diff --git a/Data/TAModel/.~lock.Sinha-etal_2021_transcript-data.xlsx# b/Data/TAModel/.~lock.Sinha-etal_2021_transcript-data.xlsx#
new file mode 100644
index 0000000..ce47613
--- /dev/null
+++ b/Data/TAModel/.~lock.Sinha-etal_2021_transcript-data.xlsx#
@@ -0,0 +1 @@
+,samiralvdb,QPE-IAMBPC156,28.02.2024 17:17,file:///home/samiralvdb/.config/libreoffice/4;
\ No newline at end of file
diff --git a/Data/TAModel/2024-02-27_gene_enzyme_reaction_relation_Ecoli.xlsx b/Data/TAModel/2024-02-27_gene_enzyme_reaction_relation_Ecoli.xlsx
new file mode 100644
index 0000000..462fe31
Binary files /dev/null and b/Data/TAModel/2024-02-27_gene_enzyme_reaction_relation_Ecoli.xlsx differ
diff --git a/Data/TAModel/GeneList_ecoli.xlsx b/Data/TAModel/GeneList_ecoli.xlsx
new file mode 100644
index 0000000..3a660b7
Binary files /dev/null and b/Data/TAModel/GeneList_ecoli.xlsx differ
diff --git a/Data/TAModel/Sinha-etal_2021_flux-data.xlsx b/Data/TAModel/Sinha-etal_2021_flux-data.xlsx
new file mode 100644
index 0000000..e0a7abc
Binary files /dev/null and b/Data/TAModel/Sinha-etal_2021_flux-data.xlsx differ
diff --git a/Scripts/.ipynb_checkpoints/create_ecolicore_pam_incl_UE-checkpoint.ipynb b/Scripts/.ipynb_checkpoints/create_ecolicore_pam_incl_UE-checkpoint.ipynb
index 6da6cce..6ffe3a1 100644
--- a/Scripts/.ipynb_checkpoints/create_ecolicore_pam_incl_UE-checkpoint.ipynb
+++ b/Scripts/.ipynb_checkpoints/create_ecolicore_pam_incl_UE-checkpoint.ipynb
@@ -26,24 +26,18 @@
    "metadata": {},
    "outputs": [
     {
-     "ename": "ImportError",
-     "evalue": "attempted relative import with no known parent package",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "\u001b[0;32m/tmp/ipykernel_10917/4068919364.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# load PAMpy modules\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'../src/PAModelpy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mPAModel\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPAModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mEnzymeSectors\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mActiveEnzymeSector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUnusedEnzymeSector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTransEnzymeSector\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mPAMValidator\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPAMValidator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/3_Projects/MCA_analysis/PAMpy/Scripts/../src/PAModelpy/PAModel.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m# sys.path.append('../')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mEnzymeSectors\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mActiveEnzymeSector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTransEnzymeSector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUnusedEnzymeSector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCustomSector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSector\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mCatalyticEvent\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCatalyticEvent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mConstraints\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mConstraint\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mImportError\u001b[0m: attempted relative import with no known parent package"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading PAModelpy modules version 0.0.3.3\n"
      ]
     }
    ],
    "source": [
     "# load PAMpy modules\n",
-    "sys.path.append('../src/PAModelpy')\n",
-    "from PAModel import PAModel\n",
-    "from EnzymeSectors import ActiveEnzymeSector, UnusedEnzymeSector, TransEnzymeSector\n",
-    "from PAMValidator import PAMValidator"
+    "from PAModelpy.PAModel import PAModel\n",
+    "from PAModelpy.EnzymeSectors import ActiveEnzymeSector, UnusedEnzymeSector, TransEnzymeSector\n",
+    "from PAModelpy.PAMValidator import PAMValidator"
    ]
   },
   {
@@ -63,6 +57,11 @@
      "output_type": "stream",
      "text": [
       "Set parameter Username\n",
+      "\n",
+      "--------------------------------------------\n",
+      "Warning: your license will expire in 9 days\n",
+      "--------------------------------------------\n",
+      "\n",
       "Academic license - for non-commercial use only - expires 2024-03-07\n",
       "Objective\n",
       "=========\n",
@@ -88,16 +87,11 @@
    ],
    "source": [
     "# load the model\n",
-    "model = cobra.io.load_json_model('../models/e_coli_core.json')\n",
+    "model = cobra.io.load_json_model('../Models/e_coli_core.json')\n",
     "# test the model\n",
     "sol_t = model.optimize()\n",
-    "print(model.summary())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
+    "print(model.summary())\n",
+    "\n",
     "# Build E. coli core PAM\n",
     "## Create Protein Sectors\n",
     "### Active Enzymes"
@@ -291,7 +285,7 @@
    ],
    "source": [
     "# load enzyme database from genome-scale E. coli PAM\n",
-    "enzyme_db = pd.read_excel('../data/proteinAllocationModel_iML1515_EnzymaticData_py.xls',\n",
+    "enzyme_db = pd.read_excel('../Data/proteinAllocationModel_iML1515_EnzymaticData_py.xls',\n",
     "                          sheet_name='ActiveEnzymes', index_col=0)\n",
     "\n",
     "# correct reaction IDS\n",
@@ -655,7 +649,8 @@
    "outputs": [],
    "source": [
     "# total protein constraint (cf. analyze_proteome.ipynb)\n",
-    "p_tot = 0.16995 # g/gDW -> Standard 0.14 g/gDW "
+    "# p_tot = 0.16995 # g/gDW -> Standard 0.14 g/gDW \n",
+    "p_tot = 0.185"
    ]
   },
   {
@@ -674,24 +669,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Read LP format model from file /tmp/tmpt4uya5mx.lp\n",
+      "Read LP format model from file /tmp/tmpb6ewyrcx.lp\n",
       "Reading time = 0.00 seconds\n",
       ": 72 rows, 190 columns, 720 nonzeros\n",
       "Setting up the proteome allocation model e_coli_core\n",
       "\n",
       "Add total condition-dependent protein constraint\n",
-      "\tTotal protein concentration: 0.16995 g/gDW\n",
+      "\tTotal protein concentration: 0.185 g/gDW\n",
       "\n",
       "Add active protein sector\n",
       "\n",
       "Add the following protein sector: TranslationalProteinSector\n",
       "\n",
-      "169.95 TranslationalProteinSector 49.92 120.02999999999999\n",
-      "-0.002944\n",
-      "Add the following protein sector: UnusedEnzymeSector\n",
+      "Add the following protein sector: \n",
       "\n",
-      "120.02999999999999 UnusedEnzymeSector 40.7 79.32999999999998\n",
-      "-0.0214\n",
       "Done with setting up the proteome allocation model e_coli_core\n",
       "\n"
      ]
@@ -721,22 +712,163 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'EX_ac_e':     EX_glc__D_e   EX_ac_e\n",
+       " 0     -3.026940  0.000000\n",
+       " 1     -3.329634  0.000000\n",
+       " 2     -3.619167  0.159000\n",
+       " 3     -4.060529  0.270000\n",
+       " 4     -5.044900  0.680000\n",
+       " 5     -5.285133  0.624000\n",
+       " 6     -6.581834  1.657500\n",
+       " 7     -6.902899  1.708500\n",
+       " 8     -7.600000  4.800000\n",
+       " 9     -0.900000  0.000000\n",
+       " 10    -1.200000  0.000000\n",
+       " 11    -1.100000  0.000000\n",
+       " 12    -1.700000  0.000000\n",
+       " 13    -1.800000  0.000000\n",
+       " 14    -2.000000  0.000000\n",
+       " 15    -2.800000  0.000000\n",
+       " 16    -4.200000  0.000000\n",
+       " 17    -4.600000  0.000000\n",
+       " 18    -5.700000  0.000000\n",
+       " 19    -5.800000  0.000000\n",
+       " 29     1.520000  0.000000\n",
+       " 30    -2.470000  0.000000\n",
+       " 31    -4.250000  0.000000\n",
+       " 32    -5.650000  0.000000\n",
+       " 33    -9.530000  3.490000\n",
+       " 34    -6.650000  2.950000\n",
+       " 35    -0.699706  0.000000\n",
+       " 36    -1.204666  0.000000\n",
+       " 37    -2.154266  0.000000\n",
+       " 38    -3.345144  0.000000\n",
+       " 39    -4.692853  0.155090\n",
+       " 40    -5.945775  1.841697\n",
+       " 41    -7.874274  6.109449\n",
+       " 46   -11.300000  7.100000\n",
+       " 47    -2.300000  0.000000\n",
+       " 48    -8.590000  3.910000\n",
+       " 49    -7.810000  3.260000\n",
+       " 50    -7.540000  2.720000\n",
+       " 51    -9.530000  3.490000\n",
+       " 52    -9.650000  6.830000,\n",
+       " 'EX_co2_e':     EX_glc__D_e   EX_co2_e\n",
+       " 0     -3.026940   6.614400\n",
+       " 1     -3.329634   7.228800\n",
+       " 2     -3.619167   8.265000\n",
+       " 3     -4.060529   8.157000\n",
+       " 4     -5.044900  10.496000\n",
+       " 5     -5.285133   9.544000\n",
+       " 7     -6.902899  10.806900\n",
+       " 35    -0.699706   1.987149\n",
+       " 36    -1.204666   2.808187\n",
+       " 37    -2.154266   4.531176\n",
+       " 38    -3.345144   6.590909\n",
+       " 39    -4.692853   8.689910\n",
+       " 40    -5.945775  10.298667\n",
+       " 41    -7.874274   8.576868\n",
+       " 42    -1.520000   4.696800\n",
+       " 43    -2.470000   6.891300\n",
+       " 44    -4.250000  13.107000\n",
+       " 45    -5.650000  17.356800\n",
+       " 46   -11.300000  19.600000\n",
+       " 47    -2.300000   9.000000,\n",
+       " 'EX_o2_e':     EX_glc__D_e    EX_o2_e\n",
+       " 35    -0.699706  -2.161836\n",
+       " 36    -1.204666  -2.886473\n",
+       " 37    -2.154266  -4.746377\n",
+       " 38    -3.345144 -10.000000\n",
+       " 39    -4.692853 -10.640097\n",
+       " 40    -5.945775 -15.374396\n",
+       " 41    -7.874274 -13.188406\n",
+       " 42    -1.520000  -4.750000\n",
+       " 43    -2.470000  -6.640000\n",
+       " 44    -4.250000 -12.580000\n",
+       " 45    -5.650000 -16.820000\n",
+       " 46   -11.300000 -18.800000\n",
+       " 47    -2.300000  -8.700000,\n",
+       " 'BIOMASS_Ecoli_core_w_GAM':     EX_glc__D_e  BIOMASS_Ecoli_core_w_GAM\n",
+       " 0     -3.026940                  0.240000\n",
+       " 1     -3.329634                  0.240000\n",
+       " 2     -3.619167                  0.300000\n",
+       " 3     -4.060529                  0.300000\n",
+       " 4     -5.044900                  0.400000\n",
+       " 5     -5.285133                  0.400000\n",
+       " 6     -6.581834                  0.510000\n",
+       " 7     -6.902899                  0.510000\n",
+       " 8     -7.600000                  0.650000\n",
+       " 9     -0.900000                  0.040000\n",
+       " 10    -1.200000                  0.050000\n",
+       " 11    -1.100000                  0.060000\n",
+       " 12    -1.700000                  0.090000\n",
+       " 13    -1.800000                  0.090000\n",
+       " 14    -2.000000                  0.110000\n",
+       " 15    -2.800000                  0.190000\n",
+       " 16    -4.200000                  0.290000\n",
+       " 17    -4.600000                  0.310000\n",
+       " 18    -5.700000                  0.400000\n",
+       " 19    -5.800000                  0.410000\n",
+       " 20    -2.660000                  0.210000\n",
+       " 21    -3.810000                  0.310000\n",
+       " 22    -4.910000                  0.410000\n",
+       " 23    -6.240000                  0.510000\n",
+       " 24    -2.780000                  0.220000\n",
+       " 25    -3.250000                  0.260000\n",
+       " 26    -4.360000                  0.360000\n",
+       " 27    -5.520000                  0.460000\n",
+       " 28    -6.240000                  0.510000\n",
+       " 29     1.520000                  0.100000\n",
+       " 30    -2.470000                  0.200000\n",
+       " 31    -4.250000                  0.300000\n",
+       " 32    -5.650000                  0.400000\n",
+       " 33    -9.530000                  0.730000\n",
+       " 34    -6.650000                  0.610000\n",
+       " 35    -0.699706                 -0.053081\n",
+       " 36    -1.204666                 -0.101840\n",
+       " 37    -2.154266                 -0.191799\n",
+       " 38    -3.345144                 -0.298272\n",
+       " 39    -4.692853                 -0.411342\n",
+       " 40    -5.945775                 -0.496032\n",
+       " 41    -7.874274                 -0.602263\n",
+       " 42    -1.520000                  0.100000\n",
+       " 43    -2.470000                  0.200000\n",
+       " 44    -4.250000                  0.300000\n",
+       " 45    -5.650000                  0.400000\n",
+       " 46   -11.300000                  0.850000\n",
+       " 47    -2.300000                  0.120000\n",
+       " 48    -8.590000                  0.690000\n",
+       " 49    -7.810000                  0.590000\n",
+       " 50    -7.540000                  0.610000\n",
+       " 51    -9.530000                  0.730000\n",
+       " 52    -9.650000                  0.650000}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# load phenotype data from excel file\n",
-    "pt_data = pd.read_excel('../data/Ecoli_phenotypes_py_rev.xls', sheet_name='Yields', index_col=None)\n",
-    "pt_data\n",
+    "pt_data = pd.read_excel('../Data/Ecoli_phenotypes/Ecoli_phenotypes_py_rev.xls', sheet_name='Yields', index_col=None)\n",
     "\n",
     "# extract reaction specific data \n",
     "rxn_to_pt = {}\n",
-    "rxn_transform = {\n",
-    "    'EX_ac_e': 'Acetate_OUT',\n",
-    "    'EX_co2_e': 'CO2_OUT',\n",
-    "    'EX_o2_e': 'O2_IN',\n",
-    "    'BIOMASS_Ecoli_core_w_GAM': 'mu'\n",
-    "}\n",
-    "for rxn_id, pt_id in rxn_transform.items():\n",
-    "    rxn_to_pt[rxn_id] = pt_data[['Glucose_IN', pt_id]].dropna().rename(columns={pt_id: rxn_id})\n"
+    "rxn_transform = {'BIOMASS_Ec_iML1515_core_75p37M':'BIOMASS_Ecoli_core_w_GAM'}\n",
+    "rxns = ['EX_ac_e','EX_co2_e','EX_o2_e', 'BIOMASS_Ec_iML1515_core_75p37M']\n",
+    "for rxn_id in rxns:\n",
+    "    if rxn_id in rxn_transform.keys(): \n",
+    "        rxn_to_pt[rxn_transform[rxn_id]] = pt_data[['EX_glc__D_e', rxn_id]].dropna().rename(columns = {rxn_id: rxn_transform[rxn_id]})\n",
+    "    else:\n",
+    "        rxn_to_pt[rxn_id] = pt_data[['EX_glc__D_e', rxn_id]].dropna()\n",
+    "    \n",
+    "\n",
+    "rxn_to_pt"
    ]
   },
   {
@@ -754,7 +886,7 @@
     {
      "data": {
       "text/html": [
-       "<strong><em>Optimal</em> solution with objective value 0.653</strong><br><div>\n",
+       "<strong><em>Optimal</em> solution with objective value 0.679</strong><br><div>\n",
        "<style scoped>\n",
        "    .dataframe tbody tr th:only-of-type {\n",
        "        vertical-align: middle;\n",
@@ -779,28 +911,28 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>PFK</th>\n",
-       "      <td>6.607656</td>\n",
-       "      <td>7.405407e-04</td>\n",
+       "      <td>7.382333</td>\n",
+       "      <td>8.177455e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PFL</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-2.353581e-03</td>\n",
+       "      <td>2.260148</td>\n",
+       "      <td>2.983560e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PGI</th>\n",
-       "      <td>3.147563</td>\n",
+       "      <td>4.035377</td>\n",
        "      <td>0.000000e+00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PGK</th>\n",
-       "      <td>-14.766376</td>\n",
-       "      <td>-7.621834e-05</td>\n",
+       "      <td>-16.251928</td>\n",
+       "      <td>-8.416445e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PGL</th>\n",
-       "      <td>5.963770</td>\n",
-       "      <td>1.118276e-04</td>\n",
+       "      <td>5.825327</td>\n",
+       "      <td>1.234862e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -809,28 +941,28 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NADH16</th>\n",
-       "      <td>27.541011</td>\n",
-       "      <td>1.236103e-03</td>\n",
+       "      <td>27.412601</td>\n",
+       "      <td>1.364972e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NADTRHD</th>\n",
-       "      <td>0.729405</td>\n",
-       "      <td>5.393183e-04</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.390978e-02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NH4t</th>\n",
-       "      <td>3.561229</td>\n",
-       "      <td>7.017980e-10</td>\n",
+       "      <td>3.705139</td>\n",
+       "      <td>7.749636e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>O2t</th>\n",
-       "      <td>13.770506</td>\n",
-       "      <td>7.017980e-10</td>\n",
+       "      <td>13.706301</td>\n",
+       "      <td>7.749636e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PDH</th>\n",
-       "      <td>9.728682</td>\n",
-       "      <td>5.719731e-03</td>\n",
+       "      <td>8.750512</td>\n",
+       "      <td>6.316039e-03</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -838,7 +970,7 @@
        "</div>"
       ],
       "text/plain": [
-       "<Solution 0.653 at 0x7f01304dfca0>"
+       "<Solution 0.679 at 0x7f2265656dd0>"
       ]
      },
      "execution_count": 12,
@@ -867,27 +999,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAHWCAYAAAD6oMSKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydZ3QTx9fGH0mWJfcu90az6cU02/QSU0IJJJSQBJxKD5BGyhtKSIAUSggQ0iAFCBBq+EMIHUILoYVmY3DFvctytzTvB0eL1yqWbMlaLfM7Rwd2dnZm7szs9d0pdwSEEAIKhUKhUCgUitUjtHQBKBQKhUKhUCimgRp2FAqFQqFQKDyBGnYUCoVCoVAoPIEadhQKhUKhUCg8gRp2FAqFQqFQKDyBGnYUCoVCoVAoPIEadhQKhUKhUCg8gRp2FAqFQqFQKDyBGnYUCoVCoVAoPIEadhQKhUKhUCg8gRp2FJOzZcsWCAQCnb+LFy/i6NGjEAgEWLJkicbzSUlJsLe3x9NPP22B0lMoFAoba9dp+fn5+Oyzz9CvXz94eXnB1dUVvXv3xo4dOyxSHop5sbF0ASj8ZenSpQgNDdUIb9WqFTw9PfHss89i+fLlmDx5Mtq0acPcnzlzJsRiMb788svmLC6FQqHoxVp12oULF/D+++9jxIgR+OCDD2BjY4Pdu3dj0qRJuHPnjlZjlGLFEArFxGzevJkAIJcvX9YbLzs7m7i5uZGBAwcyYdu3bycAyJdffmnuYlIoFIpBWLtOS0xMJMnJyawwlUpFBg0aRCQSCVEoFBYqGcUc0KlYisWQyWRYuXIlTp48iR9//BFFRUWYP38+evTogVmzZhmVVkFBAd5880107NgRjo6OcHZ2xvDhw3Hjxg2NuBUVFVi8eDHatGkDqVQKX19fjBs3Dg8ePDAqz0uXLmHYsGFwcXGBvb09+vfvj3PnzhmVBoVC4Q+m1GkAmOdDQkIgkUgQEBCAF154AXl5eUycnJwcvPTSS/D29oZUKkXnzp3x448/stIJDQ1FcHAwK0wgEGDs2LGorKxEYmKiUeVSqVRYs2YN2rdvD6lUCm9vb7z22msoLCw0WkaK6aFTsRSzUVxczFJAQK0y8fDwYK5ffvll/Pjjj3jzzTdx5MgR5Obm4tChQxAKjfvmSExMxL59+/DMM88gNDQU2dnZ2LRpE/r37487d+7Az88PAKBUKvHkk0/i+PHjmDRpEl5//XWUlJTg6NGjuHXrFlq2bGlQfidOnMDw4cMRERGBRYsWQSgUYvPmzRg0aBDOnj2Lnj17GlV+CoXCfZpTpykUCvTt2xd3797Fiy++iG7duiEvLw8HDhzAw4cP4enpifLycgwYMAD379/H7NmzERoail27dmHatGkoKirC66+/rjePrKwsAICnp6dRZXvttdewZcsWxMbGYu7cuUhKSsJXX32Fa9eu4dy5cxCLxUalRzExlh4ypPAP9bSFtp9EItGIf+vWLSIWiwkAMm/evEblWVFRQZRKJSssKSmJSCQSsnTpUibshx9+IADIqlWrNNJQqVQG5aVSqUjr1q1JTEwM65mysjISGhpKhg4d2igZKBQKN7GETvvwww8JALJnzx6Ne2q9s2bNGgKA/PLLL8y9qqoqEhkZSRwdHYlcLteZfn5+PpHJZKRv375Glevs2bMEANm6dSsr/I8//tAaTml+6IgdxWysX7+etYAYAEQikUY8Z2dn2Nraorq6Gk888USj8pJIJMz/lUolioqK4OjoiLCwMFy9epW5t3v3bnh6emLOnDkaaQgEAoPyun79OhISEvDBBx8gPz+fdW/w4MH4+eefoVKpjP5Cp1Ao3KY5ddru3bvRuXNnPPXUUxr31Lrq0KFD8PHxweTJk5l7YrEYc+fOxeTJk3H69Gk8+eSTGs+rVCpMmTIFRUVFWLdunVHl2rVrF1xcXDB06FDW6GVERAQcHR1x8uRJPPvss0alSTEt1LCjmI2ePXuie/fuDcabPXs2hEIhgoOD8cYbb2DIkCFGD+WrVCqsXbsWGzZsQFJSEpRKJXOv7jTJgwcPEBYWBhubxnf9hIQEAMDUqVN1xikuLoabm1uj86BQKNyjOXXagwcPMH78eL1xUlJS0Lp1a42PyLZt2zL3tTFnzhz88ccf+Omnn9C5c2ejypWQkIDi4mLIZDKt93NycoxKj2J6qGFHsSh79uzBgQMHsGbNGrRu3RojR47EZ599hvfee8+odD755BP83//9H1588UV89NFHcHd3h1AoxLx586BSqUxaZnV6n332Gbp06aI1jqOjo0nzpFAo1oGpdJq5WLJkCTZs2IAVK1bg+eefN/p5lUoFmUyGrVu3ar3v5eXV1CJSmgg17CgWo6SkBHPnzkW3bt0we/ZsiEQijB8/HsuWLcPkyZO1+ovSxW+//YaBAwfi+++/Z4UXFRWxFga3bNkSly5dQnV1daMX+Ko3WDg7O2PIkCGNSoNCofAPU+q0li1b4tatW3rjBAcH499//9VY+hEXF8fcr8v69euxePFizJs3D++8844RkrHLdezYMURHR8POzq5RaVDMC10ERLEYH3zwATIzM7Fp0yZmncratWshEokwe/Zso9ISiUQghLDCdu3ahfT0dFbY+PHjkZeXh6+++kojjfrP6yIiIgItW7bE559/DoVCoXE/NzfXiJJTKBS+YEqdNn78eNy4cQN79+7VuKfWVSNGjEBWVhbrBImamhqsW7cOjo6O6N+/PxO+Y8cOzJ07F1OmTMGqVasaIx4AYMKECVAqlfjoo4807tXU1KCoqKjRaVNMg4AY+teMQjEQ9TZ4XV7ao6KiUFhYiF69emHmzJka3thXr16NBQsW4LfffmtwjYmaRYsWYenSpZg2bRqioqJw8+ZNbN26Fa6urggMDMSpU6cA1G6sGDJkCE6dOoVJkyahb9++KC0txbFjxzBz5kyMGTPGoPxOnTqF4cOHQyaTITY2Fv7+/khPT8fJkyfh7OyM33//3aB0KBQK97GETlMoFOjVqxfi4+Px4osvIiIiAgUFBThw4AC+/vprdO7cGeXl5YiIiMCDBw8wZ84chISE4LfffsPp06exZs0axt3J33//jb59+8LFxQUrV67UmK2IiopCixYtDK6P6dOnY9OmTRg+fDieeOIJiMViJCQkYNeuXVi7di09DtLSWHRPLoWX6HMNAIB89913pFu3bsTPz48UFxdrPF9TU0O6dOlCAgICSElJiUF5VlRUkDfeeIP4+voSOzs7Eh0dTS5cuED69+9P+vfvz4pbVlZG3n//fRIaGkrEYjHx8fEhTz/9NHnw4IFRcl67do2MGzeOeHh4EIlEQoKDg8mECRPI8ePHjUqHQqFwG0voNEJqXZLMnj2b+Pv7E1tbWxIQEECmTp1K8vLymDjZ2dkkNjaWeHp6EltbW9KxY0eyefNmo8pfP74hfPPNNyQiIoLY2dkRJycn0rFjR/L222+TjIwMo9OimBY6YkehUCgUCoXCE+gaOwqFQqFQKBSeQHfFUjhNeXk5iouL9cZxd3eHra2tSfIrKChAVVWVzvsikYhu56dQKI2muXWaoSiVygY3fjk6OlJXTlYANewonGbHjh2IjY3VG+fkyZMYMGCASfIbN24cTp8+rfN+cHAwkpOTTZIXhUJ5/GhunWYoaWlpDbpjWbRoERYvXtw8BaI0GrrGjsJpMjMzcfv2bb1xIiIiTHbKw5UrV1BYWKjzvp2dHaKjo02SF4VCefxobp1mKBUVFfjrr7/0xmnRooVRu2cploEadhQKhUKhUCg8gW6eoFAoFAqFQuEJvF9jp1KpkJGRAScnJwgEAksXh0KhmBBCCEpKSuDn56dxEDpXWL58Ofbs2YO4uDjY2dkhKioKK1euRFhYGBOnoqICb7zxBn799VdUVlYiJiYGGzZsgLe3t8H5UF1HofAXY3Qd76diHz58iMDAQEsXg0KhmJG0tDQEBARYuhhaGTZsGCZNmoQePXqgpqYG7733Hm7duoU7d+7AwcEBADBjxgz873//w5YtW+Di4oLZs2dDKBTi3LlzBudDdR2Fwn8M0XW8N+yKi4vh6uqKGzduICQkxNLFMRn5+fnw8PCwdDFMBt/kAfgnExflkcvlCAwMRFFREVxcXCxdHIPIzc2FTCbD6dOn0a9fPxQXF8PLywvbtm1jjmKKi4tD27ZtceHCBfTu3dugdNW6Li0tDc7OzuYUoVnhYr9rClQe7sNFmYzRdbyfilVPSchkMl4pOxsbG9jb21u6GCaDb/IA/JOpueSpyMlBZW4uJF5ekMpkBj1jTVOPah9m7u7uAGp3YldXV2PIkCFMnPDwcAQFBRll2KnrwNnZmeo6DkPl4T7Wruu4uSjFDGRnZ1u6CCYlKyvL0kUwKXyTB+CfTOaWRx4fj4uxsTgaGYkzo0fjaGQkLsbGQh4fb9Z8mxOVSoV58+YhOjoaHTp0AFBbr7a2tnB1dWXF9fb21lvnlZWVkMvlrB8foe8Rt+GbPID16zrej9hRKBTuI4+Px7kJE1CjULDCc8+cwbmrVxG9cyec62w2sFZmzZqFW7duNegvzBCWL1+OJUuWaIQnJSXByckJwcHByMzMRFVVFezs7ODp6Ym0tDQAgIeHBwghKCgoAAAEBQUhJycHFRUVkEgk8Pb2RmpqKoDakUWhUIi8vDwAQEBAAPLz81FeXg5bW1v4+fkxTrtdXV0hFouZEwz8/f1RVFSE0tJS2NjYIDAwEElJSQAAFxcXSCQS5OTkAAD8/Pwgl8uhUCggEokQHByMpKQk5Ofnw9nZGfb29swfXB8fH5SWlqKkpARCoRAhISFISUmBUqmEo6MjnJyckJmZCaDWQK6oqGBGSlu0aIHU1FTU1NTAwcEBrq6uSE9PB1A7s1NVVYWioiIAQEhICNLT01FdXQ17e3u4u7vj4cOHAABPT08olUrG72VwcDCysrJQWVkJqVQKLy8vVn0DtVN8+fn5CAwMRG5uLlPfPj4+SElJAQC4ublBJBKx6rugoABlZWUQi8Xw9/dn1betrS1Th/XrOygoCImJiUx9S6VSZpDD19cXJSUlrPpOTk6GSqWCk5MTHBwcWPVdVlYGuVwOgUCA0NBQpr5LSkpQUVGBjIwMpg4rKyuZ+g4NDUVaWprW+vby8kJ1dTWrvjMyMpg+6+HhwapvlUrF6rPZ2dlMfctkMlafFQgEyM/PBwAEBgYiLy+P6bO+vr5661sulyMxMRFisRgBAQFMn61f37r6LCEEzs7OsLOzY9W3QqFA7oMHSPzmG5AzZ0B69wZEIiAnBzn//ou8zz9Hi5deQnDHjigvL2fVt1o2Q+D9Gju5XA4XFxdkZmbCx8fH0sUxGeXl5bCzs7N0MUxGWVkZ74bz+SaTOeW5GBuL3DNndN736tcPvTdv1ghXv9/FxcWcn36cPXs29u/fjzNnzrA8/J84cQKDBw9GYWEha9QuODgY8+bNw/z587WmV1lZicrKSuZavQbHGurCGKiu4zZ8kwewfl332EzFlpWVWboIJoVPig6A3vNZrZXS0lJLF8GkmEueipwcvYoOAHLPnkXFf1/J1gYhBLNnz8bevXtx4sQJjWObIiIiIBaLcfz4cSYsPj4eqampiIyM1JmuRCJh1tPxbV1dXaiu4zZ803OA9eu6x8awKykpsXQRKHqwlh2NxmBtfa4iJwfFt2/rVCrmkqeygYPHAQCEGBaPg8yaNQu//PILtm3bBicnJ2RlZSErKwvl5eUAavv+Sy+9hAULFuDkyZO4cuUKYmNjERkZafDGCYr1wDddZ216zhCsXdc9NmvsuOq8lFKLNe1qNBRr6XPy+HjcWbGC9SXp1a8f2i1cyFrXZi55JF5eDUcSCAyLx0E2btwIABqHum/evBnTpk0DAKxevRpCoRDjx49nOSim8A++6Tpr0XPGYO267rFZY8e3dScUiinQtWkBAGwcHZtt08LjsMbO3NC6oFC4D11jZ0KM2VHSEA1NWTWWkJAQhIWFoXPnzmjVqhXGjBmD8+fPN/jcvn37cPHiRZOWhdJ01LuuuMydFSu0GnUAUKNQ4M6KFcy1OeVpt3AhbBwdtd6zcXREu4ULzZY3RTdU11Eawhr0nLFYu657bAw7pVLZ5DSaw8/Wjh07cOPGDdy/fx9Tp07FiBEjcOnSJb3PUGXHTUzR58yJsQt5zSmPc1gYonfuhFe/foB6qkoggFe/frxxdWJNUF1HMRSu67nGYO26zmRr7Lp162ZUfIFAgAMHDsDf399URdCLow4L2VAs4Wdr3Lhx+Pvvv/H5559j+vTp+OCDD1BRUYGqqiosWLAAL730Eg4dOoQDBw7g6NGj2LJlC2bPno0nn3wSkydPhlwuR0VFBQYOHIgvv/ySl2shuExT+5y5MWYhr1QmM7s8zmFh6L15c6O8sZsKruux5oDqOooxcF3PNQZr13UmM+yuX7+ON954w6AKIYRgxYoVLB9M5qapDWXIlJW2efGm0qtXLxw4cADdunXDX3/9BZFIhIKCAnTt2hUxMTEYMWIERo8ejS5dumDevHkAgIqKCvz+++9wdHSEUqnEmDFjsHPnTkyaNMnk5aPoxsnJydJF0IuxC3mbSx6pTNbsBp0aruux5oDqOooxcF3PNQZr13Um3RX71ltvQWZgIb/44gtTZt0gWVlZ8Pb2btSzxkxZmbqR1Htb8vPz8dJLL+HevXuwsbFBfn4+bt26hYCAAI1nVCoV3nnnHfz1118ghCAnJwcdOnSgyq6ZyczMRIsWLSxdDJ1IZTJ49eunfyFv375Mn+a6PKaCy3rM3FBdRzEWPuoFa5fJZIZdUlISvIzYonvnzh34+fmZKnuzYuyUlSm5fPkyOnTogOnTp2PEiBHYvXs3BAIBunXrhoqKCq3PrFq1Cjk5Obh06RKkUikWLFigMy7l8eXMvVwcHfQSHpb7QVWt6TRVKLaF74Dh+OPAbQCAXF6MIaV2GNHRt7mL2mzwWY8ZAtV1FIr1YzLDLjg42Kj4gYGBpsraIAz9AteGpfxs7d+/Hxs3bsSRI0cwc+ZMBAcHQyAQ4MyZM7hx4wYTz9nZmTmXDwAKCwvh4+MDqVSKrKws7Nq1C+PHjzdp2SgN09gR4ubg2zOJ+PjQ3dqLkCjdEe8WA3jUt5ycnHlt2HFdj5kbqusoxsJlPddYrF0mszkoLioqwt9//42cnByoVCrWvRdeeMFc2eqkKetgjJ2yagoTJ06EVCpFaWkp2rVrh0OHDqFXr15YsWIFZs6ciY8++ghdunRBr169mGeef/55TJs2Dfv27cOsWbPw+uuv4+mnn0b79u3h5+eHIUOGNLlcFOOpqKiAg4ODpYvBghCC1ccS8OXxBEsXxSrgmh4zN1TXUYyFi3quqVi7TGZxUPz7779jypQpUCgUcHZ2ZnnaFggEKCgoMHWWOlE79bt+/To6d+7c+HQ44siVYj0kJiZyap2GSkXw0f/uYPO5ZFa4p6MtgIa94SuVNZjcKwRvDws3TwEbgTmd8nJJjxmCqeqC6jqKMXBNz5kCLspkzPttlhG7N954Ay+++CI++eQT2NvbmyOLZkfte+bOihXIPXsWIKTW90zfvhpHL1EoXEOpIli4+1/suvKQFf7ByLZ4ua9hCoyLys6c8FGPGQLVdRSKdWOWETsHBwfcvHmTE38EzPFFb0k/WxSKsVTVqDBvxzUcupnFhAkEwIpxHTGxR5AFS9Z0zDlixyU9ZghU1/EX2g4Uix8pFhMTg3/++cccSTeatLQ0k6Ullcng0r49fcEoetF1jJ25jmnSRnmVEq/89A/LqLMRCvDlpK5GG3WmPJbPGuCiHmtuqK6zLM1xAkhT4aNesHaZTDYVe+DAAeb/I0eOxFtvvYU7d+6gY8eOEIvFrLijR482VbYGU1NT0+x5Uh5v6vc5eXx87fRWnYXpXv36mW16q6SiGi9t+Qd/Jz9aCyaxEWLjc90wKNz4XV+PwzvEdT1GeXywxAkgjYGPesHaZTLZVKyhR7gIBIJmPVtOPXyZkJCAVq1aNVu+FEp2djazbb65F6QXlFZh6g9/42b6I9cQDrYifD+tB3q38GhUmnXl4Qqmnn7kqh4zBHNOS1Oan4uxsfp3J/frZ5YTQIyFi3qhqXBRJotMxapUKoN+xirD9PR0PPfcc/Dw8ICdnR06duzYqOkRFxcXo5+hUJqCq6sr839DjmkyFVnFFZi46QLLqHO1F2PbK70bbdQBbHn4irn0GIViDMacAGJp+KgXrF0mk66xe+GFF7B7926UlpaaJL3CwkJER0dDLBbj8OHDuHPnDr744gu4ubkZnVZGRoZJykShGEp6ejqA5lXSDwvL8Mym80jIeWREejlJsOPVSHQOdG1S2mp5+I6p9RiFYizGnABiafioF6xdJpMadq1atcInn3wCT09PDB8+HBs3bmxSBa1cuRKBgYHYvHkzevbsidDQUDzxxBNo2bKlCUvNLfbs2YOIiAh06dIF4eHhGDRoEFQqFUaMGIF4Ey+YHTBgAPbt29dgvC1btiAuLo65PnDgAObPn2/SsvCZ5lLShBDM+/U60grKmbAANzv8Nj0SYT78O6jbXJhaj1E0oXpOP5Y6AYTCD0xq2H344Ye4cuUKEhISMGrUKOzbtw8tW7ZEREQEli5diuvXrxuV3oEDB9C9e3c888wzkMlk6Nq1K7799lu9z1RWVkIul7N+AIw6/9FSZGZm4tVXX8WePXtw/fp1xMXF4fPPP4dAIMChQ4cQZqGFsvUV3ujRo7F69WqLlMWaUB9j11xK+o9bWfgnpZC5bunlgF3TIxHsYRoP6k05ls+aMLUeo7Cheq5h1CeA6MNUJ4A0FT7qBWuXySwOigMCAjBz5kzMnDkTJSUlOHz4MPbv349BgwbByckJo0aNwowZM9C+fXu96SQmJmLjxo1YsGAB3nvvPVy+fBlz586Fra0tpk6dqvWZ5cuXY8mSJRrhqampqKioQHBwMPLy8lgLI+UV1YjPKmma0AYQ5uMEZ6lY5/3s7GyIRCK4u7szYd26dQMAhISEYN++fejSpQsGDBiAiIgIXL58GcnJyZg6dSoiIyPxySef4OHDh5g7dy4WLFig8RwAdO/eHZ9//jkGDBjAynvbtm1Yu3YtqqqqoFKpsGzZMowaNQrfffcd/vnnH8yfPx+LFy/GJ598gpycHOzbt4/5Cv7ss8+wZcsWCIVCdOrUCRs2bICLiwsWL16Mu3fvoqysDA8ePICPjw9+++03lnx1qa6uRm5uLioqKiCRSODj44OUlBQAgJubG0QiEfLy8gDU9rGCggKUlZVBLBbD398fycnJAGrXR9ja2iLnv6lNf39/FBUVobS0FDY2NggKCkJiYiKA2rWXUqkU2dnZAABfX1+UlJRAoVBAJBIhODgYycnJUKlUcHJygoODA7Kyal2H+Pj4oKysDHK5HAKBAKGhoUhJSYFSqYSjoyNUKhVTBrfhw1Fw9y7g718r7PnzQEQEIJEABQXw9PdHhkIBKBTw8vJCdXU1ioqKmDbMyMhAVVUV7Ozs4OHhgYcPax0Ne3p6QqVSITs3Hx/9/oCpS7FIgE9HBsPLQczI6u7uDoFAgPz8fAC155zm5eWhvLwctra28PX11VvfmZmZEIlEEIvFCAgIQFJSktb69vPzg1wuZ9VhUlISCCFwdnaGnZ0dq74VCgVKSkogFAoREhLCqm9HR0dkZmYCqD2/sby8nFXf5nRLYCo9xiW4oOsedz1nKO0WLsS5q1d1brhqt3Bhk9I3FVVVVZYugsmxdpnMdlasGicnJ0yYMAETJkyAUqnEqVOncODAAVy4cKFBhahSqdC9e3d88sknAICuXbvi1q1b+Prrr3Uadu+++y7zsgO1O0kCAwNhZ2fHOBqtv9slPqsEz3x9oSliGsSu6ZHoEaL7Ze/UqRP69OmD4OBg9O/fH1FRUXj22WfhrzYG6pCSkoKTJ09CLpcjJCQEhYWFOHv2LDIyMhAWFoYXX3zRqAWgMTExmDx5MgQCAZKTk9G7d2+kpKTg5Zdfxi+//IJ58+Zh7NixAGq/bNUcPnwYP/zwAy5cuABXV1e8+uqrWLhwITZu3AgAuHTpEq5cuQIPDw9MmjQJmzZtwrvvvqu1DGKxGH5+fqyw+s5h6+4G8vHx0RvX0dGR+X/9Nq8ft+61nZ0d64stJCREZ1x7e3t4enoy13UPka97UoPnnDm1u2L/Mz4BAP9tArJxdESHL76Ac70y1f3DEBAQoLf8e24VIkNezVy/2CcUEeGhWuPW3Ujk6+urN9269S0SiVj39dW3VCpl1WFoaKjOfOzs7Fgj6vrq28HBgVXfQUHN42C5KXqMS3BB1z3ues5QrOUEkKKioiYbsVzD2mUyqWG3aNEiDB48GL1794atra3GfZFIhMGDB2Pw4MEGpefr64t27dqxwtq2bYvdu3frfEYikUAikRhXcI4gFAqxe/duxMXF4fTp0zh8+DA+/vhjrbuAn376aYhEIri5uaFFixZ48sknIRAI4O/vDy8vLyQnJzNfr4aQlJSEKVOm4OHDh7CxsUFBQQGSkpIQHq7/XNBjx45h4sSJjHKdMWMGnnnmGeb+sGHD4OFRuxMzMjISN2/eNLhMfMKcSrq4vBpfnkhgrt3sxZg5gLr2aSym1mMUNlTPGY5zWBh6b95MT56gGIVJDbuffvoJH330EaRSKSIjIzFw4EAMHDgQvXr1go2N8VlFR0drLKS9d+8ea1TEUBrzjKUIDw9HeHg4XnvtNQwbNozlNFWNVCpl/i8SiTSu1Q4WbWxsWK4ZKioqtOY5adIkrFixAk8//TSA2tEiXXH1UfegdG3ltHbHj8ZQf+TJXEp6w8n7KCp7NFo3d3BruNjpnvJvLPXl4Sum1mMU7VA9ZzhSmYyzBh0f9YK1y2RSLZWUlITk5GScPHkSp06dwnfffYcPP/wQDg4OiI6OZhRkz549DUpv/vz5iIqKwieffIIJEybg77//xjfffINvvvnG6LJlZGToHLIP83HCrumRRqdpLA3tTExPT0dycjKio6MB1Lp7SUpKatIu4FatWuHSpUuIiIjA33//rXPHWWFhITNd9ssvv6Cw8NEifGdnZxQXF2t9bsiQIXjjjTewYMECODs7Y9OmTXjiiScaXV4+kZ6ejsDAQI1wUyrptIIybD6XzFyHeNhjSi/zfMTokodvmFqPcQku6Dqq5/gFH/WCtctk8s/PkJAQxMbGIjY2FkCtklQryE8++QTvv/++wV8zPXr0wN69e/Huu+9i6dKlCA0NxZo1azBlyhSjy1VdXa3znrNUrHftW3NRU1ODpUuXIikpCfb29qipqcHUqVMxZswYvP76641Kc9myZZg6dSo2bdqEyMhIneuB1q5di6effhqurq4YNGgQa+3Sq6++ijfeeAOrV69m1juqGT58OG7duoXIyEjWomKK/j5nKj7/Mx5VShVz/c6wcNjamOUI6GaRhyuYUo9xCS7oOqrn+AUf9YK1y2SyI8W0kZKSglOnTuHEiRM4ffo0cnJy0Lt3b5w4ccJcWWqgPobj3r17aN26dbPlS6FkZWVpbPAwJTfSijBm/TnmOiLYDb9Nj9SYJjIV5panMTTHMVpc0GOGQI8Uo1gCLuqFpsJFmYx5v006YpeamopTp04xX7Z5eXmIiopC//798corr6Bnz55aFyM3B405rYJCaQrm3FVFCMHHh+6ywt4f2dZsRh1gXnm4BJf1GIXCNfioF6xdJpMadiEhIQgKCsKMGTMwY8YMREREQCQSmTKLRpOens5yk0ChmJuHDx9quAQxFcfu5uDvpALmemRHX3QLMu/Hiznl4RJc1mMUCtfgo16wdplMuhhnwoQJqKysxMqVK7Fs2TKsWbMGV69ehRlneymUx45qpQrLDz8arROLBHh7GDd8WvEBqscoFIo1Y9IRu19//RUAEBcXx0xjfPbZZ6ioqECfPn3Qv39/DBgwAD169DBltgah9jFEoTQX5hoh/vXvVCTmPjqg/oXIEJMdG6aPx2XEm8t6jELhGnzUC9Yuk1m2z4WHh2PGjBnYsWMHsrKycP78eXTp0gXLli1DZKT5t9pro66PIwr34ONoiDn6XElFNdYce+SM2FlqgzmDmscZ8eP2DplSj505cwajRo2Cn58fBAKBxqH006ZNg0AgYP2GDRtmQmkoXIFvuo6PesHaZTKbt83s7GycOnWKWYR87949SCQS9O3b11xZ6qWoqMiqnBQ/bmRlZWkcb2XtFBYWmnzTztenHyC/9NE5hnMGtYarffMs5DeHPFzHVHqstLQUnTt3xosvvohx48ZpjTNs2DBs3ryZubbWE3Qo+uGbruOjXrB2mUxq2O3cuZNRgvHx8RCLxejRowcmTJiAgQMHIioqiiorE/Hw4UON80OtmfLycksXgfNkFJXju7NJzHWAmx1eiKIfK6bGHHps+PDhGD58uN44EomEcy4WuADVdRSKcZjUsHvuuefQvXt3PPXUUxg4cCCio6NhZ2dnyiwaTXMdFt5c8OmLD7CuI98MxdQyffHnPVTWPHJG/PawcEhsmm+3Jh/bSBuW0mOnTp2CTCaDm5sbBg0ahGXLluldG1xZWYnKykrmWi6Xm72MloDqOm7DN3kA65fJpIZdYWEhHBzMv4i7MWRnZ1v10Gp9srKy4O/vb+limAy+yQOYVqZb6cXYc+0hc9050BWjOjXvHzw+tpE2LKHHhg0bhnHjxiE0NBQPHjzAe++9h+HDh+PChQs6Xa0sX74cS5Ys0QhPSkqCk5MTgoODkZmZiaqqKtjZ2cHT0xNpaWkAajeTEUJQUFDrMicoKAg5OTmoqKiARCKBt7c3UlNTAdT69BIKhcjLywMABAQEID8/H+Xl5bC1tYWfnx+Sk5MBAK6urhCLxcjNzQUA+Pv7o6ioCKWlpbCxsUFgYCCSkmpHnV1cXCCRSJCTkwMA8PPzg1wuh0KhgEgkQnBwMJKSklBUVITAwEDY29sjKysLAODj44PS0lKUlJRAKBQiJCQEKSkpUCqVcHR0hJOTEzIzMwEA3t7eqKioYI4La9GiBVJTU1FTUwMHBwe4uroiPT0dACCTyVBVVYWioiIAta5v0tPTUV1dDXt7e7i7u+Phw9r30NPTE0qlkjmWLDg4GFlZWaisrIRUKoWXlxervgEgPz8fxcXF6NChA3Jzc5n69vHxQUpKCoBan6sikYhV3wUFBSgrK4NYLIa/vz+rvm1tbZk6rF/fQUFBSExMZOpbKpUiOzsbQK3BXFJSwqrv5ORkqFQqODk5wcHBgVXfZWVlkMvlEAgECA0NZeq7oqICLVq0QEZGBlOHlZWVTH2HhoYiLS1Na317eXmhurqaVd8ZGRlMn/Xw8GDVt0qlYvXZ7Oxspr5lMhmrzwoEAuTn5wMAAgMDkZeXx/RZX19fvfWdkJAAOzs7iMViBAQEMH22fn3r6rOEEDg7O8POzo5V3wqFgtVn69a3o6Mjq8+Wl5ez6lstmyGY5eSJ4uJiHD16FMnJyUyhhgwZYhFv6GpvzdevX0fnzp2bPX9zkZiYaNV+durDN3kA08lECMGU7y7h/IN8Jmzna5HoGdq8TjS52EbmPG3BXHpMIBBg7969GDt2rM44iYmJaNmyJY4dO4bBgwdrjaNtxC4wMJB3J09wsd81BSoP9+GiTBY7eQKoPVh59uzZGtMCLi4u+PrrrzFx4kRTZ2kQUqnUIvmaCyoP9zGVTKfic1lG3RPtvJvdqAP42Ua6sLQea9GiBTw9PXH//n2dhp1EInks1izzrd/xRR5CCJQqAoGNLeQV1aiuUaFaSVBVo0KVUoVqpQpVNf/9y/yfMOH147CeZT1HdKRV+0yNikCpUkGpqi2PkhAolf/9+19YjYoARgxhqYgK3011QWRL63STZlLD7urVq4iNjcWUKVMwf/58hIeHgxCCO3fuYM2aNXj++ecRHh5ukZEza/dLUx8vLy9LF8Gk8E0ewDQy1ShV+KTO0WE2QgHeGR7e5HQbAx/bSBtc0GMPHz5Efn4+79aXNQa+9TtTypOaX4bDtzKRX1qFGmWtgVNTx5hRquoaNypWeG38WgNIbRwxYXXj/Reuvq5WPkqHz6is2C2NSQ27devWYezYsdiyZQsrvFu3bvjpp59QVlaGtWvX4ocffjBltgbx8OFDXjkpTktL49xQcVPgmzxArUx+jo6ozM2FxMsLUpnM6DR2XXmIhBwFcz2lVxBaejmaspgGw8c20oY59JhCocD9+/eZ66SkJFy/fh3u7u5wd3fHkiVLMH78ePj4+ODBgwd4++230apVK8TExJhKLKuFb/2uqfJUVCvxx60s7LichguJ+Q0/QHnsMKlhd+7cOWzYsEHn/enTp2PmzJmmzJJC4STy+Hgk79iB2z/+yIR59euHdgsXwjnMsOO/SitrsOroPebaSWKDuYNbm7ysFDbm0GP//PMPBg4cyFwvWLAAADB16lRs3LgR//77L3788UcUFRXBz88PTzzxBD766KPHYqqV0jCEENxKl2PHP6nYfz0DJRU1li6SWbAVCSEWCSC2Ef73fyFsbWrDav+t/UmY/wtgIxRCJBQ8+gkEEIkEsBEKIBTU/isS1jr9NpSioiL4u3LDo0djMKlhl5GRgTZt2ui836ZNG2Y3THPj7t78a5LMCZ9GHwF+ySOPj8e5CRNQ7eyMuqok98wZnLt6FdE7dxpk3H1zJhG5JY8Wx88Y2BIejpb7Q8+nNtKHOfTYgAED9J44cOTIEaPSe5zgW78zRp6isirsu5aOHf88xN1M3e5snCQ2sLWpNXBshGrDRvjI0BEKYCOqNXTEojpGkFDIGD429YwjG5EANiL2/brXYlFt+tVVlXBxtK81wkTC/4wytiGmvlfXSGMbbrXhxhhf5qS4uBguLtz08GEIJjXsysrK9C4MlUgkqKioMGWWFArnuLNiBWoUCkDLzqUahQJ3VqxA7zonDGgjW16Bb84kMtd+LlK8GB1q8rJSNKF6jGJpbqQV4duzifjzdjaqlCqtcRwlNhjV2Q8TewSic4CLxYyiWiPIxSJ5U7Rj8l2xR44c0dnIal81lqCgoACBgYEWy9/U5Ofn8+pl4os8FTk5yD1zpvYiNBT4z7dTXXLPnkVFTo7eNXerj95DefWj8wrfjAmDVNx8zoi1wZc2MgSu6rHHEb71O33yEELw04UULD14B0odmxN6hrpjYvdADO/oA3tbs50KajB8ax/A+mUyea+YOnWq3vtcGWqlUMxB5X+OWfVCCCpzc3UadnFZcuz8J425bu/njLFd+O8YmEtQPUZpbqqVKiw+cBtbL2k6opU5STA+IgATugci1NN6pwgpzYNJDTuVSvuQMRfg01mDAHg1+gjwRx5JXVcG//yjPZJAwI5Xj+WH4lD3Y/39EW0hFFrekOBLGzUEl/XY4wiX+l1FTk6TdrkD2uUpLK3CjK1XcDGxgBU+MMwLz/UORv82XrARCRuVn7nhUvuYCmuXiZs9xQyojwvhC7mGjAxZEXyRRyqTwatfv9oLHQvwvfr21flH4WxCLk7fe1QXg8JliGrFDR+MfGkjinXBhX4nj4/HxdhYHI2MxJnRo3E0MhIXY2Mhj483Oq368iRkl2DM+nMso04oAD58sh1+mNYDg9t6c9aoA7jRPqbG2mUyywT9gQMHtIYLBAJIpVK0atUKoaHNuxCcb4udqTzcpd3ChTh39SqqtWyesHF0RLuFC7U+p1QRfHIojrkWCoB3LeSMWBt8aiND4KIeexyxdL9T73KvUShY4cbucldTV56T8TmYu+0aSiofuS9xktrgq2e7oX8b63DMbOn2MQfWLpNZDLuxY8dCIBBobO9XhwkEAvTp0wf79u2Dm5ubOYqgAd/8QVF5uItzWBiid+7E1Z9+QolAABACCATw6ttXrx+7vdfSWS4NJvYIQmtvp+YqdoPwqY0MgYt67HHE0v2O2eWuBUN3uddFIpGAEILv/0rCJ4fuspZdhHjY47upPdBKZhkn5I3B0u1jDqxdJrOM7x49ehQ9evTA0aNHUVxczBym3atXLxw8eBBnzpxBfn4+3nzzTXNkrxVvb+9my6s58PHxsXQRTArf5HEOC0PfpUsx9Px59DtwAEPPn0fvzZt1GnXlVUp8fuTRtI69rQjzh3LLGTHf2qghuKjHHkcs2e9Yu9x1oN7lbihunjK8s/tfLPsf26iLaumBfbOircqoA/ipF6xdJrOM2L3++uv45ptvEBUVxYQNHjwYUqkUr776Km7fvo01a9bgxRdfNEf2WklNTeXVV3VKSgqvjtnhmzzAI5kMWWT9/V+JyJI/Gv5/rV9LyJy4dVg4H9tIH1zUY48jlux3ptjlXpd8RSWmfXcON7PKWeHP9w7Gh6PaQczhtXS64KNesHaZzGLYPXjwAM5a1hc5OzsjMbHW6Wrr1q15t6GBQmkMuSWV2HjqAXMtc5LglX507ZaloXqMom/3OkMDu9zVxGXJ8dKWf5Be9MioEwkFWDyqHZ6PDGlCKSkUNmb5PIiIiMBbb73F2lmSm5uLt99+Gz169AAAJCQkNOuWYldX12bLqzng0+gjwD95AMNlWnv8Hkqr6jgjfiKME45H68PHNtIHF/XY44gl+x1rl7sO9O1yV3P0TjbGbzjPMupc7MT4+cWeVm/U8VEvWLtMZvnr8f3332PMmDEICAhglF5aWhpatGiB/fv3AwAUCgU++OADc2SvFZHIsl77TQ2Vh/sYItP9HAW2//3IGXG4jxPGR3DT5yIf20gfXNRjjyOW7nfqXe7aNlDo2+UO1J4k8fXpRHx6JA519+C08HLA91N78MLZsKXbxxxYu0xmMezCwsJw584d/Pnnn7h37x4TNnToUAiFtYOEY8eONUfWOsnPz+eVk+K8vDyt00TWCt/kAQyTacXhONbRQe+OaAsRB5wRa4OPbaQPLuqxxxFL9zv1Lvc7K1Yg9+xZg3e5V1Qr8e6em9h7LZ0V3iPAAd+9FA0XO3FzFN/sWLp9zIG1y2RSw+6FF17AmDFjEBMTA0dHRwwbNgzDhg0zWforVqzAu+++i9dffx1r1qwxWboUiiW4mJiPY3ezmeu+rT2txncVnzG3HqNYH85hYei9ebPBJ0/klFTgtZ+v4FpqESv8xehQTG4r4Y1RR+EmJl1j16pVK3zyySfw8vLC8OHDsXHjRqSnpzf8oAFcvnwZmzZtQqdOnRr1vL8/v87a5NPoI8A/eQD9MqlUBJ8custcCwTAeyPaNphmRU4Oim/fNsq9gqngYxtpw5x6jGI8XOp3UpkMLu3b6zXqbqUXY8xX51hGnY1QgBXjOuLDUe0QHMSvNZlcah9TYe0ymdSw+/DDD3HlyhUkJCRg1KhR2LdvH1q2bImIiAgsXboU169fb1S6CoUCU6ZMwbffftvoRY2FhYWNeo6rFBQUNBzJiuCbPIB+mX7/NwP/Pixmrsd3C0BbX91D/6Y80qix8LGNtGEuPUZpHNbU7w7fzMQzX19AZvEj10Vu9mL88nIvTOoZBMC65DEEvskDWL9MZtkVGxAQgJkzZ+LIkSPIzc3FO++8g/j4eAwaNAjBwcGYPXs2bt++bXB6s2bNwsiRIzFkyJAG41ZWVkIul7N+AFBWVtZoebgIlYf76JKpolqJT/94ZJBJxUK8+YTuI4nURxrVd5Sae+YMzk2Y0GzGHR/bSB+m1mOUxmEN/Y4Qgi+PJ2DG1qsor360w72NtyMOzO6D3i08mDBrkMcY+CYPYP0ymd2ngpOTEyZMmIAJEyZAqVTi1KlTOHDgAC5cuID27ds3+Pyvv/6Kq1ev4vLlywblt3z5cixZskQjvLi4GImJiQgODkZmZiaqqqpgZ2cHT09PpKXV7kr08PAAIYSx1oOCgpCTk4OKigpIJBJ4e3sjNTUVAODu7g6hUMj4sAoICEB+fj7Ky8tha2sLPz8/JCcnA6h1tSIWixm3Cf7+/igqKkJpaSlsbGwQGBiIpKQkAICLiwskEgly/ptq8/Pzg1wuh0KhgEgkQnBwMJKSklBUVIS8vDzY29sjKysLQK237NLSUpSUlEAoFCIkJAQpKSlQKpVwdHSEk5MTMjMzAdSexFFRUYHi4tpRoxYtWiA1NRU1NTVwcHCAq6srM/0kk8lQVVWFoqIiAEBISAjS09NRXV0Ne3t7uLu74+HDhwAAT09PKJVKZoQ0ODgYWVlZqKyshFQqhZeXF6u+gdqNLUVFRaiurkZubi5T3z4+PkhJSQFQu/1cJBKx6rugoABlZWUQi8Xw9/dn1betrS1Th/XrOygoiPFD5uLiAqlUiuzs2rVuvr6+KCkpYdV3cnIyVCoVnJyc4ODgwKrvsrIyyOVyCAQChIaGsuobAJOPTCZDZWUliouL8ev1fJbbg8ndvOEmFTBxvby8UF1dzdR39sqVqG7dGrC3B4qKgMREoFs3AEB1YiJubNwI/7lzmT6bnZ3N1LdMJmP1WYFAgPz8fABAYGAg8vLymD7r6+urt77LysqQmJgIsViMgIAAps/Wr29dfZYQAmdnZ9jZ2bHqW6FQsPps3fp2dHRk9dny8nJWfatlMzdN1WOUxiMWc3s9WnmVEm/9dgMH/81khQ8Ol2HNpC5wkrLLz3V5jIVv8gDWL5OA1D8IkUOkpaWhe/fuOHr0KLO2bsCAAejSpYvOzROVlZWorKxkruVyOQIDA1FYWMgrX3YqlYrZmccH+CYPoF2mgtIq9P/sJEoqag/99nS0xam3BsJRov0bqyInB0cjI/VnJBBg6PnzBnm+bwpcbCO5XA4XFxcUFxdb9S42U8DXuuBiv1OTVVyBV3/+h7WsAgBe69cCbw8L17rDncvyNAa+yQNwUyZj3m+Tjdh17doVAoFhbhquXr1qULwrV64gJycH3f4bnQAApVKJM2fO4KuvvkJlZaWGvxmJRKL1AN+UlBReGXbJyclWfeRJffgmD6Bdpi+PJzBGHQDMH9pGp1EHmP5Io6bAxzaqjzn0GKVpcLXf3Ugrwis//YOckkcDCbYiIT4Z1xFP6/FFyVV5Ggvf5AGsXyaTGXbm8Oc0ePBg3Lx5kxUWGxuL8PBwvPPOO1bvRJDyeJGUV4pfLqYw161kjpjYXf8OOVMeaURpGOqXjmIIB25k4K1dN1BZo2LCPB1tsen5CEQEu1uwZBSKCQ27RYsWmSopBicnJ3To0IEV5uDgAA8PD43whnBxcTFl0SwOn0YfAf7JA2jK9Okfcaip64x4eDhsGjj0W32kUf2NE3Ux5EgjU8DHNqqPOfQYpWlwqd+pVASrj93DuhP3WeFtfZ3x7QsRCHCzbzANLslTH0P99NWFy/I0FmuXyaybJ65cuYK7d2t9dbVv3x5du3Y1Z3Z6sbW1tVje5oDKw33qyvRPcgEO38piriNbeGBQuGGKsylHGjVE8Z07UNy/D8dWreDSrp3euHxsI0Pgkh57HOFKvyurqsGCHTfwx+0sVnhMe2+smtAFDnqWVNSFK/LURR4fX3uyRp0PSK9+/fSerKGGi/I0FWuXySyGXU5ODiZNmoRTp04xlm9RUREGDhyIX3/9FV5NmDY6depUo57Lzc2Fn59fo/PlGjk5OczOSz7AN3mARzIRQvBxHWfEQK0zYkPXcjX2SCN9ZB45ghvvv4/qOv4dxW5u6Pzxx/CNidErz+OCOfUYxXC40O/Si8rxyo//4E6mnBU+e2ArLBjaBkIjjgHMSEyEt0hk1KiYOVG7U6r/4Zh75gzOXb2K6J079eoYLrSPqbF2mcyy7WPOnDkoKSnB7du3UVBQgIKCAty6dQtyuRxz/3PLQKE8Lhy6mcXyQv9UV390DDBuaYD6SKOh58+j34EDGHr+PHpv3txoo+6fmTNZRh0AVBcW4p+ZM5F55IjRafIRqscoAHAlpRBjvjrHMupsbYRYO6kL3owJM9ioUzsZj/v8c4s5GdfGnRUrtM4GAECNQoE7K1Y0c4koTcUs7k5cXFxw7Ngx9OjRgxX+999/44knnmD8czUH6i3COTk5vPrCrqys1Lr711rhmzxArUwQ2WDoqjNILah1eGlrI8SJN/obtBbHXPzRvbuGUVcXsZsbhv3zj0Y4F9vInC4+uKTHDIGv7k4s2e/2XH2Ihbtvokr5aJOEzEmCb17oji6BrganU3dUjDg6QlDHkLJxdGxwVEwXjVkTV//5prpT4qJeaCpclMmY99ssI3YqlUqrgz+xWAyVSqXlCfOjdsTLF7j2R6Wp8E0eoFamny+kMEYdUHsIuCWNuuI7d/QadUDtyF3xnTsa4XxsI31wUY89jlii3ylVBCsOx2HBzhsso66jvwsOzO5jlFEH1BsVq3cOaWNGxUx1xKAx7pR0wUe9YO0ymcWwGzRoEF5//XVkZGQwYenp6Zg/fz4GDx5sjiwbpLS01CL5mgsqD/fJKpCzds+52YvxUgdnFN++jYr/TmlobhT37zccSUc8PraRPrioxx5HmrvfKSpr8NrP/+Dr0w9Y4SM7+WLna5HwcZEalV5FTg57V7uHh0ac3LNnDdYJpjxi0BTulPioF6xdJrMYdl999RXkcjlCQkLQsmVLtGzZEqGhoZDL5Vi3bp05smwQGxuzn57WrFB5uM+26wUoLq9mrsdk/Y2LA/padH2NY6tWjY7HxzbShyn12JkzZzBq1Cj4+flBIBBg3759rPuEEHz44Yfw9fWFnZ0dhgwZgoSEBBNKY700Z79LKyjD+A3ncewu28iaP6QNvprcFXa2xvtO1RjtqnMyEkMDo2J1MeWaOLU7JX005E6Jj3rB2mUyS+kDAwNx9epVHDt2DHFxcQCAtm3bYsiQIebIzuAy8YmgoCBLF8Gk8E2etIIy7L1VwFzLSvPQ+c+fWHEM3XVmSlzatYPYza3BNXbaXJ/wrY0awpR6rLS0FJ07d8aLL76IcePGadz/9NNP8eWXX+LHH39EaGgo/u///g8xMTG4c+cOpFLjRoj4RnP1u7+TCjD9lysoKK1iwqRiIb54pgtGdvJtdLr1R7sEWtavGupkXGP0Twvq0T9D19w11Z0SH/WCtctktsPQBAIBhg4dijlz5mDOnDkWNeoAMAeW8wX1gfF8gW/yfHoknrU2Z/T1fbAhSo14lth11vnjjxt1n29tZAim0mPDhw/HsmXL8NRTT2ncI4RgzZo1+OCDDzBmzBh06tQJP/30EzIyMjRG9h5HmqPf7biciinfXWQZdT7OUvw2PapJRh2gOSpGoqM14hjqZNwUa+Lqo3an5NWvH6B2wSQQwKtfP4M+OvmoF6xdJrONN16+fBknT55ETk6OxkLjVatWmStbCsXiXEstxO83Hq3LapH3AJ0y/tUZ39gv7KbiGxOD7hs2GO3H7nGkOfRYUlISsrKyWEaji4sLevXqhQsXLmDSpElan6usrKzdef0fcrlcazyKbmqUKiw/HIfv/2J/+HcJdMU3z0dA5mya0dKGRsVavvoqim/fbnB3q7mOGFS7U2rqLlsKNzCLYffJJ5/ggw8+QFhYGLy9vVmOWA11ympq+HakGJWHmxBC8Ek9Z8RP3dgHvb3+vy/s5lSkvjEx8I2JMerkCb60kaE0lx7Lyqo9ycDb25sV7u3tzdzTxvLly7FkyRKN8KSkJDg5OSE4OBiZmZmoqqqCnZ0dPD09kZaWBgDw8PAAIQQFBbXLBYKCgpCTk4OKigpIJBJ4e3sjNTUVAODu7g6hUIi8vDwAQEBAAPLz81FeXg5bW1v4+fkhOTkZQO1RTGKxGLn/jRj5+/ujqKgIpaWlsLGxQWBgIDN74uLiAolEgpz/Ng34+flBLpdDoVBAJBIhODgYSUlJKC0tRV5eHuzt7Zn68PHxQWlpKUpKSiAUChESEoKUlBQolUo4OjrCyckJmZmZTD1WVFQwnhFatGiB1NRUFJVVYvmpLFxIZhvEw9t54vXeHlDkZcDTMQTp6emorq6Gvb093N3d8fDhQwCAp6cnlEolCv/7OAoODkZWVhYqKyshlUrh5eX1qL59fNDpp58Qv38/SoqKAIkECAuDffv2EFRU4EJsLNCzJwDAzcMDrWJjUf6fg9yAgAAUFBSgrKwMYrEYnv37I7emprawDx8CZWVAmza11zduwGXYMGQoFLCpqEBQUBAz+uTi4gKpVIrs7GwAgK+vL0pKSlj1nVVWBpWdHZwEAqjKylj1XVZWBrlcDoFAgNDQUKa+VSoVKioqmE1GMpkMlZWVTH2HhoYiLS0NNTU1cHBwgKurK9LT0wEAXl5eqK6uZnahhoSEICMjg+mzHh4erPpWqVSsPpudnc3Ut0wmY/VZgUCA/Px8ALXLKvLy8pg+6+vri5SU2nO73dzcIBKJWP1bqVQiMTERYrEYAQEBTJ91dXWFra1tg32WEAJnZ2fY2dmx6luhULD6bHJyMlQqFZycnODo6Mjqs+Xl5az6VstmCGbxY+ft7Y2VK1di2rRppk7aaNS+XzIyMuDr27QhdS5RWloKBwcHSxfDZHBFnqZ+sR64kYG5268x1yPC3DF82XP6H2rATxRX4Eob1cWcvtvMpccEAgH27t2LsWPHAgDOnz+P6OhoDR01YcIECAQC7NixQ2s62kbsAgMDeefHzhz9LjmvFC/9eBkPch/tfhQIgLdiwjCjf0uzDkAUpKZCVFKCarkcl6dP1zmKp2saVNdJEQ09Zy64qBeaChdlsrgfO6FQiGgt6wgsSY6F3EuYC/VXAF+wtDym8AulqKzBx/975P9NLBRg4ejOTd51xhUs3UbNTXPpMR8fHwCa9Zudnc3c04ZEIoGzszPrx0dM3e/O38/DmPXnWEadva0Im56LwMwBrcw+q1RUUwOX9u1x/5tvGrW7talr4kwNH/WCtctkFsNu/vz5WL9+vTmSplBMjqn8Qq07noBs+aMRlImd3RHkYY92CxfCRse5g4bsOqNYhubSY6GhofDx8cHx48eZMLlcjkuXLiGyoVMBeEZFTo5Z/Tz+cjEFL/zwN8sNkb+rHXbPiMIT7XUb0abGmN2t2jDlEYMU/mGWNXZvvvkmRo4ciZYtW6Jdu3Ya3tv37Nljjmz1ou/L1xrh07QyYFl5DPEL1XvzZr1p3M8pYS3A9ne1w4LhHQA8+sK+s2IFcs+eBQip/cLu2xftFi60GmXMtz7XEKbUYwqFAvfrOH1OSkrC9evX4e7ujqCgIMybNw/Lli1D69atGXcnfn5+zHQt35HHx9e+H3WMHa9+/dBu4UL4msD1RLVShSW/38YvF9nrlLoHu+Hr5yPg6dh8x0f5+vqi0pBdlwasvZXKZBYf7eejXrB2mcxi2M2dOxcnT57EwIED4eHhYbENE3VRKBQai5OtmZKSEtjZ2Vm6GCbDUvKYwi8UIQSLDtxGjerRctWFff1RHH8X9sHBkMpkvNh1xrc+1xCm1GP//PMPBg4cyFwvWLAAADB16lRs2bIFb7/9NkpLS/Hqq6+iqKgIffr0wR9//PFY+LDTtWZM7eex7ebNCOnWrdHpF5ZWYebWq7iQmM8KfzoiAB8/1QESG+OdDjeFkpISOJtpd6sl4KNesHaZzGLY/fjjj9i9ezdGjhxpjuQbhULHiIy1olAoILMyw0AflpLHGL9QugyxQzezcO7+oz8anSuzQF6Yi5vR0bh17hwz8uAcFsaJL+zGwrc+1xCm1GMDBgyAvn1qAoEAS5cuxdKlS5ucl7XR0Ih54sGDjTbs7mWX4KUfLyOtoJwJEwqAd4e3xct9Q8026KDvA06hUEDWogW8+vXT+1FpLWtv9ekFa/2QtXZdZxbDzt3dHS1btjRH0o1GJGrerzJzQ+UxDU31C1VWVYNldTZMiFQ1GH3iWwgAkP9cEljihAlzwLc+1xBc1GN8w5ARc0V8fKP8PB67k43Xf72G0qpHjsGdJDb48tmuGBhmnj/a+qaU1e+++j1q6okPXEGbXjCkHriMtes6s2yeWLx4MRYtWoSysjJzJN8orP2IkPoEBwdbuggmxVLyNPWsxK9O3EdmcQVzPSj+BGSK2lFAwaVLTLglTpgwNXzrcw3BRT3GNwwZMRdcvGjUSQqEEGw4dR+v/PwPy6gL9XTA3lnRZjXqDNmEpX6PuLa7tbHU1wum2oxmSaxd15llxO7LL7/EgwcP4O3tjZCQEI1Fx1evXjVHtnpJSUlBx44dmz1fc5GcnIyQkBBLF8NkWFKexn45J+Yq8O3ZR4ug3coKEHP3T+aaREZCcOECc93cJ0yYGr71uYbgoh7jG4aMmJPISIPXmlVUK/HO7n+x/3oGK7xva098NbkbXOwftaGppwkN3YRV9z3iw9rb+nrBFJvRLI216zqzGHZc3MlV/zgga4fKYzoas2uVEILFv99BtfLRuqmnru+DRPnorEkIhfUfavYTJkwJ3/pcQ3BRj/EN9Yi5vulYxzZtDHpnsoor8NrP/+DGw2JWeGx0CN4f0RY2otr30ZhpQkMNLmM2YWl7j6x57W1deUyxGY0LWLuuM4tht2jRInMk2yScnJwsXQSTQuUxLcZ+OR+5nY0z9x5ND0UFOaPLruvsSPWdXFrJLjddWLqNmhsu6jE+0uA5qk8+2WAa19OK8OpP/yCn5JEfSbFIgGVjO2Bij0fLcBragaueAjV2jZgxm7CcOGzQNIa6esEUm9G4gLXrOrOssauLQqGAXC5n/SyBvb29RfI1F1w77qSpcEUeqUwGl/bt9Sqd8iolPjpY54QJkQBLn+4KWf21ev+dPajGWna56YIrbWQJuKLH+EhDa828wsP1Pr/7ykNM2HSBZdR5ONhi2yu9WUYdYNg0YWPWiBmzCYtv71FdeZq6GY0rWHsbmcWwS0pKwsiRI+Hg4AAXFxe4ubnBzc0Nrq6ucHNzM0eWDWLtR4TUR9/h4NaINcmz4dR9pBc9cp/wYp9QtJI5ap4w0b49819r2uWmC2tqI1PART3GV/SdpKCr36mdDr+x6waqah5NnbX1dcaBOX3QI8SdFd/QacJbS5cafdSXMZuw+PYe1ZWnqZvRuIK1t5FZpmKfe+45EELwww8/wNvbmxMOiikUU5CcV4pNpx9tmPB2lmDOoNYAtKzVA6zyhAlKLVSPNT+GrjXLV1Ri9rZrGk6Hh3fwwRcTOsPeVvNPm6HThPkXL+qNomuNGF/clzQVWg+WxyyG3Y0bN3DlyhWEcegPGZ9OnQD4d0SaNchDCMGS32+jSvlodOD9ke3gKHn0GtVdq1eUkQHXzz/n/NepoVhDG5kSLuqxx5H6/e5WejFe+/kKa9RcIADeGNoGMwe0glCo3QA32fSfjjVihm7C4tt7VF8ePhyhaO1tZBbDrkePHkhLS+OUQiwvL284khVRVlbGq3WD1iDP8bs5OBn/6Ks/soUHRnXSfqagVCaDjVAIqadncxXP7FhDG5kSLuqxx5G6/W7/9XS8s/tfVFQ/+rhykthgzaQuGNxW/8e7ITtw3Xv1QkEd/5Na0bNGzJBNWHx7j7TJY+1uXKy9jcxi2H333XeYPn060tPT0aFDBw3/T506dTJHtnrh22JnuVwOTx4ZDVyXp6JaiSUHbzPXNkIBloxpr3d6jusyGQvf5GkILuqxxxG5XA5XN3es/CMO355NYt1r6eWAb17ojpZejjqeZrssaWiasOOiRRq7YetjyBoxfVPKfHuP9MljrW5crL2NzGLY5ebm4sGDB4iNjWXCBAIBCCEQCARQKpV6njYPfFsfQ+VpXjaeesA6b3JaVAjaeOvfEs91mYyFb/I0BBf12OOIvFKJaZsv46/77F3mQ9p6Y/XEznCSirU/p8NlSZdPP0XKr7/qnCY09xoxvr1HfJMHsH6ZBETfydSNpF27dmjbti3efvttrYuOm/O4DrlcDhcXFxQXF8PZ2bnZ8qXwh+S8Ujyx5gyz887LSYITb/TX+QeF0nyY8/3mkh4zBD7qujsZcrz2yz+sjyoAeH1wa7w+uLXO9XS6/NUBtcZZ9M6dsHVz0zlNyBiFVrpGjMI/jHm/zTJil5KSggMHDqBVq1ZNSmf58uXYs2cP4uLiYGdnh6ioKKxcubJRa15SU1PRoUOHJpWHS6SkpHDuD0tT4Ko8hBB8eOA2y53CByPbGmTUcVWmxsI3eRrCVHqM0jj2no3Du38koqLO6S4OtiKsmtgFMe31L2439FgrXdOE5lwjxrf3iG/yANYvk1n82A0aNAg3btxocjqnT5/GrFmzcPHiRRw9ehTV1dV44oknUFpaanRafJs2ofI0D3/cymKdMBHZwgOjO/sZ9CxXZWosfJOnIUylxyjGUXg3DnPnfIH5/3vAMuqCncXYNyu6QaPOmGOtGsIQh+XGwrf3iG/yANYvk1lG7EaNGoX58+fj5s2b6Nixo8ai49GjRxuUzh9//MG63rJlC2QyGa5cuYJ+DThBrI+jo+7FtdYIlUc3pvrKVlTWYMnv7BMmPhqrf8NEXWgbWTem0mMUw0n79w5eW3sEd7zYp020z7iFF4/ugff4nwBv7TM26ve+sqCg4YwseKwV394jvskDWL9MZjHspk+fDgBYunSpxr2mLDouLq493Nnd3V1nnMrKSlRWPjpaRr0b1trPfqsPX9bQqDGFPMae79gQXx5PQJa8grl+pW8LtJIZ3o9oG1k35tJjFO3EZ5XghZ9uIturDSv8iQenMPLqXghBmCnUumh77xvEgsda8e094ps8gPXLZBbDTqVSNRypEWnOmzcP0dHRetfKLV++HEuWLNEIv337NhQKBYKDg5GZmYmqqirY2dnB09MTaWlpAAAPDw8QQlDw3xdfUFAQcnJyUFFRAYlEAm9vb6SmpgKoNS6FQiHy/jsPNCAgAPn5+SgvL4etrS38/PyQnJwMAHB1dYVYLEbuf57P/f39UVRUhNLSUtjY2CAwMBBJSbXb+F1cXCCRSJDz3zSBn58f5HI5FAoFRCIRgoODkZSUhLy8PISGhsLe3p45/sTHxwelpaUoKSmBUChESEgIUlJSoFQq4ejoCCcnJ2RmZgKoddhcUVHBGMstWrRAamoqampq4ODgAFdXV6SnpwMAZDIZqqqqUFRUBAAICQlBeno6qqurYW9vD3d3dzx8+BAA4OnpCaVSicLCQgC1C8yzsrJQWVkJqVQKLy8vVn0DQH5+PvLz89GlSxfk5uYy9e3j44OUlBQAgJubG0QiEau+CwoKUFZWBrFYDKfSUvz1xRdQKZVAcDBQVga0aYMcpRIFsbFou24diJsbbGxsEBQUhMTERKa+pVIpc+Scr68vSkpKcDM1D9//9ci1grejGJM7uaKsrIxV32VlZZDL5RAIBAgNDWXVd2FhITPKI5PJUFlZydR3aGgo0tLStNa3l5cXqqurWfWdkZHB9FkPDw9WfatUKlafzc7OZupbJpOx+qxAIEB+fq23/sDAQOTl5TF91tfXV299x8XFwdnZGWKxGAEBAUyfdXV1ha2tbYN9lhACZ2dn2NnZsepboVCw+mxycjJUKhWcnJzg6OjI6rPl5eWs+lbLZg7Mocco2jl8MxNv7LyOMptHIyW2NZV4/u9f0DnEEQLUTsnWP/VB3yYJfbh162YxNxwZGRlo0aKFRfI2B3yTB7B+mcyyK9YczJgxA4cPH8Zff/2FgIAAnfG0jdgFBgbi+vXr6Ny5c3MUtVlITEy06o5Xn6bKczE2Vr/vqX79NL70dUEIwcRNF/F38qMpnbUDZIjpFmzUHwPaRuaHjztBG4s11oVSRbD66D18dfI+K9xTkYtXzn0HP3kmSHQ0BOfOMff6HTgAl//OYW7ovdeFW0QE+uzc2bTCNxIuvkdNgW/yANyUyZj322SbJ7788ktUVFQ0HPE/vv76a5SUlBgUd/bs2Th48CBOnjyp16gDAIlEAmdnZ9YPqB0F4RMyK3T6qI+myGOKxdIVOTkovn0bFTk52HM1nWXUdci4BeGsSTgaGYmLsbGQx8cbVC7aRtaHOfUYhU1xeTVe/vGyhlHXNusu3jr2OfzktSO1qPu+1ZlCNeS910Xh1asGbZ4wB3x7j/gmD2D9MpnMsJs/f75RCu7tt99mpiZ1QQjB7NmzsXfvXpw4cQKhoaGNLl9VVVWjn+UidUcl+UBT5DH0cG9t8eTx8bgYG4ujkZE4M3o09vcbhCU7Hh0pJFZWYfy135jr3DNncG7CBIOMO9pG1oc59BhFk/s5JRi7/hzriD4AGFMah+lnv4Z9dR2/dXUWstc99cGg914XOvRBc8C394hv8gDWL5PJ1tgRQjB48GDY2BiWpCFnt86aNQvbtm3D/v374eTkxKxtcnFxgZ2dnVHlU69t4gvFxcXMGjU+0BR5DFoErWWxtLb1Ob93eBJyoZS5jrnzJzzL2Lvs6vrB0gdtI+vDHHqMwubP21lYsPMGFJU1TJidWITPnumEfpJWOHfmJ/aaOX9/IDlZ49SHJm1+sODmCb69R3yTB7B+mUxm2C1atMio+GPGjNG7uxUANm7cCAAYMGAAK3zz5s2YNm2aUflR+Ishh3trO9+xvhPTFLcgnGsZzVzLSrIx6N4JrenVX8RN4Qfm0GOUWlQqgrXHE7D2eAIrPNDdDt883x1tfWuXzUTv3Kl56oOW3e2GvPe6MOS8VwrFWrGazRONRb3gsKioCC4uLpYujslQn1fJF5oqjyFHCNX9o1CRk4OjkZHMtQoCfD74DaS5BzFhs05/hfCcezrzrLuIWxu0jcyPNW4YMBdcrouSimrM33EDx+5ms8L7tvbEl5O6ws3BVuMZtV86W09P2Hl7a023MbtitemD5oSL71FT4Js8ADdlssjmCa6jdg/BF9QuQ/hCU+VxDgtD9M6d8OrXD1C/kP996WtT4vXX15xrEc0y6rqlXtFr1BkylUPbiEIBHuQqMHb9OQ2j7pW+odg8rYdWow54dOpDrp71Tvre++4bNhisD5oTvr1HfJMHsH6ZzOLHjovU1NQ0HMmKoPJoYsz5jnWNMrnECb93fJK5llZXYNyNvXrzMmQqh7YR5XHn+N1szPv1OkrqrKeTioVYOb4TxnTxNyiNhvqdvvfeNybGLOe9NgW+vUd8kwewfpkeG8POwcHB0kUwKVQe3UhlsgYVeN31Ofs7jUa5rT1zb8TtQ3CpkEMgEoFoOV2g/iJuXdA2ojyuqFQE60/ex6pj91B3sY+/qx02PR+BDv6GL4sxtN/peu8N0QfNCd/eI77JA1i/TI/NVCyf1tcBtd7++YQl5Gm3cCESgzri75BeTJhfUTr63T8DG0dHRKxb16SpHNpGlMcRRWUNZmy9gi+Oso26yBYe+H1OH6OMOoB//Y7Kw32sXSazGHb6HHyqjwdqbjIyMiySr7lQHz/FFywhj12r1jgQM4sVNvHaTvj07YPonTvhGxOD3ps3Y+j58+h34ACGnj+P3ps3G7w+h7aRddOcemzx4sUQCASsX3h4uEnzaA6S8krx1PpzOHKbvZ7uxehQ/PxST7jrWE+nD771OyoP97F2mcxi2HXr1g3Xr1/XCN+9ezc6depkjiwpFKP54a8kPCh65Lj6qVaOeO33bRrGm3oRN5emcyjmp7n1WPv27ZGZmcn8/vrrL5PnYU5Oxudg9Fd/ISHn0Q5VWxshVk3ojA9HtYON6LGZIKJQLIpZ3rQBAwagd+/eWLlyJQCgtLQU06ZNw/PPP4/33nvPHFk2iKenp0XyNRd8OyKtueXJKCrHmmOP/Gm52ovxf5MjTWq80Taybppbj9nY2MDHx4f5WYvOIqR2Pd2Lmy+jpOLRonNfFyl+mx6Jcd30HwPZEHzrd1Qe7mPtMpll88SGDRswcuRIvPzyyzh48CAyMzPh6OiIv//+Gx06dDBHlg1SXV1tkXzNBZWnaXx08A7Kqx9tjHhnWHijpon0QdvIumluPZaQkAA/Pz9IpVJERkZi+fLlCAoKavhBC1JaWYN5m//C0eRSVnh4VS42Du+B0ABXvc8bsmOVb/2OysN9rF0ms+2KHT58OMaNG4eNGzfCxsYGv//+u8WMOoB/R4oVFRXxyuN9c8pzMj4Hh29lMdddAl0xsXugyfOhbWT9NJce69WrF7Zs2YKwsDBkZmZiyZIl6Nu3L27dugUnJyetz1RWVrLOtJTL5SYvlz6S80rx0nfnWcsZAKBfwmmMu7EXccft4aFjo5E8Pr72dIk6p0ZoO10C4F+/o/JwH2uXySyG3YMHD/Dss88iKysLR44cwenTpzF69Gi8/vrr+PjjjyEWi82RLYXSIBXVSizaf5u5FgqAZWM7QCjklpdxiuVpTj02fPhw5v+dOnVCr169EBwcjJ07d+Kll17S+szy5cuxZMkSjfCkpCQ4OTkhODgYmZmZqKqqgp2dHTw9PRnHqx4eHiCEoKCg9hzkoKAg5OTkoKKiAhKJBN7e3khNTQUAuLu7QygUIi8vDwAQEBCAg1cS8eHhJCiqVEy+IqLEhKSjiMz4B4iKRDWAW6tWoeXSpSgtLYWNjQ0CAwNx99IlJH7/PVSpqYCnJ/CfIZdz7Rry330XoW+/DQdfXwQHByMpKQn5+flwdnaGvb09c164j48PSktLUVJSAqFQiJCQEKSkpECpVMLR0RFOTk7MBhdvb29UVFQwH/ctWrRAamoqampq4ODgAFdXV2axvEwmQ1VVFYqKigAAISEhSE9PR3V1Nezt7eHu7s44u/f09IRSqURhYSEAIDg4GFlZWaisrIRUKoWXlxervgEgPz8f+fn5CAwMRG5uLlPfPj4+SElJAQC4ublBJBKx6rugoABlZWUQi8Xw9/dHcnIygNrdm7a2tsjJyQEA+Pv7o6ioiKnvoKAgJCYmAqj1DCGVSpGdXbuxxdfXFyUlJVAoFBCJRAgODkZycjJUKhWcnJzg4ODAqu+ysjLI5XIIBAKEhoYy9V1SUoKKigpmg6JMJkNlZSVT36GhoUhLS9Na315eXqiurmbVd0ZGBtNnPTw8WPWtUqlYfTY7O5upb5lMxuqzAoEA+fn5AIDAwEDk5eWhvLwctra28PX11VvfcrkciYmJEIvFCAgIQFJSktb69vPzg1wuZ9VhUlISCCFwdnaGnZ0dq74VCgWrz9atb0dHR1afLS8vZ9W3WjZDMMuRYk5OThg5ciS+/vprZtvw+fPn8cILL8DJyQnXrl0zdZY6UR/DUVhYaPVbmOuiUqkgFPJnMXJzybP66D3WWZXTokKweLTuY8GaAm0j82POY7Qsrcd69OiBIUOGYPny5VrvaxuxCwwMNOuRYoQQbDj1AJ//Gc9yZeJaVoSXzn+HkMJ6f3wEAgw9f541zXoxNlb/uc79+qH35s3MNRf7XVOg8nAfLspk8SPFNmzYgF9//ZVlSEVFReHatWvo1q2bObJsEEu5WTEXfHPf0hzyJOWVYuPpB8y1l5MEC55oY7b8aBtZN5bUYwqFAg8ePICvr6/OOBKJBM7OzqyfWctUWYOZW6/isyNso65l7n28dewzTaMOAAhhHd9XkZOj16gDgNyzZ1Hx34gIwL9+R+XhPtYuk1mmYp9//nmt4U5OTvj+++/NkWWDVFVVNRzJiqDyGAchBIsO3EZVzaOpow9GtoWz1HzLAmgbWTfNqcfefPNNjBo1CsHBwcjIyMCiRYsgEokwefJkk+bTWJLySvHqT/+wXJkAj9bTiYhK+4P1zlSuf0azVv4zBtWjfHzrd1Qe7mPtMpnFsPvpp5903hMIBDoVpjmxs7Nr9jzNCZXHOA7fysKZe4/+qES19MDozn5mzZO2kXXTnHrs4cOHmDx5MvLz8+Hl5YU+ffrg4sWLnHC7cDI+B3O3X2O5MrG1EeLjsR0QuP435Ooy6qB5prLEEHnqGYN863dUHu5j7TKZZY2dm5sb67q6uhplZWWwtbWFvb09s/ixOVDPS+fm5lqNXyhDqKqqgq2tad1zWJKSjAyoCgvNclC3orIGQ744jSx57UkCYpEAh1/vh1YyR5PmUx++tREX5THnGjsu6TFDMHVd6FpP5+sixdfPRaBzoCvk8fE4N2ECahQKjedtHB21Hr9n7Bo7Lva7pkDl4T5clMnia+wKCwtZP4VCgfj4ePTp0wfbt283R5YNYu1HhNRHvVPI2pHHx+NibCxOLlyIM6NH42hkJC7GxkIeH4+KnBwU377NWm/TGL48nsAYdQDwar8WZjfqAP60kRq+ydMQXNRjzYWisgYzftFcT9cz1B0HZvdB50BXAIBzWBiid+406kzldgsXwsZR+/tn4+iIdgsXssL41u+oPNzH2mUymx+7+rRu3RorVqzAc889h7i4uObKlsJhWF/70dFMeO6ZMzhz7hyI8pEDYV0+rhoiPqsE3/+VxFz7u9ph9sDWTS885bHkcdBjibkKvPrzFdyvt55uWlQI3h/ZFuJ6R4M5h4Wh9+bNBjkbVseP3rmz1o/d2bMAIbXGYN++jXrHKRQKm2Yz7IDaI3MstdtE7UeIL/BhWvnOihWPpnD+87Wkpq5RB9Qae+euXtU5CqANQgg+2HcTStWjIYfFo9vDzlbUtIIbCB/aqC58k6exWFKPmZvjd7Mx79frKKnUXE/3TANOvKUymcHLKIwxBvnW76g83MfaZTKLYXfgwAHWNSEEmZmZ+OqrrxBdZ2SmOVGpdC/wtUasXR4Ntweiho2tGoUCd1asYK2/0cfuq+m4nFzIXA9pK8PQdt5Gl7WxWHsb1Ydv8jQEF/WYuVCpCNaduI/Vx+6xwv1cpPj6+Qh0auBosMZiiDHIt35H5eE+1i6TWQy7sWPHsq4FAgG8vLwwaNAgfPHFF+bIskEKCws5f+6iMRQUFFi1w2UNtwfBwYAB6xrUPq4a+mNQXFaN5YfuMtdSsRCLRpnHEbEurL2N6sM3eRqCi3rMHJRUVGPBzhs4eiebFd4r1B3rp3SDp6PEQiWrhW/9jsrDfaxdJrMYdtZu7VLMj0FuD7RRz8eVLj77Mw75pY98Ec0Z1BqB7vaNy5PyWPI46LH7OQq8+vM/SMwtZYW/GB2Kd0eEa6yno1Ao3OexeWsDA01/yLslsfbRR6lMVruTTs3ly4Y9WM/HlTZOxedg66VHXvBbeDng5b6hjSlmk7D2NqoP3+R53PnzdhbGrj/HMuokNkKsntgZH45qp9OoM9VudUPhW7+j8nAfa5fJZCN2CxYsMDjuqlWrTJWtweTk5MDd3b3Z8zUX2dnZ8Pf3t1j+hu6A00e7hQtx7urV2g0UbdsCN240+Ex9h6f1+f1GBhbsvM5y0fDRmA6Q2DTPhom6WLqNTA3f5NEG1/WYKVCpCNYcT8CXdc5MBmp3jG96PgId/F20PiePj6/dyVpnbWxjd6sbA9/6HZWH+1i7TCYz7Aw9EFug9nXUzNQ9LJsPWEoeUyr3um4PcupMewlEIo1dsYB2H1d1+fliCj7cf4tl1D0TEYDoVpbZ4UT7nPXBdT3WVIrLq7Fgx3Ucj2OPtkW38sC6yd3g7qDdKasuR8SN2a1uLHzrd1Qe7mPtMpnMsFu7di3at28PkQG7Gy2BVCq1dBFMirnk0TcS11Tlri1ttduD5Lt34aZSQeLlharCQqN8XBFSu6Nv1VH2jr7x3QKwfFzHxlZFk6F9zvrguh5rKrfSi3Einm3UvdqvBd6OCYONnvV0LNdE9TB2t7qx8K3fUXm4j7XLZLIjxUQiEbKysuDl5YUWLVrg8uXLnPAdpz6GIz8/n1dTsTU1NbCxMd3eF0NG4ow9CsiYtLXJY8h0r0pFsPTgHWw5n8wKf7lPKN4b0RZCoeVGVkzdRpaGi/KY+hgtruoxQzC0LtafvI/PjsRDKhbi06c7N3hmckVODo5GRurPXCDA0PPnTX4cIMDNftcUqDzch4syWeRIMVdXVyT+52Q2OTmZczvK0tLSLF0Ek5KamtpwJANRj8TVN9pyz5zBuQkTmOO99Bl1wCNXJMamDWiXRyqTwaV9e51/LKqVKizYeV3DqHt7WBjeH2lZow4wbRtxAb7Jow2u6zFTMHNAS0yLCsGeGdENGnWAFtdE2vhvt7o54Fu/o/JwH2uXyWQm6fjx49G/f3/4+vpCIBCge/fuOqczEuudMsBVtI0Y1Q9TX1cWFqK6oACOrVrBpV07nekA/ylKgQAgRGfa6ni6/l+jZQ2aIeWtG1dtVCVs3NjgNEvbN99suMIIgTw+npWPuaZwyquUmL75Ak4nFTNhQgGweHAIXhjQyuj0KBSAn3qsPgKBAItHs3066hsdN8g1kQG71SkUSvNgMsPum2++wbhx43D//n3MnTsXr7zyCpycnEyS9vr16/HZZ58hKysLnTt3xrp169CzZ0+j0nBzczM4rrapQ7eICABA4ZUrTJjYzQ3VhYUaz4vd3ND544/hEBKikY7WsmlJuyGIvz8KWrZkNhMYUl6vfv0QPGkSHnz/vVF55Z49i7ZvvWVQ3EvTpsEtIgKdPvoIlQUFBo/yGTNN/vDfO3jxh4u4J310ioSNsgYvXPoJbjuv42Iz7NQzBD5N/QP8k0cb5tRjXMSQZRJq10R6l2E0sFu9KfCt31F5uI+1y2SyNXZ1iY2NxZdffmkShbhjxw688MIL+Prrr9GrVy+sWbMGu3btQnx8PGQGKBL1vHRqaqpBvux0bRBoDEKpFKqKiianow3i6wtBZiZE9vYAIVCWl5slHzX9DhzA3c8/b9BQUyOUSgFCoDJgd1G/AweAgAC4uGh3s1CXB9du4/lvLyDD2ZcJs62pxCvnvkV4zqPNEzaOjmbdqWcIxcXFBslkLXBRHlOvsauLKfVYc2BsXejTdfXfH2Pimhou9rumQOXhPlyUyZj32yyGnSnp1asXevToga+++gpArTf4wMBAzJkzBwv1uL5Qo66M69evo3PnzjrjHb2TjQsP8pH5558oT2/4aCuL4+cHNNdB5AIBgidOhLKiAhn/+x9U1dUmT1tRVW3Qi3Tw9E3k2Dgy1w6VCkw/+zVCCjXXROjazNFcJCYmokWLFhbL39RwUR5zGnbWhrF1YexmKGZ0z8Dd6qaCi/2uKVB5uA8XZTLm/ebWto96VFVV4cqVK3j33XeZMKFQiCFDhuDChQtan6msrGT5oJHL5QbldTm5AD+cSwIcWgNtWjet4M1Fm2Ycjbr63zmSoX3MlzYKGo5bx6hzLSvErDMb4FOSrTWqoefKUiiPG8ZshqrvmsgUzskpFIr54LRhl5eXB6VSCW9vb1a4t7c34uLitD6zfPlyLFmyRCNcpVIhMTERwcHByMzMRFVVFezs7ODp6Ym0tDQUFRWZQwSKmZApcjAz40+4d2oFUu4PXL8OqF0yPHwIlJUBbdogMSEBLV1cUFRUhNLSUtjY2CAoKIhZ+O7i4gKpVIrs7Frj0NfXFyUlJVAoFBCJRAgODmZ2Rzo5OcHBwQFZWVkAAB8fH5SVlUEul0MgECA0NBQpKSlQKpVwdHSEl5cXk49MJkNlZSWKi2s3e4SGhiItLQ01NTVwcHCAq6sr0tPTAQBeXl6orq5m+mRISAgyMjKYPuvh4YGHD2tHlT09PaFSqVBQUGsUBwUFITs7G5WVlZBKpZDJZMwOL3d3dwgEAuTn5wOoPWYvLy8P5eXlsLW1ha+vL1JSUgDUrkkViUTIy8sDAAQEBMDW1haJiYkQi8UICAhAUlISgNqdpLa2tsj5b0e0n58f5HI5qw6TkpJACIGzszPs7OxY9a1QKFBSUgKhUIiQkBBWfTs6OiIzMxNA7XtfXl7Oqm9r371mKYzZ6VrfeJPKZM1q0PHtOEgqD/exdpk4PRWbkZEBf39/nD9/HpF1/Ci9/fbbOH36NC5duqTxjLYRu8DAQMTHx6NNmzY681p99B5+Pp+EKi2bITiJjQ1QU2PWLIS2YojsHSCstyuQqFQmqSexszOEYjGAWsNbKNTvfYeoVKguLEBIfjIm//MrnKoaWAdpRt9ahpCZmQlfX9+GI1oJXJSHTsU+wpi6sLRvOmPgYr9rClQe7sNFmXgzFevp6QmRSMR83avJzs6Gj4+P1mckEgkkEolGeHkDmwvmD22D+UPbNLjuhCuQ6GgIzp0zeboevXuj/XvvNTjN0tR68urXD73XPFq/Y+iahouxscg9b1i+5typZwgN9Tlrg2/yPM5YeqerMfCt31F5uI+1y2QyB8XmwNbWFhERETh+/DgTplKpcPz4cdYInqFpGUK7hQth4+jYcEQDEJrzWJLSUgCAyN4eIjs7kyQpsrdHhw8/1OsUWE27hQv15iuUSnXe13bmqzHtI7K3bzBeQ+fKNgeGymQt8E2exx19uo4L748avvU7Kg/3sXaZOG3YAcCCBQvw7bff4scff8Tdu3cxY8YMlJaWIjY21qh0dI3w1Ud9ML1Xv361ToQBQCCAW0RErW+4Ood/i3X4xhO7uaH7hg3ou2cPOx1t6Ei7QW7fhle/fujz22/os3t3w+UVCODVrx+6b9jA+Liri1tEBPr89pvBu9ucw8LQZ/dunWn13bNHa7m8+vXT6hrB0GFv57Aw9PntN635NpRHc8O1ofymwjd5Hnd06TquvD9q+NbvqDzcx9pl4vQaOzVfffUV46C4S5cu+PLLL9GrVy+DnjXU3Yk2uHzyRLZSiTadOhld3rpx1SdPOIeFNWnKpaG0DNlF15jt5XXzlXh4aNSnpeHilvmmwEV56Bq7RzSlLri805WL/a4pUHm4Dxdl4pUfu6ZSXFwMV1dX/PXXX+jYsaOli2MykpKSEBoaaulimAy+yQPwTyYuyqPeHFVUVMQ5h6LNjVrXpaWl8crI5WK/awpUHu7DRZmM0XWc3jxhCkpKSgAAffqYwf8ahULhBCUlJY+9YVfXjQ2FQuEnhug63o/YqVQqxMfHo127drz5klVb7lQe7sI3mbgqDyEEJSUl8PPza9BdDt8pKiqCm5sbUlNTeWPkcrXfNRYqD/fhqkzG6Drej9gJhUL4+/sDAJydnTnVUE2FysN9+CYTF+XhixHTVNTK3sXFhXNt1FS42O+aApWH+3BRJkN13eP9iUuhUCgUCoXCI6hhR6FQKBQKhcITHgvDTiKRYNGiRVpPpLBGqDzch28y8U0ePsLHNuKbTFQe7sMHmXi/eYJCoVAoFArlceGxGLGjUCgUCoVCeRyghh2FQqFQKBQKT6CGHYVCoVAoFApPoIYdhUKhUCgUCk/gjWG3fv16hISEQCqVolevXvj777/1xt+1axfCw8MhlUrRsWNHHDp0qJlKqp/ly5ejR48ecHJygkwmw9ixYxH/30H3utiyZQsEAgHrJ5VKm6nE+lm8eLFG2cLDw/U+w9W2URMSEqIhk0AgwKxZs7TG51r7nDlzBqNGjYKfnx8EAgH27dvHuk8IwYcffghfX1/Y2dlhyJAhSEhIaDBdY99BivHwRc8BVNcB3G4fa9dzwOOr63hh2O3YsQMLFizAokWLcPXqVXTu3BkxMTHIycnRGv/8+fOYPHkyXnrpJVy7dg1jx47F2LFjcevWrWYuuSanT5/GrFmzcPHiRRw9ehTV1dV44oknUFpaqvc5Z2dnZGZmMr+UlJRmKnHDtG/fnlW2v/76S2dcLreNmsuXL7PkOXr0KADgmWee0fkMl9qntLQUnTt3xvr167Xe//TTT/Hll1/i66+/xqVLl+Dg4ICYmBhUVFToTNPYd5BiPHzScwDVdVxvH2vXc8BjrOsID+jZsyeZNWsWc61UKomfnx9Zvny51vgTJkwgI0eOZIX16tWLvPbaa2YtZ2PIyckhAMjp06d1xtm8eTNxcXFpvkIZwaJFi0jnzp0Njm9NbaPm9ddfJy1btiQqlUrrfS63DwCyd+9e5lqlUhEfHx/y2WefMWFFRUVEIpGQ7du360zH2HeQYjx81nOEUF1HCLfbx5r1HCGPl66z+hG7qqoqXLlyBUOGDGHChEIhhgwZggsXLmh95sKFC6z4ABATE6MzviUpLi4GALi7u+uNp1AoEBwcjMDAQIwZMwa3b99ujuIZREJCAvz8/NCiRQtMmTIFqampOuNaU9sAtf3vl19+wYsvvgiBQKAzHpfbpy5JSUnIyspitYGLiwt69eqlsw0a8w5SjIPveg6gug7gbvvwTc8B/NZ1Vm/Y5eXlQalUwtvbmxXu7e2NrKwsrc9kZWUZFd9SqFQqzJs3D9HR0ejQoYPOeGFhYfjhhx+wf/9+/PLLL1CpVIiKisLDhw+bsbTa6dWrF7Zs2YI//vgDGzduRFJSEvr27YuSkhKt8a2lbdTs27cPRUVFmDZtms44XG6f+qjr2Zg2aMw7SDEOPus5gOo6NVxtH77pOYDfus7G0gWg6GbWrFm4deuW3nUaABAZGYnIyEjmOioqCm3btsWmTZvw0UcfmbuYehk+fDjz/06dOqFXr14IDg7Gzp078dJLL1mwZKbh+++/x/Dhw+Hn56czDpfbh0LhAlTXcRuq56wLqx+x8/T0hEgkQnZ2Nis8OzsbPj4+Wp/x8fExKr4lmD17Ng4ePIiTJ08iICDAqGfFYjG6du2K+/fvm6l0jcfV1RVt2rTRWTZraBs1KSkpOHbsGF5++WWjnuNy+6jr2Zg2aMw7SDEOvuo5gOq6unCxffio5wB+6zqrN+xsbW0RERGB48ePM2EqlQrHjx9nfT3UJTIykhUfAI4ePaozfnNCCMHs2bOxd+9enDhxAqGhoUanoVQqcfPmTfj6+pqhhE1DoVDgwYMHOsvG5bapz+bNmyGTyTBy5EijnuNy+4SGhsLHx4fVBnK5HJcuXdLZBo15BynGwTc9B1Bdx/X2UcNHPQfwXNdZeveGKfj111+JRCIhW7ZsIXfu3CGvvvoqcXV1JVlZWYQQQp5//nmycOFCJv65c+eIjY0N+fzzz8ndu3fJokWLiFgsJjdv3rSUCAwzZswgLi4u5NSpUyQzM5P5lZWVMXHqy7NkyRJy5MgR8uDBA3LlyhUyadIkIpVKye3bty0hAos33niDnDp1iiQlJZFz586RIUOGEE9PT5KTk0MIsa62qYtSqSRBQUHknXfe0bjH9fYpKSkh165dI9euXSMAyKpVq8i1a9dISkoKIYSQFStWEFdXV7J//37y77//kjFjxpDQ0FBSXl7OpDFo0CCybt065rqhd5DSdPik5wihuo7r7UOIdes5Qh5fXccLw44QQtatW0eCgoKIra0t6dmzJ7l48SJzr3///mTq1Kms+Dt37iRt2rQhtra2pH379uR///tfM5dYOwC0/jZv3szEqS/PvHnzGNm9vb3JiBEjyNWrV5u/8FqYOHEi8fX1Jba2tsTf359MnDiR3L9/n7lvTW1TlyNHjhAAJD4+XuMe19vn5MmTWvuYuswqlYr83//9H/H29iYSiYQMHjxYQ87g4GCyaNEiVpi+d5BiGvii5wihuo4QbrcPIdat5wh5fHWdgBBCmmt0kEKhUCgUCoViPqx+jR2FQqFQKBQKpRZq2FEoFAqFQqHwBGrYUSgUCoVCofAEathRKBQKhUKh8ARq2FEoFAqFQqHwBGrYUSgUCoVCofAEathRKBQKhUKh8ARq2JmBkJAQrFmzxtLFaBSnTp2CQCBAUVGRpYtiUgQCAQQCAVxdXS1dFJ0IBALs27fP0sUwmsWLF6NLly5640ybNo1pA2uUkaIdquu4B9V15sNadB017HjKli1bOP1iG4KpZdi8eTPu3btnsvSag/Lycjg4OBh8kPbixYsZpWJjYwNPT0/069cPa9asQWVlJRNv4cKFCA8PZz0bFxcHgUCAadOmscK3bNkCiUSC8vLyRpdr7dq1yMzMNCguhWIMVNdpQnXd463rqGFHaXaqqqoskq+rqytkMplF8m4sR48eRXBwMFq1amXwM+3bt0dmZiZSU1Nx8uRJPPPMM1i+fDmioqJQUlICABg4cCDi4+ORlZXFPHfy5EkEBgbi1KlTrPROnjyJ3r17w87OrtHlcnFxgY+Pj8EyUCh8gOo6w6G6znRQw85ISkpKMGXKFDg4OMDX1xerV6/GgAEDMG/ePJ3PFBUV4bXXXoO3tzekUik6dOiAgwcPao2bnJwMgUCA69evs54XCARMJ1RPIfzvf/9Dp06dIJVK0bt3b9y6dYu5Hxsbi+LiYuaLZvHixQCAn3/+Gd27d4eTkxN8fHzw7LPPIicnR2fZy8rKMHz4cERHRzNTFt999x3atm0LqVSK8PBwbNiwQW+dDRgwALNnz8a8efPg6emJmJgYAMCqVavQsWNHODg4IDAwEDNnzoRCoWhQhsrKSrz55pvw9/eHg4MDevXqpfGCGoJ6WP2HH35AUFAQHB0dMXPmTCiVSnz66afw8fGBTCbDxx9/zHpOIBBg06ZNePLJJ2Fvb4+2bdviwoULuH//PgYMGAAHBwdERUXhwYMHrOc2btyIli1bwtbWFmFhYfj5558bLOP+/fsxevRo5nrZsmWQyWRwcnLCyy+/jIULF2pMDdjY2MDHxwd+fn7o2LEj5syZg9OnT+PWrVtYuXIlAKBPnz4Qi8Wsejt16hRmzZqFgoICJCcns8IHDhyot1xAbd8KCQmBi4sLJk2axChWinVCdR3VdVTXWamus/RhtdbGyy+/TIKDg8mxY8fIzZs3yVNPPUWcnJzI66+/zsQJDg4mq1evJoQQolQqSe/evUn79u3Jn3/+SR48eEB+//13cujQIa3pJyUlEQDk2rVrTFhhYSEBQE6ePEkIeXSwcdu2bcmff/5J/v33X/Lkk0+SkJAQUlVVRSorK8maNWuIs7MzyczMJJmZmaSkpIQQQsj3339PDh06RB48eEAuXLhAIiMjyfDhw5m81GkXFhaSwsJCEhUVRZ544glSWlpKCCHkl19+Ib6+vmT37t0kMTGR7N69m7i7u5MtW7borLP+/fsTR0dH8tZbb5G4uDgSFxdHCCFk9erV5MSJEyQpKYkcP36chIWFkRkzZhBCiF4ZXn75ZRIVFUXOnDlD7t+/Tz777DMikUjIvXv3dJYBANm7dy8rbNGiRcTR0ZE8/fTT5Pbt2+TAgQPE1taWxMTEkDlz5pC4uDjyww8/EACsQ54BEH9/f7Jjxw4SHx9Pxo4dS0JCQsigQYPIH3/8Qe7cuUN69+5Nhg0bxjyzZ88eIhaLyfr160l8fDz54osviEgkIidOnNBZRqVSSWQyGTl//jxT91KplPzwww8kPj6eLFmyhDg7O5POnTuzZKp7XZcxY8aQtm3bMtdRUVHk1VdfZa5lMhm5fPkyGTZsGPnhhx8IIYQ8ePCAACCnTp3SWS51PY4bN47cvHmTnDlzhvj4+JD33nvPoHagcBOq66iuo7rOOnUdNeyMQC6XE7FYTHbt2sWEFRUVEXt7e53K7siRI0QoFJL4+HiD8jBG2f36669MnPz8fGJnZ0d27NhBCCFk8+bNxMXFpcH8Ll++TAAwikSd9t27d0mnTp3I+PHjSWVlJRO/ZcuWZNu2baw0PvroIxIZGakzj/79+5OuXbs2WJZdu3YRDw8P5lqbDCkpKUQkEpH09HRW+ODBg8m7776rM21dys7e3p7I5XImLCYmhoSEhBClUsmEhYWFkeXLl7PS+uCDD5jrCxcuEADk+++/Z8K2b99OpFIpcx0VFUVeeeUVVv7PPPMMGTFihM4ynjt3jshkMqYsvXr1IrNmzWKlER0dbbCye+edd4idnR1z/f7775M2bdoQQgi5ffs2cXZ2JjU1NeSTTz4hL7zwAiGk9o+jVColFRUVOsulrR7feust0qtXL40yUMPOOqC6juo6dVpU11mfrqNTsUaQmJiI6upq9OzZkwlzcXFBWFiYzmeuX7+OgIAAtGnTxuTliYyMZP7v7u6OsLAw3L17V+8zV65cwahRoxAUFAQnJyf0798fAJCamsqKN3ToULRq1Qo7duyAra0tAKC0tBQPHjzASy+9BEdHR+a3bNkyjaH4+kRERGiEHTt2DIMHD4a/vz+cnJzw/PPPIz8/H2VlZTrTuXnzJpRKJdq0acMqw+nTpxssgzZCQkLg5OTEXHt7e6Ndu3YQCoWssPpTOJ06dWLdB4COHTuywioqKiCXywEAd+/eRXR0NCuN6Ohove21f/9+PPnkk0xZ4uPjWX0PgMa1PgghEAgEzPWAAQNw7949ZGZm4tSpU+jTpw9EIhH69+/PmgqLioqCRCLRWS5Asx59fX31TntRuA3VdVTXqaG6zvp0nY2lC8B36i7CNAR1B6o1+Guprq42SVlKS0sRExODmJgYbN26FV5eXkhNTUVMTIzGIt+RI0di9+7duHPnDvMSq9eEfPvtt+jVqxcrvkgk0pu3g4MD6zo5ORlPPvkkZsyYgY8//hju7u7466+/8NJLL6Gqqgr29vZa01EoFBCJRLhy5YpGno6Ojg1XQj3EYjHrWiAQaA1TqVQ6n1MrEG1h9Z8zhgMHDmDFihWNfr4+d+/eRWhoKHMdHR0NW1tbnDx5EidPnmT+8PXo0QN5eXlITEzEqVOn8NprrzVYLkPqjMJvqK6rheo646G6zrTQETsjaNGiBcRiMS5fvsyEFRcX691W3qlTJzx8+NDgredeXl4AwNouXXdxcV0uXrzI/L+wsBD37t1D27ZtAQC2trZQKpWs+HFxccjPz8eKFSvQt29fhIeH6/zSWLFiBaZOnYrBgwfjzp07AGq/zPz8/JCYmIhWrVqxfnVfIkO4cuUKVCoVvvjiC/Tu3Rtt2rRBRkYGK442Gbp27QqlUomcnByNMlh6J5I+2rZti3PnzrHCzp07h3bt2mmNn5CQgJSUFAwdOpQJCwsLY/U9ABrXuoiLi8Mff/yB8ePHM2F2dnbMYuzTp09jwIABAGoVV+/evfH9998jLS2NtZhYW7ko/IPqOqrrGgvVdZaHjtgZgZOTE6ZOnYq33noL7u7ukMlkWLRoEYRCIWvYty79+/dHv379MH78eKxatQqtWrVifOgMGzZMI76dnR169+6NFStWIDQ0FDk5Ofjggw+0pr106VJ4eHjA29sb77//Pjw9PTF27FgAtcPFCoUCx48fR+fOnWFvb4+goCDY2tpi3bp1mD59Om7duoWPPvpIp7yff/45lEolBg0ahFOnTiE8PBxLlizB3Llz4eLigmHDhqGyshL//PMPCgsLsWDBAoPrslWrVqiursa6deswatQonDt3Dl9//TUrjjYZ2rRpgylTpuCFF17AF198ga5duyI3NxfHjx9Hp06dMHLkSIPL0Jy89dZbmDBhArp27YohQ4bg999/x549e3Ds2DGt8ffv348hQ4awvubnzJmDV155Bd27d0dUVBR27NiBf//9Fy1atGA9W1NTg6ysLKhUKuTn5+PUqVNYtmwZunTpgrfeeosVd+DAgVi9ejUAoFu3bkx4//798fnnn8PBwQE9evTQWy4K/6C6juq6xkJ1HQewyMo+K0Yul5Nnn32W2NvbEx8fH7Jq1SrSs2dPsnDhQiZO3QXFhNQu9o2NjSUeHh5EKpWSDh06kIMHD+rM486dOyQyMpLY2dmRLl26kD///FPrguLff/+dtG/fntja2pKePXuSGzdusNKZPn068fDwIADIokWLCCGEbNu2jYSEhBCJREIiIyPJgQMHWAuY6+4UUzNnzhzi6+vLLIreunUr6dKlC7G1tSVubm6kX79+ZM+ePTrl6d+/P2vBtZpVq1YRX19fYmdnR2JiYshPP/2kkbc2GaqqqsiHH35IQkJCiFgsJr6+vuSpp54i//77r84yQMeC4vqLb6dOnUrGjBmjt/z109K2CFxbPW7YsIG0aNGCiMVi0qZNG/LTTz/pLGOfPn3It99+qyHH0qVLiaenJ3F0dCQvvvgimTt3LunduzdLJgAEABGJRMTd3Z306dOHrF69mrUouH456+5qI4SQU6dOEQAkJiaGFa6tXNrqcfXq1SQ4OFgjP23tQOEmVNdRXUd1nXXqOmrYNRGFQkFcXFzId99912x5anuRKPqxJoMiNzeX2NjYkKysrAbjDhkyhDz33HPNUCrjyqULa2oHChuq66wDa3rHqK4zD3Qq1kiuXbuGuLg49OzZE8XFxVi6dCkAYMyYMRYuGaUhJk+eDA8PDzx8+NDSRdFLQUEBVq1axexAU1NWVoavv/4aMTExEIlE2L59O44dO4ajR49atFyGMH36dPzyyy9mKBXFXFBdZ71QXWeechkCF3Sd4D/LkmIg165dw8svv4z4+HjY2toiIiKC8SreXKi9YxcWFlr9GYnNhfqcP5FIZPTiZ65QXl6OUaNG4dq1a6ioqEBYWBg++OADjBs3ztJFa5CcnBzGHYKvr6/GzkEK96C6zjqhus6ycEHXUcOOQqFQKBQKhSdQdycUCoVCoVAoPIEadhQKhUKhUCg8gRp2FAqFQqFQKDyBGnYUCoVCoVAoPIEadhQKhUKhUCg8gRp2FAqFQqFQKDyBGnYUCoVCoVAoPIEadhQKhUKhUCg8gRp2FAqFQqFQKDyBGnYUCoVCoVAoPIEadhQKhUKhUCg8gRp2FAqFQqFQKDyBGnYUCoVCoVAoPIEadhQKhUKhUCg8gRp2FAqFQqFQKDyBGnYUCoVCoVgBW7ZsgUAgQHJyMhM2YMAADBgwwGJlonAPathRTIpa8ej6Xbx4EUePHoVAIMCSJUs0nk9KSoK9vT2efvppk5ctLS0NS5YsQc+ePeHm5gZPT08MGDAAx44dM3leFArX0PZuymQyDBw4EIcPH2bFFQgEmD17tkYa+fn5eOuttxAWFgapVAp3d3fExMTg4MGDGnGTk5OZfJYtW6a1TFOmTIFAIICjo6POcvfs2RMCgQAbN27UGefmzZt4+umnERwcDKlUCn9/fwwdOhTr1q1jxauqqsLatWvRtWtXODs7w9XVFe3bt8err76KuLg4nelrQ5+emz59ulFpUSxLUlISZs+ejTZt2sDe3h729vZo164dZs2ahX///Vfnc2+//TYEAgEmTpyo9b6p3gFjsTFZShRKHZYuXYrQ0FCN8FatWsHT0xPPPvssli9fjsmTJ6NNmzbM/ZkzZ0IsFuPLL780eZn279+PlStXYuzYsZg6dSpqamrw008/YejQofjhhx8QGxtr8jwpFK6hfjcJIcjOzsaWLVswYsQI/P7773jyySd1PhcfH4/BgwcjNzcXsbGx6N69O4qKirB161aMGjUKb775Jj777DON56RSKbZv344PPviAFV5aWor9+/dDKpXqzDMhIQGXL19GSEgItm7dihkzZmjEOX/+PAYOHIigoCC88sor8PHxQVpaGi5evIi1a9dizpw5TNzx48fj8OHDmDx5Ml555RVUV1cjLi4OBw8eRFRUFMLDww2pQoahQ4fihRde0Aivq9PMzZ9//tlsefGRgwcPYuLEibCxscGUKVPQuXNnCIVCxMXFYc+ePdi4cSOSkpIQHBzMeo4Qgu3btyMkJAS///47SkpK4OTkpDWPprwDjYJQKCZk8+bNBAC5fPmy3njZ2dnEzc2NDBw4kAnbvn07AUC+/PJLs5Tt1q1bJDc3lxVWUVFBwsPDSUBAgFnypFC4gq53s6CggIjFYvLss88yYQDIrFmzmOuqqirSoUMHYm9vTy5evMh6vqamhkycOJEAIL/++isTnpSURACQcePGEQDk+vXrrOe2bt1KxGIxGTVqFHFwcNBa5g8//JDIZDKye/duIhAISFJSkkacESNGEC8vL1JYWKhxLzs7m/n/33//TQCQjz/+WCNeTU0NycvL01oGXdSvo+ZA3Yba6oFLKBQKSxfBIO7fv08cHBxI27ZtSUZGhsb96upqsnbtWpKamqpx78SJEwQAOXHiBBGLxWTLli0acUzxDjQGOhVLsQgymQwrV67EyZMn8eOPP6KoqAjz589Hjx49MGvWLKPTS0xMxDPPPAN3d3fY29ujd+/e+N///seK0759e3h6erLCJBIJRowYgYcPH6KkpMSoPCsrK7Fo0SK0atUKEokEgYGBePvtt1FZWWl0+SkUS+Hq6go7OzvY2OiewNm9ezdu3bqFhQsXolevXqx7IpEImzZtgqurKxYvXqzxbGRkJEJDQ7Ft2zZW+NatWzFs2DC4u7vrzHfbtm14+umn8eSTT8LFxUUjDQB48OAB2rdvD1dXV417MpmMFQ8AoqOjNeKJRCJ4eHjoLEdTuXTpEkaMGAE3Nzc4ODigU6dOWLt2LSvOiRMn0LdvXzg4OMDV1RVjxozB3bt3G0y7sWvsDh8+jP79+8PJyQnOzs7o0aOHRv3u2rULERERsLOzg6enJ5577jmkp6ez4kybNg2Ojo548OABRowYAScnJ0yZMgUAoFKpsGbNGrRv3x5SqRTe3t547bXXUFhYaHA5//33XwgEAhw4cIAJu3LlCgQCAbp168aKO3z4cI3+qY9PP/0UpaWl2Lx5M3x9fTXu29jYYO7cuQgMDNS4t3XrVrRr1w4DBw7EkCFDsHXrVp35NOUdaAzUsKOYheLiYuTl5bF++fn5rDgvv/wyoqOj8eabb2LmzJnIzc3Fpk2bIBQa1y2zs7MRFRWFI0eOYObMmfj4449RUVGB0aNHY+/evQ0+n5WVxayrMBSVSoXRo0fj888/x6hRo7Bu3TqMHTsWq1ev1rnegkLhAup3Mzc3F7dv38aMGTOgUCjw3HPP6Xzm999/BwCt044A4OLigjFjxiAuLg7379/XuD958mT8+uuvIIQAAPLy8vDnn3/i2Wef1ZnnpUuXcP/+fUyePBm2trYYN26c1j+ewcHBuHLlCm7duqVXbvVU2tatW1FTU6M3rqFUVFRo6Lm8vDxUVVUxcY4ePYp+/frhzp07eP311/HFF19g4MCBrHWJx44dQ0xMDHJycrB48WIsWLAA58+fR3R0NGujhKnYsmULRo4ciYKCArz77rtYsWIFunTpgj/++IMVZ8KECRCJRFi+fDleeeUV7NmzB3369EFRURErvZqaGsTExEAmk+Hzzz/H+PHjAQCvvfYa3nrrLURHR2Pt2rWIjY3F1q1bERMTg+rqaoPK2qFDB7i6uuLMmTNM2NmzZyEUCnHjxg3I5XIAtTr5/Pnz6Nevn8H1cPDgQbRq1cooYxCo/ajfvXs3Jk+eDKC2f584cQJZWVk6n2nMO9BoTDb2R6GQR1MF2n4SiUQj/q1bt4hYLCYAyLx58xqV57x58wgAcvbsWSaspKSEhIaGkpCQEKJUKnU+m5CQQKRSKXn++eeNyvPnn38mQqGQlSchhHz99dcEADl37pxxQlAoZkbXuymRSDSmkVBvmrFLly7ExcVFb/qrVq0iAMiBAwcIIY+moT777DNy69Yt1ju6fv164ujoSEpLS8nUqVO1TkPNnj2bBAYGEpVKRQgh5M8//yQAyLVr11jx/vzzTyISiYhIJCKRkZHk7bffJkeOHCFVVVWseCqVivTv358AIN7e3mTy5Mlk/fr1JCUlxaD6q48uPQeAbN++nRBSO8UbGhpKgoODNaaK1XIRUlu/MpmM5OfnM2E3btwgQqGQvPDCC0yYtqnY/v37k/79+xtc7qKiIuLk5ER69epFysvLtZapqqqKyGQy0qFDB1acgwcPEgDkww8/ZMKmTp1KAJCFCxey0jp79iwBQLZu3coK/+OPP7SG62PkyJGkZ8+ezPW4cePIuHHjiEgkIocPHyaEEHL16lUCgOzfv9+gNIuLiwkAMnbsWI17hYWFJDc3l/mVlZWx7v/2228EAElISCCEECKXy4lUKiWrV69mxWvqO9BY6IgdxSysX78eR48eZf3q77wDAGdnZ9ja2gIAnnjiiUbldejQIfTs2RN9+vRhwhwdHfHqq68iOTkZd+7c0fpcWVkZnnnmGdjZ2WHFihVG5blr1y60bdsW4eHhrC/1QYMGAQBOnjzZKFkoFHNT99385ZdfMHDgQLz88svYs2ePzmf0LQxXo76vHkGpS/v27dGpUyds374dQO0U65gxY3SOktfU1GDHjh2YOHEiBAIBAGDQoEGQyWQao3ZDhw7FhQsXMHr0aNy4cQOffvopYmJi4O/vz5q+EwgEOHLkCJYtWwY3Nzds374ds2bNQnBwMCZOnKgxCmUIY8aM0dBzR48excCBAwEA165dQ1JSEubNm6cxVayWKzMzE9evX8e0adNYU3KdOnXC0KFDcejQIaPLpY+jR4+ipKQECxcu1Fi0ry7TP//8g5ycHMycOZMVZ+TIkQgPD9dY5gJAY2PLrl274OLigqFDh7J0ZEREBBwdHY3SkX379sXVq1dRWloKAPjrr78wYsQIdOnSBWfPngVQO4onEAhYfwf0oe6n2najDhgwAF5eXsxv/fr1rPtbt25F9+7d0apVKwC1fX/kyJF6p2ONfQeaAt0VSzELPXv2RPfu3RuMN3v2bAiFQgQHB+ONN97AkCFDIBaLjcorJSVF61B627ZtmfsdOnRg3VMqlZg0aRLu3LmDw4cPw8/Pz6g8ExIScPfuXXh5eWm9n5OTY1R6FEpzUf/dnDx5Mrp27YrZs2fjySefZD606uLk5IS8vDy96arXqOoyAJ999ll88cUXmD9/Ps6fP4/33ntPZ1p//vkncnNz0bNnT9bU7sCBA7F9+3asXLmStWSjR48e2LNnD6qqqnDjxg3s3bsXq1evxtNPP43r16+jXbt2AGrX1L7//vt4//33kZmZidOnT2Pt2rXYuXMnxGIxfvnlF70y1icgIABDhgzReV+9rq++/qlLSkoKACAsLEzjXtu2bXHkyBGUlpbCwcHBqLKZq0zh4eH466+/WGE2NjYICAhghSUkJKC4uJi1zrEuxujIvn37oqamBhcuXEBgYCBycnLQt29f3L59m2XYtWvXzuD1aup+qlAoNO5t2rQJJSUlyM7O1liiUFRUhEOHDmH27NmsvhkdHY3du3fj3r17OndFG/MONAVq2FEsxp49e3DgwAGsWbMGrVu3xsiRI/HZZ5+ZrbPX5ZVXXsHBgwexdetWZpTNGFQqFTp27IhVq1Zpva9tsS2FwkWEQiEGDhyItWvXIiEhAe3bt9eI07ZtW1y/fh2pqakICgrSmo7a35faiKrP5MmT8e677+KVV16Bh4eH3hF69cjHhAkTtN4/ffo0MypWF1tbW/To0QM9evRAmzZtEBsbi127dmHRokUacX19fTFp0iSMHz8e7du3x86dO7Flyxa9m0go2pFIJBpro1UqldYRVjW6Poq10b17d0ilUpw5cwZBQUGQyWRo06YN+vbtiw0bNqCyshJnz57FU089ZXCaLi4u8PX11bo2Uz1QoG19465du1BZWYkvvvgCX3zxhcb9rVu3avXRChj3DjQF2oMpFqGkpARz585Ft27dMHv2bIhEIowfPx7Lli3D5MmTtfrA00VwcDDi4+M1wtUOR+v7H3rrrbewefNmrFmzhln8aiwtW7bEjRs3MHjwYGb6gkKxVtSbCbSNXgDAk08+ie3bt+Onn37S8MUF1E5r7d+/H+Hh4cz0VH2CgoIQHR2NU6dOYcaMGToNKLVvr4kTJ2p1VD537lxs3bpVq2FXF/WoZGZmpt54YrEYnTp1QkJCAvLy8uDj46M3vjG0bNkSAHDr1i2dI3tq/aRLh3l6eppstK5+mXS1Vd0y1f/wjY+P19CpuvI5duwYoqOjYWdn16Qy29raomfPnjh79iyCgoLQt29fALUjeZWVldi6dSuys7ON2jgB1E4tf/fdd/j777/Rs2dPg57ZunUrOnTooPVjYdOmTdi2bZtOw87Qd6DJmGy1HoVCDPdjN3fuXCIUClnxHj58SBwdHcmIESOMylO9eeL8+fNMmEKhIC1atNDYPPHpp58SAOS9994zKo/6bNmyhQAgmzZt0rhXVlZmNX6cKI8Put7Nqqoq0rp1a2Jra0uKi4sJIZqbJyorK0m7du2Ig4ODxvNKpZI8++yzrE0DhLAXjqs5deoUWbRoEblz5w4TVn/h+M8//0wAkDNnzmiV45VXXiGurq6koqKCEFLrT6zuRgQ1K1euJADIqlWrCCGE3Lt3T+tGicLCQuLn50fc3NxITU2N1jy1Ub+OtKFUKg3ePOHt7c2Kc/PmTbNsniguLiZOTk6kZ8+eDW6e6NSpE1PPhBBy6NAhrZsntC38P3XqFAFA3n33XY171dXVWv0O6uP9998ndnZ2JDAwkKxZs4YJb9u2LWnTpg0BQNLS0oxK8969e8Te3p60b9+eZGVladxPTExk9eHU1FQiEAjI0qVLtaa3detWAoDx9djYd6Cp0BE7ilk4fPiw1iN6oqKiUFhYiPXr12PWrFmstT7+/v5YunQpFixYgN27dzNb5hti4cKF2L59O4YPH465c+fC3d0dP/74I5KSkrB7925mimDv3r14++230bp1a7Rt21ZjPc3QoUPh7e1tUJ7PP/88du7cienTp+PkyZOIjo6GUqlEXFwcdu7ciSNHjhi0xpBCaW7qvps5OTnYtm0bEhISsHDhQjg7O2t9xtbWFr/99hsGDx6MPn36sE6e2LZtG65evYo33ngDkyZN0pt3//790b9/f71xtm7dCg8PD0RFRWm9P3r0aHz77bf43//+h3HjxmHOnDkoKyvDU089hfDwcFRVVeH8+fPYsWMHQkJCmBNlbty4gWeffRbDhw9H37594e7ujvT0dPz444/IyMjAmjVrIBKJGqo+Fvfu3dO6Ls/b2xtDhw6FUCjExo0bMWrUKHTp0gWxsbHw9fVFXFwcbt++jSNHjgAAPvvsMwwfPhyRkZF46aWXUF5ejnXr1sHFxUWrb8Cm4OzsjNWrV+Pll19Gjx498Oyzz8LNzQ03btxAWVkZfvzxR4jFYqxcuRKxsbHo378/Jk+ejOzsbKxduxYhISGYP39+g/n0798fr732GpYvX47r16/jiSeegFgsRkJCAnbt2oW1a9cadXRk37598fHHHyMtLY0ZsQOAfv36YdOmTQgJCdFY59cQrVu3xrZt2zB58mSEhYUxJ08QQpCUlIRt27ZBKBQy6W7btg2EEIwePVpreiNGjICNjQ22bt2q04WKIe9AkzGZiUihEP3uTgCQ7777jnTr1o34+fkxowN1qampIV26dCEBAQGkpKTE4HwfPHhAnn76aeLq6kqkUinp2bMnOXjwICvOokWL9Jbt5MmTRslaVVVFVq5cSdq3b08kEglxc3MjERERZMmSJVplo1AsibZ3UyqVki5dupCNGzeyRpCgYzQqJyeHLFiwgLRq1YpIJBLi6upKhgwZwrg4qYu20Qpt1B2tyM7OJjY2NnrdD5WVlRF7e3vy1FNPEUIIOXz4MHnxxRdJeHg4cXR0JLa2tqRVq1Zkzpw5rJMnsrOzyYoVK0j//v2Jr68vsbGxIW5ubmTQoEHkt99+0195WtCnS+qPoP31119k6NChxMnJiTg4OJBOnTqRdevWseIcO3aMREdHEzs7O+Ls7ExGjRrFGtUhxDQjdmoOHDhAoqKimPx69uzJGnElhJAdO3aQrl27EolEQtzd3cmUKVPIw4cPWXEaGm365ptvSEREBLGzsyNOTk6kY8eO5O2339Z60oM+5HI5EYlExMnJiTWy+ssvvxAARrusqsv9+/fJjBkzSKtWrYhUKiV2dnYkPDycTJ8+nXVaRMeOHUlQUJDetAYMGEBkMhmprq5u1DtgCgSE/Octj0KhUCgUCoVi1VA/dhQKhUKhUCg8ga6xo3CW8vJyFBcX643j7u6u1e9WY6mqqkJBQYHeOC4uLk3e5UWhULiHUqlEbm6u3jiOjo5andpygdzcXCiVSp33bW1tTX4uaVNQKBQ6d2Kr8fLyMnrto7nStRpMNqlLofx/e+cdH0Xx/vHP3SWXS3LpvSeABEJJIJAQegmGIuVrARtEFFGKgtjwp1IUARURC4iFIiICigIqgoAEpENCEAgJJY303nMpd/P7I9xxm9xd7pK73O5m3q9XXi9udnb3+ezOPjy7M/OMkWltvB7aMC6uNY4fP97qObdu3WrUc1IoFHagHBOl62/ZsmXmNlMrAQEBBo39MzetjXtGs/GE5j4uV6Bj7CisJTc3F9evX9dZJzw8HE5OTkY7Z2lpKeLj43XW6dWrF7y8vIx2TgqFwg5kMlmLVRWa06VLF3Tp0qWDLDKM06dPo7a2Vut2JycnhIeHd6BFuklNTUVqaqrOOkOHDm2x9Jm5jssVaGBHoVAoFAqFwhPo5AkKhUKhUCgUnsD7yRMKhQI5OTmws7OjSz9RKDyDEILKykp4e3u3WKuys0F9HYXCXwzxdbwP7HJycuiC7BQKz7l7967BWef5BvV1FAr/0cfX8T6ws7OzA9C0nExgYKB5jTEixcXFcHFxMbcZRoNvegD+aWKjnoqKCvj5+ame886M8hrcvXtX69JgXISN7a49UD3sh42aDPF1vA/slF0S7u7uvHJ2FhYWsLGxMbcZRoNvegD+aeooPbKCAtQVFsLKzQ0Sd3e99qFdj/evgb29PfV1LIbqYT9c93WdZlBKfn6+uU0wKnl5eeY2wajwTQ/AP02m1lORkoJzs2bhSFQUTk6ejCNRUTg3axYqUlJMel4Ku6HPEbvhmx6A+76u0wR2fMOYqy1QKOamIiUFp6dNQ+HJk4zywpMncXraNBrcdWKor6PwiY7wdZ0msPPw8DC3CUaFbwPFvb29zW2C0fH09DS3CUbFlHqS1qxBo5YlgBqrqpC0Zo3Jzk1hN9TXsRu++TmA+76u0wR2NTU15jaBogMrKytzm2B0qqurzW2CUTGVHllBQYu31+YU/vsvZAUFJjk/hdKR8M3X8c3PAdz3dZ0msKusrDS3CRQd8HHwO9/anKn01LWy6DoAgBD96lEoLIdvvo5vfg7gvq/rNIFdZ09eSul4+NbmTKXHys2t9UoCgX71KBRKh8I3Pwdw39fx745oISAgwNwmUDoZfMqbCJhOj8TdHW7Dh+us4zZsmN7pACgUSsfBNz8HcN/XdZrALjMz02jHkhUUoPz6daOP+QkMDERwcDBCQ0PRrVs3TJkyBWfOnGl1v3379uHcuXNGtYXSfjIyMsxtglExpZ6QJUtgIZVq3GYhlSJkyRKTndtUbNiwAYGBgZBIJIiMjMSFCxd01l+/fj2Cg4NhbW0NPz8/vPLKK5DJZB1krWaor6O0Bt/8HMB9X9dpAju5XN7uY3REnq3du3fjypUruH37NmJjYzFhwgScP39e5z7U2bETY7Q5NmFKPfbBwRiyZ0/T26xyDJJAALfhwzFkzx7YBweb7NymYPfu3Vi8eDGWLVuGhIQEhIaGIiYmBgVaAqSdO3diyZIlWLZsGW7cuIHNmzdj9+7d+L//+78OtrwJ6uso+sI3Pwdw39cJCCGktUr9+/c37KACAQ4cOAAfH582G2YsKioq4ODggNu3b6Nr165tP8693DOapilbSKVGuSGBgYHYt28fwsLCVGVLlizBnTt38OKLL+Kdd96BTCZDfX09Fi9ejOeeew4HDx7E008/DWtra7i5uWHBggV46KGH8MQTT6CiogIymQyjRo3C559/zsuxEGymoKAA7jzqPuwoPYZkY1c+3+Xl5QavtmBKvxYZGYmBAwfiyy+/BAAoFAr4+fnhpZdewhINb+QLFizAjRs3cOzYMVXZq6++ivPnz+PUqVN62deea8E4DvV1FAPgm58DuO/r9FpSLDExEa+++iqkWj4fqkMIwZo1a1BXV6fPoTsMfWzXhT65ZwZt3dquc2giMjISBw4cQP/+/XHq1CmIRCKUlJSgX79+iImJwYQJEzB58mSEhYVh0aJFAACZTIbff/8dUqkUcrkcU6ZMwZ49e/D4448b3T6Kdvi2fmlH6ZG4u3fIeDpT+bX6+nrEx8fjrbfeUpUJhUJER0fj7NmzGvcZPHgwduzYgQsXLiAiIgKpqak4ePAgZsyYob8gI0F9HcUQ+ObnAO77Or3Xin399df1jmA/+eSTNhtkKvLy8tqcpNiQ3DPGvknKD6rFxcV47rnncPPmTVhYWKC4uBjXrl3TmLxToVDgzTffxKlTp0AIQUFBAXr37k2dXQeTm5uLLl26mNsMo8E3PYBp/FpRURHkcnkLf+Ph4YHk5GSN+zz55JMoKirC0KFDQQhBY2MjXnzxRZ1dsXV1dYxAs6KiQi/7dEF9HcVQ+OgXuK5Jr8AuLS0NbgZMv01KSuJVdm1Dcs8Y29ldvHgRvXv3xosvvogJEyZg7969EAgE6N+/v9aB1evWrUNBQQHOnz8PiUSCxYsXm30QNoXCNtjk1+Li4rBq1Sps3LgRkZGRuH37NhYuXIj3338f7777rsZ9Vq9ejRUrVrQoT0tLg52dHQICAlBUVGTQCy31dR1DQ0MDCgsLIZPJYGVlBU9PT9WAfScnJ4hEIhQVFQFoWnmjpKQENTU1sLS0hI+PD9LT0wEAjo6OEIvFqrGbPj4+KCsrQ3V1NSwsLODv74/U1FQAgIODAyQSiWrddC8vL1RWVqKqqgoikQgBAQFIT0+HQqGAnZ0dbG1tVWumenp6oqamBhUVFRAIBAgKCkJGRgbkcjkqKyshk8mQk5MDAHB3d0ddXR3Ky8sBAEFBQbh79y4aGxtha2sLR0dHZGdnAwDc3NzQ0NCAsrIyAE1d9Dk5Oaivr4e1tTVcXFyQlZUFAHB1dYVCoUBJSQkAwN/fH/n5+airq4NEIoG7u7tqkqSzszMEAgGKi4sBAH5+figqKkJtbS3EYjG8vLx0Xu+KigqkpqbC0tISvr6+SEtL03i9vb29UVFRwbiGaWlpIITA3t4e1tbWjOtdVVWFyspKCIVCBAYGMq63VCpFbm4ugKaXwNraWsb1NmQCqF6BnaGpQvz8/Ayq3xG0p7/cXHm29u/fj6+++gqHDx/GvHnzEBAQAIFAgJMnT+LKlSuqevb29qqHCABKS0vh6ekJiUSCvLw8/Pzzz3jkkUeMahuldfi2jB3f9JjKr7m6ukIkEqkcupL8/HytSxW9++67mDFjBmbPng0A6NOnD6qrqzFnzhy8/fbbGseMvfXWW1i8eLHqd0VFBfz8/BAUFKQag2PoPaO+rmOwtLRs8ZLQ/AuR+jiq5u2meV314QTN73nzuuq/ra2tGf83Nk/zoV7XxsYGrq6uqt/K56e6uhoSiYRRVyqVwsXFRfXb399fp03Ozs6qfzf/Mtu8rqOjo+rfzce7Nq/r4OCg+reXl5fOuurX+4EHHoCtra3WuurXWxlUKgkKCtJ6HuX4UCW6rretrS3jeje/hrrQuytWnbKyMly4cAEFBQVQKBSMbTNnzmzLIU1Oe8b8KXPP6OqiMFaerenTp0MikaC6uhohISE4ePAgIiMjsWbNGsybNw/vv/8+wsLCEBkZqdpnxowZeOaZZ7Bv3z7Mnz8fCxcuxKOPPopevXrB29sb0dHR7baLYjgymYzhHLgO3/Q0x1h+TSwWIzw8HMeOHcPUqVMBNHUZHjt2DAsWLNC4T01NTYvgTSQSAbjfRdkcKysroy9PRX0dxVD46Be4rkmvWbHq/P7773jqqadQVVUFe3t7xvIoAoFA9ZmULShnkiQmJiI0NLTtx+mAmWIUwzBkRpE5SE1N5fQ4jeawUY+xZoIa26/t3r0bsbGx+PrrrxEREYH169djz549SE5OhoeHB2bOnAkfHx+sXr0aALB8+XKsW7cO33zzjaordu7cuQgPD8fu3bv1OieXZsVS+AMb/UJ7YaMmo8+KVefVV1/Fs88+i1WrVsHGxqbNRnINZe6ZpDVrUPjvvwAhTblnhg1DyJIl1NF1IBUpKU33Qe2rgtvw4fQ+UNqMsf3a9OnTUVhYiKVLlyIvLw9hYWE4dOiQqpssMzOT8YXunXfegUAgwDvvvIPs7Gy4ublh0qRJ+OCDD9pti6FQX0ehcBuDv9jZ2tri6tWrrItmtWGst1h12P6liM/QrwkUdYz1fHPNr2mC+joKhb8Y8nwbnMUxJiYGly5darNx5uLu3btGO5bE3R0OvXpRR2cG9MmxxRaMuYwdG+CbHnW46tdMDfV1lNbgil8oT0pC9oEDKE9KarUuVzRpQ6+u2AMHDqj+PXHiRLz++utISkpCnz59YGlpyag7efJk41poJBobG81tAqWdmDPHVlvgW5vjmx4++DUKxdyw3S/kHj6MK2+/jYbSUlWZpZMTQj/4AF4xMRr3Ybum1tArsFPO7FLnvffea1EmEAhYu24cl2e4UJowZ46ttsC3Nsc3PXzwaxSKuWGzX8g9fBiX5s1rUd5QWopL8+ZhwMaNGoM7NmvSB726YhUKhV5/bHZ+6vlsKNzEXDm22op6viU+wDc9fPBrFIq5YbNfuPL2223azmZN+qD3GLuZM2di7969qK6uNqU9JkOZFZvt/PrrrwgPD0dYWBh69OiB0aNHQ6FQYMKECUhJSTHquUaOHIl9+/a1Wm/btm2MpZAOHDiAV155xai26IMyx5YujJVjyxgos6vzBb7pAbjv17gK9XP8ga1+oTwpidH9qomG0lKNY+7Yqklf9E530q1bN6xatQpPP/00Ro4cicmTJ2Py5MktMj9T2k5ubi7mzJmD+Ph4VVbvhIQECAQCHDx40Gx2bdu2DY6OjujRowcAqO69OQhZsgSnExK0zooNWbLEDFZRuAr1ax0P9XOUjqDq9m296zmEhJjYmo5F78Bu6dKlWLp0KbKysnDgwAHs27cPr7zyCnr16oUpU6Zg8uTJCAsLM6Gp7UPXmpAVsgak5FWa3IZgTzvYSyy1bs/Pz4dIJGIsr9K/f38ATUuP7Nu3D2FhYRg5ciTCw8Nx8eJFpKenIzY2FlFRUVi1ahWysrLw8ssvq5YaUt8PAAYMGIC1a9di5MiRjHPv3LkTn332Gerr66FQKLBy5UpMmjQJ3333HS5duoRXXnkFy5cvx6pVq1BQUIB9+/ap3oI//vhjbNu2DUKhEH379sXGjRvh4OCA5cuX48aNG6ipqcGdO3fg6emJX375haHPULiUY6s9y9ixEb7pAbjv1wyFDb6O+jl+wVa/IO3Wrc312KpJXwxOUOzr64t58+Zh3rx5qKysxF9//YX9+/dj9OjRsLOzw6RJkzB37lz06tXLFPa2mfr6eq3bUvIq8dimsya34ecXozAwUPvD3rdvXwwdOhQBAQEYMWIEBg8ejCeffFLj14OMjAwcP34cFRUVCAwMRGlpKf7991/k5OQgODgYzz77rEHjBGJiYvDEE09AIBAgPT0dgwYNQkZGBmbPno0dO3Zg0aJFqsHm27ZtU+33119/YcuWLTh79iwcHR0xZ84cLFmyBF999RUA4Pz584iPj4eLiwsef/xxfP3113jrrbf0tksT9sHBGLR1K+tzbOlqc1yEb3rU4apfMxQ2+Drq5/iFqfxCe/27Q0gILJ2cdHbHWjo5afxax3VfZ3AeO3Xs7Owwbdo0/PjjjygsLMSWLVsgEolw9qzpHYehqC8czVaEQiH27t2LM2fOYNy4cTh9+jR69eqF2xo+KT/66KMQiURwcnJCly5d8NBDD0EgEMDHxwdubm5IT0836NxpaWkYP348evfujalTp6KkpARpaWmt7nf06FFMnz5d5Vznzp2LI0eOqLaPGzdOtRh0VFQU7ty5Y5BdumB7jq2ysjJzm2BU+KZHG1zya1yE+jl+YWy/UJGSgnOzZuFIVBROTp6MI1FRODdrFiraMPYytJWVW7Rt57qv0/uL3bJlyzBmzBgMGjQIYrG4xXaRSIQxY8ZgzJgxRjWwM9KjRw/06NEDL7zwAsaNG8fIt6VEIpGo/i0SiVr8VubhsbCwYMzqk8lkGs/5+OOPY82aNXj00UcBAM7Ozlrr6kJ9jU1NdnZEfiC2f8mjsAfq18wH9XOU5mhbWajw5EmcTkgweGUhr5gYDNi40eA8dlxH78Bu+/bteP/99yGRSBAVFYVRo0Zh1KhRiIyMhIWFwT26HY5ykK4mgj3t8POLUSa3IdjTTuf27OxspKenY8iQIQCA0tJSpKWloWvXrm0+Z7du3XD+/HmEh4fjwoULWmeclZaWIigoCACwY8cOlKo9BPb29lq/eEZHR+PVV1/F4sWLYW9vj6+//hoPPvhgm+1tD2xbQzYwMLDDz2lK+KYH4L5fMxQ2+Drq5/iFMf2CPisLDdq61aBjesXEwCsmBuVJSai6fRvSbt1anSzBdV+nt+dKS0tDeno6jh8/jri4OHz33XdYunQpbG1tMWTIEJVDjIiIMKW9bSYnJ0frWAx7iaXOsW8dRWNjI9577z2kpaXBxsYGjY2NiI2NxZQpU7Bw4cI2HXPlypWIjY3F119/jaioKK1jhD777DM8+uijcHR0xOjRo+Hv76/aNmfOHLz66qv49NNPsWrVKsZ+48ePx7Vr1xAVFcUYVNzRGPtNzxhkZ2fDz8+vQ89pSvimB+C+XzMUNvg66uf4hbH8gqlXFnIICdF79ivXfZ2AEELaunNaWprKIe7fvx/V1dWs+wStXDg3MTERoaGh5jaHYiLOzZql0ym4DR9u8Jtee0lNTeX0ovLNYaMeUyx8zwW/pglTXAsKpTWM5RfKr1/HST3Syww/cAAOJp7ExHVf1+bJExkZGTh58iROnDiBkydPoqGhAcNbSR7bnJMnT2LSpEnw9vaGQCBokUTymWeegUAgYPyNGzeuTfba2Ni0aT8K+zHkTa8j4Vub45seTRjDr1EonQkbGxvICgpQfv16u3xsR60spI+tXPd1enfFZmZmIi4uTvUmW1RUhMGDB2PEiBF4/vnnERERoXHwsS6qq6sRGhqKZ599Fg8//LDGOuPGjcNWtS8tVlZWBp1DiZOTU5v2o7Aftq4hy7c8VnzTA5jGr1EonYWKlBSkfvIJio8dU5W1dVyzcmUhnT0v7VhZyJAx2Fz3dXoHdoGBgfD398fcuXMxd+5chIeHQyQStevk48ePx/jx43XWsbKygqenZ7vOAzT1mbu6urb7OBT2wdY1ZLOyslj3Ob898E0PYBq/RqF0BpTjmhtCQ6E+R7g945pNtbJQwfUbOP7MbDRWVwNW9yf2VJy/jIynn0XEt99A2vV+ouKMjAz06dEVVhbc9AV6B3bTpk3DiRMn8OGHH+L06dMYMWIERo0ahX79+rWY+m1M4uLi4O7uDicnJ4wePRorV65U5QvSRF1dHerq6lS/KyoqTGYbhR2Y+k2Pwl/M5dcoFK5jihms7V1ZqELWgNsFVbidX4XbhVVN/y6owt3iapAx/6d9x13pANIZRT/OdsaQbtz8GGTw5Ink5GRVt8WJEycgk8kwdOhQjBgxAiNHjsTAgQPbZohAgN9++02V9RsAdu3aBRsbGwQFBeHOnTv4v//7P0ilUpw9e1brW/Xy5cuxYsWKFuXnz5+Hq6srAgICkJeXR9eCZCENDQ0oLCyETCZTfanNyMgA0NSVLhKJUFRUBKBppYCSkhLU1NTA0tISdtXVOPXJJ1DU1QFZWUBNDdC9OwDA4vZt9PziCxAnJ1hYWMDf3x+pqakAAAcHB0gkEuTn5wMAvLy8UFlZiaqqKohEIgQEBCA9PR0KhQJ2dnawtbVFXl4eAMDT0xM1NTWoqKiAQCBAUFAQMjIyIJfLIZVKIRAIUFnZtHyTu7s76urqVOkUgoKCcPfuXTQ2NsLW1haOjo6qhafd3NzQ0NCgSpIZGBiInJwc1NfXw9raGi4uLsjKygIAuLq6QqFQoKSkBADg7++P/Px81NXVQSKRwN3dHZmZmQCauhcEAgGKi4sBAH5+figqKkJtbS3EYjG8vLx0Xm/lOS0tLeHr66tK7Oro6AixWIyCe2NWvL29UVFRwbiGaWlpIITA3t4e1tbWjOtdVVWFyspKCIVCBAYGMq63VCpFbm4uAMDDwwO1tbWM633t2jX06dOn3RMGTOXXOhI6eYLSUcgKCnAkqiltDvHwgODe88xAIMDYM2fa/ELdWj7S7LJaHE8uwK38StwurMKt/CoUVNZpOFLb+HF2JKsCO0Oe73bNigWApKQk7Ny5E1988UW7Zo9pCuyak5qaiq5du+Lo0aNaE4Zq+mLn5+eH9PR0nbnsKOaloaEBlpba19HVB9UYCpasIVtaWsqrsZ1s1GOqYMZYfq0joYEdNzCGrzM36jNYia8vBPde+ppj7BmstfVyHL6eh1/is3D6ThHaF73ohsuBXZsycObn5yMuLk416PjmzZuwsrLCsGHD2mSwvnTp0gWurq64ffu21sDOyspK4wSLsrIyGtixmNzcXEZOqbbAtjVk2RgItQe+6WmOufwapXNhDF9nbhjjlQMCmnpJmmOkcc2EEMRnlOKX+Cz88V8uqur0e8kSWwjRxdUWXRwsQX7dAaeaMu2VBUCv//s/WNo1BUyFhYXo5i5tt+3mQu/Abs+ePSqnl5KSAktLSwwcOBDTpk3DqFGjMHjw4DbPWNWXrKwsFBcXw8vLy6Tn4QJZWVnw9fU1txlGw5hfRCTu7nQ8HUUv2ODXKLqhvs74tPfltyPGNWeX1eK3hCz8Ep+F9OIarfVsxSJ087BDNzcpurk3/T3gLoWfsw1EwqZxsudOfoPCk+e02zp8OAaNvv9lMTW1AR72Eq312Y7eXbFisRgDBgxQZWIfMmQIrK2t23Xyqqoq1cLP/fr1w7p16zBq1Cg4OzvD2dkZK1aswCOPPAJPT0/cuXMHb7zxBiorK3H16lW9na3y82VJSQmvvjbI5XJezd7jmx6Af5rYqKe93Y+m8Gvmgq9dsWxsd+3BnHqMuexi7uHDiH/pJSiEQggaGhjbLKRSDNmzB2InJ4MCSH27WqVWFpgU6oVHw33R39+p1YlO2lYmUrdVXT8b25xJxthVV1fD1tbWKAYqiYuLw6hRo1qUx8bG4quvvsLUqVNx+fJllJWVwdvbGw8++CDef/99eHh46H0O5cW4ceMGevToYUzzzUp2djavJoDwTQ/AP01s1NPeYMYUfk3Jhg0b8PHHHyMvLw+hoaH44osvdC5NVlZWhrfffhu//vorSkpKEBAQgPXr12PChAl6nY+vgR0b2117MJceQ4MbfY9FQkMhuHJFtU0gEiHkrbdQcPKkXgGkvl2tAgEwtJsrHg33xYMhnrAWGxZ4GTIGm41tziRj7JTOr7y8HEeOHEF6erpqZlp0dHSbHMnIkSOhK648fPiwwcfUhvqECj5A9bAfvmnimx7ANH4NAHbv3o3Fixdj06ZNiIyMxPr16xETE4OUlBS4a/hyUV9fj7Fjx8Ld3R2//PILfHx8kJGRoXV9684E39qdufQYMz0J41hS5lg0IpcjafVqELmcUd48v11OWS1+TcjC3oRspBVVaz1XkKstHg33xf/6+cDbse1f0w0Zg831NmfQ5IkdO3ZgwYIFLXLDOTg4YNOmTZg+fbpRjTMmEgl3+8s1QfWwH75p4pseJabwa+vWrcPzzz+PWbNmAQA2bdqEP//8E1u2bMESDUlWt2zZgpKSEpw5c0Y1YzIwMNBwMTyEb+3OHHoMWXaxtS7TFsfSkCu2eVCnpKa2Dt+s/xFXBkzEqdvG62o1BH3GYHO9zekd2CUkJGDWrFl46qmn8Morr6BHjx4ghCApKQnr16/HjBkz0KNHD4SGhprS3jbDt1Un3Dp4FQVTY0w9dY1ynEgpxJ3CaggFgFAggFAogEgACIUCCAUCiIQC1TbRvbKmOvfKlf8WNqujtl0oaErToyq753yE9/ZrkEtQllMBAqJyYIRA9ZugqRuC3CsHo5y5jaBpA9FxDNwrVyhaHrv5sRT3ygxBLpejm6IMfX0d23+TWIIp/Fp9fT3i4+Px1ltvqcqEQiGio6Nx9uxZjfscOHAAUVFRmD9/Pvbv3w83Nzc8+eSTePPNN7WO9eksydipr2s/xlx2scWxbt7UfVgAaS5BOB8YgQS//pBZWgO3ilrUa29XqzHhepvTO7D74osvMHXqVGzbto1R3r9/f2zfvh01NTX47LPPsGXLFmPbaBSysrJ0rljBNe7evcur5Z3aq4cQgmvZFfgl/i72X8lBWU1D6ztRDCY2KoBXgZ0p/FpRURHkcnmLscAeHh5ITk7WuE9qair++ecfPPXUUzh48CBu376NefPmoaGhAcuWLdO4z+rVqzUmY09LS4OdnR0CAgKQm5urSmzt6uqKu3fvAgBcXFxACGEkti4oKFAlB/fw8GAkthYKhYxk1cXFxarE1t7e3khPTwfQlKza0tIShff+8/fx8UFZWRmqq6thYWEBPz8/VWJrBwcHWFlZtZrYuqioCEFBQbCxsWEkB6+urmYktlZPDm5nZ8dIbC2TyVTJwbt06YLMzEyNycHd3d1RX1/PSA6enZ2NhoYG2NjYwNnZmZEcXC6Xo7S0FABUye+VycHd3NwY1xsAiouLUVxcjLCwsDYnY/fx8WFcb/Xk4M2vtzIZe6NcDhIU1PR1TTnW/No1wMMDcHMDGhshuHABBQoFilNTdSZjl9/7GkciIgBLSxB3d+DoUaBv36bjpqQAUilK/brhok0gzgs8UWinPVj0d5JgbDc7jH3AARG9H0BOTg5yszLMmow9KSkJ9vb2rErGrtSmD3pPnujevTs2btyI6OhojduPHj2KefPm4WYr0XtHoxxwmJiYyNqviW0hNTWVV4FdW/UUVtZh3+Vs/BKfhZT8ShNYRlEnNioAK6b0NrcZKto7YcAUfi0nJwc+Pj44c+YMou5l5weAN954AydOnMD58+c12iGTyZCWlqb6Qrdu3Tp8/PHHKmffHG3J2Pk2eYL6OuNwbtYs3elJhg/Xe4yd+rHIkCEQnD4NAKgXWeI/7744HxiJFI/uIAKhxv1txUJMCvXBYwOM39VqDNjY5kwyeSInJwfd7y3RpInu3bur3nrYiLOzs7lNMCp8+voIGKanvlGBf5Lz8Ut8Fo6nFEKuMGH6cQqvMYVfc3V1hUgkUr2pK8nPz4enp6fGfby8vGBpacnodu3Zsyfy8vJQX18PsVjcYh9tydj5Rmf2dcYkZMkSnE5I0DorNkTD2E99jkXS0lp2tWpAQBR4oOAWRiMfC79eZdau1tbgepvTO7CrqanROaDQysoKMpnMKEZRKJrIr5Dhq7g72J+YjVIdXa0DA53wWLgfxvXxhI2lCHLSNO5MQci9fzeNMZMrCBSk6U+u0F1HvS4hgIKg2W+iKiP3zldTUwOp1AYCCFCTnYUbq1ZBUVsLABAQAoBAAEAkkaDPsqWw9fOHQAAI0DR27/6/AUDA3HavXHCvHPd+C1X7CbQc697YQLX99KWyshJervzJBQmYxq+JxWKEh4fj2LFjqiUSFQoFjh07hgULFmjcZ8iQIdi5cycUCgWEwqavHDdv3oSXl5fGoI5CMRT74GAM2bPHKMsu2gcHI2T7T9j0zQEctfBBobX2DydulQWITL+AgRkX4VxbikE7drA6qOMDBs2KPXz4MBwcHDRuU45JYCslJSXw8/MztxlGo7i4WOu94CKt6YnPKMULP1xCUVW9xu3eDhI8Eu6LR/r7ItCVmZesTevmGYGmz/lN46zOffgmgtMva60r/f5zvbtBzEVqWR6cbPXPIckVTOHXFi9ejNjYWAwYMAARERFYv349qqurVbNkZ86cCR8fH6xevRoAMHfuXHz55ZdYuHAhXnrpJdy6dQurVq3Cyy+/3Kbz84nO5utMiTGWXbyRW4Ftp9OxLzEbdXZ9NNaRNMjQ724CBqWfR1BxGpTvkE7h4XBTG57AVrje5gz6Py82Nlbndrb1k1P4wb7L2Xhj73+ob1Qwyq0shBjf2xOPhvthcFcXCIXsbH/GTDVAMT6m8GvTp09HYWEhli5diry8PISFheHQoUOqCRWZmZmqL3NA0+Duw4cP45VXXkHfvn3h4+ODhQsX4s033zT43BRKaxi67KJcQXAkKR/bzqThXGqJxjoCARDpZY2ef21D7zsXIJa3XI2i7/vvt8tuin7oPXmCqygHHBYVFXG+31ydhoYGVb4rNtDetQc16VEoCD49ehNf/HObUd7dQ4pZQ4Iwsa8X7CXsuQbNUWoqv34dJydPbrX+8AMH4NCrV6v1zAXb2hzA39UW2gJfrwUb21174JKe8poG7L6Uie/PZCC7rFZjHV9HCR6P8Mf/+vvCx9HaoBUe2Aob75FJJk9wHb4FdoWFhfD29ja3GUZbe7C5npr6Rry65wr+upbHqDehjyc+eSyME2M0lJqs9MmJJBDoV8+MsKXNUToXfGt3XNBzK78S286k49eEbNQ2aE42PLirC54ZHIieDnL4+d5ffssY3b3mhgv3SBcGB3YHDhzQWC4QCCCRSNCtWzcEBQW12zBjw7eJHWzQo23tweZLx+iDup68chlmb7+Ia9nMhKsvj3kAi8Y8wNou1+YoNUnc3eE2fLjuVAPDhrHe+bGhzZkKrvq1zgDf2h1b9RBCcPJWETafSsPJm5oTGltZCPFwfx/EDg5ED8+mr0apqaka6xra3csm2HqP9MXgwG7q1KkQCAQtstYrywQCAYYOHYp9+/bByYk9M+j4lhaADXqMufagUs9/WWWY/f0lFFTez88lthDi40f7YkoYuxZlbg31e2TMVAPmgg1tzlRw1a91BvjW7timR9Ygx/7EbGw+lYab+Zr9ubeDBDOiAvH4QD842TJnabNNjzHguibN2QN1cOTIEQwcOBBHjhxBeXm5avHsyMhI/PHHHzh58iSKi4vx2muvmcLeNtM8CzzX0ZYPq6MwZEKAPnh6euLP/3Ix7euzjKDOVWqF3XMGcS6oA5j3SJlqwG34cKjnJ3EbPtygL5vmxNxtzpRw1a91BvjW7jpCT3lSErIPHEB5UlKLbbKCApRfv47s9Gx8euQmhqz5B2/uvaoxqIsIdMbGp/rj5BujMHdk1xZBHcC/+wNwX5PBX+wWLlyIb775BoMHD1aVjRkzBhKJBHPmzMH169exfv16PPvss0Y1tL1kZmby6k07IyPDrJmxjbn2ICEEK3+7hK2XmOsH9vSyx+bYAfB21Jzwku00v0dcH3ti7jZnSrjq1zoDfGt3ptSTe/gwrrz9NhruLXMGAJZOTgj94APYBgYiac0aXEm8hbgHRuJiwAA0ilpOEBAq5AjPjMfDdhWYOOtF2Ad76Twn3+4PwH1NBgd2d+7c0Tgjw97eXtXX/sADD6jWXaPwE2NNCJA1yPHm3v+wP5HZXh4M8cCn08Nga8W/+T1cHnvCV6hfo3AR9ZfE0suXcWnevBZ1GkpLcXHePKT49sGxoKFIjnlU47Fs6qsx5M5pDL/9LxxlTevqnr7wD2d6FCj3Mfh/zfDwcLz++uvYvn073O79p11YWIg33ngDAwcOBADcunWLdcmAHR0dzW2CUTH310djTAgoqJRhzvZ4JN4tY5TPHdkVrz8YzJlJEtow9z0yNnzTow5X/VpngG/tzhh6NGUjEFi0/O+8QWiBS/4D8E/3Uchz0Pzlza2yAKNuxiEi4wKs5MwE8PqMlebb/QG4r8ngwG7z5s2YMmUKfH19VU7u7t276NKlC/bv3w8AqKqqwjvvvGNcS9uJ+hqMfIANetozIeB6Tjme//4Scsrvzz4Si4RY/XAfPBLuaxJ7Oxo23CNjwjc96nDVr3UG+Nbu2qtHWzYC0tio+nellRT/dh2Gf7sORZXETuNxHii4hVE3j6NX7nUIoT2dbWvJ0/l2fwDuazI4sAsODkZSUhL+/vtv3Lx5U1U2duxYVSZ15fqIbKK4uBi+vvwIGICmvHzmTkLa1rUHD1/Pw6JdiYz8SA4SEb57JgIDA7WvOcg12HCPjAnf9KjDVb/WGeBbu2uvHl3ZCHLtPXH8gVE6x8/1v5uA0TePw68sS78TtjJWmm/3B+C+Jr0Du5kzZ2LKlCmIiYmBVCrFuHHjMG7cOFPaRuEAhkwIIIRg04lUfHQ4GepZJYI97LBstDuvgjoKN6B+jcIlNGUjIACSPXrgePdRuOHZU+N+msbP6Q0HkqdTmOgd2HXr1g2rVq3C008/jZEjR2Ly5MmYPHkyfHy4kYaCK3bqC9u+PrY2IaCuUY7/+/Ua9iYw3xJH93DHZ4+HwUrIv5Xt2HaP2gvf9ADc92udAb61u/boUc9GQABc8emLg70mINdB8yoJbpUFGHkrDpHpLcfP6UtrY6X5dn8A7mvSO4/d0qVLER8fj1u3bmHSpEnYt28funbtivDwcLz33ntITEw0oZntp1Rt+jcfKCnRvBAzGymuqsNT355vEdTNHhqEb2cOgJ3EklN69IVvmvimB+C+X+sM8K3d6atHmW9OPReo8stZqksQPh31CjYPnq0xqHug4BbmnPoG7xz6AMPvnIKVvB5CLUl3RdbWENnYaNymT/J0vt0fgPuaDB5j5+vri3nz5mHevHmorKzEX3/9hf3792P06NGws7PDpEmTMHfuXPRi2WLmNTU15jbBqHBFT0peJZ77/iKySu8vIG0hFGDl1N54PMJfVcYVPYbAN01806MOV/1aZ4Bv7a41PbrW3y509sH2SW/ioqTlF2WhQo7wuwkYfeckfIszVOXN89hpGg8NwOCx0vrq4SJc19SuJGF2dnaYNm0apk2bBrlcjri4OBw4cABnz55lnQO0tGw5kJTLcEHPP8n5eGnnZVTX358k4Whjia+eCkdUVxdGXS7oMRS+aeKbHm1wya91BvjW7nTp0Tbj9c75y9i4fAdOBw6CvFlQZyFvwPDbJzHqZhxcLeQYsmcPiFyOqtu3Ie3WDQ4hIaq6usZDtzV5Ot/uD8B9TQLSfHFEnlFRUQEHBweUlpbyKpedQqFQzdZjG4QQbD6VhlUHb0Ch1rq6utlic+xABLrattiHzXraCt80sVGP8vkuLy/n9Cw2Y8DXa8HGdtcedOk5N2sW40tdnUiMf7qPxtEeY1Bv0bIrdWDGRTx07Q8415bp/YXN2PDt/gDs1GTI863XF7t+/fpBINAvWWxCQoJe9TqajIwMXgV26enprFzypL5RgWUHruGnC3cZ5cMecMUnY/1glZ8OmaLl2yBb9bQHvmnimx4++LXOAN/anTY96jNe5QIhzgUOwsFe41Fh7dCi7pBuLnhrfE90sxyIusLHzLo8Id/uD8B9TXoFdjR/E0UfSqvrMffHeJxLZQ48faKnAx6K+xrnV51QlSnHjNClaijmgvo1CptQznjNcvDGDxEzkOPYchydd1k23nmkP8ZHh6teSujyhJTm6BXYLVu2zNR2mBwHh5ZvPVyGbV8fbxdU4bnvLyKj+P6gU5FQgDcjXOC3bA5Kmo0ZKTx5EqcTElTrELJNjzHgmya+6eGDX+sM8K3dadNj4eKKI8HR+LP3BMiFzP+aHWtK8dC1PzEw8xJGv3da7y/NHQHf7g/AfU1tnjwRHx+PGzduAAB69eqFfv36Gc0oUyAWi81tglFhk55/bxVi3o8JqJTdX9LGTmKBjU/1h+WqN1CoJUu6+jqEbNJjLPimiW96NME1v9YZ4Fu706TnbkkNFu+7g4t9JzPKJQ21ePDGEYy4dQJiRQPchg9n3Rc6vt0fgPuaDA7sCgoK8PjjjyMuLk4V1ZaVlWHUqFHYtWuXagFttlFYWAhvb81JHLlIQUEBpFKpuc3A9rPpWPF7EuRqsyQCXWzwXexA+KIGR5plSW+Och3CgqoqVugxJmy5R8aCb3rU4apf6wxwsd01n12q/lvd1xFCsOfSXbz3exIjewAA9M2+gsfjd8OurunFWJlTri0zV00JF+9Pa3Bdk8GB3UsvvYTKykpcv34dPXs2LV+SlJSE2NhYvPzyy/jpp5+MbiSFfTTKFVjxexJ+OJfBKB/UxRlfPRUOJ1sxyq9naNlbjXvrEMLa2kSWUiitQ/0axRhoykFn6eSEBrUE+baxsXCdPh31PoFYsvcqjt7IZxzD1lKAGUUXEHJ2BwRqOeUCHn9ca347OlaZoo7B83kPHTqEjRs3qpwfAISEhGDDhg3466+/DDrWyZMnMWnSJHh7e0MgEGDfvn2M7YQQLF26FF5eXrC2tkZ0dDRu3bplqMkAwKuvdYBxlkjTlNlcH8prG/DM1ostgronIvyw/dlIONk2fcbWa33Be+sQ8nEJJ75p4psedYzp15Rs2LABgYGBkEgkiIyMxIULF/Tab9euXRAIBHRyxz240u6UOeiar+Xa0GzVo6q9e7Fh/lKM/ehYi6BugI8dDr0yEku+Wo4Hz5zB8AMHMPbMGfg99hjiFy1qcezCkydxeto0VKSkmESTPnDl/hgC1zUZHNgpFAqNyfssLS2hUCgMOlZ1dTVCQ0OxYcMGjds/+ugjfP7559i0aRPOnz8PW1tbxMTEQCaTGWo2yssNXPiY5ZSVlbV534qUFJybNQtHoqJwcvJkHImKwrlZs/RyDulF1fjfxtM4dbtIVSYUAO8+FIJV/+sDscX9JiVxd4fb8OE6j6dch7A9etgK3zTxTY86xvRrALB7924sXrwYy5YtQ0JCAkJDQxETE4OCVl6i0tPT8dprr2HYsGEGn5OvcKXdJa1Z0yKxcHNqLSTYGTkDXw+YidL6+8NXLOSNmHplH2asfxbZr85HRUoKJO7uqMnKwvGYGCS89BJIvea1XpVjlc0FV+6PIXBdk8GB3ejRo7Fw4ULk5OSoyrKzs/HKK69gzJgxBh1r/PjxWLlyJf73v/+12EYIwfr16/HOO+9gypQp6Nu3L7Zv346cnJwWX/b0obq62uB92Exb9Wh7q9Tnze/MnSJM3XgaqYX3zy21ssDm2IF4bmiQxplaIUuWwELLWAX1dQj5dn8A/mnimx51jOnXAGDdunV4/vnnMWvWLISEhGDTpk2wsbHBli1btO4jl8vx1FNPYcWKFZzOoWVsuNDu1HPQaeO2axesefBNnHPuySj3LsvG60c/xpib/0AIovLFqVu34tK8eWisqGj1/MqxyuaAC/fHULiuyeDA7ssvv0RFRQUCAwPRtWtXdO3aFUFBQaioqMAXX3xhNMPS0tKQl5eH6OhoVZmDgwMiIyNx9uxZg49nYdGu1dNYR1v16Hqr1PXm99OFTMzcfAFlNQ2qMl8na+ydOxijemgfwGsfHIwhe/Y0fblTBn4CAdyGD1elOmmPHjbDN01806OOMf1afX094uPjGb5LKBQiOjpap+9677334O7ujueee67NOvgIF9qdMgedJhqEFtjfZzI+H/kySmzvL6UoIApEJx/Ba8c+gXdFLmMfg7/CKccqmwEu3B9D4bomg6338/NDQkICjh49iuTkZABAz549GU7MGOTl5QEAPDw8GOUeHh6qbZqoq6tDXV2d6nfFvbcdPz8/o9pnbvz9/Q3eR5+3SuWbn3K2lVxBsOrgDWw+lcaoNyDACV/PCIeLtOUyN82xDw5udR3CtuhhO3zTxDc96hjTrxUVFUEul2v0XcpjN+fUqVPYvHkzEhMT9T6PNl/HN7jQ7rSNJ8528MZ2DcmGXaqKMOPCDnQtTtV6TNLYqHVbC+6NVTYHXLg/hsJ1TW0KSwUCAcaOHYuxY8ca2552s3r1aqxYsaJFeXx8PAICAhAQEIDc3FzU19fD2toarq6uuHu3afkrFxcXEEJQUtK0coK/vz8KCgogk8lgZWUFDw8PZGZmAgCcnZ0hFApRVNQ01szX1xfFxcWora2FWCyGt7c30tPTATQlO7S0tEThvTcqHx8flJWVobq6GhYWFvDz80NaWlPg5ODgACsrK9VYHG9vb1RUVKCqqgoikQgBAQFIS0tDUVERgoKCYGNjowp0PT09UV1djcrKSgiFQgQGBiIjIwNyubxp6nZuLsiQIU0XJDkZsLcH7k0qEZw+DTJgAGBlhZz0dPg4OOBmWiZWHsvG+bvMz9KP9PfBvAgnlBdko6HKBs7OzsjKygIAuLq6Qi6Xo/TegOGAgADk5eWhrq4OEokEbt27N13vqiq4uDS9vRYXF6O4uBhhYWEoLCxUXW9PT09kZDRN0HBycoJIJGJc75KSEtTU1MDS0hI+Pj6M6y0Wi1XXsPn19vf3R2pqqup6SyQS5Oc3DWL28vJCZWUl43qnp6dDoVDAzs4Otra2jOtdU1ODiooKCAQCBAUFMa53aWmpatyWu7s76urqVGM9g4KCcPfuXTQ2NsLW1haOjo7Izs4GALi5uaGhoUE1ziMwMBA5OTmqNuvi4sK43gqFgtFm8/PzVdfb3d2d0WYFAgGKi4sBNAUzRUVFqjbr5eWl83onJSXB3t4elpaW8PX1VbXZ5tdbW5slhMDe3h7W1taM611VVcVos+rXWyqVIje36WuGh4cHamtrGddbqc0YmMuvVVZWYsaMGfj222/h6uqq937afF1aWhrs7Ow6ta+zs7NjtBuZTKZ69rp06YLMzEyNz567uzvq6+sZz152djYaGhpgY6Pb17mOHInC0lJAKoWiohLH5Z74I2Q85AIR4/4MEhXi4ROfQdI1AKTHEKCqCkhKAiIimipkZAANDUC3bk2/L18GAgMBJyegthZITASiopq2ZWUBNTWQTpiAnKoq+Dg4dLivq6ysRHBwsGoYA/V15vd1AkIIab0ak4sXL+L48eMoKChoMbB43bp1hh6uyRCBAL/99ptqJlhqaiq6du2Ky5cvIywsTFVvxIgRCAsLw2effabxOJreYv38/JCYmIjQ0NA22cZGUlNTDR6HIysowBGlQ9CGQICxZ86g0EKK576/iJv5Veqb8Oa4HnhheBejZz5vix62wzdNbNRjzIXvjeXX6uvrYWNjg19++YUxszU2NhZlZWXYv38/o35iYiL69esHkeh+AKA8v1AoREpKCrp27driPNp8nTGuBZtgY7sDWuaqU45fzlOIsSPiadxx68aoL5VV4slLP6F3FwcITp82mh0iW1sM/flns6U8Yev9aQ9s1GSIrzP4i92qVavwzjvvIDg4GB4eHoz/4I35n31QUBA8PT1x7NgxVWBXUVGB8+fPY+7cuVr3s7KygpVVy+5Bvi0p1hY9ylmqurpj3YYNw9UaC7zww2mUVN+fhWUjFuHT6WGI6eXZJntbg2/3B+CfJr7pUceYfk0sFiM8PBzHjh1TBXYKhQLHjh3DggULWtTv0aMHrl69yih75513UFlZic8++0zrMBJtvo5vsK3dacpV5zZ8OHq++SYKVn6HD//NhkzInGHdJ/s/PBG/C3Z1VSCSIL3OI7CwaLU71tLJCYN//NGseezYdn+MAdc1GRzYffbZZ9iyZQueeeaZdp+8qqoKt2/fVv1OS0tDYmIinJ2d4e/vj0WLFmHlypV44IEHEBQUhHfffRfe3t5tyu/ENwcokUjatF/IkiU4nZCgcQKFhVSKW4/Mw/vfnke9/P4XC28HCb6NHYBe3qZr7G3Vw2b4polvetQxpl8DgMWLFyM2NhYDBgxAREQE1q9fj+rqasyaNQsAMHPmTPj4+GD16tWQSCTo3bs3Y3/l6hfNyzsjbGp3yq9yzf1n6vkErP7wd1zxCAHUgjobSyGWT+6NkQ1OuDTnR8gBQI+xkBZSKYIXLcL1lSu11hGIxWYP6gB23R9jwXVNBs+KFQqFGKIcp9VOLl26hH79+qnWY1y8eDH69euHpUuXAgDeeOMNvPTSS5gzZw4GDhyIqqoqHDp0qE0XvbX8UVxD2W9vKNpmqboMH46Lb2zEu6cKGEFdmJ8j9i0YYtKgDmi7HjbDN01806OOMf0aAEyfPh1r167F0qVLERYWhsTERBw6dEg1oSIzM1M1noaiGza1O01ZBa569cbqB5c0BXVqDAx0wuFXRmDaQD+4Dx6MoXv3NvldZRJsgQBO4eFwCg/XmDGgy6xZGLBxIyw0dLvZh4Rg+L59Zg/qAHbdH2PBdU0Gj7H76KOPkJOTg/Xr15vIJOOi7JemY+xaohwj0ujgjLf+ycbfSczGPDnUGx892hcSS5GWIxgPNo5paC9808RGPcYaY8c1v6YJY443ZBNsaXfNxyjLLKzwa+jDONuFOW7ZUijAqzHBeH5YF4iELbvxb/73HzxEIkZ2gNbWfy1PSkJJQgIsHRzgGhnJijVilbDl/hgTNmoy5Pk2OLBTKBSYOHEibt68iZCQkBbZ2n/99VfDLTYhyouRl5fXIv0AV9D00NfW1sLaCOurZpXW4Pnt8biRy+weeHVsdywY3c3okyS0YSw9bIJvmtiox1jBDNf8mib4Gtixpd2VX7+Ok5MnAwDuuHTBDxFPo1jKnMXsXZaN9U8NRMSwflqPwxY9xoJvegB2ajLp5ImXX34Zx48fx6hRo+Di4tJh//G3l6qqKs4FdtoG6YYsWQKZk1O7G158Rgle+CEeRVX3J0lILIVYNy0ME/p4tevYhlJZWcm6B6m98E0T3/Sow1W/1hlgS7uzcnNDg9ACB3uNx7HgMSCC+yOZBESB0TePY+L1g+j7nu5coWzRYyz4pgfgviaDA7vvv/8ee/fuxcSJE01hj8moamUNP7ahbZBu4cmTOJ2QgICvv4Z7Oz7H/xKfhf/79SpjPJ2HvRW+nTkAfX0d23zctlJVVdUuPWyEb5r4pkcdrvq1zgBb2l26QoL1k5ci09KRUe5cXYynL/yIB4puw2348Fa7Sdmix1jwTQ/AfU0GB3bOzs4acyqxHfUcUVygtaW/8g4dQsigQQYfV64g+OhQMr4+ycx4HurrgG9mDoCHvXlmA3Ht/ugD3zTxTY86XPVrnYGOanfaxrnJFQSbT6Vi7eGbqG8W1A1KO4eHE3+FdaMMFlIpus2Zg/Lr17WOlQP49xzxTQ/AfU0Gj7HbunUrDh06hK1bt8LGxsZUdhkNLo47MSSRsCGDaCtlDVi0KxHHkpkzhCeFeuPjDpokQaEYE2M931zza5rgoq9jA7qGvJS7+eHVn6/gQloJYx87RR2mn/sBodn/Nc1u7d8fAFAaH9/iGGyYuUrhPiYdY/f555/jzp078PDwQGBgYItBxgkJCYYeskPIyMhAnz59zG2GXuizmDMZNAh1hYV6B3aZxTWYvZ25kgTQ8ZMktJGeno7AwECz2mBs+KaJb3rU4apf6wy0t91p+xInKyhAyaVLSFyyBPJq5rKJBSdP4mA+sLf/o6huliN4iBPB+9EPwPu1r1FXWIj6igpcevFFrcNmhuzZwwju+PYc8U0PwH1NBgd2bUkOzAaaLxHEZvRazPnedHl9OJ9ajBd3xKO0pkFVZm0pwrppoRjfwZMktMGl+6MvfNPENz3qcNWvdQba2u60fYkLePxxZOzapXUFnkqxFLvCp+M/31BALaizapDh4Su/ISrtLK5/A9x0ckLoBx8gY9cuncNmktaswaCtW9uth63wTQ/AfU0GB3bLli0zhR0mx87Oztwm6I0+S385urvr9bVu14VMvLPvGhoV93vcvRwk+HbmAPT2Yc+yKVy6P/rCN01806MOV/1aZ6At7U7X5DNdfvWaVy/sHPAEKiXMrq4uRXcw48IOuFYXq8oaSktxad68Vm0p/PdfyAoKVP6ab88R3/QA3NdkcGCnTlVVVYvIlq1jO7g2bqa1pb96xMbq3L9RrsCqg8nYcjqNUR7m54hvZobD3Y5dS6bY2tqa2wSjwzdNfNOjDS75tc5AW9qdrslnmmhKNvw/nO0ymFEuUjRiwrWDiE45BiEMGo5+H0IYw2b49hzxTQ/AfU0GLymWlpaGiRMnwtbWFg4ODnBycoKTkxMcHR3h5ORkChuNAteWCNG29JdyuZkqHYFqaXU9YrdeaBHU/a+fD3bNGcS6oA4A8vLyzG2C0eGbJr7pUYerfq0zYGi7kxUU6Pwq15xUlyB8OPbNFkGdV3kOXjv6CR5MOdr2oA4ABALGsBm+PUd80wNwX5PBX+yefvppEEKwZcsWeHh4mH3QPZ+xDw7GoK1bNQ7+LUpN1bjPjdwKzPnhEu6W1KrKBALg9ZhgzB3Rld4vCkUD1K/xB30mnwFAo0CEg73G42iP6BbJhkfdPI6Hrv0JS0WjjiPoh9uwYaxaAozCfwwO7K5cuYL4+HgEc2wKN9dWnVBHomE8naenZ4t6B6/m4tU9V1DbIFeVSa0s8On0MIwNYbd+TXq4Dt808U2POlz1a50BQ9udPpPKcuy98EPEDGQ5+TLKnapLMOPCDjxQdNugcwqtraGorW1RbiGVImTJEkYZ354jvukBuK/J4K7YgQMH4u7du6awxaTUanjouExNTY3q3woFwdrDKZj3YwIjqAtytcW++YNZH9QBTD18gW+a+KZHHa76tc6Aoe1OOflMEwoI8M8Do/Bx9GstgrqI9PN46+81Bgd1Fvb2GLZ3r9ZhM83z2PHtOeKbHoD7mgz+Yvfdd9/hxRdfRHZ2Nnr37t0i31Pfvn2NZpwxqaioaL0Sh6ioqICrqysqZA14RUPS4RHd3fD5E/3gYG2p5QjsQqmHT/BNE9/0qMNVv9YZMLTdVaSkoLFZXjoAKLF2wo6Ip3DLvTujXFpXhccv7UL/stsI/fRjiJ2cNOal00avt97SOWymvXrYDt/0ANzXZHBgV1hYiDt37mDWrFmqMoFAAEIIBAIB5HK5jr3NB9/GzAgEAtwprMLz2y8htZDpxF4c0RWvxwRDJOSOZr7dH4B/mvimRx2u+rXOgCHtTlOaEwLgkv8A7On/GGSWzIXde+Vcw5Pxu9A1IgwhS35RfV0bsmdPixx4mrAPCYH/tGmq35qGzbRHDxfgmx6A+5oMXlIsJCQEPXv2xBtvvKFxkHFAQIBRDWwvfF1m53hyAV7+6TIq6+4P7pVYCvHxo6GYFOptRssolI7DWM831/yaJvjq6/RFVlCAS/Pno1RtlZBqSxvsDp+Oy379GHVtxCIsGe6HyZ5EZzBWdPYsLsyZA7mGrjkLqVRjVyuFYgpMuqRYRkYGDhw4gG7durXZQHOQmZmJ3r17m9uMdkMIwca4O1h7OIUxAd/H0RrfzAxHL2/2JB02hIyMDE7852kIfNPENz3qcNWvdQZaa3eaVpgAgBsePbBj4FOosGb6xD5OFvjiuaEIdG09V5lrVBSG/vJL0/H//RcgpGn83LBhbV4Hlm/PEd/0ANzXZHBgN3r0aFy5coVzDpAPXSk19Y14dccF/HWzlFE+qIszNjzZHy5SKzNZ1n74cH+awzdNfNOjDlf9WmdAV7vT1PVaL7LE/j6TcfKBEYy6QoUcE67/hZjM03Ce+jPgql9QZsj4OX3g23PENz0A9zUZHNhNmjQJr7zyCq5evYo+ffq0GGQ8efJkoxlnTKRSqblNaBc3Ll3Diz9dQYalI6P8yZ4OWPF0JCxFBk9wZhVcvz+a4JsmvulRh6t+rTOgqd0pg6xrH3zACOoynfzwfcRMFNgzMwF4VORh5vkf4F/WNPO5+fqt+qDP+Dl94NtzxDc9APc1GTzGTijUHkCwcZCxsl86Pz8f7hxNEnnk+GUs+v0WqsX3uw5EikZMj9+DYUXXeDHOQyaTQSJh34oY7YFvmtiox1jjyrjm1zTB1zF26u1OW7erXCDEkR7R+CtkPBRCEWPbiFsnMPm/AxArGu4XCgQYe+aMWRIHs/E5ag980wOwU5Mhz7fBn3kUCoXWPzY7v9zcXHObYDCEEGw9nYYXDmUxgjr72nK8XByHqPRzaKyqQtKaNWa00jjk5OSY2wSjwzdNfNOjDlf9WmdA2e6Kzp7FqUcfbRHUFdq6Yv2ohfiz90OMoM6htgzzTm7Eo4l7mUEdoFq/1Rzw7Tnimx6A+5oM7oqldAyyBjne2XcNv8RnAWrL3QQWp2H2mc2wD++jKiv891/ICgrosjUUCoU3lCcloer2bZQLBDj3/vstAjoC4FzgIPzS7xHUWzDHF/e7m4Dp8Xtg26Al0Wyz9VspFD6h1xe7zz//HDKZTO+Dbtq0CZWVlW02yhS4ceghziuXYfo355qCOjUGpZ3Fy3FfwEFWAaSk3N9gxrdPY8HVbnJd8E0T3/SY2q9t2LABgYGBkEgkiIyMxIULF7TW/fbbbzFs2DA4OTnByckJ0dHROuvzmdzDh3FowACcnDQJCa+8grsrV7YI6qrFNtgc9Sx2DnySEdRZ19cg9tz3mHVum/agDuZdv5VvzxHf9ADc16RXYPfKK68Y5NDeeOMNFLIs0Kivrze3CXoRn1GCSV+ewpW7ZaoyoUKOxxJ+xpOXfrq/KLX64E4evH3W1dWZ2wSjwzdNfNNjSr+2e/duLF68GMuWLUNCQgJCQ0MRExODgoICjfXj4uLwxBNP4Pjx4zh79iz8/Pzw4IMPIjs7W2/7uIisoADl169Ddu+65B4+jEvz5qGhVG3mf7OB7MnuwVj14Fu44hvGKH+g4BaW/P0hBtyNh670sprWb+1I+PYc8U0PwH1NenXFEkIwZswYWFjo13PLxnVZy8vLzW1Cq+y6kIl3919Dg/z+fBZnWzHm3z0C9zv/Miv7+ADp6QDM+/ZpLMrLy+Hi4mJuM4wK3zTxTY8p/dq6devw/PPPq1ay2LRpE/78809s2bIFSzQEFT/++CPj93fffYe9e/fi2LFjmDlzpt7n5QqaJkG4DR+O0sTElpXv+boGoQUO9JmEuO6jGJtFikY8dPUPjL55HELczzMX8PjjyNi1y2j554wF354jvukBuK9JL4+2bNkygw46ZcoUODs7t8mgzkiDXIH3/0jC9rMZjPIQL3t8MzMc9gX+OH3+sMa1C8399kmhcBVT+bX6+nrEx8fjrbfeUpUJhUJER0fj7Nmzep2rpqYGDQ0NOs9XV1fH+LLAlfWwNeWeA6Bz+a5sB298HzkTuQ7MVXU8K/Iw8/x2+JVlwbFvX/RduZKRZ84rJsZo+ecoFK5gksCOjQQGBprbBI0UVdVh3o8JuJBWwiifFOqNjx7pC2uxCHAKvr92ofLt8+xZuA0fbva3T2MRFBRkbhOMDt808U2PqfxaUVER5HI5PDya5VLz8EBycrJex3jzzTfh7e2N6OhorXVWr16NFStWtChPS0uDnZ0dAgICkJubi/r6elhbW8PV1RV37zblcXNxcQEhBCUlTX7H398fBQUFkMlksLKygoeHBzIzMwEAzs7OEAqFKCoqAgD4+vqiuLgYtbW1EIvF8Pb2Rvq93gNHR0dYWlqquqx9fHxQVlaG6upqWFhYwM/PD5d270ZDaCiQnQ1UVQFK//Xff4CXF+DmBjQ0QHDhAuRRUYiTBuP36EcgFzL/uxpeeAWTG29B3NMPOJuFuh49UGxtDSkAUluryoTg4eGBRpEIxeXlQFUVunTpgszMTDQ2NsLW1haOjo6qLm93d3fU19ejrKwMQNP/G9nZ2WhoaICNjQ2cnZ2RldU09tnV1RVyuRyl97qNAwICkJeXh7q6OkgkEri5uTGuNwAUFxcDABoaGlBYWKi63p6ensjIaHqxd3JygkgkYlzvkpIS1NTUwNLSEj4+PozrLRaLVV38za+3v78/UlNTAQAODg6QSCTIz88HAHh5eaGyshJVVVUQiUQICAhAeno6FAoF7OzsYGtri7y8PACAp6cnampqUFFRAYFAgKCgIGRkZEAul8PW1hYymUw1k9Td3R11dXWqXrKgoCDcvXtX4/V2c3NDQ0MD43rn5OSo2qyLiwvjeisUCkabzc/PV11vd3d3RpsVCASq6+3n54eioiJVm/Xy8tJ5va2trZGamgpLS0v4+voiLS1N4/X29vZGRUUF4xqmpaWBEAJ7e3tYW1szrndVVRUqKyshFAoRGBjIuN5SqZTRZmtraxnXW6lNHwzOY8c1lLlfrl27hl69epnbHAbXsssxZ/sl5JTfH8AtEABvjuuBF4Z30bgQsfLtswhAV5bpaQ+ZmZnw9/c3txlGhW+a2KiHjbnbcnJy4OPjgzNnziAqKkpV/sYbb+DEiRM4f/68zv3XrFmDjz76CHFxcejbt6/Wepq+2Pn5+bHqWjRHVlCAI2rXRBdlEgfsiHgKKR49GOV2sgo8dfFH9Mq7wdzBjLnpDIGNz1F74JsegJ2aTLpWLFdpbGw0twkM9idm4829/0HWoFCV2Uks8MUT/TAyWLtjUmY/L773FsYX2HZ/jAHfNPFNj6lwdXWFSCRSvakryc/Ph6enp859165dizVr1uDo0aM6gzoAsLKygpUVt5YR1Hf2fqJPKH4Kfxw1Vsz1XPtkX8UTl36CXX3LYSnK7ABsD+z49hzxTQ/AfU2dJrCztW19weeOQK4g+OhQMr4+yQzMurlL8e3MAQjSY2FqgD16jAXf9AD808Q3PaZCLBYjPDwcx44dw9SpUwE0JUA+duwYFixYoHW/jz76CB988AEOHz6MAQMGdJC1HUtrs/dlFlb4JewRnA8axCgXN9bh4cTfMDjtjPYZrxzJDsC354hvegDua+o0gZ2Dg4O5TUB5TQMW/JSAf28VMcqje3rg0+mhsJNYatmzJY6Ojka2zrzwTQ/AP01802NKFi9ejNjYWAwYMAARERFYv349qqurVbNkZ86cCR8fH6xevRoA8OGHH2Lp0qXYuXMnAgMDVWObpFIp59etVEfi7g634cM1TpRIcw7E9siZKJK6Msr9y7LwXNKv8LMRoFrHsbmSHYBvzxHf9ADc12TwkmK6Enoae9mu5cuXQyAQMP569OjR+o4aMPcSITfzKzF5w6kWQd3LYx7ANzPCDQrqAPAuvxXf9AD808Q3PeoY269Nnz4da9euxdKlSxEWFobExEQcOnRINaEiMzOTcdyvvvoK9fX1ePTRR+Hl5aX6W7t2reFiWE7IkiWwUAtW5QIhDoaMx/pRCxlBnVAAxPoq8OXTvRF78jAGfPYZYz91uJQdgG/PEd/0ANzXZHBg179/fyRqyDW0d+/eVseEtIVevXohNzdX9Xfq1Cmjn8PU/HEmBVO/+BcZxfczoduIRdj0dH8sHtsdQqHmSRLqiTspFIrpMIVfW7BgATIyMlBXV4fz588jMjJStS0uLg7btm1T/U5PTwchpMXf8uXL23RuNmMf3DTL3234cBRK3bB+1CL81Ws8Y51XXydr7H4hCisWTILU26vFflBOLBMI4DZ8OIbs2cOL7AAUijEwuCt25MiRGDRoEFasWIE333wT1dXVmD9/Pvbs2YMPPvjA+AZaWLQ64FgfXF1dW69kZMqSk7HyywP4RcqcveprZ4HNzw1GsKddi320Je5sntaES0uk6QPf9AD808Q3Pep0tF/r7Nh1746MeSuw9sB11KhNIAOAh/v5YPmUXrC/14uh3u7sg4MxaOtWTuem49tzxDc9APc1GRzYbdy4ERMnTsTs2bPxxx9/IDc3F1KpFBcuXEDv3r2NbuCtW7fg7e0NiUSCqKgorF69Wuc0ZG1JOxsaGoxumy5yrt7A3HV/4IoHM6jrkZeM2Ud/htej3wOezDdMXYk7TyckMN5KO1qPqeGbHoB/mvimR52O9mudmdLqerz161Ucup7HKLeXWGD5GH+McVZAXFEKSJoCNk3tTpkdgIvw7Tnimx6A+5raNHli/PjxePjhh/HVV1/BwsICv//+u0mcX2RkJLZt24bg4GDk5uZixYoVGDZsGK5duwY7u5ZfuwDdSTsVCkWHJO3MqyZ4/ftEZHuEMGwYXZWMSYr/IKqvweWtW+E3Z44qaWdaWhrSd+9Gg6srIJG0SNrZ4OaG+J07MWrFCqSlpaGoqAgKhQI2NjaMJJLV1dWMBIjKJJJSqRR2dnaMBIgymUyVRNLcSTuLi4thZ2fHm6SdUqkUpaWlqmvGh6SdmZmZKCsr42zSztboKL/WmTl5sxCv/XwFBZXMtTgHeloj9uovIM8chrKvQtlTUWZpyauVjMrKyqgelsN1TQYnKL5z5w6efPJJ5OXl4bvvvsOJEyfw8ccfY+HChfjggw9gaWnYJABDKCsrQ0BAANatW4fnnntOYx1tSTsTExMRGhpqMtuUHE8uwMs/JaCyTq4qs5TX44lLuzAw89L9is2SaeqVuFNtn9TUVHTp0sUUEswC3/QA/NPERj3GSlBsTr9mLNiYrFmJrEGODw8lY+vpdEa5pUiAl/o5o+v7L0JRVdliPwupFAFff42QQYNabOMqbHyO2gPf9ADs1GTSBMVhYWGYOHEiDh8+DEdHR4wdOxYTJkzAzJkzceTIEVy+fLnNhreGo6Mjunfvjtu3b2utoy1pZ0BAgMnsApoWFN8Ydwdr/06BeqjsVFOC2ac3w7/sbvMdGMk09UrcqbYPW5dIayt80wPwTxPf9KhjTr/GNQwd33YjtwKLdiUiJZ8ZuHlW5GFRw1X47slBqYagDgAaq6pQ/s03AI8CO749R3zTA3Bfk8GzYjdu3Ihdu3Yx8rwMHjwYly9fRv/+/Y1pWwuqqqpw584deHl5GbyvsVOxqFNd14j5OxPw8WFmUNet4BZeP7q2ZVAHtEimqVdiTbV9zJ2+xdjwTQ/AP01806OOOf0aV6hIScG5WbNwJCoKJydPxpGoKJybNQsVKSkAgPKkJGQfOIDypCQAgEJB8N2/qZjy5ekWQd2IWyfw+pGPYfvP7yiNj9d53sLKSl5lB+Dbc8Q3PQD3NRn8xW7GjBkay+3s7LB58+Z2G6TOa6+9hkmTJiEgIAA5OTlYtmwZRCIRnnjiCYOPVV9fb1TblGQW12DOD5eQnMd0XONqbmHcyQ0QEYXG/Zon09SVuFPTPqbSYy74pgfgnya+6VGnI/0aF9E1sevfCxcgFIvReG+iGgBUefrjlwmvIL5SxKivdZ1XXVhbc2KpMH3h23PENz0A9zUZHNht375d6zaBQKDVQbaFrKwsPPHEEyguLoabmxuGDh2Kc+fOtWkqsrW1tdHsUvLvrUIs2HkZ5bX3Z9CIRUKs/F9vjLPrhtMntrZwhID2ZJohS5bgdEKCXvuYQo854ZsegH+a+KZHnY70a1wkac0ajX4JABQyGRRqCZ4v+4Rh14DpqGkW1Olc51UX5eWcWCpMX/j2HPFND8B9TQZPnnBycmL8bmhoQE1NDcRiMWxsbFSz89iCcsBhYWGh0XLZEULw3b9pWP3XDSjUrp6HvRU2PR2Ofv5N10iVk+7ffwFCmpJpDhvWIicdw14996mvr4dYLDaKHjbANz0A/zSxUY+xJgxwza9pwlSTJ/Sa2AWg1kKCvWEPa1nn9VcMTjurfZ1XHbiMGYPB33zThj3ZCRufo/bANz0AOzWZdPKEMo2FOrdu3cLcuXPx+uuvG3q4DiM7O9sogV1tvRxLfv0P+xOZffDhAU746un+cLeTqMrakkxT332ysrJYN2unPfBND8A/TXzTow5X/VpHoM/ErjTnQHwfORPFzdd5LclA7PntcK/SY3KYBiykUjg++2yb9mUrfHuO+KYH4L6mNuWxa84DDzyANWvW4Omnn0ZycrIxDslKskprMGd7PJJyKxjlT0T4Y8XkXhBbaJ6L0pZkmlxOwEmh8IHO4tdaQ1c3qFwgxKGeMTgcEgMiuO//BESBB28cwfikv7SOM1bHKTwcFra2GnsqijiQaoZCYRNGCeyApqW/2DyTRJkMt62cuVOEBTsvo6T6/qBKS5EAyyf3wlORpk2loglzLJFmSvimB+CfJr7p0Qe2+7WOQOLuDpdBg1B87hyjvNDWFdsjZyLdJZBR7lxdjJnnf0DX4lS9jm8hlaLv++/DPjhYc09FRYXuA3AMvj1HfNMDcF+TwYHdgQMHGL8JIcjNzcWXX36JIUOGGM0wY6NQtP7WqAlCCLacTseqgzcgVxtQ5yq1wqan+2NAoHmyU7dVD1vhmx6Af5r4pkcdrvo1Y6Ft6IdyzK96UEcAnA+MxM/9HkW9BTNn6MD0C3js8i+wbpShVTSMH9bUU8G3dkf1sB+uazI4sJs6dSrjt0AggJubG0aPHo1PPvnEWHYZndLSUp1rzGpC1iDH//16Fb9ezmaUh/k5YtPT4fB0kGjZ0/SUlJQwcm5xHb7pAfiniW961OGqX2svqslaammWlEt5AWiR4qRabINd4Y8j0TeMcRzr+hpMj9+N8Cz9Ejk79u+PgRs26DXchG/tjuphP1zXZHBgx/VIVl+yy2rx4g/xuJpdziifNsAX70/tDSsLkZY9KRQK1+gsfk0dXbnpTsXHw9bfn7Et2b07dkQ8jXJrR0b9MOt6TPv3K9jlZeh1XgupFKErV9IxxBSKiTDaGDu24+fnp3fdc6nFmP9jAorVxtNZCAVYNikETw8KgEDQctK+ocvstBdDvz6yHb7pAfiniW96Oju6ctPJq6tRcaMpiXCD0AK/934Ix4NHM+qIFI146NqfeDD7Aobt2Q0il6Pq9m3AwgJ3f/65ZbJ1PdI9aYJv7Y7qYT9c16RXYLd48WK9D7hu3bo2G2NKCgoK4OysezwcIQTbz2bg/T+S0MgYTyfGxqfCERHUcn9dXRmGOC9Dyc/Ph4+Pj8mO39HwTQ/AP01808MHv9ZWZAUFOle5UZJr74ltkbHIcWTed4+KPMSe3w6/siwo0BQkDtq6FQ4hIQAAnwkTVC+7EAgAQtr80su3dkf1sB+ua9IrsNN3AWxNX7LYQl1dnc7tsgY53tl3Db/EZzHK+/o6YNPT4fB2bJmJWldXxumEBAzZs8dkwV1rergG3/QA/NPENz188GttpbXcdATAyW7Dsa/vFDSKmOlGht3+F1P/2wex/P6KO4X//gtZQUGLZRKN0XvBt3ZH9bAfrmvSK7D77LPP0KtXL4hE3B1XJpFon+hQXtuAmZvP40oWczzdI/198cH/ekNiqVm3rq6Mxqoq1VusKdClh4vwTQ/AP01808MHv9ZWdOWmq7Cyw46BT+GGVwij3E5WgScv7kTvvKSWOxFisvVc+dbuqB72w3VNmjPqNqNfv36qJXW6dOmC4uJikxplCnStL2svsWB8kRMJBVg+KQRrH+urNajTpytD+RZrCtx5NvCYb3oA/mnimx4++LW2InF3h9vw4S3Kr3r1xuqYt1oEdSG517Hk7w81B3UAIBCYbD1XvrU7qof9cF2TXoGdo6MjUlObkk2mp6dzcgbZ3bt3tW4TCARY+1gountI4Wwrxo7nIvHMkCCdXTD6LLOjfIs1BZmZmSY5rrngmx6Af5r4pocPfq09hCxZAgupFABQJxJjV/9p+GboHFRZSVV1LOX1eCxhD1489TXs6yq1Hstt2DCTTRrjW7ujetgP1zXp1RX7yCOPYMSIEfDy8oJAIMCAAQO0dl8oHSXXsLWywHczB0IkEsDn3tc7XTNd9Xo7NeFbLIVCaR+m9msbNmzAxx9/jLy8PISGhuKLL75ARESE1vo///wz3n33XaSnp+OBBx7Ahx9+iAkTJhh8Xn2xDw7GkD178MfaTVhv2QcF9h6M7b6lWYg9/z08K/ObCoRCQEPwayGVqvLeUSgU86NXYPfNN9/g4Ycfxu3bt/Hyyy/j+eefh52dnaltMypOTk6t1vF3sQGg30xXZVeGru5YU77FtjbDl2vwTQ/AP01802NKv7Z7924sXrwYmzZtQmRkJNavX4+YmBikpKRo7OY5c+YMnnjiCaxevRoPPfQQdu7cialTpyIhIQG9e/c2ik3NkSsIduSKsM5tLCMLgIAoMDrlH0y8fhCWikYATcFb2EcfIWPXLo3ruZoyAwDf2h3Vw364rklACCGtV7vPrFmz8Pnnn3MmsKuoqICDgwMyMzP1ymWnbaYr0OTc1Ge6GlLX2JSXl8PBwcEkxzYHfNMD8E8TG/Uon+/y8nLY29u3+TjG9muRkZEYOHAgvvzySwBNCZD9/Pzw0ksvYYmGr1vTp09HdXU1/vjjD1XZoEGDEBYWhk2bNul1TkOuRXZZLRbvTsT5tBJGubuNCHOy/oHHP79qDd46OmcnG9tde6B62A8bNRnyfOs1xk6drVu3ciaoU0c5SLo19JnpqkTZleE2fHhTriagyREOH27SoA4A7wZ6800PwD9NfNOjjjH9Wn19PeLj4xEdHa0qEwqFiI6OxtmzZzXuc/bsWUZ9AIiJidFavz0cuJKDcetPtgjqJvbxwt+vjcbsbz7C2DNnMPzAAYw9cwaDtm5l+DKJuzscevXqsJUj+NbuqB72w3VNnWblCX0wZKar0qnZBwdj0NatHf4WS6FQ2ElRURHkcjk8PJhj1jw8PJCcnKxxn7y8PI318/LytJ6nrq6OkW+roqKiVduOJuXj5Z+Y+ftsxSKsmNIbj/T3UU0YM1YOOgqF0vF0msDO19e31TqGzHRt7vQ62hEaskQaF+CbHoB/mvimh+usXr0aK1asaFGelpYGOzs7BAQEIDc3F/X19bC2toarqysCxFXo62WD/3JrAAAh7tZ4a7Q3onp7Ijc3FzKZDFZWVvDw8FDNDHR2doZQKERRURGAJl9aXFyM2tpaiMVieHt7Iz09HUDTTGNLS0sU3vOlPj4+KCsrQ3V1NSwsLODn54e0tDQAgIODA6ysrFBwLyWUt7c3KioqUFVVBZFIhICAAKSlpUEul6OoqAg2NjaqQNfT0xPV1dWorKyEUChEYGAgMjIyIJfLIZVKYWdnh9zcXABNAbJMJkN5eVOe0i5duiAzMxONjY2wtbWFo6MjsrOzATSluaivr0dZWRkAIDAwENnZ2WhoaICNjQ2cnZ2RldWUxN7V1RVyuRylpaUAgICAAOTl5aGurg4SiQRubm6qbAwuLi4Amr4EyeVyNDQ0oLCwUHW9PT09kZHRtNauk5MTRCIR43qXlJSgpqYGlpaW8PHxYVxvsVisuobNr7e/v79q4o+DgwMkEgny85smw3h5eaGyspJxvZWzw+3s7GBra8u43jU1NaioqIBAIEBQUJDqekskEshkMuTk5KiuYV1dnep6BwUF4e7duxqvt5ubGxoaGhjXOycnR9VmXVxcGNdboVCoet/8/f2Rn5+vut7u7u6MNisQCFRf3vz8/FBUVKRqs15eXjqvt1gsRmpqKiwtLeHr66tqs82vt7Y2SwiBvb09rK2tGde7qqqK0WbVr7dUKmW02draWsb1NmSmrsFj7LiGsl86JSUF3bt311lXVlCAI1FRug8oEGDsmTNmf5vNzc2Fl5eXWW0wJnzTA/BPExv1GGuMnTGpr6+HjY0NfvnlF0ydOlVVHhsbi7KyMuzfv7/FPv7+/li8eDEWLVqkKlu2bBn27duHK1euaDyPpi92fn5+rV6L7LJaPPT5v4gdHIgFo7rBQmTwiJwOhY3trj1QPeyHjZpMOsaOq9TW1rZaR1vSTnVMOdPVEPTRwyX4pgfgnya+6TEVYrEY4eHhOHbsmKpMoVDg2LFjiNLy4hgVFcWoDwBHjhzRWh8ArKysYG9vz/jTBx9Ha5x4YxQWRXdnfVAH8K/dUT3sh+ua2P9UGwmxWKxXPfWknc1hU74mffVwBb7pAfiniW96TMnixYvx7bff4vvvv8eNGzcwd+5cVFdXY9asWQCAmTNn4q233lLVX7hwIQ4dOoRPPvkEycnJWL58OS5duoQFCxaYxD57iWXrlVgC39od1cN+uK6p0wR2np6eetUz50xXQ2DbZ+L2wjc9AP808U2PKZk+fTrWrl2LpUuXIiwsDImJiTh06JBqgkRmZqZqPA0ADB48GDt37sQ333yD0NBQ/PLLL9i3b5/JcthxCb61O6qH/XBdU6cZY5eYmIjQ0FCD9mXzTNfU1FR06dLF3GYYDb7pAfiniY162DjGzlzw9Vqwsd21B6qH/bBRkyHPN+9nxSrj1qqqKr3SATCQSCDw80M9gHpD9zUxlZWVhuthMXzTA/BPExv1KO3h+fupXiivAdvuUXthY7trD1QP+2GjJkN8He8Du8rKpoWrhw4damZLKBSKqaisrGRdpviORj21A4VC4Sf6+Dred8UqFAqkpKQgJCQEd+/e5UUXhTKtAdXDXvimia16CCGorKyEt7c3hMJOM2RYI2VlZXByckJmZiZvgly2tru2QvWwH7ZqMsTX8f6LnVAohI+PDwAYlBKAC1A97Idvmtiohy9BTHtROnsHBwfW3aP2wsZ21x6oHvbDRk36+rrO/YpLoVAoFAqFwiNoYEehUCgUCoXCEzpFYGdlZYVly5bBysrK3KYYBaqH/fBNE9/08BE+3iO+aaJ62A8fNPF+8gSFQqFQKBRKZ6FTfLGjUCgUCoVC6QzQwI5CoVAoFAqFJ9DAjkKhUCgUCoUn8Caw27BhAwIDAyGRSBAZGYkLFy7orP/zzz+jR48ekEgk6NOnDw4ePNhBlupm9erVGDhwIOzs7ODu7o6pU6ciJSVF5z7btm2DQCBg/Ekkkg6yWDfLly9vYVuPHj107sPWe6MkMDCwhSaBQID58+drrM+2+3Py5ElMmjQJ3t7eEAgE2LdvH2M7IQRLly6Fl5cXrK2tER0djVu3brV6XEOfQYrh8MXPAdTXAey+P1z3c0Dn9XW8COx2796NxYsXY9myZUhISEBoaChiYmJQUFCgsf6ZM2fwxBNP4LnnnsPly5cxdepUTJ06FdeuXetgy1ty4sQJzJ8/H+fOncORI0fQ0NCABx98ENXV1Tr3s7e3R25uruovIyOjgyxunV69ejFsO3XqlNa6bL43Si5evMjQc+TIEQDAY489pnUfNt2f6upqhIaGYsOGDRq3f/TRR/j888+xadMmnD9/Hra2toiJiYFMJtN6TEOfQYrh8MnPAdTXsf3+cN3PAZ3Y1xEeEBERQebPn6/6LZfLibe3N1m9erXG+tOmTSMTJ05klEVGRpIXXnjBpHa2hYKCAgKAnDhxQmudrVu3EgcHh44zygCWLVtGQkND9a7PpXujZOHChaRr165EoVBo3M7m+wOA/Pbbb6rfCoWCeHp6ko8//lhVVlZWRqysrMhPP/2k9TiGPoMUw+GznyOE+jpC2H1/uOznCOlcvo7zX+zq6+sRHx+P6OhoVZlQKER0dDTOnj2rcZ+zZ88y6gNATEyM1vrmpLy8HADg7Oyss15VVRUCAgLg5+eHKVOm4Pr16x1hnl7cunUL3t7e6NKlC5566ilkZmZqrculewM0tb8dO3bg2WefhUAg0FqPzfdHnbS0NOTl5THugYODAyIjI7Xeg7Y8gxTD4LufA6ivA9h7f/jm5wB++zrOB3ZFRUWQy+Xw8PBglHt4eCAvL0/jPnl5eQbVNxcKhQKLFi3CkCFD0Lt3b631goODsWXLFuzfvx87duyAQqHA4MGDkZWV1YHWaiYyMhLbtm3DoUOH8NVXXyEtLQ3Dhg1DZWWlxvpcuTdK9u3bh7KyMjzzzDNa67D5/jRHeZ0NuQdteQYphsFnPwdQX6eErfeHb34O4LevszC3ARTtzJ8/H9euXdM5TgMAoqKiEBUVpfo9ePBg9OzZE19//TXef/99U5upk/Hjx6v+3bdvX0RGRiIgIAB79uzBc889Z0bLjMPmzZsxfvx4eHt7a63D5vtDobAB6uvYDfVz3ILzX+xcXV0hEomQn5/PKM/Pz4enp6fGfTw9PQ2qbw4WLFiAP/74A8ePH4evr69B+1paWqJfv364ffu2iaxrO46OjujevbtW27hwb5RkZGTg6NGjmD17tkH7sfn+KK+zIfegLc8gxTD46ucA6uvUYeP94aOfA/jt6zgf2InFYoSHh+PYsWOqMoVCgWPHjjHeHtSJiopi1AeAI0eOaK3fkRBCsGDBAvz222/4559/EBQUZPAx5HI5rl69Ci8vLxNY2D6qqqpw584drbax+d40Z+vWrXB3d8fEiRMN2o/N9ycoKAienp6Me1BRUYHz589rvQdteQYphsE3PwdQX8f2+6OEj34O4LmvM/fsDWOwa9cuYmVlRbZt20aSkpLInDlziKOjI8nLyyOEEDJjxgyyZMkSVf3Tp08TCwsLsnbtWnLjxg2ybNkyYmlpSa5evWouCSrmzp1LHBwcSFxcHMnNzVX91dTUqOo017NixQpy+PBhcufOHRIfH08ef/xxIpFIyPXr180hgcGrr75K4uLiSFpaGjl9+jSJjo4mrq6upKCggBDCrXujjlwuJ/7+/uTNN99ssY3t96eyspJcvnyZXL58mQAg69atI5cvXyYZGRmEEELWrFlDHB0dyf79+8l///1HpkyZQoKCgkhtba3qGKNHjyZffPGF6ndrzyCl/fDJzxFCfR3b7w8h3PZzhHReX8eLwI4QQr744gvi7+9PxGIxiYiIIOfOnVNtGzFiBImNjWXU37NnD+nevTsRi8WkV69e5M8//+xgizUDQOPf6OOZ0QAAD1lJREFU1q1bVXWa61m0aJFKu4eHB5kwYQJJSEjoeOM1MH36dOLl5UXEYjHx8fEh06dPJ7dv31Zt59K9Uefw4cMEAElJSWmxje335/jx4xrbmNJmhUJB3n33XeLh4UGsrKzImDFjWugMCAggy5YtY5TpegYpxoEvfo4Q6usIYff9IYTbfo6QzuvrBIQQ0lFfBykUCoVCoVAopoPzY+woFAqFQqFQKE3QwI5CoVAoFAqFJ9DAjkKhUCgUCoUn0MCOQqFQKBQKhSfQwI5CoVAoFAqFJ9DAjkKhUCgUCoUn0MCOQqFQKBQKhSfQwI5CoVAoFAqFJ9DAzgQEBgZi/fr15jajTcTFxUEgEKCsrMzcphgVgUAAgUAAR0dHc5uiFYFAgH379pnbDINZvnw5wsLCdNZ55plnVPeAixopmqG+jn1QX2c6uOLraGDHU7Zt28bqB1sfjK1h69atuHnzptGO1xHU1tbC1tYWt2/f1qv+8uXLVU7FwsICrq6uGD58ONavX4+6ujpVvSVLlqBHjx6MfZOTkyEQCPDMM88wyrdt2wYrKyvU1ta22a7PPvsMubm5etWlUAyB+rqWUF/XuX0dDewoHU59fb1Zzuvo6Ah3d3eznLutHDlyBAEBAejWrZve+/Tq1Qu5ubnIzMzE8ePH8dhjj2H16tUYPHgwKisrAQCjRo1CSkoK8vLyVPsdP34cfn5+iIuLYxzv+PHjGDRoEKytrdtsl4ODAzw9PfXWQKHwAerr9If6OuNBAzsDqaysxFNPPQVbW1t4eXnh008/xciRI7Fo0SKt+5SVleGFF16Ah4cHJBIJevfujT/++ENj3fT0dAgEAiQmJjL2FwgEqkao7EL4888/0bdvX0gkEgwaNAjXrl1TbZ81axbKy8tVbzTLly8HAPzwww8YMGAA7Ozs4OnpiSeffBIFBQVaba+pqcH48eMxZMgQVZfFd999h549e0IikaBHjx7YuHGjzms2cuRILFiwAIsWLYKrqytiYmIAAOvWrUOfPn1ga2sLPz8/zJs3D1VVVa1qqKurw2uvvQYfHx/Y2toiMjKyxQOqD8rP6lu2bIG/vz+kUinmzZsHuVyOjz76CJ6ennB3d8cHH3zA2E8gEODrr7/GQw89BBsbG/Ts2RNnz57F7du3MXLkSNja2mLw4MG4c+cOY7+vvvoKXbt2hVgsRnBwMH744YdWbdy/fz8mT56s+r1y5Uq4u7vDzs4Os2fPxpIlS1p0DVhYWMDT0xPe3t7o06cPXnrpJZw4cQLXrl3Dhx9+CAAYOnQoLC0tGdctLi4O8+fPR0lJCdLT0xnlo0aN0mkX0NS2AgMD4eDggMcff1zlWCnchPo66uuor+OoryMUg5g9ezYJCAggR48eJVevXiX/+9//iJ2dHVm4cKGqTkBAAPn0008JIYTI5XIyaNAg0qtXL/L333+TO3fukN9//50cPHhQ4/HT0tIIAHL58mVVWWlpKQFAjh8/Tggh5Pjx4wQA6dmzJ/n777/Jf//9Rx566CESGBhI6uvrSV1dHVm/fj2xt7cnubm5JDc3l1RWVhJCCNm8eTM5ePAguXPnDjl79iyJiooi48ePV51LeezS0lJSWlpKBg8eTB588EFSXV1NCCFkx44dxMvLi+zdu5ekpqaSvXv3EmdnZ7Jt2zat12zEiBFEKpWS119/nSQnJ5Pk5GRCCCGffvop+eeff0haWho5duwYCQ4OJnPnziWEEJ0aZs+eTQYPHkxOnjxJbt++TT7++GNiZWVFbt68qdUGAOS3335jlC1btoxIpVLy6KOPkuvXr5MDBw4QsVhMYmJiyEsvvUSSk5PJli1bCABy7tw5xrF8fHzI7t27SUpKCpk6dSoJDAwko0ePJocOHSJJSUlk0KBBZNy4cap9fv31V2JpaUk2bNhAUlJSyCeffEJEIhH5559/tNool8uJu7s7OXPmjOraSyQSsmXLFpKSkkJWrFhB7O3tSWhoKEOT+m91pkyZQnr27Kn6PXjwYDJnzhzVb3d3d3Lx4kUybtw4smXLFkIIIXfu3CEASFxcnFa7lNfx4YcfJlevXiUnT54knp6e5P/+7//0ug8UdkJ9HfV11Ndx09fRwM4AKioqiKWlJfn5559VZWVlZcTGxkarszt8+DARCoUkJSVFr3MY4ux27dqlqlNcXEysra3J7t27CSGEbN26lTg4OLR6vosXLxIAKkeiPPaNGzdI3759ySOPPELq6upU9bt27Up27tzJOMb7779PoqKitJ5jxIgRpF+/fq3a8vPPPxMXFxfVb00aMjIyiEgkItnZ2YzyMWPGkLfeekvrsbU5OxsbG1JRUaEqi4mJIYGBgUQul6vKgoODyerVqxnHeuedd1S/z549SwCQzZs3q8p++uknIpFIVL8HDx5Mnn/+ecb5H3vsMTJhwgStNp4+fZq4u7urbImMjCTz589nHGPIkCF6O7s333yTWFtbq36//fbbpHv37oQQQq5fv07s7e1JY2MjWbVqFZk5cyYhpOk/R4lEQmQymVa7NF3H119/nURGRrawgQZ23ID6OurrlMeivo57vo52xRpAamoqGhoaEBERoSpzcHBAcHCw1n0SExPh6+uL7t27G92eqKgo1b+dnZ0RHByMGzdu6NwnPj4ekyZNgr+/P+zs7DBixAgAQGZmJqPe2LFj0a1bN+zevRtisRgAUF1djTt37uC5556DVCpV/a1cubLFp/jmhIeHtyg7evQoxowZAx8fH9jZ2WHGjBkoLi5GTU2N1uNcvXoVcrkc3bt3Z9hw4sSJVm3QRGBgIOzs7FS/PTw8EBISAqFQyChr3oXTt29fxnYA6NOnD6NMJpOhoqICAHDjxg0MGTKEcYwhQ4bovF/79+/HQw89pLIlJSWF0fYAtPitC0IIBAKB6vfIkSNx8+ZN5ObmIi4uDkOHDoVIJMKIESMYXWGDBw+GlZWVVruAltfRy8tLZ7cXhd1QX0d9nRLq67jn6yzMbQDfUR+EqQ/KBtQU8DfR0NBgFFuqq6sRExODmJgY/Pjjj3Bzc0NmZiZiYmJaDPKdOHEi9u7di6SkJNVDrBwT8u233yIyMpJRXyQS6Ty3ra0t43d6ejoeeughzJ07Fx988AGcnZ1x6tQpPPfcc6ivr4eNjY3G41RVVUEkEiE+Pr7FOaVSaesXoRmWlpaM3wKBQGOZQqHQup/SgWgqa76fIRw4cABr1qxp8/7NuXHjBoKCglS/hwwZArFYjOPHj+P48eOq//gGDhyIoqIipKamIi4uDi+88EKrdulzzSj8hvq6JqivMxzq64wL/WJnAF26dIGlpSUuXryoKisvL9c5rbxv377IysrSe+q5m5sbADCmS6sPLlbn3Llzqn+Xlpbi5s2b6NmzJwBALBZDLpcz6icnJ6O4uBhr1qzBsGHD0KNHD61vGmvWrEFsbCzGjBmDpKQkAE1vZt7e3khNTUW3bt0Yf+oPkT7Ex8dDoVDgk08+waBBg9C9e3fk5OQw6mjS0K9fP8jlchQUFLSwwdwzkXTRs2dPnD59mlF2+vRphISEaKx/69YtZGRkYOzYsaqy4OBgRtsD0OK3NpKTk3Ho0CE88sgjqjJra2vVYOwTJ05g5MiRAJoc16BBg7B582bcvXuXMZhYk10U/kF9HfV1bYX6OvNDv9gZgJ2dHWJjY/H666/D2dkZ7u7uWLZsGYRCIeOzrzojRozA8OHD8cgjj2DdunXo1q2bKofOuHHjWtS3trbGoEGDsGbNGgQFBaGgoADvvPOOxmO/9957cHFxgYeHB95++224urpi6tSpAJo+F1dVVeHYsWMIDQ2FjY0N/P39IRaL8cUXX+DFF1/EtWvX8P7772vVu3btWsjlcowePRpxcXHo0aMHVqxYgZdffhkODg4YN24c6urqcOnSJZSWlmLx4sV6X8tu3bqhoaEBX3zxBSZNmoTTp09j06ZNjDqaNHTv3h1PPfUUZs6ciU8++QT9+vVDYWEhjh07hr59+2LixIl629CRvP7665g2bRr69euH6Oho/P777/j1119x9OhRjfX379+P6Ohoxtv8Sy+9hOeffx4DBgzA4MGDsXv3bvz333/o0qULY9/Gxkbk5eVBoVCguLgYcXFxWLlyJcLCwvD6668z6o4aNQqffvopAKB///6q8hEjRmDt2rWwtbXFwIEDddpF4R/U11Ff11aor2MBZhnZx2EqKirIk08+SWxsbIinpydZt24diYiIIEuWLFHVUR9QTEjTYN9Zs2YRFxcXIpFISO/evckff/yh9RxJSUkkKiqKWFtbk7CwMPL3339rHFD8+++/k169ehGxWEwiIiLIlStXGMd58cUXiYuLCwFAli1bRgghZOfOnSQwMJBYWVmRqKgocuDAAcYAZvWZYkpeeukl4uXlpRoU/eOPP5KwsDAiFouJk5MTGT58OPn111+16hkxYgRjwLWSdevWES8vL2JtbU1iYmLI9u3bW5xbk4b6+nqydOlSEhgYSCwtLYmXlxf53//+R/777z+tNkDLgOLmg29jY2PJlClTdNrf/FiaBoFruo4bN24kXbp0IZaWlqR79+5k+/btWm0cOnQo+fbbb1voeO+994irqyuRSqXk2WefJS+//DIZNGgQQxMAAoCIRCLi7OxMhg4dSj799FPGoODmdqrPaiOEkLi4OAKAxMTEMMo12aXpOn766ackICCgxfk03QcKO6G+jvo66uu46etoYNdOqqqqiIODA/nuu+867JyaHiSKbrgUUBQWFhILCwuSl5fXat3o6Gjy9NNPd4BVhtmlDS7dBwoT6uu4AZeeMerrTAPtijWQy5cvIzk5GRERESgvL8d7770HAJgyZYqZLaO0xhNPPAEXFxdkZWWZ2xSdlJSUYN26daoZaEpqamqwadMmxMTEQCQS4aeffsLRo0dx5MgRs9qlDy+++CJ27NhhAqsopoL6Ou5CfZ1p7NIHNvg6wb3IkqInly9fxuzZs5GSkgKxWIzw8HBVVvGOQpkdu7S0lPNrJHYUynX+RCKRwYOf2UJtbS0mTZqEy5cvQyaTITg4GO+88w4efvhhc5vWKgUFBap0CF5eXi1mDlLYB/V13IT6OvPCBl9HAzsKhUKhUCgUnkDTnVAoFAqFQqHwBBrYUSgUCoVCofAEGthRKBQKhUKh8AQa2FEoFAqFQqHwBBrYUSgUCoVCofAEGthRKBQKhUKh8AQa2FEoFAqFQqHwBBrYUSgUCoVCofAEGthRKBQKhUKh8IT/B9CvhucSR1AOAAAAAElFTkSuQmCC",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlUAAAGFCAYAAADU0AYAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAADgbklEQVR4nOydeXxU1fXAvyc72UggCSSQBUHAiIKAC6iAtqi1bq1Fra27v0rdatUqarVqS7XVWqutS7WVtmqrVqvWHayIiqDIIoKAkLBlISQh+57c3x/vzTAzmZnMTN4sD+/383mf5N1333tn7tx35rx7zz1HlFJoNBqNRqPRaAZHXLQF0Gg0Go1GozkQ0EaVRqPRaDQajQVoo0qj0Wg0Go3GArRRpdFoNBqNRmMB2qjSaDQajUajsQBtVGk0Go1Go9FYgDaqNBqNRqPRaCxAG1UajUaj0Wg0FqCNKo1Go9FoNBoL0EaVJihE5GIRUX62OSIyQUTaRORZL+dni0iFiKwUkfhofAaNRmN/7K6LRCRTRG4TkaUiUi0iLSKyXkRuFpGUSMujsYaEaAugsS2XAJu8lG9USjWJyK3A70XkRaXUiy7HHwGGAd9QSvVGQlCNRnNAY1ddVARcB/wDeABoAY4H7gTmishcpfPI2Q5tVGlC5Qul1Co/x/8AfAd4VEQ+UErViMg84DzgBqWUNyWo0Wg0wWJXXVQOlCilWl3K/icircB9wLHAh1GRTBMyevpPExbMN6xLgFTgMREZCTwKfAA8GMy1RCRFRH4nImtFpFFE6kXkYxE500vdOBG5xqzbLiINIrJCRM4I8p4Hi8izIlIjIp0i8qWIXBXMNTQaTfSxUhcBiEiWqY/KTN1QIyJviMhElzrDROQRc3qxy6y7UESSXeRq9TCoHHxi/i0MUi4RkStddN8+Efm3iBwU7GfUhI4eqdKESryIePYf5TqMrpQqE5GfYQyzHw6kAJcopfqCvFcyxjD9/UAFkAR8E3hJRC5RSv3dpe4i4IfAX4A7gC5gKlAS6M1EpBRYDuwEbgCqgZOBh0QkRyl1V5DyazSa8BExXSQiGRijRyXAb4CVQDowC8gHNpn+UO8BY4FfAJ9jTOvdAkwBvj3AbU40/24IRjbgceBi4CHgZgydeQewXEQmK6X2BHk9TSgopfSmt4A3jIdW+dh6vNQX4Evz+A0WyRCP8ULwJLDapfx48z6/GuT13wJ2AZke5Q8D7UB2tL8Hvent675FQxcBt5vnf9NPnSvMOvM8ym8yy+f6OfdwoA14KUi5jjGvfb1H+Wjzer+J9vf1ddn09J8mVC4EjvTYjvZS7xRgItCHMboUEiIyT0Q+EpEWoAfoBi4DDnGp9i3z758GcZ8U4BvAf4A2EUlwbMAbGG+4x4R6fY1GYzmR1EXfArYopZb4qXMi0Ar826N8kfn3G95OEpES4DWMF7rLg5TrNAyj6mkPnVUNrAPmBHk9TYjo6T9NqHyp/DuHIiJZGKNJnwJ/Bp4QkcuUUn8J5kYi8l3geeAFDAfOagzD6sfApS5Vc4Fe83ioDMd4Lq4xN2/kDOL6Go3GWiKmizB0zM4B6gwHqpU5VORAGQ7yPeZxT/mKMaYMezBWI9YHKdcIjJE4X1N8ZUFeTxMi2qjShJOHMeb1v6mU+lJEvgM8ICJvK6V2B3GdH2KslDnXVVG5On2a7MWYGhwJVIUo8z4Mw+wf+B7xKg/x2hqNJjpYpYv2Ykyp+aMOOFpExENf5WH85ta6VjYNqqUYRtGcIOVxUIsxUnU80OnluLcyTRjQ03+asGCuzPshcLtS6kuz+EcYQ+9PBHk5BXR5KKiRgOfqvzfNvz8OXmLzRkq1YbwxHgF8rpRa5WWrC/X6Go0mslisi94ExovIiX7qvIvhvH6WR/mFLscdshVhGFTxwIlKqR1ByuPgNQyjbJQPnbU+xOtqgkQ8Rig1Gr+IyMXAU/gOuLcNwwj6wvz/eOWywsbl/MsDHXoXkUuAv2Isg/43xlLj2zGU4sFKKXGp+3cMBfoEhqLpxDCQ2pRSDwd4v1KMFT5fmffcDmQA44DTlVL+FKpGo4kAUdJFGcDHQDFwL0b4gyHAbOA1pdR7pl/mSmAMxuq/9cBxwK3AYqXUt81r5ZnXGoXhH7rN43a7gxm1EpHHMXTfn4BlGH5d+ea91yulHg30WppBEG1Peb3Za8P/ihuF4WD5PMYDfbCPa7wONAKFQdz3Zoxptw5go3mfOzHD0LjUi8OIUrwew6BqwAiPcFqQn7MEIyzDboywDDXAR8Bt0f4O9KY3vUVVF2VhxLfaYeqGPRgvcBNc6gzDeCGrxFhUsx34NZDsUmfOAPLfGUKbXAKswIjO3gZsBf4GTIv29/V12fRIlUaj0Wg0Go0FBOSoLiKrg7yuAs5QSlUEL5JGo9EMDq2zNBpNNAh09d8U4HcYQ4oDIcACjCjYGo1PREQwHDT90assGk4VkTgGWJyhlOqx4l6aqDMFrbM0ARJpXRQMXqLFe9Kngs9SoQkTAU3/iUgfMFIpVRPQRUWagclKKR0bQ+MTEZmDsdLOH5copRZZdL87MRxH/TFGKbXdivtpoofWWZpgiLQuCgYRGehH+m9KqYsjIYtmYAI1qoqBnYFa6SJSCFQql9xLGo0n5kqaCQNUK1cWhTAQkQKgYIBqnyuluqy4nyZ6aJ2lCYZI66JgEJHpA1Sp1S+CsYN2VNdoNBqNRqOxgJAiqpsh/48C8vDwUVFK/X3wYoUPc+68AGiOtiwazdeMDIzRoGj4pWRhE52ldZRGEzUGraOCHqkSkdOBZ4A0jIfe9QJKKTUsVGEigYiMwog9pNFoIs/oSK+ws1JnicgtwHcxEvO2Y8RAu1kptdmljmD47v0IyMYIBHmVUmpDgPfQOkqjiR6D0lGhGFVbgDeAW5WR0sNWiEgm0Lh+/XqKioqiLU5I7Nu3j+zs7GiLETR2lRu07IOlqamJwsJCgKFKqaZI3ttKnSUibwH/wkjMmwAsBA4DSpVSrWadm4HbMIJTbgF+DszCCA454OiT1lHRw65yg5bdFx1799JZW0tyTg4pubk+61mlo0KZ/hsFPGRHg8qV3t5eMjMzoy1GSNTW1tpSdrvKDVp2m2OZzlJKneK6b6ZQqgGmAcvMUarrgIVKqZfMOhdhRN0+H3jc85pmYnDXcA4ZoHVUNLCr3KBl96Rp82Y23nsve5ctc5blzppF6YIFZE4YaE1C6IRiVL0NTAf00mONJob5dHs972/eywmjZeDKBzbh1FlDzb/15t8xwEjgHUcFpVSniLwPzMSLUQXcgpdQH/X19ZSVlVFcXExVVRVdXV0MGTKEnJwcdu3aBcDw4cNRSlFfb9y+qKiImpoaOjo6SE5OZsSIEezcuROAYcOGERcXR21tLQCjR4+mrq6O9vZ2kpKSKCgoYPv27QBkZWWRmJjI3r17ARg1ahQNDQ20traSkJBAYWEh5eXlRgMMHUpycjI1NUb0ioKCApqbmykrKyM+Pp7i4mLKy8tRSpGZmUlqairV1dUAjBw5ktbWVpqbm4mLi6OkpIQdO3bQ29tLeno6GRkZVFVVATBixAg6OjpobGwE4KCDDmLnzp309PSQlpZGVlYWFRXGrE1eXh5dXV00NDQAUFJSQkVFBd3d3aSmpjJs2DB27zZmWHNycujt7WXfvn3U1dVRXFxMdXU1nZ2dpKSkkJub69beAHV1xiLAwsJC9u7d62zvkSNHsmOHkRM5Ozub+Ph4t/aur6+nra2NxMRERo0a5dbeSUlJzjb0bO+ioiLKysqc7Z2SksKePXsAyM/Pp7m5mbq6Omd7b9++nb6+PjIyMkhLS3Nr77a2NpqamhARxowZ49bemZmZVFZWOtuws7PT2d5jxoxh165dXts7NzeX7u5ut/aurKx09tnhw4e7tXdfX59bn21sbKSsrIyUlBTy8vLc+qyIuLV3bW2ts8/m5+d7be+Omhq2X3cdPbm5cOyx0NEBq1dT09tL7f33M/nKK8k++GC3Puvo64Ml0JAKZ7js5gJ3YCSiXI+R18iJUupVSyQLE46h9fr6etsOlfb29hIfP1CcutjDrnKDPWW/6K+f8P6WvSQnxPH9o4r4xemlGAMpkaepqYmhQ4dChKb/IqGzzFGpV4BspdTxZtlMjByRo5RSlS51/wwUK6VO9nIdbyNVu7WOijx2lRu07K6suOQStxEqT3JnzeKYp55yK7NKRwU6UvWyl7I7vJQpBo5KGxPs2bPHtgqrrq6OvLy8aIsRNHaVG6C6uppRo0ZFW4yA2VDZyPtbjDevzp4+6lq7omZQRYmXvZRZrbP+CBwOHOfjuq6IlzKjolKdGMm/jYrm92RnHWW358WBXeUGLbuDjpoavwYVwN4PPqCjpoaUMPweBWRUKaX8pvawI52dnQNXilHsapjYVW6wX3957H33ma75sw+KkiTRIdw6S0QeBs4AZimlXFfqVZt/RwJVLuV5GH5VAWO3PueKXWW3q9ygZXdeK5BpPKXo3Ls3LEZVwIpHRJ4VkXPMyLO2JyUlJdoiaGyEnfrLjrpWXv/cOfPEMcUZHFow1M8ZBybh0Fli8EeMsAonKqXKPaqUYxhWc13OSQJmY4RfCBg79TlP7Cq7XeUGLbuDZD8r/JyIBFYvBIJ5m9sC3AzsFZF3ROQqM7WDLcnJyYm2CBobkRumBzAc/HlZGX0uE01XfyN8K11inHDorD8BP8RYydcsIiPNbQgYQa+AB4FbReQ7IjIJWAS0Ac8GcyM76yg7PS+u2FVu0LI7SMnLI3fWLP/3O/74sIxSQRBGlVLqTqXUNGAchr/CGcBXIrJaRO4UkSPCImGYcKxE0GgCwbH6J9apae7ghc/29+0jirLIj2+JokTRI0w668cYK/6WYkzvObZzXer8FsOwegRYhRHS4aRAYlS5YmcdZZfnxRO7yg1adldKFywgIT3d67GE9HRKFyyw9H6uBO13oJTarZR6xFzFkgvcCxwMvCsiO0TkjyJyqNWCxjodNTU0bthAh7lEU6OJBk99tJ2unj7n/o9nj/26Oaj3w0qdpZQSH9silzrKNOjylVIpSqnZSqkvwvLhNBpNPzInTODY5583Rqwc+k+E3FmzOPb552MuTpUT883reeB5EYkH5mC8Dc4AAkrJEC2GDbMmm064A4yVlJSQkpJCcnIybW1tlJaWcvPNNzNz5ky/5y1atIiZM2cyfvz4Qcug2R+fJpZp6ujm6Y93OPfH5aXzzUNG0Nwc0QDmMY2ddJZVOioa2OF58YZd5QYtuyeZEyZwzFNP0VFTQ+fevSTn5oZtys+VgI0qEfkV8D9guVKqw/O4UqoXeNfcvhY0bd7MR+ecQ0+L+/TK3mXL+Gj1asss4n//+99MmjQJgFdeeYVTTz2Vt99+m6OPPtrnOYsWLSInJ0cbVV8jnlmxk+bOHuf+/NljiYv7+o5SaZ2l0WhS8vIiYkw5CGb67/vAEqBBRN43fRJmmStbQkJEtouI8rL9yUf9OT7qTwz23o5oroNh47339jOoHPS0tLDx3nsHfQ9PzjzzTK688kruv/9+3n33XWbMmMERRxzBpEmTeMoMZvbkk0+yatUqrr32WqZMmcIbb7zB+vXrOf7445k6dSqlpaXcc889lst2IOOI6BurdHT38pcP9y9EKxiawhmTC4DYlz2MWK6zIokVOipa2LXP2VVu0LLHCgGPVCmlxprZ00/EGDK/ACOYXruIfAy8B7ynlApm2fCRuAfemwQsBl4Y4LwJgOuchjXx5YMgmgHGjjzySF5++WWmTp3Khx9+SHx8PPX19UydOpVTTjmFyy+/nKeffpobb7yR0047DYDm5maWLFlCcnIy7e3tzJw5k7lz5zJ9+nRLZdNEhxdX76a2ZX+sl8uPP4ikhAMuvFxQhElnaTROIj21pIl9gvKpUkpVAP8wN8zlySdgKKybgLuCuaZSys0YEpEFwDbg/QFOrVFKNQR6H2+MHj16MKdHNcCYI7VQXV0dl112GVu2bCEhIYHa2lo2bNhAfn5+v3Pa29u58sorWbt2LXFxcezatYu1a9dqoypAzOzlMUlvn+LPy/YH+8xKTeS8o/bLG8uyhxurdVYkGayOiiZ27XOByh2tZL3+sGubg71l9yTkV1kRGQucBJxsbvEYb36hXi8JI/7LX9XACQnXiEiViLwrIicMcN1kEcl0bJgZ4B1JLkMlmgHGPv30UyZNmsT8+fOZPXs269evZ+3atYwfP56Ojn6uIwDceuutjBgxgjVr1rBu3TrmzJnjs66mP1Yl2wwHb35RxY66Nuf+xTNLSE3abyfEsuyRxGqdFW4Gq6OiiV37XCByO3xpPWcq9i5bxkfnnEPT5s3hEs8vdm1zsLfsngTjqD4G4w3P8ZY3FCNx6PsYObA+VUr1+LzAwJwFZGEEyvNFFfAj4DOMBKQXYCyLnqOU8jUX5zUDfGVlJYmJic4M8BDcm6EjwJjfpI1hCDD2yiuv8Oijj/LWW29x1VVXUVxcjIiwbNky1q1b56yXmZnpzC4OsG/fPiZNmkRCQgKbN29m8eLFnHjiiZbKZjXd3d0xkwHekeE+1jLAp6Sk8Mh7W51tlpIYx1mHZjs/T1FREXv27KGjo8OyDPCu7d1UU4Nqa6NozBiqWlu9treVGeCDIQI6K6zY+aXHrrIHIncgvrSeyXojgV3bHOwtuycy8KCQWVGkD9iJEdDuPWC1uXrGGkFE3ga6lFKnB3nefzFCw5zh47jXDPBffvklEycG7d/uhq/Vf2AEGLNi9Z9rSIXW1lZKS0tZsGABM2fOZPHixVx55ZUMHz6c0tJSNm3axK233sppp53Ga6+9xg033MCQIUP49a9/TX5+PhdccAFDhgyhpKSErq4u5s6dy9VXXz0o+b4uVFRUxGSy0mVb9nLhXz9x7l967BjuOL3UrU44ZA92+sOqDPDBEG6dFS7MEfVGK3RUtIjV52UgBpK7o6aGxTNm+L+ICHOXL4+4j5Vd2xxiQ3ardFQwRtVzwCwgBfgA423vPWBNANN1A127GCgDvquUeiXIc28DfqiUOiTA+plAY319vSUZ4J0/Lh98AEoZAcaOPz6qc+sa6+nt7SU+Pn7gihHm+39ewcdlxkhTQpyw7KYTKMga4lbHatlDeZmIklEVNp0VTqzWUdEgVp+XgRhI7sYNG1h2htf3dzdmvfoqQw+NbAxsu7Y5xIbsVumoYNLUnKuUyscIkvcmcBTwBrBPRF4TkZ+JyJEhynEJUAO8HsK5R+CeDT4gHFMgg8URYGzu8uXMevVV5i5fzjFPPaUNqgMMxzRYLLFm5z6nQQVw+iHD+xlUYL3s0QglEgph1llhxyodFQ1i8XkJhIHkjnayXn/Ytc3B3rJ7Ekqamk1KqUdNhTUSmAmsBX4OfBzs9UQkDsOo+punf4OI3CMif3fZv05EzhKRg0XkUBG5Bzgbwz8iqqTk5TH00EP1slpNxPjja+vc9kt/91NWXHJJWB1lgwklEitYrbM0X1+inaxXE/uEtJRYREZgOH7OwXACHQ90YgyxB8s3gSLgr16O5ZvHHCQB92MkKG3HSCvxbaXUG8HeNCsrK2hBNV9fYm0aZu3K9by7s9W5f3jFOkY272Hvsj39ovlbKXs0Q4kMBot1VkSws46KteclUAKRu3TBAj5avdrn9Hc4k/X6w65tDvaW3ZOAR6pEZJ6IPCIiG4FK4O8YwTqfB74BZCml/IY38IZS6h0zIekWL8cuVkrNcdn/rVJqnFJqiFJqmFLq+FAMKiDq87caexFr/eXBZ91DuX1z0/5MK55TcFbKHsvTH/3FCI/OihSx1ueCwa6yByJ3NJP1+sOubQ72lt2TYEaqngFWAf/BcPb8SCnVHhapIkBdXZ2tg+tpIkttbS2ZmZnRFgOArV/t4oPk/cHyDq75ijH1293quEbzt1L2aIUSCRFb6yw766hYel6CIVC5o5Ws1x92bXOwt+yeBGNUZSulWgeuptFowkVfn+LW17fQG7f/0Z27aXH/imGcgovV6Q8vaJ2lCSuRTtariX2CSSnTCiAiQ4G5QAmggHJgSaSWSVtFtGNiaOxFrIwYPPvJTj6p3h8ob+zerUzcs6l/RZcpOKtld0x/xHooEbvrLDvrqFh5XoLFrnKDlj1WCMpRXUR+iLHSznOcrlFE5iulnrNMsjCzb98+cnJyoi3GgLz00kssXLiQ3t5eOjs7KSgoYPHixZx22mk8/PDDjB071rJ7lZSU8NprrzFp0iS/9e68805uvfVWkpKSALjjjjs49NBDOffccy2TJdaor69n5MiRUZVhV30b97zxpXM/saeLH3z6LOKlrusUXDhkj8XpD2/YWWfZRUd5Ixael1CIhNx9fYp3N9Xw52XbWLe7kfTkBHLSk8hJT2Z4erLzf29lKYm+fY/s2uZgb9k9CSZNzVTgKQw/hd8DmwABSoHrgH+IyCal1DqfF4kh2trafB5r6uhmc3VzROSYMDKDzJREr8eqq6uZP38+n376KcXFxQCsXr0aEeGNN0Lyz7eEu+66ixtvvNFpVN19991RkyVS+OsvkUApxc0vfk5r1/6A4GdteYfc1v754Tyn4MIpeyxPf9hdZ0W7zw0Gu8oeTrl7evt4fX0Vj7y3jc179v++1Pd0Ud/axZY93mO/ueJugO03unLTk+htbaC0Pck4npFMRnICIt5euWIPu/YXbwQzUnUN8LJS6mKP8tXAhSKSCvwEuNQi2cJKYqJ3QwZgc3Uz8x6LTPiaF+bP4MiSYV6PVVVVkZCQwPDhw51lU6dOBdxHlebMmcORRx7JypUrKS8v59prr6WwsJCHHnqIiooKfvOb33DeeecBICI0NzeTnp4OQE5ODqtWraKkpMTt3g888AD//Oc/6enpITExkYcffpijjz6a+fPnAzBz5kzi4uJ45513uOmmm5g+fTpXX301LS0tXHvttXzyiZE6Zd68efziF0bqxTlz5nD00UezfPlyKisrmTt3Lo899ph1jRlGfPWXSI3UPPvJTpZv2x/o88iSbG67+Cds+k3HgFNw/vr6AY7lOktEZgE/A6ZhhHz5jlLqZZfji4CLPE5bqZQ6Jljh7fy92VX2cMjd2dPLf1ZX8Oj729wSn4dCS2cPLZ09bPd1nSUVzn+TEuLIdTG+HMZYbkZyv7+ZKdE1wOzaX7wRjFF1LHCln+OPYeTYsgUFBQXRFmFAJk+ezIwZMygqKmL27NnMnDmT888/36uvxc6dO1m6dCnV1dWMHTuWG264geXLl/PJJ59w1llnOY2qQLngggu4/vrrAVixYgWXXXYZX3zxBY899hiPP/44y5cvdxpmrvzyl7+kq6uLzz//nPb2do477jhKS0uZN28eANu2bWPp0qV0dXVRWlrKxx9/zIyBcmnFAJ5tHmzuu8Gwq76NX7++f9ovJTGO335vMlk5aQFNwdnZN2eQhENnpQHrMEbAXvRR5y2MgMYOuoK8B2APHeULu/Y5K+Vu6+rhX5/s4s/Lyqhu6p8wOCk+jjOnFJCWnMDelk7qWjqpbemitqWThrbuQd+/q6ePioZ2KhoGXvDqMMByMoxRL1eDa3+58TctKd5yA8yu/cUbwRhVBUC/WFIubMEIymkLduzYEfPB9eLi4njxxRfZtGkT77//Pm+++SYLFy5k1apV/erOmzePuLg4CgoKyMnJ4ayzzgJg2rRpVFVV0dHRQUpKSsD3XrNmDQsXLqSuro6EhAQ2btxIV1eXc8rPF0uWLOEPf/gDcXFxpKWlceGFF7JkyRKnUXXeeecRHx/PkCFDmDJlCtu2bbOFUbV9+3YOOuggwHfuu73LlvULvDlYlFIseMl92u9nJ09kTE6ac3+gKThX2b9mWK6zlFJvYqS88ffD0qmUqg7mut6wg47yhV37nBVyd3T38pcPy/nLh+XUt/a3p1OT4vnhMcVcftwY8jK96+Tu3j7qW7vY29xJXWsXtc2d1LXuN7pqW/aX1bV00dM3uFSWwRhgQxLjyclIMowsHyNfuebfIUmBxZ+ya3/xRjBGVSrQ39zeTydG4lLbM2FkBi/Mj8wP/YSRGQPWmThxIhMnTuSKK67glFNO4dVXX+1Xx9Vgio+Pd+47gqr19PQ493t79/9Ad3T0/0q7uro4++yzWbp0KdOmTXMmmgzEqFJK9fuxcd33lNMhl50IJPfdMU89Zcm9nv1kJx9tdZ/2u2RmiSXX/hoQLZ01R0RqgAaMJM63KaV85u0RkWQg2aVoYKWgiUk2Vjbx0+fWuvlMORg6JJGLZ5Zw8cwSstP869HE+DhGZKYwwofR5Upfn6KxvZu1m7aRkpVnGl3m1tzlst/F3pZOunr6Qv58AO3dveyqb2dX/cAGWEZKAlOLspk5djgzx+ZQWpBJfJw9/LxCJdg0NSeLSKOPY1mDlCWimNmovZKZkujTzymSVFRUsH37do499ljAWA1UXl4+qBV/Y8eOZeXKlZx00km89NJLtLb2D+PT0dFBd3c3hYVGgMmHH37Y7XhGRgaNjY1ep//mzp3LE088wcyZM2lra+Ppp5/mlltuCVneWMExYhBM7rvB+ljt3ud92i8uSKVk19EOi4i0znoTeAHYAYwBfgn8T0SmKaU6fZxzC/ALz8L29nbKysooLi6mqqqKrq4uhgwZQk5ODrt27QJg+PDhKKWor68HoKioiJqaGjo6OkhOTmbEiBHOxMzDhg0jLi6O2lpjccPo0aOpq6ujvb2dpKQkCgoK2L59O2D0mcTERPaaaYlGjRpFQ0MDra2tJCQkUFhYSHl5OWDo0uTkZGrMfI8FBQUopSgrKyM+Pp7i4mLKy8tRSpGZmUlqairV1cZA3siRI2ltbaW5uZm4uDhKSkrYsWMHvb29pKenk5GRQVVVFQAjRoygo6ODxkbj6zzooIPYuXMnPT09pKWlkZWVRUWF4VOUl5dHV1cXDQ0NgOGDWlFRQXd3N6mpqQwbNozdu3cDhl9pb28v+/bto62tjd7eXqqrq+ns7CQlJYXc3Fy39gYjMCtAYWEhe/fupbWtnf982cSTK6ro6nUfNcoeEs+8w4cz/5uH0tXaxL49u2lJTGTUqFFu7Z2UlORsQ8/2LioqoqyszNneKSkp7NmzB4D8/Hy6W5vJTe4lI6GFGZOL2b59O319SWRkDCctLc3Z3iNGjGBvQzPb9+yjob2XhIxhbNlZTV1LF809QmOnorqhlfq2Hva19w56BKy5o4f3t+zl/S1GP0pPiuOokmyOLMpkfJZQkp1EcXEx3d3dlJWVkZKSQl5enlufFRG39q6trXX22fz8fGcy5uzsbOLj4936d319PW1tbSQmJjJ69Ghnn/Vs74KCAmdfHyzBGlV/G+D44L6BCDLQiEss0NPTw9133015eTmpqan09PRw0UUXceaZZ/KTn/wkpGs++OCDXHXVVeTl5XHCCSe4OcE7yMzM5O677+aoo46iqKiIM844w+34DTfcwIknnsiQIUN455133I7dfvvtXHPNNRx22GGAMS35ve99LyRZYwlHf4lU7julFAteXO932i9Q7NDXw0hEdZZHiIYvRGQVhoH1beAlH6fdAzzgsp8B7C4qKnL6VXnG8fGcKnE1nD19sTzrukauzs/P91s3I2P/oNmIESP81nV9yRoxYoTb/pgxY3yem5qaSq5LWiPHSmdvddPS0tx0VlFRkc+6YPwoO3C8JPqqm52dTUtLC/Hx8f18fDzrur6U9w3J4rb/7uCT8nq3OjnpSVz7jYM5Z3rh/lAIGal+r+vZhv7quu4PGTKE1NRU5/meC48827BklEv4gsO9++4pZYyA1bZ0UtNsjnQ1d7K32Rj5cv1b19pFbwAGWEtXH//bUsf/thhGUk56MjPGNjFtVDrfmDSSgmGpXj+ra3sP1Gdd+7dnmAZ/7Z1rUWotUco2dpAliEgm0Lh27VomT54cbXE0NqGsrIyDDjqIjpoaFg/kAybC3OXLB2VUPbtyJ7f+Z71zf3pxNs9dMSOkoXOH7NHEMYUMDI31oJuBIiIKj9V/Pup9BTyplPpNgNe1vY6KhT4XCsHIrZTiP2sq+MUrG2judHdjmFs6gnu/exjD05N9nG090Wzzvj7FvrYup+HlMLY272nm4211AflqfeeIUfz+3CnhF9YHVumoYEeqNJqvNZHIfbd7XxsLX9/o3E9OiOO+eZMPeF+EAxERGQ4UAlXRlkVjHQ1tXdz2ny94fb3715qWFM8vTj+UedNH2yZGlBXExQnDzZhZnn7CSil21bfzcVkty7fVsXxbHXub+8+Ezxjbf9bEjgRtVInIGT4OKQyn0K1KqfJBSRUB7LxcWRN5XKcDwpn7zvu034SQpv0cHEjLlUPBSp0lIunAOJeiMSIyBag3tzsxQi1UYaTF+TVQi5HUOSjsrKPs2ucCkfuDr/Zy4wvr2NPkbhhMK87m9+dMoWh4qo8zw0ustrmIUDQ8laLhRZx7ZBFKKbbtbTEMrK11fFxWR2N7NzO/rkYV8DKGMvI0wx1lSkQ+BM5SSu0bnHjho7Gx0bI5VM2BT0NDg9PHIZy57/75yS4+3Lo/Svr04mwuOXaMnzOCk/1rystYp7OmA++57Dt8of4G/Bg4DLgQwwm+yqx7rlIq6BQNdtZRdu1z/uTu6O7l3jc3sWj5drfyhDjhp3PHM3/22KiOJtulzUWEcXkZjMvL4MIZJfT1KZZv3M7o7OgYo1YTilE1F1gI3AZ8YpYdBfwKY6VLI/A4cD9wmQUyhgVvq940Gl949pdw5L7zNu332+8dPmhFrfu6dTpLKbWU/saZKycPUlYndv7eoin7YJ5JX3J/UdHIdc+tZWuN++j02Nw0Hjz3CA4b7Xs1eaSwa3+JixMKUg8c3+5QjKo/AD9SSi13KXtXRDqAPyulDhWR64C/WiFguEhI0O5kmsDx1V+syn2nlOKWl/pP+x2U2z9sRbDovm5PnWXn7y0asluR5cBT7t4+xWPvb+P3i7f0Cy9w8cwSbj5lYsABLsON7i+xQSifZCzgzTO+CXAsPfgKiOn06p7LazUaf3gu3baaf326iw++snbaz0G4ZbcBttRZdtZRke5zVmU5cJV7V30bP31uLat2uM8I52Ukc9+8ycweH1tTs3Z+zu0suydxIZzzGXCfiDh7lPn/b4FPzaKDgd2DFy98OIKA2RFHEDi7YVe5IbyyVzS0s9AlyKdV034O7NzuFmFLnaV1VOAEkuUgEMrKylBK8fyqXZzy4LJ+BtWph43k7etmxZxBBfZ+zu0suyehjFRdBrwC7BaRXRjOnkVAGXCmWScdw1dBo9H4wVjt9zktLnFurJr20zjROusAxsosB40dPcx/+jPe3rDHrTw9OYG7zjiU704d9bUKlaAJnqCNKqXUZhE5BMMhczyG0+YmYLFSqs+s83Ig1xKRO+mfnmGPUmqkl+qOc2ZjrLg5FKgEfquUeizIj+E3TU2sY1fZ7So3hE92z2m/aRZO+zmwc7tbgZU6K5LY+XuLpOxWZTl4b3MNN764nbrWbrfyo0qG8btzJlM4LLZXp+n+EhsEbFSJyLMYS5PfMqONvmVug2UD8E2X/V5fFUVkDPAG8ATwQ+BY4BER2auUejGYmyYnRy7SrdW4JiW2E3aVGwzZrVzpB96n/e6zcNrPgZ3bfTCEUWdFBK2jAiM5kLATIj7rtXf18us3vuQfK3a4lSfGC9fPncCPZh1ki8C7dn7O7Sy7J8H4VG0GbgZqROQdEblKRKzwpOxRSlW7bP5eO+YDO5VS1ymlvlRKPYmxYufGYG/qSKRoRxyJNO2GXeVu2ryZlb/7HYtnzGDZGWeweMYMVlxyCU2bN4d8zUhO+9m13S0gXDorImgdFRiOLAf+8JXlYN2uBr790Af9DKqD89L5z5XH8uM50Y09FQx2fs7tLLsnARtVSqm7lFLTMKIJvwycAXwlIqtF5E4ROSJEGQ4WkUoRKReRf4mIv+RFM4B3PMreBqaLSKK3E0QkWUQyHRtGslKNJiAcq4patm51K9+7bBkfnXNOyIbVcxGY9vu6E0adpYkxShcsICHd+wuJtywHPb19PPTuV5z96HLKat3jO11ybAn/veY4Jo06cKakNJFjUAmVRSQD+BaGs+e3gGbgv8CjSqkNAZz/LSAV2AKMAH4OTAQOVUrVeam/BViklPq1S9lM4COgQCnVL7+WD78tVq1aRXZ2NsXFxVRVVdHV1cWQIUPIyclh165dAAwfPhylFPX1RvbxoqIiampq6OjoIDk5mREjRrBz507AyIYeFxdHba3xQzl69Gjq6upob28nKSmJgoICtm/fDhgZ5RMTE9lr+gKMGjWKhoYGWltbSUhIoLCw0LnyZ+jQoSQnJzvfWgsKCti7dy/d3d3Ex8dTXFxMeXk5SikyMzNJTU2luroaMDJ0t7a20tzcTFxcHCUlJezYsYPe3l7S09PJyMigqsposhEjRtDR0UFjYyNgZPPeuXMnPT09pKWlkZWVRUVFBQB5eXl0dXXR0NAAGBnRKyoq6O7uJjU1lWHDhrF7t7GQKicnh97eXvbt20d3dzfjxo2jurqazs5OUlJSyM3NdWtvgLo646svLCxk7969zvYeOXIkO3YYb5TZ2dnEx8e7tXd9fT1tbW0kJiYyatQot/ZOSkpytqFnexcVFTlXnwwdOpSUlBTnm1PVffdRt307qqgIaW5GVq5EzZgBcXGwZw/Dx44l/8Ybne3d1tZGU1MTIsKYMWPc2jszM5PKykr2tHRz+b/LnTGpkuKFN35yPEkd+7y2d25uLt3d3W7tXVlZ6eyzw4cPd2vvvr4+tz67a9culFKkpKSQl5fn1mdFxK29a2trnX02Pz8/4PYePXq0s896tndBQQEVFRWMGzcOopxQebA6KxI4EipXV1fbIkK2N9rb2xkyZEhE7+mMUzVAloMdda1c99xa1uxscDt/ZGYKC8+cyDcOjc10LwMRjTa3iliQ3aqEyoMyqtwuJBIPzMF4G1xvTs0Fe400YBuG8/kDXo5vAZ5SSt3jUnYs8CGQr5Sq9nJOMuDqnJAB7N66dStjx44NVsSYoKamhjwLfHoijd3k7qipYfGMGQCo8eORLVv6VxJh7vLlAftYKaW48K+fuI1S3XbqIfzfrPBll4+FdrdKYVmJFTorHDiMKq2jQsOX76NSin99uotfvraRti53193TDs/nV2dNoqulIerPSqjEwnMeKrEgu1U6yrIwpkqpXuBdcwv1Gq0ish4jZow3qgHPlYF5QA/Qb2TLvGYn4Mx86VgO2+IjpokdaGlpiXoHDAW7ye22qig3F7wZVQGsKnLF27TfpceFd9rPbu0eKazQWeFE66jQ8JbloLalkwUvrmfJl+6+OxkpCfzyzEmcOaUAEaGs2r7Pip2fczvL7klARpWIrMGI7TIgSqmpoQpjjiodAnzgo8rHwOkeZScBq5RS3V7q+yQ+PjZSC4SCXWW3m9xuq4V6erxX8rOqyJOKhnZ+FcYgn76wW7tbQaR0Vjix8/cWS7Iv2biHBS99Tm1Ll1v5MQcN43fnTGFU1v5pp1iSO1i07LFBoCNVL4fj5iJyP4Y/w06MEaefA5kYGd8RkXuAUUqpC81THgOuFpEHMMIqzMAI7Pf9YO9t57D4xcXF0RYhJOwmt2NV0d5ly5CVK73W8bWqyBNvq/1uPGkCYyMQ5NNu7W4RL0dbgMGiddTgaO3s4Vevf8k/P9npVp4UH8fPTp7AZceNIc7jhSYW5HYQbAiXWJI9WOwsuycBGVVKqbvCdP/RwD8xcm7tBVYAxyilHOtb8zEiHzvkKBeRU4HfA1dhBP+8NtgYVQA7duzgsMMOG6T40WH79u2UlJREW4ygsaPcpQsW8NHq1XQfdhjy8cdux7ytKvKF57Tf1KKssE/7ObBjuw+WMOqsiKF1VOis3rmP659by/a6NrfyiSMz+P25UzgkP9PredGWG0JPDB0LsoeKnWX3JGSfKhGZhjFVp4CNSqk1wV5DKXXeAMcv9lL2PjDo4fq+vr7BXiJq2FV2O8qdOWECxz7/PKuee47WFSv8riryhbdpv/vmTY5Y/Bs7tns4sEJnRRI7f2/Rkr27t4+H/7eVP723ld6+/bO/InD5cWO44aQJpCT6nmrqamqiccMGywL8BstgEkPr/hIbBG1UiUge8C+MVTMNGCkfhorIe8B5AwTvjBkyMuwbrsqusttV7swJEzjsxz8mY/78oCOqK6W45aX1UZn2c2DXdrcKu+osO39v0ZC9bG8LP31uLet2N7qVFwxN4f5zJjNzbI7Pcx2jQzWVlWw2Y9IFMjrkjcFkXggkMfQxTz3l9bjuL7FBKCNVD2P4PR2qlPoSQERKMfygHiIE/6ZokJoa23mc/JGWlhZtEULCrnKDIXtKamrQSvL5VbtYtmX/b/bUoizOH58W0bdhO7e7RdhSZ2kdFRhKKZ5ZuZNfvb6Rjm73EY+zphRw15mTGDrEa2xowGN0KCvLWR7I6JDndUKZtnMw2MTQdn7O7Sy7J8GkqXFwCvBjh3ICUEptxPBx+pZVgoUbO4fFdwT3tBt2lRtCk72yoZ1fvbZ/2i8pXvjh6n/xv2NnWpbuJhDs3O4WYUudpXXUwNQ0d3DZ31bx85e/cDOoMlMSePj7R/DgeUf4NajAY3To0EPdjjlGhwbCYZh5GkXBZF4IJjG0N+z8nNtZdk9CMariAG/hC7pDvJ5Gc8ChlGLBS+tpdpn2O23D6yS+94ZbvcGmu9EEhGU6S0Rmich/zdRaSkTO8jguZgqcShFpF5GlInKoj8tpBsHbG6o55cEP+N8m9xyJx44bzts/ncXpkwsGvEYwo0P+CGTabiAGmxhaExuEYgT9D/iDiDh7rIiMwliRF5NB9Lxh1/QPYKRDsSN2lRuCl91z2u/grjpmr3/ba91AlW6o2LndLcJKnZUGrAOu9nH8JuB68/iRGAGLF5vpcYJC6yjvtHT2cNO/13HFPz6jvnV/7KmkhDjuOK2Uf1x6NPlDA0t50m/UZ+PG/pX8jA6BdYbZYBJDg72fczvL7kkoRtXVGKletovINhHZCpSbZddYKVw4aW9vj7YIIdPW1jZwpRjErnJDcLJ7m/Y753+PEucnFmUgSjdU7NzuFmGZzlJKvamU+rlS6iXPY2Kka7gOWKiUekkp9QVwEUZ+0/ODFVrrqP6s2l7Pt/6wjOdX7XYrL83P5LVrjuNSL7Gn/NFv1GfYMK/1OuvrfT6fg522cyXYxNCu2Pk5t7PsngRtVCmldpkRiL8NPIjh6HmqUmqaUmq335NjiKammEg/FhJ2ld2uckPgsnub9rtqchYjmwcwmAJUuq40btxIxauv0ujt7doFO7e7FURQZ43BSKP1jsu9O4H3gZm+ThKRZBHJdGwYxp6tvzerZe/q6eO+tzdxzuMfs6t+v7EpAvNnj+Xlq45l/IjgV5D1Gx3yMWKy8uKLffpAWjlt5wjhkjtrlvHhzHNzZ80a0GFe95fYIOQ4VUqpxcBiC2WJKI4cgHbErrLbVW4IXPYXVu12m/Y7oiiLy2eN5X8D3yBgX4mqt99m3W230b1vn7MsMTubyQsXkn/yyV4ubd92t5II6CzHL7Knh/kewF/I6FuAX3gW1tfXU1ZWRnFxMVVVVXR1dTFkyBBycnLYtWsXAMOHD0cpRX19PWBEYa+pqaGjo4Pk5GRGjBjBzp1GRPFhw4YRFxdHba0RhHb06NHU1dXR3t5OUlISBQUFbN++HYCsrCwSExPZaxr6o0aNoqGhgdbWVhISEigsLKS8vByAoUOHkpycTI05klNQUEBLSwtlZWXEx8dTXFxMeXk5SikyMzNJTU11OiaPHDmS1tZWmpubiYuLo6SkhB07dtDb20t6ejoZGRms2LidX79XyVe1HW7tMyprCAvm5HNoXhL76vaSlZVFRUUFAHl5eXR1ddHQ0ABASUkJFRUVdHd3k5qayrBhw9i927Cni3/6U+qqq+nNzUWNGQOffAKlpZCeDk1NRs7P6dMBqNm6lbprr2XMXXeRkpdHYWEh9T09pF10ES1r1xrTh0cdZQi4Ywd0d8O4caSPG0dcVhbV1dW0tbWRmJjIqFGj3No7KSnJaMPERI547DFqtm+nubaW5KFDGTtpEmVlZdSWlTF06FBSUlKcCxny8/Npbm6mrq7O2d7bt2+nr6+PjIwM0tLS3Nq7ra2NpqYmRIQxY8a4tXdmZiaVlZXONuzs7KSx0QhRMWbMGHbt2kVPTw9paWlu7Z2bm0t3d7dbe1dWVjr77PDhw53tnZOTQ19fn1ufbWxspKysjJSUFPLy8tz6rIhQV2ek9i0sLKS2ttbZZ/Pz89mxw4gVnp2dTXx8vFv/rq+vd7b36NGjnX3Wrb3NPrs3yJdaX4hSAaXHcj9J5CiMmC95eIx2KaWut0SyMOHIAN/Y2EhmpveouhpNKFQ2tHPy75c5R6mSEuJ449rjGZeXzopLLvHrd5E7a5bP+DOuVL39NquuvNLn8emPPOLVsIo2VmWAD5Vw6CwRUcB3lFIvm/szgY+AAqVUlUu9J4BCpdQpPq6TDCS7FGUAu+2iowYTl8kffX2Kv3+8nXve3ERnj3uohLOnjubOM0rJSPG/si9QnOEQPvjACPA7AJ7Pq6+gnWBM2wUalkETPazSUUFP/4nIrRjpZC4BpgNHuGxTQhUk0jgsYTvisMzthl3lBu+yd9TU0LhhAx01Nc4gn67TfjfMHc+4PMM/YjC+Eq6su+22oI/bud2tIII6y7Eu3HMOKY/+o1dOlFKdSqkmxwY0Q+zrqKbNm1lxySUsnjGjX4iQwfa5PU0dXPTUJ9z5341uBlVWaiKP/mAqvztnsmUGFRjTbsc89RSHvPwyRy9aNGB9Tx/IwUzbWYWdn3M7y+5JKNN/PwEuVUotsliWiNLb2xttEULGrrLbVW5wl91bkL8vTrqQ94dOd+5PKczi8uMPcu47lK7b23CQ6W4aN250m/LzRve+fTRu3MjQ0lKvsn9NiZTOKscwrOYCawBEJAmYDdwc7MVi+XsbKJ1K8eOPQ4hJct9YX8Wt/1lPQ5t7FIzZ43O573uHk5eZErLcDnyNrsWlppIcyHS56QPpeq7DMAvXyN1A+Oov0ZInGGK5rwdLKEZVH8YQt61J9zFqYAfsKrtd5Yb9snv7Makfks3fUveHIkpKiOP+eYf3y+03WKXbYqbPCKSeq1Fl53a3CMt0loikA+NcisaIyBSgXim1U0QeBG4Vka+Ar4BbgTbg2WDvFcvf20Bxmfa+9BIcc0xQ12zq6ObOVzbw0poKt/LkhDhu+/YhXHBM8aD9AweKep6enk5yIO3uxwcyJS8vKsaLZ38ZbIT3SBLLfT1YQgmp8HuMSMS2xs65huzgZ+ENu8oN+2X3/DHplTj+dvSFdCTuj4tz/dzxjMvz3b9S8vIYeuihQSve9HHjBq7kpZ6d290irNRZ0zFGoRzJmB8w/7/b3P8txgrDR4BVwCjgJKVUc7A3ilUdFUhcpsbFi4MKEbKyrI5vPfhBP4PqsFFDef3a4zlnbCpNGzcOKuxIIFHPMzMzBx0vKlq4PudWRHiPJAeSjgrFqLofmGDGe/mviLzkulktYLioqqoauFKM4lidYTeiJber71OoVFZWev0xeWfiSZTljnXul9SV88MJ4XnrGlpaSmJ2tt86idnZbqNUYN/+YiGW6Syl1FKllHjZLjaPK6XUnUqpfKVUilJqthmvKmhiVUcFFPrjsMMCqtfZ08s9b37JeU+soKJhf6iEOIFrThzHom/mUHvzNV79tlwJ5BkPJOq541mxygcykrg+51ZEeI8kB5KOCjWh8gnAe0Ad+IloqNFEEauHvz1/JMqGj+HNQ/cv6Erp7uCiFX+jt+4EGBmeaNiTFy70u/pv8sKFYbmvzdE6y0Ksisu0ubqZ655by5dV7gutioal8vtzJ3Nwx14+Oudcn35bxz7/PEBAz3igUc+HmfeywgcyWgw2MbNmcIRiVF0InK2Uet1qYSJJro3zJ+XZ9EGIpNwDOdIGuyInLy+PBJeov22JQ/jb0ReiZP9g77mfPUdO+76w5ubKP/lkpj/ySFBxquzaXyzEljorVnWUY3rM3w/3sIwMnz/YfX2Kv35Uzm/f3kyXR6iEc6cXcvvppaQnJ7Dikhv8jrZ8fvvtNG/eHNAzHmjU8wwXh+loO54Hi+M5DybCe6x8ngNJR4ViVNUD26wWJNJ0dXUNXClG6ezstKVjXyTlDmT4O5C4UA6aq6tJ7exk2NFHU7dyJc9NPYf6tOHO40dtX8n0XZ+RO2tW2BVV/sknk3/yyTRu3EjL1q2kjxvXb8rPFbv2Fwuxpc6KZR1VumABH61e7TMuU9Fll3k9r6qxnRtfWMdHW+vcyoelJXHPdw/j5EONaBSBjLbs++wzn8c8n/FAR9fw4scWLcfzYHE853ZMzHwg6ahQfKruBO4SkVSLZYkojiixdsSuskdKbqsSnML+WDyf3n03y844g/qVK/lkzDGsLprmrJPTspd5a/4dcV+LoaWljDrjDL8GFdi3v1jIndhQZ8Xy9zZQXKYeI4iiG6+uq+Tk3y/rZ1CdODGPt6+b5TSoIMDRlgFwfcYDdT5vjwvlJzE2cPQXOzrax3JfD5ZQRqquBcYCe0RkO+AWTMTMsaXRRA2rhr/dphCPPRaAmvRcnp9ytrNOXF8vF6/8O4Uzj455X4uvMVpnhQF/02O1ZWXOeo3t3dzxyhe8stbdGXlIYjw/P+0Qzj+qyBkqwXEtrEit5PGMDzS6VrpgAbWDv2vUSEpKcv4/6Re/YN3NN9PrJSl3/JAhTPpFv6xIUcVVdrsTilH1stVCRIOSkpJoixAyY8aMibYIIREpua0a/nabQly+nB6JZ9HRF9GVsD+jyDXTcrj8xn/F1FufJ3btLxbycrQFCAW76Chv02OOPrd8ay03vLCOqkb3vH2TC7P4/TmTOSh3f/w3T4fzxOzsAYPd+sXjGQ/E+TwjhLRtscLo0aOd/6eXlHDsc89FUZrgcJXd7gRtVCml7rLq5iJyC/BdYCLQDiwHblZK+QyiISJzMFbxeHKIUmpToPfevXu3I8+P7di1axdFRUXRFiNoIiV3II60Aw1/95tCnDaN1zsL2DVsv/zja7ZwxZzzY9qgAvv2F6uwUmdFEjvqKMdIU2VvHy/ujOfJD8vdjsfHCdecOI6rThhHYrwx1eZrUYk/gyohPZ2MCRP8+lV5e8YHcj7/uj8rmsETykiVEzO6sGdy0mASEc4G/gR8asqyEHhHREqVUq0DnDsBcL1XUJPwPT09A1eKUewqeyTlDmSo3x+eU4ibM4pYMmaOcz+ts4ULVv6D7tqTYER4widYhV37SziwQGdFDDt9b64jTRVDC/j7CfOpTMxyq1MyPJXfnzuFI4rcY635W1QC5ohVQ0O/kSXAbxJjf8+4L+dzO7W5JjYJ2qgSkTHAHzEyvrsmYRKM+C/xgV7LM2u7iFwC1ADTAP+exlCjlGoIQF5vGeBJS0sLVMyYw66yR1LuwcaZcZ02aE5K5x9Dj3Q7fv6qf5LV2RRTK2h8Ydf+YhVW6qxIYpfvzTHS1NXSynvjT+S1SafRE+/+03L+0UX8/NuHkJq0v7yjpoamzZsHXFTS3dDArFdfBaX6jSxZHUvKLm2uiV1CGal6xvx7KUbmdSsnoR1j3fUB1F0jIinARuBXSilvU4IAtwD9vPKampooKyujuLiYqqoqurq6GDJkCDk5OezatQuA4cOHo5Sivt4Qp6ioiJqaGjo6OkhOTmbEiBHOTPLDhg0jLi6O2lrD1XH06NHU1dXR3t5OUlISBQUFbN++HYCsrCwSExPZa46GjBo1ioaGBlpbW0lISKCwsJDycmPYfOjQoSQnJ1NjrmIpKCigp6eHsrIy4uPjKS4upry8HKUUmZmZpKamUl1dDcDIkSNpbW2lubmZuLg4SkpK2LFjB729vaSnp5ORkeGM2jxixAg6OjqcqzAOOuggdu7cSU9PD2lpaWRlZVFRYaSQyMvLo6uri4aGBsDw/aioqKC7u5vU1FSGDRvG7t27AcjJyaG3t5d9+/bR09NDTk4O1dXVdHZ2kpKSQm5urlt7A9TVGauDCgsL2bt3r7O9R44c6cxmnp2dTXx8vFt719fX09bWRmJiIqNGjaI2MZG822+nRClUUxPN8fEkpKeTPGoUe/bscbZ3UVERZaZj7dChQ0lJSWFPSwtpF11E88sv8+zRl9KUuF/ZHteyhcPGZDL0mO/Ql57uPHfkyJG0tbXR1NSEiDBmzBi39s7MzHRGDs7Ly6Ozs9PZ3mPGjGHXrl1e2zs3N5fu7m639q6srHT22eHDh7u1d19fn1uf7ejooKysjJSUFPLy8tz6rIi4tXdtba2zz+bn5wfc3qNHj3b22aysLJKSktz67F4LVnMNgnDqrLBhl6m/jffeS3VfEs/MuZStue4pkjI6mpjfvp6rvvNtZ5k3/ym/KAVKMfTQQ/sdsjqWVFZWVsjnajQAooJ0zBORFmCaP7+nkAQxln+8AmQrpY73U28CMAv4DGME6gJgPjBHKdXvKfUxUrV77dq1TJ482cJPEDnKyso46KCDoi1G0NhN7qbNm1l44wM8d+gZzrL8xkpuXPI7UlOTgw4gGi1iod2bmpocRsLQSE+3hUtnhQsRyQQa7aCj2vfs4ZfnX8+LU75LZ2KK27HDKtbz/c/+SUZXK3OXLyclL8+n/9RAzHj6aXJmzLBSdK/EwrPiDbsEILUzVumoUEaqPgUKAasV1B+Bw4Hj/FUyFaPrvT8WkULgRrxMGSqlOoFOx/5gs5xrvj7sTB/JS4edAWbQ54Tebi5e+XdGHTdDh0+wF+HSWV9r9jZ38rMXNrD0yPPdypO7O/hu6+fMWP40Dm3rCG0wkP+UL7b++c8RMapiDatTbXlSUlJCSkoKycnJtLW1UVpays0338zMmTP9nrdo0SJmzpzJ+PHjBy3DgUYoRtXlwGMiMgr4gv4xXz4P9oIi8jBwBjBLKbU7BJlWAD8M5oScnJwQbhMbxGr6ioGwQu5IvbE1d3Rz1bOr6XbJovGzGSO46Gcv2u5N0a79xUIs11mRIJZ11Nsbqrn1pfXUtbpHfT9o7zYu+PRphqfFOw0qR2iDQILy+iJSuepi6VmxOtWWL/79738zadIkAF555RVOPfVU3n77bY4++mif5yxatIicnBxtVHkhlPCxuRiB9J7CeANcC6xx+RswYvBHjLAKJyqlygc6xwdHAEGldO/u7h64UoxiV9kHI7cjsvlA2eqtQCnFgpfWs6Nuf66/b04Yxo++c4ztDCqwb3+xEMt0ViSJxe+tqaObG55fxxX/+MzNoEro7eHMdS/zk6UPkdNaByn7pwIdoQ0GFSXdDOQZbmKpzQNJtWU1Z555JldeeSX3338/7777LjNmzOCII45g0qRJPGWm/HnyySdZtWoV1157LVOmTOGNN95g/fr1HH/88UydOpXS0lLuuecey2WzC6GMVP0VQxF9n8E7ff4JOB84E2gWEUeegkalVDuAiNwDjFJKXWjuXwdsBzYASRgjVGebW8DYOSx+Q0MDw4YNi7YYQROq3OF4Y/M34vXMyp28/vl+G71w2BCunZFj26lju/YXC7FSZ0WMWNNRH2+r48YX1lHR4B6le3x2EvP++xAjq7buLywshJ073UIbDGqlbIRy1cXKsxJMqi2rX/SOPPJIXn75ZaZOncqHH35IfHw89fX1TJ06lVNOOYXLL7+cp59+mhtvvJHTTjsNgObmZpYsWUJycjLt7e3MnDmTuXPnMn36dEtlswOhGFXFwBlKqa0D1hyYH5t/l3qUXwIsMv/PB1yjsSUB9wOjMAKGbgC+rZR6wwJ5NDHIYJIjexpPA/kobKhs5O7XNjqPJcYLfzp/KuldgSxI1cQoVuqsrx1tXT389q3NLFq+3a08TuCK2WO57psH03lWUf/QBh6+P4EE5fVFrOWqCzdWpdoKBcfitbq6Oi677DK2bNlCQkICtbW1bNiwgfz8/H7ntLe3c+WVV7J27Vri4uLYtWsXa9eu1UZVgPwPmAwMWkEppQZ89VdKXeyx/1vgt4O9d3Fx8WAvETXskr7Ck1DkDvWNzZvxlD1tGk1ffklvW5v7+eaI15Rn/sXVb+2hq2e/I9Wtpx7C4aOz6OvLDFr2WMGu/cVCLNNZkSQWdNSn2+u58YV1blPhAEXDUnngnMlMLzFGdZI9QhskDh9O6siR/a7nLyivLyKZqDxWnhWrUm2FwqeffsqkSZOYP38+p59+Oi+++CIiwtSpU+no6PB6zq233sqIESNYs2YNCQkJfPe73/VZ90AnFJ+q/wK/F5E7ReRsETnDdbNawHDhiM9kRxyxjuxGKHIH88bmwDFd6GmM7fvss34GlYPulhZueGIp5bX7A/mffOgILp5ZErLssYKdZbcIW+qsaOqoju5efvnaRs55/ON+BtX3jyrizZ8c7zSoXEnJy2PooYdS7yMyuSMob+6sWfuTJpujWtMfecRreSRDl8TKs+IY1fNHOEbvXnnlFR599FGuv/569u3bR3FxMSLCsmXLWLdunbNeZmam2/T0vn37GD16NAkJCWzevJnFixdbKpedCGWk6jHz7x1ejsVsdGJPurq6Bq4Uo9hV9lDkDuWNLZRl28vHzGD5kP2zzKOzh/Dbsyc7/ajs2uZgb9ktwpY6K1rf22c79vGzF9ZRVuueKSx/aAr3nn04s8cP/Ez6k91fwM78k0+OakymWHpWBptqK1C+973vkZycTGtrK6Wlpbzxxhscc8wx3HvvvVx55ZXce++9lJaWuq0G/NGPfsQNN9zAfffdx69//Wt+/vOfc8EFF/DMM89QUlLCiSeeaIlsdiSUhMqhjG7FHEOGDIm2CCFjV9lDkTvY5MihLNuuGFrAi0fsX+eQECc8/P0jGJqa6Cyza5uDvWW3gkjrLBG5k/5ZHPYopfrPh/khkt9bR00NTdV7eHxLF099Vk2fhyv/OdNH8/PTSslMSfR+AQ8Ckd1X/j1f5ZEglp6VwabaCgRHlg9vzJ07l6+++srrsdNOO83ppO7giy++GLQ8BwKDSqhsZ2JhhUeoONK52I1Q5Q7mjS3YZded8Un89ZhL6I5PcpYt+NbEfklf7drmYG/ZbcwG4Jsu+73BXiASOsrhe/jp+u08c9QPqc50t/tGZCZz73cP54SJ7kbOQKNJdu1zsSa31Wl4NOEnoDc4EbnWzLMXECIyX0QyQhcr/DjyqtkRR543uxGq3P78MDz9LYJx3FTAc9POpSZzhLPsm4fkcdlxYyyTPRaws+yhEgM6q0cpVe2y+bT2RSRZRDIdG2bS93DrqKbNm3nvvPN5cl8WD3zj+n4G1eljM3jnutluBlWg8eLs2udiVW6Hr5o2qGKfQEeqfg/8EwjUnf+3wDtAcyhCaTSeBPrGFsyy7RUlx/Bp8ZHO/ZFpCdz3vcm2jUelcSPaOutgEanESJG1ErhVKVXmo67XpO/19fVhTfr+vz8s4vE5N7A32T1xc0ZvO+dtfYvZrUOo+8Zo6jCSvld+8QUbHnuMvrY24+XGTGVSs3Mn9fPnU3LPPaTk5VFQUEBzc7Mtk77X1dVRXFxsedL3pKQkRo8ePWCn0USOtrY2Zz+0Mul7QAmVRaQPI72D9yUd/TkMmOBHiUQNR7LSXbt22baTNzU1kZlpvyX+kZLbX9LW+CFDyCwtZePWSu478Qa6E4xpv3iB5+fPYFqx9ykXu7Y5xIbskU6oHE2dJSLfAlKBLcAI4OfAROBQpVSdl/pek76HS0c1dXSz8KU1PPd5/x+RqTs/45w1L5DWZRhOjkTIACsuucS/b+OsWc54cbHQ50LBrnJrBk+kEyrfFeR1XwFiOlpiX1/fwJViFLvKHim5B3LwTBgzll88+D7d9fsHMX52ykSfBhXYt83B3rIPgqjpLKXUmy6760XkY2AbcBHwgJf6XpO+h+N7W7JxDz9/+Quqm9wH8DLbGzln9fNMrlzvKpgzuGSw8eLs2ufsKrcmdgjIqFJKBaugYp59+/ZRVFQ0cMUYpL6+nqysrGiLETSRlNvfdOGNL6xjm4tBdcKEXH50/EF+r2fXNgd7yx4qsaSzlFKtIrIeODiY86zUUXUtndz13428uq5/HKaZZR9x5uevktrtnn7GNVRJsBG+7drn7Cq3JnY4IMIjaDS+8HTw/Pdnu/n3Z/udUUdmpvC7c6YQF6f9qDThwZzeO4Qgk75bgVKKl9dU8M0H3u9nUOX1tHD10of5/mfP9TeocA9VEs0I35rw8dJLLzFt2jSmTJnCIYccwje+8Q36+vo49dRT2bZtm6X3KikpCSjswp133ukWL+yOO+7gueees1SWcBKQT9WBhMOnqq6uzrZhFXp6ekhIsF80jJaqKnrr691GjSK5VPirPc2c8cePaO82VrfHxwn/+tExHOklMrQndm1ziA3ZI+1TFU1E5H6MKO47gTwMn6rZwGFKqR0BnG+JjqpoaOfn/1nPe5vdR5niBC47bgyXlwirzz/PZ6gSz5W1wfhUxUKfC4VIyd3U0c3m6vCv45owMsNnbLHq6moOP/xwPv30U2dKpNWrV3PEEUeEZbFOSUkJr732GpMmTfJbT0Robm4mPT3dchn8EWmfqgOOmpoa2xpVe/bsYdSoUdEWI2AcsXBqGhsRM9VB9rRpgJE6xoFnAlYrae/q5apnVzsNKoDr544PyKAC+7W5K3aW3aaMxlh5mAPsBVYAxwRiULkSqo7q61P8Y8UOfvvWJlq73MNjTRyZwW/OPpzJhVkAQQWXDCZenF37XKTk3lzdzLzHPg77fV6YP8OnjquqqiIhIcEtNtfUqVMBdwNozpw5HHnkkaxcuZLy8nKuvfZaCgsLeeihh6ioqOA3v/kN5513HtDfIMrJyWHVqlX9cio+8MAD/POf/6Snp4fExEQefvhhjj76aObPnw/AzJkziYuL45133uGmm25i+vTpXH311bS0tHDttdfyySefADBv3jx+8Qtj4eycOXM4+uijWb58OZWVlcydO5fHHnuMSPO1Nao6OzsHrhSj2El2t5V4xx7rLHc1phw4EhuHI9fXna9uYMue/T8Gs8bn8uPZYwM+305t7omdZbcjSqnzrLhOKN/b1ppmbn5xPZ/t2OdWnhgvXHPiwcyfPZakhP1eH8EElwwmwrdd+5xd5Q6FyZMnM2PGDIqKipg9ezYzZ87k/PPP92pU7ty5k6VLl1JdXc3YsWO54YYbWL58OZ988glnnXWW06gKlAsuuIDrr78egBUrVnDZZZfxxRdf8Nhjj/H444+zfPlyryNVv/zlL+nq6uLzzz+nvb2d4447jtLSUubNmwfAtm3bWLp0KV1dXZSWlvLxxx8zY8aMEFondII2qkRkiFKq/wS8cSxfKWWLTMUpKQHHBYw57CS7Wx6+5oGHu3taWth4773OaQQr+M+a3Ty3apdzPy8jmQfOmRyUH5Wd2twTO8tuBXbVWcF8b109fTz2/jb++L+tdPW6r2A7oiiL35x9OONHGLFNvRlQgaaGCThenE37nF3lDoW4uDhefPFFNm3axPvvv8+bb77JwoULWbVqVb+68+bNIy4ujoKCAnJycjjrrLMAmDZtGlVVVXR0dATVdmvWrGHhwoXU1dWRkJDAxo0b6erqIikpye95S5Ys4Q9/+ANxcXGkpaVx4YUXsmTJEqdRdd555xEfH8+QIUOYMmUK27Zti32jClgjIucrpVa7ForI94BHAVt4Kuba2KEyzyZRdfstw960KaDzXJdmD5atNS3c9p/9zpFxAg99/why0pP9nNUfu7S5N+wsu0XYUmcFqqPW7mpgwYufs8nDRyc1KZ6bTp7ABTNKiI8T5zS86zMZ6pT7QEaYXftcpOSeMDKDF+aH/8d+wsiBkwRMnDiRiRMncsUVV3DKKafw6quv9qvjajDFx8c79+PjjVzkPT09zv3e3v1Tzh0d/WPvdnV1cfbZZ7N06VKmTZvm9GUKxKhSSvXz93Ld95TTIVckCcWoWgwsN5OG/gZIA/4IzAOsSZsdAXbt2mVbn6qdO3dy0EH+QwBYSajO5P2WYR95JHz00cAnuizNHgwd3b1c/exq2lz8Sn76zfEcc1Dw+b0i3eZWYmfZLcKWOmsgHdXW1cPv3tnCUx+V90uAPHt8Lgu/M4nR2amA74C44Zpyt2ufi5TcmSmJAftzhouKigq2b9/OsaZbxr59+ygvL2fs2MDdIjwZO3YsK1eu5KSTTuKll16itbW1X52Ojg66u7spLCwE4OGHH3Y7npGRQWNjo9fpv7lz5/LEE08wc+ZM2traePrpp7nllltCljccBG1UKaWuEZHXgaeAbwMFQBNwpFJqo8XyaSKAL6Mp1Ddbx/UIdQWJRUuz7/rvRre39+PG5XDlCeMGfV2NvTgQddaHX9Vyy38+Z1e9+6xmdmoid5xeyllTRrm9wbtNw3sQjil3TezT09PD3XffTXl5OampqfT09HDRRRdx5pln8pOf/CSkaz744INcddVV5OXlccIJJ3hNUJ2Zmcndd9/NUUcdRVFREWeccYbb8RtuuIETTzyRIUOG8M4777gdu/3227nmmms47LDDAGNa8nvf+15IsoaLkEIqiEgc8DDwY4w0EKcrpd62WLaw4FiuvGPHDtsG/2xoaLAkQJ0/ownwmerF23JrX9dLzM6me5/hNKtGjUICSBLrujQ7VF5ZW8FP/rV2/zUzknnj2uPJzQhu2s+BVW0eDWJB9miHVLCTzgpER13+t09Z8mWNW9mZUwq447RShntMbXfU1LB4IL8Sj5Q0gyUW+lwo2FVuzeCJWkgFERkLPAuMBE7GiL/yiog8BNymlOoOVZhIEmwcDs/RHF/7nfv20V1fT/q4cQwtLfV6PrB/JEcpt7JA/u9OTQWXB9/bSJOvMkc2eUlIYNX8+T6nAzImTPD7Zrvutts4/t//dpY1bd7Mh/Pm0esx3OswqAAIIAWE69Jsh7xdDQ0kZWWROWGCX6Xf2dPL2xv28K9PdrJ82/4UayLwh3OnhGxQGdewb3BQO8tuBXbVWf6+t1+eNYkVZcto6eyhYGgKv/rOJGYOEzp3bKXDY8Q52GjoVmDXPmdXuTWxQyg+VWuB14GTlVINwGIReQP4OzAXOMIy6cJIfX29c07XHwONvnjbdy0ff9VV1CxbNmDerGBQxx5LXnw8xeedx45//cvt2t7iP2VPm0ZveztNGwOb6ehpafEa8sCVhjVreGvqVCbfcw9pJSUs/8EP+hlUriRmZ9M1dixUVYEI2WY8lH2rV/dbmg3w4TnneJUhe9o0Dv/lL91Gyb7a08w/P9nFS2t209DW//fx2hMPZua4nIA+uy/q6uocbzG2w86yW8RabKiz/Omo/KFDuOXUiWyububHY+PZ8ZsFLPYxTR+NaOh27XN2lVsTOwQ9/SciFyil/uGlPAN4UCl1WZDXuxL4GZAPbACuU0p94Kf+bIykpIcClcBvlVIBR/hyDK2vXbuWyZMn+63ry7kz2qhjj0UCcfiOEJKcjBoovosIY//+d0YNHep3RK1p82Y+/N736G1r83mp+NRUpv7zeZZ1pPPcp7v6xeRx5cwpBTxwzhTiB5mGpqyszJaOtxAbskdz+s9qnRVurNJRrtP0wURDt4JY6HOhYFe5NYPHKh0V1TQ1InIu8A/gSuAj4ArgcqBUKbXTS/0xwBfAE8DjwLHAI8D3lVIvBnjPTKCxtrbWqxMdwJdVTfzunS3sW7OGrro6r3WiiYqPQ3rtl009+6gjScr0/xYYSJv3iVA2cgLtcd7TL6QlxXPGlALOO7KIw0cPtWRIv7u7m8RE7/eLdWJB9mj7VNmJQHSUg0CNpUCNL6uIhT4XCnaVWzN4oulTdaGfw8rbG6Efrgf+opR60ty/TkROxnAm9bZOcj6wUyl1nbn/pYhMB24EvBpVZjJTV2eaDDCGeX0prH2tXSz5cg+kFMCogiA+jsYvFR3G5o9BtPmUwizOO7KQ0yYXkJ5sbbKA2tpa8vPzLb1mpLCz7FZgsc6KGP50FHiJA+cFR8y3YKKhW4Fd+5xd5dbEDqH88vzBYz8RSAW6gDaMkacBEZEkYBpwr8ehd4CZPk6bYR535W3gMhFJ9OFwegvwC8/CiooKEhISKC4upqqqiq6uLoYMGUJOTg6VVTEZYFnjhfSkOOYePJRLZk8gL6mbtrY29tVUkTpqFNu3bwcgKyuLpKQkamqM1VKjRo2ioaGB1tZWEhISKCoqoqysDIChQ4eSkpLCnj17AMjPz6e5uZndu3fT1dVFcXEx27dvp6+vj4yMDNLS0qiurgZg5MiRtLW10dTUhIgwZswYduzYQW9vL+np6WRmZlJZWQkYQQY7OztpbGwEYMyYMezatYuenh7S0tLIysqiwlwpmZubS3d3Nw0NDYCRl6uystLZZ4cPH87u3bsBI9dWX18f9fX1ABQVFVFdXU17ezspKSnk5eWxc6cxCDxs2DBEhDpzZLCwsJDa2lra29tJSkoiPz+fHTuMdHXZ2dnEx8dTW1sLwOjRo6mvr6etrY3ExERGjx5NeXm51/YuKChgbyDO0uHDEp0VadrbvQaBdxKsA3owKWkGy0Cyxyp2lVsTO1gy/SciB2NEJr4v0GXKIlIAVADHKqWWu5TfClyklOr36iQiW4BFSqlfu5TNxJg6LPCWbsLHSNXujRs3csghh3iV7fPdDdz+77U0btgQyEeJOCo1FfHjczRo4uPIOPhgANp27aK3dZD3iosjY/zB9MbF+42Yq7q7A27z9M4WLrz0dM44dgIpifGDky8Adu/ezejRo8N+n3AQC7LH2vRfKDorUjim//zpKIhOqIRAiYU+Fwp2lRuMZNAjRoyIthghEQuyR236zxtKqa9EZAHwNDAx2NM99sVL2UD1vZU7ZOsEnF7UDv+akSNH+rzB4aOzeOW6Oay45G+WrtqzCpWQgIQj/L5jOuBG9+mAt6ZOpdscUQmWxOxsZj7zDJkTJtDb2+tMa+CLQNs8d9Ysjplzc0gyhYKdpwTsLHu4GKTOigj+dBQYqWJyZ83y71N1/PERN6jAvn3OrnKDMUptV+wsuydxA1cJmF6MSMWBUmue46k58oA9Ps6p9lG/BwjKo9wxBeKP0gULSPASKj/qHH20ZZeKT01lxtNPM+vVV5m7fDnHPPVUP/+Kyffc4/cacT4SacanpjoNKsA5leSP0gULiE9NHVBmR+iFSBGI7LGKnWUPM8HqrIgyWB3lGvMt0ti1z9lVbtCyxwqhOKqf4VmEEQ7haoxpuIBQSnWJyGcYcWL+43JoLvCKj9M+Bk73KDsJWBVsAL+WlhaamgYY4cvP57C//pXNDzxA7ccfO507E4YOpcf0bwFIyMpy23ctH/d//0ftRx/tP98CVHc3ucccQ+HZZ7PrxRfdZMsyl2A3rFvnVtbb3k6zGfjTQdaUKRz685+TZE71dQFdXtokbcYMJtx3H+t/+ct+n/uw228ntbi4XxvlzJjBhOuvh/x8Zzs3NzcH1OaHL1rEhl/9ioa1a/sddsjset1IEJDsMUosyB7N+1ulsyLNYHSUt+cvksRCnwsFu8oNWvbBYtn9lVJBbUCfx9aLMYL0LJAf5LXOxfgtvxQ4BPg90AIUm8fvAf7uUn8M0IoRp+oQ87wu4Owg7jkKY6pQb3rTW+S3UcHqnMFuWKizIiSv1lF601v0tkHpqFASKls2ZaiUek5EhgN3YLw5fgGcqpRyjAXmA0Uu9ctF5FQM4+sqjOCf16oAY1SZVGL4UGwCRgPN/qvHHBnAbuwnu13lBi27lbJURvqmVuqsCKF1VHSwq9ygZbdSlkHpqKgG/4wWjtU1xMhKpGCwq+x2lRu07JrIY+fvza6y21Vu0LLHEgGNVInIA4FeUCl1fejiaDQazeDROkuj0USDQKf/Ak04+vUb9tJoNLGI1lkajSbiBGpU/QTYoJTqDacwEaQTuAuX+FU2wq6y21Vu0LLbEbvrLDt/b3aV3a5yg5Y9ZgjIp0pEeoGRSqm9IlIGHKmUir1MwxqNRoPWWRqNJjoEuiqmATjI/L8kiPM0Go0mGjSgdZZGo4kwgU7/vQi8LyJVGD4Iq8w3wX4opQ7yVq7RaDQRROssjUYTcQIyqpRSPxKRl4BxwEPAE0Q/noRGo9F4RessjUYTDYKOUyUiT2EE3NQKSqPRxDxaZ2k0mkjxtQz+qdFoNBqNRmM1B6zzpohcKSLlItIhIp+JyPED1J9t1usQkTIRmR8pWV1kuEVEPhWRZhGpEZGXRWTCAOfMERHlZZsYQbnv9HL/6gHOiXp7m3Js99F+f/JRP2rtLSKzROS/IlJp3vMsj+NifheVItIuIktF5NAArnu2iGwUkU7z73fC9iE0TrSO0joqELSOspeOOiCNKhE5F3gQWIgRBPAD4E0RKfJRfwzwhlnvCODXwEMicnZEBN7PbOBPwDHAXAyft3dEJC2Acydg5Ep0bF+FS0gfbPC4/2G+KsZQewMcibvcc83yFwY4LxrtnQasA672cfwm4Hrz+JEYSYMXi0iGrwuKyAzgOeAfwGTz7/MicrSFcms80DpK66gg0DrKTjoq2hnZw5TlfSXwqEfZl8A9Pur/BvjSo+wx4OMof45cjJVLs/zUmWPWyYqinHcCa4OoH5PtbcrxILAVc2o8FtvblEMBZ7nsC1AF3OxSlowRWuAKP9d5DnjTo+wt4J/R/i4O5E3rqIjLqXVU5OX8WuqoA26kSkSSgGnAOx6H3gFm+jhthpf6bwPTRSTRWgmDYqj5tz6AumtEpEpE3hWRE8IplA8ONod0y0XkXyLib5l6TLa32Xd+CPxVmU+uH6Ld3p6MAUbi0q5KqU7gfXz3e/D9Xfg7RzMItI7SOipUtI5yErM66oAzqoAcIB7Y41G+B+ML9cZIH/UTzOtFHBER4AHgQ6XUF36qVgE/As4GvgtsBt4VkVnhl9LJSuBC4GTg/zDac7mIDPdRP+ba2+QsIAtY5KdOLLS3Nxx9O5h+7zgv2HM0g0PrKK2jQuUstI4K5JyoEWjwTzviacWLl7KB6nsrjxR/BA4HjvNXSSm1GeOhcfCxiBQCNwLLwieemwxvuuyuF5GPgW3ARRhK1+tpHvvRbm+AyzCGmSt9VYiF9h6AYPt9qOdoBo/WUVpHBYvWUYGfExUOxJGqWqCX/lZsHv2tXQfVPur3ABHPFyYiDwNnACcopXaHcIkVwMHWShU4SqlWYL0fGWKqvQFEpBj4JvBkCKdHtb1NHCuZgun3jvOCPUczOLSO0joqaLSOCuqcqHHAGVVKqS7gM/avkHAwF1ju47SPvdQ/CVillOq2VkLfmMtN/4gxZHuiUqo8xEsdgTEEHBVEJBk4xI8MMdHeHlwC1ACvh3BuVNvbpBxD+Tjb1fS/mI3vfg++vwt/52gGgdZRgNZRoaB11H5iV0dF21M+HBtwLtAFXIrx4PweaAGKzeP3AH93qT8GaMUYBj7EPK8LODvCcj+CsRJiNoZl7tiGuNTxlP06jHn2g4FDzeMK+G4E5b7flHkMcDTwX6Ap1tvbRZ44YAdwr5djMdPeQDowxdwU8FPz/yLz+M1m//kOMAl4FqgEMlyu8XdcVphhOHv2mOdONP92A0dH47v4umxaR2kdFaT8WkfZREdFXYAwfrlXAtuBToy3wlkuxxYBSz3qzwZWm/XLgflRkFn52C72JTtG3I+tQDvGCpwPgFMjLPe/zAejC6jASGZbGuvt7SLLSWY7j/dyLGbam/1LpT23ReZxwVg6XgV0YKyqmeRxjaWO+i5l3wM2md/fl5FQvnrTOirCcmsdFRk5v/Y6Sqep0Wg0Go1Go7GAA86nSqPRaDQajSYaaKNKo9FoNBqNxgK0UaXRaDQajUZjAdqo0mg0Go1Go7EAbVRpNBqNRqPRWIA2qjQajUaj0WgsQBtVGo1Go9FoNBagjaogEZHtInJdtOUIBRGZIyJKRLKiLYuVmJ9JiUhDtGXxhSnfWdGWI1hE5E4RWTtAnUUu38FZkZFM4wuto2IPraPCR6zpKG1U2RARuTiWH85ACMNnuAQYb+H1wo6IDBGRNhGZGGD9O10UQ4+I1IrIMhG5zsxl5qh3r4h86XHuIeZ5//Aov0BEukUkPVS5gJ8A+QHW1XwN0DrKK1pH7a93wOoobVRpLMVMkBkNGpRSNVG6d6jMBXYppTYFcc4GDOVQBJwAvADcAiwXkQyzznvARBFxzew+B9hlnoNH+SdKqZZQ5VJKNSqlqgeuqdFEH62jgkLrqCDRRpULIpIhIs+ISKuIVInIT0VkqYg86OecLBH5s4jsEZEOEflCRE7zUbfEtMSneJyvRGSOue8Y/v62iKwzr7lSRA5zHAeeAoa6vBHcaR77oYisEpFmEakWkWdFJM+P7ENE5HURWSEiw8yyS0TkS/O+m0TkygHabKmI/FFEHhCRWmCxWX69iKw323KXiDzieNMY4DMkichvRaTCPHelo22CwXxjWisil4rIThFpEZFHRSReRG4y26dGRG7zOE+JyBUi8pr5JvSliMwQkXHmZ20VkY9FZKzHeT8WkW0i0iUim0XkggDEPBN41eUaPzdlahaRJ823ubUe5/QopaqVUpVKqfVKqYcxcpRNwkg0CvAhRsLROS7nzQH+BGSIyDiP8vf8yWXKdoEY00qNIvIvF+WoiSBaR2kdpXVUf7lM2WJDR0U7+WAsbcATGAlOv4HRAV7CyGT+oEud7cB15v9xwMfAFxiW80HAacC3fFy/BCO55BSXsiyzbI5yT0i50bzmYRgZ1cuBRCAJYyizkf0Z4tPNcy8FvmXKcYwp2xsu93JcOwsYipFoczGQZh7/P4yko9/FyNL+XaAOuMhPmy0FmoHfAhOAiWb5dRhvHGOAEzGSYT5iHvP3GZ4BPgKOB8YCN2Ik3jzYjwwKOMuj7E5TrheAUuB0jMSobwEPmbJeYp57jMe1dgPnYAzV/8ds+3eBkzEy1n8MvOlyzncwEn1eaZ5zPUZW9RN8yYjRd/YAx5n7P8BIgOqYIrjDbJ+1Hp9prY82eBnY6LL/EfC4y341MB14A/g/s6zQlOsbfuRytOOLGM/E8RjJUBcG8j3oTesotI7y+mygddQBqaOiriRiZQMyzE73PZeyoUArvhXWSUAvXjKH+7hHCYErrHNd6gwD2oBzzP2LMYaSB7rfkea10j2uPQFYa3bCJJf6O4Hve1zj58ByP/dYCqwJQJZ5QK3Lfr/PgKGg+oACj/IlwK/9XNuXwmoFMlzK3sJQPnEuZZuABR7X+qXL/jFm2aUuZecB7S77HwF/9rj/88DrvmQEZgJ7HbIAK4A/elzjQwJXWPcCbS77C4HN5v+lGMovHuNN8Rmz/EKMH4MhfuTy1o6/BVYE8j3ozboNraNA6yjHtbSOilEdpaf/9nMQxlvWJ44CpVQjsNnPOVOA3UqpLWGQ52MXOepNOQ7xd4KIHCEir4jIDhFpxlAmYMxtu7IEKMNQgF3mubkYbwV/MYehW0SkBUNhjcU/q7zIcoKILDaHyJuBvwPDRSTNz3WmAgJs8ZBhdgAyeGO7UqrZZX8PxptSn0eZ5/TD5x7HAdZ7lKWISKa5fwiG0nLlI/x/X2cCr7nIMgGXvmfiue8PwVAYDt4DxotIAcYP1YdKqV7gffYPuc/BUDztfuSC/u1YRf8204QfraO0jnKgdVSM6qiEaNw0RhHzr/JR7o12P8e84egErtdMDOJ8T9mcmIrgHXP7IYYlXwS8jTGU7crrwNkYbweOB9FhYP8fsNKjfu8AcrV6yFKMMYT7GHA7UA8cB/wF/583zrzXNC/3bOlffUC6PfaVjzLPl4tuj+O+yuK8lDnwVCCenIHhvOkpi+c1AuUQjDdcBx9hjGrMwZjieN8sX4XhJzLeLF8UgFyBtJkm/GgdZaB1lNZRMaujtGLczzaML+YoR4Fp5R/s55zPgdHmlx8Ie82/rks7p/ioe4yLHNkYc9iOlQ5dGMOkrkwEcjCGiT9QxqoIX5b6AuBvwLsiUgqglNoDVAAHKaW2emzlPq7ji+kYBvsNSqkV5ltygUcdb59hjVmW50WGWF5d9iWGQnZlplneDxE5GGOa5R2X4s249D2T6YHcXIxlxadgTJUAYL7ZrcRQWLMwRwSUUj3Acoxh9RJcHEB9yKWJHbSO0joqVLSOihB6pMpEKdUsIn8D7hOReqAGuAvjzc2rNa+Uel9ElgEvisj1wFYMxaGUUm95qd8uIiuABSKyHUPB/MqHSHeISB3GMO5CoBbD0Q8Mn4l0EfkGsA7Dl2EnhhK4RkQew3DYu93P571RROKB/4nIHFPB3Qk8JCJNwJtAMsZDk62UesDXtbywDaNvXSMi/wWOBeZ71On3GZRSW0TkGeDvInIDhgLLwXAiXa+UeiMIGSLJfcDzIrIaw1n0dAwH2m/6qH8msEQp1eZS9jDwhIiswlAo5wKHY0yBuJIgxjLkOGA4hkL6OYb/yX0edd8Dfmr+v9ql/H0MvwWHUvMnlyZG0DpK66hBoHVUhNAjVe5cj+En8BrGnP5HGJZ8h59zzgY+Bf6JsRrmt/R/u3HlUozh5VXAHzA6mzcWmMc/w3hrPMPhW6CUWo4xbP0cxpvlTUqpvRiOlfNMORZgrErxiVLqpxjOiv8TkfFKqSeBy83rrMfo2BfjPmQ7IEqptRhteTPGqqMf4DFc6+0zmIcuwfBt+B3Gm9GrwNEY8UtiEqXUyxgrhX6GEaPlCuASpdRSH6ecCbzicY1ngHuA+zGUyxiMYW/Pvncohr/ATow3u3PM845X7nFcwFBYGcBH5tufg/fN8uVKqU5/cmliDq2jtI4KGq2jIoco5W9K9euN6QNQgTFE/JcI3XMORkfLVko1ROKedkdEFPAdU3HENCKSg6FwCgeaLhCRxUC1UiqQeDIRk8vPNWzzPRwoaB1lD+z0bGgdNTj09J8LInIExtD4JxhLle8wD8W0ZawB4J8iUqeUGh1tQQZgGHC9p1IQkVSM6Ye3MRxgv48xND83mnIFgjmV80PrRdJ4onWUrdE6KgxyBUIkdZQeqXLBVFhPYiwd7cIY1r5eKbXe74nWyjAH/RYYFLI/+m5vCA6rMYGIDMEIoDgVw09kM/ArpdRLURUsAMSIiO1Yul2llGr1V18TOlpH2ROto6JLJHWUNqo0Go1Go9FoLEA7qms0Go1Go9FYgDaqNBqNRqPRaCxAG1UajUaj0Wg0FqCNKo1Go9FoNBoL0EaVRqPRaDQajQVoo0qj0Wg0Go3GArRRpdFoNBqNRmMB2qjSaDQajUajsQBtVGk0Go1Go9FYgDaqNBqNRqPRaCxAG1UajUaj0Wg0FqCNKo1Go9FoNBoL0EaVRqPRaDQajQVoo0qj0Wg0Go3GArRRpdFoNBqNRmMB2qjSBIyIXCwiys82R0QmiEibiDzr5fxsEakQkZUiEm+xbPki8isR+VhEakWkSUQ+E5EfWX0vjcYqfDxTe0VkqYic5lFXicgfvVyjUET+KCLbRKRDRPaZ5/9ARMSjbonLfe70IdNfHXX8yL3arHOjnzpHi8h/RGSniHSKyB7z+fydR71EEblCRD4VkXpTf+wQkVdE5Du+ru/jnkv96KftwVwryPu6taepC5WIzAnXPQ90RCRORH4oIm+LSI2IdItIg4isEJEbRSTHx3mJIlJttv/3fNS50zzeJyIHeTmeZv6GKBFZFIzc2qjShMIlwAwv22ql1GbgVuD7InK2x3mPAMOAi5RSvRbLNA24EHjX/Hs28D7wKPCExffSaKzG8UzNBH4E9AL/FZHT/Z0kIscCnwNnAn8ATgEuBiqAp4F/iog3Pd8MXOx5TETSgXlAk597TgGOMHcv81Hn28ByIBO4CTgJ+AnwEXCuR/V/AA8D7wE/BE4HfgX0ACf7ksMPZXjXT0EZaINktXnP1RG85wGDiAwB3gL+DtQD1wLfwOgf/wN+BvzHx+mnASPM/732TxdaMJ49T+YBiUB3UIIDKKX0preANgxlrYDpA9QTDIOmBsgzy+aZ514fJtmygUQv5X8071sY7fbTm948N1/PFDAE6ACedSlTwB9d9rOAPcB2YISXa99snrPApazELHvC/DvX45zLgDYMQ0f5kNnxTL1m/p3ppc77wFYgwcuxOJf/x5jXuMvHveK8lftpz6XAF1H4HhVwZ7T70wAyCjAk2nIEKOvjZpt+38fxVOD/fBx7DegE3sF4ORntpc6dLs/BTs9+BnwAPIthdC0KRnY9UqWxHGX0ykswOv5jIjISY8ToA+DBYK8nIkUi8rQ5BNwpIl+KyA2ub9lKqX1KKW9vFZ+Yf0cHec8kEfm5iGwy77lXRJ4Skdxg5ddoQqAD6ML/m/LlQB6G0bTHy/HfApuAn4lIosexzRgjSZd6lF8KvAQ0eruhiKQA5wOfAT91OceT4UCtUqrH84BSqs+jHkCVt/t51LUUERklIn8WkV0i0iUilSLybxEZ4VJnQN3j49ohTf+JSJaI/E5Eysz71YjIGyIy0aXOMBF5RAxXii6z7kIRSfa4ljKnheeLyJcYhsZF5rGDReRZj891VZCy3icijeLiXiEiD5v3/ZlL2XBzmu2aAK+bj9GnXldK/dNbHaVUm1Kq3wyEiBRgjNb+F7gPYzbuYj+3+ytQCMx1ucZ44DjzWNBoo0oTCvEikuCxufktKaXKMIZovwN8CKQAlwSrJE0jZjnG9MHtwBnAEuB+jDfmgTgRYxphSxD3jANeARZgvK182/x/LrDUHJrWaKzE8UwlishojJePNIz+54u5mNOE3g6aLzevYky5T/NS5S/AWSKSDSAiEzCmH//i557fxRgV/qtS6iuMZ/tcc9rQlY+Bo0XkITF8qzyNOgdfAg3AL8Twfyzxc++A8aKfElwNIREZBXyKoZ8eAL4FXIdhTDraY7C6J1iZMzDa8wrgKYxp0PkYuivfrJOCMU16oSn3tzGmeW/CMIY9OQv4MXA3xlTqByJSivHZJwE3YEyXvQ48JCK/CELkJRjTu0e5lH0TaMfFSMGYthOzfiCcACRg9N1guRiIxzCIlgA7gEtF3H0LXfgK42Xf9cXgUozR33dDuL+e/tNb4Bv7pyq8bT1e6guG0lTADSHe8x7z/KM8yh8B+oDxfs49CeNH54Eg73meec/vepRPN8t/HO3vQm8Hxubnmerw7Gf0n/77Eqga4PrzzfPOMfdLzP0bgXQM36qrzGO/xfBHEswpPi/XexfjRzPLQ/5LPeoNx/ixcnyeLgx/qgVAukfdU4G9LnVrgeeB00Noz6V+dNSTLvX+Ysp0iJ9rBax78Jj+A+aYZXOCkP1285xv+qlzhVlnnkf5TXhM55r7DUC2R923gF1Apkf5w+Z3mx2gvKkYo1+3m/ujzHveizGFnGyW/xmoCKIdHNPWJ3s5luC6eRwTDCNpNxBvlt1pXutEj7qO8hyzD3dgvHzEA5XAL8x6evpPExEuBI702I72Uu8UYCKGAvpmiPc6EdiolPrEo3wRxkN0oreTRGQqhmJeAdwS5D1Pw1BG/3V90wXWAtUYClOjsRLXZ+pbwN+AP4nI1YO8ruMNvd9KPqVUC/ACxpt8ginDU8r8Nel3IZExGKMILymlGsziFzAMM7cpQKVUnVLqePPzLMAY+R2PYaisF5eVW0qpN4AijFGj+4ENGCMsr4qX1Y4BsI3++ulI4Jcudb4FvKeU+tLPdULSPYPgW8AWpZS/EZ0TgVbg315kAmNUyJX/KaX2OXbMka5vYDh5t3notzcwZhSOCURYpVQbxoikQ7fPxdCb9wFJGFNomMcDHaXyiblAott1E/cVgLOBccDf1P6FUE9hGv1+Lv0ChoH9AwwDfyT72zNoEkI9UfO15kul1Cp/FUQkC3gSY5j5z8ATInKZUsrf1II3hmMMxXpS6XLc895HAIsx3lpOVUp1BnnPERhOwF0+jntdyqvRDALPZ+otESkGfisiT7sYMa7sBA4WkTSlVKuP65aYf3f5OP4XjCmn24Bc/P+YXIphTPzbfL4dvAr8QEQmKqU2uZ5gfqZVYCx1B36D4Yt1k7k56rUDL5sbIlIEvAlcJSKPKqU2+JHLk46B9BPGZ909QJ2gdc8gycX4Tv0xHKj2NHyVUjUi0uNFJk9fteEYv/vXmJs3gtFvS4DbRSQNw3j6n1KqTkQ+A74pImUYixGCmVZ0tEGxR/lmDOMYjBWy/+dx3LHS7z8u/bMRo3+fLSJXe3uOlFKtIvIcRv/eASxRSu0IQl439EiVJlw8zP7wCU9ivAU9YPqLBEMdpj+BBwXm31rXQtOgcsyln6SU8upwOwC15n29ve0eCVwZwjU1mmD5HGMV4HgfxxdjTFd4Dbtg+pGcgbEk/TNvdZRSH2H8WN0BLFZKeTW+TH+ki83dl4B9LtsPzHJ/owEoYyHJXebupAHq7sR4GQM41F/dENnLwItXgtI9FhCoTCM8fYREJA/DWPKUyXPUcR+GS8QifOu3N4KQ+V2MUalZGCNgi13K57LftyoY/6SlGH6wZ7gWKqXalVKrTIO50vWYiAzFCKMDxou8a/88HmME7nw/9/wrMAXjWQrJQd2BNqo0liMiZ2LEE7ndZXj9RxjTgMHGjHoXKDWn81y5EENhvOdy3ykYBtVuDN+CfYTGaxhvdPGOh9hj2xzidTWaYJhi/t3r4/iTGGFL7jF/VD25CWP6/bfK+8pYB7/CcHb/nZ86J2P84P8JYwrQc9sAXGhOIzlWcHnjEPNvpVkvw4uTu9e6FvMmcILpnO+LgHWPhTKNFxF/04rvYvjCneVFJsdxn5hTdu9hxBn73Id+qwtC5k8wYppdhzFt5jCqlpj3OAdjCjXg71ApVYVh2HxbRM4L8LTzMV5Absd7/6zFj9GvlPrYvOd/8B3/KiD09J8mFCY5lKcH2zCUzeMYq2YecBxQSlWIyE+Bp4KcBvw9hsJ4XUTuwBiB+jbGaNGjSqkt4Fy55Ji3vw1jWuRgV9mUUr5+nDz5F8bb9xsi8gcMxdGN8aNyAvCKUmpQD55G44HrMzUcY5XdXOA/SqlybycopRpE5LsYLwGfich9wDqMFVnnYvTh5zB8XHyilHoaYwWZPy7DGD34tbcfSBF5HHgI49l8BXhbRHZjGGubMF7gp2CsNmvBCFQKMMGs+y+M2FZVGKvvvo3xIrYUQ5cEwxAR8eoXpJRaYf57B4YP0zIR+TWwHmPK/xSMhS2bCFD3WMiDGN/bKyJyL4beGYLhK/SaUuo9jGCYVwF/M1dKrsfwXboVeGMAfywHP8GYEvtARB7FmOLMwPBHOl0pFbCvmFKqV0TexxjhKVdKbTMPfYThxP4NjH4RLNdhTBs+IyJnYPSpSgzn+IkYi4k62B9y5DKMUan7lVIdnhcTkb8D14vIZKXUOh+fZaBAoYERjFe73r7eG/5X/ymMuDnPYzhSHuzjGq9jzHMHHIwTw4n1GYy3jS4MJX0j7kEEB5Lt4iA/awLGD8BajBUxzRirrR4DxkX7u9DbgbH56LcNwBoM36Nkl7puq/9cygsxVuttw/gha8AwUH4AiEfdEvM6Nw4gl3P1H4aPTSeGgeerfhbGiq9Xzf1zzGd2i/nsdGEYJX/HZcWded5tGCMsu837tJif/zaCDFaJ/9V/CpcVYxgvSX/BMOS6MKLQP4cZsNisM6Ducflu7nTZn0OQq/9c2uNBs626MIK7vgZMcKkzDCPuXyWGUbEd+LVrX/HXX1z6wV/MNu/CGPH8CLgthD58rXmvP3uUv2OWB72K0zw/DrjAvM5e87M2ACsxQkSMMusdbt7n936uNcGs85C5f6e5nzOADEGv/hPzRI1Go9FoNBrNIAhp+k9Egs1npIAzlFIVodxPo9F8fdH6RqPR2IVQfaqmYDg1tgRQVzDilCQPVFHz9cFcvRI/QLVeZfFQqg9fMFf6VBhTY2hCYgpa33ytMTM2+IqKDcZUpdVJ2i0hWrpuMISrve38PQZKSNN/ItIHjFRK1QRYvxmYrIzUJRoNYuTEGmj1zCVKqUUW3rME8Or068JdSqk7rbqnZvBofaMRke30j1vkyvtKqTmRkSY4oqHrBku42tvO32OghDpSNQbfy3y9UUp4lsVq7Mtn7A/k5ouBDKBgqQzgnrqfxh5a32hOx//oY3OkBAmBaOi6wRKu9rbz9xgQ2lFdo9FoNBqNxgIsiVNlhoQ/CsjDI6CoUurvVtzDKsz57QIOAItYo7EZGUDlYH1H7KRvQkHrKI0magxaRw16pEpETseI45GGoQRcL6iUUsMGdQOLEZFRDJzzSaPRhIfRg1mVZzd9EwpaR2k0UWVwOsoCo2oLRq6gW5URAj+mEZFMoHH9+vUUFRVFW5yQ2LdvH9nZ2dEWI2jsKjdo2T1p+uorNj/wALXL9we7zpk5kwnXX0/mwQf3r9/URGFhIcBQpVRTqPe1m74JBa2joodd5QYtuyfR0lFWTP+NwohSaisF19vbS2ZmZrTFCIna2lpbym5XuUHL7krT5s2sv/RSelpaSI3bP/vWtmIF6y+9lGOff57MCf5Sqg0KW+qbUNA6KvLYVW7QsrsSTR1lRULlt4HpFlxHo9HYgI333ktPi/eQUT0tLWy8995w3l7rG41G45do6qhQ41Sd4bKbi5Gc8imM5I5u2dCVUq8ORkCrcQyt19fX23aotLe3l/j4gWLJxR52lRu07A46ampYPGOG/0oizF2+nJS8PGdRU1MTQ4cOhRCG1u2sb0JB66joYVe5QcvuIBo6ypVQp/9e9lJ2h5cyxcCRZKPCnj17bKuwqqurGTVqVLTFCBq7yg1adgedewMIF6UUnXv3uimsQfKylzJb6ZtQsLOOsuuPu13lBq2jHERJRzkJyahSSlkxbRhVOjs7oy1CyNhVdrvKDVp2B8m5uQNXEgmsXoAcCPomFOzc5zSRx879xe46ypWQlZWIPCsi54hIhpUCRYqUlJRoixAydpXdrnKDlt15rbw8cmfN8lsn9/jjLX8DtLu+CQU79zlN5LFzfzkQdJSDwbwBbgFuBvaKyDsicpWIFFokV9jJycmJtgghkxsmCzvc2FVu0LK7UrpgAQnp6V6PJaSnU7pggaX3M7G1vgkFO+soTeTROmo/UdJRwCCMKqXUnUqpacA4DJ+HM4CvRGS1iNwpIkdYJGNY2L3bvrH1du3aFW0RQsKucoOW3ZXMCRM49vnnjbdBMRPOi5A7a1bYlirbXd+Egp11lCbyaB21n2joKAeDjlOllNoNPAI8Yg7Nfws4E3jXzBb/X+BRpdSGwd5Lo9HEBpkTJnDMU0/RUVND5969JOfmhm043RWtb0Ij0t+TRhNtoqWjLMn950Ap1Qw8DzwvIvHAHIw3yhlATCm5YcPsm81i+PDh0RYhJOwqN2jZfZGSlxe1H2k76ZtQsEJHNW3ezMZ772XvsmXOstxZsyhdsMCyt/WSkhJSUlJITk6mra2N0tJSbr75ZmbOnOn3vEWLFjFz5kzGjx9viRxfd7SO8k6kdVTIRpWI/Ar4H7BcKdXheVwp1Qu8a24aTdDot2uNA61vgqdp82Y+OuecfkEQ9y5bxkerV1s6DfLvf/+bSZMmAfDKK69w6qmn8vbbb3P00Uf7PGfRokXk5ORoo0pzQDEYR/XvA0uABhF53/RrmCUiSRbJFlbq6+ujLULI1NXVRVuEkAhU7qbNm1lxySUsnjGDZWecweIZM1hxySU0bd4cZgl9Y9c2B3vL7oKt9U0oDFZHRSuq9JlnnsmVV17J/fffz7vvvsuMGTM44ogjmDRpEk899RQATz75JKtWreLaa69lypQpvPHGG6xfv57jjz+eqVOnUlpayj333BMW+Q5U7Pyc21l2TwbjqD4WKAT+D9gKXAAsBfaJyBIRuU1E/I//ajQeON6uXacrwHy7PuecqBpWmugRLn0jIleKSLmIdIjIZyJyvJ+6i0REedk2uNS52EediK5376ip6fcMebL3gw/oqKkJy/2PPPJINmzYwNSpU/nwww9Zs2YNy5Yt46677qKqqorLL7+c6dOn89BDD7F27VpOPfVUSkpKWLJkCatXr+azzz7j+eefZ9WqVWGRT6MJF4MKqqeUqlBK/UMpdZmp9IqBHwM7gZsA/091FBk9enS0RQgZM5O27QhE7ijnlfOJXdsc7C27K1brGxE5F3gQWAgcAXwAvCkiRT5O+QmQ77IVAvXACx71mjzq5XubshyIweioYKJKhwNH+rO6ujrmzZvHpEmTOPHEE6mtrWXDBu/ubu3t7Vx++eUcdthhHHPMMezYsYO1a9eGRb4DkVh8zhs3bqTi1Vdp3LjRb71YlD1ULItULCJjgZOAk80tHnjPqutbTW1tbbRFCJm9YVKE4WYguaP9du33vjZtc7C37L6wSN9cD/xFKfWkUupLpdR1wC4MQ60fSqlGpVS1Y8NI7JyNkYfQo+r+emZdf58lWUQyHRuQAYPTUdGOKv3pp58yadIk5s+fz+zZs1m/fj1r165l/PjxdHR4ty9vvfVWRowYwZo1a1i3bh1z5szxWVfTn1h6zqvefpu3pk9n2emns/qnP2XZ6afz1vTpVL39ttf6sST7YBmMo/oY4ARzmwMMBT4C3gf+CHyqlOqxQMawYOeH1a6yDyR3tHM2+cOubQ72lt2B1frG9MWaBngOfb4DBDqNeBmwRCm1w6M8XUR2YBh6a4HblVJr/FznFuAXnoWVlZUkJiZSXFxMVVUVEPjolSOqtL+XlHBFlX7llVd49NFHeeutt7jqqqsoLi5GRFi2bBnr1q1z1svMzKSxsdG5v2/fPiZNmkRCQgKbN29m8eLFnHjiiZbLZyXd3d3s3buXjo4OkpOTGTlyJDt2GN0hOzub+Ph4p3E8evRo6uvraWtrIzExkVGjRrF9+3YAsrKySEpKosZ8YRw1ahQNDQ20traSkJBAUVERZWVlAAwdOpSUlBT27NkDQH5+Ps3NzVRUVNDd3U1xcTHbt2+nr6+PjIwM0tLSqK427PqRI0fS1tZGU1MTIsKYMWPYsWMHvb29pKenk5mZSWVlJQB5eXl0dnY6v6MxY8awa9cuenp6SEtLIysri4qKCsAI3tnd3U1DQwNNX37Jzp/+FA4/HEpLoaEBysroKi3l02ee4dC+PobPmOH0GSwqKmLPnj10dHSQkpJCXl4eO3fuBIwVsCLi9LkqLCyktraW9vZ2kpKSyM/PD7i9R48eTXl5udf2LigosMywE8cwbdAnivRhDLs/gvGGuNpcgRPTODLAf/nll0ycODHa4oRERUWFLRNnDiR3qNnFI4Fd2xxiQ/bBZoC3Wt+ISAFQARyrlFruUn4rcJFSyu+yOBHJxxjVOl8p9bxL+TEYAUrXA5kYU4anApOVUl/5uFYykOxSlAHsHqyO8rX6D4yo0lat/nMNqdDa2kppaSkLFixg5syZLF68mCuvvJLhw4dTWlrKpk2buPXWWznttNN47bXXuOGGGxgyZAi//vWvyc/P54ILLmDIkCGUlJTQ1dXF3Llzufrqqwct49eBWHjOAd6aPp3ufft8Hk/MzuYUD1+5WJB9sDrKwWCMqueAWUAKhi/C+xjKbo0K9aIRwGFU1dfX2zYDfG9vry2zqQci94pLLvH/dj1rFsc85TnbEn7s2uYQG7JbYFRZqm9cjKqZSqmPXcpvAy5QSvm1ZkTkFuAGoEAp1eWnXhywGlimlLo2QNks01HOOFUffABKGVGljz/e0jhVmtggFp7zxo0bWXb66QPWm/Xf/zK0tNS5HwuyW2VUDWb137lKqXyMQHtvAkcBb2CsxnlNRH4mIkeGev1w4xhetCOO4U67EYjc0czZ5A+7tjnYW3YHYdA3tUAvMNKjPA/Y4+9EERHgUuAf/gwqU+4+4FPg4CBkA6zRUY6o0nOXL2fWq68yd/lyjnnqKW1QHYBY+Zx31NTQuGFD0P6rLVu3hlTvQNBRDgbtqK6U2qSUetRUeiMx/BHWAj8HPvZ7skbjwUA5m5Kys0N62DUHBlbpG9MY+gyY63FoLrC8/xluzMaY4vvLQPcxDbApQFWgsoWDlLw8hh56qA6iq/HLYGMEpo8bZ2k9O2JJmhoRGYHhPDoHw5F0PNCJMUwfk2RlZUVbhJCx67RloHJ7y9nUtW9f2NNt+MOubQ72lt0bFuqbB4B/iMgqDIPsR0AR8Jh5n3uAUUqpCz3OuwxYqZT6wotsvwBWAF9h+FRdi2FUXRWkbLbWUZrIY8VU8WAj8A8tLSUxO3tAnyrXqT84sHRUyCNVIjJPRB4RkY1AJfB3YBJGLq5vAFlKqROsEdN6oj1/OxjsKnuwcjverrv27Yt6QFC7tjnYW3YH4dA3SqnngOuAOzBGu2YBp7qs5svHMLJc5RgKnI3vUaos4M/AlxgrCUcBs5RSnwQjGxwY35smcgy2v1gVI3DywoVBHz+Q+vpgHNW7gFUYzqLvAR8ppdotlC0sOJxA165dy+TJk6MtTkiUlZVx0EEHRVuMoAlV7lhwXrdrm0NsyG6Bo7ot9U0oHAg6ShN5ysrKKEhPDylfqtUrr6vefpt1t91GR0MjHQkpdCSm0DN0GPmX/h9px86itbOX1s4eWsytYk8tF84p5dCCoQHLbDVWOaoPZvovWynVOojz+yEis4CfYcSPyQe+o5R62eX4IuAij9NWKqWOsVIOTewQTEBQ7S9yQGO5vtFoDhSaNm9m+3PPseFvf3OWBeMeYVWMwKaObj7eVsdHHaP48Pv3U7bX45HdAmz51Ou5R01ojqpRZRUhG1UOBWcOh88FSgAFlGMExAvF0ksD1mFEKH7RR523gEtc9v2uvvFFtGNiDAa7ptgJRe5QH/amjm6+2tNMenIiBVkpZKQkBn1vV+za5mBv2R2ESd/ENHbRUS+99BILFy6kt7eXzs5OCgoKWLx4MaeddhoPP/wwY8eOtexeJSUlvPbaa0yaNMlvvTvvvJNbb72VpCQj3/Ydd9zBoYceyrnnnmuZLLGCwxequ68PcSkPxhcq1Aj8nT29rNnZwEdba/lway3rdjXQF2JApdaumI0VHhSDclQXkR9iRDPO9DjUKCLzTZ+FgFFKvYmxXBoR8VWtc6C0D4Gwb98+cnJyBnuZqFBfX8/IkZ4rwWOfUOQO9mHf09TBE8vKePaTnbR17Y8NmZGcQH5WCvlDh1Bg/s0fmkJB1v6/KYm+5/Xt2uZgb9ldsVrfxDp20FHV1dXMnz+fTz/9lOLiYgBWr16NiPDGG29ETa677rqLG2+80WlU3X333VGTJdw4faFKS8Ejx57DF2og94hAI/An5eTyZVUTH22t5YOvavmkvJ727sHF/BaB1MQ4+kK1xmKMwaSpmYoxovQM8HtgEyBAKYbz5z9EZJNSap3Pi4TGHBGpARowAgDeppTyub7eR7Ri2traLBYrcthV9lDkDvRh3xOfxmMvrefFz3bT1dvXr05zZw/Ne1rYsse7IyZAdmqiu9GVlUKBaXz1NDUwLCePpATL0mVGDLv2F1eiqG+ihr/vramjm83VzRGRY8LIDDJ9jPRWVVWRkJDA8OHDnWVTp04F3EeV5syZw5FHHsnKlSspLy/n2muvpbCwkIceeoiKigp+85vfcN555wHGC3VzczPpZry6nJwcVq1aRUlJidu9H3jgAf75z3/S09NDYmIiDz/8MEcffTTz588HYObMmcTFxfHOO+9w0003MX36dK6++mpaWlq49tpr+eQTY+3AvHnz+MUvjCxBc+bM4eijj2b58uVUVlYyd+5cHnvsMesa02Lc3CN8rKAL1D2idMECPlq9up+zev2QbLYWH07tjIu4+tdLqG0ZeHIoMU4ort7CuL1byexoJqWng5TuDpJ7Ovf/39vFt956nWGjRlJeXs5BB40J7EPHOIMZqboGeFkpdbFH+WrgQhFJxUjRcOkg7uHJmxgZ4XcAY4BfAv8TkWlKqU4f53jNq9XY2EhZWZkzr1ZXVxdDhgwhJyeHXbt2ATB8+HCUUm45impqapx5nkaMGOGWoyguLs4t71BdXZ0zR1FBQYFbnqfExERnriHPPE+FhYXOHEVDhw4lOTnZLUdRe3s7ZWVlxMfHU1xcTHl5OUopMjMzSU1Ndcvz1NraSnNzM3FxcZSUlLjlecrIyHDmFBsxYgQdHR3OPE8HHXQQO3fu9JrnKS8vj66uLhoaGgBDeTryTqWmpjJs2DB2794NGAqxt7eXffv20dDQQG9vL9XV1XR2dpKSkkJubq5bewNueZ727t1L5hVXUNvZSd/q1XDUUcYXuGMHdHdTffgxvDTpW7x//9KQh50d7GvrZl9bNxurvM8kCdvIzUhm+JA4ctMSGJWdSuHwdFJUB3lpiRw6poDUuB5aW5rDllfL0d6VlZXOPjt8+HC39u7r63Prsy0tLZSVldk9r1Y09E1USUz0PWW9ubqZeY9FJgzgC/NncGTJMK/HJk+ezIwZMygqKmL27NnMnDmT888/3+vU5c6dO1m6dCnV1dWMHTuWG264geXLl/PJJ59w1llnOY2qQLngggu4/vrrAVixYgWXXXYZX3zxBY899hiPP/44y5cvdxpmrvzyl7+kq6uLzz//nPb2do477jhKS0uZN28eANu2bWPp0qV0dXVRWlrKxx9/zIyBnLgHiWv4mGB8Q93cI9p9rNsIMF+qI0bgp7/5HR9vrWVz3ng2jxhPTcYIo0KZ7xdSgIkjMzhuXA7HHZzDhPY9rDr7mgHlT2isR0bn++3rdmMwRtWxwJV+jj+GkafLMjyG978w48vsAL4NvOTjtHsw4tE4yAB2H3744c44MJ4+J54rpVzjxRQUFPitm5m5f2YiPz/fb92MjAzn/yNGjPBb11U5HHLIIcTF7R8xGTPG3cJ3PTc1NZVclyk0xxC9t7ppaWlub5xFRUU+64Lxo+ygsLDQb93s7Gz6+vqIi4vrp3A965orMACjvQsKChidne2WbmP7sGLeP/YHrEoZBbv7vzlNHJnB/x1/EKlJ8VQ0tFPV2EFVYzuVDcbfmuZOgl34qoCa5k5qmo318pQ34x58ezsJccKIzBRGDk0hf+g+5/Ri/tA+Cro6yM8dxfC0JOLihPT09JDbO5g+O2nSJLf+4q+9B+qzrv3bc0rRX5/NDWQa1z8R1zfRxlPXxCJxcXG8+OKLbNq0iffff58333yThQsXssojtxsYI0JxcXEUFBSQk5PDWWedBcC0adOoqqpyJtQNlDVr1rBw4ULq6upISEhg48aNdHV1Oaf8fLFkyRL+8Ic/EBcXR1paGhdeeCFLlixxGlXnnXce8fHxDBkyhClTprBt27awGVXONEIhxt9zc49Yu9Znvc76ep+jVZ09vazeYfhFfbC1lvUjzqQvALuuYGgKxx2cw7Hjcpg5NofcjP0TQh2BxGZ2cduwi/9gIAzGqCrA8OX3xRaMGC1hQylVZWaD95kCwhzBco5iOXy1duzYYdvgetu3b4/6EvlQGIzcjoCgmzfv5BdvfcWKqg6v9Y4oyuLqE8Zx4sQ8f355dPf2saepg6rGDiodRldDO5Uuxld9a/BrIHr6FBUN7VQ0+F7tnxQfZxpdKS5Gl/uUY1Zqol/5g8Gu/cWDqOubSGMnHTVx4kQmTpzIFVdcwSmnnMKrr77ar46rwRQfH+/cd8Qo6unpce739u730+no6P+sd3V1cfbZZ7N06VKmTZvmXA4fiFGllOr3bLnue8rpkMtqrAi22bVvHxIfj+rthRkz4KOPvNZbefHFgGGwTbz5ZiozC/hw614+3FrHJ+V1dHT3d5nwJCMlgZljh3PcOMOQGpOT5lNHBeq24TDyDhAdBQzOqEoFvP+yGXRiJD8NGyIyHCgkyikgNOFHKcVzn+7i7tc2ujmgOzj+4ByunDOOYw4aFpAxkhgfx+jsVEZnp/qs09Hd6zS21m3dSU9ihtPoqmrooLKxneaO4BVuV28fO+vb2Fnv22dmSGK8YWg5nOuHppCf5e5cP9gVjTYj6vomlpgwMoMX5od3Ssr1Xr6oqKhg+/btHHvssYDhXF9eXj6oFX9jx45l5cqVnHTSSbz00ku0tvaPpNHR0UF3d7dzhPzhhx92O56RkUFjY6PX6b+5c+fyxBNPMHPmTNra2nj66ae55ZZbQpY3VAIJtjmQg/nGe+81DKoBaE1M5YuCQ9nYOYYtj6+jJXnbgOck/n97Zx4eVXX+8c+bfSEhQBIIkIUdwiqoCCigFrcioLj111qltta6L12wamtttXZz66ZdtdbWlYriBrYiKosi+yIoCQSykI2EkH05vz/uTJg9M5ObzL1wPs8zD8y555z7nTN33px77nveN1qYltvP8UgvgwmDU4mJDt6n1J+PFkQ2j2tP0900NeeLSK2fY2mhdiYifTByajkZJiJTgGrH636MUAulGFuqH8JIjPqfUM/l+sjDbtjl7tWTcHVX17ew9JVtrNzlnef2vPyB3Hj2SKZkh9d3IBJioxmWnsyw9GTG9o9ye/zm5Fhz2/EVLpd/S2uNSVdpTVNYu2MaW9spqKynoNJ/aKbUhBhOzevP7FHpnDU6g+F+7hzter34wFR7Y3UC2ajUhFi/fk69SVtbGw888ACFhYUkJSXR1tbGNddcw8KFC7ntttvC6vOxxx7jpptuIjMzk7PPPtvtEbmT1NRUHnjgAU4//XRycnJYsGCB2/G77rqLc845h8TERFauXOl27L777uOWW25h4sSJgPFY8rLLLgtLa7iYEX/Pqw+HX6WT2oRUtg2exNahk/g8YxQdUV1HLR+XlcpZo9KZOWIApw/rT1Jc+FMEp4+Wq9sGImScdZbX480TyEZ1K6J61+uFoJRSQcefF5G5GNGSPXkG+A7wKnAKhgEtddS9Tyl1MIRzpAK1xcXFtvBZ8MWxY8d83oFZnXB0v7+3gu++tJWKOvd9CDOGD+AnC8czeqD/u2gzCXfMlVLUNrZSUtNE2dHjPl3OlS7D16uJlrZgfk6BGZKWyOzR6cwelcHMEen0TYrtlnYzMSGiuun2xqqcCDZKE5janTtZ4zER9MXs116j7/jxQfWhMjKobFBsHTKJbUMmU5je9W66IWmJxuM8x0QqvU98l23CoStH/BPBRjnpTvBP0/eWK6VWA4Ge3Zxv1rkqKipsa7DKy8sjfgGGQyi6m1rbefitz3h67X638rjoKL53/hiuO3MYUVHm+BwFgz/tXRkLESEtKY60pDjyB3uGVzJQSlFV33J8otW50nV81avsaBPtXWxvLK5p5N8fH+TfHx8kSmBKdhpnjcpgZJ8WLjw9P6Sle6vRE/bG6tjZRmkCE26wTV99lKYMYnP2KWwdPYOS2LSAXSa0NjL68F7GHt7D1x64g4lnTDbNdzPgeTMzA+4+tOvfNF909/GfRmM6O0tquf35LXxe7v4sflRmHx6/6hS/k5PepLu7dlwREdL7xJPeJ56JQ30/8mnvUFTUNXc+UiytbWRX6VE+/LyS8jrvaCIdCjYV1bCpqAaAs3bV8+x100PSpdFoeoZQHbk92V9Zz4odtby46AGKuphIpTQdZVLxdiYVb2N0+V5ilOGOMHrEr3plQnWy0e1JlYj4W8NUGI6lXyilCrt7HrOx8x2gXbefdqW7o0Pxlw8L+NU7e2htd1+VuXZmHksvHBsw6nlP4qrdjF07oRIdJQzqa4RqwCXyglKKPYfrWLO3gg8+r2RDYbXPR4nTcn0HBrQbdrU34WBnG6XpmlAduQ9WN/DG9lJWbCthR7Hj6ZSfCVX/+iomF29j8qGtDKsqJAp3e5o2daqlcqXa9W+aL8xYqXoVw6B5TnmdZUpEPgQWKaWOmHA+U6itrTUjdk5EqKmp8YprZQcC6S6paeSuF7eyrqDKrTwjJZ5fXz6ZOaMj+125ajdj145ZiAhjB6UydlAq188eQWNLOx/vr2bN3grW7K3oXO07a5Q9r3UfvIoN7U042NlGabomGEfustqmzonUZseqsz8GNlUzpeBjJhdvY2jNoYB+NGMdQVOtgl3/pvnCjEnVPOBB4B7gY0fZ6cDPMCKe1wJPAb8GrjPhfKbga5uuXYik9nAj/4J/3a9vLeGe/2znqEd4gvPyB/Lw4kn0Tw4cd6Y3cGo3Y9dOT5IYF82c0Rmdk9DS2kb+s3Y3k/08VrQhtrQ34WBnG6UJDmf8PVe7WpfYl1d3lLLiyXV8cqA6YJDiYenJzJ+UxfxJg5HyQgZGT2XHgw9SveGQ3zb9pk0jo4cjxIfKiXStmzGpehy4Xim11qXsvyLSBPxJKTVeRG4H/mbCuUwjJsa+7mSR0G6GD5Gn7qNNrdy/fCfLNhe7lSfFRfPji/O54tRsyzzzd2pvDibdSpBpIXqDrL6JzB+fbmsndQ9saW/Cwc42ShMaDclpvF3YxIq1BawvqAqYcmtov0TmTxrM/ElZjB+c2mkji1rS6JuTw8Qf/9inewIYjxUn/fSnPfUxwuZEutbDDqnQ2YFII3CaUmqHR/lE4GOlVKKI5AK7lVL+Iy32Es7tyrW1tW4pNzT+8edDBMaPNBwfok/2V3P781u8Io9PyU7jsSunkJee3C3NPUVTeTmrurrLE2He2rWWmFRZBbO2K9vN3oSDtlEnB0ebWnlnRxmvbyvloy8qA+7uzeqbwJcnZjF/8mAmD+3b5c1m501wF/GhNMeJeEgFFz4FfiUiX1dKVQCISAbwS+ATR51RgP/1yAhQWFjI5MmTIy0jLAoKCno1pL9ZPkQFBQVk5+bx2Lt7+ePqfW53Y1ECt5wzipvPGUmsBVdVnGPe3V07kaC3r5cexpb2Jhy0jep9elJ37a5dHPviCzpyhvFyRTx/+7CQumb/GRnSk2L4UnYiF08ZwozJw7sMIeOq3ddjRSvZJE/ser34woxJ1XXAcuCQiBzEcBjNAQqAhY46fTD8HTQ2w0wfoqKaZm5/cy3bDrkHxc7pn8SjV06xzQ61kzX9gkXQ9kZjaTwnMqXvvMPWe+7haF0j74+aw/9Gn01jnO9F1JSOZs4dmsDEHatJf2kZUSiqgY/DDNfSVXwojfl0e1KllNojIuMwAnOOxtiB8xmwSinV4ajzanfPYzZ2TlPTm9rN8CFSSvGvj4v46Yr9Xok7L582lB8vGE+feGs/U3cd81DSL1gBO1/rnphtb0TkRuB7QBawE7hdKfWBn7pz8Z3xYZxS6jOXeosxJnUjgH3APUqpkyqVll21d0e3L7/T1Px8yvfu4/2Rc/jf7LNpiPN2a0hsaWBy8TamHtzE6PK9RCvvkCjBhGux65iDvbV7EvZfMhH5F8b25rcdzx/fdrxsQXx8z4Tj7w1cs6j3NN2N/Ft5rJmlr2zj3d3lbuVpSbH8/JKJXDgxywyZPY7nmNtpeb03r5eeoifsjYhcCTwG3Ah8BHwbeEtE8pVSRQGajgFcfS467zxEZAbwAnAfRk7SS4AXReRMpdSGUPRpG9X7hKvbl99pc3QcL7cP5n8XfY36eO9o4f3rq7hg1zucWrSR2I6uE7N35Wph1zEHe2v3pDvOK3uAHwDlIrJSRG4SkWyTdPU45eXlXVeyKIcPeycW7imcPkSB8OdD9N5n5Vzw2BqvCdWZI9N5+7bZtplQgf8xT8jMpO/48ZadUEHvXi89SE/YmzuBvyql/qKU2q2Uuh04iJFnNBDlSqkyl5drxuzbMVbNfq6U+kwp9XPgv47ykNA2qvcJV7er32lrVAz/HX0O93/5fl6ftMBrQtWvvpqrNv6b+976GTP2rw9qQuXE6WphpnYrYGftnnQn999PgJ+IyFBgAYY/w29EZBfwGrBcKbXZHJmaSBKqD1FjSzsPvbmbZ9cfcCuPjRaWXjiOJTPzejVvn8b+mG1vRCQOmAY87HFoJTCzi+abRSQB2AX8TCnl+khwBvCoR/13CDCpEpF4wHVZqneyhGtMwel3qoBtgyfxn8mLqOqT7lUvreEI5+9+hzMKN3SmigkZC4Vr0fjGDJ+qQ8AfgD+ISApwIYbB+6+I1AGvA39USu3s7rnMZNCgQZGWEDZZWb27whOKD9GO4lpue34z+yrcg7mNHZTCLxaNY3KePSNE9/aYm4mdtXtior1JB6IBz1vkw4A/41AKXI+xAzEeuNpx3rlKKacjzaAQ+wS4G/ixZ2FbWxsFBQXk5uZSWlpKS0sLiYmJpKenc/DgQQAGDBiAUorq6moAcnJyKC8vp6mpifj4eAYOHEhRkfEks3///kRFRVFZWQnA0KFDqaqqorGxkbi4OAYPHsz+/fsBSEtLIzY2lgqHT+WQIUOoqamhvr6emJgYsrOzKSw0sgH17duX+Pj4zpW1wYMHExsbS0FBAdHR0eTm5lJYWIhSitTUVJKSkigrKzMGa9Ag6uvrqaurIyoqiry8PA4cOEB7ezt9+vQhJSWF0tJSAAYOHEhTUxO1tcZGl+HDh1NUVERbWxvJycmkpaVRXGzEvMvMzKSlpYWamhoA8vLyKC4uprW1laSkJPr378+hQ8YG0fT0dNrb2zly5Aitra20t7dTVlZGc3MzCQkJZGRkuI13c3U1lSUlxKSkMGzcOA4VFlI85yKWJU9kb7J3upW+7Q2cV7ebM9YtIzYnG2aegWpshC1bwBme5dAhaGiA0aON91u3wtChMGAANDcjGzeiZs0CERri44mpr+9c3cnKyqKuro7W1lYOHDhAbm4u+/fvp6Ojg5SUFJKTk93Gu6GhgaNHjyIiDBs2zG28U1NTKSkp6RzD5ubmzvEeNmwYBw8e9DneGRkZtLa2uo13SUlJ5zU7YMAAt/Hu6Ohwu2ZFhIKCAhISEsjMzHS7ZkWEqioj00Z2djaVlZWd12xWVhYHDhg37/369SM6Otrt+q6urqahoYHY2FiGDh3aec2mpaURFxfnds1WBOM/HATdjlPlt2ORaGAuxl3ldqXUX3rkRCHijAHzxRdfMGLEiEjLCYvy8nIyI3Sn4s+HqL1D8ac1BTyyyjtv33VnDuN754/h6JGqiOnuLpEc8+5iBe1mxYDxR6j2RkQGA8XATKXUOpfye4CrlVJjgzzv64BSSi1wvG8BrlFK/dulzlcxHjP6dBzxs1J1SNuo3ieQbl+O6IlzzmXl9Ct4eV8DHVHueUlTmo5y/q53mFm4LqRHfIHImD3br0+VXcccrKHdSnGqfOLwM/iv42U5jvmJu2QHjh07FrEL0NcW3UNHGrjzxa18XFjtVj4wNZ7fXD6FM0cZS+ElEdTdXSI55t3FztqDJQx7Uwm0472ClIn3SlMg1gNfc3lfFmqfSqlmoNn53hnYUduo3sefbk9H9A6Ej4bP4o2+51Jf2AwuE6rojjbm7l3N+btXktjWZJq2rsK12HXMwd7aPQlrUiUim4GglriUUlPDOUdPEx0d3XUli2Il7cu3FHPvqzuo88jb96WcJH765TFk5R73LbCS7lDR2iNHT9gbpVSLiHyKkUvQNdzBPIw4WMFyCsZjQSfrHH24+lWdB7im1QkKO39vdtXuT7erI/rnGSN5ecpiStK8H/WNL9nBpVv/Q+ax44+SohISiIqLo+3o8cWP2H79GH3TTZSvWePlUpF71VUceP75kMO12HXMwd7aPQnr8Z+IeD3/94fDwdQy6BQQ5lDb2Mp9r+7gta0lbuUJHa0s3vgC0w98jBB6fkDNiUl3ltZ7yt44Qio8C9yAMRm6HvgWMF4pdUBEfg4MUUp93VH/dmA/RjyrOIwVqqXAYqXUMkedmcAajITPyzH8vX4GBB1SQdsoa+FMTVWd1I//TFrEluxTvOpk1h3mnnkjGLj8ab+TIWdE9T4jR9I3P9+tf18uFXYI13IiYVoqrZ7yqbIqToO1bds2Jk6cGGk5YbF//37y8vIidv71BVXc+cIWSmrdl7aHHznA1eueJr2+yq3cmR+wOj4+orq7Q6THvDtYQXtP+1SFiyP45/cxgn/uAO5wOp2LyNNAnlJqruP99zEmXkOARozJ1c+VUm969HkZxkRqOMeDfy4LQZO2URHCl+6yrdu5/96n+O/Yc2mNjnM7ltDayAW73mbO52s4Z/l/6Dt+fMQmQ3Ydc7CGdsv5VInINGAcxjL9LquHU+jo8I5aaxcipb2lrYNHVu3lqTX7cJ2LR0cJlx7dwaz//tlnNGBn0LrM++7rRbXmoq8Xa2GWvVFK/QFjN6GvY9d6vP8lRo7Brvp8GXg5HD2u2Pl7s7p2z4mP831L+/FQB0opVmwr5aEVZZSOv9CtvagOzijcwPwdK0htrgMRmqurO9N1RWJlyepjHgg7a/ek25MqEckEnsfYeVODkTair4i8B1zlTHpqNVJS7BsKpjvaw72L+qK8jtue38LOEvcJfN6AJH5xXi7ll94SsH3FBx+Q3R5mbBYLcLJeL1bDrvYmHOz8vVlVu68dfLH9+tF65AgAauRIylNTketu5fEDMV6bbwCGVRZy2ZaXyTly8HihUmy49logci4PVh3zYLCzdk+6E1HdyW+BVAw/hP5KqX7ABEfZE6F0JCKzReR1ESkRESUiizyOi4jc7zjeKCKrRWR8OKKTknwntLQDycne+aO64uiePaxfsoRVM2awZsECVs2YwfolSzi6Z0/Adkop/rFuP19+4kOvCdVVp2Xzxq1nMTamsWsBShFTX991PYsSzphbBTtr94Fp9sbqnGw2qqdx7uDzTBDvnFABHKtt4I+M5Gvv1XhNqPo21vD1Df/gjvcedZ9QeVCxZg0fXXFFl7bVbKw45sFiZ+2emDGpugD4jlJqt7NAKbULuAkjMF8oJANbgZv9HP8+RmqJm4HTMLYvr3IEAQwJO4fFdwZyCxZ/xqSrH39FXTPfePoTfrR8J81tx5dn+yXF8tTV03h48SSS42OCzg9YG2XG5RYZQh1zK2Fn7T4w095YmpPJRvUGrjv4PGmXKN4fOZufzr6Tj0aciZLjtiqmvZXzdr/DfW/9jNOKNhJMLginy0NvYsUxDxY7a/fEDJ+qKKDVR3krIU7alFJvAW/B8VgtTsQouB140GWnzTUY8V/+D3gqRN0nDYGMib8kne/uOswPXtlGVX2LW/ns0Rn8+rJJZKYej2PozA/oOWlzJeOss4jp451UVKMJEdPsjebkwZlKxhefZY7mlSmLKevrnXlgUvFWLtn6qtfmm2Bw5unTO/dOLsyYVP0PeFxEvqKUKgEQkSEYcVrMDPw5DCOo3kpngVKqWUTex8jV5XNS5S+v1sCBA02U1ruEkmInkDFx4vrjb2hp42dv7OZfG4rc6sTHRHH3hWO5Zmae14QXgssPGGPj1EB2TmtkZ+0+6C17E3FOFhvVGzT7SEFSmTyA/0xexLYhk72OZdWWcOmWZYwt3xv+SXs5T5/VxjwU7KzdEzMmVTdjxGPZLyIHMXbj5ADbcY803F2co+4rr1ZugHY+82oVFhbS0NBgy7xaxcXFREdHB5VXq/zAASNnVEcHsm4davp0iImBigo4fBgmTACgpqSEzysaufu1vRyqdV+dGpWRyKNXTCaptZbCwkKfebVqk5LIfeopKpYvp3bFCjjlFBChf9++5H3jG1TGxlK/bx/5+fkB82oBbnmeKioqOsd70KBBQed5GjJkiNt4u+Z58hzvnJwcCgoKOsc7ISHBK6/W4cOHSU1NtWVeraKiIuLi4k6IvFr0nr2JOI2NQfgqWpSGhoaI+4S5bspxdVFojo5j5bh5/G/0ObRFx7q1SWxr4qLtKzhr34c+dzKHhEhwrhEmYYUxDxc7a/fEtDhVIjIPGIuxG2eXUurdbvangEuUUq863s8EPgIGK6VKXer9GchWSl3gpx+febW2bNnC5Mnedyh2oKCggOHDhwdV1xm4LhAdEkXhoy/xu3UltHUcvx5E4PqzhnPneaOJjwk+4q2/HYah6LYaWnv3MDtOldn2xko441SdLDbKbHzt8MuYPZvW+npWVQjLJy2gNjHNrY2oDmYVrOWi5ApSPnjPFB2B8vT1BFb4nYeLFbRbLk6VUmoVsMqs/nzg9GQbhHtaiLDyavl6hGUXQtHelb9TZVJ/np93M3s+KnYrz+qbwG+umMzMEek+23V1Tl9L3ifLmFsNO2v3Ry/Ym4hj5+8tUto9c/Q5+XRbIa9Mu5yC4XlebUZWfMHiza8wtLYY1cUNaLB0laevJ9DXizUwZaVKRE7HiBuTiYezqFLqzjD79FypEqAEeNQRhA8RiQPKgR8opYJyVLdbCggzovP6MjQK+CTnNF6adjlNMQlu9edPyuLBRRPpmxSLRmMGZq5U9YS9sRJ2s1FWYv2SJW43kEfjU1gxYT7rh01329EH0K+hmkXblnNa7T7ajwZ/SUpsLKrV114Jg+Thwzn1d7/TqblshmVWqkTkhxgpGfZgrBi5ztJCmrGJSB9gpEvRMBGZAlQrpYpE5DHghyLyOfA58EOgAfhXqLqLioqY4PAnsiL+lrDzly7lSEICubmB3MjcSR0zhlkvvmj098EHNMQk8MK0K9mU7Z57NiU+hgcWjWfRlCE9cudw4MCBkHRbCa3dGphpb6yO1W1UICJxzbluymmTaN4fNZu38y+gKTbRrV58tPDN07K4elgOaVnnk5CZScW6dXz2yCMciYlBPv4YROg31bCPRzZt8srlV7l2LbsefhjVdjyRvERHM+rmmxlz662996FdsPPv3M7aPTHj8d9twDeUUk+b0NepgOsD7Ucc/z4DXIuRIiIRI61EP2ADcJ5Sqi7UE7VbOLq3vyXsijVr+GjTJnKfegpCvABTx4zhjL//ndUbv+Bnb37B4Qb3z39aXj8euWIK2f17zlnQymPeFVq7ZTDT3lgaO39vkdDu3OG3c9A4lk2+lPJU792TpxzczEO3Xsy46e6+ahkzZpDx0kvs3baNgdHRbk8GfD0tSB0zhuFLllD+4YfUbttG30mTyDzzzB7+hIHR14s1MGNS1YHhQN5tlFKrwX9sNWU8q7zf8eoWfSwcM6mruFIVy5bBGWeE1GdzWzu/WbmXP39Q4Ja3LyZKuGPeaG6YM4LoqJ59rm3lMe8Krd0ymGZvrI6dv7ee1O7PJaI0JoUnZ13PzsHeq3tDag6xePMyRlXtY0DLHL/xo9IGDaKvR3mgXH6ZZ54Z8cmUE329WAMzJlWPYkQzvt2EvnoNq+YaCiauVO2qVSEFldt72Mjbt7vU/THx8PRkHrtqCpOGpoUrNyTs7B+itVsGW9qbcLCqjQqG7lxzviZNTeXlVG/cyP7nnqNq/frOuhmzZ5Nz5/f5654GntlURpvHhCq5+Rjzd7zBzIK1RDmeDgfK0Wfn34rWbg3MmFT9GnhDRPYBu/CIdqyUutSEc5hOaWkpmRaMdOsrSJ0XEycGFVROKcUza/fz87c+c0szA/DV6Tnc8+VxJMWZtgG0S0pKSiK+bTZctHbLYEt7Ew5WtVHBEM4158uPtN+0aQAc+fRTr/odCK8faOK1v26hLsH9j3JURztn7fuQC3e+RXJrg8/zOd0pZr34YufEys6/Fa3dGpjxF/W3wNkYvlBVnGDOor1NsHn0uqpXfrSJ7768jTV73SdpA5Lj+OVlkzh3nH2jNWtOarS9OQHx50fqazIFUNg/j5dPWUxRf2/f0tGH97B4yzIGHy310dIdf2m6NJpwMWNS9XVgsVLqDRP66jUyejHSbSgEk0evf0pKwFWqd3aWsfSVbRxpcN/2e87YTH6xeBIZKfF+WvYsdr3rBq3dQtjS3oSDVW1UMIR6zQXyI3WlNiGV5RMX8Ene6V7HBhyr5JKtrzKpZFtQSY+duKbpsvNvRWu3BmZMqqqBfSb006u0tLR0XSlCdJVHL+e663y2q29u46crdvH8JwfdyhNio7jny/l8bXpORIOsNTc329YhUWu3DLa0N+FgZRvVFaFcc8H4kbZGxfDeqLm8k38+LTHuN4Vxbc2ct3sV5+z9H7EdbX56CIBLjj47/1a0dmtgRlb3+4GfiIitEvc4c65ZEWdcqYzZs41cMWDESJk9m1kvvkibEaDMjc1FR/jyEx94TagmDEllxS1ncfUZuRGPWmvlMe8Krd0y3I8N7U042Pl7C0V7ID9SBWwbPJGHzr+b1yct8JpQnXrgE+5762ec/9nK8CZU4OZOcbKMudWws3ZPzFipuhUYARwWkf14O45O9dVIExhnXClfO2EqHcl/AdraO/j9e/t44n+f0+6Rt++GOSO440ujiYsxY+58chMXFxdpCWFjZ+0+0PbmBKPFTzTzspSBvDJlMZ8NGut1LLu6iMu2vMLwqsJunz/jrLPCzlah0XjS7TQ1IvLjQMeVUj/p1glMxpkCoqamxhmS3nYopRARDlTVc8cLW9hUVON2fEhaIo9cMZnpwwdERqAfnLo1Jx9mpYCwm70JhxPJRgXCGSZhy9KltNfXd5Y3xCbyVv6FrBl5Fh1R7oncU5qOcvH2FUzfv6EzREJ3iOnTx233n51tlNbePSyTpsauRuzQoUO2NVhFRUVsqIjiJ6/tpL7FPRLtoimD+cnCCfRNtF7evoMHD5KTkxNpGRobY7a9EZEbge8BWcBO4Hal1Ad+6l4KfAeYAsQ76t+vlHrHpc61gK+tZIlKqaZQtNnZRgX6rfsKnQBGiIR1w2awYuJ8jsW7+9dEdbQz9/PVXLDrHRLbjg+jREejgojGPf7eeylfs4aKDz7wSjnjGqfKzjZKa7cGpgYpcuTu80xw2q3kqT1FW1uYz98jSFN5OYcPlXHPuko+PNjodiwlIYafLZrAwilDIqSua+w45hrr0l17IyJXAo8BN2JEaf828JaI5Culinw0mQ2swsg5WgMsAV4XkelKqc0u9Y4Cbtl0Q51Qgb1/L/60+wud8EX6cF6ZsphD/bK92kw8eoCFa//JwLrDIEL/6dPJ+9rXGHDqqbQcOcK2++7zG3oBjAnV8CVLGL5kSZcJ6k/EMbcDdtbuiRkJlYcBv8PIGp/gegjDzzDaR7OIk5ycHGkJQeO8s/vgs8P88/SvUpuY5nZ8+rD+PHLlFIakJfruwCLYacw11sRke3Mn8Fel1F8c728XkfMxVqPu9qyslLrdo+iHIrIQuBjY7F5VlQUrQkTiMVa+nKSAvX8vntqdk5kdDz7oNqE6kpjGq5MWsilnmlcfGXXlXLplGWcOTuC0la/6nAwlZGZy5osvUulMiLx5M848XGmnnMLkBx90W4kKlHLGl247obVbAzNWqp5z/PsNvLPGWxa7LKsf3bOH9676P5YNP5f351zmdiy6o41bTh/ILZdO7/G8fWaQlpYWaQk+6eruVWMpTLE3IhIHTAMe9ji0EpgZZB9RGBOgao9DfUTkAMYEbwtwn8dKlid3A16+YkePHqWgoIDc3FxKS0tpaWkhMTGR9PR0Dh40dvkOGDAApRTV1YaEnJwcysvLaWpqIj4+noEDB1JUZCy69e/fn6ioKCorKwEYOnQoVVVVNDY2EhcXx+DBg9m/fz9g/FZjY2OpcOzMGzJkCDU1NdTX1xMTE0N2djaFhYaTeN++fYmPj6e8vByAwYMH09bWRkFBAY3793Pkj3+kOj4eoqKgshL69aMlfyL/6zOGVcljaI1230gR39HKBXU7mXNgLTGNpRxJnEBRWRlDRoygvqmJEsdGneHDh1NUVERbWxvJw4dz2j//yYE9e2irq2Pg0KFISgqVNTVUFhSQl5dHcXExra2tJCUl0b9/fw4dOgRAeno67e3tHDlyhLa2NtLT0ykrK6O5uZmEhAQyMjLcxhugqqoKgOzsbCoqKjrHe9CgQRw4cACAfv36ER0d7Tbe1dXVNDQ0EBsby5AhQ9zGOy4urnMMPcc7JyeHAsfn7tu3LwkJCRw+fBiArKws6urqqK2tpampidzcXPbv309HRwcpKSkkJydTVmbM8QcNGkRDQwNHjx5FRBg2bBgHDhygvb2dPn36kJqaSklJCWDEjmpubu7cmTds2DAOHjxojHdyMmlpaRQXFwNGTLXW1lZqamoAyMvLo6SkpPOaHTBggNt4d3R0uF2zTU1NFBQUkJCQQGZmpts1KyJu411ZWdl5zWZlZQU93kOHDu28Zj3He/DgwZ3Xencxw1H9GDBNKbXHFEU9jNMJdMuWLUyePLnL+pHmX9+6g8djJ3A4dZBb+cCjZVyz4R9MnTTcNtGACwoKLJWKwJdvh698YOGSl5dHQkIC8fHxNDQ0kJ+fzw9+8ANmzgz8N/vpp59m5syZjB49utsarIKJjuqm2BsRGQwUA7OUUmtdyn8IXKOU6vICEJHvAUuBcUqpckfZGcBIYDuQCtwGXARMVkp97qcfXytVh+xio1yp3bWLY198QbkI9c8+6/VYTgFbhkzh1ckLqU723kgzvXA9C7a/TmpznVv57Ndeo+/48T0pHbCejQoFrb17WMZRHfgEyAZsMamyC23tHfzuzW080e9srx0wZ9V/zqJ3nySuvZWKD4pDSq6sMfDn2+ErH1h3ePnll5kwwUjyunz5ci666CLeeecdpk+f7rfN008/TXp6+gk1qTIRs+2N512l+CjzQkS+ghEza6FzQgWglFoPrHep9xGwCbgFIxyEtwClmoFmlzbBq48Qnqu7pe+8w9Z77qH1yBEA1KxZiMeEqiQ1i5dPWcznmd7XdV7VfhZvfpm8Iz5c2YJIy6XRWAUzJlXfBJ4UkSHADrzjxmwz4Rymk56eHmkJfjlQVc+dL27l0wNHwGVCldpYy9c+eY6xqhppdwyzSzRgq2OltBuB0mL0VD6whQsXcuONN/LrX/+aG264gXvvvZempiZaW1u56667WLJkCX/5y1/YuHEjt956K/feey8PPfQQ2dnZ3HjjjdTX19PU1MTVV1/N3Xd7ufycLJhlbyqBdmCQR3kmxmNFvzgc3P8KXK6UejdQXaVUh4h8AowKUlcnVrRRvlZ3U/PzObprl3vFz48vytXHJfHG+C/z4YhZKHGPmZfaWMuC7a9x2oGNfkMk9GYcKSvZqFDR2q2BGZOqDIxgfK5/gRQWd1RvbW3tulIvo5TixY0HeeD1XV6hEk45uJkrN71AcksDynXrqY3u4qwy5sGkxXDNB2Ymp512Gq+++ipTp07lww8/JDo6murqaqZOncoFF1zAN7/5Tf75z3/y3e9+l/nz5wNQV1fHu+++S3x8PI2NjcycOZN58+Zx6qmnmqrNJphib5RSLSLyKTAP+I/LoXnAcn/tHCtUfwO+Ekz+QTGWnaZgPA4MCav8Xpz4W931mlABJCTQLlF8NHwWb4y/iIZ4d0fkmPY25n7+HufvXklCW7N3e2e9Pn3IX7rUFP3BYLUxDwWt3RqYMan6G8bOl69gI0d1q4XFrzzWzNJXtvPubveb5MSOVi775HlOK/rkeJLQ7GxwOPLZKRpwTU0N/fv3j7SMgGkxOumhFUCnD2NVVRXXXXcde/fuJSYmhsrKSnbu3ElWVpZXm8bGRm688Ua2bNlCVFQUBw8eZMuWLSfrpMpMe/MI8KyIbATWAdcDOcCTACLyc2CIUurrjvdfAf6B4Se1XkScq1yNSqlaR50fYzz++xzDp+pWjEnVTaGKs5qNCjbpMcDeEdNYNuYrlKR5h3iZWLydS7b+h4x6w6EYEfpNNQLhH9m0KWAcqZ7GKjYqHLR2a2DGpCoXWKCU+sKEvk5K3t5Ryj3/2UFVvXsC1enD+vOTU1MofGc3vqJ49PZd3IlCUCt7PbQC+MknnzBhwgRuuOEGLr74Yl555RVEhKlTp9LU5DuU0Q9/+EMGDhzI5s2biYmJ4dJLL/Vb9yTANHujlHpBRAYAP8II/rkDuEgpdcBRJQtjkuXk2xg28/eOl5NngGsd/08D/oTxWLEWYwI4Wyn1cXf1RpJgVncBqpL68+rkRWxJn+J1bNDRMi7dsoxxhz8DILZvX2Y8+6zbrlu9E1djd8yYVP0PmAzYalKVm5sbaQnUNLTw49d2snxLiVt5XHQU3z1/NN88czhRUcLgF180/Bic0YA3bDB1l1pvkZeXF2kJgBGrJmP27IB/JHpiBXD58uX88Y9/5O233+amm24iN9dIcr1mzRq2bt3aWS81NdVtleLIkSNMmDCBmJgY9uzZw6pVqzjnnHNM1WYjTLU3Sqk/AH/wc+xaj/dzg+jvDuAOM7RZwUY56Wp1tyU6llVjvsR/x57rFSIhsaWBC3e9zewv1hCtOjrLW2trfcadiuRkyio2Khy0dmtgxqTqdeBREZmI4Tfg6Tj6mgnnMJ3S0tKIxk3632eHWfrKdsrr3P0Jxg5K4dErpzAuK7WzzDO5crUIw/Lze1tytykpKWHo0KGRlgFA/tKlfLRpk8/HGWauAF522WXEx8dTX19Pfn4+b775JmeccQYPP/wwN954Iw8//DD5+fluuwGvv/567rrrLn71q1/x0EMPce+993L11Vfz3HPPkZeXdzJPqMCm9iYcIm2jXPG3aquALUOn8J/JiziS5P74RlQHMwrXMX/7G6S0+H5saLVNNlayUaGitVsDMyZVTzr+/ZGPY6Y7qovI/XgHyjuslPLcxROQlpaWriuZiHNC1JLaj1+ur+ClTw+5HY8SuGHOCG770ihUdRW1Ow/6vYurcgSBsxu9PeaBSB0zhlmeK4Am+3E4A/v5Yt68eXz+uc+wRcyfP7/TSd3Jjh07uq3nBKFX7U0ksdLvxdfqrhEi4TI+z/Te2Di8uYLL1vyd7JpDXsc6seAmGyuNeaho7dbAjITKUV3XMp2dwJdc3nedUdODxMTeSeniugV598Cx/OvUr1CT1M+tzvCMZH5z+WRGNJaz+Vvf7DIYZW9pNxur6fZcAdR+HNYnQvYmIljt9+Jc3a1t7uCNCRfx4YgzvUIkpDXUsHDbq0xNbSb6WFnAXQRW3GRjtTEPBa3dGpiaULkXaQslt5YvemOngXML8rGmVl6degUfjTjT7bgA1505jO+eP4aWgi+CDkbpTJVgN6yqO9J+HBqNL6y2Gyp51GiKH/gzj390iLqoeLdjsQLfOC2La0fk0nEghfhhw4iLi/Np08C6m2ysaqOCQWu3BmHd9YnIrSKS0HXNzvo3iEhKOOfywygRKRGRQhF5XkT8xrcXkXgRSXW+cCQrdeYs6kl2PfwwOxOzeHjeD7wmVOnHKvhR9WrunZ9PQmx0UMEonThzKNkNu+rWRBYL2JuI0Bs2Klg27q9m/qOr+dm6Cq8J1cTi7dz9xgNM+N7lHHjsN6SMGcPR5OTOR+wZs2eDM0q8CBmzZ5uWscBs7GyjtHZrEO5K1aPAv4Fg93X/EiNRaV1XFYNgA/B1YC8wELgXWCsi45VSVT7q+0xWWl1d3aPJSuMlhsfjxrNu7mWep+asss0s+Hwl8SOHsXfbNgYNGEB5RQXMmgXNzfDpp+DMD1dcDMeOUd7Rwd5t28gZPZq6ujoKCgqIjo4mNzeXwsJClFKkpqaSlJTkljyzvr6euro6oqKiyMvLc0uemZKSQmlpKQADBw6kqampc9eZW7JSj+SZmZmZtLS0uCXPDCZZaVVVFbm5uaYnK42LizthnBxPJA4dOkRLS4sZyUojaW9OOEJ53F1e18RPn/+Y1/d5D+XAY+VcuukV8g/v7ixzrqznPvUUDB+uH7FrTjrCSqgsIh0YMV18hU/yxURgjFLKdA9rEUkG9gG/VEo94uO4z2SlBw8e7LE/xKt2HeaHL22motHd1atffTVf3fgvxpTv7Syb/ZqxWWnNggVd9utMKnr06FFSU1O7rG817Kpb0326k6zUSvamN3AmfTfbRgVKIK7a2zn2xRf0GTmSvvn5tLZ38Mza/Ty68jPqW93/RiS0NnHBrreY8/kaYpRvd9a0+fM56/HHTdPeW9jZRmnt3dcQyYTKPwmx/nKgOsxzBUQpVS8i2/GTW8tfstKOjg5f1btF1bFmfvzaTlZsK/U6dua+D1mw7TUS21xutkPZ/eJStye09wZ21a2JOJaxN72Jmb+XQAnE3/eI11Yw/BRenXUNhU3e3iGn79/Awm2vkdoceBHwyNattkz0bmcbpbVbg7AmVUqpUI1cj+FYiRoHfBBKuyNHjpDjmkOvGyileG1rCfe/tpMjDe45jDLqyvnKxucZVekdq9B190sowSirq6stE78mFOyqWxNZrGRvehMzbVQwKWaOJKbx6qSFbMqZ5vWgdeiRQ1y++SWGVxUGd8KcHMvFoAoGO9sord0a2G57soj8WkTmiMgwEZkOvIyRY+uZSOgprW3kumc2ctvzW9wmVFEC105I4961v/c5ofLc/ZK/dCkxffr4PIdVd8poArNs2TKmTZvGlClTGDduHOeeey4dHR1cdNFF7Nu3z9Rz5eXlBRXL6v7773eLCfOjH/2IF154wVQtGmvRVYqZNolm5Zgv8bML7jEmVC4ktdRzxacv8r13fxX8hAosGYNKo+kNwvKpiiQi8jwwG0gHKjCSl96nlPKRKt1n+1Sgtqqqqltbljs6FP/+pIiH3/yMumZ3V4+xg1L4xeJJTM5OO+7HEESAyWDrtrW1ERNjv2gYvaX7aFMre8p63kd5zKAUUhNifR4rKytj0qRJfPLJJ53pRjZt2sQpp5zS+QjaTPLy8lixYgUTJkwIWE9EqKuro4+fCXxPYZa/wsmAWTbKSe3OnX59NvdkjubFUy6nPHWguwZHNPSLt6+gT0t9yOdMP/tsZvzlL2HpjSR2ta2gtXeXSPtURQyl1FVm9FNeXh62wSqoOMbdy7azodDdbSM2Wrj57FF8Z+4I4mKMRcBQdr8EW/fw4cMMGeKd/d3q9JbuPWV1XP7kuh4/z0s3zOC0PN/XUGlpKTExMW7xV6ZOnQq4T4Dmzp3LaaedxoYNGygsLOTWW28lOzubJ554guLiYn7xi19w1VXGJe85IUpPT2fjxo1eebMeeeQR/v3vf9PW1kZsbCy//e1vmT59OjfccAMAM2fOJCoqipUrV/L973+fU089lZtvvpljx45x66238vHHRu7fyy+/nB//2Ng4O3fuXKZPn87atWspKSlh3rx5PPnkk2h6jlBtlD+70XLU++9DbUIqyyZf4rUyBZBbtZ/LN79M7pGisHTH9OlDxre/HVbbSGNX2wpau1Ww3aTKLJqbm7uu5EFbewd//qCQR9/dS0ubu2PdlOw0fnnZJEYP9B0eJ5QAk13VDUe7FbCr7nCYPHkyM2bMICcnhzlz5jBz5kz+7//+z6fhKCoqYvXq1ZSVlTFixAjuuusu1q5dy8cff8yiRYs6J1XBcvXVV3PnnXcCsH79eq677jp27NjBk08+yVNPPcXatWt9rlT99Kc/paWlhW3bttHY2MiZZ55Jfn4+l19+OQD79u1j9erVtLS0kJ+fz7p165gxY0YYo6MJhmB/L4F29QFsdEymAdolivdHzuat8RfRFOse+qtP8zEWbHuN6fs3EBUwFrpBdGIiqfn5HNm0yWtlvTLW9wqu1bGzjdLarUG3J1UikqiUavRzLEsp5b0VzgIkJAQdSxCAHcW1/OCVbewscb/rS4yN5q7zRrNk1jCio6RX4rGEqt0q2FV3OERFRfHKK6/w2Wef8f777/PWW2/x4IMPsnHjRq+6l19+OVFRUQwePJj09HQWLVoEwLRp0ygtLaWpqSmksdu8eTMPPvggVVVVxMTEsGvXLlpaWoiLiwvY7t133+Xxxx8nKiqK5ORkvv71r/Puu+92TqquuuoqoqOjSUxMZMqUKezbt6/XJ1V2tTfhEMx3HmhX34effkpyTk7nsX0DhvPi1MspSXOf2IvqYGbBWi7evoLk1oagtA044wwm/OhHpI4Z49PmHSspCaofq2FnG6W1WwMzVqo2i8j/KaU2uRaKyGXAHwFLeitmBOlE2dTazuP//Zw/rSmgvcP97u3Mkek8dMlEcgYkBbxbNDtycKbNdtQ46S3dYwal8NINPf/HfsygroN2jx07lrFjx/Ltb3+bCy64gNcccclccTUo0dHRne+jo43cwG1tbZ3v29uPxwVqavKOhdnS0sLixYtZvXo106ZN6/QTCGZSpZTy8vdyfe+p06mrl7GlvQmHYGxUoF197fX1HN29m7r4PiyfuIANw87wqpNdXcSVm14M6VFf/+nTmfncc53vfa2saxvV+2jt1sCMSdUqjIjm9wO/AJKB3wGXA5bdsnbw4MEu/RU2FFSxdNl2CivdHTVTE2K4d34+l08biogEvFv0zNtnBkVFRQwf7jczj2XpLd2pCbF+fZ16i+LiYvbv38+sWbMAY3t8YWEhI0aMCLvPESNGsGHDBs477zyWLVtGfb23A3FTUxOtra1kZ2cD8Nvf/tbteEpKCrW1tT4f/82bN48///nPzJw5k4aGBv75z39y9913h623h7ClvQmHrmxUV7v6OhA+Gj6L1yfOpzEuye1YYksDF29fwayCj4iODuZhn0FMnz5M/LFXggovtI3qfbR2a9DtSZVS6hYReQP4O/BlYDBwFDgt2B15VqOuqZWH3/qM5zZ4371dOGEQP1k4nsyU43ftweTtO+Pvf+8xvRrr0dbWxgMPPEBhYSFJSUm0tbVxzTXXsHDhQm677baw+nzssce46aabyMzM5Oyzz/aZhDQ1NZUHHniA008/nZycHBZ47Pq66667OOecc0hMTGTlypVux+677z5uueUWJk6cCBiPJS+7zDvNUiQ5Ee1NuDQHSP1T1C+bF6ZeQVH/XK9j0wvXs3D7a6Q0GzZLtbcz/ZlnaK2uhpgYDr70kvdkLcCuZY1GcxxTQiqISBTwW+A7GKkkLlZKvdPtjnsA53blAwcO+A2s981nPuHd3eVuZRkp8fx04QQumDDIrbypvJxVXfmViDBv7VrTfKxqampsGSjNrro13cfMkAp2sjfhEIyNAt+2pyE2kRUT5vPhiFkocQ9DOLimmCs2vcSIKu/sPc4UWK59N1dUGImQlQrZR9Suv3W76gatvbtYJqSCiIwA/gUMAs4H5gDLReQJ4B6lVGug9pEiUKygO+aN5r09FZ0+VFeems2dp6eTUFdFU3mUm3EJdLfYiVKmRhfuiThHvYFddWusg13tTTh09XtJyMzszMSggI9zT2P5pIXUJbjnUItvbeKinW8x54v3iVY+0oH4CNQZym7lcLRbFbvqBq3dKpgRUX0LUAhMVkqtUkrdC5wDXAp8bEL/PUJ1tf/UYOMH9+X62cPJ6Z/En84fzCVvPMrHZ5/FmgULWDVjBuuXLOHonj0AwUUNNjm6cFVVlWl99SZ21a2xFFsw0d6IyI0iUigiTSLyqYic1UX9OY56TSJSICI3+KizWER2iUiz499LQtUFgW2Uk/ylSzk8aARPzL2Vf55+tdeEamrRp9z79oOc8/l7vidUuKfAMgu7/tbtqhu0dqtgxqTqRqXUVUqpGmeBUmotcAqwyW8ri3PbuaN46cuDaL/9Wi//goo1a/joiis4umdP591iIHrCaGk0Jymm2RsRuRJ4DHjQ0f4D4C0R8fnMTUSGAW866p0CPAQ8ISKLXerMAF4AngUmO/590ZFSy1Tqm9v43b4OHp59G19kjHQ7lll3mJve/x1LNjxDWlOt3z50CiyNxlxsl6amuzj9FSorK306+rqyfsmSwEmOZ8/mjL//3e/uPzCMltm7/1pbW4m1YXA9u+rWdB8rpqkRkQ3AJqXUd1zKdgOvKqW8tj2KyC+ABUqpcS5lT2Ksms1wvH8BSFVKXehS523giFLqK0Hq6tJGvbW9lAdW7KK01j2sRlwUnL/7Hc7e8Q6xHe4hLyQ6GtXR0WW6LDOw62/drrpBa+8uVvKp+nqAw0op9Wx3z9ETVFVVBZxUdbVdGaDigw9oKi8ndcwYZr34YtA5/rpLZWUlWVlZpvbZG9hVt8Y6mGVvRCQOmAY87HFoJTDTT7MZjuOuvANcJyKxDn+uGcCjPurcHkBLPBDvUpQCgW3U2zvLvCZU54zN5CcLxtO3YiS7Hq7zaYvi+vXr8eDEYN/ful11g9ZuFcyIU/W4x/tYIAloARowlr8tR2Ojz6DMnYTqgB5Kjr/u0pV2q2JX3QCHDh1i6NChkZYRFnbW7gOz7E06EA0c9ig/jOEE74tBfurHOPorDVDHX58AdwNewZ+Ki4uJiYkhNzeX0tJSWlpaSExMJD09na9NSOLdnVHUt3aQlRrPjWdkMDO3D1mpcZQ3pZB5330Mam4mta2NSqWI6dOHjv79aYmKoioxEY4dY2haGlVVVTQ2NhIXF8fgwYPZv38/AGlpacTGxlLhsINDhgyhpqaG+vp6YmJiyM7OprCwEIC+ffsSHx9PebmxY3rw4MGUl5fT2NhIdHQ0ubm5FBYWopQiNTWVpKQkysrKjAEdNIj6+nrq6uqIiooiLy+PAwcO0N7eTp8+fUhJSaG01AiSP3DgQJqamqitNR5nDh8+nKKiItra2khOTiYtLY3i4mLACCbZ0tJCTU0NYOTbLC4uprW1laSkJPr378+hQ4cAI4dme3s7R44coaqqiszMTMrKymhubiYhIYGMjAwOHjwI0DnJdfoAZWdnU1FRQVNTE/Hx8QwaNIgDBw4A0K9fP6Kjo6msrARg6NChVFdX09DQQGxsLEOGDHEb77i4uM4x9BzvnJwcCgoKOsc7ISGBw4eNyywrK4u6ujoOHTpES0sLubm57N+/n46ODlJSUkhOTnYb74aGBo4ePYqIMGzYMLfxTk1NpcQRDT8zM5Pm5ubO8R42bBgHDx70Od4ZGRm0tra6jXdJSUnnNTtgwAC38e7o6Oj0GczJyaGsrIzGxkYSEhLIzMykqMgIZ9S/f39ExG28KysrO6/ZrKysoMd76NChndes53gPHjy481rvLj3y+E9ERmFEN/6V1bY6O5fWd+3axbhx4/zWi0SohGCx6x9Ju+oGrb279OTjv3DsjYgMBoqBmUqpdS7l9wBXK6XG+mizF/i7UurnLmWzgA+BLKVUmYi0ANcopf7tUuerwF+VUj5zcfhZqTrUlY16dv0BSmsaufmckSTFWSuNqxWuuXCwq27Q2ruLZR7/+UIp9bmILAX+CXgZJyswaFCgG0f37cr+iJQDul2XSe2qG7R2KxOmvakE2vFeQcrEe6XJSZmf+m1AVRd1/PWJUqoZ6Mwo69xe3pWNuvoM78CeVsGu15xddYPWbhXM2P3nj3aMaMeWxLm8GIj8pUuJ8ZHOAyK7a8a53Gk37KobtHYbEJK9UUq1AJ8C8zwOzQPW+mm2zkf984CNLvGx/NXx16dfgrFRVsWu15xddYPWbhXMcFRf4FkEZAE3Ax91t/+e4tixYxw92sUKX1YWE//2N/Y88giV69Z1On2mz5jBmDvvhKysrvvoAerq6iJy3u5iV92gtXcXs85vsr15BHhWRDZiTIauB3KAJx3n+jkwRCnldI5/ErhZRB4B/ozhlH4d4Lqr73FgjYj8AFgOLAS+BJwZorbgbJRFscI1Fw521Q1ae3cx7fxKqW69gA6PVzvGEvi/MPwMun0OM1/AEEDpl37pV0ReQ6xkb4Abgf0Yj98+BWa7HHsaWO1Rfw5GPKxmjCCkN/jo8zLgMwzn+d3ApdpG6Zd+2ebVLRt1MsapEmA0htEbCtRFVlHIpACHsJ92u+oGrd1MLSXqZDM6IaJtVMSwq27Q2s3U0i0bZa0tI72AUkqJSKnjbV13vPwjgUuOJFtpt6tu0NpNJNLntwXaRkUGu+oGrd1Eun3+sCZVDp+CoFBK3RnOOTQajQa0vdFoNPYh3JWqU4Ksp5f5NRpNd9H2RqPR2IJwJ1W3ATuVUu1miulFmoGf4BIbxkbYVbtddYPWHmnsbm/Cwc7fm12121U3aO2WISxHdRFpBwYppSpEpAA4TSlV1VU7jUajCRVtbzQajV0IN/hnDTDc8f+8bvSj0Wg0XVGDtjcajcYGhPv47xXgfccOFQVsdNxNeqGUGu6rXKPRaIJE2xuNRmMLwppUKaWuF5FlwEjgCYzowpGOL6HRaE5AtL3RaDR2odvBP0Xk78CtSilt5DQaTY+i7Y1Go7Ey3fZNUEotsaKBE5EbRaRQRJpE5FMROauL+nMc9ZpEpEBEbugtrS4a7haRT0SkTkTKReRVERnTRZu5IqJ8vMb2ou77fZy/rIs2ER9vh479fsbv937qR2y8RWS2iLwuIiWOcy7yOC6O76JERBpFZLWIjA+i38UisktEmh3/XtJjH6KbWNXehIO2UdpGBYO2UfayUSekw6eIXAk8BjyIEePmA+AtEcnxU38Y8Kaj3inAQ8ATIrK4VwQfZw7we+AMjEz3McBKEUkOou0YjMSyztfnPSXSDzs9zj/RX0ULjTfAabjrnucof6mLdpEY72RgK0byYF98H7jTcfw0jJx4q0QkxV+HIjIDeAF4Fpjs+PdFEZluom6NB9pGaRsVAtpG2clG9WYy4956ARuAP3qU7QZ+7qf+L4DdHmVPAusi/DkyMBxzZweoM9dRJy2COu8HtoRQ35Lj7dDxGPAFjkfjVhxvhw4FLHJ5L0Ap8AOXsniMnXPfDtDPC8BbHmVvA/+O9HdxIr+0jep1ndpG9b7Ok9JGnXArVSISB0wDVnocWgnM9NNsho/67wCnikisuQpDoq/j3+og6m4WkVIR+a+InN2TovwwyrGkWygiz4tIoF1Ylhxvx7XzNeBvyvHLDUCkx9uTYcAgXMZVKdUMvI//6x78fxeB2mi6gbZR2kaFi7ZRnVjWRp1wkyogHYgGDnuUH8b4Qn0xyE/9GEd/vY6ICPAI8KFSakeAqqXA9cBi4FJgD/BfEZnd8yo72QB8HTgf+BbGeK4VkQF+6ltuvB0sAtKApwPUscJ4+8J5bYdy3TvbhdpG0z20jdI2KlwWoW1UMG0iRrhxquyA5yxefJR1Vd9XeW/xO2AScGagSkqpPRg/GifrRCQb+C6wpufkuWl4y+XtdhFZB+wDrsEwuj6bebyP9HgDXIexzFzir4IVxrsLQr3uw22j6T7aRmkbFSraRgXfJiKciCtVlUA73rPYTLxnu07K/NRvA3o9HYaI/BZYAJytlDoURhfrgVHmqgoepVQ9sD2ABkuNN4CI5AJfAv4SRvOIjrcD506mUK57Z7tQ22i6h7ZR2kaFjLZRIbWJGCfcpEop1QJ8yvEdEk7mAWv9NFvno/55wEalVKu5Cv3j2G76O4wl23OUUoVhdnUKxhJwRBCReGBcAA2WGG8PlgDlwBthtI3oeDsoxDA+nePq8L+Yg//rHvx/F4HaaLqBtlGAtlHhoG3UcaxroyLtKd8TL+BKoAX4BsYP51HgGJDrOP5z4B8u9YcB9RjLwOMc7VqAxb2s+w8YOyHmYMzMna9Elzqe2m/HeM4+ChjvOK6AS3tR968dmocB04HXgaNWH28XPVHAAeBhH8csM95AH2CK46WAOxz/z3Ec/4Hj+rkEmAD8CygBUlz6+AcuO8wwnD3bHG3HOv5tBaZH4rs4WV7aRmkbFaJ+baNsYqMiLqAHv9wbgf1AM8Zd4WyXY08Dqz3qzwE2OeoXAjdEQLPy87rWn3aMuB9fAI0YO3A+AC7qZd3PO34YLUAxRq62fKuPt4uW8xzjPNrHMcuMN8e3Snu+nnYcF4yt46VAE8aumgkefax21ncpuwz4zPH97e4N46tf2kb1sm5to3pH50lvo7qdpkaj0Wg0Go1GcwL6VGk0Go1Go9FEAj2p0mg0Go1GozEBPanSaDQajUajMQE9qdJoNBqNRqMxAT2p0mg0Go1GozEBPanSaDQajUajMQE9qdJoNBqNRqMxAT2p0mg0Go1GozEBPakKERHZLyK3R1pHOIjIXBFRIpIWaS1m4vhMSkRqIq3FHw59iyKtI1RE5H4R2dJFnaddvoNFvaNM4w9to6yHtlE9h9VslJ5U2RARudbKP85g6IHPsAQYbWJ/PY6IJIpIg4iMDbL+/S6GoU1EKkVkjYjc7kgQ66z3sIjs9mg7ztHuWY/yq0WkVUT6hKsLuA3ICrKu5iRA2yifaBt1vN4Ja6P0pEpjKo6s45GgRilVHqFzh8s84KBS6rMQ2uzEMA45wNnAS8DdwFoRSXHUeQ8YKyKDXNrNBQ462uBR/rFS6li4upRStUqpshA+g0YTMbSNCglto0JET6pcEJEUEXlOROpFpFRE7hCR1SLyWIA2aSLyJxE5LCJNIrJDROb7qZvnmIlP8WivRGSu471z+fvLIrLV0ecGEZnoPA78Hejrckdwv+PY10Rko4jUiUiZiPxLRDIDaE8UkTdEZL2I9HeULRGR3Y7zfiYiN3YxZqtF5Hci8oiIVAKrHOV3ish2x1geFJE/OO80uvgMcSLySxEpdrTd4BybUHDcMW0RkW+ISJGIHBORP4pItIh83zE+5SJyj0c7JSLfFpEVjjuh3SIyQ0RGOj5rvYisE5ERHu2+IyL7RKRFRPaIyNVByFwIvObSx70OTXUi8hfH3dwWjzZtSqkypVSJUmq7Uuq3GIlfJ2Bkbwf4ECOL+1yXdnOB3wMpIjLSo/y9QLoc2q4W47FSrYg872IcNb2ItlHaRmkb5a3Loc0aNirSGZ2t9AL+jJE1/lyMC2AZcBR4zKXOfuB2x/+jgHXADoyZ83BgPnChn/7zMDJ2T3EpS3OUzVXuWb53OfqcCLyOkSU9FojDWMqsBQY5Xn0cbb8BXOjQcYZD25su53L2nQb0xchevgpIdhz/FkYm90uBYY5/q4BrAozZaqAO+CUwBhjrKL8d445jGHAORobxPziOBfoMzwEfAWcBI4DvYmQzHxVAgwIWeZTd79D1EpAPXIyRbf5t4AmH1iWOtmd49HUIuAJjqf4/jrH/L3A+MM4xrm+5tLkEI3v6jY42dwJtwNn+NGJcO4eBMx3vv4qRVd75iOBHjvHZ4vGZtvgZg1eBXS7vPwKecnlfBpwKvAl8y1GW7dB1bgBdznF8BeM3cRZGhvkHg/ke9EvbKLSN8vnbQNuoE9JGRdxIWOUFpDguustcyvoC9fg3WOcB7cDoIM+RR/AG60qXOv2BBuAKx/trMZaSuzrfaY6++nj0PQbY4rgI41zqFwFf8ejjXmBtgHOsBjYHoeVyoNLlvddnwDBQHcBgj/J3gYcC9O3PYNUDKS5lb2MYnyiXss+ApR59/dTl/RmOsm+4lF0FNLq8/wj4k8f5XwTe8KcRmAlUOLUA64HfefTxIcEbrIeBBpf3DwJ7HP/PxzB+0Rh3is85yr+O8ccgMYAuX+P4S2B9MN+Dfpn3Qtso0DbK2Ze2URa1Ufrx33GGY9xlfewsUErVAnsCtJkCHFJK7e0BPetcdFQ7dIwL1EBEThGR5SJyQETqMIwJGM+2XXkXKMAwgC2OthkYdwV/dSxDHxORYxgGawSB2ehDy9kissqxRF4H/AMYICLJAfqZCgiw10PDnCA0+GK/UqrO5f1hjDulDo8yz8cP2zyOA2z3KEsQkVTH+3EYRsuVjwj8fS0EVrhoGYPLtefA830gBMNgOHkPGC0igzH+UH2olGoH3uf4kvtcDMPTGEAXeI9jKd5jpul5tI3SNsqJtlEWtVExkTipRRHHv8pPuS8aAxzzhfMicO0zNoT2nto6cRiClY7X1zBm8jnAOxhL2a68ASzGuDtw/hCdE+xvARs86rd3oaveQ0suxhLuk8B9QDVwJvBXAn/eKMe5pvk45zHv6l3S6vFe+SnzvLlo9TjuryzKR5kTTwPiyQIM501PLZ59BMs4jDtcJx9hrGrMxXjE8b6jfCOGn8hoR/nTQegKZsw0PY+2UQbaRmkbZVkbpQ3jcfZhfDGnOwscs/xRAdpsA4Y6vvxgqHD867q1c4qfume46OiH8QzbudOhBWOZ1JWxQDrGMvEHytgV4W+mvhR4BviviOQDKKUOA8XAcKXUFx6vQj/9+ONUjAn7XUqp9Y675MEedXx9hs2OskwfGqy8u2w3hkF2Zaaj3AsRGYXxmGWlS/EeXK49B6cGc3IxthVfgPGoBADHnd0GDIM1G8eKgFKqDViLsayeh4sDqB9dGuugbZS2UeGibVQvoVeqHCil6kTkGeBXIlINlAM/wbhz8zmbV0q9LyJrgFdE5E7gCwzDoZRSb/uo3ygi64GlIrIfw8D8zI+kH4lIFcYy7oNAJYajHxg+E31E5FxgK4YvQxGGEbhFRJ7EcNi7L8Dn/a6IRAP/E5G5DgN3P/CEiBwF3gLiMX40/ZRSj/jrywf7MK6tW0TkdWAWcINHHa/PoJTaKyLPAf8QkbswDFg6hhPpdqXUmyFo6E1+BbwoIpswnEUvxnCg/ZKf+guBd5VSDS5lvwX+LCIbMQzKlcAkjEcgrsSIsQ05ChiAYZDuxfA/+ZVH3feAOxz/3+RS/j6G34LTqAXSpbEI2kZpG9UNtI3qJfRKlTt3YvgJrMB4pv8Rxky+KUCbxcAnwL8xdsP8Eu+7G1e+gbG8vBF4HONi88VSx/FPMe4aFzh9C5RSazGWrV/AuLP8vlKqAsOx8nKHjqUYu1L8opS6A8NZ8X8iMlop9Rfgm45+tmNc2NfivmTbJUqpLRhj+QOMXUdfxWO51tdncBxaguHb8BuMO6PXgOkY8UssiVLqVYydQt/DiNHybWCJUmq1nyYLgeUefTwH/Bz4NYZxGYax7O157Y3H8Bcowrizu8LR7izlHscFDIOVAnzkuPtz8r6jfK1SqjmQLo3l0DZK26iQ0Taq9xClAj1SPblx+AAUYywR/7WXzjkX40Lrp5Sq6Y1z2h0RUcAlDsNhaUQkHcPgZHf1uEBEVgFlSqlg4sn0mq4AfdjmezhR0DbKHtjpt6FtVPfQj/9cEJFTMJbGP8bYqvwjxyFLz4w1APxbRKqUUkMjLaQL+gN3ehoFEUnCePzwDoYD7FcwlubnRVJXMDge5XzNfEkaT7SNsjXaRvWArmDoTRulV6pccBisv2BsHW3BWNa+Uym1PWBDczXMRd8FhoQcj77bHobDqiUQkUSMAIpTMfxE9gA/U0oti6iwIBAjIrZz63apUqo+UH1N+GgbZU+0jYosvWmj9KRKo9FoNBqNxgS0o7pGo9FoNBqNCehJlUaj0Wg0Go0J6EmVRqPRaDQajQnoSZVGo9FoNBqNCehJlUaj0Wg0Go0J6EmVRqPRaDQajQnoSZVGo9FoNBqNCehJlUaj0Wg0Go0J/D8CsP/T29DOUgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<Figure size 640x480 with 4 Axes>"
+       "<Figure size 600x400 with 4 Axes>"
       ]
      },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
+     "metadata": {
+      "needs_background": "light"
      },
-     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -911,12 +1035,12 @@
     "        concentrations.append(concentration)\n",
     "\n",
     "# plot flux changes with glucose uptake\n",
-    "rxn_id = ['EX_ac_e', 'EX_co2_e', 'EX_o2_e', 'BIOMASS_Ecoli_core_w_GAM']\n",
+    "rxn_id = ['EX_ac_e','EX_co2_e','EX_o2_e', 'BIOMASS_Ecoli_core_w_GAM']\n",
     "fig, axs = plt.subplots(2,2, dpi=100)\n",
     "for r, ax in zip(rxn_id, axs.flatten()):\n",
     "    # plot data\n",
     "    if r in rxn_to_pt.keys():\n",
-    "        ax.scatter(rxn_to_pt[r]['Glucose_IN'], rxn_to_pt[r][r],\n",
+    "        ax.scatter(rxn_to_pt[r]['EX_glc__D_e'].abs(), rxn_to_pt[r][r].abs(),\n",
     "                   color='firebrick', marker='o', s=30, linewidths=1.3,\n",
     "                   facecolors=None, zorder=0,\n",
     "                   label='Data')\n",
@@ -939,6 +1063,13 @@
     "\n",
     "plt.tight_layout()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/Scripts/create_ecolicore_pam_incl_UE.ipynb b/Scripts/create_ecolicore_pam_incl_UE.ipynb
index 6da6cce..6ffe3a1 100644
--- a/Scripts/create_ecolicore_pam_incl_UE.ipynb
+++ b/Scripts/create_ecolicore_pam_incl_UE.ipynb
@@ -26,24 +26,18 @@
    "metadata": {},
    "outputs": [
     {
-     "ename": "ImportError",
-     "evalue": "attempted relative import with no known parent package",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "\u001b[0;32m/tmp/ipykernel_10917/4068919364.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# load PAMpy modules\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'../src/PAModelpy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mPAModel\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPAModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mEnzymeSectors\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mActiveEnzymeSector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUnusedEnzymeSector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTransEnzymeSector\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mPAMValidator\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPAMValidator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/3_Projects/MCA_analysis/PAMpy/Scripts/../src/PAModelpy/PAModel.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m# sys.path.append('../')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mEnzymeSectors\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mActiveEnzymeSector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTransEnzymeSector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUnusedEnzymeSector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCustomSector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSector\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mCatalyticEvent\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCatalyticEvent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mConstraints\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mConstraint\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mImportError\u001b[0m: attempted relative import with no known parent package"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading PAModelpy modules version 0.0.3.3\n"
      ]
     }
    ],
    "source": [
     "# load PAMpy modules\n",
-    "sys.path.append('../src/PAModelpy')\n",
-    "from PAModel import PAModel\n",
-    "from EnzymeSectors import ActiveEnzymeSector, UnusedEnzymeSector, TransEnzymeSector\n",
-    "from PAMValidator import PAMValidator"
+    "from PAModelpy.PAModel import PAModel\n",
+    "from PAModelpy.EnzymeSectors import ActiveEnzymeSector, UnusedEnzymeSector, TransEnzymeSector\n",
+    "from PAModelpy.PAMValidator import PAMValidator"
    ]
   },
   {
@@ -63,6 +57,11 @@
      "output_type": "stream",
      "text": [
       "Set parameter Username\n",
+      "\n",
+      "--------------------------------------------\n",
+      "Warning: your license will expire in 9 days\n",
+      "--------------------------------------------\n",
+      "\n",
       "Academic license - for non-commercial use only - expires 2024-03-07\n",
       "Objective\n",
       "=========\n",
@@ -88,16 +87,11 @@
    ],
    "source": [
     "# load the model\n",
-    "model = cobra.io.load_json_model('../models/e_coli_core.json')\n",
+    "model = cobra.io.load_json_model('../Models/e_coli_core.json')\n",
     "# test the model\n",
     "sol_t = model.optimize()\n",
-    "print(model.summary())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
+    "print(model.summary())\n",
+    "\n",
     "# Build E. coli core PAM\n",
     "## Create Protein Sectors\n",
     "### Active Enzymes"
@@ -291,7 +285,7 @@
    ],
    "source": [
     "# load enzyme database from genome-scale E. coli PAM\n",
-    "enzyme_db = pd.read_excel('../data/proteinAllocationModel_iML1515_EnzymaticData_py.xls',\n",
+    "enzyme_db = pd.read_excel('../Data/proteinAllocationModel_iML1515_EnzymaticData_py.xls',\n",
     "                          sheet_name='ActiveEnzymes', index_col=0)\n",
     "\n",
     "# correct reaction IDS\n",
@@ -655,7 +649,8 @@
    "outputs": [],
    "source": [
     "# total protein constraint (cf. analyze_proteome.ipynb)\n",
-    "p_tot = 0.16995 # g/gDW -> Standard 0.14 g/gDW "
+    "# p_tot = 0.16995 # g/gDW -> Standard 0.14 g/gDW \n",
+    "p_tot = 0.185"
    ]
   },
   {
@@ -674,24 +669,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Read LP format model from file /tmp/tmpt4uya5mx.lp\n",
+      "Read LP format model from file /tmp/tmpb6ewyrcx.lp\n",
       "Reading time = 0.00 seconds\n",
       ": 72 rows, 190 columns, 720 nonzeros\n",
       "Setting up the proteome allocation model e_coli_core\n",
       "\n",
       "Add total condition-dependent protein constraint\n",
-      "\tTotal protein concentration: 0.16995 g/gDW\n",
+      "\tTotal protein concentration: 0.185 g/gDW\n",
       "\n",
       "Add active protein sector\n",
       "\n",
       "Add the following protein sector: TranslationalProteinSector\n",
       "\n",
-      "169.95 TranslationalProteinSector 49.92 120.02999999999999\n",
-      "-0.002944\n",
-      "Add the following protein sector: UnusedEnzymeSector\n",
+      "Add the following protein sector: \n",
       "\n",
-      "120.02999999999999 UnusedEnzymeSector 40.7 79.32999999999998\n",
-      "-0.0214\n",
       "Done with setting up the proteome allocation model e_coli_core\n",
       "\n"
      ]
@@ -721,22 +712,163 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'EX_ac_e':     EX_glc__D_e   EX_ac_e\n",
+       " 0     -3.026940  0.000000\n",
+       " 1     -3.329634  0.000000\n",
+       " 2     -3.619167  0.159000\n",
+       " 3     -4.060529  0.270000\n",
+       " 4     -5.044900  0.680000\n",
+       " 5     -5.285133  0.624000\n",
+       " 6     -6.581834  1.657500\n",
+       " 7     -6.902899  1.708500\n",
+       " 8     -7.600000  4.800000\n",
+       " 9     -0.900000  0.000000\n",
+       " 10    -1.200000  0.000000\n",
+       " 11    -1.100000  0.000000\n",
+       " 12    -1.700000  0.000000\n",
+       " 13    -1.800000  0.000000\n",
+       " 14    -2.000000  0.000000\n",
+       " 15    -2.800000  0.000000\n",
+       " 16    -4.200000  0.000000\n",
+       " 17    -4.600000  0.000000\n",
+       " 18    -5.700000  0.000000\n",
+       " 19    -5.800000  0.000000\n",
+       " 29     1.520000  0.000000\n",
+       " 30    -2.470000  0.000000\n",
+       " 31    -4.250000  0.000000\n",
+       " 32    -5.650000  0.000000\n",
+       " 33    -9.530000  3.490000\n",
+       " 34    -6.650000  2.950000\n",
+       " 35    -0.699706  0.000000\n",
+       " 36    -1.204666  0.000000\n",
+       " 37    -2.154266  0.000000\n",
+       " 38    -3.345144  0.000000\n",
+       " 39    -4.692853  0.155090\n",
+       " 40    -5.945775  1.841697\n",
+       " 41    -7.874274  6.109449\n",
+       " 46   -11.300000  7.100000\n",
+       " 47    -2.300000  0.000000\n",
+       " 48    -8.590000  3.910000\n",
+       " 49    -7.810000  3.260000\n",
+       " 50    -7.540000  2.720000\n",
+       " 51    -9.530000  3.490000\n",
+       " 52    -9.650000  6.830000,\n",
+       " 'EX_co2_e':     EX_glc__D_e   EX_co2_e\n",
+       " 0     -3.026940   6.614400\n",
+       " 1     -3.329634   7.228800\n",
+       " 2     -3.619167   8.265000\n",
+       " 3     -4.060529   8.157000\n",
+       " 4     -5.044900  10.496000\n",
+       " 5     -5.285133   9.544000\n",
+       " 7     -6.902899  10.806900\n",
+       " 35    -0.699706   1.987149\n",
+       " 36    -1.204666   2.808187\n",
+       " 37    -2.154266   4.531176\n",
+       " 38    -3.345144   6.590909\n",
+       " 39    -4.692853   8.689910\n",
+       " 40    -5.945775  10.298667\n",
+       " 41    -7.874274   8.576868\n",
+       " 42    -1.520000   4.696800\n",
+       " 43    -2.470000   6.891300\n",
+       " 44    -4.250000  13.107000\n",
+       " 45    -5.650000  17.356800\n",
+       " 46   -11.300000  19.600000\n",
+       " 47    -2.300000   9.000000,\n",
+       " 'EX_o2_e':     EX_glc__D_e    EX_o2_e\n",
+       " 35    -0.699706  -2.161836\n",
+       " 36    -1.204666  -2.886473\n",
+       " 37    -2.154266  -4.746377\n",
+       " 38    -3.345144 -10.000000\n",
+       " 39    -4.692853 -10.640097\n",
+       " 40    -5.945775 -15.374396\n",
+       " 41    -7.874274 -13.188406\n",
+       " 42    -1.520000  -4.750000\n",
+       " 43    -2.470000  -6.640000\n",
+       " 44    -4.250000 -12.580000\n",
+       " 45    -5.650000 -16.820000\n",
+       " 46   -11.300000 -18.800000\n",
+       " 47    -2.300000  -8.700000,\n",
+       " 'BIOMASS_Ecoli_core_w_GAM':     EX_glc__D_e  BIOMASS_Ecoli_core_w_GAM\n",
+       " 0     -3.026940                  0.240000\n",
+       " 1     -3.329634                  0.240000\n",
+       " 2     -3.619167                  0.300000\n",
+       " 3     -4.060529                  0.300000\n",
+       " 4     -5.044900                  0.400000\n",
+       " 5     -5.285133                  0.400000\n",
+       " 6     -6.581834                  0.510000\n",
+       " 7     -6.902899                  0.510000\n",
+       " 8     -7.600000                  0.650000\n",
+       " 9     -0.900000                  0.040000\n",
+       " 10    -1.200000                  0.050000\n",
+       " 11    -1.100000                  0.060000\n",
+       " 12    -1.700000                  0.090000\n",
+       " 13    -1.800000                  0.090000\n",
+       " 14    -2.000000                  0.110000\n",
+       " 15    -2.800000                  0.190000\n",
+       " 16    -4.200000                  0.290000\n",
+       " 17    -4.600000                  0.310000\n",
+       " 18    -5.700000                  0.400000\n",
+       " 19    -5.800000                  0.410000\n",
+       " 20    -2.660000                  0.210000\n",
+       " 21    -3.810000                  0.310000\n",
+       " 22    -4.910000                  0.410000\n",
+       " 23    -6.240000                  0.510000\n",
+       " 24    -2.780000                  0.220000\n",
+       " 25    -3.250000                  0.260000\n",
+       " 26    -4.360000                  0.360000\n",
+       " 27    -5.520000                  0.460000\n",
+       " 28    -6.240000                  0.510000\n",
+       " 29     1.520000                  0.100000\n",
+       " 30    -2.470000                  0.200000\n",
+       " 31    -4.250000                  0.300000\n",
+       " 32    -5.650000                  0.400000\n",
+       " 33    -9.530000                  0.730000\n",
+       " 34    -6.650000                  0.610000\n",
+       " 35    -0.699706                 -0.053081\n",
+       " 36    -1.204666                 -0.101840\n",
+       " 37    -2.154266                 -0.191799\n",
+       " 38    -3.345144                 -0.298272\n",
+       " 39    -4.692853                 -0.411342\n",
+       " 40    -5.945775                 -0.496032\n",
+       " 41    -7.874274                 -0.602263\n",
+       " 42    -1.520000                  0.100000\n",
+       " 43    -2.470000                  0.200000\n",
+       " 44    -4.250000                  0.300000\n",
+       " 45    -5.650000                  0.400000\n",
+       " 46   -11.300000                  0.850000\n",
+       " 47    -2.300000                  0.120000\n",
+       " 48    -8.590000                  0.690000\n",
+       " 49    -7.810000                  0.590000\n",
+       " 50    -7.540000                  0.610000\n",
+       " 51    -9.530000                  0.730000\n",
+       " 52    -9.650000                  0.650000}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# load phenotype data from excel file\n",
-    "pt_data = pd.read_excel('../data/Ecoli_phenotypes_py_rev.xls', sheet_name='Yields', index_col=None)\n",
-    "pt_data\n",
+    "pt_data = pd.read_excel('../Data/Ecoli_phenotypes/Ecoli_phenotypes_py_rev.xls', sheet_name='Yields', index_col=None)\n",
     "\n",
     "# extract reaction specific data \n",
     "rxn_to_pt = {}\n",
-    "rxn_transform = {\n",
-    "    'EX_ac_e': 'Acetate_OUT',\n",
-    "    'EX_co2_e': 'CO2_OUT',\n",
-    "    'EX_o2_e': 'O2_IN',\n",
-    "    'BIOMASS_Ecoli_core_w_GAM': 'mu'\n",
-    "}\n",
-    "for rxn_id, pt_id in rxn_transform.items():\n",
-    "    rxn_to_pt[rxn_id] = pt_data[['Glucose_IN', pt_id]].dropna().rename(columns={pt_id: rxn_id})\n"
+    "rxn_transform = {'BIOMASS_Ec_iML1515_core_75p37M':'BIOMASS_Ecoli_core_w_GAM'}\n",
+    "rxns = ['EX_ac_e','EX_co2_e','EX_o2_e', 'BIOMASS_Ec_iML1515_core_75p37M']\n",
+    "for rxn_id in rxns:\n",
+    "    if rxn_id in rxn_transform.keys(): \n",
+    "        rxn_to_pt[rxn_transform[rxn_id]] = pt_data[['EX_glc__D_e', rxn_id]].dropna().rename(columns = {rxn_id: rxn_transform[rxn_id]})\n",
+    "    else:\n",
+    "        rxn_to_pt[rxn_id] = pt_data[['EX_glc__D_e', rxn_id]].dropna()\n",
+    "    \n",
+    "\n",
+    "rxn_to_pt"
    ]
   },
   {
@@ -754,7 +886,7 @@
     {
      "data": {
       "text/html": [
-       "<strong><em>Optimal</em> solution with objective value 0.653</strong><br><div>\n",
+       "<strong><em>Optimal</em> solution with objective value 0.679</strong><br><div>\n",
        "<style scoped>\n",
        "    .dataframe tbody tr th:only-of-type {\n",
        "        vertical-align: middle;\n",
@@ -779,28 +911,28 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>PFK</th>\n",
-       "      <td>6.607656</td>\n",
-       "      <td>7.405407e-04</td>\n",
+       "      <td>7.382333</td>\n",
+       "      <td>8.177455e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PFL</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-2.353581e-03</td>\n",
+       "      <td>2.260148</td>\n",
+       "      <td>2.983560e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PGI</th>\n",
-       "      <td>3.147563</td>\n",
+       "      <td>4.035377</td>\n",
        "      <td>0.000000e+00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PGK</th>\n",
-       "      <td>-14.766376</td>\n",
-       "      <td>-7.621834e-05</td>\n",
+       "      <td>-16.251928</td>\n",
+       "      <td>-8.416445e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PGL</th>\n",
-       "      <td>5.963770</td>\n",
-       "      <td>1.118276e-04</td>\n",
+       "      <td>5.825327</td>\n",
+       "      <td>1.234862e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -809,28 +941,28 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NADH16</th>\n",
-       "      <td>27.541011</td>\n",
-       "      <td>1.236103e-03</td>\n",
+       "      <td>27.412601</td>\n",
+       "      <td>1.364972e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NADTRHD</th>\n",
-       "      <td>0.729405</td>\n",
-       "      <td>5.393183e-04</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.390978e-02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NH4t</th>\n",
-       "      <td>3.561229</td>\n",
-       "      <td>7.017980e-10</td>\n",
+       "      <td>3.705139</td>\n",
+       "      <td>7.749636e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>O2t</th>\n",
-       "      <td>13.770506</td>\n",
-       "      <td>7.017980e-10</td>\n",
+       "      <td>13.706301</td>\n",
+       "      <td>7.749636e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PDH</th>\n",
-       "      <td>9.728682</td>\n",
-       "      <td>5.719731e-03</td>\n",
+       "      <td>8.750512</td>\n",
+       "      <td>6.316039e-03</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -838,7 +970,7 @@
        "</div>"
       ],
       "text/plain": [
-       "<Solution 0.653 at 0x7f01304dfca0>"
+       "<Solution 0.679 at 0x7f2265656dd0>"
       ]
      },
      "execution_count": 12,
@@ -867,27 +999,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 640x480 with 4 Axes>"
+       "<Figure size 600x400 with 4 Axes>"
       ]
      },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
+     "metadata": {
+      "needs_background": "light"
      },
-     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -911,12 +1035,12 @@
     "        concentrations.append(concentration)\n",
     "\n",
     "# plot flux changes with glucose uptake\n",
-    "rxn_id = ['EX_ac_e', 'EX_co2_e', 'EX_o2_e', 'BIOMASS_Ecoli_core_w_GAM']\n",
+    "rxn_id = ['EX_ac_e','EX_co2_e','EX_o2_e', 'BIOMASS_Ecoli_core_w_GAM']\n",
     "fig, axs = plt.subplots(2,2, dpi=100)\n",
     "for r, ax in zip(rxn_id, axs.flatten()):\n",
     "    # plot data\n",
     "    if r in rxn_to_pt.keys():\n",
-    "        ax.scatter(rxn_to_pt[r]['Glucose_IN'], rxn_to_pt[r][r],\n",
+    "        ax.scatter(rxn_to_pt[r]['EX_glc__D_e'].abs(), rxn_to_pt[r][r].abs(),\n",
     "                   color='firebrick', marker='o', s=30, linewidths=1.3,\n",
     "                   facecolors=None, zorder=0,\n",
     "                   label='Data')\n",
@@ -939,6 +1063,13 @@
     "\n",
     "plt.tight_layout()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/Scripts/ecolicore_tam_incl_transcript_info.py b/Scripts/ecolicore_tam_incl_transcript_info.py
index c6af74e..96d4092 100644
--- a/Scripts/ecolicore_tam_incl_transcript_info.py
+++ b/Scripts/ecolicore_tam_incl_transcript_info.py
@@ -1,13 +1,17 @@
+import matplotlib.pyplot as plt
 import pandas as pd
 import os
 
 from Scripts.tam_generation import set_up_toy_tam, set_up_ecolicore_tam
+from Scripts.pam_generation import set_up_ecolicore_pam
+
 sinha_ref_conditions = {
     'Holm et al': ['REF', 'NOX', 'ATP'], #WT and NADH  overexpression conditions, mu 0.72, 0.65,0.58 h-1 respectively
     'Ishii et al': ['WT_0.7h-1'],
     'Gerosa et al.': ['Glycerol','Glucose','Acetate', 'Pyruvate','Gluconate','Succinate','Galactose','Fructose'],
 }
 TRANSCRIPT_FILE_PATH = os.path.join('Data', 'TAModel', 'Sinha-etal_2021_transcript-data.xlsx')
+FLUX_FILE_PATH = os.path.join('Data', 'TAModel', 'Sinha-etal_2021_flux-data.xlsx')
 
 
 mrna_vs_mu_slope = 2.64E-10
@@ -26,19 +30,122 @@ def get_transcript_data(transcript_file_path:str = TRANSCRIPT_FILE_PATH, mmol =
     else:
         return expression_data_normalized
 
-if __name__ == '__main__':
+def get_flux_data(flux_file_path:str = FLUX_FILE_PATH,
+                        reference: str = 'Holm et al'):
+    expression_data = pd.read_excel(flux_file_path, sheet_name=reference, index_col=0)
+    if reference == 'Holm et al':
+        #remove R suffix
+        expression_data.index = expression_data.index.str.replace('R_', '')
+    return expression_data
+
+def get_pam_fluxes(substrate_uptake_rate):
+    pam = set_up_ecolicore_pam()
+    pam.change_reaction_bounds('EX_glc__D_e', lower_bound=-substrate_uptake_rate, upper_bound=0)
+    sol = pam.optimize()
+    pam_fluxes = sol.fluxes
+    return pam_fluxes
+
+def set_up_tamodel(strain ='REF'):
     tam = set_up_ecolicore_tam()
-    tam.optimize()
+    tam.change_reaction_bounds('EX_glc__D_e', lower_bound=-1e6, upper_bound=0)
     transcript_data_mmol = get_transcript_data()
 
     for gene, expression_data in transcript_data_mmol.iterrows():
-        transcript_id = 'mRNA_'+gene
+        transcript_id = 'mRNA_' + gene
         if not transcript_id in tam.transcripts: continue
-        transcript = tam.transcripts.get_by_id('mRNA_'+gene)
-        #testing wildtype condition
-        transcript.change_concentration(concentration=expression_data[0],
-                                        error= expression_data[0]*0.01)
-    print(tam.reactions.get_by_id('EX_glc__D_e'))
-
-    tam.optimize()
-    print(tam.summary())
+        transcript = tam.transcripts.get_by_id('mRNA_' + gene)
+        # testing wildtype condition
+        transcript.change_concentration(concentration=expression_data[strain],
+                                        error=expression_data[strain] * 0.01)
+    return tam
+
+def get_tam_fluxes(tam,substrate_uptake_rate):
+    tam.change_reaction_bounds('EX_glc__D_e', lower_bound=-substrate_uptake_rate, upper_bound=0)
+    sol = tam.optimize()
+    tam_fluxes = sol.fluxes
+    return tam_fluxes
+
+def compare_flux_data(flux_data, pam_fluxes, tam_fluxes, strain ='REF', abs=True):
+    if abs:
+        glc_upt_ref = 1
+        glc_upt_pam = 1
+        glc_upt_tam = 1
+    else:
+        glc_upt_ref = flux_data[strain]['GLCptspp']
+        glc_upt_pam = pam_fluxes['GLCpts']
+        glc_upt_tam = tam_fluxes['GLCpts']
+
+    flux_results = flux_data[[strain]]
+    flux_results_percentage = flux_results.assign(
+        strain=lambda val: val[strain] / glc_upt_ref)
+    flux_results_percentage['PAM'] = 0
+    flux_results_percentage['TAM'] = 0
+    for rxn in flux_data.index:
+        ori_rxn = rxn
+        if 'pp' in rxn: rxn = rxn.replace('pp', '')
+        if 'biomass' in rxn: rxn = 'BIOMASS_Ecoli_core_w_GAM'
+        if 'EX_glc' in rxn: rxn = 'EX_glc__D_e'
+        if 'EX_ac' in rxn: rxn = 'EX_ac_e'
+        flux_results_percentage['PAM'][ori_rxn] = pam_fluxes[rxn] / glc_upt_pam
+        flux_results_percentage['TAM'][ori_rxn] = tam_fluxes[rxn] / glc_upt_tam
+
+    print(flux_results_percentage.to_markdown())
+    return flux_results_percentage
+
+def compare_fluxes_holm_reference(strain = 'REF'):
+    flux_data =get_flux_data()
+    substrate_uptake_rate = flux_data[strain]['GLCptspp']
+
+    pam_fluxes = get_pam_fluxes(substrate_uptake_rate=substrate_uptake_rate)
+
+    tam = set_up_tamodel(strain)
+    tam_fluxes = get_tam_fluxes(tam, substrate_uptake_rate=substrate_uptake_rate)
+    for i,row in tam.capacity_sensitivity_coefficients.iterrows():
+        if row.coefficient > 0: print(row)
+
+    flux_relative = compare_flux_data(flux_data, pam_fluxes, tam_fluxes,  strain,abs = False)
+    flux_absolute = compare_flux_data(flux_data, pam_fluxes, tam_fluxes,  strain)
+
+    plot_flux_comparison(flux_absolute, flux_relative, strain)
+
+def plot_flux_comparison(flux_df_abs, flux_df_rel, strain):
+    fig, ax = plt.subplots(1,2)
+
+    ax[0].scatter(flux_df_abs['TAM'], flux_df_abs['strain'], color = 'black')
+    ax[0].scatter(flux_df_abs['PAM'], flux_df_abs['strain'], color ='red')
+    #reference line
+    ax[0].plot(flux_df_abs['strain'], flux_df_abs['strain'], linestyle ='dashed')
+
+    ax[0].set_title(strain + ' absolute fluxes')
+    ax[0].set_xlabel('simulated flux [$mmol/g_{CDW}/h$]')
+    ax[0].set_ylabel('measured flux [$mmol/g_{CDW}/h$]')
+
+    ax[1].scatter(flux_df_rel['TAM'], flux_df_rel['strain'], color='black', label = 'TAM')
+    ax[1].scatter(flux_df_rel['PAM'], flux_df_rel['strain'], color='red', label = 'PAM')
+    # reference line
+    ax[1].plot(flux_df_rel['strain'], flux_df_rel['strain'], linestyle='dashed')
+
+    ax[1].set_title(strain + ' relative fluxes')
+    ax[1].set_xlabel('simulated flux [$mmol/g_{CDW}/h$]')
+    ax[1].set_ylabel('measured flux [$mmol/g_{CDW}/h$]')
+
+    fig.set_figwidth(20)
+    fig.set_figheight(10)
+    plt.legend()
+    plt.show()
+
+
+if __name__ == '__main__':
+    print('Reference condition')
+    compare_fluxes_holm_reference()
+    print('\n-------------------------------------------------------------------------------------------------')
+    print('mutation 1: NOX strain (overexpression of NADH oxidase)\n')
+    compare_fluxes_holm_reference('NOX')
+    # TODO print mRNA and protein concentrations to compare with lb
+    # TODO print shadowprices of mRNA (are lbs hit? how far can I constrain?)
+
+
+
+
+
+
diff --git a/Scripts/pam_generation.py b/Scripts/pam_generation.py
index 31ea7cd..2409892 100644
--- a/Scripts/pam_generation.py
+++ b/Scripts/pam_generation.py
@@ -39,7 +39,7 @@ def set_up_ecolicore_pam(total_protein:bool = True, active_enzymes: bool = True,
     DATA_DIR = 'Data'
     MODEL_DIR = 'Models'
     PAM_DATA_FILE_PATH = os.path.join(DATA_DIR, 'proteinAllocationModel_iML1515_EnzymaticData_py.xls')
-    TAM_DATA_FILE_PATH = os.path.join(DATA_DIR, 'TAModel','2024-02-16_gene_enzyme_reaction_relation_Ecoli.xlsx')
+    TAM_DATA_FILE_PATH = os.path.join(DATA_DIR, 'TAModel','2024-02-27_gene_enzyme_reaction_relation_Ecoli.xlsx')
 
     # some other constants
     BIOMASS_REACTION = 'BIOMASS_Ecoli_core_w_GAM'
@@ -376,6 +376,7 @@ def _get_fwd_bckw_kcat(rxn_id: str, kcat:float, model:PAModel) -> Union[list, No
 
     # Iterate over each identifier in the input
     if base_id in model.reactions:
+        if not model.reactions.get_by_id(base_id).genes: return None
         # Determine the form of the identifier
         if rxn_id.endswith('_f'):
             kcat_fwd = kcat
@@ -390,6 +391,7 @@ def _get_fwd_bckw_kcat(rxn_id: str, kcat:float, model:PAModel) -> Union[list, No
         else:
             return None
     elif rxn_id in model.reactions:
+        if not model.reactions.get_by_id(rxn_id).genes: return None
         kcat_fwd = kcat
         kcat_rev = kcat
     else:
diff --git a/Scripts/parse_ecoli_gpr_info.py b/Scripts/parse_ecoli_gpr_info.py
index 66464f2..757278d 100644
--- a/Scripts/parse_ecoli_gpr_info.py
+++ b/Scripts/parse_ecoli_gpr_info.py
@@ -51,8 +51,13 @@ def parse_gpr_relationships_from_Ecocyc():
          'Molecular-Weight-KiloDaltons', 'mrna_length', 'gpr']]
     enzyme_gene_reaction_relation.columns = ['Gene', 'Enzyme', 'Reaction', 'gene_id', 'enzyme_id',
                                              'molmass_kDa', 'mrna_length', 'gpr']
+
+    #Get enzyme and kcat information already available
     tam_info_merged = parse_enzymatic_data_information(enzyme_gene_reaction_relation)
 
+    #Append the information of each gene with information from the 'GeneList'
+    tam_info_merged = parse_ecoli_genome_information(tam_info_merged)
+
     #write to excel
     with pd.ExcelWriter(TAM_DATA_FILE) as writer:
         tam_info_merged.to_excel(writer, sheet_name='enzyme-gene-reaction')
@@ -82,7 +87,15 @@ def parse_enzymatic_data_information(enzyme_gene_reaction_relation):
     tam_info = tam_info.drop(['molmass_kDa', 'Reaction'], axis = 1)
     return tam_info
 
+def parse_ecoli_genome_information(tam_info_merged):
+    genome_information = pd.read_excel(os.path.join('Data', 'TAModel','GeneList_ecoli.xlsx'),
+                                sheet_name='GeneList').set_index('bnumber')
+    genome_info_useful = genome_information[['start', 'end']]
+    for bnumber, row in genome_info_useful.iterrows():
+        tam_info_gene = tam_info_merged[tam_info_merged.gene_id == bnumber]
+        tam_info_gene['mrna_length'] = row.start - row.end
 
+    return tam_info_merged
 
 if __name__ == '__main__':
     parse_gpr_relationships_from_Ecocyc()
\ No newline at end of file
diff --git a/Scripts/tam_generation.py b/Scripts/tam_generation.py
index fbbaaa3..9ae26c1 100644
--- a/Scripts/tam_generation.py
+++ b/Scripts/tam_generation.py
@@ -42,12 +42,14 @@ def set_up_ecolicore_tam(total_protein:bool = True, active_enzymes: bool = True,
     # Setting the relative paths
     DATA_DIR = os.path.join('Data')
     MODEL_DIR = os.path.join('Models')
-    TAM_DATA_FILE_PATH = os.path.join(DATA_DIR, 'TAModel','2024-02-16_gene_enzyme_reaction_relation_Ecoli.xlsx')
+    TAM_DATA_FILE_PATH = os.path.join(DATA_DIR, 'TAModel','2024-02-27_gene_enzyme_reaction_relation_Ecoli.xlsx')
 
 
     # some other constants
     BIOMASS_REACTION = 'BIOMASS_Ecoli_core_w_GAM'
-    TOTAL_PROTEIN_CONCENTRATION = 0.16995  # [g_prot/g_cdw]
+    # TOTAL_PROTEIN_CONCENTRATION = 0.16995  # [g_prot/g_cdw]
+    TOTAL_PROTEIN_CONCENTRATION = 0.185  # [g_prot/g_cdw]
+
     MRNA_MU = 0.00013049558330984208 # [g_mrna/g_cdw/h]
     MRNA_0= 1.7750480089801658e-05 # [g_mrna/g_cdw]
 
diff --git a/src/TAModelpy/TAModel.py b/src/TAModelpy/TAModel.py
index 45e187a..4dd6e59 100644
--- a/src/TAModelpy/TAModel.py
+++ b/src/TAModelpy/TAModel.py
@@ -66,15 +66,18 @@ def __init__(self, id_or_model: Union[str, "Model", None] = None,
             >>>          translational_sector = translation_enzyme,sensitivity = sensitivity,
             >>>          unused_sector = unused_enzyme, p_tot=0.6*1e-3, configuration=Config)
             >>> gene2transcript = {gene: {'id': f'mRNA{gene.id[-1]}', 'length': 100} for gene in pamodel.genes}
-            >>> tamodel = TAModel(id_or_model = pamodel, mrna_sector = mrna_sector,  gene2transcript= gene2transcript)
+            >>> active_mrna_sector = ActivemRNASector(mrnas_0 = MRNA_0,
+            >>>                                  mrnas_mu = MRNA_MU,
+            >>>                                  id_list = [BIOMASS_REACTION],
+            >>>                                  gene2transcript = gene2transcript,
+            >>>                                  configuration = config)
+            >>> tamodel = TAModel(id_or_model = pamodel, mrna_sector = active_mrna_sector)
 
             From scratch:
             >>>  tamodel = TAModel(model, name='toy model MCA with enzyme constraints', active_sector=active_enzyme,
-            >>>          translational_sector = translation_enzyme, mrna_sector = mrna_sector, sensitivity = sensitivity,
-            >>>          unused_sector = unused_enzyme, p_tot=0.6*1e-3, configuration=Config,
-            >>>          gene2transcript= gene2transcript)
+            >>>          translational_sector = translation_enzyme, mrna_sector = active_mrna_sector, sensitivity = sensitivity,
+            >>>          unused_sector = unused_enzyme, p_tot=0.6*1e-3, configuration=Config)
         """
-        #TODO update examples
 
         self.transcripts = DictList()
         self.f_min = 1
@@ -169,14 +172,26 @@ def add_transcript(self, transcript_object: Transcript,
                 enzymes generated from a single enzyme (in mmol_enzyme/mmol_mrna)
         """
         #adjust minimal and maximal protein/transcript ratios based
-        transcript_object.f_min = f_min*transcript_object.length**2/3
-        transcript_object.f_max = f_max*transcript_object.length**2/3*1e6
+        transcript_object.f_min = f_min*pow(transcript_object.length,2)/3*1e6
+        transcript_object.f_max = f_max*pow(transcript_object.length,2)/3*1e6
 
         # print('adding model to transcript ', transcript_object.id)
         transcript_object.model = self
         self.transcripts.append(transcript_object)
 
     def add_transcript_enzyme_relations(self, enzyme: Union[Enzyme, EnzymeComplex]) -> None:
+        """Adds constraints for mRNA abundance and enzyme concentration relations.
+
+            This function checks the gene-protein-reaction (GPR) relations to determine if the type of relation should
+            be included in the transcript/enzyme relationship ('and' or 'or').
+            Then, it adds constraints based on these relations.
+
+            Args:
+                enzyme (Union[Enzyme, EnzymeComplex]): The enzyme or enzyme complex for which constraints are being added.
+
+            Returns:
+                None
+            """
         # check the gpr relations: is an additional gene required or is there 'competition' with another gene?
         transcript_associated_with_enzyme = self.get_transcripts_associated_with_enzyme(enzyme)
         for relation, transcripts in transcript_associated_with_enzyme.items():
@@ -186,7 +201,18 @@ def add_transcript_enzyme_relations(self, enzyme: Union[Enzyme, EnzymeComplex])
 
 
     def add_total_mrna_constraint(self, mrna_sector:ActivemRNASector) -> None:
+        """Adds a constraint to limit the total mRNA abundance.
+
+           This function adds a constraint to limit the total mRNA abundance based on the intercept
+           adn slope provided by the the ActivemRNA sector.
+           It calculates the coefficients for each transcript and sets the linear coefficients for the constraint.
+
+           Args:
+               mrna_sector (ActivemRNASector): The active mRNA sector.
 
+           Returns:
+               None
+           """
         tot_mrna_constraint = self.problem.Constraint(Zero,name=self.TOTAL_MRNA_CONSTRAINT_ID,
                                                       lb=0, ub = mrna_sector.intercept)
         self.add_cons_vars(tot_mrna_constraint)
@@ -300,6 +326,28 @@ def make_mrna_min_max_constraint(self, enz: Enzyme,
 
     def set_mrna_enzyme_minmax_constraint_and_relation(self, enzyme: Enzyme,
                                                        transcripts: list, transcript_ids: list) -> None:
+        """Sets constraints and relations for mRNA abundance and enzyme concentration.
+
+            The lowest mRNA concentration of the available transcripts is used. This is achieved by adding an
+            auxiliary variable for the min and max enzyme concentration in the following format:
+            ```
+            mrna_aux_min <= mrna1 * fmin_1
+            mrna_aux_min <= mrna2 * fmin_2
+
+            mrna_aux_max <= mrna1 * fmax_1
+            mrna_aux_max <= mrna2 * fmax_2
+
+            mrna_aux_min - E >= 0
+            mrna_aux_max - E <= 0
+            ```
+           Args:
+               enzyme (Enzyme): The enzyme for which constraints and relations are being set.
+               transcripts (list): List of Transcript objects.
+               transcript_ids (list): List of transcript IDs.
+
+           Returns:
+               None
+        """
         transcript_aux_min_variable = self.problem.Variable(Zero, name=f'{transcript_ids}_{enzyme.id}_min_aux', lb=0, ub=1e6)
         transcript_aux_max_variable = self.problem.Variable(Zero, name=f'{transcript_ids}_{enzyme.id}_max_aux', lb=0, ub=1e6)
 
@@ -309,7 +357,7 @@ def set_mrna_enzyme_minmax_constraint_and_relation(self, enzyme: Enzyme,
         # is used to constrain the enzyme concentration
         for transcript in transcripts:
             aux_min_constraint = self.problem.Constraint(Zero, name=f'{transcript.id}_{enzyme.id}_min_aux', lb=0, ub=1e6)
-            aux_max_constraint = self.problem.Constraint(Zero, name=f'{transcript.id}_{enzyme.id}__max_aux', lb=0, ub=1e6)
+            aux_max_constraint = self.problem.Constraint(Zero, name=f'{transcript.id}_{enzyme.id}_max_aux', lb=0, ub=1e6)
             self.add_cons_vars([aux_min_constraint, aux_max_constraint])
 
             # this auxiliary variable is already converted to g_enzyme/g_cdw
@@ -338,6 +386,21 @@ def set_mrna_enzyme_minmax_constraint_and_relation(self, enzyme: Enzyme,
 
     def set_mrna_enzyme_minmax_constraint_or_relationship(self, enzyme: Enzyme,
                                                            transcript: Transcript, transcript_ids: list):
+        """Sets constraints for mRNA abundance and enzyme concentration based on 'or' relationship.
+            This is in the following format:
+            ```
+            mrna_1 * fmin_1 + mrna_2 * fmin_2 - E >= 0
+            mrna_1 * fmax_1 + mrna_2 * fmax_2 - E <= 0
+            ```
+
+           Args:
+               enzyme (Enzyme): The enzyme for which constraints are being set.
+               transcript (Transcript): The transcript object.
+               transcript_ids (list): List of transcript IDs.
+
+           Returns:
+               None
+           """
         self.constraints[f'{transcript_ids}_{enzyme.id}_max'].set_linear_coefficients({
             transcript.mrna_variable: transcript.f_max
         })
@@ -368,14 +431,9 @@ def get_genes_associated_with_enzyme(self, enzyme: Enzyme) -> dict:
         # enzyme_complex_id = 0
         for gene_list in enzyme.genes:
             if len(gene_list)>1: # and relationship
-                # gene_relations_dict = {**gene_relations_dict,
-                #                        **{gene:['and', enzyme_complex_id] for gene in gene_list if gene in self.genes}}
-                # enzyme_complex_id += 1
                 gene_relations_dict['and'] += [[gene for gene in gene_list if gene in self.genes]]
             elif gene_list[0] in self.genes:
                     gene_relations_dict['or'] += [gene_list[0]]
-            #     gene_relations_dict = {**gene_relations_dict,
-            #                            **{gene_list[0]: ['or', 0] if gene_list[0] in self.genes}}
         return gene_relations_dict
 
     def get_transcripts_associated_with_enzyme(self, enzyme: Enzyme) -> dict:
@@ -402,16 +460,8 @@ def get_transcripts_associated_with_enzyme(self, enzyme: Enzyme) -> dict:
         # enzyme_complex_id = 0
         for gene_list in enzyme.genes:
             if len(gene_list)>1: # and relationship
-                # mrna_relations_dict = {**mrna_relations_dict,
-                #                        **{self.transcripts.get_by_id('mRNA_'+gene):
-                #                              ['and', enzyme_complex_id] for gene in gene_list if 'mRNA_'+gene in self.transcripts}}
-                # enzyme_complex_id += 1
                 mrna_relations_dict['and'] += [[self.transcripts.get_by_id('mRNA_'+gene) for gene in gene_list if 'mRNA_'+gene in self.transcripts]]
             elif 'mRNA_' + gene_list[0] in self.transcripts:
-                # mrna_relations_dict = {**mrna_relations_dict,
-                #                        **{self.transcripts.get_by_id('mRNA_'+gene_list[0]):
-                #                               ['or', 0] if 'mRNA_'+gene_list[0] in self.transcripts}}
-
                 mrna_relations_dict['or'] += [self.transcripts.get_by_id('mRNA_' + gene_list[0])]
         return mrna_relations_dict
 
@@ -420,4 +470,41 @@ def _gene_is_in_enzyme_complex(self, gene: Union[str, Gene], enzyme: Enzyme) ->
         if isinstance(enzyme, EnzymeComplex) and any([gene in genelist for genelist in enzyme.genes]):
             return True
         else:
-            return False
\ No newline at end of file
+            return False
+
+    def calculate_csc(self, obj_value, mu, mu_ub, mu_lb, mu_ec_f, mu_ec_b):
+        """
+        Calculates the capacity sensitivity coefficient for all inequality constraints in the model.
+        The sum of all capacity sensitivity coefficients should equal 1 for growth maximization.
+
+        Definition: constraint_UB*shadowprice/obj_value.
+
+        Inherits from the PAModel calculate_csc function and adds the calculation of the csc for the total rna
+        constraint.
+
+        :param obj_value: Float
+        :param mu: DataFrame
+            Shadowprices for all constraints
+        :param mu_ub: DataFrame
+            Shadowprices for the reaction UB constraints
+        :param mu_lb: DataFrame
+            Shadowprices for the reaction LB constraints
+        :param mu_ec_f: DataFrame
+            Shadowprices for the constraint related to an enzymatic catalysis of the forward reaction
+        :param mu_ec_b: DataFrame
+            Shadowprices for the constraint related to an enzymatic catalysis of the backward reaction
+
+        Results will be saved in the self.capacity_sensitivity_coefficients attribute as a dataframe
+        """
+        super().calculate_csc(obj_value, mu, mu_ub, mu_lb, mu_ec_f, mu_ec_b)
+        constraint = 'mRNA'
+        rxn_id = self.TOTAL_MRNA_CONSTRAINT_ID
+        enzyme_id = self.TOTAL_MRNA_CONSTRAINT_ID
+        ca_coefficient = self.constraints[enzyme_id].ub * \
+                         mu[mu['index'] == self.TOTAL_MRNA_CONSTRAINT_ID]['shadow_prices'].iloc[0] / obj_value
+
+        new_row = [rxn_id, enzyme_id, constraint, ca_coefficient]
+        #  add new_row to dataframe
+        self.capacity_sensitivity_coefficients.loc[len(self.capacity_sensitivity_coefficients)] = new_row
+
+        #TODO sensitivity analysis for transcripts
\ No newline at end of file
diff --git a/src/TAModelpy/Transcript.py b/src/TAModelpy/Transcript.py
index 1d4e479..2e08543 100644
--- a/src/TAModelpy/Transcript.py
+++ b/src/TAModelpy/Transcript.py
@@ -31,11 +31,6 @@ def model(self, model):
         # check if enzymes are in the model
         for enzyme in self.enzymes:
             if not enzyme in model.enzymes: model.add_enzyme(enzyme)
-            # #check the gpr relations: is an additional gene required or is there 'competition' with another gene?
-            # other_mrnas= model.get_transcripts_associated_with_enzyme(enzyme)
-            # #TODO how to handle other mrna relations?
-            # # add the mRNA relations to the model
-            # self._model.make_mrna_min_max_constraint(enzyme, self)
 
     def change_concentration(self, concentration:float, error: float = 0) -> None:
         """ Setting the concentration of an mRNA species
diff --git a/tests/unit_tests/test_tam_generation.py b/tests/unit_tests/test_tam_generation.py
index f23fc9d..4125dd3 100644
--- a/tests/unit_tests/test_tam_generation.py
+++ b/tests/unit_tests/test_tam_generation.py
@@ -1,7 +1,31 @@
+import pytest
+import os
+import pandas as pd
+import cobra
+
+from src.PAModelpy import PAModel, UnusedEnzymeSector, ActiveEnzymeSector, TransEnzymeSector
 from Scripts.tam_generation import set_up_toy_tam, set_up_ecolicore_tam
 from Scripts.pam_generation import set_up_ecolicore_pam, set_up_toy_pam
 
 
+# def test_set_up_ecolicore_pam_builds_correct_model():
+#     # Arrange
+#     sut = set_up_ecolicore_pam()
+#     ecolicore_pam_original = setup_ecolicore_pam_validation()
+#
+#     print(ecolicore_pam_original.enzymes)
+#     # print(sut.enzymes)
+#     # Assert
+#     for enzyme in sut.enzyme_variables:
+#         if enzyme.id not in ecolicore_pam_original.enzymes:
+#             print(enzyme.id, enzyme.reactions)
+#
+#     for rxn in sut.reactions:
+#         if rxn.id not in ecolicore_pam_original.reactions:
+#             print(rxn.id)
+#
+#     assert False
+
 def test_set_up_toy_tam_works():
     sut = set_up_toy_tam
 
@@ -42,10 +66,188 @@ def test_ecolicore_tam_without_constraints_has_same_result_as_pam():
     # Act
     sut.optimize()
     ecolicore_pam.optimize()
-    for enzyme in ecolicore_pam.enzyme_variables:
-        print(enzyme.id, enzyme.flux)
+    # for enzyme in ecolicore_pam.enzyme_variables:
+    #     print(enzyme.id, enzyme.flux)
 
     # Arrange
     assert 'optimal' == ecolicore_pam.solver.status
     assert 'optimal' == sut.solver.status
+    assert_solution_pam_equals_tam(ecolicore_pam, sut)
     assert ecolicore_pam.objective.value == sut.objective.value
+
+def assert_solution_pam_equals_tam(pam, tam):
+    # for reaction in tam.reactions:
+    #     # print(reaction.id, pam.reactions.get_by_id(reaction.id).flux,reaction.flux)
+    #     # assert pam.reactions.get_by_id(reaction.id).flux == pytest.approx(reaction.flux, rel= 1e-3)
+    # for enzyme in tam.enzyme_variables:
+    #     if enzyme.id == 'E114':
+            # print(enzyme.id,enzyme.reactions, pam.enzyme_variables.get_by_id(enzyme.id).flux,enzyme.flux)
+
+        # assert pam.enzymes.get_by_id(enzyme.id).flux == pytest.approx(enzyme.flux, rel= 1e-3)
+    assert pam.objective.value == tam.objective.value
+
+
+
+# def setup_ecolicore_pam_validation(total_protein:bool = True, active_enzymes: bool = True, translational_enzymes:bool = True, unused_enzymes:bool = True, sensitivity =True):
+#     # Setting the relative paths
+#     DATA_DIR = os.path.join(os.getcwd(), 'Data')
+#     MODEL_DIR = os.path.join(os.getcwd(), 'Models')
+#     PAM_DATA_FILE_PATH = os.path.join(DATA_DIR, 'proteinAllocationModel_iML1515_EnzymaticData_py.xls')
+#
+#     # PAM_DATA_FILE_PATH = os.path.join(DATA_DIR, 'proteinAllocationModel_iML1515_EnzymaticData_230503.xls')
+#
+#     # some other constants
+#     BIOMASS_REACTION = 'BIOMASS_Ecoli_core_w_GAM'
+#     TOTAL_PROTEIN_CONCENTRATION = 0.16995  # [g_prot/g_cdw]
+#
+#     # load the genome-scale information
+#     model = cobra.io.load_json_model(os.path.join(MODEL_DIR, 'e_coli_core.json'))
+#
+#     #load example data for the E.coli iML1515 model
+#     if active_enzymes:
+#         # load active enzyme sector information
+#         enzyme_db = pd.read_excel(PAM_DATA_FILE_PATH, sheet_name='ActiveEnzymes')
+#         enzyme_db = enzyme_db.set_index('rxnID')
+#         # correct reaction IDS
+#         for idx in enzyme_db.index.to_list():
+#             # transprt reactions<
+#
+#             if 'pp' in idx:
+#                 idx_new = idx.replace('pp', '')
+#                 if idx_new not in enzyme_db.index:
+#                     enzyme_db.rename(index={idx: idx_new}, inplace=True)
+#             if 'ex' in idx:
+#                 idx_new = idx.replace('ex', '')
+#                 if idx_new not in enzyme_db.index:
+#                     enzyme_db.rename(index={idx: idx_new}, inplace=True)
+#
+#                     # replace NaN values with unique identifiers
+#         # replace NaN enzyme ids with a dummy enzyme identifier
+#         # select the NaN values
+#         nan_values = enzyme_db['EC_nmbr'].isnull()
+#         # make a list with unique ids
+#         nan_ids = [f'E{i}' for i in range(nan_values.sum())]
+#         # replace nan values by unique id
+#         enzyme_db.loc[nan_values, 'EC_nmbr'] = nan_ids
+#
+#         # create enzyme objects for each gene-associated reaction
+#
+#         # parse the enzyme information (kcat values, identifiers and molmasses)
+#         # create enzyme objects for each gene-associated reaction
+#         kcats = {}
+#         rxn2ec = {}
+#         molmass = {}
+#         for rxn in model.reactions:
+#             if rxn.genes:
+#                 # correct transport reactions
+#                 if 't' in rxn.id:
+#                     rxn.id = rxn.id
+#                 # are enzyme information in the PAM database?
+#                 rev = 0  # denotes reversibility
+#                 if rxn.lower_bound >= 0:
+#                     # irreversible reaction (forward direction)
+#                     rev = 0
+#                     rxn_id = rxn.id  # save reaction ID for retrieveing molar masses/enzyme information later
+#                     if rxn.id in enzyme_db.index:
+#                         kcats[rxn.id] = {'f': enzyme_db.loc[rxn.id, 'kcat']}
+#                 elif rxn.upper_bound <= 0:
+#                     # irreversible reaction (reverse direction)
+#                     rev = 1
+#                     rxn_id = rxn.id + '_b'
+#                     if rxn_id in enzyme_db.index:
+#                         kcats[rxn.id] = {'b': enzyme_db.loc[rxn_id, 'kcat']}
+#                 else:
+#                     rev = 2
+#                     # reversible reaction
+#                     rxn_id_f = rxn.id + '_f'
+#                     rxn_id_b = rxn.id + '_b'
+#                     if rxn_id_f in enzyme_db.index and rxn_id_b in enzyme_db.index:
+#                         rxn_id = rxn_id_f  # save reaction ID for retrieveing molar masses/enzyme information later
+#                         kcats[rxn.id] = {'f': enzyme_db.loc[rxn_id_f, 'kcat'],
+#                                          'b': enzyme_db.loc[rxn_id_b, 'kcat']}
+#
+#                     else:
+#                         # try if only forward reaction is in database
+#                         rxn_id = rxn.id  # save reaction ID for retrieveing molar masses/enzyme information later
+#                         kcats[rxn.id] = {'f': enzyme_db.loc[rxn.id, 'kcat'],
+#                                          'b': enzyme_db.loc[
+#                                                   rxn.id, 'kcat'] / 2}  # deduce backwards kcat from forward value
+#
+#                 # where enzyme information found?
+#                 if rxn.id in kcats.keys():
+#                     # save molmass
+#                     molmass[rxn.id] = enzyme_db.loc[rxn_id, 'molMass']
+#                     # save enzyme information
+#                     # is enzyme information NaN?
+#                     if pd.isna(enzyme_db.loc[rxn_id, 'EC_nmbr']):
+#                         rxn2ec[rxn.id] = ''
+#                     else:
+#                         rxn2ec[rxn.id] = enzyme_db.loc[rxn_id, 'EC_nmbr']
+#
+#
+#                 else:
+#                     # no enzyme information found
+#                     print('No enzyme information found for reaction: ' + rxn.id)
+#                     # Create generic Enzyme with mean molar masses and kcat
+#                     if rev == 0:
+#                         kcats[rxn.id] = {'f': 22}
+#                     elif rev == 1:
+#                         kcats[rxn.id] = {'b': 22}
+#                     else:
+#                         kcats[rxn.id] = {'f': 22, 'b': 22}
+#
+#                     molmass[rxn.id] = 3.947778784340140e04
+#
+#         rxn2protein = {}
+#         for rxn, ec in rxn2ec.items():
+#             ec_dict = {**kcats[rxn], **{'molmass': molmass[rxn]}}
+#             # add enzyme to enzymes related to reaction if these are already stored
+#             if rxn in rxn2protein.keys():
+#                 rxn2protein[rxn] = {**rxn2protein[rxn], **{ec: ec_dict}}
+#             # if not create new reaction entry
+#             else:
+#                 rxn2protein[rxn] = {ec: ec_dict}
+#
+#         # create active enzymes sector
+#         active_enzyme_sector = ActiveEnzymeSector(rxn2protein=rxn2protein)
+#
+#     else:
+#         active_enzyme_sector = None
+#
+#     if translational_enzymes:
+#         # translational protein sector parameter (substrate dependent)
+#         id_list_tps = ['EX_glc__D_e']
+#         tps_0 = [0.04992]  # g/gDW
+#         tps_mu = [-0.002944]  # g h/gDW -> transformed to match glucose uptake variable
+#         molmass_tps = [405903.94]  # g/mol
+#
+#         # translational protein sector
+#         translation_enzyme_sector = TransEnzymeSector(
+#             id_list=id_list_tps,
+#             tps_0=tps_0,
+#             tps_mu=tps_mu,
+#             mol_mass=molmass_tps,
+#         )
+#     else:
+#         translation_enzyme_sector = None
+#
+#     if unused_enzymes:
+#         id_list_ups = [BIOMASS_REACTION]
+#         ups_0 = [0.0407]  # g/gDW
+#         ups_mu = [-0.0214]  # g h/gDW -> negative relation with growth rate
+#         molmass_ups = [405903.94]  # g/mol
+#
+#         unused_enzyme_sector = UnusedEnzymeSector(
+#             id_list=id_list_ups,
+#             ups_0=ups_0,
+#             ups_mu=ups_mu,
+#             mol_mass=molmass_ups,
+#         )
+#     else:
+#         unused_enzyme_sector = None
+#
+#     if total_protein: total_protein = TOTAL_PROTEIN_CONCENTRATION
+#
+#     pa_model = PAModel(id_or_model=model, p_tot=total_protein, sensitivity=sensitivity,
+#                        active_sector=active_enzyme_sector, translational_sector=translation_enzyme_sector, unused_sector=unused_enzyme_sector)
+#     return pa_model
\ No newline at end of file