{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Differential Splicing\n",
    "Based on the alternative splicing events, Isotools facilitates comparisons of samples and groups of samples. \n",
    "\n",
    "\n",
    "In this tutorial, we will apply the statistical test to find differential splicing between K562 and GM12878 (on chromosome 8), and how to interpret and depict the results. \n",
    "\n",
    "To run this tutorial, download the transcriptome object file 'PacBio_isotools_substantial_isotools.pkl' from [here](https://oc-molgen.gnz.mpg.de/owncloud/s/gjG9EPiQwpRAyg3) to a subfolder 'demonstration_dataset'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "This is isotools version 0.3.2rc6, but data has been pickled with version 0.3.2rc2, which may be incompatible\n"
     ]
    }
   ],
   "source": [
    "from isotools import Transcriptome\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "path='demonstration_dataset'\n",
    "isoseq=Transcriptome.load(f'{path}/PacBio_isotools_substantial_isotools.pkl')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical test for differential splicing\n",
    "![Slide](isotools_diffsplice.png)\n",
    "\n",
    "To run the test, we need to define the groups to compare, what types of splicing events are of interest, and the coverage threshold (over all samples) required to test an event. \n",
    "The resulting table contains the test statistics, including a description of the tested region, p-value, the transcript ids supporting outcome A or B of the event, group wise PSI and overdispersion values as well as sample wise coverage information.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10803/10803 [00:10<00:00, 1076.70genes/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18 differential splice sites in 16 genes for GM12878 vs K562\n"
     ]
    }
   ],
   "source": [
    "#perform the test:\n",
    "types_of_interest=['ES','ME','5AS','3AS','IR'] #ignore alternative TSS/PAS for now\n",
    "\n",
    "diff_splice=isoseq.altsplice_test(isoseq.groups(), types=types_of_interest, min_total=200).sort_values('pvalue').reset_index(drop=True)\n",
    "\n",
    "sig=diff_splice.padj<.1\n",
    "print(f'{sum(sig)} differential splice sites in {len(diff_splice.loc[sig,\"gene\"].unique())} genes for {\" vs \".join(isoseq.groups())}')\n",
    "pd.set_option('display.max_columns', None)\n",
    "diff_splice.head(18)\n",
    "diff_splice.to_csv(f'{path}/demonstration_dataset_differential_events.csv',index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>chrom</th>\n",
       "      <th>strand</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>splice_type</th>\n",
       "      <th>novel</th>\n",
       "      <th>padj</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>trA</th>\n",
       "      <th>trB</th>\n",
       "      <th>nmdA</th>\n",
       "      <th>nmdB</th>\n",
       "      <th>GM12878_PSI</th>\n",
       "      <th>GM12878_disp</th>\n",
       "      <th>K562_PSI</th>\n",
       "      <th>K562_disp</th>\n",
       "      <th>total_PSI</th>\n",
       "      <th>total_disp</th>\n",
       "      <th>GM12878_a_GM12878_in_cov</th>\n",
       "      <th>GM12878_a_GM12878_total_cov</th>\n",
       "      <th>GM12878_b_GM12878_in_cov</th>\n",
       "      <th>GM12878_b_GM12878_total_cov</th>\n",
       "      <th>GM12878_c_GM12878_in_cov</th>\n",
       "      <th>GM12878_c_GM12878_total_cov</th>\n",
       "      <th>K562_a_K562_in_cov</th>\n",
       "      <th>K562_a_K562_total_cov</th>\n",
       "      <th>K562_b_K562_in_cov</th>\n",
       "      <th>K562_b_K562_total_cov</th>\n",
       "      <th>K562_c_K562_in_cov</th>\n",
       "      <th>K562_c_K562_total_cov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RIPK2</td>\n",
       "      <td>ENSG00000104312.8</td>\n",
       "      <td>chr8</td>\n",
       "      <td>+</td>\n",
       "      <td>89780160</td>\n",
       "      <td>89786592</td>\n",
       "      <td>ES</td>\n",
       "      <td>True</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>[16, 8, 26, 34, 41, 46, 32, 36, 39, 40, 42, 47...</td>\n",
       "      <td>[2, 0, 12, 9, 13, 4, 5, 10, 11, 14, 15, 17, 18...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001595</td>\n",
       "      <td>0.994244</td>\n",
       "      <td>8.728420e-08</td>\n",
       "      <td>0.296339</td>\n",
       "      <td>1.059189e-05</td>\n",
       "      <td>0.719659</td>\n",
       "      <td>0.103622</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>218</td>\n",
       "      <td>220</td>\n",
       "      <td>224</td>\n",
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       "      <td>34</td>\n",
       "      <td>104</td>\n",
       "      <td>41</td>\n",
       "      <td>153</td>\n",
       "      <td>34</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ASAH1</td>\n",
       "      <td>ENSG00000104763.20</td>\n",
       "      <td>chr8</td>\n",
       "      <td>-</td>\n",
       "      <td>18067104</td>\n",
       "      <td>18067133</td>\n",
       "      <td>IR</td>\n",
       "      <td>True</td>\n",
       "      <td>0.007126</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>[11, 13, 80, 99, 69, 70, 101, 79, 81, 83, 88, ...</td>\n",
       "      <td>[12, 14, 68, 42, 44]</td>\n",
       "      <td>0.003788</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.512441</td>\n",
       "      <td>1.078330e-03</td>\n",
       "      <td>0.010335</td>\n",
       "      <td>3.306517e-07</td>\n",
       "      <td>0.231512</td>\n",
       "      <td>0.069018</td>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>35</td>\n",
       "      <td>39</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RECQL4</td>\n",
       "      <td>ENSG00000160957.15</td>\n",
       "      <td>chr8</td>\n",
       "      <td>-</td>\n",
       "      <td>144511789</td>\n",
       "      <td>144511910</td>\n",
       "      <td>5AS</td>\n",
       "      <td>False</td>\n",
       "      <td>0.007126</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>[22, 31, 6, 20, 11, 72, 13, 33, 8, 79, 2, 105,...</td>\n",
       "      <td>[114, 147, 124, 38, 143, 205, 123, 152, 197, 1...</td>\n",
       "      <td>0.216585</td>\n",
       "      <td>0.191781</td>\n",
       "      <td>0.012575</td>\n",
       "      <td>9.366640e-07</td>\n",
       "      <td>0.401486</td>\n",
       "      <td>1.211899e-04</td>\n",
       "      <td>0.240155</td>\n",
       "      <td>0.041071</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>278</td>\n",
       "      <td>666</td>\n",
       "      <td>127</td>\n",
       "      <td>301</td>\n",
       "      <td>177</td>\n",
       "      <td>482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SNHG6</td>\n",
       "      <td>ENSG00000245910.9</td>\n",
       "      <td>chr8</td>\n",
       "      <td>-</td>\n",
       "      <td>66922392</td>\n",
       "      <td>66922613</td>\n",
       "      <td>IR</td>\n",
       "      <td>False</td>\n",
       "      <td>0.011708</td>\n",
       "      <td>0.000409</td>\n",
       "      <td>[1, 11, 17, 29, 16, 15, 7, 14, 19, 23, 24, 25,...</td>\n",
       "      <td>[4, 10]</td>\n",
       "      <td>0.994152</td>\n",
       "      <td>0.022727</td>\n",
       "      <td>0.255022</td>\n",
       "      <td>1.506108e-05</td>\n",
       "      <td>0.021488</td>\n",
       "      <td>1.779160e-06</td>\n",
       "      <td>0.128672</td>\n",
       "      <td>0.014606</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>13</td>\n",
       "      <td>45</td>\n",
       "      <td>21</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SMIM19</td>\n",
       "      <td>ENSG00000176209.12</td>\n",
       "      <td>chr8</td>\n",
       "      <td>+</td>\n",
       "      <td>42541705</td>\n",
       "      <td>42546468</td>\n",
       "      <td>5AS</td>\n",
       "      <td>False</td>\n",
       "      <td>0.011708</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>[3, 4, 21]</td>\n",
       "      <td>[2, 5, 9, 26, 7, 17, 22, 24, 27, 28]</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009901</td>\n",
       "      <td>0.097171</td>\n",
       "      <td>1.705951e-02</td>\n",
       "      <td>0.593322</td>\n",
       "      <td>2.732809e-05</td>\n",
       "      <td>0.304097</td>\n",
       "      <td>0.074889</td>\n",
       "      <td>10</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>26</td>\n",
       "      <td>43</td>\n",
       "      <td>38</td>\n",
       "      <td>63</td>\n",
       "      <td>25</td>\n",
       "      <td>44</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>RNF170</td>\n",
       "      <td>ENSG00000120925.16</td>\n",
       "      <td>chr8</td>\n",
       "      <td>-</td>\n",
       "      <td>42861855</td>\n",
       "      <td>42870003</td>\n",
       "      <td>ES</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.986423</td>\n",
       "      <td>[9, 23, 21, 34, 37, 18, 29]</td>\n",
       "      <td>[3, 4, 20, 16, 15, 10, 13, 8, 27, 33, 39, 6, 1...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034682</td>\n",
       "      <td>0.852105</td>\n",
       "      <td>5.859088e-05</td>\n",
       "      <td>0.861528</td>\n",
       "      <td>8.070359e-06</td>\n",
       "      <td>0.856429</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>36</td>\n",
       "      <td>51</td>\n",
       "      <td>62</td>\n",
       "      <td>22</td>\n",
       "      <td>26</td>\n",
       "      <td>35</td>\n",
       "      <td>41</td>\n",
       "      <td>24</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>POLB</td>\n",
       "      <td>ENSG00000070501.12</td>\n",
       "      <td>chr8</td>\n",
       "      <td>+</td>\n",
       "      <td>42361365</td>\n",
       "      <td>42369270</td>\n",
       "      <td>ES</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994807</td>\n",
       "      <td>[7, 11, 15, 20, 36, 40, 43]</td>\n",
       "      <td>[1, 0, 2, 12, 3, 10, 23, 24, 27, 35, 5, 9, 13,...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015810</td>\n",
       "      <td>0.894198</td>\n",
       "      <td>1.207652e-05</td>\n",
       "      <td>0.894023</td>\n",
       "      <td>2.830737e-04</td>\n",
       "      <td>0.893994</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>61</td>\n",
       "      <td>87</td>\n",
       "      <td>99</td>\n",
       "      <td>33</td>\n",
       "      <td>36</td>\n",
       "      <td>35</td>\n",
       "      <td>37</td>\n",
       "      <td>33</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>SCRIB</td>\n",
       "      <td>ENSG00000180900.20</td>\n",
       "      <td>chr8</td>\n",
       "      <td>-</td>\n",
       "      <td>143804825</td>\n",
       "      <td>143804954</td>\n",
       "      <td>5AS</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996899</td>\n",
       "      <td>[31, 11, 94, 79, 56, 103, 142, 17, 155, 165, 4...</td>\n",
       "      <td>[5, 0, 14, 27, 36, 4, 18, 58, 6, 67, 12, 29, 3...</td>\n",
       "      <td>0.023810</td>\n",
       "      <td>0.085366</td>\n",
       "      <td>0.852941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.854052</td>\n",
       "      <td>3.572960e-05</td>\n",
       "      <td>0.854019</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>29</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>110</td>\n",
       "      <td>40</td>\n",
       "      <td>52</td>\n",
       "      <td>80</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>CHCHD7</td>\n",
       "      <td>ENSG00000170791.18</td>\n",
       "      <td>chr8</td>\n",
       "      <td>+</td>\n",
       "      <td>56214667</td>\n",
       "      <td>56216432</td>\n",
       "      <td>3AS</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>[0, 7, 3, 6, 18, 34, 39, 2, 26, 9, 37, 50, 56,...</td>\n",
       "      <td>[1, 4, 5, 19, 27, 42, 48, 54, 33, 35, 38, 41, ...</td>\n",
       "      <td>0.026565</td>\n",
       "      <td>0.050633</td>\n",
       "      <td>0.229832</td>\n",
       "      <td>5.468918e-06</td>\n",
       "      <td>0.231743</td>\n",
       "      <td>4.678234e-06</td>\n",
       "      <td>0.230432</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>22</td>\n",
       "      <td>95</td>\n",
       "      <td>22</td>\n",
       "      <td>110</td>\n",
       "      <td>56</td>\n",
       "      <td>230</td>\n",
       "      <td>19</td>\n",
       "      <td>93</td>\n",
       "      <td>15</td>\n",
       "      <td>53</td>\n",
       "      <td>24</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>PCM1</td>\n",
       "      <td>ENSG00000078674.20</td>\n",
       "      <td>chr8</td>\n",
       "      <td>+</td>\n",
       "      <td>17993619</td>\n",
       "      <td>18006262</td>\n",
       "      <td>3AS</td>\n",
       "      <td>True</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>[68, 15, 1, 6, 160, 31, 35, 11, 154, 253, 32, ...</td>\n",
       "      <td>[43, 237, 271, 411, 335, 242, 258, 387, 266, 2...</td>\n",
       "      <td>0.153310</td>\n",
       "      <td>0.156250</td>\n",
       "      <td>0.102550</td>\n",
       "      <td>1.556612e-05</td>\n",
       "      <td>0.098836</td>\n",
       "      <td>4.151420e-05</td>\n",
       "      <td>0.100326</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>51</td>\n",
       "      <td>11</td>\n",
       "      <td>88</td>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>7</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gene             gene_id chrom strand      start        end  \\\n",
       "0     RIPK2   ENSG00000104312.8  chr8      +   89780160   89786592   \n",
       "1     ASAH1  ENSG00000104763.20  chr8      -   18067104   18067133   \n",
       "2    RECQL4  ENSG00000160957.15  chr8      -  144511789  144511910   \n",
       "3     SNHG6   ENSG00000245910.9  chr8      -   66922392   66922613   \n",
       "4    SMIM19  ENSG00000176209.12  chr8      +   42541705   42546468   \n",
       "..      ...                 ...   ...    ...        ...        ...   \n",
       "142  RNF170  ENSG00000120925.16  chr8      -   42861855   42870003   \n",
       "143    POLB  ENSG00000070501.12  chr8      +   42361365   42369270   \n",
       "144   SCRIB  ENSG00000180900.20  chr8      -  143804825  143804954   \n",
       "145  CHCHD7  ENSG00000170791.18  chr8      +   56214667   56216432   \n",
       "146    PCM1  ENSG00000078674.20  chr8      +   17993619   18006262   \n",
       "\n",
       "    splice_type  novel      padj    pvalue  \\\n",
       "0            ES   True  0.000177  0.000001   \n",
       "1            IR   True  0.007126  0.000135   \n",
       "2           5AS  False  0.007126  0.000145   \n",
       "3            IR  False  0.011708  0.000409   \n",
       "4           5AS  False  0.011708  0.000471   \n",
       "..          ...    ...       ...       ...   \n",
       "142          ES  False  1.000000  0.986423   \n",
       "143          ES  False  1.000000  0.994807   \n",
       "144         5AS  False  1.000000  0.996899   \n",
       "145         3AS  False  1.000000  1.000000   \n",
       "146         3AS   True  1.000000  1.000000   \n",
       "\n",
       "                                                   trA  \\\n",
       "0    [16, 8, 26, 34, 41, 46, 32, 36, 39, 40, 42, 47...   \n",
       "1    [11, 13, 80, 99, 69, 70, 101, 79, 81, 83, 88, ...   \n",
       "2    [22, 31, 6, 20, 11, 72, 13, 33, 8, 79, 2, 105,...   \n",
       "3    [1, 11, 17, 29, 16, 15, 7, 14, 19, 23, 24, 25,...   \n",
       "4                                           [3, 4, 21]   \n",
       "..                                                 ...   \n",
       "142                        [9, 23, 21, 34, 37, 18, 29]   \n",
       "143                        [7, 11, 15, 20, 36, 40, 43]   \n",
       "144  [31, 11, 94, 79, 56, 103, 142, 17, 155, 165, 4...   \n",
       "145  [0, 7, 3, 6, 18, 34, 39, 2, 26, 9, 37, 50, 56,...   \n",
       "146  [68, 15, 1, 6, 160, 31, 35, 11, 154, 253, 32, ...   \n",
       "\n",
       "                                                   trB      nmdA      nmdB  \\\n",
       "0    [2, 0, 12, 9, 13, 4, 5, 10, 11, 14, 15, 17, 18...  0.000000  0.001595   \n",
       "1                                 [12, 14, 68, 42, 44]  0.003788  0.000000   \n",
       "2    [114, 147, 124, 38, 143, 205, 123, 152, 197, 1...  0.216585  0.191781   \n",
       "3                                              [4, 10]  0.994152  0.022727   \n",
       "4                 [2, 5, 9, 26, 7, 17, 22, 24, 27, 28]  0.000000  0.009901   \n",
       "..                                                 ...       ...       ...   \n",
       "142  [3, 4, 20, 16, 15, 10, 13, 8, 27, 33, 39, 6, 1...  0.000000  0.034682   \n",
       "143  [1, 0, 2, 12, 3, 10, 23, 24, 27, 35, 5, 9, 13,...  0.000000  0.015810   \n",
       "144  [5, 0, 14, 27, 36, 4, 18, 58, 6, 67, 12, 29, 3...  0.023810  0.085366   \n",
       "145  [1, 4, 5, 19, 27, 42, 48, 54, 33, 35, 38, 41, ...  0.026565  0.050633   \n",
       "146  [43, 237, 271, 411, 335, 242, 258, 387, 266, 2...  0.153310  0.156250   \n",
       "\n",
       "     GM12878_PSI  GM12878_disp  K562_PSI     K562_disp  total_PSI  total_disp  \\\n",
       "0       0.994244  8.728420e-08  0.296339  1.059189e-05   0.719659    0.103622   \n",
       "1       0.512441  1.078330e-03  0.010335  3.306517e-07   0.231512    0.069018   \n",
       "2       0.012575  9.366640e-07  0.401486  1.211899e-04   0.240155    0.041071   \n",
       "3       0.255022  1.506108e-05  0.021488  1.779160e-06   0.128672    0.014606   \n",
       "4       0.097171  1.705951e-02  0.593322  2.732809e-05   0.304097    0.074889   \n",
       "..           ...           ...       ...           ...        ...         ...   \n",
       "142     0.852105  5.859088e-05  0.861528  8.070359e-06   0.856429    0.000020   \n",
       "143     0.894198  1.207652e-05  0.894023  2.830737e-04   0.893994    0.000007   \n",
       "144     0.852941           NaN  0.854052  3.572960e-05   0.854019    0.000025   \n",
       "145     0.229832  5.468918e-06  0.231743  4.678234e-06   0.230432    0.000002   \n",
       "146     0.102550  1.556612e-05  0.098836  4.151420e-05   0.100326    0.000007   \n",
       "\n",
       "     GM12878_a_GM12878_in_cov  GM12878_a_GM12878_total_cov  \\\n",
       "0                          76                           76   \n",
       "1                          13                           31   \n",
       "2                           1                          150   \n",
       "3                           5                           26   \n",
       "4                          10                           37   \n",
       "..                        ...                          ...   \n",
       "142                        10                           10   \n",
       "143                        10                           10   \n",
       "144                        29                           34   \n",
       "145                        22                           95   \n",
       "146                         3                           40   \n",
       "\n",
       "     GM12878_b_GM12878_in_cov  GM12878_b_GM12878_total_cov  \\\n",
       "0                         218                          220   \n",
       "1                          23                           35   \n",
       "2                           1                            9   \n",
       "3                          13                           45   \n",
       "4                           2                           48   \n",
       "..                        ...                          ...   \n",
       "142                        31                           36   \n",
       "143                        55                           61   \n",
       "144                         0                            0   \n",
       "145                        22                          110   \n",
       "146                         2                           26   \n",
       "\n",
       "     GM12878_c_GM12878_in_cov  GM12878_c_GM12878_total_cov  \\\n",
       "0                         224                          225   \n",
       "1                          39                           81   \n",
       "2                           0                            1   \n",
       "3                          21                           82   \n",
       "4                           0                           68   \n",
       "..                        ...                          ...   \n",
       "142                        51                           62   \n",
       "143                        87                           99   \n",
       "144                         0                            0   \n",
       "145                        56                          230   \n",
       "146                         7                           51   \n",
       "\n",
       "     K562_a_K562_in_cov  K562_a_K562_total_cov  K562_b_K562_in_cov  \\\n",
       "0                    34                    104                  41   \n",
       "1                     0                     51                   1   \n",
       "2                   278                    666                 127   \n",
       "3                     1                     70                   3   \n",
       "4                    26                     43                  38   \n",
       "..                  ...                    ...                 ...   \n",
       "142                  22                     26                  35   \n",
       "143                  33                     36                  35   \n",
       "144                  97                    110                  40   \n",
       "145                  19                     93                  15   \n",
       "146                  11                     88                   2   \n",
       "\n",
       "     K562_b_K562_total_cov  K562_c_K562_in_cov  K562_c_K562_total_cov  \n",
       "0                      153                  34                    111  \n",
       "1                       81                   1                     62  \n",
       "2                      301                 177                    482  \n",
       "3                       72                   1                     91  \n",
       "4                       63                  25                     44  \n",
       "..                     ...                 ...                    ...  \n",
       "142                     41                  24                     27  \n",
       "143                     37                  33                     40  \n",
       "144                     52                  80                     92  \n",
       "145                     53                  24                    104  \n",
       "146                     53                   7                     61  \n",
       "\n",
       "[147 rows x 32 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_splice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Depicting differential splicing results\n",
    "To visualize deferentially spliced events, isotools implements a specific plot, summarizing the PSI values in the two plots. For each depicted event, two distributions represent the models posterior probability of PSI values for the two groups, as well as the observed PSI values for the individual samples as points. In addition, the read coverage over the event can be depicted as a sashimi-plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/pkg/python-3.10.8-0/lib/python3.10/site-packages/scipy/stats/_continuous_distns.py:668: RuntimeWarning: divide by zero encountered in _beta_pdf\n",
      "  return _boost._beta_pdf(x, a, b)\n",
      "/home/lienhard/.local/lib/python3.10/site-packages/isotools/plots.py:92: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  plotted = plotted.append(row)\n",
      "/home/lienhard/.local/lib/python3.10/site-packages/isotools/plots.py:92: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  plotted = plotted.append(row)\n",
      "/home/lienhard/.local/lib/python3.10/site-packages/isotools/plots.py:92: FutureWarning: In a future version, object-dtype columns with all-bool values will not be included in reductions with bool_only=True. Explicitly cast to bool dtype instead.\n",
      "  plotted = plotted.append(row)\n",
      "/home/lienhard/.local/lib/python3.10/site-packages/isotools/plots.py:92: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  plotted = plotted.append(row)\n",
      "/home/lienhard/.local/lib/python3.10/site-packages/isotools/plots.py:92: FutureWarning: In a future version, object-dtype columns with all-bool values will not be included in reductions with bool_only=True. Explicitly cast to bool dtype instead.\n",
      "  plotted = plotted.append(row)\n",
      "/home/lienhard/.local/lib/python3.10/site-packages/isotools/plots.py:92: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  plotted = plotted.append(row)\n",
      "/home/lienhard/.local/lib/python3.10/site-packages/isotools/plots.py:92: FutureWarning: In a future version, object-dtype columns with all-bool values will not be included in reductions with bool_only=True. Explicitly cast to bool dtype instead.\n",
      "  plotted = plotted.append(row)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10,7)\n",
    "from isotools.plots import plot_diff_results\n",
    "\n",
    "f,axs,plotted=plot_diff_results(diff_splice, min_diff=.1, min_support=2,grid_shape=(2,2), group_colors=['C0','C1'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i+PDhCAgIQOfOnQucf1ZWFry8vNC2bVssX75cPtzLywtubm5YuHCh/GDGmDFj8OTJEyxcuFB+V/bGjRtjypQpmDNnToHPFFfWzbkwrq6u6NGjB/bs2YPs7Gyl7VZYndu3b8fo0aNx9+5d+YGc3r17IycnR/59qFixoryLdW6X67zS0tKUdsEuKs64uDh5EgvIunFbWFggPj4e77//PiZPniy/yz4g+/zatm0LPz8/jB07FoDsTv6xsbFYsGAB5s+fDwDw9vbGyJEjsWbNmkI/Z11dXfTv3z/fuDetszBGRkYFfna54/P+bdmypTzZBgAnJye0atVKoTv4jBkzYGVlhQkTJqgUQ+66p+xAFhERqQ+v4SYi0lJNmzaFl5cX+vTpg/3798Pd3R0DBw4s9IZbeVlbW8PLywsdOnRAixYtlCbbuaZMmYIpU6Zg7969GDVqlNKzu7k2bdqEqVOnYsyYMZgxY4ZKseReN64sOVZVUQcB8lKWAG7atAlpaWno2rWrwvDu3bsDkF3HXpiTJ08iODhYXj5X9erVUbt27XzTz5kzB9HR0Th16hT+/fdfXLp0CTk5OQCAGjVqKJ3Htm3bULNmTTRq1KjwBczD0dERGRkZSE5OLnadq1atQsOGDfP1mujevTtSUlLk12bn3rAs9wx9XpGRkahcuXKx4+zduzfs7e3lr88//xwAsHv3bkRHR+f7nNu0aQNzc3OFz9nAwADr169HREQETp48iZCQEPz999+Ij4+Hjo6O0seQpaamYu/evfDy8sp3Y7Y3rbMo9vb2BX52AOSfX+5fZXHZ2trK15979+7hl19+wcSJExEREYFHjx7h0aNHSEtLQ2ZmJh49epTv5mi506p6fwYiIioZPMNNRFQK6OrqYt68eWjXrh1WrFiBb775psTn8eOPPyIuLg7r169HhQoVsHjx4nxl9u3bh08++QS9e/fGypUrVa7byckJRkZG+e6QXq1aNQBAcHBwvi70JS06OhpCCIW7mgOyG7oBKPJu2tHR0QCQb/rcOpRNX6FCBYVnLB8+fBgODg6oVatWvrIXLlzA/fv3MWvWrKIXJo/Q0FBIpdIC7/5eWJ3R0dFKb8L3+mfi7u4OPT09XL58GR9++KG8XEZGBoKCghSGqRrn4sWLFQ7A5CabBX3OuW2n7HOuVKmSPEnNzs7G8ePHC7wj/v79+5GYmFhkL4Li1FmUBg0a4NixY0hISFC4bOPChQvy8QBQt25d6OvrIzw8PF8dERER8h4e4eHhyMnJwcSJEzFx4sR8ZV1cXPD5558r3Lk8d92rXbt2seMnIqI3xzPcRESlRNu2bdG0aVP89NNP8q6oJW3t2rXo27cvlixZgh9++EFh3MmTJ/HRRx+hdevW2Lp1q0qPQ8qlr6+Pxo0b57tTtYeHB1xcXPDTTz/h5cuXCuMKO8v+JmrUqAEhRL5HWP3+++8AgIYNG8qHKXssWO5Z6dcfDXX16lWEhIQoTK/Mjh07cOnSJUyaNEnpZ7dt2zYAwMCBA5VO/+zZs3zDrl+/jv3798Pb2/uN6qxRowauXbuGu3fvKgz//fffoaOjg3r16gEALCws4OXlhS1btsgf2wUAv/32G5KSktCvX79ix9moUSN4eXnJX3Xq1JHHBOT/nPfv34/k5OQiP+dFixYhMjJSoTt6Xtu2bYOxsbH8kXaqKKrOovTt2xfZ2dkKd9RPT0+Hn58fmjVrJu8+bmZmhg8++ABnz57FnTt35GVv376Ns2fPomPHjgBkB0D27t2b7+Xm5gYnJyfs3bsXI0eOVIjhypUrkEgkaNGixRstAxERvRme4SYiKkUmT56Mfv36YdOmTfluIFYSdHR0sHXrVsTHx2PmzJmwsrLCZ599hsePH6N79+6QSCTo27evwg3GAKBevXry5KwgPXr0wPTp0xXO8uno6GD16tXo1q0bGjRogBEjRsDe3h537tzBzZs38ffff5fYsg0fPhyLFi3Cp59+imvXrsHNzQ1Xr17F+vXr4ebmppCAXbx4Ee3atYOvry++++47ALIEsWPHjti8eTMSEhLg7e2NyMhILF++HEZGRpg0aZJ8+pMnT2LWrFnw9vZGxYoVcf78efj5+cHHx0fedTqv7Oxs7NixA82bN5ef9X9d//79YWRkBE9PT9ja2uLWrVv45ZdfYGxsLL/WuLh1Tp48GQEBAXj//fcxfvx4VKxYEf7+/ggICMAnn3yi0FV8zpw58PT0RJs2bTB69Gg8ffoUixcvhre3N3x8fN44ztd169YNbm5umDVrFh4/fiy/adqKFStgb2+vkEhu2bIFu3fvRuvWrWFqaorDhw/jjz/+wCeffCK/Tj+vuLg4BAQEoE+fPgWeqS5OnSdPnsTJkycByA40JCcnyw9UtW7dGq1btwYgu59Cv379MG3aNMTExMDV1RWbN2/Go0ePsGHDBoU6586diyNHjqB9+/bys9c///wzrKys5Dd5s7a2Rs+ePfPFnntGW9m4wMBAtGzZUuljAYmISI00d4N0IiJSJvexYJcuXco3Ljs7W1SrVk1Uq1ZN/sipgh4L1qVLlyLnVdBjhJKSkkTz5s2Fjo6O2Lp1q/wxWQW9Xn98ljLR0dFCT09P/Pbbb/nGnT59WnTs2FGYmZkJExMTUa9ePYVHiA0bNkyYmJjkm87X11fk3ZXlPhZs4cKFSmN4+vSp+Pjjj4WLi4swMDAQ9vb2YtSoUeLZs2cK5Qp6LFhKSoqYNWuWqFOnjjAyMhIWFhaia9eu4tq1awrl7t+/L7y9vYW1tbUwNDQUtWrVEvPmzRPp6elK4zp06JAAIH7++Wel44UQYtmyZaJp06bCyspK6OnpCXt7ezF48GBx7969N65TCNkjuDp37izs7OyEvr6+qFGjhpgzZ47SR72dOnVKeHp6CqlUKmxsbMS4ceNEQkLCW8WpTFxcnPjiiy9EjRo1hKGhobC2thYfffSRCA0NzRd769atRYUKFYRUKhX169cXa9asUXikXF5r1qwRAMT+/fsL/TxUrTP3+6fKOpGamiq+/vprYWdnJwwNDUWTJk3EoUOHlMZw5coV4eXlJUxMTISZmZno0aOHuHv3bhGfWsHr88uXL4WBgYFYv359kXUQEVHJkghRwn32iIiICjBy5EjcvXsXp06d0nQoROXGTz/9hAULFuDBgwdF3lGeiIhKFhNuIiJ6Z8LCwlCjRg0cOXIELVu21HQ4RGVeZmYmqlWrhm+++QafffaZpsMhIip3mHATERERERERqQHvUk5ERERERESkBky4iYiIiIiIiNSACTcRERERERGRGjDhJiIiIiIiIlIDJtxEREREREREasCEm4iIiIiIiEgNmHATERERERERqQETbiIiIiIiIiI1YMJNREREREREpAZMuImIiIiIiIjUgAk3ERERERERkRow4SYiIiIiIiJSAybcRERERERERGrAhJuIiIiIiIhIDZhwExEREREREakBE24iIiIiIiIiNWDCTURERERERKQGTLiJiIiIiIiI1IAJNxEREREREZEaMOEmIiIiIiIiUgMm3ERERERERERqwISbiIiIiIiISA2YcBMRERERERGpARNuIiIiIiIiIjVgwk1ERERERESkBky4iYiIiIiIiNSACTcRERERERGRGjDhJiIiIiIiIlIDPU0HUN6FhQGxsaqVtbYGnJzUGw8REREREZVuxckxAOYZ6sSEW4PCwoCaNYG0NNXKS6VASAhXBiIiIiIiUq64OQbAPEOd2KVcg2Jji7cipKUV70gVERERERGVL8XNMQDmGerEhJuIiIiIiIhIDZhwExEREREREakBE24iIiIiIiIiNWDCTURERERERKQGTLiJiIiIiIiI1IAJtxapXBn47TfZHQJTUoB//wUaNdJ0VEREREREVFo9fAgIkf+1YoWmIysf+BxuLWFpCZw5Axw7BnTuDDx7BlSvDrx4oenIiIiIiIiotGrSBNDVffXe3R04fBjYuVNzMZUnTLi1xNSpwJMnwMcfvxr26JHGwiEiIiIiojLg9edrf/MNcP8+cOKEZuIpb0ptwp2crOkI3l5q6qv/d+8O/P038McfQJs2QHg4sGoVsH59/mnKwrITEREREVHJy5tjvE5fHxg8GFiyRPl0pTnPMDHRdATKSYQQQtNBvAmJRNMRlKzcFWPJEln3jiZNgGXLgDFjgF9/1WxsRERERERU+vXrB2zbBjg5AZGRmo6mZGlrVsuEW0ukpwOXLwMtW74atmyZLPH29NRcXEREREREVDYcOgRkZMh615Y12prVltou5UlJmo7g7QUFAa1ayf4fGQncuqU4/vZtoE8fxWGnTwMNGryL6IiIiIiIqLTJm2Pk5eQEeHkBvXsrn455hnqU2oRbW/voF4eR0av/nzkD1KypOL5GDeDx4/zTlIVlJyIiIiKikpc3x8hrxAggJgY4cKDg6ZhnlDw+h1tLLF0KNG8OTJsGVKsGDBgAjB4NrFyp6ciIiIiIiKg0k0hkCffmzUB2tqajKV+YcGuJy5eBXr1kiXZwMDBzJjBpkuymBkRERERERG/KywtwdgY2btR0JOVPqe1SXhYdOFBwFw8iIiIiIqI3ERhY9m46XVrwDDcRERERERGRGjDhJiIiIiIiIlIDJtxEREREREREasCEm4iIiIiIiEgNmHATERERERERqQETbiIiIiIiIiI1YMKtQdbWgFSqenmpVDYNERERERGRMsXNMQDmGeokEUIITQdRnoWFAbGxqpW1tgacnNQbDxERERERlW7FyTEA5hnqxISbiIiIiIiISA3YpZyIiEjNhg8fDolEAolEAn19fbi4uGDKlClIS0tTKCeRSPDnn38qvM99WVhYoGXLljh69KhCvT179lSoY9euXZBKpVi8eDEAYN68eWjSpAnMzMxga2uLnj17IiQkROXYf/nlF7Rt2xbm5uaQSCR4+fKl0nIHDhxAs2bNYGRkhAoVKuSLi4iIqDxiwk1ERFSEiIgIZGVlvVUdPj4+iIyMRGhoKJYuXYq1a9fC19e3yOn8/PwQGRmJM2fOwNraGl27dkVoaKjSsuvXr8egQYOwevVqfPXVVwCAEydOYNy4cTh//jwCAwORmZkJb29vJCcnqxR3SkoKfHx88O233xZYZvfu3RgyZAhGjBiB69ev48yZMxg4cKBK9RMREZVlepoOgIiISNutW7cOq1evxuDBgzFs2DDUrVu32HUYGhrCzs4OAODo6AgvLy8EBgbixx9/LHQ6S0tL2NnZwc7ODqtXr0aVKlUQGBiITz/9VKHcggUL4Ovri+3bt6NXr17y4YcOHVIot2nTJtja2uLKlSto3bp1kXFPmjQJAHD8+HGl47OysvD5559j4cKFGDlypHx4nTp1iqybiIiorOMZbiIioiJMnToVy5Ytw+3bt+Hh4QEPDw/8/PPPePbs2RvVFxwcjLNnz8LAwKBY0xkZGQEAMjIy8sU3e/Zs+Pv7KyTbysTHxwMArKysijXvgly9ehXh4eHQ0dFBw4YNYW9vj86dOyM4OLhE6iciIirNmHATEREVQSqVon///jhw4ADCw8MxdOhQbNq0CVWqVEHPnj2xd+/eIruc+/v7w9TUFFKpFHXr1kVMTAwmT56scgwpKSmYMWMGdHV10aZNG/nwgIAALFiwAPv27UOHDh0KrSMnJweTJk1Cy5Yt4e7urvK8C5Pbvf27777DjBkz4O/vjwoVKqBt27aIi4srkXkQERGVVky4iYiIisHW1haTJk3C1atXsW/fPpw7dw69e/cu8oxuu3btEBQUhAsXLmDYsGEYMWIE+vTpU+T8BgwYAFNTU5iZmWH37t3YsGED6tWrJx9fr149VK1aFb6+vkhKSiq0rnHjxiE4OBjbt29XbWFVkJOTAwCYPn06+vTpg0aNGsHPzw8SiQQ7d+4ssfkQERGVRky4iYiIiiExMRF+fn5o3749unXrBnd3d2zevLnIa5ZNTEzg6uqK+vXrY+PGjbhw4QI2bNhQ5PyWLl2KoKAgREVFISoqCsOGDVMYX6VKFRw/fhzh4eHw8fFBYmKi0nrGjx8Pf39/HDt2DA4ODqovcBHs7e0BKF6zbWhoiPfeew9hYWElNh8iIqLSiAk3ERFREbKzsxEQEICBAweiUqVKmD9/Pjp06IDQ0FAcOXIEQ4cOLdb12Do6Ovj2228xY8YMpKamFlrWzs4Orq6usLGxKbCMs7MzTpw4gaioqHxJtxAC48ePx969e3H06FG4uLioHKcqGjVqBENDQ4VHjWVmZuLRo0dwdnYu0XkRERGVNky4iYiIijB37lwMGDAAZmZmOHz4MEJCQjB9+nQ4OTm9cZ39+vWDrq4uVq5cWSIxOjo64vjx44iJiUGnTp2QkJAAQNaNfMuWLdi2bRvMzMzkZ8qLSvRzRUVFISgoCPfv3wcA3LhxA0FBQfLrs83NzTFmzBj4+vrin3/+QUhICMaOHStfRiIiovKMCTcREVERhgwZgqioKKxduxaenp4lUqeenh7Gjx+PBQsWqPxM7KI4ODjg+PHjiI2NlSfdq1evRnx8PNq2bQt7e3v5a8eOHSrVuWbNGjRs2BCjRo0CALRu3RoNGzbE/v375WUWLlyIjz76CEOGDEGTJk3w+PFjHD16FBUqVCiR5SIiIiqtJEIIoekgiIiIiIiIiMoanuEmIiIiIiIiUgMm3EREROXU1q1bYWpqqvTl5uam6fCIiIhKPXYpJyIiKqcSExMRHR2tdJy+vj7vMk5ERPSWmHATERERERERqQG7lBMRERERERGpARNuIiIiIiIiIjVgwk1ERERERESkBky4iYiIiIiIiNSACTcRERERERGRGuhpOoDyLiwMiI1Vray1NeDkpN54iIiIyiPuj4moLCnONg3gdk2dmHBrUFgYULMmkJamWnmpFAgJ4cpARERUkrg/JqKypLjbNIDbNXVil3INio0t3oqQlla8I1VERERUNO6PiagsKe42DeB2TZ2YcBMRERERERGpARNuIiIiIiIiIjVgwk1ERERERESkBky4iYiIiIiIiNSACTcRERERERGRGjDh1hI6OsCsWUBoKJCSAty/D8yYoemoiIiIyh9TU2DpUuDRI9k++cwZoHFjTUdFRKQaU1Ng/34gPBwQAujRI3+Z778HIiJk27jAQMDV9d3HWV4w4dYSU6cCY8cC48cDtWvL3k+ZAkyYoOnIiIiIypf164GOHYEhQ4C6dYF//gEOHwYqV9Z0ZERERdPRAa5fB8aNUz5+yhRg4kRgzBigWTMgORn4+29AInm3cZYXepoOgGQ8PYF9+4CDB2XvHz8GBgwAmjbVbFxERETliVQK9OkjOyN06pRs2PffA926yQ6Mz5yp2fiIiIqSkFD4tmrSJOCHH2RnwQFg6FAgOpoJt7qU2oQ7OVnTEby91NRX/z97Fhg9GqheHbh3D6hXD2jVCvjyy/zTlIVlJyIi0hZ598d6erJXWlr+Mq1aKb7n/piItFHebdrrXFwAe3tZr51cCQnAhQuyPKQ0b9dMTDQdgXKlNuE2NdV0BCVr/nzA3By4cwfIzgZ0dYHp04Ft2xTL5d3ZExERUclKSpIdBJ85E7h9W3bWZ8AAoEUL2f1VcnF/TESlkZ2d7G90tOLw6GggKgr46KN3H1NJEULTESjHa7i1xIcfAoMGAQMHAh4ewLBhwNdfy7p4EBER0bszZIisa2VEBJCeLrvW8fffgZwcTUdGRESlTak9w52UpOkI3l5Q0Ksj5AsXys5y79ghex8cDDg7A9OmAb/++mqa06eBBg3edaRERERlV979MSB7YkjbtoCxsaz3WVQUsH27bHgu7o+JSFu9vk3LKypK9rdSpVf/z31fvXrZyLG0TalNuLW1j35xGBm9+r+xcf4j59nZsrsMvj5NWVh2IiIibZF3f5xXSorsZWkJdOoku7Nv3mm4PyYibVTQNg0AHj4EIiOBDh1kdzIHADMz2d3KIyO5XVOHUptwlzV//SW7ZjssDLh5E2jYUHbDtI0bNR0ZERFR+eLtLetSHhIiezbtwoWye6z4+Wk6MiKiounoAPXrv3rv4iJ7HxcHPHkC/PQTMGOG7EbNDx8Cs2fLLqGJj9dYyGUaE24tMWGC7Mu+ahVgayv70q9dC8yapenIiIiIyhcLC2DePMDBQfYDdfdu2UHxrCxNR0ZEVDRjY1m38lxLl8r+btoEjBgBLFggO5P9yy+yHjynTwM+PrJLZ6jkSYTQ1vu5lX1XrwKNGhVvmitXZDdVIyIiopLB/TERlSVvsk0DuF1TF96lnIiIiIiIiEgNmHATERERERERqQETbiIiIiIiIiI1YMJNREREREREpAZMuImIiIiIiIjUgAk3ERERERERkRow4SYiIiIiIiJSAybcGmRtDUilqpeXSmXTEBERUcnh/piIypLibtMAbtfUSSKEEJoOojwLCwNiY1Ura20NODmpNx4iIqLyiPtjIipLirNNA7hdUycm3ERERGo2fPhwbN68GQCgp6cHBwcH9OvXD7NmzYI0z2kIiUSCvXv3omfPnvL3uczNzeHu7o7Zs2ejffv28npfvnyJP//8U15u165dGDx4MObMmYOvvvoK8+bNw549e3Dnzh0YGRnB09MTP/74I2rWrKlS7L/88gu2bduGq1evIjExES9evIClpaV8/KNHjzB79mwcPXoUUVFRqFy5MgYPHozp06fDwMDgDT8xIiKisoFdyomIiIoQERGBrKyst6rDx8cHkZGRCA0NxdKlS7F27Vr4+voWOZ2fnx8iIyNx5swZWFtbo2vXrggNDVVadv369Rg0aBBWr16Nr776CgBw4sQJjBs3DufPn0dgYCAyMzPh7e2N5ORkleJOSUmBj48Pvv32W6Xj79y5g5ycHKxduxY3b97E0qVLsWbNmgLLExERlSd6mg6AiIhI261btw6rV6/G4MGDMWzYMNStW7fYdRgaGsLOzg4A4OjoCC8vLwQGBuLHH38sdDpLS0vY2dnBzs4Oq1evRpUqVRAYGIhPP/1UodyCBQvg6+uL7du3o1evXvLhhw4dUii3adMm2Nra4sqVK2jdunWRcU+aNAkAcPz4caXjfXx84OPjI3//3nvvISQkBKtXr8aiRYuKrJ+IiKgs4xluIiKiIkydOhXLli3D7du34eHhAQ8PD/z888949uzZG9UXHByMs2fPFrvLtZGREQAgIyMjX3yzZ8+Gv7+/QrKtTHx8PADAysqqWPMujvj4eLXWT0REVFow4SYiIiqCVCpF//79ceDAAYSHh2Po0KHYtGkTqlSpgp49e2Lv3r1Fdjn39/eHqakppFIp6tati5iYGEyePFnlGFJSUjBjxgzo6uqiTZs28uEBAQFYsGAB9u3bhw4dOhRaR05ODiZNmoSWLVvC3d1d5XkXx/3797F8+fJ8Z+CJiIjKIybcRERExWBra4tJkybh6tWr2LdvH86dO4fevXsjODi40OnatWuHoKAgXLhwAcOGDcOIESPQp0+fIuc3YMAAmJqawszMDLt378aGDRtQr149+fh69eqhatWq8PX1RVJSUqF1jRs3DsHBwdi+fbtqC1tM4eHh8PHxQb9+/TBq1Ci1zIOIiKg0YcJNRERUDImJifDz80P79u3RrVs3uLu7Y/PmzahTp06h05mYmMDV1RX169fHxo0bceHCBWzYsKHI+S1duhRBQUGIiopCVFQUhg0bpjC+SpUqOH78uDzZTUxMVFrP+PHj4e/vj2PHjsHBwUH1BVZRREQE2rVrB09PT/zyyy8lXj8REVFpxISbiIioCNnZ2QgICMDAgQNRqVIlzJ8/Hx06dEBoaCiOHDmCoUOHFut6bB0dHXz77beYMWMGUlNTCy1rZ2cHV1dX2NjYFFjG2dkZJ06cQFRUVL6kWwiB8ePHY+/evTh69ChcXFxUjlNV4eHhaNu2LRo1agQ/Pz/o6PDnBREREcCEm4iIqEhz587FgAEDYGZmhsOHDyMkJATTp0+Hk5PTG9fZr18/6OrqYuXKlSUSo6OjI44fP46YmBh06tQJCQkJAGTdyLds2YJt27bBzMxMfqa8qEQ/V1RUFIKCgnD//n0AwI0bNxAUFIS4uDgAr5JtJycnLFq0CM+ePZPPg4iIqLxjwk1ERFSEIUOGICoqCmvXroWnp2eJ1Kmnp4fx48djwYIFKj8TuygODg44fvw4YmNj5Un36tWrER8fj7Zt28Le3l7+2rFjh0p1rlmzBg0bNpRfk926dWs0bNgQ+/fvBwAEBgbi/v37OHLkCBwcHBTmQUREVN5JhBBC00EQERERERERlTU8w01ERERERESkBky4iYiIyqmtW7fC1NRU6cvNzU3T4REREZV67FJORERUTiUmJiI6OlrpOH19fTg7O7/jiIiIiMoWJtxEREREREREasAu5URERERERERqwISbiIiIiIiISA2YcBMRERERERGpARNuIiIiIiIiIjVgwk1ERERERESkBky4iYiIiIiIiNRAT9MBlHdhYUBsrGplra0BJyf1xkNERERERKVbcXIMgHmGOjHh1qCwMKBmTSAtTbXyUikQEsKVgYiIiIiIlCtujgEwz1AndinXoNjY4q0IaWnFO1JFRERERETlS3FzDIB5hjox4SYiIiIiIiJSAybcRERERERERGrAhJuIiIiIiIhIDZhwExEREREREakBE24iIiIiIiIiNWDCrSUePgSEyP9asULTkRERERERUWmkowPMmgWEhgIpKcD9+8CMGZqOqnzhc7i1RJMmgK7uq/fu7sDhw8DOnZqLiYiIiIiISq+pU4GxY4Fhw4CbN4HGjQE/PyA+Hli+XNPRlQ9MuLXE68+9++Yb2RGoEyc0Ew8REREREZVunp7Avn3AwYOy948fAwMGAE2bajau8qTUJtzJyZqO4O2lpiofrq8PDB4MLFmifJqysOxERERERFTy8uYYZ88Co0cD1asD9+4B9eoBrVoBX36pfLrSnGeYmGg6AuVKbcJtaqrpCNSnZ0/A0hLYtCn/uFat3nEwRERERERUKs2fD5ibA3fuANnZsktYp08Htm3LX7a05xlCaDoC5Uptwl2WjRwJBAQAkZGajoSIiIiIiEqrDz8EBg0CBg6UXcPdoAHw009ARATw66+ajq58kAihrccCCleauzvkCgrKfyTJyUl2F8HevYH9+/NPc/q0bEUhIiIiIiJ6Xd4cIyxMdpZ71apX46dPl12+Wru24nSlPc9gl/ISpq0faHEYGeUfNmIEEBMDHDhQ8DRlYdmJiIiIiKjk5c0xjI2BnBzF8dnZsseFKZuOeUbJK7UJd1kkkcgS7s2bZSsCERERERHRm/rrL9kZ7bAwWZfyhg1lN0zbuFHTkZUfTLi1iJcX4OzMFYCIiIiIiN7ehAnA7NmyLuW2trJrt9euBWbN0nRk5QcTbi0SGCg7y01ERERERPS2kpKAL76QvUgzlPTeJyIiIiIiIqK3xYSbiIiIiIiISA2YcBMRERERERGpARNuIiIiIiIiIjVgwk1ERERERESkBky4iYiIiIiIiNSACbcGWVsDUqnq5aVS2TRERERERETKFDfHAJhnqJNECCE0HUR5FhYGxMaqVtbaGnByUm88RERERERUuhUnxwCYZ6gTE24iIiIiIiIiNWCXciIionds+PDhkEgkkEgk0NfXh4uLC6ZMmYK0tDSFchKJBH/++afC+9yXhYUFWrZsiaNHjyrU27NnT4U6du3aBalUisWLFwMA5s2bhyZNmsDMzAy2trbo2bMnQkJCVI49KioKQ4YMgZ2dHUxMTODh4YHdu3cX/0MgIiIqB5hwExERFVNERASysrLeqg4fHx9ERkYiNDQUS5cuxdq1a+Hr61vkdH5+foiMjMSZM2dgbW2Nrl27IjQ0VGnZ9evXY9CgQVi9ejW++uorAMCJEycwbtw4nD9/HoGBgcjMzIS3tzeSk5NVinvo0KEICQnB/v37cePGDfTu3Rsffvghrl27pvrCExERlRNMuImIiIpp3bp1cHBwwNdff40bN268UR2Ghoaws7ODo6MjevbsCS8vLwQGBhY5naWlJezs7ODu7o7Vq1cjNTVV6XQLFizAhAkTsH37dowYMUI+/NChQxg+fDjc3NxQv359bNq0CWFhYbhy5YpKcZ89exYTJkxA06ZN8d5772HGjBmwtLRUeXoiIqLyhAk3ERFRMU2dOhXLli3D7du34eHhAQ8PD/z888949uzZG9UXHByMs2fPwsDAoFjTGRkZAQAyMjLyxTd79mz4+/ujV69ehdYRHx8PALCyslJpnp6entixYwfi4uKQk5OD7du3Iy0tDW3bti1W7EREROUBE24iIqJikkql6N+/Pw4cOIDw8HAMHToUmzZtQpUqVdCzZ0/s3bu3yC7n/v7+MDU1hVQqRd26dRETE4PJkyerHENKSgpmzJgBXV1dtGnTRj48ICAACxYswL59+9ChQ4dC68jJycGkSZPQsmVLuLu7qzTfP/74A5mZmahYsSIMDQ3x6aefYu/evXB1dVU5diIiovKCCTcREdFbsLW1xaRJk3D16lXs27cP586dQ+/evREcHFzodO3atUNQUBAuXLiAYcOGYcSIEejTp0+R8xswYABMTU1hZmaG3bt3Y8OGDahXr558fL169VC1alX4+voiKSmp0LrGjRuH4OBgbN++XbWFBTBz5ky8fPkShw8fxuXLl/Hll1/iww8/fOOu9URERGUZE24iIqK3kJiYCD8/P7Rv3x7dunWDu7s7Nm/ejDp16hQ6nYmJCVxdXVG/fn1s3LgRFy5cwIYNG4qc39KlSxEUFISoqChERUVh2LBhCuOrVKmC48ePIzw8HD4+PkhMTFRaz/jx4+Hv749jx47BwcFBpWV98OABVqxYgY0bN6JDhw6oX78+fH190bhxY6xcuVKlOoiIiMoTJtxERETFlJ2djYCAAAwcOBCVKlXC/Pnz0aFDB4SGhuLIkSMYOnRosa7H1tHRwbfffosZM2YgNTW10LJ2dnZwdXWFjY1NgWWcnZ1x4sQJREVF5Uu6hRAYP3489u7di6NHj8LFxUXlOFNSUuTx5qWrq4ucnByV6yEiIiovmHATEREV09y5czFgwACYmZnh8OHDCAkJwfTp0+Hk5PTGdfbr1w+6uroldqbY0dERx48fR0xMDDp16oSEhAQAsm7kW7ZswbZt22BmZiY/U15Uog8AtWrVgqurKz799FNcvHgRDx48wOLFixEYGJjv+d9ERETEhJuIiKjYhgwZgqioKKxduxaenp4lUqeenh7Gjx+PBQsWqPxM7KI4ODjg+PHjiI2NlSfdq1evRnx8PNq2bQt7e3v5a8eOHUXWp6+vj4MHD8LGxgbdunVDvXr18Ouvv2Lz5s344IMPSiRmIiKiskQihBCaDoKIiIiIiIiorOEZbiIiIiIiIiI1YMJNREREAICtW7fC1NRU6cvNzU3T4REREZU67FJOREREAGSPOIuOjlY6Tl9fH87Ozu84IiIiotKNCTcRERERERGRGrBLOREREREREZEaMOEmIiIiIiIiUgMm3ERERERERERqwISbiIiIiIiISA2YcBMRERERERGpgZ6mAyjvwsKA2FjVylpbA05O6o2HiIioPOL+mIjKkuJs0wBu19SJCbcGhYUBNWsCaWmqlZdKgZAQrgxEREQliftjIipLirtNA7hdUyd2Kdeg2NjirQhpacU7UkVERERF4/6YiMqS4m7TAG7X1IkJNxEREREREZEaMOEmIiIiIiIiUgMm3ERERERERERqwISbiIiIiIiISA2YcBMRERERERGpARNuIiIiIiIiIjVgwq0lHj4EhMj/WrFC05ERERGVHzo6wKxZQGgokJIC3L8PzJih6aiIiN6cqSmwdCnw6JFsu3bmDNC4saajKj/0NB0AyTRpAujqvnrv7g4cPgzs3Km5mIiIiMqbqVOBsWOBYcOAmzdlP0r9/ID4eGD5ck1HR0RUfOvXy3KLIUOAiAhg8GBZnlGnjuw9qRfPcGuJ2FggOvrVq2tX2VH1Eyc0HRkREVH54ekJ7NsHHDwIPH4M7N4N/PMP0LSppiMjIio+qRTo0weYMgU4dQp48AD4/ntZnjF2rKajKx9K7Rnu5GRNR/D2UlOVD9fXlx15WrJE+TRlYdmJiIi0Rd798dmzwOjRQPXqwL17QL16QKtWwJdf5p+G+2Mi0kZ5t2l6erJXWlr+Mq1a5R9WmrdrJiaajkA5iRBCaDqINyGRaDoC9enXD9i2DXByAiIjNR0NERFR+SGRAHPnys4GZWfLLveaPh2YP1/TkRERvZkzZ4CMDGDgQFlP2gEDgM2bZWe5a9XSdHQlR1uzWnYp10IjRwIBAUy2iYiI3rUPPwQGDZL9MPXwkF3L/fXXwNChmo6MiOjNDBkiO5gYEQGkpwMTJwK//w7k5Gg6svKh1J7hLs3dHXIFBeXvyuHkJLszau/ewP79+ac5fRpo0OBdREdERFQ+5N0fh4XJzmavWvVq/PTpsku9atd+NYz7YyLSVspyDAAwNgbMzYGoKGD7dtndy7t2fTW+tG/XtLVLeam9hltbP9DiMDLKP2zECCAmBjhwoOBpysKyExERaYu8+2Nj4/xnfbKzZY8Le30a7o+JSBspyzEA2SPBUlIAS0ugUyfZpTOvT8ftWskrtQl3WSSRyBLuzZtlO3ciIiJ6t/76S3ZGOyxM9liwhg1lN0zbuFHTkRERvRlvb1meERICuLoCCxcCd+7IHnlI6seEW4t4eQHOztypExERacqECcDs2bIu5ba2smse164FZs3SdGRERG/GwgKYNw9wcADi4mSPO5w+HcjK0nRk5QMTbi0SGFi2775ORESk7ZKSgC++kL2IiMqCnTtlL9IM3qWciIiIiIiISA2YcBMRERERERGpARNuIiIiIiIiIjVgwk1ERERERESkBky4iYiIiIiIiNSACTcRERERERGRGjDh1iBra0AqVb28VCqbhoiIiEoO98dEVJYUd5sGcLumThIhhNB0EOVZWBgQG6taWWtrwMlJvfEQERGVR9wfE1FZUpxtGsDtmjox4SYiIiIiIiJSA3YpJyIieseGDx8OiUQCiUQCfX19uLi4YMqUKUhLS1MoJ5FI8Oeffyq8z31ZWFigZcuWOHr0qEK9PXv2VKhj165dkEqlWLx4MQBg3rx5aNKkCczMzGBra4uePXsiJCREpbjj4uIwYcIE1KxZE0ZGRnBycsLEiRMRHx//Zh8EERFRGceEm4iIqJgiIiKQlZX1VnX4+PggMjISoaGhWLp0KdauXQtfX98ip/Pz80NkZCTOnDkDa2trdO3aFaGhoUrLrl+/HoMGDcLq1avx1VdfAQBOnDiBcePG4fz58wgMDERmZia8vb2RnJxc5LwjIiIQERGBRYsWITg4GJs2bcKhQ4cwcuTI4i08ERFROcGEm4iIqJjWrVsHBwcHfP3117hx48Yb1WFoaAg7Ozs4OjqiZ8+e8PLyQmBgYJHTWVpaws7ODu7u7li9ejVSU1OVTrdgwQJMmDAB27dvx4gRI+TDDx06hOHDh8PNzQ3169fHpk2bEBYWhitXrhQ5b3d3d+zevRvdunVDtWrV0L59e8yZMwd//fXXWx+AICIiKouYcBMRERXT1KlTsWzZMty+fRseHh7w8PDAzz//jGfPnr1RfcHBwTh79iwMDAyKNZ2RkREAICMjI198s2fPhr+/P3r16lVoHbndwa2srIo177zTm5ubQ09P742mJyIiKsuYcBMRERWTVCpF//79ceDAAYSHh2Po0KHYtGkTqlSpgp49e2Lv3r1FnvH19/eHqakppFIp6tati5iYGEyePFnlGFJSUjBjxgzo6uqiTZs28uEBAQFYsGAB9u3bhw4dOhRaR05ODiZNmoSWLVvC3d1d5Xnnio2NxezZszF69OhiT0tERFQe8C7lREREJSQgIADDhw9HTEwMrl27hgYNGigtN3z4cISHh2P16tVITk7G0qVLoaenh/Xr1yuUk0gk2Lt3r/xGaBKJBFKpFLq6ukhNTYWNjQ1+/PFHDBs2TF7vzZs3ERsbCwcHBwQEBMDU1LTAeMeOHYuAgACcPn0aDg4OxVrWhIQEdOzYEVZWVti/fz/09fWLNT0REVF5wDPcREREbyExMRF+fn5o3749unXrBnd3d2zevBl16tQpdDoTExO4urqifv362LhxIy5cuIANGzYUOb+lS5ciKCgIUVFRiIqKkifbuapUqYLjx48jPDwcPj4+SExMVFrP+PHj4e/vj2PHjhU72U5MTISPjw/MzMywd+9eJttEREQFYMJNRERUTNnZ2QgICMDAgQNRqVIlzJ8/Hx06dEBoaCiOHDmCoUOHFut6bB0dHXz77beYMWMGUlNTCy1rZ2cHV1dX2NjYFFjG2dkZJ06cQFRUVL6kWwiB8ePHY+/evTh69ChcXFxUjhOQndn29vaGgYEB9u/fD6lUWqzpiYiIyhMm3ERERMU0d+5cDBgwAGZmZjh8+DBCQkIwffp0ODk5vXGd/fr1g66uLlauXFkiMTo6OuL48eOIiYlBp06dkJCQAAAYN24ctmzZgm3btsHMzEx+pryoRB94lWwnJydjw4YNSEhIkE+fnZ1dInETERGVJUy4iYiIimnIkCGIiorC2rVr4enpWSJ16unpYfz48ViwYIFKz8RWhYODA44fP47Y2Fh50r169WrEx8ejbdu2sLe3l7927NhRZH1Xr17FhQsXcOPGDbi6uipM/+TJkxKJmYiIqCzhTdOIiIiIiIiI1IBnuImIiIiIiIjUgAk3ERERAQC2bt0KU1NTpS83NzdNh0dERFTqsEs5ERERAZA97is6OlrpOH19fTg7O7/jiIiIiEo3JtxEREREREREasAu5URERERERERqwISbiIiIiIiISA2YcBMRERERERGpARNuIiIiIiIiIjVgwk1ERERERESkBnqaDqC8CwsDYmNVK2ttDTg5qTceIiKi8oj7YyIqS4qzTQO4XVMnJtwaFBYG1KwJpKWpVl4qBUJCuDIQERGVJO6PiagsKe42DeB2TZ3YpVyDYmOLtyKkpRXvSBUREREVjftjIipLirtNA7hdUycm3ERERERERERqwISbiIiIiIiISA2YcBMRERERERGpARNuIiIiIiIiIjVgwk1ERERERESkBky4tUjlysBvv8nuEJiSAvz7L9CokaajIiIiKj8ePgSEyP9asULTkRERFZ+ODjBrFhAaKssv7t8HZszQdFTlC5/DrSUsLYEzZ4Bjx4DOnYFnz4Dq1YEXLzQdGRERUfnRpAmgq/vqvbs7cPgwsHOn5mIiInpTU6cCY8cCw4YBN28CjRsDfn5AfDywfLmmoysfmHBrialTgSdPgI8/fjXs0SONhUNERFQuvf4c2m++kZ0ROnFCM/EQEb0NT09g3z7g4EHZ+8ePgQEDgKZNNRtXeVJqE+7kZE1H8PZSU1/9v3t34O+/gT/+ANq0AcLDgVWrgPXr809TFpadiIhIW+TdH+elrw8MHgwsWaJ8Gu6PiUgb5d2mnT0LjB4t6zl77x5Qrx7QqhXw5ZfKpyvN2zUTE01HoJxECCE0HcSbkEg0HUHJyl0xliyRdVtr0gRYtgwYMwb49VfNxkZERFQe9esHbNsGODkBkZGajoaIqPgkEmDuXGDKFCA7W3bJzPTpwPz5mo6s5GlrVsuEW0ukpwOXLwMtW74atmyZLPH29NRcXEREROXVoUNARoasFxoRUWnUvz+wcCEwebLsGu4GDYCffpKd4S5rJ/W0NasttV3Kk5I0HcHbCwqSdekAZEfOb91SHH/7NtCnj+Kw06dlKwoRERGVjLz741xOToCXF9C7t/JpuD8mIm2Vd5u2cKHsbPaOHbL3wcGAszMwbVr+hJvbNfUotQm3tvbRLw4jo1f/P3MGqFlTcXyNGrIbG7w+TVlYdiIiIm2Rd3+ca8QIICYGOHCg4Gm4PyYibZR3m2ZsDOTkKI7PzpY9LkzZdNyulbxSm3CXNUuXym5qMG2a7MZpTZvKbnAwerSmIyMiIipfJBJZwr15s+yHKRFRafXXX7JrtsPCZF3KGzaUdSffuFHTkZUfTLi1xOXLQK9ewLx5wP/+Bzx8CEyaJLtZCxEREb07Xl6yLpf8QUpEpd2ECcDs2bKnH9naAhERwNq1wKxZmo6s/GDCrUUOHCi46xoRERG9G4GBZe/mrERUPiUlAV98IXuRZijpvU9EREREREREb4sJNxEREREREZEaMOEmIiIiIiIiUgMm3ERERERERERqwISbiIiIiIiISA2YcBMRERERERGpARNuDbK2BqRS1ctLpbJpiIiIqORwf0xEZUlxt2kAt2vqJBFCCE0HUZ6FhQGxsaqVtbYGnJzUGw8REVF5xP0xEZUlxdmmAdyuqRMTbiIiIiIiIiI1YJdyIiKid2z48OGQSCSQSCTQ19eHi4sLpkyZgrS0NIVyEokEf/75p8L73JeFhQVatmyJo0ePKtTbs2dPhTp27doFqVSKxYsXAwDmzZuHJk2awMzMDLa2tujZsydCQkJUjj0tLQ3jxo1DxYoVYWpqij59+iA6Orr4HwIREVE5wISbiIiomCIiIpCVlfVWdfj4+CAyMhKhoaFYunQp1q5dC19f3yKn8/PzQ2RkJM6cOQNra2t07doVoaGhSsuuX78egwYNwurVq/HVV18BAE6cOIFx48bh/PnzCAwMRGZmJry9vZGcnKxS3F988QX++usv7Ny5EydOnEBERAR69+6t+oITERGVI0y4iYiIimndunVwcHDA119/jRs3brxRHYaGhrCzs4OjoyN69uwJLy8vBAYGFjmdpaUl7Ozs4O7ujtWrVyM1NVXpdAsWLMCECROwfft2jBgxQj780KFDGD58ONzc3FC/fn1s2rQJYWFhuHLlSpHzjo+Px4YNG7BkyRK0b98ejRo1gp+fH86ePYvz588X7wMgIiIqB5hwExERFdPUqVOxbNky3L59Gx4eHvDw8MDPP/+MZ8+evVF9wcHBOHv2LAwMDIo1nZGREQAgIyMjX3yzZ8+Gv78/evXqVWgd8fHxAAArK6si53flyhVkZmbCy8tLPqxWrVpwcnLCuXPnihU7ERFRecCEm4iIqJikUin69++PAwcOIDw8HEOHDsWmTZtQpUoV9OzZE3v37i2yy7m/vz9MTU0hlUpRt25dxMTEYPLkySrHkJKSghkzZkBXVxdt2rSRDw8ICMCCBQuwb98+dOjQodA6cnJyMGnSJLRs2RLu7u5FzjMqKgoGBgawtLRUGF6pUiVERUWpHDsREVF5wYSbiIjoLdja2mLSpEm4evUq9u3bh3PnzqF3794IDg4udLp27dohKCgIFy5cwLBhwzBixAj06dOnyPkNGDAApqamMDMzw+7du7FhwwbUq1dPPr5evXqoWrUqfH19kZSUVGhd48aNQ3BwMLZv367awhIREVGxMOEmIiJ6C4mJifDz80P79u3RrVs3uLu7Y/PmzahTp06h05mYmMDV1RX169fHxo0bceHCBWzYsKHI+S1duhRBQUGIiopCVFQUhg0bpjC+SpUqOH78OMLDw+Hj44PExESl9YwfPx7+/v44duwYHBwcVFpWOzs7ZGRk4OXLlwrDo6OjYWdnp1IdRERE5QkTbiIiomLKzs5GQEAABg4ciEqVKmH+/Pno0KEDQkNDceTIEQwdOrRY12Pr6Ojg22+/xYwZM5CamlpoWTs7O7i6usLGxqbAMs7Ozjhx4gSioqLyJd1CCIwfPx579+7F0aNH4eLionKcjRo1gr6+Po4cOSIfFhISgrCwMLRo0ULleoiIiMoLJtxERETFNHfuXAwYMABmZmY4fPgwQkJCMH36dDg5Ob1xnf369YOuri5WrlxZIjE6Ojri+PHjiImJQadOnZCQkABA1o18y5Yt2LZtG8zMzORnyotK9AHAwsICI0eOxJdffoljx47hypUrGDFiBFq0aIHmzZuXSNxERERlCRNuIiKiYhoyZAiioqKwdu1aeHp6lkidenp6GD9+PBYsWKDyM7GL4uDggOPHjyM2NlaedK9evRrx8fFo27Yt7O3t5a8dO3aoVOfSpUvRtWtX9OnTB61bt4adnR327NlTIvESERGVNRIhhNB0EERERERERERlDc9wExEREREREakBE24iIiICAGzduhWmpqZKX25ubpoOj4iIqNRhl3IiIiICIHvEWXR0tNJx+vr6cHZ2fscRERERlW5MuImIiIiIiIjUgF3KiYiIiIiIiNSACTcRERERERGRGjDhJiIiIiIiIlIDJtxEREREREREasCEm4iIiIiIiEgNmHATERERERERqQETbiIiIiIiIiI1YMKtJunp6fjuu++Qnp6u6VBIjdjO5QPbuexjG5cPbOfyge1c9rGNy4ey0s4SIYTQdBBlUUJCAiwsLBAfHw9zc3NNh0NqwnYuH9jOZR/buHxgO5cPbOeyj21cPpSVduYZbiIiIiIiIiI1YMJNREREREREpAZMuImIiIiIiIjUgAm3mhgaGsLX1xeGhoaaDoXUiO1cPrCdyz62cfnAdi4f2M5lH9u4fCgr7cybphERERERERGpAc9wExEREREREakBE24iIiIiIiIiNWDCTURERERERKQGTLiJiIiIiIiI1KBMJNzZ2dmYOXMmXFxcYGRkhGrVqmH27NnIez+46OhoDB8+HJUrV4axsTF8fHxw7969Qutt27YtJBJJvleXLl3kZZKSkjB+/Hg4ODjAyMgIderUwZo1axTqSUtLw7hx41CxYkWYmpqiT58+iI6OLnTeQgj873//g729PYyMjODl5ZUv3ri4OAwaNAjm5uawtLTEyJEjkZSUpFDm33//xfvvvw+pVApHR0csWLCg0PlqK21v419++QVt27aFubk5JBIJXr58qdJyrVy5ElWrVoVUKkWzZs1w8eJFhfGqfHfCwsLQpUsXGBsbw9bWFpMnT0ZWVpZK89c22tzOcXFxmDBhAmrWrAkjIyM4OTlh4sSJiI+PL3TeXJfz0+Z2BoBPP/0U1apVg5GREWxsbNCjRw/cuXOn0HmznRVpexvnEkKgc+fOkEgk+PPPPwudN9s4P21vZ2X1jBkzptB5s53z0/Z2BoBz586hffv2MDExgbm5OVq3bo3U1NRC58/fYK9ocxs/evRIaR0SiQQ7d+4scN5atS6LMmDOnDmiYsWKwt/fXzx8+FDs3LlTmJqaimXLlgkhhMjJyRHNmzcX77//vrh48aK4c+eOGD16tHBychJJSUkF1vv8+XMRGRkpfwUHBwtdXV3h5+cnLzNq1ChRrVo1cezYMfHw4UOxdu1aoaurK/bt2ycvM2bMGOHo6CiOHDkiLl++LJo3by48PT0LXab58+cLCwsL8eeff4rr16+L7t27CxcXF5Gamiov4+PjI+rXry/Onz8vTp06JVxdXcWAAQPk4+Pj40WlSpXEoEGDRHBwsPj999+FkZGRWLt2bXE/Yo3T9jZeunSpmDdvnpg3b54AIF68eFHkMm3fvl0YGBiIjRs3ips3b4pRo0YJS0tLER0dLS9T1HcnKytLuLu7Cy8vL3Ht2jVx8OBBYW1tLaZNm1aMT1d7aHM737hxQ/Tu3Vvs379f3L9/Xxw5ckRUr15d9OnTp9Bl4rqcnza3sxBCrF27Vpw4cUI8fPhQXLlyRXTr1k04OjqKrKysAufNdlak7W2ca8mSJaJz584CgNi7d2+hy8Q2zk/b27lNmzZi1KhRCnXFx8cXukxs5/y0vZ3Pnj0rzM3Nxbx580RwcLC4c+eO2LFjh0hLSytw3vwNpkib2zgrK0uhjsjISPH9998LU1NTkZiYWOC8tWldLhMJd5cuXcTHH3+sMKx3795i0KBBQgghQkJCBAARHBwsH5+dnS1sbGzEunXrVJ7P0qVLhZmZmcIXy83NTcyaNUuhnIeHh5g+fboQQoiXL18KfX19sXPnTvn427dvCwDi3LlzSueTk5Mj7OzsxMKFC+XDXr58KQwNDcXvv/8uhBDi1q1bAoC4dOmSvExAQICQSCQiPDxcCCHEqlWrRIUKFUR6erq8zNSpU0XNmjVVXmZtoc1tnNexY8dUTribNm0qxo0bpxBv5cqVxbx584QQqn13Dh48KHR0dERUVJS8zOrVq4W5ublCu5cWpaWdc/3xxx/CwMBAZGZmKh3PdVm50tbO169fFwDE/fv3lY5nO+dXGtr42rVrokqVKiIyMrLIhJttrJy2t3ObNm3E559/rvJ82M7KaXs7N2vWTMyYMaNYy8TfYIq0vY1f16BBg3zx5qVt63KZ6FLu6emJI0eO4O7duwCA69ev4/Tp0+jcuTMAID09HQAglUrl0+jo6MDQ0BCnT59WeT4bNmzARx99BBMTE4V579+/H+Hh4RBC4NixY7h79y68vb0BAFeuXEFmZia8vLzk09SqVQtOTk44d+6c0vk8fPgQUVFRCtNYWFigWbNm8mnOnTsHS0tLNG7cWF7Gy8sLOjo6uHDhgrxM69atYWBgIC/TqVMnhISE4MWLFyovtzbQ5jZ+ExkZGbhy5YpCG+vo6MDLy0vexqp8d86dO4e6deuiUqVK8jKdOnVCQkICbt68+cbxaUppa+f4+HiYm5tDT09P6Xiuy8qVpnZOTk6Gn58fXFxc4OjoqLQM2zk/bW/jlJQUDBw4ECtXroSdnV2R82EbK6ft7QwAW7duhbW1Ndzd3TFt2jSkpKQUOB+2s3La3M4xMTG4cOECbG1t4enpiUqVKqFNmzaFzpe/wfLT5jZ+3ZUrVxAUFISRI0cWOB9tW5eV/0osZb755hskJCSgVq1a0NXVRXZ2NubMmYNBgwYBeLWCTJs2DWvXroWJiQmWLl2Kp0+fIjIyUqV5XLx4EcHBwdiwYYPC8OXLl2P06NFwcHCAnp4edHR0sG7dOrRu3RoAEBUVBQMDA1haWipMV6lSJURFRSmdV+7wvCvw69NERUXB1tZWYbyenh6srKwUyri4uOSrI3dchQoVVFp2baDNbfwmYmNjkZ2drbSNc68VVeW7ExUVpbSO3HGlTWlq59jYWMyePRujR48ucF5cl5UrDe28atUqTJkyBcnJyahZsyYCAwMVdrh5sZ3z0/Y2/uKLL+Dp6YkePXqoNC+2sXLa3s4DBw6Es7MzKleujH///RdTp05FSEgI9uzZo3RebGfltLmdQ0NDAQDfffcdFi1ahAYNGuDXX39Fhw4dEBwcjOrVq+ebF3+D5afNbfy6DRs2oHbt2vD09CxwXtq2LpeJhPuPP/7A1q1bsW3bNri5uSEoKAiTJk1C5cqVMWzYMOjr62PPnj0YOXIkrKysoKurCy8vL3Tu3FnhZgCF2bBhA+rWrYumTZsqDF++fDnOnz+P/fv3w9nZGSdPnsS4ceNQuXJlhaMq9HbYxuVDaWnnhIQEdOnSBXXq1MF3331XUotfbpSGdh40aBA6duyIyMhILFq0CB9++CHOnDmjcHSfCqbNbbx//34cPXoU165dU8eilyva3M4AFA6I1q1bF/b29ujQoQMePHiAatWqldwHUcZpczvn5OQAkN3scsSIEQCAhg0b4siRI9i4cSPmzZtXsh9GGaXNbZxXamoqtm3bhpkzZ5bYsr8TxeqArqUcHBzEihUrFIbNnj1baf/6ly9fipiYGCGE7PqNzz77rMj6k5KShLm5ufjpp58UhqekpAh9fX3h7++vMHzkyJGiU6dOQgghjhw5ovSaXicnJ7FkyRKl83vw4IEAIK5du6YwvHXr1mLixIlCCCE2bNggLC0tFcZnZmYKXV1dsWfPHiGEEEOGDBE9evRQKHP06FEBQMTFxRW53NpEm9s4L1Wv4U5PTxe6urr5rhkcOnSo6N69uxBCte/OzJkzRf369RXGh4aGCgDi6tWrhcagjUpDOyckJIgWLVqIDh06KNx4Qxmuy8qVhnbOKz09XRgbG4tt27YpHc92zk+b2/jzzz8XEolE6Orqyl8AhI6OjmjTpo3S+bGNldPmdi6oPgDi0KFDSseznZXT5nbO/c3z22+/KZT58MMPxcCBA5XOj7/B8tPmNs7r119/Ffr6+vL5F0Tb1uUycQ13SkoKdHQUF0VXV1d+1CsvCwsL2NjY4N69e7h8+bJK3cl27tyJ9PR0DB48WGF4ZmYmMjMzC513o0aNoK+vjyNHjsjHh4SEICwsDC1atFA6PxcXF9jZ2SlMk5CQgAsXLsinadGiBV6+fIkrV67Iyxw9ehQ5OTlo1qyZvMzJkyeRmZkpLxMYGIiaNWuWuu5M2tzGb8LAwACNGjVSaOOcnBwcOXJE3saqfHdatGiBGzduICYmRl4mMDAQ5ubmqFOnzhvHpyna3s4JCQnw9vaGgYEB9u/fX+TZTq7Lyml7O79OyG4wKr+G7XVs5/y0uY2/+eYb/PvvvwgKCpK/AGDp0qXw8/NTOj+2sXLa3M7K5La1vb290vFsZ+W0uZ2rVq2KypUrIyQkRKHM3bt34ezsrHR+/A2Wnza3cV4bNmxA9+7dYWNjU+j8tG5dVjk112LDhg0TVapUkd/Kfs+ePcLa2lpMmTJFXuaPP/4Qx44dEw8ePBB//vmncHZ2Fr1791ap/latWon+/fsrHdemTRvh5uYmjh07JkJDQ4Wfn5+QSqVi1apV8jJjxowRTk5O4ujRo+Ly5cuiRYsWokWLFoXOc/78+cLS0lLs27dP/Pvvv6JHjx5Kb2XfsGFDceHCBXH69GlRvXp1hVvZv3z5UlSqVEkMGTJEBAcHi+3btwtjY+NS+VgKbW/jyMhIce3aNbFu3ToBQJw8eVJcu3ZNPH/+vMB5bt++XRgaGopNmzaJW7duidGjRwtLS0uFu10W9d3JfSSFt7e3CAoKEocOHRI2Njal8pEUQmh3O8fHx4tmzZqJunXrivv37ys8nqKox0VxXVakze384MEDMXfuXHH58mXx+PFjcebMGdGtWzdhZWWl8LiY17GdFWlzGysDFR8LxjZWpM3tfP/+fTFr1ixx+fJl8fDhQ7Fv3z7x3nvvidatWxc6T7ZzftrczkLI7nxtbm4udu7cKe7duydmzJghpFJpgU+WEIK/wV6n7W0shBD37t0TEolEBAQEqDRPbVqXy0TCnZCQID7//HPh5OQkpFKpeO+998T06dMVbuG+bNky4eDgIPT19YWTk5OYMWNGvlv2Dxs2LF93sjt37ggA4p9//lE678jISDF8+HBRuXJlIZVKRc2aNcXixYtFTk6OvExqaqr47LPPRIUKFYSxsbHo1auXiIyMVKjH2dlZ+Pr6yt/n5OSImTNnikqVKglDQ0PRoUMHERISojDN8+fPxYABA4SpqakwNzcXI0aMyPc8uuvXr4tWrVoJQ0NDUaVKFTF//vwiP09tpO1t7OvrKwDke+V9zmCbNm3EsGHDFOpevny5cHJyEgYGBqJp06bi/PnzCuNV+e48evRIdO7cWRgZGQlra2vx1VdfFfiYKm2nze2ce7mAstfDhw/l9XBdLpo2t3N4eLjo3LmzsLW1Ffr6+sLBwUEMHDhQ3LlzR6EetnPhtLmNlVGWcLONi6bN7RwWFiZat24trKyshKGhoXB1dRWTJ0/O9xxutnPRtLmdc82bN084ODgIY2Nj0aJFC3Hq1CmF8fwNVrjS0MbTpk0Tjo6OIjs7W2k92rwul4mEu6S0bt1aoaHeleTkZCGVSsWxY8fe+bzLG021sRCy637yJuCkPlyXywe2c9nHNi4f2M7lA3+DlX1cl5UrE3cpLwnx8fF48OABDhw48M7nfezYMbRv3x5t27Z95/MuTzTZxjdv3oSFhQWGDh36zudd3nBdLh/YzmUf27h8YDuXD/wNVvZxXS6YRAgV7+VORERERERERCorE3cpJyIiIiIiItI2TLiJiIiIiIiI1IAJNxEREREREZEaMOEmIiIiIiIiUgMm3ERERERERERqwISbiIiIiIiISA2YcBMRERERERGpARNuIiIiIiIiIjVgwk1ERERERESkBky4iYiIiIiIiNSACTcRERERERGRGjDhJiIiIiIiIlIDJtxEREREREREasCEm4iIiIiIiEgNmHATERERERERqQETbiIiIiIiIiI1YMJNRET0Dm3atAkSiQSXL19WGB4fH4+mTZtCKpXi0KFD8nLKXlFRUfnqTUxMxJQpU+Di4gJDQ0NUqVIFffv2RUpKirzMkSNH8PHHH6NGjRowNjbGe++9h08++QSRkZFqX24iIqLySE/TARAREZV3CQkJ8Pb2xr///ou9e/fCx8cHmzZtAgDMmjULLi4uCuUtLS0V3sfHx6NNmzZ4+vQpRo8eDVdXVzx79gynTp1Ceno6jI2NAQBTp05FXFwc+vXrh+rVqyM0NBQrVqyAv78/goKCYGdn9y4Wl4iIqNxgwk1ERKRBiYmJ6NSpE4KCgrBnzx507txZYXznzp3RuHHjQuuYNm0aHj9+jKtXryok51OnTlUot2TJErRq1Qo6Oq86uPn4+KBNmzZYsWIFfvjhhxJYIiIiIsrFLuVEREQakpSUBB8fH1y9ehW7d+9Gly5dlJZLTExEdna20nEvX76En58fRo8eDRcXF2RkZCA9PV1p2datWysk27nDrKyscPv27bdbGCIiIsqHCTcREZEGJCcno3Pnzrh06RJ27tyJrl27Ki3Xrl07mJubw9jYGN27d8e9e/cUxp8+fRppaWlwdXVF3759YWxsDCMjI7Rs2RJBQUFFxpGUlISkpCRYW1uXxGIRERFRHuxSTkREpAHDhg1DREQEdu7cie7du+cbb2xsjOHDh8sT7itXrmDJkiXw9PTE1atX4ejoCADyBHzatGmoVq0afv31V8THx+P7779H+/btcfPmTdjb2xcYx08//YSMjAz0799fPQtKRERUjjHhJiIi0oDo6GhIpVJ54vy6Dz/8EB9++KH8fc+ePdGpUye0bt0ac+bMwZo1awDIzlADgEQiwZEjR2BqagoAaNiwIVq0aIGVK1cWeG32yZMn8f333+PDDz9E+/btS3LxiIiICOxSTkREpBFr166FgYEBfHx8EBISotI0rVq1QrNmzXD48GH5MCMjIwBAt27d5Mk2ADRv3hwuLi44e/as0rru3LmDXr16wd3dHevXr3+LJSEiIqKCMOEmIiLSgDp16uDgwYNITU1Fx44d8eTJE5Wmc3R0RFxcnPx95cqVAQCVKlXKV9bW1hYvXrzIN/zJkyfw9vaGhYUFDh48CDMzszdcCiIiIioME24iIiINadq0Kf7880/ExMSgY8eOePbsWZHThIaGwsbGRv6+UaNGAIDw8PB8ZSMiIhTKAsDz58/h7e2N9PR0/P3334Ve301ERERvhwk3ERGRBnXo0AG///477t+/Dx8fHyQkJACA0uT74MGDuHLlCnx8fOTDatasifr162Pfvn2IjY2VD//nn3/w5MkTdOzYUT4sOTkZH3zwAcLDw3Hw4EFUr15djUtGREREvGkaERGRhvXq1Qvr1q3Dxx9/jO7du+PQoUPw9PREw4YN0bhxY1hYWODq1avYuHEjHB0d8e233ypMv3TpUnTs2BGtWrXCp59+ivj4eCxZsgQ1atTA2LFj5eUGDRqEixcv4uOPP8bt27cVnr1tamqKnj17vqtFJiIiKhckQgih6SCIiIjKi02bNmHEiBG4dOkSGjdurDBu8eLF+Prrr9G1a1fUr18fBw4cwMOHD5GSkgJ7e3t06dIFvr6+Sq/XPnz4MGbOnImgoCAYGxujS5cuWLBgAezs7ORlqlatisePHyuNy9nZGY8ePSrRZSUiIirvmHATERERERERqQGv4SYiIiIiIiJSAybcRERERERERGrAhJuIiIiIiIhIDZhwExEREREREakBE24iIiIiIiIiNWDCTURERERERKQGTLiJiIiIiIiI1IAJNxEREREREZEaMOEmIiIiIiIiUgMm3ERERERERERqwISbiIiIiIiISA2YcBMRERERERGpARNuIiIiIiIiIjVgwk1ERERERESkBky4iYiIiIiIiNSACTcRERERERGRGjDhJiIiIiIiIlIDJtxEREREREREasCEm4iIiIiIiEgN9DQdwJs6fvy4pkMoU/T19VGlShU4OTlBR4fHYYiIiIiIiN5WqU24L545DIlEoukwygQhBDIzM5GeKWBRwRpDhg6Hra2tpsMiUpCcnIz79+8jKSkJQghNh1Nm6OjowNLSEq6urjAwMNB0OBonhEBkZCSePHmCzMxMTYdTpujp6cHOzo4HdomIqFyRiFL6yzXjRYimQyhThBAIj4hGwD8nkJShhwkTJ8HY2FjTYRFBCIGDBw/i8sWzyM5Mg9RQnz/WS1B2djbSM7Ohb2ACb58P0KxZM02HpDFxcXHYuvU3PIsOh64kBwb6+jywW0JkB3azkJUjgbllRQwYOBhVqlTRdFhUBgkhcPPmTfz77794+uQRMjN44Kwk6enrwb6yA+rWrYcGDRpwG/kG7t27h2vXruHxowfISM/QdDhlyvSZvpoOQSkm3KQgISEJy3/5HT36DISHh4emwyHCkSNHcPLoIbRr5YH6dWvBxIQHgkray/gEnLt4DVf+fYB+/Qejbt26mg7pncvIyMDyn3+Cbk4yfDq+j6pOVXhgp4TlHtg9fOwsnidmYexnE2BpaanpsKgMEULg6NGjOHH0bzjYVYBL1SowNDBgUlhChBDIyMzE4ycRCAt/Do8mLdG9e3d+vsVw8eJF+O/fg0pWJqj2niOMjaT8/ErQ+979NR2CUqW2Szmph7m5KSpXssLDhw+ZcJPG5eTk4PKl82jcoCY8m/P7qC6WFubw8WqNuLh4XLp0sVwm3Hfv3kV8XAzGjuyLilYVNB1OmSSRSOBQxQ4f9e2CZau3IDg4GK1atdJ0WFSGREVF4cSxQLRv1ZD7DDUL+vcW/P85A3d3d1SrVk3T4ZQKSUlJOOi/D43qvQcfr9ZMtMsRHr6nfEyMpUhLS9N0GESIjo5GcmI8atfizlzdJBIJatVwQdijUGRlZWk6nHcuNDQUttbmTLbfAanUEFWd7PDgwX1Nh0JlzK1bt2CkDzRrUl/ToZR59evWRgVzKW7duqXpUEqNO3fuADkZaNOyKZPtcoYJN+UjkUh4UyrSCrIDPwKm7Eb+TpiYmEDkZCM9PV3TobxzaWlpMDE20nQY5YaJiRHSUlM1HQaVMc+fP4ddJSvo6upqOpQyTyKRoIq9DZ49i9F0KKXG8+fPUcHCFMbc15Q7TLiJSOvxSPC7kfsxl9cDbvyavTs8sEvqkJ2dDT09Jtvviq6eHrLLYY+oN8XvZ/lVqhPutRt/R6NW3WHt1AjWTo3Q2rs/DgWezFfu/MVr6NRjGCo4NIS1UyN06DIYqamvukzfvf8QfQZ9hsquzWHt1AjtOg/E8VPnVYrBu/tQbPx1p9Jx0TGx+GTcN6ha531YVmmArn0/wb0Hj5SWFUKgW79RMLSqhX0HDqs0bwAY96UvDK1q4efVm+XD0tMzMGLMFFg7NYJbk044cvyswjSLf96ASVNnqzwPIiIiIiIiKr5SnXBXqVwJP/h+hXPHduPs0V1o27o5+g4eh1u378nLnL94Dd36jYJXu5Y4E/gHzhzZibGfDFK4+2yvAWOQlZWNv/dtxrlju1HXvRZ6DRiLqOhnhc4/7sVLnL1wDV182uUbJ4RAv8Hj8PDRU+zasgoXju+Bk2NlfNDrYyQnp+Qr//PqzcU+i7fPPxAXL19HZXvFZ2av37wDV4Nu4uTf2zFy6IcYNvpr+ZmEh4+fYuNvf2DW9C+KNS8ibXPq7CX0GjAGVeu8X+CBKiEEvp/7M5xrvw+LyvXh02tEvoNe6jjglpmZiW+/WwSPlt1QwaEhqtZ5Hx+PnYqIyGh5mROnL8DQqpbS1+WrN5TO71HY0wKn2f3nIQCy7VKvAWNg5eiBpm16IehfxevrJk6ehaUrNqq0fAQsWLoWnh36oqKTBxxqeKLv4HEIuReqUKZjtyH52mPcl4qPJjl64hzadPoIFZ084FSrFb79bpHK18rXaNAh34HTvIo6qAwAB/85jlZeH8Kicn1UcmmKvoPHqfgJ8MAulX4ltb+4dv0mOvf6GLZVm8C+WjOMnTQTSUnJKsXwrvcXAPDgYRj6DRmPKtVbwNqpEQaOmITomFj5eK7H2oEnEMv+fqZUJ9xdfdqjc8c2qF6tKmq4umDWjC9gamKMC5evy8tMnj4f40YPweRJo1GndnXUrP4e+vbqDENDAwBA7PMXuP/gMSZPGoW6bjVRvVpVzPnfl0hJScXNPIm7MgH/nEDDenVQydY637h7Dx7hwuXrWL7IF4096qJm9fewYvF3SE1Lw47dBxTKXr9xG8tW+uGX5XNUXvbwiGh8MfUHbF67EPp6ijebv3M3FF07t0ed2tUx9pNBeBYbh9jnLwAAE776DnN9v4a5uanK8yLSRsnJqajnXgvLFvyvwDKLf16Plb/8huWLv8PpwD9gYmyErn0/QVraq2uU1XHALSU1Ddeu38K3X3+G88d2Y8fm5bh7T7YjzNWiaUM8vn1K4fXxkH6o6uyARg3dlc7TsYp9vmn+980EmJoao5PX+wCA+YvXICkpGeeP7Ubrlk0xdtJM+fQXLgXh0pXrmDh2WKHLRq+cPHMJY0YOxKm/d+Dgno3IzMxC1z6f5Dtw+vHQfgrtMu+7yfJx/wbfQY/+o+Hd4X1cOL4XWzYsgX/AUUz/fnGR879xMwQvXyagdcsmSserclB57/6/8fGYqRg6qDcunfwTxwO24aM+XVVafh7YpbKgJPYXEZHR6NzrY1R7zwmnAnfgr53rcfvOfXwyblqR89fE/iI5OQVd+oyERCLB3/s24fihbcjIzETvgWORk5MDgOuxtuAJxLK/nykzjwXLzs7G7j8PITklBc2bNAAAxDx7jotXruOjfl3RptNHCH30BDWru+D7GV+gZfNGAICKVpaoUd0FW7fvQ8N6dWBoaIB1m3bA1qYiPBq4FTpP/4Cj6PZBe6XjMjJkD7I3lBrKh+no6MDQwABnL1zBx0P7AQBSUlIxdNTX+Gnh/2BXyUalZc3JycHHY6fgiwkjUad29Xzj67nXxLYd+5GamoZ/jp6GvZ0NrCtWwO87/4JUaogeXTuqNB8ibebTsTV8OrYucLwQAsvX/IpvvhqD7h90AABsXP0jHGu2xP4Dh/Fhny7yA25rf56Dum41AQBz/vcl1m7Yhpu37xW6ThZ2wM3C3AwBexXPIv+0YCZaevVD2NMIODlUhoGBgUL9mZmZ+CvgCD4bNbjAnZWurm6+mPYdOIy+PTrD1NQEgOyAW7/eXVDD1QUjh32IDb/+Ia9//FffYc2y2byhUDH471qv8H79ynlwqOGJq9dv4n3PV0mwsZFRgd+XnXsPoq5bTUyfIjur7PqeM+Z+/zUGffwFZkwZBzOzgg+A/nXwCLw7vA99fX2l4/MeVM5Vs/p78v9nZWXhq2lzMe/7yRgxpK98eO1aroUstUzugV3/XevR86NPFcblPbDrUtUR03wXIvb5C9hYW/HALmmdkthfHPz7OPT19fDzwv/Jk5wVS75Do1Y9cD/0MVzfcy6wfk3sL85euIrHYeG4eHyvfF3csGo+Krk0xbGT59GhrSfXYy3R1Ucxl5g14wv8snE7Lly+Lv+dX9S2Xl2/Z3JPIF4785c8lhWLv4NTrVbYsfuAPJ8BXp1APHt0F5xrv6/SspeX/UypPsMNAMG3QmDl6AEzu3oY/9V3+OO3FfIfEg8fPQEA/PDjCnw8tB/+2rkODeq5wafncHlXCIlEgoA9fgi6cQsVnRrB3L4+fl69CX/tXIcKlhYFzjc9PQP/HD2VbyXJVbP6e3ByqIyZs5bgxct4ZGRkYNGydXgaEYXIqFdHmr6ePg8tmjaUb+BVsWjZOujq6mL8p0OUjh8+qA/qutdC/RZd8OOSNdi68Se8eBmP7+f9jKXzZ8B3zk+o3cgbXfqMRHhEtNI6iEq7h4+fIir6GTq09ZQPszA3Q9NG9XD+UhAAxQNuyckpyMrKKpEDbsrEJyRCIpHA0ty8wPqex73E0IG9Va7zalAwrt+4jeGD+8iH1XOvieMnzyMrKwuBR0+jbp0aAGRdr1q3bIpGDcvfM7ZLUnxCIgDA6rX9w/Zdf6Gya3M09OyGGbMWIyXl1R2409MzIDU0VChvJJUiLS0dV6/fLHR+/ocK/p7lHlS2sbFCm04fwbFmS3h1HYwz56/Iy1y7fgvhkdHQ0ZGgaZtecK79Prr1G4Wbt+4WOl9VDuyePX+FB3apTFBlf5GekQEDfX2FM4pSqRQAcDbPOqeMJvYX6RkZkEgk8h6dACA1NISOjo48Xq7H2ic7Oxt/7D6g9ARiYdt6df2eKeoEYi51nUAsK9/PUp9w13B1wcUTe3E6cAdGf/wRPvnsG9y+I3u2Z26XmU+G98ewQX3QoF4dLJo7DTVcXbB5624AsqOan0+ZBRvrijh6YCvOHP4D3T7wQu8BYxEZVfCjDo6dPA8b64pKvyAAoK+vjx2//ox7Dx7B7r1msKzSEMdPXUAnr9byjfVfAUdx/NQFLJpbdHekXFeDgrFi7W9Yv3JegUc19fX18fPC/+Fu0BGcPbILLZs3wtQZP2Lc6CEIunEb+w8cweWTf6JZ4/r48psfVJ43UWkS/V8XKlubigrDbW2s5dewqeuA2+vS0tIx/ftF6N+nS4FHY/227EbH9q3gUMVOpTpzp6lVoxpaNPOQD5s8aTT09HRR28Mb+w8EYu3Pc3DvwSP8tn0vvp08FuO+9EXNhl4YOGKSPHkk1eTk5ODrb+fCs5kH3P47kAEA/ft0hd+aBfhn/2ZM+WI0tu3Yj+FjpsjHd2zfCucuXsOO3f7Izs5GeEQ05i5cBQCFdvULj4jGjZsh6OSl/MycKgeVX5VZiWlfjcHe31ejgqU5OnYfirgXLwucNw/sUnmiyv6i3fvNERUTi8U/b0BGRgZevIzHjP8uCylsPdbU/qJZ4wYwMTbCt98tQkpKKpKTUzD1fz8iOzsbkf/Fy/VYe/AEYn5l6ftZ6hNuAwMDuL7nDI8G7vjhf1+hrnstLF/7KwDAzk52LUDtmopd52rVqIYnTyMByBLng38fx5b1S+DZ3AMN67th+SJfSI2k2LL9zwLn63/oaJEbT48G7rh08k/EPLqEx7dPwX/XesTFvYRLVUcAwPGT5xH6MAy2Lk1hbOMGYxvZEaiPhk1Ex27Kv3ynz11BzLPncK3XXj7N4ycRmDrzR9Sorzye46fO41bIfXw2ahBOnr4An46tYWJijL49O+PkmYuFLgNRWaauA255ZWZmYuDHkyAEsHzRd0rLPA2PQuDR0xiR50x1UVJT07Bjl7/C2W1Adlbm13WLce/fozjsvwW1a7li/Je+mPf9FPy+8y88fPQEwRcDYGwsxZwFK1WeH8luOHfr9j38tn6JwvBPhveHd4f34V6nJgb064YNq3/EPv9APHgYBkCWcM/7fjLGf/kdzOzqwb2pj7x7q46k4N2w/6Gj8GzeCJYWys9yqXJQOUfIykz98lP06t4JHg3csW6F7IDt7n2HlNbLA7tE+dWpXR0bVs3DslV+sKzSEE61WqGqswMq2VornPV+nab2FzbWVtjm9xMO/H0MVo4esKnaBPHxiWhYv448Xq7H2oMnEJXPu6x8P0t9wv06kZMj7/5Q1akKKtvb4u69hwpl7j14BCfHygCAlFRZtz8dHcXG1tGRyL/g+eYhBA4eOqZy9yALczPYWFvh3oNHuBIUjG6dZdNNnjQKV07tw6UTe+UvAFg45xv8smKe0roG9e+eb5rK9rb4csJI/PXatYaA7Ejp55NnY+WS76Grq4vs7BxkZsrujJuZlYXsbOXLSFTaVfqvS1PMs+cKw2OexcqvU1LnATcg98fTFwh7EoGDezYUeLbi1217UNHKEl07q97lcM/+v5GSmobBH/UstNzmrbthYWGO7h90wMkzl9C9ixf09fXRu4cPD7gVw+dTZiHg7+P4e/+vRfZCaNqoHgDgQehj+bBJ40Yg5tEl3P/3KCLunUPXzrKzALkHYJXxDyj8e6bKQeXcrn15r9k2NDSAi7OjvMzreGCXyhtV9hcA8FHfbgi7cxoPb55A5P3zmDl1PJ7FxsHFuZD1WIP7i47tW+HO1UA8vXsWEffPwW/NAkRExhQYL9djzeEJxLK9nynVN02bMWsxOnm1hqODPZKSkrF9lz9OnL4ov8mNRCLBF+NHYvb85ajnXhP16tbGlt//RMi9UPy+aRkAoHmThqhgaY6Rn32D6VPGQSo1xMZfd+LR43B09m6rdL5Xg4KRkpomv/FaQXb/eQjW1hXg6FAZwbfu4utpc9D9gw7o2L4VANkPIWXXOTg6VIaLs4P8fd1mnfHDzC/Ro2tHVLSqgIpWFRTK6+vpoZKttcLNE3LNXbQKPh1bo0G9OgAAz2YemOa7EMMG9cbqdVsVuqISlSUuzg6wq2SDoyfOoX7d2gCAhIQkXLzyL0aPGADg7Q64+a1dUOj8c3883X/wGP/s35xvvc1b3+ZtezCof48Cb4ylzKYtu9DVpx1srK0KLPMsNg5zFq7CsYPbAMiuDZMfcMvkATdVCCEwaeps7D9wGP/s/1Vh21yQ6zfuAADs7RTvuCqRSFDZvhIA4I/dB+BYxR4N69dRWkdSUjJOnL6A5Yt9lY4HCj+onHvXeo/67jA0NMDdew/l+6zMzEw8fhIOJ4fKSusd1L87OrRpoTCsa79PMPDDHhg6sFe+8rkHdjf/slB+YFcIHtil0kOV/UVeuUn4pi27IZUaokM7z3xlAO3ZX1hXlNV37OR5xDx7jq6d89+NmuuxdlH1BGLutl6dv2dyWZibyed7JSgYvt9OBCA7gfhxnptyAoBHq+5YOOcbdCkgmS9v+5lSnXA/exaHkWOnIjL6GSzMzeDuVhP+u9bDq11LeZmJY4chPT0dk6fPR9zLeNRzq4mDezaimosTANlG6K+d6/C/H35Cpx7DkJmZhTq1XLFry0rUc6+ldL5/HTwKn46toadX+McXGR2DKTPmI/rZc9hXssGg/j3w7eSxxV7Ou/cevtG1ljdv3cWuPw/Jz5wDQO8enXDizEW0/2AQalR3weZfFhW7XiJtkJSULO+yCwCPHj/F9Ru3UaGCBZwcKkMikWDCmKGYv3gNXKtVhYtzFXw392fY29miexcvAOo74JaZmYmPhn+OoOu3sHf7GmRnZ8uv8bOqYAEDg1c3sTl28jwePX6KEUP65asnPCIaPr2GY+OqH9Hkv7OmAHA/9DFOnb2MfTt+KfQz+vrbuZg0bgSqVJYleS2aeWDbH/vg1a4lNmz+gwfcVDBx8izs2OWPXVtXwszURN6OFuZmMDKS4sHDMOzY5Q+fjq1hZWWJGzfvYvL0eXjfs7H8TrGA7KZ1nbxaQUeigz/9A7Fw2Tps3bi0wDvG/3PkFKpXq4qqTgUn+KocVDY3N8Wo4R9h9vzlcKhiByfHyli6XHZH5D49feR18cAulWUlsb8AgFXrtqBF04YwMTHGkeNnMc13IX7435cFXvah6f3F5q2y+3xYW1vhwqUgfDVtDiaOHcb1WMvwBKJMWd7PlOqEe62Kz62ePGm0wm30X9eoYV0c2L1B5fn+FXAE074qOnEe/+lQjP90qMr1AkB63B2VhuV19/pRpcPd6tTArct/KwzT0dHB8kW+WL6o4LMmRKXBlaBgeHd/9TzpKTPmAwCGDOiJ9Stl//9q4idITk7FuC/+h5fxCfBs3gh/7VwH6X9321TXAbfwyGj4B8jWyyateyqM+2f/ZrRp1Uz+ftOWXWjRtCFq1ci/g8nMysTdew/lR65zbd66Gw6V7dCxfct808jnc+QUHoSGwW/NqyPXn30yCFevBeP9jh+icaN6mPHfY6qoYL9s/B0A0LGb4rZ83Yq5GDqwNwz09XH0xFksX7MZySmpcKhij17dvPPtI/45chI/LlmD9IwM1HOrhV1bVhb6mKK/Ao6q1GW0qIPKADB/1mTo6eni47FTkZqahiaN6uPvPzcp3EiHB3apLCuJ/QUAXL56A7PnL0dScgpqVn8PK5d8j0H9exQ4X03vL+7ef4SZs5ci7kU8nJ0qY+qXY/D5Z8PzTcv1WLN4ArFwZeH7KRG5TxAvZTJehGhmvhkZWLD0F3z+2fBCn51amu3YfQC6xnYYOHCgpkOhcu7hw4fwW78K4z7pV+hdNt+lRq26Y9pXY9G3V2dNh1LiQu6FYtf+E5j8zQyYmpbN7VtB/vjjD6S8eIyBH3bXdCjIysqSPf/3j3UKPRvKkgN/H0P0ixyMGfuZpkOhMuT3339HVnIE+vfpqulQAJTt/QUgOzAYl6SDUaM/Lbow4eDBg3gYchWjhufvoaAJZfH7aVChZtGFNKDM3TRN3QwMDDBj6vgym2wLIRD3IgFmZmaaDoVIfufKgq4/etcyMjLQq5u3/JqpsiYnR3b8tbA77pZVEolEvvyaFvciHhPHDkdjj7L7zPScHFEuv2ekftpyHqms7y+A3M9a+R2mSbuVh++nNuHejhQ8fhKO5y+TUbOmdh4hovLF1NQUkOgg7kW8pkMBUPYPuL14EQ89fQMYGhoWXbiMMTU1xYuXiVrxY93WpiKmfT22wEellAUv4xNgaqb8uleiN6Wnp4fMzGxNhwGg7O8vACAzIxP6BqrfvK2809PTQ0ZmpqbDAFA+vp/apNRewx3z7HmZ/jHyLgkhkJ6egdBHYbh45Sac36uJatWqaTosIlSsWBEVre1w/d/bcH3Pmeu8GmVlZSH41l241qhd4I28yrIaNWrg3OmjePj4Kd4r5FFd9Paex71A2NNn6N7Uq+jCRMVgb2+POzcuISMjQ+FmY1TycnJy8PhpNDya1dZ0KKWGvb09Th9Pxsv4hAJvtEdlU6lNuH/ZvLfoQlQMEkiNTFCnfnN88MEH5fIHN2kfiUSCNm3bYc+u7di19xAa1K8NqwoW7IpagrKzcxAd/QyXrwbjRVI2urcs+EZsZVnVqlXh/F5N7N4XiBZN6+G9qo6QSg15kKeECCGQmZmFR4+f4tylf2Ft5wg3NzdNh0VljJubGw7/E4C/D5/CB53a8reMmuTk5ODoiXNITc/helwMNWrUgIGRKQL+OYk+Pbx5UKgcKbU3TXvy5ImmQyhT9PX1YW1tXeSdCok04fr16zhx/Bhin0UCQjuu5y5LJBJdVHaoCu9OneDi4qLpcDQmPT0dBw4cwK3gf5GRngKgVO4etZgEevqGcK1RG926deO9Qkgt/v33X+zZtR0GujlwdrCDgaE+D5yVECEEMjMyERYeg9T0HHTu2hPNmzfXdFilyoMHD7Bty69AdiqcHSvByEjK72cJ6jtIO2/EWWoTbiIqX4QQeP78OZKSkrTiOtuyQkdHB5aWlrCw0I67wGuDzMxMxMTEICMjQ9OhlCm5B3alUqmmQ6EyLiYmBsHBwXj69Aky0rkelyR9A33Y21eGu7s7KleurOlwSqW4uDjcvHkTjx49QnpamqbDKVM+GTVK0yEo9VYJ9/z58zFt2jR8/vnn+OmnnwAAaWlp+Oqrr7B9+3akp6ejU6dOWLVqFSpVqiSfLiwsDGPHjsWxY8dgamqKYcOGYd68eTy7SlQKZOdkIzUzFamZqUjOSJb9PyMVGdkZyMrJQlZOFjKzMxX/L/77m52FzJxX47Kys5Al/vubozguMycT2TnZr8rmKJaRjxOK/1conzvu9b//jcv572x57mZQ/Pcvd5j8/xDyk50CosDy8rIAGlg3wPXY6wBkXeN1oCP7K9GBoa4hpLpSGOoayl9SPal8uFTv1ctU3xRWRlawMrJCRZOKqGhcEdYm1qhoXBE2pjYwNjBWenRcCCH/vDKzMxX+ZmRnyNsoR+QgW2TL/uZkv9H7kqijWO9RdDl1zDtvO+dt69fb/m2GFVZ/QcPMDc2RkJ4g/64BgCTPnYPfdFje75Uqw5TVpSPRga5EV/ZXR7fA9yU2Lk+ZdzpfFccVFpuejh70dfWhr6MPA10D+f/1dfWhK9HlWbBSKDM7E9HJ0YhIjEBUUhRSM1OV7s+ycrIAyNZriUQCCSRF/tWR6Mj/r68r+84Y6hrK/uoZqvxeV4fd7onU7Y0z3EuXLmHt2rWoV0/xGaFffPEFDhw4gJ07d8LCwgLjx49H7969cebMGQBAdnY2unTpAjs7O5w9exaRkZEYOnQo9PX1MXfu3LdbGqJyRFnim5KRIvubmYK0zDTZ36xXf3PLp2WlITXr1d/0rHTZ3+xXf9Oy0mR/s2V/c/+fmVOyd9jUk+hBT0dP9kP1vx+dejp60JPoyX+85o6XD9fRhb6OvkJ5XYkujHWNX03/X9m873Pr0tfRl/3o/e+Hxn8/YwDIEgQBIU+0hBDyvzkiBznIQU5ODgTEq8QsT5KXW05XRxdVLKrIDw5ki1cHDzKyM5CenY6M7AxkZGcgLSMNsWmxigcj8hwkyMrJQrYo+M67ucl8LiEEcqDZrve5PwhVST7U8V5fRx86egWMR/Hrzf1xm7ts8uV8yyRV2bA3SYwVDhC9ZWJfkmWKc/Dj9fWpoDIZ2RmF1leseRWjHk3KTaqUJeMGugZK//96WYXpChtXSJ0q16+kbFk6YJCdk41nKc8QkRhR6CsmOUZhPXydrkQX+rr60NPRg5GeEVKzUuXrsqp/31buQeCCEnNl4wz1DGGqbwpzQ3OYG5rDzNBM9tfArMD3Brq8XpnKrzc6w52UlAQPDw+sWrUKP/zwAxo0aICffvoJ8fHxsLGxwbZt29C3b18AwJ07d1C7dm2cO3cOzZs3R0BAALp27YqIiAj5We81a9Zg6tSpePbsGW8gQGWOEAIpGSmIT4vHy9SXeJn6EvFp8UhMT8TLtJdISE9AYnoiEtITZP/PSJS/cpPikkh8daDz6kyqjuIZ1bxnWI30jGCoJ/sr/7++EaS6UhjrG0OqJ5W915e9N9KT/T/fGRkd2Q8JfV196En05P/P+8pNYoQQSMtKk79yl1v+PjP1zcb99z73jK6ys7zKzgCX9I9rPR29fD8+C/thqmycrkQXOSLnVW+AbFm8uUl7epbsIEladpr8AEpKVgrSstMUfpRJdaSwNbJFJZNKsDe1h4OZAxwtHOFcwRnOls4wNzKXH4x4m8S3LP24JgKg9OBBSSb3r/dIybuNen17lZGdofT/8rKFjStGnSUp92DnmyTxUj2pbF+T96/+q/d5///6OGXvDXQNVNpGCSEQ+iIUlyMu43LEZVyKuIT7cfcRlRSlcBBUR6IDO1M7VDarLHuZVn71//9edqZ2MNY3lvdkKKleC7m9mXIP4qZnpSsc1M37vrBxKr3/729aVhoS02W/U3J/wyRmJBYap6GuYdGJ+WvDzQ3NYWFoAWtja9ia2MLc0Jz7FiqV3ugM97hx49ClSxd4eXnhhx9+kA+/cuUKMjMz4eX16lEftWrVgpOTkzzhPnfuHOrWravQxbxTp04YO3Ysbt68iYYNG+abX3p6OtLT0xWGGRoalstnxdK7k5GVgfi0eLxIeYGE9ATEp8bjZdpLJKYnIj4tXiFBzv2blJEk+5sp+5uclYzkzGRkiawC56Mr0YWJnglM9U1hZmAGE30TmOmbwdLQEpVNK8t/IOT9sVBQ4mtsYCwfb2RgBGN9YxgbGMNY3xgGeoX/wBBCyM9sv57Ivp7M5o5LSEsocFxaturJcnp2eoFxFSRvt+u8n03eH1fG+sawMrJ69YPuDRLdtx2np6On0R8IOSIHscmxuB97H/di7yH0RSgevniIx/GPcf3ZdRx8dFDenREArKXWcDJ1gqO5I6paVIWLpQuqWVVDdevqcKnoAgN9HhSl8im3p4OeTvm4/C23d0+xk/03PUig5IBBZk4m0rPSEZcal7931mvvi0MCSb5E3FDXEHq6esjMzkRqlqznWHx6PDKyZdeAmxmYobJZZbhauaJplaayJNDYFnZmdrAxsYGpvmmByX5xE/1iLYtEIj+IrUk5IgfJGcn5fxulJxb6PiY5Bvfj7iuMS85MVjoPA10D2JrYwsbYBrYmtgqv14fZmNjAWN/4HX8KRMoVe6+xfft2XL16FZcuXco3LioqCgYGBrC0tFQYXqlSJURFRcnL5E22c8fnjlNm3rx5+P777xWG+fr64rvvvitu+FTOpGelIyYxBtFJ0XiW9AyxKbF4lvwMz1Oe43mq7PUy7aU8SU7KSJL9zUxCek7hSaCJnglM9P9LlPX/S5QNzGBtaQ1zA3OYGrzqbmVuaA4LqQUspBYwl5rDUmoJC6kFLI0sYWxgXORjrnLPACdnJiMpIwnJGbK/SRlJ8mHPU58jOT7/8KSMJOUJsZIkuLgMdA2UJrqvJ8GWUkuVyqk6zkDXADoSPhpMFToSHdia2sLW1BaeVT3zjc/OycaTl09w//l/CXlcKB7FP8Lj+Mc4/3/27ju8qbJhA/id0aQz3XuzWvamlC1UhoCooCioICii4MJXke/Fia84cTAEEQEFRBAELEtkj7L3agt0QCcd6W6acb4/QkNDU9pCQ9L2/l3XuZKc+aRPk5P7nOc8J+0wMkoyDPNKRVL42vsi0CkQwYpghDiHIMwjDJ39OyPcO5z9cBA1ICKRyHBZDiyb5apV8YBxxSB+t5BeHvQSlYm4kX8DCcoElGhKAOgP6DrLnRGgCICtxBZSsRQaQYNSTSnisuPqNOhX99pOagdHmWONB3sb0/16mJtYJIaT3AlOcif4w/++1qXVaQ0nMJSlStwsuombxTeRWZRpNCQoE3Ak5QgyizKhLFVWWo+DjYNRAPeyNw7k5c8DFAFwt3Pn2XMym1r9Orp+/TreeOMN7Nix44H2MjpjxgxMmzbNaBzPbjc+Gq0GNwtvIr0gHWkFacgoyEBmUSayi7ORVZKFnJIc5JTmILc0FzmlOVCWKVGsKa60HhFEcJY5w1XuCldbV7jIXeDr4GtoyuQod4RCpg/ITnInfTC+FZAVtgq42rtCYauosqOR8h1/xWZW5c/zyvJwI+eGyVBs9NxEoK6umbMIIjjIHAw7XQcb/XMHmQPsbeyhkCuMwuvdgm6l+UxMk0vlDL0NgEQsQYhbCELcQhDVPKrS9BJ1Ca7lXNOH8exruJp7FYnKRJzLOoctiVuQV5YHAHCycUJL15Zo69EWHXw6oGtAV7T3bw9bGXukJiLzEolEhn2Ui62LyXnUWjX2Ju3Fxssb8Xfc30jKSwIAuNu5o6t/VzzT5hl09e+KLn5d4OdUs963qwr6tQn+htfa2+NzSnIM44vVxUa/D8rPuFf5t7jjt8CdQ/lvg7sN5b+Hypt320ntHmgYlYglhpMUAYqAGi1Tpi1DVnGWUSC/WVQhpBdn4sLNC9iduBuZRZmVzqLb29gjyDkIQc5BCHYONn50CYa/k7/FWxFQ1XSCDhmFGfB18rV0UUyqVeA+ceIEMjMz0alTJ8M4rVaLffv2Yd68edi+fTvKysqgVCqNznJnZGTAx8cHAODj44OjR48arTcjI8MwzRQ2H2+YBEFAfmk+UvNTkZ6fjvTCdEOIzizWf1HeLLmpPxNdmo1cVW6ljqCkIilc5C5wk7vB1VYfoIMUQXCzddP34uzgCQ97D3g4eBjO8rk7uFcKy4IgoFhdfPsa6gpNngpUBUjNTTU8r9iE/M75yp9XbKJriqlQXD7O097T5I7RVJiuOP5B7xCpcbCzsUNr79Zo7d3a5PS0gjQcTT6K4ynHcTr9NHZc34ElF5cAAORiOVq4tEAbjzbo4N0Bnf06o1NAJ7g4uPB/lYjMrkBVgG1XtmFD7AZsid8CZakSQc5BGBE2An2C+6CLXxcEOwff8/dRTYJ+XSvTlhkdlK9qqBjSy4cCVQHSCtJMzns3EpGk0rXVRqH8joB+t/Hm6jxNJpEZrpeviWJ1MW4W3URGUQau511Hcl4ykvKSkJyXjBNpJ/DX5b+QVZxlmF8sEsPPyQ9NXJugqWtT/eDW1PDazc6N+zUz0uq0SClIQaIyEYnKRCTkJiAx79ajMhEpBSnQ6DQQPrTO28bWqtO0goICJCUlGY174YUXEB4ejunTpyMwMBCenp74/fffMXLkSABAbGwswsPDK3WalpaWBi8vLwDATz/9hHfeeQeZmZkM1g2ISqNCYk4iEnISkJibiGRlMpLzk5FSkIKUwhSkFqWiSGP8JS+CCC5yF7jbusPd1h2edp6G63I8HTzh7egNHycf+Dr5wlfhCxc7F6Pm2FqdFnmqPP3Z7loMuaW5dw3JFXc2FTv1qNjRR5XTbj0v39nY29jzzDA1aMoSJY7fOI7jKcdxMu0kzt48iyvKK9AKWoghRqgiFG3c26C9d3t08u2EHsE94KHw4I8VIrpvaQVp+Dvub2y4vAE7E3aiTFuG9t7t8Vj4YxgRNgIdfDrwu+YOOkGHYnWxIZRXPIlQ8Xprw3XWZfkmO3wtH3e33tMrdp7mLHfW3/LS3t1wosTNzg3udu63x9967Wrn+sD7TygqK8L1/OtIUuqDeKIyEdeU13A15yqu5l5FTkmOYV5nuTOaujU1hPG23m3R0acjWri34K3Xakgn6JCoTMT5zPO4kHnB0KIuQZmA5Lxko9/p3g7eCHEJQahrKEKcQxDkHIQARQCGhw234Duo2n3dhxsA+vXrZ+ilHABeeeUVbNmyBcuWLYNCocBrr70GADh06BAA/RnxDh06wM/PD19++SXS09Px3HPP4cUXX+RtweoRnU6HjIIM/fWeuYlIUibhet51XM+/jhuFN5BamIqs0iyjL10XmQv8HPzg7+iPQEUgAhWB8Ff4w9fJFz4KH/g4+sBb4Q0biQ3UWrW+aXgtg7OyVGnyi14mkRm+wKsanOXOVYZlW6ktd9BE96FUU4rTqadx7PoxnEw7iTOZZ3Ap5xJKtaUQQ4wwlzB09emKnoE90Te0L5p7N6+2bwMiIgC4nHUZGy5vwMbYjTh84zAkIgn6BPfBiLARGBE+AiEuIZYuYqNRHt6NAnoVwb3i77zskmz9Y3F2lWfcjQJ6xWBuIqCXv3aWO5st8CpLlbiWezuAlz9eybmC6/nXAeibqrfzboeOPh31g29HtPFqA1tp473cShAEpBem43zm+dvDTX3ILq97Z7kzmrk1MwTqUNdQfcB2CUWwS3C96xCvzgN3aWkp3n77bfz+++9QqVQYNGgQFixYYNRcPCkpCa+88gr27NkDBwcHjBs3Dp9//jk73LEyaq0aV7Ku4EL6BVy+eRlxOXG4mnsVqUWpSCtKM+pUTCaWwc/BTx+onfSBOtg5GMGuwQh1DUWASwBKdaXIKMzQNx0vyjB6nlmUaRScq7q9RHmv00aDrekAXf7F62bnxubWRFZIq9PiYuZF7L22FweTD+Jw6mEkFiQCAPzs/dDFqwsiAyLRO7g3Ogd2hq288f5AISJjicpELD6xGOsurUNsdizsbewxuNlgjAgbgaHNh8Ld3t3SRaR7VN4z/Z1B3Oi1ifGmOrETQQRXO9fKwfzWGXUvBy/4OPoYBm8Hb9jZ2N33e8gpycHp9NM4lXYKp9L1w+Wsy9AJOkjFUrT0aImOvh0NQbyDTwc42zrf93atTWFZIU6lnTIK1uczzxtaB9hJ7dDKsxXaeLUxGvyd/BvU7/b7DtyW4vmFJ9xt3eFhp78tQ3mzY29Hb3g7esNX4YsglyD4O/uzKUc10vPTcT79PC5lXkJsVizic+NxVXkVSQVJhttZycVyhChCEOocimBFMAKdAxHgHABnW2fYyeyghRY3i2/eDtNFt8L0rVCdXZJdabsuti7wdvCGj6MPvBy8qj0D7Wrn2qiPCBI1BukF6dhzbQ/2J+7HoRuHcD77PDSCBk42Tujo0RG9A3tjULNBiAiJ4C3KiBoZQRCwM2En5h2dh7/j/oZCrsDIliMxImwEoppE1UlQovqrRF1SdUA3Edizi7ORVZxldE91QH92tWIIr2rwtPesVcYoVhfjXMY5fQC/FcTPZZ4zHCho4trEEMD7BPdBZGBkvboNoSAIuJJzBTE3YhBzPQYxN2JwLvOc4SBDmHtYpWAd6hLaKHJavQ3c70a/i5slN3Gz6CaySrOQVZKF7NJsKMuURvNJRVJ423vD194X/k7+CHAKgL+Tv74nQpcghLiFwFfhC4lE0qCOpJii0+mQkJOAo9f1HRydyTiD89nnDbf9EUEEPwc/NFE0QXO35gh2DoargyvsZfYoE8pwI/8GkvOTkZynH27k36h03XN5iPZ29DYcKSwP1d6O3kYBWy7l9fpEdHfF6mIcSjqEvQl7sT95P46kHUGpthRucjf08OmB/iH9MaTFELTwacEm6EQNVIGqAL+e+RXzjs3D5azLaOvVFq91ew1j2o6Bg8zB0sWjekwn6JBdnI30wvTKQ5Hx64rXbAP6382eDp7wcfRBgCJA37LTORjBLrd7Ofd18r1rvz0anQaXsy4bnQk/lXYKeao8uNq6YnCzwRjafCgGNxtsda02BEHAhZsXsDV+K/Yl78PhG4cNHc219GiJyIBIRAZGopt/N4R7hJutw7z6oN4GblMEQYBKrUJ6QTpS8lNwXanvdfB63nXcKLiBlIIUpBWnIb043SgoysQyeNt5w9fBF36OfghwDECAIgBN3Zoi3CMcoe6hsJXbQiKpX0dg0vPT8U/cPziWcgynM07jfPZ5wwEJN7kbWrm3QoBTgL4jCqkUKp0KaYVphkCdW5prWJdEJIG/wt9wy4QgRZChg4LyMO3l4MUz0ERkVqWaUuxP3I8tsVuwK3EXzmWdgwABoU6h6O3XG1FNojCwxUB4OXs1+IOoRA1dbFYs5h+bj2Wnl6FYXYzHWz6O17q9ht5Bvfn5pgdOpVEhsyjT0IqzfEgrSMP1/Ns9nVe8J7iN2AaBzoG3g7hCf5ux8teBisBKJ6B0gg7HU48jOi4am+M342TaSYhFYnQP6I5hzYdhaIuhaOvV1iKfgQJVAXYm7MTW+K3YemUrrudfh53UDr2CehkCdoR/BFztXB942axZgwrcNaUTdEgvSEdijr6zr/IOv1IKUnCjQN/hV2ZJpqGJiZ3EDsGOwQhVhKK5a3M0d2uOcI9wtPRqCTdHN8hkMqsI4wWlBdgRtwP/Xv0Xe67vwaXcSwAAbztvBDkFwd3eHTZSGxRpigxd6Zd3MOYsd74dpisM5fcg9HXyrVfNWoioccgqysI/V/7Btrht2J28GzcKb0AqkqK9e3sMDh2Mx1o+ho5BHa3iO5qIqqfVabElfgvmHp2LHdd2wMvBC5M6TcLLXV6u8T2ZiSwpX5Wvzxd5SYbH8jCepExCWmGaYV6xSIwmrk3QyrMVWnm00j96tkK4R7ih9UZqQSq2xG/B5vjN2HF1B4rURQhyDsILHV7ApM6TanwrtHtVpi1DdFw0fj75M/699i/UOjVauLfAkGZD8EjzR9AnuA9PuFWjUQbumtDoNEjITcClzEu4mHkRl7MuIy47DleUV3Cz5KZhPm87b4Q4hqCpc1OEuYUhwi8CHXw7wMnRCba2tmZv4piWl4bFRxdjy9UtOJF5AhpBA4WNwnBdSa4qFzeL9eUVQYQQlxC09GyJlh63Bs+WCPcIh5udm1nLSURkbuXXj225vAVb4rdg7429UGlVCHUKxcNBD2NE2Ag81Pwh2NnyOk8ia5NTkoNfTv2CBccWIEGZgAj/CEztNhVPtnqSl6BRg6LSqAy3G0tQJuDSzUu4mHURF29eRHJesmG+EJcQtPJshdaerQ2P4R7hiLkRg3UX12HluZVQaVV4ouUTmNJ1Sp23/IjNisWSU0uw/MxyZBZlIsI/AmPbjsUjzR9BU7emdbadxoCB+x4oS5WIzYrVf0AyLyI2Kxax2bG4lncNap0aDlIHtHdrj44eHdHdtzu6B3aHu8Id9vb2kMlk9/1h0Ol02B67HT8e/RFbk7ZCgABve2+otCpkl+o7J2vh3gLtvdsbQnVLj5Zo4d6CHYoQUaNRrC7G1rit+OviX9h2bRuyS7PhJndDf//++mZ5LYfCXeHOpqlEFpRTkoPP9n+GBccWQCto8XSbpzG161R09e9q6aIRPXAFqgJczrqMizf1Afxi1kVcyLyARGUiBAiwk9qhZ1BP9Avuhy5+XXDh5gUsOrEIcdlxiGoShYVDF953GE4rSMP0f6fjt7O/wc3ODc+3ex4TO01EG682dfQuGx8G7jpUoi7B8dTj2J+8H/sT9yMmJQZ5qjxIRVKEu4Sjo3tHDA4ajB6BPeDm5gYnJ6da/9CLvhCNV7e9iuuF1yERSaAVtBBBhHbe7dAnuA/6BPdB76De8Hb0NtO7JCKqf7Q6LQ4lH8K68+uw+cpmXMm7ArlYjh4+PTCi+Qg81e4p+Lj6MHwTPSClmlLMPTIXnx34DBqdBm9Hvo1Xu74KLwcvSxeNyOoUlRXhws0L2J+0H3uS9mBf0j7kq/JhJ7VDZEAkfJ18sSthF5SlSnzU7yNMi5xW60tB1Vo1fjjyAz7e+zHkUjn+1/9/GNd+HFuY1AEGbjPSCTpcvHkRB5IP4EDSAexJ3IOUwhS0dWuLZ5o+g6HBQ+Ht4Q13d/dq70Gu1Wnxwp8v4LdLvwEAFHIFJnSYgP6h/dEzqCebhBMR1UJsVizWnV+HDZc34FjGMcjFcvT164snw5/E420eh5vCjeGbyAy0Oi1WnluJ93e/j9SCVLzc+WV80PcDBm2iWtDqtDidfhp7EvcYBfAg5yDcyL+B17u9jm8Hf1vj9QmCgJejX8aSU0vwapdX8clDn7DjszrEwP0A6QQdtsZvxfdHvseOazsQ4BiAH3v+iBZuLRASEgKZrOru8l//+3XMPTkXrnJX/G/A/zC+w3g2DyciqgNJyiSsOL0Cq8+vxvns83CQOiAqIApPtnwSw1sNh5ND7VsjEVFl/1z9B+/ueBdnMs5gVKtR+Kz/Z2ju3tzSxSKq9zQ6DdZdXIcvDn6BU+mnAABLH12K8R3H12j5b2O+xbR/pmHpiKUY36Fmy1DNMXBbyIXMC3jqz6eQXpCOhX0WwtnZGR6uHlXO/+yfz0JZqsTVN68yaBMRmcnlm5fx26nf8MfFP3A17ypcZC4YHDQYo1uPxqDwQexwjegenEo7hXf/fRf/XvsXvYJ64auHv0L3gO6WLhZRgyMIArZd2YZHVz8KuUSOfS/sq9EyfZb2weMtH8eKJ1Y8gFI2PgzcFnQo+RAeWv4QynRlNZrfVmqL2KmxCHIOMnPJiIgaN0EQcDr9NH49+SvWXlqLlKIU+Nn74clmT2Jip4loFdCKtxojqkaiMhEzd83EynMrEe4Rji+ivsDwFsPZYoTIzBYeX4jXt74OtU5d42XkEjniXotjzjAD896ziu7K1sa2xmEb0HcwklWcZcYSERERAIhEInT07Yhvh36L5LeTsXfcXvQP7Y/FFxej/bL26Lu4LxbtXwRlgRI8bk1kLKckB//55z8ImxeGnQk7sWjYIpx75RweDXuUYZvoAejm361WYRsAVFoVc4aZMHATERHdhVgkRp+QPvht1G9I/086FgxZgFKUYvKuyWgyrwkmrpmIg3EHodFoLF1UIosSBAFLTy1F0x+aYtGJRZjZeyauvHYFkzpPqnWPyUREDQW//YiIiGrISe6Eyd0mY3K3yTifcR6Lji3CqgursPTyUrRza4cx4WPwfKfn4ePGW4xR45JWkIZJ0ZMQHReNce3H4cuHv2TP40RE4BluIiKie9LGuw3mDpuLtP+kYfUTq+Hp6IkZh2YgbFEYXlzzIk5eOwmtVmvpYhKZ3ZoLa9DmxzY4lnIMG5/eiGWPLWPYJiK6hYGbiIjoPsgkMoxuOxr/vvAvrr1+DS92fBHrrq1D19+6YvDSwVh3Yh1KS0stXUyiOpddnI2n/3wao/8cjQGhA3D+1fN4NOxRSxeLiMiqMHATERHVkRDXEMwZMgcpb6dg7uC5SC5OxqjoUeiwsAO+3PElbubeZCdr1CBsjtuMNj+2wT9X/8HvI3/HH6P+gId91bc3JSJqrBi4iYiI6piDzAGvRryKy69dxrax2xDsGozph6YjbGEYXl33Ks4nnWdzc6qX8lX5mLhxIob9PgwdfTri/Kvn8XSbp9lnARFRFRi4iYiIzEQkEmFQs0HYPm47YqfE4uk2T2NF3Ap0XN4RT614CjGxMezdnOqNXQm70PbHtlhzcQ0WD1+MzWM2w8/Jz9LFIiKyagzcRERED0ALjxZYMHwBUt5OwacPfYoDGQfQa3UvPPrro9h1YRfU6trdM5XoQSlWF+P1ra9jwK8D0MS1Cc69cg4vdnqRZ7WJiGqAgZuIiOgBUsgVmN57OpKmJeH7wd/jXO45DPhzAAYtHYToU9EoKyuzdBGJDE6lnULHRR2x+ORifDfoO+x8fidCXEIsXSwionqDgZuIiMgCbKW2mBoxFdfevIYlw5cguSQZwzcNR98lfbHm6BqUlJRYuojUyK2/tB49f+kJR5kjTr18Cm90fwNiEX86EhHVBr81iYiILMhGYoMJnSYg9rVYrB65GoW6QozeOho9lvTAqsOreEsxeuAEQcBn+z/DyDUjMTxsOPa/sB/hHuGWLhYRUb3EwE1ERGQFJGIJRrcZjbOvnsWmpzdBIpVg7Pax6PdLPzY1pwemVFOK5/56Dv/d9V982PdDrB65GvY29pYuFhFRvcXATUREZEVEIhGGhw3HsZePYf1T66HUKjF803AM/3U49l3cx17NyWwyCjPQf3l/rLu0Dr+P/B0f9fuIHaMREd0nBm4iIiIrJBKJ8HjLx3FhygX88ugvuJh3Ef3/7I+nVz2NU1dP8T7eVKfOZpxFt5+7IUGZgD3j9uDpNk9bukhERA0CAzcREZEVk4gleKHjC4h/Ix5fRH2B3Wm70X1Vd7y09iXEXo+FTqezdBGpnvs79m/0/KUn3OzccPTFo4gIiLB0kYiIGgwGbiIionrAVmqLt3u8jYQ3E/Buj3ex5toadPm1C6Zvmo7M7EwIgmDpIlI9IwgCvj70NUasHoGoJlE48MIBBDoHWrpYREQNCgM3ERFRPaKQKzBrwCxce+Manmv3HL49+y0ilkVg2YFlvJUY1ViZtgwvbnoR7+x4B+/1eg/rnloHB5mDpYtFRNTgMHATERHVQ14OXlgwfAFOvXwKga6BmLBrAh757REcuHiAHavRXWUVZ+Hh3x7GinMr8Otjv+KzAZ/x/tpERGbCb1ciIqJ6rK13W+x9YS/+GPUH4gvi0X9df0xZNwVJaUlsZk6VpOSnoMeSHrh08xJ2Pb8Lz7V/ztJFIiJq0Bi4iYiI6jmRSISnWj+FuNfj8G6Pd7E8fjm6/dYN3/37HQoKCixdPLIS1/Ouo++yvlBpVYiZGIOeQT0tXSQiogaPgZuIiKiBsLexx6cDPsWlKZcQERCBaYemod+v/fDv2X+hVqstXTyyoOS8ZPRb3g8anQZ7x+9FU7emli4SEVGjwMBNRETUwIS6hmLTmE3YNnYb8jR5GLJxCN7c8CbSbqaxmXkjlKhMRN9lfSEIAvaO34sQlxBLF4mIqNFg4CYiImqgBjUbhPNTzuM/3f+Dny79hF6/9cLaI2uhUqksXTR6QBJyE9BvWT+IRWLsGb8HwS7Bli4SEVGjwsBNRETUgNlKbTH74dk48fIJuDm4YfT20ZiwdgISUhN4truBS1Qmot/yfpCKpdgzbg+CnIMsXSQiokaHgZuIiKgRaOfdDodfOow5A+dgfdJ69F7ZGysOruC9uxuotII0RP0aBalYir3j9yLQOdDSRSIiapQYuImIiBoJiViCtyLfwrlXziHULRTP73we49eOx9XrV3m2uwHJLs7Gw789jFJNKXY+vxP+Cn9LF4mIqNFi4CYiImpkmrk1w94JezFvyDxEX4/GQ388hL+O/sVruxuAfFU+Bq8cjMyiTPz7/L/sII2IyMIYuImIiBohsUiMKd2m4OwrZ+Gt8Mbo7aMxPXo6srKzeLa7nipWF2PYqmGIz47HP8/9g3CPcEsXiYio0WPgJiIiasSaujXFoYmH8E6Pd/DD+R8wZPUQHLl0BFqt1tJFo1oo05Zh5JqROJl2ElvHbkUHnw6WLhIREYGBm4iIqNGzkdjgs6jPsHvcbqSVpmHQhkGYt2seCgsLLV00qqHXtryGXQm7sPHpjYgMjLR0cYiI6JZaBe4ff/wR7dq1g0KhgEKhQGRkJLZu3WqYXlpaiilTpsDd3R2Ojo4YOXIkMjIyjNaRnJyMoUOHwt7eHl5eXnjnnXeg0Wjq5t0QERHRPesb0hdnXz2LqKZRePPQmxi3bhyuXb8GnU5n6aLRXSw6vgg/nfwJPw79EQOaDLB0cYiIqIJaBe6AgAB8/vnnOHHiBI4fP47+/ftjxIgRuHDhAgDgrbfewt9//421a9di7969SE1NxRNPPGFYXqvVYujQoSgrK8OhQ4ewfPlyLFu2DB988EHdvisiIiK6J252bvjzqT+xePhibLuxDQPWDMCWk1tQVlZm6aKRCQeTD+K1ra/h1S6vYkLHCZYuDhER3UEk3GfPKG5ubvjqq68watQoeHp6YtWqVRg1ahQA4PLly2jZsiViYmLQvXt3bN26FcOGDUNqaiq8vb0BAAsXLsT06dNx8+ZNyGQyk9tQqVSVek6Vy+WQy+X3U3SLO5l2Ep1/6lyrZU5MOoFOvp3MVCIiIqLb4rLj8OSaJxGXHYdZXWdhcs/JcHR0tHSx6JbUglR0/qkzmrk1w87nd0ImMf07iogal3vJGABzhrnc8zXcWq0Wq1evRlFRESIjI3HixAmo1WpERUUZ5gkPD0dQUBBiYmIAADExMWjbtq0hbAPAoEGDkJ+fbzhLbsrs2bPh7OxsNMyePftei05EREQ10MK9BWJejMETLZ/AO4ffwasbX0VaRhp7MbcCKo0KI9eMhEQkwZ9P/smwTURkpaS1XeDcuXOIjIxEaWkpHB0d8ddff6FVq1Y4ffo0ZDIZXFxcjOb39vZGeno6ACA9Pd0obJdPL59WlRkzZmDatGlG4+r72W0iIqL6wN7GHiueWIGIgAi8/c/buLzuMhYOWIj2zdpDIpFYuniNkiAImLplKk6lncL+F/bD29G7+oWIiMgian2GOywsDKdPn8aRI0fwyiuvYNy4cbh48aI5ymYgl8sNHbWVDwzcRERED4ZIJMLrEa9j5/M7kViUiKEbh+Kvo39VutyLHoxFJxbh51M/Y+Gwhejq39XSxSEioruodeCWyWRo1qwZOnfujNmzZ6N9+/b4/vvv4ePjg7KyMiiVSqP5MzIy4OPjAwDw8fGp1Gt5+evyeYiIiMg69Qnug1Mvn0KQaxDG/DsGX/77JfLz8y1drEblYPJBvL71dUztOhXjO4y3dHGIiKga930fbp1OB5VKhc6dO8PGxgY7d+40TIuNjUVycjIiI/X3g4yMjMS5c+eQmZlpmGfHjh1QKBRo1arV/RaFiIiIzMxf4Y99L+zD+Pbj8cHxDzBp4ySkpqfyuu4HICU/BSPXjERkYCTmDJpj6eIQEVEN1Ooa7hkzZmDIkCEICgpCQUEBVq1ahT179mD79u1wdnbGxIkTMW3aNLi5uUGhUOC1115DZGQkunfvDgAYOHAgWrVqheeeew5ffvkl0tPTMXPmTEyZMoVNxImIiOoJuVSOnx79CZ39OmPKlilI35iOxYMWo1lwM4hEIksXr0Eq1ZTiiTVPwEZig7VProWNxMbSRSIiohqoVeDOzMzE888/j7S0NDg7O6Ndu3bYvn07Hn74YQDAt99+C7FYjJEjR0KlUmHQoEFYsGCBYXmJRILo6Gi88soriIyMhIODA8aNG4dPPvmkbt8VERERmd3LXV5GsEswnlzzJB7f9DiWPrwUnVp0YmdqdUwQBEzZPAVn0s/gwIQD8HLwsnSRiIiohu77Ptx073gfbiIiaghOpZ3CkJVDIBWkWNJ/Cfq36w8bG56BrSs/nfgJL0e/jGUjlmFch3GWLg4RWTneh9u63Pc13ERERNS4dfTtiCMvHoHCToHR/4zGmsNrUFpaauliNQiJykRM2z4NkzpNYtgmIqqHGLiJiIjovgW7BOPgxIPo4NMBE/ZMwIK9C1BYWGjpYtVrgiDgpb9fgpudG74a+JWli0NERPeAgZuIiIjqhKudK7Y/tx0jW47E24ffxqc7Pq10u1CquaWnl+Lfa//ip+E/QSFXWLo4RER0D2rVaRoRERHR3cilcqwYuQJ+Cj98EfMFSnWl+LD/h3B1dbV00eqV1IJUTNs+Dc+3fx6Dmw22dHGIiOgeMXATERFRnRKLxPjq4a9gb2OPWftmQa1TY9aAWXBzc7N00eoFQRDw6uZXYSu1xbeDvrV0cYiI6D4wcBMREVGdE4lE+OShTyCTyPD+7veh1qnx2YDP4OHhYemiWb01F9ZgY+xG/Pnkn3Cz40EKIqL6jIGbiIiIzGZmn5mQS+R49993odaq8eXDX8LT09PSxbJaWcVZeG3raxjZciRGthpp6eIQEdF9YuAmIiIis3qn5zuQSWR4c/ubUOvU+Obhb+Dt7W3pYlmlN7a9AY1Og3mPzLN0UYiIqA4wcBMREZHZvdH9DdhKbTF582SodWp8P/B7+Pj4WLpYViU6Lhqrzq3C8seWw8eRfxsiooaAgZuIiIgeiJe7vAwbiQ0mbpoIh90O+PLhL3lN9y15pXmYHD0Zg5sNxnPtnrN0cYiIqI4wcBMREdEDM6HjBBSWFeKNbW/AReaC9/u/z1uGAXhnxzvIU+Vh0bBFEIlEli4OERHVEQZuIiIieqBej3gdWcVZmLVvFpxlznir71tQKBSWLpbF7ErYhcUnF2P+I/MR5Bxk6eIQEVEdYuAmIiKiB+7jfh8jqzgLn5z4BM5yZ7zY40U4OjpaulgPnFanxdQtU9E7qDcmd5ls6eIQEVEdY+AmIiKiB04kEmHeI/OQU5KD6UemQyFT4JmIZ2BnZ2fpoj1QK86uwKWsSzj64lGIRWJLF4eIiOoYv9mJiIjIIsQiMX59/Ff0C+mH1w++juiT0SgtLbV0sR6YMm0ZPtr7ER4Pfxxd/btaujhERGQGDNxERERkMTKJDOtHr0cb7zZ4ef/LOHjhIDQajaWL9UD8fPJnJCmTMOuhWZYuChERmQkDNxEREVmUg8wBm8dshqudK17d/youXr0InU5n6WKZVbG6GLP2zcKz7Z5Fa6/Wli4OERGZCQM3ERERWZy7vTs2PbMJaSVpeGvfW0hJTYEgCJYultnMPzofWcVZ+KjfR5YuChERmREDNxEREVmF1l6tsWrkKuxO3Y1PD36KnJwcSxfJLPJK8/D5wc/xYscX0cS1iaWLQ0REZsTATURERFZjWIthmD1gNn66/BOWHl2KwsJCSxepzn17+FsUq4sxs89MSxeFiIjMjLcFIyIiIqvybs93cS7zHN4/8T5CFCEY3mU45HK5pYtVJ7KKs/BNzDeY0nUK/BX+li4OERGZGc9wExERkVURiURYPHwx2ni3wWuHXsPxy8eh1WotXaw68fmBzwEA7/V6z8IlISKiB4GBm4iIiKyOnY0dNozeAIiA/xz6D26k3Kj3nail5Kdg/rH5eDvybXjYe1i6OERE9AAwcBMREZFV8lf4Y8UTK3Ak8wi+PfYtlEqlpYt0Xz7d9ynsbewxLXKapYtCREQPCAM3ERERWa0BTQbgvV7vYd6Fedh6bitUKpWli3RPruVew8+nfsZ7Pd+DQq6wdHGIiOgBYeAmIiIiq/Zxv4/R2a8z3j36Li5evQidTmfpItXa5wc+h4e9B6Z0m2LpohAR0QPEwE1ERERWzUZig9UjV6NAXYD/i/k/pKenW7pItZJXmoeV51bi1S6vwt7G3tLFISKiB4iBm4iIiKxeqGsofhr+E7bd2IYlJ5cgPz/f0kWqsRVnV0ClUWFip4mWLgoRET1gDNxERERUL4xuMxovdHgBs8/MxsHLB6HRaCxdpGoJgoBFJxbh0bBH4efkZ+niEBHRA8bATURERPXG3CFz4e/kj/ePvY+U1BRLF6daMTdicC7zHCZ3mWzpohARkQUwcBMREVG94SBzwKLhi3Ai6wR+Of2L1TctX3h8IZq4NkFUkyhLF4WIiCyAgZuIiIjqlf6h/fFChxfw7flvcfrqaWi1WksXyaSckhysubAGL3d+GWIRf3IRETVG/PYnIiKieufrgV/DzsYOs07MQkZGhqWLY9Ly08uhE3QY32G8pYtCREQWwsBNRERE9Y6bnRt+GPID/k35F2vPrUVRUZGli2SkvLO0ka1GwsvBy9LFISIiC2HgJiIionrpqdZPYVjzYfjszGeITYyFTqezdJEM9ibtRWx2LCZ3ZmdpRESNGQM3ERER1UsikQgLhi5AsbYYX578EllZWZYuksHC4wsR7hGOPsF9LF0UIiKyIAZuIiIiqrcCnQPxSb9PsDZhLQ5fPQy1Wm3pIiGjMAPrL63Hy51fhkgksnRxiIjIghi4iYiIqF6b0m0KQpxDMOfcHGRmZlq6OFh6eikkYgmeb/+8pYtCREQWxsBNRERE9ZpMIsPsqNnYm7YX22K3obS01GJl0Qk6/HTiJzzV+im42blZrBxERGQdGLiJiIio3nuy1ZOI8I/AnPNzkJaeZrFyxFyPQYIyAS92fNFiZSAiIutRq8A9e/ZsdO3aFU5OTvDy8sJjjz2G2NhYo3lKS0sxZcoUuLu7w9HRESNHjqx0f8zk5GQMHToU9vb28PLywjvvvAONRnP/74aIiIgaJZFIhG8GfoOLuRex+uJqFBYWWqQcf8f9DU97T/QI7GGR7RMRkXWpVeDeu3cvpkyZgsOHD2PHjh1Qq9UYOHCg0b0v33rrLfz9999Yu3Yt9u7di9TUVDzxxBOG6VqtFkOHDkVZWRkOHTqE5cuXY9myZfjggw/q7l0RERFRo9MzqCceD38c8y7OQ1JKEgRBeOBliI6LxtAWQyERSx74tomIyPrUKnBv27YN48ePR+vWrdG+fXssW7YMycnJOHHiBAAgLy8PS5YswZw5c9C/f3907twZS5cuxaFDh3D48GEAwD///IOLFy9ixYoV6NChA4YMGYJZs2Zh/vz5KCsrq/t3SERERI3G51GfI7MkE0vOL0FeXt4D3XZCbgIu3LyAYc2HPdDtEhGR9bqva7jLd2RubvpOQU6cOAG1Wo2oqCjDPOHh4QgKCkJMTAwAICYmBm3btoW3t7dhnkGDBiE/Px8XLlwwuR2VSoX8/HyjQaVS3U/RiYiIqAFq4d4CEztOxK9XfkVyWvIDPcu9OX4zbMQ2eLjpww9sm0REZN3uOXDrdDq8+eab6NmzJ9q0aQMASE9Ph0wmg4uLi9G83t7eSE9PN8xTMWyXTy+fZsrs2bPh7OxsNMyePftei05EREQN2Ls930WuKhd/xP2BgoKCB7bd6Lho9A3pC4Vc8cC2SURE1u2eA/eUKVNw/vx5rF69ui7LY9KMGTOQl5dnNMyYMcPs2yUiIqL6p4lrEzzV+in8Gv8r0jLSHshZ7gJVAXYn7mZzciIiMnJPgXvq1KmIjo7G7t27ERAQYBjv4+ODsrIyKJVKo/kzMjLg4+NjmOfOXsvLX5fPcye5XA6FQmE0yOXyeyk6ERERNQLTe07HjaIb2BC/AcXFxWbf3r/X/kWZtgzDWjBwExHRbbUK3IIgYOrUqfjrr7+wa9cuhIaGGk3v3LkzbGxssHPnTsO42NhYJCcnIzIyEgAQGRmJc+fOITMz0zDPjh07oFAo0KpVq/t5L0REREQAgA4+HTC46WAsjV9q9JvDXKLjotHSoyWaujU1+7aIiKj+qFXgnjJlClasWIFVq1bByckJ6enpSE9PR0lJCQDA2dkZEydOxLRp07B7926cOHECL7zwAiIjI9G9e3cAwMCBA9GqVSs899xzOHPmDLZv346ZM2diypQpPGtNREREdea9Xu8hVhmLrVe2Gn6rmINO0GFz/Gae3SYiokpqFbh//PFH5OXloV+/fvD19TUMf/zxh2Geb7/9FsOGDcPIkSPRp08f+Pj4YP369YbpEokE0dHRkEgkiIyMxLPPPovnn38en3zySd29KyIiImr0+gT3QXf/7lgavxRZWVlm287x1OPIKMpg4CYiokqktZm5Jp2O2NraYv78+Zg/f36V8wQHB2PLli212TQRERFRrYhEIrzT8x2MXDMSR5OPwtfXF1JprX761Eh0XDRcbF3QI7BHna+biIjqt/u6DzcRERGRNRveYji8HbyxPnF9pU5d60p0XDSGNBsCqbjuwzwREdVvDNxERETUYNlIbDCu/ThsTt6M9Kz0Or9FWEp+Ck6ln8LwFsPrdL1ERNQwMHATERFRgzah4wTkleVha0Ldd562/ep2iEViDGo2qE7XS0REDQMDNxERETVoYR5h6BXYCxuSNiA3N7dO13089TjCPcLhZudWp+slIqKGgYGbiIiIGrwXO72IQxmHcCH1ArRabZ2t92TaSXTy7VRn6yMiooaFgZuIiIgavFGtRsFJ5oT119YjPz+/Ttap0WlwJuMMOvkwcBMRkWkM3ERERNTgOcgc8HSbp7H5xuY66638ctZllGpKeYabiIiqxMBNREREjcKoVqNwo/AGTqaerJNm5SfTTgIAOvh0uO91ERFRw8TATURERI1Cv5B+UMgV2JW6C4WFhfe9vpNpJ9HMrRmcbZ3roHRERNQQMXATERFRoyCTyDCk2RDsTd9bJ9dxn0w7ic6+neugZERE1FAxcBMREVGjMSJsBC7kXEBcRhx0Ot09r0cn6HAq/RSv3yYiorti4CYiIqJGY0jzIZCKpdiVsgvFxcX3vJ4rOVdQWFbIwE1ERHfFwE1ERESNhoutC/oF98OetD331ay8vMO0jj4d66poRETUADFwExERUaPyaNijOHbzGNJy0u55HSfTTiLYORju9u51WDIiImpoGLiJiIioURnYdCDUOjWOpx+HWq2+p3WcTDvJ5uRERFQtBm4iIiJqVFq4t4CHnQdOZZ+6p+u4BUFg4CYiohph4CYiIqJGRSQSoUdQD5zJOXNPgTspLwm5pbkM3EREVC0GbiIiImp0egb2xNmcs8gryKv1spduXgIAtPFqU9fFIiKiBoaBm4iIiBqdnoE9UawpxpmMM9BqtbVaNq1Q39maj6OPOYpGREQNCAM3ERERNTqd/TpDJpHhdPZplJSU1GrZtII0eNh7QCaRmal0RETUUDBwExERUaNjK7VFF98uOJ19utbXcacVpsHX0ddMJSMiooaEgZuIiIgape4B3XFBeQGlpaW1Wi6tMA2+TgzcRERUPQZuIiIiapRaebbCjcIbKCgpqNVyaQU8w01ERDXDwE1ERESNUphHGAQIiM+OhyAINV4urTCNHaYREVGNMHATERFRoxTmHgYASChIgFqtrtEygiDwDDcREdUYAzcRERE1Sh72HnC1dUViQSJUKlWNllGWKqHSqngNNxER1QgDNxERETVKIpEIYe5hSCxMRFlZWY2WSS9MBwCe4SYiohph4CYiIqJGK8xDH7hreoY7rTANAHiGm4iIaoSBm4iIiBqtcI9wJBbU/Ax3WoE+cLPTNCIiqgkGbiIiImq0AhQByC/LR2FpYY3mTytMg6PMEY4yRzOXjIiIGgIGbiIiImq0POw9AADZJdk1mp89lBMRUW0wcBMREVGj5W7nDgDIKsmq0fxphWm8fpuIiGqMgZuIiIgarfIz3DklOdDpdNXOn1uaCzc7N3MXi4iIGggGbiIiImq03O31Z7jzyvKg1WqrnV+j00AmkZm7WERE1EAwcBMREVGj5SRzgo3YBrlludBoNNXOr9FpIBVLH0DJiIioIWDgJiIiokZLJBLB3c4dSpWyxme4GbiJiKimGLiJiIioUXO1c0W+Or9G13BrdBrYiG0eQKmIiKghYOAmIiKiRs1GbAONrvrm5ADPcBMRUe0wcBMREVGjJhVLoRN0EASh2nkZuImIqDa4xyAiIqJGTSqWokhXhOMZx+Fa7AoAUGlUkEvlleYNUgQZbiVGRERUHQZuIiIiatT+2+e/GLVmFDb/vrlG8/9z7R9M6DgBQc5BZi4ZERHVd7VuUr5v3z4MHz4cfn5+EIlE2LBhg9F0QRDwwQcfwNfXF3Z2doiKikJ8fLzRPDk5ORg7diwUCgVcXFwwceJEFBYW3tcbISIiIroXAYoAqHXqGs9fqilFVnGWGUtEREQNRa0Dd1FREdq3b4/58+ebnP7ll1/ihx9+wMKFC3HkyBE4ODhg0KBBKC0tNcwzduxYXLhwATt27EB0dDT27duHSZMm3fu7ICIiIiIiIrIytW5SPmTIEAwZMsTkNEEQ8N1332HmzJkYMWIEAODXX3+Ft7c3NmzYgKeffhqXLl3Ctm3bcOzYMXTp0gUAMHfuXDzyyCP4+uuv4efnV2m9KpUKKpXKaJxcLodcXvnaKiIiIiIiIiJrUKe9lCckJCA9PR1RUVGGcc7OzoiIiEBMTAwAICYmBi4uLoawDQBRUVEQi8U4cuSIyfXOnj0bzs7ORsPs2bPrsuhEREREREREdapOO01LT08HAHh7exuN9/b2NkxLT0+Hl5eXcSGkUri5uRnmudOMGTMwbdo0o3E8u01ERERERETWrF70Us7m40RERERERFTf1GmTch8fHwBARkaG0fiMjAzDNB8fH2RmZhpN12g0yMnJMcxDREREREREVN/VaeAODQ2Fj48Pdu7caRiXn5+PI0eOIDIyEgAQGRkJpVKJEydOGObZtWsXdDodIiIi6rI4RERERERERBZT6yblhYWFuHLliuF1QkICTp8+DTc3NwQFBeHNN9/Ep59+iubNmyM0NBTvv/8+/Pz88NhjjwEAWrZsicGDB+Oll17CwoULoVarMXXqVDz99NMmeygnIiIiIiIiqo9qHbiPHz+Ohx56yPC6vDOzcePGYdmyZXj33XdRVFSESZMmQalUolevXti2bRtsbW0Ny6xcuRJTp07FgAEDIBaLMXLkSPzwww918HaIiIiIiIiIrEOtA3e/fv0gCEKV00UiET755BN88sknVc7j5uaGVatW1XbTRERERERERPVGnV7DTURERERERER6DNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZmCxwD1//nyEhITA1tYWEREROHr0qKWKQkRERERERFTnLBK4//jjD0ybNg0ffvghTp48ifbt22PQoEHIzMy0RHGIiIiIiIiI6pzUEhudM2cOXnrpJbzwwgsAgIULF2Lz5s345Zdf8N5771miSPXGpaxLli5CjXjYeyDIOcjSxSAiIiIiIrKYBx64y8rKcOLECcyYMcMwTiwWIyoqCjExMSaXUalUUKlURuPkcjnkcrlZy2qNnl3/rKWLUCO2UlvETo1l6CYiIiIiokbrgTcpz8rKglarhbe3t9F4b29vpKenm1xm9uzZcHZ2Nhpmz579IIprVh72HrCV2lq6GERERI2ah70H5JKaH8S3ldrCw97DjCUiIrp395Ix+L1mPhZpUl5bM2bMwLRp04zGNYSz20HOQYidGous4iyoNCrIpXd/TzWZx1qwSTkREdUXQc5BiHstDin5KYb97N32udzHEZE1q5gxaorfa+bzwAO3h4cHJBIJMjIyjMZnZGTAx8fH5DINufl4kHMQ/7mJiIgsjPtjImpI+J1mPR54k3KZTIbOnTtj586dhnE6nQ47d+5EZGTkgy4OERERERERkVlYpEn5tGnTMG7cOHTp0gXdunXDd999h6KiIkOv5URERERERET1nUUC9+jRo3Hz5k188MEHSE9PR4cOHbBt27ZKHakRERERERER1VciQRAESxeCiIiIiIiIqKF54NdwExERERERETUGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDATURERERERGQGDNxEREREREREZsDAbSYqlQofffQRVCqVpYtCZsR6bhxYzw0f67hxYD03Dqznho913Dg0lHoWCYIgWLoQDVF+fj6cnZ2Rl5cHhUJh6eKQmbCeGwfWc8PHOm4cWM+NA+u54WMdNw4NpZ55hpuIiIiIiIjIDBi4iYiIiIiIiMyAgZuIiIiIiIjIDBi4zUQul+PDDz+EXC63dFHIjFjPjQPrueFjHTcOrOfGgfXc8LGOG4eGUs/sNI2IiIiIiIjIDHiGm4iIiIiIiMgMGLiJiIiIiIiIzICBm4iIiIiIiMgMGLiJiIiIiIiIzICBm4iIiIiIiMgMGkTg1mq1eP/99xEaGgo7Ozs0bdoUs2bNQsUO2DMyMjB+/Hj4+fnB3t4egwcPRnx8/F3X269fP4hEokrD0KFDDfMUFhZi6tSpCAgIgJ2dHVq1aoWFCxcarae0tBRTpkyBu7s7HB0dMXLkSGRkZNx124Ig4IMPPoCvry/s7OwQFRVVqbw5OTkYO3YsFAoFXFxcMHHiRBQWFhrNc/bsWfTu3Ru2trYIDAzEl19+edftWitrr+OffvoJ/fr1g0KhgEgkglKprNH7mj9/PkJCQmBra4uIiAgcPXrUaHpN/neSk5MxdOhQ2Nvbw8vLC++88w40Gk2Ntm9trLmec3Jy8NprryEsLAx2dnYICgrC66+/jry8vLtum5/lyqy5ngHg5ZdfRtOmTWFnZwdPT0+MGDECly9fvuu2Wc/GrL2OywmCgCFDhkAkEmHDhg133TbruDJrr2dT65k8efJdt816rsza6xkAYmJi0L9/fzg4OEChUKBPnz4oKSm56/b5G+w2a67jxMREk+sQiURYu3Ztldu2qs+y0AD873//E9zd3YXo6GghISFBWLt2reDo6Ch8//33giAIgk6nE7p37y707t1bOHr0qHD58mVh0qRJQlBQkFBYWFjlerOzs4W0tDTDcP78eUEikQhLly41zPPSSy8JTZs2FXbv3i0kJCQIixYtEiQSibBx40bDPJMnTxYCAwOFnTt3CsePHxe6d+8u9OjR467v6fPPPxecnZ2FDRs2CGfOnBEeffRRITQ0VCgpKTHMM3jwYKF9+/bC4cOHhf379wvNmjUTnnnmGcP0vLw8wdvbWxg7dqxw/vx54ffffxfs7OyERYsW1fZPbHHWXsfffvutMHv2bGH27NkCACE3N7fa97R69WpBJpMJv/zyi3DhwgXhpZdeElxcXISMjAzDPNX972g0GqFNmzZCVFSUcOrUKWHLli2Ch4eHMGPGjFr8da2HNdfzuXPnhCeeeELYtGmTcOXKFWHnzp1C8+bNhZEjR971PfGzXJk117MgCMKiRYuEvXv3CgkJCcKJEyeE4cOHC4GBgYJGo6ly26xnY9Zex+XmzJkjDBkyRAAg/PXXX3d9T6zjyqy9nvv27Su89NJLRuvKy8u763tiPVdm7fV86NAhQaFQCLNnzxbOnz8vXL58Wfjjjz+E0tLSKrfN32DGrLmONRqN0TrS0tKEjz/+WHB0dBQKCgqq3LY1fZYbROAeOnSoMGHCBKNxTzzxhDB27FhBEAQhNjZWACCcP3/eMF2r1Qqenp7C4sWLa7ydb7/9VnBycjL6x2rdurXwySefGM3XqVMn4b///a8gCIKgVCoFGxsbYe3atYbply5dEgAIMTExJrej0+kEHx8f4auvvjKMUyqVglwuF37//XdBEATh4sWLAgDh2LFjhnm2bt0qiEQiISUlRRAEQViwYIHg6uoqqFQqwzzTp08XwsLCavyerYU113FFu3fvrnHg7tatmzBlyhSj8vr5+QmzZ88WBKFm/ztbtmwRxGKxkJ6ebpjnxx9/FBQKhVG91xf1pZ7LrVmzRpDJZIJarTY5nZ9l0+pbPZ85c0YAIFy5csXkdNZzZfWhjk+dOiX4+/sLaWlp1QZu1rFp1l7Pffv2Fd54440ab4f1bJq113NERIQwc+bMWr0n/gYzZu11fKcOHTpUKm9F1vZZbhBNynv06IGdO3ciLi4OAHDmzBkcOHAAQ4YMAQCoVCoAgK2trWEZsVgMuVyOAwcO1Hg7S5YswdNPPw0HBwejbW/atAkpKSkQBAG7d+9GXFwcBg4cCAA4ceIE1Go1oqKiDMuEh4cjKCgIMTExJreTkJCA9PR0o2WcnZ0RERFhWCYmJgYuLi7o0qWLYZ6oqCiIxWIcOXLEME+fPn0gk8kM8wwaNAixsbHIzc2t8fu2BtZcx/eirKwMJ06cMKpjsViMqKgoQx3X5H8nJiYGbdu2hbe3t2GeQYMGIT8/HxcuXLjn8llKfavnvLw8KBQKSKVSk9P5WTatPtVzUVERli5ditDQUAQGBpqch/VcmbXXcXFxMcaMGYP58+fDx8en2u2wjk2z9noGgJUrV8LDwwNt2rTBjBkzUFxcXOV2WM+mWXM9Z2Zm4siRI/Dy8kKPHj3g7e2Nvn373nW7/A1WmTXX8Z1OnDiB06dPY+LEiVVux9o+y6Z/JdYz7733HvLz8xEeHg6JRAKtVov//e9/GDt2LIDbH5AZM2Zg0aJFcHBwwLfffosbN24gLS2tRts4evQozp8/jyVLlhiNnzt3LiZNmoSAgABIpVKIxWIsXrwYffr0AQCkp6dDJpPBxcXFaDlvb2+kp6eb3Fb5+Iof4DuXSU9Ph5eXl9F0qVQKNzc3o3lCQ0MrraN8mqura43euzWw5jq+F1lZWdBqtSbruPxa0Zr876Snp5tcR/m0+qY+1XNWVhZmzZqFSZMmVbktfpZNqw/1vGDBArz77rsoKipCWFgYduzYYbTDrYj1XJm11/Fbb72FHj16YMSIETXaFuvYNGuv5zFjxiA4OBh+fn44e/Yspk+fjtjYWKxfv97ktljPpllzPV+7dg0A8NFHH+Hrr79Ghw4d8Ouvv2LAgAE4f/48mjdvXmlb/A1WmTXX8Z2WLFmCli1bokePHlVuy9o+yw0icK9ZswYrV67EqlWr0Lp1a5w+fRpvvvkm/Pz8MG7cONjY2GD9+vWYOHEi3NzcIJFIEBUVhSFDhhh1BnA3S5YsQdu2bdGtWzej8XPnzsXhw4exadMmBAcHY9++fZgyZQr8/PyMjqrQ/WEdNw71pZ7z8/MxdOhQtGrVCh999FFdvf1Goz7U89ixY/Hwww8jLS0NX3/9NZ566ikcPHjQ6Og+Vc2a63jTpk3YtWsXTp06ZY633qhYcz0DMDog2rZtW/j6+mLAgAG4evUqmjZtWnd/iAbOmutZp9MB0Hd2+cILLwAAOnbsiJ07d+KXX37B7Nmz6/aP0UBZcx1XVFJSglWrVuH999+vs/f+QNSqAbqVCggIEObNm2c0btasWSbb1yuVSiEzM1MQBP31G6+++mq16y8sLBQUCoXw3XffGY0vLi4WbGxshOjoaKPxEydOFAYNGiQIgiDs3LnT5DW9QUFBwpw5c0xu7+rVqwIA4dSpU0bj+/TpI7z++uuCIAjCkiVLBBcXF6PparVakEgkwvr16wVBEITnnntOGDFihNE8u3btEgAIOTk51b5va2LNdVxRTa/hVqlUgkQiqXTN4PPPPy88+uijgiDU7H/n/fffF9q3b280/dq1awIA4eTJk3ctgzWqD/Wcn58vREZGCgMGDDDqeMMUfpZNqw/1XJFKpRLs7e2FVatWmZzOeq7Mmuv4jTfeEEQikSCRSAwDAEEsFgt9+/Y1uT3WsWnWXM9VrQ+AsG3bNpPTWc+mWXM9l//m+e2334zmeeqpp4QxY8aY3B5/g1VmzXVc0a+//irY2NgYtl8Va/ssN4hruIuLiyEWG78ViURiOOpVkbOzMzw9PREfH4/jx4/XqDnZ2rVroVKp8OyzzxqNV6vVUKvVd912586dYWNjg507dxqmx8bGIjk5GZGRkSa3FxoaCh8fH6Nl8vPzceTIEcMykZGRUCqVOHHihGGeXbt2QafTISIiwjDPvn37oFarDfPs2LEDYWFh9a45kzXX8b2QyWTo3LmzUR3rdDrs3LnTUMc1+d+JjIzEuXPnkJmZaZhnx44dUCgUaNWq1T2Xz1KsvZ7z8/MxcOBAyGQybNq0qdqznfwsm2bt9XwnQd/BqOEatjuxniuz5jp+7733cPbsWZw+fdowAMC3336LpUuXmtwe69g0a65nU8rr2tfX1+R01rNp1lzPISEh8PPzQ2xsrNE8cXFxCA4ONrk9/garzJrruKIlS5bg0Ucfhaen5123Z3Wf5RpHcys2btw4wd/f39CV/fr16wUPDw/h3XffNcyzZs0aYffu3cLVq1eFDRs2CMHBwcITTzxRo/X36tVLGD16tMlpffv2FVq3bi3s3r1buHbtmrB06VLB1tZWWLBggWGeyZMnC0FBQcKuXbuE48ePC5GRkUJkZORdt/n5558LLi4uwsaNG4WzZ88KI0aMMNmVfceOHYUjR44IBw4cEJo3b27Ulb1SqRS8vb2F5557Tjh//rywevVqwd7evl7elsLa6zgtLU04deqUsHjxYgGAsG/fPuHUqVNCdnZ2ldtcvXq1IJfLhWXLlgkXL14UJk2aJLi4uBj1dlnd/075LSkGDhwonD59Wti2bZvg6elZL29JIQjWXc95eXlCRESE0LZtW+HKlStGt6eo7nZR/Cwbs+Z6vnr1qvDZZ58Jx48fF5KSkoSDBw8Kw4cPF9zc3IxuF3Mn1rMxa65jU1DD24Kxjo1Zcz1fuXJF+OSTT4Tjx48LCQkJwsaNG4UmTZoIffr0ues2Wc+VWXM9C4K+52uFQiGsXbtWiI+PF2bOnCnY2tpWeWcJQeBvsDtZex0LgiDEx8cLIpFI2Lp1a422aU2f5QYRuPPz84U33nhDCAoKEmxtbYUmTZoI//3vf426cP/++++FgIAAwcbGRggKChJmzpxZqcv+cePGVWpOdvnyZQGA8M8//5jcdlpamjB+/HjBz89PsLW1FcLCwoRvvvlG0Ol0hnlKSkqEV199VXB1dRXs7e2Fxx9/XEhLSzNaT3BwsPDhhx8aXut0OuH9998XvL29BblcLgwYMECIjY01WiY7O1t45plnBEdHR0GhUAgvvPBCpfvRnTlzRujVq5cgl8sFf39/4fPPP6/272mNrL2OP/zwQwFApaHifQb79u0rjBs3zmjdc+fOFYKCggSZTCZ069ZNOHz4sNH0mvzvJCYmCkOGDBHs7OwEDw8P4e23367yNlXWzprrufxyAVNDQkKCYT38LFfPmus5JSVFGDJkiODl5SXY2NgIAQEBwpgxY4TLly8brYf1fHfWXMemmArcrOPqWXM9JycnC3369BHc3NwEuVwuNGvWTHjnnXcq3Yeb9Vw9a67ncrNnzxYCAgIEe3t7ITIyUti/f7/RdP4Gu7v6UMczZswQAgMDBa1Wa3I91vxZbhCBu6706dPHqKIelKKiIsHW1lbYvXv3A992Y2OpOhYE/XU/FQM4mQ8/y40D67nhYx03DqznxoG/wRo+fpZNaxC9lNeFvLw8XL16FZs3b37g2969ezf69++Pfv36PfBtNyaWrOMLFy7A2dkZzz///APfdmPDz3LjwHpu+FjHjQPruXHgb7CGj5/lqokEoYZ9uRMRERERERFRjTWIXsqJiIiIiIiIrA0DNxEREREREZEZMHATERERERERmQEDNxEREREREZEZMHATERERERERmQEDNxEREREREZEZMHATERERERERmQEDNxEREREREZEZMHATERERERERmQEDNxEREREREZEZMHATERERERERmQEDNxEREREREZEZMHATERERERERmQEDNxEREREREZEZMHATERERERERmQEDNxEREREREZEZMHATERHVQkJCAqZOnYoWLVrA3t4e9vb2aNWqFaZMmYKzZ88a5vvoo48gEokgFotx/fr1SuvJz8+HnZ0dRCIRpk6dajTtxx9/xJNPPomgoCCIRCKMHz/eZFl27tyJCRMmGMrSpEkTvPjii0hLS6s0r06nw8KFC9GhQwc4OjrC29sbQ4YMwaFDh4zmGz9+PEQiUZVDSkpKrddJRETUWEktXQAiIqL6Ijo6GqNHj4ZUKsXYsWPRvn17iMViXL58GevXr8ePP/6IhIQEBAcHG5aRy+X4/fff8e677xqta/369VVu54svvkBBQQG6detmMjyXmz59OnJycvDkk0+iefPmuHbtGubNm4fo6GicPn0aPj4+hnnfeecdzJkzB88++yxeffVVKJVKLFq0CH379sXBgwfRrVs3AMDLL7+MqKgoo+0IgoDJkycjJCQE/v7+tV4nERFRY8XATUREVANXr17F008/jeDgYOzcuRO+vr5G07/44gssWLAAYrFx47FHHnnEZOBetWoVhg4dinXr1lXa1t69ew1ntx0dHass05w5c9CrVy+jbQ4ePBh9+/bFvHnz8OmnnwIANBoNfvzxR4waNQq//fabYd4nn3wSTZo0wcqVKw3hODIyEpGRkUbbOXDgAIqLizF27FjDuNqsk4iIqLFik3IiIqIa+PLLL1FUVISlS5dWCtsAIJVK8frrryMwMNBo/JgxY3D69GlcvnzZMC49PR27du3CmDFjTG4rODgYIpGo2jL16dOnUsDv06cP3NzccOnSJcM4tVqNkpISeHt7G83r5eUFsVgMOzu7u25n1apVEIlERuW933USERE1BgzcRERENRAdHY1mzZohIiKiVsv16dMHAQEBWLVqlWHcH3/8AUdHRwwdOrSui4nCwkIUFhbCw8PDMM7Ozg4RERFYtmwZVq5cieTkZJw9exbjx4+Hq6srJk2aVOX61Go11qxZgx49eiAkJKRO1klERNRYMHATERFVIz8/H6mpqWjTpk2laUqlEllZWYahpKTEaLpIJMLTTz+N33//3TBu5cqVeOKJJyCXy+u8rN999x3KysowevRoo/ErVqxAWFgYnn32WQQHB6N9+/Y4efIkDh48iCZNmlS5vu3btyM7O9uoOfn9rpOIiKixYOAmIiKqRn5+PgCYvJ66X79+8PT0NAzz58+vNM+YMWNw5coVHDt2zPBYVXPy+7Fv3z58/PHHeOqpp9C/f3+jaU5OTmjdujWmTJmC9evXY8GCBdBoNHjssceQlZVV5TpXrVoFGxsbPPXUU5Wm3es6iYiIGgt2mkZERFQNJycnAPrm2ndatGgRCgoKkJGRgWeffdbk8h07dkR4eDhWrVoFFxcX+Pj4VArE9+vy5ct4/PHH0aZNG/z8889G0zQaDaKiotCvXz/MnTvXMD4qKgqtW7fGV199hS+++KLSOgsLC7Fx40YMGjQI7u7udbJOIiKixoSBm4iIqBrOzs7w9fXF+fPnK00rv6Y7MTHxrusYM2YMfvzxRzg5OWH06NGVOju7H9evX8fAgQPh7OyMLVu2GA4QlNu3bx/Onz+POXPmGI1v3rw5WrZsiYMHD5pc74YNGyr1Tn6/6yQiImpM2KSciIioBoYOHYorV67g6NGj97T8mDFjkJaWhri4uDptTp6dnY2BAwdCpVJh+/btJntQz8jIAABotdpK09RqNTQajcl1r1y5Eo6Ojnj00UfrbJ1ERESNCQM3ERFRDbz77ruwt7fHhAkTDGGzIkEQ7rp806ZN8d1332H27Nl1dn/qoqIiPPLII0hJScGWLVvQvHlzk/O1aNECALB69Wqj8SdPnkRsbCw6duxYaZmbN2/i33//xeOPPw57e/s6WScREVFjwyblRERENdC8eXOsWrUKzzzzDMLCwjB27Fi0b98egiAgISEBq1atglgsRkBAQJXreOONN2q0rb///htnzpwBoD9bfPbsWXz66acAgEcffRTt2rUDAIwdOxZHjx7FhAkTcOnSJaN7bzs6OuKxxx4DAHTu3BkPP/wwli9fjvz8fAwcOBBpaWmYO3cu7Ozs8Oabb1Yqwx9//AGNRmOyOfm9rpOIiKixEQnVHZInIiIig6tXr+Kbb77Bjh07cOPGDYhEIgQHB6Nfv36YPHky2rdvDwD46KOP8PHHH+PmzZtG98S+k0gkwpQpUzBv3jzDuPHjx2P58uUm51+6dCnGjx8PAAgJCUFSUpLJ+YKDg42uKy8pKcHXX3+N1atXIyEhATKZDL1798asWbPQoUOHSstHRkbi2rVrSE1NhUQiMbmN2q6TiIiosWHgJiIiIiIiIjIDXsNNREREREREZAYM3ERERERERERmwMBNREREREREZAYM3ERERERERERmwMBNREREREREZAYM3ERERERERERmwMBNREREREREZAYM3ERERERERERmwMBNREREREREZAYM3ERERERERERmwMBNREREREREZAYM3ERERERERERmwMBNREREREREZAYM3ERERERERERmwMBNREREREREZAYM3ERERERERERmwMBNREREREREZAYM3ERERERERERmILV0Ae5VUVGRpYvQoMhkMtjY2Fi6GERERERERA1GvQ3cX86eZekiNCwiEfz8g9GxUyd07doVIpHI0iUiMigrK0NcXBzi4+NRWFgIQdBZukgNhlgsgbOzM8LDw9GkSRNIJBJLF4mIiIiowRAJgiBYuhD34uyRrWAmrBuCAJSWliL+ShJir6WgV9+HMXDgQEsXiwgAUFhYiGXLfkFmajJ8PF3grHDgAaE6pNPpkJWTh5y8YjQPb4tnnhkDqbTeHoslokZOEAScPn0aZ86cRsr1ZKjVZZYuUoMildrA1y8Abdu1Q5cuXSAW8+rU2rp48SJOnTqFpMSrKFOpLF2cBuWjT/5n6SKYVG9/VYW3aGLpIjQ4Hdq1wsHDJ7Bn3y507doVrq6uli4SEdavX4fivAy8NO5xeHt5WLo4DZIgCLhyLQnr/96JnTt3YtCgQZYuEhFRrQmCgG3btiHmwC6EBHqiZ5cWkMtteJC2jgiCALVag8TkFGzetBbJycl44oknGLpr4eDBg9i+dRMCvF3QvWNT2NnK+f/ZCNTbwE3m0bVTW+yPOYXLly8jMjLS0sWhRq6wsBDXrsRiSP8uDNtmJBKJ0LxpCNq3bo5zZ09j4MCB/AFARPVOWloaYg7uwcB+XdGtS3tLF6fB6t6tIy5cjMNfW/ajffv2aN68uaWLVC8UFBRgx/bNiOjYAlEP9eR+thHhISkyIpPJ4OrsBKVSaemiECEtLQ06bRlCQwItXZRGoUloIPLzclFQUGDpohAR1drFixdhL5egS6e2li5Kg9eqZXO4Odvh4sWLli5KvXHp0iWIBDV6RXZh2G5kGLipEolEDJ2OnVKR5Wk0GkAA5DKZpYvSKMjlMgACysp4zSMR1T/Z2dnw9nRhE+cHQCQSwc/HA9nZWZYuSr2Rk5MDV2dH2NnZWroo9IDxG4mIiIiI6j2dTgeplHdaeFAkUim0Go2li1Fv6HQ63gmkkarXgXvW53Mhdws3GtpGDDFMz8lV4s3ps9Cm22A4+7VHs7YP4a33PkVe/u3mktk5uRg26kWEtOoNJ5+2aNqmH9549xPk5xdWu/2SklK4BnTElWtJJqefOnMBQx6fAK+QrvBtGoFX3nwfhYXG9w8/fvIcBj02Hl4hXeEd2g1DR07E2fOXq9xmYvKNSu+5fFi3YZvhfT/+zGS4BXZCt76P4/RZ4+Y+r7/zCb6d90u174+IiIiIiIjuXb0O3ADQKrw5ki7tNwy7t6wyTEtLy0RaWiY+/+RdnDz4NxbPn41/du7Hy6/91zCPWCzG8EcGYN3KBTh/bBsWz5+NXXtjMPXtD6vd9r97DiEowA/NmgRXmpaaloEhj09A0yZB2L/jD/y99mdcunwFL06ZYZinsLAIw598EUH+vti/4w/s3rISTo4OGDbqRajVapPbDPT3NXq/SZf244P3XoOjoz0GRfUGAHz+zUIUFhbh8O516NOzG155833D8keOncaxE2fw+ivjqv/jElmx6g64AcDPy/7Aw8Ofg0dQZ8jdwqHMy6+0nrgrCRg59lX4NesOj6DOeGjIGOzZf7hGZRj46PP45de1JqdlZGbhxSnvIaRVb7j4d8CwUS8i/mqiYXpNDp6ZUtUy3/ywBACgUpXhhcnvwiOoM1p3HYSdew4ZLf/ND0vw5vRZNXp/REQNAfcX3F9Ys7o4gQgAyTdSMWL0y3Dx74CAFj3w3gdf6i/Nq4YlTiAC1X/mGtL/Z73vpVwqlcDH29PktNatWuCPX+caXjcNDcIn/30L4ye/A41GA6lUClcXZ7w84RnDPMGB/nh5wjP4dm71Z4Cjt+zEsCH9TU7bsn0PbGyk+OGrDwzXEs2b8xE69xqBK9eS0KxJMGLjryEnNw8fzHgdgQG+AICZ06egc68RSLqeajLISySV3+/Gzf9i1IghcHR0AABcjruGJ58YihbNQjFx3FNY8usaAIBarcbUtz/Cwu9nsUkLNQitwptj61+3P6t33j+6uKQUAwf0xsABvTHzkzkm1/H4M5PRrEkItm9cDltbOeYu/BWPP/MKLp34p8rvFkC/Azx05BR++7nyegVBwJPPToGNjQ3+XLEATk4O+H7BMjzy+AScjomGg4O94eBZRUuWr8GceUsMB89MuXOZ7f/uw8uvz8Tjjw4EAPy8/A+cPH0B+7avxrZ/92HcpP/geuxBiEQiJCTdwC+/rUHMznVVrp+IqCHi/oL7C2t2t//PiicQW4Y1Q/L1VEx9+0OkpWVi9fIfAABarRaPjX4Z3l6e2Lvtd6Sl38TEV6fDxkaKWe9Pu+u2a3ICcdTjQ/DdlzNRUFCE//zfZ3hxygzDtstPIA4b3B8/fPUBtBotPvl8LoaNehFXz+2GjY2Nye1W95lrSP+f9T5wX7mWhJBWvSGXy9G9awfM+mAaggL8qpw/L78ACifHSl+05VLTMrAxegd69+x61+3qdDps+WcP1v42z+R0VVkZZDY2Rh132NrqO0k4dPgEmjUJRotmoXB3c8GyFX9i+rSXodXqsPS3dQhv0RQhQf7VvXUAwMnT53Hm3CV8/+Xts9jt2oRhz77DmPDcKOzYdQBtW7UAoD8S1KdnN3TuyN47qWG42wE3AIaWHHsPHDE5PSs7F1euJmHRD/9D29ZhAID/fTANi5aswoVL8Xdd99Z/9qJju1Ymb1cWfzURR46fwamDf6NVS/3tUuZ98xGCwnvhj3WbMeH5J2t08MyUO5f5e+su9O0dgSa3enK/HHcNw4b0R6uWzREaEogZH36FrOxceHq44bW3P8JnH/4HCoVjlesnImqIuL/g/sKa3e8JxB27DuJS7FVs/WspvL080L5tS3z4f2/gvx99jfenT4XsLp3PWuIEIlD9Z64h/X/W6yblXTu3x8/zZuPvtT9j7tcfIjHpBgY88iwKCkxff52VnYvZX/+IieOeqjTtuRenwcW/A0Jb94WTkyMWfv/pXbd95NhpAKjyPo8P9e6O9MwsfPPDEpSVlSFXmYeZH38DAEjPuAkAcHJyxI5Nv2LV2r/h7NcBboGd8M+u/di09qcqDwjcaekKfUCPjOhkGPfOm5MglUrQstNAbNq8A4t++B/irybit9V/4f/eeQVTpn2IsI5RGPPCm5WaoxDVJ+UH3MI6RmHcpP8g+UZqrZZ3d3NBi+ahWLl6I4qKiqHRaLB42R/w8nRHpw6t77ps9NZdGP6I6R1UeS/fclu5YZxYLIZcJsOhIydMLlN+8Gz8syNrXP6MzCxs/WcvXqiwTLs2YTh0+ARKSkrxz64D8PXxhIe7K35f+zdsbeUYMezhGq+fiKih4P6C+wtrVtv/zztPIB45dhptWrUwOqjzcP9eyC8oxMXLV6pcT/kJxOFVBO7qTiACMDqBWFZWhpKS0lqfQDSlIf1/1uvAPfjhPhj52GC0bR2GgQN6Y+Oan6DMy8efJq5nyc8vxGOjX0Z4WFO8P31qpelf/W8GDu9ejz9XLsC1hOt4Z+bnd93231t34ZGB/aq89USrls2xZMFsfL9gKVz8OyIovBdCggPg7eVhWKakpBQvvz4TPSI6Yv8/f2DP1lVoHd4cj42ejJKS0mrff0lJKf74M7rSF66zwgm/Lv4G8Wd34d/oFWgZ3gxTp32I2R+/i9/X/o2ExOs4f3Qr7O1t8b8v51e7HSJrVNsDbqaIRCJsXb8Up89dhHtQZyh82+OHH5fh77WL4eriXOVyKlUZ/tm1H8MGm95BhTVvgqAAP7z/yRzkKvNQVlaGr79fjBup6UhLv2lyGVMHz6rz2+oNcHJ0wGPDBhrGjR87Em3bhKN95FB8MWchVv7yHXKVefh49g/49vOZ+PB/36Fl54EYOnIiUlIzarwtIqL6ivsL7i+sWV2cQEzPvAkvT3ej+bxvvU7PqPrWbdZyAtGUhvT/WevAnZKSgmeffRbu7u6ws7ND27Ztcfz4ccN0QRDwwQcfwNfXF3Z2doiKikJ8fLzROnJycjB27FgoFAq4uLhg4sSJKCys+ZdeVVycFWjeLARXE4wv+i8oKMTwJ1+Eo5MD1v42z+S1BD7enghv0QTDh/TH/G8/xk+//I609MwqtxW9dReGVnE0qNzTo4Yj+fIBJFzYi7Qrh/H+9Km4mZWD0GB9U57Vf0Yj6XoKFs+bjS6d2iKiawf8uvhrJCbfwN9bdlb7ftdv2o7iklI8+/Rjd51v+cp1cHZW4NFHBmDfwWN4dGgUbGxs8MSIwdh38Gi12yGyRrU54FYVQRDwxrufwNPDHbs2r8TBf9dg+CNReOKZV+76+d+97zA8PdwNzf/uZGNjgz9+/QHxVxPh0yQCLv4dsWf/EQyK6mPyIF1VB8+qs3zlOjz95DDYVjgzYmNjgx+++gBxp3fi0M4/0bN7Z0yf+QWmTHoOp89dwqbNO3F83wZEdGmPae/dvSUPEVFDwP0F9xfWrC5PINaWNZxArEpD+v+sVeDOzc1Fz549YWNjg61bt+LixYv45ptv4Orqapjnyy+/xA8//ICFCxfiyJEjcHBwwKBBg1BaevsPPnbsWFy4cAE7duxAdHQ09u3bh0mTJt33myksLMK1hOtG10Dk5xdi6MiJkMlssH7lAqMvmqrodDoA+mYUpsRfTUTyjVRE9etRo3J5e3nA0dEBa//aCltbOQY8pF+uuKQEYrEYIpHIMG/5a52gq3a9y1b8iWGDH4Knh1uV89zMysH/vlqAbz+fCUDfqYJare+xUK3WQKutfjtE9UFVB9zuZve+w9iyfQ9W/DwHPbp3Qsf2rTH36w9ha2eLFas3VLlc9LZdVZ6tKNepQxsc27cBmYnHkHRpP6L//Bk5OUqE3rp2rqKaHjyr6EDMccTFJ2DCc0/edb49+w/jYuwVvPrSWOw7cASDH+4DBwd7jHpsCA+4EVGjxP2FadxfWId7OYHo4+WJzJvZRvNn3Hrt412574By1nACsabq8/9nrQL3F198gcDAQCxduhTdunVDaGgoBg4ciKZNmwLQH/377rvvMHPmTIwYMQLt2rXDr7/+itTUVGzYsAEAcOnSJWzbtg0///wzIiIi0KtXL8ydOxerV69Gaqrp6xVUKhXy8/ONBq1Gi+nvf4F9B48iMfkGYo6cxJPPvQaJRIzRI4cBuB22i4pLsPCH/yG/oBDpGTeRnnETWq0WALB1x14sX7kOFy7GITH5Brb8swdTp32EHhGdEBIUYLI8f2/Zif59I2Fvb3fXv9eCxStw6swFxF1JwI8/r8Sb02dh1vtvwcVZAQAY0K8ncpV5eP2dT3Ap9iouXorHS1P/D1KJBH17RQAAUlIz0DZiCI6dOGu07ivXkrD/0HG8UM2X53/+7zO8OeUF+Pt5AwAiIzph1ZqNuBR7FUuWr6lVcyQia2bqgFt1iktKAABischovFgsMhx4u5MgCNiybXeV1+PdyVnhBE8PN8RfTcSJ0+dNXidVk4Nnppbp1KE12rUJr3Ke0lIV3nhnFubP+RgSiQRare72ATcND7gRUePE/UVl3F9Yj3s5gRjRtQPOX4wzCt07dx+EwskRLcOamdyOtZxArIn6/v9Zq8C9adMmdOnSBU8++SS8vLzQsWNHLF682DA9ISEB6enpiIqKMoxzdnZGREQEYmJiAAAxMTFwcXFBly5dDPNERUVBLBbjyBHTvdTNnj0bzs7ORsOBAweQkpqB5196G227DcHYCW/B3c0F+/75w/AldOrsBRw9cQbnL8ahVeeBCG7Z2zBcT0kDANjZyvHLr2vx0CNj0b77ULzz39kYNqQ//lq9sMq/Q/TW6o9WAvp70j3yxAR07vUolixfg/lzPsbUl583TA9v0QTrV/2I8xdi0XfQ0+g/9Fmkpmfi77WL4evjBQBQa9SIi08wfNGXW75yHQL8fPBw/55Vbv+fnftx9VoyJk8cYxj36otjERociN4PP4UytRoz351S7fsgskbVHXAD9NcXnTl3CVevJQMAzl+Mw5lzl5CTqwQAdO/aEa4uCkx89T2cPX8ZcVcS8N4HXyIxKQVDBvYzud2Tp8+juKQUPbt3vmv51m3Yhr0HjuBa4nVs2rITQ5+YgEcfGYCH+/cymq+6g2dtI4ZgY/QOo3H5+YVYt3F7tQfcPvt6AQY/3Acd2rUCAPSI6ISN0Ttw7kIsfly8kgfciKhR4P6C+wtrVhcnEB/u3xMtw5rihcnv4uz5y/hn53589Nn3mPziGMjlpnsot/QJxOo+cxXV9//PWl3Jfu3aNfz444+YNm0a/u///g/Hjh3D66+/DplMhnHjxiE9PR0A4O3tbbSct7e3YVp6ejq8vLyMCyGVws3NzTDPnWbMmIFp04zvIWejuoG+/fretbx9e0VAlXP3m673690de7evvus8FWVl5+LI8TP4fdn31c77y49fVDtP1EM9EfVQ1aE5JCjA5HuY9f60au+rV35vu4rs7e2waul31ZaLyNqVH3DLzlHC090NPbp3NjrgBgCLl67GpxU6Bhww9Fn9+Hmf4fkxT8DD3RV/r12MDz79DoNGjINarUGr8Gb4c8X8Ks8E/L1lFwY/3KfajkDSMjLx7szPkXEzG77enhg7egT+751XKs1X3cGzuPiESncTWLN+MwRBwOiRQ6vc/oWLcfhzwzYc2/uXYdwTIwZh78Gj6P/IWLRoHorlP3191/dARNQQcH/B/YU1q+7/s/wEIgC06jzQaNnY0/8iJCgAEokEf61eiNfe/hh9Bj0NB3s7PPv0Y/hwxutVbjd6664aXZpw/OQ5zPp8LgqLihHWvAnmz/kYY0ePMEwvP4H4vy/no++gpyEWi9G+XctqTyBW95kr1xD+P0WCIAg1nVkmk6FLly44dOiQYdzrr7+OY8eOISYmBocOHULPnj2RmpoKX19fwzxPPfUURCIR/vjjD3z22WdYvnw5YmNjjdbt5eWFjz/+GK+8UvkLxpSy3NjqZzKDuCsJ2LHrAKZMes4i238QFi9bi5AWHTF0aNVfzkQPwqVLl/D7b0vw9tTnYGdna+niAAA693oUM95+BaMeH2LpotS5pOsp+G3Ndrz+5jvw8Kj6mi8iImv0+++/Q1OUanTm2pIa8v4C0He4lVMoxkuTXrZ0UeqFLVu2ICH2JF4af/fWBg9CVnYuglv2xrXze0zeH76+krmGWboIJtWqSbmvry9atWplNK5ly5ZITtY3BfDx8QEAZGQYd82ekZFhmObj44PMTOPeHDUaDXJycgzzWLMWzUIbdNgGgJJSFWQy081PiB4ksVgMiGBoMmVpZWVleHz4QAyK6l39zPVQ+TVQEonEwiUhIro3tTiPZFYNfX8BlP+tRdXOR9YnJ1eJLz+d3qDCtjWrVeDu2bNnpTPTcXFxCA4OBgCEhobCx8cHO3fe7pEuPz8fR44cQWRkJAAgMjISSqUSJ06cMMyza9cu6HQ6RERE3PMbobpxMysH+YWlCAys3DMm0YPm5uYGiCRIvcstVx4kmUyGmdOnwsnJ0dJFMYvUtAzIZHZwcnKydFGIiGrNxsYGarV1HKBt6PsLAChTlcFGVvlWu2SaVCqt8g5ID1pjOIFoTWoVuN966y0cPnwYn332Ga5cuYJVq1bhp59+wpQp+o63RCIR3nzzTXz66afYtGkTzp07h+effx5+fn547LHHAOjPiA8ePBgvvfQSjh49ioMHD2Lq1Kl4+umn4efnV+dvkGouV5mHjdH/wlHhhmbNTPdoSPQgeXp6wsvHHzFHTkGtVlu6OA1aQUEhTp25jBbhraq93pCIyBr5+fkhJT0LKpV1hJqGTKfTIflGBgICeIKmpvz9/aHML0auMs/SRaEHrFbXcANAdHQ0ZsyYgfj4eISGhmLatGl46aWXDNMFQcCHH36In376CUqlEr169cKCBQvQokULwzw5OTmYOnUq/v77b4jFYowcORI//PADHB1rfhTw5/mf16bYVI3iEhWycgrgoHDDuPET6kXzfmocEhMTseLXZXCQ6xDeIhSuLgqjW0/Q/dHpdMjMysGl2ATY2LnihQkv6lsWEBHVM0qlEt9/+zXCm3hj2JCHjO5TTHVHo9Hg392HcOLcNUx+9XWjfpuoamVlZfj6qy/g7SbDk48NqXRrL7p/1noNd60Dt7VYv369pYvQoMjlcgQHB6NFixa8fpusTmpqKo4cOYL4uEsoLiqymmv0GgKxWAyFswtatmqDiIgIuLq6WrpIRET37OLFi1j7xyqIhTIE+HnAVs5QU5fK1GrcSL2JMq0Yjz42Cp073/2WZ2QsKSkJK35bBo2qEAG+HrC3kg5hG4pnXnjD0kUwqd4GbiKiu9HpdNDoNFBr1VBr1bef6/TPyzRl0Ak6CIIAAfqvwYrPdTqdyfECBEPgL1++4vjy+UQQQYAA0a0OZUQiEUQQGT+WPzc1j4n5y4lEIkjFUtiIbWAjsYFMItM/SmWQiqWQiqX6DueoEkN9Vdj13TnubvPUdjv3Wr7aLlObVh/32kLkXpYrX+bOx5pOI7oXOTk5uHDhAm7cuIGyMpWli9Og2NjI4Ofnh1atWlW6zS/VTH5+Pi5evIikpCSUlpZUvwDV2Lhx4y1dBJMYuImoEq1OixJ1CUrUJSgqK0KJugTFZcUoVhcbxhsGTQlKNaWGMKvVafXhtuLzW4FXI2j0j7cGrU5rCMAanQZaQWv0vOI6NIJ+/oqPFZep+Fg+WFIXzy44fvO4xbYvFemDt0Qk0Yfwu7y2EdtAIpYYxlWcXv5cIjZezmh6xfXcuX6xBDYiG8N8thJb2EntIJfIYSe1g63UFrYSW8jFctjb2EMm1rewEYlEtQrHNZ3WEInFYuh0OksXo87VNpzfb7i/n3nqcn01Gaqbl+6dTtBBpVFBpVVVeizTllU5TaVRQa1TQxAE/cHYCgdhKz5WNU0sEhsOnlU6OGviUSwSG56XH3iVS+T6R6m8Vq8lYt6ZgsicGLiJrFhtg2+JWh9+DY+a248qjUr/WluKUk0pSrWlUGlU+ketyjCUakuh1tWugzIxxJXCmUQkuR3UKjxWDGhVPbcR3w5ohqBnIuRJxVJIJdLbQa/8uVgCmVgGqeTWMhLjAFixnIJw+yx0ufLXRtOEWz+KhdthsOJyd74WCSJ96Bf0Z791gk7/Q0un/3FVHpCMfoTdegT0P/ogwLBcxe0IEAwHFYwOVNxxEMJwgEOoeh6T44U7DnrUcDumtltbIoj0AVwi14dyiT6c20pt9c+l+nG2Elv9uAqh3c7GDnYSO/3jrfEONg76+WzsYG9jDzupHRxkDrCz0T9KxJJKIcbUo1EZrfRsck08iDPvpua9lwMl99sawdzrq6ttmUNtA3pVob28pUxtl7uXbdXkgIFGp0GBqgB5qjzkq/KRr8pHXmmF5xXG56vyUaoprTIcVxWkNbraf2+Vk4j03ydikbjakHzno63UFiWaEpMhvbrwfj9lLi93VcHcVEiXS+VwkjnBSeYEhVwBJ/mtxzteVxxnb2PPg0HUaDFwE9UxrU6LQlUhcktykV+aD2WpEnklebd/FJTe/jGQX5aPwrJCFJQVGIYidREK1YUoVBdCpatdUzgxxIagUh5abKW2htflz+2kt4JLeVipEFzsbewNAcXWxtYQUuxl9oYgU/68PLjIpLJqd6SCcOsHgk5n9Ghq3N2m1XZ+c535u9cfmdXN96DWUz7dnMp/EJZpy6DWqlGmLUORqgjF6mIUlVV4LNMfQCpSF90+wKQuMhxEMhxQ0pQYHUQyOoB06yBSqba0Vq0bZGKZ0eej/Ay8g9TB8ENRIVPA2dYZCrkCLnIXONs6w8XWBS52+sHVzhWudq5wkDtAIpHwRyXVSsUAfufzmg61XQbA7YN+dbCdmijTliFHlYMCdQGKNEX6R3URCjWFhn2eYbymqNK0QnUhSrRVN78Vi8RQyPQBz9HGEQq5whAODY/SW/tGqX7fKJPK9PvFW68Nj3cud0cANfUok8ggFlnmUh5BEKDWqY0OHJQ/L9OWmXxd62m62/OUakr1v1lUBchX5aOgTP94t+AvFon1If2OcG4I6LLKwV0h13/3etp7wtPBE252bhb7GxPdDwZuoltKykqgLFFCWaqEskRpCMh5JbePmBeoCpBflm/YwRSWFaJArX8sVBeiSF2EIk2R4TpeU2RiGRxtHI0HmePtHwoyRyhsbz+3k94OvvY29rdDsM2t4Cu1g4P8VvCVyO567W7FH0h1EWRrM+1evmpEIhHEYrEhJJY/v/OxunHmCLhkvdRatSHMF5UVGUJ+cVkxitS3Ar7mdquREk2JYVyxWv9YWFZoOChWUKb/3BeoC1CsKa5yuzZiGzjZ6H/sO9ncOvsj0wf2ij8eneX6wO5s6wxXO1fDY8XQzmvwqT4o1ZQiozAD6YXpSC9MR0Zhhn4o0g/phenILMpEZlEmlCqlyXWIIDI+WypzMnx+ysNz+WfKwcYBjlL9awepAxyk+tcONg6Qi+Qm93G1VZN9zv0+lj+v+Lq+EwQBKq3K+PdShTBu8nXZ7fF3TjN14FQiksDD3gNeDl7wcvCCp4MnvOy9DK8N4249d5I5NYi/LdV/DNzU4Kg0KmQWZCKzUD9kFWchqygLWcVZyC7JRnZJNnJKcpBbmotcVS6UKiUK1YV3bf4qhli/g7dxuL3zL/8xUB6WKzShMvywtnWGwlZx+4e1nTPkUvlddwAVfyzU5XA/ofdeQu79TmsoP0KoYdHqtMgrzYOyVImc4hz9QboSJXJKcgzjy5u55qnyjA7Q5av1j3cL7VKRFAqZolJod5I5wVmuD+vudu6GH5beTt7wdvSGt5M35DZynmGn+1asLr4dmis8phemG4J0+fh8Vb7RsiKI4GHvof+fdPC+/VjhuaudK5zlzoZ9pYPMwWxnLe92kLmuHqsaVxsV93mm9pOmhvIDc9UN9XFfKggCSjQlKFAVQFmqxM3im8gsysTNopuGAziZxZlG47KKsyqd7JBL5JVCeHlArzguQBEALwcvnj0ns2HgJqtXUFqA1PxUpOfrj6CnF9w+Yp5VnIWc0hzklOYYwnORpqjSOsTQNzVzlbvC1dYVbrZu+sHODa62rnCxddGHZDtnQ1h2sXMxhGZHuWOVZ5xMhWOtVmvyuanXpoaaqMmOtuIO916DcH3bURNZO61Oi3xVPnJLcpFTnIPcklxD65ry0G4I7BWuSy0/056jyjHZz4KzzBmuMle427rDzdYNHvYe8LDz0DfHtPeEl6MXvBy94OPoAx+FD2xltgzojYwgCMgtzUVCbgISlYlIVCYiQXn7eXJeMgrKCoyWEYvE8LT3rBSifRx9jEO1ozc87D0gFUst9O6shzlCflW/I2qitiG94nzlz8sfrfV3gVanRXZJduVgfmsoD+3lQ54qz2h5uUSOQOdABDsHI9g5GEHOQQh2uf080DkQMglvm2ut1Bo1riuvo4lHE0sXxSQGbrKI/NJ8XMu+huTcZH2Ts8LbRyuzirOQVZKFrFJ9mL7zmi0RRHCRu8Dd1h3utu5wtXWFu53+B6a7vTs8HTz1PzQd9EfZPR094e7gXqkXzvKdmEajMey8ahOUa7PDM7WTK/+hW3EnZup5QzliTUR1QxAE5KvykVmUifSCdGQU6M84Zhbe+g4tyTJ8j2aXZCO7NBtlujKjdYgggqvcFR62HvC084SXnf5MT3mQ8nPyg59CP7g7uMPGxsbw3UPWTaPTIEmZhCs5VxCfE48rOVeQoEwwhOyKgdrBxgGhrqEIdQlFiEsIgpyD4OvoaxSi3e0q7z/JOlTXIq4mB/hNLVOduwXy6gL7neHdUlQaFbKKs5BRlIHredeRnJeMpLwkJOUl6Z8rk5BRlGGYXwQRfJ180cS1CZq6NtUPbrcf3e3c+f1oRmWaMiRkJ+Bq9lVczb6KBGWCvq7yk3Gj8AZult6EVtBC+NA6Yy0DN9U5tVZ/lCkhOwGJufqj5tfzr+NG/g2kFKUgtSgV+WXGzdDspfZwl7vD3c5df0bGztPQ3Mfb8daRdCdv+Dr5wlvhDRuJjWFZQRCg1WpNDuVhuqqhKhWbcd0ZfKvbqZjaofBLmIgsRRAEFJYVIrMoU980+FZrobSCNKQVpCG9SN9qKKM4A5nFmSjVlhotLxPL4GHrYQjn3vbe8HfyR6hLKEJdQ9HMvRn8nP0gl+ubtJP5CYKA5LxkxGXHIT4nHvHZ8frHnHgk5CYYWkDIJDI0cW2CJq5NEOIcglBXfbAOcQlBqEso3OzcuH8iIxVDvKmTEFWdmDA1vboTEuW/lcoHqVRq9LqqcQ/qf7ZUU4rredcNITxRmYhruddwNfcqruZcxc3im4Z5FXKFUQhv49UGHXw6IMw9zOg3K1VNo9UgLjMOp1NP41zGOVxTXkNyfjKuF15HenG60XX9XnZeCHQMRJAiSN8KwSUIQc5BGNlupAXfQdUYuKlWBEFAdlE2ruVcQ2JuIpJy9V9C1/Ov40bBDaQWpSKzJNPoQ6GwUcDXwRf+jv4IVAQiUBGIYJdghLiGINAlEH4KPzjZOhm+nKsLyXeG6aq+0MvPElf3xV1xqBiYiYgam/JwnlaQhtSCVKTmpyIlPwXpBemGjrHSi9Jxo/CG0YFTO4kd/Oz9EOAYgECnQAQrgvVnTl1D0dS9qf5AqY0NA/k90Ak6xGfH42TaSZxIO4GTaSdxKv0UlKVKAIBULEWoSyiauzdHc7dbw63nQc5BPDtNFnO34F7+3NTJkfLXVUWU2oZ0qVRqlpMf+ap8fQDPuWoI4VdzryI+Jx7JeckA9Ae9Wnu2RgefDmjv3R7tfdqjvXd7uNq51mlZ6hOdTofk3GScSj2Fs2lncSHrAi5lX8LVvKuGVq0KGwVCFCEIUgQZDhY2dWuKJu5N0MyjGexs7Cz8Lmqn3gbu9MJ0XitkRspiJS5kXMDFjIuIzYrVN0tTXkFSQZLRNdJSkRQ+9j7wd/SHv6M/AhQBCHIOQoir/sMR7BoMJ5kTNBqNyUGtVlcbnO88AmpqMPWFy9BMRGQ+ylIlEnJvNfHLuYprubcOxOYnIbkgGSWa25cDOdk4wd/eHwGOAQhy0p+JCHXR/4Bq6t4ULg4ukMnufpeFxkCj0+DSzUs4mXZSP6SfxOn00ygsKwQAhLiEoJNvJ3Ty6aQ/e+YRhhCXEP4WogapJqHc1DhT7jwJU/67USqVVhrKL6G5H8pSJc5mnMWZ9DM4k3EGp9NP43zmeai0+tu9BjkHob13e0MQ7+zXGcHOwQ2uxYmyWIkjyUdwJu0Mzmeex+Wcy4jNjUW+Wn/A1k5ih+YuzdHSvSVae7ZGe9/26ODXAYEugQ3qb1FvA7foY30luNq6wtPB03CPPi/72z0P+jv5w1/hD38nf/g6+XKHdAdBEJCcm4xj14/h0s1LiM2O1R+hy7uKm6W3m8m427qjqULfRKaZWzOEuoTqO5BQBMLD1gMQUGWgNhWiTX253e3Mc2P/AUZEVN8IgoCs4iz92Z/yQJ5zTX+NZH4SbhTcMLqu3M/eD02cmqCFawu09ND/8Grj0wYeTh6Qy+UNdj+QU5KDvYl7sSdxD46kHMGZjDMo1eib9Dd3a64P176d0Nm3Mzr6doSbnZuFS0xk3SqeVa9JUC//vXpnHBKLxSaDuKnfrzUNhhqdBrFZsTiTcQZn0s/gdMZpnEk/Y7hWPEARgN5BvdEnuA96B/VGS8+W9arndJ1Oh4vpF7EvYR9ibsTgePpxxCnjoIMOUpEUTRRNEO4ejtaerdHOux06+HdAc4/mjaIVTr0N3JtiN+Fm0U3cLL55+7HC84zCDMNRJEDfy6a3g7chgAcoAowCeYAiAP4KfzjKHC34rsxHp9MhPiseR5KP4GTqSZzOPI0L2ReQVZoFALCV2CJEEYJmzs3Q3K05mrneenRpBgepA9RqNcrKyqBWq6FWqyt9MVUVok2F6oZ0xIqIiGpPJ+iQVpCGhNwExGXH6VtU3byIS1mXkFyQbLi9j4+dD5oqmqK5S3OEKDBUHwAAL5tJREFUu4ejrVdbdPTrCDeFG2QyWb3bnyhLldiXtA+7E3Zjd+JunM04CwECQl1C0TOoJzr7dkYnX/3Za4VcYeniEjUK5X0B3a0lZvlg6gx6+W9cmUwGGxsbw2P58+oupUkvTMfRlKPYn7Qf+5L34UTqCWgFLdzs3NArqBeiQqMwInwEgpyDzPUnuCc6nQ6nUk5h08VNOHDjAE5lnkJuWS4AoImiCbr6dEX3gO7oFdILbX3bQi6VW7jEllNvA3d1BEFATkkOUgpS9J115acgpSAFKfkpuFFw+3VOSY7Rcgq5whDAja6Jcm+Opq5N680/S0FpAf6N/xc7r+7EyYyTuJB9wdB8w8vOC63dWqOdZzu09WiL1m6t4W3nDZ1WZ/hiqaj8qF7FoeKXCUM0ERHVlWJ1MWKzYnEu4xzOpZ/DhcwLuJx9GYn5iRAgQCqSIswlDO3c2qGzT2dEBESgtU9rODo41ups04OSnJeMdRfXYd2ldYi5EQOdoEOQcxAeCnkID4U8hH4h/RDsEmzpYhJRDZT3NWQqmFd1YkosFlcK4RWfS6XGLXCLyopw+MZh7Evah33J+3Aw+SDUOjU6+HTAiLARGBE2Ah18Oljkuy6vJA+bL23G1vit2Jm8E2nFabCV2KKzV2dE+EWgZ3BP9A7tDU9HzwdeNmvWYAN3TZWoSwxBvGI4v1FwA9dyryE+Ox5Fav01y2KRGEHOQZU6JWnh3gIhLiEW7YVQrVXjYMJBbIvfhj1Je3Ai8wQ0ggaetp7o5NUJbdzboI1bG4S7hMNF6mIUqit++Ct+CZQPDbUpHxER1R8l6hJcuHkBh68fxqHkQzieehzxyngA+mvE27i2QTuPduju1x0Dmg6Ap4sn5HK5RX6UXsu9hj8v/ok/L/6JY6nHIJPIMLDpQIwIG4H+of0R6hJqdQcGiKhuCIJgCOEVg3jFQF7xkkuJRAK5XA5bW1vI5XLD8/ITWnmledh2ZRs2xm7ElvgtyFPlIdg5GBM6TsCkzpPg4+hj1vejUqvw59k/8cvpX7A/dT/UOjVCnEIwIHgAhoUNw8PNH4aD3MGsZajvGn3gro4gCEgvTDd5640rOVcM11pJRBI0c2uGiIAIdPfvju4B3dHWu61ZrxvX6XTYdnkbFh1fhF3Xd6FQUwhHqSMivCMQ6R2Jrh5dEeoYauiZUSaTGX2Q5XI5O6khIqJ6S1mqxLGUY4i5HoPD1w/jWNoxZJVkQS6Wo5tXNzzk/xCGNBuCVn6t4ODgYNZe0gVBwL/X/sWcw3Ow7co22EntMKT5EIxsORLDWgxjE3EiAnC7CXt5CFepVCgtLYVKpUJZWZnh7LipIC6VS3Hg+gGsubAGK8+thFqrxqhWozCl6xT0COxRpwfyLqZfxILDC7A6djWyS7PRzr0dRrcajRGtRqC1T+s6205jwMB9H3SCDjfybxhC+PnM8ziScgSn009Do9PA3sYeXf301y9EBkSie0B3eDt63/d20/PT8dORn7D03FIkFiQi1CkUw4KHIcIjAq1dW8NWZgsHBwejD2h9vNaNiIioNgRBwOWsy4iOi0Z0bDQO3TgEjaBBE6cm6OPbB080fwK9m/WGQqGos4PNaq0aK86uwLeHv8W5zHPo4NMBb0a8iVGtRsFBxrM+RFRzgiCgrKzMEMDLw3jFIG5vbw8HBweoJWqsjV+LBccX4ErOFQxsOhALhy5EqGvofZXhRu4NTNs6DWvj10Jho8CoFqPwcreX0S2oW128xUaJgdsMitXFOJl2EodvHEbMjRjEXI9BWmEaACAyIBKTu0zGU62fgq3Utlbr1el0+HTnp/jf4f9BJ+jwcMDDeDL0SUT6RsLR0REODg5wcHCAjY0NwzURETV6+ap87Li6A3/H/o2tV7YiszgTnT06Y3zYeIxsMxKeHp6Vrp+sjXMZ5/D8hudxOv00hrUYhmndp6FfSD/ug4moTgmCAJVKhaKiIsOg1WohEokgt5XjwM0DeP/Q+8gpzcGsh2bhjYg3at37t1qrxld7vsLnRz6HWCTGzB4z8Ur3V9hcvA4wcD8AgiDgev517E/aj2VnluHfa//Czc4Nr3R5BR/2/bBG134XlxXj0d8exc4bO/Fss2fxWofX0MK/hSFgExERUdU0Og02XN6Abw59g8MphxHoEIjnmj+HyZ0nw9/Pv1ZnvLU6Lb44+AU+2vMRWri3wPLHlqOzX2czlp6I6LaKAbywsBDFxcXIL83HwriFWH55OaZFTsPXA7+u8fp0Oh3Grx2PFZdX4NmWz+KrwV/BW3H/rXJJj4HbAuKz47Hw+EL8cPQHRDWJwuYxm6u9z960jdMw98xczO87H892fRb29vYPqLREREQNy9GUo5hzaA7WXlqLnt498UOfH9C6WesaH8D+ZO8n+GjPR5jeczo+6vdRvbmDCRE1TIIgID8/Hzdv3sSis4vw9bmvsXbUWoxqPapGy3+x+wu8t+89fNf/O7zR+w0zl7bxYeC2oI2XNyI6LhrjO4yHWCS+6w576ZGlkIlk+PrRr9lUjYiIqA5sv7Ido/8cDS9bL/w15C+0atGq2n3svqR9eGj5Q/igzwf4sN+HD6ikRETVEwQBBQUFGPb7MKSWpGLN6DXVLqPT6dB7aW8MCh6EDc9tMH8hGyEGbgtKzktG2LwwQ0/n1bGV2iJ2aqzV3fieiIiovjqbcRadFnXCpLBJGNlxJFydXAEAKo3K5IHwVze/imJ1MU69fKrW10gSET0Imy5swqj1o6DWqWu8jEwiQ/xr8cwZZmC+e1ZRtbKKs2octgGgVFOKrOIsfhCIiIjqSDvvdvh+8Pd4a/tb+PHyjzVaRiaWIaUghftjIrJKAW4BtQrbAFCmLWPOMBPegJmIiIgatcjAyFr9OC3T6X+YEhERVYeBm4iIiIiIiMgMahW4P/roI4hEIqMhPDzcML20tBRTpkyBu7s7HB0dMXLkSGRkZBitIzk5GUOHDoW9vT28vLzwzjvvQKPR1M27ISIiIqL/b+/+o6qu8zyOv/h5Ae1CqFwkhOzHqpTljG1w3VJXGYnlmLNyzvSDMWo5ujXolOyaccZfo9vgcdvsx6K5HcLajXFij5mZORI6OiX4g7T8kaTGLDvJhS0HSMsLwmf/mMMdb1wVlK9c8Pk4557j/X4+3+/n8+V9v3Jf3O/3ewEAfqLb13Dfdttt+uCDD/6ygeC/bGLu3Ll67733VFpaqsjISM2ePVvTp0/XRx99JElqa2tTRkaGYmNjtWvXLtXV1emRRx5RSEiIfvWrX/XA7gAAAAAA4B+6HbiDg4MVGxvbaXlTU5OKiopUUlKiSZMmSZKKi4s1atQoVVZWKiUlRVu3btWRI0f0wQcfyOFwaMyYMVq2bJnmz5+vJUuWKDQ09Mr3CAAAAAAAP9Dta7iPHTumuLg43XTTTcrKylJtba0kqaqqSq2trUpNTfX0HTlypBISElRRUSFJqqio0OjRo+VwODx90tLS1NzcrMOHD19wTLfbrebmZq+H2+3u7tQBAAAAALhquhW4k5OTtXbtWm3ZskWrV69WTU2N7r33Xn3zzTdyuVwKDQ1VVFSU1zoOh0Mul0uS5HK5vMJ2R3tH24UUFBQoMjLS61FQUNCdqQMAAAAAcFV165Ty9PR0z7/vuOMOJScnKzExUW+99ZbCw8N7fHId8vPzlZeX57XMZrNZNh4AAAAAAFfqir4WLCoqSn/1V3+l48ePKzY2Vi0tLWpsbPTqU19f77nmOzY2ttNdyzue+7ouvIPNZpPdbvd6ELgBAAAAAP7sigL36dOndeLECQ0dOlRjx45VSEiIysvLPe3V1dWqra2V0+mUJDmdTh08eFANDQ2ePmVlZbLb7UpKSrqSqQAAAAAA4Fe6dUr5P//zP2vq1KlKTEzUyZMntXjxYgUFBemhhx5SZGSkcnJylJeXp+joaNntds2ZM0dOp1MpKSmSpClTpigpKUkzZszQihUr5HK5tGDBAuXm5vKJNQAAAACgX+lW4P7jH/+ohx56SF9//bWGDBmie+65R5WVlRoyZIgkaeXKlQoMDFRmZqbcbrfS0tK0atUqz/pBQUHatGmTnnjiCTmdTg0YMEDZ2dlaunRpz+4VAAAAAAC9rFuBe926dRdtDwsLU2FhoQoLCy/YJzExUZs3b+7OsAAAAAAA9DlXdA03AAAAAADwjcANAAAAAIAFCNwAAAAAAFiAwA0AAAAAgAUI3AAAAAAAWIDADQAAAACABQjcAAAAAABYgMANAAAAAIAFCNwAAAAAAFiAwA0AAAAAgAUI3AAAAAAAWIDADQAAAACABQjcAAAAAABYgMANAAAAAIAFCNwAAAAAAFiAwA0AAAAAgAUI3AAAAAAAWIDADQAAAACABQjcAAAAAABYgMANAAAAAIAFCNwAAAAAAFiAwA0AAAAAgAUI3AAAAAAAWIDADQAAAACABQjcAAAAAABYgMANAAAAAIAFrihwL1++XAEBAXrqqac8y86ePavc3FwNGjRIAwcOVGZmpurr673Wq62tVUZGhiIiIhQTE6N58+bp3LlzVzIVAAAAAAD8ymUH7r1792rNmjW64447vJbPnTtX7777rkpLS7Vjxw6dPHlS06dP97S3tbUpIyNDLS0t2rVrl15//XWtXbtWixYtuvy9AAAAAADAz1xW4D59+rSysrL06quv6vrrr/csb2pqUlFRkZ5//nlNmjRJY8eOVXFxsXbt2qXKykpJ0tatW3XkyBH913/9l8aMGaP09HQtW7ZMhYWFamlp8Tme2+1Wc3Oz18Ptdl/O1AEAAAAAuCouK3Dn5uYqIyNDqampXsurqqrU2trqtXzkyJFKSEhQRUWFJKmiokKjR4+Ww+Hw9ElLS1Nzc7MOHz7sc7yCggJFRkZ6PQoKCi5n6gAAAAAAXBXB3V1h3bp1+vjjj7V3795ObS6XS6GhoYqKivJa7nA45HK5PH3OD9sd7R1tvuTn5ysvL89rmc1m6+7UAQAAAAC4aroVuP/3f/9XTz75pMrKyhQWFmbVnDqx2WwEbAAAAABAn9KtU8qrqqrU0NCgH/7whwoODlZwcLB27Nihl156ScHBwXI4HGppaVFjY6PXevX19YqNjZUkxcbGdrprecfzjj4AAAAAAPR13QrckydP1sGDB3XgwAHP46677lJWVpbn3yEhISovL/esU11drdraWjmdTkmS0+nUwYMH1dDQ4OlTVlYmu92upKSkHtotAAAAAAB6V7dOKb/uuut0++23ey0bMGCABg0a5Fmek5OjvLw8RUdHy263a86cOXI6nUpJSZEkTZkyRUlJSZoxY4ZWrFghl8ulBQsWKDc3l9PGAQAAAAD9RrdvmnYpK1euVGBgoDIzM+V2u5WWlqZVq1Z52oOCgrRp0yY98cQTcjqdGjBggLKzs7V06dKengoAAAAAAL3migP37373O6/nYWFhKiwsVGFh4QXXSUxM1ObNm690aAAAAAAA/NZlfQ83AAAAAAC4OAI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABggW4F7tWrV+uOO+6Q3W6X3W6X0+nU+++/72k/e/ascnNzNWjQIA0cOFCZmZmqr6/32kZtba0yMjIUERGhmJgYzZs3T+fOneuZvQEAAAAAwE90K3DHx8dr+fLlqqqq0r59+zRp0iRNmzZNhw8fliTNnTtX7777rkpLS7Vjxw6dPHlS06dP96zf1tamjIwMtbS0aNeuXXr99de1du1aLVq0qGf3CgAAAACAXhbcnc5Tp071ev7ss89q9erVqqysVHx8vIqKilRSUqJJkyZJkoqLizVq1ChVVlYqJSVFW7du1ZEjR/TBBx/I4XBozJgxWrZsmebPn68lS5YoNDS05/YMAAAAAIBedNnXcLe1tWndunU6c+aMnE6nqqqq1NraqtTUVE+fkSNHKiEhQRUVFZKkiooKjR49Wg6Hw9MnLS1Nzc3Nnk/JfXG73WpubvZ6uN3uy506AAAAAACW63bgPnjwoAYOHCibzabHH39cb7/9tpKSkuRyuRQaGqqoqCiv/g6HQy6XS5Lkcrm8wnZHe0fbhRQUFCgyMtLrUVBQ0N2pAwAAAABw1XTrlHJJGjFihA4cOKCmpib993//t7Kzs7Vjxw4r5uaRn5+vvLw8r2U2m83SMQEAAAAAuBLdDtyhoaG65ZZbJEljx47V3r179eKLL+qBBx5QS0uLGhsbvT7lrq+vV2xsrCQpNjZWe/bs8dpex13MO/r4YrPZCNgAAAAAgD7lir+Hu729XW63W2PHjlVISIjKy8s9bdXV1aqtrZXT6ZQkOZ1OHTx4UA0NDZ4+ZWVlstvtSkpKutKpAAAAAADgN7r1CXd+fr7S09OVkJCgb775RiUlJfrd736n3/72t4qMjFROTo7y8vIUHR0tu92uOXPmyOl0KiUlRZI0ZcoUJSUlacaMGVqxYoVcLpcWLFig3NxcPsEGAAAAAPQr3QrcDQ0NeuSRR1RXV6fIyEjdcccd+u1vf6sf/ehHkqSVK1cqMDBQmZmZcrvdSktL06pVqzzrBwUFadOmTXriiSfkdDo1YMAAZWdna+nSpT27VwAAAAAA9LJuBe6ioqKLtoeFhamwsFCFhYUX7JOYmKjNmzd3Z1gAAAAAAPqcK76GGwAAAAAAdEbgBgAAAADAAgRuAAAAAAAsQOAGAAAAAMACBG4AAAAAACxA4AYAAAAAwAIEbgAAAAAALEDgBgAAAADAAgRuAAAAAAAsQOAGAAAAAMACBG4AAAAAACxA4AYAAAAAwAIEbgAAAAAALEDgBgAAAADAAgRuAAAAAAAsQOAGAAAAAMACBG4AAAAAACxA4AYAAAAAwAIEbgAAAAAALEDgBgAAAADAAgRuAAAAAAAsQOAGAAAAAMACBG4AAAAAACxA4AYAAAAAwAIEbgAAAAAALEDgBgAAAADAAt0K3AUFBfrrv/5rXXfddYqJidGPf/xjVVdXe/U5e/ascnNzNWjQIA0cOFCZmZmqr6/36lNbW6uMjAxFREQoJiZG8+bN07lz5658bwAAAAAA8BPdCtw7duxQbm6uKisrVVZWptbWVk2ZMkVnzpzx9Jk7d67effddlZaWaseOHTp58qSmT5/uaW9ra1NGRoZaWlq0a9cuvf7661q7dq0WLVrUc3sFAAAAAEAvC+5O5y1btng9X7t2rWJiYlRVVaXx48erqalJRUVFKikp0aRJkyRJxcXFGjVqlCorK5WSkqKtW7fqyJEj+uCDD+RwODRmzBgtW7ZM8+fP15IlSxQaGtppXLfbLbfb7bXMZrPJZrN1d38BAAAAALgqruga7qamJklSdHS0JKmqqkqtra1KTU319Bk5cqQSEhJUUVEhSaqoqNDo0aPlcDg8fdLS0tTc3KzDhw/7HKegoECRkZFej4KCgiuZOgAAAAAAlurWJ9zna29v11NPPaW/+Zu/0e233y5JcrlcCg0NVVRUlFdfh8Mhl8vl6XN+2O5o72jzJT8/X3l5eV7L+HQbAAAAAODPLjtw5+bm6tChQ/rwww97cj4+cfo4AAAAAKCvuaxTymfPnq1NmzZp+/btio+P9yyPjY1VS0uLGhsbvfrX19crNjbW0+f7dy3veN7RBwAAAACAvq5bgdsYo9mzZ+vtt9/Wtm3bNHz4cK/2sWPHKiQkROXl5Z5l1dXVqq2tldPplCQ5nU4dPHhQDQ0Nnj5lZWWy2+1KSkq6kn0BAAAAAMBvdOuU8tzcXJWUlOidd97Rdddd57nmOjIyUuHh4YqMjFROTo7y8vIUHR0tu92uOXPmyOl0KiUlRZI0ZcoUJSUlacaMGVqxYoVcLpcWLFig3NxcThsHAAAAAPQb3Qrcq1evliRNnDjRa3lxcbEeffRRSdLKlSsVGBiozMxMud1upaWladWqVZ6+QUFB2rRpk5544gk5nU4NGDBA2dnZWrp06ZXtCQAAAAAAfqRbgdsYc8k+YWFhKiwsVGFh4QX7JCYmavPmzd0ZGgAAAACAPuWKvocbAAAAAAD4RuAGAAAAAMACBG4AAAAAACxA4AYAAAAAwAIEbgAAAAAALEDgBgAAAADAAgRuAAAAAAAsQOAGAAAAAMACBG4AAAAAACxA4AYAAAAAwAIEbgAAAAAALEDgBgAAAADAAgRuAAAAAAAsQOAGAAAAAMACBG4AAAAAACxA4AYAAAAAwAIEbgAAAAAALEDgBgAAAADAAgRuAAAAAAAsQOAGAAAAAMACBG4AAAAAACxA4AYAAAAAwAIEbgAAAAAALEDgBgAAAADAAgRuAAAAAAAs0O3AvXPnTk2dOlVxcXEKCAjQhg0bvNqNMVq0aJGGDh2q8PBwpaam6tixY159Tp06paysLNntdkVFRSknJ0enT5++oh0BAAAAAMCfdDtwnzlzRnfeeacKCwt9tq9YsUIvvfSSXnnlFe3evVsDBgxQWlqazp496+mTlZWlw4cPq6ysTJs2bdLOnTs1a9asy98LAAAAAAD8THB3V0hPT1d6errPNmOMXnjhBS1YsEDTpk2TJL3xxhtyOBzasGGDHnzwQX322WfasmWL9u7dq7vuukuS9PLLL+vv/u7v9NxzzykuLu4KdgcAAAAAAP/Qo9dw19TUyOVyKTU11bMsMjJSycnJqqiokCRVVFQoKirKE7YlKTU1VYGBgdq9e7fP7brdbjU3N3s93G53T04dAAAAAIAe1aOB2+VySZIcDofXcofD4WlzuVyKiYnxag8ODlZ0dLSnz/cVFBQoMjLS61FQUNCTUwcAAAAAoEd1+5Ty3pCfn6+8vDyvZTabrZdmAwAAAADApfVo4I6NjZUk1dfXa+jQoZ7l9fX1GjNmjKdPQ0OD13rnzp3TqVOnPOt/n81mI2ADAAAAAPqUHj2lfPjw4YqNjVV5eblnWXNzs3bv3i2n0ylJcjqdamxsVFVVlafPtm3b1N7eruTk5J6cDgAAAAAAvabbn3CfPn1ax48f9zyvqanRgQMHFB0drYSEBD311FP6l3/5F916660aPny4Fi5cqLi4OP34xz+WJI0aNUr33XefZs6cqVdeeUWtra2aPXu2HnzwQe5QDgAAAADoN7oduPft26e//du/9TzvuLY6Oztba9eu1dNPP60zZ85o1qxZamxs1D333KMtW7YoLCzMs86bb76p2bNna/LkyQoMDFRmZqZeeumlHtgdAAAAAAD8Q7cD98SJE2WMuWB7QECAli5dqqVLl16wT3R0tEpKSro7NAAAAAAAfUaPXsMNAAAAAAD+jMANAAAAAIAFCNwAAAAAAFiAwA0AAAAAgAUI3AAAAAAAWIDADQAAAACABQjcAAAAAABYgMANAAAAAIAFCNwAAAAAAFiAwA0AAAAAgAUI3AAAAAAAWIDADQAAAACABQjcAAAAAABYgMANAAAAAIAFCNwAAAAAAFiAwA0AAAAAgAUI3AAAAAAAWIDADQAAAACABQjcAAAAAABYgMANAAAAAIAFCNwAAAAAAFiAwA0AAAAAgAUI3AAAAAAAWIDADQAAAACABQjcAAAAAABYgMANAAAAAIAFei1wFxYW6sYbb1RYWJiSk5O1Z8+e3poKAAAAAAA9rlcC929+8xvl5eVp8eLF+vjjj3XnnXcqLS1NDQ0NvTEdAAAAAAB6XHBvDPr8889r5syZeuyxxyRJr7zyit577z299tpreuaZZzr1d7vdcrvdXstsNptsNttVma8/+eyrz3p7Cl0ydOBQDb1uaG9PAwAAAAB6zVUP3C0tLaqqqlJ+fr5nWWBgoFJTU1VRUeFznYKCAv3yl7/0WrZ48WItWbLEyqlabnDEYIUFh+nsubNd6h8UEKSfrv+pxbPqGYsnLNaSiUt6exoAAFzS4IjBsgXZ5G5zX7qzpLDgMA2OGGzxrADg8nQ3Y0j8v2alAGOMuZoDnjx5UjfccIN27dolp9PpWf70009rx44d2r17d6d1+vMn3LVNtfrq26/kPueWLfji+9Nm2hQUEHSVZnZl+IQbANCX1DbV6svmLz2/iy/2e3lwxGAlRCZczekBQLd0ZIyu4v816/TKKeXd1V/CtS8JkQm8uAEA6GX8PgbQn/B/mv+46jdNGzx4sIKCglRfX++1vL6+XrGxsVd7OgAAAAAAWOKqB+7Q0FCNHTtW5eXlnmXt7e0qLy/3OsUcAAAAAIC+rFdOKc/Ly1N2drbuuusu3X333XrhhRd05swZz13LAQAAAADo63olcD/wwAP6v//7Py1atEgul0tjxozRli1b5HA4emM6AAAAAAD0uKt+l3IAAAAAAK4FV/0abgAAAAAArgUEbgAAAAAALEDgBgAAAADAAgRuAAAAAAAsQOAGAAAAAMACBG4AAAAAACxA4AYAAAAAwAIEbgAAAAAALEDgBgAAAADAAgRuAAAAAAAsQOAGAAAAAMACBG6LuN1uLVmyRG63u7enAgtR52sDde7/qPG1gTpfG6hz/0eNrw39pc4BxhjT25Poj5qbmxUZGammpibZ7fbeng4sQp2vDdS5/6PG1wbqfG2gzv0fNb429Jc68wk3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcFvEZrNp8eLFstlsvT0VWIg6Xxuoc/9Hja8N1PnaQJ37P2p8begvdeamaQAAAAAAWIBPuAEAAAAAsACBGwAAAAAACxC4AQAAAACwAIEbAAAAAAALELgBAAAAALBAvwjcbW1tWrhwoYYPH67w8HDdfPPNWrZsmc6/AXt9fb0effRRxcXFKSIiQvfdd5+OHTt20e1OnDhRAQEBnR4ZGRmePqdPn9bs2bMVHx+v8PBwJSUl6ZVXXvHaztmzZ5Wbm6tBgwZp4MCByszMVH19/UXHNsZo0aJFGjp0qMLDw5WamtppvqdOnVJWVpbsdruioqKUk5Oj06dPe/X59NNPde+99yosLEzDhg3TihUrLjquv/L3Gv/Hf/yHJk6cKLvdroCAADU2NnZpvwoLC3XjjTcqLCxMycnJ2rNnj1d7V147tbW1ysjIUEREhGJiYjRv3jydO3euS+P7G3+u86lTpzRnzhyNGDFC4eHhSkhI0M9//nM1NTVddGyO5c78uc6S9I//+I+6+eabFR4eriFDhmjatGk6evToRcemzt78vcYdjDFKT09XQECANmzYcNGxqXFn/l5nX9t5/PHHLzo2de7M3+ssSRUVFZo0aZIGDBggu92u8ePH67vvvrvo+LwH+wt/rvEf/vAHn9sICAhQaWnpBcf2q2PZ9APPPvusGTRokNm0aZOpqakxpaWlZuDAgebFF180xhjT3t5uUlJSzL333mv27Nljjh49ambNmmUSEhLM6dOnL7jdr7/+2tTV1Xkehw4dMkFBQaa4uNjTZ+bMmebmm28227dvNzU1NWbNmjUmKCjIvPPOO54+jz/+uBk2bJgpLy83+/btMykpKWbcuHEX3afly5ebyMhIs2HDBvPJJ5+Y+++/3wwfPtx89913nj733XefufPOO01lZaX5/e9/b2655Rbz0EMPedqbmpqMw+EwWVlZ5tChQ+bXv/61CQ8PN2vWrOnuj7jX+XuNV65caQoKCkxBQYGRZP70pz9dcp/WrVtnQkNDzWuvvWYOHz5sZs6caaKiokx9fb2nz6VeO+fOnTO33367SU1NNfv37zebN282gwcPNvn5+d346foPf67zwYMHzfTp083GjRvN8ePHTXl5ubn11ltNZmbmRfeJY7kzf66zMcasWbPG7Nixw9TU1JiqqiozdepUM2zYMHPu3LkLjk2dvfl7jTs8//zzJj093Ugyb7/99kX3iRp35u91njBhgpk5c6bXtpqami66T9S5M3+v865du4zdbjcFBQXm0KFD5ujRo+Y3v/mNOXv27AXH5j2YN3+u8blz57y2UVdXZ375y1+agQMHmm+++eaCY/vTsdwvAndGRob5h3/4B69l06dPN1lZWcYYY6qrq40kc+jQIU97W1ubGTJkiHn11Ve7PM7KlSvNdddd5/XCuu2228zSpUu9+v3whz80v/jFL4wxxjQ2NpqQkBBTWlrqaf/ss8+MJFNRUeFznPb2dhMbG2v+9V//1bOssbHR2Gw28+tf/9oYY8yRI0eMJLN3715Pn/fff98EBASYL7/80hhjzKpVq8z1119v3G63p8/8+fPNiBEjurzP/sKfa3y+7du3dzlw33333SY3N9drvnFxcaagoMAY07XXzubNm01gYKBxuVyePqtXrzZ2u92r7n1FX6lzh7feesuEhoaa1tZWn+0cy771tTp/8sknRpI5fvy4z3bq3FlfqPH+/fvNDTfcYOrq6i4ZuKmxb/5e5wkTJpgnn3yyy+NQZ9/8vc7JyclmwYIF3don3oN58/caf9+YMWM6zfd8/nYs94tTyseNG6fy8nJ9/vnnkqRPPvlEH374odLT0yVJbrdbkhQWFuZZJzAwUDabTR9++GGXxykqKtKDDz6oAQMGeI29ceNGffnllzLGaPv27fr88881ZcoUSVJVVZVaW1uVmprqWWfkyJFKSEhQRUWFz3Fqamrkcrm81omMjFRycrJnnYqKCkVFRemuu+7y9ElNTVVgYKB2797t6TN+/HiFhoZ6+qSlpam6ulp/+tOfurzf/sCfa3w5WlpaVFVV5VXjwMBApaamemrclddORUWFRo8eLYfD4emTlpam5uZmHT58+LLn11v6Wp2bmppkt9sVHBzss51j2be+VOczZ86ouLhYw4cP17Bhw3z2oc6d+XuNv/32Wz388MMqLCxUbGzsJcehxr75e50l6c0339TgwYN1++23Kz8/X99+++0Fx6HOvvlznRsaGrR7927FxMRo3LhxcjgcmjBhwkXH5T1YZ/5c4++rqqrSgQMHlJOTc8Fx/O1Y9v0usY955pln1NzcrJEjRyooKEhtbW169tlnlZWVJekvB0h+fr7WrFmjAQMGaOXKlfrjH/+ourq6Lo2xZ88eHTp0SEVFRV7LX375Zc2aNUvx8fEKDg5WYGCgXn31VY0fP16S5HK5FBoaqqioKK/1HA6HXC6Xz7E6lp9/AH9/HZfLpZiYGK/24OBgRUdHe/UZPnx4p210tF1//fVd2nd/4M81vhxfffWV2trafNa441rRrrx2XC6Xz210tPU1fanOX331lZYtW6ZZs2ZdcCyOZd/6Qp1XrVqlp59+WmfOnNGIESNUVlbm9Qv3fNS5M3+v8dy5czVu3DhNmzatS2NRY9/8vc4PP/ywEhMTFRcXp08//VTz589XdXW11q9f73Ms6uybP9f5iy++kCQtWbJEzz33nMaMGaM33nhDkydP1qFDh3Trrbd2Gov3YJ35c42/r6ioSKNGjdK4ceMuOJa/Hcv9InC/9dZbevPNN1VSUqLbbrtNBw4c0FNPPaW4uDhlZ2crJCRE69evV05OjqKjoxUUFKTU1FSlp6d73QzgYoqKijR69GjdfffdXstffvllVVZWauPGjUpMTNTOnTuVm5uruLg4r7+q4MpQ42tDX6lzc3OzMjIylJSUpCVLlvTU7l8z+kKds7Ky9KMf/Uh1dXV67rnn9JOf/EQfffSR11/3cWH+XOONGzdq27Zt2r9/vxW7fk3x5zpL8vqD6OjRozV06FBNnjxZJ06c0M0339xzP4h+zp/r3N7eLunPN7t87LHHJEk/+MEPVF5ertdee00FBQU9+8Pop/y5xuf77rvvVFJSooULF/bYvl8V3ToB3U/Fx8ebf//3f/datmzZMp/n1zc2NpqGhgZjzJ+v3/jZz352ye2fPn3a2O1288ILL3gt//bbb01ISIjZtGmT1/KcnByTlpZmjDGmvLzc5zW9CQkJ5vnnn/c53okTJ4wks3//fq/l48ePNz//+c+NMcYUFRWZqKgor/bW1lYTFBRk1q9fb4wxZsaMGWbatGlefbZt22YkmVOnTl1yv/2JP9f4fF29htvtdpugoKBO1ww+8sgj5v777zfGdO21s3DhQnPnnXd6tX/xxRdGkvn4448vOgd/1Bfq3NzcbJxOp5k8ebLXjTd84Vj2rS/U+Xxut9tERESYkpISn+3UuTN/rvGTTz5pAgICTFBQkOchyQQGBpoJEyb4HI8a++bPdb7Q9iSZLVu2+Gynzr75c5073vP853/+p1efn/zkJ+bhhx/2OR7vwTrz5xqf74033jAhISGe8S/E347lfnEN97fffqvAQO9dCQoK8vzV63yRkZEaMmSIjh07pn379nXpdLLS0lK53W799Kc/9Vre2tqq1tbWi449duxYhYSEqLy83NNeXV2t2tpaOZ1On+MNHz5csbGxXus0Nzdr9+7dnnWcTqcaGxtVVVXl6bNt2za1t7crOTnZ02fnzp1qbW319CkrK9OIESP63OlM/lzjyxEaGqqxY8d61bi9vV3l5eWeGnflteN0OnXw4EE1NDR4+pSVlclutyspKemy59db/L3Ozc3NmjJlikJDQ7Vx48ZLftrJseybv9f5+8yfbzDquYbt+6hzZ/5c42eeeUaffvqpDhw44HlI0sqVK1VcXOxzPGrsmz/X2ZeOWg8dOtRnO3X2zZ/rfOONNyouLk7V1dVefT7//HMlJib6HI/3YJ35c43PV1RUpPvvv19Dhgy56Hh+dyx3OZr7sezsbHPDDTd4bmW/fv16M3jwYPP00097+rz11ltm+/bt5sSJE2bDhg0mMTHRTJ8+vUvbv+eee8wDDzzgs23ChAnmtttuM9u3bzdffPGFKS4uNmFhYWbVqlWePo8//rhJSEgw27ZtM/v27TNOp9M4nc6Ljrl8+XITFRVl3nnnHfPpp5+aadOm+byV/Q9+8AOze/du8+GHH5pbb73V61b2jY2NxuFwmBkzZphDhw6ZdevWmYiIiD75tRT+XuO6ujqzf/9+8+qrrxpJZufOnWb//v3m66+/vuCY69atMzabzaxdu9YcOXLEzJo1y0RFRXnd7fJSr52Or6SYMmWKOXDggNmyZYsZMmRIn/xKCmP8u85NTU0mOTnZjB492hw/ftzr6yku9XVRHMve/LnOJ06cML/61a/Mvn37zP/8z/+Yjz76yEydOtVER0d7fV3M91Fnb/5cY1/Uxa8Fo8be/LnOx48fN0uXLjX79u0zNTU15p133jE33XSTGT9+/EXHpM6d+XOdjfnzna/tdrspLS01x44dMwsWLDBhYWEX/GYJY3gP9n3+XmNjjDl27JgJCAgw77//fpfG9KdjuV8E7ubmZvPkk0+ahIQEExYWZm666Sbzi1/8wusW7i+++KKJj483ISEhJiEhwSxYsKDTLfuzs7M7nU529OhRI8ls3brV59h1dXXm0UcfNXFxcSYsLMyMGDHC/Nu//Ztpb2/39Pnuu+/Mz372M3P99debiIgI8/d///emrq7OazuJiYlm8eLFnuft7e1m4cKFxuFwGJvNZiZPnmyqq6u91vn666/NQw89ZAYOHGjsdrt57LHHOn0f3SeffGLuueceY7PZzA033GCWL19+yZ+nP/L3Gi9evNhI6vQ4/3sGJ0yYYLKzs722/fLLL5uEhAQTGhpq7r77blNZWenV3pXXzh/+8AeTnp5uwsPDzeDBg80//dM/XfBrqvydP9e543IBX4+amhrPdjiWL82f6/zll1+a9PR0ExMTY0JCQkx8fLx5+OGHzdGjR722Q50vzp9r7IuvwE2NL82f61xbW2vGjx9voqOjjc1mM7fccouZN29ep+/hps6X5s917lBQUGDi4+NNRESEcTqd5ve//71XO+/BLq4v1Dg/P98MGzbMtLW1+dyOPx/L/SJw95Tx48d7FepqOXPmjAkLCzPbt2+/6mNfa3qrxsb8+bqf8wM4rMOxfG2gzv0fNb42UOdrA+/B+j+OZd/6xV3Ke0JTU5NOnDih995776qPvX37dk2aNEkTJ0686mNfS3qzxocPH1ZkZKQeeeSRqz72tYZj+dpAnfs/anxtoM7XBt6D9X8cyxcWYEwX7+UOAAAAAAC6rF/cpRwAAAAAAH9D4AYAAAAAwAIEbgAAAAAALEDgBgAAAADAAgRuAAAAAAAsQOAGAAAAAMACBG4AAAAAACxA4AYAAAAAwAIEbgAAAAAALPD/uH/oVxIlQ4MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10,10)\n",
    "pos=[89780160-500,89786592+500]\n",
    "fig,axs=plt.subplots(3)\n",
    "g=isoseq['RIPK2']\n",
    "g.gene_track(x_range=pos, ax=axs[0], reference=False, select_transcripts=g.filter_transcripts('SUBSTANTIAL'))\n",
    "g.sashimi_plot(samples=isoseq.groups()['K562'],x_range=pos, ax=axs[1], title='K562', log_y=False)\n",
    "g.sashimi_plot(samples=isoseq.groups()['GM12878'],x_range=pos, ax=axs[2], title='GM12878', log_y=False)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splicing coordination\n",
    "A particular strength of long read sequencing is the fact that distal splicing events on the same gene are covered by the same read, facilitating analysis of splicing coordination. Isotools implements a pairwise independence test for all pairs of Alternative Splicing Events (ASEs) of a gene."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10803/10803 [00:03<00:00, 2798.91genes/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>strand</th>\n",
       "      <th>eventA_type</th>\n",
       "      <th>eventB_type</th>\n",
       "      <th>eventA_start</th>\n",
       "      <th>evemtA_end</th>\n",
       "      <th>eventB_start</th>\n",
       "      <th>eventB_end</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "      <th>stat</th>\n",
       "      <th>log2OR</th>\n",
       "      <th>dcPSI_AB</th>\n",
       "      <th>dcPSI_BA</th>\n",
       "      <th>priA_priB</th>\n",
       "      <th>priA_altB</th>\n",
       "      <th>altA_priB</th>\n",
       "      <th>altA_altB</th>\n",
       "      <th>priA_priB_trID</th>\n",
       "      <th>priA_altB_trID</th>\n",
       "      <th>altA_priB_trID</th>\n",
       "      <th>altA_altB_trID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>ENSG00000147813.16</td>\n",
       "      <td>NAPRT</td>\n",
       "      <td>-</td>\n",
       "      <td>IR</td>\n",
       "      <td>IR</td>\n",
       "      <td>143575093</td>\n",
       "      <td>143575190</td>\n",
       "      <td>143574900</td>\n",
       "      <td>143574985</td>\n",
       "      <td>4.436456e-82</td>\n",
       "      <td>5.501206e-80</td>\n",
       "      <td>98.895238</td>\n",
       "      <td>6.627829</td>\n",
       "      <td>0.525552</td>\n",
       "      <td>0.637141</td>\n",
       "      <td>472</td>\n",
       "      <td>45</td>\n",
       "      <td>14</td>\n",
       "      <td>132</td>\n",
       "      <td>[0, 5, 6, 7, 54, 62, 73, 17, 100, 24, 30, 46, ...</td>\n",
       "      <td>[25, 48, 129, 68, 128, 33, 133, 101, 135, 141,...</td>\n",
       "      <td>[41, 96, 110, 112, 115]</td>\n",
       "      <td>[21, 42, 1, 12, 31, 71, 44, 51, 63, 78, 92, 13...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>ENSG00000182325.11</td>\n",
       "      <td>FBXL6</td>\n",
       "      <td>-</td>\n",
       "      <td>IR</td>\n",
       "      <td>IR</td>\n",
       "      <td>144357786</td>\n",
       "      <td>144358031</td>\n",
       "      <td>144357121</td>\n",
       "      <td>144357438</td>\n",
       "      <td>1.074426e-74</td>\n",
       "      <td>6.661440e-73</td>\n",
       "      <td>1715.833333</td>\n",
       "      <td>10.744694</td>\n",
       "      <td>0.472927</td>\n",
       "      <td>0.469753</td>\n",
       "      <td>142</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>145</td>\n",
       "      <td>[1, 21, 34, 7, 32, 36, 38, 72, 45, 81, 51, 85,...</td>\n",
       "      <td>[89, 87, 71]</td>\n",
       "      <td>[4, 40]</td>\n",
       "      <td>[2, 9, 19, 41, 14, 24, 44, 48, 56, 11, 54, 17,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>ENSG00000167700.9</td>\n",
       "      <td>MFSD3</td>\n",
       "      <td>+</td>\n",
       "      <td>IR</td>\n",
       "      <td>IR</td>\n",
       "      <td>144510015</td>\n",
       "      <td>144510359</td>\n",
       "      <td>144510507</td>\n",
       "      <td>144510597</td>\n",
       "      <td>4.299909e-32</td>\n",
       "      <td>1.777296e-30</td>\n",
       "      <td>inf</td>\n",
       "      <td>38.459595</td>\n",
       "      <td>0.503866</td>\n",
       "      <td>0.743802</td>\n",
       "      <td>180</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>[0, 3, 23, 35, 5, 38, 39]</td>\n",
       "      <td>[2, 21, 19, 20, 29]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[11, 26, 14, 36, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>ENSG00000160957.15</td>\n",
       "      <td>RECQL4</td>\n",
       "      <td>-</td>\n",
       "      <td>IR</td>\n",
       "      <td>IR</td>\n",
       "      <td>144512771</td>\n",
       "      <td>144512846</td>\n",
       "      <td>144512324</td>\n",
       "      <td>144512391</td>\n",
       "      <td>1.341822e-31</td>\n",
       "      <td>4.159649e-30</td>\n",
       "      <td>11.529161</td>\n",
       "      <td>3.527216</td>\n",
       "      <td>0.331031</td>\n",
       "      <td>0.344543</td>\n",
       "      <td>1231</td>\n",
       "      <td>87</td>\n",
       "      <td>81</td>\n",
       "      <td>66</td>\n",
       "      <td>[22, 31, 6, 20, 114, 147, 124, 11, 38, 72, 13,...</td>\n",
       "      <td>[401, 191, 221, 94, 364, 68, 204, 127, 519, 17...</td>\n",
       "      <td>[117, 136, 279, 259, 271, 532, 292, 49, 184, 2...</td>\n",
       "      <td>[408, 110, 112, 28, 29, 158, 190, 215, 495, 51...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>ENSG00000182325.11</td>\n",
       "      <td>FBXL6</td>\n",
       "      <td>-</td>\n",
       "      <td>IR</td>\n",
       "      <td>IR</td>\n",
       "      <td>144357786</td>\n",
       "      <td>144358031</td>\n",
       "      <td>144356915</td>\n",
       "      <td>144356989</td>\n",
       "      <td>6.335233e-27</td>\n",
       "      <td>1.571138e-25</td>\n",
       "      <td>51.918519</td>\n",
       "      <td>5.698177</td>\n",
       "      <td>0.228402</td>\n",
       "      <td>0.451672</td>\n",
       "      <td>163</td>\n",
       "      <td>90</td>\n",
       "      <td>3</td>\n",
       "      <td>86</td>\n",
       "      <td>[1, 12, 21, 34, 4, 7, 75, 86, 15, 23, 25, 26, ...</td>\n",
       "      <td>[2, 3, 41, 24, 44, 48, 8, 17, 37, 6, 18, 33, 5...</td>\n",
       "      <td>[32, 53]</td>\n",
       "      <td>[9, 19, 14, 56, 11, 13, 54, 42, 63, 64, 5, 16,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                gene_id gene_name strand eventA_type eventB_type  \\\n",
       "52   ENSG00000147813.16     NAPRT      -          IR          IR   \n",
       "107  ENSG00000182325.11     FBXL6      -          IR          IR   \n",
       "55    ENSG00000167700.9     MFSD3      +          IR          IR   \n",
       "45   ENSG00000160957.15    RECQL4      -          IR          IR   \n",
       "108  ENSG00000182325.11     FBXL6      -          IR          IR   \n",
       "\n",
       "     eventA_start  evemtA_end  eventB_start  eventB_end        pvalue  \\\n",
       "52      143575093   143575190     143574900   143574985  4.436456e-82   \n",
       "107     144357786   144358031     144357121   144357438  1.074426e-74   \n",
       "55      144510015   144510359     144510507   144510597  4.299909e-32   \n",
       "45      144512771   144512846     144512324   144512391  1.341822e-31   \n",
       "108     144357786   144358031     144356915   144356989  6.335233e-27   \n",
       "\n",
       "             padj         stat     log2OR  dcPSI_AB  dcPSI_BA  priA_priB  \\\n",
       "52   5.501206e-80    98.895238   6.627829  0.525552  0.637141        472   \n",
       "107  6.661440e-73  1715.833333  10.744694  0.472927  0.469753        142   \n",
       "55   1.777296e-30          inf  38.459595  0.503866  0.743802        180   \n",
       "45   4.159649e-30    11.529161   3.527216  0.331031  0.344543       1231   \n",
       "108  1.571138e-25    51.918519   5.698177  0.228402  0.451672        163   \n",
       "\n",
       "     priA_altB  altA_priB  altA_altB  \\\n",
       "52          45         14        132   \n",
       "107          3          4        145   \n",
       "55          20          0         42   \n",
       "45          87         81         66   \n",
       "108         90          3         86   \n",
       "\n",
       "                                        priA_priB_trID  \\\n",
       "52   [0, 5, 6, 7, 54, 62, 73, 17, 100, 24, 30, 46, ...   \n",
       "107  [1, 21, 34, 7, 32, 36, 38, 72, 45, 81, 51, 85,...   \n",
       "55                           [0, 3, 23, 35, 5, 38, 39]   \n",
       "45   [22, 31, 6, 20, 114, 147, 124, 11, 38, 72, 13,...   \n",
       "108  [1, 12, 21, 34, 4, 7, 75, 86, 15, 23, 25, 26, ...   \n",
       "\n",
       "                                        priA_altB_trID  \\\n",
       "52   [25, 48, 129, 68, 128, 33, 133, 101, 135, 141,...   \n",
       "107                                       [89, 87, 71]   \n",
       "55                                 [2, 21, 19, 20, 29]   \n",
       "45   [401, 191, 221, 94, 364, 68, 204, 127, 519, 17...   \n",
       "108  [2, 3, 41, 24, 44, 48, 8, 17, 37, 6, 18, 33, 5...   \n",
       "\n",
       "                                        altA_priB_trID  \\\n",
       "52                             [41, 96, 110, 112, 115]   \n",
       "107                                            [4, 40]   \n",
       "55                                                  []   \n",
       "45   [117, 136, 279, 259, 271, 532, 292, 49, 184, 2...   \n",
       "108                                           [32, 53]   \n",
       "\n",
       "                                        altA_altB_trID  \n",
       "52   [21, 42, 1, 12, 31, 71, 44, 51, 63, 78, 92, 13...  \n",
       "107  [2, 9, 19, 41, 14, 24, 44, 48, 56, 11, 54, 17,...  \n",
       "55                                [11, 26, 14, 36, 18]  \n",
       "45   [408, 110, 112, 28, 29, 158, 190, 215, 495, 51...  \n",
       "108  [9, 19, 14, 56, 11, 13, 54, 42, 63, 64, 5, 16,...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coordination_results=isoseq.coordination_test().sort_values('padj')\n",
    "coordination_results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Differential Isoform Expression\n",
    "In addition to the differential splicing test, isotools also implements the differential isoform expression (DIE) test, as proposed by [Joglekar *et al.*, 2021](https://www.nature.com/articles/s41467-020-20343-5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10803/10803 [00:00<00:00, 17452.81genes/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>chrom</th>\n",
       "      <th>strand</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>padj</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>deltaPI</th>\n",
       "      <th>transcript_ids</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5982</th>\n",
       "      <td>ENSG00000070756.17</td>\n",
       "      <td>PABPC1</td>\n",
       "      <td>chr8</td>\n",
       "      <td>-</td>\n",
       "      <td>100685815</td>\n",
       "      <td>100722809</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.289084</td>\n",
       "      <td>[2, 1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5094</th>\n",
       "      <td>ENSG00000156482.11</td>\n",
       "      <td>RPL30</td>\n",
       "      <td>chr8</td>\n",
       "      <td>-</td>\n",
       "      <td>98024850</td>\n",
       "      <td>98046469</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.515042</td>\n",
       "      <td>[3, 2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4805</th>\n",
       "      <td>ENSG00000161016.18</td>\n",
       "      <td>RPL8</td>\n",
       "      <td>chr8</td>\n",
       "      <td>-</td>\n",
       "      <td>144789764</td>\n",
       "      <td>144792587</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.477042</td>\n",
       "      <td>[2, 6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4632</th>\n",
       "      <td>ENSG00000164924.18</td>\n",
       "      <td>YWHAZ</td>\n",
       "      <td>chr8</td>\n",
       "      <td>-</td>\n",
       "      <td>100916522</td>\n",
       "      <td>100953388</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.303116</td>\n",
       "      <td>[31, 11]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4172</th>\n",
       "      <td>ENSG00000147604.14</td>\n",
       "      <td>RPL7</td>\n",
       "      <td>chr8</td>\n",
       "      <td>-</td>\n",
       "      <td>73290241</td>\n",
       "      <td>73295789</td>\n",
       "      <td>1.157393e-170</td>\n",
       "      <td>1.885004e-172</td>\n",
       "      <td>0.092221</td>\n",
       "      <td>[2, 31]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3235</th>\n",
       "      <td>ENSG00000104408.11</td>\n",
       "      <td>EIF3E</td>\n",
       "      <td>chr8</td>\n",
       "      <td>-</td>\n",
       "      <td>108162786</td>\n",
       "      <td>108443496</td>\n",
       "      <td>2.092139e-137</td>\n",
       "      <td>4.088872e-139</td>\n",
       "      <td>0.166874</td>\n",
       "      <td>[0, 20]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9988</th>\n",
       "      <td>ENSG00000104312.8</td>\n",
       "      <td>RIPK2</td>\n",
       "      <td>chr8</td>\n",
       "      <td>+</td>\n",
       "      <td>89757805</td>\n",
       "      <td>89791064</td>\n",
       "      <td>6.088181e-101</td>\n",
       "      <td>1.388185e-102</td>\n",
       "      <td>0.677697</td>\n",
       "      <td>[2, 0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4239</th>\n",
       "      <td>ENSG00000129696.13</td>\n",
       "      <td>TTI2</td>\n",
       "      <td>chr8</td>\n",
       "      <td>-</td>\n",
       "      <td>33473385</td>\n",
       "      <td>33513185</td>\n",
       "      <td>9.366700e-100</td>\n",
       "      <td>2.440834e-101</td>\n",
       "      <td>-0.617118</td>\n",
       "      <td>[3, 22]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1369</th>\n",
       "      <td>ENSG00000147684.10</td>\n",
       "      <td>NDUFB9</td>\n",
       "      <td>chr8</td>\n",
       "      <td>+</td>\n",
       "      <td>124539100</td>\n",
       "      <td>124580648</td>\n",
       "      <td>4.019587e-89</td>\n",
       "      <td>1.178381e-90</td>\n",
       "      <td>0.399575</td>\n",
       "      <td>[0, -1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1525</th>\n",
       "      <td>ENSG00000104320.15</td>\n",
       "      <td>NBN</td>\n",
       "      <td>chr8</td>\n",
       "      <td>-</td>\n",
       "      <td>89924514</td>\n",
       "      <td>90003228</td>\n",
       "      <td>5.103980e-88</td>\n",
       "      <td>1.662534e-89</td>\n",
       "      <td>-0.409024</td>\n",
       "      <td>[2, 1]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 gene_id gene_name chrom strand      start        end  \\\n",
       "5982  ENSG00000070756.17    PABPC1  chr8      -  100685815  100722809   \n",
       "5094  ENSG00000156482.11     RPL30  chr8      -   98024850   98046469   \n",
       "4805  ENSG00000161016.18      RPL8  chr8      -  144789764  144792587   \n",
       "4632  ENSG00000164924.18     YWHAZ  chr8      -  100916522  100953388   \n",
       "4172  ENSG00000147604.14      RPL7  chr8      -   73290241   73295789   \n",
       "3235  ENSG00000104408.11     EIF3E  chr8      -  108162786  108443496   \n",
       "9988   ENSG00000104312.8     RIPK2  chr8      +   89757805   89791064   \n",
       "4239  ENSG00000129696.13      TTI2  chr8      -   33473385   33513185   \n",
       "1369  ENSG00000147684.10    NDUFB9  chr8      +  124539100  124580648   \n",
       "1525  ENSG00000104320.15       NBN  chr8      -   89924514   90003228   \n",
       "\n",
       "               padj         pvalue   deltaPI transcript_ids  \n",
       "5982   0.000000e+00   0.000000e+00 -0.289084         [2, 1]  \n",
       "5094   0.000000e+00   0.000000e+00  0.515042         [3, 2]  \n",
       "4805   0.000000e+00   0.000000e+00 -0.477042         [2, 6]  \n",
       "4632   0.000000e+00   0.000000e+00  0.303116       [31, 11]  \n",
       "4172  1.157393e-170  1.885004e-172  0.092221        [2, 31]  \n",
       "3235  2.092139e-137  4.088872e-139  0.166874        [0, 20]  \n",
       "9988  6.088181e-101  1.388185e-102  0.677697         [2, 0]  \n",
       "4239  9.366700e-100  2.440834e-101 -0.617118        [3, 22]  \n",
       "1369   4.019587e-89   1.178381e-90  0.399575        [0, -1]  \n",
       "1525   5.103980e-88   1.662534e-89 -0.409024         [2, 1]  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isoseq.die_test(groups=isoseq.groups()).sort_values('pvalue').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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