diff --git a/.circleci/config.yml b/.circleci/config.yml index 4ab7e6c9..2c883980 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -2,11 +2,14 @@ version: 2.1 jobs: test: - machine: - # Note that ubuntu with versions later than 2022 triggers an interative prompt that gets CI stuck - image: ubuntu-2004:202107-02 + docker: + - image: cimg/python:<> environment: - PYTHON_VERSION: "3.8" + PYTHON_VERSION: "<>" + parameters: + python-version: + type: string + resource_class: large steps: - checkout @@ -21,10 +24,17 @@ jobs: name: Installing dependencies for GROMACS command: | sudo apt-get update + sudo apt-get install -y software-properties-common sudo apt install build-essential sudo apt-get install ccache libblas-dev libfftw3-dev liblapack-dev libmpich-dev libxml2-dev mpich ninja-build wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | gpg --dearmor - | sudo tee /etc/apt/trusted.gpg.d/kitware.gpg >/dev/null sudo apt-add-repository 'deb https://apt.kitware.com/ubuntu/ focal main' -y + + # The following three lines are required to install libss1.1, which is not available in the default environment + wget http://archive.ubuntu.com/ubuntu/pool/main/o/openssl/libssl1.1_1.1.1f-1ubuntu2_amd64.deb + sudo dpkg -i ./libssl1.1_1.1.1f-1ubuntu2_amd64.deb + rm -f libssl1.1_1.1.1f-1ubuntu2_amd64.deb + sudo apt install cmake && cmake --version - run: @@ -74,9 +84,19 @@ jobs: - store_test_results: path: test-results +# we are not testing Python 3.12. as it seems to have some conflicts with versioneer.py. Will look into this more in the future. workflows: continuous-integration: jobs: - - test - - + - test: + name: test-python-3.8 + python-version: "3.8" + # - test: + # name: test-python-3.9 + # python-version: "3.9" + - test: + name: test-python-3.10 + python-version: "3.10" + - test: + name: test-python-3.11 + python-version: "3.11" diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index 5952c55c..cd0f122b 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -21,7 +21,7 @@ If applicable, add screenshots to help explain your problem. **Desktop (please complete the following information):** - OS: [e.g. iOS] - - Version [e.g. 0.1.2] + - Version of `ensemble_md` [e.g. 0.1.2] **Additional context** Add any other context about the problem here. diff --git a/.gitignore b/.gitignore index f97112b2..708d70a6 100644 --- a/.gitignore +++ b/.gitignore @@ -105,3 +105,6 @@ ENV/ # profraw files from LLVM? Unclear exactly what triggers this # There are reports this comes from LLVM profiling, but also Xcode 9. *profraw + +# Others +.DS_Store \ No newline at end of file diff --git a/README.md b/README.md index 1630864e..28485a34 100644 --- a/README.md +++ b/README.md @@ -6,20 +6,25 @@ Ensemble Molecular Dynamics [![Documentation Status](https://readthedocs.org/projects/ensemble-md/badge/?version=latest)](https://ensemble-md.readthedocs.io/en/latest/?badge=latest) [![GitHub Actions Lint Status](https://github.com/wehs7661/ensemble_md/actions/workflows/lint.yaml/badge.svg)](https://github.com/wehs7661/ensemble_md/actions/workflows/lint.yaml) [![PyPI version](https://badge.fury.io/py/ensemble-md.svg)](https://badge.fury.io/py/ensemble-md) -[![DOI](https://img.shields.io/badge/DOI-arxiv.org%2Fabs%2F2308.06938-green)](https://arxiv.org/abs/2308.06938) - +[![python](https://img.shields.io/badge/Python-3.8%20|%203.9%20|%203.10%20|%203.11-4BC51D.svg?style=flat&logo=python&logoColor=white)](https://www.python.org) +[![DOI](https://img.shields.io/badge/DOI-10.1021/acs.jctc.4c00484-4BC51D)](https://pubs.acs.org/doi/epdf/10.1021/acs.jctc.4c00484) [![MIT license](https://img.shields.io/badge/License-MIT-blue.svg)](https://lbesson.mit-license.org/) [![Downloads](https://static.pepy.tech/badge/ensemble-md)](https://pepy.tech/project/ensemble-md) -[![Twitter Follow](https://img.shields.io/twitter/follow/WeiTseHsu?style=social)](https://twitter.com/WeiTseHsu) -**ensemble_md** is a Python package that provides methods for setting up, running, and analyzing molecular dynamics ensembles in GROMACS. The current implementation is mainly for synchronous replica exchange (REX) of expanded ensemble (EE), abbreviated as REXEE. In the future, we will develop methods like asynchronous REXEE or multi-topology REXEE. For installation instructions, theory overview, tutorials, and API references, please visit the [documentation](https://ensemble-md.readthedocs.io/en/latest/?badge=latest). + +`ensemble_md` is a Python package that provides methods for setting up, running, and analyzing GROMACS simulation ensembles. Currently, the package implements all the necessary algorithms for running synchronous replica exchange (REX) of expanded ensembles (EE), abbreviated as REXEE, as well as its multi-topology (MT) variation, MT-REXEE. Our future work includes implementing asynchronous REXEE and other possible variations of the REXEE method. For installation instructions, theory overview, tutorials, and API references, please visit the [documentation](https://ensemble-md.readthedocs.io/en/latest/?badge=latest) and our [JCTC paper](https://pubs.acs.org/doi/epdf/10.1021/acs.jctc.4c00484). + +### Reference +If you use any components of the Python package `ensemble_md` or the REXEE method in your research, please cite the following paper: + +Hsu, W. T., & Shirts, M. R. (2024). Replica Exchange of Expanded Ensembles: A Generalized Ensemble Approach with Enhanced Flexibility and Parallelizability. *Journal of Chemical Theory and Computation*. doi: [10.1021/acs.jctc.4c00484](https://doi.org/10.1021/acs.jctc.4c00484) ### Copyright Copyright (c) 2022, Wei-Tse Hsu -#### Acknowledgements +### Acknowledgements Project based on the [Computational Molecular Science Python Cookiecutter](https://github.com/molssi/cookiecutter-cms) version 1.6. diff --git a/docs/examples/analyze_REXEE.ipynb b/docs/examples/analyze_REXEE.ipynb deleted file mode 100644 index 76c22543..00000000 --- a/docs/examples/analyze_REXEE.ipynb +++ /dev/null @@ -1,39 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cc81ea4a", - "metadata": {}, - "source": [ - "# Tutorial 2: Analyzing a REXEE simulation" - ] - }, - { - "cell_type": "markdown", - "id": "c3014606", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/data/expanded.mdp b/docs/examples/data/expanded.mdp deleted file mode 100755 index 93a5e11b..00000000 --- a/docs/examples/data/expanded.mdp +++ /dev/null @@ -1,110 +0,0 @@ -; Run control -integrator = md-vv -tinit = 0 -dt = 0.002 -nsteps = 500 ; 10 ps -comm-mode = Linear -nstcomm = 1 -nstfout = 0 - -; Output control -nstlog = 100 -nstcalcenergy = 1 -nstenergy = 1000 -nstxout-compressed = 1000 - -; Neighborsearching and short-range nonbonded interactions -nstlist = 10 -ns_type = grid -pbc = xyz -rlist = 1.0 - -; Electrostatics -cutoff-scheme = verlet -coulombtype = PME -coulomb-modifier = Potential-shift-Verlet -rcoulomb-switch = 0.89 -rcoulomb = 0.9 - -; van der Waals -vdw-type = Cut-off -vdw-modifier = Potential-switch -rvdw-switch = 0.85 -rvdw = 0.9 - -; Apply long range dispersion corrections for Energy and Pressure -DispCorr = AllEnerPres - -; Spacing for the PME/PPPM FFT grid -fourierspacing = 0.10 - -; EWALD/PME/PPPM parameters -fourier_nx = 0 -fourier_ny = 0 -fourier_nz = 0 -pme_order = 4 -ewald_rtol = 1e-05 -ewald_geometry = 3d -epsilon_surface = 0 - -; Temperature coupling -tcoupl = v-rescale -nsttcouple = 1 -tc_grps = System -tau_t = 0.5 -ref_t = 298 - -; Pressure coupling is on for NPT -pcoupl = no - -; refcoord_scaling should do nothing since there are no position restraints. - -gen_vel = yes -gen-temp = 298 -gen-seed = 6722267; need to randomize the seed each time. - -; options for bonds -constraints = h-bonds ; we only have C-H bonds here - -; Type of constraint algorithm -constraint-algorithm = lincs -continuation = no - -; Highest order in the expansion of the constraint coupling matrix -lincs-order = 12 -lincs-iter = 2 - -; Free energy calculation -free_energy = expanded -calc-lambda-neighbors = -1 -sc-alpha = 0.5 -couple-moltype = LIG -couple-lambda0 = vdw-q -couple-lambda1 = none -couple-intramol = no -init-lambda-state = 0 - -nstdhdl = 10 -dhdl-print-energy = total - -; Seed for Monte Carlo in lambda space -lmc-seed = 1000 -lmc-gibbsdelta = -1 -lmc-forced-nstart = 0 -symmetrized-transition-matrix = yes -nst-transition-matrix = 100000 -wl-scale = 0.8 -wl-ratio = 0.8 -init-wl-delta = 0.5 - -; expanded ensemble variables -nstexpanded = 10 -lmc-stats = wang-landau -lmc-move = metropolized-gibbs -lmc-weights-equil = wl-delta -weight-equil-wl-delta = 0.001 - -; lambda-states = 1 2 3 4 5 6 7 8 9 -coul-lambdas = 0.00 0.25 0.50 0.75 1.00 1.00 1.00 1.00 1.00 -vdw-lambdas = 0.00 0.00 0.00 0.00 0.00 0.25 0.50 0.75 1.00 -init-lambda-weights = 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 diff --git a/docs/examples/data/params.yaml b/docs/examples/data/params.yaml deleted file mode 100644 index 9472214d..00000000 --- a/docs/examples/data/params.yaml +++ /dev/null @@ -1,12 +0,0 @@ -# User-defined parameters -parallel: True # Whether the replicas of EXEE should be run in parallel or not -n_sim: 4 # Number of replica simulations -n_iterations: 5 # Number of iterations -s: 1 # Shift in λ ranges (e.g. s = 2 if λ_2 = (2, 3, 4) & λ_3 = (4, 5, 6)) -nst_sim: 500 # Number of simulation steps for each homogeneous replica -mdp: 'expanded.mdp' # The MDP template that has the whole range of λ values -mc_scheme: 'metropolis' # The method for swapping simulations. Choices include "same_state", "metropolis", and "metropolis-eq". -w_scheme: 'hist-exp-avg' # The method for combining weights. Choices include "None" (unspecified), exp_avg, ... -N_cutoff: 1000 # The histogram cutoff. Only required if histo-exp-avg is used. -n_pairs: 2 # Number of pairs of simulations to be swapped in each attempt. -outfile: 'results.txt' # The output file for logging how replicas interact with each other. diff --git a/docs/examples/run_REXEE.ipynb b/docs/examples/run_REXEE.ipynb deleted file mode 100644 index e9d44ff9..00000000 --- a/docs/examples/run_REXEE.ipynb +++ /dev/null @@ -1,65 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "87324540", - "metadata": {}, - "source": [ - "# Tutorial 1: Launching a REXEE simulation" - ] - }, - { - "cell_type": "markdown", - "id": "b2d9b443", - "metadata": {}, - "source": [ - "In this tutorial, we will show how one can launch an ensemble of expanded ensemble simulations in parallel using functions in `ensemble_md`. To this end, we will at least need 4 files: one top file (`sys.top`) and one gro file (`sys.gro`) for the system of interest, one mdp file as the template for customizing mdp files for different replicas (`expanded.mdp`), and finally a yaml file speciying the REXEE-relevant parameters (`params.yaml`). \n", - "\n", - "In our case, the system of interest is a linear model composed of 4 interaction sites, where the first and last atom have a charge of +0.2 and -0.2, respectively. In `expanded.mdp`, we define 9 alchemical states in total to decouple the van der Waals interactions and coulombic interactions. Our goal is to run an ensemble composed of 4 replicas of expanded ensemble, each of which sample 6 alchemical states, with the shift between adjacent replicas being 1 state. That is, we want replicas 0 to 3 to sample states 0 to 6, 1 to 7, 2 to 8, and 3 to 9, respectively. Each replica will be performed for 5 iterations, with the length of each iteration being 500 steps (i.e. 1 ps). We will use the Metropolis MC scheme to swap replicas, and use exponential averaging with histogram correction for combining weights of exchanging replicas. The histogram cutoff will be set as 1000 to avoid overcorrection and we will swap 2 pairs of replicas in each attempt when possible. All STDOUT will be redirected to `results.txt`. \n", - "\n", - "To have all these settings, we should have a yaml file with the following content. \n", - "```\n", - "# User-defined parameters\n", - "parallel: True # Whether the replicas of EXEE should be run in parallel or not\n", - "n_sim: 4 # Number of replica simulations\n", - "n_iterations: 5 # Number of iterations\n", - "s: 1 # Shift in λ ranges (e.g. s = 2 if λ_2 = (2, 3, 4) & λ_3 = (4, 5, 6))\n", - "nst_sim: 500 # Number of simulation steps for each homogeneous replica\n", - "mdp: 'expanded.mdp' # The MDP template that has the whole range of λ values\n", - "mc_scheme: 'metropolis' # The method for swapping simulations. Choices include \"same_state\", \"metropolis\", and \"metropolis-eq\".\n", - "w_scheme: 'hist-exp-avg' # The method for combining weights. Choices include \"None\" (unspecified), exp_avg, ...\n", - "N_cutoff: 1000 # The histogram cutoff. Only required if histo-exp-avg is used.\n", - "n_pairs: 2 # Number of pairs of simulations to be swapped in each attempt.\n", - "outfile: 'results.txt' # The output file for logging how replicas interact with each other.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "6cedf8d9", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/tutorial_1/example_outputs.zip b/docs/examples/tutorial_1/example_outputs.zip new file mode 100644 index 00000000..a9b4aaf5 Binary files /dev/null and b/docs/examples/tutorial_1/example_outputs.zip differ diff --git a/docs/examples/tutorial_1/expanded.mdp b/docs/examples/tutorial_1/expanded.mdp new file mode 100644 index 00000000..b1b5c1ea --- /dev/null +++ b/docs/examples/tutorial_1/expanded.mdp @@ -0,0 +1,105 @@ +; Run control +integrator = md-vv +tinit = 0 +dt = 0.002 +nsteps = 50000000 +comm_mode = Linear +nstcomm = 1 +nstfout = 0 + +; Output control +nstlog = 100 +nstcalcenergy = 1 +nstenergy = 1000 +nstxout_compressed = 1000 + +; Neighborsearching and short-range nonbonded interactions +nstlist = 10 +ns_type = grid +pbc = xyz +rlist = 1.0 + +; Electrostatics +cutoff_scheme = verlet +coulombtype = PME +coulomb_modifier = Potential-shift-Verlet +rcoulomb_switch = 0.89 +rcoulomb = 0.9 + +; van der Waals +vdw_type = Cut-off +vdw_modifier = Potential-switch +rvdw_switch = 0.85 +rvdw = 0.9 + +; Apply long range dispersion corrections for Energy and Pressure +DispCorr = AllEnerPres + +; Spacing for the PME/PPPM FFT grid +fourierspacing = 0.1 + +; EWALD/PME/PPPM parameters +fourier_nx = 0 +fourier_ny = 0 +fourier_nz = 0 +pme_order = 4 +ewald_rtol = 1e-05 +ewald_geometry = 3d +epsilon_surface = 0 + +; Temperature coupling +tcoupl = v-rescale +nsttcouple = 1 +tc_grps = System +tau_t = 0.5 +ref_t = 298 + +; Pressure coupling is on for NPT +pcoupl = no + +; refcoord_scaling should do nothing since there are no position restraints. + +gen_vel = yes +gen_temp = 298 +gen_seed = -1 + +; options for bonds +constraints = h-bonds + +; Type of constraint algorithm +constraint_algorithm = lincs +continuation = no + +; Highest order in the expansion of the constraint coupling matrix +lincs_order = 12 +lincs_iter = 2 + +; Free energy calculation +free_energy = expanded +calc_lambda_neighbors = -1 +sc_alpha = 0.5 +couple_moltype = LIG +couple_lambda0 = vdw-q +couple_lambda1 = none +couple_intramol = no +init_lambda_state = 0 + +nstdhdl = 10 +dhdl_print_energy = total + +; Seed for Monte Carlo in lambda space +lmc_seed = -1 +lmc_gibbsdelta = -1 +lmc_forced_nstart = 0 +symmetrized_transition_matrix = yes +nst_transition_matrix = 100000 + +; expanded ensemble variables +nstexpanded = 10 +lmc_stats = no +lmc_move = metropolized-gibbs + +; lambda-states = 1 2 3 4 5 6 7 8 9 +coul_lambdas = 0.0 0.25 0.5 0.75 1.0 1.0 1.0 1.0 1.0 +vdw_lambdas = 0.0 0.0 0.0 0.0 0.0 0.25 0.5 0.75 1.0 +init_lambda_weights = 0.00000 1.61343 2.71400 3.40859 3.60446 4.78543 5.37983 3.29843 0.36336 diff --git a/docs/examples/tutorial_1/params.yaml b/docs/examples/tutorial_1/params.yaml new file mode 100644 index 00000000..007ea9fa --- /dev/null +++ b/docs/examples/tutorial_1/params.yaml @@ -0,0 +1,13 @@ +# Simulation parameters +gmx_executable: 'gmx' +gro: sys.gro +top: sys.top +mdp: expanded.mdp +n_sim: 4 +n_iter: 5 +s: 1 +nst_sim: 500 +runtime_args: {'-nt': '1'} + +# Data analysis parameters +free_energy: True diff --git a/docs/examples/tutorial_1/standard_REXEE.ipynb b/docs/examples/tutorial_1/standard_REXEE.ipynb new file mode 100644 index 00000000..d0395e39 --- /dev/null +++ b/docs/examples/tutorial_1/standard_REXEE.ipynb @@ -0,0 +1,1345 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "87324540", + "metadata": {}, + "source": [ + "# Tutorial 1: Standard REXEE simulations" + ] + }, + { + "cell_type": "markdown", + "id": "48c42ec7", + "metadata": {}, + "source": [ + "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/wehs7661/ensemble_md/c0f6d48ce3fe746e349e4a4a9610f935cca8b0b5?urlpath=lab%2Ftree%2Fdocs%2Fexamples%2Ftutorial_1%2Frun_REXEE.ipynb)\n", + "\n", + "In this tutorial, we will demonstrate how one can use the different command-line interfaces (CLIs) implemented in the Python package `ensemble_md` to prepare, perform, and analyze a REXEE simulation to estimate the solvation free energy of a toy molecule composed of 4 interaction sites. For a more comprehensive understanding, we strongly recommend reading our [documentation](https://ensemble-md.readthedocs.io/en/latest/simulations.html) on launching REXEE simulations before starting this tutorial. For the theory behind the REXEE method, we recommend reading our [JCTC paper](https://pubs.acs.org/doi/10.1021/acs.jctc.1c00494) or the [theory section](https://ensemble-md.readthedocs.io/en/add_tutorials/theory.html) of our documentation.\n", + "\n", + "To run this tutorial, you will need different setups depending on your computing environment:\n", + "\n", + "- You can simply click the \"launch binder\" badge above to run this tutorial on Binder without installing anything.\n", + "- If you prefer to run this tutorial locally, either on you laptop, workstation, or an HPC cluster, you will need to install the following software:\n", + " - [GROMACS](http://manual.gromacs.org/documentation/current/install-guide/index.html) (version 2022.5 or later)\n", + " - [ensemble_md](https://ensemble-md.readthedocs.io/en/latest/getting_started.html#installation) (version 1.0.0 or later)\n", + "\n", + "Additionally, you will need to have MPI launcher commands (e.g., `mpirun` or `mpiexec`) installed on your system to run the GROMACS simulations in parallel. You will need at least four CPU cores to run the simulation in this tutorial. (You can execute `nproc` in the terminal to check the number of CPU cores on your system.)\n", + "\n", + "Notably, this tutorial assumes that you understand the basics of the expanded ensemble (EE) method and relevant simulation parameters in GROMACS. If not, we recommend reading the [this tutorial](https://weitsehsu.com/course/advanced_sampling/exe/) or the following GROMACS documentation pages:\n", + "\n", + "- [A very brief description about the expanded ensemble method](https://manual.gromacs.org/current/reference-manual/algorithms/expanded-ensemble.html)\n", + "- Relevant MDP parameters in GROMACS, especially the section of [free energy calcualtion](https://manual.gromacs.org/current/user-guide/mdp-options.html#free-energy-calculations) and the section of [expanded ensemble calculations](https://manual.gromacs.org/current/user-guide/mdp-options.html#expanded-ensemble-calculations)." + ] + }, + { + "cell_type": "markdown", + "id": "68b7fa66", + "metadata": {}, + "source": [ + "## 0. Background knowledge: A two-stage protocol" + ] + }, + { + "cell_type": "markdown", + "id": "318b8d5c", + "metadata": {}, + "source": [ + "In methods such as expanded ensembles or REXEE, where each alchemical state has an associated weight (see [here](https://ensemble-md.readthedocs.io/en/latest/simulations.html)), it is common to use a two-stage protocol for alchemical free energy calculations. Specifically, this protocol is composed of the following two stages:\n", + "\n", + "- A **weight-updating stage**: In a weight-updating stage, the weights of different states are constantly updated until convergence during the simulation, usually by methods such as the Wang-Landau algorithm or its $1/t$ variant. The purpose of this stage is to obtain weights that could flatten out the free energy surface along the alchemical direction such that all states have a roughly equal chance to be visited in the next stage, i.e., the fixed-weight stage. \n", + "- A **fixed-weight stage**: In a fixed-weight stage, the weights of different alchemical states are fixed at predefined values usually converged in a weight-updating stage. Unlike weight-updating simulations, a fixed-weight simulation is in equilibrium, from which the potential energy data can be analyzed by free energy estimators that require equilibrium data for free energy calculations, such as TI, BAR, MBAR, etc. \n", + "\n", + "With this protocol, one can use a weight-updating EE/REXEE simulation followed by a fixed-weight EE/REXEE simulation to perform free energy calculations. In our [paper](https://pubs.acs.org/doi/10.1021/acs.jctc.4c00484), we have compared EE and REXEE methods in both weight-updating and fixed-weight phases comprehensively. \n", + "\n", + "Notably, this tutorial will only focus on fixed-weight REXEE simulations, with the weights provided by a weight-updating EE simulation not shown here. If you are interested in using a weight-updating REXEE simulation to obtain alchemical weights to seed a fixed-weight simulation, you can simply adopt the workflow for running fixed-weight REXEE simulations introduced in this tutorial, with input MDP files replaced by the ones for weight-updating EE simulations." + ] + }, + { + "cell_type": "markdown", + "id": "451681c9", + "metadata": {}, + "source": [ + "## 1. Preparing simulation inputs for a REXEE simulation" + ] + }, + { + "cell_type": "markdown", + "id": "7f1068a4", + "metadata": {}, + "source": [ + "As mentioned in our documentation, running a REXEE simulation at least requires the following four input files:\n", + "\n", + "- One YAML file that specifies REXEE parameters.\n", + "- One GROMACS GRO file of the system of interst.\n", + "- One GROMACS TOP file of the system of interst.\n", + "- One GROMACS MDP template for customizing MDP files for different replicas during the simulation.\n", + "\n", + "In the folder where this tutorial resides (`docs/examples/tutoria_1` in the repository), you should find all these necessary input files, including `params.yaml`, `sys.gro`, `sys.top`, and `expanded.mdp`. Note that the file `example_outputs.zip` is not relevant until the third section ([Analyzing a REXEE simulation](#3.-Analyzing-a-REXEE-simulation))." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "190945e2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "example_outputs.zip params.yaml\t sys.gro\n", + "expanded.mdp\t standard_REXEE.ipynb sys.top\n" + ] + } + ], + "source": [ + "!ls" + ] + }, + { + "cell_type": "markdown", + "id": "389df378", + "metadata": {}, + "source": [ + "While you could use multiple GRO and TOP files, one for each replica in a REXEE simulation, in this tutorial, we will use the same GRO and TOP files for all replicas to keep the demonstration straightforward. As for the MDP template `expanded.mdp`, it can be inspected that it adopts common/typical settings for a fixed-weight EE simulation, since here our goal is to set up a fixed-weight REXEE simulation. In the MDP template, it can also be seen that 9 alchemical intermediate states are defined to decouple van der Waals and coulombic interactions. \n", + "\n", + "During the REXEE simulation, customized MDP files will be generated for different replicas such that the MDP file for each replica only considers the set of states that the replica is constrained to. Notably, the weights specified via the MDP parameter `init_lambda_weights` were obtained from a weight-updating EE simulation. If you would like to run a weight-updating REXEE simulation, simply use an MDP file for running a weight-updating EE simulation, i.e., and MDP file without `init_lambda_weights` being specified, and with options such as `wl_scale`, `wl_ratio`, `init_wl_delta`, `lmc_stats`, `lmc_weights_equil`, `weight_equil_wl_delta` specified.\n", + "\n", + "Now, with these three GROMACS inputs prepared, let's briefly review the input YAML file that specifies REXEE parameters. (For detailed information about all possible parameters that can be specified in the YAML file, please refer to our [documentation](https://ensemble-md.readthedocs.io/en/latest/simulations.html#input-yaml-parameters).)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6732a2d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Simulation parameters\n", + "gmx_executable: 'gmx' \n", + "gro: sys.gro\n", + "top: sys.top\n", + "mdp: expanded.mdp \n", + "n_sim: 4\n", + "n_iter: 5 \n", + "s: 1 \n", + "nst_sim: 500\n", + "runtime_args: {'-nt': '1'}\n", + "\n", + "# Data analysis parameters\n", + "free_energy: True\n" + ] + } + ], + "source": [ + "!cat params.yaml" + ] + }, + { + "cell_type": "markdown", + "id": "c2bff331", + "metadata": {}, + "source": [ + "As a minimum working example, this YAML file only specifies the compulsory simulation parameters and adopts default values for all optional parameters, execpt that `runtime_args` is specified to run each EE replica using one thread (`-nt 1`) for a reasonable simulation performance. Notably, Binder should have 72 cores by default (as can be checked by `nproc`), but using `-nt 1` is much faster than using other values for this toy system. If you are not using Binder but your own local machine or an HPC cluster, you may want to try out other `-nt` values to ensure reasonable performance.\n", + "\n", + "In our case, we will run our a REXEE simulation composed of 4 replicas, with each replica performed with one thread (`-nt 1`). With the total number of states as 9 and a state shift of 1, this means that replicas 0, 1, 2, 3 are constrained to sampling states 0-5, 1-6, 2-7, and 3-8, respectively. The simulation will attempt to swap coordinates between replicas every 500 integration steps, and perform 5 iterations in total. (Note that generally we need a much larger number of iterations. Here we use a very small value just for demosntration purposes.)\n", + "\n", + "If you are interested in trying out other numbers of replicas or state shifts given 9 alchemical intermediate states, you can use the CLI `explore_REXEE` to enumerate all possible REXEE configurations." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9cd09a1a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exploration of the REXEE parameter space\n", + "=======================================\n", + "[ REXEE parameters of interest ]\n", + "- N: The total number of states\n", + "- r: The number of replicas\n", + "- n: The number of states for each replica\n", + "- s: The state shift between adjacent replicas\n", + "\n", + "[ Solutions ]\n", + "- Solution 1: (N, r, n, s) = (9, 2, 5, 4)\n", + " - Replica 0: [0, 1, 2, 3, 4]\n", + " - Replica 1: [4, 5, 6, 7, 8]\n", + "\n", + "- Solution 2: (N, r, n, s) = (9, 2, 6, 3)\n", + " - Replica 0: [0, 1, 2, 3, 4, 5]\n", + " - Replica 1: [3, 4, 5, 6, 7, 8]\n", + "\n", + "- Solution 3: (N, r, n, s) = (9, 2, 7, 2)\n", + " - Replica 0: [0, 1, 2, 3, 4, 5, 6]\n", + " - Replica 1: [2, 3, 4, 5, 6, 7, 8]\n", + "\n", + "- Solution 4: (N, r, n, s) = (9, 2, 8, 1)\n", + " - Replica 0: [0, 1, 2, 3, 4, 5, 6, 7]\n", + " - Replica 1: [1, 2, 3, 4, 5, 6, 7, 8]\n", + "\n", + "- Solution 5: (N, r, n, s) = (9, 3, 5, 2)\n", + " - Replica 0: [0, 1, 2, 3, 4]\n", + " - Replica 1: [2, 3, 4, 5, 6]\n", + " - Replica 2: [4, 5, 6, 7, 8]\n", + "\n", + "- Solution 6: (N, r, n, s) = (9, 3, 7, 1)\n", + " - Replica 0: [0, 1, 2, 3, 4, 5, 6]\n", + " - Replica 1: [1, 2, 3, 4, 5, 6, 7]\n", + " - Replica 2: [2, 3, 4, 5, 6, 7, 8]\n", + "\n", + "- Solution 7: (N, r, n, s) = (9, 4, 3, 2)\n", + " - Replica 0: [0, 1, 2]\n", + " - Replica 1: [2, 3, 4]\n", + " - Replica 2: [4, 5, 6]\n", + " - Replica 3: [6, 7, 8]\n", + "\n", + "- Solution 8: (N, r, n, s) = (9, 4, 6, 1)\n", + " - Replica 0: [0, 1, 2, 3, 4, 5]\n", + " - Replica 1: [1, 2, 3, 4, 5, 6]\n", + " - Replica 2: [2, 3, 4, 5, 6, 7]\n", + " - Replica 3: [3, 4, 5, 6, 7, 8]\n", + "\n", + "- Solution 9: (N, r, n, s) = (9, 5, 5, 1)\n", + " - Replica 0: [0, 1, 2, 3, 4]\n", + " - Replica 1: [1, 2, 3, 4, 5]\n", + " - Replica 2: [2, 3, 4, 5, 6]\n", + " - Replica 3: [3, 4, 5, 6, 7]\n", + " - Replica 4: [4, 5, 6, 7, 8]\n", + "\n", + "- Solution 10: (N, r, n, s) = (9, 6, 4, 1)\n", + " - Replica 0: [0, 1, 2, 3]\n", + " - Replica 1: [1, 2, 3, 4]\n", + " - Replica 2: [2, 3, 4, 5]\n", + " - Replica 3: [3, 4, 5, 6]\n", + " - Replica 4: [4, 5, 6, 7]\n", + " - Replica 5: [5, 6, 7, 8]\n", + "\n", + "- Solution 11: (N, r, n, s) = (9, 7, 3, 1)\n", + " - Replica 0: [0, 1, 2]\n", + " - Replica 1: [1, 2, 3]\n", + " - Replica 2: [2, 3, 4]\n", + " - Replica 3: [3, 4, 5]\n", + " - Replica 4: [4, 5, 6]\n", + " - Replica 5: [5, 6, 7]\n", + " - Replica 6: [6, 7, 8]\n", + "\n", + "- Solution 12: (N, r, n, s) = (9, 8, 2, 1)\n", + " - Replica 0: [0, 1]\n", + " - Replica 1: [1, 2]\n", + " - Replica 2: [2, 3]\n", + " - Replica 3: [3, 4]\n", + " - Replica 4: [4, 5]\n", + " - Replica 5: [5, 6]\n", + " - Replica 6: [6, 7]\n", + " - Replica 7: [7, 8]\n", + "\n" + ] + } + ], + "source": [ + "!explore_REXEE -N 9" + ] + }, + { + "cell_type": "markdown", + "id": "f2069dec", + "metadata": {}, + "source": [ + "If you want to list all possible configurations that use 4 replicas for sampling 9 alchemical states, you can further specify the number of replicas via the flag `-r`:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b6a2859c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exploration of the REXEE parameter space\n", + "=======================================\n", + "[ REXEE parameters of interest ]\n", + "- N: The total number of states\n", + "- r: The number of replicas\n", + "- n: The number of states for each replica\n", + "- s: The state shift between adjacent replicas\n", + "\n", + "[ Solutions ]\n", + "- Solution 1: (N, r, n, s) = (9, 4, 3, 2)\n", + " - Replica 0: [0, 1, 2]\n", + " - Replica 1: [2, 3, 4]\n", + " - Replica 2: [4, 5, 6]\n", + " - Replica 3: [6, 7, 8]\n", + "\n", + "- Solution 2: (N, r, n, s) = (9, 4, 6, 1)\n", + " - Replica 0: [0, 1, 2, 3, 4, 5]\n", + " - Replica 1: [1, 2, 3, 4, 5, 6]\n", + " - Replica 2: [2, 3, 4, 5, 6, 7]\n", + " - Replica 3: [3, 4, 5, 6, 7, 8]\n", + "\n" + ] + } + ], + "source": [ + "!explore_REXEE -N 9 -r 4" + ] + }, + { + "cell_type": "markdown", + "id": "3731c625", + "metadata": {}, + "source": [ + "For more information about the flags available for `explore_REXEE`, please run `explore_REXEE -h`/`explore_REXEE --help` and read the help message. For rules of thumb about specifying the configurational parameters (including the number of states, number of replicas, state shift and swapping frequency), please refer to our [documentation](https://ensemble-md.readthedocs.io/en/latest/simulations.html#some-rules-of-thumb) or [paper](https://pubs.acs.org/doi/epdf/10.1021/acs.jctc.4c00484)." + ] + }, + { + "cell_type": "markdown", + "id": "6cedf8d9", + "metadata": {}, + "source": [ + "## 2. Performing a REXEE simulation\n", + "With all the input files in place, we can now run a fixed-weight REXEE simulation using the CLI `run_REXEE` with the command shown in the cell below. \n", + "\n", + "If you are using a laptop with 4 CPU cores, this may take around 15 minutes to complete. If you run the tutorial on Binder, this should take around 3 minutes. Note that here we use default values for all flags of the CLI `run_REXEE`. For more information about the flags, please run `run_REXEE -h`/`run_REXEE --help` and read the help message." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "443dd9af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current time: 01/08/2024 08:49:27\n", + "Command line: /srv/conda/envs/notebook/bin/run_REXEE\n", + "\n", + "Important parameters of REXEE\n", + "=============================\n", + "Python version: 3.10.14 | packaged by conda-forge | (main, Mar 20 2024, 12:45:18) [GCC 12.3.0]\n", + "GROMACS executable: /srv/conda/envs/notebook/bin.AVX2_256/gmx\n", + "GROMACS version: 2023.3-conda_forge\n", + "ensemble_md version: 0.9.0+177.g9f48c4c.dirty\n", + "Simulation inputs: sys.gro, sys.top, expanded.mdp\n", + "Verbose log file: True\n", + "Proposal scheme: exhaustive\n", + "Whether to perform weight combination: False\n", + "Type of means for weight combination: simple\n", + "Whether to perform histogram correction: False\n", + "Histogram cutoff for weight correction: -1\n", + "Number of replicas: 4\n", + "Number of iterations: 5\n", + "Length of each replica: 1.0 ps\n", + "Frequency for checkpointing: 100 iterations\n", + "Total number of states: 9\n", + "Additionally defined swappable states: None\n", + "Additional grompp arguments: None\n", + "Additional runtime arguments: {'-nt': '1'}\n", + "External modules for coordinate manipulation: None\n", + "MDP parameters differing across replicas: None\n", + "Alchemical ranges of each replica in REXEE:\n", + " - Replica 0: States [0, 1, 2, 3, 4, 5]\n", + " - Replica 1: States [1, 2, 3, 4, 5, 6]\n", + " - Replica 2: States [2, 3, 4, 5, 6, 7]\n", + " - Replica 3: States [3, 4, 5, 6, 7, 8]\n", + "\n", + "Iteration 0: 0.0 - 1.0 ps\n", + "===========================\n", + "Generating TPR files ...\n", + "Running EXE simulations ...\n", + "\n", + "Below are the final states being visited:\n", + " Simulation 0: Global state 3\n", + " Simulation 1: Global state 4\n", + " Simulation 2: Global state 3\n", + " Simulation 3: Global state 6\n", + "\n", + "Parsing ./sim_0/iteration_0/md.log ...\n", + "Parsing ./sim_1/iteration_0/md.log ...\n", + "Parsing ./sim_2/iteration_0/md.log ...\n", + "Parsing ./sim_3/iteration_0/md.log ...\n", + "\n", + "Swappable pairs: [(0, 1), (0, 2), (1, 2), (1, 3), (2, 3)]\n", + "\n", + "Proposed swap: (1, 3)\n", + " Proposing a move from (x^i_m, x^j_n) to (x^i_n, x^j_m) ...\n", + " U^i_n - U^i_m = 6.26 kT, U^j_m - U^j_n = 525.23 kT, Total dU: 531.49 kT\n", + " Acceptance rate: 0.000 / Random number drawn: 0.715\n", + " Swap rejected! \n", + " Current list of configurations: [0, 1, 2, 3]\n", + "\n", + "Remaining swappable pairs: [(0, 2)]\n", + "\n", + "Proposed swap: (0, 2)\n", + " Proposing a move from (x^i_m, x^j_n) to (x^i_n, x^j_m) ...\n", + " U^i_n - U^i_m = 0.00 kT, U^j_m - U^j_n = 0.00 kT, Total dU: 0.00 kT\n", + " Acceptance rate: 1.000 / Random number drawn: 0.630\n", + " Swap accepted! \n", + " Current list of configurations: [2, 1, 0, 3]\n", + "\n", + "The finally adopted swap pattern: [2, 1, 0, 3]\n", + "The list of configurations sampled in each replica in the next iteration: [2, 1, 0, 3]\n", + "Note: No weight correction will be performed.\n", + "Note: No weight combination will be performed.\n", + "\n", + " The alchemical weights of all states: \n", + " [0.0, 1.613, 2.714, 3.409, 3.604, 4.785, 5.38, 3.298, 0.363]\n", + "\n", + "Iteration 1: 1.0 - 2.0 ps\n", + "===========================\n", + "Generating TPR files ...\n", + "Running EXE simulations ...\n", + "\n", + "Below are the final states being visited:\n", + " Simulation 0: Global state 3\n", + " Simulation 1: Global state 2\n", + " Simulation 2: Global state 4\n", + " Simulation 3: Global state 4\n", + "\n", + "Parsing ./sim_0/iteration_1/md.log ...\n", + "Parsing ./sim_1/iteration_1/md.log ...\n", + "Parsing ./sim_2/iteration_1/md.log ...\n", + "Parsing ./sim_3/iteration_1/md.log ...\n", + "\n", + "Swappable pairs: [(0, 1), (0, 2), (0, 3), (1, 2), (2, 3)]\n", + "\n", + "Proposed swap: (1, 2)\n", + " Proposing a move from (x^i_m, x^j_n) to (x^i_n, x^j_m) ...\n", + " U^i_n - U^i_m = -0.14 kT, U^j_m - U^j_n = 0.57 kT, Total dU: 0.43 kT\n", + " Acceptance rate: 0.650 / Random number drawn: 0.407\n", + " Swap accepted! \n", + " Current list of configurations: [2, 0, 1, 3]\n", + "\n", + "Remaining swappable pairs: [(0, 3)]\n", + "\n", + "Proposed swap: (0, 3)\n", + " Proposing a move from (x^i_m, x^j_n) to (x^i_n, x^j_m) ...\n", + " U^i_n - U^i_m = 0.71 kT, U^j_m - U^j_n = -0.78 kT, Total dU: -0.07 kT\n", + " Acceptance rate: 1.000 / Random number drawn: 0.590\n", + " Swap accepted! \n", + " Current list of configurations: [3, 0, 1, 2]\n", + "\n", + "The finally adopted swap pattern: [3, 2, 1, 0]\n", + "The list of configurations sampled in each replica in the next iteration: [3, 0, 1, 2]\n", + "Note: No weight correction will be performed.\n", + "Note: No weight combination will be performed.\n", + "\n", + " The alchemical weights of all states: \n", + " [0.0, 1.613, 2.714, 3.409, 3.604, 4.785, 5.38, 3.298, 0.363]\n", + "\n", + "Iteration 2: 2.0 - 3.0 ps\n", + "===========================\n", + "Generating TPR files ...\n", + "Running EXE simulations ...\n", + "\n", + "Below are the final states being visited:\n", + " Simulation 0: Global state 5\n", + " Simulation 1: Global state 3\n", + " Simulation 2: Global state 4\n", + " Simulation 3: Global state 6\n", + "\n", + "Parsing ./sim_0/iteration_2/md.log ...\n", + "Parsing ./sim_1/iteration_2/md.log ...\n", + "Parsing ./sim_2/iteration_2/md.log ...\n", + "Parsing ./sim_3/iteration_2/md.log ...\n", + "\n", + "Swappable pairs: [(0, 1), (0, 2), (1, 2), (1, 3), (2, 3)]\n", + "\n", + "Proposed swap: (0, 2)\n", + " Proposing a move from (x^i_m, x^j_n) to (x^i_n, x^j_m) ...\n", + " U^i_n - U^i_m = -2.46 kT, U^j_m - U^j_n = 2.07 kT, Total dU: -0.39 kT\n", + " Acceptance rate: 1.000 / Random number drawn: 0.351\n", + " Swap accepted! \n", + " Current list of configurations: [1, 0, 3, 2]\n", + "\n", + "Remaining swappable pairs: [(1, 3)]\n", + "\n", + "Proposed swap: (1, 3)\n", + " Proposing a move from (x^i_m, x^j_n) to (x^i_n, x^j_m) ...\n", + " U^i_n - U^i_m = 7.14 kT, U^j_m - U^j_n = 52.23 kT, Total dU: 59.37 kT\n", + " Acceptance rate: 0.000 / Random number drawn: 0.071\n", + " Swap rejected! \n", + " Current list of configurations: [1, 0, 3, 2]\n", + "\n", + "The finally adopted swap pattern: [2, 1, 0, 3]\n", + "The list of configurations sampled in each replica in the next iteration: [1, 0, 3, 2]\n", + "Note: No weight correction will be performed.\n", + "Note: No weight combination will be performed.\n", + "\n", + " The alchemical weights of all states: \n", + " [0.0, 1.613, 2.714, 3.409, 3.604, 4.785, 5.38, 3.298, 0.363]\n", + "\n", + "Iteration 3: 3.0 - 4.0 ps\n", + "===========================\n", + "Generating TPR files ...\n", + "Running EXE simulations ...\n", + "\n", + "Below are the final states being visited:\n", + " Simulation 0: Global state 0\n", + " Simulation 1: Global state 3\n", + " Simulation 2: Global state 3\n", + " Simulation 3: Global state 6\n", + "\n", + "Parsing ./sim_0/iteration_3/md.log ...\n", + "Parsing ./sim_1/iteration_3/md.log ...\n", + "Parsing ./sim_2/iteration_3/md.log ...\n", + "Parsing ./sim_3/iteration_3/md.log ...\n", + "\n", + "Swappable pairs: [(1, 2), (1, 3), (2, 3)]\n", + "\n", + "Proposed swap: (1, 3)\n", + " Proposing a move from (x^i_m, x^j_n) to (x^i_n, x^j_m) ...\n", + " U^i_n - U^i_m = 5.27 kT, U^j_m - U^j_n = 16.21 kT, Total dU: 21.48 kT\n", + " Acceptance rate: 0.000 / Random number drawn: 0.567\n", + " Swap rejected! \n", + " Current list of configurations: [1, 0, 3, 2]\n", + "\n", + "Remaining swappable pairs: []\n", + "\n", + "The finally adopted swap pattern: [0, 1, 2, 3]\n", + "The list of configurations sampled in each replica in the next iteration: [1, 0, 3, 2]\n", + "Note: No weight correction will be performed.\n", + "Note: No weight combination will be performed.\n", + "\n", + " The alchemical weights of all states: \n", + " [0.0, 1.613, 2.714, 3.409, 3.604, 4.785, 5.38, 3.298, 0.363]\n", + "\n", + "Iteration 4: 4.0 - 5.0 ps\n", + "===========================\n", + "Generating TPR files ...\n", + "Running EXE simulations ...\n", + "\n", + "Summary of the simulation ensemble\n", + "==================================\n", + "Simulation status:\n", + "- Rep 0: The weights were fixed throughout the simulation.\n", + "- Rep 1: The weights were fixed throughout the simulation.\n", + "- Rep 2: The weights were fixed throughout the simulation.\n", + "- Rep 3: The weights were fixed throughout the simulation.\n", + "\n", + "0 out of 5, or 0.0% iterations had an empty list of swappable pairs.\n", + "3 out of 7, or 42.9% of attempted exchanges were rejected.\n", + "\n", + "Time elapsed: 2 minute(s) 47 second(s)\n" + ] + } + ], + "source": [ + "!mpirun -np 4 run_REXEE" + ] + }, + { + "cell_type": "markdown", + "id": "4e84140f", + "metadata": {}, + "source": [ + "As shown above, the CLI `run_REXEE` printed information about the simulation on the fly, including the number of iterations completed, information about the current iteration, whether a swap was accepted or not, etc. At the end, a summary of the simulation was printed. All such information is also saved in `run_REXEE_log.txt`.\n", + "\n", + "Now, let's list the files in the current directory and print the directory structure to see what outputs have been generated by the simulation other than `run_REXEE_log.txt`:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1933d59a-627d-41dd-85f9-ad0e4734a54b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "example_outputs.zip rep_trajs.npy\tsim_1 standard_REXEE.ipynb\n", + "expanded.mdp\t run_REXEE_log.txt\tsim_2 sys.gro\n", + "params.yaml\t sim_0\t\tsim_3 sys.top\n" + ] + } + ], + "source": [ + "!ls" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "dc019089-7351-4db2-92ba-b8b19fd59dd6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[01;34msim_0\u001b[0m\n", + "├── \u001b[01;34miteration_0\u001b[0m\n", + "│   ├── \u001b[00mconfout.gro\u001b[0m\n", + "│   ├── \u001b[00mdhdl.xvg\u001b[0m\n", + "│   ├── \u001b[00mener.edr\u001b[0m\n", + "│   ├── \u001b[00mexpanded.mdp\u001b[0m\n", + "│   ├── \u001b[00mmd.log\u001b[0m\n", + "│   ├── \u001b[00mmdout.mdp\u001b[0m\n", + "│   ├── \u001b[00msys_EE.tpr\u001b[0m\n", + "│   └── \u001b[00mtraj_comp.xtc\u001b[0m\n", + "├── \u001b[01;34miteration_1\u001b[0m\n", + "│   ├── \u001b[00mconfout.gro\u001b[0m\n", + "│   ├── \u001b[00mdhdl.xvg\u001b[0m\n", + "│   ├── \u001b[00mener.edr\u001b[0m\n", + "│   ├── \u001b[00mexpanded.mdp\u001b[0m\n", + "│   ├── \u001b[00mmd.log\u001b[0m\n", + "│   ├── \u001b[00mmdout.mdp\u001b[0m\n", + "│   ├── \u001b[00msys_EE.tpr\u001b[0m\n", + "│   └── \u001b[00mtraj_comp.xtc\u001b[0m\n", + "├── \u001b[01;34miteration_2\u001b[0m\n", + "│   ├── \u001b[00mconfout.gro\u001b[0m\n", + "│   ├── \u001b[00mdhdl.xvg\u001b[0m\n", + "│   ├── \u001b[00mener.edr\u001b[0m\n", + "│   ├── \u001b[00mexpanded.mdp\u001b[0m\n", + "│   ├── \u001b[00mmd.log\u001b[0m\n", + "│   ├── \u001b[00mmdout.mdp\u001b[0m\n", + "│   ├── \u001b[00msys_EE.tpr\u001b[0m\n", + "│   └── \u001b[00mtraj_comp.xtc\u001b[0m\n", + "├── \u001b[01;34miteration_3\u001b[0m\n", + "│   ├── \u001b[00mconfout.gro\u001b[0m\n", + "│   ├── \u001b[00mdhdl.xvg\u001b[0m\n", + "│   ├── \u001b[00mener.edr\u001b[0m\n", + "│   ├── \u001b[00mexpanded.mdp\u001b[0m\n", + "│   ├── \u001b[00mmd.log\u001b[0m\n", + "│   ├── \u001b[00mmdout.mdp\u001b[0m\n", + "│   ├── \u001b[00msys_EE.tpr\u001b[0m\n", + "│   └── \u001b[00mtraj_comp.xtc\u001b[0m\n", + "└── \u001b[01;34miteration_4\u001b[0m\n", + " ├── \u001b[00mconfout.gro\u001b[0m\n", + " ├── \u001b[00mdhdl.xvg\u001b[0m\n", + " ├── \u001b[00mener.edr\u001b[0m\n", + " ├── \u001b[00mexpanded.mdp\u001b[0m\n", + " ├── \u001b[00mmd.log\u001b[0m\n", + " ├── \u001b[00mmdout.mdp\u001b[0m\n", + " ├── \u001b[00msys_EE.tpr\u001b[0m\n", + " └── \u001b[00mtraj_comp.xtc\u001b[0m\n", + "\n", + "6 directories, 40 files\n" + ] + } + ], + "source": [ + "!tree sim_0" + ] + }, + { + "cell_type": "markdown", + "id": "7ea7981e-69a7-4e77-bf63-0e2362222f50", + "metadata": {}, + "source": [ + "As shown above, the following files/directories have been generated:\n", + "\n", + "- Folders `sim_*`, each of which corresponds to a replica in the REXEE simulation and contains subfolders `iteration_*`, where simulation outputs of each iteration reside.\n", + "- `rep_trajs.npy`, which contains replica-space trajectories for all replicas and is used as an input checkpoint file when simulation extension is desired. To extend a REXEE simulation, one only needs to input `rep_trajs.npy` via the `-c` flag of `run_REXEE`.\n", + "- `run_REXEE_log.txt`, the log file mentioned above.\n", + "\n", + "Notably, if a weight-updating REXEE simulation is performed, `g_vecs.npy` will be additionally generated, which contains the time series of alchemical weights for all replicas." + ] + }, + { + "cell_type": "markdown", + "id": "3ebdb692", + "metadata": {}, + "source": [ + "## 3. Analyzing a REXEE simulation" + ] + }, + { + "cell_type": "markdown", + "id": "d593175c", + "metadata": {}, + "source": [ + "After the simulation is completed, we are ready to analyze the simulation to assess its sampling behaviors and estimate the solvation free energy of the molecule. Since the simulation we performed in the previous section is very short and was just for demonstration purposes, instead of using the simulation outputs above, we will unzip `example_outputs.zip` and analyze the outputs from an example simulation using the CLI `analyze_REXEE`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c344c8b6-e644-45db-8328-e852cda6d2ec", + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [], + "source": [ + "!unzip example_outputs.zip > /dev/null 2>&1 # Here we discard the STDOUT as it's verbose and irrelvant" + ] + }, + { + "cell_type": "markdown", + "id": "e68ee68f-c7bf-4e9d-b40a-2cfefe873ed7", + "metadata": {}, + "source": [ + "In the folder `example_outputs`, you can find `expanded.mdp` and `params.yaml` used to perform the example simulation, as well as the outputs generated by the simulation mentioned above, including `rep_trajs.npy`, `run_REXEE_log.txt`, and folders `sim_*`. To save space, we have only preserved DHDL and LOG files for each iteration of each replica in the subfolders `sim_*`, as they are the only files needed for data analysis using the CLI `analyze_REXEE`. All other GROMACS outputs have been removed. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5df5d06d-e38a-448a-84b1-3d6482878785", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "expanded.mdp rep_trajs.npy\t sim_0\tsim_2\n", + "params.yaml run_REXEE_log.txt sim_1\tsim_3\n" + ] + } + ], + "source": [ + "!cd example_outputs && ls" + ] + }, + { + "cell_type": "markdown", + "id": "1e88fd87-b837-49e1-ac66-0c751071a6e7", + "metadata": {}, + "source": [ + "Notably, the example simulation is basically the same as the one we just performed in the previous section, as it adopted the same MDP template and REXEE configuration we used earlier. The only differences between the two simulations lie in the following settings in the input YAML file:\n", + "- `n_iter` and `nst_sim`: The example simulation performed 125 iterations (compared to the previous 5), with each iteration performed for 5000 (instead of 500) integration steps. With 4 replicas, this results in a total simulation length of $5000$ $\\mathrm{steps/iteration}\\times$ $0.002$ $\\mathrm{ps/step} \\times$ $125$ $\\mathrm{iterations/replica} \\times$ $4$ $\\mathrm{replicas} = 5000$ $\\mathrm{ps}$, or $5$ $\\mathrm{ns}$. Although this simulation length is still quite short for obtaining a reliable free energy estimate, it suffices to demonstrate the utility of the CLI `analyze_REXEE`.\n", + "- `runtime_args`: The example simulation adopted a different set of runtime arguments, including `-nt 16` and `-ntmpi 1` to ensure reasonable performance, given that the simulation was performed on a 64-core node on Bridges-2. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c263248a-1c53-4594-9818-70278b24d86c", + "metadata": {}, + "outputs": [], + "source": [ + "!diff expanded.mdp example_outputs/expanded.mdp" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "27eba6d7-40e7-4a13-a6d1-2925af2fe257", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7c7\n", + "< n_iter: 5 \n", + "---\n", + "> n_iter: 125 \n", + "9,10c9,10\n", + "< nst_sim: 500\n", + "< runtime_args: {'-nt': '1'}\n", + "---\n", + "> nst_sim: 5000\n", + "> runtime_args: {'-nt': '16', '-ntmpi': '1'}\n" + ] + } + ], + "source": [ + "!diff params.yaml example_outputs/params.yaml" + ] + }, + { + "cell_type": "markdown", + "id": "ca661bbf-9316-4fd2-9b39-0b79465f84c2", + "metadata": {}, + "source": [ + "Importantly, the CLI `analyze_REXEE` also requires an input YAML file, which can be the same as the one used for `run_REXEE` (as in our case) since these two CLIs only consider their own relevant parameters. Regardless of the data analysis parameters specified in the input YAML file, the CLI `analyze_REXEE` will at least perform the following analyses by calling vaious functions implemented in the `ensemble_md.analysis` module listed below, each of which can be used independently. For more information, please check the [API documentation](https://ensemble-md.readthedocs.io/en/latest/api/api_analysis.html) of the relevant functionalities, or the [source code](https://github.com/wehs7661/ensemble_md/blob/master/ensemble_md/cli/analyze_REXEE.py) of the CLI `analyze_REXEE`.\n", + "\n", + "- Read in and plot replica-space trajectories (using `analyze_traj.plot_rep_trajs`)\n", + "- Calculate and plot the replica-space transition matrix (using `analyze_traj.tarj2transmtx` and `analyze_matrix.plot_matrix`)\n", + "- Calculate the spectral gap for the replica-space transition matrix (using `analyze_matrix.calc_spectral_gap`) \n", + "- Recover all configurationally continuous trajectories in the state space (using `analyze_traj.stitch_time_series`)\n", + "- Plot the state-space time series for different continuous trajectories (using `analyze_traj.plot_state_trajs`)\n", + "- Plot the histograms of state index for different continuous trajectories (using `analyze_traj.plot_state_hist`)\n", + "- Stitch the time series of state index for different sets of states (using `analyze_traj.stitch_time_series_for_sim`)\n", + "- Plot the time series of state index for different sets of states (using `analyze_traj.plot_state_trajs`)\n", + "- Plot the histograms of state index for different sets of states (using `analyze_traj.plot_state_hist`)\n", + "- Plot the overall state-space transition matrices for different continuous trajectories (using `analyze_traj.traj2transmtx` and `analyze_matrix.plot_matrix`)\n", + "- For each continuous trajectory, calculate the state-space spectral gap (using `analyze_matrix.calc_spectral_gap`)\n", + "- For each continuous trajectory, calculate the state-space stationary distribution (using `analyze_matrix.calc_equil_prob`)\n", + "- Calculate the state index correlation time for each continuous trajectory (using `pymbar.timeries.statistical_inefficiency`, which is not in the `ensemble_md.analysis` module)\n", + "- Calculate transit times for each continuous trajectory (using `analyze_traj.plot_transit_time`)\n", + "\n", + "Additionally, MSM analysis and free energy calculations can be performed if parameters `msm` and `free_energy` are set to `True`, respectively. Here, we set `free_energy` to `True` in `params.yaml` (with all other data analysis parameters adopting their default values), so free energy calculations will be performed when the CLI `analyze_REXEE` is used. Currently, functionalities for MSM analysis are still work in progress and have not been extensively tested, so please use them with caution if you are interested in them. We welcome any feedback or contributions (via issues/pull requests) to improve these functionalities.\n", + "\n", + "Now, to perform data analysis on the example outputs, simply execute the command in the cell below. Note that the CLI `analyze_REXEE` should be run in the same directory where the MDP template, input YAML file, and `sim_*` folders reside. Also, the CLI `analyze_REXEE` does not support parallelism and here we are using default values for all the flags. For more information about the flags, please run `analyze_REXEE -h`/`analyze_REXEE --help` and read the help message. On Binder, the data analysis will take around 30 minutes to complete." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "70729fe0-7e2d-4ac1-9951-0f1183f3ffb3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning on use of the timeseries module: If the inherent timescales of the system are long compared to those being analyzed, this statistical inefficiency may be an underestimate. The estimate presumes the use of many statistically independent samples. Tests should be performed to assess whether this condition is satisfied. Be cautious in the interpretation of the data.\n", + "\n", + "********* JAX NOT FOUND *********\n", + " PyMBAR can run faster with JAX \n", + " But will work fine without it \n", + "Either install with pip or conda:\n", + " pip install pybar[jax] \n", + " OR \n", + " conda install pymbar \n", + "*********************************\n", + "Current time: 01/08/2024 08:52:31\n", + "Command line: /srv/conda/envs/notebook/bin/analyze_REXEE\n", + "Important parameters of REXEE\n", + "=============================\n", + "Python version: 3.10.14 | packaged by conda-forge | (main, Mar 20 2024, 12:45:18) [GCC 12.3.0]\n", + "GROMACS executable: /srv/conda/envs/notebook/bin.AVX2_256/gmx\n", + "GROMACS version: 2023.3-conda_forge\n", + "ensemble_md version: 0.9.0+177.g9f48c4c.dirty\n", + "Simulation inputs: sys.gro, sys.top, expanded.mdp\n", + "Verbose log file: True\n", + "Proposal scheme: exhaustive\n", + "Whether to perform weight combination: False\n", + "Type of means for weight combination: simple\n", + "Whether to perform histogram correction: False\n", + "Histogram cutoff for weight correction: -1\n", + "Number of replicas: 4\n", + "Number of iterations: 125\n", + "Length of each replica: 10.0 ps\n", + "Frequency for checkpointing: 100 iterations\n", + "Total number of states: 9\n", + "Additionally defined swappable states: None\n", + "Additional grompp arguments: None\n", + "Additional runtime arguments: {'-nt': '16', '-ntmpi': '1'}\n", + "External modules for coordinate manipulation: None\n", + "MDP parameters differing across replicas: None\n", + "Alchemical ranges of each replica in REXEE:\n", + " - Replica 0: States [0, 1, 2, 3, 4, 5]\n", + " - Replica 1: States [1, 2, 3, 4, 5, 6]\n", + " - Replica 2: States [2, 3, 4, 5, 6, 7]\n", + " - Replica 3: States [3, 4, 5, 6, 7, 8]\n", + "\n", + "Whether to build Markov state models and perform relevant analysis: False\n", + "Whether to perform free energy calculations: True\n", + "The step to used in subsampling the DHDL data in free energy calculations, if any: 1\n", + "The chosen free energy estimator for free energy calculations, if any: MBAR\n", + "The method for estimating the uncertainty of free energies in free energy calculations, if any: propagate\n", + "The number of bootstrap iterations in the boostrapping method, if used: 50\n", + "The random seed to use in bootstrapping, if used: None\n", + "\n", + "Data analysis of the simulation ensemble\n", + "========================================\n", + "[ Section 1. Analysis based on transitions between state sets/replicas ]\n", + "1-0. Reading in the replica-space trajectory ...\n", + "1-1. Plotting transitions between state sets/replicas ...\n", + "1-2. Plotting the replica-space transition matrix (considering all continuous trajectories) ...\n", + "1-3. Calculating the spectral gap of the replica-space transition matrix ...\n", + "The spectral gap of the replica-space transition matrix: 0.243\n", + "\n", + "[ Section 2. Analysis based on transitions between states ]\n", + "2-1. Stitching trajectories for each starting configuration from dhdl files ...\n", + "\n", + "2-2. Plotting transitions between different alchemical states ...\n", + "\n", + "2-3. Plotting the histograms of the state index for different trajectories ...\n", + "The RMSE of accumulated histogram counts of the state index: 4956\n", + "2-4. Stitching time series of state index for each replica ...\n", + "\n", + "2-5. Plotting the time series of state index for different state sets ...\n", + "\n", + "2-6. Plotting the histograms of state index for different state sets\n", + "\n", + "2-7. Plotting the overall state transition matrices from different trajectories ...\n", + "\n", + "2-8. Calculating the spectral gap of the state transition matrices ...\n", + " - Trajectory 0: 0.050 (λ_1: 1.00000, λ_2: 0.95008)\n", + " - Trajectory 1: 0.044 (λ_1: 1.00000, λ_2: 0.95636)\n", + " - Trajectory 2: 0.041 (λ_1: 1.00000, λ_2: 0.95864)\n", + " - Trajectory 3: 0.084 (λ_1: 1.00000, λ_2: 0.91599)\n", + " - Average of the above: 0.055 (std: 0.020)\n", + "\n", + "2-9. Calculating the stationary distributions ...\n", + " - Trajectory 0: 0.027, 0.052, 0.094, 0.149, 0.146, 0.144, 0.148, 0.167, 0.074\n", + " - Trajectory 1: 0.030, 0.068, 0.092, 0.154, 0.153, 0.150, 0.131, 0.148, 0.073\n", + " - Trajectory 2: 0.035, 0.066, 0.118, 0.148, 0.149, 0.137, 0.116, 0.181, 0.050\n", + " - Trajectory 3: 0.059, 0.125, 0.144, 0.171, 0.165, 0.168, 0.126, 0.043, 0.001\n", + " - Average of the above: 0.038, 0.078, 0.112, 0.155, 0.153, 0.150, 0.131, 0.135, 0.049\n", + "\n", + "2-10. Calculating the state index correlation time ...\n", + " - Trajectory 0: 12.1 ps\n", + " - Trajectory 1: 12.0 ps\n", + " - Trajectory 2: 15.9 ps\n", + " - Trajectory 3: 4.3 ps\n", + " - Average of the above: 11.1 ps (std: 4.9 ps)\n", + "\n", + "2-11. Plotting the average transit times ...\n", + "\n", + " - Average transit time from states 0 to k\n", + " - Trajectory 0 (6 events): 102.58 ps\n", + " - Trajectory 1 (3 events): 174.69 ps\n", + " - Trajectory 2 (4 events): 201.13 ps\n", + " - Trajectory 3 (3 events): 332.21 ps\n", + " - Average of the above: 202.65 ps (std: 95.89 ps)\n", + "\n", + " - Average transit time from states k to 0\n", + " - Trajectory 0 (5 events): 116.40 ps\n", + " - Trajectory 1 (4 events): 156.53 ps\n", + " - Trajectory 2 (3 events): 72.39 ps\n", + " - Trajectory 3 (2 events): 112.66 ps\n", + " - Average of the above: 114.49 ps (std: 34.38 ps)\n", + "\n", + " - Average round-trip time\n", + " - Trajectory 0 (5 events): 226.02 ps\n", + " - Trajectory 1 (3 events): 376.63 ps\n", + " - Trajectory 2 (3 events): 296.65 ps\n", + " - Trajectory 3 (2 events): 589.88 ps\n", + " - Average of the above: 372.29 ps (std: 157.57 ps)\n", + "\n", + "[ Section 3. Free energy calculations ]\n", + "Reading dhdl files of state range 0 ...\n", + "Collecting u_nk data from all iterations ...\n", + "Estimating the start index of the equilibrated data and the statistical inefficiency ...\n", + "Subsampling and decorrelating the concatenated u_nk data ...\n", + " Adopted spacing: 1\n", + " 0.4% of the u_nk data was in the equilibrium region and therfore discarded.\n", + " Statistical inefficiency of u_nk: 18.7\n", + " Number of effective samples: 3326\n", + "\n", + "Reading dhdl files of state range 1 ...\n", + "Collecting u_nk data from all iterations ...\n", + "Estimating the start index of the equilibrated data and the statistical inefficiency ...\n", + "Subsampling and decorrelating the concatenated u_nk data ...\n", + " Adopted spacing: 1\n", + " 0.1% of the u_nk data was in the equilibrium region and therfore discarded.\n", + " Statistical inefficiency of u_nk: 16.9\n", + " Number of effective samples: 3695\n", + "\n", + "Reading dhdl files of state range 2 ...\n", + "Collecting u_nk data from all iterations ...\n", + "Estimating the start index of the equilibrated data and the statistical inefficiency ...\n", + "Subsampling and decorrelating the concatenated u_nk data ...\n", + " Adopted spacing: 1\n", + " 0.0% of the u_nk data was in the equilibrium region and therfore discarded.\n", + " Statistical inefficiency of u_nk: 3.9\n", + " Number of effective samples: 16199\n", + "\n", + "Reading dhdl files of state range 3 ...\n", + "Collecting u_nk data from all iterations ...\n", + "Estimating the start index of the equilibrated data and the statistical inefficiency ...\n", + "Subsampling and decorrelating the concatenated u_nk data ...\n", + " Adopted spacing: 1\n", + " 0.0% of the u_nk data was in the equilibrium region and therfore discarded.\n", + " Statistical inefficiency of u_nk: 3.9\n", + " Number of effective samples: 16217\n", + "\n", + "Plotting the full-range free energy profile ...\n", + "The full-range free energy profile averaged over all replicas:\n", + " 0.000 +/- 0.000 kT, 1.546 +/- 0.019 kT, 2.623 +/- 0.022 kT, 3.253 +/- 0.022 kT, 3.465 +/- 0.023 kT, 4.671 +/- 0.026 kT, 5.127 +/- 0.032 kT, 2.395 +/- 0.056 kT, -0.420 +/- 0.060 kT\n", + "The free energy difference between the coupled and decoupled states: -0.420 +/- 0.060 kT\n", + "\n", + "Total wall time GROMACS spent to finish all iterations: 12 minute(s) 8 second(s)\n", + "Total time spent in syncrhonizing all replicas: 1 minute(s) 4 second(s)\n", + "\n", + "Time spent on data analysis: 28 minute(s) 1 second(s)\n" + ] + } + ], + "source": [ + "!cd example_outputs && mpirun -np 1 analyze_REXEE" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c9c3ebbd-6efd-4f30-9d59-7ecee9ad7794", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "analysis\t rep_trajs.npy\t sim_3\n", + "analyze_REXEE_log.txt run_REXEE_log.txt state_trajs_for_sim.npy\n", + "expanded.mdp\t sim_0\t\t state_trajs.npy\n", + "hist_data.npy\t sim_1\n", + "params.yaml\t sim_2\n" + ] + } + ], + "source": [ + "!ls example_outputs" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "9a15f8d8-c236-4f34-8cf5-5f1fbcb2fa54", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "free_energy_profile.png t_k0.png\n", + "rep_trajs.png\t\t traj_0_state_transmtx.png\n", + "rep_transmtx_allconfigs.png traj_1_state_transmtx.png\n", + "state_hist_for_sim.png\t traj_2_state_transmtx.png\n", + "state_hist.png\t\t traj_3_state_transmtx.png\n", + "state_trajs_for_sim.png t_roundtrip.png\n", + "state_trajs.png\t\t u_nk_data.pickle\n", + "t_0k.png\n" + ] + } + ], + "source": [ + "!ls example_outputs/analysis" + ] + }, + { + "cell_type": "markdown", + "id": "a288cca1", + "metadata": {}, + "source": [ + "As shown above, all data analysis results were not only printed on the fly to the screen, but also printed to the file `analyze_REXEE_log.txt`. In addition, the following files and folders have been generated by `analyze_REXEE`:\n", + "\n", + "- `state_trajs.npy`: An NPY file that contains the stitched state-space trajectories of different replicas\n", + "- `state_trajs_for_sim.npy`: An NPY file that contains the stitched state-space time series for different state sets\n", + "- `hist_data.npy`: An NPY file that stores the state-space visitation distribution\n", + "- A folder `analysis`, which contains a bunch of figures generated from different analyses (that we will look at in the next section), and the file `u_nk_data.pickle`, which contains preprocessed `u_nk` data. As data preprocessing for free energy calculations (such as truncating and decorrelating times series for DHDL files) can be time-consuing, the file `u_nk_data.pickle` can be useful when one wants to rerun free energy calculations for the same simulation. For BAR and MBAR calculations (as in our case), the CLI `analyze_REXEE` will search for `u_nk_data.pickle` and if found, the data preprocessing step will be skipped, hence significantly shortening the analysis time. For rerunning TI calculations, the CLI `analyze_REXEE` will search for `dhdl_data.pickle` instead." + ] + }, + { + "cell_type": "markdown", + "id": "1bb00b2a-9789-475e-ab74-be020c6e7777", + "metadata": {}, + "source": [ + "## 4. Analysis results\n", + "Here we show and briefly discuss the figures generated by the previous section. (For more insights into data interpretation, please refer to our [JCTC paper](https://pubs.acs.org/doi/epdf/10.1021/acs.jctc.4c00484).)\n", + "\n", + "To start with, here are just some settings for displaying saved images." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "3adffd2d-09bc-46e0-9f7a-78459e8f97d6", + "metadata": {}, + "outputs": [], + "source": [ + "import gc\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as image\n", + "from IPython.display import display, Image\n", + "\n", + "def display_images_matplotlib(images, widths=None, figsize=(10, 5), rows=1, titles=None):\n", + " \"\"\"\n", + " Display a list of images in a grid using Matplotlib with specified widths.\n", + "\n", + " Parameters\n", + " ----------\n", + " images : List[str]\n", + " List of image paths.\n", + " widths : List[float]\n", + " A list containing the width of each image as a percentage of the total width. Must add up to 100.\n", + " figsize : tuple\n", + " Size of the entire figure (width, height).\n", + " rows : int\n", + " Number of rows in the grid.\n", + " titles : List[str]\n", + " List of titles for each image. If None, no titles will be displayed.\n", + " \"\"\"\n", + " cols = len(images) // rows + int(len(images) % rows != 0)\n", + " \n", + " if widths is None:\n", + " widths = [100 / len(images) for _ in images]\n", + " else:\n", + " assert sum(widths) == 100, \"The widths must add up to 100.\"\n", + "\n", + " fig, axes = plt.subplots(rows, cols, figsize=figsize, gridspec_kw={'width_ratios': widths[:cols]})\n", + " axes = axes.flatten()\n", + " \n", + " for i, (img_path, ax) in enumerate(zip(images, axes)):\n", + " img = image.imread(img_path)\n", + " ax.imshow(img)\n", + " ax.axis('off')\n", + " if titles is not None:\n", + " ax.set_title(titles[i])\n", + " \n", + " # Hide any remaining empty subplots\n", + " for ax in axes[len(images):]:\n", + " ax.axis('off')\n", + " \n", + " plt.tight_layout()\n", + " plt.show()\n", + " plt.close(fig) # Close the figure to release memory\n", + " gc.collect() # Run garbage collection to free up memory as Binder only has 2 GB of RAM" + ] + }, + { + "cell_type": "markdown", + "id": "4378660a-e52c-4fac-acbd-6269b6fe2a1f", + "metadata": {}, + "source": [ + "
\n", + "\n", + "### 4.1. Replica-space sampling\n", + "As shown in the replica-space trajectories in the left figure below, each replica was able to sample all different sets of states by exchanging coordinates with other replicas, indicating good sampling in the replica space. This decent sampling in the replica space is also reflected in the replica-space transition matrix in the right figure, where all tridiagonal elements are non-zero. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "7327a074", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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eewy//vWvsWbNGroTH6OrqwuO4wAAenp6Sj5GKYXe3l4AOuKXTqfrtn0EMZoopbB69Wp86EMfwooVK/DTn/4UU6dOJUGkDgRF/11dXfi3f/s3HHLIIXjyySdxxRVX0DV6iJAoRRAEQRAEQRANor+/H1/84hdx4403YuHChZgwYQK+8IUv4MorrwxdPXv27MHnPvc5/OEPf8CMGTPw9a9/HT//+c9pwRNj0qRJmDp1KpRS2LJlS8l9I6UMO7nmz58filgE0coopXD33XfjwgsvxKGHHoqf/exnmDVrFglSdYYxhokTJ+K4444DAPz1r38NRXBicCi+RxAE0UTQAoMgCGLfYsKECfjTn/5U1G9kWVYomkyfPh133nln4j3Ctm1adMYYP348lixZgpdffhkvvPACpJRF8by+vj5s2LABjDEcffTR1LVDtDxKKdx555246KKLcPjhh+OHP/whpk2bFl4b9uzZg4GBAcycOZOO9yoJBG8AZUU/xhgmTZoEQMf5aJjC0KAjc4QEIyCz2Sz6+/vheR4tJgmCqBpaYBAEQexbMMbQ1taGjo6OxH+ZTCZ8T+Cco729PfH1dDpN7xkxLMvC2WefHUYfCyN8Sim8+OKL2LhxI7q6unDqqafS/iNaGiklbr/9dnzgAx/AkUceiR//+McJQQoAfvCDH+Dd7343crlcA7d0bCClxGc/+1l8/OMfL9tbJ6UMhasJEyYUdQMSpSGn1AgQQuD666/HzTffjFwuBykl8vk8zjnnHLznPe+hfDpBECOGxG2CIAiCGD6MMbzlLW/BoYceiueeew533HEH3vnOd4YLdCEEfvWrX2FgYADnnnsuFi9e3OAtJoiRI6XErbfein/4h3/AihUr8PWvfx2pVKpIjN2zZw/27t3bmI0cYyil0N/fj0ceeQTr1q3DgQcemBAAlVJ45ZVXcNdddwEAVq1ahc7OzkZtbktBotQIcF0XX/nKV3DOOefgYx/7GFKpFG6//XZcfPHFkFLiAx/4AN15IQhiRNC1gyAIgiBGxpQpU/C1r30N73vf+3DZZZdh5syZOPLII+G6Lq655hr8/Oc/x7Jly/D5z38etk3LoFqhlMLevXvR3d0NQAshAOB5HjZs2IBx48bBtm3MnDmTJh7WAKUU7rnnHnzwgx/E9u3b8eijj2LVqlUlH7t3717MnTu3zls4NgkKzbdu3YoPf/jD+NKXvoQlS5agra0NuVwOa9aswaWXXopNmzZhxYoV+Od//mf6XD9E6Go8AjjnOP3003HxxRdj4sSJAIA3velNmDt3Lv785z/j/e9/Px2ABEEQBEEQBFFHGGM49dRT8atf/Qqf//zn8Y53vAPz5s1DPp/Hli1bcMopp+ArX/kK5s+fT5/Va8xPfvITXHnllQD0DfzOzk7s3LkTp512GhhjmDVrFm699VZMmTKlsRs6RnjiiScwMDCAzs5O5HK5QeN57e3tddyysQvnHJdccglSqRQefPBBnHXWWZg5cyY6OzvR39+PTZs2oa2tDRdffDH+9V//la4zw4BEqRGQSqVw+eWXJw6yXC6HgYEBzJ49mw4+giBGDMX3CIIgCGLkcM7xxje+EccccwzWrFmD9evXw3EcLFq0CIsXL6YurlHioosuwtve9rayX7dtO7yZT1TPBz/4QZxzzjlDemwqlaJuoxrAGMOqVatw4oknYufOnVi3bh02b96MgYEBOI6DGTNmYOHChZgxYwY453SdGQYkSo2AwLqnlMLOnTuxbds2XHXVVZg9ezb+6Z/+qez3DQwMJMZC+r4Pz/Pg+/6olM+5UuGWjd3YvBd44cl14YkxszODk+dOgsUkbn3lVvTke/COg94BDgt/vPNBPPXsywBGtjDmnOP1x63A6444FL1eL6565ips79teuxdVgUltk3D+YedjUnoSHl3zIm69+xEIIco+ft7s6XjvWa9HOpXC7Y+vw0PPbQpFgZ7uHkzpAJYfnoVtW3hgSzcYgGNndUH4An+4dTWef3lDnV7Z8JkwvhPnv/VUTJk4Hk9ufxI3vXQTfBmV8k3vnI7zDz0f45xxWP3oGvz1waeKJv+MlO3b9e982rSHh/R4y+I47XVH4ujli7E3vxe/fOaX2DmwsybbAgDj0+PxvkPfh+nt0/G3F17FH+94AF6ZgsKRsPiAuXjHm04Et4Ab196Ip197ekSnUDDV4+y2szFz5kz0DLj45Z3PYPve/vAxtmXhrGMXYtmCqeh2Be5ctxP9XvljPG1zrJo7GdPaHKxdvwW/v/ke5F13JC+z7nDOcfIxy3DSUUvR5/Xhl8/8Etv6toVft7mNMw48Ayunr8TO7ix+++AmqEd2oqvrxQZudXkO2m8y3nnSYnCu8Me1f8RTrz010kttTfF9Hxs3bcQt3i3IpEf2oXVaxzScf+j5GJ8aj/uf3YI7n3h10OvJAbMm4d0nHwzbBm5++WY8vvXxEQuynHGcNO8knDznZOSEwl837ML8rnYcPKkd3QMufnnHM9jRHZ1DjmXhrOMWYen+U/Darr245uZ78IYTDsfiBXOwK+fjrvW7kPWjcypjc6yaNwVTMzZe2vsSrnnuGuR9PU0nm83itddew5//+ucwkjK5fTIuOPQCTEhPwMPPb8Xtj70CEdsX+00Zj/NOWQJuc/xp7XbkhcTr507GrM7a91Hm83kSuol9EsYYurq6cMIJJ+CEE05o9OaMeYKJY8HUMWJ0YYxh4sSJJPI1AMYYbNvGjBkzMGPGjEZvzpiBRKkqUErhqquuwurVq7Fp0yZ88IMfxPTp08s+/tprr8Vll12W+P7t27dj2bJlWL16dc23LyeB23em0CMBPLsp/HebATvXemjDTlzyt0vQJ/qQW5dDR3YyLvnKL9DTN1DVz/39n+7EFZ89Hw/3PYCvvvhVSNRG6BgKDAybXt2E0yefgUv/9zd4Zu3GQR9vWxb69u7A/nNm4ZJfrMFr3cmxnW0pC3O6LHRN6sBVu1JgAHZNdrFn23Z86qtXIZtv7sX9+nXrcNYpK3DZ85fhyb1PJr7GwbFr3S4c1XksPvXVX+LVzfUTD0tx/a334Ouffi/u2nMnrnz5Sqgar9bXvrwW75p5Lr74vT/gkTUv1/S52zIp5Ht3oW2KwCVrLkGv31v5mwbh4V0PI70rjQee78PX/vQSCteUf37wGfznOw7CYzkHd/dU7mZ4du2reH2niyuuugV/fuBvVW1bvfnNjXfgyn+9AE8MPIIvvfglSJW8nty05iZ85eCv4KYnduIHd7zaoK0cGhnHQm7XenRO6MUlay5Bj9dT+ZvqyZaRfysDw/Z123F81yp8+uq/Y+22vkEfn7I5cjvXY/KULC5Zcwn2uHtG/sMB/PrJX+Obh34Tm/1xuGGPjWkp4L0T87jn7zvxjZuLz6E7HnoGX3z7Qbjm9ofwsxvuxklHHoJLLzoL9/Wn8GBf8WDi515+FSd3uPjGK9/AndvvLN6A2L5jYNj66lacMuk0fPY3z+K5zcnrgcUZeravxyHzJ+APO1Pol4C/+RUsSNf+vbK/vx/ZbLbmz0sQBEEQBFErSJSqAsYYPvnJT+KSSy7Bc889h/e+97148cUX8Y1vfKNkeeIb3vAGzJ8/P/y7EAKf+tSnMGfOHKxcubLm25cTEo8/tQHb9vZi4qSJ8KXCM9u7IaTCnAUHYlJ6Erw1HnzpY+aCmZg0MAee78OyOI5beSg624d3x3z7zr14/JkX4foShyw5FM+tewbyRYmudBeO2e8YcFb8Qb9WKKXw6JZHsSu7C+1T2nHYYUvhiqsBAEsXL8DsGcn8uucL3P/YGuTyHmbMnI2DDj4EnnwGDMDRB89GW9rBfc9sgCck9pt/IKbNnAD/gVfAASw8+BC8lumEJwQc28LxRxyGtkxq1F7bcFEKeOxvL2Dnnm50jOvC8hXLIV7Wd/0PmXoI5oyfgwc2PoBetxeTZk3CkjmHwZd6xXb4oQsxfUr1d116evQibPz4cRUfu+W1XXj6uZfh+hKHHrYUDz/3INTLCpMyk3DU7KOqtr4+sfUJvNb/GlITUli2bDl8eS0A4OAD52H+fuVF5KGQzbm4/7E18IXEvPn7o3M/CX+ND4tZOG7OcehMDW/ixta+rXhq21PwuY9Dlx6K53auh1IvYXxHGscevB827ujBs+t3wGcOli5bjufX7gB6dmFCxsGiScX7+tW9/dgxkEfbhIk4fOlssKv1Ynr/OTOx+IA5Vb320Sa4nvhCYcmSQ7F+88uQL0h0pjpx3JzjsLV3K9ZsXwPf9nHYssPwl5f+BuBVTOlqxxEHzUIzGabzno/71myELyRmzV2ASTO64f3NG/FxUmuklNi9eze6urrgOM7wvldJPLz5YezN7cW46eOw5MDD4MpnAQCHL5yB6ROTr831Be5bswGeLzFjznzMnefC/ZsLzjiO2+84jEtXvmbE2d6/HY9vfRw+93HIYYfA38kh92wG7BSWLj8YT7+2Bkq9hK6ONI45ZD9seK0bz23YCZ85WLZ8BW5cvQYAwLiNFStW4Im/bwP69mByWwoHTOzEK3v7sHPARVvXJKxYOhNsqz6yFkxYgIOmHATP89Dd3Y1JkyaBMYZHtjyC3dnd6JjagUMPWQb36hcAAMsOmI5pEzpw/zMbkc17mDpzDg47bH/Y970Mlvdx0KKFWDql9sdBT08PTQQmCIIgCKKpIVFqBARW+CDGxznHYYcdhre97W343ve+h0984hPYf//9i75v5syZmDlzZvh3z/Mwfvx4tLe3Y/LkyaOynR84KItXX+3FCSccgl5P4h9vehx7ci46x43D+HYfDAwKCun2NNpZJ6RU6GjL4Edf/jQWzp89rJ936z2P4OwP/TukUhg/vgtWWjs4Fk9ZjOvOvQ5pa/Q+GPvSx9m/PRu3v3w7uMPR1dUFqQAG4F8ufjfee9brE4/f29OPI876MNZvfg1tbe0Y3zUBAINtcXztw6di/owJOOIff4xdPQNIt3Wgfdx4/Y2MYfz4LgyM04uHcZ3t+OlXP4N5s6sTN2qJEBJnfejfcfvqR+GkUpg4cSI414LgR474CD6w/AM4/v+Ox9OvPY10WxoTJkwAoI/lz19yAd5yyrFVb8PTf/sblFJYvmxZxcf+4dbVeM/HL4dUCl1dXeApva3LZizDDe++ATYf+WVKKon3Xf8+/P7vvwccYMKECTD6Gz56/tn4yHlnjfi5AWDDlu044i0fRu9AFp2d4zBuvHY6ZOwMvvvm7+KQKYcM6/mufe5avPsP7wY4MHHiRKQz2r22aL/JuPY/34Wrbn8aH/3mLZCKYcLEieAp7bQ5bFoXLjvxYLACKebKh1/ELS9tg+2kMHHSpDBe9I43nYgvf/ZDTSXcFHLzXx/GWz98GcCArgkTkN6jrx/7T9gff3jnH3Dtc9fiAzd+AJJJTJg4AczSYsrRi2fjuv88FxZvnlf32t5+rLz4h9jRPYB0Wzvax3mQSqIj1YEfveVHWDhpYUO3L5vL4t5778WRRx6JiROGJ0q7wsWpvzwV92+8H1bKwriuCZCKgTPgCxechDOPWZR4/K7eLFZ++EfYsrMXqUw72sfZkEqizW7D9978PRwydXjnzC0v3YJzfncOGGOYMHEC2rNagGecY+LESUhl2gAAi+dMwXX/cS5+euuT+Pi3b4Uw5xDMzRLOLUycOAlOWju2ls+YgM8efxD+96GXcNvabbBTKUyaPAnc1o9/55J34kuv/xJ27dqFJ554AieeeCIsx8IZV5+Bu9bdBZ7iGD9hAiT0+9Dn3nsCTjvyQBz90R/jxU270dbWbn4+A2PAhPHjMXly7WMYtm3TpCuCIAiCIJoaEqVGwNatW3H//ffj7W9/e7jYB4Curq6w8LwZYEwvDBgAzhgs8+FXKUAqBakklPlfXuSR9zxIKWFbFtIpZ9gfZNvSaXDG4fkCvhDI+bonK2Nn4HAHFh/dD8ZpWy9ac34Ovi/g+wJgDO1t6aLX4tgWLEv/7nwhIKSCVAqMMTi2hbRjw7YYFLTLwRNSxz+YroARUgEqyBVbTfeh3zFOPc8XkEpCKL1QS1kpWNwKhR5f+pBSQkoJxgDHqX4Bo5SCxTmUUkMq+WvPpMEYwt9ZcNyk7TRsbld13DDF0GbrRWnOz0FICdfzwp9b7Wu1bQuM65NKKhlGyxhjIzrmU1YKjDEIqX9vvtDPZ3EtmLaltfDiej6kVHDN11MWN+d3tK+VUrDN9UlIBaX0sQ6Y33OTFzCmHBsMDFJIKCXDPjSLW3AsB212GxgYXOFCSIGcq7+eSduwLQ7eRKJUyrbg2BaUAvKuQF7kIZWEzW2krfSoXxsrYTELHBwWs4a9Lbayk9deoY9bxhja0k54nQ3Q+4Lr9x3XhytcSCXhWA7S9vD3hW3pa5mCglL6Og4E73EqOocsDstiaE9H10YhJHImgq3PDaWv7QBsrt8zbXOOSGXOIXMcOpY+vznj4IzD4nrfZexMuC+EkPD8aF/o4zJ435HhNgIMvInPRYIgCIIgiNFk9PJUY5gtW7bg8ssvx5YtW6CU/iDc29uLO+64AwceeCBmzx6ew6heMIbQSSEVwm1XSiHv55HPe1AKsCwLjjN8vTKVssE4KxYXrNGfcsIZR8bSi4Gsn4UvBDzfB2NAJlUcR2GMweIcgIIvhBFmFBiLBADbsgAFvXCSMrHoCQp8tfDXbKcRg23u5gshIJUKRSmb64V+QpRSCkJKMAT7pL6k0ykwsPB3FoqZVqbq44aBhYvEvJ+HEAKup4+LdKr6yKX+/TMoBSNs6uOEYWTHRbAgF0pAQcELRSkOzhgyKRuMAa4vIWT09ZRV+mdZZv8Jc54L83i7yUTUUlgWBxggzLkZilLMAgND2tbXFU948JVAztNfTzs2ms0CZnFmRCmFvOcj7+ehoGAxLbC1Mpzx0AUbilK+AGcMKbv4OLO4/nelgJwrEvsiZQ3/nLSY/hkqFIZh/q7/zQ+Pea6vB6lAlJIFopS+8SCMqBWcO4HjTkh9Ayc4Dks5OIPjMtwXUsLzhb7eOJa+OWSeL/h5Sun35mYSUQmCIAiCIOoJOaVGwOTJk9HV1YULLrgAp5xyClKpFFavXo3u7m58+9vfRldXV6M3sSTaMaX/HDilJPQiOidyyLuudrlYPHTaDId0ygFnDEIIeJ6PrK/LVdN2uihWNBoU3q3XiwGGVDlRytILI98X4R1r/e/aZZEyi8hczCmljNNMCCM+MKadMk0EYwgdQL4voJQMRTSb20aQM1+POaXAGiNWpFMOGIvEzOC4CcSkakk4F6SE5/kAGDLp6sUAzhgY064PKbVTSpnjaCSilM2MO8i4riKXh3ZBZVL6654Q8IVMOKVKESyApRGlAqeUXUIsaDYsywJjDNK4GONOKcaYFrvB4Endixc6pVJ2s2lSsCyOlM2hEBNilNKuL97aohRjkRCjbwhIeEKCcYa0U3ycBddWQLtQA9fYSPcFZzw8ZxRiTikoSIWE21DfpNDHhyckfBmJUkKIyAWL6NwpEnZjAn+pbUk6dvU5zBlD2rHNTY9ipxSL/RyCIAiCIIh9DRKlRsD8+fNx2223Yd26ddi8eTM8z8PJJ5+Mgw46COPGjWvqSEywbWF8z3yAz/k55PJalHJsa0TiRNpxwLleRLqeV1enFIBETEsLHL5eJJQUpZCI7wUL3+BOtsUZHEcvIvOu0KKU+d64U4qz5oxdBL8/XwhIyKKFVKFTSrvEWFHUph6kHBuc82KnVA1EKcZiTimRh+f7kXMhXSOnFNeZTmmcUgBG7JQKfi9KKQgpIpeHWcimUxbAEIquQ3dKoeWcUoGzRRpRtdChkrEzYdTREx7yoSjVfK8tcEoBQM71kRM5KCg43KmqM61ZCFyqOT8H19PXU84YUqVEqdi/51wtSumoqT2ifRE6pZB0SumEdYFTKhSHmD5/fIm8q+O8vhBGdCrjlFLaKSWkicCWEdAS+8LX0XD9cy2AMVhW5JQyKXCzX4b90gmCIAiCIMYErf9puAEwxtDR0YElS5ZgyZIljd6cIcMYC/OaUiG8swwAOS+HvOtBAXCc4h6QoZBKOeCMh709cXGhHk6peEzL9TwIIcE5Q7rENKkovgf4JgqlQlGKw+I8vJufc324IponLqHjbkGnVLPFLhhjkSgV65TS8TwdfQoWctopFYvvNUCsSKUcWDyK7+VFHkDtjpu4U8r1fAghTIynNqIUY7p7LHRKBQ66EWx74AIqdErZ5nzMONrlEbikXDk0p5SOFiJcUNu21dTiORA4pQAltZgWilLGTRY4MH2pe4kip1TzOY+S1xMvdEoFvUStTDwiGwoxSplrb/FHjPi+yOZ1lFEqOeLeQc602BRE0aXSx7X+s76+Bz8XMDE6zuD5Wth1jSglhDRicBmnlLlxMZhTCgAyTqEoJU18T5+7doFTSpn3EXJKEQRBEASxr9JsZTjEKMKAcAGtzF3fuFMqb5xSgXNluDi2DcvikFIi79bW8TIU4o6YvOuaxUD5+F7cTRT0ezDjlIpHTLQopRc2QQdI1CkFsKbrlIriWcHd/zC+x7RLoLDoXEfOIvGjngTHmxTatZD3tSgVxGCqJSFW+h58oWOd6RrE94JOKagovgdVvVMqEBLDuJ3FwQCkzIJaCKUX1UYsTVmlS8uDhW4U32stpxTAws6zeHwP0KXwnHEIJeAKDzlP76ugM6iZKHIHmR6l4DW0OnFRKm9K+Mt1SnEe3xdeuC8cywnF8uEQ7L9AyI2KzlFQdM4icci4DV3PR94MPgh6Bcs5pfQ5JCNht5woZRe7xizOkXKsAoeuEaWgwmEkBEEQBEEQ+yKt/2mYGBbB594ip5SfQ87V3RopM5lruNi2Bdu2IEs4pepBQpTyXEgpwXm5+F4UVRNC383W/w7jlIoWTtmYKAUgXCQrxESJJiNc+MSdUrEuqbgoFTgEgp6tepNyHC1KKYWc69b8uAmeR0e8XF3CzBlSJRx0w4WbTjHdRSPDgvLAvTFcgiL6oLQ5PjkMTE8u44xF8b2hOqXCovNW65SKYoee1OJB0IsWilJSwPVduKbovK0JRSldrK+3KxcT7FNWakRCTLORvPZqUcq2eDhwIQ4DQgdVYl/w1Ijie0GnVDBJNhCl9E0XJCKwDAzplD6HhJS60ypwSsmkqFXcKYUhOaWCGHle5JH3PQipYFkMjomj2rGic2GcUvp1NN/7CEEQBEEQRD1ovk/vxKjBWPTBt2SnVE6LUo5jjyi+F3RRSanFBVfo56u7KOXnkXNdSKmQcqySpe3xO9Z6NHg8vsfC7hFA381PilK6Pwih+DD6r224JDqllCyavhcvOhdShCXvjXJKWRZH3pXIu24Y32uz22oSMQsWia50kfdcCCGNc6H6y19clKxJfM8IFBIy2Sllfi8px4LFOXwpkfcFfBk5pUptm5U431utU8oKY1lSitChEhy7QdzLlz5yvgu3iZ1SYacQkkLMSCNrzUb82pt3PUilRSmn1PWERb+jxL4YYZQxHt/TolLgnEo6pexA2DWdUlIqPVnViFJSGKdUYXyvYPreUJ1Sru8i7/oQUqKN27BtXsIpZXYJAxpw6SWIISOEwN/+9jfk83nYtt308W+CIAhiaPi+vqm7ZMkSdHZ2Nmw7mu/TOzFqMEST0kNRKuaUyuYDp5QzoviebduwLQsKQDaXD2NYQcfGaFJYaJ13tfhgZ6ySrpDEBDrfhy9j8T0TlQoKk3MlnFLSdEpxI2A1G7YR4rQopX/X+i69ie+xYqcUgIY4pexAzDROqfC4qZGYGcQAde9QHkIKcKtGolTs9y+EjBXgj8wpZXELnPHiTinj8kjZQXxPIuvq4xYYQtG51Gd6S03f4/o8DMS0eNE5AwvjXq5ydSee38yilO4DA/T1JJgwOdbie3mRR87zQ1GqlMit+9xKCHSWM6J9kZi+p2JOKRUc8wXCbnAOSaWv7cZhJ0x8L3RKsaRTKriOVuyUiu2LrOtBKZiJtvo1F07fUwA4mtNxSxABAwMDePe7341XXnkl/HxBEARBtD5CCDiOg9WrV2PlypUN2w56Z9mnYOGEnyC+F6BFKdPlk3JG5P5xTHwPSiGbz0WF1VZ9nVKu7yKXd/WYcbuMUwosMX1PiMDhooUmsGTEJOyUMj1cQXyveUWpeNG5dktp50ywMIo5pRLxvfovkG1LHzdKKmTz+Zo77ILCdFe4yObzkFLBsTmcGsX3eCj8yHBhPNJOKYtF7iDdKRUsqPXPSIUlzXpB7YvyTikg3odjnFwt5JTiFtfRyEJRygiqKSsFi1tQSmHAy4VOqZQ9MkFwNAliY0AgSpnJpKasvZVJ3BDwtRADBThlRKnktVWEAl3aSo9YlALM9D2U75QKtsWxOWzOkfd8DORdeH5MlIpN7yt0SoVuwwLHXiHh+5BwkXVdM1nQxPdiblQvjO8pME6iFNHcaHehvlFitc0ELzN9kqg9c2dm0DYegK0qPpaoEYKBexwdnc2ZhhireK6C5wGdnXR9qRdSKjz11HNhWqqRkCi1D6FLuZN3fYN51PH4XiadGpnLw7Jg27Z2SuXzkSjlZOqySAwXA9LFQC4HpSLBo5B4f5JvxnYrFYkM+m5+JErljcMEMIsdEXRQcTTjO5YTi+8Fiy0gcpkkOqWkCIvOGxElsi0tHEqlkM3lIlGqRg67tJXWI+CFh4F8Tsc0axjfY8GiNRbfC9wbw4VzHjqldHxPn6BB9MixLFhcl38HTikGlI5JId6Ho2JTFlvHKRV0RhUWnTPGdPSNaVEq6+pJZ4AWpZoNViBy5zwtxGSs+lwbR5uEOyjvQWEwp1Qsvuf5yHrVCXTF0/eirwmp4MVEqeBcsS2GbF4hm9Mdc4A+f0WpovPYOSRik0xLOaUKHbuFTimGSOTyTVxQwXTTDfuVE0T90ENjUuBWBjMWfQB2ZnKjN2mfgDOGT180BwcexYHxfqM3Z99hgMPZ0Y6lyzMYAwn7lmHHdoHdO4ClS+n6Ui88z8epp74fTz31QsNdsCRK7WPE43tCioL4XswpNaIFNUPKsfUiMR7fq2OnFIMWH/pyA4BSRvAoJUoh4ZQKC6U51wuEMk4pICo6hxl73oyLibhTKhSdUFx07klPO6WkgmVZsKz6v5qgIB8A+nNZeEJ3vNTaKeVLHwO5rIkWlT4uhkvUKaUghArFP8ZG5qCzmBVGkURB0TkDTE+P7m3TopQWE1Nl4rahy8MIUlIqgLWIU4rz0BVWKr5ncQuO5UBCYsDLwvOEFh2aUJQCkj1KY8kpBUQCsitcDOTz2illl3FKIdoX8SmtgXg8XOLxvXhRuS49L47vObalp8Qqhf5szCll4rdFRecJt2GsU4qVj+8FzsyBvKsnC9pW2FVoW8n4np7WSUXnRAvBLLAxMKChFWAM4Ja+ZjFyStUNZXNdSWJbsGy6NtcL29KTcm3bGhM37FqBICnTDDTnp3diVIh/8C2M72X9LHL5uFNq+M9vcY6UmXTXn8s2rOjclz76c1koBAuQUh+eWFQG7msnhjIiEzeLkESnlG/2lQIkELvD3XxRISASHYTQd/bjTqn4/2unlF4c6dde/0uCFQhESqE/l4MrXe1Uq1HsM+iU8qWPvuwAlFT6g0YNhBktPpliZRn1zXDwEcf3OONhoXLhglq/WXMoBQzk/dDV4ZQRE+N9OFqUkgBYizilWBRllCLhlAL0MexwB0op9Oezel8xINWkHyADISYbE2Iy9thySnnCQ18+DwVdKF5KaGEs6ULNGtdYtU4pAElRShl3U8E5ZFs6vgcA/TkXXuiUUuF/gD53iocFDO6UCl4HAPjCR39eO3ZTth5QAJbslBJakwpvhBAEQRAEQeyLkCi1D8FiH3yD0daRUyofilK6U2pk0aPAXdSfzYaLyHqJUmnbxLSkh/5sFkoppFJOuAhIEHOL+L6OSSlTXB4spDJGYMu5AvmwUyrmlELzd0ppYcMPJ8KVFKWEMK+dN0iU4nAcHfvsz2qnFGMsXNxVS3BcCCXQlxuAVAqOY9fELcSD+J4q6JQa6fQ9biWEmFILasfSolV/3oMwDriKnVLQUabguG0dpxQv7pQyx67FtFNKQaEvn9WuMZTv12o0bel4fG+MOaUCUUp66M9ph2xQKF7y8SVcYxkrU32nVKwTSkEf874s0SllaWG3L5sP43tCShPRK4jvxTqlhHFjgVUuOk/sCyfaF0E/XFR0rhKTcQmCIAiCIPY1mvPTOzFqcBR0Shnyfh4DObM4SKdG9tycIWXKo/uyA/Ckpx0v9RKlTFGuJzz058x0K8cuKbTEe3WC3iUFZe6O68fEF05urFNKmdJoQC+cm3EtEYpSQncTBdP3gqLzpChlXGKMlRbwRhnOGNKhwy46btJWbUSplJWCxSzjlOoHlAojPNUSLCYVon0NjNxBxxkHhxadfOkXOaUCUQoK6Mu5kEpvg1MuvlfklDIdVS3glArie0qZiG2hKMUtpLi+VvXm++ELqfdFk8f38p6PgVin1FggY0XR6f4gBu5YZYWWtvi+cCOn1EgI+9tU0ikFRJMbgZjbkOt+JwWFvv4chIhEKR3fg3lc4fQ9hNdS/fUyReemJ8yXPvrMe2rK5tr5h9jNEKE7rKCoU4ogCIIgiH2b5vz0TowKDIg5pZLxvbzII1utKMU4Umax0Rd3vNRIXKhE4DoIYloAkHKcsnfrwylIvolJBU4p8/jQ2eD5yPsFnVJmocNH6IgZbeLxPV/60XQ9XixK+Sa62KgJUJwzpI2Y2TswACFFojC4WlI8FRZm9w70m1hnbZxSYFHkMR7f011T1U3f86UocnlYnOtJhQD6cx6E1GKiXeYYT7g8TGeO7lNrflEq6HdTUAlRyjFTnwKnFAD05QaMKFV+XzSawHmZ96LIWq3K/BtN2jY3BKSH/kCIcQZxSplrq+sLDLj6Wt1mt43oZ4dF56FTKojvaSE2MSwA5uaJbUEpoGcgFwq1uuhcVnRKBf185ZxSKSsV7otesy8cO+6USnZKKVCnFEEQBEEQ+zYkSu1DFE7fCwqwAeOUMne4RypKMcaQTunv7TdOKc543USpYDEglEDPQJ/+t5Rd1v2TcEqFRefx+F5QxiuQNxGPIL4nWyS+pxfz+u4+Z1HPUbCg0hExHeNsVHyPsaiLrDfbB1/62ilVo/heykrB4haEEujN9gPQolRNnFKIFq3xKYcM1cX3AECoYqeUZRbUANCX83QXGEPYkVP0fAmXhzTT91jZxzcTnDMws52ihFOKM46UFTiltCjFWRSPajYip5TAgBd1So0FgpJyX/joi8f3ylwbg33h1mBfBE6pocb3LM6QcvSfe/tzYaQ1iIkWFp2HPYxShddSoHx8LxDo4k6puGssHt8TZmP11NcRvXyCIAiCIIiWp/lXJkRNCT74FsX3vHw4fa8tM0IxgEWCVm92oObiQiWC+J5UMhKlHKeM0JIsOg/GhnPjzgCSY8sDUQqIFvjB45tQk4qcUlKGUxbLxfc83w8jJI0QpeLxvd6Bfl0kzFgoOFSLYzmwmAWpJLpjYmVNXmtsn9UsvmeKzn1RLEoxxpB2CkWpyk4ppaKic8ZYSzmloEzvW4EoFXdh9uT6ISu4xhqJLvfW+zzvecgaIWak7qBmI2WnwKB723qMSzWTslFOl80Ek009ryaiVIAYQtE5Z5Gw29OXDW8wKKmiiXiIOaVMtK4wvlfJKSWkQK/ZF2nHDq8HcadU4MqK3zAiCIIgCILY1yBRah8jPn0vXnSe9XLIVeuUQhRR6R3og5CiMU4pKdDTr8UHXdpeYltZPOKm43vBFCTOGcAYUqabJl8Q31OxhQ5jzeeUYowlOqV84UdOKV7glFICvi/0a+c8FDHqCeeRKNUz0A8hBSxm1UyUCpxSSil09/cCANKpVE1+bwyofXzPrOT9mFPKiS2oA1GqN+eGx2xZUYohXFD7QndK6fhe81/6w04pAF4Zp1QgeHdn+yCVMud1c52PAYEQk3U95DxzrR1DTqnQpZo10cSUXdYtmE7ZYMxEGd0aOKVM5FVKkeiUKnRKMdOb55hzqLs/F4pSUumYqFJJB2TohIwVneupfKWF3eB9SCqJ7phAF1xu4p1SfsIp1ZzHLUEQBEEQxGjT/CsTomYwsEGLzrO1iO+Z7+3J9ofdQPVySjmWA4tbCadUJp1COStTPOIWjAUPxtAzIBSlcp6o4JRqvsVEQnAzMc34Qioe3/N8H4BqmFMKiI6bXnPcWNwKu4OqxeY2LKaLjSNRyqnJIpAh6iCLx/fC8uVhwhkPBTQdK0324TCGcMJlT9aFUtrJUS6OF4nQKpq+x1jrTN9jHICC74vQhRY6pWIuzJ5szCnVpIJbJhRiIqfUmBOl4u6gwZxSRrDKe15U+l5lfA9AQXxPlXZKcYZ0wikVdVD5IhK1gnMncEwFPW/h0IgyReeRQCdD11g6JtAFoql2sRqnFMruKoIgCIIgiDFPc356J0aNcvE913cxkDeLgxGKUvHv7R3og2+cUrVyvFTC5jZsbic6pTLpVNkP+/H4ni+ijqhgMZKytYSX9wRyCVFKQcYe34wknFKm6JwzXjK+5ws/VvLeiE6p6Ljp6e+FUDJRYl0tNrfhWI6J70WiFKvF745FTqkgKgkAHCMTKwOHlYKCJ/1Q/EyUNCecUkGnVOmfpTvPgqJzEZY0t4ZTioFbHEoBnu/DV+Xje93ZfkglYTHtDmtGwh4l38dAle6gZiO4IeBLgd5c3ClVmrSjBSvXF+jPR6LUSCOvpYvOjVOqQNgFjGAGYG/fQOgWllLBE8XT9zjX1ygJRKLUIE6pMC4sY1FGxwpVp8gppV1cCkhMfSUIgiAIgtjXaP6VCVFToqJzE98Li87daHGQHnm0KVPgeAmEonrgcAc2tyGVQk82iO9VdkoJIeH7QdF51BHlWDxcOOW9pCgVdQc1X3wPSHZK+cLX4gVKF517sfheo0S24LjpHuiDUhIWr118z2LadZWM79XKKRWbziW0UyoojR+xU8osdj3hFYlSDCxcUAdOKTvWg1aIZaZDKgWz4A7ie83vlAqiVgDgC7+kUyoQdXpMfM/irCER1KGQDt1BPvpjolQzXj+Gi8Md2MzWTqlcIMSUv+5nUhYYdCwzLkqNBMYYOLiO78VEKQDwA3cgkm7DQCDc2zsABM4qpUyUubhTKvy66ecLHI2liG6OFDqlkNgOEeuU4rw5p7gSBEEQBEHUAxKl9jESTikZd0p5yOZzZoLeyBwqjLHI8WI6pWxu1yyGVQmb27CZDaUU+oNek7RT9qO+ZcXie8IseK1IZAqcUp6QcEW0r6RSLRHfY0zH9zzh6aJzFkVOQqeUMkXniBVLN4A2c9z05wa0uMBqF9+zuHZdKaXC6Xu16pRCrDtKxDqlRtInBSAso9cLYD+M94Quj9iCujfrQUG7pMrpMGF8D0FfjjkOWsApFW6nUvBKxPcAxOJ7A5BSi1LN6l5MO/qcdH3do1TPIRCjjc1tM+FShtP3guO0FEHxt+f7yHqufvxIRSlE12wRi+8BgShfLOwGgllvfy7QpEynVMwpVShKAcn4XhmnVCBKSSUTrrHw60HRuYx3SpW9d0IQBEEQBDHmaf6VCVFTEh0zKnL/eL4Pz/N1WXkN4nsDuZyJ09QuhlWJIEKilBpSP1a8UypYuFix+JpjB90fKvw6YFxmsfheM64l9GtjEFLCEx6AaLIbYyzhlPKFAKBgWY0T2ILfUzbvQikFx3Jq5rCLC6MDZkR7Ncd4nHjReaEoNeLpe9zE94Qf68Oxwp8XLHAHPB3LtHn5kuQgFiQVtCNOBmXgzX/pD5xSCqXje0A0vW7A1cKGxVnTxvdSjg3OGTwjgjPGkLHGVnxPKYWcq683ukOr9C8j5ViwOIMvJDxfJFxvwyUeeZVKlHBKlehlM+dQNu+HbuGgU0oFbsJY0TkDg1QKXjy+V8Eppd+H3HBfhF+PT9+TSVcWQRAEQRDEvkjzr0yImsFYUpQKokZAMM5eaidGFQv2wPEifAko1DW+FxfA3GBhVCaKyBiDE+uU8oL4nhWL75nFvlAqcfc97jLjnDdlQ20gYkip4AqvYtG5UmioKBUUnXuuXiQ63Cm76BsuyeNCCxu1EqXAImeOFDI6LkbolErG9+JOqej3Eixwg0PSHmRqYqCxKqXgxTularRvRxMec3SViu8BkbvG9QJRuYnje7alrydChQX2Y6ZTykSnoQDXRJ0z6fLX/ZRtgXOWEPyrnb4HoCi+5wXva0gKsZmUPv7deFeglGGnFEMk9Ab/rxRC0YqDl++U4pGgHhyXbSVEKSGiyYCcNefNDYIgCIIgiHpAotQ+RhTfQ6LoHBKAZFXF9wAgkzFxFMlCUape8b2wVF0p+J5Z9KXLx2OiTqnALaSjUNGUpEigUrEVQzy+ZzVkWl1lbFt3tkgp4flJpxSQjO/5YXzPaogoxRiLiZn69xaUBdeCeNm+9IPjog5OqREsM+PCYWGnVPC7CUQpbgW/ywpOKfNnz2/B+J4RzzxfhPvW5pEDJxQyhBFbrfL7otGkHC3EQDFAWmMqvhdNy2SQUh9bbYPE91K2pV1wClCSVzWlVU+V1Z1SQkoEmpSO28lwup5tx0UpvW3xmw2hUwpRpA6I3IYKgCf9oih0IUFcGIoBykr8PBabDunHpu81azchQRAEQRBEPWj+lQlRU5Ij4qO7xHqhpGMQqWpEqWCxLzmgWBjrqAeR+MAAoV9nW2Yo8T19hxxIOqXCxX7BgiEZ3xuZ+DDaBKKDlAqu74XlvCWLzo0g1wzxPQi9+kvxVM2Om3BKmwIg9OvPDHJcDO/JI2FSFxebfclGJvAF8T2g0CkVW1A7SVHK4bzsERg4MHQEzohSnDWtmBpHF7KbfeH78GVxfC8UpaT+N8dq3ilmaccy118tVtRzMuloE7oRFdPXfgzeKeXYPCHQccbDSYrDhZnrc6n4nidi8T1eLEoh5mhUSrtmpTLHXswpxRC4Df1Q2C3nhky8D8lAlIreU+NF534svtekhy1BEARBEMSo0/wrE6KmxJ1S8U4p7WxiZuR8LUQpvfhKWamaOV4qEU5sU8HPr9ApFYvvBdP34ot/2yxwucUSEb2WcEpZegS5lBKu6ZSymFUsSimRKDpvuCgVFzNrdNxEjpRowdxWM6dULL4nq4/vxQuUPenFOqViC+p0gSg1iBBjGVFVT9/TC3bOWCh8NTPx6XueP3h8LxCl7CYWpRxb9yjFhZgxI0qF0zKL3UGlCDqlAABKTyVM2SPbFzpqZ4QeJRHz/+ri8sBtmHBKmfe4uCiFeMQ1uoGjS8gZpAJcaYZGDFJ0Hjl246JUuaLz4ObGiF46QRAEQRDEmIA+Cu1jsHJOKSMGcM6RHmSUdyXSKUf/DMFDx8tIF+jDJbkYMHfrh1B0LmLT9+LCjI4CAaxArBFKQYom75QyRedSKbi+Lg+Px/PiTinXM6JVzCVWbxJipgJSVu2OG864LpRWCB106VqJUiwe3xNVx/fibjZXePClXiBHJc0sjEVFolT5nxUU8SsoeL6ONnHOm3ZCXRzOWcIpFY/vAWbaZ5FTqjmdi4DeNv17jJxSI3UHNRvRtZcPSZSy4/uiSqdU0CmlTE9isuhclnYbJpxSgTim+7C0UyrZKRWeQ8IPf2Y5J2f0WuICXezaG3NKJeN7I3r5BEEQBEEQLU99GqiJpiB+91dP34t3SmkxwOLVx/cYA5TUccBaiguVCBcDJooYbE85bCsQZiQ8U85rW9GSNixNLriNLRUip5TVnEtgy9Lj5wudUmFfFrfNRCkJ1/fC72lUH0/Y/WUce2krXTPXVqKvRkROqdo8f+Q6isf3RuyUivcoCR9C8kREEEh2SjHGkOLlxUQOLZwpFcX3OG9Fp5QHwfSUttJOKd1nZPPmXdxrIcYKHTSRu6j1sZiFFI/cQYzpuGI5bIvrQRM1iu9xRMJwYdG5DITd2DEf9l3FDhYFBde4RgEg0LACwUi7Df2ioRGFFDqlGEo7pYSMis51fK9JD1yCIAiCIIhRpvlXJkRNCeN7KIjvCR6L71XplIKJqCgT36tTp1QY01IAgvLcQQS2yCklwylMcaeUxbQoxa3CTqkovtesi/vgdUip4A3SKSWVjJxSDY3vGYddIErZ6ZqJmQwsJlZWdtAN67kZQteRKIjvjWRfJuJ7wotKmku4PJJOqdJws9hVANxgchjjTRs7jRMvZHc9LxzMUChKMbCW6JSybR79HqU9puJ7SaeUufYO8j5icQ7HDlxj1e0Lhuj6LFRUdA4AvjSdUrF+MiAuEvGEMOX6AgrJGzicITyHgqLz+LW0kCLXGAPSMVHKiYlSnoicUgRBEARBEPsqzb8yIWpKsHyVSkEWxveg3TK2PXJRKnBKBW6lejqlAOOcMD+bYWidUkJKeH5U9h2s8C2u3VKsIKInlQq7fvTivvkWFEFpuZQxJ1Sp+J4qjO815rWEsU/JAalFqVo6B/RxgSE56IZLIPDIwqLzEW5/4PrI+55xebBBRamUVf7nRC4PBc83C2rOWiK+x8BgmXN0MFEKQCRK2eUFukaTiKwpGxazEq+llQndiIEohcrxPScm0IUT60b4s8NOqRJOKSGlcdixaFsTTql4fM+HMkXnifieOYdc45SK9/MVbQ8K9wULhxMAUbeVlLEBGwxNK6YSBEEQBEGMNiRK7WMk43txp5R2qDi2FTqIRkIkLmiBodbiwmAwxnR3kIkOgjGkU+XFB6eEUyq++Ocmvqd7eeJOKUSOmGbtlLK0KCKkhOu7AEoXnUsl4cXie40TpVL62FS1j+8BSbESGB1RSkgZ9rTVoujc9b1wgV0oSukC/kCUKv+zgsW1gu7LQdgp1QKXfhYJx67vFXVKAYEoFTmlBhPoGk0kSgGQNhzLGTuiVCjEWDGnVPn3EYuz6H1G2rC5PeJ9EXclCqU7oQLC+F6RU8oyelSy6DxySrGi6XsA4MY6pcpdn8Kus9A1VhDfC68XCn54c6N5j1uCIAiCIIjRpgVWJkQtSUzfk/FOKV1Mbtt2uBAcCY7j6AWvEQBCJ0MdCBdGJgLGmI6FlSOM78WcUnYsChXF90o4pWLT95pxOWGZnqGg6Bwo7pQCUNAp1TiBTYuZCAXFMJZVIzJOJozvMcZqKkpxKyZKBZ1SI41CskjQcn1Xi1IsKUqlHS0eDkWUisbZA67vQ5l/awlRCoBlD+6USltpLWbGnFJNqRLD9PXZkShVjRDTbIQ3BJSJgVeK71nF+8LhI3RKIdYppURx0XkpYdfRnXpgMYuSOUcCCqfv6QisdhsO5pQCUCBKMaRiAl3klFLwwrhvcx6zBEEQBEEQ9aA1ViZEzSjvlDL9NLZVlSiVcmwtbhjHS8bO1NV9k4jvVXBKBdEgIUSs6DzulNIRPl6w6JeF0/eakCCKp6RC3ohOnEc9KBbTwkZ8+p52VzWG0GFnfndhMXmNyNiRg44xVlWZf5KojFsIGQontXBK5YUXlTQnRClbC0uhKFX+fOWhU0pppxRUy0zfi79u7RorFqVSdgqMRRPfmtkpxVmsr09qEWasiFIAIqcU9PHlDOKUKtwXNhu5QMdYrFNKytJF54wlis7TKdtol1F8L3AT6t616AZO0inlmWEB5TulGAKBTrvGOGdIxdzH8aJzj5xSBEEQBEEQJErta0ROKRVGjQCEopRdZXwv5dh6ka4AiNp3A1VCiw9aFLM4H7S03Y45XErF9xhjcCxeUpSKpu81pzPDsrgWI5RC3svrf2NRPM/iVmz6nnYHaDGyMa8llQocdgAkR5vdVtPnD8XKwMVRM1GqfHxvpMd9qfieFRNb0o4FFpugV9EpZb417/smvsdaougcYFF8z/MgpSyaepa20uCwovie3ZxxWgBmiEQQWbPGllMKLHbt5bA4i5xQJeDxeF+VnVLx0nGpRKLo3PWlnpaH5DmknVIocEqp0E0YF7ri0/fi8b2hOaV0DDDulApdrIlOqSY9aAmCIAiCIOpAK6xMiBoSOKWUKjF9D4Bj24nujeGScuxYfI/rO8Z1IuzyMPE9zjmcQUUp4xYSyfhe+HwAbIsVi1IyPn2vOad9BaXmCgq5QJTiyfgeYywxfc+2rYYt6J1QzByd2GfCKcVrKErFup2Kis6rnL7nBkXnLCkipVOBU0o//6CdUoh+pa5n4nst0inFSnRK6cLqmFMqGKQQn77XkK2tTFKIceBYTt0mk9YDLcRY4Q0BZ5CbG4whiveZfq242Dgcgul7Cqqo6Nz1ZRSBjR3zqZRl3gtLOaVQ4JRCeN0MXKeVBhno603kGks6pZjp+1PwTQGWxZv3uCUIgiAIghhtmn9lQtSUSvG9lGNXNS3PcWKiluB17ZQCYuKDQkWnlFWmUyqAM8DhxaKUUAjje83qOAnjewrIxzulAqeUWVTFp+81Mr4XiqGjIEpFYiWvuVOKobZF5/HvdYV2SjGWjPekHctM0Bva9L3gnM97xuUx0r6rBmCHnVI+pDJOKV7KKWXie4O4cxoNYzDihO7Aqvdk0tFGn2M2dKQ1KcQUEhWhB/siXZNzRiiBWFMiXKFFJj3JMSZK2focSjilEHVKMUTDLfR0P12EnheenmA5iFMqKjq3QmdioVMKrCC+1yLnI0EQBEEQxGgwdj4RE0MiEqWiCXI6aqcPhXTKqapvJixKNzGsjFNfUarNbtOvRelFSMoZpOjc0hOYhBClnVKMwQmcUrFFQzy+16yOEy1KIRHfs7kdLrQCJ1Wy6Lxxrg3HtvTPD46b0XJKqWAkfO3iezwuSqlgkTnyfWnBdEqVcUqlbEtHgGKdUuVEpnj0KO/7AFQoWDY/LDwm406p+L4NhR0VOaWaFc5Zwh2UrlKIaTYid5C+/jjDiO9VI9Bp0Sg6BxOdUnGnVClRCiwxgU+7CZOdUgzR+6YrIqdUxfheKNDxok4pZrbVJ1GKIAiCIAgCY6PQghgy8U4poIRTKuVUNQnItjgc29aOF1Hf6XtArFMKWiyo3ClVKb6nO6Xie6Rw+l4zEsb3lELOc8N/CwgiUFq00l+3AxdHA3AcO9r3o+CwS3SNWRzOIGLlcLFKxPdGKlYWxfeUAmcs0Yfj2BZsOyorH0yIYbHoURBNCjptmp3C+F7YKRU7jh3Lgc2c1nBKgSGdigsxY1CUUvrab1s8cS0thDFTNg6EotRIo4zxc0YUdkoJEXVKxW62pBwrdu2O/j3v+Sa+F++UMsMClB4+AGDQonMgFmVE8b6wLGaGTCj4korOCYIgCIIgSJTax4jH91RRfE87SFgVH5Aty4p6nETtC6srkbEzgNCOGNsavLTdMnE1IYPJZIATX0gxRE6pGFIpiGD6XhX9W6NJ0BtU5JQqiO9JJSNRymqcWKHje0YUM6JULd08bXYbmORQSpdnD9Z3M1xKxfdG7JRi8fieG07fi4ufjq0XucF5WqnoPDidg/he6zilANuITK6np+8VdkrpsvCULpVGc0/fA4BMTIhJ26m6DoEYbdrstlAcdGxeUbBP7Isqo4zB9/olOqVUGbchD6J7sZ8bnCMs7pSKnUNBvK9SZ1zGMa4xpYdlxEWpoNtKKorvEQRBEARBACRK7XPE43ulnFJpx6lqcWDHC25HIYZViShCwqJIWBlsSxd7SxmfQJd0SqVsXlT8Ho8+NqtTinMW3d0PRCeWXMwHTirXjzulGkMoFI1SfC9tpY1Tygindu0ufcExIGshSsW+N+8HnVIs4aRwjNjKuHbwpQY5BhPRIy+asliNG7KeBE6pvOdGnVIs6fizmWNcKRg0MtZoGCsUYsagU8pE1hzLquj+aStwStUmvicQM0rBFRLSOJ/isXQdgQ1Vp/Dfw941FuuUMn9XiJxSlYrOtUCn94VtFzqlgul7Cq5PTimCIAiCIIix84mYGBLx+F7QfwMgEqXSqarcMokFfwOKztN2GsyIDzoSNogoZRu3kJSxBXvylHAsBm6XcEq1gijFuS7n9aP4XqJTKnRKRUXnjUJH6vRxw0bhuEnbaXAZxWlq6ZQKp+9JVXV8D4iJUp6ro0es2Cnl2JFTYzCnlH4+s6AOXB6tVHRuFewLFMT3uAObpQCpXY/N3CkFFIpStXUDNpJomIB+fSl78PgeUGJfVOEaixedq0SnlP4750lh17J46MIr6ZRC0ikV/JpCUYoP7pRK2+lYz1lSoLM5B4O+IUBOKYIgCIIgCBKl9jkCh4RSKnR1AAhFqUw6hWp6heLiAoRV8xhWJTJ2BkwErgmryOUUx7KiKUhBfK/w8SmbFwkMrRPfY0XxvYAgfpLolGqkKMVjpfTSGhWnFDMLZh3rrL1TqibxPSSLzhVQ5JSybR52SjFWEDktgDGEDpHWjO8F+8KFNB6YYqeUju/p6XbNeT4GREJM7Y/xRpO202BKn8OObQ0jvmchU0XMO94ppeN70de0U0olzgMAsC3t5iosOk84pVjSKQVEk0zjQyNKvjYrAyYd87OKO6XA9PuIK8z1grOWOScJgiAIgiBqDcX39jHC+B5QVpSq5rMx59FkMyYsHZuqI1p80AuUcBJgGWzTKRV3SjkFC/aUXbpTqunje4yFnVI5zwWsgk4p45QS8U6pBsb3OOeJLrJaL9hTdko7pRRg2bXtlOJl4nsjcX4wRFGknHEH6aLz2KKWc6RTNpiJaDqDRH/08+k/R6KU1RJF50BMlAr2BU92SlncgsNTUXyvSUXigLg7KDPGOqW0UyoSpSpNcU3siypdY8n4XvH0PV7QKaVj5ubvXA/n8Hwfec+HjWSnlO5lM6JUzCk1GIFAp1BYqh5N35NKbx9si5xSBGFgDNhv5jgctXwW5swaB9cVePalXXj8b9vQ2+9W/fztbTaWLJqCJYumYtKEDFxPYOv2fqxdtwevrN+b+BmMAQcfOBnjxw3tc2xPbx7Prd2VGLbQCiilIKXC1vWv4aWnX8aend1oa89g/4PnYv9D5iGVdoZ9fQ6ec8/2vdiwdjO2rX8N2YEc0m1pzJgzDQsOmYeJU7r0fYGC5w7crrmBPHZs2YnNr27Drm174OY9tHdmMGv/Gdh/8Vx0dnW0tJivlEJfXxZPPvEinn9uPfJ5F7NnT8XKIxdjzpxpxqU7stenlMJr23bjsUefx7p1W2FZFhYu2g8rVx6ECRPHlXze117bjXWvbgNQ+QC2LI5DluyP9vbWu7mmlMLu3d149NG/46WX1kNKhf33n42jjjoU06dPrmqfK6Wwfv1WPProM9i8eTsymTSWLDkAhx9+MDo62ko+t1IKvi/w2mu7sHbtBrz66mbs3dsLx7Exe/Y0LFt2EObNmwneQimHkUKi1D5GMr5Xzik1chiLi1IN6pQKJgk6dkWnFGNMd3t4xdP3AOOUKhSlJKL4XpMugjnXRcNhp1QmuZDiTE+PkvDgxpxSjbrgWVY0KXE0xMy0lQYPokWOXVW8rpDgGBCy+ul7QLxTyoVSMPG9WPSIM6RSWmAMJkSWg7GYy8Nr3fie67lQSoLBLjqOU7wNUPrDWzPH9xhjBUJMfYdAjDYZOwMmbSgAaWfw3rLEvlAW0vbIRNzguYLzrXj6ngyn6fHC+J45byzLRntbGt29kSiV6JSKCVR530SdmT3oOaRj5I75sxU3Y4Xnn3ZKGVGKOqUIArbN8d6zD8HF5y1HNudjzfPb0dmRwvvftRTrNnbjv751P/723I4RPTdjwFHLZ+ITFx2JmdM68OIru7G3J48ZUzuw9OBpsG2OO+9dh3/5r7sghHHlWhyf+9ixOGr5rCH9jIee3IKLPn0LfCErP7iJyGdd3PzLO3DvTQ9hwtQuzF4wE907e3DLr/+CAw/dH+++5BxMmz1lyJ8blFLYuHYLbv/tXXju8bWwbI65C2ejvbMNe3Z24/bf/BWZjgxe/9YTcMrbTkAqU7zuuO/mh/GXa+/Fzm27MWFKF/ZbMBOWY+G5x3fijz+/HZOnT8JZHzgNK09aWtPPc/VCKYVnn12Hr375l3j1lS04bOmBGDeuHbff9jC+860/4MIPnIH3nHcqHGfw95pSCCFw800P4HvfuQ5KKhy27EB4roff/eZOTJ7ShX/57HtxzLFLip737ruewJf/66oh/Yz29jSuuvoLWLhozrC2rdFIqXDffY/jq1/9Gfbu7cHy5Yth2xauu+5OOI6NT3ziPJxxxokjWtu5rodf/OKP+MUvbsS4cR1YsuRA7N3bi//7v+uxaNE8XHbZxVi8eP+i/b5u3RZ8+cs/xjPPvASAYcmSAzB5chf27OnF1VffDCkl3vKWk/Gxj70HEyaMq9GeaE5IlNrHiMf3/LLxvZGjRSnzHNLS3Rp1RDtibAgAqdTgd3eCXhARFp0X96A4VrEoJVqmU4oBiBWZxyIngSilVNQ51WinVCBKcWkjZVd3HBYSd9ClHLumv7cwvidqX3SuVPHkMItzvaA3dxidIRWdK3h+FFFtGVHKxCx10bkKj9uASJTS8b1mLjoHYu4gZY/J+B5MfC+dsiu68ZICnVPVz46m78nE9D1PSH0OFXRKxXvlbNtBm5VGd28/8p6PDiQ7nhLxPREIu4OLaBkrE0YZUwUCnWVit0oq7ZRKU6cUQTAA7zpzMT7zkaPx1N9fw79++W5s3d4Hi3OceMwcfPXfTsb/fP71uPjS2/Dqhu5hP/9Jx8zFVy49CX97bjv+7b/vwcatPRBCwXE4Tj52Lr76uZMxeVLgZIiuIYwx7Nw9gPWbe8o+99RJbZi3Xxeee2ln+NmwVRC+wJ+u+jPu+P09OOr1K3Dux85GZ1cHPNfH/bc+gt9/90b87Cu/wUcv/wDGTewc8meHR+56Eg/f+SQOXrkQ533q7Zg6czK4xeF7Ai88uRY//+pvcf1PbkE+l8dZF56W+JytlMILT72Mza9uw0lnHYuzP3g6Osa3gzGGfNbFvTc/hGt/eDOu+sY16BzfgcWHH9gyn2kCNmx4DZ/77Pexa2c3/vNLH8KJJy2HZXG8tm03vnDZj/Htb14D27bwnvPeOCxnu1IKd/75MXz5v67C3LnT8ZWvfQT77z8TUio89eSLuPQz38dln/sh/vdbn8BhSw9I7DcFwHEsHLR4Xtk0gev5eP659Zg6dSKmTJ1Q3U6oM0opPPHEs7j00m8inXbw7W//G5YtWwSAYe3aDfjnf/46vvjF76OtLYNTTjl6WMeUlBK/+tVN+Na3rsaxxy7Df/3XJZg6dSJ8X+COOx7E5z//HXzmM/+D73//85gzZ0bie7du3Yn7738K8+fPwn//9ydx8MELYNsWhBB4/vl1+Mxn/ge/+tVN8H2Byy67uKFrtdGmuT/BEzUnPn0vLDpXAIT+92pFKR46pRiYqH9vSoqnwIM71Cln0AiJZXFwNnjReUmnlFKQQadUs4pSjJui89jUNZbslOKMJ4vOGylKxcRMbiaT1RLHcmCFi0Snpl1g8fieVEFxcfWdUp7QUaQilwdnSBs7PWcYPL7HGCzzGTssVbZ4y8TGoqJzz0QZeWLfcsaRZm0ATJSxSZ2LAW3xHiWrvbEbU2O0G9Fce53KrstEp1QVrrF45LVw+p4vdRNZPIIHGLehETAt20ZbRl9vkp1S5vlj3+uZDqhKnVIJp1SBKMW5ed+JF52TU4rYx5m733h85ILD4QuJ//nRI9jyWh+UAnwh8dcH1uPaW17AvP268E8Xrqw4RKGQ2TM68e8fPw7dvXn8v/+5D+s2dYduKM+TuOu+9bjzvnXo7smXTC795b71OP/jf8L7Svx3wSduwsNPbkFfv4c//vmlloruKaWw9plXcfcND6Br0jicfdHpYSQulXZwwhlHY8lRi/Hy39fhruvvG+6TI9Oexjs+/BZM328qLDOcxUnZWHLUQTj1XSdBSom7b3gA2zfvLPkUU2ZOwlsufCM6uzrC6FKmPY2TzjoO+x88F9m+LO675eHEcItWwPcFfvzDP2Ldq1tx1jmvw8mrDodjHPwzZk7Gxz/5LjiOjZ/8+E9Yv27rsJ57185ufPfb18JzfXz0Y2/DggWzwDmHbVtYecRivO+C07DTPCaXK47DTp7ShW9+55P46S/+veR/H/3Y28A5x2lvOhoTJnTWapfUhWw2hyuv/BV27+7GRRe9DStWLIZl6e7hRYvm4WMfezdyuTy++c1fYc+e8iJ0IUopvPTSBvzkJ9eivT2Df/7nCzBt2iR9sz3l4PTTj8eb33wiXnppA3784z9ACFH0HJwzXHzxO3DYYQtDd5xt21iy5AC8//1nAwBuu+0+bNmyvWb7oxlp7k/wRM1JxPdCpxQDZLxTauQfkOPxPS7r3ymVslKwTEwrXcEpxTkH4ya+55eL75XulBIysnc3I8H0PSjAM1PX4l08CadUExSdgyF23OgR8bXE4Q5spZ+zlk4pFut7ElKE51Qt4nt+ED3irGBRy5BO2YBZLNtDckrpBTrQmp1SvvAhlSxySlnMCuN7Fmct5JTiyNil+wValZSVBldpAAxpZxhOKWUhY7dXtS/Cc6Zg+l5wnS7slGKMhc5My3bQbkQpN5hQyZLjPqzCc4hbg84DSVkpWIl9kRTEONMTMcP3kTF0HBDESDjzlAMxfUo7nntpF559cVfia0oBt9z1MnJ5HycfOxcL5nUN67nfeeZizJ01Hn+8Yy227egv+rqQCpdf+QA+//XVyeidUti6vQ87dg9ASN2RVPjf9KkdOPm4eXjkqS148dU9I3rtjUJJhXtvfhj5bB4Hr1yEydMnJq5VtmPhqFNWAGB46I7H0bunb8jPPX7iOBy8chFmzJtWdG1njGHxigPhOA76ewaw4aVNRV9ftPwAvP5tJ2DchGJ3lpOyMWt/7TbZsWUXpGwtUWr9+m24+69PIJ12cNrpRyduOjLGcNDiuTj4kPnYtbMbt9z8wJBFN6UU7r77Saxfvw1z5k7HyiMWJ/YdYwyrTlmJcePb8cTjL+CZNa8knnv8uHbM2W8aHFN9UvifUgp/uvF+tLen8aYzjqndDqkTTzzxHJ566nlMmjQeq1YdVbRvjjlmGWbPnoa1azdi9erHhyV23njjXdi9uwdLly7CgQfOTTw35xxvfvOJcBwbd9zxEDZtei3xvdOmTcK5556GY45ZWvJcWbhwHhzHRm9vP3bvHr5LtJVo7k/wRM2J4nvli86rgTGGdDpwvDg1FxcqoUUp7dRKp5xBe03i3R7BXbMiUcrRwlUc/eGk2eN7PHyj88XgolQoWjXQKcXAwmNvtJxStgxEKae2nVLx6Xuq+vheGEUy0SOrUJRiDJm0Awa9mB3MZRHvlAoWwHYwdawFCCzkIuwGSopSjLEwvmdxXtMC+9EgIcSMOadUChz6HMukhuGUUryqfaGdTJEwHF+fBFEaxpLT9zhjSDv6WLGsyCnlByJWfJGA4GaOglDRBMjBnFIOd2BBP2dhfI+bSXtKxZ6PnFLEPkwqZeF1R+tumqef3R52fMZZv6kHO3YNYFxnCkevmD3k5x7XkcIbT9wfni/xyJNbyj6ut9/F3p584t98ofD//ude/PjXT5X9vtNP3h8Tx2dw7S0vwPdbK7rX1zOAl55+BQBw4KHziz4XMcYwd+FsZNrT2L19L9a/uKnU05Rk1VtPwD9cdh5S6dLR7HR7Wk+IhkI+5xV9/XVvPhpveMeJRZ+/Ab1+cY3Lp3N8dTc06o1SCo898hx6e/oxeXIX5s2fUSy6OTaWLjsQAHD/fWuQzw+t4F8IiXtXPwWlFA5aPBcdHcUO5OnTJ2K//aYhn/fwwP1/S3ztDW88Eld+55MYN66j5POvX7cNDzywBsedcBjmzive7mZGKYXVqx+H5/mYN28Wpk6dWPSY8eM7sGjRfEgpcc89jw1ZlBoYyOH++58CgLCjKg5jDAccsB8mTerCnj09eOyxZxPPfcAB++Hf//1iTJ8+ueR25/N60E86nSr5Ox1LNOeKmhg1oul7UdF5mqcB07dTrSgVfw4ubTgNEKW4CpxSg7u+gvieUtG0j0JRKm1b4Rt1EJNKTN9rdqcU9BsVUCxKhfvGXBsb6ZRioVOKjYpTyuZ2FN+rIFYOFz5KnVIiHj0quJPWlnFM1xSr6LIIRSkVE6Va5LNEcEwKKcP4XkKUAkOGdwBgsKzmj++lU7be9VVG1poR7Q7S523asSseYpmgd0pxtFUp0EWdUsnpe0Lpyxsv6JRiDKEoZTsOMqZoNxC0dHwv5m5i0bUfSE4yLUXklELY/xY+l+n7U0C4reSUImqFnuTkw/M8eJ4HIUTTx5smdmUwd9Z4KAWs21TaCTCQ9bBtRz8YgMMWTx3yc8/bbzz2mzkO2ayHzdt6wTnD1EltOOiASVh84GTMmNYBexCH7UDWRy5fLJIBWvA6640LsXbdHjz0RHnBq1nZtW03evb0wrI4ps4qXhADwLiuTnSMb4cUEutf3DSkYymI6aUzpT+DK6XQu6cPQgg4jo2Zc6cVfX/8v0J69/TilWfXg1scK05cWnHSa7Px92dehVLAtOkT0dFRuuJk7rzpYIxh86Yd2L27d0jPmx3I4eW1mwEA8+fPKOlWTqVSmDV7CgDguWfXJ6JkqZSDzs72kvtTKYVbb3kQuWweZ58zsiLwRuJ5Pp59Vguwc+bMiKZ9x+CcY948PdTgxRfXI5vNFz2mFLt27cWWLdvBGDB//qySx+z48Z2YPHkClFL4+9/XJr4WDGsp9X26mP0JeJ6PZcsOwn77zSh6zFiCis73IYIOGiAZ32u3O+ApDonailKWdJDi1RXYDhfHcoxTqnJ8LzmFTP9/KadUsKjP2BY81w9t3Po5mtOZEZVjq0iUsmKdUlx3SpmVEQDAsRt3OWAsckrZKgWnxsdNPL6XSTs1ja8Fb87xTqlaxPekBFAivscAtGUcgA3BKQUtWilEC+5WKkkMtlVJfaCWckpleAegALsVnFImyqVMZG0soW8IBE6pyteSlGODMwZR5b4o6pSKrZmU0qpUYaeUdkrpCGwqlUI6pd8LmTlvLRb5oOIuq+AcqnTddywHHJEoFT9Dg21RQLit5JQiRopSCgMDA3jmmWfw8MMP49lnn8X27duRzWbBGENnZydmz56NZcuW4eijj8YBBxwAxxn8s1G9mdSVwbiOFJRS2LU7W/IxQirs3psDYwyzpnfCthh8UVkgmbdfFzJpG3u685g6uR3/dOFKHLViJhxbv18wBqx5YQd+8uun8fiabcPqhDrm8Fk4cN5E/M+PHkFv/9DcLM3Enh174Xk+UikHHeNLu2PslI32Tn0DZefWXfrzYg0OnReefhm+J7Bo2QGYc+Dg0w2VUnowhOdjx2Y9fW/P9r04+ezjcNQpK5rqWK6EEBJbtugOrYkTx5VMWzDGMHlSFxhj6O/PYc/uHsyaNaXic3d396OnW8dTJ08pHXFlDJg8aTwA4LXXdsN1/XCgzGDs2dOL2299GAcfMh/Lly9sqX0OAPm8i+3bdwMApkyZUPIxjDFMNeXtu3d3o69vYEjOpO3bdyOXc8EYx+TJpZ/bsixMnKj3+6ZN2yClglViWrRSytxYEOju7sWtt96H3//+dhxyyAG49NIPhjfQxiokSu1jxON7vopEqR7TKdVWA1EqeA6ubKTq3Cllcxt2cIe6wmvhFo/EA3NtKFl0bgyFGdtCr+tDKBmWxDdr0Xm860iK6O5+QGJxL40g1+AFfTomSsW3tRbo4yIFwNeF6jV8Q03E92rhlEIgxATPX+yUam9LhV+zKyxog0NahcKr1SpGqci9pxigWJEoBQBp3glAwrJ403dK6QJwAMqq2h3UbGhRKuYOqkBYhl6D+F68Uyoe3wv+aBXchWSx+J5TQpSKF50DkZNJKf31SkXnKV7glIqht8W85ZjnJacUMRJc18Utt9yCK664Ak888QQGBgbKPpYxhgkTJuANb3gDPvvZz2LFihVN8/llXGcK3CzQBrLFUS5AL9ay5mud7Q5s2wqrCQZjxlQttozvTOF//98bsHFLD/7tv+/Buo3daGuzcfrJC/CRCw7HiiXT8fmvr8afV68b0jY7NsfbzzgIe7pzuP2eV4b0Pc1Gf+8AoPR1L5UpfSPQsjgcE8Eb6MtCKoVqP0F07+7FQ39+HG0dGZx5walIVVhor3t+I6778S3o7+nHzm27MW32FHzw396DZccdCttp7htRhfi+QH+/Fl4zbemS8UQAaGtPgzF9s7O3t/x5HSebyyOf97Sbvi1dVjhqN+6s/r4sfL+0CzCOUgr3rX4amzfvwIUfPAOZttYTRnK5PHI57Xxqby8vNAVf8zwPAwO5IT13X98AfF/AsnhZ0YgxhvZ2vd97evqhlESpsFo+7+G//uuHWLt2A7Zt2wnfF3jPe96E8847E9OmTWo5MXC4kCi1jxFN31Nh30a73QGm9NtMupZOKWUjxet78bKZDUelAHgVBbZwocIYyjulLHDJ9b6xA0dMzCnVpBZWBhYu6ANxo6QoZRb7QIOLzhGJmVqUqq1TymIWUsgA6NNl/rV87hKiVKFwMqznCwQtswDmZhEbwoC2TArwfO2UGmJ8L3iSRouPwyHcVnMMc8YTLpUovtcL2+LDnspUb9Ip7ZRiiiM9xkQpmzmwjTuobYiiFGdM92vx2sT3REF8Txn1x+JJkUnHhfU2aqeUXtxGolTssQhcVgyMRaLUYNg82heFohQPROaY8EVOKWK4uK6LL3/5y/jGN76BbDaL9vZ2zJkzB1OnTkVnZydSqZTpI8mju7sbO3bswJ49e3DNNdfgvvvuw3e+8x2cc845TbHIsW39GSte/l+EQmLAzFD1tPY2/Vmio93Bzj1Z/NtX78GW10xh9x7gp7/9GzraHHzkwsPxmY8cg6ef24HXSpShF7JowSQctXwW/nTnS9HztRjCj3fuld6hLFYfoB8/8iioUgrCF7jt13dh59ZdeMuFb8TiFQdWPAbHTxqHlSctRW4gh00vb8VLa17BrVffhdxAHke/YSXsIUx7bRaUitIL+rNj6e0O1haBa2YoSKHCiPlgfbeRuz/alsHI5z3ceMO9mD5jEk46ubWcaQFSDq12xbKs8PGlpuSVwvd1RDqI4ZV/bh4+vlw5v2VxHH/8cixaNA9bt+7Eww+vwU03rUZfXxb/8A9vx8yZU1py/w8VEqX2MRLxvZhTikmemJxXDZlRdLxUwuI2HKQBeBVtjsV3z8s4pXy9eMgEF/KiN5Xmg7HYhTcQnco5pVRziBXJ+F5tjxvOuBGl+k18r4adUsH0PRE56Cw+8g9J0e8lcm0Uxvfa2xzA82EXRPsKYSxa8AYLbn2MN/+bGmOswClVXHQOABmrE0AfUrbV9Iv7jJlKx2AhXaUQ02zY3IHD9J3ATHooopTZFzUQ6MJet4Ki87joU3ieBGKR46SRSSfPER3fi3VKBeeQcTFWel+zuA2HtSV+TrSt0bYE1whyShHDQSmF6667Dl//+texYMECvP/978eqVaswd+5ctLW1wYkN8xBCwPM89Pb24sUXX8RNN92Eq6++Gp/4xCdw8MEHY/HixQ1+NYDnCd39BsAuEWkBoMVl817r+XJEE9f+ct+6IgFJSoUb//wS3vf2QzF31nicePQcXHPT84M+D2PAOactBOcM19/24rAif82EbXp1lEJZcUIpFbntncEHPFQimPZ3780P4cQzj8Wp7zqprFMozuTpE3HSWceG27Phpc346Zd+jV9d8Qfs3Lobb3n/G0MxodnhjIWftbXoUToPGQhRjLGS/UelsGwerkn8QcSm4LmtIdzIU0rhmTWvYM3fXsZ7zjsVkycPb/Jls6AnCBpH9SAin28GP3HOhxRrBHQxPTeT3MuLfJG4qB9fer87jo3TTz8h/HtPTx+uvPJX+M1vbsGaNS/hW9+6FDNmjF1hqjlX1MSoEcb3EE3fa7PatFOK11aUcpCGxeorStksEKWATHrw6CC3eHgHHGYRUrioTTlWeJcobUcKerMXnetOqcBlMogoFeuUarRTKhKlnJo7pXjolNIF+LUk+BAgaxXfK3BKFcb3AITxPccqcFGVfL6CBbdl1bRTazSx7JgohdLxvQzT8YyUXfy1ZiOYxMbAkeZjq+jcYg7sQJQahlOKgVftlIrH9+JFvEFRLreKS0RDUSrlhNcEZgXuxMjFxBhghX8eoijFyotSgUAWFvkCLVfUSzSWfD6PH/7whzjxxBNx22234Z//+Z9x+OGHY8qUKejo6EAqlYJt27BtG+l0Gp2dnZg5cyZOOukkfPWrX8X1118Px3Hwq1/9qilK0Lt7Xb2YY0BHe/n4S0e7/lzQ2+/C84Y26a4v1vX0/NpdJR+zfdcAtu/sB2O6RL3S++P0KR1444n744lntuHZF0s/ZyswrqtDT6CWMpxmV4jwBVwz/a1jfMeQRKRSSCHxwO2P4Yaf3IrjTjsSb7v4zUilBx9GFCe8lnOOeYv2w5kXnAopFP5y7b3YuHZLUxzHQ8F2LIwbp9/v+gdyZcXVgf6cnr5scXR1dQ7pudvbM8hkHN0z158tu0/6+nR8cNz49lCYLIeUEn+84V44jo0zzjyuZT47FpLJpNHWpteEfX3l45DB19JpZ8iT7saP74RlWZBSIJstHflTCmFsc8KE8YMe9/GS//HjO/Gxj70Hc+fOxN/+9iJ++cubWlYEHwrN/QmeqDmlis7TvA0W7ETZ9EhhjCFt8ucOUrDrLEpZzIJjxpKny4yiDR/LeWwxoGMeRU6plJ6+x8CQMfE9EYs+NksnQyHaZRLrjFJIlIfrAmwLAGs+pxRSYa9SrYicUlFsp1YEwmStO6Wi+F6xy6PNuAC1U6rC8wWilNnORv+eh0PYf1XGKcUYQ4prUcpxmt8pZdtaYGSoPrLWbNjchmPOsZRjri2DPd7S11+mONK8dMnuUAlFKSmS4ZLAiVQQ3wPiTqlUeCc67pSKEzqbhuiUspmFlBHo0gXREs5YtLALnFyt+kmfaAi7du3Cq6++is997nOYNav0tKdycM5x1FFH4aKLLsLDDz885IjKaLJnbxbdPXlwxjB1cunromUxTJnYBqUUNm7uKR/zK2DLa33hIq5/oHRflecJZHM+GGPoGle+iyfgDa+bjymT23HtLS/A9Rq//0bKxGkT4KQdCF+gd2/pCKLn+rp7CsD0/SqXbZdCCon7bn0E13z/jzj2tCPwjn88E+kqeokYYzhgyXy0j2tDtj+HF596ecTPVW8455gzdzoAYPeunpLnn1IKO3d2QymFceM7MHHSuCE9d1dXByZM1I/dsWNvyccopbBrZzcAYObMKUgN8nlYKYVNm3Zg9eqncPQxh2DBguFda5qJdDqFWbP01M7t23eXFHaUUmEZ+tSpk4YsSk2bNgnt7RlIqbBjx56Sj/E8H3v29AAA5s2bOeQbUYwxTJw4HkuXLgIAPPjgU8jnhzYVsBVpzhU1MWoEFxSlEMb3HKTAVO3ie7ZjFr8qVdXifCRYzArHkjvO4Id3Yf6XoTiOZ9tm4QTACRwxKj59r/QYz0bDEGSjWcn4XuikUgAUM49v7OVAi1IKlrLBanxpsmKilFXhuBj2c4fHRaxTqgbT9wYTpVKmm8guiBmV3r7grksQ32stUQqMhW6+Uk4pOxBCbKvpHScWh4msWUhbY8spZcecUratKt5R5ZYyvTDVFZ0D0fnmSz/xYTMwwlqcFwu7KRtQCpZth5NHS4lSLPb3YTmloH+/lp389Mu56YFjCMUpEqWI4eB5HqSUmDFjxog+fzDGMGfOHPT19TWFw2RvTx6vbNgLADhg3oSSjxnXkcL0qR1QCnjq2deG/Nwvr9+L/qAgvaO0EGJZHCnHglIK/QPuoPuko83BOactxPpN3bj/0U1D3o5mZPKMiZg4pQtSSLy2cUfJ1929qwf9PQOwbAvzDpozrONN9ycJrL7pIVz7g5tw/JuOwls/dAbSpoRbKYXu3T3o2dOb+Nme62Pbxu3Il3FvAUC6LQXHCCq93ZU7wJqJpUsPAGMM27fvKVti/sorm6EUMH/+DEycODRRKpNJ46DF88z3l3aP5XIuNm3aAQA4bOmCip9V/3zbw+jtHcDZbz2xpW5oFmLbFg47TAs7Gzdug+sWH1tCCLz66mYAwMEHL0AmM7RBXZMnd2HePD1B8uWXN5bc73v39mDHjj3gnIcCE6DPkS1bdhihrPR1hzGGri59DPT1ZeG6pcX1sQCJUvsY3MQF4kXnFpxQmKgw6XpoWObEUjxcWNcNxcAkB6AASw36BsoZ04ICC0pnWZFTinMOxvWbZ/BMUsacUs0c34s7pVAc34vED/36Gy1WhJsnK7sshgsDh6XMwtOu7YdwHovv+TVwShV2SpUSPoNYWzA+thwMMZdH0CnVQh8s7GCaXkxYLRThmHQAMHBeWQhpOFyCMaXLs1XrTbAZDM44uHIAKDA+BPcAUwCT2rWJKu6agyWcUvEiXhaLZRedQ0FPFIu6I0JRipd5rHFKORXixQwcDPoxzEruC84Ct1wgFlPROTE8Ojs7kclk8MADD4xIVPI8D/fccw9mz57dFG5vz5f4y33roRSwfMk0tGWKRd+F+0/ClEnt2LU3i4ef3Drk5964pSeM7R20YFLJx0zsymDq5HYoBTzzws5B4zFHLJuBxQdOxk13rsXentZ2LLR3tOGQI/UC+cWnXw5rKQKUUnjlufXI51zMmDMVcw6cNaznl0LinhsfwHU/vhknvuUYnHPRm5CKJRiUVPjdd/+I6358S+I43r55J7728e/ixafLO6D6ewaQz2phYfwQRZtmgDGGlUcsxpQpXdizuwcvvbip6BzO5Vw8/dRaMAacdPKKIXdKcc6w6vWHw7I4XnhuA/aWcL9t2rgdWzbvQEdHBsefsGzQ5+vp6cctNz+IhQvnYOURi5vyBvxQYYzh5JOPQFtbGuvXb8WWLTuKHrN7dw9eeGEdbNvCKaccPeTPk+l0CqtWHQkAeOKJ55DPJ0UjpRSef/5VdHf3Yvr0STj88INjBhGF//zPH+BrX/tZ2Wu5lBKvvaavYePGddRkIFmz0vh3I6KuxON7vnFKcaUdM4pJuCh/Z2KoSK6L4qRQUCMoo6wGpRSkH23HoAt2zopEpaLFQezCoUyBnVCxTqkm+EBXklgpqBadCpxSiDul9L81WpTyoD/gKYFw2lqtkFLC9cxxySuPkR4OiUkm5uCrZaeUXSJ6FPyD54mK+fLiovMWEqUS8T0tPhR+MJLClN8iB4nmjlIo+FDQvzPXr/FB3mB8ocJyVcEqv48oFu2LvFvd761cfI8ZR1Kp+F4+p7sfZGxSKS8T34ucUvoaWskpJWSwLxRkwXsqC2+GRP822LACgihk4sSJOO644/CFL3wB1157Lfr7+yveoAi+vnPnTnzta1/DNddcgzPOOKMpRCkAuO3uV/Dqxr1YuP8kHLV8ZuJrtsXw1tMXwXE4brpzLTZv6018vb3NxrvOXIxz33IwOtqSgnHeFfjNjc/C8yVOPnYupkwqdqiuOm4uJnSlseW1Ptz94Iay22hZDG8/4yD09Xu45a5Xqni1TQIDXnfmMRg3oQMvPPUytrz6WuIYymfzePDPj4MzjhPfcizaO6N9p5RCz54+3HHNajx85xPw3OTnKuEL/PX6+3Hdj2/BMaeuxBveeRJ8z0e2P4eBvmz0X88A8tmkuKekxEBvFk8/8HeIEqXUUko8dvfTyPZn0d7ZhoOWL6jxjhldZsycjDPOPA6+L/CnG+9NFG8rpfDE4y/gpRc3Yr8503Ham45OfOaRUuHxx57HT370R6x9KSloMcZw3PGH4ZBD5mPr1p24956nEl8XQuLmmx5Af38OJ560HIsGcb4ppfDgA89g/fpteMtZx6OjIzMKe6K+LFlyIE444XD09vbh5ptXJ0RYpRTuvPMhbN++G8uXL8YxxyxN7BvfF/jLXx7CT35yHbZu3VG039/ylpMxe/Y0PPvsy3j66eeTzj/Px/XX/wVCSJxzzusxbVpSHB8YyOHxx5/F1q07i67hSimsXbsRjz/+LADg+OOXj2lRiqbv7WPwmMgSuH2U0Bc6BYm8zFb9MzzkACh4vg+/zn0FQkh4ZnqCi9KFcwGhUwo648HiQo4h0NSkVHDNm27CKdUkH+gKSUwuizmlWLi4SnZKMTTeQZNV2sbs+wJS1XbBLqQ0vz9ljs/aYZVwSlUV3yvolLJKlDQL88aVy3lmX5X/3RUWndt264xP1vE9hOKpxZPTf5RS8Dy9Lzw1AKmaW5Ty4UJCQIEj79VWHG00vpChKOWpbDgiuRxCuZDwoeAgX2UvSxTfE0ikc4POphLxvZwRpXwhw6k8wTkSRLYDhjt9T0gFz4iOHgYS+0K/z0RFpgDF94jhwTnHpz/9aaxevRoXXHABli9fjlWrVmHp0qWYOXMmxo8fj5Qp78/lctizZw82btyIRx99FH/961+xdu1arFq1Cm9729ua5r1g+64BfOMHj+C/P3cyPvuRY7C3J4/nXtqFdMrCOacvxJtWLcBTz7yGH//66aJy6LPfuBBf+NQJYAAyaQu/+MMzia//+Z5XccyKWXjbmw7Cpf90LK78yaN4bUc/HMfCcStn4SPnH46+fg/f+OHDRdP54hwwbwKOW7kf7rxvHTZs6RmN3VBXGGPYb8FMvPn8U3HtD2/G775zA8771NsxddZkDPTlcPvv/oqXn1mHFa87FMedfmTR99/667/gjmvugePYyLSlsez4JQD0+/K9Nz+M6358M1zXw2N3P40n73um6PuhgL7uPiw/4dDCDQMY8MBtj6Fr0ngcd/qRGD+hE+AM/T0DeOQvT+D23/4Vlm3jDe88EbNbrOuIc4YLP3AG1vxtLe684zEcfMj+OPutr0M6ncKLL2zAFd/4LRzHxic+9U5Mn54UMLZv343Pffb72LZtN+7+65P40U/+Fe0xwWjcuHZ86l/ejc9++rv4/nevx7Tpk7Di8EUQQuCuOx/HH37/Vxxw4Gx89GNvH/Qzv+v6uPH61ZgyuQuvf8MRLbV/y+E4Nj75yfdh7doN+NWvbsKCBfth1aojwRjHo4+uwQ9+8HtMnToR//IvFxb1ST333Mv4zGeuQH9/Fs8+uxZf+9qnE/tv9uxp+NSnLsD/+3/fxVe/+n+4/PJLcOCBc5DL5fG7392Ou+9+FEcddRguuOCsovUB5wxbtmzHF77wHXziE+/DggX7IZ1OIZ938fe/v4yvf/1n2L17L5YvPwjnnffmuuyrRkGi1D5G8MFcQoWdUkJokUVyibyqbsGulEJODQBMiwuDjSUdDXwh4Jm7Djk1eM6ccx7F96DdQ4XxPV/p9bCUKrRk6uhj0CnVnBfqRHyvVKdUoVOKoeEjdYPfl++LQUe2jgQpFVxP//7yqF54jRPst3inVLVOKQYGFYvvFeKa8yqX9yqOxm5lp5SOKZpiIKWP2cIPRzkj7rgqG+7/ZsWTeUh4kMqu2h3UbOgbAtodlJeVOz70vvChlI2cW51Al3RKlZi+VyK+F4hSni/BeVKUKu+UGqooJaP3oYJ9ETilgh+hr8VDe50EAehj6NBDD8VVV12Fj3/843jkkUfw0EMPgXMOx3HMhFXzWU9KCCHgeR6UUkilUjjzzDNx5ZVXoquruca7/+X+dfjsl/6KSz64Et//8mnYur0PmbSNKRPb8Jf71uGKHz2KHbuKO3jaMo7+bMuAtrbiaG3eFfjKdx7Eth39eMebD8KvvvUW7Ng9gHTKxvQp7Xh5/V58/6onKnZEvWnVAWAcuO7WFyq+77YKnHOcfPbxSGVSuP03f8UVn/4BJkzuwkB/Ftm+HE4661i85cI3ItNeXACfH8gDSl/73YLI0st/XwfOOTKZNNxseeesZVlwCoYSTZ4xEed84HQ8ce8a3PmH1bj35ocwfsI4gDH07e1Df+8Aps+Zite/9QQc+foVDe9DHS6MMUyePB5f/upH8O1vXoOf/OiPuO7au5HJpLBt225MmdyFy798MU5edXjRPvd9Ea5Fcrl8UeSSMYbDVy7GV7/xT/j2N6/B5z77PUyfMRlCCGzfvgcrVi7CJz91LubOmz6oS+qlFzfimTWv4syzjsf06RNHZ0fUGcYYDjhgDq688l/xP//zC1x++Q/xox/9AZxzbNu2AwsWzMGnPnU+li8vjirm8x4881lzYCBX5GhijOGMM06AbVv43vd+iw9/+D8wY8YUDAzksGdPD84440R88pPvw8SJ44u+76KL3orx4zvw9NMv4OKL/wPTpk1CJpPGwEAW27fvRkdHG84//y344AffhunTJ48JgbAcJErtYwQaiopN3/M9PUZbsdo4pXIyi8ApVcp6O5oIIeAbp1ROlR/7CRQWnZfu9gi2XkmJfOCUMvG9ZigHL0dUdI6SnVKhU0oFReiN75QKfl9xYbFWxON7OZWFgqpYED5UgmNISAkexDpr2CllW8XRo1CUyg1BlAqdUuY4aKVOqTC+hyi+V/B7CwQNV2VDob1ZyYssJNOx4jHnlJLGKcUUcrK82yAgL3OQzAdUJCyOlOB8EzIZZw2dSDERKCAbilICjOlC06F2SlUUpYQKBbpCUUo7dFnYZRj8G0EMB8YYjj/+eNx222347W9/i2uuuQYvvPACuru7QwEqeJzjOJg5cyZWrlyJ888/H29605vQ3t7edIsbpYC7HliPR5/eikULJmHG1A64vsCrG7qxblM3/DKR5+tve9FMzQN+/6fnSj6mb8DDd37+OH73p+ewcP9JmNiVhusKbNzSi1c3diOXr3wNuu3uV3DPgxvw9xd3VvU6mw3bsfC6Nx+NFccfik2vbEHPnj6k21KYNX8GJs+YVFLUB4A3nXcKUhkHXZPH47BjD0587Z0fOQtnf+C0If38dCYpeLW1Z3Dae1bh9W87AXt39WDntt3o7xmA8AXaOjKYPGMSpsychHQm1XTH8FBhjGHmzMn4z//6EDZseA3rXt0K1/UwbdpELFw0B+PGlT4/Z86cgs9ddgEefvDvOOPM49DRWRxH5ZzhiCMX4/s/+ixeXrsJW7bsBOccc+dNx/77z0Qq5VTcb9NnTML/fusT2H/BzKZNhIwExhgOOmg+vv3tz+GVVzZhw4atkFJh9uxpWLhwLtraMiX3zdKli3DppRfhpZc24D3veVPJz9GWZeH004/HccctwwsvrMf27buQTqewYMF+mDt3ZsmUAmMMJ5xwOI49djn27OnBli3bsX37buRyeTiOg6lTJ2LevJmYNKkr4a4eq5AotY8ROqWUgm8W0L4ntSgFibysPtoUOKXiUbp64Qc/kwXiWHl46CZiYcyj0CkljFNKBPE9xiCC+B5r3vgeEHPEGNHJirXYR06pYLEVK5VuEDkZd9jVVlwQUoZOqUpi5XCxEqKUcUpVMTGgsFPK4jwZWQPgmnM37/qDuhG1+BhzSjVBof1wSBzDQXyPJfdFIBZ7LSJKKWZ6lFp4lHgpfCHhCV00nhOVnVJ5kTO9UtHvcKTE43txpxRMkXjhdV0h5pQSMhRsUSOnlC/NvmAKOZEU6PTNDx6WnJf6eQQxFBhjmDZtGi655BJ86EMfwtatW7Fp0yZs374d/f394Jxj/PjxmDFjBubMmYOpU6fCtu2mX9j09rt4fM22IT9+T3cO//uTRys+Tilg+84BbN85ss8AL7y8e0Tf1wowxjBuYicOXrmo8oPN46fMnIRzP3ZO+Pf418ZP7ATQWdX2pDIpTJs9BdNmTxnx8zQzWjC2ccABs3HAAbOH9D2WxXHa6UfjtNOPDp+j3HN3dGSwdNmBWLrswGFv19SpEzB16oRhfV+rwBhDJpPGIYccgEMOOWBI35NKOXjve89IPEe55+7qGoejjjq05NfLfY9tW5g6dSKmTh0brrSRQqLUPkYkSiFcwIVlyVxVFHKGghYXlLb01tkN4AsBz9MLo2wlpxQ3Tql4zxIvE98TppMo7RinlM68NW3ROWKOGGk6o0o5pYCoCL3hTqkswBR8Ufv4nhDSHBdA3vTd1GrAHw+LzmXYaRS6nUbyfOCx+J6OlBa+/3lCC8luBVEKKI7vOS3llDJ9WgoASjilVOSUEsjBl809Kjcns1Awjr0h3JlvJTxfQgjTdTYUp5TIQcIIxTWL78kCpxQQnUPJLrJ8Xpfrur4AM+dr8P/lnVLmnOSDL+w9X5TdF4yxRPF6KYcuQQwHxhja2tqwYMECLFjQWqXPROvS7OLmWIX2e2Og/T76NO+KmhgVgtSAQlTW7Zn4HrhCVlQvSgVikIy5U+pF6LJhQF4OJO+aFxDGKACUje/Fis6Du/lCyjDH3bxOKVbglAJsFolSHDxySinjnGq0KCUHAKhR6ZQKS/eZdkoNdlwMF8uUIgspwnOqlvG9QuFTqSi+NyRRqkTReaugu1EA3SlVyimlQkHDRx6eaG5RSjul9PZmqxRimg3XE5BKAUwhOwynFFA7UUqUdUolr+tKRqKU58swSje0TilW0Snl+sG1QMf34tsUj+8xpq8dFN8jCIIgCGJfhpxS+xjx+F7UKSWNKCWRq4EolZPa8SKkDIvh6oXreUYwUhUdMVGnVNTrUeSUMl9TUiIfjDuXSi++0LydUmBJpxRQEDlhRqQyDhSUiLjUE2V+X2BB1K62x43vCwhR4JSqEYHbrmbxPRg3UDh9rzhH7gWiVN4L/1z2+YqKzlvnsm/FnVKKwWZ2caeUccAJloPX5E6ppBDT3Ns6XAqFmErEBbpqRakwvqdEclQzAJiYdvyokUrBzecBKCPq6qOqcqcUL3KdlsLzpB6GwYqjjPH4Hph5HyJRiiAIgiCIfZgmXVETo0Xw2VcpBV8Zp1SwIOCqdqIUV4ly6Xrhun7Y95RXucGdUty4g2Jls7yMU0rfWY85pQJRqkmdUgyFnVLlpu/pBb/u12qcg0ZPbdRiZnxSXq3wfF+7r5iCixykqt1UyHinVM2cUiwSpeyCkmYFBdeUm1fqlNLb18JOqWDxXq5TSkV9RK3glMr5OYDXRohpNlxP6FgzkxVdqgCQiwl01brG4tP3Er3/RvSxOU/cnIjH94SQUAicUqYYvWZOKbMv4kIZY+BmeAFD4JQa0csmCIIgCIIYEzTnipoYNbj5GBx3SrlB4a5xSlXjIpHKlKUH4kKd3QCu52unFFNalBr0tTDjDtJF4LpjKrk6CJxSMjZ9T0j92hiaOb5X2ClVvJCyuR06UJKTCOuPgjLHTbCva3vcROXpCq7K11SU4ly7J6QQUadUFfuSM+PqCJ1SyaJzqRAOKfA8YaZ8lSdaUGuBp9HdYcOh0Cll8WSnlJQKrol6CjS/UyrnZ4GYO6iWjr1G4/pBfE9qF1SF11ZLga5cfC9wIxW6QKWUkSglVShkxeN7cfEzOX2vsijl+SJ0SuVlNrlNMEJ26JSi+B5BEARBEPs2zbuiJkaFuFNKjIJTKhQXuNLul7qLUh6EkADXi4HBxAfGYJxS0JP0Eh1TmqDZSIpohLuQMowFNqtTChjcKRX+PeiUCicRNgalVBj7lKr2YqbulNIOOlflajqljXMWxg5r4pQy0nHolLJYwuWhJ2fqRa7n+RXdiOExbf6/pZxSVuCM0m6pwmNYRz212CiZ2xqiFNfH3lhzSuU9f3hOKT+bEKWqEegS0/cKnkcLsQUTLJWC67qAUuZ6bjSicvG9QNiFdi1WdEp5AsrsC7fgfSjaHvN3UHyPIAiCIIh9m+ZdUROjAjfFrxIxp5Qbd0rlqnp+pbQYBKa0QJV3q93kYeGGCyNVMb6XdEqVju/5RtCRQoaOsmj6XjR5rRlJdkqxRM+RjrQYUcosihopsEkl4ap8GPvM1zi+53o+lHHQ1dopZXG94NWiVA2m7zFzTIaiVIHLIyZKCV+ETqGy2xdzSqHVnFKc6wW7AqCSZf2A3hd5c14q7jW9KJUV2TEb38sHU1yNU6rSOTYaTilfysQVnxnHk2Ul43u+kPA8D4C+lkvTrVdKlGLxmxVDjO/lPN/U9WmBrnBf2JyDcVN0zii+RxAEQRDEvk3zrqiJUSH47KsXttEELwCAJfVCoQrC+B5XUArIufUVpfKuq4UoBrgyH4oEpdBTmfQiAzBOqQIxIXRKSRlOhFMqKDpvrJBTibhTavD4XuOdUlJJuDIfOuxq7ZTKu56JFgEuBj8uhgsPO6VqM31PF50jIUolS5oR9kj5ngxFmbLPF3ZK6UVwazmleBSjMk6peKwqHt9T3IOr6nu9GS5juVMqfD1MIicHj+8ppRJOqbzrj3geJmMsGd8r/LkMsHmyHl8IoUUppXR8L3hopel70IJxJVEqH0y0DeJ7Bdtkx4YXcFB8jyAIgiCIfZvmXVETowI3I6h1fC+avgcgFKUqxS4GQ8f3tLgApZDP19e5kM97+m49V/DgDhrTYogXnTMwjjJOKQYlJHzT3SOVavqic6BAlIot3MKvh04pLVg08rUIJeCpvHbYKSBXY4ddJEopeGrw42K4cFOiLM05xVBdP5fulBrcKeUZp5Tvi6gTrgzFnVLNe8wWEhadAyXje1Kp8PUr1txOqUIhJleFENOM6NcTRNYqDxPI+TnTr6W0oFXFzkhM34t/ISg6LzjmfSHg+fr3oJ2vgauqwvQ9Zg1p+l7e9Y1rTBTtC4ak2Kqn7w3v9RIEQRAEQYwlWmd1QtSEYH0nTZcGAMjghr1VfadU5HjRMYpaiwuVyOXd8A61p9zBHTEMsO1kfK98p5SE5wtAadFEme9qjaLz2LS9+NfjolSDp+8JKbTLhZuR8jUWM13X0x0vXEHAq+mUNotrJ5pSMox1Vj19DygrSgmpIMxB6PtiyE4pcNbw3/Nw0Z1S0MepLBalfCGNa0xBcb+pRSmg0ClVO2G0GQiFGC7gyXxF4TexLzxR1c2Q4HzTNwuSReelXKC+L+Abp5QK4rBDmb43RKdUzjMdWUzCky58GXPFBcXrodBFTimCIAiCIPZtmndFTYwK3HxIV4icUso3H4hrEN/zpVkYcr0wyLluXSdM6bigAriEJ73kYqAAfcfaCpW6yp1S0Z31oOi88PHNQiKmZeJ7caEkFKmULpBuik6pML43Wk4pXYDvo7biRSBMKqVjntr5UIVTCsHEudKilC+1U09BQfgyLOAvR0t3ShXE9wqFVd+XYZRRcQ+eaiFRyqvOHdRMKKX06wEAJuDJCjcEEOwL/fuq1ikVilJSXz/iMAY4hcJurFNKQZ9TzLiqgMGdUkOK77nGscUFfJl0ZjLTZRhMBqSic4IgCIIg9nVIlNrHCD76KmXuKquYU4pL5IVbVQm0kHpBoh0vQC5X506pIL5nDSGmFS7QjSjFixcHcadUsNBQOpmoxYdmdkoVxvdKOaVk3CnV4PheXMysuSjlGheHhFCDi5XDxTLxPRUvwK+y6JyBl3dKmR4cQB+XlbqJQlGqBTulOOeR8Kv0MRtvB/JEJEqB+XBl83ZKKSjk/HzUo+T5YQx4LBAeh9zXotRQnFKW/h63yn0RXNsK37m08FMc39PTOEXolBJS6ccOoVOKMV7RCRn1awl4yi263tg8Ht+jonOCIAiCIPZtmndFTYwKQaeURv8p6EqCJeGKfFXOJt0N5AHW6IgLlQh/HpfwK4gPw3FKCRErOodeYOruoOZdTcSn7zEUL6TConMEE6YadznwhAehPMAyYmaNHXa5vGuUROOUqmF8j5vpe0opKFV90bn+bZUXpXwpI0FZSuQriVI8ckpF5f6tAedG+FUo2SkVxveY0rGxJndK5UOnlO7CqqeLdLTJ5UfilApEKVGVKBWIwKWeo1SnlOv5kEIAkDoGK0x8r4JTCuDgjFd0SmVjopRfJr7HTOxXvyc37/sIQRAEQRDEaEOi1D6GSQwgWPoCyaLzvMhX5ZTS8b3IKZVtmCil44mDiQ/RYiVaiMSdUgosvPMuhQwXkMpEPvQd7uY9hSpO32OmU0rFFv8NwpMefCUih12Nj5tc3jVxGgWhaluIzUOnlF4U64VmlfE93VgGoHR8TxiHh5RDc0oxaKcUGuyIGy6c8ei4rNAp1exOKSApxOQ9ETrrxgKRU0oU9ygVoF1jMVHKr25fBCJw0PcXh/Hic8j1PEgptVMK+pzijEdF5+WcUswI/HyITqkS+6Kw6NyclgRBEARBEPssrbM6IWoCj1Qp6JU0ID3zMd6WcP3q43u+jDle8vnqNniYhD+PSwj4cMXgi1TtlAr+HOuvgV7ciFinVBElnFXNhGOXj+9pQS6K7yViUg1AO6X8yGFX49hnXKyUkBWPi+FghePmoxVxpUXrYIRTwMyxV7pTysQFhaooSoW/12D6XkvF9wIHHyvplHJ9oQc2MGkEgOZ1SkmlRf/IHTS24nvZmBATdguWQSlVsC9EGEkdCcH5pko4jso5pYQQgHmv83ypRVse3aBIPn/klGJsKKKUee3ch6/84viexYOhr+SUIgiCIAhin4dEqX0M/Tk4VnwMBkhzGNTAKeUKE9uwRsfxUonQmWVJSCUqig9auIk6pQrvWAsEi3+zT5Re+LTE9D0rHt9jZeJ7+oiwOG+4U0ooP+aUqq2YmYsfF5DI+7V7fh4seI3rrBZOqcHie56UJioIKOOUGiwGluyU4q0V34sLv6VEqSD2xRTAzATHJqVQiMlXGVlrNqIeJR+ygktVQelzkJl94Ve3L4JrW5G4wwJRKnnMu64fOqUAwBVSx3CD+N5gnVLg2mVa7rUplXBKSVUsggfxPYCcUgRBEARBEM27oiZGhSieZuJBCsYtowBLLySqEaW0uNA4USouPgglKzon7FinlMV5QXwvWXSeXPgzU3TevKuJaPoewFTx3f1IlAoEucY6pfxROm6UUomuMQXjWKkROr4XCSeoMtbJAudEOVFKSG3IkrpYfahF58HIe6uFRCkWiKWxTqn4cZr3fBP70p1SzRzfk0o7UWsVWWs24kXnQg3ulCp0jXm+rGpfBOcbY7xImCrtlDLxPcScUoyBca7jdQWXQm7ckIzpTqkhx/eMQFcsSkXX2+ItJgiCIAiC2LcgUWofg8XuyjITiYEInFJKd/tUMZnMFWbqkolhhdPw6oBSKop9WbpTqpJTyo45pawCYaYwvqdk0CalaXqnVBjT0l1ZZZ1SyghyDRTYXOFCjqKYOapOKVa8qKym6LyUUyp+XHrCuKSUgpIqik2VwTIOQIbAKdW8x2whUXwPJTulIqeUjvB5ykuco81EKMQYd5DvxyYHjgESopQc/NobucYEAAXfF1Xti0gkMuJU+J8WYguP+bzr6ZsMQXxPqIRTqvBaGDmnOBizKhadx/dFaVHKinVKFU99JQiCIAiC2JdondUJUROCD7/6A3HcKQXAkvBlcf/FcNDigjTiQuBQqd8iMedG4oMqEZsopNApFV8bKKCg6Fz/YzBmHE2+mIgXnXNYRe4dhzvJTqkGvhYdG1WhKFXrgvxsrFNqNJxSjLHQzVNtfI8FQwgGi+9BC1KJqFAZ4k4pbvEWKzqPFfCXiO/lPQElI1Gq6Z1SInJKRSXtrY9S2rUGwDilBo/vSWWE4WBfSAXPH3xa32BE8T19rMSFHsYYnEJRKu8mRamgU6pMfI+zmLBbYpJpHAUg55rXwn0IVXy9icf34jeKCIIgCIIg9kVaZ3VC1ITgDjJQulOqWqeULqyOOV5ct45OKb3YAGBiWmbC0yAM6pRSSVEqfCHBAoW1iFNKGfdNgVCinVL6zxbnYA18LXk/DwUZHje1dtjFnVJhn02N4OEoeVaTonPOApGrdNG5ZwRSKbV7r3LRuf7/4HitZtvqTaKAv6xTCgmnVJMapWKilHYHeWNJlIIyQowy7qDBbwgkBTpVtUAXXYf1e5rFTTk5A8AZbDt5Dun3JRVe0z0hdfTP3Jgo1yk1pKJzNRSnVLxTiorOCYIgCILYtxncg06MOVhsAc3AtFMm4ZRSVYlSYVG6pe8U5/P1jNMo5PIedB5taOKDbZfvlJJgkPH4XuI7zT3zZu6UihWdc1bslLK53VROKRVzSuWDRWONFmtRrFP/HiuJlcOBBWKmAsLS/CqdUoMVnbvmWNRl50NzSjEYZx/nUTF7C5CI7ymm3X0xcp6vry8t4JQKJ9JxXcw+lp1SCgo5Uf4cE8pMSmQi3BdeNfG9wCkVi8QFlOqUCkXqWHyPxbrhCq/rnEfxVwY+rPheqfeheCQ3cGERBEEQBEHsq5AotY8Rd0rp+F5SlPJEdfG9vF9OXBh9lCqM7w3BKWXFnFJWcnEQ1uCqyCmlEI/vIVowNyGWZYExQCk2iFPKxF2sxnZKaaeUCmOfebe2YmbcKYUhOOiGQ6ITxsQ7qy865wiMrEWilNTnlpLa6VHRKcWCkmYT32viY7YQzmJTIUs4pXKur8VAJiKnVJMSRqMZAOi42lgRpYACIUYNfkPAl76O9/GYKOXXQJSKxfdCbxNjsAp62bQTM4rv+UKaMvNgUmmxU0pf8k3R+aDxPZUQ6IBiEdwpnL43spdNEARBEAQxJmid1QlRE6L+Cn3XF5KFE8PAJeQQJtYNRl7ExQXA9Xwt6NQBBRXF92zjlKrQHZR0ShUXnUvzvFIq0ymlTHwvmL7XvKdQXHCr5JSyeHVCSrVEDruo6LxWYqbueImLUrV1SvF4F5kKOmlGHpEzrTVl43uuWbwHx2Te8weNOlpGQA2cUq0kSjE+eKdUzvW1dMmbv+g8FKWMECOlqkqIaSakVHC9qEepkktVyJhTCgpSyuo6pQqKzi3GdHwP+hiyY8d8YhpnkVMK4ffHiU+t5cwe9FoppUI+vi9KCHTFTimSpQiCIAiC2HdpndUJUROCO8HB3yKnlBaSKnWBVCLn5xJOKdfzIOo09lwp7bABMGTxIeGUMuPAA6TSET4oQEmZFElYUGjdvIsJ2zZiiRzMKRXF91gDnVLh7yl02Hm1E6WkQj4/vONiOISRWLMvdcxn5PtSH1dWLL6XFLhcI/IqIc0xLwbdVwmnFLdaquicIRa9KueUAkJxw5MtIkpBQSoFtwohpplIvBYjxAwW3/OlD1/EBDqFSNQaAcG1LehJ1NNGY6JUxfieNH1S5afvBWe3xe1Bb0ZIVSjQFV9v4p1SFqdGKYIgCIIg9m1aZ3VC1ISEUwqFTikFUaKUdTiEd4QDUcr1IWV93ABSKrjDFaXiTqmCiIcEg+7CVZCioFHKxDma2SllBYKbqtwpVdinVW8KRSnX9SBrJGZq4cZMgbSkLiIOxNMawOMOu1rE98BgIRJLy8X3gvMq5/l6cmG57WNR5LTV4nuMBccxipxSiT4t7usYmPKbW5QSftijJOLuohZHSBkTYryKTilf+QVOqVjkbQRwxs3NFuOU4snpe7aVvLZlC0Qp15fg3AqHWJRzSulz2x7UCaldY7rAPShyz4nk9SbulCr8WQRBEARBEPsa1Cm1j8Fi/yFedM4ip1SlyNtg5PxcUXxP1E2UkmYxgLBofThOqcLFv5klFT53SNgp1fxF58F6p5JTSvdPNZEo5dfuuBmJg244hNPyAEAFDrrqhJ9y8T2lVOiU0j1nQN4Tg8owSacUby2nlOkDAjA0p5SqncOu1njSg6+CTimphZgx4pQSMu6UqnztDae0moJ6pWKRtxEQdUpFPU085noqdkqZ97hEp1RUdF7UKRUKzwxWpfieiv1eTadUyfhefPpe876NEARBEARBjDqtszohakI0fc9EHSQv6pSqNr4HIBHfq5dTSt+tD8QHldyeEjDGjFPKxKQKHCRCIZpyZorOoRAt8Ju8C8SyjHtAMVjlnFJleovqTeFx43k+RI26yGShKMVq3SkVW1QqVpOicx4MIYAuRY4TiVJKO1Jcv3J8L+iUsnhTu/sKYYjFF8t0SgEIhRBPeZBozp6msDfNCDE65jVyd1AzkejHKlPuHccTHoQMRCmRjLyNgEAYZizWKTWU+J6MRCmY+DaDFrWSzx/dyLGG5JRKilJZP5t4DDmlCIIgCIIgIlpndULUhIRLChwQ4Woa4EqLUn4tRCm9SPZ9Ab9OboC4UypYuxYuBgqxrSi+ZxdN32N6sJeC7pVSkXMqEPeaWZTSrw36V4tiUcriFpg0i7gGOqXiUxKZrfew5/sQojbHTcIpZetFaKXjYjhEnVIwomW8t20EzxcUnQdRpHKilNROKdcXg0Yd9YI6cI1YURyuFYg7pQYVpUx8TzZvfM/13RKi1FhxSknjlFJDckq5IrYvuN4XVcX3jNDKoI/t+ERMxnmxKJUL3uMCUSooOmfJaZqGUOACA+eVi869AqdUzov2hY4TRuKw7ldv3vcRgiAIgiCI0YZEqX0MxqK7wAwsdEoxzhDc/K3GRZIQpRjgCwHPr48bIOryYLAdK9yewVwktl05vhf2cKlAdWDR4qXZ43sAzCDzCk4pq6HLouC4sR0tEno1jH0KKeGZBW/8uKgVwSh5ALVxSsWm7zEG2AXHWFh0LgMBTw46TMCKObm4bYVTyVoBvYA30SzJY1PWNIEopf9ZQUDoWFgTEggxjCttzlQxUa3F8YXUbiMG2EY3rCRKCSXAubmGKiBXg/he8JGGx6bvcVbCKWWmcQZdU56QYKHbCkWiFIsJVZXie56Q8Mw5yss4dhNF5018Y4MgCIIgCKIekCi1jxF3cej4nl4Q2LYF27IqTk2qROBASaccMAYIUd2o7+Hg+1oAYwzIpB0AQ+yUihedx6SZIL4XREHiy/5WcEpZwWtTgAW7aFttZoOpmBunCTql0ikHnDHdRVaj+J4IhVGGTDqV+Hm1INkpFUzPq7JTimmxlIGVdUqlzKLW84UWBMo+V9QpZVtWa8X3WOAUY4M6pTIpGwCDUEI7cJqQIL5nW9q5o6CQGyPxPc+XEEKCgSHt6N9R1s+WvSEQCHSWxeDYXE+oC9yMIyAoOg/iezxWVl4Y31MKyJlpnBmzrb6QWiQKp6oWPn80IIQzu0gcjRMKdAiOy/LxPRaLGRIEQRAEQeyrtM7qhKgJ0fQ9QMf39IIvZTtIO3rBPtjUpMGIx7DGtXWAcwu+EPDr5JTSkS8Jxhja0xkAw5i+V+JuejBwz7a47vVJ5veSU9eaENviocRmMbsoIhKfvhcvRW8Ewe+pPZ0BY8yImbU5boSInFKdmbbw59Uq5sUSx0FtnVJgJabvmQVvZ0YLv64vBnWVxbvPLLtYnGxmGFgkACheJEpl3eD3mgZjeqpbszulUrathRsF5N3m3Nbh4vkCvgyuvZWF32BfOJaFtLFW5arYF8H5xmJOqcBhyHihsKuQN51S7W0pffNEKmAQpxRn+pkZY7AqxPcCUYpzho4y70M6vhcVsbfQKUkQBEEQBFFzSJTaxwgcE/rPRpRSQMpx0J5u03fvaxDfG5fpgM25ie/Vzynl+wKMMYxr7wCgBbbBxAfb0g4L/WeOuG7jm06plMWRti0EN/3jLqnCxUszEfZEKV3OW+SUCuN7rMglVm8SYiZjEFKEQlK1CCHh+j4YYxjfPg6AXhTXakpbND2LhdP3qhF+GGOwYAFKi4rxuJ0C4Bm1dFwmBcZYxfheWNLMGCzbaurIaSEsJsqxgul7CkDeiFJdbR3gjGunVLMWnft5fa21U2hPZ4w7aIw4pYSEEAo25xiXaQcwuCiVEzpW7VhOdAOhin1hsaATr0R8j7PEEAulovjehM42MDAoMF3uhNJF5/q+hb5W8grxvVCUYgzj26L3ocT2UnyPIAiCIAgihESpfQzGIukhnL4HIO046EhHi4mRLtgjcaETlmUlXCqjjef78IWAxf8/e+cdZ0dZ7//380w552zJ7qb3SkgDAqGH3quIghQVrnJFUGxXxMvl+rs2UFGu1wI2hAt6BVQQpap0Qq8hCQmkkYQU0jbb95SZeX5/PDNzZnbP9k2ym8zHF+5mzzlzZp7zzHPm+5nP5/OVVGYqY/vTEUyzGPBtShmjZRx/CCxDkra1WkMVmalBYN+TYQC3SXullCGN0L63O5VSUSK0qrwSIUW/zhvH1QSXFIIqn5TKObl+s3lJ2ca+10elFOAHnYswFDmAUoR5NZVpC4E+vs6sjtqOVFRK9XXfdimEKAazKxnreqYi4dhDIqTUQFVKBeq8lGFRbqf037ronDhYECilDKNISnWmuA1uFtiGSUWElOrtWIRKqfAnmCIgpSSGESV2FTnfvlddWQZCd+hUBK9tf7NBUiSeu86U0nZaQ0qGBASdG1dmts2UGrjfIgkSJEiQIEGCBDsfg6g6SdAf0Eqp4LfAvge2ZVNmF61NvYFChYXIEJ+U8jyPXKH3WSE9QcFxcV0XKSVDMhVAN4LOY9334qdDQEqlTENng0S2IwaBfU9K6RdXnSilvCBPqzgOuwNFhV0lhjRCdVN/wHFdHNdFSEFVuU9KuZ0r6HqCGDmpgkyavtn3DIoh9EZU5YGi4Fv1KjM2UggcT/mdz0qjmIfjk7CDSSlFsfugaJMppSL2t6pMGVJIPOXhqIGpPgrmuFZKFUmpPQEFx8PzlL4hEFFKdXSOBeuybVr9MhZS+plSHSilYueQUuR8pVRVRRlSCDxP4RFkStFurQw68gkEprQ7J6UKwVjElVLR7yHTkCHxNZiUiwkSJEiQIEGCBDsDCSm1l0GIYvtpESGl0rZN2upeDlNniCmlpPQLgF1FSjnhHeqqTFER06l9L9J9zzDihUuQKWWbkrRlovB5Kb+F4YC370n/bnwQdF4qUypUSsnddrdeqbZkpox1zOsrCn5oekwp5eb6TaEiRKSwVMXQ5b5ACivcdjullG/VG+Lb9zzXo+B0opQKCmohMAxzQM/Z9hD+OQp4sg0pVQwKryrTSimPgU1KKRRpM0Xa9hsx7CGkVK7gFImYtCZiOvseCc73lJmiLNX3sWjffa9oe5WGjJFSnuv5XVphSEXGtwur0PTpL+8xiMACC5jS6vT8zjl6LKSUHY6FaQiEv0+JfS9BggQJEiRIsLcjIaX2MojQhuBbHXxSKpNK9ZmUinbuq8xUYBoGnlLkdxEplc8XUErFlFJdkQ9h0DklAqV9RYptGGRs0w8519uKFvoDFUbkbnwppZQhTESgxjGM3XYsnvLIubpIrUzreeN6xcKxr8gXHDw/hLmqbAjQv/a90K6n6BelFGgSMey+F6mQPVRo36tIW0ipC+rOctuiGTmmaQ4uZYbw1YxopVS065lSkCvo4x7iK6UUakCSUtG1MWOlSFt7GinlopTyLWtFIqajtTdK0GVsPxi9j933oGjfk35Xu0DRakbse67nhd9JQ8oz4TmkwrD0Eva9yFpvSrvTtTJfcPGU8jOl/O+hNjdHTKOoWDQG0/mYIEGCBAkSJEiwE5CQUnshgg89qpRK2TZlPinVtn11TxAQWhWpckzTQHm7TimVyxfwPN31KBpo3VnGjLbvFdVCUeguZ4KUKf3W3vHue0LIAW2FklLqu/FKYGJ0nilldtzifGdDoYqk1E5Q2IVKqQhZGXT/6g8EhS+gSal+yJQK7HullVJ6v8ttE0MKXWR3Yd+TvjXJMI2wcB8MEETmZhv7nqdUSFxWZjIY0hgU9r2oOqh1jyGlHDylMA1BpU9KdWaRDQi6lGlHSKk+2PeCznkU1UeBAknKNkopT8WVUlKgFEi/M6Us2X0vIHa1fa9TpZRPSpmGpDIVyZSK2vdkcc1IlFIJEiRIkCBBgr0dZtdPSbAnodhFCIqZUoKUbZHpY6aUpzzyrs7qqEiVY1km3i607+V8pZRpGFSmu0c+BGHgCh1oHkXeDZRSmpRSPiclInfgB7IVyojsX2mllEQoXfAHapTdAU95oZ2n0rfvRXNf+op8wcH1PCyzOC8cz8Hx+ocQEIiYfa/fSKkStlJPKRzfvleesjAMSS7vdGnfCz56HXQ+cOdsKYRKKU9iiuJXlibjgtD3DKY0B7x9DzQp1R9EzEBCLu+iFFimQbmtiZjO1t5swc/XMmzK033PlArsezGlVMy+10YpVSgg0EopQ38pIg0zfG3bew1BnhTQZaZUvuDieQrTlFT4pFTBLeAqFwtNRppGsTlCopRKMJgghKCmyiSVsXb3ruwVkNK/NnUFqpCsFbsMjn8dmlNIp3+iHhJ0DcdReB5ks+7ujLndq5DP62uWgYCElNoLERSlUfteyrIos7pu5d0ZPOWRdyKklGmAUuRy/UMudIV8vqDvUJtG2EmwK1JKd53rTCmlSamM7Z8qKrDvyQHffa8YdB6QHHEY0kAOAKVUlMysDBR2irBDVl9RcAq4nkfasKlMVyAQFLxCv3Vp0zWtP3d8+15f5oVAYISZUvGQZtdTuCEpZWJKQatSoY2t9PaK5/ygs+9RnJsCiYzY96K2xcp0GlOaKKUoeLuGBO8pAiImZdiUpfYwUsq371lGkYjJO52QUkHou5Hql7Eo5rhFMqUipE+M2A2swUJQWZ4Ozwfhk58ykrsYIGaB7cK+p8eC+Fi4eTyvOBaGIZFGkimVYPChLC35t0+PZ9So0bt7V/Ya2IYk94HC25SUbLsKQgiUAcuW5HZrE6C9DZ6rSak3F25LutLuIhQch+bmgXEtmqxwexmK3fd8+57jk1Ipm4xv3wvyL3oa1txOKWXqcPDWXURKafuewrJMylPlCES3lVJQgpRy2iql4va9wZApVQw6bx/Oa4iBYd+LzpuyVJmeN0qR7SelVC5fQHkKw5BUpMsQQlBwC/2nlBI7SSmlRCywGYpKKYGvlJLSz1bq+FiCzmEAhtVeMTfQUcyUMjAi4+r6Ae8CqEhnQhXVgCWlokop376Xyztafbkb96s/EByHVkppxW3O7Ti3LbCI60ypIF9Lkzm9mZ5hplSk+15wTmr7XnGjjuPiOC5CCCrK0sV1XxiRdT2+/WhTC6OroPO8g0Lb9ypS+ju1rY3ckLJo3xtkJHGCvRxSkZ6SJzO+9w1xEvQMamOa+/9Zzxsrex+tkaBnmDLa5ovnjmD2/hnk7rs83uuwfavD9m0O0/Y1Ei5wF6FQUGTKdvdeaCSk1F6Ion0n0n0vZZO2+mbfc5UbkgvlPrkAkN1FpJQmMRSWacbuULtexyoSo5tKqbSvlFKRoHMx4O17RaWUWdK+ZwwI+57ruRS8AgJBuV2GbVn9mimVyxX8ItGgIl1eVEp1Mi96ghgJpQSSvnffC4POo4QX4CpwlUIIKLPNiNWxY1Iq2k3MGoSklOUTplLJSJe1olJKCEF5Oo0hDRSKvNo1601PUSRiMuF6ki047AmsVKByskwZklId3RBQSkUIunSRoCs4/vra88EIM6Ui9j0zzJSKqw11l1ZtDSjPpLEMiQK8MCy9/dlbVBuKLpVSwWcaKKWCmyMxUsoontfmIDsfE+zlEIDlIeyBYffY06H8JbGx1WNbw8BQM+wNGFqpb1KkUgLDTNboXQXTEkgJqTSD7lp1sEJK/d9AwADZjQS7CkKIYtB5rPueTdqMKKU66VjXEYIuagJBxs5g27uYlMrli7kmKa2I6So7yDSMsGLvuPueLHbf84clIKQG8qIppfSLNIFJ+/wHQxpIdr9SyvEcCq4moMpTZVhW/86bbD6vO4MZel5IIfs3U0oQIY5Ev9g6A7tlNBsHNBHjeB4CQdoysEwZ60JXcv8idkLTsgbd3adQKUVbUkoHvAsB5akUlqHn+EBXSmXMNGk7opTqxVo70NAaklL+2hsQMR0Qv8FYaKWUnuuB7a03KGnfi2ZKGVFSytWkFILyTEqvfQqUT0VJ0f5iOErsdhV0no2oxsrsjFZmeoUYQaeVUr59L1FKJUiQIEGCBAn2ciSk1F6I4IJbYMSUUhmflMq62Q67JnUGT3lhQZixMqSCMN9cfpcUXgGJYZlmWBg5ntNpkartewExE+lyBhRcvc+2aWj7XtG7B4MgUypqETFFCVIqat/bjUqpgCASQlBml2H3MymVyxVQCmzLoCyli0TXK6r6+oqotQcVLZB7D0NYWnUl2yqldKaUEJA2DWzT8K2O3VdKDTZZjhkqpQxkhJRyHM8fC0GZbWMbtlZKeQNTKRWE+afNdEjEZAtuL1bagYdg/tmWQcZX3HZG/BZJqThB5/XyeyJYiwUyPB9DUkqKNva9oBunoCyTKja4iASdt1dKFbv5dRV0Hh+LtFZmtrELm4ZMuu8lSJAgQYIECRL4SEipvRDF63MJXoSU8ouJ3iqlggtvIYRPSvlZIbtQKQVg2yYZq0g+BCqcUtD2vQ6UUoF9T/r2PaWtJ4ggR0gOaNWJiNhWuiSldrdSyisgkZRZ6aJSKtu/SinLtMhYGaSQ2mraT+SFQCAC7asnQitRXxC170VzlBzPQ6ELZ62UMlB0niklKBa+g1oppSRGJNwh77i4rocQWulpG5oEz3u7hgTvCRSKrFskYjJ7kFJKUSRiUpapiZhOVKoKVSTojKh9z+31WGgiuHiDQYiiLc6IEEBQVEoZUlKWssObESJKSrXLlCqqITuz70UJ4pRlUObnNLoq/j1kSJEopRIkSJAgQYIECXwkpNReBh10HiildJtZ0EHnoX2vk4DazhAtQsqs9C4lpZRS4fukbIuUmdI2LdW1fa8UKaUUFEp13/MREj4DuMKXQugOT4qS9j0pivY9a3cqpZSD42oyM70T5k3Qxc/2C2YppFb1dUJW9gQx+54SfVZKaSJKEyzRwGbQ6j1PaaNRyjSw/YK6c6VUUcllWZ2HNA9ERLvvRe17BV8pJYUgY1shKZVzc7tlPzuDUkUiJmWmSKeKlrXeqoMGDNoQMRkzHea2daiUciOh76F9z+l1W2Ipgvy8YqZUQMgbRjFbD6BQ8JVShiSTtsO1r0hKddR9zz+HpN3pvhSVUnGCLqrYNRKlVIIECRIkSJAgQYiElNoLEWRmiLZB5z4pVXALsVDW7iIgpaSQpHenUsqySZspDGF0w75XOuhc24B8UsoMlFKqmHkidFExkMsJIeJKqdLd9/yg892olCq4BRyl503KTIV2nmyud4q9tsjmNBlgWyYpK4UpzZ1v3+tDoamUwgxIqbbWI8/DU7pATvlKKeiclIKiGsO0zMHm3ovY9+KkVN5xcT1tw0pbRVKqt40adiZi6qCIfa/guLi9JGIGEoL5l7bM4g2BjpRSkaDzTCT0PSAZe4OofQ/iWWymEe++lysU8DwPQ0p9A8Py51cnSin9d/3TkqlO9yVK0KX876G2it223fcGsg08QYIECRIkSJBgZyPpvreXQQiQYVVqxILOM/5s6KhrUlcI7oxrcsGOZUrtCsSUUpYujLq27/kEgtLFS1ActFVKpVOmn/1S7L4nB7pSSsrO7XsysO95uzVTKphvUkjSZrrf501rkDVmWaQtG0MYeMrrX1IqsO+poDjum1LKEravuiKulPK0UsqSAtuQ2D5h09qZUgoGrVJKCBGx78UzpQJCxzIkKcsiZWiyYECSUqpo34sSMXnH7bU6aCAh63fKTNkGadMOSalSNwTaEnRpGSXoev69A4QdL4vd9zq27+XyBZRSSClJR0ipqH2vbVfVmNrQSHUZdA46UyodIejiSqmIfW8Af4ckSJAgQYIECRLsCiRKqb0QwTWwUNFMqRQpUxd1nXVN6gxRpZRtpEindo9SKmVboVJKoTq188SUUpGemJ5SOH6xaBvFTKlY971BoJSSvvrLLME/SySS3a+UipJSKTNFqp/nTXFemNimjSnN/ielQqVU/2RKBSRiVO0GfqaU0uoKU0pSQWB2V0opUVRKDbYaOFRKUVopZfjkXEBKtTqtu2U/O4OnPPKOnm9pM03KKpJSvSViBhKimVKBUqqjGwJKFddkHXRuItA3AVy3D5lSUaUURaWUtu8Vz6F8voCnFKZhYNtW2FhB+OSntu/FEctl60Ip1RpRjdmGjSGNdplSpiGRRjGIPUGCBAkSJEiQYG9GQkrthQgKaIEBnv49sO8FWSC9se8F5IIhDFKGTTrlKxd2FSnlB2OnUha2mUJKnR0U3JUvBUPKMKQ6at/zFO1JKUHRTiakH3Q+cAuKtva9tpBCYgSkVCRba1cjIEENaWCbFulQKdU/mU/hvPA7tAVh2f2lqBFEM6X63n1PKRUWvm1VGwVXoVAYQmAZkrSv8sjmOg7Mjnffswa0uq8UTF/BIpSMhb7rYGxN0NmmGdqPB6JSylNeGKwfEjECCoXeW9YGEkL7nq2JmCC3rRTx66qidTZtaYJOCNE3pVR4zhWVUjFSqq1SylOYpra/piwDhED460Ip5ZI+h4pKqc6QzcXHwhBGjIiDwL6XKKUSJEiQIEGCBAkgse/1Cp7nsWbNGhYsWMCqVasAmDNnDqeeeirV1dUDm6ggquowikqptE3KJyZ6q5QKSClL6nyXdKpow1JK7fRxCcivdMrGkma3FDHSLw4Eqg0ppXC8Yve9lKXv5oecVBsFy0BENOy3ZND5AFNKmdLEkhap2LzpG4cSC8BPWViGhSnNmIWorxAi0n2vH5RSQoiQRJQyHnSed6NKKdF9pZSfW6Pte4MLRaWUEVO8BJ3rDCk1ueArPbOFgUdKRZUyaStNygnWWt1BcDAj2n1PEzEpDGngOE5JlWp0Tc6YGVLCQAqdKeX0cix0plS0+54IyZ5SpJSnFJZlFkkpikopo1T3PUQsU6qz8ztbiIyFaYV24ehYBFlxLkn3vQQJEiRIkCBBgoFdVQ9QvPXWW5x00kksWbKE0047jaOOOorbbruN8847j02bNg34Ft9x+56vlLJtUqbOynA9t9OOdR0h5+RCG1aUlAoyPHY2oqRUQHC0vUPdFgEphWirlFIUPN3lzDZkeDc/zJQK7HsDuJ4QUmD4x2SUIKWEkBg+Lz0QMqVMaWIZVozMDP2SfUA4L+z4vOg3RU1b+x79oJSKdt+LTLIgfN8Q2r6X9q1HQSFccvciRLQZzuPBg2BuGpihPQt8pRT6vLVMWVRKuQOQlIoE66eNNCnfsua6vSdiBgqUUuQK+iZG2jaxDDNUB5W6IRAlpVJmSq+tUuD0YSwCpVSpoPO2pFQ2lwelsAKllE/sBsRyR3a6Yve9jpVSSmmyFPyxkFqZ2ZYEl9G1eZCdjwkSJEiQIEGCBP2NRCnVCzQ1NbHvvvvyne98h0wmg1KKsWPHcswxx/D73/+er3/967t7FztE1IYglLbvCSFIpSzSpijZvrq7yLk533qkVUrpdJGU8pRiZ9IeSimy+TgpFSilOlPEhKQUbgn7ni6QLEOQso2YUgqhyYeB3MpMRgK4jVKZUmJgKKVyTg6FwhT6M8tElVL9sP0oWWlIA9uwUah+Iy8EhAUmqtgJrNfbE8InpdoTnwXX00SM1B3FUnY3u+9FSKnBhmBuCmSMlMoWtFLKMqUm6HxSKphPAynxLVBKCYTuMKk0Oei4HgVnsJNSkIuogwJy2aP02huQUgKhCTpTq8ZczyPv9FyhC9GOl0X7XtDQw2jTfS9YVyzTxDCMMHRe+M9pG3JefA/9szP7nkK1GQsDy7DaEXTh2qxUopRKkCBBggQJEuz1GHwVygDA3LlzueWWW0indREkhGDs2LFUVVWFdr6BjKCs05lS+mI+bdukDG3oahvK2l1E7XumNCNKqfxOV0oppcj5HaBCpZThB2Z3Qj4UW4kLDKNYHLhK4XkKIbRSygsColXQfU/6hdDOO6a+Qoji3XhJ+8woEbHvGcbuE00GiqVAxdSf80YpQrIylbIwfVIK6Df7HhDrvhe1mPUWgVJKtOE9876SxJRa/ZSO2Pc6sjoKUSSlDHPwLfmBUqqtAi3nH7NtGhiGjGVKKaUGFF/sei55L6/XWjMdWtZcT1Fwe0fEDBR4EaVUxielOrNORwPQ02Ya2zaRQuB5qtcEnfApy2L3vSJ92TboPCCpLdPEjJJSgfWvw2w2/XdLdtx9T683RdVYsKa1vTkS2nJdlSilEiRIkCBBggR7PQZfhTIAMGTIEIYMGRL72/bt22lqauKQQw7p8HVtC+zov3cGaeO6HkvWbue99Q2YS9cjpUQpqGvySZpQKaU71lmGVg3l3TyvbnyVzc2be/R+b21+S9uwDF8p5QdW19Y18uIbb2PtxILYdT22bK8DiooYS1paEVPQRWp9rsDGxmxsrDfXt1I+fAhuWYEtOYe3t9QDUJctkPe02sKSEs/SpE6YKWVnaGr1eGnphg5VRkLA1DE1jKwux1UuK2tXMrxsOMMyw3Bcj3fe30Zjy84LgS8UHFq8FKSq2Ljd5YW334893tDSRE6VQWoIq7c2t3u8LUZWlzN1TA1CwNaWHJ5SjCpPo1CsrVvLpqZN4XNXbl+JQtGyvqXL/Vy8ZbFW2BkmhjRCUmrbjgZeeP3tmIKtp1BKsXHzdkDb9wJrKcA7297hhfdfAKDSrmTm8JmY0mR7QysrN9TidfOcdF2XJteCVBU4o2jdPoRXlm7ENksrKgQwaVQ1Y4ZVoIANDa00+KHunlK8n1VkzQqGjKgiU13Gu9sbqfTnybp6PZ6WoQnRoKDetL2JF5e+X1KhZZtGqN7Iejle2/har5SQOwMCwYSqCYyrHIcCNja2Up+N71utMKmaMIHMiCGs3NGKnSowJGWG6jDLNDCkCEmpzc2beXH9i90iB1NGitkjZpM20zS1tLJ2/WamTx6HbVu0FFzeb2iJBZFnLIOJVWUYQrC9dTsra1fiqa6JlO0t22kttGqllJHCEgZSSlxXq4M8pViz/gM+2FJLdy2rUkqmThzLiKFVuJ7inXXbaGjpHdGay+VYtqER3t1EZWVTj15bcFy2N+iOh6kIKaWUYtHmRVSlq2LPr8vW0VJo0QpdM4VtantdwfV49d2N1Db2vHvi9tZtOLk0olx/5lu316Ea6kEp8vkCry56h5R/0+GdVesANBkmRTulVH1TlpeWro8ppjylqPP3q7be4cW33w8Z4Pr6et7d0Ehq6QaEYbC1vhnQ56Yhit9Di7csZlTFKP2aFhdHFQCDlbXLSZsrmTV8FtXp6h4fe1cY6HECCRIkSJAgQYIECSnVD/A8j3vuuYeZM2dy3nnndWjdefzxx7n99ttjr1u2bBmHHHIIr7/+er/vV2NrgSt//jybalsQ8i1AF4Ezj53F8InDEZ4BSu/re++tptZRCCVocVq45P5Lemx/8ZSHQuEVPBYvWsyWzZqkeGflWk755DX9e3Al4PiKg9pt21i0cBGO3wVp+erlvFJ4nXs2eyyti6umlIKpx84GBfduqOUvm3aEf3d9y+Gq5e+Sq2vWd9CDC/yqCSzcuJ6Tr/l9h/sjEBy670hu/OSBvNu8hGtev4Z5w+bxnQO+w5urm7ju/16npZ86zHUExxkKY2r4vwU7uPuFO+IPKigwGcZM4r8fXMH/PLyy022Nqi7n5s8cSk11Gbetz+N48K8TbHLuFq565SrWN64vbjpQlD3f9RwK503eY9HCRWzbtgWAhUtXcvInru7R8ZZCMC/q63ew8M2F5Fs1wfOzl3/Gza/cDEDGyvD9ed9nXtWh/L8/LuLZJZtQPTAPOk4ljDkQmuay4knBqc/c3eFzBTBn0jD+51MH0yRNbl+XpalQVMt4nkJlhjPv9GEIAf/17DuhAiogynItLSx84w3qarcBsGDxWk746p0l38+UktnHzaZq3FD+sfqf/OzNH/Wqu+bOwsyamfz0kJ+SVUO4/f0cDW2siJ5nc9CnP4UAvvXcKvar3sAFoyTLV64FwCnkeGvhQmq31ALw8vqXOfHOE7v13qY0+dKcL3HeuPP49Z8e40+PPsfVl53LmcfO45Htihe3tsZmgW1IPj4hw5RUgW8u/iZPb3y620V/wStgCpM1K9dQWzARKPKOy1uLlvDeO4qrvvsbNmze3u0YNSFg/kGz+N6/fZzF7zfx7797jeY+rCfKUwixrFcKM8dXOG3dvJElbxXwCvqc/s8n/7MkOVjwCkgk61atI08lQimyeYfLfvi3XqtPC5UnMOYETQQ/+NjzNG3Zyj5nnMb6D7ZyxvduxCv4xK9vy3byeRa++Sb1/jkUqGZffWcjv/vN47HPVSCYccxMhk8cziMvreHnP3+i+MZKr3fi7qV6u76acduWD1j0lsD1z+3vPPMdrn/2egBMUcNBo39ByhrKN5/6FjuyL3LTwTdxxIgjenfwnaCpqYlcrv9UoQkSJEiQIEGCBP2NhJTqI5RSPPHEEzz00EP8+te/ZujQoR0+t7y8nPHjx4f/9jyPVCqFZVmhFbA/4WBSnrLIpAws08RT0NCSCy/KhWf6diPBkIpyJg0dxrzR81hWu6zX7ykQHD3haIZVDOPg/WcwZcJo6hua++uQukR5WYbDD5pFJpPBMv1wbwMMO8XG5jocT5E2JZZvt/KUIpfTQewZy8COqJ48z2OoJRhbXYGXSXHgPqPxfNWOQGEIqMjYJcm7vOPS1Jpn1cY6lDDZmN1IbWsty+uWIy3J+9uaqW/OYUjBkPKO7SB9gULR0NSC67rYtkFZOq7c8ZSiobFZz8O0FSrb2sJTivrmLJtqm6htdqgYarKlpQlPQU5kaPKa2NC4AcdzGJIaEnbeAjC7qY4TCI6ZeAzVFdUcNncWE594jabmrlVW3UU6bXPUwftRUVbBcZOOY1N+kybDlKIx10hDroG1zWuZN/RIlq/fQd5xqcjYsfnQERSKxqZWTX4ZHtISVKTKSn6mjuvR0JJj9aY6Cp6kTgnqcg4CKPfDrx3HIe9owkJKQSZlxYhuKQQHjCqnosxk/pwJPLFkK6250plSzdk8rXmH1lyBKrRSKutmSZtpyqyyXo5m/8DxHBpyDbxX/x45kaPRFezwVVLBWADkCh65XA5hmiAlG5tzmKlq8M9hyzQoK8twxPgjeOD9B8h6WYxuBPc35ZtodVp5t+5d7Kkp3lm9nsbmVla//wF2Ks2G5noKniJlSGxD0lxwaCk4bM97TKuQLN+xnLybp9wqDzv/dYWJQyYyfcR0mupSSD9HyTQtGpob2Lh5O47jMKSivMvOnnnHoam5lRVrNqCQrNvWTF0f1hOlFI5TwDTN0MbWUwwpT3HIjHHUVJRz9ISjeWzdY50Su+MqxjFj5AwqRTWHzhjDove29ep9C45LY2seWoYjXP096rhOuAZJQ1JZnsErFNciaUiOO3wuFZUVzN9vIk++vY1MWj8u/XU9tusi6JInsC2Tykzx8/aUh+u4uomAP+4VGZvDZo2jumIIx0w4hoY1DbGxMKgIybqUkabCrqAsXbZzrgMcZ9A1N0iQIEGCBAkS7F1ISKk+QCnFggUL+N73vsdPfvITDjzwwE4v/ubPn8/8+fPDfxcKBV577TVGjx7Nfvvt1+/753oed1yd4r01azn00ENpbC1w9nV3FcO6lQEeGIbBjBn7cuDsfXh4xsPsyO7oteRfCsmI8hFkzAz7zzmAY+cfRkvrruuGlU7ZjBpeQ97LU/FyBdTBiFEjmDFzJtaqNxC5LJfPm8oR44cBsHFbA2dfdzf1zVl+/ZUzOfnQ6eG21q9fT+0HGzl0ziykFDwwY1+ue+ptVtW3IBrXc+ToLLf/8IslC8in31rDZ370IIZpMn3ffVlmaSWcaZnMmDmDESs0MbjflJH88b/O7xb50VMUHIePffm/WLR4DZf9yzl87V8vij1e39jMOZdfx/sbt/KN6z7FRaefWnI72+pbOPu6u9lW38KY8ROYNHU0vPsmAsXkKVNpzOfhaaiwK/jbRX9jYtVEli1bhkIxe9bsbu2rFJKR5SNJm2n2m70fJx17JK3Z/ru7n7ItRg0fimFIbpp5E9c0XxN2mbz4vot5fdPrVI+oZvq+MxHGMwgBP7ryFE47ZFqX23Y9j8u+/kMWvLIIZmxg/1OG8cfz/4Ql2y+vb63azIXfuReEZPLUaYh8AVa/w6jyFN89cT/ShuS1117jqaU7+NU/VzBhdJrfXH8JQ8qKBJIAajI2tiGZNdvjQ8cdHNrz2uK63z7BPU+9jfKCrpF6rl4691KuPeraXoxk/2HptqWc/6fzQcLkaZPZ2jIMVi1lWJnNDSfuT4Vvq7r3sSe55se3MnzGDKafcTqWnWLmzFkMX9YIQEV5GXNmz2F/OZuafA0jRo9g3LhxXb7/dU9cxz1v30OmMsOsWbOwbE00VFXVMGv2bNLr34KmBj4ycxyn7zOaGxYsY0VtEyNGjmLm9GrMBZqEuP7E6/nwjA9365ir0lXUpGtYuaEW03wGx1NMmjIVt6UOgMrych747Q1MGDOy0+089eKbfObamxDSYJ/p+1KzTqtx9HryMWyzZ8RSLpfjlVdeYe7cue2s6d1FWcpiRE05Arht5m1sbd7aqbVxSGoIQzP6Js7fZsyktrG1V987L7z9Ppf+4K94SiKCSxpPhduaPH40t//5Z6QiNmDDkIwaPhTbMpk9W3HO8Qdz+6I1PL2+lsNnjOZP/3J8LIDcU4ofv76Ed3Yozj1mFp+64pTwsbq6OpYsWcJhhx+ObekbIZmUxcjqcoSAX834FVuat8TGoi7r8O2nN9GY9/jBiTdyxKQhjCwfuVOI4vr6euwObjgkSJAgQYIECRIMBCSkVC+hlOKpp57ihhtu4MYbb+TQQw+lUCiwYcMGJk+ePCDuTBpSMmZoOa11aSaPrqaptUAmZRWtVb59zzAklt8qvipd1S4DpLcQAkYNr+mXbfX4vT2BITTR43gOiqL1aVjGZnSF36mrOUu+JUe2KUu5VOHfAbJpkxapj0MIQVV5OswfQbmUWzBldHVJVcaK9bUgtBXL8xSOp0kDT3l4qtj6vCxtMXl0Namd0BUtm89hyRw4WYakDKaMiX8W2zMGhlcAJ0vNELPd4wEqy1KkLH2MubyjO8ApFY6p6+mC2JAGE6smMrl6Mg3lDSilmFzd83NBCMHoER0rDvuKlJliYtVEgFDdBZAtZMMOYEIIJowY0uGYROG4LhUW4LSCaCZTUcHk0VVhdlUU9c05pAxCnd0wuDxlGoyuSJM2JDWWwCg4ZJuyFLKKEeUWNeWlFRSGlIwdXlnyMaUU1ZWZ8HcA4Z8TwzLDevXZ9CdanVYMYegQaDdH3u8saBuS0RXpkJRKizzZujryTZqE8pTCUyo8hwyp87UEgqHWUCZUTmBSzaRO31spxfDy4YAORvc8j5wfiO+4LiiF649ZddpidEWalE/0uEppdYw/70dXjGZKzZQeHbvuqqgz6lzXw3V1rpRpSiaOHcXk8aM7ff3KNRt0DpPj4LpumK9VWZZiyuhqbKtnJHdraytrq1JMHDmEmpq+r9llVhmTqjv/DKIYUp5iSHn31GZt8f7WBh2UriQoPWeUUgQckGmaTBg7inK79BprCMHYYRWU++on2xBMHlUVywp0PZeUredDsGYH5842y2VrVYrJo6pIpdofQ8bKtBuLbc05DLkVyFNjD2NyddckaoIECRIkSJAgwZ6K3ddyaxBDKcXjjz/Ot7/9ba6//nrmzp1LoVBgzZo1XH311aFtYKBBSqEVOSEppe17hpTYg7ArV1cwfaWK4zl+Iav/Hr0Drvw8EG1f6TpnJwi/FUKHwncUgGIYuiF5WEB3QEqZUu4U6x6AwsNFH5MXUQ6Ej3sqtHK6dDxnTUNimlKHxucdCp4mD5QCT/mkn1K6l5/sf8XXzoRAxLq2Oa7+bKQQ3SYKBdoOBIDy29N38Jlapm5P73ranpcPiRURduFSEcJF4fYo26otgpD4QCkVdAk0S6i4djUsaSGF1F3anDwFV++jKWSsI5mjCiBUeAyeT4iG51Avg/CDzz3n6ND+oHun47iacPXfz5Ai1sHQ9YrnMfRuLAMiTaE/a9cneqWQXVr3AGzbQgpBwXFxnCIplbKMAd0RdGfANg3dyU5JREhKeeF6p+dL1+dQQEK6jlPy+a4XZFL1PThcoT97hbYfJkiQIEGCBAkS7M3Y/ZXJIMTChQv5l3/5FwC+/OUvh3/P5XJUVVUN2G43hpTYlkEu6CDn6Y/fMAysnaDU2Z0QQoQEiSZNCAuNaMHreUWyqisyUQidNwK6U5MhO6aTTEMXsl0ppQy/i9rOgKdUSDa5JVqta7WHB0L5naBKw5ACyzRA4ZNSKlSduUrhKk2cCFFUpw0mBHlAWSdLwXFxXYUAUnZ3j0X4ZI/wSd6OX6c7xUlyBYe844WklCVlhPAUFPxwdg8XRdfd3TpCSEoFRDR63wYEKWVYmNIk5+TIu/lwLExDEOGNcdBzU3ke+OSyUlFSSviqlZ6tuxlTq8hybg7Hdcj7xI7jalIqICkMIRCI8PMJlFKe8hBC9JKU8rentP3T9T9vKUVIHHaGlG0hpMR1XQqOEyGlzFjXuL0BKcvQa7pXJKW0fS/4tbjGd4agy6JTcNpNJQU4Kg9YuD4p2pdRDmkylZBSCRIkSJAgQYIEu78yGYSoqKjg6quvLkk+jR07tlshu7sDhhTYpiQbXq3H7Xt7EgQCU7RXSgmhC78AntKkEUqFXdo63W5bpVQHMHwFVLD9wOoTFLOFiEJmZ0Hh4VEstNsiIMygWPiXgpSClGmggGze9e17eiyjKjAp5ICwrfYEQgjSRlEplXdcXE+TDd22VAqK6hYlSnYbC2AZEtMQtOYUhYJLXgWklIiRkwHh4iqnf0ipoCoXmjwbCKSUKU0saeEq1yel9By1ZHweBYSp8lV9gfIlmNPdURaVQqCUyrt5Co5DwSelHccFVbT7BudoUSml+kkpJXzFl9KklNJKNtmNNSFQSrmeR77gkPVVXlopNbjOwb4iVEo5UfueF5JSgaqzKwQkZKFQKEFvKhw3j1Jl4fP6gkBlCiohpRIkSJAgQYIEez12f2UyCDF9+nSuvrrvrep3NWSoeInY9wDT0N359iS0U0oRKTIjRZvreUU7UFdKKSIKDqHJvI5g+JkxnqezaTq07xk7j8jRSild8JSyJiqltFIKOlVKSSHCjJpcwc+UQisFXJ9wU0phCKNTQmYgQiCKSik3S6Hg4nqqaHXt1jaKtjgUGKJjYsAyDQxDopTSain/aVZkLrW173UWFt0VrDak1EBSSpnSxDRMFMrPlNL7aBlxBaKj9LmjVISUUnH7Xm/OoNC+5+bipJSrlX/+7iCFiJHZUaVUcBw9hZACKUV4DjqOPqekEN0i2VKW5WeTeeTzBbJ5fX6nbXPvs+9ZPimFQCgdNK5UW6VUN+x7MaVUG6uzUjhePva8vsBDhfuYkFIJEiRIkCBBgr0dg6uCTNAnCCFI22Z4vS08XaBaptkpwTJYESWlgkI2asOBwL6nEz7cbmSBBeWytu91oZQSRSVWIcwj8UkpJx7SvDOgM6U6U0p5eK4HaPteR7ZTKYukVDbvhDYrpXSB7iidwSKlHHSkFBDLlAqUUkYPSCmIKN66o5SS0ledFcfSNoo5VEKIkHDxcEKVXW/Qzr4nDAKl1O5W1JjSDAmdrJONWRljqjHyfqaUFxby0aBz05A99lIJIWKZUgXXoVAoErieKo5Ze6WUV7Tv9VJ1JiMZVTrHzAWUT1Z1fQ5ZlokhJZ6nyc1AKZXuIMx7T0ZgidXnj3/OBvY93+rZFY0UtWuWUkp5yiuSUv2glPK84j45hd6TzgkSJEiQIEGCBHsCBl8FmaDXEOjMkZB88O17QYGzJyFq3yt4hbCQjeZCgX/X269augo6j2VKhfa9joLOi0qpqMXNVW4bpZTYaUHnMaVUR/Y95YHoWimV8gma1pxD3isWUW7EmtiZQmggoz0ppbSl1ezuOREhElTnuVqmKTENiVKQLbgliZioUsrDwVP9QEqFSinZayKlvxGQUkqpOCllxIPiQ6VUEHSOtuJGc9l6g6h9L+8U4kqpaGME0YaU8jtOBkqp3uSoaUVUvPteT+x7lmlgmoZWShUKYabU3khKmYb057mIB537j/dUKeU6TtgAIoCiqJTyInlVvUVwkwSV2PcSJEiQIEGCBAn2LCYiQecQkLYj9j3/47etPU8p1da+FxYBxO17jhtkjygc1+kypD5UWcmu7HtxpVSHQec7kQyMZUqVKHxczwsL/a4ypWw/XymbL4TkARBaE4POYXIQLilRxUyu4KA8hRlYXbsBEcuU6rz7niEllmloy1reCbPFopa1qFJK4eL2wb4XVUoppXyl1MCw7xnCwJLabqXDzoNMKREn6NpkSrVTSvWyg2XKTCEQFLwCuUI+PEdc140RGaFSyv+Io5lS0XWmJxBCE5ma7FBhuLohO8+qC2CaBqZh4HoeuVyBXKFo39vbYBrSt6kWlVLKUyglUBSD8btC8Hk7hfbfA0opCqFSqu/7HOSiBd33BmpzlAQJEiRIkCBBgl2BwVdBJug1gvDm8PpXaNuLZZq9KqwGOoLCW5NSWmEhIGbfc73iHfWuMqWIvFYE2S8d1MJBplSQfeO4HWdK7SShlO6uR1T9EX/cdb1iIdaJUkogNJlJ3HKm38PPlMInpQahfS/aha01lw/Jge4rpei2UiqwQioVjKUef7uDTKm+KKWEECWUUkX73u6GFJKUmUIRV0rZbTKiYt33IMyHc/yxMw3Rq3MoICMLboHWXDZUEzr+eaHtvkH3veK570QypaSQfVJK4SulAkLMkN3LmNOWaz2PWnP5vZ6UMv1zNSAnw0wp34bptTPkxRFYkUF/D7RVVkWVUv1i34tsIlFKJUiQIEGCBAn2dgy+CjJBryHAz5TyC1Q/HHZPVEpBW1LKLzKDYtCH66pwPLpHSumfgX2vo/LRkFozpPCDjCNKKddzwwLY3InjrpQqklKOzqyJIggoh6JFqiMExW4sUwqtGgmOzZCD276Xd/O05HW2lmFIrB500QzPH9V5JlDQARMgW4hnSkURklLCxe0P+16YKaVJj4FCStmGDQSZUu0JOoh031Me+IqvqFLK6G3QuZHWqjTPoTWfDS1brutqayvxxgjBuhFTStE7pZQMgs6JZEr58647p5BpaPseStGa1Qo/2DtJKcMQ4bmqbwQo8FVsAB7dVEpFgs5VmzDz/g46dyOkWUJKJUiQIEGCBAn2dux9V7B7OVK2WbTv+Rfttm12K8dkMCGamxOQUvrvcaWU43lhtkdXmVLQ1r7X8ZgFmVLKU7hukbjRXb3cPncO6w485eEJFzBKZkq5rg6ORqhOlVIInUUGPinltLfvAYNWKRWSUk6e1nwepcAyZaefb1vImH2vY3JOiqItMJvrmJRyw/wkp0/d90orpQaGfU8KScrQnQ9b2yilonBUwQ8618egQ6nj3fd6g6h9ryWXDTtRuq7rd9jrIOhctbHv9UIpFbV8Oq6HGxJsRpFY6QSmaYTzKKqUythBplLviZPBZiUzfVWjEAL8TonaqlokZD2lOj0ubfOLZEq1eb7ukpjXOYGq2Dmv3Xa6OXZBx1eFCpte9GXcB+PNgAQJEiRIkCBBggC7vzJJsEuRsaP2PX0hm7LtPfKiNkpKuSpI8GgTdO564d8LPVRKdVYLhzYcpXAiSinl2936WlB3B57y8PBJqQ4ypUL7XieZUoHCDiCbd8lFM6WiSilhDDpSKtaFzc3RmtNKKdvv6NVdGDH7XudZY0Enw5Z8IcyU6oiUUjj9rpQaSPa9QCnVWmgNybf2pJSjJ2Fn3fd6AduwkUJqpVQuGxJDgX3PU0Fzg7hSyvN0wwKlVO+VUkJbYg+eNoxKM49ZleGKi05jxNBqtm/bSqNldfr6XL7AxWceQ219A0PLbc6YO4oDx5exzwiLTZs29Xh/CoUCZWVl1NbW0tra2uPX704UHJcLjpzItobhjJ5SSSpjc8iRs8nnFKNGpbBNScO2LdDY8Zh6SrF/mcfIkTbCHMX2bVtpbrTDx3NOjuOHT8FTNiPKPDZt2hTOh5aWFsrKyti8eTOm2b3zKtuS46ThJgVHMiUl2LhxU6+6sAohSKVSVFZWYpq7v6NmggQJEiRIkCBBb7D7K5MEuxSBfS96JzllW3vkxWxISrlR+1486NwNOympHmdKdaYuM/ywZm1x8+JKqV1ESrnKRYmg0C7RfS9QStF5phQUSalcwSEXIbjaKqV2VifBnYm0mUYgfPuezpTSbea7fywx+14XmVLRToYFryNSSv/0cPtdKSUYOEqpkJRysgjRiVIKX7mifFWL13dSKmWkQlKqOdfazr4XnBuluu85niaze6uUMqTgoqOnMHtcJZNHlCFFGZ/88EmYponrOLR2sRYp4JyTj8BxXIZUlDN8WA2O61FVnu41qTR8+HCUUoOOlAI4+/CpFBwPK2UipMAduR+eAiud0mu+W6C1tfMxnTusDKcmA2Mq8FyH1tbiOqdQHD9uFpDClIJ8Lht77bBhwygUChQKna+jAVJKcfy4ahRgC0E227sxV0pRX19PfX0948ePx+qCzEyQIEGCBAkSJBiI2P2VSYJdilimlF9kpVP2oCQTukJISikntEtIRLug8wBd2fei1j/RRfc9KfX76JbvioIX5OIoHM/F8YKQ5p2slBJBRzGvnd3E9bpp3yOeKZXrwL5nyMGnlAJt4wIdeN2czQFgm7JHSqmofc/ogpxL+WPZkitQ8ErnKMXse17flVLF4OaBE3QeVam1FlqxjM7se354Nf2vlPKUR1NrczFTKKqUQoTqyDBTKhLu31ullPIcDp02lKqqakaPHY1yHXLOFjKZFJMmTeiyA5/nKfLu+7S0Zhk6Yjg0O+TyLqNGVzNsSKbH++N5Hk1NTZSVlXVb7TNQoJQiv347zdkCZUPKkIYk11CP60JZTRVSCMZXlZHqpHGBUor3G1rJFlxUtpWJY4ZhRjLlXM+lULsJQRUp02BCVaYYfO84tLS0UFFR0a3OiQCtBZf1DS14nqJCCsYNq+z1sbe0tLBx40bq6uoYPnz4HnmDKUGCBAkSJEiwZ2NwXX0m6DPStlnMu/Y7xGml1G7drZ2CoPB2PddXRCmEjJNSTqAWUt1TSoUvFZ2TFtJXUilfVeH6xILu4uSEVqGdqpTy3Agp5YY2xfDxgKgShMRSKQghiqRUwSEXUV21te8NxoIoberA64JXoCmrFRCWafQoZ627SikhigRfa87Bi3aci2QJeaF9r3+CzoN2XzroXA4IUgqKeV6tThZEfCwC6BB+pX1zPrns9VOmVEBKNbY2h+eH67o+KaW7dbbNlPL8Oa+UQsrejWU+lyOTshBWOlSs6jwk3UCha3JDr2X4xLdC/25067WlodWfvX/97oJSyifD9RjoRTr0WetxlZ03IPACGW1MCVt8vofnb1IUx8l/rvSt2j0ZOyE9v/utF26zt2tneXk5tm2TzWa7fnKCBAkSJEiQIMEAxOC6+kzQZ2RKKKVSqb0jUwoCpVTxOUX7XtdKKYgrpTorQKLdtVw3ninl7Er7HsWcnLZBuq7nafJDKApdKKWCAOVc3u0w6NyU5qBU3AXEiOM5NPo2GtvqISkVzZTqZF4IBGk/NL4175TMlAq6GurN9Q8pVQz61//rjeVsZyCqlOo86JyQPFZK6XnXx3PIkhaGMPCUR0NLU3EdcPV5oQBKZEq5/jkcKqV6MZaeUuH2fFciQLdvDmj+JCDJiuf2ntaworso9f3V4+80/0PoKHA8IC37IwY++hZ93V5AiHle722+CRIkSJAgQYIEuxMD43Z5gl0CrYoy21VA6ZTd8YsGMeJKKd3gPVrMQTToXOG4PcuU6tS+JwL7ni6etdojyJRyQtvgrrPvue3te67r2/d6limVdTpQSvXCxjQQkDJ0FzbHc2hq1WqDtGX2iF4z2nTf6whCxO17qQ4zpYpKqc5UbF0h6O5YbHEvEcIYEEopQdG+1+JkSZndsO+FSikVnkNWL88h0zAxpUmr0+qTUkWllBvk7lE6U8pVTqjQ6fW897enm3+qdn/vCsFa5HpFa+4eeG+hWyh53JE/dqexXfAUFWUJo3/rFzqqxPsOsm6HCRLsTATnQyFfoJBzEFKQStvINgra3mzX8xT5bB7XdXUn3JSF6d8k6mzb4XeD41LIFfA8rVS10zZGH/droCGTMrAtA9dVtGSdiPW/75BSkEmZmIbA81S7m5wdwTIlaVvfKHRdRWvOCa+RBjuUUriuR2trDs/zSKUsUn5N1tf5DpDPO2SzOYQQZDIpTLP7jgbldyXPZvO4rodhSNJpG9PPRR3M8z44ttbWHChIp20su+u1oDvbBchl8+TyBaSUZDKpbq0T8ddqh4BlmaT9bMzBPN7dxe6vTBLsUmhyIdqJCzJ7OCnlKL/7ntLd86JigkBtgequUsr/RXSeJSOFwJDaWhOz74VKqZ2fKeV6xaBz1/XaVWbRTClXOaHyoxTSqYCUcskWSgedm2Jwdn+yDRtDGr5SSpNSKatnVkQZse911n0PIG373fdyBQxPEx/tSKmQZSjOnd6gVPc9QR+IlH5GSEoVslTapVRjwfxSvqRI+eeU6rMF1pIWpjR9+15TeH64rheS2KDPZeGfzxDY99ww6Lw3OWoRg1lItoG+6OjurBOR/fG55UF5/vUHQoVY5PAF6K+6ng6JKqqiYn/2BznINeuLKFR1+I/iewX/BfbABAn2dCilaNjRxIKHXuStF5bSWNeEYUhGjh/B0WcdztwjZ2P0oKgGrSTdtqmWRS8uZelry9m2qZZcaw5pSKqGDWHmQfsw//RDGTF2WLvtKqVobc6yaskalr6+nHXL11Nf20gh72BaBkNHVnPAkbM54pSDqagqH9Tn6YRRFXzijBkcuf9ohlTY5PIu76zZwd3/WM6rb2/pEzlVljY5+bAJnHHUJCaNqaQsZeG4Htvrs7yzZgcPPPsery/bEntNTWWKQ+eM5Mj9xzBzSg1Dh6SxLUku77J+cxP/eGkdjz6/lqbW7jWXGGgISJEFzy7kL/c+zZo1m3AKLjVDKzn2uAM5/4KTGD68qldzSilFY0MLf73/GR5/7FW2bqlDSMG48SP40IeO5tTTDyed7rju0w00mvjHoy/z5JOvs2njNnLZPJZtMmJEDbNmT+bcjx7L9OkTBt2cV0qRzeb5x6Ov8MjDL7Jpw3aUUowYVcMppx7Chz58FBUVmV6P+9Ytddz7p6d5/rlF7NjRhGWZTJkyho+efyzzj9ovJMFLvXblivXcf98CFr65gvq6JhRQUZFh9uzJfPijx3DA3GmdiiH2BCSk1F6GaKZUNOh8T0TMvhfplhUt+xzX88ejp933ZNdB51Lqotp1Y933HM/ZJfa9olJKFQmoCBzHz5kShEqujhAopfKO204pFYS4G9IYlPa9IPDa8RyaCtq+l7LNHqlOjFjQeeeETzCWzbkCmVJB5ypu3+uLUsqQUhfSYfc9qdU9A86+l+2wE2GolIoQRbqjZd+IXVNqpZRCUd/SFN+2n7emySj993j3PTdUSvU23D9Yf7VSyv9bD14fKqVcD5Axq+GuwNKlS3nppZe6fN7xxx/P1KlT+/ReSikeeOABWlpauPDCC9tZp8N1uUj1xZRoHamctHrC87uzBs8soZQiopTqT/+eaq+UyufzPPTQQzz66KMUCgVmzZrFZZddloSYJ9ijoZRi+wc7+O0Nf2D9qo2ccO5RHHDkbFqaWnnivgXcdv0fOPljx3HOp07tsLArhaWvvsv//uAemhtbOOiY/Tn3M2dQPWwIjXVNvPz4Gzx615O8/NgbXPK1jzHr4OntzrHnHnmZ+379MHbK4pizj2C/w2aSLkuxdeM2nv7bi/z5Fw/w+jOL+Mx/foJho2sG5Tk6Z9pQ/vsrR1NdmeK3f32b15ZuYeyIcj77kf345bUn8MPfvc6fHltBb8RJo4aV8a3LD+OA6cO5/6lV/O7hd2huLbDP+Go+dc4sPn76vkgpeGPZltjS+rmP7c+lZ82krjHH/z36Lq++vZlc3mWfCdVcetYMvvu5Izj5sAlc+/MX2NGY67ex2FVwHJdbf/037rj9YeYeuA9fu+bjVFVXsOCZhdx5x6M8t2ARP/jh5xk/YWSP5pRSitrtDfzXN37DKy8v5dyPHMuXvnIBuVyBe//8JN/51m28tXAFV3/945SVpUu+fvWqDXz7m7exZcsOzjv/BA6aty+WZfL2kvf43R2P8Nqryxg2rIrp0yf055DsErS05PjRD+7i0Ydf4vgTD+Kyfz0LwzT4+yMv8dMf/5lXXl7GN7/zaaqrK3o87uvWbuY/vv5r3l+3hY9/8mSOPGo/amsb+cPv/sm/f+1X/OvlZ/Gpfz0zVJpFX7vgmbf47rfvpKmxlfM+dhzHHn8gpmnw+mvv8off/5MnHn+dL/3b+Zz3seMGXe5nT5CQUnsZUlYxUwo/KHdPJ6Vcz41kSnWglEL1a6aUEMVAdadNplRgJwTfXrWTLmJc5eLh+vvgtqulXDeaDeWESoBSCOxs7ZRSXjxTajAiIKVc5dLYqkmptNUz1VcxR0ogu1AhFUkph2rPQwiwZMf2vb5lSmkStlj46qDzgaCUinbfaylkKbgegvZj4SgHhAozpQAct/+UUgB1zQ3h372AlNLx4SUzpVxPKwsN0fuOkzGlVKhe7f6cC5SumnCW/t96tSu9whNPPMF9993HUUcdBcCKFSv4y1/+wic/+UnGjRsHwOOPP05ZWVmfSSnP8/jrX//Kjh07OO+887Dt+HeWECK+doVKJv1LZ7XUjTfeSHNLK5/+t6/7ry2lk+rnTKno7xFSyvM8fvWrX3HHHXfwi1/8gtGjR3P99dfzqU99irvuuouqqqp+ePcECQYeCnmHP/3iAVa9vYZzPnUaZ37y5FABOWXWRH527W/55x+fZsykkRxxysHdXit3bGugvraR486Zz4Vf+DCWbYZNRWYdvC+Z8jTPPPAif/jJfVz9488xdGR17PW5bB7lKT5y+Zkcf858hN8gaMqsicw4aDo//fpvWLn4PR75wxN88qvnDTpSqqYyxbevOJwJoyu57uYXeOCZ91DAwuXbeGfNDm7/5slcc+k8Vq2v59WlW7rcXhTlGZPvXHE482aO4N9+vIDn39oU8vGLVmxnyert/P47p5T83ipPmziOxzd/9TL/fGlduGYuXL6N15Zt4c5vncwJh4zn4tP35Rd/XtynMdjVUErx+GOvcvtvH2TatHF8/8bPM2y4XtsPmLsPmbI0N//sz/zwB//HD//7C2QyqW5v23U9bvn5vSx49i0uuvhkvvbvn8DySdwD5u7DNV/9Offd+xQTJ43m0n85I1RcB/u1bWsd37ju1+yobeTHP/0ys2ZPDuf0AXP3YfKUMXz5Cz8elLZzz1Pc84fHefBvz3PMcXP5r29/mrIyPbYHzJ2mrzP+8hy3/uoBvnrNRe3Io87Q2pLjh9+/i3ffWcdXvnoBH7/kFAxDopRizpzJXHXlj/ntbx5iyrSxnHjSvNg6Ubu9gR//6I9s21rHZ674EFd+7sOhXfnAg6YzcmQ13/3Wndzy879w0Lzp7DN9fL+PzUDBnku3JSiJwDoEe7ZSSggRU0o5XlH5EOvsFbXvuT2073VCKElZtPs4jtuu+94uCzqP2PfaKaVc16/ZFI5v3+sIKf9CLue0t+8Fx2bIwdl9zzY1KeV5HvWtzQCkbKNnmVIx+15XpJQFaPue42rLpBWzrBVJKYTXZ6WUEMXCN7DvDRQCsUhKteIEBF10LJTCxQn+4RMGQUfLQCnVO4LNlCamoUn6dqRUJKcpJKVi3ff6rpSKinp6nHRO0bKmOzXGCbRdAcMwOO2007jhhhu44YYbuOSSSzBNkyuuuIIbbriB66+/nmOOOaZf7upJKfnpT3/KHXfcgWVZ7R/v4LhVu1/aPK4US5Ys4a233goJTxWVrkWe161gqm5CdfD7+++/z49+9COuuOIKDj/8cCZPnsx1113HwoULue+++wZlIZAgQVdQSvHOmytY9OJSakZUc/RZh4fXV0IIKqsrOOHco3Bdl3/+8Wlamlp7tP10WYrjzjkyJKRAXwvaKYvjPzyfdFmKLeu38far77Y7xwzDYPjYoRxy/NxYrpUQgqqhlcydPweAd99cSa413w+jsWtx1jGT2X+f4SxdXcs/X3o/th6t3tDA355ZzZBym8+cOwfL7Nlafs6xUznu4HHc/9TqGCEVYNX79fziT4t54a1N7ZboguuxbM0Onn1zY7vH1m5q4KXFHyCl4JiDxmIag+u6s6mplTtuf4h83uGCi09mmG/T01m1Bud+9DjGjBnO888t4qUXl/Ro3V+2dA2PPPwCQ4aUc9EnTsW2rXDbFRUZPnHJaQghuOv//sGWLTtir1VK8X+/+ztL317DJf9yRoyQAj3nDzpoXz7z2XPYb/++3WjaHfhg03b+eM+TGIbk4588hbKyVDg2tm1y0cdPprw8zYMPvMDKFeu7Pe5KKZ5bsJiXX1rK+AkjOfvD88OaQAjByFE1nPex48nlCtx5+6O0tMSVfW+/vYb3399CWVma004/LGZRllJwzLFzGTGymrodTbz84tI9+jpgYFQmCXYZUtEAZz+/ZE8kpSCilFJuWMCWypQK7n87juMTVx1vMxZ03ln3PaG776H0l2vUHlfwnFDl0duQ5u7A8zxULOi8lH0PEBQL/w6gM5YgX3DJdWDfM8Xg7L5nSxtDGORUnobWFqAiVDN1F7IH9r2gk2HQfU8rpeLjFnBSSrj9kimFpy1JAqGVUgPNvufkybsOAhMrcoGpUEWlVMSC6nqqz80CpJDYUq99UVIqtO8RrBdtlVKEJG6f7HtE7XuBvbIn+y/C/VUQ3sHfVTj33HO7tDx//vOfJ5VK8Y9//IOWlhbGjBnD+PHjefTRR9m2bRvz5s3jlFNOYe3atbz88su8//77ZDIZ5s+fz9y5czEMA8/zePHFF9myZQsVFRUcf/zxWJal815aW1mwYAHPvfQqWQcOO/poZs+dG9uH5qYmHn7uSdasWE4qlWLWrFkccsghVFZW8uyzz/L+++/Tms3yz0ceQiHYb8ZM1PiReJ7Hu+++y7PPPsv2HdvJjKziqGM/xLChQ1EoFi58i/feew8pJQcccADPPPMMb7/9NlVVVYwePTr8LCZOnMi8efNYt24db7zxBgAjJ0xi+OTpQMTJpxQLFiygvr6e+fPnh68fP348++67L3/961+59NJLMc3ksi3BngWlFK888SZOwWHq7ElUDa2MPS6EYN+50yivLGPjms2sfnst+x0+s1vr3dTZk7jwC+cyasKIks+vGVlNWUWGbEuOD9a1VwIdedohzD1yNhVV5e0eE0KEfy/kC4OuA2bKMjj76ClIAc+/tYnWXPv1fMEbG/nMuXM4dM4oJo+pZMX79d3adnnG4sJTp+M4Ho++sLYkp+96ijsffqfk6397/9tYplFyn5QitOylQ6JxcBTqSikWL1rFiuXrGTKkjEMOndVuXg4bNoS5B07nkYdf4NGHX+TY4w7E6MbNN6UU//zHyzQ3Zzn0sFmMHz8y9rgQgv32n8rIkTVs2rSdF19czIfPPTZ8/y1bdvDwQy8wZEgZxx1/UMnzJZ2x+eyV5/Z+AHYTlFI8/9xitm7ZwcRJo5k5a2I7wm3S5NFMnTaWRW+t4snH32DGzInd2rbrejz6yEs4jsu8g/elqsRacdgRsygrS/HOsrW8s2wtBx8yI3xsR20jnqdIZ1JUVpa1e22mLEVFRQaA7dsb2j2+JyFRSu1lsK2iAkRbHrR9bzAqXLpCTCml9MVCh0opFK6r85c6Q3hHvqtMqQ7se+H+9DEPpztwPAcliuHNbS8KnPB4faVUJ+x72tZkZsH14qSUGvzd9yyjGHgdKKUCNVN30TOllJ6X2YKL43lIIWLzIOgSBNq+11XeV6f75RMVUftenzrG9TMCUirnFEKllN3OyugUrViBfS8WdN67tUsKiW3aKNoqpVS4LujzWP89VEopFaoq+5YppX9GQ60d5ZBzcmSdbKf/5dwcBZUn7+ZoLfj/dnPk3K5f2+k2vUK378KNHTuWiRM7vmgTQjB16lSGDRvG1q1b+f73v88Xv/hFvvOd71BXV8fChQv5+c9/Tn19PRdddBGvvfYaBx54IJlMhssvv5w77rgj3Je6ujr+8Ic/8MUvfpFW32Lb0NDAZz/7WW6++WamTptOeXkF/3X1l7jzV7fgeR4CwfatW/nalf/Kn+++izlz5jBy5Ei+8Y1v8LnPfQ7Xddm+fTvZbJZcLse2LVvYvnUr+bzugnT33Xdz0UUX4TgOBxxwAM89+RRf+vQnWb9ubfj+jz32GJdeeinf+c53ePnll2lpaeE73/kOr776KldeeSW33norDQ16brW2tvLQQw/x61//msbGxnCcogrVJUuWUF5ezogRI8K/WZbF+PHjWbFiRXjsCRLsSWhtzvLesnUATNxnbMnr0SFDK6kePgTXcVn+1qpub3vclNEcc9bhpDoIdlZeMU8uUx7P2BFCUDO8inFTx5RUfCql2LJhGwCjJozo8D0GKkYNy7DPhCqUgmWra0s+Z90HjTQ256ksszhg+vBub3v6hCqmja9ie0OWdR80dv2CNli/pZn3NpYuwA0pmDCqAqUUK9bVRa7jBwfeeO0dCgWHESNqGDGiut3jQghmzpoEwJIlq2ls7N66n88XePP1dwHYZ/oELKv9dV5FRRkTJo5CKcWrLy+L1QVvvbmCrVt3MG78SIaPKG0VD2qotrXUQIfnKV575R2UgokTR1Jenmn3nFTKYto+OnrgjdffpVDo3g3hhoZmli1dA8CMme3D34UQjBxRw/ARVeTzDm++vjx2nTVyVDWGIWltzdHY2NJu+y0tOZqaWhECRo8Z2t1DHpRIbrntZbBNI0Ks6EVlj1dKeVGllIgxsb3tvqczpTrpvie1UkoBBTdi31OKguv0OQ+nO4jZ90oFnbtu2EXK7cq+Z5kghFZGuR7C91o7bTKlBtOXVIAg8NpTHk1+972eKqWiQeeym6RU3nHxlPJzlKLqIIqdbvqolNIWiGJu0UCz72VMfWGQd1085WGWsO8VlVIBgaM7WkaJ3d7MOykktqHXvoaWJkDf3XJD+562VrZVSnlKUfA0iWvI3mZKaZ1U4BTT1LDH9S99i+WPv90tyVS+4FAoOJp0FBIBZFK9PAeVXiePm3wc/33af/er4jGdTvOJT3yCRx55hGeeeYY//OEPTJ8+nY0bN/L3v/8dy7I4//zzueqqqygrK9NrZKHAj3/8Y84//3yqqqo466yzWLZsGYsX6+wQpRT/+7//y5NPPskzzzxDxdDRbNjeSLo8zQ+/9Q0OP+RQps3Ynzt/dQurl7/Lbc88w+Tx+mJz5MiR/OQnP8EwDM477zzuv/9+6hsbufhTl+EqyNfV8d57a7j22mv54he/yOc//3lybo6a6aO44mOf5ZabbuSI/72NY489lrq6On77299ywAEH8KUvfQmlFOPGjeMjH/kIW7ZsYcmSJRx66KEIIdhnn31QSnHNNddwyJHz+aApG87nALW1tdi23c6iWFFRQVNTE7lcjsrKuIokQYLBjqa6ZuprGxFCMHRU6bBw0zSoHlbF+lWb2LDmA70+98P1Ru2WOloaW7FTFvvO7b4lSSnF+ys38uZzS8iUpznpvGMxepBBMxAwdkQFFWUWBcdj8472xTBAc7bAjoYcw6rSTJ9Y3e1tz546lJRtUN+YozXnMHvqUI6eO4YJoytxXY/3NjbwwlubWLW+vkcB6gI4dM4oDt9vNNvqsvzh0Xf701m906EUrFi5HoARI6vDvKcohBCMHjMMKQXbt9dTW9tAdXVFl9tubGhh0wfbARg3rjSBaJoGo0ZpYmPNmk0UCgVSKRulFIsWrcLzFMNHVCOE4I3X3+Xll97mgw+2k0rZTJ8+niPm78e4cSOLHW8HCQoFhzVrPgAIx7YthBCMGTsMgA3rt9HaksW2ux73rVvqqKtr8l9fuiFJOmNTUzOEtWs2s2rVxtj6td9+U5mz/xQWLVzJ3x95mSuvGhPalz1PseCZhWzZUse48SM46uj9B2Wd1V0MjMokwS6D7duwIK6U2hNhSX1h73pakQK0y12JBZ0HJE0HiIaXCz9TquPnFomKtkqpgluI5OHsXKUUoVKqvX3PDTK0hOoyt8i2NJmpM6RUuHDsCUqpsAubBwVHz4dMb0kpOldKCSFI+dsueCq0i8bCvVXcvteXTClTSh10Hl71SYQwBox9L2WmEAgcT+EpfW6aMYJO6aB34edJeQHJ2k/2PcMGBfmIRcBzi/Y9fTdQ/72olNLncLCNXpFSgkh3OF8pBbxXv5rXtrzWq+PpD4yrGtdpw4O+YubMmUyZMgUhBGPHjuWyyy4D4PLLL2fBggWsXbuWQqHA4sWL2bx5Mzt27CgZ7l0oFPjb3/7GhAm6+8/699/ngx1NDB0+gtaWZha+/hpjJ07j6cf+wcz9DmDY8KJ156ijjmLSpEkYhhFbE4sN8RQLFjzLtm3bOP7440OlYUVlBXMPOZRn/vEodbU7GDVKWyPS6TRHHXWU/n6QkiuuuAKASy65hHPPPZcXXniBU045hVWrVrFt2zaOOOIICtEbAJF9aLtPATzPQ0q5R3fdSbD3oqm+GSevFbHlJewroG8EllXqmxiNO5pwHRdp9+18UEqx8Lkl5LJ5Djpmf6a0sfS0RT5XYON7m2isb2b10rW88sSbVAwp46IvfJgDjmhvwxroGFGTwZCCXN6lqaVQ8jkFx6M5W9Bqj5oMUtAtEmnSmEoE+rlfvnguZ8yfxNuraln7QSMjajJ87vz9+cIFB/C7h9/h1vvfJpvv+OZbecZk2vhqhlWlOHT2KM48ahIr19fzk7sW8taKbb08+t0Dx3Go9S1YlZVlHa7pVVXlCCEo5B127GgAxna57camljCvaMiQjsmUKp/g2lHbQKHgkkrp75j1728J9/G73/5fXnl5KXMP3IdRo4ayYf1WHnrgOTK/SPHZK8/lvPOP71EXzN2N1tYcjQ3aCVFV3d5eF6CqSo9NS0uWpqbWcKw6w47aRpyCi5SCyor2CizQQoUhQ/TatnVrXex7vqIywze//WluuvFu7v3z07S05Dj2+Lm6+96r73Lvn55m5syJfPWaCxnbAdm4p2DwzKgE/QLbNJCErNTeoZRSrg4mRhe90euGolJKRexsHSOqMussU0qTVvq5BSeudsm7xeyBnW7fk8Ui3mtT7DiOhw4aUsXuex3ANg0MQ+ACUaF0rPveIM2UMqWJZViAAE+TNT3OlDKKSqmu7XuaGA7yrQ1RDMX3NxGGVyvh9VEpJXylFGGm1ECy76UMHTTpKRGen6aMK6VcXymFKpI3jufhun0jdqWQpAy/q41bHP+w+57yM6UorZQKttGbQkQQ7b5HqMr65Ox/4YKDPtqtbTQ2t1BX3wRCgmFhSMnooRW9uoOplCKXyzFj1IydWlhVVFSEF+HB+6xfv55LLrmEsrIyLr74YiorK9m0aZNvYy1ty8jn82zbto3GxkZuuukmsnmXpmweaUpOPOMshg4bTi6bpaG+jqrq6lhQYCqVYtq0aeFxt4OCTZs2YZpmO1VSdU0NTY2NNDY1hqSUZVmk0+nYMQEceuihzJkzh9/97neceOKJ/OUvf+H000+nrKyM+mwh+nYhxowZQzabjdn0lFLU1dUxdOhQUqnud2FKkGCwIJfN+2sgmB189wpEWAQX8lpt3jOTfRxKKT5Yt4XnHnmFoSOr+fBlp3dZZO/YWsftP7iHlsYWWpuy1Iys5ugzD2f6AVNjXcwGC8rT+niVUuQLpddaz1PFm3VpX4nbDWlSzZC0tqFNqmHciHKuvfkFnnl9A46rkAL2nz6cn33tWD7/sQPIF1x+c//bHW52+oRqbvrK0WTSJpVlNqvW1/HHfy7n7VXbe3fguxGu65HL6UB8HUJe+nm2HyGhlCLbJhi7I+TzDo6vnrbtjlXTqZTedi5fCHMhXdejvr4JgJdffJuRI2u48UdXccDcaUgpcV2P5xYs5D+v/TU3/fAPlFdkOOvs+YOGiC0UnNCOl+okniMYG8d1yeVKE7Vtkc3m8TwPwzA6WUN0YwWAbGsez1MEMWFCCCZPHs3nv/gR7rz9Ue7/y7P88++vIISgqamF/Q+YxmWfOYu5B+4zaMa7t0hIqb0MWvGif9ehx5DaC0ipgl/cGIIYceJ4EaVUEPzdCcKx6yJTSkBYXBdcV6s9fBRcp6iU6mE3k57A9Vzw7Xue57Ur8EJlmOy6+55lGhhS4kUUHhBXSg0US1hPYQhD77vqPSlVtO91I1PK8sk7KUBosiNGSqnonci+d9/T2ww2KJDCHDBKqbSZ9tchA513RTullLbv+cW7F8mU8jx9nvVVKQXgFrcR2vdoY9+LZEoV3CIpJXsbzRjJlNK2T8kZU89k8vjR3brw2LJtB+s2bgFpgpnGtkxmTx7Rq/HwPI/GxkbKy8t7302wF1BKce+997Jw4UJeeOEFZs6cGSqOfvnLX3b4OsuyqKqqYtSoUdx8883UNedZu6WesqoyWltaENlWsnmPsvJympuaYsWO53lks1kymeIdTf249jIrFEOHDsN1XVpatKVF+f9ramwkkymjLFNazRFFWVkZl1xyCd/85jd54403ePbZZ/nNb35D8E7RMQhw8MEH09rayvr16xk/Xrd9zmazrF69mrlz58b2OUGCviLIstvt+TC7+K2VUrQ2tfLnXz5IrjXHp/79QsZN6XrdHTFmGP/+8y/gFBx2bK3n5cff4P7fPsKCh1/m4i99hH3nTh1URWN0HerPvRaA7V/bGobg4efW8NSr68PrGk/BW8u3cefD73Dtpw7m0rNn8fBza1i/pbnk9hav2s5Hr3mElG0wfmQFF5yyD9++4nAuPGU63/z1y7y7tq4f937Xoduuw27OqZ5+hiJ6eywSYeI4Lhd/4hQOPGh6OJ9N0+CYYw/izLPnc89dj3H7bx/k2OMOZMiQjlVHgxs9GM2eD3wMhbzD//3+n9x5+6MMH1HF9d+/nDn7TcGQktWrNvKbXz3A1/7tFs796DF8/gsfoaxN9t2ehEQLvpfBMmSxABa6a1iqRIvtPQEBSeIprxhMHCv+Vfug8y6+JUL7nuzKvld8PO84ISmlUFoppXZt0HlHpJTeWWKkWSnYpp43QsrY96PnFVUjg5WUMqUZWj1RRVKquxeXMSunokuLTWDfk1IgEBhCYIo4EeOGmVJ9I6WCvKXAvieEwBDmLiUeOkPK9FvyopVqhhQh+RMgZt/zGxY4nqeJXdG3cygIWo+SUp6nKIRB5sXrwaJSirDjpCF6mykVvy4JCOGeKA3bzjMhdnlt1y9obm7GMIxQBaSUYvPmzZ0rN22bU089lRUrVrB58+bwXGpqbORrV/4r7y59m7Kycg4/6liWL3ubhob6cNsvvfQSn/70p2ltbSVowe35HQxXLX+H1195mfnz51NeXs4rr7wSkobZ1laWLHyTAw89jJphXYeNCiE4++yzSafTXHPNNcyYMYNx43SuVZyUIrSKzp8/n+nTp/PQQw/pfVKKRYsWsW7dOj7+8Y8PqoI3wcDHmjVr+Ld/+zeam0uTAbsKqbRutqOAQgfqBIXSFj/ATlmdXn91BqUUuWyee3/1ECsXv8eFX/gwBx69X7fOLWlIyivLqBo6hMkzJnD+FWdz2kUnsGH1Ju648R62bSodFj5Q0dyqx1oIgWWVHk8pBZYpUUrRknXaKe5LQVGMQnA9xYKFG0ta/l54axOtWYcR1RkOnDGi/RN8uK6ioTnP1h2tvPnuVr7565e5/6nVHDxrJN//wnxqKgePgtQwJGk/ED+fL3SoDgtUOkIIMpnuHZ9tW1iWqed4vuOmJdmsr9RKWZhBDpog/D2dtjns8DntzgnDkBxzzFwMQ7LmvU2sWrmhW/s1EGBbJpZ/7d2ZAipQsZmG7HbjgnTaRkqJ53kUCh1dr6vwfTP+80GvR088/jq/uuWvKOBb372ME0+ax+jRQxkxsprDjpjFd7/3GYYOHcLdf3ice+56IhLHsedhYFQmCXYZDEOGCp7gjnRnUsbBjGjQeaCUamffcyL2vW4FnUfte51kB1EsYtva93ZVplQ06Nzzih3dwsedYqaU63Vu3wuUUm3bzseUUsbgJKWEEKTMFIF9T9AL+15PlFK2iRC+5a+EUgoFIX/YR1LKkH6gtirSHnIgkVJGylcjWaEqyWxDHLueA7LYoQ50F0ivj+eQEKIDUsoLyepojlxbpZRC9an7XoBo2HVPOIfieah/yt3ESimlePPNN3njjTfwPI+XXnqJJUuWhJ+V4zg8//zzbNq0iS1btvDPf/6Tbdu2hcdwxhlnkE6n+cEPfsBbb73Fk08+yd13302hUODZZ58NnxuFEIIrr7ySmTNn8u///u8sfOMN3lm6mJ9+/7sMHzGSfWfNRkrJv37hy5RXVPLDG77LokWLWLBgAddffz1nnHFGqDqaM2cOa9eu4e233uK2m3/G0kVvMWPGvvzHf/wHv/rVr3jwwQdZunQpt//8NzQ3NXHVNddimBYrVqzgjTfeoFAo8Pzzz8eOOcDYsWM555xzeP3117nwwguLn1kxwCqG6upq/vu//5tHH32UH//4x9xzzz38x3/8B1dccQUnnHBCQkol6FfU1tbywAMPkMt1zx60s1BZVa4LRgXNJbpPge6S19ykHxtSU9GrUHGlFLnWHH/+xYO8sWAxF3/pIxx56iG9zmozTIOjzjiMITUVbN24nVefWtjt7qUDAVt3tOK6CtOQVJaVLsAt06Aio2uEzbUt3Q4V316f9b+/FZu3l/5Mt9dnaW4tIARMGt39Bg75gsfd/1hOc6vDnKlDOeagrvOWBgpM02TYMJ2T2NjQ3KFFvb6+CU8pbNuiZmj3xqaysixU0TT4VryS267Tjw0bNiQMWjcMGYapp1J2uI9tMWr0UCzLxHEcNmzY2q39GghIZ1KhqquuruOxCR4rr8h0mA/VFkP9cfQ8j8aG0nPd8xQN9Zr8HzmqJrzWcxyXv9z3DLlcgYPmTWfmrEmx7/kgfP34Ew/EdT0e+OtzJTv07SkYnFVkgl7DNCK2M5+UsntYgA8WxJRSPilkiLgW7Czm3AABAABJREFUIdpK1nHdTi1sUGRxhRDFHKFSiCg48q6Dp4rvk3ccXN8mZu7E4Npo0LmnvNg+QFQppXBx2z0ehW0aGFL4pF6ElPIUjlvMlBqMKAZeS62UEkU1U3cRzRfripQKOmAG88cQcXVQvPteH0mpQCmlg4t8MtUcOJlSvlJKCn1B3D5fK7Dv+Z+HfwGnSSnfkttXpZQCvAgppRR5R1tbJUXLbrB2RDOl+qPjZJApBfSIlZIRxat+qdhtmW7vv/8+lZWVfO9730MpxaZNm5gzZw6gj2316tWcffbZALz77rvMnj2b4cN1YOeBBx7I3/72N+69915uv/12Zs2axfXXX8+LL75IXV0dzc3N4XOjGDFiBHfddRd//etfue/Pf6Qp7zLvsCM44bQzSHkuroJxEyby8zv+wAv/fJjbbruNsrIyrr76ao477rjwc7v88stRQvDXP97FjDn7ceapZyCk5Etf+hIHHXQQjz32GDv+uYPK4VX84vd3M37CJAC2bt3KkCFD+MY3vsGOHTvYtGkT+++/f2wfA9JtxYoVHHTQQeG5GHzLBCH30ecff/zx3HvvvTzzzDPU1tZyww03cOihh7bryJcgwWAiQDpDRXUF1cOHsGntFrZ/sKNkZz2n4FC3TQdEj5sypsfrbpDN86dfPMCbz2lC6tATD0RK2W4cg223PTdL7ntVGdXDq6ivbWR9m65aAx0btjTT2JKnqiLFqGGlC/CKjElNZRoFLO+BTW7NxoYi997BNPXCZi+ixzeXNm1rpr45R3mmnJmTa3jg2fd69PrdBSFg+r4TePyxV9mypY583mnXgU8pxaaN21GeYviIKoYOHdKtbVdUljF27HA2f1DLhvWlCSPHcdm8WSv6Jk8ZG9Z/UkomThrt7yQd3uAKrikh0ixpEMCyTKZMHcM7y9ayadN2P9MpfpBKKTZu0DfBxo8fQaasewq1ESOqqRlaycYNOTZu3FZyDWhtzbFjRyMA0/YZFz7e0pIN33PChJElr2eFEEycNAohYMvWOurqmxhStWfaJgdnFZmg1zANWVQiBKTUHnqxa0qd3aMzpQI7TlxNEA06d12vy4u8Yvc92XXQeZAp5bTNlCrgedoG15eCuiu4nq+UEqWVUk5EKVWKtIrCMASmKTXHFRk/V3mDXikVBl4rqf9j5yqlbMtAyiIpZUrZPui8n0gpKQRSinBea7vgwFFKWdLyLXDavme2se/poHMXhBEGnYNPSvnh4L0ldgWBUkrElFLKJ6UgTvQE5360+17fyD2/8CFi3+tBMdP2uburDhJCcM4553T4uGVZXHrppR0+LqVk3rx5zJs3L7bNww8/nOeee46RI0fqDJjWVtLpNKZphs8ZNmwYl112GR+7KMfKjTvIVJXp4PzGxnBbI0aN4rNXfo6qtBXbfoBhw4Zx9dVfY01dC55StG7fhlL6jvbxxx/P8ccfT1O+iZW1a8mYutufUor58+czf/58HMehubmZysrKcLvZbBYpJZZl8eKLL/KJT3wiFlIes+8F/xchF6dMmcKUKVM6HLMECQDuvPNOXnnllV6/fuvWrWHQ8e5EuizF1NmT2bR2C+tWbkB5CtGmYKzb3kDd1npMy2DfA6f1aPtKKVqbs9xz819Z/OIyPvGV8zj4uANiCqm3X32X7Zt3cPQZh4UqrHw2zz//9AyzD9mXqbMnlV6f257Mg4gn3LKjheXr6jhszijmTB3G319Y1+45k8YMobLcoqEpz6IedLpbtGIbrTmHlG0woqY04VVZZpFO6Y6jG7cVLaRDq9JceuYMHnj2PVZvaCj5WuHfJIp2yB0sOPiQmdi2ydatO9i6pZbyKXGll+cpli1bA8ABB+xDRUXXGYYAtm1y8CEzefON5Sxf/j6FghMGpgdobGhm3TpteT/88Nnh34UQzDt4Br+74xHyuQJ1OxoZNaq9TX3HjiYcx0VKyaiRXdvYBwqkFBx62Ez+/sjLrFu7maamlrDTXoBcNh9aEg85dGbR2tgFhgwpZ86cyWzcsI13lq1tR0rpSIIdbNtaTyplMe/gfcPHdaaq/r0jt45SikLBDQncwUJ69waDs4rcSVi8eDFjx45l2LBhu3tXdhpMGbfvGXuwfS8oGHWmVGDfi+e2FKKZUp7bbVKKrjKlIGTh8yW677merQvqnZwppVkkVTJTqtAmU6ozlZgpJZZpIF0vtiA6nq9kYfBmSrUlpQSCtNUzsiGWKdUdpVSMlIorXFQ/2/ekEDjhxfLAsu9ZhoUhDV8ppQkmGZlfmizVmVJKqdBLX3C9frHApsyUzwLGLyAKISkVUUpJffEbVUoZwui9Oil0cqmwkOnJlorjFBDlg+/iPECpiyzP87jpppv4+Mc/zty5c3niiSdCq1/b1wqhw7/CrQhixWGQ2dQRVNt/qFIkYferzbvvvpumpiYOO+ww3nzzTb70pS/FtxXdtz1E7ZJg1+Opp57i97//fZ+2MWbMmH7am95DCMHhJx/EK0+8yXtL11K3vZ6hI2vCx5VSvPvmSlqaWpm47zimzJrYrujL5wqgFLafTxV9rKWplbt/dj9LX1vOJ68+n4OO3r9dl9K3X32XlYvf48hTDymSUrkCT97/HLnWHFNnT2q330opdmyrZ/vmHQCMnTJ6UHXhyxc8Hnj2PQ6ZNZKj5o7hV/ctprm1eL0hgOMPHodlSl5a8gHrPmhst42ytInrKnKFeEG9akMDC5dvZf4BYzh09kiefr19/tDsqUOpyFjUN+V5892isqem0ubT58xma122Q1Jq8thKqivTeJ5i+bq63g3AboAQgjn7TWXmzEksXryal19eyqTJceXftm11vLVwBbZtceZZ89tl4bquS2trnrKytM5TFMVrgFNPO5x77n6cFSveZ93aD5i2z/iY8u+tt1aybWsd48aN4PAj47lRBx44nUmTx/De6g0semsl+85of569tXAFhYLDuPEj2Gff8Tt7uPoNQgjmH70/o0cP5YNNtSxdsoYj5s+Jjc3q1Zt4b/UmqqorOOHkebHXa2LIIZ93wnEPIKXgjLOP5OmnFvLm6yvYsaOxnf3xpRfeprU1y0Hz9mXGjInh38vK00ycOIr3121h+fL3yecK7bKsPE/xzrK1AIwaVRPaLPdEDIzKZBdCT6wCa9euZfHixSxatCj875e//CWrV6/e3bu4U2EYAitq3zNkO+nongJDGggh/EypLux7SuFFcmo6ggyHTgfkdgQhigqOQiToHAKllPLzhHYiKaWiQeddKaU6t+8ZhsQ2JMLPQQqgO5Xp7QxWUkogsE07JKWkFKR6eE70pPuebel8roCUsgxJLFJKqf5TSvmkVDGPSdv3BgopZRu2r5TSX8KmFDFSSqGtpQG5GgSdB0op6IegcxVXSgX2PSBmV41mSuV9pVR/2/d6pJRqU/zIwcpIdQApJZ/97Gd55pln+J//+R/OO+88rrvuupJjVIqga7uSd4v88Z/T7rVt/tLVlqZNm8brr7/O7373O6677rp29kPV5peubOMJEpTC6aefTiqVYurUqZx88sk9/u+www7rdZ5Sf0IIwfQDpjHv2P2p29bAU399QXdD9r+36rbV8+T9z2PaJmd8/CQyke5TSinWr9rITV/5BT/6yi9Yu3x97FxvaWzlD/9zH688/ibT959Cc30zzz/yMgseeqn438Mvs2H1pg7379WnFrJ+1abYdgM74KN3PUlTfTPVw6s4+PgDBp2K4dHn1/L6O1uZObmGs46eHLuxMWNyDeccN4Xahhy33v82jhtfp445cCx/vvEMfv2fJzB2eNxOlMu7/OYvb9PUUuBDx05l34nVsceHDklx6VkzQQjuf2oV75Ugny44ZR/Gj2xvU6qpTPG58/YnkzJY+X4dz73Z8Wc3EFFenuayy88hnbb5412P88EHteFcdwoOf7rnCbZsruWEE+dx6OGzYnMqlyvwg+/9ngvP/wZ33P5wu+v6ffedwIfPPYbmplZ+f+ffyeUK4bbr65v4/Z2PAnDpp85k+PDq2Gurqiv41GVnYVkmf7znCTZvrg3nvFKK99dt5q9/eQbTNPj4J07tMHdqoGLUqBo+cckpKKX4/Z3/oLGxJRybbDbP//3uH7S25vjoeccyderY2LjX1zVx7TW/4pMXfoe/P/JSO2vvkfPncPSx+7Nhwzb+cu+zYX2llGLDhm3c96enyWTSfPozZ5LOFEknw5B8+KPHYNsWSxa/x1NPvhkTEGgicCXPPL0QKSVnf2g+Fd3MuhqMGJxVZB9QV1fH17/+dR555BEaGxuRUvtjHcdBSslll122u3dxp0IKgW1KHPyOG6aJ2Qm5MphhCm3f85RXtO/JNkHnMaWU12WIY9G+Jzq371EkKvKuG1tktFIqaGe/8y5gQqWU0ORRu+57TvczpQwptFKKuNLMUZ4OomYQk1JCkDbSYaaUFAK7h0opKaUfKK5Jqc4uTMNMKRlRSsXUQUFtrEB4Yae33kBKgZAUlThiANr3ZFtSqvh4aCsVKvA1Arqzj+cphOhb0HnGzJRUSuUdB0WgrNQIzn0FFPwcNUP2XikVo956GXSuyZdgTerVbgxYSCk566yzOOuss7psWx9YOaL/BoqkTxfresyBo1Q7AkvnQHXfm3PMMcdw9NFHF/et3X5Hi9tubTJBgnY499xz+dvf/sbatWv5/e9/T01NTdcviuCNN97g4osv3kl71zOYlsH5V55NY10TT963gGxzlgPmz6alsZWn/vo8Wzdu5+xLTinZKe+NBYt5b5m2nr321EImRRQczz3yMq8+qQPIX39mEa8/s6jDfZgya2Ls34ZhUDO8ivWrNnLzf97GEaccwpRZE7HTFts31fLKk2/y7sJVDB8zlIu+eC6jJ4zsxxHZNWhozvOtX7/MTV85mmsuPZgxw8t5bekWxo0s59Mfmo1tGdxw22slrXsfPXEa+06sZvqEaubPHcO9T6yMPf7i4g/40e/f4KufOIifXXMs//fIu6xaX8/IoWVccMo+7DdtGH99ahU//+OiUPkMkM27bN3RysxJNdz6/07ib0+vZtl7tTiuYsrYIXz4+Knsv88wlq+t4//96iW21rXu9HHqTwghOO74g7jyqo/ym1/ez9ev/jkXXXwKVdUVLHhmIX+9/xkOOXQWV1/z8Xb2u00bt/HgA8/R0pzlvj8/yXkfOyGmnJGG5MrPf4StW+p45OEXKDgOp51+BPlcgfvufYrFi1bxiU+exrkfPa7deSSE4Kyz57Nh/RZ+d8ejfPXLP+WCC09izNjhrFv7AX+853G2bN3BpZ86gwsuOmnQEbBCCM674HjWb9jG/fc9yzeuvZUPf+QYTNPg74+8xJNPvMFppx/Gpz9zZjsnzLKla3n6yTfxPMV9f36Gk089JPbZpNM2X/v6xdTXNXPHbY9QX9fE/KP2o7a2gXvueoJt2+r5/Bc/wpHz4+uXEILjTziIf/3sWfzuf//O967/PW8veY9DDpuJaRgsWbKav/z5GVqac3zkvGO44OITB9249wSDs4rsJZRS3HHHHdTW1nLbbbdxyy23cNlllzFs2DDeeecd/v73vzN58uTdvZs7FUIIbNPQdh4Epmli9aKLyWBAYN9zlRux78VP5nimVA/se91QSgX2vYLj4IqIfc8p+AV158RWXxELOveKypLwcbcHmVKBfa+t2spTg14pBYFiRoLSGUw9JaUMWVSQdUX4WKaBYQikqQlxS7ZXSoWj3Ff7ntB2OOUVlVJSWF2quXYVLMOKKaWskvY9L9T0tlVK6XOo91/QoVIqEnQezZQyIpY4w7dZKkWolOqKgOwOtHuv50qp4lrU5t97EKK2hM6fFyfldKA4BORPj5VIXZFYXTzeVe5Du9cnxFSCXiCdTvPNb36TM888k9tuu41rr722R8qngRSeL4SgengVV3zzUl567HUWPr+EZTevwLQMxk4ezYf+5VRmHrRPyQYzU2ZNpMIP/p22XzyLbfiYoRxx6sHd2ocRY4fFbDmZijRf+eFnWb5oFcveWMnKxatZ+NxinIKLnbYYOrKa8684m4OPO4Cho2oGbbG4fF0dn7n+CT520j4cfeBYzjxqMq05h9eXbeGPj61gyartJde8V5du5vhDxtHQlGfZe7XtHvc8xT3/WM7bq2o576RpfPTEaVSUWbRmHVatr+fLf3uW5xZuJFeIX3tu3NLMJ//fPzl8/9EcPmcUx80bxznHTsE0JS1Zh/Wbm/jLk6t47OV1bKvL7qxh2akwDMml/3IGc+ZM4f6/PMPttz1EIe8wYmQ1X/jSx/jQh4+mqqqi3ZwaOmwIs2dPZvGiVRx08AzK2oRxCyGoqqrgOzd8ln/8/SUe+8cr/Pimu5BSMnnyGH7wo6s45ti57ciuALZtceXnP8K8g2fy4APP8Yff/4PWbI6KijLm7DeVr1/7SeYdPKPT+mcgI522+erVF3DIITN4+KEX+cXN96OUYuy44fznf13KyaceQibTPuB8wsSRTJo8mg82bdeEUZu6WQjB2HHD+e+ffoEH//Y8zz7zFs8tWIRtW+w7YwJfufoCDpo3veS42bbJZy4/m8OPmM0/Hn2FxYtWs+DZRSilqKws48ij9uPkUw/hsMNm6S6lezD27KNrA8/zWLhwId/73veYPn069913H4cddhjjxo3jmGOOoaWlhRUrVpTs9LOnQEpNSrWgL+ItywiDY/c0BNaaqFLKaJMpFVNKdSvoXP/UpFPnF39h9z3HxTWLpFTOKfSL9agrOJ4D0s+UUloJFns8UEpJHSbd2bEbUivspFvKvqdJE0sOnAvcniAMvPbMiH2vh0opQ2vIumPfMw2dJRba99pkSnmqH4POpSbZCOf5wLLvmdLElGZRKWXElYye8vygc59ciHXfU352Wz/Y9zrIlJIiGnRefDzvRrrv9bHjXbwDWw/sez5hpu8viEGVZdLf0BfukbuP/s9ucz1tQqXa2/VC+WK/8Ecq/KniJHSCBD2AEIIZM2bwta99jRtvvJEzzjiDAw88sNvkSFlZGQcccMCAuQYUQlA+pIwTP3o0x51zJI7j6hs3lomQpYleIQT7HTaTa2/5IigYOW547HkHHzeXg4+b2+v9GTK0kkOOP5CDj5uL67i4fmdWIQWmaSAj3cgGM7bUtnLLnxdz6/1vY5oSz9M5UZ1dEv/psRW8unQLLdkCG7Y0l3yOp+CtFdtYtHKbH10g8DxFvuDSUVqGAj7Y3sLfnl7N355ejW1Kv+ubJrryTtdRGwMdQuj5c9jhsznk0Fnk89pmZ5qGnu8dzKkhQ8r58U+/wgebtjNx0qiS8StCCMrL03zko8fxoXOOplDQ1yu2bcW653UE0zQ5cv5+HH7EHPL5gt+pTndqHwh2375ACIGdsjjplIM5/sSDKOQdFArbMjHMjm8yjhs/gl/99mvU7Whi8pQxJcdBCEFNTSWX/MtpXPTxk8L1y7atWPZXKRimwYEHTWfugfuQzzu6s6HS17fWHjDu3cXA+CbaRQgsANXV1YC2BzQ1NYUFgWVZrFmzhiOPPHI37uXORcyaJLRSak+17wUhxJ7yyLvRjJjic2JKqRJqoraIKqVK3bELEbEVFRwHT0bse07B76Kwc7vvOW7Rvqf8zKz4491XSgkhSNkmsk13CFepkDTpWyey3QtNThiACInbniCqeOsq6Nw0JJZhxDKlonNSqYiSwielettmupgpRaiUGkj2PUMY2IYdklK2lG0IukApFQSd6zma7yellLbvxUkpfdHrAjK2XgSqKY+doJTqrX0v0rt5T1RKdRehUipy06AnFrm2Qecd2/eCp/RTQTS466oEAwBSSj71qU8xefJkMpmeZY3MmDGDP/3pT9i23fWTdyGEEJiWidnNbEcp5U63zvV0nwYr8o5H3un4WjAKx1Ws6GbIuFI6Z6q3+0Q392mwIXBVlFLndPT8mppKamoqu/VcyzJ7lRvc0/0abAhIwe522BNCMGrU0JIdCUs917atDtVoXb02lbKAwXmTv68YGJXJLoJhGFRXV/Pwww9TKBQYN24cP//5z1m2bBkLFizgzjvvZMSIEbt7N3cqhADblGE3ItM0u31SDjYEdrKosqFtAdtzpVQ0U6pr8gEg5zh4FL9Qc04+0s5+F2RKocfAaUtKBa2gJbhe55lSQkDKMtsRcW2DzgfrHUOtlDK0fa8XmVI9CTo3DYnl3/kDsNuMqadUO1KqtzCET0qhM5kEAkNaA4eUkobfgU9f+FjtxqLYfQ9KKKVE35oFpMyUJqRU20ypgMSO7qsoPh6QUv1AxEYzjHoUdC4igVfseZlSPUG0rTKUyJTqcgsqol7a+VxR9Gsm4aUS9BXl5eWcddZZzJw5s2cWYClJpVKD9ns7QYIECRLsORgYlckughCC0047jf/93//l+eef54ILLuCf//wnRx55JGeeeSbDhg3jsMMO2927uVMhEKRMA12hajZ3Z+Ya7U4E3feUUsVg4s4ypbye2fc6VUoRte8VYoRPztEyXXa2UspzCIQUSilcL36XquDElVKd3f0XQlva2pFSSlEY5EHnECGlEJim7LGtMjouBp0TFYaUMfuebciYaUvneSufiOkn+54qFr+mtAdMEWIIA0taRaVUG9WYtu95EVLKDzqPKqX60CwgbaYRSrax7xHPlPL/Lv3fFcS77/Ux6Dx61vWMlCJRSvmI8XNKk5Xd6rYXvKTtb22VUm2yqfoaTh5ba0sosxIkSJAgQYIECfYmDN4qspc47bTTOOGEEzBNE8MwePDBB3n66aepqKjgtNNOo7Kya0nkoIaveAmQ8r2ueyKCgtHD85VSqRgppWirlOp+0LmQssvue6FSqlCIbbeolAJzlwSda+WNG1FKaeVUMVPKU91QStkdKaUGeaaUEEX7nhJYRs9JKcPvvqeVVt1RSsXte1GoUCmliam+klIBMRtU0qa0+5yD1F8wpYlt2ORcS0vN2xJ0Ss9NnY1WVErlHa9oge2LUspIIZREtVNKOSCJ5QBIURzLfLT7Xm/JoEhGVfDZ9GRLoTpoDw467y7aBou3/Uy6tNtFuaioUjF4WO0k/ZTqxr4lSJAgQYIECRLs4djrSCnwC3LHwTAMZsyYwZQpU8jlclRUtO90sKdBgA5x9jwQglTK3mMD1AIbVYf2PUUx/LuHmVLdCjqXEfteO6UUfVZ5dIW29j23jX3PjSqlaK+kikJ2opQKyK3Br5TS+2/2kpQKf++ClAq6+3kd2vciyom+Bp37mUtFoksMKKWUFBLLsCl4xUypKMJMKaGJm2KmlCaQtQW2b0Hn7ZRS6IBXUkV1lN5XSiql+oroktNV17YogqDz8N+7+ObCli1bWLduXafPEUIwZcoUhg7tOoehMyilWLJkCfl8nnnz5nUQeBx/X4gQPh0F6nYQXt4uUypm7us7PRUNOm+7MaUUhUKBxx57jKVLl/KFL3yhx1lBCRIkSJAgQYIEgwmDt4rsJV588UW++tWvMnv2bG655RbKyspYtWoVV111Fddccw2nn376gCnYdhbStgmteQS6G8Oeeoc9UDF4SpH3iva9om1G4bjF8sDzFJ7XeZhiUP4KIZBdZUqZHdn38n5wNTuVEAxJqcC+58ZJp6hSSgVdzjpB2jbbtTN1PW/PsO9ZASklekVKSel3JexGppQm+ExysjQppZQO0w5Ubn0hpcIgcF8BAgNLKSWEIG2kyDqBfS++X9GgcwClAqWUi6eUJt36QOzqTCkZy5RCQb7g+KRUkewIQs89BY7Xn6RUNOm8By8UPvmido9979577+UXv/gF06ZNAzRJtXDhQg477LCwmcg777zD9ddfz8c+9rE+vZfrunz729+mtraWRx99lFSqbRvsuDpKIAjEhpEfJXHXXXdR19zKSR8p7mO77nv9ba9rkykVfgspxdq1a/nhD3/Igw8+iG3bXH755QkplSBBggQJEiTYozF4q8heQCnFgw8+yKGHHso111xDOp0GYNq0afzHf/wHP/rRj5gzZw4TJ07czXu6c5GyDGhFK6XsPVspJYVE4VHwCZh2SqlIppRSXrsOdW3RI6VUmCnlxBRYOadQLKh3etA5IJSfKdW2+57/b6E7S3WmlAJNSrVVSjlekcwa1KRUX5VSked3Zd8LVGf5aKZUpKDuz6BzKUR4fqsoKTVAiGidcZdG5ouZUlG4ga20TdB5aN+Tou/2PS/+eSm0uhECEjti30OglNdPSqlAzRMeXo/IwtC+5792V5NSruty4YUXct111wHw0EMPceGFF/K9732PI444AoCrr766S6K/OzAMgxtvvJFCodBhp7C4fY92xE8pKKV4+OGH2bKjnhPPPT/y95LPjr2uL1Btf/F/uq7LzTffzNFHH41hGDzyyCN9ep8ECRIkSJAgQYLBgMFbRfYCnufxwQcf8O1vf5uJEyeGF7G2bXPyySezZMkS3nzzzb2DlAp+Tw2cArW/IYVEConjuaF9L1q4KTSp4geJ4HkK1+vavif0L10Ww4GtKN/Gvpd3CniewjTlTisklVJt7HvEuu8pBU6boPOulFKZkqSUG5JZg5mUypgZUHr/bdPoMdGhiR8dOt1V0HmQz9XckX0vmJL9kCmlM5ei9r2BpZSSQmpSKgw6b6vEc7VqpR0p5YZqw74Qu7ZpY3gmTlQphSJXaL9eBKopBeT89cQU/TDne919rz0RsyvX8uOOO45cLheqJwPyU0qJYRgopTjvvPMYMWIEq1evplAoUFFRQXV1NUuWLKGuro4pU6Ywffp0mpubWb16NR988AHpdJpZs2YxfPjwMMNrw4YNOI5DKpXyP3d9nK7rsnbtWlauXMnmhhxTZ89mqP+6AK7jsGrlWhZt2Yht20yePJlx48ZhGAbr1q2joaGBluZm1qxaBUpRadlhR8T6+nqWLl3K+1vfR1abzJw+LmzzvHnzZurq6lBKMXToUDZt2sTatWupqKhg2LBh4T4MGTKE0aNH09DQwAcffABA1syQGlIN+BlyPitlGAbf+ta3KC8v580339z5H2KCBAkSJEiQIMEAwOCtInsJpRSGUTqcNp1Ok8vldsNe7ToIIUgHQedCkE6n9thW4oY0dNC58kpnShFXSnXHvheMlRCyZ933vDaklFK+ymMXKaVoa9+LB50rVKdB51BUSgkhkKGNycPxPD/bZ/AuJ1oxY6EA2zJ6TBbqQGxNJnVXKSU7zJQqFql9VUpp+56/faXQmVLWgCGitX0vjUAX+qWUUkqp0kHnBJlZvVdK2dJGKtNvRhokjxOSUtHzUwpBEMPer933Ij4z13XJ5wvder3n57kVHDR52YPXtt+WR6Hg4Lhuh9+PbXHAAQd0+rgQgmOOOYbm5mZ+9KMfcf/99zNmzBimT59OQ0MDS5cuZdy4cfzud7/j4x//OKlUiiOPPJL169fz8ssvc/3113PiiSfieR4PPPAA9957Lzt27ODZZ5+lsrKSXC7HTTfdxD/+8Q9OOeUU1mz4gB9971tc8eWrOef001EoWlta+PXPbmL54rc447RTaWxs5OGHH+bkk0/mhhtu4I9//CMrVqygJZfnrttvRQjBh8/+MNMmjePFF1/k2muvZb/99mP42OE88dQTjB//EF/7r+8wpnI0zzzzDHfffTevvPIKl1xyCevWraOlpYUFCxZw6qmn8tRTTzFjxgwuv/xyPvGJT7Bs2TK+//3v88EHH3DZF7/CcaedVRwsVRyzioqKXn2GCRIkSJAgQYIEgxWDt4rsBaSUTJo0iZtvvpmrr76ampoahBA4jsM777zDvffey09+8pPdvZs7HWlfKSWAdLq0FWJPQKCUUkpR8Iot3gNoNVEkU0p57Sxu7bcZte913WUNtBXIbRd0rmLWqp2BdplS7ex7LoEaRz/ehX0vZYVEStowaHVcrZTaA+x7KTOFUJqUSllGjztSSil9cqLrTCkhhCb4/PdoT8QEFWoxU0qhekV+CIHOXIqEp5vSRoqBYdkVCNJmBukrjtqNheeTUm2UUjlfKaXPod4TbJZhYSgLENiWiQIKBadISkXCxINMqWjjhH7JlPL/z/M8/uNHt7JsZefh4dFXtmbzuMq3Qdpmj22n0W25rsuxh83lxmuv6FfSsqysjG9+85ssX76cxx9/nG984xvMnz+fZcuWcffdd+N5HuPGjeOGG25g2LBhKKX4/ve/zze+8Q0ef/xxysrK+NznPkdLSwu33nprqGK6//77+elPf8oDDzzAYYcfzsqNtfz21l/yvW9cy2EH7I8sG8a9f7iTh+77M/c++DBHzZsLwNy5c7ntttswTZNrrrmGhQsXsrWugeuu/wEoRfPWrWzZsoUrrriCU045hR/+8Idsy27jiDOO4PMXXMVtN/+MH3xP52Sl02kefvhhhBDcfvvtKKX4z//8T770pS/xla98hWw2G+ZpHXbYYUybNo1PfvKTHHHqWTTnnZB8TvrvJUiQIEGCBAn2ZgzeKrIXEELw6U9/mvPPP5+//OUvTJs2jXQ6zbZt21i5ciWXXHIJM2fO3N27udORiiilMqnUgFFN9DeCTKmOlFKKiKXNL3S6DDoPlVJdE0oxpVQb+15QUO90pRT4pBMxpZQiYt+T3bPvRTOl0pYmpVyvSOQNalLKV0oF5ESPlVJCt2YTquvuewhN8IkOSKmifY9+U0opVIyUGij2PcAnpTpWSnl47ZVSrs6UklL0yQJrGzaGr5SybQtUQEq5WMRJ7Kh9r1/D/f21Ryl4Z9U6Xnzj7b5vs5cYOWxoB3lKvUdgwQOYMWMGhx56KFJKZs+ezbe+9S0Mw+DHP/4xW7duZdWqVTiOg2marFq1iu3bt1NeXt5um67r8n//939MmTKFmTNnkstmcQoF5h1+BD//4fd47dVXmHvECTz813uZtf8BTJq6T7gvp59+OsOHDw/XbyH8UPRIDtWzzz7LypUrueWWWzBNE6UUI0aPZN4Rh/P3B+7nP//961RnRgDa/n/WWWdh29oK/8Mf/hDDMLj00kv5zGc+w6JFizjkkEPYtGkTy5Yt4z+/8Q1aggNp33wvQYIECRIkSJBgr8PgrSJ7icmTJ/PnP/+Zm2++meeff57m5ubwLu3FF1+Mae75Q5K2jbDqTadTXTx78CJUSqGzj6SIF5mxoHMUXrdIKa2WELLzoHMhRJug8+J2C66rw40FfVJ5dIWoUgqlYplSREmqbgSdh+oe/5gypsEOwFVeqAIzuuhGOJARKKX07z1XShkRpZTsKlMKyKQsRNB9T7a372n0Q6YUhN33YplSA4iIzlhliAgpFcsD8jqy7/WjUsrz39vSVrymlhbyBQeL+PlZDDovnkv9pZRS/nt9+vwzOOfko7r92m07Gsg6WkU3vKqMlN27/VFKkcvlmD19SkiW7gzU1NSE37FCCEzTpL6+nn//939n8eLFHH300ZSXl7N8+XIcx8FxSs/9XC7HunXr2L59O5/73OcQQtCcLdCazzFx8hSaW1poaWlh25YtzD5gbuwGQlVVFSeddBJAifVez7NVq1ZjmibDhw8PM5+EEIwcPZod27dRX7+DCaOLpNSQIUNixwRwwgknMHbsWP7whz9w8MEH8/DDDzN//nxqampobmiNvWW/d/dLkCBBggQJEiQYRNjzGZg2EEIwZcoUbrrpJvL5PK7rYts2pmnS2NiIYRgddvfZU5D2bSpBptSeCimCAlfheB62oQv0oOhV/t/1PzQh1bV9T//UCpTu2fcKrhvE+eh/BwX1rsqU8gutUBkVPB7NlFKqR0HnGdNACHA9bQsUQvRP6PNuQspMIzw/bNsyepyzFmRK0R2lFJBJmQTR6LYRfzPP80PJpQLhdWmr7HS/2mVKgSVTA0wpVRax77XJfFMdB517/aCUsmRg3wPbspBCoJS23JZTWikF4Kh+IKWCCCulwkyrj515HFVDupcppJRi1bpN1LV6ICQzJgyjsqx367nneTQ2NlJeXr5Tu/i1JUOVUtx3333cfffd/P3vfw+79j300EM8+uijHW5HSkk6nWbOnDn85je/0aHl2xrIIvBcl+EZm9Xrt2JZFvl8vpjRRucEUPBQJpPG87wiKeb/PZ/LYZoWpml1eEwBqqurufDCC7ntttv4whe+wMMPP8wPfvCD6JKcIEGCBAkSJEiQABgYwSI7GUEGBeiLb9d18TwP0zRJ+fY113W55ZZbePvt3Wed2FVIWYav6YDMHkxKGdLwM6UIu+q1V0pFM6V02HlnCAsQ0blSCoqklFZVFLcbkEFS9K2g7gqxTCloH3Tu+ARc0H2vy0wpE+mTBhk/l8xTetwEXZN0Axkpw0b65ETa7nl4tZTSZyxEl0HnEFdKWe0sa8Fs8TOllNNrJYXOPhOxNdCSA4t0z1hlCEwEJcaiA6VUwfVQaGK3L0opU5pY6DXQtkxsX2kUKKGipLHwZ4WCnWBZ7Xn3veLzxS7vvNefWLduHalUivHjx4fH0NTU1OlrUqkURxxxBGvXrqVQKJDJZCgrK0NIyU9/cD0r3n2XsrIKZs45gPdWLifbmgX0tcDy5cu55ppraG3VaqXQXqigdttWNry/joMPPgTDMFi6dKl+HQqnUGDFO8vYZ8ZMaoYN7/K4hBB87GMfo6WlhW9/+9vU1NQwffr0cHv+DkV/JEiQIEGCBAkS7JXY40kpz/O45ZZbuPXWW/E8j1/84hecddZZJf+78847KRR6171oMMG2TH3xH3bfG5zFTFcI7HtQDI+OFpleNPxbKZTXPgy83TYpBp3Lbgadu54XKzocT/n2vb51DusMChXLlIK4UkrnacW773WtlLIiSikZbqd4LIOXlLIMG0NpciLdCwuUDjrXxEV3QsQzaZ0pJUSpTCl/VIX+z/XcmNKjJxCCcI4Fc9AyBhYRXWZWIHwir2T3vahSyieLAu64r8SuIQys4HNP2Vi+9Srgqg1RpCeFKDY6CB7vCykVdt8jQln3uOtjcbx21zKulGLLli1s2bIFpRSbNm1i27ZtsRtBGzdupKmpidbWVtauXRsSQgBHHXUU+XyeP/7xj9TW1rJ27VruueceHMdh/fr1secGEEJw1VVXYZomP/3pT9m6dSv1dTv48+/v4L0Vy5k4aRLSMPjU577Ajtpa7rv7D9TV1bFhwwZ+8IMfUFlZSTqdRgjBhAkT2L51Kztqa/n9bb/h0Ycf5KCDDuKTn/wkN998M++++y4NDQ088fATrH53FZd/+atYqRQ7duxgy5YtuK7Lxo0b2b59ezvyeJ999uGkk07i3nvv5YILLsAwjNiZrD/72Axg7dq1PPnkk6xbt47W1laeffZZXnnllTY3FRIkSJAgQYIECfYcDF6/TTfheR4LFy4kk8mglGLFihVUVFRQU1PT7rm1tbW7YQ93PVKmERZEe7RSShih4sWLFJkBlCKijPKVUm7X9j2B8BUoXSilfOKmqMbScD19V76vKo+uEMuUIkJC4Xfji5BSer+6oZSSxaBzP6qqmFU0iO17trQwKJJSPbbvCRGqVmQ3uP5M2ka05BAIrHaZUsGYRrrv9UUpFWRKeUHQ+cA65zNWOQKjJEHneq7OY2ujlBI+WWT08RwypOErpTzSKZtCYNfyJ0DcvlckwLwIKdVnUj/y0fZ0U8F7azJ0991c+Mtf/sKrr77KRz7yER555BFaWlr4xCc+AUChUOC2226jqqqKqqoqfvSjH/HFL36R2bNnI4TghBNO4Je//CV33XUXr776KpMmTeKjH/0oFRUV3HXXXQwfPpzZs2fH3k8Iwb777su9997LL3/5S774xS/iKMGIceP57o9/RuWQKuq3NrL/gQfxP7fewf13/Y7Pf/7zGIbB4YcfzmWXXYaUujPr5Zdfzntr1/Hda79GRUU5l156GZZlceONN3LHHXfwrW99i1anFWXC92/+BYceeSwoxYIFC3jxxRc57bTTuOeee9i8eTOXXnppbD8Nw+CCCy5g9erVHH300aEqq+3pHP3npk2beOWVVzjwwAM54IADWLp0Kdu3b2fevHk746NLkCBBggQJEiTY7Ri8VWQ3YRgGP//5z8NuaWPHjuWqq64KZfRR/OxnPyOdTu+Gvdy1SPmZOUKIPZqUiiqllPKLzHZKqaKNQqluKKVi9r3uKaVQ7TODgn3ZmXVkmCklfHIjqpSKhrwLfexdBWqnbSsk4jKmQcB26bisQa6UakNK9RRhJ6/uBJ0LQVna0sSKaG9ZC4POA6VUFwq2rhDMw9C+ZwwcdaQQgoxZjhRmSYLOVb59r02mlA7j1mt6X8gYU5q+UqqVdKpoawysldGPRqBJ6RgRa/T1KzQIltKEVK9so/5mdudnesUVV3DFFVeUfMy2bf7f//t/Hb7WNE0uuugiPvaxj+G6Lqapye9LLrmEtWvXMn78eJRSFAqF8DHQxztz5kx+8pOfUCgU2NzQSouvZS2OhWDO3AM55OB5jCqzMKTENItEohCCqVOncsutt7GpvhmhFNnaWhRQUVHBF77wBa688krW7lhLbX4H5fZETWYBH/rQh/jQhz6E4zi0tLRQWVkZHpPn5+wBrFmzhosvvjj2eAhfJBUlqY444ogwWytBggQJEiRIkGBvwB5PSgkhyGQy4b+//OUvY1lWyQv4888/n6qqql25e7sFtlVUEO3JpFTRUhYkaMWVDzH7Hj4x04VFIhp03lWXqpCUQsSIKeUFBFnfCuquoEkm5ZNOJZRSwbH7KhRHdU5KpWwDw1d/pc0gDFwAOlC+//J1dj0sw+4jKeUrpZTWSnWFwL4nhcBqM4/cIOjcr1j7ZN9Dz0MVqXytgaiUEno+me3GIq7m80KlFODb6fqklBKBUipLOmWH50hISkXOTxGzCuqf/asOFPSQk4qQobvPvtcVGdYdsizoWhftfuu6Ltdeey1f+cpXmD17Nq+99hqHHHJI7Ptc+ApF27ZJ2S4teafkEAohSdkdd50UUmKnUrj5PFAkcIP9smwL6UTW8Dbv3/ZYH3jgASzL4qCDDuKJJ57g5ptvHjBEcIIECRIkSJAgwUDD4K0iuwnXdXX3ncityFwuV/K5t956Kx/60Ic4+OCDd9Xu7RbYAaEgBKn0wAo97k9IIf1OaCLMrIkqpVQ02FwpPwy8c6VU9A57V9lBoQJGBX3WgjcuqrZ2ZqESV0p1Yt8TfqZUF/a9lG1iGNq2lzaDdC3pB0CLbnWdG6iwpI3ZH5lSSoS5Y50h4yulpACzhH0PAOGBoE/2PUREKeUVlVIDCRmzAkFeE3RtVGOO50QypVTMvgd9774nhcQmA9STTtnk8gWIEM4yFnQeUUr6xGPflVJx9FQpFV2PBlJHxf6AlJIjjjiC733ve2QyGYYNG8Y3vvGNWI5W/Pkljj9Y3rtJ6gbnWcfnm4r96Aiu63LzzTczbNgwLr74YiZOnFhqK6ggUSpJOk+QIEGCBAkS7MXY40mpt99+m89+9rPF1s6dYOvWrZx55pm7YK92LwIyRAiwelGADxYEljJdsLVXPnhexL7nK0m6VkqJkNDrtlJKxZVSmqTaufY9pVRR+RRkSjldKKW6Zd/TG0uZRkQlIAd90LkpTUyhrbs6U6qnNqoiwShU10qpsrQdKm/aq4Mic9IPoO+tUgqKRIoaoEqpMqsCIep8gi4+FuGcjGRKKaVimVLdyJXvEEIIUmjlTTpl09yiu7QFSikzooTRQefB6/Rc7zd1oGjzs5sYCEqpnQUhBF/5yle48sorcV2XTCajyd8ODlRGPqcA3SajuvhD2+10tdVzzz2X008/PVRqR/e5FP+UUFIJEiRIkCBBgr0Zey4j4aO5uZmysjKOOuqoLp/73HPP7YI92v2QohjYbVl77hQQQoRKqVDZIOPFgRftvqfossNRsSjVlpDOELPv0Z6U6mseTmeIKZ86VEp58ce7IKVSKRPDkCgBaSMIyxcIIVGqHwv03QBDGFgiAzi9z5QK7YzdVUrRISmloJgF1helFP6cVW0ypQaQqiZjVSJoKJkp1baDZKCU8k9ATUr14VgERVIqk06FmWnFTCkZe25IfNDPpBTdnTmR5wfWRbHnEVJQVIFF7XqdPjdYm0uMYtgltKMNdEVCKdXmiZ2fj4ZhUF5e3uHj4fZUm38nSJAgQYIECRLshRi8VWQ3UVFRwcUXX8zll1/e5XN//etfd3ohuadABkWMAHNPJqUQEfteCaWUUriRYkOhurTv+U+lO5WgGbPvRV8fVUrtJFJK+QHZQhWVUI4bqkxipJTsXtB5yjYxpMQBbEP4wczS/29w2/cEBiY2vSalIvYpobr+TFO+GqukfS9QSgVB513YKrtCMMVC+94AU0qljDKEkN1WSgGhStGQsk+EjBCSlK+Qs2wD0zT87ZfKlIqqcfpOSsXPfQFC9Xg90OuaXov29swi0e6XCLrgfMKHQ/te28fbklQ93LmukHBSCRIkSJAgQYK9GHsuI+Fjzpw5zJo1K/a3IE+nqakJx3HIZDKUlZXxr//6rx3mVexZCEKPBXIQq1u6ghTSz/qJ2Pc6yZSiTRh4SSj/nns3ikDDzxlSbYLOi6SUpA8ZzZ3vZhdKKR3y3ubxLkgpw5CaDPCU5ktCbcfgt+8JJELpc8EwelEhxmQuXX+oRkB+qPbcpluskOlr0Hl0b3QXOzCE1ett7QwYMlBuef5/RTjKKd19zx9vw+jYztUdCEB6ejykCabRhpRqlykV/K6fZ8mBMZZ7Nx3lQ8R+aHTztOlYGRX/d/C8vnJIbV+fcFIJEiRIkCBBgr0Zey4j4UNKGSOaPM/jpZde4qc//SlvvvkmuVyO4cOHc/bZZ3PVVVcxYsSI3bi3uwZKef5FtqBPgSwDHFH7XqCUkm2VUpH8nph6qAMo3+YnuxHiYhq+pUuVtu/tkqBzKJJO/5+9846zo6rf//vMzL13aza9QgppkEAghJJAQhMp0pSOUhRRUUG/VoryownytaIIKKICIkUQ5AsB6UVagNAhgZCE9J7te++0c35/nJm5c3fv9pTdzTy+Irs75Z45c2bunGee5/kUZEpRqJTqQNB5SMQpJUGp6PCFMBEY7Qa/92gogZIGoJDC6fz2IjQI0aEZpgjkir6UyGZjzm+ulFI+UnVAwdcKVLCtkiogf3rWedIEj4FUHrJZBcjm1ffiQedRplR3PlyBCj5CmT5WYEttr/reFs+U0nvt5v0goTZA94JsTip1ZCPipNM26st2g9UTJEiQIEGCBAn6Pvo8KRWHUoqXXnqJM844g8GDB7PffvtRWlrKunXr+POf/8ybb77JXXfdRWVl5fZu6laF9P1gomrGVBl9D2HQeTxTqmXQuUTPTPW8pD2llLZAKRDt24ZMU2u0lDLaCDrf2vY98kHn3cyU8qWMKkV5rh+EK2sVmogqHfZOSAlhd9iyodPbK3TfRCLE9j4v+K/nSRy3cMzlSSk9NmVEIncNzSuK+T1sAuwH14avHBy/sDJqa5lSIpBKWd1USikFjuMCCk84mJYRkK+tKaW2bqZUZxFR6komnFSh6DX4UyeDzluJjFLxnbdc3CnE29SxhKoECRIkSJAgQYK+jR2OlPrzn//MySefzJVXXkllZSVCCHzfZ+HChXzpS19i3rx5HH744du7qVsVvudrdYZp4vdhVsoIiJKIOKFwkimVIi9ACZVSbZNSmtALJybtKKWivJvtGXSuWlFKxarvhctV26SUGwRwS6lwbDefoSRMbZXsxUopXypcT5NAWdnY6e21kikMzW9/fTdgwDzPx3YK+z2ylAb767ZSKjjPIRfV00gp1w9JURfbzxYs86SnJ/HNlFJBoFm3M6WkUuQcFwBbNWGZ5cQrazbPlArvH0Lor84tqpQSzXOm2kbcfqyU2nbqnh6IQtJWEQ6TuAIqFAcX3b7N/RUJOu/mNdRi8x331CVIkCBBggQJEuxYpJSUkpqaGi688EKqqqqiv1uWxdSpUznkkEPYuHEjkH8I7YvhsZ7nI6VCmeDJvvs0nM85Km7f86XK2zyCrCivHfue73dckWBGQefNxlColArCwrcGVDwzymhJSkkp85UHw+XtKKVcX2oiTylsx4sIt9C617tJKYkb9E9ONkSB8B2FJMh9UtAR/siJkVKO28yyFo6vgCyUSnadlFL5ipJh0Lnfw655x5cB2elgey1JKaBoppTYAtX39FgOSCmylJqVwf7DIPXC9c2YUmprhPt39kjyty+15cO328HGjRtZvXp1u+uNHj2a/v37d+uzlFIsXLgQx3GYNm1a0WszvJcr1dK+1+49WwXf+a0ooVpkTnWm8UU+q+WfgmITnseiRYuYN28ea9asoaKigv3335+9996bVKpn5JclSJAgQYIECRJsaexQpJRpmkybNo3Fixezzz77RA+2Sils26ampoapU6cC8PrrrzN06FDGjh27HVu8deA4XkRIOF73Knv1ZISTxoKg82b2vfgbcKW0iqwt+IFSqiOTEq2UClRSRex7zauubUkolFY+CYoHnUtVWOUN8PwOKKWUJjds2wt1UlophdGrg85dT2qyRihyXbDvSSVRIXHSAXYgIqVcv237nlCaQOyyUkrhe17ULhVUnGxDNLLNEfWFdAqUUgUVIeOklCKvlDK7qZSSMrDvgUOWitAOGGVKFV6jUdD5lsqUioXji4LfO4Y8EaM6NO62JO677z7+8Ic/RN+RGzdu5P3332f69OnRS59FixZxzTXXcMopp3Trs3zf56c//SnV1dU89thjZDItK0iqGCnV0b5QSnHfffextraBI044Kb6g+PpbIOhcFfkl/LjbbruNX/ziF3zjG99g1qxZLFy4kLPPPptTTjmFyy+/PCGmEiRIkCBBggR9EjsUKSWE4KijjuInP/kJa9asYa+99iKdTlNdXc0jjzxCv379qK2t5eWXX+aee+7h+OOP75OklG07SCkxANvtw6RUpJQy0BXiCu17vpTNlFLtZ0pp1UnHJiY6UwpNSKnY5DYkpbqZh9MW4kopYQidl9VcKaVkEJTTcaWUQpNZtuNGbe8LSinX86OMrVwX7Hs+2vpXLGS5GJxADuV6bZFSYQZU1+17+rwHpFSwX9kDlVIAnrRbz5QylOZ2gww4zUkJrG7msvlS4gZKNZscRmTPC5VS8UwpkbfvYW75ipNdYKVkF4iYLQXP8zj99NO55JJLAJg7dy6nnXYa1113HTNnzgTgBz/4QbuW6I7ANE1+/vOf4zgO6XS66DpRX6BajPHWekYpxb///W/Wba7lsyecGK0ZiqbCodWib7vd1cXtgStXruT000/n+9//PoZhcMghh+A4Dpdddhmf//zn2Weffbr7wQkSJEiQIEGCBD0OOxQpBfrt7vz585k/fz6WZUWZUlJKLMvizjvvBPQD9/HHH7+dW7t1YDtOZIPJOW0TEb0dlmEhhBFZfGJzTHwZt7zkSYK24BdkSrXz2WH1vRaZUnoiazb3Bm1BxKvpGabAB9zY5NCXsqVSqj1SSkptjZGSnO1hBMRAOEHv3aRUoJRCkfO7kikV2vdERP60BqUUTnAufE9iN7PvhZNrYYQkV/fse6ECricGneu+CLOzbGwvV7A8r5QK1g/te8SUUt34fN+XuAFp55DFMEWQKdVSWRn/XQQKzC0ddN5p+1441raDUmr27NnYto1l6T4Iq9yapollWSil+PznP8+wYcNYvnw5nudRVlZGVVUVCxYsoLa2ltGjR7PLLruQzWZZtmwZa9eupbS0lIkTJzJw4MCg2qdi7dq1WJZFaWlpgbVWSsnKlStZunQp9Y7P4DG70L+qfwExLKVkxcoVLHtnPSWZDKNHj2b48OEYhsGqVatoaGggm21ixbJlSNumIp2hvKwEUNTV1fPRRx+xYNUCKgZXsMsug1DBdblx0ybq6+pQStGvXz+WLl3K8uXLqaioiNoOUFFRwZAhQ2hoaGDDhg24vqSOFJX9B7SwBZ599tmUlZXlCX8h2GOPPcjlcixbtiwhpRIkSJAgQYIEfRI7HCllWRa33XYbEyZMaHO9kJzqi8jZLtKXmECuDyulILTXBBW1EAWTTF+2rGrWvlJKRtWd2lPEmEaeDCtq39uapFRYfQ89cfdprpRSnc6UCrN/lNTh0OEklD4QdO54vg5+F5KczKJQncoqkmgVmc5G6rh9z/dlC2I4jDUzDYEvAmtgF6UZChVZUntyphSALx1yrWRKGaZACmJB56ES0ug8kxOD7/sRER0qpYQQRZVS0LL6XnczpQRxcjtvJ+8owoy3MJNoSxBTHc1Tmz59epvLhRAceuihNDY28vOf/5yHH36YnXbaiQkTJrBhwwYWLlzImDFjuP322zn77LMB2G+//Vi1ahXvvPMO11xzDQcddBBSSu6//34eeOAB6urqeO6556isrMRxHK6//noefvhhDj30UFav28Ab8+dz/v/8kMMOPgwAO5vlTzf8krdff40jDz+MxoYGnnzySY488kiuvPJK/v73v7NgwQKytsPfbroBpOSkE09h4ID+vP7661xyySWMHz+esiFlvPbKa+wyfirfu/RKKjIDeOrJJ7n77rt56623OOecc1i0aBENDQ3MmzePww47jP/+979MnDiR8847j7POOov33nuPn//856xZu45zvvUdPnvs8dHQ1aoswfjx41uci5UrV1JeXs7EiRM7fzITJEiQIEGCBAl6AXY4Uuq4445j6tSpDBkypM31XNdtd53eitC+B2C7XqdDnXsLBAJLWAG50LLEuwyCzsPQZEkhcVMMvu9HVr/25vaRUkpq+55ABGqaMFNq6wWdx9U1lmngUMy+F4b5BzavDpBSEJBStodRliEedL6lQ5+3JVzP1yH3QmF3USmlc8lEnuxrAxER4/lFSCl9XkxDk4ndsu8phR8qpWSoSOphpFTQLqncVkkp0zB0tct4AQoRWGC7wUp5ft6+56gcRkgUR0qp/Lqh/VeTVlod2B2llGj2i0Jx40Ov8/HKzR3eR12jTc71EErSv7KMlNW1a1Apheu6TJ80im8ev+8WvS+VlZVx9dVXs3jxYp5++ml+9KMfMWfOHD744APuuusupJQMHDiQ6667jiFDhiCl5JprruHSSy/lySefpLS0lAsuuIBcLsett94akW8PPfQQv/rVr3jwwQc54IADWLmplt/9/vdc85Mfs+s/H6K8ajAP3nsXD97zD/5237/57Mx9MATstdde3H777ViWxcUXX8x7773Huuo6LrvuV/i5LLmaWjZu2MAF3/w6hx5yCL/45S9YUruEz37+s3zr9G/Rf8BgLv7JZZx++umUl5dz8skn4zgOt912G0opLrroIr73ve/x3e9+F9/3Oe200wCYOXMmkydP5tQzvsj+RxwbEIgBEdkK6ZzNZrnnnnv44he/yJQpU7bcSUmQIEGCBAkSJOhB2OFIqYMPPhgpJUuWLGHhwoXsuuuuDB06lMbGRoYOHRqRM/vuu+92bunWQ85xojfqfV0pZRpmgX2vUCml7XuGEFqJQftKKRm377WrlAo/VRtz0mYax3dQ20ApVUhK6cu8MOhc2/eEEKRSKU1adZCUklJi2y6iXIcNiz6ilNKnU5Lzm/S57cTEXBIGnSutuGrv84JMqeJKqYCUCjLHumPfUyqfgxaSkH5Yqa2H8NCOF5JSDtnWSCnTQAqBCtWNYaZUN68hrZTyQChcHISBVlUG9wmjmVLKLFBKbdlMKaVg7qsf89Sbn26xfXYWx9ZkOf/4fdiSgyO04AkhmDRpErNmzcI0TXbffXeuvvpqLMvihhtuYPPmzbz11luRzW/RokVs2rSJnXfeOdpPCN/3ueOOOxg3bhy77747juPgui77HnAgN/7qOua98jKzDz2SuQ/dz667T2OXiZNQaIvhMcccw6BBgyKlpxD6Ph3mlAkhmDfvFRZ9/DE3/P73pFIpFIphI4cxff+9efTBf/HtC78LVWUApNNpjjvuODKZDEIIfvOb32CaJmeffTbf+MY3eP/999l7771Zt24d77//Pj/48UXUBxK5uFKqOTzP449//CNKKS6//HJMs/eS/gkSJEiQIEGCBG1hhyOlcrkc1157LX/+85+prq7mpptuYsaMGZx//vn8+Mc/5gtf+EKfVA3FkcvZEaHSl4POIWbfQ6uSjIKJjVYLGYZByrJw6YhSKgi0Vu0rpUzD0J+nAGWQsTK4vhuQUiLIw9k6Yy0iMoQglQpIKS+eKaXte4YQZKw0DuBKt819uiEp5Styjhf0pc6U6u2klO34mrQRCls2ddoul8+U6qh9L5Yp1QoplTIMbCG6VX1PK6UK7Xsd4My2KfKZUk6rmVKWaeEZAhX0g1YrdZ/Y1Qo53T8uNsLQ5IkwdFZVnMSO/64zpbZA9b3Y3g0Bc6aNYUBlWYe3qs86uJ6PUJKKsgwpq2vtUSg812O/KTtvtXsSwMCBAwsyqAzDoK6ujp/+9Ke88cYb7L///lRUVLBo0SI8z8N1i9+TbNtm2bJlbNq0iW9+85sANDkuTTmbkTvtTG1dHU1NjWxYt5bd9tgDI0Ye9u/fn6OOOgqghaox/H5YsWI5lmVFaumQVBs+YjjPbXye2ppq2Hk4oEmpsNpgSPIDfOYzn2HEiBHcddddTJ8+nblz5zJr1iwGDBxIfU1TVEEy3H8cnufxt7/9jeeee44//elPDBkypM8/lyRIkCBBggQJdlzsUKRUWP750Ucf5bLLLmPVqlUA7L777lx//fVcfvnl7LPPPowePXo7t3TrIpdzYqRU3w06D+01AiN6G24UVN/TNhDTEKRTFk10zL4Xzh/ay5QyDIEwgvhiJciYGRpF4zbJlJJK4isfAaStVNT2aHmgOBGGJqXqaVsppQOpA4WY1ERK2JVCmIEirfdOmkIbK4bEltlOk0Dx3KfOKKWk75NzCsdcyGmlLAshtAqrq6SUjJNScaUUPUcqFSnwlE3OyxXYicMxmUmlsEUQIh9ed0LEigl0Da7raaJZgCucKPQ/ypRqNqaNZtX3ukVKNWu3YQgu+eIcnZPVASgUi1Zupq7JRvgOE3YaTFVlRZeaIpWkob6B8vLyrWYpBlrcI5RSPPDAA9x+++3MnTuXAw88EIBHHnmEJ598stX9GIZBJpNh6tSp3HzzzRiGwYpNdeSEge94CE9RV9+IlUrhuk4BydyWwjVsXyZdgpQyqlwJIXHnYloWZnBPLXZMIQYMGMCpp57K7bffzre//W0eeeQRrrnmmoLTHimlYn/zPI9bb72VJ554gptuuolRo0bR0NCA67oMGDCgV99nEyRIkCBBggQJiqH3Shu6AKUUzz33HDfffDPf+ta3oqBWy7LYf//9Ofroo3nnnXe2cyu3PrK2HU3sbNfvfnXrHoyw+h6BLSM+4MNMKcMQZFL6TXr7Qed5+177QeciUhOBJqUMYUSh55ZhbJNMqXTw5t6Lt13KQCllkEnpEuvt2/d030g/IKXIT9DNwMLXW5FzA7JRSByZ6zwpRZApRUv1RTG0HXSuz1HGSmGgrU9hJcXOQimF73mgYkqp0L7XQxBmSumg80KlVKjey1gZDKGVUiFptSWUUo7r6vMlFF7MvhdemM2DzuNKKRBbNEdNV7AUGEbH/kUWQ/LkRke3bfEv6M8wX29bYunSpWQyGcaOHRt9dmNj27lumUyG/fbbj+XLlyOlpKKigtKyMizL4ubf/pJPPv6I8vJyJu82laWffIKds7VgVSk++eQTLrnkErJZbRUVQkQqx9qaatasXsWUqbsjhGDhwoVRkLzv+3y88GPGT5zEgEGD2j0uIQSnnnoq9fX1/OxnP6Nfv35MmjQp+r4VxPo6uCBd1+Xmm2/m8ccf53e/+x3Dhw/H930eeeQRbrnllq50b4IECRIkSJAgQY9H751FdgHhm8/Ro0cXffAuLS1t92G4L6BQKeXTV1kpEWW+GBBQKAX2PSkhyJRKF7G4FYPvy2AC0b7BS0/20CSUMkhb6YCUCkunb/1MKSFEnnTym9v39OQ+beVJqbZUBPlMKYXt+BGhFimleojypiuwXU+fUeF3jZSKK6X8trdVgBuGe/uSnFNoUYpIqVQKgjyesJJiZ6GKKKWk6motvy0PRcwWqhyyrdj30qm0Jg9CGVkQAmSZ3Rt1rhdTSuFAaN8TLe2+kCelDMzY/aXr6BYBpOLEuOqQbXRrQCnFpk2b2LRpE0opNmzYQHV1dQEBvn79epqamsjlcqxevZpcLn+eZ82ahW3bPPDAA9TW1rJq1SruvfdefN9n7dq1BeuGEELw7W9/G4CbbrqJzZs3U1dby4P33MWC995l2MidMEyTr3z922zasJ6H//VP6uvqWLduHb/4xS9IpVKUlJQghGDUqFFs3rSR2tpa7rz9rzzyf/9mj2nTOPXU07jppptYvHgxTQ1NvPD4CyxesJTzLvwf0pkMdXV1bNq0KTq++DGHmDhxIoceeih33303p556qrYuxlYxYpyUUoo777yTSy65hCVLlnDOOedwxBFHcMQRR/Czn/2saD8kSJAgQYIECRL0BexQ9j3TNBk8eDD3338/X/3qV6O/SylZvXo1Dz30EFdeeeV2bOHWh1JKK6WkDqnOeX6PsvJsaUT2vSKZUp4vUejsp4iUCtRErU0WQ6WUoiNKKSP2JlwHncdJqW0RdC4QlKTSQCO+L/PWQ6mPwwhJK78TQee+xHE9QqNQqJTqzbYS2/F03xg+tt8VpVQYdN6+fU8plSdivEKllCJPSpWkUggEEtk9pZTvA1ZE6PSk6ntK6XGllEIqp9Xqe5mQ0JUBKSz0tdV9pZSnz1eglEIohGHklVLNSanQvidMDLGllVLRx3YYcRKkvcILWxP33HMPr732GscccwwPPPAANTU1nHXWWYBW/9x0002UlJSQyWS4+uqr+e53v8vUqVMRQvCZz3yGG264gbvvvpsXX3yR0aNHc8wxx2CaJn/961+pqqpi6tSpBZ8nhGC33Xbjvvvu48Ybb+T888/HkTB4xEiu/NXvqOynM5723ndffnPLX7n3jtv45ssvYJkmM2bM4Otf/zqGYaCU4rzzzmPhJ4u54of/Q3lpCWd/+TzSmQy//vWv+Otf/8pPLv0JDV4Dhmnwvzf8jn0OPByAZ559lueee45DDjmE2267jRUrVvDlL3+5oJ2maXL66aezdOlS5syZE6iyCo8D8jzV4MGD+f73v1+0jw8++ODunaQECRIkSJAgQYIeih2KlAI455xzOPXUU7nvvvuiajkvvPACr776KrNmzWLatGnbu4lbHbmcjUKrYxzX71FWni0NS8Tse0GYcAgvIAYMoxNKqbD6F+0HnRsF9j0jZt/bRqSUbKaU8rTFDEJCTpNvGatjpJQdr77nehGN2ReUUhExJCSOtDtNSmklU14Z0hak0koppRTSl2Sb2/eCcZWx0lHlsi4rpWQxpdT2JTCaI1KNqZb2vXymVL4vIG992hKklJQSDIWPjxR+QfW9Fva9gkwpo1tKqZAo18MmvE90HIqYDVMprdZrg1DfmvjWt74VBY5DoQIsnU5zxRVXFIy5+PJUKsVZZ53F6aefju/7pNNpDMPg3HPPZeXKlYwYMQKlFJ7nYVlWQdW83XffnZtuuolcLsfSDTWYpeX4viRbp8lNQxjsOWNf9pg+gxFlFiUpi3Q6HX2+EIKJEydy/S1/oz6XoxSo21wNSlFZ2Y/vf//75OwcH6z7AN/wGVw+Bk8aKOCE40/ghOOPx/M8mpqaqKysjI4pvPcCrFixgtNPP51+/foFS/VLjdApCvnr8bjjjuO4447bAmckQYKtBAVkTVTTDmW02I4QIGH4gBSTRmW2d2N2GOw8JI1Siro6H9Psvc+2vQ25rMLzFPV12pGSYOvDcxXtJNdsM+xQpJQQgmnTpvH3v/+d6667jvnz55PL5RgyZAinn3463/nOd0in09u7mVsdOduBVJBh4fva9tFHq02H1feK2XHCoPMCUqqdK1N2MlNKGNtPKaWDzgWZlH6Q8fx8flgY2G6aAWlld0Ip5Ulsx4/6si8opfKklI+nnHb7ojlkjDTqkH0vCjrXSqk4mRC37wl03k33lFL6WAoypbq0ty0PhWpm32tFKRWQUvmAfrZIppRtO0GWmEIKDyV8fR6EJrFbKKVimVJbouJkQeh1p1VSheTi9rLvxQmeriwPlzX/7vV9n4suuojvf//77Lrrrrz55ptMnz6dkpKSgu2EEGQyJaTTaaRhgCdjy/V/DcPQSi2r5RedAkzLpKSkFMtzY3/X12QqnSJdksbxnZjyFRCa9DIMI2pHuPyRRx4hk8mw55578tRTT/H73/++hSoKQUTk9yCOOEGCNiGUILW5lHSmfHs3ZYeB4ypOOLCKL8zpv72bssNAKYWdk7zwwuYe9RKvr6M0k6KqX5oln/TdIlw9DZ7nkcv2jDG+Q5FSoB9i9913X+69915qampwXZeKigoqKiq44447mDlzJrvuuuv2buZWRS7noCw9AB1Ptkuu9GZYpoVAhxI3z5QKlVKmYZA2Oq6UIgiKbu+LyjCM/OcprZQSbGP7Xkwp5fv5/DDfD1UVRpQp5Utt5WxN8eTG7Hu26/WpTKmIlDJ8POl2mpTy8YPKbapj9r2CTKnYZ6k8uZCxLAxhIJXfZaVUsep7UvacoHNt3wvJMrt1pZSVxhACP6ZgE0b3lVK24wZVFxUSD4mMSCkorNYJhUopQxhbwL4nIpFUV66f+Hnsaw/OhmEwffp0rrjiCsrLyykrK+Pyyy/HNIsRS8XHdJwIa6t3wm3jGU/FEJFI7bQ9l8vx29/+liFDhnDSSScxZsyYFp8Vb1/PoYkTJGgbpinYdWqaMaMT1c62gAIWvG8zbHiKwUN2uCnbdkNdnc8LL2zmx9c8Qc5OCJJthUNmjeMH39ybXXbzO/2iLkHX4DoepeU94xlkh7nDKaWiimOWZWFZFkOGDImWrV+/nqeeeoopU6Zs55ZuXSggZ9tQHpJS/nZ7w74tkK++F1a3yi8LM6UMQ5AyOlp9r+P2PTOoptVa0HlqK5NSIcFUEgWd58O4vUApZcRIq7aIGAU4odLGlziun6/41Qeq72VjpJTbFVIqtO+pjtr3CpVSeahIKZW2UhjCwJNut5RSYXvimVI9ZRIslcKL2fdszy5YHh532tKWLqW9h1EAU3dJqZztBAH3Cl8U2veEgOaq/bhSStBNpVTIfSkR/d4ZC58qOI/bL+h8a0EIwQ9/+EO+/e1v4/s+ZWVlmGYrisxWXhIY4e23g12T37eKmCelVLTv5sH3reGkk07i6KOPRghBWVlZZDks+Cxi6ri+deoS9HGYhsC0khnjtkCoojYMkj7fhjBNHReQsz2yuYSU2lZwXR8EmGbn1eMJugbZg/q6984iOwHf95k7dy4nn3wyRx55JNdccw0NDQ0opVizZg2/+c1vOPjgg3n88ccLrAF9Egqysep7rt/HlVJGqJSipVIqqL5nGgapDlff86MZTofse9GkI1BKCbFNlFK+yge2F1TfiyulCILOrQ6QUkqPlTAHyfXy+4qUUj3lrtZJKKVi9j2vS0opqaSe/NJ+0LlUeTWV9KUOWQ/bQj6IPB0opRRdz5TypUQWyZTqKZAKvMhWqEmpkGhRqHz1PSuFaRiBSlFtsUwprZQiUEppUioMOm9+v4B4xpQmpLpbfS+Ozl4/ihjZouh0DlpPh56IGZSXl9OvXz8sy2q1jzqklGpj3IeL4ha7YqvHSau29meaJpWVlVRUVLQgpGLJWvlMqdZ3lSBBggQJEiRI0OfR55VSSilee+01vvKVr5DJZCgtLeWXv/wl6XSanXfemSuuuIL169dz9NFHc8EFF/R5pRQorZQKSSlP9rk37HFopVRg32ueKRUQM5ZpkLI6qpSKVd9r57MNQ+TtPwEpZWwj+54fVNcTCDLpIFPKK8yUQikMI2/f81RbSilVYN9zPRmNoShTqk/Y9zQp5Uq37Q2awZehfU/3T5vrKpUnYkKlVJBRA3n7XkRKqa5nSknZUiklVc+x78l4XyiHnG9q8izoi4iUMtMYpovyVH7cBaRUd8hQnSmlwJBoWsrTKilEoKxsLVPK6HamVIFSJrh6OnMocQUP5M/vjgilggwoCq1wLXKc2oERZ4nifUt4r8t/XnchCjKldtxzlyBBggQJEiRI0OdJKYCHHnqIM844g4svvpjS0lLmz5/PT37yE1atWsVBBx3Ed77zHWbMmNHmm9i+AqkUTpijglYLee1MonszUkZK5zhBq9X3TEOQsjqqlMqTMe1NJISIBSUrg4yVCayE20YpFebjhPa9eOVAT0pt3zMM0lYq+JtXQAg0hxNT9zixfurtSikozJTylY/rd46UkipPSrWnlPKlitRQmpTyg0mvllqFl2PGsjDpnlIqtCxj5CfSsgfZ9/zIvqeQysX2jLxSSjVXSvmg8mNUB513T6mUcxz9g6lQQuEpVxcnEC2VlbCVMqWiHzuplCo4hapPK17bQ8G9OJ7ZFF+nlTEfEloQEkWB8qrIdh2173W0rYlSKkGCBAkSJEiQYAchpaqrq7ngggsYMWIEQggOO+wwxo8fz4knnsj3vvc9UqlUr55QdwZSKmzHjTw8vlR9mpQKlVIhIVUYdK4VI6ZhdLj6nl9Qfa/tzzYC+4kmG4LqexhRhszWJKVCgskQRmTfk7E8LN/XRIhh6HaF27QGbd/LEymhfU9n75hYonffSuJKKYmP4zud2j5OGnXEvucFYeNS6tD4KL+GfPW9dCqF4XVPKeVLiZR+QEoFiinVcyx8eaWU0kopr1D9Ex53ykxhGLZepsvlBUHn3btv53LBeTYUCqlJKRGv1lm4fqFSyiyaFdQZiFZ+7ghUM8Xbjq2Uat++1/YOYusXi6wKVKcdDTrvKMLvo0QplSBBggQJEiTYkbFDZEoZhkFlZWVBaephw4Zx9NFHk06no7+///77bN68eXs2datDKRnkqOgJqicVbjtETG9GPFPKEKJAjBCSB1opFdj3OqGUak+ZIIQOSwR00LmZxhDmtrPvUZgp5ct8fpgObA8IuUAp1RbxURBIXcS+p/u5dxK7imaklJJdIKVkp+x7fhDYLX2F7foFYyn8ObTvQduEYVuQUqIi+17wtx6kqpFKRcHrUrk4vlOQjVRQfW8rZEpl7ZCU0ufPw41lShWx721hpZQoEEp1RSnVctzsiGiN1DEKLHLt70efA1Fg3ytUNsWr+XWtv6P8KmL814576hIkSJAgQYIECXYMUgrA8zxc18V1XTzPywd9B39zXZd7772XxYsXb+eWbl2ESqlIMdPH7Xv56nt6ghknTvL2va4qpdqeSRhC6HBm/RtpM60nsaFSytr6QeeGMEiHpJQvIzWFFxyHYRikYkqp1iZaBdk/vgwUOLr/hNCkVK+FaklKNa8C1x46Y9/zZJ4UUlJXMgxzpJTKK6UygZ04HvjdWRRkSmmfEpHYqAfAk2HVuJaklEJFOWdhJcIoR0mEpFQ37XsxUkp/XkBKQYsMOtiymVLRh0Q/d27TMNsu+K3PBZ13BoV9kUdHib580Hn4e/f216EPi+2vh1yOCRIkSJAgQYIE2wW9eCbZcTQ0NHDWWWeRyWSivy1ZsoSXX36ZsrKy6G+rVq3iuOOO2x5N3GYIM6WsKFtoB7DvRUqpwjlgeNyGGc+U8uKZ0y2glVL65/bcMkKIfLUuZWgLkrDYJplSgVLKEAaZSAkVt++Fx16YKdXq/hSFpJQvI/JEYGL2YlJKobC7rZTyQ64x6ufWJrCalNITaSkljufFCE6Vt+8FRIzepmukVGTfQxUEnfcUVY0Xz9dSDo5vFlgh85lSaX0txRg1bd/r3jUUkVKmJsa0UioVhJ2D2UzJlK++Z2IIq9uklIj91FmlYUFgvdr29j3btslms+2uV1ZWRjqd7tZnKaWorq7G930GDx7c4trS97WWxx8Kn1BtET/FM6PCe2VBptQWsO/FCbCIBIupshobG1m2bBkrV64EYNy4ceyyyy6YZu/O7UuQIEGCBAkSJGgNvXcm2QkIISgrK6O0tDT627Rp01qsl0qltmWztgukL3FcDzM2QXW9vk1KaRIoVErlUaiUMvN/U6rV0OGwqh10zL4Xr76XNpoppbpZOawtFCql9LiWUkZqCm1TVJiGiKrvhdsUQ3P7ni9VZFPTSqneO2FSCmw3IEJCpZTfBaUUHbPvuX5go1Tk7XuhUoqW1feg7cqIbbYrpmjLT7J7TqZUnqCTKNygb1ra99Kmtu/FK84JIUh1g5RSSsVIKQmCQCmVifbfqlIKQ4f7d9Oyqq+ZvPKrc+2PfgK2vX3vnnvu4Te/+Q2VlZUA5HI5Nm3axJAhQyISqra2lmuvvbbbL3uklHznO9+hpqaGf/3rXwUvmCA89pap4e1RfUop3nhjPitqGth9+gxESDs168p8EHr3k8nzJJdooZRatmwZZ5xxBgMHDuTQQw+lrq6Oiy++mMMPP5yrrrqq4BkmQYIECRIkSJCgr2CHIKUGDhzIlVdeyejRo9tc79prr8Xsph2kp8Pz/cC+mA89jibkfQxCCCzDCtRJYaZUMfueIBXY93QAeOvQCqMu2PeCTCnTiGVKdTMkuS3ElVJRZlTMeuj5OqjcMAzSZqpgm2KI7HtKTxC1AqcwU6q3QioVXAMKDB063ln7nt8F+55SWr3keIWZUqFyKBOQUt0JOm9u31NBnlRPqb7nBTlnColUPo70o2ONH3faSkWZUiGjtmWVUrqPXOVG4eVFq+9F9j0TU5hbUCnV6eJ7Le4/Ydj3tuKG6+rqOPbYY/n+978PwOOPP85XvvIVbr75Zvbdd1+UUvz0pz+lsbGx259lGAZnn302TU1NRV8eFZLp8Qyo1tbJ/+33v7ueddW13HDbPwqVVe1lSnX1EiogzQrbVldXh+M4/OUvf2HYsGEATJ8+nbPOOovDDz+cI488sosfmiBBggQJEiRI0HPRe2eSncBFF13EoEGD2iWcvvGNb0RvffsqPN8PKqflVROO2zUVRm9AWH0PCKrv5ZfFlVKRfS8WZF4MmtjRP3fNvreNgs5jSildXZICIsnzNPlmGEaBUqq1XBpfxgKpfZ1D1lcypaRU+WvA0LlaOS/XqX3ooPPg53ZIKVfKIEdZE1Oul8910xX59DlKWaYmMemufU/m5VGqZ1Xfy+dr+Sjl4foqsu8p8j+nrTSGIVoopbY0KeUpBxH4fItmShlxpZTVfXVgQaRUZ5VShURM3sK2bVipSZMmsdNOOzFo0CCAqJhIVVUVgwYNQinFAQccwOjRo8lmsyilME2TVCpFdXU12WyWqqoqKioqUErR0NBAXV0dqVSKgQMHYoWZakrhOA6zZ88OqiIWWuyy2SyrV69mU6PDwOEjCujWcN1sUxOr6zdTVZqhf//+kdLKtu0oZzKXzWKTQkqJEAYKbefeuHEj69esp6KyAtE/+FwUjuvg2jIi+xsbG6mtrSWTyVBWVhZ9tmVZpFIpfN/HcRyaci6u65ExS1oor8aMGcPNN9/MsGHDomWTJ0/GMAxWr169Vc5jggQJEiRIkCDB9kbvnUl2EEIIRowY0aF1hw4dupVbs/3heX4U9C6EQKKVGn0VLarvxSZsoW3RNEQUdB4nnYrB9/2IVOpQ9b2YUiplpiL7nmDbZUpppZQIKu7FlFK0rL7Xmn3Pk2HFOG05k1Lh+woM0fur70VKKbRSCjpNSsWDztuz70VKKalQQaGBPCmVz5RKWVZU3a3LQed+WH1PFSg/ekqmlLbrhfY9L6gG6gIglYyUUikzpV8qKBWVEeyuUqqFfQ9wcREBESgQLVRH8aBzc4tkSnU96LyoUqpbrekcjjjiiHbXOeuss2hoaOCiiy7izTffZOrUqUyYMIGnn36aTz/9lFmzZvGHP/yByy+/nA8//JARI0ZQXV1NSUkJV155JRMmTEBKyW9+8xueeeYZMpkM9957L+Xl5UgpmTt3LjfffDOV/fpRXdeAkUrx9Qt/wM47jdPfb77P3Ice5P67/8HOI0cgPZf169dz+umnc9ZZZ3HVVVfx2muvkXMcLvvehZSlLU469UtMmDiJFStW8Jtf/ZI169YgSgWb1m3ipBNP54iTvkgmk+bOv9/Jww/9m02bNnHuuefy2GOPsXLlSjZu3Mjuu+/O8uXLGTJkCCeeeCLnn38+L774Ir/+9a/ZWF3DF750Nqee8UWacVJUVVWx33776b8FBOy8efOoqKhgn3322UpnMkGCBAkSJEiQYPuiz5NSCQoRKqXCUNy+bN8Dmtn3igedW6ZBwEnpqnRtTO00saN30q59z4grpcx8ptQ2CDoPSYyQlBIizJTKK6XCNkb2PdX6sccrxkkpUSrYR9rq/UopFSNmQ6WU31mlVD5Tqj2llBMjoGRQaCAcizJOSplaKdWd6ntezLKpAkuSpOdU33OjLC2tlPJlfuwqlVdKpUwLy2yWKWUIzG5eQ3ZRpZQR5Em1tMKZRsgdGVuAiI2TXvrD3lhdzcamjllHG7IOG2ubAAWeQ8py+JS1LdRdHYFSilwux84DXfYZNahDCrD21gmXV1RU8Itf/IJzzjmHhx56iF/+8pfce++9zJs3j7vuugvbtnnvvfe4/vrrmTx5Mtlslh/+8If88Ic/5L777iOVSkUWwdtuuy1SJr300kucf/75/OY3v+HQz3yW5ZvruP4XP+ey71/A7//8D6r69+exR+dy7WWXcs3vb+LEzx1FmSW4/vrruffee/nKV77C5ZdfzpIlS1hXXcvPrv8Dw8vTrFq1nqbGRn7wnR8yZPBA/nTrn1hlr+LDdz7kkm9cQqMHp5/1Zc486ywGDejPmWeeyauvvsoNN9yAYRh87Wtf46qrruKCCy5g0KBBfPnLXwZg9uzZPPfcc5jl/Tjm8ychmvVhXOPW0NDAXXfdxVtvvcXHH3/MX/7yF6ZOndrZ05ogQYIECRIkSNAr0Htnkgm6hFApRVR2HZw+TEqZhhnZ98KKWiEi+55pYFl6iSadWt+f9GXHlVKIgqDzqPqeEiC2LSkFQleFC+17oVLKNEmZHVFKhYoWhQqCusP+6+2ZUr5U+WsgppRqq4JeHBF5EmZKdSDoXAt+NMHSnJQKbZFpy+y2UsqP2VHj5FRPUUo5vkShUEoilYenwJVaKaVQLZVSECi/tPLRMrtOCikFOadIplRwXRoiir2OkA86F9237xUJN//XgpW8vrq66/tcurnr2wKzdhrIjFGDtqjmUQhBJpPBNE1GjRrFiSeeSHl5OYceeigzZsygqqqKv/3tbwwYMIDq6mqUUsycOZOLL76YjRs3MnLkSDKZTEGWlJSSP/7xjwwZMoRjjjmGnONTUlrGsSedwr/u+jtvz5/HAQcdxq23/ImxEyay/4FzMEyTTCbFWWedFdnr0uk0ZkD+lpSUUFKSwTAN3nnnLea9+jIPPPAAVVVVrNm4hql7TWW/mftx999u5fiTTiE1oJx0Wgfwn3baaYwYMQIhBLfffjtVVVWceeaZXHHFFaxcuZKJEydSX1/PvHnzuO53f8AKjqUgUyrGSpmmyc4774zv+6xYsYK5c+ey3377RVbJBAkSJEiQIEGCvoTeO5NM0CW4rheVrA+R6+uZUnH7XpGgc8s0sMzwb36bMhJN3OiV28+UihFPYdA5hdX3thY86eUzpSwLQRB63UwpZRkGlhlYFzuilAqJKaWifQhhYvZiUipOCnXdvtfxTKm4UopAKeWGf5PE7HtbIFPK94OiBjH7Hj3Lvqeb4gO6qmNo39M2Q90vKSOlVVGB7RFAGEa3igUopbBt/VmRUkq6CCNPSrVUSoV/6L59L8zUzv8uGFpewpiqsg5tH42bwNIohCCTTnWZKJNSMqQs0/6K3cDIkSMpKSkBdLXbQYMG4TgOc+fO5f7772fAgAFkMhlWrlyJbdvkcsWvQ9u2+fDDD6mpqeGSSy7BdlyaXJ+GxkZSqTQrVyynqbGRZZ8u4YBDDsFKpaJ724gRI7jggguAluHnYdctXvQxpmkyevToaDvTMJkwfgLPPfMC1Zs3oUYOAaCkpCQKJRdCRMTRcccdx89//nPuv/9+Lr74Yp577jkmTZrEyJEj2ZzzAntoYfW9EKWlpRx99NEopTjyyCM59NBDKSsr47rrrouC+BMkSJAgQYIECfoKeu9MMkGX4LheYL8KHoOFwHb6MCkl8vY9s4VSKk/MpCz9oO/7sv3qe7Jj1fcgnykllKEn1sIiLFTeXetRWyhQSqVSENr3AsKkQClldCxTSioVTaSVAt/LK6XCffRGxEmhrpJScaWUbI+UCoLOw7Pv+RLPiyulYva9QCkVnpvOEg5eLEeMoDpbTwo6D0PfhdAh3b70I6VUYaaUFZG4YeVQo5v2PSkljltISrnKwTC1lc4QLcPHzYCEEsLANFLdz1GLbW4agm/vO77D52ZddQOrNtaD8sHJkkqZ7Dp+dFS0oTOQStLQ0EBleflWTYZrTqgopfjPf/7Dj370I2655RZOOOEELMvikUce4dxzz213f2PHjuVb3/oW1bUNVNseZibDV7/1XUoz5dE60TmKvvIKg9LjEM1yB8N1QmJKCKH3o5ptU+S6HDFiBMcffzx333035513Hvfffz8XXHBBRHoSt4cG+7NtO1JwhfsePXo0u+22G88++yy2bVNaWtpuvyRIkCBBggQJEvQmJK/cdjDYrqsnvaHaQECuL5NSzarvhQhtUwCWKbACqZTvt07M6BBqGU2K2w86jwUxKzMIOre2mVIKYplSCJ1XFBxzqHIyTYOUqbNx2lJKuYF9zxCa3NP2vbxSytoSlci2E0KllFa26b/l3E6SUjJvgW3LvqeUiggw09CTWSnzmVZh0LkgnykF3VVK5W17BGfY7yFKqVA1ZgaV7nzl4/jaUhevvmeZKZ2vpVTevtfNoHOpVETIp1ImCG3fi4LOA2IqjjyfIALCu5tB57FrRghB2jQoTZkd+pcyBBnTCP4J0kJQ0ontC/5ZJiWmQdo0tvl1/MYbb5DJZDjwwAPJZDIYhqELSrSBTCbDbrvtRm1tLTvvvDPjdtmFseMnsPPYcbwz/3Vqa6opKy9nzNhxrFqxHM91ozvbunXruP322wtUWOF9z7Ftmpqa2GXCxMg6F60jFUuWLGHETjvRf+CgdkPlhRB86UtfYsWKFdx8881ks1mmT58eCXE1wR8qpfT/7rzzTm677baC7yDXdamrq6O8vLzdCsIJEiRIkCBBggS9EQkptYPBcdyW9r2+TkoF9j1NAuSXxTOlUgEb4bWTBxSvYNcRRUPcvpcyUliGznfSy7be5C8kMUxhavue0JOq8NxHKjHTDNqkiZXQLtUceSLFIBUckxcppYxoH70Rni9xPR9DCMoz2lrUnep7HbXvpU0DQwTkSGChlQHxiQArlikVqoc6i4hEDQPCVdjenkVKpU0T0xAoFLang77DrC6BIGVYeVVUPOi8OWvUCcSVUhVlWlnjKSeWKdUy88mMLH1G9zOliAulRKer74X3oTDnKiQ2tjWUUriuq7MKIfo5Xu3RdV1839d97jgFasJdd92VxsZG5s+fj+/7ZLNZnnjiiaLrhjAMg2984xusW7eOuXPnRvt/7aUXuef2v1BSWkoqleZrX/8GSxd9zPx5r+BLiW3b3HLLLTz//PNYlj5/AwcOpKmxEd/z+Psdd3DnHX9l2p7TmbHPvtx2223U1tWipGLB+wt49ZVXOf2ccykrL8P3fFzXLTj++PeqEIK99tqLGTNm8Mtf/pITTjiBTCZvj9RB52Ef6v9WV1dz00038c4779DU1ERNTQ233XYbCxcu5Oyzzy7I1UqQIEGCBAkSJOgrSOx7OxhC+14YdI4Q5JyuWYN6A+LV98xmx1eQKdUBpRQE1qyIlGov6DxGPCldrcsUqXzQ+VbMBvGUriJnGmaQGSUKQrSbK6UgyJRq5Zi05UyRMgWZoK9czyeDVkql+kCmlGEI+pWWUwtkvWyn9tGZoPOQiClJWViGgev5UdC6Ujr7SyC2iFIqnpEWJwl6GimVsVJYwsBREtsPSKlY0LllWlimGWRK6fwkwzC6pZTyY6RU//J+VKOVUiWxoPPmd8Swsp0Qmojtrn0vuucWsQq2BV25MWiTYSCFiKyZ2wN/+MMfeOqppxg7diy//vWv+fjjj/nqV78KaEva5ZdfzvLly5FS8rWvfY2LLrqIPfbYAyEExx9/PG+99RZXXHEFDz74IOXl5VRUVDBq1Cguu+wyLrvsMqZNm1bweUII5syZw80338yNN97IvffdD1aapqZGfnjZ1QwdNgKAE044gYZclj///rc89dC/UJ5LaWkpV199daQ6OvW003jhpVf46fcvpH7TRs4+9+uUl5dz4003c/1vfsX5552PVW6xfvV6vveDH3H0qWcBggcefIB77vw7w4cP56qrruJzn/scX//61wvamclkOPPMM1m+fDlHHXUUQCFx2OyUf+ELX6C6upqrr76adDqN67oYhsGtt97Kcccd1ye/oxMkSJAgQYIECXrvTDJBl+A42r4XlVXfoex7rZNSKTMkpWSbpJTv+1HQcrsTe5HPlAqr71lCK6UEbNVMqXAyr5VSplZKhSocaKaU0oqBeLB0c7i+ziNKWyYlKd1XjhuQUr1cKeW6PlIqDMOgqrSCFdkuZErJfNB5u5lSESllkrIMbMfDDkgpGdj3EIWZUt2pvheN5+C6V+TD1Lc3or6wUqTMFMrNK6WkkpF9Lwo6h1jQeTfte1LhOB5CCAaU92MpoJCIQH1VNOg8yhwSW2TMFwSdd5JwCO8/pinwIK+G2w44//zz+drXvhb9bsVyrTKZDJdffnnBdRGGnQOUl5dz3XXXsXHjRrLZLAMHDqSsrIwrrriCpqYmBgwYAOjryjDy9kLDMDj++OM5/PDDee/Dj2kSJoOHjwApcLJO1I7Pn3YGc448BiPXwLCqCgYPHhyppAD2nTmLv9z/bxrr65g8cijrN9bheh6jR4/mz3/+M0tXLWXhuoUMHDCQvcbsw+p6B6Xg+ONP4AvHHYvneQXthMKsKsMwOPnkkxk6dGiwUP9HCBCBlTtcf/z48fzsZz/D8zwcxymoEJgQUgkSJEiQIEGCvoqElNrBkLMdIF/BSiul+i4pZRpmXillFIbYer7uA8swokmUL2UUNF0Mcetjx+17AoEZKKV0ppQQIk9YbQVE9r1AKaWziySyRaaUzroCTQK0Rko5QQB82jIoSeu+cqKqjSaW2XtJKdvz8ZXCFFopRRZsz0ahOqxeKVBKybbzcOzgHJSlLNKWzkmKMqUCi6VW2W0ZpZQer4XGrp6mlCq1UqRNXSEtUkoFyj4hBCnTipQtBUHn3biGPM/H9XwEWimFBBknpWhJZIdElSZi092374nCnzuzv8i+ZxgB6bx9zqsQos3wbSEEZWWtVxQUQmCaZlTBLkRJSQmXXHIJ3/ve9xgzZgxLlixh7NixBYRWuO8RI0bSIEzMdDoipPRyffctr6hg8NCBDCpaXVBQVdWf/v37U1VRwoZNdfrPSpNaQ4YNoTZVS8pIReNBAel0mtKUiedpYjOTyUTnb968eZSXlzN+/HgefvhhfvrTn0afFjP4BQH/hQuEEKRSqcSqlyBBggQJEiTYYZCQUjsYco4T2DyCPCAhyDldy6vpDTCFJqWEEC3tezKfKRXa96SUbU7sNKmTDzpvy/YoEAWZUpZhRZlSQtCtPJy2oJSKSAxLWNFb9mJKKdPU7dLHk1emNIcby0EqDUgp180Hnfdm+54TKKUs06Bfic4Wsn1bT/o7eIpkF+x7pWmLlGkUBG7LwFoXhuRbAaHa7aDz4J8KKvD5PYCTUkq1JKVUoVJKEqoZtVKqMOi8e/Y9z/f1dSCgqqwfNIDEiyoiiGL2PSMcEltGKRVnpbqslDIMBDqPS/UQBdyWgBACz/P4zne+w0477cSCBQu47rrrClRYIaSSIFqGgAvyajd9CRS/XytiGU+xjC69XazyHvlbQlv5Xe+99x533XUXY8eOZbfddmPq1KnRPTjcqiDoXLEd0sASJEiQIEGCBAl6BnrvTDJBlxAqpXaU6ntxpVTcjqOgoPpeyjIRaEtPW/YrX0qU6IR9L5g0C2ViCUvb95ToduWw9lColDIxDJEP0UbnQYFW44TV99pTSgGkTIOyTBCM7veNoHPHC0iptEFFiVZ0dN6+50ez1Y4GnZelzYgMzQWqM1+qKBfIihGG3bHv5TOlAKUn0z1NKVWSSpE20yhU1Pe+0sH7AqEJ3VApFbPvdccC63kenucjhKCqvBIagiy2YJdhdcQ4QmJbCF24oLuIB513Np8qPIWGEYSky+1n39saMAyD3/72t6xatQrHcRg5ciT9+vUrSioVG88i+j+N1nomTi7Fichwl82X6522zSJ98YtfZM6cOZimyZgxYwqJtBgrlRjyEiRIkCBBggQJElJqh4NtuwVKKYQg25dJKWFqyxzNlEmqSNC5oEVlwubwfQlmx+x7gnyYucDEDO17hPa9bVB9LyCltH0vHnQeLDeNyHrXUVIqVErl7SZm7yalXB+ptFKqMqNJKdu3O1XJLG7fay9TKlSdlaa0UgqVJ4ZD+6gQW4aU0jbNkI3KkxZ+DyEv4kqp0EYaklLx8WgZlr5GY0op0zC6dQ25nlZKGcKgX2kFAoGrXIKYH4rtWhPbQaaUmd5yQefQaYYitBmbQc6SoufYMrcEhBCUlJQwfvz4dtdVUiHCp5lmOeL5bm2/b+JEVrwwgP5z4dlua2/l5eXsuuuuxdtKuL/8+Vf0LUIxQYIECRIkSJCgM9h6Uo0EPQ5KqUApVWhJ6PtKKT3ZNZu9mc6TUmakwpBtZErpgGjZ8aBz8tX3hNKZUgVB59siU0qYQR8E9j2/0L5nWSaWCEgrJVvNQ3Ji9r0wUyo/WcuTJ70RtushlSJlGlSEpJRnd2qS6CvZIfueIkbEhJlSQM4Jgs4DtYtWBxlRppQru2ax9XxfT4EDYUfo5GsrN21botC+lwbypJQv40opM6aUCux7ZveIXc/z8Tw/qLpYoa8BfERwzZqtVN8L/5Yy0l3+7BCFmVKdVUrpc2jEFF07KrGh78WFtjuaZXS11jUqWl0U/Lc9Urq7PZ2opBIkSJAgQYIECTQSUmoHQ0hKxe17tuv32TwLU1gF9r04/LhSyjQBoZUqraiFUCB9P1KZdYyUyiuldNB5Kh90bm5LpZRRYN/zYvY90zA7bN+LZ0qF1jCdKdV7lVK24+tQY9OgMpPPlGp1HBSBjNn32iN8wr7MpAwyQSXDvFIqb63bYkqpvG8vmoH3BKVUnKDLWCaZZqSUVDLK9YqUUuRJKdM0MLpB7Lqehy99DMOgMlBKCQFG8DnNM+jCv4VZQN0NOlfNgoQ6FXJO80wpjZ5CNm5rtEbGFaqR2oeIV1xsZt8TgUquu0XwoqaKZnlX3dpn69mGCRIkSJAgQYIEPR29V96QoEvIK6Xy9j1t6VN0+2m7B6J59b0QClVg3zNNXcGqLaUUBHlBna6+FyqlzEApRRB0vvU44VBZY4p8phStKKVMI6+UKkbExAOp46SU5/lBQLCBZbQMGe4tyLkeCm3fC5VSru+2GvpeDIXV99pQSilwg5TxEsssIKXCIPrwUtwipJQfHkMYct5zSCnIWxkzlkXG0pXRcl5OV94LxqNAROQqUBB03h2llO24SKkwDSNSSikBRnDNagVS4TZG5AcT3VJKpdMBAZdtIvQLCjqhdFIh16iaKaXath93aNc9ZGx0FPFxHftjWHovWie+fsGqLfaYV51F+26xkipc3sq+i7Y3WC8edB4u6Erf27aN4zj069ev09smSJAgQYIECRL0BCSk1A6GrJ0vtw6Bfc/1WrU29HaE1ff0z82q7xWQUoFaqM2wYE3qqIiU6pxSyhQWlkizrTOlNOEmgnB3TVIUKKWEiSGMDmdKlWR0fzpR9b3eHXSeczxQkLJMyjO63LzjO61aGYuheaZUWxyvE5AqJZZJOmXl20BeKaXHR56U6kxb4nDDTKlwqKrQhtozLngnJOhMkxJT932LoHMhNLlaRCnVnWvIcV2klKQsk4rS8rx9K7hmiyqljDBZqHuZUqWlpZSVlbG5pgbP04UPqje72NmGDm2vFNgNjUhfUl/j4NpNSM9n06aN5Jo6to/C/Skcx6Gurq5b6rPtAaUUuaZ6lGtjmCae4yE9iQA2rPPJ+T62L6lpMPFqWz7yOL4kZ7sYQrCuKYWTa0S6HtXVm3DtJhrdRmRO4hou65y1NOVcpFJsaEpRZxpIKXFdl/r6+g6plepsFydoT6MQyFwTnhCsXet1ejwrpchmszqsv6qqU9smSJAgQYIECRL0FCSk1A6GVu17fZSVak0pFQ86T5m6tLwQgX2vvep7nVJK6Ym0Djo3sYw0oLa6UiokpSzDwgoypVAKPyCjWiilCDKnWlEHRUopw6A0HQSjx6vvmb2blFJAOmVSlioFtNKsc0op2az6Xj7jJg6FitRBhUoprWwLSVFDCAxjCymlglApXY6+44Tq1oZS4MqYfS+mlIJ80LmBUVQppe173VNKKaUwTZPyTBmmYeIJCYYm63V+VOH+8/at7imlhBCMHDmSxWvr+e/bn1CaEuw/eTg7DR/coe09XzLvg2U0ZB2mTxzO0qVLqa5rYM/dxjNhzKhOt8f3fTZu3MiAAQMiFVdvge9LXntnAebgIViZEuo3N5Ctz2GZBodOH8uarMP6xhzDykuYNKiyxfYbm2wWbKyjxDSZNqyKN9//iM01dUybvAsTx+3Ess3LeHvd2wwoGcDs0Qfx4bo6cp7ProP6MaQ8g+M4VFdXM2TIkHYJPQV8sr6WzTmHnSrL6GcK/vvucizD4DN7j6OspHP3USEEZWVlDBgwgJKSksTClyBBggQJEiTolUhIqR0MLex7iD6dKWUZsep78dBb8mqRSCnVAfue9GXERnVKKRUPOleutiRtzUwpP09KmaaBMARKta6U0iHPbSilZMy+l2kWdC6MLRL6vD2glIpUSmnLpDRVgkBopVQnSCmdKZVXSrW6ngIvGD+ZlFkkU0oiA1LKNEQBKaVQnVbm5Kvvkbd80TOUUlKpQlLKLCSlfOnrY25FKWUYolvEruN4SKlIWSZl6RJMYaLwUcEuiymlDMIKfAQEc9egM+VM1tRLrr7vHQw/xz9+/Dlmzxzboe3rGm1+O/cxlq2r4c/fP5bbHnqe199ZyM9//DUOP/jATrcnl8uxdOlSJk+eTP/+/Tu9/fZEY1OWG+/5BRWHHE75sGF8/OpSVi9aQ2VpmqMO2of3azfx1IoaPjOuH58dO7bFFbRi+Ubuems9wytK+MyeI7njoT/wypsfcs0Pv8pnD53Nw+sf5qdv/pQZI2bwhZmn858Palhdn+PHO49h37FD2bRpEytWrGCvvfYik8m02VapFH9d+gFvrM5xypTBTE2lueKetynNpDjusP0YO7x/p48/JKISQipBggQJEiRI0FuRkFI7GCJSKqaUclwv+r2vwRQmhlEkUyoW+h1mSiG0fa+tgGutlOpa0LkpzMC+52IYolsqj/YQKaWEhWEYuvpezH4YhbxbhUHnxWxi8UDqVLz6XjiGMHu/fQ9NEpWltNrA9d0u2Pf0z9KXrZK8BUSMaZBJt7TvKRVWltuCmVKhSqoHZUop8vlaGbN1pZS2MharvrcFMqWUxLJMStOlGMIAFCKIRzOMlhbMfPU9Qcpsm4BoD6FF05cK39OKrY5a53ylyDoerqfIpFOYhoHjejRl7SiQu7NtCcOye5t9z5eKbM6hJCB8XU/qf76MFLCeAk+FhGKzghfBMgmYpolSCtfzcFwPIQSOdHCkg0Rimoa2QSvw0LlmYd8ZRvvB+0oqXKm3F4b+3nF9ieH6SEWv6/sECRIkSJAgQYItgeQJaAeCAnK2tgnFg84dT/YI5cTWgCFMjCBcvEAppVpW3wuDhsO/F0Pn7XuhUsooyJQyAnvQ1kLcvmeGk6VAKRXPloorpRSqVfIjqpIWJ6WiqlR9IFMKbd8rTZdgCANPep0igqSSsaDz1ktpSaUipVQ61pdZW39W3L5nGpqMgW5W3wPCMKko6LzjhQW3GnRf5BV4JZbOlMp6WU2gBplSBkbxTCnD6JZSynYclIKUZWmllGGCUG1mSgkRVvHsnn0vhGWKoOqfVk51FK4ntf1YQFkmRSaw1Eb27B0Inu/r+1kU9q7HuLbA5rP7WvuO8+Prx85DpCqN5fMZwoju212pdKjIV9c0g2tcF5lQPcJSmyBBggQJEiRIsD2QKKV2JCiFHU5aIuuVDkNuq2JYb4YhjKjiXXOllFdg3zOiyUFrfaHovH0vZQWVvDAxhBlZfraZUsqw9Bt5I3jD78ugCl9hppQhjDYzpdxY9T0CIoWQ16QPkVKBfc9XPq7vdngf+aBzFdj3io8Nbd+LETFBX9puoX2vWNC5rpDZ8eNSSgUTaxW1J1S39QillAI3RtCVWjrPK27fA9pQSnXvGtIEjoqUUqYwQYAIbLWmaGmWDK17YgsopSBPWiNEdHwdgedrUkoAJRlrhyalfE+TUuHZKiSljIhcbG3Mh2RVRGIF58SL8vdiqlMh2t1fu+0NSanAfqrj/tp+GZIgQYIECRIkSNCXkZBSOxB0fk6zTCkhcP2+rZQyjRS+KpYp1az6nhAo2cbkQHXdvmdgYYYEmdJvyLeVUsoQQpe5D8ioiJwippQiVBMUse8pFdms0qaBERApMiiVrjOlej8pVZKyKLFKgsB7H1d2jJRSIREVKaXatu95UpeDj5NSOccPMr8kqPwEObLvqW4opXQNet3WHpYp5UmZ74tAKWX7dkSQhpay5koppRRWN5VSOTuvlMqk0lFfh0qpYoSXgOi6TRlbiJQSAfFmdZ6UMgxBSdoiE4ST74iklOdLXWUyup8GJFNwj40rpYqVH8grpfS5tSKllL7m4kopQZ4I7co1pFReYWUKka+MqnrGNZkgwfaGUvr7tHpzPXV1jZimycBB/aioKO12bpquMuqxaWMt2axNSWmawYOrSKdTRfetlMLz/A6rIg1DYFlmr8x3C49106ZaGhtypNMWgwZXUVqa2SL9nss6bNxUg2N7lJeXMGhwVYf7SimF63ps3lxHY2MOQwjKykuoqqogkyl+7noTKitKGDKoEssyqa1rYuPmhi6/pNAvmjvWH1IpXLf1mAohoLKilEEDKkinLWzHpaa2ifqGXK9+iaKUjsmor2+iZnMDSimq+pdT1b+8S/EHxfbve5LVqzahUOy085DoZVd7kFLRUJ+luroeJRX9qnS7Orp9b0dCSu1AUAps2wUUytAP2oK+r5QyDQvfL5Ip5eeVUpEKow2lVKiCUZFSqrUaa3lEpJSwMIRJSgRKqa1t31MxUip4Ix/a9vIKmlimVBv2PRmrkpY2Day0pa2O0SSvdyulsiEplbZIm2kMYeArH8fv+AS/IFNKylbte75U+FJXX2yulArHpEJPkE1DRGRftzOlwup7PShTylcqmojHSSnHc5BKFoS7b43qeyGBk05ZpK0UKTMV2PfySqnmKMyU6r59zzQMBJqU6oxSyvV8fF9iCEE6ZZHJ7LhKqdC+F54uhb5WQutep5RSoohSKnYv1fZN2txfe4grpSxDYAA+KnpRkCDBjgqlFEuXruGvtz7MG68vxPf0S7SqqgqOP2E2J59yKGXlXasy6fuSl196jztue4xPP10TvKERjB4zjLPOPoo5B+/ZwkLtuh6X//RWPv54RYc+Y6/pE7n0J2d36gVDT4BSinff+YS/3vowH374afBspxg2bCCnnX44Rx8zi1TK6nS/h2TS44/N4567n2Td2s0ACEOw665jOPe849hzr4mtfo8rpbBtlyefeI1/P/g8a1ZtxHV9PM/DMAyGDR/I7IP25KvnHR99B/YmlJelOevU2XzppFkM7F8B6O+zV17/hN/+6XE++mRNp/ZnGgbXXnoyB+43sUPrL1y0hm9ddDu20/L5cuzOgznvzIM5dPYUKsoyWJZJyjJpaMyx8JM1/OEvT/Hy64s61b6eAKUUNdWN3HPnMzz95Ntkm2wA0pkUB86ZyllfOZyhw/p3mZhSSrFubTW33foEj899nUGD+/GXO39IVf/ydrerq2vin3c9z5OPzaexUTsGUimLmQfuxtnnfpYRIwf2egK2PSSk1A4EhcIOlFIYoR1I4HoyImj6GgzDxGwtU6pZ0HkYWOv7xd8cRG+5u6SUCux7Yuvb9+LkUmjNM41QKaUJE79ZplRb9j1FISkV2vc0waEQom8EnZekLVJmCktYWinVCfteYaZUG5lkSkWT0nhovO0UKtiE0UwpFRA0nUWUKRUppWK5V9sZvsxbaFPNlVKoyLJoiOKZUpZldJnYVUpFBE4mrQmplJEqtO8VU0qJuFKqpEufHUfcvtepTClf4kmFaRikLZOSoOpbdgckpVxXV1GMMqUM3QcRyRScRxm8HW3+FiFOSglBRA66ze17hqWVUiHJ1aVMKZplShl5hW4ffTGUIEFHoJRi4YJl/PgHN1Jf38SF/3MKs2ZNpaExx11/f5zfX38fH320nJ9e9mXKyjt375VS8tCDL/DL/72LseOGc/U1X2PsuJEs/3Qtv//dfVz845v43g9P55RTDysoNqCUYsXydSz+ZKX+eytfN1IqpC+ZNHnnltUxejiUUrz433e47NJbKC8v4aJLzmL33Xdhw4Zq/njTv7nqir+ybNlavvntE0mlOjdl9H2fW/74ELf99RH2238qF11yFkOHDuCDD5bw21/dw/9c+Fuu/NnXOPiQ6S0m20op6uub+OV1d/L8c29x0imH8cMffYlBg/pRU9vIww/9l7vufJy62kbOPOuoXkdKlZWmue6np3HqCftx3/+9xk23PUNdfZaDD9iV//eDE9hv71346v/8hbfeW9bxnQoYvdMgdp00Et9r/fsktKmv3VBbVAV44H4T+d01Z1JXn+UXN8zl7feX43k+u00aySXfPY6jPzON195c0utIKaUU1ZsbuPzS23lr/iecceahHHvCTEzT4Okn3+Kvt/yH995Zys9/fS4jRg7qFAEUqjCffept/vLHx1i3roZsk01lv7IOPbvX1TZx5U//zryXF3DKGQdxwkkHkk5ZPP/su/z5pkd5752lXPurcxk9ZmifJqYSUqobUErR0NDA+++/z5QpU6iqqtreTWoTUqqIEY+UUkJn3LitEDG9HaYwMI3imVLhpMIyNWljhIGzrbyxDm1a+aDzztn3dL5VEHS+le17oQ1Pv93X9j2FJikK7HtWGN5rRERAczQP5xbBGzMVs+/1VlJKQXRNhEop0zBxPKfj9r2QzAtIKSllq19CXqiUQrRQSoV2tnzQ+Raovuc1O5+xTKnQGre9EBJ0QkDaKCSlpJKRQkUIoa+dIkqp7jQ/UkqlU6RMTUgCbQada6JD/5zeQplSQuiwc6sTb9cd10dJhWkaZFImpRlNdtuBJbEPP7O0gOO6SCmDsawgJKVCYrc9pVTMvidE/jw0DzqPrNDdyJTSAf76Z9MQmKbIvwzpAURxggTbC42NWX75v/9g5cr1XPLTs/nCiQdHL+5+dPGXWL1mE4/NfYXddhvDmWcf1eHvLqUUHy1czu+vv49MJsXlV36VybuORgjBsGEDuPyqc/nGV/+XG3//L6ZMGcfue+zSYt9Hf24WZ51zVPEbq1Lcc/dTzH34JY46euZWzQrd0lBKsXbtZn7x8ztpasrxs59/gwNnT9N9M3wgl195Ll8/7zr+fvtjTN19Fw77zIxO9fuL/32X2/82l1GjhnD5VV9l6NAB0b5LSjJ87zvX88vr7mTSpNEtSADfl/zxpgd57NFXuOjSsznxpEMiFevQYQO58DunsGL5Oj5ZtHKr9M3WxhdPmsXpJ87kpdcWcck191HfoJUxd/3rZQxD8Jurvsi1PzmF0752I3X12U7t++kXPuTa6/+v1TnKycfuy7e+8hkeeOSN6OVLiF3GDuX3155FU9bmnAtvYcWqzdGyVWur2VTdwEN3/E/nDraHQErFbbc+zryXF3Lc52fytW8dQzp4Bj/zy5+hvi7LHX99kpt+9zD/72dnRcs6Att2+eU1/+S/z73HUcfuS0VlKX++6dEOtkvyj9uf5sXn3+ezR+3NN7+TV/6d/qVDaGzM8eebHuWG3/yba37xFTIl3Vfp91TsGCbFrQApJa+99hqnnXYaRxxxBO++++72blK78H2J6wbV98z8hM/3dRntvghDtKKUorD6nmEYCCOcHLSeKSWVKrDvtQVtyYnZ94gFnYttF3Qe2vcgmGjFg86DLK1QKVUsu0ipfNB5KpgEC0HUAUKIXktKofIh45m0RdpMYQpdFt727A7vRmdK6Z/bUh16QZC5IcAyBKUt7HuBUqpZplQUdN5JREHnAYHY05RSvlRBjk+MlPKCTCnpowiUUkYRpZRpbhH7XiaVImWlIquksNpQSkH04LwlMqXCh2xdfa8TD0ABiWkYgnTKpCQkpRynS+OkN8NprpQS+ryazZVSkqJkcWSnC6yZUaaU17L6nhBghtd5V66hZplSoTor/n2UIMGOBqUU/33+Hd5682N23nkoRxy5X8G9vayshFNPOwyAe+5+ik2baju8bykVd//jSTZvruOgQ/Zi4qSdo3u4EILx40dx6GEzqKlp4K47nwgKleRhGAaDBlex25SxTCnyb9ROQ3j/vSVMnLQz++yza69TMTz04AssW7aGaXtOYN/9phT0zZChAzj+hDnkcg5/v/1RcrmOK3Ft2+GO2/Q2x50wJyKkwn3vu99u7LnXRJYvX8e/H3y+YFulFO+9+wn/uv9Zdt9jPMced2CLTJ1U2uLr3/w837rwJEp62SR98MAKvnH2oQjgb3e/EBFSoB/VHv7PWyz5dD377jWOow+b1ql9S6nYXN3Aux+sKPpv0eK17LPXOJat3MQTz71fsK1pCP7n60cwZqdB3HDrkwWEVIgPFq7ie5f9g6f/+0GXjn17Ytmn63j0/14jU5LixFNnk0rlXwQahsHxJ86iqn85zz/7Lh+892mnnqVs2yWbc7jmV+fy3R98gcGDOy5SWbVyEw898DKptMVJp80pIMMM0+DYE/Zn0OBKXnnxQ956c3GffsZLSKku4qGHHuK6665j5syZOjy6FwwSKSVOMAHHzJMPvpQt2PK+gjDoXP+cn1DGQ5bzSimjzSpIWknUuaDzcEIU2vdM8qqtraWU0pUF42/384HQkVLKK1RKmULfnNtSSoWB1OmUVgyEY16QV6P1Nujwf33MJam8UkqhyPm5drbOo7lSqs1MqUAJZRl5pZTj6jBVz9ch6eH4iEgp5XfRvhde583sez3gfuUFfSGAlCHymVK+zpSK2/d0tbuW9r3uTADi9j3L0Oe+efW95jCEiL40t0T1PdPQRIigc/Y92/WRSmEZul/ypJTbK76LtiQcxw0Ud83se4FSKp4pVaxr/BhJBLSplCqw73VJKVWYKRW2MVFKJdiR4fuSJx5/Dd+XTNtzAv36FeavCCHYffdd6D+gktWrNvLm/I87fJ/bvKmWV15+DyEEsw7YvQW5YRgGMw/YHcMQzHv1A9avr4mWWZbFhf9zCsefMLvovpVSvPrKByz7dA3HHndgp22F2xvZrM0zT7+BUrD/zKktlCGaPJpCJpPiww8+ZcniVR3e99Ila/jg/SVkMin2239Ki+/qVMpiv5lTAHjm6fk0NcWJGcW/H3yBpsYcBx8yvSjpJIRgypRxfO6YA0ine9fz5357j2fs6CFU1zQy/51PWyyvrW9i3puLMU2DE47eO2/zbwdSSn77p8f50x3Ptjo/mbHnOKbvPoYHH32DjZvqC5aN3mkwR39mTzZubuC/r35cdHvH9fjXI2/w3oLepVBTSvHyCx9QW9vIqJ0GM3aX4QVjUgjB8BEDGT9hBLmsw3NPv9Op/VdWlHLZVV9in/0mdSpTTinFvJcXUL25nmHD+zNh4sgW18qQof2ZOHknbQ988u2izzF9BQkp1UVMmzaNv/3tb8yaNWt7N6XD8GOkVN6+J/ClwumzpJSBKSx09lH+77rCiwKRD0w22lFK6Uwp2aKSWZufH1xhoX3PCEgpw+heSHN7KJhIGUH1PfJB53GlVHv2vXgOUjpUShkxUkoYGKJ3BXuGUOSVUiUZC8uwNCnVCaWUQhVkSsk2qu95UiGVJjdSQeU0AMeTQcZSUH3PCPJtuq2UCsay0sGl4S56wgQ4rxoLCDqrBIHAk170D4gyz5orpUyz65lSECOlMilMwyATkEzCaDtTSn+k2CJKKSPcn+i8fU9KhWWaWmUWI6U6Qpb3JTRXSmHoYh5GYMmOZ0oVQ2TfMwozpZorpSxhBUopEfy9C6RUTK0YKbmEka+8mSDBDoj6+iY+Wqizc+JKpjj6D6hk6NAB+L7krTeLT5iLYcmS1WzaVEcmk2Ls2BFF1xk7bjiZTJrq6noWf5KfbJumwb777cakyaOLtsl1Pf794AsMHFjFZw7fp8Nt6ilYu2YTK5avwzAEEybuVPQYR44cTL9+5WSzNu+/t6RD+1VK8f57i8lmbaqqKhg+YlCLdYQQTJq4M4YhWLliHWvX5lU59fVNvPH6QkzTZNfdxkT79H2J7/sFMRq9ETNnjMcyDdasr2XT5voWy5WCDz9aDcDuu+5E/6qyDu1XKXj9rSWt5lCZhsGXTppF1na4/+HXWzynzpwxnkEDKli2ciObqhuAQMW9FTNwtxV8X/L2W4sB2Hn0YEpLWxKdqZTJ2F2GA/DeO0txigTAtwbDNLpUqVIpeGv+JygFO+00uCixbZoG4yfoe9f7732K3YezQ5NMqS5i/Pjx27sJ7SIM87Udl6ZsjobGLLZTaN8jIKVcTwbZOKrTD9spw4hyVhzP71EP1zlHYgSklOu6NAby48asm89VMkVEEoVffEppoiHn52h0GjEMA8d18Xwvsu+5wcQ5FalZWvadFArTMjDNNJ4UKEwMy8S0DFypokm5UgrbtwtIISG0eiSy1kmtjAgzSBzfIW2mo9/D5XFyKcxBCZVSTdkcjdkcbqCgScUzpZSi0W2k0WnUy4Lw5zAHCTQplUqZQZv1GBMYOC5R34KuaJcKiLCwbdtTnROSQEIIpFTkAruc50uacvqaKE1bmMIkbaaRSlJn10V90RxRMDZ6siuViZFKQyqNFAa25yOLfDk1Bp+rrXkxpZTnU99k05RzUago4ywkpVzp0ug24kmvxbiwHbcVdZ8iZ9vRzyiisWv7PvZ2vk4bXT/KP2qulKp36ml0GyP7XjFSCiBr2xihclHKAqWQUiq6xkKEaj+9rR6vJek0QhhkrEyBUkpKD9u3yZiZYNxIsjk7r5T0BY7rkQ7CXxXgdLJPPakwLBNDgI9BrtnLgfi91fXy2X91TTZKQcoyCkipbM6msSmLm+rcm+NczsYJxiYEoZ1+6+Qq5K+pcP1ckGfVGlKWGQXlxit6hrBiqiYvRpoDgaJOxPrCi0pZ1zU0xvLRVKCUKoss0kaMRMp5fov7kB30uf7sfBXEqFJprPoe5EmucD8hsd2UzeG1Yt21LJN0ytIEdvA3UzRXSvWc780ECbYlqqvr2by5Psh5Kl5hyrJMhgzpzwLg06WrkVJhmu1PAJd9uhbP8ykvL2HAwMqi6/SvqqC0NEM2a/Pp0rXMnrNnu/sNs6remv8xJ3xhDsOG977KWGvWbKKpKUcqZTF4cP+i65SUpqnqX8GGDTUsWbKqw1mUS5asRimoqCyjoqK06DqDh/THsixyOYdVKzcwfvwoANat3cz69dWkUiYDBvbj/feW8Ph/XmXRxytwHI+hQwew38wpHHbYDPoPqOxV/S6EYNJ4rdJZt6G2VZfK6rXVSKkYNLCCwYMq2bi5odufPW7MEA4/eHeefXEBnyxZ12L59D3GIARs2FhHaUmKL3xuBocfNJURw/qTs10WLlrNI0+8zRtvL+1R87yOwHU9Vq7YCMDQYQOKjhkhBMOHDwBg3dpqmprsrR6gH2/XkKH9CwotxDF0mG7Xxg211NdlKS3t/kvRnoiElNqGWLx4MW+++Wb0u+/7bNy4kdraWlat6rgstqNoaMrxtUuvZ8Wa9ZSX3YUvJWvWb9QzM0PfCIXQCqrVa9bRv8TnnqW1LKpubNV+1BxCCKYPreT4nStYV53l0ttfYfWmlsz/doMhGLj3OKzyEn597zyuXfs4oJUiYTvramvYsMGPMqPWrV/P0pVlXP765by0/CXKFui3FMoXLF1bxfABmrGet+o1vvXIzfxk70twVZo7Fm1mTWOuoO+qG7LMOGZv0pbJpc8upKbRYJ9j9iaTsrjomfc5dPQADh1ayoLaBVzx6hXU5GqibVNmim9M+wbHjT6Ojxs8/r10M0eNHsC0qhTPrX2OP77zRy7Y6wJmD5vNm9UOT62s4aRdBjG2DBpzmkzJNmZZu3YtflBB6pd/uodb75nL4uX6LUxDQwPr1q6LSgD/8IkfctXzVwEwtv9Yrp15LQb9cT0fAdRWb8ZqyiHIZ2u5Lpx73WPIpnwwuJ3Lsf/k4Vw9aDC2MLhj0SbWNdodHldbGqUpk9MnDmZcmck/X1rK7U++r61yCpaurdZtzjayft16hNTE3o+f/DE/e+FnRfe3y4BduHbmtVSmBvDPT2vx5DnsMzsHMzJkSjJ894l3ixbqsX2JKxVpQ7Fpw3oaG+oQwPJ1NRz2gztoyOqJvZI+69aupbFen8cPN3zInL/OCfK7LM6aehan73I67370KVf94S7qG5tafpiCVes2Bj8X2veeXLKO+auru9Wn3YXtSxxfkjEFmzduoLFWH+vq+tV89o7P0uQ1aQWahHXr1tFQr6/X8BiWr17HYV/8GwRjGwW5XJbzTjuasz6fYkmjz72fbCTr5h/6UqbB53cZxB5VKaqrawDwfY+1a9agvED5FzwU3PnO37nn/ee4YuYV7NZ/Cv/4v+e4+9EX6Hf4UVj9B3D9nx/itppNXPODc9hp+GDmrmrk9bV1nXqDm7Vd9jxqLwRw/XvLKVm4Or9QwO6DKzlpTCXV9TaX3v4Ky9bpLJX6JgepFALFhvXryOX0+X/vo6XMPuXCzr+tkwrXtfnJt+HwA/binRqX/1u6KcqSK4aKtMWXJg1mZMbg9n8/zT/nPt8msTJ6xFCu/cGXGTiwHw8sq+f9jQ35vhIwul8pZ44fgHQcrrzhHwghuPyCL0I6zT8WV1NumZwyth/19Q1c+uvbWbJCl8tuaMzqz22WKSWlz7p1a2kIrqGlNY189z9vt7guqwNS2nVs1qxZjW1rG0lDQyOrVq+mvkGPOztns2b1atyA6K2rq2fVqlX889H/cuPf/00m889IZdccQwf255ofnMOQIYMitXJdbTXr7XqU0veh9Rs2smrVln/QrK+vj9l4E/QlKKWCKmX1NDQ0kMlk6N+/P2aQFdl83XfeeYelS5dGf5s0aRJTp07d1s1ugdqaelxXv3CprCyuCjEMQUWwrLamEdf1MM32s4Q2btT3zEwmTSZTfP1UyqIkUE5s2ljTIeJFKcXDD72IEHDc8bN7FTESYtOm2oDcMyhvxXqYSlmUlellmzfVdYgMVAo2Bf1eVpZpVQVcUpohnbZoaHCprq6L+n3dus24jksmk+Kfdz/FC8+/xYx9duPQz8zAc32ef+4trrnqNu7/5zNcftV5TG5FydYTkUqZDOyv7an1Dbmi1e8AauuzKKVIWSYD+1dskc8+8ZgZVFaU8I9/vdKCVDJNg51HDQRgQFU5t9/wdYYPreL//vMWL7zyEePHDuXk4/blnNPmcOudz/GLP8wlm+t4hertjWzWobFBB8ZX9mtdeRYuC9cfMGDL9H1rsG2X+jr9/FZRWVq0loIQgsp+elm2yaapqeN5t70NCSm1DfH6669zzTXXFLyN/vTTT1m/fj0fffTRFv+8hqYci5auZO2mWmBDfoElUSWBf1voinOLFi+hoWkTz683cFTnbu4b6xoYXS95beEGnpq/ZHvxDkVhWib7TBuDBazZWM+aTzcULC9NW2RrN/GptzGYHCiWLF1Kk1rNgx8/SKPfCKFYxjfA3SsK+G5wGvnn+//k0NTB5BjBqxsNVBEqojz4AlpRnwNE9PvSmkayTY0M3Sz554p7eGnFSy22/Zv9N8Y3jeeJGov3Gw38+hpSAyW3fXQbL254kUqnkkGThjB3o2BRzqCsqYbDqjzqGuoAqKuu45NPFtGvvAQBrNmwmTUbtEw6ZVl4dhPLlyynn9lPt7FuRfTZCzYsYHZ6NpP77Yfj6Yn66pUrKHVsKktTNEY5RbB0TS0N1YWqovW1WY6Z1p+GTAmvtdI32xL/ydVyUKXkr3Pf5f0VdQXLLNNAOA0sW7yM/lZ/lrGsoC+aY8GGBcxJz2F85QyeXWcg1WDKK4HgReyy2iIkUQxlBqz8dAl2fR0laYus47Fw+cZoeb8Sk0+XLsav80kZKXJejg83fhgt95o8drd35x//fpFX3/qw2EdEEEIgTIEEvJyDAOpsjzq7Z0xUy0zBqk+X0pRtoswqo9FrZOGmhdHyflY/li9Zjptr0mqT4PpzXI+Fi5fjO4VS5rv/7xlmTBrFs/UmCxpavnWam63DGijZsElfB7lsE598/An9VD+dXxWQUhsa17O09mXuMu/itBFn8Nd/zuWTlRuYMduhAli7oYY333iLB+buxCEH7MXj6wxq/c6P8bJAmr+2yQEKj2VdXSPjGlezYMlmHnvtkxZKpKpSi+XLloDnkLJMcrbDh4uKS/c7gn/8+wlGDSxh7maDRdn23P2KJ+waZpS4/O2fj7J4Rcs3r3F8uGgZB0wbxx57TOLpdQZNsrCvVtQ0Mj63Hnv9eu5/7AUEcOjeE0gNGcKL6w3SAsY1rWHpoqXMffbVgof5dEUsgyZdC8YgqkpNli7+BNtPYQqtYmvtuhRAyrVZ9PHHNAQk1Obqaj76aCGbajYB0FTfxMcff0y2KRgfmzfz4YIN3PHA4+0e+wfAvx7ZiUNnT8d29Pbr1qwB6aCkLkawbPlyPkp1/214czQ2NkYFThL0LWzevJnf//73PPDAA6xdu5bKykrmzJnDt7/9bWbMmFGQU6eU4o477uCOO+4gl8uRy+X48Y9/zLXXXrsdj0DDzuUVrqlWK16JKJjYcdwWgeStIZfTEzidn1n8/myYeSVutoNh3mvXbuaZZ+az94zJTJpc3HLY05HL6mONVx1tjviyXK7jhTSyQb+nUjqLrxjCIkPhvkPU1zWhlKKpyebf/36BH190JiefeliUB/aFkw7hf6+9gwcfeJ4rLruVm/70IwYO7Nehdm1vmIYRqW/i6uTmcBwPhe7/0pLuq3UG9i/npGP35b0FK5g3f3GRdgmqKst09tq+E1iwaA1fPP9mlizLz5ke+s+b/OPm87ngq59lw6Z6bvrb091u17aC5/pRlm6o2C6G8P4jfdkp+15XIX2JG7yoSgWVzYu2K6WV2lIqHLvvfp8npNQ2xEknncSxxx4b/e55Hscddxzjx4/noIMO2uKf57guP93YyKfLVzBxwkSdA4Tipg9/y9pKPSkPQ3Z3m7o7Y8YOhSfeJa0UX5w2hoHtVLRYUdfE/R+uxEil2Xf/vVjnLECxkBGDKvjJl+ZEtpbtCU8pHq9ppM6XnPqZiUw9Yd+C5aMGV3LoXmOoqamjtOTv1DY0MXnybozdvRLegTRpLpl9CaP6jcJ1fP73pScKAr4xYM+996TGHgrPL6AibXH2tDGUBF/iL364mDv+8wEDK8u47KyDeGXBcu59ZgFVQ/sxYsJw0qVlHHDgnjz34lOwDKYMmcJ39vsOj33yGA999BAllSUcOHs281/7FJaup9+AQcyeM5kbN1TABug3uB+zZ8/m8ec+hLU1DB4+ggNnjKZkaQnUwuTxkzl01iGMm7Ar/339vcgWAzBkYH8OP3BvSjJp7tnjHl5b/RpSSZqcJq5+4Wo2ZzczftfxzBi1H3997G1MKdln+jQmDSxn1LgpvLh4FU/V5lAILj1zNoPTerwsX1fDdXe/BMJkr733YZ1U8N+F9MtYnDltDCWdCHTuLhSKRz5ew6LNDQwZOZIDpo0h86/FQB2nHzqVw/beBYBB/Ur57IxxlKYt/jHtH8xbNU+HlzdDg93A1S9cTU2uholTJrLnsH0x/vM2StosW/AouUWC0SOHcvE3v0jKKj7+hYBdBpQzvn8Zh/qS8ZOmsGpjqAKSLFm8mCNmTuGgGbsx25vJHlP3YGNWE1bPLX2Ou96/i3R5mgNmH8CjL+lsjb2mTOCbZx5f9AutJJPmLy98wvPvbGC8BRceOBm3jQqB2xJCwNj+ZUwcUI4vZzFut3Esr1seLTeEwb4j9mXK4CkcfIDLblN249mVdbwHjBg6iN9fcSGp4JCfenE+/5z7HJmSUg448EDef2c5LFrLpEEVHDNxBC8s28D8NTVUDRzM7NmT6Xe3fqAaN3YMBx98MJNmTOKppc/y2MeD2JhTqOBN4tCRQzlg1gGkUncDRPa98GF659Fj2G/mLG577G1E1uELu45ibP/CoN7WsHTNJn5x7ysYQnDpl+YwarB+sF7bkOPeD1ZgmCYz9t0L21wKfMDgKn0fKc2kMIVg311HstvoQcw58ACmTp3CxuqOV6WK4+7/e4ZnX3mLfv2qmD17Dk/9dyFkq9l35ADmjB5SsK5UigcWrmJ5bRMjR49m1vghWKk7AfjKKUcza+8pBevncg5X3/B3NlbXssv4CczYV99PDOVz2tSdqUhb3PnuchxfMnXaNJz1a/U9Vgj2mDYNc/BQxDPvg2kwfZ+9sdD3mQH9Kvh/3z2H8rIScsrgP24pWaVQ/Rdz6OcqueHEC5m800AcX7L72to2SdiUKdhzWBWDSlLMfWkB8Ab9+lVx4OzZ9NvQDzbA2J3GctCcg3jp5Y/4aNkGhgwfwYHTx5K58QEATj76YI44qDBXxvN8/vePd7Ns1TpG7TyamTNncUcwTvbcfQp7DunHb0tGYLs+h+89llGDi9uLuoPa2lpKS4vbZxL0XtTX13P++efz4IMPRgTNpk2b+PTTT5k7dy4/+tGPuOCCCygr0xNNIQSXXnopF154ITfccAPXX399j8nliSsM22pTJKzsRL5NeJ9WqvVSITqKoHD9tqCU4ukn36B6cz0nfOGgNie5PRlGQb+3vl54TjqTK9ThfkcVrA+hdVr/PHnyaI7//JwC0qysLMOXv3oszz4znwUfLuXpJ1/n5FMP6xXEoEJFL1Taam9+jKtW1VSdwWFzpjBu9BAuueafNBZT2giBaeXPwZ/ueLaAkAJ4+71l3PXAK/zgm0dz3pmH8M+HXmNjkUysnggRVLqF9u4xefX21ipGVdiw/DgIla/FxkW8XZ25//U29M47aS9FKpUiFcv6cF03yDIysFqZwHYHlmVx3GH7s2TJEObMmYNpmkglefjuW1i7OgvoB3+FwvUVKlBNZUyDw8YOZVS/th9k31tfy4MLV2kbiWHgBCz00P7lnHv0dEq3she3I2h0PF6eO5/6+iz7TxnCGXvsUXS9bCatv/SUzivx8fGVT9pMc9rU09htyG40ZW1uLZnPZhW+oTPwlUQKiR+8CSpPmRwxfhiVwbGv+fQTVn+8BnNoFYeNGcLKj1eyZtEapOczYsJwnXdjmDhSvyXapf8unLf3edTatTz00UM40sE0DdzghuQpMEwTx9frO76DYRq4EkDgSX3DCsN5y9JlWJbFhLE7MWHsTq320x7D9mCPYbpvanO13PDaDWzObsZVrh4X6OyrkpRFKpXiwD1GM3B4Cc88/j5g8PnZu7LbkP4AvLtkHb++7xWklEgFvo4zojKd4sjxw6lo9U3olodUinfW1bJocwOu1NYsNxinB+81lq8ds3eLbaYOncrUocUtDZuzm7nhtRuoydXgKS+4ZkApj3Wr59H0Zoph2QkcuctQSkvat+KkLPjc/hPz7ZWSF19sYuehFViWRYVVwRd2+0J+AwV3vX8XrnQRhogy4iaN24nzTjum6AO1lJJ/vavtwf3SKT67y7Ae+fCWwuLICUe2utyyLE747Gwa31zCe++voKpfBed87iDKg/Hkuh7/nPuctpoKfS0AjO5XxjETR7CmIcf8NTW4SmGYRmSjKivJYJomO/ffmTOnnclba95mY64uIqVsaSOEgePpt5phplWoqHJcDwwjqAAHs3YexN4jBnTomF91bdYtWoNhmuw3tIq9JuiQzUWb67l/wUpdMU4YmkRUMKCihK8ctReVZYVjq8Ky+MKRczrY04VQSrHwk+U8+8pb2I6HYZpRNt7kwZUcM6kwHNiXildWbmJ5bROu0t8bnucjBBwxZx9OP+6wgvXrG5u46c6H2Li5RmfZGQa+AsswOGjMEAaVprn/w5XYvo9E4HkyyqvTFgOBIhCoCgPX0xOWfpXlnHPSkQyoqmRjk81zj8ynKZsFw2H8BJ8pY4YEb/lhzphCYq2tvigr1VYV2/V0lmBwry1Nl2JZJhnLJLrXChFlgszZbw++dvqxBfuzHZe/P/gEy1atazFOMimLirISzvxs50p+dxZb49kiwfaFUop77rmHhx56iBEjRnD00UczYcIEcrkc7777Li+99BL/7//9Pz7++GN+9atfMWCAzlAZPHgwgwcPZsCAjt2fthXKy0sxDAMpvUi90xxKKexATVNa2rolrDnCPCPH9ZCt2JF938cNvktbyz+Ko6EhyyP/9yJjxw1n5sypPfL7tCMI7ZBSKmyneL/7McVIeUVph4gpIXQ1MihUwTWH63r4nkQIUWAfzKRTCKFJqz33mtgiP0cIwahRgxk7biRvv/UxL7/8HiedcmivOA++J2nKalKoNJOKMmGbo6wkjUB/39Y3drwSdDFkMim+dPIBrF1fy6NPvVN0HaUUdvDipinr8PqbLUPtFfDfVz/mO187gp1HDmS3SSP476u9g5TKZKyoumSuDTVkeP+xLDOy9G5NxD+nzXblHEAXtykW0t5XkDyt7GDQYeZ+FLcqRBjU6uMGGTumIbA68MWTCsqZhwHfueCLK51qmWewvSCVit6C+ap1yaNp6hBehSLnuDi+limbwiRlhuRaENYde8shlcTxHd136IlWnF2X6D6RUgfZqsCyEj4cecHfc57+0slYmSjIGsD2bKRSUbaLE4Sw5/xcwXIvmCg7UuIrP5pIhfvpDExDH7NCkfNyQVi0rhQWHxeedFBKIoRVYM1LmQamYeD7UoczB2/CLCMfVLytEA+2dn1ZUGmypAtvNy1DV+iL+sbX7/kUEoULpIIx1/U3W21dO2GFOsdzdBB/GNadSeerjzWDQuEFY78vZCn7vq6A2byLw7Bvx3V1pdHgYMPzHx8H+iHcLdguRLhbFajJcl4Oz/ejamwEb7JCUipnO0FVRR3GnerAm/bos6SK3o7FMx7CgHOldJXCXCDlT6XMrVIFJyy5nXP0fS+832SKlKLWwfQBIedLfM8PCieIFn0J+g24DvmGnO1GfWWg7wmWYWAaunCC40tsJ28RsR0XJfMklesrco4DSpFOWVEBh7jSQaGKqhw7AiHyx2DbbsG9ttTSk6x0eOxS6nucW3wcgX7TmgkUpDnbib4rOztOEiSIw3Ec7rnnHqZMmcKdd97JlClTou8N3/dZsmQJv/71r7nrrrvIZrPceOON9O/fv8c8lzXHgAGVlJamqavzqKmpL6oWkFJSW6vtrUOCgOyOYERQ+c2xXRqbsgwc1NLmZeecKKdlxIhBbfaTUoo3Xl/AJ5+s4lsXnNhmPk1Px9Ch/bFSJp7nR7k2zeG6Hg0Netnw4W33TRzDRwwGoLEpi2M7RQOjGxqyOI6LZZkMHZoPnx4wsF9AUvqtBrBblsXgwVUAbFhfg+t6rWaG9SS4ns+aIBuyf/9yTFMfZ3MMHFCOMATZRodN3Qw532v30ew3fRduu/u/rF1fXE3t+5JNm+uD4jke1bXFi/ys31iH6/qUlqQZObxnkdttoaQkTf8BFaxauYma6uL9qZSiOlhW2a+M8g4Q1N1uVybNgAGVLGUttbWNKKmiYjvxdtVUN6JU0K5W8t/6ApKnoi7C8zxqampoaNADuLGxkZqaGny/aw/D2xJSyciCAuiAYMfD8VVQ7UoULYfeHKmAgAlJkZCUyqSsbSN77AD0G3ZNG3iydVLKMATpVAqlwLadgOyRmIauxgZ60qNL0YblaIPqZ54dVd1Kxao9AfgxUkrGqtiFcntfaaIr6+kAvoyVQZAnpVzp4kkv2r8rJX5AhAHk/JyunhiGjvsST3aPlDKEQcbUb6Y0KaWP1xAiqqgH4Ek3GEuF0u+UZWIaIiKAwrZbzfpmW6FgEikVjqtVHSVdUGzFScqsl8WVMiChfJTIn+vukFJtbZux9HlxpIMv/QJipbWe1ZN0PR78HmLb6w5c14+u67gxIBP0geN6miwIr8lgzIYES1hNMnwzXFKSL+OrIKqOFgaf50kpT5NhoVIqVsXPD/ZpCG0F6yik9AmPIh4SHl4rMiJigntrG7ko3UFE6Dma0AvvJ+lipBT5Y3R8ief7kTotk2458TCEIB38PWfnCXwhRES+hVX33FgFRYU+R+H9Q4XLbU1zp1OpvEWE/FhQSnaZlIr3he04Re+lcXLTkzJS3BUjpeJ9kgvGia64SsG9NEGCzqCmpoYlS5Zw8cUXM3XqVAzDiCx6lmUxceJEbrjhBn73u9/x9NNP893vfpfa2q5Ze7cFBgyoZPjwQSilWLlyQ9F1HNtl3TpdnGPyrmNaewfTAuN2GUlJSZpczmH9upqi62zYUEMu55DOpBg/YVSb+/M8n38/8AL9qso54sj9eizR1xGMHDWEqn4VuK7HmjWbiq5TX99ETXUDQggm79rxQPHJk0djGAZ1tY3UFiE4lFKsXrUB35dUVpYxaqe8mnXEyEGUl2tCoK05VfjtL2L/3xvw3ocrUEoxclh/SlpxlIzZaTCGEKxeW8OGbhSPMoTgiyfOxPMl9z40r1WbppSKjxavBfSLp9ZefsX/2lPsvx2BlTIZP3EkAGtWbSpasVopWLVSR2WMGTN0myiSTMtg4qSgXas3F61qGG/XqJ0GUVHZd+34yVNRF7F8+XK+973vceeddzJjxgz+8pe/8IMf/GCrVNHbklAoTSSEUy+hrRG240Uluk3RQaWUKSKJrRtTSmVSZocfGLY2pFKhsAlP2q3eRM3gbT7oyYPt2ygCpZShvzS0F1xGQcsCQ0+c/DwpZZlGgRpIkwEqaEdeDSGltgJ5UpNSoVIqfBsfkVK+iyf9aJLoSVUwUbI9G1/JvFLKl8Gk0kUgInKpMzCEERFxekKuJ5GmyJds121xo3EkC0gpXaY+fONiR6SUsX1IqWgSqQL1lq/7Jt35bKsWfRNMMhU+KiQgA6Kuq2hXKSVEQFS62IFSqq03hEoRKaV82b229QSEdqn4tQ1QktZqMcf1tFLKb0UpJfU10ppSKsxviCulXM+LPjck9IWIK6UkfkDcWp1QwPieHwZr4MUIw/BaCYmYuAp1a1xDpZmA7HRdPD9/P0m3kv9WQMwEhF2cfIrDMAwyqRgpFZC2ofIyJLsV+v6VC0kppcjZbkRiKaU/L1QHplMWRtAOWUCMyw6HIBdDqBpzXQ/X81olpRw/VEp5AfnUPikVqk41IddDviQT9Dpks1k8z2P69OmtljZPp9N8+ctf5o9//CNPPfUUP/jBD6ir61x10G2FktIMM/bdFYAPP1jaYsKolGLN2k1sWF9NpiTNvvvt1uF9jx4znNFjhuG6HgsXfNri+JVSLFywDMdx2WmnIYwdN6KVPel1P/lkJa+/voCDDt6LkaM6ZgvuqRg0qIopU8cC8P57i4v2zZLFq2hoyDJgYCVTd9+lQ/sVQjB193EMHFhJfX2WJUtWFR1377+/BKUUu00ZW6CIGjpkQEQOrlm9scV2oIUBmzbWADBi5OAoBL834KXXFtGUdRg2pB87jxzYYrlpGkzfYwwAL7++qHgGVAex86iBHHnoNF6c9zELPl7d5rovv74Ix/UpyaQZOrh4cPzQwf1IBeq6Fas2d7ld2xpCCPafuSuGIVi+bD11RYjSbNZm8aLVCAH7zdo1Ctbf2th35q6YlsHqlRupLpLR5Tguiz5aGaw7ucPW5d6IhJTqIsaNG8ctt9zCPffcwzPPPMPdd9/NH//4R3beeeft3bQ2oZQmpUI9gECz4loppSesHVW0WDGllOvLAlJqa1hMuoJ4kKIrW/fraotJfvIQKqVSZgrTCG4AqlAFo73gFCil4n2nVGCbEipSSkWklPIAFSksbE9/6YQTn9Cm5UoXV3qR6sOTEtf3cbx8ppQn/WhC6/h5+17cBtgZGMKIFDl5+57CNAoVdJ50tUIotDUGSFsmpmFEttBCFVmnm9MtCCEKJpGhugHRNfteCxWZHyrFtpxSqi2E59OTHq7vkmuFWCmEwg+qunl+obqoNyJPShUq9DJBPoMXEEhxC5qIqfxcX2l1TxGFi1L5sRwqpWzfxvW8wL6nCUjIK6VyudC+R0BKdXyQe74XWdPiSqmQAI6ImJgKdWuEXOaVUl5wjYRKqeKfFb+mPE9bGw2jFaWUIfJZDoFSSqrw5YdR0GeuL7FtJ7LA2o6LE9zbFFoRmrP1vTKdTkX3o3iYrmLLKKUcT19j4b22OCnl47heoLxseezCKCSlPF8GNs9EKZWg6wizSNsjXw3D4IQTTuDGG2/k0Ucf5Yc//GGk7u9JEAI+d+wBVFSU8v57S1ixYn2Ldf77wjs0NmbZc88J7LrbmAIyTkrF+nXVrFu3uUUodHl5CccceyBCCJ579k3sZpWrHMfjmWfmI4Tg6M/NorKydTueUvDI/72E7/mc8Pk5PeY5t6uwLJMTPn8QqZTJKy+/R01N4diQUvH0U2/g+z4HHzI9skKG8H3J6tUb2bSptsUzz7DhAznksBn4vs8zT71RcF6UUtTWNvDKS+9hWWaLIPN0JsUxxx6IYQjefnsRdXWFBIJSilUrN7B06RpM0+DgQ4qTsz0V7y9cyatvfEJ5WQlHHNIy53bMToPYZ69xNDTa/POh11r0bUV5hrGjB5PpgNr/hKP3ZkBVGf+476Xo2ak1vPnup7z74XJKS1LMmTm5xXIh4KBZk0lZJouWruPDdkiungQhBPvOnMyYccPYsL6Wt+Z/UtCvSikWfric5Z+uZ8jQ/hx8WGHWo1KK2ppGVq3cmI9y2ELtmr7PBCZMGMnmTfW8Me+jFu1a9NEqlixey8CBlXzms71rrHcWyVNRFyGEiILL4/96w2ApsO8J/X/ZYDJSLBepNUQTJ2hh32utBOy2RhSQi8L1W3/bYBRTSilFykhhCjPYl1ZKEQs6b66UShlGgUrMC2xTzZVSSrmADAg9vwUplTG1pUgrpbxIueD5hUqpkJQKVW6eVDjBNgIRkUudQWvES9tKqZjKwzSwTE1W2m7eepgyje0yLuKqDtfz8X2JgA59oTeHEKKAsMtbi/woPyy0Hm0NZMwMhjAiUipUSpVm0q3ee+KZUjqPaas0bZvBdX1NEDfr55J0GiHA86VWuDTPlDJC+54MxkJx21X47BwqpWwvIKV8HyUUSgSkVCxTKrS46rylTpBSEdFFgWw7JIClUgUq1K2hlCrIUXJdrXyShdbH5sgTM/oa96UOq82kitn3YoR/ztZ9hSa5DRFm1eWJntBWqRSF9r0gcyobV0pF9r0QCqVkoAbu2kAP+6I9pZQX2JM9P1BeFrPvxfYXjRM6T14mSBBHZWUl5eXlvP322+2+AAmJqd///vc89NBDXHTRRT2OmBJCMGXKWL5w0sHU1jbwt788QlNTLlJMLv5kFf+852kqK8v5+vknRGpG0N+3899YyFlfvJKzzriSea9+UNAnQghO+MIc9pi2C2+9uYjHHn0FP8jm9H3Jk4+/xvzXF7LblDGcdPIhbbZz48Yann7yDfbYcwJTpo7rFc/7bUEIwUGHTOegg6ez7NO13Hv3k7iuF/X76699yBOPz2PUqCF8+SuFhVSUUjz6yEucceplfPWca1j8SaFLxDRNzvny5xi10xCefOI1Xpv3QZSh6Hk+9979NJ9+uobZB+3JIYfuXdCXQgiO+txM9tl3Nz5duoZ/3vM0jpNvV1NTjr/+5RHqahuYdcAeHNxs+56OnO3yq5seo7q2ka98cQ5TJo+MlpWWpvnO145g0IAK7nrgFd5899OCbSvKS7jl1+fy3IOXcuVFJ2JZrU/jq/qVcsrx+7PwkzW8+NqidtvV0Gjzqxsfo6Exx7lfPIgpk0YWLN97j7GcceIsslmH6//0OLWt5JD1VPQfUMFXvnYklmVyx1+fYv26mmhM1dY0ctufn8D3JV88+zBGjCzMT1u/robvfvNGzjntF9xz57NFKyKG+1JBZEx+QfNlhaisLOXcbxxFJpPiH7c/w5rVm6N16+uz3Hbr4zi2y2lfOoTRY4duja7pMUiCzncwhCGwkX0PATGlFHQ8kLr5xCkbTJxK0laPsXeHZJB+y96WUqow98T2NOGUMlNYhr5MlFKFSqlimVKmKCBedI5VMaWUE1gpddaR7TcjpYJsKR2iHrfv6cyocH3Xd3F8L1J3uFLnTfkyyHjpgn1PIAosavFx0VIpFWRjxW7QpmGQskxtv3E8nEBWXSyfZlsgbtvSk0gVTKC7Zt+LE3b5CXNcKbX1LHJpMx2RUk4HlVJa/eMAJl4wKe6tUEoV2PdaKKUQ+J6P7XoxtU/evifQZILr+ThuEHgfV0qRJ1gjpZRn66wlPyCQDH2eI1IqIE7yZEPHx7nn+0FouyqwrMQt1K4vo3trZivZ97TKTOdxeb7OlBLkibzmiF9TtuMipdI2vWJqIZHv46zt6OIAKv/9YcSsbLYvycYq0ORyDukgU0RRaN/LpPNEbKg2I1izW0qpYL+O6+H6HrZvFxD8aSt27AGJ11qeFjQnpfT3Udo0Cgj+BAk6g379+jFlyhR+9atfsffeezNx4sQoU6oYDMPgxBNPxPd9vvvd7xat0rq9YVkm53/z89TXN/H4Y/PYuKGWA2fvQV1dI3MfeRk753DRpWeyz767tjjO1+Z9GGUivfLye8ycVVg9d8CASq646jwuv+xWfv2Lu3jv3cVMnjyaRYtW8p/HXmX8hFFcefV5DBpc1frLHaV47pk3Wb++mgu+c3LR4O7eiJKSNBf/5Gwcx+Vvf5nLkiWrmb73ZFav2sDch1+if/8KLrv8XMbtMrJF3zz//Nts3lRH9eZ63nlnERMmFlZ4HjN2OFf97GtcfeXfuPTiP3LssQcwaqehvPXWxzz3zJvMnLU7l/7knBbV9QAqK8v4f1d+lauv+Cu33vJ/LPhwKfvsuxuO4/HfF97m3Xc+4dDDZnDxT87plcHPr85fzI+uuIefXXISf7/xfO77v9fYXN3AYXOmMGf/ydz70Dx+fv3DLTKGRgyr4qBZk6koL+HIQ/fgf2+YS3VN8VDyg2ftyqTxw7nylw9SV5/tULueev59Lr32fn76veO560/f5IFH5rNi1SZ2GTuUE4+ZgRCCn/78Xzw4941u98G2hhCCzx45g00b6/jbLY/zgwv/yGePnIFpmTz39Nss+ng1XzrnM5x8WksV5JLFa1jwwXKkVLzw7Huc+sVDIgV4iOefeZfHHnkNFKxapW2n1ZsbuOqnfyeVsiivLOF/fngi/arKW7TroEOnccH3TuDPNz3KDy74I0d8bgaZTJoXnn2XBR8s57QvHcIZZx/aI+/dWxIJKbUDosC+pzkpTR7IzgVSxy0mTsxiUpK2egonVRDC3ZZSSsQqR2VzDtlgQpoy8va9iOmOCBh9lHFyIt2OUiq0FEkcUO0rpXzp4/hu3r6nFI7v4Und1450cHw/IoU8qbC9vFKqK/a9uO0vPDZFMIlsppSSSitv4vY90xABKdXMvmcW9s22QlzZYLs+ni8xDUG6C77seE5XvG9koHyDrWvfKyClPAenjcpfcfi4gBG9Ie7NcN0gY4hChZ4OvhZ4vh/Z0KBlppSvlCYcXC+ojpafXITXKcSUUr5NznH0mzGhUIbu85CUsm0Xx4upCTuhgPE9XyusaE7s5lWoze+tWwMl6TQgtDoouJ+INsK44+pDOwhHNwxBqhVLbJyYcWRekWsKnUsYV0qFpFO4fknsodzxZVQ2OSQhIQw6J7jZq25lSuly5LovHNfJ30vNQCllxJRSQah+XG3W1rG7gX2vs+MkQYI4QvXTV7/6VY466iiOOeYYLrjgAnbddddWtzFNk1NOOQXP87jwwgu3YWs7jorKMn5y2Tl85vB9eO6ZN3n+ubdIp1Mc/blZHHn0/uyyy6iilrnZc6bx+H/moZTi4EP2brFcCMH4CaP4w03f56mn3uC1Vz/kySdep3//Ci78zskcfsS+DBzYr021jZQKx3E5/YzDmX3Qnr1KmdMWhBAMGzaQX/z6Qp5/7k1e+u+7PP3k65SXl3DmOUdz1FH7M2Lk4KLHe8QR+/Hu24sYOKgfM/ZpOfaEEOy73xRuufViHv/Pq7z11iIWLFjGkCH9uezyr3DIoXtTXlHaai7a6NHD+O3v/4fnn3uLV15+j2eeno9lmYyfsBPnfOVz7LvfFEpKWleJ92QopXjw0Td4f8FKvnDMDKbvMYZMJsXylZs47/t/4dkXF5CzWxZnWr22hv888x5zZk7iwUfnt0k29ass5e//fJEHH+04geRLxZ33vcTrby7h+KOms9fuY9hnr7FU1zRyy9+fY+6T77D403W9NpvUSpmccdZhTJ8xgacef5P5ry9CKsnk3Xbmm985jj33Go9ZRH02cdIops+YwPJP13PE0TOKZpgNHFTJrrvpCJ9dp+zMZz47vWB5SWm61TwoyzI5+fSDmLbXLjz5nzd5e/5ifF8ybvxwzv3GUUyfMaHV56u+hL5/hAkKEGZKaYVLcFcRQpNSXucCqeMZQ9p2sXUnTl1BfpLZtlKKWCZIznbIBWqMlJm378ngLTcFQceigJRqPonzlBNlSvkxpZQS2vomlcLx/QKlVGgRM4SBr3xs34lUH75U2IE9D7R9z/G9yHLk+hLbd/CVjyGMqFJcZxGqArJeNq+UEoVkpScdFHqCWUBKmUZE+OTi5ICxfUydBcoGz48m0OkuKKUK7HtuLiJypfJQIm9l3FrV90JSSipJzskVzUVqsT9UQI5monHUm+GGljdVGLCfTlmYhibeiimlwmszrArpel4LhUt8n3GllO04OvNJECniDCMc41rNqFBaTdiJB2TP96JjKbDvCQrurZEibiupUDOZwPro+eScvPKyNXVjXimlsF0XKSXpVLr4Q1OMsMnZDk5wbzWD+4lWZsaONUZKZW2HsuakVLA8tGtCPl9Mk1OBfU+pLvVVqBpzfZ+ca+t7rcjfmwsJOQ/f94P7SWuEnL5f2I6bP3ajc4q6BAniEEJw1FFHcdBBB7Fy5UqefvppjjnmmDZJKdDE1BlnnIHneVx++eVUVFRsoxZ3DEJoG+xBB+/FQQfvVZDfGf9v822m7TmBv//j/6GAqqryVtcbMLAfJ59yaIFNry2FWRyGIfjSWUd27cB6OIQQVFSU8rljDuDozx1AODdoq2+EEBx+xL7ss99upFIWFRXFK4IJIRg+YhDnfOUYzv5y/oWuEG0XdQm3raws45hjD+BzxxS2K/7f3gql4OMla/nfG+bqvib4DmvjMa2xyebbF99BVWUpm2sailaRC3Hn/S/zj/tf7rQ6XilY+MkaFv5hTTAGmquRezdM02DK7mOYsvuYDt1jAAYN7sdv/nA+2ZxD//4VRdebttcuTNurY8UAisEwDCbvtjOTd9u5w+3qa+g57EGCbQb9FjkkVgK1j+MVWNA6ZN8TAivY3g4sM9CzSKm8mkLhtamUylcw06SUPq6UkcIIqmy19ATrdbJeNlatyiiYB7lBlk8YZByRUrhBxTZakFIQKKUQ+Mon69rRJFFX6nMiUsrzPWzPjZZ7AWnlSx/TMCMbXmcQV1jlvDzx0ryyYNy+F/dXG4YgFRBBWceLgoq3V7BvXNlgu1oFYpnaYthZtOib2PkkCMCWsntqpLa+fNJmGlOYuNKlyc22WY4+hLbv5TOlevtjRVQFL6+PAfLV2Hzpa1uVLBx3+r4m8KQi5+rg8uZV0wrse1HxAIesYyOlQggFhv58Ixg/tu1ERSLMDqpMQ3iBUkoXPcg/XOrqbDH1kBNYDbeSCrUkUB15vk8usOMJ8m2IoyUxo5VSlmmSKlKtL56rZDturBqnfqkRV2QVU0rZsYdu2/fz9r1MmvAenM9xC/qyG/a90Bbo+z5NdrZFPl8qZgO1XRffl5im0a5KzHFccp5fNJ8vQYLOYvDgwTz88MOBrRhKSjqmijZNk7POOosjjjiCsrLWQ723Jzo7ERNCUNW/YwRbR0mo1trUlxESEB1l8w3DYODA4lXamu83/t+t3a7eiHixjvZg2y7ri6ioiu63603S26u2SbLeiq7cY8rKSyjbylbRHY2Eao6ewx4k2CbQOUayYEIngkyp8OE/ZXQskDrMT1GA7RVOnHoKZHijV+DKNkip2Nt823HIugHJJNJ5i0iYTyXzmVIAWS8XqadaKKWkQxhk7PsKzwtJDAelfB0GXjRMN41pmPjSJ+c5kbVHKsjGSSnlFSz3pCLn2fjSJ2WmSBldU0qF7bA9GztUjRlGwY3Sk15Uiay5fS9SStlu3tq4nUgpnfOl+yYbhGV21b4HFCWlpHKbKaW63t6OKqWygVIqPukvuj8UHvoBxusLQeeeHwRHFtr3UqkUpmFotY/t4kkdaB8ppQJrra8UuYBI0W/mC+174S6Vp4L7hkvWzmmrqgmI5plSLrbnB5a0ztmywkwpFFEFTaAwZylO+G8l+XZIxGillCa5NVlU/FjimVI5W6KkwrJMLKsD9r3ofpL/lkm3at+zI6UlgONJckEQekmgaILCIR0GnXcVmXQKQwh8X5K1c3jSK8iSi9uBc44OeU9ZFimrlUypkhghF1NKJfa9BN2BEILS0uLqlPZgmiYjR45sf8UECRIkSJBgG6HnsAcJthl85ecryAVvH+JKqY6SB0KAZfZspVRU3h2F67dh30NPckBPnLLBsaSMVPRyJiKkCpRSgpybwxDF+y7MlFKKAqWUxEUF1dqynovrB/acgPAIbYOudMl6MaWUVOTcPCnlS5+s50STc6kUTa6NRHZZKRVvh+3Z2H5oZSykKkOllKIwD8cQeWtctgvjaksjP4mUZG0PqZS223SXlPJz0SRTKQ+CMaBk96rvtfWGxDIsLMPSFWicLI7rtlD7NIcmUwOllOx6VbKeAtfNB50Xs+8ppWiynZZKqcCWLKUiazv4vo8witj3aKmUarJzQaaURAakVMiIhAoYCC2uHT8W3/Oj8xG374lYu+1tYI3Wley0UirrajtwW6HtcWKmKedoQs40W1Ufxgl/28sT+EZgWYgrpbJx+14uX30Pgr6I2fdC5N/m5pVSXR3n6XQKI7CBNjl5Uiq8l6aDbDypFE22jZRaKVXMvtf8ZUecvEw4qQQJEiRIkCBBAo0k1GAHQ5QpFTy8i3j1vdhkoSPKQSNmMbE9ie3GLCY9RHqoQ6dBZ0q1rpSCwrf5OU+HB6aMVIFSSv8rzJTKxixuLUkpV4cjK6WVUvFMKaWtHNl4mG5ISgUB6yHplFdKNbPvSY+smyelfKVocnQpZVOYXc6Uikgpv7lSKr+OK92oimNcKSWEiCbP2SKB09sa4Xj2paLJcVFKWxGtLranGGGnqynqzKEoz2YrICSltFIqixta0NqoBCSVxCe0e/ZuKbYibt9rppSyLEzT1GRBzkYGap9QhWMFyhyJosnWSinTMEinYqQUYW6cQkh9fbvSpTGb1VXyDIkSgWw+JKVcj1xAYptG56qqRUopmpFSggKlVM7duirUkIgJ7Xu+UhiCqAJgi/Vj+VxNOQeUwrTMoiGecWImZ7tF7yfpuH0v17p9zwlUcEBBWXipQjoqSJXqRtB52gqzyfL2vTgpFZ4XXwWEnFK64mg79r24UkqrxHrGd2SCBAkSJEiQIMH2RkJK7WDI2/fySilBaN/TD8xpw+jQ47J+wx1kUvVQpZRURG/M21JK6YlTEGBtO2TdHAApESOlZNB3BdX3BLlYGHgLUioIV1dopY4X9DEiX62tKVBKxUO0U2YKS1goNMkUkj5SKRpdO7Kn+MqnybUjTYAvFU1uFqm0Uqor9r3m1fcipZTRPOhcE2u6HTFSCsgEE7Ss7eG2QthtK6QDO6pU0Gh7SKlImSZmd0kp38b29JiXAfkI3a++19a2lmGRMlNIJWmwm6Jy9CWtlKPXbZNIYkqp3sxKAZ4nI2VMXCllmQaWZaKkotF2tAUNEd2jQmWOVNBkO/ksoBiREt+nofS4cX2XxlxWX2NCRUqp0L7nui7ZQMmUMjuXV+J5XnFSChGRprZXqJTaGoT//2fvzeNsucpy/+dda1XV3j2cIXMImUhCBmQekzApkwyCAqKi4hURFbwOKOIFr9cJr1cUvehFEUF+CiKoRGWQeRKZkRASCIFMJGTOyRm6e+8a1lq/P9ZQq2rPvbtPd59eDx8+OefsoXfVrqre69nf53kdZaakwlpeQmttSanRppSLxK71zfUntabgMDVooQZ5Sf75ANOv1+6UapNSdXyv2QXmurkANVenVJIIcM4glcJqf81cS6mmTk2nFHlDTmk9cBwN2/airPwURTNMZN0vMSoqKioqKirqmFI0pXahlJ+8Z1dfROiXzaLzaRSSUr2yQmlJq+1lSq2DlCoK9MpBUspPVXNTEWAWoL2y72mgbBgpBUdKqXr6GUkwMnZZryxQqtKU6dreEkdKaa2xUvbq8mUAq0W/NqWUNaXs00qtsVr2oaGRsASCre+96ArTVdEgpQYMt5qUCovOjUli43t5uX1IKW0IGQ2NRDAfPZ1VISnlYlvKTlkEaqJuvRpnOoRG45HeiomgEcbG94wp5eKeO5uUgm4WnYekFOccSSIsKWXoQkMc1aQUI9h4n5kY1yZc3Dmu4dOYqFRlSCloGDjSGgtJAhA1SKlpDX0nb1JjkJRyhJcx/C0plW1WfC8B4wxKa6z0jMnEx8T3wn6u1X4OrbWPAA5TXfZd1QbeMFKqUujl9XV6wJSqJHJLSnXtlwhAk5irJ8yu70APibvDvRUorcBYTUq54yjcdsE5+Ih91clSY+BVEmt2imLb4I+KioqKioqK2s2KptQuk5nEJYMImo2IlHVEZJZOKRdl6JXSLxa3kykltQsqak8tjVK4cFor1wAAKWv2lhgKxi2SDCm1VvVG0kCVNSu0NotON76VSIHsqrdXlihV2SjTTbgxlDQ0VopeXb6sYf4OR04prBT94O8aa6WJ7yXcGFvrkTNeiqpA39JAA5MFg+l7zfheTUqtFRVKa1htlSnlY1taY7Vv4nvJmEXkOIUUWS5z5NKRUk1TKjTpZtUkUsoZjUf6Zhyw6ZQaTUpprSEtKaWUaizgd6JKO8GsXXRuaBXTt7Xay4NeJHPUcjLl0koDKzbeZ3qQ6uuViQTClsCZf6tUhSO9VRP7TJh3q9I0AcEQML6Dbsros1NVVXBfDozqlOqX0peDb2anFGcMSims9I3JPY6UcpRTpZSJ78Hsj1HnVNddW6vawEuC60kjvhd2SuVFo+i8V1YoShvfC0kpHf5Bz1V0HnaTHV5bgUYzCu27yXx8zxx7Yw05IpRVhTVHSs14nERFRUVFRUVFHcuKptQulCOl3OKXCChK6amPWcwDt3DqFZXpt8H2MqU83QRtO5BGL8j9wqkssVpYU4rC3hJLUbTie2tlH6UcbryY7ifzuErVnVIgCbfqXS3NtLwwvseIIeGJIaWKkJTSWAlIKW9KafcagSPWtEqYKUtfj7wppQr07CJwYNtk6bdBtkyYLBlOSm1F15iwVIJUGit9SyqI+Tul+sFkwtqU0nObUmNJKbs41tCWlFKeEBolBQWlQ1JqZ5tSZoKlrg0kq7Boe6Wf171I9n3mjMCJzDnUM4QL501Syk3YBABV1efYkbVV8xzCmFIa2kyaI0JZhvTPbF1BVRV2SjUjZ+58MxSqua27SddWR/poR/9A+/01TI5yUhpY7RmyyZBSw8+pzF5by7L0Bl54PQlNqbDofK1VdN4vKxQuJp61rs1wnVJzxveEifZqbc4xpVXDDHbXE6WBI72alBq17Z3UTAksG6TUdBNuo6KioqKioqJ2g6Iptcuk4eIpwfQ9IuRl5RfY7QjaODUXTpbaSNZnhGyG3KJVa2U7kEYvyMNC2rWiju85+ViWN/PMArRXNkmp0FTwReeAje+5ovPKT99zJlNISrkOE09K+dcArJS1KTV4u8aR3BpqPJ2flJIletUIUyqI71Wt/ZoKc9+1bdApJWz5tNIaRyzVkQi+7pHsLtpY2n1jjIyQlEJA022swmPkSH8VUkpb1j3GlNIqIKX0MUFKQTcNJKAmpQDgyFrf9yIlLVJKa1hTanBinCvMhtZQUgGaILXE4d6K/RkAYH4+59zEsqTEqo2UzU5KDY/vAeG1VaKwFGu2SaYU5wycMyilsdIz9I8YEzFzlJPSGkd6pn8vTZLRxkyWmi8/gvjeUFMqKDIHTJS6CPZRv6yQF2XwnHW0upYpOl/v9D1n8hpSahVKq0YU2nST2Wut3XYh2EhKLE0TMGKoqgq9or6WRlIqKioqKioqKsoomlK7TEora2i4XimyRefSj+qeOr4X3HfNRkzC6NZ2kPsGHdCoVDn2vllQxtsr7EIrIKW0XdBrrawxZUipXpWjlPVEKX9/aEhtCs211kF8TwMkTTk2jCkltWxMeAq7g1byZqfUSt7z5prWGkesgeZuP2Ipr4TPR0oRCIUs0B9hSjXie2qUKVWilLpxrBxtJa5LCPCmVJbwdVNbtWFXoG8pMmNKmdvnJaXGKTxGDveOQKpB2qetdqfUZr22oyF3HrnepzA2yhhDYg3xI2t9aLgIWpOUAoDDPRtxtVEtJxn2gUkCIzMB8/CaMaWIA5qk/TMDyJhJq5buCSNp06iSYdF56xxqxfeIgM4mXVsZM+Sg0trE9wAIa+INU+JpocCUSpOREbYsNd18jf6tIaZUXpnpf07htD7AxMzzsjTDFIKJk7Wdp31Efb3i3Ji8xpQ6YkmoQVJKwxxnACwpNWbbiQwplQ+/lkZFRUVFRUVF7WbFT0a7TGYxF0T3gLro3Mf3pjcy/Lf5RYXS99tsH1JK2qJzDYVKjo/vuW/ew6LzlKXebFA2uufie+RMqbKPQsqhxkulqkanlFt4GkrK7G9HSoW9JeG0pyPFWqOc+rA1ncxrUjiS94K/N0kpRus7xTOegYhQyHKkKWVIqcHpe0DTlHJ0WLso/WhJ2A4YrYEjPbstCV930XAmMmvY1RSZ0gVcN1Cb4NlIhRHPQ6tH6giamM6UUju86FyjSRQ1C/ZRk1I914uERqeUYMyaCXltSgXHZR0DM9P3OHFDSllTClx7wpFM6zmklFizptSsZoMhpcbH9/qWYt1MCpUzspPzNA5bo4VbM3eYBKsnGa6smfheJ01GRtLctbWoBkkpIho9fW+AlJKGlCJqdUrVAy0AhXk6pepuMuDg2mEAaJBSzBN3ISk12uTOEmPWyYZ5GcN7UVFRUVFRUVFO0ZTaZdK+BNYRO7CLBYnejEXn4X1X8xJSKjCi7UVKKQ23zJxUdJ7Z7o+iLLBmjZ6E6oWWNxv8qt5N38tRqnrMuZPW2k/fA5pF55okuI17rRR9SCUbvSUNIiavTSj395r/apFStlMKqI2l9ag2XopGfM89n+nIqiaTUtasBOppYkdbnNlFJHRASol1x2cyYfZrGZhS0pFStPmklIvvHVo7bEwpxsfG9ww5YkkpvbOLzrXWqIKi82bBPiEVISmlvXkCNIu768iZaERclTu9tQbXDIwYlFY4vGrjewLe4GO2h6mSEqv99REwzU6p4fG9tVKaaDQ2Mb5nSSloeFMq3HeD9zeGVUhKmULv4c/vaCEzgW7QwGvE91qkVGhK9YrSdBcS0EnDTinzX+1apeaYvsdZ3dF2cPWwPY6Ep045kY0DG3MTMHHgUdfa1G67VAor/fWZl1FRUVFRUVFRx7LiJ6NdJteLpBEwQ2RGcdfT92hqM6M2pSpUdgT7duqUcovWaTulCGZK0mpuio3b0/d8rxQAc/qYovNKKjPGvbXYkMp1SmmUQacUSIIz82dPSgWRvaYptdp4zmEmlX+NgCelMpHNTUpVSmKtHL6QmoaU6hUVKqnAaOtIKUbwXUJHenV8b92kFDeGXR5EG5XO604pzNcpNe4YJZA3pQ6uHTET5ASHGEM31qSU3vGdUo44dEZvw5QCeTPhSM+U/zsDAWhSU854SUSznFvp+spImsBgCq8Prh0BAIiEQ9toGBem3DqM781qNpjYb21K1cMnanpoLS/t9WXzDH/GTKeURmhKje6UqicZmtJ4wMSfR51RmTWsSjk8wuZij0XVLDrv5c2i8zVnSoF83Bpod0oZUmr9nVIMqahNKfNa634+zoy5Gcb3jCk1YttT07UllcKqM6W2yKCPioqKioqKitqOip+MdpkGO6VCUqoe1T2NwoWTI6U4q2mF7aAaWDHT98YpSxNr0FVYCUip+rnqonOttTXuCKtlH6UyC6Vw32loVLryZkVVyWD6noKDqlYK038jmPDxPSLy3UWGjKo1zpRyzwcAHd5Z94QnR0pVqkJvpClV+QX66E6pOtaZjMoCbbIasS1LtGSpWD9Fxo3ZV8gKeRjf80Xnmzd9rxHfs6RUO4LWltSy0Smld3KnFLSZvqct9Rl4fya+Z0mpXm4JlzqCRlSfn854SZNkML5nSSlSBAKDhsYha04kCfeTDDnnYJxBStUosJ5FVTW5U2rNG7uEziZFoxkjJNbY9PtmTD+WcJMMNbzR0smSCRE2ZkipfJC8dCZNUUnk9vcQYKKLZRBr7OUlyqqyMfHWEAofvJyPlGKMkNrndqZUaPC7+B5gpjwC1pQasbeMaWyOE3f/rZpEGhUVFRUVFRW1HRVNqV0mx0jpBmEAlJUyvSVYX3xvrTCkFGesMc1qqyWVqjul1PhOqcxOSSplhbywi4eAlDJ9POG+M9P38qpApQwplbS+AQ/je2UQ3wMpMDJ/7tsJboLq+B6hNh8OB51RABodUgA8yWSkvYnkYmbrkTNeKqWQy8FiYgAoVTWSlHIxxl5RoVS6UTh9tOVjW1pjzfbZdJLRZMMkuf0qdb1vwqJzNWen1CRSypmVh2wJcyqE7QMaLqmkfZ+ODVJKBkRRO77nOqV6RQXYCXLOQCDU5+eaM5FS0aCB3PQ9rTWgAAYOrTVW+9akTpnvlOKJ8LTQsPLuaTRNp1ReSUiljVmySddWgjFPgIAiGzMhjntTSqNny/7DOF1brlC+kmpo/5abRldJ6eO+gJm0GMYajUmlGsYR0Jyyar430GOv9eNEqA2vg6uGkHN0JFCb3EB9PRlHSiVCQHDeJKVifC8qKioqKioqyit+MtplMr1ITVIKRJBae9plPaZUIU1XjSmJ3T6HVQ0fmE6pcQt+R01UlTRj59EipazBFXZKAYRKa0itG501TpWN78EVnbvVE0mQneLl4inhhCciQocb8yFvLVadEVL/vX27eT5HO61HLq6iNPxrHhrfm0BKlcocF4Rm39bRlF9Ao943nTm6eZxhp3RNtyhnPpK2pNT643uTjERnSvX6OYDpSClNEiBj0u5sU8pM33OXrva2pDY6XNhzggcRNEbkj8GicsdB2tjfvjNOm2mbgpnCa1e+Hcb3BOcgZkgqdw7PQsCYbWnG9xrbYt/TSpnrC2e0aYa/MfTMc7sYd8Jo5PWDB7SQ2/aweLwtd4xKrVEOuZ6Y4m97LQ2uJVLr+poJc/wag441etTq48CSUlg/KUVBiXqvnwO6OTTCx0B1ve3j3hchOIQw5uasE26joqKioqKionaD4iejXSZHSbmoAxGByH2UtxGYdZhSbiEmghjIdpBb4LhOqXFKE9Mvo5QhmgjUIqVUTZlp7afvac38pK+QlKoLpi0pVUlIa1ZoKDA7yLxSNsISLHxCUorQNlCSxt+kai4cpTZ/d+bFepTwBJw4tK4phAFTSlbQdhukbtJ3frpUo2R6i0kp1GTNPKaUe59M0TZg4kI1KaVb+2Kj5d5XXZkfmAhDoYxSTUoZv2WzStiPhjTq7iWtB81QR0pVlnhKgrLukGR0tm6WJQ3CRfrpe4aUEiQAaJTWqBEJ8/vSFJ1zQNfm92YUnTt6y1Com3MOhaYUs/8dR0q581mj3vZOJx1pyKXuGKW6rzDcV4KZn2UBNS+37U7aPpa3TSk4LzGI762XlAqigTap2YjvmSiy+bO7nqRCYNTOSgQ3JCMRyG5zuo2+uImKioqKioqK2mrFT0a7THWnVLBwtgsFst+Mz1LC6k0pZh6//UgptzBRkKoau1BJbBzHrLTMvjCLUvsMdoVUl1ib/cYoAUBjSCkFDdOX4g0BUmDMPQ+HhqWTqDb0nCnFqEkgtP9OFHarAFqb55jLlGKJLfYlAKwRfaq3bXKnFGNuH2HLOqUMpVUfp8B8plTC3Xh4Zv+vDSnlTan5jJ9JhpZ/X6Vd4E7VKWWIHN0iUXaatNYj43tATUqBBcZKeLvdT8wa5wOklLJXCK2hFSAosU6YNd0TBmWPeSJmIm9EgD031hffs39umVLe2GUEEMD5Jsb3gj4uZo29lI3ulAqvddqaNZ0sG/n8Lr5HRCC77xuklCXaNFHT3GEMOngVxBjAGBhnSJNmp5T9E+btlCICMhdFtOdYOMnUUZ8agLbve5KI0f1bNr5HRP64i6RUVFRUVFRUVFSt+Mlol8lP3tOOjTIfwsmaBxRMqJpG9WLfLDgE32adUp58UGZxPmYqWmrLeKEJUMaUahad22/fgwlZxphKrPHSNKU0NKSWgO2OKitVGwIkfacUUWLMwICUAoCu6AIAGDUXe5NMKvf3jSClAA6CGErQjZu+l3CzkCbOzII66PY52iKqi5RrMyIZ95CxcuPhyf5fO1IKGiAXkV2/8TNtfM8vmNPRBdOAO26l/bP2tN6OlHaklLl6tfezm77njJWEkfc4QnPS3Z5lScMEUS69ZxFBYz4SoMzjDCll8BliBCGEufat15SSY0gpSw85Y3dT+/oapJTdd2O2JaRC3f3HxfeMccr9vmqb3IIx470FphUAEG92NREzzzFASoWdUg0aeHYRkTkuAH+OhddSGrLt6ZiJs4IzJAlvGnJx+l5UVFRUVFRUlNfmzJeO2rbS0IExExR2W1KKEc0W37MLDGa/zU8E3zLzYZhUEN+TuprQKRUQJ9KabC1SypfoasAVnRtSitmukXCSl7KmlPmZRRDfC4vOGRnqpm1KuYXQeBOKGn835pj5uzO11qOEGRqIkTTmCwhpqxNqHCklOBmOiDEQTKcT36JpU+FURM43gJTy+4aBrK+vPSk1f6fU2KLzYCqjN6XGTD0DBuN7O5qUgik6d0bOIKEXGitkSal636RtUypNG4SLdGaGJaUSR0pJR8Qw817DGPFCcGPor4OAMdsyulPKvXayJKuJRm9SfA+Dht64bTFfQFDj/t0xphS3X1YQY95oCjvmEk6GxwxoIsCayMGxTZxZU0r7qCbQ7pTCXKQUEBhsgSnljiN3PSGiel+J0dcT03+VWMIrklJRUVFRUVFRUW1FU2qXKfywri3ZQQQwWzRLmJ2UCkmrVDBjUG0TSb+tClKNJ6W44Gaho8mYUiAkrFl0XpcsuQUPA7Okk2CsYciZTikJ15JSBvE9TQrckVIQIGK+XNxpvClF9jU4U6x9+3yklGCmdJ2IQGBDJwuG0/faPkdiyTlmiamtJqUSTo34TCcV655MmHATbSQYkgxQNr5Xk1LzLIhnJ6VGd/kAruhc+RL2nWxKKaXtRM1R8b3aWHHHbLhr2qZUm+5x5obWGkoqJCz15CTQ7JRyphSIWfN1RgJGj++Ucq/dRaOF4GNjmvMqbcf3piWlksmkFGfcRNxY6Y3qNilFQ0gpZmNvTu4cTjga+0Jpx0XN3ykFBAbbSFKqacglY0gpV8ruti0k9qKioqKioqKioqIptevkpu9pb2rAdEoxE7NiRAPmwzilzsxiBGanQ40rXT7aqovOJaSuxppSgttFpsbw+J7tm6kNB2t0wHRKcdud5O+vFZSufHyvaMT3FMiTUgkIDBlvxvRGm1L1a6IWKRXefx5TijOOhCeW1Bi+kGpO31PWIjMSnKzZaQmlLSWlggW0fT0bQUqZ+B6zY+jriYibXSbeNKUInQnxPalcp5RZvM9DcW21lDXV3Ck4WHRujZWkNlaGdUoRN8XU3U7z3JF1DgxaOVMKtSmVuul72paDixb9M8u1b/z0vXBYAGMM6SZTqEli9omnyMaRUqABYyabSEoJYzjZmHi4r7iLPgc/HwBINEkpWGMnEcakc3LHg7ZT9zaDlPIvAaG5aa7F40gpbkkpYta8pEhKRUVFRUVFRUWFiqbULpNtlDJ/1vX0vbCQeiZSKvyG+ygsnGaRIZXConM59ttzwXkdCZEMjFgjvucJjcZih4EsuSRY03jR0JCqju+VpawNgSC+Rza+F0ZEABe/G0ZCGbNIT0FKrZcG4sSRstSabGxEibshpcx+bj5ecHMsORNIBFPQtkJ1wfX8phRnHAmzhh04NCQUKv8+A5tr/PhYpo/vpWP3rdQuvmcn1rXfrB0kpQzBhClIKWBw8e+7xayh0aZ7pIvmag2ttI3vBZ1SgnzROYiQpEnQk0Qzl/mP65RytKGjpRLBNtXwT0Wr92hsfK/Zz0VE6GSje9oEN6QUs/E78+VHva8MSenoypCUahKNZCNwiSCI4H7N+N78pJQvbVcE6EGDv03cjSelCGkqfEk7EEmpqKioqKioqKhQ0ZTaZXLfIrupYQBsj8f6CqnddCvXSZUmfHvF9xwpBQU1oeict0wpDt6YhueMBmdMucAjZ2Zh2953Pr7nSSkZkB0SDMr0cZGJ77VNJDN9bwgJBdNhZWKBw00pAm0MKUUK5E2peiHlDDdPSg0pOid3XMEYnVtpVoamFGE+U4qR6/8yx71Wqu6UImv8bKIp1RGdRs9RNqG03ZixYXzvWCClhndKOVKKe1OqGf3yUyG9kdKkE33c15FS3pSyZeNBfE+jHo5A1pyfxWzQcNP3alOqQRtak5vIUKip4L4TbTPkSSk+Ob4X3s5tVNJPrBuiulPKGl9ok1KGlgKRoaOsWIuUcqaZEAx8iCkVRtM3hJRSZvDFJFNqbKcUWVLKFb2DkG6j35FRUVFRUVFRUVut+HXdLpOLNfjpe2TX0usspBa2HD1cOG0lEdNWvciUE4vO3ZQk80BDSjVMKbffWqSUM43cvqjv3yw6L70ppQHSxvAhgGD6iYwJZeQKrcN4niML3LQ/N6ackDRuNybVnKYUcWO8IAGR6coSLcOt0nWnVNsc4KxJSiUsZMCOvhqkFAHdeUgpt2/sfjaGZx3fg8amklKZsOPprSk1rmAacKSUgiFIjpVOKfP3YaRUo4C6TUr542BEp1QjvqchKPWkFBGBC/LxPQ0gTa3Z4IyWWTulpASsUV5JjRDuEfYcchTqZkejU+H2XU1KjSItKbi9NvhGH4eMMaRp4ovLeYsqDUlMJszACc6Zfx99NNp1SiUcotEp5f+EDSGlOmF8jwa+MKhNKfPejyOliIBOZok6GhyIERUVFRUVFRW12xU/Ge0y1R/W6xjfPIXU3oghY2zNUyC9GQpJqUlF535KEmBIKRokpVynlN1zjWl37VJlM+mwgis6L6pR8T0OIoEOb8b33ELIkVBL1khx0/72dfbBmGKidXvqyav1ihGzpJQxXpgjGQKNI6XcceRJqTEL3KOh2oww71mWjl5ETlJNSpnYpoaCHojvrX9BPInwyHhmpv65vpvOeFOqUq7XbPMprs2W6cSqjeGhpNSYCFp9HBhqZVh8D3BEqTaDDpQjpZpF5wAZE8z3JFFjotw0Ckkp08tWbw93kVcyx22abK7hn1pjxe27SdRXO743jthztztaiFPT5A5/73Ah0M1SdLOsYS56E4xzZEmzR02FhBTm65QiInQc9SUnxPdsN9k4UgpEyNLUEnXm96XYROItKioqKioqKmqnKZpSu0z1h/VgglwjZsVmJKUYuP02nzFClogtJWLaGiClxnx7zsKFlSOlEJpSdlS8p6UIISmVMIawVnkwvjdYdO56iRjxgYVPTUoNmlIEwr7OPjBraA27PSSvZhUjU7zOvCnVIqWgfaeU2zfhGpBT05RqG3ZHW34RaRf6WTJfp5QjpQgErStorSxyaJN1cxg/k8y7TGTGvHSmVDb+fa5UBUfnHQvT91QY32vHRi2pWZsZTfOxLjo3BkEna04ulG6YgVI2vpf6XqGalDLmNAhILP1D9jo4GwGjBzulWqSUM2oYZ8gSvqnnkCttZ1N0SoW3MyHM9WKsKWXifY7CYtT88qNNSnWyDJ0srackCub3LRMCWWpik04b3ykVxPeGUKczkVL2+Xx8j2Yk6qKioqKioqKijnHFT0a7TI4U0tA+NjIwJW1WUopZUoozdFK+pURMW/UCfDIpRYyQBqbUsE4pV4LsHxOaUnbinL9/K75XlNIYW6StUSVtPIeBIIabUsQtrQQsWtOJyEx+29fZ56N/QMuUosFpfrPIxQdNP9XgcWHMjXGkFLwp5QgStoV2ZWhGGPN0flLKUWTKxxit0TtnfG8aUoqB+Ylw42JTgCukd+c9IOXOJqX8wAEMJ6WI0UhjpT4OzOTEdjl3g5RSGilzppTtlBLMv9euU4ocwTNj9NmUztdGlJtg6eSj0TDT9zabQk0T7icJApOKzqkujeemwDwdY/QyMlMiXXG5iQPXzx9GH5kQ6HYyZFkSmFK8YVqlaZuUMv91X7jUEfX1qZMl5lpuKbnG9D2iBillpjCOi++RNdi4P05mGSYSFRUVFRUVFXWsKxad7zKF3yA343t1IfUsERFO9f0ZY8hSgS30HgbkF5lTFJ03IijSTN5jFHwbr3Tj23czGYvZuBzZ0vdh0/fMz8zLqv5G35tShpQiGjSlXEwrjOcZGMcYUfs6+3z0jwAsJs60SsGJz2dKgSwplQN2qlh7wd3olNJu39gIDttGpFRjEWlMqXEL6GlkDLsSJr7XJKWAo0FKBfG9KUwpY4SanqRqp5NStu8JGDRDhTBmU13WTc0uIGas0YnxPUdKscx0SmkzHY5xeCNWayBNUx/haveuTZLW2hqEdadUaKR488Ia/lkiNpeUSkQwSXAKUsqWxpMQ4IwhSSbH9+qOKAx0SoVxwKyTQiuNIiClKqUN1Su4ea4h8T23L+uBHuuTOS7Iv/ejSCkSZrCH4ONN7swacm5bY6dUVFRUVFRUVFStaErtMjW7NgbjewmbjWdxCzEC2YjJNovvuU4pLS1lIUfet46gkDelQpNJKeW64e3/CZzEyE4ppZVZGLn4XilryIo0AGUXVpaUSlqmlMjAmQChGc8jEmAkPCllpvfVJBWzt6d8vFkxTkQm/ud6k4Trt7FqT99TLXOAMTK0lD2uJi1wN1PhAptZ03UeUspFI93+0K43jGqzd1OLzmckpfz0PdddtMNJqUZ8bwgpxVg9wa193LnOJ2LM0keji86V1kgpNREuZSgp4miSUpmN7zlSalZTStn4niPAgtsbkTYX39vEq2squN8vQHNy4dD7BxE2zvlYo5eIkLk4njVlwn1lIm01CdXNUiitUdm+qpRzpFz727M0BQWPl8HvNG0NcoX5TCkCoBUD6cF+vrCbjDE+lpTyz+dK3iMpFRUVFRUVFRXVUDSldpkcKaV1TUpxHphSLbJgkhgRRFDGO89Us81QTUqZhbnUo02pyaRUu6eEwKgV3wt/tjMDhpFSCON7pjeqK7qN15PyFJwS3xm1aDtlnBG1J9tjOqVgenQWXVExBDiJuTqlCC6+1wMwSNBp3e6Uag4ldBOmWGBKbeUyzJMNdtE9lylFhA7vwK0rlZZ1+TXBxvc2r+g84YmJlQak1LhzNiSlANtdtEPlO6UCUkpr7bc/4dz0ho2IoCXcGMfEuaGPBkgp81+tFJRSSFhqy64JnDMQg6HiYN4n05NUT4hbHyk1vFMqnNLGuIvvTf30M0sIbuK2jE9F84TGDBfjTSkAPsIGmOtJuC0EsqY+2U6pFFpr9IS5HmecoQhJqmx4fM/twHnje2maGNNLEZgejELX1xOzz8TE+F7mC+H5jDRyVFRUVFRUVNSxru3lIERtuupIQ110ntjFCGDLumf4vGy6hpoLp+2kJimlJsT3AupEmmJdFtSuuUiWJ6XIlZSbx7QXwObn6cCUknXJfBDfgy0zH/w2PrXUUwICsJAIU+JMHJw5UyqxnVRh5xQHY/ORUgACUsoaTK3jQmppKSFDsDTsOjthqjY7tzauEpJSQrCJZMM4OVKK++mJhpwhO4Fts4vOjSklAMV8ifI4maJzO30PO9uUksqcU85vaJNSQnDT3cOGH3cmvuf6ocRAObd737SNCCaU2LJrQ/2Zirl6X6Y+kmbMhlk6pZQzpQLqKzyLGFFNDx2Fa6uhzLjtyJpcxh32c3HOkUxlSpn7iNZQCKKaYqtNKfj7Z4KhL5u3h3vamf1E5v9uAt96lSUJGDFITSDNBgz+RkcdZ0gmxPe6ndQYpda4nOU4iYqKioqKioo61rW9HISoTVc7vkcAhGCALesVnGaKiLQjJtvNlKpCUmpCfA8ITSmOhJr7Qrr4HgW1WUQjTSnpOqwCU6rRKaVDUyq1xeZB/w1PwVliO6eAhYRbM4yDUYI92R5wlgIw7527nZGAoHRuU8oVnQNDSCk3fS8oOm/04cAsMkNSaivlSC1iBCE4EjHf6zH7xmw7p7qbjRiZBfEcxs9EUool4BAmvmdLlMfJF52TKWGvNjFauNmSqlliPViwzyA4NyQUxpBSjEEIMUD3NDqltIKgFKQ4tIbtlDLBMG2NsdRNlKPZ+/iMeekK8o1Z2KQN62srP0qmFLdl3ITaJBqlkJRKEjGxV6lpSlHD5DY/z5FQCbqdDErpwJTiSErZIKlCuetqHamdz5RK08QM8FAE0oP9fIm9njBmDDkx4frW3vYtvhxGRUVFRUVFRW0rbS8HIWrT5eJ75AwXRzqts5Ca2cUYYE2pbHsdUm1Salx8DwhNKYKgVkQkJKXI/YmNJaX0sE4pG6VSkL6Pi7NkSEQkhWCpKTMHYSFx07cYOKXYk+0xi2bLcy3YImSCgOApBJvvveiKbhBNbB4Xfvoe6k6pNimVcOaJla02pRz1QcxMypq0iJykuugcYOToN7MolnqTSSmWQFBiSpgxBSmlbXzPBHd3dKeUtBMwfXyvRUpxzsCFAHNl3S3aJ2Fkz18Gnoh62qZVFXRKaa3NfpYMAIFzDvj4ntmXaVDenbR61yZJt0kpqZrG7pD43mYqEdx3PhlKawIpxWpTKk2TKU0ps7+H7au6o8qYTg1TirNGEXp3wJQy/3WveG5SyplSFYwp1SKl3O9Jsscbn8GUCoeDREVFRUVFRUVFRVNq18mRUgTuY3ecM+h1Ei3h4oVxtu06pVRISk2xUAnjeymlTVJK2vlyNqalNJCwFIzMQmuAlFLSlO16U6qyXUPm/0pLa+jVpFSohCfGlALzpJQZxsUgWIrldNmTUowROsJFkzgSliFho6dhTaOQlEpaBJ2GtvE9Z0o1O6WIgETQuo+rjZbpSnOEzHzxPcDtmxwAQKR8nJMxhgrzdUpNUsJrUooIAxG0tjyNQ27K2w42paSdvjeKlLL9PmT7idrxPVfYb4i5BFlrYlyTlAIEBEgxaG2m02ko+39HSiXeaGn3JE1Su1OqbWQSmlMjN9uUErYbiqzhMilym9oOPeIcSZLMZMwIPmjMhKaUIaVUw5RKfRF6MhUpNVenVGL7BDWBKz5Anab2emiuJ/Xv0lFqRxdjfC8qKioqKioqqtb2chCiNl3OlGFE4LbEm4jMZCDMXkgdLl4YZ8i2mSk1QEpNjO/Zb8SVMaVCOVKKEXlaKgtooomdUlUzvqe1BOfGlOKUDXwbL5hAyru+M6oruI/7CdbBUroEzlJDfRChK+oi9JR35ialOqIDTuY1tYkTpZUtflf2780+HHdcVGHR+RYuxBLbYcPIkFLzdlwZiqxniUNzTDlTCsCmTt9LWAKBxJQw+4mRo2U6pVDH9+TmGWabLUNKjZ6+J5ghV4jzxkS3+nY7IY85UqoV33PXC+uyCkoAZa+NQkCTguuUUlojTVPwsCdpVlJKNTul2lMsw2vrZhv+xpQydCinyb1H7rURY0iz1JBkYxQaM8NM6nCaX4c5UsrtezZAUoXyppQl4eYlpdIk8dFjjmTA4PekFGMQUxpy3EZFZx0mEhUVFRUVFRV1rGt7OQhRmy4X3+PE/Le7ScIh10m0EMh3j3DO7YSo7fOBuzF9b6b4HkPCWqSUi+/ZmJYCIePd0aSUlgPxPaW0j+9pSL/w4ywbIKUECaSs46frZcKYT0QMCe9gIVmAYB0T/yNC13dOmdsTPi8p1QVjaR3FC95WF4V08b02sQIAiWBQ24aUsq+fEdJUbFB8zy2MKwAazJ1TWs4V35skRgwpywBNYIyQJtOYUhqAMkXnO75TCo3pe6E4ZxA25hr2FDm53iciQpIkSMQIUspeJwUSkDTGSJIIaOrb+J7Zh2magDmzodWTNEmGlKo7pdy2ORGhpoOOCill9h3shLhJkwTToJ8rTdOx5xTZ7jNnzKRDYuINUooyKK3ByJFSHKk1vYabUua/nIz5PW+nlBDcxBE1getk4FrqOqWICCIRU5FSPKmji9vnN2RUVFRUVFRU1NYrmlK7TC6+x5ghpaQGskSgb42l9ZgHjqLhgqEzYQLT0ZRGc5GptB5rShFR3VUiaYCUcl08zHaCEICMd8BQm1LNMeWqUXRe+KJz2+/j43swpBQfJKUS3oHprSJkghvKAwwJM6ZUwrrmNTFCx5NU5vaNIKVCCixcSLlFn4nvuUhVfTvB0A0lW/9xtZFKGNnYJZClwvTFzKFO0gWzFBkjQ4lxxsAYg4bcVFKKM46EUmtKTSalSlV6IxTAju6UqmSLlGqZUowR0jQFnPHUMgt4YBylnQyiFeOsSSkz1ICBgysBCSARwrVymdcAIAnie8aomIWUQqNTSindiH06U42IfNH5Zhr+xtALSKkJ50jiJhkyhixLPSU4SiEpNYxUDDuqOqkxpaiqySr/5YcQ6HQyvy/MtcjsN85M/968pJQQ3BwbzpRqkVK+1H5KUiprHyfRlYra4ZonHhu1PsV9HrWbFA/33aft4yBEHRW5D+pkqQ4pgTTlKNZLSlG9cNqO0/f8IhMSGmqiWeC/gVcMKUsRrjHNY7XvpAEICe+AxnVKeVNKm+l7SgPcGARSV96USnhnoLckjO9xImTcklIAEt7FQrKAlC8AlpTKuO3LAUMmFsBp/b1JRNTqlBqM74WklNKO96iVJQxr28WUcmQDCFk2mWyYpOZkQhcd4uCMAyjnMqUmffDkxJGiA2hjvLQjaG3V8b1joFNqQtE5ESHJjLESDmFwcn0+RIQ0ywbKuZudUhoMHExZIyXhdh/WPVBJktT0z4zHuFLKEnXa/70d36vpoc2/tnLOIKzBOW18z5FSnW5noinV7ZiicyIaMLmJCKkIO6XM/mCoY+XTxPcEq0kpPXBFml6CO1IKEENIKd9NRmRouQn7ylFixiidbcJtVNR2kpQa119bIO8VW/1SdoU0gH5P4dZbKhy4ezzpH7VxKkuNvcsd/PKLLobcwZUHO00nnbCI/hrh+m/M1/saNb2qSqPf2x6fSbaXgxC16XLxPUYEQQwFDCm1Zm+fPb5XGxZc8G1nSrlSZK0N3TB1fE8xZDYa5yQtPcGD7piUd6D08EWp29e+U6qs7ELJ/JtU0n/7L1h3qCmVWdOJMaAjmKUXCCnvopt0kfKuva+J2HFm4n0dsWCKeudQo1NqaF+WI6Xq/RwqFdtn+p7vEiKg4yZrrVNEhA7v2JJ5P7iyEYndzOl7jBhSygCtZozvmfdnZ5tS2paM16SURu0dMzKkFFnzqW1KhQRQmk0gpQBwXZtSaZJAQdXT9zQgEgGRDp9QOUmejAq2ZaQpdTTie4whsdvC2GRSykchGWuQS6PUyTLfv+Wue6FqUkqg2+GQSoFVQdF5Y/pekyoN43sMBAU1FymVCO6HIYwipThz5mZqv6QYrSwN4nuRlIrawSIiLC91sWd5catfyi6RxtrKGtJMYyHu8qOmPNdYXOR41vedijk/SkfNoLUVwje+cQTvf/vVkQw8SlJK4pZbV7b6ZQCIptSuk4/vBQvoNBVgdqG6nkJqt1jg27noHMYQ8tTICNWmFKFrDZ/6uUwnD+fMRIA0kIkF9Ku6J6Vxf2uE1aaUDHBUM33PxYuSIaYUI4ZMLAK2oDvl3NMLGV9ER3SsKWUW36l7XSB0+OKGmFKjStw1NKSqSSmt64WhU5owXzzc3jdHW24Mu1lEirmnX3WCgnvBDN3EeT2Fa56I3ERSinEkyADdh+AciZhASsmqEd/b0aaUHE9KMUZI0gSw73ebiDP0FAOIkHbShillTOs2KSXAlCUhU2HpQOUpnCQRPvKWstn4F0NGhaSUHpjaWF9bN9/w55z5fSfYZFJK2CgkEaHb7UxFC7ExVJn7NxIcnQ43nVK9+vdSg5TqjCKl+MZ2SgFIkFkCsha3JB6sKTVp27MsgbCmVBo7paJ2sBgj3OteizjzzKWtfim7QlprHDiQY/9xCiecFN2Ro6XDhxQOHVI46TSFCTM8ojZQd99OuOPuFbzj3V+OptRRktYKxYEVdLfB8j1e4XaZhk3fyxLuJw2tq1MqIKWyZPtcvbWu4zjKEj3TmFIEAIrQaZlSyi7mQ1Iq44ugIGISqlJVg5TyZoA1CKSq7Bh5M01vmCnVFUsgAMLGPoQlpTKxiIxnSPgCANPvkrhoEgidZGkDTKmup4GysaSUITxCyoOIkCXcEwTDyIijKe4W2UToZPORUoDdN86UItdnw/zidTNJKQIho46JFnE+QPu0dWzF9yz5OKLonMhMgiNLRLWNFRbQU9mw+F6LlHKdUoDpBVLaFZMbYkuIxEfeZp3oqJSypFRz20I1SKlscz8xMEZIO4ZAmmaSYOLuQ4ROtzPxuM3SxNNCQ00pF99jDN1uB91O1pgK6/dFMhjfc/uNu/jenJ1SjDEkzkBDZyAK7Uw7IkLWySbuqzQRENlwgz8qKioqKioqarcrfjraZTLf9Gszsc0ZBonYEFMqSTjSbWRKAWHRuY3vqfHxvcz20UAROmyQlALqhQ9gSClGYuikL6kcKdX+mTa+p+v4XsK7SIaYUp1kCY6EEtZ4AoCOWELKU3S44bkFJ6S8XoR3xMaYUqMmC3pTyk4hq8NhtbKEg9kpW7Mu2DdagtloI4BuJ5m7MNpQZLZ/x24aY9z36qhNNH6ICB1mYp0iiBmNko/vWfOs3NGmlCWlfDl483YiYzYBGBrfa5hSC92BiFqblCLNwHUKgJClCaQ28T1tDQ+RiLFGy9ht0dr31JltGR3f44Ih2+QhEs7QA9xggPH3D8Cgve4AAK7ySURBVCcZdhe6k02pLPW0UNvkBmqakjhHt5uh2+0MNaW4EMhGTN8TzMT3wjL89SgcIJDobOBaGhqeaZZNjO+lSeKjkdGUioqKioqKiopqKn462mWqSama6shS4acHzUdKbf7CaVa5RauLmU0ipbIkMQsMxdCxFFL9XNaU4iEptQAiAUYYmPTVJlS8yFg4Ulf+MSlfQNKaludIKcDEkAQjCGtidfgiEp6gkywDMIsxZ7wAhO4GmFIZzyDY6E4pE0908b3BBXWWcjMFDbQt4nuGlDKFy/N2ujTje3b63gZ1Sk1aTBMIGbqzk1LWQKyqnWtKOcorJKWaUx8JWbc2pdqmk4/vAeh2uwPl3G1SCoqQwNKCaWI76bQ3Y4WN7wHrKzpXuu6U8uRUIPecQvBNj0YzZsrfgYCCGiNDUwHwptT45ze9Si6+N3jMuvJzxjm6nQ663QyMcW/4h6ZUZ6BTypJSxEDE5ielqO5qS9EZGt8zHXWEbApKLE0S3z0WTamoqKioqKioqKbip6NdJkcKsWC6UprOF9/LgoXTdiKlNHSTlIJGpSeYUm6SkjZl1qGkj+8ZQkAD6AgT3yPQQETNUWkDppSlViolkfI6BshbppSZgOdMJ7MISiyllYklJCxBR5hehyS4HSB0rVk1j7IxReeus8VMNTSkwjBTyqZ7tpyU4kFx80I3mXv6VSY63rDzRed++t4mx/eI0CFbcC84BB9vVpSqtJFRF9/buRN8vCnlY7nNKWtEQKfT8b1IbWMlJKWG0T3OE3KmFxQgtD0HMhPfMz/PEFucCyTZ8HNkkpRS1vxyJvVoUkoIhnSTDX9mo2hExvyedIbwIMK2sLAw4d6WlBLOlBp8dlcAzjhHp5Oh0+mACXMNSRghs0YWFwLZhE4p82/zxffMVEtCimwgvseI/BcKCwvdifsqTRPT14VoSkVFRUVFRUVFtRU/He0yuelzjaLzRPho03rMg8a3+duMlPKmlCWlJsX30tREu0iPj++5TqKML4JoeERtPCkFSFX5x2R8AWIYKZXUppRg9UKoK5YgmPCmVMrttDG72FsQe+aOqGW8Nl6SVgG+1LLRKWXIhObjO6kZgU4wi8qtFKP6NSx0UszpSdl9M6xTavOLzk2nlO0SExxCjD5ntdbHVKdUJU3J+KhOKQDoWFJK+OL/WkR1TKyz0CSlNAZJKa0IiTbvcydLfRTSxfeY4D7yNrsp5YrNw/he+FqpUXSepZtr+DPGfKfUNKRUSJ0tLHbH3hcwplQddRzcFndtc6ZUt5OBcWZIS8686T+clDL/5UF8bz5TipB5Uqo7QJ2y8Fq70MUkTCxNRSSloqKioqKioqJGKH462mUaVnSepkGn1IzmARF5Y2W7dUrpgN5xnVIT43tpahaqmiGjeqGlff9Lq+hcLIKR+Xa+Hd+rO6XaiyNDR0gl/f5OxeKAKQUAC8keAPAl5m5h1k32gDNh433GNDIUCGs8bh6lgfHSjt81O6XMYrptpnRSU3TOWf26tkoECmJb6dzTr7KgBN53SlE9fU9tIikFwJNSaZIMRNDa8kbsMTB9bygpFR52Nk4FYCgpZYhGexwsdAcK76V2Reou90uelOpkCaogvqdhDJT1dgUNxPeCa4xTEpBSm98pZfaJ+7mTDkPf2UWEhcUpSKm0nkCXcBo4zhNuor6Mc3S7HXS6HRPfozq+RwB4MqxTypJSVJvn7guY9YgRQ2rJpgyDRF3TkFuYeD0RQqzbvIyKioqKioqKOtYVPx3tMjmjhAJSSggOLvi6Y1buQ3aaCIht9IHbxMqapNQkUyr1dM9oUooNmb5nFinNpUl7+p6Xm76npf+23ZBSycDr8aaUJZXc+7OQ7AEnVpNStt/FvYaFZM8GRNSM8UIYXEi540jbkfaGTGg+vtMxU+7CyNRWaYCUmlOOlCIAPCSlLAEyDyk1jTqWlEpd3HSMjjVSCjDxOmMUh+E9A8B1OsaUGkb7mGtcHd9r3x4WnQOAlgg6pVJIVdlj3pFS6zcbpFKNonOta1LLyRsxR2GyqSksX/A/d5JY0Nk1jSmVJCbCRhg0ud3PJAqm73U73nB100UB0+PlDCOnjY7vEcFP+MuoO3AtDa9pC4sLE0kpIYQv4E9b1GlUVFRUVFRU1G7X9nEQoo6KhpFSXDBwG5No0z7TyC0wkmR8lOhoKzRKXCH3pPie4AycE0gzT6M4hZ1Sro/Lk1IA2jUpvr9qwJQyf5daImEAoJHyLji1OqVA3pQSzCy43e7timUwYjY+SAEp5Uiq+TulEpaBj5i+JxvdOrpBpTll1uDbHqYUecM068xPnGSiC26LzonCSOz8nVLTKIUjpcREUmqYKTXPZLKtVCUVEFB5cshmhKTUsKMudb1snQ7aOc52fE9KDQEX30vs9aOO73FhSSmtIWiQ/hknH99z9VVaDxads5qUmlRoP68IVJNSU5yvnBEE1QbfJE0qhRc26kucoeNMKWfKUW1aCSGQJG1Tyr0m83tMY/Kk1UlKbbG8M4BDEaHe9mlIKc7raOSQPq2oqKioqKioqN2s7eMgRB0VDeuUIksU0BDaZxq5z9iMs4kL5KOpNimlMTm+R5xAzJBSWYuUakzfc7E7vgAiDkNONI2I0fE9W2ysJAAJrQHBhk9wyviiezYAGmSJr0yYLivBu9DQSLl5/9ztqX/c+sWZALf0Vttr9KSUi++hSawAxqR0nVJbvQxzxzYBSDYgBpWwjt03GlKXAMw5JfjRMaW4SgAQRMLHGiF1ub8+Rkgpa4WqML7XPPKSLPVGxmB8r6ZBk6zZLab1ICklKwWuBACNNBXejHXT94hxX2BNM5I5Pr5nXDZorQcIO/daOWM+Yr1pIrtPMN22mPPa7K8kyyYaclxwTxIO3RIbZWSMIUkTU7rOGMj+u/vdxDgHF81zeKNJKQBgwjyP0OnQbXP7yJlNY5+LM/+aqY2URkVFRUVFRUXtcm0fByHqqKgmpZgnpWAnpGmtodaxYHULKSJj6GwXaV0vXh0pNWn6Hpgy8ToNaNmiKORgfE9D2P8qqNZzT4rvKa0gVd88JyWgIacjI7MgUrowP0UX5inImBKMEkADUuUwBknhn29emamCxnBT1nhxGig6H0JKkTUoTUfPFi/ENKAqBRBtyOKeiNl9o1FJ9x7WRu96zqOZJF2RlfJdUaN0TMb3PCk1aIYyu/hXVTXEKgVkaU3EIeSRv15YyqYsJbSy7ymr7PXTEIKAsYrJGi2qmnBtGdgWa1r73rvB+J47jojNRmGtR4Rg35WTt0VrDVVVjceNfX7GAHd+DNlX2kfLyexTxkHEzPVDSvMYDYAYqHUOu90mLCkFPacpRQC4Ja0kw8BhpAFpt2HabTfXQw1VlRPvHxUVFRUVFRW1mxRNqV0mF2lgVBsrEoAmglIa/Xy2hRUA5HlpVlTbx48CgMZEuGk7pRQkFBl6Kc+Lxm1ySNF5Xpl/U7ryhlB9f1cwPSy+Z0ipXrViXx8bsnwG7NOjVH1oKBSyZ4gK18OszYJ4rToCpRVK2QMRoPT8p7YG2f8r/zrDbXPEiLZL//aCWrvjq1Ioq/miNPNKKo1+bhaDG2GPVbbLSEMhl6sAjFnJN4CUmiZaJ+2BUSL31M7o13oMmlIBKdWWJpN1zXv5gDmotUZvrVffr6U2KVVVEqoy/7amVgfie4VUvuS/zIuZYpFSKmtIOVNKDxw3vX5hiURs+vVVg6Ds5LoiL7xZNkpKKZSFPaeIJm67JvPth9YKeb8/cHtVVlBSmteh63NMSYmqqtDr9c37Qua1Nl6LJ6VMNFNDz1V0DgCSSgDaGJPtbbHvNwFQU7wxGmS2X8Ps26ioqKioqKioKK9oSu0yufiJoTrMh+kyKA12C/dZtNYvffRl2Ij2rVJ7+h4AVHK8KSVRQtmoX79o0UGhKWX3XS4VtAYqVaBSzcVGbQbUC8/67+a9WC0O2ufWQxfYhV1U96sVKK3Rr44AAEr7c92ae608DKmkNUjIP24emTWzWUT2ypYpZacZukmCwKBBUCkzoaysJIpya00ppZQ/tqsNOEYr+35prbBWmvdko+J70xAxuTWPC/THEiHG6Giao9WwIqYdIK314PQ9NeidSOvg9Hu9gWl2WgNrq2sAgGrIbmibUkVZobTHbk+v+OPeGYGFVP4xvV5vpu2R0pkd9fnTNnbX+iW0Mub65l9bNUq73b21tYnmbVVJ5P0cIEI5xfVG2uuxVhq9tUFTKu/XJmKhFEplDG8pJYq8QL/Xt5HHdlC6vvZwxn0p+bzxvQLmNfaHmI1KWWONprvWuvdWa421tbW5XldUVFRUVFRU1LGmaErtMg2L7xWVNB/0lUJvHaZUr1fYxe/gomorpeEWcnpqUipXuSel+q1vtJWP79VF5/3KFagXyKvmQqv+WUM6pez0vZXykL3v4L7TgF/s9crDUFphrTwMACiUMQBL+5heeRhSV+hXhtrZCFOqsp03Ggpr1gxzquN79fS99qLZvbaq3AamlNbo982xXW6AKVM6QgYKRwrzHnLiGxLfm4a26ec5gMmmFDCElLJm4U6UM/scKWXiey0z1P633+sPmIOGlDKmQKlUw9AyZGXTlOrnBSp7jq+pFUvfaMDu81yq2mxYnc1s8IXz2v50PWhmumur6cPb3HctvN6sra5NRAqrqkJhiadiinNK2muWVmqogdfr9S0ppVFK7a9hSir0+3301nq+h6u9L3zROTEwYnMXnWut0dfm/cyLYmBfSKnQ77ltn3w+SfuatdborfZ27KCBqKioqKioqKjNUDSldplcpIEH8b2++/C/DlJKa21JKe0jF9tF5jW1SKkpTClNhobIi+nie8b8KpDL5kKrbQZ4BfE9Q0ppv2BrqyalVlGpEn0bozOmSL04WysPo1IV8uD2ed+J0kXUhpFSYXzPUSutH2gSZtqSUrPHQjdSYXyv2ABTxi0wlZZYsaYUY3WnlFTrn3A3DSnltwV9yFlNKTvBbieqrJqklLZmjpOGIaC01oasGRLfW1s152kpm3Si6aCzf7Z/yPMCpe0OWlMr3ugYIKWC551W0nVKNeJ7Q0gpbc6taWikeaR1bSRPY5xIaUkpAIWcbAA5utPQQoPP3+/nxpSy2+rur6oK/X7u43uheQY0B1pwRmD2y5Z5SCmllSel8rwceK1SSeSBKTXpfPIkrAbW1mY7TqKioqKioqKijnVFU2qXycVZwvieMVZsrKK/DlKqb8wbrTWqTZ46NovMYsX92VEi482RQvahmFlgtQ06V3QexvdCUqpfNRcbpbKPH1Z0DhffOwxAo9J6wNQJF4m5XEEpS/RcfE8ZQsPFbdbKI5BKetOq2ID3wRlfWkv0ypXGwsxQUtqbfRqDnVJuT0upkBdba0o5w1VrvYEUGaB1hdXCvCecOJiL722igRAapiUVkGOO6cbESRsj3czXttka7JQajI06aKff640gpXr+WhU+siYrA1KqKHwfWk+tDhSdF1VNSvVmNBukbJFaunncaK3rTqkhdNBGq94qE0VsRx/b8vE9TEdmevJSqaH7qp/nkFUFDRffs1110ppSaz1jSmn4656Teyc5MZA1pebplFJaIYd5jUVZDRxHSin0e87cnGxyu7gvMDtRFxUVFRUVFRV1rCuaUrtM7oN6GN/rV9JSAuuL763ZiIlCbaJsB4WdUjSlKeVIKWAwvucWJowzuCGDfUtuSF0gr0aRUroxIY0xAGS+bV8tD9uoiRoS36tNp7xaRalK9C2xVCnt/29ex4q53ZlSrQXvemRoIFP+3rOxwHpfGFLKzSDUQ8wB19mjpEJ/i02pUkrfw1RuwL4xC2YTC3WkFGdBfG8Ti85NtNSUMFcoavNzhNxx6AaWHRNF556AHJyv5467vNcfMOCkUsh7NSkV7usGWekIxbw0pBQBPbXm9yWRM4uVJWD0zASMtJ1JRKbDfFinVC+vO6XaRsxGy5hy1mxfGzT02qqk9IXl05hSNXk5fF/l/cJPtCul8s8pK4l+XmDNk1KDxrKP7zECs6XrG0VKFWU5cD5LqdG3vVjmC4Dx52yp6kL8SEpFRUVFRUVFRTUVTaldpPCDejh9L7ff9Gql12dK+TJevekLp1mkggWrS0RNNKUapFTefD67bX7sOEJSqkR/RKdUe8iXI9ScKQVoSD1YZOwiO1prYzoFpJQzpEyMxdxeyMJPgivlYNfOrHJFw4aUappSlarMgp6037ft1+/MAVnJLSelqkqhsK9hI0ipwkeRJFYK0/PFqRnfW68mxfc0alKqYsXEY9pP3GQEgEyX0Q7N700ipVx8DwCKPPfRO6ewC8gZBeFjlTZGq5uul+cFqqoCSKOnVv2+dqd0v5JjjZbx22Lie4wIRKOm75X2OqY3pAttnIwpZf7c7/WGDl4IVZYVSmvcF2ryEeXje0qh1xssOnfxPcCcX75TKojvKfsc4b5wkUCg7pQCNi6+V5bVoLkZGHJST07D+sEIanaiLioqKioqKirqWFc0pXaZ/AKVuC/r9h/+lUavP7t50LOxKK2312QvpWsSwpFNk6iSXOU+btfvt0gpu2DivGnoAYDUOXI53JTi1gxw4txYWia+dwhaG9qi3RmjgyJzT0pVjpRSqAK6Kq9WkVc5Crnmb58XWvPGCyTWqkFTCgAIBEbNkmh/H2dKbQtSqjalnNk2j/w5oyXWLL0WklLzROQmk1LaU3wVjTellFaejhQWldrZpJSNZPkes8FOKWXPtaKf+5JyJ0NKmfO0jlTVT6k0ACJ/tvbyHJWlIfuWlCKytCMcZbr++B40wIj89aT93jjDf1hkbaOlNOD2Vs9OuhunoqxQuRjpFPRh6YwZKYeaUr1+H3qYKSUl+j3XKWVMwIH4nielalNqM+N7ZVWhsOfgdKaUi0KrSEpFRUVFRUVFRbUUTaldpkZ8jzXffr3e6Xtu4YTtRUoZgsFQUs6Am0SV9KseNLcT70bE93gQ33NSerBTyv0swRlC+EXY/J4pOjek1LCJUmGhby7XkFc5crkGQKOy06ncdMF+tYJc5ihlz5tZ8054cotC1yk1bNsYwS+oZWuBX5NSCvkWT9+rKoXSmlKVHOzvmlWuR0ajgtK10cv4/PG9iaSU0igKc55WlI89ps1URAlQy5TaPt7xTHKmDWfGOHJ0k5MOjJWRpJQjXJRqHrOBsequF2u93Jz3pNFTa/b6GU7ftLEsS//Mcs65onPGyHfUtU19E9+rS9U3U2ZfAtAaea8PNeEkKcoKZWGi2+UU9F3pKTeFtaGm1AhSSkr0XNG5HLYvdEBKkZ++N885KLVECduXVVUDz1VWEmWe2/MLUxpyAEZQYlFRUVFRUVFRu1nRlNplGhbf87dJ7amnWRSSUpu9cJpFSmtfgCusAedjZyPUr/oAr8fBN57PL4j5gHEgVYHeiPie4AzhvYXgILKkVHkYGspOLmx/+18bVaXqoVf1LAllitFrUwroy5UmKSXnJ6VC46Ud35PadkqR63CpJ5f5+8AQYlLKbUBK1RMAS0stzKOalKp8iT5nGxPfmySplN8WSSUKVYy8r7ZTHgEgEaaEvV3wvZPkTKksMdsSRnQBQIOgbC9Skdcl5U5+apo251C7w8mdT6ndV2u93BgSVE/fI8C/zya+Z+iffr8/036t7PnlpnlqDBJ2PTvZVGPzr63KN+8B+RDKrK2iLH18r1STSanCbq8aYcz0+jlUVUds3UQ/VVXo57m5XdWmlZOh44w4Y2CYn5SqVIWKCoCAqqxqQs+qLCsUNt4tAd+uN0rOtNPKxEfnvf5ERUVFRUVFRR1LiqbULpNboPKg6NxJK4V+Ppt54EqXXcfL9jKlLCkFQDC3IB+/fb3AlMqLpkHnSSk2xNDTBfplTUqZaVnmZ6VCNEysxE1o0xKr5RFoLW0fV2uBrLQnoUrZw1q5hkKueaIjl3byFzTyag29qodS2RJnNX+nVCFdRKpEr2pOjPLRRKqpkXanlIM+lFQDkwyPtspKoSzMsd+Obc2qcIKf1qZwHDDT97g9zjbdlLKLd8UqFNUYU8qRUqiNFinnp+i2Ss6USoUxhtsF+xo1KVXmuemDCh9fyTp25c8v+9jgubJEAADW+v0hpFRtcrshEYaUyicjM63XApg4L2cMGGJM94Jrazveu9FS2pg7WmsUQ/ZdW2UQ3yvk5KuNP2ekQr+XD9y/3y+g7D7JW51SNSk1aEqZ1153Srlr7TydUpWqUKGAoVIVyhbpWVZ1n9Y08b2QEuv3c/+eRkVFRUVFRUVFRVNq16kZ32uTUgq9YjbzwPTbVDa+h00v451FjqIgIm8ETYzvyT7AbESkbEV/gvgeH4jvlSPjeylPwIKopDOpKlVhrVyBhlnYthekMiA5StVDr+yhkD37WjTyypBSWiuUqo9e2UMpewB0o29qPQqNF6Ur9Kt+w+RyxBln3MZlhhSdW3rAHFdbS0rlZeVNgGoKqmOSCuX2TelLsTnj4C6+t4kGgpIKZRmYUnI6UioVCQA79W37nKYzqWyYUsM7pdwlqCpK5K3rWVlJHzkLzy/3WGVN7NSZUr3cvJek0Zc9lLI01xNrPvYrNyTCxAJn2a+GvtEQnIPxwU4prYF+UfljafNJqdpTK/MCpRxPGuVFicruX0NKTYj7eVNKot9vGnjm90iblLKmeGXie/28aMT76scGReeMNqRTypNSMF/ktGOgs5pSvp9PKdOdtVNPwKioqKioqKioTVA0pXaZvCk1jPZReuaYldLmMeoo9Z7MIqW1naZFSLlZkE/q3+lXPYDbhU/blLLbNnTf6cIYN7pp3ADGDAjvn4oERGSm6ZWr0DYK1yYhpNLWqApJqR4AZUkpaQkBhVL2sVquolJuIpQeMIlmVUgD9ateY+Xlo4mMe2pkMApl/7sNis6LUnpTqpyTlAKAwpZfa1Rw4SFGzJuPmx/fM2aAZhK5zEfeNySlOokzpean6LZKPr4nzARMd447mbOBTDyvLP1+cirLCmVRAtaQki1jZICU6llSimn0Vc8UnQNIuLm9Z48pZ7TMYjbUpBRDwjm0pXLqbdHIi8p3OxVysvEzj8yVhkxnWV5MjO/leQFZ2W4zNbmnre6IMnG89qb0+wWUrE2p3MX3pCGl+v3ck1TtfeGLzoPpe86MXY9KWaJCCZC5frVNqaKoUJUFoLWNjI5/vsJGZpWUyPtFjO9FRUVFRUVFRQWKptQuUz19bzC+Z2JW1UzLVQ37bb5bOE3xjfnRkrbxPUZAaheRk4vOXXxPo6yqxsJJjY3v5eaxgUJTKqTSMiHAiEFqiUL2oaFsH9dgv43UxkCoVI61cg2l7ENrQ0j1K2kiitaUWilWUKkcgF1wzxkRCWmgvOoNkFIAkFhSyr3exvYHpNRWm1J5KSErU/DdNiPWo5oiK/1+4cR9mbico7dp0vkjZd0pNYmUUlr5c76TpPVr2x6n6MyqWqRU2CcEOGPFSJbDSKm6nNv0uDWNDd0ypfzjSaPUBaSWICJ/Paknlypj0sxgRlZSAtp0zgnX9zWElBpe7r3x8p1SWqEsBvu42uoXJaQ1/eQEo7dBXkqJfl4MGKO9kJSq6vietvG9fp5DB6ZV47W3is7Nv80b3ysB0qik9GSiU14UqNw5iOYxOEyOINZSmW3fqSdgVFRUVFRUVNQmSGz1C4g6umoUnQ+J7/WVzSKM72310tp8m3+0Fk6zSFlDh4iQMkeJTFhoBZ1SRWniNU7O5OGcoTW4EEoX6I2I72U8sQulOkblTC2NCloP70mpjSWFSuXedHLF6L3SGYAmvrdSrEBqs9jbEFKqqo2XfsuUksrQXYIJb7gNi0IBgKqMKaW1njhZbrOUB6TUsEmHs6o2pUzvDOAisbaA2024W8fmTtpHlZSQlZmop5hEXo0hpRCSUtaU2sGdUjI0pWB7oBrGUl3WXQ0zpcrKRM4sFRVOC1XWqCKqTSkvpgEy9zXkZfN2XUmURTnTxLeqkrbonIMLbgzThil1dClUF0PTWqMsygE6qK08LyCtMVO1JhkOU90RNZwq6+cFVMX8fZ2tK6sK/b5GL+icahedu6cyXxjMT0oVsoCkCiBtSanmc+XWkNNaNwriRz9fbcgVeTHXZMCoqKioqKioqGNN0ZTaZWqSUkNMqVJa82G61bRSGnlZgbZx0Tkj8nGbSlXGqBqxfaEpVZY1NaYxiZQqxpJS4f0dKQUAWitolKaPq7VQcZEYQ3UUOJwfNqaTNmbUmn19Wtv4XrEKqQoACmojSKkwvieHRxMF4564a0Sh4KbvbQ9SqigrSDsprd0ltK7nC0gpJxZ0Ss0T35tkGJVl5WOdU8X3lASBkO1wUkoDqCxxkglTaO1K/50ctaK1HkpKFQEpFT6fef7adh0wpUgbYwpokFL+58oKRVHOTkoBEIIB3PSyhb1ynkKVR+fa6ix4rRRkZbZnnPpFYcgma4BPiqQ58lLLCnlRNsxEwJlSNYHmns8UnVe2U2oEKeVpRbYhnVKFLKDJGr9KD+yLvCgh7bYbI3T878uwT6soywYRFxUVFRUVFRW12xVNqV0m90GdEx80VqRCXlYzLVjdZKJk25JSJqM6e3zPlCJ7g0Brb0oxPsqU4o1/a5hSYXwvSbwpZqin4QutykchNaQucbg4jMqaThrAWmknf0FC6hJHiiPWtNKQrWjSeuSNFxTIq7wRh6lNKTG0U8qYUtZQ2wamlI+YatfVtZGklBEPSCmpXJBsdlRqEilVVhWkNAtmcDVghoZqkFIi6JTaoa6UM23cJEEAA6aU1mQirpUcNKWKElJKaKWGkFKD0/e8uPJvJSOGlDXPdSWlNVpmI6UAIBEc2pqZoVmhlEZRyqNWdC613UCtIatqYN+11c+NMaMteTjJ6PXkZSVRtPaV1s6UMn/PpfIWoSorrKkchTPB0C46r/usOKMNmb6XyxyKGVJKKz2wLwwpVVlzc4pOqZCUssdgVFRUVFRUVFSUUTSldpkmmVJFKc3inQ979JDnUxpFJcFdxKTaPqaUmUynQYxNVXQONE2pSsrGt/lyBCllTKAC/aq5P8cVnTPf56WhLW3jynvdoqpU2nbkKEhV4Eh+BFKVflrfWilt5KiC1BUO54etSaI2KKIm7XYXKFXRIA8Gi84V2oXTDkLZNqaU3b9KD/Zfzaq6b6sV3zsK0/fKqjLmBWloNt6Ucp1SRBTE99bfd7WV0tCN+F5p+swRVrEpbft9lLKF2s2+LTfBTVtarNkpVU/yGx7fs6QUmCcv/WMrQ8DIGd53T0rx0JTS/hoglUJRyaMX38OMpFRem0TT9LTV8b3K7KuGgaeR5yWUquN73pSqKqwVfWP6DTGlgGD63gYVnReygCJpzjGtkRfN4yjPa0qs3Ws2/PnqkveyLP17HxUVFRUVFRUVFU2pXSdP+7Dh8b28lBNjGKGkUigrhWybklKAHfE+zfQ9aLPAF86UqkvbNWqjwZhS7Z9VoF/pRjTQd0rZaXtOWVL/XevRpFRpaQ6tJZQubXyv9NP6Vm2fi7m9wqH+IShdmeJ0DMYBZ1VhF+xKFyhk2VjkuW3jjINbU2pg+l4rvrfOiqUNUb803TzaRo2qeeJ1AEpvcIWklNsXmx3fkzUpNYUpVZd3pyDIiaXU21lh0XkFgmrF9yTI9yJpW6gdKi9Kb0op3Zx4aUA6baOObVOqvh8bEt/TskJVVjOZDe6+ieBQI0ipspTQ1dEyperzVUqJvJxsSmkpfT/XJBM8NKVkWTWMW2VpJGW/DXGmlNbaxPeK3JhW1pRqTyoNSamNiO/lMoeCBJjZtjYp1S+suWlNKTkxvif9tpeVnMm8jIqKioqKioo61hVNqV2mJinVvE3ZqV6zRHscKeUjJtuowFUq245F1DCltNZD3RGttennsQvQqgoMOh2QUiPie7kzsQh2Yl5tSrH29D0/+NJ0SgFD4ntS+3ie8iRUZYwnDay6TilUkKrCofyQmQanBxfcs8oZL27blBxBShGv43stUsr9dE9KbaErFcb3tNZ+GtZ65SYlhqYUY2xDOqUmxfc8kWN7jvpVf2SJvNIKyr4THZEAJG2n1A40pXRoSjH0CNCqbUq5+2pTqD3MlKrk0MEMjqIjAtIxpBQjhpS34nuVtFPapjNCtNY+vieECEyp+vEDFKqd6LhZp5AvOlemSH8yKZVDyWroJMNh8nRhZWgh2YhOGhpJMuEn9blj1HRKFea9dKRUsC806i8gGG2MKVVUBTQpgDlSqmVK9QPiDk1ar63G5MHKTPKLpFRUVFRUVFRUVC02+S5Rx5LcB3VGQ4wVR0rNEPuqpEJVKf/N77YnpfRoUkppZSaZuU4pKaH9vtB+v7CBonNDzORV3phQV8f30lbReUBKQfuy7GGklKEFJDQkDvYP2mJ0Y0atFua91LqC0iUO9Q/5+2pt4n/rldZ1nNCQUsXQTinO+NBOKaXrnhVToF8NjIA/WnJTzLTdnnkpMrdvgHanFAdj88f3JhlGeVFa81NPJKWkklBatYrO65jaTpJGbfaloo4fh8ed1OQjaEpK9PttU8qZCXaKW6tTSmttrhdiWKeUvZ4EgxP8Y6sSVSUnTqwLFZJSfAgp5eN79rid10idJEf7aKmglUI/n2BKWWPGTzIcc8w3TO6qMoMHGoag+UIkjOflwf17/dzEBUtHSjWPYfdHRgROdgLmHOd4v+pD2/NLaz1gbvbywhhS9mdMJqVqg01KY0xFRUVFRUVFRUUZRVJql8lFsDjjA71ISiqUcjCGNU5lJVEpFSycNvfb/FkktTEQiOpF5qT4Xi5rU0rKZk/SqE4pwJFSbKhxk/LW9L0kAZXu73UErG1KGVMI1mSyphQktDXWVmxPk6GnJA7lh/x9NfTc8b2QlCrV8PieYBwimL7n6QbAT6RyXWVbaYQ4Ukrb3qD5KLJ63w7G9+oF8Xq3dxIp5U0pBk9KjZLSyh+TnSQFgaCUnsl43k5y0/JSwQNKpr7d1cs7w6BvJ+25fdrPTcG2soRLeM65wQhE1ChSB2DoyXFF59ZscPTTVNviSSkODDGlKqlRVQpJ5Uxye0xt0sW1ciaykn7fjZLW2hgz1sCaFN9z55yL40mpGgaej+8l5t9yWV8vVFVhrddHUZYQtljdGfYAmWvkBpNS/arfmLg4rJvMHWOTSCmg2SklpYqmVFRUVFRUVFRUoGhK7TK5BaqhOgZJqaqSM42rLi0lFU6I2syF0yyqSSlCytzkMTmS2FFaoaiK2pRSNQGmUX/zbuJ74SMN7VRU1KBcGqYUa5JSrKohxZGklPvZDVJK+g4qZ0pplNDQ/nZPSs1pvIRl3pWqUKqanHB/5kwEPUoBKWX/D232Wz5jgf5Gq1/YSVlKz23Yhfu2YUqxmngxBsfmGD95UZpjmzRAkzulalMq8a9tJ3ZKaa39tSkRxhjWujl9z02QU86U6g8xE7SGtoZQeI5o209FGBLf4/X9CK5TqjY9VGUiWTORUm76HueAjQNWwaCIKjD8ARdZ27yLa01KmXL19r5rKzRmTHxvHCml6446v6+C/WcjcooG+/VUVWG110dRVsiCTil3uTE0qfkzD0ipeU0pPcKUcuRUw5Qa81zO/PSGnJIooikVFRUVFRUVFeUVTaldpknxvVKqmUyporLFyfYx89I5GylpDQhGQGLje1LLkfEo3yllx79LGRRi62lIqdr001p7sijliTduAENthdP3RpNS2ndGaS1xT/8eW2LeJqVKAGqApJrLlNLwcSGlc0glUcralHKGW8KEL8xvd0ppOAJPm2jOFhoh/TwwpXQdp1mPQipEqdz/OyPWLDrfpM2tSSkFMExlShERMpEChB1NSoXT98h2t4XHnZ8gZ0uoBwmX3MTNZG1+OJJKNcjKmsQCAPD6ZzBy0/eC/idL/8xiNvjpeyKcvlefs4WUkDK4tsr103dTvR4ftzWRvHGkFGAm0Glf9q3HxoVDI1dWFWQlUQUGniOnFDcT7fJKgQi2G6xCr9c3/WHOlFI1lak14OZJMiIfoZ1n+p45p7TvFxxJSrltmmAUhtFFQ46Oj0ZGRe1Waa3R7xe48cZbcOed9yBJBE4//RSccsrxYIxNJInHPS8AHDp0BNdffwuOHFnF0tICzj77NOzbtzzyed3j1tb6uPPOe3DHHXej3y/Q6aQ45ZQTcMopJyBJxLpf13aR1hqrq33ceMNtOHjPCjrdFGeceTKOP34PiGiu/a41cODuQ/j2jbej1yuwd98SzjrrZCwudcc+r9bms8pddx3ELTffhdXVPpJU4KST9uNe9zoeaZbs6P3utu+O2w/iOzffhbKocPwJe3D6mSei00nn3jatzbCUr115I3prOR74kHOwsJBN9TitNO688xBu/vadKIoKxx2/jNPPPAnd7vyvaztICI4z730CzjjtRDBGuPnWu3H9t+9AsY5J4QRg375FCD7dt+55UeLwkd7Q25aXujj7jJNw4gl7wIhw4OAKrr/xDtxzcGVHTs2eVdGU2mXytA/xodP31IymVFmZ4uQGKbVNAnxusRpOyxpHSkktUcjCLkA1VEBKAW1SKog+QkHrEoXUtSkF7furEi7AA1KqaUphjCnlaBxDQpnOqLqDyk3fM+XmjpQSxpjCRhgvo0mpOr43gpTSBKXNsl6r2ac6brR6RQVYMybst1mPwlJnOYqU8lTLxiu3kTRXvt2rhv9yM69P2p4kQioECOa9naeIfaukAxonFfU5GB5X7uOEj+8NMxO0NsYLmuecG4zAYEkpItQITkBKETWKzh0Bo7WeWA4eysf3OPPxvfC4LCvVuLaWSnvzZTPkp+9VtaE3qkBfW9PKEWnA+D7BdqdU1YrvmeijNaXQ/HJDVRX6/cLQqgEppYPntvMlwFjQKaXXd4xrrev4Hp8U3zPv4fii86DkvaxMV1gkpaKiBqSUwhe+cCX+7M/+Htdd9x3s3buEsjTn/xOe8Ei89KU/jBNP3L+uRXFRlPjnf/4w/vZv/w1ra30sLy9gZWUNnU6GH/3Rp+N5z3sKOp3mgl1rjW9+80a84x3vxxe+cBXuvvsQlpcXwDnHysoa8rzAd33XufiZn/lBPPShF3lDfKdJSomPfOhLePNfvxd33XUIe/YsotfLAQ0841mX4AX/7XuxNMFAGiatNVZX+njr334A//ovnwIAdLsZDh9exXHH7cELX/R0PPHJDzMR9iGPveU7d+FNf/Ue/McnrwAIWF5eQJGXOLLSw3nnnYaffNHTcfEl99uR+11rjQN3H8Gb3vDv+PhHvoI0TZCkAocPreL0M07Ci1/yNDzskfdd97ZprXHTjXfir9/w7/jIB/4LSSLwlre/HGfd55SJjzt4cBVveeMH8OEP/BeE4MiyBIcOreJepx2PF/3c03DxpRfuyH3udM5ZJ+M3f+V5eOzFF+HISg9Ka+zbs4AvfeU6/M4fvxNfuerGmZ5vYSHDO9/4K7jg3NOmuv97PvQlvOQVb2wAElmW4Mef+1i8+AVPwqkn78eBe1aglMZx+5dw+Mga3vpPn8RfvOUDI82sY0XRlNpl8qTUMNpHKshKjY1htFVWypAXNnZStQpot1LKfkPDiJCw1vS9IZJKGuPF9sdIpfziXVuTCnD7rn6cMYoq08elZeP5ACAVScOUSgT3CyfzOouBPi6tw/hcBUChVCUI3JtYntaxplWpSjBiPt5XzDHhqUEDaTN5bxZSyhMr9pugogxL44++THwPUy2gJ0np0Z1SvujcTvrbDPXz0pxj1pSahpRixJC5+J7aofE9aG98JtySTHqw6BwIy7rzxnP0+wWgNfQQU6oR921/SA5MKUYMyZBOKRdBm1aVpbU454D9eVXDlDJRameMV0pBKWxaBNaRUloaWqltxLTVz8s6wjYhLmx6oFx8r7Rl30FHXWXifFqYf/PRPGsguuuwM6Wkro8FN7wAMPE93yk1Dykl7TkVkFLNbrLClunb92YWUkrpaEpFRbWktcZnPvMVvPzlf4zFxQW85jUvwwMfeD76/RzvfOcH8Jd/+U58+9u34I//+OXYv3/PTAZJVUn89V//M/7yL/8Rl176ILz85T+JU089Ebfffhf+6I/+P/zhH/4NDhw4hJ//+R+BaA25+MhHPoe3vvW9OO+8M/AHf/BLuP/9z0OaJrjnnsN45zs/gDe96V246qpr8drXvhyPetQDdhxFopTGu//10/jD//02nHPuafi/f/6LOPs+p+LQwVW86Y3vwd/89ftw+20H8D9+48fR7U6mbELleYk/+sO34z3/9mk86wcejRe9+BnYu28JN1x/G/7g1W/Fb//m32B1tY8feM5jB+pE7rrzIF7xq3+Jq668Hk992iPx4p97Jk46eT96vQIf/uAX8Lo/+Sf8j5f/JX7n1S/C4777QTtuvx8+tIbf/o2/w5c+fw1+4kVPxnOe9xh0uimu+uoN+IPf/Qe86tf+Br/16hfgksdcNNO2OdLw/e/9It7y1x9A3i9RFBVEMvmDg9YaKys9vPp/vQ2f/o+v4fkv+G780I9+NxYWM3zj6zfhD373H/Cbr3gLfuN3fhSPf8IDd9w+B4CzTj8Jb339L+LsM07CK3//73HZ+z4HJTWe+LgH4LW/89/w93/xy/iRn/0TXPG16Y0pIsK+vYtYWMjQ643+3NTppFjoprjn0EpjHcoY4Vd+9vvwiv/+/bj51gP4qV96PT73X9+EUgoXnndv/NFv/wT+58uei7PPOAm/+Bt/g3zCEJqdrJ1rdUatS41Oqdb1REljSIW9JpOUl+ZDtv9wvo0WvDUpBT8tS+rRpFSlKrNQtIt9pXRNSmkT5wMGSSkTHilRqtKbNWF8L+FJ41uFVIgBUwpoTgID6gWztiSUeRk1KVU/vgQCbqDuqFr/+yA1mqaUkihUfbENSSn3YaLdKaXNyzGxUNuPs1UKO6WA+U0ps280NOr3gtPGkFKTpu95o4WZiXDtqY/N11rH91I79VHpnRnfM7HDQVIqNEMrf185YKyE9M9QUkrbSJgdjBCykKEpxYlDDDGltJrRlAqKzpMh8T1DSh29a6shpchsCwbpoFBa2/ieUkMNvoHn1nXM0vQqNSNslS2JV7L1pUHwXrnHAs194cxvYLDofNK5NEr90sb3+Oj4HgAfAx13qVUNQ85MIc0nGH5RUbtNBw4cwv/5P2/G4cOr+NVf/QlccsmDsLS0gBNO2I8Xveg5+J7veSQ+85kr8Ja3/OtM57XWGl/84lV405suwwkn7MerXvVi3Oc+98bCQgdnnXUaXvWqn8Ypp5yAt7zl3/DZz14x8NxaA1mW4pWv/Gk8+tEPxr59y1hY6OBe9zoRL3nJD+HSSx+EAwcO4c1vvmymQRfbQVpr3HD9rfh/r3sXOOd4xSt/FBfd7ywsLHRwyqnH4Rd++bm46H5n4X3v+Sze/77PzrzfP/Dvn8d7/u3TuODCM/GLL/tBnHLq8VhY6ODCi87EK175oxAJx+v//DJcd90tjefWWuNfL/sUrvzqdTj99BPxsl/7YZx51ilYWOjg+OP34LnP+24845mX4PDhNfz1X73HUF07SEop/MPbPobP/ufX8ejHfRd+4qeejONP2IPFxQ4e/sjz8Qsv+wGsrvTxuj++DAfuPjLbc0uF1/7BP+Ev/+zdeOozHoFf+fXnzkQ1vesdn8J/fPyreMTF5+OnfvZpOOFE87oe/NBz8UsvfzbyvMSfvfZfcPttB9f9+3WrlAiO3/yV5+IBF52FN771w3jLP3wM9xxcxaEja3jXez+LP3r9v+HM00/E7/2PH8HCjAYsALzh//sgHvLElw/9/8Of/Gt4z4e+iCMrPfzjv32m8bhzzjoFP/eTTwFjDL/72n/EBz/+FRw6vIYjK318/svfwit+5+/Q65f4oWddiosfdt+N2h3bUtGU2kUy/St2+h41p++5QmqlNIoZfrHmpYmKhZ1S22W966JajKjulFKjFypSN0kppbXvfTHPN7xTSkMZUio0pYL4XsoTcN4kpcJfEmF8L3xpYXzP1oabZx4wpQo4U0pr5W93fTnrUR3fMyZXSEpprWtTio8gpTT5xaJWhgKTmzzSfpS0NuapobY2gpRCI9roxBn3UcbNnL7nF8gBKTWO/lNa+XJuF9/bLsbxLAqpqFTUpnr7uAPg43T9vGzYdf288D1FQJuUMv/1Refh2xCaUqw2kfxj10VKufgeNxE+tDql7HCAo2ZK2af2nVJjis41bOeUNY00xvcJNs4Z+96E+8qVxCvZJCp1QLW5x8JSUu591wGUyGj++B7gpu9hrCkVvrZxpJT5PRJuOyb2dUVF7SZprfGhD30G3/zmjTjnnNNx6aUPbvweTBKB5z73SRCC47LLPoJbb71r6ueWUuJtb3svVld7eMITHonTTjvJPzcR4dRTT8STnnQxer0+3vrW9wwYS8cfvxeXXPIgPOAB9228JiJCmiZ42MO+CwDwrW99G73eaGp5u+qyf/4k7rzzIB7ysPviggvOaOyb5eUFPOOZl6CqJN7x9o9idXX67Vtb7eMdb/8IpFR4xjMvxvLyQuO5z7/gDDz0Yefj7rsO4bJ/+kTj85KUCl/+r28CAC6831k47rgmGccY4ZGPugiMEa6//lbcdeehDdgTR0933nEI777ss+Cc4Zk/cDHStKbziAiPeNT5uM+5p+L6627DRz745Zk+xyutsf+4ZfzR634GL/65p2Hf/qWp21QOHDiCy/75PwEQnvkDF6PTSRrv2UMeeh7ue8G9cfNNd+ID7/vCLJu8LXT/C8/AM578MBxZ6eHv3/WpxpezWgPveu9ncfudB/GYR12ERz/ygqmf19BpJe45tIo77jo09P9JIvCYR16I//js13Hl1Tc1Hv+Ai87E/r1LOHRkDZ/90jcHnv+qb9yE79x6N7IswaMeEk2pqGNIzQhaYKxoU0jtolbTKrcEyrYnpdh0pJQxpcxiX2vtJ0RptIvOw0caI6iUpaejmqSUaBad8yYpJS2BVMrmxLaianZKwb6S0Agxt4d/D0mpjaGBXLwwr+pvo2pSikPYnRGOhA/je1qZnrJZuso2Ulpr5PaYVrImpdZ7mNbRoSa11iw6X/85MJmUcqaUMU9zOSMppfRcr2+rpIPIViLqwttwW3ynlDVW8qJomByecLGmVD6kU8rsK47GJzk7/AAw7/MAKVU6o2V6s8FP3xPDTam8NFSNi5xWm2z4V6gNPQAD0cdQWpn+rNCYGXe9acSBbWdVw5SqDCmlKwmEZlJgIAKoTSCl6vheMH1vY4vOMdSU8gX6WkNVU3RKAU1SSutjGr+PippVVSXx4Q9/FkppPOhB52Nhodu4nYhw/vln47jj9uKuu+7B5z//1akX6rfffgBf/OJVYIzwqEfdf4AaISI88pH3B+cMX/7y1bjlljsbtz/72U/An/zJr2FxsfmanBwdvRPLzldWevjUf1wBAHjYwy8Ab8XWiQgPfNC56HYzXHftLfjmN24a9jRD9c1v3oxrv/UdZJ0ED3rweQP7hnOGhz3sfADAf37qSqysrPnbzGdvc93PshTt3UpEyDopADK1I1v02XI90lrjy1/6Fu684yD27V/CfS+498C+6S5keMCDzobWGh//6FdQzbAmE4LjxS99Or7rAWeB8dmW+Fd+5Xrc+p0DWN7TxYX3O2PgdWWdBA96yDnQGvjkx766rlLwrdT3PuHBWFrs4NobbsMN375j4Pbb7zyEK6++CVkq8H1PftjUz9vrF3jRy16Pv33nJ0be5/uf9ggcf9wy/vadn0DZiu+nqfkStKokqiHRfimVrzsIDcxjUdGU2mUKSal20bkZ7T0bKdUvzTe/qrFw2h4LXrdooXbR+YjXV6nKmC1ceVOqjjIGnVLtonNtSCn/eDSptIQPdko1i85z+/OHk1ImIhZE4wZMqXrxqDfIlHIkgkZtiuVyiClFteEWvu8uvqftlKxZpzpupDSMeYrgODX7Zn3HqTMHdMsg5MT9hwBDSq3v+SdNo/ELZG6iZnmVj6X/AGOkpELY7u4dGt/TdUF7Krj/5RUedyEpBQSTCq3aplTZIKVsBx3sAiP84by+Jpr43iAp1TZaJimM7w03paS5tkozyTGkgzZDnpTyptRog82XdQdfSIyN7ylrSlmTqU2VFWVlSF3ZvD4PI6W0jQI2SSnzZx6QUmG/36zywwOGTN9z77P5MqYCQJNJqbATS0+ebBgVtZt06NARfOtb3wYAnHPO6QMmBADs2bOIU045HkppXH751VM/97XX3oRDh46g08lw+umnDr3PGWeciixLcfjwCr71rbpLhoiQJAm63Wzo72UpJa644hoAwIMffCEWFjpTv67toFtvuRu333YAjDOcfZ9Th27jSSftx569CyiKCldddf1Uz6u1xteuugF5XmLf3iWceOK+gfsQEe5zzr3AGMNtt92N2269298mOMd973s6AODGG24bMPG11rj2W9+BUgonn7Ifxx23PMNWb72++pXroZTGiSftxZ69i0Pvc/Z9zLF6w3W34eDB1amfm4ggBF9XKb15XQonnrgXe4e8LiLCfc41r+vbN96Be2aMFm6lhGB4xIPPBQBcd+PtQz/fVJXENdfeAgB48APug24nneq5ldL4xrduwe13Hhx6+57lLn7kBx6Nb3zrO/jEp68auP3qb34HvV6BPcsLOOPeJw7cfuIJe3HSiXtRSYWvfv3bU72mnapoSu0yuUgDoyGklO1syWcipWRNStkeos1cOM0it8gyReeTp+8V0nQngYaQUhp+IT8Y35PQkI0JdWHELeWJ710i2E4pNtgpVbYoMx/fU82Lp5xASukNMKVq4m0SKVXH9xqklCuctvG9qto6U0pZUkpD+xHu8+4b6fdN0CnViO9t3jngY1XcGGvjOqUqZWgbH9+zpNR2MY5nURjfS6wxbAjGNilF3twITakwNjUsvueuWzUpFagd3xvSKQWMN3LaquN7wn/b3iallL22GpO7Njc2Q4Ok1OhtUUoZUmqWTilrUGsb0WuaUqW53RJuTub3UmBK2dtlQPsZItMMS6SgU2ru+B4AiLDo3L4GbacsBobZ+E6pFikFxE6pqKMmMxW0wO23347vfOc7OHz4sP9yZrvowIHDOHjwCBgjnHTScUMX1JxznHDCfgDAjTfeOjUdc+ONt0BKhSxLsW/fcPNieXkRCwsdKKVxww23jH0+rc11rNfLcdllH8UnPvFFnHfemXjxi2fr7tkOuv22A+j1CiSCjzR2sk6CvXuXAAA33nD71F+23XjD7QCAxaUuFhaHm3X7j9uDJOEoigq3fKc2pUDAc37w8TjjzJPx9a/diPe/73MofQWDxnXX3oJ3/dMn0emk+LEXPGWksbMdpZTGTTcaSue445f97/5QRIQTTtoDxgiHD6/hnns23/xRSuOmbxtKcO++RaRZMvR+xx+/B5wzrK70cdddOyc2maUJzrj3iSAi3HL7PSOTErfcdgAAcK+T92NpxHE7qx578UW48Lx74+2X/ScOHVkbuP3Kq7+Nt1/2KSSC45de/Awcv78+FxcXMvz8C5+K4/cv4yOfvAIfsWTjsapjmwOLash8wzy8U8p9Iy+VRlFNj2SaiEkdi1J6+0SD3OswRecJCIRKj56+V0rTnQSmAaZND4qs43i+JJ4343tKV9DadPcUsl5shEXnYXwv4U1SypVlt+M5bpEng84o8/Pacb3W31E0Hr8eVaqOqGkYUspNpDJT0OyCmglPjYRGR4WalHKdKuUc0wDnkYnvDSOl1idpt8n0d9XnCiMWTN9bPym1nvjeqAW4IwNdfI/ZrrSdSko5KkdwVhfsh10UzgytXHyvDI5L3SSldHPipdImPsvIfFPbWBiJdtF58EFS12XcM5lSQXwP3EXO6p/Td4a/neSodNP43UhpmMgtYKKIQD3lcdgXrn6CXGAaTSKl3PtQU2X1vioKQ0pBqWanlJ3u53+uLUIPjR4T33PX+vlJqQYV2ojvmSMlNNR88foEUkoGlFj9fFFR80lrjUOHDuHKK6/EPffcg9NOOw0XXnghOp0OiAhSSnz0ox/Fa1/7Wlx11VUoigInn3wynvOc5+ClL30pjjtuuAF0tHXo0BF7PSQsLS0MvQ9j5CN0hw+voiwriPaXB0N0991m4ZxlCdJ0+EI7SQQ6HVNsfODAIf87s60DBw7hHe94P+688x587WvX4o47DuA5z3kifuInnoV73/vkbbEvZ9GBe45Aaw3OGbrd4QtwITg6lhg5ePAIlNKNjtRh0hq458BhAMDCQjbUeAGATidBkgjkeYlDdiIZEYGIcNbZp+C1//fn8Tdveh/e8Pp/xcc/+mWcc+69cPDgCr7w+auxb98Sfu6lz8ITnvSwHbXfq0ri8GFD4i4udgamDjotLXbNOVwpHJ6BlFqvlFQ4ZH9Od8x71l3IwDhDVdbbsRO00M2wZEnGw0OMISdnGnU6CZaXurjz7sNz/dwk4XjB8x6PAwdXcNn7Pjf0PmUp8cpXvw3X3XAbfvwHH4d/+7tfx6c+fzWqssLDH3weTjv1OPzpX70Hr3vj+3D4yM7Z5+tRNKV2meqy7tb0PUu0aG3op2nVs5ni5rf522PBK4OFyrSklNK2O4bBLFqrwJQaRUppacvOa1MqNG5Mp5S9Pw1O3yNIEAGVbJFSQ8q0h/0dumkiEiYvEiepNsg0YLdtJCk1ZPpem5RSTPuOrKMtpepOqY2YvucMO6JwKW9JKV5TGus9C6YuOrcL5lKWI02pSgeklDCklNbYUR0MTq5TisiRUubfm6SU/Ue7P/KiaJJSNgbg6R57zQO1rhecB2YMgXhYps2QcAZC/W9kf15vTDl4c1tqKkoIDh2QUs4ky200WvvrwOYa/uac1UH00e27IbEVF98DQFMYvT7Wreti9NCYKcoSSikwaFBw5pDWoJCcsmajRkCN6XoMxEZ0SjWoUO6OI2NuMqARPXSvbRwpFdLDblpfNKWi5pWZKvdF/MIv/AIuv/xyVFWFTqeDpzzlKfiTP/kT3Pve98Z73/tevPCFL8SBAwf84+644w5cddVVuPLKK/GmN70Jy8tbH31y/XREgBCjliXkb6uqamraq7DmN29/0RA+MxE4N5/JxvW9lWWFm266DYcPr6AoSiilcffdh3Dzzbfj1FNPBOdsRxkkhd1WIgIXw00IRuQ/1xT56C9123LXSD4mSsaCL5dG7felpS44Z7jjjnvAOMPqSg9FUWLv3kVkndSTaztlvyulfG+QOeaGv25nuBrScfO7mzTqnzPO7BWC2c9G2h8/O0FCcL9d5Zg0kOt74oyZgTdz6n7nn47HPuoi/OO7P42bvjN6QANjDGu9AkdW+zjhuGWce/YpkJXCCccvY2W1j0OHRhtpx5KiKbXLFHbMhMYKo/rSmA8pWhulvrtYam1oA7WNTCkVLjLNoe6Kn4fJxfc4ZyDBUAF+4eV+8QGDJfGMNBjpgQl1vlOKCd8ZAwBJy5RyN5VKecNMo+67IWqbTs2/t7/QcJ8tijnIJKmNQcYIfvE/zJTibMT0PfcHA1vVZMUWSCqN0hpiwr7W+Q07c7yH+55TK763yaQUEwQFoFDFyAW4I6UYMaTcFp1rtakxsM1SGN8TnI+Y+mj+K+wxW5SVp4+UNpEzczsBRM1OKWV+BiOyH7yCHriUw9kInPFG0TkBcF8cjysHb21NHd9rmVLOlXLXVk5kCLdNvLaG9qoz7PKiHGngG1Mq2JcwpfGjFgcmmmz/Yt+PcLqf6ZTSEGh2CjAALPjQToA9hmtqzMf3QBsyfU+jNqXcOZYXpTG0uYsDu203j6kaVlpTjaih3be9aEpFzak777wTP/MzP4PLL78cWZZhYWEBZVnisssuAxHhT//0T/Gbv/mbyPMcT33qU/GQhzwEWZbh6quvxgc+8AH8y7/8C57+9KfjBS94wZYv6J1h5Cokhivo9WTTmz/ObFKW/B713FrXXxKM0kknHYdXv/oXTLdjv8AXvnAlfvd334BPfOKLeMlLfhg/+ZPP8j9vJ8iZTY5qHyZzfbVfyHpDYrKE3++jh8q4L8KB5n7XWuOzn7kK/+s33ozeWo5XvPL5+J4nPhSdTgYpJa679hb87m+9Ba/4lb/Aj//EU/AzL3kWkg0wEI6GiKjRPep/4bfkjvVxhuEGv7L6S9UxnzMcuQ0crde1MQqvLXwEnQagTjto3SDX1yMi4PnPfgw4Z3jbP31y5H49bv8S3vBHP4unPP6BeOe/fQbP/9k/xe13HISGxr69i/ilFz8Dr3rZc/D0Jz0E/+0X/hw33HTn0Oc5FrRzjqiouWWKmWtSigIjijMGwcjEtGZw5fu57U0SxqiRGtuoU8r+IiWCYNOZUkorCM6R2G/kmp1Sw+N7gjFwYg1SSmkFqSUIZGmiVnwv+HvGOQiudwX+5znjpCOayHnz74Ss9SEqsx8GCjVqSTlZrt/KUGbmtbryX601KktnJSEppeufV1lSKhUMnBkjZJapjhspqZR/H92+Kub4ZVPa/RruG8BGYlkdw9oMUiokTBY6HYAMKeVMwracWUVESAS3RefzTQfcKrkPCQQy8b0x0/cy/41YBSlrSs6ZCZn9MFXI2giuO6Wa8T0iQifN/M9ox/eIgNSec6Z7aPK+NbSaIynDovP6se46LJi7tm5ep5QGebqxa6e7tEviQ1VVZUw1Muc40CyNH7i/pYU4kb9/SAvlRQGlNThjjQ+MnFHj74ITBBGUjV4CzaJzY6Lbc3C98T1to8pkzzEYksPHD6Xy36a642wsKaWtiU1Axy6cIikVNY+01njPe96Dr33ta/iRH/kRfPCDH8SXvvQlfOxjH8NLX/pSfPSjH8XrX/963HbbbXjzm9+Md73rXfid3/kdvOpVr8Jb3vIWXHbZZTjttNPwD//wD6hmqGvYLC0vL9jfnRq9Xn/ofVyPE2BiT9OaEK4PqSgKH5luq6qkJ3X27BndT+SiZYwxLCx08NjHPhS/9ms/iaIo8frX/wMuv/wb647tb4X27F0Ekbmm9UdQvlIqv2+WlxdANHnZSAQs7zExzH6v8IZfW0VRoaokiAhLS/V0w0OHVvHa17wDd9x+D77/2Y/B055+MRYWTNQtSQTue/7p+OVf/SEQI7z1bz+IL3zu6ztmvwvBsbBgPk/0+6M/L/R65jbGmvtms8QYYWnZ/Jy8X4w0UPK+IQSP1uvaKPX7BXp9cxwvjumKcj1SZVlhdXX4tWhanXryfjzrex+Oz33pGnz5qzeMvN/PvODJeNoTHowbb74Tv/G//x633HYAUikopXHgnhX8nz+7DJ//r2/iEQ85D6/8peeMjFYeCzp2tyxqqEZ1SiWcIUtMRmUmU8redylLrPmwuWW8s8i53IxMzMxRIqNMKdfNkwjhF6GelEIdrWvH9zoiQcK4KYm3XSThz0l44hedBELCmTfJAKArEgBkJ1TVr80tuvakXVDwTcqerPmLYDGpJ0QQCF3791KO/oZqklzXjmCsXnBX9QV6WNF5+EvMffTrJhyJYLarbItMKWkWsARgwS64C7l+08jtV7Nv6veRMRaQUmrdpNRY6XpBu3dxGQRCqcqRC3AX3+PEkXABAhmKb4fG95TSANVF54A1Q60h6oyVBdsdUlbSb6svqAawmJlzJOxxc6YqIxowpZa6dc8JI9YkpYjWYTY0SamkEd8zL8hdWzsJt18Y1D1KmyF3BO2xvQvjDLaqkqikMQjDc2qUHF0oOMNClvnndzKxSo2Es0bJfBrsGwDopuZaqoKYuOmUMgshFhadB+TpLApJqX2LewDY+J6bMCslysoERZ2BJyeRUlqDE8NSt16MREWtV1prfOITn8All1yCv/iLv8CjH/1onHvuuXjEIx6B17zmNfjBH/xB/NVf/RV++Id/GM9+9rORZZk3VIQQuPTSS/Gyl70M11xzDe65556t3hzs378Xi4tdaG06oIZdd5RSuOce0w91yiknTtUnBQCnnWa6nvK8xJERXTK9Xo61tT6I6vtPIyLCIx5xf5x66glYXe3hQx/6zFSP2y466aT9SBJheo4ODe8tKooSR2yPzb3udcLUpNRpp5kpYmur/ZHXuyNH1lAUJZKE4+RT6n6zb1z9bVx/3a0QguMxj3sgqEW2EBEuuPBMnHbaiej3C3zso1+e7kVtA3HOcMqpxwEADh1chRzyjYbWGvfccwRamw6n/UdhuiDnDKecYgYJHD685r94aevQoVVIqdDtZjtq6mGvX/jpeCcdv3fk/U46wdx294EVHF6Zr7/p+578MJxy0n787T9+YuRk5m4nxdOf9FAQET7zxWtw+52D5fGrazk+/MmvAgCe8Jj744QdtN9nVTSldpk87dMypVLOsJAKQ2JM2SmldU1VLXYScPcN9jahMDwpxepOqbGmlF2IJDzBYscYP0M7pXhz3y0kKRIuGosZpZXf14IJiKAYUggOQbWZsZh2zLdVOhhzDu1pnj1Zs/hzT7bY+nvz9uXUOv1yfYsy/1gNcGJYSOwidZgpxVuklP1xjpTqJgIp5za+tzWmVGVJKSLCcseaFXMYdoVd7CacoyNqQ5ATb+DPm3EWaNR9NvsWl8GI1VMjhyiM77meJI3NK8zeTLmybILpNXCn1LD43lInBRGhdEQPzLXP7TtnvIQTL5uDEVhgSpl97WTie+Q/nAvGsGiLYKc1pcJeL9EgpcKic3OOLWSGtNR6PI00j3TQy+T2jetMGaaykpCVBCPCojWZxhm9pY1pCEaNfeUWn7mdbpcKjiwgIDqJ8NQqACxkmd9XRYOUMmJEfrLpekkpqaQnXr0pVdbUmJTS/F4gwrItRx73k1wHHWOEpe5kwy8qapKUUrj55pvxjGc8A8vLyw0DPcsy/NiP/RjyPMdTnvKUoRPhiAiPf/zjURQFVlZWjvbLH9Bxx+3Bve99MrTW+Pa3bx16n14vx+23mwlt97vfOVM/9znnnI6FhQ76/Ry33Ta80+XWW+9Ev5+j08lw3nln+H83dFZ/LE3W7WbYt89cJ2655c5tN9lwnO512gnYv38ZVSXxnRF9N4cOruDQwSNgjOGCC8+Y2rA7/8IzwDnDoUOrOHjP4DGmtcZNN90BpRT27VvGvU47wd92112HIKUC5wz79i0N/ZlJwv3UvdvHTFPbjrrgotMBAHfecdDTf23d9O07obXGqfc6DvuPW9r010REuOB+5ti/687DWFsdfF1aa3z7xjugtcaJJ+/FCSeONne2m8qqwhVfuxFaa5x1xolIkkFTmzHCOWedDAD4+jdvxtqI92YaLS128PznPAbX3ngbPvzJ0RPzup0Ux+837++ddx8e+bngzrsOQWtgz/IClncQoTaroim1y+Q7pVgzgpZw5r/17ZfTlxk2SSnW6ELaarlFponvGTNCaTVyAd+v+tDQSEWCpY4xeupOKTT6DMJ9100yJCxpTKhz8T3ARtzct/12wcsD0mI5W/DRR//tv66jPIOm1Oi/ExGWLUlVzhVRM8aLYAyL6RhTKiSlgmOmspnzbiqQCLOgzrcqvicVykoZpNwuiE0Ebz7DLuUC3aQZ62KM2W619U/fG6dw8tfehT0gIlSqGhvfc6SU4NyUiu5QUkoFBlJj+l7QLeT2wnI3A2Nk43uDpNQei89XoSnlOuOIIERtPBMR9i7s8a/DHPPM04u8ZbRMI90ipZzRIm2nlNbaD5FYzBIfrZ4ndjpOCrWht9eZUlU1slPBmX2MMyx1zbaPI6WcQc4Zw0JnGClVAFojS5NGHLmTikbZ6EInRcKtQWdfm+s8Ibh+rzlNqaAbcP/iHhDQOI4qaYpqiQjL1mSSmkYuiiob5eVEWBqy7VFR65GUEscdd9zQ284880wsLy/j5JNHUz979phr2nYwUbIsxSWXPAgAcMUV1wxQGsasug133HEAi4tdPOpRD5j6uU8//RScd96ZqCqJK664ZuD3stYaX/3qN1GWFc4553Sceea9/G2HD6/i537u9/CBD3x65O/zsqx85HBxsbPl/VyzaP/+JTzwwecCAL5y+bcGjgWtNa75xk1YXe3jpJP34aL7nT3V8xIRLrroTJx88n6srvZxzTduGrrfr7j8WmgNPOCB52D//vqLn6WlLhgjSKmwsjKcbqsqhVVLsiwvdacmuLaDHvrw+2JpqYu77zqMm635FKqqJK664gYAwCMedYGffrjZetBDzsHefYs4eHAFN1x/28DtUipcaV/Xwx5xXywsZgP32a7SGvjgx7+CspI49+xTceIQWmrPUhffdcEZUErjAx+7fK4p1Zc8/Hw84MIz8c5//TQODDFlncqqwtqaMb/22zjtMO3ftwQiMxCgv4MK5mdVNKV2kcLybUdKuROgYUrls8f3FjsmvreZ3+bPIh1QR4wIgnEQJsf3tNZIeYqu7RIpK+lLzn3ZYyu+1xWJIaV0TUqF/V2CC99BQ3D9XfVCazntGlMqKMQ1xJl5/HK20Pig0zCh7O1OjBiWUmNKFVKPXChNkntswhkWRG1KmZhUPVmwMX0v6Hap7M/tJsKMvMdssdCNVCUVKqnANoiUKqXZyoQxLCT1NxY+vkfNYvyNlNI17ePie1JJlGr4LylnVpnXZs550ym19eforJK2MJWImvG9wJRyxspy1ywOTMzMHKtSKhRV00xoxPfsHwjNTinOGPYu1B+YBYlm7xERFgMCZioFnVKGlOL+9bijJveklImsbea1VSEgpezY9dCIacvtV84YlpzRO6ZYqfRU2HCqrF8U0IAxpQITKhMCnWCM+0KW+jhfEZiNDVKK6oLd9UgqiUIFpFSLuKssKcWIfBxvnP1VSl13g7j4XlHsqG/2o7aXiAjHH388brjhhqG3Ly8v4zGPeQz27h1NMtx9993gnCNNj86Cd5yICM94xuNw3HF78bWvXYtrrrmx8ftTa40PfejT6PVyXHrpg3DuuU1ip6okrrrqW7jyym8N9EZ1Oime+9wngnOGD3/4s1hZWWs899paHx/60GfAGMOzn/1ELCzUfTNSSlxzzQ345Ce/NPRaqLXG1Vdfj5tvvh1EhIc85KIdZUoxxvD9z34sOp0Un/vs13DnHQcb+6aqJD74/i9AKY0nf+8jcPwJexqPL/ISX7n8W7j2W98ZuN4ed/wePOVpj4RSCh/8wBca74vWGnfdeQif/cxVyLIE3/+cxzR6cs497zTsswTX5z7z9YESdq01rr/uFnzn5jvNfn/YfTdyt2yqiAhn3edkXPzoC9Hvl/johy8fONavu/ZWfP2qb2PvvkV87zMe3jimtNa44/aD+NIXrsHhQ6sb+jnztHufgMc87v4oiwof+eCXG8e8o6S+evl1WFru4unPfOSG/dyjpf/8/NX48levx0kn7MWTHjdobF/yiAtw1hkn4Vs33Ib3D4mE3ufMk3HJw8/HQnf8NVNwhh//wcfhyGoP//Tu8ZHeldUcX/zKtQCARzz4POzfO0jFZWmCx158EQDga9fchLvuPjz2OXeyoim1y+SLzok35j1kgiNzptSI7GtbJuoXLJzst/nbJr5nr6fcmVITOqUcKdVJOr73JJy+51xzxqjVKSWQicw/B1DHBMmWrDsSwvTVNEmppXTBTNcKelLC6VLLWd0pRSD7dyMiYDkNTaqalPJj2Nch1ymVMI5uK76ndW1KJSypp6CFhdP2jwup6YSZtUB/I1VU0hRkEwWk1Po7pdxiWLDasAPC+J6JsY6aZjNJ4z5kqAYpZWIbUtdxo7akruN7wpZIa2Boj8F2lwrOQcHZ0Ol77rhb7mbW6K1LqatK+g/GywsdEEaTUmF8jzGG5W79QYEzM/nPXQIaZkNeTHVcaWhUbiKkmBDfS4Uv1N80U0oDSpurzLLdlrKqS+LbKkrTscRYTf8UY6YsFdYETjjDgj0H3QQ6rbX/5q/TIqUywRumVCdLkHIOjWZ8D3CT+erpOfOSUgTCXhvfC81N8+cKxAjL9sOpDCKEbZXKRaEJi3bf5nmB0Y+IihovxhguvPBCvOtd78LNN9888Dtj7969eOtb34r73Oc+Qx+vlMKHP/xhLCwseGJqq3XOOffGT/3UD6DfL/C6170Nd9990PYfSnzuc1/FO9/5QZx66gl4yUt+uFFyrrXGxz72ebzgBa/CC17wSnzwg02qiYjw1Kc+Bo973MPwta9di7/7u3fbuLBGnhd429veiyuuuAaXXvogPPOZjx/62j70oc/gX/7lI1hb65tovtaoKolvfON6/MEfvAm9Xo6HPORCPPGJj9pRphQR4eGPuADP/P5LcfttB/DGN7wbq6vmy8eyrPCBf/88PvmJy3HBhWfgR3/sSQPmyDve/hG8+IV/iJ9+4R/iq1dc13huxhie/6NPwoUXnYn/+MRX8P73fQ6lTWGsrvbxxje8G7fdegDPeOYleMQjm2beqacej+c+7/HgnOEf3/kxfPrTV/rHKqVw6y1348//7z9jdbWP+z/gPvieJzz0qO2zjZAQHD/1M0/Fqfc6Dv/2rs/gC5/7BqSdXnvPgSN4w5+/F71egee/4HtwzrmnNh576OAqXv6Lf4WX/vSf4fd/++0oh3yuVkqhKCoURQVZST8Fu6wkiqJCWVRDKSDOGf7bTz8Zp595Iv79PV/Ap//jKkj7xfyhg6v4q//3Xhw50sPzfuRxuOCi6aOc20VHVnr4nT/+Rxw6vIZf+bln4v42jkpkDKdX/tJzUFUSv/+n/zzQ7XTu2afgPW97Jd739lfhVb/8XE/qD9N9z70XnvCY++MDH7sc1914+9jXpLXGX7zlA7j19ntwwXmn4df++/dj754F//lyoZvip3/8iXjcJffDkZUe/u8b33dMk1I7Y4Zm1IYpnL4XGiuZYHVZ77TmgTZRP8BETPQm957MKr/IZATOxGRSylJOmcjQzWyEJTSltImA8VZ8LxMCGU8HO6W0qqfvuaJzO9kr7JRaThdwd49QKuXpqNCgWko7YMTMND8iLKddP0WtNqmMIciI+U6pKhzDPqNcTCjhg51SGjVxF5JSynVKUR2j6iTCFBdrIB9RnLjZKivlp4UsdxJgte/phfXILYYNKVWbUoyYXxDrwOyYVWOn72mNonQRtEXwwhTsu7hRW46UquN7zHyg3pGklHnPqF10Hhi57pxf6ppiX6mUP4craUwpY7wMMaVUbUoJ278FmA9qezq1KeWmafrJpURYtMZMXpTwrdtjpENSSnBoF99T7rgkfx3uZsIau4Zg3Aw5yozI7DtGNJaUym3fVCIs+dQrUUljgje/7jByEysTxtAdWnRurpvDTKksq02pbpYi8dP+aqrU7DJqkFLzdEqVqgQRYc/Cku0mqwvz3X5hRMbAWwUUCKPOqFJqKGgb83TbXkZSKmrdIiJcfPHFeM1rXoPnPe95eMlLXoKnPOUpOOmkk/ztQgx+vFdKod/v4+Mf/zhe97rX4eEPfziWlja/r2YaMcbwYz/2fcjzAn/7t+/GC1/4m3joQy/CkSOr+PSnL8fxx+/Dq17107jggrMHfkd+4xvX+5jX179+HZ761Ec3bl9c7OJ//a+fw6tf/Ua86U2X4QtfuArnnHM6rr/+Zlx++Tfw2Mc+DP/zf74YS0tNKr3bzfCEJzwKH//4F/B7v/dXeOc7P4Dzzz8bnU6KW265E1/+8tfR7xd42tMeg5e97AXYt2/nFRAnicDP/+JzoJTG+9/3OXzzmptx0f3Owu23HcAXPn81LrjwTLzyf/44Tjp5/8B+v+qqG5DnJYqiwvXX3YoHPujcxu0nnLgXv/v7L8L//r234g//99/jox/5Ek455Xh8/Ws34Fvf/A5+4DmPwS/88nORps1jlTGG//aTTwUBeOc7PoZf/9W/wIMecl/c+/QTceTwGr78X9fg7rsO47GPfyBe9vIfxr79w3untquICPc591T81u+/AH/0v/8R//MVb8EjHnUB9uxZwOVfvha333YPfuJFT8bzX/A9A9t16OAqbrj+dshK4RtX34xev0CaNSd0f/Hz1+DP/+RfoZTC2mpuz/sCr/rVNyPNBJJE4Dd/98dw9jlNw4uIcMaZJ+G3f/8F+MNXvxO//Rt/h0c86nzs27+Er37lenzn5rvwoz/xBPzEi568YyfAffxTV+K/v/Kv8Xv/4/n4pze/HB/71FdRVhKPv+S7sLCQ4td/76345/d8duBxZ5x2Ak6/1/HgnOGhD7gPBOcohtRmEAE/9KxLkWUJ/u4fPzlVBPArV92An/rl1+PV/+P5ePGPPwmPfdRF+NJXrkUlJe5/4Zl48P3Pxk3fuQuv/tN/xvs/MkhwHUuKptQuUhgpY9SMoCWcoZvNaEqhSUrllsLYrN6TWdXslJpMSvUqk0/PROpHgfuic7jpcmQ7pZrT91JuIyk24hZ2SglWj5A35FSLlMq6YPZnlDI0pczEuKWk4ydKEQhLfhqftqRUF0TkiZjl1JFSukGRzKLQeBlHSoWdUg1ixe7iTsKRJrxB1R1tGVLKmFKLNr5XKbX+fRMYdt2kRUq56XtzxPemJaWWF5bAK96IG7Xl43vUiu9tE+N4FrlOKQKBDyGlQiPXdEoxKKlQWhOvqiRKKWvCpdd8TDgYgQekFGcMe7otUyo4/zkLCJiihNJ6KgS58pE2Doi6U8q9/eG1NbHG76Z1SmkT3yOQN/RKSxgOU1GUNpLGLClVeuNp6P39tpInpQY6pYCBTqksaZJSC53MU2OF/WY5/KmMMH98LyCl9nSXTDdZVXnKzlBi1uTudoDVwpBSIzbekVIiOE76RSSloubTQx/6UJx99tm44oor8NKXvtRP2xunj33sY3jVq16Fq666Cr1eD0972tPA+XRT7I6GsizBz/7sD+GJT7wY//mfX8ZNN92GE07Yj1//9Rfh0ksfhOOO2zvUfHjqUx+DL33p69BaD6WdiAgnn3w8XvOal+HLX74an//8lThw4BDuf//74id/8gfw0IdehI4djhGq08nwW7/1Etx55wFcffX1+Na3vo3bbz+APC9wxhmn4jGPeQge+MDzce65p0MIsaOMkVDLywt4xSt/FN/3rEvx+c99HXfcfg/OPOsUPONZl+Lhj7gAS0vdodv2g8/7btxyy1048YR9uPTR9x+4nYhwzrmn4f/+v1/EFz7/dVxx+bVYWenh0kffH7/0K8/D/b7r7JFTFDvdFC/+uWfi6d93Cb78X9fgumtvwcpKD8vLC/jh5z8RD3jgObjggjOQZsmO3O9EhAc/9Fz8vzf+d3zuM1fj6q99G3le4qnPeAQeefEFOOe8e4ExGti2U+91HJ77w4/BZ//z6/j+516K5eXB0uuz73MKXvji7x35WZIxNrKknIjwXQ84G3/+Vz+Pz3/2G/jalTei3yvwxKc8BI+8+AKcd9/TwKecfLkdpbTGu977OXz+v76FJz7uAbjf+aeDMcKfv+l9+PAnr8B1N94+9Hf5l664Dn//rv/A/c4/HX/+pn/3X3a2xRjD1d/8Dl7+W3+Lz//XN6d6TVoDH//Pq/C0578aD3/QuXjoA++DU07aD8YYPvHpq/C6N74Xn/nSNbjzrmM3tucUTaldJGeWAK5TCt7cSBjzo717U5oHYSRrMROQbuG0TRa8fpFJZiLTJFLKmS4pT7Fgp+8Nje/xdqeUIaXC5xiM79WlyZw3O6WWElPqqKt65Lu0i2WimpRyj1+ypBS0iazUJpUxHxbTDghqrvheGFFbtL1JLt44kpQKFqWO+OoIjiypO6UM6XJ0P0AUpVlcZ4nAohvhruegyJxhx3nTlGL19L15OqXG7R8pJSo7jn65uwh+hKPQxVhSyhed+/iebkQtd4qkPZ4NrUhwX9S5bQk72RZtD1NZyRYpVZlzpJOBerrZ4xZM3+O8pt44Z1juNqfv1R8WzTCABU9KFSa2OeEzm4ulAIaUQouU0qivw65TCgCKanOureZ80GAELHUyELN9XCOmTjlSSgiOxaw2ekddb0KTOwnKvh1U5kaGZ2mCTjAVpyME0iQgpTrDOqXM7yIT35t/+l4pS1TKdI/t6S6BtbrJitIUwJvjKAWhaHRyDdt2jUGizlBlUVHr00knnYR3v/vdWFszhNDpp58+8TFZluHcc8/FOeecg8XFRTz96U/fVgt68/mIcP75Z+H888+a+jH3uc+98YY3/CYAQ/4M2yYiQqeT4eKLH4iLL37gTK/nlFNOwCmnnIDHP/7hU2/LThIRIUkEHvigcwdop3GPecjD7os3vvkVYIxGmktEhKWlLr77ex6C7/6eh8z0mogIp59xEk4/46SpH7eTRETYf9wyvvfpD8f3Pn26YyvNErzkF56Jn/65pyFNk6ExshNP2ofHP2HfXK9r774lPOl7H4onfe/OikZOq5tvvRtv+YePTX3/Q4fX8NJXvBFC8LHxOSkV3n7Zp9b1mg4dXsOHP3nF2Gl9x7qiKbWLFJJSnPHGL25DSpkP//mUppRSGoWdqLbQSbDGmouFrZb7lp8xQNB0nVIAkPLMLzIdKQWtoX18jxrxvY4QSIXtSbG0VRjfC80KAhqkFIGwkHbByfVxKfvaa8ppMTClTJF5x5pixuBZTOp4H2ccC0kKon4jmjSrauOFwKk2pcyuGE9KhX0vbVNqK1RWEkppcE5YzISPba3HmNFa+21LOUPXdkoRqBHfU3r9+36cqnAc/cIiBGsW7A/cPyg6F9ZMMc+zPc7RWWS6PGyZdUAr1qSUeV8JphCbc45+UdamlKVdXA8SUX84KeWenzlSimO5s+gjsoLc9D2jhFFgSpVTve/mHApIKW9KWWNHh0XnAmqTDf/SRn2JCEvd1EYf5chvA912JkL4ARnlmOtNaHKnlhZytBUReVKqk6VIeRjfY+hkdanoQpZBtPZF+COZPQ8Bcw3W2jr3M+2LElJLQ512lwxxp5SPzbo+rSRLsZAlIJrQKRUa/MG2b4epZ1E7V4wxnHfeeTM95tJLL8Ull1zi/76dDKl5RERI02TyHaM2VETUiFdHHR1xzsD51g8o2G1yQ5OiNk/RlNpFGkZKOaWcoeOLzuU0tShmxLo1pRazBAe3NSllOqXCCYRtOdOlIzJ0O3XRubbRjLrovElKZZwj46OLzjnVRedki85DUmox6UK0+ricoUQgLKadhom1mHSafTZpPYZYMGFMKfQh9fqJmJBsYNwYL3mVewJoeKeUI4TIE18mvjd7LHQjlZcSGraYPLWLyI0w7BhDN5i+5+J7BNMptRnxPWNKBaQU42Yin5xgShED5zvblJLK0IqGhqF66qMzcl3klRxdxKGVRlE4M6GysStTts0ot0ZWbQQDxvRijLzBmCQci8GES1d07tQkpcqp3nelAlOqTUrBXFtzZ/inCYpNNvzN1D9j6C11bEm8VCjHkFJaaySCo5smxugdM9GyPmdoyL4iX3reyVKkog4/prxpSnU7KVJPSpn94zqliCzlNicpVcgCUknT12c7pcJuMkdKCc7N9QSuKH78tocxT3csRkUdTTn6JCoqKioqartpZzaVRa1LjU4pa6y4zydZYErlZYXR7SDB82kgtyRRN00GYhVbLT99j9WdUia6NLhY0Vp7yqkjOr6Mt6ykCazpoM+mbUoJjo5o9i5JJYOic+73DdlokCs6JyIsJB0/Fc2V91Z+AQ4siNSbWIaEyryhyAhYCDqnBBPoJhkAglQ1BTKLBmgga7zkMjdmG5Q3O0JSSgWROGeuZYJ5UmraWOhGy8UGOTcRVQJtTN8WJ3RFYEox3iClNiO+V0llyroZYbHThSBhYrT2uBu4f1B07o9bvTNNKTf1iFlDqib0zO2OLiQQumkCzk2pu5sm6ugUxgiLWQpG1sjyRmvdKWU6uKwpJQQWs8WG8ctZfe0UrO5JyqckYLTWUL5nqTat3QQerbUfDNCxRedA3aO00XJDERgRFrIUjLGGEdNWnhdAi5Sq9Ohzqj5napMpLwr/+ygPTKksKFBNBUenVXTuSKrckVLBz6Gg6FxpNdXvsbZKWXrqdClbAHeklD2O8qKEVhrC08Xk49Zjt51RMNW1HNnXFRUVFRUVFRW12xRNqV2kQVKqXvw2SalqqslAYXyvkwmkASm1GQunWdUgpYj7+E01ZGICUE/fa5pSlTeklAoNvToVknI2aEpp6aMpnPEmKcWapNRC0h2IpEitIe0isSOS2pQijk6S+feOMxowpRZECqJmifOsCheRLqJWyMJHYkZO37P/c4/POEe25aSUOZ4FZ+imwsZt5qfI0iHT9zgjwJbOr5eEGHfuyMqQUsY86CDhyVTxPUP31EbLTjSlmp1SLCClrJGrlY2gmfie4NxMxLSGR15WpsfHklLcvk+V1I3nacf3kkRgMehtM0YsC/5e0z/jJtaFUlo1SCnR6JRqUqjdVAR00CbF99zkPEbopGZaqJQK5QhTqp8Xhj4UvEEfDjunBkzurDbwtN1eU/w9JL7XIqU6nWxgXyjbwUUwRmHDlFrH76FSlZBKghNHN+uAc2Z/15l9kTe2ffK1tm3IEYCiqGJ8LyoqKioqKirKKppSu0iO+AGGx/e6ASk1jVTwbX43FduQlAqmaTlSaqr4XgcdSz6UpbTxPTd23JhShHrnZZwjE834nlTSxmGMIeZNKThyy5JSMKaSoKYp5UqDGaFhSgkm0BWpX5BzIvN3uxATZOJ7jKgRTZpVofHiTKlc5nXRuY23hFEmZWOOWtdF55lomp1boX5h3otEMHQEt3EbPfe+SThDRzTje8zG95RavzE7jpQqKzuOnjF0O9aUgp4Y33PmIbNE3s40pYz54OJ17S6zSjqa0V6PEt4wPIqi9LGrTiLMJELUx6p7noH4nhDopl1v/JophnW8OeHG5CKgUYg9TkrVJrfg9dRGaY8bper4nqFQN3f6nosLcyJ0UhN9HEdKuQ6oJBHoJPXwgGoKUqobUmVaA9C+OLSTpcjGxfey1O8LR5W66zOR+UBT04rrPb+N+S6YwELahRBuX5R220tAaxPfy8TYa21oyCXMbjsRyrLckRMwo6KioqKioqI2Q7FTapcpjO+FxkoSmFKmU2rygloqhbIyHS7dVNRjy7fJh+0mKcU8KTU0vhdEoDKRDZJSvnOFBorO2/E9N53OkVJm+l5NSrGAlOLE0RFZPV2r1SmVMIaOSJEwE2HhjKMjUr8gNyRVHe9LeIJMCLANJKU6Nr5XVCNIKfta3NSwMMrSJvC2Qo6USgVHljBbeK9979WsCqmPlDen7zHGAJpv+t7Yn237bMiSUmlr6mNbYXyPBXTRTjSlKqnMxElqx/eCyKvWYETIBEeSWIosbxZUC8HRSRNjSumATvSklCOxXKeUMSdCGpGIPK2YMmucEHnTcJIaReeCQ3NzNXZdZ1rXXw50M7HpQyTC3qMw+pgXw6fMOFMqSxNkwlCoo0ip8Pn9voLrVVL2PQpJqdqUygRvklJZispdK1VNSgGop+85UkqtL77nruGccSxkHd9Nlvv4niGlEiGmutaG8b1uZsiqUk5nXkZFRUVFRUVF7QZFUmoXaVjReSOCltkpSpWcKtpUVfUkAmNKbW9SihEbSUqFEaiO6NQLp0r6+J4zGdpF5yln6ARl4OZnB6RUEN9zfTjORGKMIePpgKEXjhFvx/dCUkowQiYSX+6b8hQpd9/er9+UKgPjpROQUq6nxVMeFMT3UE+dcz/XkFK2A2bKWOhGy5lhqeDIhDnutZ3Uth4VjX2T+X830/fIE3mbMX3PGSuMEbpZ5s3KSaaUM1J2enxPQ9sy62Z8T8PRhcaoTUVYsG/je7ZYWwgTKTU9btof656UYk1SKksEOkFENmHG0PLT9zhDlho6sZyBlJJS1YMPuDEzDSkVGP4AOkeBQi3tz+VE6GTmemJMqWLgvlrr2pRKEqSce2NmkimVcPKGf1VVvhzdvUfdlik1jJRKg98zhrMy/yf7noTxvfXIDXTgxNFNO8Y0RG1u9vMCWmukqUAq+MRrbYMSy1Jj4NluuKioqKioqKioqGhK7So1is6JNSaxGKLFLHCLSk7Vh+PGYxJMD4lbTJTbYMHriB3ALLQYMWNKjemU6ss6vtcNOmIqKetpanZBPGBK2W6hvux748t3ShE3Y99RR4O8KUUMmUgHioxLu1gz1IdAws17I5gwJpQvXTamlns+Z0pxGykp1xH30Rg0Xkw5eIVKVaYPR0tPgXG7GHSERxhlSTmvSalSrotcmFfelEo4Us7tvllffM+U0TcNO2dPNOJ7m0VKFWZqF2ccnTQbS0pprRudUowInO9sUkoPIaWc+ecjaKxlSlkDpZ8XgDZxuSwREIxs1HSwU4oFnVJZmiITWYOUYoSalLLXTmKOlJpsNjj6kgBw3u6U0qikQiklQBi4tm7GGRSSUp1EQAgzDdQZMW2FpFQqzPVQjjinBs4ZRwtVElLKhuHTJqXaplQnmL7nnnOAlAqm763nHHQxZcEEOmlmSCldm1Fu29MkQSr4xGvtQKcUEapKooymVFRUVFRUVFQUgBjf21VypJTrOSI0SSltiZaiklNNBiqlQiUliIAsFUjtom67kFJuseKmaXlSakJ8ryu6fiHk4jjOZCAEReeWuGkXnbuIoNba/9yQlGJEEDwwpXiGhNmIkd13pdI2QkJIGEfGjUmW8hQJExDMLZAJKU9aplRdYr9eg7AYQkq5AuCwm8ws0JtF58pSSARDRvipjltMSiWC+wW0CsyIWRT2ZYX7hkIjg6g2MTdYbmpXV3CzKJ4yvidI+JJ9YGeaUtJ2CDniy5NSdj872ocRrCllKbJ+bUqZgmqBNOHGlEJ9jrg+JOZML0dKpYkdLhDE90BBpxQhyww9Na3ZYIrOTSu74AzgJl6slBuqoFFV5nV1UuEn0m1mfE/DXE+E4EiEsJMLB0kpAOgFplRirzfTTKBLOUOWJSAYA6+S0ky2K0ebUo6scrfLhoFf99iRJX/d+7Te6Xv9ynyxkPIUqUiRJAJAvS9qU6ompYDR19owupgmAowRKilRVVsTZ46KioqKioqK2m6KptQuUoOUssaKU8oZKBEgmHLvqeJ7UqGUJkqUCo6Um8WYW+CMrmvefGk9mpQaFt9TWqGQttdEdJClCYjI7Au7cDJPR2CcecKMYAtsRd275EgpBTVgSrmy6TCOl4kUKTcLFG9K2QWXIIJg3JsPCU8gGPfT+hJGSLkYJKVcNGkdxkvYt+SiiUQ1KeX2l9keDs7ITqAy+13BLE6JbIfMFndK9ezPzRKOhLOB2NYsUtC+YNmTUm4SYouU2ozpe66HR3CGRCTeFOtVvaH3HySldq4pVdn+oYGiczvBzVx3TFn3cFLKFFSniUDCuTfo/BS3IO5LxOr4XmrMP0fgDJBSjPnrhRpTDh7KFZ0TGVJK25/lSs6lMscnESFLeJOU2oSLqzsXEsaQcLPvwj6utrwxk6ZIBTMGvdJDaSGt6/6nlNt9xcy1tZIKUtb7rJNlI0kpRoQsTVG6fWHjnI4dGzZ9bz3KK0NKJTxBwgXSJLHUWNuUMtHFSdfaRgddYq4RlYykVFRUVFRUVFSUU4zvzSGtNcqyxOrqKsqy3BQyYiPV7pQy8T1zW8YZstTQFEWlpjalKqlMh8uwhdMWyvQeTU9KDTOlWFBc7ONYPr5nHkfkprAZc6CQBaSWTVKKcSR2ohSzBcnelGLcLH5aC+RC2U4pRkh4i5Ti3Mf9BGcN0yrlKQRjdtz9+sgKrWvTIuPMR5dKWfr4noIh7twC3exD1+3jpqCR2Tc2FpqXclN6liapnztTSvh9A6yPIhtJSsEcY9zGYucpOh83fS/PC2NWco5UiMbUx2E/LzSlCM2i8+1+vWrLHZPc0oa+y8yRUlL7XiTBTM8TUFM9rqA6TQQE5wMTLxsmdkhKZak9R+sIbdgp5YwWxlhjSts4uaJzApnXwo2x4wyp5rU1iO+pje8qCyfECWZeT2Ips2GdUkBtzHQyc+3ibpKhHHxtoQHs9xUxVLKCtFHx0ptSNSnlDH9nShGRjwsCbtqiJaWgLSlFjel76znGXYw75SkSkSDz5mbdKQUAaZpAiMnX2tCQS1MBzhikVH6bo6KioqKioqJ2uyIptU5JKfHe974Xb3vb2/wi8oUvfCGe+MQn+g/F200DnVKA78NJOQNLhJ8MNM0EKdc9xVjr23wbPdtaVmqQlOLER5JSUsmWKeW6P5qdUia+V0/eIhBSTrUppQrfuWRKmY1R4Ra4rEVKJcxE79y+cwuYMlwkMoZUpI37O1LK3F6bUh3RASc2EE2aRRp1tC3hDClPG51Sggl/HNWdUmT7pHRjClcSTN+btqtsI2UiSDYalJr9xtk8hl1zgZ3y1Judrujc3W+95sG4hXS/sOPoBUeSCF+wP03ROXNRMexMUkq6njXmiJialALqc8cMEjCF3QDQz/NmF1CaWDO3ZUqFpBRqqsxE1Goa0ZTGB6SUYEiTxJgNajqzQSoFrZQxuW18DwgGBQSmVJZwuFalUm1up5QhpZinzJyh11Y/mJbHg3NqFCnlBx9whixJwJgp+y6qyht5hNrkqkvkqTalmDGl8oCUUsF5ZjqlmqTUuuJ7Zd/uC0tKpe44apJSnTSZeK0NjTpDSlnzsqqQT2FeRkX9/+3dd3xUdbo/8M+ZkplJIwUI1YQSCAGNIEVEiiw2UKQJa1u810UQdMHLutb9cXdXvHrxIrbFclldG7urrK4Fdd2L0iGE3iGEFEI6KZNk2jnn+f0xM4dMEhAiJCR83q/XFjInJ+c8Od8zOc883+dLRER0Obg0syeXOBHBN998g//4j//Agw8+iBUrVuDee+/FQw89hHXr1l2yFQhGT6lA8+26lVL+5EGgQawm/ia7P8IbqHwxpstcSpVSgYbbQJ1KqWA/nUYanTc6fc+k+KcoqprxqbtiTAVUjOkiVlNopZS/X0y9nlL1KqWCVRdWkxUWpU5SKvBg4zGSUv5KqLqVUhaT2aisCjOZYK6TlLJZbIE+VKEP3OfDP32vYeJF1VV/XynRQpJSpjoVK8FVqHQJTt9T4AgmpXzn1qvsQnN7/deyPcxsJCwEp5MY5yNYCQaExsa4LkzBSik0OQF3tkqp032RzIEl6U9XSjXGp/sffP2rbbbu1fdUo8+ayVjFEvAnn4PVOP6+SP7fgy2QzKifTLAFEkj1V7Srm8QOTqsD/IkXfyL4dFLKVOfeGWY2wWIxw2w+90qpYPVl3UopIDitT4y+fqenRte9t174m6txvzH7j8eY+ug+x6RUYPGAxhIzDe4ngamOwQSeMX0vMD3PGqgaU5Q6PagCi3LYwqzG6nuqLkYiHLhwq+8Fx5K/KtVSZxqof2VVVyAmtsC5n+1eKxLa5D0ssOqjruvwepmUIiIiIgKYlGoSt9uN//7v/8bIkSMxZswYRERE4JZbbkFaWhqWLFkC3yX6CWj9SilToDGsgsAf/1ZLoHeQfznyH+P1BSulTKHT9/SWWGOtoUZ7Sknjq+8Fk1IKFNgsNmP6XrAJrx54AFICU3uM6XsILAkfWKHOq3mh6ZpRjWVCIClVr1LK6E9jtgQqnUKXOa87nca/Ap8/+WCz2GBWTj8IWc3+CrC6lVKmOg2tm5R4qfcgZTP7p+8FY6SLDl10o/os+BAvOL3ynl5nGlWwUsrXApVSAOD2nZ6+Z64Tm8amGv0YTZeQpFQwNsHpeyaT/8HYP330wid+PB5voC+SFWaT2bgugn1w6qtbKaUoCiytePU9TdMBCfZ8qtNTSkIXWLAGkiT2wPQ9TyPTrvzTYuslpUJ6Sp2uKvMnpUKn7/kbap+uMrVaLLBYzIFkw7n0lAomuf3JL7PZ/zP1wLQ+494aqJQKHqs/4fsTgngGdSulzCbldJP4RiqlRAQe7+nG5P4xFZgSe8bV905XTvqnOp5OzGiaBp+qQlH81W1h5kDCHwqsgWmYwco0W9jp6X2qUSnl/yHB1fd+8vS9QFLKn+A3G5VSwaqxYMNzuy3sR++1AgmZvme1WGC2mKHXiSERERHR5Y5JqSbIzs7G7t27cd111xl/AJvNZgwfPhzbtm1DQUFBCx9h40TEeHD195gJTEFTAg2zrWZjWsW5NGH1qCp0Ef9UGLPpon+afz4EaLSnFNB4pZQmGnyaz/9gZLaf/jQ/0Psj2CMo+DAarGYxBR7IgtOogkkbVVeN6XuNrr53hul7wSqzusuIm5TT0wODTceDD6lhZv/UQCMpZQ5M3zM3vVJKk9PT7+pWA9VPSgWXXw8mB6ROpZQWaEhtMSmwhflXevRXfzT39D3/qn+Af/qe6SdWkWmBpAEQGhsAp6vxAhm6pialzjR2gquhBfsimU2K0WD/XKbvKYF+aEDrTEqpgYRtcAwZPaUCp2Ikcs3+5KDNdjqxIiJwuf2VLrawQKVUnetAcOZKKYfNX50YXDHTarI26ClltZhhMZsh+rklGzRND9yf/MkvsynQU0pOV0oFp0aHJvz1i9KXrW4zbkUJjV19/oRK3Uqp4OIBjY8p/z3h9P4tFgvMZrPRFD7Yt09R4K+UMp2ulAr2pAsmm/xN6v3T+4L9tYK97JTAhyzBSqnGpmn/GBExFg2wm+0wmU4nN4Ox8NTpp2U2KWe919ZP8JsDialzvU6IiIiILgfsKdUEubm5cLlc6Nq16+lV2BQF3bp1Q1VVFU6ePInExMQG31dbWwun02n8W1VV+Hw+qKoKt7vxh8qfwq25sXzvcmQXZWO1dzVcqguV7kooUKCpGnw+r1EpBV0DdA1mRYHLo+KFv2xEfLTjrPvPLaqEqukwmxXomgpToIF4qcuDFTuPG58gtwRNBEU1/piKpsHn9fn7HkHw/u73sS1/W8j2tb5alLvLAQAmMQHQYTab4PZ68eJbf4ViUuDx+mCxmKGqKsyBajiTAuiqCpP4H+ZOuU5h0Q+LcNJ50pjup/pUIPCApABQfT4g8PxiVszQfBpM8H/cn1NZi//dkYUDpVUA/APU6/HAqpye7ufz+mAJPBGbIfB5vcbrFsUCn9drZJs35pairLbxKThn4tV0VHr8PV4UXYNoArNihlt146XNL8FmscHlc/mvI58G1ezf1qvp+Mu+PPg0Haquw2IxQ1N9MAeaEJdU1mLRu98blVPNQQTYfsSfJLaaFX9sArFbl1OCwurzG3ceTUOVRz09ZjQYlVKa6l/mXQHg8frwP2//De3j2p3n8QpO5OcjOioK7do1/N4d+48C8E/f8/l8xu99Z+FOPPGvJ3A6VeKv0thyYgsAQBEFXq/XmHK2ZtdxPPH2v87r2FragZxSAP4x5/N6IYEpxoU1bvzvjuM4eqoagH9MqF4vrBZ/ciJj72E88cJb+H7zLgCA1WKG1+tFIJeAHYXl0HcIsspr/F/QdXg9XqMSymIxQ/WpRqyhA946Y8yk69A1DWaTCboI3vzoC3y3IeOs53Kq0gmX2//70HQNmqpCgYJatw/Pr9wAl0eFV9UQaTVD11Qoug4FQKXbh3d2ZYesUHch7C6qBBC4n/i8CLP4x+g/12c0mI6o64IDR3MABGJTJxb/yipG5qmakO19uo5yl/f0/QQaLCYTanwaXn5nFSIjHKiqrg1UGwKi+ROpCvz/3xRoYG42mSCiQwm8zzg9Kt7dnY2yWn+iFuK/LvRAEqiougjPfP8MwkxhOB/fZ38PwH+v9Xp9sFr919H3m3fh8effwrY9h/znbjb/6L1WFzHuMSbRoGsaLGYTdF3H2yu/xJpNO/znqii4d9I49E7scl7Hei48gZ5qRERERJcqJqWawOl0QkTgcIQmbcLDw6HrOqqrqxv9vlWrVuGZZ54x/i0iKC4uRlpaGtatW3fBj7Narcaf9v4JxZ5iIO/0182KGYf3H4YvJxyaZgUEOLhnN/TqWiiK/1P6Fat3nvPPUXQVGelbUWiyAbCgyqNi1cH8C34+TZVzLBNbTtRA8/gfZj4/8jk+P/J5o9uaFTMyD2Si0uuBSQFUVcM7n3xjvG61mLBr105Y49sDYoVowM6MbcipOQYFCqq91Xg1/VVje82rIX1zOrKz3FAAaD4PNm/eiPxT/vjobh2bNm5CocsBwILCajf+duCE8f21VeXYsH4DnCX+ZGZtRS3Wr1uPqiorADMqSoqxft0JeKr8VSCVxZXYsH493E4rABN2F1UaD5znS4Hg6IH9OIkiKLoCTTS8t+c943W7yY69u/YiylYLEStUUfDV0TpVgpqKHdvSkZVbAUVRUFXjwSt/T2/SsVwI5aWF2Lh+PTzV/tjsLKzAzsKKJu3LBMGR/XthF39sAGD/7v1wVBcCEPhUFX/6+OsLduz1qV4PNm3aiFMFpwAAmacysWTTkjNuX1laiXXr1qG60p943XboJLYdOnnRju9iUr3+MZTrCwNgwSmXFx/XGTPuaic2rF+PilNlAIDDWXlY8tZfjddra5xYv34dnJX+MXSkrBpHyk7fs4tPnsCGiuNwufzJlbKSImzasAma23//OJF9AptcG+B1WwEoyDl2FKbMWoj4+8h98vXacz4Xe5gVe/fshmYOhwKBx6fjrS93GK8romPn9gw4w+wALKjxafj00MW7t1aVlWD9+pNw1frvN+m7DyJ998Ezbl9SVIBNGzdAdfnH1Nb8U9iaf6rRbRUIMg8dRKm7CghMb/3gs++M18OsFhw8uB+FlaegiBUKgD07tqMs/6RRObV71054omKgwAqXquGzOtew6vVgy+ZNyD6VDQUKylxlWLZlWZNjUVtRiw3r16PW6f+AYMe+I9ix74jxemV52Xnda3OzMmHJckF0DQJg1Ten3/MVRUF0mI7B/Xs2+XjPpKamBi6X64Lvl4iIiOhCYVKqCcIC5fyqGjoNTA30xQgup13fuHHjkJSUZPxb0zQ8+uij6N69O6655poLfpw1vhqMLh+N3FO5iI2NNaq6esX2wvRB0xFuiYI7vwrVXg03JcVC0XUsrI3C9qMF59yo3KQo+NmgJIwZ0Qe1OuA9VoZSlxeXRFMpADF2K6b0jkc7i4Kn4p/CF5lfnLUBbnJcMqYNmgYbHJibU4a1W3YiJjbWWG58YP/eGH/TWMBsgSunHOEWM0Z3i4ZL7YPciFxknso09mVSTLipx00YmTwS/ft5UaZFYXCfTrhuWA/0dHWHM9qJ0d1H49rEa9HbIzAfK4XTc/qaspgV3JQYj9R2YYjo9R+I3BOJe1LvwZAOgxFf7cO/cstxc2IcEiMseLz740g6nISH0h5C33Z94ajw4F85p4z+R+dNATpH2DC+ZxysJg2/afcbbDyx0fjEXYGCKzteidvTbodZsaM2twJZFbXG793prELfGDtGXZmEa6704YQ7ElkF5U07lgsgOsKGX02+BsmdoxFW7saa3PKmTyVUgC6RdtzaIxYWRcNvon+DSk8lJl09CWbdiuMltdi572iTVv6CAKfKy+Gw2+EIb7xS0WKx4O7bb8CwoQPRvioevu0+VHmqzrjLcGs45l49F2nxaXg8+grEfb3HmNLY2phNCiZel4zrhvVEP58OOVaGSre/kkcgqCw/hVt7d8aQKzqgXfvOELMNzppa4/vtNhvm3D0B1/TvjTinD19ll8Fbp39euNWMSb3ao4vDjCfn2fDlmq2YO/MOJHbtgKcTnsaa3DV4cNCD6BzeBVJSi8OnajG5ZzwizcBT81R8s27bOU/bVKDgqn69cNvNP4PAhOzaSOzNLjbGkKIouH5AN/xsVCq8ArizTqGwxnNR7q26rqGq/BSmpfVCSnwkfhMWjdjY1Ub/pMbEtYvG3JnT0K1ze5hKXVifX44znroCJETYcFvPONggeLzUjXXb9tSp4FHQPzkRd4y/EZawMLhyK6EAGHtFO/g8A5BT4kJ0ZDhuGjsaqmKC63g5Tla768QKGNypHa7rFIG+7kQURxbjpLPpSVe7xY4H0x7EkA5D8Fh4HCKjv4Qr0OgcAKIiwjH//sno26MrlMIq/H1fNmLi4oypvPXFOqyY3CseURYFT85T8fXa9JDrxKSYMHzoYFyV0qPJx3wmVVVVsNlsF3y/RERERBeKIqzrPm/p6ekYM2YM3nnnHcyYMQOAv+rp7bffxq9//Wts3rwZ/fv3/9H9+Hw+jBs3DpMnT8aCBQsu+HGKCHJyc5B1PAvXX3+90VxbCawcdybn24w6uPoa4H9GuJSuqGCfEf+KaPKjKzLVjU1OTg6OZ2fj+hEjjN5hprNMSRRIg2kSwZXZJNj3pM6xCCSwgt/p1+t+e/1jb2z789nf+TLVu0Tqx67+A1jdy2bPnt0AgKvT0gAErosWaHIeFFxp8mLFpr6m9pPSdcHGTRvRvVu3kAR2KH+z/Lq/9x+7jde9DnWRSyZpfN6U0z3d6v8edV3Hxo0b0CMpCYmJiUYfOKk3qM70/YGXQ8dUYArumcbY6e8LxPY8f+/17yf1772mehfaxRpCbrcb69evw7ChQxEbG1vnOjnzDwzG5VzHVIP7Sb1YKSbTWd6VGqobC//vDOc1Jn5MwzET+vsO/qe0tBTbd+zAqFGjEBbWePKnwftQI9dJcOXOC62yshJDhgzB0qVLcdttt13w/dPloaamBkOHDkVBQT527PgUSUldW/qQLgsigl27ytA+QUf7jmwD3FyqKnVkZapISVMRaC9JzaCsSMGXXxXj8ee+4LTzZiKiw1u8Bg5LLTZt2oS0wHNbS2ClVBP07NkTXbp0wb59+zB9+nTjD8m9e/ciKSkJ3bt3b+Ej9DOWqUdglTTTud1Zzeam/2GsGP916fGv2HV+7y5mkwlms/msyShj/1DOeO7B5cpD/600fP2s39/I9k3cX1P8WOyCl40EmpwbVVXBY/kJ19WFdDFiU5+5iX/FKIoOs8nfMP1c9mH83s/xXOquWtfaNfg9ChqOibOc6zmNOaX+mAr9d/3tm/p7D/qxe+/FGkImpWHszuc6acqY+smx+rF75QWK1bnEQgFCmu//6P74lENERERkYNq9CeLi4nDvvfdi9erVKC0thYjg5MmT+Ne//oWZM2ciKiqqpQ+RiIiIiIiIiOiSxkqpJlAUBfPnz0dOTg7mzJmD4cOHY+3atbjhhhvw4IMPXpQSfCIiIiIiIiKitoRJqSZQFAUxMTF44403sH//fhQWFuLGG29Ev379ztjknIjoXHAePRERERERXS6YlGoiRVFgs9kwaNCglj4UImpDWGlJRERERESXC/aUIiK6hLBSioiIiIiILhdMShERXUJYKUVERERERJcLJqWIiIiIiIiIiKjZMSlFRHQJ4fQ9IiIiIiK6XDApRUR0CeH0PSIiIiIiulwwKUVEdAlhpRQREREREV0umJQiIrqEsFKKiIiIiIguF0xKERERERERERFRs2NSioiIiIiIiIiImp2lpQ+AAF3XoarqRdm3pmnG/tmr5vxomgZN06CqKkwm5m/Ph6ZpEBFomtbSh9Kq6Loect3RuQvGTlVVxu481X2fYOzOT93xajabW/pwGuDvk4iIiC51TEq1MFVV8cYbb+Crr766KPt3u91wuVyIiYlhr5rz5HK54Ha7GbsmcDqdAICoqKgWPpLWRURQWVkJm80Gh8PR0ofTqogIKioq4HA4YLfbW/pwWhVN01BRUYHo6GhYrdaWPpxWxefzoaqqCrGxsZfkhxeqqiI/P5/JKSIiIrpkMSnVgkwmE2bPno3CwsKL9sdsRkYGNm7ciOnTp8Ni4a/7fGzduhXp6emYMWPGJfkJ+KVs1apVEBHceuutLX0orYqmaVi+fDmuueYaDB8+vKUPp1XRNA2vv/46rr32WgwdOrSlD6dVqaqqwiuvvIKZM2eie/fuLX04rUpWVhb+8pe/YNKkSYiMjGzpw2nUrbfein79+rX0YRARERE1ilmKFmQ2m/GLX/ziov6Md999F5mZmViwYAGrB87T22+/jby8PCxYsAA2m62lD6fVEBFkZmZC0zQsXLiQVWbnwev14vPPP8e4cePw0EMPtfThtCoejwerVq3CTTfdhF/+8pctfTitSn5+Pt555x3cc889TOidp3Xr1mH16tWYO3cuOnbs2NKHQ0RERNTqMClFRERERERnJCLQdR05OSfZL7KZiAAnTpSjxq2jynnpTQ9uq2qqBfknVNijVFyCs7LbrPIyE0pLSiGqE7rOPsjNQwDRLom+00xKERERERFRo3Rdh9frRVWVE7ffPvuS7J/WVmmaQDEJTKw6bzYigK4LTOzc0axEANUncLt9LX0olxdRoSlW+HwtG3cmpdo4q9V6yfa5uNSFhYUhIiKipQ+jVXI4HPwktYkiIiIQFhbW0ofRKkVGRjJ2TaAoCqKiotg7rwnMZjOioqI4TZnaNJPJhB49esBiscBms/F6JyJqIzweD8xmc4u3+VHkUqjXooumqqoKFRUV6NatGz/ZOk+VlZWoqqpCt27d+AfYeRARlJSUQETQsWNHxu48iAhOnDiBqKgoxMTEtPThtCrB2EVHR6Ndu3YtfTitiqqqyMvLQ+fOnVv8j5LWxuVyobCwEN27d+diItRmiQhcLhdEhO/pRERtSDAVZLfbW/TDSSaliIiIiIiIiIio2bF0hoiIiIiIiIiImh2TUkRERERERERE1OyYlCIiIiIiIiIiombHrpxtlIigqKgIGzduRFVVFQYMGICrr74aFovlsmxSKSKora3F/v37cejQIaiqip49e2Lw4MGIiIgIiYmu68jKysLWrVuh6zqGDBmCPn36hDSKFxG43W6kp6cjMzMTCQkJGDFiBGJiYtp0fIPNpFevXo3Ro0cjJSUl5HVVVbF//37s3LkTDocD1113XYNG8SKCqqoqbNy4EQUFBejZsyeGDRsGh8PRJmMnIvB6vdi9ezcOHjwIEUFiYiLS0tIQFxdnbOPxeLBt2zYcOXIEHTp0wPXXX4/Y2NgGsSssLMTGjRvhdDrb/LjWNA2HDh3Crl274HK50KNHDwwZMiRktbNg7NLT03H06FF07NgR119/fYOxKCIoKCjAxo0bUV1djauuugppaWkwm81tInaapmHbtm3Izc3F5MmTYbVaQ14P3gO3bNmC7OxsdO3aFdddd12DleNEBHl5edi0aRM8Hg8GDhyI/v37hzS/FBH4fD5s374dBw8eRFxcHEaMGIH27du3uliKCDRNQ3p6OvLz8zF58uSQhuXB+9W+fftw9OhRiAj69u2LgQMHwm63N4hdTk4ONm/eDK/Xi0GDBiE1NbVB7LxeLzIyMnD48GHEx8djxIgRiI+Pb3WxIyIiIroQWCnVBokItm/fjkmTJmH37t1QFAWPP/44fv/738Pn87X04bWI0tJSTJw4EYsWLYKqqlAUBc899xymTp2KvLw8Y+UBEcHf//533HXXXSgpKUFVVRV+8Ytf4P3334eu68b+qqurMXfuXLz88suw2+34+uuvceeddyInJwdtee0Aj8eDxx9/HA899BC2bNkS8pqqqli2bBnmzZsHTdOQmZmJKVOmYO3atSHxzc/Px89//nP84x//gN1ux/LlyzFr1ixUVla2xCldVCKCsrIyzJs3D3/6058QFhaGyspKzJo1C8uXLze2q62txYIFC7BkyRLYbDasWbMGU6dOxbFjx0Jit337dkyePBl79uwxxvUf/vCHNjmuNU3DsmXLcOedd+LkyZOw2WxYtmwZpk2bhpMnTxrb1dTU4OGHH8bSpUtht9vxz3/+E1OnTsXx48dDYrd161ZMnjwZBw4cAAAsXLgQixcvhqqqLXJ+F0rwA4j/9//+H6ZMmYI//OEP8Hg8DbYrLy/HAw88gBUrVsDhcODjjz/G3XffjcLCwpA4/d///R+mTp2K7OxseL1ezJkzB6+99ho0TTP25Xa7jfcUq9WKTZs2GbFtTfe/YOyeeeYZTJ06Fc899xy8Xm/INsePH8dNN92El156CSaTCT6fD4899hh+8YtfoKysLGRf3377LaZNm4a8vDx4PB48+OCDWL58eUjsXC4Xfv3rX+O5556D1WrFunXrMHnyZBw+fLhVxY6IiIjoghFqc5xOp4wePVrmzZsnPp9PdF2XjIwM6dSpk3zxxRei63pLH2Kzy8rKkiuvvFKOHz8uuq6LruuSlZUlXbt2lfnz54umaSIicvz4cenRo4e8+eabxnYffPCBdOvWTQ4ePCgiIrquy7JlyyQ5OVlOnDghuq5LTU2N3HzzzTJz5kzx+XwteaoXja7r8uGHH8qwYcPEbrfLO++8E/La+vXrJSEhQdasWSO6roumafLMM8/IwIEDpaysTEREVFWV2bNnyw033CBOp1N0XZeCggLp16+fPP/8823u2vT5fPLII4/IAw88IG6327im3n//fXnjjTdExB+7N998U3r06GFcny6XSyZOnCgzZswQr9crIv5xPWrUKHn44YcbjOsvv/yyzcXu2LFj0qFDB+O6CI7Zzp07y7PPPmt87bXXXpNevXpJTk6O6LoutbW1Mn78eLn33nuN2FVWVsrw4cNlwYIFoqqq6LoumzdvloSEBPn2229bdew8Ho/MmTNHXn31VbnnnntkwIAB4nQ6Q7bRdV1+//vfy1VXXSXFxcWi67pUVlbKiBEj5JFHHhFVVUVEpLi4WK688kp59tlnRdM00XVdvv32W0lISJDNmzcbMf/www+lW7ducujQIdF1XTwej9x9990yYcIEcblcLRGGJnG73TJr1ix5/fXXZcaMGXL11VdLTU1NyDYZGRkyePBgKSkpMc5/x44dEhsbG3LPKiwslNTUVHn++eeN2H355ZfSqVMnycjIML733XfflSuuuEKOHj0quq6L2+2W6dOny6RJk8TtdrdEGIiIiIhaFCul2qAdO3YYlVLBqSkDBgxA7969G1T8XC46dOiAZcuWoXv37lAUBYqioGvXrkhMTMTu3buhaRpEBF999RWqq6tx0003GduNHTsWIoLPPvvMmALz4Ycf4vrrr0fnzp2hKAocDgduv/12rF69Gnl5eS19uheciCA3NxdvvfUWHn/88UanBn300UdISEjAkCFDoCgKTCYTJk6ciKNHj2LDhg0AgIKCAnz++ee47bbbjGmTCQkJGDNmDD766CNUV1e3xOldNFlZWfjkk09w7733IiwszLim7rrrLjzwwAMA/NVn77//PoYOHWpcnzabDZMmTcJ3332HrKwsAMD27duxY8eOBuO6V69ebXJcl5WVobq6GsnJyQAARVHQoUMHxMbGIjs7G4C/YueDDz4ImSZqt9txxx134JtvvkFOTg4AYNu2bdi7dy8mTZoEk8kERVGQlpaGpKQkfPDBB626QsViseB3v/sd5s6di8jIyEa3qaysxMqVKzF27Fhjil1UVBTGjx+PTz/9FMXFxRAR/PDDD8jJycFtt91mxGnYsGGIjY3FX/7yFwD+isj33nsPaWlp6NWrFxRFgdVqxaRJk7B+/XocOnSoOU//J7FarVi8eDHmzJmD8PDwRrdJSkrC0qVLjel1iqKgV69e6NixI3bt2gXAf/9bs2YN8vPzMWHCBCN2wemRf/3rXwEAPp8P7733HgYNGoQePXpAURSEhYVh0qRJ+P7773HkyJHmOnUiIiKiSwaTUm2MiGDHjh0wm81ITEw0elSEhYWhd+/e2LNnD2pqalr4KJtfZGQkxo4dG9Lbw+12o6KiAn379oXZbDam+HTs2BHx8fHGdjExMejcuTPS09Oh6zqKiopw7Ngx9OvXz4ivoijo27cvqqqqWtVD2bny+Xx4/vnnMXXqVKSmpjZ43ev1Yvv27UhKSoLD4TC+3rVrV0RERGDbtm0QERw5cgSnTp1C3759Q/qn9OvXD9nZ2SHTslo7EUF6ejp8Ph/i4+Px9ddf449//CM++eQTlJSUGNdiaWkpjhw5gpSUFKNvmaIoSE5ONvqgSWDqnsViCRnXVqsVvXv3xu7du1FbW9ti53oxJCUloWfPntiwYQNUVYWI4NChQygtLcWoUaMAAMXFxcjMzERKSkrIWOzTpw+cTqfRw2v79u0ICwvDFVdcYWxns9nQq1cv7Ny5s1XHzmQyoWPHjmftR3TixAnk5eWhX79+xtcURUFKSgpKS0tx7NgxAEB6ejqioqLQuXNnY7uIiAgkJiYiIyMDPp8PFRUVOHDggHHfDO6rd+/eUFUVu3fvvkhneuGZTCZ06NDhrLGLj4/HyJEjQ7apqalBdXV1SE+9rVu3Gu8VQZGRkbjiiiuwbds2qKqK8vJyHDp0CH379g0Z671794bX68XevXsvwlkSERERXdqYlGqDgv1XIiIiQr4eFxeHioqKyzIpVZ+IYNOmTXA6nZg9ezYURYGu6ygsLERkZCTCwsKMbS0WC9q1a4eioiLjwcLlciEuLi7kQaVdu3ZQFAWFhYUtcUoXjYhg9erVKCwsxP3339/oA5zb7UZZWVmDxtzh4eFwOBxGsqmoqAiAP1ZBiqIgLi4OHo8npEdLW3Ds2DG43W4sXLgQu3btQkJCAr755hvccsst2L59O0QElZWVqKmpafR6MplMxvVUUFAAm80WUtERjF1wH21J+/bt8e677+LAgQOYOXMmHn30UcyfPx+/+c1vcOedd0JRFFRUVKC2tvaMY7GgoACA/55ot9sbjV1wH21ZWVkZvF5vg/EZGxsLTdOMSqmCggJERETAbrcb25jNZsTExKCsrAwejwdVVVVwOp1Gk/6gqKgoWCwWI+ZtlQR6R4WHh+O+++4zvhaMXd33jmDsSktL4fF4UFlZierq6gbXa3R0NMxmc5uPHREREVFjuPpeGxRs5F13tTjA/weyruttbprP+ZLACnLPPfccFi1ahLS0NCiKYqzCFJx6ERSMZXCKX/B/68c3+O/W3ji5ruDD1ssvv4wlS5accXpQ8Lqqv5JZMHbBmAQb/jYWu2Bs25JgRcWQIUPwxBNPQFEU3HzzzRg/fjyefvppfP7558b1VLeKD2h4PZ1pXJtMpjY5rl0uF/72t79B13VMnToV8fHxsFqtWL16NcaNG4errrrqrGNRURTjelJVtcG4Dm7XFmNXX/AaOpdrrLE41X3v0HX9jNeroiht6v5XX7Ba7/XXX8eSJUuMKXgi0mjsguM1GLO698m6LofYEREREZ0JK6XaoNjYWKiq2mAFpurqajgcjpDpVZcbEUFhYSEWLFiAadOmYebMmcaDmclkQrt27eB2u0MeDjRNg8vlQnR0NCwWi1ERUFNTE9KLxuVyQUQQGxvb7Od1sei6jhdffBEDBw5Ely5dUFRUhNLSUqPCp7CwENXV1UZlXv2YeL1eo0ID8FewiAhcLlfIz6mpqYHFYkF0dHSznt/FFh4eDrPZjFGjRhkPnhERERgxYgR27tyJ4uJiozKvuro6JHa1tbXQdd2IXWxsLHw+X4PVwWpqauBwOEKqW1o7EcEXX3yBN954A0899RSmTJmCMWPG4He/+x10Xcfjjz8Or9eLyMhIWK3WRseiruuIiYkB4I9d8Fqsq6amBuHh4W0qdo2Jjo6GyWRqEKeamhooioKYmBjjfz0eT8hqjrquo7a21qgCCg8Ph81ma9D/zeVyQdO0NnX/q0tEcOzYMTz66KNYsGABbrvttpApozExMQ3eO4KxC16ndWNX/3pty7EjIiIiOhsmpdqg1NRUuN1ulJSUGF/TdR15eXlISko6Y7VLWyciOHnyJObNm4dx48Zh7ty5MJlMKC8vh67rMJlMSE1NRWlpachUKJfLhaKiIvTr1w9msxkdO3ZEx44djSbKwX2fOHECVqsVvXr1aonTuyi8Xi/cbjeKiorw9NNP48knn8T//M//wOPx4JNPPsGTTz6JH374AXa7HcnJycjPzw95oD116hScTif69+8PAOjZsyfsdjtOnDgRsgx9Tk4O4uPj0alTpxY5z4slOTkZZrMZFktoUardboeqqlBV1Tjv+tfTyZMnYTKZkJycDEVRGh3Xmqa12XG9fft2OBwO4/wBwOFwIDU1Ffv370dlZSU6dOiAhISERseixWJB7969jdjV1taitLTU2E7TNJw4cQI9evRoMNW5renatStiY2ORm5trfE1EkJeXh/DwcCQlJQEA+vfvj8rKSlRUVBjb+Xw+5Ofno0+fPrDZbIiJiUG3bt2Qm5sbMoYLCwuhaRr69u3bnKfWLEQER48excMPP4x/+7d/w1133QUAKC8vN2IwYMAAVFRUoLKy0vg+j8eDgoIC9OnTB2FhYYiLi0PXrl0b/B6C0/baYuyIiIiIfgyTUm2Moii49tpr0aFDB2zatMn4g7m4uBj79u3D7bff3mDltMtB8EF17ty5uOWWWzBr1iyYzWbU1NRgzpw5xoPErbfeipqaGuzduxciYkzXKCsrw4QJEwD4G5+PGzcOmzdvNip+RARr167FgAEDjNXC2gK73Y7XXnsN77//PlasWIEVK1Zg8eLFsNvt+Pd//3esWLHCWG1q4sSJyMzMNBJOwUbfDocDo0ePhqIo6NmzJwYOHIi1a9caU6Y8Hg82btyIG264Ae3bt2/hM75wFEXB8OHDERcXhwMHDhhjUdd1HDx4EImJiWjfvj2ioqJw8803Iz093ag+CV5PKSkpRmP5a6+9Fu3btzfGtYigpKQE+/fvb5PjOiEhAS6XC2VlZUbsNE1DQUEBYmJiYLfb0a5dO9x4443YsmWL0RdKRLBu3Tr079/faEQ9YsQIxMTEYPPmzUbsCgsLceDAAUycOLFB0rCt6dixI0aNGoUNGzYY1WKapmHt2rUYMmSI0QB+7NixsFgsyMjIMOKUk5ODnJwcTJw4EYqiIDw8HOPHj8eOHTtCEjDr169HUlISBg4c2FKneVEE3wMeeeQRzJo1C9OmTYOiKCguLsa8efPg9XqhKAp+9rOfAfCvfhuM3fHjx5GXl2fELiIiArfccgsyMjJQVVVl/Ix169ahZ8+euOqqq1rqNImIiIhajlCbo2mavP7665KWliZbt26VkydPyq9//WsZOXKkFBUVtfThtYiSkhIZM2aMdO7cWaZNmyYzZsyQGTNmyJQpU6R///5SVlYmIiIej0fmzZsnN998sxw5ckSOHTsmd9xxh8ycOVNcLpeIiOi6LgcPHpQBAwbIK6+8IkVFRfLll19KcnKyfPnll6Lrekue6kWjaZocO3ZMVq9eLREREbJ48WI5ePCguN1uERGpqKiQ8ePHy6xZsyQvL0927dolQ4cOlWeffVZUVRURf+y+++47SU5Olk8//VSKiopk+fLl0q9fP9mzZ0+bi52mafLCCy9IWlqabNmyRUpKSuRvf/ubJCYmysqVK0XXddF1XTIzMyUtLU2WLFkihYWF8u2330qfPn1k1apVRkw0TZPXXnstZFwvXLhQRo0aJcXFxS18phfesWPHJDU1VX7+85/L0aNHpaioSN577z1JSEiQV155xYjdkSNH5Morr5SlS5dKUVGRfP3119KnTx/5xz/+ERK7l156SQYOHCgZGRmSn58vv/rVr+SGG26Q0tLSFj7Tn0bXdSkqKpL9+/fL9OnTJTk5WTIyMuT48ePG+eu6Ljt27JCUlBRZsWKFFBUVyccffyzJycny/fffG9upqiqLFi2S6667Tvbu3Su5ubly//33y8SJE6WqqsrYV15engwZMkR+97vfSUFBgfzwww/Sr18/ee+991rVGNZ1XQoLC2X//v0yefJkSUlJkR07dkh2drZxHtnZ2ZKWliY9evSQO++803jvmDhxogwbNsx4X/D5fPLUU0/JyJEjZf/+/ZKTkyP33nuvTJkyRZxOp/HzsrOzZdCgQbJ48WIpLCyUNWvWSEpKinE/ICIiIrrcKCJ1GhtQm+H1evHZZ59h9erVUFUVV1xxBR566CF069btrMtft1V5eXl4/vnnG/SUAfxLfv/2t781pvA4nU78+c9/xtatWyEiGDhwIB544AFjRS/A/+n5/v378fbbb6OsrAx2ux133XUXxowZ06CJbVvh9XqxbNky5Ofnw+VyGf1lHn30UXTu3BkiguLiYrz55ps4cuQITCYTfvazn2HGjBmw2WxG7HRdx/r16/Hhhx+itrYWsbGxeOCBB4yG822Nx+PB3//+d3zzzTfQNA2RkZGYPn06Ro8ebVwrIoLDhw/jrbfeQnFxMWw2G6ZPn45x48aFXE/1x3ViYiLmzJnTJse1iCAzMxPvvPMOsrOzoSgKbDYbJk6ciPHjxxurnIkIDh48iLfeegulpaWw2Wz4+c9/jrFjxzaI3apVq4zfQ48ePTBnzhx06dKlVcdOAqtjfvfdd/B6vdA0DQ6HAz179sTcuXONKjARwc6dO7FixQpUVlYiIiIC9913H6677rqQRvEulwsrV67EDz/8AF3XkZKSgtmzZ6N9+/Yh97+srCy88cYbKCgogNVqxeTJkzF+/PhWVXUmgd5la9asgcfjga7rcDgc6N27Nx566CGYzWYcPHgQy5Yta7QZfvfu3fHkk08aVYq1tbX46KOPsHbtWogIUlNT8eCDDyI+Pj4kdpmZmXjzzTdRWFiIsLAwTJkyBbfcckurih0RERHRhcKkVBsmgdV+pM5KSa354eunOJfLvG5spM5KcPVXlKu7TTDGwSbWbTm+Z4th3QcuwD81KLjyFGMXOhbPdL51Y3e2mFxO47puTACcNXbncj211didy9gMbneh4nSuY/1S92Oxa8p7x+USOyIiIqILgUkpIiIiIiIiIiJqdmx0TkREREREREREzY5JKSIiIiIiIiIianZMShERERERERERUbNjUoqIiIiIiIiIiJodk1JERERERERERNTsmJQiIiIiIiIiIqJmZ2npAyAiupSJyHlvLyIwmUxQFOUiHdW5HYeqqvB6vYiIiLgg+/T5fPB6vQgPD2/RcyMiIiIioraBSSkiorPIy8vDf/7nf0JVVeNruq5DURQjMSMiGDlyJH75y1/ixRdfRH5+PpYsWYKwsLAWOWYRQWlpKRYvXoyJEydi7NixF2S/ZWVlePrppzF37lwMGjSIiSkiIiIiIvpJOH2PiOgsKioqcPDgQdx999247777MGHCBKxevRoWiwX33Xcf7rvvPvTt2xfp6ekAALPZDLPZ3KLHXFNTg0ceeQRdunTBqFGjLth+ExISMHPmTDz88MM4dOjQeVeRERERERER1cVKKSKis7BarUhJScG4ceNgsVhw8uRJhIWFoVevXrjxxhsBACaTCU6nE4qi4NFHHzWm79UXnNpXd3qfiIRUXtWvPqr/PQDOWqEkIli1ahWOHj2K5cuXw2JpeJsP/szgsTf2Mxt7XVEUXH/99Rg8eDAWL16Md955B1ar9VxDSUREREREFIJJKSKis0hOTsbLL7981uqnkSNHYujQofjggw+wa9cuREZG4rHHHkNYWBhef/115Obmonv37hg6dChWrFiByspKTJs2DZMnT8ann36KTz/9FGFhYZg3bx6GDx9uJIF8Ph+++uorfPXVV/B4PIiJicFdd92FYcOGNZr0AgCv14v33nsPo0aNQkxMDACgpKQEr7zyCpxOJ4YNGwZVVY193nLLLbjvvvvgcDgAACdOnMCKFSuQk5MDRVHQvXt3TJkyBVdddZWRNJswYQLuv/9+ZGdnIzk5+cIGnIiIiIiILhucvkdEdBYWiwXR0dFnrU4KCwtDZGQkRo0ahcjISLz77rtwuVywWCyYMGECSktLsXTpUvzzn//Egw8+iGuvvRaPPPIIFi1ahKqqKixcuBBmsxmzZs1CaWkpAH/fqldffRVPP/007rnnHrzwwgsYOHAg7r77bnz//fdnnDpXXFyM/fv3IzU11fhadHQ0pkyZgvXr1+Ppp5/GkSNH8Oijj+KOO+7AH/7wB/zXf/0XdF2Hy+XCrFmz4Ha78eyzz2LRokUoKyvD0qVLjZ+nKAp69uwJj8eDXbt2XbhAExERERHRZYeVUkREF4CiKEhMTES3bt2Mr5lMJiQnJ6N9+/YAgNmzZ6NLly5ISUnB//7v/+Lw4cNYvHgxLBYLTCYTbrzxRhw9ehQdOnRATk4OXnzxRcyfPx+jR4+Goii45557sHLlSixduhSjRo1qdOpccXExnE4nOnToYCTSbDYb+vfvj4iICNhsNjzxxBOIiIjAkCFDkJWVhTfeeAP3338/HA4Hdu/ejWnTpqFTp04wmUx47LHHsGXLlpCkXGRkJOx2O7Kysi5yVImIiIiIqC1jpRQRUTPo0qUL4uPjoSgK7HY7IiMj0adPH1gsFiiKgpiYGKM3FQDs2bMHJSUlqKqqwqpVq/DJJ5/gH//4B3Rdx759+1BZWdnoz3G5XNA0DTabrdHXBwwYgPDwcAD+pNmwYcNQUVGBAwcOIC4uDqNGjcJTTz2F2bNn4/PPP4fdbsf06dNDklIWiwUWi8U4ViIiIiIioqZgpRQRUTMIVkMFKYoSkjgKJn2C0+Sqq6sB+PtKVVRUGNvNmDEDDofD6AHV2M9RFAWapjX6ev1kVXA/NTU1sNlsePPNN/HZZ5/hk08+wezZsxEVFYXf/va3uPfee43jDzZCDwsLO58QEBERERERhWBSiojoEtStWzdYLBYMHz4ckyZNMpJWFRUVyM3Nhd1ub/T74uLiYLfbUVlZCRFp0Avr1KlTxtdFBEVFRTCZTOjcuTM0TYPH48HMmTNxzz334NixY3jqqafw29/+FjfddBM6deoEwF+N5fF40KVLl4sbBCIiIiIiatM4fY+I6BJ09dVX4+qrr8YXX3wBn88HANA0DX/84x/x6quvnvH7OnXqhG7duuH48eONvp6eno7s7GyICFwuF1atWoWUlBRceeWVKCwsxKxZs+B0OmGxWNC3b1/ccccd0DQNuq4b+ygsLISmabjyyisv7EkTEREREdFlhZVSRETnaNu2bVi5ciU8Hg9++OEHREdHY/bs2bDZbBAR/PWvf8UXX3yBmpoaLFmyBL/85S+xfv16pKeno6CgAC+99BJmzpyJFStWoKSkBBs2bMDKlSsxePBgvPbaa9A0DR988AEcDgdGjRqFV199FfPnz8f8+fMxdOhQHD58GPv27cOyZctCpgLWFRUVhQkTJmDTpk3w+XwNptj16dMHixYtQnx8PI4ePYojR47gj3/8I2JjY+Hz+VBYWIiHH34YY8aMgdPpxMcff4xf/epXSEhIAOCfurd582b07dsX/fv3v+gxJyIiIiKitkuRM60rTkREIYqLi5GdnW382+FwIDU1FWazGSKCzMxMlJeXA/D3iEpOTkZBQYHREDwsLAx9+vTB4cOHjeqn2NhYdOjQAUePHjX6SXXr1g2dO3cGAJSXlyMjIwOlpaXo3LkzBg0ahOjo6AbT8oJEBNnZ2Zg6dSpefPFF3HDDDVAUBV6vF+PGjcPVV1+Np556Clu3boXP58PgwYORmJhoTOc7deoUDhw4gPz8fFitVqSmpqJPnz7GORYXF2PSpElYuHAhpk6desbjICIiIiIi+jFMShERtTEigu+//x4vvfQSXnjhBaSmpoYkpV555ZUm7dfpdGLhwoXo06cP5s+fD6vVeoGPnIiIiIiILiecvkdE1MYoioIbbrgBSUlJ8Pl8OHXqFP785z/j5MmTAID3338fd911FyyW83sL0DQNDzzwAK655prz/l4iIiIiIqL6WClFRNTG1dTUID09HaqqAvD3nRo6dOgZ+1IRERERERE1ByaliIiIiIiIiIio2fFjciIiIiIiIiIianZMShERERERERERUbNjUoqIiIiIiIiIiJodk1JERERERERERNTsmJQiIiIiIiIiIqJmx6QUERERERERERE1OyaliIiIiIiIiIio2TEpRUREREREREREzY5JKSIiIiIiIiIianZMShERERERERERUbNjUoqIiIiIiIiIiJodk1JERERERERERNTsmJQiIiIiIiIiIqJmx6QUERERERERERE1OyaliIiIiIiIiIio2TEpRUREREREREREzY5JKSIiIiIiIiIianZMShERERERERERUbNjUoqIiIiIiIiIiJodk1JERERERERERNTs/j91Uj2KH/v3fQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "images = ['example_outputs/analysis/rep_trajs.png', 'example_outputs/analysis/rep_transmtx_allconfigs.png']\n", + "display_images_matplotlib(images, rows=1, widths=[60, 40], figsize=(12, 6))" + ] + }, + { + "cell_type": "markdown", + "id": "dd815dcb-a1f0-4b71-9279-1f5c63104fd9", + "metadata": {}, + "source": [ + "### 4.2. State-space sampling\n", + "The state-space trajectories in the left figure below shows that each replica was able to sample all alchemical intermediate states, which requires not only replica-space transitions, but also state-space transitions facilitated by alchemical weights that flatten out the free energy surface along the alchemical direction. In the right figure, we show the visitation distribution in the state space, with contributions of visitation counts from different replicas shown in different colors. As some of the states can be accessed by multiple replicas, it is natural to not have a flat accumulate histgram here. In fact, the visitation count of each state should be proportional to the number of replicas that can access the state in an ideal case (i.e., each replica has an even sampling across all states)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ef64451e-7f42-4577-bbf4-0ac8e7242ff4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "images = ['example_outputs/analysis/state_trajs.png', 'example_outputs/analysis/state_hist.png']\n", + "display_images_matplotlib(images, rows=1)" + ] + }, + { + "cell_type": "markdown", + "id": "e8fa1142", + "metadata": {}, + "source": [ + "Similarly, the efficient sampling in the state space is reflected by the state-space transition matrices of different continuous trajectories shown below. (Note that here we have one transition matrix for each replica.)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "c6d745c1-3e6b-4ee8-a878-ab8c67a919b5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "images = [f'example_outputs/analysis/traj_{i}_state_transmtx.png' for i in range(4)]\n", + "display_images_matplotlib(images, rows=2, figsize=(10, 10))" + ] + }, + { + "cell_type": "markdown", + "id": "a97b8533", + "metadata": {}, + "source": [ + "In addition to plotting continuous state-space trajectories, it could also be interesting to plot the time series of state visitation for different sets of states. That is, in the left figure below, each time series is contributed by different replicas at different times and the state vistation histogram for each state set is shown in the right figure. Note that ideally, the histograms on the right should be flat, but any $N_\\text{ratio}$ (highest count/lowest count) below 5 is generally acceptable for a short simulation like this one." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "a64182da-3419-435b-8567-845968ee8832", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "images = ['example_outputs/analysis/state_trajs_for_sim.png', 'example_outputs/analysis/state_hist_for_sim.png']\n", + "display_images_matplotlib(images, rows=1, figsize=(12, 6), widths=[45, 55])" + ] + }, + { + "cell_type": "markdown", + "id": "21702fc0", + "metadata": {}, + "source": [ + "As shown in `analyze_REXEE_log.txt`, we have also calculated and plotted the time it takes for a replica to traverse the state space in either direction." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "0e5d1661-47ca-47be-b294-db07b990ae50", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "images = ['example_outputs/analysis/t_0k.png', 'example_outputs/analysis/t_k0.png']\n", + "display_images_matplotlib(images, rows=1)" + ] + }, + { + "cell_type": "markdown", + "id": "8a455046", + "metadata": {}, + "source": [ + "The sum of these two transit times is the round-trip time. A shorter state-space round-trip time generally indicates better sampling in the state space." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "8d7d62f7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "width": 400 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "display(Image(filename='example_outputs/analysis/t_roundtrip.png', width=400))" + ] + }, + { + "cell_type": "markdown", + "id": "0fc9b813-d6f0-4697-839d-6e9f738f4138", + "metadata": {}, + "source": [ + "### 4.3. Free energy calculation\n", + "As shown in `analyze_REXEE_log.txt`, the solvation free energy of the toy molecule, which is the free energy difference between the coupled and decoupled states, was estimated as $-0.42\\pm0.60$ $\\mathrm{k_BT}$. Additionally, the free energy profile along the alchemical direction was also plotted, as shown below. The error bars werer plotted but are too small to be visible in the figure. Notably, the default protocol for truncating and decorrelating the dhdl data may not be optimal for a short simulation and can be case-dependent, so the uncertainty estimate in our example here may not be accurate. For more complicated systems with sufficient sampling, you may want to consider setting the parameter `subsampling_avg` to `True` in the input YAML file to dilute the effect of inaccurate estimation of truncation/decorrelation times for any replica, hence obtaining more reliable free energy estimates and their associated uncertainties." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "2dd8e040-376d-4eef-938c-98088a567f20", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "width": 400 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "display(Image(filename='example_outputs/analysis/free_energy_profile.png', width=400))" + ] + }, + { + "cell_type": "markdown", + "id": "34ce31ae-a326-484a-a82b-0ebd7b8de059", + "metadata": {}, + "source": [ + "---\n", + "So this is the end of the tutorial! If you have any questions or suggestions about the tutorial, please feel free to [file an issue on the GitHub repository](https://github.com/wehs7661/ensemble_md/issues) of the package `ensemble_md`. We are happy to help you out! If you find the package `ensemble_md` useful for your research, please cite the following paper: \n", + "\n", + "       Hsu, W. T., & Shirts, M. R. (2024). Replica Exchange of Expanded Ensembles: A Generalized Ensemble Approach with Enhanced Flexibility and Parallelizability. *Journal of Chemical Theory and Computation*. doi: [10.1021/acs.jctc.4c00484](https://doi.org/10.1021/acs.jctc.4c00484)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/data/sys.gro b/docs/examples/tutorial_1/sys.gro similarity index 100% rename from docs/examples/data/sys.gro rename to docs/examples/tutorial_1/sys.gro diff --git a/docs/examples/data/sys.top b/docs/examples/tutorial_1/sys.top similarity index 100% rename from docs/examples/data/sys.top rename to docs/examples/tutorial_1/sys.top diff --git a/docs/examples/run_REXEE_modify_inputs.ipynb b/docs/examples/tutorial_2/MT_REXEE.ipynb similarity index 83% rename from docs/examples/run_REXEE_modify_inputs.ipynb rename to docs/examples/tutorial_2/MT_REXEE.ipynb index bc7b0c3f..587e9691 100644 --- a/docs/examples/run_REXEE_modify_inputs.ipynb +++ b/docs/examples/tutorial_2/MT_REXEE.ipynb @@ -5,14 +5,16 @@ "id": "87324540", "metadata": {}, "source": [ - "# Tutorial 3: Multi-topology REXEE (MT-REXEE) simulations" + "# Tutorial 2: Multi-topology REXEE (MT-REXEE) simulations" ] }, { "cell_type": "markdown", "id": "46970a2d", "metadata": {}, - "source": [] + "source": [ + "The tutorial will be released soon!" + ] } ], "metadata": { diff --git a/docs/external_tutorials.rst b/docs/external_tutorials.rst new file mode 100644 index 00000000..06296cb3 --- /dev/null +++ b/docs/external_tutorials.rst @@ -0,0 +1,14 @@ +External tutorials +================== +Since the REXEE method combines the working principles of expanded ensemble and Hamiltonian replica exchange methods, understanding +these two methods, espeically how to run them in GROMACS, could be helpful for understanding and using the REXEE method. Here, +we provide a list of external tutorials about the expanded ensemble and Hamiltonian replica exchange methods. + +1. Tutorials on the expanded ensemble method +--------------------------------------------- +- `Hands-on tutorials: Advanced sampling methods using GROMACS - 5. Expanded ensemble `_. + +2. Tutorials on the Hamiltonian replica exchange method +------------------------------------------------------- +- `Hands-on tutorials: Advanced sampling methods using GROMACS - 4. Hamiltonian replica exchange `_. +- `i-CoMSE Advanced Sampling Workshop - March 2023 - Wednesday - Part 3 `_. \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index a2d4ca6d..53312abf 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -27,9 +27,8 @@ the future. :caption: Launching REXEE simulations: .. toctree:: - examples/run_REXEE - examples/analyze_REXEE - examples/run_REXEE_modify_inputs + examples/tutorial_1/standard_REXEE + examples/tutorial_2/MT_REXEE :maxdepth: 2 :caption: Tutorials: @@ -41,9 +40,10 @@ the future. :caption: API documentation: .. toctree:: + external_tutorials references :maxdepth: 1 - :caption: References: + :caption: Resources: Others ====== diff --git a/docs/references.rst b/docs/references.rst index 37131501..21afc7da 100644 --- a/docs/references.rst +++ b/docs/references.rst @@ -3,4 +3,12 @@ References ========== -.. [Hsu2024] Hsu, Wei-Tse, and Michael R. Shirts. "Replica exchange of expanded ensembles: A generalized ensemble approach with enhanced flexibility and parallelizability." arXiv preprint arXiv:2308.06938 (2023). doi: `10.48550/arXiv.2308.06938 `_. \ No newline at end of file +.. [Hsu2024] Hsu, Wei-Tse, and Michael R. Shirts. "Replica exchange of expanded ensembles: A generalized ensemble approach with enhanced flexibility and parallelizability." Journal of Chemical Theory and Computation (2024). doi: `10.1021/acs.jctc.4c00484 `_. + +.. [Sugita1999] Sugita, Yuji, and Yuko Okamoto. "Replica-exchange molecular dynamics method for protein folding." Chemical physics letters 314.1-2 (1999): 141-151. doi: `10.1016/S0009-2614(99)01123-9 `_. + +.. [Lyubartsev1992] Lyubartsev, A. P., et al. "New approach to Monte Carlo calculation of the free energy: Method of expanded ensembles." The Journal of chemical physics 96.3 (1992): 1776-1783. doi: `10.1063/1.462133 `_. + +.. [Sugita2000] Sugita, Yuji, Akio Kitao, and Yuko Okamoto. "Multidimensional replica-exchange method for free-energy calculations." The Journal of chemical physics 113.15 (2000): 6042-6051. doi: `10.1063/1.1308516 `_. + + diff --git a/docs/simulations.rst b/docs/simulations.rst index a561dc64..f53a4dcb 100644 --- a/docs/simulations.rst +++ b/docs/simulations.rst @@ -303,7 +303,7 @@ include parameters for data analysis here. - :code:`w_mean_type`: (Optional, Default: :code:`'simple'`) The type of mean to use when combining weights. Available options include :code:`simple` and :code:`weighted`. For the latter case, inverse-variance weighted means are used. For more details about, please refer to :ref:`doc_w_schemes`. - - :code:`N_cutoff`: (Optional, Default: 1000) + - :code:`N_cutoff`: (Optional, Default: -1) The histogram cutoff for weight corrections. A cutoff of 1000 means that weight corrections will be applied only if the counts of the involved states are both larger than 1000. A value of -1 means that no weight correction will be performed. For more details, please please refer to :ref:`doc_weight_correction`. @@ -325,6 +325,7 @@ include parameters for data analysis here. 3.4. Output settings -------------------- + - :code:`verbose`: (Optional, Default: :code:`True`) Whether a verbose log file is desired. - :code:`n_ckpt`: (Optional, Default: 100) @@ -340,6 +341,10 @@ include parameters for data analysis here. 3.5. Data analysis ------------------ + +Note that parameters in this section are ignored by the CLI :code:`run_REXEE` and are only +considered by the CLI :code:`analyze_REXEE`. + - :code:`msm`: (Optional, Default: :code:`False`) Whether to build Markov state models (MSMs) for the REXEE simulation and perform relevant analysis. - :code:`free_energy`: (Optional, Default: :code:`False`) diff --git a/docs/theory.rst b/docs/theory.rst index 3d8714b5..6a855fef 100644 --- a/docs/theory.rst +++ b/docs/theory.rst @@ -3,14 +3,14 @@ 1. Basic idea ============= Replica exchange of expanded ensembles (REXEE) [Hsu2024]_ integrates the core principles of replica exchange (REX) -and expanded ensemble (EE) methods. Specifically, a REXEE simulation performs multiple +[Sugita1999]_ and expanded ensemble (EE) [Lyubartsev1992]_ method. Specifically, a REXEE simulation performs multiple replicas of EE simulations in parallel and periodically exchanges coordinates between replicas. Each replica samples a different but overlapping set of alchemical intermediate states to collectively sample the space between the fully coupled (:math:`\lambda=0`) and decoupled states (:math:`\lambda=1`). By design, the REXEE method decorrelates the number of replicas from the number of states, enhancing the flexibility in replica configuration and allowing a large number of intermediate states to be sampled with significantly fewer replicas than those -required in the Hamiltonian replica exchange (HREX) method. By parallelizing replicas, the REXEE method also reduces +required in the Hamiltonian replica exchange (HREX) method [Sugita2000]_. By parallelizing replicas, the REXEE method also reduces the simulation wall time compared to the EE method. More importantly, such parallelism sets the stage for more complicated applications, especially one-shot free energy calculations that involve multiple topologies, such as serial mutations or scaffold-hopping transformations. @@ -156,7 +156,7 @@ In our implementation in :code:`ensemble_md`, this method can be used by setting 4.2. Neighbor exchange proposal scheme -------------------------------------- In the neighbor exchange proposal scheme implemented in :code:`ensemble_md` (which is enabled by setting :code:`proposal: 'neighbor'` in the input YAML file), -we add a constraint to :math:`mathcal{S}` defined in Equation :eq:`eq_3` such that the swappable pairs consist exclusively of neighboring replicas, +we add a constraint to the swappable pairs defined in Equation :eq:`eq_3` such that the swappable pairs consist exclusively of neighboring replicas, with each pair having an equal probability to be drawn. Formally, the proposal probability in this case can be expressed as follows: diff --git a/ensemble_md/analysis/analyze_matrix.py b/ensemble_md/analysis/analyze_matrix.py index bd70a063..cfe21411 100644 --- a/ensemble_md/analysis/analyze_matrix.py +++ b/ensemble_md/analysis/analyze_matrix.py @@ -176,6 +176,7 @@ def calc_spectral_gap(trans_mtx, atol=1e-8, n_bootstrap=50, seed=None): spectral_gap_list = [] n_performed = 0 while n_performed < n_bootstrap: + n_performed += 1 mtx_boot = synthesize_data.synthesize_transmtx(trans_mtx, seed=seed)[0] check_row_boot = sum([np.isclose(np.sum(mtx_boot[i]), 1, atol=atol) for i in range(len(mtx_boot))]) check_col_boot = sum([np.isclose(np.sum(mtx_boot[:, i]), 1, atol=atol) for i in range(len(mtx_boot))]) @@ -186,7 +187,6 @@ def calc_spectral_gap(trans_mtx, atol=1e-8, n_bootstrap=50, seed=None): else: continue - n_performed += 1 eig_vals_boot = np.sort(eig_vals_boot)[::-1] spectral_gap_list.append(np.abs(eig_vals_boot[0]) - np.abs(eig_vals_boot[1])) diff --git a/ensemble_md/analysis/analyze_traj.py b/ensemble_md/analysis/analyze_traj.py index 7119dde3..45af0bb1 100644 --- a/ensemble_md/analysis/analyze_traj.py +++ b/ensemble_md/analysis/analyze_traj.py @@ -141,7 +141,7 @@ def stitch_time_series_for_sim(files, shifts=None, dhdl=True, col_idx=-1, save_n """ Stitches the state-space/CV-space time series in the same replica/simulation folder. That is, the output time series is contributed by multiple different trajectories (initiated by - different starting configurations) to a certain alchemical range. + different starting configurations) to a certain state set. Parameters ---------- @@ -398,7 +398,7 @@ def plot_state_trajs(trajs, state_ranges, fig_name, dt=None, stride=None, title_ ---------- trajs : list A list of lists of state indices generated either from different continuous trajectories or from different - alchemical ranges (i.e. from different simulation folders). This can be generated by either + state sets (i.e. from different simulation folders). This can be generated by either :func:`.stitch_time_series` or :func:`.stitch_time_series_for_sim`. state_ranges : list A list of lists of showing the state indices sampled by each replica. @@ -450,7 +450,7 @@ def plot_state_trajs(trajs, state_ranges, fig_name, dt=None, stride=None, title_ for i in range(n_configs): plt.subplot(n_rows, n_cols, i + 1) - # First color different regions showing alchemical ranges + # First color different regions showing state sets for j in range(n_configs): bounds = [list(state_ranges[j])[0], list(state_ranges[j])[-1]] if j == 0: @@ -496,7 +496,7 @@ def plot_state_hist(trajs, state_ranges, fig_name, stack=True, figsize=None, pre ---------- trajs : list A list of lists of state indices generated either from different continuous trajectories or from different - alchemical ranges (i.e. from different simulation folders). This can be generated by either + state sets (i.e. from different simulation folders). This can be generated by either :func:`.stitch_time_series` or :func:`.stitch_time_series_for_sim`. state_ranges : list A list of lists of showing the state indices sampled by each replica. @@ -580,7 +580,7 @@ def plot_state_hist(trajs, state_ranges, fig_name, stack=True, figsize=None, pre plt.xticks(range(n_states)) - # Here we color the different regions to show alchemical ranges + # Here we color the different regions to show state sets y_max *= 1.05 for i in range(n_configs): bounds = [list(state_ranges[i])[0], list(state_ranges[i])[-1]] @@ -665,7 +665,7 @@ def plot_transit_time(trajs, N, fig_prefix=None, dt=None, folder='.'): trajs : list A list of lists that represent the state-space trajectories of all continuous trajectories. N : int - The total number of states in the whole alchemical range. + The total number of states in the whole range. fig_prefix : str, Optional A prefix to use for all generated figures. The default is :code:`None`, which means no prefix. dt : float or None, Optional @@ -746,7 +746,7 @@ def plot_transit_time(trajs, N, fig_prefix=None, dt=None, folder='.'): if len(t_0k) + len(t_k0) + len(t_roundtrip) > 0: # i.e. not all are empty if np.max(list(chain.from_iterable([t_0k, t_k0, t_roundtrip]))) > t_max: - t_max = np.max([t_0k, t_k0, t_roundtrip]) + t_max = np.max(list(chain.from_iterable([t_0k, t_k0, t_roundtrip]))) if t_max >= 10000: units = 'ns' @@ -1039,7 +1039,7 @@ def plot_swaps(swaps, swap_type='', stack=True, figsize=None): plt.xticks(range(n_states)) - # Here we color the different regions to show alchemical ranges + # Here we color the different regions to show state sets y_min, y_max = ax.get_ylim() for i in range(n_sim): bounds = [list(state_ranges[i])[0], list(state_ranges[i])[-1]] diff --git a/ensemble_md/cli/analyze_REXEE.py b/ensemble_md/cli/analyze_REXEE.py index 9e1d3a4f..62dfbd63 100644 --- a/ensemble_md/cli/analyze_REXEE.py +++ b/ensemble_md/cli/analyze_REXEE.py @@ -120,8 +120,8 @@ def main(): print('\nData analysis of the simulation ensemble') print('========================================') - # Section 1. Analysis based on transitions between alchemical ranges - print('[ Section 1. Analysis based on transitions between alchemical ranges ]') + # Section 1. Analysis based on transitions between state sets + print('[ Section 1. Analysis based on transitions between state sets/replicas ]') section_idx += 1 # 1-0. Read in replica-space trajectories @@ -129,7 +129,7 @@ def main(): rep_trajs = np.load(args.rep_trajs) # Shape: (n_sim, n_iter) # 1-1. Plot the replica-sapce trajectory - print('1-1. Plotting transitions between alchemical ranges ...') + print('1-1. Plotting transitions between state sets/replicas ...') dt_swap = REXEE.nst_sim * REXEE.dt # dt for swapping replicas analyze_traj.plot_rep_trajs(rep_trajs, f'{args.dir}/rep_trajs.png', dt_swap) @@ -141,8 +141,9 @@ def main(): analyze_matrix.plot_matrix(reps_mtx, f'{args.dir}/rep_transmtx_allconfigs.png') # 1-3. Calculate the spectral gap for the replica-space transition amtrix - spectral_gap, eig_vals = analyze_matrix.calc_spectral_gap(reps_mtx) - print(f'1-3. The spectral gap of the replica-space transition matrix: {spectral_gap:.3f}') + print('1-3. Calculating the spectral gap of the replica-space transition matrix ...') + spectral_gap, spectral_gap_err, eig_vals = analyze_matrix.calc_spectral_gap(reps_mtx) + print(f'The spectral gap of the replica-space transition matrix: {spectral_gap:.3f}') # Section 2. Analysis based on transitions between states print('\n[ Section 2. Analysis based on transitions between states ]') @@ -176,35 +177,36 @@ def main(): REXEE.state_ranges, f'{args.dir}/state_hist.png' ) - rmse = analyze_traj.calculate_hist_rmse(hist_data, REXEE.state_ranges) + rmse = analyze_traj.calc_hist_rmse(hist_data, REXEE.state_ranges) print(f'The RMSE of accumulated histogram counts of the state index: {rmse:.0f}') - # 2-4. Stitch the time series of state index for different alchemical ranges + # 2-4. Stitch the time series of state index for different replicas if os.path.isfile(args.state_trajs_for_sim) is True: - print('\n2-4. Reading in the stitched time series of state index for different alchemical ranges ...') + print('\n2-4. Reading in the stitched time series of state index for different replicas ...') state_trajs_for_sim = np.load(args.state_trajs_for_sim) else: # This may take a while. - print('2-4. Stitching time series of state index for each alchemical range ...') + print('2-4. Stitching time series of state index for each replica ...') + shifts = list(REXEE.s * np.arange(REXEE.n_sim)) dhdl_files = [natsort.natsorted(glob.glob(f'sim_{i}/iteration_*/*dhdl*xvg')) for i in range(REXEE.n_sim)] - state_trajs_for_sim = analyze_traj.stitch_time_series_for_sim(dhdl_files) + state_trajs_for_sim = analyze_traj.stitch_time_series_for_sim(dhdl_files, shifts) - # 2-5. Plot the time series of state index for different alchemical ranges - print('\n2-5. Plotting the time series of state index for different alchemical ranges ...') + # 2-5. Plot the time series of state index for different state sets + print('\n2-5. Plotting the time series of state index for different state sets ...') analyze_traj.plot_state_trajs( state_trajs_for_sim, REXEE.state_ranges, f'{args.dir}/state_trajs_for_sim.png', - title_prefix='Alchemical range' + title_prefix='State set' ) - # 2-6. Plot the histograms of state index for different alchemical ranges - print('\n2-6. Plotting the histograms of state index for different alchemical ranges') + # 2-6. Plot the histograms of state index for different state sets + print('\n2-6. Plotting the histograms of state index for different state sets') analyze_traj.plot_state_hist( state_trajs_for_sim, REXEE.state_ranges, f'{args.dir}/state_hist_for_sim.png', - prefix='Alchemical range', + prefix='State set', subplots=True ) @@ -220,7 +222,7 @@ def main(): print('\n2-8. Calculating the spectral gap of the state transition matrices ...') results = [analyze_matrix.calc_spectral_gap(mtx) for mtx in mtx_list] # a list of tuples spectral_gaps = [results[i][0] if None not in results else None for i in range(len(results))] - eig_vals = [results[i][1] if None not in results else None for i in range(len(results))] + eig_vals = [results[i][2] if None not in results else None for i in range(len(results))] if None not in spectral_gaps: for i in range(REXEE.n_sim): print(f' - Trajectory {i}: {spectral_gaps[i]:.3f} (λ_1: {eig_vals[i][0]:.5f}, λ_2: {eig_vals[i][1]:.5f})') # noqa: E501 @@ -313,7 +315,7 @@ def main(): mtx_list = [m.transition_matrix for m in models] mtx_list_modified = [] # just for plotting (if all trajs sampled the fulle range frequently, this will be the same as mtx_list) # noqa: E501 for i in range(len(mtx_list)): - # check if each mtx in mtx_list spans the full alchemical range. (If the system did not visit + # check if each mtx in mtx_list spans the full state set. (If the system did not visit # certain states, the dimension will be less than REXEE.n_tot * REXEE.n_tot. In this case, we # add rows and columns of 0. Note that the modified matrix will not be a transition matrix, # so this is only for plotting. For later analysis such as spectral gap calculation, we @@ -348,7 +350,7 @@ def main(): analyze_matrix.plot_matrix(mtx_list[i], f'{args.dir}/traj_{i}_state_transmtx_msm.png') analyze_matrix.plot_matrix(avg_mtx, f'{args.dir}/state_transmtx_avg_msm.png') - # Note that if a trajectory never visited a certain replica, the rows in the alchemical range of that + # Note that if a trajectory never visited a certain replica, the rows in the state set of that # replica will only have 0 entries. Below we check if this is the case. print(' Checking the sum of each row of each transition matrix is 1 ...') is_transmtx = [is_transition_matrix(i) for i in mtx_list] @@ -457,4 +459,4 @@ def main(): print(f'\nTotal wall time GROMACS spent to finish all iterations: {utils.format_time(t_wall_tot)}') print(f'Total time spent in syncrhonizing all replicas: {utils.format_time(t_sync)}') - print(f'\nTime spent in data analysis: {utils.format_time(time.time() - t0)}') + print(f'\nTime spent on data analysis: {utils.format_time(time.time() - t0)}') diff --git a/ensemble_md/cli/run_REXEE.py b/ensemble_md/cli/run_REXEE.py index 2337a22c..1581b490 100644 --- a/ensemble_md/cli/run_REXEE.py +++ b/ensemble_md/cli/run_REXEE.py @@ -235,7 +235,7 @@ def main(): print('Performing weight correction ...') else: print('Performing weight correction ...', end='') - weights = REXEE.weights_correction(weights_avg, counts) + weights = REXEE.weight_correction(weights_avg, counts) _ = REXEE.combine_weights(weights, print_values=False)[1] # just to print the combined weights # noqa: E501 else: print('Note: No weight correction will be performed.') @@ -248,6 +248,8 @@ def main(): # Note we use states (copy of states_) instead of states_ in update_MDP. for j in list(range(REXEE.n_sim)): os.mkdir(f'{REXEE.working_dir}/sim_{j}/iteration_{i}') + if REXEE.fixed_weights is True: + counts = None # So that this should work also for GROMACS version < 2022.5 MDP = REXEE.update_MDP(f"sim_{j}/iteration_{i - 1}/expanded.mdp", j, i, states, wl_delta, weights, counts) # modify with a new template # noqa: E501 MDP.write(f"{REXEE.working_dir}/sim_{j}/iteration_{i}/expanded.mdp", skipempty=True) # In run_REXEE(i, swap_pattern), where the tpr files will be generated, we use the top file at the diff --git a/ensemble_md/replica_exchange_EE.py b/ensemble_md/replica_exchange_EE.py index 24baf4e6..99b7fcbb 100644 --- a/ensemble_md/replica_exchange_EE.py +++ b/ensemble_md/replica_exchange_EE.py @@ -161,7 +161,7 @@ def set_params(self, analysis): "proposal": 'exhaustive', "w_combine": False, "w_mean_type": 'simple', - "N_cutoff": 1000, + "N_cutoff": -1, "hist_corr": False, "verbose": True, "mdp_args": None, diff --git a/ensemble_md/tests/test_replica_exchange_EE.py b/ensemble_md/tests/test_replica_exchange_EE.py index 2b0e9150..61ea5bdf 100644 --- a/ensemble_md/tests/test_replica_exchange_EE.py +++ b/ensemble_md/tests/test_replica_exchange_EE.py @@ -272,7 +272,7 @@ def test_set_params(self, params_dict): # 2. Check the default values of the parameters not specified in params.yaml assert REXEE.proposal == "exhaustive" assert REXEE.w_combine is False - assert REXEE.N_cutoff == 1000 + assert REXEE.N_cutoff == -1 assert REXEE.verbose is True assert REXEE.runtime_args is None assert REXEE.n_ckpt == 100 @@ -439,7 +439,7 @@ def test_print_params(self, capfd, params_dict): L += "Whether to perform weight combination: False\n" L += "Type of means for weight combination: simple\n" L += "Whether to perform histogram correction: False\n" - L += "Histogram cutoff for weight correction: 1000\n" + L += "Histogram cutoff for weight correction: -1\n" L += "Number of replicas: 4\nNumber of iterations: 10\n" L += "Length of each replica: 1.0 ps\nFrequency for checkpointing: 100 iterations\n" L += "Total number of states: 9\n" diff --git a/environment.yml b/environment.yml new file mode 100644 index 00000000..2578f5bc --- /dev/null +++ b/environment.yml @@ -0,0 +1,9 @@ +# Note that this file is only for setting up the Binder environment for tutorials +dependencies: + - gromacs=2023.3=nompi* + - nglview + - mpi4py + - pip + - tree + - pip: + - -e . diff --git a/example_outputs.zip b/example_outputs.zip new file mode 100644 index 00000000..a9b4aaf5 Binary files /dev/null and b/example_outputs.zip differ diff --git a/setup.py b/setup.py index 7a79acb9..9eb8c56f 100644 --- a/setup.py +++ b/setup.py @@ -91,9 +91,10 @@ 'alchemlyb>=2.0.0', 'pyyaml', 'seaborn', - 'matplotlib', + 'matplotlib<=3.8.4', 'pyemma', - 'mpi4py' + 'mpi4py', + 'ruptures' ], # (OBSOLETE) The following extra_require directive provides optional dependencies by, in our case, pip install ensemble[gmxapi].