{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Tutorial: Create iML1515 GECKO with auto-fitted turnover numbers\n",
    "\n",
    "We developed a series of GECKO models of iML1515, with model parameters being progressively refined from one model to the next. Our initial model was based on default values, requiring minimal bespoke configuration. For our second model, we adjusted transporter-related parameters, taking into account organism- and model-specific information. For our third model, we replaced default turnover numbers with AI-predicted turnover numbers for metabolic reactions. In the fourth model, flux distributions were corrected by adjusting predicted turnover numbers of a few reactions, considering organism-specific information and experimental data of quantitative proteomics. \n",
    "\n",
    "In this tutorial, we extend the concept introduced in the previous tutorial to automatically fit turnover numbers to proteomics data. This fitting process will not be perfect, but the model will improve significantly with respect to predicting protein levels. One limitation of the automatic fitting process is that it can only be applied to reactions and pathways carrying flux as per our predictions. This is also the reason why we corrected the flux balance between glycolysis and PPP. \n",
    "\n",
    "Quantitative proteomics is a valuable source of data for determining more realistic apparent turnover numbers, as required for enzyme constraint models, by adjusting default and predicted turnover numbers. This is due to the almost complete coverage and use of standardized experimental methods in quantitative proteomics. To circumvent the pitfalls of overfitting, we intend to impose constraints on the extent of adjustment.\n",
    "\n",
    "It should be noted, that alternative fitting procedures exist in GECKO 2.0 (Domenzain et al., 2022) for single conditions and using the PRESTO formalism (Wendering et al., 2023) across multiple growth conditions.\n",
    "\n",
    "Peter Schubert, Heinrich-Heine University Duesseldorf, Institute for Computational Cell Biology (Prof. Dr. M. Lercher), January, 2025"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Initial setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 minimal media conditions created for simulation\n",
      "2347 records of proteomics loaded from data/Ecoli_Schmidt_proteomics.xlsx\n",
      "2232 records with confidence level above 43.0\n"
     ]
    }
   ],
   "source": [
    "# Required imports and model names\n",
    "import os\n",
    "import re\n",
    "import pandas as pd\n",
    "import cobra\n",
    "\n",
    "from f2xba import XbaModel, EcModel\n",
    "from f2xba import EcmOptimization, EcmResults\n",
    "from f2xba import GeckoFitKcats\n",
    "from f2xba.utils.mapping_utils import load_parameter_file, write_parameter_file\n",
    "\n",
    "fba_model = 'iML1515'\n",
    "baseline_model = 'iML1515_manual_adjust_GECKO'\n",
    "target_model = 'iML1515_predicted_fit_GECKO'\n",
    "reference_cond = 'Glucose'\n",
    "        \n",
    "# Create media conditions\n",
    "media_grs = {'Acetate': ['ac', 0.29], 'Glycerol': ['glyc', 0.47], 'Fructose': ['fru', 0.54], \n",
    "             'L-Malate': ['mal__L', 0.55], 'Glucose': ['glc__D', 0.66], 'Glucose 6-Phosphate': ['g6p', 0.78]}\n",
    "base_medium = ['ca2', 'cbl1', 'cl', 'co2', 'cobalt2', 'cu2', 'fe2', 'fe3', 'h2o', 'h', 'k', 'mg2', \n",
    "               'mn2', 'mobd', 'na1', 'nh4', 'ni2', 'o2', 'pi', 'sel', 'slnt', 'so4', 'tungs', 'zn2']\n",
    "conditions = {}\n",
    "exp_grs = {}\n",
    "for cond, (carbon_sid, exp_gr )in media_grs.items():\n",
    "    conditions[cond] = {f'EX_{sidx}_e': 1000.0 for sidx in base_medium}\n",
    "    conditions[cond][f'EX_{carbon_sid}_e'] = 1000.0\n",
    "    exp_grs[cond] = exp_gr\n",
    "print(f'{len(conditions)} minimal media conditions created for simulation')\n",
    "\n",
    "# Load proteomics\n",
    "fname = os.path.join('data', 'Ecoli_Schmidt_proteomics.xlsx')\n",
    "with pd.ExcelFile(fname) as xlsx:\n",
    "    df_mpmf = pd.read_excel(xlsx, sheet_name='proteomics', index_col=0)\n",
    "    print(f'{len(df_mpmf)} records of proteomics loaded from {fname}')\n",
    "min_confidence_level = 43.0\n",
    "df_mpmf = df_mpmf[df_mpmf['confidence'] > min_confidence_level]\n",
    "print(f'{len(df_mpmf)} records with confidence level above {min_confidence_level}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Fit turnover values to proteomics (cobrapy)\n",
    "\n",
    "The workflow to automatically fit turnover numbers to proteomics begins with determining the flux distribution predicted for the baseline GECKO model on the reference condition, which is glucose minimal medium.\n",
    "\n",
    "Subsequently, the measured protein concentrations are extracted from the proteomics data. It is imperative to note that the four major outer membrane pores ('b0929', 'b2215', 'b1377', 'b0241') will be excluded from the automated fitting process to avoid interference with the turnover number adjustments performed in the 2nd tutorial. Additionally, the protein measurement for b2400\\gltX, which catalyzes glutamyl-tRNA charging, is excluded. In FBA and GECKO, this is used for the synthesis of biomass components protoheme (M_pheme_c) and siroheme (M_sheme_c), but not for tRNA charging.\n",
    "\n",
    "Finally, we perform the automated fit process using an instance of `GeckoFitKcats` to generate updated turnover numbers for the target model. The fitting process adjusts the coupling factors through updated turnover numbers to align predicted flux distribution to measured protein levels. In a simple example, if predicted flux through a given reaction predicts a protein concentration that is double of the measured level, the fitting process would double the respective turnover number. The fitting process adjusts turnover numbers for reactions that carry flux, and it can switch flux between iso-reactions and consider promiscuous enzymes that catalyze more than one reaction. To avoid overfitting, a maximum scaling factor can be provided. Finally, all turnover numbers will be rescaled for a given target enzyme saturation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 flux distribution in baseline model\n",
    "\n",
    "The predicted flux distribution is determined, and it is used as a reference for the fitting task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter Username\n",
      "Set parameter LicenseID to value 2731209\n",
      "Academic license - for non-commercial use only - expires 2026-10-31\n",
      "SBML model loaded by sbmlxdf: SBML_models/iML1515_manual_adjust_GECKO.xml (Thu Feb 12 15:21:50 2026)\n",
      "total modeled protein: 306.59 mg/gDW, enzyme saturation level: 0.56\n",
      "Glucose: growth rate 0.661 h-1 (optimal)\n"
     ]
    }
   ],
   "source": [
    "# Load GECKO model and optimize for reference condition\n",
    "fname = os.path.join('SBML_models', f'{baseline_model}.xml')\n",
    "ecm = cobra.io.read_sbml_model(fname)\n",
    "total_protein = ecm.reactions.get_by_id('V_PC_total').upper_bound\n",
    "\n",
    "eo = EcmOptimization(fname, ecm)\n",
    "sigma = eo.avg_enz_saturation\n",
    "print(f'total modeled protein: {total_protein:.2f} mg/gDW, enzyme saturation level: {sigma}')\n",
    "\n",
    "# Calculate GECKO solution for given condition\n",
    "ecm.medium = conditions[reference_cond]\n",
    "solution = ecm.optimize()\n",
    "gr = solution.objective_value\n",
    "print(f'{reference_cond}: growth rate {gr:.3f} h-1 ({solution.status})')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 measured protein concentrations\n",
    "\n",
    "The protein concentrations, which have been measured, must be collected so that the turnover numbers can be adjusted. It is important to note that certain proteins have been selected and are therefore excluded from this process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 996 proteins measured with total of 497.8 mg/gP\n",
      "   3 proteins excluded with total of  16.6 mg/gP\n"
     ]
    }
   ],
   "source": [
    "# Select proteins for the automatic fit\n",
    "excluded_loci = {'b0929', 'b2215', 'b1377', 'b0241', 'b2400'}\n",
    "measured_mpmfs = {} \n",
    "excluded_mpmfs = {} \n",
    "for locus, mpmf in df_mpmf[reference_cond].items():\n",
    "    if locus in eo.locus2uid:\n",
    "        if locus not in excluded_loci:\n",
    "            measured_mpmfs[locus] = mpmf        \n",
    "        else:\n",
    "            excluded_mpmfs[locus] = mpmf\n",
    "\n",
    "print(f'{len(measured_mpmfs):4d} proteins measured with total of {sum(measured_mpmfs.values()):5.1f} mg/gP')\n",
    "print(f'{len(excluded_mpmfs):4d} proteins excluded with total of {sum(excluded_mpmfs.values()):5.1f} mg/gP')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 fit turnover numbers to proteomics\n",
    "\n",
    "The automated fitting task is performed by the class `GeckoGitKcats`. During instantiation, a reference to the instance of `EcmOptimization` and the turnover numbers in the baseline model are provided. Subsequently, `gfk.process_data()` is called with the flux distribution for the reference condition and the expected protein concentrations. Finally, the function `gfk.update_kcats()` is invoked to adjust the turnover numbers, taking into account a constraint on the maximum scaling (`max_scale_factor`)  and the anticipated target enzyme saturation (`target_sat`). A maximum scaling factor of 100.0 would allow turnover numbers to be adjusted within a range of \\[1/100.0 … 100.0\\] $\\cdot$ original value. Reactions for which the adjustment of turnover numbers was not possible due to the maximum scaling constraint are returned in the dictionary `exceeding_max_scale`. This information can be utilized to enhance the model further by adjusting pertinent model parameters.\n",
    "\n",
    "In accordance with the GECKO optimization problem, a distinction is made between active reactions, those carrying flux, and inactive reactions. Consequently, the total modeled proteome is partitioned between the active proteome, the set of proteins required to catalyze active reactions, and the inactive proteome. The inactive proteome has a total predicted concentration of zero, while the total available protein is divided between the proteins in the active proteome. Subsequently, the value of the total available protein can be adjusted to be equal to the sum of the measured proteins of the active proteome (`tot_fitted_mpmf`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5357 enzyme catalyzed iso-reactions (2221 reactions, 1494 proteins)\n",
      " 394 active catalyzed reactions with total protein of 537.9 mg/gP (based on GECKO simulation)\n",
      " 417 proteins potentially involved in active reactions\n",
      " 352 active catalyzed reactions using total protein of 356.7 mg/gP (based on proteomics)\n",
      "  42 active catalyzed reactions have no measured proteins provided\n",
      "5357 original kcat records loaded from data/iML1515_manual_adjust_GECKO_kcats.xlsx\n",
      " 584 kcat records fitted. Scale factor range [0.0101105, 110.0]\n",
      "  24 (net) reactions would exceed the maximum scaling factor of 100.0 and will not be scaled\n",
      "fitted kcats exported to data/iML1515_predicted_fit_GECKO_kcats.xlsx\n"
     ]
    }
   ],
   "source": [
    "# Automatically fit kcat values to proteomics\n",
    "target_enz_sat = 0.5\n",
    "orig_kcats_fname = os.path.join('data', f'{baseline_model}_kcats.xlsx')\n",
    "fitted_kcats_fname = os.path.join('data', f'{target_model}_kcats.xlsx')\n",
    "\n",
    "gfk = GeckoFitKcats(eo, orig_kcats_fname)\n",
    "\n",
    "tot_fitted_mpmf = gfk.process_data(solution.fluxes, measured_mpmfs)\n",
    "exceeding_max_scale = gfk.update_kcats(fitted_kcats_fname, target_sat=target_enz_sat, max_scale_factor=100.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Create GECKO model with fitted parameters\n",
    "\n",
    "The XBA and ECM configuration files are updated. Adjustments are made to the total available protein in the ECM parameter file. Thereafter, the target GECKO model is created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 table(s) with parameters loaded from data/iML1515_manual_adjust_GECKO_xba_parameters.xlsx (Thu Feb 12 15:21:28 2026)\n",
      "3 table(s) with parameters written to data/iML1515_predicted_fit_GECKO_xba_parameters.xlsx\n",
      "1 table(s) with parameters loaded from data/iML1515_manual_adjust_GECKO_ecm_parameters.xlsx (Thu Feb 12 15:21:28 2026)\n",
      "1 table(s) with parameters written to data/iML1515_predicted_fit_GECKO_ecm_parameters.xlsx\n"
     ]
    }
   ],
   "source": [
    "# Create updated XBA parameter file\n",
    "xba_params = load_parameter_file(os.path.join('data', f'{baseline_model}_xba_parameters.xlsx'))\n",
    "xba_params['general'].at['kcats_fname', 'value'] = os.path.join('data', f'{target_model}_kcats.xlsx')\n",
    "write_parameter_file(os.path.join('data', f'{target_model}_xba_parameters.xlsx'), xba_params)\n",
    "\n",
    "# Create updated ECM parameter file\n",
    "ecm_params = load_parameter_file(os.path.join('data', f'{baseline_model}_ecm_parameters.xlsx'),['general'])\n",
    "fitted_pmf = (tot_fitted_mpmf + sum(excluded_mpmfs.values()))/1000.0\n",
    "ecm_params['general'].at['pm2totpm_val_or_paxdb', 'value'] = fitted_pmf\n",
    "ecm_params['general'].at['avg_enz_sat', 'value'] = target_enz_sat\n",
    "write_parameter_file(os.path.join('data', f'{target_model}_ecm_parameters.xlsx'), ecm_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading: SBML_models/iML1515.xml (last modified: Thu Dec  5 10:03:46 2024)\n",
      "3 table(s) with parameters loaded from data/iML1515_predicted_fit_GECKO_xba_parameters.xlsx (Thu Feb 12 15:25:00 2026)\n",
      "  22 gene product(s) removed from reactions (1494 gene products remaining)\n",
      "   2 attributes on reaction instances updated\n",
      "3 reactions with atom imbalances, e.g. [R_PUACGAMS, R_BIOMASS_Ec_iML1515_core_75p37M, R_BIOMASS_Ec_iML1515_WT_75p37M, ...], check XbaModel.atom_imbalances.\n",
      "2 reactions with charge imbalances, e.g. [R_BIOMASS_Ec_iML1515_core_75p37M, R_BIOMASS_Ec_iML1515_WT_75p37M, ...], check XbaModel.charge_imbalances.\n",
      "extracting UniProt protein data from data/uniprot_organism_83333.tsv\n",
      "1494 proteins created\n",
      "1203 enzymes added with default stoichiometry\n",
      "1 table(s) with parameters loaded from data/iML1515_enzyme_composition_updated.xlsx (Thu Feb 12 15:17:11 2026)\n",
      "1203 enzyme compositions updated from data/iML1515_enzyme_composition_updated.xlsx\n",
      "2221 reactions catalyzed by 1203 enzymes\n",
      "default kcat values configured for ['metabolic', 'transporter'] reactions\n",
      "1 table(s) with parameters loaded from data/iML1515_predicted_fit_GECKO_kcats.xlsx (Thu Feb 12 15:25:00 2026)\n",
      "5357 kcat values updated from data/iML1515_predicted_fit_GECKO_kcats.xlsx\n",
      "   0 enzymes removed due to missing kcat values\n",
      "1877 constraints (+0); 2712 variables (+0); 1494 genes (-22); 5 parameters (+0)\n",
      ">>> BASELINE XBA model configured!\n",
      "\n",
      "1 table(s) with parameters loaded from data/iML1515_predicted_fit_GECKO_ecm_parameters.xlsx (Thu Feb 12 15:25:00 2026)\n",
      "modelled protein fraction of total protein mass 0.3733 g/g\n",
      "1494 protein constraints to add\n",
      "1494 constraint ids added to the model (3371 total constraints)\n",
      "protein constraint: 212.80 mg/gDW - modelled protein\n",
      "   1 constraint ids added to the model (3372 total constraints)\n",
      "1495 protein variables to add\n",
      "1495 variable ids added to the model (7343 total variables)\n",
      "3372 constraints (+1495); 7343 variables (+4631); 1494 genes (-22); 11 parameters (+6)\n",
      "model exported to SBML format: SBML_models/iML1515_predicted_fit_GECKO.xml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new GECKO model\n",
    "xba_model = XbaModel(os.path.join('SBML_models', f'{fba_model}.xml'))\n",
    "xba_model.configure(os.path.join('data', f'{target_model}_xba_parameters.xlsx'))\n",
    "ec_model = EcModel(xba_model)\n",
    "ec_model.configure(os.path.join('data', f'{target_model}_ecm_parameters.xlsx'))\n",
    "ec_model.export(os.path.join('SBML_models', f'{target_model}.xml'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "## Step 4: Load and optimize GECKO model (cobrapy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SBML model loaded by sbmlxdf: SBML_models/iML1515_predicted_fit_GECKO.xml (Thu Feb 12 15:25:22 2026)\n",
      "total modeled protein: 212.80 mg/gDW, average saturation level: 0.5\n",
      "1494 genes: (492) transporter, (1002) metabolic\n"
     ]
    }
   ],
   "source": [
    "# Load model using COBRApy\n",
    "fname = os.path.join('SBML_models', f'{target_model}.xml')\n",
    "ecm = cobra.io.read_sbml_model(fname)\n",
    "total_protein = ecm.reactions.get_by_id('V_PC_total').upper_bound\n",
    "\n",
    "eo = EcmOptimization(fname, ecm)\n",
    "sigma = eo.avg_enz_saturation\n",
    "all_genes = set(eo.m_dict['fbcGeneProducts']['label'].values)\n",
    "tx_genes, metab_genes = eo.get_tx_metab_genes()\n",
    "print(f'total modeled protein: {total_protein:.2f} mg/gDW, average saturation level: {sigma}')\n",
    "print(f'{len(all_genes)} genes: ({len(tx_genes)}) transporter, ({len(metab_genes)}) metabolic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acetate                  : pred gr: 0.341 h-1 vs. exp 0.290, diff:  0.051\n",
      "Glycerol                 : pred gr: 0.540 h-1 vs. exp 0.470, diff:  0.070\n",
      "Fructose                 : pred gr: 0.544 h-1 vs. exp 0.540, diff:  0.004\n",
      "L-Malate                 : pred gr: 0.526 h-1 vs. exp 0.550, diff: -0.024\n",
      "Glucose                  : pred gr: 0.613 h-1 vs. exp 0.660, diff: -0.047\n",
      "Glucose 6-Phosphate      : pred gr: 0.608 h-1 vs. exp 0.780, diff: -0.172\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x247.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Protein mass fractions:\n",
      "Acetate                  : r² = 0.2511, p = 1.25e-64 ( 999 proteins lin scale)\n",
      "Glycerol                 : r² = 0.2292, p = 2.26e-58 ( 999 proteins lin scale)\n",
      "Fructose                 : r² = 0.6175, p = 2.71e-210 ( 999 proteins lin scale)\n",
      "Glucose                  : r² = 0.8699, p = 0.00e+00 ( 999 proteins lin scale)\n",
      "Acetate                  : r² = 0.4562, p = 2.21e-42 ( 308 proteins log scale)\n",
      "Glycerol                 : r² = 0.4894, p = 5.06e-47 ( 311 proteins log scale)\n",
      "Fructose                 : r² = 0.5298, p = 4.40e-51 ( 302 proteins log scale)\n",
      "Glucose                  : r² = 0.5498, p = 7.85e-56 ( 313 proteins log scale)\n",
      "\n",
      "condition: Glucose\n",
      "1494 proteins in model with total predicted mass fraction of 373.3 mg/gP\n",
      "      999 have been measured with mpmf of  514.4 mg/gP vs. 331.8 mg/gP predicted\n",
      "           762 metabolic proteins measured 414.0 mg/gP vs. 280.5 mg/gP predicted\n",
      "           237 transport proteins measured 100.4 mg/gP vs.  51.3 mg/gP predicted\n",
      "      495 proteins not measured vs.  41.6 mg/gP predicted\n",
      "           495 actual  proteins      41.6 mg/gP predicted\n",
      "total           : r² = 0.8699, p = 0.00e+00 ( 999 proteins lin scale)\n",
      " metabolic      : r² = 0.9105, p = 0.00e+00 ( 762 proteins lin scale)\n",
      " transport      : r² = 0.6008, p = 9.35e-49 ( 237 proteins lin scale)\n",
      "total           : r² = 0.5498, p = 7.85e-56 ( 313 proteins log scale)\n",
      " metabolic      : r² = 0.5762, p = 3.87e-51 ( 266 proteins log scale)\n",
      " transport      : r² = 0.5334, p = 5.65e-09 (  47 proteins log scale)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Fy8uL48eP06VLF6ysrMiXL1+K47t06cKuXbt019qsWTPc3d15+PAhP/30U5LISXx8PMHBwXrbFAoF7u7uKBQKNmzYQOPGjalfvz6TJ0+mdOnSREZG8ttvv/HXX39x7NixJDZ0794dpVJJz549EUIwbtw41q1bR5MmTejQoQOTJk3Cw8ODs2fPMmbMGGrXrp3pTRcePnyoi7BOnTo1U88tkUgkWYEQArVazcKFC3X1J2khPDycMWPGUL16dQYOHPjuE128CJcuGTfWFEkDtVotDh8+LGbMmCH69OkjunTpIoYNGybWr18vAgMDTZkq08guclgpyVsZksPiLQkkQMyaNUsIkVRGibcknPbt2ycqV64s7O3thZ2dnahUqZJYvXp1EhkiQ3MkSDIJIcTIkSNF4cKFhaWlpXB3dxctW7YUly5dMvo9KFKkiJgxY4bo1KmTsLW1FR4eHknkMEhGtsvX11fUqVNH2NjYCEdHR+Hj4yN++OEH3f6bN2+KevXqCUtLS1GyZEnh6+ubqhyWv7+/aNq0qbC1tRUODg6ifv364u7du0IIIUJCQkSTJk2Evb293nsZFBQkevToIfLlyyesrKxE0aJFRb9+/XR/TyqVSowYMUI4OjqKPHnyiNGjRxsthyWEEEuWLNF7z4VI+vdy+vRpUbFiRWFlZWWUHJYQ2v/V1atXi5o1awo7OzthaWmpsz0gIEA3zpAUGCCsrKz05rt586bo0aOH8PT0FJaWlqJIkSKia9euen8Phn6XW7duFWZmZjqpq3/++Ud06NBBODs7CwsLC1GsWDHx1VdfiTdv3hh1XemBRqMR8fHxYvTo0eLWrVvpMuf7LIcVHR0tAgICRHR0dBZYJslI5O8297F582bx4MGDdJlLpVKJ+fPni+PHj6d5rvhRo0R49epGraNGfQpGRUWJWbNmCU9PT2FtbS1q1aolPvnkE9GtWzfRokULUahQIWFmZiZatGghTp8+neYLSE9y+wdKdqZIkSJiyZIlWW2GRKLj8ePH4pNPPtHTU04Pcvs6Ix3XjKdnz55G3/Amx5EjRwSg01dOqxa3/N3mHmJiYsT8+fPTZa7Xr1+LwYMHi/Xr17/7JBqNEAsWCLFli9BoNGLLunXiqp9f+um4lixZktq1a7NmzRqaNGli8DF3QleZLl26MHnyZKM1JyUSiSSjEf+qGUyfPp1Zs2Zlan65xDjUGsG5+6GEvI7BzcEaH29nzJQ5L8eyYcOGVK5c2eQ2zRnBp59+SsuWLbPaDEkW88MPP9CuXTvGjRuX5rmEEMybN48OHToYpVduYAJQKEChQFy5QmxYGN8uWsTYsWOJiIgwagqjHNe//vor1YKaIkWKMGnSJMaOHUtgYKBRJ5fkXP7+++8UdVMjIyMz0ZrsSWBgYIoOUkBAAIULFzZ53hYtWvD3338b3Pfll1/y5ZdfmjxnbubZs2cMHz6cCRMmsGbNmqw2J0uYM2cOu3bt4saNG9jY2FCnTh3mzZuXRO83q/C9GsSM3wIICo/RbcvvZM201mVpXl5qiL4rNjY276SoIskdREZGsnbt2nSpH4iKimLy5Mn4+PgYLYeZhNevoWNHGDECWrZkTZ06tGvfnrEG1G5SwihVgdSc1sRYWFhQrFgxk4yQ5DyqV6+On59fsl8ADx48yPSCm+yEp6dniu9RYh1aU1i7dm2yc6YpOT4XolarGTlyJJMnT84QPcGcwrFjxxgyZAhnzpzhwIEDqFQqmjZtyps3b7LaNHyvBjFoyyU9pxUgODyGQVsu4Xs1KJkj007Dhg0ZNmwYI0eOJG/evLi7u7NmzRrevHlD7969cXBwoHjx4uzbt093zNWrV2nRogX29va4u7vTvXt3Xrx4AUCvXr04duwYy5YtQ6FQoFAoePDgAWq1mr59++Lt7Y2NjQ2lSpVi2bJlBm2aMWMGrq6uODo6MnDgQOLi4nT7YmNjGT58OG5ublhbW1OvXj3Onz+f7PVt3LgxSXe73377jRo1amBtbU2+fPlo3759Gt5BSXbl22+/Ra1Wp5u835dffkmzZs3eTf0lOlr73d4eChUiUqNh2bJl9B8wwKBEY2oYrSrw5s0bxo4dy969e4mLi+Ojjz5ixYoV2VrPUpJx2NjYJKv9KdFibm6eIe9RgQIF0n3O3MaLFy8YOXIkX331Fdu2bctqc7IcX19fvdcbN27Ezc2Nixcv0qBBgyTjY2NjiY2N1b029hGeqag1ghm/BWCoLYUAFMCM3wJoUtYjw9IGNm3axPjx4zl37hw//fQTgwYNYvfu3bRv354vv/ySJUuW0L17dwIDA4mLi+PDDz/kiy++YMmSJURHRzNhwgQ6d+7M4cOHWbZsGbdu3aJ8+fLMnDkT0GooazQaChYsyI4dO3BxceHUqVP079+f/Pnz07lzZ50thw4dwtramqNHj/LgwQN69+6Ni4uLrhnL+PHj+eWXX9i0aRNFihRh/vz5NGvWjDt37uDs7Jzqtf7xxx+0b9+eyZMns3nzZuLi4vjzzz8z5H2VZA0vX75kx44dDBkyJM1yZjExMUydOpW6deu+e+rL1avQqBHs2wfVq7OiUiV61K/PiFat3t0wY/NoR40aJezs7ET//v3FiBEjhKurq2jXrt27J+ZmEllZNPHixQvh6uoq7t+/n+nnzijSmuz/PlKzZk2xc+fOrDbjvSE2Nla0bt1anD9/PtPOmdOKs27fvi0AceXKFYP7k1ObSO/irFN3XogiE35P9evUnRcmz20MH3zwgahXr57udXx8vLCzsxPdu3fXbQsKChKAOH36tJg1a5Zo2rSp3hyPHj0SgLh586ZuzpRUZBIYMmSI6NChg+51z549hbOzs57CxqpVq4S9vb1Qq9UiMjJSWFhYiB9//FG3Py4uTnh6euqKblIrzqpdu7bo1q1b6m/Mv8jirJzF0qVLxZs3b5IoCL0rffr0EXv27Hm3gxP8HpVKiK++Ei+uXRPfffddiocYu44a3YBg9+7dbNiwge+//56lS5eyb98+fv/9d+Lj49/da87lfPPNN7Rt2xYvL680zzV9+nQqV66c5nlMwcvLK8ld1qeffqrrO59VbNy4UfcYLuHr7Y5aQgimTp1K/vz5sbGxoXHjxty+fTvVuVeuXImXlxfW1tbUrFkzSXez0aNH4+zsTKFChZJ01NqxYwetW7dOMudXX33FxIkTDQr4S9KPV69e0bdvX4KDg/n111+TtBLOSuLi4vj5558ZNWoUXbt2pWvXrowaNYodO3boPQrODDQaDSNHjqRu3bqUL1/e4JhJkyYRHh6u+3r06FGG2BLyOib1QSaMexcqVqyo+9nMzAwXFxcqVKig2+bu7q61ISQEf39/jhw5gr29ve6rdOnSgLb7WkqsXLmSatWq4erqir29PT/88EOSepBKlSrpdSCrXbs2kZGRPHr0iLt376JSqahbt65uv4WFBT4+Ply/ft2oa/Xz8+Ojjz4yaqwk5/DkyRO2bNnCkCFDsLW1Ral8995ScXFxTJkyhYMHD7J27Vratm1r+iRbt0Lp0vDoEZibsyhPHmy9vdMtlc3oq3v8+LHeP0y1atWwsLDg6dOn6WJIbiMqKop169bRt2/frDZFDyFEmm42bGxs3iknJb1xdHQkKChI9/Xw4UO9/fPnz2f58uWsXr2as2fPYmdnR7NmzYiJSf4D8KeffmL06NFMmzaNS5cuUalSJZo1a0ZISAigzQ3bunUrf/31F/Pnz+eLL77Q5baFh4czefJkVq5cmWTeFi1a8Pr1a708OUn6EhUVRZcuXejXrx+FCxfOVh1/7ty5Q5kyZejZsyeXL19Go9Gg0Wi4fPkyPXr0oFy5cuneRjklhgwZwtWrV9m+fXuyY6ysrHB0dNT7ygjcHIxr4WzsuHfhbZUchUKhty3hb0mj0RAZGUnr1q2T5Jbfvn3bYMpFAtu3b2fs2LH07duXv/76Cz8/P3r37p3pNy2yUCv3sWzZMtzc3OjcuTPm5mnrKSWEoGfPnlSsWJHGjRubto6+fg2nTml/btsW/vc/nigUbNy4kdGjR2NjY5Nu67LRjqtGo0nyD25ubm5SK8r3iT///BMrKytq1aoFaKNB3bp1w9XVFRsbG0qUKMGGDRt04ydMmEDJkiWxtbWlaNGiTJkyRdeedOPGjcyYMQN/f39dhHHjxo08ePAAhUKh12ErLCwMhULB0aNHATh69CgKhYJ9+/ZRrVo1rKysOHHiBHfv3qVt27a4u7tjb29PjRo1OHjwoG6ehg0b8vDhQ0aNGqU7Z4Itbyf7r1q1imLFimFpaUmpUqX43//+p7dfoVCwdu1a2rdvj62tLSVKlGDv3r1pen8VCgUeHh66r4SoCGj/+ZYuXcpXX31F27ZtqVixIps3b+bp06fs2bMn2TkXL15Mv3796N27N2XLlmX16tXY2tqyfv16AK5fv07Dhg2pXr06Xbt2xdHRkfv37wPa3LNBgwYZVAkwMzOjZcuWKToKkncjIiKCwYMHExkZyZ9//qn7f8tODBo0iAoVKvDs2TOOHj3KTz/9xE8//cTRo0d59uwZ5cqVy/AWiQkMHTqU33//nSNHjlCwYMFMOWdK+Hg7k9/JmuQ+zhRo1QV8vFPP38wMqlatyrVr1/Dy8krSBtrOzg4w3KL55MmT1KlTh8GDB1OlShWKFy9uMELr7+9PdEIhC9pW1vb29hQqVEi3xp48eVK3X6VScf78eaPl3SpWrMihQ4fe5dIl2Yx79+7x66+/0r9/fywsLLC0tHznueLj4/n66685ffo0W7ZsoVOnTqZPMn06dOoEKhXY2bEoMBC3f4sX0zuQYLTjKoTgo48+omrVqrqvqKgoWrdurbdNouXvv/+mWrVqutdTpkwhICCAffv2cf36dVatWqXXutPBwYGNGzcSEBDAsmXLWLNmDUuWLAG0j+fHjBlDuXLldBHGTz/91CR7Jk6cyNy5c7l+/ToVK1YkMjKSli1bcujQIS5fvkzz5s1p3bq17tHVrl27KFiwIDNnztSd0xC7d+9mxIgRjBkzhqtXrzJgwAB69+7NkSNH9MbNmDGDzp07888//9CyZUu6detGaGiobn/iR2+Gvt5+xBAZGUmRIkUoVKgQbdu25dq1a7p99+/fJzg4mMaNG+u2OTk5UbNmTU6fPm3wOuLi4rh48aLeMUqlksaNG+uOqVSpEhcuXODVq1dcvHiR6OhoihcvzokTJ7h06RLDhw9P9v338fFJVsJK8m6EhYXRrl07unTpgpubW5JWttmFkydP8vXXXxuMWjo6OjJr1qwM/9sQQjB06FB2797N4cOH8fb2ztDzGYuZUsG01lqn6+2PtoTX01qXzTZ6rkOGDCE0NJSuXbty/vx57t69y/79++ndu7fOWfXy8uLs2bM8ePCAFy9eoNFoKFGiBBcuXGD//v3cunWLKVOmGFQDiIuLo2/fvgQEBPDnn38ybdo0hg4dilKpxM7OjkGDBjFu3Dh8fX0JCAigX79+REVFGf1kb9q0aWzbto1p06Zx/fp1rly5wrx589L1PZJkPCtXrqRQoUI0adIkzVF0IQQdO3bE29ub2rVrm7aOnjkDCQGvSZPg1CnuBgbyyy+/MGLECCwsLDJkXTY6rjxt2rQk294p9+E94eHDh3pyR4GBgVSpUkWXd/d23utXX32l+9nLy4uxY8eyfft2xo8fj42NDfb29pibm+Ph4fFO9sycOZMmTZroXjs7O1OpUiXd61mzZrF792727t3L0KFDcXZ2xszMDAcHhxTPuXDhQnr16sXgwYMBbQ7omTNnWLhwIY0aNdKN69Wrl05GY/bs2Sxfvpxz587RvHlzAL2osSESf+iXKlWK9evXU7FiRcLDw1m4cCF16tTh2rVrFCxYkODgYAC9KGzC64R9b/PixQvUarXBY27cuAFAs2bN+Pzzz6lRowY2NjZs2rRJ92GyceNGVq1axYoVK8iXLx8//PAD5cqV083j6enJo0eP0Gg0aco/kmhvWqZNm8bUqVPZt28fVlZWWW1SiuTJk4cHDx4km0/64MGDJE8x0pshQ4awdetWfv31VxwcHHT/B05OTln++Lh5+fys+rxqEh1Xj2yo4+rp6cnJkyeZMGECTZs2JTY2liJFitC8eXPd//XYsWPp2bMnZcuWJTo6mvv37zNgwAAuX77Mp59+ikKhoGvXrgwePDhJ+tBHH31EiRIlaNCgAbGxsXTt2pXp06fr9s+dOxeNRkP37t15/fo11atXZ//+/eTNm9co+xs2bMiOHTuYNWsWc+fOxdHRMcUUB0n24ubNmzx+/Jhu3bphYWFhsBmUsajVahYvXkzTpk356aef3m0dnTNH20ygcWPIl4/FmzczYsQIChUqlOa0hRR5t3KxnENWVfs2bdpUDB48WPf6zz//FDY2NqJSpUpi3Lhx4uTJk3rjt2/fLurUqSPc3d2FnZ2dsLKyEq6urrr906ZNE5UqVdI75v79+wIQly9f1m179eqVAMSRI0eEEP9VmT5+/Fjv2NevX4sxY8aI0qVLCycnJ2FnZyeUSqUYN26cboyhlq1vV6nmzZtXbNy4UW/M0qVLhbe3t+41IH7++We9MY6OjmLTpk0iPYiLi9P1vBdCiJMnTwpAPH36VG9cp06dROfOnQ3O8eTJEwGIU6dO6W0fN26c8PHxSfbc06dPFyNHjhT+/v7C3d1dhISEiPXr14uqVavqjfvrr78EIKKiot7lEiX/EhwcLBo1aiQOHDiQ1abokdI6M2XKFJE3b16xePFi4e/vL4KDg0VwcLDw9/cXixcvFs7OzmLatGkZah8GFAIAsWHDBqOOz4yWr/FqjTh154XYc/mxOHXnhYhXa9I0nyTtSFWB7MOaNWtEdHS0eP36tdHHJPc/pVKpRKtWrcS6deuERmPC/1lcnBDffCPE339rX4eGChEfL65duyZ8fX3T/PlmrL+WgS7x+02+fPl49eqV7nWLFi14+PAhf/75JwcOHOCjjz5iyJAhLFy4kNOnT9OtWzdmzJhBs2bNcHJyYvv27SxatCjFcyTc4QvxnwpiQl7s2yTkXyUwduxYDhw4wMKFCylevDg2NjZ07Ngxw4oFDBVAJK6yt7e3T/H4zz//nNWrVyc7d5UqVXQFLgkR4mfPnpE//3/RmmfPniWrzJAvXz7MzMx49uyZ3vZnz54lG3G+ceMGW7Zs4fLly6xfv54GDRrg6upK586d6dOnD69fv8bBwQGA0NBQ7Ozssjy6lVOJjo5m7ty5TJgwgb1796b695KdmDlzJnZ2dixYsIAxY8bo8r2EEHh4eDBhwgTGjx+foTYkXiOyK2ZKBbWLuWS1GRJJtuLatWtERUXRqlWrJOo5KWGoG52HgyVVIs8w9ovP2LZtm+nrqJkZ/PknODhAvXqQNy/ffvst/fv3p1ixYpn29Mtkx7VKlSoGE20TJImKFy9Or1699B4Tv49UqVKFLVu26G1zdXWlZ8+e9OzZk/r16zNu3DgWLlzIqVOnKFKkCJMnT9aNfbtK3lDCf0Lzh6CgIKpUqQKk/sg9gZMnT9KrVy9d15TIyEgePHiQ6jnfpkyZMpw8eZKePXvqzW1qL3hTUgXeRq1Wc+XKFV1Pbm9vbzw8PDh06JDOUY2IiODs2bMMGjTI4ByWlpZUq1aNQ4cO0a5dO0BbkHjo0CGGDh2aZLwQggEDBrB48WLs7e1Rq9W6m4aE74nfu6tXr+p+RxLTePToEb169WLMmDF6UkE5iQkTJjBhwgRd/jVob7CyS66pRCLJfmzdupWPP/4Y0Kb1GEtCN7rEt6siXsU/G2Zwu1gN2kTZUMxYp/XpU+jZE5Ytg7Jl4dgxMDPjn3/+ITIyks8//zxNhWHvgsmOa/PmzVm1ahUVKlTAx8cHgPPnz/PPP//Qq1cvAgICaNy4Mbt27Xqvc2CbNWvGpEmTePXqFXnz5mXq1KlUq1aNcuXKERsby++//65rpVuiRAkCAwPZvn07NWrU4I8//mD37t1683l5eXH//n38/PwoWLAgDg4O2NjYUKtWLebOnYu3tzchISF6ubIpUaJECXbt2kXr1q1RKBRMmTIlic6ol5cXx48fp0uXLlhZWekVkyUwbtw4OnfuTJUqVWjcuDG//fYbu3bt0lMoMAZTOkzNnDmTWrVqUbx4ccLCwliwYAEPHz7kiy++ALQ3USNHjuTrr7+mRIkSeHt7M2XKFDw9PXVOKWjzydq3b69zTEePHk3Pnj2pXr06Pj4+LF26VNf68W3Wrl2Lq6urTre1bt26TJ8+nTNnzrBv3z7Kli2rl7f4999/07RpU5Pek/ed2NhYVqxYwdChQ9m5c6fReXzZGW9vb+msSiSSFLly5QoWFhbUrFnTJIcVknajE0IQ6b8fm2I1cGk5EnNbJ2b+fp2m5fKnXPSoUoGFBbi4gKWlVu4KwMyMtWvX0rVrV5RKZdY8RTQ1B+GLL74QM2fOTLJ91qxZ4osvvhBCCDF16lRRrVo1U6fOELKyo42Pj49YvXq1EEL7/pQpU0bY2NgIZ2dn0bZtW3Hv3j3d2HHjxgkXFxdhb28vPv30U7FkyRK9XNKYmBjRoUMHkSdPHr3ctICAAFG7dm1hY2MjKleurMulfDvHNaGTSgL3798XjRo1EjY2NqJQoULi22+/TdLx5fTp06JixYrCyspKJPypGOqc9d1334miRYsKCwsLUbJkSbF582a9/YDYvXu33jYnJyej8+veZuTIkaJw4cLC0tJSuLu7i5YtW4pLly7pjdFoNGLKlCnC3d1dWFlZiY8++kjX2SaBIkWKJMktXLFihW5uHx8fcebMmSTnDw4OFkWKFBFPnjzR2z5jxgzh7OwsSpcuLc6ePavb/vjxY2FhYSEePXr0Ttf7PnLv3j3x0UcfiV27dmW1KUaRlnUmMDBQ9O7dOwOsSj8yI8dVkv2Qv9vMRaPRiL1794qgoCDx8uXLd5ojcTe6QqN2CJsStUTeD/uJwuP3Gt+N7uhRIQoVEuKtOpGLFy+Kf/75J0n9SHph7DqqEMK05CcnJycuXryYJEJ2584dqlWrRnh4ODdu3KBGjRq8TvDQs5CIiAicnJwIDw/PMBHt5Pjjjz8YN24cV69elZXk7zETJkzg1atX/PDDD1ltSrZHpVKxceNGunbtSnR0tC4dJruTlnXG39+fqlWrZmtN7JSuLyYmhvv37+Pt7W1SDp4k+yN/t5nHlStXcHFxISQkxKQumWqN4Nz9UEJex+DmYE1weDQjf/LjTcBRbIrVQMRFYe6YtGnQsi6VaVu5gP7GZ8/A3R1evYKZM+Grr8DFBSEE27dvp2nTplhZWWVYjYGx66jJqQLW1tacOnUqieN66tQp3R+2RqORf+TAxx9/zO3bt3ny5AmFChXKanMkWYSbmxujR4/OajOyPXfv3mXQoEF88cUXOv3e3EBqzTbu3buXSZZIJJLshkaj4e+//6ZQoUJYWloadlo1anh4CiKfgb07FKkDSjODBViOyjhe7FmIpWdJlJY2KKwNr6NJutEtXAiLFsGdO5A3L/yrI3/hwgXy5ctHjRo1cHHJHsWTJjuuw4YNY+DAgVy8eJEaNWoA2hzXtWvX8uWXXwKwf/9+k+4YcjMjR47MahMkWcyYMWOy2oRsTXx8PLt27aJx48Zs3rz5nbWKsyvt2rVDoVCkWNmfnVrUSiSSzCFBe9zMzIyiRYsaHhSwF3wnQMRT3aZYWw9+9RjO+AAv3TYhBNG3TiO8q5CnQQ8sXAx3xlOg1Uj28XaG8HB48kRbdNW5M3h5wb8FsBqNBl9fXypWrIiDg4PJubYZicnPr7/66ivWrFnDuXPnGD58OMOHD+fcuXOsWbNGVxU/cOBAfvvtt3Q3ViKR5C7u3LlDq1atiI2NJW/evLnOaQXInz8/u3btQqPRGPy6dOlSVpsokUgyEbVajZ+fH2q1mri4OOrVq2d4YMBe+LmHntMKYPEmmI53v6SZ8px2vujXvNg7n7iXgWBmkaLTCom60fXtCz16gBBQuDB07AgKBRcvXuTVq1d4enpSsGDBbOW0ggkR13v37unuCLp160a3bt2SHSu1KiUSSUqo1WoOHz5MmTJlWLduHQUKFEj9oBxKtWrVuHjxYrIqK6lFYyUSSe7h1q1bFCpUiGfPntGsWbPkB2rU2kgrSdcGpQI0AqZZ/I+9N8ywKFAWp9qdsXTTVyxxtrMg9M1/2u4eTtYs9XxNTfECyA9z54K1tbb7Fdoag3Pnzul0zbPrk3OjHdeKFSvi5eVFmzZtaNeunU4KSyKRSEzh7t27DB06lE8++USvDXFuZdy4cbx58ybZ/cWLF+fIkSOZaJEkgYYNG1K5cmWWLl2a1aZkKF5eXowcOVKmrmUharWa+/fvExISgrOzc8pOK2hzWt+KtCYmPEYwwfcxzvbHiCxcKYnTCjClVTk8HK11hVs+RfJgVrkSNGgA330HiWqV/P39KV68OEIISpUq9c7XmRkY7bi+ePGCAwcO8Ouvv9KmTRsUCgWtWrWiTZs2NGnSRBZjSSSSFNFoNFy4cAFHR0dWrVqFl5dXVpuUKdSvXz/F/XZ2dnzwwQeZZE02JpkClKxECIFarc7YvusZSFxcXKaLw0uS8vDhQ9zc3Lh27ZrR+vaa18HJ5nKefRxPcWclw30s+Z9rLfZqDP99ejhaU9vTFhas1KYEmLvAX39BopQslUrF9evXUalUqFSq5NMWshFG57haW1vTunVr1q5dS1BQEL/88gsuLi5MmDCBfPny0a5dO9avX8/z588z0l6JRJIDefDgAW3btuXSpUuULl36vXFaJUYSsBeWlodNreCXvtrvS8trt2cQvXr14tixYyxbtgyFQoFCoWDjxo0oFAr27dtHtWrVsLKy4sSJE9y9e5e2bdvi7u6Ovb09NWrUSNJkxcvLi9mzZ9OnTx8cHBwoXLiwngReXFwcQ4cOJX/+/FhbW1OkSBHmzJmj269QKFi1ahUtWrTAxsaGokWLsnPnTr1zXLlyhQ8//BAbGxtcXFzo378/kZGRetfUrl07vvnmGzw9PSlVqhQNGzbk4cOHjBo1SnedksxBo9Hw9OlTrl69SkxMjNFOq+/VIPrvfpxke1iMoO+v0fxyPR4nawU1CpgRQp4k4xRA/oQCLLUaNm6E06e1Oz094V95zuvXrxMfH8+zZ8+oXr26XtOc7IzJOq6GuH37Nnv37uXXX3/l7NmzLF68mCFDhqSHfWkmK3VcJZL3HSEEN27c4PXr1zg7O5vUIS0nYcw60759+1TbZX/22WfZ8jFdhuq4JhSgJMnl+/e96rwZyrZ5J7tTIjw8nBYtWlC+fHlmzpwJaKu8GzduTMWKFVm4cCFFixYlb968PHr0iDNnzlC3bl2srKzYvHkzCxcu5ObNmxQuXBjQOq6vX79m1qxZNG3alJ07dzJ58mQCAgIoVaoUCxcuZPny5fz4448ULlyYR48e8ejRI7p27aq9WoUCFxcX5s6dS4MGDfjf//7HnDlzuHLlCmXKlOHNmzeUKFGC2rVrM2PGDEJCQvjiiy9o0KABGzduBLSO6y+//EL79u2ZMGECoC0OrFSpEv3796dfv34ARhdBSh3XdyckJARra2sOHDhAhw4dkux/W3/Vx9sZM6WCP/0fsfmn7bgTyhSL/+HMa5QKCHiuxs5CwZPXGuoUMkcjIBgX6sUuQ5MoBqkAvEKf8NPtnbj9sh2cnSE2FqysdGPi4+MJDAzkwYMHVKtWLdsUX2WYjqshSpQowZgxYxgzZgwvX74kNDQ0PaaVSCQ5mMePHzNs2DCaNGnC4MGDs9qcLMfJyYk9e/aQJ08eqlWrBsClS5cICwujadOm/PTTT8ybN49Dhw5Rt27dLLY2k0ihAEW7TQG+E6H0x+meNuDk5ISlpSW2trY6R+7GjRuAtq104vxrZ2dnKlWqpHs9a9Ysdu/ezd69e3UtowFatmyp+1ufMGECS5Ys4ciRI5QqVYrAwEBKlChBvXr1UCgUFClSJIlNnTp10rWunjVrFgcOHGDFihV89913bN26lZiYGDZv3oydnR0A3377La1bt2bevHm4u7sD2tSTtWvX6qUImJmZ4eDgkCtVO7IbQgjCwsI4ePAgbdq0Mei0GtJf9XQw50uHP6n7cgctLf/LiY+IEYw7GIOtuYJFzawokkfrtALMUHXXd1qFBo88tkxpXhO3LzdCUJDWcU3ktD58+BAXFxeuX7/Oxx9/nP5vQCZgsuOanJh2QtSgRIkSlChRIs2GSSSSnIkQgkePHnHz5k3mzJlD6dKls9qkbIGHhwefffYZ3377ra6TnkajYcSIETg4OLB9+3YGDhzIhAkTOHHiRBZbm0mkUoACAiKeaMd5p5wrnJ5Ur15d73VkZCTTp0/njz/+ICgoiPj4eKKjowkMDNQbV7FiRd3PCoUCDw8PQkJCAG00tEmTJpQqVYrmzZvTqlUrmjZtqnd87dq1k7z28/MDtI91K1WqpHNaAerWrYtGo+HmzZs6x7VChQoyrzWLiIiIICYmhmPHjvHZZ58ZHON7NYhBWy7pbtWUaBhitpsBcb9jHxr7n2YVEBiuITxG8GlZCz4s+p+7FowLM1Td2a/5r0j+G3GLdr9twPrCWcxsbeDUKb3zqtVqnj9/zqVLl2jSpEmOdVrhHRzX5MS0E7YpFArq1avHnj17yJs3b7oZKpFIsj/BwcEMGzaMhg0bZpt0oezCunXrOHnypF77Z6VSybBhw6hTpw6zZ89m6NChqRZz5Soin6XvuHQisXMIMHbsWA4cOMDChQspXrw4NjY2dOzYkbi4OL1xFhYWeq8VCgUajQaAqlWrcv/+ffbt28fBgwfp3LkzjRs3TpLHmt62SzIeIQRRUVFs3bqV3r1706lTJ4Pj1BrBjN8CdE5rC+VZ5lv8gIMiWm/cmzjBpEOxACxrbqVLMXol7Fgf34KV6nbaSKsQOMa+Ia+nK93atoTo+6COT3LekJAQlEol586do3379ul34VmEyQ0IDhw4QI0aNThw4ADh4eGEh4dz4MABatasye+//87x48d5+fIlY8eOzQh7JRJJNkQIwYsXLzh58iTTpk2TTqsB4uPjdY+iE3Pjxg3UajWgLYJ9r4pn7N3Td5yJWFpa6t77lDh58iS9evWiffv2VKhQAQ8PDx48eGDy+RwdHfn0009Zs2YNP/30E7/88oteat2ZM2f0xp85c4YyZcoAUKZMGfz9/fWk1RJuhFLLizb2OiWmExMTQ2BgIL6+vgwcOBCrRI/l3+bc/VBdesBEs618Z7EsidP6IkqD/zM1rUqas7yF/nrgxBtGme+kifICAJOPrOPnnVM5NrYhlCoFy5eDg4NuvEajISwsjD///BMHBwfatEn/XPGswGTHdcSIESxevJiPPvoIBwcHHBwc+Oijj1iwYAHjxo2jbt26LF26lAMHDqQ616pVq6hYsSKOjo44OjpSu3Zt9u3bp9sfExPDkCFDcHFxwd7eng4dOvDsWebeeUskkpR5/vw5n3/+OTt27KBDhw6UL18+q03KlnTv3p2+ffuyZMkSTpw4wYkTJ1iyZAl9+/alR48eABw7doxy5cplsaWZSJE64OiJ3vNRPRTgWEA7LgPw8vLi7NmzPHjwgBcvXuiio29TokQJdu3ahZ+fH/7+/nz22WfJjk2OxYsXs23bNm7cuMGtW7fYsWMHHh4eepXcO3bsYP369dy6dYtp06Zx7tw5XQ5tt27dsLa2pmfPnly9epUjR44wbNgwunfvrksTSOk6jx8/zpMnT3jx4oVJdksMI4RApVKxatUqChQoYDCX9W2CI7ROa3PlGQaY/663L1olGPtXDLOOxVGnkDlNiyV9IK5UANGCGdEbsFBqKDGkD6VXztc1EEhMREQEjx8/5vjx4/Tq1StFhzqnYXKqwN27dw1Wezk6OnLv3j1A+09uzD9HwYIFmTt3LiVKlEAIwaZNm2jbti2XL1+mXLlyjBo1ij/++IMdO3bg5OSkEy0/efKkqWZLJJIMIDIykt9//52xY8dSpUqVrDYnW7NkyRLc3d2ZP3++7gbc3d2dUaNG6SrAmzZtSvPmzbPSzMxFaQbN5/2rKqBAv0jr3w/j5nMzTM917Nix9OzZk7JlyxIdHc2GDRsMjlu8eDF9+vShTp065MuXjwkTJhAREWHSuRwcHJg/fz63b9/GzMyMGjVq8Oeff+qljsyYMYPt27czePBg8ufPz7Zt2yhbtiwAtra27N+/nxEjRlCjRg1sbW3p0KEDixcvTvXcM2fOZMCAARQrVozY2FjZqS2NqNVqbt26xa1btxg1apThMW+pBrx8HcP4Hf4o0fC1xQY9XzMyTnDqkZqGXma0KmlhcD4AhED54xs8nKK58bsTZkUbGBgiiI6OZvPmzQwcOFCnepGbMFkOq169ejg4OLB582ZcXV0BbcSlR48evHnzhuPHj3Pw4EGGDBnCzZs3TTbI2dmZBQsW0LFjR1xdXdm6dSsdO3YEtI/UypQpw+nTp6lVq5ZR80k5LIkk/QkNDWXUqFHUqVOHAQMGZLU5WY6p60yC05NT1qQMlcMCrSSW7wT9Qi3HAlqnNQOksLIjCoWC3bt3065du6w2RUe6y2FlwyYTpqLRaJg/fz4TJkxINq3HkGpAArWUAWy3/BqA2HjBzGOxRMfD4mYpvL/348FVCfZKeKIGJwX03AAVOuoNi42N5f79+9y7d4+WLVu++0VmERkmh7Vu3Tratm1LwYIFKVSoEACPHj2iaNGi/Prrr4A2CvPVV1+ZNK9arWbHjh28efOG2rVrc/HiRVQqFY0bN9aNKV26NIULF07RcY2NjSU2Nlb32tS7YolEkjJxcXH873//Y/DgwdSsWTOrzclxPH/+XHdTX7p0afLly5fFFmUDyrbRSl7lcKdGkgIGb048tRH3HHBzIoTg4sWLvHz5kokTJyY77m3VgLdxIwyAOLXg91vxVPM045MyKURZ4wTsiIballDfCgr8+z+RKO9bCEF8fDwrVqxgzJgxuV7JxWTHtVSpUgQEBPDXX39x69Yt3bYmTZroHnmYcsd45coVateuTUxMDPb29uzevZuyZcvi5+eHpaVlkk4O7u7uBAcHJzvfnDlzmDFjhqmXJZFIUiE8PJwxY8ZQp04dRowYkdXm5DjevHnDsGHD2Lx5sy4/0szMjB49erBixQpsbW2z2MIsRmmWqZJXkkwkuSYTEUHa7RnUZCK9EELwzTffMHny5BSLJ99WDTBEsNqBmcdieRMnmNckmShrnIAzcVDLEiwV0NcWnBNSShRah//fvG+1Ws3ly5cJCwt7b4ri36kBgVKppHnz5umSi1WqVCn8/PwIDw9n586d9OzZk2PHjr3zfJMmTWL06NG61xEREbrIsEQieTfUajUrVqygV69eOaKXdXZk9OjRHDt2jN9++03XYODEiRMMHz6cMWPGsGrVqiy2UJKV5Nq80yxsMpEeHD9+HCDZp8gJuazB4dFcCnxlMD0gAaFRc+RaMA2dHZhcLjbZcUQJxOlYREEzlEXNwSXhffkv71solIh/0xYmTpz4XqmRvJPjev78eY4cOUJISEiSykpjEsUTY2lpqWsDWa1aNc6fP8+yZcv49NNPiYuLIywsTC/q+uzZsxS7f1hZWeWq6jmJJCuJjIxkwoQJ1KlTx+T0H4k+v/zyCzt37qRhw4a6bS1btsTGxobOnTtLx1WSK0jSylRxDbNs2GQiNRKirCmteynlsurNpVETcW4XQhVLnvqf4690QqFYikb8qxQA8EwNp+MQra3Za1+HkG/q048fDaRWzEWUac3x48cxMzNj0qRJ6XC1OQuTHdfZs2fz1VdfUapUKdzd3fW8/PTw+DUaDbGxsVSrVg0LCwsOHTqkk5m4efMmgYGBSbqLSCSS9EcIwcyZM+nQoQMffvhhVpuT44mKijIoW+Tm5kZUVFQWWCSRpC+GHLke9ueZaczBmdxkIiX27duHm5tbqk7r27msSjT4KG/gRhgh5OGcpjRqoSDSbx9mds7Yldeuo/s1PgxSjWSaxWY8xUutnJUG4p/BV696c9jjY06PbAJMSpL3LRRKZs+ezeTJkzP2TcjGmOy4Llu2jPXr19OrV680n3zSpEm0aNGCwoUL8/r1a7Zu3crRo0fZv38/Tk5O9O3bl9GjR+Ps7IyjoyPDhg2jdu3aRisKSCQS04mKiuLLL7+kfv36zJ8/P6vNyTXUrl2badOmsXnzZl2FdnR0NDNmzMgVN+Om6ppKsj+m/E6TK0q6FWUHxnSgzaAmE6YghGD27Nl8+eWXJueyNlOe0zqiilDdmJlnzNkXVwVFrdFJ5tiv8SHP+Vd0vn2QTR3bEeKSl3M9SqFRmEGkinP3Q6ldzEUvCv3nn3/i7u7+Xjut8A6Oq1Kp1OVnpZWQkBB69OhBUFAQTk5OVKxYkf3799OkSRNAq3uoVCrp0KEDsbGxNGvWjO+++y5dzi2RSAwzZswY2rZt+37piWYCy5Yto1mzZhQsWJBKlSoB4O/vj7W1Nfv3789i694dS0tLlEolT58+xdXVFUtLy/cq3y43IoQgLi6O58+fo1QqsbRM2fNMqSjpnKY0T4UzHorQZDoe6RcbZRW7du2iTJkyqTqtAGduh+D1+gJdzQJAAfHCjJHmv+iNWXAqDg9rFadrnWdw/Dn2a3y0O4TAOj6WGAtrguzzcTlPSfbF+6Ays9DrwxHy+r+odYJD/b47rAmYrOM6f/58nj59ytKlSzPIpPRF6rhKJKkTExPDtGnTaNSokXRY3wFj15moqCh+/PFHXevXMmXK0K1bN2xsbDLL1HciteuLi4sjKChIpjzkMmxtbcmfP3+qjuvpuy/puuZMsvubKc+xymIpCoUChaEmE5mpKvCWlmx8AR8WL13G2LFj/2sGkYLe7OX9m/A69SV5FZF60woBAsHqCyrUGhhWU/ueaQQE40K92GVoULL0twUohWB4m/EpmrmtXy1qF3Nh586dlC9fPtdLXEEG6riOHTuWjz/+mGLFilG2bFksLPT1x3bt2mW6tRKJJEvp168fnTp1StZpTVJw4e2MmVJG1UzF1taWfv36Zcm5V65cyYIFCwgODqZSpUqsWLECHx+fdJnb0tKSwoULEx8fj1qtTpc5JVmLmZkZ5ubmRkXPE0cHDZGQ07nUcRs2MYlyWf8tNso0p/UtLdltV1TUKePJmE8X/Oe0GtCbjbZ2J7DmNCJj4qlyZrjBBsUKBUw7EourrZIhPv/5RUoFeEY954P4yxyxrMafpeoRZ5a866UAPJysqVrIkblz56bY6OB9xWTHdfjw4Rw5coRGjRrh4uIi31CJJIcSFxfHrFmzaNq0KZs3bzapC0x+J2umtS5L8/L5M8vcHMnevXuNHtumTcZ9eP/000+MHj2a1atXU7NmTZYuXUqzZs24efMmbm5u6XIOhUKBhYVFkmCGJPfj5pB6V639Gh/8OgyhtvnNrGky8a+WrEAQFy/4/mIcg6pbYm72AsUvvcHsXzsM6M1aRT+jxNHBhGGHAvTatQohWH9ZhUIBMxpaJV1H1QK+f0O3Mvs40rAaf5X8L589mSbH1Oc6wUHFpdOaDCanCjg4OLB9+3Y+/vjjjLIpXZGpAhJJUoQQdOzYkS5dutCpU6dkxyVXcJGwlK76vKp0Xkl+nUnchz4lFApFhkYqa9asSY0aNfj2228BbdFNoUKFGDZsmMEuQIY6EBYqVEiuoxKDqDWCevMOExweYzDPNSGKeGLCh1nzpEajhqXlERFP+fGfOJoWM8fRSoG1udYWgQKFoycIDbwOMjxFYumqRIz7KwZ3ewWjalnqX9v9eChkBuYKuBNPf5ex/GXz3xOOUY1Lsv18oF5AwN1WSaXXZ1k9b6rRa0duIsNSBZydnSlWrFiajJNIJFmDSqVi7ty5tGzZkp9++glz8+SXgJQKLv6VDWfGbwE0Kesh0waSITtU2sfFxXHx4kU9vUelUknjxo05ffq0wWNkB0KJKZgpFUxrXZZBWy4lG0Wc1rps1q0TD0/x+vkTfr6mokt5C+ws9e1QJGjJpoDyrSjrln9UWJkrmNPYCvO3rytcA/+LgrbWaCpaElzMnYOx1f89l9aJH/phcYZ+WFyXgnVu/y6G9uyMa74m76XTagomvzvTp09n2rRpMglfIslhqNVqPvnkE4oWLUrVqlVTdFoBzt0PTbkLDBAUHsO5+6HpbKkkPXnx4gVqtTqJhmxK7bMnTZpEeHi47uvRo0eZYaokG6LWCE7ffcmvfk84ffclao3hh7TNy+dn1edV8XDSTxvwcLLO8iczm7f9jEZAp3JJndZ3YaRvLI8iBJ+UMf/PaY0VcCZWW6XlpIT+dmgqaNNmZqi6o0GZxIk3Uyoo72ZJ0JnfmDZqAPk93FNdlyXvEHFdvnw5d+/exd3dHS8vryT5TJcuXUo34yQSSdpRq9UsXryY1q1bs2PHDp2GaGqkVnBh6jhJzkF2IJSA6fntzcvnp0lZj2xTyBkaGsrhw4cpXqUujmc2pTlf9OdrKuwtYV4TK12agY5gNRyOBW9zcDcDDzOChQszVN11Ulgeb71369ato2PHjvTq1StV5QbJf5jsuLZr1y4DzJBIJBlBXFwc7du3p2PHjpQqVcqkhduYggtTxkmyhnz58mFmZsazZ/qdiVJrny15v0kuvz04PIZBWy4lG0U1Uyq0wvlZzPbt22nTpg0NGjTg9JMYgnDBQ4QazFPVSlY5Y00MeYnC0DI5+I9oXG0VTG5ghaXZvwMex8P1eGhiDUXMYZQDUdbWTIrryzOcOacpzSdVC7OsZD49J/7ly5f4+vrSpUsX7OzsMvaNyIWY7LhOmzYtI+yQSCTpiEaj4dtvv6V9+/Zs3boVJycnk+fw8XYmv5N1qgUXPt7OabZXknFYWlpSrVo1Dh06pAs8aDQaDh06xNChQ7PWOEnGkoIeaUrk5Pz2kJAQ/Pz8qF+/PjY2Ntja2uL2+iUzVD1YZbE0SZFVQubDb/G1+cL8zyRO654bKlxsFMz5yBon67dzWYW2CCtWgJUCbBTYEssznDmjKUteWwvmdayo9x5t2LCBTz/9lFatWkmn9R0xKsfVROEBiUSShURFRdGmTRssLS0pWLDgOzmt8F/BBZBEtzBbFFzkAEaPHs2bN28AOH78OPHx8Vlmx5o1a9i0aRPXr19n0KBBvHnzht69e2eJPZJMIGAvLC0Pm1rBL32135eW125PhZya375r1y4cHBwoU6YMBQoU0D1h8vF25h+HBgxWjSQY/RvtYFz4Ib4V/c3/wEzxn68jhGDAb9GceazGp4DZf07r6Vjw/fe9KWsO/ey0Tmsi3AlFAcz5pIJufQwKCuK3336jTZs22NravvO6LDHScS1Xrhzbt28nLi4uxXG3b99m0KBBzJ07N12Mk0gkxiOEYM2aNURFRbFu3ToGDhyY5pyu7FxwkRNYsWIFkZHaDjuNGjUiNDRrPug//fRTFi5cyNSpU6lcuTJ+fn74+vomKdiS5BL+1SxNLKIPQESQdnsqzqvJ+e0aNdz/G67s1H7XZG4TiqCgIM6ePUv58uWxtramUAFPPXvM0DCtdVn2a3yoH7ucLnFfMTxuKF3ivqJB7BLamJ/Sm2/fbRX+zzTMbGTF3MbWWJmh1WMFrbyVOdoiLIUCQ3kFLooIRjYuqVsft2zZQt68ealVqxYuLlmfRpHTMSpVYMWKFUyYMIHBgwfTpEkTqlevjqenJ9bW1rx69YqAgABOnDjBtWvXGDp0KIMGDcpouyUSSSLCw8Pp2bMnjRo1wtnZOV3lVLJbwUVOwsvLi+XLl9O0aVOEEJw+fZq8efMaHNugQYMMtWXo0KEyNeB9QKPWdn5K6UG/70Qo/XGyaQMm5bcb6DSl7Yg1L1M6Yvn6+lKvXj1cXV0pWrRosvY0bz6PVZ/XYMZvAZwJ1z5JUqKhl5kvngrtDaUQgkF/xGBroeDrD82xtVBoHdQtUVDQDBpZQ43Ui6heCkcq5bPl8ePH3L17l4YNG2JlZWV0YawkZUxqQHDixAl++ukn/v77bx4+fEh0dDT58uWjSpUqNGvWjG7duiW7KGcVsgGBJDcjhGDbtm20atWK0NBQvLy8stqk95Lk1pk9e/YwcOBAQkJCUCgUyaZdZXQDgrQi19EcxP2/tWkBqdHzd/Cub3CX0Q0F2kRitqMnSZ3kf29qO2/OMOf16dOnhIeHEx0dTeXKlbU36wmRZgP2COB6/W/pcMSZ6HhBM+U5plls1jmth+7F45VHiUIBRfMq4Y0GbBTahNhzcZBPCUWNKwvqEvcVzjZ2zBvVk9cR4Xh6eqbrtedWMqQBQb169ahXr16ajZNIJGknNDSU/v374+Pjg52dnXQosiHt2rWjXbt2REZG4ujomK4tViUSg0Q+S31MKuOMaijQqhRm+5uQlsiuQYwoKDtx4gSlSpXC3NycqlWr/ndcCpFmISDP8anExi+jmfICqyyWAhCvEYz8N2d1bmNr7C0VEKWBbyPhI2uobgk+/0VZE+49DWVhaQRcDHfi73BLMDfj0vLTTG9TDum3pi9S6VYiyWEIIdi7dy+NGzfm66+/pnTp0lltkiQV7O3tOXLkCN7e3lJgXJKx2BuZt5zSOI2a5nZ32NPgKd9fjsL3dVE0/5bE6LRI7e4kzaHV499uVA9PJRvZTUIqaQdBQUHEx8cTHx9Pvnz5cHV1/W/cw1Mp2qNUgCcvqakMYJrFZgBOBsZTKp+S/tUsqeimhEdqbZtWWyW0sIbiSf9XFf9mDySkuCagEeB7J56f3bqgsHHE3NGNZxGxKUqHqTVCpmC9A3IFlUhyEC9fvmTIkCGUK1eOli1bSqc1B/HBBx8AWrmekJCQJO1gK1asmBVmSXIbReponb2IIAxHHxXa/UXqGD4+kfNYCfgOiHX2wL/8JNSlWv/nXF05aZw9RkaA1dd+Rflv2oGe6/ZvQZl/xRk4VmmDSqWiYcOG2mMSOX5lXtyhpBHnqa0MIJ/mJWMPxhIZJ1jY1JqK7kqtJuuGKOhpC17mUDH5XNa3o60PwzQ8UedhjUUzLlk2wPxflYGUpMNMbe4g+Q/puEokOYQjR45QtWpVJk6cSOXKlbPaHImJXLp0iR49enD9+vUkua7ZPcdVkoNQmmkjlD/3gOQe9Defa/jxfcBexL85ool9M6uoZ/icGwlezqD8N2c1PSK7/+J75TFVfhmFqxBJGgSEvFFjplDwcO8c2hTPDw75QVMM34AQPcevlvIF241oPvXwyQtiPKBDGXPqupnBTZXWSS1gBr1sobDxaQ0/0IHHMbb4xTtxWe2NmUf+JNKBiaXDEhozvGtzB4mW9Cs9lkgkGUJoaCjdu3fn8OHD2NnZSac1h9K7d29KlizJqVOnuHfvHvfv39d93bt3L6vNk+QmyrbRFkY5vuX8OHoaLJhSawSnb4cQvnsM4u2IJ6Bzfn0n/id1lRDZNTBaiwIcCyQf2f0X36tBbNy2DXdeJnFa773SEBwpeBmtoY1XNOzqB5taEb2gLHu2rtaLVp7TlOapcNY1FHibaJVg2EEFuy8+QwioW9gcrqvgjxiI1GjDqEXMDSevGuBRuIY/QguyNbgw/9jXwcwpZUczQTosteYOoI3QqpO7EIlxEdfRo0cza9Ys7OzsOH78OHXq1JF5WhJJJnDx4kU8PT0ZPnw4NWrUyGpzJGng3r17/PLLLxQvXjyrTZG8D5Rtoy2MSqXQKeGRdZHXl9huGZLChG/lrKYlsgugUaN+cJLTuw/SyeyC3q6XURqszRWcfaymawWLJIdaRQfzncVSBqlGsl/jgxINPsob/Kn2oY+Zb5LuWH7BaorlVXK34MfUNCuHg/8shI8likoWUMIc7P+L4QkBahSE4IQHYUmcaSEE/s80RJjl4ZK6EGYexlVeJUiMmdLcITu0zs2OmKTjamdnR6NGjQgKCpKVsRJJBhIWFsaYMWNwcXFh7ty55M8vHxvldD766CP8/f2l4yrJPJRmKRZGJX5kXUMZZtyciXNWEyK7Bguq5iYvhfVvHq1ZxFNmACTybYMjNdwJ1eBhrzTotIL2UbEGmGbxP5QqwRSL/+lkrQDUQutIx6kF3xyP5UqoBU5NBxPgVZ95p1egfKiCGhZa79Ze3zNVKMAcwTbVR4wy/wUN/z2afvpag62FggdhGvYV7YuZJnWn9e3W2CY3d5AkwSjHNTuJaEskuZ3bt28D0LdvX+rUSfkxmyTnsHbtWnr27MnVq1cpX748Fhb6H8pt2mS8WLtEksDbj6xDyGPcgW/nrCYX2QWtpuzb0d5ktFbDYwQ2FvDHrXj6Vk09WTVBJeA7i2UGVVtvvVDja9WUQ/ns6aRW8fKfMKgGp2tV5tOGp1JNCXgo8jNINZKlTtuxjgrifpjgZZRAY5uHfUX7sF/jk6qNhlpjm9TcQWIQoxzXBQsWMHDgQObMmYNCoaB9+/YGx8kCA4nk3Xn9+jXjxo3D1taWRYsWUaJEiaw2SZKOnD59mpMnT7Jv374k++TaKcls3n5knZAj6kFoksfjoJV7irH1wNZQzurbkd3kZK2azoG/JmHIaT35KJ5yrmZGOa1JTp/I3niNYP7JOC4HqZnd5iJLS3yLzaMN2JlrrzXYzMWoPNYQ8nBWU5YG4eWYU1vNwrU/kadcXc5pSqPRGC4PymNjQVi0Svfaw4BKgI+3M/mdrFNt7pAQoZUkxSjHVYpoSyQZy9OnTwkKCqJLly46qRdJ7mLYsGF8/vnnTJkyBXd3IyuyJe81Ganz+fajaA1KZqh6sMpiaZIc0YQ6oUc+UymVWjOB5LpXRQTBzp56m97ECSzMYNtVFQOrm+6wgr4P+vS1BjMFFLZXMClCg+L2c3xK3WD2h311Y4xx0INx4ZymNPFvXhH4/CGXq37AjTL9tPkJKbCyW1WUCkWKvy+jmjskitBKkmJShZUU0ZZI0pc3b94wadIkzM3NWbx4cVabI8lAXr58yahRo6TTKjGKjNb5NPQoer/Gh0GqkdpWqPyXMxqMC8st+vLNB5+lPGlq3av4zzmLVgl+vxVPgyJm7+y0JqDWCJaeiePkAzVbO9vweRVLeKkBJwXNlecArcOqQWmUg/5lREfUZmqi713EvkJjihfzJv/tG6lGSWsVdTHK4WxePj+rPq+a5PdrKEIrSYpCJNc8OxVyioi27LEtya68evUKf39/VCoVTZo0yWpzJGnAmHWmZ8+e1K9fny+++CKTrUs7ch3NXJLT+UxwidJD51OtEdSbd9igM5ZQpe9GGCHk4bymNCs/r576Oe//DZtapTgkTi2I18CGyyqG+KTNYQV4FS14FSM4fCWOvifiUHSxhaJJA2tPhTMzVD10uanNlOe0Dnqioq6nwoXJrzvxx31zLAuUxtxe+7h+W79ahEfHGfydJDCggTeTWpY1yXbZOUsfY9cZk8OmUkRbIkkb0dHRfPXVVwAsWrQoi62RZBYlS5Zk0qRJnDhxggoVKiQpzho+fHgWWSbJTqSm85lcJyZTSemRtQYlZzRaJyy/kzUrjY0CptIlK14j2Oyvon1p8xSd1sSR2eTQCMHKc3EcvhHPju62fFHfCiwVkF+byvB2S1YPQlllsZTtyo8RpVry3b261IuornPQn8bZckGU4vX14zhUaqY7Lq+thc6h7N/Am++P3zdozw/H71OlcF6TbijMlAopefUOmBxxrVSpEsWKFWPChAm4u7ujeCvJuUiRIulqYFqRkQJJdiIqKopDhw6hUCho1SrlyIQk52DMOuPt7Z3s8QqFIls3IZDraOZx+u5Luq45k+q4bf1qpYvTYyglwcXOkraVPWlS1sO0KGAyEVeNEETEwtYrKgbXSHuUNUoluBuq4e+zcQy8rEI5zB6c9bVYU6u/Eo6e3Kj0FR2OufAmKpo3149jU7wmZjYOeuPsrcy5NKUJZkoF9eYdTlaDNSFd4MSED9/rqGlayLCIqxTRlkhMJzY2lhkzZhAfH8/8+fOz2hxJFnD/vuFIjUSSmMzW+WxePj9NynqkzyPrhG5aEUEkxHCFEHx7Lo4+VSyNc1pt80HUC4O7hBCsORPH/usqdva2o8LH1lDOAvIm1WJNDUVEEEWPDsbnzUB+C4jCoXJzg+MiY+OpNecgPWt7ycYB2QSTW74miGhLJBLjUKlU/PLLL1SvXl06rRJA+wH8juUFklxOVuh8Jjyyblu5ALWLGVdgZJCEblpoo54hbzSsvaRieE0r7C1TmbPGF9Dzdy6XHW9wt0otOPtEzZs78ewI1aBQA2YKKGZ8m9bExGs0rLkYx2Srn3Cq3DTFsaFvVCw5eNuoeWXjgIzH5IirFNGWSIxDpVIxe/ZsVCoVX3/9dVabI8kGrFu3jiVLluiaTJQoUYKRI0fmyIItScaQ43U+/+2mtXDCFwyvFEO/akamBpRtx6xrzlw7FcH2RIcIIdh0MBbfR2q29balVmdbUAkwT+qsGpMioBGC52+0igZDfSyBV/jE3dDl9KYV2Tgg4zHZcZUi2hJJ6mg0GjZs2ECpUqX49NNPs9ocSTZg6tSpLF68mGHDhlG7dm1Au56OGjWKwMBAZs6cmcUWSrIDOV3n8+HDhxy7EMa4X4NQPzjJ1C0HGaFeT15eJ6ubGqJwodO2aB5FPEDJfzqrIDhyX03wvXi25FFoa2qsACvDTuua+Ba0szhHPvHS4LmEECw+HccwH0u9RgduhKX5urP9DUUuwuTiLC8vL1q1apVjRLRlUYEkM4mPj2fBggWo1WqdcoAk92PMOuPq6sry5cvp2rWr3vZt27YxbNgwXrwwnNeXHZDraOaT0TquGcGcOXMYN26cns770gO3uH7kR1ZZLAUM66YOUo3Ua6HaXJymwe65/BUPG3vY/RthJdlwqhDwfXxLnteeTGc7f0ocHax3LiEED8MFfz+Mp3ulpBHgLnFfmRRxTe6GIj1kyt5nMqw4S4poSySGEUKwbNkyPD096dGjR1abI8lmqFQqqlevnmR7tWrViI+PzwKLJNmZdC2aymCuX79OQEAAE8ePQxF4WiuLZe+Ob6Q3G089ICyFxgYzVN3/c1qFtknBrtugeJmHpT7/tk+1SPma/0dzek7bgo2lGWpNOb48+YDhqrW6c809Ece4upYGnFYFOHrSq0lXbu25TuibuFSvdVTjkmw/HygbB2QhJjuun3zyCUeOHKFYsWIZYY9EkuPQaDQsW7YMIQRjxozJanMk2ZTu3buzatWqJB3SfvjhB7p165ZFVkmyMzlB53POnDlMnDiR0ppbKJZVgIinun0VhTM1VT3Yjw/7NT4ciK2u19ggoZsVgGtkKF1+nMBK96LYtpvEvhK1CDO7xjZmp2pD6Q+6YmOp1W81Uypo2K4P9bdUpFToMYq8PM139a4ayBf+1xluPpfmZQvyYRlPas05SOgblcFzJKQCDP2wOEM/LJ4jbihyKyY7rlJEWyLR5+uvvyZ//vyywEaSKuvWreOvv/6iVq1aAJw9e5bAwEB69OjB6NGjdeNk+19JdufSpUuEhIQwYcIEFNd/gx09ebvVa4Lof0IqQOLGBgnYxMUQbWnNw4f+nLKwpFSN9lwHUCg4qynLU+FMfkINZgkIIMbGA5+GrfW2Ny+fn3pRJ7np1ZwA50YMVJ1LEu2NtfXAv/xE1FZ18dEILM2VzG5fgUFbLunmTsBQbnFabihkx6y0YXKOa04T0Za5WZKMQKPRsGrVKszNzenfv3+SRhyS9wtj1plGjRoZNZdCoeDw4cPpaV6akeuoJDFz585l/PjxKBQKFEIDS8vrRVoToxHalIB6sct00dUEfB5dpcOO6Ywr1xBF0yEG19GJZlsZYP47oJ/imuC4KOoMh6azdNsvXLjAixcvaNasGRrBfw6inQU+Zje4euMm31+Owvd1UZ09iXOHMzq3OCfmLmcWxq4zJjuuOQ254EoyggkTJlCwYEGGDBmCUmmyHLIkl5Hb15ncfn0S4zh16hQajYaaNWv+97Q1mW5Zb6MrgBKCwmHBBObNT8ylPygdcJSQ9pOJssuT5BglGk5YDceDUIMqAQA4FoCRV0BpxuzZs5k0aRKAQSfY92oQg7ZcSpI28HZxVXpGRBPP9eDFG4N6sLK4S0uGFWclJsHnldEmyfuAEIJ169ZhY2PDnDlzpMMqkUjeC4QQLF68mOHDh2NmZqa/9kU+M2qOBMmpL87vptyJHxlYoz3W9T7jQdWPkz3GR3kDT0VosvsBiHjC3ztWIfJXZOLEicn6I2qNYMZvAQa1cQVa53HGbwE0KeuRbrnFhqKrhjB0fknyvNMn77p16yhfvjzW1tZYW1tTvnx51q5da/I8c+bMoUaNGjg4OODm5ka7du24efOm3piYmBiGDBmCi4sL9vb2dOjQgWfPjPtHkUjSk5EjRxIWFkaXLl2k0yoxmZiYGBYsWEDLli2pXr06VatW1fuSSLIjx44dw8/Pj0GDBmFhYZF07bM3QmHojQaHpxEArNaomVSkMlY1O6JQpLyOpqavKoRg3olY6pRypX79+imuy+fuhxrdsjU9SIjupua0ZtT5czMmR1zTU0T72LFjDBkyhBo1ahAfH8+XX35J06ZNCQgIwM7ODoBRo0bxxx9/sGPHDpycnBg6dCiffPIJJ0+eNNV0icRkhBBs2bIFR0dHFi5cmKQYUSIxlr59+/LXX3/RsWNHfHx8MuVJ1YMHD5g1axaHDx8mODgYT09PPv/8cyZPnoylpZEdjSTvJUIIvvvuO3r16oW1tTVmZmaGBxapA46eEBHE28VZoM1xjfEVlHq0k4jqcTjUaE9MLXOjomYh5El238F78TjbKBhd2xIzJ89UW2YZ24o1PVq2phTdzYzz53ZMdlxXrVrFmjVr9ES027RpQ8WKFRk2bJhJjquvr6/e640bN+Lm5sbFixdp0KAB4eHhrFu3jq1bt/Lhhx8CsGHDBsqUKcOZM2d0lbkSSUYxePBgChUqxPjx4/VEtSUSU/n999/5888/qVu3bqad88aNG2g0Gr7//nuKFy/O1atX6devH2/evGHhwoWZZockZ3Ho0CEKFy7MZ599hp2dHWqN4PTdl4ZzPpVm0Hwe4uceCBI9xr2tQmOlgELmtM5TDX/Msa/QGIWZ8evoOc1/XbQSNxNYciaOITUsMVdCrF1+LIrUSXUuY1uxpkfL1tSiuxl9/tyOyZ/EGSmiHR4eDoCzs7Zl2sWLF1GpVDRu3Fg3pnTp0hQuXJjTp08bdFxjY2OJjY3VvY6IiEiTTZL3k59//hkXFxcWLVqEra1tVpsjyQUUKFAABweHTD1n8+bNad68ue510aJFuXnzJqtWrZKOqyQJ8fHx/Pjjj7Rp0wYHBwfMzc0N5ml6OFrR1acwXvnsyGdvxalHJbgXN4IpFpu1OalCwPE4jthb8XVII+76dMfRwjJZDdfk0KBkhqoHqyyWohGw/46KonmVDKxuiYWZ1pN95DOVUspkosGJ8PF2Jr+TNcHhMQYjoenZsvVdoqayZazxmOy4ZpSItkajYeTIkdStW5fy5csDEBwcjKWlJXny5NEb6+7uTnBwsMF55syZw4wZM97ZDomkX79+FChQgMmTJ8vUAEm6sWjRIiZMmMDq1aspUqRIltkRHh6uCw4khwwAvH8cPHiQcuXK0aRJE/LmzQskX4UfHBGbpDreJq4i1Q5W4kalYsS5WnPa+wI3wtTkLd6VFlb/aHVUExVaPRXOzFD10Gv1aoj9Gh8GxA6n2JXvGFpJYGsBSoWCp8KF5RZ9+eaDz4y6PjOlgmmtyzJoy6VkW7Ym1mlNC6ZGTdP7/Lmdd3r2mREi2kOGDOHq1aucOHHiXUzSMWnSJD0bIiIiKFSoUJrmlLwf7NmzB09PT+bNm5fqB7skZRo2bMixY8cAuHz5MpUrV85ag9KJtFxX9erViYmJoWjRotja2ia5KQoNzfiijDt37rBixYpUo60yAPD+EBMTw759+6hZsyYuLi663Gdj8jSVaHRR1JcW9tQKvMJ+a0eOvSiEVZXeuNg40Ex5jlUWS5Mcm9CcYLBqOL4a/aenQxsVp4S7PQ9eRDH7+x/xdS2OsvgK/HiCm0obsT2vKc3KTtVNcvSal8/Pqs+rJo0gp7OOamrR3beRLWNNw2TH9erVq7oK2Lt37wKQL18+8uXLx9WrV3XjTCk8GDp0KL///jvHjx+nYMGCuu0eHh7ExcURFhamF3V99uwZHh4eBueysrLCysrKlEuSvOcIIejfvz/58uVj+vTp8u8nnejXrx8zZ84kX7586TrvnDlz2LVrFzdu3MDGxoY6deowb948SpUqpRtz9+5dxo4dy4kTJ4iNjaV58+asWLECd/fkK6DVajXTp09ny5YtukKmXr168dVXX+nWs127dnH37l18fFKOEhmia9euPHnyhNmzZ+Pu7p6m4qyJEycyb968FMdcv36d0qVL614/efKE5s2b06lTJ/r165fisTIA8H5w6NAhqlevTvny5fH09NTbl1qeZjPlOWaGr8P9t2DoaAOOSqaUV+IX7IdVlRaY2TigRMM0i80ASXRYE16vtPiWoSoF+zQ1dfvqFs9HlQJ2/PLLUVaM6MTiY08Ifq3ijMYJ0Ar2rzTC0TOkx9q8fH6alPXg3P1QgiNiCI2MxdnOEicbS9QakS4RT2OiuyMbl8Qrn63snPUOmOy4HjlyJN1OLoRg2LBh7N69m6NHjybpylWtWjUsLCw4dOgQHTp0AODmzZsEBgbqFA0kkrSwb98+ihQpwvTp0ylQoEBWm5OrsLW1TfYGMy2kpkby5s0bmjZtSqVKlXQdqKZMmULr1q05c+ZMspI58+bNY9WqVWzatIly5cpx4cIFevfujZOTk66VtbOz8zs/Nj916hSnT5+mUqVK73bhiRgzZgy9evVKcUzRokV1Pz99+pRGjRpRp04dfvjhh1TnlwGA3E1UVBTnzp2jSJEiWFlZUaJEiSRjUsrTbClO863FCrAXYK3A/0k8L14o6V9ByYy6DxmkusF+jY9ROqxmCg3fWSxj4L9tYfPYWnDy8F8Ua9eEhg0bUqBAATrUKWtyQ4DUOlSFR8cx3/eG3v48Nhb0ruvF0A9LpNmRzKzo7vtIlpZJDxkyhK1bt/Lrr7/i4OCgy1t1cnLCxsYGJycn+vbty+jRo3F2dsbR0VEnwyUVBSRpQa1WM3jwYOzt7fn666+xsbHJapMkRpKaGsnJkyd58OABly9f1nVf2bRpE3nz5uXw4cN6xZ6JOXXqFG3btuXjj7WC6F5eXmzbto1z586li92lS5cmOjo6XeZydXXF1dXVqLFPnjyhUaNGVKtWjQ0bNkgN4vec48ePU6NGDfLkyUPx4sWTHZdcnmbLmydYfnQhDLBBaaNgoZcZF67Fs7yFNW52SjQCpln8jwOx1VPVYU3MNIv/sf91OZ7eucbSYG92PrvE9HYVKFAAkxsCJJubGx7DoC2X6N/Amx+O30+yPyxaxZKDt9lw6gFzP6mQZucycXQ3PbpwSbSY7LjGxMSwYsUKjhw5QkhICBqNRm//pUuXjJ5r1apVgDZvLDEbNmzQRROWLFmCUqmkQ4cOxMbG0qxZM7777jtTzZZIdBw8eJBSpUoxZswYSpYsmdXmSNLI22oksbGxKBQKvYihtbU1SqWSEydOJOu4JkQjb926RcmSJfH39+fEiRMm5eqnxNy5cxkzZgzffPMNFSpUSJLjmhGtVJ88eULDhg0pUqQICxcu5Pnz57p9GRENl2RfIiMjuXnzpu7vLLX8bL08TSFwiwwlxMEFCigxr6Dk2ks1UShoU8qcMbUtdakvSgV48hIf5Y0UdVgTo1DAlTvPqO5xm9POBTB3dOVZpIpBWy6Z3AY1tQ5ZAGv+Tuq0JiYs6t3ObYj06sIl+Q+THdf0FNFOaBmbEtbW1qxcuZKVK1e+83kkEoC4uDhGjRqFQqFg7ty5MmcvF2BIjaRWrVrY2dkxYcIEZs+ejRCCiRMnolarCQoKSnauiRMnEhERQenSpTEzM0OtVvPNN9+kSS0lMQmyVB999JHediEECoUCtVqdLudJzIEDB7hz5w537tzRqx9IOK/k/eDcuXOUL1+e+Ph4qlWrZtQxifM0pxxeS8N7F2nSdyXmjrDYRsGpk3Esb2GNp4PhCL4bYfyuqZVEh/VtImIF/zxT45VHgbvZayycKwPv3gbVGA1VjRF/+uIdzi3JHEx2XLNCRFsiSSsnTpygXLly9OnTx+iFW5L9MaRG4urqyo4dOxg0aBDLly9HqVTStWtXqlatmuJj8p9//pkff/yRrVu3Uq5cOfz8/Bg5ciSenp707NkzzbamZ32AsfTq1SvVXFhJ7uXNmzc8efKEmJgYlEolNWvWTP2gBIKDaW75mlWfV2VTTBsOFq9JbFgwdxRx9CxizqhalikGrkLIo6fDaoijD+KpWcAMRysFpfKZ8TLOBRI9xE3cBtXYqGV6dp4y9dySzMFkxzUrRLQlknclNjaWSZMmERkZycKFC6XTmotITo0EoGnTpty9e5cXL15gbm5Onjx58PDw0CtYeptx48YxceJEunTpAkCFChV4+PAhc+bMSRfH9YMPPkjzHBKJsQQEBODp6UlISAgNGjQw+riESvySHdugyJuXJgd9abyoF2Onz8Xz+E6uletCfldXBKEYcls1AoJx4ZxGq2ixX+PDYNVwVlqswEyhDXWGxwgehmtwsFRgpoTybmY8Ff8d8zamOKPp3XlKtmDNfpicpZ8gov3w4cOMsEciSTcuXLiASqWibdu2/PDDDxmSQyjJfIQQDB06lN27d3P48OEkaiSJyZcvH3ny5OHw4cOEhITQpk2bZMdGRUUliciamZklyeOXSLIz0dHRPH78mLt372JnZ0e9evWMO1AILizbQLsxm+m65gydq/WhYcU+VJ+whe1H/Wj7YW3OHd3PwFa1mKHqASR95K4R2nzVGaruel2xfDW1GKoajhBw9rEapQLi1FDN0wzzfx/Dv31MYkxxRhNyc1N6uG/Kk3/ZgjX7YbLjmlhE28HBAWdnZ70viSSriY2NZeLEiaxcuRKNRiMjXdmI0qVLs3v3bt3rSZMm0aNHD93rc+fOUbp0aZ48eZLsHEOGDGHLli1s3bpVp0YSHBysV7G/YcMGzpw5w927d9myZQudOnVi1KhRelqvH330Ed9++63udevWrfnmm2/4448/ePDgAbt372bx4sW0b98+vS5fIslQAgMDCQ0N5f79+7Ru3dqkzn8L9lymwFfj+ODiQQDuuBTkyfXj3Ny9gkk7/Yl1LY1CoWBSy7J41fuUIfEjCUb/M/+1lRu3PvjOYDesP6Ir8FlITx7G2WNtDtU9tW1ag3Fh0L9SWG+jQCthZUob1ITc3ITj355PAfSrn/zNbkrnVmsEp+++5Fe/J5y++xK1McmyknTH5FSB9BTRlkjSm6tXr+Ll5UWDBg1o2bJlVpsjeYubN2/qVAAAgoKCCAwM1L2Oiori5s2bqFSqZOcwRo3k5s2bTJo0idDQULy8vJg8eTKjRo3SG5+QSpDAihUrmDJlCoMHDyYkJARPT08GDBjA1KlT3/VyJZJMITY2lsjISI4fP85nn31mtCa1OvQVzyZMYcsHXfjuajg/91zKc7s8xEeEoLC0xcwuD64dpqBQKPUKlSa1LEtc02lsOfU5BJ7G2+o19apUwKloXexRkv/MYb2uUXEvAjGzceDoGy/Oeq5hl/oGbmptB6xzmtIGI61paYNqjIZqlcJ5mbjrCmFRSdcaQ+dOTRdWknkohImlpba2tukmop0ZRERE4OTkRHh4uHxUnIuJi4vj66+/5u7du3z//ffY29tntUnvNQ0bNqRy5cosXbo0q01Jdx48eIC3t7dey9fcvs7k9uvLyTx//pyXL1/y4sUL49MC0Dpiy7edZM2KwYxvOYKTXpURQhD5zwGibp7ApflQzB3d9I7Z1q+WUYVKCTqqmrhoNHExxD69jk2JWigU+g5qgoPYv4E3e/2D0t0pNNQ5K7ETrNYIvj18hw0n7xMW/Z8D+/a5k9OFTZgpPWSzJMavMyZHXNNTRFsiSQ/u37+Po6MjFStWZObMmVltjuRfvvvuO9auXcvp06epUKFCVpuTLrRo0YLjx49ntRkSCSqViri4OHbt2kX//v312vumyMWLhIyawMgag4mxsOGDAWuINzMnPjIUpaUtCoUCt47TUCjNkhxqbKFS8/L5mfGRG0v23yA44jm2JesA2tzSxE/XE0dAxzcvk+5C/alpqJopFYxoXIKhHxZP9typ6cK+i2SXJG2Y7LhmhYi2RGKI+Ph45s2bh7//PwyetgiL4rU5ffel7EySDfjxxx91N7iFCxfOYmvSj7Vr15p0XVWqVDE6ncqU5i2S95vXr19z8+ZN4uPjGTBggHEHqdWoFUr8QuNRP3pB3vIRBFm4olKa8ebaEd5cPYxLixHYV2yS7BTGFCpFRUURFxcHQdc5P+9zzj94pXMIqxXJy8WHrww6iFkp1P/2uRNyWUNex/DidWyKurDvItklSRsmO65ZIaItkbzNs2fPiIuLI1zpRGDlfvT6n79un8w7ynqMzbHLaZh6Xe3atdP9HBMTw3fffUfZsmWpXbs2AGfOnOHatWsMHjw4Pc2U5FLi4+PRaDSsW7eOkSNHGn2cZvX3PFu+mo87zSY0VgOffgOAOiochYUVmujX2iirWfIugbOdRapFUi9evODRo0eo1Wpd0eXbzlx2d+4M5bIag5TNyjxMdlyzQkRbIklArVazZMkSTp8+Te+vlrAj3BtBrN6YhH7UMu9IktVMmzZN9/MXX3zB8OHDmTVrVpIxjx49ymzTJDmM2NhYTp06haOjo3FOq0YD4eH4Ponhx4sxVHUuy+uoWDDTPiV9c+MEkX6+5Gs1BsfqycvEJfB12/LJPsmKjY1Fo9HoGn/kVJLLZTUGKZuVeZhcnJXTkEUFuYfw8HCCgoI4efIkPXv1pv78I8neFSvQ5k+dmPChTBuQZDjGrDNOTk5cuHCBEiVK6G2/ffs21atX11NbyG7IdTTrSHiKuXjxYsaNG2f8gV268PzxM3zqjdVzxDSxUaBQ8Pribzj6tEdhlrpk1oAG3kxqWdbgvsjISC5fvoyDg4OuWDEnotYI6s07bHKkVX7WpB/GrjMm67hKJJmNRqNhxYoV9O7dm5IlS9K3b1/OP3hldN6RRJIdsLGx4eTJk0m2nzx5EmtrGa2RJEWtVrNv3z6uX79unNP65AmEhGiP7defKWXb6jmtUbfP8nz31wi1CqfanVN1Wl3sLPnus6oGnda4uDji4+NZu3Yt9evXz9FOK8C5+6Hv5LTCu0l2Sd4dk1MFJJLMJCYmhqtXr6JUKtm5c6eus5Gx+UQy70iSXRg5ciSDBg3i0qVL+PhoxdbPnj3L+vXrmTJlShZbJ8lOCCEQQrBgwQImTpxo3EHx8VC3LrRqBd9+yzmvSvg6awsJRXwcGlUMsUE3ces4HYW5pcEpFGhv+vvU9aJJWY9kC13j4uI4ePAgXl5eJuXapiepSV2Zyrt8VnjIeoosQTqukmyJEII1a9Zw6NAhtm/fTvXq1fX2G5tPJPOOJNmFiRMnUrRoUZYtW8aWLVsAKFOmDBs2bKBz585ZbJ0kuyCE4KeffqJGjRqpO61CwO7d0KQJODjAtm1QVhsdTXDEou9dJOLcL7i2/4q8DXqkNFuqjlh8fDwKhYLly5czduxY0y8unTBUQJXHxoLedb0Y+mEJgw5sao6usZ8VUz4uQz4Hq3ST7JKYjnRcJdmO+Ph4Tp48yZs3b9i2bZtBOaGEftSJu7MkJiHvyJRWgRJJRtO5c2fppEqSRaVSsXjxYiZMmGDcAcHB0K0bfPcd9O4N/6pVADjbmBP/+gXR9y/h2mEqSovkHbM8Nhas7FaVWkVdknXE1Go1u3btolq1alnutBoqoAqLVrHk4G02nHrA3E8q6DnfxnS9MvYzpVddb+msZjEyx1WSbRBCsHHjRvr06cMHH3zAqFGjdKkBb5NaP2qQeUeS7EdYWBhr167lyy+/JDRUm3996dIlnjx5ksWWSbKa9evX8/z589Sd1ufPYepUUKkgf364dk3rtCbi8OHDzBrWgwIebrh81C9FpxVgbocK1C2ez+B6KYQgLi6OxYsX07lzZ4oVK2bytaUXKTUDSCAsSsWgLZfwvRoE/Ofovp2/mqA+kzBOfqbkHIyKuEoRbUlGI4Tgzz//JDg4mPXr1xt1jDH9qCWS7MI///xD48aNcXJy4sGDB3zxxRc4Ozuza9cuAgMD2bx5c1abKMkCoqKiWLVqFWPGjDHugCdPYOVK+OQTqFwZihbV7RJCcPfuXXbs2MEvv+zk5MNIBm25pMtdfZs8thZJopOJEUKwYcMGmjdvbpqiQQZhbAGVQNvN6sPS7iZ1vZKfKTkDoxxXKaItySiEEGzfvp2jR4/y/fff8/HHH5t0fPPy+WlS1iPdWwVKJOnN6NGj6dWrF/Pnz8fBwUG3vWXLlnz22WdZaJkkq/juu+/o0qVL6k7r33/Dxo2wdq3WWX30CGxt9YacOHGCefPmsXv3blatWgVA8/IOBh2xPLYW9K7jzdAPi+utlYnzQB3NNVzc/zNjjXWoMwFTCqiCwmP43+kHJne9kp8p2R+jHFcpoi3JKHbu3Mnt27f59ttv33mOrGwVKJEYy/nz5/n++++TbC9QoADBwcFZYJEkqwgLC2PLli0MGTIk5aeZQoBCATExcO8ehIdDnjxJnFZ/f382btzIli1bMDfX/1g31hFLnAf6+tIf2JZpQEGPypS/GpRtIo2mFts+DI0yatzbDrH8TMnemJzjumPHDl0rt8R8/vnn/PLLL+lilCT3s2vXLsaMGUOnTp2YOnUqFhapi2BLJDkZKysrIiIikmy/desWrq6uWWCRJCtYvnw55ubmqTutc+dC9+7an5s0gcOHtU5rIs6ePUvXrl2pWLEia9euxcnJyeBUCY5Y28oFqF0saQFWQh7ok2cviLj4Gw5VP8bMxiFJHmhWk1BAZSxFnG1TH4RUn8lpmOy4ShFtSVrZuHEjFy9eZPbs2VltikSSabRp04aZM2eiUqkAUCgUBAYGMmHCBDp06JDF1kkymmfPnrF+/XqGDBmCvb29YadVrYaof6OE3t5aaauE5pZvjT99+jSrVq1i5cqVRtegGCKh4Cn8/K+gNMOxWmvdvoTc0Bm/BaDWZH2TzcQFVCmhQKsa0L22F/mdrJMUW709TqrP5CxMlsOSItqSd+X333/n/PnzTJ8+PU0LrUSSE1m0aBEdO3bEzc2N6OhoPvjgA4KDg6lduzbffPNNVpsnyUCWLVvGwIED6dGjB2ZmZoYHCQEffQQVK8Ly5fDppwaHXb58mVWrVvH999/r6kzSgu/5G9w6tgeH6m0MrsuG8kCzkubl87P686pM3HWFsChVkv2JFQAszZVMa13WYIGaVArIuZjsuEoRbcm7sGrVKu7evcusWbOk0yp5L3FycuLAgQOcPHkSf39/IiMjqVq1Ko0bN85q0yQZxMOHDzl37hyDBg3C0tJwtyoCA8HNDaytYcAAbaQ1GQ4fPsyaNWtYvnx5uqyjixYtokj99thXapbqfNmpC2FC3u63h++w4eR9wqL/c2Dz2lnQvnIBnGwsUWuEVArIhSiEEFkf/89AIiIicHJyIjw8HEdHx6w2J/PRqOHhKYh8BvbuUKQOKJO5488ADhw4wD///MPIkSOTjzRIJDkcY9aZzZs38+mnn2JlZaW3PS4uju3btxusHcguvPfr6DuwYsUKBg0aRHx8fPJpdOHhULiwVpc1her9q1evsnHjRubNm4dSqUyz0/rw4UNOnTpF586dOfcgjK5rzqR6zLZ+tbJFxPVtEpQQDgQEs8fvKaFv4nT7EjcZSO8WsZL0x9h15p0c17CwMHbu3Mm9e/cYO3Yszs7OXLp0CXd3dwoUKJAmw9Ob93rBDdgLvhMg4ul/2xw9ofk8KNsmw0+/dOlS7t27x5w5c7Czs8vw80kkWYUx64yZmRlBQUG4ubnpbX/58iVubm6o1erMMPWdeK/XURO5ffs2d+7coX79+tjb2ycdIAT88Qe0bAlKpfbnBg20LVsNsG/fPtavX8/y5cvJnz/t0cFFixYxYsQIAMzNzVFrBPXmHU61Y9SJCR9mW0cvuW5aCdau+ryqjKzmAIxdZ0xOFZAi2jmEgL3wcw+SyE5HBGm3d96cYc7r0aNHuXXrFoMGDUoSXZJI3leEEAYjZY8fP062GlySs1i9ejV9+vTB09Mz+Zv1y5ehdWs4cAAaN4ZktKtv3LjBzz//zMSJE2nevHmao6w3b97k1q1bDB06VE8yK6HgKafmgabUTctQkwFJzsdkVYEEEe3bt2/rPf5o2bIlx48fT1fjJO+IRq2NtCb7rwz4TtSOS2cWLlzITz/9RNeuXaXTKpGg7TxYtWpVFAoFH330EVWrVtV9VapUifr168s81xzO9evXOXnyJB07dsTS0jKp0xocDMuWaX+uWhWuXNE6rcnw66+/MmnSJPr06YOlpWWandZly5ZRrFgxmjZtanBdTsgD9XhLasrDyTrbRytT66aVuLhMkjswOeIqRbRzAA9P6acHJEFAxBPtOO/66XLKU6dOERgYyMCBAw0/HpNI3lMSOg/6+fnRrFkzvf8PS0tLvLy8pBxWDmbTpk107NiRggUL6nVE0+PkSZg5Ezp2hAIFoHx5g8Pu3LnD77//zhdffEGbNoar/E3hypUrvHz5kr59+2Jubp6kOUFicmrHKGOLxrJTcZkkbZjsuEoR7RxA5LP0HZcKc+fO5fbt2yxatEg6rRLJWyR0HvTy8qJLly5Z9iQiNjaWmjVr4u/vz+XLl6lcuXKW2JFbuHr1KhqNhkaNGhlOC/D1hTNnYPp0+OQTbYQ1hZSQXbt2sWHDBlasWJEu6+jq1avp1asXJUqUMFpjPSd2jDK2eYBsMpB7MNlxTRDR/vnnnwEpop0tsXdP33EGUGsEG389yIPHQdT+sD3jxpfM9nfmEklWUrZsWfz8/KhZs6be9rNnz2JmZkb16tUz9Pzjx4/H09MTf3//DD1PbkcIwa5du2jYsCFmZmbkeaublY5797SOq0oFFhbJOq0PHz7kyJEjtG7dmnbt2qFUmpzBp8fly5fRaDR07NjxvWgKlNBNK7XiMtlkIPdg8n/IokWLiIyM1BPRLl68OA4ODlJEO7tQpI5WPSClfiGOBbTj3gHfq0F4t+zHiClz2HjHgsG77lBv3uFs0xZQIsmODBkyhEePHiXZ/uTJE4YMGZKh5963bx9//fUXCxcuzNDz5HauXLnCw4cPKVu2LC4uLkmd1gkTtBFWgIEDYd8+rdOaDL/88guDBg2iXr16uLi4pMlpFUKwadMmihYtSunSpcmXL987z5WTSNxN6+1PvJxQXCYxHZMjrlJEOwegNNNKXv3cA5KrE20+9530XFfuPMDXuy9hVrQu+Sq3121P6Gmd3RP5JZKsIiAggKpVqybZXqVKFQICAjLsvM+ePaNfv37s2bMHW1vjerfHxsYSGxure20oPex9QgjBwYMHKV26NLa2tnh5ef23Mz5e+93cHPLm1TYSAK3UVTI8efKEc+fOUbNmTdq1a5dmjeuLFy/i4OBAo0aN3kuFCtlk4P3CZMc1QUS7bt261K1bV7c9J4hov1eUbaOVvDKo4zrXaCmsxKLNvj9+z8/7/8b2gy8ws8ujN07KjkgkKWNlZcWzZ88oWrSo3vagoKAUi2bSghCCXr16MXDgQKpXr86DBw+MOm7OnDnMmDEjQ2zKaVy9ehUPDw+cnJwoVKiQ/s6YGKhVC/r1gyFDYOLEVOfbuXMna9euZenSpRQsWDBNtgkh2LNnD3Xq1MHW1jb54rD3gJxaXCYxHZMbEOQ0Ee33Xjj7HTtnqTWCFYdusebEfV49vgdCA0ozLFwKpVrpml07rEgkGYUx60zXrl0JCgri119/1UXFwsLCaNeuHW5ubrq6AWOYOHEi8+bNS3HM9evX+euvv/j55585duwYZmZmPHjwAG9v71SLswxFXAsVKvReraNqtZoLFy7g5OSEi4uLfvHx48dadQCFAhYtgoYNoVq1FOcLDg4mICCAAgUKUKxYsTTfrFy4cIECBQrw6tUrypYtm6a5JJLsQIY1IJAi2jkMpZnJkle+V4MY87M/b+LUhJ/dRdzTG+RtPABzB+OcUSk7IpEkZeHChTRo0IAiRYpQpUoVQCuR5e7uzv/+9z+T5hozZgy9evVKcUzRokU5fPgwp0+fTqJkUL16dbp168amTZsMHmtlZfVe6zBfv36dwoUL8+bNmyTFdNy9C+XKwfbt0K5diq1aE9i1axerVq1i0aJFlCpVKk22xcfHc/z4cby9vbGxsUmXbloSSU7CaMe1SpUqKBQKnYh24rtFtVrN/fv3ad68eYYYKck8fK8GMXDLJVShT0BphnXBsjj6tDdJT1DKjmhRq9WoVKqsNkOSDlhYWKQ5D7FAgQL8888//Pjjj/j7+2NjY0Pv3r3p2rUrFikU8BjC1dXVKPnB5cuX8/XXX+teP336lGbNmvHTTz8ldcgkqNVqbt68SWRkJNHR0Xz44YfaHRoN/P03fPABFCsGP/wATZqkOt+LFy94+PAhRYoU4c8//zT59/w2ly9fplixYtjZ2eHt7Z2muSSSnIrRjqsU0c79qDWCab9eI+LCr8QEXsG58UCsCpQ2+ngpO6JFCEFwcDBhYWFZbYokHcmTJw8eHh5pEoW3s7Ojf//+6WhVyhQuXFjvdcK6XaxYsTTnV+Y27t69i6enJ/fu3aNVq1b6O/fuhfbt4fp1KF0ajKjl+PXXX1mxYgULFizQRdjfFZVKhb+/P5aWlqjVannTIXmvMdpxzQgR7ePHj7NgwQIuXrxIUFAQu3fv1jnIoHUApk2bxpo1awgLC6Nu3bqsWrWKEiVKpPnckqTsPnaJp8HPsHAphEM107q2SNmR/0hwWt3c3LC1tU1z9xtJ1iKEICoqipCQEACTHs3u3buXFi1aYGFhwd69e1Mc26aNcQWTkvRFo9Hw6NEj7t+/T968ef9zWp8+hWPHoGtXaNMGzp7VOq2p8OrVK0JCQnB0dOSPP/5I82fl1atXKVasGK9fv6ZRo0ZpmksiyQ2YnOOaniLab968oVKlSvTp04dPPvkkyf758+ezfPlyNm3ahLe3N1OmTKFZs2YEBAS8F8LKmYUQgtWrV7N+2y+I8p9j451Usic1pOyIFrVarXNaXVxkgVpuwcbGBoCQkBDc3NyMThto164dwcHBuLm56d2Uv41CociUwlYvLy9MrMfN1Tx9+hRHR0cuXbpE+/bt9Xf+73+wfDm0bQu2tuDjk+p8f/75JwsXLmT+/PlpdjJVKhX37t0jPDyc2NjYXOe0JlaskQoAElMw2XEdMmQI48ePT+K4PnnyhHnz5nH27Fmj52rRogUtWrQwuE8IwdKlS/nqq69o27YtoJXicnd3Z8+ePXTp0sVU0yUGCAwMJG/evDg7O7N03Ta6rTtn8hxTPi5Dr7rectEBXU6rsXqZkpxDwu9UpVIZ7bhqNBqDP0uyFiEEL1684NSpUzRp0uQ/p3XPHnj5Evr2hZEjYcAArdOaChEREbx+/ZrY2Fh+//33NP//37lzR5e2kNxnZE7G92pQEs3V/DL4ITESk9t0ZJaI9v379wkODtZrbODk5ETNmjU5ffp0ssfFxsYSERGh9yVJihCC9evXM2DAAEJfhVG4emNCXsfibGdp9BwKtIuNdFqTItMDch/yd5o7CA0N5dWrVxw7doyOHTvqq+EcOQIHDmh/trKC5Nq5JuLQoUO0a9eOoKAg2rdvnyanVa1W8+jRI27dukV8fHyudVoHbbmk57TCf01sZAdGSWqYHHHNLBHt4OBgANzd3fW2u7u76/YZQgpnp05QUBBOTk6o1WqGzl1Dlx9vJllEUkPmtEokqbN8+XKjxw4fPjwDLZEIIXj9+jV79uyha9eudOzYEdRqGDYM6tSBzz+HhQtTbNGamMjISOLi4ggMDGTv3r16BcvvwtOnT7G1tcXPz4/WrVunaa7silojmPFbAIaSVWQTG4mxmOxpNm3alEmTJiUR0f7yyy9pYoQ8SEYzadIkRo8erXudIJwt0S7cP/74I//73//YuHEjhWq3YtCWSwYXkdSQOa2SjKRXr16EhYWxZ8+ed57j6NGjNGrUiFevXpEnTx42btzIyJEjM1XtYcmSJXqvnz9/TlRUlK7HfVhYGLa2tri5uUnHNQOJiori1atXnDp1ij59+mgdVgAzM+3PcXHa10Y6rcePH2fGjBksWLCA3r17p8k2jUZDaGgoR48epX379rnWaQU4dz80xSCJAILCYzh3P1Q2sZEki8mOa3qKaKeEh4cHoO2znbiK99mzZyl2fHnfhbPfJiEB/s6jIAq4OPIsJIQ//vgDhdKMGRsPp+i05rW14LOahVEANb1cUJopeBEZKxPpJUbTsGFDKleuzNKlS7PaFD799FNatmyZqee8f/++7uetW7fy3XffsW7dOp0I/c2bN+nXrx8DBgzIVLveF4QQxMbGsm7dOgYOHEinTp3g2TNtp6tly6BpU/j+e6Pni4qKQgjBxYsX2b17d5q7iL169YqYmBjOnz/PZ599lqa5cgLGNqeRTWwkKWGy45qeItop4e3tjYeHB4cOHdI5qhEREZw9e5ZBgwal23lyMwkJ8HfOHiTSbx/52oynYP5KlLvxHCcby1TTA15FqahX3FXe+WYBsuI2/bGxsdGpA2QFU6ZMYefOnXqdk0qVKsWSJUvo2LEj3bp1yzLbciMqlYqHDx9y5coVhg0bBi9eQL584OYGLVqAiR2nTp8+zeTJk1myZAmjRo1Kk20ajYaoqCh+/vln+vTp895IoRnbnEY2sZGkhMnFWfCfiPbKlStZuHAhPXr0eCenNTIyEj8/P/z8/ABtdMLPz4/AwEAUCgUjR47k66+/Zu/evVy5coUePXrg6emZoqyMRIvv1SAGrD/J42cvUL18hFun6ZjZOukS4A8EJJ8nnBh555v5+F4Not68w3Rdc4YR2/3ouuYM9eYdzvCihYYNGzJs2DBGjhxJ3rx5cXd3Z82aNbx584bevXvj4OBA8eLF2bdvn+6Yq1ev0qJFC+zt7XF3d6d79+68ePEC0D7uP3bsGMuWLdN13Xvw4AFqtZq+ffvqWlaWKlWKZcuWGbRpxowZuLq64ujoyMCBA4lLeKSLthBz+PDhuLm5YW1tTb169Th//nyy17dx40bdI/oEfvvtN2rUqIG1tTX58uVLKomUjgQFBREfH59ku1qt5tmzZxl23vcRtVrN0qVLKVq0qPZ3evIkFCoEfn6gUMDixVChglFzxcbGEhcXh6+vL7/88guVKlVKk21RUVHcv3+fo0ePMmDAgHQN+GR3fLydye9kTXK34AkFv+97ExtJyhjluO7du1cn87N3794Uv0zhwoULVKlSRZdyMHr0aKpUqcLUqVMBGD9+PMOGDaN///7UqFGDyMhIfH19pYZrKqg1ghHz1xKyaxYKhYI8dbuiMNMujgmpAb/6PTVqLnnnm7lkdcXtpk2byJcvH+fOnWPYsGEMGjSITp06UadOHS5dukTTpk3p3r07UVFRhIWF8eGHH1KlShUuXLiAr68vz549o3PnzgAsW7aM2rVr069fP4KCgggKCqJQoUJoNBoKFizIjh07CAgIYOrUqXz55Zf8/PPPerYcOnSI69evc/ToUbZt28auXbv0Ci/Hjx/PL7/8wqZNm7h06RLFixenWbNmhIaGGnWtf/zxB+3bt6dly5ZcvnyZQ4cO4WOEVue78tFHHzFgwAAuXbqk23bx4kUGDRqkp54ieXc0Gg3+/v74+voybvRolP7+2h0+PjB/PiSKdhvDxYsXadmyJXfu3GHGjBnkzZv3nW1LSFv4/vvv8fb2Ttqd6z3ATKlgWuuyAEmcV1nwKzEWhTBCjVqpVOpEtJXK5H3dzBLRNoWIiAicnJwIDw9Pcz5STiAuLo6/Lt2h26hp5Kn7GQrz5OWtnO0sePVGZTDPNaF964kJH8pFxARiYmK4f/8+3t7eJt9gqTWCevMOJ5vCkdG/k4YNG6JWq/n777+19qjVODk58cknn7B582ZAq/aRP39+Tp8+zcGDB/n777/Zv3+/bo7Hjx9TqFAhbt68ScmSJY3OcR06dCjBwcHs3LkT0EZrf/vtNx49eqSTF1q9ejXjxo0jPDyc6Oho8ubNy8aNG3W5gSqVCi8vL0aOHMm4ceNSLc6qU6cORYsWZcuWLUa9Pyn9bo1ZZ54/f07Pnj3x9fXVRdni4+Np1qwZGzduxM3NzSg7soKcsI4KIZg9ezZffvmlVrps2TKYNAkePQITm4EkBGrGjx/P5MmTyZcvX5psU6lUBAQEEBwcTLNmzdI0V25A6rhKDGHsOmNUjqsU0c5eJJf/6Ovry6JFi+g3axV5P+iV6jztKxdg/ckHKEDPeZV3vllDdqi4rVixou5nMzMzXFxcqJDokWqCPF1ISAj+/v4cOXLEoAzQ3bt3KVmyZLLnWblyJevXrycwMJDo6Gji4uKSFF1WqlRJTxOzdu3aREZG8ujRI8LDw1GpVNStW1e338LCAh8fH65fv27Utfr5+dGvXz+jxqYHrq6u/Pnnn9y6dYsbN24AULp06RTfJ0nqCCE4ffo0MTExTO7eHQ4ehCZNoE8fqFHDZKf1n3/+YcyYMaxevTqJKsS72KbRaFi8eDHjx49Pc5pBbqF5+fw0KeuRah6/zPWXGCL9hFclmYKhO1UPBwsGVnPCf/9+fv31V/yDoo2aq3FZD2p4OyedT975ZgnZoeL27Xw7hUKhty1BhF+j0RAZGUnr1q2ZN29eknnyp1D4sn37dsaOHcuiRYuoXbs2Dg4OLFiwwKSue+lBVhVqJbRdLVasWLpqX7+vzJ49m8mTJ2tf9O8Pp0+Dvz84OGj1WY1ErVajUqlYsWIFW7ZsSaIhbioajYYzZ84QFxfHhAkT0jRXbsRMqUjxBlxGZSXJYdSqKUW0swcJ+Y+Jo6PRD/355+wupodNYXXf8dja2uLjbfP/9u49Lubs/wP4a2aqaarpfkM05R7dU6J1jcIWy5LlSyw2l/oSuy67CC1Zkdva/Gjd1i2xrrmW20ouK0UitxIpIbpfZ87vj759NLqYaarpcp6PxzyYz3zmzDkfOr3nfM55H7TSUEZ6VmGN0wDKv71K8s2Xqn9NbcWtjY0Njhw5AoFAUG0ApqSkVGn6UFRUFHr16oWZM2cyx549e1bpvXFxcSgoKGACzBs3bkBNTQ1t27aFrq4ulJSUEBUVBWNjYwBlt2Nv376NOXPmSFR/CwsLREZGypyHU1L5+fnw8fHB7t27AQCPHz+GqakpfHx80KZNGyxcuLBB6tFcREREQE1VFb906ACcOwe4uACrV5flYq1hSltVHj58iDlz5mDHjh3Yvn27zHUjhGD16tX4+eefZS6rJarqdx3waa5/8H9saPDagkkUuNIk2vL3+Y4jhBCUvEtBfuJ16I5YCBZHQWzHET83M8zYGyPRNIAvffOlGkb5iltJvnA0BrNmzcL27dvx3XffYf78+dDW1sbTp09x8OBBhISEgMPhQCAQ4ObNm0hOToaamhq0tbXRsWNH7NmzB+fOnYOJiQn++usv3L59GyYmJmLlFxcXY8qUKVi8eDGSk5Ph5+cHb29vsNlsqKqqYsaMGfjpp5+gra2Ndu3aYc2aNcjPz8eUKVMkqr+fnx8GDhyI9u3bY+zYsSgtLcXp06frbXRs0aJFiIuLw+XLl+Hq6socd3Z2xrJly2jgKiFCCFauXInFixcDhAArVgAdO5YFrtrS/WwQQpCbmwt/f3/s2LEDbdq0kblukZGRUFNTo0FrLdHdtagvkehraVJSEvNYuXIlrKys8PDhQ2RmZiIzMxMPHz6EjY0N/P3967u+LVbF+Y+Frx7g3bEAKOq2hc7gGWAr8cTmPwJlc4iC/2MDQw3x0TlDDWX6bbWRamorblu3bo2oqCgIhUIMHjwY5ubmmDNnDjQ1NZlFnD/++CM4HA7MzMygp6eHlJQUeHl5YeTIkfDw8ICDgwPev38vNvpabuDAgejYsSP69OkDDw8PuLu7Y9myZczrq1evxqhRozBhwgTY2Njg6dOnOHfunMQrv/v164ewsDCcOHECVlZWGDBgAG7dulUn16Yqx44dw++//w4nJydmygUAdOvWrcoRZ6qy48eP4/7Nm/glKalsSgCLBfz9NyDFXcFyT548gbu7O4qLi7F///46CVpXrlwJZ2dn9OzZU6ayWjJJ5/rvikrC8dhURD97D6GoNvs/Uk2VRFkFKmrfvj0OHz7MpLAqd+fOHXz77bdiO8U0Bk1hNawkjsemYvbBWBSlPUZu7FloDZgCNle10nkbx1phuNWnDphObm9YsmQVKEfndjVOsmYVUFFRQXx8PExNTcHn8xEXFwdTU1PExcWhT58+yMrKaohm1Iq8+1GhUIg1v/2GhYsWgSUSAa6uwPTpwKhRtSrv/fv3mDZtGtavX89MNZHF8ePHYWpqKraQkaqd8t910qD9Y/NQp1kFKqJJtOXj7bMHeBceBJ2hvtAZUv0q5M/nP9JpAE2PpCtuqabFzs4O4eHhZbs44dNCt5CQEDg6Osqzao3aoUOHYKulhQX794M1enTZtIDz58tGW6WUlJSE+fPnY+fOnfj7779lrptQKMRvv/1GpwXUodrM4adzX1sWqQPX8iTaISEhsLGxAUCTaNe3y5cv4+rxfeg6whvvSlhNYv4jJRv6haP5WbVqFYYMGYKEhASUlpZi48aNSEhIwPXr13HlyhV5V6/RKSkpwea1a/Hfn36CQkkJYGv76cVaBK2pqanw9vbGpk2bqkzhJq0DBw7A3t4eixYtkrks6pMvzfWvCp372rJIveXrjh07YGhoCDs7O3C5XHC5XNjb28PAwAAhISH1UccmQygiiH72XqZ5N0IRQfSTDFy7cBTHti6D98SR6PuVE/bs2Q1/j7J5U01h/iNFUeKcnJwQFxeH0tJSmJub4/z589DX10d0dDRsKwZlFPbt24e3u3bBe+1aKLx/D/B4wO7dZaOtUnr58iU8PT2ho6ODU6dOoX379jLVrbi4GIGBgRg7dizat28vNl+Zkl1Nc/1r8vk6D6r5knrElSbRrlpdzEs8G5+Gy8d24L8lIbj/LAOH4oqx0VUZhWu7gecWCNfu7gj+jw3Nu0pRTUxJSQm8vLywZMmSOkm31Fzl5+Rgz/r1mDR/PpTz8oCsLOB/2Wtq4+nTp/D29sbGjRvrZKvw3bt3Y9CgQZg3bx4NWOtR+eLiz3/XSaI+81xTjYPUi7PKFRcXIykpqdEn0W6IRQXV5Zwr79YkmXdzNj4Nx/ZvxcwP63AwvgQr+nPBYZXNgxORsj9ZY/YAZu50wVUjVheLs6jGSdbFWRoaGoiNja2U9qspaIh+dO/evRgaHQ3u8eNQTUoqy8daS2lpafD398emTZvAYrHA4XBkqlt+fj7+/PNPzJgxo1H/vmuMZPl9VfG973KK4B/+5V3xDkzrSadZNVH1tjiLJtEWV5ucc0IRwa1nbyFMjoI+6yNMBO2x8ngepjzfCv/7Rdg8RBkKFX6w2SxABAKcXQhWl2HgsDn0B5OimpgRI0bg2LFj8PX1lXdVGpWPcXEIP3UKbt7eUHdyAmvSJJmC1gcPHsDX1xdBQUF1EmTu2LEDI0aMgJeXFw1apSTrnciKc/2FIoKQa0lNJs81VX+k/imkSbTFSbu/fMXpAK1ZZXNxHh0TYsAjDsb2JPDszKvyFhQbALJTgRfXAZOv6qcxFEXVm44dO2LFihWIioqCra0tVFXF09m1xM1b9u/fD/dt2zCEzYbGL78AGhqAQFCrsjIyMrBu3Tr8+uuvCA8Pr7R9sbQ+fvyIo0ePYty4cfTuSS3U9e5X0m6sQzVfUgeux44dQ2hoKHr27EmTaEPy+TTp2YXYGPEECRf3IlhxA3P8SEIJ9t4vwSZXZSgrSLBWLrealGMiYVlQm/sGUDMAjHsBbNluj1EUVXf+/PNPaGpq4s6dO7hz547YaywWq+UEriIR3gYH4yYhGDh6NFR79YKarq5MRcbExGD+/PkIDAyUOWAFykZZx4wZgzFjxtCgtRbqa/er6ua+0nUeLYvUgevbt2+hr69f6XheXl6LnKwuac655SfikV1QjGvcPQCApA8iXHheinHmihjZVUHya6dmUPlYwgng7AIg+/WnY+qtAdffADN3ycqlKKpeNbbNWeQlLDQUX2/cCJtvvoGBQRX9mRQyMzPxf//3f5gzZw7Cw8PB5XJlKi8jIwNXr17FqFGj6iRlVksl7Z1IadA815TU6bDKk2iXa+lJtMtzzn3+I8OGCD3ZCXBnX0dPdgKyC4phz36E1qxMHEkoge+5Qrh2UIA6lyVR0CoCQNTblI2kVpRwAjg0UTxoBYDstLLjCSdkah9FUXWPEIJarottmt6/R/q4cbh6+DDsevaE8u3baP3bbzIVefv2bYwZMwbOzs7g8XgyB627d++Guro6BgwYAA0NDZnKaukkvRNZ2wwA5XNfh1u1gWN7HRq0tjBSj7jSJNriqpp348K+BT/FPcwcVgB4TbSx51137E8vgbMpB6PMFMCuJmAVkbIFWRWfs1gssFxXi9/+FwnLRlpruiFzdiHQZRidNkB9Ub9+/WBlZYUNGzbIuyr1SiAQYM6cOZgzZ06Df/aff/6J9evX48mTJwDK5r3OmTMHU6dObfC6NKSTEREYGBeHdsOGQSBjVoWsrCzs378f48aNw6lTp2S+lZ+amooHDx7A1dUVysrKdGpAHZD0TmRtdsmiKKlHXGkS7crK590YaijDhX0LwYobYAjxJMhXHrzBP+fPwr4NB/qq7GqDVgDIBF/seZGKIZMKS8yL65VHWsWQTwu6qKZFJASS/gHuHy77UySUd41ACKlyu+emori4WK6fv3TpUsyePRtubm4ICwtDWFgY3Nzc4Ovri6VLl8q1bvXi1i2k2dgg7p9/YNKtG5Tv3YNg/HiZirxx4wZGjhwJS0tLaGhoyBxkhoaGQkdHBxYWFjJPW6A+qe5OZDkWyrIL0AwAVG1IFbiWlJTg+++/B4vFwvbt23Hr1i0kJCRg7969MDc3r686Ngmu3VvhF9dOWK60Byx8GjF9lS3C6Scl6GXEwbGxPAi0OKhuQ63y6QBPxt3Gtd678NhpA4QTT4L3U0LVc1WrW6hV2/OoxiHhBLChO7D7a+DIlLI/N3Sv12kfkyZNwpUrV7Bx48ay0X0WC7t27QKLxcKZM2dga2sLLpeLa9eu4dmzZxg+fDgMDAygpqaGHj16ICIiQqw8gUCAVatW4fvvvwefz0e7du2wbds25vXi4mJ4e3ujVatWUFZWhrGxMQICApjXWSwWgoODMWTIEPB4PJiamuLw4cNin3H//n0MGDAAPB4POjo6+OGHH5CbmyvWphEjRmDlypVo3bo1OnfujH79+uHFixfw9fVl2tlQgoODsX37dgQEBMDd3R3u7u4ICAjAtm3b8Mcff9TrZ4eHh8PBwQE8Hg9aWloYMWJE/X1YYdnt34vJyeC1aQNVAN27dwdbhlyqOTk5OHDgAAQCAU6cOIFevXp9+U01SElJwc2bN9GjRw9wuVwYGhrKVB4lrqbdr2gGAEpWUgWuioqKOHLkSH3VpUlbGZ6AxMN+MEQms432oQclmHKiACaabBhrsqHIYUGBVRa1fh68igjAQtl0AMfOreA06Bt0cp4Mjmmf6m/zV7VQS5bzKPmT05zljRs3wtHREdOmTUNaWhrS0tLQtm1bAMDChQuxevVqPHz4EBYWFsjNzcXQoUMRGRmJu3fvwtXVFW5ubkhJSRErc926dbCzs8Pdu3cxc+ZMzJgxA4mJiQCATZs24cSJEzh06BASExOxb98+CD5Lg7RkyRKMGjUKcXFxGD9+PMaOHYuHD8sSkOfl5cHFxQVaWlq4ffs2wsLCEBERAW9vb7EyIiMjkZiYiAsXLuDUqVP4+++/YWRkhBUrVjDtbCglJSWws7OrdNzW1rZeR7KPHDmCCRMmYPLkyYiLi0NUVBTGjRtXPx8WHIy07t3xLCEBykZG0DhxAh2+ki19340bNzB8+HAYGhrC0NCwUhoxaZ08eRJaWlpo06YNTE1NW+Si4oZQ8U5kRYYaylKnwqKoiqSe40qTaH9SvqvH9qtPofjkNIIVy4L69FwRnmWK0E2PjfBxKmKbCQDADqErhnJuoXWF6QRFKobguQVKlwXAuFdZ9oDsNFQ9z5VV9vrnC7qoxkmOc5Y1NDSgpKQEFRUVZvSpfEvnFStWYNCgQcy52trasLS0ZJ77+/vj6NGjOHHihFjgOHToUMycORMAsGDBAqxfvx6XLl1C586dkZKSgo4dO8LJyQksFgvGxsaV6jR69Ghm7qe/vz8uXLiAzZs3448//sD+/ftRWFiIPXv2MIHM77//Djc3N/z222/MbV9VVVWEhIRASUmJKZfD4YDP5zf4KNuECRMQHByMoKAgsePbtm3DeBlvoVentLQUs2fPRmBgIKZMmcIcNzMzq7sPKSkB3r4FWrfGbS0ttB4/HkJCZB4Vzc/Px9WrV9GpUyccP34cfD7/y2+qwYsXL5Cfnw9jY2OoqanJXB71ZTQDAFUfpA5caRLtsoD194tPsCMqCVkFpWBDxKS5OpxQgm13irFusDK66VcdXESI7LCq9D+wZz9CG4UsrJ44GDzT3tIHI2xOWcqrQxOB6lIyf76gi2q8pJmz3ICbUHw+Spibm4tly5YhPDwcaWlpKC0tRUFBQaURVwsLC+bvLBYLhoaGyMjIAFB2G3/QoEHo3LkzXF1d8fXXX2Pw4MFi7/88S4mjoyNiY2MBAA8fPoSlpaVY/9O7d2+IRCIkJiYygau5ublY0Cpvf/75J86fP4+ePXsCAG7evImUlBRMnDgRc+fOZc77PLitrZiYGKSmpoLNZsPa2hrp6emwsrJCYGAgunfvXu37ioqKUFRUxDzPzs6u/kP+8x+8ef4c5MQJZGppwW7ZMplHMW/evIlFixZhwYIFMDU1laksALh06RITrFf1JYmqPxV3v6KouiB14NrSk2ifjU/DwiP38bGghDlmz34EpYJ3eJBDYKTOQvg4FShyKnfcIgKkQwe3RF0gAhs3RGb441sbKHaQ4ZaJmTswZk81eVxX0zyuTUkjnbP8+ZfTH3/8ERcuXMDatWvRoUMH8Hg8fPvtt5UWP32eCJ7FYkEkEgEAbGxskJSUhDNnziAiIoJJa/T5PNa6rrs8xcfHw8bGBgCYzVp0dXWhq6uL+Ph45ry6vHX9/PlzAMCyZcsQFBQEgUCAdevWoV+/fnj8+DG0tateHBMQEIDly5dXX/DTp2Xbshob49Ho0QCbDXZODlxcXGSqb2FhIWJiYqCmpoa///4bmpqaMpWXkpICFosFJSUl6Ovr02kBVJ3r168fk1Hp7t27sLKykm+FGrldu3Zh8uTJAIDZs2fXKouN1FkFkpKSqn2Ud5KNUvJ1qVZmC0UE0c/e43hsKqKfvYdQRHD6Xhqm740RC1oBIOfxTYw9XAAhAXoaKVQZtAL/2y2kZAJE/7vsXn1MMNSiDub5mLkDc+IBz1PAqD/L/pxznwatTY2c5ywrKSlBKPzyz0hUVBQmTZqEb775Bubm5jA0NERycrLUn6eurg4PDw9s374doaGhOHLkCDIzP02fuXHjhtj5N27cQNeuXQEAXbt2RVxcHPLy8sTqxWaz0blz5xo/V9J21rVLly5J9Lh48eIXy1q4cCGzuKy6x6NHj5gvCr/88gtGjRoFW1tb7Ny5EywWC2FhYdWWv2jRImRlZTGPly9ffnpRJAKGDMG7JUuQnZ2NeABdRo5Ep06dZLo+t2/fxrBhw/DhwwdYWFjIFLQSQvDvv/+itLQUQqEQvXv3pkErVW/K1wbUdBcjOTm5yp/Tz/u5quzatQsWFhZQVlaGvr4+Zs2aVZfVr1JaWhrGjRuHTp06gc1mS5w+sKo2Hjx4kHndw8MDaWlpMuX9l3rEtaLyBNpNokM4MAbQayPRblJn49MqbSmnosRGfrFI7DxhYS5EhbnIVdDE+XEq4CrUfB2CSr/FOZE91LgcrBllgaEWrWvfns+xOQ16+5iqB3KesywQCHDz5k0kJydDTU2NCXo+17FjR/z9999wc3MDi8XCkiVLqj23OkFBQWjVqhWsra3BZrMRFhYGQ0NDsWAlLCwMdnZ2cHJywr59+3Dr1i38+eefAIDx48fDz88Pnp6eWLZsGd6+fQsfHx9MmDDhi2mNBAIBrl69irFjx4LL5UJXxu1G5WHevHmYNGlSjeeYmpoyi88qzmnlcrkwNTWtNLWjIi6XW3VC/3fvAHV1vAwORpqSErTevMG3335bqzaUKy4uxuPHj1FUVIRDhw5BR0e228qvXr0Cj8fD27dvq1wMR1F1reLagC+JiIhAt27dmOdf+v8eFBSEdevWITAwEA4ODsjLy6vVQIG0ioqKoKenh8WLF2P9+vVSvXfnzp1wdXVlnlfs13k8Hng8nkxTuKQecQXKpgt0796dSdbcvXt3hISE1LoSDUaCldln49MwY29Mpe3qPg9aC57dxtujqyAqzMVLwXC85+hUn+aKAJkcXeCredg31QFxfi51G7RSzUP5nGUA1SaRqcc5yz/++CM4HA7MzMygp6dXbWATFBQELS0t9OrVC25ubnBxcWFugUuKz+djzZo1sLOzQ48ePZCcnIzTp0+Dzf7UJS1fvhwHDx6EhYUF9uzZgwMHDjABmIqKCs6dO4fMzEz06NED3377LQYOHIjff//9i5+9YsUKJCcno3379tDT05Oq3o2Fnp4eunTpUuNDSUmJSWNWns0BKMtukJycXKu5nh8OH0ZhYSEupqbCvk8fdOzYUaZ2xMbGYtiwYUhKSoKTk5NMQSshBImJiXj//j2ys7MxZMgQmepGUfVBR0eHyZBhaGhYaUpVRR8+fMDixYuxZ88ejBs3Du3bt4eFhQXc3ev/bqpAIMDGjRsxceJEqXeS09TUFGtjXW/qIfWI69KlSxEUFAQfHx9mqDc6Ohq+vr5ISUnBihUr6rSCdavmldlCEcHykwlVjnWVExXlgZQWQ1RcAP1v/cBW5EIEYHnJRAQrbqhm1ytAY2QQ5nWrw5W8VPMkxznLnTp1QnR0tNixqkb1BAJBpdvZn9+6qmpEoHxhFVB2a23atGk11qd169Y4f/58ta+bm5vXeFt9165dVR7v2bMn4uLiavzs5kJdXR3Tp0+Hn58f2rZtC2NjYwQGBgIoy9ogrdjOnWGRlgZPT0+Z6lVSUoJXr17h9evX2LdvH/T19WUqLz09HXw+H/fv35d5BJii6pO7uzsKCwvRqVMnzJ8/v8Yg9MKFCxCJREhNTUXXrl2Rk5ODXr16Yd26dUy6wsZo1qxZmDp1KkxNTTF9+nRMnjy5Tu/MSx24lifR/u6775hj7u7usLCwgI+PTyMPXIHqVmYLRQS7opIqjbRWVJAci6zoQ9AeMAWqXfuIvXZOZI8ZJXPKtnqtkOYqHTpYXjwB906owY+k0dx11JeZuZd9sXpxvWwhlppB2fQAmh2CqoXAwEAoKChgwoQJKCgogIODAy5evAgtLS2py+rfvz/U1dVlqs+DBw8wd+5czJo1q05GjlJTU/H8+XMYGxvToJVqtNTU1LBu3Tr07t0bbDYbR44cYdKLVvdz8Pz5c4hEIqxatQobN26EhoYGFi9ejEGDBuHevXuNKmNKuRUrVmDAgAFQUVHB+fPnMXPmTOTm5tbpwn2pA1d5JdGuc/9bmV2W2uopdkYlVVp0VU5UXAAQEUo/vIb+qKVgK1U97H1OZI8LRXawZz+CPj4iA5pMBgFWViFm7I2hiZcpydA5y1QdUVRUxNq1a7F27Vq51kMoFOLdu3eIi4vDrl270KqVbP3gu3fvwOfzcfHiRUyYMKGOaklRsuvWrRtevHgBAPjqq69w5swZ6OrqiqW869GjB16/fo3AwMBqA1eRSISSkhJs2rSJSRd44MABGBoa4tKlSzJn8agPS5YsYf5ubW2NvLw8BAYGyjdwlUcS7XqhZlCW2urv+/iYXwI2ROhZRcBZmHIfH6P2Q3vgNPCth36x2PI0V5/73yQFLD+ZgEFmhjQBM0XVoHzhJ9U8JCYmYvbs2Zg1a1ad7NqVmZmJmzdvwsrKigatVKNz+vRplJSUDYTxeLxqz3NwcMCFCxeqfb38y13FxZV6enrQ1dWtcXFlY+Lg4AB/f38UFRVVveCzFmqVVaChk2jXJREp26XqUrYAMw/EAABc2LfKbvGzPt3if1asiVWl/8Hfqa+gP3IJ2FyVL5atrMhGYUn1q6sJgLSsQtxKyqQJmSmKavZEIhFycnIQGRmJbdu2oV27djKVl52dDSUlJRw9elRsJzCKakwkXfgYGxtb452H3r17Ayj74mdkZASg7Evbu3fvmsxGGrGxsdDS0qqzoBWoRVaB8iTaenp6ePbsGZ49ewZdXV3Y2NggPj4ed+/exd27d8UWYjQW5av+dxY4Yc6Bf/E95zR2KPyGrYobYFhhXuqNV6X44UAq5uRsxJjeAomCVjcLQwR8Yy5RPTJyqp9HSzUPdMSw+aH/ptJ5/vw53N3dce3aNcycOVPmoDUvLw/h4eHIycmhQSvV5OzevRsHDhzAo0eP8OjRI6xatQo7duyAj48Pc87Ro0fRpUsX5nmnTp0wfPhwzJ49G9evX0d8fDw8PT3RpUsX9O/fv9rPmjhxIhYtWlRtuQDQpUsXHD16tMY6x8bGIjY2Frm5uXj79i1iY2ORkJBQbbknT55ESEgI4uPj8fTpUwQHB2PVqlVibawLUo+4Xrp0qU4r0JDK787PxGHM4B5GxUVuLABFpQRsFnAisRSHx6hAQ5kFP/yFC0V2zKYBFSkpsOFu0QqrRlpASYGN6GfvJaqHPr9uU0NQjUd5apP8/PwabxFRTU9+fj6AyjuCUeJEIhGTk3Xz5s0wMTGRqbzCwkKUlJTg4MGDX8xEQVGNmb+/P168eAEFBQV06dIFoaGhYgsKs7KyxFLXAcCePXvg6+uLYcOGgc1mo2/fvjh79myN/VBKSopYasGqyk1MTERWVlaN9bW2tmb+fufOHezfvx/GxsZM1pjPy1VUVMSWLVvg6+sLQgg6dOiAoKCgOv+5ZZFmPoyQnZ0NDQ0NZC3kQ537KVIl/0tTVe7OayEWRBQieJgyOuqIr94eW7y40rzVjnqqOOvbV2yuqlBE4PTbRaRnFVaXPh6GGsq4tmAAnePajKWlpeHjx4/Q19eHiopK09igg6oWIQT5+fnIyMiApqZmlbf2mH4mK0vmVfeNkaTtS0lJgY+PD2bNmsUsJpFFSUkJdu7cCQ8PD6lzSVJUQ+jXrx+srKxqtXVpS1bVdZO0n5Fp56ymrDyWKBESEAC7YosR+i0POiqVR1b18bHSMe+BHSsFnxw2C35uZpixNwYsiO99VH6mn5sZDVqbufIdVDIyMuRcE6oulSfVpiojhEAoFCI4OBhr166VeWOC0tJSZGVl4ejRo/jhhx/qqJYUVT/++OMPhISEIDo6Gubmkk0ZbKn27dsHLy8vFBQUwMrKqlZltNjAFQDi0oWYd74QO4bzsHlo9bd1M6BZ6Vh1t/tdu7fClnHWWHw8Hpl5n9JrGWoow8/NjKbCagFYLBZatWoFfX19ZmUp1bQpKiqCw6F5dKvy+vVr+Pj4wMfHBwEBATKXJxKJ8Pvvv8PLywtTp06tgxpSVP3Zt28fCgoKAEDmedwtgbu7OxwcHACIbwUrjRYZuIoIQUEJsOFmMfaN5MFAreo1aiICFEIRMzl/YxLnDG6LOmO30BV8njJKhSIcvZuKN1kF+OfJW2QXlsK8jTq0VbnYeyNFLCestqoSlgyrIWgVCYHka0DSP2VDs8ZOZTk8pUg4LxQR3ErKREZOIfT5yrA30aYju3LG4XBosEM1W4QQEELw66+/wt/fXyxlT23Le/36NS5cuIA5c+bUTSUpqp61adNG3lVoUvh8Pvh8vkxlNIk5rlu2bEFgYCDS09NhaWmJzZs3w97eXqL3fj7H9eFbIXzPFeKvb3jQU5U6qQKEBNhe+jVWCyXPRVgePla5+UDCCeDkbKAgU/w4Twtw2yTRFp9n49Ow/GSC2K5fregIL0U1mJY2x/XNmzeYPXs2fH19mdETWRBCsGbNGsydO5cufqOoFkrSflT6yK2BhYaGYu7cufDz80NMTAwsLS3h4uIi9fxBQgg+FBAsv1KEP91rF7QCZRfMS+EUFnL2S/7Z//tz+ckECEUVvicknAAOTagctAJAwYey1xJO1Fj22fg0zNgbU2mr2vT/7dR1Nj5N4npSFEV9CSEEP/74IxYtWlQnQeuTJ08QGhqKBQsW0KCVoqgvavSBa3kqhcmTJ8PMzAxbt26FiooKduzYIVU5Yw8XgM0CDn6rgjbqtW92+aKuqQrhUIDkW9xW3HwAQNn0gLMLvvzGMwvKzq2CUESw/GRClRkMqg2WKYqiamnatGm4f/8+/vrrL1haWspc3qpVq9C+fXuMHTu2DmpHUVRL0KjnuBYXF+POnTtiiXTZbDacnZ0RHR1d5XuKiopQVFTEPC/PU+bXlwsWC8guqqsgjmBM6SnsFUqX8iU57S266SkCydeBt6lffkNRKhB/ARD0qvTSreeZSM2oYrS2gtSMfFy69wL2ptpS1ZOiKMllZ2cDaL6bFJS3a+LEiRAIBEx7a+v+/ft49eoVvL29kZubWxdVpCiqiZO4HyWNWGpqKgFArl+/Lnb8p59+Ivb29lW+x8/Pj6BswJE+6IM+6KNBHy9fvmyIrrHBvXz5Uu7Xlj7ogz5axuNL/WijHnGtjUWLFmHu3LnM848fP8LY2BgpKSk0gfVnsrOz0bZtW7x8+bJZLiiRBb02VaPXpWqEEOTk5KB169byrkq9aN26NV6+fAk+n98gG2q0hP9nzb2NtH1NX0O3UdJ+tFEHrrq6uuBwOHjz5o3Y8Tdv3lSbCJzL5YLL5VY6rqGh0Wz/c8lKXV2dXptq0GtTNXpdKmvOX4zZbDaMjIwa/HNbwv+z5t5G2r6mryHbKEk/2qgXZykpKcHW1haRkZHMMZFIhMjISDg6OsqxZhRFURRFUVRDa9QjrgAwd+5ceHp6ws7ODvb29tiwYQPy8vIwefJkeVeNoiiKoiiKakCNPnD18PDA27dvsXTpUqSnp8PKygpnz56FgYGBRO/ncrnw8/OrcvpAS0evTfXotakavS5UQ2gJ/8+aextp+5q+xtrGJrFzFkVRFEVRFEU16jmuFEVRFEVRFFWOBq4URVEURVFUk0ADV4qiKIqiKKpJoIErRVEURVEU1SQ068B1y5YtEAgEUFZWhoODA27duiXvKjW4q1evws3NDa1btwaLxcKxY8fEXieEYOnSpWjVqhV4PB6cnZ3x5MkT+VS2gQUEBKBHjx7g8/nQ19fHiBEjkJiYKHZOYWEhZs2aBR0dHaipqWHUqFGVNsRojoKDg2FhYcEknnZ0dMSZM2eY11vqdaHkIzw8HA4ODuDxeNDS0sKIESPkXaV6UVRUBCsrK7BYLMTGxsq7OnUiOTkZU6ZMgYmJCXg8Htq3bw8/Pz8UFxfLu2oyaa7xhSS/F+Wt2QauoaGhmDt3Lvz8/BATEwNLS0u4uLggIyND3lVrUHl5ebC0tMSWLVuqfH3NmjXYtGkTtm7dips3b0JVVRUuLi4oLCxs4Jo2vCtXrmDWrFm4ceMGLly4gJKSEgwePBh5eXnMOb6+vjh58iTCwsJw5coVvH79GiNHjpRjrRuGkZERVq9ejTt37uDff//FgAEDMHz4cDx48ABAy70uVMM7cuQIJkyYgMmTJyMuLg5RUVEYN26cvKtVL+bPn9/stg1+9OgRRCIR/u///g8PHjzA+vXrsXXrVvz888/yrlqtNef4QpLfi3JHmil7e3sya9Ys5rlQKCStW7cmAQEBcqyVfAEgR48eZZ6LRCJiaGhIAgMDmWMfP34kXC6XHDhwQA41lK+MjAwCgFy5coUQUnYtFBUVSVhYGHPOw4cPCQASHR0tr2rKjZaWFgkJCaHXhWowJSUlpE2bNiQkJETeVal3p0+fJl26dCEPHjwgAMjdu3flXaV6s2bNGmJiYiLvatRaS4ovPv+92Bg0yxHX4uJi3LlzB87OzswxNpsNZ2dnREdHy7FmjUtSUhLS09PFrpOGhgYcHBxa5HXKysoCAGhrawMA7ty5g5KSErHr06VLF7Rr165FXR+hUIiDBw8iLy8Pjo6O9LpQDSYmJgapqalgs9mwtrZGq1atMGTIEMTHx8u7anXqzZs3mDZtGv766y+oqKjIuzr1Lisri+lnm5qWFl98/nuxMWiWgeu7d+8gFAor7a5lYGCA9PR0OdWq8Sm/FvQ6ASKRCHPmzEHv3r3RvXt3AGXXR0lJCZqammLntpTrc//+faipqYHL5WL69Ok4evQozMzMWvx1oRrO8+fPAQDLli3D4sWLcerUKWhpaaFfv37IzMyUc+3qBiEEkyZNwvTp02FnZyfv6tS7p0+fYvPmzfDy8pJ3VWqlJcUXVf1ebAyaZeBKUdKaNWsW4uPjcfDgQXlXpdHo3LkzYmNjcfPmTcyYMQOenp5ISEiQd7WoZmDhwoVgsVg1PsrnRgLAL7/8glGjRsHW1hY7d+4Ei8VCWFiYnFtRM0nbuHnzZuTk5GDRokXyrrJUJG1fRampqXB1dcXo0aMxbdo0OdWcklRj/b2oIO8K1AddXV1wOJxKq5zfvHkDQ0NDOdWq8Sm/Fm/evEGrVq2Y42/evIGVlZWcatXwvL29cerUKVy9ehVGRkbMcUNDQxQXF+Pjx49io4st5f+RkpISOnToAACwtbXF7du3sXHjRnh4eLTo60LJbt68eZg0aVKN55iamiItLQ0AYGZmxhzncrkwNTVFSkpKfVZRZpK28eLFi4iOjq60H7ydnR3Gjx+P3bt312Mta0/S9pV7/fo1+vfvj169emHbtm31XLv601Lii+p+LzYGzTJwVVJSgq2tLSIjI5m0KSKRCJGRkfD29pZv5RoRExMTGBoaIjIykglUs7OzmRG25o4QAh8fHxw9ehSXL1+GiYmJ2Ou2trZQVFREZGQkRo0aBQBITExESkoKHB0d5VFluRKJRCgqKqLXhZKZnp4e9PT0vniera0tuFwuEhMT4eTkBAAoKSlBcnIyjI2N67uaMpG0jZs2bcKvv/7KPH/9+jVcXFwQGhoKBweH+qyiTCRtH1A20tq/f39mxJzNbro3e5t7fPGl34uNgpwXh9WbgwcPEi6XS3bt2kUSEhLIDz/8QDQ1NUl6erq8q9agcnJyyN27d8ndu3cJABIUFETu3r1LXrx4QQghZPXq1URTU5McP36c3Lt3jwwfPpyYmJiQgoICOde8/s2YMYNoaGiQy5cvk7S0NOaRn5/PnDN9+nTSrl07cvHiRfLvv/8SR0dH4ujoKMdaN4yFCxeSK1eukKSkJHLv3j2ycOFCwmKxyPnz5wkhLfe6UA1v9uzZpE2bNuTcuXPk0aNHZMqUKURfX59kZmbKu2r1IikpqVllFXj16hXp0KEDGThwIHn16pVYX9tUNef4QpLfi/LWbANXQgjZvHkzadeuHVFSUiL29vbkxo0b8q5Sg7t06RIBUOnh6elJCClLibVkyRJiYGBAuFwuGThwIElMTJRvpRtIVdcFANm5cydzTkFBAZk5cybR0tIiKioq5JtvvmnSHa6kvv/+e2JsbEyUlJSInp4eGThwIBO0EtJyrwvV8IqLi8m8efOIvr4+4fP5xNnZmcTHx8u7WvWmuQWuO3furLavbcqaa3whye9FeWMRQkhDje5SFEVRFEVRVG013YkmFEVRFEVRVItCA1eKoiiKoiiqSaCBK0VRFEVRFNUk0MCVoiiKoiiKahJo4EpRFEVRFEU1CTRwpSiKoiiKopoEGrhSFEVRFEVRTQINXCmKoiiKoqgmgQaulMSSk5PBYrEQGxsr76rU2q5du6CpqSnvakglKioK5ubmUFRUZPbGrkpiYiIMDQ2Rk5PTcJWrQ+/evYO+vj5evXol76pQlFzRvlY+JOlrBQIBNmzY0KD1osTRwJWSWNu2bZGWlobu3bvLuypYtmwZrKys5F2NBjF37lxYWVkhKSkJu3btqva8RYsWwcfHB3w+v8HqZmJigoiICInO3bVrF1gsFlgsFthsNoyMjDB58mRkZGQAAHR1dTFx4kT4+fnVZ5UpqtGjfa18SNrX1rUHDx5g1KhREAgEYLFY1QbGW7ZsgUAggLKyMhwcHHDr1q0Gq2NjQgNXSiLFxcXgcDgwNDSEgoKCvKvTojx79gwDBgyAkZFRtSMYKSkpOHXqFCZNmtRg9bp37x4+fPiAvn37SvwedXV1pKWl4dWrV9i+fTvOnDmDCRMmMK9PnjwZ+/btQ2ZmZn1UmaIaPdrXyo8kfW19yM/Ph6mpKVavXg1DQ8MqzwkNDcXcuXPh5+eHmJgYWFpawsXFhfni36IQqlERCoVk1apVRCAQEGVlZWJhYUHCwsIIIYSIRCIycOBAMnjwYCISiQghhLx//560adOGLFmyhBBCyKVLlwgAcurUKWJubk64XC5xcHAg9+/fF/ucf/75hzg5ORFlZWViZGREfHx8SG5uLvO6sbExWbFiBZkwYQLh8/nE09OTJCUlEQDk7t27Yp919uxZYmVlRZSVlUn//v3JmzdvyOnTp0mXLl0In88n3333HcnLy5OojRXLjYiIILa2toTH4xFHR0fy6NEjQgghO3fuJADEHjt37iSEELJu3TrSvXt3oqKiQoyMjMiMGTNITk4OU/bOnTuJhoZGtde/vI2hoaHM9bGzsyOJiYnk1q1bxNbWlqiqqhJXV1eSkZHBvM/T05MMHz6cLFu2jOjq6hI+n0+8vLxIUVERc07fvn2Jt7c3mT17NtHU1CT6+vpk27ZtJDc3l0yaNImoqamR9u3bk9OnT4vVpap2fi4wMJDY2dmJHStv68mTJ0mnTp0Ij8cjo0aNInl5eWTXrl3E2NiYaGpqEh8fH1JaWsq87/Xr12To0KFEWVmZCAQCsm/fPmJsbEzWr18vVv6KFSuIh4cH83zbtm3EyMiI8Hg8MmLECLJu3Tqxa13VtV+5ciVhs9kkPz+fOWZiYkJCQkKq/TeiqLpA+1ra19amr/28L3zx4gVxd3cnqqqqhM/nk9GjR5P09HSx9/j7+xM9PT2ipqZGpkyZQhYsWEAsLS0lKr+cvb09mTVrFvNcKBSS1q1bk4CAgGqvcXNFA9dG5tdffyVdunQhZ8+eJc+ePSM7d+4kXC6XXL58mRBCyKtXr4iWlhbZsGEDIYSQ0aNHE3t7e1JSUkII+dQRde3alZw/f57cu3ePfP3110QgEJDi4mJCCCFPnz4lqqqqZP369eTx48ckKiqKWFtbk0mTJjH1MDY2Jurq6mTt2rXk6dOn5OnTp9V2pj179iTXrl0jMTExpEOHDqRv375k8ODBJCYmhly9epXo6OiQ1atXS9zG8nIdHBzI5cuXyYMHD8hXX31FevXqRQghJD8/n8ybN49069aNpKWlkbS0NCbwWb9+Pbl48SJJSkoikZGRpHPnzmTGjBnMZ0vamZbXLyEhgfTs2ZPY2tqSfv36ibVz+vTpzPs8PT2Jmpoa8fDwIPHx8eTUqVNET0+P/Pzzz8w5ffv2JXw+n/j7+5PHjx8Tf39/wuFwyJAhQ8i2bdvI48ePyYwZM4iOjg7Jy8sjpaWlJC0tjairq5MNGzaItfNz7u7uYvUpb6uioiIZNGgQiYmJIVeuXCE6Ojpk8ODBZMyYMeTBgwfk5MmTRElJiRw8eJB5n7OzM7GysiI3btwgd+7cIX379iU8Hq9SZ2pnZ0f2799PCCHk2rVrhM1mk8DAQJKYmEi2bNlCtLW1vxi4BgUFEQAkOzubOebh4UE8PT2r/TeiqLpA+1ra19amr60YWAqFQmJlZUWcnJzIv//+S27cuEFsbW1J3759mfP37t1LlJWVyY4dO0hiYiJZvnw5UVdXlypwLSoqIhwOhxw9elTs+MSJE4m7u3u117i5ooFrI1JYWEhUVFTI9evXxY5PmTKFfPfdd8zzQ4cOEWVlZbJw4UKiqqpKHj9+zLxW3hFVDETev39PeDweCQ0NZcr74YcfxD7jn3/+IWw2mxQUFBBCyn54RowYIXZOdZ1pREQEc05AQAABQJ49e8Yc8/LyIi4uLhK3sapyw8PDCQCmfn5+ftX+4FcUFhZGdHR0mOeSdqYVR/wOHDhAAJDIyEixdnbu3Jl57unpSbS1tcVGO4KDg4mamhoRCoWEkLLO1MnJiXm9tLSUqKqqkgkTJjDH0tLSCAASHR3NHNPQ0Kj22385S0tLsmLFCrFj5aMlT58+ZY55eXkRFRUVsZERFxcX4uXlRQgh5OHDhwQAuX37NvP6kydPCACxzvTVq1dESUmJfPjwgRBSFmwOGzZM7PPHjx9fY+D6+PFj0qlTp0ojxb6+vqRfv341tpeiZEH7WtrX1ravrRhYnj9/nnA4HJKSksK8/uDBAwKA3Lp1ixBCiIODg9hIKSGE9O7dW6rANTU1lQCo9G/5008/EXt7+xrr2xzRCTSNyNOnT5Gfn49BgwaJHS8uLoa1tTXzfPTo0Th69ChWr16N4OBgdOzYsVJZjo6OzN+1tbXRuXNnPHz4EAAQFxeHe/fuYd++fcw5hBCIRCIkJSWha9euAAA7OzuJ6m1hYcH83cDAACoqKjA1NRU7Vj6JXNI2fl5uq1atAAAZGRlo165dtXWJiIhAQEAAHj16hOzsbJSWlqKwsBD5+flQUVGRqD1VtQkAzM3NxY59PrfI0tJS7DMcHR2Rm5uLly9fwtjYuFK5HA4HOjo6lcotb6c0CgoKoKysXOm4iooK2rdvL1a+QCCAmppalW1JTEyEgoICbGxsmNc7dOgALS0tsXJPnDgBJycnZh5YYmIivvnmG7Fz7O3tcerUKbFjWVlZUFNTg0gkQmFhIZycnBASEiJ2Do/HQ35+vhStpyjp0L6W9rW17WsrevjwIdq2bYu2bdsyx8zMzKCpqYmHDx+iR48eSExMxMyZM8XeZ29vj4sXL9b6c1s6Grg2Irm5uQCA8PBwtGnTRuw1LpfL/D0/Px937twBh8PBkydPavU5Xl5e+O9//1vptYodlaqqqkTlKSoqMn9nsVhiz8uPiUQi5rOBL7exqnIBMOVUJTk5GV9//TVmzJiBlStXQltbG9euXcOUKVNQXFwsVWda1Wd/fqymukhSbnk50razKrq6uvjw4YPUn1d+TNrPO3HiBNzd3aV6DwDw+XzExMSAzWajVatW4PF4lc7JzMyEnp6e1GVTlKRoX0v72tr2tfKgq6sLDoeDN2/eiB1/8+ZNtYu5mjMauDYiZmZm4HK5SElJqXGl9rx588Bms3HmzBkMHToUw4YNw4ABA8TOuXHjBtMxfvjwAY8fP2a+3dvY2CAhIQEdOnSov8ZUQ9I2fomSkhKEQqHYsTt37kAkEmHdunVgs8sSZhw6dEim+kojLi4OBQUFTDB248YNqKmpiX0bry/W1tZISEiQuZzOnTujtLQUd+/eha2tLYCykZuKQXFubi4uXbqE4OBgsffdvn1brKzPnwMAm83+4v+7+Ph49OvXT4ZWUFTNaF8rOdrXVq9r1654+fIlXr58yXx2QkICPn78CDMzMwCf+saJEycy76uqb6yJkpISbG1tERkZyeSXFYlEiIyMhLe3d900pgmhgWsjwufz8eOPP8LX1xcikQhOTk7IyspCVFQU1NXV4enpifDwcOzYsQPR0dGwsbHBTz/9BE9PT9y7d0/sdu6KFSugo6MDAwMD/PLLL9DV1WX+wy9YsAA9e/aEt7c3pk6dClVVVSQkJODChQv4/fff5d5GSQgEAiQlJSE2NhZGRkbg8/no0KEDSkpKsHnzZri5uSEqKgpbt26t1/ZUVFxcjClTpmDx4sVITk6Gn58fvL29mY69Prm4uGDq1KkQCoXgcDi1LqdLly5wdnbGDz/8gODgYCgqKmLevHng8XjMCMXZs2fRqVMnCAQC5n0+Pj7o06cPgoKC4ObmhosXL+LMmTPMeyRVPsK1atWqWreBor6E9rW0r60Lzs7OMDc3x/jx47FhwwaUlpZi5syZ6Nu3LzP9w8fHB9OmTYOdnR169eqF0NBQ3Lt3T2yKR3FxMTPwUFxcjNTUVMTGxkJNTY350jN37lx4enrCzs4O9vb22LBhA/Ly8jB58uQGbXNjQPO4NjL+/v5YsmQJAgIC0LVrV7i6uiI8PBwmJiZ4+/YtpkyZgmXLljFzEJcvXw4DAwNMnz5drJzVq1dj9uzZsLW1RXp6Ok6ePAklJSUAZXN/rly5gsePH+Orr76CtbU1li5ditatW8u9jZIaNWoUXF1d0b9/f+jp6eHAgQOwtLREUFAQfvvtN3Tv3h379u1DQEBAPbZE3MCBA9GxY0f06dMHHh4ecHd3x7Jlyxrks4cMGQIFBQWJNwOoyZ49e2BgYIA+ffrgm2++wbRp08Dn85k5tMePH680TaB3797YunUrgoKCYGlpibNnz8LX17fKebc1OX78ONq1a4evvvpK5nZQVE1oXysZ2tdWj8Vi4fjx49DS0kKfPn3g7OwMU1NThIaGMueMHz8eixYtwo8//ggbGxskJSVh0qRJYn3j69evYW1tDWtra6SlpWHt2rWwtrbG1KlTmXM8PDywdu1aLF26FFZWVoiNjcXZs2eZubotCYsQQuRdCaruXL58Gf3798eHDx+a3HZ7TdmkSZPw8eNHHDt2TG512LJlC06cOIFz587VabmvXr1C27ZtERERgb59+8LAwABnzpyBvb19je+bNm0aHj16hH/++Ufiz+rZsyf++9//Yty4cbJWm6LqFe1r5aMx9LWyGjRoEAwNDfHXX3/JuypNEp0qQFHNhJeXFz5+/IicnByZtn29ePEicnNzYW5ujrS0NMyfPx8CgQB9+vRBZmYmfH190aNHj0rvW7t2LQYNGgRVVVWcOXMGu3fvxh9//CHx57579w4jR47Ed999V+u6UxRFNSb5+fnYunUrXFxcwOFwcODAAURERODChQvyrlqTRQNXimomFBQU8Msvv8hcTklJCX7++Wc8f/4cfD4fvXr1wr59+6CoqAh9fX0sXry4yvfdunULa9asQU5ODkxNTbFp0yaxW11foquri/nz58tcf4qiqMaCxWLh9OnTWLlyJQoLC9G5c2ccOXIEzs7O8q5ak0WnClAURVEURVFNAl2cRVEURVEURTUJNHClKIqiKIqimgQauFIURVEURVFNAg1cKYqiKIqiqCaBBq4URVEURVFUk0ADV4qiKIqiKKpJoIErRVEURVEU1STQwJWiKIqiKIpqEv4fug8hP4gt/kwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x247.2 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 file(s) exported for \"Load reaction data\" into Escher maps\n"
     ]
    }
   ],
   "source": [
    "# Optimize model using cobrapy and analyze results\n",
    "pred_results = {}\n",
    "for cond, medium in conditions.items():\n",
    "    with ecm as model:\n",
    "                \n",
    "        model.medium = medium \n",
    "        solution = model.optimize()\n",
    "        if solution.status == 'optimal':\n",
    "            gr = solution.objective_value\n",
    "            pred_results[cond] = solution\n",
    "            print(f'{cond:25s}: pred gr: {gr:.3f} h-1 vs. exp {exp_grs[cond]:.3f}, '\n",
    "                  f'diff: {gr - exp_grs[cond]:6.3f}')\n",
    "        else:    \n",
    "            print(f'{cond} ended with status {solution.status}')\n",
    "            \n",
    "er = EcmResults(eo, pred_results, df_mpmf)\n",
    "df_fluxes = er.collect_fluxes()\n",
    "df_net_fluxes = er.collect_fluxes(net=True)\n",
    "df_proteins = er.collect_protein_results()\n",
    "er.plot_grs(exp_grs, highlight=reference_cond)\n",
    "print(f'Protein mass fractions:')\n",
    "er.report_proteomics_correlation(scale='lin')\n",
    "er.report_proteomics_correlation(scale='log')\n",
    "print()\n",
    "er.report_protein_levels(reference_cond)\n",
    "er.plot_proteins(reference_cond)  \n",
    "er.save_to_escher(df_net_fluxes[reference_cond], os.path.join('escher', target_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "G6PDH2r        :    3.516 mmol/gDWh [b1852]\n",
      "EDA            :    0.000 mmol/gDWh [b1850]\n",
      "PGI            :    3.749 mmol/gDWh [b4025]\n",
      "PFK            :    5.560 mmol/gDWh [b1723 or b3916]\n",
      "FBA            :    5.560 mmol/gDWh [b2097 or b2925]\n",
      "TPI            :    5.473 mmol/gDWh [b3919]\n",
      "GAPD           :   11.884 mmol/gDWh [b1779]\n",
      "PGK            :  -11.884 mmol/gDWh [b2926]\n",
      "PGM            :  -10.831 mmol/gDWh [b0755 or b3612]\n",
      "ENO            :   10.831 mmol/gDWh [b2779]\n",
      "PYK            :    3.057 mmol/gDWh [b1676 or b1854]\n"
     ]
    }
   ],
   "source": [
    "# analyze reactions selected fluxes\n",
    "rids = ['G6PDH2r', 'EDA', 'PGI', 'PFK', 'FBA', 'TPI', 'GAPD', 'PGK', 'PGM', 'ENO', 'PYK']\n",
    "for rid in rids:\n",
    "    print(f'{rid:15s}: {df_net_fluxes.at[rid, reference_cond]:8.3f} mmol/gDWh [{df_net_fluxes.at[rid, \"gpr\"]}]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5 – GECKO model limitations\n",
    "\n",
    "The optimization results of the final GECKO model have been obtained, thus allowing for the discussion of some of the limitations of GECKO modeling. The cases in which GECKO does not predict protein usage for proteins that have been measured at high concentrations will be discussed, as well as the suggestion that proteomics measurements have a slight bias towards cytosolic proteins and that small integral proteins are not measured correctly, which impacts correlation of protein concentrations. While the AI-predicted turnover numbers should approximate the in vitro values, we will examine situations where there are substantial discrepancies. Additionally, we will analyze the impact of automated fitting to the AI-predictions.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 inability to predict protein usage\n",
    "\n",
    "The GECKO model's inability to predict proteins that have been measured at high concentrations prompts the following inquiry:\n",
    "\n",
    "b0197/metQ and b3460/livJ are proteins associated with transporters, which are measured at relatively high concentrations. These proteins are utilized in methionine and branched amino acid uptake systems (ABC transporters) and function as periplasmic binding proteins. Given the nutrient conditions lacking amino acids, these transporters do not carry flux, and the related proteins are part of the inactive proteome. However, the cells utilized in the proteomics experiment expressed these proteins at high quantities, suggesting either constitutive expression due to cells evolving under rich conditions or in anticipation of such conditions.\n",
    "\n",
    "The b4015/aceA and b0605/ahpC genes encode metabolic proteins, which are measured at high concentrations; however, these proteins are not utilized by the model. b4015/aceA catalyzes the isocitrate lyase reaction (ICL) in the glyoxylate cycle. In the previous tutorial, we manually adjusted turnover values in the TCA cycle to generate flux through ICL. During automated fitting, this flux was switched off again, indicating the need for another manual adjustment. b0605/ahpC is a component of alkyl hydroperoxide reductase, which protects against oxidative stress. In the GECKO model, the protein is required to catalyze the NADH peroxidase reaction, which is inactive in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b3460 pred   0.0000 vs.   6.1215 (rank 24); livJ: Leu/Ile/Val-binding protein\n",
      "b0197 pred   0.0000 vs.   5.9980 (rank 38); metQ: D-methionine-binding lipoprotein metQ\n"
     ]
    }
   ],
   "source": [
    "# transport related proteins measured but not predicted\n",
    "for gene, pred in df_proteins[reference_cond].to_dict().items():\n",
    "    if gene in df_mpmf.index and gene in tx_genes:\n",
    "        exp = df_mpmf.at[gene, reference_cond]\n",
    "        if exp > 5 and pred == 0.0:\n",
    "            description = df_proteins.at[gene, 'description']\n",
    "            gname = df_proteins.at[gene, 'gene_name']\n",
    "            rank = df_mpmf.at[gene, 'rank']\n",
    "            print(f'{gene} pred {pred:8.4f} vs. {exp:8.4f} (rank {rank:2d}); {gname}: {description}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b0605 pred   0.0000 vs.   8.2787 (rank 15); ahpC: Alkyl hydroperoxide reductase subunit C\n"
     ]
    }
   ],
   "source": [
    "# metabolic proteins measured but not predicted\n",
    "for gene, pred in df_proteins[reference_cond].to_dict().items():\n",
    "    if gene in df_mpmf.index and gene in metab_genes:\n",
    "        exp = df_mpmf.at[gene, reference_cond]\n",
    "        if (exp < 1 and pred > 5) or (exp > 5 and pred < 1):\n",
    "            description = df_proteins.at[gene, 'description']\n",
    "            gname = df_proteins.at[gene, 'gene_name']\n",
    "            rank = df_mpmf.at[gene, 'rank']\n",
    "            print(f'{gene} pred {pred:8.4f} vs. {exp:8.4f} (rank {rank:2d}); {gname}: {description}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 biased proteomics\n",
    "\n",
    "A subsequent focus will be placed on protein concentrations related to transporters that have higher predicted than measured levels (red dots above the diagonal in the log10 correlation plot). Four proteins have predictions that are 20 times higher than measurements, and it is expected that automatic fitting would balance this out. The four proteins of interest, b3737/atpE, b2278/nuoL, b2276/nuoN, and b0430/cyoC, are components of larger enzymatic complexes that exhibit a fixed protein composition in the GECKO model. Consequently, they cannot be adjusted independently from the other proteins within the complex. It is hypothesized that the proteins may not have been accurately measured in the proteomics analysis due to their shared characteristic of being integral membrane proteins. \n",
    "\n",
    "The cytochrome bo3 ubiquinol:oxygen oxidoreductase complex, composed of four proteins expressed from the operon cyoABCD, serves as an example. The GECKO model predicts a protein requirement of 11.1 nmol/gP for the transporter and each of the four transporter components. However, the proteomics experiment yielded protein copy numbers of 0.1, 0.9, 6.5, and 17.9 nmol/gP. The low quantities of b0430/cycC and b0429/cyoD, which are small membrane proteins, may have led to their inaccurate quantification during the proteomics measurement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b3737 pred   2.5617 vs.   0.0161; atpE: ATP synthase subunit c, 159.35\n",
      "b2278 pred   0.3739 vs.   0.0008; nuoL: NADH-quinone oxidoreductase subunit L, 442.83\n",
      "b2276 pred   0.2929 vs.   0.0071; nuoN: NADH-quinone oxidoreductase subunit N, 41.17\n",
      "b0430 pred   0.1640 vs.   0.0028; cyoC: Cytochrome o ubiquinol oxidase subunit 3, 59.29\n"
     ]
    }
   ],
   "source": [
    "# transport proteins with overpredicted concentration\n",
    "for gene, pred in df_proteins[reference_cond].to_dict().items():\n",
    "    if gene in df_mpmf.index and (gene in tx_genes):\n",
    "        exp = df_mpmf.at[gene, reference_cond]\n",
    "        if exp > 0 and pred/exp > 20:\n",
    "            description = df_proteins.at[gene, 'description'][:50]\n",
    "            gname = df_proteins.at[gene, 'gene_name']\n",
    "            print(f'{gene} pred {pred:8.4f} vs. {exp:8.4f}; {gname}: {description}, {pred/exp:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b0432/cyoA (34.9 kDa): pred 0.253 mg/gP (7.3 nmol/gP); exp 0.623 mg/gP ( 17.9 nmol/gP)\n",
      "b0431/cyoB (74.3 kDa): pred 0.539 mg/gP (7.3 nmol/gP); exp 0.485 mg/gP (  6.5 nmol/gP)\n",
      "b0430/cyoC (22.6 kDa): pred 0.164 mg/gP (7.3 nmol/gP); exp 0.003 mg/gP (  0.1 nmol/gP)\n",
      "b0429/cyoD (12.0 kDa): pred 0.087 mg/gP (7.3 nmol/gP); exp 0.011 mg/gP (  0.9 nmol/gP)\n"
     ]
    }
   ],
   "source": [
    "# check cytochrome bo3 ubiquinol:oxygen oxidoreductase complex\n",
    "for gene in ['b0432', 'b0431', 'b0430', 'b0429']:\n",
    "    pred = df_proteins.at[gene, reference_cond]\n",
    "    exp = df_mpmf.at[gene, reference_cond]\n",
    "    mw_da = df_mpmf.at[gene, 'mw_Da']\n",
    "    name = df_mpmf.at[gene, 'gene_name']\n",
    "    print(f'{gene}/{name:4s} ({mw_da/1000.0:.1f} kDa): '\n",
    "          f'pred {pred:.3f} mg/gP ({pred/mw_da*1e6:.1f} nmol/gP); '\n",
    "          f'exp {exp:.3f} mg/gP ({exp/mw_da*1e6:5.1f} nmol/gP)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 large difference predicted to fitted turnover numbers\n",
    "\n",
    "Assuming that the AI-predicted turnover numbers are close to the apparent in vitro turnover numbers, which are required for the GECKO model formulation, it can be expected that all of the turnover numbers should have been fitted, given our maximum scaling factor of 100. However, 24 turnover numbers exceeded that scaling factor and consequently were not adjusted. \n",
    "\n",
    "An analysis of the corresponding reactions indicates that, in most cases (21 out of 24), the fitted turnover numbers would be considerably lower than the AI-predicted values, resulting in proteins being predicted at a significantly lower level than measured. This discrepancy can be attributed to one or more of the following reasons: Firstly, the AI-predicted turnover number is, in fact, too high, which can be verified by comparing with experimental values derived from databases and/or literature. Secondly, the model may predict insufficient flux through the reaction, potentially due to the model's preference for an alternative pathway. Thirdly, the cell may require the protein for tasks not included in the model, such as repair or housekeeping. Fourthly, the gene may be constitutively expressed, thus not directly linked to reaction fluxes, as observed in the expression of certain periplasmic binding proteins. Finally, a portion of the cellular protein may be inactive due to regulatory mechanisms.\n",
    "\n",
    "Additionally, there are three cases where the AI-predicted turnover numbers are lower than the fitting to proteomics data suggests, and again, different causes can be identified. Firstly, proteomics may be biased to specific types of proteins, integral membrane proteins may be more difficult to isolate and measure correctly compared to soluble proteins. Secondly, the model predicts high flux through a reaction, while the cell uses another pathway. Thirdly, the AI-predicted turnover values are too high.\n",
    "\n",
    "\n",
    "An illustration of proteomics failing to adequately measure a specific protein is the gene product of b0175/cdsA, an integral membrane protein. The AI-predicted turnover number for b0175/cdsA was 1.65 $s^{-1}$, which is well within the measured range of turnover numbers for CdsA (see BRENDA). The protein mass fraction of CdsA is comparatively low (0.009 mg/gP), and its copy number is significantly lower compared to neighboring proteins in the pathway, indicating that not all copies of b0175/cdsA were measured.\n",
    "\n",
    "An illustration of the potential overestimation of the AI-predicted turnover numbers is evident in the synthesis of iron-sulfur clusters utilizing b2528/iscA or b2530/iscS. While the AI-predicted values are within the range of 15 $s^{-1}$, the proteomics data fitting suggests turnover numbers are three orders of magnitude lower. The turnover number for b2530/iscS has been measured at 0.202 $s^{-1}$  (Dunkle et al., 2019), which is more aligned with the proteomics-fitted value. The protein mass fraction for IscS, as measured, is relatively high (4.208 mg/gP). IscS has been shown to be required for Fe-S cluster repair and DNA housekeeping, which are not reflected in the GECKO model. \n",
    "\n",
    "Increasing the maximum scaling factor beyond 100.0 could improve the protein correlation. Setting the `max_scale_factor` to ‘None’ would result in unlimited scaling. However, it is important to note that limiting the maximum scaling provides a greater degree of control over the parametrization of the model and the potential for investigating and addressing inconsistencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24 kcat values exceed max scaling, concerning 18 different gene products\n",
      "(3 kcat values would be much higher, 21 much lower)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orig_kcat</th>\n",
       "      <th>fitted_kcat</th>\n",
       "      <th>factor</th>\n",
       "      <th>reaction_string</th>\n",
       "      <th>genes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub_rid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ORPT_REV</th>\n",
       "      <td>8.66</td>\n",
       "      <td>1403.143135</td>\n",
       "      <td>162.025766</td>\n",
       "      <td>orot_c + prpp_c =&gt; orot5p_c + ppi_c</td>\n",
       "      <td>b3642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DASYN160</th>\n",
       "      <td>1.65</td>\n",
       "      <td>297.703107</td>\n",
       "      <td>180.426128</td>\n",
       "      <td>ctp_c + h_c + pa160_c =&gt; cdpdhdecg_c + ppi_c</td>\n",
       "      <td>b0175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DASYN161</th>\n",
       "      <td>1.65</td>\n",
       "      <td>297.703107</td>\n",
       "      <td>180.426128</td>\n",
       "      <td>ctp_c + h_c + pa161_c =&gt; cdpdhdec9eg_c + ppi_c</td>\n",
       "      <td>b0175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          orig_kcat  fitted_kcat      factor  \\\n",
       "sub_rid                                        \n",
       "ORPT_REV       8.66  1403.143135  162.025766   \n",
       "DASYN160       1.65   297.703107  180.426128   \n",
       "DASYN161       1.65   297.703107  180.426128   \n",
       "\n",
       "                                         reaction_string  genes  \n",
       "sub_rid                                                          \n",
       "ORPT_REV             orot_c + prpp_c => orot5p_c + ppi_c  b3642  \n",
       "DASYN160    ctp_c + h_c + pa160_c => cdpdhdecg_c + ppi_c  b0175  \n",
       "DASYN161  ctp_c + h_c + pa161_c => cdpdhdec9eg_c + ppi_c  b0175  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# kcats not fitted due to exceeding factor (max_scale_factor set to 100 during auto-fit)\n",
    "not_scaled = []\n",
    "for data in exceeding_max_scale.values():\n",
    "    sub_rid = data['iso_rid']\n",
    "    fitted_kcat = data['orig_kcat'] * data['factor']\n",
    "    gpr = eo.rdata[sub_rid]['gpr'] \n",
    "    react_str = eo.rdata[sub_rid]['reaction_str']\n",
    "    not_scaled.append([sub_rid, data['orig_kcat'], fitted_kcat, data['factor'], react_str, gpr])\n",
    "cols = ['sub_rid', 'orig_kcat', 'fitted_kcat', 'factor', 'reaction_string', 'genes']    \n",
    "df_not_scaled = pd.DataFrame(not_scaled, columns=cols).set_index('sub_rid')\n",
    "n_higher_kcat = len(df_not_scaled[df_not_scaled[\"factor\"] > 1.0])\n",
    "n_genes = len(df_not_scaled['genes'].unique())\n",
    "print(f'{len(df_not_scaled)} kcat values exceed max scaling, concerning {n_genes} different gene products')\n",
    "print(f'({n_higher_kcat} kcat values would be much higher, {len(df_not_scaled)- n_higher_kcat} much lower)')\n",
    "df_not_scaled[df_not_scaled[\"factor\"] > 1.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b0175 cdsA (31.4 kDa): pred 1.583 mg/gP (50.40 nmol/gP); exp 0.009 mg/gP ( 0.30 nmol/gP)\n",
      "b2585 pssA (52.8 kDa): pred 0.099 mg/gP ( 1.87 nmol/gP); exp 0.107 mg/gP ( 2.02 nmol/gP)\n",
      "b4160 psd  (35.9 kDa): pred 0.368 mg/gP (10.26 nmol/gP); exp 0.397 mg/gP (11.06 nmol/gP)\n",
      "b0914 msbA (64.4 kDa): pred 0.062 mg/gP ( 0.96 nmol/gP); exp 0.067 mg/gP ( 1.03 nmol/gP)\n"
     ]
    }
   ],
   "source": [
    "# enzymes in the CDP-diacylglycerol biosynthesis pathway suggest issues with proteomics\n",
    "for gene in ['b0175',  'b2585', 'b4160', 'b0914']:\n",
    "    pred = df_proteins.at[gene, reference_cond]\n",
    "    row = df_mpmf.loc[gene]\n",
    "    exp = row[reference_cond]\n",
    "    mw_da = row[\"mw_Da\"]\n",
    "    print(f'{gene} {row[\"gene_name\"]:4s} ({mw_da/1000.0:.1f} kDa): '\n",
    "          f'pred {pred:.3f} mg/gP ({pred/mw_da*1e6:5.2f} nmol/gP); '\n",
    "          f'exp {exp:.3f} mg/gP ({exp/mw_da*1e6:5.2f} nmol/gP)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orig_kcat</th>\n",
       "      <th>fitted_kcat</th>\n",
       "      <th>factor</th>\n",
       "      <th>reaction_string</th>\n",
       "      <th>genes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub_rid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ICYSDS</th>\n",
       "      <td>15.24</td>\n",
       "      <td>0.014410</td>\n",
       "      <td>0.000946</td>\n",
       "      <td>cys__L_c + iscs_c =&gt; ala__L_c + iscssh_c</td>\n",
       "      <td>b2530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I2FE2SS</th>\n",
       "      <td>15.24</td>\n",
       "      <td>0.014410</td>\n",
       "      <td>0.000946</td>\n",
       "      <td>fadh2_c + 2.0 fe2_c + 2.0 iscssh_c + iscu_c =&gt;...</td>\n",
       "      <td>b2530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I2FE2ST</th>\n",
       "      <td>11.34</td>\n",
       "      <td>0.018765</td>\n",
       "      <td>0.001655</td>\n",
       "      <td>4.0 h_c + iscu_2fe2s_c =&gt; 2fe2s_c + iscu_c</td>\n",
       "      <td>b2528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I2FE2SS2</th>\n",
       "      <td>15.24</td>\n",
       "      <td>0.014410</td>\n",
       "      <td>0.000946</td>\n",
       "      <td>fadh2_c + 2.0 fe2_c + 2.0 iscssh_c + iscu_2fe2...</td>\n",
       "      <td>b2530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I4FE4ST</th>\n",
       "      <td>11.34</td>\n",
       "      <td>0.018765</td>\n",
       "      <td>0.001655</td>\n",
       "      <td>4.0 h_c + iscu_4fe4s_c =&gt; 4fe4s_c + iscu_c</td>\n",
       "      <td>b2528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I2FE2SR</th>\n",
       "      <td>15.24</td>\n",
       "      <td>0.014410</td>\n",
       "      <td>0.000946</td>\n",
       "      <td>2fe1s_c + iscssh_c + iscu_c =&gt; 4.0 h_c + iscs_...</td>\n",
       "      <td>b2530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          orig_kcat  fitted_kcat    factor  \\\n",
       "sub_rid                                      \n",
       "ICYSDS        15.24     0.014410  0.000946   \n",
       "I2FE2SS       15.24     0.014410  0.000946   \n",
       "I2FE2ST       11.34     0.018765  0.001655   \n",
       "I2FE2SS2      15.24     0.014410  0.000946   \n",
       "I4FE4ST       11.34     0.018765  0.001655   \n",
       "I2FE2SR       15.24     0.014410  0.000946   \n",
       "\n",
       "                                            reaction_string  genes  \n",
       "sub_rid                                                             \n",
       "ICYSDS             cys__L_c + iscs_c => ala__L_c + iscssh_c  b2530  \n",
       "I2FE2SS   fadh2_c + 2.0 fe2_c + 2.0 iscssh_c + iscu_c =>...  b2530  \n",
       "I2FE2ST          4.0 h_c + iscu_2fe2s_c => 2fe2s_c + iscu_c  b2528  \n",
       "I2FE2SS2  fadh2_c + 2.0 fe2_c + 2.0 iscssh_c + iscu_2fe2...  b2530  \n",
       "I4FE4ST          4.0 h_c + iscu_4fe4s_c => 4fe4s_c + iscu_c  b2528  \n",
       "I2FE2SR   2fe1s_c + iscssh_c + iscu_c => 4.0 h_c + iscs_...  b2530  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# enzymes catalyzing Fe-S complexes suggest lowering the kcat value\n",
    "df_not_scaled[df_not_scaled[\"genes\"].str.contains('b25')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 predicted vs. fitted turnover numbers\n",
    "\n",
    "Less than 25% of all turnover numbers of metabolic reactions were fitted to proteomics data (542 out of 2349 kcat values). Although all turnover numbers undergo slight recalibration to attain target enzyme saturation, proteomics analysis is only applicable to reactions that facilitate traffic. The application of proteomics to these reactions has been observed to augment variance in turnover numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 table(s) with parameters loaded from data/iML1515_manual_adjust_GECKO_kcats.xlsx (Thu Feb 12 15:21:28 2026)\n",
      "1 table(s) with parameters loaded from data/iML1515_predicted_fit_GECKO_kcats.xlsx (Thu Feb 12 15:25:00 2026)\n",
      "2349 metabolic sub-reactions catalyzed by enzymes\n",
      "     mean TurNuP kcats 32.1 s-1, fitted kcats: 34.5 s-1\n",
      " 550 kcat values fitted with mean factor 3.58\n",
      "     mean TurNuP kcats 52.4 s-1 (std: 456.8), fitted kcats: 62.5 s-1 (std: 245.0)\n"
     ]
    }
   ],
   "source": [
    "# collect original vs. auto-fitted\n",
    "import numpy as np\n",
    "\n",
    "df_orig_kcats = load_parameter_file(os.path.join('data', f'{baseline_model}_kcats.xlsx'))['kcats']\n",
    "df_fitted_kcats = load_parameter_file(os.path.join('data', f'{target_model}_kcats.xlsx'))['kcats']\n",
    "\n",
    "metab_kcats = []\n",
    "for key, row in df_orig_kcats.iterrows():\n",
    "    if row['info_type'] == 'metabolic':\n",
    "        metab_kcats.append([row['kcat_per_s'], df_fitted_kcats.at[key, 'kcat_per_s']])\n",
    "metab_kcats = np.array(metab_kcats)\n",
    "log_x = np.log10(metab_kcats[:,0])\n",
    "log_y = np.log10(metab_kcats[:,1])\n",
    "log_delta = np.log10(10.0)\n",
    "\n",
    "scale_factors = metab_kcats[:,1]/metab_kcats[:,0]\n",
    "fitted_factors = scale_factors[scale_factors != 1.0]\n",
    "fitted_kcats = metab_kcats[scale_factors != 1.0, :]\n",
    "print(f'{len(metab_kcats):4d} metabolic sub-reactions catalyzed by enzymes')\n",
    "print(f'     mean TurNuP kcats {np.mean(metab_kcats[:,0]):.1f} s-1, '\n",
    "      f'fitted kcats: { np.mean(metab_kcats[:,1]):.1f} s-1')\n",
    "print(f'{len(fitted_factors):4d} kcat values fitted with mean factor {np.mean(fitted_factors):.2f}')\n",
    "print(f'     mean TurNuP kcats {np.mean(fitted_kcats[:,0]):.1f} s-1 (std: {np.std(fitted_kcats[:,0]):.1f}), '\n",
    "      f'fitted kcats: { np.mean(fitted_kcats[:,1]):.1f} s-1 (std: {np.std(fitted_kcats[:,1]):.1f})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x247.2 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "marker2 = mpl.markers.MarkerStyle('o', fillstyle='full')\n",
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize=(12.0, 4.0 * .618), squeeze=False)\n",
    "for gcol in [0, 1, 2]:\n",
    "    ax = axs[0, gcol]\n",
    "    if gcol == 0: \n",
    "        xy_range = (-3.0, 5.0)\n",
    "        #log_x, log_y = er.get_log10_xy(all_kcats)\n",
    "        ax.scatter(log_x, log_y, marker=marker2, label='metabolic')\n",
    "        slope, intercept, rvalue, pvalue, stderr = scipy.stats.linregress(log_x, log_y)\n",
    "        ax.plot((xy_range[0], xy_range[1]),\n",
    "                (slope * xy_range[0] + intercept, slope * xy_range[1] + intercept), 'r:', lw=1)\n",
    "        ax.plot((xy_range[0] + log_delta, xy_range[1]), (xy_range[0], xy_range[1] - log_delta), 'k:', lw=0.5)\n",
    "        ax.plot((xy_range[0], xy_range[1] - log_delta), (xy_range[0] + log_delta, xy_range[1]), 'k:', lw=0.5)\n",
    "\n",
    "        ax.set_xlabel(r'TurNuP kcat (s-1) log10')\n",
    "        ax.set_ylabel(r'auto-fitted kcat (s-1) log10')\n",
    "        ax.legend(loc='upper left')\n",
    "        ax.set(xlim=xy_range, ylim=xy_range)\n",
    "        ax.plot(xy_range, xy_range, 'k--', lw=0.5)\n",
    "    elif gcol == 1:\n",
    "        ax.hist((log_x, log_y), bins=20, linewidth=0.5, label=['TurNuP', 'fitted'])\n",
    "        ax.set_xlabel(r'kcat (s-1) log10')\n",
    "        ax.legend(loc='upper left')\n",
    "        ax.set(xlim=(-2, 3))\n",
    "    elif gcol == 2:\n",
    "        ax.hist(np.log10(fitted_factors), bins=50, linewidth=0.5, label=f'TurNuP ({len(fitted_factors)})')\n",
    "        ax.set_xlabel(r'kcat scale factors (log10)')\n",
    "        ax.legend(loc='upper left')\n",
    "        ax.set(xlim=(-2.2, 2.2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (Optional) Track progress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 table(s) with parameters loaded from protein_predictions.xlsx (Thu Feb 12 15:22:11 2026)\n",
      "1 table(s) with parameters written to protein_predictions.xlsx\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>lin r2</th>\n",
       "      <th>log r2</th>\n",
       "      <th>lin proteins</th>\n",
       "      <th>log proteins</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>iML1515_default_GECKO</td>\n",
       "      <td>0.033244</td>\n",
       "      <td>0.190225</td>\n",
       "      <td>1018</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>iML1515_modified_GECKO</td>\n",
       "      <td>0.135981</td>\n",
       "      <td>0.201613</td>\n",
       "      <td>999</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>iML1515_predicted_GECKO</td>\n",
       "      <td>0.078803</td>\n",
       "      <td>0.268769</td>\n",
       "      <td>999</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>iML1515_manual_adjust_GECKO</td>\n",
       "      <td>0.205413</td>\n",
       "      <td>0.333470</td>\n",
       "      <td>999</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>iML1515_predicted_fit_GECKO</td>\n",
       "      <td>0.869850</td>\n",
       "      <td>0.549757</td>\n",
       "      <td>999</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          model    lin r2    log r2  lin proteins  \\\n",
       "No                                                                  \n",
       "1         iML1515_default_GECKO  0.033244  0.190225          1018   \n",
       "2        iML1515_modified_GECKO  0.135981  0.201613           999   \n",
       "3       iML1515_predicted_GECKO  0.078803  0.268769           999   \n",
       "4   iML1515_manual_adjust_GECKO  0.205413  0.333470           999   \n",
       "5   iML1515_predicted_fit_GECKO  0.869850  0.549757           999   \n",
       "\n",
       "    log proteins  \n",
       "No                \n",
       "1            299  \n",
       "2            322  \n",
       "3            308  \n",
       "4            308  \n",
       "5            313  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy\n",
    "import numpy as np\n",
    "\n",
    "number = 5\n",
    "xy = np.array([[df_mpmf.at[gene, reference_cond], df_proteins.at[gene, reference_cond]] \n",
    "                for gene in df_proteins.index if gene in df_mpmf.index])\n",
    "log10_x, log10_y = er.get_log10_xy(xy)\n",
    "lin_pearson_r, _ = scipy.stats.pearsonr(xy[:, 0], xy[:, 1])\n",
    "log_pearson_r, _ = scipy.stats.pearsonr(log10_x, log10_y)\n",
    "\n",
    "predictions = load_parameter_file('protein_predictions.xlsx')\n",
    "data = [[number, target_model, lin_pearson_r**2,log_pearson_r**2, len(xy), len(log10_x)]]\n",
    "cols = ['No', 'model', 'lin r2', 'log r2', 'lin proteins', 'log proteins']\n",
    "df = pd.DataFrame(data, columns=cols).set_index('No')\n",
    "if number in predictions[reference_cond].index:\n",
    "    predictions[reference_cond].drop(index=number, inplace=True)\n",
    "predictions[reference_cond] = pd.concat((predictions[reference_cond], df)).sort_index()\n",
    "write_parameter_file('protein_predictions.xlsx', predictions)\n",
    "predictions[reference_cond]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Closing remarks\n",
    "\n",
    "While artificial intelligence (AI)-predicted turnover numbers might serve as a valuable starting point, manual adjustments and automatic fitting of turnover numbers to proteomics data hold significant potential for enhancing the prediction of protein concentrations under both reference conditions and other nutrient conditions. This enhancement can be attributed to the fact that the AI-prediction model is based on experimentally derived turnover numbers that are scarce, noisy, and acquired by non-standardized methods. In contrast, quantitative proteomics is a standardized, high-throughput method with almost complete coverage of the proteome.\n",
    "\n",
    "Nevertheless, there appears to be a potential for further improvements, particularly with regard to different reactions and pathways. These improvements could involve additional cycles of manual parameter adjustments followed by automatic fitting to proteomics. With varying nutrient conditions, different areas of the metabolic network could be explored and subjected to manual adjustment and automatic fitting workflows.\n",
    "\n",
    "However, achieving perfect correlation between predicted and measured protein concentrations will not be feasible for a complex metabolic network. This discrepancy arises from the modeling of enzymes with integer-based stoichiometry of participating proteins, while the measured proteins exhibit variable copy number ratios. Additionally, proteins may be components of different enzyme complexes, precluding their independent adjustment.  \n",
    "\n",
    "The final GECKO model of iML1515 was created through a gradual improvement of model parameters, commencing with an initial GECKO model that employed default parametrization. The configuration data of this final GECKO model will serve as a baseline for the construction of a resource balance model (RBA) of iML1515, as well as for the creation of thermodynamics constraint TGECKO and TRBA models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "## (Alternative) gurobipy – automatic fitting to proteomics data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SBML model loaded by sbmlxdf: SBML_models/iML1515_manual_adjust_GECKO.xml (Thu Feb 12 15:21:50 2026)\n",
      "LP Model of iML1515_GECKO\n",
      "7343 variables, 3372 constraints, 28438 non-zero matrix coefficients\n",
      "total modeled protein: 306.59 mg/gDW, enzyme saturation level: 0.56\n",
      "Glucose: growth rate 0.661 h-1 (optimal)\n"
     ]
    }
   ],
   "source": [
    "# Load GECKO model and optimize for reference condition using gurobipy\n",
    "fname = os.path.join('SBML_models', f'{baseline_model}.xml')\n",
    "eo = EcmOptimization(fname)\n",
    "total_protein = eo.get_variable_bounds('V_PC_total')['V_PC_total'][1]\n",
    "sigma = eo.avg_enz_saturation\n",
    "print(f'total modeled protein: {total_protein:.2f} mg/gDW, enzyme saturation level: {sigma}')\n",
    "\n",
    "# Calculate GECKO solution for given condition\n",
    "eo.medium = conditions[reference_cond]\n",
    "solution = eo.optimize()\n",
    "gr = solution.objective_value\n",
    "print(f'{reference_cond}: growth rate {gr:.3f} h-1 ({solution.status})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 996 proteins measured with total of 497.8 mg/gP\n",
      "   3 proteins excluded with total of  16.6 mg/gP\n",
      "5357 enzyme catalyzed iso-reactions (2221 reactions, 1494 proteins)\n",
      " 394 active catalyzed reactions with total protein of 537.9 mg/gP (based on GECKO simulation)\n",
      " 417 proteins potentially involved in active reactions\n",
      " 352 active catalyzed reactions using total protein of 356.7 mg/gP (based on proteomics)\n",
      "  42 active catalyzed reactions have no measured proteins provided\n",
      "5357 original kcat records loaded from data/iML1515_manual_adjust_GECKO_kcats.xlsx\n",
      " 584 kcat records fitted. Scale factor range [0.0101105, 110.0]\n",
      "  24 (net) reactions would exceed the maximum scaling factor of 100.0 and will not be scaled\n",
      "fitted kcats exported to data/iML1515_predicted_fit_GECKO_kcats.xlsx\n",
      "3 table(s) with parameters loaded from data/iML1515_manual_adjust_GECKO_xba_parameters.xlsx (Thu Feb 12 15:21:28 2026)\n",
      "3 table(s) with parameters written to data/iML1515_predicted_fit_GECKO_xba_parameters.xlsx\n",
      "1 table(s) with parameters loaded from data/iML1515_manual_adjust_GECKO_ecm_parameters.xlsx (Thu Feb 12 15:21:28 2026)\n",
      "1 table(s) with parameters written to data/iML1515_predicted_fit_GECKO_ecm_parameters.xlsx\n"
     ]
    }
   ],
   "source": [
    "# Select proteins for the automatic fit\n",
    "excluded_loci = {'b0929', 'b2215', 'b1377', 'b0241', 'b2400'}\n",
    "measured_mpmfs = {} \n",
    "excluded_mpmfs = {} \n",
    "for locus, mpmf in df_mpmf[reference_cond].items():\n",
    "    if locus in eo.locus2uid:\n",
    "        if locus not in excluded_loci:\n",
    "            measured_mpmfs[locus] = mpmf        \n",
    "        else:\n",
    "            excluded_mpmfs[locus] = mpmf\n",
    "print(f'{len(measured_mpmfs):4d} proteins measured with total of {sum(measured_mpmfs.values()):5.1f} mg/gP')\n",
    "print(f'{len(excluded_mpmfs):4d} proteins excluded with total of {sum(excluded_mpmfs.values()):5.1f} mg/gP')\n",
    "\n",
    "# Automatically fit kcat values to proteomics\n",
    "target_enz_sat = 0.5\n",
    "orig_kcats_fname = os.path.join('data', f'{baseline_model}_kcats.xlsx')\n",
    "fitted_kcats_fname = os.path.join('data', f'{target_model}_kcats.xlsx')\n",
    "\n",
    "gfk = GeckoFitKcats(eo, orig_kcats_fname)\n",
    "tot_fitted_mpmf = gfk.process_data(solution.fluxes, measured_mpmfs)\n",
    "exceeding_max_scale = gfk.update_kcats(fitted_kcats_fname, target_sat=target_enz_sat, max_scale_factor=100.0)\n",
    "\n",
    "# Create updated XBA parameter file\n",
    "xba_params = load_parameter_file(os.path.join('data', f'{baseline_model}_xba_parameters.xlsx'))\n",
    "xba_params['general'].at['kcats_fname', 'value'] = os.path.join('data', f'{target_model}_kcats.xlsx')\n",
    "write_parameter_file(os.path.join('data', f'{target_model}_xba_parameters.xlsx'), xba_params)\n",
    "\n",
    "# Create updated ECM parameter file\n",
    "ecm_params = load_parameter_file(os.path.join('data', f'{baseline_model}_ecm_parameters.xlsx'),['general'])\n",
    "fitted_pmf = (tot_fitted_mpmf + sum(excluded_mpmfs.values()))/1000.0\n",
    "ecm_params['general'].at['pm2totpm_val_or_paxdb', 'value'] = fitted_pmf\n",
    "ecm_params['general'].at['avg_enz_sat', 'value'] = target_enz_sat\n",
    "write_parameter_file(os.path.join('data', f'{target_model}_ecm_parameters.xlsx'), ecm_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SBML model loaded by sbmlxdf: SBML_models/iML1515_predicted_fit_GECKO.xml (Thu Feb 12 15:25:22 2026)\n",
      "LP Model of iML1515_GECKO\n",
      "7343 variables, 3372 constraints, 28438 non-zero matrix coefficients\n",
      "total modeled protein: 212.80 mg/gDW, average saturation level: 0.5\n",
      "1494 genes: (492) transporter, (1002) metabolic\n"
     ]
    }
   ],
   "source": [
    "# Load model using gurobipy\n",
    "fname = os.path.join('SBML_models', f'{target_model}.xml')\n",
    "eo = EcmOptimization(fname)\n",
    "total_protein = eo.get_variable_bounds('V_PC_total')['V_PC_total'][1]\n",
    "sigma = eo.avg_enz_saturation\n",
    "all_genes = set(eo.m_dict['fbcGeneProducts']['label'].values)\n",
    "tx_genes, metab_genes = eo.get_tx_metab_genes()\n",
    "print(f'total modeled protein: {total_protein:.2f} mg/gDW, average saturation level: {sigma}')\n",
    "print(f'{len(all_genes)} genes: ({len(tx_genes)}) transporter, ({len(metab_genes)}) metabolic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acetate                  : pred gr: 0.341 h-1 vs. exp 0.290, diff:  0.051\n",
      "Glycerol                 : pred gr: 0.540 h-1 vs. exp 0.470, diff:  0.070\n",
      "Fructose                 : pred gr: 0.544 h-1 vs. exp 0.540, diff:  0.004\n",
      "L-Malate                 : pred gr: 0.526 h-1 vs. exp 0.550, diff: -0.024\n",
      "Glucose                  : pred gr: 0.613 h-1 vs. exp 0.660, diff: -0.047\n",
      "Glucose 6-Phosphate      : pred gr: 0.608 h-1 vs. exp 0.780, diff: -0.172\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x247.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Protein mass fractions:\n",
      "Acetate                  : r² = 0.2511, p = 1.25e-64 ( 999 proteins lin scale)\n",
      "Glycerol                 : r² = 0.2292, p = 2.26e-58 ( 999 proteins lin scale)\n",
      "Fructose                 : r² = 0.6175, p = 2.71e-210 ( 999 proteins lin scale)\n",
      "Glucose                  : r² = 0.8699, p = 0.00e+00 ( 999 proteins lin scale)\n",
      "Acetate                  : r² = 0.4562, p = 2.21e-42 ( 308 proteins log scale)\n",
      "Glycerol                 : r² = 0.4894, p = 5.06e-47 ( 311 proteins log scale)\n",
      "Fructose                 : r² = 0.5298, p = 4.40e-51 ( 302 proteins log scale)\n",
      "Glucose                  : r² = 0.5498, p = 7.85e-56 ( 313 proteins log scale)\n",
      "\n",
      "condition: Glucose\n",
      "1494 proteins in model with total predicted mass fraction of 373.3 mg/gP\n",
      "      999 have been measured with mpmf of  514.4 mg/gP vs. 331.8 mg/gP predicted\n",
      "           762 metabolic proteins measured 414.0 mg/gP vs. 280.5 mg/gP predicted\n",
      "           237 transport proteins measured 100.4 mg/gP vs.  51.3 mg/gP predicted\n",
      "      495 proteins not measured vs.  41.6 mg/gP predicted\n",
      "           495 actual  proteins      41.6 mg/gP predicted\n",
      "total           : r² = 0.8699, p = 0.00e+00 ( 999 proteins lin scale)\n",
      " metabolic      : r² = 0.9105, p = 0.00e+00 ( 762 proteins lin scale)\n",
      " transport      : r² = 0.6008, p = 9.35e-49 ( 237 proteins lin scale)\n",
      "total           : r² = 0.5498, p = 7.85e-56 ( 313 proteins log scale)\n",
      " metabolic      : r² = 0.5762, p = 3.87e-51 ( 266 proteins log scale)\n",
      " transport      : r² = 0.5334, p = 5.65e-09 (  47 proteins log scale)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Fy8uL48eP06VLF6ysrMiXL1+K47t06cKuXbt019qsWTPc3d15+PAhP/30U5LISXx8PMHBwXrbFAoF7u7uKBQKNmzYQOPGjalfvz6TJ0+mdOnSREZG8ttvv/HXX39x7NixJDZ0794dpVJJz549EUIwbtw41q1bR5MmTejQoQOTJk3Cw8ODs2fPMmbMGGrXrp3pTRcePnyoi7BOnTo1U88tkUgkWYEQArVazcKFC3X1J2khPDycMWPGUL16dQYOHPjuE128CJcuGTfWFEkDtVotDh8+LGbMmCH69OkjunTpIoYNGybWr18vAgMDTZkq08guclgpyVsZksPiLQkkQMyaNUsIkVRGibcknPbt2ycqV64s7O3thZ2dnahUqZJYvXp1EhkiQ3MkSDIJIcTIkSNF4cKFhaWlpXB3dxctW7YUly5dMvo9KFKkiJgxY4bo1KmTsLW1FR4eHknkMEhGtsvX11fUqVNH2NjYCEdHR+Hj4yN++OEH3f6bN2+KevXqCUtLS1GyZEnh6+ubqhyWv7+/aNq0qbC1tRUODg6ifv364u7du0IIIUJCQkSTJk2Evb293nsZFBQkevToIfLlyyesrKxE0aJFRb9+/XR/TyqVSowYMUI4OjqKPHnyiNGjRxsthyWEEEuWLNF7z4VI+vdy+vRpUbFiRWFlZWWUHJYQ2v/V1atXi5o1awo7OzthaWmpsz0gIEA3zpAUGCCsrKz05rt586bo0aOH8PT0FJaWlqJIkSKia9euen8Phn6XW7duFWZmZjqpq3/++Ud06NBBODs7CwsLC1GsWDHx1VdfiTdv3hh1XemBRqMR8fHxYvTo0eLWrVvpMuf7LIcVHR0tAgICRHR0dBZYJslI5O8297F582bx4MGDdJlLpVKJ+fPni+PHj6d5rvhRo0R49epGraNGfQpGRUWJWbNmCU9PT2FtbS1q1aolPvnkE9GtWzfRokULUahQIWFmZiZatGghTp8+neYLSE9y+wdKdqZIkSJiyZIlWW2GRKLj8ePH4pNPPtHTU04Pcvs6Ix3XjKdnz55G3/Amx5EjRwSg01dOqxa3/N3mHmJiYsT8+fPTZa7Xr1+LwYMHi/Xr17/7JBqNEAsWCLFli9BoNGLLunXiqp9f+um4lixZktq1a7NmzRqaNGli8DF3QleZLl26MHnyZKM1JyUSiSSjEf+qGUyfPp1Zs2Zlan65xDjUGsG5+6GEvI7BzcEaH29nzJQ5L8eyYcOGVK5c2eQ2zRnBp59+SsuWLbPaDEkW88MPP9CuXTvGjRuX5rmEEMybN48OHToYpVduYAJQKEChQFy5QmxYGN8uWsTYsWOJiIgwagqjHNe//vor1YKaIkWKMGnSJMaOHUtgYKBRJ5fkXP7+++8UdVMjIyMz0ZrsSWBgYIoOUkBAAIULFzZ53hYtWvD3338b3Pfll1/y5ZdfmjxnbubZs2cMHz6cCRMmsGbNmqw2J0uYM2cOu3bt4saNG9jY2FCnTh3mzZuXRO83q/C9GsSM3wIICo/RbcvvZM201mVpXl5qiL4rNjY276SoIskdREZGsnbt2nSpH4iKimLy5Mn4+PgYLYeZhNevoWNHGDECWrZkTZ06tGvfnrEG1G5SwihVgdSc1sRYWFhQrFgxk4yQ5DyqV6+On59fsl8ADx48yPSCm+yEp6dniu9RYh1aU1i7dm2yc6YpOT4XolarGTlyJJMnT84QPcGcwrFjxxgyZAhnzpzhwIEDqFQqmjZtyps3b7LaNHyvBjFoyyU9pxUgODyGQVsu4Xs1KJkj007Dhg0ZNmwYI0eOJG/evLi7u7NmzRrevHlD7969cXBwoHjx4uzbt093zNWrV2nRogX29va4u7vTvXt3Xrx4AUCvXr04duwYy5YtQ6FQoFAoePDgAWq1mr59++Lt7Y2NjQ2lSpVi2bJlBm2aMWMGrq6uODo6MnDgQOLi4nT7YmNjGT58OG5ublhbW1OvXj3Onz+f7PVt3LgxSXe73377jRo1amBtbU2+fPlo3759Gt5BSXbl22+/Ra1Wp5u835dffkmzZs3eTf0lOlr73d4eChUiUqNh2bJl9B8wwKBEY2oYrSrw5s0bxo4dy969e4mLi+Ojjz5ixYoV2VrPUpJx2NjYJKv9KdFibm6eIe9RgQIF0n3O3MaLFy8YOXIkX331Fdu2bctqc7IcX19fvdcbN27Ezc2Nixcv0qBBgyTjY2NjiY2N1b029hGeqag1ghm/BWCoLYUAFMCM3wJoUtYjw9IGNm3axPjx4zl37hw//fQTgwYNYvfu3bRv354vv/ySJUuW0L17dwIDA4mLi+PDDz/kiy++YMmSJURHRzNhwgQ6d+7M4cOHWbZsGbdu3aJ8+fLMnDkT0GooazQaChYsyI4dO3BxceHUqVP079+f/Pnz07lzZ50thw4dwtramqNHj/LgwQN69+6Ni4uLrhnL+PHj+eWXX9i0aRNFihRh/vz5NGvWjDt37uDs7Jzqtf7xxx+0b9+eyZMns3nzZuLi4vjzzz8z5H2VZA0vX75kx44dDBkyJM1yZjExMUydOpW6deu+e+rL1avQqBHs2wfVq7OiUiV61K/PiFat3t0wY/NoR40aJezs7ET//v3FiBEjhKurq2jXrt27J+ZmEllZNPHixQvh6uoq7t+/n+nnzijSmuz/PlKzZk2xc+fOrDbjvSE2Nla0bt1anD9/PtPOmdOKs27fvi0AceXKFYP7k1ObSO/irFN3XogiE35P9evUnRcmz20MH3zwgahXr57udXx8vLCzsxPdu3fXbQsKChKAOH36tJg1a5Zo2rSp3hyPHj0SgLh586ZuzpRUZBIYMmSI6NChg+51z549hbOzs57CxqpVq4S9vb1Qq9UiMjJSWFhYiB9//FG3Py4uTnh6euqKblIrzqpdu7bo1q1b6m/Mv8jirJzF0qVLxZs3b5IoCL0rffr0EXv27Hm3gxP8HpVKiK++Ei+uXRPfffddiocYu44a3YBg9+7dbNiwge+//56lS5eyb98+fv/9d+Lj49/da87lfPPNN7Rt2xYvL680zzV9+nQqV66c5nlMwcvLK8ld1qeffqrrO59VbNy4UfcYLuHr7Y5aQgimTp1K/vz5sbGxoXHjxty+fTvVuVeuXImXlxfW1tbUrFkzSXez0aNH4+zsTKFChZJ01NqxYwetW7dOMudXX33FxIkTDQr4S9KPV69e0bdvX4KDg/n111+TtBLOSuLi4vj5558ZNWoUXbt2pWvXrowaNYodO3boPQrODDQaDSNHjqRu3bqUL1/e4JhJkyYRHh6u+3r06FGG2BLyOib1QSaMexcqVqyo+9nMzAwXFxcqVKig2+bu7q61ISQEf39/jhw5gr29ve6rdOnSgLb7WkqsXLmSatWq4erqir29PT/88EOSepBKlSrpdSCrXbs2kZGRPHr0iLt376JSqahbt65uv4WFBT4+Ply/ft2oa/Xz8+Ojjz4yaqwk5/DkyRO2bNnCkCFDsLW1Ral8995ScXFxTJkyhYMHD7J27Vratm1r+iRbt0Lp0vDoEZibsyhPHmy9vdMtlc3oq3v8+LHeP0y1atWwsLDg6dOn6WJIbiMqKop169bRt2/frDZFDyFEmm42bGxs3iknJb1xdHQkKChI9/Xw4UO9/fPnz2f58uWsXr2as2fPYmdnR7NmzYiJSf4D8KeffmL06NFMmzaNS5cuUalSJZo1a0ZISAigzQ3bunUrf/31F/Pnz+eLL77Q5baFh4czefJkVq5cmWTeFi1a8Pr1a708OUn6EhUVRZcuXejXrx+FCxfOVh1/7ty5Q5kyZejZsyeXL19Go9Gg0Wi4fPkyPXr0oFy5cuneRjklhgwZwtWrV9m+fXuyY6ysrHB0dNT7ygjcHIxr4WzsuHfhbZUchUKhty3hb0mj0RAZGUnr1q2T5Jbfvn3bYMpFAtu3b2fs2LH07duXv/76Cz8/P3r37p3pNy2yUCv3sWzZMtzc3OjcuTPm5mnrKSWEoGfPnlSsWJHGjRubto6+fg2nTml/btsW/vc/nigUbNy4kdGjR2NjY5Nu67LRjqtGo0nyD25ubm5SK8r3iT///BMrKytq1aoFaKNB3bp1w9XVFRsbG0qUKMGGDRt04ydMmEDJkiWxtbWlaNGiTJkyRdeedOPGjcyYMQN/f39dhHHjxo08ePAAhUKh12ErLCwMhULB0aNHATh69CgKhYJ9+/ZRrVo1rKysOHHiBHfv3qVt27a4u7tjb29PjRo1OHjwoG6ehg0b8vDhQ0aNGqU7Z4Itbyf7r1q1imLFimFpaUmpUqX43//+p7dfoVCwdu1a2rdvj62tLSVKlGDv3r1pen8VCgUeHh66r4SoCGj/+ZYuXcpXX31F27ZtqVixIps3b+bp06fs2bMn2TkXL15Mv3796N27N2XLlmX16tXY2tqyfv16AK5fv07Dhg2pXr06Xbt2xdHRkfv37wPa3LNBgwYZVAkwMzOjZcuWKToKkncjIiKCwYMHExkZyZ9//qn7f8tODBo0iAoVKvDs2TOOHj3KTz/9xE8//cTRo0d59uwZ5cqVy/AWiQkMHTqU33//nSNHjlCwYMFMOWdK+Hg7k9/JmuQ+zhRo1QV8vFPP38wMqlatyrVr1/Dy8krSBtrOzg4w3KL55MmT1KlTh8GDB1OlShWKFy9uMELr7+9PdEIhC9pW1vb29hQqVEi3xp48eVK3X6VScf78eaPl3SpWrMihQ4fe5dIl2Yx79+7x66+/0r9/fywsLLC0tHznueLj4/n66685ffo0W7ZsoVOnTqZPMn06dOoEKhXY2bEoMBC3f4sX0zuQYLTjKoTgo48+omrVqrqvqKgoWrdurbdNouXvv/+mWrVqutdTpkwhICCAffv2cf36dVatWqXXutPBwYGNGzcSEBDAsmXLWLNmDUuWLAG0j+fHjBlDuXLldBHGTz/91CR7Jk6cyNy5c7l+/ToVK1YkMjKSli1bcujQIS5fvkzz5s1p3bq17tHVrl27KFiwIDNnztSd0xC7d+9mxIgRjBkzhqtXrzJgwAB69+7NkSNH9MbNmDGDzp07888//9CyZUu6detGaGiobn/iR2+Gvt5+xBAZGUmRIkUoVKgQbdu25dq1a7p99+/fJzg4mMaNG+u2OTk5UbNmTU6fPm3wOuLi4rh48aLeMUqlksaNG+uOqVSpEhcuXODVq1dcvHiR6OhoihcvzokTJ7h06RLDhw9P9v338fFJVsJK8m6EhYXRrl07unTpgpubW5JWttmFkydP8vXXXxuMWjo6OjJr1qwM/9sQQjB06FB2797N4cOH8fb2ztDzGYuZUsG01lqn6+2PtoTX01qXzTZ6rkOGDCE0NJSuXbty/vx57t69y/79++ndu7fOWfXy8uLs2bM8ePCAFy9eoNFoKFGiBBcuXGD//v3cunWLKVOmGFQDiIuLo2/fvgQEBPDnn38ybdo0hg4dilKpxM7OjkGDBjFu3Dh8fX0JCAigX79+REVFGf1kb9q0aWzbto1p06Zx/fp1rly5wrx589L1PZJkPCtXrqRQoUI0adIkzVF0IQQdO3bE29ub2rVrm7aOnjkDCQGvSZPg1CnuBgbyyy+/MGLECCwsLDJkXTY6rjxt2rQk294p9+E94eHDh3pyR4GBgVSpUkWXd/d23utXX32l+9nLy4uxY8eyfft2xo8fj42NDfb29pibm+Ph4fFO9sycOZMmTZroXjs7O1OpUiXd61mzZrF792727t3L0KFDcXZ2xszMDAcHhxTPuXDhQnr16sXgwYMBbQ7omTNnWLhwIY0aNdKN69Wrl05GY/bs2Sxfvpxz587RvHlzAL2osSESf+iXKlWK9evXU7FiRcLDw1m4cCF16tTh2rVrFCxYkODgYAC9KGzC64R9b/PixQvUarXBY27cuAFAs2bN+Pzzz6lRowY2NjZs2rRJ92GyceNGVq1axYoVK8iXLx8//PAD5cqV083j6enJo0eP0Gg0aco/kmhvWqZNm8bUqVPZt28fVlZWWW1SiuTJk4cHDx4km0/64MGDJE8x0pshQ4awdetWfv31VxwcHHT/B05OTln++Lh5+fys+rxqEh1Xj2yo4+rp6cnJkyeZMGECTZs2JTY2liJFitC8eXPd//XYsWPp2bMnZcuWJTo6mvv37zNgwAAuX77Mp59+ikKhoGvXrgwePDhJ+tBHH31EiRIlaNCgAbGxsXTt2pXp06fr9s+dOxeNRkP37t15/fo11atXZ//+/eTNm9co+xs2bMiOHTuYNWsWc+fOxdHRMcUUB0n24ubNmzx+/Jhu3bphYWFhsBmUsajVahYvXkzTpk356aef3m0dnTNH20ygcWPIl4/FmzczYsQIChUqlOa0hRR5t3KxnENWVfs2bdpUDB48WPf6zz//FDY2NqJSpUpi3Lhx4uTJk3rjt2/fLurUqSPc3d2FnZ2dsLKyEq6urrr906ZNE5UqVdI75v79+wIQly9f1m179eqVAMSRI0eEEP9VmT5+/Fjv2NevX4sxY8aI0qVLCycnJ2FnZyeUSqUYN26cboyhlq1vV6nmzZtXbNy4UW/M0qVLhbe3t+41IH7++We9MY6OjmLTpk0iPYiLi9P1vBdCiJMnTwpAPH36VG9cp06dROfOnQ3O8eTJEwGIU6dO6W0fN26c8PHxSfbc06dPFyNHjhT+/v7C3d1dhISEiPXr14uqVavqjfvrr78EIKKiot7lEiX/EhwcLBo1aiQOHDiQ1abokdI6M2XKFJE3b16xePFi4e/vL4KDg0VwcLDw9/cXixcvFs7OzmLatGkZah8GFAIAsWHDBqOOz4yWr/FqjTh154XYc/mxOHXnhYhXa9I0nyTtSFWB7MOaNWtEdHS0eP36tdHHJPc/pVKpRKtWrcS6deuERmPC/1lcnBDffCPE339rX4eGChEfL65duyZ8fX3T/PlmrL+WgS7x+02+fPl49eqV7nWLFi14+PAhf/75JwcOHOCjjz5iyJAhLFy4kNOnT9OtWzdmzJhBs2bNcHJyYvv27SxatCjFcyTc4QvxnwpiQl7s2yTkXyUwduxYDhw4wMKFCylevDg2NjZ07Ngxw4oFDBVAJK6yt7e3T/H4zz//nNWrVyc7d5UqVXQFLgkR4mfPnpE//3/RmmfPniWrzJAvXz7MzMx49uyZ3vZnz54lG3G+ceMGW7Zs4fLly6xfv54GDRrg6upK586d6dOnD69fv8bBwQGA0NBQ7Ozssjy6lVOJjo5m7ty5TJgwgb1796b695KdmDlzJnZ2dixYsIAxY8bo8r2EEHh4eDBhwgTGjx+foTYkXiOyK2ZKBbWLuWS1GRJJtuLatWtERUXRqlWrJOo5KWGoG52HgyVVIs8w9ovP2LZtm+nrqJkZ/PknODhAvXqQNy/ffvst/fv3p1ixYpn29Mtkx7VKlSoGE20TJImKFy9Or1699B4Tv49UqVKFLVu26G1zdXWlZ8+e9OzZk/r16zNu3DgWLlzIqVOnKFKkCJMnT9aNfbtK3lDCf0Lzh6CgIKpUqQKk/sg9gZMnT9KrVy9d15TIyEgePHiQ6jnfpkyZMpw8eZKePXvqzW1qL3hTUgXeRq1Wc+XKFV1Pbm9vbzw8PDh06JDOUY2IiODs2bMMGjTI4ByWlpZUq1aNQ4cO0a5dO0BbkHjo0CGGDh2aZLwQggEDBrB48WLs7e1Rq9W6m4aE74nfu6tXr+p+RxLTePToEb169WLMmDF6UkE5iQkTJjBhwgRd/jVob7CyS66pRCLJfmzdupWPP/4Y0Kb1GEtCN7rEt6siXsU/G2Zwu1gN2kTZUMxYp/XpU+jZE5Ytg7Jl4dgxMDPjn3/+ITIyks8//zxNhWHvgsmOa/PmzVm1ahUVKlTAx8cHgPPnz/PPP//Qq1cvAgICaNy4Mbt27Xqvc2CbNWvGpEmTePXqFXnz5mXq1KlUq1aNcuXKERsby++//65rpVuiRAkCAwPZvn07NWrU4I8//mD37t1683l5eXH//n38/PwoWLAgDg4O2NjYUKtWLebOnYu3tzchISF6ubIpUaJECXbt2kXr1q1RKBRMmTIlic6ol5cXx48fp0uXLlhZWekVkyUwbtw4OnfuTJUqVWjcuDG//fYbu3bt0lMoMAZTOkzNnDmTWrVqUbx4ccLCwliwYAEPHz7kiy++ALQ3USNHjuTrr7+mRIkSeHt7M2XKFDw9PXVOKWjzydq3b69zTEePHk3Pnj2pXr06Pj4+LF26VNf68W3Wrl2Lq6urTre1bt26TJ8+nTNnzrBv3z7Kli2rl7f4999/07RpU5Pek/ed2NhYVqxYwdChQ9m5c6fReXzZGW9vb+msSiSSFLly5QoWFhbUrFnTJIcVknajE0IQ6b8fm2I1cGk5EnNbJ2b+fp2m5fKnXPSoUoGFBbi4gKWlVu4KwMyMtWvX0rVrV5RKZdY8RTQ1B+GLL74QM2fOTLJ91qxZ4osvvhBCCDF16lRRrVo1U6fOELKyo42Pj49YvXq1EEL7/pQpU0bY2NgIZ2dn0bZtW3Hv3j3d2HHjxgkXFxdhb28vPv30U7FkyRK9XNKYmBjRoUMHkSdPHr3ctICAAFG7dm1hY2MjKleurMulfDvHNaGTSgL3798XjRo1EjY2NqJQoULi22+/TdLx5fTp06JixYrCyspKJPypGOqc9d1334miRYsKCwsLUbJkSbF582a9/YDYvXu33jYnJyej8+veZuTIkaJw4cLC0tJSuLu7i5YtW4pLly7pjdFoNGLKlCnC3d1dWFlZiY8++kjX2SaBIkWKJMktXLFihW5uHx8fcebMmSTnDw4OFkWKFBFPnjzR2z5jxgzh7OwsSpcuLc6ePavb/vjxY2FhYSEePXr0Ttf7PnLv3j3x0UcfiV27dmW1KUaRlnUmMDBQ9O7dOwOsSj8yI8dVkv2Qv9vMRaPRiL1794qgoCDx8uXLd5ojcTe6QqN2CJsStUTeD/uJwuP3Gt+N7uhRIQoVEuKtOpGLFy+Kf/75J0n9SHph7DqqEMK05CcnJycuXryYJEJ2584dqlWrRnh4ODdu3KBGjRq8TvDQs5CIiAicnJwIDw/PMBHt5Pjjjz8YN24cV69elZXk7zETJkzg1atX/PDDD1ltSrZHpVKxceNGunbtSnR0tC4dJruTlnXG39+fqlWrZmtN7JSuLyYmhvv37+Pt7W1SDp4k+yN/t5nHlStXcHFxISQkxKQumWqN4Nz9UEJex+DmYE1weDQjf/LjTcBRbIrVQMRFYe6YtGnQsi6VaVu5gP7GZ8/A3R1evYKZM+Grr8DFBSEE27dvp2nTplhZWWVYjYGx66jJqQLW1tacOnUqieN66tQp3R+2RqORf+TAxx9/zO3bt3ny5AmFChXKanMkWYSbmxujR4/OajOyPXfv3mXQoEF88cUXOv3e3EBqzTbu3buXSZZIJJLshkaj4e+//6ZQoUJYWloadlo1anh4CiKfgb07FKkDSjODBViOyjhe7FmIpWdJlJY2KKwNr6NJutEtXAiLFsGdO5A3L/yrI3/hwgXy5ctHjRo1cHHJHsWTJjuuw4YNY+DAgVy8eJEaNWoA2hzXtWvX8uWXXwKwf/9+k+4YcjMjR47MahMkWcyYMWOy2oRsTXx8PLt27aJx48Zs3rz5nbWKsyvt2rVDoVCkWNmfnVrUSiSSzCFBe9zMzIyiRYsaHhSwF3wnQMRT3aZYWw9+9RjO+AAv3TYhBNG3TiO8q5CnQQ8sXAx3xlOg1Uj28XaG8HB48kRbdNW5M3h5wb8FsBqNBl9fXypWrIiDg4PJubYZicnPr7/66ivWrFnDuXPnGD58OMOHD+fcuXOsWbNGVxU/cOBAfvvtt3Q3ViKR5C7u3LlDq1atiI2NJW/evLnOaQXInz8/u3btQqPRGPy6dOlSVpsokUgyEbVajZ+fH2q1mri4OOrVq2d4YMBe+LmHntMKYPEmmI53v6SZ8px2vujXvNg7n7iXgWBmkaLTCom60fXtCz16gBBQuDB07AgKBRcvXuTVq1d4enpSsGDBbOW0ggkR13v37unuCLp160a3bt2SHSu1KiUSSUqo1WoOHz5MmTJlWLduHQUKFEj9oBxKtWrVuHjxYrIqK6lFYyUSSe7h1q1bFCpUiGfPntGsWbPkB2rU2kgrSdcGpQI0AqZZ/I+9N8ywKFAWp9qdsXTTVyxxtrMg9M1/2u4eTtYs9XxNTfECyA9z54K1tbb7Fdoag3Pnzul0zbPrk3OjHdeKFSvi5eVFmzZtaNeunU4KSyKRSEzh7t27DB06lE8++USvDXFuZdy4cbx58ybZ/cWLF+fIkSOZaJEkgYYNG1K5cmWWLl2a1aZkKF5eXowcOVKmrmUharWa+/fvExISgrOzc8pOK2hzWt+KtCYmPEYwwfcxzvbHiCxcKYnTCjClVTk8HK11hVs+RfJgVrkSNGgA330HiWqV/P39KV68OEIISpUq9c7XmRkY7bi+ePGCAwcO8Ouvv9KmTRsUCgWtWrWiTZs2NGnSRBZjSSSSFNFoNFy4cAFHR0dWrVqFl5dXVpuUKdSvXz/F/XZ2dnzwwQeZZE02JpkClKxECIFarc7YvusZSFxcXKaLw0uS8vDhQ9zc3Lh27ZrR+vaa18HJ5nKefRxPcWclw30s+Z9rLfZqDP99ejhaU9vTFhas1KYEmLvAX39BopQslUrF9evXUalUqFSq5NMWshFG57haW1vTunVr1q5dS1BQEL/88gsuLi5MmDCBfPny0a5dO9avX8/z588z0l6JRJIDefDgAW3btuXSpUuULl36vXFaJUYSsBeWlodNreCXvtrvS8trt2cQvXr14tixYyxbtgyFQoFCoWDjxo0oFAr27dtHtWrVsLKy4sSJE9y9e5e2bdvi7u6Ovb09NWrUSNJkxcvLi9mzZ9OnTx8cHBwoXLiwngReXFwcQ4cOJX/+/FhbW1OkSBHmzJmj269QKFi1ahUtWrTAxsaGokWLsnPnTr1zXLlyhQ8//BAbGxtcXFzo378/kZGRetfUrl07vvnmGzw9PSlVqhQNGzbk4cOHjBo1SnedksxBo9Hw9OlTrl69SkxMjNFOq+/VIPrvfpxke1iMoO+v0fxyPR4nawU1CpgRQp4k4xRA/oQCLLUaNm6E06e1Oz094V95zuvXrxMfH8+zZ8+oXr26XtOc7IzJOq6GuH37Nnv37uXXX3/l7NmzLF68mCFDhqSHfWkmK3VcJZL3HSEEN27c4PXr1zg7O5vUIS0nYcw60759+1TbZX/22WfZ8jFdhuq4JhSgJMnl+/e96rwZyrZ5J7tTIjw8nBYtWlC+fHlmzpwJaKu8GzduTMWKFVm4cCFFixYlb968PHr0iDNnzlC3bl2srKzYvHkzCxcu5ObNmxQuXBjQOq6vX79m1qxZNG3alJ07dzJ58mQCAgIoVaoUCxcuZPny5fz4448ULlyYR48e8ejRI7p27aq9WoUCFxcX5s6dS4MGDfjf//7HnDlzuHLlCmXKlOHNmzeUKFGC2rVrM2PGDEJCQvjiiy9o0KABGzduBLSO6y+//EL79u2ZMGECoC0OrFSpEv3796dfv34ARhdBSh3XdyckJARra2sOHDhAhw4dkux/W3/Vx9sZM6WCP/0fsfmn7bgTyhSL/+HMa5QKCHiuxs5CwZPXGuoUMkcjIBgX6sUuQ5MoBqkAvEKf8NPtnbj9sh2cnSE2FqysdGPi4+MJDAzkwYMHVKtWLdsUX2WYjqshSpQowZgxYxgzZgwvX74kNDQ0PaaVSCQ5mMePHzNs2DCaNGnC4MGDs9qcLMfJyYk9e/aQJ08eqlWrBsClS5cICwujadOm/PTTT8ybN49Dhw5Rt27dLLY2k0ihAEW7TQG+E6H0x+meNuDk5ISlpSW2trY6R+7GjRuAtq104vxrZ2dnKlWqpHs9a9Ysdu/ezd69e3UtowFatmyp+1ufMGECS5Ys4ciRI5QqVYrAwEBKlChBvXr1UCgUFClSJIlNnTp10rWunjVrFgcOHGDFihV89913bN26lZiYGDZv3oydnR0A3377La1bt2bevHm4u7sD2tSTtWvX6qUImJmZ4eDgkCtVO7IbQgjCwsI4ePAgbdq0Mei0GtJf9XQw50uHP6n7cgctLf/LiY+IEYw7GIOtuYJFzawokkfrtALMUHXXd1qFBo88tkxpXhO3LzdCUJDWcU3ktD58+BAXFxeuX7/Oxx9/nP5vQCZgsuOanJh2QtSgRIkSlChRIs2GSSSSnIkQgkePHnHz5k3mzJlD6dKls9qkbIGHhwefffYZ3377ra6TnkajYcSIETg4OLB9+3YGDhzIhAkTOHHiRBZbm0mkUoACAiKeaMd5p5wrnJ5Ur15d73VkZCTTp0/njz/+ICgoiPj4eKKjowkMDNQbV7FiRd3PCoUCDw8PQkJCAG00tEmTJpQqVYrmzZvTqlUrmjZtqnd87dq1k7z28/MDtI91K1WqpHNaAerWrYtGo+HmzZs6x7VChQoyrzWLiIiIICYmhmPHjvHZZ58ZHON7NYhBWy7pbtWUaBhitpsBcb9jHxr7n2YVEBiuITxG8GlZCz4s+p+7FowLM1Td2a/5r0j+G3GLdr9twPrCWcxsbeDUKb3zqtVqnj9/zqVLl2jSpEmOdVrhHRzX5MS0E7YpFArq1avHnj17yJs3b7oZKpFIsj/BwcEMGzaMhg0bZpt0oezCunXrOHnypF77Z6VSybBhw6hTpw6zZ89m6NChqRZz5Soin6XvuHQisXMIMHbsWA4cOMDChQspXrw4NjY2dOzYkbi4OL1xFhYWeq8VCgUajQaAqlWrcv/+ffbt28fBgwfp3LkzjRs3TpLHmt62SzIeIQRRUVFs3bqV3r1706lTJ4Pj1BrBjN8CdE5rC+VZ5lv8gIMiWm/cmzjBpEOxACxrbqVLMXol7Fgf34KV6nbaSKsQOMa+Ia+nK93atoTo+6COT3LekJAQlEol586do3379ul34VmEyQ0IDhw4QI0aNThw4ADh4eGEh4dz4MABatasye+//87x48d5+fIlY8eOzQh7JRJJNkQIwYsXLzh58iTTpk2TTqsB4uPjdY+iE3Pjxg3UajWgLYJ9r4pn7N3Td5yJWFpa6t77lDh58iS9evWiffv2VKhQAQ8PDx48eGDy+RwdHfn0009Zs2YNP/30E7/88oteat2ZM2f0xp85c4YyZcoAUKZMGfz9/fWk1RJuhFLLizb2OiWmExMTQ2BgIL6+vgwcOBCrRI/l3+bc/VBdesBEs618Z7EsidP6IkqD/zM1rUqas7yF/nrgxBtGme+kifICAJOPrOPnnVM5NrYhlCoFy5eDg4NuvEajISwsjD///BMHBwfatEn/XPGswGTHdcSIESxevJiPPvoIBwcHHBwc+Oijj1iwYAHjxo2jbt26LF26lAMHDqQ616pVq6hYsSKOjo44OjpSu3Zt9u3bp9sfExPDkCFDcHFxwd7eng4dOvDsWebeeUskkpR5/vw5n3/+OTt27KBDhw6UL18+q03KlnTv3p2+ffuyZMkSTpw4wYkTJ1iyZAl9+/alR48eABw7doxy5cplsaWZSJE64OiJ3vNRPRTgWEA7LgPw8vLi7NmzPHjwgBcvXuiio29TokQJdu3ahZ+fH/7+/nz22WfJjk2OxYsXs23bNm7cuMGtW7fYsWMHHh4eepXcO3bsYP369dy6dYtp06Zx7tw5XQ5tt27dsLa2pmfPnly9epUjR44wbNgwunfvrksTSOk6jx8/zpMnT3jx4oVJdksMI4RApVKxatUqChQoYDCX9W2CI7ROa3PlGQaY/663L1olGPtXDLOOxVGnkDlNiyV9IK5UANGCGdEbsFBqKDGkD6VXztc1EEhMREQEjx8/5vjx4/Tq1StFhzqnYXKqwN27dw1Wezk6OnLv3j1A+09uzD9HwYIFmTt3LiVKlEAIwaZNm2jbti2XL1+mXLlyjBo1ij/++IMdO3bg5OSkEy0/efKkqWZLJJIMIDIykt9//52xY8dSpUqVrDYnW7NkyRLc3d2ZP3++7gbc3d2dUaNG6SrAmzZtSvPmzbPSzMxFaQbN5/2rKqBAv0jr3w/j5nMzTM917Nix9OzZk7JlyxIdHc2GDRsMjlu8eDF9+vShTp065MuXjwkTJhAREWHSuRwcHJg/fz63b9/GzMyMGjVq8Oeff+qljsyYMYPt27czePBg8ufPz7Zt2yhbtiwAtra27N+/nxEjRlCjRg1sbW3p0KEDixcvTvXcM2fOZMCAARQrVozY2FjZqS2NqNVqbt26xa1btxg1apThMW+pBrx8HcP4Hf4o0fC1xQY9XzMyTnDqkZqGXma0KmlhcD4AhED54xs8nKK58bsTZkUbGBgiiI6OZvPmzQwcOFCnepGbMFkOq169ejg4OLB582ZcXV0BbcSlR48evHnzhuPHj3Pw4EGGDBnCzZs3TTbI2dmZBQsW0LFjR1xdXdm6dSsdO3YEtI/UypQpw+nTp6lVq5ZR80k5LIkk/QkNDWXUqFHUqVOHAQMGZLU5WY6p60yC05NT1qQMlcMCrSSW7wT9Qi3HAlqnNQOksLIjCoWC3bt3065du6w2RUe6y2FlwyYTpqLRaJg/fz4TJkxINq3HkGpAArWUAWy3/BqA2HjBzGOxRMfD4mYpvL/348FVCfZKeKIGJwX03AAVOuoNi42N5f79+9y7d4+WLVu++0VmERkmh7Vu3Tratm1LwYIFKVSoEACPHj2iaNGi/Prrr4A2CvPVV1+ZNK9arWbHjh28efOG2rVrc/HiRVQqFY0bN9aNKV26NIULF07RcY2NjSU2Nlb32tS7YolEkjJxcXH873//Y/DgwdSsWTOrzclxPH/+XHdTX7p0afLly5fFFmUDyrbRSl7lcKdGkgIGb048tRH3HHBzIoTg4sWLvHz5kokTJyY77m3VgLdxIwyAOLXg91vxVPM045MyKURZ4wTsiIballDfCgr8+z+RKO9bCEF8fDwrVqxgzJgxuV7JxWTHtVSpUgQEBPDXX39x69Yt3bYmTZroHnmYcsd45coVateuTUxMDPb29uzevZuyZcvi5+eHpaVlkk4O7u7uBAcHJzvfnDlzmDFjhqmXJZFIUiE8PJwxY8ZQp04dRowYkdXm5DjevHnDsGHD2Lx5sy4/0szMjB49erBixQpsbW2z2MIsRmmWqZJXkkwkuSYTEUHa7RnUZCK9EELwzTffMHny5BSLJ99WDTBEsNqBmcdieRMnmNckmShrnIAzcVDLEiwV0NcWnBNSShRah//fvG+1Ws3ly5cJCwt7b4ri36kBgVKppHnz5umSi1WqVCn8/PwIDw9n586d9OzZk2PHjr3zfJMmTWL06NG61xEREbrIsEQieTfUajUrVqygV69eOaKXdXZk9OjRHDt2jN9++03XYODEiRMMHz6cMWPGsGrVqiy2UJKV5Nq80yxsMpEeHD9+HCDZp8gJuazB4dFcCnxlMD0gAaFRc+RaMA2dHZhcLjbZcUQJxOlYREEzlEXNwSXhffkv71solIh/0xYmTpz4XqmRvJPjev78eY4cOUJISEiSykpjEsUTY2lpqWsDWa1aNc6fP8+yZcv49NNPiYuLIywsTC/q+uzZsxS7f1hZWeWq6jmJJCuJjIxkwoQJ1KlTx+T0H4k+v/zyCzt37qRhw4a6bS1btsTGxobOnTtLx1WSK0jSylRxDbNs2GQiNRKirCmteynlsurNpVETcW4XQhVLnvqf4690QqFYikb8qxQA8EwNp+MQra3Za1+HkG/q048fDaRWzEWUac3x48cxMzNj0qRJ6XC1OQuTHdfZs2fz1VdfUapUKdzd3fW8/PTw+DUaDbGxsVSrVg0LCwsOHTqkk5m4efMmgYGBSbqLSCSS9EcIwcyZM+nQoQMffvhhVpuT44mKijIoW+Tm5kZUVFQWWCSRpC+GHLke9ueZaczBmdxkIiX27duHm5tbqk7r27msSjT4KG/gRhgh5OGcpjRqoSDSbx9mds7Yldeuo/s1PgxSjWSaxWY8xUutnJUG4p/BV696c9jjY06PbAJMSpL3LRRKZs+ezeTJkzP2TcjGmOy4Llu2jPXr19OrV680n3zSpEm0aNGCwoUL8/r1a7Zu3crRo0fZv38/Tk5O9O3bl9GjR+Ps7IyjoyPDhg2jdu3aRisKSCQS04mKiuLLL7+kfv36zJ8/P6vNyTXUrl2badOmsXnzZl2FdnR0NDNmzMgVN+Om6ppKsj+m/E6TK0q6FWUHxnSgzaAmE6YghGD27Nl8+eWXJueyNlOe0zqiilDdmJlnzNkXVwVFrdFJ5tiv8SHP+Vd0vn2QTR3bEeKSl3M9SqFRmEGkinP3Q6ldzEUvCv3nn3/i7u7+Xjut8A6Oq1Kp1OVnpZWQkBB69OhBUFAQTk5OVKxYkf3799OkSRNAq3uoVCrp0KEDsbGxNGvWjO+++y5dzi2RSAwzZswY2rZt+37piWYCy5Yto1mzZhQsWJBKlSoB4O/vj7W1Nfv3789i694dS0tLlEolT58+xdXVFUtLy/cq3y43IoQgLi6O58+fo1QqsbRM2fNMqSjpnKY0T4UzHorQZDoe6RcbZRW7du2iTJkyqTqtAGduh+D1+gJdzQJAAfHCjJHmv+iNWXAqDg9rFadrnWdw/Dn2a3y0O4TAOj6WGAtrguzzcTlPSfbF+6Ays9DrwxHy+r+odYJD/b47rAmYrOM6f/58nj59ytKlSzPIpPRF6rhKJKkTExPDtGnTaNSokXRY3wFj15moqCh+/PFHXevXMmXK0K1bN2xsbDLL1HciteuLi4sjKChIpjzkMmxtbcmfP3+qjuvpuy/puuZMsvubKc+xymIpCoUChaEmE5mpKvCWlmx8AR8WL13G2LFj/2sGkYLe7OX9m/A69SV5FZF60woBAsHqCyrUGhhWU/ueaQQE40K92GVoULL0twUohWB4m/EpmrmtXy1qF3Nh586dlC9fPtdLXEEG6riOHTuWjz/+mGLFilG2bFksLPT1x3bt2mW6tRKJJEvp168fnTp1StZpTVJw4e2MmVJG1UzF1taWfv36Zcm5V65cyYIFCwgODqZSpUqsWLECHx+fdJnb0tKSwoULEx8fj1qtTpc5JVmLmZkZ5ubmRkXPE0cHDZGQ07nUcRs2MYlyWf8tNso0p/UtLdltV1TUKePJmE8X/Oe0GtCbjbZ2J7DmNCJj4qlyZrjBBsUKBUw7EourrZIhPv/5RUoFeEY954P4yxyxrMafpeoRZ5a866UAPJysqVrIkblz56bY6OB9xWTHdfjw4Rw5coRGjRrh4uIi31CJJIcSFxfHrFmzaNq0KZs3bzapC0x+J2umtS5L8/L5M8vcHMnevXuNHtumTcZ9eP/000+MHj2a1atXU7NmTZYuXUqzZs24efMmbm5u6XIOhUKBhYVFkmCGJPfj5pB6V639Gh/8OgyhtvnNrGky8a+WrEAQFy/4/mIcg6pbYm72AsUvvcHsXzsM6M1aRT+jxNHBhGGHAvTatQohWH9ZhUIBMxpaJV1H1QK+f0O3Mvs40rAaf5X8L589mSbH1Oc6wUHFpdOaDCanCjg4OLB9+3Y+/vjjjLIpXZGpAhJJUoQQdOzYkS5dutCpU6dkxyVXcJGwlK76vKp0Xkl+nUnchz4lFApFhkYqa9asSY0aNfj2228BbdFNoUKFGDZsmMEuQIY6EBYqVEiuoxKDqDWCevMOExweYzDPNSGKeGLCh1nzpEajhqXlERFP+fGfOJoWM8fRSoG1udYWgQKFoycIDbwOMjxFYumqRIz7KwZ3ewWjalnqX9v9eChkBuYKuBNPf5ex/GXz3xOOUY1Lsv18oF5AwN1WSaXXZ1k9b6rRa0duIsNSBZydnSlWrFiajJNIJFmDSqVi7ty5tGzZkp9++glz8+SXgJQKLv6VDWfGbwE0Kesh0waSITtU2sfFxXHx4kU9vUelUknjxo05ffq0wWNkB0KJKZgpFUxrXZZBWy4lG0Wc1rps1q0TD0/x+vkTfr6mokt5C+ws9e1QJGjJpoDyrSjrln9UWJkrmNPYCvO3rytcA/+LgrbWaCpaElzMnYOx1f89l9aJH/phcYZ+WFyXgnVu/y6G9uyMa74m76XTagomvzvTp09n2rRpMglfIslhqNVqPvnkE4oWLUrVqlVTdFoBzt0PTbkLDBAUHsO5+6HpbKkkPXnx4gVqtTqJhmxK7bMnTZpEeHi47uvRo0eZYaokG6LWCE7ffcmvfk84ffclao3hh7TNy+dn1edV8XDSTxvwcLLO8iczm7f9jEZAp3JJndZ3YaRvLI8iBJ+UMf/PaY0VcCZWW6XlpIT+dmgqaNNmZqi6o0GZxIk3Uyoo72ZJ0JnfmDZqAPk93FNdlyXvEHFdvnw5d+/exd3dHS8vryT5TJcuXUo34yQSSdpRq9UsXryY1q1bs2PHDp2GaGqkVnBh6jhJzkF2IJSA6fntzcvnp0lZj2xTyBkaGsrhw4cpXqUujmc2pTlf9OdrKuwtYV4TK12agY5gNRyOBW9zcDcDDzOChQszVN11Ulgeb71369ato2PHjvTq1StV5QbJf5jsuLZr1y4DzJBIJBlBXFwc7du3p2PHjpQqVcqkhduYggtTxkmyhnz58mFmZsazZ/qdiVJrny15v0kuvz04PIZBWy4lG0U1Uyq0wvlZzPbt22nTpg0NGjTg9JMYgnDBQ4QazFPVSlY5Y00MeYnC0DI5+I9oXG0VTG5ghaXZvwMex8P1eGhiDUXMYZQDUdbWTIrryzOcOacpzSdVC7OsZD49J/7ly5f4+vrSpUsX7OzsMvaNyIWY7LhOmzYtI+yQSCTpiEaj4dtvv6V9+/Zs3boVJycnk+fw8XYmv5N1qgUXPt7OabZXknFYWlpSrVo1Dh06pAs8aDQaDh06xNChQ7PWOEnGkoIeaUrk5Pz2kJAQ/Pz8qF+/PjY2Ntja2uL2+iUzVD1YZbE0SZFVQubDb/G1+cL8zyRO654bKlxsFMz5yBon67dzWYW2CCtWgJUCbBTYEssznDmjKUteWwvmdayo9x5t2LCBTz/9lFatWkmn9R0xKsfVROEBiUSShURFRdGmTRssLS0pWLDgOzmt8F/BBZBEtzBbFFzkAEaPHs2bN28AOH78OPHx8Vlmx5o1a9i0aRPXr19n0KBBvHnzht69e2eJPZJMIGAvLC0Pm1rBL32135eW125PhZya375r1y4cHBwoU6YMBQoU0D1h8vF25h+HBgxWjSQY/RvtYFz4Ib4V/c3/wEzxn68jhGDAb9GceazGp4DZf07r6Vjw/fe9KWsO/ey0Tmsi3AlFAcz5pIJufQwKCuK3336jTZs22NravvO6LDHScS1Xrhzbt28nLi4uxXG3b99m0KBBzJ07N12Mk0gkxiOEYM2aNURFRbFu3ToGDhyY5pyu7FxwkRNYsWIFkZHaDjuNGjUiNDRrPug//fRTFi5cyNSpU6lcuTJ+fn74+vomKdiS5BL+1SxNLKIPQESQdnsqzqvJ+e0aNdz/G67s1H7XZG4TiqCgIM6ePUv58uWxtramUAFPPXvM0DCtdVn2a3yoH7ucLnFfMTxuKF3ivqJB7BLamJ/Sm2/fbRX+zzTMbGTF3MbWWJmh1WMFrbyVOdoiLIUCQ3kFLooIRjYuqVsft2zZQt68ealVqxYuLlmfRpHTMSpVYMWKFUyYMIHBgwfTpEkTqlevjqenJ9bW1rx69YqAgABOnDjBtWvXGDp0KIMGDcpouyUSSSLCw8Pp2bMnjRo1wtnZOV3lVLJbwUVOwsvLi+XLl9O0aVOEEJw+fZq8efMaHNugQYMMtWXo0KEyNeB9QKPWdn5K6UG/70Qo/XGyaQMm5bcb6DSl7Yg1L1M6Yvn6+lKvXj1cXV0pWrRosvY0bz6PVZ/XYMZvAZwJ1z5JUqKhl5kvngrtDaUQgkF/xGBroeDrD82xtVBoHdQtUVDQDBpZQ43Ui6heCkcq5bPl8ePH3L17l4YNG2JlZWV0YawkZUxqQHDixAl++ukn/v77bx4+fEh0dDT58uWjSpUqNGvWjG7duiW7KGcVsgGBJDcjhGDbtm20atWK0NBQvLy8stqk95Lk1pk9e/YwcOBAQkJCUCgUyaZdZXQDgrQi19EcxP2/tWkBqdHzd/Cub3CX0Q0F2kRitqMnSZ3kf29qO2/OMOf16dOnhIeHEx0dTeXKlbU36wmRZgP2COB6/W/pcMSZ6HhBM+U5plls1jmth+7F45VHiUIBRfMq4Y0GbBTahNhzcZBPCUWNKwvqEvcVzjZ2zBvVk9cR4Xh6eqbrtedWMqQBQb169ahXr16ajZNIJGknNDSU/v374+Pjg52dnXQosiHt2rWjXbt2REZG4ujomK4tViUSg0Q+S31MKuOMaijQqhRm+5uQlsiuQYwoKDtx4gSlSpXC3NycqlWr/ndcCpFmISDP8anExi+jmfICqyyWAhCvEYz8N2d1bmNr7C0VEKWBbyPhI2uobgk+/0VZE+49DWVhaQRcDHfi73BLMDfj0vLTTG9TDum3pi9S6VYiyWEIIdi7dy+NGzfm66+/pnTp0lltkiQV7O3tOXLkCN7e3lJgXJKx2BuZt5zSOI2a5nZ32NPgKd9fjsL3dVE0/5bE6LRI7e4kzaHV499uVA9PJRvZTUIqaQdBQUHEx8cTHx9Pvnz5cHV1/W/cw1Mp2qNUgCcvqakMYJrFZgBOBsZTKp+S/tUsqeimhEdqbZtWWyW0sIbiSf9XFf9mDySkuCagEeB7J56f3bqgsHHE3NGNZxGxKUqHqTVCpmC9A3IFlUhyEC9fvmTIkCGUK1eOli1bSqc1B/HBBx8AWrmekJCQJO1gK1asmBVmSXIbReponb2IIAxHHxXa/UXqGD4+kfNYCfgOiHX2wL/8JNSlWv/nXF05aZw9RkaA1dd+Rflv2oGe6/ZvQZl/xRk4VmmDSqWiYcOG2mMSOX5lXtyhpBHnqa0MIJ/mJWMPxhIZJ1jY1JqK7kqtJuuGKOhpC17mUDH5XNa3o60PwzQ8UedhjUUzLlk2wPxflYGUpMNMbe4g+Q/puEokOYQjR45QtWpVJk6cSOXKlbPaHImJXLp0iR49enD9+vUkua7ZPcdVkoNQmmkjlD/3gOQe9Defa/jxfcBexL85ool9M6uoZ/icGwlezqD8N2c1PSK7/+J75TFVfhmFqxBJGgSEvFFjplDwcO8c2hTPDw75QVMM34AQPcevlvIF241oPvXwyQtiPKBDGXPqupnBTZXWSS1gBr1sobDxaQ0/0IHHMbb4xTtxWe2NmUf+JNKBiaXDEhozvGtzB4mW9Cs9lkgkGUJoaCjdu3fn8OHD2NnZSac1h9K7d29KlizJqVOnuHfvHvfv39d93bt3L6vNk+QmyrbRFkY5vuX8OHoaLJhSawSnb4cQvnsM4u2IJ6Bzfn0n/id1lRDZNTBaiwIcCyQf2f0X36tBbNy2DXdeJnFa773SEBwpeBmtoY1XNOzqB5taEb2gLHu2rtaLVp7TlOapcNY1FHibaJVg2EEFuy8+QwioW9gcrqvgjxiI1GjDqEXMDSevGuBRuIY/QguyNbgw/9jXwcwpZUczQTosteYOoI3QqpO7EIlxEdfRo0cza9Ys7OzsOH78OHXq1JF5WhJJJnDx4kU8PT0ZPnw4NWrUyGpzJGng3r17/PLLLxQvXjyrTZG8D5Rtoy2MSqXQKeGRdZHXl9huGZLChG/lrKYlsgugUaN+cJLTuw/SyeyC3q6XURqszRWcfaymawWLJIdaRQfzncVSBqlGsl/jgxINPsob/Kn2oY+Zb5LuWH7BaorlVXK34MfUNCuHg/8shI8likoWUMIc7P+L4QkBahSE4IQHYUmcaSEE/s80RJjl4ZK6EGYexlVeJUiMmdLcITu0zs2OmKTjamdnR6NGjQgKCpKVsRJJBhIWFsaYMWNwcXFh7ty55M8vHxvldD766CP8/f2l4yrJPJRmKRZGJX5kXUMZZtyciXNWEyK7Bguq5iYvhfVvHq1ZxFNmACTybYMjNdwJ1eBhrzTotIL2UbEGmGbxP5QqwRSL/+lkrQDUQutIx6kF3xyP5UqoBU5NBxPgVZ95p1egfKiCGhZa79Ze3zNVKMAcwTbVR4wy/wUN/z2afvpag62FggdhGvYV7YuZJnWn9e3W2CY3d5AkwSjHNTuJaEskuZ3bt28D0LdvX+rUSfkxmyTnsHbtWnr27MnVq1cpX748Fhb6H8pt2mS8WLtEksDbj6xDyGPcgW/nrCYX2QWtpuzb0d5ktFbDYwQ2FvDHrXj6Vk09WTVBJeA7i2UGVVtvvVDja9WUQ/ns6aRW8fKfMKgGp2tV5tOGp1JNCXgo8jNINZKlTtuxjgrifpjgZZRAY5uHfUX7sF/jk6qNhlpjm9TcQWIQoxzXBQsWMHDgQObMmYNCoaB9+/YGx8kCA4nk3Xn9+jXjxo3D1taWRYsWUaJEiaw2SZKOnD59mpMnT7Jv374k++TaKcls3n5knZAj6kFoksfjoJV7irH1wNZQzurbkd3kZK2azoG/JmHIaT35KJ5yrmZGOa1JTp/I3niNYP7JOC4HqZnd5iJLS3yLzaMN2JlrrzXYzMWoPNYQ8nBWU5YG4eWYU1vNwrU/kadcXc5pSqPRGC4PymNjQVi0Svfaw4BKgI+3M/mdrFNt7pAQoZUkxSjHVYpoSyQZy9OnTwkKCqJLly46qRdJ7mLYsGF8/vnnTJkyBXd3IyuyJe81Ganz+fajaA1KZqh6sMpiaZIc0YQ6oUc+UymVWjOB5LpXRQTBzp56m97ECSzMYNtVFQOrm+6wgr4P+vS1BjMFFLZXMClCg+L2c3xK3WD2h311Y4xx0INx4ZymNPFvXhH4/CGXq37AjTL9tPkJKbCyW1WUCkWKvy+jmjskitBKkmJShZUU0ZZI0pc3b94wadIkzM3NWbx4cVabI8lAXr58yahRo6TTKjGKjNb5NPQoer/Gh0GqkdpWqPyXMxqMC8st+vLNB5+lPGlq3av4zzmLVgl+vxVPgyJm7+y0JqDWCJaeiePkAzVbO9vweRVLeKkBJwXNlecArcOqQWmUg/5lREfUZmqi713EvkJjihfzJv/tG6lGSWsVdTHK4WxePj+rPq+a5PdrKEIrSYpCJNc8OxVyioi27LEtya68evUKf39/VCoVTZo0yWpzJGnAmHWmZ8+e1K9fny+++CKTrUs7ch3NXJLT+UxwidJD51OtEdSbd9igM5ZQpe9GGCHk4bymNCs/r576Oe//DZtapTgkTi2I18CGyyqG+KTNYQV4FS14FSM4fCWOvifiUHSxhaJJA2tPhTMzVD10uanNlOe0Dnqioq6nwoXJrzvxx31zLAuUxtxe+7h+W79ahEfHGfydJDCggTeTWpY1yXbZOUsfY9cZk8OmUkRbIkkb0dHRfPXVVwAsWrQoi62RZBYlS5Zk0qRJnDhxggoVKiQpzho+fHgWWSbJTqSm85lcJyZTSemRtQYlZzRaJyy/kzUrjY0CptIlK14j2Oyvon1p8xSd1sSR2eTQCMHKc3EcvhHPju62fFHfCiwVkF+byvB2S1YPQlllsZTtyo8RpVry3b261IuornPQn8bZckGU4vX14zhUaqY7Lq+thc6h7N/Am++P3zdozw/H71OlcF6TbijMlAopefUOmBxxrVSpEsWKFWPChAm4u7ujeCvJuUiRIulqYFqRkQJJdiIqKopDhw6hUCho1SrlyIQk52DMOuPt7Z3s8QqFIls3IZDraOZx+u5Luq45k+q4bf1qpYvTYyglwcXOkraVPWlS1sO0KGAyEVeNEETEwtYrKgbXSHuUNUoluBuq4e+zcQy8rEI5zB6c9bVYU6u/Eo6e3Kj0FR2OufAmKpo3149jU7wmZjYOeuPsrcy5NKUJZkoF9eYdTlaDNSFd4MSED9/rqGlayLCIqxTRlkhMJzY2lhkzZhAfH8/8+fOz2hxJFnD/vuFIjUSSmMzW+WxePj9NynqkzyPrhG5aEUEkxHCFEHx7Lo4+VSyNc1pt80HUC4O7hBCsORPH/usqdva2o8LH1lDOAvIm1WJNDUVEEEWPDsbnzUB+C4jCoXJzg+MiY+OpNecgPWt7ycYB2QSTW74miGhLJBLjUKlU/PLLL1SvXl06rRJA+wH8juUFklxOVuh8Jjyyblu5ALWLGVdgZJCEblpoo54hbzSsvaRieE0r7C1TmbPGF9Dzdy6XHW9wt0otOPtEzZs78ewI1aBQA2YKKGZ8m9bExGs0rLkYx2Srn3Cq3DTFsaFvVCw5eNuoeWXjgIzH5IirFNGWSIxDpVIxe/ZsVCoVX3/9dVabI8kGrFu3jiVLluiaTJQoUYKRI0fmyIItScaQ43U+/+2mtXDCFwyvFEO/akamBpRtx6xrzlw7FcH2RIcIIdh0MBbfR2q29balVmdbUAkwT+qsGpMioBGC52+0igZDfSyBV/jE3dDl9KYV2Tgg4zHZcZUi2hJJ6mg0GjZs2ECpUqX49NNPs9ocSTZg6tSpLF68mGHDhlG7dm1Au56OGjWKwMBAZs6cmcUWSrIDOV3n8+HDhxy7EMa4X4NQPzjJ1C0HGaFeT15eJ6ubGqJwodO2aB5FPEDJfzqrIDhyX03wvXi25FFoa2qsACvDTuua+Ba0szhHPvHS4LmEECw+HccwH0u9RgduhKX5urP9DUUuwuTiLC8vL1q1apVjRLRlUYEkM4mPj2fBggWo1WqdcoAk92PMOuPq6sry5cvp2rWr3vZt27YxbNgwXrwwnNeXHZDraOaT0TquGcGcOXMYN26cns770gO3uH7kR1ZZLAUM66YOUo3Ua6HaXJymwe65/BUPG3vY/RthJdlwqhDwfXxLnteeTGc7f0ocHax3LiEED8MFfz+Mp3ulpBHgLnFfmRRxTe6GIj1kyt5nMqw4S4poSySGEUKwbNkyPD096dGjR1abI8lmqFQqqlevnmR7tWrViI+PzwKLJNmZdC2aymCuX79OQEAAE8ePQxF4WiuLZe+Ob6Q3G089ICyFxgYzVN3/c1qFtknBrtugeJmHpT7/tk+1SPma/0dzek7bgo2lGWpNOb48+YDhqrW6c809Ece4upYGnFYFOHrSq0lXbu25TuibuFSvdVTjkmw/HygbB2QhJjuun3zyCUeOHKFYsWIZYY9EkuPQaDQsW7YMIQRjxozJanMk2ZTu3buzatWqJB3SfvjhB7p165ZFVkmyMzlB53POnDlMnDiR0ppbKJZVgIinun0VhTM1VT3Yjw/7NT4ciK2u19ggoZsVgGtkKF1+nMBK96LYtpvEvhK1CDO7xjZmp2pD6Q+6YmOp1W81Uypo2K4P9bdUpFToMYq8PM139a4ayBf+1xluPpfmZQvyYRlPas05SOgblcFzJKQCDP2wOEM/LJ4jbihyKyY7rlJEWyLR5+uvvyZ//vyywEaSKuvWreOvv/6iVq1aAJw9e5bAwEB69OjB6NGjdeNk+19JdufSpUuEhIQwYcIEFNd/gx09ebvVa4Lof0IqQOLGBgnYxMUQbWnNw4f+nLKwpFSN9lwHUCg4qynLU+FMfkINZgkIIMbGA5+GrfW2Ny+fn3pRJ7np1ZwA50YMVJ1LEu2NtfXAv/xE1FZ18dEILM2VzG5fgUFbLunmTsBQbnFabihkx6y0YXKOa04T0Za5WZKMQKPRsGrVKszNzenfv3+SRhyS9wtj1plGjRoZNZdCoeDw4cPpaV6akeuoJDFz585l/PjxKBQKFEIDS8vrRVoToxHalIB6sct00dUEfB5dpcOO6Ywr1xBF0yEG19GJZlsZYP47oJ/imuC4KOoMh6azdNsvXLjAixcvaNasGRrBfw6inQU+Zje4euMm31+Owvd1UZ09iXOHMzq3OCfmLmcWxq4zJjuuOQ254EoyggkTJlCwYEGGDBmCUmmyHLIkl5Hb15ncfn0S4zh16hQajYaaNWv+97Q1mW5Zb6MrgBKCwmHBBObNT8ylPygdcJSQ9pOJssuT5BglGk5YDceDUIMqAQA4FoCRV0BpxuzZs5k0aRKAQSfY92oQg7ZcSpI28HZxVXpGRBPP9eDFG4N6sLK4S0uGFWclJsHnldEmyfuAEIJ169ZhY2PDnDlzpMMqkUjeC4QQLF68mOHDh2NmZqa/9kU+M2qOBMmpL87vptyJHxlYoz3W9T7jQdWPkz3GR3kDT0VosvsBiHjC3ztWIfJXZOLEicn6I2qNYMZvAQa1cQVa53HGbwE0KeuRbrnFhqKrhjB0fknyvNMn77p16yhfvjzW1tZYW1tTvnx51q5da/I8c+bMoUaNGjg4OODm5ka7du24efOm3piYmBiGDBmCi4sL9vb2dOjQgWfPjPtHkUjSk5EjRxIWFkaXLl2k0yoxmZiYGBYsWEDLli2pXr06VatW1fuSSLIjx44dw8/Pj0GDBmFhYZF07bM3QmHojQaHpxEArNaomVSkMlY1O6JQpLyOpqavKoRg3olY6pRypX79+imuy+fuhxrdsjU9SIjupua0ZtT5czMmR1zTU0T72LFjDBkyhBo1ahAfH8+XX35J06ZNCQgIwM7ODoBRo0bxxx9/sGPHDpycnBg6dCiffPIJJ0+eNNV0icRkhBBs2bIFR0dHFi5cmKQYUSIxlr59+/LXX3/RsWNHfHx8MuVJ1YMHD5g1axaHDx8mODgYT09PPv/8cyZPnoylpZEdjSTvJUIIvvvuO3r16oW1tTVmZmaGBxapA46eEBHE28VZoM1xjfEVlHq0k4jqcTjUaE9MLXOjomYh5El238F78TjbKBhd2xIzJ89UW2YZ24o1PVq2phTdzYzz53ZMdlxXrVrFmjVr9ES027RpQ8WKFRk2bJhJjquvr6/e640bN+Lm5sbFixdp0KAB4eHhrFu3jq1bt/Lhhx8CsGHDBsqUKcOZM2d0lbkSSUYxePBgChUqxPjx4/VEtSUSU/n999/5888/qVu3bqad88aNG2g0Gr7//nuKFy/O1atX6devH2/evGHhwoWZZockZ3Ho0CEKFy7MZ599hp2dHWqN4PTdl4ZzPpVm0Hwe4uceCBI9xr2tQmOlgELmtM5TDX/Msa/QGIWZ8evoOc1/XbQSNxNYciaOITUsMVdCrF1+LIrUSXUuY1uxpkfL1tSiuxl9/tyOyZ/EGSmiHR4eDoCzs7Zl2sWLF1GpVDRu3Fg3pnTp0hQuXJjTp08bdFxjY2OJjY3VvY6IiEiTTZL3k59//hkXFxcWLVqEra1tVpsjyQUUKFAABweHTD1n8+bNad68ue510aJFuXnzJqtWrZKOqyQJ8fHx/Pjjj7Rp0wYHBwfMzc0N5ml6OFrR1acwXvnsyGdvxalHJbgXN4IpFpu1OalCwPE4jthb8XVII+76dMfRwjJZDdfk0KBkhqoHqyyWohGw/46KonmVDKxuiYWZ1pN95DOVUspkosGJ8PF2Jr+TNcHhMQYjoenZsvVdoqayZazxmOy4ZpSItkajYeTIkdStW5fy5csDEBwcjKWlJXny5NEb6+7uTnBwsMF55syZw4wZM97ZDomkX79+FChQgMmTJ8vUAEm6sWjRIiZMmMDq1aspUqRIltkRHh6uCw4khwwAvH8cPHiQcuXK0aRJE/LmzQskX4UfHBGbpDreJq4i1Q5W4kalYsS5WnPa+wI3wtTkLd6VFlb/aHVUExVaPRXOzFD10Gv1aoj9Gh8GxA6n2JXvGFpJYGsBSoWCp8KF5RZ9+eaDz4y6PjOlgmmtyzJoy6VkW7Ym1mlNC6ZGTdP7/Lmdd3r2mREi2kOGDOHq1aucOHHiXUzSMWnSJD0bIiIiKFSoUJrmlLwf7NmzB09PT+bNm5fqB7skZRo2bMixY8cAuHz5MpUrV85ag9KJtFxX9erViYmJoWjRotja2ia5KQoNzfiijDt37rBixYpUo60yAPD+EBMTw759+6hZsyYuLi663Gdj8jSVaHRR1JcW9tQKvMJ+a0eOvSiEVZXeuNg40Ex5jlUWS5Mcm9CcYLBqOL4a/aenQxsVp4S7PQ9eRDH7+x/xdS2OsvgK/HiCm0obsT2vKc3KTtVNcvSal8/Pqs+rJo0gp7OOamrR3beRLWNNw2TH9erVq7oK2Lt37wKQL18+8uXLx9WrV3XjTCk8GDp0KL///jvHjx+nYMGCuu0eHh7ExcURFhamF3V99uwZHh4eBueysrLCysrKlEuSvOcIIejfvz/58uVj+vTp8u8nnejXrx8zZ84kX7586TrvnDlz2LVrFzdu3MDGxoY6deowb948SpUqpRtz9+5dxo4dy4kTJ4iNjaV58+asWLECd/fkK6DVajXTp09ny5YtukKmXr168dVXX+nWs127dnH37l18fFKOEhmia9euPHnyhNmzZ+Pu7p6m4qyJEycyb968FMdcv36d0qVL614/efKE5s2b06lTJ/r165fisTIA8H5w6NAhqlevTvny5fH09NTbl1qeZjPlOWaGr8P9t2DoaAOOSqaUV+IX7IdVlRaY2TigRMM0i80ASXRYE16vtPiWoSoF+zQ1dfvqFs9HlQJ2/PLLUVaM6MTiY08Ifq3ijMYJ0Ar2rzTC0TOkx9q8fH6alPXg3P1QgiNiCI2MxdnOEicbS9QakS4RT2OiuyMbl8Qrn63snPUOmOy4HjlyJN1OLoRg2LBh7N69m6NHjybpylWtWjUsLCw4dOgQHTp0AODmzZsEBgbqFA0kkrSwb98+ihQpwvTp0ylQoEBWm5OrsLW1TfYGMy2kpkby5s0bmjZtSqVKlXQdqKZMmULr1q05c+ZMspI58+bNY9WqVWzatIly5cpx4cIFevfujZOTk66VtbOz8zs/Nj916hSnT5+mUqVK73bhiRgzZgy9evVKcUzRokV1Pz99+pRGjRpRp04dfvjhh1TnlwGA3E1UVBTnzp2jSJEiWFlZUaJEiSRjUsrTbClO863FCrAXYK3A/0k8L14o6V9ByYy6DxmkusF+jY9ROqxmCg3fWSxj4L9tYfPYWnDy8F8Ua9eEhg0bUqBAATrUKWtyQ4DUOlSFR8cx3/eG3v48Nhb0ruvF0A9LpNmRzKzo7vtIlpZJDxkyhK1bt/Lrr7/i4OCgy1t1cnLCxsYGJycn+vbty+jRo3F2dsbR0VEnwyUVBSRpQa1WM3jwYOzt7fn666+xsbHJapMkRpKaGsnJkyd58OABly9f1nVf2bRpE3nz5uXw4cN6xZ6JOXXqFG3btuXjj7WC6F5eXmzbto1z586li92lS5cmOjo6XeZydXXF1dXVqLFPnjyhUaNGVKtWjQ0bNkgN4vec48ePU6NGDfLkyUPx4sWTHZdcnmbLmydYfnQhDLBBaaNgoZcZF67Fs7yFNW52SjQCpln8jwOx1VPVYU3MNIv/sf91OZ7eucbSYG92PrvE9HYVKFAAkxsCJJubGx7DoC2X6N/Amx+O30+yPyxaxZKDt9lw6gFzP6mQZucycXQ3PbpwSbSY7LjGxMSwYsUKjhw5QkhICBqNRm//pUuXjJ5r1apVgDZvLDEbNmzQRROWLFmCUqmkQ4cOxMbG0qxZM7777jtTzZZIdBw8eJBSpUoxZswYSpYsmdXmSNLI22oksbGxKBQKvYihtbU1SqWSEydOJOu4JkQjb926RcmSJfH39+fEiRMm5eqnxNy5cxkzZgzffPMNFSpUSJLjmhGtVJ88eULDhg0pUqQICxcu5Pnz57p9GRENl2RfIiMjuXnzpu7vLLX8bL08TSFwiwwlxMEFCigxr6Dk2ks1UShoU8qcMbUtdakvSgV48hIf5Y0UdVgTo1DAlTvPqO5xm9POBTB3dOVZpIpBWy6Z3AY1tQ5ZAGv+Tuq0JiYs6t3ObYj06sIl+Q+THdf0FNFOaBmbEtbW1qxcuZKVK1e+83kkEoC4uDhGjRqFQqFg7ty5MmcvF2BIjaRWrVrY2dkxYcIEZs+ejRCCiRMnolarCQoKSnauiRMnEhERQenSpTEzM0OtVvPNN9+kSS0lMQmyVB999JHediEECoUCtVqdLudJzIEDB7hz5w537tzRqx9IOK/k/eDcuXOUL1+e+Ph4qlWrZtQxifM0pxxeS8N7F2nSdyXmjrDYRsGpk3Esb2GNp4PhCL4bYfyuqZVEh/VtImIF/zxT45VHgbvZayycKwPv3gbVGA1VjRF/+uIdzi3JHEx2XLNCRFsiSSsnTpygXLly9OnTx+iFW5L9MaRG4urqyo4dOxg0aBDLly9HqVTStWtXqlatmuJj8p9//pkff/yRrVu3Uq5cOfz8/Bg5ciSenp707NkzzbamZ32AsfTq1SvVXFhJ7uXNmzc8efKEmJgYlEolNWvWTP2gBIKDaW75mlWfV2VTTBsOFq9JbFgwdxRx9CxizqhalikGrkLIo6fDaoijD+KpWcAMRysFpfKZ8TLOBRI9xE3cBtXYqGV6dp4y9dySzMFkxzUrRLQlknclNjaWSZMmERkZycKFC6XTmotITo0EoGnTpty9e5cXL15gbm5Onjx58PDw0CtYeptx48YxceJEunTpAkCFChV4+PAhc+bMSRfH9YMPPkjzHBKJsQQEBODp6UlISAgNGjQw+riESvySHdugyJuXJgd9abyoF2Onz8Xz+E6uletCfldXBKEYcls1AoJx4ZxGq2ixX+PDYNVwVlqswEyhDXWGxwgehmtwsFRgpoTybmY8Ff8d8zamOKPp3XlKtmDNfpicpZ8gov3w4cOMsEciSTcuXLiASqWibdu2/PDDDxmSQyjJfIQQDB06lN27d3P48OEkaiSJyZcvH3ny5OHw4cOEhITQpk2bZMdGRUUliciamZklyeOXSLIz0dHRPH78mLt372JnZ0e9evWMO1AILizbQLsxm+m65gydq/WhYcU+VJ+whe1H/Wj7YW3OHd3PwFa1mKHqASR95K4R2nzVGaruel2xfDW1GKoajhBw9rEapQLi1FDN0wzzfx/Dv31MYkxxRhNyc1N6uG/Kk3/ZgjX7YbLjmlhE28HBAWdnZ70viSSriY2NZeLEiaxcuRKNRiMjXdmI0qVLs3v3bt3rSZMm0aNHD93rc+fOUbp0aZ48eZLsHEOGDGHLli1s3bpVp0YSHBysV7G/YcMGzpw5w927d9myZQudOnVi1KhRelqvH330Ed9++63udevWrfnmm2/4448/ePDgAbt372bx4sW0b98+vS5fIslQAgMDCQ0N5f79+7Ru3dqkzn8L9lymwFfj+ODiQQDuuBTkyfXj3Ny9gkk7/Yl1LY1CoWBSy7J41fuUIfEjCUb/M/+1lRu3PvjOYDesP6Ir8FlITx7G2WNtDtU9tW1ag3Fh0L9SWG+jQCthZUob1ITc3ITj355PAfSrn/zNbkrnVmsEp+++5Fe/J5y++xK1McmyknTH5FSB9BTRlkjSm6tXr+Ll5UWDBg1o2bJlVpsjeYubN2/qVAAAgoKCCAwM1L2Oiori5s2bqFSqZOcwRo3k5s2bTJo0idDQULy8vJg8eTKjRo3SG5+QSpDAihUrmDJlCoMHDyYkJARPT08GDBjA1KlT3/VyJZJMITY2lsjISI4fP85nn31mtCa1OvQVzyZMYcsHXfjuajg/91zKc7s8xEeEoLC0xcwuD64dpqBQKPUKlSa1LEtc02lsOfU5BJ7G2+o19apUwKloXexRkv/MYb2uUXEvAjGzceDoGy/Oeq5hl/oGbmptB6xzmtIGI61paYNqjIZqlcJ5mbjrCmFRSdcaQ+dOTRdWknkohImlpba2tukmop0ZRERE4OTkRHh4uHxUnIuJi4vj66+/5u7du3z//ffY29tntUnvNQ0bNqRy5cosXbo0q01Jdx48eIC3t7dey9fcvs7k9uvLyTx//pyXL1/y4sUL49MC0Dpiy7edZM2KwYxvOYKTXpURQhD5zwGibp7ApflQzB3d9I7Z1q+WUYVKCTqqmrhoNHExxD69jk2JWigU+g5qgoPYv4E3e/2D0t0pNNQ5K7ETrNYIvj18hw0n7xMW/Z8D+/a5k9OFTZgpPWSzJMavMyZHXNNTRFsiSQ/u37+Po6MjFStWZObMmVltjuRfvvvuO9auXcvp06epUKFCVpuTLrRo0YLjx49ntRkSCSqViri4OHbt2kX//v312vumyMWLhIyawMgag4mxsOGDAWuINzMnPjIUpaUtCoUCt47TUCjNkhxqbKFS8/L5mfGRG0v23yA44jm2JesA2tzSxE/XE0dAxzcvk+5C/alpqJopFYxoXIKhHxZP9typ6cK+i2SXJG2Y7LhmhYi2RGKI+Ph45s2bh7//PwyetgiL4rU5ffel7EySDfjxxx91N7iFCxfOYmvSj7Vr15p0XVWqVDE6ncqU5i2S95vXr19z8+ZN4uPjGTBggHEHqdWoFUr8QuNRP3pB3vIRBFm4olKa8ebaEd5cPYxLixHYV2yS7BTGFCpFRUURFxcHQdc5P+9zzj94pXMIqxXJy8WHrww6iFkp1P/2uRNyWUNex/DidWyKurDvItklSRsmO65ZIaItkbzNs2fPiIuLI1zpRGDlfvT6n79un8w7ynqMzbHLaZh6Xe3atdP9HBMTw3fffUfZsmWpXbs2AGfOnOHatWsMHjw4Pc2U5FLi4+PRaDSsW7eOkSNHGn2cZvX3PFu+mo87zSY0VgOffgOAOiochYUVmujX2iirWfIugbOdRapFUi9evODRo0eo1Wpd0eXbzlx2d+4M5bIag5TNyjxMdlyzQkRbIklArVazZMkSTp8+Te+vlrAj3BtBrN6YhH7UMu9IktVMmzZN9/MXX3zB8OHDmTVrVpIxjx49ymzTJDmM2NhYTp06haOjo3FOq0YD4eH4Ponhx4sxVHUuy+uoWDDTPiV9c+MEkX6+5Gs1BsfqycvEJfB12/LJPsmKjY1Fo9HoGn/kVJLLZTUGKZuVeZhcnJXTkEUFuYfw8HCCgoI4efIkPXv1pv78I8neFSvQ5k+dmPChTBuQZDjGrDNOTk5cuHCBEiVK6G2/ffs21atX11NbyG7IdTTrSHiKuXjxYsaNG2f8gV268PzxM3zqjdVzxDSxUaBQ8Pribzj6tEdhlrpk1oAG3kxqWdbgvsjISC5fvoyDg4OuWDEnotYI6s07bHKkVX7WpB/GrjMm67hKJJmNRqNhxYoV9O7dm5IlS9K3b1/OP3hldN6RRJIdsLGx4eTJk0m2nzx5EmtrGa2RJEWtVrNv3z6uX79unNP65AmEhGiP7defKWXb6jmtUbfP8nz31wi1CqfanVN1Wl3sLPnus6oGnda4uDji4+NZu3Yt9evXz9FOK8C5+6Hv5LTCu0l2Sd4dk1MFJJLMJCYmhqtXr6JUKtm5c6eus5Gx+UQy70iSXRg5ciSDBg3i0qVL+PhoxdbPnj3L+vXrmTJlShZbJ8lOCCEQQrBgwQImTpxo3EHx8VC3LrRqBd9+yzmvSvg6awsJRXwcGlUMsUE3ces4HYW5pcEpFGhv+vvU9aJJWY9kC13j4uI4ePAgXl5eJuXapiepSV2Zyrt8VnjIeoosQTqukmyJEII1a9Zw6NAhtm/fTvXq1fX2G5tPJPOOJNmFiRMnUrRoUZYtW8aWLVsAKFOmDBs2bKBz585ZbJ0kuyCE4KeffqJGjRqpO61CwO7d0KQJODjAtm1QVhsdTXDEou9dJOLcL7i2/4q8DXqkNFuqjlh8fDwKhYLly5czduxY0y8unTBUQJXHxoLedb0Y+mEJgw5sao6usZ8VUz4uQz4Hq3ST7JKYjnRcJdmO+Ph4Tp48yZs3b9i2bZtBOaGEftSJu7MkJiHvyJRWgRJJRtO5c2fppEqSRaVSsXjxYiZMmGDcAcHB0K0bfPcd9O4N/6pVADjbmBP/+gXR9y/h2mEqSovkHbM8Nhas7FaVWkVdknXE1Go1u3btolq1alnutBoqoAqLVrHk4G02nHrA3E8q6DnfxnS9MvYzpVddb+msZjEyx1WSbRBCsHHjRvr06cMHH3zAqFGjdKkBb5NaP2qQeUeS7EdYWBhr167lyy+/JDRUm3996dIlnjx5ksWWSbKa9evX8/z589Sd1ufPYepUUKkgf364dk3rtCbi8OHDzBrWgwIebrh81C9FpxVgbocK1C2ez+B6KYQgLi6OxYsX07lzZ4oVK2bytaUXKTUDSCAsSsWgLZfwvRoE/Ofovp2/mqA+kzBOfqbkHIyKuEoRbUlGI4Tgzz//JDg4mPXr1xt1jDH9qCWS7MI///xD48aNcXJy4sGDB3zxxRc4Ozuza9cuAgMD2bx5c1abKMkCoqKiWLVqFWPGjDHugCdPYOVK+OQTqFwZihbV7RJCcPfuXXbs2MEvv+zk5MNIBm25pMtdfZs8thZJopOJEUKwYcMGmjdvbpqiQQZhbAGVQNvN6sPS7iZ1vZKfKTkDoxxXKaItySiEEGzfvp2jR4/y/fff8/HHH5t0fPPy+WlS1iPdWwVKJOnN6NGj6dWrF/Pnz8fBwUG3vWXLlnz22WdZaJkkq/juu+/o0qVL6k7r33/Dxo2wdq3WWX30CGxt9YacOHGCefPmsXv3blatWgVA8/IOBh2xPLYW9K7jzdAPi+utlYnzQB3NNVzc/zNjjXWoMwFTCqiCwmP43+kHJne9kp8p2R+jHFcpoi3JKHbu3Mnt27f59ttv33mOrGwVKJEYy/nz5/n++++TbC9QoADBwcFZYJEkqwgLC2PLli0MGTIk5aeZQoBCATExcO8ehIdDnjxJnFZ/f382btzIli1bMDfX/1g31hFLnAf6+tIf2JZpQEGPypS/GpRtIo2mFts+DI0yatzbDrH8TMnemJzjumPHDl0rt8R8/vnn/PLLL+lilCT3s2vXLsaMGUOnTp2YOnUqFhapi2BLJDkZKysrIiIikmy/desWrq6uWWCRJCtYvnw55ubmqTutc+dC9+7an5s0gcOHtU5rIs6ePUvXrl2pWLEia9euxcnJyeBUCY5Y28oFqF0saQFWQh7ok2cviLj4Gw5VP8bMxiFJHmhWk1BAZSxFnG1TH4RUn8lpmOy4ShFtSVrZuHEjFy9eZPbs2VltikSSabRp04aZM2eiUqkAUCgUBAYGMmHCBDp06JDF1kkymmfPnrF+/XqGDBmCvb29YadVrYaof6OE3t5aaauE5pZvjT99+jSrVq1i5cqVRtegGCKh4Cn8/K+gNMOxWmvdvoTc0Bm/BaDWZH2TzcQFVCmhQKsa0L22F/mdrJMUW709TqrP5CxMlsOSItqSd+X333/n/PnzTJ8+PU0LrUSSE1m0aBEdO3bEzc2N6OhoPvjgA4KDg6lduzbffPNNVpsnyUCWLVvGwIED6dGjB2ZmZoYHCQEffQQVK8Ly5fDppwaHXb58mVWrVvH999/r6kzSgu/5G9w6tgeH6m0MrsuG8kCzkubl87P686pM3HWFsChVkv2JFQAszZVMa13WYIGaVArIuZjsuEoRbcm7sGrVKu7evcusWbOk0yp5L3FycuLAgQOcPHkSf39/IiMjqVq1Ko0bN85q0yQZxMOHDzl37hyDBg3C0tJwtyoCA8HNDaytYcAAbaQ1GQ4fPsyaNWtYvnx5uqyjixYtokj99thXapbqfNmpC2FC3u63h++w4eR9wqL/c2Dz2lnQvnIBnGwsUWuEVArIhSiEEFkf/89AIiIicHJyIjw8HEdHx6w2J/PRqOHhKYh8BvbuUKQOKJO5488ADhw4wD///MPIkSOTjzRIJDkcY9aZzZs38+mnn2JlZaW3PS4uju3btxusHcguvPfr6DuwYsUKBg0aRHx8fPJpdOHhULiwVpc1her9q1evsnHjRubNm4dSqUyz0/rw4UNOnTpF586dOfcgjK5rzqR6zLZ+tbJFxPVtEpQQDgQEs8fvKaFv4nT7EjcZSO8WsZL0x9h15p0c17CwMHbu3Mm9e/cYO3Yszs7OXLp0CXd3dwoUKJAmw9Ob93rBDdgLvhMg4ul/2xw9ofk8KNsmw0+/dOlS7t27x5w5c7Czs8vw80kkWYUx64yZmRlBQUG4ubnpbX/58iVubm6o1erMMPWdeK/XURO5ffs2d+7coX79+tjb2ycdIAT88Qe0bAlKpfbnBg20LVsNsG/fPtavX8/y5cvJnz/t0cFFixYxYsQIAMzNzVFrBPXmHU61Y9SJCR9mW0cvuW5aCdau+ryqjKzmAIxdZ0xOFZAi2jmEgL3wcw+SyE5HBGm3d96cYc7r0aNHuXXrFoMGDUoSXZJI3leEEAYjZY8fP062GlySs1i9ejV9+vTB09Mz+Zv1y5ehdWs4cAAaN4ZktKtv3LjBzz//zMSJE2nevHmao6w3b97k1q1bDB06VE8yK6HgKafmgabUTctQkwFJzsdkVYEEEe3bt2/rPf5o2bIlx48fT1fjJO+IRq2NtCb7rwz4TtSOS2cWLlzITz/9RNeuXaXTKpGg7TxYtWpVFAoFH330EVWrVtV9VapUifr168s81xzO9evXOXnyJB07dsTS0jKp0xocDMuWaX+uWhWuXNE6rcnw66+/MmnSJPr06YOlpWWandZly5ZRrFgxmjZtanBdTsgD9XhLasrDyTrbRytT66aVuLhMkjswOeIqRbRzAA9P6acHJEFAxBPtOO/66XLKU6dOERgYyMCBAw0/HpNI3lMSOg/6+fnRrFkzvf8PS0tLvLy8pBxWDmbTpk107NiRggUL6nVE0+PkSZg5Ezp2hAIFoHx5g8Pu3LnD77//zhdffEGbNoar/E3hypUrvHz5kr59+2Jubp6kOUFicmrHKGOLxrJTcZkkbZjsuEoR7RxA5LP0HZcKc+fO5fbt2yxatEg6rRLJWyR0HvTy8qJLly5Z9iQiNjaWmjVr4u/vz+XLl6lcuXKW2JFbuHr1KhqNhkaNGhlOC/D1hTNnYPp0+OQTbYQ1hZSQXbt2sWHDBlasWJEu6+jq1avp1asXJUqUMFpjPSd2jDK2eYBsMpB7MNlxTRDR/vnnnwEpop0tsXdP33EGUGsEG389yIPHQdT+sD3jxpfM9nfmEklWUrZsWfz8/KhZs6be9rNnz2JmZkb16tUz9Pzjx4/H09MTf3//DD1PbkcIwa5du2jYsCFmZmbkeaublY5797SOq0oFFhbJOq0PHz7kyJEjtG7dmnbt2qFUmpzBp8fly5fRaDR07NjxvWgKlNBNK7XiMtlkIPdg8n/IokWLiIyM1BPRLl68OA4ODlJEO7tQpI5WPSClfiGOBbTj3gHfq0F4t+zHiClz2HjHgsG77lBv3uFs0xZQIsmODBkyhEePHiXZ/uTJE4YMGZKh5963bx9//fUXCxcuzNDz5HauXLnCw4cPKVu2LC4uLkmd1gkTtBFWgIEDYd8+rdOaDL/88guDBg2iXr16uLi4pMlpFUKwadMmihYtSunSpcmXL987z5WTSNxN6+1PvJxQXCYxHZMjrlJEOwegNNNKXv3cA5KrE20+9530XFfuPMDXuy9hVrQu+Sq3121P6Gmd3RP5JZKsIiAggKpVqybZXqVKFQICAjLsvM+ePaNfv37s2bMHW1vjerfHxsYSGxure20oPex9QgjBwYMHKV26NLa2tnh5ef23Mz5e+93cHPLm1TYSAK3UVTI8efKEc+fOUbNmTdq1a5dmjeuLFy/i4OBAo0aN3kuFCtlk4P3CZMc1QUS7bt261K1bV7c9J4hov1eUbaOVvDKo4zrXaCmsxKLNvj9+z8/7/8b2gy8ws8ujN07KjkgkKWNlZcWzZ88oWrSo3vagoKAUi2bSghCCXr16MXDgQKpXr86DBw+MOm7OnDnMmDEjQ2zKaVy9ehUPDw+cnJwoVKiQ/s6YGKhVC/r1gyFDYOLEVOfbuXMna9euZenSpRQsWDBNtgkh2LNnD3Xq1MHW1jb54rD3gJxaXCYxHZMbEOQ0Ee33Xjj7HTtnqTWCFYdusebEfV49vgdCA0ozLFwKpVrpml07rEgkGYUx60zXrl0JCgri119/1UXFwsLCaNeuHW5ubrq6AWOYOHEi8+bNS3HM9evX+euvv/j55585duwYZmZmPHjwAG9v71SLswxFXAsVKvReraNqtZoLFy7g5OSEi4uLfvHx48dadQCFAhYtgoYNoVq1FOcLDg4mICCAAgUKUKxYsTTfrFy4cIECBQrw6tUrypYtm6a5JJLsQIY1IJAi2jkMpZnJkle+V4MY87M/b+LUhJ/dRdzTG+RtPABzB+OcUSk7IpEkZeHChTRo0IAiRYpQpUoVQCuR5e7uzv/+9z+T5hozZgy9evVKcUzRokU5fPgwp0+fTqJkUL16dbp168amTZsMHmtlZfVe6zBfv36dwoUL8+bNmyTFdNy9C+XKwfbt0K5diq1aE9i1axerVq1i0aJFlCpVKk22xcfHc/z4cby9vbGxsUmXbloSSU7CaMe1SpUqKBQKnYh24rtFtVrN/fv3ad68eYYYKck8fK8GMXDLJVShT0BphnXBsjj6tDdJT1DKjmhRq9WoVKqsNkOSDlhYWKQ5D7FAgQL8888//Pjjj/j7+2NjY0Pv3r3p2rUrFikU8BjC1dXVKPnB5cuX8/XXX+teP336lGbNmvHTTz8ldcgkqNVqbt68SWRkJNHR0Xz44YfaHRoN/P03fPABFCsGP/wATZqkOt+LFy94+PAhRYoU4c8//zT59/w2ly9fplixYtjZ2eHt7Z2muSSSnIrRjqsU0c79qDWCab9eI+LCr8QEXsG58UCsCpQ2+ngpO6JFCEFwcDBhYWFZbYokHcmTJw8eHh5pEoW3s7Ojf//+6WhVyhQuXFjvdcK6XaxYsTTnV+Y27t69i6enJ/fu3aNVq1b6O/fuhfbt4fp1KF0ajKjl+PXXX1mxYgULFizQRdjfFZVKhb+/P5aWlqjVannTIXmvMdpxzQgR7ePHj7NgwQIuXrxIUFAQu3fv1jnIoHUApk2bxpo1awgLC6Nu3bqsWrWKEiVKpPnckqTsPnaJp8HPsHAphEM107q2SNmR/0hwWt3c3LC1tU1z9xtJ1iKEICoqipCQEACTHs3u3buXFi1aYGFhwd69e1Mc26aNcQWTkvRFo9Hw6NEj7t+/T968ef9zWp8+hWPHoGtXaNMGzp7VOq2p8OrVK0JCQnB0dOSPP/5I82fl1atXKVasGK9fv6ZRo0ZpmksiyQ2YnOOaniLab968oVKlSvTp04dPPvkkyf758+ezfPlyNm3ahLe3N1OmTKFZs2YEBAS8F8LKmYUQgtWrV7N+2y+I8p9j451Usic1pOyIFrVarXNaXVxkgVpuwcbGBoCQkBDc3NyMThto164dwcHBuLm56d2Uv41CociUwlYvLy9MrMfN1Tx9+hRHR0cuXbpE+/bt9Xf+73+wfDm0bQu2tuDjk+p8f/75JwsXLmT+/PlpdjJVKhX37t0jPDyc2NjYXOe0JlaskQoAElMw2XEdMmQI48ePT+K4PnnyhHnz5nH27Fmj52rRogUtWrQwuE8IwdKlS/nqq69o27YtoJXicnd3Z8+ePXTp0sVU0yUGCAwMJG/evDg7O7N03Ta6rTtn8hxTPi5Dr7rectEBXU6rsXqZkpxDwu9UpVIZ7bhqNBqDP0uyFiEEL1684NSpUzRp0uQ/p3XPHnj5Evr2hZEjYcAArdOaChEREbx+/ZrY2Fh+//33NP//37lzR5e2kNxnZE7G92pQEs3V/DL4ITESk9t0ZJaI9v379wkODtZrbODk5ETNmjU5ffp0ssfFxsYSERGh9yVJihCC9evXM2DAAEJfhVG4emNCXsfibGdp9BwKtIuNdFqTItMDch/yd5o7CA0N5dWrVxw7doyOHTvqq+EcOQIHDmh/trKC5Nq5JuLQoUO0a9eOoKAg2rdvnyanVa1W8+jRI27dukV8fHyudVoHbbmk57TCf01sZAdGSWqYHHHNLBHt4OBgANzd3fW2u7u76/YZQgpnp05QUBBOTk6o1WqGzl1Dlx9vJllEUkPmtEokqbN8+XKjxw4fPjwDLZEIIXj9+jV79uyha9eudOzYEdRqGDYM6tSBzz+HhQtTbNGamMjISOLi4ggMDGTv3r16BcvvwtOnT7G1tcXPz4/WrVunaa7silojmPFbAIaSVWQTG4mxmOxpNm3alEmTJiUR0f7yyy9pYoQ8SEYzadIkRo8erXudIJwt0S7cP/74I//73//YuHEjhWq3YtCWSwYXkdSQOa2SjKRXr16EhYWxZ8+ed57j6NGjNGrUiFevXpEnTx42btzIyJEjM1XtYcmSJXqvnz9/TlRUlK7HfVhYGLa2tri5uUnHNQOJiori1atXnDp1ij59+mgdVgAzM+3PcXHa10Y6rcePH2fGjBksWLCA3r17p8k2jUZDaGgoR48epX379rnWaQU4dz80xSCJAILCYzh3P1Q2sZEki8mOa3qKaKeEh4cHoO2znbiK99mzZyl2fHnfhbPfJiEB/s6jIAq4OPIsJIQ//vgDhdKMGRsPp+i05rW14LOahVEANb1cUJopeBEZKxPpJUbTsGFDKleuzNKlS7PaFD799FNatmyZqee8f/++7uetW7fy3XffsW7dOp0I/c2bN+nXrx8DBgzIVLveF4QQxMbGsm7dOgYOHEinTp3g2TNtp6tly6BpU/j+e6Pni4qKQgjBxYsX2b17d5q7iL169YqYmBjOnz/PZ599lqa5cgLGNqeRTWwkKWGy45qeItop4e3tjYeHB4cOHdI5qhEREZw9e5ZBgwal23lyMwkJ8HfOHiTSbx/52oynYP5KlLvxHCcby1TTA15FqahX3FXe+WYBsuI2/bGxsdGpA2QFU6ZMYefOnXqdk0qVKsWSJUvo2LEj3bp1yzLbciMqlYqHDx9y5coVhg0bBi9eQL584OYGLVqAiR2nTp8+zeTJk1myZAmjRo1Kk20ajYaoqCh+/vln+vTp895IoRnbnEY2sZGkhMnFWfCfiPbKlStZuHAhPXr0eCenNTIyEj8/P/z8/ABtdMLPz4/AwEAUCgUjR47k66+/Zu/evVy5coUePXrg6emZoqyMRIvv1SAGrD/J42cvUL18hFun6ZjZOukS4A8EJJ8nnBh555v5+F4Not68w3Rdc4YR2/3ouuYM9eYdzvCihYYNGzJs2DBGjhxJ3rx5cXd3Z82aNbx584bevXvj4OBA8eLF2bdvn+6Yq1ev0qJFC+zt7XF3d6d79+68ePEC0D7uP3bsGMuWLdN13Xvw4AFqtZq+ffvqWlaWKlWKZcuWGbRpxowZuLq64ujoyMCBA4lLeKSLthBz+PDhuLm5YW1tTb169Th//nyy17dx40bdI/oEfvvtN2rUqIG1tTX58uVLKomUjgQFBREfH59ku1qt5tmzZxl23vcRtVrN0qVLKVq0qPZ3evIkFCoEfn6gUMDixVChglFzxcbGEhcXh6+vL7/88guVKlVKk21RUVHcv3+fo0ePMmDAgHQN+GR3fLydye9kTXK34AkFv+97ExtJyhjluO7du1cn87N3794Uv0zhwoULVKlSRZdyMHr0aKpUqcLUqVMBGD9+PMOGDaN///7UqFGDyMhIfH19pYZrKqg1ghHz1xKyaxYKhYI8dbuiMNMujgmpAb/6PTVqLnnnm7lkdcXtpk2byJcvH+fOnWPYsGEMGjSITp06UadOHS5dukTTpk3p3r07UVFRhIWF8eGHH1KlShUuXLiAr68vz549o3PnzgAsW7aM2rVr069fP4KCgggKCqJQoUJoNBoKFizIjh07CAgIYOrUqXz55Zf8/PPPerYcOnSI69evc/ToUbZt28auXbv0Ci/Hjx/PL7/8wqZNm7h06RLFixenWbNmhIaGGnWtf/zxB+3bt6dly5ZcvnyZQ4cO4WOEVue78tFHHzFgwAAuXbqk23bx4kUGDRqkp54ieXc0Gg3+/v74+voybvRolP7+2h0+PjB/PiSKdhvDxYsXadmyJXfu3GHGjBnkzZv3nW1LSFv4/vvv8fb2Ttqd6z3ATKlgWuuyAEmcV1nwKzEWhTBCjVqpVOpEtJXK5H3dzBLRNoWIiAicnJwIDw9Pcz5STiAuLo6/Lt2h26hp5Kn7GQrz5OWtnO0sePVGZTDPNaF964kJH8pFxARiYmK4f/8+3t7eJt9gqTWCevMOJ5vCkdG/k4YNG6JWq/n777+19qjVODk58cknn7B582ZAq/aRP39+Tp8+zcGDB/n777/Zv3+/bo7Hjx9TqFAhbt68ScmSJY3OcR06dCjBwcHs3LkT0EZrf/vtNx49eqSTF1q9ejXjxo0jPDyc6Oho8ubNy8aNG3W5gSqVCi8vL0aOHMm4ceNSLc6qU6cORYsWZcuWLUa9Pyn9bo1ZZ54/f07Pnj3x9fXVRdni4+Np1qwZGzduxM3NzSg7soKcsI4KIZg9ezZffvmlVrps2TKYNAkePQITm4EkBGrGjx/P5MmTyZcvX5psU6lUBAQEEBwcTLNmzdI0V25A6rhKDGHsOmNUjqsU0c5eJJf/6Ovry6JFi+g3axV5P+iV6jztKxdg/ckHKEDPeZV3vllDdqi4rVixou5nMzMzXFxcqJDokWqCPF1ISAj+/v4cOXLEoAzQ3bt3KVmyZLLnWblyJevXrycwMJDo6Gji4uKSFF1WqlRJTxOzdu3aREZG8ujRI8LDw1GpVNStW1e338LCAh8fH65fv27Utfr5+dGvXz+jxqYHrq6u/Pnnn9y6dYsbN24AULp06RTfJ0nqCCE4ffo0MTExTO7eHQ4ehCZNoE8fqFHDZKf1n3/+YcyYMaxevTqJKsS72KbRaFi8eDHjx49Pc5pBbqF5+fw0KeuRah6/zPWXGCL9hFclmYKhO1UPBwsGVnPCf/9+fv31V/yDoo2aq3FZD2p4OyedT975ZgnZoeL27Xw7hUKhty1BhF+j0RAZGUnr1q2ZN29eknnyp1D4sn37dsaOHcuiRYuoXbs2Dg4OLFiwwKSue+lBVhVqJbRdLVasWLpqX7+vzJ49m8mTJ2tf9O8Pp0+Dvz84OGj1WY1ErVajUqlYsWIFW7ZsSaIhbioajYYzZ84QFxfHhAkT0jRXbsRMqUjxBlxGZSXJYdSqKUW0swcJ+Y+Jo6PRD/355+wupodNYXXf8dja2uLjbfP/9u49Lubs/wP4a2aqaarpfkM05R7dU6J1jcIWy5LlSyw2l/oSuy67CC1Zkdva/Gjd1i2xrrmW20ouK0UitxIpIbpfZ87vj759NLqYaarpcp6PxzyYz3zmzDkfOr3nfM55H7TSUEZ6VmGN0wDKv71K8s2Xqn9NbcWtjY0Njhw5AoFAUG0ApqSkVGn6UFRUFHr16oWZM2cyx549e1bpvXFxcSgoKGACzBs3bkBNTQ1t27aFrq4ulJSUEBUVBWNjYwBlt2Nv376NOXPmSFR/CwsLREZGypyHU1L5+fnw8fHB7t27AQCPHz+GqakpfHx80KZNGyxcuLBB6tFcREREQE1VFb906ACcOwe4uACrV5flYq1hSltVHj58iDlz5mDHjh3Yvn27zHUjhGD16tX4+eefZS6rJarqdx3waa5/8H9saPDagkkUuNIk2vL3+Y4jhBCUvEtBfuJ16I5YCBZHQWzHET83M8zYGyPRNIAvffOlGkb5iltJvnA0BrNmzcL27dvx3XffYf78+dDW1sbTp09x8OBBhISEgMPhQCAQ4ObNm0hOToaamhq0tbXRsWNH7NmzB+fOnYOJiQn++usv3L59GyYmJmLlFxcXY8qUKVi8eDGSk5Ph5+cHb29vsNlsqKqqYsaMGfjpp5+gra2Ndu3aYc2aNcjPz8eUKVMkqr+fnx8GDhyI9u3bY+zYsSgtLcXp06frbXRs0aJFiIuLw+XLl+Hq6socd3Z2xrJly2jgKiFCCFauXInFixcDhAArVgAdO5YFrtrS/WwQQpCbmwt/f3/s2LEDbdq0kblukZGRUFNTo0FrLdHdtagvkehraVJSEvNYuXIlrKys8PDhQ2RmZiIzMxMPHz6EjY0N/P3967u+LVbF+Y+Frx7g3bEAKOq2hc7gGWAr8cTmPwJlc4iC/2MDQw3x0TlDDWX6bbWRamorblu3bo2oqCgIhUIMHjwY5ubmmDNnDjQ1NZlFnD/++CM4HA7MzMygp6eHlJQUeHl5YeTIkfDw8ICDgwPev38vNvpabuDAgejYsSP69OkDDw8PuLu7Y9myZczrq1evxqhRozBhwgTY2Njg6dOnOHfunMQrv/v164ewsDCcOHECVlZWGDBgAG7dulUn16Yqx44dw++//w4nJydmygUAdOvWrcoRZ6qy48eP4/7Nm/glKalsSgCLBfz9NyDFXcFyT548gbu7O4qLi7F///46CVpXrlwJZ2dn9OzZU6ayWjJJ5/rvikrC8dhURD97D6GoNvs/Uk2VRFkFKmrfvj0OHz7MpLAqd+fOHXz77bdiO8U0Bk1hNawkjsemYvbBWBSlPUZu7FloDZgCNle10nkbx1phuNWnDphObm9YsmQVKEfndjVOsmYVUFFRQXx8PExNTcHn8xEXFwdTU1PExcWhT58+yMrKaohm1Iq8+1GhUIg1v/2GhYsWgSUSAa6uwPTpwKhRtSrv/fv3mDZtGtavX89MNZHF8ePHYWpqKraQkaqd8t910qD9Y/NQp1kFKqJJtOXj7bMHeBceBJ2hvtAZUv0q5M/nP9JpAE2PpCtuqabFzs4O4eHhZbs44dNCt5CQEDg6Osqzao3aoUOHYKulhQX794M1enTZtIDz58tGW6WUlJSE+fPnY+fOnfj7779lrptQKMRvv/1GpwXUodrM4adzX1sWqQPX8iTaISEhsLGxAUCTaNe3y5cv4+rxfeg6whvvSlhNYv4jJRv6haP5WbVqFYYMGYKEhASUlpZi48aNSEhIwPXr13HlyhV5V6/RKSkpwea1a/Hfn36CQkkJYGv76cVaBK2pqanw9vbGpk2bqkzhJq0DBw7A3t4eixYtkrks6pMvzfWvCp372rJIveXrjh07YGhoCDs7O3C5XHC5XNjb28PAwAAhISH1UccmQygiiH72XqZ5N0IRQfSTDFy7cBTHti6D98SR6PuVE/bs2Q1/j7J5U01h/iNFUeKcnJwQFxeH0tJSmJub4/z589DX10d0dDRsKwZlFPbt24e3u3bBe+1aKLx/D/B4wO7dZaOtUnr58iU8PT2ho6ODU6dOoX379jLVrbi4GIGBgRg7dizat28vNl+Zkl1Nc/1r8vk6D6r5knrElSbRrlpdzEs8G5+Gy8d24L8lIbj/LAOH4oqx0VUZhWu7gecWCNfu7gj+jw3Nu0pRTUxJSQm8vLywZMmSOkm31Fzl5+Rgz/r1mDR/PpTz8oCsLOB/2Wtq4+nTp/D29sbGjRvrZKvw3bt3Y9CgQZg3bx4NWOtR+eLiz3/XSaI+81xTjYPUi7PKFRcXIykpqdEn0W6IRQXV5Zwr79YkmXdzNj4Nx/ZvxcwP63AwvgQr+nPBYZXNgxORsj9ZY/YAZu50wVUjVheLs6jGSdbFWRoaGoiNja2U9qspaIh+dO/evRgaHQ3u8eNQTUoqy8daS2lpafD398emTZvAYrHA4XBkqlt+fj7+/PNPzJgxo1H/vmuMZPl9VfG973KK4B/+5V3xDkzrSadZNVH1tjiLJtEWV5ucc0IRwa1nbyFMjoI+6yNMBO2x8ngepjzfCv/7Rdg8RBkKFX6w2SxABAKcXQhWl2HgsDn0B5OimpgRI0bg2LFj8PX1lXdVGpWPcXEIP3UKbt7eUHdyAmvSJJmC1gcPHsDX1xdBQUF1EmTu2LEDI0aMgJeXFw1apSTrnciKc/2FIoKQa0lNJs81VX+k/imkSbTFSbu/fMXpAK1ZZXNxHh0TYsAjDsb2JPDszKvyFhQbALJTgRfXAZOv6qcxFEXVm44dO2LFihWIioqCra0tVFXF09m1xM1b9u/fD/dt2zCEzYbGL78AGhqAQFCrsjIyMrBu3Tr8+uuvCA8Pr7R9sbQ+fvyIo0ePYty4cfTuSS3U9e5X0m6sQzVfUgeux44dQ2hoKHr27EmTaEPy+TTp2YXYGPEECRf3IlhxA3P8SEIJ9t4vwSZXZSgrSLBWLrealGMiYVlQm/sGUDMAjHsBbNluj1EUVXf+/PNPaGpq4s6dO7hz547YaywWq+UEriIR3gYH4yYhGDh6NFR79YKarq5MRcbExGD+/PkIDAyUOWAFykZZx4wZgzFjxtCgtRbqa/er6ua+0nUeLYvUgevbt2+hr69f6XheXl6LnKwuac655SfikV1QjGvcPQCApA8iXHheinHmihjZVUHya6dmUPlYwgng7AIg+/WnY+qtAdffADN3ycqlKKpeNbbNWeQlLDQUX2/cCJtvvoGBQRX9mRQyMzPxf//3f5gzZw7Cw8PB5XJlKi8jIwNXr17FqFGj6iRlVksl7Z1IadA815TU6bDKk2iXa+lJtMtzzn3+I8OGCD3ZCXBnX0dPdgKyC4phz36E1qxMHEkoge+5Qrh2UIA6lyVR0CoCQNTblI2kVpRwAjg0UTxoBYDstLLjCSdkah9FUXWPEIJarottmt6/R/q4cbh6+DDsevaE8u3baP3bbzIVefv2bYwZMwbOzs7g8XgyB627d++Guro6BgwYAA0NDZnKaukkvRNZ2wwA5XNfh1u1gWN7HRq0tjBSj7jSJNriqpp348K+BT/FPcwcVgB4TbSx51137E8vgbMpB6PMFMCuJmAVkbIFWRWfs1gssFxXi9/+FwnLRlpruiFzdiHQZRidNkB9Ub9+/WBlZYUNGzbIuyr1SiAQYM6cOZgzZ06Df/aff/6J9evX48mTJwDK5r3OmTMHU6dObfC6NKSTEREYGBeHdsOGQSBjVoWsrCzs378f48aNw6lTp2S+lZ+amooHDx7A1dUVysrKdGpAHZD0TmRtdsmiKKlHXGkS7crK590YaijDhX0LwYobYAjxJMhXHrzBP+fPwr4NB/qq7GqDVgDIBF/seZGKIZMKS8yL65VHWsWQTwu6qKZFJASS/gHuHy77UySUd41ACKlyu+emori4WK6fv3TpUsyePRtubm4ICwtDWFgY3Nzc4Ovri6VLl8q1bvXi1i2k2dgg7p9/YNKtG5Tv3YNg/HiZirxx4wZGjhwJS0tLaGhoyBxkhoaGQkdHBxYWFjJPW6A+qe5OZDkWyrIL0AwAVG1IFbiWlJTg+++/B4vFwvbt23Hr1i0kJCRg7969MDc3r686Ngmu3VvhF9dOWK60Byx8GjF9lS3C6Scl6GXEwbGxPAi0OKhuQ63y6QBPxt3Gtd678NhpA4QTT4L3U0LVc1WrW6hV2/OoxiHhBLChO7D7a+DIlLI/N3Sv12kfkyZNwpUrV7Bx48ay0X0WC7t27QKLxcKZM2dga2sLLpeLa9eu4dmzZxg+fDgMDAygpqaGHj16ICIiQqw8gUCAVatW4fvvvwefz0e7du2wbds25vXi4mJ4e3ujVatWUFZWhrGxMQICApjXWSwWgoODMWTIEPB4PJiamuLw4cNin3H//n0MGDAAPB4POjo6+OGHH5CbmyvWphEjRmDlypVo3bo1OnfujH79+uHFixfw9fVl2tlQgoODsX37dgQEBMDd3R3u7u4ICAjAtm3b8Mcff9TrZ4eHh8PBwQE8Hg9aWloYMWJE/X1YYdnt34vJyeC1aQNVAN27dwdbhlyqOTk5OHDgAAQCAU6cOIFevXp9+U01SElJwc2bN9GjRw9wuVwYGhrKVB4lrqbdr2gGAEpWUgWuioqKOHLkSH3VpUlbGZ6AxMN+MEQms432oQclmHKiACaabBhrsqHIYUGBVRa1fh68igjAQtl0AMfOreA06Bt0cp4Mjmmf6m/zV7VQS5bzKPmT05zljRs3wtHREdOmTUNaWhrS0tLQtm1bAMDChQuxevVqPHz4EBYWFsjNzcXQoUMRGRmJu3fvwtXVFW5ubkhJSRErc926dbCzs8Pdu3cxc+ZMzJgxA4mJiQCATZs24cSJEzh06BASExOxb98+CD5Lg7RkyRKMGjUKcXFxGD9+PMaOHYuHD8sSkOfl5cHFxQVaWlq4ffs2wsLCEBERAW9vb7EyIiMjkZiYiAsXLuDUqVP4+++/YWRkhBUrVjDtbCglJSWws7OrdNzW1rZeR7KPHDmCCRMmYPLkyYiLi0NUVBTGjRtXPx8WHIy07t3xLCEBykZG0DhxAh2+ki19340bNzB8+HAYGhrC0NCwUhoxaZ08eRJaWlpo06YNTE1NW+Si4oZQ8U5kRYYaylKnwqKoiqSe40qTaH9SvqvH9qtPofjkNIIVy4L69FwRnmWK0E2PjfBxKmKbCQDADqErhnJuoXWF6QRFKobguQVKlwXAuFdZ9oDsNFQ9z5VV9vrnC7qoxkmOc5Y1NDSgpKQEFRUVZvSpfEvnFStWYNCgQcy52trasLS0ZJ77+/vj6NGjOHHihFjgOHToUMycORMAsGDBAqxfvx6XLl1C586dkZKSgo4dO8LJyQksFgvGxsaV6jR69Ghm7qe/vz8uXLiAzZs3448//sD+/ftRWFiIPXv2MIHM77//Djc3N/z222/MbV9VVVWEhIRASUmJKZfD4YDP5zf4KNuECRMQHByMoKAgsePbtm3DeBlvoVentLQUs2fPRmBgIKZMmcIcNzMzq7sPKSkB3r4FWrfGbS0ttB4/HkJCZB4Vzc/Px9WrV9GpUyccP34cfD7/y2+qwYsXL5Cfnw9jY2OoqanJXB71ZTQDAFUfpA5caRLtsoD194tPsCMqCVkFpWBDxKS5OpxQgm13irFusDK66VcdXESI7LCq9D+wZz9CG4UsrJ44GDzT3tIHI2xOWcqrQxOB6lIyf76gi2q8pJmz3ICbUHw+Spibm4tly5YhPDwcaWlpKC0tRUFBQaURVwsLC+bvLBYLhoaGyMjIAFB2G3/QoEHo3LkzXF1d8fXXX2Pw4MFi7/88S4mjoyNiY2MBAA8fPoSlpaVY/9O7d2+IRCIkJiYygau5ublY0Cpvf/75J86fP4+ePXsCAG7evImUlBRMnDgRc+fOZc77PLitrZiYGKSmpoLNZsPa2hrp6emwsrJCYGAgunfvXu37ioqKUFRUxDzPzs6u/kP+8x+8ef4c5MQJZGppwW7ZMplHMW/evIlFixZhwYIFMDU1laksALh06RITrFf1JYmqPxV3v6KouiB14NrSk2ifjU/DwiP38bGghDlmz34EpYJ3eJBDYKTOQvg4FShyKnfcIgKkQwe3RF0gAhs3RGb441sbKHaQ4ZaJmTswZk81eVxX0zyuTUkjnbP8+ZfTH3/8ERcuXMDatWvRoUMH8Hg8fPvtt5UWP32eCJ7FYkEkEgEAbGxskJSUhDNnziAiIoJJa/T5PNa6rrs8xcfHw8bGBgCYzVp0dXWhq6uL+Ph45ry6vHX9/PlzAMCyZcsQFBQEgUCAdevWoV+/fnj8+DG0tateHBMQEIDly5dXX/DTp2Xbshob49Ho0QCbDXZODlxcXGSqb2FhIWJiYqCmpoa///4bmpqaMpWXkpICFosFJSUl6Ovr02kBVJ3r168fk1Hp7t27sLKykm+FGrldu3Zh8uTJAIDZs2fXKouN1FkFkpKSqn2Ud5KNUvJ1qVZmC0UE0c/e43hsKqKfvYdQRHD6Xhqm740RC1oBIOfxTYw9XAAhAXoaKVQZtAL/2y2kZAJE/7vsXn1MMNSiDub5mLkDc+IBz1PAqD/L/pxznwatTY2c5ywrKSlBKPzyz0hUVBQmTZqEb775Bubm5jA0NERycrLUn6eurg4PDw9s374doaGhOHLkCDIzP02fuXHjhtj5N27cQNeuXQEAXbt2RVxcHPLy8sTqxWaz0blz5xo/V9J21rVLly5J9Lh48eIXy1q4cCGzuKy6x6NHj5gvCr/88gtGjRoFW1tb7Ny5EywWC2FhYdWWv2jRImRlZTGPly9ffnpRJAKGDMG7JUuQnZ2NeABdRo5Ep06dZLo+t2/fxrBhw/DhwwdYWFjIFLQSQvDvv/+itLQUQqEQvXv3pkErVW/K1wbUdBcjOTm5yp/Tz/u5quzatQsWFhZQVlaGvr4+Zs2aVZfVr1JaWhrGjRuHTp06gc1mS5w+sKo2Hjx4kHndw8MDaWlpMuX9l3rEtaLyBNpNokM4MAbQayPRblJn49MqbSmnosRGfrFI7DxhYS5EhbnIVdDE+XEq4CrUfB2CSr/FOZE91LgcrBllgaEWrWvfns+xOQ16+5iqB3KesywQCHDz5k0kJydDTU2NCXo+17FjR/z9999wc3MDi8XCkiVLqj23OkFBQWjVqhWsra3BZrMRFhYGQ0NDsWAlLCwMdnZ2cHJywr59+3Dr1i38+eefAIDx48fDz88Pnp6eWLZsGd6+fQsfHx9MmDDhi2mNBAIBrl69irFjx4LL5UJXxu1G5WHevHmYNGlSjeeYmpoyi88qzmnlcrkwNTWtNLWjIi6XW3VC/3fvAHV1vAwORpqSErTevMG3335bqzaUKy4uxuPHj1FUVIRDhw5BR0e228qvXr0Cj8fD27dvq1wMR1F1reLagC+JiIhAt27dmOdf+v8eFBSEdevWITAwEA4ODsjLy6vVQIG0ioqKoKenh8WLF2P9+vVSvXfnzp1wdXVlnlfs13k8Hng8nkxTuKQecQXKpgt0796dSdbcvXt3hISE1LoSDUaCldln49MwY29Mpe3qPg9aC57dxtujqyAqzMVLwXC85+hUn+aKAJkcXeCredg31QFxfi51G7RSzUP5nGUA1SaRqcc5yz/++CM4HA7MzMygp6dXbWATFBQELS0t9OrVC25ubnBxcWFugUuKz+djzZo1sLOzQ48ePZCcnIzTp0+Dzf7UJS1fvhwHDx6EhYUF9uzZgwMHDjABmIqKCs6dO4fMzEz06NED3377LQYOHIjff//9i5+9YsUKJCcno3379tDT05Oq3o2Fnp4eunTpUuNDSUmJSWNWns0BKMtukJycXKu5nh8OH0ZhYSEupqbCvk8fdOzYUaZ2xMbGYtiwYUhKSoKTk5NMQSshBImJiXj//j2ys7MxZMgQmepGUfVBR0eHyZBhaGhYaUpVRR8+fMDixYuxZ88ejBs3Du3bt4eFhQXc3ev/bqpAIMDGjRsxceJEqXeS09TUFGtjXW/qIfWI69KlSxEUFAQfHx9mqDc6Ohq+vr5ISUnBihUr6rSCdavmldlCEcHykwlVjnWVExXlgZQWQ1RcAP1v/cBW5EIEYHnJRAQrbqhm1ytAY2QQ5nWrw5W8VPMkxznLnTp1QnR0tNixqkb1BAJBpdvZn9+6qmpEoHxhFVB2a23atGk11qd169Y4f/58ta+bm5vXeFt9165dVR7v2bMn4uLiavzs5kJdXR3Tp0+Hn58f2rZtC2NjYwQGBgIoy9ogrdjOnWGRlgZPT0+Z6lVSUoJXr17h9evX2LdvH/T19WUqLz09HXw+H/fv35d5BJii6pO7uzsKCwvRqVMnzJ8/v8Yg9MKFCxCJREhNTUXXrl2Rk5ODXr16Yd26dUy6wsZo1qxZmDp1KkxNTTF9+nRMnjy5Tu/MSx24lifR/u6775hj7u7usLCwgI+PTyMPXIHqVmYLRQS7opIqjbRWVJAci6zoQ9AeMAWqXfuIvXZOZI8ZJXPKtnqtkOYqHTpYXjwB906owY+k0dx11JeZuZd9sXpxvWwhlppB2fQAmh2CqoXAwEAoKChgwoQJKCgogIODAy5evAgtLS2py+rfvz/U1dVlqs+DBw8wd+5czJo1q05GjlJTU/H8+XMYGxvToJVqtNTU1LBu3Tr07t0bbDYbR44cYdKLVvdz8Pz5c4hEIqxatQobN26EhoYGFi9ejEGDBuHevXuNKmNKuRUrVmDAgAFQUVHB+fPnMXPmTOTm5tbpwn2pA1d5JdGuc/9bmV2W2uopdkYlVVp0VU5UXAAQEUo/vIb+qKVgK1U97H1OZI8LRXawZz+CPj4iA5pMBgFWViFm7I2hiZcpydA5y1QdUVRUxNq1a7F27Vq51kMoFOLdu3eIi4vDrl270KqVbP3gu3fvwOfzcfHiRUyYMKGOaklRsuvWrRtevHgBAPjqq69w5swZ6OrqiqW869GjB16/fo3AwMBqA1eRSISSkhJs2rSJSRd44MABGBoa4tKlSzJn8agPS5YsYf5ubW2NvLw8BAYGyjdwlUcS7XqhZlCW2urv+/iYXwI2ROhZRcBZmHIfH6P2Q3vgNPCth36x2PI0V5/73yQFLD+ZgEFmhjQBM0XVoHzhJ9U8JCYmYvbs2Zg1a1ad7NqVmZmJmzdvwsrKigatVKNz+vRplJSUDYTxeLxqz3NwcMCFCxeqfb38y13FxZV6enrQ1dWtcXFlY+Lg4AB/f38UFRVVveCzFmqVVaChk2jXJREp26XqUrYAMw/EAABc2LfKbvGzPt3if1asiVWl/8Hfqa+gP3IJ2FyVL5atrMhGYUn1q6sJgLSsQtxKyqQJmSmKavZEIhFycnIQGRmJbdu2oV27djKVl52dDSUlJRw9elRsJzCKakwkXfgYGxtb452H3r17Ayj74mdkZASg7Evbu3fvmsxGGrGxsdDS0qqzoBWoRVaB8iTaenp6ePbsGZ49ewZdXV3Y2NggPj4ed+/exd27d8UWYjQW5av+dxY4Yc6Bf/E95zR2KPyGrYobYFhhXuqNV6X44UAq5uRsxJjeAomCVjcLQwR8Yy5RPTJyqp9HSzUPdMSw+aH/ptJ5/vw53N3dce3aNcycOVPmoDUvLw/h4eHIycmhQSvV5OzevRsHDhzAo0eP8OjRI6xatQo7duyAj48Pc87Ro0fRpUsX5nmnTp0wfPhwzJ49G9evX0d8fDw8PT3RpUsX9O/fv9rPmjhxIhYtWlRtuQDQpUsXHD16tMY6x8bGIjY2Frm5uXj79i1iY2ORkJBQbbknT55ESEgI4uPj8fTpUwQHB2PVqlVibawLUo+4Xrp0qU4r0JDK787PxGHM4B5GxUVuLABFpQRsFnAisRSHx6hAQ5kFP/yFC0V2zKYBFSkpsOFu0QqrRlpASYGN6GfvJaqHPr9uU0NQjUd5apP8/PwabxFRTU9+fj6AyjuCUeJEIhGTk3Xz5s0wMTGRqbzCwkKUlJTg4MGDX8xEQVGNmb+/P168eAEFBQV06dIFoaGhYgsKs7KyxFLXAcCePXvg6+uLYcOGgc1mo2/fvjh79myN/VBKSopYasGqyk1MTERWVlaN9bW2tmb+fufOHezfvx/GxsZM1pjPy1VUVMSWLVvg6+sLQgg6dOiAoKCgOv+5ZZFmPoyQnZ0NDQ0NZC3kQ537KVIl/0tTVe7OayEWRBQieJgyOuqIr94eW7y40rzVjnqqOOvbV2yuqlBE4PTbRaRnFVaXPh6GGsq4tmAAnePajKWlpeHjx4/Q19eHiopK09igg6oWIQT5+fnIyMiApqZmlbf2mH4mK0vmVfeNkaTtS0lJgY+PD2bNmsUsJpFFSUkJdu7cCQ8PD6lzSVJUQ+jXrx+srKxqtXVpS1bVdZO0n5Fp56ymrDyWKBESEAC7YosR+i0POiqVR1b18bHSMe+BHSsFnxw2C35uZpixNwYsiO99VH6mn5sZDVqbufIdVDIyMuRcE6oulSfVpiojhEAoFCI4OBhr166VeWOC0tJSZGVl4ejRo/jhhx/qqJYUVT/++OMPhISEIDo6Gubmkk0ZbKn27dsHLy8vFBQUwMrKqlZltNjAFQDi0oWYd74QO4bzsHlo9bd1M6BZ6Vh1t/tdu7fClnHWWHw8Hpl5n9JrGWoow8/NjKbCagFYLBZatWoFfX19ZmUp1bQpKiqCw6F5dKvy+vVr+Pj4wMfHBwEBATKXJxKJ8Pvvv8PLywtTp06tgxpSVP3Zt28fCgoKAEDmedwtgbu7OxwcHACIbwUrjRYZuIoIQUEJsOFmMfaN5MFAreo1aiICFEIRMzl/YxLnDG6LOmO30BV8njJKhSIcvZuKN1kF+OfJW2QXlsK8jTq0VbnYeyNFLCestqoSlgyrIWgVCYHka0DSP2VDs8ZOZTk8pUg4LxQR3ErKREZOIfT5yrA30aYju3LG4XBosEM1W4QQEELw66+/wt/fXyxlT23Le/36NS5cuIA5c+bUTSUpqp61adNG3lVoUvh8Pvh8vkxlNIk5rlu2bEFgYCDS09NhaWmJzZs3w97eXqL3fj7H9eFbIXzPFeKvb3jQU5U6qQKEBNhe+jVWCyXPRVgePla5+UDCCeDkbKAgU/w4Twtw2yTRFp9n49Ow/GSC2K5fregIL0U1mJY2x/XNmzeYPXs2fH19mdETWRBCsGbNGsydO5cufqOoFkrSflT6yK2BhYaGYu7cufDz80NMTAwsLS3h4uIi9fxBQgg+FBAsv1KEP91rF7QCZRfMS+EUFnL2S/7Z//tz+ckECEUVvicknAAOTagctAJAwYey1xJO1Fj22fg0zNgbU2mr2vT/7dR1Nj5N4npSFEV9CSEEP/74IxYtWlQnQeuTJ08QGhqKBQsW0KCVoqgvavSBa3kqhcmTJ8PMzAxbt26FiooKduzYIVU5Yw8XgM0CDn6rgjbqtW92+aKuqQrhUIDkW9xW3HwAQNn0gLMLvvzGMwvKzq2CUESw/GRClRkMqg2WKYqiamnatGm4f/8+/vrrL1haWspc3qpVq9C+fXuMHTu2DmpHUVRL0KjnuBYXF+POnTtiiXTZbDacnZ0RHR1d5XuKiopQVFTEPC/PU+bXlwsWC8guqqsgjmBM6SnsFUqX8iU57S266SkCydeBt6lffkNRKhB/ARD0qvTSreeZSM2oYrS2gtSMfFy69wL2ptpS1ZOiKMllZ2cDaL6bFJS3a+LEiRAIBEx7a+v+/ft49eoVvL29kZubWxdVpCiqiZO4HyWNWGpqKgFArl+/Lnb8p59+Ivb29lW+x8/Pj6BswJE+6IM+6KNBHy9fvmyIrrHBvXz5Uu7Xlj7ogz5axuNL/WijHnGtjUWLFmHu3LnM848fP8LY2BgpKSk0gfVnsrOz0bZtW7x8+bJZLiiRBb02VaPXpWqEEOTk5KB169byrkq9aN26NV6+fAk+n98gG2q0hP9nzb2NtH1NX0O3UdJ+tFEHrrq6uuBwOHjz5o3Y8Tdv3lSbCJzL5YLL5VY6rqGh0Wz/c8lKXV2dXptq0GtTNXpdKmvOX4zZbDaMjIwa/HNbwv+z5t5G2r6mryHbKEk/2qgXZykpKcHW1haRkZHMMZFIhMjISDg6OsqxZhRFURRFUVRDa9QjrgAwd+5ceHp6ws7ODvb29tiwYQPy8vIwefJkeVeNoiiKoiiKakCNPnD18PDA27dvsXTpUqSnp8PKygpnz56FgYGBRO/ncrnw8/OrcvpAS0evTfXotakavS5UQ2gJ/8+aextp+5q+xtrGJrFzFkVRFEVRFEU16jmuFEVRFEVRFFWOBq4URVEURVFUk0ADV4qiKIqiKKpJoIErRVEURVEU1SQ068B1y5YtEAgEUFZWhoODA27duiXvKjW4q1evws3NDa1btwaLxcKxY8fEXieEYOnSpWjVqhV4PB6cnZ3x5MkT+VS2gQUEBKBHjx7g8/nQ19fHiBEjkJiYKHZOYWEhZs2aBR0dHaipqWHUqFGVNsRojoKDg2FhYcEknnZ0dMSZM2eY11vqdaHkIzw8HA4ODuDxeNDS0sKIESPkXaV6UVRUBCsrK7BYLMTGxsq7OnUiOTkZU6ZMgYmJCXg8Htq3bw8/Pz8UFxfLu2oyaa7xhSS/F+Wt2QauoaGhmDt3Lvz8/BATEwNLS0u4uLggIyND3lVrUHl5ebC0tMSWLVuqfH3NmjXYtGkTtm7dips3b0JVVRUuLi4oLCxs4Jo2vCtXrmDWrFm4ceMGLly4gJKSEgwePBh5eXnMOb6+vjh58iTCwsJw5coVvH79GiNHjpRjrRuGkZERVq9ejTt37uDff//FgAEDMHz4cDx48ABAy70uVMM7cuQIJkyYgMmTJyMuLg5RUVEYN26cvKtVL+bPn9/stg1+9OgRRCIR/u///g8PHjzA+vXrsXXrVvz888/yrlqtNef4QpLfi3JHmil7e3sya9Ys5rlQKCStW7cmAQEBcqyVfAEgR48eZZ6LRCJiaGhIAgMDmWMfP34kXC6XHDhwQA41lK+MjAwCgFy5coUQUnYtFBUVSVhYGHPOw4cPCQASHR0tr2rKjZaWFgkJCaHXhWowJSUlpE2bNiQkJETeVal3p0+fJl26dCEPHjwgAMjdu3flXaV6s2bNGmJiYiLvatRaS4ovPv+92Bg0yxHX4uJi3LlzB87OzswxNpsNZ2dnREdHy7FmjUtSUhLS09PFrpOGhgYcHBxa5HXKysoCAGhrawMA7ty5g5KSErHr06VLF7Rr165FXR+hUIiDBw8iLy8Pjo6O9LpQDSYmJgapqalgs9mwtrZGq1atMGTIEMTHx8u7anXqzZs3mDZtGv766y+oqKjIuzr1Lisri+lnm5qWFl98/nuxMWiWgeu7d+8gFAor7a5lYGCA9PR0OdWq8Sm/FvQ6ASKRCHPmzEHv3r3RvXt3AGXXR0lJCZqammLntpTrc//+faipqYHL5WL69Ok4evQozMzMWvx1oRrO8+fPAQDLli3D4sWLcerUKWhpaaFfv37IzMyUc+3qBiEEkyZNwvTp02FnZyfv6tS7p0+fYvPmzfDy8pJ3VWqlJcUXVf1ebAyaZeBKUdKaNWsW4uPjcfDgQXlXpdHo3LkzYmNjcfPmTcyYMQOenp5ISEiQd7WoZmDhwoVgsVg1PsrnRgLAL7/8glGjRsHW1hY7d+4Ei8VCWFiYnFtRM0nbuHnzZuTk5GDRokXyrrJUJG1fRampqXB1dcXo0aMxbdo0OdWcklRj/b2oIO8K1AddXV1wOJxKq5zfvHkDQ0NDOdWq8Sm/Fm/evEGrVq2Y42/evIGVlZWcatXwvL29cerUKVy9ehVGRkbMcUNDQxQXF+Pjx49io4st5f+RkpISOnToAACwtbXF7du3sXHjRnh4eLTo60LJbt68eZg0aVKN55iamiItLQ0AYGZmxhzncrkwNTVFSkpKfVZRZpK28eLFi4iOjq60H7ydnR3Gjx+P3bt312Mta0/S9pV7/fo1+vfvj169emHbtm31XLv601Lii+p+LzYGzTJwVVJSgq2tLSIjI5m0KSKRCJGRkfD29pZv5RoRExMTGBoaIjIykglUs7OzmRG25o4QAh8fHxw9ehSXL1+GiYmJ2Ou2trZQVFREZGQkRo0aBQBITExESkoKHB0d5VFluRKJRCgqKqLXhZKZnp4e9PT0vniera0tuFwuEhMT4eTkBAAoKSlBcnIyjI2N67uaMpG0jZs2bcKvv/7KPH/9+jVcXFwQGhoKBweH+qyiTCRtH1A20tq/f39mxJzNbro3e5t7fPGl34uNgpwXh9WbgwcPEi6XS3bt2kUSEhLIDz/8QDQ1NUl6erq8q9agcnJyyN27d8ndu3cJABIUFETu3r1LXrx4QQghZPXq1URTU5McP36c3Lt3jwwfPpyYmJiQgoICOde8/s2YMYNoaGiQy5cvk7S0NOaRn5/PnDN9+nTSrl07cvHiRfLvv/8SR0dH4ujoKMdaN4yFCxeSK1eukKSkJHLv3j2ycOFCwmKxyPnz5wkhLfe6UA1v9uzZpE2bNuTcuXPk0aNHZMqUKURfX59kZmbKu2r1IikpqVllFXj16hXp0KEDGThwIHn16pVYX9tUNef4QpLfi/LWbANXQgjZvHkzadeuHVFSUiL29vbkxo0b8q5Sg7t06RIBUOnh6elJCClLibVkyRJiYGBAuFwuGThwIElMTJRvpRtIVdcFANm5cydzTkFBAZk5cybR0tIiKioq5JtvvmnSHa6kvv/+e2JsbEyUlJSInp4eGThwIBO0EtJyrwvV8IqLi8m8efOIvr4+4fP5xNnZmcTHx8u7WvWmuQWuO3furLavbcqaa3whye9FeWMRQkhDje5SFEVRFEVRVG013YkmFEVRFEVRVItCA1eKoiiKoiiqSaCBK0VRFEVRFNUk0MCVoiiKoiiKahJo4EpRFEVRFEU1CTRwpSiKoiiKopoEGrhSFEVRFEVRTQINXCmKoiiKoqgmgQaulMSSk5PBYrEQGxsr76rU2q5du6CpqSnvakglKioK5ubmUFRUZPbGrkpiYiIMDQ2Rk5PTcJWrQ+/evYO+vj5evXol76pQlFzRvlY+JOlrBQIBNmzY0KD1osTRwJWSWNu2bZGWlobu3bvLuypYtmwZrKys5F2NBjF37lxYWVkhKSkJu3btqva8RYsWwcfHB3w+v8HqZmJigoiICInO3bVrF1gsFlgsFthsNoyMjDB58mRkZGQAAHR1dTFx4kT4+fnVZ5UpqtGjfa18SNrX1rUHDx5g1KhREAgEYLFY1QbGW7ZsgUAggLKyMhwcHHDr1q0Gq2NjQgNXSiLFxcXgcDgwNDSEgoKCvKvTojx79gwDBgyAkZFRtSMYKSkpOHXqFCZNmtRg9bp37x4+fPiAvn37SvwedXV1pKWl4dWrV9i+fTvOnDmDCRMmMK9PnjwZ+/btQ2ZmZn1UmaIaPdrXyo8kfW19yM/Ph6mpKVavXg1DQ8MqzwkNDcXcuXPh5+eHmJgYWFpawsXFhfni36IQqlERCoVk1apVRCAQEGVlZWJhYUHCwsIIIYSIRCIycOBAMnjwYCISiQghhLx//560adOGLFmyhBBCyKVLlwgAcurUKWJubk64XC5xcHAg9+/fF/ucf/75hzg5ORFlZWViZGREfHx8SG5uLvO6sbExWbFiBZkwYQLh8/nE09OTJCUlEQDk7t27Yp919uxZYmVlRZSVlUn//v3JmzdvyOnTp0mXLl0In88n3333HcnLy5OojRXLjYiIILa2toTH4xFHR0fy6NEjQgghO3fuJADEHjt37iSEELJu3TrSvXt3oqKiQoyMjMiMGTNITk4OU/bOnTuJhoZGtde/vI2hoaHM9bGzsyOJiYnk1q1bxNbWlqiqqhJXV1eSkZHBvM/T05MMHz6cLFu2jOjq6hI+n0+8vLxIUVERc07fvn2Jt7c3mT17NtHU1CT6+vpk27ZtJDc3l0yaNImoqamR9u3bk9OnT4vVpap2fi4wMJDY2dmJHStv68mTJ0mnTp0Ij8cjo0aNInl5eWTXrl3E2NiYaGpqEh8fH1JaWsq87/Xr12To0KFEWVmZCAQCsm/fPmJsbEzWr18vVv6KFSuIh4cH83zbtm3EyMiI8Hg8MmLECLJu3Tqxa13VtV+5ciVhs9kkPz+fOWZiYkJCQkKq/TeiqLpA+1ra19amr/28L3zx4gVxd3cnqqqqhM/nk9GjR5P09HSx9/j7+xM9PT2ipqZGpkyZQhYsWEAsLS0lKr+cvb09mTVrFvNcKBSS1q1bk4CAgGqvcXNFA9dG5tdffyVdunQhZ8+eJc+ePSM7d+4kXC6XXL58mRBCyKtXr4iWlhbZsGEDIYSQ0aNHE3t7e1JSUkII+dQRde3alZw/f57cu3ePfP3110QgEJDi4mJCCCFPnz4lqqqqZP369eTx48ckKiqKWFtbk0mTJjH1MDY2Jurq6mTt2rXk6dOn5OnTp9V2pj179iTXrl0jMTExpEOHDqRv375k8ODBJCYmhly9epXo6OiQ1atXS9zG8nIdHBzI5cuXyYMHD8hXX31FevXqRQghJD8/n8ybN49069aNpKWlkbS0NCbwWb9+Pbl48SJJSkoikZGRpHPnzmTGjBnMZ0vamZbXLyEhgfTs2ZPY2tqSfv36ibVz+vTpzPs8PT2Jmpoa8fDwIPHx8eTUqVNET0+P/Pzzz8w5ffv2JXw+n/j7+5PHjx8Tf39/wuFwyJAhQ8i2bdvI48ePyYwZM4iOjg7Jy8sjpaWlJC0tjairq5MNGzaItfNz7u7uYvUpb6uioiIZNGgQiYmJIVeuXCE6Ojpk8ODBZMyYMeTBgwfk5MmTRElJiRw8eJB5n7OzM7GysiI3btwgd+7cIX379iU8Hq9SZ2pnZ0f2799PCCHk2rVrhM1mk8DAQJKYmEi2bNlCtLW1vxi4BgUFEQAkOzubOebh4UE8PT2r/TeiqLpA+1ra19amr60YWAqFQmJlZUWcnJzIv//+S27cuEFsbW1J3759mfP37t1LlJWVyY4dO0hiYiJZvnw5UVdXlypwLSoqIhwOhxw9elTs+MSJE4m7u3u117i5ooFrI1JYWEhUVFTI9evXxY5PmTKFfPfdd8zzQ4cOEWVlZbJw4UKiqqpKHj9+zLxW3hFVDETev39PeDweCQ0NZcr74YcfxD7jn3/+IWw2mxQUFBBCyn54RowYIXZOdZ1pREQEc05AQAABQJ49e8Yc8/LyIi4uLhK3sapyw8PDCQCmfn5+ftX+4FcUFhZGdHR0mOeSdqYVR/wOHDhAAJDIyEixdnbu3Jl57unpSbS1tcVGO4KDg4mamhoRCoWEkLLO1MnJiXm9tLSUqKqqkgkTJjDH0tLSCAASHR3NHNPQ0Kj22385S0tLsmLFCrFj5aMlT58+ZY55eXkRFRUVsZERFxcX4uXlRQgh5OHDhwQAuX37NvP6kydPCACxzvTVq1dESUmJfPjwgRBSFmwOGzZM7PPHjx9fY+D6+PFj0qlTp0ojxb6+vqRfv341tpeiZEH7WtrX1ravrRhYnj9/nnA4HJKSksK8/uDBAwKA3Lp1ixBCiIODg9hIKSGE9O7dW6rANTU1lQCo9G/5008/EXt7+xrr2xzRCTSNyNOnT5Gfn49BgwaJHS8uLoa1tTXzfPTo0Th69ChWr16N4OBgdOzYsVJZjo6OzN+1tbXRuXNnPHz4EAAQFxeHe/fuYd++fcw5hBCIRCIkJSWha9euAAA7OzuJ6m1hYcH83cDAACoqKjA1NRU7Vj6JXNI2fl5uq1atAAAZGRlo165dtXWJiIhAQEAAHj16hOzsbJSWlqKwsBD5+flQUVGRqD1VtQkAzM3NxY59PrfI0tJS7DMcHR2Rm5uLly9fwtjYuFK5HA4HOjo6lcotb6c0CgoKoKysXOm4iooK2rdvL1a+QCCAmppalW1JTEyEgoICbGxsmNc7dOgALS0tsXJPnDgBJycnZh5YYmIivvnmG7Fz7O3tcerUKbFjWVlZUFNTg0gkQmFhIZycnBASEiJ2Do/HQ35+vhStpyjp0L6W9rW17WsrevjwIdq2bYu2bdsyx8zMzKCpqYmHDx+iR48eSExMxMyZM8XeZ29vj4sXL9b6c1s6Grg2Irm5uQCA8PBwtGnTRuw1LpfL/D0/Px937twBh8PBkydPavU5Xl5e+O9//1vptYodlaqqqkTlKSoqMn9nsVhiz8uPiUQi5rOBL7exqnIBMOVUJTk5GV9//TVmzJiBlStXQltbG9euXcOUKVNQXFwsVWda1Wd/fqymukhSbnk50razKrq6uvjw4YPUn1d+TNrPO3HiBNzd3aV6DwDw+XzExMSAzWajVatW4PF4lc7JzMyEnp6e1GVTlKRoX0v72tr2tfKgq6sLDoeDN2/eiB1/8+ZNtYu5mjMauDYiZmZm4HK5SElJqXGl9rx588Bms3HmzBkMHToUw4YNw4ABA8TOuXHjBtMxfvjwAY8fP2a+3dvY2CAhIQEdOnSov8ZUQ9I2fomSkhKEQqHYsTt37kAkEmHdunVgs8sSZhw6dEim+kojLi4OBQUFTDB248YNqKmpiX0bry/W1tZISEiQuZzOnTujtLQUd+/eha2tLYCykZuKQXFubi4uXbqE4OBgsffdvn1brKzPnwMAm83+4v+7+Ph49OvXT4ZWUFTNaF8rOdrXVq9r1654+fIlXr58yXx2QkICPn78CDMzMwCf+saJEycy76uqb6yJkpISbG1tERkZyeSXFYlEiIyMhLe3d900pgmhgWsjwufz8eOPP8LX1xcikQhOTk7IyspCVFQU1NXV4enpifDwcOzYsQPR0dGwsbHBTz/9BE9PT9y7d0/sdu6KFSugo6MDAwMD/PLLL9DV1WX+wy9YsAA9e/aEt7c3pk6dClVVVSQkJODChQv4/fff5d5GSQgEAiQlJSE2NhZGRkbg8/no0KEDSkpKsHnzZri5uSEqKgpbt26t1/ZUVFxcjClTpmDx4sVITk6Gn58fvL29mY69Prm4uGDq1KkQCoXgcDi1LqdLly5wdnbGDz/8gODgYCgqKmLevHng8XjMCMXZs2fRqVMnCAQC5n0+Pj7o06cPgoKC4ObmhosXL+LMmTPMeyRVPsK1atWqWreBor6E9rW0r60Lzs7OMDc3x/jx47FhwwaUlpZi5syZ6Nu3LzP9w8fHB9OmTYOdnR169eqF0NBQ3Lt3T2yKR3FxMTPwUFxcjNTUVMTGxkJNTY350jN37lx4enrCzs4O9vb22LBhA/Ly8jB58uQGbXNjQPO4NjL+/v5YsmQJAgIC0LVrV7i6uiI8PBwmJiZ4+/YtpkyZgmXLljFzEJcvXw4DAwNMnz5drJzVq1dj9uzZsLW1RXp6Ok6ePAklJSUAZXN/rly5gsePH+Orr76CtbU1li5ditatW8u9jZIaNWoUXF1d0b9/f+jp6eHAgQOwtLREUFAQfvvtN3Tv3h379u1DQEBAPbZE3MCBA9GxY0f06dMHHh4ecHd3x7Jlyxrks4cMGQIFBQWJNwOoyZ49e2BgYIA+ffrgm2++wbRp08Dn85k5tMePH680TaB3797YunUrgoKCYGlpibNnz8LX17fKebc1OX78ONq1a4evvvpK5nZQVE1oXysZ2tdWj8Vi4fjx49DS0kKfPn3g7OwMU1NThIaGMueMHz8eixYtwo8//ggbGxskJSVh0qRJYn3j69evYW1tDWtra6SlpWHt2rWwtrbG1KlTmXM8PDywdu1aLF26FFZWVoiNjcXZs2eZubotCYsQQuRdCaruXL58Gf3798eHDx+a3HZ7TdmkSZPw8eNHHDt2TG512LJlC06cOIFz587VabmvXr1C27ZtERERgb59+8LAwABnzpyBvb19je+bNm0aHj16hH/++Ufiz+rZsyf++9//Yty4cbJWm6LqFe1r5aMx9LWyGjRoEAwNDfHXX3/JuypNEp0qQFHNhJeXFz5+/IicnByZtn29ePEicnNzYW5ujrS0NMyfPx8CgQB9+vRBZmYmfH190aNHj0rvW7t2LQYNGgRVVVWcOXMGu3fvxh9//CHx57579w4jR47Ed999V+u6UxRFNSb5+fnYunUrXFxcwOFwcODAAURERODChQvyrlqTRQNXimomFBQU8Msvv8hcTklJCX7++Wc8f/4cfD4fvXr1wr59+6CoqAh9fX0sXry4yvfdunULa9asQU5ODkxNTbFp0yaxW11foquri/nz58tcf4qiqMaCxWLh9OnTWLlyJQoLC9G5c2ccOXIEzs7O8q5ak0WnClAURVEURVFNAl2cRVEURVEURTUJNHClKIqiKIqimgQauFIURVEURVFNAg1cKYqiKIqiqCaBBq4URVEURVFUk0ADV4qiKIqiKKpJoIErRVEURVEU1STQwJWiKIqiKIpqEv4fug8hP4gt/kwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x247.2 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 file(s) exported for \"Load reaction data\" into Escher maps\n"
     ]
    }
   ],
   "source": [
    "# Optimize model using gurobipy and analyze results\n",
    "pred_results = {}\n",
    "for cond, medium in conditions.items():\n",
    "    eo.medium = medium\n",
    "    solution = eo.optimize()\n",
    "\n",
    "    if solution.status == 'optimal':\n",
    "        pred_results[cond] = solution\n",
    "        gr = solution.objective_value\n",
    "        print(f'{cond:25s}: pred gr: {gr:.3f} h-1 vs. exp {exp_grs[cond]:.3f}, diff: {gr - exp_grs[cond]:6.3f}')\n",
    "    else:    \n",
    "        print(f'{cond} ended with status {solution.status}')\n",
    "        \n",
    "# Analyze results\n",
    "er = EcmResults(eo, pred_results, df_mpmf)\n",
    "df_fluxes = er.collect_fluxes()\n",
    "df_net_fluxes = er.collect_fluxes(net=True)\n",
    "df_proteins = er.collect_protein_results()\n",
    "er.plot_grs(exp_grs, highlight='Glucose')\n",
    "\n",
    "print(f'Protein mass fractions:')\n",
    "er.report_proteomics_correlation(scale='lin')\n",
    "er.report_proteomics_correlation(scale='log')\n",
    "print()\n",
    "er.report_protein_levels('Glucose')\n",
    "er.plot_proteins('Glucose', plot_fname=os.path.join('plots', f'{target_model}_proteins_Glucose.pdf'))  \n",
    "er.save_to_escher(df_net_fluxes['Glucose'], os.path.join('escher', target_model))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "## References\n",
    "\n",
    "- Domenzain, I., Sánchez, B., Anton, M., Kerkhoven, E. J., Millán-Oropeza, A., Henry, C., Siewers, V., Morrissey, J. P., Sonnenschein, N., & Nielsen, J. (2022). Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0. Nat Commun, 13(1), 3766. https://doi.org/10.1038/s41467-022-31421-1 \n",
    "- Dunkle, J. A., Bruno, M. R., Outten, F. W., & Frantom, P. A. (2019). Structural Evidence for Dimer-Interface-Driven Regulation of the Type II Cysteine Desulfurase, SufS. Biochemistry, 58(6), 687-696. https://doi.org/10.1021/acs.biochem.8b01122 \n",
    "- Wendering, P., Arend, M., Razaghi-Moghadam, Z., & Nikoloski, Z. (2023). Data integration across conditions improves turnover number estimates and metabolic predictions. Nature Communications, 14(1), 1485. https://doi.org/10.1038/s41467-023-37151-2 \n",
    "\n"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3.12",
   "language": "python",
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    "name": "ipython",
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