{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. Tutorial: Create iML1515 GECKO with manually adjusted turnover numbers\n", "\n", "In the previous tutorial we created a GECKO model of iML1515 with TurNuP predicted turnover numbers for metabolic reactions in a few simple steps. The model predictions of protein concentrations are still not satisfactory, e.g. the model predicts high protein concentrations where the measured values are very low and vice versa. \n", "\n", "To achieve high prediction quality, a GECKO must be constructed using apparent turnover numbers that account for *in vivo* conditions such as enzyme regulation, enzyme saturation, and substrate channeling (Araiza-Olivera et al., 2013). TurNuP predicted turnover numbers can only give approximate values. The deviation between predicted and measured data is estimated to be a factor of 4.8. Furthermore, the quality of the training data is poor, consisting mainly of *in vitro* measurements using enzymatic assays. Measurement points are scarce, highly noisy, and acquired in a non-standardized manner using enzyme-specific assay protocols.\n", "\n", "Compared to enzymatic assays, quantitative proteomics is a fairly standardized procedure with almost complete protein coverage. This justifies the use of quantitative proteomics to adjust predicted turnover numbers to apparent turnover numbers, as required for the GECKO model. \n", "\n", "In this tutorial, we attempt to improve protein predictions by selectively adjusting turnover numbers. This requires a more detailed analysis of the optimization results combined with a certain amount of organism specific knowledge. We will analyze predicted flux distributions and try to redirect fluxes to physiologically relevant pathways. We will selectively compare predicted to measured protein levels and adjust GECKO coupling factors by modifying turnover numbers. Our focus will be on the major carbon metabolism. The methods and code snippets provided can be used to adjust other parts of the model as well.\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": {}, "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.utils.mapping_utils import load_parameter_file, write_parameter_file\n", "\n", "fba_model = 'iML1515'\n", "baseline_model = 'iML1515_predicted_GECKO'\n", "target_model = 'iML1515_manual_adjust_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: Analyze baseline GECKO model (cobrapy)\n", "\n", "First, we need to identify deficiencies in our baseline GECKO model. Using Escher maps, we can visualize predicted flux distributions. By applying organism specific knowledge and comparing predicted to measured protein levels, we can visually identify pathways and reactions where predictions appear to be incorrect. Our focus will be on the major carbon metabolism for the glucose medium, the chosen reference condition." ] }, { "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_predicted_GECKO.xml (Thu Feb 12 15:19:56 2026)\n", "total modeled protein: 306.59 mg/gDW, enzyme saturation level: 0.68\n", "1494 genes: (492) transporter, (1002) metabolic\n" ] } ], "source": [ "# Load model using cobrapy\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", "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, enzyme saturation level: {sigma}')\n", "print(f'{len(all_genes)} genes: ({len(tx_genes)}) transporter, ({len(metab_genes)}) metabolic')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Acetate : pred gr: 0.453 h-1 vs. exp 0.290, diff: 0.163\n", "Glycerol : pred gr: 0.609 h-1 vs. exp 0.470, diff: 0.139\n", "Fructose : pred gr: 0.625 h-1 vs. exp 0.540, diff: 0.085\n", "L-Malate : pred gr: 0.695 h-1 vs. exp 0.550, diff: 0.145\n", "Glucose : pred gr: 0.653 h-1 vs. exp 0.660, diff: -0.007\n", "Glucose 6-Phosphate : pred gr: 0.691 h-1 vs. exp 0.780, diff: -0.089\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Protein mass fractions:\n", "Acetate : r² = 0.0286, p = 7.71e-08 ( 999 proteins lin scale)\n", "Glycerol : r² = 0.0433, p = 3.17e-11 ( 999 proteins lin scale)\n", "Fructose : r² = 0.1063, p = 3.62e-26 ( 999 proteins lin scale)\n", "Glucose : r² = 0.0788, p = 1.51e-19 ( 999 proteins lin scale)\n", "Acetate : r² = 0.2768, p = 5.65e-23 ( 303 proteins log scale)\n", "Glycerol : r² = 0.2684, p = 1.77e-22 ( 307 proteins log scale)\n", "Fructose : r² = 0.2718, p = 3.60e-22 ( 298 proteins log scale)\n", "Glucose : r² = 0.2688, p = 1.38e-22 ( 308 proteins log scale)\n", "\n", "condition: Glucose\n", "1494 proteins in model with total predicted mass fraction of 537.9 mg/gP\n", " 999 have been measured with mpmf of 514.4 mg/gP vs. 442.5 mg/gP predicted\n", " 762 metabolic proteins measured 414.0 mg/gP vs. 289.0 mg/gP predicted\n", " 237 transport proteins measured 100.4 mg/gP vs. 153.5 mg/gP predicted\n", " 495 proteins not measured vs. 95.4 mg/gP predicted\n", " 495 actual proteins 95.4 mg/gP predicted\n", "total : r² = 0.0788, p = 1.51e-19 ( 999 proteins lin scale)\n", " metabolic : r² = 0.0340, p = 2.92e-07 ( 762 proteins lin scale)\n", " transport : r² = 0.4967, p = 6.70e-37 ( 237 proteins lin scale)\n", "total : r² = 0.2688, p = 1.38e-22 ( 308 proteins log scale)\n", " metabolic : r² = 0.3028, p = 7.92e-22 ( 258 proteins log scale)\n", " transport : r² = 0.2613, p = 1.49e-04 ( 50 proteins log scale)\n" ] }, { "data": { "image/png": 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", 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" ] }, "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 extract results\n", "pred_results = {}\n", "for cond, medium in conditions.items():\n", " with ecm as model: \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", "# 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=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": "markdown", "metadata": {}, "source": [ "### check outliers in protein correlation\n", "\n", "The presence of issues in the model parametrization can be identified through a visual examination of protein correlation plots in both linear and log scale. Data points suspected of having issues should be queried and subsequently analyzed in detail." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "b3389 pred 30.1695 vs. 0.0809; aroB: 3-dehydroquinate synthase\n", "b1276 pred 11.4470 vs. 0.3967; acnA: Aconitate hydratase 1\n", "b1851 pred 37.5290 vs. 0.0359; edd: Phosphogluconate dehydratase\n", "b3737 pred 10.4876 vs. 0.0161; atpE: ATP synthase subunit c\n", "b1136 pred 0.4634 vs. 10.5086; icd: Isocitrate dehydrogenase [NADP]\n", "b3236 pred 0.0974 vs. 11.7189; mdh: Malate dehydrogenase\n", "b2414 pred 0.1408 vs. 17.2441; cysK: Cysteine synthase A\n", "b3829 pred 0.0000 vs. 29.7986; metE: 5-methyltetrahydropteroyltriglutamate--homocysteine methyltransferase\n" ] } ], "source": [ "# proteins from linear protein correlation plot with poor predictions\n", "for gene, pred in df_proteins[reference_cond].to_dict().items():\n", " if gene in df_mpmf.index:\n", " exp = df_mpmf.at[gene, reference_cond]\n", " if (exp < 1 and pred > 10) or (exp > 10 and pred < 1):\n", " description = df_proteins.at[gene, 'description']\n", " gname = df_proteins.at[gene, 'gene_name']\n", " print(f'{gene} pred {pred:8.4f} vs. {exp:8.4f}; {gname}: {description}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### query results records of interest\n", "\n", "The supplementary information columns present in the optimization results tables are useful for filtering records of interest. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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reaction_strgprgroupsrankmean mmol_per_gDWhabs_mean mmol_per_gDWhstdevAcetateGlycerolFructoseL-MalateGlucoseGlucose 6-Phosphate
rid
EDD6pgc_c => 2ddg6p_c + h2o_cb1851374.4519824.4519825.0543090.00.06.4809020.010.15101210.079977
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" ], "text/plain": [ " reaction_str gpr groups rank mean mmol_per_gDWh \\\n", "rid \n", "EDD 6pgc_c => 2ddg6p_c + h2o_c b1851 37 4.451982 \n", "\n", " abs_mean mmol_per_gDWh stdev Acetate Glycerol Fructose L-Malate \\\n", "rid \n", "EDD 4.451982 5.054309 0.0 0.0 6.480902 0.0 \n", "\n", " Glucose Glucose 6-Phosphate \n", "rid \n", "EDD 10.151012 10.079977 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check reactions catalyzed by a given gene product\n", "df_net_fluxes[df_net_fluxes['gpr'].str.contains('b1851')]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### visualize flux distribution on Escher maps\n", "\n", "The predicted flux values were exported in the ‘.json’ format using the command `er.save_to_escher()`. Given the central carbon metabolism focal point, the subsequent step involves accessing the Escher web portal (https://escher.github.io), loading the map designated 'Central Metabolism (iJO1366)', and selecting 'Data > Load reaction data' to upload the file named 'iML1515_predicted_GECKO_Glucose_reaction_data.json' under `.\\data` (Data > Load reaction data).\n", "\n", "A visual inspection of the flux distribution indicates that the upper glycolytic flux appears to be inadequate, as the majority of imported glucose is directed towards the pentose phosphate pathway (PPP). To address this issue, we will adjust the turnover numbers to shift flux from the PPP to glycolysis. Additionally, we will attempt to enhance the flux distribution within the tricarboxylic acid cycle (TCA).\n", "\n", "### query critical reaction fluxes and related proteins. \n", "\n", "Having gained insights into the predicted flux distributions between glycolysis and the pentose phosphate pathway (PPP), we return to the optimization results and query critical reaction fluxes in those pathways and the related proteins. We can observe that the protein encoded by the gene b1852/zwf has a predicted concentration that is higher than the measured concentration by a factor of 7.5. In contrast, the protein encoded by b4025/pgi has a 8-fold lower predicted concentration than measured. The gene products b1723/pfgB and b3916/pgkA are isoenzymes of the PFK reaction, which has been sub-divided into two sub-reactions: PFK_iso1, catalyzed by b1723 with a molecular weight of 32.4 kDa, and PFK_iso2, catalyzed by b3916 (34.8 kDa). As their predicted turnover numbers are similar (13.37 and 13.34 $s^{-1}$, respectively), GECKO selects the sub-reaction PFK_iso1, due to its lower molecular weight. However, proteomics data and literature (see BioCyc entries for these genes) suggest that b3916/pgkA is the preferred protein. The FBA reaction is catalyzed by the isoenzymes b2097/fbaA and b2925/fbaB, and the predicted flux should be carried by the sub-reaction catalyzed by b2925/fbaA." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "G6PDH2r : 10.911 mmol/gDWh [b1852]\n", "EDA : 10.151 mmol/gDWh [b1850]\n", "PGI : 0.154 mmol/gDWh [b4025]\n", "PFK : 0.092 mmol/gDWh [b1723 or b3916]\n", "FBA : 0.092 mmol/gDWh [b2097 or b2925]\n", "TPI : 0.000 mmol/gDWh [b3919]\n", "GAPD : 10.154 mmol/gDWh [b1779]\n", "PGK : -10.154 mmol/gDWh [b2926]\n", "PGM : -9.030 mmol/gDWh [b0755 or b3612]\n", "ENO : 9.030 mmol/gDWh [b2779]\n", "PYK : 0.000 mmol/gDWh [b1676 or b1854]\n", "G6PDH2r b1852 pred: 2.960 exp: 0.469 mg/gP zwf , 55.7 kDa, Glucose-6-phosphate 1-dehydrogenase\n", "EDA b1850 pred: 8.981 exp: 0.552 mg/gP eda , 22.3 kDa, KHG/KDPG aldolase\n", "PGI b4025 pred: 0.086 exp: 1.931 mg/gP pgi , 61.5 kDa, Glucose-6-phosphate isomerase\n", "PFK b1723 pred: 0.160 exp: 0.208 mg/gP pfkB, 32.4 kDa, 6-phosphofructokinase isozyme 2\n", "PFK b3916 pred: 0.000 exp: 0.344 mg/gP pfkA, 34.8 kDa, 6-phosphofructokinase isozyme 1\n", "FBA b2097 pred: 0.294 exp: 0.561 mg/gP fbaB, 38.1 kDa, Fructose-bisphosphate aldolase class 1\n", "FBA b2925 pred: 0.000 exp: 5.353 mg/gP fbaA, 39.1 kDa, Fructose-bisphosphate aldolase class 2\n", "TPI b3919 pred: 0.000 exp: 1.368 mg/gP tpiA, 26.9 kDa, Triosephosphate isomerase\n", "GAPD b1779 pred: 10.363 exp: 11.410 mg/gP gapA, 35.5 kDa, Glyceraldehyde-3-phosphate dehydrogenase A\n", "PGK b2926 pred: 4.157 exp: 11.229 mg/gP pgk , 41.1 kDa, Phosphoglycerate kinase\n", "PGM b0755 pred: 3.473 exp: 2.348 mg/gP gpmA, 28.5 kDa, 2,3-bisphosphoglycerate-dependent phosphoglyc\n", "PGM b3612 pred: 0.000 exp: 1.018 mg/gP gpmI, 56.1 kDa, 2,3-bisphosphoglycerate-independent phosphogl\n", "ENO b2779 pred: 12.648 exp: 11.257 mg/gP eno , 45.6 kDa, Enolase\n", "PYK b1676 pred: 0.000 exp: 3.080 mg/gP pykF, 50.7 kDa, Pyruvate kinase I\n", "PYK b1854 pred: 0.000 exp: 0.666 mg/gP pykA, 51.3 kDa, Pyruvate kinase II\n" ] } ], "source": [ "# analyze flux split upper glycolysis and PPP\n", "rids = ['G6PDH2r', 'EDA', 'PGI', 'PFK', 'FBA', 'TPI', 'GAPD', 'PGK', 'PGM', 'ENO', 'PYK']\n", "\n", "rid2genes = {}\n", "for rid in rids:\n", " gpr = df_net_fluxes.at[rid, 'gpr']\n", " print(f'{rid:15s}: {df_net_fluxes.at[rid, reference_cond]:8.3f} mmol/gDWh [{gpr}]')\n", " rid2genes[rid] = re.findall(r'b\\d{4}', gpr )\n", " \n", "for rid, genes in rid2genes.items():\n", " for gene in genes:\n", " pred = df_proteins[reference_cond].get(gene, 0.0)\n", " if gene in df_mpmf.index:\n", " row = df_mpmf.loc[gene]\n", " exp = row[reference_cond]\n", " print(f'{rid:10s} {gene} pred: {pred:6.3f} exp: {exp:6.3f} mg/gP {row[\"gene_name\"]:4s}, '\n", " f'{row[\"mw_Da\"]/1000.0:5.1f} kDa, {row[\"description\"][:45]}')\n", " else:\n", " print(f'{rid:10s} {gene} pred: {pred:6.3f} exp: not measured')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 3: Manual adjustment of model parameters\n", "\n", "In order to improve protein predictions, we propose a redistribution of traffic flux between glycolysis and PPP. By decreasing the cost of proteins in glycolysis relative to PPP, flux can be redistributed from PPP to glycolysis. This can be achieved by increasing or decreasing the turnover numbers of selected reactions in these pathways. \n", "\n", "First, we assess the impact of different parameter sets in memory. Once we are satisfied, we generate a new GECKO model with the modified parameters. The method of choice for modifying the turnover numbers of selected reactions in memory is EcmOptimize's `eo.scale_kcats()`, which, in fact, updates the coupling factors. The turnover numbers are scaled by a factor provided in a Python dictionary; for example, the factor 2.0 will double the original turnover number. At the termination of the optimization loop, a reversion to the original turnover numbers is facilitated by invoking the function `eo.unscale_kcats()`. Alternatively, when the initial scaling is executed within the context of the cobrapy model (`with ecm as model:`), the reversion to the original values is carried out automatically. Modifications to the turnover numbers affect the predicted growth rates, necessitating adjustments to the average enzyme saturation. This adjustment is achieved indirectly by decreasing the total protein available, utilizing the cobrapy assignment statement `model.reactions.get_by_id('V_PC_total').upper_bound` to establish the upper limit of the variable ‘V_PC_total’ within the model context. The identification of a suitable set of scaling factors that enhance protein predictions is an iterative process, and the resultant set is presented below." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Acetate : pred gr: 0.513 h-1 vs. exp 0.290, diff: 0.223\n", "Glycerol : pred gr: 0.656 h-1 vs. exp 0.470, diff: 0.186\n", "Fructose : pred gr: 0.648 h-1 vs. exp 0.540, diff: 0.108\n", "L-Malate : pred gr: 0.664 h-1 vs. exp 0.550, diff: 0.114\n", "Glucose : pred gr: 0.661 h-1 vs. exp 0.660, diff: 0.001\n", "Glucose 6-Phosphate : pred gr: 0.676 h-1 vs. exp 0.780, diff: -0.104\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Protein mass fractions:\n", "condition: Glucose\n", "1494 proteins in model with total predicted mass fraction of 443.0 mg/gP\n", " 999 have been measured with mpmf of 514.4 mg/gP vs. 406.3 mg/gP predicted\n", " 762 metabolic proteins measured 414.0 mg/gP vs. 250.6 mg/gP predicted\n", " 237 transport proteins measured 100.4 mg/gP vs. 155.7 mg/gP predicted\n", " 495 proteins not measured vs. 36.6 mg/gP predicted\n", " 495 actual proteins 36.6 mg/gP predicted\n", "total : r² = 0.2054, p = 9.22e-52 ( 999 proteins lin scale)\n", " metabolic : r² = 0.2173, p = 2.22e-42 ( 762 proteins lin scale)\n", " transport : r² = 0.4309, p = 1.36e-30 ( 237 proteins lin scale)\n", "total : r² = 0.3335, p = 8.69e-29 ( 308 proteins log scale)\n", " metabolic : r² = 0.3800, p = 1.33e-28 ( 260 proteins log scale)\n", " transport : r² = 0.2661, p = 1.75e-04 ( 48 proteins log scale)\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "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 with fitted parameters\n", "\n", "scale_kcats = {\n", " # 1. rebalance flux between upper glycolysis and PPP\n", " 'PGI': 2.0, 'PFK_iso2': 20.0, 'FBA_iso2': 10.0, 'TPI': 20.0, 'ENO': 2.0, 'PGM_iso1_REV': 2.0,\n", " 'G6PDH2r': 0.2,\n", " \n", " # 2. flux through usage of TCA-cycle\n", " 'PDH': 10.0, 'ACONTa_iso1': 7.0, 'ACONTb_iso1': 7.0, \n", " 'ICDHyr': 0.2, 'AKGDH': 8.0, 'SUCOAS_REV': 3.0, 'SUCDi': 8.0, 'MDH': 0.1, \n", " \n", " # 3. fix the low predicted flux for DHQS reaction\n", " 'DHQS': 16.0/0.23,\n", " } \n", "\n", "# check that scale_kcats dict contains correct reaction ids\n", "for rid in scale_kcats:\n", " if rid not in eo.rdata:\n", " print(f'{rid:15s} not found')\n", " raise ValueError \n", "\n", "# optimization loop\n", "eo.scale_kcats(scale_kcats)\n", "pred_results = {}\n", "for cond, medium in conditions.items():\n", " with ecm as model:\n", "\n", " new_sat_level = .56\n", " model.reactions.get_by_id('V_PC_total').upper_bound = total_protein * new_sat_level / sigma\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", "eo.unscale_kcats()\n", "\n", "# results analysis\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", "\n", "print(f'Protein mass fractions:')\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', f'{baseline_model}_adjusted'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### confirm flux redistribution\n", "\n", "The updated turnover numbers have resulted in a significant redistribution of flux from the pentose phosphate pathway (PPP) to glycolysis. Predictions of protein concentration appear to be more accurate when compared to measured protein levels. Furthermore, the selection of correct isoenzymes has been confirmed, as evidenced by the observations of b3916/pfkA and b2925/fbaA." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 4: Create GECKO model with adjusted parameters\n", "\n", "Assuming that the model predictions meet our satisfaction level, the adjusted turnover numbers can be updated. Subsequently, the XBA and ECM configuration files can be updated, and the target GECKO model can be created." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 table(s) with parameters loaded from data/iML1515_predicted_GECKO_kcats.xlsx (Thu Feb 12 15:19:33 2026)\n", "1 table(s) with parameters written to data/iML1515_manual_adjust_GECKO_kcats.xlsx\n", "3 table(s) with parameters loaded from data/iML1515_predicted_GECKO_xba_parameters.xlsx (Thu Feb 12 15:19:33 2026)\n", "3 table(s) with parameters written to data/iML1515_manual_adjust_GECKO_xba_parameters.xlsx\n", "1 table(s) with parameters loaded from data/iML1515_predicted_GECKO_ecm_parameters.xlsx (Thu Feb 12 15:19:33 2026)\n", "1 table(s) with parameters written to data/iML1515_manual_adjust_GECKO_ecm_parameters.xlsx\n", "loading: SBML_models/iML1515.xml (last modified: Thu Dec 5 10:03:46 2024)\n", "3 table(s) with parameters loaded from data/iML1515_manual_adjust_GECKO_xba_parameters.xlsx (Thu Feb 12 15:21:28 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_manual_adjust_GECKO_kcats.xlsx (Thu Feb 12 15:21:28 2026)\n", "5357 kcat values updated from data/iML1515_manual_adjust_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_manual_adjust_GECKO_ecm_parameters.xlsx (Thu Feb 12 15:21:28 2026)\n", "PaxDb file E.coli - Whole organism (Integrated), year 2023, covering 3743 proteins (total 1000006.5 ppm) loaded from data/511145-WHOLE_ORGANISM-integrated.txt\n", "modelled protein fraction of total protein mass 0.5379 g/g\n", "1494 protein constraints to add\n", "1494 constraint ids added to the model (3371 total constraints)\n", "protein constraint: 306.59 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_manual_adjust_GECKO.xml\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create updated kcats file\n", "df_kcats = load_parameter_file(os.path.join('data', f'{baseline_model}_kcats.xlsx'))['kcats']\n", "for rid, scale in scale_kcats.items():\n", " df_kcats.at[f'R_{rid}', 'kcat_per_s'] *= scale\n", " df_kcats.at[f'R_{rid}', 'notes'] = 'manually adjusted'\n", "write_parameter_file(os.path.join('data', f'{target_model}_kcats.xlsx'), {'kcats': df_kcats})\n", "\n", "# Create updated XBA parameter\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'))\n", "ecm_params['general'].at['avg_enz_sat', 'value'] = new_sat_level\n", "write_parameter_file(os.path.join('data', f'{target_model}_ecm_parameters.xlsx'), ecm_params)\n", "\n", "# Create 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 5: Load and optimize GECKO model (cobrapy)" ] }, { "cell_type": "code", "execution_count": 10, "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", "total modeled protein: 306.59 mg/gDW, enzyme saturation level: 0.56\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", "print(f'total modeled protein: {total_protein:.2f} mg/gDW, enzyme saturation level: {sigma}')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Acetate : pred gr: 0.513 h-1 vs. exp 0.290, diff: 0.223\n", "Glycerol : pred gr: 0.656 h-1 vs. exp 0.470, diff: 0.186\n", "Fructose : pred gr: 0.648 h-1 vs. exp 0.540, diff: 0.108\n", "L-Malate : pred gr: 0.664 h-1 vs. exp 0.550, diff: 0.114\n", "Glucose : pred gr: 0.661 h-1 vs. exp 0.660, diff: 0.001\n", "Glucose 6-Phosphate : pred gr: 0.676 h-1 vs. exp 0.780, diff: -0.104\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Protein mass fractions:\n", "Acetate : r² = 0.0895, p = 4.31e-22 ( 999 proteins lin scale)\n", "Glycerol : r² = 0.0838, p = 9.63e-21 ( 999 proteins lin scale)\n", "Fructose : r² = 0.2062, p = 5.73e-52 ( 999 proteins lin scale)\n", "Glucose : r² = 0.2054, p = 9.22e-52 ( 999 proteins lin scale)\n", "Acetate : r² = 0.2912, p = 4.52e-24 ( 300 proteins log scale)\n", "Glycerol : r² = 0.3015, p = 5.08e-25 ( 300 proteins log scale)\n", "Fructose : r² = 0.3129, p = 9.11e-26 ( 296 proteins log scale)\n", "Glucose : r² = 0.3335, p = 8.69e-29 ( 308 proteins log scale)\n", "\n", "condition: Glucose\n", "1494 proteins in model with total predicted mass fraction of 537.9 mg/gP\n", " 999 have been measured with mpmf of 514.4 mg/gP vs. 493.4 mg/gP predicted\n", " 762 metabolic proteins measured 414.0 mg/gP vs. 304.3 mg/gP predicted\n", " 237 transport proteins measured 100.4 mg/gP vs. 189.0 mg/gP predicted\n", " 495 proteins not measured vs. 44.5 mg/gP predicted\n", " 495 actual proteins 44.5 mg/gP predicted\n", "total : r² = 0.2054, p = 9.22e-52 ( 999 proteins lin scale)\n", " metabolic : r² = 0.2173, p = 2.22e-42 ( 762 proteins lin scale)\n", " transport : r² = 0.4309, p = 1.36e-30 ( 237 proteins lin scale)\n", "total : r² = 0.3335, p = 8.69e-29 ( 308 proteins log scale)\n", " metabolic : r² = 0.3800, p = 1.33e-28 ( 260 proteins log scale)\n", " transport : r² = 0.2661, p = 1.75e-04 ( 48 proteins log scale)\n" ] }, { "data": { "image/png": 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fP8+BAwcICgpiypQpnDt3LlM2u7i44Obmxq+//sqdO3c4cuQI48aN0xrz8ccfI5PJGDx4MIGBgfz999/Mnz8/3Xnr1auHra0tX3/9NcHBwWzcuJG1a9dq9sfHxzNixAj+/fdfHjx4wMmTJzl37pwm79PHx4eYmBj++ecfXrx4odFNNYRMJmPNmjXcunWLRo0asXv3bm7fvk1gYCA///wzz58/11kufPXqFaGhoVoPtaPdo0cPevbsSe/evZk5cybnz5/nwYMH7Nmzh5YtW3L06FEdG5ydnTl06BAuLi4a57VXr1507dqVfv36sWrVKu7fv8+VK1f44osv2L17NytXrsTOzi7da9PHgwcP6Nq1KydPnmTq1Kn4+PiYPIeEhIREfkIIQUpKCnPnzuXDDz/MmiwVqrSAQYMGsXbtWoYMGUK9evWyydJ0MEXSQKFQiCNHjojp06eLAQMGiF69eomRI0eK1atXi4cPH5oy1Vsjr8hhpSdvpU8OizRSRoCYMWOGEEIl06Rvv1rqad++faJ69erC3t5e2NnZiWrVqomff/5ZR+JI3xwlSpTQ7B8zZowoXry4sLS0FJ6enqJ9+/bi4sWLRr8GJUqUEIsWLdI5Z2oJpbQSUC9fvhSdO3cW9vb2wsPDQ0yePFn07dtXS5ZK3+vZuXNn0a9fP83zwMBA0aBBA2FjYyOqV68uDh48qPUaJSQkiP79+wsnJyfh7Owshg4dKiZNmqSRZVK/zsbKYR06dEhUrFhRWFlZiapVq4p///1X51pPnz4tqlWrJiwtLUX16tXF9u3b05XDEkIlf1WmTBlhY2MjPvjgA/Hrr79q5LASExNFr169RLFixYSlpaXw9vYWI0aMEPHx8ZrjhwwZItzc3IySw1Jz69Yt0a9fP1G0aFFhbm4unJycRJMmTcQvv/wikpOTNeP0fX4AMWvWLM0YhUIh/Pz8RJ06dYStra1wdHQUtWrVEkuWLBFxcXFCCG05LDWRkZGiQYMGokyZMuLx48ciOTlZzJs3T1SuXFlYWloKR0dH0aZNGy15OWNRKpUiJSVFjBs3TgQFBZl8vD7eZTms+Ph4ERgYqPW5kygYSO9twWP9+vXi/v372TJXcnKymDt3rjh+/HiW50pJSTH6PioTIuOqh/j4eBYsWICfnx/h4eFUr14db29vbGxsCA8P59q1azx9+pTWrVszdepU6tevnw0udfYQFRWFk5MTkZGRUo9tiTzNrVu3qFChArdv385UNxGJjHny5AmjRo1i3LhxGlm67KCg32fSu76EhATu3btHyZIlpc56WaB///68evWKP//8M9Nz/PvvvzRv3pyIiAicnZ1Zu3YtY8aMybRMmfTeFhwSExP58ccfmTBhQpbniomJYeLEidSuXZvPPvssy/MJIdi4cSPVq1fH19c3w/uoUSXt5cqVo0GDBqxYsYJWrVrpFMYAmq4yvXr14ptvvmHw4MGZvwoJiXeM8PBwtm3bhqOjo1QYlAOof59/++23zJgx463ml0sYh0Ip8L8XTlh0Ah4O1tQt6YqZPP/lWDZr1ozq1auzePHi3DaFnj170r59+9w2QyKX+fXXX+nSpUu2OK1CCObMmUO3bt14//33szxXYmIiy5Yt48svvyQqKsqo44xyXA8ePGhQR1FNiRIl+Oqrr/jyyy95+PChUSeXyL/8999/tGvXzuB+db5tQeDhw4fpOjpq3dKsMHDgQC5cuICfn1+WCujS4116z1Lz7NkzRo0axcSJE1mxYkVum5MrzJo1ix07dnDz5k1sbGxo2LAhc+bMMVpnN6fZfy2E6X8FEhL5pnixsJM10zpWoq2vpCGaWWxsbDSyVhLvHjExMaxcuZIxY8Zkea64uDi++eYb6tata7QcZkasWLGCLl26aGm3G4NRxVkZOa2psbCwyFYdSYm8Se3atXWKx1I/ChLe3t7pXqtatzQr7Ny5k4cPH/Lxxx9ng8X6eZfeMzUKhYIxY8bwzTff5Jg0S37g2LFjDB8+nDNnznDo0CGSk5Np3bq1lmJFbrH/WghDN1zUcloBQiMTGLrhIvuvhRg4Mus0a9aMkSNHMmbMGFxcXPD09GTFihXExsby2Wef4eDgQJkyZdi3b5/mmGvXrtGuXTvs7e3x9PTk008/5cWLF4Bquf/YsWMsWbIEmUyGTCbj/v37KBQKBg4cSMmSJbGxsaF8+fIsWbJEr03Tp0/H3d0dR0dHhgwZoiWpl5iYyKhRo/Dw8MDa2prGjRunW1S6du1anJ2dtbb99ddf1KlTB2trawoVKkTXrl2z8ApK5FWWLVuGQqHIlg6NAF9//TVt2rShd+/eWZ4rJiaGJUuW8Pnnn+Ph4fFmh7FBT2MTZ2NiYsSQIUOEt7e3KFSokOjZs6cICwvLQiru26GgF01ISEjo8vz5c9GnTx9x48aNt3K+/HafCQsLE4A4duyY3v0JCQkiMjJS83j06FGOFGelKJSi/szDosTEPXofPhP3iPozD4sURdb7peujadOmwsHBQcyYMUMEBQWJGTNmCDMzM9GuXTvx66+/iqCgIDF06FDh5uYmYmNjRUREhHB3dxdfffWVuHHjhrh48aJo1aqVaN68uRBCiFevXokGDRqIwYMHi5CQEBESEiJSUlJEUlKSmDp1qjh37py4e/eu2LBhg7C1tRV//PGHxpZ+/foJe3t70bNnT3Ht2jWxZ88e4e7uLr7++mvNmFGjRglvb2/x999/i+vXr4t+/foJFxcX8fLlSyGEEEePHhWAiIiIEEIIsWbNGuHk5KQ5fs+ePcLMzExMnTpVBAYGioCAADFz5kyDr49UnJX/ePHihfDz8xNKZdb/ZuLj48WECRPEn3/+mQ2Wqfjxxx/Fq1ev3mw4c0aIn34SQggRGR5u1H3UaMd17Nixws7OTnz++edi9OjRwt3dXXTp0iVzlr9FcvML5cWLF8Ld3V3cu3fvrZ87p0h7I5TImJ49e4r58+fnthnvDImJiaJjx47i3Llzb+2c+c1xvX37tgDE1atX9e6fNm2aXsWI7HZcT915YdBpTf04deeFyXMbQ9OmTUXjxo01z1NSUoSdnZ349NNPNdtCQkIEIE6fPi1mzJghWrdurTWH2qm/deuWZs70VGTUDB8+XHTr1k3zvF+/fsLV1VXExsZqtvn5+Ql7e3uhUChETEyMsLCwEL///rtmf1JSkvD29hZz584VQmTsuDZo0ED06dMn4xfmNZLjmr9YvHixiI2N1VEQyiwDBgzINqf1xYsX4qfXDqoWP/wgRO3aQiQnG30fNVrHdefOnaxZs4ZffvmFxYsXs2/fPvbs2UNKSoqxU7xz/PDDD3Tu3Dlb9CG//fbbLHe1MBUfHx+dAoOePXtma1/4zLB27VrNMpz6oa/i9caNG3Tq1AknJyfs7OyoU6dOhvnXW7dupUKFClhbW1OlShX+/vtvrf3z58/Hw8MDDw8PFixYoLXv7Nmz1KpVS+dvYvLkyfzwww86gvsS2UtERAQDBw4kNDSUXbt2Ubt27dw2SUNSUhJbtmxh7Nix9O7dm969ezN27Fi2bt2ape5qmUGpVDJmzBgaNWqEr6+v3jFfffUVkZGRmsejR49yxJaw6ISMB5kwLjOkbh9tZmaGm5ubwbbUly9f5ujRo9jb22seah3MjBqYLF++nFq1auHu7o69vT2//vqrzv2oWrVqWh3IGjRoQExMDI8ePSI4OJjk5GQtNQwLCwvq1q1rsLNcWgICAmjRooVRYyXyD0+ePGHDhg0MHz4cW1tbTffIzJCUlMSUKVM4fPgwK1eupHPnzlm2b8GCBdja2jJkyBAQAkaOhIULVTu//BLOnAE9nRkNYfTVPX78WOsPplatWlhYWGi620hoExcXx6pVqxg4cGBum6KFeC0+nFlsbGy0c1JyCUdHR0JCQjSPBw8eaO0PDg6mcePGVKhQgX///ZcrV64wZcqUdCVdTp06Re/evRk4cCCXLl2iS5cudOnShWvXrgFw5coVpk6dyubNm9m0aROTJ0/m6tWrAKSkpDBkyBB+/vlnndaovr6+lC5dmg0bNmTzqyChJi4ujl69ejF48GCKFy+epzr+3Llzh4oVK9KvXz8uXbqkabN46dIl+vbtS+XKlU1qo5xVhg8fzrVr19i8ebPBMVZWVjg6Omo9cgIPB+Mklowdlxn0tY821JY6JiaGjh076uSH3759myZNmhg8R3a3mM4sUqFWwWPJkiV4eHjQo0cPvW25TUEIQb9+/ahatSotW7bM8n30yZMnrF27lnHjxmGTnIxMCJDJwMkJ1E1jLC0hTWObjDDacVUqlTp/4Obm5ia1lHyX+Pvvv7GystJo2kZERNCnTx/c3d2xsbGhbNmymtacABMnTqRcuXLY2tpSqlQppkyZoukPvXbtWqZPn87ly5c1Eca1a9dy//59ZDKZVmHNq1evkMlk/Pvvv4BK108mk7Fv3z5q1aqFlZUVJ06cIDg4mM6dO+Pp6Ym9vT116tTh8OHDmnmaNWvGgwcPGDt2rOacalvSJvv7+flRunRpLC0tKV++PL/99pvWfplMxsqVK+natSu2traULVuW3bt3Z+n1lclkeHl5aR7qqIiab775hvbt2zN37lxq1KhB6dKl6dSpU7pO95IlS2jbti0TJkygYsWKzJgxg5o1a7Js2TIAbt68SdWqVXn//fdp0aIFVatW5ebNmwDMmzePJk2aUKdOHb1zd+zYMV1HQSJzREVFMWzYMGJiYvj777/zlIa0mqFDh1KlShWePXvGv//+yx9//MEff/zBv//+y7Nnz6hcuXKOt0hUM2LECPbs2cPRo0cpWrToWzlnetQt6UphJ2sMfT3KUKkL1C3p+jbNMkjNmjW5fv06Pj4+lClTRuuh7t6mr9WysS2mL1++THx8vOb5mTNnsLe3p1ixYpp77MmTJzX7k5OTOXfunNHyblWrVs2wa6NE/uDu3bvs2rWLzz//HAsLCywtLTM9V0pKCt9//z2nT59mw4YNfPTRR1m2b8GCBXh4ePDpp58ie/kSSpeGjRtVO7//Hr74ItNzG+24CiFo0aIFNWvW1Dzi4uLo2LGj1jYJFf/99x+1atXSPJ8yZQqBgYHs27ePGzdu4OfnR6FChTT7HRwcWLt2LYGBgSxZsoQVK1awaNEiQLU8P378eCpXrqyJMPbs2dMkeyZNmqRpgVq1alViYmJo3749//zzD5cuXaJt27Z07NhRs3S1Y8cOihYtynfffac5pz527tzJ6NGjGT9+PNeuXeOLL77gs88+02nnOX36dHr06MGVK1do3749ffr0ITw8XLM/9dKbvseQIUO05ouJiaFEiRIUK1aMzp07c/36dc0+pVLJ3r17KVeuHG3atMHDw4N69eplKOx9+vRpWrZsqbWtTZs2nD59GoAqVaoQFBTEw4cPefDgAUFBQfj6+hIcHMyaNWv4/vvvDc5dt25d/P39SUxMTNcGCeN59eoVXbp0oVevXnh4eOi0o80rnDx5ku+//15v1NLR0ZEZM2bw33//5agNQghGjBjBzp07OXLkCCVLlszR8xmLmVzGtI4qpyut86p+Pq1jpTyj5zp8+HDCw8Pp3bs3586dIzg4mAMHDvDZZ59pnFUfHx/Onj3L/fv3efHiBUql0ugW00lJSQwcOFDTFnratGmMGDECuVyOnZ0dQ4cOZcKECezfv5/AwEAGDx5MXFyc0St706ZNY9OmTUybNo0bN25w9epV5syZk62vkUTOs3z5cooVK0arVq2yHEUXQtC9e3dKlixJgwYNsnwfDQ4OZvv27YweORKLY8dU8xUqBN9+C82aZWluNUbHladNm6azLTtyHwoqDx480JJJevjwITVq1NDk3aXNe508ebLm/z4+Pnz55Zds3ryZ//3vf9jY2GBvb4+5uTleXl6Zsue7776jVatWmueurq5Uq1ZN83zGjBns3LmT3bt3M2LECFxdXTEzM8PBwSHdc86fP5/+/fszbNgwAMaNG8eZM2eYP38+zZs314zr37+/RkZj5syZ/Pjjj/j7+9O2bVuADOWYUn/ply9fntWrV1O1alUiIyOZP38+DRs25Pr16xQtWpSwsDBiYmKYPXs233//PXPmzGH//v18+OGHHD16lKZNm+o9R2hoqE7k1tPTk9DQUEAlCzdz5kzN6zhr1iwqVqxIy5YtmTt3LgcOHODbb7/FwsKCJUuWaC0dent7k5SURGhoKCVKlEj3WiXSJyYmhmnTpjF16lT27duXY7q32YWzszP37983mE96//59nVWM7Gb48OFs3LiRXbt24eDgoPlMOzk55frycVvfwvh9UlNHx9UrD+q4ent7c/LkSSZOnEjr1q1JTEykRIkStG3bVpNX+OWXX9KvXz8qVapEfHw89+7d44svvuDSpUv07NkTmUxG7969GTZsmJbMFkCLFi0oW7YsTZo0ITExkd69e/Ptt99q9s+ePRulUsmnn35KdHQ0tWvX5sCBA7i4uBhlf7Nmzdi6dSszZsxg9uzZODo6ppviIJG3uHXrFo8fP6ZPnz5YWFjobQZlLAqFgoULF9K6dWv++OOPbLmPLly4kNGjR1OsWDHM//kH2reHgACoVg2yc1UpW8rF8jC5Ve3bunVrMWzYMM3zv//+W9jY2Ihq1aqJCRMmiJMnT2qN37x5s2jYsKHw9PQUdnZ2wsrKSri7u2v26+vnrq+3fUREhADE0aNHhRBvqkwfP36sdWx0dLQYP368qFChgnBychJ2dnZCLpeLCRMmaMaUKFFCLFq0SOu4tFWqLi4uYu3atVpjFi9eLEqWLKl5DogtW7ZojXF0dBTr1q0T2UFSUpIoXbq0mDx5shBCiCdPnghA9O7dW2tcx44dRa9evQzOY2FhITZu3Ki1bfny5cLDw8PgMWvXrhVdunQRoaGhwsnJSQQFBYkjR46IwoULi4SEBM24oKAgAYjAwMDMXKLEa0JDQ0Xz5s3FoUOHctsULdK7z0yZMkW4uLiIhQsXisuXL4vQ0FARGhoqLl++LBYuXChcXV3FtGnTctQ+9CgEAGLNmjVGHZ/e9WVX5XmKQilO3Xkh/rz0WJy68yLHJLAkjEdSFXiLKFKEuHtciCtbVf8qUrR2r1ixQsTHx4vo6Ogsnyo5OVl88MEHYtWqVdkim3X9+nWxf/9+EXfqlBCLF6s2KpVCnD9v0jzG+mtZy+SVMEihQoWIiIjQPG/Xrh0PHjzg77//5tChQ7Ro0YLhw4czf/58Tp8+TZ8+fZg+fTpt2rTBycmJzZs361Stp0X9C1+8bmcJaPJi06LOv1Lz5ZdfcujQIebPn0+ZMmWwsbGhe/fuOVYsoK8AQqlUap7b29une/wnn3zCzz//bHDuGjVqaApcChUqhLm5uU7eV8WKFTlx4oTBc3h5efHs2TOtbc+ePTMYcX7x4gXTp0/n+PHjnD17lnLlylG2bFnKli1LcnIyQUFBmupkdVqEu7t7utcpoZ/4+Hhmz57NxIkT2b17d4afl7zEd999h52dHfPmzWP8+PGafHEhBF5eXkycOJH//e9/OWpD6ntEXsVMLqNBabfcNkNC4u0TuBv2T4SoVMXujt7QehbXn0QRF/6UD6pWwNrSAtIpMM4IpVLJsmXL6NChA5s2bcqW++iyZcv4/PPPKV26NFa//AIrV6ryV62tIVW6ZHZisuNao0YNvZVmakmiMmXK0L9/f61l4neRGjVq6FSRu7u7069fP/r168d7773HhAkTmD9/PqdOnaJEiRJ88803mrFpq+T1JfyrnaCQkBBq1KgBZLzkrubkyZP0799f0zUlJiaG+/fvZ3jOtFSsWJGTJ0/Sr18/rblN7QVvSqpAWhQKBVevXtX05La0tKROnTrcunVLa1xQUFC6y/QNGjTgn3/+0WqPd+jQIRo0aKB3/NixYxk7dixFixbl3LlzWj8aUlJStF67a9euUbRoUa28ZgnjePToEf3792f8+PFaUkH5iYkTJzJx4kTu3bunWab38vLKM7mmEhISuUTgbtjSF9UiSCqinrJx2sd0KKty05xuyeCEN7SdA5U6mXyaxMREunfvTocOHShVqlSWFQOuXLlCTEwMn9y5g+XcuTB5MgwdCsOGmSRtlRlMnr1t27b4+flRpUoV6tatC8C5c+e4cuUK/fv3JzAwkJYtW7Jjx453Oge2TZs2fPXVV0RERODi4sLUqVOpVasWlStXJjExkT179mha6ZYtW5aHDx+yefNm6tSpw969e9m5c6fWfD4+Pty7d4+AgACKFi2Kg4MDNjY21K9fn9mzZ1OyZEnCwsK0cmXTo2zZsuzYsYOOHTsik8mYMmWKVgRUfc7jx4/Tq1cvrKys9DpdEyZMoEePHtSoUYOWLVvy119/sWPHDi2FAmMoU6aM0WO/++476tevT5kyZXj16hXz5s3jwYMHDBo0SMuunj170qRJE5o3b87+/fv566+/NGoLAH379qVIkSLMmjULgNGjR9O0aVMWLFhAhw4d2Lx5M+fPn+fXX3/VseHQoUMEBQWxbt06AOrUqcPNmzfZt28fjx49wszMTKsP/H///Ufr1q1Nek3edRITE1m6dCkjRoxg27ZtRufx5WVKliwpOasSEhIqlApVpDWN03r1mQILM6hXxAwn61QOZlSIysntsd5o51UIwYoVK/jggw9Ys2ZNtgRPVi5fTu8+fZBbWWFz9Ogbaass5NyahKm5DIMGDRLfffedzvYZM2aIQYMGCSGEmDp1qqhVq5apU+cIudnRpm7duuLnn38WQqhen4oVKwobGxvh6uoqOnfuLO7evasZO2HCBOHm5qZp+bdo0SKtXNKEhATRrVs34ezsrJWbFhgYKBo0aCBsbGxE9erVxcGDB/XmuKo7qai5d++eaN68ubCxsRHFihUTy5Yt0+n4cvr0aVG1alVhZWUl1B8VfZ2zfvrpJ1GqVClhYWEhypUrJ9avX6+1HxA7d+7U2ubk5GR0fl1axowZI4oXLy4sLS2Fp6enaN++vbh48aLOuFWrVokyZcoIa2trUa1aNZ0OIE2bNhX9+vXT2rZlyxZRrlw5YWlpKSpXriz27t2rM29cXJwoV66cVm6xEKocJE9PT1G8eHGxZ88ezfb4+Hjh5OQkTp8+nanrfRe5e/euaNGihdixY0dum2IUWbnPPHz4UHz22Wc5YFX28TZyXCXyHtJ7m8PcPS7ENEfNQznVQezuZSNCxtuLl/9z0Nr35uEkxIKKOjmw+oiOjhadO3cWixYtypZuWhcuXBBXTp0STz09hfDzy/J8aTH2PioTwrTkJycnJy5cuKATIbtz5w61atUiMjKSmzdvUqdOHaKjo7PDt84SUVFRODk5ERkZmWMi2obYu3cvEyZM4Nq1a1nqZCGRv/Hz82Pnzp0cPHgwt03J8yQnJ7N27Vp69+5NfHx8vskJzsp95vLly9SsWTNPa2Knd30JCQncu3ePkiVLptvgQyL/ke3vrVIBD05BzDOw94QSDUGeN2Xs3gr7JsFZP0AVZXWzlREWK6julfFrcr3VRio0aK9XKk4Iwe+//06HDh2Ijo6mePHiWTJTKJVsnjyZ1uPGYWVtjf3vv0ObNpANXUFTY+x91ORUAWtra06dOqXjuJ46dUrzwVYqldINDOjQoQO3b9/myZMnFCtWLLfNkcglLCwsWLp0aW6bkecJDg5m6NChDBo0SKPfWxDIqNnG3bt335IlEhK5iKECpEzmbOZ7Dk5BnPVDCMF/DxQUc5JjaYZRTivAL3tPceKIBd939qV91TfSm69evWLQoEHUrVsXR0fHLKdYnT9/nkJ371Jn1izcOnSARo2y1DwgOzDZcR05ciRDhgzhwoULmi5B586dY+XKlXz99dcAHDhwgOrVq2erofmV1IU+Eu8mqXNvJXRJSUlhx44dtGzZkvXr12daqziv0qVLF2QyWbqV/XmpRa2ERLZjsADJ9JzNAsG1PxGnfuR6mIJijnLM5FDKxbRV2TCcCY9NZtjGS3zx+BWT2lVk586dtG7dmh9++EGrxiIzKK9cYf+SJVSdPh2HMmVwOncOXuvQ5zYmr19PnjyZFStW4O/vz6hRoxg1ahT+/v6sWLFCUxU/ZMgQ/vrrr2w3VkJComBx584dPvjgAxITE3FxcSlwTitA4cKF2bFjB0qlUu/j4sWLuW2ihETOYaAAScXrbfsnqcblIxRKwengl+wKeMLp4JcolEZmXSoVKPaM5XKoAqWAJIWgcXHTYogKIceFN6mYPx24QrN2XQgMDMTKyirLTuuFCxeI+PdfvA8fpqiTE07OznnGaQUTIq53796lVKlSAPTp04c+ffoYHJvbnVgkJCTyNgqFgiNHjlCxYkVWrVpFkSJFctukHKNWrVpcuHDBoMpKRtFYCYl8zYNT2ukBOgiIeqIaV/K9t2ZWVth/LUSn01vhVJ3eFEqB/71wwqIT8HCwpm5JV00uatDRPygW/ZJnMYI2ZdJ3wYQAfYsxcpQst1jC0OQx/BlsgVWRCoSVbs9XX3+epfbIyVOn4n//PoW++QazPn2oPnTo21MKMAGjHdeqVavi4+NDp06d6NKli0YKS0JCQsIUgoODGTFiBB9++KFWG+KCyoQJE4iNjTW4v0yZMhw9evQtWiShplmzZlSvXp3Fixfntik5io+PD2PGjMmd1LWYZxmPMWXcW0SfA3ooMJShGy7qxI9DIxMYuuEinzcpye7LITpO7eT25SljE8+Fq1dxTc7YaQVQAvoyXmUyCI8TvNi/gET7DlgXr0K8Q1H874Wb3sQjPh7kci7fvEkZGxuEjU2WI7Y5jdGO64sXLzh06BC7du2iU6dOyGQyPvjgAzp16kSrVq2kYiwJCYl0USqVnD9/HkdHR/z8/PDJ5orUvMp776UfRbKzs6Np06ZvyZo8TB6sOBdCoFAoMM9hQfWcIikpCUtLy9w1wt4ze8e9DZQK/P/9iz2nAgiKs8NfWQElcrwcrUhIUaaX9MAvx+/p7Hv88CFD10diFnKFej6ufGybcZZmpLDFSRand9/ZxymUcZUzqR5YuNfmjFL1+QyLTtA73iAJCSRXrMiNDz8k+eOPSR46lMbOzqbNkQsYneNqbW1Nx44dWblyJSEhIWzfvh03NzcmTpxIoUKF6NKlC6tXr+b58+c5aa+EhEQ+5P79+3Tu3JmLFy9SoUKFd8ZplTCSwN2w2BfWfQDbB6r+Xeyr2p5D9O/fn2PHjrFkyRJkMhkymYy1a9cik8nYt28ftWrVwsrKihMnThAcHEznzp3x9PTE3t6eOnXq6DRZ8fHxYebMmQwYMAAHBweKFy+u1bwkKSmJESNGULhwYaytrSlRooSm+QmoUkb8/Pxo164dNjY2lCpVim3btmmd4+rVq7z//vvY2Njg5ubG559/TkxMjNY1denShR9++AFvb2/Kly9Ps2bNePDgAWPHjtVc51ulREOVegCGzisDxyKqcXkAxfVdRM+uQN3j/fguZRGbLb/nhNUo2sj9CY1K5FWc/rbq+hBCSUr0SxJfPEAokhHF6+CvrMBT4YqhlFghVI/tKbo/eF8lCAbuimf7jRScrGXUKWKGB680+z0cjAggCgGHD4NSyY1790iZNIlnVatSu3ZtnPOB0wpgso6rPm7fvs3u3bvZtWsXZ8+eZeHChQwfPjw77MsyuanjKiHxriOE4ObNm0RHR+Pq6mpSh7T8hDH3ma5du2bYLvvjjz/Ok8t0OarjaqjiXO3o5FDFeWRkJO3atcPX15fvvvsOgOvXr9OyZUuqVq3K/PnzKVWqFC4uLjx69IgzZ87QqFEjrKysWL9+PfPnz+fWrVsajUwfHx+io6OZMWMGrVu3Ztu2bXzzzTcEBgZSvnx55s+fz48//sjvv/9O8eLFefToEY8ePaJ3796qq5XJcHNzY/bs2TRp0oTffvuNWbNmcfXqVSpWrEhsbCxly5alQYMGTJ8+nbCwMAYNGkSTJk1Yu3YtoHJct2/fTteuXZk4cSKgKg6sVq0an3/+OYMHDwYwuggy23RcNe8xaL/POfseG4s6JeDRyc10v/s1CEidKqp2Mocmj+GAUpUmKUdJXflNPHhFGM74KysAaLY9jDUnQF6J2PuXsSvfSOt8beT++FksVs2T5pYgBKyTd6LSe92oe/xNK/XA5wrsLGQ8iVbSsNibFYBeSZM5o6yEq50F575plXGOa0AAKTVq8HDNGu4XL06tWrVwcnIy4dXKOXJMx1UfZcuWZfz48YwfP56XL18SHh6eHdNKSEjkYx4/fszIkSNp1aoVw4YNy21zch0nJyf+/PNPnJ2dqVWrFgAXL17k1atXtG7dmj/++IM5c+bwzz//0KhRowxmKyBkWHEuU1WcV+iQ7WkDTk5OWFpaYmtrq3Hkbt68CajaSqfOv3Z1daVatWqa5zNmzGDnzp3s3r2bESNGaLa3b99e81mfOHEiixYt4ujRo5QvX56HDx9StmxZGjdujEwmo0SJEjo2ffTRRxr5vBkzZnDo0CGWLl3KTz/9xMaNG0lISGD9+vXYvW6xuWzZMjp27MicOXPw9FQttdvZ2bFy5UqtFAEzMzMcHBxyT7WjUieVc6pXx3X223Va06Sk7I8pyfQ9t3gWGccJq/nI0C2IkstUDuU0i984lFibVvLzTLNYj7fsja8TLlS60y5E8yoB9j1OYV45d+ZX7M8B7W7qHFDWZWjyGNUcvJnjhXBkSnJfPm5Wi7qFkhC2bkRFvGDioQRszGUsaGNFCWeV26YUEIqbxmHuWr2IYac1KAi2b4evvuKBiwtux49zIzKSDu+/n7XXMpcw2XE1JKatjhqULVuWsmXLZtkwCQmJ/IkQgkePHnHr1i1mzZpFhQoVctukPIGXlxcff/wxy5Yt03TSUyqVjB49GgcHBzZv3syQIUOYOHEiJ06cyGVr3xJ5tOK8dhrpn5iYGL799lv27t1LSEgIKSkpxMfH8/DhQ61xVatW1fxfJpPh5eVFWFgYoIqGtmrVivLly9O2bVs++OADWrdurXV8gwYNdJ4HBAQAcOPGDapVq6ZxWgEaNWqEUqnk1q1bGse1SpUquZ/Xqo9KnVQ/QHIzj1lPE4SqwpWqyX2JlNtrOaJpkcnAm5eMMNvJGPPtOvtdiCEqUfBcITh2X8HHVSxQilf4sVgrUqvmgLIuhxJra0VtXYhmisVveJ9aBsCjSCWRiYIelSx4v9Qbd00dAZ6e/CnK1xmf71dMJ0f48mUUP//M886duXjrFq1ataJDPm7wYrLjakhMW71NJpPRuHFj/vzzzyx3bJCQkMhfhIaGMnLkSJo1a5Zn0oXyCqtWreLkyZNa7Z/lcjkjR46kYcOGzJw5kxEjRmRYzFWgyKMV56mdQ4Avv/ySQ4cOMX/+fMqUKYONjQ3du3cnKSlJa5xFGukgmUyGUqkKt9WsWZN79+6xb98+Dh8+TI8ePWjZsqVOHmt2256nkJvlnuSVgZQUL8Lxs1jMGkVbo6YZY75dJyorhCAuGTZdS+az6hZ8VFn1OZDLVE6mOlKrTFNWpETOGWUlQJU+sNxiCQCxSYKv/kkEYHFbK2Tu2pHUUNyYnvyptjOcdtFi/nx4/BgWLyasSRPkp07hf+ECXbt2Neo68zImNyA4dOgQderU4dChQ0RGRhIZGcmhQ4eoV68ee/bs4fjx47x8+ZIvv/wyJ+yVkJDIgwghePHiBSdPnmTatGmS06qHlJQUzVJ0am7evIlCoRJft7a2fre6aOVyxbmlpaXmtU+PkydP0r9/f7p27UqVKlXw8vLi/v37Jp/P0dGRnj17smLFCv744w+2b9+ulVp35swZrfFnzpyhYsWKAFSsWJHLly9rSaupfwhllBdt7HUWWNJJSVGvrnc3O27UVHKZttOakCJ4GCnYfyeFIbUtsTKX6Yz3lr2krlz3b18zBiXTLNYDEB6v5PIzBR+UM+fHdtbIZTIEqjSC0UnD6JU0mcaJS3QiuC9iEyExUSVvBeDggNLBgVcREfy9bx8OhQrRqVPB6E5msuM6evRoFi5cSIsWLXBwcMDBwYEWLVowb948JkyYQKNGjVi8eDGHDh3KcC4/Pz+qVq2Ko6Mjjo6ONGjQgH379mn2JyQkMHz4cNzc3LC3t6dbt248e5b3tN4kJN5lnj9/zieffMLWrVvp1q0bvr6+uW1SnuTTTz9l4MCBLFq0iBMnTnDixAkWLVrEwIED6dtXVbhy7NgxKleunMuWvkVyueLcx8eHs2fPcv/+fV68eKGJjqalbNmy7Nixg4CAAC5fvszHH39scKwhFi5cyKZNm7h58yZBQUFs3boVLy8vrUrurVu3snr1aoKCgpg2bRr+/v6aHNo+ffpgbW1Nv379uHbtGkePHmXkyJF8+umnmjQBHYSAxGh8ihXh+L9HePL4MS9evDDJ7gJBBikpchkGpacMIYQgWSHwO5dEEUcZ3SqlL9Sfuvo/LXXlN3FJecn/DiUw41gSDYuZ07r0mwVxuQwKyaJ4hitnlJV0IrcAHjbmUKMGzJ4NQFTv3jwePJjj//1H//79sbKyMun68jImpwoEBwfrrfZydHTk7t27gOqP3Jg/jqJFizJ79mzKli2LEIJ169bRuXNnLl26ROXKlRk7dix79+5l69atODk5aUTLT548aarZEhISOUBMTAx79uzhyy+/pEaNGrltTp5m0aJFeHp6MnfuXM0PcE9PT8aOHaupAG/dujVt2xq3ZFkgkJtB2zmvl3Bl6K04bzs7x/Igv/zyS/r160elSpWIj49nzZo1esctXLiQAQMG0LBhQwoVKsTEiROJiooy6VwODg7MnTuX27dvY2ZmRp06dfj777+1UkemT5/O5s2bGTZsGIULF2bTpk1UqqRaSra1teXAgQOMHj2aOnXqYGtrS7du3Vi4cKH+E8a/gsjHoEzmuzH9+WLiD5QuU5rExKQC36ktbeMA65s3yc67k0IpCHqpJOilkrENjHMIw3A2uM85KYz/nilo5mPGB+UMO8A6zq8QNHh4lUeVa1G3rAd8/TWiZk3i4+JYv349Q4YM0aheFCRMlsNq3LgxDg4OrF+/Hnd3d0AVcenbty+xsbEcP36cw4cPM3z4cG7dumWyQa6ursybN4/u3bvj7u7Oxo0b6d69O6BaUqtYsSKnT5+mfv36Rs0nyWFJSGQ/4eHhjB07loYNG/LFF1/ktjm5jqn3GbXTk1/uSTkqhwV6i2ZwLPL2K85zEZlMxs6dO+nSpUvWJ4t/BRG6QvgaXEqCjXOG0xj93uZi84i0TmpEbBIz9mq3Y60vD2Sz5fcZzmWoxWpqlEIw92QSExtZGpXWo67+b5y4RCdSKlKSeXVqE14pIdxqfznDudTSV2pKhT/hyIovuLRwBTXGDiIxMZF79+5x9+5d2rdvn+F8eY0ck8NatWoVnTt3pmjRohQrVgyAR48eUapUKXbt2gWoojCTJ082aV6FQsHWrVuJjY2lQYMGXLhwgeTkZFq2bKkZU6FCBYoXL56u45qYmEhiYqLmuam/iiUkJNInKSmJ3377jWHDhlGvXr3cNiff8fz5c82P+goVKlCoUKFctigPkBcqzgsKQqgirekR+RisnQx7aUJAUgzEx0JKgsoxNYTeHx3eqkh6Dv/o2H8thOl/aTupoMoZrZ+qWv+8shxPhStehOvopoLqciF9p1UIwYUQJS/jBJMaGxdl1Vf9r5lPkUxcsD9WnmVILv8JT8Uog/allr4q+iqUD68f5ceGvYgvWZpTm/fT4KNWJCcns3TpUsaPH1/glVxMdlzLly9PYGAgBw8eJCgoSLOtVatWmiUPU34xXr16lQYNGpCQkIC9vT07d+6kUqVKBAQEYGlpqdPJwdPTk9DQUIPzzZo1i+nTp5t6WRISEhkQGRnJ+PHjadiwIaNHj85tc/IdsbGxjBw5kvXr12vyI83MzOjbty9Lly7F1tY2ly3MZXKz4rwgkRQDygy6OymTVeOsHHT3pUoxIEVAzHNYNx6ajtF1RA01j4gKUW3PwcYC+6+FMHTDRZ1yqzZyfx2N1afCld0pDfncfA9KPc0FMtLsF0Lww39JfPOecVFWNfqq/4UihcgzWxHJCbg0+0xlAzA9uS9+Fov12gdvnN8yLx/TO2A/jsO/4LMejUEoufBaD/pdKYrPVAMCuVxO27ZtsyUXq3z58gQEBBAZGcm2bdvo168fx44dy/R8X331FePGjdM8j4qK0kSGJSQkModCoWDp0qX079+fxo0b57Y5+ZJx48Zx7Ngx/vrrL02DgRMnTjBq1CjGjx+Pn59fLlsokZtkW96pwsiWpPrGGUoxiH2u64jmVPMII9IOFErB9L8C9Tqt6o5UqfEinM/N9/Brygd0Mj+lJfofiT0uxOgco+b4gxQAJjcxvrhpaXJnTooq+CsraEVahVJBbOAxLFy8sa3YROsYQ00JQnHj2JkKNAs9w4F2dfm3VC2afbECu6B4PklRsGjBPCZNmvROqZFkynE9d+4cR48eJSwsTKey0mCiuAEsLS01bSBr1arFuXPnWLJkCT179iQpKYlXr15pRV2fPXuWbvcPKyur/FE9l4s5QRISxhITE8PEiRNp2LChyek/Etps376dbdu20axZM8229u3bY2NjQ48ePSTHVSJ7MEu/ut3guHRTDF67iKkdUSObRyjun8SsVJN0xqXCyLQD/3vhetMD1JJSaSOoaj3VTuanaZK4mNryIE0agQwlmyxn6lr/OspqisOq5pSorJWLKpQKovx3IJITcX7vE4PHpW5K4KV4SYTCnv/Mq9HF4ijlrR8gE0qETE6CmQWRNy9Sddg1fhzT/51yWiETjuvMmTOZPHky5cuXx9PTU+sFy44XT6lUkpiYSK1atbCwsOCff/6hW7duANy6dYuHDx/qdBfJd+RiTpCEhLEIIfjuu+/o1q0b7+fT1oB5ibi4OL2yRR4eHsTFmSbFIyFhEEt7kFukny4gt1CNS02GKQZpupgZ2RRi+sYjNOxSlra+hdMfaELaQVh0gs7hdeU30+18JX/d+aq2PEjLqZSj1Ml/3Xc7GQ87OZObWBlVsJUWd97U1gghiAnYh5mdK3a+Gd9Hlcg5q6jA7nVjOVvMl2MtarDDt4XWfFGnt+DUsCdJwNANF/H7pGbGr28BwmTHdcmSJaxevZr+/ftn+eRfffUV7dq1o3jx4kRHR7Nx40b+/fdfDhw4gJOTEwMHDmTcuHG4urri6OjIyJEjadCggdGKAnmSXMwJkpAwhri4OL7++mvee+895s6dm9vmFBgaNGjAtGnTWL9+vaZCOz4+nunTp+f/H+Ngsq6pRA4hk4FT0fRVBZyK6npjelIHVPmVQrs4S+2wGtkUolD8PdZu3AC9e9O2SlH9gzJIOxDISNrzP8zLtcfM3BwPB2vkKLXapXpi2GlNTVpJKSVyTX6pQimYfSKJr1/nsmY2eyMMZ4RSQfT53QhFMk4Nehh1XN1H17hcuByJ5pb8Uq8bd9y00xzjg88ht3PBqWFPre3T/wqkVSUvzDJK1i0gmOy4yuVyTX5WVgkLC6Nv376EhITg5ORE1apVOXDgAK1atQJUuodyuZxu3bqRmJhImzZt+Omnn7Ll3LlCTuUESUhkI+PHj6dz587vlp7oW2DJkiW0adOGokWLUq1aNQAuX76MtbU1Bw4cyGXrMo+lpSVyuZynT5/i7u6OpaVpBSwSOYDMGmyLQHQoiJRU283BwUu1PyFN1DJFqB68FhVQwvPIBOTx4VjGh70Zp3ZY1c0jokLQ952mjlSOsviTUfzJs+1+KOSLMKvcWdfeDNIOZAis4kJYPfMLfBt3pK6H4LT1eDx5qRnzQugpNNODPj3VA8q6tL3Whm88T/L1e9Gaz28IbsxI/oQIHPDgFT6yp4w136GyyYA6Qcjr6v8o/+3Ire1xqNbFKLu8ol6wcdPXTGw3mu1VWrAnVQ5s6iirzjmBkMgE/O+F06C0m1Hnyu+YrOM6d+5cnj59yuLFi3PIpOwlT+m43vsP1n2Q8bh+e6TqWom3SkJCAtOmTaN58+aSw5oJjL3PxMXF8fvvv2tav1asWJE+ffpgY2PztkzNFBldX1JSEiEhIVLKQ15DCFAkglIJcjmYWaUvgRUdAsrXjq4yBdvnlyh8cw2WCS9QdTHzhjFX3wRWXq8gqsIuQmsq0D6VUqjSCZUfrdN1Xq9ug+0Djb+s1/+mvhKlePNc3yUa0lMVSgVR53biWPdDzGRoRXHTFlcBTDLbyBfme3TOIQQohKDZ2cYEKovjWKtjhtfhHRVGr4ADLHqvD0Imp3LoHa57lgaZDEszGUkKQezNE1i6l8DCLf0i8yW9qtO5epEMz5mXyTEd1y+//JIOHTpQunRpKlWqhIWFdoL3jh07TLf2XcHInCCjx0lIZBODBw/mo48+kpzWHMbW1pbBgwfnyrmXL1/OvHnzCA0NpVq1aixdupS6detmfKARWFpaUrx4cVJSUlAo0tH8lMjb3LmvWhUUArPkKMyTol47pAa6mFXqBD3Wk7D7S2wStL+30jp2qgIpwfOtYwlQ1tJOGzAy7UCDnrxTdQGWDF2JK0N6qrGBx7AqUhHHOl2RyeQoQSv/VR+zFR9zWZTme4vVuMmiNdtDcKPjkaLcsymOQ80ORl1Gkcgwel85wLYqLXnoUpjrXmU0+xKTU4jy34Fjve5GrWB4OGSy+Uc+xGTHddSoURw9epTmzZvj5uYmLQmZgrF/nKb+EUtIZIKkpCRmzJhB69atWb9+vfS3nAPs3r3b6LGdOuVcbvsff/zBuHHj+Pnnn6lXrx6LFy+mTZs23Lp1Cw8Pj2w5h0wmw8LCQieYIZGP8G0P8hSV8xob+Wa7o7fhLmaVOhFgUZ8la9bRUH6NUeZ/GgzqymXgxUvWbtoEsk/eFBRlkHaQlvTmB1AK7fbBafVURUoy0QH7cKjRHpmZ6eJK+5T1OJBYh7rym7iLCM5euc09URjbRq1xzOA+2uPyQRo8vMLYjl9yrpgvjYesJtHcUmtMzPWjWBetbJTTKgO8nKypW9LV5OvIr5j8jq1bt47t27fToYNxvygkUpHhH+frpZgSDd+2ZRLvGEIIevfuTa9evXjvPSktJacwthmLTCbL0UjlwoULGTx4MJ99phI8//nnn9m7dy+rV69m0qRJOuOlDoTvMJnoYla3tDsPHGriEf3KqFN48Eq7oEhuplLV0aQdZA05AqWA1Yq2HFbW1lryj7l+FBufGjhUb5spp1WNEjlnlJWIOLoaua0zjnVaGnQyzZQKrJMTibWyJdrKllc2DpgpFSjkZlpOq0hJIvriXhzqdEYmk+udKzXqs03rWOmdKcwCyPiVSYOrqyulS5fOCVsKPuo/TkD3T9PAUoyERDaSnJzMjBkzuHjxIn/88QcfffRRbptUoFEqlUY9ctJpTUpK4sKFC1rts+VyOS1btuT06dN6j5k1axZOTk6ah9TE5R1D3cWsSnfVvxl8J5nJZUzrWElv4ZM+wnDWFBRpqNQJmn2VZacV3kRk25ud0zitysQ4oi8fxLZsA8zsnJGliXKaghCCmGtHiL3xH85N++FU70Nkhl4jIdi46Wu+OboKgH0VGjO95Rco0oyPuXIIZWIcDrU7GeW0girS+q5JYUEmHNdvv/2WadOmSUn4meV1ThCOaT5ojt6SFJZEjqJQKPjwww8pVaoUNWvWxNw889EGifzDixcvUCgUOhqy6bXP/uqrr4iMjNQ8Hj169DZMlciDKJSC08Ev2RXwhNPBL1Eo9S/lt/UtTP/evXmGGwaGoBTwQjjiSTj15YGERcVqD3DLvqCYXAbespfUld8k5vpRQGBXoTFyy6zngkb88yuK6BfYlm9o0GGt+fgGtknxIJOxuk5nNlZvp3ecMjGO6Et/Y1e5mcqhNiJwNaJ5aTYNrs+Jie+/c04rZCJV4McffyQ4OBhPT098fHx08pkuXryYbcYVWDKxFCMhkVkUCgULFy6kY8eObN26VaMhKiFhiHzTgVAiR9l/LYTpfwVqdakq7GTNtI6V9DpMbasURSFfhGxrP5RC6BRIyYBCsiiWWKpkLRMPrQSreZqAjcLOg+z8FgyPF0TdPo1NqR7ILG2znMcfe+M/5JbWuDT7LN2IrUtcJJs2f8337w/kt5ofcKCc/vS/6MsHsavQCPsqLZEZ0fFMnc86tlX5dyo1IC0mO67G5mxJZIB6KUZCIgdJSkqia9eudO/enfLly0sFWO8ghQoVwszMjGfPtKu+M2qfLfFus/9aCEM3XNSpxgiNTEi3W5NZ5c4oWMeLrWO1dFb13Xks455pGu/sV9Zhxu4EtgpXChNucreqtGy+lkyn8uZYFquCmY1xGq/p8fLgT5jZOOHUsIdeJ9Mj+iV9L+1lUeM+RNg68eGn8wn0KKl3LkV8FPF3L2BXsYnREeB3NZ9VHybruOY38pSOa06iVEgRXAkNSqWSZcuW0bVrVxwdHXFycsptkwo0ef0+U69ePerWrcvSpUsB1eejePHijBgxQm9xVlry+vVJZC8KpaDxnCNakdbUqCN/Jya+b9CJ2n/1MWs3bcKTcKZa/IYL0egfKiPexhPfiPkokNNG7s/PFouNclwjhS2OxGmNDYtVEhCqpGIhOXKHQryX9KOOFqspxAWdRm7jgKVHSeRWdgbHVQ69w7qt0+jdaya33UsYHBdz5RC2Fd8DpSLd+dKSXqS7oGDsfcaod7OA+7b5n8DdsNhX1dxg+0DVv4t9Vdsl3jni4uLo1KkTlpaWFC1aVHJac5Fx48YRG6vK4zt+/DgpKSkZHJFzdqxYsYJ169Zx48YNhg4dSmxsrEZlQEIiNf73wg06raDdrckQbasUpf/Hn5Bs54WbzJDTqprNJj6UOnJVU44DyroMSx6NQhj2XJUCngo3JiUPRvBGp3XHjWQcLGWUd5NTxFHOdyl9M+20CiF4uX8ZiU9vYVW4vF4ns1PgMX7aOROE4LpXGRoNXWPQaU2JCSfuzllsytZDbmFtlNM6pUNFlvSq/k7ns+rDqHe0cuXKbN68maSkpHTH3b59m6FDhzJ79uxsMU7CCF53LtFplxcVotouOa/vDEIIVqxYQVxcHKtWrWLIkCFSakAus3TpUmJiYgBo3rw54eHG9VPPbnr27Mn8+fOZOnUq1atXJyAggP379+sUbElIAIRFG3ZaTRnX1rcwyzp6GzWXB680/9+nrMfw5JEoBTqFXqmbCexT1mNo8hguRzty9nEKvh5yrM3BwsmdocljNLqtphIffJ7ksHs4v9cHl2b9kZm/SQ2QKxXYJ6qK06Os7Hhl44ilQvWDNK0eq5qY60cxs7bHyrsCZjaqSKKzrYVBBQUZqghr/0Yl6Vy9CA1Ku73z6QGpMSrHdenSpUycOJFhw4bRqlUrateujbe3N9bW1kRERBAYGMiJEye4fv06I0aMYOjQoTlttwSo0gP2T0S/JuxrNbz9k1SFYFLaQIEmMjKSfv360bx5c1xdXZHLM780JpF9+Pj48OOPP9K6dWuEEJw+fRoXFxe9Y5s0aaJ3e3YxYsQIRowYkaPnkCgYGNuFydA4hVLgfy+csOgESsXYUMWIudJKae1X1mdospxpFuvx5s0PvlhLD36New8rUqgvD2TXHRkHii7A1+IipRzMCEvW36rVGIQQhB9cjszcCucmnyK30L2+ldtnEG1lx+hOE/i3dG3+LV3b4HwpUS9IeRWCdbEqYGaBma3KsR3bshzlvewZuuEi2q0SpFxWYzApx/XEiRP88ccf/Pfffzx48ID4+HgKFSpEjRo1aNOmDX369DF4U84tCnRu1r3/VGkBGdFvj1QIVkARQrBp0yY++OADwsPD8fHxyW2T3kkM3Wf+/PNPhgwZQlhYGDKZzGDaVU43IMgqBfo+KqGDOsc1NDLBUKscvJysmd+9Gi9iE/FwsKZWCRcuPIjgUGAofwY8JTxWtUIrR8kJq1F4Ea43XUApVJ2tGicu0etsylFSV36T794vRDnzMMSFtciin/I0WklkguBhsiPb3QZxUNTL0jXH3w/A3MkTZDIsnLWLFquE3OahsxeRNg40Dz5HpLU9F4tUTHe+2FsnsSlVC2VCLOYOblr7FvWoRteaRU1WbSjoGHufMUlVoHHjxjRu3DjLxklkEzHPMh5jyjiJfEV4eDiff/45devWxc7OTnIo8iBdunShS5cuxMTE4OjomK0tViUkcgp1QwFDEUEBxCcr6LPqrGa7XKa7rA+qDlPTk/viZ7EYpUBHIgtUy/6GIqQCOQ8calK6cAxsHQsITjxMobybHHM5tCocSyuWZDo1QCgVRPzzKwDOTfsjt7TR2m+XGMfGzV/zU4Me+NX/iKOl66Q7X0pUGMr4GCwLlUBmboW5nqi02qlv61uYVpW8NNFpDwdV61Yp0po+kgJ5fsbeyPw0Y8dJ5AuEEOzevZuWLVvy/fffU6FChdw2SSID7O3tOXr0KCVLlpQaP0jkadTL/IkpSsa0LMcm/4eERr2JCDrbWhARl8yruGSt4ww1HQBVwdXQ5DE6y/6huDE9+VONw2lw2fyD8pgdaMXTaAUKJaQooZCtDHc7uebc0yx+41BibZNSBBIeXcPCtSj21dpimUq6yjUukgHnd7G0YS9irWzp+fEcbqajFKAm/u4FLL3LIwfMHQ3/QHW1f6ORbCaX0aC0m8GxGZE6LeNdcXylO2h+pkRDVcetqBD057nKVPtL6Bc/lsh/vHz5kuHDh1O5cmXat28vOa35iKZNmwIQFhZGWFgYSqVSa3/VqlVzwywJCQ36lq69HK0Y06IMKUoQCDaefZCpuQ8o63IosTZ15Tfx4BVhvMlFdbE159N6Pqw/84BX8W8cYi/1srndHS4HPcLRSkayUtDMR9t1kcvAG1WXrDPKShnaIhTJRPy7FpGcgEvzgVpOK4BzfDS9A/ZzsGx9rhQuR6BnqXTnS4kMQygVyG0ckFvZIbO2T3e8l2P2NIF5V1MNJB3X/I5aVQDQ+1tVaiNbYDh69Cg1a9bk3r17VK9ePbfNkUiFMfeZixcv0rdvX27cuKGT6yrluErkNoYaDuQGTjYWDGhUkhHvl+Hli+eY3drLyZ+G06l8+t2lRiWNYLcy/UBN4tNbWLgVI+n5fayLvnFy2wSdoseVQwzqNgUhk2OVkmRQJSA1CY9vYO7oDkKhypHNgMIZ6N8ai6H3Sz2roQYReZkcyXGVyINU6qRyTvdP1JbEcvSGtrMlp7UAEB4ezujRo/Hx8eG9996TnNZ8ymeffUa5cuVYtWoVnp6eklSZRJ5BoRRM/yswTzitAJHxySw6HIRj8ktqFbHFOskqQ6cVdJUJUiNLScTm1DJsY15SoWU3AorWQCkEDklxRFvZEWHjSISNIzbJicRZ2mTotKZEPUcmNwdFMuaOhTK0LTvVAtJ7v17rCTH9r0BaVfIqkGkDRjmu48aNY8aMGdjZ2XH8+HEaNmwo5WnlJSp1UkleSZ2zChwXLlzA29ubUaNGUadO+kUBEnmbu3fvsn37dsqUKZPbpkhIaJFRw4HsQK1EkJCsICJNfmxaFPFRyMwsmb56F8F/zMQs8C5CJkcmlHrHq5UJ/JX6U6dqv/iL2e77CC71gjZlzIEFPBWuPN1qy6skOwZ1n4Z/MV/8i/lmeB1CCJKf30dmZo4ws8C6hHEpPs62Fsz6sEq2REFNaRCRlfzZvIpRWcx5RURbIh3kZirJqyrdVf9KTmu+5tWrVwwcOJA//vgDT09PyWktALRo0YLLly/nthkSEjoY23AgKwigejHnjJ3WmAiSXz5CERsOpRsT9O9G2NYfDDitIh1lAqFIxu3EXCzPrqKQiFA5raEKiFHiRTg1a97ncr30Za1SkxL9EpEUT0pkGBZuxXRks9Ijo+s2hexqEJFfMSpsmpdEtCUkCjq3b98GYODAgTRsKBXWFRRWrlxJv379uHbtGr6+vlhYaC99duokpfVI5A7GNhzIKvuuhRrcp0yMRWZuSVzwORyqtQZUGq5FznwLCINdphTIGZk8UkcKKzn8CRZ2jgz1vsGAFrYqGa4UAb/FQS0L5O9boyxtTm/+Y3li93TVCIQQpEQ+QxkfBbZO2JY1XTM2O5fvs9ogIr9jlOM6b948hgwZwqxZs5DJZHTt2lXvuLxeYCAhkZeJjo5mwoQJ2NrasmDBAsqWLZvbJklkI6dPn+bkyZPs27dPZ59075TITeqWdKWwk7XBhgM5jTIxlsTHgVgUKqFxWgHqym/imBSW7rHmMiWvsNM8F0oFUWe3k/TsLm0/aMOgkglwNBEaW4GlDD61BXeVk2qMGoEi9hUySxuSnt7CrlLTTF9jdi7fZ/R+qdMy6pZ0zdJ58ipGpQp06dKF0NBQoqKiEEJw69YtIiIidB5SCoGEROZ4+vQpQUFB9OrVi4ULF0qFOwWQkSNH8sknnxASEoJSqdR6SE6rRG6ibjgAGIxspkUGuNlZsqhHNaZ0MH65PTXKpASEIpnYwGPYlK6DuZO29qkHr4ya5yeLH2kj9ycl+iXK+CjMHN0p1HkiRSzjIVHAhWQIef035mUGZtpXaeg8itgIkp7fRxkflSWnNTXZsXyf3vv1LrSMNamZb2oRbScnJ70PCQkJ44mNjWXUqFHMnz+fWrVq0axZs9w2SSKHePnyJWPHjsXTU2oIIpH3aOtbGL9PauLllPHystod+qGrL11rFqWQg1W64/WhTE4kPtgfRXw0DjXa6x2TnkpAauyV0ZQ9PweLw7OQW9nxgbUDG/6YwgulAzjLYaw9lDC8wJz2PIqEGERKMvF3L2DjU10ld5VNZNfyvaH3y8vJOl9KYZmCydIAkoi2hET2EBERweXLl+nYsSOtWrXKbXMkcpgPP/yQo0ePUrp06dw2RUJCL/pakEbEJjFjb5qmBGlE7k1xxoQiGaFUEHv1MA41P0h3rL+yAk+FK16EYyh4GBEviEgQOFrByc6xNJab88LOmQhbR64nlOCplSteZuF6o3T61AiUibEkPriCZZEK2FdpafR1ZUROLN+/qy1jTXZc87OItoREXiA+Pp7JkycDsGDBgly2RuJtUa5cOb766itOnDhBlSpVdIqzRo0alUuWSUi8QV8L0paVPPnt9H0ehMdRwtWWTxv4YGn+xhVU51yGRcZRR09nLDVCqSDm2hFsyzXQOK3W5nISUgzIXCFnenJf/CwWoxRoOa9KIVjun8yR+yls/ciGzx8r4dpT6va6wRmvyozq9D+AdI7n9X6VGoEyKQFkEHvzBA7V2mTlJdSLIGeW77PaMjY/YnLnrGrVqlG6dGkmTpyoV0S7RImM+/m+TaSOLxJ5ibi4OP755x9kMhkffJB+tEEi/2DMfaZkyZJ6t4PqR//du3dzyrwsI91H3130t4G1pnfd4vgUstVE+a4cWo/nqW/xlr2pdXkqXJme3Jf9itqIxDhiA4/hULMDoIpAutpZ8nW7Clx69IoNZx8atGGS2UY+N9+LXKZyV+KSBcHhSv4LTGZIDQvkzmZwPwUUMKrYGJ3uWW3k/kyzWJ/GNjemJ3/KAWVdREoSsTeOY1OmHmY2DtnxsukwtmVZRrcslyNzFxSMvc+Y7Lg6ODhw6dKlfCOiLd1wJfICiYmJTJ8+nZSUFObOnZvb5khkMwX9PlPQr09CP8a2ge1lH8CslHmkla1SCpWUVJPTDXlUZRBySxuTbWgj98fPYvHreQUrLiZzIDiFbd2skf0YCxUtoO2bVIVeSZP1KgTIUVI3TTRYoVAgkhNVUdbqbU22zRSW9KpO5+pFcvQc+Z0ca/mqFtHOL46rhERuk5yczPbt26lduzYffvhhbpsjkQdQxwsk9QiJvIqxbWDlKBmVvBKB0FqKF0LwIk7w500F2xsF0TDBCv0JAenPPc1iPQApSsGF+wpiA5PZ2s0GmZkcPrYFN1UqQkbds5TItRxaoVQQc/kAdr4tctxphYKrqZobmOy4SiLaEhLGkZyczMyZM0lOTub777/PbXMk8gCrVq1i0aJFmiYTZcuWZcyYMQwaNCiXLZOQ0MbYNrB15Te1luDVzD+VxKh6lnxeywJ4yV8d5QTZVmfGnuuExxrXRaqu/CaFecm6y8nsv5PCpjZW1H+mhKdKKCMHT1WHyLT5qukhhBJlXCRxd85lWByWHRR0TdXcwGTHVRLRlpDIGKVSyZo1ayhfvjw9e/bMbXMk8gBTp05l4cKFjBw5kgYNGgCq++nYsWN5+PAh3333XS5bKCHxhtAo4/RG02qgPnil5NiDFCY00pbIquwYT5SttdFOK4C7iODI6SRCzyWxYagdMks5jLEHc+2VikjsmZQ8SKd7VlqEEET5/4ljrY5ajQ5yindBUzU3MNlxVYtoT5kyRdIjlJBIQ0pKCvPmzUOhUGiUAyQkAPz8/FixYgW9e/fWbOvUqRNVq1Zl5MiRkuMqkacIj0k0alxqDdRZ/yUyoZElfatZ6g609yQ0It6oOYVSibh6iD1PTrP0PXNaPFFAMmCJjtMKMCx5FKeVvobnEwJFVBgJj67jVE+VriWXQbNyhTh7P4LYxOwJuMlkkLpqKK1smET2YLLjKoloS0joRwjBkiVL8Pb2pm/fvrltjkQeIzk5mdq1a+tsr1WrFikpKblgkYSEYVzt9DifevBXVuBYmD1hLyKY1NhST962DBy9oURDwh8/yHA+IQSddswg5Vkw+79YwVO78Xh1N6TjKiPexpOzCfrbtaqJOrMVx3rdsPd9X7NtWe+atPf1QHH/JHeC7xAQYcUjx+r87v+EiDjjo8KpWfJRNdydbN4pTdXcwGTHVRLRlpDQRqlUsmTJEoQQjB8/PrfNkcijfPrpp/j5+bFw4UKt7b/++it9+vTJJaskJPTj5WScAkDE6W383nAgfoUWI2SyNC1IXz9rOxvkZrjaG+6w5RP+hGf3A3jy8hGPGvbCJT4KYWap0WEVyJDplIoJHtaZjPKg/rzW5BePSHr5EKcGPTTbCqujoPJzsHgiZlFPKQ+UB3D0pkW9SXx4tNDr2U3D3cnmndNUzQ1MdlwlEW0JCW2+//57ChcuLBXYSGTIqlWrOHjwIPXr1wfg7NmzPHz4kL59+zJu3DjNuLTOrYTE20bdVMBQgVZi6B2UcZE41u/OISFneMpYFjltxiY+9M0gR2+V01pJVbTt5ai/sl4mlHTZ+BXHbJ2I67uQAPM3fsUBZV2GJo/R0WFVU9x/Bm3kvXXyWyNP/YFjgx5YFCoGQNNyhRjStIwqCnrzL9jSFx3XNCqEGqdHs6P5EoZdLGpUcVpqwqJNGy+ROUzWcc1vItqS/qBETqBUKvHz88Pc3JzPP/9ckjV6xzHmPtO8eXOj5pLJZBw5ciQ7zcsy0n303cSQjmvk66V3kCGTyd5EMSt5wINTKKNDuRFtyx3bKng42mmWzBVKQeM5RwiJTMAmKYHP/Xfwk3Nhwl4+oHal5jx29iLRQn9Utp38LD9ZLgHQiuoKZAghGJo8hgPKuiSG3EYZH4V1yZo69+WfP6mpsnGxL0Q9NXDVqtQGxagr+D+IJCw6gRfRiczYeyPD12vT4PpSxDUL5JiO671797JkmIREQeCrr76iaNGiDB48WHJaJYzi6NGjuW2ChESGKJQC/3vhmjzN5R/XYMbeG4REJpDw+AagpGyLXvRpUAqfQnY6uZz7Y8swfW/S62jlVSDV8rxvYaZ1rMTQDRexVKYgzu/CtpgvTp3+R7ABhxVUeq5TLH5DpGnbCiBDqNqpWvzG1n/v4lBfpeKi7748/a9AWtnexsyg0wogIOoJZo9O06D0ewD8fSUkw9etsAHJq7Svp5T3mnVMdlxTI4loS7xLCCFYtWoVNjY2zJo1C7k8fb1ACQkJifyEvvauhZ2smdy+Ivs3r6TZ6L54OdtRv3Qhvc6XoQhtaGQCQzdcZJOvkrY/z+HzHgPx23uCn4evI8nCOgPlVcNasWpOPkxB8IxWDX05Kwz7IyGRCQTffYhRjVdjngEqx3PG3sAMh0/pUFHnNTH0ekpKA1kjU9+8q1atwtfXF2tra6ytrfH19WXlypUmzzNr1izq1KmDg4MDHh4edOnShVu3bmmNSUhIYPjw4bi5uWFvb0+3bt149uxZZsyWkMgSY8aM4dWrV/Tq1UtyWiVMJiEhgXnz5tG+fXtq165NzZo1tR4SErmJ2ulMm9d5/+o5Bi/eRosP+9Ctjg+NyrrrdVrT67TlkBADwJwrUSyLiibk8gmubVnIzg+t2droCa1sg5Cn01crrVasGiEEc04k0rCYGe8VN8NTFpXhdYYJ5wzHAGCvUk4ythGDi512xNjQ66l24pccvs2ugCecDn6JQmlqGdi7jckR1+wU0T527BjDhw+nTp06pKSk8PXXX9O6dWsCAwOxs7MDYOzYsezdu5etW7fi5OTEiBEj+PDDDzl58qSppktImIwQgg0bNuDo6Mj8+fN1ihElJIxl4MCBHDx4kO7du1O3bt23slJ1//59ZsyYwZEjRwgNDcXb25tPPvmEb775BktL4+SOJDJAqYAHp1QROntPKNEQ5Ga5bZVJ6HM6hRDEXNqLnW9L5OYWzDl8n461Suk4reql8JN3nut18L48vp7WQWdo1uoLjr14yLgtBxih9IdV9an8esm+DhBhZc/qlLYsV3TR6X6VWitWzeG7KbjayBjXwFJjk75xaTErXh9OyUGk04BWZgbF6qnmNLLg6lBgqCa/NT0nXr1t0eEgzTYpCmsaJjuu2SmivX//fq3na9euxcPDgwsXLtCkSRMiIyNZtWoVGzdu5P33Vfpra9asoWLFipw5c0ZTmSshkVMMGzaMYsWK8b///Q9z8yxl1ki84+zZs4e///6bRo0avbVz3rx5E6VSyS+//EKZMmW4du0agwcPJjY2lvnz5781Owosgbth/0TtQh9Hb2g7R1NJnx9IG1WMvx+AuaM7tpWaIbdUKQGERCbgfy9cq/hI31I4QNHIZ1imJHPXrSh/l2/Mxuhwom/8h0vzAVjd3Q/+Y0hb0e8ii2G8xTY+M9/PV2m6YPkrK/BUuOJFODIEi84kMbyOJZZmqlRFpYBQ3PBXVkj3Ot3sLKltdjt9pxVAKODRWSj5Hh4O+pUQ0rLl/GO+6aDqkGVslFaNOgrr90lNyXk1ApO/iXNSRDsyMhIAV1dVgvOFCxdITk6mZcuWmjEVKlSgePHinD59Wq/jmpiYSGLim44fUVEZLx1ISKRly5YtuLm5sWDBAmxtbXPbHIkCQJEiRXBwcHir52zbti1t27bVPC9VqhS3bt3Cz89PclyzSuBug5JKbOkLPdbnG+dVHVUUSgWx1//Fpmw95JY2yNJEjlNHHw3lsyIEfjtn8sDFm0G1OuEf8RTb1kNws7BGjpJKl79BIDC03uBCDH4WizUqAQBK5ExP7kun+/Mo5SJnSG1LrF530FKvsk9P/lQnUpuWl7FJzNh8EqPCa69zXOuWdMXF1pyIuPT9m5jEFM4Ev6RR2UImy2IJVEoJ0/8KpFUlL6l4KwNMTtRTi2inJasi2kqlkjFjxtCoUSN8fVWt20JDQ7G0tMTZ2VlrrKenJ6GhoXpmUeXNOjk5aR7FihXLtE0S7yaDBw8mMDCQJk2aSE6rRLaxYMECJk6cyIMHGXcPykkiIyM1wQFDJCYmEhUVpfWQSIVSoYq0prcYvH+Salw+wMPBmvj7ASjiIrH2qY6Ztb2O06oeB7pL4VbJiQw7vYWikc9AJmN0xwkMcPch5soBbMvWQ26hOq6u/Cb2ic8MOq2gapsKKpUAdd6rUCrYduEF271H4+BWCFuLNzOE4qbl5GZEUJydUePUOa5mchkNShkncXX67gsAo6O0qRG8iWpLpE+m1j5zQkR7+PDhXLt2jRMnTmTGJA1fffWVlg1RUVGS8yphFH/++Sfe3t7MmTMnwy92ifRp1qwZx44dA+DSpUtUr149dw3KJrJyXbVr1yYhIYFSpUpha2urky8dHp7zX1h37txh6dKlGUZbZ82axfTp03PcnnzLg1Pp6ICCWlKJB6eg5HtvzazMkJCQwNOAYxQqWooYmQ0yM908fhnglUruKe1SuFwIPg7YxxNHd4LjIrkW9QKrGu2xtdFeYTBUZJUWuQy8eUld+U2O3I7F0r0kthXe44i5A/8mNaSu/CYevCIMZ/yVFTKMtKZGk3YgCzdw1JsWtWpKuTsAxhSFqxxqdfOG0MgEk7tvSU0MMsbkiOu1a9eoWbMm7u7uBAcHExwcTKFChahZsybXrl3j0qVLXLp0iYCAAKPnHDFiBHv27OHo0aMULVpUs93Ly4ukpCRevXqlNf7Zs2d4eXnpncvKygpHR0eth4REegghGDx4MGfPnqVatWqS05pNDB48mJCQEM0KSnZhjBpJcHAwXbt2xd3dHUdHR3r06JGhGolCoWDKlCmULFkSGxsbSpcuzYwZM0jdo2XHjh34+/tnyu7evXvz5MkTZs6cydKlS1m0aJHWwxQmTZqETCZL93Hz5k2tY548eULbtm356KOPGDx4cLrzf/XVV0RGRmoejx49Mvl6CzQxRirbGDsul/jnn39ITEzklbUnseaOep1WUEUDp3Ws9KYIKjqByqF32P7blzgmxBBvaU2Lwb+wPiac6At/YVXcFzMb3bSYEjL9K6X6SEgRRFz7j0ZFoIvjTRrZPUKOEiVyzigrsVvZkDPKSgadVjlK6ssD6SQ/RX15oCZ6q047QKAnYUG7Ra0aY5sKqMeZyWVM61gp9YxGc/9FnIlHvHuYHHHNThFtIQQjR45k586d/PvvvzpduWrVqoWFhQX//PMP3bp1A+DWrVs8fPhQo2ggIZEV9u3bR4kSJfj2228pUqRIbptToLC1tTX4AzMrZKRGEhsbS+vWralWrZqmA9WUKVPo2LEjZ86cMShlNmfOHPz8/Fi3bh2VK1fm/PnzfPbZZzg5OWlaWbu6umZ62fzUqVOcPn2aatWqZe7CUzF+/Hj69++f7phSpUpp/v/06VOaN29Ow4YN+fXXXzOc38rKCisrw4Lw7zyvl5GzbdxbJi4uDn9/f0qUKIG5hSUrr6Qf5XO2taBVpdd/yxEReDhYE2bvRritEw6JcbyICiM+Lgq7Sk0xd+imd442cn/Gmm9DiDfpAIb4+3Yy9YqYsaD0OXydjmm2PxWuTE/um2FaQBu5v06L2NTHqtvIzrffiENS2JsD07SoVVO/lBvOtha8iks2eE4XWwvqp0opaOtbGL9PauotXkuPxYeDKO9lLxVppUOulkkPHz6cjRs3smvXLhwcHDR5q05OTtjY2ODk5MTAgQMZN24crq6uODo6amS4JEUBiaygUCgYNmwY9vb2fP/999jY2OS2SRJGkpEaycmTJ7l//z6XLl3SrLisW7cOFxcXjhw5olXsmZpTp07RuXNnOnToAICPjw+bNm3KdIQ1LRUqVCA+Pj5b5nJ3d8fd3d2osU+ePKF58+bUqlWLNWvWSBrE2UGJhionJyoE/XmuusvNeYXjx49Tp04dnJ2dKVOmDKeDX2boWL2KS1YpCmz5FVasoO71QMyLFObzblN4dXYHSaG3cW35BWZ2zoAqymhraUZskirHV46SaRbrVfvScVpjkgT/3k+hiocZjlYyXGy0o49ehOsUbqWljdwfP4vFOtvTHntAWZdDUbU1aQcpdh50atWNtpWK6hxrJpcx+8MqDNlw0aDtsz6solNU1da3MK0qeWk6Z91/Ecuiw7cNvwCvkYq00sdkxzUhIYGlS5dy9OhRwsLCUCq1ZSUuXjT8xqZFXeTVrFkzre1r1qzRRBMWLVqEXC6nW7duJCYm0qZNG3766SdTzZaQ0HD48GHKly/P+PHjKVfOqB4qEnmYtGokiYmJyGQyrYihtbU1crmcEydOGHRc1dHIoKAgypUrx+XLlzlx4oRJufrpMXv2bMaPH88PP/xAlSpVdHJccyKt6cmTJzRr1owSJUowf/58nj9/rtmXE9Hwdwa5mUryaktfVG5aaudV/3JzbhMTE8OtW7c0nzN1fnZGOZVeUS9wTIwhLLo6dOkCJUtiZmVJ/4pmTN8ZhF3ZejjW7arRJVa/GmqnFTLufAVw4E4KjYubUdZVTnEn1Y+rtE6uXKZSEZhm8RuHEmtrpQkUdrKmc1UP+vqP0IzN6Fh12gGALBr2/X4Zv0/M9EY72/oW5udPavLt7kBCo4zvhGUml+mkGqTnvKYu0jI2ReFdw2THNTtFtFPnjhnC2tqa5cuXs3z58kyfR0ICICkpibFjxyKTyZg9e7ZUtFcA0KdGUr9+fezs7Jg4cSIzZ85ECMGkSZNQKBSEhBjuOT5p0iSioqKoUKECZmZmKBQKfvjhhyyppaRGLUvVokULre1CCGQyGQpF9legHzp0iDt37nDnzh2t+gH1eSWyQKVOKskrvTquusvNuYm/vz++vr6kpKRQq1YtrX0ZVcAv+WsesZY22HzdC0oXgYoVWbhwIadOnWLesG9Ydla7UMvLyZr4ZIXWsnp6RVlRiYIrzxT4OMuwNAM3Nxdk6XTASl24dUZZiRHNS9OojDt1S7pi9uAEnDPsIKc9NjXGSFKljaB6OKgK1kyJjPoUMk7VQCrSMozJjmtuiGhLSGSVEydOULlyZQYMGKBz45bIv+hTI3F3d2fr1q0MHTqUH3/8EblcTu/evalZs2a6y+Rbtmzh999/Z+PGjVSuXJmAgADGjBmDt7c3/fr1y7Kt2VkfYCz9+/fPMBdWIgtU6gQVOuTZzlmxsbE8efKEhIQE5HI59erV0xmTtgLeMiWZfhf+4kjpOgQXKsakdqOQF/biYElXgoKCEELQtGlTTRCgVzOh5cgplYI+q85qncNQR6t/76dQr4gqLaB8ITO+S/6ElwpHllhmvKrqwSsKO1kztlV5QKV0YH49kDpGvC4GW8iScbRTXwTVFIyVysqMpNa7gsmOa26IaBcE1G3xMvsrTSJzJCYm8tVXXxETE8P8+fMlp7UAoVYjOX78uE40sXXr1gQHB/PixQvMzc1xdnbGy8tLq2ApLRMmTGDSpEn06tULgCpVqvDgwQNmzZqVLY5r06ZNszyHRB5EbpYnJa8CAwPx9vYmLCyMJk2aGBynroAfuuGipgL+48v7iLay5W6hYtx3LcJP3WuwbOmPHDlyhGXLllG+fHmt41M7crsCnuicI3XnK7kMIhMEDyKVOFjKMJODr4cZT4UbaxVtqSu/qXO8PsJwpkftYhwKDNUUQNWXR7HZiE7GGbWGzcloZ0ZSWWmlxyR0MdlxVYto//zzz5QoUSInbCoYpOpf7f/cnLFnbHkS9WbpROpNnPOcP3+eChUq0LlzZ8lpKEBkpEaSmkKFCgFw5MgRwsLC6NTJ8PJtXFycTkTWzMxMJ49fQiIvEx8fz8uXLwkODqZs2bI0btw4w2PaysK5uH8afdpPItDchrYDlpNobklhJ2s+r25HVVeBddWqjBo1KsP0wEJ2umoUagkqP4vFnHmsoLK7nCQF1PY20+l8ldbJ1ZkrVXtX59AofvzntsYBzOhYgYwQ4Zpha1h915BdpP2hoCc7Wkt6TEIXk8tLU4toOzg44OrqqvWQQNUKcLEvrPsAtg+k7vF+bE38gjbyN9XJ6t7E+68ZzrmTyByJiYlMmjSJ5cuXo1QqJac1D1GhQgV27typef7VV1/Rt29fzXN/f38qVKjAkye6URs1w4cPZ8OGDWzcuFGjRhIaGqpVsb9mzRrOnDlDcHAwGzZs4KOPPmLs2LFakaIWLVqwbNkyzfOOHTvyww8/sHfvXu7fv8/OnTtZuHAhXbt2za7Ll5DIUR4+fEh4eDj37t2jY8eOOgWAOkREqP4tXBiX4t781a8qmwbXZ+4nddk4qB6fON5i27LvORv8nCiXcpy5G45CmUFutAF/a1+8L33C+vEwyR5rc5XTCrqdrzQ6q7xp56omrZO7//ozLccvvWPVmq0/WgxEZOD6jN96We93s0IpOB38kl0BTzgd/DLj18IAaqksLyftdAAvJ2v8PqkpBbQywOSIa2oRbU9PzywVZxVIDPSvTivFIfUmzhmuXbuGj48PTZo0oX379rltjkQabt26pVEBAAgJCeHhw4ea53Fxcdy6dYvkZMN6icaokdy6dYuvvvqK8PBwfHx8+Oabbxg7dqzWeHUqgZqlS5cyZcoUhg0bRlhYGN7e3nzxxRdMnTo1s5crIfFWSExMJCYmhuPHj/Pxxx8bp0n9448wZw4EBYGrK+zerfL27oXzPOQJ1p5uhCRZ87LhaMb9/QRQ/ZhUrxYaKlJ6EZOoc6qkFw8xs3Hg31gfznqvYIfiJh4K3c5X6sp/tc7qNIv1ePOm2CoUN6Ynf5qujquhY0OEKz9aDMSpZlc4fk8n2pmaZ1GqwFJqJ3L/tRAdTdbCTtZM6VARFzsrk9MAs6PQ611FJkwsLbW1tc02Ee23QVRUFE5OTkRGRuZ8Fy2lQhVpNdAKUL3E0ThxiZaMx6bB9SXZiyySlJTE999/T3BwML/88gv29va5bdI7TbNmzahevTqLFy/ObVOynfv371OyZEmtlq9v9T6TCxT068vPPH/+nJcvX/LixYuM0wJCQ+HZM6hWDe7dg+PH4ZNPwMyM/ddC+Hb3dW7/9xdxt07g1nYE5o4eOlOoHb60gvxqh9bJxpLeK84AoEyKRyQlkPj0BjZl6yOTpR/pbFXJg8OBYRqHUo4y0+1d9R2rjrR+3qQkuwKeEhql62Snvk4vJ2tOTHyfQ4GhDN1w0aj2rVIaYOYx9j5jcqpAdopoFzgy6F8tl4G37KVO8rkke5E17t27R3R0NFWrVuX333+XnNY8wk8//YS9vT1Xr17NbVOyjXbt2lG5cuXcNkNCguTkZGJjY9mxYwfly5c3KpeVgQNh5EjV/0uWhH79NE7r4J8P8/RFJDKZDI/u0/Q6rfAmSpm2i1TI6/S3iNhECjtZo4gKQxH7ipSoMGzLNczQaQW49iSK5R/XoPDrJXRj27vqQ9+xatt3Xw5hXvf0g29qhYEzd18y/a9Ao5xWkNIA3wYmpwrkhoh2vsHIvtRppTgk2YvMkZKSwpw5c7hy5Qpr166le/fuuW2SxGt+//13zQ/c4sWL57I12cfKlStNuq4aNWoYnU5lSvMWiXeb6Ohobt26RUpKCl988YXhgcnJ8PPPKBo2wt+5BDHDJuNU2INaSqFZkk5RKBk5Yykv/Pfj1m409lVbZdouAXy99TwTW5dl1Jy92FZ+H5mL8ZHHkMgEXOysODHxffzvhfPD34Fce5K5Fsvp2RgSmcDZey+NGr/t/COTWrZKaYA5j8mOa26IaOcbjOxLrZbikGQvMs+zZ89ISkqiRIkSfP3111KudR7DqBy7fIip19WlSxfN/xMSEvjpp5+oVKkSDRo0AODMmTNcv36dYcOGZaeZEgWUlJQUlEolq1atYsyYMRkfIJMR/eNPrDxwkyW+6pz/cAofuM+0jpWo5WnOtWcJvHz5Eo/u05CZZa0LvCIukrDoF0xe/wD3mq2JSzLdHwiLTsBMLqNWCRcCn2av06qNcd8ZOwMMr6IaQup+lbOY/CnNDRHtfEMG/atTy3jkF9mLvKY/q1AoWLRoEadPn2bz5s188sknuWaLhERGTJs2TfP/QYMGMWrUKGbMmKEz5tGjR2/bNIl8RmJiIqdOncLR0TF9p/XOHVUKwMaN7I+2ZHTnmSSaa4ubhkYm0HfyjxR9dpJRM5bgWDtrXb5ESjJCKIm7dRKHGu1RQKacVoAX0YkolILfTt/XUQbIThqUdmP7xccG9VSzAykNMGcw2XGVpIXSIZ3+1WolSLWMR35I4DZURZlbdkdGRhISEoKLiwvbtm2ToqwS+YqtW7dy/vx5ne2ffPIJtWvXZvXq1blglcTbIrNBAPUq5o8//siECRMMD4yIABcX8PKCQoVQREUzfc8zHadVmRgHMhnJEU+xaP81ZX2KAaZHFTXzJcWT9CwYuaUtDjWyruQyY+8NVp64h6tdBlJemUS90lm/lJtBPdXsQkoDzBmyti4goYuB/tUyxyIE1fiG9i7N6J8HIpcZsf9aiN4qSnXi+dvUmlMqlSxfvpyjR4+ybds2KlRIXzxaQiIvYmNjw8mTJylbtqzW9pMnT2JtLX3BmUpeWw1Kj8wGARQKBfv27cPHxyd9p3X1avjf/1TRVmdn2LUL/+CXhEQ+0BoWd/ss0Rd2UajzJJwa9OBZrILAp1E421jwKt6wBJ0+hCIZZHJiLh/EsU5nk47NiJDIBJPySo0l7UqnWk817XuTHeeR0gBzDslxzQn09K+WlWhIebkZ5TM+OtdRKIXBKsq3nXiekJDAtWvXkMvlbNu2Ld1e8xISeZkxY8YwdOhQLl68SN26Kh3Ks2fPsnr1aqZMmZLL1uUv8tpqUHpkJggghEAIwbx585g0aZL+iV+8gAcPoFYtaNcOUlIglaJK6mVqkZKEMjmBxJBbeHT/FlmqKOwPf98w+ZqEIpmE+wGYOXlmu9Oak3jp+Yyo9VQXHbrFsqPBWT5HfkkDzM+YrOOa35D0B03ndPBLjQ5feuSk/qwQghUrVvDPP/+wefNmKS1AIk9j7H1my5YtLFmyhBs3VM5CxYoVGT16ND169HhbpmaKvHQfNeQIqu8QeanzkEIpaDzniMFoXmqtULWTI4Tgjz/+oE6dOpQuXdrw5D17wu3bcOEC6Lk/qu/j8XcvEOW/Hfeuk5Fb2Zpkf1qB/ZDwGGbtv0XUuV041fvQpLlyAxnwQVUvWlbyyjAqb+z33pQOFSnkYIWHgzURsUnM2Js/fkDlB4y9z0gR1+xGqdCKtFKioSr3NR9hbEJ5TiWep6SkcPLkSWJjY9m0aZPktEoUGHr06JHnndS8TF5aDTIG/3vh6S5Bp60+T05OZuHChUycOFF3sEIBq1aBry80bAjz5oGtrV6nFaBmMUfciOHOvYu4d5uK3ML4dBRnWwuW965J/dJumtdRoVAwacEKUiLN87zT2rRsIZqUc+fTBj5Ymhu3Sle3pCuFnawNFmupf2T0b1RS67PVxlfqfvW2eXfWXe+fUjmVOUngblXnrHUfwPaBqn8X+6q25yOMTSjP7sRzIQRr165lwIABNG3alLFjx0qpARIFilevXrFy5Uq+/vprwsNV7SgvXrzIkydPctmy/IEpjmBewJQgwOrVq3n+/Ll+pxVUDurKlaBW9ileHAoV0jv0yJEjdOr4AdN61MO1xWDMTHBaQdVcQC6XYSaXIYQgKSmJhQsX8mG3j7AwQZc1tzh++wVFXGyMdloBzOQypnWsBOgKZaW3/G8ml9GgtBudqxehQSpHXyLnMCriWiBEtDf1APciqqr/SlmT/tBL4O7XagJpfqtFhai291ifM+fNAYz95ZmdiedCCP7++29CQ0Ol6mqJAsmVK1do2bIlTk5O3L9/n0GDBuHq6sqOHTt4+PAh69evz20T8zy5vRpkKsb8uFcmJ3D4j1UsnTVVd+fDh9CnD6xYARUqqFq0plPIJ4QgODiYrVu3sm3bNhwcHLC0tMpU8VFYdAJCCNasWUPbtm2ZMGECCqXA1c6S8Ngkk+bSR2YKwkwhM5F3Q8Va+nJjJXIPo36OdOnShc6dO9O5c2fatGlDcHAwVlZWNGvWjGbNmmFtbU1wcDBt2rTJaXuzhtqJzO4IqFKhUhEwuIAF7J+U8xHfbCKzvzwzgxCCTZs2MWTIEDp06MCkSZMwN5cyWCQKHuPGjaN///7cvn1bS0Wgffv2HD9+PBctyz/k1mpQZlEHAQzdKaMv7sXTVs7iH9IU50VEqP719FRFVePiUCgFp5/EsivgCaeDX6JII3J64sQJOnXqhI+PD35+fjg4OAAqZ+zExPfZNLg+S3pVZ0qHikbZ7miuZNGiRQwYMABvb29A9d3Qpbq3Ucc3L++e7v7SHnZGzZMa9evYzjf9Zj9Zibynfb02Da7PiYnvS05rHsIoD6HgiGi/zoLaP0lV9Z9duacPTmlJX+k9b9QT1biS72XPOXOYt/XLc9u2bdy+fZtly5Zly3wSEnmVc+fO8csvv+hsL1KkCKGhoblgUf4jN1aDsoI6CJBWK1SZEEPs9aM41PyAGT1raQcB/vgDvvgCgoLAwwN27lSpKKQp8kpdBHT58mXWrl3Lhg0b9P7wVy9ngypPeOWJe+kK76dc209CfDlGjxmrs69VJS9Wn7yf4bVffhyZ7v5LD19lOEdafVX1909iipJ91zJusZ7ZyHvq10si72FyaCv/i2jngBMZk/EfkEnj8ghqmZCcSDzfsWMHJ0+eZMGCBdlgqYRE3sfKyoqoKN0WlkFBQbi7px+dklBhyBGEvCtDlDYIEHV+N/ZVW1G2eXe+7VRZFQSIiFA5qvXqQcuW8MMPKj1W0pfTGjB3E+VeHOfo3p2sXLnSKHvSew2VCTHEXD+KY62ODN0eROHDD3UCFeofD+kpJbjYWWSYTmBMV6zRLcpQr1Qhne+f08EvMz6YvBN5l8heTK58UYtopyXfiWhnpxNpn/6yhcnj8hA5kXi+du1aLly4wMyZM7PBQgmJ/EGnTp347rvvSE5W5fXJZDIePnzIxIkT6datWy5bl39QO4JeTtrfN15O1nlKCis1bX0Ls71/Zfo4B7Nyzjf8Mbw5Jye1eGPrl19C376gVIKbGwwfDpaW6aooJDy5QdSlv4mt1c/k1qj6XsOoc7tAboZjrY6abWqt2f3XQjTb1I6vDMOpZF2rFzHNIAOUdLfX+/2TUQqGDFVEOq9E3iWyF5MjrgVGRDs7ncgSDcHRW5VDa2gBy9FbNa6AkJmuNXv27OHcuXN8++23ksSVxDvHggUL6N69Ox4eHsTHx9O0aVNCQ0Np0KABP/zwQ26bl6/IydWgnGDJkiUMGTKE6V8OUy3lK5Wwfp1KGeD992HGDJDLVY9U6FNRSHoWTPSlv3FtM4JCRSryPBmNnJYpqF/Dv/1vMGzmrzjU7qRzXzYkMWYolczT0YredYuTrMgeeXhDEVMzuYwpHSoxbKNuMXhejbxLZB8mO66TJk2iVKlSLFmyhA0bNgAqEe01a9bkE33CHHAi5WYqtYItfdHNynn9h9N2dr7TczVEZrrW+Pn5ERwczIwZMySnVeKdxMnJiUOHDnHy5EkuX75MTEwMNWvWpGXLlrltWr4kP+QhPnjwAH9/f4YOHYql5ZtuVchksHatSo/1/ffBW3/BU9oczfgHl4m5fADXll9o3Uczm8u5eNFCarfrjbxiS4P35bRas2rS/ni4/yKOTf4PWXT4dqZsSU1Gucr7r4UwY2+g3n2udpZ0ru6Nk40qYi05rwWPd6dz1iQHHK1e/5rNKWmqwN0qdYHUhVqORVROaz6RwsoIU7vWHDp0iCtXrjBmzBjMzAqG4y4hkRZjOr6sX7+enj17YmVlpbU9KSmJzZs307dv37dhaqbIS52z8gtLly5l6NChpKSkqNLoQkOhd29YuBBq1ICEhHSlreBNN6ek5/eJvfoPzs36g0yu42Sa2sXwwYMHnDp1ih49erDnaiijNwdkeMySXtXpbCAFwND3QmaRYbgDminnkrpY5S+Mvc9kSt0934poO3rnrJ5qpU4w5hr02wPdVqn+HXO1wDitGXWtAdWSklqmZfHixfz1118MGTJEclol3nk+++wzIiN1K62jo6P57LPPcsEiiZzg9u3b7Nu3j88++wxzc3Os4+NVOwoVUj0SE1XPjagJqVvSFevQK0Se3IRD3a7I5GZaTmtmcjkXLFhAkSJF+OijjzAzM8uyxFh63wuZwdnGwqDTauq59OXoSuR/TE4VyLci2r23gG+rnF+ul5vlG8krUzG2a83Pm3ZjEfuMoUOH6kSXJCTeVYQQepdjHz9+jJOTUy5YJJHd/PzzzxrdUzs7O9izRxVlvX5dlc+6davRc928eZMtW7Ywb3Q/RnpWQSaTZUlF4datWwQFBTFixAgtyaysSoxl9L2gpkt1b/4MSE82UkX/hj4GI6TGnktNXmwDLJF1THZc1SLac+fO1Qgcg0pE++OPP85W47IVn4YFJsfUJJQKlfRXzDNVQVqJzL8OxuRRRZ7dwZ7gZLasXi45rRISvOk8KJPJaNGihZbToFAouHfvHm3bts1FCyWyyo0bNwgPD6d79+5YJiRgee2aKn+1aVOYOVOlx2oCu3btYu3atSxdupSiRYtiYWGZJU3tJUuWMHz4cHx8fHTuy1mVGDM2v7ZpOXfO3A0nNCr98ZvPPWRki7J6z2fsueQoqSu/iQevCMMZ/8gKmSpgk8ibmOy4SiLa+Qi9ObfeOm1vjVUISG9JKeHxDRRRYTjUaMfU4c21ftRISLzLdOnSBYCAgADatGmDvb29Zp+lpSU+Pj6SHFY+Zt26dXTv3p2iRYuq7nujR8OOHXDvHjg4wMiRRs91584d9uzZw6BBg+jU6U2Vf2ZVFK5evcrLly8ZOHAg5ubmBrsSZqXhjLGpBl5ONvSuW5xFh4PSHRcalWjQyTTmXG3k/kyzWI+37E3XrKfClce3pkHp/kbZKpG3MdlxlUS08wmBu1+rHKRZ/FG3vX2d62uKQoChJaXIM1tJDn+K6/sDKeLuImnnSUikQt150MfHh169euXaSkRiYiL16tXj8uXLXLp0ierVq+eKHQWFa9euoVQqad6sGXa7d4OTE7RvD998A//7H5jYunrHjh2sWbOGpUuXav24UWOqisLPP/9M//79KVu2rFEa65l1jk1JNTA2YmpoXEbnaiP3x89isc52L8Ip7D8GfFyNqjnJjNyjxNvD5OIsSUQ7H6BUqCKt6ZVR7Z/E/quPGbrhok7OkKGEdvWSEqhuRokhQcQFn8O+amvc24/GzNpe0s6TkDBApUqVCAgI0Nl+9uxZvd0Is5v//e9/mp7zEplHCMH27dspXLgwxYsXp3iJEvD773DggGqAhwcUMV6A/8GDB6xdu5amTZuya9cufHx8smTfpUuXuHDhAt27d8fa2tqkxkCZaTiT9nshNWlTDbJaCJbeueQomWahqrFJa7bm+f5Jqu/HdNh/LYTGc47Qe8UZRm8OoPeKMzSec0Qq8MpDmOy4LliwgJiYGC0R7TJlyuDg4CCJaOcVHpzSTg/QQdX2dvfu7UYrBKhRLykpAnYSfeEvrLzLY2brlKe71khI5AWGDx/Oo0ePdLY/efKE4cOH5+i59+3bx8GDB5k/f36Onqegc/XqVR48eEAlb2/cevXC+epV1Y7t22HJEpPn2759O0OHDqVx48a4ubkhl2dK6AdQOdTr1q2jVKlSVKhQgUKFCmV6LlMxtptZdnS8MnSuuvKbeMvCdZzWN3OnavduALXUlrHBHIncweRUAUlEOx9gZDtb89gwoIzefYZEpwMCArCLjubYz1MIVdhLSykSEkYSGBhIzZo1dbbXqFGDwED9YurZwbNnzxg8eDB//vkntra2Rh2TmJhIolq2CfSmh71LCCE4fPgwFby8sHVwwKd4cXB3V3XAAjAx/ePJkyf4+/tTr149unTpkmW5wAsXLuDg4EDz5s1zTaHCmFSDrBaCpXcu65tP4ZwRhhr4fsxI7lFSJ8g7mOy4qkW0GzVqRKNGjTTb84OI9juDke1sw3DOeEyqXKM5c+Zw6dIlfvzxRzw8PCiZWfskJN5BrKysePbsGaVKldLaHhISYrBoJqsIIejfvz9Dhgyhdu3a3L9/36jjZs2axfTp03PEpvzGtWvX8PLywun2bYp17gyXLoGbG2zcmKn5tm3bxsqVK1m8eDFFixbNkm1CCP78808aNmyIra1trhfFGpOHm5VCsHTPJa9gnONq4PvRWLlHSZ0g9zG5c5aZmRkhISF4pJH3ePnyJR4eHigU6eePvG3eyY4vSgUs9lUVYhlIl0+09aJi+DyUGWSLbBpcH8eEUBQKBebm5lSsWFFq2SohkQZj7jO9e/cmJCSEXbt2aaJir169okuXLnh4eLBlyxajzzdp0iTmzJmT7pgbN25w8OBBtmzZwrFjxzAzM+P+/fuULFkyw+IsfRHXYsWKvVP3UYVCwfn//sPp8WPc2rTB3cEBfv4ZPv8cjIxcpyY0NJTAwECKFClC6dKls/xj5fz58xQpUoSIiAgqVaqUpblyg2wvgDLiew9Hb1VTID2SkLsCnmS5g5hE1jDWXzP5L6egiGgX6KpBuZlK8mpLXzCwIGPeYQ6eu20zrAT9b8dqzp45w9KlS6XCDgmJLDB//nyaNGlCiRIlqFGjBqBKvfH09OS3334zaa7x48fTv3//dMeUKlWKI0eOcPr0aR0lg9q1a9OnTx/WrVun91grK6t3Wof5xo0bFC9enNiVK6l34AA8eqTqdDVmTKbm27FjB35+fixYsIDy5ctnybaUlBSOHz9OyZIlsbGxoXBh4+sK8tL3nqkqCRlixPcebWcb1DHPauGYxNvD6IirWkT78uXLVK5c2aCItilRg7eBPg/eFAmofI1eHdciqj/e11JYQzdcBHT/xJPDn/D9h9Wo4CyoV6+eFGXNBAqFQqO+IZG/sbCwSDcP0dhIQWxsLL///juXL1/GxsaGqlWr0rt3bywsLHLCbB4+fKiVn/r06VPatGnDtm3bqFevntFL1e/KypVCoeDWsmXEJCRQauBACsnlEBUFmaz0f/HiBQ8ePACgatWqWX6fL126ROnSpblx4wb16tUz6Vjpe292ulJYCqWg8ZwjGQZzTkx8v+AEufIYxt5njHZc1flO06dPZ/z48QZFtC0tLbNoevaS9oVQO2tpL1r9MSxwlfEZdM5KezMTQiC7vg/vuGC2rF+Z5RysdxEhBKGhobx69Sq3TZHIRpydnfHy8tL7Iy6/OHbGpgqkJb9cX1YIDg7G29ubf9q04YMyZWD16izNt2vXLpYuXcq8efM0EfbMkpyczOXLl7G0tKRYsWK4uLiYdLz0vWdcx8j0gjlQAF+nPEa2pwrkhIj28ePHmTdvHhcuXCAkJISdO3dqusyAygGYNm0aK1as4NWrVzRq1Ag/Pz/Kli2bqfO9k1WDcjMo+Z7B3amrM6/cuEVRD1fkzTvRtk1rlAJOB7/ME8tK+Qm10+rh4YGtra0Urc7nCCGIi4sjLCwMwKSl2d27d9OuXTssLCzYvXt3umM7dcpYGF0i+1FGRvKoa1futWyJy5AhfHDokMkqAamJiIggLCwMR0dH9u7dm+XvymvXrlG6dGmio6Np3ry5ycdL33vGk12FYxI5i8k5rmoR7bTLFGfPnsXMzIzatWsbPVdsbCzVqlVjwIABfPjhhzr7586dy48//si6desoWbIkU6ZMoU2bNgQGBpokqqxGqhrUj1wGAQe3sHfvXvz8/ChWt827s6yUzSgUCo3T6ub27nyGCjo2NjYAhIWF4eHhYbR8UZcuXQgNDcXDw0PrR3laZDLZWyls9fHxwcR63IJLRARP4+NxdHDgohB0rV0bXLPW9e/vv/9m/vz5zJ07N1NOZmqSk5O5e/cukZGRJCYmZnq+vPq9l5fybVOT2Q5iEm8Pkx3X4cOH87///U/HcX3y5Alz5szh7NmzRs/Vrl072rVrp3efEILFixczefJkOnfuDKikuDw9Pfnzzz/p1auXqaZnud1cQeThw4e4uLjg6urK7t27kcvlBpeV1CLM0nKJYdQ5rcbqZUrkH9TvaXJystGOq1Kt85nm/xK5izhzhhfNm3Pqu+9o9fnndD16NEvzRUVFER0dTWJiInv27Mny3/+dO3fw9vbm7t27Br8jjSUvfu/l9cBItheOSWQrJrfpeFsi2vfu3SM0NFSrsYGTkxP16tXj9OnTBo9LTEwkKipK66HG2GrAF9GJ7Ap4wunglzqdowoKQghWr17NF198QWRkJD179kQul2e4rAT6O2pJaCOlBxQ8pPc0nxMfD//+S3h4OBGlSnGsZ0+6DxmSZTWcf/75hy5duhASEkLXrl2z5LQqFAoePXpEUFAQ/2/vvMOiuL4+/t1Feu+IhQUbGpUqiBJFRbHErsHyKhiDWCDB3qJYohixG6OxGzWKxKgoihGwRH6gRooixWBAFAELCNJh975/bHbi0txlF3aB+3meeWDuzNw5d2Dvnjn3lKqqKomVVkD+ouVpdSqKpIhtcW2qJNo5OTkAAGNj4WTBxsbGzLHaqC9xtqDcXF1RgwB/2XxjaDKzL09vgdIiOzsb2tra4HK5uHLlipD1SF6XlSiU5siePXtEPvebb75pREkoZNcufNi8GRe3bMHUr77CpOPHJeqvqKgIFRUVyMzMREhIiFDAckN49eoV1NTUEB8fj9GjR0vU18d86ntPEC1fX5lVadEq/W0pUkdsTXPYsGFYuXJljSTaq1atwtChQ6UuoLisXLkSixYtYvYFibMFTOnTETvDn9Z5fXVDojjL4/LqsyOAEILTp0/j5MmTOH78OLy8vGqcI4/LSpTWh6enJ96/f4+LFy82uI9bt25h0KBByM/Ph46ODo4fPw4/P78mzfawc+dOof03b96gpKQEOjo6APhzp5qaGoyMjKji2hhcuQJ8+ICSsWORP2EC/qehga8WLJC42zt37mD9+vUIDAzErFmzJOqLx+MhLy8Pt27dwvjx46WqtALSK7MqDahhhCINxFZcpZlEuz5MTEwA8OtsfxzFm5ubW28al7oSZ99IysG2mw/q/NCwWTWVVkD0t0B599l59+4dVFRU8ObNG4SGhtZpHZe3ZSVK88bFxQXW1tbYtWuXrEWBu7s7Ro4c2aT3TE9PZ37/9ddf8dNPP+HIkSNMEvrU1FR4eXnB29u7SeVqLZCLF1Gen48jb99i7ty5mOzrK1F/JSUlIITg4cOHuHDhgsSpwfLz81FWVoYHDx5g2rRpEvVVH/ISLU8NIxRpILaPa7t27fDo0SNs3boVPXr0gJ2dHXbv3o3Hjx8LWTYlxdzcHCYmJoiIiGDaCgsLce/ePTg5OYnd36KghDqV1km27WpVWgV8/BZYG/LusxMcHAx3d3eUlJRg4cKF9bp0CJaV6nr3ZoGvkDfFslJrhssjiH72rsX7WjclqqqqNUpVNyVr1qzB3r17hSondevWDTt37sR3330nM7laFGVlwIQJQHAwKisr8WzRIlybPh2+vr4SJ/+Pjo7GF198gbS0NCxcuFAipZXH46GoqAjnzp2DgYFBk6RCG96zLe4uH4wzXn2xe4o1znj1xd3lg5vUsEINIxRpILbiCgDq6uqYM2cO9u3bh23btmHmzJkNmhSKiooQHx+P+Ph4AHzrRHx8PDIzM8FiseDn54fvv/8eISEhePz4MWbOnAlTU9N608rURV1f+ywA4cmvRepD8Bb4sVIRlfYW60KeyGUwU1FREfLz85GSkoKrV6/C0NDwk9cIlpUA1FBem3pZqbUSlpgN5x8iMfVQDL49G4+ph2Lg/ENko78Aubi4wNfXF35+ftDV1YWxsTEOHTqE4uJizJo1C5qamujcuTOuXbvGXJOYmIgRI0ZAQ0MDxsbGmDFjBt6+fQuAv9x/+/Zt7N69GywWCywWCxkZGeByuZg9ezZTsrJbt27YvXt3rTKtX78ehoaG0NLSwty5c1FRUcEcKy8vxzfffAMjIyOoqKjA2dkZDx48qHN8x48fZ5boBVy+fBl9+vSBiooKDAwMMH78eAmeYP1kZ2ejqqqqRjuXy0Vubm6j3bdVkJ/P/6miAhgagtumDXbt2gULS0uMryXVojiUl5ejoqICYWFhOH/+PKysrCTqr6SkBOnp6bh16xa8vb0brWpabQii5cdat4NTJ/0mn8epYYQiDURSXENCQpg0PyEhIfVu4vDXX3/BxsaGcTlYtGgRbGxssHbtWgDAsmXL4Ovrizlz5qBPnz4oKipCWFhYg3K41gUB8L5UtLKcRpoqNZSK6YfvIaewvN7+67PWNhYhISEYO3Ys2Gw21qxZI1ZFM8Gykom28HM20VahqbAaGVlb70+cOAEDAwPcv38fvr6+mDdvHiZPnox+/fohNjYWw4YNw4wZM1BSUoL3799j8ODBsLGxwV9//YWwsDDk5ubiyy+/BADs3r0bTk5O8PLyQnZ2NrKzs9GhQwfweDy0b98ewcHBSEpKwtq1a7Fq1aoa5aIjIiKQnJyMW7du4cyZM/j999+FAi+XLVuG8+fP48SJE4iNjUXnzp3h5uaGvDzRPmuhoaEYP348Ro4cibi4OERERMDBwUF6D7MaQ4YMgbe3N2JjY5m2hw8fYt68eULZUyhi8ugR0L49EBUFHo+HhPnzEaakhKVLl4LNbpBthuHhw4cYOXIk0tLSsH79erErVn0MIQTl5eX4+eefYW5uji+++EIi2Zoj1DBCkQYilXxls9lMEu36JoKmSqItDoISYh38zoGtXHeaEh1VRRSUVtYbdblmVHcs+DWuTuttfeyeYo2x1u0acKV4VFRUIC8vD7t374a/v79ESr68B5vJI2VlZUhPT4e5ubnYz15QK7sul5bGrpXt4uICLpeLP//8ky8PlwttbW1MmDABv/zyCwB+to+2bdsiOjoa4eHh+PPPP3H9+nWmj5cvX6JDhw5ITU1F165dRfZx9fHxQU5ODn777TcAfGvt5cuX8eLFCya90IEDB7B06VIUFBSgtLQUurq6OH78OOMbWFlZCQ6HAz8/PyxduvSTwVn9+vWDhYUFTp06JdLzqe9vK0qpwjdv3sDDwwNhYWGMla2qqgpubm44fvy4TN0YPoXclXwtLweiooDBgwEeD9izB8TTE5v37cOqVaskTl0mMNQsW7YMq1evhoGBgcT9JSUlIScnB25ubhL11RKQ95gQimyQasnX1pBEe1Z/c+wKf1pn1OWaUT2wMbT2NB6i0BQ+O2FhYdi+fTtCQkIQEBAgcX80CXPTIg8Rt71792Z+V1BQgL6+Pnr16sW0CdLTvX79GgkJCbh582ataYCePXuGrl271nmfffv24ejRo8jMzERpaSkqKipqBF1aWVkJ5cR0cnJCUVERXrx4gYKCAlRWVqJ///7McUVFRTg4OCA5ORmiEB8fX2tmjcbC0NAQV69exdOnT5GSkgIAsLS0rPc5Uerg0CFgyRLg5UsQfX1EOzigLDYWq1evlrjrR48eYfHixThw4ECNrBDiQggBj8fDjh07sGzZMondDFoKtDoVRRKkl3hVzqnPp8ZEWwU+gzujm4lGnVGX2qpK9SoV9d23sXPkcblcZGZm4vr167h06RJTnpLSvJCHiNvq/nYsFkuoTWDJEgSXjB49Gj/88EONfj7OBFKds2fPYsmSJdi+fTucnJygqamJwMBAsaruSQNZfU4EZVc7deok1dzXLZ4bN4CsLMDTE5g9GxgyBDAwwOZNm6SisHK5XFRWVmLv3r04depUjRzi4sLj8RATE4OKigosX75cYvlaGqIYRuiqH6U2RJo1W0ISbTZ4cGQnwQjv8Ro6uM+zBPnXxVfgU1PfW+Cl+Cyx79kUPjuRkZEIDAxESEiIxNYBimxpbhG3tra2OH/+PDgcTp0KmJKSUg33oaioKPTr1w/z589n2p49e1bj2oSEBJSWljIKZkxMDDQ0NNChQwcYGBhASUkJUVFRMDMzA8Bfjn3w4AH8/PxEkr93796IiIiQOA+nqJSUlMDX1xcnTpwAADx9+hQWFhbw9fVFu3btsGLFiiaRo9ly9SqQmgp4eACqqgjPyoJGQYFUlNbk5GT4+fnh6NGjOHTokMT9EUKwZcsWrFq1SuK+WivUnYBSFyIpri0hiXaMzmoYkzfM/iuihz2KX8Nl3FdCH4K63gIboiw0Zo48QgiSkpJw/vx5BAcHN2lkKqVxkKcKN6KwYMECHDp0CFOnTsWyZcugp6eHtLQ0nD17FocPH4aCggI4HA7u3buHjIwMaGhoQE9PD126dMEvv/yC69evw9zcHCdPnsSDBw9gbm4u1H9FRQVmz56N7777DhkZGfD394ePjw/YbDbU1dUxb948LF26FHp6eujYsSO2bt2KkpISzJ49WyT5/f39MWTIEHTq1AlTpkxBVVUVrl692mjWsZUrVyIhIQG3bt3C8OHDmXZXV1esW7eOKq7VqaoCZs4Ehg3jW1m3bAGUlEAAbPr+e6mkECOEoKioCBs3bsTRo0fRrp1kcQiEEEREREBDQ4MqrRIgCFKtPg+KUxCI0nIRKeQyPT2d2TZt2gRra2skJycjLy8PeXl5SE5Ohq2tLTZu3NjY8jYY1TLhlFdtWfkIqArEcHbd6XM+RpQ0HiZaylg9sjtmOplhzajuuL10UKN8uO7evYtJkyahe/fu2Ldvn8SlBinyQXOLuDU1NUVUVBS4XC6GDRuGXr16wc/PDzo6OkwQ55IlS6CgoIAePXrA0NAQmZmZ8Pb2xoQJE+Du7g5HR0e8e/dOyPoqYMiQIejSpQsGDBgAd3d3jBkzBuvWrWOOb9myBRMnTsSMGTNga2uLtLQ0XL9+XeTIbxcXFwQHByMkJATW1tYYPHgw7t+/L5VnUxsXL17Ejz/+CGdnZ6Hgoc8++6xWi3OrJT8fIARo0wYwMgIEfs7Kyrj0b2pEaVhZ//77b4wZMwYVFRX49ddfpaK0btq0Ca6urujbt6/E8rVWPlUWFgDWhTxBVNpbmue6lSJSVoGP6dSpE3777TcmhZWAhw8fYtKkSUKVYuQBJkpthSa0lGtRB7RMAb/HAFvhk30J3gKBmgFcBICOmiLel/yXWqsxljUePHiAn3/+Gdu3b2dK7lLkB0myCgigS2TyiaRZBdTU1JCYmAgLCwtoamoiISEBFhYWSEhIwIABA1BQUNAUw2gQTZZVIC0NsLEBfvsN+Cj6nsvlYuvWrVixYoXEGQMAfiVBLy8v7Ny5k3E1kYRLly7BwsJCKJCR0jCin73D1EMxYl1D58eWgajzjNhJ7lpWEm0CFGaBmxEl0tl15TfVVuMv03+stALSzb157949eHh4wN7eHocPH250pZVWbpId8lDhhiJ97O3tERoayuwLFLDDhw83qBpgi6GyEggP5//eqROwaRNgb88cPnfuHDIyMrB8+XKJldb09HRMnjwZysrK+P333yVWWrlcLjZv3oyxY8dSpVVKNCT4VF6qVFKaBrFDWgVJtA8fPgxbW1sAzT+J9onTJ2A6zgLDe7X/5LnVA7gM1JWxODgBQM0iBgR8a+z6y0kY2sOkwUu8t27dwrFjx7Bz506pWBs+BbX4yR6aiqzlsXnzZowYMQJJSUmoqqrC7t27kZSUhP/973+4ffu2rMWTHb/+ys8SkJHBLyTwb5yEIML/m2++kUr2haysLPj4+GDPnj1Sca86c+YMHBwcsHLlSon7khk8LvD8f0BRLqBhDJj1E2n1sTFpSDyJtL5rKc0DsS2uR48ehYmJCezt7aGsrAxlZWU4ODjA2NgYhw8fbgwZG52vuL+h92+fI+76CZHO/7hsHpvNQk6haLk3xSUuLg6+vr4YOHAgTpw4AT29xg/KkXXlJgqlpeLs7IyEhARUVVWhV69e+OOPP2BkZITo6GjY2dnJWrym5c4d4Oef+b9PmwbExfGV1n85ffo03rx5Ax8fH4mV1hcvXsDDwwP6+vq4cuUKOnXqJFF/FRUVCAwMxJQpU9CpU6cmMSY0CkkhwK6ewIkvgPOz+T939eS3y5BPxZPUhayqVFKaHrFnhJaaRNsEeWgb/Q14vGSwLUcBZv3ABfuTOeQaK/fm9evXceLECabOe1PwKad4+kZLoTSMyspKeHt7Y82aNVJJt9TsCQ8Hbt8GvLwARUXg32X2kpIS/PLLL/D09JRKae+0tDT4+Phg9+7dUunvxIkTGDp0KBYvXtx8FVaAr5yemwlUn+0Ls/ntX/4C9BgjE9EEQarzTsXWKAgkCo2Z55oiHzS4kDOHw0G3bt0wcuTIZqG0fspFk83iK2bse/uBE1+gNLAHVm/ejKmHYvDt2XhMPRQD5x8ia1gcpZ17MzExEatXrcQQCyWcXjEehkUp/OWcJkCcyk0UCkV0FBUVcf78eVmLITsI4bsD/Pgjf/+774Bbt4CPSoifOnUKZWVlmDFjhsRKZnZ2NubPnw8Oh4PQ0FB069ZNov5KSkqwd+9eTJ8+HaampvWWPpd7eFwgbDlqVwn/bQtbIbXvnYbES9QVTyIK8pLnmtJ4iP3pE+RJVFNTw2effYbMzEwAgK+vL7Zs2SJ1AaUFEXPhQbkkB5srt8KN/V96nNqWy0VJk9VWxNybISEh2LBkLnyVfkOb0+PA+v3rJl2+kYfKTRRKS2XcuHG4ePGirMVoWgSprVgswMAAEEQKKynx28DPA3769GmMHj0aurq6UFdXl+iWT548gYeHB+bPn482bdpAQUEyn82jR4+irKwM3t7eLaPS2fP/AYWv6jmBH7SM5/+T+FZhidlw/iHykwag2qgepHp6tiNMtKTzXUtp3oituH6cRPvjt2JXV1cEBQVJVThposASb8FBsBLur3gSbPAA1J5D7n56HtaM6g6g4bk3U1JS8MMPP2BYhwoEOT6GCYRzzjLLN42svDa3yk0USnOiS5cu2LBhAyZNmoSAgADs2bNHaGtxZGUB5uaAwNL8ww/8ggIf8euvv6JNmzYYMWIEtLW1JVp+f/36NZYvX46uXbsiNDQUPXv2lER6vH//HseOHcO0adOgp6cHJSUlifqTG4pEzP4j6nl1II14iY/jSfp3McC6Mc0nzzWl8RD79fHixYsICgpC3759W3wSbTYLMMU7OLBTEMPjf2AIgJzCckw//F9d9bbaKpgzwBwhCdlCH1JB5ayhPUwQ/exdrb6y58+fx6lTp7B39y6o/Da85ieSuSuLv3xjOarRoj6bW+UmCqU5ceTIEejo6ODhw4d4+PCh0DEWiyW3VQfFoqoKuHkTGDoUaNeOn9rq889rnPbmzRvcu3cPQ4YMgbq6usRR/rGxsVi2bBkCAwOlUkXw6NGj+PLLL/Hll19KxTdWrtAwlu55tdBY8RICF4LqWW8as0olRf4QW3F98+YNjIyMarQXFxc3b2f1ejDC+3qP5xSU4eCddOybZgtddSUhBfVGUg6cf4iskVrKy1oNFc8TMG3aNEyYMAGsjLuiL9+Y1/wikAb1OcXTN1oKRTLkrThLo3DpEjBpEpCaCnTtCixYUOOU4OBgfPHFF7C1tYWxccOVIwDIy8vDzz//DD8/P4SGhkJZWVmi/l6/fo07d+5g4sSJLbcioVk/fuGdwmzU7uf6b2Ees34NvoU48RLipv2rnpKyrsBpSstFbFeB1phE+zV06j0u+OhvDE2CnZkujDRV8PpDGX6M/Btza1kqeXYvHD7f+EHV3BZaWlr8Z9hEyzefoi6neBNtFVofmkKREoQQiFm0UH6JiQH27uX/Pn48EB/PV1qrkZOTgzt37sDe3h4qKiowNTWV6LYPHjzAl19+CVdXV6iqqkqstJ44cQJaWloYPHhwy65KyFYAhv/w704di+7Dt0i0stfY8RIfuxA4ddKnSmsrQ2yLa2tKos0jQA70cZ9n+clzBW+QfQMikFdcUes5VQW5KM9KhoqZNVS79cPOe+/R2eIt+nbSh0ITLN+ICn2jbZ24uLjA2toau3btkrUojQqHw4Gfnx/8/Pya/N5HjhzBzp078ffffwPg+736+fnh66+/bnJZpMadO/wSrfPmAW3aAFZWNU65fPkyhgwZgo4dO4LD4Uh0u4KCAvz666+YNm0arly5IvFSflZWFp48eYLhw4dDRUWl5bkG1EaPMfyUV2HLhVf6tEz5SquEqbBovASlMRHb4trSkmgTwt+qZ+gQ7K+vnAGeGI+pLqW1OPkO8v7YD6W2XaGgrgMWi433JZWYfuQeP8qyyBylqib/hoHVBgvQaifR8o040DdaGcPjAul/Ao9/4/9sopRo9UEIqbXcc3OhoqL2z2ZTsXbtWnz77bcYPXo0goODERwcjNGjR2PhwoVYu3atTGUTC0IAHx9g61b+/sKFfKtrLRH32dnZSEhIgLm5OVRUVCRWWmNiYjBhwgRYWVlBW1tbYiUzKCgI+vr66N27t8RuC82OHmMAv0TA4wow8Qj/p99jqeRvlWa2HQqlOmIprpWVlfjqq6/AYrFw6NAh3L9/H0lJSTh16lSzrdPMYvG3PGgKtedAH/Mq/XCd5yBR/1WFb1H67AGUTS1hOHENFHVrLo/lFJRh7ukE+BVMAepQogkg8fINpZkgg4o2np6euH37NlPwgsVi4fjx42CxWLh27Rrs7OygrKyMu3fv4tmzZxg7diyMjY2hoaGBPn36IFxQa/5fOBwONm/ejK+++gqampro2LEjDh48yByvqKiAj48P2rZtCxUVFZiZmSEgIIA5zmKxsH//fowYMQKqqqqwsLDAb7/9JnSPx48fY/DgwVBVVYW+vj7mzJmDoqIioTGNGzcOmzZtgqmpKbp16wYXFxc8f/4cCxcuZMbZVOzfvx+HDh1CQEAAxowZgzFjxiAgIAAHDx7ETz/91Kj3Dg0NhaOjI1RVVaGrq4tx48Y1rCMu97/UVjo6/DZFRaF8rAIiIyOhqqoKdXV19OzZU6Lcpx8+fMCZM2fA4XAQEhKCfv0ke4HPzMzEvXv30KdPHygrK8PExESi/potbAV+zESvSfyfUvp+EcRLADQDAEX6iDWTtOQk2jtZHphS8R2+qfDBlIrvMFn5ALoPmg6fQZ3gM6gzTn7lUG8OudooTv4T767tRhttE7TRNgKrjklBoKde5zlgXqUfciD8FpoDfaxssxRcy9ENGxyl+SCoaFM9UK+RU6Lt3r0bTk5O8PLyQnZ2NrKzs9GhQwcAwIoVK7BlyxYkJyejd+/eKCoqwsiRIxEREYG4uDgMHz4co0ePZnI6C9i+fTvs7e0RFxeH+fPnY968eUhNTQUA7NmzByEhITh37hxSU1Nx+vTpGta4NWvWYOLEiUhISMD06dMxZcoUJCcnA+AHg7q5uUFXVxcPHjxAcHAwwsPD4ePjI9RHREQEUlNTcePGDVy5cgW///472rdvjw0bNjDjbCoqKythb29fo93Ozq5RLdnnz5/HjBkzMGvWLCQkJCAqKgrTpk1rWGdnz/J/rlsHzJlT6ynZ2dl49uwZVFRUoK2tjc6dOzfsXv8SExODsWPHwsTEBCYmJhLneb18+TJ0dXXRrl07WFhYtNigYllD4yUojQWLiBkh4OHhAWtrayxcuLCxZJIqhYWF0NbWRsEKTWgp1z1BvSOaiO6+BlzLL5Dxthhn7mcip7CcOd5WWwVjrNri4B1+ZHB9D41blI/K99lgK6tDUb99nQprXbDBgwM7BUZ4j9fQwX2eJXhg44xXX7EjMClNS1lZGdLT05mlUbHgcfmW1TqzS/wb7ev3uFEs79V9XG/duoVBgwbh4sWLGDt2bL3X9uzZE3PnzmUURw6Hg88//xwnT54EwHczMDExwfr16zF37lx88803ePLkCcLDw2tVHFgsFubOnYv9+/czbX379oWtrS1++uknHDp0CMuXL8eLFy8YRebq1asYPXo0Xr16BWNjY3h6eiIsLAyZmZlCOTgb6uNa39+WmWcKCqAlSLJfDV9fXygqKmLHjh1C7UuWLEFpaSn27dsnljyiUFVVBQ6Hg/Xr12P27NkN7ocZX0oKtOqpQvXgwQOYmpqiuLhY4oqKJSUluHPnDrp27QpDQ0Noamp++qJ6eP78OUpKSlBZWYlevXpRhbWJ4PIIjZegiIQo8yjQgOAsQRLtqKgo2NnZ1Xj7lddchHX7jvLRwweMSlmGWG1l+N02rKGYClJe1ZavVU9dEXnFlQCA4pS7KEq4Dt3Bs6FkaNZAWdlM3tiPoRWrWjjiVLRppJRotVHdSlhUVIR169YhNDQU2dnZqKqqQmlpaQ2La+/evZnfWSwWTExM8Po1v7iGp6cnhg4dim7dumH48OH44osvMGzYMKHrq2cpcXJyQnx8PAAgOTkZVlZWQvNP//79wePxkJqayvgr9urVS64Sxx85cgR//PEH+vbtCwC4d+8eMjMzMXPmTCxatIg5r7py21BiY2ORlZUFNpsNGxsb5OTkwNraGoGBgfUm6C8vL0d5+X8v7oWFhfxf2tZuJcvNzQUhBHl5ebC3t5dYKbx37x5WrlyJ5cuXw8LCQqK+AODmzZvo0YM/p5qZNWxepjQMQbwEhSItxFZcm2sS7U/5RLBY/JgDi3vfgYX9INWuECRNDknIxu2lg/DweT7zBmlnpot+6y4iOzsbbTQNYDTJHywF6ZcGpBGYLRw5SYlWneovp0uWLMGNGzewbds2dO7cGaqqqpg0aVKN4KfqieBZLBZ4PP4rpK2tLdLT03Ht2jWEh4czaY2q+7FKW3ZZkpiYCFtbWwBgirUYGBjAwMAAiYmJzHnStAT+888/AIB169Zhx44d4HA42L59O1xcXPD06VPo6dUeHBMQEID169eLdI+UlBQAAJvNhpubm0TylpWVITY2FhoaGvj999+hI/CjbSCZmZlgsVhQUlKCkZERtbJSpI6LiwuTUSkuLg7W1tayFUjOWbduHTO37Ny5s0HZXcT2lk9PT69zE0ySzRUWC9DFBziyk2o9Lkh59fB5vlDE/dUrIeCG7wTh8aDSzlJIaRVMkxrKDVdkJYnA5PIIop+9w6X4LEQ/ewdu9cgvivwg45RoSkpK4HI/nb0gKioKnp6eGD9+PHr16gUTExNkZGSIfT8tLS24u7vj0KFDCAoKwvnz55GXl8ccj4mJETo/JiYG3bvzyyt3794dCQkJKC4uFpKLzWajWz1L2YDo45Q2N2/eFGmLjIz8ZF8rVqxggsvq2lJSUpgXhdWrV2PixImws7PDsWPHwGKxEBwcXGf/K1euREFBAbO9ePGixjlv375FYWEhEhMTYWlpKbFrwIMHDzBq1Cjk5+ejd+/eEimthBD89ddfqKqqApfLRf/+/anSSmk0BLEB9a1iZGRk1Po5rT7PfYwgQLa2TbB61Vg8efIEEydOBIfDAYvFEilNoihjXLJkCbKzs9G+ffsGyyaRWVDgHtvSJgQndhKieXX/A95IyoFTJ33k5+fj/fv3UFVVRcztcNz8O69GKTodNUXkl1SiqLzu4IuPq1RJs2JVWGJ2DXna0tJ48ksTVLSpDw6Hg3v37iEjIwMaGhqM0lOdLl264Pfff8fo0aPBYrGwZs2aOs+tix07dqBt27awsbEBm81GcHAwTExMhJSV4OBg2Nvbw9nZGadPn8b9+/dx5MgRAMD06dPh7+8PDw8PrFu3Dm/evIGvry9mzJjxybRGHA4Hd+7cwZQpU6CsrAwDAwOxZJcHFi9eDE9Pz3rPsbCwYILPBMvkAKCsrAwLC4sarh0fo6ysXG9C/xcvXiA7Oxu6urqYNGmSeMJXo6KiAk+fPkV5eTnOnTsHfX3JlpVfvnwJVVVVvHnzptZgOApF2qipqYmcmSI8PByfffYZs1/f/7u7uzuGDx8u1Obp6YmysrJaK5hKk5KSElhYWGDy5MlixzTVN0YNDQ1oaGhAQaHhcRoNyk9y5MgR9OzZk0nW3LNnTxw+fLjBQsgdnzBKHo3KgPemQ5gwcSLy8/Ph5uaGNopK0FZVwjK3blgzqjt2ulvj9NeOUG7z6UdsrKWMA/9niwNSjMAMS8zGvFqqduUUlGHeqViEJTZdNDVFRJqgok19LFmyBAoKCujRowcMDQ3rVGx27NgBXV1d9OvXD6NHj4abmxuzBC4qmpqa2Lp1K+zt7dGnTx9kZGTg6tWrQimT1q9fj7Nnz6J379745ZdfcObMGUYBU1NTw/Xr15GXl4c+ffpg0qRJGDJkCH788cdP3nvDhg3IyMhAp06dYGhoKJbc8oKhoSEsLS3r3ZSUlJg0ZoJsDgA/u0FGRkaDfD3z8/NRVlaGyMhIODg4oEuXLhKNIz4+HqNGjUJ6ejqcnZ0lUloJIUhNTcW7d+9QWFiIESNGSCQbhdIY6OvrMxkyTExMarhUfYyqqqrQuQoKCoiMjJQo0FJU+vTpg8DAQOYFXxzEGWNDENviunbtWuzYsQO+vr5M8ER0dDQWLlyIzMxMbNiwQaoCyoJ7pO4lL155MUhVBX5PToP5wMV4rdS2TsvmlD4dhDIT1MX2L63RvzPf6iONilVcHsH6y0m16t8CX931l5MwtIcJje6UNxq5ok19dO3aFdHR0UJttVn1OBxOjeXsBdVq0tfmOiAIrAL4S2teXl71ymNqaoo//vijzuO9evWqd1n9+PHjtbb37dsXCQkJ9d67paClpYW5c+fC398fHTp0gJmZGQIDAwEAkydPFru/+Ph49O7dGx4eHhLJVVlZiZcvX+LVq1c4ffq0xNajnJwcaGpq4vHjxxJbgCmUxmTMmDEoKytD165dsWzZMowZI/qc/ssvv0BNTU3u/8clGaMoiK24CpJoT506VUjI3r17w9fXt0Uort1YWYiCdY320ox4FESfg97g2VDvPgCvS4G5p2Jr7SO7oAw7w/8W6X5vi/5TbqURgXk/Pa+GpfVjBL6699PzRL4XTWnShPQYA1iO4mcPKMrl+7Sa9aPFJygNIjAwEG3atMGMGTNQWloKR0dHREZGQldXV+y+Bg0aVG+aGlF48uQJFi1ahAULFkjlCy0rKwv//PMPzMzM5P4LndJ60dDQwPbt29G/f3+w2WycP38e48aNw8WLF0X+HBw5cgTTpk2DqqpqI0vbMKQxRlEQW3GVVRLtpqQj643QPq+iFCA8VOW/gtHEtWArSTe6X9rZAkRNmyXqedRXVgYIKtpQKBKiqKiIbdu2Ydu2bTKVg8vl4u3bt0hISMDx48fRto7UWqLy9u1baGpqIjIyEjNmzJCSlBSK5Hz22Wd4/vw5AODzzz/HtWvXYGBgIJTyrk+fPnj16hUCAwNFUuqio6ORnJzM5MaWRyQdo6iI7eM6Y8YMoaTgAg4ePIjp06dLRShZk0n+W7Yqy3yM1+c3oKogF5o2I6WqtDZWvWZRFWFRzqO+shRZQAhpeFlSityRmpqKUaNG4f79+5g2bZrESmteXh7u3buHt2/fUqWVIndcvXoV8fHxiI+Przf+x9HREWlpaSL1efjwYVhbW8POzk5aYjYJ4oxRVBqUVaCpk2g3FYQAXLDxC3cYeJXlYLFYKM9KhtGENWArq0n1Xo1Zr9nBXA9ttVWQU1BWV2w6TERQmKmvLIVCkQQej4cPHz4gIiICBw8eRMeOHSXqr7CwEEpKSrhw4UKTBKhQKA1B1MDH+Ph4kV7iioqKcO7cOQQEBEgqWpMj6hjFQWyLqyCJtqGhIZ49e4Znz57BwMAAtra2SExMRFxcHOLi4oQCMZoDgsK3h6tGojgrDW/Or0dlfja0nb6UutIKSFav+VO5WRXYLPiP5kdf1xGbLpLCLI6vLEUYMSspU5oB9G8qHv/88w/GjBmDu3fvYv78+RIrrcXFxQgNDcWHDx+o0kppdpw4cQJnzpxBSkoKUlJSsHnzZhw9ehS+vr7MORcuXIClpWWNa4OCglBVVYX/+7//E+le1TOs/PjjjxgyZAizn5WVBUtLS9y/f7/OPioqKhircUVFBbKyshAfHy9kPa3eryhjlAZiW1xv3rwpVQFkBSH8ggMCuGBjf5kbtvG+REnaaRiMWwUFFY1GubfPoM5YOLRrg6yUovqbDu/ZFvv/z7bGuSZi+KZK21e2NSBI+1FSUiK3DvSUhlFSUgKgZkUwijA8Ho/Jybp3716Ym5tL1F9ZWRkqKytx9uzZT2aioFDkmY0bN+L58+do06YNLC0tERQUJBRQWFBQIJS6TsCRI0cwYcIEkYtyPHv2DG/fvmX23759y1TrA/ixSqmpqcycVhuvXr2CjY0Nsy/wkx84cCBu3bpVa7+ijFEasEgLNyMUFhZCW1sbBSs0oaX8n6J4vNIVmTBBR9ZrZBIjHMqywNtbJ6E3bD4U9do1qkxnvPo2KHOAwN+0+h9MMKraLLiSZAOIfvYOUw/VXdVDQEPH01LJzs7G+/fvYWRkBDU1tRZXoKO1QQhBSUkJXr9+DR0dnVqXvZh5pqBA4qh7eUTU8WVmZsLX1xcLFizAsGHDJL5vZWUljh07Bnd3d2hra0vcH4UibVxcXGBtbS1SZSnKf3A4HPj5+QmVfBV1npGoclZzxpr9DOsqvwLhVgEgKHh8BAZjl0NBtXG/dNgsIL/407ldq9NQf1NJ0mtJy1e2tSGooNLYJfkoTYuOjo7I1XFaG4QQcLlc7N+/H9u2bZO4MEFVVRUKCgpw4cIFzJkzR0pSUiiNw08//YTDhw8jOjoavXr1krU4cs3mzZuxefPmeq29n6LVWlwrCBvmL9YjL/Io9Ed+izZajVs+7WNYqN06Wh+ysn4KrLxA7aVoG+qnK03kNccsl8tFZWWlrMWgSAFFRcV6SxS2Zovrq1ev4OvrC19fX7i4uEh8Lx6Phz179sDb25u621DknqysLJSWlgIAOnbsCCUlJRlLJN/k5eUhL48fF2NoaCi0kkItrvXAIwSVlVwMjl0KvwlGUFMPRBLpiIs8Z3RjZcGenQo1lOMt0QRYgD4+oJQo4z6xxC9cN1RJ4bEJWUd5XCDjLpD+J18jNHPm5/D8KOG8qH6k1/5NTyUt5U0avrKNiTznmFVQUJCoHrM8Ia8vBxTZQQgBIQTff/89Nm7cyJTjlaS/V69e4caNG0LLhxSKPNOuXeO6FrY09PT0oKcn2Spts7C47tu3D4GBgcjJyYGVlRX27t0LBwcHka6tbnFNfsPFwutlODleFYbqYidVAJewcKhqFLZwp4l9bXXOePWFU3kUcPlboLRaZL6qLjB6D1PiU1SLqwBpK2/yqLg0xOeXIj7y/HIgL7Q2i2tubi6+/fZbLFy4EI6OjhL3TwjB1q1bsWjRIhr8RqG0UkSdR8XX3JqYoKAgLFq0CP7+/oiNjYWVlRXc3NzE9h8khCC/lGD97XIcGdMwpRUA2CDwbnMFKxR+bdD1H6OQehk4N6Om0goApfn8Y0khAP7zNxVVVZR2gQCBr+xY63Zw6qQvc6X1Uz6/AN+qXT1VGEU8aAEKSnUIIViyZAlWrlwpFaX177//RlBQEJYvX06VVgqF8knkXnHdsWMHvLy8MGvWLPTo0QMHDhyAmpoajh49KlY/U34rBZsFnJ2khnZaDR+2IED86zahaIOGl7hlgwerRBGSCV9bDvC49eZmrY2WrrzRHLOND305oFTHy8sLjx8/xsmTJ2FlZSVxf5s3b0anTp0wZcoUKUhHoVBaA3Lt41pRUYGHDx9i5cqVTBubzYarqyuio6Nrvaa8vBzl5f9F7RcUFAAA/Acqg8UCCsul9SVL8GXVFZziip/yhQVgsMY/KM/PxifzC5RnAYk3AE4/9Ouojm3jumDLtRTkFoqWmSDrdQluPnoOB4uWFfmfkf0GvPJPRyVmZL/BZ4bUitMQ7v+Th6zX9Sv+LfX/S1wKCwsBtNwiBYJxzZw5ExwOhxlvQ3n8+DFevnwJHx8fFBUVSUNECoXSzBF5HiVyTFZWFgFA/ve//wm1L126lDg4ONR6jb+/PwHfIEQ3utGNbk26vXjxoimmxibnxYsXMn+2dKMb3VrH9ql5VK4trg1h5cqVWLRoEbP//v17mJmZITMzkyawrkZhYSE6dOiAFy9etMiAEkmgz6Z26HOpHUIIPnz4AFNTU1mL0iiYmprixYsX0NTUbJKCGq3h/6ylj5GOr/nT1GMUdR6Va8XVwMAACgoKyM3NFWrPzc2tMxG4srIylJWVa7Rra2u32H8uSdHS0qLPpg7os6kd+lxq0pJfjNlsNtq3b9/k920N/2ctfYx0fM2fphyjKPOoXAdnKSkpwc7ODhEREUwbj8dDREQEnJycZCgZhUKhUCgUCqWpkWuLKwAsWrQIHh4esLe3h4ODA3bt2oXi4mLMmjVL1qJRKBQKhUKhUJoQuVdc3d3d8ebNG6xduxY5OTmwtrZGWFgYjI2NRbpeWVkZ/v7+tboPtHbos6kb+mxqhz4XSlPQGv7PWvoY6fiaP/I6xmZROYtCoVAoFAqFQpFrH1cKhUKhUCgUCkUAVVwpFAqFQqFQKM0CqrhSKBQKhUKhUJoFVHGlUCgUCoVCoTQLWrTium/fPnA4HKioqMDR0RH379+XtUhNzp07dzB69GiYmpqCxWLh4sWLQscJIVi7di3atm0LVVVVuLq64u+//5aNsE1MQEAA+vTpA01NTRgZGWHcuHFITU0VOqesrAwLFiyAvr4+NDQ0MHHixBoFMVoi+/fvR+/evZnE005OTrh27RpzvLU+F4psCA0NhaOjI1RVVaGrq4tx48bJWqRGoby8HNbW1mCxWIiPj5e1OFIhIyMDs2fPhrm5OVRVVdGpUyf4+/ujoqJC1qJJREvVL0T5XpQ1LVZxDQoKwqJFi+Dv74/Y2FhYWVnBzc0Nr1+/lrVoTUpxcTGsrKywb9++Wo9v3boVe/bswYEDB3Dv3j2oq6vDzc0NZWVlTSxp03P79m0sWLAAMTExuHHjBiorKzFs2DAUFxcz5yxcuBCXL19GcHAwbt++jVevXmHChAkylLppaN++PbZs2YKHDx/ir7/+wuDBgzF27Fg8efIEQOt9LpSm5/z585gxYwZmzZqFhIQEREVFYdq0abIWq1FYtmxZiysbnJKSAh6Ph59//hlPnjzBzp07ceDAAaxatUrWojWYlqxfiPK9KHNIC8XBwYEsWLCA2edyucTU1JQEBATIUCrZAoBcuHCB2efxeMTExIQEBgYybe/fvyfKysrkzJkzMpBQtrx+/ZoAILdv3yaE8J+FoqIiCQ4OZs5JTk4mAEh0dLSsxJQZurq65PDhw/S5UJqMyspK0q5dO3L48GFZi9LoXL16lVhaWpInT54QACQuLk7WIjUaW7duJebm5rIWo8G0Jv2i+veiPNAiLa4VFRV4+PAhXF1dmTY2mw1XV1dER0fLUDL5Ij09HTk5OULPSVtbG46Ojq3yORUUFAAA9PT0AAAPHz5EZWWl0POxtLREx44dW9Xz4XK5OHv2LIqLi+Hk5ESfC6XJiI2NRVZWFthsNmxsbNC2bVuMGDECiYmJshZNquTm5sLLywsnT56EmpqarMVpdAoKCph5trnR2vSL6t+L8kCLVFzfvn0LLpdbo7qWsbExcnJyZCSV/CF4FvQ5ATweD35+fujfvz969uwJgP98lJSUoKOjI3Rua3k+jx8/hoaGBpSVlTF37lxcuHABPXr0aPXPhdJ0/PPPPwCAdevW4bvvvsOVK1egq6sLFxcX5OXlyVg66UAIgaenJ+bOnQt7e3tZi9PopKWlYe/evfD29pa1KA2iNekXtX0vygMtUnGlUMRlwYIFSExMxNmzZ2UtitzQrVs3xMfH4969e5g3bx48PDyQlJQka7EoLYAVK1aAxWLVuwl8IwFg9erVmDhxIuzs7HDs2DGwWCwEBwfLeBT1I+oY9+7diw8fPmDlypWyFlksRB3fx2RlZWH48OGYPHkyvLy8ZCQ5RVTk9XuxjawFaAwMDAygoKBQI8o5NzcXJiYmMpJK/hA8i9zcXLRt25Zpz83NhbW1tYykanp8fHxw5coV3LlzB+3bt2faTUxMUFFRgffv3wtZF1vL/5GSkhI6d+4MALCzs8ODBw+we/duuLu7t+rnQpGcxYsXw9PTs95zLCwskJ2dDQDo0aMH066srAwLCwtkZmY2pogSI+oYIyMjER0dXaMevL29PaZPn44TJ040opQNR9TxCXj16hUGDRqEfv364eDBg40sXePRWvSLur4X5YEWqbgqKSnBzs4OERERTNoUHo+HiIgI+Pj4yFY4OcLc3BwmJiaIiIhgFNXCwkLGwtbSIYTA19cXFy5cwK1bt2Bubi503M7ODoqKioiIiMDEiRMBAKmpqcjMzISTk5MsRJYpPB4P5eXl9LlQJMbQ0BCGhoafPM/Ozg7KyspITU2Fs7MzAKCyshIZGRkwMzNrbDElQtQx7tmzB99//z2z/+rVK7i5uSEoKAiOjo6NKaJEiDo+gG9pHTRoEGMxZ7Ob72JvS9cvPvW9KBfIODis0Th79ixRVlYmx48fJ0lJSWTOnDlER0eH5OTkyFq0JuXDhw8kLi6OxMXFEQBkx44dJC4ujjx//pwQQsiWLVuIjo4OuXTpEnn06BEZO3YsMTc3J6WlpTKWvPGZN28e0dbWJrdu3SLZ2dnMVlJSwpwzd+5c0rFjRxIZGUn++usv4uTkRJycnGQoddOwYsUKcvv2bZKenk4ePXpEVqxYQVgsFvnjjz8IIa33uVCanm+//Za0a9eOXL9+naSkpJDZs2cTIyMjkpeXJ2vRGoX09PQWlVXg5cuXpHPnzmTIkCHk5cuXQnNtc6Ul6xeifC/KmharuBJCyN69e0nHjh2JkpIScXBwIDExMbIWqcm5efMmAVBj8/DwIITwU2KtWbOGGBsbE2VlZTJkyBCSmpoqW6GbiNqeCwBy7Ngx5pzS0lIyf/58oqurS9TU1Mj48eOb9YQrKl999RUxMzMjSkpKxNDQkAwZMoRRWglpvc+F0vRUVFSQxYsXEyMjI6KpqUlcXV1JYmKirMVqNFqa4nrs2LE659rmTEvVL0T5XpQ1LEIIaSrrLoVCoVAoFAqF0lCar6MJhUKhUCgUCqVVQRVXCoVCoVAoFEqzgCquFAqFQqFQKJRmAVVcKRQKhUKhUCjNAqq4UigUCoVCoVCaBVRxpVAoFAqFQqE0C6jiSqFQKBQKhUJpFlDFlUKhUCgUCoXSLKCKK0VkMjIywGKxEB8fL2tRGszx48eho6MjazHEIioqCr169YKioiJTG7s2UlNTYWJigg8fPjSdcFLk7du3MDIywsuXL2UtCoUiU+hcKxtEmWs5HA527drVpHJRhKGKK0VkOnTogOzsbPTs2VPWomDdunWwtraWtRhNwqJFi2BtbY309HQcP368zvNWrlwJX19faGpqNpls5ubmCA8PF+nc48ePg8VigcVigc1mo3379pg1axZev34NADAwMMDMmTPh7+/fmCJTKHIPnWtlg6hzrbR58uQJJk6cCA6HAxaLVadivG/fPnA4HKioqMDR0RH3799vMhnlCaq4UkSioqICCgoKMDExQZs2bWQtTqvi2bNnGDx4MNq3b1+nBSMzMxNXrlyBp6dnk8n16NEj5OfnY+DAgSJfo6WlhezsbLx8+RKHDh3CtWvXMGPGDOb4rFmzcPr0aeTl5TWGyBSK3EPnWtkhylzbGJSUlMDCwgJbtmyBiYlJrecEBQVh0aJF8Pf3R2xsLKysrODm5sa8+LcqCEWu4HK5ZPPmzYTD4RAVFRXSu3dvEhwcTAghhMfjkSFDhpBhw4YRHo9HCCHk3bt3pF27dmTNmjWEEEJu3rxJAJArV66QXr16EWVlZeLo6EgeP34sdJ8///yTODs7ExUVFdK+fXvi6+tLioqKmONmZmZkw4YNZMaMGURTU5N4eHiQ9PR0AoDExcUJ3SssLIxYW1sTFRUVMmjQIJKbm0uuXr1KLC0tiaamJpk6dSopLi4WaYwf9xseHk7s7OyIqqoqcXJyIikpKYQQQo4dO0YACG3Hjh0jhBCyfft20rNnT6Kmpkbat29P5s2bRz58+MD0fezYMaKtrV3n8xeMMSgoiHk+9vb2JDU1ldy/f5/Y2dkRdXV1Mnz4cPL69WvmOg8PDzJ27Fiybt06YmBgQDQ1NYm3tzcpLy9nzhk4cCDx8fEh3377LdHR0SFGRkbk4MGDpKioiHh6ehINDQ3SqVMncvXqVSFZahtndQIDA4m9vb1Qm2Csly9fJl27diWqqqpk4sSJpLi4mBw/fpyYmZkRHR0d4uvrS6qqqpjrXr16RUaOHElUVFQIh8Mhp0+fJmZmZmTnzp1C/W/YsIG4u7sz+wcPHiTt27cnqqqqZNy4cWT79u1Cz7q2Z79p0ybCZrNJSUkJ02Zubk4OHz5c59+IQpEGdK6lc21D5trqc+Hz58/JmDFjiLq6OtHU1CSTJ08mOTk5Qtds3LiRGBoaEg0NDTJ79myyfPlyYmVlJVL/AhwcHMiCBQuYfS6XS0xNTUlAQECdz7ilQhVXOeP7778nlpaWJCwsjDx79owcO3aMKCsrk1u3bhFCCHn58iXR1dUlu3btIoQQMnnyZOLg4EAqKysJIf9NRN27dyd//PEHefToEfniiy8Ih8MhFRUVhBBC0tLSiLq6Otm5cyd5+vQpiYqKIjY2NsTT05ORw8zMjGhpaZFt27aRtLQ0kpaWVudk2rdvX3L37l0SGxtLOnfuTAYOHEiGDRtGYmNjyZ07d4i+vj7ZsmWLyGMU9Ovo6Ehu3bpFnjx5Qj7//HPSr18/QgghJSUlZPHixeSzzz4j2dnZJDs7m1F8du7cSSIjI0l6ejqJiIgg3bp1I/PmzWPuLepkKpAvKSmJ9O3bl9jZ2REXFxehcc6dO5e5zsPDg2hoaBB3d3eSmJhIrly5QgwNDcmqVauYcwYOHEg0NTXJxo0bydOnT8nGjRuJgoICGTFiBDl48CB5+vQpmTdvHtHX1yfFxcWkqqqKZGdnEy0tLbJr1y6hcVZnzJgxQvIIxqqoqEiGDh1KYmNjye3bt4m+vj4ZNmwY+fLLL8mTJ0/I5cuXiZKSEjl79ixznaurK7G2tiYxMTHk4cOHZODAgURVVbXGZGpvb09+/fVXQgghd+/eJWw2mwQGBpLU1FSyb98+oqen90nFdceOHQQAKSwsZNrc3d2Jh4dHnX8jCkUa0LmWzrUNmWs/Viy5XC6xtrYmzs7O5K+//iIxMTHEzs6ODBw4kDn/1KlTREVFhRw9epSkpqaS9evXEy0tLbEU1/LycqKgoEAuXLgg1D5z5kwyZsyYOp9xS4UqrnJEWVkZUVNTI//73/+E2mfPnk2mTp3K7J87d46oqKiQFStWEHV1dfL06VPmmGAi+lgReffuHVFVVSVBQUFMf3PmzBG6x59//knYbDYpLS0lhPA/POPGjRM6p67JNDw8nDknICCAACDPnj1j2ry9vYmbm5vIY6yt39DQUAKAkc/f37/OD/7HBAcHE319fWZf1Mn0Y4vfmTNnCAASEREhNM5u3box+x4eHkRPT0/I2rF//36ioaFBuFwuIYQ/mTo7OzPHq6qqiLq6OpkxYwbTlp2dTQCQ6Ohopk1bW7vOt38BVlZWZMOGDUJtAmtJWloa0+bt7U3U1NSELCNubm7E29ubEEJIcnIyAUAePHjAHP/7778JAKHJ9OXLl0RJSYnk5+cTQvjK5qhRo4TuP3369HoV16dPn5KuXbvWsBQvXLiQuLi41DteCkUS6FxL59qGzrUfK5Z//PEHUVBQIJmZmczxJ0+eEADk/v37hBBCHB0dhSylhBDSv39/sRTXrKwsAqDG33Lp0qXEwcGhXnlbItSBRo5IS0tDSUkJhg4dKtReUVEBGxsbZn/y5Mm4cOECtmzZgv3796NLly41+nJycmJ+19PTQ7du3ZCcnAwASEhIwKNHj3D69GnmHEIIeDwe0tPT0b17dwCAvb29SHL37t2b+d3Y2BhqamqwsLAQahM4kYs6xur9tm3bFgDw+vVrdOzYsU5ZwsPDERAQgJSUFBQWFqKqqgplZWUoKSmBmpqaSOOpbUwA0KtXL6G26r5FVlZWQvdwcnJCUVERXrx4ATMzsxr9KigoQF9fv0a/gnGKQ2lpKVRUVGq0q6mpoVOnTkL9czgcaGho1DqW1NRUtGnTBra2tszxzp07Q1dXV6jfkJAQODs7M35gqampGD9+vNA5Dg4OuHLlilBbQUEBNDQ0wOPxUFZWBmdnZxw+fFjoHFVVVZSUlIgxegpFPOhcS+fahs61H5OcnIwOHTqgQ4cOTFuPHj2go6OD5ORk9OnTB6mpqZg/f77QdQ4ODoiMjGzwfVs7VHGVI4qKigAAoaGhaNeundAxZWVl5veSkhI8fPgQCgoK+Pvvvxt0H29vb3zzzTc1jn08Uamrq4vUn6KiIvM7i8US2he08Xg85t7Ap8dYW78AmH5qIyMjA1988QXmzZuHTZs2QU9PD3fv3sXs2bNRUVEh1mRa272rt9Uniyj9CvoRd5y1YWBggPz8fLHvJ2gT934hISEYM2aMWNcAgKamJmJjY8Fms9G2bVuoqqrWOCcvLw+GhoZi902hiAqda+lc29C5VhYYGBhAQUEBubm5Qu25ubl1BnO1ZKjiKkf06NEDysrKyMzMrDdSe/HixWCz2bh27RpGjhyJUaNGYfDgwULnxMTEMBNjfn4+nj59yrzd29raIikpCZ07d268wdSBqGP8FEpKSuByuUJtDx8+BI/Hw/bt28Fm8xNmnDt3TiJ5xSEhIQGlpaWMMhYTEwMNDQ2ht/HGwsbGBklJSRL3061bN1RVVSEuLg52dnYA+Jabj5XioqIi3Lx5E/v37xe67sGDB0J9Vd8HADab/cn/u8TERLi4uEgwCgqlfuhcKzp0rq2b7t2748WLF3jx4gVz76SkJLx//x49evQA8N/cOHPmTOa62ubG+lBSUoKdnR0iIiKY/LI8Hg8RERHw8fGRzmCaEVRxlSM0NTWxZMkSLFy4EDweD87OzigoKEBUVBS0tLTg4eGB0NBQHD16FNHR0bC1tcXSpUvh4eGBR48eCS3nbtiwAfr6+jA2Nsbq1athYGDA/MMvX74cffv2hY+PD77++muoq6sjKSkJN27cwI8//ijzMYoCh8NBeno64uPj0b59e2hqaqJz586orKzE3r17MXr0aERFReHAgQONOp6PqaiowOzZs/Hdd98hIyMD/v7+8PHxYSb2xsTNzQ1ff/01uFwuFBQUGtyPpaUlXF1dMWfOHOzfvx+KiopYvHgxVFVVGQtFWFgYunbtCg6Hw1zn6+uLAQMGYMeOHRg9ejQiIyNx7do15hpREVi4Nm/e3OAxUCifgs61dK6VBq6urujVqxemT5+OXbt2oaqqCvPnz8fAgQMZ9w9fX194eXnB3t4e/fr1Q1BQEB49eiTk4lFRUcEYHioqKpCVlYX4+HhoaGgwLz2LFi2Ch4cH7O3t4eDggF27dqG4uBizZs1q0jHLAzSPq5yxceNGrFmzBgEBAejevTuGDx+O0NBQmJub482bN5g9ezbWrVvH+CCuX78exsbGmDt3rlA/W7Zswbfffgs7Ozvk5OTg8uXLUFJSAsD3/bl9+zaePn2Kzz//HDY2Nli7di1MTU1lPkZRmThxIoYPH45BgwbB0NAQZ86cgZWVFXbs2IEffvgBPXv2xOnTpxEQENCIIxFmyJAh6NKlCwYMGAB3d3eMGTMG69ata5J7jxgxAm3atBG5GEB9/PLLLzA2NsaAAQMwfvx4eHl5QVNTk/GhvXTpUg03gf79++PAgQPYsWMHrKysEBYWhoULF9bqd1sfly5dQseOHfH5559LPA4KpT7oXCsadK6tGxaLhUuXLkFXVxcDBgyAq6srLCwsEBQUxJwzffp0rFy5EkuWLIGtrS3S09Ph6ekpNDe+evUKNjY2sLGxQXZ2NrZt2wYbGxt8/fXXzDnu7u7Ytm0b1q5dC2tra8THxyMsLIzx1W1NsAghRNZCUKTHrVu3MGjQIOTn5ze7cnvNGU9PT7x//x4XL16UmQz79u1DSEgIrl+/LtV+X758iQ4dOiA8PBwDBw6EsbExrl27BgcHh3qv8/LyQkpKCv7880+R79W3b1988803mDZtmqRiUyiNCp1rZYM8zLWSMnToUJiYmODkyZOyFqVZQl0FKJQWgre3N96/f48PHz5IVPY1MjISRUVF6NWrF7Kzs7Fs2TJwOBwMGDAAeXl5WLhwIfr06VPjum3btmHo0KFQV1fHtWvXcOLECfz0008i3/ft27eYMGECpk6d2mDZKRQKRZ4oKSnBgQMH4ObmBgUFBZw5cwbh4eG4ceOGrEVrtlDFlUJpIbRp0warV6+WuJ/KykqsWrUK//zzDzQ1NdGvXz+cPn0aioqKMDIywnfffVfrdffv38fWrVvx4cMHWFhYYM+ePUJLXZ/CwMAAy5Ytk1h+CoVCkRdYLBauXr2KTZs2oaysDN26dcP58+fh6uoqa9GaLdRVgEKhUCgUCoXSLKDBWRQKhUKhUCiUZgFVXCkUCoVCoVAozQKquFIoFAqFQqFQmgVUcaVQKBQKhUKhNAuo4kqhUCgUCoVCaRZQxZVCoVAoFAqF0iygiiuFQqFQKBQKpVlAFVcKhUKhUCgUSrPg/wFGV/XQvimCQgAAAABJRU5ErkJggg==", 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" ] }, "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", " 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", "# 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": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "G6PDH2r : 3.747 mmol/gDWh [b1852]\n", "EDA : 0.000 mmol/gDWh [b1850]\n", "PGI : 3.949 mmol/gDWh [b4025]\n", "PFK : 5.872 mmol/gDWh [b1723 or b3916]\n", "FBA : 5.872 mmol/gDWh [b2097 or b2925]\n", "TPI : 5.779 mmol/gDWh [b3919]\n", "GAPD : 12.553 mmol/gDWh [b1779]\n", "PGK : -12.553 mmol/gDWh [b2926]\n", "PGM : -11.417 mmol/gDWh [b0755 or b3612]\n", "ENO : 11.417 mmol/gDWh [b2779]\n", "PYK : 3.173 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": [ "## (Optional) Track progress" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 table(s) with parameters loaded from protein_predictions.xlsx (Thu Feb 12 15:20:18 2026)\n", "1 table(s) with parameters written to protein_predictions.xlsx\n" ] }, { "data": { "text/html": [ "
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modellin r2log r2lin proteinslog proteins
No
1iML1515_default_GECKO0.0332440.1902251018299
2iML1515_modified_GECKO0.1359810.201613999322
3iML1515_predicted_GECKO0.0788030.268769999308
4iML1515_manual_adjust_GECKO0.2054130.333470999308
\n", "
" ], "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", "\n", " log proteins \n", "No \n", "1 299 \n", "2 322 \n", "3 308 \n", "4 308 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import scipy\n", "import numpy as np\n", "\n", "number = 4\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", "By manually adjusting the turnover numbers of seven reactions in glycolysis and the PPP, a substantial flux shift from the PPP to glycolysis can be achieved, thereby enhancing the predictions of protein concentrations in these pathways. Consequently, the overall correlation between predicted and measured protein concentrations has been shown to improve. The adaptation of enzyme-related parameters to known flux distributions and proteomics data emerges as a promising approach to refine our model.\n", "\n", "In the subsequent tutorial, we will proceed to enhance our GECKO model through the implementation of an automatic fitting process." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "---\n", "## (Alternative) gurobipy - manual parameter fitting\n", "\n", "The manual fitting of model parameters using the gurobipy interface is nearly equivalent to the modifications applied using cobrapy. However, it should be noted that model contexts are not supported using the gurobipy interface. Consequently, it is necessary to explicitly revert model modifications at the termination of the optimization loop. The function `eo.set_variable_bounds()` updates variable bounds and is employed to modify the total available protein. Consequently, at the termination of the loop, it is necessary to reset the total protein using the aforementioned function. The update of turnover numbers and the creation of the target GECKO model have been demonstrated at step 4 above." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SBML model loaded by sbmlxdf: SBML_models/iML1515_predicted_GECKO.xml (Thu Feb 12 15:19:56 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, average saturation level: 0.68\n" ] } ], "source": [ "# Load model 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, average saturation level: {sigma}')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Acetate : pred gr: 0.513 h-1 vs. exp 0.290, diff: 0.223\n", "Glycerol : pred gr: 0.656 h-1 vs. exp 0.470, diff: 0.186\n", "Fructose : pred gr: 0.648 h-1 vs. exp 0.540, diff: 0.108\n", "L-Malate : pred gr: 0.664 h-1 vs. exp 0.550, diff: 0.114\n", "Glucose : pred gr: 0.661 h-1 vs. exp 0.660, diff: 0.001\n", "Glucose 6-Phosphate : pred gr: 0.676 h-1 vs. exp 0.780, diff: -0.104\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Protein mass fractions:\n", "condition: Glucose\n", "1494 proteins in model with total predicted mass fraction of 443.0 mg/gP\n", " 999 have been measured with mpmf of 514.4 mg/gP vs. 406.3 mg/gP predicted\n", " 762 metabolic proteins measured 414.0 mg/gP vs. 250.6 mg/gP predicted\n", " 237 transport proteins measured 100.4 mg/gP vs. 155.7 mg/gP predicted\n", " 495 proteins not measured vs. 36.6 mg/gP predicted\n", " 495 actual proteins 36.6 mg/gP predicted\n", "total : r² = 0.2054, p = 9.22e-52 ( 999 proteins lin scale)\n", " metabolic : r² = 0.2173, p = 2.22e-42 ( 762 proteins lin scale)\n", " transport : r² = 0.4309, p = 1.36e-30 ( 237 proteins lin scale)\n", "total : r² = 0.3335, p = 8.69e-29 ( 308 proteins log scale)\n", " metabolic : r² = 0.3800, p = 1.33e-28 ( 260 proteins log scale)\n", " transport : r² = 0.2661, p = 1.75e-04 ( 48 proteins log scale)\n" ] }, { "data": { "image/png": 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", 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" ] }, "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 with modified parameters\n", "\n", "scale_kcats = {\n", " # 1. rebalance flux between upper glycolysis and PPP\n", " 'PGI': 2.0, 'PFK_iso2': 20.0, 'FBA_iso2': 10.0, 'TPI': 20.0, 'ENO': 2.0, 'PGM_iso1_REV': 2.0,\n", " 'G6PDH2r': 0.2,\n", " \n", " # 2. flux through usage of TCA-cycle\n", " 'PDH': 10.0, 'ACONTa_iso1': 7.0, 'ACONTb_iso1': 7.0, \n", " 'ICDHyr': 0.2, 'AKGDH': 8.0, 'SUCOAS_REV': 3.0, 'SUCDi': 8.0, 'MDH': 0.1, \n", " \n", " # 3. fix the low predicted flux for DHQS reaction\n", " 'DHQS': 16.0/0.23,\n", "}\n", " \n", "for rid in scale_kcats:\n", " if rid not in eo.rdata:\n", " print(f'{rid:15s} not found')\n", " raise ValueError \n", "\n", "new_sat_level = .56\n", "orig_bounds = eo.set_variable_bounds({'V_PC_total': (None, total_protein * new_sat_level / sigma)})\n", "eo.scale_kcats(scale_kcats)\n", "\n", "pred_results = {}\n", "for cond, medium in conditions.items():\n", " eo.medium = medium\n", " solution = eo.optimize()\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", "eo.unscale_kcats()\n", "if orig_bounds:\n", " eo.set_variable_bounds(orig_bounds)\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", "\n", "print(f'Protein mass fractions:')\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', f'{baseline_model}_adjusted'))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "G6PDH2r : 3.747 mmol/gDWh [b1852]\n", "EDA : 0.000 mmol/gDWh [b1850]\n", "PGI : 3.949 mmol/gDWh [b4025]\n", "PFK : 5.872 mmol/gDWh [b1723 or b3916]\n", "FBA : 5.872 mmol/gDWh [b2097 or b2925]\n", "TPI : 5.779 mmol/gDWh [b3919]\n", "GAPD : 12.553 mmol/gDWh [b1779]\n", "PGK : -12.553 mmol/gDWh [b2926]\n", "PGM : -11.417 mmol/gDWh [b0755 or b3612]\n", "ENO : 11.417 mmol/gDWh [b2779]\n", "PYK : 3.173 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": [ "---\n", "---\n", "## References\n", "- Araiza-Olivera, D., Chiquete-Felix, N., Rosas-Lemus, M., Sampedro, J. G., Peña, A., Mujica, A., & Uribe-Carvajal, S. (2013). A glycolytic metabolon in Saccharomyces cerevisiae is stabilized by F-actin. " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.12", "language": "python", "name": "python310" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.0" } }, "nbformat": 4, "nbformat_minor": 4 }