nRkaGcMfRgoKC9PR08x5rAQBOgJ9mAEiai4tLSEhISEjI5MmTb61rtdqmpqb29na1Wu2gj0Od\nTFZWlmh93LhxFp136tSpAQEBwnpzczOB0BRNTU2iddGdQu+Ii4vLihUrRE8NSUlJiY6OtvP1\n7QBgZQRCABDh6ura86M7YAXFxcXCYmeYt+i8AwYMGDBggEWnkKbr16+L1s3ytnNISMisWbO+\n//57g7pGo0lNTV21apXpUwCA02ALZgCAvdPpdKLrRdnZxXFduHBBWOzbt6+5nrvOnTtX9NFu\nenr62bNnzTIFADgHAiEAwN5VVlaK7i9q9rMHYR1VVVWiB9CPHj3aXFO4urouX75c9FJycrLl\njp8BAIdDIAQA2DvR9aIyAqHD+u6774RFuVxu3h1foqKiRF8xra2t3bNnjxknAgCHRiAEANi7\nkpISYVGtVvv5+Vm/GZjozJkzGRkZwvr48eODgoLMO1dSUlLv3r2F9X379pWXl5t3LgBwUARC\nAIC9q66uFhZDQ0PZ/dXhFBUVffrpp3q93qDu7e39i1/8wuzTqdXqBx98UFjX6/UbN24UbkMK\nABLELqMAAHsnekSBj4+P9Tsx3e7duy10mKfw5D27Ul9f/8MPPxw4cECYBnv16vWrX/3K9AMn\nRI0dOzY9PT0/P9+gXlFRkZaWNmvWLEtM2hOVlZWbN2+23PghISFTpkyx3PgAnAaBEABg70QD\noYVilaV1daCik2lvb29tbW1ubr527Vp5eXlRUVF+fr4wCspkMjc3t9WrV0dGRlqoE7lcvnTp\n0jVr1rS1tRlc2rVr1+jRo/39/S00dffq6uqOHDliufFHjhxJIATQEwRCAIC902g0wqKHh4f1\nO5Ga9vb2X/3qV5YbPyAg4LHHHrP05kD+/v7z58/ftm2bQV2r1SYnJz/zzDOsPQYgZbxDCACw\na1qtVqvVCusEQocml8snTZr0yiuvWGdVhVbQAAAgAElEQVSr2OnTp4eGhgrrBQUFojvcAIB0\nEAgBAHZNdL2ozGGXjEImkw0ZMuSVV15Zvny5Uqm0zowuLi4rVqwQfRKYkpLS2NhonTYAwA4R\nCAEAdq29vV20zjI/x1VVVVVYWCh8qc+iQkNDZ8yYIaw3NjampqZasxMAsCsEQgCAXVOr1aL1\nrp4cwv7V19dv3br1tddeKygosOa88+bN69Onj7B+4sSJs2fPWrMTALAfbCoDALBrSqXSxcVF\neGScgwbCNWvW9O3b1xIjb9iwwbG2MK2trf3www8feOCBe++91zozKpXKZcuWffTRR8JLmzdv\nfvXVV93c3KzTiUwmi46Ofvrpp602HQB0hUAIALBrcrlcrVbfvHnToN7c3GyTfiRFLpePHz/e\nuO92dHQ0NTU1NjZWVFSIbgvUaceOHQqFQnQxpyVER0fHx8enp6cb1Gtqanbv3r1w4ULrtAEA\n9oNACACwd6KBUPQsCphXr169Vq5caeIgHR0dpaWl+/fvz87OFj2KcMeOHUOHDu3Xr5+JE/VQ\nUlLSmTNnhP/+pKWljR07tn///tZpAwDsBO8QAgDsneiGovX19dbvBEbo1atXZGTk448//txz\nz/Xu3Vv4gfb29m+++cZq/Xh6eiYlJQnrOp1u48aNwsXJAODcCIQAAHvn4+MjLJaWloo+boLd\nGjRo0O9+9ztXV1fhpZycHGue/ZCQkBAVFSWsl5eX79+/32ptAIA9YMkoAMDeRUREZGdnGxRb\nWlquXLkSHBxs6dnLysp++OEHYX3cuHHDhg2z9OxOJjg4+IEHHkhJSTGod3R0FBYWxsbGWqcN\nuVy+bNmy119/Xfhy465du2JiYvz9/a3TCQDYHE8IAQD2LjIyUrReXFxshdnz8/OzxFhhaqc0\nadIkDw8PYb2kpMSabQQGBs6dO1dYb2tr+/LLL3n4DEA6CIQAAHsXGhqqUIgsabFOICwtLRWt\nh4SEWGF25+Pm5jZmzBhhvbKy0sqdzJw5U/Q3MT8/PyMjw8rNAICtEAgBAPZOoVCEhYUJ67m5\nue3t7ZaeXTQQqtVqb29vS0/trMLDw4VF6x8s2atXrxUrVsjlcuGllJQUa77TCAA2RCAEADgA\n0VWjjY2NOTk5Fp336tWrN27cENZDQkJEgwR6QvS5nE3OEQkPD582bZqw3tjYmJqaav1+AMD6\nCIQAAAcQExMjWj9y5IhF5z158qRondPqTCF6+IStDpacP3++r6+vsH7ixImCggLr9wMAVkYg\nBAA4gAEDBohmsKKioqqqKsvN21UgHDlypOUmdXpKpVJYbGpqsslWLu7u7suWLRO9lJyc3NbW\nZuV+AMDKCIQAAAcgl8unTp0qemnr1q0WChKXL1++dOmSsO7l5TVw4EBLzCgRokcR6vX6lpYW\n6zcjk8mGDx8uus9NTU3Nnj17rN8PAFgTgRAA4Bji4uJUKpWwXlBQ8OOPP1pixt27d4vWY2Nj\nXVz4C9R4HR0dovXm5mYrd/KzxYsXi/7btW/fvoqKCuv3AwBWw99nAADH4ObmNn78eNFLqamp\ndXV15p2uvLy8qx1rrHZ+urPS6XSidRuuz/Ty8lq4cKGwrtPpNm3a1FXDAOAECIQAAIcxc+ZM\n0dfPWlpaPv30UzM+X+ro6Pjqq69EL/Xt25f1oiayw0Aok8kmTJgwaNAgYb2srOzAgQPW7wcA\nrINACABwGD4+PnPnzhW9VFxcvHbtWnPtVJmamnrx4kXRSwsWLODACRN1FQi1Wq2VO7mVXC5f\nsWKFQqEQXtq7d6/1+wEA6yAQAgAcybRp07o68qGsrGzt2rU3b940cYqDBw8ePHhQ9NKgQYNG\njBhh4viwzyeEMpksKChozpw5wjpLRgE4MQIhAMCRKBSKX/3qV+7u7qJXKyoqXn311aNHjxr3\nE3x7e/vmzZu7Wiwqk8kWLVrE40HTiT6Fk8lkDQ0NVu5EaNasWf369bN1FwBgPQRCAICDCQgI\nWLVqVVfBTKPRJCcnv/POO+fPn+/5cRR6vT4nJ+ftt9/u5qT7mTNnhoeHG9EwDHQVCCsrK63c\niZBCoVi+fDmxH4B0iP+JDACAPRs5cuTy5cs3bdrU1QfKysr+53/+p0+fPjExMTExMREREaIH\nRbS3t5eXl58/f/748ePdH3A/cuTIBQsWmKF1yGQeHh4uLi7Cp7j2EAhlMllkZOSUKVMOHTpk\n0VkqKys3b95s0Sk6TZgwgRsZALpBIAQAOKSJEye2t7d/9dVX3TwGrK2tTUtLS0tLUygU3t7e\n3t7eXl5earW6ubm5qalJo9FcuXKlJxuZhIaGPvroo5w9aC4uLi5+fn41NTUG9dLS0vb29q6e\nH1rTAw88kJubW19fb7kp6urqunkcbUaDBw8mEALohu3/zAUAwDiJiYl+fn6fffZZa2tr9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yQaCMPDwy20YtNAYGDg3LlzhfW2trYvv/xS\nr9ebd7qcnBzRHXQsQafTib6sCAAAnBKBEIDj0Wq1XQVCq/Uwc+bMkJAQYT0/P9/sqy6tvIyT\nvUYBAJAOAiEAx3P69GnRzTCtGQh79eq1YsUKuVwuvJSSktLY2GiuiRoaGgoKCsw1Wk+cP3++\nrq7OmjMCAABbYZdRAI7nxIkTovUBAwZYs43w8PBp06YJN5JpbGxMTU195JFHzDJLZmam6BrU\nqVOnDh8+3MTBd+7cWV5eblDU6/VZWVkzZ840cXAAAGD/CIQAHIxGozlz5oywHhER4ePjY+Vm\n5s+fn52dLXyeduLEiYSEhCFDhpg+RVfrRe+55x5fX18TB79y5YowEMpksszMTAIhAABSwJJR\nAA7m2LFjHR0dwvqkSZOs34y7u/uyZctELyUnJ7e1tZk4fnV1dWlpqbA+aNAg09OgTCaLjY0V\nXfVaVlZ29epV08cHAAB2jkAIwJHU19d/8803wrq7u3tsbKz1+5HJZMOHDx8zZoywXlNTs2fP\nHhMHT09PF62b6+x4Hx+fQYMGiV5iaxkAAKSAQAjAkaSmpopuJ5OQkKBUKq3fT6fFixerVCph\nfd++fRUVFUYPq9frRdeLuri4jB492uhhDYimWZlMlpGRYfbzMwAAgL0hEAJwGKdOncrKyhK9\nNHHiRCs3cysvL6+FCxcK6zqdbtOmTTqdzrhhy8rKqqurhfXo6OjevXsbN6bQ6NGjRVeNXr16\n1ZQ0CwAAHAKBEIBjOHfu3Keffip6adSoUf3797dyPwYmTJgguvayrKzswIEDxo3Z1XYy5lov\n2snT0zMqKkr0EqtGAQBwegRCAA6gsLDw448/1mq1wksKhWLRokXWb8mAXC5fsWKFQiGydfPe\nvXuNGFCn04nmMVdX15EjRxoxYDdYNQoAgGQRCAHYtfb29u++++4vf/mLaBqUyWRz584NCAiw\ncleigoKC5syZI6wbt2T03LlzDQ0NwvqoUaPM/rZkTEyMi4vIXwf19fUXLlww71wAAMCuEAgB\n2Cm9Xn/mzJk1a9bs3LmzqzQ4ePDge+65x8qNdWPWrFn9+vUzy1CW3l/0Vmq1eujQoaKXulq2\nCgAAnAOBEIB90el0RUVFKSkpL7/88l/+8hfRXVU6+fv7P/bYY6KPtmxFoVAsX75cdI+WO6LV\nanNzc4V1lUrVVXIzUVxcnGj95MmToqc+AgAA5yDyugsAmEVJScnmzZt78kmdTtfY2NjQ0NDQ\n0HDjxo2ungfeytPT85lnnvHy8jK5TTOLjIycMmXKoUOHTBkkLy+vpaVFWI+NjRV9TdF0I0eO\nVCgU7e3tBnWNRnP27Nnhw4dbYlIAAGBzBEIAllJdXd3N8z1T+Pv7P/3004GBgZYY3HQPPPBA\nbm5ufX290SN0tVCzq+d4pvPw8IiOjs7LyxNeyszMJBACAOCs7GipFQD0xODBg3//+98HBQXZ\nupEuubu7L1myxOivazSaM2fOCOs+Pj6iJ1uYS1dpMzc3t62tzXLzAgAAGyIQAnAYrq6uSUlJ\nv/3tbz09PW3dy22MGjUqJibGuO929dremDFjLPrC5IgRI1xdXYX11tbWU6dOWW5eAABgQwRC\nAA7AxcVl8uTJf/rTn2bMmGH6li3W8dBDD7m7uxvxReucRy+kVCq7WhrKXqMAADgr3iEEYNeC\ng4PHjRuXkJDg7e1t617ujI+Pz8KFC3u4rc7Prl+/fv78eWE9MDAwNDTUTK11KS4uLjs7W1g/\nc+ZMU1OTSqWydAMAAMDKCIQA7IVCoVAqlR4eHgEBAX379g0NDY2KivLz87N1X8abNGnSiRMn\nLl682POvZGZmitbHjh1rhUejw4cPVyqVra2tBvWOjo6cnJwJEyZYugEAAGBlcr1eb+seAAAA\nAAA2wDuEAAAAACBRBEIAAAAAkCgCIQAAAABIFIEQAAAAACSKQAgAAAAAEkUgBAAAAACJIhAC\nAAAAgEQRCAEAAABAogiEAAAAACBRBEIAAAAAkCgCIQAAAABIFIEQAAAAACSKQAgAAAAAEkUg\nBAAAAACJIhACAAAAgEQRCAEAAABAogiEAAAAACBRBEIAAAAAkCgCIQAAAABIFIEQAAAAACSK\nQAgAAAAAEkUgBAAAAACJIhACAAAAgEQRCAEAAABAogiEAAAAACBRBEIAAAAAkCgCIQAAAABI\nFIEQAAAAACRKYesGAMCu1dfXFxYWVlVVVVVVVVdXNzc3t7S0tLa2urm5qf/D19d34MCBkZGR\nQUFBcrnc1i0DAAD0lFyv19u6BwBO4vPPP09PTzcoBgUFvf7662af68CBA1u3bhW95Orq+pvf\n/CYqKsqU8a9du3bs2LFTp05VVlb2/FtqtTo6OnratGkDBgwwZfZOL774Yn19vbA+Y8aMpKQk\n08cXamxsfO6554T19evXi37+0KFDW7ZsEdbHjRv38MMPm7c3AyUlJe+8846wPnjwYNF/BAAA\nIIonhAAcz+HDh7tJg08++aQpabCoqOi77747e/asEd/VaDQZGRkZGRkRERHTp0+PjY21xAPD\n/fv3jx07NiwszOwj36nY2NitW7fqdDqDek5OzrJly1xdXS03dXZ2tmg9Li7OcpMCAOB8CIQA\nHMyxY8e+/PJL0UsKheLJJ58cMmSIcSNfvXr166+/zsvLM6G7/1VcXFxcXHzs2LGHH37Yx8fH\n9AFvpdfrN23a9Ic//KFXr17mHflOeXp6RkdHnz592qDe0tJy5syZmJgYC82r1+tFA6GLi8vo\n0aONGFCn012+fPnChQsXLlyoq6tramrSaDRNTU0KhcLT09PT09PLy2vAgAFDhgzp37+/iwuv\n3wMAnAeBEIAjOX78eHJysuglU9KgXq8/cuRIamqqVqs1rcH/o6Cg4PXXX1++fLlxKaUbFRUV\naWlp99xzj3mHNUJ8fLwwEMpksqysLMsFwkuXLtXU1AjrQ4cO7d279x0NVV1dvW/fvoyMjJaW\nFuHV9vb2lpaWa9euyWSy3NzcHTt2qFSqUaNG3XvvvUFBQcY1DwCAXSEQAnAYGRkZ//rXv0Tf\nfFYoFKtXrx46dKgRw7a1tX3++ec5OTkmNyhCo9GsX79+yZIliYmJ5h159+7do0ePDggIMO+w\nd2rkyJFKpbK1tdWgnpeX19raqlQqLTGpWdaLVldX79ixIycn547epW9qavrpp5+OHz8eGxs7\nd+7c4ODgnn8XAAA7xLoXAI7h5MmTn3/+eTdpMDo62ohhNRrN2rVre5IGFQqFv7//oEGD4uLi\nxowZEx0dHRER4eXl1ZNZtmzZItxux0RarXbz5s023xjs/7d35/FRVnffx2eGyTpm3wADSQgE\nMJCQBcKm7HFDFBARWSpYxd2qtfWubS223rUuoN5uFF9YWUSJgjdBUQlLQQyBkIQ1CyRkgZCE\nBLJNtpnMPH/keXhorjNhMvtkPu+/+vpdM+ccgi/Kl/O7znF3dxfuBGo0Gos03woJA6FSqRwz\nZoyRIxw/fvz111/Pyckx7Qeo1+uzs7Nff/31gwcP2v23AAAAc7BDCMAJ5Obmfvrpp4bS4BNP\nPGFaGmxtbV29evWFCxd6+ExoaGhKSsrYsWNDQ0OlJ8To9fr6+vqysrKcnJxjx45ptVpD4/zr\nX/9SqVSjRo0yYZ2G5OfnZ2VljWjHfy8AACAASURBVB8/3oJjmiAlJeXw4cPS+tGjR8eNG2fx\n6SorK6uqqqT10aNHe3p63vDrer1+586dO3fuNH8lGo1m06ZNhYWFy5Ytc3d3N39AAABsj0AI\nwNGdOHFi3bp10qMsZTKZUql8/PHHTUtZWq32k08+6SENxsTEzJ8/PyIiooeTQuVyeUBAQEBA\nwJgxYxYuXLhv375du3YJY6FOp/v8889XrVrl7e1twmoN2bp1a2xsrI+PjwXH7K0RI0b4+vo2\nNjZ2q58+fbqlpcWyv16Z2f2iBw8etEgavObo0aNarfaxxx7jsBkAgDMiEAJwaKdPn167dm1n\nZ6f0Ub9+/VauXDl69GjTRv7qq68KCgqEjzw8PObNmzdlypReXRqhUqlmz56dlJT0r3/9q7S0\nVPqBxsbG7du3L1682LQFC6nV6rS0tBUrVlhwzN5SKBRjx47ds2dPt3pnZ2dubu6kSZMsO50w\nEHp4eBjzX0JpaamhC0u6DBgwYMSIEYMHD/bx8fH29m5tbW1sbCwrKzt9+nTX0TJCubm5X3/9\n9QMPPGDM+gEAcCgEQgCOKz8//6OPPhJuuPXr1+/xxx+Pi4szbeTjx48fOHBA+GjgwIFPPfVU\ncHCwaSMPGDDghRdeeO+994qLi6VPDx48OGHChCFDhpg2uFBWVlZKSoppTbOWkpKSIg2EMpns\n6NGjlg2ENTU1Fy9elNbHjBlzw6ZNjUazdu1aQ229MTExc+fOFf7WTJw4USaTFRcXp6en5+fn\nC7++Z8+eqKgobkEEADgd+lsAOKiioqIPP/zQUBpcuXKlyWmwubl5w4YNwkfBwcHPPfecyWmw\ni4eHx7PPPhseHi59pNfrt2/fbs7gQps3b5ae82lLgwcPFl7DUFBQIG0lNYc5/aKHDh26cuWK\n8NHcuXNfeOGFnoN6dHT0b37zm+XLl7u5uQk/sG3bNsteWwIAgA0QCAE4onPnzn3wwQfCv14r\nFIqVK1fGx8ebPHh6enpzc7O07ufn9/zzz1vkHnlPT8/ly5cLXyorKiqqrq42eeTIyEhpsa6u\nLj093eQxzSeXy4Vn2+j1+mPHjllwImEg9Pb2vuH9kzqdbvfu3cJHixYtuuOOO4xsDx4/fvxL\nL70kvE7jypUrGRkZxgwCAIDjIBACcDglJSXvv/++cMtLoVA89thj5qTBqqoqYbOoXC43p1NU\nKjw83NDF8Ya6VY0xb9484V0XGRkZZWVlJg9rPkMHih49etRSU9TV1Ql/jYmJiUrlDd6AOHbs\nmPAu+/j4+ClTpvRqGREREY899pgwQO7Zs0d4+hEAAA6LQAjAsZSVlb333ns9pEHhrXfGy8jI\nEP6VferUqREREeaMLJWamipsL8zMzDS5t9Db23vhwoXSul6v37hxox3TSHBwcHR0tLReXFxs\nqFGztwxdF2lMv6hwo1KpVC5evLhXRwd1GTVqVNeLhd00NTWVlJT0djQAAOyIQAjAgVRUVLz7\n7rttbW3SRwqF4tFHHzUzDba1tR05ckRa9/HxmTNnjjkjC3l7ewsbKdVqdWFhocnDJiUlCU/U\nrKiosG/LoqFNwuzsbIuMLwx1vr6+MTExPX9Rr9cXFRVJ68nJyX5+fqYtZs6cOcJtyby8PNMG\nBADALgiEABzFhQsX1qxZ09LSIn3UlQYTExPNnOLo0aPCvcd58+ZZ/Lq8LrfddpuwLswnRpLL\n5Q899JDwNbYdO3YIGyNtIzk5WfjapEW6Ruvr64Wbb4YmvV5lZaVarZbWe9ssej1/f3/hi4vm\nRH0AAGyPQAjAIVy6dOndd98V/q1doVD8+te/Nj8NymSygwcPSos+Pj4pKSnmDy40aNAg4St/\n5gRCmUwWGBh47733SusajWbTpk16vd6cwU120003jRo1SlovLy835xydLub0iwp/2l5eXsIT\neownPOe2vr7enDEBALAxAiEA+6uurl69enVTU5P0kUKheOSRR5KSksyfpaKiQngkSUpKSr9+\n/cwfX0gulwv3kcrKyoSdscabNm2aMM/k5+dnZWWZM7I5DEVr8zcJheeLBgUFRUVF3fC7lZWV\n0mJ0dPQNtxZ71r9/f2mxqamJc2UAAE6Ei+kB2FlNTc3q1auFt9XJ5fIVK1YkJydbZCJDb7JN\nmDDBIuMbMm3atJCQEGm9tbXV09PT5GEVCsXSpUtff/11afzYunVrbGysj4+PyYObLC4uzsPD\nQ9qXe/To0bvvvtuE41u6NDY2nj17VlpPTk42ZsyGhgZpsedbB40hfP9Qr9c3NTWZ/GoiAAA2\nRiAEYE+1tbWrV68WdtnJ5fJHHnnEmIZAIwnfQAsPDxfeIG9BUVFRxuximSA8PDw1NfWHH37o\nVler1WlpaStWrLDGpD1zd3dPTEzMzMzsVq+qqrp48aLJP+q8vDxhH6yR/3kIN5/Nv2KktbVV\nWDd0cz0AAA6IllEAdnPlypXVq1dfvXpV+qhrb9CCaVCn0wn7RYXvvDmR2bNnC7cfs7Kyzpw5\nY/v1yKzTNSrsFw0LCzMyYQp3CAMCAkxeTxfhi5EKhcLLy8vMkQEAsBkCIQD7qK+vX716dV1d\nnfSRXC5fvny5oTsMTFNZWSk8X9Tidw/amJub25IlS4SPNm3aJPwlW9vw4cOFDZNHjx417bQb\nQ7d0jBs3zsge1ODg4CiJoKAgExZzvaqqKmnR39/f5M5YAABsj5ZRAHbQ0NDwzjvvXL58WfpI\nLpc//PDDFj/209B14c4eCGUy2YgRIyZMmCDt0qyrq9u5c+f8+fNtvB6FQjF27FjpjYh1dXXn\nz5834c2948ePC49pMf7l0hdeeKG3k96QXq8X7sFGR0dbfC4AAKyHHUIAttbY2LhmzZqamhrp\no640KLzM3Uznz5+XFlUqVWBgoMXnsr0FCxbcdNNN0vru3bvLy8ttvx5Ded60G+qF/aKDBg0S\nHvJpM2fOnCktLZXWhw4davO1AABgOgIhAJtqbm5es2bNpUuXhE/79etnpS07Yf4cPHhw3+ju\nU6lUDzzwgLSu1+s3bNhg+1sQBg0aNGDAAGk9Ozu7t4tpbW3Nz8+X1i34fqkJ9Hr9d999J633\n69fPIhdmAgBgMwRCALajVqvfffdd4aVwXbRa7WeffdbZ2WnxqVtaWqRFf39/i09kL+PGjYuN\njZXWKyoqpN2b1iaXy4WvgDY0NAhvj+jBiRMntFqttG6py0hMs3///uLiYmk9ISHB19fX9usB\nAMBkBEIANtLS0vLee+9VVFT0/LGysjLh3ov5s0uL3t7eFp/IXuRy+UMPPeTu7i59tGPHjtra\nWhuvx9CZQL09azQ3N1dajI6ONv9IGJMdO3bsq6++ktblcvntt99u+/UAAGAOAiEAW2hra3v/\n/feFFz9I7dq1S/jKnznUarW02MeuBwgODp4zZ460rtFoNm3aZNoJn+YsRni8Sk5OjvE7wO3t\n7adOnZLW7dgvmp+fv379euEP87bbbhs8eLDtlwQAgDkIhACsrr29/f333xdmPE9PT2lRp9Ot\nX7/eglcmaDQajUYjrfexQCiTyWbMmCHMJPn5+UeOHLHxYoSHA6nVauE7gUKnTp2S/sbJ5fKk\npCRzF9d7Op1u586d77//vrCFNTAw8N5777X9qgAAMBOBEIB1aTSaDz74QPjC1dixY//0pz8J\nM2FNTc22bdsstQZhv6isb7WMdlEoFEuXLhWelLN169bm5mZbLiYpKalfv37SuvFdo8LzRYcP\nH2779/Rqamr+8Y9/pKenCw/FcXNze+KJJ1QqlY1XBQCA+QiEAKzrypUrRUVF0npycvKKFSuC\ng4MXLlwo/OL+/ftPnz5tkTUIt3RkMlnfOGK0m8GDB8+cOVNab25uTktLs+VKVCrVqFGjpPW8\nvDzhhm03Go3m5MmT0rqhtxOtpKOj47vvvvvrX/8qvGRCJpMpFIoVK1bQLAoAcFIEQgB2kJSU\n9MgjjygUCplMNmHChPj4eOHHNmzYIHz3r7cMbd0Y2jl0dvfcc4/wzJXDhw8L71K3HmF4a2tr\nEya9bs6cOSNtG+7Xr9+YMWMss7gb0ev1WVlZf/7zn3fs2NHR0SH8jFKpXLlyJVdNAACcF4EQ\ngK0lJiZeS4MymUwuly9ZskR4r3p9ff0XX3xh/oweHh7XprteXw2EHh4eixcvFj7avHmzoWxj\nDfHx8cKWYGO6Ro8dOyYtxsbG2qYzs7i4+I033li/fv3Vq1cNfUalUj3zzDM2C6gAAFgDgRCA\nTSUmJv7617/u9mqZr6+voQCTnZ3d24sKpORyuTBFtLa2mjmyw4qNjU1JSZHWa2tr09PTbbYM\nNzc34e7ZyZMn29raeviiVqs9ceKEtG6D80Vra2vXrVv35ptvGuoR7RIdHf2nP/1pxIgR1l4P\nAABWRSAEYDsJCQnSNNglMTFRGGBkMtkXX3xRX19v5tTCQGiRflSHtWDBAuGvOiMj44a3QVqQ\n8LdVo9EcP368h28VFBRI47qbm1tcXJwlF/efOjo6vv3221dffTU7O7uHj7m7u993330vvvhi\nQECA9RYDAIBtEAgB2MiYMWMeffRRYRrs8uCDD/r7+0vrLS0tn3/+uZnX6AkPFDU/ZzoyHx+f\nBQsWSOs6nW7Dhg3C0zKtISYmRvjb2vPGr/B8UUMNqBZx/Pjxv/zlL7t27TJ0BFGXxMTEVatW\n3XnnnT38lwwAgBMhEAKwhfj4+J7ToEwm8/b2XrZsmfDRmTNn/v3vf5uzAGEmKS0ttfF17TY2\nfvx4YU9jeXn5nj17bLMGhUIh7PM8ffq0oR1anU6Xl5cnrScnJ1t4cTKZTCarq6v76KOPPvro\no7q6uh4+Fh0d/dJLL61cuTIwMNAaywAAwC6U9l4AgL4vLi7uscceUypv/AdObGzsbbfdduDA\nAemjr7/+euTIkWFhYaatYciQIdJNp7a2tqqqqgEDBpg2pvHKysp++uknaX3ChAnCixksRS6X\nL168+LXXXpNe87Bjx46EhITg4GDrzX5NSkrK7t27uxV1Ol1OTs6tt94q/XxhYaE0K3p6elrj\nZ5Wdnb1x48aeX2gcMGDA3Llz4+Li+uQ9JQAAF8cOIQDr8vf3X7lypTFpsMv9998vTCkajWb9\n+vUmNzpGR0cL6yUlJaYN2CunT5/OFrHB1KGhobNnz5bWOzo6vvjiC9tskIaHhwtTt6GfgLBf\nNCEhwc3NzYKram9v37hx47p163pIg/7+/suWLfvzn/8cHx9PGgQA9EkEQgDW5eHhYXwa7Pr8\n8uXLhX/5Li0t/f77701bxuDBg4XLsE0gNHReZXh4uA1mnzVrlnCi06dPHzlyxAYLkMvlwqNl\nCgsLGxoauhUN9Yta9nzR+vr6N9544+effzb0AW9v73nz5v3tb3+bNGmS8M4SAAD6Bv5PDoDD\nGTp06KxZs4SPvvvuu7KyMhPGVCqVERER0npeXl7Ph4hYhDAQqlQqPz8/a08tk8n69eu3dOlS\nYcbeunVrc3OzDdYgvKFer9dLLxssLi5ubGzsVlSpVBa84KGmpubNN9+srKw09IFbb7319ddf\nv/322y27JwkAgAMiEAJwRHPmzBk4cKC0rtPp1q9fL30jzhjCrtHm5ubc3FwTRjNedXW1dB9M\nJpOFh4fbrAsxMjJy+vTp0npzc3NaWpoNFhAUFDRs2DBpXXrWqLBfNCkpyVKnelZWVr711luG\nzo8JDg5+4YUXlixZIjyWFgCAvodACMARubm5LV++XNiqV1VVtW3bNhPGTEhIENaFZ9hYkHQT\nrMugQYOsOm83c+bMEd6bd/jw4fz8fBssQLhJWFJScn020+v1wkAo/K4J1Gr1Bx98IN2BlMlk\ncrl85syZr7766vDhwy0yFwAAToFACMBBDR48WHgaikwm27t3rwkZJioqSpjBioqKLl261Ov1\nGc1QIIyPj7fepFKenp6LFy8WPtq0aVNHR4e1F2Bol+/6o2XOnz8vvRzS399/6NCh5i9Ar9ev\nX79euDfo5eX1/PPPL1iwwN3d3fyJAABwIgRCAI7rzjvvjIyMFD76/PPPW1paejWaXC6fNm2a\n8NFXX31lpfM2L168eOHCBWnd19fXIiGnV0aPHi28yq+2tnbnzp3Wnl2lUgnvjbi+a1TYvpuc\nnGyR3tqffvrp1KlT0rqfn99vf/tbNgYBAK6JQAjAcSkUiuXLlwsP9rh69eqXX37Z2wHHjh0r\nfDcsPz//0KFDpizxRtLT04X1pKQku5xduXDhQuFPYPfu3RUVFdaeffz48dJiRUVFVVWVzHC/\nqEXOF21qavruu++k9dDQ0N///ve2Oe4VAAAHRCAE4ND69+8/d+5c4aOsrCxhfuiBu7v7xIkT\nhY/S0tKuXr3a6/X1qLy83NCJNUlJSZady0i+vr7z58+X1nU63caNG02+5tFIo0eP9vT0lNa7\nNgkrKipqa2u7PQoODhYeD9tbP/zwQ3t7e7eiUql86qmngoKCzB8fAAAnRSAE4OimT58eExMj\nfLRp0ybhAZ49mDVrloeHh7Te1tb2z3/+s7W11ZQlinR2dhraw+zfv7/t+0WvmTRpkvDAz7Ky\nsr1791p1ajc3N2ESPnr0aA/HyZjfL9rY2Lh//35pfc6cOf379zdzcAAAnBqBEICjk8vlDz/8\nsDDFqdXqDRs29Or1P39/f0Nn1ZSUlKxZs0atVpu40P+UlpZWXFwsfDRv3jybXTghJZfLly5d\nqlQqpY9+/PFHa88uPC+0urr6woUL1usXzcnJkd42OWjQIEPXXQIA4DoIhACcQFBQ0MKFC4WP\nTp06dfDgwV6NNn36dENXPpSVla1Zs6apqanXS/xP+/bt27dvn/DRsGHD4uLizBzfTGFhYXff\nfbe0bu2WUZlMFhMT4+/vL61/++231dXV3YoDBw4UXkfZW8KoOWnSJLu8xgkAgEPh/wsBOIeJ\nEyeOHj1a+CgtLa2mpsb4oZRK5cqVK4Uvs8lksoqKildfffXgwYOmpSOtVrt58+YeDry5//77\n7bg9eE1qaqpFslZvKRQK4Sah8PxPSx0nU1RUJK0LjzwFAMDVEAgBOIeuRkeVSiV91NHR8dln\nn/Uqv4WEhKxYscJQMFOr1Zs2bXrjjTfOnj1rfD+qXq/Pzc39+9//3sNN97NmzTJ0kYaNKZXK\nJUuW2CWapqSkGPlJ4SUZvVVSUiL9TQwLCwsJCTF/cAAAnJ3gHRIAcEx+fn4PPfTQunXrpI9K\nSkp+/PHHO++80/jR4uPjlyxZsnHjRkMfKCsre/vtt4OCghISEhISEoYMGSLsMNRqteXl5WfP\nns3MzOz5gvv4+Ph58+YZv0Jri46OnjJlivC0Fau6+eabBw4cWFlZ2fPHIiMjQ0NDzZ9OeJ1G\ndXX1ypUrzR9casSIEc8//7w1RgYAwBoIhACcSXJycl5e3vVXmV+zY8eOUaNGGXo5UGjy5Mla\nrfbLL7/sYRuwrq4uIyMjIyNDqVT6+fn5+fn5+vqqVKrW1taWlha1Wl1VVaXRaG441+DBgx95\n5BFHe2lt7ty5eXl59fX1tpxULpenpKRs3769549ZpF9UJpNduHDBIuMAANAnOdZfTQDghhYt\nWuTn5yet63S69evXG5PNrjd16tQnn3xSeIRpN1qttq6urqSkJC8v79ChQzk5OQUFBRUVFcbM\nGBkZ+fTTTxszi415enouWrTI9vMKXyPsxlJXNRIIAQDoAYEQgJNRqVTLli0TPqqsrPz22297\nO2BcXNwrr7wSFRVl9tLEZsyY8dJLLwlDrCMYM2ZMQkKCjScNDAwU3oV4zbBhwwICAsyfSK/X\n23j/EwAA50IgBOB8Ro0adeuttwof7dmzR3ikZM/CwsJ+97vfzZ8/39vb2+zV/X833XTTk08+\n+cADDwgv/XMcDz74oKEzV62n56NlLNUv2t7e3ttNYwAAXAqBEIBTuv/++4OCgqR1vV7/2Wef\ntba29nZAhUKRmpr6+uuv33333cKzTHslJCRk0aJFf//73+Pj480cygb8/f3nz59v40mTkpIM\n5WSFQpGYmGiRWcy/UhIAgL6NQAjAKXl6ej788MPCWxOuXLny1VdfmTast7f3nDlz3nzzzZUr\nV8bFxfX2rT+VShUfH79y5crXXntt6tSp7u7upi3D9m699dbo6Ghbzujt7W3oYsmRI0f6+PhY\nZBYCIQAAPZMbf8UWALiazs7O0tLS4uLi6urq6urqK1eutLe3t7e3d3Z2enl5qVQqb29vlUrl\n5+c3ZMiQoUOH9u/f3xEunQcAADASgRAAAAAAXBQtowAAAADgogiEAAAAAOCiCIQAAAAA4KII\nhAAAAADgogiEAAAAAOCiCIQAAAAA4KIIhAAAAADgogiEAAAAAOCiCIQAAAAA4KIIhAAAAADg\nogiEAAAAAOCiCIQAAAAA4KIIhAAAAADgogiEAAAAAOCiCIQAAAAA4KIIhAAAAADgogiEAAAA\nAOCiCIQAAAAA4KIIhAAAAADgogiEAAAAAOCiCIQAAAAA4KIIhAAAAADgogiEAAAAAOCiCIQA\nAAAA4KIIhAAAAADgogiEAAAAAOCiCIQAAAAA4KIIhAAAAADgogiEAAAAAOCilPZeAAA4DZ1O\nV1ZWVlxcXF1dXVNTU1dX19bW1tbWptVqPTw8PD09vby8QkJCQkND+/fvHxMTExoaKpfL7b1q\nx1VfX19YWHjp0qVLly7V1NS0tra2tbW1t7e7u7ur/p+AgIChQ4dGR0eHhYXxwwQAwOLker3e\n3msA0Ef8/ve/r6+vl9Znzpy5YMECa8zY3Nz84osvSutr16614Cwajeb48eNZWVlFRUVtbW3G\nf9HX1zc2NjYlJWX48OEKhVkdGXl5eR9//LE5I1jKiBEjnn/+eXNGuHz58s8//3zixInKykrj\nv6VSqWJjY6dPnx4VFWXO7F1ycnKE/5GsWrWqf//+5o8vtW7duuzs7G7FsLCw1157zRrTAQBg\nJHYIAVjdnj17xo0bFxERYe+F9Fp9ff1PP/106NChXuXAaxobGzMzMzMzM/39/adOnTpt2jRP\nT0+LL9KJFBUV7dq168yZMyZ8V61WHzly5MiRI0OGDJkxY0ZSUhIbhgAAmI9ACMDq9Hr9xo0b\n/+u//qtfv372XouxWlpa0tPTDxw4oNVqzR+tvr7+22+/3b17d2pq6syZM5VKl/uzt7q6+ptv\nvjl+/Lj5Q5WUlJSUlPz8888PP/ywv7+/+QP2PXq9/uWXX762Xa9SqVavXm3fJQEAHBaHygCw\nhYqKioyMDHuvwih6vT4nJ+fVV1/du3evRdLgNWq1evv27a+//npJSYkFh3Vwer3+3//+91//\n+leLpMFr8vPzX3vttZycHAuO2WeUl5cLm7cBAJAiEAKwkfT09MuXL9t7FTeg1Wo3bty4du3a\nxsZGK01RWVn55ptv/vDDD67wCndHR8fatWu/+OILjUZj8cHVavXatWv3799v8ZGdXV5enr2X\nAABwGi7XtgTAXjQazebNm5977jmHffWroaHh448/Pn/+vDEflsvlPj4+/v7+/v7+Xl5eDQ0N\nDQ0N9fX1ra2tN/yuXq/fvn17WVnZ8uXL3d3dzV64g1Kr1R988IEx26FKpdLf3z8gIMDf31+v\n17e2tra2ttbW1hoTy7ds2eLl5ZWSkmKJJfcFOp0uKyvL3qsAADgNAiEA28nPz8/Kyho/fry9\nFyJQX1+/evXq6urqnj8WGhqalJSUkJAQHh4ufCWyvb397NmzOTk5eXl5arW6h6FycnJaWlqe\neuopYzJhUFDQpEmTbvgxoUOHDkmLgwcPHjRokAmjDRgwwJiPtba2rl69+sKFCz18JjQ0NCUl\nZezYscL7OfR6fX19fVlZWU5OzrFjx3po3/3Xv/6lUqlGjRplzML6vDNnztTV1dl7FQAAp0Eg\nBGBTW7dujY2N9fHxsfdC/kNDQ8M777xTU1Nj6ANyuXzSpEnTpk27+eabe97h9PDwGDVq1KhR\no5YsWVJYWLhr167CwkJDHy4oKPjwww+ffvppNze3nlc4aNCgZcuW3fAXIiQMhAkJCXfddZdp\nA96QVqv95JNPekiDMTEx8+fPj4iI6OGHKZfLAwICAgICxowZs3Dhwn379u3atUsYC3U63eef\nf75q1Spvb2/L/AKcll6v//HHH+29CgCAM+EdQgA2pVar09LS7L2K/6DRaD7++OMe0mBERMTL\nL7+8dOnS8PBw4/tdFQrFyJEjn3/++V//+tc9HIZZUFDwxRdf9LH3Cb/66quCggLhIw8Pj0WL\nFr3wwguRkZHG/zBVKtXs2bP/+Mc/RkZGCj/Q2Ni4fft201bblxw5cqSoqMjeqwAAOBMCIQBb\ny8rKOn36tL1X8X/p9frNmzcbem9QoVA8+OCDL7/8sqEcckNyuXzs2LGrVq2aMmWKoc/88ssv\nfelklOPHjx84cED4aODAgX/+85+nTp1q2nukAwYMeOGFF6Kjo4VPDx486FLHt0qVlpZu2bLF\n3qsAADgZAiEAO9i8eXN7e7u9VyGTyWTHjh3LzMwUPlIqlY8//vi0adMUCnP/qPT09Fy0aNE9\n99xj6ANff/11VVWVmbM4gubm5g0bNggfBQcHP/fcc8HBweaM7+Hh8eyzz4aHh0sfdR3VY87g\nzqvrrpQ1a9YYc6YRAADXIxACsC7h3lpdXV16errN19JdU1OToR0VNze3p59+Oj4+3lJzyeXy\n2bNnz58/X/hUq9Vu2LChDzSOpqenNzc3S+t+fn7PP/+8Re6R9/T0XL58uTClFxUV3fBYoD6m\nvb09JyfnnXfeWbt2bVtbm72XAwBwPhwqA8C65s2b9+mnn0rvD8jIyBg7dmxERIRdVtVl27Zt\nwvQil8ufeeaZ4cOHW3zG1NRUvV6/bds26aPi4uJDhw5NnjzZ4pPaTFVVlbBZVC6XP/XUU2bu\nDV4vPDz89ttv37Vrl/TRgQMHFixYYKmJHFBbW1tVVdXFixcrKysrKyvPnTvX0dFh70UBAJwY\ngRCAdXl7ey9cuHDdunXdpViKrwAAIABJREFU6nq9fuPGjX/4wx/Mb8g0TVVVlaFm0TvuuMMa\nabBLamrqqVOnhCd/7Ny5c/z48Uqls/7JnJGRodPppPWpU6daPPmnpqZmZGRI77vPzMy87777\nbnhqqzPasmXLyZMnuVICAGBZtIwCsLqkpKTRo0dL6xUVFRkZGbZfT5cdO3YIWzTDw8Nnz55t\nvXnlcvnDDz/s4eEhfXT16tWDBw9ab2qramtrO3LkiLTu4+MzZ84ci0/n7e0tvNBSrVb3cM+H\nUzt//jxpEABgcQRCAFYnl8sfeughYQTasWNHbW2t7Zd05cqVnJwcaV2pVK5YscLae3RBQUGG\n2hr37t3rpG8SHj16VHhQ0Lx586x0PeBtt90mrHPvAgAAxiMQArCFwMDAe++9V1rXaDSbNm2y\nfQQ6ePCgcNKJEyfefPPNNljA5MmT+/fvL63X1NQ46QaXcG/Tx8cnJSXFSjMOGjTI19dXWicQ\nAgBgPAIhABuZNm2a8MTR/Pz8rKwsW65Ep9MdOnRI+Gj69Om2WYNcLp82bZrw0c8//2ybNVhQ\nRUVFWVmZtJ6SktKvXz8rTSqXy0eOHCmtl5WVcd4mAABGctajCwA4HYVCsXTp0tdff1167sjW\nrVtjY2N9fHxss5LS0tKGhgZpfeTIkQMGDLDNGmQy2fjx47dv3y6NLidPntRqtc51tEx2draw\nPmHCBKvOO23atJCQEGm9tbXV09PTqlPb3h133CE8FPd6DQ0NO3futM16AAB9gzP9hQOAswsP\nD09NTf3hhx+61dVqdVpa2ooVK2yzjLy8PGF9xowZtllAF09Pz4kTJ+7du7dbva2t7dy5cyNG\njLDlYsxUUlIiLYaHhwtvkLegqKioqKgoq07hOBITE2/4maqqKgIhAKBXaBkFYFOzZ88Wbulk\nZWWdOXPGNms4ffq0tOjt7T1q1CjbLOAaQxtop06dsvFKzKHT6YT9orb/eQIAgN4iEAKwKTc3\ntyVLlggfbdq0SXhMpWW1t7dfvHhRWh86dKhcLrf27N2Eh4cLD189f/68jVdijsrKSuFvnMXv\nHgQAABZHIARgayNGjBDujNXV1dmg2628vFx4vujQoUOtPbWUQqEYMmSItF5eXi684d0xCftF\nZQRCAACcAYEQgB0sWLDgpptuktZ3795dXl5u1akNjT9s2DCrzmuIMIh2dHRUVVXZfjGmEe5n\nqlSqwMBA2y8GAAD0CoEQgB2oVKoHHnhAWtfr9Rs2bLDq5tjly5elRaVSOXjwYOtN2oPo6Ghh\nXbhOx1RTUyMtDh482PYtuAAAoLc4ZRSAfYwbNy4rK0t6vktFRUVGRkZqaqqV5r1y5Yq06O/v\nb69rHoTX08sMrNMxtbS0SIv+/v62X4n50tPTvb29rTFyaWmpNYYFAMBMBEIA9iGXyx966KFV\nq1Z1dHR0e7Rjx47ExMTg4GBrzCsMWsL+VdtQqVTCel1dnY1XYjJhILRSrLI2QxcqAgDQV9Ey\nCsBugoOD58yZI61rNJpNmzYJj34xn/QieJnhVGYDbm5uws1JtVpt+8WYRrhULy8v268EAAD0\nFoEQgD3NmDFD+PJefn7+kSNHrDGjdENSZtcdQrlcLpxduE4HpNFoNBqNtE4gBADAKRAIAdiT\nQqFYunSp8PSRrVu3Njc3W3xGYdCy4w6hzEB3pbMEQmG/qMxpW0YBAHA1BEIAdjZ48OCZM2dK\n683NzWlpaRafThg+rdSeaiQ3NzdpUavV2n4lJjC0To4YBQDAKRAIAdjfPffcExQUJK0fPnz4\nzJkzlp3L3d1dWrTGVqTxhO/gCVOiAzK0uWpo5xAAADgUThkFYH8eHh6LFy9+//33pY82b978\n6quvClOcaYRD2fcEF2Ec9fDwsP1KTODh4aFQKKRXRzppIFy1apWhi0DMtG7dOo4wBQA4IHYI\nATiE2NjYlJQUab22tjY9Pd2CEwkPO7FjINRqtcKDT50lEMrlcuEmYWtrq+0XAwAAeotACMBR\nLFiwQBgtMjIyKioqLDVLYGCgtGjHllFDWVS4Tsck/F1zomszAABwZQRCAI7Cx8dnwYIF0rpO\np9uwYYO0KdE0wpcVr169aq9TPQ1dQC9cp2MSHihaX19v+5UAAIDeIhACcCDjx48fMWKEtF5e\nXr5nzx6LTBESEiIt6nS6kpISi4zfW2fPnhXWhet0TP7+/tJiaWmpfc9uBQAAxuBQGQAORC6X\nL168+LXXXpPedb5jx46EhITg4GAzp4iIiBDWz549K8yi1lZYWCgtKhSK8PBw2y/GNEOGDMnJ\nyelWbGtrq6qqGjBggLVnLysr++mnn6T1CRMmjBo1ytqzAwDg7AiEABxLaGjo7Nmzt2/f3q3e\n0dHxxRdfPPPMM2ZecDdo0KB+/fp1dnZ2qxcVFZkzrGl0Ot25c+ek9fDwcAserGpt0dHRwnpJ\nSYkNAuHp06eFp3dOmDDB2lMDANAH0DIKwOHMmjVLuD92+vTpI0eOmDm4UqkUbhKeP3/e9q8R\nlpaWtre3S+uGIpZjGjx4sFIp+OdF23ThlpaWCutOtMUKAIAdEQgBOJx+/fotXbpUuBO4detW\n808EjY+PlxY1Gk1mZqaZI/fWzz//LKwLV+iwDGXsvLw8rVZr7dmFgVClUvn5+Vl7agAA+gAC\nIQBHFBkZOX36dGm9ubk5LS3NzMHHjBkjrGdkZNjyHJSGhoasrCxp3cvLa9iwYTZbhkUItzSb\nm5tzc3OtOm91dXVDQ4O0Hh4ebmZrMQAALoJACMBBzZkzJyAgQFo/fPhwfn6+OSP3799f2E9Y\nU1Nz4sQJc0bulT179gg30BITE4UdmI4sISFBWD9w4IBV5z127JiwPmjQIKvOCwBAn0EgBOCg\nPD09Fy9eLHy0adMmM9/3mzJlirD+ww8/2GaTsKWlxVBYmjZtmg0WYFlRUVHCDFZUVHTp0iXr\nzWsoEDpXzy0AAHZEIATguEaPHp2cnCyt19bW7ty505yRU1JSPD09pfWSkpK9e/eaM7Ix9Hr9\n5s2bW1tbpY+GDh3qjLtbcrncUI796quvrJSxL168eOHCBWnd19d36NCh1pgRAIC+h0AIwKEt\nXLjQ29tbWt+9e3dFRYXJw3p4eMycOVP4aNu2beaMbIxffvlFeFOCTCa75557rDq19YwdO1b4\nO5Wfn3/o0CFrzJieni6sJyUlKRT8vxsAAEbh/zIBODRfX9/58+dL6zqdbuPGjTqdzuSRZ82a\n5ePjI61rtdp169YJb4OwiEuXLn355ZfCR7GxsSNGjLDSvNbm7u4+ceJE4aO0tLSrV69adrry\n8nJDJ9YkJSVZdi4AAPowAiEARzdp0iThqZtlZWXmtHd6enred999wkfV1dUff/xxW1ubyYMb\nUl1d/cEHHwhfgFQqlffff7/FZ7SlWbNmeXh4SOttbW3//Oc/hS2ypuns7DQUqvv370+/KAAA\nxiMQAnB0crl86dKlwoM3f/zxR3NGnjRpUmxsrPBRfn7+O++809jYaM743ZSUlPzjH/+ora0V\nPp0zZ87AgQMtOJ3t+fv7z549W/iopKRkzZo1arXaIhOlpaUVFxcLH82bN48LJwAAMB6BEIAT\nCAsLu/vuu6V1c1pGZTKZXC5ftmyZSqUSPi0vL//HP/5hqUMyc3NzV69ebSgRDRs2bNasWRaZ\nyL6mT59u6FCcsrKyNWvWNDU1mTnFvn379u3bJ3w0bNiwuLg4M8cHAMClEAgBOIfU1FRrbKD5\n+/s/8cQT/fr1Ez6tra197bXXtm7das7W1qVLlz744INPPvlEo9EIPxAcHLxy5cq+cQ6KUqlc\nuXKl8ARXmUxWUVHx6quvHjx40LQkr9VqN2/ebKhZVCaT3X///WwPAgDQK33h7x8AXIFSqVyy\nZIk1/ro/bNiwX/3qV4ae6nS6PXv2/PGPf9y9e3dLS0uvRq6trd28efOqVatOnjxp6DMqlerp\np58WHm/jpEJCQlasWGHod0qtVm/atOmNN944e/as8ddR6PX63Nzcv//97z3cdD9r1qzIyEgT\nFgwAgCsTvJMDAI4pOjp6ypQp+/fvt/jIKSkpnZ2dGzZsMBRRWlpavv76623btg0bNiw+Pj4u\nLi4kJET4Sb1eX1ZWdvz48ePHj1+8eLHneX19fX/zm98MGDDA3F+Ag4mPj1+yZMnGjRsNfaCs\nrOztt98OCgpKSEhISEgYMmSIcINUq9WWl5efPXs2MzOz597d+Pj4efPmWWDpAAC4GAIhAGcy\nd+7cvLy8+vp6i488ceJEDw+Pzz77zFBjp0wm0+l0hYWFhYWFW7dudXd39/X19fPz8/X19fLy\nam5ubmxsbGhoaGxs7OzsNGbG0NDQp59+OiwszHK/CAcyefJkrVb75Zdf9rANWFdXl5GRkZGR\noVQq/fz8un6YKpWqtbW1paVFrVZXVVX18NtxzeDBgx955JG+0XMLAICNEQgBOBNPT89FixZ9\n/PHH1hg8KSkpLCxs7dq1NTU1N/xwR0dHbW2toSNDb2jMmDEPP/ywl5eXaV93ClOnTg0MDPz0\n009veKmjVqutq6urq6szYZbIyMgnn3xSeN0FAAC4If49FYCTGTNmTEJCgpUGDw8Pf+WVV267\n7TbrnU3i7e29ZMmSxx9/vG+nwS5xcXGvvPJKVFSUlcafMWPGSy+95OfnZ6XxAQDo89ghBOB8\nHnzwwfz8fGtcHC+TyTw9PRcvXjxp0qStW7cauuzONAqFYsKECXPnzu1LR8jcUFhY2O9+97uM\njIxdu3b19lSeHtx0003Lli2Lj4+31IAAALgmAiEA5+Pv7z9//vzNmzdbb4rIyMiXXnrp7Nmz\nP/744+nTp40/D1PIw8NjwoQJqampQUFBllqhE1EoFKmpqZMnT87IyNi/f7+Z19OHhITMnDlz\n4sSJ7u7ullohAAAui0AIwCndeuuthw8ftuwOXjdyuTwmJiYmJqa+vj47Ozs7O7usrKxXF+h5\nenrGxMSMGzcuPj6e9OLt7T1nzpy77rrrxIkTmZmZhYWFN3y38HoqlWro0KHjx48fM2YM58cA\nAGApcjP/2RsAXEd7e3tJScn58+dramouX7589erVtra2jo4OrVbr7u7u7u7u5eUVFBQUFhYW\nFhYWHR09aNAgooshnZ2dpaWlxcXF1dXV1dXVV65caW9vb29v7+zs9PLyUqlU3t7eKpXKz89v\nyJAhQ4cO7d+/P5fOAwBgcQRCAAAAAHBR/NM1AAAAALgoAiEAAAAAuCgCIQAAAAC4KAIhAAAA\nALgoAiEAAAAAuCgCIQAAAAC4KAIhAAAAALgoAiEAAAAAuCgCIQAAAAC4KAIhAAAAALgoAiEA\nAAAAuCgCIQAAAAC4KAIhAAAAALgoAiEAAAAAuCgCIQAAAAC4KAIhAAAAALgoAiEAAAAAuCgC\nIQAAAAC4KAIhAAAAALgoAiEAAAAAuCgCIQAAAAC4KAIhAAAAALgoAiEAAAAAuCgCIQAAAAC4\nKAIhAAAAALgoAiEAAAAAuCilvRcAAP9Br9dfuXKluLi4uLj48uXLarW6paWlubm5ra3N09PT\n29vby8tLpVL5+flFRUVFRUWFh4crlfxRBgAAYAq5Xq+39xoAQCaTyS5evLhv376TJ0/W19cb\n/y2lUhkZGTlp0qTk5GR3d3cz11BZWblq1SppfeXKlYmJiWYOLrR169Y9e/Z0K6pUqtWrV9/w\nu0899ZRWqzX09Lbbblu8eLG567uREydOfPjhhz184MUXX4yJibnhOG+++WZxcbHl1mWUSZMm\nLVu2zMaTAgDgUPhndQB2ptfrT5w4sWfPnsLCQhO+rtVqz507d+7cua1bt06cOHHGjBlBQUEW\nX6Qzys3NXbRokUJh3VcDjh07ZtXxAQCAVREIAdhTY2Pjxo0bT5w4Yf5Qra2te/bs+fnnn+fN\nmzdlyhS5XG7+mE6tqampsLBw5MiR1ptCq9UeP37ceuM7Eb1e//LLL1/b3DZyjxcAALvjUBkA\ndpObm/uXv/zFImnwmvb29i1btqxZs+bq1asWHNZJZWdnW3X8/Pz81tZWq07hLMrLy3vV6gwA\ngIMgEAKwj8zMzE8++UStVltj8MLCwrfffruhocEagzuR3Nzczs5O641Pv+g1eXl59l4CAACm\noGUUgB3k5uZ+/vnnPX9GpVJFR0dHRET4+PioVCo3N7fW1tbW1tbm5uaLFy+WlZVduXKlh6/X\n1ta+++67v/3tb1UqlUXX7kzUanVBQUFsbKw1Bqdf9BqdTpeVlWXvVQAAYAoCIQBbKy0t/fTT\nTw0dcaxUKpOSkqZOnRoVFdXze4D19fWZmZm//PJLTU2N8AOVlZUff/zxiy++6MrvE2ZnZ1sp\nEBYUFLS0tFhj5Gv8/f2ttPguQ4cOtcg4Z86cqaurs8hQAADYGIEQgE3p9fotW7YYuixh5MiR\nS5cuNfKYUH9//zvvvPOOO+7IzMxMS0sThpOzZ88eOXIkJSXFrEU7s7y8vMWLF1vjqkYb9Ive\nfPPNjn8thF6v//HHH+29CgAATMQ7hABs6vDhw6WlpcJH991333PPPdfbSyPkcvnEiRNXrVoV\nHR0t/MC2bdva29t7u84+o6WlJT8/3+LDarVa3prrcuTIkaKiInuvAgAAExEIAdiOTqfbvn27\n8NG8efPuvPNOk3s7fX19n3nmmcjISOmj+vr6vXv3mjasM5JuBlrjrFFhv6g19iEdXGlp6ZYt\nW+y9CgAATEcgBGA7RUVFwpM/R40alZqaaubgXl5eTz75pKenp/SRSx2GKX3pLi8vT6PRWHYW\n6Y80JCQkJCTEsrM4Mr1en5OTs2bNGi7eAAA4NQIhANsRNhkqFIqHHnrIIue++Pn5zZ49W1qv\nqKhwnWsJx44d263S1tZ25swZC07R2dkp/a1MTk52kcN72tvbc3Jy3nnnnbVr17a1tdl7OQAA\nmMXl2nsA2IterxcGwltuuaW37w32YNq0aTt37pT+Nf3EiRNTpkyx1CyOLDo62s/Pr9tObHZ2\ndnx8vKWmEPaLJiUl9dVbKNra2qqqqi5evFhZWVlZWXnu3LmOjg57LwoAAMsgEAKwkZaWFuE2\nXVJSkgVnUSqVsbGx0obGkpISFwmEcrk8KSmp22uTx48f12g0bm5uFplC+uMNDQ0NDw+3yOAO\nZcuWLSdPnuRKCQBAH0bLKAAbEb49KJPJQkNDLTuR8Oa6xsZGy87iyJKTk7tV2tvbT506ZZHB\nhf2iSUlJfbJf9Pz586RBAEDfRiAEYCOGIpmvr69lJwoMDDR+9j5pyJAhAQEB3YqWOmu0oKBA\nrVZ3K1p2mxcAANgMgRCAjRiKZIYuqTeZMGG6VCDs6hrtVjxx4oRF7mN0nX5RAABcAe8QArAR\nQ5fU1dbWDhw40IIThYWF/eEPf+hW7JMNjT1ITk7OyMi4vtLR0XHy5ElpN2mvuFS/KAAAroBA\nCMBGvL29hfVTp07FxcVZcCKlUhkREWHBAZ1RZGRkYGDglStXri9mZ2ebGQgLCwtdql/0jjvu\naG5u7vkzDQ0NO3futM16AACwOAIhABuRvtXW5ZdffrnzzjsNPYVp5HJ5cnLyTz/9dH3x1KlT\nbW1tnp6eJg/rav2iiYmJN/xMVVUVgRAA4Lx4hxCAjYSGhqpUKmldo9GsXbvWIq+34XrSzUCN\nRnPixAmTB+zs7MzNze1WpF8UAACnRiAEYCNyuTwmJkb46Pz58++8887ly5dtvKS+bfDgwcHB\nwd2K5pw16mr9ogAAuAICIQDbmTx5sqFHZWVlq1at+uabb5qammy5pD5MeNbo6dOnW1tbTRvQ\n1fpFAQBwBQRCALYTGxs7YMAAQ081Gs1PP/30u9/97t133z148CDJ0Hxjx47tVtFqtcePHzdh\nKJ1OR78oAAB9D4EQgO3I5fJly5b1HCF0Ol1+fv6mTZteeumlNWvWZGRkXLhwQa/X22yRfUl4\neHhoaGi3omldo/SLAgDQJ3HKKACbGjJkyL333vvtt9/e8JN6vb6goKCgoEAmk/n6+o4cOXLk\nyJEjRozgPFLjdXWN7tq16/rimTNnWlpaDN0CYohd+kUrKys3b95s2TH79+8/Y8YMy44JAIDz\nIhACsLU77rhDq9X26qT+xsbGrKysrKwsmUw2YMCArnAYExNjzg0KLiI5OblbIOy6XH7ixInG\nD2KvftGrV68eOHDAsmOOGDGCQAgAwDUEQgC2JpfL77nnntDQ0C1btphwwMmlS5cuXbq0d+9e\nhUIxZMiQkSNH3nLLLZGRkQoFPfACN998c1hYWHV19fXF7OzsXgXCwsJC6f3sZt5xDwAAHAGB\nEIB9pKSkxMTEfPvtt0eOHNHpdCaMoNPpzp07d+7cufT0dE9Pz5EjRyYlJcXFxXl4eFh8tc6r\n64b677777vpifn6+Wq0WXgspJOwXvfnmmy2zRAAAYD/8gzoAuwkICFi+fPnf/va3mTNnmvlm\nYFtbW25u7qeffvriiy9+9tln3TbEXJx0K0/YAmqITqfLy8uTjsn5ogAA9AHsEAKws6CgoAUL\nFtx///3l5eUnTpzIz88/f/68aXuGMplMo9EcPnw4Kytr3Lhx999/v6+vr2VX64wGDhw4YMCA\nS5cuXV/Mzs7u4VrI6xUVFUmvAOF8UQAA+gYCIQCHIJfLIyIiIiIi7rnnnra2tqKiooKCgvz8\n/MrKShNG0+v1WVlZZ86cWbZsWVxcnMVX63SSk5PT09OvrxQUFDQ1Nfn4+Nzwu/SLAgDQhxEI\nATgcT0/PuLi4riDX2NiYn5/fFQ6vXr3aq3Gampo+/PDDBQsWzJw50zordRrSQKjX63Nzc2+7\n7baevyhsLrVZv2hsbOyzzz5rg4kAAHBZBEIADs3X1zclJSUlJUWv19fU1HSFw8LCwpaWFiNH\nSEtL0+v1s2bNsuo6HVz//v1vvvnmixcvXl/Mzs6+YSCkXxQAgL6NQAjAOcjl8rCwsLCwsKlT\np+p0uvLy8jNnzpw5c6a4uPiGLxx+8803ERERMTExtlmqY0pOTu4WCIuKihobG3t+zZJ+UQAA\n+jZOGQXgfBQKRWRk5F133fXb3/52zZo1Tz755OTJk93c3Ax9Xq/Xr1+/3oQ7D/sS6baeXq/P\nycnp4Sv27RcFAAA2QCAE4Nw8PT3j4+OXLl363//939OmTTP0satXr/773/+25cIcTVhY2KBB\ng7oVs7Oze/jK2bNn6RcFAKBvIxAC6CN8fX0ffPDBF154wcvLS/iBPXv2aDQaG6/KoUiz3Llz\n5+rr6w19XtovGhYWRr8oAAB9CYEQQJ8yfPjwZ599Vtg+2tjYWFhYaPslOQ7pDfV6vV6a+roI\n+0WTkpLoFwUAoC/hUBkAVtfS0rJ79+5uxX79+s2ePdsa0w0ZMuSuu+763//9X+mjwsLCUaNG\nWWNSpxASEhIREVFWVnZ9MTs7e8aMGdIPnzt3rrGxsVuRflEAAPoYAiEAq1MoFN9//720fscd\ndyiVVvlTaMaMGd9//720QbS4uNi0AfV6vdmL6sXI1tuFS0pK6hYIS0pKrly5EhgY2O2T9IsC\nAOAKaBkFYHUeHh7CHs7m5mbrzThixAhpXbrl1Y23t7ewbvy1h72lVqulRZVKZaXppF2jMlH2\n0+l00gNI6RcFAKDvIRACsDq5XO7j4yOtX7lyxXqTBgcHS4s3zHXCdcpksoaGBgusyeiR/fz8\nrDRdUFBQZGRkt6L0rFH6RQEAcBEEQgC2IAxaBQUF1pvR3d1dWvTw8Oj5W/369bvpppuk9R6O\n4jSTMBVbLxDKRJuEpaWltbW111foFwUAwEUQCAHYQkhIiLR46tQp680o3Hnz9/e/4Rd9fX2l\nxcLCQmu8RlhbW1tTUyOtWzUQCjf6rk+A9IsCAOA6CIQAbCE2NlZaLCkpqa6uttKMly9flhaN\nCVrSjkqZTFZTU1NZWWn+qrrJy8sT1qOjoy0+1zWBgYFDhgzpVry+a1TYLyp8+RAAADg7AiEA\nWxg9erR0f0mv12/fvt0a0zU2NpaUlEjrAwcOvOF34+PjhfWff/7Z3GX9J61Wu3//fmldqVQK\n87MFSdNdeXn5tb1KYb+oMT86AADgdAiEAGzBx8cnKipKWs/NzbXGZfGZmZnCDs/ExMQbfnfk\nyJHCM1H3799/8eJFCyzu/8nIyBBuY95yyy03fNfRTD10jQr7RZOTk+kXBQCgTyIQArCRhIQE\nYf2TTz65dOmSBSeqq6v77rvvpPWQkBBjjkXx8PC45ZZbpHWdTrdx48aOjg4LLFEmO3/+vHCR\nMpsc5unv7z906NBuxa6u0eLiYs4XBQDAdRAIAdjI1KlTAwICpPWWlpb33nuvqqrKIrOo1ep/\n/vOf7e3t0kfjxo0zcpvr7rvvFtbPnz//0UcfSe+7760LFy68//77wmw5cODAcePGmTm+MaRd\noxcuXKiurqZfFAAAl0IgBGAj7u7uc+fOFT66evXq3/72t/3795t5kmdNTc1bb71VWloqfeTv\n75+ammrkOBERERMmTBA+ys/PX7Nmjcln4ej1+kOHDr311luGbkR88MEHFQpb/MmcmJgojcdH\njx6lXxQAAJdCIARgO+PGjROe4SmTyTQazZYtW956662cnBydTtfbkZuamrZu3fqXv/zFUPfp\nwoULPT09jR/wvvvuE95kKJPJiouLX3vttZ07dzY3Nxs/oF6vLy4u/p//+Z8NGza0tbUJP5OY\nmDh8+HDjxzSHn5/fsGHDuhV3794tva6DflEAAPowpb0XAMCFyOXyFStWvP3229K31LoUFxcX\nFxf7+/uPGzcuOjo6Kiqqh4sitFptTU1NcXFxTk5OQUFBDzEyPj7e0BuMhvj7+//qV7/69NNP\nhZuWWq02PT39+++/HzNmTHJycmRkZEBAgHAbTavVlpeXnzt3LjMzs+eLKwYOHLhkyZJeLdJM\nSUlJRUVF11ekSZV+UQAA+jYCIQCbCgsLe+6559555x1DPZMymay+vv6nn37q+t8BAQF+fn5e\nXl4qlcrDw0Or1bZYoINFAAAEcklEQVS1tbW3tzc0NFRXVxuzl3jLLbc8+uijJjQ9Jicnt7a2\nbtq0ydAHOjs7jx071vXSnY+PT2hoqLe3t7e3t1KpbG1tbWlpaW5urqqq0mq1N5wrJCTkN7/5\njUql6u0izZGYmPjll1/23KZLvygAAH0bgRCArYWHhz/77LNr1qwRHv3SzdWrV69evWryXLfc\ncsuTTz4pvEbCGLfeemtra+s333xzw082NTU1NTWZNktQUNDzzz/fw16olfj6+g4fPrygoKCH\nz3AfPQAAfRvvEAKwg6ioqD/+8Y/Sd9gsSKFQ3H777eakwS6pqanPPvus9dLahAkT/vSnPwUF\nBVlp/J71/H5g//79BwwYYLPFAAAA2yMQArCP0NDQF1988cEHHzR0dos5IiMjX3nllXnz5pmZ\nBrvExsa++uqrFt8r8/HxeeKJJx5++GEvLy/Ljmy8hISEHg41pV8UAIA+j5ZRAHYjl8unTZsW\nHx9/4MCBQ4cOGTpppldGjBgxefLkpKQky17eoFKpHn300enTp+/bt+/YsWMmnIN6vbCwsGnT\npk2YMKFXB59ag4+Pz/Dhw/Pz84VPOV8UAIA+j0AIwM4CAwPvu+++e+6558SJE4cOHTp37lxr\na2uvRpDL5QMHDoyPj580aVJwcLCV1imTyaKjo6OjoxcsWHDw4MHjx49fuHChV8nQ19c3JiZm\n0qRJI0eOdJydt+TkZGEgpF8UAABXIDfzGmgAsCy9Xl9TU3P+/PnS0tKampqWlpaWlpauQzv1\ner23t7eXl5eXl5e3t7evr29ERERkZGR4eLiHh4ftl9re3l5WVlZcXFxeXt7c3NzS0qJWq1ta\nWjQajbe3t0qlUqlU3t7egYGB0dHRQ4cODQoKcpwcCAAAICMQAgAAAIDL4lAZAAAAAHBRBEIA\nAAAAcFEEQgAAAABwUQRCAAAAAHBRBEIAAAAAcFEEQgAAAABwUQRCAAAAAHBRBEIAAAAAcFEE\nQgAAAABwUQRCAAAAAHBRBEIAAAAAcFEEQgAAAABwUQRCAAAAAHBRBEIAAAAAcFEEQgAAAABw\nUQRCAAAAAHBRBEIAAAAAcFEEQgAAAABwUQRCAAAAAHBRBEIAAAAAcFEEQgAAAABwUQRCAAAA\nAHBRBEIAAAAAcFEEQgAAAABwUQRCAAAAAHBRBEIAAAAAcFEEQgAAAABwUQRCAAAAAHBRBEIA\nAAAAcFEEQgAAAABwUQRCAAAAAHBRBEIAAAAAcFEEQgAAAABwUQRCAAAAAHBRBEIAAAAAcFEE\nQgAAAABwUQRCAAAAAHBRBEIAAAAAcFEEQgAAAABwUQRCAAAAAHBRBEIAAAAAcFEEQgAAAABw\nUQRCAAAAAHBRBEIAAAAAcFEEQgAAAABwUQRCAAAAAHBRBEIAAAAAcFEEQgAAAABwUQRCAAAA\nAHBRBEIAAAAAcFEEQgAAAABwUQRCAAAAAHBRBEIAAAAAcFEEQgAAAABwUQRCAAAAAHBRBEIA\nAAAAcFEEQgAAAABwUQRCAAAAAHBR/wemBfR4eh5VLgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=10)\n",
    "gs = getGeneSummary(dfx)\n",
    "gs = gs[MutatedSamples >=1]\n",
    "set.seed(42)\n",
    "wordcloud::wordcloud(words = gs[, Hugo_Symbol], gs[,MutatedSamples], \n",
    "           min.freq = 1,\n",
    "           max.words=50, \n",
    "          random.order=FALSE, \n",
    "          rot.per=0.45, \n",
    "          colors=brewer.pal(8, \"Dark2\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_genecloud_01.svg'"
      ],
      "text/latex": [
       "'Saving : ../Suggesting\\_Variants/plots/Suggesting\\_TT-Other\\_Solid\\_Tumor\\_genecloud\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_genecloud_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_genecloud_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=10)\n",
    "\n",
    "out_svg = paste0('../Suggesting_Variants/plots/Suggesting_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_genecloud_01.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=9, width=12)\n",
    "gs = getGeneSummary(dfx)\n",
    "gs = gs[MutatedSamples >=1]\n",
    "set.seed(42)\n",
    "wordcloud::wordcloud(words = gs[, Hugo_Symbol], gs[,MutatedSamples], \n",
    "          min.freq = 1,\n",
    "          # max.words=200, \n",
    "          random.order=FALSE, \n",
    "          rot.per=0.45, \n",
    "          colors=brewer.pal(8, \"Dark2\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_genecloud_02.svg'"
      ],
      "text/latex": [
       "'Saving : ../Suggesting\\_Variants/plots/Suggesting\\_TT-Other\\_Solid\\_Tumor\\_genecloud\\_02.svg'"
      ],
      "text/markdown": [
       "'Saving : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_genecloud_02.svg'"
      ],
      "text/plain": [
       "[1] \"Saving : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_genecloud_02.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=28, repr.plot.height=28)\n",
    "set.seed(42)\n",
    "\n",
    "out_svg = paste0('../Suggesting_Variants/plots/Suggesting_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_genecloud_02.svg')\n",
    "paste0(\"Saving : \", out_svg)\n",
    "svg(out_svg, height=9, width=12)\n",
    "geneCloud(input = dfx, top=total_genes)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Drug Interactions\n",
    "- plot shows potential druggable gene categories along with upto top 5 genes involved in them. \n",
    "- One can also extract information on drug-gene interactions. \n",
    "- [check this documentation](https://bioconductor.org/packages/release/bioc/vignettes/maftools/inst/doc/maftools.html#98_drug-gene_interactions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PwAEAAABEXk5nF0BUff69916sqWl92jMnZ3DXrhcUFd3au3dRdna6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\nSUVORK5CYII=",
      "text/plain": [
       "Plot with title “Druggable categories”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.height=6, repr.plot.width=12)\n",
    "dfx_drug =maftools::drugInteractions(dfx, top=gf_sx )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_drugInteractions_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../Suggesting\\_Variants/plots/Suggesting\\_TT-Other\\_Solid\\_Tumor\\_drugInteractions\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_drugInteractions_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_drugInteractions_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../Suggesting_Variants/plots/Suggesting_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_drugInteractions_01.svg')\n",
    "paste0(\"Saving drug interaction svg to : \", out_svg)\n",
    "svg(out_svg, height=9, width=12)\n",
    "dfx_drug =maftools::drugInteractions(dfx, top=gf_sx)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Oncogenic Pathways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pathway alteration fractions\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Pathway  N n_affected_genes fraction_affected\n",
      " 1:      NOTCH 71                1        0.01408451\n",
      " 2: Cell_Cycle 15                0        0.00000000\n",
      " 3:      Hippo 38                0        0.00000000\n",
      " 4:        MYC 13                0        0.00000000\n",
      " 5:       NRF2  3                0        0.00000000\n",
      " 6:       PI3K 29                0        0.00000000\n",
      " 7:    RTK-RAS 85                0        0.00000000\n",
      " 8:   TGF-Beta  7                0        0.00000000\n",
      " 9:       TP53  6                0        0.00000000\n",
      "10:        WNT 68                0        0.00000000\n"
     ]
    }
   ],
   "source": [
    "options(repr.plot.width=6, repr.plot.height=4)\n",
    "op = OncogenicPathways(dfx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0eOpKen19XVNVjBxMQkLCxM2E4BAACg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\nOAAAgHIEDgAAoByBAwAAKPc/UAi02aVrMOUAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug interaction svg to : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_OncogenicPathway_01.svg'"
      ],
      "text/latex": [
       "'Saving drug interaction svg to : ../Suggesting\\_Variants/plots/Suggesting\\_TT-Other\\_Solid\\_Tumor\\_OncogenicPathway\\_01.svg'"
      ],
      "text/markdown": [
       "'Saving drug interaction svg to : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_OncogenicPathway_01.svg'"
      ],
      "text/plain": [
       "[1] \"Saving drug interaction svg to : ../Suggesting_Variants/plots/Suggesting_TT-Other_Solid_Tumor_OncogenicPathway_01.svg\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_svg = paste0('../Suggesting_Variants/plots/Suggesting_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_OncogenicPathway_01.svg')\n",
    "paste0(\"Saving drug interaction svg to : \", out_svg)\n",
    "\n",
    "svg(out_svg, height=9, width=12)\n",
    "op\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=3)\n",
    "# for( i in 1:length( op$data$Pathway ) ){\n",
    "#     pway = op$data$Pathway[i]\n",
    "#     PlotOncogenicPathways(dfx,pway, fullPathway = F, removeNonMutated = T)\n",
    "# }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathway_lst = c(\"Cell_Cycle\",\"Hippo\",\"MYC\",\"NOTCH\",\"NRF2\",\"PI3K\",\"RTK-RAS\",\"TGF-Beta\",\"TP53\",\"WNT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=8)\n",
    "# PlotOncogenicPathways(dfx,pathway_lst[1], fullPathway = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=3)\n",
    "# PlotOncogenicPathways(dfx,pathway_lst[2], fullPathway = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# options(repr.plot.width=10, repr.plot.height=9)\n",
    "# PlotOncogenicPathways(dfx,pathway_lst[4], fullPathway = T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the MAF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving MAF file to : ../Suggesting_Variants/maf_res/Suggesting_TT-Other_Solid_Tumor'"
      ],
      "text/latex": [
       "'Saving MAF file to : ../Suggesting\\_Variants/maf\\_res/Suggesting\\_TT-Other\\_Solid\\_Tumor'"
      ],
      "text/markdown": [
       "'Saving MAF file to : ../Suggesting_Variants/maf_res/Suggesting_TT-Other_Solid_Tumor'"
      ],
      "text/plain": [
       "[1] \"Saving MAF file to : ../Suggesting_Variants/maf_res/Suggesting_TT-Other_Solid_Tumor\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../Suggesting_Variants/maf_res/Suggesting_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)) )\n",
    "paste0(\"Saving MAF file to : \", out_file)\n",
    "maftools::write.mafSummary(dfx, out_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving drug Interactions data to : ../Suggesting_Variants/maf_res/Suggesting_TT-Other_Solid_Tumor_drugInteractions.tsv'"
      ],
      "text/latex": [
       "'Saving drug Interactions data to : ../Suggesting\\_Variants/maf\\_res/Suggesting\\_TT-Other\\_Solid\\_Tumor\\_drugInteractions.tsv'"
      ],
      "text/markdown": [
       "'Saving drug Interactions data to : ../Suggesting_Variants/maf_res/Suggesting_TT-Other_Solid_Tumor_drugInteractions.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving drug Interactions data to : ../Suggesting_Variants/maf_res/Suggesting_TT-Other_Solid_Tumor_drugInteractions.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../Suggesting_Variants/maf_res/Suggesting_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),'_drugInteractions.tsv')\n",
    "paste0(\"Saving drug Interactions data to : \", out_file)\n",
    "write.table(dfx_drug, out_file, sep=\"\\t\", row.names = F, quote = F)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving mutation data to : ../Suggesting_Variants/maf_res/Suggesting_TT-Other_Solid_Tumor_mutation_data.tsv'"
      ],
      "text/latex": [
       "'Saving mutation data to : ../Suggesting\\_Variants/maf\\_res/Suggesting\\_TT-Other\\_Solid\\_Tumor\\_mutation\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving mutation data to : ../Suggesting_Variants/maf_res/Suggesting_TT-Other_Solid_Tumor_mutation_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving mutation data to : ../Suggesting_Variants/maf_res/Suggesting_TT-Other_Solid_Tumor_mutation_data.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../Suggesting_Variants/maf_res/Suggesting_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),\"_mutation_data.tsv\")\n",
    "paste0(\"Saving mutation data to : \", out_file)\n",
    "write.table(df_ss, file = out_file, sep=\"\\t\", quote = F, row.names = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Saving clinical data to : ../Suggesting_Variants/maf_res/Suggesting_TT-Other_Solid_Tumor_clinical_data.tsv'"
      ],
      "text/latex": [
       "'Saving clinical data to : ../Suggesting\\_Variants/maf\\_res/Suggesting\\_TT-Other\\_Solid\\_Tumor\\_clinical\\_data.tsv'"
      ],
      "text/markdown": [
       "'Saving clinical data to : ../Suggesting_Variants/maf_res/Suggesting_TT-Other_Solid_Tumor_clinical_data.tsv'"
      ],
      "text/plain": [
       "[1] \"Saving clinical data to : ../Suggesting_Variants/maf_res/Suggesting_TT-Other_Solid_Tumor_clinical_data.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_file = paste0('../Suggesting_Variants/maf_res/Suggesting_','TT-',gsub(\"'\",\"\", gsub(' ','_',ttype)),\"_clinical_data.tsv\")\n",
    "paste0(\"Saving clinical data to : \", out_file)\n",
    "write.table(df2_ss, file =out_file, sep=\"\\t\", quote = F, row.names = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               _                           \n",
       "platform       x86_64-redhat-linux-gnu     \n",
       "arch           x86_64                      \n",
       "os             linux-gnu                   \n",
       "system         x86_64, linux-gnu           \n",
       "status                                     \n",
       "major          3                           \n",
       "minor          5.0                         \n",
       "year           2018                        \n",
       "month          04                          \n",
       "day            23                          \n",
       "svn rev        74626                       \n",
       "language       R                           \n",
       "version.string R version 3.5.0 (2018-04-23)\n",
       "nickname       Joy in Playing              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.5.0 (2018-04-23)\n",
       "Platform: x86_64-redhat-linux-gnu (64-bit)\n",
       "Running under: Fedora 24 (Workstation Edition)\n",
       "\n",
       "Matrix products: default\n",
       "BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n",
       " [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n",
       " [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n",
       " [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n",
       " [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n",
       "[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n",
       "\n",
       "attached base packages:\n",
       "[1] parallel  grid      stats     graphics  grDevices utils     datasets \n",
       "[8] methods   base     \n",
       "\n",
       "other attached packages:\n",
       " [1] wordcloud_2.6       RColorBrewer_1.1-2  knitr_1.22         \n",
       " [4] corrplot_0.84       maftools_1.8.0      Biobase_2.42.0     \n",
       " [7] BiocGenerics_0.28.0 vcd_1.4-4           ggpubr_0.2         \n",
       "[10] magrittr_1.5        xtable_1.8-4        mixOmics_6.6.2     \n",
       "[13] MASS_7.3-51.1       factoextra_1.0.5    FactoMineR_1.41    \n",
       "[16] DescTools_0.99.27   gtools_3.8.1        broom_0.5.1        \n",
       "[19] d3heatmap_0.6.1.2   pheatmap_1.0.12     reshape2_1.4.3     \n",
       "[22] mosaic_1.4.0        Matrix_1.2-15       mosaicData_0.17.0  \n",
       "[25] ggformula_0.9.0     ggstance_0.3.1      lattice_0.20-38    \n",
       "[28] xda_0.2             forcats_0.3.0       stringr_1.4.0      \n",
       "[31] dplyr_0.8.1         purrr_0.3.2         readr_1.3.1        \n",
       "[34] tidyr_0.8.3         tibble_2.1.1        ggplot2_3.1.1.9000 \n",
       "[37] tidyverse_1.2.1    \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       "  [1] readxl_1.2.0                uuid_0.1-2                 \n",
       "  [3] backports_1.1.4             circlize_0.4.5             \n",
       "  [5] NMF_0.21.0                  plyr_1.8.4                 \n",
       "  [7] igraph_1.2.4.1              repr_0.19.2                \n",
       "  [9] lazyeval_0.2.2              splines_3.5.0              \n",
       " [11] BiocParallel_1.16.6         GenomeInfoDb_1.18.2        \n",
       " [13] gridBase_0.4-7              digest_0.6.18              \n",
       " [15] foreach_1.4.4               htmltools_0.3.6            \n",
       " [17] BSgenome_1.50.0             cluster_2.0.7-1            \n",
       " [19] doParallel_1.0.14           mosaicCore_0.6.0           \n",
       " [21] ComplexHeatmap_1.20.0       Biostrings_2.50.2          \n",
       " [23] modelr_0.1.2                matrixStats_0.54.0         \n",
       " [25] rARPACK_0.11-0              colorspace_1.4-1           \n",
       " [27] rvest_0.3.2                 ggrepel_0.8.1              \n",
       " [29] xfun_0.5                    haven_2.1.0                \n",
       " [31] crayon_1.3.4                RCurl_1.95-4.11            \n",
       " [33] jsonlite_1.6                survival_2.43-3            \n",
       " [35] zoo_1.8-4                   iterators_1.0.10           \n",
       " [37] glue_1.3.1                  registry_0.5               \n",
       " [39] gtable_0.3.0                zlibbioc_1.28.0            \n",
       " [41] XVector_0.22.0              DelayedArray_0.8.0         \n",
       " [43] GetoptLong_0.1.7            shape_1.4.4                \n",
       " [45] scales_1.0.0                mvtnorm_1.0-8              \n",
       " [47] rngtools_1.3.1              bibtex_0.4.2               \n",
       " [49] Rcpp_1.0.1                  mclust_5.4.2               \n",
       " [51] flashClust_1.01-2           foreign_0.8-71             \n",
       " [53] stats4_3.5.0                htmlwidgets_1.3            \n",
       " [55] httr_1.4.0                  XML_3.98-1.16              \n",
       " [57] pkgconfig_2.0.2             manipulate_1.0.1           \n",
       " [59] labeling_0.3                tidyselect_0.2.5           \n",
       " [61] rlang_0.3.4                 munsell_0.5.0              \n",
       " [63] cellranger_1.1.0            tools_3.5.0                \n",
       " [65] cli_1.1.0                   generics_0.0.2             \n",
       " [67] evaluate_0.13               ggdendro_0.1-20            \n",
       " [69] nlme_3.1-137                leaps_3.0                  \n",
       " [71] xml2_1.2.0                  compiler_3.5.0             \n",
       " [73] rstudioapi_0.10             png_0.1-7                  \n",
       " [75] stringi_1.4.3               highr_0.8                  \n",
       " [77] RSpectra_0.13-1             IRdisplay_0.7.0            \n",
       " [79] pillar_1.4.0                lmtest_0.9-36              \n",
       " [81] GlobalOptions_0.1.0         cowplot_0.9.4              \n",
       " [83] data.table_1.12.2           bitops_1.0-6               \n",
       " [85] corpcor_1.6.9               rtracklayer_1.42.1         \n",
       " [87] GenomicRanges_1.34.0        R6_2.4.0                   \n",
       " [89] gridExtra_2.3               IRanges_2.16.0             \n",
       " [91] codetools_0.2-16            boot_1.3-20                \n",
       " [93] assertthat_0.2.1            SummarizedExperiment_1.12.0\n",
       " [95] pkgmaker_0.27               rjson_0.2.20               \n",
       " [97] withr_2.1.2                 GenomicAlignments_1.18.1   \n",
       " [99] Rsamtools_1.34.1            S4Vectors_0.20.1           \n",
       "[101] GenomeInfoDbData_1.2.0      expm_0.999-3               \n",
       "[103] hms_0.4.2                   IRkernel_0.8.15            \n",
       "[105] Cairo_1.5-9                 pbdZMQ_0.3-3               \n",
       "[107] scatterplot3d_0.3-41        lubridate_1.7.4            \n",
       "[109] base64enc_0.1-3             ellipse_0.4.1              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
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    "sessionInfo()"
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