diff --git a/.flake8 b/.flake8
index ffb0b3c..cfd3540 100644
--- a/.flake8
+++ b/.flake8
@@ -5,9 +5,10 @@ max_line_length = 120
# B007: it can be intended to name loop variables even if they are not used
# B023: leads to a lot of false alarms at the moment: https://github.com/PyCQA/flake8-bugbear/issues/269
# B027: it is totally valid to prepare more methods in an abstract class without forcing them to be abstract
-# B028: currently broken: https://github.com/PyCQA/flake8-bugbear/issues/329
# D*: pydocstyle has a lot of irrelevant checks by default. We are mainly interested in D417 (checks for missing arguments)
-ignore = B007, B023, B027, B028, C408, E203, E501, E721, E731, E741, W503, F841, D1, D200, D202, D205, D212, D400, D401, D402, D415
+ignore = B007, B023, B027, C408, E203, E501, E721, E731, E741, W503, F841, D1, D200, D202, D205, D212, D400, D401, D402, D415
extend-select = B902, B904
per_file_ignores = __init__.py: F401
-docstring-convention=google
+docstring-convention = google
+# rich has a print function which is explicitly named the same way for easy replacement
+builtins-ignorelist = print
diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
index 2ae3704..c1287aa 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.md
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -2,21 +2,24 @@
name: π Bug report
about: Create a report to help us improve
title: "[Bug]"
-labels: ''
-assignees: ''
-
+labels: ""
+assignees: ""
---
## :bug: Bug
+
### Description
+
### Dataset
+
Which dataset are you using? [`HeiPorSPECTRAL` | `private`]
### Environment
+
diff --git a/.github/ISSUE_TEMPLATE/dataset.md b/.github/ISSUE_TEMPLATE/dataset.md
index 2eb6939..ee97cb9 100644
--- a/.github/ISSUE_TEMPLATE/dataset.md
+++ b/.github/ISSUE_TEMPLATE/dataset.md
@@ -2,9 +2,8 @@
name: π Dataset
about: Everything related to the public HeiPorSPECTRAL dataset
title: "[Dataset]"
-labels: ''
-assignees: ''
-
+labels: ""
+assignees: ""
---
## :rainbow: Dataset
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
index 16652ca..2adf831 100644
--- a/.github/ISSUE_TEMPLATE/feature_request.md
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -1,10 +1,9 @@
---
name: π§ Feature request
about: Suggest an idea for this project
-title: '[Feature]'
-labels: ''
-assignees: ''
-
+title: "[Feature]"
+labels: ""
+assignees: ""
---
## :climbing: Feature
diff --git a/.github/ISSUE_TEMPLATE/question.md b/.github/ISSUE_TEMPLATE/question.md
index e0e3698..7cc8860 100644
--- a/.github/ISSUE_TEMPLATE/question.md
+++ b/.github/ISSUE_TEMPLATE/question.md
@@ -2,9 +2,8 @@
name: β Question
about: Ask a question
title: "[Question]"
-labels: ''
-assignees: ''
-
+labels: ""
+assignees: ""
---
## :question: Question
diff --git a/.github/workflows/dataset.yml b/.github/workflows/dataset.yml
index 1c4ef03..44f64f3 100644
--- a/.github/workflows/dataset.yml
+++ b/.github/workflows/dataset.yml
@@ -29,7 +29,7 @@ jobs:
strategy:
matrix:
os: [ubuntu-latest, windows-latest, macos-latest]
- python-version: ['3.9', '3.10', '3.11']
+ python-version: ["3.9", "3.10", "3.11"]
steps:
- name: Checkout files
@@ -51,11 +51,11 @@ jobs:
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- cache: 'pip'
+ cache: "pip"
- name: Install htc package
run: pip install imsy-htc
-
+
- name: Run example
env:
PATH_Tivita_HeiPorSPECTRAL: HeiPorSPECTRAL_example
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 04c7142..bb0ecf5 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -9,25 +9,25 @@ jobs:
strategy:
matrix:
os: [ubuntu-latest]
- python-version: ['3.11']
+ python-version: ["3.11"]
steps:
- - uses: actions/checkout@v4
+ - uses: actions/checkout@v4
- - name: Set up Python ${{ matrix.python-version }}
- uses: actions/setup-python@v5
- with:
- python-version: ${{ matrix.python-version }}
- cache: 'pip'
- cache-dependency-path: 'requirements*.txt'
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v5
+ with:
+ python-version: ${{ matrix.python-version }}
+ cache: "pip"
+ cache-dependency-path: "requirements*.txt"
- - name: Install dependencies
- run: |
- pip install -r requirements.txt
- pip install pytest wheel
+ - name: Install dependencies
+ run: |
+ pip install -r requirements.txt
+ pip install pytest wheel
- - name: Install htc
- run: pip install --no-use-pep517 -e .
+ - name: Install htc
+ run: pip install --no-use-pep517 -e .
- - name: Tests
- run: py.test --doctest-modules --import-mode=importlib --collect-only .
+ - name: Tests
+ run: py.test --doctest-modules --import-mode=importlib --collect-only .
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 3fd3e34..9d946ee 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,7 +1,7 @@
# Order plays a role here: we first need to run pyupgrade because it changes the code and the new code may not be correctly formatted so we need to run black afterwards. flake8 again should be run after black so that it doesn't e.g. complain about whitespace issues.
repos:
- repo: https://github.com/asottile/pyupgrade
- rev: v3.15.0
+ rev: v3.15.2
hooks:
- id: pyupgrade
args: [--py39-plus]
@@ -22,6 +22,7 @@ repos:
- flake8-comprehensions
- flake8-use-pathlib
- flake8-docstrings
+ - flake8-builtins
- repo: https://github.com/asottile/yesqa
rev: v1.5.0
hooks:
@@ -31,8 +32,9 @@ repos:
- flake8-comprehensions
- flake8-use-pathlib
- flake8-docstrings
+ - flake8-builtins
- repo: https://github.com/nbQA-dev/nbQA
- rev: 1.7.1
+ rev: 1.8.5
hooks:
- id: nbqa-pyupgrade
args: [--py39-plus]
@@ -43,7 +45,14 @@ repos:
- flake8-comprehensions
- flake8-use-pathlib
- flake8-docstrings
+ - flake8-builtins
args: ["--extend-ignore=E402"]
+ # Avoid potential problems with py.test if __init__.py files are missing
+ - repo: https://github.com/lk16/detect-missing-init
+ rev: v0.1.6
+ hooks:
+ - id: detect-missing-init
+ args: ["--create", "--python-folders", "htc"]
- repo: https://github.com/citation-file-format/cff-converter-python
rev: "44e8fc9"
hooks:
@@ -56,3 +65,36 @@ repos:
rev: 1.7.0
hooks:
- id: pyproject-fmt
+ - repo: https://github.com/pre-commit/pre-commit-hooks
+ rev: v4.5.0
+ hooks:
+ - id: check-yaml
+ - id: check-toml
+ - id: trailing-whitespace
+ - id: check-case-conflict
+ - id: debug-statements
+ - id: name-tests-test
+ args: [--pytest-test-first]
+ - id: mixed-line-ending
+ - id: end-of-file-fixer
+ - repo: https://github.com/pre-commit/mirrors-prettier
+ rev: v3.1.0
+ hooks:
+ - id: prettier
+ - repo: local
+ hooks:
+ - id: check-notebooks
+ name: Check notebooks for common errors
+ entry: python hooks/check_notebooks.py
+ language: system
+ types: [jupyter]
+ - id: check-public-readme
+ name: Check for common mistakes in the public README
+ entry: python hooks/check_public_readme.py
+ language: system
+ types: [file]
+ files: ^README_public.md$
+ - repo: meta
+ hooks:
+ - id: check-hooks-apply
+ - id: check-useless-excludes
diff --git a/CITATION.cff b/CITATION.cff
index faadcf9..c8088f4 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -12,5 +12,5 @@ identifiers:
value: 10.5281/zenodo.6577614
repository-code: "https://github.com/IMSY-DKFZ/htc"
license: MIT
-version: v0.0.15
-date-released: "2024-02-05"
+version: v0.0.16
+date-released: "2024-08-05"
diff --git a/LICENSES/MIT.txt b/LICENSES/MIT.txt
index 13c8533..7bc47f1 100644
--- a/LICENSES/MIT.txt
+++ b/LICENSES/MIT.txt
@@ -18,4 +18,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
\ No newline at end of file
+SOFTWARE.
diff --git a/README.md b/README.md
index f3df227..49ce1f1 100644
--- a/README.md
+++ b/README.md
@@ -4,15 +4,17 @@
[](https://pypi.org/project/imsy-htc)
[](https://pypi.org/project/imsy-htc)
[](https://github.com/IMSY-DKFZ/htc/actions/workflows/tests.yml)
+
# Hyperspectral Tissue Classification
+
This package is a framework for automated tissue classification and segmentation on medical hyperspectral imaging (HSI) data. It contains:
-- The implementation of deep learning models to solve supervised classification and segmentation problems for a variety of different input spatial granularities (pixels, superpixels, patches and entire images, cf. figure below) and modalities (RGB data, raw and processed HSI data) from our paper [βRobust deep learning-based semantic organ segmentation in hyperspectral imagesβ](https://doi.org/10.1016/j.media.2022.102488). It is based on [PyTorch](https://pytorch.org/) and [PyTorch Lightning](https://lightning.ai/).
-- Corresponding pretrained models.
-- A pipeline to efficiently load and process HSI data, to aggregate deep learning results and to validate and visualize findings.
-- Presentation of several solutions to speed up the data loading process (see [Pytorch Conference 2023 poster details](./README.md#-dealing-with-io-bottlenecks-in-high-throughput-model-training) below).
+- The implementation of deep learning models to solve supervised classification and segmentation problems for a variety of different input spatial granularities (pixels, superpixels, patches and entire images, cf. figure below) and modalities (RGB data, raw and processed HSI data) from our paper [βRobust deep learning-based semantic organ segmentation in hyperspectral imagesβ](https://doi.org/10.1016/j.media.2022.102488). It is based on [PyTorch](https://pytorch.org/) and [PyTorch Lightning](https://lightning.ai/).
+- Corresponding pretrained models.
+- A pipeline to efficiently load and process HSI data, to aggregate deep learning results and to validate and visualize findings.
+- Presentation of several solutions to speed up the data loading process (see [Pytorch Conference 2023 poster details](./README.md#-dealing-with-io-bottlenecks-in-high-throughput-model-training) below).
@@ -20,12 +22,12 @@ This package is a framework for automated tissue classification and segmentation
This framework is designed to work on HSI data from the [Tivita](https://diaspective-vision.com/en/) cameras but you can adapt it to different HSI datasets as well. Potential applications include:
-- Use our data loading and processing pipeline to easily access image and meta data for any work utilizing Tivita datasets.
-- This repository is tightly coupled to work with the public [HeiPorSPECTRAL](https://heiporspectral.org/) dataset. If you already downloaded the data, you only need to perform the setup steps and then you can directly use the `htc` framework to work on the data (cf. [our tutorials](#tutorials)).
-- Train your own networks and benefit from a pipeline offering e.g. efficient data loading, correct hierarchical aggregation of results and a set of helpful visualizations.
-- Apply deep learning models for different spatial granularities and modalities on your own semantically annotated dataset.
-- Use our pretrained models to initialize the weights for your own training.
-- Use our pretrained models to generate predictions for your own data.
+- Use our data loading and processing pipeline to easily access image and meta data for any work utilizing Tivita datasets.
+- This repository is tightly coupled to work with the public [HeiPorSPECTRAL](https://heiporspectral.org/) dataset. If you already downloaded the data, you only need to perform the setup steps and then you can directly use the `htc` framework to work on the data (cf. [our tutorials](./README.md#tutorials)).
+- Train your own networks and benefit from a pipeline offering e.g. efficient data loading, correct hierarchical aggregation of results and a set of helpful visualizations.
+- Apply deep learning models for different spatial granularities and modalities on your own semantically annotated dataset.
+- Use our pretrained models to initialize the weights for your own training.
+- Use our pretrained models to generate predictions for your own data.
If you use the `htc` framework, please consider citing the [corresponding papers](./README.md#papers). You can also cite this repository directly via:
@@ -37,20 +39,25 @@ If you use the `htc` framework, please consider citing the [corresponding papers
author = {Sellner, Jan and Seidlitz, Silvia},
publisher = {Zenodo},
url = {https://github.com/IMSY-DKFZ/htc},
- date = {2024-02-05},
+ date = {2024-08-05},
doi = {10.5281/zenodo.6577614},
title = {Hyperspectral Tissue Classification},
- version = {v0.0.15},
+ version = {v0.0.16},
}
```
+
## Setup
+
### Package Installation
+
This package can be installed via pip:
+
```bash
pip install imsy-htc
```
+
This installs all the required dependencies defined in [`requirements.txt`](./requirements.txt). The requirements include [PyTorch](https://pytorch.org/), so you may want to install it manually before installing the package in case you have specific needs (e.g. CUDA version).
> ⚠️ This framework was developed and tested using the Ubuntu 20.04+ Linux distribution. Despite we do provide wheels for Windows and macOS as well, they are not tested.
@@ -63,52 +70,65 @@ This installs all the required dependencies defined in [`requirements.txt`](./re
We cannot provide wheels for all PyTorch versions. Hence, a version of `imsy-htc` may not work with all versions of PyTorch due to changes in the ABI. In the following table, we list the PyTorch versions which are compatible with the respective `imsy-htc` version.
| `imsy-htc` | `torch` |
-| -------- | ------- |
-| 0.0.9 | 1.13 |
-| 0.0.10 | 1.13 |
-| 0.0.11 | 2.0 |
-| 0.0.12 | 2.0 |
-| 0.0.13 | 2.1 |
-| 0.0.14 | 2.1 |
+| ---------- | ------- |
+| 0.0.9 | 1.13 |
+| 0.0.10 | 1.13 |
+| 0.0.11 | 2.0 |
+| 0.0.12 | 2.0 |
+| 0.0.13 | 2.1 |
+| 0.0.14 | 2.1 |
+| 0.0.15 | 2.2 |
+| 0.0.15 | 2.3 |
+| 0.0.16 | 2.4 |
However, we do not make explicit version constraints in the dependencies of the `imsy-htc` package because a future version of PyTorch may still work and we don't want to break the installation if it is not necessary.
> π‘ Please note that it is always possible to build the `imsy-htc` package with your installed PyTorch version yourself (cf. Developer Installation).
+
Optional Dependencies (imsy-htc[extra])
Some requirements are considered optional (e.g. if they are only needed by certain scripts) and you will get an error message if they are needed but unavailable. You can install them via
+
```bash
pip install --extra-index-url https://read_package:CnzBrgDfKMWS4cxf-r31@git.dkfz.de/api/v4/projects/15/packages/pypi/simple imsy-htc[extra]
```
+
or by adding the following lines to your `requirements.txt`
+
```
--extra-index-url https://read_package:CnzBrgDfKMWS4cxf-r31@git.dkfz.de/api/v4/projects/15/packages/pypi/simple
imsy-htc[extra]
```
This installs the optional dependencies defined in [`requirements-extra.txt`](./requirements-extra.txt), including for example our Python wrapper for the [challengeR toolkit](https://github.com/wiesenfa/challengeR).
+
Docker
We also provide a Docker setup for testing. As a prerequisite:
-- Clone this repository
-- Install [Docker](https://docs.docker.com/get-docker/) and the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
-- Install the required dependencies to run the Docker startup script:
+
+- Clone this repository
+- Install [Docker](https://docs.docker.com/get-docker/) and the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
+- Install the required dependencies to run the Docker startup script:
+
```bash
pip install python-dotenv
```
Make sure that your environment variables are available and then bash into the container
+
```bash
export PATH_Tivita_HeiPorSPECTRAL="/path/to/the/dataset"
python run_docker.py bash
```
+
You can now run any commands you like. All datasets you provided via an environment variable that starts with `PATH_Tivita` will be accessible in your container (you can also check the generated `docker-compose.override.yml` file for details). Please note that the Docker container is meant for small testing only and not for development. This is also reflected by the fact that per default all results are stored inside the container and hence will also be deleted after exiting the container. If you want to keep your results, let the environment variable `PATH_HTC_DOCKER_RESULTS` point to the directory where you want to store the results.
+
@@ -129,46 +149,53 @@ pip install --no-use-pep517 -e .
```
Before commiting any files, please run the static code checks locally:
+
```bash
git add .
pre-commit run --all-files
```
+
### Environment Variables
+
This framework can be configured via environment variables. Most importantly, we need to know where your data is located (e.g. `PATH_Tivita_HeiPorSPECTRAL`) and where results should be stored (e.g. `PATH_HTC_RESULTS`). For a full list of possible environment variables, please have a look at the documentation of the [`Settings`](./htc/settings.py) class.
> π‘ If you set an environment variable for a dataset path, it is important that the variable name matches the folder name (e.g. the variable name `PATH_Tivita_HeiPorSPECTRAL` matches the dataset path `my/path/HeiPorSPECTRAL` with its folder name `HeiPorSPECTRAL`, whereas the variable name `PATH_Tivita_some_other_name` does not match). Furthermore, the dataset path needs to point to a directory which contains a `data` and an `intermediates` subfolder.
There are several options to set the environment variables. For example:
-- You can specify a variable as part of your bash startup script `~/.bashrc` or before running each command:
+
+- You can specify a variable as part of your bash startup script `~/.bashrc` or before running each command:
```bash
PATH_HTC_RESULTS="~/htc/results" htc training --model image --config "models/image/configs/default"
```
- However, this might get cumbersome or might not give you the flexibility you need.
-- Recommended if you cloned this repository (in contrast to simply installing it via pip): You can create a `.env` file in the repository root and add your variables, for example:
+ However, this might get cumbersome or might not give you the flexibility you need.
+- Recommended if you cloned this repository (in contrast to simply installing it via pip): You can create a `.env` file in the repository root and add your variables, for example:
```bash
export PATH_Tivita_HeiPorSPECTRAL=/mnt/nvme_4tb/HeiPorSPECTRAL
export PATH_HTC_RESULTS=~/htc/results
```
-- Recommended if you installed the package via pip: You can create user settings for this application. The location is OS-specific. For Linux the location might be at `~/.config/htc/variables.env`. Please run `htc info` upon package installation to retrieve the exact location on your system. The content of the file is of the same format as of the `.env` above.
+- Recommended if you installed the package via pip: You can create user settings for this application. The location is OS-specific. For Linux the location might be at `~/.config/htc/variables.env`. Please run `htc info` upon package installation to retrieve the exact location on your system. The content of the file is of the same format as of the `.env` above.
After setting your environment variables, it is recommended to run `htc info` to check that your variables are correctly registered in the framework.
## Tutorials
+
A series of [tutorials](./tutorials) can help you get started on the `htc` framework by guiding you through different usage scenarios.
+
> π‘ The tutorials make use of our public HSI dataset [HeiPorSPECTRAL](https://heiporspectral.org/). If you want to directly run them, please download the dataset first and make it accessible via the environment variable `PATH_Tivita_HeiPorSPECTRAL` as described above.
-- As a start, we recommend to take a look at this [general notebook](./tutorials/General.ipynb) which showcases the basic functionalities of the `htc` framework. Namely, it demonstrates the usage of the `DataPath` class which is the entry point to load and process HSI data. For example, you will learn how to read HSI cubes, segmentation masks and meta data. Among others, you can use this information to calculate the median spectrum of an organ.
-- If you want to use our framework with your own dataset, it might be necessary to write a custom `DataPath` class so that you can load and process your images and annotations. We [collected some tips](./tutorials/CustomDataPath.md) on how this can be achieved.
-- You have some HSI data at hand and want to use one of our pretrained models to generate predictions? Then our [prediction notebook](./tutorials/CreatingPredictions.ipynb) has got you covered.
-- You want to use our pretrained models to initialize the weights for your own training? See the section about [pretrained models](#pretrained-models) below for details.
-- You want to use our framework to train a network? The [network training notebook](./tutorials/network_training/NetworkTraining.ipynb) will show you how to achieve this on the example of a heart and lung segmentation network.
-- If you are interested in our technical validation (e.g. because you want to compare your colorchecker images with ours) and need to create a mask to detect the different colorchecker fields, you might find our automatic [colorchecker mask creation pipeline](./htc/utils/ColorcheckerMaskCreation.ipynb) useful.
+- As a start, we recommend to take a look at this [general notebook](./tutorials/General.ipynb) which showcases the basic functionalities of the `htc` framework. Namely, it demonstrates the usage of the `DataPath` class which is the entry point to load and process HSI data. For example, you will learn how to read HSI cubes, segmentation masks and meta data. Among others, you can use this information to calculate the median spectrum of an organ.
+- If you want to use our framework with your own dataset, it might be necessary to write a custom `DataPath` class so that you can load and process your images and annotations. We [collected some tips](./tutorials/CustomDataPath.md) on how this can be achieved.
+- You have some HSI data at hand and want to use one of our pretrained models to generate predictions? Then our [prediction notebook](./tutorials/CreatingPredictions.ipynb) has got you covered.
+- You want to use our pretrained models to initialize the weights for your own training? See the section about [pretrained models](./README.md#pretrained-models) below for details.
+- You want to use our framework to train a network? The [network training notebook](./tutorials/network_training/NetworkTraining.ipynb) will show you how to achieve this on the example of a heart and lung segmentation network.
+- If you are interested in our technical validation (e.g. because you want to compare your colorchecker images with ours) and need to create a mask to detect the different colorchecker fields, you might find our automatic [colorchecker mask creation pipeline](./htc/utils/ColorcheckerMaskCreation.ipynb) useful.
We do not have a separate documentation website for our framework yet. However, most of the functions and classes are documented so feel free to explore the source code or use your favorite IDE to display the documentation. If something does not become clear from the documentation, feel free to open an issue!
## Pretrained Models
+
This framework gives you access to a variety of pretrained segmentation and classification models. The models will be automatically downloaded, provided you specify the model type (e.g. `image`) and the run folder (e.g. `2022-02-03_22-58-44_generated_default_model_comparison`). It can then be used for example to [create predictions](./tutorials/CreatingPredictions.ipynb) on some data or as a baseline for your own training (see example below).
The following table lists all the models you can get:
@@ -197,7 +224,9 @@ The following table lists all the models you can get:
After successful installation of the `htc` package, you can use any of the pretrained models listed in the table. There are several ways to use them but the general principle is that models are always specified via their `model` and `run_folder`.
### Option 1: Use the models in your own training pipeline
+
Every model class listed in the table has a static method [`pretrained_model()`](./htc/models/common/HTCModel.py) which you can use to create a model instance and initialize it with the pretrained weights. The model object will be an instance of `torch.nn.Module`. The function has examples for all the different model types but as a teaser consider the following example which loads the pretrained image HSI network:
+
```python
import torch
from htc import ModelImage, Normalization
@@ -213,26 +242,34 @@ model(input_data).shape
> π‘ Please note that when initializing the weights as in this example, the segmentation head is initialized randomly. Meaningful predictions on your own data can thus not be expected out of the box, but you will have to train the model on your data first.
### Option 2: Use the models to create predictions for your data
+
The models can be used to predict segmentation masks for your data. The segmentation models automatically sample from your input image according to the selected model spatial granularity (e.g. by creating patches) and the output is always a segmentation mask for an entire image. The set of output classes is determined by the training configuration, e.g. 18 organ classes + background for our semantic models. The [`CreatingPredictions`](./tutorials/CreatingPredictions.ipynb) notebook shows how to create predictions and how to map the network output to meaningful label names.
### Option 3: Use the models to train a network with the `htc` package
+
If you are using the `htc` framework to [train your networks](./tutorials/network_training/NetworkTraining.ipynb), you only need to define the model in your configuration:
+
```json
{
"model": {
"pretrained_model": {
"model": "image",
- "run_folder": "2022-02-03_22-58-44_generated_default_model_comparison",
+ "run_folder": "2022-02-03_22-58-44_generated_default_model_comparison"
}
}
}
```
+
This is very similar to option 1 but may be more convenient if you train with the `htc` framework.
+> π‘ We have a [JSON Schema file](./htc/utils/config.schema) which describes the structure of our config files including descriptions of the attributes.
+
## CLI
+
There is a common command line interface for many scripts in this repository. More precisely, every script which is prefixed with `run_NAME.py` can also be run via `htc NAME` from any directory. For more details, just type `htc`.
## Papers
+
This repository contains code to reproduce our publications listed below:
### π [Robust deep learning-based semantic organ segmentation in hyperspectral images](https://doi.org/10.1016/j.media.2022.102488)
@@ -241,7 +278,7 @@ This repository contains code to reproduce our publications listed below:
-In this paper, we tackled fully automatic organ segmentation and compared deep learning models on different spatial granularities (e.g. patch vs. image) and modalities (e.g. HSI vs. RGB). Furthermore, we studied the required amount of training data and the generalization capabilities of our models across subjects. The pretrained networks are related to this paper. You can find the notebooks to generate the paper figures in [paper/MIA2021](./paper/MIA2021) (the folder also includes a [reproducibility document](./paper/MIA2021/reproducibility.md)) and the models in [htc/models](./htc/models). For each model, there are three configuration files, namely `default`, `default_rgb` and `default_parameters`, which correspond to the HSI, RGB and TPI modality, respectively. You can also download the [NSD thresholds](https://e130-hyperspectal-tissue-classification.s3.dkfz.de/models/nsd_thresholds_semantic.csv) which we used for the NSD metric (cf. Fig. 12).
+In this paper, we tackled fully automatic organ segmentation and compared deep learning models on different spatial granularities (e.g. patch vs. image) and modalities (e.g. HSI vs. RGB). Furthermore, we studied the required amount of training data and the generalization capabilities of our models across subjects. The pretrained networks are related to this paper. You can find the notebooks to generate the paper figures in [paper/MIA2022](./paper/MIA2022) (the folder also includes a [reproducibility document](./paper/MIA2022/reproducibility.md)) and the models in [htc/models](./htc/models). For each model, there are three configuration files, namely `default`, `default_rgb` and `default_parameters`, which correspond to the HSI, RGB and TPI modality, respectively. You can also download the [NSD thresholds](https://e130-hyperspectal-tissue-classification.s3.dkfz.de/models/nsd_thresholds_semantic.csv) which we used for the NSD metric (cf. Fig. 12).
> π The dataset for this paper is not publicly available.
@@ -261,6 +298,7 @@ In this paper, we tackled fully automatic organ segmentation and compared deep l
volume = {80},
}
```
+
### π [Semantic segmentation of surgical hyperspectral images under geometric domain shifts](https://doi.org/10.48550/arXiv.2303.10972)
@@ -269,7 +307,7 @@ In this paper, we tackled fully automatic organ segmentation and compared deep l
-This MICCAI2023 paper is the direct successor of our MIA2021 paper. We analyzed how well our networks perform under geometrical domain shifts which commonly occur in real-world open surgeries (e.g. situs occlusions). The effect is drastic (drop of Dice similarity coefficient by 45β―%) but the good news is that performance on par with in-distribution data can be achieved with our simple, model-independent solution (augmentation method). You can find all the code in [htc/context](./htc/context) and paper figures as well as [reproducibility instructions](./paper/MICCAI2023/reproducibility.md) in [paper/MICCAI2023](./paper/MICCAI2023). Pretrained models are available for our organ transplantation networks with HSI and RGB modalities.
+This MICCAI2023 paper is the direct successor of our MIA2022 paper. We analyzed how well our networks perform under geometrical domain shifts which commonly occur in real-world open surgeries (e.g. situs occlusions). The effect is drastic (drop of Dice similarity coefficient by 45β―%) but the good news is that performance on par with in-distribution data can be achieved with our simple, model-independent solution (augmentation method). You can find all the code in [htc/context](./htc/context) and paper figures as well as [reproducibility instructions](./paper/MICCAI2023/reproducibility.md) in [paper/MICCAI2023](./paper/MICCAI2023). Pretrained models are available for our organ transplantation networks with HSI and RGB modalities.
> π‘ If you are only interested in our data augmentation method, you can also head over to [Kornia](https://github.com/kornia/kornia) where this augmentation is implemented for generic use cases (including 2D and 3D data). You will find it under the name [`RandomTransplantation`](https://kornia.readthedocs.io/en/latest/augmentation.module.html#kornia.augmentation.RandomTransplantation).
@@ -292,6 +330,7 @@ This MICCAI2023 paper is the direct successor of our MIA2021 paper. We analyzed
title = {Semantic Segmentation of Surgical Hyperspectral Images Under Geometric Domain Shifts},
}
```
+
### π [Dealing with I/O bottlenecks in high-throughput model training](https://e130-hyperspectal-tissue-classification.s3.dkfz.de/figures/PyTorchConference_Poster.pdf)
@@ -307,18 +346,19 @@ You can find the code to generate the results figures of the poster in [paper/Py
```bibtex
@misc{sellner_benchmarking_2023,
- author = {Sellner, Jan and Seidlitz, Silvia and Maier-Hein, Lena},
- language = {en},
- url = {https://e130-hyperspectal-tissue-classification.s3.dkfz.de/figures/PyTorchConference_Poster.pdf},
- date = {2023-10-16},
- title = {Dealing with I/O bottlenecks in high-throughput model training},
+ author = {Sellner, Jan and Seidlitz, Silvia and Maier-Hein, Lena},
+ url = {https://e130-hyperspectal-tissue-classification.s3.dkfz.de/figures/PyTorchConference_Poster.pdf},
+ date = {2023-10-16},
+ howpublished = {Poster presented at the PyTorch Conference 2023, San Francisco, United States of America},
+ title = {Dealing with I/O bottlenecks in high-throughput model training},
}
```
+
### π [Spectral organ fingerprints for machine learning-based intraoperative tissue classification with hyperspectral imaging in a porcine model](https://doi.org/10.1038/s41598-022-15040-w)
-In this paper, we trained a classification model based on median spectra from HSI data. You can find the model code in [htc/tissue_atlas](./htc/tissue_atlas) and the confusion matrix figure of the paper in [paper/NatureReports2021](./paper/NatureReports2021) (including a reproducibility document).
+In this paper, we trained a classification model based on median spectra from HSI data. You can find the model code in [htc/tissue_atlas](./htc/tissue_atlas) and the confusion matrix figure of the paper in [paper/NatureReports2022](./paper/NatureReports2022) (including a reproducibility document).
> π The dataset for this paper is not fully publicly available, but a subset of the data is available through the public [HeiPorSPECTRAL](https://heiporspectral.org/) dataset.
@@ -338,6 +378,7 @@ In this paper, we trained a classification model based on median spectra from HS
volume = {12},
}
```
+
### π [HeiPorSPECTRAL - the Heidelberg Porcine HyperSPECTRAL Imaging Dataset of 20 Physiological Organs](https://doi.org/10.1038/s41597-023-02315-8)
@@ -365,6 +406,7 @@ If you want to learn more about the [HeiPorSPECTRAL](https://heiporspectral.org/
volume = {10},
}
```
+
### π [KΓΌnstliche Intelligenz und hyperspektrale Bildgebung zur bildgestΓΌtzten Assistenz in der minimal-invasiven Chirurgie](https://doi.org/10.1007/s00104-022-01677-w)
@@ -389,8 +431,9 @@ This paper presents several applications of intraoperative HSI, including our or
volume = {93},
}
```
+
## Funding
-This project has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (NEURAL SPICING, grant agreement No. 101002198) and was supported by the German Cancer Research Center (DKFZ) and the Helmholtz Association under the joint research school HIDSS4Health (Helmholtz Information and Data Science School for Health). It further received funding from the Surgical Oncology Program of the National Center for Tumor Diseases (NCT) Heidelberg.
\ No newline at end of file
+This project has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (NEURAL SPICING, grant agreement No. 101002198) and was supported by the German Cancer Research Center (DKFZ) and the Helmholtz Association under the joint research school HIDSS4Health (Helmholtz Information and Data Science School for Health). It further received funding from the Surgical Oncology Program of the National Center for Tumor Diseases (NCT) Heidelberg.
diff --git a/docker-compose.yml b/docker-compose.yml
index f618dba..4236959 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -10,10 +10,10 @@ services:
- htc-base
image: htc
container_name: htc
- network_mode: host # e.g. for Jupyter Lab
+ network_mode: host # e.g. for Jupyter Lab
shm_size: 10gb
volumes:
- - /var/run/docker.sock:/var/run/docker.sock # This allows Docker containers to be start from inside the container, but as siblings and not nested (https://stackoverflow.com/a/33003273/2762258)
+ - /var/run/docker.sock:/var/run/docker.sock # This allows Docker containers to be start from inside the container, but as siblings and not nested (https://stackoverflow.com/a/33003273/2762258)
# Required to get copy-on-write to work: https://github.com/moby/moby/issues/18191#issuecomment-159280820
cap_add:
- SYS_ADMIN
@@ -23,4 +23,4 @@ services:
resources:
reservations:
devices:
- - capabilities: [gpu]
+ - capabilities: [gpu]
diff --git a/htc/__init__.py b/htc/__init__.py
index 1ed6a36..31ae312 100644
--- a/htc/__init__.py
+++ b/htc/__init__.py
@@ -19,6 +19,7 @@
_import_structure = {
"cpp": [
"hierarchical_bootstrapping",
+ "hierarchical_bootstrapping_labels",
"kfold_combinations",
"map_label_image",
"nunique",
@@ -56,6 +57,8 @@
"FlexibleIdentity",
"copy_sample",
"cpu_only_tensor",
+ "group_mean",
+ "minmax_pos_neg_scaling",
"move_batch_gpu",
"pad_tensors",
"smooth_one_hot",
@@ -127,6 +130,7 @@
"utils.parallel": ["p_imap", "p_map"],
"utils.SpectrometerReader": ["SpectrometerReader"],
"utils.sqldf": ["sqldf"],
+ "utils.Task": ["Task"],
"utils.type_from_string": ["type_from_string"],
"utils.unify_path": ["unify_path"],
"utils.visualization": [
@@ -150,6 +154,7 @@
if TYPE_CHECKING:
from htc.cpp import (
hierarchical_bootstrapping,
+ hierarchical_bootstrapping_labels,
kfold_combinations,
map_label_image,
nunique,
@@ -170,6 +175,7 @@
from htc.fonts.set_font import set_font
from htc.model_processing.ImageConsumer import ImageConsumer
from htc.model_processing.Runner import Runner
+ from htc.model_processing.SinglePredictor import SinglePredictor
from htc.model_processing.TestLeaveOneOutPredictor import TestLeaveOneOutPredictor
from htc.model_processing.TestPredictor import TestPredictor
from htc.model_processing.ValidationPredictor import ValidationPredictor
@@ -187,6 +193,8 @@
FlexibleIdentity,
copy_sample,
cpu_only_tensor,
+ group_mean,
+ minmax_pos_neg_scaling,
move_batch_gpu,
pad_tensors,
smooth_one_hot,
@@ -254,6 +262,7 @@
from htc.utils.parallel import p_imap, p_map
from htc.utils.SpectrometerReader import SpectrometerReader
from htc.utils.sqldf import sqldf
+ from htc.utils.Task import Task
from htc.utils.type_from_string import type_from_string
from htc.utils.unify_path import unify_path
from htc.utils.visualization import (
diff --git a/htc/context/context_transforms.py b/htc/context/context_transforms.py
index bca56ac..80f0ff0 100644
--- a/htc/context/context_transforms.py
+++ b/htc/context/context_transforms.py
@@ -522,6 +522,10 @@ def _apply_transform(self, batch: dict[str, torch.Tensor], donor_indices: list[i
donor_regions = {k: batch[k][donor] for k in regions_keys}
valid_donor_labels = donor_labels[donor_valid_pixels].unique()
+ if len(valid_donor_labels) == 0:
+ # In rare cases, it may happen that a previous (affine) augmentation removes all valid pixels
+ # In this case, there is not much we can do here because no donor pixels are available
+ continue
selected_label = valid_donor_labels[torch.randperm(len(valid_donor_labels))[0]]
# Apply selection to organ acceptor
diff --git a/htc/context/extra_datasets/run_dataset_tables.py b/htc/context/extra_datasets/run_dataset_tables.py
index 231c9de..c29b8f0 100644
--- a/htc/context/extra_datasets/run_dataset_tables.py
+++ b/htc/context/extra_datasets/run_dataset_tables.py
@@ -14,7 +14,7 @@
if __name__ == "__main__":
# For the context runs:
- # htc dataset_tables --model image --run-folder 2023-01-27_23-59-37_random_erasing --metrics DSC --test --dataset-name masks_isolation
+ # htc dataset_tables --model image --run-folder 2023-02-08_09-40-59_elastic_0.2 --metrics DSC --test --dataset-name masks_isolation
# For the MIA runs:
# htc dataset_tables --model image --run-folder 2022-02-03_22-58-44_generated_default_model_comparison --metrics DSC --test --dataset-name masks_isolation --output-dir ~/htc/results_context/neighbour_analysis/masks_isolation/image/2022-02-03_22-58-44_generated_default_model_comparison
runner = Runner(
diff --git a/htc/context/manipulated_datasets/run_context_evaluation_table.py b/htc/context/manipulated_datasets/run_context_evaluation_table.py
index 515df2b..081f4ef 100644
--- a/htc/context/manipulated_datasets/run_context_evaluation_table.py
+++ b/htc/context/manipulated_datasets/run_context_evaluation_table.py
@@ -1,6 +1,7 @@
# SPDX-FileCopyrightText: 2022 Division of Intelligent Medical Systems, DKFZ
# SPDX-License-Identifier: MIT
+import gc
from pathlib import Path
import pandas as pd
@@ -43,6 +44,10 @@ def produce_predictions(
self.rows[k] += rows
+ # There might be memory overflows without explicit garbage collection
+ gc.collect()
+ torch.cuda.empty_cache()
+
def predict_step(self, batch: dict[str, torch.Tensor], batch_idx: int = None) -> dict[str, torch.Tensor]:
prediction = self.model.predict_step(batch)
prediction["class"] = prediction["class"].softmax(dim=1)
@@ -73,6 +78,9 @@ def produce_predictions(self, model: HTCLightning, batch: dict[str, torch.Tensor
rows = self._validation_context(batch, batch_idx=-1, dataloader_idx=0, context_key=k)
self.rows[k] += rows
+ gc.collect()
+ torch.cuda.empty_cache()
+
def predict_step(self, batch: dict[str, torch.Tensor], batch_idx: int = None) -> dict[str, torch.Tensor]:
return self.model.predict_step(batch)
diff --git a/htc/context/manipulated_datasets/utils.py b/htc/context/manipulated_datasets/utils.py
index a7cc18e..197bb8f 100644
--- a/htc/context/manipulated_datasets/utils.py
+++ b/htc/context/manipulated_datasets/utils.py
@@ -46,21 +46,21 @@ def compare_performance(
mapping = LabelMapping.from_config(Config(experiment_dir / "config_reference.json"))
# Select all dirs in the experiment dir that start with exp
- all_subdir = list(experiment_dir.iterdir())
+ all_subdir = sorted(experiment_dir.iterdir())
exp_subdirs = []
- for dir in all_subdir:
- if dir.name.startswith("exp"):
- exp_subdirs.append(dir)
+ for subdir in all_subdir:
+ if subdir.name.startswith("exp"):
+ exp_subdirs.append(subdir)
exp_subdirs = sorted(exp_subdirs, key=lambda i: int(i.name.removeprefix(exp_string)))
# Get all the reference dirs
if reference_experiment:
assert reference_experiment.exists()
- all_reference_subdir = list(reference_experiment.iterdir())
+ all_reference_subdir = sorted(reference_experiment.iterdir())
ref_subdirs = []
- for dir in all_reference_subdir:
- if dir.name.startswith("exp"):
- ref_subdirs.append(dir)
+ for subdir in all_reference_subdir:
+ if subdir.name.startswith("exp"):
+ ref_subdirs.append(subdir)
ref_subdirs = sorted(ref_subdirs, key=lambda i: int(i.name.removeprefix(exp_string)))
assert len(ref_subdirs) == len(exp_subdirs)
else:
diff --git a/htc/context/models/configs/context.json b/htc/context/models/configs/context.json
index 106637c..ccf922a 100644
--- a/htc/context/models/configs/context.json
+++ b/htc/context/models/configs/context.json
@@ -8,14 +8,18 @@
"checkpoint_metric_mode": "class_level",
"checkpoint_saving": "last",
"context_transforms_gpu": {
- "isolation_0": [{
- "class": "htc.context.context_transforms>OrganIsolation",
- "fill_value": "0"
- }],
- "isolation_cloth": [{
- "class": "htc.context.context_transforms>OrganIsolation",
- "fill_value": "cloth"
- }]
+ "isolation_0": [
+ {
+ "class": "htc.context.context_transforms>OrganIsolation",
+ "fill_value": "0"
+ }
+ ],
+ "isolation_cloth": [
+ {
+ "class": "htc.context.context_transforms>OrganIsolation",
+ "fill_value": "cloth"
+ }
+ ]
}
}
}
diff --git a/htc/context/models/configs/organ_transplantation_0.8.json b/htc/context/models/configs/organ_transplantation_0.8.json
index 7747d02..9c526b5 100644
--- a/htc/context/models/configs/organ_transplantation_0.8.json
+++ b/htc/context/models/configs/organ_transplantation_0.8.json
@@ -1,10 +1,12 @@
{
"inherits": "image/configs/default",
"input": {
- "transforms_gpu_extends": [{
- "class": "htc.context.context_transforms>OrganTransplantation",
- "p": 0.8
- }]
+ "transforms_gpu_extends": [
+ {
+ "class": "htc.context.context_transforms>OrganTransplantation",
+ "p": 0.8
+ }
+ ]
},
"trainer_kwargs": {
"check_val_every_n_epoch": 10
diff --git a/htc/context/models/context_evaluation.py b/htc/context/models/context_evaluation.py
index 38ce2ff..4a575b7 100644
--- a/htc/context/models/context_evaluation.py
+++ b/htc/context/models/context_evaluation.py
@@ -7,6 +7,7 @@
import pandas as pd
from htc.context.settings_context import settings_context
+from htc.evaluation.model_comparison.paper_runs import collect_comparison_runs
from htc.evaluation.utils import split_test_table
from htc.models.common.HTCModel import HTCModel
from htc.models.common.MetricAggregation import MetricAggregation
@@ -45,8 +46,8 @@ def aggregate_removal_table(path: Path) -> pd.DataFrame:
# Take the minimum for each used label, i.e. keep the worst performance per label (this corresponds to the performance of an organ if the most important neighbour is missing)
columns = [c for c in df.columns if c not in ["target_label", "dice_metric"] + additional_metrics]
df = df.groupby(columns, as_index=False).agg(
- dice_metric=pd.NamedAgg(column="dice_metric", aggfunc=min),
- **{m: pd.NamedAgg(column=m, aggfunc=min) for m in additional_metrics},
+ dice_metric=pd.NamedAgg(column="dice_metric", aggfunc="min"),
+ **{m: pd.NamedAgg(column=m, aggfunc="min") for m in additional_metrics},
)
# Implode the dataframe (to keep the same format as before)
@@ -63,7 +64,9 @@ def aggregate_removal_table(path: Path) -> pd.DataFrame:
return df.reindex(columns=column_order)
-def context_evaluation_table(run_dir: Path, test: bool = False, aggregate: bool = True) -> pd.DataFrame:
+def context_evaluation_table(
+ run_dir: Path, test: bool = False, aggregate: bool = True, keep_subjects: bool = False
+) -> pd.DataFrame:
"""
Collects all the context results for a training run.
@@ -86,6 +89,7 @@ def context_evaluation_table(run_dir: Path, test: bool = False, aggregate: bool
run_dir: Path to the training run to the context network.
test: If True, read the test table instead of the validation table.
aggregate: If True, organ-level aggregated results are returned. If False, a much larger table with metric values per image is returned.
+ keep_subjects: If True, keep the subject column in the aggregated table.
Returns: Table with (aggregated) results.
"""
@@ -237,7 +241,9 @@ def real_data_tables(names: list[str]) -> list[pd.DataFrame]:
config,
metrics=metrics,
)
- df_agg.append(agg.grouped_metrics(mode="class_level", domains=["network", "dataset"]))
+ df_agg.append(
+ agg.grouped_metrics(mode="class_level", domains=["network", "dataset"], keep_subjects=keep_subjects)
+ )
assert all(len(df) > 0 for df in df_agg), "All tables must have at least one row"
return pd.concat(df_agg)
@@ -245,13 +251,14 @@ def real_data_tables(names: list[str]) -> list[pd.DataFrame]:
return pd.concat(tables)
-def compare_context_runs(run_dirs: list[Path], test: bool = False) -> pd.DataFrame:
+def compare_context_runs(run_dirs: list[Path], test: bool = False, keep_subjects: bool = False) -> pd.DataFrame:
"""
Collect all scores for the given training runs and combine it into one table. The network column is adapted to distinguish the different runs.
Args:
run_dirs: List of training runs which should be combined.
test: If True, read the test table instead of the validation table.
+ keep_subjects: If True, keep the subject column in the aggregated table.
Returns: Table with the combined results.
"""
@@ -260,7 +267,7 @@ def compare_context_runs(run_dirs: list[Path], test: bool = False) -> pd.DataFra
# run folder name without the timestamp
name = run_dir.name[20:]
- df = context_evaluation_table(run_dir, test)
+ df = context_evaluation_table(run_dir, test, keep_subjects=keep_subjects)
if "context" in name:
df = df.replace(to_replace={"network": {"context": name}})
else:
@@ -316,7 +323,7 @@ def find_best_transform_run(name: str) -> Path:
return best_run[0]
-def glove_runs(networks: dict[str, Path] = None, aggregate: bool = True) -> pd.DataFrame:
+def glove_runs(networks: dict[str, Path] = None, aggregate: bool = True, **aggregation_kwargs) -> pd.DataFrame:
"""
Collects the test results for all glove runs. There will be two test datasets (glove and no-glove) corresponding to the out-of-distribution and in-distribution, respectively.
@@ -325,6 +332,7 @@ def glove_runs(networks: dict[str, Path] = None, aggregate: bool = True) -> pd.D
Args:
networks: Dictionary of (name, run_dir) pairs of glove runs which should be included in the final table. If None, the default glove runs (as specified in settings_context.glove_runs) are used.
aggregate: If True, organ-level aggregated results are returned. If False, a much larger table with metric values per image is returned.
+ aggregation_kwargs: Keyword arguments passed on to the grouped_metrics method.
Returns: Table with all aggregated results.
"""
@@ -340,7 +348,7 @@ def aggregate_run(tables: dict[str, pd.DataFrame], config: Config) -> pd.DataFra
config,
metrics=metrics,
)
- df_agg.append(agg.grouped_metrics(mode="class_level", domains=["network", "dataset"]))
+ df_agg.append(agg.grouped_metrics(mode="class_level", domains=["network", "dataset"], **aggregation_kwargs))
df_agg = pd.concat(df_agg)
return df_agg
@@ -389,3 +397,94 @@ def best_run_data(test: bool = False) -> pd.DataFrame:
df.replace({"network": {"context": "organ_transplantation"}}, inplace=True)
return df
+
+
+def baseline_granularity_comparison(
+ baseline_timestamp: str, glove_runs_hsi: dict[str, Path], glove_runs_rgb: dict[str, Path]
+) -> pd.DataFrame:
+ """
+ Compares the baseline performance for different spatial granularities.
+
+ Args:
+ baseline_timestamp: The timestamp for the model comparison baseline runs (MIA runs).
+ glove_runs_hsi: A dictionary mapping spatial granularities to run directories for the HSI glove runs.
+ glove_runs_rgb: A dictionary mapping spatial granularities to run directories for the RGB glove runs.
+
+ Returns: A comparison table with class-wise aggregated scores for each network and dataset.
+ """
+ table_name = "test_table"
+ df_runs = collect_comparison_runs(baseline_timestamp)
+ config = None
+ n_bootstraps = 1000
+
+ tables = []
+ for _, row in df_runs.iterrows():
+ for modality in ["hsi", "rgb"]:
+ if row["model"] == "superpixel_classification":
+ rgb = "_rgb" if modality == "rgb" else ""
+ run_folder = settings_context.superpixel_classification_timestamp + f"_default{rgb}"
+ else:
+ run_folder = row[f"run_{modality}"]
+ run_dir = HTCModel.find_pretrained_run(row["model"], run_folder)
+ if config is None:
+ config = Config(run_dir / "config.json")
+
+ df = pd.read_pickle(run_dir / f"{table_name}.pkl.xz")
+ df["network"] = row["name"]
+ df["dataset"] = "semantic"
+ df["modality"] = modality.upper()
+ tables.append(df)
+
+ for folder, dataset in [
+ ("organ_isolation_0", "isolation_0"),
+ ("organ_isolation_cloth", "isolation_cloth"),
+ ("organ_removal_0", "removal_0"),
+ ("organ_removal_cloth", "removal_cloth"),
+ ("masks_isolation", "masks_isolation"),
+ ]:
+ table_path = (
+ settings.results_dir
+ / "neighbour_analysis"
+ / folder
+ / row["model"]
+ / run_folder
+ / f"{table_name}_{dataset}.pkl.xz"
+ )
+
+ if "removal" in folder:
+ df = aggregate_removal_table(table_path)
+ else:
+ df = pd.read_pickle(table_path)
+
+ df["network"] = row["name"]
+ df["dataset"] = dataset
+ df["modality"] = modality.upper()
+ tables.append(df)
+
+ tables_agg = []
+ for df in tables:
+ agg = MetricAggregation(
+ df,
+ config,
+ metrics=["dice_metric"],
+ )
+ tables_agg.append(
+ agg.grouped_metrics(
+ mode="class_level", domains=["network", "dataset", "modality"], n_bootstraps=n_bootstraps
+ )
+ )
+ assert all(len(df) > 0 for df in tables_agg), "All tables must have at least one row"
+
+ for name, run_dir in glove_runs_hsi.items():
+ df = glove_runs({name: run_dir}, n_bootstraps=n_bootstraps)
+ df.drop(columns=["surface_distance_metric", settings_seg.nsd_aggregation_short], inplace=True)
+ df["modality"] = "HSI"
+ tables_agg.append(df)
+
+ for name, run_dir in glove_runs_rgb.items():
+ df = glove_runs({name: run_dir}, n_bootstraps=n_bootstraps)
+ df.drop(columns=["surface_distance_metric", settings_seg.nsd_aggregation_short], inplace=True)
+ df["modality"] = "RGB"
+ tables_agg.append(df)
+
+ return pd.concat(tables_agg)
diff --git a/htc/context/models/data/pigs_semantic-only_5foldsV2_glove.json b/htc/context/models/data/pigs_semantic-only_5foldsV2_glove.json
index cd0b13b..df8cff9 100644
--- a/htc/context/models/data/pigs_semantic-only_5foldsV2_glove.json
+++ b/htc/context/models/data/pigs_semantic-only_5foldsV2_glove.json
@@ -2299,4 +2299,4 @@
]
}
}
-]
\ No newline at end of file
+]
diff --git a/htc/context/models/run_baseline_tables.py b/htc/context/models/run_baseline_tables.py
index 7b9e719..a0f2f70 100644
--- a/htc/context/models/run_baseline_tables.py
+++ b/htc/context/models/run_baseline_tables.py
@@ -9,8 +9,10 @@
import htc.context.manipulated_datasets.run_context_evaluation_table as run_context_evaluation_table
from htc.context.models.run_context_test_tables import compute_glove_test_tables
from htc.context.settings_context import settings_context
+from htc.evaluation.model_comparison.paper_runs import collect_comparison_runs
from htc.models.common.HTCModel import HTCModel
from htc.settings import settings
+from htc.settings_seg import settings_seg
from htc.utils.general import subprocess_run
@@ -134,17 +136,40 @@ def compute_context_tables(runs: list[Path], table_name: str, recalculate: bool
# The main difference between the baseline and the context networks is that we store the context tables for the baseline network at a different location (settings.results_dir / "neighbour_analysis") since we do not want to change the existing models
# Additionally, we also compute the validation tables for the baseline network, but not for the context networks (as this is done automatically during training)
# This is why we cannot use the same script for both
- runs = [
- HTCModel.find_pretrained_run("image", "2022-02-03_22-58-44_generated_default_model_comparison"),
- HTCModel.find_pretrained_run("image", "2022-02-03_22-58-44_generated_default_rgb_model_comparison"),
- ]
- runs_glove = [
- settings_context.glove_runs["baseline"],
- settings_context.glove_runs_rgb["baseline"],
+ runs_main = [
+ HTCModel.find_pretrained_run(
+ "image", f"{settings_seg.model_comparison_timestamp}_generated_default_model_comparison"
+ ),
+ HTCModel.find_pretrained_run(
+ "image", f"{settings_seg.model_comparison_timestamp}_generated_default_rgb_model_comparison"
+ ),
]
- compute_context_tables(runs, "validation_table", args.recalculate)
- compute_context_tables(runs, "test_table", args.recalculate)
- compute_context_tables(runs_glove, "validation_table", args.recalculate)
- compute_context_tables(runs_glove, "test_table", args.recalculate)
+ runs_other_granularities = []
+ df_runs = collect_comparison_runs(settings_seg.model_comparison_timestamp)
+ for _, row in df_runs.iterrows():
+ if row["model"] == "image":
+ continue
+ elif row["model"] == "superpixel_classification":
+ runs_other_granularities.append(
+ HTCModel.find_pretrained_run(
+ row["model"], settings_context.superpixel_classification_timestamp + "_default"
+ )
+ )
+ runs_other_granularities.append(
+ HTCModel.find_pretrained_run(
+ row["model"], settings_context.superpixel_classification_timestamp + "_default_rgb"
+ )
+ )
+ else:
+ runs_other_granularities.append(HTCModel.find_pretrained_run(row["model"], row["run_hsi"]))
+ runs_other_granularities.append(HTCModel.find_pretrained_run(row["model"], row["run_rgb"]))
+
+ runs_glove = list(settings_context.glove_runs_granularities.values()) + list(
+ settings_context.glove_runs_granularities_rgb.values()
+ )
+
+ compute_context_tables(runs_main, "validation_table", args.recalculate)
+ compute_context_tables(runs_main, "test_table", args.recalculate)
+ compute_context_tables(runs_other_granularities, "test_table", args.recalculate)
compute_glove_test_tables(runs_glove, args.recalculate)
diff --git a/htc/context/models/run_glove_baseline_runs.py b/htc/context/models/run_glove_baseline_runs.py
index 5562f17..9921620 100644
--- a/htc/context/models/run_glove_baseline_runs.py
+++ b/htc/context/models/run_glove_baseline_runs.py
@@ -1,7 +1,10 @@
# SPDX-FileCopyrightText: 2022 Division of Intelligent Medical Systems, DKFZ
# SPDX-License-Identifier: MIT
+import argparse
+
from htc.models.common.RunGenerator import RunGenerator
+from htc.settings_seg import settings_seg
from htc.utils.Config import Config
@@ -11,10 +14,32 @@ def glove_adjustment(config: Config, **kwargs) -> str:
if __name__ == "__main__":
+ parser = argparse.ArgumentParser(
+ description=(
+ "Start training runs on the cluster for the glove baseline models (MIA runs with the glove data"
+ " specification)."
+ ),
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+ )
+ parser.add_argument(
+ "--model",
+ default=settings_seg.model_names,
+ choices=settings_seg.model_names,
+ nargs="+",
+ type=str,
+ help="One or more model names to generate runs for (each time with RGB and HSI).",
+ )
+ args = parser.parse_args()
+
rg = RunGenerator()
- for name in ["default", "default_rgb"]:
- config = Config.from_model_name(name, "image")
- rg.generate_run(config, [glove_adjustment])
+ for model in args.model:
+ for name in ["default", "default_rgb"]:
+ config = Config.from_model_name(name, model)
+ rg.generate_run(config, [glove_adjustment], model_name=model)
+
+ if model == "patch":
+ config = Config.from_model_name(name.replace("default", "default_64"), model)
+ rg.generate_run(config, [glove_adjustment], model_name=model)
rg.submit_jobs()
diff --git a/htc/context/neighbour/__init__.py b/htc/context/neighbour/__init__.py
new file mode 100644
index 0000000..17e71a8
--- /dev/null
+++ b/htc/context/neighbour/__init__.py
@@ -0,0 +1,3 @@
+# SPDX-FileCopyrightText: 2022 Division of Intelligent Medical Systems, DKFZ
+# SPDX-License-Identifier: MIT
+
diff --git a/htc/context/neighbour/find_neighbour_valid_pixels.py b/htc/context/neighbour/find_neighbour_valid_pixels.py
new file mode 100644
index 0000000..d741ac5
--- /dev/null
+++ b/htc/context/neighbour/find_neighbour_valid_pixels.py
@@ -0,0 +1,82 @@
+# SPDX-FileCopyrightText: 2022 Division of Intelligent Medical Systems, DKFZ
+# SPDX-License-Identifier: MIT
+
+import torch
+
+
+def find_neighbour_classes_valid_pixels(
+ labels: torch.IntTensor, label_index: int, valid_pixels: torch.BoolTensor
+) -> torch.FloatTensor:
+ """
+ Create a matrix which has True for label_index and False for all other classes.
+
+ Arg:
+ labels: torch.IntTensor containing a label of a class (int) in each entry
+ label_index: int which describes for what class the neighbour pixels have to be found
+ valid_pixels: torch.BoolTensor containing which pixels of the image are valid (bool)
+ """
+
+ class_matrix = labels == label_index
+
+ # The kernel defines our neighbour concept, does not work with torch
+ kernel = torch.tensor([[True, True, True], [True, True, True], [True, True, True]], dtype=torch.float32)
+
+ unsqueezed_matrix = class_matrix.unsqueeze(dim=0).unsqueeze(dim=0).type("torch.FloatTensor")
+ unsqueezed_kernel = kernel.unsqueeze(dim=0).unsqueeze(dim=0)
+ unsqueezed_dilation_matrix = torch.nn.functional.conv2d(unsqueezed_matrix, unsqueezed_kernel, padding=(1, 1))
+ dilation_matrix = unsqueezed_dilation_matrix.squeeze(dim=0).squeeze(dim=0).type("torch.BoolTensor")
+
+ # Superpose the class matrix to the dilation matrix and set their class values into the neighbour vector.
+ superposion_matrix = ~class_matrix & dilation_matrix & valid_pixels
+
+ neighbour_vector = labels[superposion_matrix]
+ neighbour_classes, counts = torch.unique(neighbour_vector, return_counts=True)
+
+ return neighbour_classes, counts
+
+
+def neighbour_class_percentage_for_valid_pixels(
+ labels: torch.IntTensor, valid_pixels: torch.BoolTensor, n_classes: int
+) -> torch.FloatTensor:
+ """
+ Find the "percentage matrix", which indicates the neighbour class pixels percentage
+ to every class. EX: class 0 has a neighbour the class 1 to 0.75 and the class 2 to 0.25.
+ In the matrix ixj the i represents each class and j the neighbour to the given class.
+ EX: (0.00, 0.75, 0.25)
+ (0.50, 0.00, 0.50)
+ (0.50, 0.50, 0.00)
+
+ Arg:
+ labels: torch.IntTensor containing a label of a class in each entry
+ valid_pixels: torch.BoolTensor containing which pixels of the image are valid (
+ n_classes: int number of different classes that appear in the image
+ """
+
+ class_vector = labels[valid_pixels].unique()
+
+ percentage_matrix = torch.zeros((n_classes, n_classes))
+
+ for label_index in class_vector:
+ neighbour_classes, counts = find_neighbour_classes_valid_pixels(labels, label_index, valid_pixels)
+ length = sum(counts)
+ # Set the percentages in the spot matrix[class, neighbour_class]
+ percentage_matrix[label_index, neighbour_classes] = counts / length
+
+ return percentage_matrix
+
+
+def count_rows_sum_eq_1(neighbour_matrix: torch.FloatTensor) -> torch.FloatTensor:
+ """
+ Count which classes appear in the image.
+ Arg:
+ neighbour_matrix: torch.FloatTensor
+ """
+ length = neighbour_matrix.shape[0]
+ ROW_IS_0 = torch.zeros(length, dtype=torch.float)
+ rows_diff_from_0 = torch.zeros(length)
+
+ for i in range(length):
+ if not (torch.equal(neighbour_matrix[i, :], ROW_IS_0)):
+ rows_diff_from_0[i] += 1
+
+ return rows_diff_from_0
diff --git a/htc/context/neighbour/find_normalized_neighbour_matrix.py b/htc/context/neighbour/find_normalized_neighbour_matrix.py
new file mode 100644
index 0000000..6160ce6
--- /dev/null
+++ b/htc/context/neighbour/find_normalized_neighbour_matrix.py
@@ -0,0 +1,66 @@
+# SPDX-FileCopyrightText: 2022 Division of Intelligent Medical Systems, DKFZ
+# SPDX-License-Identifier: MIT
+
+import torch
+
+from htc.context.neighbour.find_neighbour_valid_pixels import (
+ count_rows_sum_eq_1,
+ neighbour_class_percentage_for_valid_pixels,
+)
+from htc.models.image.DatasetImage import DatasetImage
+
+
+def find_normalized_neighbour_matrix(dataset: DatasetImage, n_classes: int) -> torch.FloatTensor:
+ """
+ Calculate the normalized neighbourhood confusion matrix for all images.
+
+ Arg:
+ dataset: A DatasetImage class which needs to contain a matrix for the 'labels' key and one for the 'valid_pixels' key.
+ n_classes: int number of different classes that appear in the dataset. (The neighbouir class percentage will only be calculated for these classes)
+ """
+ result = {}
+ rows_diff_from_0 = {}
+
+ # Group all images per label (what pig they are from) and add them
+ for sample in dataset:
+ # get neighbour matrix
+ subject_name = sample["image_name"].split("#")[0]
+ neighbour_matrix = neighbour_class_percentage_for_valid_pixels(
+ sample["labels"], sample["valid_pixels"], n_classes
+ )
+
+ # Add the matrices that have the same subject name & keep track
+ if subject_name in result:
+ result[subject_name] = torch.add(result[subject_name], neighbour_matrix)
+ else:
+ result[subject_name] = neighbour_matrix
+
+ if subject_name in rows_diff_from_0:
+ rows_diff_from_0[subject_name] = torch.add(
+ rows_diff_from_0[subject_name],
+ count_rows_sum_eq_1(neighbour_matrix),
+ )
+ else:
+ rows_diff_from_0[subject_name] = count_rows_sum_eq_1(neighbour_matrix)
+
+ # For each label, divide to get the average
+ normalized_result = {}
+ for key in result.keys():
+ normalized_result[key] = torch.div(result[key], rows_diff_from_0[key][:, None])
+ # Make sure that NaN turn into 0s
+ normalized_result[key] = torch.nan_to_num(normalized_result[key])
+
+ # Add all labels and divide
+ result_matrix = torch.zeros(n_classes, n_classes)
+ result_rows_diff_from_0 = torch.zeros(n_classes)
+
+ for key in normalized_result.keys():
+ result_matrix = torch.add(result_matrix, normalized_result[key])
+ result_rows_diff_from_0 = torch.add(
+ result_rows_diff_from_0,
+ count_rows_sum_eq_1(normalized_result[key]),
+ )
+ result_matrix = torch.nan_to_num(result_matrix)
+
+ normalized_result_matrix = torch.div(result_matrix, result_rows_diff_from_0[:, None])
+ return normalized_result_matrix
diff --git a/htc/context/settings_context.py b/htc/context/settings_context.py
index 05e4186..159cd60 100644
--- a/htc/context/settings_context.py
+++ b/htc/context/settings_context.py
@@ -69,6 +69,14 @@ def __init__(self):
"elastic": "#F4A460",
"baseline": self.network_colors["baseline#HSI"],
}
+ self.cmap_diverging = "PRGn"
+
+ self.labels_paper_renaming = {
+ "major_vein": "major vein",
+ "kidney_with_Gerotas_fascia": "kidney with Gerota's fascia",
+ "fat_subcutaneous": "subcutaneous fat",
+ "small_bowel": "small bowel",
+ }
# This also specifies which tasks we include in the paper (e.g. box plots)
self.task_name_mapping = {
@@ -82,6 +90,18 @@ def __init__(self):
"glove": "occlusion",
}
+ self.scenario_mapping = {
+ "semantic": "isolation",
+ "isolation_0": "isolation",
+ "isolation_cloth": "isolation",
+ "masks_isolation": "isolation",
+ "semantic2": "removal",
+ "removal_0": "removal",
+ "removal_cloth": "removal",
+ "no-glove": "occlusion",
+ "glove": "occlusion",
+ }
+
self.transforms = {
"organ_transplantation": {
"class": "htc.context.context_transforms>OrganTransplantation",
@@ -263,6 +283,9 @@ def __init__(self):
"masks_isolation": self.masks_isolation_dataset,
}
+ # The original superpixel runs got broken so we had to re-train the HSI and RGB models
+ self.superpixel_classification_timestamp = "2024-07-24_15-20-46"
+
self._results_dir = None
@property
@@ -286,6 +309,12 @@ def paper_dir(self) -> MultiPath:
target_dir.mkdir(parents=True, exist_ok=True)
return target_dir
+ @property
+ def paper_extended_dir(self) -> MultiPath:
+ target_dir = self.results_dir / "paper_extended"
+ target_dir.mkdir(parents=True, exist_ok=True)
+ return target_dir
+
@property
def best_transform_runs(self) -> dict[str, MultiPath]:
# Best runs for each transformation (found via find_best_transform_run())
@@ -327,5 +356,29 @@ def glove_runs_rgb(self) -> dict[str, MultiPath]:
),
}
+ @property
+ def glove_runs_granularities(self) -> dict[str, MultiPath]:
+ return {
+ "image": self.glove_runs["baseline"],
+ "patch_64": settings.training_dir / "patch/2024-07-19_10-26-33_default_64_glove",
+ "patch_32": settings.training_dir / "patch/2024-07-19_10-26-33_default_glove",
+ "superpixel_classification": (
+ settings.training_dir / "superpixel_classification/2024-07-19_10-26-33_default_glove"
+ ),
+ "pixel": settings.training_dir / "pixel/2024-07-19_10-26-33_default_glove",
+ }
+
+ @property
+ def glove_runs_granularities_rgb(self) -> dict[str, MultiPath]:
+ return {
+ "image": self.glove_runs_rgb["baseline"],
+ "patch_64": settings.training_dir / "patch/2024-07-19_10-26-33_default_64_rgb_glove",
+ "patch_32": settings.training_dir / "patch/2024-07-19_10-26-33_default_rgb_glove",
+ "superpixel_classification": (
+ settings.training_dir / "superpixel_classification/2024-07-19_10-26-33_default_rgb_glove"
+ ),
+ "pixel": settings.training_dir / "pixel/2024-07-19_10-26-33_default_rgb_glove",
+ }
+
settings_context = SettingContext()
diff --git a/htc/cpp/ParallelExecution.h b/htc/cpp/ParallelExecution.h
index fe514c8..edb736d 100644
--- a/htc/cpp/ParallelExecution.h
+++ b/htc/cpp/ParallelExecution.h
@@ -16,7 +16,7 @@ class ParallelExecution
public:
/**
* @brief Provides simple methods to parallelize for loops including helper functions for critical sections.
- *
+ *
* @param numbThreads specifies the number of threads used for parallelization (if not specified otherwise). Defaults to the number of cores available on the system (virtual + real cores)
*/
explicit ParallelExecution(const size_t numbThreads = std::thread::hardware_concurrency())
@@ -27,9 +27,9 @@ class ParallelExecution
/**
* @brief Stores results in a thread-safe way.
- *
+ *
* A mutex will automatically be locked on entry and unlocked on exit of this function. This is useful after the parallel computation when a common variable is accessed containing all the results.
- *
+ *
* @param callback includes the code which should be executed in a thread-safe way
*/
void setResult(const std::function& callback)
@@ -41,9 +41,9 @@ class ParallelExecution
/**
* @brief Writes messages to the console in a thread-safe way.
- *
+ *
* Same mutex behaviour as in ParallelExecution::setResult(). Useful if you don't want your console output get messed up.
- *
+ *
* @param message to print to the console
*/
void write(const std::string& message)
@@ -55,9 +55,9 @@ class ParallelExecution
/**
* @brief Executes index-based containers in parallel.
- *
+ *
* It is save to throw exceptions from inside the threads. They are catched and re-thrown later in the main thread.
- *
+ *
* @param idxBegin first index to start (including), e.g. 0
* @param idxEnd last index to start (including), e.g. container.size()
* @param callback this function will be called from each thread multiple times. Each time an associated index will be passed to the function
@@ -95,7 +95,7 @@ class ParallelExecution
}
std::deque threads(sizeThreads);
-
+
/* Calculate the index ranges */
const size_t n = idxEnd - idxBegin + 1; // Both are inclusive
const size_t nEqual = n / sizeThreads; // 38 / 12 = 3
@@ -103,7 +103,7 @@ class ParallelExecution
size_t d = 0; // The last part should be portioned equally between all threads
/*
-
+
# Thread 0
d = 0 -> d = 1
0*3, ..., 1*3-1 (+1)
diff --git a/htc/cpp/__init__.py b/htc/cpp/__init__.py
index 96e3ef3..70509ed 100644
--- a/htc/cpp/__init__.py
+++ b/htc/cpp/__init__.py
@@ -38,7 +38,15 @@ def _automatic_numpy_conversion(*args, **kwargs):
# Call the actual function
if conversion_happened:
# Return value should probably be a numpy array (because at least one argument was a numpy array)
- return func(*new_args, **new_kwargs).numpy()
+ res = func(*new_args, **new_kwargs)
+ if type(res) == tuple:
+ return tuple(r.numpy() for r in res)
+ elif type(res) == list:
+ return [r.numpy() for r in res]
+ elif type(res) == dict:
+ return {k: v.numpy() for k, v in res.items()}
+ else:
+ return res.numpy()
else:
return func(*new_args, **new_kwargs)
@@ -133,16 +141,22 @@ def tensor_mapping(tensor: Union[torch.Tensor, np.ndarray], mapping: dict[int, i
assert all(
type(k) == type(v) for k, v in mapping.items()
), "All keys and values of the mapping must have the same type"
- first_value = next(iter(mapping.values()))
-
- if isinstance(first_value, int):
- assert not tensor.is_floating_point(), f"The tensor must have an integer type ({tensor.dtype = })"
- return htc._cpp.tensor_mapping_integer(tensor, mapping)
- elif isinstance(first_value, float):
- assert tensor.is_floating_point(), f"The tensor must have an floating type ({tensor.dtype = })"
- return htc._cpp.tensor_mapping_floating(tensor, mapping)
+
+ if tensor.ndim == 0:
+ # Map scalar values directly (in-place)
+ tensor.fill_(mapping.get(tensor.item(), tensor.item()))
+ return tensor
else:
- raise ValueError(f"Invalid type: {type(first_value)}")
+ first_value = next(iter(mapping.values()))
+
+ if isinstance(first_value, int):
+ assert not tensor.is_floating_point(), f"The tensor must have an integer type ({tensor.dtype = })"
+ return htc._cpp.tensor_mapping_integer(tensor, mapping)
+ elif isinstance(first_value, float):
+ assert tensor.is_floating_point(), f"The tensor must have an floating type ({tensor.dtype = })"
+ return htc._cpp.tensor_mapping_floating(tensor, mapping)
+ else:
+ raise ValueError(f"Invalid type: {type(first_value)}")
@automatic_numpy_conversion
@@ -262,10 +276,11 @@ def hierarchical_bootstrapping(
def hierarchical_bootstrapping_labels(
- domain_mapping: dict[int, dict[int, list[int]]],
- label_mapping: dict[int, dict[int, list[int]]],
+ domain_subjects_images_mapping: dict[int, dict[int, list[int]]],
+ label_images_mapping: dict[int, list[int]],
n_labels: int,
n_bootstraps: int = 1000,
+ oversampling: bool = False,
) -> torch.Tensor:
"""
Creates bootstrap samples based on a three-level hierarchy (domain_name, subject_name, image_name) while always selecting all domains equally often in every bootstrap. Compared to `hierarchical_bootstrapping()`, this function takes the labels into account and always selects images with the same label for each domain tuple. For each domain and label, one subject and one image is selected, i.e. selection of different subjects is preferred over selecting many images per subject.
@@ -284,38 +299,46 @@ def hierarchical_bootstrapping_labels(
>>> print('ignore_line'); seed_everything(0) # doctest: +ELLIPSIS
ignore_line...
>>> domain_mapping = {
- ... 0: {0: [10, 11]}, # First camera, one subject with two images
- ... 1: {1: [20, 30], 2: [40]} # Second camera, two subjects with two and one image each
+ ... 0: {0: [10, 11]}, # First camera, one subject with two images
+ ... 1: {1: [20, 30], 2: [40]}, # Second camera, two subjects with two and one image each
... }
- >>> label_mapping = {
- ... 100: {0: [10, 11], 1: [20]}, # Images 10, 11 and 20 have label 100
- ... 200: {0: [10], 1: [30], 2: [40]} # Images 10, 30 and 40 have label 200
+ >>> label_images_mapping = {
+ ... 100: [10, 11, 20], # Images 10, 11 and 20 have label 100
+ ... 200: [10, 30, 40], # Images 10, 30 and 40 have label 200
... }
- >>> hierarchical_bootstrapping_labels(domain_mapping, label_mapping, n_labels=2, n_bootstraps=4)
+ >>> hierarchical_bootstrapping_labels(domain_mapping, label_images_mapping, n_labels=2, n_bootstraps=4)
tensor([[20, 10, 30, 10],
[20, 10, 20, 11],
[20, 11, 20, 11],
[30, 10, 20, 11]])
Args:
- domain_mapping: Domain to subjects to images mapping.
- label_mapping: Label to subjects to images mapping.
- n_labels: Number of labels to draw with replacement. For each label, images from n_domains will be selected.
+ domain_subjects_images_mapping: Domain to subjects to images mapping.
+ label_images_mapping: Label to images mapping. Every image must occur in the domain_subjects_images_mapping exactly once.
+ n_labels: Number of labels to draw with replacement per domain. For example, with 3 domains and 2 labels, 6 images will be selected per bootstrap sample.
n_bootstraps: Total number of bootstraps.
+ oversampling: If True, instead selecting the labels randomly, the least currently chosen label is selected first. This is achieved by keeping an account for the already selected labels (including every label for each image) which is updated whenever selecting an image. This may still not yield a perfect balance across labels because some labels appear on nearly all images (e.g., background) but underrepresented classes are at least selected as often as possible.
Returns: Matrix of shape (n_bootstraps, n_domains * n_labels) with the bootstraps. It contains the values provided for the images (final layer in the mappings).
"""
- n_domains = len(set(domain_mapping.keys()))
- subjects2domain = {s: d for d, subjects in domain_mapping.items() for s in subjects}
- for label, subjects in label_mapping.items():
+ n_domains = len(set(domain_subjects_images_mapping.keys()))
+ images2domain = {
+ img: d
+ for d, subjects in domain_subjects_images_mapping.items()
+ for images in subjects.values()
+ for img in images
+ }
+ for label, images in label_images_mapping.items():
assert (
- len({subjects2domain[s] for s in subjects}) == n_domains
- ), f"Label {label} is not present in all domains (only the subjects {subjects} have this label)"
+ len({images2domain[img] for img in images}) == n_domains
+ ), f"Label {label} is not present in all domains (only the images {images} have this label)"
# We are generating a random number which will be used as seed during bootstraping
# This produces different bootstraps when the user calls this function multiple times while still allowing to set a seed
seed = torch.randint(0, torch.iinfo(torch.int32).max, (1,), dtype=torch.int32).item()
- bootstraps = htc._cpp.hierarchical_bootstrapping_labels(domain_mapping, label_mapping, n_labels, n_bootstraps, seed)
+ bootstraps = htc._cpp.hierarchical_bootstrapping_labels(
+ domain_subjects_images_mapping, label_images_mapping, n_labels, n_bootstraps, oversampling, seed
+ )
assert bootstraps.shape == (n_bootstraps, n_domains * n_labels)
return bootstraps
diff --git a/htc/cpp/colorchecker_automask.cpp b/htc/cpp/colorchecker_automask.cpp
index 8707b4e..5180123 100644
--- a/htc/cpp/colorchecker_automask.cpp
+++ b/htc/cpp/colorchecker_automask.cpp
@@ -67,7 +67,7 @@ class ColorcheckerAutomask {
{"square_dist_horizontal", this->square_dist_horizontal + this->safety_margin + best_param.delta_horizontal},
{"square_dist_vertical", this->square_dist_vertical + this->safety_margin + best_param.delta_vertical},
};
-
+
if (this->cc_board == "cc_passport") {
// Search for the right part on the right image side
this->generate_parameters(/*offset_left_min_start=*/this->img_width / 2, /*offset_left_stop=*/this->img_width);
diff --git a/htc/cpp/evaluate_superpixels.cpp b/htc/cpp/evaluate_superpixels.cpp
index 4cbda77..e75ab31 100644
--- a/htc/cpp/evaluate_superpixels.cpp
+++ b/htc/cpp/evaluate_superpixels.cpp
@@ -8,11 +8,11 @@ std::tuple spxs_predictions(torch::Tensor& spxs, t
spxs = spxs.flatten();
labels = labels.flatten();
mask = mask.flatten();
-
+
auto spxs_a = spxs.accessor();
auto labels_a = labels.accessor();
auto mask_a = mask.accessor();
-
+
// Count for each superpixel which labels the corresponding pixels have
auto spx_label_counts = torch::zeros({spxs.max().item() + 1, n_classes}, torch::kInt32);
auto spx_label_counts_a = spx_label_counts.accessor();
@@ -23,18 +23,18 @@ std::tuple spxs_predictions(torch::Tensor& spxs, t
spx_label_counts_a[spxs_a[i]][labels_a[i]] += 1;
}
}
-
+
// The label of the superpixel is the mode of the labels, i.e. the max count
auto spx_label = spx_label_counts.argmax(1); // The index of the max count corresponds to the label of the superpixel (mask-only superpixels are assigned to the background)
auto spx_label_a2 = spx_label.accessor();
-
+
// Project the calculated labels for each superpixel back to the image
auto predictions = torch::empty(shape[0] * shape[1], torch::kInt64);
auto predictions_a = predictions.accessor();
-
+
for (int i = 0; i < spxs_a.size(0); ++i) {
predictions_a[i] = spx_label_a2[spxs_a[i]];
}
-
+
return std::make_tuple(predictions.reshape(shape), spx_label_counts);
}
diff --git a/htc/cpp/hierarchical_bootstrapping.h b/htc/cpp/hierarchical_bootstrapping.h
index 188f649..c29f699 100644
--- a/htc/cpp/hierarchical_bootstrapping.h
+++ b/htc/cpp/hierarchical_bootstrapping.h
@@ -9,14 +9,16 @@ using Domain2Subjects = std::unordered_map>;
using Subject2Images = std::unordered_map>;
using Domain2Subjects2Images = std::unordered_map;
using Label2Subjects2Images = std::unordered_map;
+using Label2Images = std::unordered_map>;
+using Image2Labels = std::unordered_map>;
torch::Tensor hierarchical_bootstrapping(Domain2Subjects2Images& mapping, int n_subjects, int n_images, int n_bootstraps, unsigned int seed) {
std::mt19937 gen(seed); // Offers a good uniform distribution (https://www.boost.org/doc/libs/1_61_0/doc/html/boost_random/reference.html#boost_random.reference.generators)
-
+
auto n_domains = mapping.size();
auto bootstraps = torch::empty({n_bootstraps, static_cast(n_domains * n_subjects * n_images)}, torch::kInt64);
auto bootstraps_a = bootstraps.accessor();
-
+
// Cache domain2subjects vector mapping for later use (we don't want to do this all over again inside the bootstrap loop)
Domain2Subjects domain2subjects;
for (const auto &[domain_index, subject2images]: mapping) {
@@ -25,64 +27,141 @@ torch::Tensor hierarchical_bootstrapping(Domain2Subjects2Images& mapping, int n_
domain2subjects[domain_index].push_back(p.first);
}
}
-
+
for (int b = 0; b < n_bootstraps; ++b) {
int col = 0;
for (auto &[domain_index, subject2images]: mapping) {
std::vector& subjects = domain2subjects[domain_index];
-
+
std::uniform_int_distribution<> random_subject(0, subjects.size() - 1);
-
+
for (int subject_index = 0; subject_index < n_subjects; ++subject_index) {
auto& subject = subjects[random_subject(gen)];
auto& images = subject2images[subject];
std::uniform_int_distribution<> random_image(0, images.size() - 1);
-
+
for (int image_index = 0; image_index < n_images; ++image_index) {
bootstraps_a[b][col++] = images[random_image(gen)];
}
}
}
}
-
+
return bootstraps;
}
-torch::Tensor hierarchical_bootstrapping_labels(Domain2Subjects2Images& domain_mapping, Label2Subjects2Images& label_mapping, int n_labels, int n_bootstraps, unsigned int seed) {
- std::mt19937 gen(seed); // Offers a good uniform distribution (https://www.boost.org/doc/libs/1_61_0/doc/html/boost_random/reference.html#boost_random.reference.generators)
-
- auto n_domains = domain_mapping.size();
- auto bootstraps = torch::empty({ n_bootstraps, static_cast(n_domains * n_labels) }, torch::kInt64);
- auto bootstraps_a = bootstraps.accessor();
-
- // Cache domain2subjects vector mapping for later use (we don't want to do this all over again inside the bootstrap loop)
+Domain2Subjects construct_domain_subjects_mapping(const Domain2Subjects2Images& domain_subjects_images_mapping) {
Domain2Subjects domain2subjects;
- for (const auto& [domain_index, subject2images] : domain_mapping) {
+ for (const auto& [domain_index, subject2images] : domain_subjects_images_mapping) {
domain2subjects[domain_index].reserve(subject2images.size());
for (auto const& p : subject2images) {
domain2subjects[domain_index].push_back(p.first);
}
}
+ return domain2subjects;
+}
+
+Label2Subjects2Images construct_label_subjects_images_mapping(const Domain2Subjects2Images& domain_subjects_images_mapping, const Label2Images& label_images_mapping) {
+ Label2Subjects2Images label_subjects_images_mapping;
+ for (const auto& [label, label_images] : label_images_mapping) {
+ for (int64_t label_image : label_images) {
+
+ // Search for the current image in the domain mapping
+ bool found = false;
+ for (const auto& [domain_index, subject2images] : domain_subjects_images_mapping) {
+ for (const auto& [subject, images] : subject2images) {
+ if (std::find(images.begin(), images.end(), label_image) != images.end()) {
+ label_subjects_images_mapping[label][subject].push_back(label_image);
+ found = true;
+ break;
+ }
+ }
+ if (found) {
+ break;
+ }
+ }
+ }
+ }
+
+ return label_subjects_images_mapping;
+}
+
+Image2Labels construct_image_labels_mapping(const Label2Images& label_images_mapping) {
+ Image2Labels image_labels_mapping;
+ for (const auto& [label, images] : label_images_mapping) {
+ for (int64_t image : images) {
+ image_labels_mapping[image].push_back(label);
+ }
+ }
+
+ return image_labels_mapping;
+}
+
+torch::Tensor hierarchical_bootstrapping_labels(Domain2Subjects2Images& domain_subjects_images_mapping, Label2Images& label_images_mapping, int n_labels, int n_bootstraps, bool oversampling, unsigned int seed) {
+ std::mt19937 gen(seed); // Offers a good uniform distribution (https://www.boost.org/doc/libs/1_61_0/doc/html/boost_random/reference.html#boost_random.reference.generators)
+
+ auto n_domains = domain_subjects_images_mapping.size();
+ auto bootstraps = torch::empty({ n_bootstraps, static_cast(n_domains * n_labels) }, torch::kInt64);
+ auto bootstraps_a = bootstraps.accessor();
+
+ // Cache common mappings for later use (we don't want to do this all over again inside the bootstrap loop)
+ Domain2Subjects domain_subjects_mapping = construct_domain_subjects_mapping(domain_subjects_images_mapping);
+ Label2Subjects2Images label_subjects_images_mapping = construct_label_subjects_images_mapping(domain_subjects_images_mapping, label_images_mapping);
+ Image2Labels image_labels_mapping = construct_image_labels_mapping(label_images_mapping);
+
// List of possible labels
std::vector labels;
- labels.reserve(label_mapping.size());
- for (auto& item : label_mapping) {
+ labels.reserve(label_images_mapping.size());
+ for (auto& item : label_images_mapping) {
labels.push_back(item.first);
}
std::uniform_int_distribution<> random_label(0, labels.size() - 1);
+ // Keep track of how many times each label has been selected
+ std::unordered_map label_counts;
+ for (int64_t label : labels) {
+ label_counts[label] = 0;
+ }
+
for (int b = 0; b < n_bootstraps; ++b) {
int col = 0;
// For each label, we select per domain one subject and one image and repeat this process n_labels times
while (col < bootstraps.size(1)) {
- auto label = labels[random_label(gen)];
- auto& label_subjects = label_mapping[label];
+ // First select a label
+ int64_t label;
+ if (oversampling) {
+ // Find all labels which have the current least occurrence (there might be multiple labels with the same count)
+ std::unordered_map> min_count_labels;
+ int64_t min_count = std::numeric_limits::max();
+ for (auto& [l, count] : label_counts) {
+ min_count_labels[count].push_back(l);
+ if (count < min_count) {
+ min_count = count;
+ }
+ }
- for (auto& [domain_index, subject2images] : domain_mapping) {
- std::vector& subjects_domain = domain2subjects[domain_index];
+ auto& possible_labels = min_count_labels[min_count];
+ if (possible_labels.size() > 1) {
+ // From the labels with the lowest count, select one randomly
+ std::uniform_int_distribution<> random_possible_label(0, possible_labels.size() - 1);
+ label = possible_labels[random_possible_label(gen)];
+ }
+ else {
+ // If there is only one possible label, we do not need to select anything randomly
+ label = possible_labels[0];
+ }
+ }
+ else {
+ label = labels[random_label(gen)];
+ }
+
+ auto& label_subjects = label_subjects_images_mapping[label];
+
+ for (auto& [domain_index, subject2images] : domain_subjects_images_mapping) {
+ std::vector& subjects_domain = domain_subjects_mapping[domain_index];
// Select the subjects which have images of the current label
std::vector subjects;
@@ -98,7 +177,15 @@ torch::Tensor hierarchical_bootstrapping_labels(Domain2Subjects2Images& domain_m
// Select random image
auto& images = label_subjects[subject];
std::uniform_int_distribution<> random_image(0, images.size() - 1);
- bootstraps_a[b][col++] = images[random_image(gen)];
+ int64_t image = images[random_image(gen)];
+ bootstraps_a[b][col++] = image;
+
+ if (oversampling) {
+ // Update label counts for all the labels which appear in the selected image
+ for (int64_t label : image_labels_mapping[image]) {
+ label_counts[label]++;
+ }
+ }
}
}
}
diff --git a/htc/cpp/map_label_image.cpp b/htc/cpp/map_label_image.cpp
index 0699b6b..993e1b7 100644
--- a/htc/cpp/map_label_image.cpp
+++ b/htc/cpp/map_label_image.cpp
@@ -5,10 +5,10 @@
torch::Tensor map_label_image(const torch::Tensor& label_image, std::unordered_map>& label_color_mapping) {
auto mapped_image = torch::empty({label_image.size(0), label_image.size(1), 4}, torch::kFloat32);
-
+
auto label_image_a = label_image.accessor();
auto mapped_image_a = mapped_image.accessor();
-
+
for (int row = 0; row < label_image_a.size(0); ++row) {
for (int col = 0; col < label_image_a.size(1); ++col) {
auto label = label_image_a[row][col];
@@ -19,6 +19,6 @@ torch::Tensor map_label_image(const torch::Tensor& label_image, std::unordered_m
mapped_image_a[row][col][3] = std::get<3>(color);
}
}
-
+
return mapped_image;
}
diff --git a/htc/cpp/nunique.cpp b/htc/cpp/nunique.cpp
index c1566ee..35479a7 100644
--- a/htc/cpp/nunique.cpp
+++ b/htc/cpp/nunique.cpp
@@ -30,7 +30,7 @@ torch::Tensor nunique(const torch::Tensor& in, int64_t dim) {
*reinterpret_cast(out_data) = values.size();
};
-
+
// Unfortunately, we need to execute the loop in serial because the unordered_set is not thread safe
iter.serial_for_each(loop, {0, iter.numel()});
});
diff --git a/htc/cpp/segmentation_mask.cpp b/htc/cpp/segmentation_mask.cpp
index 5f89b98..24f65e9 100644
--- a/htc/cpp/segmentation_mask.cpp
+++ b/htc/cpp/segmentation_mask.cpp
@@ -5,10 +5,10 @@
torch::Tensor segmentation_mask(const torch::Tensor& label_image, std::map, int>& color_mapping) {
auto seg = torch::empty({label_image.size(0), label_image.size(1)}, torch::kUInt8);
-
+
auto seg_a = seg.accessor();
auto label_a = label_image.accessor();
-
+
for (int row = 0; row < label_a.size(0); ++row) {
for (int col = 0; col < label_a.size(1); ++col) {
auto pixel = label_a[row][col];
@@ -20,6 +20,6 @@ torch::Tensor segmentation_mask(const torch::Tensor& label_image, std::map None:
results: Results from the computation step (one entry per path).
"""
pass
+
+ def _compute_necessary(self, image_name: str) -> bool:
+ """
+ Check if the computation for the given image is necessary.
+
+ Args:
+ image_name: Name of the image.
+
+ Returns: True if the computation is necessary (file does not exists or has no valid size), False otherwise.
+ """
+ target_path = self.output_dir / f"{image_name}.{self.file_type}"
+ return not (target_path.is_file() and target_path.stat().st_size > 0)
diff --git a/htc/data_processing/run_l1_normalization.py b/htc/data_processing/run_l1_normalization.py
index 8a8ea04..a936e84 100644
--- a/htc/data_processing/run_l1_normalization.py
+++ b/htc/data_processing/run_l1_normalization.py
@@ -16,14 +16,21 @@
class L1Normalization(DatasetIteration):
- def __init__(self, paths: list[DataPath], file_type: str, output_dir: Path = None, regenerate: bool = False):
+ def __init__(
+ self,
+ paths: list[DataPath],
+ file_type: str,
+ output_dir: Path = None,
+ regenerate: bool = False,
+ folder_name: str = "L1",
+ ):
super().__init__(paths)
self.file_type = file_type
if output_dir is None:
- self.output_dir = settings.intermediates_dir_all / "preprocessing" / "L1"
+ self.output_dir = settings.intermediates_dir_all / "preprocessing" / folder_name
else:
- self.output_dir = output_dir / "L1"
+ self.output_dir = output_dir / folder_name
self.output_dir.mkdir(exist_ok=True, parents=True)
config = Config({
@@ -36,7 +43,7 @@ def __init__(self, paths: list[DataPath], file_type: str, output_dir: Path = Non
clear_directory(self.output_dir)
def compute(self, i: int) -> None:
- if not (self.output_dir / f"{self.paths[i].image_name()}.{self.file_type}").exists():
+ if self._compute_necessary(self.paths[i].image_name()):
sample = self.dataset[i]
img = sample["features"].numpy().astype(np.float16)
diff --git a/htc/data_processing/run_median_spectra.py b/htc/data_processing/run_median_spectra.py
index 7af4411..39af479 100644
--- a/htc/data_processing/run_median_spectra.py
+++ b/htc/data_processing/run_median_spectra.py
@@ -112,13 +112,16 @@ def compute(self, i: int) -> list[dict]:
current_row = {"image_name": path.image_name()}
current_row |= path.image_name_typed()
+ # Avoid std nan values for single-element spectra
+ correction = 0 if spectra.size(0) == 1 else 1
+
current_row |= {
"label_index": label_index,
"label_name": label_name,
"median_spectrum": spectra.quantile(q=0.5, dim=0).numpy(), # Same as np.median
- "std_spectrum": spectra.std(dim=0).numpy(),
+ "std_spectrum": spectra.std(dim=0, correction=correction).numpy(),
"median_normalized_spectrum": spectra_normalized.quantile(q=0.5, dim=0).numpy(),
- "std_normalized_spectrum": spectra_normalized.std(dim=0).numpy(),
+ "std_normalized_spectrum": spectra_normalized.std(dim=0, correction=correction).numpy(),
"n_pixels": counts.item(),
"median_sto2": np.median(selected_sto2.data),
"std_sto2": np.std(selected_sto2.data),
diff --git a/htc/data_processing/run_parameter_images.py b/htc/data_processing/run_parameter_images.py
index 365e31d..ed799e3 100644
--- a/htc/data_processing/run_parameter_images.py
+++ b/htc/data_processing/run_parameter_images.py
@@ -14,14 +14,21 @@
class ParameterImages(DatasetIteration):
- def __init__(self, paths: list[DataPath], file_type: str, output_dir: Path = None, regenerate: bool = False):
+ def __init__(
+ self,
+ paths: list[DataPath],
+ file_type: str,
+ output_dir: Path = None,
+ regenerate: bool = False,
+ folder_name: str = "parameter_images",
+ ):
super().__init__(paths)
self.file_type = file_type
if output_dir is None:
- self.output_dir = settings.intermediates_dir_all / "preprocessing" / "parameter_images"
+ self.output_dir = settings.intermediates_dir_all / "preprocessing" / folder_name
else:
- self.output_dir = output_dir / "parameter_images"
+ self.output_dir = output_dir / folder_name
self.output_dir.mkdir(exist_ok=True, parents=True)
if regenerate:
@@ -30,7 +37,7 @@ def __init__(self, paths: list[DataPath], file_type: str, output_dir: Path = Non
def compute(self, i: int) -> None:
path = self.paths[i]
- if not (self.output_dir / f"{path.image_name()}.{self.file_type}").exists():
+ if self._compute_necessary(path.image_name()):
cube = path.read_cube()
sto2 = path.compute_sto2(cube)
params = {
diff --git a/htc/data_processing/run_raw16.py b/htc/data_processing/run_raw16.py
index ab5d276..f0935ec 100644
--- a/htc/data_processing/run_raw16.py
+++ b/htc/data_processing/run_raw16.py
@@ -23,15 +23,17 @@ def __init__(
output_dir: Path = None,
regenerate: bool = False,
precision: str = "16",
+ folder_name: str = None,
):
super().__init__(paths)
self.file_type = file_type
self.precision = precision
+ _folder_name = f"raw{self.precision}" if folder_name is None else folder_name
if output_dir is None:
- self.output_dir = settings.intermediates_dir_all / "preprocessing" / f"raw{self.precision}"
+ self.output_dir = settings.intermediates_dir_all / "preprocessing" / _folder_name
else:
- self.output_dir = output_dir / f"raw{self.precision}"
+ self.output_dir = output_dir / _folder_name
self.output_dir.mkdir(exist_ok=True, parents=True)
config = Config({
@@ -44,7 +46,7 @@ def __init__(
clear_directory(self.output_dir)
def compute(self, i: int) -> None:
- if not (self.output_dir / f"{self.paths[i].image_name()}.{self.file_type}").exists():
+ if self._compute_necessary(self.paths[i].image_name()):
sample = self.dataset[i]
img = sample["features"].numpy()
if self.precision == "16":
diff --git a/htc/data_processing/run_standardization.py b/htc/data_processing/run_standardization.py
index 8b31596..38fa05a 100644
--- a/htc/data_processing/run_standardization.py
+++ b/htc/data_processing/run_standardization.py
@@ -38,16 +38,16 @@ def channel_params(self) -> np.ndarray:
def pixel_params(self) -> np.ndarray:
# Pixel params based on the channel sums
total_elements = self.total_elements * np.prod(self.sum.shape)
- sum = np.sum(self.sum)
+ total = np.sum(self.sum)
sum_squarred = np.sum(self.sum_squarred)
- mean = sum / total_elements
- std = np.sqrt((sum_squarred - sum**2 / total_elements) / total_elements)
+ mean = total / total_elements
+ std = np.sqrt((sum_squarred - total**2 / total_elements) / total_elements)
return mean, std
-def calc_standardization(datasets: dict[str, DataPath]) -> dict[str, float]:
+def calc_standardization(datasets: dict[str, list[DataPath]]) -> dict[str, float]:
rs_hsi = RunningStats(channels=100)
rs_tpi = RunningStats(channels=4)
rs_rgb = RunningStats(channels=3)
@@ -87,7 +87,7 @@ def calc_standardization_folds(specs: DataSpecification) -> dict[str, dict[str,
if __name__ == "__main__":
- prep = ParserPreprocessing(description="Precomputes a filter for all images")
+ prep = ParserPreprocessing(description="Precomputes standardization statistics for each fold")
paths = prep.get_paths() # Must always be called
assert (
prep.args.spec is not None
diff --git a/htc/data_processing/run_superpixel_prediction.py b/htc/data_processing/run_superpixel_prediction.py
index 462f007..a1c459a 100644
--- a/htc/data_processing/run_superpixel_prediction.py
+++ b/htc/data_processing/run_superpixel_prediction.py
@@ -39,7 +39,6 @@ def aggregate_results(i: int) -> dict[str, Union[dict, Any]]:
if __name__ == "__main__":
config = Config.from_model_name("default", "superpixel_classification")
- config["input/no_features"] = True
paths = list(DataPath.iterate(settings.data_dirs.semantic))
dataset_all = DatasetImage(paths, train=False, config=config)
diff --git a/htc/evaluation/ExperimentAnalysis.ipynb b/htc/evaluation/ExperimentAnalysis.ipynb
index 1eaa43a..33e269d 100644
--- a/htc/evaluation/ExperimentAnalysis.ipynb
+++ b/htc/evaluation/ExperimentAnalysis.ipynb
@@ -33,11 +33,12 @@
"from htc.utils.visualization import (\n",
" create_class_scores_figure,\n",
" create_confusion_figure,\n",
- " create_confusion_figure_comparison,\n",
" create_ece_figure,\n",
" create_running_metric_plot,\n",
+ " create_spec_labels_figure,\n",
" create_surface_dice_plot,\n",
" create_training_stats_figure,\n",
+ " create_training_stats_label_figure,\n",
" show_class_scores_epoch,\n",
" show_loss_chart,\n",
" visualize_dict,\n",
@@ -55,7 +56,7 @@
"outputs": [],
"source": [
"# Parameter for papermill\n",
- "run_dir = settings.training_dir / \"image/2022-01-27_15-52-09_generated_default_lr=0.001\""
+ "run_dir = settings.training_dir / \"image/2022-02-03_22-58-44_generated_default_model_comparison\""
]
},
{
@@ -68,7 +69,7 @@
"output_type": "stream",
"text": [
"Model: image\n",
- "Experiment: 2022-01-27_15-52-09_generated_default_lr=0.001\n"
+ "Experiment: 2022-02-03_22-58-44_generated_default_model_comparison\n"
]
}
],
@@ -157,8 +158,8 @@
"
0
\n",
"
49
\n",
"
NaN
\n",
- "
0.925478
\n",
- "
0.716378
\n",
+ "
0.814283
\n",
+ "
0.676449
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
@@ -169,11 +170,11 @@
"
0
\n",
"
99
\n",
"
NaN
\n",
- "
0.677840
\n",
- "
0.642946
\n",
- "
0.151312
\n",
- "
1.192136
\n",
- "
0.770478
\n",
+ "
0.610165
\n",
+ "
0.570003
\n",
+ "
0.101304
\n",
+ "
1.109594
\n",
+ "
0.730523
\n",
" \n",
"
\n",
"
3
\n",
@@ -193,8 +194,8 @@
"
1
\n",
"
149
\n",
"
NaN
\n",
- "
0.502476
\n",
- "
0.495842
\n",
+ "
0.358511
\n",
+ "
0.425239
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
@@ -206,22 +207,22 @@
"text/plain": [
" fold_name epoch_index step lr-Adam train/ce_loss_step \\\n",
"0 fold_P041,P060,P069 0 0 0.00100 NaN \n",
- "1 fold_P041,P060,P069 0 49 NaN 0.925478 \n",
- "2 fold_P041,P060,P069 0 99 NaN 0.677840 \n",
+ "1 fold_P041,P060,P069 0 49 NaN 0.814283 \n",
+ "2 fold_P041,P060,P069 0 99 NaN 0.610165 \n",
"3 fold_P041,P060,P069 0 100 0.00099 NaN \n",
- "4 fold_P041,P060,P069 1 149 NaN 0.502476 \n",
+ "4 fold_P041,P060,P069 1 149 NaN 0.358511 \n",
"\n",
" train/dice_loss_step dice_metric train/ce_loss_epoch \\\n",
"0 NaN NaN NaN \n",
- "1 0.716378 NaN NaN \n",
- "2 0.642946 0.151312 1.192136 \n",
+ "1 0.676449 NaN NaN \n",
+ "2 0.570003 0.101304 1.109594 \n",
"3 NaN NaN NaN \n",
- "4 0.495842 NaN NaN \n",
+ "4 0.425239 NaN NaN \n",
"\n",
" train/dice_loss_epoch \n",
"0 NaN \n",
"1 NaN \n",
- "2 0.770478 \n",
+ "2 0.730523 \n",
"3 NaN \n",
"4 NaN "
]
@@ -267,83 +268,107 @@
"
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "rows = []\n",
+ "for fold_name, splits in spec:\n",
+ " for name, paths in splits.items():\n",
+ " r = {\n",
+ " \"fold_name\": fold_name,\n",
+ " \"split_name\": name,\n",
+ " }\n",
+ " labels = []\n",
+ " for p in paths:\n",
+ " labels += p.annotated_labels()\n",
+ " label_names, counts = np.unique(labels, return_counts=True)\n",
+ " r[\"label_name\"] = label_names\n",
+ " r[\"# images\"] = counts\n",
+ "\n",
+ " rows.append(r)\n",
+ "\n",
+ "df_labels = pd.DataFrame(rows)\n",
+ "df_labels = df_labels.explode([\"label_name\", \"# images\"])\n",
+ "\n",
+ "fig = px.bar(df_labels, x=\"label_name\", y=\"# images\", color=\"split_name\", facet_col=\"fold_name\", facet_col_wrap=2)\n",
+ "fig.update_layout(height=800, width=1200, template=\"plotly_white\")\n",
+ "fig.update_layout(\n",
+ " title_x=0.5, title_text=f\"image-level label distribution for each fold and split for the spec {path.stem}\"\n",
+ ")\n",
+ "\n",
+ "compress_html(target_dir / f\"{path.stem}_labels.html\", fig)\n",
+ "fig"
+ ]
}
],
"metadata": {
@@ -240,7 +3264,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.12"
+ "version": "3.11.9"
}
},
"nbformat": 4,
diff --git a/htc/models/data/SpecsGeneration.py b/htc/models/data/SpecsGeneration.py
index 6d26956..6098a40 100644
--- a/htc/models/data/SpecsGeneration.py
+++ b/htc/models/data/SpecsGeneration.py
@@ -27,6 +27,7 @@ def generate_dataset(self, target_folder: Path = None):
specs_path = target_folder / f"{self.name}.json"
with specs_path.open("w") as f:
json.dump(folds, f, indent=4)
+ f.write("\n") # Add newline at the end of the file so that all JSON files are formatted the same way
# Make sure we can read the specs file
DataSpecification(specs_path)
diff --git a/htc/models/data/data_spec.schema b/htc/models/data/data_spec.schema
new file mode 100644
index 0000000..a0e43d7
--- /dev/null
+++ b/htc/models/data/data_spec.schema
@@ -0,0 +1,38 @@
+{
+ "$schema": "http://json-schema.org/draft-04/schema#",
+ "$comment": "This schema file defines the common structure of our data specification files used by the htc framework to define the training folds and splits. We are storing the data specification files in this repository because they are fundamental for every training and we want to ensure that everyone uses the same splits. There are also several tests running against each data specification file (e.g., that they adhere to this schema definition).",
+ "type": "array",
+ "items": {
+ "description": "Each object in this list defines the training setup for one fold. The folds are trained in the order as defined in the data specification.",
+ "type": "object",
+ "properties": {
+ "fold_name": {
+ "description": "The name of the fold (will be used as folder name for the training run).",
+ "type": "string"
+ }
+ },
+ "patternProperties": {
+ "^(?:train|val|test)": {
+ "description": "The configuration of one split for this fold. Usually, there is a training, a validation and a testing split. The test split is usually the same for all folds. There may be more than one split per split type, e.g., two validation splits. However, for train and test splits, all paths from all respective splits will be combined (e.g., paths from train_1 and train_2 become the training paths) and sorted by image name (not relevant for training but for testing). Only for validation splits, a list of datasets will be used (e.g., in HTCLightning.datasets_val) with the index being defined by the order of the splits in the spec.",
+ "type": "object",
+ "properties": {
+ "image_names": {
+ "description": "List of unique image names which should be included in this split. The name may also include the desired annotation name as defined by the DataPath class.",
+ "type": "array",
+ "items": {
+ "type": "string"
+ }
+ },
+ "data_path_class": {
+ "description": "Per default, our htc.tivita.DataPath class will be used to load the images. However, with this key, it is also possible to specify a custom class which should be used to load the images. The custom class must have a from_image_name() method which gets the name of an image and should return an instance of the class. Specify the class in the format module>class (e.g., htc.bias.bias_aware_ML.SimulationPath>SimulationPath) or refer to the type_from_string() function for more details.",
+ "type": "string"
+ }
+ },
+ "required": ["image_names"],
+ "additionalProperties": false
+ }
+ },
+ "required": ["fold_name"],
+ "additionalProperties": false
+ }
+}
diff --git a/htc/models/data/pigs_semantic-only_5foldsV2.json b/htc/models/data/pigs_semantic-only_5foldsV2.json
index 4807bdc..b926ddb 100644
--- a/htc/models/data/pigs_semantic-only_5foldsV2.json
+++ b/htc/models/data/pigs_semantic-only_5foldsV2.json
@@ -2,8 +2,6 @@
{
"fold_name": "fold_P041,P060,P069",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P044#2020_02_01_09_51_15",
"P044#2020_02_01_09_51_31",
@@ -262,8 +260,6 @@
]
},
"val_semantic_unknown": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -342,8 +338,6 @@
]
},
"val_semantic_known": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P044#2020_02_01_09_57_47",
"P045#2020_02_05_11_01_20",
@@ -360,8 +354,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -535,8 +527,6 @@
{
"fold_name": "fold_P044,P050,P059",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_39",
@@ -819,8 +809,6 @@
]
},
"val_semantic_unknown": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P044#2020_02_01_09_51_15",
"P044#2020_02_01_09_51_31",
@@ -875,8 +863,6 @@
]
},
"val_semantic_known": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_01_09",
"P045#2020_02_05_10_59_32",
@@ -893,8 +879,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -1068,8 +1052,6 @@
{
"fold_name": "fold_P045,P061,P071",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_01_09",
"P041#2019_12_14_12_01_39",
@@ -1343,8 +1325,6 @@
]
},
"val_semantic_unknown": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P045#2020_02_05_10_54_19",
"P045#2020_02_05_10_55_07",
@@ -1408,8 +1388,6 @@
]
},
"val_semantic_known": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P044#2020_02_01_09_58_48",
@@ -1426,8 +1404,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -1601,8 +1577,6 @@
{
"fold_name": "fold_P047,P049,P070",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -1870,8 +1844,6 @@
]
},
"val_semantic_unknown": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P047#2020_02_07_17_28_15",
"P047#2020_02_07_17_28_38",
@@ -1941,8 +1913,6 @@
]
},
"val_semantic_known": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_01_39",
"P044#2020_02_01_09_52_12",
@@ -1959,8 +1929,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -2134,8 +2102,6 @@
{
"fold_name": "fold_P048,P057,P058",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -2376,8 +2342,6 @@
]
},
"val_semantic_unknown": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P048#2020_02_08_10_34_35",
"P048#2020_02_08_10_35_01",
@@ -2474,8 +2438,6 @@
]
},
"val_semantic_known": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_01_39",
"P044#2020_02_01_09_58_31",
@@ -2492,8 +2454,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -2664,4 +2624,4 @@
]
}
}
-]
\ No newline at end of file
+]
diff --git a/htc/models/data/pigs_semantic-only_dataset-size_repetitions=5V2.json b/htc/models/data/pigs_semantic-only_dataset-size_repetitions=5V2.json
index 1efcaa3..386b68d 100644
--- a/htc/models/data/pigs_semantic-only_dataset-size_repetitions=5V2.json
+++ b/htc/models/data/pigs_semantic-only_dataset-size_repetitions=5V2.json
@@ -2,8 +2,6 @@
{
"fold_name": "fold_pigs=1_seed=0",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P044#2020_02_01_09_51_15",
"P044#2020_02_01_09_51_31",
@@ -26,8 +24,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -191,8 +187,6 @@
{
"fold_name": "fold_pigs=1_seed=1",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P047#2020_02_07_17_28_15",
"P047#2020_02_07_17_28_38",
@@ -207,8 +201,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -372,8 +364,6 @@
{
"fold_name": "fold_pigs=1_seed=2",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P061#2020_05_15_10_26_26",
"P061#2020_05_15_10_26_51",
@@ -396,8 +386,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -561,8 +549,6 @@
{
"fold_name": "fold_pigs=1_seed=3",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P069#2020_07_23_09_55_15",
"P069#2020_07_23_09_55_42",
@@ -587,8 +573,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -752,8 +736,6 @@
{
"fold_name": "fold_pigs=1_seed=4",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P049#2020_02_11_19_09_49",
"P049#2020_02_11_19_10_20",
@@ -774,8 +756,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -939,8 +919,6 @@
{
"fold_name": "fold_pigs=2_seed=0",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P044#2020_02_01_09_51_15",
"P044#2020_02_01_09_51_31",
@@ -968,8 +946,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -1133,8 +1109,6 @@
{
"fold_name": "fold_pigs=2_seed=1",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P047#2020_02_07_17_28_15",
"P047#2020_02_07_17_28_38",
@@ -1173,8 +1147,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -1338,8 +1310,6 @@
{
"fold_name": "fold_pigs=2_seed=2",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P048#2020_02_08_10_34_35",
"P048#2020_02_08_10_35_01",
@@ -1377,8 +1347,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -1542,8 +1510,6 @@
{
"fold_name": "fold_pigs=2_seed=3",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P048#2020_02_08_10_34_35",
"P048#2020_02_08_10_35_01",
@@ -1583,8 +1549,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -1748,8 +1712,6 @@
{
"fold_name": "fold_pigs=2_seed=4",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -1778,8 +1740,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -1943,8 +1903,6 @@
{
"fold_name": "fold_pigs=3_seed=0",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P044#2020_02_01_09_51_15",
"P044#2020_02_01_09_51_31",
@@ -2012,8 +1970,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -2177,8 +2133,6 @@
{
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"image_names": [
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"P047#2020_02_07_17_28_38",
@@ -2222,8 +2176,6 @@
]
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"P043#2019_12_20_10_05_48",
@@ -2387,8 +2339,6 @@
{
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"P048#2020_02_08_10_35_01",
@@ -2442,8 +2392,6 @@
]
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"P043#2019_12_20_10_05_48",
@@ -2607,8 +2555,6 @@
{
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"P044#2020_02_01_09_51_15",
"P044#2020_02_01_09_51_31",
@@ -2666,8 +2612,6 @@
]
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"P043#2019_12_20_10_05_48",
@@ -2831,8 +2775,6 @@
{
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"P041#2019_12_14_12_01_09",
@@ -2866,8 +2808,6 @@
]
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"P043#2019_12_20_10_05_48",
@@ -3031,8 +2971,6 @@
{
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"P044#2020_02_01_09_51_31",
@@ -3122,8 +3060,6 @@
]
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"P043#2019_12_20_10_05_48",
@@ -3287,8 +3223,6 @@
{
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"P045#2020_02_05_10_55_07",
@@ -3350,8 +3284,6 @@
]
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"P043#2019_12_20_10_05_48",
@@ -3515,8 +3447,6 @@
{
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"P041#2019_12_14_12_01_09",
@@ -3574,8 +3504,6 @@
]
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"P043#2019_12_20_10_05_48",
@@ -3739,8 +3667,6 @@
{
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"P044#2020_02_01_09_51_31",
@@ -3803,8 +3729,6 @@
]
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@@ -3968,8 +3892,6 @@
{
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"P041#2019_12_14_12_01_09",
@@ -4013,8 +3935,6 @@
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@@ -4178,8 +4098,6 @@
{
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"P044#2020_02_01_09_51_31",
@@ -4288,8 +4206,6 @@
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@@ -4453,8 +4369,6 @@
{
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"P045#2020_02_05_10_55_07",
@@ -4554,8 +4468,6 @@
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"P043#2019_12_20_10_05_48",
@@ -4719,8 +4631,6 @@
{
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@@ -4800,8 +4710,6 @@
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@@ -4965,8 +4873,6 @@
{
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"P044#2020_02_01_09_51_31",
@@ -5047,8 +4953,6 @@
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"P043#2019_12_20_10_05_48",
@@ -5212,8 +5116,6 @@
{
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"P041#2019_12_14_12_01_09",
@@ -5272,8 +5174,6 @@
]
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@@ -5437,8 +5337,6 @@
{
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@@ -5562,8 +5460,6 @@
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@@ -5727,8 +5623,6 @@
{
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@@ -5843,8 +5737,6 @@
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@@ -6008,8 +5900,6 @@
{
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@@ -6099,8 +5989,6 @@
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@@ -6264,8 +6152,6 @@
{
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"P044#2020_02_01_09_51_31",
@@ -6364,8 +6250,6 @@
]
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@@ -6529,8 +6413,6 @@
{
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@@ -6611,8 +6493,6 @@
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"P043#2019_12_20_10_05_48",
@@ -6776,8 +6656,6 @@
{
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"P044#2020_02_01_09_51_31",
@@ -6919,8 +6797,6 @@
]
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"P043#2019_12_20_10_05_48",
@@ -7084,8 +6960,6 @@
{
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"P044#2020_02_01_09_51_31",
@@ -7218,8 +7092,6 @@
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"P043#2019_12_20_10_05_48",
@@ -7383,8 +7255,6 @@
{
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"P041#2019_12_14_12_01_09",
@@ -7492,8 +7362,6 @@
]
},
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"P043#2019_12_20_10_05_48",
@@ -7657,8 +7525,6 @@
{
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"P044#2020_02_01_09_51_15",
"P044#2020_02_01_09_51_31",
@@ -7781,8 +7647,6 @@
]
},
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"P043#2019_12_20_10_05_48",
@@ -7946,8 +7810,6 @@
{
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"image_names": [
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"P041#2019_12_14_12_01_09",
@@ -8046,8 +7908,6 @@
]
},
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"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -8211,8 +8071,6 @@
{
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"P044#2020_02_01_09_51_31",
@@ -8382,8 +8240,6 @@
]
},
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"P043#2019_12_20_10_05_48",
@@ -8547,8 +8403,6 @@
{
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"P044#2020_02_01_09_51_15",
"P044#2020_02_01_09_51_31",
@@ -8709,8 +8563,6 @@
]
},
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"P043#2019_12_20_10_05_48",
@@ -8874,8 +8726,6 @@
{
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"P041#2019_12_14_12_01_09",
@@ -9021,8 +8871,6 @@
]
},
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"P043#2019_12_20_10_05_48",
@@ -9186,8 +9034,6 @@
{
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"P044#2020_02_01_09_51_31",
@@ -9326,8 +9172,6 @@
]
},
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"P043#2019_12_20_10_05_48",
@@ -9491,8 +9335,6 @@
{
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"P041#2019_12_14_12_01_09",
@@ -9609,8 +9451,6 @@
]
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"P043#2019_12_20_10_05_48",
@@ -9774,8 +9614,6 @@
{
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"P044#2020_02_01_09_51_31",
@@ -9983,8 +9821,6 @@
]
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"P043#2019_12_20_10_05_48",
@@ -10148,8 +9984,6 @@
{
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"P041#2019_12_14_12_01_09",
@@ -10314,8 +10148,6 @@
]
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"P043#2019_12_20_10_05_48",
@@ -10479,8 +10311,6 @@
{
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"P041#2019_12_14_12_01_09",
@@ -10650,8 +10480,6 @@
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"P043#2019_12_20_10_05_48",
@@ -10815,8 +10643,6 @@
{
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"P041#2019_12_14_12_01_09",
@@ -10959,8 +10785,6 @@
]
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"P043#2019_12_20_10_05_48",
@@ -11124,8 +10948,6 @@
{
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"P041#2019_12_14_12_01_09",
@@ -11261,8 +11083,6 @@
]
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"P043#2019_12_20_10_05_48",
@@ -11426,8 +11246,6 @@
{
"fold_name": "fold_pigs=10_seed=0",
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"P044#2020_02_01_09_51_15",
"P044#2020_02_01_09_51_31",
@@ -11659,8 +11477,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -11824,8 +11640,6 @@
{
"fold_name": "fold_pigs=10_seed=1",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -12009,8 +11823,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -12174,8 +11986,6 @@
{
"fold_name": "fold_pigs=10_seed=2",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -12365,8 +12175,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -12530,8 +12338,6 @@
{
"fold_name": "fold_pigs=10_seed=3",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -12693,8 +12499,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -12858,8 +12662,6 @@
{
"fold_name": "fold_pigs=10_seed=4",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -13023,8 +12825,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -13188,8 +12988,6 @@
{
"fold_name": "fold_pigs=11_seed=0",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P044#2020_02_01_09_51_15",
"P044#2020_02_01_09_51_31",
@@ -13439,8 +13237,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -13604,8 +13400,6 @@
{
"fold_name": "fold_pigs=11_seed=1",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -13811,8 +13605,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -13976,8 +13768,6 @@
{
"fold_name": "fold_pigs=11_seed=2",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -14185,8 +13975,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -14350,8 +14138,6 @@
{
"fold_name": "fold_pigs=11_seed=3",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -14541,8 +14327,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -14706,8 +14490,6 @@
{
"fold_name": "fold_pigs=11_seed=4",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -14911,8 +14693,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -15076,8 +14856,6 @@
{
"fold_name": "fold_pigs=12_seed=0",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P044#2020_02_01_09_51_15",
"P044#2020_02_01_09_51_31",
@@ -15337,8 +15115,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -15502,8 +15278,6 @@
{
"fold_name": "fold_pigs=12_seed=1",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -15749,8 +15523,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -15914,8 +15686,6 @@
{
"fold_name": "fold_pigs=12_seed=2",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -16128,8 +15898,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -16293,8 +16061,6 @@
{
"fold_name": "fold_pigs=12_seed=3",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -16494,8 +16260,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -16659,8 +16423,6 @@
{
"fold_name": "fold_pigs=12_seed=4",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -16882,8 +16644,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -17047,8 +16807,6 @@
{
"fold_name": "fold_pigs=13_seed=0",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -17312,8 +17070,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -17477,8 +17233,6 @@
{
"fold_name": "fold_pigs=13_seed=1",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -17744,8 +17498,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -17909,8 +17661,6 @@
{
"fold_name": "fold_pigs=13_seed=2",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -18142,8 +17892,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -18307,8 +18055,6 @@
{
"fold_name": "fold_pigs=13_seed=3",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -18530,8 +18276,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -18695,8 +18439,6 @@
{
"fold_name": "fold_pigs=13_seed=4",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -18934,8 +18676,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -19099,8 +18839,6 @@
{
"fold_name": "fold_pigs=14_seed=0",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -19380,8 +19118,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -19545,8 +19281,6 @@
{
"fold_name": "fold_pigs=14_seed=1",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -19830,8 +19564,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -19995,8 +19727,6 @@
{
"fold_name": "fold_pigs=14_seed=2",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -20256,8 +19986,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -20421,8 +20149,6 @@
{
"fold_name": "fold_pigs=14_seed=3",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -20684,8 +20410,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -20849,8 +20573,6 @@
{
"fold_name": "fold_pigs=14_seed=4",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -21126,8 +20848,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -21291,8 +21011,6 @@
{
"fold_name": "fold_pigs=15_seed=0",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_12_00_16",
"P041#2019_12_14_12_01_09",
@@ -21592,8 +21310,6 @@
]
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27",
"P043#2019_12_20_10_05_48",
@@ -21754,4 +21470,4 @@
]
}
}
-]
\ No newline at end of file
+]
diff --git a/htc/models/data/run_pig_dataset.py b/htc/models/data/run_pig_dataset.py
index fbf89d9..1837328 100644
--- a/htc/models/data/run_pig_dataset.py
+++ b/htc/models/data/run_pig_dataset.py
@@ -90,23 +90,15 @@ def generate_folds(self) -> list[dict]:
fold_specs = {
"fold_name": f"fold_{subject_name}",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [p.image_name() for p in paths_train],
},
"val_semantic_unknown": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [p.image_name() for p in paths_val_unknown],
},
"val_semantic_known": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [p.image_name() for p in paths_val_known],
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": imgs_test,
},
}
@@ -134,23 +126,15 @@ def generate_folds(self) -> list[dict]:
fold_specs = {
"fold_name": "fold_" + ",".join(fold),
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [p.image_name() for p in paths_train],
},
"val_semantic_unknown": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [p.image_name() for p in paths_val_unknown],
},
"val_semantic_known": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [p.image_name() for p in paths_val_known],
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": imgs_test,
},
}
@@ -182,28 +166,18 @@ def generate_folds(self) -> list[dict]:
fold_specs = {
"fold_name": "fold_" + ",".join(fold),
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [p.image_name() for p in paths_train],
},
"train_masks": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [p.image_name() for p in paths_train_masks],
},
"val_semantic_unknown": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [p.image_name() for p in paths_val_unknown],
},
"val_semantic_known": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [p.image_name() for p in paths_val_known],
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": imgs_test,
},
}
diff --git a/htc/models/data/run_size_dataset.py b/htc/models/data/run_size_dataset.py
index d3d4ba0..7830077 100644
--- a/htc/models/data/run_size_dataset.py
+++ b/htc/models/data/run_size_dataset.py
@@ -131,13 +131,9 @@ def generate_folds(self) -> list[dict]:
fold_specs = {
"fold_name": f"fold_pigs={n_pigs}_seed={seed}",
"train_semantic": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [p.image_name() for p in paths_fold],
},
"val_semantic_test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [p.image_name() for p in paths_test],
},
}
diff --git a/htc/models/image/DatasetImage.py b/htc/models/image/DatasetImage.py
index ab337b9..b0ad48d 100644
--- a/htc/models/image/DatasetImage.py
+++ b/htc/models/image/DatasetImage.py
@@ -5,6 +5,7 @@
from htc.models.common.HTCDataset import HTCDataset
from htc.models.data.DataSpecification import DataSpecification
+from htc.utils.Config import Config
from htc.utils.DomainMapper import DomainMapper
from htc.utils.SLICWrapper import SLICWrapper
@@ -63,11 +64,19 @@ def __getitem__(self, index: int, start_pointers: dict[str, int] = None) -> dict
sample = self.read_experiment(self.paths[index], start_pointers=start_pointers)
sample["image_index"] = index
- if self.config["input/superpixels"]:
+ if self.config["input/superpixels"] and not self.config["input/no_features"]:
if self.config["input/n_channels"] != 3:
- # # We always calculate the superpixels on the RGB image since we only want to compare the features and not the shape
- rgb = self.paths[index].read_rgb_reconstructed() / 255
- sample["features_rgb"] = torch.from_numpy(rgb).float()
+ # We always calculate the superpixels on the RGB image since we only want to compare the features and not the shape
+
+ # Load the RGB image via the dataset so that potential transformations may be applied
+ config_rgb = Config({"input/n_channels": 3})
+ if "label_mapping" in self.config:
+ config_rgb["label_mapping"] = self.config["label_mapping"]
+ if "input/test_time_transforms_cpu" in self.config:
+ config_rgb["input/test_time_transforms_cpu"] = self.config["input/test_time_transforms_cpu"]
+ sample_rgb = DatasetImage([self.paths[index]], train=False, config=config_rgb)[0]
+
+ sample["features_rgb"] = sample_rgb["features"]
spx_features_name = "features_rgb"
else:
# We already have the RGB data so we can directly use it for the superpixels
@@ -77,7 +86,7 @@ def __getitem__(self, index: int, start_pointers: dict[str, int] = None) -> dict
# The main problem is that the border values get mirrored leading to duplicate superpixel indices or missing indices
sample = self.apply_transforms(sample) # e.g. features.shape = [480, 640, 100]
- if self.config["input/superpixels"]:
+ if self.config["input/superpixels"] and not self.config["input/no_features"]:
fast_slic = SLICWrapper(**self.config["input/superpixels"])
sample["spxs"] = fast_slic.apply_slic(sample[spx_features_name])
diff --git a/htc/models/image/DatasetImageBatch.py b/htc/models/image/DatasetImageBatch.py
index e5f9866..2f6ca97 100644
--- a/htc/models/image/DatasetImageBatch.py
+++ b/htc/models/image/DatasetImageBatch.py
@@ -1,8 +1,6 @@
# SPDX-FileCopyrightText: 2022 Division of Intelligent Medical Systems, DKFZ
# SPDX-License-Identifier: MIT
-import torch
-
from htc.models.common.SharedMemoryDatasetMixin import SharedMemoryDatasetMixin
from htc.models.image.DatasetImage import DatasetImage
@@ -74,24 +72,3 @@ def __len__(self) -> int:
@property
def buffer_size(self) -> int:
return self.worker_buffer_size * self.config["dataloader_kwargs/num_workers"]
-
- def _add_shared_resources(self) -> None:
- self._add_image_index_shared()
- spatial_shape = self.paths[0].dataset_settings["spatial_shape"]
-
- if not self.config["input/no_features"]:
- self._add_tensor_shared("features", self.features_dtype, *spatial_shape, self.config["input/n_channels"])
- if not self.config["input/no_labels"]:
- if self.config["input/annotation_name"] and not self.config["input/merge_annotations"]:
- for name in self._possible_annotation_names():
- self._add_tensor_shared(f"labels_{name}", torch.int64, *spatial_shape)
- self._add_tensor_shared(f"valid_pixels_{name}", torch.bool, *spatial_shape)
- else:
- self._add_tensor_shared("labels", torch.int64, *spatial_shape)
- self._add_tensor_shared("valid_pixels", torch.bool, *spatial_shape)
-
- if self.config["input/superpixels"]:
- self._add_tensor_shared("spxs", torch.int64, *spatial_shape)
-
- for domain in self.target_domains:
- self._add_tensor_shared(domain, torch.int64)
diff --git a/htc/models/image/DatasetImageStream.py b/htc/models/image/DatasetImageStream.py
index 555a5f2..34c6323 100644
--- a/htc/models/image/DatasetImageStream.py
+++ b/htc/models/image/DatasetImageStream.py
@@ -54,27 +54,6 @@ def iter_samples(self) -> Iterator[dict[str, torch.Tensor]]:
yield sample
- def _add_shared_resources(self) -> None:
- self._add_image_index_shared()
- spatial_shape = self.paths[0].dataset_settings["spatial_shape"]
-
- if not self.config["input/no_features"]:
- self._add_tensor_shared("features", self.features_dtype, *spatial_shape, self.config["input/n_channels"])
- if not self.config["input/no_labels"]:
- if self.config["input/annotation_name"] and not self.config["input/merge_annotations"]:
- for name in self._possible_annotation_names():
- self._add_tensor_shared(f"labels_{name}", torch.int64, *spatial_shape)
- self._add_tensor_shared(f"valid_pixels_{name}", torch.bool, *spatial_shape)
- else:
- self._add_tensor_shared("labels", torch.int64, *spatial_shape)
- self._add_tensor_shared("valid_pixels", torch.bool, *spatial_shape)
-
- if self.config["input/superpixels"]:
- self._add_tensor_shared("spxs", torch.int64, *spatial_shape)
-
- for domain in self.target_domains:
- self._add_tensor_shared(domain, torch.int64)
-
def n_image_elements(self) -> int:
return 1
diff --git a/htc/models/image/LightningImage.py b/htc/models/image/LightningImage.py
index cb34e39..9f84346 100644
--- a/htc/models/image/LightningImage.py
+++ b/htc/models/image/LightningImage.py
@@ -53,7 +53,7 @@ def __init__(self, *args, **kwargs):
if hasattr(self.dice_loss, "class_weight"):
# MONAI >=1.3.0 uses a class weight buffer which breaks loading of old checkpoints
# Since we have our own class weighting anyway, we simple remove the buffer
- del self.dice_loss.class_weight
+ self.dice_loss.class_weight = None
if "optimization/spx_loss_weight" in self.config:
assert (
diff --git a/htc/models/image/ModelImage.py b/htc/models/image/ModelImage.py
index 13f943b..48c159b 100644
--- a/htc/models/image/ModelImage.py
+++ b/htc/models/image/ModelImage.py
@@ -7,7 +7,7 @@
from htc.models.common.HSI3dChannel import HSI3dChannel
from htc.models.common.HTCModel import HTCModel
-from htc.models.common.utils import get_n_classes
+from htc.models.common.utils import get_n_classes, model_input_channels
from htc.utils.Config import Config
@@ -23,7 +23,7 @@ def __init__(self, config: Config, channels: int = None):
channels = self.channel_preprocessing.output_channels()
else:
self.channel_preprocessing = nn.Identity()
- channels = self.config["input/n_channels"] if channels is None else channels
+ channels = model_input_channels(self.config) if channels is None else channels
ArchitectureClass = getattr(smp, self.config["model/architecture_name"])
self.architecture = ArchitectureClass(
diff --git a/htc/models/image/configs/default.json b/htc/models/image/configs/default.json
index 3125e7e..d439116 100644
--- a/htc/models/image/configs/default.json
+++ b/htc/models/image/configs/default.json
@@ -5,23 +5,27 @@
"preprocessing": "L1",
"n_channels": 100,
"epoch_size": 500,
- "transforms_gpu": [{
- "class": "KorniaTransform",
- "transformation_name": "RandomAffine",
- "translate": [0.0625, 0.0625],
- "scale": [0.9, 1.1],
- "degrees": 45,
- "padding_mode": "reflection",
- "p": 0.5
- }, {
- "class": "KorniaTransform",
- "transformation_name": "RandomHorizontalFlip",
- "p": 0.25
- }, {
- "class": "KorniaTransform",
- "transformation_name": "RandomVerticalFlip",
- "p": 0.25
- }]
+ "transforms_gpu": [
+ {
+ "class": "KorniaTransform",
+ "transformation_name": "RandomAffine",
+ "translate": [0.0625, 0.0625],
+ "scale": [0.9, 1.1],
+ "degrees": 45,
+ "padding_mode": "reflection",
+ "p": 0.5
+ },
+ {
+ "class": "KorniaTransform",
+ "transformation_name": "RandomHorizontalFlip",
+ "p": 0.25
+ },
+ {
+ "class": "KorniaTransform",
+ "transformation_name": "RandomVerticalFlip",
+ "p": 0.25
+ }
+ ]
},
"label_mapping": "htc.settings_seg>label_mapping",
"optimization": {
diff --git a/htc/models/patch/configs/default.json b/htc/models/patch/configs/default.json
index f869efe..f54d464 100644
--- a/htc/models/patch/configs/default.json
+++ b/htc/models/patch/configs/default.json
@@ -8,23 +8,27 @@
"patch_sampling": "uniform",
"patch_size": [32, 32],
"background_undersampling": false,
- "transforms_cpu": [{
- "class": "KorniaTransform",
- "transformation_name": "RandomAffine",
- "translate": [0.0625, 0.0625],
- "scale": [0.9, 1.1],
- "degrees": 45,
- "padding_mode": "reflection",
- "p": 0.5
- }, {
- "class": "KorniaTransform",
- "transformation_name": "RandomHorizontalFlip",
- "p": 0.25
- }, {
- "class": "KorniaTransform",
- "transformation_name": "RandomVerticalFlip",
- "p": 0.25
- }]
+ "transforms_cpu": [
+ {
+ "class": "KorniaTransform",
+ "transformation_name": "RandomAffine",
+ "translate": [0.0625, 0.0625],
+ "scale": [0.9, 1.1],
+ "degrees": 45,
+ "padding_mode": "reflection",
+ "p": 0.5
+ },
+ {
+ "class": "KorniaTransform",
+ "transformation_name": "RandomHorizontalFlip",
+ "p": 0.25
+ },
+ {
+ "class": "KorniaTransform",
+ "transformation_name": "RandomVerticalFlip",
+ "p": 0.25
+ }
+ ]
},
"label_mapping": "htc.settings_seg>label_mapping",
"optimization": {
diff --git a/htc/models/patch/configs/default_64.json b/htc/models/patch/configs/default_64.json
index a0bdc33..816378d 100644
--- a/htc/models/patch/configs/default_64.json
+++ b/htc/models/patch/configs/default_64.json
@@ -8,23 +8,27 @@
"patch_sampling": "uniform",
"patch_size": [64, 64],
"background_undersampling": false,
- "transforms_cpu": [{
- "class": "KorniaTransform",
- "transformation_name": "RandomAffine",
- "translate": [0.0625, 0.0625],
- "scale": [0.9, 1.1],
- "degrees": 45,
- "padding_mode": "reflection",
- "p": 0.5
- }, {
- "class": "KorniaTransform",
- "transformation_name": "RandomHorizontalFlip",
- "p": 0.25
- }, {
- "class": "KorniaTransform",
- "transformation_name": "RandomVerticalFlip",
- "p": 0.25
- }]
+ "transforms_cpu": [
+ {
+ "class": "KorniaTransform",
+ "transformation_name": "RandomAffine",
+ "translate": [0.0625, 0.0625],
+ "scale": [0.9, 1.1],
+ "degrees": 45,
+ "padding_mode": "reflection",
+ "p": 0.5
+ },
+ {
+ "class": "KorniaTransform",
+ "transformation_name": "RandomHorizontalFlip",
+ "p": 0.25
+ },
+ {
+ "class": "KorniaTransform",
+ "transformation_name": "RandomVerticalFlip",
+ "p": 0.25
+ }
+ ]
},
"label_mapping": "htc.settings_seg>label_mapping",
"optimization": {
diff --git a/htc/models/pixel/ModelPixel.py b/htc/models/pixel/ModelPixel.py
index c3bc490..482a464 100644
--- a/htc/models/pixel/ModelPixel.py
+++ b/htc/models/pixel/ModelPixel.py
@@ -7,6 +7,7 @@
import htc.models.common.functions
from htc.models.common.Heads import Heads
from htc.models.common.HTCModel import HTCModel
+from htc.models.common.utils import model_input_channels
from htc.utils.Config import Config
@@ -45,7 +46,7 @@ def __init__(self, config: Config):
# The adaptive pooling layer ensures that the output of the conv layers always has the same length
# This allows a different number of channels to be used as input which could be helpful for pretraining
# If the input already has the correct input size, the adaptive layer does not change the conv output
- in_dim = self._conv_output_features(self.config["input/n_channels"])
+ in_dim = self._conv_output_features(model_input_channels(self.config))
self.adaptive_conv_reduction = nn.AdaptiveAvgPool1d(in_dim)
self.fc1 = nn.Linear(in_features=in_dim, out_features=100)
diff --git a/htc/models/pixel/ModelPixelRGB.py b/htc/models/pixel/ModelPixelRGB.py
index 132cd13..353e21e 100644
--- a/htc/models/pixel/ModelPixelRGB.py
+++ b/htc/models/pixel/ModelPixelRGB.py
@@ -7,6 +7,7 @@
import htc.models.common.functions
from htc.models.common.Heads import Heads
from htc.models.common.HTCModel import HTCModel
+from htc.models.common.utils import model_input_channels
from htc.utils.Config import Config
@@ -27,7 +28,7 @@ def __init__(self, config: Config):
DropoutLayer = nn.Identity
# FNN
- self.fc1 = nn.Linear(in_features=self.config["input/n_channels"], out_features=200)
+ self.fc1 = nn.Linear(in_features=model_input_channels(self.config), out_features=200)
self.fc1_norm = NormalizationLayer(num_features=self.fc1.out_features)
self.fc1_dropout = DropoutLayer(self.config["model/dropout"])
diff --git a/htc/models/pixel/configs/default.json b/htc/models/pixel/configs/default.json
index 74a9e67..0184da4 100644
--- a/htc/models/pixel/configs/default.json
+++ b/htc/models/pixel/configs/default.json
@@ -6,23 +6,27 @@
"n_channels": 100,
"epoch_size": "500 images",
"oversampling": false,
- "transforms_cpu": [{
- "class": "KorniaTransform",
- "transformation_name": "RandomAffine",
- "translate": [0.0625, 0.0625],
- "scale": [0.9, 1.1],
- "degrees": 45,
- "padding_mode": "reflection",
- "p": 0.5
- }, {
- "class": "KorniaTransform",
- "transformation_name": "RandomHorizontalFlip",
- "p": 0.25
- }, {
- "class": "KorniaTransform",
- "transformation_name": "RandomVerticalFlip",
- "p": 0.25
- }]
+ "transforms_cpu": [
+ {
+ "class": "KorniaTransform",
+ "transformation_name": "RandomAffine",
+ "translate": [0.0625, 0.0625],
+ "scale": [0.9, 1.1],
+ "degrees": 45,
+ "padding_mode": "reflection",
+ "p": 0.5
+ },
+ {
+ "class": "KorniaTransform",
+ "transformation_name": "RandomHorizontalFlip",
+ "p": 0.25
+ },
+ {
+ "class": "KorniaTransform",
+ "transformation_name": "RandomVerticalFlip",
+ "p": 0.25
+ }
+ ]
},
"label_mapping": "htc.settings_seg>label_mapping",
"optimization": {
diff --git a/htc/models/run_generate_configs.py b/htc/models/run_generate_configs.py
index 2f9e849..667b5a9 100644
--- a/htc/models/run_generate_configs.py
+++ b/htc/models/run_generate_configs.py
@@ -28,7 +28,7 @@ def generate_configs(
if params is None:
params = {
- "seed": [settings.default_seed],
+ "seed": [0, 1, 2, 3, 4],
# 'model/architecture_name': ["Model3D2DSeg", "DynUNet"],
# 'dataloader_kwargs/batch_size': [8],
# 'dataloader_kwargs/num_workers': [8],
diff --git a/htc/models/run_inference_timeit.py b/htc/models/run_inference_timeit.py
index 736c0b1..80dbdbd 100644
--- a/htc/models/run_inference_timeit.py
+++ b/htc/models/run_inference_timeit.py
@@ -4,7 +4,6 @@
import pandas as pd
import torch
from rich.progress import track
-from torch.cuda.amp import autocast
from htc.models.common.HTCLightning import HTCLightning
from htc.models.common.torch_helpers import move_batch_gpu
@@ -44,7 +43,7 @@ def __init__(self):
self.batch = move_batch_gpu(sample)
self.batch["features"] = self.batch["features"].unsqueeze(dim=0)
- @autocast()
+ @torch.autocast("cuda")
@torch.no_grad()
def inference_image(self) -> None:
fold_predictions = []
diff --git a/htc/models/run_training.py b/htc/models/run_training.py
index 23cc087..648a2f8 100644
--- a/htc/models/run_training.py
+++ b/htc/models/run_training.py
@@ -28,6 +28,7 @@
from htc.utils.DelayedFileHandler import DelayedFileHandler
from htc.utils.DuplicateFilter import DuplicateFilter
from htc.utils.MeasureTime import MeasureTime
+from htc.utils.Task import Task
class FoldTrainer:
@@ -40,14 +41,15 @@ def __init__(self, model_name: str, config_name: str, config_extends: Union[str,
adjust_num_workers(self.config)
# There must be a label mapping defined (class names to label ids)
- if not self.config["input/no_labels"] and "label_mapping" not in self.config:
+ labels_requested = not self.config["input/no_labels"] and Task.from_config(self.config) == Task.SEGMENTATION
+ if labels_requested and "label_mapping" not in self.config:
settings.log.warning(
"No label mapping specified in the config file. The default mapping from the images will be used which"
" may not be what you want (e.g. it is different across datasets). Best practice is to explicitly"
" specify the label mapping in the config"
)
- self.data_specs = DataSpecification.from_config(self.config)
+ self.spec = DataSpecification.from_config(self.config)
self.LightningClass = HTCLightning.class_from_config(self.config)
def train_fold(self, run_folder: Union[str, None], fold_name: str, *args) -> None:
@@ -111,14 +113,14 @@ def _train_fold(self, model_dir: str, fold_name: str, test: bool, file_log_handl
)
# Create datasets based on the paths in the data specs
- train_paths = []
+ train_paths = self.spec.fold_paths(fold_name, "^train")
test_paths = []
datasets_val = []
- for name, paths in self.data_specs.folds[fold_name].items():
+ for name, paths in self.spec.folds[fold_name].items():
assert not name.startswith("test"), "The test set should not be available at this point"
if name.startswith("train"):
- train_paths += paths
+ continue
elif name.startswith("val"):
dataset = self.LightningClass.dataset(paths=paths, train=False, config=self.config, fold_name=fold_name)
datasets_val.append(dataset)
@@ -128,8 +130,8 @@ def _train_fold(self, model_dir: str, fold_name: str, test: bool, file_log_handl
if test:
# To avoid potential errors, we activate the test set only temporarily to get the paths
# If other classes access the specs, they cannot accidentally access the test set
- with self.data_specs.activated_test_set():
- test_paths = self.data_specs.fold_paths(fold_name, "^test")
+ with self.spec.activated_test_set():
+ test_paths = self.spec.fold_paths(fold_name, "^test")
# We use only one training dataset which uses all available images. Oversampling of images from one dataset can be implemented in the lightning class
dataset_train = self.LightningClass.dataset(
@@ -145,13 +147,6 @@ def _train_fold(self, model_dir: str, fold_name: str, test: bool, file_log_handl
" calculation of the metric but just the name of the metric (e.g. used in the checkpoint filename)."
f" Defaulting to \"{self.config['validation/checkpoint_metric']}\""
)
- if "validation/dataset_index" not in self.config:
- self.config["validation/dataset_index"] = 0
- settings.log.warning(
- "No value set for validation/dataset_index in the config. This specifies the main validation dataset,"
- " e.g. used for checkpointing. Currently, only one validation dataset can be used. Defaulting to"
- f" \"{self.config['validation/dataset_index']}\""
- )
# Optional test dataset
lightning_kwargs = {}
@@ -226,6 +221,13 @@ def _train_fold(self, model_dir: str, fold_name: str, test: bool, file_log_handl
),
category=UserWarning,
)
+ warnings.filterwarnings(
+ "ignore",
+ message=(
+ ".*Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0.*"
+ ),
+ category=UserWarning,
+ )
if self.config["wandb_kwargs"]:
wandb_logger = WandbLogger(save_dir=model_dir, **self.config["wandb_kwargs"])
@@ -247,7 +249,7 @@ def _train_fold(self, model_dir: str, fold_name: str, test: bool, file_log_handl
settings.log.warning(key)
self.config.save_config(model_dir / "config.json")
- shutil.copy2(self.data_specs.path, model_dir / "data.json")
+ shutil.copy2(self.spec.path, model_dir / "data.json")
# Inform the system monitor that the training is finished
monitor_handle.send_signal(signal.SIGINT)
diff --git a/htc/models/superpixel_classification/DatasetSuperpixelImage.py b/htc/models/superpixel_classification/DatasetSuperpixelImage.py
index bda515e..d0057cb 100644
--- a/htc/models/superpixel_classification/DatasetSuperpixelImage.py
+++ b/htc/models/superpixel_classification/DatasetSuperpixelImage.py
@@ -38,7 +38,6 @@ def __getitem__(self, index: int) -> dict[str, torch.Tensor]:
x_image = F.interpolate(
x_image.float(), size=self.config["input/resize_shape"], mode="bilinear", align_corners=False
).squeeze(dim=0)
- x_image = self.apply_transforms(x_image) # [100, 32, 32]
features.append(x_image)
@@ -48,9 +47,8 @@ def __getitem__(self, index: int) -> dict[str, torch.Tensor]:
"image_name": sample_img["image_name"],
"features": torch.stack(features),
"spxs_sizes": torch.tensor(spxs_sizes),
- "spxs_indices_rows": torch.cat(
- spxs_indices_rows
- ), # We already concatentate the ids since we make only full image assignments later
+ # We already concatenate the ids since we make only full image assignments later
+ "spxs_indices_rows": torch.cat(spxs_indices_rows),
"spxs_indices_cols": torch.cat(spxs_indices_cols),
}
diff --git a/htc/models/superpixel_classification/ModelSuperpixelClassification.py b/htc/models/superpixel_classification/ModelSuperpixelClassification.py
index 87a9e13..51bff45 100644
--- a/htc/models/superpixel_classification/ModelSuperpixelClassification.py
+++ b/htc/models/superpixel_classification/ModelSuperpixelClassification.py
@@ -7,7 +7,7 @@
from htc.models.common.HSI3dChannel import HSI3dChannel
from htc.models.common.HTCModel import HTCModel
-from htc.models.common.utils import get_n_classes
+from htc.models.common.utils import get_n_classes, model_input_channels
from htc.utils.Config import Config
@@ -27,16 +27,17 @@ def forward(self, x):
class ModelSuperpixelClassification(HTCModel):
- def __init__(self, config: Config):
- super().__init__(config)
- n_classes = get_n_classes(self.config)
+ def __init__(self, config: Config, n_classes: int = None, **kwargs):
+ super().__init__(config, **kwargs)
+ if n_classes is None:
+ n_classes = get_n_classes(self.config)
if self.config["model/channel_preprocessing"]:
self.channel_preprocessing = HSI3dChannel(self.config)
channels = self.channel_preprocessing.output_channels()
else:
self.channel_preprocessing = nn.Identity()
- channels = self.config["input/n_channels"]
+ channels = model_input_channels(self.config)
self.architecture = UNetClassification(
self.config["model/encoder"],
diff --git a/htc/models/superpixel_classification/configs/default.json b/htc/models/superpixel_classification/configs/default.json
index fe0b701..81f6ffb 100644
--- a/htc/models/superpixel_classification/configs/default.json
+++ b/htc/models/superpixel_classification/configs/default.json
@@ -10,23 +10,27 @@
},
"resize_shape": [32, 32],
"epoch_size": "500 images",
- "transforms_cpu": [{
- "class": "KorniaTransform",
- "transformation_name": "RandomAffine",
- "translate": [0.0625, 0.0625],
- "scale": [0.9, 1.1],
- "degrees": 45,
- "padding_mode": "reflection",
- "p": 0.5
- }, {
- "class": "KorniaTransform",
- "transformation_name": "RandomHorizontalFlip",
- "p": 0.25
- }, {
- "class": "KorniaTransform",
- "transformation_name": "RandomVerticalFlip",
- "p": 0.25
- }]
+ "transforms_cpu": [
+ {
+ "class": "KorniaTransform",
+ "transformation_name": "RandomAffine",
+ "translate": [0.0625, 0.0625],
+ "scale": [0.9, 1.1],
+ "degrees": 45,
+ "padding_mode": "reflection",
+ "p": 0.5
+ },
+ {
+ "class": "KorniaTransform",
+ "transformation_name": "RandomHorizontalFlip",
+ "p": 0.25
+ },
+ {
+ "class": "KorniaTransform",
+ "transformation_name": "RandomVerticalFlip",
+ "p": 0.25
+ }
+ ]
},
"label_mapping": "htc.settings_seg>label_mapping",
"optimization": {
diff --git a/htc/settings.py b/htc/settings.py
index 7af4fe0..b8aef6e 100644
--- a/htc/settings.py
+++ b/htc/settings.py
@@ -19,7 +19,7 @@
class ColoredFormatter(logging.Formatter):
- def format(self, record):
+ def format(self, record): # noqa: A003
# Apply level-specific color
levelname_prev = record.levelname
record.levelname = f"[logging.level.{record.levelname.lower()}]{record.levelname}[/]"
@@ -97,7 +97,7 @@ class Settings:
- `PATH_HTC_DOCKER_RESULTS`: If you compute something in our Docker container, results will only be stored in the container and deleted as soon as the container exits (since the container is only intended for testing). Let this variable point to a directory of your choice to keep your Docker results. Example: `PATH_HTC_DOCKER_RESULTS="/my/results/folder"`
- `HTC_ADD_NETWORK_ALTERNATIVES`: If set to the string `true`, will include results and intermediate directories on the network drive (default `false`). This is usually only required for testing. Example: `HTC_ADD_NETWORK_ALTERNATIVES="true"`
- `HTC_ENV_OVERRIDE`: Whether environment variables defined in the .env file or in your user settings override existing variables (default `true`). Set this to `false` if you want that variables defined elsewhere (e.g. before the command: `ENV_NAME htc command`) have precedence. Example: `HTC_ENV_OVERRIDE="false"`
- - `HTC_MODEL_COMPARISON_TIMESTAMP`: Variable is read in settings_seg and can be used to overwrite the default comparison timestamp (e.g. used for the reproducibility of our MIA2021 paper). Example: `HTC_MODEL_COMPARISON_TIMESTAMP="2022-02-03_22-58-44"`
+ - `HTC_MODEL_COMPARISON_TIMESTAMP`: Variable is read in settings_seg and can be used to overwrite the default comparison timestamp (e.g. used for the reproducibility of our MIA2022 paper). Example: `HTC_MODEL_COMPARISON_TIMESTAMP="2022-02-03_22-58-44"`
- `HTC_BENCHMARKING_TIMESTAMP`: Variable is read in settings_bench and can be used to overwrite the default timestamp for the benchmarking networks (e.g. used for the reproducibility of our PyTorchConf2023 poster). Example: `HTC_BENCHMARKING_TIMESTAMP="2023-09-03_22-48-13"`
- `HTC_CUDA_MEM_FRACTION`: Used in run_training.py to limit the GPU memory to a fraction of the available GPU memory (e.g. to simulate GPUs with less memory). Example: `HTC_CUDA_MEM_FRACTION="0.5"`
- `HTC_SYSTEM_MONITOR_REFRESH_RATE`: Refresh rate x in seconds for the system monitor (an event will be logged every x seconds). Example: `HTC_SYSTEM_MONITOR_REFRESH="0.15"`
@@ -285,6 +285,7 @@ def uuid4_seeded():
"tag_blood": "#f51505",
"tag_cauterization": "#9d9e9e",
"tag_malperfused": "#03ffff",
+ "tag_tumor": "#ff5100",
"instrument": "#636363",
"fur": "#FF7830",
"ligament_pat": "#FFB46D",
@@ -299,6 +300,7 @@ def uuid4_seeded():
"vesic_gland": "#00469C",
"Exterior": "#00000000", # Unlabeled parts in MITK
"network_unsure": "#AAAAAA",
+ "not_suitable_for_semantic": "#AAAAAA",
}
self.known_envs = (
diff --git a/htc/tissue_atlas/data/run_tissue_atlas_dataset.py b/htc/tissue_atlas/data/run_tissue_atlas_dataset.py
index 1607c2e..ea81a00 100644
--- a/htc/tissue_atlas/data/run_tissue_atlas_dataset.py
+++ b/htc/tissue_atlas/data/run_tissue_atlas_dataset.py
@@ -46,18 +46,12 @@ def generate_folds(self) -> list[dict]:
fold_specs = {
"fold_name": f"fold_{subject_name}",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": sorted(imgs_train),
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": sorted(imgs_val),
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": sorted(imgs_test),
},
}
diff --git a/htc/tissue_atlas/data/tissue-atlas_loocv_test-8_seed-0_cam-118.json b/htc/tissue_atlas/data/tissue-atlas_loocv_test-8_seed-0_cam-118.json
index dded86f..33f1d8d 100644
--- a/htc/tissue_atlas/data/tissue-atlas_loocv_test-8_seed-0_cam-118.json
+++ b/htc/tissue_atlas/data/tissue-atlas_loocv_test-8_seed-0_cam-118.json
@@ -2,8 +2,6 @@
{
"fold_name": "fold_P041",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P042#2019_12_15_10_14_19",
"P042#2019_12_15_10_14_44",
@@ -3739,8 +3737,6 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -3779,8 +3775,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -4488,7 +4482,7 @@
"P086#2021_04_15_19_44_11",
"P086#2021_04_15_19_44_30",
"P086#2021_04_15_19_44_49",
-
+
"P086#2021_04_15_19_55_01",
"P086#2021_04_15_19_55_24",
"P086#2021_04_15_19_55_48",
@@ -9081,8 +9075,6 @@
{
"fold_name": "fold_P042",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -12814,8 +12806,6 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P042#2019_12_15_10_14_19",
"P042#2019_12_15_10_14_44",
@@ -12858,8 +12848,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -13567,7 +13555,7 @@
"P086#2021_04_15_19_44_11",
"P086#2021_04_15_19_44_30",
"P086#2021_04_15_19_44_49",
-
+
"P086#2021_04_15_19_55_01",
"P086#2021_04_15_19_55_24",
"P086#2021_04_15_19_55_48",
@@ -18160,8 +18148,6 @@
{
"fold_name": "fold_P043",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -21834,8 +21820,6 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P043#2019_12_20_10_05_27#overlap",
"P043#2019_12_20_10_05_48#overlap",
@@ -21937,8 +21921,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -22646,7 +22628,7 @@
"P086#2021_04_15_19_44_11",
"P086#2021_04_15_19_44_30",
"P086#2021_04_15_19_44_49",
-
+
"P086#2021_04_15_19_55_01",
"P086#2021_04_15_19_55_24",
"P086#2021_04_15_19_55_48",
@@ -27239,8 +27221,6 @@
{
"fold_name": "fold_P044",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -30965,8 +30945,6 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P044#2020_02_01_08_52_55",
"P044#2020_02_01_08_55_38",
@@ -31016,8 +30994,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -31725,7 +31701,7 @@
"P086#2021_04_15_19_44_11",
"P086#2021_04_15_19_44_30",
"P086#2021_04_15_19_44_49",
-
+
"P086#2021_04_15_19_55_01",
"P086#2021_04_15_19_55_24",
"P086#2021_04_15_19_55_48",
@@ -36318,8 +36294,6 @@
{
"fold_name": "fold_P045",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -40043,8 +40017,6 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P045#2020_02_05_10_16_23",
"P045#2020_02_05_10_18_37",
@@ -40095,8 +40067,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -40804,7 +40774,7 @@
"P086#2021_04_15_19_44_11",
"P086#2021_04_15_19_44_30",
"P086#2021_04_15_19_44_49",
-
+
"P086#2021_04_15_19_55_01",
"P086#2021_04_15_19_55_24",
"P086#2021_04_15_19_55_48",
@@ -45397,8 +45367,6 @@
{
"fold_name": "fold_P046",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -49140,8 +49108,6 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P046#2020_02_07_08_43_49",
"P046#2020_02_07_08_46_51",
@@ -49174,8 +49140,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -49883,7 +49847,7 @@
"P086#2021_04_15_19_44_11",
"P086#2021_04_15_19_44_30",
"P086#2021_04_15_19_44_49",
-
+
"P086#2021_04_15_19_55_01",
"P086#2021_04_15_19_55_24",
"P086#2021_04_15_19_55_48",
@@ -54476,8 +54440,6 @@
{
"fold_name": "fold_P047",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -58221,8 +58183,6 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P047#2020_02_07_17_03_35",
"P047#2020_02_07_17_09_00",
@@ -58253,8 +58213,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -58962,7 +58920,7 @@
"P086#2021_04_15_19_44_11",
"P086#2021_04_15_19_44_30",
"P086#2021_04_15_19_44_49",
-
+
"P086#2021_04_15_19_55_01",
"P086#2021_04_15_19_55_24",
"P086#2021_04_15_19_55_48",
@@ -63555,8 +63513,6 @@
{
"fold_name": "fold_P048",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -67301,8 +67257,6 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P048#2020_02_08_10_03_45",
"P048#2020_02_08_10_07_50",
@@ -67332,8 +67286,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -68041,7 +67993,7 @@
"P086#2021_04_15_19_44_11",
"P086#2021_04_15_19_44_30",
"P086#2021_04_15_19_44_49",
-
+
"P086#2021_04_15_19_55_01",
"P086#2021_04_15_19_55_24",
"P086#2021_04_15_19_55_48",
@@ -72634,8 +72586,6 @@
{
"fold_name": "fold_P049",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -76367,8 +76317,6 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P049#2020_02_11_18_39_13",
"P049#2020_02_11_18_47_14",
@@ -76411,8 +76359,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -77120,7 +77066,7 @@
"P086#2021_04_15_19_44_11",
"P086#2021_04_15_19_44_30",
"P086#2021_04_15_19_44_49",
-
+
"P086#2021_04_15_19_55_01",
"P086#2021_04_15_19_55_24",
"P086#2021_04_15_19_55_48",
@@ -81713,8 +81659,6 @@
{
"fold_name": "fold_P050",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -85451,8 +85395,6 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P050#2020_02_18_17_31_22",
"P050#2020_02_18_17_32_22",
@@ -85490,8 +85432,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -86199,7 +86139,7 @@
"P086#2021_04_15_19_44_11",
"P086#2021_04_15_19_44_30",
"P086#2021_04_15_19_44_49",
-
+
"P086#2021_04_15_19_55_01",
"P086#2021_04_15_19_55_24",
"P086#2021_04_15_19_55_48",
@@ -90792,8 +90732,6 @@
{
"fold_name": "fold_P051",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -94536,8 +94474,6 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P051#2020_03_03_19_02_24",
"P051#2020_03_03_19_04_06",
@@ -94569,8 +94505,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -95278,7 +95212,7 @@
"P086#2021_04_15_19_44_11",
"P086#2021_04_15_19_44_30",
"P086#2021_04_15_19_44_49",
-
+
"P086#2021_04_15_19_55_01",
"P086#2021_04_15_19_55_24",
"P086#2021_04_15_19_55_48",
@@ -99871,8 +99805,6 @@
{
"fold_name": "fold_P052",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -103612,8 +103544,6 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P052#2020_03_04_12_22_54",
"P052#2020_03_04_12_31_04",
@@ -103648,8 +103578,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -104357,7 +104285,7 @@
"P086#2021_04_15_19_44_11",
"P086#2021_04_15_19_44_30",
"P086#2021_04_15_19_44_49",
-
+
"P086#2021_04_15_19_55_01",
"P086#2021_04_15_19_55_24",
"P086#2021_04_15_19_55_48",
@@ -108950,8 +108878,6 @@
{
"fold_name": "fold_P053",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -112686,8 +112612,6 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P053#2020_03_06_11_09_56",
"P053#2020_03_06_11_13_21",
@@ -112727,8 +112651,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -113436,7 +113358,7 @@
"P086#2021_04_15_19_44_11",
"P086#2021_04_15_19_44_30",
"P086#2021_04_15_19_44_49",
-
+
"P086#2021_04_15_19_55_01",
"P086#2021_04_15_19_55_24",
"P086#2021_04_15_19_55_48",
@@ -118029,8 +117951,6 @@
{
"fold_name": "fold_P054",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -121790,8 +121710,6 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P054#2020_03_10_17_50_00",
"P054#2020_03_10_18_06_00",
@@ -121806,8 +121724,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -122515,7 +122431,7 @@
"P086#2021_04_15_19_44_11",
"P086#2021_04_15_19_44_30",
"P086#2021_04_15_19_44_49",
-
+
"P086#2021_04_15_19_55_01",
"P086#2021_04_15_19_55_24",
"P086#2021_04_15_19_55_48",
@@ -127108,8 +127024,6 @@
{
"fold_name": "fold_P055",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -130847,8 +130761,6 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P055#2020_03_11_10_35_25",
"P055#2020_03_11_10_35_55",
@@ -130885,8 +130797,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -131594,7 +131504,7 @@
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- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -294299,16 +294105,9 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
- "image_names": [
- "P074#2020_08_19_18_40_44",
- "P074#2020_08_19_18_41_23"
- ]
+ "image_names": ["P074#2020_08_19_18_40_44", "P074#2020_08_19_18_41_23"]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -295016,7 +294815,7 @@
"P086#2021_04_15_19_44_11",
"P086#2021_04_15_19_44_30",
"P086#2021_04_15_19_44_49",
-
+
"P086#2021_04_15_19_55_01",
"P086#2021_04_15_19_55_24",
"P086#2021_04_15_19_55_48",
@@ -299609,8 +299408,6 @@
{
"fold_name": "fold_P076",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -303325,8 +303122,6 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P076#2020_08_24_10_01_46",
"P076#2020_08_24_10_02_16",
@@ -303386,8 +303181,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -304095,7 +303888,7 @@
"P086#2021_04_15_19_44_11",
"P086#2021_04_15_19_44_30",
"P086#2021_04_15_19_44_49",
-
+
"P086#2021_04_15_19_55_01",
"P086#2021_04_15_19_55_24",
"P086#2021_04_15_19_55_48",
@@ -308688,8 +308481,6 @@
{
"fold_name": "fold_P085",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -312353,8 +312144,6 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P085#2021_04_10_10_47_08",
"P085#2021_04_10_10_47_38",
@@ -312465,8 +312254,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -313174,7 +312961,7 @@
"P086#2021_04_15_19_44_11",
"P086#2021_04_15_19_44_30",
"P086#2021_04_15_19_44_49",
-
+
"P086#2021_04_15_19_55_01",
"P086#2021_04_15_19_55_24",
"P086#2021_04_15_19_55_48",
@@ -317767,8 +317554,6 @@
{
"fold_name": "fold_P088",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -320763,8 +320548,6 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P088#2021_04_19_08_31_51",
"P088#2021_04_19_08_32_10",
@@ -321544,8 +321327,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -322253,7 +322034,7 @@
"P086#2021_04_15_19_44_11",
"P086#2021_04_15_19_44_30",
"P086#2021_04_15_19_44_49",
-
+
"P086#2021_04_15_19_55_01",
"P086#2021_04_15_19_55_24",
"P086#2021_04_15_19_55_48",
@@ -326846,8 +326627,6 @@
{
"fold_name": "fold_P090",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -330041,8 +329820,6 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P090#2021_04_22_08_35_05",
"P090#2021_04_22_08_35_25",
@@ -330623,8 +330400,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -331332,7 +331107,7 @@
"P086#2021_04_15_19_44_11",
"P086#2021_04_15_19_44_30",
"P086#2021_04_15_19_44_49",
-
+
"P086#2021_04_15_19_55_01",
"P086#2021_04_15_19_55_24",
"P086#2021_04_15_19_55_48",
@@ -335925,8 +335700,6 @@
{
"fold_name": "fold_P094",
"train": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P041#2019_12_14_10_50_54",
"P041#2019_12_14_10_51_18",
@@ -338919,8 +338692,6 @@
]
},
"val": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P094#2021_04_30_08_36_33",
"P094#2021_04_30_08_36_50",
@@ -339702,8 +339473,6 @@
]
},
"test": {
- "data_path_module": "htc.tivita.DataPath",
- "data_path_class": "DataPath",
"image_names": [
"P086#2021_04_15_09_22_02",
"P086#2021_04_15_09_22_20",
@@ -340411,7 +340180,7 @@
"P086#2021_04_15_19_44_11",
"P086#2021_04_15_19_44_30",
"P086#2021_04_15_19_44_49",
-
+
"P086#2021_04_15_19_55_01",
"P086#2021_04_15_19_55_24",
"P086#2021_04_15_19_55_48",
@@ -345001,4 +344770,4 @@
]
}
}
-]
\ No newline at end of file
+]
diff --git a/htc/tissue_atlas/median_pixel/DatasetMedianPixel.py b/htc/tissue_atlas/median_pixel/DatasetMedianPixel.py
index 0237413..809cb9d 100644
--- a/htc/tissue_atlas/median_pixel/DatasetMedianPixel.py
+++ b/htc/tissue_atlas/median_pixel/DatasetMedianPixel.py
@@ -7,7 +7,7 @@
from htc.models.common.HTCDataset import HTCDataset
from htc.tivita.DataPath import DataPath
from htc.utils.helper_functions import median_table
-from htc.utils.LabelMapping import LabelMapping
+from htc.utils.Task import Task
class DatasetMedianPixel(HTCDataset):
@@ -15,42 +15,68 @@ def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# Load precomputed spectra
- label_mapping = LabelMapping.from_config(self.config)
+ df = median_table(image_names=self.image_names, config=self.config)
- df = median_table(image_names=self.image_names, label_mapping=label_mapping)
- assert not df.duplicated(["image_name", "label_index_mapped"]).any(), (
- "Found duplicated rows (same (image_name, label_index_mapped) combination found more than once). Cannot use"
- " this table because it is unclear which median spectra should be used in this case"
+ self.labels = torch.from_numpy(df["label_index_mapped"].values) if self.config["label_mapping"] else None
+ self.image_labels = (
+ torch.from_numpy(np.stack(df["image_labels"])) if self.config["input/image_labels"] else None
)
+ if self.labels is not None:
+ assert not df.duplicated(["image_name", "label_index_mapped"]).any(), (
+ "Found duplicated rows (same (image_name, label_index_mapped) combination found more than once). Cannot"
+ " use this table because it is unclear which median spectra should be used in this case"
+ )
+
# We need to set these variables again because an image may contain more than one median spectra and we want to use all (and find the corresponding path to each annotation)
self.image_names = df["image_name"].tolist()
self.paths = [DataPath.from_image_name(image_name) for image_name in self.image_names]
- if self.config["input/normalization"] == "L1":
+ if self.config["input/normalization"] == "L1" or "L1" in self.config["input/preprocessing"]:
self.features = df["median_normalized_spectrum"].values
else:
self.features = df["median_spectrum"].values
- self.labels = torch.from_numpy(df["label_index_mapped"].values)
self.features = torch.from_numpy(np.stack(self.features))
self.features = self.apply_transforms(self.features)
- assert (
- len(self.features) == len(self.labels) == len(self.paths) == len(self.image_names)
- ), "All arrays must have the same length"
+ if self.config["input/meta"]:
+ self.meta = torch.stack([self.read_meta(path) for path in self.paths])
+ assert len(self.meta) == len(self.features), "Meta and features must have the same length"
+ else:
+ self.meta = None
+
+ assert len(self.features) == len(self.paths) == len(self.image_names), "All arrays must have the same length"
+ if self.labels is not None:
+ assert len(self.labels) == len(self.features), "Labels and features must have the same length"
+ if self.image_labels is not None:
+ assert len(self.image_labels) == len(self.features), "Image labels and features must have the same length"
def label_counts(self) -> tuple[torch.Tensor, torch.Tensor]:
- return self.labels.unique(return_counts=True)
+ """
+ Calculates for each unique label in the dataset the number of occurrences based on the task, i.e. either based on the labels or image_labels attribute.
+
+ Compared to the parent class, this method counts the number of annotations and not pixels.
+
+ Returns: Tuple with label values and corresponding counts.
+ """
+ task = Task.from_config(self.config)
+ return getattr(self, task.labels_name()).unique(return_counts=True)
def __len__(self) -> int:
- return len(self.labels)
+ task = Task.from_config(self.config)
+ return len(getattr(self, task.labels_name()))
def __getitem__(self, index: int) -> dict[str, torch.Tensor]:
- sample = {
- "features": self.features[index, :],
- "labels": self.labels[index],
- }
+ sample = {"features": self.features[index, :]}
+
+ if self.labels is not None:
+ sample["labels"] = self.labels[index]
+ if self.image_labels is not None:
+ sample["image_labels"] = self.image_labels[index]
+
+ if self.meta is not None:
+ sample["meta"] = self.meta[index, :]
if not self.train:
sample["image_name"] = self.image_names[index]
diff --git a/htc/tissue_atlas/median_pixel/LightningMedianPixel.py b/htc/tissue_atlas/median_pixel/LightningMedianPixel.py
index 60ad64a..269fd4f 100644
--- a/htc/tissue_atlas/median_pixel/LightningMedianPixel.py
+++ b/htc/tissue_atlas/median_pixel/LightningMedianPixel.py
@@ -18,6 +18,7 @@
from htc.models.common.utils import get_n_classes
from htc.models.pixel.ModelPixel import ModelPixel
from htc.tissue_atlas.median_pixel.DatasetMedianPixel import DatasetMedianPixel
+from htc.utils.Task import Task
class LightningMedianPixel(HTCLightning):
@@ -48,7 +49,8 @@ def train_dataloader(self) -> DataLoader:
)
weights = calculate_class_weights(config, *self.dataset_train.label_counts())
- sample_weights = weights[self.dataset_train.labels]
+ task = Task.from_config(self.config)
+ sample_weights = weights[getattr(self.dataset_train, task.labels_name())]
sampler = WeightedRandomSampler(sample_weights, num_samples=self.config["input/epoch_size"])
else:
sampler = RandomSampler(self.dataset_train, replacement=True, num_samples=self.config["input/epoch_size"])
@@ -57,14 +59,13 @@ def train_dataloader(self) -> DataLoader:
self.dataset_train, sampler=sampler, persistent_workers=True, **self.config["dataloader_kwargs"]
)
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- return self.model(x)["class"]
+ def forward(self, batch: dict[str, torch.Tensor]) -> torch.Tensor:
+ return self.model(batch["features"])["class"]
def training_step(self, batch: dict[str, torch.Tensor], batch_idx: int) -> dict:
+ predictions = self(batch)
labels = batch["labels"]
- features = batch["features"]
- predictions = self(features)
ce_loss = self.ce_loss_weighted(predictions, labels)
self.log("train/ce_loss", ce_loss, on_epoch=True)
@@ -76,7 +77,7 @@ def validation_step(self, batch: dict[str, torch.Tensor], batch_idx: int) -> Non
len(values) == 0 for values in self.validation_results_epoch.values()
), "Validation results are not properly cleared"
- predictions = self(batch["features"]).argmax(dim=1)
+ predictions = self(batch).argmax(dim=1)
self.validation_results_epoch["labels"].append(batch["labels"])
self.validation_results_epoch["predictions"].append(predictions)
@@ -116,10 +117,9 @@ def test_step(self, batch: dict[str, torch.Tensor], batch_idx: int) -> None:
), "Test results are not properly cleared"
labels = batch["labels"]
- features = batch["features"]
image_names = batch["image_name"]
- logits = self(features)
+ logits = self(batch)
self.test_results_epoch["labels"].append(labels)
self.test_results_epoch["logits"].append(logits)
@@ -133,6 +133,3 @@ def on_test_epoch_end(self) -> None:
np.savez_compressed(Path(self.logger.save_dir) / "test_results.npz", **results)
self.test_results_epoch = {"labels": [], "logits": [], "image_names": []}
-
- def _predict_images(self, batch: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
- return {"class": self(batch["features"])}
diff --git a/htc/tissue_atlas/median_pixel/configs/default.json b/htc/tissue_atlas/median_pixel/configs/default.json
index 7030d0d..410d07b 100644
--- a/htc/tissue_atlas/median_pixel/configs/default.json
+++ b/htc/tissue_atlas/median_pixel/configs/default.json
@@ -40,7 +40,6 @@
"annealing_epochs": 0
},
"validation": {
- "checkpoint_metric": "accuracy",
- "dataset_index": 0
+ "checkpoint_metric": "accuracy"
}
}
diff --git a/htc/tissue_atlas/model_processing/run_median_test_table.py b/htc/tissue_atlas/model_processing/run_median_test_table.py
index e55f214..974677a 100644
--- a/htc/tissue_atlas/model_processing/run_median_test_table.py
+++ b/htc/tissue_atlas/model_processing/run_median_test_table.py
@@ -11,7 +11,7 @@
from htc.tissue_atlas.median_pixel.DatasetMedianPixel import DatasetMedianPixel
if __name__ == "__main__":
- # htc median_test_table --model median_pixel --run-folder 2024-01-10_15-45-57_median_18classes --spec tissue-atlas_loocv_test-8_seed-0_cam-118.json --table-name test_table_pigs
+ # htc median_test_table --model median_pixel --run-folder 2024-02-23_14-08-16_median_18classes --spec tissue-atlas_loocv_test-8_seed-0_cam-118.json --table-name test_table_pigs
runner = Runner(description="Create a test table based on a trained median spectra model for a new set of paths.")
runner.add_argument("--input-dir")
runner.add_argument("--spec")
diff --git a/htc/tivita/DataPath.py b/htc/tivita/DataPath.py
index dfb9976..1164e23 100644
--- a/htc/tivita/DataPath.py
+++ b/htc/tivita/DataPath.py
@@ -11,6 +11,7 @@
import numpy as np
import pandas as pd
+import torch
from PIL import Image
from typing_extensions import Self
@@ -89,7 +90,7 @@ def __init__(
Args:
image_dir: Path (or string) to the image directory (timestamp folder).
- data_dir: Path (or string) to the data directory of the dataset (it should contain a dataset_settings.json file).
+ data_dir: Path (or string) to the data directory of the dataset (it should contain a dataset_settings.json file). In case of a subdataset, data_dir should point to the subdataset folder instead of the root dataset folder.
intermediates_dir: Path (or string) to the intermediates directory of the dataset.
dataset_settings: Reference to the settings of the dataset. If None and no settings could be found in the image directory, the parents of the image directory are searched. If available, the closest dataset_settings.json is used. Otherwise, the data path gets an empty dataset settings assigned.
annotation_name_default: Default annotation_name(s) which will be used when reading the segmentation with read_segmentation() with no arguments.
@@ -148,15 +149,10 @@ def __lt__(self, other: Self) -> bool:
@property
def dataset_settings(self) -> DatasetSettings:
if self._dataset_settings is None:
- if self.image_dir is not None and (path := self.image_dir / "dataset_settings.json").exists():
- self._dataset_settings = DatasetSettings(path)
+ if self.image_dir is not None:
+ self._dataset_settings = DatasetSettings(self.image_dir / "dataset_settings.json")
else:
self._dataset_settings = DatasetSettings(path_or_data={})
- parent_paths = list(self.image_dir.parents)
- for p in parent_paths:
- if (path := p / "dataset_settings.json").exists():
- self._dataset_settings = DatasetSettings(path)
- break
return self._dataset_settings
@@ -172,12 +168,11 @@ def cube_path(self) -> Path:
"""
return self() / f"{self.timestamp}_SpecCube.dat"
- def read_cube(self, *reading_args, **reading_kwargs) -> np.ndarray:
+ def read_cube(self, **reading_kwargs) -> np.ndarray:
"""
Read the Tivita HSI cube (see read_tivita_hsi()).
Args:
- reading_args: Positional arguments to be passed to read_tivita_hsi function.
reading_kwargs: Keyword arguments to be passed to read_tivita_hsi function.
Returns: HSI data cube.
@@ -185,7 +180,24 @@ def read_cube(self, *reading_args, **reading_kwargs) -> np.ndarray:
from htc.tivita.hsi import read_tivita_hsi
cube_path = self.cube_path()
- return read_tivita_hsi(cube_path, *reading_args, **reading_kwargs)
+
+ if getattr(self, "calibration_target", None) is not None:
+ from htc.cameras.calibration.CalibrationSwap import CalibrationSwap
+
+ cube = read_tivita_hsi(self.cube_path()) # We need unnormalized cubes
+
+ t = CalibrationSwap()
+ cube = t.transform_image(
+ self, image=torch.from_numpy(cube), calibration_target=self.calibration_target
+ ).numpy()
+
+ if reading_kwargs.get("normalization") is not None:
+ cube = cube / np.linalg.norm(cube, ord=reading_kwargs["normalization"], axis=2, keepdims=True)
+ cube = np.nan_to_num(cube, copy=False)
+ else:
+ cube = read_tivita_hsi(cube_path, **reading_kwargs)
+
+ return cube
def read_cube_raw(self, calibration_original: Union["CalibrationFiles", None] = None) -> np.ndarray:
"""
@@ -204,6 +216,7 @@ def read_cube_raw(self, calibration_original: Union["CalibrationFiles", None] =
t = CalibrationSwap()
calibration_original = t.original_calibration_files(self)
+
return cube * calibration_original.white_image.numpy() + calibration_original.dark_image.numpy()
def compute_oversaturation_mask(self, threshold: int = 1000) -> np.ndarray:
@@ -213,12 +226,12 @@ def compute_oversaturation_mask(self, threshold: int = 1000) -> np.ndarray:
Args:
threshold: Threshold to consider a camera count being oversaturated. The maximum count of the camera is 1023, therefore a value of 1000 is chosen as default to account for noise and slight miscalibrations (e.g. due to the corresponding calibration files not being available).
- Returns: Oversaturation mask of the image.
+ Returns: Oversaturation mask of the image (True indicates overstaurated pixels).
"""
cube_raw = self.read_cube_raw()
return np.any(cube_raw > threshold, axis=-1)
- def is_cube_valid(self) -> bool:
+ def is_cube_valid(self, strict: bool = False) -> bool:
"""
Checks whether the HSI cube is valid, i.e. not broken. Unfortunately, the Tivita camera may produce broken images due to unknown reasons. Here, we basically check whether we can read the cube and whether it contains invalid values (zero, negative pixels, infinite numbers).
@@ -226,6 +239,9 @@ def is_cube_valid(self) -> bool:
>>> path.is_cube_valid()
True
+ Args:
+ strict: If True, will also mark cubes as invalid if any value is zero or negative. Otherwise, only a warning is issued.
+
Returns: True if all checks pass. If False, then the image should be excluded from the analysis as the spectra may be completely wrong. R.I.P.
"""
is_valid = True
@@ -238,7 +254,10 @@ def is_cube_valid(self) -> bool:
is_valid = False
if cube.shape != self.dataset_settings["shape"]:
- settings.log.error(f"The cube {self} does not have the correct shape ({cube.shape = })")
+ settings.log.error(
+ f"The cube {self} does not have the correct shape ({cube.shape = } !="
+ f" {self.dataset_settings['shape'] = })"
+ )
is_valid = False
infinite_values = ~np.isfinite(cube)
@@ -255,6 +274,8 @@ def is_cube_valid(self) -> bool:
settings.log.warning(
f"The cube {self} has {np.sum(cube == 0)} zero values (the cube is still used)"
)
+ if strict:
+ is_valid = False
if np.all(cube < 0):
settings.log.error(f"The cube {self} contains only negative values")
@@ -265,6 +286,8 @@ def is_cube_valid(self) -> bool:
settings.log.warning(
f"The cube {self} contains {np.sum(negative_pixels)} negative pixels (the cube is still used)"
)
+ if strict:
+ is_valid = False
except Exception as e:
settings.log.error(f"Cannot read the cube {self}: {e}")
is_valid = False
@@ -332,6 +355,31 @@ def read_rgb_sensor(self, *reading_args, **reading_kwargs) -> np.ndarray:
rgb_path = self.rgb_path_sensor()
return read_tivita_rgb(rgb_path, *reading_args, **reading_kwargs)
+ def align_rgb_sensor(self, *args, recompute: bool = False, **kwargs) -> np.ndarray:
+ """
+ Align the RGB image from the RGB sensor to the reconstructed RGB image of the HSI cube.
+
+ See the function `align_rgb_sensor()` for more details.
+
+ Args:
+ recompute: If True, the alignment will be recomputed even if a precomputed file exists.
+ args: Positional arguments to be passed to `align_rgb_sensor()` function.
+ kwargs: Keyword arguments to pass to `align_rgb_sensor()` function.
+
+ Returns: Aligned RGB sensor image.
+ """
+ if not recompute:
+ precomputed_path = (
+ self.intermediates_dir / "preprocessing" / "rgb_sensor_aligned" / f"{self.image_name()}.blosc"
+ )
+ if precomputed_path.exists():
+ data = decompress_file(precomputed_path)
+ return np.ma.MaskedArray(data["data"], data["mask"])
+
+ from htc.tivita.rgb import align_rgb_sensor
+
+ return align_rgb_sensor(self.rgb_path_reconstructed(), self.rgb_path_sensor(), *args, **kwargs)
+
def segmentation_path(self) -> Union[Path, None]:
"""
Path to the file which stores the segmentation image(s). These are not the raw annotations but the processed images, i.e. numpy array with the same shape as the image and annotations for all labels merged in one file.
@@ -407,6 +455,29 @@ def read_segmentation(
else:
return None
+ def colorchecker_annotation_path(self) -> Union[Path, None]:
+ """
+ Path to the colorchecker annotation file (automatically or manually created).
+
+ Returns: Path to the existing colorchecker annotation file or None if it could not be found (e.g., if the data path does not point to an colorchecker image).
+ """
+ annotations_dir = self.image_dir / "annotations"
+ if not annotations_dir.exists():
+ return None
+ else:
+ mask_paths = list(
+ annotations_dir.glob(f"{self.timestamp}#squares#automask#*.png")
+ ) # searching for automasks
+ if len(mask_paths) == 0:
+ mask_paths = list(annotations_dir.glob(f"{self.timestamp}#polygon#*.nrrd")) # searching for MITK masks
+
+ if len(mask_paths) == 0:
+ return None
+ elif len(mask_paths) > 1:
+ raise ValueError(f"Too many colorchecker masks available for {self.image_dir}")
+ else:
+ return mask_paths[0] if mask_paths[0].exists() else None
+
def read_colorchecker_mask(
self, return_spectra: bool = False, normalization: int = None
) -> Union[dict[str, Union[np.ndarray, pd.DataFrame, LabelMapping]], None]:
@@ -436,22 +507,15 @@ def read_colorchecker_mask(
- median_table: Table with median spectra (unnormalized and L1-normalized) for each color chip.
- label_mapping: The label mapping object to interpret the values of the mask array.
"""
- mask_dir = self.image_dir / "annotations"
- mask_paths = list(mask_dir.glob(f"{self.timestamp}#squares#automask#*.png")) # searching for automasks
- if len(mask_paths) == 0:
- mask_paths = list(mask_dir.glob(f"{self.timestamp}#polygon#*.nrrd")) # searching for MITK masks
- assert len(mask_paths) <= 1, f"Too many colorchecker masks available for {self.image_dir}"
+ mask_path = self.colorchecker_annotation_path()
- if len(mask_paths) == 0:
+ if mask_path is None:
settings.log.warning(
f"Colorchecker mask cannot be found for {self.image_dir}. Please refer to"
" ColorcheckerMaskCreation.ipynb or use MITK to generate the corresponding colorchecker mask!"
)
return None
-
else:
- mask_path = mask_paths[0]
-
from htc.utils.ColorcheckerReader import ColorcheckerReader
if mask_path.suffix == ".png":
@@ -643,7 +707,7 @@ def _load_precomputed_parameters(self) -> Union[Union[np.ndarray, int], dict[Any
return decompress_file(params_path)
- def compute_sto2(self, cube: np.ndarray = None) -> np.ndarray:
+ def compute_sto2(self, cube: np.ndarray = None, version: str = None) -> np.ndarray:
"""
Computes the Tissue oxygen saturation (StO2) for the image.
@@ -666,29 +730,34 @@ def compute_sto2(self, cube: np.ndarray = None) -> np.ndarray:
Args:
cube: If not None, will use this cube instead of loading it.
+ version: Name of the function to use for computing the StO2 parameter. If None, the official function which will be chosen based on the Camera_CamID. Currently, `calc_sto2` is used for the Halogen formula and `calc_sto2_2_helper` for the LED formula.
Returns: The StO2 parameter image (as numpy masked array) with values in the range [0;1].
"""
- try:
- from htc.tivita.functions_official import calc_sto2, calc_sto2_2_helper, detect_background
+ detect_background = self._code_from_official("detect_background")
+ if version is not None:
+ calc_sto2 = self._code_from_official(version)
+ else:
+ if self.meta("Camera_CamID") is None or self.meta("Camera_CamID") in [
+ "0102-00057",
+ "0102-00085",
+ "0102-00098",
+ "0202-00113",
+ "0202-00118",
+ ]:
+ calc_sto2 = self._code_from_official("calc_sto2") # Halogen formula should be used
+ else:
+ calc_sto2 = self._code_from_official("calc_sto2_2_helper") # LED formula should be used
+ if calc_sto2 is not None and detect_background is not None:
with np.errstate(divide="ignore", invalid="ignore", over="ignore"):
cube = self.read_cube() if cube is None else cube
- if self.meta("Camera_CamID") is None or self.meta("Camera_CamID") in [
- "0102-00057",
- "0102-00085",
- "0102-00098",
- "0202-00113",
- "0202-00118",
- ]:
- sto2_img = calc_sto2(cube) # Halogen formula should be used
- else:
- sto2_img = calc_sto2_2_helper(cube) # LED formula should be used
+ sto2_img = calc_sto2(cube)
param = np.nan_to_num(np.rot90(sto2_img, k=-1), copy=False)
background = np.rot90(detect_background(cube), k=-1)
return np.ma.MaskedArray(param, background == 0, fill_value=0)
- except ImportError:
+ else:
params = self._load_precomputed_parameters()
return np.ma.MaskedArray(params["StO2"], params["background"], fill_value=0)
@@ -1102,6 +1171,18 @@ def image_name_annotations(self) -> str:
def datetime(self) -> datetime:
return datetime.strptime(self.timestamp, "%Y_%m_%d_%H_%M_%S")
+ def is_timestamp_folder(self) -> bool:
+ """
+ Check if this data path points to a timestamp folder (as it is usually the case for image folders).
+
+ Returns: True if this data path points to a valid timestamp, False otherwise.
+ """
+ try:
+ self.datetime()
+ return True
+ except ValueError:
+ return False
+
def annotation_names(self) -> list[str]:
"""
Returns the names of all associated annotations for this image.
@@ -1158,11 +1239,26 @@ def _build_cache(local: bool) -> dict[str, Any]:
assert len(table_path) == 1, f"More than one meta table found for {entry}"
table_path = table_path[0]
- dsettings = DatasetSettings(entry["path_data"] / "dataset_settings.json")
df = pd.read_feather(table_path)
- df["dsettings"] = dsettings
+
+ if "dataset_settings_path" in df.columns:
+ # Subdatasets may have their own path to the dataset settings
+ dsettings_mapping = {
+ f: DatasetSettings(entry["path_data"] / f) for f in df.dataset_settings_path.unique()
+ }
+ df["dsettings"] = df.dataset_settings_path.map(dsettings_mapping)
+
+ # The data directory always points to the folder which contains the dataset settings (may be the subdataset instead of the root dataset)
+ data_dir_mapping = {
+ f: (entry["path_data"] / f).parent for f in df.dataset_settings_path.unique()
+ }
+ df["data_dir"] = df.dataset_settings_path.map(data_dir_mapping)
+ else:
+ dsettings = DatasetSettings(entry["path_data"] / "dataset_settings.json")
+ df["dsettings"] = dsettings
+
df["dataset_env_name"] = env_key
- df["data_dir"] = entry["path_data"]
+ df["root_data_dir"] = entry["path_data"]
df["intermediates_dir"] = entry["path_intermediates"]
# Append the metadata for the current dataset to the global cache
@@ -1266,8 +1362,8 @@ def from_image_name(image_name: str) -> Self:
)
DataPath._data_paths_cache[cache_name] = DataPathClass(
- match["data_dir"] / match["path"],
- match["data_dir"],
+ match["root_data_dir"] / match["path"],
+ match["data_dir"] if "data_dir" in match else match["root_data_dir"],
match["intermediates_dir"],
match["dsettings"],
annotation_name,
@@ -1277,7 +1373,7 @@ def from_image_name(image_name: str) -> Self:
@staticmethod
def iterate(
- data_dir: Path,
+ data_dir: Union[str, Path],
filters: Union[list[Callable[[Self], bool]], None] = None,
annotation_name: Union[str, list[str]] = None,
) -> Iterator[Self]:
@@ -1305,6 +1401,8 @@ def iterate(
Returns: Generator with all path objects.
"""
+ if type(data_dir) == str:
+ data_dir = Path(data_dir)
if filters is None:
filters = []
@@ -1340,7 +1438,11 @@ def iterate(
parts.pop()
if DataPathClass is None:
- if not (data_dir / "dataset_settings.json").exists() and (data_dir / "data").exists():
+ if (
+ not (data_dir / "dataset_settings.json").exists()
+ and (data_dir / "data").exists()
+ and not data_dir.name.startswith("Cat_")
+ ):
settings.log.warning(
f"No dataset_settings.json file found in the data directory {data_dir} but the subdirectory data"
" exists in this directory. For the default datasets, please point data_dir to the data"
diff --git a/htc/tivita/DataPathMultiorgan.py b/htc/tivita/DataPathMultiorgan.py
index 832ee5e..49382f2 100644
--- a/htc/tivita/DataPathMultiorgan.py
+++ b/htc/tivita/DataPathMultiorgan.py
@@ -13,17 +13,29 @@
# We use a decorator to wrap some of the path functions. This is important for the files
-# which are stored in the overlap folder because then the image data is stored in the semantic
+# which are stored in the overlap folder because then the image data is stored in a different
# dataset (due to multiple annotations)
-def use_semantic_path(method: Callable) -> Callable:
+def use_overlap_path(method: Callable) -> Callable:
@functools.wraps(method)
- def _use_semantic_path(self):
+ def _use_overlap_path(self):
if self.is_overlap:
image_dir_old = self.image_dir
- image_dir_new = (
- settings.data_dirs["PATH_Tivita_multiorgan_semantic"] / "subjects" / self.subject_name / self.timestamp
- )
- assert image_dir_new.exists(), f"Cannot find the path {image_dir_new}"
+ potential_data_dirs = [
+ settings.data_dirs["PATH_Tivita_multiorgan_semantic"],
+ settings.data_dirs["PATH_Tivita_multiorgan_masks"],
+ ]
+ image_dir_new_found = False
+
+ for potential_data_dir in potential_data_dirs:
+ image_dir_new = potential_data_dir / "subjects" / self.subject_name / self.timestamp
+
+ if image_dir_new.exists():
+ image_dir_new_found = True
+ break
+
+ assert (
+ image_dir_new_found
+ ), f"Cannot find the overlap image name in any of the potential dataset dirs {potential_data_dirs}"
self.image_dir = image_dir_new
res = method(self)
@@ -33,7 +45,7 @@ def _use_semantic_path(self):
return res
- return _use_semantic_path
+ return _use_overlap_path
class DataPathMultiorgan(DataPath):
@@ -78,15 +90,15 @@ def image_name_parts(self) -> list[str]:
return parts
- @use_semantic_path
+ @use_overlap_path
def cube_path(self) -> Path:
return super().cube_path()
- @use_semantic_path
+ @use_overlap_path
def camera_meta_path(self) -> Path:
return super().camera_meta_path()
- @use_semantic_path
+ @use_overlap_path
def rgb_path_reconstructed(self) -> Path:
return super().rgb_path_reconstructed()
diff --git a/htc/tivita/DatasetSettings.py b/htc/tivita/DatasetSettings.py
index 014a329..bd9bcf3 100644
--- a/htc/tivita/DatasetSettings.py
+++ b/htc/tivita/DatasetSettings.py
@@ -2,6 +2,7 @@
# SPDX-License-Identifier: MIT
import json
+import threading
from pathlib import Path
from typing import Any, Union
@@ -43,6 +44,21 @@ def __init__(self, path_or_data: Union[str, Path, dict]):
self._data = None
self._path = path_or_data
+ self._mutex = threading.Lock()
+
+ def __getstate__(self):
+ state = self.__dict__.copy()
+
+ # The lock cannot be pickled but this is not a problem since the lock is only for threads anyway to ensure that inside one process the data is only modified once
+ del state["_mutex"]
+ return state
+
+ def __setstate__(self, state):
+ self.__dict__.update(state)
+
+ # Just create a new lock for every process
+ self._mutex = threading.Lock()
+
def __repr__(self) -> str:
res = (
"Settings for the dataset"
@@ -82,18 +98,19 @@ def __contains__(self, key: str) -> bool:
@property
def settings_path(self) -> Union[None, Path]:
"""
- Returns: The Path to the dataset_settings.json file if it exists or None if not.
+ Returns: The Path to the dataset_settings.json file if it exists (either at the specified path or any parent directory) or None if not.
"""
if self._path is None:
return None
else:
- if self._path.exists():
- p = self._path
- if self._path.is_dir():
- p /= "dataset_settings.json"
-
- return p if p.exists() else None
+ if self._path.is_file():
+ return self._path
else:
+ possible_locations = [self._path] + list(self._path.parents)
+ for p in possible_locations:
+ if (path := p / "dataset_settings.json").is_file():
+ return path
+
return None
@property
@@ -102,10 +119,14 @@ def data(self) -> dict:
if self.settings_path is None:
self._data = {}
else:
- with self.settings_path.open(encoding="utf-8") as f:
- self._data = json.load(f)
-
- self._data_conversions()
+ # The data should only be loaded and converted by one thread at a time
+ with self._mutex:
+ # By now, another thread might have already loaded the data
+ if self._data is None:
+ with self.settings_path.open(encoding="utf-8") as f:
+ self._data = json.load(f)
+
+ self._data_conversions()
return self._data
diff --git a/htc/tivita/rgb.py b/htc/tivita/rgb.py
index d316ddf..4cd079c 100644
--- a/htc/tivita/rgb.py
+++ b/htc/tivita/rgb.py
@@ -1,10 +1,12 @@
# SPDX-FileCopyrightText: 2022 Division of Intelligent Medical Systems, DKFZ
# SPDX-License-Identifier: MIT
+from functools import partial
from pathlib import Path
import numpy as np
import torch
+from kornia.geometry import HomographyWarper, ImageRegistrator, Similarity
from PIL import Image
diff --git a/htc/utils/Config.py b/htc/utils/Config.py
index c36adfa..88e6dba 100644
--- a/htc/utils/Config.py
+++ b/htc/utils/Config.py
@@ -123,30 +123,45 @@ def __init__(self, path_or_dict: Union[str, Path, dict], use_shared_dict=False):
self.data[k] = v
if self["inherits"]:
- extension = "" if self["inherits"].endswith(".json") else ".json"
- inherits = Path(self["inherits"] + extension)
-
- # We try several locations to find the parent config file
- possible_paths = Config._get_possible_paths(inherits)
- if self.path_config is not None:
- possible_paths.append(self.path_config.with_name(inherits.name)) # Same directory as the child config
-
- parent_path = None
- for path in possible_paths:
- if path.exists():
- parent_path = path
- break
-
- assert parent_path is not None, (
- f"Cannot find the path to the parent configuration file {inherits}. Tried at the following locations:"
- f" {possible_paths}"
- )
- data_parent = Config(parent_path).data
-
- # The existing data (=data from the child) has precedence over the parent data
- self.data = dict(merge_dicts_deep(data_parent, self.data))
+ if type(self["inherits"]) == str:
+ self["inherits"] = [self["inherits"]]
+
+ for parent in self["inherits"]:
+ extension = "" if parent.endswith(".json") else ".json"
+ inherits = Path(parent + extension)
+
+ # We try several locations to find the parent config file
+ possible_paths = Config._get_possible_paths(inherits)
+ if self.path_config is not None:
+ possible_paths.append(
+ self.path_config.with_name(inherits.name)
+ ) # Same directory as the child config
+
+ parent_path = None
+ for path in possible_paths:
+ if path.exists():
+ parent_path = path
+ break
+
+ assert parent_path is not None, (
+ f"Cannot find the path to the parent configuration file {inherits}. Tried at the following"
+ f" locations: {possible_paths}"
+ )
+
+ config_parent = Config(parent_path)
+ if self["inherits_skip"]:
+ for key in self["inherits_skip"]:
+ del config_parent[key]
+ data_parent = config_parent.data
+
+ # The existing data (=data from the child) has precedence over the parent data
+ self.data = dict(merge_dicts_deep(data_parent, self.data))
+
+ # Extend all config keys from the parent (but not the own class due to the possibility of multiple inherence)
+ self._extend_lists(config_parent)
del self["inherits"]
+ del self["inherits_skip"]
self._extend_lists()
@@ -157,9 +172,12 @@ def __init__(self, path_or_dict: Union[str, Path, dict], use_shared_dict=False):
else:
self._used_keys = {}
- def _extend_lists(self) -> None:
+ def _extend_lists(self, base_config: "Config" = None) -> None:
+ if base_config is None:
+ base_config = self
+
# Users can extend additional lists by adding the same key with _extends appended
- for k, v in self.items():
+ for k, v in base_config.items():
if k.endswith("_extends") and type(v) == list:
k_original = k.removesuffix("_extends")
if k_original in self:
@@ -168,7 +186,7 @@ def _extend_lists(self) -> None:
" supported for the extends feature"
)
self[k_original] = self[k_original] + v
- del self[k]
+ del base_config[k]
def _copy_data(self, dict_data: dict) -> dict:
new_data = {}
diff --git a/htc/utils/DelayedFileHandler.py b/htc/utils/DelayedFileHandler.py
index ae06226..2d6c26d 100644
--- a/htc/utils/DelayedFileHandler.py
+++ b/htc/utils/DelayedFileHandler.py
@@ -29,8 +29,8 @@ def set_filename(self, filename: Path, **kwargs) -> None:
self.file_handler = logging.FileHandler(filename, **kwargs)
# Apply existing settings to the new file handler
- for filter in self.filters:
- self.file_handler.addFilter(filter)
+ for f in self.filters:
+ self.file_handler.addFilter(f)
self.file_handler.setFormatter(self.formatter)
self.file_handler.setLevel(self.level)
@@ -39,11 +39,11 @@ def set_filename(self, filename: Path, **kwargs) -> None:
self.file_handler.emit(record)
self.cached_records = []
- def addFilter(self, filter: Union[logging.Filter, Callable]) -> None:
+ def addFilter(self, filter_func: Union[logging.Filter, Callable]) -> None:
if self.file_handler is None:
- super().addFilter(filter)
+ super().addFilter(filter_func)
else:
- self.file_handler.addFilter(filter)
+ self.file_handler.addFilter(filter_func)
def setFormatter(self, fmt: str) -> None:
if self.file_handler is None:
diff --git a/htc/utils/DomainMapper.py b/htc/utils/DomainMapper.py
index 19af53c..132d8cb 100644
--- a/htc/utils/DomainMapper.py
+++ b/htc/utils/DomainMapper.py
@@ -75,7 +75,7 @@ def _init_attributes(self) -> tuple[Union[list, list[str]], dict, Any]:
elif "subject_index" == self.target_domain:
domains, domain_mapping = self._pig_domains(dataset, paths)
elif "species_index" == self.target_domain:
- domains, domain_mapping = self._species_domains(["Pig", "Human"], paths)
+ domains, domain_mapping = self._species_domains(paths)
try:
from htc.human.settings_human import settings_human
@@ -124,8 +124,23 @@ def _pig_domains(dataset: list[str], paths: list[DataPath]) -> tuple[list, dict]
return dataset, {x.image_name(): x.subject_name for x in paths}
@staticmethod
- def _species_domains(domains: list, paths: list[DataPath]) -> tuple[list, dict]:
- return domains, {x.image_name(): domains[1] if "SPACE_" in x.subject_name else domains[0] for x in paths}
+ def _species_domains(paths: list[DataPath]) -> tuple[list, dict]:
+ domains = set()
+ domain_mapping = {}
+ for p in paths:
+ if p.subject_name.startswith("SPACE_"):
+ domain_mapping[p.image_name()] = "human"
+ domains.add("human")
+ elif p.subject_name.startswith("P"):
+ domain_mapping[p.image_name()] = "pig"
+ domains.add("pig")
+ elif p.subject_name.startswith("R"):
+ domain_mapping[p.image_name()] = "rat"
+ domains.add("rat")
+ else:
+ raise ValueError(f"Unknown species for path: {p}")
+
+ return sorted(domains), domain_mapping
def domain_name(self, image_name: str) -> str:
"""
diff --git a/htc/utils/DuplicateFilter.py b/htc/utils/DuplicateFilter.py
index 6dfaf75..1b32fe2 100644
--- a/htc/utils/DuplicateFilter.py
+++ b/htc/utils/DuplicateFilter.py
@@ -10,7 +10,7 @@ def __init__(self):
super().__init__()
self.msgs = set()
- def filter(self, record):
+ def filter(self, record): # noqa: A003
rv = record.msg not in self.msgs
self.msgs.add(record.msg)
return rv
diff --git a/htc/utils/LDA.py b/htc/utils/LDA.py
index f60c555..2006e9d 100644
--- a/htc/utils/LDA.py
+++ b/htc/utils/LDA.py
@@ -74,9 +74,9 @@ def LDA(data: np.ndarray, labels: np.ndarray) -> tuple[np.ndarray, np.ndarray, n
) # Using the pseudo-inverse matrix gives stabler results
# Sort the eigenvalues descendingly (https://stackoverflow.com/questions/8092920/sort-eigenvalues-and-associated-eigenvectors-after-using-numpy-linalg-eig-in-pyt)
- eval, evec = np.linalg.eig(scatter)
- idx = eval.argsort()[::-1]
- eval = eval[idx]
- evec = evec[:, idx]
+ eigenvalues, eigenvectors = np.linalg.eig(scatter)
+ idx = eigenvalues.argsort()[::-1]
+ eigenvalues = eigenvalues[idx]
+ eigenvectors = eigenvectors[:, idx]
- return evec, np.matmul(data, evec), eval
+ return eigenvectors, np.matmul(data, eigenvectors), eigenvalues
diff --git a/htc/utils/LabelMapping.py b/htc/utils/LabelMapping.py
index 29725bb..e436f6b 100644
--- a/htc/utils/LabelMapping.py
+++ b/htc/utils/LabelMapping.py
@@ -1,9 +1,7 @@
# SPDX-FileCopyrightText: 2022 Division of Intelligent Medical Systems, DKFZ
# SPDX-License-Identifier: MIT
-import importlib
import itertools
-import re
from pathlib import Path
from typing import TYPE_CHECKING, Union
@@ -15,6 +13,8 @@
from htc.settings import settings
from htc.tivita.DatasetSettings import DatasetSettings
from htc.utils.Config import Config
+from htc.utils.Task import Task
+from htc.utils.type_from_string import variable_from_string
if TYPE_CHECKING:
from htc.tivita.DataPath import DataPath
@@ -243,22 +243,22 @@ def map_tensor(self, tensor: Union[torch.Tensor, np.ndarray], old_mapping: Self)
return tensor_mapping(tensor, old_new_mapping)
- def rename(self, rename_dict: dict[str, str]) -> None:
+ def rename(self, rename_mapping: dict[str, str]) -> None:
"""
Rename existing label names to new label names.
Args:
- rename_dict: dict with key being what label should be renamed and value being the new label name.
+ rename_mapping: Mapping with key being what label should be renamed and value being the new label name.
"""
self.mapping_name_index = {
- rename_dict.get(label_name, label_name): label_index
+ rename_mapping.get(label_name, label_name): label_index
for label_name, label_index in self.mapping_name_index.items()
}
self.label_colors = {
- rename_dict.get(label_name, label_name): color for label_name, color in self.label_colors.items()
+ rename_mapping.get(label_name, label_name): color for label_name, color in self.label_colors.items()
}
self.mapping_index_name = {
- label_index: rename_dict.get(label_name, label_name)
+ label_index: rename_mapping.get(label_name, label_name)
for label_index, label_name in self.mapping_index_name.items()
}
@@ -315,6 +315,9 @@ def from_path(cls, path: "DataPath") -> Self:
Constructs a label mapping based on the default labels of the dataset accessed via the path object.
These are the labels as defined by the clinicians.
+
+ Args:
+ path: Data path to the image.
"""
label_colors = path.dataset_settings["label_colors"] if "label_colors" in path.dataset_settings else None
return cls(
@@ -335,31 +338,34 @@ def from_data_dir(cls, data_dir: Path) -> Self:
return cls(dsettings["label_mapping"], dsettings["last_valid_label_index"])
@classmethod
- def from_config(cls, config: Config) -> Self:
+ def from_config(cls, config: Config, task: Task = None, image_label_entry_index: int = 0) -> Self:
"""
- Constructs a label mapping as defined in the config file. config['label_mapping'] can be defined as:
+ Constructs a label mapping as defined in the config file. For example, `config['label_mapping']` can be defined as:
* a LabelMapping instance.
- * a config definition string in the format module>variable (e.g. htc.settings_seg>label_mapping). module must be importable and variable must exist in the module.
- * a dict from a JSON file (as saved via to_class_dict()).
- * a dict with label_name:label_index definitions (like settings_seg.label_mapping) in which case settings.label_index_thresh will be used to determine invalid labels.
+ * a config definition string in the format module>variable (e.g. `htc.settings_seg>label_mapping`). module must be importable and variable must exist in the module.
+ * a dict from a JSON file (as saved via `to_class_dict()`).
+ * a dict with label_name:label_index definitions (like `settings_seg.label_mapping`) in which case `settings.label_index_thresh` will be used to determine invalid labels.
+
+ Args:
+ config: The config object.
+ task: The task for which the mapping should be constructed. For segmentation tasks, the mapping must be defined in `config['label_mapping']` and for classification tasks it must be defined in `config['input/image_labels'][image_label_entry_index]['image_label_mapping']`. If None, the task will be determined from the config.
+ image_label_entry_index: The index of the config['input/image_labels'] list in the config file (used only for classification tasks).
"""
- assert "label_mapping" in config, "There is no label mapping in the config file"
- mapping = config["label_mapping"]
+ if task is None:
+ task = Task.from_config(config)
+
+ if task == Task.SEGMENTATION:
+ assert "label_mapping" in config, "There is no label mapping in the config file"
+ mapping = config["label_mapping"]
+ elif task == Task.CLASSIFICATION:
+ assert "input/image_labels" in config, "There must be image labels defined for classification tasks"
+ mapping = config["input/image_labels"][image_label_entry_index]["image_label_mapping"]
+ else:
+ raise ValueError(f"Invalid task: {task}")
if type(mapping) == str:
- match = re.search(r"^([\w.]+)>(\w+)$", mapping)
- assert match is not None, (
- f"Could not parse the string {mapping} as a valid config definition. It must be in the format"
- " module>variable (e.g. htc.settings_seg>label_mapping) and must refer to a valid Python script"
- )
-
- module = importlib.import_module(match.group(1))
- if not hasattr(module, match.group(2)):
- # In case settings is an object
- module = getattr(module, match.group(1).split(".")[-1])
- mapping = getattr(module, match.group(2))
- # Now load as usual
+ mapping = variable_from_string(mapping)
if isinstance(mapping, LabelMapping):
mapping_obj = mapping
@@ -389,5 +395,12 @@ def from_config(cls, config: Config) -> Self:
mapping_obj = cls(label_mapping)
- config["label_mapping"] = mapping_obj # Cache for future use
+ # Cache for future use
+ if task == Task.SEGMENTATION:
+ config["label_mapping"] = mapping_obj
+ elif task == Task.CLASSIFICATION:
+ config["input/image_labels"][image_label_entry_index]["image_label_mapping"] = mapping_obj
+ else:
+ raise ValueError(f"Invalid task: {task}")
+
return mapping_obj
diff --git a/htc/utils/MultiPath.py b/htc/utils/MultiPath.py
index e30e704..2891a60 100644
--- a/htc/utils/MultiPath.py
+++ b/htc/utils/MultiPath.py
@@ -124,22 +124,22 @@ def __repr__(self):
/y (exists=False)
/x/y (exists=False)
"""
- repr = f"Class: {self.__class__.__name__}\n"
+ text = f"Class: {self.__class__.__name__}\n"
root_location = Path(super().__str__())
- repr += f"Root location: {root_location} (exists={root_location.exists()})\n"
+ text += f"Root location: {root_location} (exists={root_location.exists()})\n"
if self._default_needle is not None:
repr_needle = f" (considering needle {self._default_needle})"
else:
repr_needle = ""
best_location = self.find_best_location()
- repr += f"Best location{repr_needle}: {best_location} (exists={best_location.exists()})\n"
+ text += f"Best location{repr_needle}: {best_location} (exists={best_location.exists()})\n"
- repr += "All locations:\n"
- repr += "\n".join([str(a) + f" (exists={a.exists()})" for a in self.possible_locations()])
+ text += "All locations:\n"
+ text += "\n".join([str(a) + f" (exists={a.exists()})" for a in self.possible_locations()])
- return repr
+ return text
def __reduce__(self):
# Called when pickling path objects (e.g. multiprocessing)
@@ -181,17 +181,17 @@ def name(self) -> str:
# Some methods also rely on this property
return self.find_best_location().name
- def iterdir(self, filter: Callable[[Path], bool] = None):
+ def iterdir(self, filter_func: Callable[[Path], bool] = None):
# We also need to override the iterate methods to return paths from all alternatives
- for location in self.possible_locations(only_existing=True, filter=filter):
+ for location in self.possible_locations(only_existing=True, filter_func=filter_func):
yield from location.iterdir()
- def glob(self, pattern, filter: Callable[[Path], bool] = None):
- for location in self.possible_locations(only_existing=True, filter=filter):
+ def glob(self, pattern, filter_func: Callable[[Path], bool] = None):
+ for location in self.possible_locations(only_existing=True, filter_func=filter_func):
yield from location.glob(pattern)
- def rglob(self, pattern, filter: Callable[[Path], bool] = None):
- for location in self.possible_locations(only_existing=True, filter=filter):
+ def rglob(self, pattern, filter_func: Callable[[Path], bool] = None):
+ for location in self.possible_locations(only_existing=True, filter_func=filter_func):
yield from location.rglob(pattern)
def mkdir(self, *args, **kwargs):
@@ -330,7 +330,7 @@ def find_best_location(self, writing: bool = False) -> Path:
# There was a match, but the path does not exist, still better than the root location
return matched_location
- def possible_locations(self, only_existing=False, filter: Callable[[Path], bool] = None) -> list[Path]:
+ def possible_locations(self, only_existing=False, filter_func: Callable[[Path], bool] = None) -> list[Path]:
"""
Lists all locations which can be accessed by this multi path.
@@ -341,7 +341,7 @@ def possible_locations(self, only_existing=False, filter: Callable[[Path], bool]
Args:
only_existing: Include only locations which exist.
- filter: Filter function to select locations. The function receives a paths and must return True if the path should be used.
+ filter_func: Filter function to select locations. The function receives a paths and must return True if the path should be used.
Returns: All possible locations for the current path.
"""
@@ -364,8 +364,8 @@ def possible_locations(self, only_existing=False, filter: Callable[[Path], bool]
new = unify_path(new, resolve_symlinks=False)
locations.append(new)
- if filter is not None:
- locations = [l for l in locations if filter(l)]
+ if filter_func is not None:
+ locations = [l for l in locations if filter_func(l)]
if only_existing:
locations = [l for l in locations if l.exists()]
diff --git a/htc/utils/Task.py b/htc/utils/Task.py
new file mode 100644
index 0000000..9b3197d
--- /dev/null
+++ b/htc/utils/Task.py
@@ -0,0 +1,26 @@
+# SPDX-FileCopyrightText: 2022 Division of Intelligent Medical Systems, DKFZ
+# SPDX-License-Identifier: MIT
+
+from enum import Enum, unique
+from typing_extensions import Self
+
+
+@unique
+class Task(Enum):
+ """This enum can be used to distinguish between a segmentation task (with pixel-level labels) or a classification task (with image-level labels)."""
+
+ SEGMENTATION = "segmentation"
+ CLASSIFICATION = "classification"
+
+ def labels_name(self) -> str:
+ """Returns the name of the labels attribute (e.g., used in DatasetMedianPixel) or the name of the key in the batch which stores the labels."""
+ if self == Task.SEGMENTATION:
+ return "labels"
+ elif self == Task.CLASSIFICATION:
+ return "image_labels"
+ else:
+ raise ValueError(f"Unknown task: {self}")
+
+ @classmethod
+ def from_config(cls, config) -> Self:
+ return cls(config.get("task", "segmentation"))
diff --git a/htc/utils/blosc_compression.py b/htc/utils/blosc_compression.py
index 7136ba5..c69ca79 100644
--- a/htc/utils/blosc_compression.py
+++ b/htc/utils/blosc_compression.py
@@ -61,24 +61,36 @@ def compress_file(path: Path, data: Union[np.ndarray, dict[Any, np.ndarray]]) ->
def decompress_file(
- path: Path, start_pointer: Union[int, dict[str, int]] = None
-) -> Union[Union[np.ndarray, int], dict[Any, Union[np.ndarray, int]]]:
+ path: Path, start_pointer: Union[int, dict[str, int]] = None, load_keys: list[str] = None, return_meta: bool = False
+) -> Union[
+ Union[np.ndarray, int],
+ dict[str, Union[np.ndarray, int]],
+ tuple[
+ Union[np.ndarray, int],
+ dict[str, Union[np.ndarray, int]],
+ Union[tuple[tuple[int, ...], np.dtype], dict[str, tuple[tuple[int, ...], np.dtype]]],
+ ],
+]:
"""
Decompresses a blosc file.
Args:
path: File to the blosc data.
start_pointer: If not None must be a valid memory address. It will be used to store the decompressed data directly into the provided memory location. This is, for example, useful if the data should be directly loaded into a shared memory buffer. If the compressed data contains a dictionary, the pointers must also be a dictionary with the keys corresponding to the (expected) keys in the compressed data. A pointer can only be used if the size and dtype of the decompressed data is known in advance.
+ load_keys: If not None and the compressed data contains a dictionary, only the keys in this list will be loaded. The other keys will be skipped.
+ return_meta: If True, will return additionally a tuple where the second value contains (shape, dtype) information for each decompressed array.
Returns: Decompressed array data or the given pointer address. Depending on the file, this will either be directly the numpy array or a dict with all numpy arrays.
"""
res = {}
+ array_meta = {}
with path.open("rb") as f:
meta = pickle.load(f)
if type(meta) == tuple:
shape, dtype = meta
data = f.read()
+ array_meta = meta
if start_pointer is not None:
blosc.decompress_ptr(data, start_pointer)
@@ -89,7 +101,12 @@ def decompress_file(
res = array
else:
for name, (shape, dtype, size) in meta.items():
+ if load_keys is not None and name not in load_keys:
+ f.seek(size, 1)
+ continue
+
data = f.read(size)
+ array_meta[name] = (shape, dtype)
if start_pointer is not None:
blosc.decompress_ptr(data, start_pointer[name])
@@ -99,4 +116,7 @@ def decompress_file(
blosc.decompress_ptr(data, array.__array_interface__["data"][0])
res[name] = array
- return res
+ if return_meta:
+ return res, array_meta
+ else:
+ return res
diff --git a/htc/utils/colorchecker_mask_sketch.svg b/htc/utils/colorchecker_mask_sketch.svg
index 4be4d2c..f7a0505 100644
--- a/htc/utils/colorchecker_mask_sketch.svg
+++ b/htc/utils/colorchecker_mask_sketch.svg
@@ -1 +1 @@
-
\ No newline at end of file
+
diff --git a/htc/utils/colors.py b/htc/utils/colors.py
index 5b73449..9886f12 100644
--- a/htc/utils/colors.py
+++ b/htc/utils/colors.py
@@ -5,7 +5,7 @@
from pprint import pprint
import numpy as np
-from matplotlib.colors import to_hex, to_rgb
+from matplotlib.colors import LinearSegmentedColormap, to_hex, to_rgb
from scipy.spatial import distance
from htc.settings import settings
@@ -13,6 +13,27 @@
from htc.utils.helper_functions import sort_labels
+def lighten_color(color: str, amount: float) -> str:
+ """
+ Lightens the given color by the specified amount.
+
+ The color is interpolated with white so that this function has a similar effect as if a transparency is added to the color on a white background.
+
+ >>> lighten_color("#FF0000", 0.5)
+ '#ff8080'
+
+ Args:
+ color: The color to be lightened as hex string.
+ amount: The amount by which to lighten the color. Must be between 0 and 1.
+
+ Returns: The lightened color as hex string.
+ """
+ assert 0 <= amount <= 1, "Amount must be between 0 and 1"
+ cmap = LinearSegmentedColormap.from_list("lighten", [color, (1, 1, 1)])
+
+ return to_hex(cmap(amount))
+
+
def generate_distinct_colors(n_colors: int, existing_colors: list[tuple] = None) -> list[tuple]:
"""
Generates distinct random colors by maximizing the distance between the colors.
diff --git a/htc/utils/config.schema b/htc/utils/config.schema
index 754902a..8223d62 100644
--- a/htc/utils/config.schema
+++ b/htc/utils/config.schema
@@ -3,11 +3,36 @@
"$comment": "This schema file defines the common structure of the config files used in this repository. It is not a complete list but describes the most important properties.",
"type": "object",
"properties": {
+ "inherits": {
+ "description": "Path to a parent config file where this config should inherit from. Absolute, relative or package-relative paths are supported. Properties of the parent config are available as well. Properties of the child have always precedence over properties defined in one of the parents. Multiple inherence is possible by passing an array of paths.",
+ "type": ["string", "array", "null"],
+ "items": {
+ "type": "string"
+ }
+ },
+ "inherits_skip": {
+ "description": "List of keys which should be excluded from inheritance (via full names, e.g., input/hierarchical_sampling).",
+ "type": "array",
+ "items": {
+ "type": "string"
+ }
+ },
"lightning_class": {
- "description": "Specification of the lightning class used for training. It must be in the format module>class (e.g. htc.models.image.LightningImage>LightningImage) and must refer to a valid Python class.",
+ "description": "Specification of the lightning class used for training. It must be in the format module>class (e.g. htc.models.image.LightningImage>LightningImage) and must refer to a valid Python class (see the type_from_string() function for more details).",
"type": "string"
},
+ "label_mapping": {
+ "description": "Mapping of label names to label indices. This will be used to remap the original labels of the dataset to the new labels for the current training. Can either be a dict with label_name:label_index mappings or a string in the format module>variable (e.g. htc.settings_seg>label_mapping) in which case it must refer to a variable inside a Python script.",
+ "type": ["object", "string"]
+ },
+ "task": {
+ "description": "Sets the main network task. Can either be set to segmentation or classification. Segmentation tasks use pixel-level labels whereas classification tasks use image-level labels. This is for example used to determine which labels should be considered for class weighting.",
+ "type": "string",
+ "enum": ["segmentation", "classification"],
+ "default": "segmentation"
+ },
"input": {
+ "description": "Common attributes which affect the loading of the data.",
"type": "object",
"properties": {
"data_spec": {
@@ -16,10 +41,14 @@
},
"preprocessing": {
"description": "Name of the folder inside the intermediates/preprocessing directory which contains preprocessed images (e.g. L1). It is also possible to specify the folder relative to the results_dir or results_dir/preprocessing. This may be useful for preprocessed files which are only needed for specific projects or on the cluster. Finally, the relative or absolute path to the folder can be specified as well.",
- "type": [
- "string",
- "null"
- ]
+ "type": ["string", "null"]
+ },
+ "spatial_shape": {
+ "description": "Explicitly set the shape of the input data. Useful for cases where it is different to the image shape (e.g., cropped images). If not set, the input shape will be inferred from the dataset settings.",
+ "type": ["array", "null"],
+ "items": {
+ "type": "integer"
+ }
},
"features_dtype": {
"description": "Explicitly set the dtype for the features. This determines with which dtype the features are transferred to the GPU. Usually, this is automatically inferred from the training precision (e.g. 16-mixed leads to float16) but in some cases you may want to have control over this parameter (e.g. for benchmarking).",
@@ -36,12 +65,83 @@
},
"preprocessing_additional": {
"description": "Additional preprocessing folder names which will be added to the batch as data_NAME. For example, if L1 is in the list, it will be added as data_L1.",
- "type": [
- "array",
- "null"
- ],
+ "type": ["array", "null"],
+ "items": {
+ "type": "object",
+ "properties": {
+ "name": {
+ "description": "Name of the preprocessing folder.",
+ "type": "string"
+ },
+ "parameter_names": {
+ "description": "Name of the parameter images which are concatenated along the channel dimension (see input/parameter_names).",
+ "type": "array",
+ "items": {
+ "type": "string",
+ "enum": ["StO2", "NIR", "TWI", "OHI", "TLI", "THI"]
+ }
+ },
+ "n_channels": {
+ "description": "Number of input channels for the additional input.",
+ "type": "integer"
+ }
+ },
+ "required": ["name"]
+ }
+ },
+ "meta": {
+ "type": "object",
+ "properties": {
+ "attributes": {
+ "description": "List of meta attributes to load.",
+ "type": "array",
+ "items": {
+ "type": "object",
+ "properties": {
+ "name": {
+ "description": "Name of the attribute. This name will be passed on to path.meta().",
+ "type": "string"
+ },
+ "mapping": {
+ "description": "Mapping which is applied on the loaded metadata. This is useful to map strings to numbers.",
+ "type": "object"
+ }
+ },
+ "required": ["name"]
+ }
+ },
+ "dtype": {
+ "description": "Data type of the metadata table (also used for GPU transfer).",
+ "type": "string",
+ "default": "float32"
+ },
+ "missing_replacement": {
+ "description": "Value which will be used to replace missing values (nan values).",
+ "type": "number",
+ "default": -1
+ }
+ },
+ "required": ["attributes"]
+ },
+ "image_labels": {
+ "description": "Specifies how the image labels should be constructed from the metadata of the images. Each entry in this list results in one image label which can be used as classification target. The resulting image_labels entry (e.g. in the loaded sample or batch) can be a scalar (if only one image label is requested) or a two-dimensional tensor (if more than one image label is requested).",
+ "type": "array",
"items": {
- "type": "string"
+ "type": "object",
+ "properties": {
+ "meta_attributes": {
+ "description": "List of names for the metadata columns where the label should be extracted from (via DataPath.meta()). Specify more than one name if metadata from different datasets should be combined but the corresponding columns have different names.",
+ "type": "array",
+ "items": {
+ "type": "string"
+ }
+ },
+ "image_label_mapping": {
+ "description": "Defines an optional mapping to map the string meta values to indices. The format is the same as for the label_mapping attribute.",
+ "type": ["object", "string"]
+ }
+ },
+ "required": ["meta_attributes"]
}
},
"no_features": {
@@ -53,48 +153,44 @@
"type": "boolean"
},
"n_channels": {
- "description": "Specifies which data should be loaded. 100 = HSI data, 4 = TPI data, 3 = RGB data.",
- "type": "integer",
- "enum": [3, 4, 100]
+ "description": "Specifies the number of input channels. For example, 100 = HSI data, 4 = TPI data, 3 = RGB data.",
+ "type": "integer"
},
"n_classes": {
- "description": "Number of classes which should be used for training. This key is only required if a label mapping cannot be specified ( usually the number of classes is inferred from the label mapping).",
+ "description": "Number of classes which should be used for training. This key is only required if a label mapping cannot be specified (usually the number of classes is inferred from the label mapping).",
"type": "integer"
},
"epoch_size": {
"description": "Length of one training epoch in terms of number of images. Can also be a string like '500 images' and then it will translate automatically for non-image based models (like the pixel model) to the appropriate number depending on the image size.",
- "type": [
- "integer",
- "string"
- ]
+ "type": ["integer", "string"]
+ },
+ "target_domain": {
+ "description": "Specifies the target domain which should be taken into account in the model or for the sampling. If set to \"no_domain\", assigns each image to the same domain.",
+ "type": ["array", "null"],
+ "items": {
+ "type": "string",
+ "enum": ["camera_index", "subject_index", "species_index", "no_domain"]
+ }
+ },
+ "hierarchical_sampling": {
+ "description": "Use a batch sampling strategy which takes the hierarchy of the data into account. The first hierarchy level is defined by input/target_domain and the second by the subjects. If set to true, each batch contains images from each input/target_domain domain while maximizing diversity between subjects (it is preferred to take images from different subjects over images from the same subject). It can also be set to label or image_label to additionally ensure an equal label distribution in the batches (label uses the labels from the segmentation masks and image_label the metadata defined by input/image_labels). For example, with a batch size of 6 and two cameras (first hierarchical level), there might be two colon, two liver and two kidney images (or more precisely: images which contain at least colon, liver and kidney), one from each camera and from 6 different subjects. You can also add +oversampling to the string to enforce selecting images which contain underrepresented classes.",
+ "type": ["boolean", "string", "null"]
},
"transforms_cpu": {
"description": "Data augmentation specification as list of dicts (each entry denotes one augmentation step). Will be executed on the CPU (by the workers).",
- "type": [
- "array",
- "null"
- ]
+ "type": ["array", "null"]
},
"transforms_gpu": {
"description": "Data augmentation specification as list of dicts (each entry denotes one augmentation step). Will be executed on the GPU.",
- "type": [
- "array",
- "null"
- ]
+ "type": ["array", "null"]
},
"test_time_transforms_cpu": {
"description": "Similar to transforms_cpu but the transforms will also be applied during inference. This is for example useful for context analysis (e.g. removing organs in an image).",
- "type": [
- "array",
- "null"
- ]
+ "type": ["array", "null"]
},
"test_time_transforms_gpu": {
"description": "Similar to transforms_gpu but the transforms will also be applied during inference. This is for example useful for applying normalization.",
- "type": [
- "array",
- "null"
- ]
+ "type": ["array", "null"]
},
"patch_sampling": {
"description": "The strategy to extract patches from an image. `uniform` yields so many patches as a grid-based tiling would yield, i.e. the number of patches are simply a function of the patch and image size. `proportional` constraints the number of patches to the number of valid pixels, i.e. so many patches will be sampled until theoretically (!) all pixels are used. However, this it is not enforced that really all valid pixels are sampled. `all_valid` is similar to `proportional` but now makes sure that all valid pixels are part of a patch at least once. This is especially useful to ensure that smaller classes are sampled as well.",
@@ -107,10 +203,7 @@
},
"annotation_name": {
"description": "The annotations which should be loaded. Either a list of annotation names or 'all' if all available annotation names should be included in the batch. If no merge strategy is set (see merge_annotations), the annotations will appear as separate tensors with the name labels_annotation_name and valid_pixels_annotation_name. Please note that it is also possible to define the annotations you want to use on a per image bases by using the format image_name@name1&name.",
- "type": [
- "array",
- "string"
- ]
+ "type": ["array", "string"]
},
"merge_annotations": {
"description": "Merge strategy in case there is more than one annotation per image. 'union' merges all annotations in one image. It assumes that the annotations are conflict-free, i.e. that there will be no pixel with more than one class label (overlap on the same class label is fine). Later annotator names overwrite previous ones.",
@@ -119,15 +212,44 @@
}
}
},
- "label_mapping": {
- "description": "Mapping of label names to label indices. This will be used to remap the original labels of the dataset to the new labels for the current training. Can either be a dict with label_name:label_index mappings or a string in the format module>variable (e.g. htc.settings_seg>label_mapping) in which case it must refer to a variable inside a Python script.",
- "type": [
- "object",
- "string"
- ]
+ "optimization": {
+ "description": "Settings for the optimizer and the learning rate scheduler.",
+ "type": "object",
+ "properties": {
+ "optimizer": {
+ "description": "Settings for the optimizer. Except for the name, all attributes are passed on as arguments to the optimizer.",
+ "type": "object",
+ "properties": {
+ "name": {
+ "description": "Name of the optimizer inside the torch.optim module.",
+ "type": "string"
+ }
+ }
+ },
+ "optimizer_layer_settings": {
+ "description": "Layer-specific settings for the optimizer. This can be used to specify separate learning rates for different layers.",
+ "type": "object",
+ "patternProperties": {
+ ".*": {
+ "description": "The name of the property is interpreted as a regular expression which is matched against all layers of the model. The value must be an object with the layer-specific settings (e.g., learning rate).",
+ "type": "object"
+ }
+ }
+ },
+ "lr_scheduler": {
+ "description": "Settings for the learning rate scheduler. Except for the name, all attributes are passed on as arguments to the scheduler.",
+ "type": "object",
+ "properties": {
+ "name": {
+ "description": "Name of the learning rate scheduler inside the torch.optim.lr_scheduler module.",
+ "type": "string"
+ }
+ }
+ }
+ }
},
"model": {
- "description": "Settings to configure a neural network.",
+ "description": "Settings to configure a neural network. Most settings depend on the lightning class.",
"type": "object",
"properties": {
"pretrained_model": {
@@ -152,18 +274,22 @@
},
"dataloader_kwargs": {
"description": "Keyword arguments which are passed to the PyTorch dataloader.",
- "type": "object",
- "properties": {
- "batch_size": {
- "type": "integer"
- }
- }
+ "type": "object"
+ },
+ "trainer_kwargs": {
+ "description": "Keyword arguments which are passed to the PyTorch Lightning trainer.",
+ "type": "object"
+ },
+ "swa_kwargs": {
+ "description": "Keyword arguments which are passed to the SWA scheduler. If this attribute is present (and not null), SWA will be activated.",
+ "type": ["object", "null"]
},
"validation": {
+ "description": "Arguments which define how the validation is carried on (metric, checkpointing, etc.).",
"type": "object",
"properties": {
"dataset_index": {
- "description": "Index of the dataset which should be used for checkpointing (relevant if there is more than one validation dataset).",
+ "description": "Index of the dataset which should be used for checkpointing (relevant if there is more than one validation dataset). The index is defined by the order of the validation splits in the data spec. If not set, the checkpoint metric will be calculated based on the results from all validation datasets.",
"type": "integer"
},
"checkpoint_metric": {
@@ -176,15 +302,6 @@
"enum": ["best", "last", false]
}
}
- },
- "trainer_kwargs": {
- "description": "Keyword arguments which are passed to the PyTorch Lightning trainer.",
- "type": "object",
- "properties": {
- "max_epochs": {
- "type": "integer"
- }
- }
}
}
-}
\ No newline at end of file
+}
diff --git a/htc/utils/helper_functions.py b/htc/utils/helper_functions.py
index cfd71ea..4c9ad41 100644
--- a/htc/utils/helper_functions.py
+++ b/htc/utils/helper_functions.py
@@ -24,6 +24,7 @@
from htc.tivita.DatasetSettings import DatasetSettings
from htc.utils.Config import Config
from htc.utils.LabelMapping import LabelMapping
+from htc.utils.Task import Task
def basic_statistics(
@@ -92,11 +93,8 @@ def basic_statistics(
df["label_name"] = df_median["label_name_mapped"]
df["label_valid"] = [label_mapping.is_index_valid(i) for i in df["label_index"]]
- # Only include valid labels in the statistics
- df = df[df["label_valid"]]
-
# Sum together the pixels for labels with the same name
- df = df.groupby(sorted(set(df.columns.to_list()) - {"n_pixels"}), as_index=False, observed=True)[
+ df = df.groupby(sorted(set(df.columns.to_list()) - {"n_pixels"}), as_index=False, observed=True, dropna=False)[
"n_pixels"
].sum()
df = df.sort_values(by=["image_name", "label_index"])
@@ -111,6 +109,9 @@ def median_table(
image_names: list[str] = None,
label_mapping: LabelMapping = None,
annotation_name: Union[str, list[str]] = None,
+ additional_mappings: dict[str, LabelMapping] = None,
+ image_labels_column: list[dict[str, Union[list[str], LabelMapping]]] = None,
+ config: Config = None,
) -> pd.DataFrame:
"""
This function is the general entry point for reading the median spectra tables. You can either read the table from a specific dataset or provide image names for which you want to have the spectra (also works if the names come from different datasets).
@@ -123,7 +124,7 @@ def median_table(
Besides basic info about the image and the median spectra (`median_normalized_spectrum`), all available metadata is included in the table as well:
>>> df.columns.to_list()
- ['image_name', 'subject_name', 'timestamp', 'label_index', 'label_name', 'median_spectrum', 'std_spectrum', 'median_normalized_spectrum', 'std_normalized_spectrum', 'n_pixels', 'median_sto2', 'std_sto2', 'median_nir', 'std_nir', 'median_twi', 'std_twi', 'median_ohi', 'std_ohi', 'median_thi', 'std_thi', 'median_tli', 'std_tli', 'image_labels', 'Camera_CamID', 'Camera_Exposure', 'Camera_analoger Gain', 'Camera_digitaler Gain', 'Camera_Speed', 'SW_Name', 'SW_Version', 'Fremdlichterkennung_Fremdlicht erkannt?', 'Fremdlichterkennung_PixelmitFremdlicht', 'Fremdlichterkennung_Breite LED Rot', 'Fremdlichterkennung_Breite LED Gruen', 'Fremdlichterkennung_Grenzwert Pixelanzahl', 'Fremdlichterkennung_Intensity Grenzwert', 'Aufnahme_Aufnahmemodus', 'camera_name', 'path', 'annotation_name']
+ ['image_name', 'subject_name', 'timestamp', 'label_index', 'label_name', 'median_spectrum', 'std_spectrum', 'median_normalized_spectrum', 'std_normalized_spectrum', 'n_pixels', 'median_sto2', 'std_sto2', 'median_nir', 'std_nir', 'median_twi', 'std_twi', 'median_ohi', 'std_ohi', 'median_thi', 'std_thi', 'median_tli', 'std_tli', 'image_labels', 'Camera_CamID', 'Camera_Exposure', 'Camera_analoger Gain', 'Camera_digitaler Gain', 'Camera_Speed', 'SW_Name', 'SW_Version', 'Fremdlichterkennung_Fremdlicht erkannt?', 'Fremdlichterkennung_PixelmitFremdlicht', 'Fremdlichterkennung_Breite LED Rot', 'Fremdlichterkennung_Breite LED Gruen', 'Fremdlichterkennung_Grenzwert Pixelanzahl', 'Fremdlichterkennung_Intensity Grenzwert', 'Aufnahme_Aufnahmemodus', 'camera_name', 'path', 'dataset_settings_path', 'annotation_name']
This function can also be used to select specific annotations, either globally per dataset:
>>> df = median_table(dataset_name="2021_02_05_Tivita_multiorgan_semantic", annotation_name="semantic#intra1")
@@ -138,15 +139,40 @@ def median_table(
Note: In the original table, one row denotes one label of one image from one annotator which also corresponds to the default of this function since the default annotation is used (similar to DataPath.read_segmentation()). If more than one annotation name is requested, a row is unique by its image_name, label_name and annotation_name.
Args:
- dataset_name: Name of the dataset from which you want to have the median spectra table. The name may include a # to specify a subdataset, e.g. `2021_02_05_Tivita_multiorgan_semantic#context_experiments` for the context_experiments folder inside the semantic data directory.
+ dataset_name: Name of the dataset from which you want to have the median spectra table. The name may include a # to specify a subdataset, e.g. `2021_02_05_Tivita_multiorgan_semantic#context_experiments` for the context_experiments folder inside the semantic data directory. If a dataset consists only of subdatasets (e.g., 2022_10_24_Tivita_sepsis_ICU), it is also possible to use the name of the main dataset to get all tables from the subdatasets (e.g., 2022_10_24_Tivita_sepsis_ICU to get 2022_10_24_Tivita_sepsis_ICU#calibrations + 2022_10_24_Tivita_sepsis_ICU#subjects).
table_name: For each dataset, there may be multiple tables for different purposes (e.g. tables with recalibrated data). With this switch, you specify which table should be loaded. The format of these tables on disk is `dataset_name@table_name@median_spectra@annotation_name.feather`. Per default, the normal table with the original data is loaded corresponding to tables on disk with the format `dataset_name@median_spectra@annotation_name.feather`, i.e. without the optional `@table_name`. Requested image names (`image_names` argument) are only considered from the tables matching the given `table_name`. It is not possible to select images from tables with different table names with this function since they may contain the same images.
paths: List of DataPath objects from which you want to have the median spectra. If annotation names are specified with a data path object, those names will be used. If specified, image_names must be None.
image_names: List of image names to search for (similar to the paths parameter). Image names may also include annotation names (e.g. subject#timestamp@name1&name2). It is not ensured that the resulting table contains all requested images because some images may lack annotations or are filtered out by the label_mapping. If specified, paths must be None.
- label_mapping: The target label mapping. There will be a new label_index_mapped column (and a new label_name_mapped column with the new names defined by the mapping) and the old label_index column will be removed (since the label_index is not unique across datasets). If set to None, then mapping is not carried out.
+ label_mapping: The target label mapping. There will be a new label_index_mapped column (and a new label_name_mapped column with the new names defined by the mapping) and the old label_index column will be removed (since the label_index is not unique across datasets). Only valid labels will be included in the resulting table. If set to None, then mapping is not carried out.
annotation_name: Unique name of the annotation(s) for cases where multiple annotations exist (e.g. inter-rater variability). If None, will use the default from the dataset. If the dataset does not have a default (i.e. the annotation_name_default is missing in the dataset_settings.json file), all annotations are returned. It is also possible to explicitly retrieve all annotations by setting this parameter to 'all'.
+ additional_mappings: Additional label mappings for other columns. The keys are the column names and the values are the LabelMapping objects for the respective columns. For each specified column, a new column with _index appended will be added.
+ image_labels_column: Specify how multiple columns should be mapped into one `image_labels` column indicating one or more image labels. Each entry in the list specifies one dimension in the `image_labels` columns and the dictionary contains information from which columns values should be mapped from. It is possible to map values from different columns to one image label and to have multiple image labels. The specification is similar to `input/image_labels` in the config file. See tests for examples.
+ config: Load median spectra based on the settings of the config. This can be used to automatically retrieve common options (e.g., label_mapping) which otherwise have to be passed to this function. If no dataset_name, paths or image_names is given, the data specification is loaded from the config object and all non-test paths are used. Options passed as arguments have precedence over the config options.
Returns: Median spectra data frame. The table is either sorted by image names (if image_names is not None) or by the sort_labels() function (if dataset_name is used).
"""
+ if additional_mappings is None:
+ additional_mappings = {}
+
+ if config is not None:
+ if table_name == "":
+ table_name = config.get("input/table_name", "")
+ if dataset_name is None and paths is None and image_names is None and config["input/data_spec"]:
+ spec = DataSpecification.from_config(config)
+ paths = spec.paths()
+ if label_mapping is None and config["label_mapping"]:
+ label_mapping = LabelMapping.from_config(config, task=Task.SEGMENTATION)
+ if annotation_name is None:
+ annotation_name = config.get("input/annotation_name", None)
+ if image_labels_column is None and config["input/image_labels"]:
+ image_labels_column = config["input/image_labels"]
+
+ # Make sure the label mapping objects are created
+ for image_label_entry_index, data in enumerate(image_labels_column):
+ data["image_label_mapping"] = LabelMapping.from_config(
+ config, task=Task.CLASSIFICATION, image_label_entry_index=image_label_entry_index
+ )
+
# Collect all available tables
tables = {}
for path in sorted((settings.intermediates_dir_all / "tables").glob("*median_spectra*.feather")):
@@ -169,7 +195,8 @@ def median_table(
assert _table_type == "median_spectra", (
f"Invalid table name for median spectra table ({_table_type} instead of median_spectra, the general format"
- f" should be dataset_name@median_spectra@annotation_name.feather): {path}"
+ " should be @median_spectra@.feather or"
+ f" @@median_spectra@.feather): {path}"
)
_table_identifier = (_dataset_name, _table_name)
@@ -178,7 +205,12 @@ def median_table(
tables[_table_identifier] = {}
tables[_table_identifier][_annotation_name] = path
- def read_table(dataset_name: str, table_name: str, annotation_name: Union[str, list[str], None]) -> pd.DataFrame:
+ def read_table(
+ dataset_name: str,
+ table_name: str,
+ annotation_name: Union[str, list[str], None],
+ requested_image_names: list[str] = None,
+ ) -> pd.DataFrame:
table_identifier = (dataset_name, table_name)
# Find the default annotation_name
@@ -189,6 +221,9 @@ def read_table(dataset_name: str, table_name: str, annotation_name: Union[str, l
annotation_name = dsettings.get("annotation_name_default")
if annotation_name is None or annotation_name == "all":
+ assert (
+ table_identifier in tables
+ ), f"Could not find the table {table_identifier} in the tables\n{tables.keys()}"
annotation_name = list(tables[table_identifier].keys())
if type(annotation_name) == str:
@@ -196,6 +231,9 @@ def read_table(dataset_name: str, table_name: str, annotation_name: Union[str, l
df = []
for name in annotation_name:
+ if name not in tables[table_identifier]:
+ continue
+
df_a = pd.read_feather(tables[table_identifier][name])
if name is not None:
df_a["annotation_name"] = name
@@ -203,21 +241,34 @@ def read_table(dataset_name: str, table_name: str, annotation_name: Union[str, l
assert len(annotation_name) == 1
df.append(df_a)
+ if len(df) == 0:
+ return pd.DataFrame()
+
needs_sorting = len(df) > 1
df = pd.concat(df)
- if len(df) > 0 and label_mapping is not None:
- # Mapping from path to config (the mapping depends on the dataset and must be done separately)
- df = df.query("label_name in @label_mapping.label_names(all_names=True)").copy()
- if len(df) > 0:
- label_indices = torch.from_numpy(df["label_index"].values)
- assert (
- settings.data_dirs[dataset_name] is not None
- ), f"Cannot find the path to the dataset {dataset_name} but this is required for remapping the labels"
- original_mapping = LabelMapping.from_data_dir(settings.data_dirs[dataset_name])
- label_mapping.map_tensor(label_indices, original_mapping)
- df["label_index_mapped"] = label_indices
- df["label_name_mapped"] = [label_mapping.index_to_name(i) for i in df["label_index_mapped"]]
+ if requested_image_names is not None:
+ # Select relevant images so that we don't change the labels if we don't need to
+ df = df[df["image_name"].isin(requested_image_names)]
+
+ if len(df) > 0:
+ if label_mapping is not None:
+ # Mapping from path to config (the mapping depends on the dataset and must be done separately)
+ df = df.query("label_name in @label_mapping.label_names(all_names=True)").copy()
+ if len(df) > 0:
+ assert settings.data_dirs[dataset_name] is not None, (
+ f"Cannot find the path to the dataset {dataset_name} but this is required for remapping the"
+ " labels"
+ )
+
+ original_mapping = LabelMapping.from_data_dir(settings.data_dirs[dataset_name])
+ label_indices = df["label_index"].values.astype(np.int64, copy=True)
+ label_mapping.map_tensor(label_indices, original_mapping) # Operates in-place
+ df["label_index_mapped"] = label_indices
+ df["label_name_mapped"] = [label_mapping.index_to_name(i) for i in df["label_index_mapped"]]
+
+ for name, mapping in additional_mappings.items():
+ df[f"{name}_index"] = [mapping.name_to_index(x) for x in df[name]]
if needs_sorting:
df = sort_labels(df, dataset_name=dataset_name)
@@ -225,150 +276,232 @@ def read_table(dataset_name: str, table_name: str, annotation_name: Union[str, l
return df.reset_index(drop=True)
if dataset_name is not None:
- return read_table(dataset_name, table_name, annotation_name)
-
- def parse_paths(paths: list[DataPath]) -> tuple[list[str], dict[str, list[str]], list[str]]:
- image_names_ordering = []
- image_names_only = []
- annotation_images = {}
- for p in paths:
- image_names_ordering.append(p.image_name())
- names = p.annotation_names()
-
- if len(names) > 0:
- for a in names:
- if a not in annotation_images:
- annotation_images[a] = []
- annotation_images[a].append(p.image_name())
- else:
- image_names_only.append(p.image_name())
-
- return image_names_only, annotation_images, image_names_ordering
-
- def parse_image_names(names: list[str]) -> tuple[list[str], dict[str, list[str]], list[str]]:
- image_names_ordering = []
- image_names_only = []
- annotation_images = {}
- for name in names:
- if "@" in name:
- image_name, annotation_names = name.split("@")
- annotation_names = annotation_names.split("&")
- for a in annotation_names:
- if a not in annotation_images:
- annotation_images[a] = []
- annotation_images[a].append(image_name)
- image_names_ordering.append(image_name)
+ if (dataset_name, table_name) not in tables:
+ error_message = (
+ f"Could not find the table {dataset_name}@{table_name} in the registered median tables (from all"
+ f" available datasets):\n{tables.keys()}"
+ )
+ if "#" not in dataset_name:
+ # If the dataset consists only of subdatasets but the main dataset is requested, collect all tables and merge them, e.g.
+ # 2022_10_24_Tivita_sepsis_ICU = 2022_10_24_Tivita_sepsis_ICU#calibrations + 2022_10_24_Tivita_sepsis_ICU#subjects
+ parent_tables = []
+ for _dataset_name, _table_name in tables.keys():
+ if _dataset_name.startswith(dataset_name) and table_name == _table_name:
+ parent_tables.append(read_table(_dataset_name, _table_name, annotation_name))
+ if len(parent_tables) > 0:
+ return pd.concat(parent_tables, ignore_index=True)
+ else:
+ raise ValueError(error_message)
else:
- image_names_only.append(name)
- image_names_ordering.append(name)
-
- return image_names_only, annotation_images, image_names_ordering
-
- if paths is not None:
- assert image_names is None, "image_names must be None if paths is specified"
- image_names_only, annotation_images, image_names_ordering = parse_paths(paths)
- elif image_names is not None:
- assert paths is None, "paths must be None if image_names is specified"
- # Theoretically, we could also parse the image names to paths and only use the paths function
- # However, it is faster to use the image names directly if available (and we need image names anyway for the table)
- image_names_only, annotation_images, image_names_ordering = parse_image_names(image_names)
+ raise ValueError(error_message)
+ else:
+ df = read_table(dataset_name, table_name, annotation_name)
+ if len(df) == 0:
+ settings.log.warning(
+ f"Could not find a table for the dataset {dataset_name}, the table name {table_name} and the"
+ f" annotation name {annotation_name}"
+ )
else:
- raise ValueError("image_names or paths must be supplied if dataset_names is None")
-
- image_names = image_names_only + list(itertools.chain.from_iterable(annotation_images.values()))
- image_names = pd.unique(np.asarray(image_names)) # Unique without sorting
- image_names_ordering = pd.unique(np.asarray(image_names_ordering))
-
- # First all the images without annotation name requirements
- dfs = []
- remaining_images = set(image_names_only)
- considered_datasets = set()
- for _dataset_name, _table_name in tables.keys():
- if _table_name != table_name:
- continue
- df = read_table(_dataset_name, _table_name, annotation_name)
- df = df.query("image_name in @remaining_images")
-
- if len(df) > 0:
- dfs.append(df)
- remaining_images = remaining_images - set(df["image_name"].values)
- considered_datasets.add(_dataset_name)
+ def parse_paths(paths: list[DataPath]) -> tuple[list[str], dict[str, list[str]], list[str]]:
+ image_names_ordering = []
+ image_names_only = []
+ annotation_images = {}
+ for p in paths:
+ image_names_ordering.append(p.image_name())
+ names = p.annotation_names()
+
+ if len(names) > 0:
+ for a in names:
+ if a not in annotation_images:
+ annotation_images[a] = []
+ annotation_images[a].append(p.image_name())
+ else:
+ image_names_only.append(p.image_name())
+
+ return image_names_only, annotation_images, image_names_ordering
+
+ def parse_image_names(names: list[str]) -> tuple[list[str], dict[str, list[str]], list[str]]:
+ image_names_ordering = []
+ image_names_only = []
+ annotation_images = {}
+ for name in names:
+ if "@" in name:
+ image_name, annotation_names = name.split("@")
+ annotation_names = annotation_names.split("&")
+ for a in annotation_names:
+ if a not in annotation_images:
+ annotation_images[a] = []
+ annotation_images[a].append(image_name)
+ image_names_ordering.append(image_name)
+ else:
+ image_names_only.append(name)
+ image_names_ordering.append(name)
+
+ return image_names_only, annotation_images, image_names_ordering
+
+ if paths is not None:
+ assert image_names is None, "image_names must be None if paths is specified"
+ image_names_only, annotation_images, image_names_ordering = parse_paths(paths)
+ elif image_names is not None:
+ assert paths is None, "paths must be None if image_names is specified"
+ # Theoretically, we could also parse the image names to paths and only use the paths function
+ # However, it is faster to use the image names directly if available (and we need image names anyway for the table)
+ image_names_only, annotation_images, image_names_ordering = parse_image_names(image_names)
+ else:
+ raise ValueError("image_names or paths must be supplied if dataset_names is None")
- if len(remaining_images) == 0:
- # We already have all image_names, we can stop looping over the tables
- break
+ image_names = image_names_only + list(itertools.chain.from_iterable(annotation_images.values()))
+ image_names = pd.unique(np.asarray(image_names)) # Unique without sorting
+ image_names_ordering = pd.unique(np.asarray(image_names_ordering))
- # Then all images with annotation names
- if len(annotation_images) > 0:
- remaining_images = {name: set(images) for name, images in annotation_images.items()}
- is_done = False
+ # First all the images without annotation name requirements
+ dfs = []
+ remaining_images = set(image_names_only)
+ considered_datasets = set()
for _dataset_name, _table_name in tables.keys():
if _table_name != table_name:
continue
- if is_done:
- break
- for table_annotation_name in tables[(_dataset_name, _table_name)].keys():
- if table_annotation_name not in annotation_images.keys():
- # If the table does not contain any of the requested annotations, we can skip it
+ df = read_table(_dataset_name, _table_name, annotation_name, requested_image_names=remaining_images)
+ if len(df) > 0:
+ dfs.append(df)
+ remaining_images = remaining_images - set(df["image_name"].values)
+ considered_datasets.add(_dataset_name)
+
+ if len(remaining_images) == 0:
+ # We already have all image_names, we can stop looping over the tables
+ break
+
+ # Then all images with annotation names
+ if len(annotation_images) > 0:
+ remaining_images = {name: set(images) for name, images in annotation_images.items()}
+ is_done = False
+ for _dataset_name, _table_name in tables.keys():
+ if _table_name != table_name:
continue
-
- df = read_table(_dataset_name, _table_name, table_annotation_name)
- df = df.query("image_name in @remaining_images[@table_annotation_name]")
-
- if len(df) > 0:
- dfs.append(df)
- remaining_images[table_annotation_name] = remaining_images[table_annotation_name] - set(
- df["image_name"].values
+ if is_done:
+ break
+
+ for table_annotation_name in tables[(_dataset_name, _table_name)].keys():
+ if table_annotation_name not in annotation_images.keys():
+ # If the table does not contain any of the requested annotations, we can skip it
+ continue
+
+ df = read_table(
+ _dataset_name,
+ _table_name,
+ table_annotation_name,
+ requested_image_names=remaining_images[table_annotation_name],
)
- considered_datasets.add(_dataset_name)
+ if len(df) > 0:
+ dfs.append(df)
+ remaining_images[table_annotation_name] = remaining_images[table_annotation_name] - set(
+ df["image_name"].values
+ )
+ considered_datasets.add(_dataset_name)
+
+ if all(len(r) == 0 for r in remaining_images.values()):
+ is_done = True
+ # We already have all image_names, we can stop looping over the tables
+ break
+
+ # We cannot assert that there are no remaining images anymore because some images may get excluded due to the label mapping or some images maybe don't even have annotations (so they can't be included)
+ if len(dfs) == 0:
+ error_message = (
+ f"Could not find any of the requested images (first image: {image_names[0]}) in the tables"
+ f" ({considered_datasets = }). This could mean that some of the intermediate files are missing or that"
+ " you do not have access to them (e.g. human data)."
+ )
+ if label_mapping is not None:
+ error_message += (
+ f" Please make also sure that the label mapping ({label_mapping}) is correct and does not exclude"
+ " all images."
+ )
+ raise ValueError(error_message)
+
+ with warnings.catch_warnings():
+ # The same columns might have different dtypes in the dataframes depending on missing values
+ warnings.filterwarnings(
+ "ignore", message=".*object-dtype columns with all-bool values", category=FutureWarning
+ )
+ df = pd.concat(dfs)
+ if len(dfs) > 1 and "label_index" in df.columns:
+ # label_index is potentially incorrect when paths from multiple datasets are used, so it is safer to remove it
+ df.drop(columns="label_index", inplace=True)
+
+ # Same order as defined by the paths
+ df["image_name"] = df["image_name"].astype("category")
+ df["image_name"] = df["image_name"].cat.set_categories(image_names_ordering)
+ df.sort_values("image_name", inplace=True, ignore_index=True)
+
+ # Make sure we have all requested image_names (it is possible that some image_names are missing if they contain only labels which were filtered out by the label mapping)
+ image_names_df = set(df["image_name"].unique())
+ assert image_names_df.issubset(image_names), (
+ "Could not find all image_names in the median spectra tables. Please make sure that the median table exists"
+ " for every dataset where the image_names come from"
+ )
- if all(len(r) == 0 for r in remaining_images.values()):
- is_done = True
- # We already have all image_names, we can stop looping over the tables
- break
+ if label_mapping is not None:
+ assert set(df["label_index_mapped"].values).issubset(
+ set(label_mapping.label_indices())
+ ), "Found at least one label_index which is not part of the mapping"
+ if len(image_names_df) < len(image_names):
+ settings.log.warning(
+ f"{len(image_names) - len(image_names_df)} image_names are not used because they were filtered out"
+ f" (e.g. by the label mapping). The following tables were considered: {considered_datasets}"
+ )
- # We cannot assert that there are no remaining images anymore because some images may get excluded due to the label mapping or some images maybe don't even have annotations (so they can't be included)
- assert len(dfs) > 0, (
- f"Could not find any of the requested images ({image_names = }, {annotation_images = }) in the tables"
- f" ({considered_datasets = }). This could mean that some of the intermediate files are missing or that you do"
- " not have access to them (e.g. human data)"
- )
+ if image_labels_column is not None:
+ image_labels = []
+ for _, row in df.iterrows():
+ row_label = []
+ # There may be more than one image label to predict (e.g., sepsis_status and shock)
+ for level_data in image_labels_column:
+ # Multiple attributes can be mapped to the same image label (e.g., sepsis_status (new sepsis study) and health_status (old sepsis study))
+ for attribute in level_data["meta_attributes"]:
+ if attribute in row and not pd.isna(row[attribute]):
+ value = row[attribute]
+ if "image_label_mapping" in level_data:
+ mapping = level_data["image_label_mapping"]
+ value = mapping.name_to_index(value)
+ else:
+ value = int(value)
+ row_label.append(value)
+ break
- with warnings.catch_warnings():
- # The same columns might have different dtypes in the dataframes depending on missing values
- warnings.filterwarnings("ignore", message=".*object-dtype columns with all-bool values", category=FutureWarning)
- df = pd.concat(dfs)
- if len(dfs) > 1:
- # label_index is potentially incorrect when paths from multiple datasets are used, so it is safer to remove it
- df.drop(columns="label_index", inplace=True)
-
- # Same order as defined by the paths
- df["image_name"] = df["image_name"].astype("category")
- df["image_name"] = df["image_name"].cat.set_categories(image_names_ordering)
- df.sort_values("image_name", inplace=True, ignore_index=True)
-
- # Make sure we have all requested image_names (it is possible that some image_names are missing if they contain only labels which were filtered out by the label mapping)
- image_names_df = set(df["image_name"].unique())
- assert image_names_df.issubset(image_names), (
- "Could not find all image_names in the median spectra tables. Please make sure that the median table exists for"
- " every dataset where the image_names come from"
- )
+ assert len(row_label) >= 1, f"Could not map the row\n{row}\nto any image label"
+ if len(row_label) == 1:
+ row_label = row_label[0]
+ image_labels.append(row_label)
- if label_mapping is not None:
- assert set(df["label_index_mapped"].values).issubset(
- set(label_mapping.label_indices())
- ), "Found at least one label_index which is not part of the mapping"
- if len(image_names_df) < len(image_names):
- settings.log.warning(
- f"{len(image_names) - len(image_names_df)} image_names are not used because they were filtered out (e.g. by"
- f" the label mapping). The following tables were considered: {considered_datasets}"
- )
+ df["image_labels"] = image_labels
return df
+def add_times_table(df: pd.DataFrame, groups: list[str] = None) -> None:
+ """
+ Adds a column "time" to the table with the timestamp converted to a datetime object. If groups is given, another "rel_time" column is added which contains the relative time (in seconds) within each grouping (e.g. time for all images of one subject relative to the first image `groups=["subject_name"]`).
+
+ Args:
+ df: The table to add the columns to (in-place).
+ groups: A list of column names for grouping of the relative time.
+ """
+ if groups is None:
+ groups = ["subject_name"]
+
+ df["time"] = pd.to_datetime(df["timestamp"], format=settings.tivita_timestamp_format)
+
+ if groups is not None:
+ df_group_times = df.groupby(groups)["time"].min()
+
+ rel_times = []
+ for _, row in df.iterrows():
+ rel_times.append(row["time"] - df_group_times.loc[tuple([row[g] for g in groups])])
+ df["rel_time"] = rel_times
+
+
def group_median_spectra(df: pd.DataFrame, additional_columns: list[str] = None) -> pd.DataFrame:
"""
Groups median spectra per subject by averaging all median spectra from that subject.
@@ -514,7 +647,7 @@ def utilization_table(run_dir: Path) -> pd.DataFrame:
def sort_labels(
storage: Union[np.ndarray, list, set, dict, pd.DataFrame],
- label_ordering: dict[str, Union[str, int]] = None,
+ label_ordering: Union[dict[str, Union[str, int]], list[str]] = None,
sorting_cols: list[str] = None,
dataset_name: str = None,
) -> Union[np.ndarray, list, dict, pd.DataFrame]:
@@ -532,12 +665,15 @@ def sort_labels(
Args:
storage: The storage to sort: numpy array, list, dict or dataframe. If dataframe, it will sort by label_name, image_name and annotation_name (if available).
- label_ordering: Alternative sort order for the labels. The mapping must define a key for each label and something sortable as values (e.g. integer values).
+ label_ordering: Alternative sort order for the labels. Either a mapping which defines a key for each label and something sortable as values (e.g. integer values) or a list of label names in the sorting order.
sorting_cols: Explicit list of columns which should be used to sort the dataframe. If None, will sort by label_name, image_name (if available) and annotation_name (if available).
dataset_name: Name of a dataset which is accessible via settings.data_dirs and which contains a dataset settings with a defined label ordering.
Returns: The sorted storage.
"""
+ if type(label_ordering) == list:
+ label_ordering = {label: i for i, label in enumerate(label_ordering)}
+
if label_ordering is None and dataset_name is not None:
dsettings = DatasetSettings(settings.data_dirs[dataset_name])
label_ordering = dsettings.get("label_ordering", None)
@@ -610,13 +746,13 @@ def sort_labels_cm(
# Swap rows
switched_cm = torch.zeros_like(cm)
ordering_indices = [cm_order.index(l) for l in target_order]
- for i, id in enumerate(ordering_indices):
- switched_cm[i, :] = cm[id, :]
+ for i, idx in enumerate(ordering_indices):
+ switched_cm[i, :] = cm[idx, :]
# Swap columns
switched_cm_final = torch.zeros_like(cm)
- for j, id in enumerate(ordering_indices):
- switched_cm_final[:, j] = switched_cm[:, id]
+ for j, idx in enumerate(ordering_indices):
+ switched_cm_final[:, j] = switched_cm[:, idx]
return switched_cm_final
@@ -740,7 +876,7 @@ def get_nsd_thresholds(mapping: LabelMapping, aggregation_method: str = None, na
Args:
mapping: Label mapping of the training run which is used to make a selection of labels.
aggregation_method: Aggregation method (e.g. mean). Must correspond to a column name in the table.
- name: Name of the table (e.g. semantic for the MIA2021 thresholds).
+ name: Name of the table (e.g. semantic for the MIA2022 thresholds).
Returns: Tolerance value for each class in the order defined in the label mapping.
"""
diff --git a/htc/utils/mitk/__init__.py b/htc/utils/mitk/__init__.py
new file mode 100644
index 0000000..17e71a8
--- /dev/null
+++ b/htc/utils/mitk/__init__.py
@@ -0,0 +1,3 @@
+# SPDX-FileCopyrightText: 2022 Division of Intelligent Medical Systems, DKFZ
+# SPDX-License-Identifier: MIT
+
diff --git a/htc/utils/mitk/mitk_masks.py b/htc/utils/mitk/mitk_masks.py
new file mode 100644
index 0000000..913ea43
--- /dev/null
+++ b/htc/utils/mitk/mitk_masks.py
@@ -0,0 +1,343 @@
+# SPDX-FileCopyrightText: 2022 Division of Intelligent Medical Systems, DKFZ
+# SPDX-License-Identifier: MIT
+
+import json
+import re
+import xml.etree.ElementTree as ET
+from copy import deepcopy
+from pathlib import Path
+from typing import Union
+
+import numpy as np
+from matplotlib.colors import to_rgb
+
+from htc.cpp import nunique
+from htc.utils.import_extra import requires_extra
+from htc.utils.LabelMapping import LabelMapping
+
+try:
+ import nrrd
+
+ _missing_library = ""
+except ImportError:
+ _missing_library = "nrrd"
+
+
+@requires_extra(_missing_library)
+def nrrd_mask(nrrd_file: Path) -> dict[str, Union[np.ndarray, LabelMapping]]:
+ """
+ Read an nrrd mask file from MITK. This file contains all the information from the annotation process.
+
+ >>> from htc.tivita.DataPath import DataPath
+ >>> path = DataPath.from_image_name("P065#2020_06_19_21_02_33")
+ >>> mitk_data = nrrd_mask(path() / "annotations/2020_06_19_21_02_33#semantic#annotator5.nrrd")
+ >>> np.unique(mitk_data["mask"])
+ array([1, 2], dtype=uint8)
+ >>> mitk_data["label_mapping"].index_to_name(1)
+ 'stomach'
+
+ The "Exterior" in MITK always has the label index 0 and means that pixels are not labelled and are always considered invalid:
+ >>> mitk_data["label_mapping"].name_to_index("unlabeled")
+ 0
+ >>> mitk_data["label_mapping"].is_index_valid(0)
+ False
+
+ With the mitk_data you can easily map the segmentation to the desired target mapping:
+ >>> from htc.settings_seg import settings_seg
+ >>> mask = settings_seg.label_mapping.map_tensor(mitk_data["mask"], mitk_data["label_mapping"])
+ >>> np.unique(mask)
+ array([0, 6], dtype=uint8)
+ >>> settings_seg.label_mapping.index_to_name(6)
+ 'stomach'
+
+ In case of multi-layer NRRD files, an additional dimension is inserted at the front corresponding to the MITK layers:
+ >>> path = DataPath.from_image_name("SPACE_000069#2020_11_05_11_43_51")
+ >>> mitk_data = nrrd_mask(path() / "annotations/2020_11_05_11_43_51#semantic#primary.nrrd")
+ >>> mitk_data["mask"].shape
+ (4, 480, 640)
+
+ Args:
+ nrrd_file: Path to the nrrd file.
+
+ Returns: Dictionary with the following content:
+ - mask: Array with the raw label indices per pixel.
+ - label_mapping: Label mapping to interpret the label indices.
+ """
+ data, header = nrrd.read(nrrd_file)
+
+ total_n_labels = 0 # to be populated with number of labels across the layers
+
+ mask = data.squeeze()
+ mask = mask.T.astype(np.uint8)
+
+ if mask.ndim == 3:
+ mask = mask.transpose(2, 0, 1)
+
+ mapping_nrrd = {}
+ max_label_index = 0 # used to keep track of iterating label index in different layers. MITK assigns labels starting from 0 in each layer.
+
+ # new MITK version NRRD files have to handled separately as they contain JSON meta data
+ if "org.mitk.multilabel.segmentation.version" in header:
+ label_groups = json.loads(header["org.mitk.multilabel.segmentation.labelgroups"])
+ n_layers = len(label_groups)
+
+ # in the new format there is no exterior label, so the total n labels are incremented here
+ total_n_labels += 1
+ mapping_nrrd["unlabeled"] = 0
+
+ for layer in range(n_layers):
+
+ if label_groups[layer]["labels"] is not None:
+ total_n_labels += len(label_groups[layer]["labels"])
+ else:
+ label_groups[layer]["labels"] = []
+
+ if mask.ndim == 3:
+ # MITK assigns the label index for each layer individually according to the order in which the annotation was performed. This leads to different label indices for the same class in different layers. Therefore, a remapping is performed using the label_index of the previous layer(s).
+ layer_mask = deepcopy(mask[layer, :, :]) # needed for remapping
+ else:
+ layer_mask = mask
+
+ for label in label_groups[layer]["labels"]:
+ label_name = label["name"]
+ label_index = label["value"]
+
+ # in case the label name has the label order number as a prefix e.g. 12_kidney, then extract the label name
+ match = re.search(r"^\d+_", label_name)
+ if match is not None:
+ label_name = label_name.removeprefix(match.group(0))
+
+ if label_name not in mapping_nrrd:
+ mapping_nrrd[label_name] = (
+ max(mapping_nrrd.values()) + 1
+ ) # remapping to the smallest unassigned label_index
+
+ if mask.ndim == 3:
+ mask[layer, layer_mask == label_index] = mapping_nrrd[label_name]
+ else:
+ mask[layer_mask == label_index] = mapping_nrrd[label_name]
+
+ max_label_index = max(mapping_nrrd.values())
+ else:
+ n_layers = int(header["layers"])
+
+ for layer in range(n_layers):
+ n_labels = int(header[f"layer_00{layer}"])
+ total_n_labels += n_labels
+
+ if mask.ndim == 3:
+ layer_mask = deepcopy(mask[layer, :, :])
+ else:
+ layer_mask = mask
+
+ for i in range(n_labels):
+ root = ET.fromstring(header[f"org.mitk.label_00{layer}_{i:05d}"].replace("\\n", "\n"))
+ label_index = int(root.find("property[@key='value']/unsigned").attrib["value"])
+
+ label_name = root.find("property[@key='name']/string").attrib["value"]
+ match = re.search(r"^\d+_", label_name)
+ if match is not None:
+ label_name = label_name.removeprefix(match.group(0))
+
+ if i == 0:
+ mapping_nrrd["unlabeled"] = label_index
+ else:
+ if label_name not in mapping_nrrd:
+ mapping_nrrd[label_name] = max(mapping_nrrd.values()) + 1
+
+ if mask.ndim == 3:
+ mask[layer, layer_mask == label_index] = mapping_nrrd[label_name]
+ else:
+ mask[layer_mask == label_index] = mapping_nrrd[label_name]
+
+ max_label_index = max(mapping_nrrd.values())
+
+ mappings_nrrd = LabelMapping(mapping_nrrd, last_valid_label_index=max_label_index, zero_is_invalid=True)
+
+ assert nunique(mask) <= total_n_labels
+
+ return {"mask": mask, "label_mapping": mappings_nrrd}
+
+
+@requires_extra(_missing_library)
+def segmentation_to_nrrd(
+ nrrd_file: Path,
+ mask: np.ndarray,
+ mapping_mask: LabelMapping,
+) -> None:
+ """
+ Converts an existing segmentation mask to an nrrd file which can be read by MITK. This is useful if existing masks should be loaded into MITK for visualization or adaptations.
+
+ >>> from htc.tivita.DataPath import DataPath
+ >>> import tempfile
+ >>> with tempfile.NamedTemporaryFile() as tmpfile:
+ ... tmpfile = Path(tmpfile.name)
+ ... path = DataPath.from_image_name("SPACE_000001#2020_08_14_11_11_22")
+ ... segmentation_dict = path.read_segmentation(annotation_name="all")
+ ... mask = np.stack(list(segmentation_dict.values()))
+ ... segmentation_to_nrrd(nrrd_file=tmpfile, mask=mask, mapping_mask=LabelMapping.from_path(path))
+ ... labels = nrrd_mask(nrrd_file=tmpfile)['label_mapping'].label_names()
+ >>> labels
+ ['colon', 'omentum', 'small_bowel', 'fat', 'instrument', 'background', 'blue_cloth', 'unclear_organic', 'tag_blood']
+
+ Args:
+ nrrd_file: Path where the nrrd file should be stored.
+ mask: a dict of masks, each key representing an annotation name e.g. {{annotation_name1: mask, annotation_name2: mask...}}. If None, path must be given.
+ mapping_mask: Label mapping for the segmentation mask which gives every label index in the segmentation mask a name. If None, path must be given.
+ """
+
+ # create a copy of mask
+ mask = deepcopy(mask)
+
+ invalid_pixels = ~mapping_mask.is_index_valid(mask)
+
+ # We need to remap the labels to consecutive values starting from 1 because 0 will be the unlabeled pixels in MITK
+ mapping_mitk = {"Exterior": 0}
+ i = 1
+ for label_index in np.unique(mask):
+ if mapping_mask.is_index_valid(label_index):
+ mapping_mitk[mapping_mask.index_to_name(label_index)] = i
+ i += 1
+ else:
+ mapping_mitk[mapping_mask.index_to_name(label_index)] = 0
+
+ mapping_mitk = LabelMapping(mapping_mitk, zero_is_invalid=True)
+ assert mapping_mitk.last_valid_label_index == i - 1
+ assert len(mapping_mitk) <= len(mapping_mask)
+
+ # Remap segmentation to a valid MITK mask
+ mapping_mitk.map_tensor(mask, mapping_mask)
+ n_labels = len(mapping_mitk.label_names(include_invalid=True))
+ assert nunique(mask) <= n_labels
+ assert np.all(mask[invalid_pixels] == 0), "All invalid pixels should have been mapped to 0"
+
+ # MITK/nrrd loads the image as (width, height)
+
+ if mask.ndim == 3:
+ n_layers = mask.shape[0]
+ mask = np.expand_dims(np.transpose(mask, axes=(0, 2, 1)), -1)
+ else:
+ n_layers = 1
+ mask = np.expand_dims(mask.T, -1)
+
+ def mitk_label_header(label_index: int, label_name: str, label_color: str) -> dict:
+ # 0 = background/invalid in MITK
+ opacity = 0.600000024 if label_index != 0 else 0
+ locked = True if label_index != 0 else False
+ r, g, b = to_rgb(label_color)
+
+ meta = {
+ "color": {"type": "ColorProperty", "value": [float(r), float(g), float(b)]},
+ "locked": locked,
+ "name": label_name,
+ "opacity": opacity,
+ "value": int(label_index),
+ "visible": True,
+ }
+
+ return meta
+
+ header = {
+ "modality": "org.mitk.multilabel.segmentation",
+ "DICOM_0008_0060": '{"values":[{"z":0, "t":0, "value":"SEG"}]}',
+ "DICOM_0008_103E": '{"values":[{"z":0, "t":0, "value":"MITK Segmentation"}]}',
+ "org.mitk.multilabel.segmentation.labelgroups": [],
+ "org.mitk.multilabel.segmentation.unlabeledlabellock": "0",
+ "org.mitk.multilabel.segmentation.version": "1",
+ "type": "unsigned short",
+ "space": "left-posterior-superior",
+ "space origin": [0, 0, 0],
+ }
+
+ if n_layers == 1:
+ header["space directions"] = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
+ header["kinds"] = ["domain", "domain", "domain"]
+ else:
+ header["space directions"] = [[np.nan, np.nan, np.nan], [1, 0, 0], [0, 1, 0], [0, 0, 1]]
+ header["kinds"] = ["vector", "domain", "domain", "domain"]
+
+ # switching back to MITK increasing order of label index
+ curr_label_index = 1
+
+ mask_copy = deepcopy(mask)
+
+ for layer_index in range(n_layers):
+ labelgroup = {"labels": []}
+
+ if mask.ndim == 4:
+ label_indices = np.unique(mask[layer_index, ...])
+ else:
+ label_indices = mapping_mitk.label_indices(include_invalid=True)
+
+ for label_index in label_indices:
+ if label_index == 0:
+ continue
+
+ mask_copy[layer_index, ...][mask[layer_index, ...] == label_index] = curr_label_index
+
+ labelgroup["labels"].append(
+ mitk_label_header(
+ curr_label_index, mapping_mitk.index_to_name(label_index), mapping_mitk.index_to_color(label_index)
+ )
+ )
+
+ curr_label_index += 1
+ header["org.mitk.multilabel.segmentation.labelgroups"].append(labelgroup)
+
+ header["org.mitk.multilabel.segmentation.labelgroups"] = json.dumps(
+ header["org.mitk.multilabel.segmentation.labelgroups"]
+ )
+
+ nrrd.write(str(nrrd_file), data=mask_copy.astype(np.ushort), header=header)
+
+
+def segmentation_to_nrrd_annotation_name(
+ nrrd_file: Path,
+ mask: dict[str, np.ndarray],
+ mapping_mask: LabelMapping,
+ annotation_name_to_layer: dict[str, int] = None,
+) -> None:
+ """
+ Converts an existing segmentation mask to an nrrd file which can be read by MITK. This is useful if existing masks should be loaded into MITK for visualization or adaptations.
+ This function can be used to directly convert a dictionary of masks read from the path.
+
+ >>> from htc.tivita.DataPath import DataPath
+ >>> import tempfile
+ >>> with tempfile.NamedTemporaryFile() as tmpfile:
+ ... tmpfile = Path(tmpfile.name)
+ ... path = DataPath.from_image_name("SPACE_000001#2020_08_14_11_11_22")
+ ... mask = path.read_segmentation(annotation_name="all")
+ ... segmentation_to_nrrd_annotation_name(nrrd_file=tmpfile, mask=mask, mapping_mask=LabelMapping.from_path(path), annotation_name_to_layer={"semantic#primary": 0, "polygon#annotator1": 1})
+ ... labels = nrrd_mask(nrrd_file=tmpfile)['label_mapping'].label_names()
+ >>> labels
+ ['background', 'blue_cloth', 'colon', 'omentum', 'small_bowel', 'unclear_organic']
+
+ Args:
+ nrrd_file: Path where the nrrd file should be stored.
+ mask: a dict of masks, each key representing an annotation name e.g. {{annotation_name1: mask, annotation_name2: mask...}}. If None, path must be given.
+ mapping_mask: Label mapping for the segmentation mask which gives every label index in the segmentation mask a name. If None, path must be given.
+ annotation_name_to_layer: Maps annotation names to layers in MITK. Layers must be integers and define the order of the segmentation masks in MITK. The dictionary has the form: `{annotation_name: layer_index}`
+ """
+ mask = deepcopy(mask)
+
+ # take annotation names from annotation_name_to_layer attribute
+ # if the annotation_name_to_layer is None, then the default annotation names are used
+ # use all of the annotation names from the mask if annotation_name_to_layer is not set
+ if annotation_name_to_layer is not None:
+ annotation_names = list(annotation_name_to_layer.keys())
+ else:
+ annotation_names = mask.keys()
+
+ assert (
+ type(mask) == dict
+ ), "The mask has to be dict containing all annotations, of the form: `{annotation_name: layer_index}`"
+
+ stacked_masks = []
+
+ for annotation_name in annotation_names:
+ assert annotation_name in mask, f"Request annotation name {annotation_name} not present in mask"
+ stacked_masks.append(mask[annotation_name])
+
+ mask = np.stack(stacked_masks)
+
+ segmentation_to_nrrd(nrrd_file=nrrd_file, mask=mask, mapping_mask=mapping_mask)
diff --git a/htc/utils/mitk/run_mitk_dataset.py b/htc/utils/mitk/run_mitk_dataset.py
new file mode 100644
index 0000000..24895d7
--- /dev/null
+++ b/htc/utils/mitk/run_mitk_dataset.py
@@ -0,0 +1,89 @@
+# SPDX-FileCopyrightText: 2022 Division of Intelligent Medical Systems, DKFZ
+# SPDX-License-Identifier: MIT
+
+import argparse
+import json
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+from htc import LabelMapping
+from htc.tivita.DataPath import DataPath
+from htc.utils.mitk.mitk_masks import segmentation_to_nrrd
+from htc.utils.parallel import p_map
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(
+ description="Collects all images from a dataset and converts the existing annotations to MITK nrrd files.",
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+ )
+ parser.add_argument(
+ "--input-dir",
+ type=Path,
+ required=True,
+ help="Path to a dataset where data paths should be collected.",
+ )
+ parser.add_argument(
+ "--output-dir",
+ type=Path,
+ required=True,
+ help=(
+ "Path to the output directory where the MITK files (images and results directory and task_list.json) should"
+ " be stored."
+ ),
+ )
+ args = parser.parse_args()
+
+ input_dir = args.input_dir
+ output_dir = args.output_dir
+ assert input_dir.exists(), f"The input directory {input_dir} does not exist"
+
+ images_dir = output_dir / "images"
+ results_dir = output_dir / "results"
+ task_list_file = output_dir / "task_list.json"
+ for f in [images_dir, results_dir, task_list_file]:
+ assert not f.exists(), (
+ f"The output directory {output_dir} already contains {f}. Please select a different output directory or"
+ " clear it first"
+ )
+
+ images_dir.mkdir(exist_ok=True, parents=True)
+ results_dir.mkdir(exist_ok=True, parents=True)
+
+ tasks = {
+ "FileFormat": "MITK Segmentation Task List",
+ "Version": 1,
+ "Name": "Segmentation",
+ "Defaults": {"LabelNameSuggestions": "dataset_labels.json"},
+ "Tasks": [],
+ }
+
+ paths = list(DataPath.iterate(args.input_dir))
+ assert len(paths) > 0, f"Could not find any images in {input_dir}"
+
+ def handle_path(path: DataPath) -> dict[str, str]:
+ rgb = path.read_rgb_reconstructed()
+ rgb = Image.fromarray(rgb)
+ rgb.save(images_dir / f"{path.image_name()}.png", optimize=True)
+
+ mask = path.read_segmentation(annotation_name="all")
+ if type(mask) == dict:
+ mask = np.stack(list(mask.values()))
+
+ segmentation_to_nrrd(
+ nrrd_file=results_dir / f"{path.image_name()}.nrrd",
+ mask=mask,
+ mapping_mask=LabelMapping.from_path(path),
+ )
+
+ return {
+ "Name": f"{path.image_name()}",
+ "Image": f"images/{path.image_name()}.png",
+ "Result": f"results/{path.image_name()}.nrrd",
+ }
+
+ tasks["Tasks"] = p_map(handle_path, paths)
+
+ with task_list_file.open("w") as f:
+ json.dump(tasks, f, indent=4)
diff --git a/htc/utils/mitk/run_mitk_task_list.py b/htc/utils/mitk/run_mitk_task_list.py
new file mode 100644
index 0000000..9545188
--- /dev/null
+++ b/htc/utils/mitk/run_mitk_task_list.py
@@ -0,0 +1,118 @@
+# SPDX-FileCopyrightText: 2022 Division of Intelligent Medical Systems, DKFZ
+# SPDX-License-Identifier: MIT
+
+import argparse
+import json
+import math
+from pathlib import Path
+from zipfile import ZipFile
+
+from PIL import Image
+from rich.progress import track
+
+from htc import read_tivita_rgb, safe_copy, settings
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(
+ description=(
+ "Searches for all Tivita images in a folder and creates a task list for MITK to annotated those images."
+ ),
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+ )
+ parser.add_argument(
+ "--input-dir",
+ type=Path,
+ required=True,
+ help="Path to the folder with the images which should be annotated.",
+ )
+ parser.add_argument(
+ "--output-dir",
+ type=Path,
+ required=True,
+ help=(
+ "Path to the output directory where the MITK files (images directory and task_list.json) should be stored."
+ ),
+ )
+ parser.add_argument(
+ "--wildcard",
+ type=str,
+ default="*_RGB-Image.png",
+ required=False,
+ help="Wildcard file pattern which should be used to select RGB files.",
+ )
+ args = parser.parse_args()
+
+ input_dir = args.input_dir
+ output_dir = args.output_dir
+ assert input_dir.exists(), f"The input directory {input_dir} does not exist"
+
+ images_dir = output_dir / "images"
+ task_list_file = output_dir / "task_list.json"
+ zip_file = output_dir / "mitk.zip"
+
+ assert not images_dir.exists(), (
+ f"The output directory {output_dir} already contains an images folder. Please delete it or select a different"
+ " output directory"
+ )
+ assert not task_list_file.exists(), (
+ f"The output directory {output_dir} already contains a task_list.json. Please delete it or select a different"
+ " output directory"
+ )
+ assert not zip_file.exists(), (
+ f"The zip file {zip_file} already exists in the output directory. Please delete it or select a different output"
+ " directory"
+ )
+
+ # Find all images in the input directory
+ images_dir.mkdir(exist_ok=True, parents=True)
+ paths = {}
+ for p in sorted(input_dir.rglob(args.wildcard)):
+ # We use a dict to get a sorted list of unique images
+ paths[p] = True
+
+ assert len(paths) > 0, f"Could not find any images in {input_dir}"
+ print(f"Found {len(paths)} images in {input_dir}")
+
+ # Create task list and copy RGB images
+ tasks = {
+ "FileFormat": "MITK Segmentation Task List",
+ "Version": 1,
+ "Name": "Segmentation",
+ "Defaults": {"LabelNameSuggestions": "dataset_labels.json"},
+ "Tasks": [],
+ }
+
+ n_digits = math.ceil(math.log10(len(paths)))
+ for i, p in track(enumerate(paths.keys()), total=len(paths)):
+ timestamp = p.stem.removesuffix(args.wildcard.removeprefix("*"))
+ image_name = str(i + 1).rjust(n_digits, "0") + f"_{timestamp}"
+
+ try:
+ rgb = read_tivita_rgb(p)
+ rgb = Image.fromarray(rgb)
+ rgb.save(images_dir / f"{image_name}.png", optimize=True)
+ except Exception:
+ settings.log_once.info(
+ "Could not read the Tivita RGB image. The RGB file will be copied instead. This is fine if the image"
+ " does not contain black borders"
+ )
+ safe_copy(p, images_dir / f"{image_name}.png")
+
+ tasks["Tasks"].append({
+ "Name": f"{image_name}",
+ "Image": f"images/{image_name}.png",
+ "Result": f"results/{image_name}.nrrd",
+ })
+
+ with task_list_file.open("w") as f:
+ json.dump(tasks, f, indent=4)
+
+ # Create zip file of the task list and the images
+ with ZipFile(zip_file, mode="w") as archive:
+ archive.write(task_list_file, task_list_file.name)
+ for p in sorted(images_dir.iterdir()):
+ archive.write(p, f"images/{p.name}")
+
+ print(f"Stored the images folder at {images_dir}")
+ print(f"Stored the task_list.json at {task_list_file}")
+ print(f"Stored zip file at {zip_file}")
diff --git a/htc/utils/paths.py b/htc/utils/paths.py
index 07e08be..61d9232 100644
--- a/htc/utils/paths.py
+++ b/htc/utils/paths.py
@@ -36,7 +36,15 @@ def all_masks_paths() -> list[DataPath]:
class ParserPreprocessing:
- def __init__(self, description: str):
+ def __init__(self, description: str, inplace: bool = False):
+ """
+ Helper class for the preprocessing scripts.
+
+ Args:
+ description: A short description of what the preprocessing script does.
+ inplace: Set this to true if your preprocessing scripts operates in-place and hence does not need an output path.
+ """
+ self.inplace = inplace
self.parser = argparse.ArgumentParser(
description=description, formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
@@ -121,7 +129,7 @@ def get_paths(self, filters: Union[list[Callable[["DataPath"], bool]], None] = N
# From now on, we write to the intermediates directory of the selected dataset
settings.intermediates_dir_all.set_default_location(self.args.dataset_name)
else:
- assert self.args.output_path is not None, (
+ assert self.inplace or self.args.output_path is not None, (
"Either --dataset-name or --output-path must be given (we need to know where the generated files should"
" be stored)"
)
diff --git a/htc/utils/renderjson.js b/htc/utils/renderjson.js
index f8905a3..4304184 100644
--- a/htc/utils/renderjson.js
+++ b/htc/utils/renderjson.js
@@ -8,154 +8,212 @@
// Code from: https://github.com/caldwell/renderjson
// Code is adapted to avoid conflicts with Plotly (the originally returned define object from renderjson causes problems)
-var renderjson = (function() {
- var themetext = function(/* [class, text]+ */) {
+var renderjson = (function () {
+ var themetext = function (/* [class, text]+ */) {
var spans = [];
while (arguments.length)
- spans.push(append(span(Array.prototype.shift.call(arguments)),
- text(Array.prototype.shift.call(arguments))));
+ spans.push(
+ append(span(Array.prototype.shift.call(arguments)), text(Array.prototype.shift.call(arguments))),
+ );
return spans;
};
- var append = function(/* el, ... */) {
+ var append = function (/* el, ... */) {
var el = Array.prototype.shift.call(arguments);
- for (var a=0; a 0 && type != "string")
- show();
+ var show = function () {
+ if (!content)
+ append(
+ empty.parentNode,
+ (content = prepend(
+ builder(),
+ A(options.hide, "disclosure", function () {
+ content.style.display = "none";
+ empty.style.display = "inline";
+ }),
+ )),
+ );
+ content.style.display = "inline";
+ empty.style.display = "none";
+ };
+ append(
+ empty,
+ A(options.show, "disclosure", show),
+ themetext(type + " syntax", open),
+ A(placeholder, null, show),
+ themetext(type + " syntax", close),
+ );
+
+ var el = append(span(), text(my_indent.slice(0, -1)), empty);
+ if (show_level > 0 && type != "string") show();
return el;
};
if (json === null) return themetext(null, my_indent, "keyword", "null");
if (json === void 0) return themetext(null, my_indent, "keyword", "undefined");
- if (typeof(json) == "string" && json.length > options.max_string_length)
- return disclosure('"', json.substr(0,options.max_string_length)+" ...", '"', "string", function () {
+ if (typeof json == "string" && json.length > options.max_string_length)
+ return disclosure('"', json.substr(0, options.max_string_length) + " ...", '"', "string", function () {
return append(span("string"), themetext(null, my_indent, "string", JSON.stringify(json)));
});
- if (typeof(json) != "object" || [Number, String, Boolean, Date].indexOf(json.constructor) >= 0) // Strings, numbers and bools
- return themetext(null, my_indent, typeof(json), JSON.stringify(json));
+ if (typeof json != "object" || [Number, String, Boolean, Date].indexOf(json.constructor) >= 0)
+ // Strings, numbers and bools
+ return themetext(null, my_indent, typeof json, JSON.stringify(json));
if (json.constructor == Array) {
if (json.length == 0) return themetext(null, my_indent, "array syntax", "[]");
return disclosure("[", options.collapse_msg(json.length), "]", "array", function () {
var as = append(span("array"), themetext("array syntax", "[", null, "\n"));
- for (var i=0; i type:
_type_cache[class_definition] = getattr(module, match.group(2))
return _type_cache[class_definition]
+
+
+def variable_from_string(definition: str) -> Any:
+ """
+ Parses a string for a variable definition and imports the variable.
+
+ This works for any variable which can be imported
+ >>> mapping = variable_from_string("htc.settings_seg>label_mapping")
+ >>> len(mapping)
+ 19
+
+ It is also possible to import a variable via the path to the script
+ >>> from htc.settings import settings
+ >>> mapping = variable_from_string(str(settings.htc_package_dir / "settings_seg.py") + ">label_mapping")
+ >>> len(mapping)
+ 19
+
+ Args:
+ definition: Variable definition in the form module>variable (e.g. htc.settings_seg>label_mapping). The first part (module) may also be the full path to the Python file.
+
+ Returns: The imported variable.
+ """
+ match = re.search(r"^([^>]+)>(\w+)$", definition)
+ assert match is not None, (
+ f"Could not parse the string {definition} as a valid variable definition. It must be in the format"
+ " module>variable (e.g. htc.settings_seg>label_mapping) and must refer to a valid Python script"
+ )
+
+ try:
+ module = importlib.import_module(match.group(1))
+ is_path = False
+ except ModuleNotFoundError:
+ # Try path importing (https://docs.python.org/3/library/importlib.html#importing-a-source-file-directly)
+ spec = importlib.util.spec_from_file_location(match.group(2), match.group(1))
+ module = importlib.util.module_from_spec(spec)
+ sys.modules[match.group(2)] = module
+ spec.loader.exec_module(module)
+ is_path = True
+
+ if not hasattr(module, match.group(2)):
+ if is_path:
+ name = Path(match.group(1)).stem
+ else:
+ name = match.group(1).split(".")[-1]
+
+ # For example, if settings is an object
+ module = getattr(module, name)
+
+ return getattr(module, match.group(2))
diff --git a/htc/utils/visualization.py b/htc/utils/visualization.py
index 430b83d..dcff46f 100644
--- a/htc/utils/visualization.py
+++ b/htc/utils/visualization.py
@@ -5,6 +5,7 @@
import gzip
import json
import math
+import re
import uuid
from pathlib import Path
from typing import Callable, Union
@@ -14,12 +15,9 @@
import plotly
import plotly.express as px
import plotly.graph_objects as go
-import torch
-import torch.nn.functional as F
from IPython.display import HTML, display
from matplotlib.colors import to_rgba
from PIL import Image
-from plotly.colors import n_colors as generate_n_colors
from plotly.subplots import make_subplots
from scipy import stats
@@ -38,9 +36,10 @@
from htc.utils.ColorcheckerReader import ColorcheckerReader
from htc.utils.colors import generate_distinct_colors
from htc.utils.Config import Config
-from htc.utils.helper_functions import median_table, sort_labels
+from htc.utils.helper_functions import basic_statistics, median_table, sort_labels
from htc.utils.JSONSchemaMeta import JSONSchemaMeta
from htc.utils.LabelMapping import LabelMapping
+from htc.utils.Task import Task
def compress_html(file: Union[Path, None], fig_or_html: Union[go.Figure, str]) -> Union[str, None]:
@@ -358,89 +357,6 @@ def show_loss_chart(df_train: pd.DataFrame, df_val: pd.DataFrame = None) -> None
fig.show()
-def show_activation_image(df: pd.DataFrame, hist_config: dict, dataset_index: int, epoch: int = None) -> None:
- # Combine activations from all images
- if epoch is None:
- activations = df[(df["dataset_index"] == dataset_index)]["val/activations"].values
- else:
- activations = df[(df["epoch_index"] == epoch - 1) & (df["dataset_index"] == dataset_index)][
- "val/activations"
- ].values
- layer_counts = {}
-
- for key in activations[0].keys():
- layer_counts[key] = np.sum(
- [a[key]["counts"] for a in activations], axis=0
- ) # Sum over the activations from all images
-
- fig = make_subplots(
- rows=2,
- cols=1,
- subplot_titles=("Activation Distribution", r"$\mu \pm \sigma$"),
- row_heights=[0.7, 0.3],
- vertical_spacing=0.1,
- )
-
- values_range = np.arange(hist_config["min"], hist_config["max"], hist_config["step"]) + (
- hist_config["step"] / 2
- ) # The values of the histogram are predefined in the training config
- colors = generate_n_colors("rgb(5, 200, 200)", "rgb(200, 10, 10)", 16, colortype="rgb")
-
- # Calculate mean and std during the sample process (not perfect and this information could also be calculated from the histogram, but it is simple ;-)
- layer_mean = {}
- layer_std = {}
-
- for (name, counts), color in zip(layer_counts.items(), colors):
- # It is a bit stupid but in order to generate the Violin plots we need the original activations instead of the counts
- # The approach here is to use the counts and sample n values according to the distribution and then generate the Violin plot (of course, this is only an approximation)
- counts = counts / np.sum(counts) # Normalize to probabilities
- samples = np.repeat(
- values_range, np.ceil(counts * 5000).astype(np.int)
- ) # ceil ensures that every value with a probability > 0 gets sampled at least once
- layer_mean[name] = np.mean(samples)
- layer_std[name] = np.std(samples)
-
- fig.add_trace(go.Violin(x=samples, line_color=color, bandwidth=hist_config["step"], name=name), row=1, col=1)
-
- if all(excluded not in name for excluded in ["pool", "logits", "input", "Model"]):
- samples = F.elu(torch.from_numpy(samples))
- fig.add_trace(go.Violin(x=samples, line_color=color, name=f"elu({name})"), row=1, col=1)
-
- fig.update_traces(orientation="h", side="positive", width=3, points=False, row=1, col=1)
- fig.update_xaxes(title_text="Activations", row=1, col=1)
- fig.update_yaxes(title_text="Layer", row=1, col=1)
-
- # Mean/Std graph
- means = np.array(list(layer_mean.values()))
- stds = np.array(list(layer_std.values()))
- line = {"color": plotly.colors.DEFAULT_PLOTLY_COLORS[0]}
- x = list(layer_mean.keys())
- fig.add_trace(
- go.Scatter(x=x, y=means, mode="lines+markers", line=line, legendgroup="stat", name="stats"), row=2, col=1
- )
- fig.add_trace(
- go.Scatter(x=x, y=means + stds, mode="lines+markers", line=line, legendgroup="stat", showlegend=False),
- row=2,
- col=1,
- )
- fig.add_trace(
- go.Scatter(
- x=x, y=means - stds, mode="lines+markers", line=line, legendgroup="stat", showlegend=False, fill="tonexty"
- ),
- row=2,
- col=1,
- )
-
- fig.update_xaxes(title_text="Layer", row=2, col=1)
- fig.update_yaxes(title_text="Mean/Std", row=2, col=1)
-
- # General settings
- fig.layout.title = f"Activation distribution throughout the network (epoch {epoch})"
- fig.update_layout(xaxis_showgrid=False, xaxis_zeroline=False, title_x=0.5)
- fig.layout.height = 1200
- fig.show()
-
-
def create_class_scores_figure(agg: MetricAggregation) -> None:
df = agg.df
mapping = LabelMapping.from_config(agg.config)
@@ -604,9 +520,11 @@ def show_class_scores_epoch(df: pd.DataFrame, mapping: LabelMapping) -> None:
line_ids.append(f)
- button_states.append(
- {"label": fold_name, "method": "update", "args": [{"title": f"Dice over training time ({fold_name})"}]}
- )
+ button_states.append({
+ "label": fold_name,
+ "method": "update",
+ "args": [{"title": f"Statistics for the validation set ({fold_name})"}],
+ })
# Calculate the visible states (find out which lines have to be activated for which fold)
line_ids = np.array(line_ids)
@@ -642,6 +560,8 @@ def create_confusion_figure(confusion_matrix: np.ndarray, labels: list[str] = No
y=labels,
x=labels,
text=hover_text,
+ colorscale="Teal",
+ colorbar={"title": "%", "thickness": 10},
hovertemplate="true: %{y} predicted: %{x} row-ratio: %{z:.3f} % pixels: %{text}",
)
annotations = []
@@ -650,8 +570,8 @@ def create_confusion_figure(confusion_matrix: np.ndarray, labels: list[str] = No
annotations.append({
"x": labels[j],
"y": labels[i],
- "font": {"color": "white"},
- "text": f"{value:.1f}",
+ "font": {"color": "black" if value < 0.5 else "white"},
+ "text": f"{value*100:.1f}",
"xref": "x1",
"yref": "y1",
"showarrow": False,
@@ -659,8 +579,9 @@ def create_confusion_figure(confusion_matrix: np.ndarray, labels: list[str] = No
layout = {"xaxis": {"title": "Predicted value"}, "yaxis": {"title": "Real value"}, "annotations": annotations}
fig = go.Figure(data=data, layout=layout)
+ fig.update_yaxes(autorange="reversed")
fig.update_layout(height=max(len(confusion_matrix) * 50, 300), width=max(len(confusion_matrix) * 50, 300) + 100)
- fig.update_layout(title_x=0.5, title_text="Confusion matrix of misclassification (row-wise normalized)")
+ fig.update_layout(title_x=0.5, title_text="Confusion matrix (row-wise normalized)")
return fig
@@ -838,7 +759,7 @@ def create_median_spectra_figure(path: DataPath) -> go.Figure:
df = median_table(image_names=[path.image_name()], annotation_name="all")
df = sort_labels(df)
df = df.query("label_name in @path.annotated_labels('all')")
- line_options = ["solid", "dot", "dash", "longdash", "dashdot", "longdashdot"]
+ line_options = ["solid", "dot", "dash", "longdash", "dashdot", "longdashdot", "5, 10, 5", "2, 10, 2", "5, 2, 5"]
annotator_mapping = {a: line_options[i] for i, a in enumerate(df["annotation_name"].unique())}
fig = go.Figure()
@@ -882,11 +803,12 @@ def create_median_spectra_comparison_figure(
"""
n_cols = 4
n_rows = math.ceil(df["label_name"].nunique() / n_cols)
+ n_missing = n_cols * n_rows - df["label_name"].nunique()
labels = df["label_name"].unique()
fig = make_subplots(
rows=n_rows,
cols=n_cols,
- shared_xaxes="all",
+ shared_xaxes="all" if n_missing == 0 else False,
shared_yaxes="all",
subplot_titles=labels,
vertical_spacing=0.05,
@@ -923,10 +845,60 @@ def create_median_spectra_comparison_figure(
)
if col == 0:
- fig.update_yaxes(title="L1 normalized reflectance", row=row + 1, col=col + 1)
+ fig.update_yaxes(title="L1 normalized reflectance [a.u.]", row=row + 1, col=col + 1)
if row == n_rows - 1:
fig.update_xaxes(title="wavelength [nm]", row=row + 1, col=col + 1)
+ if n_missing != 0:
+ # Manually add the x-axis ticks to the plots in the last rows
+ fig.update_xaxes(showticklabels=False)
+ ticks = [600, 700, 800, 900]
+ for i in range(n_missing):
+ fig.update_xaxes(
+ tickmode="array",
+ tickvals=ticks,
+ ticktext=ticks,
+ showticklabels=True,
+ title="wavelength [nm]",
+ row=n_rows - 1,
+ col=n_cols - i,
+ )
+ for i in range(n_cols - n_missing):
+ fig.update_xaxes(
+ tickmode="array",
+ tickvals=ticks,
+ ticktext=ticks,
+ showticklabels=True,
+ title="wavelength [nm]",
+ row=n_rows,
+ col=i + 1,
+ )
+
+ if n_missing != 0:
+ # Manually add the x-axis ticks to the plots in the last rows
+ fig.update_xaxes(showticklabels=False)
+ ticks = [600, 700, 800, 900]
+ for i in range(n_missing):
+ fig.update_xaxes(
+ tickmode="array",
+ tickvals=ticks,
+ ticktext=ticks,
+ showticklabels=True,
+ title="wavelength [nm]",
+ row=n_rows - 1,
+ col=n_cols - i,
+ )
+ for i in range(n_cols - n_missing):
+ fig.update_xaxes(
+ tickmode="array",
+ tickvals=ticks,
+ ticktext=ticks,
+ showticklabels=True,
+ title="wavelength [nm]",
+ row=n_rows,
+ col=i + 1,
+ )
+
fig.update_layout(
title="Spectra for organs and cameras (with inter-pig deviation)",
title_x=0.5,
@@ -944,6 +916,7 @@ def create_overview_document(
navigation_paths: list[DataPath] = None,
navigation_link_callback: Callable[[str, str, DataPath], str] = None,
nav_width: str = "23em",
+ searchable_meta_attributes: list[str] = None,
) -> str:
"""
Create an overview figure for the given image. It will show the RGB image with all the available annotations plus the tissue parameter images.
@@ -954,9 +927,13 @@ def create_overview_document(
navigation_paths: If not None, will add a navigation bar with all links sorted by organ. The user can use this navigation bar to easily switch between images.
navigation_link_callback: Callback which receives the label name, number and data path of the target image and should create a relative link where the corresponding local html file for the target image can be found. If parts of the link contain invalid URL characters (e.g. # in image name), then please wrap it in quote_plus before (e.g. quote_plus(p.image_name())). For example, ('spleen', '08', DataPath) --> '../08_spleen/P086%232021_04_15_09_22_20.html'.
nav_width: Width of the navigation bar (in CSS units).
+ searchable_meta_attributes: List of meta attributes which should be searchable. If None, the annotation_name will be searchable per default. You need to include the annotation_name yourself if you change this parameter.
Returns: HTML string which is best saved with the `compress_html()` function.
"""
+ if searchable_meta_attributes is None:
+ searchable_meta_attributes = ["annotation_name"]
+
seg = path.read_segmentation(annotation_name="all")
if seg is None or len(path.annotated_labels(annotation_name="all")) == 0:
# No annotations available, only show the RGB image
@@ -965,15 +942,17 @@ def create_overview_document(
# Similar size as create_segmentation_overlay()
img_height, img_width = rgb_image.shape[:2]
- fig_seg.update_layout(
- height=img_height * 1.5, width=img_width * 1.53, template="plotly_white", margin=dict(t=40)
- )
+ fig_seg.update_layout(height=img_height * 1.5, width=img_width * 1.53, template="plotly_white")
fig_seg.update_layout(title_x=0.5, title_text=path.image_name())
fig_median = None
else:
fig_seg = create_segmentation_overlay(seg, path)
fig_median = create_median_spectra_figure(path)
+ # Remove the Plotly title because it cannot be selected
+ # We'll add the title manually via HTML below
+ fig_seg.update_layout(margin=dict(t=0), title=None)
+
if include_tpi:
images = [path.compute_sto2().data, path.compute_nir().data, path.compute_ohi().data, path.compute_twi().data]
names = [
@@ -1002,10 +981,11 @@ def create_overview_document(
fig_tpi.update_layout(yaxis_autorange="reversed")
fig_tpi.update_layout(width=1000, height=800)
- annotation_meta = path.read_annotation_meta()
- if annotation_meta is not None:
- meta_html = "
Meta annotation for this image:
\n"
- meta_html += dict_to_html_list(annotation_meta, schema=path.annotation_meta_schema())
+ skip_keys = {"dsettings", "dataset_env_name", "data_dir", "intermediates_dir"}
+ image_meta = {k: v for k, v in path.meta().items() if k not in skip_keys}
+ if len(image_meta) > 0:
+ meta_html = f"
Meta annotation for the image {path.image_name()}:
\n"
+ meta_html += dict_to_html_list(image_meta, schema=path.annotation_meta_schema())
else:
meta_html = ""
@@ -1084,12 +1064,13 @@ def create_overview_document(
else:
meta = ""
- invisible_meta = p.meta("annotation_name")
- if invisible_meta is not None:
- invisible_meta = " ".join(invisible_meta)
- invisible_meta = f'{invisible_meta}'
- else:
- invisible_meta = ""
+ invisible_meta = ""
+ for attribute in searchable_meta_attributes:
+ attribute_meta = p.meta(attribute)
+ if attribute_meta is not None:
+ if type(attribute_meta) == list:
+ attribute_meta = " ".join(attribute_meta)
+ invisible_meta += f'{attribute}={attribute_meta} '
link = navigation_link_callback(l, label_number, p) + f"?nav=show&link_index={link_index}"
paths_html += (
@@ -1099,10 +1080,17 @@ def create_overview_document(
link_index += 1
# Add an image for the current label if available
- if (path.data_dir / "extra_label_symbols").exists():
- svg_path = path.data_dir / "extra_label_symbols" / f"Cat_{label_number}_{l}.svg"
- else:
+ svg_path = path.data_dir / "extra_label_symbols" / f"Cat_{label_number}_{l}.svg"
+ if not svg_path.exists():
+ # Try to find the label symbol in the masks dataset
svg_path = settings.data_dirs.masks / "extra_label_symbols" / f"Cat_{label_ordering.get(l, '')}_{l}.svg"
+ if not svg_path.exists():
+ # In case the label ordering is different, try to find the symbol by name in the masks dataset
+ svg_files = sorted((settings.data_dirs.masks / "extra_label_symbols").glob("*.svg"))
+ for f in svg_files:
+ if re.search(r"Cat_\d+_" + l, f.stem) is not None:
+ svg_path = f
+ break
if svg_path.exists():
with svg_path.open("r") as f:
@@ -1199,19 +1187,19 @@ def create_overview_document(
}
}
"""
- nav_html = """
-☰ Image selection{}{}
+ nav_html = f"""
+☰ Image selection{prev_link}{next_link}
-""".format(prev_link, next_link, details_html, search_function, nav_width)
+"""
nav_css = """
"""
@@ -1437,7 +1436,10 @@ def create_overview_document(
{nav_html}
- {fig_seg.to_html(full_html=False, include_plotlyjs='cdn', div_id='segmentation')}
+
"
+ ],
+ "text/plain": [
+ " set_type n_pigs n_images\n",
+ "0 test 5 166\n",
+ "1 train 15 340"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sqldf(\"\"\"\n",
+ " SELECT set_type, COUNT(DISTINCT subject_name) AS n_pigs, COUNT(DISTINCT timestamp) AS n_images\n",
+ " FROM df\n",
+ " GROUP BY set_type\n",
+ "\"\"\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "658c79e0-0a04-4a2d-b880-37c6ba4a2fa4",
+ "metadata": {},
+ "source": [
+ "Ratio of background pixels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "1d7df6f5-9111-49e1-b7db-4912fb903cd7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.48073018311511867"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_background = df.query(\"label_name not in @labels\")\n",
+ "sqldf(\"\"\"\n",
+ " SELECT timestamp, CAST(SUM(n_pixels) AS FLOAT) / 307200 AS pixel_ratio\n",
+ " FROM df_background\n",
+ " GROUP BY timestamp\n",
+ "\"\"\")[\"pixel_ratio\"].mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1f096dff-9b2f-4054-a215-77ebf9abb823",
+ "metadata": {},
+ "source": [
+ "Labels which are available for all pigs in the training set and are hence suitable for the dataset size experiment"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "455d7d04-4741-4146-9e58-5d211db36b73",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
label_name
\n",
+ "
n_pigs
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
1
\n",
+ "
kidney
\n",
+ "
3
\n",
+ "
\n",
+ "
\n",
+ "
2
\n",
+ "
kidney_with_Gerotas_fascia
\n",
+ "
3
\n",
+ "
\n",
+ "
\n",
+ "
3
\n",
+ "
major_vein
\n",
+ "
3
\n",
+ "
\n",
+ "
\n",
+ "
5
\n",
+ "
muscle
\n",
+ "
4
\n",
+ "
\n",
+ "
\n",
+ "
6
\n",
+ "
heart
\n",
+ "
5
\n",
+ "
\n",
+ "
\n",
+ "
7
\n",
+ "
lung
\n",
+ "
5
\n",
+ "
\n",
+ "
\n",
+ "
10
\n",
+ "
pancreas
\n",
+ "
7
\n",
+ "
\n",
+ "
\n",
+ "
12
\n",
+ "
bladder
\n",
+ "
9
\n",
+ "
\n",
+ "
\n",
+ "
13
\n",
+ "
gallbladder
\n",
+ "
9
\n",
+ "
\n",
+ "
\n",
+ "
18
\n",
+ "
omentum
\n",
+ "
13
\n",
+ "
\n",
+ "
\n",
+ "
19
\n",
+ "
fat_subcutaneous
\n",
+ "
14
\n",
+ "
\n",
+ "
\n",
+ "
22
\n",
+ "
colon
\n",
+ "
15
\n",
+ "
\n",
+ "
\n",
+ "
23
\n",
+ "
liver
\n",
+ "
15
\n",
+ "
\n",
+ "
\n",
+ "
24
\n",
+ "
peritoneum
\n",
+ "
15
\n",
+ "
\n",
+ "
\n",
+ "
25
\n",
+ "
skin
\n",
+ "
15
\n",
+ "
\n",
+ "
\n",
+ "
26
\n",
+ "
small_bowel
\n",
+ "
15
\n",
+ "
\n",
+ "
\n",
+ "
27
\n",
+ "
spleen
\n",
+ "
15
\n",
+ "
\n",
+ "
\n",
+ "
28
\n",
+ "
stomach
\n",
+ "
15
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " label_name n_pigs\n",
+ "1 kidney 3\n",
+ "2 kidney_with_Gerotas_fascia 3\n",
+ "3 major_vein 3\n",
+ "5 muscle 4\n",
+ "6 heart 5\n",
+ "7 lung 5\n",
+ "10 pancreas 7\n",
+ "12 bladder 9\n",
+ "13 gallbladder 9\n",
+ "18 omentum 13\n",
+ "19 fat_subcutaneous 14\n",
+ "22 colon 15\n",
+ "23 liver 15\n",
+ "24 peritoneum 15\n",
+ "25 skin 15\n",
+ "26 small_bowel 15\n",
+ "27 spleen 15\n",
+ "28 stomach 15"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sqldf(\"\"\"\n",
+ " SELECT label_name, COUNT(DISTINCT subject_name) as n_pigs\n",
+ " FROM df\n",
+ " WHERE set_type = 'train'\n",
+ " GROUP BY label_name\n",
+ " ORDER BY n_pigs\n",
+ "\"\"\").query(\"label_name in @labels\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "42c5f742-8b1f-4640-b1bc-a6f1ffc11df7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "4192.15625"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.query('label_name == \"major_vein\"')[\"n_pixels\"].mean()"
+ ]
+ }
+ ],
+ "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": "3.11.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/paper/MIA2021/DatasetSize.ipynb b/paper/MIA2022/DatasetSize.ipynb
similarity index 100%
rename from paper/MIA2021/DatasetSize.ipynb
rename to paper/MIA2022/DatasetSize.ipynb
diff --git a/paper/MIA2021/GeneralizationError.ipynb b/paper/MIA2022/GeneralizationError.ipynb
similarity index 100%
rename from paper/MIA2021/GeneralizationError.ipynb
rename to paper/MIA2022/GeneralizationError.ipynb
diff --git a/paper/MIA2021/ImageExamples.ipynb b/paper/MIA2022/ImageExamples.ipynb
similarity index 100%
rename from paper/MIA2021/ImageExamples.ipynb
rename to paper/MIA2022/ImageExamples.ipynb
diff --git a/paper/MIA2021/Intro.ipynb b/paper/MIA2022/Intro.ipynb
similarity index 100%
rename from paper/MIA2021/Intro.ipynb
rename to paper/MIA2022/Intro.ipynb
diff --git a/paper/MIA2021/NSDThresholds.ipynb b/paper/MIA2022/NSDThresholds.ipynb
similarity index 100%
rename from paper/MIA2021/NSDThresholds.ipynb
rename to paper/MIA2022/NSDThresholds.ipynb
diff --git a/paper/MIA2021/RankingDifferenceLR.ipynb b/paper/MIA2022/RankingDifferenceLR.ipynb
similarity index 100%
rename from paper/MIA2021/RankingDifferenceLR.ipynb
rename to paper/MIA2022/RankingDifferenceLR.ipynb
diff --git a/paper/MIA2021/SpectraVisualization.ipynb b/paper/MIA2022/SpectraVisualization.ipynb
similarity index 100%
rename from paper/MIA2021/SpectraVisualization.ipynb
rename to paper/MIA2022/SpectraVisualization.ipynb
diff --git a/paper/MIA2021/SuperpixelReference.ipynb b/paper/MIA2022/SuperpixelReference.ipynb
similarity index 100%
rename from paper/MIA2021/SuperpixelReference.ipynb
rename to paper/MIA2022/SuperpixelReference.ipynb
diff --git a/paper/MIA2021/annotation_protocol.ipynb b/paper/MIA2022/annotation_protocol.ipynb
similarity index 100%
rename from paper/MIA2021/annotation_protocol.ipynb
rename to paper/MIA2022/annotation_protocol.ipynb
diff --git a/paper/MIA2021/interactive_example_spectra.ipynb b/paper/MIA2022/interactive_example_spectra.ipynb
similarity index 100%
rename from paper/MIA2021/interactive_example_spectra.ipynb
rename to paper/MIA2022/interactive_example_spectra.ipynb
diff --git a/paper/MIA2021/model_overview_samples.ipynb b/paper/MIA2022/model_overview_samples.ipynb
similarity index 100%
rename from paper/MIA2021/model_overview_samples.ipynb
rename to paper/MIA2022/model_overview_samples.ipynb
diff --git a/paper/MIA2021/reproducibility.md b/paper/MIA2022/reproducibility.md
similarity index 99%
rename from paper/MIA2021/reproducibility.md
rename to paper/MIA2022/reproducibility.md
index 8af7c3d..388165c 100644
--- a/paper/MIA2021/reproducibility.md
+++ b/paper/MIA2022/reproducibility.md
@@ -1,7 +1,9 @@
# Reproducibility (DKFZ internal only)
+
This document will guide you through the process of reproducing the main results for our [semantic organ segmentation paper](https://doi.org/10.1016/j.media.2022.102488). To reduce the number of required training runs, we are only reproducing the results for the spatial-spectral comparison (Fig. 5).
## Setup
+
Start by installing the [repository](https://git.dkfz.de/imsy/issi/htc) according to the [README](../../README.md).
> These instructions were tested on the `paper_semantic_v3` tag. However, for reproducing, we recommend to use the latest master instead as there are some general dependencies (e.g. dataset version, cluster access) which are not guaranteed to work on an old tag in the future.
@@ -9,36 +11,47 @@ Start by installing the [repository](https://git.dkfz.de/imsy/issi/htc) accordin
> Please use a [`screen`](https://linuxize.com/post/how-to-use-linux-screen/) environment for all of the following commands since they may take a while to complete.
## (Optional) run tests
+
If you like, you can run all the tests (some tests may be skipped) [β 1 hour]
+
```bash
htc tests --slow --parallel 4
```
+
With the test `test_paper_semantic_files` (usually one of the longest running tests) you already reproduced all paper figures based on the trained models. We will now re-train the networks again to see whether we can still reproduce the main results.
## Start fresh
+
We want to train our networks again based on the raw data, so please delete the intermediate files
+
```bash
rmd ~/htc/2021_02_05_Tivita_multiorgan_semantic/intermediates
```
There are 20 pigs in total in the semantic dataset and the pigs `['P043', 'P046', 'P062', 'P068', 'P072']` are used as test set. Please move the corresponding pig folders (located in `~/htc/2021_02_05_Tivita_multiorgan_semantic/data/subjects`) somewhere else to a location only you know (but outside the repository). This ensures that the following training steps cannot accidentally access the test set.
+
```bash
for subject_name in P043 P046 P062 P068 P072; do mv ~/htc/2021_02_05_Tivita_multiorgan_semantic/data/subjects/$subject_name YOUR_SECRET_FOLDER/$subject_name; done
```
## Preprocessing
+
Create the preprocessed files by running the following scripts (this basically re-creates the intermediates) [β 10 minutes]
+
```bash
htc l1_normalization && htc median_spectra && htc parameter_images
```
## Training
+
Start the training runs with the following script. This will create and submit 75 cluster jobs. It is recommended that you have set up filters in your mailbox to ensure that mails from the cluster get sorted into their own folder. [β 1β2 days (depending on the cluster utilization)]
+
```bash
htc model_comparison
```
If all jobs are finished and succeeded successfully, copy the trained models from the cluster and combine the results from the different folds (some unimportant warnings may be raised) [β 20 minutes]
+
```bash
htc move_results
htc table_generation
@@ -47,25 +60,32 @@ htc table_generation
All run folders are stored in `~/htc/results/training/(image|patch|superpixel_classification|pixel)` and there will be a `validation_table.pkl.xz` with all the validation results and an `ExperimentAnalysis.html` notebook with visualizations for each run. You also need the timestamp which was used for the runs later (e.g. `2022-02-03_22-58-44`). Every algorithm is prefixed with the same timestamp.
## Test inference
+
During training, we computed only validation results. It is now time to move the previously hidden test pigs back to the data folder and re-run the preprocessing steps from above [β 10 minutes]
+
```bash
for subject_name in P043 P046 P062 P068 P072; do mv YOUR_SECRET_FOLDER/$subject_name ~/htc/2021_02_05_Tivita_multiorgan_semantic/data/subjects/$subject_name; done
htc l1_normalization && htc median_spectra && htc parameter_images
```
For the NSD, we need to make the inter-rater results available (they are also shown in Fig. 5) [β 5 minutes]
+
```bash
htc nsd_thresholds
```
After this, it is finally time for the test predictions and validation [β 4 hour]
+
```bash
htc multiple --filter "" --script "run_tables.py"
```
## Main results
+
It is now time to take a look at the final results. The main figures are produced by a notebook and you can generate a HTML version via [β 5 minutes]
+
```bash
-HTC_MODEL_COMPARISON_TIMESTAMP="" jupyter nbconvert --to html --execute --output-dir=~/htc ~/htc/src/paper/MIA2021/Benchmarking.ipynb
+HTC_MODEL_COMPARISON_TIMESTAMP="" jupyter nbconvert --to html --execute --output-dir=~/htc ~/htc/src/paper/MIA2022/Benchmarking.ipynb
```
+
Fig. 5, Fig. 7 and Fig. 11 are directly shown in the notebook. Fig 6 is stored in `~/htc/results/paper/ranking_bootstrapped_test_dice_metric_image.pdf`. Due to non-determinism in our machine learning, the results cannot be expected to be exactly the same, but as long as the results are roughly similar to the paper, everything is good :-)
diff --git a/paper/MIA2021/run_generate_variables.py b/paper/MIA2022/run_generate_variables.py
similarity index 100%
rename from paper/MIA2021/run_generate_variables.py
rename to paper/MIA2022/run_generate_variables.py
diff --git a/paper/MIA2024/BootstrapRanking.ipynb b/paper/MIA2024/BootstrapRanking.ipynb
new file mode 100644
index 0000000..83e45ee
--- /dev/null
+++ b/paper/MIA2024/BootstrapRanking.ipynb
@@ -0,0 +1,477 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "import copy\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import torch\n",
+ "from torch.utils.data import DataLoader\n",
+ "\n",
+ "from htc.context.models.context_evaluation import compare_context_runs, glove_runs\n",
+ "from htc.context.models.visualization import ranking_figure, ranking_legend\n",
+ "from htc.context.settings_context import settings_context\n",
+ "from htc.evaluation.ranking import BootstrapRankingSubjects\n",
+ "from htc.fonts.set_font import set_font\n",
+ "from htc.models.common.torch_helpers import move_batch_gpu\n",
+ "from htc.models.common.transforms import HTCTransformation\n",
+ "from htc.models.image.DatasetImage import DatasetImage\n",
+ "from htc.settings_seg import settings_seg\n",
+ "from htc.tivita.DataPath import DataPath\n",
+ "from htc.utils.Config import Config\n",
+ "\n",
+ "set_font(24)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
[INFO][htc.no_duplicates] Found pretrained run in the local results dir at HTCModel.py:481\n",
+ "/mnt/ssd_8tb/htc/results_semantic/training/image/2022-02-03_22-58-44_generated_default_model_comparison\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1m[\u001b[0m\u001b[38;5;28mINFO\u001b[0m\u001b[1m]\u001b[0m\u001b[1m[\u001b[0m\u001b[3mhtc.no_duplicates\u001b[0m\u001b[1m]\u001b[0m Found pretrained run in the local results dir at \u001b[2mHTCModel.py:481\u001b[0m\n",
+ "\u001b[35m/mnt/ssd_8tb/htc/results_semantic/training/image/\u001b[0m\u001b[95m2022-02-03_22-58-44_generated_default_model_comparison\u001b[0m \u001b[2m \u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "
[WARNING][py.warnings]warnings.py:110\n",
+ "/home/j562r/miniconda3/envs/htc2/lib/python3.11/site-packages/torch/nn/functional.py:4343: UserWarning: \n",
+ "Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please \n",
+ "specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for \n",
+ "details. \n",
+ " warnings.warn(\n",
+ " \n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1m[\u001b[0m\u001b[33mWARNING\u001b[0m\u001b[1m]\u001b[0m\u001b[1m[\u001b[0m\u001b[3mpy.warnings\u001b[0m\u001b[1m]\u001b[0m \u001b[2mwarnings.py:110\u001b[0m\n",
+ "\u001b[35m/home/j562r/miniconda3/envs/htc2/lib/python3.11/site-packages/torch/nn/\u001b[0m\u001b[95mfunctional.py\u001b[0m:\u001b[38;5;145m4343\u001b[0m: UserWarning: \u001b[2m \u001b[0m\n",
+ "Default grid_sample and affine_grid behavior has changed to \u001b[33malign_corners\u001b[0m=\u001b[3;91mFalse\u001b[0m since \u001b[38;5;145m1.3\u001b[0m.\u001b[38;5;145m0\u001b[0m. Please \u001b[2m \u001b[0m\n",
+ "specify \u001b[33malign_corners\u001b[0m=\u001b[3;92mTrue\u001b[0m if the old behavior is desired. See the documentation of grid_sample for \u001b[2m \u001b[0m\n",
+ "details. \u001b[2m \u001b[0m\n",
+ " \u001b[1;35mwarnings.warn\u001b[0m\u001b[1m(\u001b[0m \u001b[2m \u001b[0m\n",
+ " \u001b[2m \u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
[INFO][htc.no_duplicates] Found pretrained run in the local results dir at HTCModel.py:481\n",
+ "/mnt/ssd_8tb/htc/results_semantic/training/image/2022-02-03_22-58-44_generated_default_model_comparison\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1m[\u001b[0m\u001b[38;5;28mINFO\u001b[0m\u001b[1m]\u001b[0m\u001b[1m[\u001b[0m\u001b[3mhtc.no_duplicates\u001b[0m\u001b[1m]\u001b[0m Found pretrained run in the local results dir at \u001b[2mHTCModel.py:481\u001b[0m\n",
+ "\u001b[35m/mnt/ssd_8tb/htc/results_semantic/training/image/\u001b[0m\u001b[95m2022-02-03_22-58-44_generated_default_model_comparison\u001b[0m \u001b[2m \u001b[0m\n"
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+ " ce_loss ece \\\n",
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+ "\n",
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+ "\n",
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+ ]
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+ "metadata": {},
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+ ],
+ "source": [
+ "run_context = settings_context.best_transform_runs[\"organ_transplantation\"]\n",
+ "df1 = context_evaluation_table(run_context, test=True, aggregate=False)\n",
+ "df2 = glove_runs(\n",
+ " {\n",
+ " \"baseline\": settings_context.glove_runs[\"baseline\"],\n",
+ " \"organ_transplantation\": settings_context.glove_runs[\"organ_transplantation\"],\n",
+ " },\n",
+ " aggregate=False,\n",
+ ")\n",
+ "df = pd.concat([df1, df2])\n",
+ "df.replace({\"network\": {\"context\": \"organ_transplantation\"}}, inplace=True)\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Lightning automatically upgraded your loaded checkpoint from v1.5.8 to v2.3.2. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint ../../../../../../mnt/ssd_8tb/htc/results_semantic/training/image/2022-02-03_22-58-44_generated_default_model_comparison/fold_P041,P060,P069/epoch=46-dice_metric=0.87.ckpt`\n",
+ "Lightning automatically upgraded your loaded checkpoint from v1.5.8 to v2.3.2. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint ../../../../../../mnt/ssd_8tb/htc/results_semantic/training/image/2022-02-03_22-58-44_generated_default_model_comparison/fold_P044,P050,P059/epoch=70-dice_metric=0.90.ckpt`\n",
+ "Lightning automatically upgraded your loaded checkpoint from v1.5.8 to v2.3.2. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint ../../../../../../mnt/ssd_8tb/htc/results_semantic/training/image/2022-02-03_22-58-44_generated_default_model_comparison/fold_P045,P061,P071/epoch=75-dice_metric=0.84.ckpt`\n",
+ "Lightning automatically upgraded your loaded checkpoint from v1.5.8 to v2.3.2. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint ../../../../../../mnt/ssd_8tb/htc/results_semantic/training/image/2022-02-03_22-58-44_generated_default_model_comparison/fold_P047,P049,P070/epoch=52-dice_metric=0.85.ckpt`\n",
+ "Lightning automatically upgraded your loaded checkpoint from v1.5.8 to v2.3.2. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint ../../../../../../mnt/ssd_8tb/htc/results_semantic/training/image/2022-02-03_22-58-44_generated_default_model_comparison/fold_P048,P057,P058/epoch=79-dice_metric=0.86.ckpt`\n",
+ "Lightning automatically upgraded your loaded checkpoint from v1.9.0 to v2.3.2. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint ../../../../../../mnt/ssd_8tb/htc/results_context/training/image/2023-02-08_14-48-02_organ_transplantation_0.8/fold_P041,P060,P069/last.ckpt`\n",
+ "Lightning automatically upgraded your loaded checkpoint from v1.9.0 to v2.3.2. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint ../../../../../../mnt/ssd_8tb/htc/results_context/training/image/2023-02-08_14-48-02_organ_transplantation_0.8/fold_P044,P050,P059/last.ckpt`\n",
+ "Lightning automatically upgraded your loaded checkpoint from v1.9.0 to v2.3.2. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint ../../../../../../mnt/ssd_8tb/htc/results_context/training/image/2023-02-08_14-48-02_organ_transplantation_0.8/fold_P045,P061,P071/last.ckpt`\n",
+ "Lightning automatically upgraded your loaded checkpoint from v1.9.0 to v2.3.2. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint ../../../../../../mnt/ssd_8tb/htc/results_context/training/image/2023-02-08_14-48-02_organ_transplantation_0.8/fold_P047,P049,P070/last.ckpt`\n",
+ "Lightning automatically upgraded your loaded checkpoint from v1.9.0 to v2.3.2. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint ../../../../../../mnt/ssd_8tb/htc/results_context/training/image/2023-02-08_14-48-02_organ_transplantation_0.8/fold_P048,P057,P058/last.ckpt`\n",
+ "Lightning automatically upgraded your loaded checkpoint from v1.9.0 to v2.3.2. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint ../../../../../../mnt/ssd_8tb/htc/results_context/training/image/2023-02-21_23-14-44_glove_baseline/fold_P041,P060,P069/epoch=77-dice_metric=0.89.ckpt`\n",
+ "Lightning automatically upgraded your loaded checkpoint from v1.9.0 to v2.3.2. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint ../../../../../../mnt/ssd_8tb/htc/results_context/training/image/2023-02-21_23-14-44_glove_baseline/fold_P044,P050,P059/epoch=82-dice_metric=0.92.ckpt`\n",
+ "Lightning automatically upgraded your loaded checkpoint from v1.9.0 to v2.3.2. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint ../../../../../../mnt/ssd_8tb/htc/results_context/training/image/2023-02-21_23-14-44_glove_baseline/fold_P045,P061,P071/epoch=41-dice_metric=0.86.ckpt`\n",
+ "Lightning automatically upgraded your loaded checkpoint from v1.9.0 to v2.3.2. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint ../../../../../../mnt/ssd_8tb/htc/results_context/training/image/2023-02-21_23-14-44_glove_baseline/fold_P047,P049,P070/epoch=48-dice_metric=0.85.ckpt`\n",
+ "Lightning automatically upgraded your loaded checkpoint from v1.9.0 to v2.3.2. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint ../../../../../../mnt/ssd_8tb/htc/results_context/training/image/2023-02-21_23-14-44_glove_baseline/fold_P048,P057,P058/epoch=57-dice_metric=0.81.ckpt`\n",
+ "Lightning automatically upgraded your loaded checkpoint from v1.9.0 to v2.3.2. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint ../../../../../../mnt/ssd_8tb/htc/results_context/training/image/2023-02-21_23-14-55_glove_organ_transplantation_0.8/fold_P041,P060,P069/last.ckpt`\n",
+ "Lightning automatically upgraded your loaded checkpoint from v1.9.0 to v2.3.2. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint ../../../../../../mnt/ssd_8tb/htc/results_context/training/image/2023-02-21_23-14-55_glove_organ_transplantation_0.8/fold_P044,P050,P059/last.ckpt`\n",
+ "Lightning automatically upgraded your loaded checkpoint from v1.9.0 to v2.3.2. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint ../../../../../../mnt/ssd_8tb/htc/results_context/training/image/2023-02-21_23-14-55_glove_organ_transplantation_0.8/fold_P045,P061,P071/last.ckpt`\n",
+ "Lightning automatically upgraded your loaded checkpoint from v1.9.0 to v2.3.2. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint ../../../../../../mnt/ssd_8tb/htc/results_context/training/image/2023-02-21_23-14-55_glove_organ_transplantation_0.8/fold_P047,P049,P070/last.ckpt`\n",
+ "Lightning automatically upgraded your loaded checkpoint from v1.9.0 to v2.3.2. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint ../../../../../../mnt/ssd_8tb/htc/results_context/training/image/2023-02-21_23-14-55_glove_organ_transplantation_0.8/fold_P048,P057,P058/last.ckpt`\n"
+ ]
+ }
+ ],
+ "source": [
+ "networks = {\n",
+ " \"baseline\": SinglePredictor(\n",
+ " model=\"image\",\n",
+ " run_folder=f\"{settings_seg.model_comparison_timestamp}_generated_default_model_comparison\",\n",
+ " test=True,\n",
+ " ),\n",
+ " \"organ_transplantation\": SinglePredictor(path=run_context, test=True),\n",
+ " \"baseline_occlusions\": SinglePredictor(path=settings_context.glove_runs[\"baseline\"], test=True),\n",
+ " \"organ_transplantation_occlusions\": SinglePredictor(\n",
+ " path=settings_context.glove_runs[\"organ_transplantation\"], test=True\n",
+ " ),\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Seed set to 42\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "P043#2019_12_20_10_10_19\n",
+ "isolated label: stomach\n",
+ "P072#2020_08_08_13_14_14\n",
+ "isolated label: stomach\n",
+ "P042#2019_12_15_11_15_55@semantic#annotator5\n",
+ "P043#2019_12_20_10_11_20\n",
+ "most important neighbour of stomach is liver\n",
+ "P043#2019_12_20_10_11_20\n",
+ "most important neighbour of stomach is liver\n",
+ "P062#2020_05_15_18_46_30\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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OS42YWapiMaOYcT6AwwfPmnOv3WsHz9dev99CHn9H5RWrI6od1ILWglKQ8SMxNjBdUUC0RQNInVAVTK44ijdCbAvJnZQ6LL9iVtah3v4NhSumBUOpBKoZlm/UecLnikmAIJRlpJQTFgFJeK0YdR24VCjlGQkBZUH2D3g0zEYs/jAEv74iRFhuYA1MIyEqOn1HlB6pBUstVSolCaXM1NiTry9UE3z+vMYB14rXK64BKxOy3PA4YDFQY0+ZXvA8ketMyVdKrFjT4dpj0yu2XPBaoFRICWPGLt9S84Q3R/zyTMkVmj0wEb3SlPP69/EeTQGxEckFtYC0LUWU0B5wDyQ50MSGTo1eMo0tiBjxzdeU+ZliQpk/QtxTHHx4orBQbzMpz6T5CvM6eA0PX5PDgVxnzCdMW7h7RPp7tLsHNXzOkC80wz3t8RGfPpGvH8jNDg89ZmeoFxivyPiK2IQ2CYkNuGPljIfMsnxgud2gvcNdEAk/bC0nJDUYTrEr1jT4MuI487jg3R0WIza+UOoVZAa7QD5BPhH7B2x8Rtt7WM6YJ3JZqDmvhwGmz8QwE+yM5gnshbA/gF1wmyE/IzWjsScvI9UXvBoqGWl7hAnVDML6c1XAC94OeL2ACn59wcoFuX9YXzcCpQh2+YhNF5CAdY/UUlnmT9R6xvdfkr0g+3dr1Pv+iCHU5YRjWH5eD0Bcn7Gg1PmGpwbRgiwTWm94ECSlNX59yqTQEuYXhv2epo0M+z2dCokFsU+oZprdG4JlYt8T7u4RmUDqehgjX5F6RpIhXknDPWG5EHcH/PYBk4GyZMrthXR4oKk3Ajcksv4M84W0ewN6T53PVLsRmfH5iiyvaFBYLlCvCJVwN4BPBB2BAsxoA9kmZHckl5GaP62Ps+mR7sByecGvn9DhkTqdCdwILvj0TGwOqAt2+zXZJrLfqO17rM7cPv0lNuyRoYXQUvyGvvsZtUuU8hmbXnCvLJe/pubCdJvx5gtKKJTrNywvv6VON0IKYOvvY2xE5lfk/Etk+ob2/gkfX7ESmU+/pn7+JT4XludfUU437PQd4dOvSdcTkhe4PmMvv6N5cw/nPyfUz4SQUFvQZsA+/wukf6B5fI9NnyiAnX9Lvv0ObQPtL/4pEmfKy2dEWkqoeGyoCH79TNj3yDStseubzWaz2Ww2m81ms9lsNpvNZrPZ/DvmD24ADJlSR5wOmj2SM75coBY87PE04GmPaYO3LbUJ61ZuPEIIVE5UqVSJOE7pDlSfcIuYZaxO2HjFlgIeCGHA7EbVSMXwZo+p4pLAM9Uyxa8gI0Eyodmj/cPaiaktRVqKRipQY6LmV5bySg6K7L7A2iN5ubBcv8FCSy3Acka8EqRDYkTj2mkqtxd0ekYdQq6oV2g6XNfH4ipYGfHTMyoVLb5uscYOtwlHsbpgomgYcAx2PTXfkHhEpFnjn4ND24L5OtxjB97A9Lp2Jbd3SNNjy5kqvm715oxXsFzRENZNThvwmrF5xErFPGBLRpYzdblSpxlrDoAg/SOksPY154zrfh0Ui+H7O6oVqjglDNC0WNtRxxfIr1hdYFlQuSDdG6z+TedzBJ8pLFxv31FevyPc1g3HdPwJ4f4e7Y9YvlBu4xq57JlcMksB6Q/k/IrXG2jBsiFWiMcfU/KNPH2kTCe8O+LdgeoJk47U3hP7A8zPyHKFvFBrwbxgywj7N8j9j3ECMlZSFtrlM22MREBZB2g6vFljwqlI0+LFMO2oOUPsWOrCcj0xzxNmHXY9UaeRmi/U2EBjhN1bpG+oEWQ3oMuEzldS84D5jM/T2iXtN4opOjxiywWbbjAcqXWiaqCWwjKfKL6Q8xnsSpUK03XtaQ0dGgcqheqOdANeR2pILLGHbofXTO6OLLWQJZJJ+O0FBOpshLuf4fsf4c2OdnhD7AbEM5LuqcsJW15/2Ir+YXiu3brxP18Qi+vrWYyaJ3TokK6jtjvy8EAZHqgG1j1iy3XddBdB+zuwAZOOUk4gjk1n6u3Mcnol387rxv84o2UhqpA0Eb0SOyM0BSRB06Kv36wx8P1Atkpxo+7eUucZ0YHYHfDYsIwnvHnDko0yvGW5fkaGhzX+uonU5RMx7KnT53Wj+Trh6Z5sHZYTPhZshsoAzf36+pw+EY5fobcLEUWWGb+9rMPy5ZXQ9cjuPW4BP7+yfP4rJB7IL9+hux3p+AA1IKVSnn+FaKV/+MkaCV4g9ANBHA976u23LPUVz1BzS/n0Qj39Dj+f0PmVJr/SH79EY4AiSGjQh5/i7YCZEVKPkckff02ZXkjvfwH9QDVjuf4GG79do9S7N3j/Hgl7YKCm+zWyXm8s45Vw/4R2A9Qb0TN+fsa5X5MS5ityW9B9i00fwSM6PBEPX2NSsN0TxW+k3R/9/m5lm81ms9lsNpvNZrPZbDabzWaz2fwt/uAGwDa/AAEvVyRkXBdk2BHihNqJWAuiBuaYNZitnawp7kl9B6zDyabZIQW0CincE60S2+O6gRcT6fAlukxIGTFxqkCtM6Br3yeFGod1mFyUXDrm2lEkspQFN4daCM0BYSGK0TVHQoNdqgABAABJREFU+nRHWyDMF2JQghguDSI7arlSreK6w5aM1RH3TDh8icSEh34d4ISAhwXymWAjzfA15Bkj4mMFiwiO0CD9HRWjdg+UqCyl4l6oscHTACgiBl5xFGTELK+dwM0BUkedvsWnVyQEJO5AM7QNJoqYrv2484Xh7h27BvqHr9ehpRs+Tvg8IwiBkXB8gPSAUHFx3Bvy9QNuI6S3a2T06YbHPdN8Yw47cinM8/fMt1eyCXWBUjI1V4oruKCeoc5oI2ho11het7Wr+DIR6Qh+Q9uOGBtS2pFKQxreEA9PxH7AbKJcntdtzzxBc0DiHlsWfJyxl49w+h2haWiGO8L+K8oyYtOFOr2gsaV9+jkhtqT+nv7tnxEevob7L9D9O+LtGVkuaIygiu6+oC4npM7rgYLphC2/wcYrXiqWF8pyo/YPQAOWCf0BLyMqCS43zBUrI3Q7igtlWViqwOELluxUTdSysMwZDzuq29pVrIVw/xZipcwj1QOkA4snplxZcsbqjjC8IS1XWl+IuhD7AZWBfDsh7RNWAFdQQbodvpwIqcebHfP8jBzf0xx/jIwTdjsTlpGIkfo7mv4OM0PmidjvaYY9ze6Azhnv9+ugNtg6SPZCpsOtRZsjmEGIiDlSKyoVkYDEA6G7xz1SrwXGK7GC1IXSv2HKI9N4o1w/UPMNK8aSrxSFYhnrD0gz4K5kVSzucDPk/h6TBrIj3QO+vGL9j7i9PFObI16uEBQdDvh4RVKzxnbnhVgqqT9S8wnyZ3w6I0GZL78hDu9QbdbXfSlo/xaNR8QmuruvibtHpFyQ/g06n4ndI16uNMOe/vAevX5Aa0LmM23XIG2HT2esfCLefY0vJ5hO+Ol7qAvaJ9r9Han9iqQV71sY3lGuZ8rpe8pyxbqe5XamjFd8meHrf4Kb46cRbfdUS5Q5482eohG+/JKqiXk8k5dMbh/JGgmPfwT1ho8n6ulbhEBsekgJ14Acn0hPP0eZSaGhafaExz+lXr5F3zwh7Q7xFtGKFiG1d9jnM6E4oY6IHKmfXqFW4uENitJ/8SPimz9Bbr+E86/o3v4xernSPb4n3H4DwSFfieGJGhr8zfvf381ss9lsNpvNZrPZbDabzWaz2Ww2m7/FH9wAmNABN6SWNd413uHNGzztqCZUiVithLsHVBXxC4SK+AWzBZGE1bL2wtpEsIKEBo8DogdE0xqPLJG0e0cUI3qh4YduX3c0DgiOqkAxNDVoaEAT7oYuC3XJ+HwmaiFZoJpjGqntQNEKwbGasdTh7R3SdUhNxHYHQSmhYNqSs1PmK3UJYBFvD3jcY+0BiwlfrkhwiAksgABNiy+FXCu5LGRryVXJtxHzRF1OkGckHvCqiBzWSOOy4CVQloDdLpgPVHNCvIM64qEFFsrlE2WcyOEey5U6LwQSQkF0RlJP1UiZX3G7EQ57VBQd9qh0hFaI0pCaHlg3hOv1invGqmCSwSbwRLWZvFyg7pD2ieSVmDORADnTDgNar6SHd6gY2rSIOlIXZPgSspBSvw56+wOhe4DlRswntC7I/JlwfIc+fI21R8L9j1AzdDFirTTNANcb4hVCwIiU+UrZv8WGHTZdyOMn8rRQz5+RpSDTGZaM7g7o4Q06PKLHL+Dux3h1zIyljNTUkJsdi00s85Vlmcjsma6fKL6jSkM+vWDG+nwvE14WQNZ+aMlInQnNDiuFOo7U8Yo0LUFb5HLBpoLmSiiVIErsOpoYkOUF8RmmK7HpaA7vaQ7vSLkQxhOhLIgtaGohgEyvxOFrNHSQBswaEF+3dMuIzBckZ+LwHkWICKl/j2gLDuSKZCAbUJD5BlTi4Ql8QbvjGrcsCixIHaFWpLmHqjAXNB7QZkBLJkoghB1SC0hP2N0T8g0NmZBaghsaApxHTKCaoRTickNixOIebRo0NRB/+LtUvDpIQJpEE4+E2FDHE0aH9A/4MiNdwuLa7Rw0QLmtj6+7gzSALMT2Dr1+Jsi6Acv4TFhGxCPd/dfIdEalRRFC2+LSAo4shXj3hNoVbQYIBdQQv6JqpAZYJuRvHjuF8OZLXASfK7p/h6ZMSAc03RGPf0xo75Bhj1XAe6zdEbqGspxI7ZHATJ5eMGnw5UzgAJKw64kQHwntE7oo8fErbPyEjFdi09P0b5DpIynd0bz9BeHhR0BD6N7ir59gOjHNH5jzC3mcyJcr+fQX5OdnZIGmf6Dp7pDlA03IaFJi0xHf/Cnd/Z8it2coBaMBq7CcCTuH+ZX2Z/8LlI56fsFjxPJngl2ITY/mC8wXCDsYX9Hxl6T2HneF82+p1xErJxRFRf7eb2GbzWaz2Ww2m81ms9lsNpvNZrPZ/KvE3/cF/L2bPxIOX2K5YNVRZryMGP3aP6kVkR4mgeaIG9Q6QlnQ5IgGWBbm6w3q2pXreofWCY1gpSOUiiSw2K+dufOMJsW7w7odGiOlFtxA64SkPYmMYXi9UWtF0x0kpxaw0FCqEEyR0DDbQl2c1hvSNBFSC7JDwg1JAa4LYhCGlloz5hFhBilIVlx/GAJqT+1apFRkqahGMMP7HT5eCWFgmT4joVuHesOANB0+OrYUnBnmM5Lu4DZizQ69ZVJq8GlG5RW/3eDwDqYRL/ywPddSmAlpjy8TbTC0e0P1ymwdnF8JmtDbR+LbHwEz5XzG5xZpyrrNnFoYC6HpMNnB9QLDHh92TKfv0fAWt4LSErSnGSLeRuR8AYXm+ITNryiRmhWCYD4j07IeBrheScM9rkb2itmExIFaDVsUixUkEEzR8UIxkMPXaFDKy79E6yt6OqOPP8LGM14umDhCQG+vqDbkpaD9FzCPqJwJzdv1568QlorYK4QIcoW4w7s95fQbZKqErHDo6O7fMz//BqaFtHuD+0C1CzrcEe1CGgaYL3iZcYkQB6BA2xGmC7Dg7Vvs8kplIaQ97d0TulwJXcTdAac/3kG9YOUzzgEwpCiYoXc/xjXg0w3mZ0IFvf9qHWR7C/kM+3fQv6Uur4g5bdzDVCAllAtoWt97qYPpA5CQcIDpBYoh9UZQX3uOrYeg1NsV7R+geURCi88fUZP1t5oUiBWpJ2yBWp0wdGtvcFR8nmDfgXVIWTCcOi/gC1bXAxh6vMPdSNKSyxlpIyYRFQjtG5bz7wjdEc0fEVpqERYWlnGhS4KbIoBoA/MrpAYn4ySQAerMWtO9nsMRD2AFZMDqjJcJ2T2SXz9izQ5TRecCKtSSIUTm+hmt6wGT0D3A7QRZ8PmKhyNeZkiP+HxBmz1WF5b+QDx/Q3UIydHlRmgPWGjwGChLIdz/GG7P6OEBKXvcHG1a8vUv8OURNFAvJ+gjjRZCd8C79zgLPp2Q1P/wMd8i7Q4vr7QPf4p+/8+pd+/xPEO3Xz/+5VeE/T00PV4Wwt0X2PkDy1zXLfrdzxAqpB1ye6W53+Of/or88S+g6VBTlpjg8iti35G++k+w12/Q41uoF2L3JXW6Qr0QHn6On/8SFcfyGQ9Gevgn8PznSHTEhfr65yBPxLdPUF+IP/tPqArx3Z/iH/7fdF/+FK8X4te/wF/+4vdwE9tsNpvNZrPZbDabzWaz2Ww2m83m7/aHtwEcB2rTYetkBpMWd8dqXYe1+YaGhFiGclqjgJe8xiOXee0NdaNYRroBQoNIwEUwr3h3h9mMWaGUQhGhlIlSFYktFvWHgWxA047QvEXzBHmGbkDo0bQHL3gwqixUiWAj5ldqCIT+Cc0L1GdKfqXkQpGCdwNLKZS2wULAkqIxkZISugO5LGtUci0IQF7wnDFZr9NDIlfDlhvVKiSn6TvS7khQR5IivhCI1DJC21CRdXCTR2JURCrSBCQquCLNYY33PXwBZd3upRvQnFG7Efd30D9Sls/k6YzLQJ2ndeDZvgEiNk+wf0CaHW6s/cp5hGjQJ8LdEyRdez+bB6I3NIsRm0LYdaRW8bggdUI0o3ePID8MwOsCbhAMIeHzMyItokpdFkwDy+0DZZkp4w2/ZYy69j2XT5R8xcuVkC9QFnK5IM0dVjOLJmYyediRl2d8PGHLDcTBbqQ60u7vaFOg0YT2DWIjWhdcT3gFlgmqw3RBvdKERFwKIVSCAwSah1+sQzMXvBjSDsQQcAeNCWkCxITohaoL1WaqGWX+vA59Y0SCEjWS9ndQCzXPmGdoe3wpeBjwurAYzEuloFjsqOmIma9Rv+UKasSHt+j+iHQthEIpFRseMF/jru32gtcz7nkd1DYN0vXUWvAyYWlHLqzdzAGQEY8Cfb++hVOLpGGNAb98QGXdcCUo5mClYLeCjQvWHMhlJqSWGCLiFW0PQEA0rEPqFLGakdStPeA1gIMuldQNaB+IVFR3JIlrf3aeaZo7Ilfi7kBwQ6ikpiPWjEyvBM9IGQnD4xppbgXt9/h8wvMFpFm7rtMjeIdHx/K0Hq7IhjQPeL7i5RUPO/BA0ULRiEUh7L5AikHoId+Q3TsqrK/VlPByxfMNabofuo0zPo0ESUhzJB3f0b79EZJfIAjSNZgKeIR0Ty4Ldb7iYrhUTMG7H1HzBfcWJSLlBd89Ik2DM4JXqBckf0b3D+j+DTGP6PEJ0YKhxP0T9faRefwdoX2AdqAuFcZnJDb49RmO79G7I/H+pzT3TzCfSF2LTM809+9JX/97CJnYDshwRzo+Ed/9ZE1oGJ9xG9H9I/X6Ct0d5fQd0+WMlzNMI3b7jMRMun8gdh1SbrQ//p+ibVi3v19+jS6fUFcIHbK8oFrwENHjT/HJ0DCAdb+Pu9hms9lsNpvNZrPZbDabzWaz2Ww2f6c/vAFw2uHXF2S5gDuqByRPSBCEC9r2uExQJ8QrQW7Q73AvlOrrlu5wDzZj2lJyJi/PVO2pxQga8KbFKLBc0WkmuBPyM2qKz5c1mrYU3Ao1gmOYJigjnk8IhmjAl4UynpHqWB0py4SNFQroMkHOeFF8Pq1x0OZ4yWjs0XZHvVwp8xWirT3EKZGt4iSsCHWp1HzBZtAk4DOaQM0I3RG3iogjyxmJghXFxoz2A9Eu6DIRBdxGzCo+naBeYRYIByQ1aGrXWN76AvsjNA1eM6SBmoUyfSJPN6wWxBdiamlqQesa52vzDc9K1AHRBpGEBoOQYdjDMhJig9q8blrPZ2J3AITYPeHLhWU5MZaZzLR2hxbDb88YiUKLpR5fMnQ7FEEkoocjtdwwHL8sNN0bQtoTU8TmD9RlRK95HdzmCc2F5DfS9EoTGkKjeL0h80zqviB170nNG0JzT64zPhZCcGLXI4y4zygLmCDLhC8X6EB07T5Wm9efe/sG9bA+Pzatgz9m0m5PXC6EWtdobL+h6iCCBgh3d9C/XYeQ7Z4okNon0vAlNl/Xn1//BgktNp3JmqlWoBQQcAo5j9C8xZt76mKAr53MyxU7fSbPV8ptxOIBnz9jbtgsuAwsRJZpooSOGgey7sgS8JjWbuHimAp5nn742g0alTqN1FumjBc8HCnLJ0oZyWZrB2vfwfyKVaecL/iY1230+YqV87oJT6XdPRDFicOewETYP6JWEDKEBp9f0e6OQCDKTNSMLiPqFZmuqGRUJsrlE9ocYHlZt46XdQCby4VSZ8p4QpsevIP2npoaqk1Y1+FBcAtrfHroEDOEsF5/fgYCFIP5ZR2kHh5Zri9Y+4jfnqnjd/j1RpkzVFAHqYXYDNTpFa0jMUVCf7927hKQZgckPCulzGQr+DgjKRKiIXUBU4QWLi/ofMZKxcsrVVtEEmhHef0Wu/6Wdv+G3eFndPs7mjjT3v8MnV+R/Ew4/YpQviEOR8hXwulXNId3yDzit5FyPq/93TmjRNL4Srd/S3P/hDQQUiLdPSKSiQIRJ4YBTp+x8Rk7faAeH1jOH/CopIc/IhzukHpF7BXt3yHHJ5ifqeUTnF/w/AzT94THd8RywqdPsHuH24xfvkXHBcor8ekXSByw+UxdCnYY8OQUz9ScsdNfUz78JWiCcqFcP1Ovn7Bx/P3dyzabzWaz2Ww2m81ms9lsNpvNZrP5W/zBDYA1DkjaISGtQxZuqAZCULwbqCieF6wWyK8gLSEmyNO6NbpkPBfqMlPHC0pGpQIZLRXNhaj3NLnSSCVQ0W639oiWCWnvwF/RbofXM1ILFhpoezDHmo5lumG353Xwy0ApGe3uiWUhTB9IoUXbA8UzpgqekVLx25lYHc1nAgWYwTNcX9DpRmOKaqSQqW7UJlGlI5cri7QspeDar12xtsByWvtTJeDSIOML1JmaC94+rR2idkPKGdm1mFekP0K5YdmwfMPyBab5h6H3DbGMhojMMzFEgjeIVEJ3j4QI8ys1v1LHK2hYB7bdEah4yHj9m63GNX7YbzdkGgntAWVEMeL+gMlpjeu2ii3PhMK6cVk6IoGmGsEh+hmVBbG49sjWCzKeYF7jrUN7T3r8MZ6vhBhJbcOu7+i1kEJE8iuaAM+oT+sQNybi3R+DRihXZLkQHn6KPv2M8PCWpAsxFsRGpJzQw0/Qxy+woPh0wmxEWQfqYme0ToRaiD6j/Q5PQrZljTNerlAnar6xxMjcNmQpmFWmfGUUJYuQxxNGoI4jCtTxFbl/woYDiyukQEhKmS4sS6E8n6keqfkMQw8+IT6goSGwoP0RIa/xycsNacFqRfoWbMTLTF1uVFkQNdLtO5rlTEulaYTUCtFnTISlVvJ8o9xuuLdUc+pyok4jyzizlAW7/4IqIzlDXi6U6wfqPGLzgqU76u1EHl+pyzcwHBAEbR7x5UrY3eMKVmZoEmW8YFEo44WqCa8GYYdrpS6fqbXi2mMxUrwylhtz2FPHgsQOjUBMeK0sNlFtIltGDl/iTEhM2P6eXEdKTVRP2FQQT0gT1x7mfo/ZFe6eEB/RpkdyXQ+BxESdRixPYE4MLTaekfBI7B9p6oRcrhQrlGlau6OlgfwCUlAxYv+IjycyCbMb5foZ73a4V3R3hPKKjZ+otWC6Q7qOfHmGxYnDF9Tbae1ABiRF4vErIi1++4CHhOSM7N4j4pAzEo/QHyl1R3VF9l+QfcZtxGVcD6SUjNRCnZ6JuztiasBHQr7Rdg1xeE9oEmIVrT/0QtcR9wvSDeTrBfWW8umZ6fKZosr8+pl8OjFdztTbB3y6Yr5Qxht5fiHcf015fUbDkfTmTxHdo7uvoXwkh4Id7iivH3A15pe/Znn+juIR/fI/gu4LLGekayneUNUp2pFvL9S2w8Yr9eWvfi/3sc1ms9lsNpvNZrPZbDabzWaz2Wz+Ln9wA2DPZ6Rc8NDgqpgWrDxj+QxpQKSgoUFiQJoDLr5uWdqMioBfEXMCiVQmmt37NUrWK2Zl3QT0cY1NTS0W1shnk4CoEEMLXjCpMF3RWoleiMtIckjSILFBY6JJkSZWgjiiCcFRClYuNPs9ffeWNnZESQiZ1DWQErKM6/V2D4TmCLFDyohYxUVRm9B8o206ht2O5JloCzKd0dAQJROkoN0eklJV8VLR/kCILYgifYtZgeYJiqGhx0OL1w5UCUODWF67O4OtzwfNGm3rjgwHZH5BUkto9lg2ynSmzCdKueFlXDeNibRUIrc1ZtnndTgtB2Q8IyqoTcTDERVFfAYKMSTEX0n1Qte8pw2JtEyEQel3Hc2bB7AL9fZKSAPqnwl1IuyeQCZIA4QO5hvStMD6nIlNhLufoPsn5P0fQbtb42T9GUkLGltkmVEvaLOnTGXt/z2fyMtCXgqyewf5E1YNs4IPDZ5261YpM6F/ix6+xG7fr9vduVJtweyKkZHhnnD/Y8QXxDMynvDlhDc7KAvUGbt8RrwlSUDGTDQlmtAwI8tCaPp1uzXf0PKCWwWbkVAQL8S2pe0PqAO6p9wKWQO5GOV2JcRm3UDPC/i6KZzUSbsnkipRA7E5wjKjzRugUn0dVNc84RgSoF5PVCvUKogrocxEC2sXa824ZlBDU7fGtPdHVBNSZ4I0YBW8Uqfzekji+AY9PqLDAYYjOc/k2DLPMznfKNcXqkXKdGauCzkNlNt5jU2eCss8sZRMDcJSZhYikjpCjOh4oekf8HoBiYjMxNggZSJqhPmCLZWcC+brUNuWC5SCj2dkfEFFUWkJ5UqQgLiCduj+DW43KDfi8Ej0aY2R7h/xeUYPe9rdD+9HL8j9HbJcESmIXUmHHWbL+md+RWOL44Tb94go4eENLZkmzMTGcc9rRLgGoo1QFEsty3RCfCZerzA+w+1GuX2PdA/o7h2MI7J8Jubf0h0OaDkjGpHuDZpaUkxEn4nNQDz8CX49Qy6ktqcNQrM7EkWJSfG2R1B8fsWnF7yO1PMHynjGm6f1IM7lI4wzcfgKOTwgsUF2AcYLnH9Lnb4jvP85ng0fb+TLt+TzhTzCMmdcO6QZIAo5VHL3QPGZ5eUz2v8Et4xVodRAWSYodb12NYIvpNbR6YbERBieiMMAtxOh72F+Jbz7R7/fG9pms9lsNpvNZrPZbDabzWaz2Ww2/y3i7/sC/r6FGIAemEFabH5BmzusTNhyQ4tD28L8DDUAO4yK7t4RZcGsICER+hYRCLEhTEqNGWmHNUqYQNVu7dw0Xzs4vUItoC1N2uMmWNsTghAloTEjOGH3iIyv4BUzo+3vkNtljUzmgC0zUjJS7Ict4AmqItWwFAhLxmztKw1iSBQI90iacYwwvkJ3wItAOeHakoYj7oGwf4uUGWK7/rGMpB1xqbBrCXVGmhbPI+a69m0qkG/Q7AnRCG2D3y6oRur+LbEb8Olb0D2UvG6TomhJuHwgxIBJpC4nvHtat/8e/wi9fKSXK13XUOmxUImxZcr8/2K0PbUQDVHFNEL/APmE5DNh9w6fT3D3E9yVMp5JIdPu3qGpZ1kKOIT0iARB+wOSjLj/ivn6O9wzVpyomaSRIi2+TEiM0LTggkiA/RM+xXV7e56pPiPaQwHZ7cCd0ZzIgtwuRDXYP1BoCbHFlhliWb9eOKBvvkBDBFHC23+ChoKGDjxQb5/XYWu/X3uBd0/oMiKxJcWFms/UcgNpITbEvkMWR/ZvUAVfZmT3Ja6BODxQpu9RNaIGansEd4yM+kI4vMVLRdo3a0d2mUh3XxLzKz4kJAWk2WPLiFYDL+hhD12zDniXgqcBujeg64BYiNA2qPu6Qdrdo+cPiAE6EHYtjK/Q7Gi7N5TTt4TDe0QWxCvFDTNHWL+3NAP2+lsqQu2OpLuvwUccR7o7uF0wy7hNIIFaMpoiKQ2QHaRBr6+YCI20SKzI4R0+Z2h2xHICN0RbwOFuj7SVUMCWBW9atChiDdo21OlK2D1SypU6RbACmpG6Du1rnZA6oX27Du93d+TreY2Mny94mUnDI1pveApAQMTXYWR3Dzaj8ysmhWb/FX5+Qd/8EX76HSodWmfYHbDzM+KV9vgEMWLjJ3T3Bl6/QY9fowp6fA8IogpMMC+kwxP59gKWwRaah/f46TdoOGLzCY1K2PXENODSgQXQlvT0I8p8xsuyvifc18Mh6mBGeP/HSEz4+C1h/4TUZ6qsB1qCsPaCixDm31E8rK+RBPL4BX77SMkXmI0YHasTcTig1kNqCcUJzY503EMcyN0d9Zv/hv7pxxALsd/hdYa+owlH6vlX6PEXuNzRHb6E6XuYP6C7H1E+/Dn65mmNm68zMr3SPP0Z48d/SXj4Bfb5O8Lbr9D6F+jbf596+it0/8Xv5T622Ww2m81ms9lsNpvNZrPZbDabzd/lD24ATB2x2wUPgvYDLhHigRAHxNa4WjEnxrcgEbe1w1cCqDmqe0Qqqj3kzxiA7hB3oEPqCckVaVqiNbCcUCsQnyi3z4hA1SNeKoSOWhdqM5DaHplfCZbxWqi5rJHItGgohGkhDndIvK0DUEagIwVH7h6oNaM5Q9sRtF0HbCmuA0MWTAW3hKQDEveoTPjiuBhCAgokXTtJGXDXNQa56tqVqj2eM57i+pwRkG6H0uBxQZoeyc8kFaxt8ATR23XDVHfrALCPOAXLjgbF2wHcUG54Epqmxa6vEB4hjpQqODeauwdKE7heRxzDQ4RpxESQ5Uy2itQz0iR8zHjXor5gOWPaU24vSK6kxz0pJaxMQIMER7sd2BXtHjCfyAFsnsEDIaW1D1Wcpt2BCjLsIV8hRnx+hu4eOfwRUMkv/xJ8QBbDyyuu76nMNIuQ+oCIgATEnfj2p6gn6vU7ZJnW7t9yJuyf8HLC2K99pr5g1uLkNULXKj5lPAjS7KnLjJULMiuWKtJ06xC9OSDSQr6h+w7GD+trITb4dKKGgHvEPJO9J2gkL2dCyZQYCNJi9QSWURNi0xBTRKTBOWLzZzwdIC+QInQPWLOjnEdinRA3VBY07rH5ezCgO0DswATyiCDEeMR1Aq84AfoOKVfQHXR7Ylw7bMtt/RiVjuoLAaXWmbh7Q10muuEBLVcQh+UGmpDlTGqOZEt4ngne0NBCmXCERgRNEQkKaQ9lXjftd8Pa09us/bx0d1AFrBJcQfo1Vvk2rtu14mifCLlB24GQAl4CFgZCkHXzt38CX7fDpT0g+YzNjk8ViRHPF3R3B6mn3l6Q/it0eiV0e0wdL0YVX7ek2zuYblgZ1+dy95YqGdeK1AoP7/GSkeYel4qdZkgZFCT21OsHZP8lzBO13mjufs783T8nFyXuH2G8UJuOGO6hrXjNeL1RPULqqBLX902ZkP0dnkfkdoa6rD/D4Us8X9bt/2aH92/wy6+Q9oCWBdQIuye0NWT5jPSPSDhAvWDXV3S3h+WV1D2S62+p0wdCCtj5I9o8gM5rN3E4EsTw5XtEQTCa/g3L/VvS8ED9/GskFurtFdUIRdYe7esz6af/Y8Qz5foBpjM+z9TxhO6+woiU82donFoWxH9HiD+jyISOE7YI6kYMPU3T/h5uYpvNZrPZbDabzWaz2Ww2m81ms9n83f7gIqBLVXy4Q6Ii9ZkQB6JdaUKlAcQVmU6oNGt0shgiAfWM6ICEhNcLqgUQGBdCELAFlQXXiVJnrAqh69E4gCwEndCuWweuTSI0HRJ6JLbE2BKrEXSHzRm3SNM8EDWh9QaNUqhYOeNtgKZbo29DXrf5PJDaHWpr9HFU0KSE3Q457EAEv16QUpG4XyN1+x1h1xHCjMgEIa9dv9WQsFufg/4OyTPS79bNvj6t24q2oAhBK4GJeHwk1gndHbFgWEp4XdYt3eqQdrgIXiI2GULEy4QPB8yEmh0PPV5GqmVqrZTYcj3fuPjAWJzr82eW528py5UyjmSvlPGKNXeIZagjvixYe09+/UitBgQwR0JPOO7phkeQzDyfsNhA6hBtMTNqcCxf8XxdtxuXEWk7fDmhVMDR4yOkwzq0tgmhwDICC3gg7n5OuHvP7BOFFrt9omnv6QP0jRJiBStrXHM1zAWxBqdi+YJ1b9fI4jFTpyvldqHMeY1NXjK099TXz3i+rZG5LPj+gbnAVCs1vIPmCDUg7R73CZOZMj6zLBO5ytpxnRpMCiwzjBOhaSjLRLndyNqs8dPlgtmybm1jhLaF2yf8doHpgleljCNmETl8hbRvoPjaCVwy7B8p+YzbGWwGb9HuDWYVU4Nhz1QL3iYkBqqPzPMrrgmra2Ry1AG7PFPOL6gbvRV2bWC/a+i6SFyuSDOQuj0aAj6d8eWGlQWmG6o9GpTGDF1upG639j1LRiho1+PNHrQBLVg+4+N5HZrPN7R7QNKAdC3SOz4cqSFQm4HaP1LrTOh3mGdEGrQJaFCYJmq+4FIwKywxsoxn6jxTryfMjRoGah2hqVS7UtXWX1DmyHDEVSl+wUSwXNf3TwykZofEHbkY9HeE+UTII0qPxh4fz+g8w3Kh3L7DLt9i8YjdbnjY4XPB9Q4bRyyf1q7mPBGaR8KuRcKABUP376HckN0DYbgj9Du0H8hlpJYbNk+UfKVeXinLCYsZH57g8ITlE2aRMk6U+UK93Zg+foT2PbXe8PYJO3/CSmF5/kg9n/HpM25g80Jz+IJgEzJ+g8RAevMnxO4t4fhzRBRPb5hPZ+p4pkyveMkYiXz+LYx/Tff1P0IbIz0cEK7ENz9GyoRaoXnzjxGJiCTq6/egPfr1/wSfR2T3FkLHcnnBxhckPFCff4sP/z6VBn34Y/L1W7j7Cp9HePNTynj6fd3KNpvNZrPZbDabzWaz2Ww2m81ms/lb/cENgEHW4W5zR3XHTLDYYZKw0IBcsfaOog1Vwhqn6wVzoZhSg+DSYnOhpiOLCoYSYwRJiHRInYgYIkK8v8cxSghY2+GuhFIRX2iHlrZJpCCITdD1uFY0BAJGSjtCVYImkgqh7QnaEmJCpZJCJGhDUCOIE4Z7VGWNjw0CUtet02aA3VvcFqxO+HjGl2UdQId+jWCtDRISVAOf4IcYYm0Tqe0IOtM0B4JVYtsj9YIRsOIQAtUDEnaYTXhI+G3B6oKVSo099G+oecQqa2TvdMVd1o3QmGDJeFbC8CV1vkB/xOpIZWB++UQprNt78xWZb8g8onlCKUjswW9IBEkDwQLiBXEnVKeLib7tEQmM12c0NnRJaCQQ6mXdcCWgccDGBQ8duU54caoHmNfuYfMZ9ys2Ttgy4ygyT5DP+OnbdRtTerR/jyyf1j7bEPD9A0uZyWVkqTeqJWxcyNdXvOsRiagrIe3XXle/rT3E8ydsmrG64MsVlonQduumemjxKYNFwvCG0N6jVqFkpO2oLus1dnuWeaRKT8kLtco6IA578nihVAWJxFwIXbdGfFfIRaghIV27bkTTYDavm+TLCLHFY4/F5oct8htqC6pGaAa03SMpIXlBpEfv3iNRiVaR8YV6myCv3b9uEenuYSlgIM0R1x6rBQ2JUCsaF2R/gNSg/QNhd08IEfVMVJAyghqeHRaDIrhnyAXqmdD1SEr4MqLdsPZQa0QtgzlMGSmK9kdknNbNWTe83SOewNIa7W2CX1+JFNrjExqUdDgiPkI0LArmBfdCub1QxwkPd2tktzje3eN5wqcLElvEW7R5JMgOt4iVikxlPWARH6AY2WZKt0OkoQaB2KOhI6QegqA6oFroH59o+hbyBebLGgtfHNIRrwtmCVsqomGNZw5H5O495JHQNSRRNCYUI3QdzFfIJ7xe8VKpuWChYf44cTudmcZKnjJVB5aafuj4hjzOlOWCd3dY6pH8SrM/4jFTx+/xpoNyRepI6J+Qbo1Ut2UkhJ5y+YS191SJ+O5r4sOf4ObQdix1InZ3hHc/Z3z9LZmKpQHv32HNE1YDlAW/jcjd13iZkfYBzxmbrlijzMtn8nKihEppd3g8YOMH9OHneJ6x5Ua9fsKvZ+zlI7Vk7PQNlUq5/RYf9jgTLh3z5eX3ezvbbDabzWaz2Ww2m81ms9lsNpvN5r/FH9wAWLSBsPa3argniAMzLhEJApZwr3h5Re0FVUOHIzR3OJc12lYbyvzCurRneJ5Bjmg2Uuxp2j2xyUS/UJcRZUHzQhuVNiWaWGnIJJ+JPqB5QSrofCPYRNvt0QQWC1VGJES0SYgsqBVCc0BKxa2sG4/1QskTVSC7YDHizXHtlS0LTC8oDjQIM3F4ILYNlCu+ZNwVTe0avSsGISJNj90WKg25ViwoVcFqxkJLNcGQ9UmtBssFu31CbiNaQGMgUEldS6wzQSGkhtgf8dDiaaAW1qFiccp8XgdPu4HWjZjPtIf3CHntlw0N2r5BPCEBxAMS1muWbn2sfruuPahdT5kWcjtQmMEKYpmlzJTwxEjLeHnFRDF3RBKhGHigjBfchSpKXc6k+zukvuLNkToZ5fzDlvByg8sr7oYvvj7XNlOvV0wSi1W8GqEK0h4oNVLGgljAxxO2nIiWwcHmVyyP1HzGlorfbnA5g9m6qWoZzRX1gu4fkVSR62fUKmpnmtQTd8d1Sx1bN3xtouRCKRk30JTwMBDM0FAgX0ntQNrtSeJoGWnSnq450OQTqU7E5UrJUKhrr24WiEe8PxK80reRdtihrBvYhAm1ifDwBpmvJI9IjGtXdorrxniC0LTE5DRNIATHmwbxGY0NKrZusXctIQmSF0LsCcuINgNar0SpUAqEDpWIixOCotoiYqgVVGbEZkLTEVxpdntEZugHkEgY7qFmfCmoDgQ32t2B0HTY0GGa0HohMOF1xq4veJ7XaONhj5SJFAfUJrRpwYRaZ5acmWmZ84jFPdmFWmZK0B8it6HMN0wWvI6IKqozVk54dawWSjlT64QPD/g0onJApyv18i0+f0TrRBsDMTa4g/Q99XJmGa/UIOuBi/YNqEI6/PA90rqt3yyEkAlaaYaepiw0Xulit24Se1kHxOWERIUUkXQkj8/UeSHFgXB/R+gesOtnTDtsHvHbTH39Bp9uNO2RML0i84nUrbH45IqUMzI8YVkxV5g/E4aemA6Yt1hV6Ps1kvn6jHVH5NABE2X6bn1NdXewfEakEHZfAXeUWvF6Jd19gd59uW7PT5+o4zPL9RPz+RssBvIyMX/8a2y4x+oNv52x8yt2+4Tvv4LY4/N1fVwWMLtC52jTIXnEz98iu3fQ7bDLB+zyARb/Pd3JNpvNZrPZbDabzWaz2Ww2m81ms/nb/cENgPvdj4k4SSpBJjQmUjoQpEKdoNuj7kBFciZKJGi3DiG9JegAaU+Vjlwd8YqUz3Sh0DMTUwvdI7aUddBkCR3eIFWRoGi/x5sOuX+LpUjpldq01C5hqYF0x5JnbrOz5ErRQM0Vsx67ZUot6yC2P2LVsNBjskckghmKISpoVCgVM5CqaLmhTYM0j4BD2lPSsI72bIQ0gPWEdIeUvH69YIhGPBs1J8r5guWCaiTGjjb2RBU0Bdg9YNrg7Q5XXTco1TBVimWyGxZ3GAtMJ3yqeHGWaaHWER0OGFDnC1UFPKIx0ChoO6Ae15hfjWvP6CGhw37d0kwJ94GSAzYJHjvqMqEa8WWh5pGikUUKdc7otNCixK4HIrJ7pGpDjfcwTUjNSNNSS0WaAd99CbGHMFDTnuoN1Q1nQuYX1CqhB1UlxsqQnP3Dz9do8OWKuxLvf0xz/Jr27kfEmAgu62ECDEl76Ae0rJufkjpgRqoTltsP3dNAVIgNOHiS9aCClXX7Oc9I00O/Q0wQqwSPhBqJ0lDnhZB6rCxIe48sr6TH9zSHR1QL+jex5CbkGqk1Y80BtzNNOKzfH0AiEgIeGup0W7e9l7x2L2ejSgNVCFRUFXGB1OP1vEb1ph3SHAnd3drXKw2WDRsroj0aBqQWKDPaHZCYib0jd0/E1NN17RqBPV/BFtAAKLUs+OkFu53XAV8UZDgiTYL9Ax46mCvEA14ztHvwTPFKDUp2o4hiU0FqhBgwd6on6nKD/ojgeHGwsD4ubI3IzgvOiDSP6DIS+z1x94hKQGKPeCJUXzukU1ojiJt7yBnaAZ9GghwJqSUEJQwPqK4bw5KcsD+wjBcsHqnNe1QjebxQa0OeX1nmETfD5hHLBURYQmJa8rqNbIU8X/H2HaUGavvuh8MMz7i2NG1Hih1abI3rliNMgusA9JANjUfC/Exkpuki0UZie0/TKMEWUtcgocGTIK0SHr5A5Yx6xpcLJTZMGYyWWm5485YyLdSysExXPLAmFUhDnifmKtTLJ1gG6u0ZOOCXE6IDy3TGxiuxOcLySrm9wuWEzxfq+IwvV3yasdcX9P4X+PkD1r5H9u8IGkhph02vTM9/gTUty3jGrGC3K/76O2Iz0P3iP6a9+zGpe0cKActX6m3EcsRfPiHa4eOZcln+vm9hm81ms9lsNpvNZrPZbDabzWaz2fwrxd/3Bfx9KzhUgAXzdfBS8w00YLoHyz8MlVpq6HEUvCAkYorrIICwfpwtwA4LlWILWCUvI6F1LDqZdaMvRKUGpWZDS0HsRqAjzxN0geiGaSLkso7kfVnjaWOC4oQgSHAYHakFZYHuQKgLLhG7vRASeH9EJK4byc4aFasOMqE5I9HwUHBN1OUK0iDdHuZnYAYmpOkRC0g0pM4IIzG+w0rBY4IA7guhT3i5QtsgNhKGnjqvW8a+3PDYw3ICv6Ex4iHgpuvWcOpgfkaWhVAN3d2BOzm/4LeM5ysp7nEe8dgiPmLzZ+qUyTjYjch7rFwoJOT8ijaPsHyD+4jGO4RX1CvqJ8JwQALUKSN1InQ7+sc7likz1xnswDR+pqaWpV6RM2vfsWfqOCPNHsZPVAMctDtSiqC7t0h+ReorskRqI2uEb9dDGsjLQK0FGc+kNiDdYe1Gbnvy7SMigVQGpL1HgxHyBR8/Urr36zbi+HkdhpcCLmh0NO3Q4e0abXz6HlKHl4AgSLvD62dILZonaBOK47NBdTQ5HhyJgvZPuDtmGVcBM9wKwkioM0ggNj02zciux5cZSQ0iBckjenjAZ0fIuM64ZHAj3n1NcENFiEOL20KJe0wK+fJKWSaiJuLuDVock4oSQAWNIHWE4Qj1Fbcz2lXcRqR/wFjI2lOWE9UWNA0IhqQB5o9Y36PVsKZFSkvYPVLHD9DcrzHdlmGWtYN3PMOSicMer2fAKBjiJ/CMhA7TSM0O2pNSg5+fkf09nguaWtwmPHaYXbHqIBWbTqA3gnbUMgIdpB02fsYXQfZvIV/QVsA7tK5x0ezfQL7h5YI299h1xrWhakO5fQJ3tBkIMcL4PS7Kcvkd7oLmE7J7XDuMa8DzhdA9YvPr2hXegSah+kSdvyVMDTJdCNFRO2BNQLqeko7Y9YwcvqCeP2HLmSA7KJm4vyccDqhf8Zxxd9LxEZZPUF8JD3+CLBPYgo+/Q3fv8PQVfv68xqi3EV/OiByYpw8Qd2jcUacZxu/Q4Y7Q7SBfiX5F7n5EvXyHXz8gdqG5/wLTj0jbEhbDZsHkexhfaB6e8HHEXp+RuEdSBBnQABo7iBHRK94qEg6oGna50Dz8E7R7RC6fkAjUT0gL0vSE/SPy8jtMG2Su+NSixw5tApILEgd8mYkPw+/hLrbZbDabzWaz2Ww2m81ms9lsNpvN3+0PbgBc/QVpArCD0lOK4QK+KKF3ghkSIqE+U6TBagWbiWGA1FHnGVdF2sMap6sBk4bYKDXs0JLXnlA54JczTWwIcsS1rDGpsaVMmaBnIhnNE6IBERBxJLXkum6VpVjQ4UAoV4gthP3aVWqsMb6xR1QhCsUFagM24fNM7AJtm37YxG3RGCh1wfOItBEJESllHWD5EaFiEXCFOq7dwMMdjlAoaNNT8w2fDRVfq4KLQJdgvEA44uMLfujXntHQITrg3Y5yfUVDj2mF0CN+IR4O2GWGfQKHejtB2CNR103WcsU9kOsE0wWaHZYCoTjVb/jlFTBC/0C5fkZTIu73lMszJUQsHSjThXj/IyQFXANmF5r+kcfjE4+7jqtMzHIlywNLOYHfw+49ZXxGfKCWG6G2pHgAHJ/PaFBkt6fRt2snLHuYPlHzFTs/4/WGNj+BWmiGe5bzBcYbIQpBFHGD4S28fkfwBh3iOiD3jKvB258RZYfKDMnwmHARsEjrC0FnvNlT2oZaRsQzFn8YEIeFUhbMARTt9mCOqxBCoA2OdgOeDO13lNuEYZRcYXcEJlLqCM0V7R4JIbHEB8wKzgJNB9EI2tGGhO52SC3U3GCS0e6JOOyJZR1EytBT5wsqMM0zZgWlAQMvV7Tt4PKMD3dod08ISrl+D/IA6R6/faamR0R6ZF6wakgMuEFqj6Smo4xn1CrSJji+wc0xMsJXiM1rR644BaiSoP6w1V0m0B4zW7ulm4hKRFLASsXoYLwSk6CSkGrIscVyBq9IU7GacFMkOyEd8XpF+yeszJixdo1zBa/EYY/lEcWR4xPcToTDO5hOyOFx7SuuN+jvqblC01LyssZGZ0f3D0QBlhtzceLhgXz6QPPwBcv5O6JVzBqku4OX36AdNLtHxAPUTDi8o0wn0vB+jbLe3ePDYT14wYzfTnjTUZfv0bNT5wvx8AauF0iGVMWbAYtHiDN6veGxwYvC3R9TygK3V9xmLCvSZLTbUxzi8IB7R7ELuj8QzmvUtNULcfcTSpuo1xP4SIxp3azXiPZv8fEVPKPySuzBW0f6PVYVrw0cBqS5w+K0Ho5p7qnf/4qwe0NoFBfQ3R7pW2gjjK9IOEDIpHe/wOfPECr68AX++deEw8/QZgfTN9TlGTn+mDJ+Rh/uAJD2CZ//Cnn4Y+zjf0O8f//3fQvbbDabzWaz2Ww2m81ms9lsNpvN5l/pDy4CWvF1s9VAVJB6JclADEYqQiChIVJ0j5uBlDWOuFyJXIkhIZJJjRJ8RGIBrYzZqRIoOMtypWKgiaUUlnkkI2QqRQu1PVCs4s0e3EEd9bQOMDyQQo/ajBfDi1CXShnXQa5pxqaCLwX3irlB6HGGNbrYK6R23S71QhuVKEYVxWPAYsJDi+V149PzCBLXTUJ6qA6ppbhRxxEzp1wuWK24tVhdsPGFWkZMIzZPWHasXtYBlucfoqgzTdOhdSGJ09pEF5TWZoQCIeJhRtyROaOl0nQNbbmS4h2qPSozcbrSHp4IqSPGnjDsSYcvkNRA02DTBWpByghtS1WFZSGIrEO+ItiSWV6/Q33grt3zs8eBn7w/8nDf0Dx8Ta2V0D4Ql5FWhRATUjNBB8wrZSmgO5rQ0bb3pFpJCFEyISq6/5Kw+xoNA01zJJULgVc0FEIoJDV0uhCsIFLQ8srw+BOGu3d09UJfbzTzmTYX2vaAyhWhEO6/gt0bvP8C7w5M6cCl3LhNV8py5fuL8JtvPmFLpfpIrWfcW2xeo82DDpBvhGYghIBnQ7s9wR1qoZSJy+cTp48fEW3w8YKNV7Q9kvoj2LptLoC4E9KASoPuBzxmvI0UMiTw7oANuzWSWISKsSwLc9gzLjPlfEYqdMe39HePqCgp9YT9AZWZIBUzw7Pg4yvBjShh3eaWBpknxBwtE2qKRkAq3f5Akyrt8IRownASsI+Z5CPSHnAcpgkvI0hFfSGFQIiZIIUUA6m9I0hZh+3NgNi0dm77RJCMaoUaUDWIC2UGiq8d2yKYCsGddr8j7QdC2xDanmZ4IAXQdoe0B5gnRAKeGtwqlQwkzC5YDLgmfDyD9ITxQqhKExuaekLyDMtE6g7odCO54lVRHWBakGUiAmF4h40vyHCHHu+gLpDzGpstDTJ9IqYBsuFktL0jnz/j+bTGg3sh1ldkd4S+A2nwPOHX0/qY0x7Z3cE8IhwJuRDOz3A5kacZUoMsMzJdCWRScyQsIwkDK6Soa/fv7gllRFUJhztC+4A0Ab3/xXoIJQ6UZaR6Jd9eKdMJXyb89h1SJ9QbVAfq6Tu8ZiRB1wf2x8rh8YFgZ0K5oX5bB/g24+fvcHPSmz9DquAvH6nzR1gWZPyEhBFSoM4X8nyj+JV6+n+iwx359g21VqbnV2y5UsaFGv7gzlBtNv9gHI9H7u/v/40/X1XZ7/f/nb7GZrPZbDb/Q7TdQzebzWaz+TfjOLfhxGX3vP575b8B08rYnbnsXv7tXtxms/kH6Q/uX68bM6pValh7fmOzx6loUGz+hGkHFTTeE/tAzTdsemVxWWN3UwN5xooCCXUQWrwq3iR8uqDuNKak/T2YroPIJCxVCQiijqaGGJq1b7c/QrlhBmZ1jY7tB9wqIo5XEJmh9mj/ADZS87T2D6cGid3awzu/4O2AFKdWYwyK5EKxgFkBImiklhncMQTxH+Kla6ZqA26ItJgXtD3iyxnSjppnMEfSDp+ua+yu9uAXRIZ1kDw84Nmhf0TthqeAFCd1PaiDKsUmXKDWAOkAZcFFsG5HkPVnoiHjbYdYptsf0f5Aun5DTgPLfAMy1g7UeiaEAJ6AglvFzX/YLt0jkpDgeBBi/yV3w8BPH1v+5Gd3DF/9lMuf/wr9fMG/+xW621F8Bu1wWTeppe2w5UxsExIa9PhAaDqoEzGM60BeGiQGRO7wvSKpoZz/CmuPyLjQ7B8pr7/BeItVRzSAB8Juv25nLjNZZkqqUNbnsZqAF4KthwM8ZyDiTULGjAcj243/1//1/0ZdJm7Pz3zxs6/puieIBeSCtHvURzwIzoLnC1kDthgujnnh+19/5NOf/wt+9h//z9Ze5bmsPcPNjmkZ8VJwUagV2iNmgpQrJkfmckO0AxRp73Dv8OJIqTS+oLt78nSlVKh5xlHS8T1JC6gi9OATxIRNBdMMFHR3j9gNqRMyvIUyYbXg2UAL3u+R4FQ38nIjdg8kabAQyLf1oIIqLI1TQ7sOha1QlxGG9xAd9xl3X18jqj987YjnBVFDKHi9IPs3SD4RmgGrM24Rq4bLDkkRnU5Y05JvJ0J/wOOZQqWWCqpoEEyUWh2dJ1DFy4LKgqQ9MRacgPuISIf0LVEF390jIZEX8H2LzGXdVi4GzY4gE1YzNB0y3YgmaN8TpAXOSGphKtgyYiKYBKyOWHOPL0qMB3wZQZ3KgtoDiOKeoD1S7RvC2x9j0w3f3SFuazy9zfj1I+gDhAaPE3ZbD154UuzwDqnL2kd8fMLcsMbQoJTld6THP6Lmy7pJXDKhe0N++RcQO6S5p8aETc+E3SPTb/5raM7UAqE70h4HWCZMKsgj/vo7bPcVACXPeHWYZubspLs/ItSZMlVoKvRfUMcT4qAPv8Cvnwlf/I+wj7/ETUl3/wj7/BEAyw12fYU8YUTC8ws6/CM87Ilv/oz68hfowXCbCV9+Dcvt7/0ettls/u346U9/SgiB7777jo8fP5Jz/tf6/KenJ969e8df/uVf/vd0hZvNZrPZ/Lvppz/9KTFGvv322+0eutlsNpvNv6bv3/01poX754nD+Q2xJAT5/+tzHef1+JHX+w+8//Zn//1e6Gaz+QfhD24AXG0iNHdYPq/DTs2QWigOaUfwtT7X44jPLa4RNAE9C6xbY5YonpHUUfO0RtnON3wMiAXUFqoXgs1ou8PyRPEOd0U9k9oBXxZoE55B6oxLQdo9voyE0CDsKTox327Q9KTlhkoFyaACJusWrSlIwW3B0n59HPNItYLHgQjrUBRdB7dNQooBgsYGJ68dsVRIUH+IxA3jd+jdT6E6oVO8Llj3hOcrnipSCyGcCU2PuDJPGXRHmF/RpqHUCvONNjWwe0teCr6cMDEkDGhdh5qogjgBg2KYPsKYoW1Z5g8shyfCMqEeseWKzSMuAeKCLxkaJaQjIjPVIB7eI6dvCCkgYY+mDnUlSCS5MkXjr0pgmIRT7BmvV8xG6hTJZvhtRDRRcyHcPyLjmcpIFIf+HpsXKJWliVi+QcyoL7gqHlsosPhAPX2kiUKIdxAP1OUTGh6RsiDJyEtA3YjhkVzP+JzRw9O6pVoWJIDPE64LWEJsJMobsCseFIg084083fjm/3Pm9eOJtz++8PjjL+jbIxDJvuBxgOkVQsKmCzXcMT2/8Pyr33L99iM/+sdf0x0G7PRx7WYeHqm0eH5Z+54l4NMVDR1wXjfAy23direMV0WaHXW5IvmKSiWHgZITZQavM0xXdPcE7sxlhgrilaUs60a6LeA9dn1FuiOSDtQy43YDq3g+47Yg3qASYEhYyVheqD4hKVGvM3WcQWBRp5QZ94AnxW8/dFI3ip2/IQzv1m3XpNTlhuu6YYxGRCO2FKwKqkJIeyqCWcRjpI7P1DwTDu/xvoOwQ2uB5UaNO9TD2sFdMxIjAmhqMSKCoUNHFP+hW3vAw0CxiNYJKQseWyxGRBTp76HOYBe8+wpsRn3GNWFUQrunjCe8aUg2wuGB5fwRlhG/vSAEarkRhyf0+j2h76Be0KaBoCCCp5Z8+wwxIWFHoaF6hPYt5fW3hJrX7VmN6HCHx2bt2k173ArUjFkPMiB9IJaATBeir1H51ArTGY070EQdvyHc/yPs9DuqBsSVYC1lPhFCpJpSrh+p8QhlJB6OpDc/R8snvGtZPv6atHugNu+w12/x9gFrf0TQik0fyNMF7e+x6Ybc/4x8/oZYWsrtTPKKNG8p4ws6vSB9IvXv0H5Pef2v0Mf/EMpMPX+DjC/I26/RoKh2+OUbwt17IBDe/hn++jvi2x/D5cPv4za22Wz+LfgvfvNf8J//8j9nF5zGB/6X7//XPOQHLNvf+XnDMPD+/XsOhwO//OUvuV6vf09XvNlsNpvNvxuu5cqvTr/iv/z0X/LP3v8zmluD3pRpmv7Oz9vuoZvNZrPZrFyd5zffcj5+Zn+553B6Q5O7v/3jcZZ25Pn+O6b+wrvvfko/7f8er3iz2fwP1R/cALhYRVMl9W9g/kjVhlwcFUdrxr2s0cw1UKRALqhHRALV5rWLt21RM0Jq8HqFbGhoEIzYHMn5Ba8LJUckrBuB1W9obKi2bl9GjMqCNR1hnKgU6EA9IM1Aya9EBqgZLAAOWrAKElucC1YbLJ/QNGDV0BhhGRGUmFp8uVBTBKk4CS8FvCAekH4dvLkDUcADEhNhekaalpDeIgXodog5QSIuhYwhaYfWae0YNsc1EjSiodI2Ee0bbiaIBtq+x3SBJpBzQeWeWCY8BGq94KJYLYDjQdHdA3b5HqVH0htYJlQidPeIrNvVljM+T0js8DRAABuvQIMFRZoOESV1kShKG4W+gRgWRtnzm7OyXH/N6ftnruONLOswt8YeXNGmQbxDrKDDGzx/htRjdYSgCAaZdQBtQnUFzetGqyZiPCDjZ7y0kBa6/g73BZvPaJtY1xoDBIG+Jby+UG3djPV8IyyO9qwb4axb4aEm1AvePuHzC1IKXSnE6mgUGCc+/MUv+fTXv+MX/8GfsX+KVCuYR8wT4sZ0Lbz+5X/F+ZvvCV4RHIY7rM4gAdKeKgmfT3jNsBSKtdjipEaRRdD2HhHB5xmGFg0tPr9AMSgLZhnvH5BlwkzJy4xIR5KE+YLnBRdHcsWbddBb447gjtVMzWeIB/jhHYJlIMBscN9TyxmNe4SIag8Y5XpCwg7qjJWyvr+aHl+uyMMd1RZ0uAcKlvY4E6G/x7yQpxndDdTllRAGgiUsOrkG0vW6Do5lgcsF6Y/IkompReZnZLjH/UboB3w64SXjfsBpsTISYgvuiLZo6HAtyGLUcoWgRIxSM2VasCUT7o6wVLArEh1PPXX6bu13Xi749EypgsYD0EBq8Zuu27hlwj0jVqARXO4RMml3DyFStcXKjdg67nHt+k4H6nLC8ojQUPMF1YWcnTpV3O/Q6oR2WLt4x1d8eGKOM03qsNM36P4LqmWYzvhtQsOB0B6x22c8RKS9BztDTHiuBOlxN5bxQvRC2r3DpIF8RsuM919Qb2dSo3h8IHYH1Efy6SOCIgJm4/oeje8RIqWcEHbEu5+CL4gbfv2AqMP4LVUbKg3UEz6fkN0X+HLCp5my64g148MvkMcfUz7/Fb58JJcXmvBznIgnh+XTGiMu1/XgzPRLdLqn3E6/h7vYZrP576p65T/7r/8zRn/lf/tm5nnZ87/7P/3f+Wfv/+f8053xi7v19HUQsB8OBn53c/6Pv3PevmmQ37zwH/3UeV/+9TaeNpvNZrP5h+Cff/7n/Kf/j/8UgP/Dr//3/G/+6f+K/+DpP2T47cD1ckVFcf8h1lKcpm2Z7s/cHiZ+Kc+846t/7a3hzWaz2Wz+ISpp4eXhe86Hz7z/9md00+6H//I3G8FObmZe7z5wOn4GWf8HNdQ/uJHOZrP5N/SH99vCGvLtDGEEaYAWKPjtDG1H1DtKHdeITwxzJaSB4jfICxo7tAmILYg0WOihTtAcqfXGIh2eekKdqXWB24KEQCmOhIZiTriN1KbF5gJLodYFVwfvCDZjPiLjhDWQXcEWkES8XYl9DxWs6ciXExJ7zA3PFVkuSGMQWswUH6+4NYQoBFVqf4dTcRwPDeZnsAoIIgmbFtAjsozrU+MXTPZUH9HUIjaBjYj2RJkJJLKv286uARNhShFyxoioBBav1CpARdsHalUWMtrtMSb8tsbtejzg84L2gMsaBSuRGgxpDBOnloK5/hBTHBDpyOdnvLujThn1C5JaZP8Fbbmw3+/QMqKiiCq5e2KpwvjtR27jFatGmQsm7bp53XYwf4/UPaaOTzek268buGlYN1WrUC+/xSoUbUn2kXh4Ao9rp2oAD07p7mF6WQelTUQOT/iieKOwfFibapdCbQw3w2vAxxt5nKFkxAOeDFnWGOF0ewUq0r/Bp5n5cqIP4E0LQanjGdMGj5Ff/5//L6R+h/jC0z/+9/j0V3/JMk7UYpRpQbUSukSMPZdff8fusMetQhB8vuD5SpknvBvwfKZqgHkklBu0PSJCrYVwzoRjwqYZt4LVmVIKaSpYPiPVUZsx1TVKXAwIuAesSfg0YRr+v+z92a8t27beCf1a60VEjGLOuapdnXPPtdPp60KZaQRKgQCJBxBCvIJ4yH+Ad975B3iHfOYBCYkXkBBIFEJYINJKOZ22b6bT1/atTrn3XnutNeccY0TRe2+Nh77uJY197XNucfY53vGTprSKOWLGiBERfczxte/7utO3GTFMuENbb4SoCIpdPmAi6HhC4gG7vaf5FUOgGhpH8BFdbthypUnD5IDOz0jI2HaleUOptHnGw0i9vEOOmbZu+GLI8EdCeiP4ikhArHzs4DZYb5AHWD+ACHq8h3JFgbbcIA609YKcPmO7PhLyiEVBwgjqBALUJ2I64jlhqxOPL2ntQpOR7fZTmg/kUqAZrQiUBkOgXYx09wnt9gExxT2y1oaVDyjGtr4jDYFabnBTXCIQaXjvfx4zRMXCSji9ol5mfDhQ64fe0RtDdzCP99QPv4sOn5LiPTLfkAQaJiQIng7Y/JZWjTDe0bYF0gHxFVmXHk9eHFjxcGarEZsX8lnx5UarG01mJJ+ol7eEfMLXwhqMpgtRE+XwgAr4dsEcOBxQDfjNemz8ekXGF5R3PyYd7wjpAO2G397ik5JTQVC8Ge4Nn58g3bF++BnpxW/0RIflA/LwGV4vbMuPEXtJkSsSj/jzN7T3H7DhwPjmrxDv72jLE5LP1McfUZcVyyfS+gj5Fc0qZf1Xuxx2dnZ+Nfmyfkn1igDL9oL6/Fts7/+b/F/er/ztACnAFOB//Fcj/+ffb7xdnGZwq8AffgnA//4ffMX/7L/9Cf9WnL/V57Kzs7Ozs/PLZBxHfvv5t//47188fEo+wj9qf4/4SeL+05f8pfhb/PhHP+LTzz7ja/0pf1B/hAXDSgPg97bf4b/xxX+X+Z/ta+jOzs7OzneLLS+4/IupUy1WfvrF76KmiCmvvvkeH158SY3dSGOh/XPf//b1j/neT/7qL2u3d3Z2fo35zgnAMr6AuvVoUruCDt1lmM8ogDTUZkRPKAMaG4oTt4rrhNQrwggWkLKCKGag5UatQFwhjvg2E0NCq/UI2PYBvHQRqDaoEdZL79ElgCi6PpPTQF2uSIw9vrgUbFkI013fz9ogRlSOpAwhBbwGCBu0jLRCq1uPrQ65C2S2QVvRjx2/BAOLiB7waHD9Bhkysj7B9ICKgY6gG2wzYLgbjuCt4qps6Y62lo9xtIKGkdZuCN1tSjiBL5St4QgawEvBJNHMaddL1wPVQN50F3Kde2RwWSFlbJzAB5o77fk9VirYhqQRI9KaghzRsuF5QLdnYrpnSImzCMOh93lamLjGe9bLM02VZgPl+RlzoayGE6FuxOMJPOOScE3INhMQPL2iWUM10pZH5PAF0m6oZ9rS0KUiVEhH0IC4kU+f9u5nn7F2RmRCDhPOgq8Zvz1T6obXQBzuEXHK7Qlbr4TzKyKNevmAa0TtmeaRuFR8KNhwz+13f9IFYHEsCKYJr4ZII6gQ1gWs8fQPfpskEGgYiqWAh4z4hlK6qC1GVYFasGBQZlpZGY4PNFmIwz3t+gFXQYpBWQnDCW7f9G7oOuMi1GqE4TVSb2irNJRQCjq9wkuF6LBt3UqVTvj2TExDj2APFVpFmkI8YNuMRKXUijvku3Pv6B3useXGtl7JaYJtgThh9YoDQiIdH5B6Q8YDbCtxfA31wrYWPDnmsfcrb1fS8R7f1t7lrAN1mbtDV7v4aBIwSYR8wC+P6HTASxd9rVVqqzQTrDqUFd82ggQ0PrDdHtEwoiniZUHcYbijrCstXJG1IYN21/nhhC0zst3Q02esj++hbUAXxLFCmF4SzKiXR2CkbLc+eLEt6PQK1gulgMuMjq9p6wfC/B7Phz4U0gxvRmmFenkixTvieIe2jwkA4QGvK3E60eYP0AoeGnULMGYYX8H6AdmUakY8vKJu70Bm0uk38Nu1x9Gvz8TDKyw5Pj+yXJ8Ip0+QFnvX8PyBMEz44YSnCO9/CuMBd0ekonrDhpcwf0NLhpZMOH9OvX2JWoAQqfMNLxvVZ+T0Gdze03CqKNIGfHlC1pVw/xuwbOAL1burOhalPn0N4UQ8/gB//gkeN3y7wfOX6PFAePE5dvuSOl8JkhDNMH6KE2i3CyYP6NZoev9tLGM7Ozt/Rv7j23/MtTpuD/yH/+A/wObf/OP/W1r/egb+V/+g/onbWKrxv/m7X/I//6/nX8Ie7+zs7Ozs/Grw8tULnpYv+R/8zf8OAN+7/7QnROEU3XjLz3jbfgafw0/53f67X/jnt9Go/LP6jzjy6pf/BHZ2dnZ2dr5Fns/v/gUx949wNZp2cfjLz3/vT96IgOu/fBs7Ozs7//985wTg7GCSqMFAetSsioNfIN31/l+9R1sl5oDkkXW5wXBAmuM24TIS9IYHenSqzwiCimGmqDVSHgh1++hWdaINAEhwtDSSVyQO2BCgGq6RqAGdzsS2gARUJySMeHkECi0pVmaCGKQjerzrEbNl6REQx0irQt1ueDOGwx2aE24Bv804H2OWpKJ2QeKIpAjTCcVow4B7obQNjYoSkZx7tK/0QnoJI3hBLNF8xdM9LO+xdI9LRBC8FXSKyFqReEcrN7ZNcTfQpUfXVqOWhXR4QYhOuz1SGZFtQacDlIUghqijUtF4xOVCWQ1rc4+praDSI3s13iE4OSZeHEfG/Jo1CEt8YIsj82Wmzjd0vAe/YesFayteS3eWqqOrYOMdtj3j8Q7FEKs0IrKt0GYgEI4nsHtCBLtlfHmmlUtP6Z4XJCcoCZs+ocxv0ViJ85U4VRogx09Z3/4hajfcjlSfiFYYNCP3n+PjHcWUdptpZYGYGWKiSkPXGTm+IJ/vsftnPAjlNqM+oq2gCiEIulYkpT5bgOFxwF0wjKaJJhPVDNxZlhWmV+i2IWI0H+B8TwkJjVMXfnMmDOc+PLAF0EwbT138rjPaCsN0h0wRqfVjQvELTECyIi69M1YUYsRZ0MNEqxsaJlQjTu8bJiQYEvXSY5YTFZHW+4ARUCdPRyRMsLyHECEEwv0XYBWNCbjHTXB7j3rCMEIIWFVSvqOJEKYzTQWvGzEmxBuWjvhmyHSm2rVHY+eE1wLphGjAyhWR0OOBwxlZV3R4ga8VD0dK3dCtElRQXZE8QUhdgrcVHe/wsqLjx/js4UipK759g4RXPYK7XNB4RPOJWisxjBSgtCutfU2c/hJeL+jdZ32AQ6FaQYd7bC2EUOF4pKVEm5/g8Ib2/LYPuiwLaXxNyBHJAfUNCc/E8z3bhy8R3VBWGB8o809QEiF/ggxHPDjNG3X5GplOYCvx/D2szJgYbAsiC7RIyA9sxZG710gcqI/vCfIKV6ECIiChIMPU+5qv76gh4q2i9w94HHEX1u0dcT0Sh4FSF3x6gWnDW/s4QHGCtlERvAW4fkU+fobmB9BAHBuqG/kwYs9f0YYT7o388Jdhu+AuyN1r1ndfo/cvCYcH6u2RdnlCDy+x5RmJ99j2TJg+QdaKPnzB+pM/IJ5f/9LXsJ2dnT877fabXP/Zfwu3sSd47Ozs7Ozs7PycCL/58vssfvszbeV9+3oXgHd2dnZ2dnZ2dnb+gvnOfeq1eu9sdMmYRWhPhBAQSYBhbQEbQQWrz1i+o1qFlpAUwDdC8x55Whuwokm6o1gz4pXYjGE4o+UbTBTz0t2EdcUxCJliBaqQNSDahTgk4AFKaQQNqF1xHSnrTJwyGjN1ufVeYr1BzaCGB4FawaFtT3gciCYwXzCZkDDAMPXvsQ1kpK0zbq1XB7hhmqApLkaTRFsrwkY4KLb1iFbqBmzggeoGKG43ZJ6RQZBW8Th8fJ5CSBmxRp2faEuF+8+g3BAvXaSzDb99QxuO2FZhPOO3J0gn0IJ7Q+WJ7LEfMz2Apt7fCcQYaGVBbhthCNydX/D5w4QcAu9K5en9N9Rwpt2eWR+/Bp0IsmCtUZdnpBmkDOVKlIDVZ5geEJlpZWZrC8wRYiXi4A0RgVKRqHjZ0JxBH7Btoq0XGg1pgF4I+Y64jYSloJPRrguSRogbeRiQ9ADXJ5DYHcTmyOElXq+ETfH7N3gRUKVJo61P5PWjKHmI6BevUHP0RwuxrMQhEIaJEBUd+msrakgIYIJLwRBqg+rKilDXgpdKzgWl9A/CpaDTG3R+R0qK+4xRkGQgjgyJVi+EoIhvXSA20HRE3TC/ISETZaYdD5gLXj7Ge41nzBvUhsQDrQmtGbmtxOFEbQ1fn9GYQTfS+ArEaMuCVcAXVBqEEVFD4wErC+F0RKIjFoANN6WVgvuIr1cApBVCPKKshBr6diTAMKEqiCgaIBzvkfKI0GjJsTDA8gHNGXfvvdnlhroTNCHZwZ2yVVpImESgEsYB0ZF2e8LHl5jdsNtGyBlfF1QUW2ZWUbysWJqwGGnvf4Kme3R+RscjVitLE9ryjNeKy4G6XmD+hpDvkHCkbKWLoccB1RPr5T16ft1jxp8viN5Rl0fCeKRd36FvftBj1Z/ekU9H7PIeTyd8jKzzhdoqMU6YH1GcerugueLmlJhph+8j2zOqJ7b1hl9/SpETUp5Jh+9htxtsv9cj7+8+o14e+4CIQhMByQQveE3o8Z5WGmKOrc/49ClyeULCwDZ/QGyF68+QdsUs4h7Q8YiOD7RQ8Kf3fbhFj4R0opSfUqeX2PwW15G4GJqUFlLvkh5X5PQ5WyvUd/8IiSN5uYfyAR0+B8mUxx/R5kIaR25PP2b45G/QvvynpHRHXReogdZmdPzOLaE7O/9GUJ//Hbz92Semi8G1OMck//pv3tnZ2dnZ2fljTJymlWD7++mdnZ2dnZ1flH0d3dnZ+Xn57t0lLPXOWa0EmZCoaDoAhlsBTZgaMR6xdMLtggio3bA2kQRUV1Ie2FZHrCKaqLWBJbCZplDDgXA6UeZHvAguiWKNUCMJxTBkmKDekPEOJFGrEKuRqYhnxB0NlRQH1KWXvB8eYL0gLeBewR0djjCMsD0zHl9j3ro4GQN4xOsKdYXhHlp/jOcBSuliVhPEDXHDXUjTQFnXLpLPM7SG6ArQ42SX5y7EhgMoaEiILZgUzBySIBgaEtCI6UzbnsC3fpzXCx7fwHjEy4IvjxAOqJXuaA7S42BLw8WQ6cQmxlIMXHDu0O0DTQ3TEamFKM5wvmP6JPFej7z/8cq6KHCjLVewiE4P1HqF2wWNB4QPyPFT/PGHpOM9y+2R6BtBV8QEOZ2QspLGMwxHmG8Qx95vy9B7l6Uhp5fk8YjXEXD86SvEodlMmg5w+aZHVbcVloYMA+HwBrRR5vdoNGzdCMOIC3jZYGvolBimQ4+ftgjDEV+fYH4iBKV9/Y4AjCkTEVIMxKhoHnt/azWCFyQGiAOO0Rzq1ii1oNaYa8EvBT9H1rYSg5MOrxG2Xo+tgVKvWADxSrCGhUyrN0IY8bYR8hk/PXR3aLngFoBEXUqPQ2+FVio+vMJ9I2gkjAeslu64vz1RElQRZDzSlud+vsYD2AJN8LJSCKScqJsBhtq1uz8rVAdbu1AvqYErvjnFIx4CrE+QR4JsuBnihXC8Q7PgzTCN+HqhttjPYRN0vGd5/jE+vemx2s0hJEwMjRnbZjQqti6QzmzLB3Sc0BABxcRwHHNFfKWRqfPXhPCApECpDTOoUnrvtenH+PMjJvQ/H17hfsWDIFURAuQJrRt+fImXJ/pRBNeNOj/jIVLXDc0blI0wvu59x/kNVjfCdKS2C1YaWmbi8QWtFGq5YOOpJw6MR3y7kY8vMQFzaGXFrOEsiCWsbUgyrGZqvUNThPCA2UYxBR3Rrcfty/IWjq/ZliseBlQDjGfKuz/EpoEUz6BHWGZCeqC8+y/QeMbrDRlGZDhRtgDb2m+z13eAEIzuvl8XJD7jw5nhzV8iqzN/OcMh4+lM2VawGU4vCduV+NnfpH74GTa8JBxeU9cZ227oWNBSoDyS7j9FpKDTPS4ZefM9mB8hCGxPxIcviM1/qcvXzs7Onw9/86Xyt3/cWP6MGvBPrs7/56eN/94Pvntvp3d2dnZ2vps8PT9hQ6P3Z/3pKXnh+fyOh8dP/nx2bGdnZ2dn59eAw3zm6f4trv9iD/Avwr6O7uzs/Lx85z6xcndEz4hX8LWLl3WFoFQJUCsisLVKSIpaBplpHsCFEoToihTDpeAWCB5pfoPaMGlUA7aFFBJ2fI1frjCMBFaCCfEQ8JIgCa6BZg62IpKw7drFx0OiSsBKRdJA8xmkF8GrKtYKEhQo0AIgQMBrw27PvYf3cN/dcziejkiZwVZcAqIDyIBY6eKSVcgTUnqHKVuB4YBt74nTAS/X7rr0Ec9nqIW2PqMCLWp3r8YJv13g8EBZSxeCveI6dpF6q5gVGO9gfkRCxlFMMxoCWp2qhs/fEIYXtHalxhPXslG3jVoETyN+uVJVaW2F4Y4QM24rb59vvM8Htqd/SlsMVsPqinmGoCgFKwWaEVJCDi+p7YYZ2FpoFvFiqEzY8pZxuKNaod1WbJ1Bj0gtZApCozZHHFhu+LYSD3fdbRsOeJ2RsKHjC2o+Um8X8A0hEuOBtazghnskLFekNvTwgF0WlrJRS0VsIcQNQ0g1kIeIl2dM7pDxQCpCcidHJ+dMAjROKIYOA+ILWno6ODgeEyb9d3URwWqlaqJ5YL0+UYIQNBCrQ73h5mhw6tz7jWVekHoBi7glqt5QceT+TMTwNmM47o7qAV/fY82o84wNmVIqPj8TowIBaxtjPuIuzKtjy404JupqYAaxdtc6DmJgT7ThczRPve+3PFGKo6o9Rt0Km23diU/EtystRNrWgEQOR7bLe+LxjrZcuqD7vGFlRtMBXa54PtJKo5YnQsyU9BLfDN2ckBN22aBt+EGxYgQ16lqRsqARTCGuFxCnPYGGiuXYrwsi1qBdLkhIgGBFkaSEMBLbh37sQkTDETNDt5U2XxlefYIvN7wF3AtIwjTTyg2JGasVTXfdbQz4OuP6AbeCDgfq7ZGQJhxjU/AbKIGYX/S+5Xzu9wzNmCshPbA9/T5tfEkrhoRE1Io1wZZv0HhEoF+T3nvJJQ2A0+Zv0DAR0x1bfYttF6IKIUzU60/Jr39AffoJpC+QYkhU6nZB9IZapC03PJ6pFHw6kO5/g7rOSIzU21vC4Tehvsfff4U+fE6YXkJu1OUJu90YTp/Rrh/Ib36D9vxT4v33qO//KdRCuPt38Hf/BOYfIcvMcPgUObzk9gd/j/TJXyFG8Md3xOYoM42Enl5R1rfE8QX1+vvoix/Qtp+RH/4m3N7+klewnZ2dPw/+xkthjPyZBWCAtzNUc6LuLuCdnZ2dnX/zeX56pr20P7MAjEBNG44hf+aN7ezs7Ozs/How3c6ohT/u+v1Ts6+jOzs7PyffOQG4uRFEuzOPgIv0+N0yY3rGKYQ0wTr3vlRvUAvYiPsM4Z5iF8pakXQEeyZFJcoIsmFhhHLpTkRZwI+0KAS/9jjotuBtxGxGa0DChG8GrBAD1A0dzqh1kXGtc3czWkG2pcdVe4UKyRXVDSsVk4zpAV+veAzoUtG6ImEkyoaL47bSzHqHr9feS2qAG9hKiBHWuf8fggQnopgVrCxoOlG2haBCXZ4JegBf8HiC7QqqXRQvN5oqda5daA2KpIjdbrTYEAuIKt4qkGjlCbZCGib0/Irt+g0yKMxCaY32/ERNB6yArwusz1i9InFAhok2jqy3G+H6ji0PlHnD1gbNQQOGQnmiXmakOhKVsj0iJGqZaZujYghC294STp9DninbBkgXmplo2wzSe511E1pVjJVQFQlQ1hvWBEKiXr8keCDFgo8TZX6HbAVJmfX2iOSMkpDpRL1+idZKrAvYRtgWrDm1BVrNJGYsZ0ozbHiFbQVKJWIkhUQitplIIJSCjqmLvDGC96EGrxcYJkLKNDeEDTUntEr98Q/R018hjnfIeKLWGdsWaJWQD9Q6g07djTuce8R1XZBw6neQsiLuuBjb9kiI9zQrmEe8Choi8fCCOl/x2qjmuFUcoZxHVLwPBlyvlO0KOXX3q/dYcajgDYaXcLtQY3fZmmVa3QiiBAmUOuMCtD58UWvBjyO+zHC8p9QNt0rZKu6KLwuyPOPDkXZ9JqYRD0pbCsaZdr1hCFKNOj9iZ8XrjEpCliuaJtq8QT5j84I3J7iwlQ3sgk6f9FQBlx6trYKmhG4rvm54iqgVBhLiT+hwQt3xekXjANMD7fIOOQ6kCM0XrNzw8QEZJmIYWGxD04Qv79Fw7q+tRPR4BlE0HWlEQogEc0yE0FIfiEiBkCNeCi3024BgWDUwughrClTs9lOY7kjHV2zPMx4H2vaeMH2KFYHrE24bFjJheoV/+ENkNMLhBe36ExgzRCFMp4+DLhXdnsgvXtNIlPKErwm9/wS2Zzi+xsqMn14iBOzpS2SYEFOyRvTVb7LE34eh3wc9KaR7bKu0UlB7JnDAqWA3PCvWNlL9Bjkc2W4bpT0Sa0NnQY9n0v1n1G9+l1aeaNszwV/iy0wdBny5UqvgcmCKQrk6fn2kPv7oW1nHdnZ2fnX4f/yo8d//zcDL8dvek52dnZ2dnV8vHu/fcv/hE1LN3/au7Ozs7Ozs/Nqxr6M7Ozs/D985AZgwgt0wqSCZ5hFIXWS9PCHTgG8VvGK1gmYgAE4jEFpFwgTlhktCKlSf0TRB66IaGmnbQvOAqeHVcBXCdKRtV+qyUKsTk6JpxZtTvDtg1RTFUTZcIyyFGieoBmWhpUTKJ/BnlnrrUdCh9Tjo0KCsNI6E4QjrgrERaORDRCzRTKgeewTrOoMVRAKSz9j1ipqgm+CHiVIXNI3UUnBJ+HIDr2g60TYjDAaldZejB/yyYFUJI4hLdyknR3TFqYgrwQfa7al34eaAX56o1TBtmG+oJNqtUnXrubPzjbYB+YiHDVueEFV0OOAysM1XQjpQSyO4E5YnXM/U+oGggrZCYMU8EYeIZMet0G4X/PB9tCoyFDDDh7ELc9tKHA/UZUOme+z6Q+L0QJJEWZ4pH94RhkDzHodsuhIlwfMCYcBTxMIn2O1C5RGGM5ZeoKGRUiYsT4gJHqTH59pAqBVdFqoYbeuxxzkppARboS0OOWEpI/XS/d4BghghJgIJXVbUnBjPaGhojjBEfNuwOWBL6XHEqrBVZDNUleBG+/obwsNrvFVsvuG1EUTAGjGeCSFRyyPCA6JGs0IIV0Tf4Ns3GCO1rlhp8DDAuvbO6BAgBWxb0dtMTJlmig+Gl4KUK63Wfu3EjLSC1EKlQhqhLqAGQYgxs11WRDasLjhCikrwBG0j1gVXQ1UIVEQK5E+o6xUlU7cZ4hEpj6TxJZS5C50SKK1AGtC1omEgcKE2kLZigOSItA0vhpwP/VpgQcrSe23bBeKRVh0Zj1AGPEyYLUhzMENdMVNEMzknbLvBMCBtQ3xjiC/wNtPSgThod5PjxMMdtl5RoQvDErDyjKsTbEMs9nQAVobxBNR+jOMRsRkn4qF3Qasr4fxAnB+BBrcLTQXCHW35GheHdID1PTGNxDhAjTS7IeszafwEufuMVlZ8HQgGut3QcSRIpViF4Q55eAPbExruUVU0DGi9kaYXiK3o6VWP0g5OaIWcJmoYCGGg2SN++wZRIbjD7StEN6Su5OOZrTz3/uHxE1hX2voVkk7EYUDSQLWFUm+wfA0SKR++Qu9+A1l/jzZf0MMr7HaF20yRRIrP5ONL2voe94pT0Vd/jTDe45evCRYpyzfAI/F0D8XBKzK/g+d339JCtrOz86tCc/jf/ZPK//TfTd/2ruzs7Ozs7Pya4Xzz6sd89uVf/rZ3ZGdnZ2dn59eQfR3d2dn51/PdE4C9uzrFlLZd0HzoLtFtRvIBoAtSEhGT3rGrCW8rTsLairr2iForGNDqTCRCqzRVVDJWHgntjOuMIdQyE6wBitcV0zvadsUYqNXxEPC2YAKyXlEqms6YLbAZ5AHqM8GFtl0ROdBK7yJt5QqELhJbINQKKM0dcaFZoWwbohkSsG7dXRkT0Yy6FoiOtYanI7U1pDi0leJGbaEft5hAIu4KMXdnsIHfZsIwgK7IMOJbwWOAttLKtbt9A7jk7jJ0hXKDFrurGiGE/jw8KLZdaEsixEhzg5Tx+oQ3QTT0ruCWQRqhXgl6hjCg7RkuM+FwwNZHUj6hrSC+0IaXYIa1BlVATsStYN4gR9r8DvHXtHVGNCE+4d4dx5LvSNszLiPiQjw8YO2JlN+wbT8BArUYWIS2ABMyjvj8Fr8VvCk53UEshJSgblhdqdXQHImnl3i+Y5kfsfoe8n2Ph7aFsAXEHHwGCpo/g3RHwAgKQTMhKBoPPUZ7uaHLhZDuUMmINBgjFoX6WPCnZ0IKhKZoK8QQMFGaB9pcMNuIZvjyDeH+C6RsSDri20yQoQu1pSJhgNIQrlAbpW0EW5Gpf69WwyNdUA4juqyMttIMUgp4HCjrFdaIeCBtIL4heQJrmG2oF6rPQCZOXyBu6BgJttBqReKEIYQUkK3gLjSPyHgPZUbTp9hyIQ5nqDeir6ARt4jYFbYNy2fqMqOSaSroeiOEiKZMpFHpr5eMd2Abw+GMfnSBg/RoagpVHE2KzFckHykpIfVGFNBwoC7f9KjyfIct33Th14WQR/y2EM+vgQatEA4PiG2QIJwm3HoUdjy8Aa+wXrAQoG1gG1Yzks9QZgKgYqT4QF0/gAx4KHgayWmiPb4nnF9Sbs9YnIhpQhUIgtsGKPHwAi5fE7dKHIVmlXT6Ars9IfN7Uj6g5YaeXuL1SmsfCOfvEVqh1YJvT8R8T/OArDNtWwnTJ1hdaKGg7kg+UJfn/rO3td9TgtKuX2LlSji+gvktbBewQjh9ht9mJB1gfu5d4TwhW4Hja9rzzxjaiPgM8Y6trVSDfPc5fPNVH8xJL0jTAU8BlwLHE/rwKfH6Fh0EVti2G7E8Mtz9LXy9UMqVMEIYD7T4AhkUXTdCuANx0un+l7l67ezs/IpyLc7WnBz2GOidnZ2dnZ2fGwELrQ+G+h5fubOzs7Oz8wuxr6M7Ozs/B989AVg2mmQwQdKJbgFc0QCaEqaKikItaIqgQ++3TQMaEm2daYQer2q1O89qAYfauoDSRBA/dOFGFUX7v6O9m7RuhHRB+p2ayBWpaxdDiFQJuCZMVjwO4A3xRgoZSoEUURFSOtBSJKxfg2UYR6oGmhmpOUkzOSTMuoijarAVrFTwhB8Cnh5wrj2O2Q3zhcZGtBMigRwSAcdlQhBcClFm9HCkXD+gw0CrF6ixC8qimBQYMtaeiPnYxUx656y6INMEm4I04uEFgym6XdnUERPC4Y5gFY0nYluo6xULA60seMjQFkLKBBJNjoTYIBypxYGKYkjqp3YQCPk1SMW8oDJAcrJ/dPG1C5Jf9LhkqYR8hO09mu/Isnw8aSYqK2oNbTPp7nPgJaZGeH6Fre+Ieo+mleaCq+Fa4P4NrBcSlSAXxANeBB/uqM//DCeSWiRrhBhY0kBLXxCO93i7UN//FPdC0gFPvfeZVpDziShCTAeSG1GMiBNjJEyHLu6uGyFnRBVR8KDI2J2yrBupFkwDXkt3LH/9E8LdSH75Ai0bfn5NiAkLA54mvK7Ewxuk3qhBiPmILU9kViwKwoLHRDic8dsVi4bmE7KsMB6o27su7kvA6oyKEIcjXlcaiTSNiEArK3o8UbcRX5/RYYKQyAG8bEhSqGOPK48ZlmckgQYI02uiOmk4Uscj4oLfFnIMNN9oFVBHTkfcGgwCyYmz4UFREWzIgEM+AxfydMLWlTEHaIqmCbELpAwScEt4a7QQaDGCj7RS0eiIOSGC+toHOGwjqtPiCVZhnM6AI+dXyOEI2yMSR5CCXZ8QHSGekPJEGO+Q4HgDc0PPL+H2SLz7hPVyIaZMKDfk8BJbvkS1EVLCVZF2xcOJts2E4wldnknjGzge0bph61tYQE6fU9+/JZaVEB2JIPaOJidUN/T+JbI94739l5gjVgrh/lU/rhKJ5RnKiJYPpMMDbfkRbbwjjPfUy0rQgWoX6tbAK54mWJ/w1vBaUQ2EcCAd72lDxtuKbBWNAZkOaIpoeoCP/eNMEO/vsHjt0f5xIny4MBw+QTwgISIPLyEocn5DXT+glkjtA/mT38KKoVF7jHcrhDzC9Fv9eV6/Ibz4AmlXZJgYqEi6x+a3pMMdcvkKvfvil7l67ezs/Iryn79z/uFb47/2afi2d2VnZ2dnZ+fXinl65nZ44nR9+LZ3ZWdnZ2dn59eOfR3d2dn51/HdE4BLRWLCzQnhgLUPkA/QnnA2pCqqoTsnPdG12+6sbVYRFVR6d6/6AmyYF9BEzHddYAsZ0YDblWatd3TSYLsg6YjGSNCAEcALbgJBMIc4DmhpIAk1IURo63t0OECYkPUJqHjs4mSQQIhHWq00bx9dfAE8IXVDUiTqgeaGu6FuhPGAbx8QzmgzwjDQlhVCIGkm6EIUg9YQGVDZaMOIlw0VwZt9VFZjjxSOE5jRTJAhI9WIXmGYEElstycQQ/OEupOi4PmAX54IzbC60bYPpLsf9LjccaSujyBGwIjRaSiWj8QYiT72TuToiGeqN0IQUryD2weoDTk84HUl5DcUr3DbwCCcDjSv2OUdKkdEMpgRD/dQCno8U+eAYOCPwAMeEqkFsCsxQPAFy8fuZr17CRcI48D6/EOcRLBEK4YfzjC/R9LQBUdp1Lah4xEfXiHSHc21rgQPDFHhcMfmgB8YH/4Ksd3wNEIa8MefkmwDuhifrBBaIxCIMaDiPeKXim6K3hb0fKSrkIIcI2iE+YY/FaxWXAUXw1C2H3+Fn1/SPMN0xq6PXWTFQA1Ro0qAKFhZ8PFIa5W2rWCGnA5o2WgKwYygjh0PeFsxKi6NPN3TSkDrSjie8BpwB02Rin506hphGmjlAodPMBVs2cA3QoGgkaaGuNNUCLcrcbqHHPGPsclJM7ZdYQy4KkpARZCQkRTwqtTWiKYwjb0zWaGGA00DsVaoDdqVECLSCk0jsII75t5duqY9upqG3RZ8uiPklWAGaUSC9U7w80uktu66Pr2A5S2aTlBXGAdoVzDH8wva7RGJx96DHDPVlCCO1w0vhZLvoBlaNnR4iQ4VtY0wDsQpYPFTzBs6vcIJrG8f4QhtvhEefgPWJ8LpDhEFWQjn17Bd8azEF/egFW6BcP8DfPmKLBMw4+2CTPeIVnT4tKcgDC97bHT50MMVWkBjQr3A/I6QzoR8husT1RomAbVMCI7rRIygd5/i3mhbQdqK5AHKRggnynYhKEidkW1BTkeihy6Ux4zkEcoVsRHTAHoH7SvC8R7xiBBouiAx4wHa8yPx/AL55K/j+QF7/5/j3oV/u70jnT8DgVaeaRiaD5T3X5Jf/QCe3/c1RK4EfdHv8+Ppl7l67ezs/Arzf/y9xr/3Rkm6u4B3dnZ2dnZ+bgTev/wZx+sdwu5e2tnZ2dnZ+YXY19GdnZ1/Dd85AVg1Q3PUVgiKhgxhJIzgawUPCBUd77GqUBtqQvUrASGEA0IjBcfKDZeBljLeDGMCL6isxPwGmlDqrTsAJUNoiDuiB0QDQYG6YjqCClafoZwI1nCtqAmkiXE4dGE5DzA8dMFNA7pueHjG0hGGAMsVjRn1SPBG0gPeoDYnhI2YRhAgjVg7YwVquEGNXcwJiVYKcbrDS4M0dJHNK1oSbanoeEbaBQ2Q05GtNdi2LtZWI+qImSOlINMdXm7EdIDtEdKEqiLrFRmERsNrQ0pF4z2UircZ1zvUJwKtO3THT6AFaBVE8CGjm4KDtCuJALcb8uIN3N4hGAJY3dhso7Ur2ho+PlDb2sWl9AmUC4yvYHsi5hPLZSFM97TwTPWFOHwKpRFcCOmAilDzxOaNMDeUDY0g509pQXpstiWkfEPQA/p8g3imrgtxOJEQolQoG/n+NV4a/vxTpJ0wjDiMeMmwPEE2fDwjfu7R3UHJh5ddIMNQiaTDEV1XQoqoN4Ioqhm1hZiPyLrAZYYhwZAJAj4ECAdQxee1J5mLYSK06wX95ivk089pHnHv3djSNiRGdFuIBqqClxXXiNUVHc+EcEI1wnohtIIcBqxVSANYJVgjDK+7U3Q4UIMSA2g4sd7e4bcN1EHPUFeiKmE64XH46LCtVAu4j92pLYLYDT09YLcLTEcoV7CCewRZ0boh6eN5s96o4UACqIBUYj4jWZD1GeKJttyQaoQshFbR6QD143OoC5oGBIc2YTjqNwgjslyRw0RKtXfVEvo+aAVJfVDAHYkZKzOaAh5e4N7wpjAXxBueB7xWrEI8PCDbI6RMLCugIIaETMSxy4J4wreNII00DQwSkSDUeIdfv8IsQSsMecS3DR/fEJrgoojPaGugRhqOuNwo2yOS7qBV5DBBW5HhJdQNJ4CN+PyIDB/d1SK4gzSBNuDDgdAapCO0K5KBVWA648uPiNMd0hY8FGS4g+aU5xvplJFwJKDo9oxOL6lPP8TDht5uSDoQpjtQoa1P4AISiPFljwu/PIGPGBvqC3L8qxASvrzDNYGf8aXgCUI64tcL8uoH8PyIuaDjHWrvcbv1IaAlsC3PyOFIaM/ENCGueLzDnq54VcIpYHrsXdc7Ozs7wM9uzt/7yvj3P1VEdhF4Z2dnZ2fn56WkhevpkePloaek7ezs7Ozs7Pzc7Ovozs7Ov4rv3GhI0EbUGxozQRoxjURboTXiIGiqIBBCYJgGclRcnKBdwFE1gjiBiuQjCGg64EEg9OjfVhutXRFRQhrBlaiRqJEcIeeEqn90HUY0JFSVlO6JYSNwIR4+ugd9I4RM0tQ7ViUQtBLLioaM1IiEAKG7b+NwQFkRKfgBCE7KH8WaVjENtLoQpgECBCBo6L2hAfIohDyh0QhDxrYL5AOSC/l4IgxKiwm3GWnPDLUwxIHBYIiKshLPd4gUaM+IFUIOhPFE2Dba7S2uERrE8YDERh0S2/kTijQKUH1Dsnex/PwppIz4TAwL6le03BBAbCMMU+/sFYF2gdMdtr2jhkQLI4SNkA6EdAQryOUbUn1kPI7k6Y7kMykE1I0xFEZ7ZhwnclkZ8pGohnoDK3C8I6TA4EbWG0NcCeqobcT1wnC8Zzof0CEhMSO+kYYDQ0wM7Yq2Da1XdEyE0tBiuGaqrVRr1JTAbmAV21Zsu7AuC7Y8IeX2UcxcoM64GyLez6OQ0BjRnAlTJJzOxPOB9HAixgjzCs/PUCvBjahCGhNxzERpRGuEbSXYRv3Rj2mti/Qat56tbI0QEx4S2IrWBUGxuoFkwnQmxIbIFcKCECA8dOHVNrxc8ekePXRxOrSZhIEZXhaiOKpCaE4KhnugXW+YOywXZFuIrRC9EEaBaYSQ2VqjzFdaTtR2Y9tuWFlp2wyl91xjka1WVhI+jGxlpoXYo39zF7ebO942Wtswb/068yvkBNOIxAyxEZKiAmIzaRiIwYlRCFMg5ITGisSI1YLUS+9PLhfS6eGjo7xAzn3fygUXp8SAD0eajtRtxetGzKBREDWkbRAKejygYehCvAbyIZLy2FMGHFwFzg+4L3j5gNkFthtS5i62ihOOJ9AZCSPe5h5rP97h7ki8R6cHfMh4VBhf4NsjsGHbtQvv8dAHSG4Xgj0RWYntgupGHEZSHiEP1PqMs/ahlnDpkfCp9Vjs5S3h8IpYZ1JQYrjAtuG3J6x+AN0gGJoTgZl0mEgvv0BCQu5eIl4Jh9eEYaTcvqbVDY8jFhK+VXz5Cj0fgCcYjqhfiMME9RlZ3xEevoeXZ7zMUL8h5Tek4xvCeiOdf4CkAdavSNt7goGsjRAPPTliPGD1K+Tue/jlSrj/Ddhu39ZStrOz8ytGMfhf/6PKHzz7t70rOzs7Ozs7v1a4Ol998oesw/7eemdnZ2dn5xdlX0d3dnb+VXznBGAsI/oKCZEomRgECRFiQkIGcfAINSDuoIqkQEj3TDmT84COER8z5BOY4JUe4+yNMD5AqzgVQiBMJ1DFVCFPNAMJmayQ0kQ63pNTJAcnD0doK6R7Win4MCK19h5iVfAAvmLTC0orFLPu5muVIIE0nkmSyJLICMGFOAyEJAzTiUAh2kqyBfVCHgMxDoSsZAmkMBIRlPpRFG6EMZCyYkuFaUKSEtOIWEXCiAyxOyOjdmdgMUQGJAyoOYQA0z2cPwGR7rocMkjtXaLpjMhAWD4QWyHlF6RmaDp2l7Nkgt1Ih4mYBLUIzaFewGfC+EA6P5BSIQJRR8QzSZxoTpJE9IwQoVYEJY6fE3DylBHbMFuwVomSkWjEdCSxEepGThOx+4lxV8Tp01QSaetGaSt1u1JR6nqj1oKNb6i3n1FUqEFp+cxshVJnagXWK2W9UduNcPq8u6/LM8GdJnSxUQPy/B6xtXexinYRephAA95usG6oN1QhpISm1IXIw4TEAHlAYkBrg7nCXFAPhDQS0kDIkRiUaNqHDNJExNGf/AFDUnJQcoSoEbRHQeswQJ6QHMgpkacRlYawEFRQHDneo1qR8Yw4hBDJ5xNuC6aOqYEJRRJFEzXfY8c7KkY1w/JIDQOtOsWN4hu0QswnYl16fHoaiWEi1BWdZ/Q6I1WRAomuU4rXLoqXjTCc+uBGT7AmDRNBFDBCnhAK6TCQD7nHBad+basOiBtsgjBiteEx415get27o4cXuDkqR1QOH89PA1EKQmsRTyOeD4R4RHT7eC/qndVtM2ot4BmJCazibe3OWQerhpcNbwusK9KkO3KHM9SIDvf4ZlQTqkRaC8B9PwohdxFfEl6uqErfZw1UcZpErFZsXamLU2ujLQ2TkbVmlsVZm1Gfv4T1gk6vkNTP6VZnONyh4wkLCfMNVycM97RqEAbk8Lq7ssMZyWc4PvQEhHUjbE/k40vS8UWPfW4zcnwNthLHO8LpjD587+NxeIa20cxAJ3T8HI0v0dZQGQnHA+nlZ6T77xFqg3lD0oTV9xjej3W5EtYbIQykdkEC3e2d+gBNePn5x7gcQctCbKF3xevYo+XLBfeGTAPEAmEG3R3AOzs7/z/WBv+3P2yUtovAOzs7Ozs7vwiuxuP915jYt70rOzs7Ozs7v3bs6+jOzs6fxHcuApoUAe9Rz0EgjSQHazdkq7QWEBHMrngNmDhCREQoVZBpQCRRtxkPgdYauBNCIuOgkXK4B6vkoDQqEhS3iqaxO0q1QBMk9P5T8xuaAoRKChM1DCTdsDaDGxIcrKFRUBsI4hQE10YQI2gGW/mY94yPE26KqPaIVK/9z0npqodieoDlfe85zRlyxsra427L1nuRqTC8gFqIQT46Fxd0uuv9pPkA6yMeBaOibQVtiDVUIB0mrIXuVlWhHDM0ukDmoYsyrRJFUHVcB/w4wdMNQdE49TjuADoeaTURfMN9wbZAs0TcZvR8xNqAtw1RRc+v8fkDGhUvhdoWWrkghzskTazqeNlIhxM1j5T1ipQVme6hvqdJhNP3EDYCQtueiHd/Ga8BqTO+PuEy0tqKt4IMD4S2kSThywz5QDi8opUVWZ7Rwx3mL5EyoyFBraRhgvrRHa2Ku/aOYUDa8rEPeoLcwzu8dvEuHN9g2xO0K9Ed1dz7doeREAIBEIlgrduRWiFMGWmGzzd8W5DziTBkchvwXLFtJrn1TuIYKF9+yapC+M3vIa3hDnhjKzOSTwig7sQQkOGMto+u0nXG1g1GwaziK5gtqAisla00Qsu9a5eE3lZkOiC+4k0gn4lWCVZg9N6RXQp4w8XQ8QzrW9RKF52T43HA5pWYJrStWFv7J/BJEc2oRDwFPE+Ib+jhJVoW/PgC6hWVhqcTbStoHEAUEcVbpaxPPU49RBgOWLtCWeB4h2y9a9vrFR3uCS4wnWgI6XDCakbywOCGMHcRUaQL+s3wdELaQk4Ddn3GtgU9vkJk6deqGIQ7/PaWkO7x2xNYBRSNAVtvMEYkgbNRto1UCuIKISKl4C54sC74yobkEUf79f64YFbAuhNZVFlvT3g7IGGiXZ7ROBA1QxQa9Mj00xE5vsBFcU9oOML2jK03VAMqgXCYaNuAXZ7h/g28/8M+ZFMS6AGrtx6J3woaX4BvaKvI8AJPCV8f8RqR44DrCZ+vtPWGsCHhDfb8FeQJHU5QVtrlh4h+Tpjue7dwKfjyhOcBZKJdftqv/U1p8w1Pd7RlpS1PqK5EFTi8wbaCX5569PX9F4T7e9rtER9zdxfPH9DpAVrDq9Genyjz47eyjO3s7Hx7OPwrQ7X+zs+ML46N/+Ff/u69xd7Z2dnZ2fmzcDm/J28jLz589m3vys7Ozs7Ozq8d+zq6s7PzL+O75wBGsLbhHqkm3eVLQ1sXSsJ0h1C6yGVKsETQCWHD2fBlha3ACrI0VJ1kK8M4kqIyDMKUB7IbEgoxRPI4kn0jB9DDiATQ8QQYZpESDhCPiDVkOpISaK3QUncgU9AhoWFEFSQE0uGe7EqUA7oWpNJ7cjWBBiQdoGz4VmFpuBkeDjgDLgFpgsgJTWMX2fKEpoimhKZAIKAmSC0IShiOhLChORCiE4ZMkoaKI2zo8Q0igk5KVCMOmUAkqJPjypSNQXrnslpDokCeqMV65DFTd1qWmVo+YPWCqSP5iE4vabXSLHXxmoinAU0J9yseE40TuAK1x7iaddelOCzfIOMdBOkuRTPcK/2gKZrvIQ5YHGmlEreNpAPBMjKcCcdXXVwXw8MRjq8AR8LYHbi24m7dtRkTEp10foWo9H1qBucXyIvP4PSCqpnmDaFhgJ9e44cXSBF0LYR0JGnsAu624tvcO5lJuAAKIR2YTveEoL3z2D5Giot3F26zj73OQjjkHgd9N6HuyHVG57W7f1Mi4oRSCW0jY6Tg8OXPYFOoTpruiAghCkmNRCGOR6TO/UsE5WMk9XhGrDsnNRhRHUmR5hUauLQugEfINpPVCUBUJWVFx6EPWcTUneRlhdqw/KY/loSsDSkFTGh5JJwfeqSxVwLA6SWkEY4PmDXkcIfKimoDKVSvWJ0xL/hwj7UCEvAKrAtsFSyhmrpjVkFzhrIi4wkVQcYMapgLrd4o60Lz0M8BBB0GklTG7IzHM9gNq8/YNFG9gcbu3texi8nDPVYr3DaY7nuCgM19REcNkYiML2G8w+rcHb04rV5o5QppgrpgywJl7p3I1ijbgq1Ld69LxC5XbHHaWmDb0DQiTLgcEHPYHM0HVFakrkhY0dzID2/Q8wtCW1HfkHJleHiDxBXYiMfX/bULEWxBT/fIdEa2BbYZ9ND7q23tgxCHM8SM2QUrH5AYkGGA2yP1tmASsccrUhYoC/H4BSIQj3eIKhJB24qWJ+J4h88L7fqe9fkDzRxpC7I8IuNrzAQkI/keR3r3crkgDITxAa8r3sC3Z1jfU63gp+/TLFPXR9yUZgGLE5pfomVGUkDiQIgvf8nr187Ozp8HIUb+1TLun8y/7lEO/O2fNH74vE9e7+zs7Oz8m4dpo692fwEIPN1/w5rnv5jt7+zs7OzsfMvs6+jOzs4vm++cANylTSdogeBdQKwruBDyQKAxiDGEQD5kdMoQGirKMB4ZkkA4oMyktHA4PpCHI+IFDFqLECfSOCKtIX5BovftaATG7lD01h1wOaAhUDajlICJ4M3ROJAPB1KewB1JU+/HTLVHEOuETCfiIROmHv8rSZFh6P274pACMozoZGgriFU0GGFtJHXGw8QwnUljRr0haiiOFkNjRJKgof+7WcP10HtD3dBpwplpw4m2bqhUNE+onpGPrsqtGBaPGEppgrkiaviQsNLwtqEBPB5xbxgVLys1jHiFoJkpCANC8JXkt+4UtK0LuDGiIZO2jTx2Z3SSSNINhiNerli7EO8/YxgzyQrRM1G197mSCHUmSiLXJzIzebwnKITQuvO6KZJeIRhaL6hsxNML4stPyS8+I+VT7ySuVzQqytIFXxdkOONWqPNMKCtRIik4w+kFsW1gC9QbISrx+IDkjOeE3N0jpwcsKbUW6tawreLt2t2a00s+ff05h3/r+/01d0c/OniD01/LFJAhIHlC8kg4HImvXpE/eU3KmVAMtUbMiXSIvcuWQDAnY4xu2N//u4gpjN0NnvMIdkNkJU73hHFABagLrRbatnRHr1RCUNKQCBFCPpBxRqtErPcO64IcMlJuaPqjHusE2607f9WhPlM10ACPSl0uVB2pIVM1UYcRqRd0AMaAnO/h4QWMCcYTJonaNpoMNFe8VqxsPbLcDbcucNt6xcUQAYKCr+iQu6N1zEgO/Xwjggi23LCimClUepR5jLgVcMdKxZbKtmzcnq9cLyt1U8r1hq+ON8Guj10sbQWGPvQRpOAx0taFcrt1RztHvDgeRjwmsI1WFzxP1OUJDwO+rsQh4hg6HpG2EoeEZu3pAF4J04DXCyTD2xMkQcdjd+PPTwgbw90DQQrUDfEN0Y12eUeZF1q5YXHABFpzrK1Ud3x+6kkAMeA1UetGKTeaG8Sxx67nl6T71+SYiMcHYnKCz11FiR/j4v2K3L5GbCXhpNOIDgHxK2qXLu7HF6grYXjdo5rL0nup775PEJCouBxoyyMWz+jwgMYjmg/dEa8BCREdRnx5i4/3mCS8bohmZCmIBjSdUHWkfk2cvof4B+JhIkz36Jhw32B6g82P6PnVt7eY7ezs/Kn5/NNP+WsMf2HbfzvD//LvF/7w2XDf46B3dnZ2dv7N4f2LL2mh/vwP+AWXwRo3fvb577LmG/4X9QH5zs7Ozs7Ot8S+ju7s7Pyy+c4JwB4HPN5hmrCaaCs0EYoXtqW791qYWN1ZlsJ2u+LbQmOgtkrZjLbeYHrAmtCsIRZQBHJCmGG7dWGiATYimgGFdUPWGQkDmCM40m5Iq0hMBAUh4EHxIIg2dDojOnRxVI+AEKQiFIi5JyqL4j7joYHN/f+sgEVEQeLxo3Cr0Aqu3sVXK4Dg8YhIxPVAk0CLkeaObRstJNwUPZxxc1rprsJmTrWRao7V2GN63bGQaXrCvOEf47ateReEcgTt/bldqFrQ8z2kiDWw4cDWKimekdYFdUJDgyF5osURH+9QidAashWm8cgYYTpOjOeXaBKQRDjeI/FMOH8PxgPGgpughwdUAppHXCvohmaBcMLnAuOp9xnnA15nvF4RKwBYWfDtitKI+URIET29IYyvmY6fkqVg2xXBu7Ccj5Az4yGQWBHZUFWOY2Q4Teh4QmojGEQaKR04vfwBx+mOOE4Mdy8ZHj5hePGa9PoLNCaCFUJw7P0H2rvHj1HiCbzHMhMiPSY4d0f3Hx1zQDUQh0T+9CX54UzwQBAlHs+kIRGtEiWQQ2acJgac8tv/Ke39ezCQMBDSHSG/gLoi8R5xx9eCzzd0+hwhIxKw7cbz5UYbPjosRbAU0eFAyRN1vSF6xGLDtgWzRm3Gtl3x1jAfe1/y+JI2vabOK3Vr1O1GbSvVP/ZDpyPWKpqOSDpCmIAEYQQKIo74AhQ8pB4bnDLm3l3i3tAwEHQk5Ewc75DDGXej1Yqb4AZi3vuVpXcah3Ih+EoYhCBCjJEYKnHMyKDIEAmHY3f6txXGE+HwGpeGpFN3d4dDjzXOXQgu64qljNdK1AGv0FT7GzYPMD/idUHTGWjYWpFwQvPUo5DXG+Qj4fyiu3fzmTi8hDJj4YDPFUsPlK0ixzdISt0FfzyDViRIv4f4jFglHO6RWEEcLxW2CyYRSRMaD1gpuB5xEr5d2C7vqbcrbBG/rsj8CK7IdMLb1oX9eMZkwuSIl7VH4KcJGw54OuFWkPFM2xqeXmFboa2Ful3/+B4naet97dMnyPQaL0+QEqIHdBip84KeX6NTQNqK0pDU4/vxa78WTl+QpgeohtuAa8LqI3L+nPTwBW4FWx4J44gUR4d77PINrgrhDjwB1961vbOz82vHP/5/fc0P3v/F/jL8duki8I8uf7qfczgcUP3OvU3f2dnZ2fl14BcJ0fhFAzcEatr42ee/x5aXX/DBnX0N3dnZ2dn5leZXeB11nGW4YdJ+4cfu7Oz8avKde1fs64yU96g3ApVmDSR87JttOI0WDU1DN+xiiNSPYmlA80SUHm3rqrgKWzQ2G2kpQw1UGua9N5S24r3QFax0sS4ExK27gq2hCtFX1BeQlVZbFybccQxzw6tiJjTLlFpxb9AqXsDjCR+OSK0gAjJAi71j1wWvDtUhKGoQxjuIUKz1juK6gTVCdKKCSgJa76UlIX4DDEJBqF1ccifmiayZeMqIpP4zmkGdEW9oPBI+Oju9FdzBrUK5ojhE7UKTzYRg4IpLoKVDF5LrjdvauDFROOAIRsKHl4ThhCRhayubrdRSMc2949QdN0fahpjhteLzlRQzWTbGUJm8cRDjMH5GuF1JwwNCQ0ngjrYbcTozDDAkJQ5HggpxPPf+WzYilZQj0+vPObx+jR5fks4vCaPgwQntSk6hx3Zr605ZM+bqtPELbHxBXS80a7hXmldmGkvdKC7UNGL5hAz3SB7RfId5o60rW4Ptj84DDd1Rvm1g1uN0o6IxoilDzP2cGzLheCBOI+nuQJwyuqzodSU4PYq5VpIGhhiZxshQNsrf/Y+wp8feJw24WT/3qHide2Tw8YEwBNRn1uf3/Pgf/w7/6G//v/nJP/zPsG2mPD3RLOEuXYCPZwzH/ISVQtlu1LJh8YHtulLWFSQSYiJr6M5rM2IIDDmS6xWxGQlH2BoSjr3HuW4fe4Mrgn/s9Q19YKA6iHZBtjSETAiJkCM6DT32WCoSMhqcNIxQl94BjqCiSBiQfIDTQx/eSBkNgTCcwTdQIYRMjIk4HNHpDsEI1oghoXWG6MjxrncSA8KCmBCnM0F6RkHII1oWYgTVj078FBEipNBjlqeEBLoYKbnHudsGw0BrGy6G64brAV/nj9eEkyQR2oyXFds+OqJLw7YVhjNIwyViApy/Rzyf0BTBV6jX3s+dj/1YxgitDx/k45FxeCAdzoQhoykRVAjjCO1CGE+IXQhsxDET714TWQnFUTl1t7oc0fSA3J4ItXdoizlxOqLZiHIl8FGgHoSQK2q19yOvFV0bAYE4YcuMSENDQL32mP90RsqN+OIHiBdkfYdSoW5QZ0IWlBWZ3yN+31+7PJLTkRgj/jzj1ycQ0PETdMjf0kq2s7PzZ2G5VMz+5cLsn6cs/M0C/+E/KPzw2Wg/hxO4ufMHT8b/9nca/4cfDbTDqz6wtLOzs7Oz8x2jpo2fffaLOZimaeL7P/g+v/XX/yrf+94Xf8F7uLOzs7Oz86vLL7qOOs463Pj6zQ/58ff/Md+8+snuIN7Z+TeE+G3vwC8dMUxHhK6VMt1jbqQgvYPWnWBOOGeaRbItdCtvJQYnokjKEAbUFrwZSQbMK7IFcCNq6N3CIYNM0K6gEx4DrSyIKnhD5QiqqEasNtQdq4JvNziMXYCtK1jAW0HGhITUhRpToBHE8ZAxD3he+6RrWZHQ3b9ExZtAqWg69JhdcWy4Q+qCWsFDRBGCKm6VMN3R2g3JI1INPbzqzrv2R47ShIgSZCOE2LuKvVGjf3QSDrSyEtQptUI49bhb6dtwPsbmDg/4fOvPRRULDd0aao2QM4GNKWVyErZ6pXqlWusxvHGAVpgOL0jjCasX8IkQzsDGskXKmGko25YhvySc7ontAzEkxofPqG3tPay3DXRDFAIrYbrDfMXyQFueukhI692ycaC1K2760dntbL5RqmHxjIQJe/opTSJSHTvc05YLoX4U0KugKeK6IUSiRvAZ06G7kJcFqGhTPAYIAW9LHwgIAz5/QDO0D1fq23cohmoXOrWsSFlQnRANvZtX6D3SeUBVEet9wWKVlBSmETUHG5Ap0erWxXodCPmASob375n/k3+Iv3hBePNA1Ynxs9eoHiAmLEw8ffkWkQ9cfvoTrt98xbo1Ypl597v/hGArr//tf5t0OLI+PxJTF6mpQug2b7S1PrAwjNRtI7YNSanHmYeNHBoyCPFwRDRiZFq59B7Hw7HHYOsfXakGTnfuhkjQgmnoMcqXCzIdoD1DOuIS8PmCjwGbC+E4EdoKVvHpHq+JIJHWVjyOmDWoM8QXeDpDGAjtieojRkJRsCcIE7XMuAmSXyAU3NaeDIAjUVANwIgGMN+A2sXqnPE40MzR7YJVupAfpn4v8AYmSJyQWlH1PpkX76Hc8Bax7UoLA14cxjNsMzbcE1ohHhKuipKRNCCtonlACLA2WjPk/nNkveCi1LahwwF1wAq+Xmi14BJQqZAHPE6ID4Rk2PqEpIzrBNsVryCueIxEW4jnl7T1PWE4U9cbNiWkOXVV8EgcAs0PJNngeE+b7vAQcD0Rbj/Dwj1abnj5CvQOSQe8gs9PiM3Eh9/A1hkZJkBgfAGXn+CyotNn1Pd/CMsZblc03aHnV7TrT4gvPkPjQLtdsFaR6Yy3G3GYqOsjcvoEvb5D7l5g6zfI8RV2+9O5EnZ2dn51+dM1A//JfDXD/+LvFv6918q//6kiwN94qeTQf9LanP/ineHA3/mZ8dvfGHMF/vBn/J/+s8B/9bXzP/mtyBD+vPdsZ2dnZ2fnV5uaN37yvX/K4XbHi8snvImf8/T09Mf1CiLC3d0dm6/YXaGervxH7T+BZ/jrd3+LF9+/58OPn/Y6hp2dnZ2d7yT/5XX09PwCgGk+94RQwMSYp2cALud33A7PWOjO36f7tyDOq7ff/+Pv39nZ+fXkOycAOwrRoRRUlMSKR8eLwfjRReixO0NspSUltBVPkeqO2a0LVWvFCbgbQx5gXUCF4oKWK+EYQQsSDzAXUKW5IduCnO7AwDUh0aBcEAeGEUojaCSEhOYBM8e3ClI+fqUuEm0XGA4YjRAjgmJNcBFcM7RCimN36UlCxvQxptmgKVpmNASQhAZBtxmGU3cE2oZ7Q0LE6wXT1F20OlCtoEGQckOmHvXchhG2GRkG8IJLxLdHWuxCeIjKMEzo+gGfEtUP+O09ljI2RNgct4qa4cdz7x/NR5jpncbquG80Gm50cbYmKDOFSojgm2PlGT3e47cbFhULARcQCWATZqX/8henHnVtgbZ8jTOj4Q7R3lk7ZKUUZ6NhIrRywVtAg0FpPaY2TLhIj9wuGwAaItUDLR6o13fEkAllpoWIL0/IcukiXgxgAcWwfMTbBdkWdMx/3H/r+Mde6QbuQOiuQypBD/28QdGgBJwYFNUBrQ1tFQ3d/StWetS4te6IdUdFIGZ0EEIaSKcj+nRFny8Ud6o7shUYj4SDwjLA5Zntwwe2tz9jI7F89YbmTvPKSuab3/8dGI5M40gaAsk2kIrIwOWHP+Z4uuPubz6g64xJRnyDHICA5wekLfj6hFCY7s5oW3DAWoOYcVmR6YhbxUOm5ZGtroS14F7I04BJwSwh7kgEdCakA229dNE4RCQHRCOeD6AVthkfBqgV8hGJEWqjmnenejxgVqCWj7Huty4ue+kd2xSqj91VXBdcB0Bot+cefbzMoIkaQu/bbQK2ITjVE2wLDJmtFRxIraBecKW76cVR2ZA04W0FL2g64A00T4jNbCGhVDQHfBPcKyIJdcEIKCvEhm8bxETTBAIhBiQIYoWUM5VIawtBBiQGfF1AJtQrXJ9AHTnc4+WGxOHja3js96SyEeRGlIQFxcqtdyePI5RHZBghbrS1QgBvK54jbThi6YisM26PEA8fh11O3S0nAQ4n7Po1WGC73tCXLyG+wZ5/BikjBjIOWKjY+gFhg9bwOGEf/gC/+wxvDVkj3i6YBXS9Qrsi4wkZBK4X5PB9bLvRlkfq7QNRJ5gOWKm4O+oLbjNy+gzmC/0qvX07C9nOzs6fmfJL1FPn2sXdv/MzA+DffaV9CaQLwL/9zb/8g+nntfH//DEkbfyP/mog6S4C7+zs7Ox8t7DQuJzfs54vWFx5Oj7+cYqHqjLf3VF85av2U/gvVSr+/fXvcHd4wW99/rf48NNdBN7Z2dnZ+W7yR+vo5fweHA63O+SPBeDGfHj+l09BCzzdfYO48uqbL/74MTs7O79+fOcEYNRQE8wzlQ1a+BiPDKmteC3EYcSqgRXa2mjS+1HdGh4mNhPwQg4BqYUWG2HI+LoQxiNLKYS2kZt3h6wEqI0gGZcG8wxmUBaQgDSIwXECra2QJkwcaFidIR7ABQF8W3qcdLiCC7hgpfSY0yCYVcT4GDldgQQecfEe4foxhtnkQF0v8DEitZKQreBroaYu3MnHGGhbF3ybsdCjf8kHqBVbC4iBOW6GrAVpM3ocujjWFAkQ3QjBYUyYDngttDz2zuBtxpvipdJC6O7Y5Qkh4iEwX1cswja8xpZnGje8OF42kImyGX5daVsliKGb4zrh2w0XxSxhyw0oyNidiuQDrM+4gZeGNcOXBRknKOVjp7LibpgJvq3oufeRIgUuCxzA/EazEZcCVdCw0lBqDdQmWKsEn4nHM14DIR2R6QGrhWgFwZDDC+zDe4QRqoBd8SIfI7MTMuQu4pcNy6D5gVpveFsIYySWQmgVMUND6np/owvB0ZHQP2HW5gigMRJzRGPCy0atjeZOHCIxK8vjhTIveGn4XAmnDKcTrDNbzJSkbAbb25/hFapXcDimTHNI3kgEVANBRswV9cI3v/NP2JaVN3/trwCKI92NnHPvCKaxyUhqhtcbcTjgEti2G+6GN0OPJ3z9gNeGlRkVRwS8Gk5Am8P1hoeVEMbeT5wCtjQ83GPrRogjVh9BH/DlhtuKHI/ddZ8feuy6SI8Rv620/FFAdUdsBVvR4aG7+vPYu3FjhPmGTvcEnBYPSLniErvj3rTfZ9wQEQg9Oh6v6HTsguq6kM73yLZ1l/xtIRzO+PNX6PlFd4HPH8XGLVKWR2hK3WYYA+Y3nBOaE80BE2iGjwPcKlB7ckGD5olQZ7BIrSuijk0TdnummdBESXVG0tTvOeML2uNb3Bc8vEDCQ48cv824JtryRPSCu1IMfHiJlw/IbYHhiLWCXDfcX9HiHWEDrwGen9D8CraKeMJJMJwo5YJWAQRPd9jjW+z6hCoQP6E+fSCmE2F4Rbk8wfYBGd8Q4gH83LPMWbAl4EzYbUbSPWwLrc3o8ZP+OrcbWIJlxafPqCVitwvtcvk4EHKmFcPzCXv+IaaKt4F4XWjlglwC5fbul7h47ezs/Hnx42j8X6ftz327zs/nIP6H39gvtN3/+w8bmzn/wV+LxF0E3tnZ2dn5DlIo/Lj+Phz++X9/rn/y+/Ene8/vnP5Tfut7/5XdCbyzs7OzsyNwOz79Qt//eP81Jsabr7+PfPeaRHd2/o3gOycAi44IDrISFSRIFzVNeszucKCUGxIjFSFIQ6JgHjAviDZCUFhWdBiJDtpqj0veoAUlTidSuSDDiLkhSI9OFcXjAPWG5ISZUVx7/LI7roaLY2VBxXEJIOPHrOoDvl1BD11QiifK8kgMJzRVmga8VnqtcwB3jA3UQGLvAtYzzoaXpTsZK2guSDUYD4ivkE/ACm54a5ASSsOHA1QDUzQG0BH3hi9XQjogMmBe8OGE/3/Z+5cf27ItzRP6jTHmXI+9t5mdh1+/fuORkRGZZKiofACdEpSU9BANkJAQUgkhlVQSolP/DA26/Af8BXRA1SrRoBJUlMgHqkxFxH25Hz/HzPZejznnGDTmjpBQFZkRcW/4cQ9fv6sjv3I3O3vuteZay2x/4/s+dcQuRFSiCFUbrayETbRbIdZnghFU2HfvQlgaKLEgdcPXgkUjDwKxEvmxi8aagYTY1I/FdKFtDRsqoolabtTxik4X6nKlxUArC9K+JaYLZgmx3q8MTgwzkWecoUc0i+JrQfO1Ry8XR9IT+PNfxHO3rGAD+1ZRGYnyCsMIreE2Uvcrq56J+Qt4+YYkgfqIn38CdJcw12+otuNesHShnt6RMUK7sC0yILU7jUNAkvRuWs2gA759oF6vPerb6UJ7CKihA0iAuPdO4HFAVRAXpFTEDB0GbBqhGrI1TL33AU8TOozUWnAb2D9+wltDHx6IrWB1YxgvjPtOMWEL2FtjcyevV7ayYTqSRfEhU6tTW0E1A86nf/NvkHnmi7/7+3hkfP01mgaaN4oL4YWaElZhHxviRmwLngbk9BMcBcZ+LAQsCjIkLM8YjaCAbej0iCbF0om6XpH0iIog+0r/aF4Jc9wLbhNaK5pGJCpt3ZAwiIxLhW1HkiPjiba+Qj6BZyIq2jLq/TyG9z7JWlccQWxCWoXWwAz2a78BTSfCN3Q448sHQh6I9Rvs9IhIgnYjxieoNwKH0wmX0p3C8/k+OLIBGerWRWmdkOFE3bZ+rcpERCbWT9jg6KT40qimWLuheiFuK1UaMr8lqH2QZL3RVGnzE1ZWtEDETm3g0xnZIK8bot4dz8MDsW3U245czrh3Md5LQU9vkfqCtB3fXpHzI1FXoq6QQHXC94pzI0pBbewx+fXWhzBGQ9YVRJFYYHigbTfIgq8bURxN93uiO+3lA3VcCSmM+7k7gX1D7Ezsn7D5C3pAeMZFqeu30J7J+QnfV0QGYvs13G7o/A5993uI7MjyAU1viARSGzI/IO0Vm556moRN3+0D7ODg4LfCv8iN5W/gd9e/KWk2gP/sT53HofG/+Hs/uh/dDw4ODg4O/to8+0f+36f/gr//03/E8y9eP/dyDg4ODg4OflgIvDx+Q2qJdx9+53Ov5uDg4K/Bj+5TpJwMp5JtREpBdOpRq0lwDWLfejyzg4gTaUS1kmpjtwHZd8Y0352sjWID5jcSA6KgrZHE8EioV+q2kscBMcVJlGRIW8jDmbY7oU6pUIYR3V9JYhgCbncXXwHfuvCkhuzP6DyT5pk0WE8HZsUCIk9ddG2td5va2Pt6Xz/CkNF0795VQSzQPHeHaL3hAdECsZ5wLakLoNQGiR4ri8BgCE5IJvYF0oRTsWFAN+lu4NJ6FG55ReyC0FBX6rb0WGPtEdaRhRxAHolw0t4IKuHd1eyWUJ1I0Sj7K+q1x2HjpPO5C5eyMcQMw4jXhVEVV793LDckFnyagGAUwWvDtdJSpnnpPa15xstKkhPhjertLtS/Ipp7/O7+CjHgt504v0HqgsvYnaAvr/19De8QOaFrwaYzcZqwUknsXaQrjZwa8XjGyystKlI/MA5nJMDLDqWS3n6F2zP1+gHhCpwhgUTFFOT0BZo2JK6IBmSjn9lAc+5xz5q7sF8KTCNowF5gcRgSMk7IeEKkEK3hgyCaQBP19QXHsMcL9XbDc8DlhD4XYjoRY6LuQdlWVlF2D7Y8sNZCmGAZILHHRgkl8gRlwb1x/bM/4+ndgExv8DxiWjEb0CQUBNqODPeBgnLFC1B35OEJ80KenqjlpbvR7dzfs42E74iNMCR0eOR0SQzDxPbtt+wyUvaVyACCTQ9diJwS5IFSbphmCEOiorL3ft9aEFU0KSA9njgn8B4pLeqYnWC/4nnoIvCydLd+yogIZd+JMaNasSaEA2F4vYI+9VjnfEbMiH3t8eqp4duKxNCv7+aQJyQ9EnWDspJPb9jXG2mcaGUjDU9dbMagvILOuIOVnfAg8oXWXmiS0bVQqpPmkcEyvKz4vnen6/VrJD/0lIKklNKI+i06PWDpHdF2YrsS24JejLo84z5QdshU3B1ZP0H6EpnfwOsvcRfUzggb2QVKEL6BnEEFbwtRbqQxIflM2Abjz4i23ePolZQHQsC9oHYhpBDtiqaKPv5d4vY1WR0JwaIh6QFJZ+x0AR4ZsyDtxHb7mppG6vYL7O0foPkB6itE6m7y1CPnJRmxfITpiajfggU2XYjhoXdAR8OXT+Tzm+/0+XVwcPCbEwF/UJUhGvvnMNP+ZW3C/y3f9n//lfM//Fnw5dx7Dw8ODg4ODr5LgqA/kX5YvPhH/sv8f+Or/EfkMn7u5RwcHBwc/Ej5oT5HEbieP/Hw8p5Uhq4PHBwc/GD40QnAdX8FU7I3xB4Qv5Ki0KowzEbJAgxQC0QCjCRBpC6sQSMnI6aZ7faKDom0aReTMagbmuYulPrKOE09CrYt5GnE6krMCaSQehEnmENSKANiDkO3pSjAMOPbC6hATKg5KYSTBT4/cF1eiW2jaQN/6D2d9H7YaN3xxvCIsPaO0kiIF0BgUkKC5mN32ooTApQMrfW1aMbbBh6IaHf/RSM0dXeqdpdmSEJNCOsWVKEryXkeSayoZkJvuCpuQHqE9Rmm3B3Oy0KSkYhKG3qXsNhMazdqATSjDNS0EeXaRa75gXx6i+V+HH0aaUCtO80Mr607LjHitrClRkxPSHOiOqUFbVvwKlAbUTZiekCboFWIUom8Iilh2nDZMN0gv0HHtz0ivD7Q2o2kCdlvpHDyMCOs+DwT0uO+xTcIoTZHxxNRdlSG7qg2Q6T3yfrwVY+gHk6IdMFQ80hSw/zG0G3mnL75GhMwEzQNWDZ0MDQbKmMXy+/OX2p0ATjZ3Y0OUbqgrzkRyaDupJyI84zmRK1OmjI+jmzbTppGTu0tJSfKlIkatDYzN2drjb1NrPvGXnZknIiUSOPMvl6pGC2UFEJ5/sSf/r/+a372D/+Y85ufoDQqQYQgw0gsOykJyRKFhl7e9BjpujIkY7BGSjPZB1LOpLbRSiXNE5FmltVpFNJ0wdSZ5gFqpRVHh4xYQixTaqFFgW0hSTCkAdDewSyCuqCpYjbRNdiKjkZ46U7xsnZBMoSI1nu2S8WmM3XZKKWQpoQko26v2PnhHpe+Emb49YZNcx/w0Azr2iPDH99Debl3XztoQqJ21//+DHUnNMBXjAUfL9AK+3IjJUVPb4naoDxjpwtBoW3PMF2IvSD5AX/9hE1P5JywqMSQkfZK2IzpjEm73y0NKQuB4rFi2VEd+99lgkdhOF1AjeYbsu+YCgwTtE9QnJCGPXzV3/vWYH5LlBcaGd+fSfIFlh4IXXpcd1t7f7Hv8PAT4vqRti/UbSEVQU9zvzGKQhph/IIoW49/ThkIeP4T5PxTVEq/b6/P1HEiGtSyEekBvbzFzj+B65+BZ3Q+IdsNeffT3jfebkgBnWf8+Vfo5XfwT3/SO7wpaH7EP/4SHc/f2bPr4ODgt8P+Am/+hTHM5fMIwL/Ba/7pNfg//LPCf/pPMl+e/t1ff3BwcHBw8Nuk5I2Xxx9mBco+rpz/YIR/M7Dvv/0aiIODg4ODg38XP/Tn6M+/+v/ws5//Ebkew1QHBz8kfnQCcM4zEY6JYLPRSiMe3mHrMxIzSRoaDfJw7wa23impA7ZtyKSgCkOGG1gdkKE7MWLuvaqSApUzMOGtgAWSJrpwMEJTwgUZAySI09wdj80h9deVDEbrzt/IiGoXiwlaXbm17vB0MuS3fYpo3wm3HiHcbbzQCqKJiBmRAq0S6UzsK+V27RHPYeATEXsXakQR3WEHkiBNkPHSO4QJGq2nqUpCxGCYwTLuG5YSURZaAju96eJZJForRL7grRK03oMqRnjrQvvuyOUB9500zPj2LdDwJWAcMEmUcBjsLobvIEHyQrIM84DbhbYuiENKD+z7R5rkLubvN1wcGc4IQo6KRyOd34ELOnYHp6QTZg1RJ+bcu19VUDtRtcGmiG9ogLSCTxdsOKG+I9IIdpR073rNBE7QiBBKOOIDuTl6/immgV9/jfu9S1mEsKnHSiNIPiE2I9L3RV2d2oKplnv1UaAhJBVsGFHtAq+oIqFoSkAXu6UGEt3JTalAIAqooCJd2Gp9KCFECNmJfIK3b+DrD8yDcrbM88dXbjjMhpjQSNR1oayFW1nYS6Fapg2JfS9o7JQwChOxb7RW2L/9lpdffcPl/U9wnWF/RWiICZIVjw33AbUTTRSJgBoUnFJ3BhXUHEfwy4n68ok2TUQ1Kgut7SzbTk0C44mt3LqAa0poxvdrr8+Ose91E/Iw0+pG3fo1L7KgOoBUdBihQG2lp7GTex9tc5okRKJ3S7drd6DOMxbahzFUYP2GiIQnJ8qC5QkTg1KJtuEknECHRBCopu4IriuSHkFq70mujWhBjGeoFSHha+tiQhIUhbaT5hOxFpp5dzXLgOxOKifEjaqJlFpPCqgLjF/AsuJtwYYJ9UIQyHQmpXN3ZRsgleYbyQ0Zu1CsZtRyj54fZ6DC9ADbQpOtO2zTdO9MPuHusNGd2vMb8ALzCZGHLqS3j0R+i29XpCbidUEwZJoJX3vMtzdA0WnsXdll63vEtQ8RDF+iecKXDUkVUuChwN4d3bGhj7/Xv6clohWkrRA3bPwpHplYfonMxnwZED2xjxdK+Vkfegm6sH0+I9PDd/PgOjg4+K0R3m89zJ97JX89/uwa/F//tPG/+u/86H6EPzg4ODj4zIQ4ru3f/YXfU/45/0/+zps/Rn81fO6lHBwcHBz8CPmhP0fLuPLp6Wu++OZ3P/dSDg4O/gr8+D498h2n0aLSVsCsd3vaQEgQpcJ+Raepu+6koWIQSxfM9kTTAhUEp7RC1IIkRSJBLbRqpJzABvCGeEGmGd+9qwfe//TY5b2Lpql1MXjbCBqqZxobtA1virSCILgYNKjbDjXw9RWZnrrQtC0wTV3UsSA03V+u4fuGT6ceCe0VKQXyiNYNMKoWxHfURzTPgBED0BbCZiJKjxCeH6FsqBhpCKIZoTPRdlQz2q5ggkZ0N2Cs2HSiSmBth9hpe0UtY3mi3FaIhZgnIm5o3VEqTAOUHckJG05Iee1Rx5JpseNFaNeVMjySRNFYCK+0VhEFD8e9IdDf7+kRmc5EuUFU/OEnsD2TTPD9FZkeKLdXTBJNHNwJmYn1IzJkNPcoWjR3F6bV7kquL0Q6oTpBCsLe0V5+DnE3KuYTsb4SLdCykm0g+8748JayLzQBdSfCcZuIcuvObXGkBRFBTkMX5cYn6usHRnMuf//3WX/+K7Q1NCkqgqmiooiA5IypQq2gqXfHritxu+IpoeWEW/T9aQqmiCg0kN1hK7Rtp7xeKbcb8fTIw3/w32X4+a/Y/uRPQLrQnOaZx/dvsa3y7cdv2HNiK43ruiLhMF+QfUc8E8uG50ygbC876+sNHUrvm66FiIymC9F2qm+IZFrb7zHIFfaGTG9o0rDqtG3BihFtgKsQ9VO/fhF8ubGbQUBdb3jdaHYhtk+4TuAFSSMQtFi5thm2G+EJfOnu6eENxIJWJWrFPd3Vv+5O19iQnIgaveM7CaKC1EpU78KsJPLpEdluYBltCdYFJEMsuDnFBRIkUeAePy7WneNC78wenyAqNRxplbZtWB5gK5ASkgdkOEN9pbUglhuuTjPF0iO0jTSeqXWhqRLbDlLx0wMDTlgmwmjDSF0/ovMbdHsm8glvO0kmGr17O+wTNENspPnWxVW7R6W7o8srjA+YGL59hCbEdkXOXyH7B/JlxFuD4US7fYC9EDaDNGQpyGD9HuCvfY/OJ/LpgSaP8PpzeHiAvcL1W5jfoNNDP3e3r4k6Ym9+HynP6HQG7W5wygsujv3sD6nf/gosUZfXfl2c36HLt0R+InbH949EWZH5C5bbjWxn6u0Vnx6J658R1veanN6j2/VzPMUODg5+A9b7wPVXTfmX6p93MX9NfnENbiU45SN66+Dg4ODgu2Obbp97Cb8Rjcotv/BkX9LaD/cD+IODg4ODHyY/9OcoQBlWmlbMf3yS0sHBDxX93Av4rgkBGy6g3dUXGC7a+3g3J1wIMh7S42BbEPsOzTEEGXpXpLYFIZC20HSktKCiNFWaF1qr3UHM0MUgFA2Fde+OVxFCJtBMtABf7t24G1TpMc5pglJQb0isIKU78aIR207sjRrGvlZ8azD26GRKj37WPIBNhE0wnHsf7P29kBOiSjASrrQaOP31Ihpelt4XXAqqgkZF84TGTlLF2BBN2JCwKWEmiA44hg5vkNjx1nAClwGGR6o3mgeRht5LytAde2nsblmHYkN3WpKRUjCb0NIjqKmvxL70jttIEIINiotRl4UoDRsvpNowv3fiaiApE8NE80qULngHG5pnvDXaXmhbo7lQt5VWlfX2yt6caEKsz7StgHMXIoOIRMNxceL23IVKTQiNcKOsC6VUXBKMMxKK6QhScJy238B3REfCIaJgASkaEgL7hmwrWiuUjeR7d8jGztOy4DkhlrrI3AKku9AlHI3oF7YmxLrDVXNCLzOcz7R1ozw/U9cFX9f+Z9vw0ohW8Va607Q2IpzYdravv+XTv/ivKZ+eCe8fOIsLEYK+f8vlP/jvcfryC+Yv3zN/9SV5HElpYMiZpJBUyNNEwrCA1z/7N5Rl6+dRZiTN3aldNmjgbaPtV9wL0Tba642iQcRO0B3Feyi1bJTSWLcrdS3U5tTW2NvOvhdKjT48kU5IGiml0JpQq1AcylYpbcCXa78ubSak91yL7Kimfk3VDdre9+K+dHduHntEsg44FaFi0wMmO7QFCIgdoULWu3O1d21HVDyP1CaIZboGYfdre+nR0HkgWsFdIBRSYDimCRkGmkw92nsaIBoyZCg7lAASEo6tN4QdkUBY0baTUiAmDOMTWXPvwLUTav0eZWLoMEOrtGgQQZQrEQvp4YJ46UMiviIqpDyRsyBx673beSbF1uPJhxlxR9KAqJPGmXx+QGUlW0XnGRkfEKlkaQwP75nUGabH7ka+vCed38Pe106r6DSh00RMAyTDTfDLe/z8Fc1mijvbutNsxLeFfXXW60fK7RNlvVHXlbLc8K0S6QRpppYNeftTPHZaqezXr7uzfAtqUWLdYLvCdsXyE6qNNA7wA57cPDj4sfLxXwmK8E+2H+4vrP/sa+eb9QfYHXVwcHBw8IPm09PXv1GVwfeB19O3lLR97mUcHBwcHPwI+dvwHL2dnqmpfO5lHBwc/BX44X769dek6EjGkTDQSkrKtu14CSwL5gU5ne5Cg0JxqEKWAR0GImfktqB6j4IuexcBYuhu4dMb4vWlR53qjqaBuoKUHcRw3yEmxHdMIGzAl29RFyLPhMzIPNFK6w7a0pA0EnFFWkN8o6VEW1aSNHLroqPK3gVjKpwSWMLcEVMcpUVC12fQAawhZe1fa4ZY5uSNyIa0SijE8AARMD12Ibg1NA1oCGEZ329IzJj1yNoWBbMBrSA+oDYgUlEbyGY09d6Xms5QvMdRx44mQUOJoMfOjk+U5QWqoKd3qGXCHYaJWMFkwx1CF3QwkMB1YLvu1LaSxgm1gVoXwhRkwH2jrs+ITbSWyeNAW1ZwpW6FimBRMRv6ulBgoK0bDCPp+oFIT6glRBqpbQw24gGlSXd6B8S+Ejg2nmG5oRjKRh4vRHwktopYJkkwmRMYeRrZtxegYeLUVrAQPBn4gIjjYmy1Iv6KpjPrv/6XmAraKgQ9xhhFNKPSUO8x3rGthGjvrg7osdIjXjbq85XWwC4zqtoNqeI0d1rZca/dFTwk7HyiXjdefvmB9PaBvVWSDwjg1xu3P/saLZWIwF/3Hmes2juGTckp9z3kBd8aXhsNWD5+Yn7/M7yUHlmeMs27eGmSiKTU/RVxCKuk9ATrjXv5NLRGQ9i3hfE0IexdQDbpbu22E8NjF8enE1E3cpqRthN5oLbGYDs6vkHaithAi50un49QFiKEcKfeNnQ6IdqQ9EhsN9xyF+ttRHwj8rlHnMsJphVk6scFwAUmI9aK7wtMb7ByI9kJrxUvO1yeoI+R9KjpNBHLt6TLW4hCrP0eEOlE9gUfhKiCSMKWZ9JwJp0uiCh1a9Ta4PIOTO/934pGJk8XZN+QtiA2oiQwqEtBU++IJgqeT6hM+DRQ1k9YqTAK8vgzwIhSEH3oHeWl9G5tHZDLe/z6a9RWmN7j2y/wNJBouArbBmEnvDwTdsE1AKUNCU0T5eUDTl+rjG+oe6E+f01YgkikD1/3Y2MXWHda29C8gU3UVsn71odcYsPr0uPqeSSWr8E/9R7j6FHufdpgQKdLP0dbJdYb5g9YfurXjCkmtQ+enC/IYJSXlbZeidvrd/j0Ojg4+G0RBP86/TDdv3/Of/mN8/sPP7o5zoODg4ODg9+Iq7/waf6Gt9tPP/dSDg4ODg4OfpDcTs+M+w+0U+ng4EfIj04ADna8JiJWbMh4MkyfcK60suE6Ym3FHaw0tDRkvNBapS0bZk9gsDdAen9q0kaoENuOyIzMA7E3ohRCGjINxB4wKC1PiGxYHol6I3xGdALbUSl4KtAMWHscbTaQgvqMGLR7DG3gUDdEB9DaRWILPAIsAw4eiNw7aLXBPHUnbXNinBGN7h418Dzh2wJ6grqjNhC1i2EuO3gXwjx6LC1K77ytK2EPyN1JaMOM6EYNwcMQD0rbSDpzmidqDbb1BfJ8j/6t4BshCc+ABLa/EskJDaKBZGEcZtSEzQthCdkKmjKyBTr28SkNIUswnM54Wwg9dVdp2zDpHbhmoKVgsWLTpcfnSkIo6OUtvP4ScWcwJ5Ybaf4CphnGLu6JCVBp9Vs8jf244j0+WTMS1sXOy5l6+8gwvCOnIJ7esq87DYPySonuri0y4jbQaqXhCCtResepUxCZEJQGaBJsfALASyFlQ0XRlLtL2wRLI5SNaE4sC9SGmCHJQA2JQAYlQvFtp9aKTgMyFEKhtcBLxZvjAKJoVmwwyjcfKC8vtLIST09wGoi9Uqvzaa+4F0opVCDKTpQdWsPSQCqNsMzwYMS60LbC65/+KT/5+3/YI5VV+yCBrwRByt7F61IJc5jeoKKQMoL3r/ENVyWlEW0F3249otkmpCyoTERbiXRGA6I1JDLsVyKNaKx/4T51pLudvVD2FbEzSQLFe5LzOON1oY2GqqJDpiuLjsWGxA6MeF1Ry32gTxJMgoj1LnCEmEaQDckgtyDPgi/fEmnGzJG6Yw+Pfd8mIBvWCq0stOkRdIN2o9YVGwyJGzDTLo+YCpG6IyzyGV8/QFyRcaSuKz6ciOUTli9YNCIKIjMh3jvGa6GJoOMJ9p22Nyw7bk4eL2i5wW3H5wHYwE6IO2y3Pnxiqd9f9hU5XaC8Eu0GNASnrSvVnDQYLTL1dcWGF0IUq4m9joi/9nh9bXi5oTkTpdDmB8r1EzLMsC+oOzJmZHvFPCH1Fc83qA1S6zHtFeT8BbF8BDsRdkGqw/kCqYDeYHhP1BXShbjd8OUFvGDv/04fqNh+QdLfoeWGRCEQLAU2P4IaPr/5m35kHRwc/A0QwJ+kH7aD/z//hfM//bufexUHBwcHBwc/MAReH77l7cdDAD44ODg4OPgrczxHDw5+cPzoBGD1AR2UqhOlwVCdun7sPaMSpKTggQ0jEiNhr10cQdAwou6UErBdScOExp+LlArqUBc8ZcptQZOQUDRNPS5VgjxNyPaK6EDRBF5JGSQ/ASs6nYhWwBJpvODrDYYMORNtQyIwMdLDOyS8O2eLE75AeuyalPQPNT0yihHbCxpO5NzjoHUBb4SOPaJ527F56n2x4wCt4dHjgFsY4ZUwJdeG5IGQhOhElEZLI9IctQlJc4/I9t5j2sozahM56r3PFipBpKkL2EJ/TTshyWi3b9G8EFKgNWQYGZOQ8ozKhuRGtYwiFD93B97cGCTgNMPrK+OUSIOyfqq05kRZu5v79A7xHfaALAQnassgr8R4obw8Mw4VGwwsoE7962RC5j/va44u+E7v8fKKjm9g+yUqINa7aiN6hLhM77D1hbjtLGmDccax3nXriVg2gkLTQO2huxW3Db824vyGaBt1/YStGy2PqGbSPIE1jEZKhk1Dj3vW3vurck8SsQHZ+vuWPKDjiGSDPHTXeu3Ht9Udv260b259gGE0nKDVhmt3V4e3HiudFd0r5fWVenuBWtH2REKo28a+F9yi76cWXWyFLkALqNfu6jXHTRlSZl+uPP/iFzz89EtEoO6OqqNpgKg9Br01ojR0fkC9EoMRoURskE748kqazoRvME49KllPRA5CL8TyCeYT3lYiokcrDyPhFSGBTtTy0t2fgNSCtcDmAYuGEBCOmvV+2+GM+QrWzwkkaAVRI3DqPeZZouDRRV/JA8IKMgC53w9k7+sSpU5PpOEM+yskIZrRyrULzZYp3q8FbOg3sWVDT094W6jDe3zbeDi9ZZ4K+EjZrkg6oUPCI4im2PCIlgWXAY3So8yHGcKg3VAEm2ZYFmLIXWStFYlPWOzo+Qmxcz+f9aUL2vnhL8RVGcceCV5eCX9B0juwB2grkieYn6C+oCRif0HCyDYSLkj5BtqARk8k8HJDnr4E3YEKdUXTAzYKJtpF+RBkWZCU0fkNUT/2XvPYwMcuRoeC2D1mPSHTE+Fbd2RfPyEpdefv/owg/TxKkMYJmy74/msiJ1q99W7n+SsihForMjwQZUdz/i4eWwcHB79FXjX4hQX/4Zr5P132z72cvzYft+Cff+v8g7eHC/jg4ODg4OCvQs6Z8+XM9fX6uZdycHBwcHDwg6NZYZlemdfL517KwcHBX4IfnQA8qhNipDwgsZOiEgQ1Ap1PZE341oi9kA1iGKjRurhgI7I6jIb4BbMuVkVbwAQpvdtXJMiD0KKgxdFhgpR63HIyZAdJQlJ6jGoI4bVH+aoiIr0L1Hs/aLgT4ncX4w6uME2QUxd8l9q7dQcjmlOjkVQIAq2N5iMpNtgqYgp1h3DQMyKOqtyFsakLbkIXprAeLSyJbIoNXeyQaCAz4Z8QOUNdEZ3wCLYivetUA8NIdoJ2JXKwLY1G4A7OTqsNiQExJepG2ICQSeffgfqKaMINnJW9JLZ1x8PxfcVbIywRMuICKWV0nGn1lWYXigzUqN29OT8S+wJRiTz3KO7WHYDbdqMkBamoOORHYrkipyeiXgluSAD7K+4V9cCHM4zvCDEY3tLa2mO4vZLEcRuoyzMxf0lI7X3Hy4qXhrKhpwcoz7Ta453dDAhKOHZ5i+pw76EuNKtgA02N8vyMPTj7uzec1hsyT6j38yHSPwCO1hBvKBXNI2K96xnrf8SUsLFH9kbDp6GLr2vBX1a8ln6MpwzTRGCQArXAvOEVXJUoW49uvpxJAXsrhAcuQbh2J2ar/di1hmhC6o5UJ4mRB2O/bawfPzC/e9cFVBF8dRAHMrI/I+mMpIb4TkQhIkipd003FywlxCsSM/n8llJ2Gg1LGYuCjiNKQc0JTWzJCUmYt/7PtCMVoIImdHwijRVVwaLQJCFpotZG+EBEo+HU+pE0PiIoUQsuA0Hp/cG1d2F4LZBGvKyIncAdbxshA/vrRyz3IQbGJ9hKjyF/+Alte8XDiL0SNqCiiEJsPcrcI8ijQhFkSrS9EdZoaqhkNJ1o3tDxsfdLX1+R8Qx7w6YniB3mh36fISASodL3yv4M0aDt2Pkt5foBzRNx24nsyDj373Pp11Ma+2CB1z6sEsBoRHtFmPu9a5h6h/K2Iw9vYPtElIDYkOEC569guSI5QVRsnrrLe3yD1xUJwV4+oAQynfqQiSZgR8YHVKClJ8hCrity+4CM79Bh6unX4uiQAKXdXhG7QBP09Dv9OlgW5OEJiys2f0mLQisfibJjb76kvXwN+Qk+fg0Pv4v6jgwX6u0bPJ2/y8fXwcHBb4H2FPxXa+MPi33upfxGvBT418+HAHxwcHBw8N0x7BP7uHzuZfzGFNt4HT4iHMOcBwcHBwffHX9bnqMtVbbxdgjABwc/EH50AvCuBttONMeGxNoEsxHKC36FlgzRC6ormIAmqI4PI80FkQbVkTT2uFMSki5UHBsdjYaLocNEavRYWRyxHXC8JQpKKjsigUTp0cEm9/7QAdSgOnhFsuEvr0gOGB96h3DtDkvNBuJEHnpH6O2VttceiTs4koIIJSchigGCaiDTBerS3XJRiWHqguifx0fXDWkb2Ss6PCD1huqAREO9QJoJ2XGbCHfEBpKACHjc+2cJkmTMHDMhrAvMbXulRo+orrcNHTLSCrpvpPkRlisxnbqQVHcqDbjgCu7QXLs4WjYswOsr2wqihp4ekfUVSiVqJfYVySMRgZRbd21agnYD747kdLqgS0GmB4QGee59zXh3/bkT6kSeer8sGdwRqeClHy8Zu/BfXxHJUBVpDbWMt52IBmJoVFR6H2/VJ5wNbjeYQNwZ64o+PkEUmmb0zTvcu9BN9f76NfiQLlyu35DHmRgHvAnqQeyFIKC0PmzwMKND6oMD7khtfc9mI8aJECBW3BsqQVjQXitx2/DtRji4CtF2IiUij8iloTixFurtSppH8vlMak5bNyK68Bll6y7TnKHeHeDQj78IqRaSOJZ6pG+kBGKgifAVk4QMAyEzeEPl/t6C7lZ2JWJHhpnYV2w4YdIoXmDf0WRYNFrKtFZJKVBJCJmyb31/2YBXJ9Tu8d2B093ubV+pqngsUO+DF5FxdSDhu/S+aEt4dciNWFfIY48U14TaSKgR60dCT10s3hZiVJBMK96PkW+EKWGnLpiG9q7utoE0JM2Eb7S2UaKSxzPiTvXo1xuNtlf2UMY0Ynkm1mdcB8QDGYSgwhCQhFZWzAbw1gcXxrdQPkHcXdPthlugWZHHLygeDPvS933diPGEjnNft8b92FR0HMFrv1Zaw3UBnRAZ8OW1xzdTidK6CKulpxXYQJwu2JiQ9Ybr2O9BMuDXG+6CToYuG+yVUEWoyOmEq9C2D0hL9/h5kPERmwakvWJ2wr0hVgkMHZSUCvrV38OrUz99A/dBIFtv8HQGzsT+c1AhmuARqJ2oXLH6iq3X7tTeFUk/ukfowcEPnrd/HPw/vm180X7YwqkApvK5l3FwcHBw8CPizbdf8nr59h479cNlljNf2u/wa379uZdycHBwcPAj4m/Lc5SgJ+kdHBz8IPjRfXrdthcsgqQCGFoKoZmwmaCyOeR4ZtCBsEwtDr4SzZE0oDnRvOJJuhCrBfGCunaxNpw9VnJ6g6czpX3EWiXlGd9XdnGiAEqP0d0Ldn7s8aOmPYp6XyFPvV+zBM0MbSvSZkhn0BvJIIl2gXSeoc1I24iUSHShTai4tC5gpEw3iUaPqhalx9c2UEcsiHBYX3svaooe3do2GAwPkH1DhhlvhZDU/47a///qK0hCNUgSGEIJqOtOygm20kWuFrB/RNPAdOouV2+1C3L7htQbQsY10djQaIz5LWVvNDfwvffFzg/EvhE6EvtO6EyIoi2RYkctoyz9odRqd8FOcxdtVdBhwtdXNF2obEjKUJ6RlHERJEDyCZUdtYnmCckFsbELrMl7jLYkxDLoRKgynGbq6y/wXHFuhJ579G0WQlKP1N4LYlMXjwZHkhAFYn6kbTstKqEVU0Ul98hmgjg/EW3DB2XRTGqORHQRUnsvMKVhql0oDboQPE2YKtKc2Er/OnFCpA85pO4cdwkiKTIldIfy7cfecjtmwgqehi6ineduAN2c/eWV9HBinCekFtZ1o3rt3cRiNA9M6J3EpRKa+jkJZ3x64uHv/D6+vkALoN0HEBaaCTJM/XwReIPwHdIDtUF1R/d7NLMNNBHWfSXqDkkodadGhXqjtcS2FXR6hH0nfIC6Qhpo1aGs6HiGWAkZaW0nqBgzUQtkQ3TokesiEGCnN/j+8e7sbV3oDKPVBlIxG5Ct3gcE5v56MqEh7K0g8yNSXwgdSdaHJ1QVqdHFaO5x2GO/Bl0ykkdGBej9w2mYaPuNPXpTsQ5TF8o3qFtBoqcLeB6h7Wg+EV6wKpAagcKgiJQekyyCqlOXK2LdAa96wsKJecbaBvVGtF4CHi54rZAGiAmdpr5Xy9IFbB2Q2ysxQLl9w/D4Hq4fQOfeXZwfiNdfEH5GhnMX9Nmw/AjLQou9R9EzYHaBxxPUGxLa92/tvdOQe/7589folEnTO9xBWuDLS4+jX54JG9H5Pa1eYcj49qnHnJ+/ZN8/kIYBqU4r38BW4PL+3is90vYbMp5p6zMhA9pucHrozvqDg4MfFOcs/Owi/F8of63vD74fv6//7kX4p7973IMODg4ODr471BN5nyjj+rmX8htxKU98/fXXn3sZBwcHBwc/Mv62PEeHfeLx0/vPvYyDg4O/JD+6T47MRlI6IenSBRJrgBOqoILEhkSlKlALQwJNA5pGsoKlDDUh1XHrna+QwJ3YlSiVtFcyO6YNmS/UCvselEiYVwaUwbV3COuAUFEC07HHq4r0/s+cEYM0jgiJaAVJDdUGNAIIevGrG8Q4YcNAlI1o93hWG3sXZkAQuCmOEvkBEsRw6u8hGqoT5AnMYFkwepw1DMR27d29NqI29JhbL0g4aCVQ1AYsHNWM+4Y3wcuCy4inzF5WvG0wjHj0KN4oFSx1l2sLQlIXKIcT1B2xN5g3UluQ9oqEkwlMnMTKkIwxZzKFLJXx8kAeApMAhZAMNhHjA6GpH5u6I2lCpifcFVol9opLpq21RxG//hKsEBV8WdH9hukIkkFBJFAU9Ya0DfZXsg2kccAtEyqQBnQaEesio98WYl167Gx5gbYSkfG1C7MhAx6Vsjp1rZTdif1Kz+RuveO37rSk3OYTbdu7g5Ue7+wihBkyJuTco3IR7U5PUXSesccLdrmgmrrYSO69wNrdt5Luf8x612opcN3wpeHbQqtOcydOZ7hMtK2y33ZiHBjePTE/PXAeRk7JmE0ZIxiGicEUKdEFOw1sOnH5o3+ANIF8Qm0mEag7Mj1CSqgoRheGwzc87vtFDMMwdUx2RLhHeIMNSk6BKqBK6ARRgUxEug9DDP24RIXYaJqIqLgYQaJ673sWUxjOUPceUZ4Twoqwge9g5+7Yn84ojkYhWSahyL70TmjvDn9ICA0ZJ5Inclv61zVHxVD1+1DI0qOyTXrnrzvupXdm40hxxK2LoHlCRZmGCaN1N3VrpLIy0MjWK70Hke7eT4JoRcYBSfneWz4QdUfNIE1dpG6KnU9o27C2Y+xoWYioyMMX0LrTG78hklAGTBNaC8JdEF4XtCz9OO8bmUDFkAjkdCLqjaivxHCC4dTP77XS2kx4wfMENqDjE/npHVBBog8LjCdieEMry73nVxCZYXoEn2hutP2VOl6oUYlxJmoQ24YXpz4/s334QFle8Xzq98gd5M3v9SGV/UZsG/FyI5YrURJxW2Bz6re/xmWglBXPSls+ffcPsYODg9+I95PwD98r7a+p4n4fxN/J4H/+h3YfZjw4ODg4OPhuyHXgdHv83Mv4jRBXTt+8ISI+91IODg4ODn5k/G15jr798DPkxycpHRz8YPnRXa3mgaYRHKgwjmcsJbJUBlOyGDldSHeXrLcdlQIp0aKLrWaVwRfUFPGVuHfDYu3uZAuaV8JXBndGdgbbmeYTqYLljKTAJKHzI16DVoLmDbYrIj2OFqSLoGpIu/f2bg3aCW+w7zttb/hSe+xxceq+shNU6dG0WgqqICrEeoNt7TG09aX3u3rpsbtrxUulRQEqMp1pUvEadxdm73EN712qnqzH2ya6MHwXqUp7Yas7uwsujaqJdS0sFdrdBSkh3a1cC5HO7MtGIwircP4SQlBXkl7QUGpp6PCEaiJapdK6O7D0OOiSZ0r1Hjus0HSmYXgNImrvjq0Lvl2JBjK+pdRKXTZiu4EY4d77lGMn6o0iAjLA+NCdhsPc3bDrFd+60zTqtXehloZERWKhLBvwSNxusK9I7Fh6wncQ37t+XHv0toSi+Yy2HfGV5AtjypzEmdQYs6GSoGyoOKaBnU4gG9uY2NeFuu14KbRt627McAJ6nDLSxV1JfVjAHVTRMWOnCR3uEcEVpDY0QE3QcULnGT3P2GlARoNa8GXHl5W6blR3fDR8gPWbj6zfvuApk58uPPzsPW+++oK3X77n6e0bLg8PzJcLo4K6EGEMX37J9MUXXXQGLDs2zogZuJM0k1S6MzYCtg2xuR93bwSVsJkmI9E2hOiR6qYIEGWH291ZXBuWzhhgnFF30njpom0rpJTJppzmR4ZkTGliyDNWK1JbjwKXQKjdKW0Tsa8oCfEbmqbuCh5mUlIsn1DN6DT2fxJozl0YpSLaKCLsy9qHJCq0co8F0IFoO80bkUciFmS+dLF6W6H0azHiLhCr9PmA8kywEWG08UQjsZGoacDNCBca0ruF5YwWwULRSEgkLDKy7sTeIHIfDpCxn58m+LJR1wVvBuktxAAt0NMjOgrCM1JfoQQSE0QBX5HxAq2heezrTkApBIkoa080EGDvEfSSu9uWumKm5PMjOkyEr7SyEzL2G3nbIT9AE1gqEOjwAPlCawttX2i10baF9voC6dIHTF4+9e8rO7JcifWKtxuRBKlC7Ddkrfh2uwvvAjmQ+gLrL7Fp7jHvt2vvaW/X7+zZdXBw8Nvjj98K4w+4Avif/q7xP/jyR/cj/MHBwcHB94B5uSD+V3gGfY901kTm71//Ee35e7Sog4ODg4MfFT/k5ygBj5++4Hx9+twrOTg4+Cvwo4uAFp3wfaMp6DCwM+DmFHeiXsl5ooj3bt5G77Q8n2ArSKlEPhH5xL7dAIGtYKqk+aGLb+G0dCaKYzWDNsLvggpbdxXS45Z9X3s3qkO1RF5bdwBbpqmj0uN9BYH5Eak7pIzkE1Gfe5yyWO/xbQVJMxaGnC7dGVdWGtxjco22T6hIf00VYq1gFWSHfO5CjWQU7cbRCpIGRB2zBG0nxAm5QNl6N2nsiJwRGYgGyox5dy8T0CJAd6IGTXJ3MjqInbug3VYkKrpHdx9LgFf8Hs0obWMTQxXMJiwWQpRSGjafMQGxASwQBqgNyTOJF9p0wmPt3bhhhAxIqmgLlJVmCjFQAkR3NJ2I/YpojzS2AE1GWELyidoWQmqPxfbx3mFa+z5oG9XH3juMkU7vqfWFaA1JI+npLeH9uLO+9GhgnDCFpIhkTGE2WMdgXz+CPfbeWypRlcCI8UKUhVs2lhrY19/0CPCUQMGyEWnGJfqQQ/EuVHkQWdHc35MIKI4RuNL32t67iFt4145zj1ZmrzAq4g2aEx54KVRXdBiRrXD7xa9ppTC+OTNeLgxPI+aObTu1VHRQqhn+8kKZZsbf+3uoBmbQWgUZKXXt+9l3PATVJ7w1ai3oeEYFPGWk7IhCIxMSvUPWg5ACqnjEvfP2hCCk02Pvfm0NGQfq9twj31tDxhHRQEVJqQ98JK+ECVIbFnfH8mC01mhN+zWqA+G1C7Ntgah4OvX9nAdCRjQl1LwPdhB9D9pIqdfekxvPyHzB15U0DX3QwwuMMxE98jzcsALSdtTOyLmCCL5cEXV83/ARQgd8rRgOKqTHJ/z2QiBEMkIctRm/vqKzdmdy3QkMsxHLA+X6TR++OJ3x9QWxERkzatod2a/fwvKKDqe+Bpv7flhfe9qA3nuqPUGa8LWiskHdKPnUj79LH/SYz8j2sa9hq6AjaO5R4evS48Z96cMpZYPxAq8vaL7gy6ce8Z4feoyzF4SA2vu5CWBbgI8ID7C/otMFGc54bYQOoDvogtiXyLIj0xt8vw/HDAN6+l0wQcqCDBdi6h3oRAXb0OkELy9Iqd/5M+zg4OA35x99ofzeRfhXn75Pv03/5fijR+F/8gfWn0UHBwcHBwffMafbI+M2s85/yUHI78njapCRf8//+6zfNJzjZ/iDg4ODg8/DD/U5CjBuJ958/PLo/z04+IHxoxOAaTtNE96gtYVM3N2fRkqXrpt6I8fUI3RrBZTYN3IaSbrTVClDhX0HJrS+9sLVfSfIaEo9QjRDqRBq6L5j0rB0IrZXIs2EBo2gsXc3baxIHnFWqI7k3HtPSf2GLwUVAVYszVhtVIHwSgwGDTwa1gRtO9ECnbqDlEFI06k7I7WiFkjOqBjsL8SY8PUK+YR4QTRjEohY78MdTqh092/TSuRzd/kVAa4QRrSNJkaN2ntGUWAjCYCiHjQH99odzTqwlxU0ULlAvcG+oG1D7QlxZ8rGfH5EBSpGSzNY4hrKZMLDCDIKlZkoC0PO6CmzW4MQym2jVcWzIqMh2wrayKaMmti26NHCtcB5hO0TphVNI4pgYjD0mO7wnTaA9IZjfPgJsXxNcI+vzhseFZW5R9uWa3dzWoY8IKEYQgwLQSNaRWQm5icCp5VnmhoyvcPLB8rtFZIhHiQFpgsmCgz4dObldxrTn/wKjYpGQlVpGLJXIoQ0jIjcI4Sj9/xGvb8fAUSRYUCD3jecBzzvyLYReyBF0TDElh7tiyFJURGaKc2huWNTpi479ZtvKbXQMPIsXdjOXUBULmRRhvOEv31DJGVvK0QQTft1VpY+iKAZqrNzZV9eaOvG9OZnaDiaZ2gr7r1rV1rvy8Zbj/EKaNrdqWmYobwy2Fs0Gmre3b95ovhOL9K+4NszdRiJvWG1O1EtP2CpYl5pXpD5jN1WYhLCHbMMKB4z4RXf9u4MrSsS0ePWl40QQaaJWF+IGjA8YO5oNDBD296HF/LYY6VVMBP8dkVTxtKA+YJZF4/DDd9e0WnCo9BSRraF9PDY723JCF/Zfeou4ipQQGTCtyu1NXLd6bHahkjC/cqeFB+f4PYBx2ilYnXB28ReN3J+g81vaFJo5RUpG5EvxPqMOGBzvx9Uh/YKw4zI1IVWv6HTQxd7NSMhRPXedZyAbSeyo3XF89D/ntNPYfvUY7MVNM/EtBPM+Ab4AvWl75nxLcR+vx9VJAYiDJWMze+IxcEaJgPkSpNA1g/I9ITMZ/y2dCG7LXjbidpgeotEH0iQ2oAueLsm1J0Yz30QIb39bp9fBwcHvxVUhP/ZHyb+9//Ff7MH+PvS8fvfxh8+Cv+7f5x5M35fV3hwcHBw8LcdQXj77Vf8fP5Xn3spf3kCHvd3bD93aj3E34ODg4ODz8cP9Tk6bid++os/JLX8uVdzcHDwV+THJwDrRNjee1ObEKK0UsF3Is2wd8Ghto3sO4xn2BphJ5okYnViSFDG3ptqQ3fTlQZkVLS7KDWQtjPYDKl1V1wEIg4EiuBlR08j6SY0CjCAOFoVVJHIoEZEQzA0JdCGIEgaIIHVQoyORRCjQUgXeXPqopI4kbWLfdIgK7EVYO59u6rATrSKe4ISEI4rWNvQpIQqEV30FBm6OK2JdrtCHojasJwB657SsuFjJpUbNp778QEsNswykc7UYnhUkmaQGU2ZKAMyJYQeg6vjGbXEkIOaEsvSqKVCudGaUHVkzzODOTJf2OSVFiDbxqqJpXXxXE6n3vW73brgKiPu3qO6t2cYJva9kdtOJWO+k5gRglp3JCe03WgqRBNk/kl3AusIw1vi9VtEhNhuxDj2ruFQYnhH1AXZb8hwBrQ7oPMjsXwEDyRVREc8KqEjWz4RMsMURPmzuwt17sMI+xU1w05PGIqsL9ScsHGgDYmwLu6SBsKdtleooIPd+157L7ISQHeyinZHMCTCFDFDTPGkeHF8WbCmRBoJse6IF8PqTnOneXTn6zQiHvjeqJ9e0WVDh0Q6nUGFKkIbjPruHVkyIq0Lmvtrd8XrCdc3sKww5+6C3VaMmTTOSHPEAo0KmkASkpS27/3cupPefkmrgaxXdH5CaHhLtGj8eZtwqOKD0cr9I/7YCd+RJXrfbz4jXjGNPiBQBVcl9tLdVmVHqaR86R3D4jQGLKfu9M0PaN37taBCUADvLtLm4BXNJ3x5JcYLe7mRp0dCGtKcyE+0egVGvECw08KQSHeBdaWV2t332ys2/gT3T1B3miQ8z2hAjuixxynhy4cee3z9RD69hXbr75PUI7UtaGUhLNFa6rHo1fHTE1GWfr3HFYbUB0LyhDJQKbQI0viALAVsQfKfx5CvMIx9uOb8Bq2OLw19eoOUrUfS74pMEzwo7Evvr66FSKceC356C2XHfSecHh0fBRkGQjMhDpcTMk6wv4APcFHk9QNyfocMA0jDxWCYgCDWZ9LwjlqNeHxPLAUpK3J6IgL08mXvSN+uxPYJLj/Fb0u/r0m5JyWsyHCC20diuHyXT6+Dg4PvgO+rtPqHj8J/+k8yT4f4e3BwcHBw8Fdi3E6cf/6e0v6bg18HBwcHBwcH/3bG7cRXP/+jQ/w9OPiB8qMTgEMLachIq3gbkNiYQ+6xsfeoViYirhBKKq1H3w4z4oFlw6WyJyG2iiXQnLrQitLajo0ZkwGpW48IjoChd8qKGcwjKiDnsccmJEdsIAjUpi5s1A289VjYuiEpg53x8oyWKy5dzAqDaOkuEtPdlDi0RqOi0ZA8Qd1RojvvLOPhSNQecz2MECAWuDjVez9oVPDBiLpDEiQaEq3HEkdDTQntAnCPq477e0wkE0TPcHeKhhgpzyDd/eeaqcsL2IiUguMwPfRuUx57T2jauQqUlytiZ5aWcQ9ghvqRXZXbVri64ftGtoS0Ah4Un9heP5DnC6oZ2rUf01OPng5xYr2C7z1qN09EVXK+QPnU18iGDUb4/Vh7wFohCxrd9dvGS+9yNsW3F1QTpgmvO6J6d2zTX9MrbiPISExvsbqBBuFXJKS/N5mh3jATTk+/i3sFutjJfiVCaXch2ZJRakE9d2dxst4HqwqmgBHe0NKQuqE5YQ6oYUmQlCApOgS0EY9Ay0bUhG0jbdu6GzUPPWZXFA3vDs5ipOq9D3ndukieEyZC2ypbqbBncgk0GTVnXuZHbHwgWaa1FXzCU6OWG2E7WTLEBkUhBImKnR+h0bu28d7J6oEMgqOIzVgEmieiCbQK9z3g0R3rta7AgIhSvPTX8B6fLkIfCtFEI/on/zpSykLToHrgouBx3xOG2EDz7rAHQdRQMSS8RxYno5dNK0ImykbYBaaA/QZJaJbw2l3q7n0wJNS60B3d8UrZwU59TbGgCp4EOT92l2pkVCpiCTywZMxzBpvYtldYIcK70EvrbtoAr1vf7+5QF2J6T2sveNsoSfr9xzcGeyDCYH6Lb4XqlTAQVfI0IWvvKfa29IhqvDvbhxGR+4BNDMg4EtcrTRSNRrQbzO/7cEu7wTwg0/t+3vYrkh5ANpwJiUrsjYiNKDc4zbAuSD5BHvsev34EBM3KMCZibbThRFu/pk1PeFvAZ3qSgxN2Qy5fQctwfUbU+/XZntHhS8SD8JXQEyB4ee7d5MsHNO+wvZBGpUom5ofv8Ol1cHDw2+SPvzzxT36n8c/+7PVzL+XfyqDw+w/C//YfHuLvwcHBwcH3gyd9x6YvfPBffe6l/FsRV/5B/GOWX+zHh9YHBwcHB98fQvof+X5XEokLw3bip7/8g+M5enDwA+ZHJwDXtmFrQaMgMkIV5HKG1+cuCiVQ39Dpgu8bldK7MGMj24TMRl1vSMuEv9DqSPMNsiD5TCs30jqRxxmZcxdYGMAmqJ9wn2h17wc+G9SNSCeIoNUdjx7pbKK9W9ROxN15KGGIXAjzLkrWQohCGKSR2BZkzETrcbJWFkIT7K8QhlvQy18zwtpTpUXvYpKDdZNoSgq1IHnu4ndo7zvVCgxoK0hSIs+0WhADqmMSDPmBUl4IBlyM5rW7YcszNr8H3/DyiuhD7xstFWKn7dL7dvcVzSdojdgE31/g4T3aClmDqql38E4npqSMU6asN5IrTJnYV9gVbRtjFiQM2kKUpb8fqWitWJ5oCSK9hbozqCG+InZGS0IMNF0wb6gNuAotGpwSKVZUDDVD1RiezozjxLY/dHd2vVGpFEnsJvgOiNNiw2sFLaThgian1ReCsT/3pxNSl+6Y9oakTMoPvd82Ggwn8LW7Vy3w91+xf/0tVhZKdXZpaBJg68IcBTFBw7r4XwrRGt6clgdsSIhKFxgtITjQhxIUw1SJlGGs6L6h+4bpiEVDk6LrhnmmIWDKPF94Gvt18+1246aOInw6nSmWiOlMq40minvG2xUhSNGQckXSCaY3eLkRsZLzjOA0KYgGre2QTpgJYkb1wuXhkcsAe3EkBbdr5UYg0t3CiuG1Yan147pvuCbAe2d0S3SNXaC89m5ejL1e0XRBo6BqffhBM2oVBuv7+/UD0+ktCWfXTL0tSAlERwTFo6BJ8ZYINjwakbqDPw2J6kJ2BSmYDZgOfd3jQ+8bjhsQCA2bZ3zbgAxegApJCV9x39H0gAwjEpW2BLG2LiCL9d7w2PFpgLbe99EGhX7fskCade0boZVX9PyELisyZOqyEWqkYWa7vUBcSbnvlxwrERmZB1RnvK142e/R6Yq0HYlE7DfGpzNRt+5Gbg004+WGr4pQUEC995XHcgOttNdvYZiw6RGZHnFfiCSEFFT64AfrDWGC5ZeU+gaffkooiBpoQR/P/R4oIJc3xHYjPb6hrR9hcGS6oFpxegd0lELsCwzv8PUZpgcUhfktUa/Y5QJI75hu/t0+wA4ODn5rDL4y+m/XCfTbjo8+JfhP/v3EP3yvJD3E34ODg4OD7wfltbI/F/geh+F8Jb8Hvxioi5Bj/NzLOTg4ODg4+Avm5cL59Ynrw8fPvZT/v2gzvvzlH3C6PSDo517OwcHBb8CPTgBObQcyLRIaFUmGrDt5mHETvBZyMmpWsAcktu6oXBdsCGgnvIFoI80zvm+k6dwdfykjNyNn7eKqKsjQI5ltRHTqUaY+E1G76LZvyBRQKymPiCpeNnQ4A3v/NDG6CxWkx+XWQrcQW++mjbsTMWeQgaYVqQU1Q7x04YMKeiJCQRyxCRFFVHB1xA3RhHtBbIJYkWSwLcg4EV7wYkQ2RNq9E7c7Y0HwbYU0E7Eg6XR30CWoDdIZUaPUFdyJGAm/i0S6QSiWeux1d0w7jBeiXJFhIKuQNfDq1CjdCSlC4/4taaRuC+mWCBvQtjDZ0LtutVCqYOMFtZHwT4wZxstbSlHW23rvcjY0B6IFP2VMGymNJG/kwYiys6aRZl24rNH7gUUDb051J8aBJErcPrFLYw+l2gTp1p3TzREB95UmCcvCnEaqG7UVRB2rhUrfDypBeOvnzwPyA95ekZRIQ0JSICFQnCYLxTPWBnDFQuAu/iLtHj+u3eneCk4QEojQo42999aiiuQBkdZDkyUQBaGR6Oe7SSJXJ4dQrQt2YDz94e/xe3//j7rI/S//Fa+fVj4+PlI0EJv767eFRulDB+KIJCKf+uRb652r6NyjiXMlmlO99uhkMSIUHwYiGl6ChYXCSFCxurO2RovoXx8BoXgLZC/IcKG1G6SxC+x5Qr0gqpgIIYZrwn0jSY/LFoa+plZ7VHbboFVCZ2SYGC4zA+CvLzQyKgNKI0o/b+GODhPVex9wtNz3d92xlHrv73Cmy4i1v543EMEZEKkIjeZTd003R1URlBgGYi946v8t23u87KToLujWnKqBTuN9gETw+3RhtAb5hA5nrNwQhEjS91kBs4HwFaGSW8Gt96QPUxdTNXrvsni6C8+tu+/p3c/aBNG9C+F1o+WGSCB6hXGG9SOYwDigJGAlvOEy9B8rxfAakCfk6WdQXggfiOsH5PIEjMTtiqYBTu+747gqza9EZNhv3Z273tBxJJZnAkVPF2J5oeQXWFckDcRwonz8U/TdV0gzonzCt4rLhtFIpxM6OHjGo5EfzsTrR+zhHe326Tt7dh0cHPz2eXfKCBu/rbnr35ZEK8A//V3lf/x7xu9fpD+nDw4ODg4OvkeMTHwvMzQCHp/f8+blZ5T16Ps9ODg4OPj+Iff/fS+5P0cfP33BsM/f33UeHBz8pfnRCcCDJZgu+LohCkwjmgLDQBN+cyQnhrYRUZFhoLZGzGcqQfYeqGoKkk+ICZIyvhWiOUN+RLndhVsgj+i2I7V3V7pXdFRkB5EgphPRGqqODiPNGzbMiAQME1F2wjKyXBEZuoCbM5QulnlrXaDaG5wmvBb2tYvRgyra0r072JB9Ibx28W0+99jpcNBM+NJ7RmvtC3eFCALt4nNZe0RzA2zAm6M2oKV1l6gKTsa3G5rfdPdp0OOgxXEbaeUbRE/IkPHlI5ozIgPu4NKDjXn4grit3XUtjSmPjNZI00hdF1pzxAulJbLAZTpThuC23vB6w8cLsQu7L9SyI+OFkACHWlbwzJ7PRBPKDmXbqXuBBNIcDUeHmVoKjcRmhm0bZkoliLrhe+88TlEpRbm1u+CeL9116AP19mvCHiANSLpQX76Bfe9R3uOA14USAy0LEn2zRAjF7ybtUXGbeucuQE4YBZEMAeGKl0DePnG5XanVqXVlzxViJmIF1e6q1oEkgdg9XSS0/72tO2nVHZrjavdhgdSjfkmoCaj3NVoCS3T/LKQh0ZpDVMQGhvOM/r3fJ0plebnxkq99r7Uu5LG/9O7tGiAjEL1zuBTchSYj+P2Dbs3ggu87xSu6r4yPb3BfejduBFRhR9j2K12lDqJV2Bd0OgNyH7x4pdkj3grY1GOwZSLa3oXLPOF1QYYzIgVQIgQpO5pHNApeNxgydd8JOWPsYGdelwXyiWjRe5e1dTFZK2EjtJ3wjbhHvJfYEAfPU/8RyhLaWnfkTk9dvK4b6NBjoP2GDo9odeL1iqsR80OPaZYEUTHtaQRK693QY8aaMKVKNaF5wzVgHOkbbKSJY/7nP8YJIorhZDIhIxHCFn2QwCWIdcFGQeYnNA+IjfjrL9HTI1Kc2G60cGScYXyDP/8K5QE5XfDnj+j8QDAQ/KTvh12BBlPvUhcPYt/g/JN+LLL17urzTzF16sut9wq7wB5IHpHU711eV2QApgc8Mvx5tL0A6ytUQB+RcsXLDRm/QHylyY1wxdrY+7y9D7BEC7wVtG19iEYcX679PisDrRq+7YStxCEAHxz8oPnf/PuP/Ot/9S3/lXyHHxD/JWzC/6OfKf/rP07Y4fo9ODg4OPie8nvb3+Pb869waZ97Kf8/PLy844tf/z6FQ/w9ODg4OPj+cro+8nr5+L2Lgf7z5+gh/B4c/O3hRycAk86UBg1lSEZOdnfd7kRpiLZ7H28mJDDfGaYuzPLy0gUPVUTAcuqCLgmGibpuyDhCm/uHey3+wulLAc+wNyfVK1pB7EwVg3Xp/aOxgyleHaKQ9x3tmczdBbytwIpOT7j0/mII1LpjEypRHRUwBqQsSFRkukce5YyGEqU7I6UVkIaq4GnAW6OqklrrDmHfsXHCvQHW+3lj69HNIr1bNwdBARtQqaBgBlUGBMFomOy4ZhJnRAZavVGnc++TxQkzxA2o3YlsidieiWxEFLwpS8ws5Up1oBnBjd1nXq/PhBqtFYonBvf+/b5ASoQ7Eo5HQQRCTrR9Ay9E9HNvY3cqS5qhFCRGNCuwYXYmlm9pwxfgPaqW+pEoCR/PWB6pgBdH4i785wnkQqw3ZGjE+AT53KOek0JdIDkehlcjbq+o9Y4iwqEtVNN7x2z/d6oJSYKev8C3D9Sy4DIRT498cTLWj6/cvv1ILDv7slHTgAwZU0jjSOSEmyEiCDuaMlGjdxdH3K3UAa07xEOcECAM8YamjAz3yA9RPByELl7KRHijfnjm1//ZP2MX4Vevr9A2LM2gDYsNly5Ehlt/vbYhkbto2xx8R9VwwERAZ1rbiXsctnv0vdd2JAKvpTvqjXtP9kY0J6JiOG4JTa2LrQHauvgqXpE8987Z5F04Rvp1TXdKiyfUKoMmmlfQRNsXvPQNXtcd5YqLk4boztn5RNQVHWYwAMEl3/vF995pbIlar5g+YPsnZHqCdUfGEcFIbNg4kO7Ob5d+j2p1oeYTqn1/UHbkZD1GWydEldga9ZRJteDjCHvpQwVNYfceOV+CyKD7As2JfO8+TlN35baGZMPqjkahuhKToHLBlxuxvZLGE+orkqfeW1xfkKyoC6KOENRxhFiIkpDWkNjRbLh2N3uMZ2L52FMYbOjHSx5BdoiCeMHbDbMT4iuaxt5fPj8S5QUxI02K7x/wFuAD4YLUjbATbC9oc8Qu3W09vu17Wh2xCib4uhOnN/iyIGmCshDLRygbaX6CPBP7MxGJPqOxE82IrfZ76OuN2I8I6IODHzLXl2f+l3/0Ff9nX/nnv3rm22X/m3/Rf8fv0ecM/+Hv2CH+HhwcHBx8r/n06RPxNn673Qe/Ie/5KV9d/5CV7XMv5eDg4ODg4N/K6faIhHTT0vcEbcbD8/tD/D04+FvGj04AjiER64aHs7bAizLQaGrU/RUdzkitxN5QU1QaEiO+l+6cjCBsJKIS0YgIfN16HGvssIEPU4+zJdNVMiXqDfzSuyQdZD6DGSZBzCN61/owoXkQteDe+25VE+h4j7Ht8bWajGgJHe6/c1VH7269QQORHSzQSOCNhJJTpgJF73G2XnuMdFTEZrxdSWpIK0gNMMdbxcPQIQGV8B0ZHpDwv3BGSp6I8YL7ApJx34nQu/C64DLjNe6i9b2/OMDDIEp3Mafcl8MG6ydifACH3RUjw74RMhOyAA38ROSBfS34ZBRLeNvQfSBfTujtE5ou+P56d5wG2AmlgjrQnZHNQW0CWfu5338JTUAfsFiYk7HngdogWiFo96jrRuQZb7cuYkePBxcUakWmN/hy68cxO5HHHrkbDfcNltLjtKczMV3ott4KcYN0QYguoGF9v+TpHukcSCjuKyqQ0sjl3U84X05M0Vi++ci2V+rdAulRiXXrbvEkqFqPrs4ZGccusosienf4Ju0G8ObdeazdLYz2Llm8dTHMu3ivKoQo4UFdFpb9V/zJfOYq0SPJqYzjjESjYBALqBMiPW5cByT1zuUUCi5oUigLqo1Mj4q20xMqdLd4c1QDSfd1S0ZSwTSDKPl0YcqJpRnb9hEPRWLvkcXQxXQtXXjOF6IWJA843kVG934cFFx7zDopE16QZMR+6+kBBKpGUiGZU1WpdcNrTxOgvBJFYEjQKtRKTgPGiCK9FzyckIqkh76vRcEmIl5RVbCRvdwo4cQ49f0XG8gFqV2sjrbD6S1EAcnQHElC8kpx6/ecuxDqbSOPAy0cV0W0oSKkQalxprWPRJqI7QXxnTSd2WuQxgE7KxEV1a3HVF8usFyR0SA9EXVDvMK9b1sFKAUtN/TNe6w5tdQuAgMypX4OeUUl06yh3vC6EuM7qB/wsgINS2eCHVKiSQUKbAUvr8T8sx4X7wWKI7YCI9QgnSaiCdzd7y5BWO5u4woaCWkL2IhKHxRhGJCH38PXD30gg+jOYz2BVXx77ekPLvDw5Xf16Do4OPgbQLLx/h9/yX8kyp98vPF//M//JZ/WHf9Mv4Nnhf/430v8g7dHx9LBwcHBwfcbke/Xs0pcSL88sV4P8ffg4ODg4AfA90j4hf4c/cmv/g7zevncSzk4OPgt86MTgGtxQjNKQT2IKmytgnaB0EvrPZvRkDx0x1epiCthiboV1AZaa2RbIT0QVLwJ3gaStd7ryQ41CEmEKWoZ6tIdkd4gdaccmrvDd+8CUUTc+1ujOxdXv3+9QCTCBAnv0b2pR+ZSF8AQMVSdgiPqqGY8DcS6EghVMtB6j2oLMEMlwe4Ehagr2ACquAVU+t8ZpYu10iN1TRyTDCykPNJa6b3AIpgO1HCaZmL7hKUJC0e1UX1HADfDvIIr1aO7Y8uGth10ogVItP7+VEipx9PCipYbLoHn3rsc1ZHNET1jsZK1krUx2UDUwo53YbPWLpCFo/TIahGF9Qr6ANW7gJ8EESOZke2CqDOkCYtCiUbFYPgK8YWiI9E22DbIA+0ukBOBlwLTXfguC2oZFUdUUZuJ9gHw7pwepntsrSB6IU0XvGxEK+CNhtGWKzENNFdiuICfobzy1esnsDNzGpi+fM9NhOvzlVurlGy4GO3e/2sRfW/LXbyl9V7niN73nHLv8/0LkT6QdI9kNifCCYRojtdCA9z7EERrTvPgF+eJT+rggcYJ0YLmAbZGlJWQhLQgpEA6Eb7R87elx/CWV0hnGB+70zVN9+5kR6PHBoel7ty2hPuOSGXKyvjwBCV4fHri4fLIN998yze/qGxY7ztWJWhESgRBSCb2QL3gFNxyF/AVognVhWgQPiCtgiTEnVgXwowYExpBC6g18FghDPYdte5mD+/d1ZBxF6Q2ImdavaF57uJ8PhO1ITQkn4h9oTn3wYIesW15oHkgXomtwjx2J/f0QLmtmFfUjOk0QAtKLbTpjG+OtBWbT7TrDsMJ9wLT1PuXveCi1LoSqtStUGMjfGdIJ8rLK+SZJiumguQZj4rI3O9ZETC+xetO7E6Y42WDPCKnR6S+4sMbAqXGjWYTIQ0JvQvTA1H7MAaAsxBVkcnALn04JQoutd8DRZA0IsmoPhBMIEPvEmfuTuu6I/NDd/GnBJXuPE8zlFe8JWL9AKd3xFoQNfT8Dtle0Plt3/flmf8ve//WK8uWZOlhn5nN6R4Ra629zzl5qepusppig9ADKVAC1IAoURLAPyAI+i36Y3og9CJA0IsgCHoS2GiIkkCyqy/VnZdz9l4rwt3nNDM9WGTyomaziszKzOLx72WdXDt2hN+me+4YNsbI7Qfk8rMa3pEbcgzsciPHO/LyU/j6F6T0P8BT7OTk5HfFsfwpv7Eu/Z3PV/73/8G/zf/9P/sF/4f/6M/xP4AK/L/5N43/yc/+uL5QPzk5OTk5+Zdx+9MVqdnhPwq+/dXf4uXj8x96M05OTk5OTv5S/PIn/6Tq3f5IOJ+jJyf//eVHJwATO54Gc0PsBUHQJthxx/uFcEftSjdBhkMzfHugcsGyExxoCzQ7OQcsCbFXf6wIh5dz1toNOwYwgBXiDmGkrLgKy9jLaTeT3L9WbnIsMCG14xHEMTBZsMcOi5Q4N3dSQJc3EEGBbo0QxcWJPJBZgnHmUwhu62/FRHyUW1UXRDvCQVorAdguILM6OpGKRb69kEl126aRx0Y0BdnJSuQlqOhc9mAuAiYV7yslso7ciZCK+RUhxCkv5gS9Pt3S89l5C1w/lxidgcGz+7YhaURf8bmhc2BNMAvCNsKVtCtxHMRxIW1hzu25j430Ozwe5OWNQLA8kGw4oDaYx0T2HbPPKIPOhJns4RUzLODzgdORRcpBKw62IGuSuhD7DziTct5qubXlUbHC4ytmK6prifLr57p2xFEmIkHmwC7fABusC4yXiiYmiLww5zsqQfqGSvX1HjH48KqFvr3cWMy4XDrrx8Y2nT2iIn4R4je1sWolYFpFMZewm0+n77NLt0mZH6VivfJ5PaRIudM98WNUv2wGM5JHa3xZOhKgPjELul7ow0EOXMGXa+nw4716gC2QSDRr46IlkgOhVwR2u4IM/DjKhXzs6EVJaU/x2cj4YLcbrg0juUdw7aDiJTiOSetJpuJTieMB1vEUNDZQI8MhRznXW+3jzIm4o7o8hzqEjIlel4qYlqg/80mkEnFH9IpoElDZ1C3g+IDlCq3WD72TvdVnxlGRyMHT/Tuf0dSdiA1prxhCzuqrTbwi3eNgHu9Ee+PQhs2B+co2Egvj8f6otRLg+wdBkmOH3ontg+zXim4/HtjtG3y8E0NxUdgGuJNvDYaCKJk1iKCzuqVTErZA1xtxbOT+pXrDj4Hc1hKG9w+EHX35jG8PMpVcet0Srx3JScZE2kv1MYuSc8OubzC+Eu1KxjMeX7IGXWSF8QOZn5H1hdQ6vpIT0QVuC/nx63K3747blRy/BvuGzI187HVO+g273oj8FzQbLO0bPD7I9Ts8lXj/nlRBbSG2X6OfPsOYgBL7gbwcZGyIvvx+n18nJye/UxKtBzwgIly78e//mz9nePB//If/pIbUfk/8yU34n/5JVTWcnJycnJz8sfOfzv8XwR/HF9f9WHl9/+aMrDw5OTk5+RtDSvzR1Cicz9GTk/9+8+MTgJekTQdtiAo9y0UnAjEnIh2xwNMQEk2BXEmtnl2dk/RBLCsegjhIBmYdacLcD1pkCUNWrscRQreOTvCZzKlo7+gMyK1cwCnl5l2NnAfTobOhtyscDVdBrKPydEF6kkKJ0G0ltRH+KFGZZ2z0/IBZYpmgiNXHwEtF+U5wftOBagSOjAaS1cspQYTj9zv28gK+QwPfJ9k7okJmvWl69ZwqDkPAGq2/celJaGfsD4Y1PBOmAyvcf4BWxxxpQMD+QG7fIINy0n68M65XLGBKI8gS5eNAFA5P8tgQbmR2Rk4WEqyTDIQV952YCe0z0hqZB+nJOO4kL+zpsL6yZMUeR0wePLtwfSAoGQp6RVRKRHfHFRBD7QWz53EwiO0BMUkMMWXJJGwhpJejWhq8VPdqbL9EVLDWCX3BY1RccwYhrYzpeq3OXSvHbKbiEsjywvjFL7jHO8s3b6yvF9bWaM1Y3wbHHGz7wbFNxv5gRpa7MZ3EcHnGN9fFD0FF8FpFOlf0LSUQa0CWWBweeDjzOOpnBkckX9++JZtybWCXGyaTfrsgc6KrYes3fBxO6ETkVvHD7YI/fl3XQzjKtTpg05B1AVGWVNK1opnX13Lxxk6kglyZj0ZPI359p2ljdOP+vjHdn8dWQOrLfaSmFsSzUp77tf4PTh7M4yvOgqKIDpb+iuWk9xs5goEgutCXhrbG/v59dX7HQH2p9eEOYlSrsJf7XFdI0Eh4DlNkNtJA+ko6YIIsS2nAeoMmxObkcw2m3khqreQQMjfQF8YMYFYcuSTT6/yET2ImHgfRFiCQtQGC9BvuAzYnl4alkrM6p5uuDN+hf8OYht6+JeckxRBX8He43BDfQbTc3DnIdsHWl4qp1wF9QUxhr9hmeqL9hTzeyfVancnyBsdXdFGC5en0vkJficcdtY2UgxTBrp9Ib+Sxk+2tYtZ7xdATJczm/oUgkNefINoJeWDi6PpG+F73mMu35P0XyLd/l5x3/O7w+jNym8QQyPqZ7zvSPuE+QZday28vzPsHuTSEgNt3iPz4HqEnJ/99R0X4D/6tP+W2NP4ff/4r/j+/+Pp7+dy//yfKT6/nP7hPTk5OTv6m8EfyzEp4ff+GPtc/9JacnJycnJz8FTifoycnJ78ffnTfXveZzExyAH1jRqCb01pHJckEnRNsJRdDYieakfMr3ZRYOhGO+Ya1zoyBiTyL2xsyNswnsl5L5MqGPr4gfQUSzR1TkDnxvpDHgH4l54PWtAyXmSyXBZla4pQ4Eo5oK7F47KSuMAPUCFvK1ZuJGqR4RR77QuZkZmARiCjaFjSCbA2eYmLMmjqSVPAsl968l2sxHLusz37aLFfrtj9f6xXJSoIORBsSkKZIOPTg8EG2F6IJeRzIsZc4m+9wWdEoEVKY1DsFOTdkvcCxEb1DdkY8ha142nExJDbMvmWmlqAN9Esr82wEPGOimdX1Kq0jcYd5oNfvsP0HZOnkxwOWjhxfQH8CuRLjjvTP5HiUvRYhtVc88NjrfX2CLEQLMg1Z1xIu9Y6qPYXThePxjruy2CCW9ux+pWKIU5kpxDGx/kLM93KkplbfrySi0LUEd2biUsfXQrjeXmldmdPZjoFeF5bbjbZOFoSbGj4nY984vj4Y98fzcxMXwRHy6exVKQEsj6S0ykB4ipkJqBAZFffszpwHLsYIZ0N5f73BcKYIbgs2Hvhex0q8E2JkvBNUlzY+QDspVyIc9o86Z7FUHHLU9vgoN7EgpArCUtecH4gObO2IBl0Ea9XZej/uTN9Qc9yd8IB+Bb8jfUHSEVt5WVcuOI9U7q2DGiaKSkfUWJYb11tn+yJELsy54zMARVhgPgc30pGpZFfyuNf5tQY26xrz2q5svQT8Y6MtBswSSFUJn+Xs1kmE4X4gYcxjIOsVREgJUr1c2W7I/k6uF5gDLp/r/IaTxweit3JXi5QIrxXjnghmjVwXdOmYP0oc7UKzROcdua34/hWiYT4xM2x9I/IV7l9gXet6lKwe50gwRa5Wtwo/SLmVcL2Vw19Q5Aja5yty3GGCH+CLVA/vmLACTbD1RsZBumLXT2h6xcqPB6k36Ikfz+GZdgOM9A0fv8T0J/i+wfJCzq/o8i35/T+FfkHXKxEDzQ0I5HaF60ruX9HLt+Sxoz7Kod4E8Ya2W7nS+0rkHXl9Q8Y7dv2WeLz/IR5jJycnvwMSJcX+pX8mIvx7/8bP+Hf+1jf8p7/64D/8B/+Yf/6+1RDbXwNvHf4Xf/tfvi0nJycnJyd/bIQ4of6H3gwAzBtvX37yh96Mk5OTk5OTvzTnc/Tk5OT3yY9OAB7aYP4malUpzdPJYZg1ehtE60SUJxZTuu/EIrgrJonMQV86KkLLqOjkeQdXWu/4eFTPqghy6bTRkN5ADIk7rV8Q37H+QkbAdFwF2kJm4CjqoMuC5SCXFdn3cp1FlHtw+4Iu1+rRjP3ZodpLtEuFZtASQlExwh2TCWOQspaLUvrTjUuJTqz4+AHtL7Q5QScyEllX4rgjlysiC7lA5gDpz4hiRcYdubwic3sKQh/0MEwdB3z5hG+/KPGzrch8lLjdVzIFqMjqfLlV3K506IYYiHh9SRuz+oh9QgRK0L/rtK5sX38gUtD1E9msHqSxkXqB/lpiY2zkHGh/Awyxxhx3Ig7aY9YxjECWBseAtiOiJWrPR3UG6wLHICXJ9EpMtuUZkTzrvFuH+y+hXwi9wXojPr5netLdSsgXQzJRW5AZMO9ob3Q1IrP6XnOCCSFCy0HXwC4NVcFH4zK+8o0Ey+0FyWQ7HDVHeqOpYU83sMiFfLnh1we+7fiYuEdp4/vg7hs7s4RnBdzxsZPSqjc5KrBbzHCenbdPUdUlmSk8vv2WoCHx8ezFdqZ25piQCsu1RLmZiNQwQaKQhiKIrrBkuZGtPR3hAnFUX7QJtl7JMRDqeFs3JAPVwF2Y4oTDfNzR0eA4SvQ9fqjhhFSkaa3LdgUmf+vvvHDpnf/vP/xH6HLFZKWZoDE5Itjdya2Oi2QgbIR8Io8NtZWYjxoumB+IAdqYquRwWBvEQOJA24UIxY87jAO01QCBK9hzmKB9JsiKcZcFsUtFXrcV8VnuWdFy/Ishy0KfKy4LMe+ICK1fUHe8DawL0a74thNY9W+7k+sVjVlrJCdYq3UWQuwHjmGqyLKgNLCOjHcy7ki7kNcr2a7oeEdfviGOA+5fSBsVw2w38vG11krveG5INiIC6ZcSXTKBQD59AwwyZ8XfaLmEtS0wL+ha7zG//6fIekXzIHMpV3VMphjkHW039OVK82+rp9mlRPP9HfId0RLJtX+QlzdEEtVENCEnwmTtL/gxmXFn5A55Q6bC5dk1PQfEQW6z7kkOuf3w+3+InZyc/E5wu+H2+q98zdva+bf/9DP/9p9+5v/6n/wL/m//6S/4Rz/c+V3rwP/Lv2P89PK7fc+Tk5OTk5O/LrbrB/fblz/0ZgDw9uUntLn8oTfj5OTk5OTkL835HD05Ofl98qMTgJtemBqIjGpGDUVTsE51jY4B6/XZ5RrkcEIV6KhUDK60V8KPElmprsvUS8UeW2M0YY5Jp2KavStNgL7AsTNH0DKR6eUSnBvtKQSTAWtHIhEr16tIR01Kn0SQ1itadU7QQLvhAZBIVnxzhBMhiCZmAnol9zvZEvwDaW/VCzx3xBqEYdJRe7qO19eKm7528A1RQ6WTfq8+3hhg1xK4rMOeENuzR3UFW5nSQVdiVKwwdNDqPk15Hk9Acq8OVBZQKWfzQkVe50DmxuXtJ0gTPu7B9vFRjskuzHBSyyGMKO6N3RXf3pFYS6gdA/GjxJ72iq0XFh5slvSxk8ut4oWXbyp+uBsqF1KF7Ats78+o4AVk4nEnwxBJ6Eu5xmPScMQaY1mI7UZuB1ycaRVxPbNhcsEm1UMqgi5L9cKur3gomQPJJHNWJHkuSEtmbLivmN6Ix1dyTi77VjG7UdeNe7B9HWTv9KVhKTTAFFSSdlnoa+c35c3r2ye0Nb7/x/+E7//il+zHhrvW56O4TyKjzpcnaBAVFE48u6QleMaXT5SncK1GzB0Rr2vFGjLviB8lfENdC7GhKeWoSsrpngJjR7rVQENWjHNGI7wjOVAzkMAPJ/tKNUVPwPFoMEC7oyF0aaReSROQAHsl5x0UPA7+0a/fMXvhjpKyoOlEGoGgT/e460TbCmPAFMQUSUO1umlrPxO5vlV39eWFWKLE9Hx2S6IgYP0Cjw9YX5GY6OVaEco50CYkx7O3OmlrQ0ZgfWXOO1wvT2d2Evc7uQg5DqQrLSZdJtf1gkYS6xspgxRlz1ds/0Lqij++oOtSYqwn0hQ1Q/oL8f0/RfWCX67VS92XiiNHyCawv6O2QvtU19z6qUTe8V7JAFCJABLIstT5RNE88Dlp0ghpMAZmb8Txy0q8sQv0a/U092cUeG7PyOZPcNxRfUFzopfPzP0DZEWubzDvxO6IDpISrNUgx0TmV2y5ktsDu9xAJ+TELhdsKXd9yq3uqf2NMf05YBPI20/LQXwMclnI8YXcD1hvYEoeEMednH8cE5snJyd/dS6XK4+/RN/ubzp5/+f/g5/z7/6d7zg8+D//v/8Z/+Cf/cAvPvbf2fac3b8nJycnJ39TuFwufzTJlcDZWXhycnJy8jeGJDmWx/kcPTk5+b3xoxOARStiVV1JhEbDL9U/K5n09UYcG5iRfjBC0OVC+kHEYDEl7cqxO8uqwLNYtzXSD/SyYHNlzgfZpNyN7YJEIBmEKRqzhAR5ikzPzt3MHdGFRZKMBzlbpR1jZLsQuZP+FGjtQsZGLq3iiFF4dthCr9/td2hSQooJ0hUMlBXaWgKyKWIB7Uoed2TtMB+IVhdr+iRjgCaRgxKZO8gFZK9I1n5Bby+oKCFCpmMmCI4jTJ/lnuwXZAzCx9OxnMg8yKiYaI6ttmv7Qqih6pgYqguGYy9viDv5uEC8k/Za0bbXhevLJxgbix4s9oY3ZV5f8ePXdZwyELlAg4iNI4PQC7p2Yu6MfbK0Bo8PcnRCgjx2pL8SScX8ykHiFUs9Zjmtj4NcHU8hsyNJxVSvr+T4HpXkIpAvPyEzMRXMH2hCiJbI2SoSWmKAv5P6Wl2nCalZ8b5BubT3nUyHhLf3L8TLlfFQQivCO5h4Om0aZoO21FCCNcNMaaKYQltW1j/71+hvL0irB/3jhy/4DGYGHsEcg2MMZiThWf2qATorElqsV49xBmYNZJLW8bkDQdIQP1jsCjqYI8ijxFJdDQWaBNGknK+j1QBAUhHN2spZHSDLlfQDdCUlkAiwXv3VorDvpDsY2Hqpc9PXEpDXK3kcIEnIMwLbJ4QQH0nkr9FQyEG3SW8LUzpCx1MZx6hO79zR9Vo/e61p7R1rA4lgmuKHYz1pvTGOOx4d0YXwZwyyXkACYX9Gp0M+duT2CuqIJ8iC+jvYitjCYnW+Z7uAOzkP5HJD3JkszONBv71AHhWlbELmwvaxEeLIPFCMDCWmk82Z+4YxaLefQuyIHOjthm6jRNM4iMcDax1Xe3acXyueXZ8d5JdP+PYFVMh+gTHx1pD5IOWFxJEB8Xjg7YqMgaiCHxULL0mS6PwCslZvMAdB0l++Q99/YMxHif4vN1QOUi84gfuG+gHxAf0N/F4Of5/kequEh7jD+nPSDpCBEUQDSHw8KklAviHHB2kLxIOIO7Gu6PIC8yByB16RqC5hf+w1EGEX8gjk9rd+vw+wk5OT3xl/+qd/wvd/AX8VM+/L0ngB/rf/oz/j3/u7D/6f//R7/k//8T9ln/FX/vyugqrwv/57f8L/+CcHcEbKn5ycnJz8zeBP/+RP+U/GPyD/Sk/RvwaSGiA+OTk5OTn5G8T33/yLP/QmFOdz9OTkR8GPTgDOOavrt1+I/c6hCe/v2HJBloUIZRBkTrq9IvtX1AdHBIuUO1LSkB4IB7IspDhqRuRAYiJi9BnlrJWBRi+H635HF4PjQBetzsvWn67KEmhMvUSyZzyuaACDFGNG9fY2c1heyXkh0pBxgCboKzEPPEuA07UhrOQ8yNhRvSFAxk76BmpIdiL26uw93mF9gW6kKJkQcyBmFUGriUp178qyIHMv1yyOaSs3sjUkkm4LizjZhUOEtEbMILIik2N+AFJRy5fXEjh9h75C68ixk5cL3hacjoyAZTDTCVdEX1AvITbbg0RwT1oIGckcyXF8IbajXIdZx10AVSO0BHnmjraF9RhkTLRdUIyIDvmlYp2XW0UWaxIuSHurQQJxcvwAj+zLAVMAAQAASURBVAekkP2lREhdqlN4vcB0VLNESzMWM7S9Mt9/QaqSuSL9WtHcOK5L/W8M7r8kjonsG/SOtXISC1eyNT6uX9lo5DHKacksXV0T1frZjmAR5yd50HvDemO5rCzSkH/xKy4BvLxw+el3v3WSp0qdjuNgfNwZ++A4Bj4PMpQcO0kHEYYIrsLX7z4TvpPLKzkniBKmzBmkDDS8rncVmgqak4myjTuBlVFWOmz36qVdXjAmB1Ludy2BWU0qml2VrhcW29nSGMNo67dkRMVfC6QITRxIHhZkOplCtI4RZDRQJ/aB+wN0IbXjYyKXTgwI76hKuX9jwedR6160op5p1cWbg9g3QpSYCTPKmO0H2aVcvUeW2315IX2HdkHmhl5fQBTmQbu8ol73mKr4bkQOgk64E+74diBLI6ajlwXuo6LgHdJWYrzzmMGYUl/JjA25vJAf72TrVby7LCALHpMMsHlAe4H4C8x7DSBcrtX3q41DJr01TIUcUekH4yAPR9ulznkEft9p4kifJdDPDaxjvSPzQPoNjQFzQ1SRtqA5yXAiPup4Hg/2/A7pL+QYMByb78jlBnMQciV1J0iSBWwl4jeDKhf8Y4frDaIRMxFWaNXrzjGITPJ9h+sb4hVF35YbzAdpjTwmoqMGaNZydYt1woVcXpiPr4jesOOfg333+3+InZyc/E7I/Xu+MeHr9t/w5bUY097gv+LQ/dNPV37+duHf/zd/xn/4D/4J/9E/+55fP45/5Vtdu/Hv/Ok3APz9P/sJ//q3L6wyeXv8x/9dduXk5OTk5OT3Sv965V/3f4tf+S/+la8Lde4vP/y1uZza7Hzz/Z/89bz5ycnJycnJXxOv79/gf4kO4PM5enJy8rvgRycAP8ZAjafr9rWcqa0cn0HFgiICA8SCdrtWL6kujBi0cWAXUHkjEVIbWCM1S6zcD4JntPT9K325EWa4TZoLIgssGylPRy5RscNj1Ab6AtJIqU5WDwURUhKk4/GViEbLRPWCSCPGsx94meWuo+MOmkprWUJ0CsRAeTpPg+riFS1RL5/CpSaVv6zomMhlJSOhtXJ5+oE2RUWqx9SkOmJt+e3kkGaivTPnM0Z2HuBBxkSykzqpIuEgr+WMZb6XU3k69BuWSTmZo0Q0lJitHKiyU0pyR5e1Om2jE3rB58Cx6i1lh6WjYQQbcjhir9U16lEi97GVOO9OzoBlefZ7JumtXMoRZAxCnu7olwu6XMA30n5KjA8yJjkfRCzQeu1zu8L+A2LKguE+yjFrC7O/EcdGyjOaegYYoN+Sc9Q1tXwq9+u6AkbGB6YTegluv3z7Dg9lPq+djJ2gY4vBSHS5oASqF14PyPvO344Pfnq70T8r6y9/zeXXP9CE2r9uTzO5YSRNhWXpzOkVBz3rGMU8SnAneczJY9uZx0TbteYQJEkfaIJpp+XO9aJE69BW1sWYQ9AQ5lG9zGJKqoAkILS+onPQb58Y8eyqTa2ObJGKhV7gkQtjuxPaMbtUjHvu1Z87NybKnIPpA6yXIDoHAcQIPCp2OjGYCVrDIT2rb1vzgWjHj7oGJQatKdY7hw+gop7T6nqQfiHGgEyEC9kmeCKtEerE9kCWRoZClPApc4AFguHueCSw4scDXRshCVnHPdzr2vDq85brSl8MgPn0Fbd2hXDSXoi5EwGyO8SO9W+J8QWTEvCZj3LQMis1wITpO6hjaczlQmirhO1IpHfSFGQlfSD9Vv3aviNOrZd+Q0SImKgAy3dMJsQAobrHTfAQVATRG9msYsb3D2Ah3n8AfamhgaikgjY2yEDmgckC2vEM2D+QVHJ26Ar+FZUVsdoOxju6fCJEyfGV0CvYJ7TdwL9H5h1bPqNz4m6IdHL/CkcgXZHjjqw/wb//Z8j1jX4M5NKewx7npOTJyd9U/vGf/zkA1/+G1yWC2w2A0b777X+nGCrGtTf+d//un/Hv7sav7l+4K/xfLoPYHV3tt+/zP/z5J/5nf/en/Nm3L/+luGeJGlQ6OTk5OTn5m8I//vN/TOeFP+HlX/m6EGe73AH4+umXbJdKuwgNwn4XVSpyOpdOTk5OTv5GIQg//cW/9pd67fkcPTk5+V3woxOAjZ0YgrYgQtE4kPWN4/GVnlfIA2sXXD5gfpBT8bVDHhXJa0834HopkU+9OigBehIOykCXhSad9B0PQ7WTlABCW0s8pJW4LA1ZV+I3EcymzJFoBKoLOR9IW2nSMF3LPZizREynuioJJHZEjNYVGwHZSugV5akfgwpmFUkrjKd7OckQpBmIkVkOyzRqX/NAL58qfjm94pRp5RD0QGIh3VEqytdz4r6DNmQ/yCGIbqTcyNwrTrmv5ONL9bKmAAfZF2TfkdfP5P4DSsdS0esr0RZEFFkXpN0gj9ovAYuBWJLLSu6PisJdFvQRiDbiOIhjf/YqW4nMNNI/SrhLq97WcJBOPp5dtQlsByGCWKK5gzUsDkyd6SXiC1dEDBkfWArhdyTWclhbZ5Wd1q7sAjOjhLDLCzQjt++R+EwaZASiXs7kbNCfAn9bkZggL9VROp9ier/B/gX1vUT5dkGXBY2dXJZKjfbA5GA35xjKP+w3xEG+/8pn7nwHWG+8SPLTuVevrwitG613tBm2GC2M7BVxGb5SJlfnOCYyndZvtOXGGPfqGG6G+IG2K5GTmc768onwYEZDF2GdA9zI2PAo7TeXF4SNYx60XBizP6PFDroqJp19v+Np2PFOu7wRc5KszP1OhgOT4YZkoi1KDJ0Hqk8HawaRJbiWY1rLVeqPGkY4Ap+OyULER11jqsxIaB23zpyN2N6hJzkBFOZ4CujPWHav48DcnlXASaSh4SWixiyHfkpFWve1hhHSAUMUWjdsOgk4NXSh+2BEELqgx8dzELBEhhyTFOOtTba58zU+KhKaRw2U8Ki391Hp9THIBu6BsoGszLHTbQEcbUsNcpjRrCHHg+rq9rqnNKv+YhXQGyWIP3u+I0lJtCW2v5O2luitDRQ0e0VWr6+oNyIdUtHlBRkfoO+QD8QMXT+hcydir3UfA+kXfBsw3iEF7YroBf3252AQj+9BP7FcVhgHYwO1z9VF/fknaH4Qj4FeXplTiPc7uX5GfiNSX9c6tjPQxZHrZ8R2vCWSG/L6BvrX+bQ6OTn5Y0BImn8AYM+fAG4vuL0CELry9/7+3+bv/f2/DcD/KmH7z75w+buf/gvvc/b8npycnJz8uNA0bo83AK6P19/+fru+c79+BWAsOx8v3//nf+l8VJ6cnJycnADnc/Tk5OR3w49OANaQMm0FJVqIIj4wSVSUFPBjI9fO8Alh4CUiEQLN0KYlFGUgSMU/B6gYi2xIW7BFgEZko8cAW8pByCT7StW+B3Fs2OUGkihB+gOiY7YiOTADiU7ghCUsb8j9KPGolXiUmdWF+hQ2iUBykLaUuzIVUZAQst1KxJWlxKIcJBMwRFaEJKU9I12tejUx9mPQ07H1RsaDkVLxyAHZLuR8QDphdWwqlnolM2j6TLhFidgw6Yi9kMsg56Mcr9lAGx6O+QbrG5HOmHcWPrP4YKagU2nLQqayLp1+acy+IGHQjEUatxZYLPjlyuP+IPYEu8DFyjk5N2R9g+HQX2BU/K+NHbIjliUoy0ocH6j0isyNADVmBjEEF0VmHbukBFiB6niWINlg+cRje4e+17naNuR6RVBMwPq1HJ7hiHXwR7nQs8RrcDLu5fhuC9c8wJJoiqzGLi8MH2hbqqfWErWGtoVjBimdtGq7TnkH+0QySIxf73d+GYrqJ0wGV0/Qzr/22LhtO2258KLVc9uaoSJkBJHJTBhqPHQyrBEqjOApUB/kcquBh+NAFFiupDT0+Errwd/66c/45T//NQ9Rul7pAhEHLg1yJeIg+oWWG9NrSKA1YbGF4VE9y9OJcSCyoniZ0ZuU4zU3VA2xFZGJNGVdDPPBpgszB7SlhOB8xsJrgGkNF7A+Xe8vKIkzqg/3eOD7ndQDHLTXoICIQupzKGGCONECErJfkNzrvF7L+R6jtqliz281eDEnKTUTIbaTAjmqe7wcxjuaTmpDfRAaeHbWZjXYsd5Q3wgaj9wZxwFjL6dvKhqNmI+KZs6sXtvLK+p1j6jft4ptXm/E/gOoQQTt0hHriEMev0JsQZZrnVtdAQeXikqPiVhgfSX2CWwQG3q5EOOBvXxL7O+oXshUfOzkvMM8kFhJEazdiPlBRiKXV1CIZcU/NkReCNuR+w9I68jsxLIipuDvhL5UosDyGckH+fLK/OU/Rewztnby45dIvlW38LrC8pnMD3j5ae3HPMiPLxXFvhiyfiLiQC5v+Ps73H6GsKGvP8eP+x/gKXZycvKH4r/4b+nmH78VhhNlkQ7Asfyc0IXXP1uJU/A9OTk5OTkByvH0G66PN67PL7RDnNn+FIBff/cXjL4T6oxl/4Ns58nJycnJyR8j53P05OTkvy0/OgFYZMHaM3rZq1MUjXKrSoKt6NNlxmXFZ8UqiwSJoW0hRyBrVl9kPpB2QbeDlAvYIHPgbjiUMDOyhBK5kSPLkRd3aC8k1VXKAJFLOdnS0Z6IriXS9E7ODQ0DnOxWEdZ5lAMvlBRF1mvFGu97iTFNyX6BDCqfdSGHk1LdotkauD4fIkmKkZRzjzGR9VrRrrbQfcLaK974mE/3s1Q8NUuJ2dZqu9qCZ6BxlBi93JDLC7IdgEO7YjExXXALMgW1GylJEJCJiRH+wNYrSwbd7OmwdFwGM4IjJm1+QuSG5IA5mL2xMTgCxubMFNI6VYpbnb9Yx579oTkm+CiBdO6wK1zeYO6EKbFPpEX1MtvLMyr6gcsF4RkXng3NDb19JuNArJUIrlmuVl3geC8h0jqg5cBUEDNMhPRJ6kqOxLqR4oAQKUiUkVvFOSKRbhwO8b6BXZn6Uu85HBmDY31B94oXRo19UE7TeAd/lFBpCttAnncAPw7edQXr/MN1IePBsg++88Et7vwsHZY6z26Ng2BDOR4bMR25OOQdyQnrJ0ICYSmhUSDshTEDTy8H9uPBNgekYm2FdJIDIkk11JL12mhz8PUjmbEx9VvmtpHZkXiQ8uzrZYA0wqOc8GT1vCas1lmomOPWV3j61EklUoEN9ArHhvJKXy40AbGDwNkjmDNQKXFYMmnWiBS8vwADtRfieAdJrDUkBomgas/o8wRrgGJRbl5UYNSJ1aUi2MUaEoHwIGNB9EKGMOlIJBwPYlZntoSg2pE4oK3osXNbGt2NcQwe04mkOrV90lQQM/LoYAuSjtAqgjmCtF6xL6Korqgm0i6A12c/3pE1YbmAvCLtFfwr2K1irPftGVu/INqQ8YU0qUGL+QHyQoQR2UhfyeMr2h/o8gbRyO2XJBN9/e4ZR97IXJD1m4qR336J9Qv4HdEbspbrPrXuXbFeka7MzWvwwB1pC3H/ypFRsc+PHwh1shnmG5GjEvhR8uMH+PRvEPsXZB/gIKuDLEirjmdkIzWBQewHsh7k+PJ7enKdnJz8MSMElvUP7Ov+jwAIacz2mdALo33z21f+V7uEz/jnk5OTk5MfM5rGMqqQ4U/+4t8AYNrg4/V79uXB10+/er4y//9cTX/W/95/6Qvxk5OTk5OTHxv/XZ6j5z9FT05+HPzoBOBlbWVqc0PaJPRKpsH8wDOI+aAtF4QDGQONxCeIXVF2lI5IwPYgr68wg8zq6+V4wLLCgIiKRZWhFe8agQgl2HSF+4RXrQ7deZRQti5YTrBbuUaePaFiWbGpmc+o6Ss5tqd7ubpjRaK6g9nrrNoLIo61CTPJtjzdxQtiYCrVryuTECHGLCfw/AB7KydpjHICZsXqql3QCFK1nH6U21fVEZxmvfp6peMIXRRdGtaF1js2drbLtwiJsOFzIq2T6YRv0Ffs9Y3Y73hS4iYr+zGw9QXGwWQy5nw6sAWVwETJ3Eg10q9sH8nY72CKBYRM4jGgK7q+gEq5qX1B2o67lvt6/YwoqCZhHcLR2xXmwPprRQdXTjFICf8lclIP0f0DNZDlwjw+QC/Ajl3e4BjEgxLR40C0V2+pNmLcn0LyDhb48QC7QsuK+tYLGc7MJPsreXzBPUtMCwV99rnGrMhh5NkR+4Esb+Vw1U6ub2SU41RzR25XtF9RDbQvpC5M7aQ+EBGmXviLXFB2/lxfyP0L330cSByM44F/HNjaef/ZT+sz08vZ2hOtLGOqxLiRnkgMTFYmwvdfJ7ENLuuCWDAPZ+qzizqzOmozefggwsnHZKygc1TfcgxEGrYsMCfOU1hNhzHI1mpNWMPtQsrC2HeyNebxBbELFjvXvvD26TP+FWZr2KJIMyIUH5P0yWwGGDI3Ugaib4hvWJtoTnqHSGXkpYT6yxViVA/4sZMEYITwdKk62lZivCOXF8i99ABd63y3FwxHftOlvFzAH+SiNDXSFM+BcX2u2Z1UQJJ27aBGpHB83EmCnEJqkLpifaCm+HEnQ+DyhraK8Gb7FVOS5bWj4mRrpK7oUk5mZsV76/UzlgJyLbl9HuWazwSdaGugN3I80H7D5yQ0EALtF9I/0MtnNN5RUUQMvd3wzAqRlwd4q2t6MdTvSG/keGD9DeUgYiX7Soyd2O5oX5CDp4vZYXyQas+Y9XdkeSVHDV3Y5dsawCBxGnJ8wPJNOfd9qy7nywq3T+DlRJf9C/hE1lvFPluQ4zloc3JycvIvQXOyjF+SCMvxFwAc/eeEdlIaYTckDi7bn/+Bt/Tk5OTk5OSPi+adzz/8jJDgu1/9LQB+/e0/Y/aD1jqvLy98im9p//iFB48/8NaenJycnJz8cfGveo66TfbLnTY6P//nf/cPvKUnJye/D350AvBQgf1OEGgvMSW3R4mokdiyMPyAudN1RQl0XfE5SqxlgsbTCVau0jwcXRZi32AKYgsW5UjN1sl8CrlNid5Rn5RqdKCiJSrqAtaJCFQE/Nk5PB7I5QJqZDgqz97g3ssRaL3E0Dme2bEJ2hixocfE1EukFKlIYwEJJczI4yCPQS4rYldERgnPuZf70A9MF0SyvqicjuijBEnKHZfjKxmN1Csjyu0oCqJKqFS0bQhKItcXeByM7aOicWUtocYPVMrh6yawv8PlM4wDcpD9jZRyH+ahpGxEBku/0NUJW4mj4mfRZNI54odygUon/Z3wjbx+YsqBRkdzEGJgjdCBT7C1EceG9F69rC2R5afI/RfYcqno5JEgB10ErJGxlTBvrcQmoWLC7Uoe72h7QxXy9sKlvxCS+P0HsI4TqK0wKzo4fyOaypUcA1NQUTx2MgNlecYGJ+IHIlqfT0PMkK7lLI0kY0dtrRjk+EBUoXfmxx3roAnL63fMfeK+PTtmlfQ75CTSSb3A+J7oNzycEPjnP/k7pN+R+YZdvyLXT4wudd3F012eJV6iSw03WKf3oI/gMQ48rAT//c6uySo33P05NKFEgvsgHsI8tnKIXz+Rx8TTwMezi1qwayB2JfY74g/SJ2IXIKB/U5Hry4V930gO/Eg8ko5XfevlQtqAlwv6VPLdd2a7IaIVDjAOdFlhf5C8EZG1jkjSVlyCyEHqrfq53enrFXnsDEm8NXJ/B+0lxLuSzcu92o2YD4TqdyaynMr+FWyhrzfSk6mG2RXpBz4nunZiOhnV760vLwT5jE8OllwZ8lEidLPfzihwOHETglkOXVNMwVbB/Ub6gxAjY2Kyk6wkjti1rgeZWHyQ05HLp4pTT6/BF3kmAriTbUUI3CcxnLCG+Aah6PWGqRMjmds7Yiv4ARJ4SnVx945vHzSsBN31Ahi6XmH7JUwId2K/V8T5/uW3/eXSLzDB+CDbJ3L7vgZ0Lo3w6oMWgtgecPs5OT+e68Rh35HltXrHY+Ifv0LWV2RZabrA/iAYxFpJB/aMfD05OTn5r0PISscALsc/Acod7HpDc2BxfnF9cnJycnLyL0NTUVcAfvaLfx2A1hqvr68cx8H9ftaxnJycnJyc/NfxL3uOThtsl3faXLnstz/k5p2cnPye+NEJwDGVXFZ8P7BHOVc5qg8US2SfEAPEGAxEFFNh7jt0Qbkj9ok4AuRBzmA/nM4kZmA5sKtWvKsqzMEhKy0+SL8xEHR/sKw3MjdoL8SuaG8lyPrTISyQaSTBcTg0RfYNkZWWgqxr9dJ6xf6Ws7OcijMdn5MMSL9BPErMMsMRfAs4EnIlteH7O9ZekLET/UpsH7RmJEKMDelK+ni+/wXSEWnVi6wKMwnl6VaurtwSRg5iHqAL81DUOrpvMGe5NU3q77VPSFaHcXriaViMckuijDE4tnuJuemUHXEhpOMJ2/7A9IocD+SSSEv8eJDZkAzcruRtwUzRCMwStQvuwdgfJQLHQRNDe3XKyrKgHkg+0MuVl2VyTMUD5PIt1xWWZeXLl+/x+QXTG9Gjopydch96I3NnPnuVQ4yWE9YL4XfCgabPGNydeWxwBHK5kun444GbkR7AAtcD5kralWzLs9/V68vleZQje0y0r2Qk3i7V5SxruZbHxPqKzFEO43DgIHMQckHE8VGu0+k7aQ3VFxpK5gHtFZ97RWa3lfz8HR4BY2DqIIEuV9y3ui6kOnabw9g2hgdzOsfcSTNUje7B9dr46obvK+6/6bitL8zTnwu3reT8QNRADVUlSXw/CGnVZ81CW1/LEe0TWzqWG86CRw0NiDpmDaRBwv2YbHvFYrfW6cuKpDFzcMyDOLIivjeHbEQG7luJlKxYb4QnoUbTpzN9HDRpoMLMckMH1LVBKzFyHuT6gvskdcFE0BxoX1AG1loJ54shMbF8Ont3ZY7qcta5IculOpclySyX/hgbx0g8hSQrhtoW4thAFhgPUq8cDrofBL+5FhcYv8TthYxgp8F4J2Zia7DaQgRMmVhSsc8+kH6pbuixI1opALrWAEG+b4zHQb58ph2z5gR0q+3CCJT4+AqMWkM28Lmx6CumV3Lf8QzwrIETZq0FmWTsFXl/+1xuXAI9Kn0BVXI2RCdyfSVmRaw7QUOIjy9gV2RUB3lcjDzuNeww7rSlE9uvGcvKMrzippsilxu5fa2UhONe8e4nJycnf0U0J+pnhPzJycnJyclflTkn33///R96M05OTk5OTv5G0rzz+vHtH3ozTk5Ofo/86ARgNJBUWrNyU6JlhxToVN+jHZARmC6EKqpOe3kjhqM+kE51BqvCcoP9gYjRNLCWFd3aV3J/wLrSxRBvkBtdlopKTaso6hwojjJBS5izZkQ8t1deaJEYSi6vzPsX0E7OJNu1HJ9+lNNyeWX6hk8BWjkCm2FTES+h0ERQHxXlLCtigukNJIk0JCdmWv20ZImwEtXDqVkuOyYqhoiTspIRqBjWFsiGyM66GHNqiSy5I9kZc5TgaytEQgSq1+f+7GVRnBuiHTzR3jFNTBxPJeaBiKMk6YEN5WKNppCtczwmzQZ9WYnlwvsxyWy0ZeI0MgEm0hqaiVPmwqmKREXYpt1K117LUOyeZAZ+f1SU8TjIfiPXK7ku8BjEdjBGYrmjkoRdYCraLvRqNcZnEARuTtrKnI7GUywupR4Rg0sJmLoY8IDeYGzI000bdgd9AQtkTtTjGRUNwqB1Qyyrb1WSHKNia+OAOYh2gZioCPu24Zngg3X5RMSOIMRjq97m6ZiVqJ9Z5x5JsBcyBqkd2J7OeEd0rd5ZrM6vlJgeWXKfpIN1VCCPHbUFAd63nXlEHf/HB7pekOWKZCDXb8rxywPR30RiG2aNtEaMBxITpKF9xfpS3cdtQecglhUfE4tEsIpVJ0EPMq0GQUzRrAhqTFlywY6dawgzR62LGLg0/OnMF1FEEyMxVcgV2qT1S90nlpUcXlHqY0PWN9wDxqOudxeUo+K5lyvkgSS1HklUqoc55gvSGzEPfB74seNcyy2sdR9CBKYz953j+onklZjvWL+SDvL4FaoNHzt6vcEMwm6oDGy7k2tH5sSyMdOYxx0fO/3yE5BBW+zZ5a00uxCalSrAfA4SlPiLJxIb2i4wE7GD7MJi39Y16g7LrdawKGQQotC+Yc4HiKHjTls+lyO3veD374GAEEQHkutT1JbqB26U+C9rDa3YJL7/5+jlAm1FbCB7oksnQpAYMBT0il1/St5/CfOBXF5BPypK+vIJ5o5PxdYbHF+I60+I/QPrV2IMxBq+Tcryf3JycnJycnJycnJycnJycnJycnJycvLHxY9PAM4JkUgEy+WKjAHLp3KQNUCcdjFwQKNieG0h2yDQ6rZEKvI3B6rOZfXqBraGNCrKmVb1pzJLZbQL4qMcjOsr2AQq/g8CtJdwZAp9JWcSjw9QRecoAbBfMNtB5lNE9hLmbIVeze0WoCpkCE7QFbhcSywMIaMipUUETGE6qks5jns+Y1y93htHlhtQMcOJIDlAFE9HrJHzN/3G5d4V63Xs9lFu5aXRMxHdGdnwlBIAeyMev4a+wsxyXbcFOZy2LDA3QFCE1q90M0YqewCyoOpMgrtPxh7IpcTmPB6klovWtl9A+4axBz5+QPSC9V6iLoPIJNSxTGT9BB4VR0siE0QaEklmEmbIeFQEN5P7tvHYJ4lhL99gZgh3fB7VHZyTpV24cCDN2E3YZ3WpSiStvyI2UP8g7dMzvvcGz65TUeCygl2Idqv+1JgIEE/nI+sNuX8lh5MKEoG9fiaOr6StSP2SnAfkREQwSTKCJkkKhI+KJUYQQJsTr6+YNGQMLg1kUbYtCY3qHGaWmJeCpCPh5UjWBttex68tYEGzhsrktlyY1vCZNK3I6BRD2oXjcSfHAaLPvttA5471BbUFByQmupb4FnOi2iCS9faJ4+N70loNV0RwjL3Wp1QEuJnQ+sLMCkKePispWxrWtAY2RJGcuE+GdMIda8ocs1zfIngMkES6AQ2Jgwgh/IOgw+Pg6IakkGNCBI6RmYiXSB8+IRu0FfRA3J6udif1iqHIWv3Hx0zU6pj68Ir8piEq1a9rNeRBDMIuRO+ITjCv+GLW6rpdyrVv6wVTw62hOlmOHcSwdkNyEHGAXenHB11a9RAvL7Wuu2C9w9yBXg5vT0Sre1q11TGxRNQqpj4H2hpiL+Tj1+TlO1TK/Z+21j1mbNAMW17rPpmOLS+IvxMTyAB15PIt6l8RjXKhh5dDeB64H6g/16s02ts3aO/E9kD7G7Dh8YEsn2j+CeLAXr5D0skc+LrW/VAm6CsoxHTaekGaIrkgpqRe8LHBVPLYKnb/dsblnJycnJycnJycnJycnJycnJycnJyc/PHx4xOA2wL7hggwvVynmdAvzPgAFsQSaytIEAg5A4lAzPFo6DygNfzYMbtUDKltFXUcE58B4vSlocBMYaaQEYiXiNeiUmiPAGGhz+DYJ20p4S2z4bFjslb0qySQSL8iuT27gwPUEGmkNtju2LqWgDECmVLOQq+uT9IQWUlbngKNlMDZ9upUtQXiQJaKB046EIheAS1npRiWAQKRCUzUJikgGPLs8iy36CQ90NsbPgceiQmIRkW/tpX0cmKqGIbgmSTx7EMeSCYrr2BKJsS+VZyyTnY/YF6YY2cF+uUFmYGPSeyDtFcER+egbQFvK04iY4f1lRzvZHslto22GjkmIhvsE/QC64rEo/YrpWzBvSM+yRSSgYigciFzIu2tBPztlzAVt8DX6jc9HCDxmdCeMdaiRAipezlTm4E/gCBzlFDIJETLcd0WxN/LAR2JtBfi8lLRyDLI9sq+D3IEdEPF0b6QEcABYYQHKcnMxF2IObD+RuYkxgPaii5Xcu5kC2a/wfASOOdExQmshDkZMAUub2QKZPy2x7hdXsnYUIWmyuV6IUMYP/yKMZ2UjowDHxNMy/2cifaVmDspsFyMiIGno9ZpCt6vpE0mT+Fx7mS7EHEw8o5kJ0UQzVofYxBRoqH2FcsE7XiMiqGOwAykGcogdmd6lIhvKy6vzHyAT9COkOWmFiEwRBU5KAf9dCR2EsHnUaJhKNhSDllVcrmU2B4V2Y04klb77xuhV2YYIgFUJ236gHYph7BAkiRL3RPigKV6ciMWYqsIauZAZeJZPdzEBlrdvwIVC02D1kmfhLSnq1lJvcPyicgd80mMA7OFOcuJy9gQ2RFNxPe6p4r9NlY7jq9kuyB6QfzAeBC+o/2Kbw98fYPHDqbM+4FdDCEqmvx4EMsNax2JSWwbunyu/bNvIT6g30AnfnwgdNBGdnAx2O/w+h0xH+Ryw8cAuZDvf0HcVmx5gccHLG+4f9QxuPyEnN9DfyPDiY/36gK+vpL7IFLRcSDtpd5/eYNjQz79HI7t9/n0Ojk5OTk5OTk5OTk5OTk5OTk5OTk5OflL8aMTgGUErJ3YDmYH04bORLrT8xMcP1SEL0qgjBEok44jumAq5fCbiWJMd3IGi0MuicuCDwcrN650UK/63Oi9eny1YlOTZ9S0BEzHdKm4WzekCX35RLlv7en6HeXaDYE5MTMyB5m9hDdbQJWIgHmUmzK8BJJjJ7siuqJA652IJJoC9uzLDAgrkcn3EkuOB3L9jmSjNQXpxByQWb3DnmS/QA5C16cw2PFjgDYajixRouG+oblj/cpxBKl1HCsLu6J2QaurtSnsTvSF4Rv5HgyPEs04gIVwJ2Yj6ex7Mv2DxRrz/UGgiAeRR4nd10913o69dFWojtsxiNzIoaQ/kOOl3KvjQFt1x4pcy+24rGCNHB8lXIeXMNxKLAyCDCFYMRskk23WAEFEOXFVDbJeKwkagmQSOcqBSJb79xlZLRno81inap1vLYe55iSsk/IKcZQIPweiKykrxKOGDvIZ2zzvpAb4ZGqQ4ytkRVDHdFJX1K16XJmEH8SqZExESqzMTKQ1EOU5RfHstPUaEMDQtWOrwdEQM4jJbFdyDjwVn04NFjxj1EURg5wTjw17vnfQmLGVeGsNWW/I40HmWt3GNI7hZBwQSQiQO9QuYjVtQIwHwUpnZeVR62254gKXptw+vXH/+pXhF9bLgoxkuiM6sV7CLiS0hnmQWVHXojeYFYMs7mhenlHdj4rFrnxiIqWivFPwOQEnA/x5XBflmRJwQ01qzYqA3io2vl9gCp6TjBo6EBUSe0Zid4gDFaG3RqNzpJOy0MwYMcmnOJsR1QOM1OlrgsSOtYaRhCndJ4ggYaQlIQ29H8iqYC+gg7a+EPtXaJ9oLbCcTN+qj3m51r1KtRzsflTX+HxU+kLr5HgHXWjX1xpi8Vkx9uunSgNYVwTF9E+Q1vD7r58R8Xd0/fbZ8zxqvyRqvSwLIlYR9+kEK37UUEwu35If7zVEsV4xmcBBvlxIgdy+Ypefg99JrKKsYyV1hb6iAeIfuD+QpWKwbRHyN1H9JycnJycnJycnJycnJycnJycnJycnJ39E6B96A37fLIuyrDf0eivx11YwAwyIiuCdAkegI2jp5NiZdOKY5MgSLQlgJRUsEswY4QjKMibr4agEZIfYK543lPBgTsfjQOZgtYa6gR9oBhZevb7iqCUqDbMFcxDKmSgs5WzlQMKrU1MDXUqsY86KmG1a+yYH2FIOuxhoTDTqj1Uq+heexbf7DhmIdEigd9I3ZAbSLggODMR3tC1IK/dv+kSBngddKmJbfJDpzK3iasnJjM6IVtvpDlpdr7+JmKatiJfoaX3F9EIeTorVvo+9nKi50eLBhaSbob7RUpBudIMFpTeht16CWR5k7GjvNLsiz/7VSMfUaC7YcqtDv5TTN0VIWZC4P2N/gaMERg1DI1ERVAST+kxl0JqhecciIJVst3KGpgM8O5EBpAQvFUAREsVr20jAyazjGwIjwWUhAnI+iH2UA1QN0aUEZTVau2DjAzk2EMFax0SQVmJs2ivpO5HK1EsJ6TlBV8JWwic5HbFOxqg/x4lxR/Qpdu4ftf+2IpmIj2eE7yB15Xg8OMZgP3a2EXz/vvHl+wceNUChpk/3tFWM73QygwzKsRvB43EwtiQDxtgYO8xjQGwYR4l4GdVVLSCS5eZnIEzISTYjqPhkyUD0RkaSCDnuPMbkPmF4sB2Jz6AbGJM4EvaJeL2/xgEEYh3DWVpgHLS20NVZFsE0kHUFWcgK7q7jEs/zm47tg94vtYawitHmQHuDcCIgUshQYvJ0Cu/lEM5nhPL2Xj2402EM2AYxJiOTBw2/fCLUyP6Ma07B9LVejwETZEc4sGUpkXUO5r4xfhOfvXSwC+1yQZY3JBSRidhaVcra6v6RENrwoIYAZpKPnfx4r4jssZOHQHa0X6sTub3C8Pqde4nRdkHWpYZB9jtxAHqtPu0j0flOi8Ae79gcWO+YBeZf6rj7VtdAHOTcMO61bb6Rl0/osta1sr7hEc/7fsDcq0/4N4kGEdCu5PKKLA1tSs53fH/A5a0GPeYgjoP58eu//ofWycnJycnJycnJycnJycnJycnJycnJyV+RH58DWBJ8sjwFT1FFlurAtWPH+o1hQuSBiKKA9Ss5SlxSEi4LmRUpu4xALk934DEqbrndoDlJoE+HXngJav22INqIx3impiqpShydKYH2G12UmFF9o5koQVp1nqpSfb2x4APSgiop9XJAepDLlUTJ8UFHSLmQC6RXZLSLEBnYHBU9TCC9E7P6gaM1SEWYiC5kJorgVcpJYmVSTH3GRCuCks9+5VRFLFC7Vlxt1LGz9VKiTwyehbPgB2mCZKuY6qb1RxgiSfeNZW0sLyujG9v9gUci88CWK9aSLo2xO3ZRIgW7vpL7VyKlnJpNCS/XqqD1mZGINVpquX1F0PVCjPfq822NUnqvyDzoy42WEDo59g2Zd5IV8iAQ+mJPId0qOrx/QnzHqP5Ylm9RDRYx9PiALoxwUhdyBrosBE6moxmoBJZgrXFodfWKNuiv5P1foJHYKjhJhAMdsaUE9yinsAIiiueANGgv5XRcFcFol58y54bP/K0AmXOWy7hldVGPLyTXihVOqxjhUGjl+tY0wkc5RrMGITwOoNXNZQ7UOn0+EHFaE9yMtlzhuIM1Dg3cHYkE6+VSHfeKFmbUNWUXxkziOfwgSIm9IohdyLz/59sIFZesF/yYwFrHIQ4e2phUx7Bn9cYex8bYnw7mGbgpI3q9CYlqVBf22Ei5PLt9Id0xU9ZFMG1MaYzjoJFwuRI5yBAyFXKiovS2kNNBko6iFTKPLt9UGn0k0lv1PVuSXBjHBzIf0L8hcyf9YKQiOTBq0ILlCnPiPvB9EKLVhU2tSeglhJKIBbk/SAG7fMeyrjQRhgwkP7Dl7blGHaVc76wNGV+re1oMyQO53kpsJQm9IatVH7RPcnmp62Juz3NB9VBrIBimRpgSHLU+UOSilSoQoK0Tx3sJ7+7k7VsiNsRu5PF9Ra5nIDmhX/D5AYCHgDVkeiU7tGsNsIgQ1gkCzXvF1487rD8lfQNtRNyR8YGZkK8/IyXguCNtIWVWnENC5lbOZrxioU9OTk5OTk5OTk5OTk5OTk5OTk5OTk7+yPjRCcDTDZs74MT+gd0+Q8zq6VQh2wU/turLbZ30US7FvHN4IrZh8QIIzJ0E2HbkstJc4DJJ6U/X8MqIoC0NfEOOg9SV1IR+RcQRaYQIrgEBklIOSwaRndzvxPVzRcLGXoLXdNDqdg1ZSnRJI8RxWUrwiQMZk+gvwEEMfXaYwkjBXEA7TRM/Btl+0wEK+CzRVqjtFiBGRSk/u4O1VS9mRBI8yNYqWjgDGQ/MbgiOJ0CgObhYsk3BcIYKIUbMLygXWm8QExch506sV5g7kaB0bDougWurPlBp6HKjKUQky/U7NCYmTmsr4wHdGnI47s7QiYrBrMxWQ4ilEXMit7eKoNUFiaAxEGl4TrTdUF5pEagMUhVbXsohHVQfr07yaMxuaG54Jtgzojk2ZhiqDdMbIwbOih517QROWEMTcg7mNrDbN8i4IwyOEYQJ8ZjIRVCtztsczvTad3BE5jPa9wAmoqDawBoNwT1IP2i9A5O2rGWW9UkTQVORuTMiIUetiUzghuha3at2rZjecUC/UCpooBrgR8WTtxumAE6zxhwJ9kb4RDOYY4IMLBt7BipbxQKLVGwygYiSspLjAcdBhCC3C+n3WosuqCgiyjEfoB3NiXABVcQuNFMul07MjV3XWg8JMfffqJFIGhbGOhNryjaF1ozpBwfPvl+rNdN1YUgQQM6D8uOX2xkPOjDnIOKAlHLAxlFCai6Ak0cJ5FyuJBt6fS1Hubbn+r5jyyu2CHEIGRuRNQgRyxtowmwVVd5v+NxgudGOj3Lsxk5rb7Q44D5qLkQNlwNpRh6O9E7GTmgno9ZsbA9CP5Nyg+2XNQASE1vXinLeDuhX0Cu572AHgWGL1cDA9ZWYgA8gie0oEdYu5CjBut1e6nqRrB5q/6jhG2lk7MgMcu7QbmRs5KdvSRLmgT+TD6ATIiXqHgcxD8wUWT/D9oUcXoLwPsqZ3Hp1crcLTqBLJ03q3n7/FZkX0InOLIHbIUhUV0DJeZD5jIa3tZ4HdDiOGpzZN2if/3ofWCcnJycnJycnJycnJycnJycnJycnJyf/LfjRCcD7NrmsC5oH2m742IkUWpQIWnHHW4mF0qEtpDv0pUS2hJBEpCGSJRD0pTpr21ICLQ7iNP9CxopRLuMMJ91RcWgL4gECKsnSnEAwd/pyI9JwD/TpPsts5MyKEW5UrGszDAHfy21svU6oBSEg+oJYxcpKTEwUNSNjQDO0LbSc5QrE8cpyRnJ7OtsSqL+fAFR8LxloEzQPkAt5bGQaKaDqTxdwkvFA7EbLQH1wRBIEoTdi3gnfQF9QbUhOcgbSk4yO7g9yXZExUAX1nR6BZwmQYSVSqS007bRuzAwacOmwv9zI7QHD2dcr7eGIZB2H5ULrggeE7SXqjB36QrbGEQNrHXGw2MEEn3cq5HridkV6o1pYDYnBzJ2YFc8bba04ZQ1iPgVB3es6ap1onfH4Qs4SdZEVFQGZ6MtnxJS+vsHYGD5KcH+5IOmoQCyv0BPuX5D2n7tghb06eqXOVWp7Rj53RILkgVirbusoYYv0cqBeXol0wrNES12fkdIOchAkmFRs89JJgmxC5oAop6nIlSDAy8WMKr2tTL8zE1Z1milHXNmOidm1en+9upGlvcL8QrIQ2iHe0bX6lzMHohAx0H6hNSdzAd9IL8HR1hdMO+4HsnREg752dAY+G2MepGi58mWtaN/c2WavnmA39tnwNGLuiERd67IyxgY4Gom0yuTOrqCJxSRRIhO9vuFzwAikX5GhEAF2rf7lLCdp7rPWsEb1JPuDFCdphA/W9co47oxsaDzPsQ8aya6GBJhkCeFtJSTJlmRTml2wbZDLpeLsg3Kl+0FrVxgbsIIm89jJZUXyoJkRIczHVt3luTxj2QOOnewVkR/ZUO24Vyd5OuT+pSLWPUAuNSigiTCw5YpKMOYdWV+AQYiVwK6KakdaksNq8KTdyGOgbSG8ouJTDnLeUfPqGe+KtG9I3ZB4YK2BVpJDfv0F7faGieFEOXV5OrrHneifanjj07fI+IHQRLvh80Gun8gI0h/EfoflE8w7tM/kfkes3iPXb5H3Ddbl9/4MOzk5OTk5OTk5OTk5OTk5OTk5OTk5Oflv4kcnAGtPQgLa5emeLNepzoG20gHNLggDcitXpRxgRhNjHnsdtDQ8R7nTCLoE4YGqIhpkewFzdA5EhSHAPrHWiHjGAreOSpJxECgzy2EoGOkHMYNgKdeqQfYbEl+BEp7SE9VEupFjBxZyTiL16ZgzUoyQ/oybrmZZ0V5do3MyNMGuxByk5DP62RCMiB36jZw/IO1awi4TRIipKCspWh+lTqbgciFjQ32Q0rBQpjg6siKS01FTXAWZCWKkKCJOrgs4yPUF9gcNI3RWjO26Vp9o8hTSDyJgzxc8B9MS1BjHjs+dHWPfDpLlKRBmiYXrN7gkPg+SiY+JmBBq+NwRu5brMRLxA56CamYAE9LI3HGBzCTagrWV6U76TspAhqJjgF2Ao4SnAJcDDUCM0AtuTpsHqg1VwTHUFjRrKEDWK+SNGU54wHigHqytukvz+sIxH7gKyV7HcblieZCzwrpFnLQFHxviQSgoO4Tj4aRQTnSDSEMzyLkg7UZsv4Roz3M7auDh6QDPuohrO2JWZLYuJfxR17cQtHXBjx3NDVtWhit+OIESJois+HzHKTETGozaLmbDdQH1pxvcQEDmzpENcodnhHfItfRUHI9Bj4YkHBPmVNx35kxSBUKxmNiyoDTcg+mdiMk+HMnaJ2uKRJIyWVsjPXFrqLQS4n2QDTIryrvPB2NojQV0Ye2NaMbx+IHs19rn/QfQRC/XWjdS4jbaUbvUMWvCclHclWadFRjHBFNkevVSs2OtXP+ZgDUiGtvmNGB6kCaMmKRJxTKTuO/lytWoauIm2OXKzDsyd2y90PZ49uhOQiBTcUl0Bun70w0caDrpgoyPuh7aC9FnDRlsgUhULHnrzOPAWRD36lC/vtR8yfEFMUHWV5CA7SvoSh4PrC/PYRoBu8F8EJlIHIgEZmt1Ro+vMMvRHaJgjWkdH+9ke31GlHvdN/NCHgd8+mmt630Sms+e4i/k5RNpndy8oq4Bptd5FoUE2SeyNLJdquf95OTk5OTk5OTk5OTk5OTk5OTk5OTk5I+MH50ALPOOi6Fp1WU7HS4roQ5TKx7WdzLBHbQ5IgrpJELqwmQCVBxqX1FthDsDheOBtqU6gSMRopyhZlhUdDOZ9XeXlZiTyAYjYAZ6aWTcnxGtB9haGyIG2shs4A9kKqTii6Ba8aTIUW4+KsUXKrKYTCKTVq2wpGX99AN1JeMgM8mQZ//mSv1HJ/3AZ2KLwhygAFkdrNqwnOjaynnqW0WsEmQqooZJYCJE7KQtFZkcB0hDAoINn0KKQYP0UftqhvtGPjbul1fGFvicjDnIbNWRDNgejONRQv31StsGswlzwjF2xDrpO/HYYb2iUYJ9xch+geuV3L+SLMgI1EDnJPFyBk9HmqDaSA9EhZBJjAESuFzJywp2Q2Yrp+1xR9qFzIOKkn2Q2gEr1ySDZblU5+z+lSZJZqC9k2ykQNBwtARCocSyvhI5iJw1eLBcKqrZH6VtaYNx4DmruzgD0yvqB5lB6jPVuS8gDcajhO5+w4cjRHU59xsxNkI6IOAT7Lm9EoDV8VfQTLAbKR2e3byRTrYLcw4Of6++VhEiGnF8MIbT+opso7YravhhuT+Q9VKDFznRvtIsnyMWWp9t13LBi5JjK2d6X0iCse9oE5omaaPioA9BzYkISrWd5TbOQHQh8ighV6S6XdOqy3jecf0JRG377k56IlLdvPQXUBhixPHALskkcB/P4yS4OnMa/hvXfQjydPhmUtc5k+RC5lIx3BxILMyQchSr436Q2XCClF5rKY+K5B6DSH3Gn7/Sjuq3Hj5gB8mJtBXRRGZ7xjBbRbc76NqRqK7vMSeo0/hancfxTmKQSbOlrg0ViA12RVIhq3NceEbI9xuMO7Z2RDo+HogskHfsWtHNstwgHzWEM3fwBvsoZ+82kFdB1BB/EPEAvYF2eFnqnOsFSycf3xN6BV/AOjE2OB7Y209ruCEExqj77f3XsKxkf2F+fE+T7xB7QA9C38B3yI4Px+aExwPWF6RPxG7Vs3x5I7cf4OVn5HigL5/q/U9OTk5OTk5OTk5OTk5OTk5OTk5OTk7+yPjRCcDaDeuvMDa6UL2jCSoXEMNImBuyvtDs6aR1QBUToZlUnCpBWgl0WDnFugc4KFJOyEa5QDNpCPQViURsKUHBGhFeam03eu/l7CURNXLptdG2lGAlQtpSfasqqCqqUmJqc8iGqhI+8cVRWSrGNoz0QCUJsxKfKoCVzCxxJZxQQfJ4Rgcf1XuZA7vdUJmEQva19i0mmh1TJYSnIHhFQqA9X5OJy0TXV6wtxNzxpxAnArreyGNHrY5JuJNRpk7EYExYFxJnbpPIEkrjOEArgtuaoXLluH/A7IhdqrfYtXqR5/ytMzbFSB9kBLpcoF1JGsNLTNf5URHZAOOOthfSD1I7kSWCYkEcWw0LqBM60F1R6niFXEgeFQMsILqQ8YAMwu/QX5BIfJlPEX1lHhO0nJPuByKdmU74/hQJBTLRtiBRwwnhs2KIWydVyaAGFXwjwtBuaEq53APEegm5PM3hAvEcVhCpOOvEyDSQhukBYQwMjRJLhdrmwNDnpUkm0te6Bhmolrhp1ummjEjGDKS/oSngyiLAFKQlGoL3lR4D9Q2lEesK0yoaWcuRW9ZLJX3W+kmlphFqvQRZAxYseDrbdtB1QbT6uD2f7vYIkI6IMn2iJKIN5g60ukbSny75UesxkhkB2tA8mLMx80GG4eooCzoa7o5KxW6jxvD2FKsbeJAoqYbMUc5SID3rPsJeQifK9APfGiM7ue3lCM8sZzSzuoDTgOrwpSscd+ztW14X4djujBhMbYQuqDuRgZjW2teVHAFLKwd+HNUPLg3EK9J+eSG3QSKEBIscNTSRF8gDaa/IfmdKrwrjOZ/ucKOmKBJVIblUL7IopJS4ris5v4Anam+Utn8g7sj1lRSwywX1hrcLcIHxK1QvxHFH2yvaLkgaIp3MQarB7Q0OARxyIpdvyP2jIqkl4H4vERkq4SES0Y42Iz425OXnWBzgd5ANlU5LI5fb8xo8yPkg2mek35H+iTx+/df0pDo5OTk5OTk5OTk5OTk5OTk5OTk5OTn5b4/+oTfg901vlxJyl44tDekrSiAN6E9HZbsiWdpuilYHL4PEib4QNALB24U5D2JMGAcqwrJeseZIp7o/U6kSTqW1FfUSldCydZp1mnW6CmYVoaspSAaqKyqKimKxobmjkvTlhWZgJsABVLdp6guoEtLqc60TonhrZF+YPstxHPO3LsrQ6uJUCZammG7VZ/qbuOWsHtQYg9RLxca6EHsQOTjCGSHMTFwV8YHiCF6u4kxM9SluH0BgutBjZyXo1xfUqgNYzdDbJxTKqbuAWkPjwJ6vMRG0WfXlSrCqYOu10oi3R2mcI0rljKfTWXs5GaHiZJcriCKmMN6R6dWvaw3JcmlrGr0ZfV0wNZSl3JSxo+OB6lHbJkZmIKIYgcYD6yvKhnFULPHlCnhF3Y4vaJu1f1niY+6P2j7JGh6QgfioiOn8jbAWxHAyOmMOPP9/7L3RkuRGkmV5VNXMAPeITFbX9M7+/weudFcxM9wBM1XdB0Wy9m23e6d7SoQ4JSUkgxnucMDMXIRX772ChxN0Ur4j9iwxE0PHg4iD6T/wFCbgaSwM97N6W3VBg7Y1mjQ0F9IHbTzYNGjNUBUajqjUtVsg44luz2v4oNPHE2Wh/sXAaSLY+I4ZbFs5iFVbudLPN6KKWUONcjZbrz5uEvqD0BL5xS5xUhV3J/y8hh8OYh3l9kQRKZdrk6QZaKuebHEn4yDSmT6JLHHV2qPeSzfMA1urhi1yETRyrRqKSC1RMhYQSEmhaCZmg5wnSaI+aaZYnLS+07aBbd/QsWHbVkMWDkgjxPA58ZUkDTyR/kDdabqQ3i53Lkg4Q4we8Ng/6RaYVh82l8aJOB4nuSqI3jP5KYNDOtOeBNQeaBsmStLIFEQ60gzRqH2jSQ4FeZGSLMlyHVuHbUP3z1pvftTzad/RYchuSNtr8ADK6b4m2TZyBRmT1EFEEN6ILHc5eJ1N7kRupI5ydrdObh9EzMriF6kEBA1iCv7zCyKJ+YXHUSkJGTC99nZAjoqEz+OLfH+Rr5/k63foVhHa8WY8H2jrGFIDOeFI6zVk41H3xRTZN1y9nlu8CN5kBzTgcOQ4kdf6r/qqurm5ubm5ubm5ubm5ubm5ubm5ubm5uflP86dzAHtMmm5ICsRE28cf/9Gf/EFYOWbJcvOWm68ByVwLVUdDWBGIJR2FEMAgnHwYJeNVjCl5lIDHC9oTZatIZuIPV2BF+Fr1aGYibZQAEwECqla/q4onuIClEDlZM5GWcP6sWFpfpEsJSX5Q5cNvZBukzxLgKGeiksSasBaMiqbGO6KLbFuJslpiTtBLTFKvLliqQ1iaEglrOpa/PJlKOmQXUOWIxNebuSo2WkkkrJzGvkrUI6uXVKrnNajaTmOVy7c/kRT8fJOrOmG9DyYgEgSK+MloO/r4wM8JfqI4xBceWZHA/Ylpo+XJXB1xp49eItB4luovYG2QLKJ3Yv2O2DfIRiwnzkR7UK7NhqgS6cSaeI7qW84rwluclJ3s5SQVSchFakIehF6ORK945YxA8qAakxuiHcNrYECSkA8yfqAZiO41MCCLVgW4LDpaQdsV3d2SyEmmXgLxutb2Qvu/IGo0j3L1AisnEU7LhG0n338ndSu19nBkC+ALTUH1rEjzGZgkoylHGppOROPrPYlIVDrui0VcMcjU3liOm1enMFIDE61EWHVHtwc5nXCHVDISbEBCLMcAtXLFiyzIgzUhI0l/4/aE9cbkQYgQcda6jkmyEfFm6QNZJ6I7Eid5nmTrdX1cwxuxKtLbD9w6ERMRpV39r+7Bef5O6g7zREyx3lmrergRL9d+JKodVpT4LYKIkuuLGB/YPEvQlQfJBIxIJb5+EjHJ7BVV7av6uA/Hoxz/+thp74OzNcIT2g7zq/qsZxJpiK2KsheF9a54c/0L6UKeg1hf5LkIGch04MTU6pq0VSd2vGj9wZwvlA/Cf6+Bi/1ZUctxwqqtETOhOcyfEAPOWaL3kDoT5xtJqT3RNlIGuQ7CTzx6RYdrkPONtA/i+IGM70gcdW5IDcqgZ11Draw6p+YscfdXYoEAY5AS4IL0IN6v6gg+3uVOz4msN6KJfvtXUhr+fmObgo9yTfuAfsDo9YweH/89X1w3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3/wH+dAKw2iByggqWBiKkbSSrxNikRIdmVJRoR0WJXLQ0xCcug/V+lejWR4kcWbGnTgJXv6q0sv+1J8TE3UlpyHyT7hAnbkFMo/VGiCJdYL1Zs6JuiQNlJ9SImMQM1Dqu7YoyPcvtqqPEFyruV/wkMi/9KSvOduyElCtXWwlZaZcgKR/Agu1R6uua5XpUudzCCZyXeLlV72rrwCwn6AaCIzR6A+9P0hPlxPJFxidijxJ74yTb1SnrPwnppBiKkyvw80DMkBTUjNE+GLswx8bPH6t6Q7WhY0fXCfrArq5js4U+PrH1BRgrgjWTGYmQbDmRhJmCy4RmsMoBmvogXv9WDtn9NyScTENjvyJsA9GO9M+KuB1Gz4NH73hWp6pocrDh3eB9ouKENkSfyPoVhfuq2Nq8RKvtA7pWBK6fsFU3qqwTHWAhZFzroQu+DMuKnfZmpDsrFqoNYmJ9r/jpK8I7pURmOEjdERSx7ep9PjmoAQdS8LlKCL1+DxpYJ9dBbjtoOWStdboYSXIAEZNjJlNGiekiiG6kvMjW4DjoXF3QYdiuZNYQAW1UJHI6zCuSfOuIJrG83NLbt8tKaxXv2wy5On1pHdbJomGhJSKHkwxGHpgJy7+ILGer6MDyLHeqJBqJ6MIEpO+VCGBafcKycRyUyJzxh6tdroiAsIEvJ5aSbZVOHwl+xVbHpGyjgsQsZ6kpaNT+iSDHQNVITiKt+n1XEBr1OVQQ10oOoIRTl45K0Lfr2VyitSEl3s9VryO/BOF6LrROeg05yBU5r5E0ddw6+A/MOrkcl3Lfiy0kFddBcyfyDSZInxUVrx+k17CLYMj7JLdnRUH7gY6G8IF//SSvwRQzJfuOtOpHlpyIOolibaLPD/j6OzkTwjGbsG9or0EW3Kqm3Bw+/oKvQNarOnnNkMdvJTC37Yr1XhXBzyKlk8er4qjTy11vHfFFyqTZjowHfP1AbEciaiihbWR7kOdPoJGRaH/8t3133dzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3Px/5U8XAU0biHWSIFwJFUK0xFozgmCV2ZF39oo2jlXdma2TU6qLVww1QbcSUWRIddUuytFoG+d6s7J6elXLFaxyxSr36l01wMo2i8wF83JrzorFVRlERnWFzgNdJ5KramHVwDqiIPkTCFgg81Udq9pLvBOr15dEw8h5ViwrdvWhWl3AqlhbuIQsSXwmcf0+0kAeqCjSR4k2MhB/IwESSoqXmzezuo2lYwpGolkOUFXDYpYD+Vc0bkx8vkqgdidfB7TB0uTlwev4Yp1vYn0xzTmls9w5l3N64GElxJmBU33BcZKvv6FiNOn0ZXC+q0/XBLWOuqBjgMzq8W0D8cRUEA4sT1o3Nt7sCqMp/fFgfDzp25OwwbEO3tN5Hc6cP9n0RCWQ7UkK5JqIg2QiEmjb4fgi1wE+K9I6As8g25OUjdSPWh1eBvOQhmSwSzD6A7eduYLlTlwdwbgjJNO/WJTAGDiMjTQlspzhevU0hzvLJ2t9lbC5zortTcfT8HMR2cC9BhakER5kKivgRXL4m8TxVNyBUE5fnPPEry5cvBzKZpC2oY+dbU/Gt88rCn3V+4qRc17t1FpOTeukbYSvGhhg1oYRIa2V2zwWuQLsgfcdp5Hto/p+bbt6b7Wc2QbgpJa7N9dZPdwrSARpSh/P6vr2IGNh0jF/MaxhkhWbjJboLxVxrntHVSpKWDvpk0hjpZYQ2wbW9+oYNyVPR9ByYttAmCXMJshydOw0cVozmjZUA4vE1qpY+G6IfyFaEdvmC43F2B6MtvN8Nvbn/4G2J2KLPgRhoTJQAt3+B2Kf5Xo1o+Vibw9MN1omzU+G1RdEQ4m16J60baNtD3ob1QOue0Wh+xfiC/VKSZA4Klo+FiqfiFjFp28DaQ21ztgHbQxs+0CbobpXLDMPOBdYRbUbWmdelqvXULQ1tG+Ifl4pAkmy12DJeSKZ6POBblLxz9sOZuSxkAXymtj2L4gszBx7/EZTsP0b0Tb8/CLOL6TvFTfed3KeqPaqENgfaDrk+d/8BXZzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3Pz/86fTgCex2TNybHAeydjXpHMJTyeqZyuxJeTZ2BnQkAF1wa0rB7eXrHBAC6NuQKRTuOFSIlJSTkyEyey+jfJQFuJvyKCkOVoo6KMkVYOTZESiLJEohKgHyWEHV9wnmhkvVYa5F7CWS4gyd5Ra3QzemuYCpYgsarvNk+aCiZA26uqWDqEkK6INUQUGb2iraVXF2wsRBMyyJCK1s2KtE2MjMY6gwxIL0HzV5+ws4imBMaSB56QMqqqN66u3jXJx4NsibhjYhX1m0quuic6g3EetGw0MTrJ3gbj+UlLZ7Rkf3yjK+j2QLXR1kTnFyoDw9AAi1UuwJhIKkLQHhtt9BKrrw5ooVW8sv/OfH8RLCIhCFw/eeUHxxTW+4v3Er7C8Zigj/rsy0k/yHAiqq8199+AUe5trvspkNdwguhAtw9k+yTaN2I8mSwOJkv8ipa2csDmCTjBoiTgoDNoYXgs4jyQNekijDYQqtu2iaCR1Q8rT3yVEBsJ0j9qzcWCWolIJCpW9ypPJKjhhmtsIKRDToxyg2ecFSHuq+LKHUKsxONDmLMcn+f6IuasmO5rP0TCvFy82h8IjjVFpMEKzAQzoA0yFnEJvTonmpPGglysUFYGPlcNMXjWulxHrVEuMfly//s6OdM5I1givNdkXl3eEbPEeMAxSGW0pCuYNiShW0OtXXtJUfko4T+TkGD6iaPlio6Kv8YnPk8itETTbvQumCm2beiAECEly0E+vpVbef92ZaUDqiCBSNI+Hthm6AbajLY/6JugwSWWBukvIit6XbfPqtvFaU1gZTmge0Men0h/0vtAfvUrq6Jt1KrQciyLJNo2aBv0B1COXFVD+g7+RrYNMSG9eqVVtCLoZSDtibRHxaTnB3n8AIlyfwu49NpD/sbnQZ5HHehxRUyvN7BQFdInqXWtGcB8IWshWQkQmif22/9Etw21wLphtmqIxhroTsb7ck0fcH6Rstc120GTqC9OWUjr/5VfVzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzf/Kf50AnC+ZkV3prDiLHFVq8czzkX3hYQgudjSUauOyoxgJghZQllWRbD7guklpFIFvZkTVejbjiClz0mSrxcZl0AxhQhISlDWCESVZiVEYlvFx2qWSzImiCH7E/oGrVH20IX2Dd13LJPRAjFBJbEMGlExqVrduiKB6oZSPcfhScyTnEc5KSnxZ4UzowSnTMElmevk9Be+TlhakcR+lCi1HNarxLQoIdragw4022i82badbgMlaQYDp0tieaBxIG1H4o1CORdZECetdawbYuVAtrYj2wMkqn5ZEumB7juoYSpoHyW0eZLzDRzlhrbGioMV4OckZCuX7PYkJoTn5TT1EmH9INbJDGOdkK26colVnaCx6rraDtsT6Q9yQfhJxknJ128yVj1Df0E40p7Y+IbMuFyfWUJzOun1DDCDodXxLFGu8ney3kdFdNtebvP335CVqA5UBunOzMaSRO0DzcAy2NrO3gfdlMGkm2MmYA2RE1FHFbRtiHVMrARfJtJHrVOBZjUOQQSxjrrP2kjJK9Z6IvoEXxUzHlkdyb9ET58sV+bLiRW03FENNE6iNUIE/MQMmilmSVOhm9H2gfZe7lBPJJ2FcV77CpwG2B+u6KOipnNVovuapNrVsayoBLgTIax5kKEl2K9JxqhIY63PF7oRIUDS9x1VJ2iQQe8bJoG4o6vWTMyD9BdgxK9I9jbQTEo2hxQhAGRD9DqOE+Z0PBOfwXy/ER2kdsIqCjp8EZFETPCrh7w9QKsLOcPRXJgshmxsoTzak4cl34ayy0JSaG1nx2mX4KvjEpWzw9eJzPPq2zWQDU5H5oTjAHdYsyK7U6uvvO81QJIDHQ9Ua0BBbGGP3zBOtHWWn5wxWH6UGO8LmWedlxY1HDLPEnzXWe7s8Y0giP69ouvnSbKAVj3taxHzd/Tjr4gaqVvd8z6Q+KrP8fiNzAkazHhV8kD7DusLtw9gIPlGGdj2HRUDHai/0I/vgKOPRw3yPP4Hott//ZfWzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3NzX+QP10HcHDih2Nq6AL2cbkopcRYM3R+IdtAiIqglVVuYIwUQVqymSFZok3b+yV6gkegsSGt8malPa/OTyMa5HFC7yUchiBNCHv88fsRCRzQHiVoxcKv17W1MBvw/oJWwjShV4xukCpE/yjh5DILulhF/84ToQQnMcFtlHgEsBK6kQ1Yq8TM46ho6ThRa+XxTMH6NyQntIoMlgDpSkpAU0wheiuBtAuqi9Z3iMbpE/PANXFKOPL1VS5b28l5EMcLthKX/f3CewMP9D3JCIISb+oDbkR2ziU4oDMqmvt8E7qVY9msvNsyyunsixSw/QFMxI18n2RTxF8IH0gbkAtlg3yVw9I+0Gcr97QE07Pujya5JqDlfFQj1wvNK7ZWn9XB3FYJ1ZfwbvnGtJGjIap4E0wH7gcuXk7VLEFaxZFFxRhH9QiLCZlBtorJDQw1iHORDLx3WD/JrK5oa42VzlyrXMkCrHIRS9/R+a4+Whuo7sR6kUrFMrNospN5YA1CL5e4GnEKaCNpZByQAT6uAYVewwwE0r8jAhkHIobGKuHTpAYJ0oj35GzluFbjWrdHxYmTeCa5FqINs8Qvx+wQZSolnkfDlT+6erVv4NWFW92vjpiWAKzV/1r+/oW1hrQNUSp+OgLVHViYTGgfiL8B6LlgDFwE+qOc3Rl4Guo1xOBXv2zaA1gIUqkBuSpyurdy1NpAre6niqJyXZd0Ip20HZMoIZuK0Y6cuNcQS7eBroDe6/ank/6F9A9atxokkKTtDRwWA0mndaPvHZlvoj2xLsi71tnqA/OTSEfcYR1gAuNB+AvZP2H+rHMOKRN7lsMdhUhHtRO5XSJ6B66e7DZIP4n5RWJETeCUYxerznFrBAn2QNoO/gORnTze6LffSDf8/ROxJ2KXi30DP5xsA44vtD+J9XfInZhndRS3N0jiBBlB9m+gT3K+GPavrDwJP5HzJHsDTkQeoFekvE/m9llCc/+E42//tV9YNzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3N/8J/nQCsDSjWYc4ERqxBNdJphBm2PHGJUGqdzNW/RxpuAcyGhpvLA1XWHEyZBDAigA1mp+AorZIUzIDCYP+AXoCVmJV7yTKDKf5QnTAuZDdLpGqk3GCbqT/rK7etci2ke+znp4ZGVKilX0AkCkV5as7KgMox3I2SnyKiUwvYTcoZ+w5kf1BWsXiiiTSSpiRvqGvL3T7ABHwciBCIK0BDT8P2Ac5v1B7krHwcGLCVK33Ot5XH+sO8SZp1deaq/qH08GeMJ20E5qAdPz9A3t+kv5VHcdZ0dMzThqQLPL1Qm1nfG5ELNznH2KtjI20jcyF5iRlI9asDuj3Cz/fiAdiJ3Em2I6+HNsanAoNRCdqikcifhC+gcxyVoeU+7ltBIJII+cPkCdmjozvxPvfKmY7EtkVDavOXxsQJ4Sz5CzRUBRPKbfseeJaAwbiVtHUKkj2cpUzSpwSrTRpA6NzjTNUFPUVC31GA51V5+yLCEdHR1qw5rqieD/KCZ6r3NBMSGMyISbhO4QQS8ESeJYTlAPSyyG6lVjM5bO11rGxwXyVGMkldsasyN1wMg15JJHl6hVK1E5NJLV6lMPQcLQLmkGqkmol3AsgdoVRZ8USqyAqpC/6+CxhNCsymvYB/oNwR8YHzB+Iar1OJplKcuKL6u82pVMRztG/M1dizfBzlsg6D9AnkQG6V2y5BrEm6dVnTUZFE0eiY0PSQfs1yHGCbYg6EZcLvT2J83XtpzfLnZSscyAd0x0PJ2Ux/XfkeLD2RluNd34Qs84L4sT8DVLRzJFaYeF5sk6vGZJU9JxMv+LEJUjt9CbV/Y2iOktEt1Gu9QD2Zw0aSPUpS9b+4fU3ohmmG5HBGA9adF6aKELITvqB6HYlLCwiFHv8Rsy/1+ulII+/lGh+OukntI6+/0bYkxzfSBYxD2R0lA1p3yirvhPH7/D1E33s+Mc3mP+GMeHzf5awHgswpL1JNrwHeUxyBX6+KuK6N2RTPF7oEeWOnk7MiY2j4vhvbm5ubm5ubm5ubm5ubm5ubm5ubm5u/sn40wnAoz8RhFMUQivyFUHNYMFcTu4Nff3kNEBe4B1s4OebzZ4000sHEGSe5N4RdxpCjgdrTrrM6kdNK5GVIEVJVnX1ypvMRGSj6YksR3UQI0nZIK5yzwTzJEIQUWhR8azzBxwN3Rzpv7pknZSBmCDaMQWRipGO3Uj/QbkyuZyZIK2VEEgguciAyBfYIHORqvArplogc15C5kRsqyhgBdE3KkouJ+UHIq3icAHmiQYs1XL4SaJRDlklWelXF3ODbcDrR0XKhiL9pCc8Hw/mEeR84VnxryJKMhEPMiYuxnSDgHWeBErKRuKseRAJY3TEGrHerFisnGQfVKnvBn7Q9r0iZOOEyxlMNDwocXQdiJ9kT5BREbzzclP7gW/f8EiG/x30N7IJuXVWNCIO5P2T3LRcpqbI+kL6ThAloq55ibuC8ri6YgORRdqjImrzvAT6hWcJVcQEsl7nfIO/ynG8yvXc9yct34Qak0TkWYLsmgwRrO3QFNZieoJCzi+wT1IF97xE31Xh8flG9EkczmLSJKiaWqPylgNJx1oj/VVOetXquG5GnG9wqyjpBM3Jo/UaEqgrhKs/d9Eux/UDw/HlRCpi0Ftj+SK8OmAHwrmEJQYS9PGkdSXnwsfgmF8ob5xVAp+Wch4ott6oVmywr3Lchp/w+R0Lp7edM6/rjUQJ8vpnFBpOquEY4mc9X4QawqiucZbAw+B8lxt2ntioveTzJNlrHczqX05Ztf/UCLFLuEyIoEciLVnWiTxJCYLkDMNfP9A4sL6zQoCKWkdHRZRzgn6SS6AD55uIF7Hv5PlCE+gPMhRaI44TkTdp5aSX/kQ5a8jAHiD96qWepO74OshernXRjRRFmfgKQpXwN5yJ9EFauenX/EHMVfs/36CdjIX0b6S/Qb7h59+JeNXwwPwi83KU2wlsBBM9X+W6H5XwIKp1piHE+QUiyEzYrucQShyzEhrWCR9/IfsO/nfSjJwnIQOZq4YoFhzHC5X9v/Lr6ubm5ubm5ubm5ubm5ubm5ubm5ubm5uY/xZ9OAJZYwEKjXIQ2PvDskI7GF953LBfZDKKR8yf6KIefmGJR8cR5vGkYplZxwVuHeYJIOUfXLPedjXL95eU2G/t1DTspg0RRacijl7sWvaKCr7jmFCSDMXZEShBCk9y/wflCWsckq6MzHZVJ9IbPEqYyIT1QVYKtOoWz3Iis84oVXkjfkJxEa0ga6YJMh62Vq6/vlzP0rFhWOtIaeZ4k4LqR75+odYQHolIZ1ApdJqEGqUhAny9UDEcqKtYX6E6SVBJ2w9qAPDAdtK1BKCEPaFnXHV/I/iTOiveNbPjxIlC27YnqBDYyXiRvMgO3hgMtoxyxDoQgqzqS9flAfjomUo9iTXJ74ucPmnYyLlEyFGwCJd6he4m5CbRA1kHrO/nj/yKeD9I6yQPVjnZB5t8hAc1yUbYPUgXOLzKkIrjPH4jGL9s24m/08RupO3K+2fdaH0eCYES+ERotFi6rnLQyathAFWmD1gLLDmJYKGoDfCEJqga6iEt0VQLaE4+FmhGxmPVvaQlqDZYTUlHf7bzc4uMDrXZsQnp13/IoF2wmxMKuCOnwWq+itcYyN0TAzCAbYgNLRwhO2zGF0TsxrwhfUQTHtm/I8ZNzTT62B9v+5G///u+IdIYp375/AsEBnLJoMUihxLvW0XWQfcPyGoLIRGXDbJWb3l8kxiJwabV/+8ARkIE6iDjp5UCWy2Vf7mkHNiRPJKU6wKVSAdgeiFekdfad9JNMI8URnxUUYAPBkdZLo42omHgZdR58fJJ+YraTK5mvIPIgJ7DeBCXc0p+I/7xE9ahO5tAaNMlE4oWOD+T1hQIjFbFr6CCFpCOjopCJAzyh7fjxVR3AEtQFU2el1tmWx0H6yVsHsg7yPUmD8InEDuYkJzkVaVGd4ivRxzfydVy/H0iL6uRuhugnrBdyftU5rb3OIV9IvpCxIdsTyYU9n8h6V58y9cxEuRzkC/UBpnXp+UXET0QSHb8R8UXMVmkIy2E0smklFKTA9NqrNzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3N/9k6P/uC/jvxnpHdadvYC0RU5oGTa3EKwuMxAz6brSP75g9SK1+YPF5ddYqJoa0R0UiB4R2YiUrwOebOBcewRJjupAzL2cbhA2SIAUqL9Yo9aQhEeWeJEtUFFAtH6FoiV4iiowNZJAeFe2cQnjiK6jS2EoszZwkUV22IuQ5r79vSGi52kSIVCJhHRUbKxGISDk2c1bXsFaccLmOr5/lSTfD2gNtn1fncQMBEcUVwoXwE+dgmXFinAgeSdqOjIbkQqQjSUVSp4EGKQPnvByyCbLI9kGqlWAUDroja9JjsfXEciIaSPxEbK9eWToyq6dW0+le4imjlRC6JtEdj0m2EkJZE9UP8jzgWPha0D+hfSLtk9SNtV7Qjor3ziAlmaowPhGiYotVaQTdGtq+g7+R8HI+9k+gVbRz37HWsPEdFSFzIQLSn9VJKy9s35H+/R/PPQ40AlgEidAwaagOJLzck9Lq5zZIF1J30muZWBraBiFJzHIQpyitCX08EWuIwBifNGvY6DQVtG/VWUsglqjWvxMoodM6yEaTwMywDFQbKULGG7VeJ5AkYqB27Q1RQqunVlsHM5QDjzfLD6IJzQa9VX9scPL47Dw//spLPvi3aSx70qwzHo2ukBG8z8mZl+s3qX10zorljhfWBOkdGTvK4jkaj5Zs/ckzYOhGbzAUNL1i3DXqzLCBqSNipEe556VVtzdJ8iCzl+BqSc43pJbRXxtxHuAT1QFegwwZq3pyE0iruGf3EtozK3aaQDIxBMtErbFL5wOhj0/a1WedcRLrIHQjfYHsiI36/J7I2xFPZF6CsMzqIZ8g5yLXq6LDpfa2dENyotKxttUQx/UZZDoSjmJIJmIdWU76JMdWMdamtN2qP1wbXLH7IopIBwTtOzVHktWHfh4gJ3TFmiHbRus7ZoaMge4fsD/JVaMKuT+JZrg0wqwGbl5fNXzw+98QfRBSncnRq0tb+pN8/obIQtaJ9Ee5qB9/Rd6vEtJVsefnH6kINzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3N/9s/OkEYCTRlggDac/qiI0J58Kk0RPERnXPTmWKsjzJbJhZdbSyoDXYAumgoyHSKka4PHfo1kooXfMyzR0Q73I6+lm9lle0b3oS60rwjSQ9CFFQJVUIryhaMYE8SIRkgXWwRmSwVuApuCg5zyvKWYgcBKNEn0jC/erXXOVEVEHaoxaCz3LakmTvhPUSUkQvMU+rA9SdiEXG5cJ0Q9uGSiMkiIhyM+dRDtaZsBxiAANpD4KJzBOVXm7hmIhVL6xuO9qy+o2lkz6J9SbjIHQRXvG9kWDaMG2MnrRtZ9sazRQZj7oO2zHbMetsvtCro1eA1pMWL9ooh2auE+vfUQTVhuaitQ1tguJITrpZuZxtJ1Ow/qBvn+j+BClFVcLp+aZt+xWJXBvN5MS60R4PrD+QSBrJEGeoYZKYDkCx3rHxW/Uub09kDFImEiepnSVGaqu1kddznav6jaku3cCrqzkngoNPpgTLf8I8sHhjGhCTNjbqrhwVO76VwBwe5awMsN4YTVBreEyc6m7FJyqJbNX36nUFqDupG0qiUgMILobbRzlRTa9nEeDXwEJeYl+cNBVsDHL7ZAEeyXkerHkATtfksW3IhHAFaeRKZC6aVgvydOPndN7vF8sdUujrqMhrsUtgVEyfpHTQhrWrWToDT0cEIiZzvZlncLrjGB41EODrjWfFrmsvB3eixHLy6khOkhQjVwm2aCPXLEHYo/Y9nZRWQx22XynwHQRiHSw/yJyIbKQ72jbsGrJQlGaNz8fg8fFB70a3Ru+CKeAngmKcaE70CoLPTMR2IlbFpLcOmfgJvlYNZ2wPFKshA7/2rO01/GH15yvyGyKC3D4reUBmuWv74+r5rkGTXFJ959rIuUAHsj2Q/lm12iqInzVEgtewinZSfhDrJOJN4mh7AFuJ53ORftQ9jgndSD/xw/H5RXqWG1udmCf5/Beit+oPn0f9ddXekvZBzDdkYpIIq+6BGRJvyAMZowYzxq0A39zc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc/PPxpxOAU42VgvsiVOoObFuJsTFByvVq4WgDXSctDkwTD4feUFGkdWJOwoPwWWKnlWAR5xuPjVM75/FG3OnNqJ5RZ6FX/69WzHJcsaS8kRQMaEwUJ1HAyVi4PIjcyelXdOvl1NXqtc2YMCepWjGx6XVdEoQO3IM4r37fOIFZ/ZpNCElSKl6aBsecnJn1HucXv8SihHIgouUITMcMmk5GmzRVVBxTwXSDrA5iDKQNDBB/Y3a5+6SEST9fuBgRB2lSUdD+KpejQdqDlAeZjbRvWCZdArWOxQsT2B7fMOuoJq3vdDN6/45FoOKIOrp9v/6+owJ932gZ9D5oCs0Es4acBzIamu8SpFol3XZLmi7ETzQmkYLLgPYJz79CeyJ+INHJ1FJ/w8kIXBX3y4m9PZDHB/gL91Vxwe0biQM1AJDtAe0D7EHaVj2rgHgg8SrHJNXZm5Fo60jbiDUJrHSz1oCF4hX7uxTDMPGKcb7iuc81WbkIgWh7rYUMVhy4TwLQXNX5jJe4uY5rEMGgPRARMs9aUzqAE4tJ+CyncHvSJNgVHtsnDUXVUC2HceYCq88kIiX6aSc1S2yTJGxnZav1sZwk8On8eAuv44XF73Te4AepWZ8zqi8XEUw6Nr5dgw07IlbufzVYX2RMVsD0ydthRs0vvKaDNrQZGUl4EuusaGiHFKNp0qUh16CH+kIkSsgMLyF+K4HZRK/hAAVNVBUzEN4VS61eDtRMOA8iS+BGyxEtqeCBZiKX2x6BOQ++1uI4F8vfhDj0b1dXdytR9xTCv6ob11pFRnO5kaXBcaJtqyGVtchzAQ1aZ63Jev0kzjcRzponsa7+7tHq9ebXFXsvtX8jqs+4PSDPWqckIlZrSk6aLJokbWgJ8Bk17LImkSeMjbRvsJKcSk5IP1nrTawkjYqhPn+g+7fqXVYDVvV0rwOWI+Nf0XijY7/O1Te5fVyx8uu63z9KMJeOUH3EMp3++T+wZujW0fUTHYaY/Td8a93c3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nz8x/jTCcB+fHG+XsycrBnEXBCgW8cxZgaxJrm1Eim0k2KoJ2Tg6yRmkGsyA851ci4t924mvB0PKxHSBtDI05HsEJDrACDe6+rnnYDBFMI20owQQ9ogEryCajlzcoRzSOMIIRaQVLyqlWScq/pLq993ErnKWRmrRFvR6mMVBfsg0og0PFc5AG0AE7ONno2upSqFWb3uPFExJIVy5lkJwdpYCefl/IRyQ4e0cuG2nSaJSXWiZiSqJXJW3HDC2EtMXVEOZjU4JhELz47MyRChqdGbYa2DdEwNa1bu5YzL3VzXITpIP/CkBLi2k7zLvSyNBFJ3/PWzujyPk1xf1UXsE0dZx6tcqfR6PrrjKRVjrHGJ7PUsRQUdn7huABXx3QZ+nkS8yuWZi1gHeJAkrv2PKHDtvfpG64kDC/yrYq7dSXuWw3adJfBpI7cHgRAe5a6dPy5RVohceEKmEyIVU04Q4rgYc501CNEMX5NYQYaSNGKerHAyWn1+21geLF/182akGikNHZ8l7ouX21QN2iAEEK/rE63o41zM9WKt81r/q5y3qRBXxLBUl+6RzvvrxXx9kRklul5/XvqTcGe+nRlZTlx3zuW8Tsez3PiYsdw5Zg1/zHnyWsE7hZVBXKK2aFQcMJ2YZ4nsIiBbXWcmohXLDGCyMN3RSEZ7sjXj28fGt28PhlmVq5vU87SBmiAmqPVyA+cEvfYpRkriUSKs+sRC0PZEMgCtNSyCaNbaMCEMXARpvcT3tvN+H0RMnAOPyaoFAAhJI0Xx3svVT0VtZ0R1UFuSXQmBHKPi0cWr9FnehGYNJORJyiJWMCMJqcEWUsrN37Xi362DDUiv12yCxIE8txJ3/YuwvZzAKeXg1YZow3qlG6BWneP+QsSILFe/jg/ieNdZN8oxnfoNaVrx9JKVqO+zOroTJCemoCbVYxwJsqHbE3n/QPffkGZIHuR64ecXHgn9SXidG8wvxD4qav3xG7m+/hu+tW5ubm5ubm5ubm5ubm5ubm5ubm5ubm7+Y/zpBGAkYQyIhjogQc6DEJCmJVr+Ekva5XKNYMbC/SDSyhU7yj1o6UjrJbKu+rlGoDOxltjjUSLK4HrfrG7MX/GpMhAWZo5qL6HPv1jeIBuaILrTsmMR2ApMEjDyPcnzB5FeAmTbQMF9sa7uYdcG0epJazK60MRLTEJoAiLlYmu2o7LRFdrwcuhGlJCTgfRyoWYrgVcTzARUwRP0G0g5TIk3GS9SJxmJtO+YJujVfxyTtEFmAwyJRCNoOmBBYjiNlB3zg02FZmd1r3oJk42F9UaODxbJOd8sMY6fP5EElQPPqMfev5MuxOmobOXqVcg4UFFcFmdTyIbIBiF4GOlOukA6guGqFbe8FhkgBOZO02RXYVgytoZycNlWy/noQZw/ifcXrHnFbSdoJ/zFmm/i9D+WacU6T8iFpNf/5wlnRUyDVKdqCrp9YuN5icqK2KNE+ogrYlnQK4e6ETTZyiGZC7Edax2ToG8b1nYsz4rg9UBVUDVEKtJ8pZD5UU7sVJBewwGrHKG5JnL1UtslAGZrhAxkJoQQkcx5lnteLrc3Wo74BM/APcEVi4VMJ+e8+qFPJCd+nEwGHkFw1PORun+ZjmRguUiBNd+07clH3/ncBs/WeH588jFga439+cS00doTI1AZ9P6gadJ61N6UYJ7lZm7jk6HQx0BZyKOhmpyunO9ZseWqmDQMQTNRuTql02uvxICoM0CEioPWihxuWXtcrBHnRPoT64ZIoLFQMdRPiE7G5crPV0Wlt4ZLkv1BhkJYdWtTa4bLSRyi15lVUceZAUtq3YsiTZBu6NigbeCC+MK6ofsTsUGI07cNSa9Bk1WCrI5n9QivBW0r8TzKCY+MslWvTixD7AF0QgzHy+Xv69KsBX38tV7v/CLnmzxfmCZba+j4QFSQdZRQ3irOnrlqj0XC+yfkqnXNROIL+jdq6ueNdkdzwnheP8/6Pvg6yZRr4OKLNRfz/EmK1Dp//U7MhZ/nf8e31s3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc1/iD+dAKz9gy6t3GUCpEHriCmWZ3WDbhsiCf6m5byEgE5kI0NYrSMRbI8dNaELCK3cfqPhOIv8I1Z6ZbK+XhxHcBwwZxDngmNVQnBWayosYr6YDOI8iDiRPFELNA4sZ/XIGkgeQJJUvGzmLMdbJrIaqgOdV89pv0S8dSJxllPz6ufMS/TmcCRO5PxJLi+3ss8S06SuLXHEX2gzxF+gQUSSaeR6V6+wv0lZFX0sDejgi8jFmhMCwJhRkb+iIGuxZpDrQMfAWtIkyW0jJTnnySsH7/dk+YnHKgdiXkIpILOeExhi1UW7UgggMoj19+rQtYaYYb1VhHIY0htNoHer+6cgY8ci4PmJ8oUIkL+EunIbc56XiFtdptMDj0DZSaycuDgy+iUsa4mYoigg4egV8Q2dyPmPmG4vUT/JWlfh1VM8HogGGmeJifOogQQFs4ZZdUunz3LLZuCRrNdPqkzWmdJY6w3aCDPmOfFcLDpcAveKRNSqLzYdiYOcr1oXcRJR7ys4FrO6aC+BUbl+LyY9T3oI4hP0RM3oZtjYKrJXkvR3idRc0eY+0ax/Z+o8mpXzVUvgVT/IfNPkrK7okOqH9klmoulkrurwXSdYY+yGykS3huRJi4mvkxnB13FyhJdz1oO13pyX45h5wjoQrDpiQ9HMctJ62fBtLSQXpwvnfIEqglSXNnDlFV8CsBDrRRjEmjU8glcUdMY1aPEoh3kDto3lX/iaZFKOfV94BKkJfkW506sPOpL0VsJqJLGOcvyL1jrKBUw0r7WpeTmVjdYEI5Ctl6ib1ReOJDyfqEb9vhqE0/uTRsD8qiEH64DW/YpLFA5HOEBXvR5Wsc6i5YbPWTHMPglpZANn1b31674tJ+yTXAH2gR9/41wn4OT5JpgQX8j6QqMGH/x1kudJyk68fzDngdPw5cR6EyHkDDKf1Z0dkMfv8D7I94S+1/ngUU5sSZD63Pgk+STeB5TX++bm5ubm5ubm5ubm5ubm5ubm5ubm5uafij/df72WUFSSFovIDjRIiOmEO9asxAu5Il/XwnuH800GYIEl1RFpvVyN8q7YUfKKQO0IL6AiXEUNpeJbE4PppEzwgSMICS2uuF253JvlAEQSRUn7JCKQdaKPUX+fb9CtYlPdQRYg2N7JuDpEV3W0iirRHyUiqV5RzBN3L7F2NGCSzeqfU8AcbK+IWBkoimWwNAl7YIxy/WmW2Ouzol4jkV6uXqQToog7agP/QyDij57iSKf1HdYPUpLMF3jDuBzNopwzSAapXrLa8YNpD+x94LYRYigVMSzbB+D0/ROf/4YfP/H06mXdHrgk83yTlVGMIiWGs6Fekc5IlklQd7AFfcekHMF+TvKcoEJLL7ekWt1zDVBBxwe8/w3tT9I6uX/Ue6wg83W5QhOAZEMwBCfyQC4POutVblqdFQFMQ/oHvv4dyUSYIEr6ApXqtA67hPgXhOJUBLi2zlU0jSIkhvQP8nzVGhdBM1DJq915IfaJ++tygC+EDVWI+Flr0ycZUQMERDmOTUFOyCBSmGvV7/ivVF5nkuCXeN02RKUE1bhi16WEffyNN2GRhG5IcDmOg0YSqsRaIB3iBb5q0EGNiMRnZ/qLtn3H3KvjNsq17iwCwbPhZwnoFZS8atBCHwhJaLlsJQXTDruR/kbZaNKZksx05nqXx3Z5uZj92l8KqoMML1eqDpCd9CDNIP3aPqOGLRBEFPsllrZG+IGqYPJR7v7zC7eBxoL+UcK0GRITk2SLE8+tHLW6X89kq+elWsMmMev9Ral8ZMHbRsyDZkaekzxfyEp0FzIa0MjwimxPR/tWArRcwzRmJcJH1Prn6nV2qW7qKGc2KaRXZDpJOXszwL7D+SaOF/L4C+IHuc4ahGmP2qcCaY11vsm1iPMNn38h5080DlYuUqxO1TPIbVxDJ1JpA02I9UbSkNcbPr/X55SBQMXmA/LtX5D4WX3wY5EGIht6nKQo0q+JkL7/l39n3dzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3PxH+dMJwPhRgigC/ibaB8wfyNgukWyRXKJNBphjDViCddCI0kc9EU2sPUj/Quwbef6OtMEYHd7ltpV2dWnmiUogzQhLRD4hpVx1XL2VmQwdl5PX0SHkOy6BFeR8IdsGJDqq1zKtkRHAiWSr3tJ01EBlw1VhvsjK/yVXEF7dt+QP1DoxX9AfVy9piX1VShukn6CGZHX1htV1Yr36U3/FZOsAccSDjBO1Z4nestAMjMBTy+mXV1exCDkn2kb1h6ZccdYljGJlErXe6PKG1onZURZre16xtge9DaR9w9//jmkg6sj6gYVUZLA2zNolwr1JF0Rbvb8FsRRWYt2An+AB9gDNcj2bUf9TtAeHfeK2oSQarxLTM0iEcOre0aA9gYqJTslLAOxkdla8gIbS0PasGGQPRDqMJxLr6r09iNbhcrKSCtqRtiH+Vf5hTSQCleqolfCK5e7j6rcdWCsh07UTfiL7E3yW2CxXT7B/VTS4VGx2skjbSITMSVp1OgtGiYbtcm5W3G4Ji1bXQq2VzINq9E2QIFDCoxzLfUcI0BJ+U5Qq0+YScRfH2QlWdQNnIHFWdLgORIz0nyQHxERSSC1XvkR1MBPg6+AQQRI4TqYN1nmU45ZJKYSC6EDMGNvGtifpwntKRTpLQB/VOyuLMQabASf4OvFIMk9UlXSv9G/bL0GxItfLIRtIl4qoth3LiYwn1fechO5ofNGbQSTnmjUE0pRhcLxm3Tu1ijq+4twznGwNbYPw5Hy/ybbVgMB8kWkQJ6FPYJLyLDF4vWlqqAYNSBFYzpon2R90i3LeEqQYqTUcEmnIWnWOju+QX3AuiMSXg07MejnVtdUQwTlh22G9QCaZG5iA1/CJZVz3Z9S6uPqDJQTNA9UgtZGt1RZjIv2z1sZ4AEm8T3K96txtHe0CutVZa0LOE+k7+fU7sn/WWddHRVX7C5rD869I1pBC7r/h54HaBjGJ/huyvuh9Y/lRgwo3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3/2T86SKgUYW1UOu0NnAmZwprLmidlAFsXHaw6r2VjmyftP0bNgzMoFu58kanPZ6YJdo6mZV8KqJIVLdmqpCppF9mNH+TfpL+rr7b1cgFccoV+xtkKrEEaf0SdQUYyIrLqigko3oy0yH1inotcSQSXCDVCLZyzvrEVwlSGqvcw6LYLxdbBGTiKSyV/4dzzohVUbv/sHIu8ngTtHJzqpSLVTqKlss0v0AuJ3BWZy0RSAoK5ahtRmivLmbtdRn6QUQroVEgw/FYFZWdQTIRoFljiKHLERaglFbl0B545hUL3Ui3ii9e5RCMSHxdUcbbAJyYJ6cMVlgJ5hLkzOppzjfkycpE1a5hgUaMv+DaKuH39OvzRUXX6icZSng5sxPlsqBe0d1STtmYSDhmDe3Py6nbwD5AN0Q3ou8V6ezrj9jrSC3HpSeRiYcTYtdQgFUseZ5oKi3LVSoJEVE9q/MgIyFfqHUksrzHEUR74DFJat0SgaiWMCdaa4VaH7FWddXKDmo1v8DVd6uPEvTTr2ehaNtpAtb2+pyrhEMir2GGOpgEQdsDs732I17XW9Ju/Z4NWEHKRm4fZCbujpNELMBIn6wzmaczvTGPA1/VM6tiWNsYNkqojsXMxrmUmEHziaTjPplrkSSRnUlnrVleajOk1UBGZMVq03rtCbFL1G6kDvL0cr/mdZ8pgdrnxD3xmJzROOXB8iBXQtbwwjy/mD4rQjy9opjn5VR3Q1OQCDKCc01Sy+EbLmRcnzcSnWe5pteCRbn01wI/UW21d1eiYnWe2KOE5HDwQFIx6eCUM9h/IBE1nGCAbeTM2u8oqU/8PMvVfp2JjA0ZW0Wrb09k+w3tTzQF1V6PWzpEBWlHZK3/OGqI45yEPklpsE5y1vvL/gCtwR1tDrbBeiOSdT9moCFYG9j+xDQRfyNZXeWM39CPB8JJrnft19MhO75WXUdMvD9BDNPtf+33083Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3/Av58AnAK05RjCtF7xQDLAHfWfHOuIEimT9ZK1jrxWd2c6QtfeTnvegkBebJCWHm5KzNInGgbEYN8H2hmibilmBAYLlavqdVfmpKIccWp1vUI63L3naRKiRu9/SGGVYdxg7YjtlfHp39VVWVSwlsEMkZFT4ehqaj0cuaWZxW0BFu0X25Zo1FCUOsNiTeMjpiUCzWu9x9bRda2+itXdLFcHcAqn5h0REuMSwRRRfuG9kHGRK7o6DXfZB8lIomwdOFGff7WcRGWKyvBvRHri2RCsxLnJiz9ZM5J5iSycU5Q2zEzSml3cpToLTFRKdesZCL2QERo7VGfiXJeC+UsNSl3ta9ZAlNJsIQp0j/Rj9/ABiFCRhJXL3PG1euaXo8LL5H88RdojWDVmhG/hOGoh8flmtyeSN9Qe6D6pKTzrM7l/lGOXWb1/mpHxwfCFeXcGirVTR1+IGqICEq7YqMHThDyLEFaqRhqGVdEuJPrTcyT6g8+K+k5F5EH4W8yao3SO2kdskRRtO65RvUlq+jl1rz2gQqwatAhpURyNVIMR/BcJT63RCwhkwiFVoKbxMJzlitTrTpzfUIugiDCQRzVvOYVXngsiDemFQOP7iCGtqTtg9SD8MWaB++vgyM3Tu1MlEDgipF2D86pvI7JeyrvufAwMq71fwn1OWcNPqSV09QnYUlKOW0zHTAySlhMU9Q61jsxjFRB9RokCWMtrdfSDmk1FLKoOGM1TAZ5RsW6s0p4zln7XyDHRkiS1pA8kHS0135DkqbJZpRYrILPBQzUsvZ96zAPRKtnWTXqPcRIV+gd2pPWFdsHOnakbdCsOoC3vVIF2rPOmmE1LXO8yv0/34SfQFZqwXoRnKgpsj3I/iCkhmxSA9YXrDeRE4kDma/6TI8PtO+1LvwFlNhtmdjje8V8f/5PMqnY/LHXnvWz1vO6xN/2cQ3F1JCFtu9X1zT4fBNtoOMWgG9ubm5ubm5ubm5ubm5ubm5ubm5ubv75+PMJwOuAOYETmS84X6hAaoNMbE1yLcgOqqQ31B1bC8UxMTjjci0Kmb2cmJEl6qwJoRCN7A2XjVhZ0aXneWUag8wFPMo92wdEoqp0U4yKiiaDRCuyOSZJ9fWacHlsoeGYDlSlXHfSSNd6sDkrdnh9lfsVrdcVEO0VQ+sTu3pH0er7xQWVRMYnCSVKk/g5WSFEnEBpeEKQ4UQs/IRMIb2TeRIKc75ZoSVKqeI+8fN9OTCD9Bc5DyKirvX9RlCMDXkdyDyu+/mrSFbLgW0fCIb1naaUOzUdXYnQwScaJ9YUYaEiJZSnwDlJ0RLA27Pig9tWwmWeFcstoP07JknTgWbFNZvt+HmCn5cIdZKe4FbipDzw9b5EzxLOMiaRVwxyJBonuk6QraLEc6GxMBmox9XvG7RckPVexCrxPytSu+qDFayX6KhZxlw/yQTpo/6cKLGclcr85RoVR4kSdP0EWgnWkcBA9KP+XkY5uMXh1xqBEump/mxJpfUnbd9QDjQSy6T1b2gadt3n1gafvTFyYr5oNNrlZK245IbEWe+HXvc0icOJuYg1y3ScQsog6ISD+kJFQfQSnvMSPr36ruP6rCiqzxKSaQRbrUV/M9/J6+tgvVYNhIRX96tEbVdtaKmFSAYaQfODFoCDSaPjSE461elbSnE55VU7+mvf6gOZ5eIvP3SUU5vrd3LVXj+qczzTr3PACBRtA70i4V20jqmu5XBFmOvNXC8i5iXsXs5dEnPYNuUxHvVczMqhnFH6vnVSO75eZB8lYsdJnBPmKmf/4zf0uj/MiZqhl8greA2hNEOvNQOBxhuNcvinvxHt5Pmz4uVJxKyE3PWu66ZBexA4HFmCr5+XO71cvWo7qonaQPsD2b7B9q3OtvZxvfcgp5D6CTiyXhir1j1ekdghtSfWgY7f6uw4JuEGociaaPus/ZcO5w9sPNA8//jrzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3NzT8bf7oOYG9SLtj3F7p9Vi9ngqtUf+3l4GwZiFX/p7SA84TpJURquWBDtH6mICJkCuHB8iR10bQj1kArUjVswxxIK/FFtV6bfjn4QE3xELCd0ET8ijHNK764NyIMITBpNFvMPEpMFYjsEEf19OpeIqVfvbjaS1CmooDR6vRMsXLRUQ5MsYb47+UQ9Vmx2SFgQvlYO4YTy0t0a48SmzWJ5ah08jwut/AJ41HRxr4ISSK3ukZtFYXbgm6KhBNtIH5gbb/6a42K/l2IVCy05MIFPJX3cbmd1wRfjI9vSAahDWkbfrzLnUm/xLWAFqA7kYJnIPON6WD59TGt03yirQFa3cdtMJpVjPH7ZFG9vyZ5RduW5GQC0fbqB5YkmhHvjrBKNFuL7CCtXMZpiqohLIJV99qrBxcRLMuR+w9BLaszWhSVCdt3GE843oBCOtaeJZLnItNqDfzq1/VFFyNU8PUD6w9GGzBPRAcuVxx3JI5XnDRX1zQL0a16bk9FuiEeSL+GCjwRCfq2Iy1p8iCZTG9gTzK9OoVj4ulkGEFWxy8VQy4qJYSaIpe7OVPIw5HR4DhhVIRwtRSPP1zIUtno1fcqQU6H/RuZPzErEdCz1hliZJwl6PZWLm3da41pYhaIJk0N1iRVSjS9HOmCl+A/3+iv+2+dsB2R2n85SlwNoZ7NFcOeLcAv/32v6PY4T/T5URHM8UVThb5zrAPZvwOruo5dkdYI0eoUD65e6RL+PXt1Fht4Bkpc/ct1bmUmUzuhTkaQGRV/zM7pwAomBl2RGEQkYgPxF0m/zhCv6OWcFX3NRPVNeiNFqi93fdXGbL3OMDEyDzJbuet7iarZthJd4yTfX/D8RuKoDKw9a0/0Rn79O6qDmD9BDGyH8a3E+vTLEa2g1SeON6wref5E5Intf0Hf/wYS1/10lIanI7Kh7Q3bkzx+EAnqoLuiKmhX4nJz921gHwN/vWtA49devbm5ubm5ubm5ubm5ubm5ubm5ubm5ufkn4k8nAK+wSmJuHckke3W0hlcUaPcFnCT75Syc6HmQ4xM/Xsi+oV5uX2mC50Rmlotue6Dbhhx/h7P8faIG0xFTbBsQjs6FZJDNUN3JXJCBXz2TKUnGu/o1cyFs5HyRzfDXJMZGzDfWBp5ZUckRoELE4pwB1jGviGT55T68HLfSBlXxmxWxnFI9mRaXwM0l8CzwKJFlLuzxifgkUy8XnZEe0C+XcXvCOWFkRR5LicCiilgj5t9RNlInGRWFLFbXludRUdRjENOqZ3hrJSKtAx2jRDe8hG5JZL1Q+Ubqhr9/lnimVBysGBKzhPWu+Pt3iAaSqHWUhYugodBK3NRY6AowQbShokTb6hmEsGistUoYzbzk2MvZmwel0oNIJ+Jdju1M9LHD+a5+1HAijhLgpZejM2c5MQEosT4iSzM0Q6Ac1ALoA6ioY/HqsM3ecd+BE80aTpB4owizdVgnrIPsnfCF9gc5f5LaaL0EvJBJ9o86ENZkapJLyDxwF0TK7mpUB6owYXlF8s7j6rxeuE+SbzUwkbNMqFLX9CZZsWroICpem2vroGd1/ooQ6ZfT15CMiq0egraOtKMM6dQwAbaQX47UzHKJxwnSEHeUA5GOGbivWod5lsgvgqdhveHz63LhHtXtLEpkiXvhk2TgWU5yUSVylZjIIuZRZ0Yqvo7LZV9dxcgVsZzrj7WRc6FjkOdXuazjRLtiEjQ50P6txFHyiopfiF9x8Co1CHK+CRt4JOP8KtFzXL2/Klj/II4fePxyLgMeTJ9o7+Uyjij3a3sgOuo1Ecbjs+LjR5Cq0KSGSdZXRZC3HdZPsj9AJxJv0h4lqF/x1k4gIRVf7QtZ1zNCrqESg2PVWZOLbAL7E5qW65jqC89tI9dC+pPwA9pGvF/IcLI90HBSFmidJ6iR8yesk5AN6U8yv/AYuBoak5BGRSLMa0hlgj3gPOr5cyIjoG34OkhancdZjux8nYQLcZ6VKHBzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3PzT8afTgDu+NXeWn5BRbE0uASilYZGkhJ4JurgOtBjkqGwFis7qgeZneWLlo7oE+IsgUc20MCbVN/kelekNDu+TjTssoqehAf4IkVhdNRPaIPMEl6hfI5IK/eoltPRY8EEtoasd4myvoDErphnI1EvV6loiT7aLhewJoSWCMR5CTyz3isuATiql5UEuhARSGolAANidnkuhbQNYiF2iU39SaRjCpEncQSwkShQvaHErLbdY5V7ci2wilXGrhhg8YoQ3p5k6BVxXBG/tI3kILWD5CXGneT2qO7U8Q1//TuSjmcSOM0q5jr8TU7KvUqrzzg6c05sRTlo8eo9PX4g+wdzTnw54id5CFggvWKwhUbk5dAVLWH09YXuj+ps7UZmEDqQ83dEJ6qCtAe5FoGViB8LjQNhXM9BET0xTZxE20acP0u0p6M4Mf0Pl2plVyuahqdXN3DOKzI68AjWIUQk2nfcy3msmqiAR5CZxJpoG6x3ILbRpJzzAsjl5MVf5K99JFnOSW3gC80akrBMWmvQknn41S2cZLa6LgSRhmXdx1hnPds0yFlCIAPt30iZlzv9irbOGtCgtRKBs4YFkl4u2f2JZmLjWSKy1J9pvVU8t3TUDXzSdave6XDEJ2EPlnulA1z7issdvqgIcfFysaOA177M/BUhfzlTxUAauQ6oVu1yyOZxOesnklFCpDsujZRgnec17qDILDFfrGK9M0p4t5iVwx7rckRDRoVFpziRdbYgHXKQnCAO8ar3jUR6RwkUwWwQ5xdmjXwfzPN1nR3j6vF9VIS9v//R/Xuctf9QaKMOhvkmZMPSyeVXn7nCuSAb2r1c2CHX+XP17ar94zmaEl9/qz2fSbPtEpcVmZNEIA8i3kj/QI6v6tEGiIGE1XlpDVmT8CQt8aiOYzEHfyPjrxAHKXu5qOMN1tDtXyFehB9E9IribhvJXkLwCpKJbncH8M3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3NzT8ffzoBWKMjElgEouX2JB3GoKHMSHwuOtWpSgSpO378xGznnCfZFQ1nzMGQiiSWJqXXmrD1AfPvoB2JRJ+/QQQhgfatBJjxLAcalHjzK8K2KdW3aiW+nD9JE0wFn44BIoFsv9FYjO2DGV/Mc13hwIo2LeclC6IhEhUfi5RolmCtV9+tgM8JNkiva8nzILVVd+qqiGuagR/QBkIjbZRAuxbQyumYszpptaSuQMu1p8/L6ZmXSK6obYhM1vxJtg1m1OufCnOBlWMyM4ixkT6h79WrWj5UtHVYevUUb7i/IUH7hsRR/bIriXXWfTEDDiQCUpDRr3vzKFFZFfPfoW2I1DMxDB0fdW+lk2akBKrXfV2/l9O37eWehnJct1Gu46tjt6zJgFm9hke5mGORerl3/ShnrD0hkvBJRHUJJ4lqR2SBPEFOYp0s6Vjz0gEzKsrYjErdbVg4ySjn83Kk7eVa10etjwwyhUVHp4OU0B9J5WHro4Rqjet9L5HOBtEaLK+Y5gi41rdeUdYqG12csX0y/Y0wWaI4WbdKNswdVbucsobkC+SjIpRxyKvfVpNcfgnO1dtqqqzjqM7jDFQabf9LdeOmEDgO1bOtVFQ7QN9Z86g+7+sZmCo5Wg1n+LyizgGViqeOcv56nOVO5lePtKAuBBXzrQhksGpqoq4rYdGod19XL3SJkxl+xZwnEbUWIwKaoj5Z4peD2EropT5r2oD3GxsDueppEWd7bvSWxPusPdwe+PyJNiuxvX0nj1ft4/xHbLnh6L4Rq6LdMWiPT2Sd4FlOfwnEdiCgfZaYj6L2INcXzFmueFGaGmJWEdJqdX68ftQeZtUQgSx0PAhvFaVsQYaB/5ow+UQisdOR8b6SEU7EtoqP15PkUS5ce1xnkIO/rkGIAbJBv7rDc5HnC3Qj/QttWwnRakT57EH36jLWSRwHsn2W9m+jRHPt4DV0Er6IGf+rv6Jubm5ubm5ubm5ubm5ubm5ubm5ubm5u/n+j/7sv4L+bVC+33ehAolc8q5Job3RVrO3QtxIath2XhcvG/BXjGlIdnZrlehuXIJFacc5xCQZrXW5ZKQevl7ArVj8iAlonw68OWS/x0xcmiaqRrZWTULM6V62ih1UhbWOa4CvI98+KPzUrASMvx610PC9nJ1KOwKTEylRWNMKua3CvPF4zpkh1HPer65VE2w5UpLRIQ2Oi28Ca07tiZqgKpoaJoQLadzSjHKZaMdXSG8FZ7aSqiBl0AS1htiJmvQRarRkFjUTXUQ/RDyQrXlZFaGK01qs/VvOK203ST2CR/YmOJ+qOUp3KebmXYyVpxsoJCr49S/qTjUgtcVOSdX7hPq/+0m+gDaWh/bPEp0jI1x/vTTh6uTJFrmuKisvVsZMYGRPijSkYiopi2wb9o+5BG+XwNCvBOa7no4m0R7mzYxJ+ubA9CDqeQko5ejUX2loJgE1pmnTT6rgN/ojlFa0oYsJr/dij+ma1hDP3uBzdgbXqdlbrmDrWgrZRTltt1++cNBH6/qDtoMMJWej2RMejhgt0XJHFVaCcUdHXkU7IJf7rhtvG8hMncA9WSDkyWYQoOU9SjLQnp8CSIFQvZ6fi6wWZCHI5bRfpwlwnHpOokl6aLJo69I7Eu4YXYpaTngNkkmWBJiNRki51T7ic0Mhe7nm1Ol2lEzEvgbvVIENWvHOGl5Nb9eogFkyCoTCeH4w2GJRwm+JogoaXCJsLRq2PNEAT7R3tRpOOqGLjQRIl6mdWJ7JLnQ2ppAjEG+2jOpPfB8kgA9K9Blj2Z3UvayLSSO2kbnXWiKKjhhdEQUcrN3YzRJJYWaLxeNTaBbQp1hsyPmrwQ7L6gG2QsjGXl/ieDqNi+K0BMcmvL5YHMyY5vyoiP5LwEnHrWKt7nGrkNaTB+arzJDoyT1QdXYqOb8AJ86uej0+yfSDtOzHflZhAQ94/ykU9XxX/fZxcZv+7A/jm5ubm5ubm5ubm5ubm5ubm5ubm5uafkj+dA/jIS1yQSdcdXRU/zHzTmpVzrxn4IlI4IsAnrXdWCDIT60rIhmegDrIc7cHMSb7L8da2HZ+wXm/2Zzk0iQm5ofYkJpTi6EwHacrAKz46SvARnwhGrN9RviFtI0JQd7Ak0/GleGo5iiO5zIQkUi5OKcds9cIG0rJEqPASzbLEE9ZZfcGqzHNhnghXxG1v1et51ZqmlkiI6BV7KyVoSnXgznmUYLRO2L9fcbcTMEw72EDzRfhCZCNnRTqL9RI4W9YjSqme12NyaqnmGUGi5Hozo1UvqjkZndBPDldkZcXZ8mZJr985f1YfqWzV43pFwa4M5P2DtEDWpMtArRyeOl/E+MAjy1AoAZmYRImAAuiG2ZPwCbxgHXXNqmAfJRr9chnmIq+u3+wPyJOUVn2rmVfErJL+wmiX0Kak7mS+iONVLmlpFcWrnfSDyCTyV9fyBv47kQ0PK4HRHSFIFcIMWW9UN1wWgtNUUDXa9iAXTIdIZcVJ4lcv7yIZLOoepAQ5/14uYY8ScmmQbzIEbONYbzwbr59frHMSucNxIK1XNK+UuF7DCecV6yuE1/pKrfmUDLkEdsoZn0G4ILQS5rT2UsZRr6VcUcJUFLWC60G6VtS6lUCd2cptq0Hakzh/4B6IdYxy4FMSagmo134ka5+uOclhiC9cnkR8VSS6NWRBXJ8hIkoszMstmtSzyPLJpy9EDdFOqLKs1TNpiqx+RcQLHolaq3kSBQ9F3JFoFZl8RXfL1kC83s7P6hleR8Xen18VLS9JEARCzKDHQSK4J+ssN7ysF9k21IwMZa2knT/LwZxxdRJvZIJaxbBnjoryjtrHrLhc5pDjA5kTuhFaHbsxT9xPtH2gtmjXvsr3F4y8usANpZFcz9pXOcObl/MZrw53d9h2ZH+iuZHrjfQHsj3JmASCPL8hayL/8i9oKn5OaBvKm7j85SlCnoB+lFNZFejI/lfSf1bnNxXRr9vnf/E31s3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc1/nD+dANz8wDMR7eXmHAL5JOJvLJ8lIEUgITAaRGAuuL5hjXK4kmQq61ysSDpAJGYPcn5dqcgKPWEKIldvbiTYvCJFlYwOa9HEEM/qDlWq+zUDdCIy0HZ1b16CV17/kwxYCxW7ukB/gm7159IhKrpVtKGtlfuYJDSr5zUBvMRfUZJGhmC/hDwUkQEVQAzzQPpAWWQaYYqS1Y0sXkJuM/AS6bT1664bvF/I1vCYcC7SlHi/wHYyHD9mxdnmqs5cLcdiToex4SqYgIriTclZburRB+uKQu7NGDjNSmQVD5o2Mt6sWKSMctqS1WG7vmjWYTnWdzi83Kv+rrjg9iSi4qy1DbqU2y+9YpPdT1BH2iAFQkbFLpPlJkTIrvWZkeu5JACqiciGxCTjIMWx7CSzupnFy2mdgsbC3UvIiwmWSESJbv2Bz/flDN7weFdMtrXqi7YHHF91T3HClWblCF6HkL5g+8B9sl4H6GDJRsbPEt+kEX5W32sufJUzHeyKQi7xtNTwhca6hM1yqa7jQCRRHXQdrPl3ck6sCyrGigPsSbLQawghI1AgpRPhaGatUwcZCoyrd3miEcTl5nZfRF7dsiJoUr8vBmcNLIQf0DdwJ7Fyj3qQNgkPUgYSC0Tp2vBL8I1MyH7FwgNS8qnqEyNQEpeKrVb3f3QZN1DZII4Szluvc8C99r0qYlJdw7HAk8ggVqfl1f9NVmS6Vsx75mXiD0dnIvsGc4I11pq4wJonk3ZFc8u17gxyIW0n18I9UTNiHjA2Gp3MN/RZrvN1if0+6xxpHbSR54LRESb4RFujRk5GuccFzEqlziwHfJ5vZPsGeZB8IOffaH0nVFAV1PKKGR/l6rZH7WEW1h7lUBeQ+TtsW923nEQb0BrpJ6lRww19w0TI9lGR0e0DeX2RuioWer3R7f9EXj+QyDoz/UDbdyKFWF8kq4YN3CuSfL7J/YN8J+x/gTzKzc7tAL65ubm5ubm5ubm5ubm5ubm5ubm5ufnn408XAe2paO9IOKpJBDgHUzY8lRTjdDi0M0NLvO0bXTo0wZsw58l5lPOyRGOvSFxJUEFNYCUgtK1XzO4VAYt49Z+qIH1D2kCboeoVYSxZIk8bpI5yLKaQ58Lnm3Qhzll1lQiqo5y4KiXQtK1ch654Qkir7k8EzYqTRaziXUkkE5FE0hFLQgLX4BTDtw2Y5Vr1EpHEDGSjjKVOrFX9uZnlfEQuB+IVxxwvUMH2vaKQz8tB545ue7kR00uID7/+eVYXcc5ya1ogfmKRiAniFS1dOq8iGBJvLF/lvl1vRBPtimaWkDOe0DvhJxGQTWHbyVTcXxXHzQJ1FknEJFCUF2LldPZsHLlzLlg+SWloCuTEsmKY0XYVzQaejoeU29u9HNFrlShG9TqrjlqYa7Gy1lIAnk6sSfjJjJMFhFSfcVCSfGSQNFQ3kF5xubYh/Rti1YeaCQxD+igxMA+cRq4Ti4mp0qJE/pC9InQp93VdX69nKQZcYqp2PLOEOtES0CQg1rWHlFDF9Yo3pmFjB01MLwcuQmqtT8dJNUK0XJhWrlMRuQTFRCOQpldE9SyhXgzscoVXEXB18NYdJDyBy+1LCcty9QXTdmCWwGhbOXxNq3dYtnJm69ULm9UILFaR24iBeImyccUN54lIQzBWKN4GtCd/2HXbVvHTUc7blE4uvz5j9WeLbogrvA/gpI2NrtC37XKfzyv+vBzFTQzTVZ3LsZA0yMZUxaMGFS6luAR6BVO5nkGWw7jVx0kBsYDWUK7eXrFywwpMP0it+xbd0D7q2tuogRafxPpRPb3tcgjzy9UuyPaE5bW+xEnZ8Uxy7KQpzknEImhXWkFHpEP7jtuTdbzIPK99Koju0B5Ir/0nNuqaxjcy93JVY6ifJb63en8zRb//SznRI9FuFTN/JUFkTHgfiJ/kmXh6PZ/miL/ItkjeFZdunYif/+XfWTc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzc3Nzf/Uf50DmCxgGhgLyIMWQe0vVya2sEn0jud6kdNSeT8AtvRLOdrikNrDPESW86kyRuVAW3g589yWbKV03V+VV+nXfHT9kDmC+klxMT5Ls1qPGBW/HAuqlt0zT+EywzDbCFj0EhMk2BWnGo4aQPJSyg0wa54YRVD8yzxWTq5XmRWxK2IITL+cBdKUP2pOmlZDsCyV57QejnitF8OxrP+Xgbi5QpkrnrdpERhESSzOj7nu7qVr/uMlvBNgqwAMcSeuP8AP1EzENAMLDuwqmI3ogRxMWI30gQ9/85cHUzxY0Ea63gRK/8hwHuQIUSDnAvmUUK0Z7lLqVht2/bS/VhAh1gETugotyROuJPrIMdAvOJvJRdcAru/fyf7JWL1nYwDZWKlDqMJkklqR65IYPFFEyFXVH8pEFnPVqKE0BwbwoJImm00U9w+mD4xNbrB4V6CII6wEdlIf5G6s9aJaK0TpRyv8au72Dayd3SeNRQgieUqcZYGeWJ9q/uYTqai41kO5JiX6zyq03X9vCKdjW4JUYMC0gfqk7SNyEXQSX8h3ipKmCT9wLVE0bzWSeCoPq548yRjITHh1xBDWv38nNX52kbFZ/dRccRmdQ2i5DqqR/fqb00WuSrqGfdaH1ER6kIS8csNWjHm6ID4Qi7BcC0r0dT2Eu3lV0Fsuc3JScSvluDGOn6gj29Y+0L0AZTjWCRpLHRs1fnL/83eH/VIkiPZmuAnIqSqmXtEdt9Z7P7/3zfAoO9URbiZKiki+yD07DuP3bertxbJAyQqMjLCXJVKpQF15HynEumC1NpJvQ/IRMab1CfZW32uQXLXr+cBxw/kehU2OXUNoCjhX2ielSDPrLNQIVKY/iYHSDQ0ovDcBJaGtCfx/oJHw9qDzDLqySjUsp31PsSos+X6wsQK554DmWP9rFbPrp8w/l4DEXbW3m5HpcvnXdhvUWjrbEklY9SQhVeqXI4HPm/SL0QrWV9J7SA8sPwCN/J9Vye7GT5vsKPw9jkruz3eqD3B3/UOdyXvgTwhQ4ku6DWI8Op4TyfnQPldPcdbW1tbW1tbW1tbW1tbW1tbW1tbW1tb/2T6yxnAEa1MYA5cQOxAUoAO90Cs0UTBb+YydzUa/VRkCCqVYhzqZDQkLzKDjJM0QyVpqUyp3lVzAw+YDnaA659IWRMBFPpH4ag1EIRgGdGUKQSJtkeZHBGgD+Z8k0Qlf7VXCpHqe808EB3VPZtlYqc9yPle/apWiUl04YKTSC1zbdzI0WjnB4yBSEfTCVlGKqvOdAjRPpBxkQrtKNxs+kDso3CuWejojKxrlwB5likm1VkqzYjpTO2YTOR4kC+B6WQDcpJTkHYiUIlEBomTtBVuNEJb9Sxnkr1hNjk+/gfC3/BZmGsVqz7jNHwOIiaWrdKODaYfaE4khbTqos32QOIL5LEQ3Q2dF5mVfqYdqFSCNa0jMWna0Y8/iPlGpD4jozprBSNjkhmVRjclxk3hva2SqJLY/4KyDmBK/b3WfyLxRcRFMnFvDC4QCJJrjLruDERBxYjxd1L7nynd9FlGa/uJ5yzAtyh2PPD1fAXQ/kG8f5FY1eZy1HbBUD0IfxH3RC2qG9cnoYVMRgsDLnnjPMoYpPYXOciQ2isp9bM0ibn2i99ECsZRyV9Tsj0IUVRldcrW/qpW3QZM8nbIu5K2CbQT4V4dyhPJdX2hZSw7lfrMLKM+Fw5dIbUQ1qJFAej6QLjRdiIk425oO2o2Igp7TGuIvzEJPFj92/WOC79hNqQJ/ayEqiOFUY6BygeSgepZxvL4zY0wpeHzXlhqq3vzgP5Zyde1XtJ/1rkSUlj51gkFNSUzmfmo1KqsfaCgsbDiIoCRbjVw0BsSV/Vam0E3DhrMqIGN78Rxey6zthHNKuWMIzmQ/llYeBYaXlsliFHQhuW9Os0Diwt6pdBVE+LG7Vmp3HGTOdH2gcQLFyPHF6mK3L9rcCEbeb3XbRQxoa4riPMT4kJ91uCEKUwhzepM7p/kfIF8IC1RkfozH/8vwjr5/p8ox0J1H8h397QpguL9/Ad+W21tbW1tbW1tbW1tbW1tbW1tbW1tbW395/SXM4CxJIuxCz1INSQLHevXQM6DIxxap00H98IXjxvGJFRR7VgkqkFeUemyEJh/R/oH9nwyhmMOonel4/yNtZNsn8R1QTM8QcRIq2SqZKXeLJMQyKyEMICoVX+pNTQuUCXkQKUhrL7flaiULvgFksvom0r0Sh+WQWIrmbjScHlXwnNO6CvxmgFXmcFhZxlzrDSwZGGeY+LmmJWhW0Z6Q2NWUBLFIwqLnF6o4PmCgGgPiAkRBEqoYNorFeqBM7HbaKbQrXpeVdeaBHo4ah2RWWlR7TAGwVlp45XunAhzvEj3SgpnI30U+7ydZd6htQfU4JplRoUUcpgyB9UMPDFL+tl434aZIVR3cfiFy4EaTFWQToy/Q/ZKGqqiWZ2njNef+O2QDvZZxr47EUk7TzqTDKcdyZy+UqsncggMQ/NRBmjeqGWhwTNXKtYWOvkk8ib1oKDTE+kHkUloW/v+hjCyQeZApxOx0pFTcO1kKBnL5J53rcn5JO8sMz4XdjkpQzwC4t8ToqLVxztn4a3TwdRJUUy8ErWZWKuE/Hx8VGJUAqXezzz+IMavej9U0TTcDO43iK+fP+D4UaanKKoNjbtw37lMaoB+kO5I62Q6EtB7x1OYvZe5+D2cIYY0xTOQ9sSAvH6X8ZsXghEk+FrL1IUxlxoSWMMOSa/uZsoMxe9Cd/vEvvdXON4ajHfVDOPMMcoYlrPuM28CRcwqUa+d5fTX+gOhRswXaY3gQc6/k70Rs+6ZmCSt9onEeleynsPrF6nGSGFoJ96Dpl5DDe1ZDy8dMUNmvb96nEg44YPUGwnF7IPISo2LatEJ5hfSAB2k1zmCVTd1dZyDTxA60EjVlWSHyBcR1eMs5xORwojX8h5FJ9CAeCEz0MdZCXVpyNEQopLgPmsNfBDWEan+5rDAUmpgQxcKe76Ixw8yB2nA/I0eP9Z6NSIGKd8951tbW1tbW1tbW1tbW1tbW1tbW1tbW1v/PPrLdQCrPWnHUalb6XSSHi9aS3h8kB6EX2gE9nhwPA7MKqVo58n6FcyAENrjifmFNgN9kDNhJj0Gcv8mZ3X6wlEdlhnAjdARL3NDshfCOYL0N8kkUkkT0rOMVwrXmmMSCbEAvwn4DNxHpf8iYF5oDmSW2ZuZhJfBkpEIidp3d2sl/4DqAVZYnNnaHVG4XXdfv7e2jHbkftPaAxNBVeuv9epxVW0LL+1ITiwS5lV4VtHCL89RicvvZN37jbgQIrT7olGfkRmID5qDhaMJ6BOn8LPmAXGgKWgOcjh5D3yCj2WaU52lMb9AAsmBERxM9Kh09AiHQ8m8SUuCJHwws+NjoFrr/fITtwd5fBAcjHsy7zc53vg9iPtNjAmcEAnpqEclEaPSjAA5X5XklYUvnmVOae/QHtwYv9/JPaNQtGPg15s0q0TsGJCGTyd8EiFMGlPLcIwI/H5VSrqiqpBW/ddImbweZdxFJZHDvfDX8ig0ci7jOJfxawb2JMe7EMlzIOOuvaxW/a1k9Q2HYP3B0Q1JxcNJ6Uj7JMeofmFOLGonC62w4QjWOyZJa0+kdYSBhiM+6EC3wpU3terARdGPP7B+ItrQVr25Zk9OEU5VWnvQ2qP2CZXQFwSzAxOp90IbZg80B5ZZzyvKPtcIYECT6rVOICaao7zi9BoK0I6ihV6XA01QPQp9XouLrMETMqpeOW8gcL9xvyqhGstQLlg37pPpd3Vyxw2DRR9ohXUnSX3g2nGn/jfeRAYxLmLcpBemW+xRKe1xI/LAUiBGJd/HYLxehUdPwbLX+3R9rWGNTmZhubOfsDDZcXthnmmVZJ8XOYLMBtck399IdSOk4zPr37Wh7Yla7YsZSfhKPKeTHviY614n+Hv1MB9o+0ClBmSsNdrjD1QKly4KYklnYiiSjfS7KAwepH2QUd3cJt+Y7EcNZLgTV2Gr0xXhE53fQzoNa4qm17DL1tbW1tbW1tbW1tbW1tbW1tbW1tbW1j+Z/nIGcGtSxsMc1a2qCo8fheG1gD7hfKLHiSpIM/J20h7VxenBVOBxVO8pglqrRHFOciXI8vHBJSc+KjUrHoARFAZZZHkY6jDeqNjqqj1AOtoaKoK0lagVkEcj4o2MNyaGpJNR5quILpTrACbSFOlZnbttJUIzMZL0d5k3KkQOoimVoMwK4lkDpMysoyEi2GGghcBWBDGpPtfVXSpZ3agirS4jFXKi9qPWSZKmHbEHqSehyxs9PlCSJl7miwtqD1KtjLZxo9loInS8kssOucxkrNfPo0FfiVP/wp4HcJMuSPtAVOB+EXOUESeA32RUUpkEMSkzWnsZ5u8XkiAEmk5Lrx7YTDSEro3z8Qe9/0T7z3JZIyrZ6IWHTukLt+0r7ZgUcfyTbD8KU5uz9sVxEtq578EVUnuu/YD2SeqD8JvwgU/DIwlRpv8usnYKErP6fe83QRBqiFSaVfhOBhdmVy1qf6OVHM8ybCvhKaR2nBvT8vyT+pxOIkxyOhI3qlbPHwovTBAZqAmoM5kMv/H0ld41yJuk+qmxStfmHAT36uEd6160+qjlUclNE9DOSOH2Gixo7QORA+0HaoWqVnFUakQixWv/RqJ58+iNsylNDLVOOw6O3ut9F0G0Eu2twXk+6K3TmtEkUZxQZbZnmYcr3VuG9VGdyZqYGa0ZZoW2ruGN6mQGq3dtoZcTW8lowbVVUh0prLn0SrkrNG2I9uoMNlYK/0CQ8i3FsBRaDroETWsYohscHx+oRH0WWn99XosAYIX0ZtIFrJ8oxtGMM5KzJaaCnWXU+v27hjgyqz+3uN7QDP080dbKWCbXOVJdvTSQ81ld2uMqI7edEG+gk051/U6vvt/7BXEhOGqKHmeRC0RBH+A3xkTmu+5NJoTXe9of9OOThtNUaFoDOdaEdv7ETOg//w/a84ky6iy3IPhFnNUdLTqQswM33H8rFP/xs6gFOEEj6ci5EdBbW1tbW1tbW1tbW1tbW1tbW1tbW1v/fPrLIaDlmrS4kAg4n4jM6iQd1+rObIXUvX4RRysMbiQ2J8Rd/cAzmQZzDAhDPOh6oXqgcxT21ypZqPmCcYM0Ml4QD1j45eqDNVJimXRRvZiZwF3mivQKDUuCGHY8K6mWswzQEOgHgqHzospgk8VNBV39pJ4VJpQoY2ZGGVhzrjRwwnHUSIDMMloEkMKniig+J6GCykTQ6sGUSsVGrOsRqiL0mJRTVYjfCMeOj9XvOqj+4Ru0Ep8iA84n87pxrZTqfV80HNFkihEquDRSBK4vJBrdlJkToxC9Pr5wGhpJxoW16nuN8SbE4Dwr0WpWSWENUpx2PuB1wfMJLpg4aifkVWlA64wAl2/zGZpVN7FapQulnZgKEQkapJcZ3GKQeoLf5bGGIFr3oWOg1olwpFmZ5TlJsUo2kqAnYZXKLXN9VJpX6/o0JiGGyIkadZ+0Kg/WDjKRWP25eD33qOv3uIGD9CT0RlTI9lG4XQ+8nm6Zej6Zs0zbICEFz++BBilTL4NcScpKsydzSBmx1Bqk35XGDqntoYKIEnNgj09agvtVSOLpdf3pmEhh1BMChXgRBppH4ZZzElKJddFW0OtxIe2kJ9X36xfQEOkcdjD9xZxX7WcKx4wHmcKgUqfYUSlpM+S6Vpy6MzzRs1XydUwsnYlAXoRLIYelgQoxvAY0rDNiwBy09kBCSFMypUxrjSI5e71jEQMVpZ8Hed2196XQ3MyL9ESODhEL0f2CfmJSJq2YVrKadSYgkI2QIEJQbZU4bkc9NyZhk0xD7olYQ3LiDkME44Fdb6QvAz+jJgREFh164b97q/7gkTV8EtXNrAu9zPxC2idkoDoqnSxegxpqkGehtONCng1FSGnkeJcJHhd+/67+c2kYjmeD8YUcz3qP2pNMx9sH+f6/C5GtD/z+G9EfqP+dmE4eP4pIIAf5vkEc8ahBkTlJP5ix/j0utB34/BvRV5/51tbW1tbW1tbW1tbW1tbW1tbW1tbW1j+Z/nIJYMmbEEd6VC/kSPIWQv8FtDOncH994XQyjBFORDD9ZsasdK4J5lnoWALthWdlpfZGOiMC61rpsgY8lsnTHVUnMXIeZPgymIRIJzNJH2Se1F/UZb5Vpy3tkxy+OkJbGbUREIZB9dhi5PsmFxI6vYwnVVCxMqXsgbLMNyjTa5l60AoF3To5LhLBQ5j3rD5Tv8sn/k6MAslN4mWYdUNlmaA5y5iiUM6Vns2VWK7u3dIBkdi86eIcnz8QH2UyaSvsddZ2zVS8PaBV968gNCmIsBSX+M+0qahh4mQ/mHTi/SKnk3KQakx9cN8XMYXhA+43ki9kvAqHe93EHQQwcpJygjuEMDx4Xzdz3vUcEboZKtW9bBpYP8harTLvImkCpkLXzuP4KDwxgdlZpq8kEpVw9gzGLIyv6oGk0AR6g94arf/B1E4gpMwaNrBHJUwzqjc2kpTO9LuSwy54BNf1VZ2rraEETMfdVpfwBXh1T2fWPUQSgNMKDS0Lc2wHKoJm9amK6kpRWqVFw1EeSEL6pHxDXZ211YOc1qtbO6mBhFRyRv1ZgpQEOYhZHbhQezqj8OceUhjioBLMSM1BaCN9MrURKdzzZvhVCecoBPaUhof/OxJdGynKnBTq+L7JOcEh3k64IVHPWMYL1Y6mQjQ8FXdZCW0j4yZHpWFFy0xtHjQxYILO6vPOiWcSkfh84TFJa2QMMmHe7zKhRxReW4I0w7MQ8/l6E7Gw8N7wGeRUsrzm6nCOQr3nd0pXGwGECMEsKgBSZ4odcHREC0A/rzdgtPMEtXp+EdCfYFLXOUcZvq0tXPMAe9afU6uzLHwZ0mdRDfoDWUj0CIf2UUZ5a6AH2X7gcRD3RY5BXpMMSHtC+1EDDnbi+iTT8FDiDuYdNR/z9ZvhEP2TGF81fGInOd+kD8QaKm1huj8hhHxNcgYxop6fCVCd7yaOWUcmhVH/ev+Xf0dtbW1tbW1tbW1tbW1tbW1tbW1tbW1t/e/qL5cARoImn4S8mT4hH7R4I7wK89ml+mLLiSIkcS56GjEDPQJiYmKVmr2v8ky1Uoip4B4wbqz1wkOPMr2ws8ys0KLAdql+zJgwX+TjWT/Dk2SUOZJjGV1XmVBaxmm+b0J7PcFcqUfRSja7E81WB6+UqTwdaZ0UJ3wtRYJpIaZdG9DrZ3SB6w16oDnR9kDS4fEEIPUgfBbOFwft+ATVhmYQ0oj5u8y0uOD4F+x4VjqRMj+DIPpBuYACrS5KHx8YDu1EH2XiMW8iXmT7RKVhOcv0mk6IYPbAx5uMF6HOjERiMJNlzBpoFIY7lbSDFYFF70mEkwZ5WKUg+wnzb0gC2VB1NAaqDXzgKcScYJOjnVgEt1fac9yBY8j8jXyb7fqozlYcOGvsIieIEKroSqxmDkTKJG96QOvM641LR3N1oDIXrlohLpAndv7k0IC8iVlmuUeZ0qo1QCAiOL32mv1AxYkMpBlNE0Hx44mllknWGjKC9Audy1TOAC0UeqSTkmWI511pTYGIG9MDma8yFKUhcgDVB512FEZcD9JroCLntd41ZY5BzBpcCKH6k6fgOdH+rPQ2Xl22y2wWreRpioA7IoH5rC5iPch542aIO6YnMwYpEPMiGIWujjeiB5FJ08Al/h9dsiFaQw3nAa2h9wtpT4Q3Kclkko8D7llmpyw8tOaiq8fqAjd8XNjHgxk33Tq6upbBiDlIFm4+IEVX6nxUwppWePF7fd64EWukgcqgn8/Cb8vPMsstyXkXwlu/U/qNFlH7RANRqc7kuEk5EatEsbZeiXAPrIPNv6OnIUev9Db1fHLcEFZpXaukebxfoA05TsRvJKhOcymUNB00B4oQ/lWpajnKKL/fYIUnp1UfbyKI33XP8VXvlNUaxet/InbUURK2UPizPlNrYCXbQeZHvYPHGqwJq3vN2g/IDVlYafSG52cNVOgHxJsQQThgRp2zWXtoa2tra2tra2tra2tra2tra2tra2tr659Nf7kEcPYHmcGYkGNWz20/kf5A5eBIo9mB6IOcYO5YNLoZXQ2zwjbPSG7reDhhVkatVIpRNTmsow6FXNVC9nKQo9DJzDcxy0TIOasrOKS6Z3shpNMHOieqitqJaiDiaC90LP4i7wuJXiawHGUkqaFyrG7cREXRdi6TJcsgyYkeBiaEKeFeCV6+e2z5MwUcHoTHv3e9ZhCvFzP4E5Fd5mBHLMsYtkpTqp7V1SnUz9azenGLL10dtCT4G8ZdRmx4oVctqud43NB+oO3AMiqhmIHMwst6UzLuMnJpZAh5J5laPaWUKSSSha9WqQ5mT9TgOBoWycEDGQKh8PwDOzrt5/8AU1IboUoKpAWZF/N6MzO5WZXHkgyfpDQiyxSPpAxx7aQcIIYH1XtLkNKYJK5GhiNZRu7HU/hxgmlU97FF9S4fHwgCI/7slVXKdDcUa4rqKOOXNTDAJPPGtPaJioMvlK51yhevNHkapAgZgZ4fhWq2s5C6EZgZh15Y7yuHW0lPFeEwaGqodKw1zvbgsyXP0/h4TA4d9OZY+y4Wrh5fkUJi19ZTIn3t0ygEtimij3qX2kelwmMiETRLpBW6XGKiM5AUICqBHG+wViF5INpRv85RSGQ7iTEI6UxRYt6MOYisftvMMgtbf9T7YwchjosyYzBCuX2WcXuvXt1vnHtUV3IAcwY+C6jdzrOS+alESiWMpQZBEkH7E5O5zGWrdC2AfZJyELcTprjPOs/GIPXBcGfOCfMGp5LRVxSKWqt/GmatQzqZjkpf1xH1Hsd79Qkn4l8oiZrQzwcijyLLR+LzXVj7MYiAbIadH6i1Ras/6rlIVJLejEwpIzwnJlFJ8SjcNGqENmKu3u004n792fUtdiJ2wvlZZ+lw4nqT/kbbicRVCeT5JnPUEEwmef5EBeCu550QY+HC54UnxBh4FOKe+673/PxR1+gXKV9EGhmzgAfZ6O2BKcjnH/8N31pbW1tbW1tbW1tbW1tbW1tbW1tbW1tb/zH95RLA6ZMxKx0o1jAtI62lEvNNHA80vXDODPy60XYSJKnOFbNc8wwIJY5GI8E6+As5Hhzns7ooVZAUohmqAcyVmgSaIf2uNLAH1q0M10MRFibZKEzrHKQawoVKL//2owMdVUX6jXhU2lYSESsz16PSktIrdZsJqZV0bka8Xkg/kJiFXE2pBKoY9CdJmTQqUghnKXMthT/RrzAQ6cjRyPAykXRU+pioDlJtlThtjzJpYixzOpC8Ec/VVVs46kwnNJGZhFykftBEMC0TJtwLJds7cd342XDtlZCMRh7GZODSSSYRA8YAU8x+YKs/OftKxU6p1LMd8HoVoju0zNgOORsiRjPDU8iRy8BuZUDpudKZZfyRhcbN178hi36b/UHO6thlOjGcNGNqIa7TyyxEBmEPXq83djRi/Ca1I3nU4ECUYS4CE0N9kOHc8+ZOQfmFWmGb0U6mI2kkuUxPq/1jz3Kn56gku6xUtFdNtOQEVzLb6mUNpJ8kF1MEohNSvbKSCozVl3vWugmggoditsz2ZowxVqLzRShlMGqnN+M2wceN9GclW3PWmEBOtH9g6dWLS1S6VBvzvgg5ah+Jo+2gPT7IdGZWStzOTksHT4J3pY8zET3QGMDE8qzbt05moHTMqv865Fi9wgeZZTKHavW/ygF+QQqKklHYYJFKzoZQezUHRAO8nqMPejuQrH1cr8Ms05RKm+vCmgeKauGukwtaIPbE5nqPJEhzVFgo40qtjwmp9Q6IUYMN8gPNQOzAmdUjPJI8HekfyP2bCGVOR48H6gMNr57i8yRGJdAto9KyRPWSkyhOzJvsHWlnvdM5iPxC7BOjnh8569keT0Dg/S5se3bEDJmToJF5LAO215mlUT3Wus4mB7xh/QeCMfMi0hCTMqcRrEWt9wCGkyo1QPE9nENWb7S06uC+34h1pD+qG7n/RGPWM9UT+gFcZOt11rbzH/2VtbW1tbW1tbW1tbW1tbW1tbW1tbW1tfUf1l8uAazNygyxQpHKQiWDQO/c4bgHElndkNLoh1QS8/nJ0ZVmjZaNbtA4EfdKMPYTPFGkcMHZC7esnXCtRJqdy2RocAeqDem9UMEhSAQ5HK7q8J1jlvmEYhiKV4LXPlDtiAh4Yu4cmjRrqPXV9Vu44zJBHVZmUxduNs1ItEyR1e0r/axc7gqQxnA8YAbkEEJaoW2PVkY1Jx3o2VAGaoJadfxWFWyjInhlFqryp8mcCXM2oj1Je5D3RSRk9jLAe0c4ES1TzXOSU3AgMCJBcyJRvZ6Fwg7QA8kyScm7kpv9R+Fmxyzo9EIjA9BqHee4uOeb+f6FWFTvrReuGLLw0ADtE338QXt+ItJWyaoW5nlhp4WBWq9UN0rOi1QDrX8ngohBzkpFi9QAQOoT5OSOk9dX1Ee6ENNJL9Sy2A/keKIZ9RxzJbDjJtxwr25ltV6oWjkKQZ6JWJmYKUnmVSRcMULL5OzqHHKXAcpCIKPoInW7Q8zqtoVe3dQ5cXdul9VBnYVBlw7txD24rTNW6jX1qH5qabWvM1ZXs5R5TGHGNbNMN6yQ10xMJiZSqWxZz9Hv6q7WJ/r4A+0P0A80FNUnTYJTK5F8aPUsr5ritU6GilbCunWwD0yDpoFp1vZdwxTWyhBXlQr3x0DpoAe+Bjuw6hGmdQQjZ6B2Iq1XojkTmY5JVrpfqmsXS6prVrD+QNdAganSmxSKfK2VZFTSXgWsLcPYyNbJ48ATxJ4LN36BzzJnyertll7ngya63uUiBAhmBSFoK7WecsDMShZrg+OBPn4Wcl6/zy4nwlc/M6TfxHxXypt/RXAibpBHrVsacY8afLBOcNQgSSa0ByKgR69hFElQqaGdvGoQJkF6x84PhDXAYmc9fwW6rL2RtGgIUpjoa1RKmVnndY4iA6ggPpDjEz0KT57jXd8T6cjjJ8REpZF5FzLfB1yvf+C31dbW1tbW1tbW1tbW1tbW1tbW1tbW1tZ/Tn+5BLAkWDa0G2KGVpiRuBNJw/KGQ8usRDhOKnlnhoQhsxChLhOfgWnHhqBRCODvflRJKhVqZeMUthZgwhRyRPXeKmC9koAGhJEjq4OS1fkp1dMZPJB4oeF4Oi4gUgniwqt+m1L3t7sFWQZlSIBR6bvbkTYXornMVEFQA2US2pZhOMtsEugihAJ+FV44s3ps7VF44HaCdIKsjtKcSF603iBuRJ74fOEJ0FbqF0SzulrHxYpLwlGmSxLoWjtD0VjJu/vGZ5n0PH+SYuDVuRxhxLh4HtX5OqKXuZkDz5sZAvlJkyTmwO8XevxYidPqLEWiOmbViLzLKI5gzkqkWitTPROsfwBK3r/JORBrhZdG0OOstHI6gWPhQC8jCqrr1wamxiQJtcKIU8agy4F+HIgZ+CDmG5FJO4Im4K5lRmFcaZAXYk8yLkQryZuZpL8BQaRMRBMBnCFae9QHtAN1Zy7zWPoDH78XftjX/nms55KISAWSCXR1tIY9iJBK42YS8wXHJzFv5vhFeL1XwRq4SAUT5rwZ7y+w6rL2+41iCAZ6lME43oUejovBUUY4N6GP1d/by3xefcRmjfREpCEyq4s7vfxQaYUVDhjWIZzUXHu2LcS4Exr4GHQNQpwMp/EE2sKeO80aMX3t+1oPYhmzUunTvG6kNdQUpoMK+ngAldYnvbDraOGL5cCzQQ6wAw9HaXhW36xwQtxks0rvp4EnqCNxE3ngchfeOLzWxdf544PUROJVQwPT0d6QVCQuaAfc73q/qc5mpNLSrH5xNavZiSwyAFxoUyIVjk5K/Ek6SKT6fL2Q7GigaBnlWaZrzTG0OrPui6SDrEGXrPMB+npnZE2jTJBGcJEtaZJITlIfMLy6f/PG9SDevzFrdbZYGcp6POoz5xt8lpE93tD/IFKw8Xvh5Cdy38gxC3Ev9QxCClNu5/Mf80W1tbW1tbW1tbW1tbW1tbW1tbW1tbW19b+hv1wCGBSTrJ5QgfBcvZAvhikhxpwQ943EFyD4mNzZuMZdJkaOSu2m4CGECsjE06q/FIH2JPtZyc3xBgLmVYnKoyEtkHPhcv1a3bsOJNIBM4RZOFOcnINqvDUcJwUiAo+Bq+JyEmJkJInhYWT2MpQWcrY6Mh36gWirDtockFL9uSlEgnABkzF/V6Jvzn9PmbKMoaxEcWQQEgWqDceibpUMHJhDV5/qxOxRvazaKuWogh7PMjx7Ih9nJT2bEEL1hTbIozHVCDsLeW0HJokphdmds/yz9lnriJDtUSZsOzET8BsNRR4n0lbvcNyVzNTqmbWedHugnJgVvraStQMQNN4cTE5/YzHQuAt7TBlB9viD1owmiRKVzJTq6ZWAEP0zcUg74HhW6FJzbU0FgmSU8aVaax1lFIodBMZ931x34HSGJzMmxLVMPilDMxdqtx0r7euoFP42IgkMJSoNLgc+LqbfuDuhnYi5hgwUxElRIgbhheBOd3y+yUhcG9k+qh+WQYoyacyEMS88637seBD4MmgV1QS/cIRIAwdtn6hAt6iUPEnr1cEtKsx2ghX+OVfC246Gts6KrJNRJnWmE/lmPQrUhJkXamvftgaSZI4yhX2iOCqDsyXdTux4kMcDsYP2eCK6ktP4MpTrM1o7MU0MQSXWc5uggp3PMjF9JWhRIiZl+Obaswqi1d2tra5Jg5QJ/iZsdYP3o3DJIqRPICvJb9U1nRH4cBgTS0frxSBlkr0TOOmz2r4F6L2uQaz2ay9jmtYLPT++IF4ggWagrSHiZZpKIjIW/FlWanxhqEXQ1TUuCJoTrFeyVvtCcJepnTLqHUkq6Wt1DlVS+UVKvR9JUQPoP2tIZFxo1iDGdK1E+f0m9YOabTrABe0/a89JoucPVCb2+AQZhcnvP8vwbw05OiYJTQpff4+iE6RAa0g42B/EDIK+nsHW1tbW1tbW1tbW1tbW1tbW1tbW1tbWP5f+cgZwZkC3wuCSkF+EB2NO4noXgtnvMpHsg7BeyNr7LrTzs2PnQW9GE6FLZeCkGUphhEknJZlq3NoYkkR/ULagEJogWh2tjDJ2UHzOZco6tLNMKZWV4kxilnni80XEMvs8wIUUYV6VBi1L0om4iCnETNBHpd/aQYgUzjguYt64JzOVkEZkEvogvHDEM38TVhk8ZmFlk7Y6cFc6L2D6xKO6e0FIhWyfOEH2BzMnI+ZKIRYqNqOR841HEOvzcrwLga0Nrhf5/ipcdP1XaE9oB9I+4Dix1nngtByIDzqTbiBZCT2VwHCaGtYahyuMd6FttZU5N0c5hNdXuVe8ySyzzuKL1k7UOtafqJ3LnAexXvcTgSZYP+tZihelmwB7EvOuq78TDynDP1hGpTHHJHwg9EpeOkRO0h3JMtKUxNoDzYucwfRghhL6UQnuhSEXmYidpHScJDLKzGonKVG9vxmEB44XVre16orOQnOHNHx+wfTqXV745+8UsbRjYY77Smt+gPTq4KWSnbQP0jqaXmjd9klrhqlhGrVsmdWpLIUhR7LWzh6IdLQpzSaWN6laww3TiYx6dtLoK60eBB6BZ9bajEn9rjDDGdNJE2YEN0H0k5HgMcGelSjVo4x5bSSdew5ub2W2z5sxhesajDjwDCIbd/gyu0fhx9tJSJZZDuADVaNbR9tRgyej0uXfCVe0OppVBUUgBuGD4cYcd+GkM1GPf8eNL451ekA6431zh+KphBUZoDfDmtZAQMg6L5Ls/0K1L3fCg3TIOUGV0CDvmwxjZj3z5CDHVQlfaYRPnM6cTobVmRXOjIn7IAIiZuHN6TAHLjUIEtOJEHw4c7wJOav7NwQZX4h+FmLeOmmN5EnKs8xgKkGtKoW5Pz+R84HaifYf9M+fWKthANNK56tUGri1E1VfpP0GtyNjFhXBkvSbdv4rFgYy0copIxJI72ivgRVZJAWbE9UnGvnf88W1tbW1tbW1tbW1tbW1tbW1tbW1tbW19R/QX84ARiZkEuH4+wWuhXc+f5ShxKQl0DpJ9aNiiopjeVWnagZJ0Ezr955n5U5joUVzwBy0cFoqORuaSfaTewb3FcS4CU+mV2cqJB6NHMD1jYtuGAN7PJAuSKvuWNFnGZyrL1W5EUmyKykOMckYCIVvrr7Uq3qKraFSP5Fsq0uYQl8LIEJkMMcbUYGhgCI5wcpsShVACI/C8uqJp5Ji5Bh1N6MSxxk3pGL6gEjc70qkrtS0ZJZhGdVHLHpUGBJB+g/oT4hE57uM9vm7unZlwrhAGmoPFKM34zh/oOlMD4YHHrPS0tIJO4ic+EyYE+lPMm+SqBS4LKNbDmKuvlr5QUOwhbUdrZKtiFT3sUyafhvqlbBOeQCFGBY1oJNxkfMLbodUNJYx+p2IFaVrVHrYBLWjPD4JNEetVxTS1xi0LNyvNUXbB9gDaUfhub+7jal+WXWwdISDzCSkjGYZhfhVaYg0Uh+oGpZehGaRMt68UqSaoHpUP/MyvlIBBsobAIvBeZx0HYiXoYxWcvl7j7g0Mip5n9pJHwuTrUQKmcFA8WmMSMZM7uvNxAqp7WWwTpXCNEelWr/7gyMmEWPhxo3pk7cqrysJDub1qp8534U0xtCYlTie1fVbSX5H/DfMN5lKzF/1rADxlfSPN10Vcyf1ICJqQKE/CyW9hheQRR4wg3yDGZGzhgh8kp5kdmYOZmSlrDMhKiEsEQuFvI5svwArI/+utL0xK/l9K2nPwp3HAfmNlafwzev9lUyISsNr78QM/Bo4B7c7qUZaAzvIdlRqOQM4yqTVOiPTy2AWLpAkVMjri6yJEWrj6p8/jzlXb/BEsiMonuDSCFFy3tWT7om0HysJr0jMMmC5ai2PoygA400lsgPaB/iNz7sw2dNJ/yKsEaEwXmR2wm8iko7SMLQ/MBNCXyQ37i+yPdCjY30Z4OMiPVEfaBuoDHLsDuCtra2tra2tra2tra2tra2tra2tra1/Pv3lOoAThV6mF+NdHZaZSNyVdJOD1s8yfbXTYgIT7514TWIG2htEIr26XDPL8JwI/dHLoDRDPDEMZUCuWl6BtE7qrFSnGYQTPjHryJzQOnh1YAZGToc50N5IfOFfRxmM1teHTsysMM9K3V/KShQnpCF5k9lgJUqjPyqNmY0YE1Eh5y+kfaJmy92byOr0pX+WYSkN5gs5PupayvGF9oHEjWoHDFEwNRqg3fAxSOmoHZCDoCG9UMVxXYge6HEUcppKv2IN/A3Hx1qjDywHoeCiuCStKRZK78Hwg5xGn86IBO2UAfsbzUSywZEkA/FKPIudZXLaAxhIeyCs+0hnEqQomok6+OpH9WCljCdEElF5yUyrvlstA8+ef+D3b+jL2E5HJGovZof4RcrBzFHrTKHJy2IVLIPw1YeqH8BY6G2lC0gTJscy6bWM0FkJWEQQQFUQgTETaOT4Xd3TaqhMQilEtxg5vyqP3R7gv5H2oO7suwf4JgMkHGlGuuCzOptDKLN6JjALS52Kzy9Sztorkag28LtSswQRXr3FmZhUcjftgPqpiDU0owYyGGsQQ3BpqDSsAuRoOiGJiNa9EKR0mhqYV78riZngfiP9A5OgWeG4Pd+o1xkhoWWoW4Cdlb6V9Y5JoiJkgDMLV55zYYsb+PdsTT03H68yfwHaiUR14Wptf1IPKBh2DWrkjSGEWqV8WyDtWeeUO3CgXLgdOIlYwwgUp/FC9Acz36SWoa79AA0stRDkOnA6KiciAz1socOF6AYzkXkjh6533gqZnCstH6/q/Z6jEM/nExnVjatAtAffmGxiGcER6wzMwp671PnynUUXgfmFUIlvmRfSzlpDeyBcpDYipQY4RhLmZbTPN6TW+Ugi/ibViNb/7MBOksgG7vV4Iuo6/UbNSHfCwUNI6YgqeV+kniSCyIHGxESZxx+4B9n/cl+hW1tbW1tbW1tbW1tbW1tbW1tbW1tb/3+gv9z/ez0cyJsYAmmoNSQqkZZ5gX2wyoHLaMkbUHJMZgQNIXyuHtMHmYMxWnXQ2kS0I/a5kKqJdEO1IeoEnVPAT2H+PTjya5m3ICqYNuhKSlsduytwp4q0J2QgWDnJXcp8+ZNAquBZBqMsbC8rxSgN2onH7+raJNCF81V6WSPHA+ELWwhbtMw3+gfio0wrKYOOTKQ/6tJ9kHoSTOR+V0q0sUz1AFXcEh+TUOG0xvl54FfyuqtbN6ejjweElOniF7ReRkwmFolbR7U6TD2CvN+F953VEzxT4Z1gibSOqtL7k/H73wh/Q74rxSi5EuCJUphnSa8kaHtUQtAu0k5k3pg9Cf+/yfavZeTLoDchvTGiMOIRZULlwvkChdr2qxDLKaj9IFvhl/P6G2msdKjAd6J7llmmKOEvaAYYIYqqEAiqDySF4EZoy5Z1YOICOu/qShWwlfRNtMxpC0wVUcefDzKqCxevdKmpEFL35J4rbWnlYfqoRLgX4lz6J8zCUaNSOOsYa72ycqr2IOYb6w2JpOlkaiValUpUpyjICfpAcqLLQBWL6sxlVu9z/4mP6vMVEm1JhuBxl2Hvr8IPi0J71DWqwbjIfjDed/1MkrCTnDfCgcck0gk/SDVcrFDi1lFrEELXQCyZ0rivi4yJiALVzewJHmu9kjpPPIn5/S5UmlrWUIdoq3R0P5DIhTquVLUdDySE8MJCCw3Ji0zBrPDO6ROzgyMS68I13kwKhS0sknaMOsOkEtHIgYQT1rFwRE4knKSemw/D1jqKKtomRKvucLEaKnn8BB9/YsT/NOJbr+EDWcMvLdB2rnMpyRFwnqBruCG8njUdkUmMVRyuFA0AReyDeP/CchZ9HEFUkJUSDr8QCdSOeody4lm9z5mNaB8wX4R+gB54DJiQcYEdyBzknEQ7UTFCnuBf5HijLeD4l9rrx7Ow3KrY8VF9zBKkNEQNO77vc2tra2tra2tra2tra2tra2tra2tra+ufR385A1hmmSZ3Bn4VilUZ9PDCt+obD8faB7nSqOgH+N8LsTtv4vHA1CrdZp3mQT4+SH+BgATgr+pHNYPpRC8nSJpgubpsof6wHaAXMSciXujUKIMIJpKt0MjXi5RO9ko4Yp1MQ+KrOkHLQakkXBZiVzAkK2FJdrDqWg3KwJrZkPEu1GsqWPtfEqSC4ogpMibRKrVaydyOxo2jSN4Ld1umXpAV+MukZaA+8AhUOh7O719fhfxN+bMPGDHEf9W6iIJ79dh6YtbR+YZnx12BhkfDmtG0EMk3nfv+G3pCa53QqOvJ6oz1VLidmYNm7c+uWncnURJorcE0EEU1VwI7sP4vpAju9d/aafi73L2Yb4ibaAbS6TlprREkY0y0NSBQHTRtpAdDG0R1kzqBtIMeN0Qy7bMMtfdXJRdVVtNyFfGGZaXC44HMNx59Jb0N/CZWX6+pVLJ5/AZ7lhk6f/+JJO6tkXMwZqV2mxiZN34PPJaTSFbi13MZfCfEQMQAJ9sTmV/EGCgTFUOlkeMiLZkZSAaaWj21lxPyjbJenbNRfc2Stbc9vdpXs5NxE9owl2UIL8yzNmKZx0kS8wtIQo9K6gpAq3dIgYBuxiGCCtxkee0eK1WthDiE1Tqq4HPiMQiUoQc6K8XqUZjpxXyvH7US8pkdsY4RlYRtwopKI/1Z77KPGvpYXdmehdfOHKQAIVhCt16GdiahSkjHx13IdDWCyU0yUhA96HxfljHlUUnh9kGmI34BF+k39LMSwFiZwv4m8yDcMVsJ35Q6HXonr7EAA4H0SsqiB4KQ8a4UeWaZyZ6ET2QltyHICNIauszxWAMsghYm/uiVOM6rENy5hg68hgwq7RxkCilONkdQVIyk47Cec8M0K3k9f9dz0QOhkuKIImnkvKrDPB07ftKy9j9ZyX9bhAY8YN5I+xfwL+z4RP2LbJ2MCSo0edH0+V/8DbW1tbW1tbW1tbW1tbW1tbW1tbW1tbX1v6+/nAGcUUm8oxu3PGk4XVldqAMPgdZWSjGXGTBo7YmNiyRpUslWrurwFHuRYmhvZZyoIvpA5k2OuzpBozpvURB/l5HihU0mBpmFfValTAspsyyHV3LTTvLjB/P6tbDVjURR00Kp+oD+A4mBWVL1oUcZ0tcvzLK6gEPK5ACgVyayCeqzekYtiMzV3bpw0yKkNYKorlgmzKgOz9YhKpnqAqlZ5on2ug+zMrKiOj9ZwNfw1VGMgSXcgaQuanEZYq0JLWKZN9VTXP6bY+bInKj+S6WZDSIajUGkEjMZcq3kakfbQY5JD0etekJTBb0rtZ05YHTQg2ZJpqIaiAwEBQ8OOxBT7mE4AjbQ7JSnaaR/MbMwz0lhZH2WIaZyg0/UDMmzjOk5ak94lgkVUnsokn5+cA8r416fZdrlFzIFOT7/RIFb3pV09kqopx0QA1VoLaja1vcyV6urWFaSveLjvlKcn0Tc9fNiou1JBoTflSYfE2yUsSZU+ldGmWH+KvR0e1ZPbTo+vUxRPQuZTN1jfxz4FURWMl2tkNOQqBg+HdfEsvqDVXp1IvvNzFnm9BpugCC1176yXqnjlVpu0vF4kZxYOi0nvVulW33gGbxTmF5/R6URGqjP1Ud9A0n6Velm0zJTTUi3GtrIaxmiDm71PmC1X77fg9ar61ml3tnvZLccdR8qkKM6iM8PNBUFTCtJfCHECNQCdCJShq97pX4LRW31LJ0y+FXXGjkZvrp6JyqK+oWIgPY1AuJEO2H8QkIQfUJchN/1s85epntIDRpE1rVgFW+WRUtIoJ00PkGoXuaM8rofT/A6O1WFlOoyzmaVlmaZ4HTwX4gcZeZmrDizkpL4dMQbysQyiZbkVIT1jqYgWs/YxZDxxvpJ3mtt5S6zvwnCEz0PLAfpAxWQ6294/1Fnm1+oKOGOeKCmeBg5bjwc0cZ4XcTn47/ja2tra2tra2tra2tra2tra2tra2tra2vrP6S/nAHc+0DkiZpwpoC8kXaQWWZuSyMZlTyddyUwl7kiYkgro4EUxOzPzkn1N3IYQhmk2RSRjliQUQnK8KjUrj3J95s0q75Ln9B+IhpgjZCA2Qq33B/gQcokaKRnoZibopGVNmw/q3c1HFEFFQzKV/EyoEGQmOS8iX4AUZ8vtv53oGbEDSFRidZlKDHf0J9lxsSrunrFkNZAKyULgmalI4PC8yqzULM5CKm+ViIIVaAMsDxOxF+kvOGszl05jLxe9RmtIynVGzqc1F7oYA5o8LrftP4sQ1lPwr1Suf0kphPyRCNhXEy/aPZAmIgk4eUx5SF1n00q9ZuJMMkbshsZE7WOzUnTwLpyjYGko0eH9omnE5eUSZhZpnm2Sp8ySVWmJ+IX3R6E/WAwK1nOMvCOAyVrHdPo1spAt07GQHggR+FvNSHtZMQAf+OhpBgZjvuN6oMYVEJ4XuDVz5ozMYX7epf5l1pY6hz1ZybQHpXKzkGmwhgL8wvCBNfC4LqTcVefr53ViSpBmb1HGegkqqA4bh/EyNqTYoU5X8jnTCdiYH3dqzpdnkyFtIbPINeeUxEiJpGTwjAL0heGeJZRaiY1auBCWiVmpyvuN0hjJvVeixORSFMiygjMuHAmdvyBUO9pZhmRjSQlmREYSVtQdbMnGb9BHFCIMr1JARXUKwnsclKtzL4M1DJJRR/VUb2S83dSgw+yjN9mhVWeVw2RhBUum6ikd0ww4dBANZgzKkUb9Y6nKzVaEoVZlzWA0GpgAHngSSXF/QZRImYl5ZGVRKeS6HOCOiZJ+pvUHyDQuqLWGWMgpoCiORAJnG8M9er1jaInMN9/9jbjWgMvH60SuanL0C/TW46Oz7tS3jialeYVq3MDIONF2LP2bnsUGKEb3L+LbvD4CeML+fyjUunuNDuRGAvD3Uiu2sMryZza8Pt39QenF6Vg3GR84Ff847+0tra2tra2tra2tra2tra2tra2tra2tv6D0v9fX8B/t9QNzTeMX0CZeAhlRsQN3KgYzb7RocL0wbiTzCQ9C/mKVuptOnAipmQqMy7SBTBSDzKkULYBOGQE83pzC9zFG2XGiY9JzupxzRiVOHRHRBGz5VQCxwd4dfsGSWg1ZCLLyNODFCucdV5kxkowGrESe0l160Y6QiXzwoXhguckMDwUz75+bgcE7urXxB1HyvBzqQRrDty/Fs650LchjTknkR9l7GSZ1zkusgmRb8J/Fw54GiFltCWrx/Wa5OtFpoEILkf12+pzdRxrYaTvN6aVmgwC01ypT6OJ0RXMEjt+1DpMyrRKIdtRmFs7al3nRcyb9CQNwgeZ1QFKU0QVSzhVKyE+BzlvTJR+PFE9ERzToAGiyvTBnBcuQaQxIoi8scOwZqhBxIDVJXulc82LO5zbJzG+iEhcT2JUYjhFILQwuQiR4CH4cHIaEQc+Cr+tqqBBjhvBy6S0VvjwnGj7wKIMtUONbgcGWFPMErVEm/6JPQ89AQMUnwL6SXCQEUQEqkZrJ6caTaExEXvSzAoVrFmJy6B6rbWDnrUvA8SMiCSp9DfjhmX4Rs4yu/W5DDlFpSEzMK+OWRIyJrnmW9IH04MrhOu6ec3JiKxUa005QM5KrmLVS5xaaGH7rD8XA1MrXLUEkjdisoYn+jJxG8FKfWvD1DBNVI+FRle02UKlf2PZK/2qKrRDOU3X++WEdiQdsw+ags2bHC80o65hYa4zs4zdoyM2UUnME8FoWA1rrCEE0Qb2qD1d5c5ITFQNzMiMdaY13B2fb2K8yPFaJrwSWRhrz6zuXAkkR6VjhyNi9cWSQmCEz+qKzoQpkEc9I3egE1L9xcRXmdwRTFdmJO6By0H4gDp16p71ifOooQeSqY3sz0UIqDM31QgP0nohuENBtbDUM2sY5r4r2RuBtn+FDMyzuodzgBraCyuuVu9DN0XnL9rjifj7H/VVtbW1tbW1tbW1tbW1tbW1tbW1tbW1tfWf1l8uATx9kK5MuRE1ulEJx2jAwb36Mrv+IK2RMitVuTo7pZ3o7aA3stDDqJPScBRwdFyFeaUXKla8UK+6MKV5InIh8cQN9Nnx+YsZHXOIOGin4NEr+YqV+SUOM0ntiBxozEq4mVXqVhPRif1ppBqpkDnRLGwpTYFZCFilzJw8ECtTJ4ejamjO6i9OR85PhAGHrsTghEjSZ6V/JclYWNgMrDV8zjKfUwi/CC3jqxLEik5AH0XydSEPEHfE7/LdrZPtJjlICaxsabo2MgbXUejfQ5XeH3i8qwfUTnS8MFXUWAnZQPqDNu4ykibQblIVRwgROorML7DqanbeJB8YvnzCyXgcZDPiGoQPZhbsmQgYL2KuoYFBGUw2F2bZcHck3pVYTEXCV03qMtTCKxHLMj8TTJOMJK0SmMggZmHEa8ggUfkJ9hOJC8sLMcXbSTKAiazuabMDCS8MsCxUNkH0B2mQV5LLuFWcawZEJXgL2Rtl0smjTPwUQuHoH7WveIMkZpQRKUIPw1URN2idOScpUsjiBGQSPMi8CpcsVqY71GBESnXmplffLEclRxkoV+HNRRGN6h+mfj2jGOeZgfZP/P6FtHPtq+pOPh4/8eu1sOcPNC7s+IHP1dFrDcTLgJUAtExQg9QPMl8kjTsmgWLm9c5ngJ2kOGptJbLfoGfhs0nwrM/PgbQPNCfhjveflTA/FBFoadVdTb0bMW+C1aGcWUliT1IcqAGSOB8owvn5E5mTETBjkNFIbYgm6AeSv2rQIkaRClQRAidrUEQVkbPMb8vC0/usn6ltDZEIqorILFz4IgGgnRg3iSBWSGnRlXJvikoS2sl5YXqus+oGFNoJVAI5ScR+1lnAb8RnnS8SZIw6d3wSMwtjnfe65kTugGOZ25lgJ5JO+u8y7Lnq3aJVLnoO9NmR+xfKJMzQ9omHE2GICGqTfDuiA8sG4zf5+p//qK+qra2tra2tra2tra2tra2tra2tra2trf+0/nIGsDfFstE5V/LVkH6sFPBB/loptG6kKZGN5LHMpol2ZUTDEqwfMEcZmEw4lPADjxctFfGJ9g5+1WfrQfqkdaF5GW4iDvHGDGiGZqIGrRsGzDERKUQ12UCvQiL7C20do9KjGUGGEQFhs5DGAhKJ6Fkpyn6vzs66FxEDaUhKpf60k62MxULHJum9ul4JlHMlggPuN9I/qqtTDY+5TC5nZQvXP2WQinUkRiV7MdQnSHWTppXRiQgcn5USjYD2QeLowliX0VVmnMpNMMEOXIzrnlhTMIGYtPyAmMyYZMqfpuU9hd4qIe2qzHGBGmggTQrdzVndzZGkBwSkTcZlzFA0HI8o86zX/WRaGZk4+vGTfP/GgJSTaIKOL3IKRbWNmjeYd6Vfrws5nrUXNFAq7a3SyN4oh7aSlWnnSgt79cjOV5n6CtIfSA5spXPJAdLBvwDQ1iEcOz6QfHOcwhzGGGW6YydhHXHB2oXHk5y/anABo7DFB0LUYEImdva6n5QiH0dg0SFvrpxYNJBl/kb1yiZJRCHO1SBnmYoiVNdxJKmBHYbOMiVTOyrPMrkTPCYmbe3hhW3HyVnGurQHpmUIh9UgRvpFiNTARCSoI/oJOJEH4rEMzoVuDl37rbqGc971LJhl5qasHl+v/c9BxKz6bJQIJ9xIBM1KEIPhGiCCRKXxw7P6bwdcBNKfqH8hTUnrxBhIjtUt3Qq9fc+6PH1A1DBLehmZ0svQHTm5x6z6Xq0kLh5Iexd2PmYh5zPKwM5ECPQ4iUzMWQMMAseBzHs9o0Z4Iaw1HVJQ7WUfZyyjdqHlFcDq96ytdfXqLs5YQyFl/qYdZFTPMfmND5dFaXiS+YXwwHPUeZFAXGQa6hcaWrQAAhhotlq/OdGWpJ4wX/WeRxau/fzAGaC9nqFUtzNhCJ3wvyH2rAR+9hqEmYW4lnTy+B//wG+rra2tra2tra2tra2tra2tra2tra2trf+c/nIGcCVJldSOJ2RrJMoYb/JdfcDHeSIy8VG42bDktITpZJTBqXciEstIUKw3GmXo0I9KtHUhsvpJkay06yyTLzMh34VijSQXRnZkYO1gjLmQvhPRN72DxBufb1QPBMMjUcbqfq3UaAKTVt6UZ3Vb5rF6NwdQqUlSVp9wVl+mKQqkHmVu2Qk+ERWCRDzKGPNJ9o6YY/ZtMrbqHpYgYxbKOaJ6iZdhhlR6unqEqye4sLuCjxf0D5IyfZizUM2ezBwcKfhRPcYojOtNvC/0caLzLuMIYXigFpg9YCZOq2RwODon4ra6SRPS0WyceuA+wIRbf5B+8bCgqZThdj7J8YX0D9SM06yeg4CvjtPWjIkyQ8iV7pXzQOMicaSduH6UEZ5O3mOZbwepWvslve7dkxyFGA9uaB3TILNSxKGN9Kv2sgZCx4bjmStdHihl1LX+ZCxEcipM72CKT2fmBD7w+Z08BsTwcZHaKsEshe7N9oHkVZtLAxFH2ic5f6MSqwfbVwf1SfpdCHBdgwsLpS0EEoKHl+HMG+NR7wcKOSv1qb0QzvNeBmGS8lloY63kd0QvI9frvnMNGmRW4jryRuwHMt+kPMrojyiEsD6Z7qicqJTBmGW7I1nPCF09td846Kx0a5m1o4xfspKtmQtJPuvvzsItl0l5Ie1BzsQzUBNEGxKjOrHHjUqi0iF+4/YsE1ZbociT2iv9A0FJfxcyvT/w8ZvEwF9k/6hEcSh5/2b0H8wZBFoY89VHLtJQGbjUuYNZnVUTWvvE+cVq0y3Pf5nerR+EWRnsIqhU8l9wRH8i5qgb6V+gjrbHn53BGe/qMg8HkxpgWNj9pLD4mIJYPYV5Vc/ufBfe2i/EWv2Tde5JBumTjEoNS84ysOcXeRw1zBBRwwsieAxUO06DVrj/wpUvM7h30geMN8jEfvx/qjN9aA0LzAvMEHuQs4YQJLPoAFtbW1tbW1tbW1tbW1tbW1tbW1tbW1v/ZPoLGsDKzC8sOy0b3o5lYmYZW6Hk+4s4DtKFZloI2Pti6AOmkpkMDc5MxDuMm8xOGmVy6erLlQOVUf3CTuFXMdCFHg0pkwVB2+oT9URXZ2Y7D1o3MhQTYDipy7SyB5VcLBRsU8F9Imgl6CJxAqPQwRW1BNGTBoR7dQCL4XkDB7SkpxFxk3EDkBgychlnN2FWhls/gEqBMm7QJxkvAqtUb1bHqkuiKUhMxE5yvtd1tDK04kb6dyfpIDnhPaBDVCEs9KPMTQ6S6jHV7rQunG2WudlO5vvfUHH64ycSg7ATvaXQzL2BQB8GPtCupChjVD+zjIkfDX3f8CxMbXWkFq5XqU7cpqDHH1ifjGsQ/mLerzJms8ystAbRuMdXdcVmopyF6cYgb3Le1SkqR/UQ5ywEtR7o8SCuNymCLJx44rUWWP0ZkTKCI+u3I8pATUVN8CxDds43cjxoquBvEiXjTXIsZHWZyYgtA7cGE8hlcvYDiDLp9Cyjuj3RZWBGVDMrEagqqYZKVlA8g5lFEFYgw8E+wAcRN6IHvkz7lLM81vz+HMVzkhjhjnWBvJhehpxoK/yvA3mvRLQh8lG4XhEkwWhETjLWEAJCk4m0RmbQswznGZNpRooQUT28IisRii4zuNXgAIU7ThytGOr3m4aKLnNY6u8h5HRSH6gIEW+glalLq/V/fBBRwyR0w3yiVMpcvJDskWMlfO8a+MjJGOVPd3tWAjsFk4N2PECFsEfdgw9SBvgNx78Co4YPSMQHIlqYbw24C+UuK3VNViI3fSIYNFbq+vsWPxAZ4InEJCQL/RyBqYI4ro9KOOcgvZXZi4NXN3lKr+f3nRaOVmvXP+uaI0n/ItVqzQhSOhmJtLPS6NrXdXVy/g3kUQMpdUqh8iCiupOr09nR1lC/15nZ8JyVCrcPZE5svgj9UQMq0mD8HT3/ZYXDjzK7czvAW1tbW1tbW1tbW1tbW1tbW1tbW1tb/3z6yxnAeCUjBQdxeio5J9qepC7cMIFwIE3AFsoVJRV6TFAlRsDpZeCOWQaKRBmt08sAnb8qndYFxXAmnAZjVo9mXNWR2aqztIyrCUz0BFqlQVUdsroyNSZ6Hsuoq2Ri+iyDSM8K2I5XoYVFcFWaJElUYjgHocLMWCbO6nhNqfTb8bn6WDs5V4+sKiGxDBZZOGLI1w2HIQLY6gBmJZ21o/OFnT+BgYgVtjkNy1EGtJQxrNpwd/CBpCHnEzzQBjYmakIz42HOlIZnJWF9Cm5SCccI1IwuyaErneg3My/mcZY56pV0lLOTIvWM1InW8BHY5Vh7luFkWqYZByKVKJ3x4B1Os8D1YIqT+qOwsH6VmY2R2ZYZ+Vhp5kRtohFkRhnNNMyEj6MRYvh4cU9dhlZDs5PzjXZDtNFUGOM3moraARL4SDJHpXUjyXBaf+L+Jq0zpFKt6U4Mh7gKw+s3YQ6u37nXGlqQINLJOTH7icZgppPrzyQrrT7euJYxqlQ/b6aD/ES1MNPivyA/OB4fHC2Y05ixdoh2mC8yGmoH4V8Ir8JV44VhjuqnFjsIPRDeqAvBqMEFd0DAeqU/9QQMa51m1fnMeOMIc84yCW0hlzMQH2QqV7G2K10a90qcUn2yKeT9q9bMBLxM69SzuoljYZhTEToiA9Wz+rMlcFEipRKjWsYx7jVUoX0NgBjildvvR0esesXVD3x8odbRqAEOn15EZj3IeNPsIJqjYahqXQ/JFKm+XlNaBKlAJi5Kjt8LrZyECGog7QcaX4WZ7h3uhRhHkL7eHanEfobUuViWfp0rPlGtRLsKuE9SZiGuTRB9kHEtlHgia82Sui7tDZEH0oB7rOGQuUzd6pr2tDKhVVAfhFyFNGdW4j4FaQeYoXEgGpW67q0w6XnVNQ+BY/VH96PM+XGjvTrKxT4r3eyTouBPxL+Qx/+xznjq+6FppdTbz3/cd9XW1tbW1tbW1tbW1tbW1tbW1tbW1tbWf1J/OQM4mqLiSG+kNMLvMj38C1Gpmt08l41ZiTFtRtoTvQZphsWFPj9BlJw3wxqpQgtHMW6FMKOFYWNixydezk3Rh9sTTSlMLMC8kNYJwLOT1mk+KAzrY6VxBelPxF/ku6J/gaBV/lpGH0q4I12RDIIOqozvTt0IVAKnkVIp5IxYfZ0XKZ3ISv6FF+5atRLLwgNpVilOpToyH9Wf+idaWkFW6hGi1nXVqFa4WStB7F73HS+wZ/V9jt+kPCpt24w5LnRaIaPdoAmunUhnevJ+Xzweyognnldha+Unw50clWqc802KQRoxg+HQu5IY7gkyKinrwvQb1RM7WyUVBfJ25CzDjpmIDUR1mZyJ6zIc2weMF+/rNylGU6lkdusEV6GVfSAYIoKhNSiAM7yQ4B6TsA+QE+JNNkP0Sep3OnHg0lG0UowC7fwD5t+YCS4dSCZK5iJtCyi5jE3KIKOXaeys/Z0AuK9UJgCNUEMFZHr1vGorJLTK6oW9IBWPUSlkOQoDHjeoQHsSBE4wU4hxE+1HmYPpaP8kM7EG4h/gswYL7AeaN/goDDmN8jbfpDS+k7UVFr5Rk3o+0oBR3cwLA824cWf1VwvqVqli+TYilYir9kv/QP3GtVXqNY1sJ5GAdsikWUdMyRyEGTIGatXBawt/XvlUqTQzjqShZhC1p9ZLgkgvI1Qeq9tacBeEiwzDIvGZuDjcb+zjBxwn0gxCkOyYXtid8CgMtVil8sOjunpxtAsSNZRSZ0Ug2jGC9IR2kgxiraO0juWN+zJcRUEU6StxGxNyVLoXqcETO/AQKr0bNUwiJ6MrxKuGVeaAdtbwSxqqUSn/OWECXdGQ2oN2FH7ZLyKDVMP6D1KF8F+InijfJnUH7M+BDpHCpgeQ5hBlJON3vXPnA8YXnD9WD/KNnQ/wN6Sixw/Ef0Pe9F44/LefBBM5PmrYoXWEKNR9jv+qr6atra2tra2tra2tra2tra2tra2tra2t/zL95Qzg+744VFEz0oNkEvLAX5U2NBNCVw8lCfpRWFuSpm2lCE88qk8YFxonORNtDc8bU8N4Ms+E1xc67jKc5+9KeJpUtSiVhERtIZCj6KiSheQdY/VVrr7cQwkaKCitOoQxxD4ARfwmLSDLNBOdC6tL/XcrMyzFyHGBSoXyIlb6MiGj7kuFbPV76YAaEmAiZESllbUTXoafpONjkrZM35yEaOFnQ8j5Czl/IOmoHYX2zTe5ULtiZ5m1Ub3L4GXw+CSy4ZFYCIc9iLPh902ZmaAMNBW6QQSmT7QL7ncZl+NdpmCutZMBNG53rHXmTKydhbJNAwYMQxbuGmp9fP7C5WCIoJEQkxkT8gA5iWZkXAy/0VwJbT2XyZ6keBm6SCWFRbjeL7Qd+JhEf64OWl1I50FoZ0QAQVvJX/kGDluCfjLnhcZEWod5E7Iw2fe7jP4AtJNzgnjtNan0eBDQHmW4BthRpmtE9a+KHuT8AhIRrZSoPVBNPHWl2T9QAY0ktAYRxM66lpFMn4TUtakGqZVcJpQMQ2hgBvEGJq4HNSIgqL9IEzJnIZLNwB5Y1oiGSCyDNqo/meqqjlFYYkSQ1IVqFsIn3wTjDCfxWi+f9Z4hiGjtc03UTiIDTUeOj3UuRCXvTYiQSgBL9V17NOL+Ii3KsJasAQ0fqGh1JocTWtjjlLu6gH0iCpodJJgySIyMgejBDBCZaN4E5+rnjdp/3mjhcBy438xZwyvfSfREgZviNxsh1VXt4sgsA18fndQyoXM4KSek19GQc3WYKykdZXUcr8GAVVSMMFFdCeu1zzJ7zQvYNx3ggv6JzhchNSigVPLcl0ktKUVpjiB9IAhxXWg/6hC3jnIQYyXaSVSPQqW//i+yfZD3BXYieUNraGg9b+Pfz5b0OvPsuYZYOjFHnVERzHQklQglfIJLdbj3TuYFPOps2dra2tra2tra2tra2tra2tra2tra2von01/OAPYUhgR6X0h7FL6UhTT2SWYglmUcnk+gIzlgJhGz+knd8VCyBdZANQpBqr3QvyQckCOrNjS9kp0JmQPJjoohrfDJokpkJTGrtNKhHYRR6bt2Vr9mDlJXCi9elSJkolzrr5aJreTqHv6AGJWG09XXaw2RRlKY6oYgreOU4SIr8Wgx4GjE/SbbWYm9HIScSAxoQsRFdXkm6WWmiBQlmjHLsFEn9QB9YgjKRZOTaMHIE7+/SFlmZibJTXrU+ucbOVqZhuHMeSGaCAOzBvPN+PhXJKL6gudFSmPev1A5kYVFznkh2TkOJecgIuBhqFe6Ef/Czic9o5KFLiQTa71MyfaJaKxeUcX8xqQRzDKHgKaDhyWigc9klnOGiuHh1amKID7RdoC8KwmqQLxRTTRuRFvV7s7CiU+8/CqOMjsp892nQ6t+ZGuG31eZxkx6f5Y537y6amMZoSLVP4wgOpBw6E+yP8jr75gZ0lqZXTFWipUy+DSKfmu2PgdUAjmemB6FP46JKlh/MKYzJFErjC80/H6h0jBRMpXgqgy7TpoqmUJIYbK1gVNJVpWFC7cyTU0npsL0Mv+qY5fClH/3NuOgZQz6/Zu0E2sNyVbvY3vC/F3DDHYW8tnaQrHXPYvPZZTPejHHVTjtdvJtqGa8QY4yKk3LqG5t9dlKdf3mVe+/RJmxMUEHcJE8kBjk/ULOj9VZW+9PSCtjUh+Iv/88GtBYidpG3AOLXB3IEzDIA28G92+wRERWatyW6dzrGsSInIWnzkpIJ8mMWWjuLDx8mtU7n1nrJvbneWUc+HgXcl1ru4g+Id/Vm8xBk0AwRqzr81o78TfWThBFXYmstRWvtHTYAXog1sh5VYr9fkOr67Dj8b/0OwdMKfM3ZnVm5yjzmsLK5/VVBn7/QMKrdzul3vGcSP+sdLdHPVdr5Fx48xmQNzBRaaRfqJ4w/v5f9+W0tbW1tbW1tbW1tbW1tbW1tbW1tbW19V+kv5wBbL1jKpXGZKKcQKKPgwhB5ijj9uOJrFRs3gMZiVouZOubNEhRfFSHZcYAEVQ6ef9CutG6kPpJzlm9m8cHiCNWCFFI7ChDbHhUJ63fKyGZlQD0wEwrrdyOStbaAR6IOeJR1qIKsnpZM25S20rSyQroRZmAdJolGUpImcJqnekvpJ+EV8+rJGUcjglnr7SdHpUMthNi4bHVwA4yXjR51jqkQFvGE32Z6icWlf6dWnjZiuP2Mrt0GUjSqBipEPTqVg1FpdLM814GrpdxLiL1GfP15/p73MTt6x4btCeijbhvUqQMKgWLgxwvVJScSbijrS+UrlfaOsF8YYVtIbwZzJxEBKGBkERQa9caMxz3C7Vn/XlrxHhV525T3L3MVFHEHpU8phFzoHIz/ahhg8xac4nq7dVKm6pkuYQeZYAB6BPyovEkUphzEDGWaeikRJmfXoMP4g5Z6VjmF6Sg/VF7eb5RUYIsU9FarW16JYljEGJVIa1WpmfWPSWgraHi9NnAQDI5VauLed60LiDGfVvV65JITqYIj/ZALHi/J0pD0sGSiVXaVEA5IByTCdKYfiFUOv07xa45Kw0sjjVD9IFwcR4H876qH5tAtK1e6+rMzpyFRra1zoCqoXKS8yJEK23ug5Cj8OVALrObjPVztfqSMwitwC6ehdbWrM5s1+ru9UCOB2oNIXA9QaM8ZD2IdFSkuqilUO6VIlfUHmgG2s+1VopKox8NzQdeIwSkPepdcTDTSjUTNdzBgfWGZKDh8PgD5trvaGGgrZOvX6QE0f6dXEBOAkfswJkUzfpeaWqv0PEaXACrHvGYuPQyw+dErM4b0eNPhHwGSGR1kgfQG//6eaDnwf/19/9JhGD9LJx8gIev3u3qj5b7TXYAgfnC7XMlxWeZ/CtdzfNfgS98IbtJJ8eLINGolLy0E8nqGc9R56noUeSC/i//qK+qra2tra2tra2tra2tra2tra2tra2trf+0/nIG8PH5g3gN3L+YYejq/w0My4tYGFy1Vh2YzQhVhglzvlG9EYTGjUoHGaQ3pGkZP2mQZxFGxfEEzRcRibRzGSvO9EHmSXjgGkQEE8ihaFcEL7MyC1Gtrfoo54RBYuo010r1lqNRqWIcx4iU6tYMRTSq3/e+kaheXPeAtkyfphxyQiR3XqR2Uo7qZT2O1SFbCcL0m0xBPNCPQhazMMNi/LuprCcqR11HlomWBEM6RKVSw/3P7s4xOw1BHx2dk3QBaYzhiF5o+4mIkkgFXc0LPT2cfhwMhRAtE10M8wl6Mqcvk61Mv7Dq0dUI3CcDw/qBeCKPhWs+6zmlR6Fw41XG/3xVktNnGX3ToR84DcmstZg3TkPlWWYSUunn3svUFsjxhaZB6zVkMBYOl8SviTevDmeptKTw/WyBfqLjRiVpClOCucxA5KgOXk4877UHl2ElZfoRgsTqgu0/UAL3us6ZE70gp0DcpHxUitKsEpkVE69hht6JeRHxrm5ZgtsncjyYQ8hppCptJSUjB5FBZBI+SQncb5wDNaHi7rUnCON13QiJ6cJd60mqot4YcZGehCSWL2g/Yf4iwxHJNRSQwEn6SvgK1f/rE+sdUSf8wAMkBqrVLxyRZE7UflDlw2VOp9S1yzJp0bOetx7L0DXokPddJmc6SRA5gHouvrp5s7V6kaUh4khK4aKjEOqWkyBJAdUaUhDtYE9y/L2S/u0kYmJdiHRsrZ2IkXyR+ge33wStkrXZIO8y9sfA3Yh0xGZh8POjPnN+FeHab4DCynOQpqQ98ZjYeKHf73xeNQQQUYMTepX5K58kg4zJyFjI+lH90Fihk00XPaEw4bSOxBvGXX3H4wWjupr18UH/f/9g/puR11XP8/5dRrv1hXiv90/bCcej9r59AC8krhq4kF5habkRf68EvmHnR6W73VGtIRBNqTW2R+19PeB8oPLdle1wPP57vri2tra2tra2tra2tra2tra2tra2tra2/gP6yxnA8vWLyuhVz2zMCzNFA/I90fOBZiPGu0yhGCAdjRvVjrli3SC++E61Fab1ZBVXQm/49SKaYSMhOkl1dZb5A1AGc8qLiEbKifkk1RCPMlG6VXIznNSEHKg1ThTrP8n7b4g+MAF8IL3Ss9X1S6XdlHLurNVnaCOjAU6+v5jNyHuAFA47I6kLrHQf0slZmOi6hzI9U5XwKMPRDNFeqd5IUpNILYyy6QoCGqlOplb6uoppwcuwzrPh98AQ8vUi2sHMgawEogk06czU6lUFxBqKV6eqfVTKVgZYX2lixdOJ+3cZUGKYJ3CvNK+jqbRMBHBR5vsX3azugzKDckrdI49Ki1qHHGWU318gE5GjHHBPeldaP5njaxlTN9Y+gatSwladzoKQnkh70mKQabhMBCujLgqrrUghdu1RJrzWkEHEb9ROOlYpYZXqhGU9dlmY5naSIkReqz910toHGTdoIvMGObCAiGv17YJaYYIRIX2QdhQqWToSleLNHPUepdeQgxvwtXDUjSTwmEx1GI5iRCrhjmdi88azFeJZDe4vnINMBXE8gnnFSneuTucUUhSzB+lf5LzJqM/GTlRb7f9vQ9khdZQ5unp+yZuMGlYIkoyoQQHRMtNjlFmuRuj6ewYZF746tVOqXZdcSXmnurTj5rs7WjCgrpeFg8+ZMAfS1zDBSsWmWRmUDKyd6zlchVHnQLyQ6+lSif90XAR1JdcdqxjanugYNOvcYaS/0dDCPfcfhTyWSlPXYEQZ0SnV3Us6MSZyPOvMKEA8YkGL6joXOQu1rL2uJa5agwWxVn0RLoVUNq1hBoRi48+FPw+0ndUDTpAhCAunLwdxJEL9N3m/+dv/+W/4e6zDrdeBrh1biGnyrmfjL8SOen8lK/1tUohor/3tPqB9IH4hJGIfZN5k3Gh71LBJ/ELbR6GitZPxRvuPQvCrlEn+Jyd9a2tra2tra2tra2tra2tra2tra2tr659HfzkDOP+/7L3bjiTJsWW5RETVzD0ii2Rj5v+/cBqYQ2aGu5mqiMyDaCTP67CbMwRKN1G8VWSEX8wtgFq614aCdb7AbguatFIwtw+SZNxv7NGJOavVJqDN6fRqmJqCfxTEaEqOBBoptZWqzbBxYOMi9EBb1japrgfga7+VQfIBJKmtIN4YBLbqlg1mAVMOQyZoXmRbKtP2hFQiRmmTJco8i5L3hRwK+aztypilawYcL4jahNCz9oJjluXYesFPmaTULudv0JcJXZe+2Mj3T1Tbgr1U81I6IoHYA8svTJQmYOqENGLOUt5qq4YjQTOjpQMXmZ8EjeGzWpxnRzSIGGuTtNfr3I96P1MZriCGsrS0UrAmTWq3OFtpbMnVwA2aNezxxN6zWtpW8Jm2WqPtRLM01/SP2qK1huAgfxTcjoHnRUo1GVWEmI66EdJLWRxBM8XU8dU8jHZic9BaNbfpHRzGvFF7Vtsz7gLUzAKjKJEXOarNm5JkCJaJ5iDqjUfak/Q3OZdCnFGNSL9rD1ULbs9vqEdpugXDqxqJ56z2KxOyFeyNRFsHUWpy9iKtYKm7oyS0DzyrjSx5ETNwe5AySKe2qc+OmCHXq1TiGIxXFUNzMj1AJ2LrAAK2/qrDA+Qgog4ywI3oSfqroH/7AM1q9Wqp0PU8Sb/oanTrXFlAUfXAeSPNIHvBVqVApBv4RHpDc5CctQnMgrgcBT+99MekkWLU+YpGipdOPgztQsy79MMa1fwFaK32qUUL0pdQG/BSm7dG3OszEzdoYKzDD1aff9GDHrP+XBa87o+T4+zIVfeJ5kHmWYp3KbCdoz5bQhKe6GPB3DnR/sDiJnjV9WG9GuQ+FqC1UtgnoFrvW8y6Z2Ug7UB8Vrs/AqSRdtbetICdwC2k9mrxRtSWrkY975jIeQCBmkF00InnwH8FmU5EoCqYHkv9vO5nWT/P/VWf25zQjPSk2YGLlMa5P+A961CLKPgk8147wLX7jv8iccQEnxf0H3BLbYHHgP4Heb3Anv+231U7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7/2r+dABYQmlqtObQDVKRHKgpFlJa10cBCOtG4vUP+vsTcUfyBhSOavvmvAuW+axdzRmlzW1nFXDTC8ylk3KQarXZGwr+DVRHaWZTwA7EIdusVuNZ6tQUrcdw/R282qn4/K2GdtZO7bcKVYRMQxilsY2oBmMu5W8kZg0Tx2lkzKW0Nfwb3ETBFtHvFmWrzWGt1pv2g8wCirRq4CGGZGmCa/PzgyE/yVjN3+9/uSMkaQ8Yb7jXxnAGHB0ZP1F5FpSdTpoQqiAT9Sio1x601phSG7T02ssVBlgjuBGHZq0ae/fNjKCv7dDaA3XMTooaym8YK7p+blCvPwl5IJaQX8ACQ/YDFDIG7o5nw6Meo2rH/U3mwHMSUaAabfD4YM4XEaDTaYDYo/S08QZ9IscgZmmAJSYyIJug0hAKAAZJLAAXGbW5PJLIBZddiBwkgYgtra3DGPU4vLaoazt1NdozCqaGs8TTDK19XI2JRoIJOe/VUhZStThkvEg3jGpmdoN0I7lpx5OQgqy0B33e0ITkwO9JiiHtQGOSOIEW4F0gHK33wJoU9MsbpNqa2s7als1EM9FUPJP0XwiN1qBr4gopDZ1vWnssRfPA5SDmeyncJ+6JtKPaojFQlfqcZ4FFESlGzkFyQzSSqwAygmY9xgxDMsm46jUGyFwNePn9WUWrIRs+0OjMHKSXathfF/rHj7qvyH/bgKajcREqqDhpis+LvEvRfo8b6yfnszPnrHsDE5HAvpvP1pEUTDtGMr3a6e14MPOuQzKatRe99O2QpA8kSy9f+9BKaEMF6jt3sKy2LAH5BvmBj4lEoGKlbPcLZ9S9b77LHi1HtdMz0QSXufZ7lyYbJdPxHKUud2oP/XZCRl3DZuS4UU9EIK43AkQvvbk+/1oHSeIirVdrmEDlsw7iOGT7ZK57vBjI+Uc1pvVA4r8d6tnZ2dnZ2dnZ2dnZ2dnZ2dnZ2dnZ2dn5D8uf7h9fhyXBqIaev+sf4stBq8lZMKkd0RxwTeKejNkBI63hRZCqXXk5I7MakAlNDOmQelTTdCleI2DQyLgKXLQEHcipIIOAAlGSoI50RayjUqpRlYOGgExon6Qv2GsdUYX+B9Efta8auRpxILQCua0jra/9Ta8n6kE69fUupXT9vaFLNZBzFljLiRyG+mobeilcsaOgmC3oI1KK3rVHbP2D8lMn7j8Ja7WDrA3aal1GVNNWIdpBjIlmciwgJenVstOGmtDbUYpfeeDAZC4V64T2ABRRwwzSs1qmKXC/gcTsQFbrNudFSXxrj5X4VTCOFxl36XpTKeTVasM2gsj6mRlae8giaPtEj0+0dVQEzaCl0BYUI5KMgYrQmTTrcPwgmGReeNwFGbkoNHjTgCQLHpvC8Vyw+Y1/7+Ji1NpsNVjDRwG5Zqg42joiWQccTLDe66/Wl774jVpDbaJMVAU9/4JgmDRa75iC2olqtb+lWwE7aaga2j+x87PmWNXqeRwPckHTlGCmMFUZ42a8BjELbkpUW5zjQeasJqs+URqm1Sa31YBOT1BFtCP6RKR2e+V41OfA34i/q1VO0hNMFGsFa10MnzcBDKnN44gkv9+blKUX19JsTyflARr4vIhIfATuAxHBRFF1wJDe6/HYUU1rv/A5CArMFuwVMoOMWVvgmtWcVlk68gWEY/zerCYTezxAoq5VVdYbQsZNeK4Wc5ZefTqpwow3Yw7GvBnjJuJdW+MJac86TKHHasa/iAYXMHM9xwTum7zf5KgDHylBnPUZLYP+Xddest7rwMOJgMhJDC/bs9fGc2ojrtI0u08kaw+YkPoMqv43rbhUG93H2gvP1fKta7HubVmvuRiZB9EftUXcPuq66D9AlLST1LNeb30Aszh/XOS74D0Yog+0tYLCmXA7OSbS/ihoHYGmYP0v5KgWcM0A7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Pxn5U/XACZ/QTbMPgGB8VWKX5XaOB21i5ryYB5ebToCHzfZhDwfGNUQmxalPPbVuj0OZFQb1eOgSbVZmU70hmVDE0Qmkg21Jxl3NW6jlLX0H6i/qxmZtT2amUhAqFDjtqUzLq6YpF8FKLjAPgoO3hd5jNXGpVS11lARRBreotiFX4R4Nfx0AU1s7V6eCwI/108btWEcSbE9IQmYiRyUhjpqG5d4Y3IiXUn7IBgFscYbiaXhlgZebdpshs5qgSJazUPPauu1ExRCtbZtVUhbqt+oLVDLQOdV0NwHYidiT2z+IhugBxGK6UTTEfmDSzrWDLlGNX+j1WsyowqZZuR8F0QWqa1QSrVbNt4AJum1i6pdwB7Ee6wN4sSOA94vphYow2+kHcT4RYYiCCq1KRxejeRMwSMIsWpuqyESpQm/fpb6G3ABlZNGqYgjjEgK1FoHLsQasnZmhaveTxVCEsk3suBY5l2PuRkZncz/qjK5fJKsdniUQlqpNndrvbZp21lt7vGFrC1e9VJQKwX7vD3I91XXzVFNUpFWhwHS6QbDtFq1WlvC6cFcG6uC12buvMCS1k6kd2Ik7ndBbzVEtUAqsrZ0f0C+SemMEYxI8n6vze4XuJOtYd8q4ryrbT8nUxKNjug6NOBenydtGA70ui50Fe9bI60h3giVtf87EW2IxPeaLXm94XiiRG3TZgIBllga4aNeV2sgb8x67RxnQFIHNe43tM49B60ZGg7S8VCuKxCeJBc+FF8gNnPtQid4Sn0esxq5d8q6q3Q8JtI7PD7WwYVq7tdJlUlq4jQwLXg/3zDvajingD7g/bPufdZZ+oDSats6JBKx2tcn2DIkpENb7V+v+5FYR+0k7v9CchLTy0hgP4APiFGN6KjnJu0A8frckHC94Ujy/IHcE/ELlQP1uv+5fm8hJ/r8pIlzpxAzSrXvjqB1yCQaTS5y1GED7X/Bx69/26+qnZ2dnZ2dnZ2dnZ2dnZ2dnZ2dnZ2dnX81f7oGsPW/YP2TbDfpA1pDxEmZIG+Y1++9yjmEqYbKBQxUoK9moEjSpdPnjR6NFEGTAm2RHOKlycUQJp2G9l4bstGqHRdCSmdVbiE7jBcxRxVwU+B9kVJ632/9Ki3qceaAcCQnaoK2Z4HQ8a53VhNtirUGFihOt4Nu0PTAWkPtoPVW8I9E+NbdBmkHOX8VtkovNTOg2ZEM1G/MoD1OWiaHGYYuRbFiMmmSiCban9WMJKvAGC9YO7kRAjTordTRM4Feul47yLyxdCRHabaR0hnLpKlxNMHEC4LqWXukJJaOalYr9Xgg6oiP0gyLc7QTnaPeExfSOxkD2rOamlLbxoyrWpnWSZFqFuesprgpklJ7s3Ni/sLMwB2VwEyR84GMe+nAo1qoXluw2n9gmlgrmMh67Vo7aZQm28jaXmUdNPAbibseNzfZfhD6g8QwWa3h+frdVIVRbVGO2mu2g9RGahJ6MuPNjEHYY6l2B4jRutEZq0V5gCbWDswUa4FI7czmfNd1ol763OMgczDnxWu8uQgkxj91xzPrsIDXe8S4YE6O3jhb47QkEEbWZqtItXShIf1Ew9F5I7OA6Kpokwr4qE3sZhyPg3YG1gQTg987zhOLN5YUzBWjN+E4Sq8sWrp0EMQvWrwwNUyqgd6PD7oeWG9IXli30oSnFCDtB9IE6WBWK8sprEMVdZ0jkDnqesq5lOuUUlutNrgV1DqwmteiBfSZJJOLJM4nI6Pa8+KkLKW3Vas1c4BApEJOYvxarXMnw1cruVd7mHWwoT3ITDK97gvWAIV5/z4E8Pu9zEHmTcpR9zEF1KFrwVNVRLyuHQ3UOhJaQD3ekF9Ivuq5Zx2xSJcC8wpyfFab/fE/MG11rwJgAgNykjEQrUMEJrWmzHyhOdFDES5y/lza7SSl9M0mjrUPGBcewRwTn6+C7eMLRdHMdfCmEeKEKWIB/SQkVot5Z2dnZ2dnZ2dnZ2dnZ2dnZ2dnZ2dn5z8rf7oG8Ji1TZlhCG+gI9OhP6o11ooopWi1/eZPBoLEib7fyKNXs086emQpnHNWu1IMUSNDCU0yAucbcv2dbH9Z8CQhBhFXQUYHfGLtLLWwdvy+UTsKsviNpKGiBVy0Iz7Iy9FTUDOSSdpfkHxjVeVbWmYlI6q16s6UFyFSbTs7ENfVai24Gn6XTheDcEKehFfDlt5XA3FCTMJO1K+CWUDQcP+F0ggzXhHYTNyD8wiYitsnPt4gBWsCI+0i3RBT0ho5FxxtBj4Z40YeTyyzlLVx1deIlrJYOzN9NZCr3ap0TCaDjqiVPVYKZAlS8CmE1F7gricxS39rR6cqhYa0T5hfJI30WWVNvxEVZFzY848F2GCGUortk5DJSKp9m0r2o1qv7YHkVdvCciGqeAwyOuElcyZ9KboLsokYJMQMRD9wmSiOZrUuJbXajGLEfJPWV3tXYL5Wu3wiHKXcdi0wqB+rcdxgTuIAjYHmYOIMGu43Lo2IV8E5mbXzu1qpol+YCe6TSAEURiAYkY0Yb7AHkoLogeXFTBDr5HiDPkqiK8qJltK4C00GnrVZrRjZHyQNaWAUqE8PMhJtD/AvPKrmKiSMSRwNvW5ED+bMpQr3avhqvVcqjTnfuAoHASlEZu3dSsG/VIWI1T7vQHLFBTSEjjiQXu1W9zo04AueaoNMwp3ICWhtzo51XeRqV5uUNjltvXY3MlfrWX7VB7r9AfEm5yD6Bzm+wDs5XtzPP2BeNIXud2na/Vf0u13cAAEAAElEQVS9vmNU7fcqcEkGKrKuEavr2RWOD4xRsNodMkjJ2lW2jti57qL+W0+Nj2qwS9kQqu0bdW+xuT4bVhA/wecAFSQnpmfpm783gOsiX030D8J/AceyHiSxoDY+VuX6rsbxt1p7Bnn9IvsHRBBZ1176AL/X/vSBhJNH4iKlqM+JRIMZjDFJdeTj/8Qy6ixKSu2x9w+y3Yg2zkxGDEIe/8bfVjs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozv/Wv50ADj8LjDmjuQDM8NcyXEhYricZKMUq4PSrzZD0rHoBZfUIG5SW+1bEtxitHcgVUODdylabQrSflAtTIG7tk+ZSur4vWs5HWgTkYZKIFYAb0Ygs2FHgdxUQTJIfdTuJ42cQWiD/InH2vDFSB+ILV2rtNK4phTwk6wWsVoBETnIuMAfFM6FjBekgV/kcRQgz8DFF/SCxOFYDly/ARAdyPEkvTOn4zGx96D1ZwElydIwp5AzyQEcpdrOVOb4QvlA/a7H2k/SnrTTEHd8zW5KM3om0hueBzMnpkLTBxo3eT5QXy1BkQLM2kneZAoTLUgeQd6Tfj6w9i42GIK1hFYwV9SqIZlZgFaUbM+CjoDoUTvENDBH9QPiq4qK+oO0E9FAVQkXkpuUjkWAPMm8QALzURpe92phjhs5kohG6oGoEO8LMVmNzdUWXRCs3vwF0LFqM+oJODlfaDfMLjKeuALxriaxQN7OYD2/mMBBSC8dbwy0NYQo0BtJiqEUeK82MqAHSlYjW4RMRa0UuunJ0NIi57zrsecAVUKUK4PhA5EHdjyQ+YuciXsuDfYg7lL0UgLl2tONN8Soyz4FtZNLGjKpdrDUWnR6MudqlqqiCDEHczVhkShgO2f9mdBq9ktb7XKrz55fZBrkgeis7y1WjWTV+vurta2i9Rmxjkxd75ESM7DHt3L6qMc/Az0fcH9B60gOZBY8hboHsTZ2tXUOMVIEX032aUZHaXKC3GsrW4F6bep+8yQJ1Nat/77XVnbUzvhchxNm1mGMtxNyI30prX3WY0Gq6ZtKWoBHXfephENIHT6xe9br2dehCmngV5XdRQk7yJkwXmClepe1i1wa8PlPFbMqSuDS1lZyQP/AaGVDIIGzAHGcxY7NSP2BSb3nOQf0g8w39cMmNAPrhJbaP2mINsJfZP9AcxAa+Pwi+xPNrIMaHmvfeWdnZ2dnZ2dnZ2dnZ2dnZ2dnZ2dnZ+c/K386AHyYV4szE8WROKvxtWzYjbtahO0kmTAvehcYvrZXZ22eDohrkFg1787n71dT1AooDQcUsjZAibv+533D8QR/g3YU4bBBXokepaoVFExoDujEkgJlGUiCHCdNIVyIMQpgHx+oVJMVGesZRTVY26OmdXNgZoQI4RTw8lftn0aS7Vgg1qrlF68CJOWIRjho6sQhpe69BfQNx1lwyA5ILX7ov+rnZzDiyXQhxlXwHCMoqIIdtY0ctVdshxOsXdcMlIbGgFk7n6Xa1QVxB9BxO4n3T1yFrgc9kpmCsFq1kQW6/SIiC7AZ5Jw4Clw4B81HQaPjWSrh79dr1XyFG+kPxBoxx2rBJjleeOu1WawfZMxq7EpCvGpHlLGguZFxoam4RqnBTeGeoAfIScabnBeY4HXBIv5GWsOOz2pw+j9g9lIKU9bibAarURlEgW8J1A4kLoxEE6YMbFRDM91BFZHEc6KZiJ1ELB044HkDZ21J61GfA4yMUcXr8NKdh+HpuERpzCXJ+4VkQk6s/RUI3N+r8GmI3ghCZCOHo+rg38rmTko1TX3c68BEI6WVxjgL/mUG1k8IqQMPYcCNyInGLP23Kl0Nz0BzloZYCihaJkQUiEerNZoNkU661/WJkzmxdhRcp3TTKonD0gqXBWBkVrs9BY2B6IE2ReckJPFHqeGr9xr1/c8D1UE2QduJ6ORQw70RHnX9AO3xQOLCe2MIhCctE81Jyomb0Sb12KmTGvU5aKX3jgVYkWo2H71eKx00Ld28M0oZ3mr3WmswvBThIYhlHYpIqYMKTGqi/IGpI/OuAydqSwk+MWqjN91RKTW0xIVYKdpJK0iNIjlQPUtDHV+oNVK8VOC5rrX2Ufcq/1V70ybo0fBfvwrozoHZgcVczWcnxwuxBx4XtL8hPjCv3wXJXLprAf9FXr/Q4w9wB1O4vQ6gzIvonxCvOmCys7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Oz8x+WPx0A1q5gBnTw2qdNa8SIaiS+b+w0wDBL6J/ISMT8vzVoqUYfTsxg3JPTanc0Z1ax7PkoXWsMxAQZtWEasfTD6dUmVYM5SpPbZumaOcj5xaqtAYJnr+aZJ6mO+iTkgBy1edur4RZzrh3SDtEK1rWJzlk6YesQExWBtvaHtbSopC9QLaXIvd8gC/5mICjiL0KOpZB15Mcf1cQLIflAZYBOoOMxUH2geWMx8RGlLO4nMSZBFjimmpFSmBVtf0WuL+RRm7U2BtKVOZfleQ5SlURqN/SunWT6Ax8TRfDeiRx4ZLVYIwmCiCiA76v55wP6J7y+kD6Z7ZNwx5j1enivVnVWW1vFEKlF3hFCtrP2TTPJ8QvkXO9NIucBdxSkZKLSqmVqDyQEZaAhNCA9mFhpqvWi0fBupApdjBDw+2eBO+v4Pck8C4DNao4TBXLx2hqmdXIINSM7CTE8BcYA6r3QFBJIPdAcSAQBqD5L9bw0zOENWu3jpk8ivdqcAK83dC1d8bxJrZ3kwNH2UQ3y9fWl7i5FdGYSEmiwdMOTVKmWfiikIvqgaTJyIjwgKSDvV+3dSimUg7MU26oFVu2gS6By14EBVdQNTmUoqA9CTiJ/cUQ1RU1huhTwzI4eHSXq/4+BY6AN68qpxrwHkUZEtV0jrtr1zllAXE4gySx9OKqkrmvAEyh4KmqlapdGhiDa0AxiTK52EPckpSEJRunZJdaA+wwkWx0sCWFm8NDJaScqgzsSt45GgiRJ7RJr+anJ81nQM5LGoD8O4r7JfCASpfpGqXMACnmR1sCDkLIRWBNCnpgewCDcoXdMGiKltY75hqM+W0jWe6c/aPnG/a7P87fGvT8gf9bXxVLfHyeMuxrU09bd3JGY+LcCOgHx0sS3hszERMCM8H+A/kCPHxAX6N/q90EW9BUPqjn/RPxdDfDzuQ7DOGJ/JR+ORNRuewRBIs+Pf9evqp2dnZ2dnZ2dnZ2dnZ2dnZ2dnZ2dnZ1/OX86AHyF0iLRfIM25n2h7YC4CTugd9IMmRPsXsC3YJ7ek2xUC1F0gWBBTsNV0QGQpGqBDzoxvjDNateq1Pc8a/d0TmgyCz60dwFh+yzVq5yljDZB7xfyOLHzASN/b2P6vFCB1pWQs/SlcxQMRggPrE1QCFVSrdSnKUgocjzrcTEIUbL1gkkCwlww22tXtz1ImWjr9ThlVptPqtmpDpFv3D6q5eoTshW8USX8RvsPUMUj6jWMalmWnncS/QMJRzKYQMyJeqLS8VzcXhokOBPPqN3WEEQmqmdtEEu1QDODtIO4L/CB2NpAFQr+XzcRjS6BPD5QhBRFxRFZw8Ys33QkgpDqHF3RZoS/yVC6tGo+U61kbGI0GkLIJPLGcwFXD9Bqa5ON8BsfE6YTdhA+an6YtSV7O269DinQS5dNNYq1n+S4lkYYJBWNUWpkPUoRfQgSNyKfC1AmIa32kwmCWa9pjAKZKgWT/aqGrhjgaG9IOhpRgI0C6oyrDhJoL1BW+BjVvlTSV8HdnJg+SL9gNZOz6tyrdV4qdFNDWjVw0zqo1wZt1OYz7VEtXs1qaCMgvYD0vNeuc6PxxkzxqH1iS6f3ht836GPNyjrVb633WwRMTyJr7/dbsT5R5p1kbwiO3zdvXU17O4m8yKyta/dZAB4Hm3XAw1rp0eO7FR3gk6Ta8pjW9eYDsV4t7wzQk1ibvGg9PlBQqe9535h4/RnxUj5nkvNipDNEiW9zgAshAp51voMElIhBxoAUhjUIxW+va39cpXPu9RxEHaHDfZF0cr7JVteZiDPjhvDamVYh/KrnZtWgZi59tp7I+EUcRobjSe2o64Lf4XW4IQP1CzXDQon+hPgiHmcdqMgL2hND6j2TQMaEx6Oe5/HExEnpxGwQF2KfxP1CWyPjhd8Xdp6Q7zosk7l+U+QyvQe0k5xvaEddy+1RDfb+We/zzs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozn9Y/nQAOH1AdLAgI1eLNDFVJEC6Lk2qw5hw/AFnwHRSC3xB1E5mOiLQLUgWlMlJ+VwfoJP01eJ9WLVos4G/UT2Q3hl+1S6rdMKdfhiaN7TOeL/rz54JdhWb6KXZlX8+EkwEs7M2KrUBVhuuWiAY6YhKATl9FNyyBByVSWTWDq7Yb1VuaZLX9/EEjfp5rfZeg2r7cS9ltimarQrDUi3jbMr0gdoH2H8h1goAhYCtjdyckIljzJhYDGQI0jvt7QWrPjvaDpoeWIMLYXrStdrELW5UD1yFtAePs9HbUXuvBG9/V+FSn9XUjRs9TmK+6O2BjDfYicy1I4ujTKQ9avNWBajXsOzTUWrqBnbfxBj0U5DjE+YXhBIt8EjEDmS+sdXQjDTEE+wLrJMuRBies64PBaJgXkiSfuNiSIyCxvNdpW1RTDuujRgFUuV44nGRqb8bwQqYnaXGvW8yDc1R10Y4Zt+HB16kJCoHaifpL2AimsXqU0gc14ZpQ8ZVuuNDoJ8ITzReRNZ1onas5q+REqj+qHIyq9lbZfpqxSK4tNp81kHms1S/4QWGDbpkAeEY0BsqjekC4fi8F85UUjqSSSbM6ybaDxJnpvGKas/CRPuJv7+QGGQ7CR9o/wORCWPgPhE6djSaae1My42QmBTcLvA6sefJuN7VXhVBREtb3hrJLBBvZ91XTBF30JOMga6GvcwvaJ+kCBY32s7aUH5/Ib8/K0YC/t0obh0ZLySVOb30zSiSUpB7OqaK+xJUm1X7un2C3PV5z9Kxq4JqImKEztJxW4eWdb+IG6TXveQsPXzeoP1ZMHs6oqNAdevVrG+JSC81tepSsTu0s7bSpbT6Eo5E1vXWP0iv+55qENmI9kHOV91z/AtRLXW9/CDdiazrREyQPEEfpP9EJesggSqiT7j/b4hG2knmhfhA6KgYcw7y+QNhYOtzEw0kraC3fxE+C5aLYTnQ86/4+x///l9aOzs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7O/8v86cDwAKIXKQ3NF7QW7Vm9UBaZ46ftEikn6T2ajLGALOCpNpg3NCkmpPuSP9AXVGzAihMfG3aGg3GhZxngYvpBYkyOQ4h84loAwI7W0HG2UozfHTAEHsC39vFgeja7JQPMm5CEot3tXK7IWNUk9OEFEFV6zkQ9QqIAEaOieckhWr7Hm2N7ArpkzSDWZCVHAvWan2fuP/ZlmulZC3gFYSdcF/V8tVezUP9a70OOQt6xfq5Ue+KiaKSkAaaaHsUUIqBaRI+CR+kHKRfmCdNlUOy1LMieIzasX39pD0+qm2cieqxGrSgTEDrOmgfBXXuGzlabfCmVQs8blLPer7xBhqpJyZGzptpN+6JSwMSvd5Yd9xZu78BeZIShExkQs6CX5ql88UnorO+1gwkMSh9dETtOaPVIM8E8WoGZ72FOV+Inqid1RpvHZ2BtVKFj7wR/SSYqL9IbZjfZPtB5KsgZv+kZYPoZOvVZPY3yAPh6781oasrK3qUElsK0GovhbdZYDyY8yaez9rTDVmbvQfogcdXfQAxUg2htOnqC+AppBgxX/88UOEXOEwc1TqoMK53NV2jtL+SWYAdQySxOtZBkiiTiLXbG0LKUcBSwdLx9qz3NANLcB/1OOwArf1qn29cWmm7rZHZgTdHOwmZTHciACmTgCyojTQkJ5lL7Z6BtVZabW3AqA3w7/1kwCJQezDHRTZDpbaYM4UmDRMhuerAAIq0A7kDNaBBM6N9PnjaJ79+/oP02tJ2sdK+S2K8Id51jwuBHGQeZFQjPGZtO9M6Liy18yTHDTZQreepvVNvzqu00Fp6dCWJeQNJnieeiThoW23vfNdny1/r3lCHSnK9D7gXiBdF+19IAZeznssw0l/IcaD2qF3s4eu6bYRqKZwzISYuBnmBPldLP1BOkoGooOdH3dfM6nEEyNE4AT0+ucerNqWlo1Kt/GrC36hfTH//m35T7ezs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Pzr+dMBYLNOamO8Lo7qxyJW+mPEEKwaihgiStOJx5vkUd9AGtIo1W2DlJvITvLGIqu551664Exww+Oubdbjo7ZurTZooz1qtzRLqAtSzcsZBWvMkJjQtJpzoghZoCQmjBeBoiLMTFJ/IEvxDFowJGpfNyNJn2h6wRqZYB2yIx7//PkMiEZMYaRzqoIOmDfy+FsBrWhIDkSPgiELCPXeYX4xZydNWSJnZN70sx5zaCvYlwMRkNYKRM4JrUGXalvPQQgF1e5BNqvXfF5kOIoRY3IPp9vB9DeeCXIwU2nCgsYTvLaFC9xb7dhSgLKgfqeJQjNCgtAHeKAJ2gy8VcPU3+jxgYUg6YRT8EiMtMbM6mRLeyLxqhZhFmiu1mn1T2VBr/Li/sDHr2oZZiwAtkzXMzCEQw0nC2a3B/n+WQra9lkQ2QzkQBYZnjMLkFLt2/QkV0MSXfDeBdEHpNK4CHHSHsx4kTF/q29lvEistN5aO9H4XWronKRXi7y2bgNXQ6YC67rI0n1HDCTrWsdqZzep5n1GoO3AeDMjwAVVaNrweNW+czRKfwwmdaBhrga+9oMuzj0vVJdmWzrtOABnHgfWj9oSdhj3gFmHIEQodbUqntUWNbzAJwoJY6nQBUhpaExCOxcBw0udPW/QhqZXI1tBdaJrN7q2uetaw55IjgLrfiMh1XzVQUpnmhB3kGmUrHnt2toSb1+D0La08geTi5BGZ6CR+H0w+lzwPFCE0NomRhQnQB7kmED7fa/LfqDjRo4OI8EHDQO9So9sC/jmXdd5KDCQ9iDFV/McIoNoD8QDyQnjLn1zRr3eKBmvajTHhPFG7EG6r61xkNbJeFOHViYcDRk3mZ8gTsYk8yc5aqeYnGSOpSc35rih1Ya0zEHojYWABeQX6Ur6RbQPcAXtmL/xcOKayOODPgbj9Xfk/CsqMLPu/02A9sAjqwG9s7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Oz8x+WPx0AziHMnNwRTAnO/kmbA/zijkDTSj0aDvfN1EYcH8QY5PVCz9L7MhI5jHFDjBtrDZXBtIMpQvfawpzcWD9X8/cmpmOR0Koc6eMugBfJt9RZ7FGNzvDCsj5R6QV8ZBVvhyHp9dehpAdYIvMmj4amEBHc15t+nCAGPvEsFat2RWKgUX9LzEgS5l2wVZMWQYgSUzF3NGq/tQBWtZYzHc8bECQGQVuNRUUifmuTZyyFsisz3kDVfyPqZ0ozCFA9iPGrIHafIAfhAgJhVrDcE+KN6wPxZEaBcEsQC7J17hFY6wX3mTXVKax/O8j4Oz4ND0ckq9l3PpH7jQWIPTAmj14br64niiBNkVRwRdqstnVG7UTzBKlrR9uTmKuJiBDaaitVFaU2Y9Olmpt2FISXgRLQnkz/SaYhTMa4SWml/9bGFAMTTIXWYYxR6l/ParPLgYgj/aiWbnvWtTQHZg+MhOMgsnaYszVSa8PVF8QTBvhB0KtBngNpzzpAIIlKw1VqDtuUzFGwV1tt3NJINRi/CjQTZFZDXEJWm1eIccFquUZ28AttJ/YArol7VmG9P2rfF6lDFX6vMn1g4txMYgbMgqY6J+1RkFOaMeaFWCBxcLuS6ZhWo9fv19KYS11v81ftXpsh4dVij1Ha4fFGSSRaXVIh9TxbQyJJ0drPjiS8rn3sLIW6UOBeD5gFqTNvsh9IXsSY6HGQ91ddN5cjEmujuOEJ/vtgRGeOfxBmtcMNdXhFOnkbkRdgyPlE55vIZI5ErFdL2draQJ4gN8gTiVLKa8xq7aqVMh+rxrB0RGSB3jIBiB612+vvOiAQkHPW47Bq9EoTqlrrBfItsIj1ep/ggo8L2qO+LgP0o/aS5wsI4r5r45e6xphKhtX+tNQOb+BlFvAksxXUJoh2IGK4JR0wE8a8CM3aay7ijPtN3BORk8GBj1+EfaA46TchDcmzvg5DtRr9Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7O/9p+dMBYJ0DgD6vgiuezBnYeVbDE2Fm0KKalRG52rwDT0GHY4eRehPay47rE3s80BxkBF3WVqUILYBeGuUCNIlkFpwjseMoyESWOliN0j0HMWtBtKmQJvC+iFYgWM9HtWijxlSl2bLrNvBZ+6GzVMQyJ9I7eXxUW3EU5CUGaQcy/w79L+BB2rOaswgjE5uBkeSjwA/2BJQIhQVWBamG8ZhIfxCzNlSNwMaAdhB+YWYFm+JN6qMar/NC2gkBM5zWLmAUWBlZPdF+1vYqB6K1vTvluWBUIJKl3/aLUxXVE8kbvZPmN86kZomTFCl1MoLYSddExvjn6x8XEopKJ7TjmYQdiJ40nHY8oBk+gzkcxv+FinL00uaOGYicCMrZat913NUeDREy4Q4Bv+n9IMJJeRD+j4JbvaHWkOxkVPtWM5kEMZ3UQNsDzaRLUe1ux9JDw7DVykaJMMhBa4KaMqfQclSDthnz+3lHctgDnYMrE0lBxFCCkMRjbZ/mJxE3oq02iUXIsAKvJEgjRQumctd1aR8IhspV7WE5ED2wqDZ2iiHuaGZts3pgpnAJY/yqIxE5wA3VBjEY4og1uiW6IGWkoI9SGms7sNYwH3B23KupamjB65ylswZsBI4gqogBTLI3fAzwKAjZDMkoDTq6Tkw4zFnt5AwMrda0tIL9WdeaaG0hi55IOJHvOtxh1fBGtHZq40bkiYnQ1EjpS538qs9bztr2FWh24ikc/cEYg5lJD1DLgvWPUsxbP5lc1R73uVrvF+rOerLk9SZ7gX0YiBjEVZrs47O09bGa3+nQe9XTta5hxEh8meET0bOa7VlNbgzQ2mXmdjgf1QS3J+LV+k+xtandibzXfvEAa0hUy1zCEXVUO+6jNPF61KGAnAXB5SBNwb/QLsuEAMRAz9qNFv9C7RPmQOxv5HyjTZfOH7Qdte0co/TwbR2U4ETGV2mvRWvnXCbG/e/8dbWzs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7PzL+XPB4CzIVZNvdYokIEXrF2aVUbUP+BXAS2wZyj6rH1S4QH9IH19XQbhTmQr+CGz6r1jllJ4GipZ6lCMfL/IyGr9tkZ6FsBSxdJAAp83SQEt94aqwPFEZsGOAqe9dLoyVgvugSPVLmwHmCNpeATmQdq9hKqB+Y3SEIGpj4I3a/czPZDjxBjEBdYTvRXprVSv7qCBpqPHga+mL61XixchROG60ONEZNbmaSSWA+xBMgg76nn5rBbu+w3nczV15+/HQnxV+3BM6E88oJrSyqHUc46ASO4Iut+lCibQTIZ9EExEL9IvPMDkRC3gusl2kPNGMehPBCmVsMGhhh3CvG5mNloEpo3ojR5J9r8hlszxwu+LaEqjlMduT+b4VXu0kQgNz9WolAfpd23qjhcpQnAQeaAupHysdqExNMlwYrwQO0sfncFNATKNAE1Sqg1ZhdDahfUB9/1FF0M8cbWlFHb00Rl3Mpkc8lGbqPnG5422TzRqrzkzSGm1wQqk36hoNdfzXmC309snk4lQBxNCBWjVco27wLMUHHRJMhV3ACenIzMQOZAZeFabPBtkLtDPKGAaEzOjZOIHLhfIo64jsraWSbyGeYm8yAhMlZADbR8oAzIZMcgs57ZMx91JFTIcEVvt1fpMZoKaotoKevoNWMFsbfgcJI2M2opFrbaB0yGvguoi1MgwSBMiZl0PPBGzgqL9iYhi/oL+gee7FOG+PhdMyCTV8LjWxq/UZzwnPoQrg8goxbJUA1uyIfVNCt5i6PFZ27c5S82sra4jC4hRjeF2gFxkWoFqW+9XQjJRIGmonlgTAikLgDgpgtmBxA3HiaoiRF3CYkvPHuTzk/SBzKUkiPf63ExiOrSjDAWiiBSMFzlIXiRapmh+IvIBodB1Kd9PJLUOMGgjs3HPAXZWQ1iFGLUV7ZnI+cTUYbzq/v+t97YftW0eNwb1GXQntwJ6Z2dnZ2dnZ2dnZ2dnZ2dnZ2dnZ2fnPzB/OgDMwyGUSCMfD1SCdn4ULCBBHH18wp21DRqQKrgqgiMKgqIaMAoqyGloT1Sy4JGUajbff0clC9DeSR5SrbhWQIYMGBc+Ezk6GRAHpXy1TmbSRBB5YfIEO/Go1mym/rfmopDDoCUzHGIQAa0p7SzVMFaNX4uCL9psNeTkn0DLBBUtsJuBRpCfT2R+ofqB6k1IR4+lTE5DTLDMgooAeqAxaYB0Q5tWw1mUQwW3E78GGYCM2vLNatOmPYpYHp3p1aqEgHuSzyfZpF7/cMIK7oHTe+OaWZAoJu6D3p58P8VgEvcLESc9sSa0VBC4PNEOuIE7dla7VgSIaoeaO2Iw7wv1xLJxZDII4lSCIPQgM0tTK47iRBz49AXnBfK94HaHFKYL4l+lYI4ktP1ukYMW1JYo1a0cSHNUtWZ4s75GPQlJTAQzSh1cbwRG/n5vm9YO8KwKKXoYPt7Vjmwf69BCHXTAjvVhCUwaKaP2rPMmc9TBAasG7Vyw01Rx3qQk4RPLaiIno7TlouT5hNfP1XSv51dKX6s95qzP02SSc9RTaVa81FkKdEHsSaow5ouUV11LmrXH7L7Aa0MYBJALmLs3hBe9d2ROpjwI7oLzVkBPpK6hpIE9/mkN516Pc+Ieq32eiBmeExLca++YOdCm5O8d2qOgfNbnIMLJuNFvfbFqNWQzQA8y73WPWl+b3+p3LwV3tvX9Zumm7UBi4MeJSvLoE5k31zRMDJrgofVxjwBrLDE6iPyGv5ETn0Lc9VwdJyML8ErtGqP1+ogpMt5knITV1rNoYArIRFqrO4JPVKU2j/WJyFWt4e8t8rgghbxnbftmNbSh3o94X9BbmREEYkFbvOHjLuMBdVCHSMhfcH5We71pXcbtIOICLkQ7+fWTfP5AfMCY8PxBjJ+IHNQ8+vz9ngix3qeJtg8sbrQ15nTk8aBuFDs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozv/WfnTAWBPQWUgTRkjOaM0pT4CEcXDUXlh12Q2aCKENSKVVOWkIZlkdEQHNguiig+ifcI1kANSD/jxN+LvP3F1jqZoRu1iMoAGehaQaQVSmUkmZJ5lWR1fpUOVauipCHJ0pjoZDn4jaah91vedF5ZG2lnFZhKSAjBYQWBGMYuc9YIEa79VUas28/d/0o5qU2ojW20KVysPFhohPGvT2Ayfc7X3gkwDOZk+EDuQMRjHg3kPxuuCoxV4CmBO4F2NV5+IHrQQ5jWASeuf1XrWjtoDPTsRL/CEbox7Foh3JdyxftCO5L4nw47VWP4iecDRIV4FbOONPg6IiZgQ80byIP2LzI70k3j/Qo9HqZn1C9MCkmQBOXIS7wKaYkcByFEqZ7gRO6q5C5BGEyXMCW1I/0Rj0JsReuEGcziC0LQ2oYNSjmON9E/Cb0IT4UCaoWLEPXGHpoFF7SaXShxQRYPSlc83guNpjHe1eSUVRKu9HJPQA7FWOmt3VLLalVaq48hWUH0mqBBuSO+ENHLO0vsK9V7Gi3Idl9Y3bq/t2LhROkscXSA4FJG23v+z2qEkmlnXfTPcg8Tp1nB3IhXNi2adSGdmYmp1UfOtJ7bSVdtBW7u3MSfIWe3YKA1zeDVxE6/WrjuZL1Alv4F4JPl2OBuCFtSeox43DmhdB73Vn5MGeVd7V7X2i3NdF3KWOtsNUV8g/AHUnnLrrZrP9y8SIyMKSPqNHk8ik5xJx5ij4fMf5PGg5UHYiWnj0Hrv6oCCrhe7YXrU8/VYGDio9d+DUCFlklIA12gFqe8BPWu/OJ2Mhoehj4MYk/SbZDKzgz4KYjuI9N+bvLXdPeF7E32+QTtqB4xfhAfZHkR6XR8a8HygNPz+e7W0FdQehHygmojdpEs1le0g7l/1/fyF9pOcX6Q1JA+YX4hAfB+UiAvkRrVT889PFCFF8LUR3WhY/2COn8T9JmXiCuI3+fgL8v763/47amdnZ2dnZ2dnZ2dnZ2dnZ2dnZ2dnZ+d/NX86ADznjfaD5rEabQXvtB3oYej9Bgl4KE0D5fjnDuYETCFHwR09IS9AqzEYUnBkrq3bNLI3JB3RXgXjLtUSdCF7g9uK1UXiEhjzd0s0e0E84SChtNES1ehNQVptEPt4lT5WBXptqs502rwgG9IORBxWY7d+YIHa8IT7QhBof4EYqy0MYorLjWZHwlEPpGm1MMNrezOEkG8Fs0CbpBxkXKQqmgWHVWqPVnxUCy+z9m0l6nWYB80vOD8KOveG3BeugbQsNayNAnoWSBqY49aJPCBeOBPaE8mbRiNN1kbyhdAJOSHeBZyPjmYDotqmepQSNmf9DHfSIGTymoG6c3CgYcRdfHzeiU9HUMgL1YOpsvZvCyCZPbH5RsUZUUhX6BCOatKtA3cBKIKoN7p2lUlEOk1rA3mokne9rsRA8knoQVjWNm5VOklpCIHPgSwQLLNeb3IgUfCdvNF+EhkMn4gIIUftJ6cg0oh0EEH1BH/Xz65aPKBo72hXThXmgJSktZMQLy06Wu//YcTXm6QasSla26ptQVScZopYw+ddjWQzuiRiToyJ9Cfig8RRkWp46yeS1SI1ldoMjqzHF17A0v8BejIkiRnknGgrmPr7dY5BctT7Y9VcTelAkrGunxxIL2064ggHMX9WW58oJXt4qZJz3XAkS9dcnXkCRd1Lp+4O5wlxkdKw46idYe0ggamRvddBBwdRpZ1/Y/gs+4AYcnZkRCnFpZC6qNH04Br/WBvmVsDTg/CB9hOdjhMFpkOQbnVNwtorF3rdIOugQzMYN9KrIS7ppaTHwd/Vko1lAmiQ/lUtZavPYyRrO7i2trU13C9UDJVRynjVtX8NqGNqaPsrOQboH2u72cn7i0Tx4wcNReZdTWZRVIWMm/CxgDdIDMw6qQ2JG2kHMGo3vP9A8gs5O/b4gNdPYvwix0V8/B8QN9ITzUb4u3aC1ZlI3SPuvQG8s7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Pzn5c/HQBu5xOzA/S9dLGl+tRDkAZOo0Zmo8CvF7gxB6whZuT8Qvos0GQs+BlIC7IVaMGUtECGQlyEQzbD5iSR2p8Vh6MXfDJo2cBH6W5VoRtwk3Iw7zecnwswOXxvEXsgvcCT2BNNIXzSrRqmog3NJNJrZzeCpLZAFdDWCP0gPCCz4EuwnkOtrKYkcd3YxweZVnrjLGgmWRuheb/I1lF39KgtYHKAFGyy84G5k9ZpJoQHasqcN8mFpeCp+H0T9HoQdtRLMEc9hhDUo/Y8MxC/MO+odTwTw6ArGkbrxmED7igNb3sw/C5Q14/SeTclR4CWBrf8tdQ+aBYQFU5MB56DSUOa105rTDRvlKWllsDFsQzseMD8B46ScWHtScRFcFcjcsFv0+A8Tq774s5GTPAciGc1RElMIVKx5ugEtRPjwpGlLg5aP+uQgE/mCMTf9Vw8UY1SFcddgDve1cgWELW6xrNax3b+QN2XAtlBTiSCtPpzZBTG9FmNy5ggivpFcBbkI5lZyuVMqfeKg3yXThgmaoLqB+JXQWltqBrNguFRmvNMgmQkMIOZgeRAMJyJBsx0QgIEOkKzxhjv1eoVVEB0Qn4WiB43mfJ7JziyNM6RiUqv9zGznn87MK12NH6Ramu3t5fOOPu6RzQQkBmEJsxAWh2ukO+9X5yIuxTT/QPpz/oco/W9xFH9JD0IVjv5vqApob32eqXuXeHJnG8CA0q/7f5G7APzRFrScxQQJzkeRusn8+78/PoHqgcmAzkEn0kqiGXBUBW6Ki6f1VqPBTetodqq2S+JipcpQAWmk62h7Vj6camGt57EalfneIN0kBtRo7eGRH3mQ2qnO0UgJhk3pUuvPeToAxDUPsB/Yvpg6hcSgXLBDML60sxPvEhzHWRRq8c9fvENxmMO8Hfds/yG838Q80VYg+sFDKZ0AkMja6f7dhiDDCUzgIbIIMfEbW8A7+zs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs/OflTweArZ3VYMSg9dIrMyG19LUqZK5dR196ZlHSCspkUKrfuEGeMC5opZZNH9AfBUKsINg8grwe+Hhz6GepmyULXGXUtmTVFqFZqVLPE7lf4A1JIVWQdlYx+KrNUnuUihcSIpH2xExoPRnZEWulf/UkTKrNmI51IfKuH60HGTfWDswmkWtstR9I72S+aKrgE6IRAcq9YM1ArRqAQe39igrdah85+kGGL7nsxO/av5XWwQdiQXgUSLwcsQRRpJ/kdZcut4N4goIcj6UP9gJ4GfWeaDWS02pDuYsjrRN+k3pSPcc34l+Q1XAtzDuBaiNm9mo4ttpJVnVIMA3k+Rfi9fdSXjeHOZhSr4kEFNwbJE+cm2A1vOUHMV40C8ROXIzID0SDnDfkxOPBzeT2YHq1qRkBJriPalnmQBuI2wK6xhjgfq1h3A+UBA4yJ/iFWbUxtT1+g7AC8hcpz4KTjweSDQ/H53ttr9bhgii7M2QSRKl3M3EayQLmepB5AV6vaw6QSYShTEyM7A+YL6x15nwXdI7V3mYg4kg41p8cjyfpN37dWD4IroJ8IgSC6GcBZwBXwgTopF+IdGhLB93Ao5rx0j+QmKVq9rVRTUFJNYhQcmZBWOvo8ax7wf2FyIFaabhTn4Q7qR3TWc83BmpH6apz4rbuGUZ9xrU06YxcG7Z9fVa9oPe8axO5Poh1AMETPT5qb1kUXYrqiCCl49PRGHV/adV8zUykG90EAUy07g/qgJGpTPe6R0muHeNBWv/9eQLq3tdOIi7SZ33tt54+RoHs9kAYv3XUMX0dkmikrwMx/kaOD1RPVFlbzoJYNXStlUp83userIbHRO6JtqUFj7k+w4q8A4na5M1MHCX6Yx0uaYQIqbHU1FE72zGg/0CitrvTnoSUYjxt3TNFyJnQ7mo0a0NX67n3k9kfTKnPS/hdP689EOsoSfgBVOt/Z2dnZ2dnZ2dnZ2dnZ2dnZ2dnZ2dn5z8tfzoATFxALtCmZD9JV1Bq35RE5iT8IlunzQGHkWMwpyD9hfIk54GSuBxYvBE94a6GoxwH8ALvdCCP0r1KCyRPcl4FiKP0usFSn6oQ3GTq2l2FmM5g0E9DxyzLNILwUbAJITNQv3h+dNI6Y5zEgoFljW7QavM0sNI5iyHpqDQ+DiP7D75+/sL1XFAKBKPmggtUZwQxDT0ayGcpl+eCtzkhnEmDUJBEU8k5odWuqPeTlrMgoAAqTL+QNkE7MgVVIfIN8sSvUYC+GRpS+65Lj8v4BV0QLVDV+xMfX8R08nxwjxvpxpwX8XrVa5mGiSCS+HWTrTFmtWNbdkiBeZXCOo3wwEYpvk0fxLyYdKSdtZFqwfjH/4SmkNUyRKLamdIXQDXmGFVqxEvlG4lIgN+M6wA+kfyv1bo2xErZG3MQYnAnqlkQ2iAwkk7e69BATDReBen0k2+4DdVibmZAZ143KYlkqYJTgZFEFPBWCZyOah1OiNX4lBjM8NrC9azXZr4Re5QCvbWCnqlgfe1AO2i9n60rEYm7o+0sYBsXmUqixJiojmoCc+MtgAeMn9A7Op1UKfgcBUfJBdoViDdxN6ZYbStbomL4dEIo5itJU7hD8eGkOyFZnycrFbnEKA23HAWOod43aevAw9pD1tJkSwhwl459fVbQTvq3eluhS+mireB5xrua0aYFWCkoXocjBBg0U87nB5I378uAN5lB13odv9u2KQaaWC5rfCQBvMM4bieOg+lOfJUinnCCXKp6iAwyb5o+6tqco66cqAMW9AeSXgA9FlxVqx+miqTUdnEA3HVIoZ1kgMdcW+H32nYOnKh2f7xgxmpqZ1kCrD7nluAyybkA+3wjWQC8tsXfdWhDGonWNrePagBHYNRBl/BqNddt6Qb7qNfI36Wu9ok+/lKPn1mqf+p6cTV8vshroOdfIC9cftTjtqMO/agR14uU69/2q2pnZ2dnZ2dnZ2dnZ2dnZ2dnZ2dnZ2fnX43+//0A/j9PWgGocuuSDtdMrqmMCXckMyfog5EH7zjxtxPhtOeDJh+oGm6TSyZ5GMwFlFORfqCmMBWun+gctb+aJz5lbfl6/f3sEIZiIK0Uud5LTd0EzBjWSbUqexqIOtm1WqdJbV/GDQxeQ/n6NUu96hDZQY7aBs2JaEdGIqFoPwpm2MF7TF6vSYy7mnYC/t22y7vgkK3d4KO0uGJKjb7W6/i9ayvWwQpo4U5dYo1AcYdrDDycGYsB3TcuH9xDmK83nrP2lMcv5PzAzyf3+rqI9wKp1drLbMRdYNRDChqpkPPF9InPC+ar9kmxtc1a8Eh7rxagON4bOW8iJsM7fgHaiAwiJ9kac/4i05l+sRzZ0IHHB9I+arOVganWrmle9F4t8IKxUZuhGdDOAv8O6S+CQajVtWG9tLliYMZ5fnB+/g3pRuQLH6MOCOiJNMHEMS3zrtmgHwVGUwVsgeaE4P1bcZ2maNZjFL1RCbQplo61G7MJ+qNgn9Zrp1mqXkFQtFavNTDRauZmIscTEdAITFjt5SceY71mQsoBLet90MfSkSv3dXG/b+4pxHWvFurad+5nqclT6/vEBA/OJhztSUTg0nH9wEOJNEROji4cDURuVBOOR13fZvj381LBJGjaMfXamU0nmfWiJkt1XTvXYo9q91on8ibSiPAFTSfBXOrr+t+QpROvTwBCQ83Q46jDC1qqc9XSLGcmoQqm+GgEB7JeoxHJyCAiq5EclL5aG7p01eGTmMGYjkxQrya0AmaPgv8xwe/Viu61GZ1aVgNroEL4m4xZqnHqcncHn4NVc67r2hd9trP2jO0BnLV5LGsfe13TIrI2pM+6TxwHQmBiyPEkMom8FtS/C5aTpDaqBm/IUbD+ew9YYqzH8UXmXYdNojTjTF1T1UrGvX7mRx1Q6E+stXoeYyDqWExSzzroECy4PfD7RWoj9CDHV4FsSXIcoB//zt9WOzs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7O/9S/nQAWM1Kd2qttKgx6JL06xeG00dUSzQHhyRNC/6qWAEHLajS7cHRjMPADqtWogwsJ4Zj9oD2hNsBwVJo6eRdylzUEW5UW+lfZdlYrSHWkS6IBs2MIyfWFLEDPRpmvvaJo1q8xwe21LGZpWEFgaWl9TtIfyNxIXphFsi8wbW0uyK4XwVtPEvRm1HtXSuVq5RTl4zvjdiJmKFWjVp8onagTVCSDGfGm2ytmnOZC+AeII9qByZIf9T2p1/40WEmYZ30am3a+6rXTGQ9vxcxZrVZczVrM/Dxwu3JHI7PxPMoxas9CPsgMsn5JuciWTGQCNr1xkYwZ8AAlXruEgPGgBnkfRVwj6BJ7cT6NYjrvUqohthJRCfpkIOI5E7DZxBeqmMR0CwolvZRkFYUmRfiLIDqoFn7sdYZEbjfaxtafoMpJej95JDkIDiacbZPzFezNBtwgHXcJ+ETTFbL1OtfqRAFrLsoTXu13/NADq3GZNYer+pZEJhEVBYkvIm4C0K2J6aGitX1eT6IGNzDa786HJFWW8i341IbtrXFfBKyXje09najtlaTIK0VZMVJMVIfSH+ureCJ2QdmgshFU+hyc2pwptNFUTPwScyB+kAFNCcqgqoV0JZEfRLZqtCKlgpeO2KGECRLLT5HNaHDq8WcpVOvJnADdXJepGftdWsptCGx1lFxcibpQo5ZamyR9ZkQmML9GqgZZ3fASQMRXYdF6n1xLyW2dK3rxrSga9wcfdJavb6aXsrmZanGjrWRLGQqMS7cJ47i09fWb689ZZ/rIEe10KsBWzu9dd8KUqQMCBllWMgXgiBqdZ/IOjCh7fitNU9/158NgXwBd91XQoATkZOMauGHlHJaz4/i/1r72OGBzERSgEe14scXcnyCCG5R7xFWG+X+C2ufaDhoZ/i7Phf9swA3pcoPH/Wb8fFR1ycCcWH6qPugdjRAeq/7xc7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs5/WP50ALggS5DXgLt2QbV1QgS//oHKRI6PgnBRul61rAasCPikHUprDfOJeCliXd5gnbhnKZC1NkqRagBqjNolVap9q6VLRb1KtDhpiWc1JdEGmVgT5LDaS5VqCsvwUuHmgpQomYaPwOcg8yyIJQIyseeBtrOIawSeMOPG8QKWo2istBM1QXMWS1q7w6mCPE+IxKPh7wJfovb7MWGGzFJWe1Jw7fhAuWnHB01vmrb6awGdHL9qY5akzYklcE/chat9wjURcZoJtAOxT8SeJAfSjRbJqQXWuxrWHyAFR8USpdqvOX5CBiMND4gciJ3I/YX2J2pGO57VlNZqsIoItIa74yGAITpomljeoI0ckB4kA5GOPj6hHaVOhqo4A+ZR+6erQSuMBf06QkeWjlsyEBSJBdemk2Ny3TcjjbQn2ALJVnvKmZ/1fmYwMEY4mQ3aBx7JdU9GBKGN9FEb03lgPLA5gYvj+ReaCpGBS0OPT5oa9t2CXc3d2nBt6PFA7ajWp5yIflS/dbxQ1sZ1BIKh3AUAjz9o5wOVIJilD05QBUUxs4KlPhFtmOk6XCDIuOv7etT+rBgZwhxCRqA1xlxt1AQP5T0v3tG5huNXQepcH8CCstVKxeu/u08G32BU+d1y1UZVfrOa1V4b3CJry1dA9VG9aBVUAXI14Vu9z9oKXIuREfWtQgo69wdQGu5MpYvQciAIabIe67nMAXU9f29ge+tkDJjJ/fMLrw89Zkb4yZiCLzX3nE5MiBRiHQ4QOUqfnYGHEHrWwRgE0wfWH6hKdYDnr7WdrNDAWsf0QO2BSgNWc1dibeoW9JU41mvHahlfdTDmWDvl7YPgXJruumeYGmrtN+iWPOqePSfhCa6gj7qn6YGootax41kNbauDKqod8YGooM8fiMf3LwA0cjXVg6YJDPj4hO5Yen2NjyrBHx8YrPtio/eTjAt9PEt9v7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs/Mflj/dBnC8/ify+CC19nM1fwGfYIqlAUnki3m/sEZBzmikv4ADvrdIc+l6Y0BLlAM8CBPGDFITLAt0jPpP0lcDE8BKz0tt5OIFZi2DvH8h9kHRntL4RtTOb4qVkpmAIWAT8iKjFKvWD2K8IKSAn7UCHzIJ0VK8sjTOfld7L77A/lg7v1l7m2sLt6jZUaCJgbQnErM2N3XUa6HPUiVfFyJ9NXjH2oA1gk6K0ACNgcddBBMt1W8GchqSk0BIvRE7GeMLjeB4PCFu9FBEgR7oVFKqLRx5l+J1TDKrWVotaKpV6BPU6Ja1v5tRu6TnsxrPcxK94fdNbwo2ibcjH39FmESUwpk8eTlo3LVpyyBJNBxhIr0BRojWzrNaAcn7TZO+dlgD94QU1Izhd0G+O+AsQCWqBaPbt0J7IK6odvJ6o+cnTRSloR8w/cRHYl0IKdDv8SbjJtsDi4vWlJkd4sas0/KNR+2spiRDBfOb7D8KusvELJHphETtwOagH58IsWAjxOOozwKjWq15Fxg1oasxi8nW1/obwUqT3ToWA44T4Y3NwPXEF1ATgS4FzB0hciJSbXmJF9oOtB2YJtf7VSCYhnBXa9k67jdBw+8LOT5QDkpsXo/IctSWrh6kv0q1vrasq6lahwckF8TNsU5wKBkTUUNzoN8NZVGCqAY/kDHq/5tR/xuv6eLw2m4eN9o/YA58KHZSCvTvFjoJY8K46zoTUG0FS8Nprdrewwv+StwYBy6Bng9sTKx1Ii9ush5/Xmg/UC91slvjeyhZ/IXag1BBMuhL+I0IcT6Q8RPpfyXbUX/nKDDt989S17PsBlaa+1QpTfpKUs1hlbpniIwFeetAi2oizeD6RXwfhDk/S4k9rtogzrv2te1E2oGMq15PUcw7Llr3onyhChk3hCLS1mf1qz6n80Ik0dZxIP2u++S46hDNeBH9ExlfYJ9Y/fL457axKCIXen397/nFtLOzs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs/O/MX86ADz0wZGGxyTy5mgnOFhvtT2bE/HEHn+g2fDw0iQjNX/beumCrTY4MxyVjsqa9HUB/4mePwpu9B/k+MXy6SIupbhVBzUyT2YMdA5MhXldSEILLfDhXnB5vpbC9V2ANq6lPi01srAgFVkq6RnQDihpLzkCLNFc7WTtwIAMIjrCBFWinMrImNBl7fqeZL5Aj2rQHQ/ISaQumKYFyfoTMaFZw8VLQxxZMEdPhv9C9QAOIOAbRM2CwSITN6Nrwa9sH2hr6LzBFHFHDbood84FYTvajfH6SVLVWPdBYAhJZCO12oUmSVCt70xIM+Z8YxjQVrtRgD9gvDE7lv71rp7ycBwFXZCSqPcQQISGMjJKVx21/6r6IM8nkwFRADATTArooUIk2I/Pgoie2AJvnmvbV8G0Hnv0XhuoBIQwh0M7ab205jG+SOuYy9ppHURO5hWENYhfwAl5IVptSfVAORhywAym5Gql1w6yakclqeL52pnNAt+hBxlejWWUEEX1xFBcwH25vmft9toCiknD4yLvpLdq2mYG58NQYIaCGc3LZB7Z/tnGlTooYRkYwS2KJhxH6YA9DM8CjZmOHkfpy3EOM0YkycTM6P1BRjDoBNWgr+v1qHZnVJM8EchWqmNxcr0xOUdBwYzSQces9i/1UKv277jfuDvWTiQNiXq/NCEl0GO1XFXI/lGa5LuVcvm+IRVXIeggAz0eCI63Jz4ueBxkOj5LM69d0LwhhRlg54nMC39bPeZ5ESFk7+s6VISC1VgdAKlKfx1qwKxMAR6IQqaTZjBHfaa1r91mR7TU0ukD7KCroCm4GcE6xOFZOmfupTVfJwXGVd+bA8YLHj8gHbUHOX+R+qy9Y0qnbgZCw33UdjuUolnXPdAeEKAySuedCi6gYC5I+yTiBe3AR5Lu5Lzx7LR2wj0R/aD1qza7R71WenxAOPHx1//tv6N2dnZ2dnZ2dnZ2dnZ2dnZ2dnZ2dnZ2/lfzpwPA2g8AWutIGHQDD6Y7kYlpryYYziBgTlQbqp0YAvNG2wEkmcYcFy28djhvR5qSGJEFHEgnbDDDaHaAXySCxSyVsYJF7YvOGaXvXSpczQQm0IlUhERuoB/Ml2A60QHJBXYWJFutRpohxu/tXLTXnqcGIhPFydZxDzKU9Cxtq1+gB9kXlM3vDUyFo2EzS1vtb0T+th7fXH8+UV9gi4aLIKaYTMQamYb0Dl2I64261uOejmggIXAccP2CvrZY70FOR56l242s1nDOiRxGxECnomlMH/R+os+/Mu+rYLZOjIK1KVZ7xf2s1ymoBmgTfJYyNv1GDKQ3RK7aIh1ftRetQsaNzwvkqNfV5Jvt4ymlilVFjhMjia7cVyPlJEVI7toXFkFSsHYS/lo7qQ2/BvmY9YjdkRZI6Go2RzU0Y5A6QQ7cBWmlpo174MPRCcmb0AOJBWbXyLTKEw0hjwcihkrwOBrX+004S6/suBfkmxGoPWn6JltnzFHtyfYDaY5RTViykzkQ7RxHw9R5h5Jj1meKhskEBimlQfZ0xAejdZBOyEQNIo3hDuNmRG1L8/FXdHyV5TkAEyKcMX3pkSceypHgkUTcaHsCjvQHGbOguQipBxEwMYjJ8HoDTZXWH+R4U57jLLifhpiRktVAjdrzJahGMKVW/95Glvhn0zVSStsutZddn0ch5xuOA/F3bXsL9T0wcrwQ64Rf9WRF631ByVj71f2E6WgGbQbZZ91X2sHzR+fZkusOZn/i10VGEsMJKbWytCc5biSdlKzPXvsBcdf9wxfQ9qvuJaL1eLPa8NK0vlbqOZNj/X+CmIMcpdAXq+Zx/0Hev6q9Ox3iTUpbYD0KOB/Hqos7ageed91fkoLy/QSktNqZ5AxSjbCjoL9fvz+/5Efdy9AC8kJtCltDxSGc6Rfan6Qq5IOcToxBjIDPH6g7IdVcnuML5IEdTzJuYk6w9fh3dnZ2dnZ2dnZ2dnZ2dnZ2dnZ2dnZ2/sPypwPALSY5k4FAOkapeEU6cf+dOJSDQboRDBRBmmD5VdrRvrTII9BezVeuNzNOVBJJY/YTeV+IvJFUpP1BT0cdSON+vzhNsUdCTNSU8KR3IxXwTqYgoihWGmM18l4wU5T+OKudl75AVC41dEJ7IjEQD5KvahG2oxp8rTY+EUH8ptEIGXA+SXEk29rs7IDCqUhmabAzStOqDecPVBPNZIrV/nEk1rW+/9HRCCIcodFEQY7SzeZEW+dOJ0iQC9NPRJ1c4JOjQbwRCeR4otpo58n5MMbrF+9saDPOPsEe3PKE9wC5gEe1Zq2VXtcDlWW61QbxAgKh09qDeQ9iOPI8wEephPtRlW5RxATRej9qJNhQs2oKji9COv1oqBnEjUc1D6e/yDlBEqPjFIyVGaQ5WGDSEevMOQs0myB2gt8ga+vZqzXpIYgcOE6kYT7IJnAJ2aKAdMZvSBpa7VnhQNTrsVGa86N1TGFewfsKfC59L1KNXqq5LVqvyczaLFZp5KmcGvT+YPgF00td7AJidahAdGmi6z8l33hOWu/150ZpdCMCvQv8ZsD1840eT8KDGAOaYT5RTcKDjIG2RxVIU0rTHKULjgl3Owkz4C4IKXWYApF6XAmOl9oYGLPe2wgnUfQ46SSZzprcrSZvzPVnvIBuFsiNvFejmWrLa+1FR2btY5NkJiqGZb22iFZ71gtgklr/nURaR6LApMYkQtYOs2MahDupB1Ct2vp2WocupHj0pOGA20lG0vKu13vOMgWwoG1v5LjrsEUkFtVKj29pt3xbBJyk1OSl11bSTkjHPcnh1bBF69bhjdAJmrDsCTHLmCBmdRgjlUgHe2CtrTX6TtZINxlfZWSIG/E6EME9oB9gQkbdc1S1nleW1cC1IbNsBfX6130e62UFyAvVVnru45OY71Wzn5gm2EGY1a6x/8L6X3AfpDaYv9DHib8djieMX8vCsLOzs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs/OflT8dAAao4l8Hv9Djg/BRauT+KNiFYL1xJqVGdQqOHQ9klio5mxEykaMhP/9B+/iDMQfWFctgiAIXTZ/V8l164kyD8Ys8/4IzEDkKUAh4Jk6s5t+BZK2WalfUOnEXTGQCJujRiKlwv0u9aopor5/jg4xSyOp81Z7scdaT5xvyJLQOa6+3Zk+tgK8ZkoOcQSBEOpKP2qYNKVg1b7CkSWdIx2zSmhACogfJhakh2pki2GnIeFeLN5WmnXH/HY4Tl9pTRQXvguWFRoAbeULkwKcRU3E6yURCaPEADm4pSOSchAuHJcdnR37evLUx19YwPsjHZzUvRVH5wuxJmNPW3nN5ZRfsihvauZqKSwesR23iLiV2IrhPgldBKwF86aLDyTELppnW7nPr5P1iJqUaNi1gF4EcJyoH2Yyck9QHIYZ4I+OFcyPtQBPu+IIrF8A0MgPOB/n6R4HezFI1S67/LnVEIAfjNZiSzHjXvHH/KBhGATg5P7B4gzQ0JzOTtFG68aifOX3iw4m48Jlk+wMJYd6jNo3zKOgvBW9JCO201nH/SZcnjtJsoFGPK+2gJaQ07nbQm3GY4DG5vgG+BGivtvV5MN8vUj7Iw5g13Ys+H7SYhD3gvqoBrI2Uhs5XHdYQQ6Q+2tofkG98JCqK4ogdZExCssBtTmgHJvVaeyY5s5r5YvU51mfpv9NJEuSEuMCymqmRtZv97YeW+jSC13awlFJZmyIBXZXIhrReym1Jps/ScPv6893IKYS8QTvXTGDUDvId2NGR9yjsrY5EqepTo2CqPojrC3ihxwPGwNNRO6vgHArpjDurya1SqmaAaAx3zLQU6lQLOKMempoBJxE3AGpJjKv09giZk8BQq1Z2+AQ5kajdcpxqVUttnEc4kUu9/zgI0wKyMeremI2UWHr2ev0zZ2ntNVFtWFNGntA6Ot91b7OO+AXniWXCvHBRiNpYhw9Ca8NbBXoX3JOZ9u/7PbWzs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7PzL+bPB4C1M4na0ZQDzWSsvdij92oHq5DvN+fjBLU1O2rAIF//BR//g5mz9jZngD6wTBqBOWRTpAVwFIyYjjQpdXIIR/9Rat4Mst1IGtqluJYaOQw6MBPtDSExHHueMH+tJlsSoaAB5wkxyXnD2WB8FWjWgn9pwJyIONKO9X219kQzfgNjiyDtQEwIjEyK4iSI3IhmbQVLNaZFpCBKDlQSZSlzCSITH05rDcuL9GDcQBpIFOyLrCah1Wauk+CDJkcpkduTlIH7u3aQRVFuMu6Cz0fHTkoVe03Unpi/kDiZrSPvN293ZiqpnXAvDfC4cCg4GxS4mncpuO2kAS299pjtJJlkjKUpXo1THwheO7okIV4NTxNyOkgi9gnzi5CC6IJXaxWF41Gq5TkgDL/f0A4SYcYL4SzwrFYKW53kEJQJoQRWr2UMtJXiFqu2Lt9AUqOakumIOGrCgZAe+HwX/AzqTR6+Gp5eVl8UpBExcW6ShrAgvT1Lob005dVCbZAXqrYa0x2576UcfjPnRNpJF6ExqlkqQBv0/sDviURi7YHEJDyq9d0OGkK+L1qr5unRDCUZ7zfCSWsHWCnPMyeeiXht6zadWDOuWa1m4sL6A8mgt4P0wT3eq7lb4HBGbcuqHuT/w97f9UiSJFmW4CEiZhFVM/fIqpl52P//63aA2enKDHdTFWYmonkg8eh+3OqebGTD+QCFCkREmuuHiBgQl++5bvWZCoi0uhnSEWlIvLH2JP0L7nBS/FUHKEjSgxSphm7mf23WxwSkNrvnQI4TNNCobWHPQb4NlaSr4as+xyQxRim8qfeKgx0HOcdfG842gyOTlYveDJPO0EkzJaIayeBkgurdtD+4Dz1UjTi1lUI56kBDWkPj1l/nIOPegvY3Yr2uo1/K+xikGFir5rwHKx0RwVcdKiC8DtKMVxXPSfRWa4e2euYkZQAIRVrDxep146QmeV14K6W/yt1Y95+ktGpEOyRBtk6ugaEV9M8vevsGOVj+Bv1E8kIkavb8Dv3FOkoS2SvAVkFmNYhjOc6txt5sNpvNZrPZbDabzWaz2Ww2m83mX4zfLgAOE3QZidBbstakPw/i/YVkhQkMubWsFYaJ+1/7k3l+QCatKXwN1nBcFjovVAXyhaSgepKhrPGF2FFG2jlBD9yTloGYwpqQizyecAdv8lF7tcMHolqNxeOJquDtWXuZKJIDEUH1UVuYVMiUNbB6N+I6Kp9gC9UKDOUOTawd1W6zX1OjFSh7VkATa1RQ5xdi9S+le7WPxcksrW41/cBXEEsRcVJn6WszeJx/IExe76jdVz1ISdb1hWgFXYiD3M3l20ac+RPPs9p5Vjrp97vj4yLu9/i6LkKUFVGhtYBqIGl3c7mRDHK+iaxwUiXRUCLfFciZQDbicrSfhM/azE0q+PUJGSigveHhpAxII2VWoGWN0I7kqgMDKSATmpLTkVYbxqWqBZGztN0+yXvnVEVLV2yKyK9G48JCEQQ7nki8S73LQtoTi06uL6KVSlgk0Nbo/QlMfExUFMvGYtY9oAbtgfhFkyStM33CrM1jez54dGFcSeqB+sCsVWCf1f52OQifpceVVuFoBEigJNZOWIFqNcalP+r7zeDlimfUNepCsojlrGuxjglYKZhHoo/ArVWzfIILaDeaKOqKSJLPauXO6Sx3FDBt1T5PqZ1oUdrjRO/2fHrSz06+Jnqc1ML2E0kYJGpCPxtgjK/aFxYUX38SNDAH6v1lnjhJjom0XwrijrRe5V5tdyM4qrnOf6PaVkPCS4usJ+lC5iAYyOPJmqOuxbmglYK69U/wN9lPMt/1c+ONPv+gadJa0s5O82DNwRpR2uv7+7tHcqvlHJB5ISHYYXX9jD9rrzy5g/5Ajl6HCnqvv9dOWBPpB5qBRiAxq/3MrbfGmCzqXMEstfm6kKOjohXY9s/6DteCmGR7lGrbQNYi5xuRg0ytP3tdqNTzTtNQbfWa7UGOqHayPbAcSPtkrT9RDqSfFc5Hq633lsR74HPSngfiF9JPzBpzvBAZqDxJv1X/r7rOwcnHB/7n/xc+/6jvZbPZbDabzWaz2Ww2m81ms9lsNpt/MX67ADivxWKhBGJPzBx5rwqGuAgAqlELH7VrarU/mWKEnqgvRBXTE7UXI6TCvHbiPlDriDrBAdFwATdFV2AZhC+GJIcI8nY4PiArSL0FtuSMarplKYdr71QpRe9Ajm/35mivDcvIUiZLJ5tBvokB+hCSe883ndSGSAcmxAvJIBZE1mCsRJamVbVClbUgonTQKNlOWKO2hCVKkatagWwGag3pDzISOQ4kB+8UxA09OjZeXPFG9SQQ1niDCU2MzGStaldmOxjjT3ItjuMbkkbiLKmGsmTprIUKqBUQvSDyVlEf9ONkrYtYUSF8JoKR64LjSc5qFcYYt7Z6VhDsQUqgdpA+oBv4A2mCSP1fZkc8SU/EFtBIETIUlUBVkKw/N/Tvf+2RVl9Q7sap3MrgC0xvK3A1XxEhVh00kExaq13XGItshmKoQjufZJwVYJkSKMgDzQqX1U4irgrH3QGvNvStvm2tgzV0XdXsPk/MFPFAM1G9kGh4BE6idoA7KxfQSdVqjjLQrEMByxt5Ccdtx017VPHbJ9jB28vOS0CaEV/vuuZNeNyf3ZiOiNFF61BDOEd/YiwkwNesGLU3xCfaoEUSvdTZQbWujYPwF9E7YsbHpyI44xJyvuoAyPFJ+GCIED4hkpiO2qRht8K7EbYIOeq7JZF+kp6kdnJdRO91mKEddZ9l1oa03IcTIkkEmS/k/IZcFzx6HVBIqZ/rk1ShqdO494RFybhQh8MepCa+4r/ZGJ6EnLQ54VHNcWkHub4YKxncg9AS5LiI8zuMC+udFEPyJDWJBBMl2gH561eDw1E7wMwBR70/QqpxLvXcqfY4ZQXQg7z+C+jHfa29IH/5ARSWENrq72uS0atVn1kHblTqTmkK+gna7j32SWqiPWvPWY3wQa43t7QbkwM1Ia8XSOBjkL12tSWS8Ikd34hVW92p35g+Uf+Cx5OYb5YvNJJleW+sSyngBWJd5ADngNXJ8eOf9atqs9lsNpvNZrPZbDabzWaz2Ww2m/9ufrsAeK4J54n4m2zJfC1EG/p8QC7mnKx8o5Zok9Ig+4IRiA3k+I4wwBsiiWtUGw4n5mSa0EcQWsGPHJ/w/gn9l1q1ggQEUp+lijbj9kwTK2C+WMcT4kQ80aOT/q4GWgbSSh+cqaVCVq/dVuv1eqeTy6AZSxRZF0LirQFB5ih16RJUldCsJps9SysbWv9fGpKLaCcjHfFJu7XPnl6BtCgZR+2cakcyUD/B/0FkI9KZcyByYMOpqmtjZRJZTVAPxSWx7PgS1vpC9cmcHbMgRillVSDzTaCkJvhPVj5Y0iqYESG8FM2tOREdD6n2oDZyBcMvjt7uxmVnuZNrIkenq5I40xrhL8xOpBuCEh1SFPSk4WQksOoOkltXGwNrJ4/WkHyVWlyVod8hvgBFrZEI1rS0yzmraNmPX0uw5N1YRfPeGF64c7duB4mCHmguYnVcBeyJZTWfa+/4izt/JNYFrNI45yDoFXQiYNXozKzvReRgjlGzr2iFZOm4UNeg1nmD3h5IJgOD9QUkkVYNdntAJJ5xq4Ep3S+lDRYMSUVl4ZK1lZy14TwJ1KP2Vy25ZKFIaZPtwZhCzFG24hVIBErUfROL9FXfaS5GBtIgVw39JgPC0FisKXfDv+E4w50ZXo3vrID+mpPJHTQK971WBxdEHWudnD+qwe0LU63NbJU7bM/afHZlrTfW6j4PUyQn0kBu7THaa/92DTg+QRzThLMxX191XaOM9kBlkJm1w90f8KrWed6fhQggicdiJaULt0bGm5lC+KDHIKZAO1FrqFS739fXXb+f9z6xVNirHY/BvdxLxp+gT4hFZH3nqg6A5ERbq2tKqjGOX9DsfobNUkin1/Msq0WvdtYz53aTxxxk+6jNZzmJ9UWG4kzSBZUgWCRZWnh5kHKh0usAjQ2yPWBVMzmlmslIbV0nUtdlLlw/yDVhvmG98QX6ccAa1R7GSZlV6E7g7CSOtN/uV+hms9lsNpvNZrPZbDabzWaz2Wz+F+C3+6/X58cTcSHlhOHYcRAqWAhXBrjQjoMmic5gCeR7oM2wR0NjggUiCf2JLcfGBc8PRBsnAW6ggueqAEgXbQX2fDCvRfZG8wvFK2RZbzJOOKiNTAyfQXI3YpXS566JzqsasvEnOGT7QCuVqY3KdEI7HJTXOQWYFYCGw3K0d7QdtWebdyBtdyrNqm3PBagjw+v9usP5LKVtlppY7KhwhgR/VwCTgvtV2l/NckNTYXjLL8IOfrWRVZKjt2rkzsmUoGsg6fj44vj4wN4/ab6QD0X03g8Oh7hQkkdz3vlgjH9AtAoLaagYGs5hENbuTWFH0uHXxnBCZ7G6VUM5lXTnYQuVjlki7ZN4/yQJxJKP86C7814XL2Zt9S6ouqvg/uaKo6aT05n3fi7ySUvH2oFH0BxWJnp+IuMnS0Az6qO59byoEevehbUg4yKP73B9gQ1c7Q7r7utNGjn+vNviDnagBCln7UPLIuhIONY+EQXJoKlWc1bPUoLnYo2J0SocN8NwgobQEHUwxeaFZrW/k0BxznaSJKFBPz6Y7xeqxlyT9El4bamGnRW0jovWD0yfZA66njhRbWEUf71xE/rjG7lgptW1N51gYbGw9kGsnzArCNSo7d3QJxkLs47oJKcxou6JTMNU0XUxx2DeauskyVwopW8WKW164iAfSPp9+CLwGCAHpGMq1QBnQEKEEBE0sdqujiz9OrVHTVaDWH5tiCtEOnI8gcQ9Sy9vVJDaDzKDlROd79rbnReSilhQ5fUgfdD8BW5c15uIVlvV0giBfrd2OZ8VymYQr4E8GugBMf7rlvQcFfBqIMevQzITPEmOO3B/I+0DsXswfFU4z8d3wKtBLxPOB5LUQRFppHM36ZX0W4OvDyJHdbePE89VW8HpmEWptAHS0CbI+ELtRNqT8BcetbuMOmmdHC+w0suLGNIeRFzknGBK/PwP+Pg3xAdoKaQZTl4X+fnvFbCbYSkwL2gN6R9wd8tVA+T5P+G31maz2Ww2m81ms9lsNpvNZrPZbDb/OX67AJhM0FKWhpyEVfswV4Uh8vxASdp61e6lJ3mcqASsVY0yeSJRHtsV9yZnVDCVj0+wwNJRtVLVHk8kF5JCM/Bm5AxwCMkKZW0wouNh9Gb0mKDVUINfeuBSOKNK2nfEpALZ8Gr0OqQvxLTaqjwquNAG2WH+HWkfFQYDKdViraZex3QSQVU81YBBmqIIR1NSD2AiZJUW1YiR0EGOj1tRnbXfqWf9GC3psYoi54FFB7gFvVkbrSnQT1qAqNHGGxOYMWjHiRJoe9B7A1PW9WaGo/0DA56HIP43xqyW9HE+630dDUlDxoXGhegTDq2WJdUBTDEaWS3E1ghfmJz1mWmAKMshWv+rWTlUcTmxx4O4vkrJLLXbHAFDFeEB4wfStAIxfYKs2lJmMNfCUU5z2vc/8B8/CJyIqO84E9Yi4o3qZzW8pdV+a/9AmlMWaUF8oGJlAlcrzfWdxxEXKg03wEuxC/Xdqa5bMuz1bcSooIy8G9IK/Syt9PoJeqLhBLCuIK1DDCLArNFZNGusWFgzUmvn1r32b4MgpVdjOoIQQ6WjWRvHok/CL7J1kgMfX0guVDuEERJoC6CjruAHmZMcbzLXHfJ/I2LUFq2WPjoUzKPuJ2+ITJZPPBULJ1UrFFRD9FfDViAC11XBbipqkMtRU5odtKbESiIb0g/GXDW97QMTufezs0zfR6na0x3RsgeoShkABBBDxe77HVROxEDXJCXRdCIWuZyQA5e8A+eS1osJ2p6lTbYHKUn0z2qr+iCp54ccirjUa/kVIrdORNY0sEjdw5JwHAidmF+YO2IHog3sE5DaINdviOm9/+vEfJGtnnfkqq3nOEprnkFoBeaowEo4BOw7rC9SufeMJ5on2r4Tseo6nxOxhiioCXAiz16BelbArUeF+oyf9fftSdqvPfTjr2YxdhDrRT6/c0iWhlytPqMG0v5G9ifMF9K/VWjdeh2GeH4H3jAHEVE/c7PZbDabzWaz2Ww2m81ms9lsNpt/MX67AHi8B4cZ2iFFEDo6JiujAok2yblYGLkgfdLPB5qLaR/kGDQ1EiGngDxIcfQ8iRCMCmk1JxlSwQ5A3i3Yzye6Bnx+I1atYkZW07CTNEuEiRxUIy9Lq5pYtQbzbu1mqwBYB1wC3UiDzF/qVa3gAifnqrBOvpN5kdlL6zpnaWnXC5rerUUj0yusRBFZd+PRKtQRBfmoZum60Ga3LlZAokKa/BWKlD6YqHaxt15t3rzDOFopd9sTOND4k5TGOqCl0scE7eTzAVQI53GWkbZ1MhfvIaQrYid9/SS00U0r4A9F4ycak4ja8xSiQveoPeCQKA1vXAidpnYHcoKHIPFGj0Q50KYIjXw8UKsg/D1ri7ZJXUMZIKwK10hONVpTXnPiWGXKCMEs9bgqdpyoXazXq7TgdpB5VTu2fStdNK1ap+nkcWB6IPMnYgPEiLspmfQqfkcFn6xVu8UYmaOC/SztbbijNKY7LhXmmlqFdtLr5yW1MbtqezXgDoQvMoRcsxS6bozWWJHV4H5f6KggVyLrdXug7YlIImtiRx1msFVNz7SgHQ2RRj9gvjsuJ+J1cMK8GqUuUtu/GXjvyHgh+axwPL0OO9hZjVttELX6LTRMATrGrB1ZqT1lWpAYhzasN9Z8s1bg/gV6VON/vhAzpLdSn2egMuja6/mhjvCEVDRvBXSz0grrUepjquWN6L1jfCubrQJgCa/v+vHEYxHSIX8iTWnWERHcgfVG9IHEi2mKaiN9Yudn3evu9b5utXbkQqTf93Bdo9pKja0fn8T4gfbE+GB6Qt6HQTjpx73xzX3ftIBsqNbIs95a98DQ89fhgwQxWgS0B6IBAWYKHqQkeR6VNavA0esZswb0E+aFmtbP0V7/Tji1ITzRXp8hnrj/RDIxaSAPIgZ47aKjD5gD6YuybTshSWKofYPxA/c3nEaXgMcHBMzxg8ysMzgS5PkH8vP/h8XE/aod4fF1H3rZbDabzWaz2Ww2m81ms9lsNpvN5l+L3y4AFmtoN2iKqWK00u9i6MeBr8VhjVh3E+/xrABtNVrVXivQFENy0h+K3DpT0Qo+hSRm1sYoHXoFZmiWLvia0DriE/WG9laaUjoiWg3edZENkI7kBbGQ8wHjqu1KWSRKqiJdkPVG+rM2hp3aRb1DVjxQdcIAOoKQWRu4AUh7Ao7JgZowUyuQQ2rzNRXNpOrG1RlF1t1ybGRE7eTCHbKMet0ESrVqoUNcxHJMGxGzVMFXII+zdK0Z1XD0hudE+oOZgo430ltlOu4QpYlWFUIUzQqptT9QaRhvpJ+4R70ge6JM0gc5L7CjtK+m6JqISbUsLakPHWDVd6VUi9icKoEnEQuPQUjpfFX7HagOyI7htTXaPzFGaaKx0mj3syZNu5JrMq8XriexBNoHmoHEhVqDxzeanWANv0a9fun13WSrPdJVO6TWTyKFjEXkC5X6a6RhsiAV13a3gpOIQFm17RsKBoahmrgLkbUNHNTXrlLabkm7m5SztnVj0YBUJVLr0ENmKZQzkaYoTo5FSi+1dfDXpqyEsvoT06BJQhoRExXjPE+8BMbE9ZOIEk2L3oczYsHKeg9qaCZG4AEzF5r12iKDkLKcmzipFbCbPhBJWgwmB7km7ey1ER3J1A+MoJkRc+DakUxwISyQEPy+3iOtrNsSqCbhd2AqerdLJ7/2r/PeakYamS/QA7NGk4Xno4LONcGS1qzOpbQHQuBz1rVlnVyDjMDuJj+WREzW9cLCQRLLZKXiq8JuYpGrDmCoQ4jh46qDH79K0+G1G8y8N3UVs17tWZ9IrNonpr4DD0OaVXk5Gz4GdL1V9Uk2RXwg7RsRb5C7FQ9IrHuXt5daPAaiHwhXHWqQDr6QdoJy/72s9yBxt7aftVPtA+IF9qj2vjsiC3804voH2r7VPvV8I+0bwhvHyX6i0gmZGFY/QzrSDiQvNGqDW88/6lq2s65zcm8AbzabzWaz2Ww2m81ms9lsNpvN5l+S3+6/XieKL0Elq7nGm9aEXM4a1bCVptVOBCSSzIloteyC4K/RXRmw9G4SOyqL/LVv2T5BXzBGNWIfB4KAJGq9FMOWZDdyvMGe1ZJrDRwkT4ggTWAp0jvIQTIrlNCOaMMz0Naw47M2gH2RdrcKg9IrNyMiSissb5BPVOXe1MxqrUaSWjpcuzddwyfSDzSDkH6HLgNrE+S8w8P691RKOy2h5HojXe+WbwCOxw+Ug8Rqk3WOCsI+TtJn/fWtjbaU0hV7o4ljnsihtaWajqfDGGRvlfPowvPeNl0XoHwcDc3F8lL5xlxEvMis9xxxQiQztN77OpDlFR5lUn7uRG9tbPiqIJTB0ru5LCcZvbTN/eCwTqwKcEMU0caVB3JNxBJTQVVJAV2KiBJ+IdcXoqs2iMcXYVbZYTtwAV+OWiOjIVk7p0ltpnq+0TVQqb3liFEHBLTRVAj3Cug1Eb+3S81whZwJ1Hdcu7fQWyekkX7dJmCDHGg769DBusA+yDQik5hv2uP7HfZFqbQla2s4L5iCWuK/DhD4LF26QLWaJyYHS5WxFmaKtgr9SWFJJ1nIKgWyc0IGihAeSAaRQsSFZyK6yKDuz6wgMzORCOQQrDcYjnNgArkWS7SasNbuewpGNsKTj28Hpyo//QvlJOcLvEEoM9/3/e4VOK6BPk9a1nNmRr1msUcpqqFU7eHVoDeDfFQo7M7SVtddc1Q6n5+fyBp8xcGK+OvzEz3vIH/cQXGvvd7z497Ldaw12jUZuVhh97axVJv51k4HUgdNfIB2tB+1ne1/4tlhJaoTidImw0FYJ2Mi2qsFfuvplYnYBznfVR7POmTCfIEIcb3gMGRdZP8g17ht0wIq1XifdUAiYkJ2JAbICzByLGjV8BZ94jnBL0w6oGQoKYllkNZgXMBJ+BvoYN8gFvR/q2tek7xe5Bzw7d8gJh4dHwI4ef0J+SCOTuiJrC8QY/hPVJ9kfpHtA1nvf/rvrM1ms9lsNpvNZrPZbDabzWaz2Wz+s/x2AfBhs5pjIRVqSIMIWIKeisw77JpeTbzeKN+oc/t97w3LRqihKEopbKV/gl+11WpB0isEjQEWqPUKYqQ2aEWV8C+iPTFLcip51J4sclTwRm0NC07mIs1q/xStQmHMCnMCJKqFnBiRdxA2r3oPZqUDzg8Eg7j1qlGBaSqk3a1nPfH1RlpH8t4K9VmKXXuWRtq0PsdMiEnqSY4v5Py4wyIv/WomqR1hVh6cC/dg2UG+/16BdzrhF5onokGzBtbA30Aix4PWDp69M63DNfFR34E0BSb4F2YVQppB6w3pgkeyfryq/infQFa95ofB++I4O+nr1vLOUiW/B5wHIlbfXTtoYhVOpiJ60NMIUUJHKb7lRNTRtvBZjdoKSw0UTlOOo5S371BWvCvw1rtUHkETYbVOx1ASWxcAbo/6mI5vzK//wvyl6Y6TsO/Aqz6ntFL0qlb+uSZCQ1VuTfFVDfFYd9M5IWp71/zHraQ+kAzUR23ZZuApdw+34XaryGOhovjxB6mG1BWJyv3aDkGnISqlNZfvrFhIKphDnqWjFiB/taQVkcHj8YD1JOZijRdOkr5o7UR83e9JuUdrId4VCAtoSN0vqhCNVKnX2p+c/UGzYEjSHwcazpgOHNVrbw1P5z2CzOCwi4YgYYic4OsOwrVCUzqaUfuwAdHOOjghVqb0+8BC+ECt/zdq9EW2z/ueKjtASKnAmRe+DtqpqDU0B2YHa100FHl80EVYaVyijD9HPZeOAwi0nbR+Yq2RU4npHGfnVMWn8J5fpAniXjppldvs7MAg5VEHLUTgOOsZkXHv7lZ7muxwa7ORDusLz8bJxCUQ7eT7QprA+Y30SbQHIr2+e+ukZOme9agrR4zUCzBiBqJRn6F2zA7Sq6nPvOpMSTbQB05Arro+taF2kA6Rs9q5Y6CHIe0kf15gPxD7ZSWozWUJJeZP9Pwk1leF9NrqsI4aaoqvHxVou5OPZzWBxWH9+Kf8ntpsNpvNZrPZbDabzWaz2Ww2m83mf4TfLgBeAsaBzDeRF2KlE8Yc4wNPiPVCrNe8o+ldvPsBj7/h1omvL3gm4YqOnxyPE8lFZG1j5prVjJOsht4KVCC1mrXZH4CWolkaFoqkIP3Wql4/0POzQjxKCc1dqHWTe1s4yXgDrXY+08Cq05y5arvVk+gPYFXbuXPvv1Z4K5kQlYFXOzmIzGqXjjccWu0/OUsbfedXKUeFvvemJjRyjNpJJRFtFbrdmmWhcRyfEI6vBWpYLkL93uoU2vEEu7XFCqIn6W/UAzOlp3OYoq2zxoBenwHdsHA8VymQpeFhLPf6vNwJFqlS27gTaImGQ+8IWaFSO36VfpH2gHUhR4eU25ociDRWg5gXroJFIL/apv5FprGkNL/cTVyzg29PKshncr3fjCG1L9oESfAlmBkzgkxYUfuxCJytYZK0CCa3cphSO5M/UPkGeuLNqiG57G6attIOSxLzxTCvhjgBxwNd474WvyM2AVATlkU1I2PdW9arAt2UUntbaZN/qYw7gkj9c6WBT+zxIERIHBHFaUyCXBM0MGmIJtgbsxOfpYtOFkEDX8w5GX63d3OSEYAh4khzJBWRRCVJPcr07Ve9P6k93ZRBcpRx2jpCfa7dApFAJAk9CKmt75btfh16b9wOltf/pplBXuhxgCczDO2NvH5AKPFLBxwLt5OIdzWDqZ+X87p1wUqmIBFEegWtBGoHrTcI5zgPng/l8Qhids4cnNo5e2cifP8QNJL/8/+COQ5y3CFwLkRP3CchQuSrDnYkjDxY8wdzOWl3YO9CaDWsEUN8ETKgfRLzZwWqNEKp3Wz/qmeGGuFvRBuZP8m50MeDuZL0JGMQa6IJeXySPqu5awcpRsbFXQGvGzLt1jon0lfpoTNqE7h9J6hDJOqv2uB2r4Mr7YCxqIeY3AdSqOehVzPXmpG+6pptx/1bwDExIp04nhBvaB9IE2QF6W+4HPnjbwgvsA/gAX43umPVn1dXxv97v5w2m81ms9lsNpvNZrPZbDabzWaz+X+J3+6/XrdUJCnVrzupXoFjHOQSbC306KRLqVsxWBPpn4jU1mU+OmgyfWKqFZhGoh4khrSFRCdkVQCqgfWjcsKsJmXEuJvFHYlZ+5ytkz6Q86M2dROuNTj1QKjNX1FIPeH9JknQ2tcVCdS0tn0zCTHSFvIrFBn1WnK8oDdEDZpAe4LWdighpVAWQdqBSTV30yokaq1C6yZKhlMrwUY2A52onqQ7Zme9LkkaiR1G/+isny+Wnhi1EZvPP2q305Oc824DfkC8S7ttR6mLJZgBf38rkT+oNvZE1KoZzQMf/wALpN063EyQIKV2gkkhdZISqJ0AFRay4PhAYuFRIZIeB5qjdpm1A9ettAWf93fk1TRNOiBo3m1Bn4Q96pq524hrPsgcHHZ/f3yV6rrqr6z5rh3iNDKCjEFqAwxPUBWmT3w5cEK+gXtnOUv1TDb88lJpi+L+rg3nyGpphmIkkVknCbKC0jRnDkcyiSyF+JoTyYB2QtzN75r2RUVRavvZ54vgQGPV4YZ8odrp3VhjgZ20HGQq6omLoyiijpkRC9TfhB5/BWoEvK9krcRjIun0fjIkCSb9bJg2cOE1F0li/UBGtaDxoELWFy4PWIHZk/PsdOJ+20KdvTg4/MLXm6HKTMf0AeNdWm/r94Y1rOmEPMjlaAQmF4mxpEEGErPazUDGmxiDDC91u/Y7lFyoHXBvRms7sZhIO+jHg44TrYHCGhc/M4lI/D0RPXi9Ao+L689A9YP3j3/AcvC6mkHx68WbJEyY10LaASG4KCHcxoMFctSu9Vz12kwRO+lqrDT8vrdYdR2pHSSP2rmO+xJyL/NAO+/37eTwynN7q2r7uuqMyPEEvypoXnVIBakDAxlOSpTGOUDTwb6T8886nBOgkogoKUbIrGfavFvB2kiAaES+kfaBnH+7d8uTXA5+kXcz3g5FMeY7ES3Tgj0/YL4Qvw+2PD4h3gRee+E+7iC4QSSeVu1ve/4Tf1ttNpvNZrPZbDabzWaz2Ww2m81m89/HbxcApysuoHEh3UANSSUJVN7kh5HaKqDwF6Varp3f9BekVpMsFmbG8snhWnu98a7WqBgwK3TEQAWPUYEmXg3cteBoSDNEFkKWklUElYbkm8yOuIMBnLXh6UB6BYRChZ2iyCl/aZqTUriGKuLU+1ODMcEDbYq2CmjCS+GsNDTB0fv1WIUe/VEt36DC7ZwkjRiBHoaGE9T+Zm3NgqhUAJ3KCgUSvxbrmqQF6UJmbeFWwP6F2AMYFYJfjv7a4k2pYNY6Sb+3mwOscZjxMGVGMrRVYBtWW83pZLba/cXQHDQx1mmoJnpvsmZq6a7tpJvVHnEEtfHspe1eeWuwHQiU4GhPRgyUQRfo9qQdwpm9GpP5DX994WoskpQTXYGo0nsnYhGSqB1VzJYkY9Zn0j4I/yKlEeuLmQlmREBoI7yhUpvCwpvGE+vBGsIkCKyamtZgOalPhFXhlxiSTshJdHi0Rj8ezGvhacT6uhuqQMxq5IpXkKyPCuf6E3CwE5nVHha82uINpr/rOhVlpZO8yTCE/KuZ7qta2Su1dlvbWe329WbpgaNEvCETdTjaJ3O+CYWmgjVBJkCQvvBcEAOzEzMlvQ5EVOgqGJMV8F4LUjiOxGSxVkDWfRxqNLnV7TlYY+DTSTHcr1JdrzrYgIPYuNvRHTjInPdzoCzFWCNXom1h/RMVx1IZEZCO5q1xt1IKSyqZib9/YB/fYSyuuYgVqF5MX8QazP4A/yJWlI5aVxnXe+2CJ520xvH4TuuCv97ErfKGuj+b1m6u2SRcSQs0FtLvsHXV8Y4Uq+teVx24kGrei3Yyq7kskuDVKCdBRfCMO5iu55cQhCQiHYlXPYzDif5Rz5Th0I6y0jNQDegnHotOEumYSTX2gwrVRWvLF+o5Ha2C5LUgHU9BtaHd7u1vYC6WdjTfJLWNTQgS1VZHrA61PHvtF5/fsVhM535O/YEy78MJSiz/p/2u2mw2m81ms9lsNpvNZrPZbDabzea/l98uAA5NhMWMSfcTmiJaCtQ5HQsDFrEWlWcJHMIaqwKIdEJKXSzN0FA8LpQTjl5B13Eg/i4bqQftUduvIKAnwioNrigeXqHs8agXeIfHgaPy5GgfaFB63xylt7Un0hXyTZ5KroFkYHoS1pHMaoL6u9S9nrXnmQLnJ2lOSjWhxaIUra0Rc8H9HqUf1UiVqFCrAblQ7aQ0ODrujluFXuJKxhvtT1aMUmjHYuUbHxX8RFabml+qZg+Yb6BXAzbubePw0mnHQrXRWke11NJmQprh7y9SKuhrrfN4fmONP9ED+vmBUVvOzaDFF2evvc+fV7VbVYIK1RdAhb4GrCR1Er1CU1ch9ETopez2QHsnPz4xP8mf/4FmklyENCzeiHxShWpFVIABtAr+IjgIBk6kogTRHrj/gEysf5KaxJWIf5VO2xJH8RwIiVqrIO1uEqvWfnDawI8H5hM3rT/fB2ig8oC8aAoWzpBG5sCaYAShSvhPkAOVC+tPksCzVMnIuremE/VVgV/ll9XSTCHVCAQuqUME1lCehE8wIyeIKalC+K1atoOjBWbOQpnh9e9n3AcdTqQZx1PqHvWA+aKrECSIEeNdm7LtSdqzrjtrpTa2g/DJNYVUYc64d7jj/jOqDR0Y7kHKHcLHVRZuWcgt8I55IaoIimpDcv2lxs4o2bPQCJ11HUMZA3wSYVV4jfpn1Q5/gSiWC0IIaYS8QQ2PJNyIcNIHMW89e+YdcJYqOTNYqWVA9lla8/NANcnDmHMRJGpBa4AE7oNwBVr9vKMR14vVQTjJSGJU4JtiYNWVrz/ztgeIlB5dtO5haaWS//ZJuiPrqsYujthZ+vhxlW5cAVEiAmEieqKHV4vc5723/AN4loEgBZH63rD7YAr3ri9OeFRTPS54fIP5Z4XTgFlHvA4bRJ6E/EDyIGLB42+w3sh4Q3+QOXEBrMNwcgZpyVhvkJMYL3gMYl1Y/yDm32sTebPZbDabzWaz2Ww2m81ms9lsNpt/MX67ANgjSQ4q7XM8g/CGxUKbVYvNHSfQ46xN0AgkWtk/aUgmsi6CR23sXgNtR22vWhK+yGhIvNFerTKr2AnHYPxA+t8qoEJI6aTUDq9ItU9VztvqaiSLlV4hi3/BGsjjo9SpeTeUqcacppcFNxZizwqI1htdiUi1kcGqiZlCSIeVSED4Qo+DzAq5ck1S5K/2HmnVfCyjMuEXQrt3V7PeRwpo3DuqQVVC36QpTKn3ZFYKZAH3e5sYyFk/B/uotrLVXqqPBQ0OM7Q9GdebCIHlTAusJynOnI6m0/QiurPGxRxO2gly4Gkw/0/COmTgkqz3hdwaX1onn9QQsC9EBV21cWxJNRWlkyFc71maYE7yODG/mF8vQgRGYLnuTVznwJm5WKuuv2lGctBkkCksr+sAO1DrEFkNbRS6YMcDjYVFNbRRJSUIVzpCqpKieH9UgzkFSUFNEH0QAeREm5ARtQgdCwvBLydU8KxN6wr5lX4ezPWrqVnBI7FKL75GbUqv2rhdkZg0tH3e28f1/YuPao0j5LrqQEQsGv3ee64d3DmNuRYqgsqDNSvQNW2Inagqsry2bacxVQlJ0gepvdTU9o3UxH3UoQrRe0f2J4ngK++5bC8teCRqn0iuCrDFkDFBFW1KqiJyYjjH+WReX4QpTRogLDHuKW1Yk1Sq9YqjWRvQeIBlBakxyKmENCDJ+zWmO/NubOt41fPJGumL3gzCcBdUz2reSt2XJIQnnlFqY7hrxwuLu40dDb/eBB1dC7ODNRfirV64Oma9QmA6InAeRrwuRmuIZ0XfRy/zQUZtomcgXs1rsUDsgVjy7E/WWswcyNGRFCKDQGs3t38HvJ5zGHk84P13pGntJK86ICH9UQ12EQgI8VsznWhMRDsxB7H+Xi30hBQFJjK/EAzRB8Igc9WzIgOVQcoH6Qtt3yEbklc9Y2PAWMjxcW+rB5zfcH+DJ/poiPw7whdpVPuYjvYdAG82m81ms9lsNpvNZrPZbDabzeZfj98uANYM3BI1Koh0oTWp/U5pRJYWuDMqCBSrJuqRiAfu0HqH53d0TrrBahUOpyxEzgpNWifTql0YA5Yjduuh84PwN5C4GwxH+4nHrFarWGU5nvdu8IRQOCrQzbiqgZkAWorffNV+LEqui2oZKohg54NcSWVXvwKYFykPVCF7BaLqrZJd07vxV4ETKMpBrElaK31sJqEHQqBpaOuYz2oLhtTOKLfOeP0o5bZ10Go+4wNVxc8KdgRBj7M0wSalidaz9L7mkMlQgXlVczjifl2GmMEE6U9gVKDpjZj/IDNRPZk58PjV5k0kjwowVdB+kHgFbO1AfKFaRUVtJ+IXooalgFIBVyqHwFLj1IYcjWu8S6vLRUsn26P2Y0NZOB7VBFYzlipzQs5V1xwCImReCFQQrkB1fiv0knlv5d56W2lELvq6kPPE0sgWtb2qJ8LkfFodSJhG+CSlMTMJ6zSdhC8ieqmWrfTKyYNAai+XUkYnFZKHZ11zlAY48n5NchAxSM8KCPuT8Kta6zlr01oaCmQ6gmEquDvLDG0PfH0RoaU314lIJ8OJFbzDcU9MFaxh3Yj3YvkqVXFG6dK9lOhIIumotHpvCmCEJJGLlINmwhGGUqHw0k77+MDTea8BElg/USbCIuQOxTNIPW4FcifKIk1ei+wnEheaQWa7NeedvDXamYFQB0ZIyEzWHDQJ0I5EEOviPP7gbx+fLPni70OICMyyRABq+EpmTNLhaAeMf8D570hOThMkDMdLkZyJp7PSWZHVPi5RPI7X9WFJWmfFr9aw1DZwUJrvMOiPCqDHT6JVez7SEHFkLS5VfM26Ne8DIOG1L8x6I63VwROsNsm7gj0Jkoyr9M5mdd1HR2KQt5UA8dsqUI1hjtoXVmuoNhDDV4f1gvMbxhuRow5EyN3G1kQxclzo+UDihaWTj78RekHvpCSRlEr/r5bzE40KzImE/u8IA/n8A53vf/JvrM1ms9lsNpvNZrPZbDabzWaz2Wz+8/x2AXDTC7PSlDIctKMp5LzIuHcurSFhQDVLNQTxqr32dlEuVSPjwmOhC9AL+pMYE+xA1Ek5K/xardptayEPRSxre1S1tmhjoPIEvcNTgkprFx5R4fGRwHW3Z2tXF78VqnaAPUmfqGk1XO/N1wwqWDTw+Ual4bTaUDVDJcGcDPDeKjwjkAyy9dLsvuf9nvNW/q5qDppXUPSrkUglYUJtfqZIBTLygJikBCJKxCStw/qJ2BPx0gyLGrFK75oatQEcX3D8ASr3xVpB/fKFWaM9Gi7ByrvBvcDfgbaAqM3SuF4Ek4wDokLcmlJOtJ8wVym2STTuAN0OAiPjTQggfufxPwj5RGORmtUgRSAOmh64KrGSGYH4RLH6jrM+FZEkc6AudeCAKKXu+kL0SRfBdPBaSbhUWJkvrD3uHeZBRH13KU+WgMqL+bUIucilICc5k1xvpnU0kry3TVXWrTa+vwcXog1EhFPh+PzGGnDNUWpxhHRHWyf8fq3SwBfWTiwbS5RIhZgIgZnSLYjUaryrgDzqmkonElokrdXBAvyLx8c3VijDJ7W/XPvcmkIzreAP4D6YkQOEfrd3HclZgaVQ90/WprZoI9cLp0Lq2vFN0EbOi4EDnYyBqxFjwbruFn9pol0Tv7eiSy1cm7G0sw4iWIcY9dwAUs97P1rrHpS87+cKvvNOjE0W2c7bDQCcBy2SIwbf/7cP/vY8+XH95DxPfF74ctIXaMNjgT4IuTDybjxfZJ68V+c8G1xf4HW4YK4oDXMk2AOVd9kGQlARoDbFMwWofWl63fPQ6tlItbdX/7gbyb3U3/Gupv41SFGsfxLzT0K0noEjqpGbpZdPPSoo54XYiYjeOuuslnDOe8Ocum5NS48uC8SJHKW7b6Uf16iWfx14OZFuyDtQM+b1IuWoZ/L8ibYn+nhiuZAc5NFQVVgTOf6dWPWMJRwffwd7IKdh8ydghF8kBxkv2vHvpOs/89fVZrPZbDabzWaz2Ww2m81ms9lsNv9d/HYBsPRv4ELezUVbX9A+kccn6VFq1Ljqr8+DjFv1Otfte/1AqRAROzAEeiLcmlQ1ZK3aFrZ+b6pWI5hwJLOUqKKkdpgXeT7wNEQaYk55lu/2WXayHZjegSqJpFb7V09UqgFZoTFYfxASxAoyVmlmA3RBtgfLk2ZC2idktU9VFDSQBLQCH41FSOVbqQLToYNax3OR6WRe9Z7twAiYk+xWPy8SUanXYM96/+JIrGofaoN2oCghUW1pEdCDXKv2PdegdkCh/NuJmCAoIoLnYmWFpDIHAWQ2Ynyh8q02aceb9FtTK6WTjTGR/oEcC8IhJ5nVMk4atNIOh5zE/FnhndX7lNQK9UMJgpVCjtpjrbK04b5YGchKzJRrTdL9ft+KSIdsmE3EOr4GyAnhLE3mktqjZSJxlOJ5jvva+onak3aeqCUeBvatNmjnvZ0rkCyCDg7uEwisP0i/QKX2jQNEj/q+/GLwYA4h5yDdOawRkSyZqAktBafhLKRBt1n7rr6INUEX4o6df4BSwWIsIjvWG+pCrFe1rtuB4GAQLlzvq84RZGmlVfqtVBe6JcMNa41cC18LDHoG0s/a4JZ7Vzjr3hEUCa+DBWKYPSnTt5CpeOR9z5xAbfkaUs3tTDS4dfALsYPe6md61KER84uUhuiFGazRShkugWhHosLikAqzFSfIOgAgnaRavaVkd+So73DOF/35INbg/RZeBGNOlkPGrBDbo1rjEZgdlJagNsS1K8eH8OiGh9ZbI4jWq5FNkFwQUa1oM+hHHUaIQNTR/kSvWfeVKlhtFjMvXCeiD1QcaU8kLpYoqh3cq41vBu9qLNNOyDdYu4PVhgFhStabovWO5YGvrPawGJIXklYHRZA6UJOlSvcVJD/rfhDF53WfdLmfudfAxyAfcv+ZFdJm+yAkMG2oOK6NCCvTQrZ7l3iRa2JM+vGBx0IS3E6q1XzVwRR7sGLgfv2zf2VtNpvNZrPZbDabzWaz2Ww2m81m85/mtwuACaAledUOamu39lhBrNqCyAlHbQTn0ZB37dFKvz+uuCoQ9Dc5nTDl4FaHIveOrSEsUIjIO0QpvXPem6OgyHEAgbbaDSb83sGscCvmq3Ym4wVxgka1F8dFaq0Ko4LdwaWvWYGce+1n5t0U7g7Zatc3G6kXYtVQDtNbqUz9+QQRtd3JHNWslfp8IImUWwVc+79igl+zQqx8E3ebEBKsVVDMJ+JeW6wIsl4QRsQFTVCxCgS9QtmQqGYeTo4XebyYdMxXtSWltNHNDlYMUhcRB6JS4ZglHpMl4CnVzJTSZ6dWqKpUozBFiJdj6sT5BfpgjoEcVLtTApVJE0XV0BgcXRgBvkrDbJocnKwVJL2uEe0Mn9VizIlhqAoZjpOwFLHaEq7PvJPXu7adrWEmt67ayTxImTgHLRPNC3Gj/dpJjWqlE7MCNKICYq9LUgWSq0JlOjkv4FGBf9bGtKQgw0trm8E8OvH1roMKrqyoAxKZQG+MWw2c1B4sGKLBvIO9nnUJZG+QiorTm1azWI877HOO86jP2Bo+S1Oc9Hr/BnJ2ztaINJiL1xyYKRKKZZDtQNcF6SSOplQr1hqoIq3TzEhg5YVI3puwBiwiL1KMxiKiMcNJsbqPpeGRNMm61qG2kNVqo/j+WSlBZENJRGYFie5gDTnusJNZjfq7ORxiFVLbQWsNncG0Xu1sT5YKTQ9gkEzof6NLED4IeeL+BSvB7/uchUrHv36yHnCNwZSjDmuIEdkIEwQn8KrBQ/0MvRvSr4Hk3cT3Wa3v57MOrGSQy5FWW9d6K5FVeoXCDWL+rPfaKzBVf1WIitUBGleyGVx/gmZppN+LMMXRu01cWnn/+gEf30hZ9Fbb5GlPIkcdsplO9gPpvZ5V7mUcaCfoWQcz5ID5Aj0QeQKLzIEvZc2fyOM7mkHIg3xf1QqeL/J83mH7u+5ZF+gnobfxnLpvsv1+v0I3m81ms9lsNpvNZrPZbDabzWbzr89v91+vUxNSkaOjyd3SpUyncjfp7sakilfpV95I+wAqmMO1gsrekVxAIhKIDsSeFeDdgRqi5JiIBrQDWJX9emKSuGYpoPmoPCYCaSeSQfiFeEB+kVgpZVNIStWay0kVRMBTaTEhBdUgzxPWRIPa2lyDbMpxGEmpaVEBV9STuCusOf4B7YHaSeMiUu6mL2D/BgzUkoy8m7hKuuByILnQFUjPCnvEqgEZUcFwLoSGiICPaik3I79+ok+r/U3r5JyoNtQqhBYHsArYzfB5Ee6odub4weTA3cj1huOTzIO5Vm0Bi5BdwCcpcjd8BfEvMmftkbYKZtetBZf0em9zIP0BP3+Qj29gC8MAY0xDBFpMQoNcgrMIs2qA5slhwQjDM/9S3aZA+nWHj47Yg6cmkcE1X6wM0hXJQM2RDOzetHVPWvvEKI1y+KywTLlHaK0C/RDCr1JYe6DaSRyJ0mJnOqEHpfa97jD91v2u+3vKhCW0o5N2QA5SHqQ7poJZ57jD8eFCqpYGuT+QW1P+eD7I609WCmsNNCdJEqqcqlwZhE8cxY6OoYQIprd+3YOcF6/xQB8n6pN1TVy82rfppWWWOswREaQoQbKidOPik3DF1hdqhte6NRGDkFatcK97f/iBZN4NUxARkkCkIxKYQMbCEbIdHD1ph5HZYSk+FtKUnItLkmm9mtBZ7e3wo5494eitPEcCPQ8knd4cy86ag3cTPMDHJH0gPks3fjR6NGbG3WKWe+84WDTq8jwrfHargyrjC7UDiVlt8qg/OzHIhRDAhahWsMlVm+b2vP+5159P3aPAbTQIQrJmqeWN+as2l+mEyr1lfv+ZVlvSokHTxTJAqxUcepDzIuzeFL/e0L8hjxPJ667Wz9LXZ9S12uoAiLUnwrr30J/AF5pOHLeJQZR41DXZ+oGsxfRkrK+6N9Yopb4+kBjEWGQ76jOMF7SPOiygi8w39IO4fqCZpfCPXyn6ZrPZbDabzWaz2Ww2m81ms9lsNv86/HYB8PBECXxdWFcshKZC+rrDsLOaaW74mEQfWOulzmVBPKA9qzEriuhV7cPHd3LVjrBahXypBzmDlYnFwjjrRXg1gzNrCzRDWavCpoxEfJJRe5prJWqJ6AMfE2tWQa0dtZsZt9IVGNNp1iuom6NUylIN4ewf1W6zTm35NtIHiRLzDceT9BdEI8eo0DQXmSd4Q1vC+EnqWWEfiZqS64XYvaPpE7qSt+6WyGo6592YQ8FAcdwOsAEpRD9v1eoi9KwSqywi425hnmT2CvfCiTRWTloaMQdhR+2NRhDXRbOGxwT7RPPNGhM0ETkQ7eSYpQDXVnW+tQhXxIylg869ixp1JiDlAdKqgahK05NQI03I+V9IfxEciCoqxgpHpBH+JjLKyhyJ99pSLdfxF9I/sOm8tOFLyKzGYp6N5E3MWa3YPIhInET8wrVh2avlfcd3VbhehD0ggqZPEsHthYihaZi/0fPAfSJyoum4Q8RE7YPURIVq2kpiZuhh5HoRMzGrNmZK4rGY/QOTdTdjAxfB14QV9KbMyxjzIHyiOMsXaEeOEx31PrAnEFit9GLWaZF4Rn1+NCQhfbHGhUnSTavlTkfsAXGhKvT+rOuHVZpeX9A/kesLacbj44P3fBOrNq41F56dumGz1NESWDtY/ob7dQBEU9InS41MQTJ5jzc9FBEjYzEjiHngc0FIHbT4dVBD6uCAzkEeSoYiOYAgXm9onePzQSyYMxhfyc/4CZloOibgnozXgONEY1RQHkFkHXbQVEwNF2G6QVckf5KiNBJ6w9cgZ0K3ajLnQPoHGV8ISthZ6nkXaAvyJNwxvZ9/WXu6pbAuvXX6F8IHKzpYR6Sz1iDzIjNJbdVej0DtifsqK7PHXyaEwOp54Rf0DvGGdpC5yAzE73axGulvxD5IUzwmOVe9Np21je0XsgRf906zKGqN1JoAxxKZSp4nmUn4G22NzEEeH2ivQzwRb7Q/EVk0PZh/Xmiv7zX7QfoXmPxP+s212Ww2m81ms9lsNpvNZrPZbDabzf///HYB8MEAO2kqcE04H8SMuwHcSkmcCdaJNCwG2EclgXoA1cojF9o60xVCsbulqrqoDd1Grh+QT0x7TVQuIbuS17i1sBdinfRErRS6S87SB6MknZQLVRCJanaOn0g7K5zTRtyNWNYLbc/SI8eo/VFq/9LnRTtrt5L1BdIQd9QGoZ+QvZTOHreSOiuw8g55ISHkOqrZyQJKqXtqMvoH4ZMIry3Ze5FY5YD5hi7VGkxKA8y7AsmYOA2JifUDjSSGI12ww2oz1+t7kKhNWm0gt3ZX9EDWwOSBmaJxcOlFSu2sniTt0fiak2z9LnkHsihtr3boUhvG0tH3TzgaPR+QA+iUW3lBb7WBW58uKZOUBfOo1xQfxIzaLPXaLo1Uhkc1wa1Dq53dJOs6Wq0+1yaQfoeoDbmVsqkPWv/EjidiDV8veP8AGiadboavJH2h2jmOXjGqgMmk90A4eV+TcZVW3OygIeghrFvTbCKoPFE7cOq6MwX0rAB2NMIV0rFcKAMnK5DPUhNHVqO1HZ+4T5oI2k/mGIBSC6zlzhVTTJJUQyPRFnRJulQDeQEhinji0QltWDPOhEuUMMgVkF7713ayQuHspBzkqnZ6ZEIs1AdqgiDk9aKpMAE8SDVE/A7SD4j3rczWv/65I3Bfi4kxV6JN0Gx4Jr6g5UWG3SrvF4pUo36N0q5LIDLq0EcvpTe5qo0eibT6mZVZD1Ia7qMOCuiTiArGI5yUjnrtaGcqmY43K5tBBqxFXk7nRYjhK+pQQV7MPEqBjiMz6k6VE7LawikAUnvm5ih1ACNb7RYLoCGwZj0uibqnrHOagJ7MNMLre89Y1TJWQ3OBHaATknv7+77216Bbx3OSMUn9Dv6jgl+hFMzZkFbBftoBrws5FexRO9TLyfkGO+v5J89bhT6o3eA3Gd/u4N2R84mmQNQBCaL+vfqcF8SbTCX8QnMR59/IR8f0IsYk86yDHcfzn/BbarPZbDabzWaz2Ww2m81ms9lsNpv/MX67AFho4BVfpJ34DOgl9l0L3AVrSvNxb94egEA2JKU2Rnv9NbFojVLvCqV5TakwrzfEH7Vnq405HdUJGEOdNl6ofa+Qwi701j47q0LJCCQm/fGJ+EWKwHGQ7wGxqukWVLgqHbwCpDSpzeFb/TxDIcAJWpTKNaWRMlh0NJy0RJkVPnaDcfFrzhg6WICt2h5mIFQ71k0IlJlUaCaJSv35yb2bjJZjVwPRUg2349ZYTyHdEEAY1a6z0iUz/iTkUQHU/CoVq/baUZ5/ItmQ4w5LszaLUx9EDsBx7Ry6MO2YrNqizYWHk4cgY9SfQ4I6tH6Hs/VZIoJ6lDJcFYm6HjKT5Y1cgegXKp9YF8KifhZR6tm8g+c5kUaF95HVRlQhj2o5Nu5AKyfWHqWoZZFi9PPgcRpmQqzOzwkzIXLiaffn88akwlAxYV6O+0XkJ7EGc2rpsu+ATRHCjRXUwQVJtFltS6+sTWCtBrDoUS31tUCtWunpSDtRlN5O0geq1bBsUQG9qaOHkmnVHI4BKkw7ICaHOcGjdLs+0eeTiRKvn7h2SCVyVpAbk5TglVKhrnUk3qXszUmuReD4OkkZFZKG1y0egA+aKaYBchCjNOmpRt57zHEFnKOeD2pk1L41dJpWaK15IdpQvcgI6oCGgBruWUr5Oe5w/0JJwqrVj7XSb9eQNoJXMzazNO9qzFH71+EB4ahatVzjTeodKOu98YwS0qvFnvmXItweD4gXZlHN4AhUGpmBB39dy3XDJWqlA89YhFSDfYmjcYHcVoRc5K8w/fGtNrH9hbRGrGp0m3VEKxDPOeo7+2WnDiWltry1fxDrTehBXP+o6577mbEmGUKkltIeZQ2H+ULPxvE4Sf1E1heKkf1V+8ON+iztgWirQzXH99J3r4OUC+xEYtzBcCfHD+z5qA33qEMESd7b69RO+r3FjrQ6CJACWpvI2HeQgZx/wPzxz/2FtdlsNpvNZrPZbDabzWaz2Ww2m81/B79fAGwPUEdod+MskVbqWodbGRoQ7wpW7oYmphW2vh1OJ7UR44J8oumIJL5qNzaNOzwzYgSzAXIw1puUE/dSOJ9N4PqTsA+Ga+2CNquAKlrtC+eiWnlWocrxrEAurnuPU6r1prN2iFPr73Wla2AZxJ1PiysppUSVWGQTQrPCKW2lNp2ztknRUtOKVQtYDqQluR74+sLbg6knZAVqko3UqMahRoXY+iz9tCQZAzMwa3gaPgfMJMSq6RhSKt0xqn0sHbVOrAHn31BRxCq80vM7Hou4i9sRE7esdvCciCSqB4TRnp8M/yLmq4KoZ4XreXRYb/L4A4mBHSfgrJSyTrdSc0fOakxKIwk0sz6ze4s0cDIPMMhIMhQk8IxS09oJ16jWs9UmNGUbBnsQOTE5KtRdtQtNLCJhzEEsrdAOcDuRlWRMlmsprL0zfLJegZyCc2HtwDVxSdIacu/ujgyWU+1qqUA8UoAGS0qZvA6sP+7A8k2KEOG1XwwkHdMOmbgPMupanhhzviocVaWrsqQT+aoNWTlxHI3gujpqimcnCPKrAvo5J2hg0okMQpVcb2QZ3nqF8zHAHujZ4UoiF0ZWwzQGuQK/Q0VB67XGYDlwKp5GhqPSifkCFfRxAEEIZCr8Cr2b383XRYjdmm2pzeH0+swigIaMUkZnOuRRe+AYsQLc655KAYLMIK1U5LTnfYBh4MdZ4bNMVJP+bPgroT/pJHpdLLwU8HeevOZAzgeWA+QJ7UGYEvLBslJn+4raRFbqcEY7ajdXWwXgw5jiWALnga/E1f6rxpqEmKRftOOo/Wp9kmagwhovIgcpUY3ndhLrBfLAzIkYZCrqF8wgm5Ja5gOkkXbvf/sP+KVitvs7YSFyEuOL9vhW96w9kP4NWKV0Dq/XaGdprYEcP+t6maMOoUgj1p/Qn3A+SbE6VJOQ7QPGf9A+/g9yzTuol9pIl9qAZr7INYlZiurMF/Z44OP1P+PX1maz2Ww2m81ms9lsNpvNZrPZbDb/KX67AJj1VXZf6QhKSydWQ4ElAvlVO7e9QbxgGRlGklhv1YAjyIRphswX5k5mr8BNAgtoamBGPJI+F7QGq0EEa9ZOKv0T+qNeV14V/kyvLVmErBVXwO8Wnd7btNV2RRWVVspVzwpaj7P2M9cbN4GxUGk0/SB0ktErNNEDrbHiCpY9qv2Ik3IQvlh2VHOXg+ZKjoDDcAwjifET1QfiE5FF+kIyER5oOqlvVATXA2hlVMaJFDIPRN+YJf6WvxrEYlqNQ44KW3zCcVZjUSll9v251PutP79LI9IZkRADPz6JdNYMMq473Ov1OueogDd7BeyRJF6KYqqFrOnQlPRERGuHVnppkjPKHJ3rblsPlNpmFhEyLiQcsQqGQSp0XYbEF7SjQl3rSChr/CRiAuCiVJosFWbqVRpdhFhGXl9EE4iJaCCihNyfzxTCA9eA6cT4qj8nHLF57xJffwVfsQbaPxEvpTJZSt3whWiiKWQI6YH2jrLAnrU6nINIQ+VRyvTxRWSSPpD2BybJkYMvFqG9NpJ5o9Kw3vl8dt5fb0Y2WG8sOzMdTUFkYlXZJtQQoVrF7UCnsHIhVWGuVjDQJIgUQgSNwDAkHZUHufLeeA2WR4XIcsvK8z6wsO52dJV7cavGcyMQbaVvXgNpBxJUC5mT8HXv2Ppf1zjpda3LImMhrdc1oPcBgXvvONOrbZ6L0Cc+HeablEZIEEtKCz6dywfugSdoVGAOyZITS2j9WZu7nPhI/FDW9ROP+vdSD8QvWFHq5PJgA4l2oyXIWnXf9gPxaheL3aro1uE9SH0TdiD+IlVgJTIXcfRbyx00Omv9B9FB7CzrgAnJgfZFcCHSEA3sPOqwAZ0lj5oNLwk1TRZ+fMcOwyQ4NIh2MucFx0nrDzIG7gMxQbWR7aOCaEA0yankGEjvpYqG2vWNVU/YxwPlQltHEdxfWHjtwT/+HeILMa1DCLLI1tDmSH7WIZSxG8CbzWaz2Ww2m81ms9lsNpvNZrP51+O3C4BTq8GWqwK30ikrGUbvxvq5iHGxzqxwzwBJek5EDugNuCoYS6NJQx/c+7aG3ppbsQp5mhv0d+3AiiINnu2JrBdqC22NNQcskG4krbS4KhWsoWBPlKxmsq/6/2sgYUifpXm1rGCjG/peuLVqvp3V5HMNBANG7Xjqva9pR4W2ogjgdNIONC7keJA5K47JQCyRFEQeKEmq0jzwxydcf2LSKxzsBzBRga4dFYh2IutNokgGZhB3u1Rbkr9eUyrpX2CfiCTZThAhfKI9kRAkBMUQ3ogHrTfsuuDjE21P5o//qAZmdnpbzOOsddkZrGtivSP2QA7wdcG6yH4CFeCiVtu6pjQEca9/pkHkQVyvui4UlEZIBWkZQYiRccL8Wd8TDdEnef29vu91ES2BJxaT1JO4RrUp71YumWjrWCbdkpDS6F5iFd6TpeYNp7VqSidCrIsIQUQhR7WMgfQKS1OduC54fAO/d44zSmcuRrYnmlF710k1M8dAH5/3YYEvDjtJEfJ4QFbrVWKBJZKNgGr86gFuEAnaMCampeslF5GdboImeHYsFYuqRvfWabpYS3jH4Dgbj/PA2gfrUtbrVbvdXrvZYgeRyUonPWqbNp1WlVfCjlJC+x2aawMC6RUApwN23gXd2ufFBMmOtP6rCIqr3lvWE6ShmhCCEaWeNiXHJJsga5Y+W+ugSeYi1q2wlkd9/q0Ok6j0+1k0EHti0sn8Ite7VNVr0lpDJYi1iBz3wYuBdavwXR/3fTX5aAfH2fBR28J40lsHFmEPYg3k0UkfBEpG0EyR/oH1JOnEvGrPuZ1kq9crarWHHC9SnJzVlqc9bg2+VKM9B2JHbWGjqJWSXriQiFLIMxFRrNQEqEEuAxaqT8SE+PGGU+p/o0/Cf6Af39DhhL/IpfXcPZ4VWEdtdqvVpSf34ZF8v+rARmtYBlxfpXwfE46/EflGz0+EQCXJ8SZVq/k9uLfCr3o2yX1/qaCR8O3/88/5RbXZbDabzWaz2Ww2m81ms9lsNpvN/wC/XQA8SWx5hcBzAp3wiSv0KxGDJq06emkV5Ljj5wPLRDVxeyLzookRcaGxUHsgzcFgLUXiKjVyOyAUbVlBybzQnEwEXUGawhQksvZuM+/2YKug5A5qjfufi8JRzTdyEWEVoABGwPjCQ9FfzU9p0LKCzVgVBopUg1JrP1ciUKH2SccFZyPpqDXCJ6RX+KsnIIgu1AfJo0KzNUnpROvYeldrWR6lsKYh16TphQq4PgkGK+6QxQVV8AgIqc+KAzCyZzUo10T7g3DBLGu7dVazmRis9WQuiK9g+SAI5jVpcRLS8DWIzGrxRpDyDY034a02QNtJJmgEGRP6QYhV25IPYr0QEv/yCml9wDK8H4gIkidLHbAKrU2Q84H4C1IRO9Hzf6v95PYNlfnXxm4F/LfaWA0fTuTkAHo/0RAmpQjWEOT8Roy/VxBlikagK1nh9f3GIlMQVn3nuZB+UpXPE+kVZqb2+r5FUQk87+Y0Xte9DEwaol4btjjpjfc1yFy1s+pC5hsIzBq5/kT0G0q1i0NaGcpvU3nMgAzWBS/tmIBEskLvQmuvz7efZBjBRXqw3pNxfOOMRSi4PUif96GFhaB4vEtrLqVLV0mwhvvCRaotH45mJ+XXZ9TI9NqNzoQUzI4KxaPuh5JvC56rdnTFMaTUxamlXVdFteMRZABz1vavJ3WyQwADTSS9dncJ1BpQ96pkNfhP64hepHxUeO2DMDjVWO51QKAdtdErDY1JSgWtaP0ZTuDjjUnDWsPjDQZ+DVwOUqy20MUqiM4oy4FOeM8KprWhZyfXgnWBLtIOxIMgEf2GyCxdtijCrF3sTCJehHYwuw99BHp8IKv2iyWd9NrU9RmoHvj7J5FHKd39J8oT+kd9LjiZF4ESl5N+4Z6kQns+UPdSUuNEOib1fSDlCpDH3aiHvxrRMi9yfdW9Z8KKu/19veqwzPENv+pZIL6IGKWpbyfx/kcF+zEgf7tfoZvNZrPZbDabzWaz2Ww2m81ms/lfgN/uv177upW9qkiv5l2GkctxFNMnYtBz4QjGwXwk4cmcTjMIH9jdVMxVLd08eoUp0mld4fWGj14bw9rJl8N5q4DtIP0iVtZOrALipBoQ0DoqitLwHAiJKaXY1WrQoq02Pb2ap5lBrqiAWHs1L90rWBPF7LYstw7RwNZfe5qeUPVMx82qBWmCxrt2ML0anYLjCU1L7aoIkYEqtSfKIrV+jppjYjRV/FB8JQn0rsxUKsmrvdiIgaiSKGGCv50uA8+8v5MTIlFp6PlJrBe5jNUFfy+yOUMN5tfdVGxkJFGRUIWXcxC02kCOF+4QVmG4r4t0OI4/qtWa1ZpNGUjWDqoJ9PPB0ZQ1D665QBWZf6LaqunYGsJFyh9lA345qdyN8Ae5Ji5ZbW6fFTiZQXsgS5B06IbJAX7xen+BWe2waqBpEL1009ZQElXDNEmyyqt6oFoHEtInnoE0qzb4eiPNaCjZP/D1g4eBmuBZ94JjjDWJUHoGqo2VQlCfa2miO3DVFi4Vasa6QDoppUePOWn9GyalZUYgVRCtsH2NidtBphD+ItJL36wKsXh74sshBQ+Yw1m9tnk1soJe1frnOWp7mF5/r75xvKq9tevrde9ggmgj1sJvzTGptY0rELHqfs1Z28lR4W2KV1BKh3aU2ZsgfJFYaZIj64mqvVTQrfTU9TMNVSu1MIreQajUh06qonLcm7ZGHh0RJ8MhJ5dfrOkVaNLr+ZHCymrPqgiSCXIQreMpJI01Fmut+4DJUc+fOaAtoBNrIa2h9YLR9kTUkJilwVaF9ar7cw2ynZjVljEpcHwg/gPRrDaynfX3Y6HW61CCPKs1bkK2E11vUK0dXhPkaCQf9aycb0rgfEH7IPxPiHrO5HqBdtIn2KN2lJFq8KsgHGg7Yf2sAD/qwE5EIv2owyrjBf3f4Pq/kONR2uex8PwCFcQ+yHYAi3z/RM7/nYhZ6vn338neEJU6pDAh47f7FbrZbDabzWaz2Ww2m81ms9lsNpv/Bfjt/uu1XF+040QOQ9SR9mC+k7N1It4sAxsXrT+rmClOMyNk3ruiUeFPJFiUHvT6gjBYAmcFUJDwvhBV1t3m1elAZ42fZBprrnotK0mLatpJR3sgGSzNagBqBxpV3i3dsLveDU4h1/9doZDlrRAGmKgetYc5L0IPJAUVJfVWOaMVOsessMkd7FEBMA+w992uM3RNaE9EA1VBRZi+yFYBcfobRIms0Fy0EXnhGgTKvIPQHF+oz9oAlkWOL7I9SiUbF+iD0IMga6NWErWTTMFlsdyBg+hvTIQpjvCiczJ9IBghSsw30oWmMKI2gwWQDHItpFXb05cTHFguSpMMrAvVA7JCuHZaNaQ18FjMLHUwBNafFSy9vohebVB0kl6a4/QLldISI1khmlaAmZmIzAr7RYg0mh2gjbAG7x+0Vi3jiIuMiYjgraNiFXyZVOjbH0T8yZ2eEX4R7gQXNleFkBykAcfi0MlF4BmsaxG9V+C5Frl+ov3fiHxXW3QFQfx1SIB1b+iK0roRIyootQMicKvGuTEYavgatwY5UO3VzhUvRXK8a/fagt6eaDuQ9QaPuoz7JybKw5TzPDg+v/Hz5w/eP38gveMIEgr6HWLVrG0Enqt2na0jmZjWd2MKqieRb1BjrWrdazeWv8kESUM1b/W5k02xUPRW/7ZupB6w3nV/+t3wlbx3pJMMr69C6tqpMNkRFFFI2q3aro1vImp/OWrruzM5mhD6wXz/Byt7XWfofZ8IeNCjEVa7wjhkS+a4d6+j9M6143xBaL2uVY1Y0UX2e496vRDugwKReExgoRghZ+17ayNjVggudXBF8h+kNqDVdvqaeCxSkhjz3jBX8rqqKazzblwbcIJ/1bY4Cj6IXMjxrdq1XMgdvmszclH6ZRx+/t/k5ycuH2jUsxsctbOeiWpEvMrYvRzmD+TjE9EO/hOx76Xcv37ULnM6/qq2fPqoz6l9Q33h64UefyMf/wbzRYZiRxIy4ez/lN9Tm81ms9lsNpvNZrPZbDabzWaz2fyP8NsFwO3xRI+OWKmfeU1kLuyh2HEga8HR0dMIT0wUQdHe7mDVqykWcm9uGnl+lmZWatM0JEuj6gs8aa2zYlQ41ahWrAri995sXxBaAW1TEGVlkn7v7coCBPv4JK9/3AFtaWrFgOMbGkJmBXGLgUkirSEsZhrmggosEua72qp6626xCpTXgp4VGksSwyu0SSc00JxEwky7N4mlWp9Rr1VbvR9PJ/yNpKMoGQNByBTy3iHFJ2hi/cTTSF6IB6KBqcJ4o+1RrdjXBQJynOCv+t9JIHTUTh7ngySZ68ACLKJakDhB4NrAvqH+YvmrWohWYbCvSUojSiQM0mA19DRkObRWYW+UUntJI5igDTRIe1Yj/PFxXxsdss4HiHaISddSHJuBtoNJtRXFf5C6IFp9Vu2ArJBYNcnjWSFiCqkf5PgqS3YokpNUYcWLNpP0INJqa/b9Ro4PMmDRGSSWHWuGoqyluDV8KRMHO5HICi5zgJTae0Uj0oGFSKu8MiaoIWKoaelxE8otDWSgqaidjHkRHDSbCAu3D8QXSdCsEwQhgn08aQ0OTUSV13US8wciHREjM5jXQHCiJWMJkwPGhP6oHe7W8RGAk+RdUFUUUFVarmqYUrriueqvK1XMej/aEWt1WccDaBVWRiPDkf5Ac5Fr1TOEIOyDiOsOuEsjLcy/njeiDaSXglxAPOs71tKm5wK6VZs+XiBH3XPumD3xORguRCySRO+97lyJz8DnRPREZ1ZA74OMg0zlPBuE8hpR1weN1I5agFlZBRAyFsgDKL26+ELkIFdp7FkLDiGtms2RC0g0JyJnBcCVtBI+CR9133Ar4+X/Ye+NlhxJkuzKo6pm7o6IzKoaDrki87r//2Ers9yd7qqIANzNVHUf1DPJR/aQ3ZyVsjMyUt1ZmRmAwwGI9LV7rt5q9L0OD9g3ZH6VwSAhfVa7Or0a5norriPI17O2hdsB/TvpJ9J+QY7r1myXlhup1m7Ek0y9N7V3wJH2Vpr8SKR/Q/yk7e/E/KrX5e29Hpc2wjby9YG2RGIQ3gErDf7+Tg6H3Ah3Ut7RWxK+WCwWi8VisVgsFovFYrFYLBaLxX8k/nQBsAnVXowLFSWa0PsOWSFl76XRzajtXSFL9ZtBem2M4tWek+zVwJwO0wk6Ms9SmnaDZnCdJINQkDkQ2ZA88PEk9YBrQnwBjSZO79+r0ZgTzVFNvxioGhIXVxp5DqS9A7MeCzupozZFw+txZZLjVfrgSPJ5kYeS8WOQNchWIRkxEVNk28g5iFTUz3v7FTgDtp2QqIZx+J08672DWS1hydL8xhxIKmobQ62ajwm0jtRvQsyIMcioneNkhy2BwJqQGODIALZqAIsKYj/2V09EJqpRG80YrX8nv/4N3QxLJX2QtqN5kuMizUoba9Uw1jxh67TrqnQ2Eu1SzVffSTuBX1EC6dR1GxPufV3NunbGoG2d5I24d1tTIdPIZsz73hHNel7agAH2gPOTMCWvEzkeRJ7VDJ4K6jBvhTiDbK3UuXHhlAbcOIh4kV46b6jf320jGSCd6V+YaG016yRkqwDZJ5leKvR0wi9EGm3bIcFzYqqobsx0hERtI9qGkMgm+HMg9oDwCo/rzVXBIhB5IpmlFo4g5lX3jSbSOoLSNGjSmOdJmlczlmpFbzIJLjKTy3fCnL4bYwrit7q41b6z5IWIMNJr01mkmu0JV0y0PUqp7kJQQesPVXHmidIQUUyTMV6EnxWOy6z7PSuoxHqplKOaq6atrkcmphu4E3a3aXHI/NnsTe4NaT0Qv6o4HBXg67HTekMINutQa9a0fSfnxYws/bc2OF/01upwiMWtlm+1xYui2mo/NwVVISKQzW798gOzRNPBtVTZMUEfiL1h8ReybbdCO1BT4pogFaZHgPQDYQeymuiRuBwgjmwbNcEcMA3tpa32lNqPlo2QTknaN1I6Gp/1GdsfdV/+OIzRDXSH10cdMvAPsv+K9IbErGllUaTtdRDl/CCs3yr4js+Bdq32+/U7Kg0RRyNq+32c2FvUNezHbXYwRLO003Ir+dXJ66/1/u+NyED6AfOPf8wX12KxWCwWi8VisVgsFovFYrFYLBZ/A3+6AHgOaFSwmhbgUcFnRLXQ3O7d1gkxCNuQeVajTQ0hKmhgqzDUjaQxUghq79TyRORRP/BWH5soqRfmncSR/iBbw8YTp9SyHoElcP6OSCs1qRr0XqHrqHAw5biDWCqI5KKCmg4qxKw90umJxcQyKsyVDi3IrOhFvELuYCL3RqqplkZYKnDNcES8tnDDa3t4ONIquCVrA1m7ITFRUdq2AU5IEDnQbYcpiERptHWS9uAcd3icUT+DH83sIGUjXr9X+3p/xwC1RusbHhObhmo1ajNOMhSJB6HCHBetlzrZBDwHUy9Sv9X2cUzQrUJADMnr1uBWGzhRUhJ4IPEktREY4UqOT7IBCO4BUtcw0/GsrVvOq9rlvYFt+Odf4Q7YCSE4SU7o76AdGVeplv0sTTSTFKs27Y+OoRhhhl9PhPteC6m9Vt2RiArrVUAepWyOk9bsbmonSLVWRQzxCXlVyOalvyYcPQ72wyAm55WoNdRnNWLnE1q/G90gl6D9rXaUh1eYHxPbq/lJ3XZoCkbHNHAL0hr7thExONXQTCwSF6tudE6kt2rX+6sOJORA2YjzAjqZDZUL1c58vZhZzWINr0BQk4xE5Y0Yf5Se2IMQRTygCRKvu6GbxAxgYC73ezaJrJarWaO1nbi+EOtsvbZ0r+wwB2EdmV/3vcN96CBrSxqpAxB6q7S7wOylgb6iAs4YqG11sMEH0g7m/Wea3KG/XKWAzvuziVkHAOTAI5gk6Y7lhqXD8SDOJzNqR1hoWAZzOPQLH4NUxUWJwf2+vkB+x0WIKA1y3R/3fTVesLW6Z+csLXNAzrgPAQSIomJIvqpt/XiADyQUEyeaVfgrSY4P6Ecp0tt7HSbRVlvqKkjcB12kPjvwJ4mQ4wVtr8Mk1yDCYYt6D2mDEPK6yBallabep8wg88Led6YPZiS5/VMF9h7kviPxUZ8HZ6DHG4Rj9kaef6nNbrwOAlylR5/n+Xf+xlosFovFYrFYLBaLxWKxWCwWi8Xib+dPFwBLy2rdySDlgftFnoZYNchETnJuVHTzqABWo8KPdiC6k7OCoIxebTVraDzp+ztxXeQ10R1qFzOQeFV4GAotSZPSPRN47iClNNZuEE5kYNkImxCBxkZWvFeh5r4zxgsSlCDNaq9UJsiGyEmMC5EN5EK2hqhVsJVWwYoqt9walXvTVg4kJ2pJ5ImxVxi5WTXfdEebVBsZv3d5FbUKQNNKMdybEu6ItdLLciFy1K5rTtL2CpeolqsiJA3VaklWuxpEeumFX4O0qB1bK9V2iJBj4mcwdSfjxPUkuiFzYIfRtOG3cljbe+25usC44LivSZxkTEQ6KZ1Qw2SD8arAdj6h166zSB0WiDGoputVbdC0aljqBSTRgXQ0DXyS+zdMB+TEM6E1RN8r+M77OnT77xTKAIGYYQRqRqAYo7ZkVdgEECNlAzrkdQd9VIORCe2N4EIrhry3nytI5/pEyo8MoYSciB1EBtdroCSZe7W0xSin8gA6ZtUulfTSIPso/bEkonKH8rMCy4CIDT/gUAU2sMkUZ46LEGNoaYCHX6UnzsSjguqRpRQXDIlAsyO8iDlIvNrVIYS/6v2bjqmAXER08EHQgKzHpA3pgqE/tdXu1VJOCVIbPoMQQ01o246qIk3Y5CC2OvjhEfchgSiteSb1f7P03Zm1lxu9UnAuaIbIjj4M4sK71fsglNh3MuswQd+EhrC/7cj3B/Pzd1w6LtTe8PVBtk7OUeG+nzTdGdck7QIa+ItrPutQgjawA80XdCVzkClwBdlq6xeU1B866EbOC56fYN/qAIplHXbJBAYpkF5Kd2xHjndkftw75VmfPUBcfjfRE20H5gfkq5q1Wlr1CpM3lPq8S03U3pH5BW2DnIi91QGdOm0BPkh/4WJoL6W8ZrWUxaw+8xyyPfDXB/AsPbsN4hXo/guKIscbXL/XvrafqEBmkB20NfL1SWaH7ajd5tfvpSZ//wWJE+lvf8dvq8VisVgsFovFYrFYLBaLxWKxWCz+ffzpAuAZRoagMpnXJKz2QZt01Brhk0lWEzYFrlHa0u0BUmETspeuVDtcVwUJvcM1kNbw592avHct08EkiL4BQUzB5wm9I7uh4bTrhcgbOWdpmdXvbcxWQZ0MVHfESkNrs0IV0uCqXWKXncyTjED3HeIOsbN2R4lBeKJJBXjtATkxsQq/CFBqe5Pa1szwW8csSEvSsxp3GWR7VPCYO5FPVIxgMkMREdIvMqzaxXYikUQTUgWfSWBkntXYi4lbQyzr54/E+46Ni2t8Id6wvRHnCxBSFdzZDpgCr8/SD4coKgrXBTaRH6F9eGllNcntHaFC5iRx2WmtFNstIU0rNBNHj++on8T8QOiYwvgR/Fm7FdoJJEqrACt7tTZtIqqIPQh/wlS01++9q5xk24n5Wfpp3clx3o1QA+1kTKZ6hfSyoZsS48WmAibMW+WsUErdDMgXkVHBZUBou3ejk4jEx1e1M9s3yBOYSNzN5+EMCyQSpCF+of1XiBeOoVE7wYrg9Ho/UCrkusaNOScBJB3mJ7p1aiY2mfOFDhhtI2iIT9xbaXVNmecLaQe0wElinnXoob+jTMyUa1rt6AYV4FP68QqKQXKSrtBKP36Xkaus7hOxo/aO9SBj0KSTDSbKpCPxVdevdUStZnBPZ4rjH78DYG0jCeJu5aZwh6dJnBfStmrEaqLSqGQy67Gl1MsvwrwPFrT5BWJM6+RnbXR7fCCZnNNJd0aUDl5CCG0VOMcs9bwo3A1Yw5CADCkd8ziZmYzXbTIg6nCGJJmjpqtN6+BAlLw7Q8jeQUa1m9uG2AEykVQkkzAQGm3fYXzUNR4TNUfaXqr3DHJczO0bOp6QtcsbXv9ZzaDJrZEHz1Jio7NCdL+Q+CrjAq0eY173zrahj2+IJJqtDl1QnyF1QCMBkMcb4XVPiIPuWvfNeFWL2KR07AhxnuQV5PdfyHERvpXK+/UHaCPtGxJOvv4N23ekb3/nb6zFYrFYLBaLxWKxWCwWi8VisVgs/nb+dAFwzslUpUZaAzCsb8Ag/CT7hmTCNSsA7UkMpY0L74pI0uxu7vUsJW4OMoIYUtuX2u9NYJjq+FSUQd/fmPOF616a076R40L9YmaCT1pLlJ2QCfsbPh3LSdLI80S6gUwy9dYHP5G2kaJkSv36CNJgJhACBp1AI8iphFS71DkRBLKCzNLmeoXOGDEHNRQ8K9BMIWKWZRivkMYHkPeearV206sd6VRbt1quFcgode3y+qMa0aZ3i9p+tv+YA9HGZkKIkRNs2zESTOv3XZOwxnwNfG/QDvIaJJ2QzuscPI6twrf8JOZFhFXoyKyms1aGjt2bxraVXlYF1WATIw38igpRbSvls8kdKNbGqoTf+76DRKr9aFmhXGYpqgG5N1szL/AA20uZe3yHHGj+OCSgyLzI8w9ia2hUwE9SjXTruBmWlZtF+J1BT1w67d55djHCv3A9an12BkhyuaHS6JL37m0SJrdiOGuCNwPkBVSQ51nXIDwglbSs9mmegEG+qr0uRrhjEjhCqtFsZ2sNyYuQzgSMF/1xME8lxkCl341fAU16a+R41pKwKMhGcKI+StkbpaeuQuqFqGIkYhtqgnsFiiJG5qRJQ7phosi2k35ybELKznhJbTATEKNa9PqFyr2NK+UD8HEBoO0BZLVx751c0XqfpChsHVLqnkbu989OpBPjLM271K8bE6FVO75ZvYfniYbh1yDJOmThUWZ2wH7uA9/XJpPoyi5e++MoY0zENpoaLg3mV93s51Wf+vKG2ERdyd5AzjIKaG0Xixhz/wVJJ1+JRFQYrK0av/chBuutdsyT2uTu/X6tBBUwBmEGOZizWvKZTsZW15ujtM4CUNr9BMKztNJEHSTwOkBC2+rQiG5wHGhr6HwiYWRrZCrio/6pRgDx+gSjmvz+KvPD9UXoVp9Z6XWYRVqZFt5+rbb4+SSP79XilwNe/4a+d7hO0O+1lx6ff8+vq8VisVgsFovFYrFYLBaLxWKxWCz+XfzpAuB9t6oDWq+wZ5MKeugkgUzHMgjLat7OoO0PyIFhgIPtFfxlIqZwTUI30pyMwHQjxomYErMhbRIuhAumhsjAv+/YKei2QcLQjs4kpSFyVfOQhsdJSjXlqk08MGmk3rvFKFyfaOsVMkmV7DIDBrQ2YZ4gFTa2rVVdsXfS/0D0GzFfpNmdg3bkDsEyvHY1cURKW40aqkrKG+nPatURIDuRE/KFWSOl3Y3HUlGnHnWd57Mai1paXolA+lGt3TsgzLyQBuIDUtjevleIrNU4xEA0K9QMp2Ogxoivmm8FMgaq38jrqxrGdDDQPMG1AlPpRDpKabFT93uD1JHWKhz3rO1nO+rPW0fkF/y6QC7wC7m1umRDBNAgU4lUyuYcmG23hlnI2SFORL12maWjMjG/mHoQNPLxTlxPRKweqw+IifQNtU4C0y9EhGZbhdvzq7Zk4yKnItIgGhqBT2hdAKWlY9KrOSmQ7j9fDyVoBNIfzKhQ0AwEZbCR97arKZSreit9ru7V4o0Kvk3AspG28ejCvilzNGyTUnTvDfMgFbQZPQdb2+GXd0QDxHjNjalKSGLyYgthuoNfNBVMNyYbEY5Zx+ygtUYVSKPao2qkCF0bb3ti28blyXhmPYcIPIOpHeJViuy+E6M+J1IcsQPTRLIxU4iE8Grpk3crFbnD4vpcIL3CRE0yvd5TCSYdVQNrmHRMnDGCyKg9b9vr0IbUdrC2g5wX1g7afT+JX0h6KdMjfurdVbQep0btfquQw39ujKt0Uus1Zb7Iroi2Mh+M824Q70g6YYrkrM+Kff9R8kYQUmqbWRU0lbAk2Kr5Th2KIH48jQ0RJeNEQ0vhHFI/a9sgLyQN2ToGRASTRuYTzR1tjZD3+uFcddDFk8xS5msoTXfm+J2YCfs/lYa7ddJLeS4O6S84vpPtgYXi24HoO+nP0rfHicYotXYE0oFtJ+eE+Tv2/i/QAskoZbi9E+MP5PjtH/PFtVgsFovFYrFYLBaLxWKxWCwWi8XfwJ8uAK7mqpCieCbNO9l+7ErCJJGY0HaQhlmVIWMMFAAnzas5tu0122oGYjQg7Mf+7Y7nxMJL1zonqZPsDfWsIIUT5AGqdMsKeSOpDc+BGLS24fNCdINe256J1BZnQtpW+lMbtx7VK7SJQFSQmaVjntxJXkBcSBgiR23yqkEKkRW8Vetwljl63u3WuENxlLiDrryuu5GXSN5tRduqzScNmZ9Yr53dGIM0uwPRRFzua6vVlNUK0mtz2cCrHcx0/PWk9V5BrDSUWUF3ttoCjlktPX6ofoNQ4+Uv1Cep3xAGxIsYpeXN6QxpjNeT3g7G66RtG6FGXVg4x0B6J7JamETDEzKeVFiahNbhgJwvCKHvB6qBz6gQ/TrrmstEJG9lMtDf8XFidiFa6uahb3XPQd1Dcmug0yDPasRuDyReoI3AYF6oZZWD+1abzX4h7YEYiFeQJyalhBboasS9WBso2IEQCI5pQwns2NELPBLNq3aqqRYqGkQKuKM5SSYehtqOxgnmeHtDXp+AMcaJPN9r83a+Khx0ZU6vgwpsXAHj+aL1jgjMPBljEAGitbUtmbhtpCSNBjLRkaRspD3IOBlX7VtnJpGJxiciHU/n6wUyJhHGnJOhnTiftSes1VyX9qitYTmqYXs3rfGz3ic+qvUrR4W8YqRfqHUwI+PelWbU4QG2akyrI1JbwiTVGm9WYfm2oeFIJtbAJxBXHQ6JJMRo3cCFiFFbwhg5TyCRRt2f+iAuRyKIfCHZ6nVqHUJr2zYS9tIdV+u9XpOklUI8SnnvCPm6kFvZXodA6r0dr78g20FeH4w4QJREISoYlnSy1+eL4LgEXBPZHzA/q2Hts96s8yT7Gz68/lyNWINuRJxoVJhuveGidQiifUf8RDJA789X/1Z73dMRA02BfpQy+/EgRhIEsv1CvP4r7dd/QTzw68L1UdvvJKoP4vpERunbXRLkG4aD7rgPZPsF1SDZ0O1P9xW6WCwWi8VisVgsFovFYrFYLBaL/x/wJ/xfrxu53c1MD8JPtB3g1Qy1OInd7vabIrKhDHTbiOHkHOi2kybgoxqdsgFOyoWHk/Kgm2JyEOMLxt0iZUC+kT3wWSHLHgORavWqT0qJSjVO0yuUygnsqELmRs649zpnmWSPnQhD4yLiJEYikljbQK20yK/PautKBWcmT8Q20B9KVUey1banNFKo/U4r5TQoZveuL9XWVT1KGSwd1NEUVDfSP5HWwO7tUy01dqah6J3tGtIED4i4ylfdeu3pSqmxxQxC0C5I2xA1VGbtvM6T9Ia3b8j5yXSB/lZtZ05SG4wnvn2H6Wh84TjSDU2IJug80a1Cd2u9Go5N7/ZulMYWgYx6bc5PYjvAHbTjamQ8S+KrByrV8J23flgl0cPuXedJpJM8bqWw4ykwL6TdSnFThIuIi8xWzx0v/bYo9L3uibB7X7YBHfdblUzUtRFAGxJXNSFTafJGnP8PbBuZE7XOdEfihfW3e/c1UUno75yfH2QaYspgEGF3q/VuePoAPe4t7DdCS3UdEjBrh1cyYdZ27csH5twNXgcXUjuqiUlgrXOdn3W/y0Zk4AFQO74xHc9ADPxWczdRUhLVgPy6W9hWSmuPOtMxS+cdBJdtiN9aYunEOQh3cirWJmJ3A1xu/fWEbG8ERkTdx3V/JsKrNrol675PYV5+b2hnbQCrkFn7zSGG3uFm5qj94evCBcwSjaiAcZ5EnogcWNuR87MOIbijMSCSlF5BMJ3wV7Vq50Q3J6imvuijgmakwmGvez+F+iyKavXneELUYQ+1ow6AjA+qAlsbuupSqu2m3B1gRB6E3XvK0yGeoIpYkHKQDCRmHSrRum8iBcZ9cMaq0Y5u1ZqOSYST0uswTsY9Wz7qgMisdrNrqb+xDZrW4Ylx1T1//ALjgu1XYjwRD/R6EdJg+5X8+FeSVnvafpJzkNdEHgnzRPbvCPdBnOzEfIK1Uuxfz1tXf0H/jn/+K7z/F+Tr4x/0vbVYLBaLxWKxWCwWi8VisVgsFovF/zh/vgDYk5wDT2jSoW13+1crUJg7NgPiVVu77Z2MQFUQ1dqUjQEYoQljQDiYESiWgfRSwiqKWsfMCO2YvyqwiFEBEQMf0Kx2e5FA9rzDUuq/N4OX3LpdqX+kgwxoUspo4t6InagoiVfQOwfaSvUrx35vfAL5wEzrj+QgJCqAbo05L2pplNINj4EwqnmbRliv/VptP9uMKY4ihCTBHVJFVnCWiUkHqkGdJmg4aQced0gjWn9OO5rVS+XYqCRxYE1v7XRWYGRCzldpopvDBFMn2kG4orqjInStneRXnngG8E7Gi/BqGmdW6IoYoVbBep61T9oCsqHPJ9Ib2AMRJc4X7agN2BSYOes16RXsugg4MIPYA/RA4wNS2LWRClcqKoo+dnI+63XVJGVQKbSCRCl2Eeqv7NAaRpDWCL8qi7T32m2NSUpppiU7YlFlb7m3leUJfYMIQkpDnj5I6aR7NcZjIO+/ke44IPJCMcKT0Gr8Ig3RrI1WAhcp1XFE/X0ZFZhF1OGALB12NoNzkJEkpTruKvW6pCMzMOv13/3Epd5zQdyHDiba38ioMDOzkX1DWzKvkzvBxEQwMdDAXye5Paohvu2lSL7vbomse1Ih9h2JF6q3CUCqmZ5cxPUk5Ulmhf2iO6qGzScjq0mt2nCf1QaOgN6BOwimWvWqWU3g6hVXk5UoZTeO3+pwIlF7r3+Ogafi08mY1Uy2dmuQ74a5Cpp175OBaEebodawvGDCzKyDKgm3T7wCT6kPhJwgbUcy0Sbk/l6bw7sg0hCV2jQfL7QdSH+D/ESsTAX41/1rXu1+E/z8An1D+jvMv8C9GZxbR+THJrjD9o40IV6zHtv+QP2qa207Kg+ygZ8fqEQ1/WOS0uvfXU9iPEkahCOtV7B7fkB7q93mrYL4qWCaoL+QodTc+aMOK0iw6Y6PZ2mf40LffiXnBa1jPgite8vnB8JeNoR5/X2/rxaLxWKxWCwWi8VisVgsFovFYrH4d/DnC4BVSLL2ZfuGjBPZjwqoRCrQlVZqZhEwQW6VKJnkdodsulUI27cKmPLCTEsdzaygWBNegDhKB96IK5HWkRB6Pyr4SyPL13trl/VuGSZqAo+9UquICixtQ+IsHW9E/bs7NJZ+YP0kQ5A4kWi3ZldKCZ1e+mC9W4SZ9TPdyexkWAXImkj2CkPaTjjEj4BFFQnI8UL2DSkZMqQBk2x7BTO2YVDBlm14jgobaRVY1sNGMzGt/dgkqo1qDeasMHRCmiMWRBylpLa99oMD5HjDz08YF2IbOT9JgaFvtBhEjNpRjrNeq+nItpHbNyDJ1ye57WCB9F7aZH8RfQO09oblQvqD1g+aeG2BorDteCR2N8ZTOtzh+mb1DIcZMQfneSHHRmr/ud2aWm1zszcig4xZzcP2Rs5Z7UVVJC5oBz6fIEcFtAQmgfRfIQcyEzhxn8RMFCGlQm2i1NY5XtC/VePYoz4BJEoF3h4VwMbr3geu+y5iVBs4r2qG+rjbo191kCEvuB8RlLK5gkBDidqVxuse1Q3NZN8Ptrx4epDZaeb46QRazekURDeMszZcRWj7Ro7aM1YBzYusEWGQC0VJd6YEPgY+A+2z1Och9bbGUPnRoq2GK3FWi5Yk8yLdCbG69hHUGDC4ZNkA0hlI7R7Hq5To7vebMOoQApBzoi1LtRwX6QZtR6SCfW0bEk7iZH/UaxNWAbl/MOW4Ved1oCNbq5ZsDrBO+lf93QSiECKIV6C+b+9oXFwZdT/3AxlPUhsjEgmnXyeyvyFNENvQqLa72APxF+In3NcKuzfAkwrj5xey/Urg0B7QNswVqPdeyIEwUc66jlnNenK/d5Fn3X/PL1Igm90tf8EUfG7VTLZvd7v4nTn/CnIgEaRM8voDYtR11cBQyIG1A9QISegNuV5MqgVvWl95OgPVRh4bOf8g2sGUCtfhBK563cdEW2npuzXgydB3pgb4H8j27e/0RbVYLBaLxWKxWCwWi8VisVgsFovFv58/XwC8K403Mj4gJiETzYaaVZtru9W7toEoIRVWZNwtyd4RtMJfDyIHoo24ILXTuhJegZJQgTHnCQxSG+KO6AF5kcNrc9aAGUxPlBe4ItrAkpilnCUm0QW9Egz8eSIMVN9gf1Ro21oFNNcgXeA47lB73qF3lr7ZqokpUhpmCaHakBUeudxt4h9bv5G1K2rtR5EPCce2vcLDGD83SCUv0hXtG3JOMCWYP4Pe1Nr7zRh3XphkeGmXhVqZTe5A7aht4+cHmY9qRe4v/ArkLjJGJh3hCmArvXOqIggyP5H+DbNO+CRFUO3YsVUojVYLUQ3Tk9SJyDvSDWIDOtGr1ZjXRLaOGMz5JNJR3RDbMeYtxk0krlLXZuAe8ENrq+D7ndRPLz12XJAbXC+kXWjbifB6/e26ldd1PWTbMCbR9tLqhpPRqimZV+mFo98N0wqS3RpoINFuNfD9nAnA0M0gwDJqS7kZylXNc4RdhRkn3jYsHahd6yRR21DphFRgL9z7tHez01TZts54fVXod+9KWzqtgcjBiKz3UDz5jM68m/W0jRxntZYz631njTwr8JW77S40pp+lfVatJqjcM9eZsG0Ad0M3CKcUxSkkjrVSOksoqRVYSyr4iZkxw7G+leLcn5DtbiZbHULIwYwDNBHOugr+SXgDnHAnsmO7Ah3tUppvbcQYFUDHKBX7+UJEahc4XxV+a4MMunU2M14eOIGmkrIxxqhbOJ2c9+eIVQga55NxN7er6R0wnkjfaD6R/TvKrPt/npg90M1QEVoKL02mSP07ElSwfhA+EU9E38FHvd+aVaM5QZhkfGDHL3VQRgKLIPY3JF4ItZ2bGdVIF6k2fDqSWu38OFExSP25pyy6QTvqAEH4vR/8hO0NvXe4LSc5A7UBj4Oc1Vivzx4nZCPmqO+BvsH8QNp3gmoDk1/IeBHjQh//VI1k22rjOy5mdNx+qUMxoz7/7O3xd/7CWiwWi8VisVgsFovFYrFYLBaLxeJv508XAI9UDEd7J0VxduJ80vY3QjtcjjbFI0hT/LowCbRtpD+BVnrlHGTr1RhGEFGk383VqNBY7tZtqiB7BccZn6QIOSfednRUgzesAigXQa2DVwAoZIU8Ezif0DqiDe0HSalmdU6yJfgg5CLMYGtoVutSmiCT+rtMS5XKCe0NEKwp5HsF1zFQ22vHMwPUAK/NY9E75kwwI01rilZ3cCUTJg/UsvTKXUitX5esHVbRt7vtXMEV0kohu72hDHI683rS9l9Ae2mZ+wNaq3A6kswkzyfs30A23O/mqHs1vLXartK+3YGbwSxNbKoyszZAiRNQHCevpPWDzAvVnWwKvBDdifPfUHurn4sx9bh1vYC/IORuoWo1m9VI6t6hbRVqS4WUgoA64bfuOYHjqFA3hfDrDvKrzRshRNTPGnpUS3I+SU+w+/mm4J73VjP3AQUBAbsPBriDzEnqRnpt1OrPHevahRa/iBQ0G5EXoUmzNzwTEyExYp61T6y3BjwGiRASxOWINSSS1Is5rHZ8NVFN1BRNrcBtvEB2XILhX9Uozw7+OyG/3erqUQGlQ+v73YSOClzbOya1jXxNvw8glD47tdfGrSq9bTSFU2FvnW0zAK7hRATb3jFRzpi4trtavJPjWep0mXUgICm9s5SWWmarLV//QHu/3xsT9p0ckzlbqbpN6RGYtWrUX6/SPWeF5j8+I5C6T9Ua4on1bxAXLRIlGHPiXnr3KQ181qZuAn7vE4veumlFxWF7ELMauIkQoRCByoaKYO0N5iexfa9rqoJJkPOsgy12ILaTcdVnR1PMOhmg7cBygApjXhUMN6vWOHs1fAmwDt5QE9KFbLXDnNnuz6YNNatDERlknPXrQjXkcdIHEbeyOqqdrdph/6U+GNNBOi6G7Ac+v0g/Sd3vsL+VZUGdfD7h7R1PQbejzAfiJJ3UWXr+479UQDw+aftO+InPJ8ms8Dr8PvgC+lwbwIvFYrFYLBaLxWKxWCwWi8VisfiPx58uAM648KwQFAHvnd57JQ7nIJuScZLtYOQgx4XuCtMZumFzlto4o5q5tpGfX8he7d5Qr5LlnPeOqCKtl0VW5h0UGblVfJuPTvpFy1u/2rYKFQWgoTqq6dkM5qtCR7V6nICPIJnEy6G3UkcbFaZcVzWZdUO6wwhs67g0BLDwCh0jSj9dcQs6r9I4ZwUjkrPao6YVdGsHvBTVEqW3RQivnVbNF0LlUhWEl5J10w6twqwMgai2NNbQiPrp7cBEaShTohS82kFArGHbQYZXOCQbwoumb4y+kdfvtP0X9r4Dn2TfSA8kX0CSenuy8xPUkHs3FBxtpaBmTGQXJFtF3VFbq9Gq7RoupfC2jQwjnv8vMQfBi9Ta6MX2UiSHVzhFbZASWrrby/HxiW0PpCkSHXgR/gQ7Ksxzx69Z/753JBURqsEoB9hEYyC9kRGlkhZBTclZYWDc7erIIFLvjdqBtrslGtTjnE90P6o9SYV8Ayt98P24Jyf0AyVJZqmOSbg3euNyaPvPPWDPBE+Q2riVqJboFEFaQ3Xi6bjHfUBgQjbQd4gvqs6qiCrJq+6DrJ+d0RD/5GWtlMn+IjFE34BE8j4EkCee1fBVETKTMRyzjjCZbj+b+pcbzNoSThHCE6SUy5pJRm36/rhvEm51+8D93jqeUQcaSMzq96IKc1Z42QJCAatwGb+1ynEHng1Tw6yjdj/Wzer53PdqZP39yUA1ifQKWcUhq2lPewedqE9kRu0854kc/dbVO0kQY4CUfj7ni0Dw/k76C64LMam2cquDJj6jPge0Iz5K6R5yt2cbMxpmOxIXQdZm+rhobePyFzES6ER8oe2ogy7zIpy7Ta/I/KMejwfZ9tpD11ZnJWLeNgMnm9+HKmqH2Ocn2kuxXfdJq31t+0Zcf6md48dvOCddsnaE+69EfOGvgWyfwI68/VPd4/5F+iS2d1QmbP9cBzroyDix4xeYv5P2/e/+nbVYLBaLxWKxWCwWi8VisVgsFovF38qfLgCOe//S+gZzYlXmunWlwHCogio9IFuvEPVyrBtjgvoTzQZ9AgktCTrSqnkKVhud8w5YtCIbsiH6dgcvF2l6b49WxVekV5PR7P6zE7K0sQoV7MTrbqJRP2fvpCfaHFqv3081cFMM2Tb0crLv9xOdaMp9NaQaje4VHIfQ9K2U12oVIqfXzyEqZGMiUQ3X8Hm3aye0DW21fYu2ahv+CI65UCqYiTPQTEK1foY7wvbTTCxZj8sj6lqKwxzAG+FBWu2sJrVVS25ETmIaIxrjfNZj8oFsz9oLBdylgvHr92pp+xdi30pLbDs5XqS81+PwC/qOZO0wxw9VrVXQJwndnL4ZV3/j9QR5/eXWVns1qAHsDbme0BqpwJjVomwdyeMO4xqZVwWLfavbRyBsg72ui7TjbhrfqmXxOxi9Fcay1f1hWocD+Lzbvw2RJACJicsPLfKPMN/qucpWwaQlmcI1X6AbmY7ZjvOqsHhMZI6fDW6Q2rCNO+jV0vlKSN0fVGCGB55XBZVxISOqGT1HNVK1l8K8qqXkz/D43pa2o36+CSG99nhT8edXHQRIRUxAIeZZrftUcMj5qoMAFVtjVocL/DwJNeI1bnW1odpKf53Q7ABLhMetaJ+YVfszqKZzXYGtDhHoff3rhyJBKYTpd+s+6/91Q/OEFGTrWAStHcAkmGzWsJyMEWQInso5TlJryzsiUBGERsRVTeJmpQTXBjHx8VXhve6keO0f00jq0AuW9y6yImm15b0dpA8mJ5kX0Y56zyeIX2Tb4A51a8+5kTmJKKUzIqXp5iLjSbIR+g7xO9hGxJOwDjGBOnRQdoFBhpUSnVfpy2Wr131ccH/WSdtvDfQLpZHxQiRQ67XRrr1awte9d24b2d/AQbdHfe7ZhRw7cl3o459r3/j5qo1qNRiJHkHPOhgwe4d0Ij+w9s/EXmpyWmnOU6w02YvFYrFYLBaLxWKxWCwWi8VisVj8B0P/dz+AfzRqG3s/MBWU2rvMbohMTIRmiSj4vCqv0YAc0MDyYveLLg00YQaEgXTI4DxftTs5rwp4GqVi9SDOE4kXzKuarankrKavRzKzkWrEdDKjWsKReMB4fjHmvVGbVMA0vBTG41W7uu2Baq+w5tYLt2NHRaA3IoLwD8bri3Fd+EymVxNO2AC7PdNOIhiCqSLipF8VMueF5qy2sEQ1m01LzyyBaKIyyQg0rtJYW4PhuJyc42QEt5J2kvOsBqQH+ETSKrB1Ia4/SFHQrVZt50kSeJxEJulKTscz0TREZ7UBM2qPVBSZSZO9plH9RPyqoDMVZoNx/gyiBw/QQLWRnuS8SJKgfh+v38lrICSWAkMY5wdyJTvOsf/CfnzHHt9BsjS8LZFjAxsoA/3RqGWixzvSHsCoIDbLeyvp92EEB/X6dR9Y66gkandrOSa0BxlJ+geSLzRB54UmiLwRmbWFGwLNkOl3eGgoDfIFXjvPEdR1n05Iv3edA8+T9IE4EEEI1ShlqzAxAxVDttJZSwamo5TNlLY7fdZhAWnISGI68xp3wF7t2N4OiEn+2FZWr2uDo0TtVZOY9Hpt897iJVAzFEP9xEQQFMmBSOncxRRVQ7she2fvsB9vdBxlUH866V3ox85+GG9bslmj7YqJo2oc+85Dg85gE6WrsLeNh+7stmFaLV6xDd021BKYcP9s5N6cJqsZXB8N9X71WU3zKcxZ275ta5jkrZ8fcF0YtQksWlvg+FmHPa7z3sVt5BCCRmTpruVu2KtttLYh7aitY/0RCgcxS1OOO7zO2gWP2kNmAmMg8kOPfZH0sg/UmQe4XsT1JM6on02Af4EaPqpNi9Tnidk7IlYK7TRy67UNfI3aD897Ax0nrwHnicxERFFNrDWsf0P7O9u2Yy2xfYf+re6Z9iDOPxB3jC9s25G3b8T5V0TrnkUbef2OzL8ipnB9oq1j0cj5ex2MCKup8Oj4+UHOC3BkfyPH70Q7iPH8R311LRaLxWKxWCwWi8VisVgsFovFYvE/zJ8uAO6XU9par6ZqBj5g3ru9P1q6Glk7tXOQV2lwdSoie1leW7t1rxd51d7lphvpZwWM0kh9I1NgJtYfSAraD3ROnAa2w3WRAbZ1JAayvZXyuPcKiUxxg4gJaP26bcjRoHe0t3oRtcI2+oO0anNGZIVvGMEgfEPs7dbyJuC1kRm1/ZshRGSFxTmrwBhRDV/rFVBp7dUKE9VAJe/gt64rYZCGY8RUZEapjs+7oShU2zM2xG997QzyckR3oErMQ96q3TouRN9oj3f2/k7HaNbvuddOk2pgKkEzoZndmbTx1pOj652ybSA7qUcFrts3EgN7AEnfHmhmbaGi1cYmUc4Ksft+tx+DQzv7vqNSWuPaU4Xe+91IrWuhaP0Mf9YhABNAiFkhKGqobBVKkRWkpeIO6V5bujPITDwF799qmzqDFEPF0O2dzI0fRvLUHbZv1Vz0wRTDb/2ztH43mJPwScyNqKgfU4V4EWp1aEBvTa5nPTajNOl2IDGRqHZ6aiPtqF1sSVIF1/2n1rn+tKDHb2gq9K0eh0gFu7rVfRsT1XaH04rIhtBK0+1RzdJs+HziWWposw2zDZHqG0Mj4G4oNxShtwObtSurdCSUzKAJaCRVWD8qj81SdqsZUEGsX59kBCKNcb64Jkx2rpiMgOHCUGPM2qee4cQPZbMeqBotDU2rcLo1xAztguTz3rkNQnZQIeSs12tSKmMv3bblRDJRvbXImaQ00Lc6oLJtFd5Tu9thb2CdwG4NeB0wqGD/xz/PalnbUYcA5gW9I/sOLcsP0Tu8fa+QNGr/O9qBM+B4L935/Lq13oluVhr4mKgm0o7qXovUtc5AraH9ex0saJ32eHC8P9DtQI8daYHmqIB/e9C+/4r4hfgkrhN80DF6N+J6MT7+Qlxf9ZluG6DEnGQmMU58TmJC+Dfy5WT/RpwfEEnot1JJeyDbNxzHdWOmklota3n8E9Y21B15PpFxVes8k/z8y9/9O2uxWCwWi8VisVgsFovFYrFYLBaLv5U/nQIambe++ER9KwXt1sq2rELKSQ4n5ots73g7MHNynvU/+h87OT9BNzBlhuH6pA0jLMhINuLWSCvzmkQmTTvpVvpU2zEJxHaIF711CKnwLanQp5VOVlIwNcIdn16/PsetaBZivJBWIefMrIDXjbxOZhjJpNkgWkd6hV+1D9uQuEg/K5Ajap9W5GcDGttql/Vunc44MWuk9tLR+kSozVTNQPys561G+KuUsONCdsj+uJXSpb9NqgHIcKS32gnOu41Y6SH4JCKZkuj1gr6TbSfCcQSJgSuYNrhqG7Q2XwchifSgo3cTtBOSpd6+QB9KSq/XY7zAAs+BupIGwhcpD/x1Abemtx34PHF74S/nnF9kdHKeeLtt3SjySq74K9o2pmwwBfyLSOiP9wqab0VxMEAETwE/aa2U4KIV+ldAG7cyN8D/gNkQUdxPyAdiG04QKojV5nLwqt1an8j1Q0VsyPkk7FHBmAe2NRTH/cKz1S6zBooTuldgTENUS5Xsd5AoQua4u7PJbe6uYLEd1SxFEUDcahc2ox4DTtLweZJIHTzIqAB8S9w/69c5yHjdKvFJWiNDkDhxvBq1tyY6qV3jvNXhEknMwez1+olkBZwzeY6K5sd0UhTNZzWVZxA+CIFgY44LNUXkRFtdG1e9LetXHfLIV+0YRx0osbudnQRidh+zmBVii90bvoqMLEvA9WJuDbPajPZMVA3GV+0ht/3+zyD7vUOdg0SQCEKDmI6kMtKZ+cXD32qLNy8y6j0ECfN3wGAMuD7rP+9vFeqKQjto0pDjDf/8IP2r3v9bbeoyzzu0bzD/QPQgxqzQuL/dautRfyae5IDIjUwgre5puV//+UU65Kb0KIuBajW8UWc4yHVix3+q8H8/yHyRfSebMTmRExwnOMgxEHkSbrV3nkrEX4GNfL5gvvAU9GgIpbgXT3J7wPVByE7GVZvm14n//n/Bb/8ncZ207bfab/ao92NO2B8oTr6//2O/vxaLxWKxWCwWi8VisVgsFovFYrH4H+BPFwAP2TARPDdUnDAnUKI34nrR5sCOxPY3XBqTxD2QnGw5kbmB7/dfNojhbO0Ad1Q6vh8VlOgBgO07+vpEYhLNyJzMTdEBPRLagc5BmIInEU9gkmdHm+EJw53mIJrk+QGyl6J0cLf6QHbBtN/7wJOJkaaY1EusOIwsrbIlIiditacq0kol3SpYFLzCYVUk+bklqnaU4ha/24D3PifvpXvtv4I6Qi+F77yQ3hHu2cy0uta0WyO9cUcqqAmVADaaOqkb+AAV2vUip5LSKsSOgcwTm4ONUl9jhkRitkG0KtTOk6GTDEXUK4iaozaWh6NSPV1agwhkvrjkvdS7l+MbOBsaFyJCzhNs55WfyIwyIMurtm9DuFPb2vy9BIlGa8LYHxV0p5d91qyC/AgyD/L5b7VT2o8KUH8orAVM9Q41K+AUt9Js+wX6DWFg3SA7Stbza0+QuoczXrBJBeoxoW+YJKYd6x1kVjirhpreoS5ICJqO5IU8foExkWyQk4yk7Tvh5/36C1T3FFFFtRGyVcgdJ3QjMzFJ5vOTiAoV44q6r3SrDV9JxKO2rufX3VDl3ggunbXemu9MyAw0J2nb3fCtJn56Ei44Bi9Hj0ftJc/Paty6MahQU81LO57VzkZ2wj9BRrWebUe1Y8wyOM8Bpvemt+Opd8BKBfvc07+SFUjPANuYqqXi/rHr7QHaEauDIGKt1PGy3e1cxfJC6CDB1E6G3Xr3hNbvrV3qFWhCpyNz1jWMq7alZfupYia1rAKSsP9TPZcIcjiyN1TrGk6vzw6iDnpUHVnRtiExSZ9kNlKueh/rO5oXhJD5dd+D3yognyf4ibQ3NAZRQ9ylmtYNP4XXeNHQ+zPouoP9B9G89sJn1vPRsi4EtXVMvEhtyNsDk6By9mrs69tv5Pk7YUcdnLCoieZmaAx02/HnH7T3DUWYvGNR2+DxOuHxn0kNZOsQH/gshbXuG9YbTRMymfH4O35bLRaLxWKxWCwWi8VisVgsFovFYvHv408XAOcMstWe6DAqVEklx1n5o30rNbE4llEBjE9kfyBfTwQhm9b+qULbAgRCOmaTFgp0CCetVWi3V2Bsd1ux0xEdgMDsFXj4C7G9lLvSESsVM+7gd5PSgHOrVy0M54mx34FudQ3jKnU1bSv1K4ZaohG124uACpn3liqlL07dkcxqW9KQ/lbtQXNigqSjvQOjWsL385Fxkj1wASTw80I6YFLXU62Uy2Q1ErWhYqQHqKPtAH9VCEvtjlq8CAE3wccLkYZxkbqVelqc9Ge1KVWw3VBPIgSJC+5Y0rmbxXqCV/Mb7aRuJKWvVp6EbOQYeG7gL9Q2RHf6PLFtuzXdE20HxiTCQKKeP9UaxUrhixhhQrN3jBPUMO2o7WAbTSaSTogSEmTrhP6CQL2OQ8kOcGGpqHwBb0AQupFbA3/egbMgTJgJBGkNlY5MJwByYCrE3diGXjdRnkDpqmMMQu6bWeTeIoaIs7TZZjAqANfWiVAyLtzv+0S8NoklyKi92/Cz7q0GcSXJBWnV7G4NjVtBvO00srag5SDVKtyPUjiTT8Sp96s+ICHjukNZQW3H5sm829ISjqhBOGaC3NddLYnLoY4aIE2wVHJOMqzM5TnvRvj8b5u751Xhedd6L8asbds7kK/3QIL1Uqnn3URmgiSRUhvZOOhe92U4RGmxyepOZwSZA48ATiSDlJ1wx/KsP5dZjVOVUn57bSCDky4/7zPThqihdyhOKCKt9sHjJFNw9wpC+1YHThqAoKnEOEEEU3BrdZhhftUOcNvQrdWRDW0Qr1J1i5T2XAzsHTEQr8Mh2oS0NwTBrxe6f4e0n4cxpCkipaDWoxPzrHtLZ+nNhyN+Vui8HYgooFgTtL8RUbvjuh/Vuk6/LQ9nvY8k6nNj0/rc8hOznbwutBligrGTckA+yTHI9gaPDeWJtPdqmHuAvMi2ETEZ44W0gznn/+qvqMVisVgsFovFYrFYLBaLxWKxWCz+p/nzBcBqyMsxBq4HYoKqQtQcsvS4d4ADricWJ+zfYDq0gxnVSjVRaDsmUmHCXsETZve28ESmg2UFI/NJztpSVUkqcAMiiHQke1UHReE6QRU9fiHzycGEfsA1oFn9uW1HdUf9KnWyKsiO9ouc0MVpLZHWcE+ybahfaG+lT85qHKo2eiRTd2a+SA8yrwqyvMITeJJ9q1AnE7SRroRoBYNSYVT4gL6BOOLURrE72hPTRnAg1vGolp/0TorgkhWsxx3Qzg6b4JejtqG7IfJGKuBJomR7Bw/cJ30Gre+M66NUtlCvRdvQvPD8gKBCKxKPCTjRG+SjmsqtVfB7/o4EFWiZos2ADnTEGk2CaBsplGb4HERQe6cBaCAzAKvt20wkvUJxuUhXGBP3L9jeUKICpwg4/0L2XxGV2ngdT9Cjmt8haIKLoO2dTIX4JGXH5URod7tYaaLImLhqhYAZiCV5DZyzXkPR0n+LoUykHbiXPlkxeLyTI0nJ2lQWcFXifEFq7WWr1XsGhQymfsO0hsU1zmpr2gOZr/p5OVGpFmp40rgPK2gF92oddyV91GstvULkO2w9tgYBl5fim5kEWx0SAKJ9u1XMWUrprFZyRhAK4lIabt2ROXHriCauBi6kX/WacW9uNyNmgIzauI6sgFmoQwN+1v60TELq3hKUNKuDHM3uqe0XSLvDSwHLCkDDiQiSQXjU7nROUgIIjImpMU/Hm0IKBpB5t88bRDI1SU9aPLG2V6N/fpH2BnlVK1sS7XsprlWQjGp/I+i2w3zV+6o/yHkxny8wQUTAGimDZNx6+rf6pxrCVYcG1OqfbKTcSmyZFXQrpQ9nQ7SDJ9rfUXVUGiHQescUri+Y+YVwYu0XPM5SzMcO4yJ6IPMLbXsdWIhSn+fHX4lMQhtyzvqsJSrAf57I1qphP4LYWjXTt38h8hP3F/r+C/EMspdSXfIDeV312a+BvP1KjA8kW+nYt38mXn9B2vb3+qpaLBaLxWKxWCwWi8VisVgsFovF4t/Nny4A1jyRBn45bT7Rx6+Ef4BtlWdJ6VddO7k90OuqbVwm1jdMlUjBM2lqQIM2MLNqzlk1iiWjmoUxcDGSDXBUd8gXaf0OTpJMY1JN294E3R/Ava+LIU0qkN4N7gZvcAeQCSp2N46rnZuto/MiAetCyHX/OSVGVrs4IUlUN1wmSFRAJr3Cr7S7yTeq5RdBuiBtQ1HCAH+RXQkuMhPJREyBILWjWWF55CQ5qh0agcd1XyMj/d+gfccxxF9kJiE/Apzalr0ykXGx75TSNrIaojbxc3C1F/MSwhMsiKjHoHFrn3kn4rOC2NbQAM6rWps2QTvMgaow+4PIQO3euk1B9Ki91RAuFdJLlSxotVFlB0a1uKUh0ghOUnq1WPU7PryyPyqYfLHRZyCtXpeUgO0byUSzXofapN5RGuhX7arqjlNK65iC+EB7/cy+7+An1+t1b6AGkfPWPG93hdxKTZwDoFrZAviJ6oa6IwFNH3AEngFhTILMSbqTURrwiEBFqh263c/Ng9RGWAMvnXSmk/Miba97IrP2oTNArPTXs5rb+v5AvH5PmqLWgYki9KMBjfl84TOY4aVlBsTsVlEnkbMCwnnWFrFHaZEzq0zrtRctupGacF31lzDBGzMNSStdcQtE7r1rbQRJhlcoWf50YjjZuNvTRvqFj4HNE9kfgNYBEs16H+e9mWwKl0BWC5m4KhCXCtQ9D1InrgLsiEwCv7XP1DXLrPd1JFO0DhTkJNTReFWA2xqSFyE7boL1rbTfIaWBF63nFkn6QNJx+fFa1afQFKNpY/qFcpEkMp6lY942ZCax9dKVXxc0JVWJ64m0Bqno8VaNdYUIr43t+SQxXASfF9fzk9x/JXTgz48Kn7OT2L2rnmCN6Z91zfobGla7y+Hk+KrHIJ3MHUlB2kHkC/VEtk7OJ7L/AsT9umx1IEMGuv0KcSHDif29PnvdaRb46Az/Iq8L2U6SjtgKgBeLxWKxWCwWi8VisVgsFovFYvEfD/3f/QD+0WQzHJBjx1Vro7J1RLW2YV3Jc4JPwGBThiZDt9K1nhMhsKBac5EoCqN2ggUhLQh5ELLh2sjphExoUhvANewLo/Zjw19crnizCth0lspXkmxB5CBSagMTQX3U5mWekNWiDYe41bAxZgV3ItWoRer5QWl63VG8Zkr9whHcJ5FOUi1Q8qrAVDs1AzrBDI/EowLE8FnBi9ituO2la06tcBqpcCXvDWV1TOMOTWZtF+teYfn4ql3UhBCriVBt6LYhc4AMpk/G9TsZH3C9YA5clBgTtaTtb3cbWWpTNYMmSbNG9q1C7xlIjLoeYxIhuChXBHmeFTAJFcrlQPxJUsGbzA/IgeYLITHT+nuUWzN7gJaWGk/AkFtBLGJI1m6wbjv79g0RqYajf5Ln72TbK3CLu0EtVpuxEqDbz8Ztjo9SHtOrqewVyM1rMl5OXhdYR4V6baRV8D5fZM5qbWsF+aJGaw96f2BChdF9I6nrEiH3pu4krnrNkET6VvdWOLIp1h+YgpgiMlDR+/9Lx52pd+sYJKJa5CYQ4956vpXkY0BohecR+HUR58Tn5PPjg+crSLFqt4pyy5RJpPaO771m9YkKqEzMGoajzdB2B+u2IxJoXmQ6kQPohNylaQlUoCFI5q14hpxe7dfMatNG3Ep4gfR6X1wX1jakb0gGKg1tpS0mpcL3SIRADSRnXQfryN0MJgYhgc+zDmfkRfog/Cp5gFarf8yBRyLXV+0jz/rsEtkrXBdoKoiUAr4s1LMsB3LU58017mC/NN45LtpmiAqI43MQ56jDF/177WXrDrIj/XFrl0ftkvsX2UCtodytZ1GIF9oCC6e1RhqQd0Df2n0o5Qu2u62sD7CGn18wTkSet7I+62XffiNbGQXAye0gtcO4QN5rRnj/BRVHt0D6A/MnKhvpE2kKfJHjj9oWP5PUHc0LGVfd5zHrUEcafH2SOhBx9P3/gDHg7a0+uxaLxWKxWCwWi8VisVgsFovFYrH4D8afrgGcZ5J7J85B7g9MEpHaSE0vNfDcOu6DlgoBvU9ojRmd9AtmYmpoNMRnbX3m3eacg9BGipMxSHbcomZSZ2Kbl/J5TMQakiA0jp0KE9oOAmlajbZsjPFFT0P3rXZfJcAFRrUjsdqDxU9EGsmJSBIK55jk1VB1cnyAdqztFSbpRqoQcZU21i+07YQmeTppRhDM+cSsY8MhHdcXun2D1gkEjSCyNM7u1c6U3pEAR1A97jnghsaFei9lNS+Qtwpa7v1YuZ71+4RS6MakmVZghaD5ozH7ee8gwxnQNTB7kPnCJIgwNC9a+84YT/TKChVN6kYwQ25NrnhirTHOi9SkxUDfFMatpU5q0xWpAFk6aJBzVPM6ADXcDkSEyM87nKyd3Pk6K5iXjZBEkToQoIamVelTNuJ8/jetbbmDSW2lmxatQNgn6IZglOF5gwhyXKRkBeD7AxEqUNe6hsgG5hX6ZaBM0IQMQio4C59gO+KKxyTmVzV13avpSbU6Re7gVjeQnb69I+a4QEbW1u+9DRvh5EyyH6WcViHYIb3EztbrUELm/dpUYCxTfvzH+71puCg5XhU+x7OCR+QO7CuIkx+7uKrkDIgDmc9qu7YNzUnOQarj1yA1qumbAgRCuyd3lRGDptD6G5gi4RViS0PKKU7e96PM0ntLN9px1J53XPdBDGhipCTYfmuqG3k9kbbD9Pp3GiC1EYxwbxq3upd8VKO579V09pPMXhvhNKYoeV5gG9vWa085o1raHqUet0k8HfYdZNx72QK3fj38ifALGlJN/jAiHEVrn/j6grYT2tHQ2ksXh+uTtCTnrdO3Ts5PsANpR71WYuAwfaKm5Eyu/ETsN4wqM/uogxqqEPPrvghK2kacH7UlPV/V4u8T+js5L4ILfAcacvxW9/zHJ2Jf1fZtD0wMj3pvyfadHMA4IUutneMP6P+ZmBeRgfAg3JGYhB2EXxX+P39H3nYkToidfP3Xv+8X1mKxWCwWi8VisVgsFovFYrFYLBb/Dv50AfC2Oaon/njg4qRY1aAFpCkxTnzkvXWppCViBwBNHHoFRdxBBzFhGNKrdZrzqjApYEi1/TZrRO8MD2ImMr9wO1Dr9Fkhnfqo8M+sQrnM2oV1pamgeodjUirirHojULumeW+Rav9G3wQJqRD4biSnBiIPVDppjbRGC69McUgFUK2ep/IovTINz5MrBLOgpWN2gBwV2MWE6KWD/hHOtR16v9vHQA6CCjnHHCiB5YW2g4xntY5zgAnJTjapTd7jIMeLRJitI0BzSBOkGbI9KBvsgeB4NNTOyk9Tagu3vQETF4FmqHXCB35+YfpO6hsNIWVUA9gm6DcmYK8Xsr/DPEEbYp1MI+dEu5AhpNZWKzORjWpks9UWME7GVkXgy8nmxJnoBrm9IaGgR+2JkhVYOvBjHzpntYh7EnHWZvO956tWoS9CNSDtAPdqI2dWmDyvamRqq8eYA5FfIE9EO5JB+pPQagfHOQFBzLB51uORhs+okNHsDkDvfWRp9aqa4nlVgVU76i9c+92GnaV5ruyY6UlD0XRgh5jEvYMtMus5Nu5GczVHhVlv3HGib78heeHXZOaAmFh73Hu2vTZqmQS1cZtZumVJYeqBesmTReLe3s16T6jf17MRMgmM0IZJMvPC/ET7A40gTau5KwJieHg9PjOwexvZ2v3YBaORUp8LYobqRbKR7hBSW8UzYD/QTHDIdsD1gWzvVFl+QBp5HGTbkaw2rrhgxw7zovUDwrFe4XI9vlk6ZKv3Y/oALpj1GSJyQXsHqC1ub3QJ2L+ReZJc972stMcDXn+pQwN6EON3dOulateO2P4zkI9UwLBUkmBeX0gqbLP06F6/nploXuB3AC+CZrv1+bPa4sf3+hyJJ5ZRLXnRCurnF2JyB+gTwksrT0C76h7rvxLX7/X5FHXNlQciZ2Xfj18rwGerzweCfL3w+Rfk27+QX38g+z/VbnJcsB/1HRDfsAxCH//LvpsWi8VisVgsFovFYrFYLBaLxWKx+F/Fny4ArvBMsQA1JySQOWF7w90Z58YcgraNMZRdA5hEGpX53EpYTxifpDneN3RO1KQ2K8eE3uhRAZlcF9aosMwH0JB5b7daqw5eKmmCjidoJ0TIqJBV9YH7RDXQ6RX8jlcFGPpj+9TusOuqVptIlRrzKuVuQqLVPp61o1lB9aw93XihtpdK994gVRtYTrbeUUbpmKntYUJqe1grAIJJbh1BkNaQ6aWcjdry1bZX6zTvJqRUKzVjliaZxP1V19lK9VobuhDPamxqCtrf0XSSVsFxBrI/iPMPxktJqeaohuMqhI97a7Y2W2OMUuVqtUWTARhbt9oQVkd7I6cgooQawqzWL0LbNnSepQq+7vanDEpzfIdaCvH8RLZeSun+QM4n7Q74ZE6kt1Lg6gHnvyLbN8SM8FHP/d6XFhTBaqsUkJhAVBjd+q1TFugbOU8ykiaBNGPmdrdZo7acewXEUC9LhCLtQGRibRL6juRAhbpP1FC+YHvDmpJzIiGENhQrlTZSjVrJupaSiOy1oaulMm5CNZq1g38C7W5U37u8omhr1Xx1q/v3Dn7zViKLgkQ1tFWV5kqKol67yEKgGXgEmlnvVa3d7dROy7tlLEKpyifYRoojPmvbuzWUDuOFit4N7a102rc2PPy8Vex2y9L91jk7Ilvp0v36GVyLeDXWzUhLFCooTYdtBxx6Q6Tax6kbjC/CQTmJYQhBpGNqMC8is5rh/sJRQjZ6nQZAzoscE9/eIT4I/U6edbhEosJdvDTZ0JF08npC2+nbGxInqkZE7QojP85yTGT/9lO3zX7UIYUM2L6DOJk7+fqAPm41c1kVNDby+BWZT1LvT6scIEdd53mBVytdtYP0u937B8oLwhD7FZmf1TpnEO6luu47Im8goM2IOUu13n+7W8QVJOes+yQy6O2JtjfyeiH2BmdtNWsEMSdx1ha5XK/6nI+z2usomYqpIq3DGEj/7e/zRbVYLBaLxWKxWCwWi8VisVgsFovF/wR/ugAYM9BqtAo7I6jGWJ4Ipffd1GFruDQ8nJxPYn+nC8gQctupOmYFapoTz6yWmyhoIGa181sjnzAnaqAjkL7XnnDfKP3qSaaSofgE2xSQWwVbGuoZwtYnYbUjigJjIttA9EFIBSuZ9+NiJ/MiwmtLOEvbWy1RR0lUdzLOn5cmyQqA7B3vCgIWDekJrwFbv/dJ/b8FqFohN3nv7sZJbxuo4mPU3m8mkqA56ds7I7XadFoqXY9bow13o08Ij2or56Bp1q5r21FxJBSdT1xLg7ybcLWOpTCxCmYs6/masYkyECQc23rpcEkkKqQWKc2zAGOcCHs1qMcAfcD8y70ta0RaacClM/1E2RBJOAe0CpZTDLFfIE7ob4QovH+rsG1Wu9siK/DD773bvIM2JTKZXx9Yu0M028FKqSz9O3n9cbcsvXZ2yQpHCZJZAV22uvZxobrXdaWCQ8RIbchmWIvbOv3AVGhaAX/MJ8i3UoNTZmLRowK8eYJRGuOoUB81fL5KHSxOHTfohFUrVCLu1nE10IWJ9toQznPU47k3j82MyHY3O6NC7/6owwop0DtqUn+Xv+p9NycpE6URHkgHkY4Yd3P7ye1bJ0VQrVY6JHprqCWknkrfUa7SAVMZZ4giOQlX8GC06z5AYPebRyoAz4AMUhq62b0FPatdH9ya5Tq0oVW/x01KEc6zlM+A7Fu1Vv1ETDFRkiSilM/pJ4jcCvtAJBHdEFXUDNUkeEe0oeGA3jvGF4Qgx61enyfQUX5on6U2ibMCcqQzr7+iUv/e+oFH1vOWAzTr9/sFoXWoRW+HtRktk0sD1UAyCYyUTnKhFmjf67HZC5GtDqP4V22g7+/kKE13+u+o9DpgkA2xsi5UG/iiHd/wDPL5RP0ETbq8E/WhS+4PQNm0dtrVX6RaCRf0wDNx/yCef0Xsgb3/huYXrv+E5CRjIPNE3n6jWUOv/5uQo1rFi8VisVgsFovFYrFYLBaLxWKxWPwH408XAMfnrLncPkm5yHwn+qPCwnC2vSP7xBmlyBUj+kEnEL8gO8RWW6xd4TqJ6ZjtFYwykVTkfCFIFWwrZSBlu6/4RKNC2Tg/8GtgtuMInkpa1A5tU3I49I5do5p79ka5j7cKkC8HTtSOOwhSqiH6Ak8kZoUlakRepfJtFQILE5ONjJNgqyCRjWDUhG343TAe5FYKYhmBbA1CEe33puqFK8i8ajfUqhmYEyJfSHvDctRc6ay94cAJH9V09tLvkgbSyOsLzAhxQpORyY7deme7W6gGqmiAZnD0b7yuP+p1ab3CV1Oyb/j5ewX10gndYD6rAYogHhVip2PW8fmq5z2rWSu7ECQyohqbUYpesZ2WD/z5r+SV2PtvQCOnE12Q1vHzA83OVEMsMBq0hs4kzxeOQ1Po7wgnERNECLQUv9eJbO9I1gGC7HfLW4+7iV7hvqRzR9qE7iSK5ryDqwAJUjaSqwLVnCSdnF52YQLtb0hOZjRkvvDsFepVhZgg0O1RvyYG17ibsNy7xS9C8raUD8SjFMmZhAT80EH3DTW930teBxPcIQdmikcy/W7P5iQ9Ca3WuN2HLuI6yfBqseqDGF/3c2yoGtmUGYHKhegB86pgWOLe31Winni1pxU8nPAviIamEEyqXK33AQGpkLgZQR3qEE/ELyKFTAUZ96RuBeIVxis57s8NkgxFNWvhOV51z4xZ78t+VMuUWX+HO806Sj238AsJq4J/AFnX0WdgstVGsHVSO5Gz7tv5quZ1OtI6TQLvSc5PRKT+rsdROmVR5PhOnF/E+So9tDi6fSdj4FHt9dQO8QKbCLXtTEp93rUOskG+UB91MGZ/R3XUoQTZiTyRtpUtIErfnPJALMn5UYp7LkQf2HYwr69qUZ+v+nyI0jHL9p1wR9Xx578S0iskHy/wC8fI92/k+KzDEG0j/BPt78zzRPo78fyse61vRCTSv9+vg+Ov35Ht1zp0sP1C+l8xBcZJPv4Z//wLLvMf8K21WCwWi8VisVgsFovFYrFYLBaLxd/Gny4AlrtcWwOTiTbQGISCuFfAByCO3Lu22/aOZOlG6b32Qnupg7EDiw+07aBJ1hjqHSBuqEkFVVK7pCENGQPppT9WMaTV9qiFYFatQBFBrBMxuXzQxCCEJEGsGnlCqVhnwJhoV1ozHCUzCT+rITcHbIrqdm+BttoHjgo/Yz7J9s6cjqqgtDvI2u7IzWurN04SQ+QACzKdyCR8Aq12iM1LuUonstSzKk7qG8KLSbLbhWrnCeT1SeqGCOQI0ga+NbhGBVyH0Q2ggmLR6rlGGpIQKowJHp9c87wD7SBzInzHLsdlI0LQO/wUDTir+UhqtVmvQTalbbWDLG0DG2AXdiryeCPjC2JDuiESmIK8/0bsFQ7y/Ldqt/bvtbncNjK/UPleN186gqIZFfK2rHBeWjUMM+5gDJQD8Y7uWzVXM0sBzCREMW3E65PESBu164zdW6cv0qN0wjZJf8E4f27DpnUk8r6X/J4KfmHSEVVcqD3UTMiGSanCNSeRJ5qViRH3bnXcCmh9q+se1D2UA733diWCDO52qBNZzWkR4PhWv56zdNcxSa0OcVrp0TWCbA9yvmqD+L7HoZqntA0UIu9GKkZItfOr6ls7vzkD2X609RsZJ34Nwu73Vlwk9R7X/rgPRSSRjspBMO4PkFu9zQFx1WtEI5Nq7QrIOSt81V4BddZzr0z9x1ZvaY+19dKpp1fLOgESaUqOibSOOQSz9py1NPIhRjs2CCdbg3hWOO6JqNVnFbWFLW0vFfLrWZ9f7Xsp2X2SszatJc46bKCN9EFSKnTVUkwHjqLgwJiw/QH2qNdCtYL/+KOM9NpQnZg9YPwV11+Aus9VEpGGzk/Cs17/Kk9j/UHMQfgLckfFCGmwPeD6g2wPRB3xZx0ACCO1k3MgrogIbm8V2E/AG+hVu9vyGzkGbL8BncDrQEVO5N4OzybEOYB3MpO8/oI8/gWOfybnJ9frC7TV496+/T2+phaLxWKxWCwWi8VisVgsFovFYrH4n0L/dz+AfzTRgaNacpKCNAEbZYY+BNkq2NHesO0N274jZtXwywReVPoh6HBUBLa3UqZG6XfTlZCj9M96B035Q98apCohE84vaEaoVBjWs/7IvpHCHb4cSDhTO2m9QhcfhCcxX/h84iFEGk5nhBIeFd5IryaicAfSBqOal0HgIsx0su0/w0AEcl4QSQ4v3TFZ4aQYaRfOVQ3DWQEzdg+Ftp3IhvuP6d8K4uZ43ZrrSXgwovM6R12yaGRYhTK9gTaGG8MHKoKiyEgYVLtXG9YF2xspgs8vpvx4RRoBWFPa/q22hOVVtuKYNINmgehWWtl9hy2RDmxOKoxUIq1CXckKJHsHrnqem5BRat4E0jawRuSDYQeO3kFmorohr4ExMSl1MJnEvsFbtWkl7/3YayL9vVrQkUgD2asVnRiRwpxXvdYeREzC3mBcaJbWOjPAP4h54nJUmuZJZiPsIDOqvRoTqK1iVdgfG72/0/eOioPtiAqajuJo1mGH8ElGErRqbNvdhP+hnL4l1Pxo99LqkMH0eswKY76IcdV9dQeiIlGq7QhUBGt7HZbIIKGaqiK4V5vVegdriNWBCXt/oK22uQFS7d6CdiIGIeARhADdUAFr39hMqzmtCqml6pZOilTjNiDYcDUiHI9BDsdfX6Vnz6wGt5b+XLQRApcn1xWlAldKw6xGaxu2bxVo9wNVgzAwIf15H0ZoyPxEfNR74TqZ2vC4mCQZF6ob1o460JGzwmSfRDO4pELoVu+l1AY2S03uddCFvoP8et/HSc4L0SRlJyLJ+32d85YajAvmvDXg7+T5hO29PhdlA7IOrdxNd3TWKRrZSDPA8as01RYX2h+EC+4bYySuDY9Rr9E1ydygP0g78HiSedWGNV7NaAPaOx5yK+gh+zvYO+QgdEO2Azu+oxno8UA44LyIVPw8CTsY/qzXkqzDLDFJNeL8hDiRZqi9gX4n8+t2gUO+/zPpghz/Cev59/7KWiwWi8VisVgsFovFYrFYLBaLxeJv5k/XAB5xa3StsWVDngG7lYY1snSvDQgwmRWMuiK9kT5hcqtnG8ELNInrDiZ6q6B1SzQ7IVRw0RKwOyyDap7J/fsU8xPXXi20VlvAtPfaTCUReql1VatdTOlhUSFSK4zowfRBm0aoI3TUfrQXt6psulcbOATZG9o2YgwQQ6zXRmlFqJXgzie5dZgJ/SASPBSb191g7ogKzFJKY1ptUQ/YDMZOmqA87vC8msWZQDSadcIgspp7aYr6ZO8KssP2QJnIvpOe1ZCcn6AHMkFIpP9CZiIhtz66WsZqHREn+3dyJpNPGNWeJpyZ39GPv6LbAS1B35iAaZD+YsYBL2drJ6JKRv05mpfeOvu9C+u13Xs0jF8QCRqBqDFzkN9+Q2RW6bx1UqJKoBJ4+46ME3qrsD8rDFVt1TQNI3PW4QPaHWrq3RSFsANxYc4PMKOKsUZKqxZsBNJ6Td/aASMw7eR8EdbJnNXibUc9Z487KJYKccesLeeU2lF1JbSCbPET6b3UxbahCCGGPV+AQvuxU9uqsd2UQDEG2g5SEgg0H3i8ELkIqbY5raG61Wse/51iV1tpg6XCZsSAIK8vQhTNRNBbMR6kC20r/XCqEeykXBjCvoGftYWdfcPnhemto9ZWjuW4SpueFRDXnq9g+4Zq7Rrn9Go7Y8BJt0S3NzyiDiBQYbQYFdJHBbWqG8wXYRt5DaQJTRR1BztK3X0H68oG84uMBO0VvM5n6bVdaF3qMysT3g+0NbQ9KmQ37l3iRE1hJK4G1wWP7W5wG6TUe23rpL6DNNS+UNnq/eyO7RWUpu383DVWJf2JWKtdXRLZfiXnF8QX9KMex9ZRKWuASEc7uH+R+zckA1Mlxqhw9W5tC3Hf0wrxicgDxifS7qa7NXKO+rCWXg1n/hPSFD1/B1HieiHvv5EE/vwijx15fEdjopmkTkQFIWmPX5C46vCKG3H+gT0atn8vdbR+ggfWBble2LffmJ9/+bt+Xy0Wi8VisVgsFovFYrFYLBaLxWLx7+FPFwA3GTAUlYlsG+BoChlOppHq5BDCJ61VEJa0CiGy9kUrlBA0j2qmzoAm1ZqdgcQFhyDUdiiRpCh5Xeh+3K27wf/H3r81SW4kW7rgp6pmANwjMklW7ZucMyNyXub//6k51+5dRWZGuDtgpqrzoEjWa1d3154tQnwlFBYzGZ4AzGAuwqVrrSmNhQPPSRw7qkJYx+ekqRPzRbYN94nlAO51HbvDBgxHrZUwGQYZaBPMyr2YsZOhCFKC0pwV09sNI4l5EPEq8dSdmOVw5YeIawv4DxdwA9+xZojpKdJlXYNW/HO5eBfo73B8QLfqFZUVkx21hs9REbZ9gfGsxzO8ukBPF7WEIcsG46Oemy5A9b1mE9xWiB3TRlusglyzOpW9LUgoTUqQbjqYvsN8kctSbloSeuJzQXxBxMEEG5NhSveG+SBkhTzg7Eemb4i/CGskAfEiZ5YDVAciHaiuVmaQU0uQHQfSBxKt4rF1x92r5jVeaNzBlhJt58BtqSUVL+fwGaEsrcP+Cd1Ki1Ql+gL8rXo2yXKAxkTGrOjhsSNtRzBiPks8Hk88XoQr3ZI4AsLRxSv+vC018HAKqWnnMAOd1CxRej5J29BIRBLyKKf4uqESNRSgC7TE9FaR0OtCX6TuzwPRUTHIEohUzzT+RKhhgmjnwIa/0HYn5yiXqs/qeBaplGMcSMQSo0pyQxy1wOYg9EZERUw3mTB2PBNkRcJL+I2o/mTidP0nOef52QtkIGtdD+mkGXEc5fA9BxA4npi0iuvOH9fUYQ4ijxok0CDzOOOm61pkeSc14AhUGs5e4rwa6Y+6NlG01fs+ZyIm9NuN9Ccp9U6Lgmqn9RoYGTPQ/PF+DGIm9Chh/Ui0r+V8VkFyYtpg+YIfH3gkmNeQhCo5PyEHLF/An6TewAeMgWw30AVxJ31WhLKBZJLucAzk1itee+xAEJ9PZKs4+nJePyBHicyfD4SA9V9I9+r69Z3EkfGsz7ZG7t/qTPQgNavS2d7Loa9rubuff63r7wuMUUK4GqIT+hcyPoljx+ynipk/XsTziXz5GdFWn6tvwITn9xp+WBsxHzUscXFxcXFxcXFxcXFxcXFxcXFxcXFxcfGfjD+cAKzZGCnEcZAz6Ay0r6d6NjlCyfnCwsm2QDZIZ7x2YlnooxHHC7vdf+8S1rUBSgoMcQxDp4A6gldPK0q2jkiSuqMYC1Ku2fWGHQcytURDKVFRlzdQWHQtwaYHHGfs7HSQXjHQS0M1EBpmUn8WB4wn5EZaq59pKwAmTtpWvbV6K9HOFFrFtWId4kVonjG3K+xPRAyRitAVdzLsd/EoKXdqzgM9U2EFL3FajGwdnzseUQIzSqpXF68o2K0EsxQgUVlIbURGPWaRipTORuwvMgyxHR+dZp0xXri3ioOdk6nQRZljEqrYsmJNiP1BIMhQZOnlyp4vMlfIZJk7aHUvW3ur2OEmqHZMIFyrb9UnYwaxvIPvdDewgbROppCvT0J/qpB13WAeSO4lpIZhISSNbGs5ajVBNxJFNGmqzHQ4HHovsSqFVC8XbCQpB5NOVzs7Y4UcE1eneSCLYSmkdIIgdIHxQNoGDFRv6NKAgTVBbEFsJeNA/BNvjZiTXKyWLEatlUjdpwtyRgD7eJFWUqkhkELoHY4HtF7uZhL8xXgItBvMIPQT7EZQ91D3UetOnN3T6WQmFnXv5YIGxpMwI2i11zJq7Zm41iBDjJ3Qijj3nJDOMztqE+jE3MkcuGq5a0VL6NWz51Y7Yq0c0etSgrpPPAWzBcxLhN7LxQt6Om9riCKz1dpyDlVIki44NbyRY1QfdArqA5a19qSDSKJJrftSkfXp5aJtXQj36px2IyKrk1aeJb7PFT+C1BseE3TB/VVnmpfLmrASZyNIS9J+JsYTsQOfju9PEEdvNyTLKZwo4U/wQBlkvKC/V8R9Hug8kG7QDFHFdBAp2O1O5CT9+L3nWbqB7tX3G4paJ/0gxYj2Ruzf6HnUOdHfYX4v13m7QyQWT2Tt5551nIGPo4Y89IbMGt7w4wNpgYzJDEGyIv+JJLXjfoCs6PkezO+/AR0Vw/dv2O0rmhA4LkZGvSvEE/pP/yHfWxcXFxcXFxcXFxcXFxcXFxcXFxcXFxd/D388AbgHPRX3hexJYtV9q1KOsXmU07XfT0FUkHGgsmAqWHNMGtq0+lR9UiWyXlW4BiJWT3aWS1DGAa2jvZXo2jfy2E9x8477gYZiS8UMpxhhZ1ZyTDSEam2tKFnJJEZgemDWUeu4BypBzAHRqu9XbkiHQBj+wtodzaMin30i2oBZHaQzoTuZTzSslDh3iB10LaH7dqt703JMk/WIoq3k/Kze1Lkj2hBLMrIcdGTFCIuQaogsJR6pVRzr2JkzSxhOQUwr+tgUspVIJYHGwLgTAqEBYxK2s6cwQk/tuBx5klLO1PWNPl8cPyJ1tQQ2lSBGIuJ4v+PHTg5nWRrThSM7XQ7s9hMyP8p1qg2RTh5PMo3pjvogtER3oByM2mDZ0HyBd1gFcjuFb4jDEShn77Ih8ayUXrzWmxfhYLKVkJh5Cq9AvxP794roJdAfgrBkRZhrskRU1601JCbSNpTJFKnYbhPQDtPKiWrV1SsYGVFrezhpiguYWgn8ditHdparVEyBWYK3dUZ8ICakzOq/bbXWZUutvR0orgsWE9qt/NiRdW8RRHpFeYtWXHYOJlru0pyY9RJoQ8h2I4+dYYGmIzHBjYkT4kh/wyQR7ZATFQj0XIZz2EET6PUsmyIiiLcSuZn1rHKebel5RoEP3AfajnqOMckm5y2U+5gE2gLHTmVbL/U8YhCZ9cdHxVXb1soNrMu5jxJbvgKvs0ebiqp2KkK5306htCK8IwaZxugNyw0LZY7zGqzVsEhGnUf+rH1ovYZTYjBH9Wcjs9zWzBLj169nGnytd8q5DxDoC5GC2A2JnSxtm1AlDyeXOyJWQv58KniB3gABAABJREFUIrcN9cR5A5lI7nDMinFmwXPSyEoBGN+h/wLLVyJ2IgIY0NaK34+j+o3bvZ6JD1I3MhZoGx5+ivglcAeOUMMOmgNvb2g6+XjB1itS/O2N1jr+/f8iXeHtHZ8Psm0Vo398kiHEsgJOvj5pt47Exz/uy+ri4uLi4uLi4uLi4uLi4uLi4uLi4uLiv5M/nABMdhRHW8Js0BXJZ4kv01FxdP1agocf5b5rcbp6nyVmLQtwIGxksxJ4hpOdikuds/pCrYRSkeS0E579rOVgnNmQfEJOUoSJgVYPqkaS2UlAJbCs/tuYDbEVbQ72Bn4gA8wrxpiI+owfkbQx0W60WBivJ61r9RpLOUolJzmT/OEW1a36f8MRKVGctFMok4r+9UnGxNqCTMFaEtrwmKgFMp2UBqcLU5tSDsgS26qv9klE9XzGPM4/p3qCxQ+i9RKyZ0nfSCez4T/+2RtiRu9vaFOSAf5CaDjQbcFap3cw17qlSNIUiwZtL3HbGjJfJaTe7hWjjbLkjuiKWXVA1zUkpCKtYwTWVpj734S57Vb7IBVpN+R4Vr90lrBoIqh0YjnI+R3aUjW2dkfygBGk9ep/nt/geMIC+Apm1YuqN3L9CvOo31/LfZn5o6O4ungllZiz1i1qvTy81jZBZMGaEpNy+YYiDSQHIiDrgqRW/6xkxVOvW4nUM0BKYFPtCAvuD1T6OQxREclyRu2S1DXMifQ7LQ3mB+is9248keMgbSPmC1hr72QNGdgI5G7IBHiipoSXIElb6f5AfUf7G9mFhiLcgEBsxdJPEfpGMMg8e4Sz+n4zo86GKBc9WZ23UHHMolpdxv4g0xBrtDzfEQZkDTukCDQtp64aoh258bfub6n4ZgUQrb5jSUKOek56J2NUFPbZf1tDGgcZvURpM9L3c207EaP6bxP64VjXis9GSTN8PlC91RmXAgnzcKSfh38kfbuTx4HIq4Y3UKwrCoRIdYDPvc4NesWzR5zDGkbMDyIVljtqifaGjrM/naN6hcdk5ifoDaQh9k72J0JD24pa/buZcZ4/B2aKiKJS/cuanygrOR9Ie6uBnfEis/6S5R1lRXgQxws1xdWwylqvXvTjA1n/hOROe/+lHOav32rv6CTT6T//v8mczNdfzr7gAdnKSbxuML4h/Sd0/npGQ19cXFxcXFxcXFxcXFxcXFxcXFxcXFz85+KPJwC/DmRt5exbsrohYyEBxqM6MOPAUdAN8Q9yecP9QRuCbDdSIEeizBKqxgFLgwCwcs3FAVEuzOx3iKPcaxkIA5ONhSyxKMs9qnjFBccTUKQJMhJaO3t6G0YJvMit4kzbDTl+Y0RDc6EvDW3VQys+UQyOHdEb0hVTLcEKKwGx38AetHZHNMgxIQfeNmQeZJSbkrUimoUol6d2EsN1Vhesl9wjYiTVCZsxSsTzN6Ql2m+YKh4Tl1ZC8hxnj2aW29E6OhJBmLlTmngr97NWt2pmkC0RWVh0omrldPQS3qCE8NY2TJOhgpJEJorg7tWR2zaAchg2w5qS4agGlhBq1W8agbS3ck6qo+1eYrUq6p94LuC/UrHWvVzCc8ddsHkQuSBdy5HIrN5XbUgYgYPd0HgBUvdCJ+1GzAPalzOWeCJZ8b/nZqneZ1lA674QIwkERTPpa8MiOMZkitOsQQdcyhlLnpHUA+k3iIm1TsyBtK2clSlI+jnI8AIPQhLNXoJzBpIvlMTbV2I8UK34YstJihIhvz+v8rLHGTNOrbmcHdz+Auu4lAgvejpnDSTn6ShVMgQXwdJLX3aF5a3ESpW6NlGEmokwC6Ynsb/ABFk6Ei9ENxTwUbHf+FEGcj+fcTMiwXQ5nfC3U+yt51MDC05GuajLOZuEVIe4+MBadTSTA3BEOZ3Np2AcDWm1oJJOZNY5QUWji3TwURXflIs3xl7PjKwBFelgE8PP/aOkP0g5XecieGQlFnQgswICpKNnmsHMWougYUxyHGS/VSS3ZQn1UfchDCpXQGDWIIBkh76BD9Lj9/5f0iAFP77D7Zc64+aBz4O0O5JZf1FR1PiO9Pez8zhQO6P2Lcm5kU0JWWoYZk9YFNE3hETGb0i7kzLQbanraAv4ju6fsPyM3H5COFAz2taZjxepaw0+pNPe/4nwnRx7neUxgB17+zMsb2QeZPuCLivMG2Hr/9zvp4uLi4uLi4uLi4uLi4uLi4uLi4uLi4v/Cej/vy/gPxzz+o/6tpNxEGQJZCkcsnBgHHQOhSnBtJUxEz+SXLaKGA4lYpK9RC0aJYquEPxwyhmMJxlHiQbjs6JaUXIKGaDHQLQzszN2IfbERzlKidfZIWq4xyly3eHs1ZVIGgOzgHYjVdFlqfjkeQpEUubkitMFVQE/e1VVyNYodeVGJvjhOJ2ZjRizXKXdyLYQPkiSnF5u0aTE4Ux8Ohklvp3GQEKcjFf92eM4xZ08+zal4obFiNwRK7cmeZQjGAgfiK7n72U5Xj0JWgmJ2us5K0gzYg48GzMNl2BIsseT47ETYkyMQJj7jmvCGCWOjQPtjfCADGLmGVvba81TyeUrWCMzgSQZ1cWbUTHEthLtCzEGQkUAp2r10+47KcmMYB4Vl4w00r4SeUZexymUSUdJLB/VT3z7Uu7OZUFtOaOeo7p0ZYHtDhzlrs1ZIjGOxMC04S48Q5kZoA3V/eygnkTr5RRGSLmVsCyJHy8ka5ghAkRrXSqC2kC1hPVu2LIhkQRRYu3Z4ZxhJAtBr27aGERMwuq9iTiq/5hWexUll62E/VRU1xI+0XqPRGt/RjnlRaxEWZG6b4tyHp/if54dzUHgOc5u4TNuWIIMw3UlZJ4idxIeRFase6oRkrVl6ThZ73uWGJuH43GQ4oRW7y9qREjd8/NJ7Ad5Rmsj5eSP7Hg24tgrTlkByzMeW//WjR0HIjXIIZKI2Rn3nUDQlhVtp1N530mf4C/oKzF3Ig7mbHhUd3JEEF5xzCmQ1pm2Qe8V8z32en5tQVVpOaGtxHyR40H4Qe47okvdy/FE2hviWWulDbVZ766/iGNHZKkE+/EiMggFtYaRiG0VQS07wifCo8TrrmjraP8ZXX9BdUHjA5mvitW+fan47PYV1j/X+JJ0RO+I3mD9E3GUmz3VKqkhBqw38u2f0Q5266AHrD/hxySP78Ryx+dgvAYzwQf17rPUetPwTOroMbT/BMeOe6tQg4uLi4uLi4uLi4uLi4uLi4uLi4uLi4v/ZPzxHMC3jRzVhToD1J2whZZBtF4xtQQ95RRahWAwrFeErg+yLUhrcAzE2ul4hchGDke7QCq0e3WG5ouIBU1BGGTr8HqU+zE2NILowsx29tMmyAouoIbPJ7IsJZgsC+JHKbstYSpCq0bTfGGpyDGhd9ClXIYImqOcmd3I/YmIld4kE2wpEUkUkUkqpDby+I7kDZMBsqDSSBkVB1wlpme0r8Midb22nPGwRuoNMEImMT4rXrf1ElXHq0Qwu4M6MiDdyPGJtOpVRbazX9RJE+CTGA0ZO9IU2o19VMqxy0YuSRw7nFHPYy+nccydOepzXEA9oL/DMasGtlcfKiOwvsLzGyxvqFQvruQEfyLHRLY30idmQsop5DNJOixfyZyIKGbl7qS/EylEKk0CyYoZTqKuZQzoSc4X9FZiamZFUWciDPSMO07bMBHCdxypyuSz5zTjKCEOIJUZkDFIrRjdCMAbLFaffzqqmd/Rdq/rCj97fpP0QfjOiJU+HGlnDzG9nOBzVp8yUs5YtPqz5ex+NmrQAGX4E8Ro0StOGAc5SO2gWzl/MaIZMwbd5RSeBfyoCOmZOEJbgjQr923poRWR3QJx/92BDlmu9FzY00vMbG9oUveaHeZR66UN0k/3OBVDfMYwa+7kFCQFGd8BRXp1VB8x6bKeP/9DCQx0WVFt5fIe5YiVda0oaaUE8gSJANvqHJjlfjekeoirRJiMZ50XY0f9IOkVixwVm54tUAsqAV6g31Cxsj772dfL6dJebjA/WdbtHGYo927EOXwhHV2WigSXXk5otRpGsQUVh6UjkRWNfluhreT+ggDNg+wrersj84m0RrSfS4Rdtho+YGJtRewLGXtF8uutYsBjon1D5VldyNaInXLyju/IzZHjSciA9Y4t9zOZ/SiXvBnRF6Jt5OP/gP4FmZ+IrmAbcfyKciv3+dzJ57+T+4DmxOMb+uWX6pLOA2wSU0lx0DvEN3Ah/BwYOb7B/d/I1//zD/7Curi4uLi4uLi4uLi4uLi4uLi4uLi4uPj7+eMJwMcgKRem9s7wQHA0qj9Wu1Yk6RxkrqQnYQ5sEA/AmGakCYtICai/V6sGYXlGrFZkM1LRr9qUkOqClUzoK2pvIFGm5HWrKOVeYlyKIFnOP5NOyumyNUFtARqiSSSIC7SlooWlwbKeDuLznltD0ipuWaWEKHdSE4lRomNrCIFKCaqlX94ghXBBVCrqtcsZYSzVe2rVrwmCzIm0tZyJ4RUXHBPp1QurtmA0wPG2IvNbCdQOaX46WxXRchiLVR+otIUMpWJnA+SGBJgqbWlnf6mUQ5SkN6tKVQI5nZyqjTTDSNy9XJFMrDXycKxviO+IBIGSDQxDeCJ+J62BJjmfJfpnCWuqSR5eQusZi5151L1kVq/t61dab6dgq/h0wEnpjHQsQbRjqoQfhE+gUVXRQrjWn22NnAepAXOStpRTVoTMQGUtJ255tSETk0YyURHSFM1AAJEJx5OgV1ft8Tqd2fW8Qo04ZsVGIxWRHI5nUE8mUXGkL7X8TEJhvg5a38qpmSAqmK0IG3CABoidIv9Rz9EHmJ57qhG+o9JKuxxZMevHTrttkDunvlufJx1PxzIw0erUzVOPzaiI6FGOVJWGipTbtAkhyvANSOQ4kL/5z89Y4zO6Oo0gq/9YG0jDjg8iFyIP3N5Rn5jUO5migNfwgDTq7Rjo2WkctpVgKVJdyjGRrSE66Vlxy8RALXFpuA88Hc5o68hB+kC6QWvlotfExyfaOrSO5qy46SgHMctaQypk7fHw0/1dZ0rmUoMTHmT7coq+jcwDWnVjx/MDud0QW8j5qo7wocThYHeCRHWS/iL3b+j6XibuFHJO0g5UFJMSv8P3iu52L1ewCtob7E9i/5VcNmT9BVWvSOqxn1Hw5WzWlsTnb4RJ/Xx28hSz0xPhKPEdgfit4qAzod2QHNAa2VZAEVO0vSF+is+HIFs9b9qtROw4YHhFhXuHYyc/P/7R31gXFxcXFxcXFxcXFxcXFxcXFxcXFxcXfzd/PAHYjcmgzUGogwbSYMSthJg5MZl4KHCQ2nBWJCdte0NSWXBKgC1hg3a68qiO37L3yRlNCzI70LAUBMgxEVuqMjidFBB3PBLrZ2SyGRJeAqWuRJYzEImKvY2DSCOlIbljZwwtokx1Mho2DmRVkok3IV2xCGAjjbOfN0GdnHF24lY3bvhHaWzTS7S2Bjkw7acMO2CWsCrLUi5dDfCDECuh0QSx6jBWFXrrWO6nm7cTXpG6iAGNtOC0aJa78YwKlowScsxQWcESbUoS5HDacqP5i52VkYM5d4yOqAED0OrsDMf9QDIqDnu9wXwiMSAarok4+NLI2Onty7m+kLpCb/CqzwuqH1eXDXRSgl+i7a3EKgKaILrTlhXxg0BrYED1FDAnoYr6jrQVx6tr1joSikyv3mYB8NP0Oupn5XRwTkeakfbl7GhOshmaR7k2c+AetdaipA+0bcgcRB5YX3A/zijln0hJRBxzR/q9opOXFahfVy0BX2RFMohs1Vl9dvJGE7SVGE4cgKOSiE7SHQ+n8YL2c117KmFr7TEX1BJckSnkkvUMMxm5o7qhLIh49R3HJKOhLXFX0gKL/Yw7TiQN13Jp1x7dK45YFDkeSOtlcI2zRxfq2bqjywJtw49HdRADZEPEUA5oDVMls5+iJBWR7AdE9U1H7jQr0ZmAkCy3sDbSnzWIkYOURkqiCQcdmUHmQAP6eiM8zqGVOK/R0OUL6QeRQu7BIQ3EsTgQJtruaAxCOxI1oMDzUTH2UdHnoY70tWK4bSNtPbumrZzarx33F7bdQAxvDT0cUcGNcyDik+wr2Iqkw+uBdiOWGzFHvcv+JGKiYydSmPlJplXf8phEPJEwVCuOPGLC8qVcz28dOc9DplTXd+vlhj4+q793ea/zcDoxvqHyhiy/AAfS/0TsH2hp8oQk+fkX5O3PaHS43eoM0gaxkxnE46/E8aR9+d/Ij/+KtJ/qrJEGqaQt6PtScxFf/+0f9lV1cXFxcXFxcXFxcXFxcXFxcXFxcXFx8d/LH08AXgTDYDiNwLKEpMhR4q07SEPQcmDOhDbLtZY7ksqcg6bvpA8Ydoq9DosiTrkUtYFtzMPJGLTWkJgltrmcnZ5LCU/mJRha9XpqX4Ak5hN8gixkVKevxiSnVjQya0XYWpINJJf6eQZDWn2cNtQH+dphuZ0dtYrEq2KsxRCJU+hVNBTywPdTNJoHbG8VkaxWDsmIuk/JcudJuWFpWwlgmmeXK/yIitZUYr7QJrh33B9n1+6O9hIAIylBDC3HXZYDOJ8vZir9JmRbTyG0wXziJqQ6/nwRtqJMHEW8Ymp1WfAZyKxoYuikeX02SUojFkNJZN+rB7e1MyYa6F/L2SxaUb2rlJPaB2la7kVViKgeXf+11nEfqPb6meWNnEYEjPksUVuVloPWBV4/nLBLCW/yqphabUjWYEB6ElQ09HRoquWaNS1HL+V+jvB69ijWjRizuoVVS9xyOYXfd0Q3Ir1ilOdA2iRF0Uwyd7R9qfVkK0f32bEbcZASuDZkHtXdC+ADS2oAwl8l8lFx5uk7GVkdrX3FwuvZkzUkYVoO6UxYvyDUe0MGGY7dfkKblKM2kyp/viOaIIalI+dAgGr1MNPeStjunVP9A4zAkezI8PqYFNSqv7dcw4LJ2W1sHTVFc5IYIhNJ8MwSfpO6VijXbHLGl4OGlKBefwhYCcg5PkiXWvNzUIIZRCaqBvMJraKlY/rpKA8SPWOjO+6jXPxmuHZIxXrFwZOVYhACokbOV/UXE2gG7ju+3Gki9Z65oDJJlJCJqZVDti9IK/c124q1dg4uJLo0ZBx4OsmK+Ceqcg4IHKh9IWSe+wfEvqKzEhL8OCpiXBe0JZqt3ODtTvrAfCBvd9ys3MBzL4G9LzVwcPbxIl8QAZUk+w3VCfK1Ir5lQAzCQfRO6iDHRLcvqOxIvAh/ILaibSXbvVzhRzJDkNu/EoeTt3/Fc0f2o+L713ulEhwP7H4D/RGzcHFxcXFxcXFxcXFxcXFxcXFxcXFxcfGfhz+eAOxRgqh1kgOxFUGr3lQakQeB1H/XdzhUMCnhh6mErOX0C874VOA40HWDFHwxLPR0P+6kKT4CFceakCi6KDEPJAw00HRCDR0Vbyq9l7gsW7nhZjKHo3JAP7tEf/R7DifFkClEDrQpmlQMsvQS86xjE3J8Yn0lDVSrAzhO0S2WBUmvLuCmaF8r7rctJTrJ2esbR4lMcfYfZzWuRpYQmdIRm4jk2ccKuBMKmTvBT+V89olqJ9c3Ih3NQaOBbsTcSRsQiriTrdNUqu+WRM9OXrWNpqDLHUfJMclDaM3YmtLMkNudeLw45hMxqxjt6MT+JHQiqahmRSC/3dHjoNnZZZxZTmHZq+eZhOzlapa1ROFZrlLV5Yww1ooAzka+yj2Z7OUe1IZalNjqA4lOiICuJZz6E44DtjtxvJA8yPC6lmVFrJzIpgHHByJL9dVGuYMxLV1bJikQQ8ptriuJk+7lPpZWXbQZiCaqENzL5RrlhG22IR2wjTHBcyDyVl2xrWK/VSpqO8OInIQmIvezN/kJcvbp4mjfyOevyG0tQY5XCYwpFbubWQMHo8Q+NYU5SMkSulvtT/kxsHDGNJstJfpLktnR262ip8NL6NU64rSVAJ4+Qa0GGtCKtk4IGuRRjnZdSH8Rx6uc7dKIlNOd3RC5g+6IJ6kTOYXgCIMcSC+HdbaOhJAjYO2QjTg+SsA2O8VXyJh1jthGiqNtqehsqa5mE629n0IyKk45J9LOvm0B04mFYq2Vm1qTwOr8yFvFIqvBLDHZYkdE69p6J3MH6aBrDTJEDaSoWHU8xwHh2PYTBuVoxsqhPV71HCWQvoK8k+EoScogckFalLu4rch+kE0rTl2/Ym0luWF9I/bfCAnwT8iOz6gBhd0RHTVcMRy2FdqNnC88A/l8wP2ONEH3B7E/kHYjuyKjuoZzaaTsaLOKqTZDj48arLkZmkYcT3T7GVnucPwG5gg/xPgXsii5fyLWma9fIfQf/511cXFxcXFxcXFxcXFxcXFxcXFxcXFx8XfyxxOA4yAVpijKikYDyxIGJ2hquQ5lqShlfyHL10p1ziidQyoSVmWF+IRFSljqDXEhzU6Xr9GYxHor92M80X5Hx47YAkOgZ/XGTi2RWY30WcKZLhV/OhUaRDQMEFFi+akiV1XJfJB6q05Pspy9OSuNmlFR0RiqN6S/IZlkQsRR4tc0ZDHSIHID34GsfltLRJa6rjlQoiJYl07KrBhkzYr59QPaQriA3ZA40Ayy9eqo1bUEx9f3UwBzMp2QRLVhsuAIHI8zQjjKSd3fQCpal9eDbAbTy4Wahh17iffjgUi5FA8SdCG/fWfMnZmJEiSGkyQTYymH6hyECCYNZNLP7tQS/xTpDcnqViWD1I5oIink84VoQ3Mg0kinhKXlToSjUuJ4ZqIyaLaQueP7PJ3YC/RW1+Ed+foO6Yw5kZz0tpZwnHkKalJR1Hav6NqllXAXDtpRSfAnjV7ilAC8iEzSyv1NviAMM84O2oHrrWKH5Un6Afe6DsJpUbG/skAOiOmggsVBYzJREqWvP53dvwm8UepmOWyBctT3Dc1JTClB0Iy0lcwB4bU/JVFRsIUob3yJpdrI9BJ3RVBrNGtEPsvpWiXLpCepUj25AiJZAxWZZ4qygjjSegmpaHVE+3EK6p0MqXUlEdlQgtBG4Gdse5zdy1aO36huY6whEWQK/vqA5Z20qE7e00VdkmFUf68fFR0vG+CVNKCthPEEaUbsVIR5TmIoLl4R2TJAhWW94/5ijIlanTscO9neyTjOWHonZNSgQoI4dVqEgRsjG5YTaytmDZ/fzz5kRRZD/BSWx4BtI8ZAAItJNCO1E1iJre2oyPhQ8AXpC8JAtq+IHNVx7Aey/ITEJyp39HYj950MRXjH92d19ooiyw1ZFfaPM1kBLB/gziz1Hpoij+/o0kpQ7wupda7E/IB8ous/15kVIHajbT+Rj9/wiErD90msb4h0ZH7WeRMryo5tCxL/jI/PSi7Y7uQxaLef/iO/vS4uLi4uLi4uLi4uLi4uLi4uLi4uLi7+m/jjCcC9gVcMahyOvf9EHANpC6aBNCWPTkQiAov+LZrWe8PSSQzmC3Sr2ONxAA05K2xjZjktxwARuip+fCfvX88YW8ipQDla0194GNoqihgAN8QmqeU+9Ax6N6aDMqkCTaXUyjsc8xQDS4tilDiU1mHOcszpgmc5Fkk5+0dXxCr+GT/IrqRX9DAhyAxCjxKrxaEZplKichwlKEbCBBAyGhkfoAvh53UqJZbR4HiV6KSng9pfpL6BDnwcYO/lznaHGGi7QVYUdmYiy1aCz9prTY6dKYKaYq1XPGtlTzOJ+p9YuZwp53XGEx8HbVvwPHC7k/t37HAMJfZBNjBpqM/fxXWJio0WEsHICLJ/KfHRn+VATgObzEhSkxhPsr9XtLEIcEZ6b1Yx2pZEBpb++95EgLYhMZD1K7Tq8pV4kY0zFngiBJx7V0VrX/7ouS3lFWyD+SJUz3hwELnhCcwk86j1toBUZgTJynxWf6tgFT/shh1HxTKrleAuC3s8iX0gTUETGSVMpmzV2/yjv3g8wKpzOWU7Ha1eYunZAV1CZQm/fnwnUNKsBOV0mAHdSFmqC3s+melkNkIaMQehFfJM/nBsz3L0ppc4LRWXLVR/r0SW+EwjUjlfTjAhWcpdn7MGPKSRI5j2KiGSKNFfOJ+Vk2mE1z7Rfqu9n0a6o7ZQizvLLQ8gQbSGeHVLuy1IJpoOLOR8oe1ernR3dqrbuaK/pVz7Esy5V/90OK5OHBPyUTHtGMhaZ4sqMjqpQkirqOfXji69+nrxErSXL/jjN7w1+n5gKuSykALz9R36O/n8hixL9TELJYp7VtevAPTf+6EzB2nnIMXSyUMQPUjf8bjh+1F72Ct62rJV1bVVxLYu9xokyAfzdochuAvSFLN77TmcOD4JdlS/gkr1h7//GeYO44msN+L1V+z2J+Tzk+k72e8Vf/35K/rlX+H1G9isrl++I9Errr9vSOygN3x8QvtSbvqLi4uLi4uLi4uLi4uLi4uLi4uLi4uL/2T84f7rteRKNlh9h606Wm2pLlukInxl2WlSgoOKQg58EZyKlXVRersh/oTZySMQ9YrH7R1LgzBSqdhaheyGeCJSbj+1JORFzhKzLMFcqTzXBBzG6ab0nWX9CiLE2ImkoonNKsNZtVy/dkP8AZnlqtSJ0CqOmSTmROITYSk3pfRyGZqCChFbdQ7PWR208SSW6tVlTmitOnmPB/Tb6YaUek69kxGnyPlWwhZZ0b0iqAoZe7mjFXI8EbmBvqFqpfGpIDmBBD/Q7Us5bvdPIhuZSosHauVaDO1kW8qZmYG0jh87non6QPxAMcRuaL4In2QE4jfMjJjCxMnxvZ5Hcw5K2DaceTj9tiHHk7St3LUoEQNJgI6aVzSzvIFN/PmoiOezEzk9MHHMlNTqoBWVcq7O2jeaCocjdgOeSC4sdsZv67PiokXO/uVJeO0vbm/IsYPvFfVr5bhNV3y9k/4qByVCesD4jq5fyXwiupUIGnnGIOcZ851IW8oFHJOUhvmEdifzBbKcotcgOerP3W4QAz+O2nvhyNJRDTInMY9yMUsr5zyJtI7QynUZs35uBqllT9VJRW37JLL6ciOfmN/OiO3BLFs86VG9zhIkk6gy6TIiRxKM36O1z5JeoJPxSbojtpSwLY3KYgbGDhrlfKdc1zaAzBKS/XmK6YGLIi2rf7gpiBGcwxwemADNEFVEhZxOsoAPpFUEdTJRu9X7H84iwmyNHEdFcafgY1RX94+IeFUi6/51n9i7ktOBhtzuqHY0Zr2L4xtiW62zLX9zRTMqEj4OQFFVTHa0G8MXdGSdMaG1z5vUvm0g61oDHqIwPmreQDt2e6uBCAlieTsF9iyxOSaybNjSiZyIvKOtE3Pi/km0DdRRa6QHcfyKxp0cL9p6B6LOiOUdbeXmjf2DtLcSaDG6NXL/C7r9AvtvyPpGmODf/73SHpavpN2IeNQgT7uhcaA//wsakF0h9jq7E8IUf76Q/K1E7mUlvn/D3jr++Mt/wLfWxcXFxcXFxcXFxcXFxcXFxcXFxcXFxd/HH67AcD92cjxBTrddcAovWdGhAF4uOOFFkgSCZzJTwRObiUhCfyPbCk1ICZJOuhGPvUQqhJRqj5Xtjtskqf5OfEB0NCfqTnRjyusUt8qhm72TfWFEcLy+M+YT60ZrIBh44n7GOWsj4lEdsFi5P+esq+8GTkXX2o1AiDzKZSsDo2Ei1e0aXvefQVq5+0KSaGuJwmYVzdoUkVY9w0QJdcck3GEmKoIsC2pSAuQcpbuVLxPsVr20KuWk9YMQYYqTBpxiac4A2RD26oWVG7CVW9paiVkNbDmvLQWbO5wu577d2aT6S0lF+oI2xVrFcy/9C8t6Y3t7Z/FEh2DhWNswrchgmR0LLzcpQdO1YnQJVBUNrxdJOrKuqB9o6+AJXchjr3tMqyjmPPub1fAjK/ZahBSpruCzoxkEXImsWQCXjvvpJFbQCWZb9db2W62zVX9zxo7ERI+97h2v/SqB2Ib2hvoTy4r1FhKNoPcvNEY9fh+1jjhmYGqoUF2sY6/PtFv194qVGJcBtiGpZzQyQEOXr+XMtk4wSRdilkNcot6JoIRCnmcnrhpyvnvj2EnW2pue5QbOEqwzteKjM9DsZ6f3Au6kVa9rtA7WacuKkdW9jZ7Ry/XuZ8wzBlv+5lr10/XshseDMEhpSC4kd4IoYVcMbXeglyA6nR9mfoLTlR4QjWQjEWJyxr8P6BsgiA/MFiCQ2FHbwF/AXh+3fCXjqGELPSOaw2l05HigzSqJmsCYZL7InORca53mAEqYl3RkvaO3Vu7z8/ib7Z1BR9WwxdB1gS4Vw50NXb9gDEzuWLujOLq+YX0tkVa19mlUJEJlHSiia5nHM7G10yJPZ21D3m/I7Q3tK5rAnmguyPIv5PsvRFvJ8WR8exHHIMdOZuCxM/cHMT8qcl4dsai1ZMcD/PXBnM6UOzkSlYWMAxeB9lOdX8d30AX8A8ERX8j1rQYPZGNaZ4oRjwPfnYwaQojd/6d/R11cXFxcXFxcXFxcXFxcXFxcXFxcXFz8j/KHcwDH8cTXGxIvot8q9nivTtPwIFBMFNkfhN8QG+SygBvdB6MZLZ/kXEtkVamnOALRjYxBLA2LQCUY01ELVCjhhlZ1pqtWRPA0GCvCIHyiWhHNszfYn2i7I7YREeRhsAn52pG17kcDOJJjJtacbDciAtkf2LLAGMSPGGgLxO5IVxifiCimEOGkN5K9InJvC4SWQEy5XTOSyHEKWYnFqO7UU7iMLL1LZEIfMF60vlWcLoa7QFOIJxKC2Rsxf0X6z8h8kWpAEF7CCu29OkHToSciXxAVkFZx1UwkoelEvCKCZYBZJ2QgsZTb8njgKWTWvXom4Y5ghAaSr4pClsCZRAjMRn58w7Y7c/9E253JpMsGx5NUJ1VBoj67dDhyP9CtQa4wXqh08FadxX6AP0htwHY6qhdIp82BLNWZG2NAWyoee3q5pXtCGCJn9LgpKb30z6X+DLF6bpKD4OylhYpMnk9wq///OshF8flAX0/y9gURBwJtnabBzA3J72i7oVkOWcMJnwxO13n/GfxXUrz6ZO3HURK1PqKE2NlTLWQcyOkYR1ZEnDweyLKVAxxBNFBNorcSM28Vs9wlyqXaFYkzalkEmwPdNjKrKTj1jGuWGjMQtXK6WkN6R+NR/a4CxIFI9cRWJ/AgIxENfDxI7WRqfQYA/rtD1/PH+p8ufTH0HO5gVq81CkxH21rPY9ZQSGn/VrHG24bkTm4rWDuncRqqzpgD0eryjnTSF0ITmPXO6kYe3yAa+E7eV9InOh6oLki3GjbpK8wBS4VOZ1vAYcZExsC0ISHUA04yBu4K+wNiMt2RvmBq5PGqBANt1VOugZrU/hZF7A7i5P5AbEEri55kJ3RFfYcE7QsxnhW3PxMeH6QqwgG8Eb6T4RWHfv8zjAcSyvEMsq917M6Bz1ln9/sbZBDzRYyJbSXaxu5I3piPvzCjYeKgQXx/IV//Fzie1Tntn4RviH+WqPv8xD9/Q/70/0H2vyBLYtZIvZO8MIH88v9C4kDel3/UV9XFxcXFxcXFxcXFxcXFxcXFxcXFxcXFfzd/OAEYuePhuAvmL6I1Ig5M33Cc6ZMpFRVtDZr2EilkMmWWCzFaiSXiMBwxA7zEsOULNj6QLhBCW7dy3UnDdCVJ6EZIQBqiSTZDImjtDggxBkqSkiVUaSuxrK0cmbR2Q44nHuVEVtsQc3y5E+E0NbC1hCYx4EWuWwnWOZApFfvLQspR/bBMJGa5aDEyJmKtnKuh5PGJ9K0cyiLg5bLzdEBI39HWIHfQRrRWYk+WsOhS7uIcgvatHJPLz6QLYiuZjmaCBRzt7Eht0JTMnTkeGIqtX0kr0S33X2H5BVtv+OMT0YXpgxRBCWLu0De63VBGOTqPg8x6plgjj+CYE01F5qwSZ22IBxowxZjxotmdQCsG+xioHvWs+k8IR0X4bh2LSaxJHgHLAjiiXwl/wXiVA5hAtg5HoL0T+xPJB/KjM1YewFpimtR6qARKOcL9eCJajtYYXj3PgNKwfJLazp9LJCbSbnA8yDh7nFMIFnJVlOpvlRSQpdpp/QXPB+iG5yTXd3xSjtqlw0yEHVA0BFTOnlkI6Yga/QY+OjESs4UcNfxAPkt8txvZv1ZMNRVLrHZD2GlqyLZSltQg5wPxcvaWaP4qV7ncKvJYBQ1DwwmSGIFoligdXnHTp0PZZ5TrWBvQ0HgQAiKKNKtuZDmAit3OUpNRPcXs8Tqdw/Xr9EZyMEeckc+D+g1BtCM+oW8kCyELyjid5FRktbRyMfuBp6J9I+Yn8UN8zyBFSuzMRCXPM6iGNkRr+AIPpK14zNMFbPjxIpfqqhaMGA+0b6RGCbqtke7k8YR2g74QowYrqvcckAV5PiqevS1MbzDAlgXRUZ3R8+3sEw9yDqQLieG2EOHEsZNdUHek35HjQc4J44W9/7ni8sfzjMZ+Eud7RTgSL8RftRduK6bUeTjLKR1NoLXq4SbJfJZru1c8uIrQ3joaCrtgXYhFkfgVjg9YqHj5W0d0JeVRzuwvf0LjRd5+QXHCX7T+E/71C5JP8nkgtz+fDveLi4uLi4uLi4uLi4uLi4uLi4uLi4uL/1z88QTgtTo6TQ7Yd6YK3YyYBy5GupZbsHkJhLkz9xfWbzRbiAjKAuZkrsjxWfGprSJcMz+Q1hBt5VTESshDcYT0F4kTnwcsb6gPmAfTQFs52dqyIS7ImKQOfBcmRmeSR8O7odMqUjUeWBNU7tA6jBJnZemodMQUwaqvFflbJywASeQCcZTbTgXi7OJVQxFcyhWdulbkclq5LWMgYkQkYVQ0rFSXbKKIKyHlRhUP0BVyoLcvZ4/tC6A+m0nOHdHqCyYOEgVbSR/E8aD1FWlCjicRDRkP5py0zdDhuDQ8lGwKocQsca2pgkzCgjji7ClWQoDXg2nnuk1FsmPHi9g2sq+4Ktg78txRdiYDNUdzkGGQDeYH6YGj0Pr57BY8D/h8lpvaX7BssHTy80D9gY8NWkXgRgO1rDhmdURX4jUrtlsn6oasvdYsFah+VOxMGZ4D4YmkkUtDIiFKCEcTkUb2eu40IeRA2BDbzq5fIVtH8iCmkeGkrSXC20KIIgQpa+2PtiDHR8UsZwBC+Bk1LEoTwZ8LwSC91pOAyB3sDZUB8QAa0lY4XnWdbUM9keVOzgd5vAgVyBXd+il6HkhfyZinKLugude7rbUPpZ3irApi5bKVDEIhs468GhEYCB1JraEAMTSDkI7Og7By3CZW0dZ51P20TnjFVysJbQUfRHgJqcdEFkWs4s0lq69XCCIDTU7ncZ49s2cMutZ9IQ0VRbPejTgF4pTGzHpriCx3748uZDufszUihTmd0HMvqFXfr61wnkSJAOWyjf5ervscFWMuvdZ12bBoeAoWB0SWGL5aubXlVj3f+04yyHmQb18R3dDhkDvStATlGGe8dqLWIYSwgYbUXotGyIuIgT9/w26K2oade8rHA+1fEFXi9Ruy/FTx/B7kXv3AhhKigBLPD2x7J+RZ0e+p8NZreOL+U7mO2wbjdIz3QH0j9u/k+kZbGnJ8J5Z/QgncFubj/0SWf0FEyQY2vzP3z3/wF9bFxcXFxcXFxcXFxcXFxcXFxcXFxcXF388fTgBuOJqTyaTdV6Qp2jYkE40DlgZYiSkaSCptuxMeqBrLYmSW81bESHkD30EcGY5s5TYknhBKpeFKRb+q4YfSWpZgY5OS8BqjbWSCtrO/lx3uN2R/0m+KirH4hKWVO2+9Ia2RE+T1QDYBBzUlzMpI6gN4gd7LtZyjxCAoBy9PcJDeS8TSKCGY+ivdkXTseJFmiHpdrzRCNlSU1E+QG7pECUvZkJILzy5jEAPVWZ2uOsl+h/ECPzuWXxP6HR872jrBgbZ3mCUSsv1UjmsDyxViJ6ej63sJj/ILsixwfENPcTvzwNpG+uDwAwjEVthu5OsB+wGts2QyGiV69hXdnWXdibmUe7Z3pB/lGu0bpMNtQ/WGpDOPF3P/gGViequbNQPeifhGY0G7EDhkstyEPED2X6HfgIb+iOkd35DbF0Rv1T/tH2h/Q3rSyBoksBo2SH+icZBtIVlKE7SOSKPcq1bdtExEIDTI3EtUdiGXgUS5Z+U4XdwoIlGx3SYIVvv3FOtFNvAH+JOghD1VUAae5eIVcdK26lXNqGuNHbWG5oH0AboiM+pZSsetQQzMhL40PILQBVajueNrCbUmgpphBK6wtBUyGGeXrtidRZzIGiwQwJqheUdb4g5j7iS9+myloctKMyFyIY6PM6a8xHXObuamQlcnssOSRF+QUPBJaqIyyGZkVNwxTatD2wMxJX2HWMo9r1rvtwuhA/Ukp0BfarDiR5/yPIiomPB8vmB7K7EYLdd067WOosi6MvedOR8s2xupRh4f6HpH8lUO8chaW1Gydc6HASjSejmp5Tx/SPL5WV3O3Wm3FV5Rvz8PdHurbmoNwJGWiK7I+obJ2U28COKJiiNvP8Gxk2QlHozvxJhoW4n5QUSr2GxJJALd7qi+UAVkKWF//Sfi8RuxLrBP5Ph35P5GHgN5/wrzgOMDWW7gL6wJPD/I+xfy8VmDDrZVH7N/kjZh+YLsD6Qpi92RnBzbL3QMYZDiGAG5097/pZzWJP7x78jP/8b4+C9we/9Hf2VdXFxcXFxcXFxcXFxcXFxcXFxcXFxc/N384QTgeH0n2huRgXPQ+ldyOJkTHwe2GnIIqUp2xeeBWUO7IOOAvlafb5aPNn2Szxd6X6uHlaWiU6eQY8CiSC4lhMZBa4b4q9x7KKEl+C44dIPMkjAzkBQCwVqniVRcsCd5JtCSCU3IdgNPZLHfnX1xKOQkdYHHINfq9RUEkSCciiKmE36grZWwTYAPUjqkkDjSgNwrCrp1JEv8jjwQFBFHosQrPMB6uR+nkLETvpXzWKViXscLEcejYqE5+2M9s/pS20+UCK0wo7p6l17u05zlmHy7lSMxglRnjnJSI1vVDs9JqhO+l+htK5mzRMmZyJzIVvHcloHHAfYV6zfkeGHm0N/g2KHfwQw53a4qADuZgtpKuzV4/nut2QqIo+5o27B8IWwVI0tDBWYm3L+i6SSnqG6O2E9ECDJHdbjKRm4bIrUPlSeZ74RMMhoxJzo+YQnQdvY2P2sgQVu5hc0qrXiC0EGinO3Sqm+YRMxREVQ2kMGcgi6/VPxuBqJB+cCdcGB5r+ceAeqEe8U7A+JJ8km2DXMnTSuyN7w6YnHSn+V01Q5+INZ+GHYJq/hiPUVSOYVOEUWkY1hF8y4bOXci69pCG5aK/3ieEkhMYk6QtZ714RV5rEpkObUbjs6JyIIn9VmZtV+ln45rOPZZ0e2+w3yQTWAENCNyUgJ6xz3RHDAG2AKrIrRymYpXb7OAu2OphIPe7hXNfvYWV2Rzgr1V1PayliCfRsRREclx9inPF9YWJhPdvuBjp9LXK/47RaoHWSBGRYCHO6IbkkrkAccDaRUXH+4VM23GiAeWN/R41OJYBwT2o86TMaCB6B3RiUivIYN5QH9HWgM/Ks4+f4iz4KJkNmZ2OH5DrKPaUBU8H+j6J5QzaIFxDiZAvP+CpKM/b0i8yLHTvv4ZW1csD/wjQZ4Eia3vhJVLPO9vuB+kO7r8BJ//FXn7V0R+POe1HOFxoPc/I/sHeXzg+V576vVfMANpPxHPvzDbHX052f5UyQYXFxcXFxcXFxcXFxcXFxcXFxcXFxcX/8n44wnA61dUFcIrMngmPSYshs+VOCaaDmFMB3WDIwg1zLz6c60ihjEhZWNosqSQMitmN4ABsMA4YBXyGIDBYqRudTG+16/9EExxmFpuvWUpl+LSYAZJw5sR84WOQS5vpAvRBVvs7GVVMiFjkraguSE4rEHKAFMknUyQZtXDaYlKI/3FFIUUWjawJHyQsmAcyLKRWr2aSbkBy1XXy0WMVuyuaTk7dS0Hcu8wPkG/1q9nCdSZgbUFz+o2zjnQDMT3alANKlKZAbtzaKNzIH0p0YoVUUE1kemYdcJuMAaiENaIeWBqWP/KnM8SaCNhWyCdiODYnb5uhCl2VISu5FZ/9v5Z0c0RiBg+DtQWIkDkQFkRHYglae9wJI6i2RCbIFbdsOmgjmj1ubLeK956vOo5ZpK71z11IXVHVdHWSnDsPxPi+C7ovdzlqhvZhYzaL9U17CCTlABp5UKlVYSwCowX0itmXMTKwTn36tUVQF/Vxbv9gvqOZTC0Ef5ZPdMxsfWncsZHkMtCvl7l7O7rGVMM5KwuXfoZ/zxIQGRBx8RTyFSYE1GBIaR1xnjBMct5mgMSIp0kMQHyIGxBtOEzGGefbzokR3Vwt3q3q5A4yk3OhJEgnaRixGW9Q+zsftC04+zMNEYKxEQyaPkClKmNtrRzHYNM+z0qHYSUc3Bh7ui6nlHJCb3XO2BviM7zBDLy9SgxlAMzRZm1p20Dgkwhk3KxhtFbOdqnBz4CekN9EtZJSSKO6qVebigdaW9IVySOcv/mKIdvtxoUsQ5kXVNbmMeLFhOVFelBfn5Ht5/Q1wOJAH8iqciyku2NPL6jYtATsRsZDnOgLcjUEp1zR0yJbEju5/nzpa7DbqAD1UEuBvYGsQMT7EZTqb7d+ICPv9Ygi92QZUFyQt5B3mD7An7Anuh2J1dhjCfy9Z8w34mm5P6oGPfWib/+7+R9wnYr0Tc+iBhoGj5eeA7EE+YHxEHMRixBbv8GT4j2HY6BrL+Qn/8F/vRv8Nv/8Y/+yrq4uLi4uLi4uLi4uLi4uLi4uLi4uLj4u/nDCcBdo/pGWfBM9Dhg6fjjg9a/EA4uQebAsmMRSFTsqeaKzOrBFIMcjujE5IxLbr2EWDNYtSJfxymY9ka+vqN8KTFMFbVbxbDGLKHGBV1aiamjemtzwDh2bIVkwaUh82Bhos2QNMBLKM5Z7uOg3HeiJILEC0tIDQKDsaPaq4tTgvRJ0AgB86OcmdFwHG1JHoPMt3JDxkBlJfMgo5WoC6S16u9N+d3FnE1QXWC5QYVCo9rJtoAPRBS1hu9PMgbR1opcHVldvpKIj3LlagOEOHY8BLUdW27QOj4eFSXMcvYtV+dyZiLLz4jGKYxaXaMG2Y2cg7beIA+6Ab4jw6vfGGVGYtmJozqS45jY4qTdSE5Xa5fqVNYOveK+mUH66c62hfF8YmuD+ToFc60YawR57LC9AxO1OK9xqQ0mL1QayFF9v+sG8UJTUXHo5SyPo8R9dKkOYtsQXWF+lviZDbVOLF9O5+iOmoFNYp6xvJ64J9Z/Isdkzknsn+RagnHQKtg7SjjHR4nALOe+qH2AT1IamJIZcCTo2UN7HNDWs0+43qOysh9kBKrncSQDtFdvLYpKlLhs1XlbfdWnyE2WS10bplo9vBFADTjodoexQ2gNQLgjvf2tT9g2SEXV6QbqAa2VqJx77eV01ARON3FSTmLc0UWq99eDbFYOehT6WsMSKeCjBHuO6o4Ww6RauEUEzxeIVh9uTrAVC0cSUgw/nevhUesbWWcXQfSVlpPsnTlfqNwwldofcvY5Z5IzcQvi9aAvX5jjAH0h/UbvFQmdx6Oi5bf3EqCXjthCjqz7eP2GrF/KEZ87ylqd336AtBqQaIbInXj9O7K+ISxn73Yj4kWOF9r+hHYnx0GesekpN2J8R778CTkOfP+VXFZcNzCYj096U3I8iAaSUt2+Deb8JD93yIWUL+T3D/ZvfyVvv5RDWWrPhL0DB6bv5SAOiFnx4/P5K/LlX5H5vfazfIU+YTwRvROn21pjVje2gvgk5viP+Nq6uLi4uLi4uLi4uLi4uLi4uLi4uLi4+Lv4wwnAIkLMJOWAqcxWoclBQ44DVUrgiYmPT5ps6O29ImFbiTY5opJhTUA6toAoJQKOnRgDp2PdyTggNyLAMfoxiFT6mkAJktMHeKellvNsKFCCbl20wXRmvCBbxch6QksiB+IgmSWxGmhbwR+ErUCCJ2qdFAfK9SeM38WZRECS5lHCtipI0JqdSnd1mnI4aMWyCopYVnxznmWweQp8WSIjtpSgaw3UUBTLZI8naUYcn6QmYtVf3IAq+t1JXfDXCw6lLYlKxe1iC+qJeBCxM/12im6zrhuB+ERso1lg7OQ4fo/hRQSZB6mK6AuxXo5QuZF2BwJxJ3WjlvOD6J3WGs2EjIm0cjGjVg5EAbQhesaCP75DE6R3TA1pyymQOkyH9qVcxUzEFCGQ+1eEgyDqmcdRGrAmkklKq0GC34XSAZ649DOWe9bPZHW4EgcZ9WTEP4nRQYTwhP6V+fggxgvfbixZccG0FR+D8CciSixrZUfPWqM8xVBbggjIsaP9reKcxTBNIpRcbuWIxdGlletZak9xRgyL3cnxQF0IM0zBTtE8slynJdieTl6Psxt7I+UFabXP0Iq7tl7CfVRvb2UuN0wgRMC2cpMr9e9oQ1s/Y4qjlhAncpbLVs4uYF3rfPBy+9d7k9UHvUKOB+Ln2sgZzey1TsTpuLZzT7qgLYl+Dk7IQmRUfzGNlKP6yEURP+p+dcX3SWorp7EG7BNbNvQUmGPs6LJgwxE+yGMnZatzThvkgeuCptTQhClxJDGU3q0cwfkCEsk6e1IOcnzgwzB7gwjm64HZgbaV3H/F+1Y9xJHQ27ltHdqoNAUSazUoECNqICL0PL+CiEnmAeNZww+5Iu54PMkjybEjb38GP+hfb+jxG24L4hMwkEkc/J4KIMcOXQg18u0LQr0POQ8YD5oYEmvt1WUlZ5C3n4j9GyNXFlvQkUgI/tv/if70r+RxIG+KqYD8ifz2oC03sv8r4R/Iz//rP/w76+Li4uLi4uLi4uLi4uLi4uLi4uLi4uLv5Q8nAM8ZpBs5PmH5egpTraJYA5iTaIKg9H4voVOTiFHxpiPJOfGlEyQtRsWh5oAWuAIoqYFPyNzw1wvPhqriJO5gUXHNUzqBE/sntBs9ltI2aOSs/t1OAxcWHOl+xrom83FgW6sOTXNi6ul0TLKvdT+6QMsSpyhhqXpdFyIdhiPW6c0IUWBSRZ1ZYmwOWG8lTKsBvXQ4CdIHmYm2d8S/gd2R4yBEkNDqG81ENjv7TRfcBxCoKKlWjkdR6PcSclMhN5xyQ+p9OUVSqW7RtpXTuTUYwbIFaneGf0dUwRZybOVCdSd9ghlKOf3ISYyJSINciTNW15qi4wnbvYTgMdDtTsNJFUwFQyvqOAYZD4hS/q11JD4Qu1VP63qH13dCj4oDtjtijzJLR4I7AZAbIoPcvyGyIH0j5yeaivSGD4dFQEddOx2nEUeUmK2zhMZu1flqggiYKD4DkQ2YyPJniCf+e+T3xJm4rDQxSvBLVBdUtNzC/kKsYsjl/hXxQeQLsfcaBBCFtpZL++xq9ajrEqjOaClRNjBEa9hBJSC9+mcRQqIcymqEVDw5tiBUlHrGcTpIFUhSJ9rvGIE7kAcqApFk1LWUCDuQpfqt0YZwOuX7ShOn6emYjh1XBZ9MlOxvyOsbIjXcIGr1+QKSQUTU7+WAbCXuZ5DHXhHO8SpxPw1pZ9S7VLe3tob7Uc7RH+uVRwmwcfBDJ9d0XBaIRry81jpmxVfPQKyRTEacfcXZ6gxaFuZrRyKQ7nXN2iA6XSsSXXq9c7Z13M8y8ed30taKpZ4HaLm3nU5k9QIzJtJvJTjPHWn36lgejq4/nYMuSeyDHA79b3HWJf7e0ZhUr/cDfIDdz97slfS9usc/n9jyhqx1TiSOzCeIEf0dFQXpxOsbsQcsdxQl2jvKUT3k4wPrG/hnxZP7rOGcBF0WMr6BK/n4jvQvgNG7oDExPfDHX8jtHWkGIZgHkhWHz+3PiH+QASLviC7/8O+si4uLi4uLi4uLi4uLi4uLi4uLi4uLi7+XP5wAHNPAHyAlULSj4mIHTu8LJgND0eUL1iFdGMdAWvW+YishEL6TdCYP1n5GnVKdvbYPmlWksifYcVSMLW+EJYtR3ZoGDcPV4IBhCfOJtYqopTVgImZ4fKK9ldtXSzDqXU7hSYgJuZTAGzOhL6iAMErH9Yq1zjlAhJkBtkAzRIUYT9AFwdG2gCYRO35Q7t7Q6oo9r5ucJazFByK3Er00CVECRU1RCySTlgN7fyeO7yXGjq2EXwK0lyP37LH1bGhOmnZi+VoiM8+Ke/3hNhav55dgkqhOPKgI4JxMWf72+RI0bYTPEm5n4gYcg8Tx6YQo0pw8HD1KBCYD8YnT8f0Tpp+CUCtR2RVPw9rERwINEUMcom/V5/zaka1D87P3eSU08H3g6QiTvr1xiMIctHEQY9LXG8Ze6/H8LAHefnQsfyXzryVARj/XZuI4LVsJoCYVs6yBxijjrWYJxtmIMZDlDV0UnS9UldBWnc8BMIixI1n90OrH2Sd8qyhcWYicSN9qTVNKzPQJqghZ+5ZGuCNLQylHbLZ7dfH69/q7gOwP0O0cIPhOkiUETy/n+XTY7mjOii/f2hkZvdTQgEHMg9QEEdIF+oLQgVE9y/oEFrQ1NOr6fMxyGVsSkdWdnQp9I8cB6mROwicicg4+KOlC5ignc1bUeKm0E7zVtlu2cqhGRbCjS3Ufx9kDrb3e+wCbUXu/r5U8EAepK/iLRBFJYvzozx6wVKIAbcNHdd02T4a/4PZ+dhKfXb8WZGzgO6nVNT2ZeL+hGcThqGq5+sOYovV8wvF2pzZ00jJQHGk3wGs4YApsHfyj1sF3ctsAQXih0svtq+V+Tzuw279BPGuIYHygtxvSVyIb3B1lkvOv0N/KwT72itJ+faDbO6pC+kcNU9zv4JQgfXawiwa6rOT4xNYbmVrm+yXJNMJf6O0rMQexnuedGGx3TAf5/VecQN7fqhv99k+Qk/HxHb0FakYCY+40e5Kf+h/wrXVxcXFxcXFxcXFxcXFxcXFxcXFxcXHx9/GH+6/XgqP3G7IuqAnZG0O8UnMjYFnIdnZwvgZMJ8eLjAM5SjxSa7TW6RIgC7iWsDS1xJxW/aVC0CLp9oWlrbQt0XT0+Kju3xkwJzYO9EcMr2f1ak5gn7C/iLGTGPvnJEcAAe2tXHZtJcQ5fOI7kP3MozaStTpAXcmc5cyTdoqtILkDA+Yk6VR8cj9103IWivUSa2agORGrPmPpvZy5Wj20tA05RaIu9XctxZLQleMI5h4w8xT1jhL55ExHFqB3bCn3dOaTjB21ut3UG8lGzIMYTowsh+cEJ0hbmf7CPYkppCfJQrOtqmbnRKciKMFSnbQofVNEnfQdff/KSJhjIDEQedEJ+hHoa56OXyNMyeUNbUYeCi9HvHp0Yx7n+nSCQFKR1xlNDFg61hJLWLYVXZRNF1bbsBj07R36RowVxqM6X8cgx9kbzY7+cKbHXlHJEZg00EZoL+e5HGQ67jvTgzgCDUV8otZQM1alonDzqP2gK8Ik5hNsRbyc26QRLki/YTjKKEFwPlHdKkb7dZBTynktZ0S5ew0JuJcwO5MMIVPIbEgmgpO9nQ77Z7mKYyB4xV/rAraWcxpBeiNeO75PPJ3IRnqc4ngHernDtYLdsa2c4QEwifFkhjEmOCUOhk9CbrWPjxc5d9JnOZgzQG91eKiQVNx5Wgm6PpzITphV9HNbSqwPP0VqEM+KAs9JdoWm5UaNHZlJ6lGiMErK+RkxqvO3NeLzG9Ia2srBir8qivz4RpsTHdU57fsDmd8xDpIzPn4ev58hfjgxgVT6/mAJxyyRdauaZhIDxAJ/fSIedIlyvtvprt8/SgyfR3XiZiDt5zoHt5+R9gVb3lBbkK6YBKp6RoLfER/IPsl1RdYbhCBjotbryyi91uP1gnAkjuqzvr0h/sBI8vNBjllnMS/SlNz/KxKKuaPqmC5oDCwXYn5j7kGEEa+s+Ov9E2krpgnHb4gL5s7Uhn35l4qY/+v/Trw+mY9Pcv1KPA+IFX8N9P7neieW9R/zRXVxcXFxcXFxcXFxcXFxcXFxcXFxcXHxP8AfzgFs1pFIxpiwCp6GTD/FpJ3MDcOJDI5QVAasHTyZkrQu6NTfY5UlBOarBCC8XI2pjFcgbdLbvUyaCeY7Zg1hO/tFW/XWZgNNWuv4/sS90+YT7XeglcsNRfV0GabAHEjr0DviL5oNaOXkbLZRluAAh/RHRSfrWj2sTSuSd36Wo+94km0js+KOc0ZpyEh1j/aOxKNErbTqAU7DZ/UVd0lyDlwaEiBNSeukV1dryouMXtGtM6ormAYEMvaKPk7Ad7BekcG7MeJg6Q3hBtIrrVs2aBNBUTMiDho3DMNzIiRqJXw3e0fzWTHVJN4VCejTyQyaGhErEp9YJG5PcjHaLBEuUdAgupF2x1Ww4wXayZYlPG8dmSCPB7YkLAsyHLFEtOEBar2c4GZkQvhBpJPzA2030qwEd22Q1dM71ci80XUhVEAn4Xu5n/MUWseObI00LaFRBfIoQREAQexOeRYT5FaO5XQCRdhPx2+rn7cDzeWMTV5QizIeS5Jn7HEen7B+JWUvkTEDOT5JbuRtK6ewB0gH99qjODkehBky94oPHwNRA9WKA5dyvQs/oqudzHKaqkbdjVQPcFLhwDKfJfCKkNnrnk3Q7OderYEKYfmbM1my+nr1b8MLGR/IuTYSWf3HTarTOXs5z6ei0vE8O7Nj1DBFVxifpC71fsqP4YWsZyDVNxxjnO716hwXMwBC8hwsiOqibguoIyooS0WWr3dE+hkV/QHthqG1HuyYbqQHtt3JqIh6mpajmUboIIeecd2JUJ3JlWqgFTW/rPUsMrBI2rKS8QTvpENaPcM4HM0kl6/k6/9bke03RfQNOMCfZHCmCNyJ+CxRf34n21tdnxn5+g7rz+TxJOIAi3KRz1nR/Fl7QzKrt5nznUsj395hf5KvF7IIIgbrn6tXmU4cv6LLn/BQMgeH3Mj9oLcJ2y/kx3+Fr/+MWsfiL7D+QubOnAa3f6r97wfZNvBHVZzLDbn/M+Q3ojWsL7gu9O3rf9RX18XFxcXFxcXFxcXFxcXFxcXFxcXFxcV/M384B/CUg8MP9vnk9fGdnA7zQFHSjchxOlMNuzXSVpSEJkif5ZZcpMQGqV5PGNCEFAUOIgZi1RkLR3VxhoPd4EdkbN/I+aBk1lYRyvsH1lckG1gi6aBWTjYd9Ptyir8lisR8lUgWDbU70s6+0hxnfC/lihRDJFBzkMCn48ejNOI9iISYgY9Z7kcC9yBCmeNg6lFdniF4HIw5ec3BmAMzIeLJFGWmkMuP2NlP/HhWlPTxQY4H+K8lSkkrp6NKRUJbQ8xhPpHjRVNBSdZtQVHQWe7onAiBCqgkMXc8okRIcbItJcTLAzEltXpdsZ+Z0io2N48S781KgPcH1jZGKaisplgXIGEmmR2VhnUnx072RJvRmHSFZkpfO7q94VJdodLy3BO9Ir2XybKW6xOCvjTWL1+R6OQQGM54fCPVSuD1gQlnjG9WFLgrzAlzAFTE9r32E/NV3dX7b6S/yHkgdCSDlBLcpC3E8Y0RA58lqEZkRWP7QMKxnBXd29/J3M/YcCXGQXgQ80nqUr3JadUP+/xgRKt+6Pk8nd07ool2qb5cHyWAhjDnk7lP4EfndCOpeHLmi5Sof1ateGsciR1hh/EkwysaW6u3OGLHI8v5vb+I41GuV9cS8OcEHSgDVUOboIx6j2Li4wM/XcC0jvSFNAXZkeQUXXf0x9rkC2JHRODYgXLGioLPnUxnZCflVnHx4WRASC1VxYQ/QHvt297QHvVrvRNZ3ceogQhxfCLdIB/EfBHHAQjJgWfW+5nOzCfWFjKTFCPOjuX6Z4h8IF0QHOtKW1bEVnJO4nkwP3c8BimDeH3gmeWCnkGOA//8dyIms31lfDzx3Jltw03J/Rvx+VdyHuWCT6v9kg/cg8xv5P4k9wN/PUvA72+n89xhCYgnuf/KHFTPugTIIB7/hfj4FT6+117MF7K8I1/+jNw6tDvZDLtvFf2sTt7+BQ8nmOTxF3pT+taQ+UR5wnKK+vLAxwvWFeaOxwEayL6TR+IyQY6anZi/knwjfGLLV9rn/8Py058w//Yf9dV1cXFxcXFxcXFxcXFxcXFxcXFxcXFx8d/MH84BDELrHdVkeKKqsHwBTfoA2FGtns4c1R+qvePRYbxIP5Bbx13QSGQB+lLmMz8AI3yiImdc66MEK7PTkViiZ44AuXNKjYh0qhG4YTJLpMg8+0UPLFqJs9ZgHOUUDC1R1KycfGFwfBAGSEOilZOwL+Q4yHB8vniNRlNjWYLMYGYvUdUVNJnqKIrEi2adcXwiWt2eEYrKJ526P1Ul1dCIEi1JYhxoW0iDikO+lRuZrUQ9DQg9xb6G+EEJpluF0OZRMbq6QnwvASZKjEwcmEj/Wo5Za8y5k8sbhBNzh6z11Xhgy4IzSDlOoT1RWxgMYnyg0mliZL+j4tXxG0cJyNOR1jD5Br7Qz3UUdQxBpKG6lEB4O+OHJyXe+dmVzEKOeQrcCaao3skZsN0Jr7Xs2wLLAjEr+jgDAZgPpGsle8eP5025SdfqKs7j10pIblv1v+oNxIljkksn56g9nUYOZx4v2ptVL3UAywqS+HRYy00KVq5uTYhRbsgMRJWWgzmTbEbaiiw3NIJMSJKYwWJrrWl4vRvtDcRRWyoCGUUtyy2+13sGSoYjMiAaUNngIknMAVgJujzOjuy1hjXCzw5qQZCKQJeJjAPajdifyPpWPx9n9LJE7TVRRIUMQXLUsIS22gdZMcziQaghJrTljgTVl9yALLeuRFTKdDbM6q0uEbN+Tk1PA3ASy1vtNR/ljh2JtPc6A+YkFwU/iDS83bFwMhKRv3XwRiYpK9LvaAaurXq5e/V6I72GVKLuyXOhAa4HrX8h909Sq1dbbuc58trRt1+Yi9YeigMhsa7k88Cfn8jaCb0T+yexbCycbl4Vcn8h/Q62ka/viK7o/R1muaJFAo5PWL5gKmgEsa2oNaDho8F2g/FCju/o9pXsa4np83u9v89PRBK1N7ANgPz4tZ5j2+rcozO//9/o/Qs5ArUgWkeen0h/Q7Y/1/1Oxw/QdRKffyF++d/QYydjgu3Y8o4xyftPsH8g44W6w/Erx5j04985Hr/9A76jLi4uLi4uLi4uLi4uLi4uLi4uLi4uLv7H+MM5gPGJ708iQXSgOYAkHg+cU0zVxnx+llPxtRPPnZwHHoqqIR5YM2RRRKN6c31Au0MmTRvaz0fbtsp/FkN8VmSpterUXBoZA3jQtoaZQytHZLkAE9LJ6ExfKtHZk9AOoYg74YN4HTCS3AfZVmZ2QoQKKfZyAP7/2Pu3HkmSJM0SPETEzCKqaubuEXnpqupG12Cw+7KY//9bFtgBFthZdN0yI8LdTVVFhJmJ9oE0cvZp0VWzlV2DkAMEEnFxMzURUTUgPz7fp0afBV9ulGuFksGsVMs8rjthg5C0iGMGPgvuRoS8NnoDrYrqBa23VyUvGb4Rr53RJ1hWQVsUDMVEEVkyGNXI66CWweLIvVKZgnghxHLX+NcgWAVtF1huiK4EV8KXrKsmEGmEB75/MMUzbJ8b4sfLZBZ8PgHFtRI0pHdMLGuq6xvhO+65A406MSZj/8iwej6gXTIAN4itAxVnRdyzsnZaBrITqEaM72lcM5FWCDnwY8Pnxux3XA6iTFxgenlVTTfwycw/BXPHXRgIEQpTiNfhggjBtWQIGRB6I7Ty+hvEg6DiEnlI4RVkhgkxD1g/5fXbPtBaUDHCnRDy+jmoCLIuhAh+OO7jVd0thJGW83xAqygDPJgD+v1rBoAuzL3jQQatmoGkzJm7sbWlYX0EHisRhhN5MMAN/MhgMIIg0ka2hVDDPb15Ds/KbAnCPbdp8+qlCV8kTd1SEG25czt3cEMxTIWyrrlRy/EKbMnXaRckSt5jXRA1qlaqVtZLVjJTGhwPBMmQt61oPDPclQARtBhYwfuAsYM7WhpmDjVrjr1k/beKwLqAGb59AE4pBTVBq6H1gpaKzHzu1TfMgnR9DRlCi0DGfB3IUFQV9Um7fcqPE5b8bPEOx/aqDfdXLfRCMJH6KQ9l7E94/oJ4oPUTMDCdlEva9DUCoyPlgrTPyPoODDT2rL4+BtGfAEhbEOvIYojW/IyJfP/7duRrsoqOA3XAjbltUD4j7ZrV6tszDfgp+P4gjgfc/4z1rNIe/sT7IMaGlzUb+W+f09wed+BCHDMtcZ/5uVy/4I+NGRXxkp9JTOLjZ0QWfPvIreryCZEL49s/pM1dPtGfypDf/XV+b52cnJycnJycnJycnJycnJycnJycnJz8K/jNGcDNVkYcDHNKvVG1pkVY3yG2DE9qZbk0sCUDB2vonIQ4woLw2g8V8PF8BW8zt0jNwCNN0CJID7CONH+ZoQMXRcSReNXHvvZAqSt9dHzuVLtC7yDKaIr3B6pvlFaIo0M/cB/McoExEMv9UVAohdl31BXKJEZPG68CM7dvub6qrXFUFC2Kh2V4pkZoEDYJd2pb82ffO3r9AYtJMF7WJKiuMO6ga5rIQu4L1wuIIFpyH1VXvG+5ZxovI9Y7REGkIMsF8QNnUpZG7N+gXvJnkgI8ActA1gpIzwC9GM6rGjvSCg6XDKHmzEphGnMOyhgghhao+g4y8HFgyzVtW1mIdsE8kLoQ/YFGRaoRWgmZsH8QUokpiD0hKuGBlgxpIxTf70g0hEbMRh8D15JftxVcBMidaD8+EG9pFI+fEQyWBTFDteT9ECfMM+jsO1oHQSNkoEheU3Fwx8oVl5HBmW/4eG1HqyMI1chnlGs+gyKAoGVFgamBTABFjgdeFf11W7YFc6Q9O2bHtCDhEIEiTFkJEUQ6UgtEx2vL78+vAdvI/9VA/GX/agUfGQJbRRCUgoi/dqMNhFfNugK5b03MbIquAk5WHo+eErC9YXTc1gxpfQIFqR0tK+GNmBm50y4wHYlgem5cU5dXjptGP4C7E8eBquEeecADiH3gtmRVOb8+ex2Pmoce3JC65m7vHMzZ8/7FQOyKxMjqbHvH+wPaGxDEsSGmeYvI55myZGgfM793OBqCrAbzgYyCNkEY+f7xZ26O9wNp78jr5w0nP7vGAVZgfQM2ChMXwefApWBzINVo9ROMTkiwtIp5HlSRUtOo1bTIozvRXp+TDGQcqNbceV4aVgvSB16vWJ5ZyPcLIOVGlBUpC96/g4H4hsoFWkFvnxEka/anwPoOekEF3CdzPNHZKUva/hKvAXYqIVlL7v0bKpX+/Irc/g6NgsoNYcsDCB9/yjr2YxBxzX3u+IqWG379Q/49B7GsmP7mfoWenJycnJycnJycnJycnJycnJycnJz8n4Df3v97XSCkYQwkYOx3ipTc+pSaVp5HhpmlpZ05H0xdiOFYrYg53jemgfQnWpfcWqWATiYDQrBUEfGYqGuGgxIZ2OQgLdSSVc9zI6ylPVgsq5mlML0zMWgrYIzoeDN0e2LLOzIHdmmAQy2ET3QMpFYUSRNxkLXGvGxYVXT2NBj3J24LxIF4SyPRLXeEtUDfYKkZwC2KaSf6JOJArL0CuJFBnVbi2KAabnm9REHLhtmaYZ+SAed0sIoVS7O1Hy/DdCIIfW4IVywOODZCjqxmLTdEsxabPmA1oi3wuCPNCDe8pxXZ54bVvG4STjWIUXETZDwQnbmpWioyDoiSm8r2a4jcmXIj+jO3fU2RWDLU9AndUWnQBCFeE78DrOLHwDjSDl8+YZcMIMvc8pqJEuT37FyoNVB9MqOjQR4c8I3Jjox41RorEsC4E/6qUg7yeZHIQFAC8S1rxf0g4kDrFWRhzgeYZkipKyzxut6Bo0Q4c4y0zucGL/tbdMGj5xYxAb7jsmLtBkFa25YWOOuNkInHTHN2Kkra2vHaHpZWAQiVtMvnByrvhGRNMkjaqr7j4yCOg6gLMnckBFmueARSDiQCwggP4ni+arcrAqRTLKBB+JHhsRaYgxgQvuUzbkrsW25Ci2TIO5+vuu6SgZ8pYWlnqwBjy7rpokSkFR2eFcNE2uSiWWOd+9QGSNbAhyJa0+D3wMoEH4TlPUyzfgXZCamg8qqDH4TnHjmkNYxPZOxMVhxHqWgt4AbWcmfZKjCIks8uh8P62lHefgIMFZB4vnaqR9rX6zvYZOwHdjyR5Yq0FSGQqtk6oC2D02PPQLnegILMDShoyVp5sZbNBhRiet4/36H8CCWtdhFBRZljy8+WtiDzQHxHfeBiMHITXPWCo+D3rDh/+wLPHfQN+EbsPyP1RoRm8F+uKE7MAfWdMTb67tT3yL1s9TTZn//ENEe//D3iG7QvzJjE1w903WD9Ay4H8/4TxX5gHvd/z99WJycnJycnJycnJycnJycnJycnJycnJ/8mfnsV0GEUMegVjk60a9qlE3IjdDAcYgjhnlW4KIEywpk6CJFX+FZAb1m56zNDzGmYXighmDoiBdqV0AUBxr3jj4HvPcMqh3AhbIVhaBhFL7ktS8FUqc3RcEI8jUsJ5PoFsYpMZe4H7ncIz7BixmuTtBKuuY/pmnW5dsNnwJjEHFkv7AbxqrQeE414BT2gt9zVRAwNRbyjS8mgmoC+4V7w+gkR0Gq80iSkGLRKWMOnMvqEUDyCSSAhhKRJGaWkFayV6I7J8gqyKnG8aqhpr4BzR/oTKYGGUd3Q9gUdE+Y9A6a5g11Acyc2RgepSGsgNUO754687mM4UBQZA+ECHZgdE1BvablKVvZS37D1Snn7hOiWlrhI2qc+ELtRru+ET1BFdGAyKTiiRowdfx4wwCRY1obVNavF1x9Qa0j/ju7fKFPRGGgMTAtWLsTtj7l1KqBmGfJHB11exvEg5sTxrMweTmyDceQ+c3QhHESO3BvG0kKfnoaplNxttkLUlkFoKej6u/+9Xnp7pjHrkdvX3RGcpSnt9awh8jLTR4bXMpFlIWTNAwmhaZ7SYAxUyf+GDEkJIUYg2tKeHYWYHR9bmt1SkHLJgw4EstyQ8o7UC2K3V/X5ku9lCcQkQ3K9pvVKBS34KHnAwyUDaykoigVICNRKsYoNf1nzECgSjs8nkuu/aCcDSlHEXka3vsximRlwAx6a87w+CH/Vg4dAvOX11DWnsodTbEFj5mulgLV87zKRCAyweqWoZfW55GEE8T2r1eORVdBheZ+JDJjHgN6RUim3BZGBRIPyBmpIP9DaELlQ2kpIZT6dmHn9/LERsmR9dlSob/m+KNesfNdrhvJ7B/uCx0AOAe/48wOi4bvjcwMcLQsawHgQ2wc+sqZZhPz8rDdkP/JzbQ9ie0AEM3LvOD6+YuHUtWA1UALpT5CGDyU+Zpr52xPEmQeUL/9XTBX2ryg38CtDP6E//N+QUKJvxPEd9g/0/T8TIahC7Dvl7fdY/xldlr/SL66Tk5OTk5OTk5OTk5OTk5OTk5OTk5OT/35+cwawSuA+Kb/Wujq4CrM7ehyUuuQusE3CwUdH2hXd7oiVV2my5W5mBJTIMK9ZhqLakTkY4dQjEHsjpKICUYx2qcQxsvJWgkziyL/KK5F3x2fHykJMzUrdVone0eosYq8/M5DVmSOyytUnVfM1IUGMDdG0RGNR5BgUK7khqkL0HWSB+R3apww9aieOD0Q/p9U6yR1VXrkuI8MbXRBXiAq+IWIZkDLAJY3VcNieRCkEgYcQxyAK+bX7nmGezzQnZQc+I3OAGtMDj05dFVFBZCU04CgvG7WC7+gwrFxwz9cpOjIEf9mqEUdWWs8dgbSHY2RwZwv+fKIOUt8yaD7+BFPR6zXN7TJyK1hzB1dkoJbbw8gFxfHtF2IHqQvSGhGeRvn8DuMN88BFkGowDEcRHKk1zU6JfE5Kwc3QpxH+LU1QbYQqMF9BoRJ2JebjdV2uBDNrhyWyKjkMrStsHxkOF6WEvA47BKIT8V/vV+7cYpYm+jgQ+wxMNFeI02jtT3xktbSUDRdLa3LsSL1iyw2RyKbnIWnRyisI9dyhDgTx7/n91GB0pLVXrbqjugCBKBBg6xXwrBxvgnAlJMNwj4mpgzhR12yZDk8Dtyj48ZcKZ0HzOfO8Fq6aQWdMmFta39ERAXsF12oXugy0Lvh2x/SdGD/n/q4UhgP+2vctiq4lQ8bnTxArsl7AIsNi70R8ZGWyDNwnUkqGpHrBtMPcCXNUKu7H6/VpPueSNdtIz8MY0vJexUDtQjBpukB//Swq6IAoBTSrtJlbBtjueEwYG7q+EyNAP+chiP5EjdzYneMVGKfNHTrBD4g7YpXYvpMv75KfM7qi84nPV613aa//vmdI3xqigh+d+fg5A/vjO4yCmmfIPAeyXlHJPeIgUM1K8Fnf0ySvFf/4QI47XG5gV6Q7PH9GYzIfX5HrO/7cUIdpbxD3/JBdP8HzSbl+QcqgsjAvn2EIsX9Dllua5/MXaCu6T8Q65VaY698hxzdkecPWN/j6jVUuf4XfWicnJycnJycnJycnJycnJycnJycnJyf/On5zAXAPOAbI6KwmTO3M2VG/EHVhDrKq2dKu9OOg9lf4YgOhA5W+HQQTn47OgraKxYbOTpbh1gyP2ZCiePcMpUrJELJW3AdZwWvMDhIDMUeLoOq5kUrFYzB9oGsGPCCIHGkiz0CjMOckqlKiZMgoJYMmAvoHqbKm2ewzLTkAKSsx7BUKvWqh1zWN1qlpb3qGZK5XogsSHShEPDMogzSJZzDRDIxFmPuGUGF/EnUwRTK3npZV25cLbJ4VveH056BcyWvXO6KCaQN/ErMRsmedbLHX3nHuKbtP5v6Bh0MoSsm6WAHvH2h7w9nT1B3xsrcHrDfG/QP33H9WV+aRNdR2+QQY0zcCR2LPrzkrGj0D15hofcPHgzFXBoNmDWwyx47Iq+LXjPnaTFYPaGlUx+OeYalW/Hhk4D88d5+rccw3ijVKbTB22B9Ea6AzA/Co+NgIydzT9ichQoxvsPyIjx0ItFyIUpF9JySY5tirahuE0PKqCn/mm6R8AgKJAf6qfUYyRK8VzNDlE8EV9n8Gb+TFDoSe5rw1nJYBP4qMDW1XXI48IBCRu61FM9BGmP2JuKBtTYM4GmjuDTOOrGa3QPQtnw2fRNjr1QnhO6gRKhk6euBxILagvuOvGnbpRxrg8WD6JCLQ8Ay0rWAC7p3BwB1ie8IIXO8ZnPIk7ILLQagyplNDiN5xg0Gl+I5HJXoQ7YbGjkRua2tE1o7/aslzMJ/fkfb7/H7HgTQhtOYuta558IBCPB/ItSE+CFvx+wexBHO/o5dPuYVtDZEKOmB2xC74uKfN3Te8FDCDtuAzEL/DFFgXPBz/+EbUG2MGUipy7IRv2O0HqI3YH2lyH+DlgsyssBcNBgc+j6wYV5ijgAY8P2CpGS7X9wzES2RFcwSuLb+OV2y5YTEyEN6/MZ9bhuUx07C+Nvj8OxgHWnPbGr1BW5jPb0T7gokS1wvefwH7TETNz4LlQoRgdSXGg/7Lz8Tbj5j/Ax7XPOjx/JoHR/YgWiVY6c+fcwN45HWf93+G6++Isf9Vfm+dnJycnJycnJycnJycnJycnJycnJyc/Gv4zVVAz1BEDVVhWAFtWLtSFijmSC0Ek7I/0dkpohk02MIihspKIHhRXASs4M0ZE2YsBBWJhWqG1BVRQxiI9awuFsnNVjqMDHMZG2LHq/614lHSnAwHnVgErQg1AlwJmUxXYhYCoxs4kiHYeKbFJxOtnhXH9YJiaR/6/WXyNqTknmfuy5KBUUzUM/DMLdJKxMGgMh1CsvLXfSeG4gWmCN5H1k+/alZj9qzxrQ1EX5XD+gq3D2Q+YM40Zr1khbBd0sw1MHdUG2U+kTEzgGfC/YHvneggfSCxM/yOy0ibeWyoWVbr9gHlChjKG0QQ80BmZY4bMSNrsqszraQ5enkn6kqMwdx+Blvx+cyq38cHKh0t5RWCB+5HBv7LG1zeAPJ7jEBY8GjI8cgt1/J6u1lLo7K84eMJDHxOwLLyFkHFaVqQSoaOIgQrUtesJyZQqVk1/NzQ3RFbESboks9VTKQ2YBKzI33LP3cM5LW3630ntBCvFWOtF6oqxUCOHbGB2AE19XSt13w2xgfCA9GC3q5IMXxsDBd63/NZ8bRfGZ1YPr02aC1rviU3bXHBx473jfCCk03UHq/dXn9C9L9UWnvsgOPjibvicxLu+MzDALkhrBCWQSQVxobveQ0FYO5pX8eGAmYrKtnuLWqENiImsz/xYyeOA4rhHIQshNY0vMdAtVBahSKETqQ06uWGXN7yWRbQnrvPACq50y3khq6I4scDrxdor+riekFQVAKKge5pVseGXBZUAz8OIhy/XMAEKRcYM21qE6QMrCy5NaxHGq4e+NzRYmh0dDzTtLYFaQuy3TEt0Ba0LogNGAe+XAgxJCIbAAq5Y7x+ykp1Bn1/5h6uC1I+E897fn6sBbBX/fXEt19yF/wmWBxofUf0mtX2vqH1DYlORCfohBxZ8D2feY2Pf4FjQ+dOKSvqK/78OQNyNWhXaI3x/RsRC32/53a4BTy/ISPQt095YsI3Zv+K+MbclLh8yq9jYO2GrktWo5eFcCPGgP0fwe/49mTuH2x/+od/719ZJycnJycnJycnJycnJycnJycnJycnJ/9qfnMGsGrWO5sZUJnumAneN6wI4pPiNWuEx2t70wKqwMyAVctK02COICKYRbACqgP6hLrSD6eYpv0WmnucE4SOe9Y+R1ny+6oQsTL7T5h9QgxcWlquMrPmdWSFsKswj4mqYSWYHmgo5fIKTuqV4MC9Z7UwCyLg0YneiVrQThqsM3dxVT1/Ngohk9Anopc0NS33ZK3dkJhI5MaviuRk8hzEdEQG7PoKk9cMfOurFvt2g8e/4HZBbEFcXhXV4xVAKeKadqkoQkfKBTHFd0O0QGnEuGetchx4FLS9570JR7Xi4mkej0cKqWND7AvMrNzFFqRemPNAKtA/KJd3RApzOuCIVmbweiYUjifeQbWgfqB01G65ZUxB4qBoY8RBfe24Mh+wFmJOyhTYv6Ex8KiwrOADlw6LItOQUmkXBR9pdJeSBrfdiahAywrmZmifqBWEgaO4rnAh7405WG42C572rirR76hV4rLC2NG1EkfPyuO6oMx8zqRAqWlUTkGWFY+XeRmduT+Q4nlQwFoGwe2CxJGHIjDwPUN3ibR/bYEmaFkR9ryuOC4XInbw/bWxLfn+MCX270S55GuZATHznq7vWYkcA9FJ9Im0G3hHJHCpL0M/AEvb+7pAP2C5oiaIg4flfrZWSinEVPq+E3OmF/0KrkUKKr9eo5qt5hG4LET/nvvLxwMrL2O5vRM+CCcrj0VfoX/Woac9XPKAgQ/E4lUE7mipecghJuXXunK7IvYO83ta6LXlc3d84CX/pClZ4V0VH3mQQAOiFZRBKMgc2O0d9ZFGNBPfdsIVveZ7WcuaHe9lQW+V6Ftu//YDtMD7e9rHegPq6x6t+V6eg3L9kk0BAWKTWBfkeKLrG2KdwRMpbwiKyRMJwb3D/g1xQcoboS8benumMS2OXv8r0e7INGLc8XpD5pHb6XPH+8yt6u1Jnz8huiBvv4PlK1Lu2PoF9Z04Hsj777Olfn6DuOBa0LcfsDKZxaE4OvOgzDz+kfrpvyDbN7j+gTlnmv8zDxHE5/+CDxifzgrok5OTk5OTk5OTk5OTk5OTk5OTk5OT/3j85gJgD4E9GDEJy7BkRoUjcIcSgVjHKUyCYgYzw6YMahbEHPFKuTRi7lmFOne0LIwJJp0ilTmfWDSwLQOyPZB2IL3mfm9VwusrhA28LRmghbyqlwVMcSo+ANtgLtgMhCczatYdT83NYgERR2SB2PBROebAGJR6Q5ZIw1Q9d2H9koZbdAJHayPmAeULIpNoAIZXwWIicxC2wAApE8ZMI1pH1vC2HakXoGWFquQuMQSsX1ApyPYgWgFW4JGhlyrYmqGl2Kted8DzK6JXpFna0aLoZUnL0zve7xlISW7XSqxEucI4qM1A3wnpiBdCHB95TQVLozUu2SQdAy1GjI42pxaw6Nj6hj8+0PWS30Nbmrp+z9B6jjQuBYoLgWcddd/g40lq5Ste3pDxyIpkAdYV6VndzFTm/p0woK5ojAxfZ1rOVMAgLDLg6xMrSogRJeNOE3JTVwyZH6/55Ve9rh+57XsEUSOfKUbuNc+OUAkc9gH1igxn0mFIhrd+T5PawPuByoIcE9YbbHdYch8YAhXHRx4MIAQtry3kgBgHIZ5VxfWCSNZoi76eFb1kkFsWolbieLxea+CedcAagZRLXiNeLdH9G1reiNlRyHAzCkrHlzdUHNoNxBF38EGEpAVvNfebfcsvRsVd0ZiYGNTXxrUZ6oKEMjlg7oDmjrMoET03aFvjuB8EB1ULWl515/baDc+0NsNLH0hZwDvaviA+8/BIu+Fyx+fAHNw/AEdKBdKKRhsqFcZOqOVBlVKhXZH5QHxi7TMcv2D2hem/5Nb1+MjAfjjSLGu/2YnZ8dKI+pZ7yKFwGDEeoKQJjBFW2EbHQqAI1iOrtm9vaZH3B759wFiwukDR1+ejI7whLsRsSLnBfIK9ET5wjNgfuVXskfXm7UfwjlVlDiVG1nGzfslDDfPJnOQ++vqFOAZSb4znn6i+o9c3om/UeiOe/5IHYmZAeSP2gaz5XtHLp3yv1HeM18GK/hMyG7E7vk+0DTBjjgPWH1Bb8X1DilAvy7/vL6yTk5OTk5OTk5OTk5OTk5OTk5OTk5OTfwO/uQA4Ik3QuQ1kPrMKtDVoDQ84ONAZuDWYSmkTGHQTtHcijDEcC8dp8HhmWKYjg1wC9h2xoIQQrowxERF8CnEoYk4xQy2oEqAd5qS0hQBGd4oD8wAKFsYxntCuhAxqKP154LPRzCmtI6oElXDJgJeViKAUz+BRHQji18VUWYhS8HkHlgx0ZUJbCc+fU00QJibkBmpkGI68siMrEI6I4fsOrWTNtRqp0XkGVv6GWqThGp1sHp+vWmQQKUikDRk2MqgNQFesrcR+z8CprGmy+sEMgzGxcsGKMvafCHWCguie9dPjO5QVsYVKgO15AMAnMQ5oS9ZAj4OQToxJYclQtN8RVeRyxbwTBaAQQIwnMpRoNaucPZC+M0WJOGD9nLu4ZYfiIDeYC8IOw/O+amXuG1pza/kIze7jvmNyUDX3V3OpWdPQttxkdn9kBa++Y0WRSdYF+wZkzjh7XvvoHb1dYdHceG6XXP6VCuVVM+6CV0XV877GJI4NWSsxQUrWSFtLi5aSddBc3oEDPwwpTkzPqmONPLgQivgO9fp66n41lO1lvzZCKh6RVrMUwre0gOtC9An9wP0DWXOTGd9xK+AFl5mG8NwzUPUPhBW3yK9ZNWuwhb8E/4G/nltgDgZCRCX0TqbtkZvIsaOuqCgeA8LSmsfyfgRobaDbK2zfoXfiJXJ3EYpU1EEItF7TAh4PRAwouUMtRx628AUi8gDH9DTXAeYHIQZ95vVlIHbJ+12vzHjmax4TagW7gjoyBiGX1+GMSvQn/nygInkwoF0Q36DvMBWfW96fYyOsoYuiuuZ7dH689skbbQBF0FaggLKAb0g0mAOxa97fl80MMOaen23bV2ifiIis5Z4B13e0LfjHf4PL71Ea1v+MFsXjyhw7vudnSmmBh2e1dKlZu/4ykO1yRXQi9fd5kCA8q7QdpH2CCnNO2BxpX4ix499+QX9oSH1HqoJ0OP5E4NgP/wUTGLURIvjP/090/RG//E3+3ugf+bNuj////5I6OTk5OTk5OTk5OTk5OTk5OTk5OTk5+T/Iby4AHiLIOFCz3OOdWwa4DGgFpmR4RFBaSVlWKiU6EQUJfe2Iem5rmiJLI2RBRV41ra861/4KM0XQYshiHOM7Ni1txPopTdcx027FCXcgDTvKirxqXKt5hnWlMpnUeHuF1iNDo18rao8nESDlgjVQUYIbMTvRN2ivmmV3VJ3pJWuY3QkM94HKa+t3OCFOuBBHR5eW5qg4Ts3Ka5+ghi7ttSPbUSYxJO3BUjMQxGDscPk9dCXkO+irZtcnEjMtW29wbEj9tYr4gU9HKMwaWRnsgc0nXgyZOz6VcQTaXkG7N1AnJDAEiT2ty4jcXkbSVD2+UaRkVe9oeAm8f8e0EnWFEog0OH6G4z0PCgDUFbyDXYnndyImapfcS9ZX0H0BnlkfjN8RKUCF1/eAglaBWKAqOnpWcodR25q7sQxUBpXInWV7h7IypuH7ndI3TK4ZdvO6F1LTUpaadmRTYkxCPQ87zJn/vObONL7j45HBYQRT0m6lNqJ3UENMoQ9kvb3M5E7ISJM6BlKNQAk/kHZD8QzIXGE4Uh0h8D7ya5nkvTBDZkXjeG03V5wJIw9TxNwhdswWxBZCgnnsWBHmviGtIHbJ1zMmUZfX4QTNqt94hdXjyMA7snqdgDEPZJBV5GT9OeMg6hXxtP1D8v3g01AJQgKRSlFjcocY+VlRFJcCz4Paah4MiECBKHmNgoMsD86tZbGF0Gc+76ODHFBvuS0tBQkn5sDtBuOe/85mBtHecTouFXBcFazCeGB1zdrp2EFveL8TcwKO1DXD3xhZUy68LHVBxSCEaNf89z7SkvcBFMJ52fqB2Bu+fUfF8tBFqs6ILGgtWW+veSBAIrB2zQ1rb2itODtiBRl3DAFx9MvfEGHQPzIYPo58z2jHmoELcd+xm+H7A8olt9Xnnbn33BpvBrMhCrN/x2IlijKfz9x89pZm9XVlfP1v6A9/BwLz+Aq3T7lp3oHb3+IuzH2Hy4KMjXj7W/rT0fUDf3zF1h/g+Mj6/JOTk5OTk5OTk5OTk5OTk5OTk5OTk5P/YOj/6Bfw12bevzHnkVWnBFEuREa6MHfmXwxVmDGY28crH36FKHS0LBmMVYPaUB+ISW6eBqjaKxid6MWIayWaojrRtsLc6HOw78LxPPC+0fvBRBEximVUpFYIn3AMmIW5j9zz9ECKozopYsQe+P7EZycsEDEEz2DNA5kdZjCm0YcDPXc9p6PWsgq11vy5fRJTYQbRB+4BPTK8ijQqsXjZya/a6QAJQY87OhUf4KTRGnsjZhq33p2ISdiBiBE98H1j3L/Rx8E4XvdkaNY8z2DO146qdLxnXfcckXvFtjK9M0vA2xsxNrCG1oLsH0B7vf4dn88MmkcgYVidWClQK2ITLY4aeAThJa/RsWWOT9ZTSxyoFcxWrL1lJTaFCGfODfpXZNwzXJ9ZgYwf0J9IP3KLWHP7dBwHEYJER2PQ3FE3altQM6Iur/BR6d3oDtN35ujMULALfmyve+LgSmiDEISWP1PLsHf2nZgjg8h5/CXoD99zs3U4ykRFUWmvTV3y2RBDjp7fo+9EbFk1HI5ozxB43zIsrivCJJXblnHn8gk8UgYdA2Y+7xGSe8TxkfXFOHM+CFle77+AYuj1E7p+Iphp+aoRw5FWICYWjmiglxVt1/xrWXLTNiJNb4QIyWfweBAu+PDX5qxAP7IK3QX1ARyE5qEKfwXp4Vva0XIAD6K0NJGtZsCqBaoiMjA6djwZ8+BwZ/cMv33kdq0gfznY0PuTKCU3k7c/4Z672sSRgbPGXzaOX3o2geJi+Hjk967tVUsdxHji+87x/c7YPnLr2a6v96+kMX10Yg6kT4yKWQCdmFsa/VbwsRPj/moDSBN9xGS6IzKhNPz+jfn4AIfUgYPwO9G/Z1hcKrH3NJAj0OUKvuF94nHAemHGzjy+ZeX3/g32O25XYvtO+JHPo6xZ+3z5lHa6fmE+P5iPP6dlvrwz9p35cYd44vdv+Liw3/8Fj5VgzUMkNtDlSjz+GSmvz+/ZwVoeEug78/J7wjVbB3wHP/DnP+d1XmsWG1CYy4Vnj6wuPzk5OTk5OTk5OTk5OTk5OTk5OTk5OfkPxm/OAF7qApKBSwaMguigmuBHoHXB3UEd3YMeRt0DGZ0olSgVtGQFq1ZiARkzK2wvK6IVALGSla0aFF0RNvC0Auf6TrEg2JF6Q+eBMtKCjcig8VcLUCo0y++haS9Tbi/7EtBCWEXNwQyoiFWEAZOsAT4CqQulkAGbADKzNlYLlEZEIbdhc2NXFFjfoP+MFAGZqCkRLYPLYsT4AFvSDkSh3gifCB1pFxhbbryiRJHcqR3bKx9cUB/EMWC5gRVk24GCtjVtZw2YjpeAWFCfIFeiBh5L7rrOTrE3pD+YCj6OtE1ZEW24D4ZUJDqhC2Lfcov2mFh7x48HMyry3NGloLIQdESvef/GxJYb4SVfdwyQQFUIKi4FOcAZxHGHdgEJpCh+pAkql0+EpVHNEEJvyNJyB3W5Zeg9P6jrFXEnxkRqhl7ghK4ZNkfuKZfheYOWFenP7HzWCS7MKPjzG7QFtOKvXV0wov8E9RPKK/hkT+NcC4TgMZE+idJyG/lld2M9jcvw12a1gTUiRm4xA6oBVlDvec9EsyLdn3kgYn8gl0/QnxlGHx1ZLINCArcLcfyMaiDlHfyRz7suwCDcgAN5HdhQPI3RmFi5MpWsbWZFYhJYbgTHDvaO+IYX4CiIPPOZhldw+tr5bpYmvVRifMPrJMIz4A/JQwuv5wtpaOyEVTg8X2cdEIXY7nkAQrNiW5gIJf98Dvsiy5LG7qsO2n2H9iUPFHCgsoIFOp/E5Q/IfBAK2ndkvTE90OX3cPyExqvcvVzox4GqY3UhVF/huiFD04b2A5YGumQ9e0x8GDEHsVT8GPj+JKIwxkSLovtXoKK3BWbQP76hn37HWA9qCdBJjB2sogWYlpvDBFGvmAwkvuH2nte3XohtJ8oVGU9oK/P5E6KXrBxvIMs7E4cta9ulPKG9wzSsXbJkYTuQZUFiIcoPqOzw/BN9Cnp5Q7RmJfzbFfyW++OrEI/APv1fiHHPCuz1hkUQ/pW6/g2zf0XECR/o/p0RLd8ztx+wY2f+cMMfP1Pe/wY5vv37/sI6OTk5OTk5OTk5OTk5OTk5OTk5OTk5+TfwmwuAta6EB9CyXlZgypISmwxi+wBZKEURa8wYyHSiLkwmeKd4BqKMg4jGmAumuUUJ8b/vzAIugnEg/trzlZLbkj3Q/sRWheMBZckq2fkgdIGAkAZmxP4ABV3e035kEKIISohAdVw6Oh3pB7LcEJm4XMAG0RxREEpu+B5Pol4QKngnuL1qYQ9MV1wFUYF5IFFfQV+FWNIcdHlV7Cogr3Aza3cDQSZpX86GNgM/XoHiDtpyh/Q4CB2wXl+VtxtUAf8OpRD7lgYtEzkczHN6NZ7AlkGfGmF53QMh4kKMLau91bLqu7QM9eNl+0kwS0vxLyYyHW0rc0wUxU0ovqEa+HbPAPswRHrasJd3InZcC3AQMfBWMvSqwjwcXSMtaBfi2Kla8zrNjlIy/GPi9y1tWTFESz47pOnp/iB6oEpum5YCfQMKoZJnGORT/kzjW27mhqHRM8gViLlTSn0dFFDc0/KePrCapnaMHamfUFHkVU1sVpl+AEduucqCxkRkZRYhfBKzv4L2nsEcQcyJ/1r/XVfQA3TJkFgrpRiiFZ8KboQWJhDzDmpYuSIWeU2mpgisWXksEi8r/EDKQoQjv5r6xwfe3mEE6JYb1LKn1VlWGBvhO1recLM0flu+37GWwWj0V033QYgRXvG5pRFOQRDk2P5SjS1M6A+iNlwKOh0fO1KVWC4wwDTD39wjXtKIn5FWvxvRB5TP8Pyan0erE887crE0fwPC0u71eRD7BK1oCDx/Rt8sq8rnxGXmpq+QCaoWECf2DSmkXc2F6ANpn1D3vA+WW+G9f8e8QX1jf3zHrm+5+1sLWm/Ec0MJvC6EKOqdutxQVaLf816NQYigy+/w7/8I6xVd35C5E33JGuf+gLeGrH8ga/ffwGoG+3PA5QdEC9N3tFTG+Ao40Q8sGr5/IPoTUj/D7YaoEI8/U24/YnVh90/oRYj7f4PyR/q3n2lffkCr4/dv2PgEty9EfOThGO/gMGZHypfcMB9CaMGtI/YZyoRDiPuT2H4iLu/073/G3hrz65/+vX9lnZycnJycnJycnJycnJycnJycnJycnPyr+c0FwBIbIZZ1tDSkGIUdDset0GXFTNi6kwHYzJC4XPH+QF0zeMSIYyd8gI4MrEKI8YR2QeqSJiwvixOF8UDkBv1BqcaoFXMAQ4ulHesXwoBiGTjOTsiRW6d+gAXhOzJLVjF7y+C55Zatl5r7wkKGSLJQzIk4mP2ByEq4wn6Hes3w8TigpOEoBiqACz56Bo/7HZYvKK9aV7Y0ne2diE426SocO7UZ3iehgVZFmWBX/Hii7T2DY1fcZxqKkdW6qp57wZ7BlxcQUWbfiFCiGIYQ80AL3JoiBbpc056ulb7BOBz2iVbPulsqzAM9HgwthC6YGFYWZP+FqDcEp65X8ElVzwCZeFX6ToKJqyHbA5MCRlqPIWh42qlz5p5q79AnLLm1HHJlfvyCtpbBX7sgDiEK1y9poMqrPhfSug1BqUQZ+bX0teFbl7TEee21+hMpCzNuMAOr75geaViOSGNY85kUbhnGHk+GgMyFOHbcVqw0YmxEB18z+I/Y0mQmDzOYGO79tWnraZKqIvYFeJDJo+KHQ1tzy/Z4Qk3L15Y36B3PZJpQI44NLVlzHh5MU2QIxDeYJS3zmbXqMdMODzrMJXPOkRXrE8G0Z5CL51+mxIgMgPdvacJKPmdTNb9m5IEFXHAfGeoyCbkhbSGkg3sG6JL158LMJoDxwK3g20DqTFPflryHWP7ZGDAl94y3D8LKy+quhBxgF/DOGGDXBgi6VkQ07fCSB0BkPwgpSFVUF7x/pLW/72n+jsnQRoyDUCHEUd+AJWufY0K55vtJ8pnt+y/ghVryAIKtt3yfWKW+/wF0Qy3rvbl+IWQltn9G6o22XHMXO94ILRncmuahiwDGE7dA5kEUcj95Bh4d6OjsRPT8GvVH2P+ELn/A/SPfBxEwnsR4ou9/k60Jj2/48+f87DIn5IHGYG6PrKx+3pn79/wsrD/A8wN5f6eY5c71/EpUw6flgRHbkfYHfKTNP7cH+vZ7ZP6S4fnzA7lV/HlHb39A6yDCmFsndEfrH1A3fK3/rr+vTk5OTk5OTk5OTk5OTk5OTk5OTk5OTv4t/OYC4HF0NBzc4GK5MxpXmBumgkin2pXZvzOkouVKB7h/IGWmwbjtYAvUCzInRQUt+trYnBliysTLio8HouCsuePLQLxie1DWBaRnpa5U3CTDtQjcLSuHNdB6BYwAIipQgS3DZZ0wBPZJ4NAaoQV8gxiY8NoOLVDfiGl0LVQNcMlaYgW04e5opAVKTKS110bmGyoKcWSoqpcMt3iFZntBy8zX70rUCwxPq9onFIG24jh2OG4dSkVmZ3oGiu6GWlb+ik+iXJnHgeNwWQn3DO61Eq7cx4LEoMjOdIVjEKwQDSkPkJZ2amzEULCGqOJjx7c74YbFQszJ7L9QLj/gOO6CR6UcE23v+LFDDKSszAoxvmVN9MwA2MVQj1cY35FGbvc+t6xoTtmVGM+0P6Oi14J4fVVcN0JnbkxXzcBwveQG8/FklErVkteR8rJhHY/AVTAteG343FE6PmZWD8tEyprheRhzexC1MPsH+vYJ5kB2x96u4EeGePUC0hDZEJzhhs0dBq/g9oBuGYaaEWMg5UgzV4yQYJbANOvJxQoSjZCZRrpDHxtMp6wgr5A26pq1y/5EdMHdIHZCDJGWJqor0lbcO1ggshB+x13S4n2FukyH2PMZ1UbMnu8HLlmLHhOX1+uTgveDyYaUls845LMvCq/abxeAtG+9H69AteT9kJ0Zhu1PpC3I9gB1wgPvE6xil/c0TVGkCuIDn1khTw/KcgGeyCxIXfA4iD3QCNQnTj73ok70nrXjUmHveClMcUQcWd4Y3/+MXARdl9yIrgvsD6JVmD2/l4BcPiGe9duMO2oXWD7DeOb2MxXf7/l87x8gjSkrNibM3MYO34BBvP0eHR+I3fD7n4laYPnyCtc3Zt9xuRE68RgoDWIDy89dXPD9J6JLNhVYQ9pn6A+0XPK5a2/5HlLJBodRmLYQLbDlh3ydeuDxQMZ3YvmMREXeVmL/mbAVeDBtB9fc8JbvWWtNWtHSgKPC139kHk/q7e8Z/R/Q2DBZmL7T9aC+/z12fKXZgrz9zV/ht9bJycnJycnJycnJycnJycnJycnJycnJv47fXACM3bLKmUAZ8DygrshSEBH0OTBxplVKWXEha1TNWWru8WYwBjJ3nAH7ABOonzNEmw9CDelPXAKfE1FhilPciTKYNIopMSWDHSKDGZnIr0adOBJCqMH2gSwVohF9J/oTuayoNbxFhpxqucfrDziezHrBLdLolfx6xMDcwQshGcLJmFCepBZJhoHzFTxtE8LpPPAoFC1I/mcZDiNILUS8dks5ssLXHeVAdUHkyN1grYgObE6kwPCRIdQUKAUfhvCqHrZL1v3Wgu9PqA3vX2H5A0RnzAc6A60tf+bYwTIH9KMTC8AEKu1SmSi+P4htMOZOtEao5ZazBEMKkweuE2Fljg0dQqiTU86DycJ0RT86FncIxdc179XoTE8r26Uyjx2NHbMVvXzBxyMt8Zq7pJQDYjABf9zRapgJyAP6Aa5oKVgo0JFyy13mEa9gDERW8ANzw1CQIDgQLYQFMgY+BpQVYmf4RlkXTMjK6U8FkQ4Krg1MUe25YzsOqpU0dS3fOi4VTR8TaERRIgYiSmjuZBcrCD1rvWvJZ9UWgpHP+Myt4PBJaEWlI73jteT13De0Vbw0BEHj+RdTN/r3PHghloca6o3wDa2KSGXOX8DzEAcuFLtlnbNKHrQIB58oAg7hT7DAUGQeSL2lNS0Qx5OIida3l/3bCZ9pt9cgwilKGu8MPIQ5B8VqmvoTdFkIb1n/bgWsAv3lKAdGYHXkVnUoSCGYGagvBUzp3UF3zDciah4gsSWD81VzVzwCD0N1UNuCtgU8d6gFJWqDcISOakNKfb1ntgyR+4FdhRiDiIFGtiOwXEAr8/4vqB/Y8gNiJa1oWYkRuOX7skxHxi8ZNIvAsUMtrzB8ojUbCkRWmHtuKaug7YaUK957Gu9akf2Jvn3CLYg+8PGBzwntU+4v+5bXRxqm+dkDLa1xKYQeyOWK7DuugbTb6yBAhX1DLl8QP4iv/yv87n9BawPfEToxfmIeH3D5gTk/4PZ7UGNu/4SI0N7/gOgA/07Uwtj8r/Jr6+Tk5OTk5OTk5OTk5OTk5OTk5OTk5ORfw28uAG6qaFP2Y+KjE95ZFEQquwReV/r8TsSCTyfYGf3JdbkhEcxwRhckNqyW3GkVYe9B+FdaM7oX9JjUaphU2O9oHZSalbyIwbYjWnBVbDRkHNnsq4KUzyhKSM+q4OF4LOgRhDmhC+479IbyBFmz+lQUkQyTZX2jIBkqxZ4WJQCBFUNGz2DkuCOlwQDKivQ7USYxjgzk6EhJW1jKq1aag5iP3BDWSngnRkfMwAOngA/sGJTrCgSdSWx3pKyoGj4GwRXRe75+BqEDHzMD69FBLsCB1QuMiY8L6D0rkwMinGWpjLawH57VvMcBzZDwVwX2oPfcZXYCCUPLZ2JMJjvaN6R9wuRATJj9ZS7LhZjP3LId3xAXvO+oZViENWYIOr9BLFBvlMWASjAo+oY/PrAy8VlAruiSZqmEI30ipmAFvZasnd033AoMA+9Y+Z7GrRfkpggGxdN0Pu7oIoBlyEWgoaA3IGAqEVlBHv2BSMe6pOU6BiwLEvm8gCLq4ANhIeZOREMig3xBUdX8ulqzOxlQCrDnzz8/iDEJbRCaVdXioCX3hUURDkptWRX8snGlVGIG6q+g+PhOjIFpgzgyuAsHe0tbXY78vrMj+gZ6wec9DyO4QjGQBVAcR+T1/asRo+NeEGmvQDdQfdUqzyMr26ul3K65Q+z9gbYVyNprvbzj4szxQDHwgVojbFAcRF4W+yJZ0x0Qh+d9Did8oFpRE2Ck7f+q9Y6XkatomvyP70ireb/sLdutX3u7aq8a+nnQTSkT5PlALrmpTf+Wxr9vuQ09HK4/pLn/em8zZprQy5V55KECyhVRQaYQ1pBQZmkgFZHIfWW9wfZnNBxbL/jUbLZ20FqQ8kbsf0ZCwTeigYni4xv6OsggdSX6hvJ6v9cr9qqzdtmJ8ZE/X3FC12wauP8ZFiG2D3TcsM+SRrZPNK7ZmCCCdMW//wQNZPmR+NjyPbcqgVKWAo9/gLe/R2Mjfv4Zrr8ntgfjPpmf/xMWMw9irIZuD2YtcGwoB/HLPxHXL/j9l9wWPzk5OTk5OTk5OTk5OTk5OTk5OTk5OfkPxm8uAJ7FmYCwoeFIW4k5XtOzAw9jzpY1rG6oGq2sIM7egyIXwpxjGiUKokL4d6IUfBwEN7BXGNXBNYgJ3Z9IXYj9nuGfCAVFx8T7jizX1wYpsG8ZvhQjZs9qaVHm/sCLIabY9QImOBd4fkCtiB2AwZiYz6we9orPQNWQOEBLWo6mCIOohvgriIsN944Mzf1RsmoX96xnjpn2rzYirjn72rc0J9FXzS2IjtzqXBYOSOMYcFVEBUyZc2SgNSfuj6zUlUoUZxyDUt5QPcgRUcNLZAhVBuiCzA0pJWuebQFKbt1qJbrCvmOXHxAdjGPLIF1Aror5yvRvRB9EW9A5CGqGW2NQV2A/0jItCkdjHobpxPUJl8/E5C/boUUfaTXqgtbPUITQFCXNOxIO7QKyMGJS9o8MzK+foBTEYXrPAO1ywyeMMZBdCCuMgLK9LMqyZu23ZYAmTVEOkCUrdyErtXuG1+LAcNQqVgXvG+ELcUysvV4kD4R3RBrqAx8baivCDlYJFebsiBZkbhkwzgdIfdUY7/jRXyGnphFNhfGRe9kirz3hgVhNG1WUiB18IXPUgSrIbUGnvDavJ3Dk4QQpeQ1VkYgMWXXmFu5huBlSKqoZlDsDQlFRwg+iT3zvhARqwRyBtCWvlx95eOH4AG65ux0D6g1i4KIQBa0GGBoTtR8y4LbJHIMBmKcZrTyRrvlzLo1QRWUQAT4DkSAV5Gz2Fp0IEDPfh0rk919XjIWx7/BWAIH2nvvKmk0AaFDsgpgy+gMbT4Qtt2vLAhGEOLQKz+9ZoWyk5a2Sddqq4I1wxefL/t1/gaVBFEq95aEVNqQ/0aUwS0G2O759Q+t7NgmowvjIP3v5A9CJ54DlnZCDoBGzEeOOjgLizOcHXiZcfk88N+BB7JNZICJyl1wKYYrXBv0J5Zph/vfvRFmy1cEP9Aj0qvTnT/gU8JL74MuKj1+Q+obYDcbOjAt2+5HYOrt0rD8Q/yC0UdsbfPwE8zvqB1MNXX7E7QohdP9G4Q90cbTJv8vvqZOTk5OTk5OTk5OTk5OTk5OTk5OTk5P/I/z2AuDnwE2waDgHNo0pr1LWLhgHQTC0omMwfVLqlSETtUBsUMSYMcEcicnRB6EF4Up/RgaW45mB3GVlWkG7o9IZs8HYcLPc+ZyORkN7J0ohxmDO3IStnruoSGFgeDEQp9kVmQHFU0ysjTl2jCsmgrYrPkdu3aoxQxDtRJGsW6ZmYP18EqvR56DoTBOxXV4xouSuqE4QIfYBqxCuhExmUSwC8RXmkbvHFXJQGESVOQAG0g/09gMhDfqWldj2Mk2nMEvNheP5RK0SVomx4S6IjZxFRrL+Nd4yaBJDgN13bLQM88cDKe9MDFQIcXx4Brt0RC/QBzG/Iz5oIeA70yujH7BcoR+43FG5oGtFXLD1hvtA6ydk7zD33K/13A7FhHimyezxhGj5M9Ylw/d1yaprf2Je0qC1C+M40GavAG7SLws6BLMB5UL3g5iClSvTg9mfmJNXQis+OsyO2ZIBtxZkDuTIsI3QND6lEjjhB6NcMGnoks9SBLg3xB1dKzHuUC55z/21BS2SYS8Qe8CtEn7LgG7fGVvHqqHtQjCh71npLWvuJ8dOTCVoeN9wESQM0wIj0gotWxrLfmGawdhQkQyg8bRPY+S2ryghFfp38P6yUyvCjvggJNJiFc1gHogwMINxxzWyijs6MXawCxpKWNqzYoYcG2KC64r7RMbES80AcjpSHCkVxPB5oO2K9Ce0ytwU+kQuLXe6q+aGcib8MJ5M0de28yCaEtueYboIPjsSGd76/EBvF2CCKz4OrC3ocFyV6AehaaeHXYnjiSwrcnnPZ8KuSH8QHmlYWyABghGkAe5zQCkEFZ8bPB9Z0/3cMyT2J1avGcobzMc3WG54UcbsFJkwPPeNp+f777gTbSWoqDT6/gS9UkwI+0wc/wiXK749kbIgfUAJ5mNDb79H64Xpe9bpR80DK9YICbAvRBv4/I6O3FmOshA2mI9/AunUT3/E+xOJfFZkeYf9CW1l//bEPv8d4/4ta55Lw02IbWDvn2H/QI5JlBvebvRtY5mCWA5C6/IHYv9K2DtSLn/dX2AnJycnJycnJycnJycnJycnJycnJycn/x3o/+gX8NdmWuCqjDmY/qq1tQxjTP1V0Ru01ijLglSD6szXJmgcg5iFOgf2fJCXsCGuSL+DjKx+rVd2qfRpGdx4z81PM7Q2iijMjq4tcx8taUlaYAaUhTBDlyvaCmWp1OWGoojvhG1ZjztHVsJSMcvgVT1QM/T6A1IN5UC0wsgaZPcnzI2o+fMrToigYq8d3yAYyJyYVgRHbg0pF0IVIlDIsLHY6xoEQkEjKAGqgcwN1QICFo7MjpqgIVh0tFVYGvYSIulO7APpE2JCC8QK0UfOF4vnpuj+RKYgbswhxAyKgrbPMD2t1tLwcMbziXvgeoEeMD8wGcR+5NbvHoQoUwXEscsfsXKhXhrx2NJ+1o4p6AxKM6ytUCoShXJpUAujXhm1phWtjmnBioAf0HfUQKVlNfDlU95XIut95xPpg/prUD2cJkLRC80aZbUM/YLc+T2O3DItuXMa8xvid3TulOiIdaTmDixmUIUoSh874cqIR4ao3fH5RFDEHD8+8FjyiZiDOQ7CC7I9YOzI3Im25Fbs3EB2Zn+ilwVdFlyUCCf6zG1eDlQmsW3E9g0hMFEMUCugFa0VkQ2pypxOjD23f82yJjnmK8CPrGb2HTXBrCPlkmH48YS55zB1ODI9bfrYwAzVfMBUS24pzz1/5sgtbCQPC4gqIgsSNT8XGCCTMjsqjoxv9P1Iq9aEOO6Ep0ms4ewcuGz5ui8V0ZafGWMHMtAVU3xCsQY+oCzwfMJrI5vjkfu8moFjkIcQOJ748xsyJzGE+fgJVBmzIUOY+6RIQVpLG9yWrGifGwDx+AZ90D++MaO9AvUHYQG6MJ5/Zo4HYTcojixXolyJYvhSif07jI0oQSzr674o5fIG8w6PPxP7jg+gfYZ9g2mIVWJ8A1FKFWTeM/S+vOfX+PwH5Po3RDjeH0j5jBRB/Mja6O9/RvZfkP1bHi44fobnP+b9aFe0rsi4o2zopaEs2NvfInOjrO/w/CBMsgp934n7L+jtCxLfYdyRr/8bpTR0/xmt7/l1LZBLbiIzDbNPWb39vMN+R6Ti9/+VooH88t/+Sr+5Tk5OTk5OTk5OTk5OTk5OTk5OTk5OTv77+c0ZwNUFSiUWJ0Jh/QRzMMcz66B9R4ZBfxLlHWZPU7e84XSO0UF33CNDyeHM6JhIVjzPBjZAG1wK4FnJXARpSqEyiyPiNGvZAUtuwKo6oZKBLwbhhBXi8R2tAQa2rojk/q0Ds0808s9iV5hH1jFrZOBEhRpZd+zBITObZGVidsk6WutZlasKkv9dsZWILU3LGCCRL9UdIet+sVe1q2+o3jJ8tYUY39OMMydkEuXC7Afa3qAG9P1V7zsJyTBePGuvlUCL5Ndqyrzf08w0EAJ/fofuWQV8WdJMdDLg9Z6Ba5G0h2NHLzXDNN/TrLQl67dXxbfvhBpagyJvIBtB4H1QWmFaRZnEyGsZ/ki7UrMKW25Xwh04qLea93FabgQ3Q8ZC3Ao8vmaVrSkhGfSHCsQNYoeypGk+syoZLYgH0ioyJzInFCNaSwt6qVkBPJy4fsrQUBuo4lKIWBE1ZD4zrNoeyNKw2hA6k8Lcd+I4iAllldzZjQ7vF8QDH5PZJ0vphBsUya3VMgkfWfctQLtkNbV3FMk96HZNg3Q8EFmRBthnJIBiWCacaS+XL3h/AI7VgtDSsP61NjqAGESMDKmnQ+9YDALNgLuUrDQ+nmCWjekD3AfUBfavIFe85Med4PnewqBaXnNISzaeSARRMnjVOQitRASzT2yp+ey5Ei4ZepZCiKRJOwOiQhXcD0IK4RPpDzAHXq8JcMlqdkLR+p6b32VFaiP2r0i9IsXwY8/DDgW0NMb+jWgLNg7KZYH9ibYLzAPRicrz9b4F/FXVvb7nwQ9dsV/tf3li64947CA3vDv1cgGPDKEJ5vYz0f4IUin7A61L1pCPJ5TB62MmjWMq0hzRQg8wf6C1whRk+4befsfcPvLPlRXpAy0B8zu+Nnx8Qayi8SC2r8jyTvnxf0YU4tiQfmfcf4YGtAdmSzYt9A2xT+jjO1y/ZCX8/Z610e0TMZ+ES1Y5G+j8hi2/w8uGfPqfEGAMxz5fspK83In+HW5/RF2xtaTtfezM7/9I6EJ8/l+wshC/ud+gJycnJycnJycnJycnJycnJycnJycn/2fgN2cAa3R4fkN6oFKovlEriAc2IzdE1wYMfPuG+DvYDY2D8qozrdXRZUHWG1oKpgttvWDWEDYwTdMVEA7UFGsXGAEINiYmBkxgQ3ggscPes355AKNDTGJMQgAvCAXRBseRm6iHYa0i0V+2ZAawHmkJih+IO+YTLQVplXIxVrtQWJAx0pYNI44O0VGfqF2IyCASMzyE6JEBkwMY5oLKA/FAY8kwcBwIuYPsA1j+BtWGLp+hvSEy03gF0AWGwwjMJqYrRRvWruhi6Lqk3WwFKxMzQ3RFy4pePyG3G1hFzAnZER2YObKUDPFLQSlpeY4JHsjlB6BCV5hK1IV2vWL7jhwfsN8Jc+QqzO0DUyX6lsZiXfG5czy+puX6OjshIkikDSzaQCVt1I/vMGdunZaKf7vjc0f6R248zwGmOM487sATkY5RUAmoDebr31vFo2SYO3eQgfiOMVBRdPmE6AJ2zTCuCGYHtpRXoDyhPzIYHkYz0OMDef4zZgZjBzaoV+SIPLAghWUpwCS0IwYhQgwHP5jamFGw8rJlBcQ7RMvnZvaXjT0yQBYAx8dMI3sKPiXDTA6gpuEbM+1kHOdlnOP5Gu2KlLe8DlyI8USnoLIgKuiygE5Ecvc275ET0RAz1DsmHbWCKGirmBbUJ/RBdpYLc+5A5CGKku8BEcfWFfVAxkSN3PeVQFDm3FBt9P2Be08Ddn8i+0jb+dhhP/J1amR9OBCxI9ffITW3eLV+QqLkh8AsGSg/vxICWgXZ95S6i6DHho6eldW14CH4445/DGII3B+gl6yXXlbCB3pZkLEx545HgwHqTl2ulHbDHz/nzzwD2e9Mr0g8cJvMmBkiHw/gQKLj4wGXH5Dlil4WOJ4w7pTbF5SGiKGes9Cy/QRWUQwZAzlGVm1HoMcDiYnFE9kDZ2GSn7NZ311xd+btP1M+fUIk8Mf3rGR++1vgHacRR8fmQOpn/Pkd1wWfjkcH8dyg7k+UnaotQ3YxrK6I3rI2/es3+rcnVp3x/H+DPbOVYb3gb3/A1lseoAnHfvwv/56/rk5OTk5OTk5OTk5OTk5OTk5OTk5OTk7+Tfzm/KV6+8wxnvjR0VA0BBkFqxCi+JG7qNCwSoZRPgipWKm4FGwOrKSVyDjwBdCsS1ZZCRkoE6Tg5UqMJxIL0xqqIAq4EiXrZ8UsTWATVISQLFRmHDCcaBd8pH35F457/q8HWAMEGU9oufOJH8TeCXXkGHCrqCoVQQpptCoZ0InkBmo0UH/9OyFQdN6hvsJlzxAq3LOe1n+9ViBxILWlEVhh+sxNVnnV30oldGQQSwExQl5BkChSK1GCzLGNkIaMn9HlB0wF15pBXVuZsgMV9++4f6GUhsgkEKJUbCwQSvie9b6/7sH2B7E9MmislwzhHaRVaqnEMFxheM3QU4UxSlbdaiVu769g7DuUA6bCcoNisKdVqgTeO/R/gfY/581uX4g2Gf1Ai5CFxiV3T13o2wNdViTuhBiMQGpa3hRQFLWFiGc+N/2J1gZakaFENSQOYtwzuJ+vZy8ig+ay4v7M6xeAG2jD2w+oClIrjInaitBRKcT2gZsSU6C13AqWipQFvGCQdcsKqKIRxDgIM4gjQ1N9z8pmiXxeNGCfRETWMdcb0Y+sapYJYhzHk1pvvDrBgUA8g9nccTaQFeWJ17fcZjZFuuN+4L4TI49ehGlaydby52YScxJW8p/PrGtHAtEAXV8V5wvgWS8tQvQneVamQ50oFWUQIfmZIYb2A+qawbe/TH65viraKyy3rBaOmRXus742hAciW167PuFacqvWvgCDeXxAq7nH7YGIoOsnfHuCNVQLMe4Zgi8rzmeoS1Zvq2PLypSSldjVQDq4YetnQh0fG7LfkXZF2g36B0RFqxK+siwX4vknmBVr19zRZqDWCMB0gjg+D3h2olSKGGETLXkYwI+fkGVFQ3PLd27Efkeun9M8tgvz+Me04kWRuqHtj/kcRdr7cz6xyxsyD8Q0GxbqDlzTRm9CcEGOe35WlQBZkPmgyI6VhWFvgKPXL8g42O/f0S9/QOXCbAbckdIY4xe8tDwcIzeiO33/J+z6nzCr2UTw+JlYfwA/K6BPTk5OTk5OTk5OTk5OTk5OTk5OTk7+4/GbC4D3DqFXIn4itu/0o0B13AdLU0IN5gfF3hFVOoOQg9ouRGwUe9muFOaYTJ9YafTnnuGQODaNqTDmwA7HGUwzwgpyfKc9HbkV/LgzIqhh6DygXNJQKw4zcEA065tnZDhsI3dDY3zP/dApTAKKY7pm+DRB1fD6snerpgUcnZi5eRwur5regxgF4kCrQQjOIKYjLrhHVgPPA/SNCAEURifqW1rBrf3Fkh39g6BleLzfczNWDlQ7DMdzajdNa5nYZcUjYDzRuKSlWIzpT6S+I6NDGLaklekiGdyGkxn7q8r32NCm+CxMQHwQYyKXhmGEFGROvHVYjGDgPhAWxFYkFH8+YRyYNlSuSIEiFXXJ2mspiA6iCGqFKUsG32OgVhFmfu/LjwRvyHggyxsxDmhvGGvW/I4HMjthIFZQblTAx6SPg1or7L8Qo+KxIQa+7oQ1PAw5JsEOl5bhnS1/qe4WKsROROAHuDtFFJ2FMZ6o1DTMRbGlpZXuG5QG8xu0d2J+MMZXRN/SnC1v+NyyOhknjg+oBVQJMWJ/glZinxnwtZL3TUe+V0LTDD4er73dBjIJPqBUxhN07iDgx04XxeyG9A3Zf8rg+fKWVvzc0WpMn4CBVtxHmtFzEHMQxCucf8s7kicq8qCBORK/3iklFEQMvOYz6oLgxNGZGrhPtCy5dy0jK8StMcbOGJPenbZ25rLCHEhZUGtY9Kz63oA5X3XeI7+fXbLKOwY+IkNYMbRe0vrVlvZ/35H6CXl+Q9aKMLJCfRxZYV2yXppYMlyeD6ylhS5zIteFOHYkZrYL1MbcH/golBV8TnhscLtgsyNjx5ffYTrQvjOXKyrBLBdEYI5OKZZG+573UktlbN/xkQc/SlkJjNi+4VyxuhDe0bnQ9zvy6fd4GCzgX/8Z+fyf8HDi+iPiuf/smyP8jF0usD+YY+SO8vET8vaOPn5Glv9EaGGOD+gHPCbc3l+fWU/m4xvSfsjmg/Fk9gcxGmN8pX36W/rjFwZC2b4xxh2pKzKuHD//P4j1E+36A/HxE3oxtO7MLRj/8n9HfvivzHEn7MK0O3OT/xG/xk5OTk5OTk5OTk5OTk5OTk5OTk5OTk7+f/KbC4CbBqNA1IZIpTRDpBD6qj2VSV0+w1AwoZW0LNU0G2JVXzW2Dq0Qx0ocDm1BJtS1ZdATAxUjqjC9wdxgVEIU6sS7E+tK3J+MVWi2IqWl+euRAacuwIGVAq4ZQulrs7SsBBW0Y6MjvUDtAK9dUUdomEVuob4MWOQCx3iFmTBnYAtpZL62UEUbMTcIy0peCkhFtGSYpiuj74CDP7B6Q63hceCiOak6DV6vQ1URLcxtg5ohe8xBREGsEH7k64uBiOXOb30jXInouAVgyHLJUMwD4aC0zxStIMEQw/dBaKACIgXWK6JG8TszFC8NKZMgGH0w9kGxA0bWeVs15rCsEp65P6sRyL6BGeoPoq5E+xFEUcC//oLUvGduDZoRFsjV0KNgbWUMw48BTFR2YhzMngbkrBVYoA9mTcNa243YDfavaFHwgWhF1NFWieX3aVC6o9dP4B2xC2hD/EjJ9xiIKjaCiJ3QypCgsBN7RdYFcQcmvUOp4FqRebwC6z+mLSozq5GPAcsV+j3fL/uElgG2aOD9mXvApmmR04CJlJY1xuMDCNSyEhg3BEeqEEtFcUKEevkMMpESyPaA9XdZcRwOVVEpWW0+jwwg2yWfVXm9fs3QMnSkhVyXrKIOxY87EgFqxHZH1vc0cucByxux72ncOxCCWkV1IqXg8YS4EKHMcMJaPoNYbhTPeH1/YHRU0uDXdiFkIExEFVFDTdIStwK2IaGo1tyJ1oFEIZg4UCzwEpgZLvm1fQZRFpAMp73l28fvTyiCxgbfvyFvP+JxgAuYvDaXP6FywPEVLVe43fIP14HaFb0UjIX5fBCWtclWvyDlSXRH+gciN1wbcf/A7QHLG1JKfjbFDuMO9Ub4whh5D9078/IJdSeKoRN4+5EYH3D8hC5/C8zceLY3YjyIe4f19ylfl3cmSjsmvgmx/wNxeUOWGzDgOYhv/4B++gPx/AbxJI4rulxwvxDHE71csfGOTyXshl0bzMEYG00c0Y7aO3r5Ao9voI3SKnJMyu2/Mu0N3Igx0Mtnxj//vyh/+/d/jV9bJycnJycnJycnJycnJycnJycnJycnJ/8qfnMbwKhRjg3tB3URpFwy7EHxKahXPIyp8qpl3VFrzMjp3X44Q43hG2O/w3Tm8UFxaE1hn4wZeJ+I5s6waUN1paCIwJiCi6PhsC5YCKnkBW7g2pESlPoyFnWi7DBL1kl7ICzgmsFsuSI68Ji4GlhhijKP78TYkLkhAh7rqwrXEFFie1JaQ/oDfW2tZgXxA+07wpahswvEE9G0OqM7+QVHmp29pxE8JvQ9a3AtMkAeBzGfaW6uBRGAndCZNbx+R4Ksw/UgdM294dER/yAIPEpagsfIr6kBE3zfGcdBuBBRmGKIXBG3fL0G6k8cT4taD2Y4PgQ5Bq1eEVmZx4FvT+JyI8SZY2PoE9+3vCdlIUqDJQNDnZ3sRA64fCbkmtXL+y+5iayK2AFtEnMj5BXoyytPxwlVou9YP7ApoAfoBS2fiaMzamFYZayfiU/vxJxZiauFOD6ICePxHe9fCd8YveN9z/9uvMzb1/NOWfGh4MGQgrQLsKQFjTCHZ92tkraoNrAOZuCG968QE4kH0T+IZrmxHCUDSR+wLEhbCLJCHKuIkOZ0dDg2fE6iH/k6+06g0A+sNMJqbu8KuaXdB0FDtKDRc1/YQdBsbbYr7sEce15TqRl+2oGaoygSA5kbFgONIy1Z1QyYL18yqJ477jWrriWNYtReP+MVrVegMI6RFcrjJ2LcERFMK80CRWil0VypVOQ4iCP3fkMcWGFMRDSrmSN3mc0MY6HWK/raxhYpxPZAtGZ43CdYw6XiDv35INTyZ6wX/PiWD9WxI7cfYfuAYxKXH/BSoNbcky4NypL3txqyfMrn1CcaE+mT2R+Mb9/wr3/Gh+P3X/D7nXn/E75lFbzbD/j+zOsdnbAbQUdky+pwF8Z+5IGSVon9z/m5EQ9srai+TP6PB0FhPgaOEd7zMMjjO2hB3v6OWb7g+y/EkVXjtlzyMMj772H9TEwHnkj7I/rp97D8gHz7GX8M5O0/I/Vlt1tFbj/gz3+Afmf80/+GLDfUcrK7lDdUKnH/Z3i7EccvhAseQN9zT3m/o9JQRu40M/EvPyLz/6uW/+Tk5OTk5OTk5OTk5OTk5OTk5OTk5OQ/CL+5ADhm0AlmLRzbwdE3+vZgzombg3fGvjFmEKUR3Rk+OY4NaiVE0miUAtMo+mvgB96fRAiTiutCPAfRnxhOqQUrjlohquXGrg6W9Z1yuaAiqAUaT9ItNdw1K3PDiBi4kIFSTGS8gi6FsKB3J1zSWvWdpoK1ksEe8toPBYojLS1XMUW0wlxeYUrJ2loRaG+gV6gXpGqGrhEwHkjpaToeB96Macr0ABThZQqXFR8Ppq7M+y9ppfY7MfYUS4+DiaRVHU/wSQwn5g6XT7mNHDO/z3YHXnXL/RscD1wXXCfeDyTuaHMUR/2B1QpiGMrcn0Q03ArRO/HY4dgyTBSIfUdvNzw2VJ2yLFRVml0Rn3CpcDVEJ8JKlDUDyHlAfCBtTTO5XfB2zZCQyIbqqRwTYM9/bpqB7PKOXd7TgFbDmiCaNnqJDSnvWaO7vDFFcC2wfML7IJhQDFkFvb7DtmXg3u+IP4ADHxPqNQP6Zmhr2LWi0v8/7N13nBXV+fjxzzkzt21hqYIoIEWxi4rYFVTs2CCW2I29xBaNUaPmayyJRo3G3rGXWGKLxkjU2NEYxF4BUZC2sOWWmTnP749Z7m8vW9gGGHzerxcv9u49c+bM3Ln37J3nPOfgewlEarEZwfoetlBH0vewJoGVAhiH9eLseKmtQSJwErfb4EO6B9gkYuOs+agQxJnHJsSIYIV4WnDJgm/jNXfD2nitXr8M4ycQ3yM0Lg602iSYAvGav4JxEl/fLgAXNQSNLZGz5Gt/IMzXIZFP1BBEdw5cZMD4WJLgiIPgTrCRBYmz16PIIYkKIgwuzIJxDdM6A8l0/Np4FeAlIJXASQBhPbgAsnUkkuUYl8PgY/2KONOZEElWEJEA64PkMYTYVDmhn8L55UQRhIU84vs4L4HzkrhCLaS6I7g46z+sIcLHSUSYryHy0xjrx1m/LgBjcLlqwnwdpqwMm6mKB5Z4PvgVGN/DhQHGFYjS5UTpckil4/eOSWP9FAQFjEng8nXgZeKsYPHA93ECQZjDRYX4tamvjz/7UhlcMkPoPIJ8Ph7EkIbQekRJH+lWjst4RLk8LlcP+bkYE2K79cPVzwfr4kAsSTBgwgK4FHjlmMpeGOsw3fpjKwaBl0CCfDy1eCqNFGogKhDV1scDHmrnYYICBAL5OiSRwZZVYRJV4GrBRSQzPZBkOaayH0RZ8FJENbXxoBZJ4eoDsHm8fr3wysviKennfo7nOSS/APErsIk0YipwyRSFECRVBn4SU5iHC36AmrmQSODCAomy3ojJLt8OTCmllFJKKaWUUkoppZRqg59cABipw/N9rKTAeXiRg0SKUGw8tXIinlI4EQV4EuFbryE2lYLQ4WWrSRYKJARMFOIih3MRgROCyMNEdfgmJDJJQj8BksAFIa6+gIQuzsB1Bt8FeC6BJw7CHFiDdYI1FXEAODJIlMUVQPJZjCTxAAoCLo9JWIxn43VHsfip7liTiacALsRri1qvDC9VjpcqiwNTvsOzfhy8JMImy5BCiEkkGoJCXhyIlQhxBVyhHmfSuDAk8spxzkcKgsnn8W08fTP5PGTj9UCJ8uAlMeEivCiMf7YeJl0FAlbK8EjEa/xKHlyElSRIAhGDhGCyWayXiqd6TlVi8nlMmIewPj6P2RwuF+EKOayk8JIpIvGQwEOcT1RYhAQ1iHgE9QvjNZVtOdTPw4UFcIJJOEwiCUGeMF4wGd+EccazNXEwdfHasSHYvIuDxkEN1qbwBWwqA5RhoizGRpA0UFaJKTgsfpyB6vuIWCTrxa9tPsQuDhJaQ8I3mPoabCAQGSgEEAFRgURYwHMFEr6PZ8J4GmQ/jTEOz3pYk4zXQrZlDcnFPtZLAQnwU1gCjBGMSWKCApYIIx7GOTwEKyaeTjmdwM9UYq3BhDmsl8GIxAH6wMRT+grxdMUSxZmdGGxYjyGPJJLx+ZWGac6lEE+7bDJxJrIzmHyETVZgAGMSeCaB75djEIyxkA+RXA0WgzEOyeURAZMqxxAihTySrcPaciRyiBQgiLAIVixEAcY4kADneXGw0RjExWseS6GACaN4gAA+kIqnIA4DwMNKiCnUY1wdRgpY5/BMGhOEUMiC58cJ1ZFg05VY42FyNUh9FoIaPBMhLkskPgYfYzwSPvEU3cRrDIt4uEJdnAEtGVyYw+XrEBfhcnnw4yA2eYNJQFS/ALwkNlEev+cji+/ZeKrsuvlELoR8LdZEEEQEQYGwfiHGWqyY+DVygsnXAgYjLp7SOpnBlwAT5ZB8Diselvh1tWGEb31I+9gwj2d9bFgg4Zfjl/UgCiIwHolMD/xcDT4JEkEWU5gbz0jgJfH8NH7Cx4QOk18YnzeXpWH5aFzdIjwb4vlpbCGLDesgqsUiGJPAlPWKB7EEWax1kKrCJHwMHmQDgrpFhOQxURYvKkDNgnht5CALhVpMohxMnmjBd4gIQa4GqZmHq1+AlPXCJsrxkt0wLsCGAZKqgmw9YXU2zq7PZTEYTBCQSAjOZiDZA/EMxkEQxgOATGiwhESiAWCllFJKKaWUUkoppZRSPz5GRGRFN0IppZRSSimllFJKKaWUUkoppVTn/fQygJVSSimllFJKKaWUUkoppZRSaiWlAWCllFJKKaWUUkoppZRSSimllFpJaABYKaWUUkoppZRSSimllFJKKaVWEhoAVkoppZRSSimllFJKKaWUUkqplYQGgJVSSimllFJKKaWUUkoppZRSaiWhAWCllFJKKaWUUkoppZRSSimllFpJaABYKaWUUkoppZRSSimllFJKKaVWEhoAVkoppZRSSimllFJKKaWUUkqplYQGgJVSSimllFJKKaWUUkoppZRSaiWhAWCllFJKKaWUUkoppZRSSimllFpJaABYKaWUUkoppZRSSimllFJKKaVWEhoAVkoppZRSSimllFJKKaWUUkqplYQGgJVSSimllFJKKaWUUkoppZRSaiWhAWCllFJKKaWUUkoppZRSSimllFpJaABYKaWUUkoppZRSSimllFJKKaVWEhoAVkoppZRSSimllFJKKaWUUkqplYQGgJVSSimllFJKKaWUUkoppZRSaiWhAWCllFJKKaWUUkoppZRSSimllFpJaABYKaWUUkoppZRSSimllFJKKaVWEhoAVkoppZRSSimllFJKKaWUUkqplYQGgJVSSimllFJKKaWUUkoppZRSaiWhAWCllFJKKaWUUkoppZRSSimllFpJaABYKaWUUkoppZRSSimllFJKKaVWEhoAVkoppZRSSimllFJKKaWUUkqplYQGgJVSSimllFJKKaWUUkoppZRSaiWhAWCllFJKKaWUUkoppZRSSimllFpJaABYKaWUUkoppZRSSimllFJKKaVWEhoAVkoppZRSSimllFJKKaWUUkqplYQGgJVSSimllFJKKaWUUkoppZRSaiWhAWCllFJKKaWUUkoppZRSSimllFpJaABYKaWUUkoppZRSSimllFJKKaVWEhoAVkoppZRSSimllFJKKaWUUkqplYS/ohug1I9NGIZMmzaNzz77jNdee42XXnqJ+fPn88knn6zopinVRDab5csvv+Sjjz7ilVde4aWXXmKrrbbitttuW9FNU0oppZRSSimllFJKKaXUCqABYKUaufvuuzn66KMJw7Dk9wMHDlxBLVKqZcccc0yzgd7NNttsBbRGKaWUUkoppZRSSimllFI/BjoFtFpmbr/9dvr27csGG2zARx99tKKb0yY77LADl156KUcccQTW/v+3R3l5+Qps1dJFUcTJJ59MVVUV48aNo6amZkU3SS0HRx11FJdddhk77LBDye9/7NerUm11zz33YIxp17/KykqGDx/O6NGj+c1vfsMbb7xBFEWdbsubb77Jaaedxs4778zqq69OIpGgV69eDBs2jPHjx3PVVVfx3//+F+dch+r/4osv+POf/8zuu+/O2muvTY8ePUgmk/Tv358NN9yQXXbZhYsuuoiXXnqJQqHQ6eNRK8Zzzz3X7HU7atQo8vl8u+racccdl/p+2HHHHZvdVkR44YUXOPHEExkzZgx9+/YlmUzSp08f1l57bQ466CBuvPFGPv30U0SkZNsZM2a0+32ZSqUYPHgwW221FcceeyxPP/002Wy2w+dRKbV02odqH7qyqa2txfO8Jtdtjx49+PLLL9tV18UXX9ymvmvJgeGLffjhh/z6179mjz32YMiQISQSCXr06MEaa6zBuHHjuPTSS3nrrbea3X7o0KHtel96nkf//v0ZOXIk+++/P3fddRdz5szp0DlUSimllFI/LUaWvKujVBf46quvGDZsWPGm4dixY3nhhRdWcKvaZ/fdd+e5554DYNNNN2Xy5MkruEUtu+uuuzjyyCOLjy+55BLOPffcFdgitTzl83n69etHdXU1AGeeeSZXXnnlim2UUl3giy++4OGHH+aHH37g4Ycf5vvvvy953lrLjjvuyKhRo6isrOSHH37g22+/5auvvir5zO7fvz/nn38+Rx99NIlEol1t+P777znmmGN45plnMMYwZswYtthiC+rr6/nwww955513iu89gCOOOII777yzzfV/8MEH/O53v+Ovf/0rAAMGDGDnnXdm8ODBBEHAnDlzmDx5Mu+++27xJnz37t3ZZ5992GGHHdh9993p1atXu45pWamursbzPCorK1d0U5Zq0aJFzJ8/n3Q6TSaTIZPJFH+/cOFCFi5cSHV1dfHnsWPHstpqq3V6vzNnzuTyyy/nkUceYfbs2SXPHXPMMdxyyy1truu5555j0qRJvPjii/znP/8peW7kyJHstNNOjBkzhp133rnkuc8++4zDDz+cN998k2QyyU477cRmm23G3LlzmTp1Ku+88w719fXF8hdddBEXXnhh8XE+n+eaa65h4cKFPPPMM0yZMqVJ2zbaaCN22mkn+vTpQ3V1NTNnzuTbb7/l9ddfLwa6M5kMJ510Er/5zW/o2bNnm497RaitrSUIAnr06LFc9lcoFJg3bx6ZTIaqqiqMMW3a7q677mLkyJEMHz58qZ912WyWuXPn8vHHH5NMJhk9enQXtFz9mGgfqn1oa6IoYu7cufTq1QvfXzYTw2WzWZxzXTY4VUT47W9/y1NPPdWk79loo414/fXXKSsra1NdU6dO5ZFHHmHy5Mk8++yzJc+tttpqTJgwgfXXX5+jjz665LlFixZx6qmnctdddwGw5ZZbss0222CMYerUqUyePJkffvihWH706NFMmjSppI67776br776irfffpvnn3++yUCr1VZbjXHjxjFgwAAKhQIzZ85k5syZvPPOO8ydOxcAYwz77rsvl1xyCWuvvXabjnlFKRQKVFdX06dPnzb3Z2110003seOOOzJkyBA8z2uxnIhQX1/PnDlzmDJlCqutthqbbrppm/aRz+dZuHDhMmm/UkoppdQyJ0otA9OmTRNjjAACyOjRo1d0k9rt9NNPL7Z/m222We77//bbb+Wvf/1rm8ree++9xbYCctFFFy3j1qkfm4033rj4+p9//vkrujlKdbnp06dLMpks+ay7//77Wyw/bdo0ueyyy2S11VYrlh82bJi88sorbd7njBkzZMiQIQLI6quvLv/617+alJk3b54ceuihxX1sscUWbao7iiL5/e9/L9ZaAWTw4MHy1FNPSRiGzZafPXu2nHvuuZJKpUrOwbXXXtvm41kWFixYIBdeeGHJeR4wYICcd955Ul1dvdzaUV1dLZtvvrkA8vbbby+1/LXXXltyHpf27+OPP+7S9s6fP1+22mqrJvu57bbb2l2Xc05+8YtfFOs49thjxTnXbNkpU6ZIr169BJD11ltPpkyZ0qTM9OnTZbfddivWd+CBB7a47yiKZP311y85huOOO67F/VdXV8sdd9whm222WbF8VVWV3HjjjS1us6Jks1n505/+JMOGDSu2tU+fPnLqqafKzJkzu3Rfzjl5/vnn5ZBDDpH+/fuXnM8ePXrIIYccItOnT19qPauvvnq7ruvF/3bYYYcuPR7146N9qPahIiK5XE7+8pe/yCabbCK+7wsgvu/L8OHD5aijjpL//ve/nd6Hc05efPFFOeKII6SyslLOOeecLmh5qSAI5JRTTmnyWXbIIYd0qC+55557inVssMEGks1mmy1XXV1d7L+6d+/e7Hf1+vp6OfPMM4v3Qvr169fqvk888cSSY9hkk02kpqam2bKFQkGeeeYZGT9+fLG8tVZOOumkFtu8ojjn5K9//atstdVW4nmeAJLJZORnP/uZvPPOO12yj0Kh0KE+D5Cjjjqq1bq///57Oe+882SNNdYoblNeXi4jRoyQ888/X77//vsuOQallFJKqWVNA8BqmfnLX/4iPXr0kM0220w++eSTFd2cdjv//POLf+xvv/32y22/b7/9tuy3337ieZ5sueWWbdomCILil+wJEyZIbW3tMm6l+rHZZpttitfrhRdeuKKbo9Qyse222xav82QyKUEQLHWbBQsWlNxc9n1fbrrppqVu55yTHXbYobjNe++912r5xTfwevfuvdS6c7mc7L777iUBtpZu9i3p/fffLwkQnXzyyW3abln44IMPZOjQoQLI2muvLZdeeqnceOONMmLECAFk0KBB8sEHHyzzdjjnZJ999imek7bcwD7jjDPafJOwZ8+eyyQ4+c033xRvii7+l0qlOnRj9L333lvq8edyOVl77bWLQdcZM2a0WJ9zrniNjhw5stV9//a3vy05hldffXWp7Q3DUP7whz+UBKSOPvpoyeVyS912eZg+fbqMHDmyGIw5//zz5dZbb5UxY8YIIL169ZJJkyZ1yb6+//774mfNgAED5KyzzpJ77rlHrr/+etl1111Lbjw/9thjLdYTRZEkEokO3Qh/4IEHuuRY1I+b9qE/7T70yy+/LA4YHTJkiPzud7+T22+/XU4//XQZMGCAAOJ5nvzud7/rUJ+3eCDL4gDpuuuuK3fffbfk8/kuO4bGwjCU4cOHN/k8u+666zpUV48ePQSQP//5zy2WO/LII4v7eeqpp1qt849//GOxbGvX5z//+c+S9l988cVtavPTTz8t/fr1K263+eabd/ngpI7K5XJy+OGHCyBlZWVy/PHHy5133ilHHnmkGGPE8zy5+uqrO72fb7/9tsMB4DfeeKPFel944QXp06ePQJwI8Oc//1muu+46Ofroo6WsrKz4t+GTTz7Z6WNQSimllFrWNACsVAsuvPDCFRIAXnxzEZAdd9xxue1X/W/bfvvtNQCsVnr77bdfSTCkPf7whz+U3Pi57LLLWi3/0ksvFcsed9xxS62/tra2mCWwYMGCFss55+TnP/95se5DDz20xYyllrz++uvFQM8uu+zSrm27yowZM4o3HkeMGFFyczObzRZvMq+66qrLPEvisssuK3lt25Ktu/ha2nfffeXEE0+UAw88UHbeeWcZOXKkrLPOOrLxxhvLVlttJTvttJOcd955y6ztAwcObHJTcuDAgTJnzpx21TN79uzi9nPnzm22zB133NHm619E5LvvvpNu3bpJVVVVq8GAJbOp2xPAfvnll0sy8nbfffd2vx+62sKFC2XdddcViLMWG1+/URTJHnvsIYBUVFR0OjhTX19fzKDeY489mg0SXHzxxcXz07179xZv8M+dO7fkdejWrZusueaaMnz48Gb/LQ749OzZ80cTeFfLlvahsZ9iHzp79uxiNva6664rs2fPLnm+tra2GLBvTxBysXnz5hWvr1QqJVdeeaUUCoVOt3tpDjvssCZ9qO/78tprr7W7rnXWWUcAefTRR5t9/ssvvyzuoy3XTRiGxZlJ/vOf/7RYbsqUKSXtv+KKK9rc5u+++04GDx5c8vfDis5Mdc4VB41Ya+Xll18ueb5xYPyuu+7q1L4mT55ccu569uwpa621Vov93uL33Prrr9/i3zWvvfZa8e+SAw44oMlAmc8//7yYse/7vvzzn//s1DEopZRSSi1rGgBWqgUrKgC8yy67FPc7bty45bZf9b9NA8Dqp6Dxjb723rx2zsmpp55acqPo8ccfb7H8L3/5y2K5++67r037WHyDvLUA2P/93/8V6x01alSHg11/+ctfBOJpL5c355xsvfXWxeN4/fXXm5R5++23i8+3NoVwZ7344ovFKUAX//viiy+Wut0mm2wiQLumM10WBg0a1Gxmyk477dSuayMIguK2LW231157Fcu09eb4CSecIECrAenGgeX2BoBFRP7617+WLBty+umnt2v7rtY4uNTcFLnfffddMXN7q622kiiKOryvk08+WSDOups3b16zZcIwlFVXXbXYppaypha/56y1cvvtty/1+vn9738vgJx22mkdbr/636J96P/3U+tD999//2J9//jHP5ot891330l5eXmx3Pvvv9+mut99993igJKqqqrl2q8uzjJd8l9HAueLX5MXX3yx2eevuuqqYv2XXHJJm+p86KGHBJBHHnmkxTJfffVVhwPAIiKfffaZ9O7du7j9FltssUKng77llluKbTn22GObPN946YiqqqpOBawffvhhgXhq6b/97W9LzVw/+uijBZBrrrmm2efr6+uLf5eVlZW1OA37U089VTzG/v37S319fYePQSmllFJqWbMopX5UUqlU8eeysrIV2BKllPpxMcZ0ats//vGPrLnmmsXfHXLIIXz77bfNlv/qq6+KPycSiTbt47DDDuOYY46hZ8+ezT4/ffp0LrnkkmJ7br31VjzPa+shlDj++OMZOHAg06ZNI5/Pd6iOjnrkkUd47bXXABgxYgRbbrllkzIjR45kgw02AODBBx/klVde6fJ2TJ8+nQMPPJBtt9225PdtOafTpk0DYNCgQV3ero5YZ511Sh6/+OKLnH/++W3e3vf94s8tHX9HrukTTzyRE088sdX3XmfelwD77bcfhx9+ePHx1VdfzRNPPNGpOjvqrbfe4v777wegT58+jB8/vkmZVVddld133x2A119/nXvvvbdD+wqCgLvvvhuAIUOGtPi54XkeI0aMKD7+/PPPmy336aefAvDb3/6Wo446qtX3QRiG3HTTTQAcffTRHWm++h+kfej/91PqQ999910efvhhAIYOHcoOO+zQbLlVV12Vgw46qPj4d7/73VLrfvvtt9lhhx2YMWMGq666Kq+88kqTPnl5GDx4cMl36O+//57999+fIAjaXMfifrQr+9C9996bY489lsGDB7dYprN96JprrsmVV15ZfPzmm29yzjnndKrOjqqpqSn52+WEE05oUsZay5FHHgnAwoULOffcczu8v8X93g033MC4ceNaPZcLFizgvvvuI5VKceihhzZb5oYbbij+fXjAAQdQVVXVbLk99tiDYcOGAfDdd99x6623dvgYlFJKKaWWNQ0AK/Ujk0wmiz83/iKrlFKqc5LJZMlNsrq6On77298udbvnn3++TfX369ePW265hSFDhjT7/AUXXFC80Tx27Fg23HDDNtXbHM/zOOGEE3DOldyUXB4uvfTS4s/jxo1rtowxhr333rv4eHGwqavk83kmTJhAEARMnDixXdvW1NQwb948PM+jf//+Xdqujnrsscf4zW9+U/K7yy+/nMcff3yZ7K+t1/T666/P9ddfT69evZZJOxa75JJLKC8vLz4+++yz23XjvqtcdtllxZ933XXXkr/JGuuKazsIApxzAIwaNarVsgsWLCj+3FJw7NNPPyWZTHLSSSctdd9/+9vf+Pbbb9lqq61Yb7312tFq9VOmfWjXWN596O233178edttt8Xalm8BNW7P008/TX19fYtlP/74Y8aOHcvChQvJZDK89NJLnXpNOuPss8/mscceKzm2V199lbPOOmuZ7K+t13QqleLmm29m0003XSbtWOzQQw8t2cf111/f4mChZen222/nhx9+AGDAgAElg5caa3xtP/DAA1RXV3dof59++imrrLIKP//5z5da9s477ySbzTJ+/PgW+9HG75Xtt9++xbqMMSXvlUceeaQdrVZKKaWUWr40AKyWqW+//ZbbbruNF154YUU35X9G46Bve0YEiwgffPABV155JV988cWyaFqb1NXVrbB9t1dNTc2KboJSajkbN25cSQbT3XffzYcfftik3OKR/YvLdDaDdc6cOdxzzz3Fx4uzHzrj+OOP59FHH12uWaxTp07lv//9b/Fxc5lLi2200UbFnx977LGSIFZn/fKXv+Sdd97hxhtvZODAge3adnF2x+qrr16SObsiJZNJfv/737PPPvuU/P7www8vZrh0VuNr+oorruCTTz7pknq7Qv/+/Utu4H7++efcdttty7UN8+fP59lnny0+buu1/cYbb/Dxxx+3e39lZWU899xz/PnPf+aaa65psdysWbN4++23i4/HjBnTbLkDDjiAp59+mj59+ix139dffz2g2b+q/bQP7ZwV0Ye+8cYbxZ+XNuhpcdYxxINU/v3vfzdbLpfLceCBB7Jo0SIgHjyz9tprd6h9XSGZTLL77rvzpz/9qeT3f/7zn3nggQe6ZB+Nr+l//vOfXVZvV7DWcsYZZxQfh2HYrllEukrjGTFau7YHDx5MRUUFEF9LDz74YIf2d8opp/DYY4+1OFhrMeccN954I9Byv7dw4cKSvrw975U333yT2tratjZbKaWUUmq50gCw6lKvv/46Z511FnvssQeDBw9mwIABHHPMMcXp9Frz0Ucf8Ytf/IIRI0ZQXl7OKquswiabbMJee+3Fb3/7W/72t78tdYouEeGFF17gmGOOYZ111qGqqory8nKGDRvGnnvuyZ133rnMgn7OOZ544gn2339/NtxwQ8rKylh11VXZfPPNOffcc5k9e3aL2/7rX/8ik8lgjOG+++4r/v7uu+/GGFPyr/E0kQ888AAnnngiY8aMoW/fvmy44YacddZZLX5ZX9Ls2bO56qqrGD16NKuvvjrJZJJ+/fqxySabcNZZZ/Huu++2un0URXz22Wc89thjnHvuuay77rqsv/76JWXmz5/PZZddxrbbbkvv3r1Jp9NstNFGnHzyyV0aDGiLuro63njjDa677jp22GEHunfvzowZM4D42rnvvvsYNWoUlZWVjB49utWb7lEUcd9997HnnnvSr1+/4rnbcsstueCCC/juu+/a1Kb58+dzySWXsMsuu7DGGmuQyWRYc8012XXXXZk4cWIxO0gp1TWMMSWBJhFpto/af//9iz+HYciee+7Jo48+ioh0aL/PPPNMyft59OjRHaqnse7duzN+/PjlulzAkhmprd3wbXzzOp/P8/rrr3dJGx544AFuueUWDj744JLpKtvqm2++AX480z9DfPPWWsvdd99d0s/X1NSw7777dsnfLo2v6UWLFjFmzBhefvnlTtfbVZbM4Ono1Mod9cwzz5RkHbd2ba+33nolmWYvvfRSh/a57bbb8stf/pLevXs3+7xzjsMOO6z42bHbbru1GABef/31GTt27FL3+fHHH/PSSy9RWVlZck0o1Rbah3bO8u5DnXN89tlnxcdLC2oNGjSoZArkDz74oNly55xzDlOmTAFgu+2245RTTml327rS4s/jU089lUMOOaTkuaOPPrrF42iP/fbbr+Rz//DDD+eGG2740XxX22uvvUqu5SeeeGK5DnaeMWNGyb2D1q5ta21Jtvg///nPDu1z1KhRbL311kst98ILL/DFF18wbNiwFj87lvzev7T3SuMBAWEYdtlgPaWUUkqprqYBYNWl3n//fW666SaeffbZ4g3WpRERzjnnHNZff33uuOMOunfvztlnn81RRx3FokWLeOqpp/j973/P3nvvzaxZs1qs5/PPP2fMmDHssssuPP/88+y8886cc845/OIXvyAMQ5555hmOOuoo1l57bZ588skuOuLY66+/zrrrrst+++3HrFmz2GabbTj33HPZbLPNePvtt7nssstYZ511SjI4Glt99dW57LLLuPrqq9lss82Kv99ss824+uqrS/4tHr0K8Oyzz3Lbbbfxr3/9izlz5rS5vSLC1VdfzZprrslZZ51FZWUlRx99NL/5zW/YbLPNipnEI0eO5LDDDmP+/PnN1rPDDjswfPhwxo8fz2WXXcbHH3/MtGnTCMMQEeHmm29m2LBhnHvuufz73/9m3rx55PN5pkyZwvXXX8+GG27Y6mvalc477zy6devGVlttxS9/+UsmTZpUvCninOPYY4/lkEMO4Z133qG2tpaXX36Zo446qtm6pkyZwkYbbcQhhxzChx9+yCGHHMKFF17ImDFjeOedd7j44osZNmxYSabCkkSEiy++mIEDB3L11VeTTqf5+c9/zumnnw7EU4sdfvjh7LLLLmSz2WVyTpT6qdprr71KHjc3ze6WW27JhAkTio9ramr42c9+xo477tjiZ3lr/va3vxV/HjRoEP369Wt3HT8GkydPLnnc0jR6AKuttlqr23bEtGnTOOGEExg4cGAxi7EjdcD/DwCLCDU1NcycOXOFz2LRrVs3nnjiiZJ15z7++GN+8YtfdDhwstj+++9fMtXwrFmzGD16NOPHj/9RZANvu+22Jcf9xhtvLLe/EaB913YmkymZFrsrru0l5XI5TjrpJP7xj38AsPvuu/Poo492er3IG264AYCDDz64ZNptpdpK+9COW9596Jw5c0qmcV7aYGprLZWVlcXH8+bNa1Lmm2++Kel/L7nkklanlV6ejDHccsstbLLJJsXf1dfXs99++3V4muHFBgwYUJJlGwQBJ510EiNHjuT555/vdB/dWRUVFey4447Fx4VCgeeee2657b891zaUXt/Log9trPGsFy31oV9//XXJ46W9V7p161byuLn3ilJKKaXUj4Io1cWcc/Lggw8KUPx3+OGHt1j+T3/6U7HcpZdeWvJcGIZy8MEHF5+fNWtWs3W8++670qdPHwHksMMOk3w+36SeSy65pKRNt9xyS6vHceGFFxbLbr/99i2We+GFF6SsrEwA+etf/9rk+RtuuKFYT79+/aSmpqbV/R5++OFtOm+LZbNZOfXUU0uO7c4772yxfBRFcswxxwggPXr0kDfeeKNJmY8//ljWX3/9Yn0jRoyQOXPmNCn31ltvyXXXXSfDhw8v2X9dXZ2cccYZAsgGG2wgV199tbz22mvy4osvyp577llS9owzzljqMXaFyZMny6WXXiojRowo2f+zzz4r5513ngDSq1evkufWXHPNJvW8+OKLxdf7vPPOkyiKSp5/++23JZFIFOt48cUXm9QRRZEcd9xxAsigQYNk3rx5Jc/X1dXJ9ttvX6zj5JNPXurxNS5/4YUXtu/kKPU/ovHnY3l5eYfryefzJe9TQL777rsm5RYtWiRbbbVVSbnF/7bYYgu5+eabl/qZvtjAgQPb1Kf82K2++urF47DWNvkMbMw5J57nFcvvu+++ndp3GIay3XbbiTFGXn755ZLnGr82X3/9dav1/OpXvyp+/g4fPrykjYD0799fjjvuOPn888871d62GDRoULNtfvrpp8UYU9KuK664otW6FpdrzcyZM2WttdZqcj0bY2SnnXaS++67T3K5XLuO4c477yyp65133mnX9o017ssAuf/++ztcV3tts802JfueNm1aq+WHDh1aLLvxxht3WTucc/L222/LBhtsIIAMGDBA7rvvPnHOdbruRYsWSWVlpQAyefLkLmit+l+ifeiKt7z70CAISl6rU045Zanb9O/fv1j+2GOPbfL84u8wgKy33nrFz6b6+nqZOXOm1NfXt7udHbX4ml7yu++0adOkd+/eJdfcuHHjWj3fi/ufSZMmtVimUCjIvvvu2+w1vd5668mf/vSnZr8zt+brr79uV1/fmsb3L1p6/ZaV888/v2Tfd999d6vlf/GLX5SUr66uXibt+uqrr8QYI77vy/fff99iuZdffrmkPc3d12nss88+W2F/ryillFJKtcePY6imWqkYYzjggANYa621llpWRPjLX/4CQDqd5swzzyx53vM87rrrruIUPI3Xx11swYIF7LPPPsyZM4d11lmHW2+9tck6MJ7nce6553LhhRcWf3f88cfzzjvvtPv4Gqurq+Oggw4qjqzeY489mpQ59thjWW+99YA42+b222/v1D6XlE6n+fWvf93m8tdccw233norANdeey1bbLFFkzJrr702zz//fHF0/fvvv89RRx3VZGTzqFGjOPnkk5tM/bbffvtx7bXXcvHFF/Pee+9x2mmnsdVWW7Hjjjvy17/+lW233bZY9plnnmlz2ztj00035Te/+Q2vvPJKSYbRgw8+yKWXXsrJJ5/MrFmzuOmmmzDGYK3loosuKqnjs88+Y/z48dTX13PSSSdx8cUXNxnxvtlmm3HSSScVHzf32jz66KPcfPPNQJwhseQI6bKyMv74xz8WH99888388MMPHT52pVSpZDJZMs0uxGvWL6myspJJkyZx4YUXNlkr9s033+S4445j9dVX5+yzz252+8VEpCSTsUePHp08gqWrq6vjm2++6dS/JbNhnXPMnDmz+Lh79+6tZv0YY0oyiebOndupY/rjH//IK6+8wplnnsl2223X4Xq++eYbKisrGTFiBIceeig33HAD999/PxdddBFbb7013333HTfffDPrrLMO11xzzQrJ6tljjz34/e9/X/K7X//610yaNKlT9fbv35/Jkydz4oknlvxeRHjxxRc5+OCDWWONNfj973/f4uwfy1LjKU+h+fdlPp/v9LW9cOHCJvUuua+lvU+78tqGOFPv1FNPZejQoYwaNao4ZWlVVRWffvopn3/+eaf3cc8991BTU8PGG2/Mpptu2un61E+T9qH/O32o7/sMHTq0+PiJJ54giqIWy4tIyefjktNj19XVcddddxUfr7vuuhx77LGsttpqlJWVFf/v27cvP/vZz3jhhRdWSB86cOBAHnnkkZLprJ966ikuueSSTtWbSCR49NFH+ctf/tJkBoUPP/yQM888k4EDB3L88cevkCmB29KHigjTpk3r1LXd3NJWnelDYdll0N54442ICOPGjWt15oDhw4eXPH7sscdarXfJvyOW51TySimllFLtsqIiz2rl13jEd0uZrHPmzCmWSafTTTJ3F1ucTZrNZps8d9JJJxXruPzyy1ttUy6XkzXWWKNYfrPNNmsxo6ItGcDvvvtum7KOfvOb3xTL7Lrrrq22sb0ZwCLxaOTG7WgpA/jbb7+VZDIpgFRWVi41y+eWW24pqfeZZ55ptlxdXV1JuVVWWUXee++9Fuu96667imVTqVSbjrErbbLJJiXtHTt2rIRhWHx+xowZ8tVXXzXZbpdddhFAksmkzJ07t8X677///pL6v/3225LnTznllDZlMay66qrFcg8++GCrx6QZwOqnoKuyl0RExo4dW/I+ffzxx1st/9///lcmTJjQJFt08b/Kykq58847m+1T5s6dW1L25z//eafavjTff/+9dO/evdl2tuffcccdV1LvwoULS54fOnToUtvSONtpgw026PAxvfPOO+L7vgwbNkzq6uqaPN+Wvnixjz/+WAqFQrPPRVFUMjMJIFdddVWH2700LWUAi8TZX+PHjy9pS58+fWTGjBnN1rW4TFu98sorsvPOO7f4+vft21eefvrppdbTlRnAS87Wcuqpp5Y8n81mS/6O6+i/XXbZpcm+e/ToUXze87ylZtw2zhiurKzs8DEv9u2330r37t2lf//+0rNnzyZtTiQScs4553Q4u845J+utt54AcsMNN3S6vep/j/ahbbOy9aGNvysD8uSTT7ZYdsk2/uEPfyh5/qmnnmryuh177LFy++23ywMPPCB//OMfZdSoUSVl9t57b1m4cGGH2r40LWUAL3bttdeWtMUYI88991yzZduSAdzY119/LUceeaSkUqlmX/9EIiGXX355yXfMluppvF1nMoBfe+21kro22mijJmUOOuigTl/bVVVVTe7LLJkZ/eqrr7ba1iUzht99990OH3dL6uvri/3ps88+22rZxn0kxPcoWpp9TkTkySefLGn/W2+91dXNV0oppZTqEqVDcZXqQolEYqllevToQSKRIAgCcrkcjz/+OAcccECTchdccAHjx49vkgE8f/587rjjjuLjvffeu9X9pVIpjjnmGM477zwA3nnnHSZPnlyy7m57rLfeegwbNowvvviC8ePHF9cTXNLAgQOLP0+dOrVD+2pNW841wHXXXUehUADiteSay6hu7NBDD+WMM86gtrYWiNfP2X333ZuUW3LE63nnncfGG2/cYr0bbLBB8ed8Pk82myWTybTpGLpCRUVF8edkMslNN91UMkJ89dVXb7LNG2+8wfPPPw/AxhtvTE1NDTU1Nc3Wv/h8Lfb111+XrHO08847c9ttt+GcK8kWXtLAgQP5/vvvgfi6ae69oZTqmO7du5c8XvzZ2JINN9yQRx55hOnTp3PjjTdy++23l6y9XlNTw5FHHsmLL77I3XffXfKZEgRBSV0LFizo/AG0omfPntx7771N9tse6XS6ZC05gEWLFpU8bksWljTK/OnoeuZ1dXUcfPDBhGHIbbfd1uksi7XXXrvF56y1nHHGGbz99ts89NBDAJx99tnssccebZrZpCsZY7jrrrv45JNP+PDDD4E4S3TChAm8/PLLS+3Dl2bbbbfl+eef5+OPP+b6669n4sSJJf3a7Nmz2XPPPTnnnHO49NJLO732bFss7X2ZSqW4+eabS9a0bC/P85pc21B6fffo0WOpx9sV13Zjq622WslnQz6f57nnnuPaa69l0qRJBEHA5Zdfzn//+1+eeuqpks+Ytnj55Zf58MMPyWQy/PznP+90e9VPm/ahrfsx9aFnn3029913X3EN3BNOOIH11luvJDN4scYZykCTMn//+9+LP/fo0YPp06eXfK8C+NWvfsVVV13Fr371KwCefPJJDj30UJ544onl0o80dvLJJ/Puu+9y9913A/H5/PnPf867777L4MGDO1X3GmuswR133MEf/vAHbr31Vm6++WamT59efD4IAs455xyef/55nn766eWSIdqW9+WZZ57J/vvv36n9jBo1inQ6XfK79l7fskRmeFf0o0t66KGHmD9/PgMGDGDnnXdutawxhksvvbR4Pymfz3PwwQfz5JNPNsn2hqbvlc5eT0oppZRSy4oGgNUK5XkeW265Ja+88goARxxxBHPmzOG4444rCWpWVVWx1VZbNdn++eefL35ZSCQSTabuac6ee+5ZDAADvPLKKx0OAKdSKT777DOqq6tb/ZLTOPjXFdMEdtTjjz9e/HnDDTdcavl0Os3YsWOL273yyiuIyFK/vK+//vqtPr/klE8LFixYrgHgxu3fcccdGTJkyFK3efTRR4s/v/XWW+36krfkNFl77rknc+bMwfO8Jl+eG/uxXDdKrYyW/BxbvNTA0gwcOJDLLruMiy66iPvvv5/f/OY3Je/x++67j6FDh/K73/2u+LtevXqV1NF4KstlIZlMNrskQWcteZO3LZ/bYRgWf248/X57nHHGGXz22Wccf/zxbL/99h2qo71+97vfFQPAYRhy5ZVXcssttyyXfTdWUVHBE088wWabbVa8ef/WW29x6qmnctNNN3XJPtZZZx3+8pe/cPnll3Prrbdy4YUXlgSCL7/8ctZaay2OPPLILtlfa5b2vjTGLPUmbkdVVFQUp3RcXtd2a1KpFPvssw977bUXRx99NHfeeScAzz33HHfccQfHHHNMu+q7/vrrAdh///2XSXvVT4v2oe23ovrQgQMH8vjjj7PffvuxYMECvvvuO0aOHMmpp57K2LFj6dOnD/Pnz+eZZ54pWarIWssOO+xQUtcXX3xR/HnAgAFNjgnia+PMM89kypQpTJw4EYC//e1vvPjii4wdO7ZDx9BRxhhuuukmPvroo+LSTwsWLGC//fbj9ddf75Lvn3369OHcc8/l17/+NU899RS/+tWv+PLLL4vPT5o0iRNOOKEYhF6W2vK+3HTTTZfJEgDtvb4bX9uwbPrRxf3eUUcd1aZBU3vttRdXX301Z5xxBiLCP//5TzbZZBPOOOMMRo4cSXl5OTNnzuSBBx7ggQceKG63ySab0KdPny5vv1JKKaVUV9A1gNUKd+WVVxbXhcrlcpxyyikMHjyY888/n2nTprW67b///e/iz7169WrTqOIlA3efffZZB1r9/xljmg3+zp07l6effppzzz2Xc845p/j7XC7Xqf111Jw5c0qOdcmbKS1pfL5qa2vbdNOltfWsoGnG8tKyBpalJddKaslLL71U/Hnffffl8ccfb9O/Z555ptms6fLy8ibBX+ccn3zySfHG7uKMY1g2o6KV+ilrnHkEsOqqq7Zr+1QqxZFHHsknn3zCnnvuWfLc5ZdfXvJZmUgkStb6njp16v/ke7qqqqrVrKzmNL7B15F1G5988kluueUWVl99df7whz+0e/uOGj58OKusskrx8VNPPbVC1jEEGDZsGA888EDJ3zg333xzMSDYVSoqKjj99NP59NNPGTVqVMlzZ5999nLpqzv7vuyM3r17F39eHtd2W1lrueaaa0oykG6++eZ21fHtt98WB/MdccQRXdk89ROlfWj7rYg+dLHRo0czZcoUTj75ZPr06UN1dTW/+93v2GabbRg+fDjbbrstkydPplu3bsVttthiiyb7XDwzEZQOVG3OaaedVvL46aef7nD7OyOdTvPYY4+V9Onvv/8+xx9/fJf2657nsc8++zB16tQmA3QmTpzIf/7zny7bV0t+LH0oLP36XjIA3NX96FtvvcXkyZMBOPzww9u83Wmnncabb77JPvvsQyaTKQ5AHDlyJOussw7jx48nn8/jnCtus+uuu3Zp25VSSimlupIGgNUKt9lmm/Hyyy+XfImcOXMml1xyCUOHDuWAAw7gm2++aXbb7777rvhzcyOQm1NZWVmSgdrZAHBjn3/+OZdddllxFOi4ceN46KGHluuXr5Y0PlfQ9vO15Jf7rjxfPwZtnUKz8fmrqqpin332adO/3XffvdUR0GEY8uKLL3L88cfTr18/1llnHY477jg++uijNmdTKKXar/FNMs/zOvw53b17d+6//37WXXfd4u8KhQJPPvlkSbktt9yy+HMQBLz11lsd2t+KZIwpucG35M275jQus+TNwaXJZrP84he/AOA3v/kN8+fP55tvvmn2X2Pffvtt8fczZ87s8A3exgOgZs2a1eK0/8vDrrvuymWXXVbyuxNOOIH33nuvy/e16qqr8thjj9G3b9/i7+bOncukSZO6fF9LWvLmdeMlNJa15Xltt1e3bt3Yaaedio/ffffddgXkr7/+eqIoYrXVVmO77bZbFk1UPzHah7bf8u5Dl7T66qtz3XXXMXv2bD799FP+9a9/8cwzz/Dee+8xf/58HnzwQWbMmFEsf+yxxzapo3HgfWkZmxtvvHHJ7Fwff/xxp9rfGauvvjqPPvpocdA5xEHZG2+8scv3lU6nueGGGxgzZkzJ7x9++OEu39eSfix9KCz9+l7y+caDPLrCNddcA8SfHe2dnnnUqFE8/vjjVFdX8/777/Piiy/y97//nY8++ogffviBI444ojio31rLUUcd1aVtV0oppZTqShoAVj8KW221FZ999hl/+ctfWGONNYq/j6KIhx9+mBEjRvDss8822a7xWqs//PBDm/fXOKjZmXXkFps5cyY///nPWWuttTj33HOprKzkjjvuYPr06Xz55Zecf/75nd5HZy25Lm1bz9eSAeCuOF//ixZPvQlNg+kd9fe//5311luPsWPHcu+997L33nvz/PPPU11dzWuvvdbstOdKqc6rq6srGcyy4447dmpttsrKyuKNpsU+/fTTksdL3ghsPHXc/5LGsyYsLSDqnCvpMzbZZJN27SuTyRTXHzzppJMYPHhwi/8a23bbbYu/HzRoUMkN7fZY8sZpXV1dh+rpKmeffXbJun35fJ599tmnJCOrq6y22mpcfPHFJb9b8ppeFt5///3iz5WVlWy77bbLfJ+LLXltL23gQOProb3XdkcseZ3PmzevTdvV1dUVM4YPPPDApc7SotTSaB/accuzD22JMYa11lqL7bffnt13352NN96YyspK7r333uL+Bg0a1Oxa4Y1nkGrLd8lhw4YVf/7222+7oPUdt+2223LttdeW/O60005bJoObfN9vMlPD8u5DgSbZ9cvSkrNqLe36btyHrrvuul26HNSMGTN45JFHADq15n0ymWSjjTZixx13ZJdddmGdddYhmUyWLMFx0EEHNbuetlJKKaXUj4WuAax+NMrKyjjppJM47rjjePLJJ7nyyit58803AVi4cCEHHnggH3zwAYMGDSpu03ik6KJFi1i0aFHJ1FUt6d69e/HnxhkuHfH3v/+dCRMmUFdXR9++fXniiSfYYostOlXnsrDkqNq23hBvfK6g8+frf9XAgQOL6151NJiwmIhw3HHHceuttwLxekMTJ07UNfmUWk7+9a9/lWTPNQ6qQTyop66ujrXWWqvNdY4ZMwbP84iiCGg69d3PfvYzzjnnnGLGw/33388VV1zRpj6rI+rq6ppkgrRX7969m8wWMXr0aF544QUApk2b1uq68DNmzCg5Dx3pG1944QVef/118vl8q+X23Xff4s8333xzcarHDTfcsMMZMI0DbMaYFb6+mzGGO+64g48//pgPPvgAiM/xnnvuWbJMQXO++uorfN9v17lYcq3dtkxX2hkLFy7kjTfeKD7ee++9myyVAHHgu7NB7+7duzf5+2b06NHFdZ7z+TyzZ8+mX79+zW4fBEFJ5vny+LtvyeUz2potdc8997BgwQIgvlGtVGdpH9o2P4Y+tK3q6+u56qqrio9/97vfNfnMgdIsz7Z8DjcO6rU3C3NZOP7443n33XeLax0HQcC+++7L66+/3mrG6rx58/juu+/YYIMN2ryvNddckzXWWKPYVyzrPhTi+xKLDRgwgM0337xJGRFh+vTpnZr+Op1ON+kft99++5LH33zzTavXbOP1pLv62l4864W1lp/97GddWvfUqVN54okngHgmsR/DQH+llFJKqdZoAFitUAsXLsT3/ZJ1zXzfZ/z48ey7777cdNNNnHLKKTjnqKmp4YorruAvf/lLsexGG21UMp3Sa6+9xm677bbU/TbO5hw5cmSH2//ee+8Vg7/GGP7xj3+064vh8jR06FDKysqKI7tfe+21Nm3X+Fyl0+mSKdp+SoYNG1b8ovrxxx/z1VdfMWTIkA7Vdd555xWDv5tvvjmPPfZYyZpgSqll6/777y/+XFVVVRI8BDj99NOZNGkSM2fOJJlMtqlO3/fp1atXMSOmcdYLxINIfv7znzNx4kQgnpXh0ksv5fLLL+/MoTRr1qxZrLPOOiWf3x1x3HHHlWQ5QGkWVi6XY9asWS1O/dk4Q6xbt24d6m+rqqra1K83tvPOO5fMJtJRX331VfHnddZZp2TqyBWlvLycJ554gs0224z58+cD8d8iS5vW98ADD6RQKDTJDmpN4/USoek13dUefvjhYvAH4Mgjj2xSJpfLsc4667S4NEhb7bLLLiU3yqHpzeuvv/66xQDwN998UwwWGGMYPXp0p9rTFrNnzy7+PGDAgDYtYeGcK2ZWrrXWWsslU1mt/LQPbZsfQx/aVueee26xz9tvv/047LDDmi234YYbFtfybUsAuPGsSWuvvXYXtLRzjDFcf/31TJ06tTiN+MKFC9ltt91afb3/8Ic/cPXVVzNz5swmfWNrVllllWJ/taz70E8++YR33323+PjII49sdsaHgw8+uNMZ9FVVVcyaNatkkFb//v0ZPnx4MdP566+/brWOJWcR6Cp1dXXFwVw77bRTlw5eD4KAI444otj/X3nllT+K61oppZRSqjUr/k6WWmk1HlXa0gjTww8/nIULFzY79ZK1lhNPPJHPP/+8ePNq8uTJJWX22GMPzjvvvOLje+65Z6k3ivP5fMkXkpbKt6X9v/rVr4rTF+22225dHvxdctrmlizZvubam0wm2XnnnYsjVt9++20+/fTTkrWZmvPJJ58Uf95hhx2azcZp7wjizow4XlGGDx9ecrN44sSJXHTRRe2u5+uvvy65WXXmmWd2SfC3LderUv/ruuLanjx5csnN66uuuqpJJl1FRQVz587l2WefZZ999mlTvdlstiRbqLmbWRdccAGPP/54cVq8P/3pTxx88MEd7jvCMOTkk09m+PDhnH766cXf9+zZk3vvvbdT2SbpdLrZY9h8881Zb731+PDDDwH48ssv23Tz+rDDDuvS6f2WtXnz5jF9+vTi43Hjxi2T/Sy+idiWtSAXGzJkCA8++CC77rorzjkApkyZ0uo2FRUVTJo0iffff58RI0a0aT+NZ7tIp9NsvfXWzZbrivdlbW0tF1xwQfHxoYceyg477NCkXCqV4uabb+7UchSe5zV7bffv35899tiDZ555Boiv7cbrjjbW+NreY489GDBgQIfb01Zvv/128eclA24tef7554s34w866KAWMw3VT4P2oaW0D4298sor/PnPfwbiQPutt97a4mfFTjvtxKWXXgrEg4QXLlzY6gxGjad9bunztDM60oemUikee+wxNt10U2bNmgVQ0t83p6KigjAMuf/++znttNPavK/G/Wjjddwb66rvbWeffXaxrjXXXJNzzjmn2XJnnnlmk6z99ho1alSz9wSOPvpozjrrLCC+tltSW1tbHBzQq1cv9ttvv061p7GJEycus1kv/vCHPxSD7OPGjeOkk07q0vqVUkoppZYJUWoZ2WyzzQQQQA444IBmyxxwwAHieZ7MnTu3xXqeeOKJYj2bbLJJk+fHjBlTfD6dTsu8efNabdfzzz9fLD969OgWy5199tnFcptvvnmzZcrKyoplzjzzzBbr+uc//1ksB0g2m22x7OGHH14st80227R6LIvV1dWV1H/jjTc2W+7ll18uKXf22Wcvte4tt9yyWH7SpEnNlslmsyX1/v3vf2+1zo8//rik/CeffLLUdnSlzTffvLjvX//6123a5o033ihp82qrrSYLFy5s974ffvjhkno++OCDFsseeuihS30PLTZq1Kh2H5NS/2v222+/4nVeXl7e7u1nz54ta6+9drGO3XbbTZxzTcqdcsopAsi2224rQRC0qe7JkycX6915551bLLfkZ8Aaa6whs2bNavex1NfXy1577SVDhgyR6dOnt3v7zrjzzjuL7b/oootaLDd69GgBxPd9+fjjj5dpmxqf06+//rrFcgsXLpSLL75YrrjiCqmurm6x3MUXX1zyt8W33367DFotkslkBJDJkye3e9srr7yy5LgX/2vOuHHjBJCDDjqo2Wu+OY8++mixzmOPPbbFctdee23J/t955512HUcQBLL//vuX9K+t/V24LE2aNKnYjiOOOKLFckcccUSx3IsvvtipfS5YsGCpZd57773i/pLJZJv/bho7duwK+1tL/fhoH/r/aR8ae/vtt6V3794CSP/+/eWzzz5rtXw+n5eBAwcW23/vvfe2WPaLL74oluvbt6/k8/mubr7stttuAsiVV17Z7m1fe+01SSQSTfrQ5r7v/ulPfxJA1lxzTVm0aFGb6p87d26xzuHDh0sURc2WmzJlSsn+r7jiinYfS+N+OJFIyCuvvNLuOrpCdXW1VFZWFt+bLf29cddddxXbe/7553fZ/qMokuHDhwsgqVSq1b/z2uu2224Ta23xHlJtbW2X1a2UUkoptSxpAFgtM2uuuWbxD/vdd9+92TKLb6C1Fjy94oorivWccMIJTZ6fOnWqpNPpYpmDDz64xS8bURTJ9ttvX7yh29oN1+OOO65Y59prr73UY2wpQBdFkZx44oklX+xau1Fx+umnF8sNGzas5LlCoSDvvfdek21mzpxZUv8f//jHFus/7LDDSm4iTpkypcWyjQPX+++/f4vlZs+eXbL/hx9+uMWyIiJvvfVWSfm33nqr1fJdbfEXQ0COO+64Nm3jnJM99tijpN2tXWsiIosWLZIJEyaUvGavvfZaSR3PPfdcs9vOnj1bBg0aVCy36667tvmYjj/++DYdk1L/a7bddtuSG1wt3Uxrzocffijrrbdecfs999xT6uvrmy170UUXlQS+lhYwc84Vb0L26NFDvvrqq1bLX3XVVWKMKe5jww03lC+++KLNx/LWW2/JiBEjZPDgwTJt2rQ2b9dV8vm8bLDBBgLI4MGDJZfLNSnT+Gb+b3/72xbrmjRpkqy11lrSvXt3Oeecc9ocnGysvr6+5HO1tRvl559/frHcqFGjmr2RO3fuXFlllVWK5f7whz80W1cYhnL00UdLt27dZMSIEfLuu++2q93z5s0r7uPJJ59s17Yi8XV30EEHtSkA3Dhgeemlly617kKhIBtuuGHxNW5tcN1vf/vbkv3/+9//bvMxzJ07VyZMmFDcdujQoUt9/yxLzjnZZZddBJBu3brJnDlzmpSZOXOmJJNJAeTQQw9tsa4pU6bIJptsIpWVlXLkkUdKoVAoeT4Mw2JgfvPNN5cffvih2XpyuZxstdVWxXN0ySWXtOlYpk6dWtymuQGU6qdH+9CY9qGxF154QcrLy4vBuqUFfxe77bbbim0bMWKEhGHYbLmzzjqrWO6uu+5qtswNN9wgffv2lUGDBrVYpjWLz+Ppp5/e7m1FRG6++eY2BYAbByx32223Ng1sOOGEE4rft1v7rrvkQPHf//73bW5/oVCQSy65pLhtWVnZUgdiL2uXXXZZsT3/+Mc/mjwfRVHx74u11lqrxc+R6upq2WuvvaS8vFy23357mTFjxlL3/cwzzxT3vd9++3X6WETiz6dLL720WO/OO++swV+llFJK/U/RALBaJr788suSLzK9e/dudtTv8ccfLxCPbG5upOpHH30k/fr1E0C6d+/e4g2BRx99tGQE73HHHSd1dXUlZRYuXFi8AWqtlYceeqjF9jvnSka4e54n3333XZNyhxxySEmZ1157reT5L774ojh6u/G/xYHn5r48LplJc9ttt8krr7wi1113nQwdOlTWX3/9Jl+0G48kB2Tvvfdu8dhqampku+22K5bt27evvP76603K/eMf/5C+ffsWb5IveT4be+SRR0r2/8tf/rLFsiL/fxT10m6uLwszZswQ3/eL+15rrbVavHGxpNmzZ8taa61V0vbDDjus2XPzwQcfyMYbbyyHHnpoyetcV1dXvHHcXADCOScPPfRQcTT+4n/rr79+8abOktfNjBkziiOSAVl33XU7FERR6sdsxowZJe8dQJ566qlWt3HOyfvvvy+//OUvi+973/flV7/6VZNgTGPPPvtsyX7Gjx8vU6dObbbswoUL5dRTTy3euG5ppoQlPf7441JVVVXcR7du3eSGG25o8bM2iiJ588035Re/+IUYY+Sggw5qMWC0PHz22WfSrVs3AeS0004r+cyZPn26DB48uHijrLmb24s1ziRq6cbr0jS+4QfIPffc02LZxjfxFveXjYMgc+bMKd6YBOSYY45p8fN0yb5v1VVXbVeG04UXXlhyQ7mtfVFjdXV1MmLEiKUGgG+44YaSMscee6x88803zZadNWuWHHjggQLIgAEDWp2pIooiWX/99Uvqbm1Q32IzZsyQK664Qnr16lXcbty4cfL999+37cCXoTlz5hSvy5/97Gclr+mCBQtk5MiRxcDH/PnzW6yn8d9agNx5550lzy85G8rWW2/d5Pr5/PPPS7J4zzjjjDb370cffXRxu45kx6mVi/ah2ocuFoahXHfddcXvzvvtt1+bZiJYLIqiksFHxxxzTJPPrueee05SqZQAcvjhhzf7uTV79uySQD7Q6sDkJTWesWGVVVbpcLZn40HfLZ3Djz76qKTMmDFjmv3+LBIP2ln8t0Y6nZYHH3yw1f2fdNJJJXVvueWWrb7mIvEg43vuuaek/11vvfWW+6Dq5kRRJHvuuadAnPncuF+PokiOPfbY4r2d5ga2L3bBBReUnJfDDz98qfveaaediuUfffTRTh9LdXW1HHPMMQLx/aP/+7//69DfakoppZRSK5IR0cUiVdd5+OGHmTx5Mvfcc09xTZ3FtthiC3bbbTe22247Ro8eDcAZZ5zB1VdfDYAxhrFjx7LBBhvQvXt3pkyZwuOPP04YhgwePJgHH3yQUaNGtbjvf/3rXxx77LF8/vnnAKyyyirss88+DBo0iOnTp/PUU0/x3XffMXjwYO64445iGxqbNGkS//73v/nnP//Jyy+/XPLcsGHDOOCAA1hzzTU5/PDDAfjuu+/YYostiuv7JBIJ9t9/f4YNG8arr77KK6+8wnrrrccpp5zC0UcfXaxrk002Yeedd+aBBx7gpptuYtdddy0+98MPPzBkyJDi2sKN7bbbbtxzzz306tWLIAi4/vrr+eijj5g4cSL5fL5YzhjDhAkTGDFiBAcccABDhw4tqSebzXLWWWdx4403FtcP3GmnnRg1ahTWWiZPnszf//53rLUcc8wxXHXVVZSVlTVpz8SJE5kyZQq33norixYtKv7e8zyOOOIIttlmG4444oji7x999FFeffVVbrnlFnK5XPH3yWSSo446ik033bTkPHWl6667jlmzZvHQQw81WZNo1KhRjB8/niFDhjBhwoRW6/nhhx849NBDeeGFF4q/69OnD/vvvz8DBgxg/vz5vP322/znP//h//7v/zjppJOarPF77733cuihhxYfr7HGGuyzzz4YY3j66af56quvOPjggykvL+fGG28sljvmmGMIgoBXX32Vjz76iAceeIDPP/+cBx98sMkxjR49mh122IFtt9222Wtdqf8FX375JY888gg//PADDz30UHG9sMWMMWy99dZsuumm9OjRg/LycnK5HAsXLuTzzz/n/fffZ9q0aUC85ttee+3FxRdfvNS1z6urq+nRo0eTfW2zzTasueaaDBo0iEKhwOeff86//vUvfvjhB8aMGcPtt9/O4MGD23x88+fP549//CPXXnst2WwWgB49ejB27FiGDRtGRUUFs2fPZtasWfz73/9m5syZbLDBBlx22WXssccebd7PsvLqq69yyCGHMH36dLbeemt23HFH5s6dyz333ENNTQ0HHXQQd9xxR7PrxC02cODAkjXyJk2a1KbPrP/85z/84x//4Msvv+S+++4r6TPLysoYN24ca621FieffDKrrLJK8bkwDLn00ku5+OKLi2sGjho1ij322IPq6mruv/9+Zs+eTffu3fn973/PiSee2OJaiI899hjjx48v+d3TTz/d6mvz5ptv8swzz/Dyyy/z6quvljy39tprM27cOEaNGrXUvqixb775hpEjRzJv3jyMMcV+vbEpU6aw0UYblfzO931Gjx7NkCFDGDhwIIsWLeLzzz/nxRdfpLa2ln333Zebb76Z3r17l2yXz+f585//zKJFi3jmmWd4//33m+xv7bXXZsyYMfTq1Ytu3boRhiE1NTVMmzaNDz74gKlTpxbXKtx666254IIL2Hnnndt8zMva1KlTOeigg5g6dSobbbQRu+++O4VCgYkTJzJnzhx22mknHnrooSZrnza23XbblbzGd955Z8nfRABXXnklF1xwQfH9v8466zBu3DgqKyv54IMPePzxxwmCgNVWW40///nPTa63lsyZM4cBAwaQz+cxxjB9+nRWX3319p8I9T9N+1DtQxtzzvH888/z29/+lnfffZe1116b//u//2PChAntXh88n89z1llncd111wHxd5nF/cl///tf/va3vxEEAaeddhp//OMfSSQSTeqYM2cO/fr1K+mzfvWrX3HFFVe0uN8ZM2Zw77338p///IfHHnuMKIqKz/Xs2ZMDDjiA4cOHc+KJJza7z+YUCgXGjBnD66+/DsDLL7/MdtttV1LGOUefPn2YP39+ye8322wz1llnHdZYYw2MMXzxxRf8+9//Ztq0aWy88cbccccdjBgxosk+J06cyDfffMNbb73Fc88912Qd4FVWWYVdd92V/v37U1VVhbWW2tpavv/+e6ZOncr7779f/C695pprcuqpp3Lcccfh+36bjnlZq66u5sgjj+SJJ55g1VVXZcKECfTq1YtHH32UqVOnMnToUB555BE23njjFuu44IILuPjii4uPDz/8cO66664Wy3/wwQdsuOGGAFRWVjJ79uwOr5udzWaZOHEiF110EbNmzWLnnXfmkksuYeTIkR2qTymllFJqhVqx8We1shkyZEjJSM3m/h111FHF8ueee26zZVKplAwdOlTGjx8vTzzxRJszavL5vNx3330yYcIEGTp0qJSXl0smk5FBgwbJhAkTZOLEia2OWD/qqKOW2v4hQ4aUbPP999/LhRdeKBtssIH07NlTUqmUDBs2TPbbbz958cUXiyOeb7rpJlljjTUklUrJ2muvLfvvv3+L6/NMmjRJRowYIel0WlZddVWZMGGCPPfcc01Ghy+trYBMnDixxeP9+OOP5fzzz5eRI0dK3759xfd96d27t2y66aZyzjnntLpeXBAETTIJlvyXTCZLslWXHKW+5D9r7VJHPHdEW9raXHtb4pyTf/zjH3L00UfL8OHDpXv37pJMJmXgwIEyevRoueaaa5a6Htm///1vOfDAA2XIkCGSTqelR48eMmLECDnrrLOKa5EtWrRIDjjgAOnRo4d069ZNttxyS/nVr35VrLu97zel/tdMnDixTZ9zS/7LZDLSr18/2XzzzeW4446Tu+66q92ZITNmzJB33nlH7rnnHjn99NNlxx13lEGDBklVVZX4vi+rrbaabL311nLKKae0miHZFjU1NfK3v/1NTjzxRNlyyy1l6NChUllZKel0WoYNGyZjxoyRs846S/773/92aj/LwsKFC+Wcc86RrbbaSiorK6VPnz6yzz77yAsvvNCm7V966SVZc801paqqql3TV/7f//1fm66FV199tdntv/nmG7n00ktlq622kr59+0oymZT+/fvL2LFj5eqrr25TNlQURXLllVeWTMF/zTXXtLpNR/7OaIvJkyfL8OHDZZ999mmxrV999ZW8+eabctttt8lJJ50k2267ray++upSWVkpiURCBg0aJNtvv72cc845rU7B2ta/P5rrY3v37i3rr7++HHzwwXLVVVe1mIX8Y5DP5+XSSy+V0aNHS48ePaRHjx4yduxYefjhh9t0nf73v/+VESNGSEVFhRxxxBEt/v05Y8YMufLKK2WnnXaSAQMGSDKZlMrKSllzzTXl0EMPlQcffLDVv12bc9111xXP+3bbbdeubdXKQ/tQ7UMbGzt2rFhrZezYsXLvvfe2eX3m1vznP/+Rk046SbbeemtZZZVVpKysTIYPHy5HH310m9aD/9vf/ibbbbddMRO4pT5ssbZe0+1d13nu3Lmy0047yeDBg2XhwoXNlvn+++/lvffek4ceekh+/etfy2677SaDBw+W7t27i+d5xffML37xC3n99ddbfS3a8h2uue/J3bt3l6FDh8ree+8tF154obzzzjs/2lmfnHPy4IMPyp577ikDBgyQsrIy2XTTTeXKK69scdrnxqqrq2XcuHFSVlYm22233VKngD7zzDOL5+qwww7rcLsXLlwovXr1kvLycjn44INX2HrKSimllFJdRTOAlVJKKaWU6oT777+fgw8+mBtvvJHjjz9+RTdHKaWUKvHee+/Rv39/+vXrt6Kb0sTXX3/N8OHD2W+//XjwwQdXdHPUT9y//vUvNttsM8rLy1d0U5RSSimlOu3HMUeMUkoppZRS/6MWT5O8+eabr+CWKKWUUk1tsskmK7oJLerVqxfWWu1D1Y+CLp+klFJKqZWJZgArpZRSSinVQTU1NWyxxRasssoq/POf/8Rau6KbpJRSSv1PEBGOOOIInnvuOT744AP69u27opuklFJKKaXUSkPvUCmllFJKKdUB77//PltssQWLFi3irrvu0uCvUkop1UazZ89mwoQJPPzww9x1110a/FVKKaWUUqqL6RTQSqkfnWnTptFVkxNUVlbSq1evLqlLKaWUWuykk07i1ltvZf/99+faa6+lZ8+eK7pJSiml1P+E++67j6OOOoqRI0fyzjvvsP7666/oJimllFJKKbXS0SmglVI/KnPmzGGVVVbpsvr22GMPnn766S6rTymllAL49NNP6dOnjwZ+lVJKqXb64YcfqK2tZciQISu6KUoppZRSSq20NACslPpRERGeeuopnHNdUt8aa6zBiBEjuqQupZRSSimllFJKKaWUUkqpHzsNACullFJKKaWUUkoppZRSSiml1ErCrugGKKWUUkoppZRSSimllFJKKaWU6hoaAFZKKaWUUkoppZRSSimllFJKqZWEBoCVUkoppZRSSimllFJKKaWUUmoloQFgpZRSSimllFJKKaWUUkoppZRaSWgAWCmllFJKKaWUUkoppZRSSimlVhIaAFZKKaWUUkoppZRSSimllFJKqZWEBoCVUkoppZRSSimllFJKKaWUUmoloQFgpZRSSimllFJKKaWUUkoppZRaSfgrugHL2wBThqnKYijD5B2mexXkchAW8FMhNhJSZd2wXgrrRXimDM9zWCwGh/EtxgD4mEQZEuYBB4UsJpHEeD7YJBIKEtVgxMckU2A9iIAoAELwEuAC8HwkDMFYjJ+EqADWYIyPNWA9n0gMmAjPeES5erxUJUT1REK8PwN+shIbFpBkEnEhEkRYCxJkkaiA8RLgpSFyCGCsQcIsGIv10phEEgp1kKxCRHAugqAGCtVYP4WxSUzFKri6ebiwHpfsRlQQCvl6CkEAXg7jdScsVOOiPLgkQohNdcNFtZCfj0l2xyb64KxDojwU5mMlg02lSIjBRIvwTAavrArjJD6f1iGFHCZZAcF8jF9Owq9AIgHPIblqTLoHYi1gicI8kl2EhAGRTVBwIQ6DSySgECGSR0warIeLskh+HjbTD2NSuPwCbKIcrOAiwRJgCDCJCqSwKN5OHNg0iAFbQIJFmER3XKEea3ygBhcKiMP6vcDzwSZw1bORZIQpWwWTzCD5AIxBPB/COqKoBhMWsH4frBQw0SJMpi8mCnFRHaSqsC6I90seIg/P93FGsMkM1liiqB6XK2CMD8ZgfR9xDluoxVgQl8PYMhKpCkyyG1FUg8vnwCbwK7pjjQdhPSIOMJhUJS63COOnMGEW46XwM91xhVrEBRibwHhJEIcr5DB+Gi+sh1QlUAAniOfjwgKR9UA8XKEek0wThQWi+lrwLV6ykihbh01V4KI6As9HghzWS5FKpvBTZXiRISEB4grgQmxZJYQhRBF4SSDCiIBnwfcxGIznI1gkyAEOCbLYRCXG84nCAr7ngwvi97DnIc5BkEdskkiEfGSpzy2ikK8hiCx+tz4QhBg/BYXZSH4BXsUQkulKTLCAhDFYm8AYhxFD0vcwEuLCEPwMFodJJjCm4XMjqIdEElPRE0QgDLBhAeN7GGvBCJCIP7g8H+MlMVEEEiBBPcaF2GQZJllBtOgHIgHPeIhEhPlaqOyLiECQxRVqcSaJV7kKEoWE9T8Q5OoJ6uZhyvqSq19AlKtGskC/oZhkEpvI4EtIMt0NE1RjDVBfg/V8/ISPMWlMvgabKsNmqpCwFsnnEImwiTReRR/AEtbPwxTymHQCQxpjDRSyhLl5CAabqkL8NFLIYyp64goB+R8+hihCUknwMuCVgXFIsoLcgmmITeLSfQGD1FcTVn9LZo0NSBSq8YxF6n7AAH5Ff5LdVyWaPwOTn48kKogKWSQ0mLRHlM9DIhl/Nia7IZmeRLkaCrM/g3QS63l4gcGmEgQLF5JafR1Y+A1e1aq4+TMx5Jn0Tc1y7MFWvAULFqzoJiillFpJ9OjRY0U3YbnSPlQppVRX+an1oaD9qFJKqa7zU+pHf3IBYL9PGS7vwIY4YzGFHCbhMNbgrMGSxOVz2HQEphs2lcASYP1EHASUcpzx8XCI5MFaiBKYlMFYD4jAFTCJDHjlGAnjoK+LED8FXioOCuMQsXEA10tAlMcgmEQ5TgKM73FrcIEAAQAASURBVEOQA3w8P40LqsH62GQGYwT8DEYisBaLgzBCfItIgER5jEmAl8SIwdgEEtVjRaC8EpddhLU+VPTEFRY1BJoCSJQhHmAMJvQwfg9Mohyb9DGSQzwwmSrIGRCDJCwmshASB8/8COP5GK8MwgLGJDEmxEtX4oyHkSTOhUi0CGMymHRvTD6HxcPr1gdr+mByOTARGA9MAREwmQxYH5PohynUYxIGfJAwAj+NGIPxk7goxKYrcNYHAalfgHEpJAFEBQQAEwdxvTTGsyBZTLISClmwAaQqIDcHm6iKA5h40LAl4XxMahWgAMnukMtiqwZhCiHGGUxUC7YMv6wKF2bB1eN5PTDJNGH3brhsHUR1ECawqXQclM3nEAEv2RPj5zFShydpxFbhJwwmWUmYdZgoxBgLJoxbk/QxRvC8JFGuGpvpGQfbC7PxvGQcUPQEU9Yt3l+iPN7OhRhr4+BYqg+SysbBUr8CJA+JDMZFSFCLEcHLdAMXASlsqgxjDdZL4CTE+OmG54REphwxBrzyOBgfhZCsJCrUIc5hvAzOZRuC6xFRoYCXqQQ/hXERBoclT+QEm0gShTmiIEsBgzEWY33Ec1gDpqIHxnhIGCASImKwnocxFiFAIoM4gzEGiBAngMWkumMSaUTAekmMNRhTFgeBPQ8JQ5zUQhRik2m8fEDSeIR4eCYER0OguIAJgUQPTCpNFGbxIwNJL34+jDB+ArEemCQiOcAiEmLwEUJcuAib6hUPvHDx9WkQQOLX2YGzxAFt57BRiIsarkMJMOLAJuPHxmIre0HdPIwYJAyx6fKG4HwBV4hw1jS8lvVx4FwsJjIkeg7HRbV46SpcTR67Si+ci3C5ekwUEnkQFjwSqTLEy2BsGVI3F+c8vLIMzuWwxoFx2PJVcCyC+nkggrgc1ivDK++Nkx8gcDgvjAdKSIQk0hAJkquDlEAiBZHD+B7JfusQLvoOl5uPeGkgH7+GYkj3WQsBwiBHFAaEFPBXW5cwcoi1+PUL8Kv645f1wvcySJjD77kablESV8jj9eqDxWA8jyBXT1g9A0wCKSzAJCrwyirxyvsSZb9Bkj2JwnlE+SRej9UgzEKyAsnXIFlIrLHBsuqqlFJKKaWUUkoppZRSSqkO+8kFgD0vDRUGcT6mQhqycCshEqwNMRLGQY5kCDaNSBhncXouzjTFi4NxfhqI4nhiwmC8cohyQAr8OACJM3Hmmg3jgGZQwPgJ8MoQlwUTAF4cyIkEjANCjO9jIoN4lQgBxkQYk8BYLw7qRAXEJpAITKEO/AxiIgQf4xzGJTDGAzGYRApDGgkSDQGyhoCwBYOHzfRG6uciEYjnI4UcmDg72SSTDdnLtiF46MDzIFGOC+rAL0Osh1fWjTDK4Kwg9AApgANDHRIYjO2GSWXigLkTjKsE5zBeCi+TwkQFXFCH9dOQSkNhASZVBdSBKW8IagXYVDew9XFAz3iIZzGJKiRfi6OAkIiDyelyoshhxOFqZmPCBGKSGD+PK8QvC1GASZdh7WpIIQthgEl0j68Hvxw8A6YM4xko5CHRk0SiByZTTljIxlna6W5xMNXUYkwer9sqWC+JxUJUgQvmY20SPEeisheBV04Y1WIteK6ANQYSCUj68TmPHJDCS5eBCKZQgFQCI2XYKAB88FKQSCGJJC63EAkDEuV9EFeHpRKvrDcmCiDM0xDlxSa7IVEBLBjxIV8TZ1Tj4uvWhBjJxxPCh1GcYZvpDS6M/4kBP4m1HuIChAD8BI4IEzmsBYfXcF16caDTZDDOgV8GUkCiEIkCxK8gytbgZ6riwL1x4CL8VBliCnh+ApfPIa6A1IeECMbzsNYRYkiU98Rag3FxG0hXIPWLcGEeW9YNSMYZ/X46fs9FEUaCOODvJ3BhDuMkfo+GEg8CsAmiMA72m3Q3COrAT+MHBmd9/FQG56WRQl38GpgAsHhlvSCfa8jC9yDVG7GCSVs8Y7CJRBxMd4bIhXhlfeL3YH4RtmwVbKYSE+WRML6e46hvAQIfRxhn4Zs489p5SYwEcXDdeJAqx9o0Ub4GG+UxXgpb2RdyddiKHlhDnPkcZDEJH8r645xDogDjZzBBDpssx/hJQueTcIuw/dbAeR6F+jqMMURBgBGD8yKibIi1AV4yCT0G4Gpm4wl4FasguXlIGIAfYMuq4iB4UBcHd10B45k4u7t2ASasgVQFprwXvpeMB2rUzW8YKBBivAATgU0kSFT1J18/D6J6DBlssifOOQwRLsxi6uowRBDV4iVWJcxlieoDokwVXuXqREEdRNX4xIF4r6ov5PKYVArrp5CgHr88jdQvRHBIqjtSWARkSPZZjWB+QDB/AViPZI8+eBXdcYvm4Xfri9TNwvYfCJ4s3w5MKaWUUkoppZRSSimllGqDn1wAWKJsHATIGAgKiCmDYC4ULCE+Ygy2MoB8HmMcLl2BMwZLKs7QMw3TzFqDiQqITcfBKLx4ulUvgfE9pFCIg8PWQGQAB56PiwKs8eMMRi/TMJWuj02V4fI1GN9gjY0DmtbHOYcXFjAkcEEOz69oCIAWsIkkzjVkAeLi4HUQxMFn3xJnI0eIl8Ik00iYwwVZ8JNIWIsJTByoS2RwhTxCGGd0GsG6CBN6CAXEM3jJDCIWl88ixgIJJFeLsRYXZuOAdK4esXkgGQcJ8cFLI1EWEYtExPWH9VjPghOcLY+DOGE9xkX4vgUBk0ggQQaMxeHi6ZJdAS/dPc5OTPngPIyXQBIZpFCH8U08fXU+j8NgvDT43eLgErUYDKTLoFADoSB5D/xEPKtyVI1N9gBXi/EzcTZrOB+iJNZZEpVVWOMRuQLWJDGuHvCwURi3o7wXXqI8nro3yGMMcXA9qMEAGEeyvJxEYOKgql+GlVxDYN1gfIs4H1vWA1wufk3EgER46Yp4inBcPJW48XFRHpuowPhgEkkkdBAVsKlKAAyCFGriYLCLQKI48GnTGJsEl2+YKjrCYhBxSCEEK5hUBptMQUHApoiCGvxkN6RhOmMRDy+VxgV5hAJhKHEQlIaBAlGA8dNIIRtn1hqDC/I448fTETdkIUdBFmsiPCfgJYny2fi1lPmYVCVk0lD/HWHBp+CAdAqbdhCG8VswU4VzEba8CslngQTYRPypFuTAZIgz29MYm0LCPBJEmGQCwjzO8zFOcEKcXRsWGgLyGSgEWCMkrJBK9sQVagnyOUhXgDQE7cNcPNU7YMqShFEez/p4Iojx4sx/L4Epq8BGriGbN4wzjFNlcTapSDwbQBiAhPF0xBIhoYvPobVIIRu/H/GQhI+EgC0nQjB+iijIYyPBJlNIuhwx8TZGJA7yGh9r42xX5+JgvJesxJYlCevqMGEWv7w31mYI8guxOKJCNYgjLCTiWQ78FDbKIjZFwk8g1ifI1+L7lUiyByZYhJ83WB+8TAWRKyBhNn5PFiJsphu2WxIp1OElfUy6nDAqIA6o6InNGlxdNbhEPK13WAfJCvyqNeLBHgS42u+RRHdEDDiwkscFIZ7JEC2cC4V6XF09tmpt6hfNJ2kCEsYirh5cLs5gT2dw2YWICcB5QAK/1wCCedNwtfMwqTIohCBCsudqQBKvvFucqZ2rxngOl52Hy9aQ7N4fl1u4TPsrpZRSSimllFJKKaWUUqojfnoB4NoaTDqFOIOENl7z1LOYiso4KJaoIAxzmEQa62eIwhwEDusnMdbHJDIYcvG0rXgYFyJ+CnFRPGerKSB5kEJ9nCUc5OMpoa2H+Kl4nVKieEpjk4iDGWEtJlmJSWbAxdPAYryGqVUFEcGkyrGRiTOCPR+JcuAsnp/GuAIQIWEYBzOxGBfEa3EaryGAIggGCfOYpMWQjDOO8wXwfWzSj4POLsBIvJ6qIdUwKa2PYHD5OgjChhMJ2AicF0/hWlYV79cmIAzAJpBQMASIrUOkDCcg2SxeWQUuXIQXeti0RaIAz8vE01JjIeUhhdqG8xNnWdtEWZwdSQJSZXGGaBQSFeoxqTKMMbjCgjhrm3g9V5E8Jp3BSBZk8VqiCawHLqrFRHmQVDxdtZ+Og3+Rj0gSCvVY2w0b1WESKcQ4oiDOVDaexTMJwGAJicThJZLxOrkCiMN4XhzcS1VAvj4+N34C/Cpc3VxwObBeHKC1Ppg8prwScPEU1YY4KFzIxgFLz4KfwBgTDy7I18Vrr3p+HDhMVuHq54JEWC/VMGu1F197frLh1QekgJcpI8rVIiaM16wVhyDxVMKJiniKbVdoyCA2WL87IuCCCHEFbLp7PE2wiyDhg03FtUfxWtZi4t2L9eIAqSsgEuHEIi6e+jcK6hEXYTyLiRz4HtZLEwlIlIR0Jg5iJ8qRYBFSviqSSBEU8oi1eNbgJQwELr6+0+UN00GDSaUR65BCPRgPU1YWTxceBBjPxVM0e348bXoiHohgJD5dRGH8ekSCibJ4mQoSgZCLHMaGhLn5GAN+ZT/IZ0HAFRYR+uV4Hkghh/N9IgQvkcB6AcZG+H45EuQbpmfuhgkKYAIEG0+9bcAkMthEGkSwLsR4mfiNlkwi2RokymGiBCZRiUT5OPDrl2PCApLw4s8qm4zfw1EUDyRIdQMvFX8eRTko1IJNYpNpXFCPZwvx+yVZBkEdCSNEwSIkjIiCHJIQgvlzkPJuJDJpbN7hpC7+DAqzhIU81mZwQYglAqkDK5CsxNXPRqI6SCUxUh8PkEj2wAIuqsdls/GsBokMNtMbL5EmnP8tkshAogxTqMfPdAc/Bcbg/DSFRd9BojLORC8swHhVJJLdMVGBSOqwvQYgFHA1i8j7BsmUxZ/B5PHxsL5BojAe/BDUgU1gEt3xe6yOm/UZUnDYZDec1CBSRrL3aki+GsnNjYP3nhdPZV7ZEwlrMC5a9p2WUkoppZRSSimllFJKKdVOP7kAsF/h4/xMvE4s9YgPnvGRsAbPSxEVFpD0y8DP4JzDRiDpDBEhCRtPGWsk0RBUTQEREsUBT+IJluMgl03GMVJr4zVxcQ1RsbBhcU8TT8Ns40Csi6I40GGDODBmPay1YDJEQT0mDDBeChMF8TqmYRgH3STEJMvjaXoJ4jVevXQcrAjrG9YXzSPGYixgbRyM9rw44G1dHADzU1iTwkU2XpPVb1jPmAgig2TjQCwIkXHx+qZhfPn45b0IogLGSpwxbIBEEonq4zV6w3h6YCsGU9kdL5mGvAdRHZ5x2MreSP4HbOSwtgwrPtb6GD8VBxOjAhJkMRISYfESSaJ8HeIlkTDE8yIMKWyiCpedC4kKpOCIrIezDpKVYAwAkq9pCAInwBOcW5x5W44REJfHZMoxzpJMZjChQKYSgjpcEJ97zzd4LorXu80twkt2wwpxdq6E8dTH+OA1HIMIEhTiQHSqAlPWB6mbj4jDpHtifJ8oyMZT/Das2UyUx9gUUB+viRyFGFJgBOMCTMKPp0+O80/joLOfjtecNn78uqe7IWE9hLl4zVg/Ga/3KxabzMRZs5KLr0XjINEN/CSuUBu/lhIh+SiedjoKAYskMkQuhLCARHnwe8fntVDbMGW0QUKHSBBPQW4FEQ8XObxUmqi+FptIxNP9BiGSFyRTEQ84SCQhl42za4McEgSIn8H6aSKbiM9jFOAiwSUqCPN5PJE4oO8lCYMCxoVgkvG6zonyeCBAQ6apsQYKhfh1Ch1Yg3MRxksjhUJ8Tj0/DhASIIkUJlWJiapJeY5IPCwekkjEHyZi4/dQt95xkD23CKyJ1442ltCFeE6wRsDVxq9LwmKTSSTIIQ4kzIF12EwvjKEhKO5hSOKiHNZLYLCQqSBehDuB8SDM1jQE+UMwCVyuFpMow9kQEKwIJlEGiXgQipEIg+CV9wI/TRQG8VTtYQ6/shdREOCcQJglVd4HolpwPaGsB+IKRNXfIHVJqKzEEuGVdUeyWZzJQ8LiJSqIstWYTBkuZ+Jp7DM9wDkkuwCx3XFSjfEz4CeJ8gEG4oEJQRZMFK8xXNY7ngmhUB9/joX5OJjv4n0mjJCf/wPOhZDshbEWryyFLMrjdRtAlConrJ8P+Vrcwmpy6QzSew2ibC3OBnhBAIV6MAWMGPAKEJRhUgmSq29AWFeNy87DiofDxgN0gtr4s6+uGtI9MekeeN1Wwy38FuoXLKuuSimllFJKKaWUUkoppZTqsJ9cANh4DiMRLKiNpwFNphrWKbVEYYjx4kxV8nUYBOclIYhwvo8TDw8TB16dA+tADGJ9wGGsjTNs/XIgjNe8DSMkkYnX7sWBpP9/Y8I8JNNx/I4469J4SayLEN9DxGKMBRtnBRKFuCCHeElMMo0Jc0jk4kBdwo8zlE0yDiRZA5LERRHWhFg/ASaB2FRDkDiFcTZe49gk43any+OpoyVC/DgLViJByONMApsqj4NbhTrECi4UokgIiQAPm8gQFeogyoJYROoxyZ4YP4L6erAG66cgVwupFJ4rQ8IAk0jh+T2xQYDBFc+VsQ3ryooBPDAOcULkIiTRLc5SjXKIc/EUyX4FlCdwuUU4F+IkwDmLczaeQjoqQAC2zEBZJVJfjTGCKesGBEjUEFCNIgSIClk838bnOSQO/uezeImqeM3VKIv4Ns7sdvG0sViDGCmuwSuhB4kMks0jxsWBYOtBRc94SnE8JIqXHnaeh8HgewaJHDbpYzLdkCCM136NBGOEhjmL4/35tiFoaOLptqUQT/stApEHXiKeXhiLEeLtowibSuNcFK9FjY9NdceIIQqy8bVtDMbPxNdbGMRr6VrAr8CFOQhdPAAizAMmnlrZj6cpN4kMLgpwOGwYYhKZOFNYIvDirHaCQhzk9ZOIMUS5OkyqLI5x2gSuvh5HLl5HNl2BCwNC35I0DrxUPMW3c/FU0DaBi+LpywUbry9NHuOlceJwQQRhgCB46Yo4sBgFGD+BZxwuV4NJZeLzWchjEuXxUr+JNC4M8Qz4xiPVfXVwecSzSLYOYwJsZU88PwWFXJwd7yfi7G4/hQvDOJs70Q1TiDC2gEkkiSSKg5/5WkiUYct7YiREXBhfe2E+DiSLh4uCeLp5PxUHsAWiaPHgDwCLX15FWBsQhVkkkYIowBXy+D3648UjQAADfga/rAdRGEJQh3UGvGQ8zbpz2KiAi0JsZYaklEEUEeIwyTJM5WoYm8EVFsTB60IeEmlcdjZIT2ymOyZZRZitxa8wYJMYP4nBxuvrFnI4a7AERFLABUHDutQ+kkzgCnXYRApT3h0vVYmrnQ+uYf3iKIg/d/I5rJ/BJNNx5npZ73it5PpaJAjxK3vFAfX8AqQuC6kqbKYXkuxGvr4eqZlHIl2JJxFRYPDK0g0DK4J4+vN0FbZ7X2ymisKsqfFnYVAfD8iwlshL4mV6Ei2aj/Fnw6L5GMksn45LKaWUUkoppZRSSimllGqHn1wA2Ev2jbNRu6WJ8llcecM0w1E2nsq2Po+zeazpjiQzOBwS5bHGJ7Q+tpDFeAmI6uIMQ+vHAZvQxWtwYhCXw/hJyHuI78VBoDCIM4L9FAR5RLLgp5AoAlIQ1ccBWxfGQVmXiIOKCR/reYgU4lmn/XjqVc/6DZP6WpwJMA5oCMLh+yA+JD1MkIt/F+TB+thEgigXYP1CHPwJCgjx1NG2PhsHg0wUz0KdqkRcASNClK9HiJBECsnXxWt3WpDINcxaXUC8dBxM9gwuvwAyfZBsTRzE9MuxST/OJPUE48CmeyH5BbgowC+Lp8U1OCQKEXEAcfZzpioOXJKJg5GhK04fjE/D+QoR4sxsSOJ8IV+fQ0w8pa9xDglrsImKOMCJYMp64go1caZ15OLXJ1EOhVwcjHJRnFUb5nGRa1iPtyw+hkQ5EuWwqZ7xeQkdEgVx9miqOyJ1uKAOm7DFAQSSzxfbYrxkHNgLGtZUTibiLGNniMTHhA5j6uNM8rA+DoD5PsYaxAkGH8TFa/e6HGISGN9HcgF4QZxl7iXj7F1P4sxlz4DLxWtIhw6bqULCDHGk2sTrtkZhnBlbyEHKA+vFGe4Sgm1YtzZyRC5AxENcHo8oXifWWsTGwV+pr8WW9Yh/ljrwy4miPNbLxMFDJ9iKXliv4f1hIsQF8SAM3wMECW3DGrLZOPgvSZxxeOmGKanzeaKkxUuk4v0biwS5eHrfZDpudxjEa3S7IM5ItRJnAPuJOABvibM6vWScJZuNwNRj0pVxHBzBSoSfTEMiSRQYwkJ9vJ6wZxDjEeWykK/GS8YBZ2tT8TXmJREDLsrjGYsL8phECoIIyddjkt3i44viKbKNCxoGH9h4bWZpGNggFhflcSZeQ1jCHBLk44EEocWkUnjlVUhNDVIoIC4PiXJcGFAIHCYKkSiLKe8Zv65SwNgwDpp2Xx1XyGKlFhBs935EWEzo4mm+oxBxDpusAutwfk8K9QtIF2qxFb1BuiFRHsFBeTnGh7CuFi8l2GQ6DnCnuyOLZmEiIGHjz0Ij8UwJhTzGJTE2EV97Jg7ce1V9kNrvgQAiG6+Z7nKIyZDovQYSBLiwgDFgrMPr1RcXBZjCIqz18fsPwySrECkQ1VVDeRX5nCOqq6GszyD8snT8mZnPIzXfY5IZXCiYdAU2mcFLd6MwZyG2Rw8IaojyBtO9T7yOeaoMyeUx1uD3GbD8Oi+llFJKKaWUUkoppZRSqo1+cgFgKyHOQGSqseXlRHWLMKkqjJfBOQPpHJGXwWLB1WBsAmsSOCxeaHB+hDWJOKvRxZl8YDBJ25CQ58dBJXHxFKsmA8bGa3xGBXAeJBLx1LT5LNZPYPxyxDVkSJooziiOsvH6lFG8nqyTeP1VbBrjHM6L80eRAI+GjFkBiAORJiqA52HTSQx+PLWwixAJsYZ47Vnfh3QGydfEQTMXT08tNl5n1iAYYxHPx5kELlsHXqKYWWmsxQQRfrKMMB9n3eJ7SAhS1isO9FmLlTCeVjkIIQoQ40EUxsHDZAaJojiw68eZdpJbgPHLcdIQRHIFJIywiQzGZeN1XbGYKIWxlbj8QvAbzqk1kChDbAqTsrjcbIypiNvhl4Hn4iBpOonx/DiAm18IJsLYTJyBbMD3U3gSYgxELsBLJjBeZRxfFodpmHLZJiyukIunjk6Vs3hKZklVENUFuCgbr2WcSOJcAcnVxmueujzWGiwGcQbnQqJ8gPgeTpJ4Np4e2CYMkRdnnFsJ48BpogI8H5erhVwtJpmOp9uWTLz2cpSPg5/GYqIEJDIYCXDiQbICqZ2LsWkIBMGL1wCOcvEaui5EnMH4aVwhHwe0XYjzktjIEUkUT8uLAA5cRGh8rLg4ixkwxNNNOykg6QrCXE283nIkiO/hSQiZMozvx9tHAWJ9XJQH38OFhThgi+DCOiwZDBAlHX6mHCcBYRRfV9ZUUsgXMC4fT2+dSCNhFE8xbSW+hiWCVFk8fXZYABthrYdFwIsHRTgX4gFeWRVhUBcHcSUXr9Prp/ETGVyujkShHhfl48EXyco44OwnMamKeKCDc3FwOZGMk+ldhBTqCZ2L17M1Ns7S9pOYZBlIPg7wG6/huYbjcHEwG89HBCSRiYOsxCMvTKoCEhlcdiGF7CJssqJhuud6TKYX1vcxDe+bKIriNXWDPDaKMMZgnGDTZfF7XnJYP8Jk+sSvaqEeQ4hPiPg+URAR5ubjPA+bzuDKqgjy8/HDHDbZA6n/gSiXxXMhNlUGXgJXNx+sh2fj19GWVSF1c7B5QayHzfTAVFhcEOCyi5CwEE9pHgYIeUy6G7aiD1H19Hj98UKE+AlM2mIligeJuDCeQjpaiMl0h6A+HtCRqCJV1oMoN4cgDCEI43OeqiKy1YSJBJ5zmMjglyWQLLj6+ZiKVeL3QS6P33MAkqwjmv8VYg0mVYlNlRHVzsGr6AX18zGVPWmYF18ppZRSSimllFJKKaWU+lH5yQWAI3GIKyDZJKYyjSlPIXU1mIyHzUa4dBJMhiDK4ZEBV09DmA5JJuMsXFePn6gEIsQYrJeM17os5DDJdLzebBRAMgNeAsKgYecRYgOMSYGXwbg4o88YMMkkEsTT6QLx+qmAI8STeDpYBAwN2ZFBiGcTiBFcGDTEmKM4W48UODCYhumlwzgwiImDp34SglzcTkO8nnGQi6djhfhoI4CGKY0ljDOZCwaCODMXFyLGIAaiXC14FpetRZLJ4nLERPl4LdN8Ns62jEIMYTw1cSINQRZJl2NcFJ8/wvg4nY3Xx3V5rFeJyy3E+B7GOBJlq+CyNXGQ3QVxoNdvmALY2Dgz1NTixMXrHEsi3g9BfOyZXgj1cYZ0Poqn8SaA3AIoWxUbOTw/zg6M8vUYzwN8vGS3uP6otmGabRevZRtGiASYRCWSTMfT9ubriHL14JXF2dIJL17b2SYxLg4Y46XibGcbgU1icYQITuLAr6Qy2EIW8vl4LeF0eRwUtR4QB7vxUvG6sqlyKNQhLsQmUohJY0SQXE08FbRN4iRsuN4E/AyEEdgonpY6ysevZZSHQgiZbgg+oQTx9OIuwviWIIwDiCISryHsckh9Pc5PYNOVuDBAfB8KWUwiDVEhvmaTZUihHiM+JirgogK+yRAVsoBFJEC8BJKL4szdwmzwPWyiO2HNbAgarlvjiKQCG4QIESbdLZ4K2voYK/iJFJGz5KMayOWwfhooxO9fP4MUCkCIMQYJHM7GwWHjQqxIvF53Io2fLIuPWRzGhXiZ7hAV8IzBT1ic85F0Mg7uRnGw2SbLcUEBGy+0DZHgRDDWx2YqGwaLGAhDCPOYit64qICJCnF7XB4hApNqWLtbsJkKxDlcUI+EEfjxJ4IhiZcqByNIqiKepjtXD34ZtjyJwYsHukQR4PAy3eL3fqEeY+PAvS3rATaBy9ZgohCb6Y7YFFHdAkwYZ9RbiUi4AtZP4SIfFxTiKecre1JIlCNhPYlUMn4dCrmGj5McNpWJB5FEeSS0OJfEz1TgdVsVk6vBIVibxLkAazxsZR9cvhbJzcVQEU8Rnq8Dk8KU943Xxk46XF01JsrH12RUH0+VTxKb7IWE2XhtbBzpHv2gvgabc3giuKA+DnSTQMr6kav+AUkk8P0M1oLXczBSPYuwfg6mvB/GM5ggwivvhkQ9sOlVEd9H6udj/ARSyBFlF5Co6EWYnbtM+yvVtUQEYzoXtO+KOv6X6TnsPD2HnafnsPP0HKr20mum8/Qcdp6ew87Tc9h5eg5VR+h103l6DjtPz2Hn6TnsPD2Hy99PLgAcBxnjVMWofh7WZojSSZwBUq5hvd1cPPVt0uDyliBbjylPETqL8dKYyCE2DiQRFRA/AQgkDEJDoA0TB2SjhmCwCxE/iRFDvOKvxAHiQg5cLs4gNAbn/h97f9YkSZJka2Ifs4joYubuEbnVcruBmaEBEQj//9fgZUCYueh7u7oqMxZ3M1MVEWY8sHgUHtGVVX17KOU0BWWsZmqiImpF/fE5p8efawBg904nQVoQvwHRkYof4IIue3SymgcM7AdGRSUjyUZXrIRztywRdy2OlzLgXkPyO7StAXG6I2pYXgJAmUUnqSrUGi7blON9NYcjVEfXp2v0xQqIZmhvsFxBCtbeEM/osgCviDwj532YZu+ge/z7ZSd6SxXEydtLxE27gzckZdwrSENYSetHeNwxe8O70zXR6gG+oJcfA6iJ4mWL+F9x7H5Dlx6f9byh248MWk5KCej4foHjhi4LwhGX5I7IiciKuEY0MhqA/fEa6+z3cE5zIvsz/vgMXlBN2LJgtwfYW3RGu6B+YELcS2v4/RdMJVzR7uCdnBdcM4rh/sB7j32Q456jG14/Y1nD3esOOT4rWHxuJXqmyw7Hl9gXkvD3gQVZ4Po0HME14GhasXwNmGs93JBlB28Rfb4+oTnHPmkNUiZGCAT3DO0R6dCiyKJRdiyKvbtZvSO941rCAd5PyCuyfMDEI6b69hds/xHZLrR+IFpIvWG90jnQ5RnTHbFO94ouJZz09Y5j6PoS0dcSezn5EfHMGEJAaUnRRWztwHU47vsZLl0q9IpSSW4Ua3hZOE/DzzumOoB+dDH32tBUMBHEHV0uaE7hcK4Humx4u8c9Eo8BERFYnsY+XQIKS4r7m3f89S9INWS5otsV7PjrkMfjBssajme7oFKR3uNer+H+jgjvRj9eSXkn7xrO4Axigi7P9McXsB4jJj4c+adj91dwRayO/uM/Q96pjxuuN5a0IwVQobcDeiddf8DvX+jHG2l1zFak7MjzipwH/fgUaQS6oqrkyws95/icSUcv8R2WJwQjrUQMen+gInRP9PsNthRu6OOGt6/kH/7XSBlIjqSD1DvN6zgfB377RLfGIYZrJdV43sv+Aauv6OOGSx97EfT6e7S+4XWjtTuaCu6NdP0xosnP1/+gb66pv4f+Hv/j8Lf+PzDnGv56zTX89Zpr+Os113Dq36u5Z3695hr+es01/PWaa/jrNddw6m/R3De/XnMNf73mGv56zTX89Zpr+B+v3xwA1v6K63fIk4d7tT/IrHSveLkOGLlBN+ztwLVSLnsg2+50NVQ3LMyxAV57j19YgZTAPSJNyYiGS9i1ABIQsxuSBPoDSsAmqQeCAmnEEBuS1mF0vY2O1TVeW0D7X7tS5R0kiyLlY8TeInh9jc+jKQCfJqgniAVU6vdw24qiaR3w0qNb1ip21nBJ9kqi0HuLnk5kxCUf0c+5LohodM2qRvo1ivgr5Cs8fobyAZGC5hXZrqhuuN0hv6ApIbdXqB3WBdIWcLVsEXsrGjBUHDslepDribDEetZHQOMKnJ8wcfzdXa0C12foHsDtuIXrWWVAzoBoOT/j3kkphzO33knbE14WtMR+QEA1QxKkVWS54v2OliX6R93CyWxGUkWaI0umpZ3eKiRDk5Cu3+Nf/w3XQt4/4i7Y+RXzE04f3aUGZYfHV8SVfr5FRC4ZxFGzcDXnbUR1+3B53kEDaMqy47fPSEkB4nUFFNcyHLFfcUkRp9tB1gUkhZsaRy0imGW90s4zXNl4rEFvAMh6ARx7vOGtIYXhaO2oE73MgLWDVBYgRXTxeSIGTg2HsHl0RN9PnBLd2O0AEn75KaKdzei9I9rRlMMx7wlPd7BMFcGthuN3e8ZzuJWhh1O7nni/Y3lDuEXUshiuhvUaTmYy/t4ZrBLu6XqLNQeKKLLuuCgtEXvh+n1Ax8DNcT97Q1WCv7cDR1HriCqkcI4LNaLQU0GWJ9CE9BrnWgp23r49W2R/QdRRKdFZfd6jb1szlBJDALmg0gcgPSMe2RNmYx3c0aefSGWHfqCtRQfv+ozZidBRe8D6AbMcz6KnJ3i8kv7yX+lvB75m7Gmc9wZ2HrQlkQX67XMMMSRIGOnyHZwP/HjDyorWAysb5j3cz6LhIu8d/IKmlb7scL7GHtQd2gMf/c755Ufs9gv9y59Hx3WKwQKHfh7kpz/iecePL/h5x883rJ8sTz/RX7/Q5Q3rgt+dVgo9f8Vf/mfWNVPySn7+I1Jv+P1npEo4wPuNXk9cewzPsKAeSQL29mf6lwmAp6ampqampqampqampqampqampqam/vPptweAZcUl0aVB+wXRJ9Aj3H6e8P4VyncgDc2ARPdm14RIRnKmmSO0AGG5hGPYc3T9uoNnZHuGeoN2RuSy5HBbWgv3nmyE4zRcmFiAKBEF13Ah2iOAqQJJkbxg5x3NCyo7IoQj03rEtErCNeAv5hFbWh+IOSIJaosIU+8BlHuCfkfEMZFwJkoAaNqK9694e4A0ejM073TzgE1yIC2ia6FDdnRdsfOOW3QHoxnNCS8/4o8viF6Q63fgJ56uEWvbhlv58hwdyNgA2IZKgXbSCDermCN2Bug1x0um1/vobz7xJPj2Qr/9JYy5QlzL2XDJAdNoiCZ0uSDnVxIn6eV3ZEn0xxuy7mhSXBa814CqDq4jBrwd6P49yhmAPa2kdUWPB60R158LXg9UgV7JeaHXO9YFmpLyil++I+XRG42hHnDXtAUYq9GHS17xVvEu6FJoGKl3uhmyrPH6YTiH/B67bKNTVZByxf2IP7czXMNW8bLiacUfb2PdPsSgwnELR2uP/e3FwCsisYd0vWD1wO2MTmY6XituHpG59YYsazhjPUUEOsAakNdbQzWNSPCDVDKqmd7uOE+QTvL2RKv36CKWiAv28451B7tj647nDQeaNaSlWGtv45RH5La8x6+PrlgE9PJDrEELYEomBjFkwXsPl+6IYdflGcMwc/zsyP4R1URuFXv7RGp32D/gonG2Xz/jJSK5nYjO9iS4g3pBbDiKfVxrO8M5vD7H3Em/QzMg4X7Eo6QeYTBfNkQXRA3pDXeNZ4km0uV7+u1TVDKfJ6YOrZGWZ7CT3g23Hp8rbbi3OAveQB2yIK0hDro9I/vz6NhuGDGwsMgCLw+OL/8VPw5EM74ken2F7KT1Jdzu5x1PQm8d3S8Bp+/hcvacsHbDzcNN3k9kKSNK+xUQ9PI9XD5gn/+Etc9I+oB3QyUBRrp+iJh7LaT1govB44bWO+n5R3o78HrD7/8K6UfSdSNvH2nnDb8/8J5BDuxx4s8rt1/+jf78E9sK5XIlP/8Ij9/Rf/l/guyxV/SIiO5PD/I//xH75V/Q7Qnur+jz5R/2XTU1NTU1NTU1NTU1NTU1NTU1NTU1NTX1t+o3B4AlvwwA8hXhiSwbZk6WTPcE6QL3OwDmAssZ0b3daDa6fEnQwZuRlwzyhPcjXLQ9YIGkBdJCZE0bZI1Y5LKH09UGhNUBGDHEEiwlQK1mxFt0ga5PeD2D32hCTYYjuQeYC0swimDVII1EaC14quHuFEVSRnCsebiOU8BFzEADRnqPeGwQ0npFXBDvtOMzJgL7MzxG9LEGYO2kcPqlHXdBigT4RuMalydIHRkxrKqQ+h3dPiDHz4ityPI0Iq8P9N2F7OGeJm/IuwsaUO2YK91qQDI3EAlQLwqX75Hb13AJW4P8FCCwVZAOJFIRcv6A+knOlxEbK6T0hNXPSFmgj17l3qAekBVdnuB+Q5YNP07SZUdap/f27V7rcsVM8HoHHHdIecUen5CU6FUjKtglPpckpOxoa6gY1ip2VGRPiJYYBFhKdMzSwFfAwkWbE/08ScuK5pdwTDrRtVwTbFewjOhLuMLbPUD78cDzBlvBHRAfn4Fwk9cT9iuie/RD13O8VvQ12+lIAm8HkhNJV1wTnja8nxFPXEr0wHoNV3M3NJXofK538uU7JJcRz6x4uw2ndAB04cS9YMcr3QoCJD/Jy+/p5nivJI0hCk2JBNA7uuVworcjIs7RcHOuTyA5zuG6A+DkcN5qwbVjdgPraN6w3mJYwi26oDVeR6WRRFiWjVMz7fGG2ytsG5hAuyN5DQAtOeBh6xHBPUC0W0XyFi5vSYi1uG92gq3R8ewgKgF7W4PkUWXbT7zF84CmIAXKFe9v9NrDyZu3iJe24UJ2R9dn/DzpEmvH4wt6+V04cHtH7EG6/ICbonYGfG4N8oWkhWUFu/yR80//FZOCXr4LV/btpPmd5flH3AR7fIE+BlOWK6k84fdP9NYgj2fiANJ23hFL6B7dv1YPJBX0+hP+9ufRnawR7XE2zI308ju8O2Yx3OIG5af/ORzovSGWSD/8PyLRwDq0B+X7/4J9/u9wr8jL/4ItBcjY8ZXz7V8w/44tx3pnXZH1e+T+GfMH3jJWK/rjT/B4Q9cLfvwbvN3QH/9v/8Bvq6mpqampqampqampqampqampqampqam/Tb85AKxOgJX0A5Z6gFev8HDyXqgtunwjjlihZ1r36JG9KP0USM/RfXsajpLyA00LURIrEV1sPZyTaYk4WQAzRDxceP0kCGp0/uKCJ6A7YhE36jrifpvgbiSraC4R/5pXHMfPz6B7uJc1HLTvwNe6IPkZKWnAsOg+FbW/rsOyw+Mtft4OaER8b0rhQEUDssoCvQ1QJvhxQHpG7DXgjAfYiX7SgDJiHWsVSkOWD7H4ZnEtaQW/o8tLrEk/SXmJHuDbZ+T6jPVw64o33A3VJZybHjG6mER3a79HL7E1sITnHX36Lpyqx88REW0CNKgBTbPAsu5Iig5Qt5O0PgMnTsBwdcPSBv0NXde4l6UgHcQbad0RTXHPBHS5xj3RjOpL9MieFV03RAM4Wm9oaWB5OE9z7IuUoJ8RG6x7uLlbRzJx3ceXuBes6PMVTUp7fMEe9+h2rh3RcLp6P5GcIzrYAF2G6zzil/32C6QVt3sAVgyvDXUfcccJLk9Yt4iObhVNGvvhuMevlXDxIvRHrKmZDShp4ZZXkLyDbUhvMTSQtzgP1+9wMt5HrHQS+nGSlh2rD8QV9xLDCWmLgQBx2H+HIbTRHSx5p/cjnN7LFX2PQx5nBrPYRzlii70NN/Q4W2IVyhpAlIjdDvd1/qsjXQrsF2j3cVYNLRqx4ccDX3ZajZjmJIbSY1+JxHUfX/D+gPUHXEsMJuQLsmxIq8Nlr9A6boa0MzqWewM3WLboEl/GflNHlgtSdvrtM663uJ/6AvUrun0AKbR+h/OGk0fENNAr4h6DAMseNdvmdDq6/4iI0D06ibX3+AxWca94byxLwX76A/Xtz9hZ0PWK3/4Nazvt/kpeLrFP71+wlPD7DV8yWhb89ecYRCkref+ILFt0QD9e8fMt3LbEPdLlAvsP9Lf/I/qMXWB7imQCzjGUcMCy4PUtrkMaHG9QlFR2zCrSD0Q6IhvL8/+ELn9BdKGr0t5+Qa9X+pE5P/0CJMQMLxvl5XcRlf75f0ftQXr6PXp5pn36V/TyHfZ2oP/8u9hvU1NTU1NTU1NTU1NTU1NTU1NTU1NTU//J9JsDwC5E/KidAW3TQjsc3Qq914BfZMjRL0oWaI2+PSFJ0drw9hXXH+luiIE0xQpoP1HNyABHeIVewgksGjHMGKQcEKoT7mDNAVrOIyBuWsDDBeyt0uUk6RIu26IjxtXj3+Wn+LkwujQtel21R7Ty6HOV0e0qDAjcGrQencXrE1IfAXhdY41kdGB6dMuKaPTotjvuhqdMtQcdH92vT/jjDTs/gzmijpugacNqQ5JCPxABTNB2Qs8BgagoKSBcSlhW6IafB2m9YOdXVBOeDEkv4azVDJzRD6wpoow10zH8uAVM1wT9AtRYfy2klyfUnMJwZHvDu2HN0eQ4HVk3vFWsd8TvATrTU/S31hoRz2Kj67nSrUPZUC0Bv9uJuaLlissN7zdEL5T1Sq8HtT7w7HF/jjMifhOQIJWNsz3Qdcdub1gHKR9iz2hDdMVFaL3iZUe8IOsa0bfthm4vaG+xH857gPJmOD26n3tCiChy740uFkAdi65YTUjeAvTTxr+BvO6IGz3n6Dpe97CZcyLqkDLJBVwR7Uhew6UawdkBMtMCLvTWyCUF5PQW+0tXSA0tWzjoy05eE+3tL9j5wDhRDLUL9rghSUn783C2r2AHrhuUOH/x3oSzNRXIsUbUc0DiM64sxXWIR/Q2YrEnex9DHA1ZniJuGY0hBi+oOlo7yTq5dyQvdGsjinuPfWThwk/X75EeExfuAVtzuYzu5RbDId5igCGv9PMWZ3b/GPfxuMV79xrrJTmAp1lEYZ+xn2S5ItsH0Iz1I0CzFKRscf7rfUTUNyjX+JzngVsAa8kZrJOk01uHnNFeY5+r4rWSemdZn6BXrJ6QL8j+e3r9DDWjlOjOtgf9eENWGa7sF+T6e6y9klKGvMR5MkGuL/jN6Y/P5LLgZhElXzb08vvoJTfD3n6Gywr1FQU8pXAQlxV/fAE6nox8+YlebwH73VCvY/DihXz9I/b2M/m8Iea0x4389E+0t/+d+vUVb4Y/f4S8kC8/IO6xl9OKHV+Rotj5C3Z+QT/83+m3n//xX1pTU1NTU1NTU1NTU1NTU1NTU1NTU1NT/0795gAw/jUcsvkJsxrOvHqCCtrfwJfo/FwGmO2PcPrqn2nte6QkTBS4kcsHer3D9Yqa0V2wepLX6PtEd9wrNIdi4Yr1AV01x+rbgGO6wkq4AIXo8e3HiIpOoAnTgiIRG90HKC4K5y3ie93jz0XDVZoH/E0y3JYBVzQ5uCMqEZ2cMsg1HI7JAlZZj2hg1QA4kjF5xWUHEoZgreIk8Ps3+IxsSPa4Br4GRDfHU4mY2/NG3r9H+oGuS7g4C0DH6BGtrTlAczvoKSN5x6yjw03rOVyitAdWrlBWRJR23OmPG55l3OMLcr1GD3KOWOCkkHNB6leQEvCchO5XnBrdpS4RV7xs2PEJLRfET6x7wMUjYSmhWaMn2BoiO72fJBEkCXRDUkbzlf54xXtFlxWWjW4Wfbw8AEHf71fZcQXBIv5bws3rKGl575GGbh33iqaM7FsA+Vyo7WDtbdBkg7IEfPYEajH94CDlGbc31DLeG6YyMsOFvHyIuuB2ACdJErptoI5bJ4lj+wfUDe83XAtawkUsrSLlip0gKUM9I6r8vKMeUd54I5ctotLVUVV6N0RGd7K1GBBYC80dWRfS+QbLBU8L1iu6rbg1ulWSeMRK5+HiTR7rKB07H+O1lgDCrUf/bjtiLfEBdj0i0D0cwaIgang/IO3h1u8tzoopdtxh2Ug1eqcZ8NiPO007eCd73P84ExnPBat3/Hgj7QFQvR3xTOotzuL2Ej3LmqAL6Ibn6Aj3+1e8Vlh3vFzwXjE3RBXr8ayhP0BXrDbgAedXZP8BTQVr93jPemJJIpLbnOjcbpCvuB1h0j8DFLvVEVDQok9aE4pTpOHaqVbx8ytcXjAbLuyucFa0POPtE3q+4vszbhW2C4kLIg3rd/wEbQ7bjuzfk5cdXj/FsENKcP+KXF+QtKDuSHL62ytSNvCKP16h7OjyffT+1k55+gPdDWt3/GyQhN6dlBPcP+Epx/GggH1GeIo+9x9+5PHnf6X2O5JKOPLTnXx5jkGRo4ZLmYznC/Ky4o9P4ZCfmpqampqampqampqampqampqampqa+k+m3xwAFs/hqJQHtDfcM1yew9WYrySi89byHtDEV1gqyHe4FoyK+EI3kHZDrcHtNaClKikJjgJLQD078bLj3VEa5g2RDl7ClTqgHBZxx54FsYZgqGS8lOiErWe4MsWQdQ3YUyu0AmlDxSIyNudwRXqP6OOyxu+roouGK5AFUsK9jQjj4X5cLtE9yiOcggPqioNmwRr4+QayomknpYoheFvDzakaUdbWUbEwQWvAUK13RA70+fektZDbipyv+CpQXrDjFRmuXtEMktDrh4DxxPp0d8QsomytQd7BO35Gry95h9IiklkT1I5uBVueAFCv0Wk6AK3rI4COtehNNofmuIbDNueFni/h/jsPNGl09KqgdEgf6PUTpC3gMIpJHVBZkN7RkvFlG1BOEJRlf8JvP2OqpOfvAyzWL/FZuyOyxz1ZV+ztz6gLdgTs894DZp8VXxOSOu6OpwHB252cd0QM0gq3r3i5Il0C7ouDdCTt+PEZLQW1RreGrk/RqWsNFcddwx2cc+xPFPISTt/ekKUEIDUP5zfh8g4HavQyiyqpXJDzIKkGYM870k+8nnQpeF7oZqhn3APoOo7UiqYX9OWCmFBbp7VP5HtF9mfwk4bg1chKxBe3Gs5qz4juyF4iQhgbINciuFrARbBake64gkoJhz0W8c95jQGINpy9EgMKkreY2Rjdwnr9ntwbxhvWlF7jvJE0zpV1pL9DfQ1QKhVxwcZ6yfocUeq06AlPHe838HBjsz1FXHJaQRr9HA5nVSSvEYcsG3BDUkRrszwjpcQQiQPVwiEsEUONNfx8Q68foR2Y9egv3l8QM1QSRkZaw1//Fa014s05WfdntBvVjF7vOBv25S/wtOAslKyky3e4HbT755gvOB+kvJNzDJUEdO/4cUPzgpYLXKEfX/DbZ1ivUA/UOqoJyc+wFfrbv46BE0cWCTd3f5CuH0A2/P4JxTA/kfNOvv4z9vhCXhc07Vj9GsML9Q2pCS0dXT+y/eFKq2+01//Kgx/h+hL8vvd4PYx0ecLvnbT/SHv9BW/2H/TNNTU1NTU1NTU1NTU1NTU1NTU1NTU1NfX/v35zANjrCekWUcv6ElCl3yMZVVO4H70F+HKPGGe+QvoRvNJZ0LPSuJFffh/uPBFMokvTcsYMVCJO1ayj5vHeCogF3KIMF2QecbECVGCLyFxZMIsuTvBw2qrinAOCjq5TAuihCdGCSAr3sAjuAUGlOLQaPagpuk0lb/gZQEy6Q8nDOQq+rOASTkc8IpZbi7jp4zPuHXFHJaHtoNsb6CUimM8a0DhLwNmkqALnA9k/oksJcJ0LyBLXby0glh9QG6zL6LLtAevsgJSJlQgntUn0h3p9gEp0q8oSr+tHuCuTYF7weqBuqCpZDRWBywf8/iXWYt1xP+G8o2bIsqMker1hkujd0GaoNTKKXBZcLGKXPaFk3E7cDUnhtJXuaAY3J5GIbuEaEduE2/Pdze1uYfTWgElu4baVvKHpJdyTo5taUgBFaKiHg9n7iYkg5nhvqDZUFekVyztiJ0I4scPomyAVpCyxv71Rtpexp26jZ1YDErcHY0IBsIDzvQbYRwYYdtQFzWsMTaQF6j3gr419pwv4QY68Y3rgtOFaV7DRM+wdlhWOA6fRzNC8Uu9fRyduo/YBZS3Rk6BFoL5BWaNbmejV1hz923G9cfZk20hpiT7ssiPN8PNrRGsrATtFR0/4Mpy68W91vSJZcet4veMC5fIdTTfcvpLOB+ZCtwdNFHiQbImebXf8PAPuejiOsQPyiqyXOLv1Dq3HR0iOYNDvoAuaN1wT2BnnoleEiqQn9PoRf/1lDGEQTu9+wPZ9uKsJxzi5QCm4S+yhMTDCcJTj0N1RWYaz2cZgyI4+/Rdye8DbnyOGuuzIy3fo2xcexxuen2lpp9/Cue9bZ91eSOsz3hp2v6HrSu8d7QnxTkKGw3nFariP0+WClR2+/ml0YqfoxXbDRty52DNIQdZruN/rA7ca57C/Iv3Ab2/o9kTe/4AI9JoweyBU1C36sZ//ifP+SkoFtzfy+ozu39P2K+enfwEKbRUynSxtuNQveP3f8HWH9gvo9R/6fTU1NTU1NTU1NTU1NTU1NTU1NTU1NTX1t+g3B4D1+XfYeeC+jXhWcBaQIyJxJeJ83Q2ooB8ixrgTgMwdS4qmjVZfyfqMpgAzrBfsrPQCVgTRTpIdtztmiVQi+ll6x6US8HNcmHeGDTFeSxes1YBpeYV2RhxrSuEqRsORiYSL2CwcfRpAUPOKruH+FX/v1x2x0a0P8HmNnxcLIEVUvtpw8Xop79Qae7yGo3H7Hm8PrD1wXTA5Eb1iDWRJIA1N4XLU9YcBgwuiKyqdfh4RaWyOXj5A/Qz9xPcXxFc8W0C3/oC0Qa9o2YCOa8bqAVIChBWFlnDRSNL2c4CyFc9n9Lie0TusIhTViJJ1ATKyPIOA5LE+64qnDTtuqCx0rwGb61sAJi3ItoDdkeUZb0f0w/Zb7BUCDupyAemY1nDzZietT/Rb9P2CRsQthCvVIS17dM4KZHWs3um9oTlz3r8i/Yb2Dc+F5BadszWgnZ8nWEeXpxgqqA3Piqui+QK06Gr1Br0GtBXB96dv3bSQwR8jurtBiz0YELThrUfvr8ffFyIiPXpyjZRK/P3SSSgdIZUL2s8xcHABe4Sr2BpqjVSuoCve75ifwBoOYWw4biVg5vkal9id5p2y79Gle7whWTHfYCkBJqtg2VEFaUcMNSSPXurteUSk5wCfCFoU15dvvdz++Ir0iiwvyHBBu92QXKIf3B1oCA1JKe5HPcPtvH+A8wF20N0jpjlf0HpH84KLQ16gO1bfkOWCljXisa3F4Ie1iC4ekeXQkNFNLt1in/oRYFsTXitSNtLLR/rtM96OcL7mJ0R69AT3e7jitx3aI9bUGt5vpOvvAxafcW/y8w9Yb7E3qbgpohlNEq+ZF/rXf8G8I+kC6Y3sgHZ4+QP19ZXz0/+B//TPcP/C0it5/4B/+TM2nkvdK1ojXlvOO6oHsn6IPvFaMcnI9Qfs8ZVuhoyI/i4noivp8kPsQG+IGV5v6PMfowf6eIBXyvf/F6QsqBj9uJFefke//Xf0vFE+/l+RtGLniZUL/f4XnAz1FWylrB+wcnL/y38jv9zIpuRc2b/7A7QDvfyEnY94xv34+3/k19XU1NTU1NTU1NTU1NTU1NTU1NTU1NTU36TfHACO7OYHKhvqnWYdFwuHnjwABX8F72h6wtpb9PVKhf6Ke0bShtiByYrxQJoCGR5vIEo/e8TPtg5ZwVPEvbaKlh1LKUCMKZIbkgq0B24awFbzcM8u0E9gRDr3cAL31gJgOIgfw6knqPtwBadwBecVzRGny0i2xQMAums4QHMO4Ozjz2BEDVfoCU8RgYoO56MI7o71RhXCsVt2aC3io5PGayu4nYh1ZFkQzRjDVXge4NHzKcsT0OC8gxZEE1a/Brj0A8oFp0MDyYXe7wgnnteIgU4Zv3+FvGIidHq8fwpnNY8TUWMpBdGOyAL9QNOK5xIg9jzw/kBI4Sg0QxZBmqKeoj/WE55SuKFdsPOIOGWzcOFaR9YtnNiSsVWx8y3Ao3WSSbhy/YGnJwzDuw3HdsQ442fsIxI5JYSDlqNHuJ2fMPtE4nv0+oTnBY63iAJf9wC2Lkg2vDV6TQE7F0HKggSShZyxxy/h8H3vwk0pIoS9B3TtBuWK24gCJ0EZn81qbKRUAipSI/JcBfwMFyadvGQizjjHXrJwq0te8H4fgDUij90MkYXeOkjBrOGtxrnjRERRGiJQ1u/Bld7PmFkoO9YPejV0fcFLop03UsqkssZnasPxjoQj/92B6+CpR9fziOhmeQqw7w4DmMuyBeztNdYmss2R9YJ7Q7WH03t7RvKOfHuuhHvezMPhfInP7P1AdEWWFQmyHR3CfcQ+a4JaI/rcPeCqpOjhDWsyrCUGUo43rJ3x87RBF2Tb0ZSx8zXOZxJkvyIIPoC/tzPuhdV4NrjB+hQx5K2GI3jc5+guN4QHogXdP8Lja3SSby/kXmNAxYWyP9HSP9PePqHbB1ouaH9D9w3/+glbKrKspMvvAnLj+P0T9vhCWi6YO50jYs6XC/blX+iqsW7rdayX4cdrXDujgzlpdCSrAyu67fj5Ru893M71jm4/IuUR7moRxE7ysuP3SDtI245053ztpKcPuNywT//CoVf85SNZrvSvf6Jcf8Te/hvy9NNIaJiampqampqampqampqampqampqampr6zyX9H30B/9HyVhED4YD+b6SkAbF0JSJtDzS/oLKGW7I8xyppgvI9pCewz/jyEfFEd8VcBxhUXDKeM3bcMCSMuQLkhMiKuIIpLgXJC+Y2OmgXPBp14zr7AD3dAs7qEiDDJCKnHTyvmGSs1wCRmuMaMLxZgJw2IGzOAYUlYJ7kNBYEnDxcpPLN0RmOR0daRXqPiNreAxJrotsj4mH3l+gy1hKxuSiiFkbSfoO04u0t+obN0O7DlRmOS0kFzIeDuSJ+opSIOiYHXOsNlwPXiizXiFBGAzir4MsTjTOAZT9xr3i7I3lFUiH1Bv2BeELWjVSexrU2ZPQv0wXyhvV7QPrzDJdzP0AgPX2A7SNuBUkJUkLTihAuXFk/RiytLgHK3aIft9a/gvWy4loQAh5LIvqgsYiXJSHLBVEhrx/ImpHWyetK3r4nX35H2i/hyjXB1xdYPgQALivWDshX2F9I2zOoYOrDtPoYMb8dTSt0D7how+0rPuzfEfVNvgRUHPtOlg1575KV9/sXgxOCBxDGIj7aDVhiv3TQtCF4OMHFAUWWFfdK7+FeJiVEHV0LUGG7QLlAJVyudkfFQdYYirAHafuI5w3fP9I0h5m+rHjK9Frp1rD+gCWiwb3FOUF7fE6rAVqPV4Q29sFBKiV4tnvEepeNGAzpSIq+YC9bzFP0cFPndUeSk1chZyVrbOvDhZqveHmO+ybgyWC9jusZkc7nPeBluUIbkHnfA8BiETkNmDWsdkS3eGYsO24tngFpQ64fRkdxi/sE6PqMpgtuY0ijPtD9imzfxQDB8Qr798hywetbXFOrmFX8vMU9U4nz2g9SuZIvLyQamlfK/h2FTsHR80ZeP2C2014/0c87vRteNvzlRzxlLBVs2WlYDIVcfoDthV7vmAx3cj2jK/n6O+TpD3D5ET+PgL5Wx2d7H9bY8ccb1ir98RW5POOPL1g74jPXz9BHEsD6gj/e8PMz+IHayfr9P5HyBq0hkijJSedXyvVHZPmJ9OP/hH/4Z+6vP9PSTj3fIsZ/ecLffvnHfmFNTU1NTU1NTU1NTU1NTU1NTU1NTU1N/Q36zTmAA4+WiKLV7xA70e54LridIBek3cEFTRo9vG8H7AKykFRwnpHmuOwE/dNwDaqCOm7RAcuSsd6Q2vBsyLIC5+gHzrgK7gXzcEBGHHULRyg+ul4Znb8ZkkDJuCXo0evqZISOWSWhw5Ea3bt2Gpo1Ol9NcYmoakkb3iRAGBZQLYO0cB26VdwChniP6GX1hElQre43RDf62QJAnbeAz+0+gG7DZUXkEm5HN2iCq4I30nYZTmTHrQ7Hc8QKKxpR2u0BGNIzOWWchDUL8FouAXeOByxAuWLHSZf2DeaKAHJS9idyXdBiaNoR3ZDFkdHhalbDAV5W3BpJ1gCm3SHv0WlbCv28I+kgieB3YAHPGu5Ka+Eg7wHdSQK1IuUCZYH6NuAb6HJF/UDOB+qZVsPhChbdt84AoorISuIVEyeVgjdwS1h9xBBDzjFY0Hv0qPZbOFHTgoqHi9Ij7juiwsOJ6mmJAQh8xIiPjl/Jcb2aRjS140IAUGuI94DveUBYiEhxzeFgrQfvZmCSRCS0teju1QRewgmsBT/fAt6RopdVNBzvPiCybuAnsq3UR4N8JUnBjrcBcRd43HBRfL/EvQL6cZCWPfb8eUYMcFoD5OLhZLXoyDbv0YU8uq9FHPGOyIbR46zkZQDRHvHaKTqQpVwCnLcWHd5li2hld9SMZJ3cKqef9LxipdCOO4qRlxd6rSRrkBreLbqAyxYQWBOiOSKWd4tu3+Mzfv8MuuDLZQx9DAdsPXD1WHNz3ByOO2RB9+dhkr1Bt4i4zjviGe8nouEKFoj4as1w3PB+xmcVwe+fIy7da7jI/YwzDeANyytpfYkebYN6//9Q1iv++Ex7vMVwjAhZV2T7HkmZ+vYJ7ZWeMrI/RXR7XvDXv0S8/XaF9iBtF1LeMDeUDMcrTnQgex1u4OUZeXyGdpKe/zn2x/EAlvG5TygHWKNrJokBLfZCO5G8snz8I+df/l/0t09IuaLqcAr54/eYN+zTf8XOG77ubJcfKR/+CO2OtLd/6PfV1NTU1NTU1NTU1NTU1NTU1NTU1NTU1N+i3xwARgRJ6+jBPHFbkG1Dz1e8FKyHy1A0RQcrhl2eAljKEvBDMt0cESP5G92upCx4KmASUEUU2g1YSMseccbu0StK1AobAeasn6PyNI2eUQiwLIiu0c2KgyxYPwn62APcaPQKu4d/WPMSsaqq+PmG2AqlgPVw2w0DZESgesAhja5ZloIdd1oT1ISAgiP6WQMGeu9w1ABw2gOGieJJ8WaI5wDAycA6SQV6ik5XO5DtA1ouaH/g9YbIFV02/PgMHpG0WlLE1eYd6xaRvSlBfQtYmgrOA5Yd7w2Tiq5P9HrDbz9D0gBxvgRUXnbSviE9rsnEI7a49oj2thafx88Ax2lHeBuRuwXXEhDXd7r2ES3smFtAxneX7HskdOu4ZGjhkpa803sLB3YTpOxAQ8tC2a6jvvmBuyMGJBnOxoxWCaNtOwjYJwGsVWMIwVtsjVQCxtboQHVNIAt+vJFKwlOP/SsRz01a8fsrrhEZLik6e01SwG8kwLH1iBa2M8y/w+EbTvGG5CueFrzeAiB7x0loWKzjs3qPc+UDbkrEKQsWnar7c/yZR3e0AOInjlPKhqSVRsfPE3Ho7YD8RJMTcUEPsJ5pZUGA1E4kZWTNEQ0M2Lj3QToFeodWo7u6hPNfNMW6eEf6SdIUrvD2iDUj4Q3Ie5wLCZCs24eAuO2BN8OIiOmUFlI/6ecbBztZnUUTXWIQwjWuS3KBZQ9I3iuUNbqYPdznohm2j+NejNhkqxHBnRYsFfx4oFuKZ4aA54y3hi+Ec9scvEZ8c1rxeke0xTqsH8LJbA52x/OGaDwnJceaej8jpl0yaX2KjnKviFWyKC019PUzpbwguuKyYtc/4ub0r5+oKeMCZb+EY9zfh08ce9ygKPruPD9vcX1phVbp3eJzLwtmG/3tT3F+8wIoYg3zhj79iGmG+5d4/fuf8FSQyw/Y21csQ84ZsXuclfeY/ccN3a4sP/2vtC+/UI+viAq9vUK6cNw/Axm3TGPnOL6iT39A7Q71yz/+O2tqampqampqampqampqampqampqamrq36nfHgDmM/gznAyAA157RPN6jthj6bg9UMIBnNNO93CMqSZa76gciD6BXON1zPDzFcmXgDC5DLAYfbvK6BzFv8XQOhHn7JpovSJZEVGSV4QMrUL2cLxZjc5UM3yAL7yGW3XAPsuCm8QvzXBNEbF7Rqw0OQAgvaJlQXLCvOG1YR49qZLWcHBqg/bA7I6Tgx+VjV4f4SROGemC9+hN9tpwj85W728knhG5I9vvIsa63dFlR8sK1G9dueEsbWi5QjdETlQF94wiwetoiC1IWnAz3O+AIkL07CbFvERUd36CdkB3khCuwww8+ogtDpekpoL1e0C09SXu1XkG/LUT3ZaIVE4HdB0R1Teag5aNJCWu1zuyXCJKt1WQEtfW7tEzm7eAwedbOHw74ZjdP0ACLPqU3VrcUwRkxbmheiFtT6TbGzYuATfwA+8STt4+eosTiB7BUdkivlgcLStmJzqGBuBAZA8Xb1lirVQhX7EkaK+YB6yV5Smu2xkwWANU9hH/vVzDKeoGrf51HSSNPZ7i12i4471DPcIJ73W45gu4Rne1ZqAj+TLus+DjzNEiBpn2hqujSeOz9oNeBZJD6yQvGB6DG8slAKfXiLh2Q9KOLDmGE8oWr0sPCN0OBuFFUkHzhjHimNXJ68tw0feIK+5ndCOPHlrGWsRbGXI+KO6Ir7R2p6JIyeTzjvYzApDTPtzDNnqgH9BzpGgnjf0qxHlLMcjhRFy6t9HZnDbgxI43tIwzYoZsT+HgHbDbZUXWDTHHE0Ae/eACLpGA4EDeEdbYpymDCtJzDLXUO+7vHcYL3F9RCvn6I5Rn+v2N3AzjQS0XzCPmvr4d+Nbh7YaUC3K+4VIwV2Qp4aROB1y/g/0pBjnqG908esrbgSaNQY3yEoB6fwrHfYthBJzoRO4NP/6MXH4kyQLrd9EvfX8NF3wOoC2X7+F44GrI+UpaXpDv/gD3lfrl38AeiCnJGq39DPeEXD/y+PO/wvJCev1K3r//R39hTU1NTU1NTU1NTU1NTU1NTU1NTU1NTf279dsDwOn3iFRYLMCeaMAEHx2i4njzgFEipLTQrYI94Ei0i6F5x9ojCpQ1BUw8D1J5jn9PD4jiGVqnnydsG9BJskJ7QFdcPP6+KNY6SQLaiidUBFEP2KMX3PtwSVq4l1PC2wBBEJ+jSbgDUcRB047bA+sR8yvShzOU+JFXqI63x4AsZ8Dr0a3LckF6wVTw8xGO43zF/SsuFvC4W0TpArpstONOLh+RLkh+xh+vsH5AskEp0ZkLCBm9fIgYVYS0rgFUWaMv2Hu4cdEAgh4OVms30Iggjh7OMoAjQEaKIOuO1ob4EUA76TcHpHtGVeO+bc/hknRDPEVkcV7xfpDyDp7wvgSc9AhMZt3o7USL4t7Q9Tn6ml0C2uMBxo6GSThaNRmyXQIyE85yUcUR7HgAgpQr9BMl0a3FfWyG6kJebvQmWKvDHc6opO3RISx1QNwnOG70sS9ScVg2sAD2KgI+opvdok85LQEJB4zvJiN6vIOViAu3AaiXl299zWbRJ+2uEQ2dcrBKWcY+PQN+n+F8VjLuJ7Jsf93D64dwr5uNDuuOtRb3VKHVYwwfWEQAe0JlIS0BNu2IGOJundQWXFrc5rRiZkg9A/gpYIZulwDOEk7mGAB5d8cK6Ir3e8DsbcdxrB5Yr2jewu2rY31E8bKGG/09qj3vZKtYfdBF8VRIDt4f1DNhZpyyoFIjbnp9CtexGWYnklJ0hHvsE94d+pwx0JFSQHQPNzQ43s54Vq0rPN7wI1zDLHvc535CbwF6UwkQrAI4dh6wfzf2ZbyubC9Ai/PWIw5e8jo60C+IZPx8xfOJrBe0OdbuaO/k7Sn+3uu/UtIOrdFUYf/A+eVPuC6k/Snir3vHcsb7Az/C9aytIvJAUkGefsJf/wzSIzqdOC+CkC4fo4e5tYiOtjfyh38KIN8E94p++F/Q9Yp1p5030uUD3RPmN/L1nyGBkyJG//gCebiiV6U8fY+1hn39t/FoWbC6wve/Q/1G/v5H2tu/Uetn1g//69/7G2pqampqampqampqampqampqampqamrqV+u3B4DfI2BTiR5Lt9Freccp0O/RP6qxNNYbxgmy45eVxAPpD5Je6f2Bpgv9/ErS0X3b30aE8ANxR9OKUeitBcwpRioXen2L63AJN6UqzY5wMCq4dVISzAn42CopecSiWhv/NuCrGgSwA5cyAFXCrIXjVqKXVBRcUrh2UziMRQroidUjom29xTq10bOaUjjs2onQUM1App83SDuuPeJUH1+wlklpQ1IKsJYIAKogy450D7dvkm+OUNEEfUCevARE7B7XX3tE+EpAdM97fO52RHQsBVKmn290TQESUbQoOW0kXwMMuyDJ8R6w113AJByOueD1wP0IEEpAeUkZrZWiGT9PrOxIXgIK4sOh6ljriN/DMZv2gHiSYc1Qb4g9QK9oLlg1vL2hyxV73HGNa3PvI246oLdqoecnxCqQECuodcRvARsvPyFakPMWgwnpireG5AE7zzNmG9pBXtaI8sVwEUQWvNcAiTKinhHgiA7Zfo608RWvZziJvUHah5v0MbpnC+4jXhiJe24tHOYYEMMPpEvEHb/vq5TwWiFvIxa8h/NUF+gNzWsASzviHnnFpAz3spMuTwgL9XiFYkj6Djtf6ccDvV6wXCIO2BQVjdt/nOFuJ/aIj89t1qMvOi+414hgz1doB645XKk9OrJdV3rvpN5R3k25AZojqj1DzjgLul5JBtINayfcP1HsQc9PWDNOP9j2nWEBx+sjOsF7jUj1tIBauJftRMpwa+NAOPhlfwkAf/scv845Bhp6gxL71M8jQHzS4VS2cENbir7qvCK9Ri9za8h6AQTvxxiK6Egpw90en1rKAvqCtbdwvC8F+g0wpD7I6xX4CW6/oKWgfnCeJ6UUzp//jD59hu0j6fnHkZLQsXbQ6kGy6EvXrOj2hJQn/OufQBUTIV1eInbeNqgN7ATLSH2DZcPPO/3xBcoV3a6xrueBtIbrRtqv4BkXj61PQ7bnGIDpBlliOMcTy/NPuD9on/8behfyplhx+v2O7Dv29c9w/QPttL/7V9TU1NTU1NTU1NTU1NTU1NTU1NTU1NTUr9VvDwAPt6uYQdKIdG0nyDqg2IKI4P0rhpB0wVuKvl6pdDspaQMvUTXboz+2o4h9RdMLmhOIgenoAxU0JbpH72kXC3ekV7weKBspF/rxgFRBCwFrEuYVhmvWzFExpFbcHE8rYmc4LomoV8kNN38v+o0oWA2gJ1ZHxPUasbBJwmGcC+IN9yNcxRqAHKsRNdxbOGNd6K3SU4ouWnvg3iJ9VXe0vWGyYXRSvwFbvFZ9oL6A1oDVrePZAyaVEnGsfuLNkZwCImbFe7gV+3kGvKTB9ozdPuGkQLEjZpn6GBHKDamQckJ0QyRgFscJCrJdok/XKkKPpfOAOJJyANBUBiQdnbPZI75ZF7y+4pIwAUiot3BclxXSgiSgN8w83JJyQj8xUyBBWuj1HLCVcIsPmAzgS0RLa1mQ6gHy9o3FV7zt9C6YBZiMyOUCywXOO3bcArQXifeqr1hvqARIxkeksDtOQFx5h4rYt+7ftO0Rv/wOIbshJUE/EasDJCvezrh3yxoA9B3ymoVL207QEYkM4bLsNeLGRXGPfY1bOKK9I5Lo9UQkkZeV9nglqdHOByKC5hU7bnT7iuj3iHtEHdcHLoKmZZwdQ63HGy9bDCkcRzDXknESNgYSsOjmxg0khUvZDPceeydHZLXoeySzIizxDPHxHqPXVyQhCiaN5DFI4JfvWeqd5s55PzBxuq6ot3iNvCDnEZW4KeF2ICRk2aEFlPduAc/fz7PmuG/LDvS4T2lFx5ACvcUeY0HyEg7/esSzwe7I8hRA/PE11r1c8Vzw4204wDVegwpl/7am1hqSd7ymOHPLFV12/LgFZLZKXq94PTDrFBEonbR+F8/J85VOgHmsIyiyPuH9Rrv9K3n/MYZtzjuiCbl8jHOL4PURkeZyDzewpNhzyxU7X4crOqH7C3b7HFDXejwTTpBlQ8sT/ohIdXWwUtHrD/iX/x5/X5YYCKKyPP+E5IWSnukinLfPmDVor2AZLYX+9d/+vt9PU1NTU1NTU1NTU1NTU1NTU1NTU1NTU38H6f/oC/gPV8pAxCtDi+hXAtAGsMoBfWRH83P8uQSowDsiG639Av7ABLq10Y/a6LYFUPUW3aJbDiffADQiGTPD7QyAoWV0oFp0/6Y1roOIe3YX1BzFw0HqYO74smIabld0BxXcaoBBH++nYR2WbhHT3C04lfeAq9aw84GfLUzReYcBZqx2rN4AH5CmBxC0ikl0IUdktCPpMrpvHfMjXNC1I+UJJIfDkAOXGpG23UcX7/s69ajXrSeIRdxwE7x13Dwin4UAxaP7lLzR3bDzRu89ImOXWGsZP3AQIqYWTbCsyPKMSELLjmARs9wtXJbpEtA75fhvbwHgkHBde8db9A3j7b2QN3qczcIZ3Cq91vgsvaG5kNbnuB/WERU8reGINcLl2v/qPPZc4vVoJHXyUshiaC5oWkhlQXOOe2gVM4GyjjXJ8d9WQTJkpa87rd+/dfoyILOUZRyGaFgWcUQsAOv2FL3UA26KpoinFiJCWzTubdJw/+Zl9CoHIIw4ckeSBzDFCbIZDl63PtYz9pGMzlyUcAFDnLd1B1HSsiC9omVD1qc4swbiinnlrG9Yt+gjRuhjiAMy5g1JhpQlAK+N+OTexpnzEWk9+mMR6DUgdat4H8+Dfgy36QGM3x8R4OFer8MpHbHE4eTtSD+QfqDeSWmhYBTuiBbOR6XWRu2G1SOi35cNygWWK6wbLDt6+YBcPgCG9SPO8HKNc9TPOPuPtwE7Le5xHxHT3mKQQBLUM/bGGW51UsbtwCWgN2UNx7A59Hucj+KRTnC+xTDIiHqn3hEj9oc3dL2iy4qYxFmqd/LlOV6rvVFcKFZZlgvp6ffY/sLj+MpxfKadr/EsWi74/kfa7Y3+9gVqHWfwA7K8INsLUq7hqG4Wz43bp4hF1xygXIV0/WEM8siYdXjA8RWxFv3Fx9cYHPGG9ftIBDD0+iNeK377CvUt4rTvB7p9RJPFaxxv5OU5zuKH5xgkSenv+e009Q/W+6DN/+jX+D+z5hr+es01/PWaa/jrNddw6t+ruWd+veYa/nrNNfz1mmv46zXXcOpv0dw3v15zDX+95hr+es01/PWaa/gfr9+gA/hAtj36ZS1F9GsqeH+gZY/u0CS4RaSt2S3gmBc6wzGrH6kYyRMynHHf4JV7OC29h9NVM+oV1BGzAMDiJA13IPIesQqiipjimqIHNyWEjPU3TFpEFo94XTdFUo8I6LyCPfBcRncx0VFa37BEAKBW8Ry9oqIOqrh1PDGillO4CgU8dcQH4HKwXnFNo/M3gG1EJWu4p1OOaOG0kiQ+Z3hJzwDL/Q7c0XxBlgUtG+DQ7qCGlg07v6Bd8LwAHe8RN+utBThtfQA3D9jWT9AVaQ8QI+tOLwnuX8muSD6Hc7MjZUVFkRpR1z7AZzilE946kmx0wfaAW3TEl3BHS/TyWh+OWE1Y/UTaf4wo7JzRdqCuoxO2orJExHZOSN6wduK9R/zs8gLHLwFql2v0956PAGHdAlzVE7Y11qHHNSUT+nFHu2HS8XbHPAfcWjZIG94jxlaa48tOq4+I8tWGSgIW3EZMtd1i/+HRlbtu8QBtPYC0KFhDZPTcesRm02uAfhUkFaKQuMY99XAI4x7XlXJ8zt5BR1SySAwq9B77OV9jv2vGegXdAqr3E8mFtC+AoN2oj1dEhbXsHBb7v7eDJuFgTuoRv60bngTycLK2Bt6wDmoC3IJp47in2BHecFWcWG9NKxHBXVERzALyaioBIbHYv+kSr9Nq7NUewwG8O5DfgaQqZXlGVKl2cFaFLmhZR/wy2HkjpSUc0tYjploV1gvSw61PSrFfoswYz5c4Y6J/dSRbg1Jif9cj7mvv6PYS0L6dEcVt0QXNuyveHfIHJDmkDTRFOsDjLc7MgM6IhHvXDbGGl0ucCdmwEbGdrh+wtmD3V1LO5OM+BjcSTRqmQMlIPdBeozs7ryO+/EayEj29KRIJ0v4BPwR7+xTndNkRhqO939HlCdOEH2/4+RYDCubo0++xx5dwe6uAPTAXBEO5xaBKLuiH3+OPT3i9wRhSoDpWb9E9bBUrF6we5Jc/UD/9b/TP9R/4ZTX195ZEsfz/8Nf4P7PmGv56zTX89Zpr+Os113Dq36u5Z3695hr+es01/PWaa/jrNddw6m/R3De/XnMNf73mGv56zTX89Zpr+B+v3x4A9ho9kF4AiQ3ThzvVakSNioO9Ir6i6SMm5/B6hhvYydB+Ropi3QP8WcftDuVDOBClB3SS4chEMBfAcJOIX/Yef5YWcAngkxS1Y8CciH21znBvKp1GCqpEbwEzlYC3SaKncyRCRwepP0B2Eg1rgqZ4XUkDMPUzTINWSakgeUWtQUr04y2AT+/hNtUVb58Q73i6hMu4Bxg0f2CyQbuTUwn41x7hUM0jDtgHcLWKkHHNiJ2IR0+tdUHsQHQNN6ooIkY3jRhh77gUbPTUujS6VdwKyoOkBdme0X4A4bKlEm5HlXBPm6GqkFesviEWUbvah3MTAEXzhln0wrobKhWXBqbx+fJ30evcLRyv3fDkf3Wn2gGyIeaRrOwR9Sy54NYgPw8QnwZwZdy0FM5EFD9XnAWoqDQynZ4Fmo3u2RSgWATpNdzT3UkimCjSKqSNXu94VpakAd2zjkhgBT+AhORL3J/hIndrfAsI8ACD7jn6cusjnLwy7qWkGAjQJVyppLgWHOkNz4aUbcQPQ+Rqh2uetAbg7AbSEEZsMgRgdkcRzG/gEQ8uduAKao74SW8C1tEs9J6o/URRpCz03tDWR9SyRix4HnHWucT61Ypr+atzd8kjhnwJ56wYnjJpuSJJERf8ccPFyOszhkSsd3sM+E040N0GQM/j/J5IUjJGPx+0arTrC/m6hev6OMbwxBlDJBp7D01IXgKE8u5YNqQ3TNaIk39E57jLeFa0hpRL9F63sfd0CVe7++gkHy5tAejxfONE1mu4ghmDHqqwPg/n+R3vB6I7sl2R84ZoRI7LcsXrG3jCxKN/XF/wZni9k1VprUVHdW90eYXtBW8XylKg1+Hgdfr9Z8QfaM2IbIhW/OwBq+sDKSsprbhqOO69R0/yeRvP8RPKBc0XvOz48QA6aifeDJEHun2Ho9AeGC1isZ//gLU79vXPMZTQjxggeTzwRSnrFX/8CV4Lcr+Ttpd/1DfV1NTU1NTU1NTU1NTU1NTU1NTU1NTU1N+s3x4AvnzACbch7Qzgq4JYgNT23oUq14CM9hbAx+8BoJIExGKnNYdcaNbIXkn5md4d5YFs24hlZUT6hvvWlh3OGnBXMlDDKSg5IlxTRNeqZroZSYxcrrR+EpHVRERuKlgfka2c4eT0iI5G0uiIfQrYJj46aQP4SD2hbOiyY8cjontd6EdHkmAqeO24brjk6B49PwdsXp7p5x2nBbQaEb/uRiobyhLvbw1dPwLh1MMSKiUcjmUJ1pm3AR0Nludw93IiKqNfNVyWqkqXDGT8/IxoxrVg0jEbkE4TlERK4eYTd7y/hdNZHPdw5sZnVcQTUnbcnJTfQSpouWD1EY5ALXTzcDkfB8kSsu2YH4gUaAe2rtEZ2h9YrQig+3eIx/1y83DQpg38hnt0AUsabmzrRGR3wFa3BrIEmLeGaBpRu4YqZFV6zmhPEVMMA0D3cFu3z/SmsYbdYX/Cy05aN0Cjw7SfJAfkAslGjG30Jwf4s4g7ljLixYebNafojvZ392sa+9HHdUjASsJJjS5/dcLnEnHXWsIdLMQ9lxLudbdwGHuL17UjAKoLXWz0sjrqDbanALXnJ8SPEf3snHVDeEM1keVEzjjymjXOQdninKkiGiBU1w2WFZcc0dTvEcnLFl3O3RCvEQvuglfDeh/AeUNLwVtDe8WXLYCrOYLhuaCeMXuAK949XKutks3x9UoXoR43pGSS9ICs/g7ew2XvQvRwjyEUxyLmefxdKQrbE15v3/rBZd3ifnZD6NGju8SAhjuj57wh+3cB/c8bmKDbx/g1DW8Bf7XkiKgmRWpAjz7klAsuW7xvvpIuH7AjU19/JmnBe4qhjPU5hlWssSal7gn7etBsw+9vsKUYBFk25HhDyk66/ER/fEJoeDsBh7Ti9Y48/UjyiG6ndfx8hctPEVveDdqJPv0ByRtugp8PystP2Jf/dziKn38kpTFM0E6s39HmIF+gp4g4z1f868+Qv0b09plI3/0XxA7Wl3/CutH3DXn+4z/qm2pqampqampqampqampqampqampqamrqb9ZvDwCfx4hRNXRbI/5YJOBfg+SNLgSEEgNb6d4R3XBaxDWnAnairNh7DC0X8Ia6B3SoCmKI7rg/wokoSvIWHaz1DrSIMS4rfj6+OUmRFF2kugS40YjPjajpjHuPFFY84n0ZMb5YxPZK9BI7GZEUsbQW3cDiGVEQ6biMCF8/0XYGHFGNuFhrAUhEvzkF7T3aWQt+3vBlDSBUdqweaOvh6CwZOcNxBwoGSVZUBZFCLHfE52KFLo6cdzQvaC7h8MtrgOG8YV2w/sC00OqJC5gqnRRO694wUVLrWHa07N9AprcTFYVkEfnbe9Q6ezizJWnEDvcBPw1cjD7qTqOrVpDlgiBIUvCAgbpExK497nQcykLW7a+R4P0e7twEkjYQsFpB+4gJJpymBIC1fovhgrzFW59vkJcBiwE7UFHy/hwOznv0p4oEJIZ3R/EDlxXDUTN8WWmjv1UE7NGQJaFZo9u6dxwjiXzrspXyMpymAzKnca0iAYE1xW/6A7ccwxHegI6PXl8RDUCshMseEE3h4FYde7qPzu0U90BTwOZuuMY+Se4DDhu+faA1x2snpwvkF47jv0ONeOq+LpifdFOSJBDBaiUtEeeMNVwG/KODaQw5tAopIduGt2NUaEe8sSwXPGe6hfM8eoIboiu9HnEWtw0k0buF09xj+IN0ovc75ie6XIcTuFEuT4gkToez1oDdY7mgo7XFeU4JR8O1bLHvEUfLhqvC4w1aONpleYL2QPc99o+1cIGLBvwlHOLuBEhVjW5fO+KeL0/jOeHfnieIxpCJ1JFckKOLuZ1Y7xGTbQGY1U48LeT9O/zxNWC4Suyd7Zl+/4zaSfaMv/wYMNsaVt+oONSItU7WkHxBl416fCbLSfIYLEGJoYn2iNeXJVz3KcUztHd02ZG8hxvdApSLC+n5jzEYk1KkLvRHpAPcDdcb8N1wo39F92eUjt2/gBn6ckWLYj9/Qi/fwf0L69N/4bTH3/HLaWpqampqampqampqampqampqampqaurvo98cAHYk4lFxzH3EL1fEa3TMmqC6YN7ChchKTkpzR/UJYw+HcHnB+h1kRVJBzLH2RspPeN5HNK5DqgEccoEePawigq+XAEROgLBlRc8jnIo5onU1SUQpU9Bew3iccoA5nJSvoGeAs9QHsI1+XJEMGCI5YFROiBMR2CWct948gKaMHlmcqM2sjJJkIMCb6kpvJ07D0oJrRC9bWumtIlnwfgcKHAS0Ihi69hNZFpyGagoArOP1PeJ7PRUiHhvMnKQZtyMgYrvHzesN3b6n3j/hEtBclm3EXffo+LQcXazZkXIZ71HBJMDf/hSgskeHqWPx/jhSCr03PO1Ir+GwTitCQ5bnWJN2Q9MyOnEziYZnUFkDfqPx+uawXNB2AhYuwiCaSK8BnlC819HlmnAXJK0B4MseLmiI3y8rsJH0BC84hl2uUI+A5b0CB6xPAeiE6EK1BkenKaA5IHzJ0VlLBZYAZKIDXD4g7RGP3MBNIk43vUREslfIKZy7fbjXYfxeiy7oPvqX+4lrGnD3iMEJCQBKeorIdCf2gIxe2bzixz0c2KQAlSIBG8tKTjn+fXd8ueK1U+T31PNnbNkgXXCDfjzwPeMphzN6dBeLybgfFUkeEcASLtmIUC6gK9ZadH9LQijY2QFDARWJSOvhvPYk3xz8IiADVsdtTaTLD9AMFwsAqw3PT+jxSsE4u3LUB7Jm8rKi7YjhjbJA6+PzvvcvH3HGthLvkZfYby5QdrSUcWQHgJbx7Ek54qX7A6yh6zOaSnRT14aUiLemP0b8dDw/vsW4e46eYM3osgfU93g2UfYYCEmO5hUpV9xPrFskBDjIdsHzDucvUA0pF8QPWl+x5YKdf6Ger6T1Gcex9og1yE+4gonjxytpfULqHTcjWPUdXbbRt2yQJfZ/f2DHHfcYXnGLc6LpPcL8Le65ZvTjD/jbn8DuoJfY22Kk60egkD4s0Q19/0J6+gFrD7A3ND2THuff/0tqampqampqampqampqampqampqampq6lfqNweAI3a54akEDHJDPOCSmaB5obdHAJW0R4ervUaMcoegGQ2aQLqi1gPQIYjuwVzsRMwDPnmHCpoKkvO3uGU0BfhtNdygkiAvqIaDUGRBxQMyEY4/WkO9hcvXAzahedgGZbhBo4+TuKIB6DrCErHArIhoxMfiePcBKzfsfAQEcQWzgHfu0VU8on6xcCdLWvEjupPdG6qK9BR1ouhwrVpcw3unbOt4soinbX383QziJBrSE94znkpExjYL06ZIOGHPRwDl5Rm7fR0dq04uO/hbQHezgPpmiJ/IcglA7iC6hAHTFPfRI9zCJUzeg9n1Fq7R3kZM8AFpG8DXwnVdj3CLhm2SrAHf8ROXjAmoGG4JSxEVjWjEOeseUM1qgDqGw9IPZNnjVrYz1gyLrt1yGXMCCU8b2u6U0afctxf8PALSHRVNHqDbHU8L1CM6f7vQl4x1I+eM9ROxjLxfs3dEC5LjGrwdeBuOXN0D9vYajsryHO5s3sKRrgURjcjgzNhnNmKTDXobAwECZwONblpcYxjCh9O1OdrG0IEuqBueV7yfaN4QzViPmGTNK2qGtgeZlVZ+ithsaVjacdFw52rCJdF6I401pcnowfXhkI9rMemQ/VuRvJ+PMSxRI2a8OV0NV0XXHWiY20jwltFjPPqqe0N8RHzrAjnRqyH9wMuV3s7R3m2kftCAU3aETNoSousA4hGxbscNtYboAum9c9xx0ej/XRSRhqcErYVLu9WIstY4i95qQPy8oMuK9IoyIuM14WclBhjqtyEGIfqOpSzx/LQj3NZ2YqMZXVPBRMNdSw+j7uUj/vYZqW8B5VsjkZHykbRDe7zGHjs7lA1fvsfLB463n8klsywvaAfdX8LpjMf5PQ88xzPBz1ekLEh5ws8Rmb6+xDNvnC8Rx+sR95yMv3ezE73IngVRRS8/YK9/wY+vsCzhqtaFtF3j+VdvsZ4mqBb8+Z8wN/T48g/8spqampqampqampqampqampqampqampr62/SbA8DeWzjdjoqUEV2rW7jiygotXH89e3A5f0XlgvXPkK5EJ21EP0s/MX8l6QWX9VvkbTcJ/6trwJesAdgoSG8BoUafJymcibhDUroZKingBQppDbeiBmgROyLutR0IPWKp0zJAbw43rwogiB/Rx8rokdVtuDglgJudeAuAiy7oumPHOWJ/3zt4Fe8HpgtmlWYNlxV6wrzhZUM68R4afbIqCVkSnLfRvwvi0Ykqy4aWDckWTtHRWdxNSBnAortW43NZa0DG6xluxTT6gzFkKQGkLCKWdVE43xAO8I33Klp0HevA6Fsev2qG++hMdQlnYEojphccG5HVo59ZHe/vXaoV0ehd1nSNWF0aCPRaIUlcR9khS0BoE6SEy9cOBQLuxnZ4BLTDIoqbsaZ2BqBuJyYEsBRQcbIJ4g2TcPD2lALe9Ygg1hQ911iAWK8nuISPtQQARATRI1zt2xoDAV6BPvKiawBlG5B72cf69Dg3uiN2RlR2AqiAh4u5VyQv0Zvd+gCNQM5Ii8/rSSGVcKzmMqKVdbhYPfZzXkAT7gGg6Q1NSwwDJMVaJYtQrVPrg5wU268BJSVj7YyPYjH08O4CHlngI958RIKbjc/nICUAa68RA0zAYSfH+feO946sV6x33MNFrCJISkjrSMqxj1xQOk5CTb51cnuvlJIR3WndOb2SyhMp5+gVdkc0kbfriK0e69B79Hl7wpZLRJp7CQjtxMbPMgZdzrHHOmzPqI6IeesxvHF5jl5j7Xjrcca8Q1nQsiLu2O0T3j1AdwLyht9fx56XcL8/PsdewXAt6HYF3/C3n/HyBK2iSceTT8hpI22N8/HKKRfIC11X/HjFW2HZwhXv6wXHSWUHB3t8JpWC5CWGWUTwekPLJeK86w3XDMcveIpOX29v+HlH0gL1EUMR3qGecT3lij7/ETtueP8CugY4hji7veG9RUe4dHS54J/+RPqWFT81NTU1NTU1NTU1NTU1NTU1NTU1NTX1n0e/OQAMo+vVA8FqVkQ9OlVtdN7mBe0n3g9UMj1lsj/TrIIMQOcPkl4x+Z7uFQW4HaR9QQVQwThJpqAbLjn6VVWxfqI+HINpJSzCOuJnI9I1QQAKGZ29GC4peClAWnA/EQkgGn2qBejh3pPoBcY70VcbDmIXD7DmhBPOBXfFzo4kCXAygLK3O9BxCuYVyRvcX8HOEXGb8fpn1BqqLwgZsYpzInUFSUjeSPSALmuGFL3BSQCFbg4qsWa9AhLvP7qDQXG/j9/PdO+YK7ZFjHVEPzsZj3u3PgWsPF9h2YbDGdAcENgsej7Tgh9fA9qVJwII3sG2v65bi8V27ePX4XlUXUYfrkV8cMoRo+uOe0X8CJcxf31ftzPWY9xzWXKsrUi8X7NvO9TpwV/ziJTuPVyfkgFH3FACjIOTSkRCOylAXjLEGFHSKdyLkuH8jOuFbimGE0Sw8w65UEq43eGI67GK5Es4vC1irNESEc/2COArBfEjPoMTXdb9xAmne3Rl91jvZQno6GfEGdNiYEAUAPOOm5JpAZZbODhjjfNwpBoqFdeMiyJyoiOCWnr8ualxnpCywLrjpBgCILqfWXdUFW99xFyfEZm+Lt9c3o4jAp4XrFvsd3PYP4B6OHzrAaMfWEcHs53H6DbWgMi9DxdvBumogmwL1iGJY+cj+qrLDudBt4PaO+kB6hsiPe6JJcgp+rHNYmijtxi8KCUimauE07yMOOTzAZcX8DbioOMZIyN5wHuPDl0noqFHBDwlRV81DPd+DKLI+hxu4PoVabeIcF4v2OM14rK3J1iu8T6ewuGdN8R1dAa3SCzoLZz9dsY1YuSS6MeN3g7k+gR8oNcbrVWyPuAMV7ibkfcd8RaAfX8OYF1PUI+OXj/CsXt8gu0JNAZO7G7QjtjLZUWsI+sFrzeg4fZAdEP2K94Kfvsl9nD12OsGkhY8F+ztK0kzxok+P/8dvpOmpqampqampqampqampqampqampqam/r7S/9EX8B+u4Y4lLXg3ImA5Ibog6kgWxO4IThZBkkO7BdTjDZFClgCWYkJWUHfE7+j1KRyD/hYrqxnIEdHbW7gLNX+LWEYUTQqyxTVZXJ4QEFq8B3yR/ZuDL+KYHVDMwl3oA1qBBeSCgJ1pC9gtgrUHXQxKwYg+UpYtnKpCgK/eomPU63AQbiDr6EkOV6SuV0gRPYso6heQ57j+dUd0DdjcNYAKLYD6eglHaj/itdXDhffuNM4XWK5ILqN7FVDD/cSlYCY0T7RW6RBArlekewRea3Qra8rIssP2QrhR33uGCccjBNRzID9HZKymAVgXRAJiS9rCSa3DPdtjnaVcAhTKEvHNegEUaQdeH4g5KV2jB9b6iE42kH3EZUv8e5W4rnbG3ylbxPuOjuhv90BL3E8PCC3f1i66iZMq0ipeI8ZcREgpRzy1g2wvsO7jx+/jXvYD6xVPF3x9RtKOyzIg7DgTbmObFd4jxRHB/QhHb1zUgIoM2CuQLrHnXWLYAaL/eLhZkeEuJUF5Al3HkIIBNVz34/+sn+PexT0UHJFMWq/hsm0BG8U7ogl9nFBrxHrXRu8WsdAeTnMpW7i2RaOT+j12vVwQXdC0Rsy39YiexnBxrMceDsiuWHfcGpYyLoJZuEMpGy6Jbh7zESlHFLqPPegKKkjuCJ28fyBvOwlDBbIJ1MrjfuM4DnrvmMY6Riz3OeKNG/QBPr2FCzbvIOH8xh225/hvaxH9jEC+4BYx0NYrdt7wvGHW8XrgtUVcs7dwRks45I029mdCRseyG4iOzzeuSdIGZcUeX6H2b/tctidcZPRdG4we8HgWZ7Q2coKUFDmPOG/7R2oTand6j6jt7obViq8veHnGjjaeDR3df4g9ZRXB0MsfkPW7cAVbRfePUEYvetmQ7SOyPEVEvDtuFes3vN0j3j5fIiZdIrrc7RHPy36SLx+gVmiQ9x/+Ed9SU1NTU1NTU1NTU1NTU1NTU1NTU1NTU79KvzkHsLNEP2lvSFY4D6Q44g2XTLcWULRpQE4rqIDxIKdnzG9UNkBRrWAJ40C4Yr2RuCHpGu4/ldF9agH1TBASWq7Qb+Hq9Qa5DAdoG1BRcasBy7BvnZzSR3ywj25dBe+PEUUMLg0hE7bVuDZPW0TNWgBoM9BuAbpFkGWLrlOr4AEO/9p3W0ZKrtO74ilisc0y1r4iaQ0umC4BemmwDJch8XmsG1kqulzCudceEelrOdyL64K0E9oDSTlA8PkaLk/veBa8QasHtuwRVawVJJHze0pxDdae1ugtTYqUjLencBkmwb2Fc9pPVJZw2KZl9BrX6A/WEn3OeR2bJWCV9xogVMtwYDvujwBDSkRda0bLMtyfCWcBewQYTCWgqTHuJ8gZUccmBLRrA4I2i4jyvA0HeCKQqEI9sZJR3ZGUAuodbwGxzgNRJacUUdYCogEAJW9x/xeNSN7HDelgcielgrvRz1dyGOPH516hP6L6GeJzGQEVPdbfIfYNEnvWYz+6Ddc1R0SYm0cfLgaMvmEZ/djDbS3ueD9wXeM1zcf9asMBPKhjugTcpqPLjq2J9PVf6dVgy1HPXW/0bQBc94jWzgvixBoyjojF/bVc4nxpDA94q9FpXAMmSrlAyuHwTYKKxD7QhNFIJCyluFeav90zUUG6Yf0e+2DZYlCjNnTd0Lxho7sWIaKFW6dL43x8QVthKRuaHHWN/ccYANk2BB3AssX9yReQFudAFD/uY9BEwmXsPu5tDuaeLtFR/D5Usl5iyKFXsBbXKnk4oh+xEfISSQH1CAf48gTHKzAisGXBlyeor9A0OpIhBm7qDWmGkMmXH7He4sf9C7ndkWQ4C+ftFd8dXzfOr/+NXjJWnin5pNpGlxjmUDewByLve8WjW7zsSF7xeselx333Ey1PeNohZURzDGykJ+z2J6REDD5k3I7oHs4Ze/tzPCfXZ0QSrX9F8xOosXz3u0hMmJqampqampqampqampqampqampqamvpPpt/e//faeoAND+erlh1ViS5Na7gK5hvIA/REfaN7dJKKawAUe4DumN9Q2SKydoAx1WsAF8DNR9JuAinBhSxcm5533E7UBKTjKYerj4r4FiAmJWBAsvRXF+Z71HAApzX+nOFs9h6wmcCGWIBPHfG8AWwTdBvv2f4KT8TwVL7FRrtVnI4huBZ6r+Em1I6bBNBTxaVGx2k/Axpjo6fXEQKyurXoBk6jf9UUWiPniIr23nHV6Lx1hmu54VqgP4ZjtpP2C/3xFckRWZyXDbPhmrUaYLG3iAjO8VmsR6+zJEF0x7shjLjfEWVNO6Kb1jp4GdHD0X9Md2Qb8K4fAZBcY91rC1ieC1L2WFevI7I7R3y0KGLhIsVqxAGnEnCs9/i7pACPaQ1omVf6+TUc4WmJqO/UA0gPsKhpxRZHdCEXoqt02UAvSHfa+Rb3osa9FXp0ypYSgK8LvVdSAskaXdN0pFyRtAaYV/kW9/0NSIsDJYYXrEU0NDL2UAY/hnN6uJYJp7ekJUAo0fuMNcwbZj4iy0FFA+rpguuG1RPVHuvOAllxb7g18voUgPP6E/lxC/fyorR+Yv2k10JPQipbdNaaxdq5h4NcAsZ7Y5wTgxTOXW9tuJIdKeFSdmFcd3QbuxmCYrz3JLdvgxnxwhpnUjeQNpzdcW/f10KFiMc+7oh0tBT6caM15XzegU6ujZIzRo/+XpUYljCPIQz3+KFlwOcc0dwAoqT1EjHd7Yho6+MeIFcy/n7P1x2RBWu3b+kE3oeDPhdE+2jR7t/ex60hqWC64ucRvd8pHLZuDxgOWreISdf9I1LvEd+cwFC67OjLSr/fkdvPeDrpyWntHs+i5SO2JKoUuL9SSMBBlRajLr2j2zNmFakndEfXDe+POJfDZS9ywvKCJhvO8j6eT5309BN2fILDYbGY0UgHsm5Qr+h2RUjY8UbaXkb6AOhW6MfjH/I1NTU1NTU1NTU1NTU1NTU1NTU1NTU1NfVr9JsEwEEnFSo4DU8SvZDjr8jxGD2xH7DjHg69/oaLI/oC1lDqAHsHRZ6p/YamLdiWNap3ND+H65aG9gPShpqhEhHO5EvQJzcsin0DttKJ8NzhzjMgJ7ADvAw3pQ6X5xLg7x1oMj6eS/yeJtwMs4YOSOeiEQFNj45cl3Cp1iMA7HsyuACesPcoY4vOYDHwlEbn6or3I4CWA1kDdPWAJCINd/2rk9U7KYXLUxE0Rb9tFx9RsgY5Y9biszdwUTTlcGymBVkv8PaKasaToXmLNehn3EtJEYssDdJO9NrmuGZvoOE2DS7ZwpldXmI9so61JMBmr5AyY7PEj/F7jsbPZcD6egsHqA1wqDr6gR+AIqkgGoDUewsHr4x4YBWk7ON+WMBEyfR6IkVRNVRHZHZr8Z5lI/mKmZHyivevuBQcIaeMpAUzMALehRvSRwz0baRjC+ZLODNTRtIeELe30Wec46yI4nZGtHhe4zqOezjctYzo4Ro91mbjyRJg3wZoF4h9kQr0E+udJgHcNEG4gyNaPa1LALyUA5ovT+P8HuBC0iWgnxkpJWzZo0e2GzntOBU7HtjlKYYvxtnorUVk+/sGTwTQdjBxaI4mCdd8i2hubyfmDekVaRFHrGtBuoOd4eJGhtN/AHKXcELTwhEv+te477z9/8BzGeuYSMsz2TtdC0aiuoKsaDZ6qwGb3WNNWry3pBiQcC1474gUkNFZXO/o+jI+aYt7dj6ik5jhim8tnMrrhrcjfr+PzmCRcT7ACuG09RGX7gFPYx8u2FnBLAYdVCE/hTO+HpAU6aCpoPt3SBLs8Rqd0M3wtJGuF8hKe/s5Hr3eaLcTnl5i72nB9k7vB7S/Pr9SKYhrROPj6OUFJ5532BGx5O3Ey4bYGeeyj7h8B+RA0oZuH7HbX/D7HcoCPeNNke1DPM/qieKInRgZXa8xUNMmAJ6ampqampqampqampqampqampqamvrPp98eAAZcUkSVSsPM8JxJrQWj7KOIV1fcO+YEvMlP4dTt94BPQJIdtzvd/kLKHzGroKNb1Bzzims4NK02KA0TR9Hol4XhFo6eSZESfZVCgBYIcOrhLHTKcF/m0XWZcIuYXekMiia4COItXK0DVGM9nHdExLOrj25VR0XQdcVyQWqAXusdw2juGHHduGLtAFF0RPlCuH/pjouEy7cTMdrWER1xv4TzOi0XtGTUOlI7ikPZ6LfPAbpVERtxuhJrYOKYKSIVOFE03mfdMYzkhuSMp2e833A7w6XYW0BJBE8EhPeIIR5FwvH+YgGT+4CUWSKKWcBzBt4BU8TpygDYAUaHw9Mk4qMx4EBIQIq9liz6nzEoG/iCtUfA9LyHK7XdeYdyKAETCRjtbvT+ANfoJH03maoFlEsDNuYt3t8DPKay4K2R0oK3G1SFnOkYlCf8fIWcsHqHnEj6FAATwxnwFUdKjsGBNsC/KLQa+y5l8AQ+nNvW4s+dAbgzKkJvZ/y+OqoaznIzrJ2k5Rr7WjN4Q8sW8LcPWF0WIEWsOiOiWKNn+N35rS7k2Km4ZzQXvKzhuqbF2bE+en2FZOHo9d7RpEiPfmEkj4j10ZGtKRyk3TAXVBzVZbjKezi4lw3vjreGL/FIlXe4q3l8lgH8R89zTDZ4uP7TiuQF7ZXcDWfj7A07H1SHtCwB+8sSDmnvsf/yiDLuw+WaF6AH7O4HuuzRdWzjjOYy4G8KwG89BiLyJfqFPZz6LCMC3QQp4XT2xx3xGgMSKRIJ/KiItuH2V+T+C7K/RB6Bd3z069KPMf/RAYXaBigPVow3vMd51n4h3b/i1Unm2OMW6Qelo9v3eHvQjy8kEXy9YBKR/n7eSeszjsYz0epwcQuyP8P5GpH46uFQ7xrPBl9xif2kTz/i/cRun8a9A2+PiLZWRYg4dc43uP6Av72GS35qampqampqampqampqampqampqamrqP5l+ewDYzwAOViGNntVuWEoj1hfIBT9OcCPljLWOa0J6IumIvsUD/kkh69PAdWX8noy43wPrBmVBcqGJojhqRtIR2SwBc7UHqJQ8OopV4j38r/2q74DsGywzD2dvynF9Onpm3QAdsdHD8fz/Ze8PmyQ5kiNt8DFz94jMqgYw5O6+J+/9/x93cre75ADoqswIN7P7oJ7Vsx9OTrgYkrOCUJERDhpdmREe7lEjfExVSw5b8ya3Jp08P9RQ3DbyUGwzdFSuG8R8UDkV/ZwF+zeI5az0TnpbjtVJ66n7f35Xz7Ip0tpvNzyNOg/RHmsYCxTv4GbQjHZ7J54fAmm2orZN7uPKAivciqyGRWDbjnXHY67+2KKsCL8ReVLP3+UMHHJU16ElsQrwHWtvkJ/0TPr2i7qfa0HyfOUCgyyiguW24ozNBNMqlhPXXU5AMzEuoOLAaJj56ib+1D0dJ+UmN7iBuZGhcN3Kc/XtNmoeuO+KCq/Emy+Hd6q/l4mDvhutKWvLaKhAe6CbkfGg2k55kWut2ujkPrD5naTh7WeyDOLQ1suA/g7EVxxyrR5kW2Adb/qafCDH807FJ7QbVaf2WU5oGz5eBcNQFcT5CRmMtuPuzHxAmjzwbUVUW2neIV7u9+USxwXs6hSIxRSf3NQ9PPNgywGtrXhzU5xxFdZveFPkd+uDyEnMJ33sFEXGob5da4KcX1HQgd++yQFtBo9PisTvP2OWEE8qDmoGhIYGiEKdvNu6F/XvGkWdBecJfcf7UCrAMzXQkIXnpOKkynnUxL79THONBFhq2MJyKjZ9/4n6/i9YfWLj21qnwnZFkuv6FeXMeId5rgh5g2yAgDzzqUGFsfqlvX3Fm7MDdVMce5yr17pTxwe0GzY26P+s554TWDHVfVsDKYfWqRS1nfMBsSC/Q8SkvOHjhtlG8Cv9eRA8idqp5++cFGPcwDo1vlHbnWr6WZ9J20wd1POA86no7nHXczwb1OcCv42a37H9G+ShiPReVO/YeMe/beTn/xQgt9W/TgcG3m4UH4q8rg/6+89/t19Nly5dunTp0qVLly5dunTp0qVLly5dunTp0t9Lfz4A7EMwy04qndaGAFE57htmIcbaHcKoKLwEQSmnrMS88nea/QVrm3onSyDTYpItsH7DGOJVVXiUEl97p/Kg4oH5bV1UF0g0+UapWK7SBWVtwT1WXHCl4G5M3Acp6yUeCqL9gtN+W12362fZsO6Kwo0DHzsZU0CHv/n8EPxp2x0y8DiIjw8qptyO8yQz1KuZDbeE6XBzSLkSLU/s7U0/33b15po6ifOZ9NYZ9x2rZGZi3vDxRtpJlpHxJCkiTtIEopIN5gfu9xUPrcjosqKYzDQmjfIu4Hk8qKciejlXHG9XnLWPN2iDsODMiccDy6TzBiU4WGnYiom2nILC2zcqPrHtrj9v9wVBFzDOgu0O9TtViis2BtYV0VtMyNVHzIr3zoTqCzq+4qcb1YDaYH5XN7E1IvWEzQcsXzjxpLogXMWnAHbEipXeFE/ty7F9CNAFk9a/kWzqabXSnmyr6xdb7mmpqK8e5spjxWG/OqcBTPfmQ+tcoR5fa+qZbV3AfEHjMochGFvzpM7E9yArsIi1VqlBAa+1/3U1tr+pSzsmTjFR92w8DMuTjpHRyeNBZDH7TsfkiPW+annv2jt0aI3sK2I8PqkI6LsGD0pxwtblZKZQRHIV9DVcsKKPa9wVFZ0n3nesD8Wnn78rIt0V/W4klk/s9ka1pjj3nGteQ+c3MzXgEZ9k7Ty//47fbjA6PSd1fuBtKIa+oc96fGLxIXjbtrVe53pmhflNZ6JSgxtxYvu79t+cmrnYf9K+BEVLL9ezjbGGUIp8/AsWb+sMnOrb7T9Bu5HTqee/4vsuuGqmBISP3zES398F4mPiZmSTE7fjZEJUhzbYfvpvzPEveJz4DM7zIH0jxo1+/4u6xE1u73p+0t5+0mBOHeo1v71j/a6dG4Hd/0I9v8N80L79E3X/Wfs17jC/LxCcsH370ef9+S9gG3U+qbbT3n+GeNDuf1EUdRp2f/97/Fa6dOnSpUuXLl26dOnSpUuXLl26dOnSpUuX/q768wFg2+RetF2duOs/lU5mCn60BrZjeeCeckhGkW5y1GHgP8uNWA51YIwv12DmpM0VGTwUj5uoTzabHLduLDhrinFN5MLD5USuuZycyLWbhyCc8NbXdVsmzVdEccl9rJ9ShHEzgRv9+wAGZvp3idHaIFI9li+mR5UckywHII3qAwjdc3PID2h/gXzQ9nfslBNXPbKOpdPOg1XJi7VNDsBEfbbd6H0AJ3OW4FJzqEE9P/Sc8iEnsRtxHvr7Z8CYiu21Ln56fJAOUUba4JWCnOHMeXCSitqtD+zptL5h9QH+E603+Pyd5o5bJ9oJUbQ6ae60bLqv7Y6VKUJXpkCcu9yE+Ykc10PxuRXU+Cc4f1/9zCwXqKm31aHmk3BFLOMGufp9a37tB44HxVixzuvvVlDb+4o+llPZhnqgy7cF8PWdRi5AuJG1OqD7jfn4K9TqlfWuHwnF8maWHMDjRtlUxG2u3ldKEDennM2l768y9dxW6l4rFWceU6C6FdTa3Wbkih1uFFYm3t23NfCguPKsSRV4X6589Bk2tnXulpvWBj7V023daT44n0+aTchBmhHzQRtv2uDzSbUi2FcPc8lpfp6YvXqY5f7PmRSBtU0g+vgEG+ptbomNN/J86Nlkg32AbXLRuq+Y6oB4QhzY7U1u/vnA92+s6QJFpQPpjrWiWaMnVBkxg6qDCOdx6LtqDMVmt6FeYe+Cwfcu923r6zGs3m7vWrcFq6ugjgfW93Uu1Hds47bGR1bZtZmio1c0tByxwPhZa5Qntn2jHv+yIqjBWqNu73JOe4Fpv9v+pk7g5wMbDXv7RR3gGPnxV/Uquxze8/yOtTfcb8znr9RUK3k+H0x32DaYSW/b1xtcpufCzwNvm55jnnLim84k4471XS56d0Vn9049FYVt+8/k8cDbTtu/ERnU41d8NK1fa+TzUzHezxN/+69fvfGXLl26dOnSpUuXLl26dOnSpUuXLl26dOnSP5L+fAB4QTaqC5S0BbLsBUAbtgDJq2zVE8wVt5wUVZPe7mR80NiBTvWNiol7o/l9RQirkzfSqPzEuGPhZNsIg8LVRepAuxPnB41cLuX46v+VIa8JXLxckBi0nTLFUVsVVBMcs5db8yRNjlxnCCIvV7GVr7/ruA1iflC+4mALwbgMwSIrzBr1chLGa7FSnaTnie+OxVM0Jm40a3hLzHddT57qT7UXMilac2hvWAjgFUZZUggIJ0YYzKm+YGPS3v+JOn6D2qkqxdDWuk4Ci0PO3XJdcz7w/M75PGF/h3Yj5ncqBq09mGeD+ISCMd5omXjrzAKvydYarRndu2KJ4ynYViXeOR/qXB3vgn4Zq1s1od+peS5guTpwjRWRW1iuSGcD2+/Y80n6phTnNhShnYecjLa6ZS2VLFwFzSkftPFGHocg43gDX2sYT6DkZKUE+gjatpM5qXK5V5tRlWTJCZ7jfcVgJ24bFqeeY6woaFxQjeUetwGomxhzwUGVu2rYArXwWhvYAtFm/Ss6Op0FrgPvg4gT802x148PGPuKfk6qbcttnepjpeEYVYHfd/oBbf4O+TsVjfqEfLtDU3R7YoodDnVZC66/Itfbiknf1dn9ek5t130XKwIa6O+KuC7BfGuCkjRTLPMM0rXm7HcFiZtjcWrYo68u39UrXfPQcES/k8evuMPY7rSe1PFBzCfT4HOeVCb3+4oe7vtywoLZkNOa0LOZBV9uYKfqkOs5Erutvud8YnEI0Lrrea5+ZDIE6GvT/S8XOn1bz/nUm2j/Jsf2ecp13jb1KK+fZzM9q0Ku8uoLpCviu739BTsO8vgNmoYuIk+aN9h+hvtGzOT47b9TRxKxQTPYnFaJbxvn43c8J60543YX6F/vFGNCOuYbuAZMyrXf6Ind/xmO38n4jo93xWNPx26/YD7wccfbIJ/f1fF+fAcL3Is6P/5ev5guXbp06dKlS5cuXbp06dKlS5cuXbp06dKlv5v+fAA4TzknLSlO9XXWiTWnjoltQ7HFvckJF8ugWY3eGzWDmSF4ZxuFKYL5lDswlyvSMwWdZgClLkxv5HEQQPWdrUE1Vy8tRWtdwATI1DUZ6rbFOjWT8qI1OYLN+3KLqnOTfOpibcX31m0Z+Wr9X8Xc+tZpCFRmJAmKvI1jxREDKOoaQj2sBfH8pLIIb2A3bE7aGNi5CnbbhlE0lzvXSLnzMjHPFRfcMdvAmgzL/oKiSZqRc1J9I8+DM4Jp8iT2PmiE+kPbitE+IbcB7U1ux8cTa6XuX1d07f72Tjwdq4MaAmEzN2o+iEAg6xnYfiMI/Jy0Cjgm4+1GHSd+f6PPJwNHrDIwCmu+KpabYHMcCySmIG429f/Ghxy7uJ5jPRdIra96Zo6nYF7KrWzmgOGL4bHcvBSQB0lhtmHtBpz04avH2WDbiPOg6FoXmwtCm2CwN6zmci8WVo05g2woopcmWPj8pMYmp2hNOT7NsHYXOM1TcLF17a/VTYutPdvU+5up/l1vHavlaK+TLBTPfQQMl9PbDMxpbhpyGJug5LgJPubLGf/a94pO7+Odmg/KgltvnCkHKNuAvsthuqCm4qy7zt1aZ8pwSzLWc2V99najaq7hh1Qt7gzoipuuFJB1HxBPLB0bXTHMU4MA5rU6wVd0um8rDlvrT52UNdg2rCYtf1aktXUsJps5j+//L2reiP2dmY1znlhrtNbUy2ub+mrNvwA5eUDfMTPFI89JnYdgtQ9FQFMC1tawCt3nPDW0ges/zw+5y5vpWSzwbcgJnuYkE3NdP2dg2xt5/IbNB42T6ncNALSNKoPjsUIMOnjDbzesG3z+BkzcdyITu/8M1mgtqPonzs/vFHoH5a//k+1+w7fb6rd22vaT3sExdbDmoW7fcnUwW4N5Qm8aTkgUt77t8PFJ5UNx7dU0lDAUN19T757KJ7bdMH+XE36+4vMvXbp06dKlS5cuXbp06dKlS5cuXbp06dKlfxz9+QBwFfQb+HLkLbBWZ2KjK07ZfcUmg9kTGFglVom3RtUmOGMpEMtJeuB0QTEz8C7o5gbVyYjlvA1yTswbs5zeOljhK5YUWA7eFfm7InFpDq1R+RRAyZSDzgpzuRsZbVUGGzAW9EN9vObYuAmATTke87UehkClyeXKfEIJJGWte6wOWUQcVA7w5fhMQTLKwQx3BWt7c2CQedBWXG/6TZ3LY4dmZCTYpHD12BpkGOFwkmRrkIW/3MIVchL7zjw+KDNiPmE0WhvqQT3+lepvingtxWz7+zvt9o2YD+I4cHei75xlcP4PeDRq3JiPXwW2E/j4zrTibLDHnTgPZga9N/rY5Db0G1TS2kbWA6uiUh3NhnqbzYov97aV+nj5Rj2+a+3bgHaHOihyAdvOV88zy6H+5VxH/26tfU3FA1d32n4jKXI+Ke8rbvhYLvK59ktXpHdtWJ3L3l7gU07jciomaYomZ57gitB1c3zcvq7BrFNdXcjkKfhLAn05yNUJbD5oMTGMNMe8Yan+3swD9hXRW8CcuHfAqFJ3sPam/dirKYew4Gm+DjatNbwKO4Fxw3AYY60fq7s3BXjzXPDddI35xOhinPHURzbHesNmkYnwf4LczqWhhTLa9k3PN9saBjnx9qPXWzXcJ6zO5+orZjlW33Frcm6nupdt3/BA3bMk3hv95/8nMyZF58zCHh/Y7SaDLwLF9CGA+8pE3vcfsNlKMH3csdbkRK5JHU/Yv+naSsMevn/TAIo3DaT4nTq/wzHl8MWw/S5wnytvnUGe3/HlsiVTMdcVVPz+NTBQ2b6GAyqOr3ei0bHtjueJHeokNnNmPBSf78Z+/xnyg+PjU49vd2YFzSA//5V++2fCnHp+CM4fgrnGII8PvL9BT/AX4A4sDGsd7wPe/wucJxy/w3aD+g75ps7kmnKNjzeIQ/f2+FU9wpf+j1FVrd+P/7mf8X+yrjX847rW8I/rWsM/rmsNL/1bde2ZP65rDf+4rjX847rW8I/rWsNL/zu69s0f17WGf1zXGv5xXWv4x3Wt4X+8/nwAmEIlroJU1nw5K/MLbPly9VqdAmI2BYAt8Uo6h8Cjy5XoOK1CUKgNfUcuh1sk9G9QjapJvPo2m1H9BhSeKTOgL3BcpZhd5PyVVbPwgllN7kJ7RciawKJ1da3mq7tTHt6KJ+HQx2118L6ArwscxwMrE/ABoFE+qIT0lJPUh2DNtsMxqfkb+LsAm+VX7LQSXgt6U/xuPWhjB8AygJfL9Qn2RppRUXKm9htxPsiczDRmLhdtJI0kreG+Kd62AhtN/uKYeB3UnHjrRH8XtDyf+NZxW03P9VQnshtZ4H3Hzl9V4fxP70RC/fWEXtSbUwnH9wdzOEf8Srek4ww3+rsxyug86eZkNTmMWd3NTARu5W5m21ckbejZmsvRSi7Hd0I+qRq8epdfHbcsZ7H1bW3fgJgyLg6n6GROyEZZI8+n3KT5qahd3xRx3dQl3BYMZeu6zPlBeadvd+0lDuZyZDfva3jhxMkVN/xynwN0LI8VMZ0arDg/FJkcU27nmHhr0DqRQWZom89T1+1yCVesvuJM9f5Wrm5h1CVbtQYAjnWOcznTFUtcFJaTliWn5yxqG4K088H0jrnT+i6nep1fsebEJ9bfBcNn0lonK8B32lMuXyuw+zcBy5lYPMgKfNyhdex8kvGUmzSLqNR7oAwfcj7XPKk2sJgCh1F4H9qLOdd5rhW9nOiuJpmTbpviyyvI5+8cpKDq+8942zE7dDYCdShn4Pu7BgqUUwB0RR+jKGtzo7ovtp4648iV79Z1vptTNGx7hw3q+X2d+Sk3b3zKPewD7J06vsulXxNvd3VDh/ZD0dTVPEzu7echZ/S263xm4befYbyTH//61W9uOalIyhrj/t+o+T8Jd+rtF6pOnp+/Y6fR3jfyOKjzgZ1P2n7Hb98UP+1D53Ke1CyI3/HtF7mg89A72W9yHPdBPn5dgzwPrHcqDqUKuAZXaj7hOEnf/j6/li79h+jv8T8O/+z/A/Nawz+uaw3/uK41/OO61vDSv1XXnvnjutbwj+tawz+uaw3/uK41vPS/o2vf/HFda/jHda3hH9e1hn9c1xr+x+vPB4D7WBC0CSr5JqjUOuBUBD42vJJMV1xuyr3rtpGmeOfyB04s8OpQH4K/dS7X3JvgEoERtHEjM6k25OQ7Jr2eTL/TDWZNjIG/AHHfoQ5ALlG55UzRuJXgG5YvJ9v6s1gxw6+e4P4GeWAYNZcDtN+p+cCqYy3IbIpoLjkaZR80OYZ7w+TTpB7qRPb2jSxf0cRyUBcTTsP3fUVQNypOfNwxCm8bxMRWPDLNwYqgkRWkpdZ/7kx/rChqganOSWt3Eog0uUobchGWU9kxQqCwgtY36Dcqkpwf6hQFmndyTqY51gatN1r/C/ncwTeSk/PtDd93zo/fyHGnvKjHk/lpTHvgGP3nf+L+/UHugzmTff+JVociv6nlppWjV6TZ5LhtdwjFMssxqV5lyNXLvH8NIpSV9k6FAGmanJSg56uyarmxzWgr/lb9rud6/oVZaH9XLABWgsk4VkUbG9k6aYKtVUm8QKABuckJnEnmgbdNjuIXaPQfQwhYx2aArfNVyOVpte7JSORSzVQUr28bgT4j58S7oqSjikbidULd/yb2Ode+fpez+nzoGpbj1TDIA28dn5M8D9IKLyc86befePX84k2fNXbMGjb6ynr/lNt57Cse2CAS2950m6/O4CzcBpjjkdT658qpyu4wVj44dTxxc6pcke2VECWX8hhgCg1fJ0/f4w2a0ayvQYcf+6LyJJ8HD00y4G8/MejY8Qmmjl/atvaQ+sHLTXHdcax3n9zjjG96VvEEOra96TytTu2KAJsCvJScsY+nYHnvEBvMT2wba42M+vwN620NAbi6yo/vq5u66V5iTWrFpA6j2lAkfCqC27/9E/XxVyoEgStCCNsS//m/cVRwztXJfk76t/+LmcWIEzjxn/8r1vqC/E9s2+Esvfv2b9D+greB2ZM6PqgwihOLrmGL9k49f4VN7zLmxPefNGgy3nSGm9Pe/unv/Rvq0qVLly5dunTp0qVLly5dunTp0qVLly5d+sP68wHg1lh2PMD/pg8y1etZJjeeD9yKqlPApiEXcBa449yXU7coHrjJBZlhNAMIZgTdB+RJzANevKx1Kk7y1J/N/oaF4Zy0lnLyZeAFrRVkMuME73QzuevcKUs4PxXda//roywS41TkbysqC6NhltiQM7LMKRtUPb9iYgtFJ0edhDKiqZiUTaqeRIHZVPTr9gs1HwK9cahjtQ3BNX/BMnXP2tgEjTOoMLKnnJBV6kVe/Dqrk/NBa06rxNsd+sDPgzKoGbpu5OS13nTttRzTs6A71jtub+pm7qYEbQesES6Ha+83yjv1fABBv28kA7+/kTiR/0rcv6n7mCS3wQlUnmyHYzWpdtAJvIK2vdF8Ezi3jTo+oKbAXxusG5CZlQWcaIK7pk5Sy9WdawbzgH7D7KbI4nhS5vj2rqjnnGRM3DZszgX9hqD/6nA1d/XdmgNBtRs1P2nWAKPcBH9nCDCen5CG3b6RaDAg86D5oDB1vFYoYjpfHbpT4LdSrt88BGYTdcQSvIKaSSCT/OreDfJ4KjK5EuuNtlA3vvGCqF9Rw+0bosRz7bUhF2rqGmx0OP5VUdw2KSvyMPztp4XFzwXgm64voKxTZ1BN8K9egDKm1nG7ySU7D8o6vmKnbR9wyJ3to1Otr87v1V/spY7t80Hmie/vmLl6ePsd705Vkqe6hSkUy5wI3EbgNenAtFKUOnp/cN/J0Xk+v6s7/Hanb29YPGBs6p+uNQygXGd1+6LY5spT0doY9Yq8HttKLSgq1RGuKHo0vFBoX267nnmVwOrxoU5fd+gDu/8Ez+9YuNzbvoZYHt+pDjCw/SdB7rWvmQ+wnfLVPdw7fnsnf/0fmDvJiVWBbbgnfU5yfpB0Ij7I+dCQyPnA9zedq+NJUHgVbVvA/+1nsCGoXOeKz3clNbiGH6pMsfYW1PGr0gfGfTnaP7BmcDzw2y9wPv/3fg9dunTp0qVLly5dunTp0qVLly5dunTp0qVL/4768wHgY8EqK9xMrrNC0a3zQXjDsotlYKTvqjQ9g/RGRin+FIGvAigjfRMMtRuYkznlenSj0umVYlkYziTZyJr04wQPGS7nQdouCFUT9xVRC1+O5YnJsZeB+062WmAk+XL/piJeIRX1SoexkZG4yWeYdazU67Zipw/BJzMCZ1YSM0leZcg/A5+Cue2NVgc2P/mC6GNQEfQWX99ZYcvVqWjbeoG9SEHAtpynacQ8ianoWsFZOU/xAfOpSO62U/d34jyp9sKETsagneeCgNtXhLL3jbIJ5yQa2Bj42LHnh2DWcs6mGdV2Wne8ktZ2IsDtnZalOOr3dyKM/Pz/cMbOvIccn3zSrRhvNxowCPo8cB9UH1iog7fmAu/nudaD9awSqoG5Yoz7+OHYXVHImijQvmiVRL5ctU1Q2ccaGlgua7/h3rB8olppwUiiUONyCrxHLPexyX2aJ+DY7SYzcqFOW9uwPgQEl+Mc61TFGpZoQJNb+JXA4Ip/NuTUpk5ekdDqIjYyYz2rEvOrvmLJn5Q3QefUHrNcYJBY7tWJ3d50FuxG+QHeaGV4v+Hzd6KmnL/bf4HWiJw0B7eGrcjpmivO2oqakxpy6ZJBrt5vuGHnXEkBBpS6kNfaffUKV1K94QVWy9HbGsUdbGrd1vcyVi90FXCIp7ahszg/BMrHXew2ggZ6F6TAt9nGmUFG8ng+sObY2GnjhuUaLqjQmRvvFMuhjukZ9LvcujGhJtb3dWbXfZbSAGp1PSt6Ww5tdQOvvdtv2Pamn5uHrr9tZHsoDODl3O4bdneoB1TXwErVio5u0E7q+YlvjpXDeQBO//ZftF/Ok/r8leYQ88ApttEUUd9/oWYw66Qs2FpTD3aeq+966vO2neaNiie5UiBmTHz7ifr4H3AkbAYnmhbZ35UC3W5ys9eBbd+0TyjcfqQtXLp06dKlS5cuXbp06dKlS5cuXbp06dKlS/9I+vMBYHI548SlrA25uMbbiuxdjKaS1hplocjcJrdg70UcC8zZIHm5NwEbyz0rp97AyJxyA+LQgJnkizpbZ1rB4zu+b1jbSQybEzOjCM48cdtwWJHB+rlEPaHW7hDnKr9eHaLr75h33I0oFGNbwDnlEDS5D43l2jSB3soSCI7Sn025IcOdOp9QQ93J1sQB25Abs/hyfeKlPk+bEI1qjsUpfmcNuhydNYM09f1mBpFyFfrYtTzL1Vw4uDPP55c7to6PtaYbbbth0wXHci6nbUE9ab4RvZE41g3PxMadev4VS8PawDan0uV4ba61ez5o+0/E519pP/0CdZLunNk4SSoeBMb3fOLN2B8bg+9E73Q3Ri9a68Dta78p3rZTNbEyrXWGoGI9sTbkwI6n3K2YoqTZAAHUNP/Rg2wNX25y63csPwD17Zob7jcZOKvkGk1oZxH+Rs0PLJc7uCZQWNuUON264PA8cO/62RKALAIb71BFxsTcV2xxaR/6ABLLU3HHmRiBR+EDThUyA6lHVCbw7R2m+pPDgt52oPR5MbXPfCzIrw7lyhWxvM5SZhI1sdsdnt/J+cTbP5Gj02bgXa7fsvOrExmDqnPt24adRfUfa1z9pp7YSl5dzZgJVJ+B1wvGKyLZvqCxXPA+V3y1jdXVDLZtX2eWKjwLxhvupv29nlWZUQwsTuzxiVXDzGh9U8Q6QbaN+fErH5nUe7Lf3mg1MVI9x/1dfcYRwPzq7n1FW1NT7nBz7bVMgft8RXsvp7b35ZKVq5tcAx3xsV6rJ7Sb0rjPAx9v2FzuXm9y6LeO2c8rAvu73gXeNADRdtg7dT6g63k7he87ZsZc79g4PlYsesD5AX1n2MY8Trl/f/5nzhm0eNC8YzyxXMMSU8MI7uh5rVjtTOD2z4p8fj50ftbgA9u7nv8MuZRZbv72s4Yn5vn3/fV06dKlS5cuXbp06dKlS5cuXbp06dKlS5cu/R305wPA5sul5nKnnSf0bcXZGlmmOOe/cTM2G+CPZdwsWi8iDmCHmIJOvOKZP3F/J/NBWYdyCieY+JxiX+yQDwFj4MzEj0+6bYpRHk0J1WPD6EQeVGu0VwcpDnOSDZrlgor6czNWJ3EtSLlhGB6Jj23lIKeuqwRuK0MAtw05H2cR528rqriTK9Y2SpDVrC1gfnz1B1tTny1tk7u6DSwDW25Po9Ms8XHDumKmK5OoIiyYn78DDXNTZHUV1m4UQdSKo60Tol71rQLVuCCzd8Udn08qJ7hjFVCNauodtazFqQ1G13fkJ5aJb7cvKOd9o45PXf/tJ0XyplFhitTed+b5K1Ub+T/+B/nzTzzm/5vZ71TuhBdRJ3vdaf0OOF5BzeXw9Z2aj7W57oJ+U928Fafc5TnhKwy5+OrafTkOK1d88QKQ0fXvS7Hbitk28jzBGp4CYaRAnPuNnE96c7mB3fDWZWiNlCO8d2KeuL+6ieUurgyyYg0ldHVMr55sAdr+4xoxDQa4K1rXG5h6p+P81Ja53XV/ffXM+qZu1yrc+tpbK37aisonZhsviPx1tCsEU+dJv/1CHG9y+x8BveA4YSzHdOh67QU5X5DUHc4mp6rd/hc3vf4e2NC5h0m1Qdtuut3Xs8PlyDfT0EeEzucLqGeQhWBrnTA22r7Rus411omppAGlVXes335E0OekcRIV5HLlzo/vfKy+6H00hqsfGHeI1RVeaGDEBxZPuagtsXFbdu+A88TGTg1fDt01dHI+YT4F6keTWzkevDrD6Xc4fofxDhYYN9gGVo16fGjIIgLrA6vVU9xR3DTLEd9u1JnrrAY23jTvkVMO3P2mZ/74DZ/nAsoGw8nPB7a/qWc9DuAkH3+lGbTbf6OO74T/hMWEOhVhHom3RjUNftjtm9b8fCw4rEoADRI07YM+VvT1wGISXAD40qVLly5dunTp0qVLly5dunTp0qVLly794+nPB4BfoCOfcnh9uVYdPL/ieSNCUaQG3pzynfTCGdjjAfmpzsyUQ7N4MAuaN/l9+8uZC7hR5WTrtEJuOftfIV+Evt+90c4peHM88T6wkiOTPvB8uSEBSjHTVoKH5svVaF/9mhVyE39RUyAyxG1KLuACqorKJMsFR8Y7fH7XehhkqY+WearH1w7BWl5QrzAbikk1J8tolVi/rUs9MNtxN10rEDWZc3UNb3f8+CS9KPblwEytW4buwVwO6QWKnRLI64qUtj6obVDngp1tI8nV9XoAgjjZO/g7nIqj9fsbES8ItZFxYP0md6S7HKFnYX7SDvTs+l+ImPB+x8ZGxIP5+we/34PNG9sM6n2wxSf9BRw9qPC11/Z1PfMrEttY3bkrptbaAMZyynZBQzMqTsXb+oZFkKs/180xprqJCyqH3JXxUL9tFlYhZ3XfcIKKSVjRfMdMkc9VCVn01slmWGg4wlvpGdQkAfOOUdrjlRogOA+sbVQ91cOKYr7pQ/tu/hi48ALrHQM5Shf4dx+KhyaJAve+4G7CcubKufs39FdZ7F97ESbWbEVIdxIj3Ol+W+7fWFHPKZA57vqQmmCJ1dB1Rug7DXx/A9CemJNKUwfuC8wvNzCxXMVmq4O5Qer50BWrzFpj+qDancwUM/b1GegeKNQPPjotcg2c5HIOB907cf+J+PggHr/zaQkM6Ma2Lbet2XJi+1cXNIac+Dqd67yt9WtjvRcX9Pahv9d36vwUYO27nvM8KfR88W+rAxmtv29U26m91Evtm9y4hZzBYXJ4t01rwRO7/QUI8vhNvcPseOv4GlKx2ztdAcykdSImeTzoDWid4/GdrCeZib/9BfobtIE/T+L40DvCDcsHbf+LzmWeQNO7dvtG2qCef4V6qgvaFY9uVRoeOL5DNOLjdxjj3/ob6NKlS5cuXbp06dKlS5cuXbp06dKlS5cuXfp3158PALcXCN3kYlwu3zpP9Vm6UyQZSURTbKwlXrXcY6F41BoQJ+43ek4iB8VgVmGcNDbCDuANKui2onJbg3PFK8toDGUCkDHJsRFxiMGQ1GkCFkyCTmbRkMM1Z5CjaJE0V2dummOh2F3rb8uRKQep+n4bNY0koHfq+BQorkFQRCUZAnHcv8EpoOjnJA25V3lCCOiaF/RGzaI2X32ygcWxekXB+o5np42N5RckKcIbMx/Mc9Jw2O9y3r3AdhmVgo0yOA/8OOQorpSLO3L1m0KVA43m71ATz5PwseqR1UWs6OzlVh77iucuvCbhhTcjj1hR0zdF18YpF2cU7f7P2POvtHFn5ge8/8JMrW0+vsP54PhFMbT5/YMcG+MOzRqjUnG+adhye1fq2untR9zuq3e1Utd9PhZfnHz14KbLmdgbkYecxq3DPOTONJObtHWwXcMOPWEmxALq+x0O9ctSpU5fK9zUSQ2rwxcEZBcUr1qOcV/gc1nl5ZBsC4QatE0g9PX3q5bzUhHjbb8rJvpU9HC63J+62fXQDbKeuOy6K876rv8+19qp6BjVXx80b6StuOYE7KCqwf7zWmOBPFxnG1fHq3nHjg99x9C9arkD2g1Y+y0nOQ+8bVrHCjmtfWC27vd1Te7UfAKd6qsrOQ+5b8cb5k6eD6o6lSaIX/kFt62KMuXSN+SW1dIofjqsoDl+vxHff+U8E+4b9tMvWAaeIWdyJbZ9074vAeo6Duz+Fyqn4uAroA0931idxXPKPe0OaYLAzw+9UsZN4D6f2i9to8YJz+/aE+cTPDRUcTxpTY7pFS2wzlhh5wfVXecNDZBw+wv5/J35eFDNtB7uNMDufyFyYnFqzSjs23+hzk827zyPB2x3qt04KzRcsX+D5+9EJGw/Kd7fu77vOOXw94B60MZO1Dfy8VelG/hyv+/fNBSyv5PHB9mNNt7/N34JXbp06dKlS5cuXbp06dKlS5cuXbp06dKlS/+++vMBYGvLyeUUpq7bMaAJfhGfX47F5MSaI4+s0ZqRE7w1fG7MPKGM5FC0rp+UdYpgZgJ3zJqiiivx2r66RiuDmUWb4G2QTPWJnoJqcwZO4vbE2w2Lidkn9ME8ntguh/Gsk2wbaRsDwVfoAjA1aV3RwIpjhloOzloQCFf/rcKl5f6Ua9YEPZpTaaRvgkKt0duGz0P9uX05P20CCd5wb3LsmaAieSr6WB7EH33DkWTJyRmp3lv3TubqDY2TLF+dv5/4flOf6QxgfQcOzyd9DLxOspLyTR2y5YranoBvxOoQ9lSUrW13Aaw6KTe5ZavwsZHpVB0CgTlhu+l70zD/htVk238Bc9rnB24H8y+/EJnIrJh8xidVT8Kc0TvhwfCG0+guSOg0mcGzyOXshAVwlwsWV3zuy2nb2k7dnJqf5Dxx38jjE7/9hIjpJ2U3dQsv52fNuYBtk3u9CUbatgnUZaw4Xz371m/LFR4kc/UTF0oglisyq7SvVqwzK2IbBJ4FrE2R0Cmo7O5UBrbvxPp5N0WIRyTW1vk8FVtsHlgbZGodvK246Crdjw11JhOYDZot4FpGKzgziCp83HV/FeC5+pdP3JtgcAWYy7Eah9ywuXq1+y7n6HmCmqLFvfsrGru0X1vX+rYUELXVdc1yPm876k4WTP+611I/dMUkTSkAynWeEOjzVme3N9da334Wy318Jy2VPnD/iZhPpm08Qs7tLU+gsO3tC6AXUGeAbz/cxpW6JvMfz41YkfEhF/2KAbf9Th2HoqX7oGaqJ9ddZ37bsWO5jFutYY2dPD+weWLbjr3/ovdxQvEJ8wnWqengIU4/bsznX8lDzmmzjvvQQMt8Qk4815p46fHmwfb2jdk25vEJEZxdndGt37DeiViJCXngY6fl2v8ckJ06DWs32AIe/6LhiXHX9Z2fWAuMSbv/sgZILl26dOnSpUuXLl26dOnSpUuXLl26dOnSpX8s/fkA8AtAptyU6hdd4CoErswG1gK8E2Y0c7k1GfSm+NExijpPTjOqbvTR1PtLkX5X1Gl+Yu1nqG0BMwR5rWPeME+oJ5FGY3AycSu5hTPkuM1J8Ilbw7NkLEzDjk9BijqJOEmcMqMbNHtQvkHvRJxYa7R9E+Sah1ylIdcl5UCn6gHViHPqj0iYkGXkK0L6PAWYXxBP9kpsTti63K1W6oV1l7vV2pch1DzUuUlyZhJnquu4Ui7HLKalmNTqZ7ZtkI8PqKZ42e0Nzs/Vcww2GlaDYv4NcH4yfdB9YPOgfMVtb3cByDoVXZzHF/viPGnjviKlO5Dq/OUBqc7PlxvXhkFtQIc6YWs0/sJsO+cM5vEgrVHW+Hz+C8+A23Zju93JedL9iXPDXT22magvOicrb3bF7ioWGBt43xXtbI4vh+jsAubWd3WXEuqnZQdc0bZeyyGdELYAnxyliuUeK353OYwr6X0TDM2grGFRf9PrO+X6bhuVQWTQvKvr1UKdu/HU86/4csgWhXvDKoko7bHjqXPXu+KYzxWNHg8YXXtuHrquPEnbsHKiElXc3rQf2756gRVxbN3w43ccJ+rE+jesCXaG1Yokr2W2noKwFNSxotIbzPPLWW6j4zkXQF0O97GpjxcNDyiuWwMJla/eayh3bLtRXa5trV/H+qYkAXMBzDzWd62Bg0jt9blAazXMHV9xxdXQPtnuZAnuB+q7roTz8UmLju2N3m9rXTWIUsvhbN61pzKwtkOZzkbmV3Q0vjrH6QLPcWrgY6Cu3m5429RpHerlpd2o9h3LE5S8rj1qb3rXFlgpaQFz7P4LzIci57cENj2LNmB8U+x7TioeeOtydrtj3vE62dqdzIN0p7aB9zeYH9R5aCDDOqcJwHvrECdxClibGWw/kY9/xeLUv1/d6D5uVP0X7P6GYwLYvrqAVz/wqzLg0qVLly5dunTp0qVLly5dunTp0qVLly5d+kfSnw8A14qNTceHHHxMw1sDO1dUsKJFFdd8EG5yp5r6Y71O2DZaQMVk+iDmv+B2B4yecqOlv+OZJJPEgUFw0s2hnG5dLssqvDm9hhyxlswQ7D2Z9BAgDZv00ykrnJQ7sQ14fuDzwTSHrUNvP5zAVYKIhDp+v/pKY7kTS446H2TMFf0KNZNqDSKICBxF0/ZyvALbN/DE7IaVoNQqc10u0NUPXKvzNBJ8MbS+EYe6RHN1Mpc1Mp8kQ0bU44nVhGnqq22dOp4r0ng5Ste60RUrGxnr8ereJibnXsjJ2FYqr4HclS8IlQe+3Uka1l1uUyv1u56fcv9yqjrVBJ7pXXHVceDlsL/Rz4fim7sLyvug7Gfi+J2PeRIFt/5GxolZYHXINcly+prc2hpOQA71Wg5Za5QN9UnHE8spt3RjwTpnxmR4Yf7qJXU5G5UzTrnc32aNylP3YefaEQL3As+1IoEPnE6NN0VAr+5aazdFNp8TGztuTc8qc7lyTeva2lf8sxyxLvi43QQ2m7pxKyeBay1eLtO2ruP1GeMdMCLlWE1rgrKrt/brns3xBr7fsPh1dXKrgxZfvcdrkEKxwsttWxoK0VCAnokB3m5wxtrXy9mLEgTyPDBL6E7zpvVm9fu+wrMDOelBLnkLvG3kVDy1NadaCFB7W7HUilqGhu1D5+P4DvNU7/bY8PNJ5lQX9jlpNtQFXizXu/GMho+f1Zqd6k7WgMMOra3+b62BGVjN1c89dY+Zcr/OU/Hh1TCTK5x1RgS0FzTPlNt/uDq0nx86NzUEztuCyCX3NTMFsblrEGH/iTq/U35QfSj1oG0/3t1+W2dzvd/iADq+7fiZzJr02z8zP7/LCd4b9flQXPP7T3qPV9LiKbdzPjlXogP9jfz8K0Tg213DK96Xw/kkV48yManese0blc+/6Ty+dOnSpUuXLl26dOnSpUuXLl26dOnSpUuX/nH05wPAEYr1bYqBZaXiGst9GXL+mq8IVBd8I6e6NsvpvjGfn4y90w6BsmzqhyQPaG9Efsf8jcjAzcn4pBycJ2ftNIxmSfWBn9+Brs7fOqFuMulSVDlnfizn78aMAx8bHoHXJ/ZsMJq409jEsapRVfTziVvHfKLa4AERpIUgl7mgUCEnXuvQkANzRdWWl5x+rdEs1Yn5goCEAE735RZcAKsmgs/xN4AtIQf4rtheDjgOGomx4lq9qPwU6Cy5fS1zJfdORcnO9Z3zIMxXX6mAmdtJHR8LNBrWCrOU69dMzsxMvDcqnpSdlDVBTe9YnmSi+Ovzc8G4JlfrmYJ9ZYrOPR9U7xBGjRueE6fo7lg8aR5ENdI6ZCfzk+OzU3tn2zo8PshxY7//jLt6eb86bo3lyk2tcXXg1P6dIahuAvFVgrnqM02yjLZA7av3WO7fCbPU4cpy9Nb8gsPigH05kZ8AlO8rmtdWNDXLKSoY6r3hZuqOTTnDreprAODL0dy6wCMlNto0HuAkmfPrOdOa+nVt4IU+1ztuG5Tg9Ssau+GKbLdCGd8LsuYJOLRG299pR5I1SetknkRNvK+eW+vqv7YFj+uJ3KeC3dXGcqav/uvXUIOVYo9NwNv6fa1PLjexLZi9YKmhz7QGdMwM775SAgJjYOMNmGsdNoHtWn29mIBqLwHuzLVNBG6rNew88dGJ44RZ1HxS28bzCFp9kqNpiKPvAvTzpErR1G17+4qCrprqeg5Fb2OmNIF8rq7wFB9vTh1PDMU+W+9UHoL3OQWZ339WT+/x0DOahTcNIjDX+7eCMsW/22hyDx+f2POktpvWtfW19+6K535+at3T5LS2JEudzLjR1iAKGVQ/yTaY3/87k/9K9kHEycjElNbNNFPM/tvPVBX5PFaK/gnjRqY62JkPfP+ZOr/j4xvYQcbx9/m9dOnSpUuXLl26dOnSpUuXLl26dOnSpUuXLv0d9ecDwMRyWCaF430sEDoRmSroy4WX0DA5HN1WnG1hVvq5LmA8UjG7ESaA4Y6lUfnUZ483MmzB174ckXJmuhnFrphhN5p3MLkPI0+6v1HeBJIdQawTzIp2KMrYMNIHbR6kDbIOqjlhxW4Q2fCWX+CNYyruFTkokyQCwGBsiu+1oM4P5nEqnneeK1r1BA8BSutyxLXlyLWuPtnm6v70ho0hYBkH6YNZTsQkyikbeD6p3nDrcgPXAr3WFv+MH8+GhjpxOwyodOL5hM2xxwfpnbbfifMTJhgHbdsIICJplYqoLsNud+bzg6Ro453KpCIpbysV+yaHYUfxs5SeUaWguG2CQi7X8Mtl7ATkwM3o1QgDu//CPH6nCOL44BGN3O9kG5CTUYZnYrWej6mL18wFyys0hAAI0i3YiGMVlBWWkyy5w+G5HLGvrtu54GzIPdwF4amTzBDE3W6QDYsFfEGwse/LKWpyLdtGoIhiN8fgCwLC0ABDW8DQxo8hCzMqT7KEfquCnBPfd50rK61PKTbZa5IUVqVntJzE6mqFai9wvSKKK142YznDMWwM+vnJ5zyJR9BGh/s38E5W4G0sM/wUAMQEORH4V2z0BDQk0fd33I2K5Hw+KCZtvCkSfcWrF66oZyuMqX21npyn3KOU6wjF6s9uG4LYK6o7Dy2ZK07aszDfKPKrJ9rN8Jo6J/1Onv8Kz5Nmg+mrz9wHcXxw2I5Z4Nu77hk0SDET6+tXQKJBgjKsrWGQFfdu3iA7hCAwoXentfW8fUV4N6jnARRmB7CB37Au1zXN1KB8nmuL6ZnJ6Z4rQXvHtkGdH+tdcoP40OcbGmCopG2b/qzUB1w+aH0TVPek4oHtN9z/b7IafPx35vNX6tyJ1sjjXxlv/zeWJ+WfxHnit3foQ9Hiz98o79h5fjmlrXd1hbeNjIM6z//VoXzp0qVLly5dunTp0qVLly5dunTp0qVLly79g+jPB4AXdMNX72cUNjYBzQytSE2BEFGY5extLBuuPsYDL2fub5BP8jjxntSU47C2n4h5QhY1DxoIHtgulyOJxYrudaNXKWK0bZAflO90EnJi7Q2rh2ALoc8gmaHeW0+jYWQURtAa0BvVN9zUdRlHYF5Uc/AmR+bXvaWu0wR1X0DGxjdaHbgFNT/BN+z2rn5NFDdbBDYN+qvf12U6tcT6rq5Wa+qeNeM8HkyKPJ56BtYwJtZ2XXvW6gwuQdfzxG5jQXPDSNya+oOtqDix2Vb6rlM0Wn+j6sTjlDuYhgWKG66DuN0Ap2zHvAmSvgBjORUf0HaMfcXMxoqMLth1nfRBPT+w221FB4dguDl++0nrtVzBlnJeVvzOtEHV5Hk+mBnUeBJjoxtsJmj+wwncFUter1ho9SorprcgTw0FlOKjnYbFA5ZL13DBKlJuzra6gqswkvJdPaa29r6tc1EgwGw/IKQtR2wlliGgaK4u5ZIz1Zg6X9oAGgggFKtsTmG0PjCC2Rqty7GuuGmoKOr5hH2533PSemH0FVEtMO19kBQ1pyB507VWyVFq5jiFy86NV3JmsXXFSFOC14LHO+aJ9QG2Ued3gf62q8O29D7w7X1FZrv2qRllGxVG1RMo3Ad5Hlq7AO8NmmloAgN3vG9kTjIPbNzVQV6TCvuKUBf4R+8nq/XeeUVwo3vMk7aGUtIc37/RHh/U+VyDC7vSC3JyfAbURh/FiEODA2iQw8Y3ucNzaj1vb2uv9bXH1C8sINw1FzO/YxEw7nJCn5Pa5FJuA/LjN/A3sFDQQNvh8zfBb1cHOBFYrZ7qqX7uslgp6A3GOzl/xeapZ7NAfYHSB/zVmW24Ff72T+R8MOOkFdhP/w/MGzEPYga8/Vf4/JWYB/Sf4PaXlTRgxPmA9kY9v8P9J70TMGx+fg1gkCc+/gLngY2NPA/KnH7/9u/ya+rSpUuXLl26dOnSpUuXLl26dOnSpUuXLl36I/rzAeAqwYNWkLZcf6nuUzOIp2JZW4dmcqM2dbOamZxvXnK2VdIpZhq2dXXsVuCJgKo3ynJF1hpVDhTpTpxP8iz63eRIJJdreOKoD7TqtmKhnUanvONsRJ5kuSJo4yDttsAXgm9p5DwY3bD7wAHHybnihDMEFW25XfNJUcTqAa4QtLNmtP6KrVWPqlJ4BdHKmyJve1vfnXgf4lVmchlHwO749qa65Uwi1N+LG1TXf19dtdV3PE7KiqwTbjcaRsQJYyOPT61/bzhGjbuczNnAg7LAfcf3DejkDLIFzVNxtG0IdLWOdcXxMg/iOPBN8Fvx1StWeA0MyCGrXmiboajb7b6AcWBp0HZ824CC8W31tgbDoY0782hYqAvY6yDOg+e8UWdQtzfMgp4T3+4CmnloH0YKAtaU67FvWNbqq1VPb8aE3gUlz6dc7LWgo48vqFvminUeBdVWxG9fUcRyhlOFjfsCuQsIr47benXcWv8Rc1wTqi1H5upENXXMqh92aO+U46Z+YXyjShHQVakO3ZywjxV9rCdhtVp3SwMbbdyoMio/dS5b05ld+5k2IA6MhrvTgSNLgLEppvjrrC/zsHmn4sB8YO0OnAKzq/fYtjc5sGsq6dlXMngfco1HKhK5NbA7vFiuGdYNi6Jiwn7TvURomGLFZ1etwYxzkvHEt53mTWA/QgMqtd5dlKD89k0R2g/17BoNtjfgO9ze9E47AzyhGbN2znNizegNSA1osHY8adj9J+35DH2mrTjvfMF17QPbv8F8rH2+wfG5ovMn5hv29vPq/12vXAP2Ozw/tf596DNYazw/IR46i1mKgqZj2xv1+VcMnTe8a+DAIY/51QFe1mjdsWykTfj2M8128ngs13nSfOP29gszJ1EQZcx40reNmFODE+zU52/kuNNuP+HHQwMBmM6zNWp+apglA7+9XR3Aly5dunTp0qVLly5dunTp0qVLly5dunTpH1J/OgAcWbgJgPoYAi/zkGM2Ut2c3pRo64v0UAJnr57RZcqzFITtrZF0wh40biRQ54Oe6hnNZoS5YldBXZUY1QMjGQVYJ+oATsrv5DxorP5c07UY+RVBLcvkWFG+36HeMFbsb51AY3JiZwML2j7wKoHD1qn5IFfccNiu2OiZ6uTMuYBTKu6Ztr5LfajqzT0VfewbhtFsLGdkqpuUJnewNRLUG9ydOoJ4/I73XWtpAwh89Z+2CmK5I3k+8X0jI9QPawa3O3l8Cjpn4dtOTIM4v5yLMBXD7SvG9vEQEN12aHfifOCpPlIi5bzuBr3jEWRvy/krIEUBY19O3ANfsbnlCyZ7wvaGm5HWFD9rYPs76U/KOhaJbXf8FFRMc6wgzgePcsHw1oh9Z8tJ821165pien0Ifr2uhxX/y7a6Wk+oUy7fNrRn89SeYTlI51yQEmwq3recda8uMIvJ/ToGdtZXB2tZk1MzA2sbzTrEQcUk+8BfXc80uTp9X+7ydb02cIOISZFUKyJLz3QGRcMwug0iD9qCuxXHciwXVk2O4VzPerxpD7npu6zJodtueDxwc9yDNoZc589JdCeb6Szw2uNyXleEQKrtcsVSek+UkykHvcWUc7/vgrqVeGmwIl/AWzXAcmubhgi8NwHz9S+9rQjxtccqDpl92y54u+4DzsXgc4UPhJ5n7+RrcCUnxcTbTm3oPVOn9mXb6NtN53xOom84QdveMVMXdsaJby83/IQ8vyLHFd28rrt0Xujq8q7zxMYut3HO1X+cmA+qrz1oy0neNmpf71pyDQiE3mPjru96/o4NX+/Yqeezvevvu5PPD5r7Ot9KZIg4NXRSQdYB7U7b79SclCXexooPL6w1/ef5O8wgW2NGYf0djt/w+qRv36jj1P22AfaGW8e763z19X6v1ABDnP/WX0GXLl26dOnSpUuXLl26dOnSpUuXLl26dOnSv7v+dAC4KLIKZ1n5qgtCxXL9glyM3paJNRR32tXBqdhkgaJakbTWB5aJ5U4Nw7Oo6hRd8blxYgWzNTkt8xQwc9dnr2tRLO2mDtGalE3mcv0N1/eWO1aNlkFrBmycp+6p8sBhuSI/SIzznBQ3DhwfRjOocmq52CJX+6gP6nwI0LiTmbidX65DzH9A0a0jV+eJjTesXo7LV2QsX92hZSa3XAqiFxMbA87vioZFHauQdIdZjbJBIXguM2gtR3GoK3jcYE6tXYPed7I16lDscvlGReEmJ2sbDb6cvQdtDGo+5X20JDjVoxr5o3+Yoo+dOp5k63LVlolv5pPmQ2Zyh2wCoFZJW5G5hdyQbewrdtaoMrz/TD1+Z+ZkKzjsIGJyPA5i+8ZoRjmMvvqnOeTgfQE4Qs5vSk5EFFXcW2cek/JSd+nqSpZzdgqQtq7k83b7crcqo1d7J63jNrBy6jixmop57juRRdRTScZtLECa1Av+Fus5vjqM6+u8FYLs1jaBalxx3HEuMGdit+XMCpSfrG7VRNHovSu2O+LQ57ed4KTXikv2HVjPrw111h4PrDU8G3k+mEDvG3Em3rrAsaH47mRdc3w5TS3BtzvESWVQkbTlCK/mZEzUguxyUx+TbHpfZN9wY3Vjm9zKqQELAftXFDdUnFTf9f6x1Hf7wGlUc6yKig9i9R1Xa9Tzk8r1ufNBAmd8MqM0DGECqa0NWiXFwTyeVGz42zd8W6AePZda6QdfcfCjfjzD+VhdwfbjPZCpfVOB9U0R63XqnUdg40aeD2oe6grO1Ze9r4jp87Fc2K8e5KZ+5s/fKOvqIl8x06asefzeKI7l2J7U8cTaRvhGHc/lzt6pmXhO3E1DKvOBeWe6YfOklc5PzpOyJHFqu1OPX7H8ldZ2ogbVd9p2U/fyPOR/N92Hb+96J3A5gC9dunTp0qVLly5dunTp0qVLly5dunTp0j+e/nQAuMOPONuZWE+cAahf10gxNuuKd7WV55qFuZysRSdTULIKBOOK5i5I0xxmyv3a1RVqOemIM0VNmhUZq+81l8PNFdVsVvQ8KXbAsdVB/HKNeimCWeDppG/bgj5d0LM1fGzEKSdbRTLnQbZ99Q6rG5V4yEn3chpvd6o59Xion3MupzGn1qx3fQ8nRseaCXZmWyDNllN5OaXztepax3CAG86JdcEwdcMOCEWrer7iqg/oN/USv3plZ1DbcmLHSWaj97X21qntHc4HkSfVoM5J2++Y7fqZnBiNLGGbnqdiXV8R3yUg33qDbAJx+x3SiPoUD0vDxw4ErXylh28rxjrVgxyJlWEpAIfLMYg1VRW3v2Cff5Wr3N+IMGp+EudJRFHDiM0YfaPXk94ds9VRzFrfYnX3nooeN+23NueKKDd1yVaoK3lFbGMuQM8CYzRBYAxvvmJ/5dzN1vAO5bZ+Hq1lBq/8ZF+9wDDVm0ouALwctPjihtpjbo3Al1v79VdWbLY7ZFDreVklrd2olxHeSnHCTHWzUgSJJ5jJvcyKIqdttJuivHse9Nm0Z2aSvjPzpPWB1ymneMaXg7gySCu8DawZidNK0cuy/m+rN1qR2DZuGnQ4p5zMa91JU6I2YGdQljpfgd49Lne/bTc5U1+x194x07m35rg3yu+KQa/X8y/MkhmQWcz5YNqmvZeB5cTHYPRBy5PKItPIcWeWY3PSMrE4sO39dWJ13dubBjBoK445qHiu7uep+O4+YB4YK769DerzQTU9x8LxtmkA4Xio2xzFlJu3tWfW9801SNOGznyeEOocZz6V0oCtf4Z8/K79sTqk/TzJfEK/ycmfp2K1Tw2s+O0bPD81nFElyD4a5/OAKuIszN9g/ELUk6yTlkZNk4veoJuisW17g5j4/abO6uPxb/sFdOnSpUuXLl26dOnSpUuXLl26dOnSpUuXLv0HyP+zL+A/RaaoUmiK/80Vs1xQ1dS/2xDoMadY0bIsWOgu4EMuV19hq0/Ym+MYPt70M2W07Y6z467O2WbqEzaTYzIq1f1btqJXA9qdSoGH1gQjvYKM7xSKrLVQbLKnnLFuLCjdiCiayf1ZxydxPIgqAc8sqozpYwGk5caMU05GH9imaOaXG1owd3XJxnI8vuJ+y+Sm7cthuiKtqxTxW+bMOskZcoKyomtpQGLW8XZf4JDlzm4CeRnU8anIVQ9yprp4vcHWiQgBoyrcnXa7C5CfE6skz6DmJKcikitfPa6Kf404MbupwzSeeB+YKa7aanXA2sTNcC85PuMVN6wO0rbCupsDc9JKMeOti7c2H9hyJXaDvnW2b78w3n6i98HWTLHSBRmTY8Lx/OQ8J08Gx3kQmeu5Bfl1z4kZum9cz79pkKDIxYsXELOdWUnkScyToBGtkyUnr/VGmROrk1dRyzDPYn4+yOdTvJ6XI1yR1dTEMtZAxIoKts6rL7YiySqtVwVRKz43T52d3il33R+m2OYFOEnBTK/8EX8dy3FZpY7b+dRZztVNXCm4XHLeNjq9C0yra/dGbfvqD1a0dmVQC0K/XMmvrvCM1L154fdv2H6DfcNMgwe13UhD9xkHNZ/EDL1TcmroIHSOMuVm1r2d6n42OZGrdDbkdtcSWjPcwJtiwN27+L1p0ODrpTW+Mc8iz0N7owIcxv5OY50Lg9bUtx1zEnEKXvvAKcHnWl3TVuv9ZAv6N7zf8P1NAwOnHMD25QY+oAzb31f89BpQeQ2N3L7pXWKlgYSYcK73DQjakopY3u76nOVCf51tAfWpYZD9XX+v3wXfgZxTO/N8QEzyfGDbG+3+jb6/473Rsmjmilc/n/T9hmfR6oT5AV7Q37D9n8njJM8nmUWi4ZUcu/Y0QZ5Pwd92+3f4BXXp30tKRPjP/4z/k3Wt4R/XtYZ/XNca/nFda3jp36prz/xxXWv4x3Wt4R/XtYZ/XNcaXvrf0bVv/riuNfzjutbwj+tawz+uaw3/4/WncwDjK77WgZKTtbphoYhhGx1rbbkcC2NQ9RA4MeR+g8XAGnRfsakn9E3AyoxqhdtGxEHNT2zsipPuN/J4ylFZRrSOl+vzXNBFe7jhK/r0FQmN/ejaxVa/bQlkLwuuIIkZNMfrBqmo1SKJOOhtY1mcf8Cv8yn4bZMsl8syFO+sv9ugDvDQn+UhqGUN0rGWVMyvDmL6oPKJ+ZDb2AYVpdTYfCDo29Tzm6sT1A2zO85JznPFb5uADh1wOalrUpSAdDnWXj3NSaXW3W43+hzkfOI2ieiKp45YTs8Q/Bs7TiMjcBPcNV/Ps+0r8psF8hKOoN1u4IH7RsaD3rsixe0Fyx36jtUCV1bqBm66BjfBLm+7uo4JouS29GbMFOA9569kwnbbqZxYbeo9fjlArcFczuyYeN+ovsN8aP/kFFhrm+CcN5wO8xTYq4nZJkBnXXskTsUNv9zCtRzwr27X7SYAPEMOztbXPbav+9JeSe3XGqQZGQfu/rWXYoYQ/77JXX0c1DxgL0UF2659kwu0Nwdcl/SioyTExHpbccSrS9dszRGsXwQVVD5xjNlupLnQeBvan69n1gaEgR2rX/guJ3BC5kH1jmUotrmCiqDo4qUziPmAvgmIFj/ima1gHgSdrFDE9mvA4XbTEYr8G+e0wZwaCrGitbFi61/vI/RcCL4it/OA7UZ9/1XPo4Hf3pBBf8Viu+G+0XIyz8LK8WaCo2ZUHupA7ncq5Mw3X05v61+x2Iw3Kn7D4pRT+BkQctXSO7bv1OM7DIFRM9cwSNQCyqXP/5ojKOp4wtgoHHvFzI83Kj+w1HPWc596Z2/bWuOJhYC03X5R9LRBnes9ZQ2GwL7f/wnqdzg/8e0dGzfCO/Zo+PNXMgOOD2rcSNs15HN+6qweRXiD289UnfRxp+zVK7z/fX8/Xfp3lZn9//9L/wGf8X+yrjX847rW8I/rWsM/rmsNL/1bde2ZP65rDf+4rjX847rW8I/rWsNL/zu69s0f17WGf1zXGv5xXWv4x3Wt4X+8/nwAmAWuXNGlueJSrVbP77I+Ktq0qG6UNW0sd/0//XHF6sax4qRX7DMsp5qr17SK6o08DeXUdnn2xg2en/gLkvlYzsOkynn1DKujdMX2TpPB0oYA7OvrlgOXV4QuCMaE7tP66tw05Lx7OSEz5GxuwONQ1DKbXLKRZJy4+3Kmrtjomcsd2OTWc8eYeJOTzt0wV1frj2sDa471QZ4nTuJVuq0Vi1zIFcr6c7eGL2Bdtum+TnWJch7Qh+Cva60zn+pDbjewlQI7Bo7AtPNyXqqDmGVUxW5kpjpeSXXgxqku1pwCa21gcdD6gL4LVFdb/cJDUD4F4a0Nym9UHvrsOGipTtk+9hV7PGk2gLn47M5sel72fALGZBD5znH+zsxiawJpg8L7xmhjPWftN2urj7m1BceTJPFakLC54pRBaxcPxenacr4TEHoukamo8zaWI3wB4NYUnVsrZpnXPl1x4i/X7std7zvUpHnHbCeryMoVew3Nd8yMSep57BtRSfNOxEma074gLsAURBx3wc8E2wbmG5bLBdx3AcB5oChnrUNV0vvOzCSen/QYxGZyyJvh1rA8wAbWblQ+134RqC5vGE2gegFmd1NE9KFeZHWCO4z7GgL4MZQRORVpPm7qZz4eeg7OcjW/en/lbi7fqDnl5B0CxMn65ZYhGNqGoqsfHwLqCYxvGvi43Va0t1y6Zo1qO1aBEzSKPJ7E2zeqdZKQc3+76VzVGnxgTVTV1BAKCATv79RcgxzbDc5PDYhEFxDd0VCJKdaafsK2wfO7YHHbsf19+ZdTfcCx4urXO9T6Hd93+Pzta6il+sB4nc01cFFdEdEVX1Dcth2Q+zzOwOsAHN+GAHUhx/Pzk9rvej4U5UZSZJzUeCdR+kCzor39M+l611gNWjyw+19+uJgvXbp06dKlS5cuXbp06dKlS5cuXbp06dKlfyD9CQHwinctOWAxdb/STHGt/eUwFLQ1e8UVr2hb5jLZGWlyJoqPGZEl8NJd8NBQV24Pxa0aVBmtO5WDrE7NuXjaRsUTB8wGycTbIPK5HKkD6rnA3RMvsH5nmi8H3sDyKRY8zy9YrVuWG9lqgZIy4gz1dVZSo5PHp+4xBJrcfDGg1dVqO7QpCOgd8hXz6zL5uqmreNmjbQU5y5mZZCaWC3SZnLaGnMo1Q+BpOUibOemd4InFcggW2OiCaHMBzCx87JQL8FoFng0jaU3AKCLkKjzBxhu+36k6yQiIQ53D3chIVeFWYCvS2vp9OV03aLsAdQZkyiWbhrcV9dtvKxIZolJx4N7WQIAieRWl/XJutuWyhb4ifs94Qm34PKn9jfM05pxM2zhyJ5vTbcOARmrP5NTzqBXZPe746v0l5oqrFqw1FmhPE9AHQcblKo54Lo+pXMNr4kDQ3w0iKTcYA6dR8aRWxzR9aO+7L7en4GqtfuyqABtUTXyx4jgnK7dc5ygns3fg1ECF3+B46r5W7Lfgb8pp29/R8MUb1LHcpm0Nd2jfZhz08Y5tTvvtr8Q0sheZk7RGM7QWZsAhOGl39fXGikAeu2KFY/XWupM+lts35R5t2o90xaXXLKJCDnW/4f0FpadA5hgaMKnkR2766hiOQ0x9u5GU3LZVGjBY7mvzsRIDhn6+njqLY6e1gZs6lM1MbtgpaOro3qffsOic58FggXUDjodc8MYXBLe2rfUJDZ9Yh7ZRpS5mus7Ga82tNepE/+xdXeL9ht18uYtT31GheYHtG3U+qfMD2+9Ymt6LzajbmwZnZlDzA0ZX7LePNTxSWOtyjq+uY7vt1PkBUVg6aQWY1gwDJp6BdScx/KefOD8+qDpW1H4xKWr/RtqgbW9rXx8Yrjj0CpqvfXnp0qVLly5dunTp0qVLly5dunTp0qVLly79g+nPB4BtdXDWAnDuy+26YSGoBlNQdbsLmsxzOXGL1o3iidPw1qg6FXGKg50LbCblbdUDhxx0CDZlnRRdoI6TisCr5ChuO8ZJVgIlg2aawKQlVo1i9fQaC3RuRDyAE1GbBa3qd8ifoCWvLt8vIElXJC35xYhpb3Au2CZyqxjjMxdATMx1TawIXeIJ1bHjgzZuAjYplzCO3KLOlyvVkUuY5bCtV4/wV9du09q3hs0Tz1TsdjdFcR8PbHM5tWNSNKxCDtM28Zhyvo5XzG7R2p0so26D5q4o7AI/PrH9G24JBWN7Wz2sYrVWE3xXl6g3MufqqE3FSWOCfYau2wS3quaqpD3xNsC21Um7+lV9QMrp+8M0WzQ37HajTeMM7QGzgfmTqmQ+P8hwdQCb0Rx67zRrC1xp6KBcPdLG33Rbq7iZWnsjW19wcEVp+3KHuz7PzFeENYrVbWNFlrNgcdOezBccrNUbnFguF7CZYGwalWtPuy/ntQvA2+ruxRcg76xcYJyx1rjr/Cy3daXOBj6gpozu7oK/VXKmw4+/6wPwFVGexJzMo2itFKvtimkuSnHweWpIoUxxz97hXEMQeVJfEeGNOs/Vjb3W2IGpPtxwoXTPFV/dOsyDjMT3gc0nVJMDnQljx9wVpe2uc+Gyqpcvh3KeeHPMdmIqrttbw5/n+ryhQY0IRVMTcuc3Uzp8pvalDdwg5slsRR9t9TK/Yt0P8KKqr7jw1a2shQXT52eUrt07GR9y/K7kBN/uehZ5gO3rPJkGVQyIQ0kEBEZifVC5w/FJtabhhkrtr4Iahvk39e72Judthp67N71PMvD9m2BwAb6cy/PU+2p0zELw105W6zSJ0cZGPn+jYiPrxNtOnYX1bwRTW/p8rgSHQRs7dj6+oPelS5cuXbp06dKlS5cuXbp06dKlS5cuXbr0j6Q/HwDOJsdaPpZ76wRXRyU4pFxjtt/UAVpOOnQTxKzoeL8td60Joi0o2qx9/XmmnJdujTD1h5o5DSgzwooKx/c7OQXw1K9quPfVyzmxrsjnesUjl+NNUGwmgqfbNzwfMnX6pn8fr/7fXJDScd9YucHQjXp8QjbB6wT66uTNU+tkQfpOMfFMvmpKM2Fb8HAMbJicjizYhVzMlGDYC3ZShrUNGzs2V+8rC3ilIOUre9abM9obZDCPQ9e4YoBpGxwHdIErs8TbJhA0n+rkjYl5W0bvnxb4R9dEw9sOFbQ+FBPtXc5NwLzEa2vifUDVCqe15VpWj7S6oqe6dyOoCsFDkvobmKlbSsEoa+BvMB+C5OOuyN35WM7NxPpPzKnoYHsezEIRynPyjFB9sQ8Sp3vhpoMc1VefcSrWuO+Ke07TPmm6R3PXoEMcX8+zMLo3XZ+ewHKLCyQL/Keczlb6PN8w3xbom+oLplYX9Ct2eznqX+5Q+IoZrlgds2P7GgCoDGi3r65b86GBiRc0JuQwbuqk1b4QRK6/hZS+yQVdUHHoZ2gUkyMm4zRO39hyUv6654m9hjlesdfetQ55/vh7PlbvMJQl1u663zgWxJ5YKRo+s1adsuKkravrVokA+QMq++pTzqDa6jj+6jTu2PamO3PHYxK5Bj2aadCCxOygoquL106sv1HWBb8N7fG2Ezj+/GAymceTuP0zjVRkfZxf/b0Y6j7GsPmg8lBvMk6ZKzbfx7qXDeaHepBp0DbMhtb+PPTP9TpBeidUhlzjJvewb3cqV7d1nGuwIr72jY2dyiKP70K3fdc1eqPmgfVd1zVPweXzxBj4tsM8qXhgvuG9aH4n5oOKwhn02xs5GjkDjs91DpyYn2DJxKBOxniH+SBvd0Xc5xUBfenSpUuXLl26dOnSpUuXLl26dOnSpUuX/vH0pwPAZay+z1cRrP5jVSuOVrGmFavj1hIvl9u0mWBN/YhsLYxIuYCb+eo1XdCrgshPaHd1XZqikjGnLXcl3nA3RbtaCarlAdUwN9ydiMR6w1LgxKxI73gkQWB50HDSBUnlzFwwL55QJ8a2eoiHQNmKo6441soY1KDMqXHXP8cnNk8hG3ds26jzg4rEp+Nt0Fanq+hW/c2aBtjE2k4lxDxoPlYkrJyqcoQul98A5upeNnQ/DFqFemJz0dk4tP7ucnnGkzSj0mhNYDMzyZjYMJqbnNmJQBWOWWHbG1VPqp54fyNT3ajWulzbzZcD1KgsvE4qFVltAH3/sUdeTu+ZVAux7JqkD1ouKBgHihgeQIPxBqQipW1B8uXo7FG0DtNuzJhYTLKcWQczkgwIP9ksKN8YvTFdbkYsFg59xRUv+Gr9yx1r9gJ8TdHBNAF0M+3T1ceMN7x2PcsqXXfJScqKFxbYVTy6WPdc7Hh9f50YjvdB5iFnrSkSnYau628gNGtwIs1oyIVb5trTFXJiuxyscvzGupf1fa9zXSdOp+pAgFjR01vbib4RaGhg1qTXpNm25jimHPYUxkbFKaAPijFG35tT58b6DWtOzVM/G8uhbLofA9y0lhEsoL66r+eUM3hsxJyK7nZTBHgm1vQOKCYwyDY0iFETbFLnpHB8v9HPh6Ko+1xx2R1sW/DVlit31/cdDw1GzJO0zvl40m+73mZNyQaZK5q6lkO7b8shvTqfrVNtg5oCtX2Ava14dlfseh+rnxqtT1vu8kw5wmPqtTGG9mZMbLxR1tY5jwXtQ/uLgG3Hmsul3zYE3p9kTdw0GFLkAtk7tEH5DvPXdR2Kvk9O6HesTo0R5Cfed2o0vA/m52/AVA9w6fdDNddgx3Tq+VAiwQuWX7p06dKlS5cuXbp06dKlS5cuXbp06dKlS/9A8v/sC/iPliBTKuI51IdqhZhG1XIFr8jezOVXC8GHBXZEItsXHHHfX6nJwsK5XGvmNGu0OOm24o7brvjdlFfWXj3BX3G8yw04xopGdmzf5BL1Tb29Vli+op2LXoU1te56GeYFEXJW2g1rbz/AmOnWKeB2F9QwRWJXnkRM5jnlYG53bB/YvkMJNtl4x8aGN8dHX45XdYaqP3XB2Yrl4DNFr/q+gOFTi50mOIS4e+s3bH/Dtg23oe7SOgSY+oDRFP366hq1DpXqoF0m7JgCaNXGirKNr/jasCIpZgroYEmzHbc3IeuqBekFfvGdfPUYk1hMHLmKRfX0PMucLEUcB0/xP98AJ+PlXK4F5Nae+YoFh1c8N76tWGNwN9xgNGe7vzNGo3EyqtGy5ExPmM8n5/lkxmRWEOYCr6/I3iqwTdC5bYr5djmajcTawNo71nYNJnzBe9b+n3pWL8jaxupaXefjK8PaMF/xvzaWmxs5632QNQUnIyjrX1+B76tbmdVLrC7glws85knkpPIgp9arytUPDMupHj/utXI5MgWDrabOdK2+5tsbfd8F1H1dp2/rOm4LKP4YzKDO5f6NL4eu4OJ6fmsv1DlhhtbXXPcZr5tEsDPzB6QuI86DGoP0Rp4ndTzJLGpO3VLJIVul/uw4nuTzwTyfzGMyD8VJy51t9HHHIrHzqTNmTXsLDV9Uhc7fnAKoVjS0784IzjiJSg02rBRvvU/WvZrJTUwjz6nI8DbWFljr3Dasv62zL5hvFLYNuYXXOahXmoIv9/ZKHDBQtDOG9Xe979a7gjmXozzxsePbHWvaszmf+P7ta9/k+cDGG9buclvnQe1vVDkVJ+VvBDtZTe/XU/3RjpIexrbTx0azYLjTc1LPT1rd4Pld52Cqb7h8+//5u+bSpUuXLl26dOnSpUuXLl26dOnSpUuXLl36z9KfzgHssOCOyRFLqjfTJuW7uk9tE0BtL2jqmBtmScv24gzAj//abLlAq4gC8776U3eI5ZJ8uY2bYk/bYiLZB5C4KUa3vlicgKHnihq2pyCGBXAjOQSpFtA2cvVsbupOJeXMq7HiZlfMNcGrl9f8RsaDqlP8DCBPuXVRv2bGXEB28tWfPCc+dqwCN8XdkgG2YDW5orUDwnFz/AXYTfDaWsN9o+aJedK8Y3TCT7zkxO1dkPh8nlRr1GxfgBkU+1zHh2K9+yAycO/UtstRWYr1zaoVu9zEXG0I5gIZ6lMtHPNSVC7LkV0mODpuC+Iut+6KhKaMzNDn241laxXYnJ9kJV5t9RvXApSmftIXOM9VPJwI7rdNjvOctGaw38GLeTzY6QzfCYPn+UmPQ+w9Gj5MfcIEQcMVDI35huW5vm+s3mQUr2vrelmDDS9u2W5AqeO60PVX/Iizbq9+2FjR4Au6u8tVbq9nVNQrMrk6RRGx9uULINeJN8Wkp3cMAcWkrehtp7nAd203qCAr8LYptvoFgZeLmXiugQd+PN+haGLLB04Qx4Mw8Npoo1M1cRplGmrQIVyf+1qjPNcaveC3k+epfdQa1hrV7np+tRzuBjUPImJFK+ts2LhhZjofaeoUriAqMFdcuOPYnHq5+MBSnd1y5+5479jzO47Reqffv3EeTxh3rO//S4y2jdvrcONRlDs13siYRDx51sDvP2FpOOfXe82sCe6n9muZE/0O56d6e9sGz8/lUla6gd4LpWcbGgixvlPPT8oPbNtge9ewSBScn1prFA2ugQi0lx7ffwyRlOudOgPcZEqPBJrc+XnC+YBx1wDNPOB4kJhi50dfgx26r6rAthvUAfGkbdsaRkj6/WdFqH/8Fawz7ndog5wnlFF54PvP/Dgwly5dunTp0qVLly5dunTp0qVLly5dunTp0j+O/nQAmFe06cuVySbI1ZrgL7HAMOCveF4BW1v9tn8Lc4EFwlw9oQbeBNmC9fNmfxM5feC+k7Yip+e53LMNm09sa1TbyGPBSuZijeoCdnc59HzSvEGr1f3rKDg5qJhUPKArQtZo6m4lBalpgr6Zcti5K8Z6ydzJKlpNMqZ4jKujWFHZL5doYOXLAfxySrOitBckXfDPKgWoYUG1Un+vD6xs/Tv1MXumIq5rgu30+SA9SXNq38nPDwHuOhd0Deo45WztjVj9rIpefsq9nR38Rusd4pSzMiaYXN5VpbjoTK37q/c2D/AmkF+hztZc/bBmEEnGIbhXC5vVScfI5X4lS89q7YGySdEVLU0qNnnB/vLbV78sK67afdBvjnWHgDgTD3XShhmBo0LoA9t2zASqM05ogx4Cl1bxw+FcqEP35Zx1wVgrBGx9xfBia7/9jUO4ja9o7FecMWsGgEqqkrIuFzmJTf2ZhgnO5eQEWx7rxHVEeqcDeZ7gA29OzAM45WVt7cs9nZH4GAr19kHmpBla4+X+Lhcsdb8R7jAP/WsrIk9mOH1AVOFsJBMzl/113QeU1rNSPde1oqpd0Liq1Ffb2uo0rhVZPNY+d3X6rshqW+vgzfVzJQctEQsYd2oqPh1f7ugQHNXshq7J+85XTHeqB7qNTrSmoYgMqqEY6Tb02THl5q3C9xs1DX9+5zyezLEzK1f8/MRNse+VpfebG601OZsLatfghluD1sk5qdXLTBvU+alBlQqs9E6zm6BrrXeAKSud2t50XcfvMHa9r3J1s+9vus8san7CuFGcUE27MZ6YdzIeaxBA78I8HnoNOT/6uW1F3M/n1/AHBOw/Qz4pN73jKxW1PXbY3vE2yAgyDmwMMp5ETEWDf0XoX7p06dKlS5cuXbp06dKlS5cuXbp06dKlS/84+vMB4BecrJWF3BxqCnQkchHGhO5iYnTBVFtQrBYgoss5lrDIE8lYoFiQpvu23KHq9MUGvkBfM0gctwEzSHdq/wYtsVTwdJm6O/WdUC7XpVyKhkcuEJkQKUDYu2BgGJYdb4qZ9gDrK1bWXA7FSCoNE7UWeCIoS7JcENSXK8+a3IhmWG/AjjFp/aZI4Lbx5QJ+wZXlwqwF1gxbJsEukNyHXJc19Txe3avmWMidarWitPsmWNacGu+Kcq2SK3m7QRwUp2puN4czBKq3u/pUHZqva/CNmk99JZ3KuSC2y91XAuXu2wLJQ/eV82/in4vClql6OcqrwEIdwyVwS/mKv12OVBsQJ2n6zqoUYPxyFS8n+QKZVerE9UI9ugbNHkQYNnciDuYMqm3E84HTtAe6UWV4FmHroL/cu97hBZ2Xx1ZdxKuj2VfMdk2B+7YtKLpcz7Tl6NVegrWHMtc/rwhw1llrru5VWE7drjNmvuKVjXSjzbU3veFda0N3mIWNm5b40FCFlRGlwYLwpm5b2W3Bd4qDWXJWmzW5Z71pWQGLZM6DzE373U6aL1cocjYXifkGhIDyC4Z+9SErO77MtD55rhrs1wCCU3NqTawrtr0CG52IU+cL1pkZ2FiRxD11Zm25imOqU9cM84GvTmQA7508f6dhWJRmEObU+6SMum0LXMfq3Q187KhnWd/T+51M43ge+N51DRQVgbuTeQpY54G54xiZ6kcXEB9ACP5SgqIrzlwDAoUxBYHRQEjlq8u76Xz4DfYG8buOiTWtf9MZKl/7NqbeP2fI4bvevRqmmbDd9f5r6P0Qp/qHra+/qwGESjALqhwrvU/j8X2977qSDbwrlj4maRAkkaw/v8MpWH7p0qVLly5dunTp0qVLly5dunTp0qVLly79o+lPB4BrRfiaKQqZPBeIMMoBV0RzWSMxmKk4XlyONUusmiKjW1+puYpX7hVkKMLZvC2wlz9giHeyTvG1KjlrTR5Ie6XwniYnmje5AdOh1OnpK+62tQbzKWBnHbpjUYI0YywH84FX4H2jEQwHaxthpa5PakGuUqQsDsMVRxtB4UTG2iC1nH3LIReJb7vSpl+9trZAtdsCf68OVb5AGX2j+cC7wSnXo7tR2418PNYCGGWunlKKnBNvg54nGU+5U/fO/DwE1uaDau/qH06Tuy+ctnppdU36LIG95T41MEuyimxN++HVdbvcsbGikr1qgcWXq1nPIc3ISkH5UjyvYGhRaxCgtfXsX8DVGtadFgdlhvW7HJ7rZ8jVnYz6jaOKWi5lz6S7q3e0F/jEDoH5jCdnFv186rPqBQ9PcCNWN7R7X3t/k0MY9Ly9qWsZUw/rF/he0BvWoo0F/WLh3Vfs84LWtYDn6hku+a/l3o4F5VpbjuWQ+97veMlJn6hLtiqJNMw7beyUGfF6PnNC76uD1QSvle0uQMukfNCqmDWJmOSM5aQtmglixjnJx4O4veP2wNjWmUX7hLb6pf8GZpdR/nIiy3FqbcUr51P7wYfgPi8X+QlmP8KCZ6zBjtWr3Tq+Kb7ackFRjJpJ9VTUc/MfaQIRy6iu8+htI+PAFwRVMvJJ9TeBcbMvZ6yNdz2n84R5YiS9d86cxJkc1dnv3zBCSQZ9JyuIkNvZPammSGzhazmTfb9T88GrkxtrmA+oBzUf0N/kpn8NQuRrEEHDKtiUc9tuy1WbcpqvtG0qVuewqYe3kNO41tBJhmKm15CDRVCnIvNte18O6wZRGghpoSGEbFT39R79Jod/BeYNd1/PKXTVBba/MY/fcNtIjDqe/7ZfQJcuXbp06dKlS5cuXbp06dKlS5cuXbp06dJ/gP50AHgyadYpTjxWlK+CZ6my5RTsy/UKvTXwXa5Ok0u0WG5Ie0XCrohcW6DYkSOyVmeob8spVqt7cq4YWdkRvdtyMkLFhCl4RwBjp+aCI01A+ivmtlKfm45vQ85PXxCpd3WzzpOxD8Ubow5eRdcaOYtkLiDcBSh9w5oJqgSQAstygt4FB5sL672gqcnFZ+4YXX/mchDSbnAGlR+UKa63ZcEuuGjbWIA8yfOkQnHZthmeKsbNCFpz3DfSTPcwdjkR67kidF1O1VuXW5PEM+SOdtfzMEUNl4G1ncoDq5Nm23Iz6pmq73StL67PAF4OXVJ7RQusXlTyKcScgueJ4y2xOFckryJtbcWCW+9f0dm0AfnQd9oaKKjlCiUEs7yRBlhR5wkcjHGn+85xOpGKyY2YZB10c9xvAOrcbV3J3TEV22tdfdM58dahdUX25lzGTTmYBcbRWviKMa+5ILXWpFZ8uFnTAAEsh6oTFTpX3skFTGHt2zlh2wXWI6jWBFA1lgDLUypuL4Br9VRcenc85PC0vq1nk18mZWFngfQv124V7D/hvdjsgzOLSMPng7y9A0GrNYyx+ow1IGLquB3v63Ncey5DsJoVhV1rD62zrvR2AeMvd+yrI9oLi8D7TtuWKzbm6gNWJHlVUUfgW6dWrLeh73FCwyLdYH+DjwPLE085VSuT2hVdzIraxge9NSz12a03io20RsuT+XhyVmIjGa0xyom5HM4M0hLPiaXAdWXKFdwH1m/Qbth8rpjoA9omgAwLlht6qaw9EOcC1AgOl8klbE09znNCt+UmR/uur35l054iD0W5xxrMOFN7OA8NVzQ5oOv5AfNTwyXdNdjgQz9H6fn+f9n7n2Dbtiy9D/qNMedae59zX2apJJckgwFBSBbIGEKiQUA4wDigIVuAG3SAHhEEASYUgp47YBoSNNSgYQQRhMNh/kRgG4nAoArLokyVq0CBrZBdtkuucFF/5LJKrtKfTFXmu+fsveYcg8Y31j43UUqqyluZ76Xv/Cpe3fvO3Wfvteaaa52X9ze+77Or+pNzwvgmlujZZxsxbuBPhDdsuyj6O96TT8/f6Y+ipaWlpaWlpaWlpaWlpaWlpaWlpaWlpe+aPjkATHTSUkDkW/p5EbyJUJzwRHCyKQ71AXofbkgK0jU5RGMo5jXPHlmvTlTgAaNSjt8UiBTiktssA8KD9Eu51TYs78B4iwIeVp/jsG9y0abek6wIVNNBmiXeOr1VFPHpQLSU4y6AvmPH1PFS0Mq74o/TBKZmU5zweNU67Jfq9K01wx+dwHJGl1MvB7Y9EXEQbhgb6Z2wpG0bfTMyu9zHc2BtQxW8Ub2q8hdaDnprhDlkLzdl0rZO5GD4Z+rnzHJ/eqf1iq+OO2Y7jmEPeAbmOnbzTd2+GMkdvmVPNPX2ZsptnTegkf1KjBve9zdXYug65HHDtgtt2+QcPPOGCWgXQVWjunib1i6r47mOkQzC5Ky16rVNgjEnWGfarOGARo7AHfoOzgUbCS+DGAeBooPbVb2s5/pmyAGZPstpvVeYNbUPqGjlilOOfDsHGnb23somXZ3StQ9iVhJ26PUmh2iMg6hY6bY9Aamu2u2i+yQnM4NWcFhR4akBiugkU1HbsrFC3/FMnHtB8qj6YcFjfNe/p5y2cdzkxN0u+po32n6FmNr1VkMVOBFTe6VivuvBoCGOGOX4H3JPmyvKPO+YyflLjLfjdEWuE6+k7bXnUnAyguzP0DtmWey/qTc5/bGuhsm97IE39d4+nkenPXamOr6PieeNOSdcPmPGwO6KbU9L/LrXcARkS7jdcH+CPEgmzdXdPG8v2L7XQMRR16NSE1qH+00pA75jY5Dj0J95I7OTx+d6tsTxcALn/XPF7Z+uYXRvKB49yUePuAYm6O/I20sNmNTzqnUgsX2vHumBtYaPW4HYfEB0f/rqB9HsgT99Rrx8Xddre8LU0k36UWkKlX6wX3Sft2cyDqx3WlR/dndsDPz6mYDw/YptCwAvLS0tLS0tLS0tLS0tLS0tLS0tLS19+fTJAeDeYKThbIILoThZMDy9Io0n1grm5pRhz1rBoQLCFXOrP08o52sU/LEUyGpN/aPTEot8xPBmvArS0QTmMmnVe5keghH1HrZvxD0quvRKMiEcbMMZldBbPcNYdYsG3p9oTTDLTlcgpzPZwA7oW4G/chamnJg2q++0lVuxXR5O0NY3LGZ16gZmG6BIZS/Im7U2GUmk0dozmUFEChDOcjm7kcdU3LNteEvididdx+l1DI0k6QQHnr1cgZtig60AjhkxU7G47rg3LHXOVpcSu8PscpG26vid5Wqs+F9rO8wg7jeid5yJ2UWklQl9I4YRJ0gm1aV8fX4AZVoS4xU37Sntm0lNDugfO2OjW3WhDuCovVmuY+uEy+WtWGVofQfbmIdis3u7MBK2S8fzwnh5zxg3cbO4QN8qPll7bY6ClGm6XqhL1wn1vjZ16lrUBEOEnJSWcl9W7LmYbFZk7lbOTCfjrmjjWe7r7jCOt9POA2uboF9B23RnElqvTF2TlBM4KnKbHLA9P5Yw2xVaF4wdd6JfaK4hgcQIswKFOn45TYf2fyo6O3My9ydmHBoI8I1Enw2TmBPru9zacTyQsLqku2ZGctJrwMLOLmya9tgcxHkv+y437Ty0nkAcgxHgXfsjqD7sOYmZ4sih/vG0c77D8YtAetzvZap1bL9iJH7pZBR8dire+yLn73nNZpDtKudrBE7Hrl8lIpgxGfcDdSCnopBdT5ncdg2UjBfFUvcrFq8F2wV2bXsij1e5k8+O8u0qB+6csF2xy2dyy3vC7a6o8taqj7fcwv1Cvv4qZxw0AGGYhfrKrYYPvO6vOORG3za8dfL+Kje+yZOfXWtmaY/wgvRG3tURTgPGXc9Ld5LOuL3Stiv0C5YpQD0PfBzsn/0mRZgvLS0tLS0tLS0tLS0tLS0tLS0tLS0tfcn0yQFgEjxvJJv6S3vHpnpW5a6bsDW58s6o5dMZ2jqPPlsUT4zpa+kJodfaw1EsZ6G5KU7UvRzEimNVF3AIWPbOMY6Kmm7qHd0v4oBzYq3AmJ3xs4H36sRkyKnrUYfWgInlHeNSx78Br4xAMbRxLwYpgJvxCu4Eic9yBY8s47JhWytQbNVdXJ2ornUwHLIVYBbQE/SzijSWwzZx5v2gbdBIttaZTZ8rOJi4Z3XGpuBbDPXMutHtwhxH9cwGrTnN5ezMnHijgKuOz+asy64uYPwdxA2LWjfL+vPTCX5mCAfTgkjBxN6vqFtVfc/WBFCJIEfAfiknNoBclt6fag3uBespYJqVLD0xS2aoa9iynKYGtCeYr2CBc8GbICKuAYQ0aP0E704voLw9PzG2C7fbwby/kreDiAYMcuu4mQKqQ13LM+6VSCwfrXvDbFfEr5vcqHHXZ2YI3j8iyKfcptZoGBGJ2VTk79kAnAKwWV3MYdWtW67ibkYeSW6NSHBcMc++0awxby9ymM+pfmtCcD4T3/fqpe24tXLtyr3t405gWoO26d6dd6w6uiFwUwzznK9EPGuwojmRZx+04HOja68Uec6c0LpcoYmGJnKrM4ZHz+0MSAe71J7IclQ3jE5OAeUIw467gHfFThsO2w7pRA6cQYyOZwi81rkaTpj6bM2Mtj0x50FaMjJodHq7CqzPQZSDOzfFg+d9aPDD92psBotBxKzubHT8A7AgYmD9iqfjoQ5vzLExab0ct1bO+To/yplv22dg6r4my5kcjm1XGEYeL9il4rzr87k8Y2GKcb5/Uz+xWsc8yCgcn3LmkgPzLgg/7m/DOeGKwu6KLY9xV/9wua3t8o64vcfnHfpevcJU53AnNj1D8/Y51p8FgdmgNeK2OoCXlpaWlpaWlpaWlpaWlpaWlpaWlpa+fPrkALDCYuUepVkBmeo0jQn7EzbusBWszXiAPScI22n5Wg5gvaNct+f7NiwP+AACG0Hz7eF+NRJzF0QNRaBCo3V1oGaUS9C9fi1gtJngRAq2EhPb9rf+0RkPoGmG+kzz7LicTGtyMUZUrG8jZp2LG8TtEX+MF4SmYm1bOy1zgp8PkN0L1iBwW5GullGQNmk5yzW94dWFG/NQ/+nWyTaI2yBnCIxuHR/JtKy+XX2ejYH3C9acOSd5Aua+oQJlrTX4o/fV+uXx73kWxLaLYr5pj65a7LwV2uPzaJteZ40x7xCTsOqVrQhbMqCXG5ypr1c88tnvm2NiGNlqz4wzG1xOR89J0qsLuZyu1sqdHPoYN7LgLzHehgFMvmL3hoXc6tveCTMshmB53jSb4JDuhHWMQcOYc2LNsGykdzknM9TdG0PDEadLPCti3PsjKp2+AU0O43JbE0meYNsVT26keobnwH17i5EmSQ4BYnNBZjP1Ekdi204wsX7VfjqGXO29M2OS0/Ct4d7wsze4NSI3skBf9lZdvhMYEHKMphmOMY5kthu+X2HecDNGpkBsf2YyaKle5plJJoLHWYC7orsTiEwsh1yzqbOjjqwVDMc3ogYi5B4fmO9ymYYij7N39Yq7usBj3BUl3S7aJ9ReN4d5Kye59hlzaCvbhH7h0YNMMsddcdGcENZ1T5mRQ477YHKMCa2xZyfjhrVG1l60Wc715ooSbw3PqcjnRGB2v8rVO27QNgFob9i2K5V5joLDLiDfN8yNjBdAzztnktaxTfdvbE8wXsEaOQ65gN11j1vCBN+e5T5uGrphCNBa22Co+5zetE4xEMjVe+TxSo5Rjl8997JVj/K81z0+cO96fQa2fXI/QpeWlpaWlpaWlpaWlpaWlpaWlpaWlr4P9Mn97bUlmH0mfjkH1jrYLveeCZbQLtU3GaQlkYNuraDukEszo6CponUFuj5wB2eq8zJQt6WdKarlHA45+cycyHIjply+ZpD9Ko7YK8bWXJ8x71jfBFe8Iou3Kzm+qeN2p22t3scEkUAOWTppN8hQ1+64CaBkQH+u2Oog5tkbCjZfBGk4sDmwPGjbO5rt5YAMnacrchdQfLX3chkrt9bQsdj+RGtyVAbJMbReSWI2yCkwhydmV0GWFJRNP7tlGzbucj+6VwJ3g2mCVHY6tD90cJfDNs8Y3Kma2Tw3hWKlma+Keg6reGu9R8xR0dshmDTVP0sM/XrGEltWnHAnYuDWySb3ZWaTO7ldyPGebJtcq77heQJWexyLmqpDEbpR7lAzzLq+VkDbH9nKvQ4i2Bq0d1fuL4M5gjgGEWDd8U00eNxfBKjnrIEEZzw6iVHMtN0F83NWdO4mp6pF3SMC72cHNqSg9EyYNQRAbf7M8qwCmcQ4GG607aq62bTaL9UzDQLBdLmKM9XDGjWIkJCuaPOwJuhKrZUZ9K2c+YccxHPUfVtO3Gy0TDIHx0291nZRJ7LuuRrmyDMm22rNnYhznxnNz+85BHofnclayqB6br1h7EK3U65qjQs0oOPnIAeC5nk/NJzSNsFhEHRt5TXOpid4drirq9fioHlyHENw8nFtat/vnwkqH/eK1Z5wucpNb8GMg96fCe5M3xjuevaNb8LUOWRFrM+YGkKZDbbOcXvBbauBFa847CEXfNu0h8yx9HLcU1C+bh6vqOt5r7j0Vg5eRZYLupo6hqszGE/FyN/fY/tnus+9Eg4IaBoOyFk9xRjc9NzjjON2x3on7Z0eX3ngUXsxp6Kw6TBesctncsT3Kz5uzDOqfWlpaWlpaWlpaWlpaWlpaWlpaWlpaelLpE8QAJ/OuaJcbng5D81NLtpWLk9rih9uF4JJO/tJz27gOCNPQ7+iTltccbN6//YAevYonizomSmQZS64hOKLGwl2kEy8XZgHArJzyEk6BAFtGFy8ei+fwRvenM1Ckbgm12Km3KSZdwjXr9sGx3i4hhv6mqWTfqX1jt1udX4Gh8P+DHkrZ6YLOJ3O2miP9cSqPzh0fm4N5sRp6pd1w20n4068vggk2gZ+uqGdDC+AbnJyppExIFsZlBX5q17j6vXsG5gTMaDckY/I4tNxWs5n+rNgeibpm5yYqHeUUNSyiGk5JaHgcWDTC3AXhD/7Tyt22gMyDrlU09SZyoGfbmw3Zr9WjLbg+AN2mSF4LSdouMtd6XKREgKWZrOihE9aixzLca/L4Fgz9utXud9fFYN7JDnvikjemlyOs9yhzQUET1CYdZzlQnczDSCQRAYzQl/LLNDdHmsWyaOLlVlueG9vx+WGD8VNp2uwIsehPl+SRg0SVC+11kd7mQHRW7nyh2KG20W1uTnxQqqZFSMe94pirnv08k6AcJTzuRn+emOyMceBu4ZCbCIXa06IVyKMtl9x9jLP3rXv2yaX75xETGLeFd2MWpIrVBncGWOAH2RWlHwm4U6WM99NcdJmBvMEkAZ5w9umzuxAn5HlKN42rO2YVyy8mZ4lkQT90YM8M6vzuu4Fs0qqvlR8O4Lo6H5vbX+4ncMCbxc5dMcrjBdiEyx1Jnn2OWfH5qBVh7JvXWvprnU8r2u5hTNqMKD2leWh4Qt7qqSCSZqT5bb2DHLbIC9Y0/2j6HdIv+q6HnetfMyKnb5g1oj7i26pqEhwyxqcuSsEop691utZdH+v65JaG7OhDmHrdf0nedyx7fId/BRaWlpaWlpaWlpaWlpaWlpaWlpaWlpa+u7Kv+gD+F7LKj5V9sITUoZqTU2RxmYCSRnHI5Y3szonH47S6ht1ucqyumZl+aso4EAQsWKG7YRo1st99hYbO3I+YnEtJ61fcBxP1PlpU05ASx1rd2gJx4QpJ6i1N8jpJ2StaOOIwUwj3ORunUNwxLUJDGAMRdhi5drbFc26X7C46/j7czk94w14YgLrfYdInEY7DkHq053aW8HUKSdwc7xfBQZjFiAqoE45lGcSxyDNyNaZtmstp9ZdqcsViY0iX5sb3p6qr7ncrJH1GfbWG0rTtfaOoevLrDhozghdIzHmHMR4JY47bnvBs61c2Zdavfr+CPBGMxMEPqOyz0jnFKjEjEhFZucZB/3w8gqikkkzuZ8jdIwxK3KccqNmAEPGYUOgPJOMqS2+dbatsz1/hl+1H8ZxZx6pz2g7bHvBSh7u6BkTRu2t6cS9OnWrwzgzCIxhSaYzwhgRjKn9DDUEcEaHg4YiTujdu1ytzctta4roHeNxXgKXs3qXeVtn0/BETMU5M6eAddb9Vksex9A5jAlHwuWpBjIM6w1oWH/Cr0/0vmN+IUaS94PsOzknOQZ5HIrOftzekzkPRjgTmDEZ42B6J9OIGBwkg8YMU6z71HmM7IyYHOPGMGfijJiMGIwY3DMZkVrLFLgdAWPcmfcb4zgYYzKPF455MI+pfbg9Y7Q6wHis85x3MgSn02uN59CzLQO/aPAg511O8O0K5uqJHnfGeOUYd0Zm3YcXRqR6hmnMCOL+TebtzsSY1nRc4yAOxVx/yz1ernVFVpe793h9zONYDeLYdtEe4NA9H0PDHjOhPWFnHDrA8YJvF72m4Ky1d3IEu54Zvl11j2C0/Ql7+iq27eo/H3f1eB8vMA+tozeYN2Ic5PFK3N9r+GK8amihILyGIpa+X6SfsV/8e3w/a63hx2ut4cdrreHHa63h0q9Xa898vNYafrzWGn681hp+vNYaLn0nWvvm47XW8OO11vDjtdbw47XW8HuvT84BTIxyeR7Qy81lhkVCs0eHbALN5QQGE0i1D/+yvyJeIwUd3MvhGVgeipM+ncHJB847f8T3mkGzxK0TcSdiYtbKNZgPyIiBHXfCEtouF7I3udw4AMPsjs1O69CzgnabCWoVsMqo2GDTceOO7RfieMVTbsWzQzSYNK8Y3FCXrc1Ba13rEhOrqGn3XnDnRtsEtzMKRBokDUdrGWMSdsO2K943mhvzEKQSWJWjVh8xMU8COTabVcQvdS7e8C5A683JMMFCPxST7eqg+e+kAAEAAElEQVQKVdStnLNv0cmC/OadnDexxeqKTdvJFOSKgjxJp7VdMD4RePVGonhdyg1q3tX46ldsvirKNl2uwkywqAjcxD98VpWjNY7A+la1sgduG269opl1PdIVE20hUK3e6SiInswxHhDPzGjtQkbQHHJ34vZKZKNt6su1SDJgNOiRAul90zU54e329LgzJpNHv20kaQXOs7qkkTM1Q+7f1ja9pvdyjGvtYk7Cz4hfgFQ8ePXthoXc66fjvoFvV6h+YfpOwyumeWLIbRpZEBkU+zwD9l0wO8ZbvHt1YZs32ji0X3zXfWCpc5yJtYt6q8dRkdMTmNo745X0TW5qN7hcmOnF50N50HNoaKFvxDgUv1wdx5Ojhi2aZlHGjbF1GBUH7gLVtE5rTqt4+eaN3vdywOpzbN+w26TvjRnJiIM5JnPr9P1JIDz0vNA6bMSYtFqH9vRUMc9gU05sn1NO6jlI0zmzPRHHrSLfTbML8UrLhtmuoYqYWDgMdT5b2+B4IeKO7U/qIU5qEOaVjFG9z2dcdSP7Drdvkn2vTuWjBm/q2Zd1L7f29vtx01BDc7nh6xCNgMtGcqmd5thxaE+B0gBadVvHgW1PpHXi5a/jfcPaOw0ezBuGEfOG789v+2zp+0J21hR8we/x/ay1hh+vtYYfr7WGH6+1hku/Xq098/Faa/jxWmv48Vpr+PFaa7j0nWjtm4/XWsOP11rDj9daw4/XWsPvvT49AOwFZLwpDtmAHOUMLsBoG3Z2Rvbyx5YbNGOIKXh1nxLMMNzsEUFrfikr5ekQRlAjEyjncHXLknLKekEKs87wgmAkeQy5JFMxqerUVESxeRN8sQOzjeZOD8NaE6vOSYZhOQW2bjf1ZSb1HgdG0FJQyU1AL9zwcJiBd8Wv0nfM1bNqlhVZ3aAl3lwRyDmU1EsQBYkpNppMIh2fUb2Z6t5s5qSXozOyoGJg3rC4k30rGDYV8TxN3cV9J1IdvWYu5+jD0B7YzILJAusnsMWv5ZBE1zRPt6RAjoJ7jTEPZiiyW6neBayoHuJ8u65k0PuTBgkA0gTArAtUNdN1q+vdChgn2n/qOdZ6t159wtYESw3SHMu9opCNycAiKi4caDuZguLhnewNIpjjjnev4F0BTQ+dX4474TvukxhTMcJzMs3qGKrT2arL2eQ4lru3axuPg0rs1j4/+2ujeqGRW/50XVtTR3NYYqFYYIu6L1KR5dYaVjHsxgWO9yhTW69Jz4db2/YTqssZL5e1Yq0jDhipruz+XPfKUSvhBeJr7THx9znVz9s3GIdcqYngbnUbx1CXsO1PvHVHC7jnUDS4FYzMOWrApOu+m0F84IKNQzHvHEdBzFR/9vEi13q/ymkaA/pGPxMA+s52ecLy7ZlUHng5gccdxnvFIfdNkcdBuVtPR/67gq93wfPLk2KvLWmpGOyc0PNgzKPu5xpoOcH0HOokziSP9+T+jNmdre1E3sl50BPorsj29kR6dQ/b+YyzctEHkXesupAjte/pz+oDp0PcMJyoiGlt7MB8q9hnfc2aP3qDAczat/yaQ2saptQA39+Rt8/1DOzas5ahzub2DrtcdXxz4GdEPJvWYmlpaWlpaWlpaWlpaWlpaWlpaWlpaelLqE8PABuc8bL4xNgUIdury3W/IGjVC9zNigbemeWRFSShft9pGTKifcv0QZ78gQ8oWTncBIAtg7QNUk7PbC6TZ3WKNqP6LyH6Xu69TrbEjkOdnt1hdvWmni5aM4ggq4s4Ho7VJEOQmR6KPXWvDl4EyGLiAVRMc1Jdv+lYTMFNV2ysxUGjQQ4dv3dFZSdYKNI6ZaOUc/V0PPYLOW7iJ12RyuoMpqDZVFUyLthWQDjnIdciTh533IJ0x9pFsHLICUo2YKhT2LeKlz0zkivyNmfFXSeZN7Ar8reG4ndjCBYfN2gXbB5yPmcFRJ9gPwJoZBoeUy5trBJ4jTBX36mp61Yga5AxyNaI0Ln76UCGAmu6NR89qICZroaM3AH0x3GkbYy4M7NAuMnhHccrZhfSdZzWHLeduL2HeRCReJMLk96hXRjnfomKp24XzIYctBkFJkPrfbm+9SXPA9oZiV2uXVzRwedx5aC1C5bjAZXHeX1oWATYBNtrbbUvaDtg5fw0gcc5dU+alzs8gUMu1Rjqyu2XGvbQvtJ5FSzm0HuFq4J6a3C/M++D6K5K6uuVvN90Tx73RyetR/VLn3JXN/Mxaq+F4qSp006I45U5pzqCX170HEo5it33cgU3Of+bKYa+XzXccP+cux1Ya7h1eP2cWVC43e/sbce7ywk/JruZYtStFzC/8ZjGOJMNUl3ZYY5XTDqmoQoncB+0CWMecNzItus+rS5zs8BjY1on4lbg3ok4lEBwvJJt4g5bGJ41HONGHoeOoRW69g7+TM4XHfcZZ+1ydeOuYZfjpudPhNz3vutazrr3t8+wrP71qdfk6fTPeMu7T91n6Wqctv2JfP1cLuOtk7bDCLx37VW09/Ou9ALbdiKGYPvS0tLS0tLS0tLS0tLS0tLS0tLS0tLSl0yfHADObIp3tSY3cIJte0EmClYG3nc50DKxVv2tVu5UFDmarl/NQxHHZ6RpOfwev1eerwCgCQRndZbamTLN+efiztFa1Z5mOWBPd2vFP5OkyQXatyaAg8tJeJ7brN7WSFK5vsgdO8E2kledb5dzkd71/vMQPMkD56K45wI5Pg+sddyaXLnecDOsC54aHUaQzctxqVBtMxOkc5eL15x5ey2T9Kb4YpdzN7MReVSUbGItSGvEUTHHvaufM4E0geymmGpGdY2aHKOCabuucw7IQxHbOYEQZM4uX2TOgkOGheE55PRtTVHOhvaDdblWo1phW5MrsSDRRO7uyMTdiWyPztOOy7HL1PFbE4MskElEOUjrnFOOXScVq41ipB89uSmoP3GOSIFl7BETThr4JMdr7bOGtw17eiJeXhj5CtOwtuP7VlssgKmo6nlgWwr+VgS5epcrevcE91Q8+TmEkBUfba71DsfcSKuIaBKrGGZvncDEsjMFf0ntp/1CZhG7TJ3POGpoQd3AadWl7eCnU773cqkG5F2DFHEOZaTgYZ2H9a3u3cR9Z447x/2g7xt2KO7ZQfddZg0jTA0rhAmC3w4y7oBXV3eQhyK5w8vRWs7RnLPWqdzA1uVcPq9tGG1r2BhYb+TWyrGrHvEcQYw7ud3J65XpRoxXdja6K1q+u7NtO8MgMsA67oFXrHxmMiPl8t+a4sRTbn/bd4xB279K3m7MY3K/vX84b80u6g8ft9ovE788YeMFbGfSMGtYu2IEcXthWsNrCCFHPQ8dcibJrOeJkWww7rpX+wWbr+r/zo7ZBjbI2wu0rucyAAOOO7bLSUwmHPGI+bfWYbzquRhdX59e+/NQX3bv2OVZe2IO7f/zOTFmPVsGtm1wv8H1CeaN5IMhgKWlpaWlpaWlpaWlpaWlpaWlpaWlpaUviT45ABwxaH2X03AIhNJPILE93KqKag2ZgG2TW65iSdMUa+sFeCv4t9x1Hyxpxb2at8f7kkMONavI6TBIrxRidV9aFlAmiQZzznI+fuA6NcNm0MzUAetyWnpFVQu8CclCdeBGxfTmLLiyAdWXGnfSn8op2WHrcH9VLG83WjNsKzdthhzCXtGqW9nqJoq5jXuByYHZRuLEPPCWwIXIgbGROchx4K3hdLlMe8CU+zEC3LzApUAcM2EU4Esg7wV26ribAJKinaEoq9YaATnMsLaR864/b3vB3am+4Ri0XlHEBcWtYp2DqJ7bclzT5KzWqwDwnOVa7oJsZnJgWoPmGI6154dD3KqzNp3HsUVMxSHT5FY2OZcjKXAZj6jmY0yOWm+5WyeZXj22Bjc5pzOH+k3dsL7h20Y/BJrTNzkac2ABFgfsV0Wjz6nzLVczVuC7XLeC4fMxYEDbIW66FrOcv00wGTfmCHq5b601/HSE2vY2iBEDmy54SUFjMzk0rRzOoyBzu9QxmG6pTMKiHNCKJbf6ft+fdJ2O+1uE9Diw/VpDGrMc2jDCcQ7gYGajXZ4hveDxXYMZ7YkcyZiTHBouyGOW47nu+dgwpqLIT6MygxZg1rG+Y3FgYY9Idhvq6CXvQMP6tZ4jikqmaTAk7gPbdo4x8QTvE/fEHLbLMxkHMwbpTqTuJ6vYcX2hqW83UhDYK7q7K6HA/MrWDuZVoJYYMG60/k57arxittPapvWer2SEnpGAtx3Mmff3+P5My0FODV/YmNX1q3vOcoBf1KserwLMrdf+S63d5VpuYT3ziEnEHbr2Up6x5Hlg13cP97Ci5Q/FmU85xdmvgOlnQSKofe0wbuT9pqEZlTnrWbbJTZ59kvlKjjvWL9/Rz6GlpaWlpaWlpaWlpaWlpaWlpaWlpaWl76Y+OQBsphhZKycg5fKFFIC0DjSYE28CNGdUquBUxzPrPbpcm2ecczkJ5fIVNMtmcg1ae0AoAYpRTM0LpE0dX8tyCrrifwM5LW8HWMoV6/nmXiTp1gSQDblHScFY78S8k2bMMdSTaU2uywjssolrPzqRXyt2VRHKtu9YS0XLng205gUCE6fhNmGaoKiZ4FTf4DjIdi3IpK7dpAkiJaQNfNvruO6kO46cttZdNc055bR0QXLOXmGr83f1pqYp9tksde36RWAwBBztBIhRUcS2gXXMkux17Qs0tkzBo3bBLAhTx+rMSWbg/VrrtemcXc5n5tT2cMN8w04IjDp4s23kPBjZ6rNOl7IA6jTDU0MFSuMNmof2TBR8qxByMFrrBfImY74SYQQdO170ib2Rh9Yy5xRktF79pynYtu9438T/52DcJr5teAZ+dsU2wbeMxNpV3dFWDmVvcl/neZ4V6TuHwHM791Ipq/vVhmqCt50JRET98aj1HGQGYZMWnXC5n1XRXU72CMHIrbqnp2v/GXJOT4h56M9GQE9oT3UPJ7Zv2hdu0OVIz3AyX+RMNWcc7/GAYZPeLow5cPlbyUO9vFFDDOQEb8w5mJRz/HT+xysPiF0x156J+RXbNtqcctFvHZsHNMfPaO9sj+ZnwciBWwhwm2lfTA07HPOOeaNlp/legyRZUdAH2JUIOfmJO5YTtndyvucgSXVBV/S3zUnLJFuyYdyz+nHjBePAtyt5DNzKLds3zI3x+qv1k6U91oU4yOOF6Vv1EOusMgZ5hPZAk7M/3Wnblby/AKH7NByjoph7f7uHCMVC7195dC6TgT19BUzgXdH+z/D6ORGv2PUdj5SGGILPjoYjRgKd4C7oG+egxwHXHear4qTnJOseXFpaWlpaWlpaWlpaWlpaWlpaWlpaWvqy6dMDwK1ijr0LXLaKTo6QExd1n5qbYGfBFcMeDs+0pohW5QqrFzML9mRF5ZoL5Gag1tiKeHUDv1SfL8hB2sopmYq7rbjpCDjMlFbcGsV89D77rn7acKxZwU8/uQoRk5lJ2iZXYtwFlN3VR5uK+LW+keNgzgIwUbZUJtYS+gVPuT5b8XKrzl7vTqZcrafT2dom+NUMy6PgUisXdVZkL5ApRyQQ9yA70B1vV5gver1PMmGGfMy+7czbe0H8k3hX32uR8jd3tHU5+OYA7xXnfdOxxahrpKXMEFw138iUc9fP4xMtA7/I+Iuud5Tz1dH1SUOdzPa2RzKGHOQ4gamHNyDdBN8SON3jOG6pr3uD1tQX62gPNjk4k4PWnjDXfpsRBF2dvu18yyhgjlyx/QozNTjQLlrf1vXJXWtm08g5uM3Paf7E5elJhzWQW/ZyfcBejiGoX25pZgrSR8OakXnXRjkOXZ9yNtOanNSpyPGZBeRnPuKN5ZZGfNzAutMI7eUMfH/ikeS8Pwvc3V90X7WO0TiOQ9B8RN2fE/OnutyKbdYuV/xyspFjCHn3K3O+QMqpfLzeyMvOyAnHN2jeOJG2G+S44d4BI7zrfsihSPKTc45DbuVtp5kGAPr2RGsb7nL9Wu/l2NZ1krv+jg9TD21rRDpsGxmGX654Kvo5XV3LYTv3cbBtTc+vMc8AdrJdiAjFcNMIcz1DaqhgYrRNbtaYGiyxMq97DDwCUBd3az+A5ZA7//pEHp8rDQEnrGOX30SOl6ojj3oc7sS8YXPg2xW/PmGZxDGZx+fVL671yjmw5gLKxyvuHctXsKvcwzH1DPOK2PeNyBTonQPru56Fea+IZsXh07bqL6/48Dzvh6nI9EyIG2ldtQAHYHc9T/pVxxYV/Z4hqBwrAnppaWlpaWlpaWlpaWlpaWlpaWlpaenLp08PAJ+dvFNux0cksrVyqyqjVY7deq0JsDrGzAQmZqcbtamL94wEPrlPTszU65vlnNNntXLyVq/w2ada7lZDoCznFFOeUR3AoIxXgRbH8P2q7z1BolVMagBZ3x/GZMK2we2Ghfo2zcoBTJJc4PUmWBp1jPuGd8MYWG7Q9weqxFJRsgU1Bb1SLsI4e00Lvpaz1qsrM7NgsdWaVKxvRtdHJ2cdsq5L79j9BjaJGLA/YfcX3DfCg6TrmoKgTlMsLBkFt+VuJOVZtr6R8/VtH1REdlU0Y0xaXZs0k2PW5BQNDIsCke7Qu2KsZ8Lu6omteHAzq/7fwBkC0NUdLUj9gUMUBHPnpHcNCuTpds5yPHorV+6FlqnOZ9d7ZKT277jD7qRtcpbfX2H3coyfNJLaa+WEhuodnjQmx3RuOAzYfeo8t10QPcp1uVV8+bzrH98KeOZbtHB1QsOs92/lrNXeyIrcrgMQfA85q23e1MtrDvNV12EC3QX6xlC/9P6k6Of+ROTQrWuN5EbcX3Qcmdh+wfoJq+Oxh7Nt5HEQKGaZQ/3JLb3cpEZcNkHCIeg9xsGIwLcrdtz0/nlXVPg5PGEVJZ9RAxpXvAXNFLNsrbPtF4zEM+WUttDASd8xDzpRvb9DgxS2y8UbNSTijuNgW9VByxEf7ByZ+Ji0VJS6t86IiRNkuCLY9yftnXHTuVaUcaYAK071iCvi3nF8BLOl3OcAcdAuT7B1xShHKghg2wlTJ7Ztuj/dtYcsD6BrzyKnNfuznnPHTc84gxy6btl27Vfv5PG+jtMe9zSRNdByR3H2oedn3Avo1jPR1MttvsG46dyiXO6XJ/K44XHo+ZAVCd037Yvzvp036Ls+O6j7dAHgpaWlpaWlpaWlpaWlpaWlpaWlpaWlL58+OQAMFGXkAQnJcomO4+GgfUA1psBGb4DJQRdDbjwzLBOzgqCPAtMT7Pm3fqxfMWbx2q7397tiisOw3qt/tAJ/Zwo6pGP7BcbEIvGWihduTd2bcZOD8tHzm+qqzdO3rH5ZKzisj276kxFybu4X8l4AJqYciy7AbG3SUdypeXtEuFpOrO0FxU2QLCqCOk8+c0bfFtn1D0BoGs07s2KlI6loXxRfmwkt5fZtm0BiM7JtEBOPJJsRrRzV86hr1AvYTGg7WU5vC8PSwPfq5JVz00isd8HrYqQxg/RyizeB0qDWxo2WAm4jg9YNrBFMgU4QEGtdQwNxf4N2rRFjCoSOqe2SLo9wUxdr5KD1Tni5yGs/NbNaxcQsBXlBLk/fifmCzY3sTe5tuhzAUI5dnYW1vZzRco4KmiXkBfdgjoPb+wG90bvTLheBtpB71vZWIDUEg1ODFMmEowYRzu7eAuYi7Bs2hyLNC87iKK4aKzBd990ZHe07kaMgdJar2Mu5G3UcjvVGSwMGtE76LnhqHdu0HzLtEdVN7wLms/ZlrYO5EVtXZPnttSBo9f2OO1wCjk60Xp+div2OUQMFCTbqKSB3bzcNbCgOudPaRfcSJvd+BGyXR3+4paLoDYNNruJx3PScsk3APWAyadaxOAh3ZrnfmcEwwy017JHBEZPuHSPkPDYUvzyz7hcjojrPMwTcz2GY+m1zyHkQZoTLbu5z1jBLPSNTwyF5eUeOW91zTlrgu/bK2Q+eqW5tCO0B2+D+oqGD9kyMge8XOea9kf0C8677MjVgYmawXSsuf2D7ZzXEInidVJ96arhDQzIVP33G/5tDv5Djc/WLWyvnfLwlF9RAgK7BDvuZPHAOMSwtLS0tLS0tLS0tLS0tLS0tLS0tLS19efQJAmD1kFoUPLJZvaLlEs3E0onu4kwpCDIjBBtCPa15Ai4qOvrhJuUBv8y8XoeihVPdpngjOQTuEkXEeoO0tyTp6uHUJTrUDWpGc1cvsclRKPfkFfJ4xOhmuY7zeFEnKgbjwPqVnK/q6MXhdlP/pTnZOlzfwXitjliD+x3fHTcBIyMEvL3RdsX5hptAZniBO05TrXphm8Ec1XAs17D7LmhdwNUw8niVk69gq2WQ1omZ0FyQHYEjbyaQaF5R0CekdXWIzkNuPxUh6/ezepZD63SyeaMchNXDHOkVZ1vX0Y1WkdrujWlyq0Yc1YEM4QWSYhDdaTGrUzYqEVrgyNnFnvpOxKHIbZP7WzMJSViIi8bUuUVBw8zabdUCLAutHLAkoI7oOV7w1mG43I45q0P4qAEDgI10F9AEAc5QnLbNyaa2aMaEmQebdczv+Az88gzHvT7znCrI6gY2AWvzivQdJA5RDvFeZnX3t/svUq7KcstHCEjm6Zhvjcj+5uL2XgMPDY7TfTnkWq8E9J7GbI0Ix1qr+QMn5ysZQWDw+qJhihMIFqCfNMYcjONGs04zgeHYGtlNgL3Jcep+w7Jh7TfjOch5p/cdsxQANgffNbAB2qPm5YSdikkfQ8MXXqCR+QDBkVFp3k2DDLaLxKYGDDKT6WrOzjEfsdNZ6QS2XcAbfr/pHNxo2461Ts6Um9oSt500xa2fMdsGRDnfSfD757QZTHsb58g5GJ40njWwcHvBt465Y3Rsu5BxwDGw1rA5Bf+pGHALOeTnufd3stXX8obRiXHT2rTEvZF2gZfPBazd9FlzkLfP4fKOiIGnHO05D8BJ3/B5U6S0OZlHraEGYwjDbIPtKxpyoAZN5lC6w4cDLtsFjlfwJ6he8KWlpaWlpaWlpaWlpaWlpaWlpaWlpaUvmz5BAGwYrdysyC3mXVGnrcvVCxVnyyMGOokCgwXeUDSqnIM83H2UO/YEWMkHTmIUJ51ZTtMTHlin2mRRX2wyrakf1V3wCkWvUkcgyAKns9bOSGHvMAdOMiKZdna29uo2FjizS3UBe8opGwPznfS93qZheRdsNcX7mm+YGa2bun6xR8zqA+xhDyduROjoKmrVm5Pp1bMrSDUjFOtqLjg7ErYmd+QZl8wkabRmzEiYBbl7E2CyOhbr5cK8aY3flofHwnlBy7ySeeh4fSNyVtp3V0x2QbosZ3UvEJhuOmXfC9Kfe0LWavUJ5+OYiYPmGzN0TSzlBrX2ROJE3nSYMdWjW3HeMUOgzgWmjXJ2GxhDeyy7rr3KieUYnpMcQC+7dzPyOIBG3u61Tjs0J49Z57ST5sxxL6ekabjAIKLzentPc6O3nT4TG8cDbOsXV7cuFwHgZjAC6xfF+pLkvgv6GjrGmLV/5cLmPlXPHBM7HbhEOZgLvlU0b7FvSHXcWuv0hx8axZsb6pEtkJ7zTg51cTdDDurrpRz9d6JfmLcb434jM2nmbPtVYHoO3ar9t8gEmgnHTZ/Yv6J47gzYdrzvj7hpy4ButLYLPE85vs0NT9de7pc34A3qJ85gktp7JDkVCZ4j9RgJDZKYpfZu25jjtQYqyuVrBaBTzy3Lc3jAHvHFJNAu2rOz7rO2C5rX4ACp42W/0rLRbp8TMQmv6PUxCL/h7aoBk7zrvGVxxrdLDTKEHLPVLZwWb/HSrj83S2bftc/nDc9BIKe2ddPARL/AflVE/5kaMDXcYqYYb0NwnO2KtR3vjbyH9scZcw/Qdj37LMEDo6tjOZN8/eu6Jm0DbwWsG2aNaInNQ/fVtv8tf9osLS0tLS0tLS0tLS0tLS0tLS0tLS0tfRH69ABwgVB1xY6KmnXMJ9jEaIKEFb2bTZjKUxCFcvWq47fhpwOSitM9XcDW3lzCeeYup36d9+LEidnpdlTfa5px5GAEcmj6XrBDnZ+K/lX8b6TAoDGrB9MEh0gmgfUdO+5kf5KD0s/444rD3Trcs2KfIVHE9GmQ3bYnnInbLqjmTt823ATvMhNCWCnnEBzJO+DlpJOLUd2kH6zFFOQVgDnK5bph3eXaC4FAuaJ1jR6wJ5NwOCN7M6bA8wkFmRXbWr3JuNZdR6NjtYqIxUnTOpJCcPpH8FNx1+U2nncwx9uFiDt5RhDXfso5Kv62qZs2Bpk3otzmBnWermM7W5HPKGXvD2imNRIATZODeuTEmyvW1mHUXhDwvRVYNwHLHOUm3QWt9kvFBgc57oK0Wd2n1Q2cMbR8BcfcTLMB6LXjmIzobHmj24QBvXe5gZuiptUHjSBb3Q/WL9XHOvVPuwq6ZZL9grUkj6Nc66E1ugWToV5Yq67sc1hjBn55py7dWa73TUbvmcGIcwBDbuQ85Oj0SLw/01oqvrnV+xrkfWMed0Y2YrzQtiv96au6Qmdc/L5VJ6xh41ZdtE/Y5aq48mnYvkPcFaveHG+79lHMAvmNcKehNfLtCn6ed9Q9GBTr134114DFuKs/2879eGhvmBObOrt5fYEWuBvez37z6mGek7Y1xcTnlAs2JmzPMNGwCeheHfV7JkwD1Cnd6DTv6lc2DU6QodU2sG3DJhAv4IpltnPo4jgqIcAf+5sm965FPqK+G13Pu/0z8vi8+o8bMVN8O4y2dZJJTiNiksed/vyZIqFdgzt++UyR5Gb1+K30g0zY3+nCV1c4xwv4O8wO0jpnJDTH50p4PkI93PszOQ+5maMGTHz7tf3cWVpaWlpaWlpaWlpaWlpaWlpaWlpaWvoe6pMDwBZgrcvVdnafAmmKE6Vcv95asbgoF7DyZS2TMOEQO+FUGlXmWgQxBFUzy5FKxT1npUMX2Dytqd7egA4w54BDINNwHWvMgrDq3nXrj9hhQQhFUc8IwvIRE0vfgagIZOp4BjnLMdw2mIHZgdGrr1V9xmmN5nUM8k3jnnjbFBdLddT6Toz3glPVJ5o5SFo5FUMxz7VUSfV3+v5wXxOvgnmtl3Oyw0hFCKf81vQmmBhO2lR3sXdB5Hm6H1GcNeXgDrmRy7oJJDapTt5WACuq27TR/ATWCWPi7QpW4NIEut7Ov6jzOLTG1skmoJfuusQ5FSNceysLHM2H+7sc5CAgNUNg3jfFKjcncNxNABzANpoZc7wQFftt86CbIZ9jPGptvTUc7Yt01F16U+903lIgexwEcuWS6s1t12eIQR9Dbl5vikGfyLXddP+426PvWIfWyXErMD4Uu0uvmPPzGgx1rkY+ulsnxjGnHL/z4IjxuLeaVQcsMK1jL6/0FNDctguRB8cxmb7JKTsPmDcsvQY1kv78jG+C9jEFRmNMZkwYyTzudDfs6bdgFnJrx9DUwfVaDvXqeG47FoN22cWQrWFto7mBPxFdAxqJEUyYdyzvtP6EpwN32C+4GTNDfeOZAtbzgK7Bgzw7fb3gfgOmHM22PxUoDvI+sO7Y9R1tDrZtf3T56r5wrLv2XMXVZ6QGMMbU4wuD7aL785j1uabHRXUdGwfuHe9PxBx4A/eNtJBjF93PmU94pOB+dX3TNjg+V1T8dsWuX9W9EwHjhRwH3trbQEdvmD+R779BmO5J2ZgHOcpFHHfdM1Cd5RD3Qbs+6V6NGsxJ9UKfMfFZQw4ZU/szz4GFph54HGsXwfj7e8x2rD1hNIFzk8vfLlqHpaWlpaWlpaWlpaWlpaWlpaWlpaWlpS+bPjkATPMCA1FOUcP8IB8FpUk7o23zjFYWpElrcvtBxT2XezPL2csE+iMR+NE37A0ol51FvaYVjA3BDBqZk/sZBlv9w0bg1svTCA25+PCC0rEDWXBw6IjK4SvXqaJVW9vL+adY05yvigKmySU672A3OZxnCCiFA45vVCQyuDv0Ttzueq9MiAOzxkwUm5xRHFzOzmkGOQhzdaXmwPxCxk0Q/VzDVlA6AveNyU0wN8oZOtSpmvMVWTcHtK3Cf3VNE4M8CjRWFPcj7lbxsnLztgcQVi8stK41EnAOrFVXs53XW39mVMpxVmSzG97f1ceYoKKfDskCRKHXzRMsWqvXJ+QN4yLHd9t13OUM9VDEsWB1CpBmEpa0fiHnQW7PeL4yjuqSbjtscrE7+YhEzhjlGlWXck5T7HaUgzPVw9v3q5zm5RLfTEC8x6B5km1nEsz7wbTEe6dZL4dvxaA7ev85tMe8InyZBfUnmXdGGjknYwbzeI+3zxRFXZ3YzINhBsdB+kb6JCIYTLo/kbcb29bxOQRbu2N3QcHWwTbDrl8tB/lkzkmMIGatV0Brjd66QPresNyw1J/R2ltnr7WK207adsUtcaB9EA1vVjHomQKuZrDtGF0gHoBOWmdkxRFHlNu59v8Ycgz7Jnf5/aUGBRoZqWhrf7tXMMW4t75x2Xf12eahiHvTns8M3f9ocIO+69yO9+WKvQBece2Bpcnl6u3NSQ84SXNTNHPM6m9u5JwEQwMiXc8Tyw8gdnPw55oAmYKu1vT++2ek3WC84NulzkcDA+36jKec5Xm/gV/Apnqcm+PeFFE/Q7DZqJj2oafCmQowU3HXOXTcBYHNG9afIV7VeWxnYsDE2gV7Umeyou0VyZ8xaujHHvHrS0tLS0tLS0tLS0tLS0tLS0tLS0tLS18mfXIA2HIi4Njk/nU5d/UX/HfYr8g+awIUFQWNybXZTRhHHbMfAIOMD+BBdf76JidfjoqA9nKcnu5eBCtJ8E4kzHmUCxkwo3kohrntOElmCppGRR03I2IoChVBvXD1iBrlmiOIPABoOOGO5S54aVOQZr+StxtYdYtG0vquiOuCTr4LWscIOWTn8ejPPaOKM++KTfVOGATBWXlMBpFRXcCBWStQXU7aqZhg9dFOMuRmTeLhpC7iWtclBXbsgrnAbupCwRnFfQJkHKL6jK0A5JnWbYG1jUg5lQPI445vVyG7vldcbLnFKUeyNYg7uOva1P+d3am9NbCNEbX2IWA1Geo6zju0RmbHpvaLhgd0vlZuacfLyWlvey0gvZepdmJtx26HgpdzQhdAjZl4q2jwOYlZnbJzFuR1Ti9z9w3rF9zkzLRM0hLbN8UOW8ddPax2DKbfuadzj0aPA7PJpvxuDSw4goNxr9sogIPIjTHvcAw89TkdZ2tP4Elu6l3NORkRuk59JzBiCOx1a2xj0i1oOLY1ueJ9wvOVNqf6q91IN45jMuJgvtw4xnjc8701mtfQRmtQ60JWVHfbsUy5nKEGKhxrjn2AdM/+a6thg/Kuy2lr6DMIfE5oG7MGDbQXFSmd2XQMVNdx5gPUxss3tNf2C2a9wLopljoO3BvdUm7VR59wweIAjhvhzpxysJu7YssNoD+c10kwcygC3M6o46lr3hzPwGl4HNrt8w40wubDYS0H7a645DnJtmGt4f1KkBqmyYFVSoCZ41sj+zvFMWdAe9K92fZHB/m0jo/qv45JHjesXSt6+9C1ubyraxWV2FC7uwYg9DDSsImhZ7H7Bn4lbt+Uu7o1iFadw7ue1/NQDLeZnr/ea5WXA3hpaWlpaWlpaWlpaWlpaWlpaWlpaenLp08QACd09XEC6hJtJylQrLJZx02xwTEL9JbTKyxxKmIYe8BFQaMzo7e6Jenlkq0ezjR9vsEDHJiRmSRDSciz4pvNaN4wWrnumvys1nQ841WwKxqTpljS7FhTZG4QOvbkrWe0dQFY1DUccZAhd2YC9Ct4YvPAbBfsq2hsMy+w4mQeMqPON48tXhHTowusm+FR6x1R7tCd9I0w9aBmRPWqOswXRUJnYOly47VGztAaOm+9m8ADG1fXr3FRxPF8RQt4Oqw7OpAmaBZacysnbVo++pVnwUbHibYXClZH9COuO2/gV2akIPIM0hvGlFs8Klo8Q2viG5ZyR07T9cR3iBtnRy6RREz1B2eAHVh2Mo0IaOg6psfj3GHqV0Mgsk3S5IKMSPJwbNvJHIxhZNyUlB1B0HDbsLiXcVXAuZm/vW/rWHcYN7BBttqX+0bmxH0KQPvOPA6O+U1oOyN03nYctAi8bUQaI6dieucgPelzYCS9Of3yJOhmGlxISzKczBuXyw+QJGFwHDeB3gjaZae1C25B3zZBeANomDvR5JmfBGMMxv3GuL0Qh+J7W2+CmW7YVp3JNdBADMHffpULOt9c5NYarSkjwKv7Fr+WC/3gHP7QtQkaFMAst3cNnOheOZ8BFYUOAs/m5JDTmTjkAN/f8eYwrgGT03UcUYMb5cK2csXaXs+8Q9H1I2Hf8S7QHVnu794FctPIOLD+BM2wXm5umiK8cyqCngmeHHPiXs8jGnG7wXbBW6q7uF3BDzwOrc88HlHwSlw+4921XmZO2kVrPw/M93IQg6XRtq3i2u/13N0efyb4+6T1jajabx27nf3SVDR3jnIZJ/Ry+/qm519OPdkdctz1nD/d8WPA5R15f8Eu5e5P/5v+rFlaWlpaWlpaWlpaWlpaWlpaWlpaWlr6ovTJAWCs3G4uB23a6bZU3K3HhFZ9taT6WLMioxMiUqDJFKUa1miPSN8CO+W6JYYcfr6/dVWWU/ABgdhIBiODY+ozyCwHXuLe3kATZ8y0Q5PnNKiO21Sfa6C4VNwFPO2AmZjveAThxojUMVqTAzm7XKS+Yb1j206vhFmzpHWnWWK9VYVrKB7aTE7oAGzUcRScqghmc6+E5yBcncKRUbAuYA516vaLgHBANBcUjzuJy1WYrYCsupi1joLSSagn1JoiYuOor1OAZlaHchNMxuWQnaf7eoc5cUIgKJNm9XnuD8hPTshd3bzegDv0rm5QKoKWUGdqc0YmloPZGnMc9NoTYa5O0YdznLffU9fZaq/aICIxNyK0J9RBSvVRO+aJT6NdnojX9zXboKGDNCeOV2IacdyZMbUXWsNanaNteH/SkMO81RKfDvet1jMFaWloeCFJ27DjhmcS2cnpzJiEJxw3we18KbelYzFwS3Zr7M3x1uiXjXa5EmNWfDnqZo2Bb42+XzCMOe5sfsHiwPulvg5ZruS0LLTozIpfDnPiODhuB/P2qjmO62f03bE5FM2eFOzd69rdBC7T5HyuCHWbhl0Fes0Mz6EoeXvSpWLU9YtCw3F60KWc6sU2raG6oEPA2VtFQOvexwtGZw1czHJ0W/nT43jcBobhlyuNgZl6u3Xt9OzIFGhnTvVT0wRVK05cr7fa3wdVcs35AEhznce+wWwwX4o9d/DOnIPe5IZPa8S4k+1K2Kz99VSx5npuWYbuzZzkvVztrb/1Q2+7hh/mC8mo+890vpbY5R2MjqMBEaU1j+pg7hVzjz6vX6vjHOx4AQ65ycvR7e366ConpsB39a1zfF6Xo57TOWB7Eoy/PmsY4Lhh27u/7Y+cpaWlpaWlpaWlpaWlpaWlpaWlpaWlpe+1Pj0A3IDjLrB1OkNNjjg6pAnmyfBpb8DAP3Td6WtOQUZrgoOPvsyuf2K8wWOrqN1Wbjo7nWMDCEYEMW7qpEQxpOqwzPrEFKyuHs9JgTLz+vemY4my5raKMM0zDnlW3KycjK31eo+EMQVqOeB+KHZ167g5fbvICRonsDamgdldpmPQ+0Y5fDnAUrG5ZvjZ7UsjcuLljk0M843MQczEtiu0UI/rvFeALjxgspUL0+p8pnpALQPvG8F4Y+q2kVZdn3a6eANsykFYwMu8IrIf7y83o1WHrYihv8G4Mz46kkYw0ysudpDzRV2xtsGlnIs55bAMwDfGnDRPZt4Vowu1Fo2IoDUByUni4441ExeLwTyCbHIdurv6hDPlTGTQMJwu9/ccBL16Vl3XGMfaBW/3ii9XLK817TU5S8vVnoENL8fxqC2T2KNDeRIztCUs6AYpvM1h4KkuasPlKA/YPLGW7O1Ka8m27bT9ieZy6eIGx8CO83oLqjYrJtkb9E6zndYvOta4Ma3r+0OO98gkMhk5dNmOUd2wxnZ9qljojtumz7Qko2PNdc3iLgf2dq3bfeIx8O357d6Pqa5ra4KSVo7vttWQQCIreQ2FVOxwZJMzNkKftT1pT5dT34wCxYqKZ8wCsbOGSerZRFYkd6O547arhzgGoEjy3C4VV900BNJN8DIPYCcy5ITtVz1H6rlidlGfrsF0uXLPam6lYjdyvlYfeNeah2CztZ3M5Hh5pe8X3CbZTPfZGWGO1S+z9pru5VMKOKi46HEHQs9Ei/reQW4XPUeoZ4EN7PpcsdznY/pCnMfNhO0q9/4Y2PZMc8PdiOnkGNqngNF0i7dOHq/nYxvmXZ3g41Vu7XlA23QvLi0tLS0tLS0tLS0tLS0tLS0tLS0tLX3J9OkB4AgefbxT0am4YpCtnLY5BwJkQeUyC+4AuDOZeAr+CAoN5KBD8IHbW6SrVSRwAd+cKKpVB1Pxto05bnLDpvpCvaJR3XdBvAJJmTDKTRcRZB5Y2+rPz7jaIrNh4Ls6YI9DZuQm196DS28bGYdAZ7aKng6aQXone6FKm0Ta43MeMIsJfSOPWY5jrR0nrM4KxXXF4EYiVJ1W/b8Vz3rcCrJbQS4rKJaY94LdFUlr8nqaq3N0xkRbudaYUX26/gGENxkqDUjF6KbJoVvkq+jT+dkVqzsnORN653Ram8l+2ZpcuCMh2PFwsgum0ow5FAE86zOxqehocyJTXwonLPDWiXknor43NzwO8EbMAqR5KBrb94LHNxo67oMh2EcyCVrciKHeVmsXxdk2l4P5/tfVbWpd33NCSlzmYutw2fW1aerGtq4OVW/EUMR5y4obz2D2nZYBYURO2nXHGGzecWtsTc73vl/wTPreFdUcwZwHEeCtMd3h9T2WhredMOhnLytGa5vOMYK0nWHGPF6I+9B+DRivL/Lfupz27om3K947RpRz13RtDcHfIdCYl8/guCH3emhuoj/X/RGQRvfkjGouXC+46h1CQxaGqy82IPMVCJpvJMk0RZ93VAUNTftwHnKwHjXcccYOezlkw8mmwQr8CXrDxsBj4t6xXtes6V6IHIprjwl2qfdsdXyAX8ihZwjunJHR5I0cW0WYo37vGGTI4e/9iXb/XCi67XKVx1SvdHPoF92TQ5H36TVkk0fdi47tX9GfzQlxr2tRUe+G7jXf4P4C3aB7OXIdy3vdyJWY0C+Kpq5Y675dBcuPuwZRHjH1O1x2Qd5ecfz9oucf6ky3Keeyb08C8cd7XdvtqxU/fbyB7H4l8/7r/hG09MUpazDpi36P72etNfx4rTX8eK01/HitNVz69WrtmY/XWsOP11rDj9daw4/XWsOl70Rr33y81hp+vNYafrzWGn681hp+7/UJAmArEHbG+Bo279D66ScreKtNZK1Vv63VP4mnV0dwbbZsjz/DXS5QFCWaVv28hJxu3qsztqKISWZaOTA7HtC36vBk+8B5ChnB4OxJVfdshKPOTCs3aUIObNb7mzo7fe9yHhbAjBiAIp2DTh7HIxa49y4YHgVAu5x6OQ4SE7yccpdaphyl3hXFigDTAw6fALy6kCMbLbJ6mAP6rveqrmDBpin4wxTknbOMv6cDM4Guc+07Nu7kvClqNk1/bjcR2qzPJwS8rEE5GKWK1U25wcWKTwpVjukHwDKBbRM8OvucrdyxEwRBUx3LrXV1EvsuN2crwJwnxDbB6pHEVlHTrev4u1eH84DLVp+vY4wMIo3uu1pNXQ20jSD2Z+YxGBjbHBo2KBetAHPD2fEzSvyxr2sPNQe7FoT3Ap2b9mP1sbJ1iA2Om47Fdg0O0Gk9i0FuWGy4GW3f9Lat655JSHOOCOY4mDOw1kkP8khyBm3f8csVxqHbKwN6Z6Qiv+c4yLYxj8kMuatzzLp/G5ZTDDGT1p91q89brcesPbs9nLa6llF9u7tc2x/e/6Hr7vKuQ6+u2hNC1n1ofYdIMrKOwbTvCN0roXvEXfu4kwwPAdRzT0YNK/Rnwfc5YG66RhGKpm4dT4O+aT9alKP9Up24U8DewNqVlnfmmGRMIo6Cxak0hOor1zNL52Qcldq8ax+EaeDEUEJAD9p8YdiuW/e44TiZDfpODEWDZ/daa0U714SB4stzKh68P6vH+Pj8Yep9xKHvVzmt/RzmUNS8HgWtzlP3ZUTBb28VaV9x/SEXfFqTYz4OsE6k3Nven4hxe/R/63nltH4lmjrA3Qzmobj66h2Xa3o5gL+f9BvxH4ef+n9grjX8eK01/HitNfx4rTVc+vVq7ZmP11rDj9daw4/XWsOP11rDpe9Ea998vNYafrzWGn681hp+vNYafu/16QHgPCB6Ab5Zna9Wca0CbAZYTswbbqaEUqNgZhJMxTSXS/WMgLbTnWoINDzcv3A62pKK7TW5jiMmMw/MHd/lljS3Ap5BnJHO9b1yeJpifU9YmQnHAe3c/IreFc4oYFV0KePsta1u0YKDbDs2J45j0+WQa8jxGflwiUZWOrYOj2wGx42sWGIBJB2/m2sdz3Wa5YRuXZHH7uS4Cb4113Fbkm0X6KLWzbLidU938VHdw4kNdR+npUCZiKzA5ciC8QjKQrn9XJHRdB6Pi36tnub6/jjAd625RcVJn3DozOttZNwxq7ju09kcU+5Pb0Tbda2sXKOnuzgph22t5oi6xqa9ZAXkRq2NyznMeS1TvcjR1AvtbYPxHtzwrRHDyJaw61pY1DVg4NsFi1lzBbWPZgqebbucorMAIkBrxDwqJfvsAX7FcuK2Y93wCO211rG2472rH9sdbyfZdPU/uzPHnTmT8fpCpkFPzLXXvXX65VlrWs7baDvq4J4Chwlxe2UMRVFnKMa8W8P2DcZde3G7yPmbU/ecycUruF3uTVcU9mNta7iDqb5hm1GzGoE/MoHPCGL0e+t6ImRqj+UjO1hR2FYglMS9M6unt46E6Ls+PxJ8Ql44u76VZa290voF6xd1QXPuCcWIM3WvpVNlymgw4xxSqFdqwMUFl4H0TU7wipnWOEGd3zw0NGOOpdcTaMjpG17R4Jti3HNUv68JAsfQUEjf9Nylab8Y+rNUb7a5oK1imgcm27Wek651BfUV56x0hczq9Xbs7A/OBNuIoYEO8w4cECFwSw2CGHJbY49r734h40WPybZhNiANb04co7j06Zx2bNvJMet5srS0tLS0tLS0tLS0tLS0tLS0tLS0tPTl0qcHgLtAkiyJszpsN2xO/cU+qU7L1min09NS0ccPt2R/gIuHA7Y8vYo55e3PrWCtCb9wOn8zyEqYzjHpZjQvIJmgGOdWFZnqyMxMhUYncpamF9TqsAksPtzHPuWepAtGWpNrOCZ5Am1XdyozsOhwudLcBO28FzQUBJohl2FEuQWzeo/niUVOp+3piGvAwMywuOHpSqQ2yqEZAk7uAjUxSDsEUCu2FWsC1jiPblUK5OVNUCbnG1gFyCHknROsOlmtCcyfsboK3i4A6Cje+INrnPn4TDMvKFvQlQnt6VvdnzqQR20wWbshJu6NuB8Cjx0dSzNdPwP2awH5Q9AuQhDwOAS1xyEgdy6t+4M7BoM8OZ+D9w2/H3iGOl5Rv2kaxHEw6njdHbYLHC9k6wKv5oK7MckZD3iYxyRtyo3sRje93iYCe75B18CDu3qjrQYRPILpk8imc5qp90rDxx1eX7FR3bmjOpS70/YrbgWuW3tEj0dU9/D9BdomR2s5sa01WgbmE8tQLHrfy1lfHbMxtedT53m6sq0ZHHLEyxV8E4hsT4rz1gV9u/9dQPmxX1r1CZ+fUa+3tJq7qD7opIYhNCwiP/g5wOByMM8gm2ugI9QzS7/I4VtDBc0Mp5zoZMHXA3cNR9isYy33KvOmJ49lOeubYq7z0BDIdi13bWhfb9cPzlUDGzkPuZe9kdZr2CKqrjlpbdeebpvW1YzwRo6EeKWd0Nr9DTCfcR0x9Gw6o/kJLIc6sXVTa62tCaQfr1q9rphqd3Q8+xV30/MsJ4wbWMfP4Z7WsNA+ykxFz1cCQOvOzCed48nWzcmh+zLRgInt7+B4r2dYDLmOl5aWlpaWlpaWlpaWlpaWlpaWlpaWlr5k+vQAMFZuUyBNsGiT885xdc42RQAnAhRe0cBREMgiivdkuXI/tJ1XfPAjRpiCGqmI1hRdSMvyEepYmrtMhvXaTEg3QbyEyFTy8cOZegFLxRf7VFxr3yqqtr0dUx71+QVdDEVRt7L+plVs86C1neZNDk7vmE1m3AW0zcnWBU3jALvwcMt+eN4PwCow4r7J1JivBWMEz6PW0SxJLmWulXMW63ofK+fgGQdrBWFVZozRBHFslDOvk9mweBW4syzw5mcL6APQYhWJbbXmOfSa1rE5iIJrWkN46wZWX2uWG9x8w9LkfiQKhgvksV9Ih7ZvxPH65izFyP70tn6JAGmUW9utzqvV+g6SncfEwFTnauwXQf9uyH+r47VIWh1vHALnU4el9YjAmqCvEWC7epzPSGog5wv4TljAHMw5yWjYNnBMUcmtVexwlltyU8QwBnMQGPl6QJvaY8crPoe6g8e9+m0F+acH2Y3YHd/APfD5QLfEMQgmcRzy1M7EPGhRQxDMgqLt0SVtamCuIYdZMPNZ6x4H9CdsHBAD267VqxxMb4p09v6A+wLi29s9fe4l2zmdso89dk4FFGDM88UFUAF6Cmp663LUz8kjmvpmct4+nhuBdXUmW8Ffqx5fdyf6xhx3zKacvHUe+PUxVBDjBnap51Z1Sk8Um/6QXOjqQ5fL1luvYZmL3N1T1yENDZvkjZk7rTluFYudIZew7+BOHBN3QeE0qz1Vsdsu166OeeDeSNRp3gz1HBPETJ1Hu1Tscq+4fR13elMAwTwe7+vtmTNpIcftLcIcxYhnv9S9sOGWBA3rm1zM40X3Rr49A3x/wrZGZD378o5vTywtLS19r/X+Pvn8/jaA4ga/+XlbMUhLS0tLS0t/C9nr19l+7k+z/8wPP752+3v/W4z/4H+WvHz1CzyypaWlpaWl7wPFZP/pP8H+Mz9MmvPy9/2j5OUHyOe/41v/vnRpaelLpU8PAEeRsByKf27lHjUjOLBZTsiZRDtjjOW8JQJxs3KIPvKTDRgFCp0Po3rP2Ga9zOR2nYf6e6dcsM0LDJpDGpGDs6MSKAhc7tMssFnuULwJ5BTuSne523B8vz5iYYmh3lAzpg+5IA3gDhyYOS1SDmADc6u+XPXZZgEka1eI2wNMWtoDtGbb5Z701KEi552aUzvmJmdqwavoG035xAKy7pBHuX+dzAPe8JmO54RfVsDT7PF+mKmzlc/kbsxZ31vwNk4HtsBrFizLlGsy7RBUfri06y9XjeoVBmu7jjH9AattHhjqA20ozjen3NVuhrkT+4Wcd7mNXagYUi5RkvRyWxZ4Y8hJLqduweNu5CjLb2+PmOwZSWej+U62g5xJTiuHq3puM+XI1lKUI/3yLMfjGY8b6gXOGHL8zlegkXPIgelwvP+cHkFrE+vvakgBaI0xXgCTqzSn4OgA7q+ClfNgt0abU3ut7xhy0Kc5QXA/r1AmkyTSGOMuZDfkQHff5Yid89Hn7c2w0P2jyxf6s1HuXXM5Wy3lQN3UW2zbTk65RA0Y9xvmTvpOIjd5nq5pC9IuOmYqbjzf/gLe2kX3dlZ8+Rk5PYegpZ9DF0FY1GeUC78gugYc6p6I1HW2BsfUoMdW920OLLT21poAcZ5d1ui6x5S7Nqfee6vBl3MwwhLzCxaHBiAe+2roOMOxs0faT6AOdtwwDPcr/Ti4x117d7vCbJCvGHudd8e2jTlf8f0C5MM1a17PsnkAcn9nRXDbdiVi4hG6T1uCTSwdP2PVYyhGfwzYnsiRNZITeLvqsTgDi8TpZB5EBvSnijevoZjq3m4ZihxvjvmTHMTV20wktm3k8aJ7Poais3P9B+7S0tL3Vv/iv/1X+b//m3+ZH//Zrz2+dunOf/c//3fx+3/P38Fv/crlCzy6paWlpaWlL6HmweWn/k9cfvJ/h3/tZ/W/m0r9L/wY87f/Pr75D/7j5Ge//Qs8yKWlpaWlpS+39p/+Ezz/yD+qvxMDtl/4MdiufPP3/+OM3/H3f9GHt7S09DfRpweAEVylX8khN+MJUbuBmRyWp9tTMGe8tenaCX/l3y06WJzSygGn/lZ7NEcCbBghxovDDDIGnop+jjg4UIxunpGnqVhaxxTBao3mhoWia2c4Nu9YF0gyeTMJpiqAK8qachEz71jbaThzKjpXfbuT7s7uTmtdka9xyFVqTZ+dCEra2fV7vAGutpNzFMhpBbnuOv6Y0HfMn8i4P1asQpjrOCdpcvaabWTc39aVlBvRyqkaN4E4smD4GfcsOPxBYXNFQcuVm3Zer9D1hse1mTMUYWxd7l6MZBI5aGYwUxDMIHOcqP2xD8ySlpDewBstk/TOHK/VaeuYOZOtvnMSj/c4/7+LUeeUuzZ1zRz1CAeKZrY0sl8U651D3bd914BATtw3zCaZN2YarRWMjAMwWuuCVjEqUhpy3qriuSBgNmjvgKPWE7pvjDmxcWDvX+DaYXupWyDI7cLZqcp4X/HnAuW9HMtbvqfbO7Z4T7cfoBM0U5TxNGMcN7o/cbTBrM7rEYK+cdy1ny9XwKtHe8d8J46bzqdfMe+K7c26j89fu9zScoRPxZs3uUPpnXm6d7cLOaIGLpJ5HPi+a6/NAL/X/i6AWbHnaVpX6+UOjVmdxkbyIrd40+NWTvemOHWrGOiKWs8cZFDDHwMfSiQw6/X9BYr7s2BmBO3cu97AhoYUCHLOR3Syb00JAudzJROzHcsB59mXG/uMTZf/eqthE/UguzveL8zjRferNzKMkcE27wK/lCs60CBGN7BG3F+0Zt6hX/DW5baNe3XpXvRsqJ72BObxgpli7M9nqvZWVAR3qcB7xgH9QswPOoZx3ZtZPdtTbmRmAeR5dvzWsyTBW2eak8d7uaovVxInslzHMbF+/ZYBgKWlpaXvlt7fJz//V1/4oz/y8/zMX37/Le5fgHGf/C9/9C/wf/nJX+Yf/Ht+iN//e36I/9APXr+go11aWlpaWvqNl3/9F7DjPU8//oe5/65/iPF3/j7iB/7DsD1/+28YN/zrP0/7az/L9V/5X9H+6k9/C/g9ZRn0v/Rn+cof/29y/4//w9x/9z9M/Kb/yHf5bJaWlpaWlr5PdLzH//q/Q/9Lf47nn/gjWCg5UsGoA+7f5N2f+h8xf9t/is//K3+UfPdDX+jhLi0t/Y2y/Ja/Rf/3v37v7/5dAopnh2bbAcXD9rSKxK0I5/p9Q9GljgkAmstRaQre1eu6wMwJGDP1emsCGtY+AJVyFY4ctAhwZ+YbrBFpLLdqUs7gUZ2oXc5Ml5tRpr8BJJ6NaP9/QLp+SRpx3PFNgHYcR7k6BYEubmz7pl5Xq47jAllhXsbCLCgkhGt9K4iyC+COQw46lc6SUajXnEkwxiTccaac1dZoZPWsVhy3N32v7YLItW52dh17wngl7YzjdTlhrVyW8pqeR0jmXS5ks4q3dZj3un7JSCfnDYtJ65eKpHb1zXpdM4zuBcJO56Ayp8vheQhqcbrBg0xjpjB3M61cZOI5cDtd4pQbUvstZjk/vZdRVM7NDAiXo1y7IsgRBYon1hpugZejOebBcbsz73eyNcwaY97IgN4bnoGj6OTsusYZU8COBNsr9vwG4YKSGDHutG9+TtueaB2MQX5+I54GXH6rnLhu2Ey6uT4nBm0Mem80S3Y0aLHZTiNoTWHV4c6IYPjO4XDfLxzuvN5eeJnVwdo3bLvomsSA/RmbWUZOOZkxh+Mmt7VvBW03RQmHIqepDmO67u1ALnINCei65pgP13zr/fS+Yq6nB+aP2t+se9jccdvO26MMvUnGoWGEihROsiqRtS8jJmMK8McY1fktp6oxYdzxbcdbDZF0p1d0s9XwhnnH6I+Y+bSpIZPUuMEtJpHJU9vZPLF+Qu279qsrvjtBgwQkZpv21t7lgDWTY5/GHDfmSEYkL8cNs+CyVb8wG8T76vttio7und5MJugwrG+0TcMSM5IcctFj5ayH6iuON9f6vGG+67n0NkKie2/eC/An9KdyH9eznPrFej0vhvYAlcjgRt7eK/a+dbm9W2POoT54gNbLUa1ecHUKd/K48c/92I/+On4Cff/ra1/72t/+RUtLS99e3643/MPKkPNloSySr70/+D/8K7/Ev/erN/7UT//VX/PH/NBnO/+1/+QP8d/5z/1dPO/tb/8NS0tfkH7wB3/wiz6E76nWz9Clpe9AmfRf+pd59yf/Eezlr3zLHx1/93+V+Mp/4Nt+m71+jf2n/hn9/tf6UUC++2188x/6Y8zf/nsf/5t9aenLqE/tZyisn6NLSx8j++a/x/Vf+ycBuP+uP8D8od/z7V94vOf6Z//X9ffh4N/4JbZ/+/+m9/hbvH8C8+/8z/DNP/C/Ia+/+QO2srT05dSn9HP0kwPAv+93/y7FE2diW6vI4MDbjs2pzksmjqtfM1M1lRVLLBeoYpGDpKW9/b2dcl/rgVgQ0rLcmXBGQ5vJ4ZepeGa9zpkJ5KTVO8ioqd5Qd69jEOA5+2bTCmhkwckCHREhF1soOjXxiszt+r4Y8v0F7NtGb47bCS9PkKqDSAt9P0NHe7rpkooYznKsVlRw9dwqchmwZCYcEQQDvNMi5FYlIQ85GDHcUk5Sa4LLJshlbdN7lusyM+r3pkhfQlHCmZg1eQXPmOiK5DU/XbxOjheB95jqDfUL7fw70jRG3DDfyDGgbfQzpvlMFH5c8IJQpNYB078K9zEz8RSIinIwtohHJPipJJhR3dMZ6mKuIQF9TiMtiUzmUQ7O3usHqrCbxaFjJjhGMl4+FzQ2YxbIbtYEC48b1gWmiXKF2i6YZoHXmp1mYTv+Gn680F8a22c/QGPCyzdh/4rWxQzGIbA3B947DaMbtAi6Gd2cZpOtd1rWMVvTuRvMdGYGkXC0zj3h85j8qsGxX/Eu1/R5f4EJ7D5A7BTot0YeBQRpdXnUd2wxKx56f0A8kPsZazJ05uTssrXrM93aY6SgA+aGeftggrz2K4qMP6+onqxJPPqjy5FsTXsVmDmZGUwaOev6zoPIRtvOePcDO25y3+4Xet815mDgdCzvBZ8b5jtZDt8zWnvE5HbcSW9c3LjsV61LhM6zhhcyyjl9DkxYDZxkYshF3JpjI4mczJmMOLiNySS4dEForXug/vFdvbrmuBut6zljQOtdqxohh3Om3P/uZCF3PWvOW20qgeF89iibG3wT3M+AdpXb/Tz/ecid7ZVcUPvdzuSAtr25nePAKFezOxFZ0deOV5qBPrLDtpPHjZw3/rl/6Sf4lLT+R/fS0ncm/9W/yLt//g/i3/ilb/n6v/x7/xf8tR/8T3/L1/7pP/eX+Olf/pwRyV/9/PiOP/O/+Dt/kP/p7/+d/ODz9h2/x9LSd1Of0v/ohvUzdGnpO1H/xT/Dux/+7+OvX/+efWZcvsrxd/8B3v8Df+R79plLS79efWo/Q2H9HF1a+hh99if+22y/+P8CIK6/SeaJb6cM7PNf/jUPT33LtwL5/EPgneM/+l9aP0eXvtT6lH6OfnIR0IpP7liqG5UB9CZweLpXcfVq4rS86wnm9sGoiyCxpZpf2wl908ptewJa50yWFfzV94lIyd1pZuVglVPU8Dcu6AWBUp2ccp4leMMKMCUpNzBHwVg5VM075KC7g3y22Cbok2akN3qqJ9UNgXCrLthxfIsDOdMwr+7UnJCt3JV3mALMnMdW3yPYeq/+0U0Apbp+1bM7iRhy29GIGDTfSOvfAsowI9vp9q33pwkuzfocZrn7kHv48e/xiIwVdZ+KiYUC5gJ4hiBilANS7mRdF2+tAGqQs76nNTrl1FWurnB8pi5zwX7jdEzmo7c4rKvPNA8e7sUMLKfqbM8O1Dz3UcF1035zyvkbp/PcmAVDraKtPV3wvO0wKt45FHs7UkML3jc5nx+9swL75jpuawJmdgSNSc8LW062z75Ct2o7fvebtOYJ6n01LF9pqGu6Iydw2zb1/rak2UGzC55TYDyTdLmBpymSO72zZbDlINuF99Y42g6Wgq5xKPY6yqU+dU9oTRAk7BVDjOu6U9246eUCVwduTnUt5xQApO2c/c/W93Kno710QnuzAqXnHr1wAs/EKyY+C/en5iE4hyG63PLleNdAwQny1dFs3mmkOpwNHe/1SXvFvdzHUVdd52gPCJ0CmPPtnhgx5C6PiT9/Juh9zEevuLWNPM7I5IG1q47eGzSnYczjRUwcQdcs97ClOsMHjRmDbmeCwhkFPcnjFbwT3skIfNtomVhM/EwssKlOdk6AXXHz5UoXay/nbdRQhCt1wSIrCr1ivgEzI4+jnudJjldsr17MCLl+8+wG1wCO+bWu18RmfW8Y6fnmJO4bzAPLrj31SY1PLS0tfaeyz3+Fdz/8P6D/8k8+vpYJPx5/L//jP/05f4U//1353B/7/36Nf+OXvsF/4Xf+5u/K+y8tLS0tLX1XNQ+uf/aPfU/hL4DffpX+Cz9K+5WfYv7Wv+d7+tlLS0tLS0u/ocpg+7n/B+1X/s3Hl/Rz9eu/4R9lgL3/y/r953+lEvg+OfS0tPSl0yd3F2Zv1edqnB2yWGJzYr2rY5cNgdpZDsECnyf4sVGwrzPHJF2ozq1yX0EdrZxAqpWTtoDUI5nZiAT3Jlch+Tgu9QU38QyrmGS/kHFXLK256lnT31x8GZBXBIFcwLIAkZd7VWAHoJNRgNGBmaSf4NULqN31XrK06ntzQpzO14LirSKv3Yn51rOq09nJnMwc6rO1Vu7djs8bxFbnL6j96BY+41tTzttMQdQHCE6H5uWzRW6/R0dqAkNw08+u3dMmKgdpFiz3MNh2iCG4ZQWpqov5jAKXi9seIHmW89mt2qFT1ylJMhKBtQueAtYj7zzc41jtwV7A3h6uS6uu08xkukMYvaDjBHo62ZGzu7qde0FpjLc16gKLOQekup9zKO532sQeoca1NtuzHOBxfMDaG+Rki4MrN/bLZwK6hFh+dUBrMOAZb4KMbeu0TLoZLeXl9N404GBnBDXlMq0Y7Zg1NgFpSa/BipHBtTVe8tC+y4CuKG/LWnfvAv/lRM5JgXp/xKMzD9I7tnW55ql1j0MQuF9rv73WgMNFn/NYo6R5L6cqdaT+tr/yqH0y6k7h8dwwvK51RVlTz5E4UwXO4QntT+ZRx6B1sdBxUwMlbonnee8KXMIQWM7QHsiB+YWIof/gikHbr7j1urXK9d0uNaQRjyhr7HTV1150p23PBd9DseqtOp9N3R/NUCz76Rw/4+6tgc2CugZpxDCwwM0I35RwgIEn3nYiOoxXBH5nPU/rXve9Hp713qnjMWs1f3IvOD+x/QkKyMcwPe9qsCUnWO9l2q9I1tzwzbFw4ngP5z2SWde1aTjEu+7jcUC7fJufMktLS0tv8l/9d3n3w/8I7QP4C/BNnviDx/+Qr/OV7+rn/+Rf/AZ/33/sB/XfgUtLS0tLS98visHTn/mj9F/8f38hH9++8UtsP/MnmT/0n2BFWC4tLS0tfb9q+9l/gXd/6g9h4/V7+7k//yP0f+cnGL/j7/+efu7S0tLfqE/vv2St3HFRXb2y2OFdAFJOzTMCtf6yLAp6Rjn+ELhSBLPinMlZTOeEjfX9FQ1Lc0HeyhnOdIa8qowMIhXprEjSIFt1lXb1ZbLv0CpaNSZhBluBLOWo6jO9HHAfOBgFoAXcrF8w3zDf8LZXPG4SGURFKGdGOUZ7ARfBE8xg/4y3vuOCRFngkSyDpAu01P9QMkNxz2a0h7N4YlY9pAWdFbBbWzKtIGQ8OvMi1aObdR0p0KUoaORcPp2Q1ZEKqDe4YnHtjLdtpuhY3x5RvlaO2xM2+wkYx4Qj3uB4EdLMYGKMSEYmc45ymG4ETsSh15GK5g3F12ZhwMyzxxQSxd4mjpqlp9bKGlFQ27Pc1akuWioW3HIqwtZ6oUoNK1hFHbt19QPv7x7QecZQz7Jd32Jz59D7AoyA+0EfB5cYXLiye2M3Z6exm7E77GZsFuxubG5ceudixsWMvRl7a2zAlskWwcZGx3E67jvuFxzo1ujN6eZ0YCPpGHsE27jjoyJ+s6mbNqg+itT9csLccQgok4LfM2qoIKFvxLmvvOtZwCaIZ13DHrMSAPqmtIAMXUf3t3ufSTLKCYs6vpPqrE0yT5BtD8OwcT4SvABtEK7rrf7lKEd6nZMfj31vzbA56hpD5mDGUduxi1O27e0ZkAltV/w8KbA7rdzdo+D7BwA+vfaMY+1SULV6lqf6oa2B7x3vHcvq5W5yWXtOPJIwh34ph+6oIZAaEKi46ZyTHHdiGjOcOQTg9czRtXRP2n4t9/XpiK9eZLLu7bqO59fcdf0LMmd7R55963F+j5y/bFfY3oFd5PAdQ8cQBznmIwI/4660hJzV1d3IOfS1UdHS9snNUC0tLf16lEH/hR+l//JP/g0RWkbyxO27fgj/13/9V5ifVtvL0tLS0tK/D3T5V/8JLn/un3h0EH4hx/Dn/5n63zZLS0tLS0vfhzrec/nJf+p7Dn+huMT6Gbq09KXQJweAM0+H5RklGorxjXLJnu5HvboMpanu2jg7ex1Lx2KoHzgq9NlOhFgAGH2GAIwAj4Uct5NB5EHmQeZkjIM5BnEM5ozinvORNJvjEKgwJxiChcfgjHxWrLORESQCuSJEJ6AotxshiBGjAOTpnJPDNR8nX6DSq2e4Ym3JKSPiAzbdH8AoaQXWTmd1OXjpBXPOqOzAw2jW6a2pwTcEoPFLRfnaSY4JusDdw82bAjJJdSDH23pnyvmZTtom596YBbnKtZmOxRkP/RbvKuRotNZ0DhEPaEtvb7EVdnYgn+B/lsN0k8vUTD271dMcBlH9veeQwIzJiIMZ6iqdLtA74mBiTGvMUMRsZD6GBTA5z5tttJDjU+BS68GczCPgJiekbWD7jhNskbS+6bK5aejg0al6Qu0CrfsTTmNLuMR7Nje2grMtb3gzzLTbvfzE3ZzNnI1Jb0afoFZYraxur6hUYxe3zCm377bXe3Q6gt09k91gJxSJnJQTVpeRtgm6jqHI69Tey5gFVc97ufqNJ4KqY/KIEjYqLlp7nv2J04GetZ5WwHFmOVkBUdfq0M0TpDbB9L4JDHqH7Vqw/5DL3V0H3/aCh6dLv5zz8y4YOuU6N0ssBwprEAhNM2jP0Da5l2dB43YlvWldaMRMZtzUQfx0lcPbEqz6rPul9szpZt70/DgHFc4BhUC9uaTuwctOjlBHrl10T4cczZkmuDrhLSY7H4M3Wc802kZ0Xb85DsWvZxDHQYyKlKdpqGPc69mbtX/OdT97ngsIV6y2nXs8huC8ZR3CBdvf1XUQbMZaDfjItZ9xJ+cd/AlsI8erPsOvShMnmAWLBcvXf8wuLS39zbX9/I/w/ON/+Nv+2Tte+UP9j3+Pj2hpaWlpaenLL/vGX2L/mT9Z/ztoaWlpaWlp6detefD8o/8Y/d/9M1/0kSwtLX3B+uQAsJ9wNjcBozSZUHlzgT4gcALmhMs9R++F4cqVhtHa2ZfqAiaWFfU8BBDLbqf3n4ppDUGgaU7ECYudmIMRdyKTGIOIJMadmEGMECSJJKcRx63Aksu5Nqcg8Ty7POcbCKvjzcw3qDzzRIr1ZwImRJYbVXG7OW48+mrjBOIFP2tFCSsXbrn+5Hktt2uUIxOdfw5BnYr+NUNQqV6beddxeUUv42QOxbxW5HJECGAlQkKZcmSeF27OOuaT04ZgERQUPmNiH15tYX2r3ZHlLm0bgbpJW7vUC2f1DBf0O6F428AbEYK7QZDeGPMQKIspCD5vkPcHeM2YjCkHouJ6kWOV9gFI70CjWUHonHKRNsf7LoBZbkz6pn/fNjgOneeYWEwsDrmIw+TstGTknbRRa1170zs2buzjxjWC3Z7prSvK2RPvF5o7jsm3bEazJv+yFZvlhLwp8ywC187ETd/jTPmd3WgOrTnt0mkNetvofadbcjFjqw5jWrnN5+ngp87/Ar0XzDW433UPHjfBUnPIo6K6FeNNRSYzDsHglGP74SiP89Y5Xel5PiHANkgrKDnlLPWp9ICkHKk1+HEem/vb/UgITpKKKp8FdgOYkzEG2TedywjMBpZBzgOPPFu9a8gCmBUh3ur6W2AccoW3q3qS58RmEOM4rxDqIh/qlW5yN+trtQDlAMbOZIDxlp4QQz3R2xXLg4hbndP5AC3XMiko267qFXZdixx3DSG0qxzzx60eMUkcr3JQ70+wXavft44rRoHlclj7fHv2mBzy53BPPbC0F8g3p+88qm8b2J5r2GPIvZ1BMhQp36+Cv2YVJ271Gnj0Oi8tLS19O90/Z/+pfxYbL9/2j83gt9iv8o5v/+e/Ufr6y8E/9f/5pe/qZywtLS0tLf2GKZN3f+oP0X/5X/+ijwRi4N/4S1/0USwtLS0tLf36dP+c5x/7x9j/rf/z35BEtbS09OnpkwPASTkcuQNT6DUnZkYck5kIsobcm5kTixB0rEjeByV0QUA3V6/mPOiZgspZTuCHm1jOU6bgqVov5UROmlycm4BPmhzCIyYzgohRILTiSDlhMIoMjiDjzpyDJASQT9hLCqpYQe4MHXtBUEGPchDnAOajM/QReXzGWpsJBiXgVgD5hEhnZPMZ0VTg54xwzgJ3kXJU1vtHJhY3OUXLbT3P2NX6d6A8popntXIWz5hyBtuFsvwJ/oZAUZ4xtd4FrKxcwmcPMXUuGRWV/QH1q45VtygeeBSwtOqcnfgJl1xdscQQeK+I8QhIa8SM6uyFaFciBAIjRznRz3jpfPQpy6FKxQB/ANvT6vWBR93AMQTWrAClQ3oroFhdrH72Xg/8hNk1DDBmVmdrqg8ZZzsmTzG48ir4m1GRyEl6lw/TBL69NTwHnql44eza66H4XzfHDRpJM8e9YxwYTtsbbd/xvtGuV1o3fLvQ9o3uxmbJJZLuF8w2GHLQ473W/wTDXk7O6pC2Lqdv3yu+OWvPDjJuGo6Y57rX3g2r9ztg3rB56L6s87bTNXz2Cp/7xawi4suBamAxMKpX+uzcPju5m+AxUIMad/Xn+kXAsTe5iGnYfpUrGbmDzXo5fLVHlEYgNzsF+TMEN7N6ogW1k9YLmPuu8z3ueh5Z02vH7eHk1X385pSOQ4MMOdXLmxXlbpjSEFwO5aCc4b26caPisaOi1/tVTuNjkMedeb8TYzKzMX1nzlcig1nx23gX1LYGfpXjmXocxemqvvCIkm+n47pcvce97qF6PpzJBSDnOAXO9ws2JzleyDiHMZT4kKgfOSKIWde+P+n+je9+fOvS0tL3oTJ5/ok/wv5z/8Lf8mX/5fav8j/p/3vaOZD3XdBM+Gvv79+1919aWlpaWvoNUybbz/8I7S//+S/6SADw169z+df+yS/6MJaWlpaWln7tisHzj/9hLv/G//FRefiF6TRyLC0tfaH69AoMpynhNQXMDKoT1woyGiODbg3FBh/qhT1LPE+AUP9umWRBQjk1rSJKQQTPHq8vjAogp2DFA2ccglsnTH18n8tjlnLrnt2hlbla7xs03wo2JkGjW2PmKAiFnHJzIPukoFFW3KuczBOjEb59cIwnwK4+WSrqFnVhKna4InRR9282g9kRWHdBGHdhoQyCTd9qQI76rFluylFrZ2S6HHblDLZav4GXC1huvKz4VjOBICHGglhNZ5BpmLdyXvLmFM4AejmUG1bQGeuYu+BgDMF9Q9HdIDDUdrlp/aojdtflPjuK3XQMLmgeeSengze8K/43xo00UyTzCSEr+jiavzHfELzTNhhaH1f8+Nmx7OXZHpG1jlFcspNxxy2JeZQ7EpoJCse8a1ghgmkdtx0D2rxxzYPdOj2TVq3Ecmtf5JbMqXvI6001FYBnYC4gXA2sNDe8HjVGYN2x0TAHbxu+XbFm6q0dd2ZP7DggnL5tXAZcMd5TUdUjoWtoAQqIz6m9GhUT3nceduRR4K+crDU5odedXdb1nupILri793KiQ6veZZl793KcKn4dKyDLeBs0CNPxZZLjjqV9EJsOMJnl6A3T8yfjgA4eVzym3i+uWi+6uHdrlSZQcLd1zu5gM9eemKEhhNO5HFPXzlxuXTeYo+Y1CnJnVH/uELw1rUWi2z3GwO4H+ehCnrS67oMpAHz7Zo19XPHeNVgy7opH71c9K+eow6oNnnUvTyNbI+nkfSqiuQUWlT5w3iPm5P2mQQlvuj96AAe0i1zScQg8H1PnZq1c9XXsJ8A/u5ctda9seyVxTz3DvWFoLfUsPLD2RB6vcNl0TZcJeGlp6dvIv/ZzbD/3p39Nr/1vtB/jL+Rv44/N//p37Xj+4tdvfON18JXrp/ef/UtLS0tL3z/yX/1F3v3zfxA73n/Rh7K0tLS0tPT9p+M9zz/2P2P/8//sF30kADz9xP+c43f8A2VkWVpa+qL0yTmAAUV7ur85eFEEqxt4mmDsGfeLOnAdJ8weXZhW8EkYo9y8JGkuNykUKDmjggV/NPgy5NwjBTEt5RSk+kknxIyH+zWAEVGtvFQEqROod1fdpFHQBoElgpnByGCOwZyTGQeR5dw1J2IQ8yCyziQFUeWg9IJmKRB+uoH54OsVbZ3nOijvWsfoXg5hKzibteSmiGXfFDpcYCd9Z+a5nnpXNW4akVOwOsrRHPnWS1yO6CTkYm5yp3KcXaYIdp0ObhD0802vr+uTpzvZmpzQ5Rq2urbq9TXcN1pMWt91vep6O+K0cqVSU06BZdLahrmijzV9FXjfcd8+AJEpzmsFJB/OUb1nt8QqXnzEIDhqxmAKGteanSCaCF1G2+VadLnUT67cvFyVkQLT1gS9jxv7mFzGC7sFrXWtwIdu8LPfuTpRM1PQ1wxLUyQ0Ou9m4JGY7bjteL/g1mlboz89sT1/Rn960j/PV7bP3rE9P9OvF/rTRt8al+vGk03aPGDeBexcjvg397TAnaqMKxLYyrGbA+bxgP54ucL3J0UzT2C7vN2nrvenbeoern/MOu7lbJU/vNzfFR+dQY5QV3eWG/m8rhSgrmjzsx/X2wXvnYfzfOoZMI87OdTJrCEFcN9qmCJ1PUDnv3U9e5IClYLcMrfXRY88q64fUe5QgPQcS8ko6NvlSE5FWxsfRI1HOZG3DWtKQzAz3B3vVz23ADj0HNyu0C/1fKmO4zkL+iptIEli3olxaJTBjJjq+VY3cLmRkSOYtmP9CtuT7tEZmFX39gdueXv+Cu3yrHjrM679hL4xYbsWvEdr5rsA+X6tvVJ7xlwDO62ufSsHdQzy/NrS0tJSyT7/FT77k/89/PNf+bV/z+PZ9d3Rv/SzX+MXv/76Xf2MpaWlpaWlj9K4cflz/1v4ksHf7Rf+n/hf/wtf9GEsLS0tLS39bdW+9nPsP/VPf/HO35J/45fY/60//kUfxtLSJ69PzwpgqehPo4An+kv+JngJgbkLMhICv1RKbAJe8FH5xPU+AihuO48IZC+o83hNvb9buVqn3KUEM03R0UD2rlhob/oLQTMszr8cbEwOdVzSCAaWg1aQOlNOUHX6GuE7GQfdIONVgMMK/tjE286M480VyRTADgomb9SHo25gUwRtHpB6rb7P1aPadtS1azDuQCt2JgBsrdPmYFqBLksIJwyB1naBPHBMcPoD598b2DrdePW5Mf9/7P15rG7bmt6F/d53jDm/b619bld1q69yVYHLUBWDWzDGNhY2TWw5QsZOQ1ASK5IDSAhhIiURCYmUACIEcCQQBMcSFkksxcE2NpRtbKqcVOcGG1fj6quuq+6t7rbn3ObsvdY3x3jf/PG881v72veee+49zT7n7Plc7bvPXutrxpxzzLGa33ieh7BGhNFy4u5kc8KzHNC9TNsPMN3K+WkIIssdLDBr1wjrcg7X72TN6vOtyc1bkdTmvYC4rrW5yYFt1ZdajuKFUzmMA0sv1yEF4vQfUZsK7AGp1/WfWCuwngK50zuegllZHuCwRo47rvHRKVBr3klO0BQT3irqePHGNFe3cCbOxjqTUwSLnWmEnMa+1olbgALrDuBYhKaIGZZTgDyDhtOs0yzU83sWQLflXC7gW9r5Eb70At91KlrD5SkFBm7G6saj08JpBI/NoJ9wgjmr43iPQ84k5p2gYACznPrVDcty83AjVyeu+oPrHmWB+8eaK6dbXYdEc6Kv9ZjJPpPMq6NXdy70lRwVXYxhdV6T2ljg5a2vOdbbwigQrDhqQy5yQf19zjJTY2TDzGh7EoDXXIkg+w057hUBnSa4n8GMQd4/wc83ODvwNyyHNj1c3cy1VrkVuB7X+5PWsG7ldl/J7V6P7We4vHydy8uych+TmPe0fqN7PhOskfNejl33pzY51O097sh+JmLiU2uMLQ3G3VMbdeo6m8FyUirA7syNgH7SNYmaSG0VgHbHxyDaWX3mbpifazODoLw2DgRkL6DtZFth3GnZzkFaYp5yJLtrnWuO7z3Uhw4dOlRaPvBf45/4qVf9+D8xfwv/8fxdr/s46kvroUOHDh069NbXuOf2L/7vWH/krddV2D75s9jdp+A9z3okhw4dOnTo0Cvr5rv+9Wc9hM+SzQvtYz/2rIdx6NBzr+cOAKcvWCRicKaoUe9iC74Ak8jqhrWKIo7A2hUFl9eXcgkvYAP2blqqT/Ma01oRrRXHKqirqOeOsxECqexmUPVoKiVVsaxmJiDEpNlCzoviaiPKrXoi8wFu2tiY5kDHvYsDPv2bwB0MkgI6cRHoACITT0VL72BS5NEqQtcKyHJ1Gto1aracpmlyhe6QhQI/FUVrBZOxhWyTmRP5du3qUjRvesvdFZtcX8N3BGfIgbnD8+vxJc4JXFA8TIh0Rip2udySLqT9FNDrunZdUOzqeo6K9qXV2Ch41h5AUjmJNYeCxomRd7iVg9iNmOpnbnb1RuMu6Df3fmaouF/BS2ut3KsCcGC0rj7fObcC9U1Rvt5rz0ErqJcC1N7JbUBrNKzmazlQrWMVG+1pnOPCKZ2l9YoNrg7c1iFnGX+T/f+pLljPRge6Qc9JW2/U97t0vDe8d2xZ5RwFrJ/x7lhvVwcpBmFG3N3B/R3MBXenu3PjjZvtjsd9KTCbFZ8Nc246huWkzRGjYGOrzu0ZcvvG2ClzQeFUPHSvTQ8OnG/1MetAYpFYXzHaw6aD6/XWJgD2bvAZ5QhOaDo/2jdwL8BIK/4seGwknpNMxWSPy0Zc7mVUJfEIGCiSeX+93YHtizZjMMlwvVHrOqzm5FRXcc6AdcGXU60qFQGPXPeaJ9Xh7fuGjtpw4Q1zHXNsSTPDmmPrmdjkyg9bYO49uLpfZjpLTLwtDy58X8h5B9Z0/dupbmCdDyqKOmZgdo/5qqj07QneJrlf95g87BiYdT9WfHvFbdNX3A17alOGhUFfKpLedd+NZJbbPiMxD82fBGtKCGB7QnrT5hnvXHud58DXFypa/9ChQ4ek5af/PLff/W+8ql9efzC+gv/V+F/wN+ObGW/At+N/+xj+8Pf9HP/u7/576nvVQ4cOHTp06K0j/9SHWH/8T73hiRhfqvzxR5nX2qZDhw4dOnToraflp/88/SM/9JbbSHXo0KFnr+cOAIsoptJ7Fy8oIrgKFLg00uQSdRLruztYDuGMgHYq11w5Rq8xzxUfW691dbtlXF285gasGIFbyPmZWVHHk9aaelnTMB+4dWYmHhSwrm5gX8pNmEU/Q0yl9VrwFYtsV6dfBSt7wzMYWT2hbkQUAKW6S3mIkd3jrBVBbAWoJuDq491d0HvELoKjWcAQV/SqFYk2gnTDkXuXhGmKDk5zIgpmzoG3TuTEcpJ0sE7koDXFMz9E/j79w6IxuYB5xc1OnePqA53zUvBOnbt7rHTvDbxig/eO5RwC3tYKwlYcratPmf38t3PhcvWLGk7vNwVNyyHeOuQFY9Jt2cOywRNPJ2Jq7piOjdh07rqcpna9qkHsLk+9MtPkUmQ5yTFajyRDTmou5DCmQ8Sm406n+YBQn+tpBksOFn9BLt0ZDw5M313CgfWueTzu8XhC8xPdjIXGYk7rJ9r5jC+rIG8atjT8fFIcrzvWuzZVtI63dgXAlupZttsbdp87p4XzmNyMlRZZpvC6T5uikSN69R4j12pMmK7I6HYq8OvXea1ZXU7sWW5aK8fwIojrltAFp+3ay009rqKfn/4lRdZb7G51Tzn5XecLkNu8rXIEjzvavvqMgtPNYUz1Jy831XU7gV6bGcDpgr9e952b4HNTwXbGJHMwQ/Oadqo+5nIZV481Yz7MX69I833jAKZbAEgb2mQg87mSC1rIhNwaEQ2bm9bWacS6EjWn3Dsz7wXGq7NXy8veYe7gNxCboPXSCBe4t2ZYW2pt7er2NbmyE7+mkT+kAwyyeUXOB+n1XmaANo5YrZMeipo2M7njp5IVrJejGMWa53Kj10utW9ZqE0jaA4w/9LbR/vX9Wb/G21nHOXwFbU/kXNpefsWHZcLP5lfxz21/gB/Pb+BL8ul+CfbeD7745It/n7eojnn42nWcw0NfrI4589p1nMPPoUz8xQ/wwrf/C/q57S2qm+/9P7N90z/6rIfxuuiYh69dxzk89KXomDevXcc5fAW9yp9Fn4X6L/0N7DMfJl/4qmc9lNdFxzx87TrO4Zuv5xAAXyBdkbrhpA9F8vrCHFPuuXL/whSISwq8QJZ70GLsVtArYNDk0ynNLIhqs15fANkKklrBLncj5mTu7tHc5Dg2KpoXiKB7Y06BzrBW4Fcxuzbvufa5Frgwi3It7uNTlHMSVN2tXI4FOPHODAHhQT704RpYTvWf5sByAUuBoIS0rthZK4coG9eeYIpLZ2K2kCZntVnHYpLlLmQG9GTSyumMIJWBx6Zu27oeci4LQieT5o2wis3OIK5dy1nws1yTLgenmamfOIbcf4b6gb0xc8OrEzp3sFe9qDtkTtPcsOpffgBZs65tPc+y+FyTE7O6ghtymUMUnBL8zNjfh5p3gs4ZBeHNBOATIqvX1XegWedhXAhb9bpLOdG3csB6g7HhMdWrPAduim/2MJZ4zInO0h5hntfOZKMc8rEDNjl4fW5YPKYv76MDy7ywemNZOr402s0JvzmXA73czGv13S5rGWgdLwjcqqM1YuLrGb9M8hSkTdwbqy3crkG/u2Nzf4CJcfWzMjIgHU5n2O415/sq8B8Al3Kx+0OENeXcz4DU/WauzQrJlPOzSKjVhgm7Vqfvru2967vg6r5ZJABXFLpZqvM2pzZuxAY00obAZqsocWu1YWITJPfq5B53Arys2lzBJlerOcxRj9HGhoxJTnV/Z+vQ9hHL9Z3uuidSndqYoo8ZtfFh78TOhDmvVeYznVZmYQMBcgR5YwzcoFkwYyPPN9r0wVAUtuna2Fr3w7jo3mfovFv1h18eE97k4qXJ1Vz3X2anxYDuZGxab1urvS8KDceX+uWR6EgY1cluNHOs1drWIGdtzHBFthP3MBau8e4m528S+rphVNx5w9a1vgYcDuC3k16Pbw6f928wj3P4uWV3L3H7nf9blg/8hS/42A/mV/L7t3+Fn8xveA1v+MU/5Wc/cccf//4P83t/zVd/6e/7FtExD1+7jnN46IvVMWdeu45z+HfKX/pbvPBf/n7aix94g9/JYPkG9hoa2nvhXb8dPvXtEHf62PhFyPvP8/y3pjP5S9ExD1+7jnN46EvRMW9eu45z+Pl1893/xqv6WfRZqH/4B/CXP8J8hwDgYx6+dh3n8M3XcweAva2ILOZDnG0i+NIFey2CbAtQDKfACaCu3Ypk3h2cGXsfZ4Ekb3JvXnsmo8CwQGEFvCr61eQpTROY7dcYZL9CEcHlh4ntJDEnZXSDfsJm0huoYxaKcOg1jAdqU7wyMgQsTXR7Xl2eAOPhRoqCRLsdUGHFmDlBYrnp95AxEArzPWFakGmfYjnqcFb8qRjnZBLNiEjcH2Am5WrONLoZQRIi8zRD/a2ezHTMmqqVSfXuemPH9W4uV6yBjY0sd3aMe7yfBMILpCbIieirQCIP0d1FfgvSuSJn6wgoUJ8FneqRitDd4VpqU0B6h2xyClvD+iKwN++xYWRuetnWYCYZGxMB/+ZWjF+z0vPhqlg6njpPe++0RSNmgiU5N9KUbpzssdFDMdRPXmZdG8typlmUs9HrfphVvyons7UFGxc8Bt1uWR2WlizeWNZb+mmlnc74qWGnFXYHp5sioMv5m5jqYJeOt1O5UxOPRjsF7SLAV15UlpicW2c1Y5sVwbzfKylgr3Ne/9UXSEVv5zavcWK5X9cwOWuvHd0Dn7V5Ik2xxNf7oe4FE3BNHKNitPFyz46He/46A6ygsmn+o40GXNccdTjjXV3jfcJ2Eaz0BS4DVoPmWL/RNLWKmNYkqTVm03vTtBHCksiNqSewG5sxl4O1OTFr/maW6/hyvd9pa10jrXX0BtuAsRGtoyVUGwoy1XPt/URcHmNs5JbMNrHzSosJcWFGbeAIxS3LurtvFCmA3RTRnOOeGBu2GGSWYx+8LcodiHEdu9L2HRsXaOc6NXUnpjbfZAtd4n6qdWk8AOPaFNL8RNqCx9T6512rXl60HcQ7ycD6DcQTzE8CxHO/Aw8dOvTcKia33/Gvsv7Un/mCD/1gfAX/3GuFv1+iRiSfuj82rRw6dOjQobeAMvGXfoYXvv2ff2Phry3wwm8HfwFufrU2vj6tL/t9D/9996Pw5K/rbyaHDh06dOjQ20Htoz/K8rP/vzewRsG4/m7fz/DCP8bnTMOLl+Ez36nfUf5tWn/k/8OTr/yVn/t5hw4desP13AFgmmCf5R7K2gQ4QlDIHHC7xopmGfkUpNvU2cnuRq1YV3bYufOHp6NB7QEE75G95ValwnndklmgjoJ8Pgc0K7gUmAXNvRx5TY7iq/uw4Km5gJMXmMoCTfO+XsevzzNziEtFPy/l+HWCiVHxyvtxWLs6BTN2F69cq2lWUaqAySuZMeR2TcEkKDdlVjz0FZIPDLlALUP9nldILVdy5MQDfI+9toKCrdEt5XZkP2R1DU/yGuOKB1bxueaNlvpxztcX6rwAJpjqGeoe3WPAd4dvomhoKJg39cNkzIoO3nth63rXESsq2Wj9Rj2wkUQ2wiBDcJuZgmixbx5woJFD8D6qizZBzux9fuVQVO5+jQK5Pal5MC/lcIccd8yEEYMZG2ELLRp2d8Hn4NQHp+UFOonvlzy3mksLpH5hbNbxueEk3ZzVjJVkaSvLyennR7TTgp/PWDfSVjkrlwVfFjAj21O9yd4U8bvPa3PMg74sxKMbMGOOSRq06azNOWfwuC1yals+dDHt8cvlbCVRIXGmooQLwNtplZt8PGGPAScGrGfdfjmv0cDmXT2yFZ2cfkKAP55isEvNkay5PXf6X5sIdkd4gfXd5bvfB6YY9ZihTQ1jk/N7j7gOvYd5r00ZwcysjSLlqvUFxp3WLIPIyTRtViGCXufK6l4m9giA/d6f5dQPrRP7zpIsF6w1WApmR5BzYr1rM8wc+lMA1+833IIZg7QV1gWzxLaNWvJqrZ21GcYe1sWoTTisMAcx1YesXTiNzAveOxFZILvW2HJVq/NZnca5x1o3vX/MC3m5r7Wyvuzt3eupWHdfV3pd47y8rD5imu68GFhrWDPIVeA3LorpPnTo0HOt9tG/Sf/gd3/Bx30gvpp/fvsD/MQzgL+7fuqjj3myTW6W9oUffOjQoUOHDr2BevRf/QHax3/ijXnx9uVw+nvgXf8Y2PlhU/cr6fytcPrlsH0YXvqjMD/xxozt0KFDhw4der2UwfJTf5b2qQ+9/q9tJ30t7e+HR795/2B9Xf0cIDcDbn8DfPJPwd0PfNanlp//y7xzCokOHXr76fkDwBmCGDmZ3vGl4yG3XloqFhivKFeFxELqMd1IFgGgQC4yX3GGelctnopylRPT2KFvKzDUyBwV+QtmjWbJNLkBo/eCqYucahmYl9u2AA9Z8dBRrtqcQIGd6w83chYK6uzAg4ItOspsKx7jylgag5bVxboDpL3beEb1mRZcrChZFChceLwzc6uzFoqHTkUlZ3pBMEVly9JXEBXwHU7vADO2YmSC8ALlLgDtWXHZAjPeFOkrM2jl1fqAecdDPG/Kd51xdRjrsYqXdQS1dd1i95OyR4HnFGg27w9ObwoQxcQqDlcXZyVzYAy8nzBzPM8F2owYMDOZIXCYFRuc5rWboOYpe71rXaHdhby7up8CwtPBrWHbPclGVmdykmwJlzQi5Uy3DIKkn29Zx4W1d3oE3RV/7RVrfQXBdTaaNVpudIMVOK2NZb1hWRaW04qfbvB1gWUhCKY72VfFre8xy9PqWhkWDpepGGR3aOrPdu/0RBC8IrDpjbV31tZpQJjAuRy9CNhOaj7JYcwsB7cNbdpoa0HUhHZT907dP1enfIH+umesIHPzxt4DnPLsC44Ce2+u5lpdpx24Vjx15cpjXXMj54R+gphETLnbU6+eMa5Q/zreKzgO3SuVTWA+tcGhn2BciEjGCIY3QUug0TRnW81l6pgzNFfnvSDofr/n2P3wmmbbpud6U+d3DF2b1gWtrSLi25m2BNvlM8zR2LYLzc9YX/Gtotwr9f06lwmdP1uADbYu0LoILmtHiTZIREJ6aiNMzuut4jGhr+TcrucZP5FNADwdgfrtojnZCqZnMrcN5gVrXmuVX6PZjQsQZEyMwNtJY2kNm0oaSD92Lx469DwrXvoQy7f/K/jl06/4uA/FV/AvbP/yM4W/AP/Vj36Mf+m3fuMBgA8dOnTo0DPV8lN/lvaJn3wDXrlDew+875+F5Wu/+KfbAuvXw/v+p3D3N+HxX4F863YTHzp06NCh51hz4/QDf4TzX/u/vc4vbOCP4F3/Xbj5da/etWsOdgvv+aeAhLsffJ3HdejQoS9Vzx8AJuQcy4WcgwhTLG2WmzLKEehN0Md2Bx9EKKrUsYp0rvhSEKmzHdTs0a9BWlzjgK/OQDp7FDSpHmAbFbQcYBm06gxutdDG7thrjUxBn2uHzQ6ZbUe5WRG3iXsrt+msRwZhO5Y2RuswBu4P4AoQpN0BssgUM8fVRajYax1P7jClIJJArc5ZRhDl3rUQKErbIZfAolzVos5OB7aCXbvzVK/v1lGD7R4da1g7q4uzAF0i8NbaKif33Mp9TF0PCkIjkIPJEmwJuZRz2K+P13UCfFFsNq7rVe5rJQRvkAUXvVzhvhDbBtxjbSFdXb/mhqFjsQxGbpCK29V7D9TzWv82q5jg+tscr3HPsXedOjmGrmWxxoxkzju26NyFOn8TcDtheUdnYcng1BdOBL06iy0uYI71PZbZsRx4Uwd0M2MhWU+PWM4rbVloNwvtfIufbsCdMSczG9P2a+owJjYm5oG54X1lMoiacx6OD1NHcLHBvi5YphyfEaxunNvKsg02T7C1eOUdSZC+QDvBdl/fnyxAkKMcowV4LcppPeW43zdtGOWmjagpH+WCXx5cy7jWhP37nz2OeN94UI58XP21lsAQlKfd6N8zsH4uN716hDPlWtd+A2POSTeXyzes3K5W2xlSQLM1mJvup3TSVyw2Vfpu9dqL5osvt1w7i0Nd0umrIs8zubq9oebgjlfrryiH8qnrcaO6vtuij7ND0k73E5ccjMvGbJ1uTdci7oELmQ3arY4ppj5e68HeYUxFo+f2WPc5F+hd93N2rpH8rnMfszbsxFAkPglzKCEgqHWnK9lgRB3TJpc/judGTnuA/f0kCH65BzOsP7q+p5SC+fOIUz106HlVZvJHfhS+/MW/m9/Xf/rzPu5vxVfzz2//Mj/+jOHvoUOHDh069JbQ5WXWH/0T2Pby6/zCBu/+HXD7D/Hwe5IvUctXQ/8qeOEfhcvPwM2nXo8BHjp06NChQ6+b2sd/jJvv/jdf/+jn898H7/3vI3bxJZge/FbPfynh7ocAsCcv0j7yg8yv+lWv71gPHTr0qvQavzN+G8o66s5MWJqYTtRiGeXcbQ5mRCoaNTHBROtiQ+VKzB3csEf3Vv+nuV4zd1i5x8PuMb/qJi2rmaBiXxSnPLYyKU/M/SnHbOKZeJrexwLvSzmUBTSa2TWAWC67EATJIaiRk8hQzK9sgLSKYJZnMj57cc8HSK1Y7CQiFFmco4yJk7n3BBdkzszdwIh6eeW6jkxmDnLcwxVlJVFx0xGDZDIjFIMbo0C4Oo+xqU7XGFezrZmD92LrVr3OAXmR89Yc40Ijad4V8Rzlcs26hmSxHbk/0/RxrFUPrU6pYsFnQb+8bhiwNF3/vqh/NXaHdyNmEHMQIcd0AOmBrY12qjjhMbGc9dqUe7cpipiCl94Up22tzuPuCO3QGm0pAN472RcyG2Mmd+OeOS6CauVU9jFZ5saC0TPoBt06LR/jJjjrtncZV0dq6zSSbsay3tBvTvhpxU+Lun2Xk7plW2f0zrYszEWdrbFdmJcLM5NhyTDYCC6ZXDLYIrmMyWUbjLt75pMn5GXDW2ddV5be8Ob0ZeVkRttnVhniaauuq5d73Ts2p5ysd3fQatPBJhicpg0RaXm9nm66JxvIYbp7YFPAXy7Vp+KfTe7qyKvHt+ZPuX4zC4ymgKkt1/vRrm7jqc0eqS5tbGozQmhseTrJpR6bXivlFlanr6uv2PaY8rr3ag2KciVbGl69wRYbObQeXPt2s2BoW7huaKl7w/bXzb3f2JhTse+0AvWua251LMakLQvEVpHjWlPt1PGu62Roc4rcww59rYj6Os8xYNxr/WytIrAXGGO/OR/WqBlyUM8khgLsdfyz4uG1/mTuvcGTnBdyuyh2Oyb0sx43dT9HhNYb71hbsX7G2u4Ib7gN3BWHfdSXHDr0/OrFJ4P//Ac/yh+ev5MX84XP+ZhI4zvj1/Dj+ct42Dn07BQJ//H3vgHxYIcOHTp06NCrlN1/kuVnvvP1e0F/BKdvg/f/i4K/1r60X1j/7drrc05/N9z8Fuz+/rW/5qFDhw4dOvQ6yB5/nJvv+bdeX/jr74KbXwvv+d1lhHkNX0ttgff803AW8PXHH6V/8Ptep4EeOnToi9Xz5wCO3aUb0Be8N7nsbLmmBe+gNnExPupjCLzE3Gimz1vm1fWpB3hFNVdXsO1g5qmHxKhQ3Q6mmFazzt4BfAUzKCpYTGfBmBjO3jws6NmxuOgY6nMTAUy3pRzICq31+h8Vx6so44qutafgy74vIBGEdvXhzrjUsamzl8gCnoBbOUhnRRkHzdSlvB9LzpBr0VeMrcByJ6Nio6sbNaIRFniYYK4l1nXUXjG4cvqWO9hO9bHLw7it6OBywmcdc0UiT7OHGOdy2GZrdZ0Ds0Yw8QLTdo3VLth+3TdR17d6epkbV/sq6Dx4I3Jic2rUdW7kBhbUHMxyREO6AG9S1we5Ivdo4lbxyZNU/GzWY7zp+uHEuGOGcZ/JmJN4+WVab7T1xOrGcvtu+rzQY7DkPc1uaTHx/khQMROrnmoLaMtCH5PFjeW00h+9QFtXrDu2rtiiXt2IZDRjdiew6rQNJop2pjWyjttQD7eHunHdve63xCJpBDYn3py2qpd2xTh/+jOs98ldaBNEYLg5E6eVW332BrbK4XuqbopZ8c05Ifa4dG3ccDM8J14bMpyGm2LiaYscw4Zif83Ff8uxK1d5w65rQDnGU7sscm6KCmbRZg5D3dCVJoBpzK2vZFuwlti4yN+/R66bVSqBVSpBVzz8dNKbuqxl6UXcOcjp4IPeb7RJpNz5aXV/597D69fNILmPXTdQOYOzjr9dNzyE27W3m7mBm/bOVK+156T5mYhBzE3d1Kb+5RZoQwRJWpTr3bDeyDzD5XGlJZic/ctJ4w2trdrs4mRs+kbXGmm9NqQEtLPuN7huzsnY7+H9vsrC+3WvRpK5kOOetEUOY1dPerauuZOX2lgCXlA4t5fJeB2/2T506NDbSn/qBz/Ch166w3k/f3r+Rv5n/S/8HY/59viH+LfH//AZjO7z6xc/efwC+9ChQ4cOvUNkt/DefxbWb359oO/nkX/mMzz6S9/Hy7/pN5Pr+oa9z6FDhw4dOvRqtP7IH6N/6Htfvxd89Fvg5ldD/9rX7+up38B7fo9+X3f/w2WoyTf06/WhQ4c+t54/AGwT86Y+23QihyBU9WNeezwLIEaEnGeGHIJmzOZXZ2SmXJpyEadeI02ASBQCdth49cTaw2MRuGs5mJaEmUCYCWC01hgVtxyZuBXMiIm5XsN2917KqWzlaM24w72XK46HPlGsoKW6YPe4W7MTaXEFP7A7ZKu/140ZgQe0tsjBiNeOo4qI9YWs8zd2l2pOmZIbD07paQ8OY+vqFc0QhDWdmnC5YL2ie8mBWye9k3Gv2G5Hr9EWmM4oMNRoBbaMtKXg6AYIrOUcisPGyF7+5ylHX4usptVZ7y0g39yw2F3dyMVYEPyh57VhjIoUdwGqCdMVZew9Me/kEAC0vtIJZjZiKhrbypUcIIcxU/Bvh/7kQ4/y/gU0TXHbcyPu77nMYIyN+eQOPy1gj2ksLEDzRp+d9cmn6edJi7n70GUG3aG3L5gHzaD3xtIX1kcvsNze0JZF/dne1AKdg2AhcoqXze3q8Ex39bo6185nDJ3LTEHn0PyKhBxBjAvhsD56xPLCI9w7a0xu1pWTfapapnU2IgNLqie7vpnwps0UieKKY1Z/tK6hSJ7hQXUfOy31Mds7fb1f75mEihnnCiK1CaHpcQV8d1ifOQUw8SskTGpd2FMHLMuBW927EdVv3fAx9ZhKI2A5CVRbZ7ekJyH3+Z5CAOW0V3eytVUR8iaQL9B61nvOjWv89HXzwJ5QcK57tulP67W5YUPR9r020jz1x5yMlzW3C/inNUZMYlzwvmAVue3NyFFwNmdtuGmKSu8nuY2jNtjMWV3NtZ5GkOOJPmYr19j7ucFyo/VqDJypWOi+6Bpsd3Kp2765RnPcsvqhfYF+q1M41TnsKLXAAF86FpW6EANQn7f15y9E49ChQ5+twPmT8zfzO9tf4SvsISLyk3nLH5+/hXveWr8o/uTd4KOfufAVL7y1xnXo0KFDhw69ermg7wu/7Q2Hv6AfQ/pHP6KfKw8AfOjQoUOH3gJ6Xb7yLV8n1++eoPF6y1e4/bVw+SlOf/0Pcf+tv5d84ate//c5dOjQK+o5/e21gE/Kegu2FojYu1+fAhsR0DvWlorirehTYQEh1HIICjy5HLzervHR+0tiVvGvFeO8w6prTJEJUhGFZk2O0d0Ial7u3qzX3GFudWyKiigqul4vhlx26p0tx1tmjS0prybNGm7Vf2lG915sNa8uTcpJqKTdcgdao1uTs7itpMGsuGemopLDvMbayAIw2RbSnJFRPGvv9d1PrQOt3IsC42GtUFyCLaR3ghSKm0Pw3BeCVrw967qOp66ZroG1BbKg6g7GI3BTZHczuWvDG9OCgQA8XnsmvCJ9bY/+1jHYjtvrK3FGEt4rBlrHambQFrKihs2N1p3WkmoDlpu1ILxb0gv+WlJux3n9fGQyC1bFtrFFMmfBTm/Yk3uW9l7Oy0nOYxLvK+2m0Z5UtHhbaRg9Js2NxmSxoGtmsfaF9eaGfntDuzljp1V91MCYk8s22cZg3N0Rl42YoeOr+8IQ8PVI2pj0MfEpgOk7GNVsYVpyMbhY4wJsBWbb2llOjRvrLPvmiqfOk+3/PeIBUMYGBDk3Yo86XlbwTusdb53mTu8L1hZFQZsrBrsvuj1rUwVthXZ+cPuaV3QxRG6KGo6tIrx1PXK/Tlm90a45nJkwhiKF24InOAlDUd3MTc7TWb29ufdjP9x7jEnOIH0lU+7mMEWKYwNvi5j0U9H0GVmbL3bneNc8j6go99p9sX/j5657tq+KarYQAN33koyBhdag9HMNtRzWaaSdBXJB3cm1UcZyU3/u7qDdu7H7ivWbWi+mXMljVIxzkn0heod2hnXRmDKf6i/W+YoJ9DPYivtChGk9ylA0dEVxa59PCvqjOOtr97n202DVVc5yUmLBctI1jvGwHhw6dOi50iefbPyFH/vY9d/fn9/Cd8fff02ov8+F/8P2+/j/xq9+NgN8Bf3Yh1/mu37qE7XOHjp06NChQ28ndVi+Ad77P4Iv+58rnvlNdBKdPvCBN+29Dh06dOjQoTdGDv6C3Lnv+33w6De9MfB31/m/A/1rsPtPcvqh/+cb9z6HDh36vHr+fnttrRxgXjTNGXPSXP2V+ngtfHkvp2wKZu0eX8UHCxh4RYvKgacIY3l87Wq2ffj8qtfPUf5OvwIsy8Bik7tubuotzckMjVF+R1d3bUUSZ2S5gCt6OGv4wrHQTuDBjHsdqi2CMFBuW0UhmwWWAtYj5tVx667jU0Js4G4YCz43aI6ZFfBUsGog91x6ExTfoYx7pS0LwkQKagZOzqkoWHfMVyICm0F6YmwVSrsfm6KPd8djhu2fxdH7gtF8hZwPcb+2uyTlwk1RVLzJ4egMzJLsGqOAjxGWzLRyS07SjWynAnCT3Ul9jbHYwZktmlsh2GRU5HdFaMtb6FeIxw5lHWxdmdtG5GRJY1bcdnlday61q7s8Q65hQXZnziH3ber1/QTt9IjTeqb1hs+pmuu8p/dH9Ecnmne6QWPQAB93mK1KI0cO2b4s9NtH+HKSI7R3ImHe3ROXizprI8nesJYP0NcdN7mKSeRubQ2Lco97Qfn9Gqccq+FGNLma88mFuU1yQu8rN61xmhv3MXSuIypKfGhOWy/XbW14GEMOUzspYroZfTmxUu5yc3xZ6rFyidP6Z++ma6daE3aX/8BsAaoPmiR9KTCve/saQ56BHKMVGR9D17JV7+286N8hR2yODVrNu1PXecrdOV0wd3fO2r7OdGLcyXHLBv1Mb0azinpuSj1QmoHVGjSva1ACliZX694LbJCzuniXOn7vxJh4NGgNGyjaOoZgeDpsLxckDyaTXG7Ief+w0WKbZNbmgBi1Dtdky70z+Exe7jUvnr6XrKBvxW8rHnsTnM0Jm+L97aReXy29ga+3MJ6AzRonpCuKW+d7gbgHOtYXbE5yXJTyQDC3wG2tKO66zl598ocOHXru9GQLfvwjL3/Wx/6P2/+Er1s/xrfxM/xvtt/Pn4nf8IxG94X1H3zXB/kd3/YV3K7HGnbo0KFDh94msht47++F06/Qz4Bv9tsD6899iMs3fTPxwgtv+vsfOnTo0KFDr1nL18H574Pb31ipem/SJqp3/y7s4/8B60/9OS7f+nuJ937jm/O+hw4dAp5TAMzc8N6Ys7qAK46WJhft7okwPxW8KehYcNNM8ab7MpkZD67Ain9l7/StHmGr3t3d/WtxKa5Zrl2gtc6wKHfaIgcrexNsEKEOUA3OiLyQOTBOPNhDQUBNfZVg9HaLoFUI3pUjsYo1C0hpzC15gC4FN6KiYneolf0EuWnUdexJys1Z4Ewx1gX26p1iXHSOSUFg72TKreogh7LB9Li6HWfmtffX0grqFIA1Ad+MC9O6QOkVPLbipUEWJEuSyEkzJ8wKrO1wNTFfdqt2jXl3mM7r3IjqE43ySvqOqM2Bro7jKOBfsbo6D9ocMGOPGa7I4KXidL1j0cAm7XTCto2Ys+ZizZ16R7Pqeo5NEDaNHPdENGZMgdYChm4n1r7Qe8N7x1qHyx09G4tNuV8zaJZyAKPr6f1GjnALltOJ/sIL2PmMLas6X82JMYjtIujcuzYIuJzk5tqE4BWvbGTFQDesOU790N4eXMIxFbfuU8B+Ijf5ZbsQs+EZNFOM9WkbtPXMNAc2Mi4CsIDH0MdzAzN1JS8LGUNu43AaSWuGeWfv8DYa2V3gdYeSZtVZrbmkqGnT/Wlebl90v1NRzij6Wh+bZF4eXn9v6faTXntW93MkMeV2JUyx4jkgOnTH9+j0fQtKPystIAcZTetDa5pf7UauZUs9pS0ay6y1LJKk6xcnKSAs57tdv/ezfb5Zk5N3XOqfXQB/atNGturOzQYtqsd7pY3JiCDmYGybXM7jvhzVTnNFWWcb5HanSPda8/b7Gl/I7V5pAKm5xUysKSPBN/R+e0+vJTCg6/gJraXqT0+yKTL6uqaOoWsXA0u0ecEccmIVHc2411LpTWsaFRGdgS9rpRwcOnToELzEu/iXLv8iX2sf52/kL2f/XuKtqPsRfPLJOADwoUOHDh16e8hO8J7fDadve1Mdv3+72qc+Rf/YR7kcAPjQoUOHDj0j2eOPc/rhP/ZFPukE7/7vKTmjve+NGdgrqX8FnH8V7RM/wPK3voP7X/37rua2Q4cOvfF6Du+2oZjXmLgroNlNEaL65X8BT1D/alZcs7erY88w0toVjCqJ1uq/jaSTykKuH1DymlSbGQIzfREsJB9+R5g7M+7knOUt1P+Yu4tYoc0JeDsXxLm+OIQ9/PfuPs7dnVzxspHlkE1BLG8PPt76gWpiFXOqCNu03Xlb0GrKERu0CpG2Aqz51FvLuWeZtMwy+G1kTIJkxKjIbCMyGBnMKCepm6KizRh5T8yt3JS6dori3qOXOxFTEcuZFQo9K745mXMoIrm6PmdAZu4NyHIWPgW+E6toRMVVRytnYyQzg1H9qZ/1o6c1QbZysArqLoVs1RmLd2LbFJOc29UxHXFRf29s1flreF/Au+CpLQKG3hXHSzUE7zHS7syRbHNTf+t6Q3aB+N4bS7NymCdmigJ3kpZBJwv+Nrl/cXy9oS9Gt2R99C7W97yH5V3vop9PV/g7x2Tc3zG2TXPRHW+N5oKJ3RvdO6012tJoy0JrHW96nPWOn9QjbL0JhK8dTgvWnNYWep3/EcEYF3Vv366cTis3BOuO7n2BdsLqfa3J2UkEbE9I60RazRfFey9uuLdyHmueZ8UHJy44OEc5z+VUNu/42ss13K5Q3jK1mWB3AxtaM3YXujW5Rs0gL3o9m3Kf7n3Dc+g+d4Ou+zlPj0jr2Jh4pFIA9q5xq3jvSgeInMxEkfEYbtpy4Hs8fFZqQW1Qyac3FWTK2W+t1o59SwpyOqM1I6fGm80F7kMbSrLWoJxJhmPtLNBrkHMyU/HbuaxyEvde1cI6p7beCo6PoXVmbuS41z2z3CiyuxlZTukcDw53bV7Zl7ks8E7FcVfsvcvtnO51z02tLVGbQ5ZHtQ4IGMd2YV42oq1kP+PLC/hyxjO1cWN3/wP52avAoUOHnnN9mC/jb+S38FaGvyAH83/0PR981sM4dOjQoUOHvrBshff+D+RYeobwd1f/pV/S71QOHTp06NChZ6F5j3/yZ1/94/evoze/7tnAX9DvtNt7Abj5y/8ebI+fzTgOHXpO9fwB4Io+jurUbF7xyhXxjOukxBhgk2SIu44NSHAn2J2ZgrHhcuDt3bxX8OpecAU50lL9vng5glkq/hdoK62v16fqcS5HJ+XcNb031XwbOSv+6KkfhHZn6bUfuD6dg2vH8bW3NiE2QTCXezRiE2AMgHZN5b2axbPcjWkyuRLMKYgV5Za2DDHmen8rTGKt0UwOVqvzcoW4u/txP8cxmTvqngLnMQWI1AWssSSQ3hXnWrB7AiOCGRf1AZtGkLtD2qz6gQVzBpPYr391NMeYeJ3DHWbnHBiKp752NFevK55ynKqlmbRGpmF9pTyjdU0XuZ63YDx5wtiGql6HOoxpTf89Bfr0M25dc4MwjXv/AwLu6Z2cA2fQImjead5YDEHZvkDP6rc9qdfZ/Npx6ky8OXa+rejnybI21lNnffSIfj7hy0K6MWcw7y/McYFm+GmlNcMtCyxXr7A7zZ1mrl7hZaGtC62vtNNCW9Srba1h1ipuFzid8PMJ75ovkEx3Zmvk6US/OXPTVm4yaPu5KN953Wx1ngE7Yb3L1ckFJ+ku9zm7S3Z3fF+hb93Lc9NGhEhd5xxy6WY5ZHd3uFulxhdkJTA2FM2ue8doVFJ0RQcrStoKwNJX9Yz3jrUTtnSMXm7sPeJsCiR7J6PWo9ah+obHNtV/bOgYzVBEue7T3FMIqPXFajOKd6ztiQD7AjSuMFjrlqCnjYnN7WGTyRh1f2V1Jd9j877czU+IuTHGJMYkm8vFa4qitjFp+/qwnLB1JWetdzuYrs0CtsNjN8xd6wUhEE2tDznJ9VxAX5HaEYMYW63PUZteUg55UypCVqLDDo1zbEQoijvNtTkgtqpNrsj2001dzuOXP4cOHXp76sXHg8/cHykGhw4dOnTozdXNX/p3n/qZ5FXo3b/rmTt/n9b6Cz9fyUqHDh06dOjQ20Bvla+jj34T+Lvg8jI3f/U/eLZjOXToOdNzB4AzyiFW7jF1ryqWGFAUqjnWVtwWyCaw54Injh56NdsqW5iZU7G4WbCIp6JMUUSw+Q5dgZBz0L0jF2Fgsb9+QjZyyl0cqY7dTEUn7/DW9oF4K3C5CjpbRb6WFTeuYLpccwhgWSpCVUmxhvsJs4UosKh42OupKYhcLru9h3O6oBN27fjUe5W7+epAFkp177T2FJSaem/B4KLVUTDYOpFeTtfOnIqVHWbEnMw5GAkj5SROc2YGc27MmHL6sncR78deUdhjkEPOYFDM7e6iDJIoh6PXsNIhrq5PV2Vrzup0LVe1aew5Z/UGh55YsbIZ5XC0cpgGJE5EMGeSFPDfXyPKQbrPiYCIRF5VY2QohvlyB+Nefb0ZtAw8J+uysjSjWcPiHg/N726N3heBWhO7dndsOeMBDccj6acb+s0N/eYWX1bCtHFiXC7M7V6Au62C+pH4NvG7O9pMPBQr7VhB505fOr11ems0b9WErG5rK8e14old/bJNz2umGRKpSGM/LZzWlUetse4wf06cLIdsuX3bqr7Xy1251su/byYAGClQmCGgGIpUz3IPJ42Y5Tidgxyh6+J7vHtFjvdzRR+DWdfmhp1FU93iOT8bwmrxuAJFzU/FRpvX3/tcdcAb1s5cU6nLeVzVwcxt07FYyERsCCRbuc9j6BcVvjx0cyO4iXvtYq8NHL5AP2FLRX73tdakoXvca06PScTAcipKe1xqfajH9BvAmUObN3Rt5aL1vcd678+OwOjQ1nIx19qxjzsS6yuWQW5PtBaprFwvM+4rthmds+0etjs9Nxtxf68xzMBy01Izg9w2dTB7F1ivBIOMqWjrOckZxLYxtwvTGnNs2owyBrkd8OTQoedRH/lUXL81ervqez7wIj/4C5+pzWSHDh06dOjQmyN/+cOvPidj+XpYv+XZ/9L6ac3J+nMfetajOHTo0KFDh76w+te+db6O+gtQFYHLB/4C/uIHnvWIDh16bvTcAWCa3JqGC9oMOfj2zNK9R9ZblsPWHgCimWKGTa5h3+FobAKIMQr6lKs3y91ncgizfxwQjdp7Np/65Zs71nZoMwssFRCxTrNy6uX+tHH9nI6hsfd6XiOfqV7iCiSWE9UU2hwPvbltB9++0KzANOAknoH7SWPMSUNuVeuUw1JH13CaqYc154Vrv2h1Cpt1ndLWsYCWG+5GW040b4La1gRzrhHWW0EwgSJCDuPwh6hfOYbFYQN1hKZFxUKXg/Fpt+hyEjROA+8C+KmrRMi9amZic5lkTsKCMDSGvSu6OqE1fep61zm3Olc7DLfkYbewrWD9wXWYQWz3xJjM6hjOa2eszsPc3cWxqTd1DiG72nxgvuq6N2dpzrp2WlsF7ha5S5ttij/vjca+CaGueyaNTSC5Ga0ttNMt9KbNBzEZYyO3objdUHSxzYFH4DOuMLkvndYXVdDOSdMWA9wbrXlBai+nsisaus6hldPS6jV7JG1MfNvIMbHWWNeFL1tveF8/0TzlYE0E03e3Pugec8csMDfaeqL124LDXXMhJrBobgKYletdTlNFoSfZBEYVCd3UrdtbuYhHudqH/m37rvC6DxmaxzU/8poG4JrvpCLp0yp1oNallNvezYCmzR6hua15szEdZgxy3IEFvVW/NSbwnRs5N43F+3VtsNQmC9t34O9rhDWuEcu21Psu0M5MKjp559XW6imOta7jqo0RvVzdkZMZQY49dj5qDQ6Mcqvb3ifeq6e81q45C8abIqj7CVtO+PkWW87QF20sOd/UmjJqEUjB/1WR04q8rnUgXPO3dcVLWzm3fRVk7yddI0IbBfaof0wbALIR48KYT7QGHTp06LnTi4+Dtz0BBv7gd/4MB/89dOjQoUNvTXV47z8L/RnFVX4eWSb9Ix951sM4dOjQoUOHvrD6l7/lvo4CtBd/Gv/MLz7rYRw69NzouQPAMp0Jmk6D6Of6TNQnCwZfI5RtN/Ei+2HFKO8xsfvH67EPIap7/HI9eY9mtka1+pZTOK6PF7vrWJjgsT/lqK3Y54kgUUuwdEHFLJftDpV2Zx1dcKn+XIdaUbneVqx6MbPcu5ZyUrqZel2t0XzF3QqInnFzxf0auIVOUXKFu5jjOL2daOl1LgRJWyqi2IDW5cY2sqB7o1mjW+I5CnrtcbA7vEZv5uWuDkXyKok1C3DpuIksT6Ou695RHHUFsrVyX8qZmdWlHASRTpiR2XRKR+BzK5ApV65nzZc95tuiQOvuCH3qF8TLinVThHRUBysBsZUzUVA1dvfhvFc/LEZGMGJUT7CczSNRvO12R045ad1dBuVQ5LSHos2tL9Bu6rws0BILsKbYYe8njT/VK9ssaH2hVR8v5swZbJfJ3EZdCs13z1QM780Jv73FHz2i3ZxoN2d6QWYnMU+8G23ptN7py0I7rbTWcG8VF93wijonDJtJG4HPiY8Nu9uwJwOjs97c8vVf+37+rq/5Cs59wUnB8yjIWEnlOhdT8cTLWeejYsx1i7fqV3bNnbqfzNaaT8m1Fzj3ayc3t3nHUk5YIrDW4Omo51on9tjnh40hNSfaSddjT6G2ct2PcsxPRYpnRiUNVPd2wdTIjai4dXVxTyg3tbe1DL1Bzn2jicvtGhv7MMz6U2tH0zmITWvk1d1ed1EGZCfKkUtbKlY9r45r85NcxdUzbmmKl8/BTDmoZcRPEeRZ16fcvgL1Cyy3da/vu2wGOXWifFn14djXzlnnfo9jNux8C+ezgLIZfjpzPejlBMtZgHh38k5tNLFazxStva+VF628ifq6zci5EeEHODl06NDbWp++H9yNI8by0KFDhw69BXX7D0B717MexedU/+hH8JdeetbDOHTo0KFDh15BDW5/w7MexFMyePQbr/86/cD/45oYeujQoTdWzx0AJg1vZ9LLzZub1puKIZ07TA0qerTifJWzyv4bf/W/whWs5ICsaFlmwWC9PruTGCNdkCgj9WdW9C1DztrqUaW1gptyDmdWP2qiPuG+FIHcH/MAaVTPWXHK5QS8jovq793hlLWKxy0IVB2m7NHI1rByJjo1voq2bkCLRrMFz9C/zelutN5oXsCzYoAbey5uq2hlwb+HntEs3t6w1rGCT+atIoIbZNITPLZrdyju0BwLo2XSM3FbCmrHU4A8CwJPIuKp9NlU3Ha9PjRAnbyxc+d1xZcb0jvh6l325hWJC9Yb/XSm9451RRiXPfypawhkOYe56HNmcmru8dxxkZO14mjJScxJphEVbx3WSVuIy0ZsFUMZumbDjVCmd/XrLorw3SGgN11LB2PQzHBXF6znPYyJp9GWR/hywlrXrWBNvaiZpLvOx6qOWtK0yWDttJsb+vmk84DhbnjveBoWiUVgMeQE7p22ruoGbos2AKwrvXW8mTYYtIafVvzmjC8Nj6TNYHn3I77iV38rv+xX/Qrec3uWu3gHsAb4rD0ZBYOzgChyc6dVLHIG6oQtt6cvur9sYlZA0daaQlt1yuocCFrKWS73dcfaKgdpP0G/wfqN7v05dC285sU1lvzBnc4MMu/kyJ0XRU/XvZ37/KiNKWmdRL3d2xikL9jyiNY6rZ01n1wuVsywdqp7j5prgC0kW93rjvVzRanvm17qBrluo1C8s7mR40LOAryRsJXL2E1g1bL6elOwOoy83BN3imLOZcEXbT5QhHeNY+h4rTVsuYFxp2jpGXJYVwezxiaXsLk2aXgA4x66Y14bD7I2s5jRmnqEvbrFrdbDqF5vq2M2HmK9My71/ehazvAV3MiYqggYRwfwoUOHHvRW2hPyasby4U9f+CN/5eff8LEcOnTo0KFDX7SWr9EG5reg2uPHrB/6IMdu0EOHDh069NaVw/K1z3oQDzKD5euu/+w/95dpv/T9z248hw49R3oOAXCQU92/oM7emBV9muU2u3bg5oMb2CiH4Ci761J9kcGIO0Gxa1+rYI1FAVZ4Kkq23HwFYvLqEhVUtUzaIgC2OxIz5QK0edEwC17q+a3AcFRS6WDMwaymWL1RASezOm67QhQn8NYe6gD2Hl72WNuKtd4dcZYFD7N6jRV7670Jou2x0RnluZXr2ROwUB9sdcO2NLAuQIPVYyhXsOOZ9LbQSCwHzZ3eXNHAbaVl0twVkhz3NAbugldWcbaWQyA5R0GxrjG4AH1MOblnCATKZTuYYxTymjXuVrWvcl9eI2FrnqR1zYFrL6kJApKkmaDXTMX77vMLwbM9gjflZWRkKjTY5PTNvhDemN7BzuQYRAzxuLZgduIhnlybA3aXpGVdix0qRnURz1HXKqsbVj3StnT8fEM7r/h5hdNKuDNiMKfmYXTHlpW2LoKyKPa59U5bFrwtkI5Nqy7cfDC47pHXFffsrdFPK+3mjJ9XenMaQfNGu7nFb06wruTayaXD2vAmx/3cFC39nkfvZvUOvcmNnIr0tYrutfNNAfmHDRUC4oKvmYOs6Gmre8X2jRLXG2PfLYCc2b5UBLNfr6XFfpwuZ/AOmPuquOhr3HQ9JbaHdWZfO2ypeG0UOZ1179QGhj3Sek8buKScz7ld8K7OZN+v6R6dbEu5qkdx3K1+mVJzF6vY5VGPGYpyttwvmmKvqTh4HDeTc7f5dW0hJzmH1rxyh3tB6zAnrDPNajlNrDV8bdhpkZt4FvyttcOaYf2kmOa2ABVtH+pz3t3a7EkN+1pjTdciFcdttQkjveLoQ9dcDzfdQ3ucOa7n5BTUb2eB/N0dbto8kr7WvDgA8KFDhx5kX/ghb5pe7Vj+1A9+hJ9/6e4NHcuhQ4cOHTr0Ral/NZy+9VmP4hV1+ls/hT35xLMexqFDhw4dOvT20fL1cPoVAPj9S5x+5D9TUuGhQ4feUD2HAHhTN6xsgQJ73pFzMK9sNK/wl4IPFBBagSB3eGNGs5UdQrgtmPcH50Wk3LvWivttAi3lWlUsr4Antsgpa41r7+TYwILWGlgXg7IQmESQeAIzkpkbGeU0DoHNB1KCoGSmomtNjb14w3zBbKmoaC+INQV59shrA7em5F+qvzNT7lwvQLxHaO+/dkydj57z4XOZ165Vk4cR2yOnDTr5YFux6o1Nufe6Ja0tsHSNozVBzNbprbpNM+q1g+6N1s41NoH4Zh23TmYUuCwo6e0hecLlIKSApWDZJD2r/leA3wvoJY24DLa7O8Zl1HkPvWVf5LCOWTHCcoBmJjEHE8HQyMGco0yeBYnTatNAReRmkmz62NhgWSqyNom2MOcop3qo57Sc5e5GM8juzDTMurp6dxBtjo2LZkhbsGXFTwveV8ycCEHGmEPNxtaqx7nhpzN+OtNShvTG7vJN8MD6Q59zupFmTFydsCbXbmuKhO7LSu8LvTXacsLPN9jNGbpc19E72Re4OUM69x/7FHY3+Nr3vIcvv3nX9bxf76vMuvam2GsvWJs6T7urPvdeXqqrO4biwC0xFt3vGcwIIra6Ly+C8AxtAnCvWzkwDwHovQd4ztpfUufbG9jc70gsg2ZyRHt13NLPeD8JsHujTb0XaZpHc2PGpmMYA1wO/E5FKls5z5flOt80iL2T2p6CztUrXE75fZ1Q/PMOrBVFb442Smhy10YUE7Teu8pJrVUhEG1uzJiENVhPWF+wWa8/A0/DvRy/VNLCTLlrF3U1uzdYz+r4HUObGGr8Wf3BmaFNF8l1YwaX7boW6lpHQWRthNB6aJqnpNZn1AWfFtB6bZSYWNRmkth0raze89DbRvk6uDRej9d4O+s4h6+DXo/Dfx1P4Uc+c+HP/sjH3lbX5ZiHr13HOTz0xeqYM69dxzn8InTza9+y8c+77OUP8+jb/wXs8cef9VC+KB3z8LXrOIeHvhQd8+a16ziH7wD5Dfij6z/Xv/lH8Zd+5tmN50vQMQ9fu45z+Obr+QPAtve+ChY2A3sq9jgTYkw5OVsrEKF4Zu1Kqa5Ms6srzoxy+7qgxA7wgJmTQTB32IIVdSyXXo6CLlMmNpczsrUmeNj8wXxo1GNdoC9D4azeCXNBmdbo7ekOWgFVjbNiUfcIVCsAZYKV3lZ9zBzrXTCQ3ZVn9ZwuYL6cBH9NQNQwgWArh+CyKsbZFRnbMNy6XHXIbemW1TPcK4JXILz7Qsf0t02aQzfFO7ecdPNy9VVEdIL5iu/nNQtOE+VWXMHPOq4cet8dUs5ZXcDlHHZ7CnxPPANyB2ZJxgW3in6uc0pWH+8YxLxXZG0iR2PuHpwCULbHPc+6do2wxsRJ84Jwus564Xw4zjGh6lXJrNjdUB/pVm7T3UWdG/RWjmtFcfsIWhlgcatzlOWOdOhyebuj67euArZjEJd7ueV7x5rXn4avC21daGa0SNjmw4YBb3rvKSidkcRUZ21iRCbpHeuLIHDr9Nbo61nu4rXh64qfTrAsZCsXcO+wNOwyeMEbf+8v+zr+nq//al5oC20/B61XTLrAnqWXE7zA4O7+3WPWc+j+TmotGFwTAXYgmslMI7IVVFQ0t+KVUwbzpu5sOY5dEDGnIPC46NqbItX3yGF8KSd3zTNv0DTGvYubfgLvmquxESSzEgiSkIPejGat+nBrc0YMOY3nvTa49DPGenXwkxpL7gB13/jird6zYqtrHqqDWhsh5rgQOyR2I+NSDlr1RntfoBncP4ExGPPykLbQr3nUWl888UVR4i3B5qXmTsLQ2My6aoPHHTE2gefUPGZuqE9ZPnr1NG+w1Je4ccG3irqfU+v6nLBdsFnnqt/IMR3VUxyz1r+HzT74+Tpubew5APDbSfu6/axf4+2s4xy+Dno9Dv91PoX/9+/7EN/+wx99fV/0DdQxD1+7jnN46IvVMWdeu45zCPb4Y9j9p77wA8/f9sYP5jXKEvov/Df0X/xrz3ooX5SOefjadZzDQ1+Kjnnz2nWcw3eI2pfz9A+0N9/zbz27sXwJOubha9dxDt98vTVLVd5IJQX0FCcac9KaIlX3jll6qwZc2RrTdZrMn4K4JoCac7c+NiXMOpBGmOE5aRkMK2dcxY9S8cQP8bJZLrZZtbBGIWo5U2cw44L3pWJYBVp3B5ztIbtu5VYLmu+uvKzPVqStO06SzYskCrqCk24YJ7n8Mq8g06yijW3RGKvz1FNOaguN08pZJyZaEbsmlypPd3fuMCVTscouoKZTUV2sJpCU5Sp8emOHVWS0pSB3Ipe1W4Om3uZZ7kon8NwqmTkV+4yicuUGdLo7GSlA6F1AsEAd1hU5ayh9ty14bLTlJPDkFQMd1cWb1H9vtdlA1zZjEmyQFVmdqz5XvcRyhtZFr+lBXQbSlBDuBiPJbTIZELuDVJ+a1XnsUXNrZrlhwVqn7ddzBA2BeYtUZ2pfIYZAWOv4umLLUu5ybWBIb7XpIXFLQcsmN7HtGxtqt4G5IewfZNfBpJlAsyWR5Rhtiv8lh6J715XWF2ZMZsq5aYQ2WOyzxxt+7py+6st4/zd/AzfvfRf377vhJz7+Szz+mKBkToPmiiROv7qR2xW07vfjpmvgTRsZYggquqCuIo1rk4c/eGyjOq9p6mrOOUmMZlpXBP/lSDZr7A5aKHd2ZgH4TlqSORg5iSGH995XrDXBBNzda65Pwd85NW/bSeevNj+o2taw3slZvd+EzkMU/K5uYfeutVATV9cI05qwR4OD7gt0vogk+1pcfABNG0agHMNyzKYZrZ+YM2rMgrIxwdPAawNC5HWdacuqTRDzgqG1Mmdi0cCH4qwXJ00bATTlB9lP4vVzXmu3rZ9qA4CuKym3duJas3rXWM3wyErhX2u3zX1F0+sxkXIDuxug85rbY1hv/vavMIcOHTr0ttNlJi8+VoLJ8YPUoUOHDh16o7T87HfRP/yDD5uWP5dO3wr+wps5rC9Nfoudv43zX/332b75t9fPS4cOHTp06NChV9Sj3wIvfy/kEwzoH/5++oe+j/EN//CzHtmhQ+9YPX8OYAfiArNceHu3bUZhOPV32pTDkoIKyRQ43l17u5GwGaRhKfhGuWs9qwfW1f3pe6yzyxkoyopcw1YAlnKHUmA0BU3TOmnlskXAL3GmJVEOQAjFxJab2Wyl+Unu2t3Z6/sOicACRUHv8BHKGVz/TLDlTDu9QOur3LCtVzy1QFM0yq1Yr71HJ7tzpZmWipcuWFiFpvW8hZ3WmJ00zrbgy4J3uYe9t4qmbjVeue8MVyR1TPWSVr+x43i7ZWkr3ZzmNTZS7mRrdG+4dbp3ejvRU7Df3elu9Hq8m9GtaSNAbDoHZgLx+6XCrzAtYxJz6nohV3AWxBxAZD78nGuUWzweoF3NB57uk3YjcjC2e0Uw5wCfqAZaMPAa190WarY9AOSC23iD9RZa6pyYuluznTQH54Axab3RTh1bO9kU3TtjEmmECyQ6iUc5xXlwyUbIIbv3/GIhx2qrY5rzeuyYc+oL71te4H3LI9Z2Q+sn2umE3ZzwmzOtORVUruvq6lmO3snmPInB48W5cyPWhUfvusGnYoVlLE1If9hcUNHjimDeY9ub4tnpiufOfa44Rd1rU8jeCyyImTEEIXNUJ3fFPNPFWM0UdYzXvdfqfTsPmyDqY+Xo9qj7vqpuLWt+GZDqbyYGkZMxN+Z2rw0jS6OfT7qudfNaWx82TeS27yQg41IbQ+qctFUdxalI7H18Wevh9YJ5k6u6da1TUd3G5loDDVhPcmnPO/aNJ26pCPAxiJnMRC7iUS7cUfdls1paBeZ9PVfqQHUzm+Z486ZI6ObXY4qI2nxS8c4kfr55iO63xNYXKu7+hK23sN7IKe1wjcmf5RBXzjVJVuzzgHGvORHzek1ZbmszwaFDhw69/fWHvu9DvPRkPOthHDp06NCh50GvtNdo+bpK3XmLy0+wfD3toz/K+sN/7FmP5tChQ4cOHXp7yFZ41z95/ac/+QTrj//nMO6f3ZgOHXqH67kDwOq9XQU+mDDk9ZV11wQjY2CtEX0Rzg3I6WJy1c26g1oA2qJ+z97KOco1JjgwwUXsCkitO2a7M7eckrl39QrOKPq1XMkmd6qbHHtJMM3KYFgAEUVE7x5gJ+VWber31bG7HMU7JnK5OIuW6bhyqzjrB3fyDvCyHK+2rFfXHjmxLDDocirSBWrdFrk6TV2sGlkv6BUCiG2t99kUV7yuukZ7dHI45ote21wwqEasrUJn3NWbLAisLmH39epeNJoAfEUhG0OR09av57k3o3ur126KnK738Ob0fqL1heZ+fV25d6sf1lwdp60rQNg7A+TqvPaEJpFTHdSm+GAlC8utCi4nojmwXF2RaQ2ykaHYWbLR+y2teohlaA16c6w7vrSqPK6o6+smh6wNCRPbYSzUKiCHo7i9QzpzTMaYzDk032JiTb291prmeNlj9/jqyCDHJCOIOYkxyE1u2Cw4hznLsvBN73kfv+3v+ib+0W/5FfzKr/5a3vuuR/RV89HM8L7iveNLpy2ONcOXBdZOmvH4Fz/BR3/wJ/n5v/KDfOJv/jj2qSc0d9KzzvZ8cN3yAFqJTRsRqu/W945nEtqpOsF5cLPaioXVfVb3qTuxg9JUn3EWoLa20NYTvizYenpwu183T6AY67Y8bCgBIoKcF91T1e1ssdNgv/bWjlB7dvqJjA3LxFuDdiLbgi0LrZmcsWNo40pbSfwaQ544Zl391HNU1HN/GF8Gu2dZec9bfTO2wHq+XkdfVtxqN0zIuU8/63zrDqoI60ZuTxhz0yaJvqrDer3R5/d1zCGryzz7qhhn22PrURz/DGJGjWti3oisCP0xwDuWE5u7szlF05eTQL5R/eHqGNfpj4dI/5yKv7ZOxmSG1pmMp7rMR7nln8Lkhw4dOvR21sv3kz/6137xWQ/j0KFDhw491zKw07MexKvT/Aw8+W+xnJx+8D/FP/Vzz3pEhw4dOnTo0INu/8G35tdUMzh/K/SvuX5o/dE/gX/6F57hoA4demfruQPAAM0M5h1pSzn9EHjAHrpoy31LqMeXHERF114je/eO3bkBrYx9A/3gArD37Lrygy2qN7cLBvmD43CHRKLMgstOw3KD7V59oxNg7ggK6AXr8oGK7hZhNsxmJRkPYehI9VvuwHcEYcV9Q2DSWlev5ZRTMHISofFlbMxQ7GxmaDxRTmdrhGVBJMAca4kzytV4X+djdxq7XNM71G2t+HtFW5spXtYa18hsN1jOAj2mKGIrwG3Mcm5ydSibLzpHTOSKdrmIrVWH7e6KbnWNumJ599jYeg+sF4RuSsKOjchkYMxMOWQRWItE52gKKOkc7pDeyEzErmoOxcbMe9Q2GupULRBWFlCBqGWF1gT43PC44KcVX86w3NS8clq/xc+30LRreofmkqKqbbvHQxB8d1E+ONtd1zJhzhD8ncmMILy6r/uC9c4VrPVGLo1wBMMzBYBHEpvcspBYLwjfnOX97+Wrf+dv4ut+3+/l6/7pf5Jf90/8I3zjt3wT/XQq0GbYumJLQfpMJfS6JmwkPHn5MR/9wM/xSz/2s3zyZz+Mf/plbtwVgT3uycuEeVd9vOq53fuHM4au+R6zvMN8g/Sl5nXdr7UxhHLq65xZAW+IeZELeLvT/C3QnftmEV/Vx+xNseHWdZ6znFYm+Ivt45wCnK6+bYuNPUI6Qvf/Pk2sdZoZy4zy3C9gSzl467q2tdamTU8KxZOLh89ySPerk32Hppaj1raajzStj6A5kBVvTTljx8Smtrwwt4qLRq/bVvBV8dnWtYEkdI9wegRt0caTWX3jsencLGesnfSaY7tudrG9I3s/gRkP82zvAje/vk9GbXiI6n2+9qA7tix1rfTxh7XlRNpaoL7XPgmtLenVFXwkpR46dOgdogT+4k9+gg+++ORZD+XQoUOHDj2val8Gj37Tsx7Fq1NuMD4GQPvYj3HzXf+nZzygQ4cOHTr0PGj54PfwWT2Jn/eBX1W/Z3wLqr0H/Pbh3zG4+d63VxfwoUNvJz2HRSXqiGytP0QPxUb2cue5AGjGJpjSnG7VFelNUGWHvOV+VOftlPOUih0tgGy2wzW7RpoKGjhWYJI0uTyv0b+CHM0aszWw6te1fHjfWT255Vq7dpIa5WyL6qK9+vj037sZdf/MViDbOjYGYaNgk0HeQ7ZyDlv1+1pxF6/+Txcg965TG1sBR0Fh3GXuq4hWs71ndwqI1+gyQv2wVyBZ8a6McmpmQagVCgimmcA6i15lj7rd3dVxEUSzk7pSU4BPF2w8FYUbBeWGHJxPwaXMBKZgWP07ECAVZx6K4A5F6CpemYduVAzmw/uaCURyvZQJNGIOOb7b3kF85Yy1H2DvGB4C4dQcbkZsAX3Bc2BtVfNub8Q2iRgVd92YlnJ7tpNi0DN1/kKdtbkssCykGTMHWxhjFnAsVygJNrSJIFp/cFBGwDbkPF+NzLqWLnewlTs8kIN6zODjH3+Jj3/sl3jv7Qs8frQSjx6pZ7jJ4ZtZXdWtabPAUNy1BcQYbHPAp5/A4ydslzuW7QmPZvJy69xd9k0DJwFBEgure5uCuXvZ8u5gzes5twKh5k7aRg6BvwbMPSc9q8u4NgxEgs+C/WmaF9bkmt8j4K9dvBf12Ybgszc5/fN0hm1o3nTdP1ljzTQuaYpRnve6//uJZhPzpaaTrvGMmmPtVE7m641f3dqzIsjRPbLfc1n3xw69Ca1VMYFeXeAh53nFrqc3GOX+ZdRTloLHGzFrk03v2uhg/lR6wYRhSinowLhTt3f1NCuhIKv/OgTm3fGcMJ6QfdGaUJtEbD1XlPPUNYhNh7icwOXWJqLW44qOpisOfd5rU0ikeplDa6SWM23OsKbecXwhxwV7ykh/6NCh50Mf/0zwqbt3pvv/pz/2mO/96Zf4+l93xo8u4EOHDh069Ez09vv6Y0D/0PfRP/S9jG94mwDsQ4cOHTr0ttT6E3/6C6fR2QJ28+YM6EvVC78dPvEBqN+72f0n4f7TcHrXsx7ZoUPvOD13DuBWccRBRShbwb9wOdZAUcVtVVRzloPWKh4arnGwZvV51FUqyLmDlSyQtPe+FsmbAxs7JPKKYy0Qsve+ov7RxAVjIkkTeIqY5TCUo3iPsW3umK+Kda3IWD02K6rYKgK24B27++9SzuBB0tQtWy7nCMX5xhzkCGImMfT+6U3m50yCJEYobjbkKMzU82MGOQu2GmRbSO+Ak3OS6eoNjUmOe2KM4kKhjtXYYWyrGO4CsSYnX4wLkYKwidd77927Vo5iqudVDmOmnptxIeeFkcbIycwQXMuoPttgZhChj6uyNJnoYxGj+nFdx5+z/gi6j0jS5ewdqP808QLdcnqbn3FfVP1LucFJKJfwGIOIVP/vZcBQj7E6UCteOhPvYKcT1k2bDmJC84qcNmZuclAGclNaI+dFrthxz+7azgjmDEbAnKnzMMvhnJrDmXl1NofBzGDMjQHMSALXeazjYBvE/WTeb7Bt5DbYPvEpPvpX/iZ/67/8XsZ/81PcfvBFvuzjTzjfB6rxDcVJh6l12BqT1By83JHbRjRjtiTuH2P3n2G5v+PR5Qm3Gfhywnov1zHl3NzKBZzV6VzfZOwQmP0aXgTwy5VutOqhVUev7ffkuMixjTFiI3Lo3pl7LLSRNuVGnpte0/ceccWmP2wmUfyyjal5UKAxau6HdcWMY+WqDnCjNcP9qXEW1NYd/BR03o/UF2jncjXbU1Hu5dTPAFMvtP5esa6+5MzLU87p0PHWvU5fyLjoNa3c920VCI6L5mNMiCTGvdaFfTMEiVngrdHWk+6BrPjmOWoNUER5eMK4MOfGTCeiNsbEdo3uTrSGxby/OvNzbLWvZL+RH8akPudGtrNWa6/UhowHq3UmEZPAiTkUPV0z59ChQ8+XtplqD3mH6j/+vg9xeScf4KFDhw4dOvQGyC+fVhdwjGc9lEOHDh069Lyrfy2cf+WzHsUrq73vs/7Zf+4vs3zwu16du/nQoUNflJ47AGxmWCbla63+VUEzbysW5SC1PfY1qx/TuBbQ9iZY4U4zOUSF9hDc864/tj/HC9Q6aV11w66414dNO/sv2wopZNAY6qVtXkBzErtj1U0AGe2VUWNtQQmLAk1drt39tc11LKlOVAGrEIAm6/WbgBuNmcmIQcaQ4zBT3ZxjE9RlAoKQmVtB5kZEuee2i2BuFvCMENAZl4e+2BgwIWm1xs9y6FZkckXuZs5rZG1EClSzA96ppNcCZXl9fH1ubhrffg5zEHRGwAjTnwxGJAPYcmM6XALBYYIRkzE2xkjGjILxckCr31bnYM6Ncbkw5wVIRlQ/7LgTCE5dwexLXbsd1WkuyKG9ux+rL5nQPgUG5uowZkKGwaa4bsaUG9zQ9Wd3Sy4F+WqzQcWZ6+0KqFv1Ym+DuNwLeI9BzFGAX//L6hoWM1TEbiIXbJgJBveFcNMcX1borg0CkQL+5YAfBC85fOT976P9vd/E+e//Fr7l138b3/gNX6mNC/umhdQmglnvO8fQVEKAedgkuIOx0efGeU5e2AarZfW8phydsSm211vdn/vftZmirexQNsvNTZicr1Zd3XaL9TNui9y6rWN9rQ0Dk5mTiIprz+rWnVOwfd+IERMYkFNQ2BxsrQv/sPaYKdK5ZRbUdUYmY9vIywZz4JF4mtYt1G+9w9DrffTUmqJY86bHZbnkW6+o832JUB+5lXOZpSuZoK9YU59xzotg8Ux1PJejFl+0JpjuYxmJG21ZMa/ZPidCu0NdwDHKEb9Hbi9470o9iA1rBedDf3tbYF1o50e00w2+KBGAdiNwGwHU6/Yztt4KDBtyKc/tuh5qM0q7OusVB31SVzByAbMnL+Sg9ZO+bljTZoJQD/ChQ4cOvZP0qbvBH/q+o8fw0KFDhw4dekXZAu3LP+tD69/6DtrHf/IZDejQoUOHDh16Sm+zRCcDbr73/6LfcR86dOh11XMHgAUpqmcTw3x3eVUMrBeRjZ3MqhN1T16+at+RYl3wNeXYtHL9iu4J0upxXjC53JmxAeUstvp8uf2gFaiR49B3ByP9mlhMdpiJm4KGfe/mxPQascfXdnVYMtUJnNWTS4J1olytGZOYkxlb9dpW56l3EhM7wZne2OIpN1y64Gg5aLccDFJ/FxgT4nQ5j0MwUOBZ8dFzbiTJrPHsl0LxrXuMtXpfRwQjBiMmMwZzbFdQPeaFYRC2CCjHFLzNqOdNRg4mzhaBMJkJ2o7Btl0UeTxM7t3tnhkw5mAbF7lx/alo6IruTis3871c1PRF137eI6dkuSmH3Mcxo6ZXxQpPuaWnye8ZkYw59B6Wcu7O+5qC8zofMzcyLook9lXXa07mAMyw3uo5WfOvwLJrswC+kG3VHwRdcR3LDudibAX5UiCtNkLEfpHKAZpl9g2ryHFvYuPNSYdpSbiixHMOctvYXr7j0/MJL54Gc2nMb/p67Jd9pZ4XArGzPOuTmjOmaOQAtm0yN7lQLbUBYyG5HZPzSDwL5s5RHt9O5L6xQ+CTdlLstjXSGiODmeUIpaKxx6aO2op73zd8mDuBXPByd2vThDYYVGQ3Dh4wEMT0xrU41kwbQnJw3QDSykFr8vM2c0i4bPeM+wt5qQSA1upur00ffdXmgJja0OHlpI3QLvSC/mR182Y5d63Vuak4bKsNIrHHs1fKgIEtTUB2BjNMc46sXe5JW1dsuREQrmNyc0FbEuZWqQPqzZ6RV+f8FXxn6Fwvi9IMZl5TFCwSc2h9qeVUcJ+9b1vWX8H7fha43ru/l/q3N1hPsNxqg0WiqOq9p3vK8RuWAsYp57hlbaSJ+9qgAu5+AOBDhw6945QJ3/HjH+dnP3F0AR86dOjQoUOfV+0FuP11n/Uhu3yG0w/8J1XBc+jQoUOHDj0j3f6GZz2CL6y9ku0p+Wd+gfXH/uSzGc+hQ+9gPYcdwHKiha3XfsjWFiynYK1Zwdly0O4xrbGzcv0dBBZWcK3Qa8pdfHWNIXhA6rUyprpMaQ9AxhBIjD0KVq7arDheMy9QUXGtXo+xBbPAhsFiBSKsnME78KrjqbRZde6eNCZQcLX7Nb5X3bW787aOtRyr6sBM0st1HBMcwTdzxTd7vb1eSO7QsWGt4QXFzQRzZ0R1HifORuyA2x2Loe65NCI2tSXvncAjoXlFzbpeY1wIq37muZWjVT29EQLfRGPscbJ96hq4Fcqb5OWiwx0XpnftOMogx70AUFvVR1vR3pkVwc0kxwSfsNw8dMj6CvMO8ySp7tAcBdcgiYrr1rn12gQQc8rs6K3coimg1x0bdY76CnaBu1Hx1k6Me6y36jVGUM674FsBfzPkbrz/tIZepzHn7gRH7mwm5rpGGo9B7w9xyQa5dMVbJzD1OFqDvpKWOoY5qwN7770OPKZ6gWcwXv40L/3Aj/LDH3+Zr3zfu3k87vjgz/8cc8qNmZl6/1SUti0dN1fscGzMCNLVs+y2YQkN5zzvuR3BHSceg+6bZVWEM5MML2C434/UPSEgq5NajnVLzE/q450ChGrkrcDumNf7OAjcO5OkWS/n/w4+E5ZTvd8GrLopY6s1SeeRyz20TjfoBFYO9G27Y1wG4Q3PxNezUgjMBIG9a240HoKJ43rjX+H/dUxY9etW/DxA63LQ7t3a+3NoBZKB3utcbKQvclHH5bquenciu5y21Z/dMrB0xv1j6IpljuVdTO+4pyLxG7j3cicHbl1d0JfHciVjlT7AdXPBdbeIAWOIRefQ/ZFgObWOuGl99yDtdD3mnILFMTb1p/eKfR6O2Ur6BvNOx9hWrDY+JOL0vpwf0hUOHTp06B2kD754x0tPNr6Rt3hv1KFDhw4dOvQW0/qjf4L7X/nPML/61zzroRw6dOjQoedV6y971iP4wvL3wAu/FT7zX18/ZPPC+uN/iu3v+sfJ2y9/hScfOnToi9Hz5wAucKOoUK8IVntw4JYL0poeBxQIafV3oVXrArTuxXAdjCuoZHcB21KAWFApd9BmOwguqFcu4qIs9b7yvnkoAliYqIE5ZklvDfcCPplYNqweq0GNgpiB0fBouBUEQo/ZYSMR5LzU0LNORXXrZpDhZfgMZgwi7plT/bBzBsMW5hwCgrE7duWkHbEx5iyX7uQy7tgy2coYOcZkYoy5ERGMMRgJI4MtjftMxuUJY07CnLFd5NSMCqFO5Fgd6ioeMbjMe7ZUXPMe8ZzeyL6IZc0hF3MorlcRvU1GwG0w76pjdLuglGq5l3MkMS6CRwUpBVvXupsS9nhsX+AisKgX3on/IHHBpEzBdVwOY0Y5LmtuRGL9jLUbaGc5Vue9Llw/Cchde2XzCujSmvqm4WEu7teWwspz70rm2tmcU93Uc26MrSArJmCKnN/q/7VrPPS0JK0J1DVFK0caYc4cyRzVmVzzJYacudvljpdeepHv/+DP810/9rN8/098kI995BPk/YUY5Ui/XJjjwqyNCd4MUu7R2TpzXRi3LzDbGbzTlzPdjdtx4TQ32rgrB3xZlLPuq4odZk4sHcv93qaco3Z1RJs7Ni4P4DShueOZtN0BGxDbqKhqObhnVNQ5va5H1kaP9hA9vScAZNAscXe6OUtveD+BNea2EdtkpuMEvpzx3unNMe865zGVRGAd9dcCexyzL7Xu5bWn+hpBX4DY7CQ3LXtEdQjizoFVNzo51Z9NbS5h6Dh8wbL6wrOiy9nXMp107x1fb8FXmW7nlLsaq5SBvRd7d5bXBpl+EmjuXVDaHdvTDqoLPd3kwJ6DytcHEmuG+UPD88OGHqhsehm0T49Q//Wm55orjrp1bH2ErTd4X2vNdIggrBzY/hx+CT106BB/8Sd/ad+6847VH/krv6Cvg4cOHTp06NChz63zr4b23s/6kMXg/N/8h9efuw8dOnTo0KHXS/2D30378A8962G8PjIDf8TfjqaWn/tL9F/6G8fX0UOHXkc9f7+9jskM9aC25jRcrjxr5XpTT27KGimwegV4Xt5YuTSzOZmCtK3icJUQvD44dsvhCy6QUY5PKrB2jxjV+7aKKRVUNnP1FFuxC0sB5gw8CuS2G3Yojc3qf9U/8c4V/JQ72K+Z0uXIzcTN1KXpXa8VuyvQCmAlw5pSbCNhGHMaMcslOOQgTFMv6wRyBjOmHKapmOmRwTaTwGEqmje2yUirSGmY40Jkss3JNjZmbgLLs+J8y5Gde99qzLoOUcB6Kjo4q3fWnJhyImdaOWZNscaXKbB2f0+4ETHKceqKAC73aTQj7l8mL0+IcU/MphTdIegc7vXfl2t/bOam91lXdb0mZBPoyjCSYM4nRAoGK4YcBP9NZzH3UO9UH21vFRHcNR8JCKPh+HLWa1g9rXcd+xU8Uy7OWQ7irKpldfjOFMTNtgjqjmBsF+beERxZ76kY8IxgzlAPc/Vam4E107k0iAymuyD/NpgzmLNCvaeu6/39HZ966UU++rGP8JEXP8HLd0+IPSp8KOJ5bKMqbQtnR5BT48neiEe3zPMt2RswaH7ihHE7Bms741nPtYpELgh4DRfez2VO/a3A6YKlrSKNXW7dGOCu6HUzuXFpV0d6zsFMRaJrs0R1T5dbOCN0/uvKGq7/7yeW5czp/C5Op0d4OwGu3uNsbNtGxEZYxxevlm7HvdX40fi2Czk33SNUF/jT3zOlHL17DD474K/jfoi8t1o3eDgXoHlQcNbccDcBaLTpwCqO2+wE2bR+7PtolgX6reZYVAdy1PrWVtJXRY1HXiGttS7Iap3scsybVd/x3K4OYHeBZNa1HMQKndcxaaNO1kYZ2JfF+pzVXVax4xmzkhBC70c5iLMAvlEx73HEux069Jzqw5++e9ZDeA1KfvvNE37bzStHPH//z32K7//5T79JYzp06NChQ4fehmrvBVv/jg/3X/hrtF/862/+eA4dOnTo0Dta/vJHsftPPuthvH66/Yegv//v+PDN9/ybz2Awhw69c/X8AWAzWlvw3nAvgLLHOO8geO6QoFzA1acLFU1sXeAkKuLYYJqpt9f82olJaw/AaY9izXgAMnkRxN3dm4CaP/ehmiBwqh9TYBb1e4oQY6SiU33Fe6P1BV/Pch5P2Ps8iSCtaTg0rC2YCf42K+7cy61s+oDNwHdQVGA1xyQtChYPuSm73HCZVl2tU0CRJE19t3KPJumdtIX0ciOG+n/jclEPccYVvmbIAcucZGvlWk3SF/XtXgTZcgy5PL0XGC434tDrRUJYZ2ZByxnktskBOzZd99kVwWtO2mROxSvPMMYWjGiMbIyAyxxs816vPwY57dprHHlPxoXcTJ8P12aB3W3rrs7dMQgak07QBFJbI62gVSa0rL7oVDU0gU3NpawOV8so6B/aSDDK9Z2afzkvwEVRzI5eG4gUXGTvyU1n2spgMrZR7l85eDMGpLpm6Qtpxsy923m/bXaXMALiRrl9N7lilyaQPjdik9M7UF/uvP808/GLzPvPEOO+op8vzMt9nd8haFxx09o8MBW/HAnNyfNK3pzw00rvzsk7tyycI/FWqHfeExZEqg86YpTrM8pVmvRy4mcGYV6QVBtGAHXtmgvcpvqU3RvmBUYxrubiCOa1s1rHgHuB4E3O4g7WOu5drt6uvy0FjEdOPW+5xduZ3hbMvPqBtV6o93aS3qAXsN03Z1CR0zbkbPXzQzrAfk53Z2ytQIqnXqGdFFO+nAr+ys1rrvXIKQhcMcs573Q+p9ZLawuWhm+bun3HgO0Ocu9zFhzez0cShDeyaTMLlgLKU1DWWtd6U/285g5NAN3mBV8X7W1xK2gtfG2UGz4Ge7y3oqb3COmKOF9utUTvGwH2z9VWAZmd9w031L166NChQ28v/drThT/4FR/jdz56/IqPe+nJ4M/88Ee5jCPq/tChQ4cOHfq8etc/+Xd8yO9e5PRjfxLG/TMY0KFDhw4deqdqfM2vI77sVzzrYbzh8k9+iPWH/l/PehiHDr1j9NwBYDPDvToc7anDz4pj3WNTzQQtCMxM0LMcrl4xx1gTZMvEUk62hAIQ/uAi28FvzKoLrV+mmQvsWa9/Foy+cgW58YxGC8XPCmwMue1A/bYVd4p1rC+Kr27tGvcauxswg3R1nJrpuLwgszzKjdYqyvcayVo9qblpaBEwKyLW1Ib6WSAlkrhshCV5GfUcE6QNf3ATWodRsdhAtlO5fLl239I6EbszT7HTOS/lGjQCJ+cUFBxbOQ4bMS/E/YWMuxpe6jXHhHKTstxo9nvB7N2B2xo2Nrp1uSzLJd6g3I1TsMkUI5226LlzUxfuZXK5XBiXOyKHxhIQcyMvdwK/cxP0rajkkeVsDsHGDNMxouuIpcDvDJl5Z2BzwKhYZDf1T9tZczJDn8sk+0kA/DoH0bVwV7cvIUd4cyLvmZfJHIOxXTROdwFIM/USp9y7ulYF9Xdo37p6ks3Kd9l2/yXhnWlG9E6YInt3AByxEXEhxj1zXBhjY45Z8eJT+G3u8xdBP6PO+yC3WdfC8OY0h05ywrhhsMxQpzOKKw5f9JpeTtdE/bfGdT5OdF+QdXy06yYDWnuq61v9tG2/72OQd/fkJjf5zEHU/bH/9x4/nTmUge6d9KiPT4iNiMEIwfjYBm7Q1441JRZ0K2c4oV3n1/vSsafXjwyyoH1arzWu/mSC9+uGE8hrGr55K4iryGu3RQ7ecqPTC75mYjmUQt5XiMT3/mQa3s94WxWV7drQIPfvhTm3cvEK8EZFMMs9vG/CqfdCm0jcGtZW7HSLn25oy0nr6XLWeCPxUZtefAG/gX7SWhcDm+qKVhf46brOixs3bD1h7aT7nEp1CH19sNbl+vaGx1QsuLcv4qvPoUOH3gl699m5Xe0LP/AtKCP5Z971Gd7dXnkDy/7ZP/79H+YXPnn88vrQoUOHDh36nDIDf/fn/NT6Q38U//TPv8kDOnTo0KFD72T1X/zrtE/8xLMexhsuiw1//NGH3w0eOnToNem5A8Dpewyw4IjMt/Hgrk2ruNdJWmK+CJ7F3qtp1T+JXJUmf5jZrH/v0alR0axW0bUNmsNyEmGpuF7lo+4O31GRsFPwicCYNC9WbL4b4yqSVK+faTATR72asVV0dT8J7prAcoZiXg2BWWsVYzrlpLRq9mx1GqyfcV/0B5NrehFgtJ0UzRSQLHCeWZHBcwOmeHpzaKuo+wjYJoyLgFp2QcRIoMtFmYo21rg2Ii+Cx8PVG7oNxbmaCf6mCSbHDo0WWJYK1x24lQPUAu+Ge+Kt4W3F1zPWFrwlvrT6XMcXw0l6P7Gsne5GY9LoLJxo3nDzurblEp0XpcO2s2D+0McoN7P6cyGHeplzG8T9Y+Z2IcZGUCDVEOjOmo67G9qQ27cBy4qZjolMdZ0uHRH3yV4anRFEyJluvtR8q4hb72RfBW4zamYk2xzMMRljIyqSO/f7ozpX4enbRcC3tiKQrvsrLQs8Wjk+ETxtLliaiueWU9yYYzDv7plPnjAum95/j/8uGEdtEtjnbc7BDPVQ27jHL09oCT2TNe+4udxzGhsWDfoZQMfjRux92773F5+v9wAGMyeR+7mqucasiOLErNcGCrl/da85uDYcJE1x4kOAeuxzYG61KaEJLG+bnPUh5/xMYzIZuRFDTms5/7V5xS10fc2u7m/zgqZz1PVvEEPvVdHkFDDWho+6Wnv3OXVtllVR0oyKvE45dwHM8Nb1+slDNLhp+0j2LsAeQ++TQ2tV0wYLuyYZbLWypXp75+7Y3923E2sVoy8rtTZuZGLN8bbUZpnaXFNd7FrUXK7k5eb6mMwB65lczthyxpdV9z7gua8hSaY2cdAatDO+u5vjvvqyQRHj4O7Yeq6UiEOHDj1Puj0Z5+VZj+JL1/taMBJ+/PL5D2LH2wn8e3/xZ96MYR06dOjQoUPvMCU33/WvP+tBHDp06NChQ29Lnf/bP3xspDp06HVSf9YDeLNllJsXufuaG5GdmAPrcvZFxeSSk8wdkAieOE66XHGZE3cnU723Zo4XZHWvqGhTnKy48P46LnNvdXeaydkrY97eKelX0LJ3Ac+GgGuOaxzq7sRNE7oygmxl3s28uvLUVRnVL5yCpAnZFiwv9du+3ZksB6XGpT5Xr5jqbAZ2unaqZoKtHeE/I1nIVue3LQhOFXCfQ/ByBvhazmrDZ43LDHLVeQp9jARn1XnogmXmnRgDlo651esqerstZ7lEVZ4qh6btMdZWrEoxvOpiDTkWp9V4JyxLuYOrVzRNH7sausupaXLO5kXR1uBw0uOTTt49ESRPh2WRYzDLiTrKodgUTxvN4BK46/pBw7aLjovEGHpbB2snQdbLhcyUk32fW+blRp51PBXbrKxfOJ3Iy53iiW3QXP2maoI1xhxl8pRPPczkNj6d1LVcUM3yGgB9hfGT1H0RWTC0XMPoeuiROwhEc9Lbg8t2TI0bYATT9udo00XOh8jvrM5iTeKhDREj8AE57mRqxhRmPCe+npkU7LNgNtN18iCzy91tiXnHyqmf1D3bV81Fqy7ZGLpuuHalYWRb6WaMGpcl2vAARGizhpvrDjNo1nT77t2yGYpOH4P0Jhg87q492+6uebr3OduZB/jZsJa6d2LWvV1d2Ts8tqZ4Zu/Vf1uOZ+YVYlpba6NB0/26r5geupeak+7q2d02ghUnsRHQHYsgW/WIZ/UPkzBcrt3t5SswznUBW0lvzEy1EheHTtvXKIPtAvSKg97HusNYuebd28NcsoH15bruqsMdrREm13JmqnI9A4tUV/m+/kxtvvHlBOOuNgLVZpm2z1XB4ubn2rhy6NCh51PJqV24n6cv6dlf5pP//Ze/yMvh/DsvvodPhhNPZTK8EUqM//VHv4xfe77nOx/fvKrnfPLJxmUEaz82vBw6dOjQoTdaCWyokuZtInP0a7Xx2R8G/O4lxUD3L+17hUOHDh06dOgdr9vfCJ/6U3/nx7eXOX3/f8KTf+Rfe/PHdOjQO0zP3W9zEoEfT2hmcty6C0CmYKArx5krH69IaKwxCblkzXFrcmlGQHa5LffHX6Ue4cyQG27OAjaLQEXbnXNesdFy01G8TsAjChsb2TphFQ0tssru6Mzd5KeyVH0qFQ987QNNCmqWw9kEOZVwPetwq9/TNPrFkmYNN6PbwuLQTfDcm5yw7k6ryGRrSzn4sqBpKKY1Ki46st4jqlNVr2OtFWQBFoFG76uiXW3qWi3qMLVF7ks/3WCLYJr1Bg3MmuKb+5nmSWOjtYW2nGmnG6w5rTU6SUt1qXZfWE+dU184NWfBWNYzq01O7pz6ymlZOK3O2oLlfKafb2ndaOcz/dEj2u0t6nBOPBM/3+jcdPA55D70leZyHvvieEyxxdmKZ92TM2WCHhdyC7CT3KvLWdHVJDYn6Qu0k0CvAfOi+b3cPMTb7g7lnV5nMjEZ3iMUsRzCvzHV1zvNiMzq6YXoC5n7x6bmfO+k2RXWC+AO5jbI7aKY7+YC1czqew5ihBy4IIi/XWBsFZk8uPpUc8oVGuVoLldybKOizUPvD2RboS24dbk+Tc9xkiWTc+ucY8iNXa79HbrODJLtGq0N5eyu+yitaYNFVER8lLPf1nLqmyKQ0aYQVSHLhWp1z4s27u7s6luGByi/R3GnFxCdjDHIVPduzFmR7Y6HOr/l3tXYzLLiwB18qRSB2lyCayNKgeCs2GVrXRteepMDut1onai4dcal7tep9IPqOM5R7nwWLTOtKT47QnM+N+hdmxDG5boeeTtj/awY6bbqHFIx8usJWoccAso7uLUGywpLJ7sRzOoL3juZ82GNrHWS7BUZPhXj7FbXuza3zJpzUb+gWU/Yzbtp3lky6Rm0ObBxUbC3r/j53dh6g5O4a831UBpBjO2L+fJz6NChd4h++fvfBRi//+/7T/m6F37h+vFXuyXka9rgD33VR/mnHj3mf/yuz/CXvuHn+Wfe9ZnXdYyfbywfj8ZfeHyrTVGvQj/0C5/hz/3ox16/gR06dOjQoUOfT/MT8Jnvedaj+OLUvxZuf/3n/FT7pb/B+hP/xZs8oEOHDh06dOhtIjNYvv5z/vBqwPKB78A/8VNv+rAOHXqn6bkDwIIyD329VpHNKr40zFs5yvSL/f3TFDgGY+7O3dwdxV6frwhSTJAkLhghB6PtXZGtnKXFRmbCNtT7mnIzimuMcsTuzmAvN1tisxyy7NbcLMYxmXMjx6bO1Bh6nIW6Q70XrKMcwVBW1wJUS0UlCwwbyE1oOicNaCYA1cxoNDkZSbxAununA52gt4LrYdhM3FY8wZcV8w0I+g5/reKac+LN6M3VbWtWILrhBN473hqtOW5OI+jLid5XujudpFvSvdMt6MtK6ydaW+hNXb6dRndnbc65O0trLOtCB5o7rXW6mf40py9Nr2WT7rD0GwFih8U7a18VEe3gzXV5HZp3bFnEp5oL9LaGeQoKW7nOabgF3gzvHbN7BG2djHuwSaoxF5ujIsgLlZbjHO+Y9QKtUxBu/+9ILA1PgdHZnMljBqFpajV3cGY7MSMYEXKzzku5U6MirDV/osCxRiFAGjX35oQ5Qs5K0+cjs4zoU5XNEcSYzMsmqLu7eklm6r1jqrM5tgsxpuKox6b3ngEXxWtfOyFy1mss1Vuta3CK4LxNWgyY9wUnFTWeZEVcZwHtWUcEZB1ffSzHUOd0xYnv97y3E807zdUP7g87MWB39nu5z+ueyx2uMgRcaerx9kbQFM1tTvZbfL3VPUjSe6dbdVJbCqjOTZB6Vlc2UT3aJudtHQvX2Pg9+GEHrXKBa80rx3DuxxcVBX3BY/usOGYitH6ZXPhaN5rm5LIKGs8NPDEutN606YWGzQeQTxY5b4vmeHWTa4H163F40875zNpwcLnT+2PXTTfWO271tETnNgX2r+t8kys/MsgIRcS3VhHb2tjSzbQeAFbdwRmDjKl79nQSqJ4HAD506HnUP/zN7+cf/tq/xrd9+Y/xD3z1f4tfN9C9Ov3ydePXny/avGZwdvhXv+wl/t9f82F+5XrPrcVrHqOTfGPf+Oa+8eW+jy/5X77vJV74Il4/gf/0r/48jy/zCz720KFDhw4deiVt3/hbGV/1q77Ao17718A3VwPy/nN+xkjOf/0PweXlN3lMhw4dOnTo0NtIn+cH6fbJn2H96T//FMM4dOjQl6LnDgBnVBfpDtgSPIcij9k3nSStIl3VhVmuv6iuyDGZoVhnq2jXHfR4PB3TWs47KgLaIE1uyJhTDkjyCp0wJ7OrF9TKuVcxvnaNeZYLmNyujlozgVtyCsa1VX2ebljPgoUD6y7AHDp2mwI7Zon1ji3loPRVjmEH8iLIZV7OyIKy3jFmRdPKadhJnEEzo7sev7TO0pxTbyzd6etK7wvNTnIVZ9B9oeWkh9Os01ujYbTWZDBcV/pZAHdxZ9lhb0uaN5o5bVnpvuJzo2cI4jenW6O7ntsNFpLVGwuhXmNMoNYfenKV/Fz9rr4WfAezXsdfkFdnW47jNHzCYsZixqk3eoO+LIKDzWge1aO8lit5pa8n+ulMO6340gp+LdUlC6ScweyRt1e65bilXNQVrXx1VU4KTPVrT27sMcmWzNbY7JZxd2GOC3M8UQ9zc+YcjG1jbBszUlN+DGIMuXRHMrepTQYRzCkwHDOIdIJUj+2czBGMbRIzCYyRgzkH83LPvL8nxn05fO0KciP1/KhY6iAFlO834rIRlwtx/0RwGG3a2MGx+q+H4GBb5US3hSU2TnOw3g2YaOwEMe7liCXI2Igcdevu4NaZOZnjQoyLVoZy/1Kxzlbu3jSB8eucst1HD5ZJq40nPAWZr458c9IWYefaXJIBuV2YQ3HT1uVg722pePmnkwKSjLungOoe5b5Hg++bUCrSHCfjot7sMQS/c9R82+Teb6tcuf0k2OkQe0y2VTxzjOqYrkj5qfWszfu6/+TeJwdVmwxhuCm2OtM1Py+DnCEIa/16PXf3cM5RDuxFrvSpzmTamdwhcXUw72kJet5W56lpHYutHM1xhbkx78lxwVurSPZV6QOrYa3Gun+jmak1eSjGPaMcxofeNsp9s8gzfo23s45zKH3z+xsfe/Ll3I8Tv+ObvoMX1i/iF7uf5/AfefIPnu75L772w/zb7/8Ev+3myed/8Bc8hck/cfuEP/d1v8R3fv0v8oe/6qN8VdMGst/zwsv844+evPrxAj/7iTv+4k9+4ot6zhupYx6+dh3n8NAXq2POvHYd5xDy9svJ07te+UGXD0A8fnMG9Hpo+yV48jc+76f9pQ+wfuDPv4kDemUd8/C16ziHh74UHfPmtes4h+9AZcLdD7/iQ85/9d/HLq9vWtZr0TEPX7uOc/jm67kDwLuLN6huUiawYN7xLtg3zarDtNF8EcRArl3DoDUyU2HQZnKtWtCvvaeosxIqYlpRz1jFRI/5lOOxyHA5BgVXypmXiaWjNlMXnBVlujpzccW4tmWFdSkYYooiDgEeItS9SQrmMAWT91hqM8XZRgpyWqu42z39utyMvuzMTY9zOQkFT3XOmi20gr8OcuJ6U8yymKwgqavXuLuzNGftJ3rrFSstN21vSzl55arsRrl0dzi80svQ14HWFpb1BZp3uu/XQztvmy0FdB1rHV8eyZHrcn3b9bepWam8AjtWHbS7fTPNC7S3gsKtIsGhN5MDufs1QLwnLL3TMRZrtFT0eOs3iqU2k+PQncUFoz0XPa7f1NxTn6s2LISu6byvsWb1n3bBufT6WIHV1gUErRHVxzuBYY3RJjMhbCXbKowcwRiDuT0hYmPG1EuRRJoAXKqLd4Y2MswxBYEjGDGZJJHBzGSMyZwXddlmEtX7zHXGJ3MMYm76MzYikm1MRgHmOSdjbIz7e8bjx8z7TVHEjAeH8wiY5QwlmbXRw/KCZ7LOC+fUxg7WG8HWfkNkq3tDnb6Cs1zjsyucWde9L1g/1fzwinZeIW0Peq/o5cBrhbECvWZ2PQdlyWXGrBuskWyC4xHqOh53ZAy5fTNoS6d5e4hcb6ibF8hefcA2KMJdc3mHzXvKQVdiAQG56GNt0TpBgzF1XzfXZpDaeAKOr7fYcsLd8Nb3UnBybnJFN4Fwm3oNj0FrDV9X3Ls2SiA4baS6sPJeoHefDFEx1uz/Xc5r3zug79XF6wu2nOX2bavArsuvm9fu4xCApjZT1LFrHVOfs8D9Su4d2X3BllVj2Gb1bSua3kEbChJ8PaszOhPr51fxRefQW0X7uv6sX+PtrOMcSu+9WfjlX/Mb+dTlC/wS+3PJkm/u43N/qr7d+F0vPOb/+pUf4/e88DJf0z7HY7/AKfyV68a/8xUf58YTM/g15wv/0Vd+jH/ty17kK9vkm/qGv+rAahiR/Okf+gifevK5x/1m65iHr13HOTz0xeqYM69dxzmU5nu/+ZW/Ar3dAPAXkMVg/ZH/DLt76VkPBTjm4euh4xwe+lJ0zJvXruMcQty+nzi9+1kP4/XV/Y+88ufHHee//AffnLG8Ch3z8LXrOIdvvp47ACxuoThUxbByBTLEwKoH0nZ3bQE7s5TT1NVNi0GE3GRmQUth5Ss4LPiY+//GECzJAnbskawVKRuBxx7rO/WcnGRuYjsZFUs7ysGW1Rds1/HsnJa4qCvTd0jVdSxz0IaOSYBFDmW9boHG5jIf+1I9xSf18O5RzL4AXYAlorqCBbjcK8qYwNB5c0u8V8eyydlrmbhB7wuGCSr5gq8nuhu9PtbwimJuLG3FW6/rIGDave8B1uVQTo2ht3LlRjm02xXkysEb/3/2/j1mu20968N+9z3GnM/7rbXP2/vk07ZjG4NtjGNICOCYGGhECUnTQAM5NEnVf0pVkqpNiPpPpRIlraq0VaNGUEVqVCQiWqVVpSaINiJxkQGHEgKYY42NDbbB3tv2Pqy1vvd55hj33T+uez7vMttmH9bee629vnFtf/7ew3OYc8wx5ru+9zeu65Izunfw6hyOeXd1Zp5YKuo65FM3avlSySBMna1nF65ZKN7ZdwxF0XZD59Q39RIj53Cr+dFM47QZbG3jsm3sm9H3ztYM33cByigH+rjq2h5TCd8AOfFMOb8twSrK1ybMwFydx44zsxOZHA2O/R0M3wnr99jgoMBuwDwO5u1GHgcRQ25br3MfkxiCfxFDjvZ7h2/KPTyrqzcVr5xmhDvTBIIDr9cZRCQxg5zBHDe5i2My5iEXbsHlMQZxm3Dc1DU8D+z2SJs3PF4XKxwDGwOLQYugk+wG+xyCeNa1p6N1uToNdb6mwLnbhoc2YcgGTcUkx72j1tt+jy7Xmu9YPgUsG0bPoE9FJ2fFzDdSZm7X5o67KzWpSPi696DOWWvgUVtWYgo8TkUzW8pam63eNapr27Y6CvUn36OWmVgOjKPuG02Pqcj7tm3A1Bq2mhOZRBxYa+TmRB4ai7brPnf2S/cOfat7VGLnsfr2tNHEUzHSZrrmOYg8at6mXpdG+kUx3uhbmVn3EKO1rTZ2oE0dZphtQKj3F8P9Qe9r5ebeLnJSm6Km6c8Ek0myHN45z17hwiLjqte0WX/Onx2hY22dZQBeWnox9bA53/yB/Qt6bgN+z3s+9Vkf9y5P/jcf+Dn+4Ic+znv984u8+ruj8W//3Hv5Q596B8/DeB7Gt+83/pV3vUI3+B++51P8rs+zc/jP/Pgn+aG/8+nP6zlLS0tLS0t/rx5/ze/R7yj+fsrP7+fem6ZMeP5ffdaHbX/7T9J/8v/LvbpoaWlpaWnpC9D46PcyP/Sdnz0R6ivl5+jnICPx135mxUAvLb0BvXAAOOyM87S7I+zsyDxrPX/BsFT/qRN32OKuPl+v+FYokEhFrp4Al1Tk9DzQLSv1GOvcS4hPr+gZ6Xq62M54V4y7E25OrF3AkrAm0GKB07Co7uF7nHQU3FYj7/1nQ7PqqS13X+sEAlDUeeEIEnm5gUk5irPcjJzGRbmjvTd97g1rmzp+24b3yx2WmT8rGNQFmEHR1a1XfPWhfubeUY/yGbMs6CwoXTHN5S5Wd6ccx+J4p1/T9LqtV0RvPrmYEeTRYZ0ezerxtK3+UaZxzCwQFAWGyudJgUKLWVGyZ5S3Y/agY9wuAlbVzau/vdzChqd83WaG42BNgN2S1jda1zl3d0V333tWd+g7uT9jxJWYg7SOjVHG83yKgE4qPnjX7DO7v9aBc6RzACODGUPRzSMYJNN2wjuzb4wzWtzUT51EOcgRIDw3JESQE2YkaUl6ghvpTlgyzeUOxhg0ZgRHTI4YjJBrd0bcu4JjHMzj4DgObnEwcjDc5FB+7ZF49RGuE7tdaddHrWYriHvPzdacaObsEezHjcbUnMisSOfkjM+2dhHYxRR5Dve1GaMcxmZaK17zrZVD3zrWL1h7UKy58ssJd2JGAV9n5pCb11rt2tDRqpN2kFmdyBHQQnPFrI5J88TaTmvPKvr74N7lbRvYQ9mrqx/ZvRIDBLnt/IVLE+zNrLtgfyAS3Ot8Iis2WWvFIrWxYbvgMXFTx3PUetI6l31Nf6fuUamNJYrTVycw/SVoG2FKU5jHTU7i2nyj+8kOtyuMQ++dIdhsdV1Dr2cYFrrHKhG6yenvteZzYnPqXujqRHbhfsVOz0pjyNBcNgSMfVP0+QjSevWpe73kgL7/wp8VS0tLS19kfTqMv3X0z2b4/Qx9PBp/5NPv4Pf/7Hv5tX/7q/m1f/ur+ef/7ofu398MfvXl9nkfz1/+O68Q65fXS0tLS0tvRP0Z8/2//O//mE/9J1+eY/li6Pajn9PDnv3Av/22+oX80tLS0tKbo/GhX3Vvnvwl9ZXyc3R8DOLxsz5s+5H/jP4Tf/rLcEBLS29PvXC/vbb0igQ9LbNPQ5Dn/xNhKafXE7CTuzerN1aAwc5yX5picpmc3ZuJFSDWd9TFKZeoNasIZVN/KFR06xR0zVGUNCFcrkVXvyipDlNcUDZywr0D1srJGIqbzlku2IrenQIkOuETNNdPjnnAOOCo986AllijnK1eLldTTGxvtK5o2t4vd/dt7xu967FUd7Cbzsva6diTY1e9uyfYReeKYX2r99gFq2l463jbMAJvO+1yoW+boDEVeUzT33OUuxHBXASrsnVyamyYBxnH0/k7d8cqkXUd6zUL0kfOcrRCmmsn0qSc1n7Oovt1NDuvezkh3QSxvByR91BmKoq4nNbWCyqaeoP7hm0b9C6+maH43vaA9U3u3EiybQLEbReytidH6v3YCnYdTI45uI3BgQvAZjLTGXMQx42IZKbJBTqCOQ8yjWyNyGTMUTHNcu/mUD/rk2lZHcFzTuYxiHEwhoDziCF37xwc4+AYByNC7zuGYPT1kePxOcfjI7dDbtTZgunqC47HK34LepzriYLyOh5igCv+2yPYY7JdbzDkuFWUtSKrE0VmW6t5Y6G+XW+aP9Uvm6k14CFXqEVWBPcJnfMcZspmTG7VKU3QfK81aHWfOJMAqEjtQWRgz57JLe5JP2OnSaw36EYYRFo5jyvq2c5NCudGkHLD18YNwWLBUMMFVasDnHPeV7R3zomFYSFH7zwd0FDdvuWmhXJta4OMbbuc2TPwcsJ722ojR3+dO9nAOukP0Lbq+a2ceKtUg/0B23Yd4zw078/57w2sVTd6yol9ph5U369AsOC5tab7byUQYOCb489eVrW2PV07IknboV/I7UEAPfNpw0Oem22CpaWlF1O/5Vu/iq9+9+Xzft4E/uAn38XPDL//+SOffpn/+cff+wu+9v945SX++z/9AX7vx97Pz0X7go5xYHwqGp+Kxl+7bfzR154ButW/Ep9/hMH/9b/+u3z6cf3yemlpaWnpC1c+ey+3b/0nP8uDPv9NSm+KHn8I5ic+p4fa7ZW3VIfh0tLS0tJXpq7f+S+R+2eJgf5K+DmaAc//HMRnT8eyOHj4c//BcgEvLX2B6p/9IW8zpQvw2QloFPFrrujSO7RhFgg+7aqtXMMV3Uwrl225DcvtZnb28FbnacVCm3vFD8NJH2TWDVQ22cEHJ8oQral42Oqw9Jw4SbJzBxVtFxA+HRlWECYhm9fN0fW4uBGWtGyYV7QxWXCjomOzwKmoLwxFnaaf51pOZZtys26COVadwnI6ymGX0cimIzYDmpNz4r2pp7iZnKVzYn3D52AS1U2sXmLM1XXq5a5uXfGyrStiO00xzDPlxA0KghXAhycQjB4vuD1r3Otj3zVuxr2j9h5t660g8gnhjKxrbTlVbnzPno+6DHUMuMbUvb7m5OmKzLjvPzhjkh0Bxnk6WC0E2qoLWI+bAtcVQQzAtpE5Tyupxi0CN5fTGBhZTs1+IeeNI0IbCgj6fE7z/Q66bE5tV7g+x/u7yDGZBLY9kA7t7JU+DdPlCm4F0Wyqk1f9uRAxaikJmmfcytWZT0AbraGc8z6SIysa+uRyoJhhb+T1IOOmUfZGTnXKhnXFZDOrs1nRwg3oY9LN8a4obDsdw+eGh7jpGpWT3K0RFUGNBQ3ubn9DDtmkepfvO0jidedjlMeb884CgtSVHC7obSYY7p2x7/RMvHc2d7o1PA88D7lifau5XtB3HOer1juMc1LJEY1A5d27b01O8HO99Eu5e430rB7lAq0EFq6s8mlMD1o5fU/gizeYhwBqmvrR2wWPo+4pAstuTt8eOCJrDbjOo3foXXNh1oaMvgOzes/lIue8X5EFhG/lbC53LghI11olFe+MOd50n9U0muek1Jrsju0PMG+6/pZ6TcvaA5QQiinXTpyOt6bznefYLy0tvWj62vdcsFd/Az/103/k83ym8Yc+9Q7+8Kfecf9K/dcG/9Gn/96vffFy5l9N5//+ysv8xmeP/OnnD/yvfv49n/dr/OyrB//hD/4E/+Pv+4Yv2nEtLS0tLb14mh/8TuLhPfhbpBf3C9b82c/5l+z22sd5+LN/gOff8z/7Eh/U0tLS0tLbWfnyB3n1t/37vPzHfu9X9s/R5/8VvPonPueH97/z5+g/9WcYX/vrvoQHtbT09tQL5wBuJrjIyIo+rfjXVNSpEImAo+KJrVxxeXcATlJOMtsJE2SKTJJWv6qrX+VlyB2Y1YWJnK+WIQBKVJflGW1arkBDAOLuJjy7KY1mYNYFObIiiEGgxzZx28uu14pREGdCDJJGxmSiaF7Rxqj+zynoUq5k5hDciENdyca9C9dSIMwiFIUa5dar6NcEaJ2293vkM03n2bcN2y+w+T1+17wpYnvv0Jtcgjnucb6JKT7XrdyWm1yltxtzVHyrV5crwQm8sqKLAyO963HzeIrlthpeE8DTNahrfncpUq9zQkJdrVng9HVIShDYa5zuL346PetXuWbabKBXRs5xK2epVRyyIpVnwohCwRGKoy3omkDPIUiO3lNu26uAugV+ueBuVbH0BPYSCLtwjMnj7RO8+slPcQ3jMGdYMBgcBDMb0zqToY5gt4rrPRi3G3MIjMaYcv7OqN7fhKG5GXPcv5YzyXEQ89DXyqkLpxM3GMdNjuIxmOP2BH8z7xs3zk0bOYNmQc8hQJfBTEVIpzdyfxDsPDupc9Aj2eegx03jWJ3BkVQPtHpvtTarOzoVby0ndI0DkxnlpK0Yc/2p5WtWznd15GrPxxMKtuqZjXLhBwVU4xAcbtUV7U5vHd9fwvYHvFl1iWve0+x10S8Fn9206aA2smg+VupBf1YbAAyma9MAcvm7VwS1dW3e6JscuJdneAzB0ThqrmqDgc16H+/kMcotPCsZfyPPGGii4P+sOWs1byHGDZ+hPuazy3gO9SaX255zEw3oPWPoPnh2EJcL+7yDc3ZB1z03Y2quZcVQZ0FyJjZGbQLoZDZinPH92hCT41bR0Eb6Rk5BZSUJ/BI/aJaWlt72MjO++qv/B3zkQ/8c/8J3/Bme9eef83OVWfD0p7IVfpGvfXH1x197xu/4qQ/xb3z8fTzmF/ZPgJ997eA61s1vaWlpaekL1/i6X8/80K96sw/jjWl+Cl79wc/54Qbsf+U/xn/+c4uMXlpaWlpa+kVlxvj67/nK/zn6/M/z+aTq2e3TbD/6x79kh7O09HbWCweAJwUrXZHIcQd7WdHQLkcfTT2kWdHBokXMSKAXVBG8yQJH92hp5AhNbwU+zyhhK2BbkKLJvaY+1YpwznHvGk6iQOQvjP5LzujmE1I4OU963SHKwZvIxXbGQ7uT3u+wWvC1wGU9PnF1lrZLHeeEOfCYtOrnxU2O4rYJ/GTKWVuwhQwikpwD96R5x6ep23arnt1odxce5mRr5c49HbYO2cmKvW4zabPw+0xiTAJB5JwHzHL1nR2dOdXxmwVH5wnDYRik708O4TPe9YTB9QNI8LgxzwDghOGNEZO0zjjBGllAbhSwDDKuFdM9obW6hgVgrVyT3u/h4LPmZpJPSbzInMwxBSdnkkcItM3qrg7NhTmTOAZxm3JbZqef8c+paOc4JnHcFNHcnJsFj88Hj+9+meftwmMERx4cMznm5GAQ5kSq11eADJiTOA7GcTCOG8ftxnEEcQzm42A+3pjHjTEno3p883TEV1xvRmjbQznH7zA9BZEjFBmdU+d+duLmccAY5OMjxMDHTaBwjqe4alM8dI68u0aT2rjQNjZzLhE0M9KcGdrEkd4KkgtoeldfrCVMnPDT3U/11aIY9pM5nunLNXs0+SpJgKxu8BqHGMyC++GmaOE5mSMEQy3wZni5571vtE0bKjQ5TPNoTK3rfJrfFkPO2bZr44nVuql0A7l9a9uCb3r9gJiH1sHUZhRFtTdB7P2ZNm6cTn+DMKs63sC3nezazGDesTCtx9b0x2ojzOkavj0KZDPuGycsUjHN26Vc5XJuW47anKGIbkUMXKA96GsZ1UfeyrWfgum+6R7VL7qnVyy0XPiAW90L7Qkg9x3cidtNG07ca/2egDmxvmm9HwfZXrwQjaWlpSe5P+Nrv+Zf41/+3v8ev+83vZu9mSLl3+wD+yVl/PVj5xNfYKQ0wH/ylz7Gj378tS/iMS0tLS0tvXAy49X/xr/L81//+4j9nec/YwBtkrr9st/+ph3a56wcn1Ns5evlz3+W/a/8x9zjrZaWlpaWlr4Q1c/R46v/4frd/VeYjp9Qisbnqf6TfwZ75ae/BAe0tPT21ov322u/lHsMYGDZCpw6uKibYF6rQFVFGJ+/zjOTOyxy4tmwtIoGRuAWwJ4y6QPF0yo62J+cuVYu29YK2Jogn1cX5wlw8wSbgpJyOE6MTgCtIpOjGTYOnqKPB4aAFv4EW89o1DDDbSPzgO5ybGbFO2fATMHnrudGDlVuWiePg0nUeyvmmseJty4Tbjl37Rw2c2xvgu6ZZdCNOnc5m9MgsuKSW5djOYdcxptD37CZxPWmY6YGPQ+YFbUMQBMYo8ZO9lciFV2LQecEtwW/y817PvYOIlMOzzzBs5ffNxSP3IDRHDmsAyJwb7SKICZngaWaY/W808hqBHE6VjMZ5jiKno0TLpkrxjaboOlxKI43qXjcA45Hct+IiiX2Puhtp5nOr/AnsRlcJ5lXxhGM9hLx7kFvFzlcMbo7LQaeXR2380qMJNyIsWt6OhVj3l73b1cBOnfk+nV7AmgYfkLZ0Bi6gWdB8Zjlnx0a9+oNjlHrAc3HjHK9WiOOg31O7LiS10kQArlAWKtrOKsnF6Bj2wMWA8+gj017K7qR1uXKd8god793LOWad5v0Zqjh2+UIRjHklTlem0o20srdmopc9gKeYXISu+8QU0D9hIcZwt/lhu6b0cy5YHRvcvUzsWyka+zmHK/brFKO9Ry1cQRB0vM+k1F7IxyLWjsnMO7POKPq07pc95H3ruqcg5md1g33xjTI46bx7J28XbEzNb5vgse3G1Cxze6K5J433RPcdZ+yJxCe0cjhxOaaE2ZYu5C3mzrI656UObTBxXs5qAvCU2kNebrcwbYH7kQ8E2ubEg1MveJlJNddI+p17Qa+470i8WdFSU9F7Nv2gM2JbYqLZ3946oteWlp6YWVmXC5fxz/1XV/Lr/vmG5nw/T/8c/zvv//HeXybOmX/d//Fj/F//N3f/lRDsbS0tLS09HkqX/4Aj7/m93D9Fb+Dy1/4P9N/5i8CcPwD/zjj6/8J+g/8AP78c0/X+LLr03+U+783Pg9tP/Zf8Pjr/40v/vEsLS0tLb1Qypc/wKv/5H9A/zt/lsuf/w/xV/4u7ef+xpt9WJ+bjp+G+YnP+2n9Z/4i20/8aW6//J/+oh/S0tLbWS8eAE7FHbt1OUjlsRMczQ4FFeYZz1zORUfwIs5+WK+hu8eSVk+tbJngFywP/E7IZmG4go1RFHBWyF+M6hZtgCCNFZR8AtBG0ogU2PPqjVUvcUFeEuwQDHHDsqsvdVaMsRvMgHkj26ZxGFcIr+M/O1MVUAhdh3RL4hA4TcDmlWkd93nvzI0MfID1dge8FgZdUbNzCrBl1nG4F+AOdQ0XcCMUea14bJ3zuAVExUZnjbFZOZ5HQd+CXWfcLQXVzSrCm+oplcvXfNeUODtTrZyY5uVHrehmA6EigSFv7enfenloPM0L9FY/s+s8wiggf7rA5XVtKOI5KEN0gfuZrwfR6ptOH2UOHzqMfpF7O67asNCMvD5i1nH5pbE49xBkQWedf/rGPG6EwWRCf5mjAOy0pMeN5oG70TA2wMdgbhfGuNF91wxou4YntUvbzIvrVwcyhqfRLGremuZCdaZm3+HsOZ7xBO9GwDgExs0U9+tykE4QpBw3OV8TLJwZN60ub0R6ufZd45tRLk/UN5t6L+dKewyOZ8+gbQrSTkgaHTliTwe8QHtUXLPin/EuqFotyrialuP1G0kAmmKdW2rs7+b//iDQX/cXpq69m2Fbx1sX2jWjWcWoJ9jMSnO+CFBmXehzk4cllrVOQK5bKnY8qcfUfatfyuw+Bebbps0xWWsk8+5ejqnEg9Yac7vAMcm+4dsGx1GOYhfw74LIilqv9/QN4jUY2lwQ28u6B1QRcoyDcK+u9K5h2jrEY61paj3Wpo4cZJZ73HttRgHiwGwjXPdW3asLilvHmqKrtTFCdQARiXvAdIyheOt+gXEUuDay7U+bOSIE0vvlC/iVz9LS0ttVbsaH3nkB4Hd994c5Ivn3vv/HGfH2u1P8tZ9+lT/5o5/ge77pvW/2oSwtLS0tfSXLjHzHh3j8Db/vM751fPgjXP7mWzQu+fY34fZjX9BT/dM/Sf/x72d8w/d9cY9paWlpaemFUz68m+MbfzPHN/5m/Of/Jv2n/zzb3/zP2X7sT2PXH4HLN73Zh/hF18Of/QPcvuW36ffSS0tLn5NeOACcFVE8gUzhxbtrjCknYDlIM430EyKWWzETEOyxPNQJGYccZncc0AUSMPVUlkvXlGuKCE79zekWfh3csNd9XH3Cgh1ZcdSDCCfcaPN09inalP66lwTIQ7G5Vu+d5RB2wc2wjp50yPFnjlUvnLsLepVzL2MArVzSAlFRTkSqZzN8w+eQKyQFMe3s8pxUPHJZ70Y5JzPruI57N6r1rc69kSME/by/buzQdclNTsOYio2uqFnyhMAIooJgMU1Ov2yc3cp2xsDSSQ4iCkrbpihpc13vs9tZ9ltds9B1tHJjytVLwTMncsi9ewLrOIBkWCNJZlSPqIdikkOXI83gOCAM/BlmgW16T6uxZ7uUi9LJCFpP/PKM1gXVLG7QH+Q8Tjg7oWdMQd88NFcNZg4mNxoXmnca0CKwCX3fmBhjJlxvtH1TlC8TbILtcvBGVgyyMHSesdrzgCMxa7RaIs7BsIoAz1pyMcnrUKT35UGO8ePxyRV8OnspA/IIYobGMitS2k0vGYdgsGdFrMs1m7UGLQduid12clfku3qeyzEdJ9SUu9xSGzVmCt57KJ5Ycd4BTVHolvY0F1H3rbsih73WtVkjTXBfKyTkqo1Qb/CUC9j7jtOIeWB0rCmUHLYnB/2sfvA0fb0As2m3RUHoXesxbnVcgfWH+9ogE7YOU5suRL+ds3PZmGRo3UWqlzi2rPjphiqtC8ibYdvGfHysDRUFWm3D/EJjI0g8gpxy9iuaXpsvmHlfK+ZGtme1QeaGjdpsYucsSJ1/Umu0EuUb6iavjSw5zo0x531S6yMLWHu7EMdz3BWfryCCqc/NayON7it5bvCpjTIcB0tLS0t/r8yMf/5Xf4RLM/7d//zHOObbCwJ/+jr50Z99bQHgpaWlpaUvmcb738/+4z+mzZtvNY2fg3jlC3qqXz9F+7m/sQDw0tLS0tIXVfHeb+T23m/k9i3/BO3n/gYv/8k/Tru92Uf1xZf/3A9z+aH/iOt3/Stv9qEsLX3F6IUDwGUFFHxwP2kBE+G/PK125aAz8wIBAoSeUf2lU9+ruNgsiOgFCLJ6fE9gadYVLW3yAJ/9mhnzyT3KuINfK6CiHtkOczBzABsZLmx09gefgcQ5YDj4Vk7ZctueJ35+2LrgKQlWUKzv+sfVPMGzYmuJCcMUDdvOjloBJ4tJuper2armNtTJStPsikGa+nazYpKznM86z3I4n7HZcdwrU/VXknHUdSonLa8D2ii+Ne10eJpgl5eD17pcu3lCfIBevaL6PFMO1SDOSl0BtTwEwhwyorziVi7As9t1FjSk5tEJnUPHRSfGgTc5tw2IkbR20yaEsCcn7axrPaKOQZDLKqpaccrIkYoc3jEGaYFdnmHcFNULtL5BOHNORlKxyorcHTRGwVAjaO6a9/YSozVmyqm7Y3RPdhozkxGBey+HsXpq1Qt8xjNTDugC2XdXpuaFNV0bw8lx7lKo6PFMbKjPOd2hCXWKESfRNiKNOB7JGBrlMZhzqJ83jRlJmEDxLOivDR9aL2DM6uCONHxObZAIU4Q6BQirUzdrrDMHTqrXmsHA8K6Yc2eS0QrKl6P13IQA9xj0M8ZY9wOtoQhFYsc4iLbjbrgbfXNa2/G24b2ps3nOisr26rce5JSzN2dFv/s52K7NG3F2iKt7997pXe5tOe0DeisHdq13AA5tkDnvldagOcyDsDMGu+6VDsTEW8NN9wZvnTkOrT1H4912dbBnva8lxJX0B22ksSTmazS7VLe4oHYaOuYM3Repe+u4KeGgXPaR5Uw/O9IJQW/TfdwIXes6xsxB9l4O5U7kUZHd2uAjd7tXagFYjIo/d7ztut+M8Yv8kFlaWlqC5sbv+K4P84nngz/wA3/7zT6cz1lm8KF37MxMPvbK2uSytLS0tPTm6PbRb+Dhr/1V2itfGGj9kilv8NqferOPYmlpaWlp6RdX25kf+DbG17xCe6smafwiyv5APHs/dnsFv37yl3ycZZS5amlp6XPViweA58BMrj71tKbiQi0ZQ/GrZJZbzyouFEQbowBWKs6VWX+qE/IkeXeHmkAvVrGtPMC8YZbVmxYCTDHqPSoe+h4DXL22kYpsnQ4mkJYDck65QL36Niv6V9bc0ykHZ6SwIl7lgDud0Peo5EQxrdPvcDhn3F19ZH0epwO2kTkFtiiAPRRzPcstzXVCU6fsTMot2TEzchw8WT/lNrQo4G0dK7CZ1WGKd723V99vHFSms2CNbwJ+44an17jLMWzEPZL5nqWriyNHsjUxypzljKYgXmIW+CwneLlUT5e2ALWr6zbKhBmJnJ9P7xcGNgfRmkDa1hjz+gTV0gTez8hsqp/Vm5yqDtFdkcAhl2ZgjDHIObAwvE28C1x5Hc+0RmSQx0GMRzKS6YK5iUNccd+q79kLspUjvjlXnGbOM4aua3amyTnqbneAHgUX0wuEJqQNQhbx6r9uGDBSIF37EvS9mZDjqB7emstjlhN8J6aRChPyLwABAABJREFUeTBiEHOQMfGpgHJMUPdIOCKI1khrjO1CRKtrfSj1OG8kV8aRjKNzvGTE1UmXU9jouHdtOoCnmHcMM+TOzWDSOWKyG4R3fJMj2jI0Z897AfNpfZ3T7lx3ISf8GIOkQ3M8YducS+uK2rZaa5ur9zmiHLxAuOaKOW4DyykAaqae8nmu7XIH14YC8702RdQmipDLOQv8yti+l/n+yTmb4zl2BOm93M4BakSu9V/QPLve0jtsro7mecJevWeLqM5rvfeIgzaMvOyk7XLmWlSU9Y17WoI33Tcy6x7clbDgpnXQNo1NKk6eOHRPq3PPDDn1YxYADt1DrWmTipmWQky8Eh0CgeWzKzjaAzmuJF3g/a3oSFhaWnrLqLnxfd/yPv6ff+lj/MQnHt/sw/n7yg1+13d/hPc+6/zLv/ZreOU6+MN/9u+QwA/8yM/zwx977f7Y97208R0feeebd7BLS0tLS29/ufP8O76Tl3/wT90DwN4SyoTxsadPAcy5fue/RG7P7l83YP+hP4xdP3X//P74paWlpaWlL7GOD3/4rZmkkQOuP6wPgdu3/27i2XuJ930zt2/9p2k//RfYfvQ/+4yn7f+//xT/1N/6Mh/s0tLbQy8cADYT0GhujGz3KtkscJsxaOQdLBhBFbk+AVVvcgq2BmFkVqwoT3HBd7epFSjLUORuRsESezoe+hNYfX28KAjWmHo5zy7SKicmrBEJrayS5g16HWvv5HFF0awpJ2DK3SgzpICNnYwKoBm+dTIFhOQsNEjHbAiUnPnSrQlantC6ImKxpshfr6/TVMUZIYeiVYdmr9jmcpHeo19T309ON+Wmj+9g+3Yfa6suUMpFaxjWLuW0PJ2GBaTMMR/FxBTZTAw5/Mxh3sp9vWm8vNdlUKyvVVxva4pzTnNcTdEyP1swqNjfUZsGWiso58x5LaCo65rt8nSNEabOuhjuTeZ0cwjF+gq4akwjJnPcyGwnP8eb0zBamZ8zyy0aQeRV8IxgHlfmrLnmcYfotF7jps0LGa+CXzjYuIXRZsO7Kxo5agMBhk2BtegnID8wM0WHVx9DzgP3RgsIq2OaR43RVjHRqahwDmB/GqdZJvQYjBjMcYN0GINt3sgIHhOu5ox9I5rD9sCoMWDcIM65upGuyOdbO7h644Zh42B/eEZgeBQktHLFomtpudE8753CM5PDjD2bAHpt3Mg5n1z3+m2APsj6Qk6trxxyI3fdg3wmvhndnOZNZmLXhg7FsCuB4CniPKHpntD6g/pqszq840BbBHbNR7e6v7ncrfa6jRxN88uq/5peMesNnb+5bn39QZHYxxXyqh7qKeBt1gSFj2Debrg39ehuBvsDeQzcnDkmlrO2jKTim93w2pSRJN726unOX7A+kln5zp3T9W9e0evjWqC318aQQ/eRdtGas1n94nWOeEVo7+qLJhUxXRs0giDHVf746lnHXJ2/8yBpzOMRxiPWHn6JnzRLS0tL0rd88GX+t//tb+Xf+n/9CD/8M6/xON5i/wAHHjbnX/3ej/LPfveHaYqy4X195/f+xo8C8Lu/+yN8+jr49OPgB3/sE/z2b/8gX/vedf9bWlpaWvoSyozjIx9hfNUH2D7+sc/++C+X5s/f/52SwPGNv4Xn3/NvEu/5xqqsKmVy/fZ/Fru9xrM/8fux8RyAeOdXc/sVv/NNOPClpaWlpRdJx0e+mmxd9YBvJeWE618BBH9f+8f+F/odY2l+5LuZH/nuz3ja7Vf+i3C8qpd46QNfnmNdWnqb6IUDwFgrjJa4qee2bK6Ep1JUXb29J4c4nXT3/sh7TDTA2XH59CVBXUXOcgfLCCr5GQVbIMIMRRo/xROLGel55orTrSDgp62jFCQORebKqZyCY2dEqjtkueIoUniCbNswNywEEkXBDzggmxUs3srNp/5fMxddnKNcwv50jl6xqynXa96jZIHIcgqfrlqNYbqcsWc8L7ZD3p5ck5y9w1NwNOEekW3VMcvEbMcqfDntPBcTjHSBLs9R1+V0X0P62Y97EFauW85OVfUuV9awztkabFbnk2QK1M0I1LZ6E6DaevXDVneoudy9915no9EIxn0eti4Harq6opuhDtTqbp3TCG/EGIRp+CMOMfXLpnncGhGHoLnBJJmk5sDmxDE4MmX6BLy9R9ereTlBT+f5cyw3Mo0xbxyPzr5tuL3EbLr+LZLZy81tVK9zXcdxgzCMQ+A57e62966x0T4MzeGodSlwB+QNqyjfDDgyGZnMMRjDaZbM48ptHngmr6XzuDVy32DfsNaZA44EfKcdR63TjTFvTEtGbzxi3HC2bPQ69Znq7DVXF7DVXLACws0UHTyPYG6dySgHuqBxkrXx4Fyj88nBeioPdT+3ijmOKb5ojc2chuPNdCkmeBozZq3b09kvKOyp+Pdoe417rYNzg0EA8+CMeT/va43GtKw4+Kh1hpzyh66bbovVI5wG3SF2Mm4ak96xM+kgpyCwueCobVjItet7Zz5e6x5mdyau3u/zPtEK+k6tITdi3rTWCwbnucklda+0M187ge7VaV33odYLeFdE+/YyeTyve2q5hsni9qNut1mbVLpc6cdzsu+Qkwi5p9Ut3cAmScdfB6mXlpaWfil9ywdf5g/9d7+TP/ZXP86n/8aP8wf/+mt8ejoKrnpzfU1u8K9+70f5537NR37Jx3zwnTsffKc2dX3X177ry3VoS0tLS0svuraN2zd9E/3jH7//u+NNVSa88v3AQQLjo7+R1/7xf5d8eM9nPtZMUBh45Xf+X76MB7m0tLS0tPQW1uNfhBzEs/dx/bbf+Qvg799P8a6v+RIf2NLS21cvHAA+G2vlqEtBKAMPIUrs7LktVxwwM+mUG9R6sdSKGfaGpyJVzfdy58FJb/Le/VkOy/vv+RT1m+xynYYika1dnnqBT6cfG3A9g2n1OqfD9nSnIfdfMwG1PKZg8AmT7w7mxOKEwAnbA4yKIz4di1Gut7PDOCeKia3zqk5faQi2lNuXbIpLjoJVWT2kFoLUfpLyAjXe630mZK/Px9O5VfyzlfP57B8WcERjk3Kdpu9y8prrewWwOZkZyIHJ6fQVHDayXMR+EnzOTQEQcni2BwFzBJBGhpyUoGhY1MfqJpdm0GBeMXM6KTfsrPPyDctQVy+ujQh36C33+Ok+jfr87F4OE3hmv2DPXxGEc8X+agZ39d/m0KYDb3Kcz0FEaD5ypXlHLmrkyDZXDG48F7xrF83fHFxnsu0bDUUWC/g3PCfWTXPDdE0s5Bin94rEDqhuXrzXEkh860/LIE4nKUCr3lm5w0caI+F23JiPj4DTImAObrkxCV4hGA187zqvVM/rnOoRPnzStgdsBnGgXuR5EHEjsnOYqad520lrzETnpqxtrPWqlE1aCmrGZkRB9lb7LYxUl/bcyRwClTYEtkmtGyaTQ5HlGXLLm9E2Z/PEPdWVTMAYuMuRqmhqxRYTE2tbOcSjrh9ypweYORFTDmgzRSqf8cnucrp60lz91Gmp9d8bjKPWfBFxhQ1ok8aRSkWwB8hBy6YNC+NGtnIOO6TXxodhWFNvbjoC1HVPaftL9NTmizMC3e7O6Toe6+TxKmf8szZ9PIWn2YR0xZWfbmLmxPql7ntRzmHH8txko/jqxGAGYU7crvX6U07xHKQ3gsZ87RVs2zDv2Dw0lznIOLD2QN7e2pGuS0tLby391l/xVfDN7+a/9Y4f5Pipn+Lf+bn38slw/uTjhTcDBF+68z/5vm/gn/muD33Z33tpaWlpaelz0fGRr2a++930T37izT2QTHX/Pv4lHdc3/iZe/a3/HuzveHOPa2lpaWlp6ReTGfM978Y/9lZK0XgFnv/XQPDab/pfMj/yq9/sI1paeiH04gFgP/2/Z9PnADYMOc8yjZmCxIKGivU9I3U9oxysTyDzdKIlg3ueckUG41nQ4nTMljM36nFzvA7iyhls5k+RrUJYis2tp9gcUBHJ6l+dojTRSUvBJy84dMZTW4cYgiM55M60JqBkZ4dmr7jlUP9n5pMr8/6LScNcwDQRFM+wiiI+3c51PFNA01qWizCw0FjSHGH1ik2ucxXgDcy3cto2wV4qUtgcm7cCWylnJuVAzKMcmOr4tOpbNXlMBanSyu1Y8cd5FBc3wtSxDBNm4puTU7DTXPHXmcmkru3pZDTKNV5zKwuG+ybX+Okg906mIsazjqoxcBpm+t7JtxKYFYud5qSZunvbLtA6DuzZS5g3ursA+dnrYM7IRuZBHgiyjWTMSWRg+Qj2snpS3dWZzCRikPOR5u/AXK5b0qpfd7CF0073NNBmKB67FeSHAo8npOzYnPd1EplyUraGmWMVDR3zyeHZTP3FZjBojHjkNo3juTqMPbUWZsKN4GrGY38G28bmO7M2DTTXpoK8PcL2wOzVS+0XPCb9aMQIZqiP+nY7aG1Dic7n5oekmQv+5em+NdqE3ZxbTI6CnZsZfq7vBjZN6xLDUJ9sZhI5CbrAfkU7t+7s5jTbsOa4lROVjRjl9G1yro9jkq3jrjGUx9sUuewm0/+sOeiubuLzNl+bPmIcZL+U67s2a/QOJ9znpus5b3i5etUjbIpuNvR+xFOs+bkhJBxvHW6KgU9rxIxf0MWreOqkZxJ+0Vr1uhf22rRSTmHrO6c7OKcA9mn6xbLuF7pP5xzV1xtkhGLztcifYvrnJD0150JjOfsOt+fqWPdW358QRrDB6NAo0J7knEROPK6rA3hpaenz17Zx+XX/EO/7M/8l//7Dx3h+TP4/zx/4W0fnD3zyXXwyXP8t9qU+jGb8j7736/nv/IMfuiejLC0tLS0tvdWU+86rv/438PKf+gH6Jz/5phxDbBtc/zL26f83MBjf8H289lv+1wv+Li0tLS29ddUa11/2rUrReLPT6+K5fm/4yf8beftRxjd8H+Orf/UTQ1laWvqS6oUDwHLvbgVBTY7VHIIkOQQB56gI3bP7FsV92hnZPO8vZZicra3LXXoSPKsY5RMK4WBnZ+55461+ShNcyYiKnKYceI17nGzb8TjILHedtwIsVs5Qx+eN8OoBPd/H7OkYCojSBQd1nllwp1fMcyu34/HkiE1T52/OcuY52FHRt+r8TW/q0Tx7b6LA7klrQqAmLfVe43hiyjl1PlGu4HtM7AG+q6s3AuMRs+3+PMUIG9YFq+SwRZC4IqTl+ht1pSho3AWEyiEZCHjbUCRztnL+FhAzr+5hNzEz1zE6JrjqBfg4txYU8D0/PgG6g7HhEaRVKLYpGljQVI8ZNW+iopUjZ7lJo/YIVOwuBVJPuDwHaUakkeHkDSIfCXNGTI4x6vhfUk9xa7UvoXpQzfHt3TQCGLSE4JHwl7mNA3d48IuOIWc1IDc6XeskU9HJieDa48S6YzhzZvUoD9wftInhdmA2sDQBz+bMRLDdnRHB4zW4Pn/OKGdxz2SmDvuwUACzdUHHGff53wqzhj3ITXyPaRfEa8245IX04BbJGDfmzbD9AWtZDnZIV8yxNZc71OZ9eopZ3ph9o8nujI2ohIG8r/RUwzNJME/neypyvPXGxY3en8mZu/nTuggUz52pNeBgveGtlWtdMdKni5gI4vXu9ZjgF91jQpsx8ox2d2qDSK2xezR7020rBJ5j1GaSTD3PNMd1KwnFPFO93+1MEXDSOhk3chhJV+d336EbdgRzPMfNMQZpHdqmeOdjkH3TPdao9eu6b7DrnEwXIMcgW9Pcj6nbR9sEb1MbfKySFfCLrkPqHjtyvs6B3bQOj1fI9owTUZO675996DGumt8BfnmHer3P6OylpaWlz0P58MAr3/OP0j/+cfYf/RF+69/6cTLhX3zXK/xPP/Z+/thrL72h13//yxvf9y3v+8z3Tfijf+VjfMdH3sFv+/YP8E/9yg8u+Lu0tLS09JZXvPOdvPo9/ygv/8APfFmdwNkat49+lMdv/RXEs9/Ow395wcYjz3/dvw77y1+241haWlpaWvpCdHz11/D8O34lz37oL74pxUPnz1Fe/QGYn4QPfTfZ/pH1c3Rp6cusFw4Azwz8LEEFAZOcxegqYjQMWpQbU07aYKqvNR3nhCVZ0bUucFqOM3WiVuzz3QFaMDgnOV/XhWld8CuFFLFyqP49rmHLEzeD9Qs5b3LYmt0BTfZdcKoif4moLtMGTIG2eUA0QZeQS+8Om21X1OsJI00eVatezUy55mjlYp7qq82CT5BkVISsGZZNMcm10yjdoG1wuoxFoTA2PS/l6D3dylBQOOGMqDZLfT/j/kfodRMRziE3d/1oM5cTVybgISjnZ9TtiWkFYa15xflmAaDEKnpWVEh9z2df7jwjojHOSHE5t4NW1zfcyXH2IlOduWfELph1QeMM3EzdoxFMUq7vuEGWAxEEIU/8mIHfQVUQYxKWAsdpTE/GEcy4cdxeI73jdF0bC5ivgj8gQ/fG5tt9DpON3hs5m/hjBMdNMectb2weOA1PGLOc9N4qLnjD0Ry2qTkoh6bA5JxD7sypvlqzBz2/NxiDzOQYg+vxyPPb4JiNEYM2b5rDn/55xuWBvEDf3klv9mS8JwjrNOu0S6f5YFyTPK5aTU0+XWuOT+gpnm4kYxy0y8N9z8Os+ZkVp2wYll7O2ql55pptIwc9E7f9KZ79dTHpMhRrI0jqpHFLdjda3/FeTtoxNT8tiTHL6WuwdfUBuyvuWyZ3jEosmBNrJretofXeNnXUlnt3plz0VpHJ4uFRbmPHU85W2sYZ/6w3sdpgcSW6YHFa6n5Ya8Hc9ZhsSgXoncBhCPoz+tOeGEZtqsjqip6op7rVvYNKEah1ZbMS452s+a/AgNPbH3DcoFd8/ph1Dw2lGpgRj6+CNbw35lUbafLsST9d+/aMOQ+iwDLmzEpA8JzEDFp7UMpCTGzWxomlpaWlL0TujA9+kPH+93P95m+hvfIKD3/lL/PvPDyn/UTyn776Ek87gn5pPWzOB9+hft73vbzxr/3Gj/LypfEtH/jMf1BnJv/Md32ID7/rwvte2r7YZ7S0tLS0tPQlU7z8Dl79Dd/Dyz/4p2mf+iQ2vvgbMROIl16G3njtu76b3Dfme993dyg9/oZ/84v+nktLS0tLS18ymXH91l/O+MAHefaXf4j2cz+HH8eX/G0TOL7u63n81m+tn6P/8Jf8PZeWln5pvXAAOKo71l3QMVPAJVMYsJkxumAoGdWjSVn5Qn2cVo7aLPdmdcEqOTYFikyOXjnYTiDkZEZxjdMZHDBfHyP6FDV7d46aIla9YpKV3Ooo7FTRxV69u9mbwKx1kqPA5009pgUr0/IpujTkZlZ8M1g00lqRsXJJex2ad0Ge8HLNzXqdAOuKa04Q1C2A3TYdfhYolXWv3KcHystFDtdynt4fBxo/b4pSPuOzswCydUHqcdPrYHj1DWvs0TFEAdczxjdnnctBYXdmZrkcFZvrrQlg1sUvTFTPP6qrODRPznPN6m6eyXT1+lom2TaBqTnlLr73/cphHSkAfEJnORtdmxGyyfUdwdnhrGjuCf1yd0FPYLqRs15rHgw6M5JjBoMNj1fxbNBfgu0ZDANupL9M9yaHbjlflUEtR6+V43jijNtkZjDCaD3oHjiJe42VynCJTPUT53wyvp+wPAq4nW7qPDQ+18m8HXIrH8mYV25zcpwJw3PCeI3x8gXMaP5MXNxr80EZd3MOYoNunX1zYGNcA4tJ2KbYcLthDy/Trs/pOTnMOHD69REeLkDe3dUzJuaO7Ze6vhOLQfcNT2NkxWtb0v1GP7t5azlH3RcUhawNJo1g643WN/p2wcyIlLV5xqx5m3dYbNbx7hVtfAJQ9Xm7d043u1sSdd/xrvWYJD5HbWZw7TdJJ+YB/UGvP6fmIU2u4wKx2giCEg6slcveoMlFH3nD2NT5DHhoc8yMSgvoW0HgCt0fWieWFdOM14aIIQjcaqdDTqLOFdeGhpyp75v618NSm0puV7J3vVZMYlx1/H2XO5ggckAMomKiZyb0C8xbufQdrDN8I9tBPP9UdQmH5nZC3x5w7zSfNT8M21bs21eS8nS6v8mv8ZWsNYZvXJ9x/q0xv+qrmO9/P7ePfpT26U/zb/343+bVv258/4984hd9je/6mnfyj3zDewD4uvc+8N/8tq/6Bd//pcbXzPi2D3/l37fWPHzjWmO49PlqzZk3rjWGb1zx8st8+jf/Fraf/An2H/8xtp/8SeBz2S71mfp7t3HevvEbiZde5vrN30Lu2lj1doymXPPwjWuN4dIXojVv3rjWGH6Bcmd+1Vfxyvf+Y/Sf/mn6z36c7Sd+glaJGl+s0UhgfOCDjA9+EMx4/GXfqrq5t5nWPHzjWmP45dfbbyV+NrVyjcUV940y8pa7T326ivYd3H/Jn3K7gVcMdL2WQdkv5RJETrs8X8/LJZxnZO+8RzZbVjSpVTQzE8HQEy4X7Kv3V+etVYco6vnNRrZK5EVx0RZWLt2GtQsQ6octB2NywtAzirYiYUNuRjmABUTMTXG81slx0+P69tShGwJ66kBWDLbNWXGtkzP6OR2e4qQPgUXQ+xAVAdsp+vW68TU5gc9O3cxiiPk0/gUcrfpNBdi8wOKE7Jzxtll9w5bVr+c7xtBrxiygu8k53RpwBXY8hs65XThBc110Aei7Y7sAce9Ydd5iJz3PSk4O0o1J0lC0b4SprxdFH9NdTljfyAwyHvHsBb2tjq3pupo2NQTqCY6QgzECJleOMFU6tx3Y7tfdYmK+YbbTvGscrIEX1D+B7cz79Yju3OJGPwTQxnSO3vE5aa3jTWC9+VT3bB6YtWKhRznOL8ycWi++M+NWbm8jbzdiqjk3c3JkCP5SCL41gmek7bjBnoG7rkeOyVRYs1xVaTQTwL50w9kY0xkzCAt83+mAPTyD58/JgNtMnueVBybbs3fWSdeV867uZTvkXjaX0TeTFsHIyTQnY5A42wn5c2IRzLpvWMVTOw3vO77vuGuOtHIJx315GkHS0iuy+yl5QBDa8WZPbt1xrfjtxMupayRxHMzWaa3VWLuiy1+3xjKq27xt9V48bQ7BcVPcewLMQ67d1is+/2DOlIs5Ur3L58YRa9AfyOMGdrzulyjq6M5xQHOci46t6TnpjbgdqDf83PQgoHz2YZNBDqvY74bPmzZPeBPcRRHd0EnbmHFog4A/kCbAnMdR99oGXYkLaeg/UuNK23Y8bhBJ2y44B2ZdDvrWyH6u76WvBH0x/uPwRf8PzDWGb1y/5PnX1+Nd78J+5bfz+3/Z4Id/5jX+D3/ixwHYmvOv/+Zv4KE777x03vMCO3jXPHzjWmO49PlqzZk3rjWGXwTV+R9f+3UcH/ow7VOf5Nlf+AvYOOif+MRnfXq6M9/3vnv62/Nf9Q+Su36exrOXnjajvo215uEb1xrDpS9Ea968ca0xfIMyY3z4w4wPf5jrN30zNg4e/upfxT/9KWyMz+nn6C+mNGO+7/08/7ZvY77v/eTl8sU97reY1jx841pj+OXXiweAC1omJ0Swe8opmXgabkFU5q4pyJRIBxNCtKz/l9zBrIohzx5bAVn3gr1WXbR5yFVqJidwVhQvr/+HRkWjugNnnHJBxjhdtCmwGZA2wDoZVlHFs1xycoxac4HiEBQ0bwVZrXqEUddt9WNqbCry2TbwLPdpgzgEKNPublcZFOUuNg+yXNFyLz5FPAtkT0jX65tjTccl+PoEvO/dx+FkDrKcmFgreF47dlN9vudx437S8AKv5fptBW3nreKbB/d+4IradnfiBKS9YCtygUcmtj2r2tR6bhonlI6piO4z6ph7LPZR8L/Gk0lmKjI5B9O7HMcNMna5YI8b2Tp5DL2NQ4wguWG4IFvf8XRsPidpTJycNzGyYzIzmSOYtUHBuyB4Q32pFrd7dLfg47n/uVzWllh/0PHk1PXZLrqmmYwpyOZz0mZwEORMuidm2hzQ+gW7XsEbM4ysSGjiFTlCW9fcDfXzpjWCAbYz8yCqzzZTwPycv7QHPAKzifWXBDpjgnVaU7z2HEPAz5OOYe5se8Njwu3GOIIY6gtuzYn9Zfr1FY6EmcZ1Bm1M2tbAJmE7QdAiwIO0LN+44Wh+tgYj5ZLONNwbDdcmjHmlZSi+2Zs2KPSO91Z10rWJhMT8jEyfdTk6dAFoOYFNc7j1Ms8rB0Cx1TsO+FYbTyJ0j/MG1og5BXLh3iFOnuvoXI/VSU5UnHm7pxmkDUVS906Oq/Z0eCduj0prn4qCjm0vK3Z1Vjev46lNFk1zWEkCHYtHLCfNLhqHOQhcEdzjOe5d7nIzck7imLXWg4xUp/mcWtpMaA863iFneRAkk5lGWCfntUD2JLPD8RxsYKYNEW7q+GU8Ysdr0Dfas3fQzLVpgtpQs738lPK9tLS09EXWOy+d7/66d/F/+hd+5Zt9KEtLS0tLS289bRvz/V/FK7/pN2OPj2w/9ZO0T3yCy9/80c946O3rP8p4//vJ1jm+/uvfls7epaWlpaWlz1X58EDywGv/kKKZz5+jX5DcuX39R4tjLC0tvRX14gHg+w2pkfOoz00QsZ12WqebXJDj5Lzu5aBNrDozOWNsQVGjGVgepO36N8UZg3pGPXN2lRZAOuGbWzlPK17VUgA3TSDUvDoqHf1vEuYF0IJw9eDee30zsL5XL+cJLHW8GYk6duXSxBT5mnMIMDmoI1jRvGLQ53GboMkJCdvDk0vQNvV7to7NIVAcWXC3oDWTpM6lYHlWd/AJ06HGzTUeiSsGlimYfUKkM87Zi5fZ4Izk5oxzPh3Oc5KeQJc7MA/FxZ5OaPOnLs/zUGNgfVdkLQWd73C6Yq+9aRy9Q7OaR6bYcORanRW9G3EIet5uZAq2kVlOX0F6Zp1Xmvqc5yPkjm0PGE2QuRktobUk2wMjk5iTqOsn2NZIbrQ52O2MB261c8EFk9uzGnIRrJ5OxGQatO0BO6OpbSfbeIJ5JLltYJ0YN3XGuqKG1QUcEM/lIs0kx1VJ0zHYmrH5hdY2jVc5mHW8GvdgwkzcNsIC4lUsNuDA7RnkwNzYWpOj1R3PxJth2yZoma5497npunsXGHWHbSPnlXHciMszWhqtJVu7MO3GNZwZxhFXfO5E6zT3ey/wGYNspjWRKRDppn7agbjtkbNcwom1jZYTbx1rmkvRmgzrCRaTOabijBMYmlOtdyKDGUEnZfTNgr8G5JTjla552jtnMv2TSx/FMNe6OKPYrXcN/Rx1H5ID31wAVpsbam3GuemDe1855kROQdO2ac4PZ1pt1Bivgvf7mrXekem4HOizPm47tu34uaYwMpKZ1ZtsTlhTB+94BN+14WYqmj9rDBSaoJ524jlntPrM0GaLTD1v3BSNf99FYtAfsHhFh7P1cvhCu7yDbJvc7a7xkKl502YMXpdWsLS0tLS0tLS0tLT0pigfHrj9A98EETx+x2dunMrWXgh379LS0tLS0hei+8/RpaWlt6VePAAsSqkIZe/FgxPjdIMi+JhXMT539QObAELgirY942YzK4m4ik7bQwHLEymmOlzNMdPHSfUAF/i0TIwuoJgVoZxWfyqq2fa7izJSXaLmDROGYIL6VimoPINsdncqmjcybzUIURGmLqdsRLl4kfM1HfKm97+7l6kuYMOCJ2co5WbuBjPJedM4OJxR0orBpgAUgstDzmVoJAVR7Xx/vXZWV29WfPX9/Spq20DQtDXuHbwnmD+7hQvcKD72AnETIMwTjAtMu3XBIgbYhve9GLNXR/TJsQv8zqnxyymW+foe3/O6Y6R3XTf36lPteo8op3UTKM+46mltAwK3BqiLVu7sifUN705zzbtIOcw1lmd3auBHsvcLeBP0nL16cjcgie092HHUcwSADTmlm3dwY8yh51bXcuYhsOYXsB3mYGZUpLg2OUQGySDmhOMKj4/w7CUcw9tG753mO9bVn2yZRA6sNVpF/EYMxn1enhHaHfNnguZxwywU315ucN8uqq1N8AzCGhHJTMPle66NG42eQWw7MW/EuDH3C607Ho1+c45UR+xxa2xmtK6NCLOufTu7jvuuGOQIPIxAbl6PWTHWTvisuOiG+Y45NMuKk37qkc5jYNafesFbg96guzp1Y2pziO16HMhNa5C+we2otTN1OUf1hDvYvuPVuxwRcm674fG6DQ/lrK8Cb83xUfczQ8+h+rZrvUWea29gzcljllN4yunuTffFcdPaRuCUSMW1F3w1gr4pBjtikMcgh7rWMx3aM811l9c5Z9SdVXMtsxFp2Dw44N7nzjjICOLQusqme41lpQR4aA3vF7wbFu9QtPW8yZldzmprD2AKF/fU5plm+rpbaJ4vLS0tLS0tLS0tLb35cn/q711aWlpaWlpaWlpaegEBMOXOtVSf7+mOdCfiRiv4G7SCjCHnZyHfIJlhdE+5U6m45qhY0ixX6qzY1gBQXy+tYVOdk3mCWtt0DK4Y4fR2MjmwWSCsjjFc4DYbeVzlxvO9oochbFMvabugztVy+zJJU+wuMTE2rDe5b7O6P3uB3nml7HNwIuypeGXLUJy1Gxkuh+7dGUy9j8C1YeWSrG7fhMxWY02BXsFWOZWnABO93NCHzv3u9j0JdK+vX/W+VrAQxzzLyVzAmgat3MuG4HSErtOYyCEp+KwzPWOdEWTndBtv970Ben6raysIF16wnqmo4oLEcjA2uWG7CwS3pkjs26PG3LuMy7MJlssCKki2GV7RwfQNP0+EYEQS4wYzME+cRtwE371Rrs5NzspWx2eak26NpJHjWk5IRXx7axxYjY2cnGGTYJAk0y66NkeNIwPsAnkUnHcyrqRfdM16p1ly6Tt967RuFZWteZcxYG9YdvJ4rO5kw60Rp9O7v6w5nHkH/C2turj9yS1/Rn5TTN03AmMWZGwV8W7e6XbjYDBu6u1tlx1v0JqzR+MWgzEOjm2nFQQ957747Q7M+6vjDWPQp8ZsuKK+bzHobnQMz0GGE16JAFlgOgRLfWv4zJqvtcEhQ5HR993qiu1WlHLtc4gsZzKcfdNm0OZB9k093qOcvwa+N8yaQGsGxIHtBU1nkiOgPxNwbu3u3o1D8elGwgSjy11tJsDdjJznmgzITpphPhS97juWiTfHKhXBmnMGIYgHN4LjHgGOd5iHzptB+l7uZMWBhzkzpqbGGES7kFObR+LxCuM52S56nesjftmVtjAPoOn9TVtktsszCMWJWwau7HUlHTBp/qAI+30nb6/hve7929u722RpaWlpaWlpaWlpaWlpaWlpaWlpaekrUy8gAA65Ys/eX9sEdguCZbloT5edADDMLLeoIVjMFJPMQcbGxPAImsdTL2QOTohqeYNUdK5RUc8Z9THljk0BGd8qGrl6eplYDtwUa+rmzNaxOeVky4rCPSFp3ARppyCVTMAGWZfbJ9AFk0PQKfMQ/HHDyw2cVNwzQ65m3wRREwRiBSPBy62rzlSiYrBzVs+oleNaDmeLsyv3WjGx+z02+exJltu4OoYpJzEC5d6MPFqBwUMx117xzG2TM/Hss83G6+O61SFs0CpiuTWyIKGRclTGrSBzEzQ/Y3LP/tLjBn0jQvHOVv3FiuHWuHNm8faic0eBrF6AfHuoOF6eoHQdt2XDLfF+wfNWcb1ylUcm43YTkKseWEwdy2dPM2bq+W0PmutxrfM5Ya9X7Hev7zWyP5AzK4ZcburIAo0oOlz90ceTUxTNzUaS1uRC902N1vac9tK72CzZLGnWoUY6I9TtOxEo9xpXAkt1sWrRUMdc3c01ztY3miVtc3xzvJ3nBmfk+mkynTnwcM0k0zm0/YHdknicxBgMT7a+kV3vkUdyTeM2B30cdHe5WzGgk2NgLQtwNo33lKveM2mZDIJ5XMl+IZuxOVgEcUw5YS1gDKxrA4SixsHSsWZPc8IKutaGkbNTOkeUQd8F/2vN0ysyfToWrTab1PBtWzFydVPHvNWcCZrF06YVUEcxQN8EStl0vtdDGz/Kha61ftqNy6V/5nm7YW0HbvfL6RGaNjmwkdB3zQUzZprirB1iPt43WEDCMLCrYtUTBk6GETHJQ7Hq5I0cNVfGFS7vVFyzQd6G7tcxFameQ+5fS3Zvcvv2pg0N89DmBG+YG456s33bybiVq3pi44YtALy0tLS0tLS0tLS0tLS0tLS0tLS0tPQW1AsHgJupibZVVK9Vv25mCvJU3HBiMhcWfpE7VT2YVj2xSji+CJSd3y4XnlyuAr12dydauYXP7xWzQpCNlJtOkKYAy8y7Azmt4p+zwDQD84dyzxZYK8gF5fRDfaVysw0dy5waCCsQFVPRqAZ3O6EXQKKcl1lxvNYhB26TMBMkLTAnV2id/DiKl6vv2PpeEDqewGc+019xqz7dgqze7o7jPGGSCdAbAt7WLwJRdVz6esGytsmdHdWLC9WvW669lMMSv+gC5CCiAG45wm1WpHTfBbsOdfV6N3WtgkCdORl5H3swObDvcbpA38jwAsNPXz4jrYGKWtZ8NJzmQWuG2UPB2NCfOYks26Q5T6Zgk6v7GHgatNOdDvCg2GWbcLvVnHN828jRNPduz8k0mrXauNBJg8jJVpsOnq5LzVc0XxLNBY/kiAtmk9bew97V0+ut5sQ5xmXqxh6qA7misIfgdBByxmbI4RuAGc0cb8bentE3w9w0jU+z+tAGAZo2YwgoJrMFzR2zndPZvG0PjLhxm53whNbp1sgYbCFH+HGbHHnFLxttv1QFsrp1cwhuZrnvrQkUkjeisgGCC3EMNV/nhnsvV+vQdeu71vvZV22m40ebHDS+gv73pIGKXo6oJIG+42cn8DjAg0zTRogz1nkqhtkinwLKCziz75DJHNrk0Ny1FqYRps0yNifprvtdb+RR83bbtSFk6PiyXci4CZ5a6P1ax/dn5O05vu3lYofIRsyJHQfpRpgzRtTpJWkX5vFJrF/kBhc+1vdNc0Vx05DHDfYHnb/Vveuld2Gta2OHO2zvgjEUn943Gg33hs1rubyVOmCbge8qf68NHs3AxsQ2k+vbNy2//SWti6WlpaWlpaWlpaWlpaWlpaWlpaWlpaW3mF44ADzLGijkkmQccm1ieE6sXI2YkwROA8qVOg+8bUV+GxbV11ndsWZ7fSzgamc0MQUVIhWJyukLLkBj5TI1I3OT49FPF6TgsUCn3LaWQ2Dam5ylrdyVSYHUEOxzx1ydslYAlC5wZmYClu4wbnLiRlR8q+KolYvrT+7Ls9MXSGsIjx9kAdhM9Q0LVJXjNQ/oFwHaiILAVjy5zi0mZ4Qy1rCsfk6cjEG2csAWvbIMuTHTdNw5ngC2eR0PmAdeYye0dAhU5UGVuhYjO4lkuXBPquqbjnlMonfcK1o6T+ezEZbEuNLarvM16jomjFmXLCt6NgpyFljHIA85XrtjEbTWaG60NKap6VQvNck5yRmY77C9pM7S0w2ah1yb0TQPZkBPjXHXHDVz2B9wJhmoO3XrgrDtQUnR84a3l2DbiHGQGbhNOTmj5mwrJ7BGl8gkc8fiOXuTA7XvjZYu4GcNLIhRmxnyEPyNG4mpM3dWjLRfyPmcI9ShbKFoZjejkWz7RR227vi2ywAdU874fcPMmBG4NyKTGMEkOLyxZdIs5UxP0zjHgIA5g+bQtgciHukzmDaIcXD0rWB9Mtzpta7TDLO8R51HTNx3No9aP5MZTsRk2KBZe+pVzuocrzVhHX0c6B7SvcBsufJ9q15szSv6XtcUYGqzh9u9+hqs3LAhB3nrpyn3tEbL0Z+DnEHQaFvHTHfAGNq4wdltHdVhTb33uGnDBo3MA+sX9R9vGxxJEljbsfN/fbvPmSTIyzPSO2MGcXvOjGBGkO71dyPZyE9/kvR+B7twxoPXhpPUfLS8yZV9O7D93VhXdL7Z0329bS9hlriF9rIw8Xb2YB/1A+KGtQtsz2C8hsUVbMMvL2FNIB+SnBPvuzaZLC0tLS0tLS0tLS0tLS0tLS0tLS0tLb3F9MIB4BZG2GmOK/DorgThNIK4cy5oRI4nm67XL//Ra7Q0Mg7F7vpTfLSAl9xqchjPApqKkBV8kFtQjkS9F6jfklbgkYK0UbGvUcZi8/oAQcCCGQKYkzAwQu60KVhFvwismnp0jYpNjTrXOfSabgU5n2BUM8CbRqZNcqCOzrOb1IzMrmM/u3HLIaoq3RQALuu05YHRKmZ1KxpfYc9lnDXvGs8Czdwjlfu9BxRvWDYSF4TygscxSSa2dcHlUcSfTec4r5hlObsF/8gD2gXLOqc51NsbQ47B0+k3p65JaxWte4B3gqSbF9OTSzfLTZtRtnHfIV4TwGtbuT0dbGIkjcCt4QWII1OvNeWQtG5YP2Oxwawx0TjlMbARWCtIba0geG1ocB1/d8PC5OQ0+cjN5SzPUL+ttZ1srSqtU/Csl0/9EFzNbWfOA5sDmVFvmDnunc0Ma02OV9NY5tnhe99EcNO558FMCui9Jhib0LyDJz1TDkyDtj/DLxdUcZy1wQK8O5Gim44RNiuuuSKK2wOkEUwNOQ4WbM2IYQwmM2EzObwjNnVmz4NZ0dZtTi2NNCaTbl3dtrZXTLPc2OmOhdGrDztMjvRwTcNtXLG+Y+ZEBn5uhFB+di0Yr00qtaYyyBC0ZCIAW3OQqA5vUps7zrDwiJoHLkd8co8qt0z6w8tyR6ec+t4unJH1FiEXOZNsXg7rqJ5v7vHPNg/YNkjd47wpNjovF/L2qP7q2qhhbSfozEwiBnFVz+/U7YERwTxjzWMKEqeRdtG9MR6rN/tB88cTtk09ya3mOpPcHNu87g9Ow+itCWzf+66tuqYTuzzgEcTtNd2Luu5H5oZf3oGTNDM5qHNgMZW2nihG+oyuX1paWlpaWlpaWlpaWlpaWlpaWlpaWnoL6YUDwGGm7ttCbu5nrLABR3FVV2Ry73Lqzbi7T7McpG7lpAXCmiDISS8B0rGY4IolzRPNRAjgeJJschGnoK9lL0A2FePquzpas4MJQASO2U2vD7BfiOOqY3aBJ6BAbyuncUGiADJpLSE23BUt3PZnTAs5BmcUNCxoaQV7XOeT6ffYaDkCT0jVC+JVB+gJpazcouZgo4D7pTp/6/WZchMa9doIGqUJtA6BFmuboodjCsgYFY8NpBzTWbDep+FjYr0T7ewiPoH/Vg7liju2KAAGwdDXu84hm59IDTIUF2xNkblNLvAk5OIGuTLR28XmMDQ+qm41AcOuxzADNqdZU7xxN7mMUfvynCEHMDXW1cvaqs94eLvDMjksp4B/L4AVgxZRDmDFW5v3ezy0u9Mi8a0Ds+bQhRM+s+k1vZWtNENxw03wfs4gRxKpuG4Htl6dvLYJjJqRvpPjuZyc89D8nzfOeG5H75/+EqTR3djajnlgBRZtf4nNHbNyMpthmQoFTqOXWxXOLu+CmftObT9QRW0mnorGdjMuWyOPZB4H0zrNFa/tHOotbo15uzLDaLERPaF3whQVz3iUW/qMHh7njgo5lpNq+Q6YJpe/gqgDT0FiJvimzQMnqI9zb0Dvin+OUIR4KvqbKdf7/f6lRY+d5cfVV259O033Wo6Ziih3FCMdidd7qEdXLuM0h2Gc3vn7jhPzWs9n17Iem7UJ5gTQs21kjNpMY0RoswGZzEiOQ67fQN8jYI4gXvs01jc8n5dD/aLr7bvWZatzJ2EOfHuGbdr0YNPoz3YyE6fjODYfaeHQn9UmlLoTN/U3uyk63XhGjhs+jprvDbOOVxS95VG9zLpn+cMzGNWXvbS0tLS0tLS0tLS0tLS0tLS0tLS0tPQW0wsHgK3gTGYIsjDJ1uVUs4pdTgO7yi2Wd/ILQKuIZcUX16s1I0NgQeDFgBtZMMciZe6TtUzu0gJUp1v4dPwCckuewPI8bpObUi7eDbNBngfVd7kfzVAGrPpe8w7/UEest4qrdTlkhxyncbqQvVypZyy1tTs0zTEEBTOx1srdemDZC25DhgnyumKyTX7AOm+NPvtFXz9mDVVibasxOd9Xz/He8UyideSKNEEp34CjEqM75k5mAbS40eyGbxuWTrqRoVjizBDY967hv/cyX2pWjKee1QRiqELXFfkc5gSTVhDSMWY4tNTj5hAor2hkInU5BgLDvb1uXG80e1CPLV0g0+QCnzEZGDEnOYagbTe6CVqSjZlJRjKvctL6JninA5Fr10/ohZWrseZ3XZdWvddn9Laba85gWEbx/SYHZCZhcg5HJiPU+xs0zIJmRus71hK2vVyj1XtsDfyCzSBPh2Vr5BwYnV5rKu2CMbHtJdw67lPvX/O2wz2WWLAT5NKfBclRPDRGWBcrjIBDvv6GMzHSglZR5u5G90bkjWOqe9ZxwjtcOh7BCLgetWGgQ09jxiRINmtAx2ZWZ+zpgFcncHd9fcxkutz7bcqtPhNtEumde5/zGTdwAlsH53STF+jUgYNrY0ZGbSgwryed8HdHszzL+Yyc5waRQ5sXMuXUztrIMAv8jwl7x1z0OOcVzySt4HCTs9zyTETYyHHVJo0Iwg3aTt4GGY/McM3ZDIHeecgdHZM5tfnDsxHWYc5y9HY4brCVa3zbYT5iEXXbvOBn7PwctO0Z3dD558SZ+OWdguJzQH/QGp+P+NyhXNtmhvddADtC0fGmzRVOw9zIUddleyDHlZxRY/bC/QhdWlpaWlpaWlpaWlpaWlpaWlpaWlr6CtAL99vryIlnOVzTmAXHIDBSrtPqr2yZjCj3r6dcmFEOxEjFPhOQg25WAAZFtuYZdxwVmSpQqc7PLOBTUCHzKfrVrEy3Q8CHBjbvfZdyEVcIsJ39w01uN+rLJhdi5sCGIHJaRyWv1dXbOjMKFKFuWncnW3V9duB2hTCygZkgXM4rFpRLeNcxhSJSFQVd4PrsIvauMai4Zx1PlHt2QBxYuyiq+XRIV/yvkl6NZkZmI+a8w9ksWC5mVnHAbhV3O+7vF6MgviXWnLSOzQn+QOYNgF7OxIhZXciz3IzqrM3UeBqKFh4TujsxFUMtIEn1QU+yelsbpi7X7uRM3HUuYeD2TFy5lGlEDkaB3chZcwb13ZrVVZZXN3MSoS0AllNu4nMsXDDYzjjpDKIBJI1kItexYr7jKVK4piYGNuUQ9qa1AQJlM5NjBuMI5jyIqIhmd6ypc9rSKhZ9A4w8ruDlmG9NrvaZ9TXFqlvUMVjD9k5LozX1Lvt9kELz359iwbO6eaM+j0y5Os91nbruxGTEoPft7jyWS3nStxvDN2KiWGusxtMYYxKhmOrHSDyb0rwrTn2a08asOaIob7lM5Yg2qw0hrXqcCYY7Leuesl10fIT2P0TNJXtdnLPV515d2Dm07DEypmLH58G5oaG2kZyLQzzU0D0HxUlndW+n1WvU5og8B3tTr/gZd5/ZmZuR47H2xBgxB15R1mEbMQZcr9CciCDNiTTy+pzAyaOgL+ixDALDtpdpDMyN7fISOQ7seFTSgfe6Nxt4CGr3LnfutrFl9bi3C1t1U0dOjf/2Mr5ttYklsXEDC8wfdP36peZI4POQi766lZlXqO5xzyAtBOGjjiGDiAPrD5/xM2ZpaWlpaWlpaWlpaWlpaWlpaWlpaWnpzdYLB4CxXpDIBW9pd9fuTKHUZiEIaCazZDnr7s47KKNeFpcN1KvLHcrejcDlFBacKQgMBUuPp/enMJbV55kIKHl11TawILIcnXMW2CmoQxLWaFjF3xpm7fQTQsoxK/A71bXZHcaNNEGxjLi7fi0d6xdOHkUKCEInZpSrLrgTo6yY5VEdm/Lwyr3orUynp5tazs+0DeIm52zfUfxz1vMLtItuYa3TrDFjVF9qw2zgOQkvN/LcoDfcNsHZmIorPl3NYwhiWwHSVkA+TQ5SUxf06aI9Z4iid4P0XjCWO0RsfoLofLpuvfpCT05vXed89qRaU1wwCEymOpVHhPqCKTDbsgDUoNlOIxhUZ20EcUw4jjv4pu1YHBgD88uJDwnXxgJPq7GvCewmRzJWGwlO57lc3ooNj9qQEIQ1xhzMxxvzuII3mpfz11GUbr5uHOZN89sMphV8c8gNTPDM0sutCk4qYtqCbjp6OxfS3d2u8Tx7tRP1RjeSORXLHX7aZ+WYjpTTld6ZJC0LIpsii93hQnJlMq+PpKc2gNyO+7zJ1vCpDRVpyfBUbHcGmaE9BrapM7lRa+Mc0qCHHNVhnRnBwOldLtQseK+w8YTm+GkIBzKTIKp7VhsFcgZG0z3IjJxNDtzetTZ9U9R1QfKYhm26BgnkmERoo0vOIIbmUdLgmIX8FQs+T0g85eiOmMAhZ/8cRIbOvV43b5/GrBGt1z3IietrepwrbjoM9OMnaXlg+1Yu9IT9os0iea0Yfm1m0GV7wLvc6+6dxiS94b6X419ufG8vKwadoXvq9kzd397xftH9zpI41OFu+4PSCMja1LFh44ZdFPOOWaVeK/Yad6zv9P7i/QhdWlpaWlpaWlpaWlpaWlpaWlpaWlp66+vF++11wtlrq8/lvI36Zqagq6JmEXTMCfOG+V4vIsdeZAr8IMAXJveoMXHfOXs4i+AKkpalT87FQPnArT7XSysyuomj1TEngrqeim9174zbAI6CJFbw9LRwyvaniN9yLLcQr52B5ah82f7Ux+q9OoCtQIqigA2HqQ5SNxcjpuBvTjmLz05g3wRRSKx3cg6y7xBdTtVyP0NFSbPJ2Vkv19wLghYOy4FbV9Rsa9gY6pjNQaTch3f3q+dpeixgTvV0Zr2momGF+VE0dHlq0+R+PoFjlqvYqGvWN0UDZwIDY6e1Lvifhrsp8tv2cpI3OG402xSj2x8KzNcUmIrczQxGwpyTnIK/cr06OHTrlQSezFSHbYQTM8jbc7lEC6ybDRg157YDb+XMTrl7o8beY1JXVd7KmOVGPrOvz3kD6Y2IYJozxmAcB+N4BDrNOrY57qhrtq48Y5C9YoVnPl1zh5jlprYHOcmV+F3QznFXdLifc/h0nqeunfml6qVT4A8EqQnYdm2EOGOsXY5py2CcPcjlFvas/lZrmO00v3FJ59o7E0WnewePRosHLqGIcrdZ699qM4bmSyCwb91pdgLOAuhXrdfdBO/HmBzeFVsc2lihKx/Agdumt5j3lVQ0WJ3QEUDrRCQ2DqJtir32jQzdvZhT4DYHaY10Uz+xydcbRzDd4Rhw3JiWuO/EvAryEqrmtklmuflnKPUgDnIectubwfURb4lttZmg7brOBZXNtRGjzVA6Qeu0h5d0T8qE62vYccXaBWu1SeGyy0k/p9zllph3euoe5CZHvpy+OxZ232TS9peqU1yQvGWS3aBf8LZVbHW559uOcWBxA3u498Nbv5DuxO25wPT2UCkIcnXHfMTaw33tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLb2V9MIBYDe1ooaniKP7k/vRKm70iSJWfKigxZkcGzbleMtRbt9BtE0xvybnrZo3KZBcMdLUW52p0/U9K2cjkeVu7ffHk4ppFaEuOHdS5S5g6af71BHAthCU5nyOHGuZhs28x726b3jfmDg5Bp6DNHlfCWOm470pXrcga5BkU6SxnfHH5tCQU7cieq3gZpaDV5RP7jyLVDdvjXt69RSfnZye0DYCOS8xDYe5waULMG4X2ghmuXPxTrNJRid7VI+rk1cUB40/uT7LIU31xp5Xo53dyelyTFuTK9q3+/XwOt9mYOYIB6p/Wa9Sr2sdLhcsotyrclfPuMlkixMxmYSAZcUmW1Mf6W7VsWoO7QFojLgRATEfiRhE6zR3HV8ojtouG9aclpPI0NyYR51jk0NWWcPMYpleIPScmOZdgD/U0zxwxgzG48Ft3MA63Tc5msfAtgc6Xj2+yWRAtIK2WRHDE6bWV6bRLnKuNwzriXtTLHK5vxWR3nXtzlTktmlsU5sa1P0c9zXsrvdMrwjwTCIhunpl07SjYtS17IDlJC2wtuHcaHRiTPUTO/St6xqwawzTGFkbBjK0doBGEHmjzY1Zc7aZgqBtf0mu2Rz0oQ7qEZPBVKxxnveIRrSGzSvkTrau7u15kNvOCZSjnNl577U+GFMO9ZhaZ5k3mNqkIoe8ax6EMLPijEddmwPsAvFc67Sc/ZkGe8Pi0Jg3wBLzXRsjGlojz16u9Pspl/l2IcdzoNE8yXbBfYPrawLR1uS+d+Sk3XdsDsiJt6atH2Zy7faj3OuhjTX9IrDvSktoONYrij8Cz473VvcWx63useXQdnQO+l/DWsD+bhiPus5GudSH+qrN8O1CI6paOckIWr8IDJ+JDktLS0tLS0tLS0tLS0tLS0tLS0tLS0tvIb1wAFhKOdoQ5EgzwkzQzKjeVIE8u/f6QlRE8Iyz+/JOphSVbFltlSJMYVNxs8jJmPW3XLMNC0FSLHHrFUMsuJWUwXYcAqzlKFSzrMC0QNlG2I27y9dULWoMFHetCFqs6Rt5ep1LUaB768QZkRxnJPAkhvpTFYta0bDD7+DUy8kYWLl0TwdtHa+l3Mt5OjDLKTsrVrVg1hnzm4Y6epvjxyCsEVaRxOXKneUItmaKkkbwNOYku2Phd9hH6/drjXXB5RNKIzff6fbVIddYa/ChIoKNgrSAtX53CvdqqD1ToZ/AfkFVF/w9nZxpxqhOZBkg9bqWB+aJbxvdNW6WXleq3OaoGzgyyCPlZmxN79127h2wWZHQOYjqqr4rBFeZFXld0d6ZoT7egmOkERjHPJg0xnFjHjewRt82WlP8sfVneGuCxefGBK/byrnbofcCfHaHZmap1wCab0+A3xKy1SYKapy9HMn3XRGCvjH1FXdBexRN7hVbrG9VDDZyQee8Ee2CPMMmh2qNi5mzuTq7x/VKuDEfdjZTxLQ2YzQSI9KIhBGT3hppSY9kFhK2Ltdt80q/no6HQ99okcyYzDAwbWDobav56WTvREXDR3MiO3mr7l3f1CkdU47waXBcyZzMWU5pr8jmY8K+k/NRQzcK7u5d6/x26Jy2DYuKn3fIeQVGAV5TFHnbapOJkgJse9B1yFprMen9glZKYjwjc9K6M73hJHl5CUvDTH3j6q4eWN9ovmM59cdadYwb+EOtczmY+9ZrDcoZbucaOzul20NdWQW4W+q6ujfSrKK1GzmrR/tMZPCdnK+pW9g6eBPM7vvdqe55zunE2oM+nrdf7AfM0tLS0tLS0tLS0tLS0tLS0tLS0tLS0puqFw4AJydsLPdqDCIG2XZyTNq+A4rqxSr6tOKUswCmPJ9ZjmDX11KxxUrbDdwUZAujYkIFm3IWrK0IYGwv0OiCzV69tGdk8d0ufDrNZnWgHvqaJdhFbk8z1brSC4BWzHPBaZBj0GRzIzOZ85AbD4d2AQ84Djn1AOJ46jGuPlSsl9NXnZp3yFgdvjlnRbYa1jZy3qontSvi1RvMoXHOLsdyHhrfUY7LBNpWTuIaggxBO06Ae7pBO/im63C7QetyL57Q8IRJPEFIuR4LmL1+3HODPBSdHAM3KwdyjXWenc7nn6io6NPxHXrNE4aWA3FmRT7TNLvm0Pzqm1zj2WgmUGZZUdb38xQAjUjm7RGmuk11HQTMMLAxdB6O5px1OZnjdCD7fYOBzveQczkgrNGp8agw4pnGkcZ8vHHMK2TSLxvNDdU693LOQjDxrmhqXd9aZ0eoe3ZrEOVY3zvNaoOFqyf2dGVrmlZsNYm1C0+jUc7ttLur3lwu7RPzatOB09tOeYmJGBxu5E1R5zkPZjrWGoRpnnKUIbWzWRDemd6ImQzX1VXnLPSKxT5GKCLbGo3aSJBT/byZ1f1cvvBzKla094XkOpN5u5H7MzmgzzmTxsyUE5nUPSWqs5YBz7URYI5Qn/X1ChtaBxX3nTE15lQkuDv4wLYL1k2R6Ls2jfRtq/h0x/JGbO/i7OBOErdGzhvu54aXLJfvqH0nCc2xGJhvgv19A3vA4grz0NepBADzcvlueD9vx5uALhOfQ+5p17x213Wxvcklb4AVsD6TG2xCe7hHlzMGRGJ9UzR33oBN5zDnfe26q4c4WwN/lzYQ3OdV1hyrGRgDtgesJX462+/35aWlpaWlpaWlpaWlpaWlpaWlpaWlpaW3jl48AJxVNisSgXnSUg5ec8fyBHn5Czp4BZzK9VuAUsmzggTpwAhB3hN25umQA+OAqKhjK0cq9Rgox2gn5+SMPAYKho3zE7kCuRUgFvDKqehUdc8iGKI8aO5R1GFPcc2m98wYcsbNQ447F4SJbcfObt0oF2q5jOXOQ4A1ncxG+iynq47V5oHvGx7qAJ1PbbN63CgwTeKWtCZ39Ewjw9VHWtHYJ4iN10VQO0mcUL0gvrhsI63DuGls6xmKUT4hvRymsJHjPKcULMpbxVGnoKmdXdDz9d5TjY13uSeFDQUfUy7fkaFrgQvYJeQYpCsyWpfSwQ23pGPqVjYEsO72bLkts2Ki1Xfc4PaaGK7N6os1mhu0/T61ZVjWBLbxKOcocQfEeEHtui5uctQGgrlzDNW+Xm8cI8GSbdsFf7sArtk5B3UdM7N6cY9yqjuxA+PA2jPMJrReWybU9Ss0P+8LLY1ybT4tPrmA8w6nzZ4cxor3bnoN7A7hdSVatXuX81V+44KWk3DXUojQ/LeGp9yp296wdOYMRgattQLuSaup15ozTD3Udt+wIWAaYYzbld4a7saMuLuMG45Z0PLGaBuZMGPSmm4zkU7mlZimmPDUpoJZzm2tzdR5usOzBzKOupp1/VPwMwm8K0o6L8/Un0uQFoqW33b1FoPgql+e1jJgvsldXeuVeeV0yE8SS8Wtp3fdk8atouEBJvgFO2ZNu5qnJnd9wwSGTW567w23ju8XOLQ5wXsRYsCaY9tLArE4rauDO+aVbLvu33HOicAuz+Q0BjgSY1TiQ90btwdBa4yc6pBubcOY5Dg4kxa8bxrzM4L83LQQo5zQS0tLS0tLS0tLS0tLS0tLS0tLS0tLS28tvXAA2G0jYpRvK9WFiYDjSc4E/SAtFD+c5QJV1i6QcrhipFv5JYGm6OO0zoygmVx7XpHIRtx7TM+Y3XtUMXKR0lpBXJMT9Yyoziy3Z5aDNdQXTOJucqSZHnvvMPYLIoEF1opiZgqO0ZwoR11aggWeZ3cpZDPoBXPmFesVmT0qHnY6OQ8BlTjkibWAreNRkLQ5bo2ZATblctwr3nZWTGtzIhQxm3W8AOmCqkEoeTU0ztbkzktDDtl2EYw9Xae+6/lRYBsK6NaYuaDhvdvXqPc8gbyRFnLP2lZQ9nRyvs5ZXAOaBSkzkjBFA0cemJ39sUa4EzEEjsywLhDaqN7ou0O3AH0IimnvwWREErdDDvJtk1vTHVzwNGyD5nB7rnFpG9OgmUO/aCOBU87rpGGCdmjezHIuJ8acg2MGcUyOcWBm9N5ol0a3LhdlpCKj69pEOSppXVHDlqh7tWNbq55kxUx7zUP3VkNocpDm1HWxrOGuTQM1ytRaVIT41KaJNGq5ak1xrtFaUwUivXWt0yhnqHcqfVtxyAWPZznpe3+A46ZO4+sjc3uJRG55T0Hq1hoW6hlO+f7JgO6BV1LAzEkLRcenN9q2K6p8ONu2QyYjJ2NCyyyoOWEGGck83eOmUOMypL8uyQCaG2m73Pqni/jyrOasXLXZrfrJT0R8kN6xtit6e95IV6T43VUb6jhXPfRWm12sIPCgtV793jXH26Z+4uOArjXjZvj+EhaPtO1S6QMN6DiDllMO7O40N83pZtAaGddy1+d9nVozaA/EnLo3zxCg7l73uerf7s8oqq1I+O1lJRGYQH/drGr+yqHs9/jpjZwD5oHve21GSKwrrUHd1oN8eGl1AH+FSXH/9tkf+CV+ja9krTF841pj+Ma1xvCNa43h0uerNWfeuNYYvnGtMXzjWmP4xrXGcOkL0Zo3b1xrDN+41hi+ca0xfONaY/jl1wsHgNMETmTYmwh2NDlk2wlMIM1JM1qhHavOz3aC37NjEsWZjrjJvUYQVs+nOFianKw2BVuzQV6LPm363rwCruMA5GCVMzaQq1hgUpHCns6IG7RJ2lYx02dE9elGrLNpeo4SiY07ZzshbTkKIQmTo8/T9b5TUFqxuxPYSA6YimB1ss41CuhV/PEIpp8RuHLzEsG0iaW8p95cjt/bQXgjRzmJp5E2SXYsjoJzBluH240ZWS7qTfBvXBXhmrOs01tFt/Y7sM8ccoEWSyJTsOnsYSYFjuOoBzjYpu8b1TUqp2mKmJcjVXHScfpO08kcRKobGRf8IzukHJBugl1nlLgOVtdBTtZyMudBRjAiGMdgVieyW1Ns97iSvZWDUSDOfCvjbOK+6bzaVtfOoHVaDLloDUYoDnha4CEAOocxszHGDbzTcuDbBW+tIsatNipEbWQIvHVGnk5eA/e7c9fTaf66rl8qivqcK1T0M17X69BxJ2TOOwQ2A1pFpltAe1bg/XofQ7uva0H1p9D2WhlnL7HlPTL6dLfOLH+2aX721kkO4vISOQ9iOoftqsU1Bby7aU9BpDaDJK41ao0sSD9zQARt3/BmMmL3Cxw3nQuDMYKrGfscchhjzJj3H2gGuJmisxPY91rjgZkinHF7+uGXkKhfGNv1GOoe4w626xgjsTlJNqx3QXi3ikOvMZpnDDnaNOEN4obRBePzhjYVOO47o8sd3qzc9a3j9gCpSHVzp+3P7hsfcg4sh/p77exlR+vgeE62zj2+/LgRoTmTM9Q9bQZjKHY7D/Adi0HOYLqTTL1mBHStS13yrPMMOcWtw+3QxpOmH405p9IHauOD7h0V8+2diOOz/9BZesvoi/Efhy/6f2CuMXzjWmP4xrXG8I1rjeHS56s1Z9641hi+ca0xfONaY/jGtcZw6QvRmjdvXGsM37jWGL5xrTF841pj+OXXiweATyepVw7uCUstMYuCrpKjLs922mctCj5t5aJNBKuMVvGwWbbC09koM28WyCrnHl0ABsUX4x1o5WZ1gSwr12uC52TEQVovHFyTvLXKfC3nb6qvONLv/cM6kC4nJomxg9/0/BOqmOm9o6KZvb5GKmo4qyOTKMjq9bcRJiexzfO1gBZypN4OQe8mEJmhDt7kyrTG2Z1sFeMs12tXVGtTfG16F7iJo8bNBHdakwuPcvTNgl2zztsROIr6eoF+Mp9gejzFbyvS+lbzwVDUa52zuaKeszymMQXP0oCjAGDI+WvUeVTPbqq/FQ9663Sbcg1aBfam4HLSEKCrm1gq7jpAXbTlxnYLOWkJOZ/D5HSMocPum9y4OWihLtY8I4oraneOg9adaTAzmWOSBvN2I9IYmIYtk80bbb/g3vCpNULfKlFbLlVDPcIeod5WZrksnfPMdLXPDuKaJ5zDXdcjExiap7YVaB/cu57N7+8JlFvb5XTPKdft2dN8zlPk9vVMWhzl1TeynNdQ10svKAdrlhvek+adbRqDVDX3DA6H9EYHjEkjSGtk6pIbxqwxz/Q7oE2MmBPcdU9xweJtKDp94IwIdcye8efHTQ5Zr7jsLHeueW0uScwEyW2r+ObQfMgZZNvBKsIY07lmRReP0FrDFP2cU/NkDuKMD9duGO6f5FGQvsbOeq0nOYuNpLcHyOcFewX927OXIQ5aJta1UcRah+zq201t0gjUIZ61vsyVDBAR5O2qjREVze6eREzSHSfxsxc9Z92KEotZqQwoln8eOpWKlD/dyIq/RhsMOK3hrcBx07126h5u3sjWtBbGAsBLS0tLS0tLS0tLS0tLS0tLS0tLS0tvPb1wANgsSbfXAaLqfHVB06riFWC7g185Pt2yHn/gtnOWqVqEHKtZsbZ0Zg51g5ozIwSXrQCM3QQe3OUSTfSmXkDaTTGwtiHA7Hjb1COK631ImjWiOxmTSPWwzgja2W+c6gWu/5Pbrh0671nguaCtekJvZM6q1T2gNcVYn0Avknvsrmym+msMvfrp/gxFQtO9gIkROe/gKC3l3KNiiU9o7gX4TsQ9jicgE+rvNBM083Aib2U4VafoE6gqMBgJ8boO5V4u4ZycwCqrfzZPuJiUOzcqcjrJiDvIt5TjeRyvFXiNGlm7g1fx4wl9KwfmQbemjlLf5dZOAah7dHHeAMNax5qThy5aWDJvkzlnndqsnmLDel2HNKw/3CG4ulV7ATP18d6BfiT0C2MOsEaE4pbjeE4OY3gjIvAM+mWjxcT7s7vblb5VLG+oXfrsdI35BPQLbls67kZr5QZ2RQVTLnYNQMF0jMpJruvv5YYuIO+9rumsjRZZMP/Q2uFc01afny74DgpnprsagWcObRSw83XhjN221BYLPJlmWs+uTSA5B3mbTDdyQ18XdafLG05mMAtoRxp2FDh1L45dm0NS94OWDdpk8wtWcc+TjTPuum0P6r3OAuNt0yaKun95vwjo1pEIwh73WHpvu4B5gWO7d/Bqk0M6yDF/QvYUss8h93ROOZrxp57jk//mofvhff1pThmz5vnACdrWaHYmCVDXNsl51KaKrg0UM5i3V8vx2/X+tYlC0fOOuaLZtWnkUKSzX3TPnqmUgDlhTtxNrmDL6tt+cjKbOcRQpLQ1Jh1uV8KnjOiueO66VNUrnti24XHDtmfEMevrS0tLS0tLS0tLS0tLS0tLS0tLS0tLS28tvXAAOE9IeXf/Ba16dQG8n3CqXJwnrLKykRHcofHpYvRy49VLE1OuuOrqNcuKfhYQlYP0fP1ejmNBVDuBpwlcibPGHYjpPdQH3KxVLa7AVvO94EarQOJZEAeBztYVg9zkms1yjVo60VzAciZzFPyZSbYgo5Ecd7exeYOszuIMgRxczsuCZ2WVJd3wiqwOj4qPPaHnAdFIa9g8qrvWIDs58u6SzTgw24SFvWKj41ahvmfMryK7M64CnickO8fVNgHlAszFf8vFK/AYIdev+oXr/GjqxyUqXrjcnk0dqXMKbCrpuqKpm8PWaER126rr1kncmiKZ5xW8K97XnRyjpttGxrVAOMzjYEYQx8ALvtlEEPkEqu4Fq6JM0B23pO0VIR5JjqS5DMOZVsd7JSKYx0GkM+nl6IWtN8Eu2+ju5R6nYn1RRy1wd1WH1caKwHzXZgm30wOvNVLu33tXLwL1ZtrEIIoX2gAR5xrcOJfV2TQMBejuXdqjXut1qudBOcz1TrTWiOsgPYiwilt2nhZzfRS6ruemBjOnbzvjdisnb3CQpHd6rbs2D2Y+Qf2oa051bgeCv25y+OIHMyZuO2bqiA6moqhTz3BruHVFboc6lWFW/zLq7B0hh2/191rFoVvv2qAQcprTHGu6xtp4YmQcijY/QWkG+K5bnuzM9bhbfU+9x3gn41DnLrWRRqHxOsdtw/0By4kH0EJrrnrP05EbGph5JcZBphMT5rjiPBLu5fA/n9eJ26vYTN2nzYlIjAMeD7q/hNmh850HRxz49kzx7cdrWH8JmNrc0PT+PiZsF4Hy9oDFDUZidsO8YXnVXLJG5hC6noa7UgmybSwtLS0tLS0tLS0tLS0tLS0tLS0tLS291fTCAWBRNgTgbJcjzltB0vbkFr1H9M4CmydgEmShuOH55RNQZTrjjAFGz80IoslBLBiVd8B7uicFUZCjrG33SF+zTtrTMek4C3ZmFEx2WjM4XcZ2Oh/P+GNTzPDpzpwHirGeZXycmHdslHvSLxA3MoIZA+8GOLYlHILCAqXn+bdyCcrtK5dgh0OdvLM5HAV/s1zE2FM8c98I71iMylBOwaITYKUrcjVmxUgXYkt7ArN4cTw5Bxm3yuMt6JjP1XNcxymwf+P0cI4YdXWTQJG6EbrOSZA5mWiuxHHVuYy8d4XK8QsWiY0Db07zC1EuUp9Z518u3gKnlgGh7labNS/Lsh0JM5KYiTMxd8UWz0CFvZvO8TigBYkLFHriU8A/4iBikFaAmwQ20hoxb8xjVk+qXLONoHfHU1HjrclF7jjtdMtGoNbUM/IcdSynzsktsQha32pdFATO2tiQta7gCZ5bjQupcVXhrK7v6ejOfN3cfv3CO92+J8id98VptT3AEFje/ALeOYa6YmdOOlE9yaEO3HMjw2n/TDlGmzfywYljEMdzZjRyV3zwhhziLQZBY2bFgjfDC37bVL+zbQ3mgcWgtQthTjLoN73fkclxuo8N4MDY1ftsWVA3sW3XGPQNbs+xqXU4qe5gr1h5zo0rilPPUV3ZUQ77c2NDDMVOQ23yODepKIo5c9a9w5+AfNZ6ahcYj4LbreHuWn5Zx5BOzOO+8SSvNyKTNCOyVaz3DXUTH4zxGqD4Z2uNyKmkAzbMksiueWHBuD3X69okj6DlwQzd62wMzAYxExuvYXNgW+N2JN5fwg38OLAsWN+00cTjkWZJpLqujSQLnrs7jBqL/cLS0tLS0tLS0tLS0tLS0tLS0tLS0tLSW00vHAB2kyMS3wvsyOcp0lJu3dyeXMD1mExBqaRiRE+oZY7lqLRowV4LQSdRoAF9l8OxvS5S+g7Fnpyu+viENglZPZPIqedmRMWsAuXI8wKC6hQ2wM7e1DMK1yoeN86YXSM9f0GULFRMM1aRqSC7ade5e8iJuBlz3iAEb4hZ/aIF5iJIgzbV54l1mEZ61/lNQb4sKC0AbjW2do9dpbkemzr2E3xTLmjBeoHENJfrGEFD/WWn3bscpycYP2H7CeFhxhT4nxDbprGgXgM5ZoOstOc6ztOhGilw3ZwWB9YeKrY2gaCXyxufek4zYp4gsxywTP3ZGsbGnINxPGekEzOZt+dszfGoqOztonmFYXMIlLeLzisHOUYBZupa73fXb44bcCXYGOEVx60x7q3RLxtG0v2Cl+v0vmmi5ldm3H3XMeVwxQXt3bs6istBbfa6ru1yhXN34Bc8zHmPjQbKVRy11uy+9UIftbqm88nNXZ3UApgml2xGHbMpStq8MHDSW7v3Np89vB1tfpg5ZbH2B84NIIGVE/f/z94fNUmSJEl64CeiauYRWd29SwDhnvH//xmIgNsjdFdluJuqyD2wqHn20u4tZhI3U4NUbqrOysgINzM1Ncum/oSZk9OM0Z3BoYTxSIZrb3XK0V1nHNa0W3Ni44tww/wkZ+Bp2PlJmNq652h4E+hvMZmgHmHTDiF+SBxYQwxzaCigNYHI68JNO1xufvScZsW+j1G9ylQkd9evmbXWR72TZt13yHhHZVs42UzPSnbt4ToHc+D8hsXAZ+haZmC9k96Yry+50yvOPSJqsCJul69ul6Kbnd+I+YW7QG/W45x2QDc8l/PbmO1Qv29erApnb6ee3+tFuhy819SASUOQ10ZidtHS8KGYfo7EjweeRr6+CqK/sHTslIM4/BQsxvHxXznPt7a2tra2tra2tra2tra2tra2tra2tv4E+uUAsJKbTU5SdyLlqLPW9X/qR4BFAaouGFTRpln9uze+TUUGC9GoPzYT3BTJnK2cuwgQCugsF6xcmQJGXZ9VLj2YcuYFOjcq7jRdTrk6v6CiXX8A05FZtb4Fp+0oeFSOy1bAOIom+QnzS9doZzmPl/sxwY8Ct0Y66j2dCJRFxflaoyx0Ql9+FDRWfy5ZvctzKv41FnMuOBrVBerqGc4FNhcwNPlyMS8un5BPoBUIheTQ96bdfbHJVa5DL2OxOk3NeqVZP4gc5T507Gw6X7cC2QfJqATdo+DXVfd+xS4nbkG3jrWHwJI10v0GUhAVTz0FzhB8N3vDbmvV2xvlbLaDuJ5ETHmUc91Pg+4wTrj+IHtXJG8G1l29vzjjWAMFps+cwu4jO3k9CVvra1hzmkP3cvr2ztkKDq6eY6vfG8RI6Iu5az+mP7CmiGuvoYk152AFYm+AuYq2CzbKzT3rWVj3HDlxbQG2GoRgDUsEuQYcyrVvriji9Yzb6njGbqhcXnZOjEu7StHj5liG4pyz4CR13t6AQaThBt0Vlz4S4hpMGvMwogXdGxVcrMpsjIkAdzvOOxra+ofc3Llc+nWohAPDIxiRXIdx2oGt53cB5n7oMc0s4/uKKVbUsoW6ctOC5fgmLurtp/df/9C7KdSjbFbpBjVEkes9YHrOLLJiphvMl95R7YD5wsxVB90fNdswSFfqQFoyMXJ+EdH0XujnHU2d848C7E3vkAhoH7g1LF41I9P0TusVq58Vhx2J90ODKMcn9vwDHh9kOoyvCg2fmn1pB3FQgwNdYNyNvP4QaG71royg98YcE+IpJ/bjxK/Aj47NQVwv7PMvRMR/76+ara2tra2tra2tra2tra2tra2tra2trX83/XIA2K1hPpnRq2d0dY5O9WNWxLP98DMyki44AjMHTpNrMec7WnkZGy2FhLOTDVigNoLRHKFmK+dscVBzuV2tYzYFJVd8a+vkeN3A4p1+6+ry9C5HHBP3csaaq6PSqr+1emjJcn1WPLW5kzwE4FDXroyZ6j62eYmQNZPpNeI2K2eBPXHEVuBH4E+psur1pLVy85YLFiC8HLY/9LeOAup5laNvOZuX23N1CC8nryJbtRjLUavPTBysERbkFNS2dpDm5XpuRL5qU8BMdRWbOVYx1EEIYIWunzH1Oel4E1ByM4xGc/17OnIFY3Llzpd+xgTpcvVJsyKOy1UbRs4nMeQ4jTRGTCwH9vgQiK9zFdOc0D+hF9y8XoLujw+BwDFgJNldLsuczBjMyIKuDcxwczrQeseORrNGr9hn9SsLTJspwjlj4sdBWJJjEPPCutznLYNOxQOb1+DCe0hB1L+ODTf8vUF1gVs9k4r6FeAvJ/x63vgRui24C8vxnjkEOXPqs/vHG7JqQgO3To/JNbUmrUEzdfXSGh5Z7lSnp0CocL+R+eRoJx6T2WA8LzIbV5tk7zS3u2IcTLHM6djrwnwQ7cBaVCR5MfT5AvwNOk17buZgxOA4TJHPY2DHA29gqUEWs3KGFwBvFmLiIfDqlsy8CCbkVP9wOwTOY+i4/aw4cqo7e+r83N7DGg0sqkscRZ3TDrCO28RqYMbagX1dtVbFVK2R/UPDJ6a+6hUv3frfFM1sV/VaI0d8a9r3/dRgSDy1t3rXM5eKQs9EEfEZ2LdP7YORYB/QLrJ15nipu3vMmqt5EamBAmufzPkHxKWe4OsLRtd14UR7YGHYfOEB/ejQHRtfMAZbW1tbW1tbW1tbW1tbW1tbW1tbW1tbfzb9cgA4c5LzEjRtraBOxdLmD2BpdZtWh6wAmBeyE/aMAsEyzRmKbC6HX1JOwtv2WE5FwS935HDMrHjps1x+8+1yNEFY9fxamSAFjtNdUMX12RGBuzEDOg5cmDVBuOWeJe9uYebUSYaptzddHbsMMqx+rkClI3A5YcHsTMXxZsXkmi9wBJnt/X1MHYsmB2ytZ/HFWvbl0hScjQiweffQ3nHLq/+0ol/XR8gpqNjrLBdkVJx2Zisjqa6lGcz8AZKnAHH6WTxuyokY+npU/C1jYC6X82GNRmgPFZC3FQ0eBUqZWJ7VlRxo8frahAUyy9E7XuVUTDKdSaqTONetMywG1jowCoIPrFdMMUm2cpbPJOdFtkbOJ3nJETyp6POYYIJ/3U+8O26HnN3toJkcrJYoWjtW3/MQrPZWT0DtaT8Ap0WyEr3lsfXbtV02z9pPtadivP99RRq/v8Ky2mu4YrnAqXXkh+c11k9oQEJZ7PLvJnoGgoLurn1iVpHTWaA9Ga6BCLepuF/kPnbrZHXqRgRpcjkbcLjTmmOPZEwjxmDEJI5TqclWUcU4YYNIdXg3g7gSawWA40V7fJAo5juviVVkeFR094yBXeD9oDWroYEUBL4jxRWZbiS0JMKgHer8zVFR56nI5ju2XUMnzHwPhlQ0s55h9RVr0KLWPC5B4KlBB3PXfjwbOUOguXXi9aWBC2tKAFjufKfgcw1cmGPnqQGLcWH9wGnqNW8fgOLp89J+NdM7wVIdxukHFpd2RTv0555678yDdKO1Dw3D9ImW46VYdwzsYPIXrtffYf6O9YPZtD7hitZulli8CIeIiT/+ouGODYC3tra2tra2tra2tra2tra2tra2trb+hPrlAHCQeHvQkAsUkpFyQr7hFPp3X/BngYosdxnljNT3WzsK6Anq+d1zGoXBFiQ+BYRue2B93nI9pqJ2b762+mwJ3BrZ5Pqd1mC54Vzfo3jacua1zg1xMuXq1RkXhO117avnuFyUZ8NmVk/vKKiLLHzeSa5yJ0IiiKgu3/q+FQk8qjvTqC7ail5urRy9Ts7vb6cwjqJog3TT6TCZJmDn5fLMFCT1imbOmVWfnNAeZAxGPolpWDkCMVOEsjtp877XOZ7QPwTYlrt2DrK5ekrppL3kgk7B9Q7QOt1MwH7BMH9UdHVy+0RNscFyNNfarPtdkcWYg58F5i85JZm8rkuwMfT95hSwE0Q1S7Kdgqpm5HzpOOMJGNmcmFHQUvCQvATz2iGISMfzhbcHbgPvn3Q/aAXXsSi4V3vJINJp1ivKOjHvdENANaCZngNFJocGHGLKBbxcuuTbDZyv2ovr++oZ9APLWZXYdW+weg4X+PVaC8UMU/e6Hoo6zvv5EUwfrL7tzPHeryRxXbz6yYHTMrX/rd0OXRK6dyJmDYUkloLm5gdpF3mpKzyu0GFc8dq2hkm8msNnyJU7J5aN5g/MhMyzqS87X4FnctCZQqqY6Vh6kZXL3JtSAGKQrXp5DfVAe2Wy12CGotGPgt/lkiY1ABJDP5uBnZ81IOILret5qXeS0fQ82kvDFea4dWxO7bFI0g/icPJ6QjxR9DngTdAasOaQhnHROOHxF2I88Zy4yWVfb2ytz3nCfGKRlR7/SXrFhnvDj0oyuOF1qgvZTc5lb9CN4/qC84MY6oyOCfM4OHojpgYd0jsjXvfwxkQDIe3jNzJhjC/y9f2fh4a2tra2tra2tra2tra2tra2tra2tra2/iT65QCwHH5N4FTlkivYFTIEgt2o8lM8FdI7I8oMOzGvvktv1furbk2BzuVIrM+Kd2CtnLY6TqZ6c+9uzxSkzQJwtrpPK/ZWXbOCqQ7EHe3M21XLck3OApAFQeJZALRxF7PiZZaMckgK7KRVZHMKkmUVG1sI1sW8dCSLm4wpIrvA4e04Xp2jXqA4y1lp5Awdq4CctYMMda5GJQLfnb/Vp6w47FEgPpgFi1smacbM6hfOLrekXYpoJgvYcYP1QB2jsxydt6M5L+DzB8exYr67Gc3lHra1hgtWWgHSRXlzkumYDe2PWAed5RKvNfGzzmnU9erar+VernvkNhSX63HHZuc1sB6YPaqfV/ss08mYRMoBru7XN+Q3FNPcVkxvOs2Tdjxwdxrq4dX5rv33jk4273dsOSv2HCC99vGydBdYNEXsRsx33/HqvTUEC9fww6Ksufp91/OUpDUi5FhXR3O//4wc7z2dy9H6dgXf3cEFiM26jp9TtyETt0nQYQazB5Zdz5p5Ocddj2HreKzOarn73Q6aBYcNwmCOAQ0ydN3EgTUNHcyp3nHzhEvO2Xbo7eP1zFtm3X/DODAmy/tMekWE173KCSaXeby+dH2tkbPcvxULb66oeOcoNzX62Qy95+r75CR+u76XWzibaT9YOX0JvHU9khl4awLx4yWQ7OrjtjToH+R41kBI7cVIOXa90bxpX7nuUTs6RINxCRrXfnT0rtKgxws7HnUvA67v0E89J6BBkpRr2fzEMIIvbF4F/g2LoD8+gaT3JDKI9qlBhxGM5+/qcreprTUm4Q/yuhhmMJJ5XXICb21tbW1tbW1tbW1tbW1tbW1tbW1tbf3J9MsB4MxG5MCi3IEhF5ityGCzivNtNyAG4RhBOX2PcJYBs2DZgmBe0BbgYsbAXVHTDSctiEiByYJqAOlT4CQKamVZD7M+k1nAawqG0Jn51HnOcg+71/GPckQeYKNAyorg9bqSieHEuGitCSwa5Ljkrl1O5ETO20zwwpz3uuQN+0ir6OvEI4FLEJdxO6ix6ixejkRb8OkqqBfkXNHbcvwu56tVdHZmMhO5huPFTEUPZyzobHICMgUeEWiPguVGggUjvaBW0depvmIbQ/GxgEVyHop7thUnfB4CZRllaPW6XxRUdOhdsbmZb8e0+9u5Wp2tAshZ1wUjBzMKJ389sfYB7ZSzt/kdm2x+wHTw6+6RpZ8Czq/viqemETFwM7kg22cd7lIE8Ol060L2frdSA1HrUq8GUwR5JLDcr7mGJQzPcmybF6t7g1vsB6dzRHVLV/x2lhO9vn+58RUrnm/QXHHhGp5YoB20SgjmmZWLusBvW5HSa23Xc9lJfoieBlrr4I2JnNJxvcgWcHz+s+PYDqy6pNOyuHK73yutnzy88XINJ4CRZkRcEMa0rvhiwGKQNFprBF5u3qRM84KT7kSOMi+3GhR4ktnJqU5nr3cHzfHzQz3mc/4AcHXdMZ5g6riWAVvvFkNR2LkCCOyQS39EhQ8MRT9fdg866PZVc/XxgY8nHh3rhh0fxJzk60uuehVfV3fxC2uuKGjUW271zDgO8SQ4aj82OJy8XnBWV3vKMWxu4CeWNTxjjeyn1jtq4CWbnpsF/itBIcZQdHX/FAQ3E4g28NV1DLouT+L1O5mKn54OvRn5epJ+qp+51Xt2a2tra2tra2tra2tra2tra2tra2tr60+mXxAA678EccohN9vtKqzy04J0CPbGFG4yu0GwnGRHAQlQD+4HGa8CRggOtqbDVN5x+kHYxHNwNwqbySFavbRy2AourQjWH52NRiOZAoKhuGLlpS74XMe/o6gLcprdDk/DywU9iQxFpKYgaGSB7R9AWcalz1tm2pQT0eMF/sGYf2DtvI+zMF3mrKRd1+fzUIRxwc801MlMuTpN4EzwLRRtS1NaslUELylQXBG/MwJHUbvNDbfqTrVGpHpSI0Og3HzVMK8iZlgR303dr+7gDKyfHK1BNryiZO/1qHjtWhx93dvtxpXz8dIwQfJPENTaISgZIbhHVKdyUqgammEt5FYHVrQvfghwuaC3Ne0HwVm5Ta3cu/546Ee9YxYV560/b+YK8zUNP5gv67WgmtzLl9Y6wVpXRLXJodpbv5PMzcDsRNnoC/DpuixTIwc3k604Zu+Cjz84irUvg3vzlNvaaq0jqOdUbmPzQ8eLS3HG3mt/r57gdTwjLcrNG3XOCar91dOQGtEY/YNp0Av+JlnAUfD+7ecHak29dTyG1vn8ZEzh6TmTtCBfT6wNMj4ggqtBa+rntjEIa1g3mILq2kuJzUk3jT+oh1n7bkTCcdKotTGH7hpgIMmZtBqkiByCzNYKyus9Y7ggaQw8pxIFLMtpn7fzNodgLTHkUPYmkH59wfFB9I7PF7SHno/WoZ/1LrX3vgNBYANax88PfV6gfm0Ct3G/ByJNgxRf3zWU0w454FtoiIMasomv2hNym5NPrJ/aB3PUkM1KdfjQezOvdzQ9DuO79mPrFfMPx+OTaBDP70RvdD6xo5MfJzFgXL9zdsXgb21tbW1tbW1tbW1tbW1tbW1tbW1tbf3Z9MsBYONH927idNxWCPSUg9RM8aUOOcv9aA2ul5ycONEalgPPVlBYnaZWACdNMEJQIuoYE0/1WYYdFfxabtCkYAUFNhcYrr5ROjDq8waKB/6mHk5bDmUgjPQBnCzUCxTsDLId2OpCzepdXeBvdaeuc1o9tiJ8dY0LFEZB4IZxYe0hGLa+1xJSUFaOw4HI6xQ894ZcqkkoAJdm0KwTUVAlg2BBazmn8U7Gi3Cdj5krPnpBfVMUcjCJ1TubyyFdgHaB7EycEKQ1E/h1OX2djlnXsqGYaizI66o1khsx51Wfudar3Kvzeu853SC5KXNi88K8EWY1kKC+1hgwh7p67fhQD2tDjkX3twt9zrofBxwmMDYvzBPzT9yNmAXKGnhUJLgl2QSDO3JA+ooazxXNrOvWfadA3lGAVO5rd637HfFttca1LnLRr1mAtafk7l1JwpqHKOB/d19HRZeXG571ufrVCirLBdx1LPLuDPblgM+srut3P6zV8anhhsTA4z4zVUmrD1djIfVyzIpsXqA11/NUEeumveWtw5Tj3JoiydUd28nDyJw0c9oYhDc9HuOl9wgBQykB6a/b7e7nJzkHHlGPtpHji7CTiGT0Rss3W9erpyBwTmiORSsj9tD1Lqe8HdwDIxjuRs5ZzuCKaDcqkcDqPob4eXMYioX2dlT08uR2a7eT+fxd+8C7nMaPh1IXQvfLQ25ib13QeF5wfdWwQuIVw5/HN/V5jwvmk/RHXW29p0wgfXWIg6PbtfrJdT2a7dEwQ4x6rprjrcHxF3J8F8TvD12Da/3NGs2P6nuPGhpwnJMxjbBf7q/Qra2tra2tra2tra2tra2tra2tra2t/wD6Bf/f63JSVlytGxhJmFxwyxWWFW+Mm34fEw5BKSso4nfk7QQ7FKucb2dt5oqCtvJ2Lg+hPLy2Am0Lktp9jhVnWtwQr5jn1WdqfjvbjFOwZ46Ce4OJ07kEkpb7dEUOj1e5Tl8F2QoAesWZxnif7W3dS0VCC8eCGXOtTbkks9YhzASMMgUSbbmIaw1BX4u4AVMmipt1we072jUmbn7DHJpBhOp0PcWsW2DtYI4vnVsURF+w2qp7NHUOzQ2b5TZdjmp3mveCZ4a3VoHICRZY7zCugrUVRRwLVlKu3a54ZVqtyburlrrj2mxd0DgRsMpgYIwxGM+X1qplRR67HJlmgv5e5NR1nZwVqV09ys1qf2O4OxEvPE9xvjuiWYC73ehzWboLpq2BgNqHtzsY9e82L0dygUTB7RXVXXHQKahYK3gPOIhUFoBcOysLHKKOXkHhFT/NDfWWe11x5G+Hvkhqe0dT26xnZmHR9v41K/55weRyzN4PXnNaJiMmo9zR3g6sIpUzpmBihCLTEVTEU33fLWAqMtgztd7jBZYVsw3tOGm9aTAiBfMTyMcBzcmocZLWb7jtBFxfcsV7x3HidTFmwNmx0J5Y4e4yXMtdG64BjbUz8I4SA4D5pXXvHzUTMe73FTXYkBkYrwLeNaQSgxVdbpZyEmfcWzNHYr2To+LEj6ahAf+omy53rta+YtO9Y/0kx4s8D31uGtbPOsdyvteVWFwV1tCgL7DstH7SUSrANQc5vnA/5AI217PfTFHVK1K9kgRifsdyVJJBQd+ud2SrxIjoD5gXx2//Gb+e5OvJ1tbW1tbW1tbW1tbW1tbW1tbW1tbW1p9Nvx4ALp4kyGWsXlY3OXrVkTvvFFq5YYNe0cRgRIOWCEhYlpt3RaeWmzZX1CzvzuBIwuR+XTzs7bZM3p2o6khVrG4U5PtgxaoSCGB4gVE3nYu3AjPlTixXrzpVDeiLxco1GqN+MyGN9K4o4lQ/cN4OUCqimoJl5fSMYPobEreKY04zwa8C41DA09sNdpOsX7VIkYGnuoDjBudUhLQJ3qQxSFpzMpLgEtyxpPWTCPX+5vUscCOw6AVEF9pX1/Kk9wcZoxyt1UFrpk7oij6mgGPWUsjxq4XIFPq9S1TNf+iMpSKfy4ft1NpmQeCnnKZmBK4BBNlQYQywU3tq1mf16nRNx6IcnpfAqp9Gy8ndG40gV2vlIjaASUvTdXrBUuQ2VsyzngF1Oi8374pGF6R1c6wGHeT49AKKUz+znqHVH429BwkKLPuP55jI2W4Vgf0DwH6v6bqPk8hZLmjtxcy6NtNgQLrctFkx6m/4O/RPzDcYLkDrqdbhOaujuOB0RDD6qf1Cq2dlnZ6AY1qoq9oOJq6jtY6e9onNguMB5oLn7uWmrdh3weRTztQ5NQhS56h47Can6jx/GIBQTHjMwTUa+OQwPQeKgdY5ZqsBAirdgIoyB/VKo3dYRt1vcywGdp5KijY5vf3o2Hwyr3Lx+6H9GNqnd4z3nHLk+9RzSRBjCtbSaoBBUdPZ38c2i/rMh56NuDA76jV91f00snW4vms4whq0pr1RiQnWH+AwZzCfX3pWj4d+PirKmoGNq7p9gzTdF/Ou7vAAUnCZaXI3GxUD/h1vHxrDMcdbkr99Y2tra2tra2tra2tra2tra2tra2tra+vPpl8OAKejaGAMjwJIy7FbwDCAVsAqshyGCFREeSchCa+uWpIglDK8OjzhDVBzwbQmSNLWd1Q87urtZRS8MZzV5VsRu7wUA5wCT7fZLwUivbVyzUGzeIM8GSsFPyyXRY/0Q27fvModt86zQHGjQPSKgC1wl/rAhiJpKSQXBsZB8xDwsU+dMwtAVldxxU5HJpkv1LnbsSwQngtJW3XEUsDHCxLLmxuZWDuJOWlN7tNmML3BTHwOvB1kgek0p2WUw9dF8GNiTRHgd2ywn/q1HzCmoPhroOjqWd2/gDciXgKntXMEmJZTu+5bE1zK6yX4XeuJHbW2g0kyryn4O+UoZVw1EFAuyezVi1yDBlHQzFGcbm/lNm333pBZO8k5oHWmGb2cv+a9eqx1zRYFXFcAcqy+5xpVyBBwM0VcZ4Hu5NKexLHl5q0+Y00/OJVB/IanP/YnawpCz8eKC78f1h/OqSCjzpkb+pFJpqKcfb6qB7h+3tb11N72836ezAQWcw5sTtxbwfwCpgkjJssLrmjzE2sO8SK4ZDptdY/ixYyEfGr5y01PhJz6prh384ecuqkhCPOuWPkxqv05sONEKwrkC+sdO48a5Ji0ZkQMAkWQDzvxEfTu2jsh563mNmr/BIrKHrPeS6YI7/5Rkc6Xzrc9BF+PeiZcoNzaAw8XVI2BHadu3hQUtt6qNvzS+c7Em2NdgyZKAl+e8CTHC/N+A/nMS+8S76iXt4Y4jLvn2DJ1fpTDeM2ejJdipCPIEcT1BzMN63/RMIc7vCZWPdDWDng+oZ3kHExXLL4BPD5gtgLN6lZ3SyUnHL9hCc0OvIUg8QLqW1tbW1tbW1tbW1tbW1tbW1tbW1tbW38i/XIAeMS8I5M5DoY13AzPxAsyDWvMBZxWFKwdb2dkBCO43Z1ZEDfuMORyy1KdsNbKqGjQBSrUoSoIETnKP7hAGqw+2dsKi5y/WW7dQqQFwRCgzgKyVlg2u4AQkAzgqKjXAs/e7ghmSRHNdkPshC7QI+eryR2d6jptK7Y3jVaOSgHsciCz1mA5McsNm0EjmH5CTvXwetfPpX60AdOa4M38gvbA5/ujBNWqQ7cA30Qn0M5vOBdugnfqL6XiegUu3bruhRtW3aditoKzOcrFPQ3yKpfsj/el4d64HdQxCx5Wz3BemHXdk6zvi4q2zkmaMXMwU27TnENu3+MQLBujHI4IcPlT8HEGeRyKI26AJxEvGg9B/QURrekuO4rv9i73bcWIw8T8Q/fS5CIV713DCK2c6ykX7NoSVo7LqOEE1j/lfqfgpwlyyknu92GD1cBt0Fp1s+rPFAncashgQeK1d8qZb6u3ViA+7w5hF5C9xzNmPQ/8cH71/CiMWZDUuiB9QekcLxgB54PMYISGBnrrimKuaG7NUhTInXr+vTUyrJyx5XZNuXy7O+6t4rcn5o2ZXevMIDPq7WIwLxqN7A7tKEZqej91DTikdzwuMuXEH+4wJ80UQW6PD2JcurdxaW1zCuguZ7Y/sO4Vq13R3H6QmXr/heB/kDQSPxrZ1C9NTg1uzOt+JBaUjddTiQrNFKfsDeaLNZWgoY7j/dy0hltX2ncOrDfs+CTzhdz3dg+OWDvq/QXGJUzujrWOjUHkJO3Ejq5nfO2j4xvML+z4xCLwz0NrPpIo2GthNL80BGKOx9AQUCQZT7xVn7E3Wuskv3HN9Z7b2tra2tra2tra2tra2tra2tra2tra+vPolwPAc1aHZWtMTLW3pphgM8cTvNzAiqtFYMhCLl+q29RXtK3JIHlVb2v1lXIbGSsadsWULogYyy05MevVLwqsCFtHttoFapeTuGCt2VEgVvAr17mavy/2Ln0VkF3+XvC79xLr6vt0k4P4dvoOQdmkujIHq0fXTPBmgSOj3decsfpVo8BnOZxJyOs+RzOnJ/WZBSZD4NF+ONfMC2unzrs7EVddj/ppfQFAXJvZDLfE63PWmpmVuzQp12GWGbVoc7pgc7zIPBSdfDSBYIB1jeizbvAMqFv2x/V2zB76uoUgKAtWi4TO1F5KghxDzt9DgN3o6lWd1QlckeWZYB8PoGPtAdd3yFYGawG8OSa963untXIEV7f1cuTWmIKVI/o+/3VN3lnQVL3KUS55/XmWU9xuh/Clc4blW61jrOvlHYW91sAf3BHlOSj8W53CtX/44Tm6j6kobO332lcx6jkS2M3lJLaj9lz5aSveHIt7rGLtZy/n7qtpaCJRx/TwSeuP2r9DsFe0s6Bq6ucdrKuHd1yGz4k3aFZRyQlmjzI8N8HEUJ+whgHAY9TIRyPq+WoO9IbZxBzcGjEu2vEghxORxAwmTU5ia3T3GvSYGrY4HvxT9Lohx33TPbU5SKue3BwaQkGO5TsUIIO8Qj9jTpsCwtHsfn+Ann3vn3LNrjj8ih2f41VdvQ07P2vgIBSHngPz4/2OdSOjw+u77nNrxdSDnHquzV39zO2omO2OXV9w/IZZDeKYEhJy7WuL2v3c74K8LiXtd5cbPF5Y/yRff8D8B9k/oH8jI/HzgTPI1xcjG6/nH2z9x1Gu9+C/82f8R9Zew5/XXsOf117Dn9dew61/qfae+XntNfx57TX8ee01/HntNdz612jvm5/XXsOf117Dn9dew5/XXsN/e/1yAJgMsnpyBfcGZGdm0HtT/6R3IbvlhDUE7FKuswXSMl+CTJOCRoGPSTYDdzynnG4ZBengjgZeQC3zDV6jYnbJ+t6CJ61XL2z1e8agmRHWiBCYzcWKV+xtLqckt3tOQHK5UfX5hlcnsfpWKw1ZTssCSFadroJB5RRMe59zwT2dRzmY893rasiZKmIYFQlbzuCKs850amF0XLNy7HYayxVaLsucBCmXoTszBBs79j5vBNoyB0Z1O5+fgvTjKhdrgc+5QL4cmQKUyJGbgnKCYDr/rCjbLKfvuqfmvRKLx9utWPCMtaY5y0+ZRE7m60lcL+gnNuSSzKMXKJVLFnftAV/Ox5SDvZ80rNyUAwhaXw5lAd4st/gN2SuC2SzWJy1f9g9gdzmFK5Z6ddaSFXmuKOiMIRc2htm85yEyhuBcXSflpMS7oPxyTZdbmRWiXXBW3+vawzm01tQ+SrlSKRe1bot6rn0NXsTyGY+6jgerp9jMyjlescKrv7js0j317OWIigJX9Pa0BZ+1f9P7fS/SXoomTg0YGHmvLwbZDoFGG1h2RRDXuRoVqQ2EHzAHGZNoHe8fEBcRgfdz+Zix86AD6Sfj9UWGEddXOeAPvB1ai4ofb2YFrk2Rx5FwPDSEEkmYY74GJqaioY8PcpQLeAbhDa+9084TmwNM0dmU81zvi4Z3h9B5WVtQ+VT0ukcNtMS9r6x/kHOS47vWMV66NwlprbqBU6AZvUN9vQvdiHVfc2owwgLL0HBI7VnB6Rqa8cBnMs0gFY0f43fdy+PU5+XEH9+weejdQSUOjJfuKY0xnlzP53/rb5mtP6n+7/gfh7/6/8Dca/jz2mv489pr+PPaa7j1L9XeMz+vvYY/r72GP6+9hj+vvYZb/xrtffPz2mv489pr+PPaa/jz2mv4b69fEgATy/XYCEzAwIwRE/cHB4ZcjQU5M+lWiajW5Rr1Bi3JSHJM8mhYNsLlYWsFknCr2NjUB3QXaJAND/W8ugAUl76nFYDrvbhKuV5zFKhst3vS7WBmgRxSN9QLrNhyugniqUt3rcECVKlu1Pgi5rNoYEXEqvyzYOqHYGdMdRBrdQpYLzgo/+Lt/bRW57AeykB9wp23G7iXezPKPbris+VAbHZg+axPXADUcHfM5OVrFats5QI1qx5Yc0G7Rd+b40y8IpTV5zzJEYKLs3qgW1P/b1wFlCnHNnIvchSYhLTEsvqXy3EIpvvcalBgPDV0ULbTmYMIgbgYE8KwHGAdO7oA7eR2KkPF+saKmNYHmXtBOcAe6uC9X4BO5oUTy4Neq7fA9QKgRfYtK4Z7RUBToLeGB2g18LBacZfjttzkeRWwq/2JK27ZXD3KsaD+gvwLPa995vUs1CRDnWsRVUHGMP3oev5Mz7F7uXuFUQseBxlX3a/ql7UD8lWDDFbXqX2k4QXD0jUwYMFy1wdZBvxxg3k9zhreWMZy2a7f7lLPLDjpJKFq3pa4gcXEm+v9MwNrDacT3mHtOzOynRhBXJfOr596tiuS3Y+DeF56FkYyc3Idjd7K4d0fWAwZv0mCRveTyCCyor/X+qWeraT6pFeqwJjk6aTFDYHf+2YSaer99sd7PZZDelKu4flfDaWgZyhdzuV+kNcUXLeGtYa5orjvYO/mBda1TTIDa3rGLaY6yfuhOO9omhMYQ++E1hV3Dyhx4VUA3DV80RpxfWd8/R0/v9Es5Uy3Q++nrGEYf6ifuH0jxx/M8Q+2tra2tra2tra2tra2tra2tra2tra2/mz69QDwcjRmwa1MGAnHCSOwnuSKjG0VnVtgxE1+xXCwfCmq1UygLxJ8KLo3IcplmSEo5+aYZwHUVs7B9na6rUjo5Y5rhz4zypVXccxyNernV7yweSNn4C2ZftJtAd53r6e4ZEVJr3JYopyck/SHwG4OOW9p5RKOcnlm1Q033j2xF+owLcZaHcJJwbQF8zLe5+2Fg+Os41nF8y7g7oqjBkFJk8PZvL9je8tJa4FcpVa/ZoFxS61379XNKzCUEaSrk1YADMF8egGygvWjztlObhfpcgAnYIosFpBN8CDd3uefgYXAZUbq8mOBqw5MAiPSyOMb+MDGs2Dlgv/l5J2joqnH2nhVuxt1n+rcWPdknYOcshap/Wlycwoslgu83Mty7Pb6sYtc8dlefatpZK7uXK1NkgJrdA0LgKBqLEf0EPyOi7RkWsOL5bb7fBXje99//6jBjFrnugeCq+UQDvsnFzCsYYi6fyuz2JoGG8hyC69n1t7wMlPAOgUTFaPdcVwx8f2svm4jczCzizunkTFIop7lrl5ehnpxQ4MZ2YMegzHr2W5yS8sZ37A5aTHI41D8cfVAh53a069BHo2wpkjl/ob/oPdJSyc9IRQePqeetbiC4+ODlpTrGdKPexiAS13eWeBW+1lgNx+f1UF9vAdXXs91WAHXdGxcRAFX80NQumnNzU/M1X+cK7b9Tguw2i6r31n95d46OavbPF71TE8NEnjX8zyeArz1tayPjBi637MGLeJZMy4C0TYn2Ro2nXz9jqXJcd8OnVcY2Eng5PN37PwGzVCfNuAHYU7rjcyTOS+u7//gNX+9v0K3tra2tra2tra2tra2tra2tra2trb+/Prl/t/riIH1BzDkmAyD46gu2K6G1nKSzgjxtTlpvamSt3yqueJdx4tIp5lhI8kugOFp5VoUaPReDsccQlBe8C6rj9XknRXES3K+buel0nevcioKAgarRzhpNEZMxU4XhNUPXeXqKxBsP8BB6xBfLNekGJq9XZgFg9Gfsnp5V3Tv2ymcBeAUketEdbA2mC9FPVcnqeDPAS0EnzLLXdzIlGNU3aMF+Pyhjk9TRLUt0Lh6Zt3lDTT1tr67h4PmRjuciEZGqFOVIEd1ghpa49VzPMt5bF5gFRQPvpylxuouJqdA13jJTWhZULHgbcrVmlD9o/pVzljtoWAZ0Z8aNDiOur5y5pbDNeeKIq+I7Ib6iZl4N7JikY0sh+w6X90frcmo86l7l/aGzYkipO9zK2gfs75V9yKXExl4O9eTFdG8oF7Wf6+O5PBGjJeiejMw94KSa58e9TwVNF7nXkt5r30MotZWHdSUJf+qb/6vncN+3wvFUKtr15HLOfO1yGHdrwOzC/XGQj9OgiRSHc3ZDrI1ZpliYyoKPu3QEEi86q1Q7thaJk+5ZrNihZlG1BrggZ0PEfERusMONvXOACPnJByy+smJUC8wcq+6N9rjN+Lr73jqPuX44rIDn4mb9o9WyAW8Y6iTlwV5a9kqJtmi3PgzlBrQDWvftD/HSy5ctzeMd6su3Skn+3FUHHPDrDNj1HDEe2/mfGkIp1sNo9Q5diUN5EzIZ92XcpyHkX7oPGlyfmPk/MLtABoRXzWsoKERX3Hb44nFJTczB2kTP79p8CI1BCGD8gH+ybyeeD60zXFyvBRnPTSwMMffucZ3ov3G1tbW1tbW1tbW1tbW1tbW1tbW1tbW1p9NvxwAzmxwvbBugMChXIudnBfRjfSGk4I02cimBtFczsRyMBpWAOUicTIdN5Nrci53ZgDVA1yxxGkLFi4gOwuYrYjmvJmMMQSo3Mulxg0hzRRDS068y9voTIxekcQfsMBtUr2dq281yxH8QrBs/buuTXDZy5GsqF11fRqMS5GqPARDc95u6cLYgjbtVNyz+d3Xal7u4tXxWwDa7CiIOgQnXdHY5l7R1FovM1dEc+p4nhP3ck2iKF045JiMIL0TkeRA8GtGLX0r958DQ9+bgTVF7ApQr15idI0F6c1ce8a1PhokENA0b4Ky86Wfn3UvK3I4l/MYI0c5iafcvelgXdeuXliDx4eA73KITw0qGAeZTuSFB1hXzyzzCa3jpj2MRwG2WreoDuuIcsjCj/3QGjaY9Wd13wu8W7mcZTz+uu/R6kDWkEKrztWFubv6i2NWOvPq/7XqdkXXt+C9L9fp9YbvdqBe1wXTqye4hiXWLWJFYFu5p2sY4HafW6/PBLOueGY/7ujszA7xkjvUXT+eBTd9QVwnIpk15OA561lXdHI4ularnmgM/8HdH63rfveOHef9fWZKLSa74qETzCeRTs5BGkwO3E1R5ZnYIce6p9HPT67rRZtBTHUXj3nhx6nnyloNV+iZsPEHbg0OOf3neGkPsTqgEyzx0FvIPO49FNeFnw/y+BDIXYMEj2+15hfpJ5aCz94f5BoGsYq/bw/doutLe6Sv99HQ8Ih3cn5hR8XZV2e6u8t97LVvTF3T2vsXTiPc4TjVLB0BNNJOIv6A9sCbnN85QwkDr6963zbwC9onkc71+s7h6uZOb/DxDeZ35IhP4vzP5Pj67/xNs7W1tbW1tbW1tbW1tbW1tbW1tbW1tfXvp18OAMecuE0sToqyIHoxkNPRYL6IdkJreIFbm4r5XdGzuf6zYnDnoLWTTMMYAp3WgJDx9Y2DdCIiktAEKIhXOeRWp66cerY6M2+XY4BN3J05BNPSFSeMUe7K6tptWeyuop8JOVfnEETzAr+xIJn9ANOyoFj1dK5frYNPRQmHHLLmAm7qeAXzB5mXzrpcfzKxBsapwxxORsXLlmNWPZ0I6swhCGSjepPXiVGgOHDvNNTv2ZopQrc6ctM7OeWGzAxd/vwhyjlQJK+t2Ooh919GdeRSruflSG2CaLWOd9xzubixXgBLvaTEB8xnwdDl5g3CkplJvJ4wqsvWW8Ve63OtQDWuWN6MCzvK9Sq6Log9JtYCa0dtj+XgRoMAtXfMWjl5U2taDvdEvbyC+NX7XO5dc1P8rmkYwHxZcguK21m/z1qzDisOejHzhIgLSJoL5q8ZCqzLLYr6mzXckIJ6N0Be9zvKSb1cyFGO6kPfNdXZu+7RDZiXsmuN7o5mAVs5dRdErPUrAN1IYiYRCb0RGbpUsmLDeXcpr2e6G2mN1jphATPKcfxdn1OQO82Y3upJr6EJT2xomENAuMEcqvNOmAbMi0hjhqsrOsBT99Bb5zBTvXd/MWcS48Xrjws+v3Gs99wC4/2g9U5ykfGktUMduzM1/GBOs1quZQ6fGs7gcHJMBRqYYfOFHR8VbW11jya5+nOtaUDkUmw23vWs1fsmrwvGl95NdU+9mVIOrj80KEB1/VZn83Ki6z3WiZiqWmfgpoEDNxSt37SXzT9r2KeGMRi01hn5/ymI/Fdd46VkgGzO9cc/MDfOv/5PGFMw+xr1rjHm2z69tbW1tbW1tbW1tbW1tbW1tbW1tbW19afRLweAx3zRj1OQLgvk4W8X4uoINn9Dp0wmicUsgCH4k2Fgs/il4CFRqLKtpe1EjnLlqn9WwKgicFc08nJALjdluRiz4mwVhewFEi/SznficKrLODOYzQSO2qkI1wJehuAUMUkU7azK2MYNMZfzdh0r5eTVBVXkb4xaL8UfLzBt67zbgUXifhDzImPizQuWFuQLOYjF0+/AXJ2jH+UoFsAz6zrCzILGWjvviXvDmiKjMybuD7l9IwQ304kI9fAiYJiZkC/9zOyCSOMNDy2H3IGWWC5o5gXuyy2MVR/riTEEsv3U7ckGDLI5cJDzWesnZ+IY35lz6vbHhOuJHSd2qIf1hp+ta62bw/HB6hZW1K2iiaM1BJflhu6u9cvl2L7jvqNo6gR7yOlt1e1be6MslboPdzdrqeKozVsNJfzw56lOazmFK943Qv3AZlh198r9bT/s8eXi9fvapHI7t7PA9MWPkP3Guu51HlkJwgXjgYxEIcZyulvrusqsB846N8TPuNdHoLzpmJH3o3jHSa++4FgubJ1H5sDsQVJR8KgvHHdFj2cr57tc6bRHpVMrEltG5xoYmcnqnbZ+lqN20JT4rMevd7m+PYkIWpdzu/XaG2HqDx4v5mxcK4oalCjAcpkreSC83/Hp5jCvBeYDO7sGP+oJ1bqY/oxy6tbzp0e8k941/AAYrSKjP+AY6oROez/xfsCjkcNu57R6xWf1fn/ouQVsXnjTMTXMUFtuqt86brisrnFvjTQjpu6dY6Q5My5sTrwisdvjPzFeL8Hm44McF3Y0sj/AD6w31IstB7nlJKMR8w/yx0GDra2tra2tra2tra2tra2tra2tra2trT+JfjkAHC7QkK8pF50doirLaVtduuQkYzD9xGfFQq/oXJrACRfMVMenGXNOOQ3dsaQclQtUFYziElxesaX5kqvUXEBXFljIC/ODjNWxSjnsCoBE4mZEOyouN0gbRBruXfBsQVcmSVfP5aJaMcu4KPgnUAyMZxkFKw4ZF8D5wT1o5aI1W67Gybvf+AC7wLogsEUdopEhSGgpp6G5ju3Wiq1OARsQSJ4D9wWtUx2nBzASb2d1qjrZK8KY6kdOgxEEQcbqry13ogHZ7q/pnCjnp+AvqfXIGLrsJtJu5fxWdHSCPQUWF8CMvHuNBWA7iv8eNUyg/ZDjIkdF7B4Prf34QwD6/JQb3M/7/lkGC5llxhuUWVfMM4Kjy6VqXrBuubhrFyi69irIP2tIQQMOLDcwpj2yYp/XfsS0N+wkW4N4at2qp1pDBUniTGs0QvW6RsHz5aTWemnPr3243O3Vr+y9vtu5X1FWzugCxTpXuWXxNQSx4PJB5lVR39UpvYB4DLBy3y9gXP9t9znq31teWH8UCza4ngV8k5yTbM58XXjvcvJXavra45B42kpu16BAuVRnOC0dqxhjWkWg07HQOqZXvPF0RQ3PEBTNZBJ4zUkIvgq0Wnp5m4OZ4G7E88nrOOgErak3WHB+9VlXTy6Q18RJYk5B69eLOLoC72fCeeid0FotVZDZCKYAc1yQTliHMXROKjbGWxcoj6l3ljVds5lgayRcv0PXM5Fz/hBL7dAr9p1JjItEDm3rD5jqGbZ+6HlJDX7EeBHZ6K6hCTDa8UHYSz+TXg76S+/Hr39gxzfyeuoamwZkIi5yTNIbMV9kO5gjmH/872xtbW1tbW1tbW1tbW1tbW1tbW1tbW392fTLAeA5B9EEVoKGE+BBRi+Drb+jRcttOQk8YXqv/teJu9HwcgMrJTX7sUKkWVjJyju2+n/1JxVTa4KEWU5bS27n7Tu2uSDa+noO0jqWBVkJpimr1ejlo5XzjnI8Zv15Zody/wYNixDcXZ9PQnbSAlkUwViA6iTnVd9rlQRcMbpGwWwn40vQpjpxnSwnZ4FwS1o/CyyW63M5Q5ugZWteXyvIbA0/FVerlOz+7oMFQaF2EjPkOp7qGZWZsGOu+GViKrTVD3J8x1rl27pAlkD2KUcnlANxRQavuO+p33tB0khorYC/IqVvV21B4Iyn+npJwhrZTpi/Y49PwacZyvjtB+9uaIExi6D15bRGTkdvWBots7ZUfX8OAoHFe6Bhgd0JePUXu+CxxUCB1llOzV57dsH0BWbr2OVqlXM0SPrb1ZsTRUAPFKC8tl85Z1d09A1Z61/dNNAwn3UdoNfSuN2gkDf8TeFJOYfLsZyLvNYAheH0ivO+3fYVdZ0/dE/f/50VlZ1RUNLAgtY+dezq0c7eiam464yJ5SSOB7Sj7nnFRFeHszq0DYteTuba8rZW3d9x22tBHDw7keVkD50LfghIpqkTmMn0RraGX0E/Gu6GuSK8W2t0GjODiCSu4PKE4yEX7YrN9uWM1V4beeH9E7MvOD7vNO2YTzmbY2iAwFeCAgzTc5vz0t40avjgIOMFNrD+Tc8gNVRgB2YVsJ5xD9Fk/4BxacjATWkB7pidAsghZzNRPb7oXWAO3j+1R2J1emvwRaeqIRXPi/n8vd6vofd/TLx/EH7U2qI9PrJuWdK8MTPp3pj+YD6/eD3/wB5/Y2tra2tra2tra2tra2tra2tra2tra+vPpl8OAKcdXF3Q74ipqNH2ELSIIYefCb6apbouofo+Z4HhRkbgCzpVVLTZ6qoVuDQ/CrSsozcyXwhWrf7YtwvxjqhNOeagKVq6AC5ZADbr9wzkJRZcxOWoW9zujq+NSfpypAp2efMCX/X9K4LXdW6Wyy3Z1ZVM9dWW+1TxtHa7f63W1tzl7usHXFe5C4Fy83mdl7ljY8hh50aGomQtXtUH2+DokKZzxbDs5DR0aoP0lFM6O9HWfWjVPzzfkDOtFliOYDyhfwhOZZMrM2dxXjli3xBPsb1i8wH0d2x3TPUWl5s1s+KYxyhwbBiT8M4cL4LGnIOcF3b+BScE0JuT7UOO5tUZXPBfMcLqjhYwc3IMQcyu+2lrr86ge+2N+LHjeZZLVg5IC6t9NcsY3WrYYUHwuq+++qGrH9i6gHzBPbmKg0wr/2wU0p/MigrGGjmnyqkXqM0oRF7XWD2umOl5aako7ftcFkBeXdUNLMS88weH8rI613X9GN1sWU5od8hyqXvDxktxxr7ifdUBvTpqW0zGVCeu9Q/cgxzLKS73P5h6lTFmOh6rNjeAWYMljZyjemyRg9ghj4eOXYMF79Ruxb5roMBofuhxi8msPmlSuDvbgY2X+obdb8jfm+PTGFcy5hfpH4wxsAxa9UubNf1Te92Ph3h+/9R69FYDHp84SV7ao9YahC92Dn6qq/p6weMbC2ZjZznpVy/zYM2MqB+4czvdzch+EN7I6wuzs/6o9qE3vU+yQ0894iG3L03DOpZBzqFnpp1KMVizGFlwt59a4zzI13eaGzO0B/38pnv5+r1mKEJ7PwZ2fDLHiznhui7SnP75n/6bf89sbW1tbW1tbW1tbW1tbW1tbW1tbW1t/Xvq1wPA+WRcJ82/CP/E7UNwUrY5wcfWKlY5BVsxcbkboE0iB35+EhGEyekqOAateicjnwX5TkSk5DAlK1p4uTdBkHY5gBcwzpC7NC7gEMyIBBPUNExxxzlxa+VOfXuABfAuwchMzKp3+D6G+k6toNzqXbXlIF0uyhSAMe9ySObEqiNV5zl1nijSNtvqoQ0sGhzqz5XzrxXQgzw+xInM1HWrhSBT8dYrljZiKILXjKyo53Qn04jxoh2ODUX6ujWye8Vdl1M5KnpZRy0WlZifOt4cus4femQpd+zbqip4bO0sV7LALX7cTlpLwes0OT4Vlazo8MkQtC5nrTtaLxabLserF3ydAf3Uvph1/Az1zx4fVQabgtAJ3jrRHgSDlhdUXylr3ayg+vLmljvZKHiWvNcgJ2RT5HjdEy1kq2co33HkWd7XBaGtuovNiDGw1gqSl5PcFBdt5uXe9BqqWG5yVx93RQO/u3fVM5srHjqjvrZuUfXmphf4rr2O3RHr97WRQDl9C/ziZ33e91qHh5zQZVx2X+dZYPtK9UATt2M8Me1/ktkbnmLRbqvLu9Uelut++iGAHLPAtdVlm1zHbthKCFi5As3Ia2qXxqgO7SbwPCbeZjl0ew1bGHkN+e2vJ3MGPA5Wn7TXHpTjvNMwwgIbQXoKsnvFPePgUa7uVBzy83fohyByf5DeyNcX1h/V/eu6/+O73if/NDBT7y4grRE5Kg2hwflNkds1AJCgbmPrGpTJiux2yMgb4BOB9Q8NmCQ1fIAitsfA+idzPdmRpD+xCNyR63q+wJ2ZQZsTbx8wtfdoQc7JvCavuIj2SfN7umdra2tra2tra2tra2tra2tra2tra2vrTyP/H3/L/8OU32E+eQ25B3NFz6aTI8m44OsSyJsXeFbc7avchq0AlBOZBF1dkzGJhNVLmQWc3NoNfOoElv2tYpIFjW+4d98Sr2rWwhXxfLt0kWtPMbQX8MLmRYwBM4Fy1eWQy9ery5dy3npBpt7Vn1kdsMSrznAK8OV4xzPbAocr3jexSHxBwiGIml7Abjx1DhE/QK2Kx7a61jQykhyKg84ZhDWCzhyTnEGOq4yp872OAcxBjIKO1YFMOjMuwZp0uSTngt4oNrbciJZJplzQd/yuveOAyZBbtG4VVk7NHPVxjqWuKRMB44TsB7RG+kleg5wvcrxIc2ZMcg5Bv7WmaTAFlgA8DPwgC75nCLhllAv2VD+qeYfxqvhbL4iVeATEWveoO/XjY659JQS34OrAmHKtL6em+xsgV0+vMQUdU/HH2i8F5d2JBWzXz7XVfT0Jd51HjNrTqzvYKoZZ0DutkZZkxSTDchfPApGCwTEV50w7mBkMBulnObNXhLU6jrMin9UFnCRPQeE5NNBghuUoKH7q+apIZ8tJX3BxDmZEuW+NzCTWSlrT8dqnBim8Qe/YeWI0Wpq6d+tFYNbFqOes+zzJ60WOWc8dkJecp677aJ44Tjej9wfeHuK2U4B70JnRiVSMvWO0nPTz4Hj8psSCNOZr8HwG1zWZCXO55P2s4YSGN8ddEd+WqfWKC+uu7vQailCE+dBAyJxy4HcNlmD6Gb3njHz+QY4BGNYe6vpth95v86le8DQNuWDiuV/fmddTX6oBE6rrOhPdC2/ENcgIYj5rmCD0Ti5jOJnY8U0DCRkC9fOloZp51TtW6831xNpfGW7Mr9/JGXg7wJx043r9wfXHd6w5nhsAb21tbW1tbW1tbW1tbW1tbW1tbW1t/fn0yzmAsd8QQHXGnChE10mbb2BkJsDXevVWJgz1veZRUaXeKoZYECJN7ricSVuVrQkDo5NyFvsCjf7PsLHcuOnlYCuX5J2iW+5Vi3LQrnhoV49lo5PN8JhgIZBRbl1MvberT9XcBY5bv6Eprus04nbpyXVajmhX5O7tHkUuZjseWJkXs1u5L5vcyf2jXNSx6oTVlToFCxWDO8BccGd1+k6tQdhamq5FGCi+OZO0IG2olzjO6vuMu4dYsHrqWhc0ni+wA+sHFhdp4OUSTZaz0G8nZKbiiuUCbYoFZ0IhP11QdfY2h/yUwzq9YLXOPyKZTHG+XA5Ur2ra6kT1pB8PMgeRF+RRid8vAcrjQSuAHljBvcTOT/15OXoNVzQ3AtW3g1fl0hW5XF2ra9uBrqGCps0caxVXrAsop+8Cwqn4aHeYX4rLNZgp57ZRAK/F24kegutp5fq0j6J3F3fntXWg4r8zFCm8wDDcEdRklANbrnK56it6G907fNS5lrPbj3WX39+7zNG5GpvXhlux7nomrZ84SR8vZkTh6np+DDmZY3UxK4bbDntHnbcawBiNlskcL0XOu2Oe1aEMaRWnXI7uJDScMS+9E5ZDPhM/TzpyrM458KZzCDemW9Uha+9bO2nm+Lyw1nmNSwMq54MxJjSjm6knO68bkluv6PDVSU2qQzzKmR2BdyePA8Jud7/uzQOe3yEvuYJJaB+QFfNthwZXplescwOuure6jxk632zf6p4B8dKaOJWAkIX1y+lf0eR6HORCt5jq1/aj3q9D118d1hBwfiP/8f/WfXLFQ898kvaBdz1T9viAeDJfFxfGOB6YZbntt7a2tra2tra2tra2tra2tra2tra2tv5c+uUAsLtcYkEnvcvRGrPg2HXDQ9LfkcxucgY2K/gKWJMjMdSbajgMI3FGGhZg+cKPkwinlRNyORhXHOw/OYOXE9mSTMMXME4KbpZD2KzipuvfQzBzVB+vCFKBZPNK0hUQyyFoJZfbxw2GdX0ALxhZvbAV3Vu9navv162ipgO8tUqEDdISn0FrOq+suGerHltm9WkuE3Sda0QKJDvQpkBb60SoY1Wuv4H5SUxjTkFUt8UBTYB1dSpbFlQCm9UxjL8hnx84A3ojrwEm2Gbz0j0wUOz1VfDb8BjcHbZYAeKE6YJ5BcAyhzqIU8AyeHHNSWTSTPckuGDKwcv5EMg3yDA5EjPF3Vq/u2OjGz39nZR9+3oLynsBfZNTVzHUFSt9O2nhjriu8yvSWQC9kxwF0hpykr/qs9fatPqs+c/APeXqtXvtJzYVYd6awLcv8OrqajWrdTSBuFy9wmsQAzlj9Zz0N0jO6kQmKDMuhuG3a/iHgQU/6r61+mfoXuF6Zi3kbl1Zz1wC9UyB0IKbXg74mBMzZ86sQYlLHbyWJFd10C73saLgdR6GzaB5v/ujMVOs8QxoXXCfds+JYMDxUOdwCLRb6+ounhNv6nK2mFUbHsxpTE+8Q2TgxwPLgTkcpxPZGM2JOcgYjK8Ojw/UJhwVTjDxmVpDk7t8ufjTWgFwq/dQvU/GVY55DcrQ9TWsqy+YqM7qio8mBKmtPttbgdsvzGvf176938E45hqHGfPFyv9OP7FxEdbKFT2gGzmTiKHYdq99E3Kvpx/vPvNs+PFX5vV3GF/QHiSd5CJaF5wf/4AZjJg8XxccD7xNnNd/76+ara2tra2tra2tra2tra2tra2tra2trX83/XIAWJHPH2CNK6GVc9aOghSAwFe+q0+jAJIbRpMT+O48DQGOTPCs/lH9ebYDIuQA9gcCUQv0rvjdgqmZtwM1rX4PBeOWe9EKyjRyvOqjFDWdEXh76PS9zidGXU/9fjmPvRcKnThJ4LBiXRcLy+qYRetgzaqLNLHeccDjwqzjlszqz017RwHLAVku5CgHaBPImwVEdQ11rrmcnOUETsC1ZkGQYxBNYNIIYjzx9iCtY6eRz6fiiEOAEJCTMcu1CHfUMmTF7YZA+B1Z3LTe1TW8ZEeTA3lO7j7bcplm1mMUcoBm3eOMofW4KtL3UZ2hcRDP73D+hveOVzR0a7oHcXfvouN5gexG3SOBcc+405ZXRPf7J9WJuxjaDXC9laPSBLhXVLifhZSngFyWo7N1faq1Owo6Y9wxvVadrr6uOetZSaBV7Heqy/aG6/A+7nLfZshJPcc/gWlrJzd0p64hBWHvQQLWY1X3S6WywKHnJVvdn9cNX9WLbXLhIid9lIuZVH+0Ir9nPX+KRu7mzEySwXxdzOOBNytA68j+T1UQFyQH7e/Q4IZF6Mkag7SJnQ8g8dD9C+Ud47Pczs3JKfhJOfi1R5cTWjDVPDCaXlmvFxynOrK94X5iNjj8wCIZ8UXkZIwgWye80ZiYG60/iAzMTg2hNIf5UiS+US7uri5w5K6mHTovL5d7P7V+M2A+y0G/HO/c5830e+9iHfpDeytHbRfdQ/OGt07GJOIl17+73gU5yXHRHqee23LPx1QseIRSEbzJuR7P30lXVHzSIBt+PHA3YnxpqCEusEbEReYBeTDnZE4TfPbk8EY//8LW1tbW1tbW1tbW1tbW1tbW1tbW1tbWn02/HABu/leSxnRF5EYkWVHBdk3FhPZOjsC9IZdnxTePS5Dsdj6aXKOxnIa9QCYVnSv3WgBejlE5HpuYx+3GfPe0qnd0fX7FGqO+UWvlSh1ZTmGFAitiVXAEjIhJ8y4Yuty/y+lbjt7sJzYnsdyQ1W37djy7en2tICMdc/V9EkbgmCk6emYIToaRboLTZrSK0cWBob5QSxcc92BG4CRuTf7DtW65nJz2BpjWiZiLC96QFdTnG+U2FVm7KtJaa6AQ2wLTrpjjpMvtt8D1qH5bbxVDWzDUy7E9L/3TPmsNq4+2AVm9sNbUgXypn3ZGCvl7g/nELzl5BQK7OkRDUclpzkztnYgVQyzHudmBN3s7wFH8r6/4Z1PM8hocoMJxca9I5VEuy9W3q+jhtHJV2qFE5nIMa8tMRVoj53nW3rB49/LWiRSel8N4psuBHJPmRz11g7RWLdQFY3MoPtofNWSxHody4nPUEuh8My8ds8Cc1bfGGqQAgWnz2u9gVs8iVv3dlOv0QIB8ObZnOd6XO389k+VuZQpgW6cxiTlwJuEHGSnHesVy53xhXl5xd2wWSAaBTIyeMGrAwtoDWsMiMZ/ElbW36/4p8bsGKaz2WZJzKADZDrBDzlkzyphbgHkSFTGNJW4d96DnwN14mdPtIOfFHJc+r3fSBt1Nbma6gG8/yun7FOSug5hN9ZzjyD099JwjcI138npW1HXt1azI9nKdm9VzyxSMt5Cj2Cqa3ysFAJ1j5kH2Cu42J8aFHd902xx1oQPWPyF03YT6rfM1ytE+sfYg54U1w8bUAEb7K6/ni3H9HTs/K1H/D2Y4Ixvfn78z+4n5QWuN89s3tra2tra2tra2tra2tra2tra2tra2tv5s+uUAsDcnZnDOxnQnMhi4uoDPbwXLBGHSTWDUT8GKfgoSToHZnAPyFEBwJ+dLsb2+4BiKOO2fcmC2TmRXJOsd89phxZPe/s0GVt2SeReScsfUehOMtEZEEGGkwcyUK7S9XZ3k5C4kLSCbMbARpDXF9K6P7i64Nsr92uVMFGe9CnLbDU0UbuvqP56JHQ5XgB3gycxJI7BscjF6OUtrfTKdiEFrKPY1YcYlUJTV+9kb5MBawyZ3b7DhpHdiXNXveZAMXUM8BEIz39HZOcvVutbB1EsaCfmSi9pNDmXAaKvyVucayTu2W+tAfWdGRSXHpfVtB3O+GHMw0iAblq34lzpqV4RxKkO3ekmtQKwBX8AppyUBt8t4Qmu3Nzkr0pb+EISLS67SimrWWi/INpWMvByy/vEeVFjwjXkPKZBy296wfV3yvTD6ufUHmXJUZnWxzhg0b4roJe+oZuH7Q92qGDAE72O8o6sZWg9h5QK7g6C/r/2HPmYH5pyK+nXFfTc0WJEEvpy4rEjs5UhWn3UuADy/a/394I5sN4FMi3lvqZiOtRV9HYTVc5B6Hr01gk6LSeQLw2l0Oe3nC49D4Ldi5M2cTH2mxZRTvVvtX+RUX+EBYyLW7UrkbgbjqH0uaO7HSVwXOYzpF5mNfjwwEm8HnpO0b4xM5jWY8UX4By2cfA2yN5qHgrK9HN7WoH9ADMwNO04NMLhVO/ZZfcCJ0bVPWtN76Hoix7icwmnq1baYArLNsXi/57J/wPzCvRf419221hRFHTX4kUNbudUQD0A7BHcjcZ8C8f6NfP0O7cDtW8VSJ+P1BzkGvTeYehy8NTh+I+ZLbuh4cc1B+l/5/vqd8A++HSePx2+c/YOtra2tra2tra2tra2tra2tra2tra2tP5t+OQBsGTIcZtKtgBeTHE7263bwWnW/QnWrVoQrNIEZosyYcsKteFQRiBCkDfVsRlxEMzwSJ5gJiyx6WVwtFU2c5li85ERldeaW+3OOOl6S1sm4BI2dgj9RUNbejjrzMnFGwdcmI2kOaKd6kOcFZvhMvCfhrZyGFU/tFVUdQ+fSTuJ2W045AjMgBBndjHgJVCahxGE3LIxJaq1cV+/NywGaZQAtl6BN3DuMQVgq3jaFltx68Uh1eeaYmDfsWi7WVlHTV7ljjfRGxlPR0MvpWa7QjAZ5CUhGQDNFhVs5WONSZ6gvx22yEm0Fa8cNiAXyJoODMb9DHjpeN+AiLlcstX4YM91lublXNPiUY7priXMG0wZE0NpBw8R1x0sstguwmtUGrPhqOZ2z9kCrdZh17NX5uty8iTAeAnPm2pNernF/d8ESE/NewM3v4Gk5MuVOhVbDAUYwaZlknqzYc7MDy0v7Pd9x0Kt792399vs+WSbuBWxT0FUPXzmQvVUscIHQ+jxVbndgEjkwa3oWlgPflit5gh1i36F1CjPMPgq+dhzFRJs7/XYJ355r8KZ9W093Hk68XjRPOBzLFETuTVw3J2R1KueEeVW0c9PzXu7ftIpITg2GeF9g+II88C6ALAe3Yq7dUlHJdijlICburcIAOodzH4PrIMwYMe7eaYVjW91LgXjzDl1w3Pohp33K/2sO4Yq9d7P7HRqzkeUEzjmx1jE/NfwQTQ7scZHtrBh8MAuyfZKv71pgPxSLH6l36nrHzldFZKszPceFtVNu8wyyfOdB1iBOYjH0HEfgfjDjqZ2WieO0nLTj5Pr6g/n7Fxwf0D4Z15M8/4bPwdGNx+OgXdf/r79utra2tra2tra2tra2tra2tra2tra2tv5d9MsBYGxA67gfcn56v112MtgFtG+YC9DlLLfu0VEZKwVDU/woFwDN2zFJE1BzN1p7qMszekHPoJkTCold6ayEGZGBV+qzZRbAsop5LdDgXlA4SBqTWeeRrG7aGYPuHU9oTZAsrCueOMrdafUzOlM5Gav32PvBtHWNrhpVvKKMh3ppzd/Yb15YOwRu+kPRwct5y3Lddp13eQWt+lcJKxeuupTdT/L6Iqyru7M5zeocW9L6A+alGF7rgswdRdN6LxdrXZt3mIr6Zo5y91Yn8eoaDnQ99IqttfqzBeVSLuR2yN3M6m4eBZdXJ7BBGpGDiMmcEHZiY+C9HKWtYrVDDmJBqCdYJ1G8d0a5U49PXZM9oAuW9dYq7Xn1GDveW8FduZzTm9YF7TWrYQC5VyvSmRQERZtv4V9WHLg/9JxMOXvNKop7wdhWzvB8VfSv/+iHVryvd/UF1/EnqGvbbRFTFrxVD2tHEb9AmqKRqaGD2mnpp6KpM7RlRApxK7BqFU2egacgdBh4OwW3c3VDC+Kr71dR65arS5dyMFNO/xoSsYpynoan0V3Pq0Y4ElA3cENObkMgP6fj/ZQLNaMcr6snXACdFaNOu13Y1kxRyhHkNaB38IARcrgSui9XvbgcgVVSwwhTIB+vfZ/BDHVet34q0nlOjoLZHF0JBjHJvFTdO53ek2g1rNIERy0VqZ0vRUmrI7p2kTc9L9dLfdc43o3wD72mWrnn14vPG0Yn+YLxkjPXrYZUTI5jV/e25aj3VA0GsCLyBagjtX/cG1Ed7Z6hZyN++JmsdUzF0+fxCa+X3oTNsQktII7/zOQP4vhkzBdjvMj5ov/2N/r5G25Js/Usbv1HUOYalPn3/Yz/yNpr+PPaa/jz2mv489pruPUv1d4zP6+9hj+vvYY/r72GP6+9hlv/Gu198/Paa/jz2mv489pr+PPaa/hvr18PAPMpkGBFTXAYznDjAMhWkbtOMrB2FsAcRWYbVrGjlr0+p5yS8UJLeggcRgg2tyw+MgTk7BDUTAoetnL3TbAkMBq+7J91Hke5eAVb0hSZ3DIZOYkpt26+XszWmIfT0t5dnNd6OArizQJIXo7mPGAmGQbjEqhsgniEvIB5Xdz9pJngAj4WoLLMILMxl6fUrCJ4FVWbV0XbtoqMdi938SXInVS0a+HRkENzmuBm80P9qhGC0ZnYNRTNXVAPmgD8gp3WycgCZhWhncidTStn90tx0DOhCXrngvnmOs7rq/aPASdZkd73PaXiaFEUd75e5bRUL7JZnV4/tL7W5GwOQVu45Dr1U4dJOZGpDlw33XtDfdBEOTCtxggqotiWY3y5Wisue7l7b8gbyyl93PDY7Kz45MrCtVqf+4Wq/WzmcnGvSGzAI5iL67YTMCwS9yxX6rrXgdlD1ct4DRNU17atPT9YTlcNLHg9H6GI80gNKwCkyW1qVoHPQVhTbTeOWy/WLCBYNl3e7l+/YaoitOfbzdsa4GRcOv5Uz676dHV9mYmn6Z6TTJs4TlpieVSEvOm+hpG9VUfxvNdI9/KhiOOkoKic99bquRira7xpHaP203HU2rFeKDUMoj3Uzg9ijnIaJxHA8cBDYN9aV9/vDIyTlk+9T64/yPaNOb8T3hX3PC44uoBxGJ7jfg/BwNqH4uPvWPNZz7gGbPw4yxFcTvJEQyHesPaNnJOM74qP9hOb1UntTfsuCt6mepiNIWewG4wa9Oin3htx/eCoTzn4I8CiXNCudQnkeHYTBL6+BOFjEpF6xvLieg6u5wu+/UbL4DwPjn5g2wH8H0r/d/yPw1/9f2DuNfx57TX8ee01/HntNdz6l2rvmZ/XXsOf117Dn9dew5/XXsOtf432vvl57TX8ee01/HntNfx57TX8t9cvB4C9dWIMQaZWMCafciBeKE7XvWDLSfKEgUDDuBQ3asbKPLV4kXYKZPkpWFHutmimyGcajlf07FRMLiF2Vh2kjhMrnpipANxM7C5tpUCYwLXF4G7MtINwE7g7FI8cKadoWLkIy9GYBcswueYs8o7HpXe5Gpd7NpZDVede5FS/5pRDtK3PWVHAA3cnREIhIfIqR2LHjor8taj4aofsAopZ13icuAUzRck9BLHNKCAo8HpH7hpEPwWtudRD2rtib0eWi7MBQ9DOGzlGxQUb1r8BJocl5Y52q3jaAnSJ4qZj4i53qWKvqyPXdE3XGIz5gmbli3Wy4rIzTefeDnI88f6BnQJbNifkUW7lFxwPHT8KjhdsTQMwwhqW0HNFka845OXGVHy4TaAJmuK+jJr86IY0AusnSdN+Xq5hL/c2WhfFXVt1/VJ7CCImIybZulyzBcRFedd+8orf5gaxGdU77K3czj+A5QLV6p1GILBM99OoeyqKGCTtdvcGvpyny5mc5fheMdOyGVcntNzQxKXnoh0CvONF0GqUAQ0JuGCrtQfMSVrBfz/xvJhDgDwLIWdMwoNuXTBz9RCbXOYtNPzAqQhvD+oe1bWkgCXWsONR7yTd2xvKm+nd0wrGJ+UCnnL5Gri73L9hZDuJmQSjWpDVCdw86J68Evz4jTFOVs55RiOf3wlzhhneDPdGUs81rueer/tdZXbU3hgw9MxmptzavdW8SOp9yNDv3cEfGhIZrzVNgMWUM7d14voi50sR9d61/jMF6Y9PDUHkgHmRDOL41N4dL7wLwse8MDdyfmGmuOg8Hoo+f/0O10Uzo+dkoM7zYPAcF98e33h8nhztwOdXPRNbW1tbW1tbW1tbW1tbW1tbW1tbW1tbfy79cgCYvARRUrBKPbIOacycdEIdpt6BQb4EuzwCGX5fAkW5Io7ltExDMDUrrjUFwOZEIKI1OrJIWgG11W0KYBl0DzkJreHXS12x2VGUbEVR097ALyeCxYG54ykXZpJqNk7Dx+q9PQR0RIT08/Mpk+ws4JvQ3ElrxFW9x5gMhW6kFwzFoQvaZnWGAnLaNsFVCoQLjgq0W2vqJU2BrAgUA20muDovPCE9bgZtZjAFzPRZHe/JvEbBuYFl0szI3nXezg/nYFgIKppbsb8EfwAXdFfMNaFr6YcA1ILtmWTdA+ZTMcAx+SfYWq7Ga07mjLpXDfpD93tW9PNZ0cnu2HnKeTgvwJUebrOGDbqgWWtUIa1Q51A0sMCzYd0E9BOwFZWctxvWqs+W1Yssaqt7aopAz7h0TWuaoDXF7YIck+v74wX+UcHVa9+qD3eiSGA5O7NcmkZrifmhPloQ6DO5QDOviloOOTuLa94gm2WR115QXHLFX68BDSZuGgD4ETrXT9fnjDqeIKStYQpguY0zl/N6CB5iNZgxmTm0LNb0s8cBMTBPbC4on1iqg9YzBPC9BgrykMPbTDDXG5aClljAedZwwOLl1V9scutnTPUku2HNFa9dayz26BrgiKFny7RuaU3u2zW0MYaGCnKSrxf0zpwTP07lIDSjZeP4/AueYIcTEUR2xWvHNzlvn1/QH3p9EvSm91n6KaA/v+P9wWSSzZUoEIE1PS9B6tkcTzCjtXa73tWdLJgbKSc25nrVmmldrYC794qCTu3N9lDMszXtrdbBP7Wm5uQM8vlVaxlEnlj/TZ9Ze8watM//xPQv4vk7/fggx4uBc/39O9E+aAzO/knrCc8X4Z//w79ytra2tra2tra2tra2tra2tra2tra2tv6t9esB4HIfGo1sRnIIpNokvOJ1TfGj1l3ArTk5p6JJZ0XsNgNv5EwBDncB09YEHMYFbZLZCOvYmEQ/aKnmXqueViPU1ZlTUJnEY+KtF/BVVKzAR5ebNBboqajfHFgqeDlN3aJhil7+gVECkBZv2NcPmFfB37ydw0QIRraOcQiQRQje0cAu0hrpFUmdg4yKpp3z/nihQcVm40nGINJxdwHljFrXcgXaCderelBN5sfqi003MpKIwL16S6k1zwMo5+TRBNyGALY6RQ889bOizoKnZPXMetz3NcMEfOPC7m5fan1WD3R9zSsqOC5mCADHXOs0y8dqZKuY3DGqSzq1jvPCGswZcjy2juhbVpz4b2UID5KnYmxH0Hoj24pA1n+ZHYqxjZdcveZaR1uR0OpKhSer7zj7QzG61rWPWV2pqSjo5Uim4qBXnHi4jmPCwZpLOGtfwurSpfpXFbkbdX0VK50FVKv7WAqcRlI90yRhTXsS09dlmxc4TuR6T51bVuwxloQZzeq6M7mj2VmDAQvgyx1u5mAPcnyve3tgeelZALI9BJarTzYD3M4Cyw3zvNG1xh6MRGB2Xjpv944fXYMiZnIUWz10rmvETe7WGg5JX5HRuYKp5eYeF/Sm/V3vh8zQsEROrPcyUSuO3NvBSgFPa+Tri2iHlmaG3nfW5OyNi06STV26mcZ0q1fUyRxfFeENeT2hnQLUXe+oNr7w9iCn1dp2cj5xekW3x7tTObk7xc2HBgfciHYSr39ob+P6rBqIWD3Ty2Of7R2b7iZHcvqhQYgY2iu9EzP13m31154BQy/GFUUPnXY25nBsfld6wvffefnvtG9/wxN6Ji2N9G+M8Z2tra2tra2tra2tra2tra2tra2tra2tP5t+PQA8ZvXpXnhMYvXvRsCAPA3LIW5UMc0ZQ666GFir4NR5FQCzAr0GXXBLPb+twF2CjTJFyn08M/EcuB94d4HAgPSk4WTBrQW2sIJsMcgMPL0ieAPLKZxnQnELjuQIroqfTbe3M5MmUOQumOUPGF8CZd4KMskRXfmugk+WiqRuRoYL2l3X21GcsxycVvG8Qx2j5eQUnEpoU4ByTrI1uZ1zkqMArDmWF04veBe34zMwGH8QVk5BB8uu+NfWFC+bk4zlllwO04Kqo0CT6b6IaRbEB533fN1rpOjlKZd4U1QsObXKLkicOYkYXCMF7hrqEbaHomZNfa7eT+b8A2c5er06pbXO2bL6dgtsW8HbWe7NhzPjRe8feEUJj/GF+UORx7cLuJBslos3BeFk5A2gqyPVFCNuVj287tUxzRuy5ixndBSshByviuFeD1Q5kte9KjK4tlzPWZ/p2h/xRFDvUed78U9DDnmVg1UfUDhaMN6dhjEjEMiVe/V2ZLv6p4nEkWt8QWm59l865VxDEPYDINc12+pgRsMcPZPLncnQz81erFZA0tIUJe2nnvMFolGUdFZUsqXJRRxT974rmv2Oc57VYbvOz40cNZzw4zNMqlv3eAhOjyfWjwLYruXPrphtIJ+zQHG94wy4BmWrJcdU4Pxxamig9kRD9ywiFPoeTyKbBgDyg3x9Qf9k5gHPv+PtofeVn+Q1aY+BV+R9OmQaMb9wnlh/QP+mIZoIdYvbk46+llSnc3/o+rLp/kXWa8wKBM8b+OveGTmecHzTu3Kqa9us3gXe6h058eNBji8Nkry+48fBxCFfuDXax98Yf//fGH//P7lI8vE/4Z4cHyfNDuLrH3h7YLEjoLe2tra2tra2tra2tra2tra2tra2tv58+vUAsKtzl+ZEOW5xrz5bZ4bhLlduTvWoyqGm/s/MgQCdC/C6yVWWKYgDclFWn6yAJJVsm0QIDEdzLIM2Bk7+APFS8NPtDWxy5eNWXymhX/PgnXfreA7168YkrZdrUI44OXEFPXPKn1h1wsBBxksQ26luVOS8s6lvigtrjZbJ9HIFty6wK9wmFpgL3C2XLBUlXYpkRjnxKgZ32sKWhrmiirOtHuZ8w+i6f2Yph7Wd6iCegosRVBzui2wPOE64vuTGTNPvvZH50mcGgpsRdV8XhKQc2nIZ3l2jXve8XM8RSeJMOuFyC1sOfHrVSysu2lrHgHZ8I8Ylp7Jrb8xR61eu4SyHMnYIWGPw8fF24N4g0OS6tUO8PofO28sReu+LBajq5xasR1CfmOUAFzDWHjhqv9Wh3LX+M8rZqpjwjIuZkzQj8qXzs1P3ojmHdcydzFl7qs7BVl9xOayr3/gNwCv+GB3DDaI6gWMB6jzkFjYq2jsY959Vx3aaopDNazah9tgNfJcrOsUR0V43b/W4Dcw7TsgJqzxhIgM3gxw0l8s3rNU+VVwzMQvoOtChN8JW93VFjhNv5zVy9katk4Vhx4MYE8+hZwX116Yf2JUCqNMrArsu5XY9X8X7G96OOtYod3+DUwMMjEGaM21ieSlKnkk7H/p+k1u6+UPzEVPXFVNDMEknszqqLwN7wvFBfn2n9U5rnRwm4N2P2ksVfx1oTXqHcOaY1VE9iDrPsEPPXzzhemL9syLIl99agweKz37p2kLPAmlkDsWdR2B+YjnISHyahke64e0kvv5PaIa1T3wGLSet/w37S1c/9wyaTfq4aI/A/UPr8J6E2Nra2tra2tra2tra2tra2tra2tra2vrTyP/H3/L/LEWmzHtmcqhhAiE+YQrwplfHqaXgmLtQuSWkC0BUR6wVyDIvSNnPG8TIgdblrLODtKnPRxAiw5jXd2DW98h5qsThgn3lQsZ6uTjR+XhTPHU/Kxo11F8aX5D9hmkTI+ckGAUJ5WAFg2M5jAuEZQg6psBgzinAA+8/J3X+qX7YrLjmdC9ncDmBvSKuzcs1DemTsCB7I20UzJzYrI7Y5pgJsDFh5mR6EnU+LbUOicH5WRHCFQ3L0GdGAXrK5ekn5gfWO605Tsj1C3JN5xRsWjHYfkBbUcEF7EyuQZYTHHXRBsnIwRWz4NqoiFknLXW8pg5V3R/Bv5gD8lUADXXumr5GOwXEDK1PU7/y3Yc7F0gN3A7MZt3TVvB3OUH7fWsN5Ag1DSlgHfJVZl1FZWdoX9JM+9oKwlu+j5/vbuTMwcy3c9eza6+LnuPmzOrXXYZiucWb/rGCvfc9DQHWOnczuZZngXasy21r5QbOKVduPYu58svD7ovWma+9cOkLUZ2/WTHbOdeboQYFBOF1unIEu3KSybjIvMogL/A3x5O0Q+746qQ10zAJ3u7PSYyIWZ3gQc5Xue1DTvo06B3rp6LevRFlwJ9pzOtiPL+I1Tmel1y1lszrIua4AXJW9DaZ2HFiFsL2zbDe8fO43drmQ7MFU58Rc6g/fF7lvB40D7o7ncnRnH443j4wE3z2pj5xC+TSvb4IOtdrMr6+6/la7uaoTucZ5DWJMSti+9TzNL6YcxK0GoY49H6xk7Cu10tCzCc5vr/3bA4NJbT+ji2v99Ud0Y/cwt6qDzteWE7cHD7+pqSH6zuWTyxemCftOPGWtAZHe3B8/hXnwrsGScbzj/8rf+1sbW1tbW1tbW1tbW1tbW1tbW1tbW1t/Zvql3MAd74rtnQOGp30zrDlMh0YHV5J+sCPRuQldzAD/CCa43N1w7YCXK6Y3taqE1egzrorSthXN+oh7tJN0b4B6Z0Z0CyUVuudOSdmE6djKbelRXWNmlUX7JeOS0FsHPcHbi9muADqfOHtQ5BGB9b3r/7YETfkFGw+5KLLgdHKffsqR6yigtNb9csGoHPKin82Zrkkoz67ERnYLPhp5Wx2F2yfWvdszrwuvOXdjTxX7+wlDzMc1aw6Kx644l/DsNbl/GuNvC7FW0/0cxU/69aJGBWNazAusnfBKMtay+qxTcVAv3t/Kecs5YhWb2zEi2sIVgk2KTa6ZSguuuKxYz6xqdhu7yfQibjUB5wFWfNS7yxJztfb8Tu/I4h5CpC7MyNo3vDliqYMravw+Y5ULoiWcqTKELwicbv2qKFoc38INq845RgC4fbuoMW77u/tIPYCm1GJyn7H7mYE7k7koFl9zu1kn/r3db6+Tnn1w/oN73r1uw7UAc2KWF49viTYFHhekdoFlQVgHXLUWg7d63VAq/O549G5+2EVk37AfNHMmDOYyKVcAeT3IEjQMOSA9nJ7WySjorptJozvpJ0Y6id2d+yqSOrjFDAeNWAR5bovh2kaWEa9B+RaTzO4nmQzbFzQT4xRSQboPVQu83VvaIcc0KbzIy8NbbiRvcOYeg77h/qVXbHqWMPjIgncGlEu6xmpZyK6qqPnxFu7nd1+fpDzhcUghnqSbQ2wpFzbHk5mw+wpgMtLe6H32i6XnOoJtAc5vyrKvavDOsFCQy6cnxrmSTRckQH9gcfU19tD9+31RfhBvr5j5ydhqbV9/E0R8GNFiL9oryc8n/T2jdaS3jpW7w1GpQ5sbW1tbW1tbW1tbW1tbW1tbW1tbW1t/cn0ywHgQdDC8F6dkisr1y4wV31rAY6YWVCqIPAcMC/oB/l6YUeDqO5UU7RsxgWog1aQCnJOrAUWisNlLhh3CsxUhqubMaciWTMbQdCXS9ldDt9sdd6tXIdBeitIfeH2geWr3IwwYwoWZjkjWXDN1ceaXt9bvap4OUQLRJlgdTbF6ca85KjN5O4chgLc7Q3TrFyGHII6tcwOxCXgwnjdbs80Y44XrRdMzXf/r/jlS+A6yu0cYK3dbmFrR0XWVvwuU0C1nJARF4SRnhVhfMD1AuQwFAfUHsic5TjNu8c44QbOmQJwY2oN5OAt0OeXOmojsfOhSG6cDHVPG3IymzdqF2BuxPDqSa747RzY+RDsai43YqH+nEFk0FpBVVOEcy4IvRzeDoQXaG2KRbZyhlrTMscU/DWXezbLrVkQMjMVuZ3UnhnlTlX/8cxlTg6dJ2CTe8jArQvSUd2+/qghiYLXCzAD5FAYcu2VzMWIU5diVFR3OaRzEulUI7Y+Mwxc98zqiRfktjo/K6duXQ9XfVY53dceag2j4Z7kfHJ4546NzqgBCLmZs6y6xqzEckH3NispoDUsBUbDG41BjiSjyZHbrKLaU677HESue1hx1HbA0bRscQl4miKgsz2IeOFXRZWzOqbtDbYDrJ+Kns7Q0MQM7FwOfyAm7o5T74mR6qa+3dkPPAfNHDsO2ksR+GHBtAfJF56BtQ9Wcnh6de1akCOZXn3m14W3qe9N5DrPAP+NbK/qGG73vmLqmTM/oQfWzopRv+D5BcejYrcDxlOx99Y08NCcvF64idpmNuI1oH3QUjHXVhHm1j+Y+STHH7TWtY/9wP3FcfyVHn/g9ld1Oc+ngPfW1tbW1tbW1tbW1tbW1tbW1tbW1tbWn0y/HABu/a/YKMC1encjcO9wNDwmZh/gijpWPLSgoyJ0mwyM7hBN3ZSYIOyVFd9bSGW+Ck662OpBAcYA7zhJc6+I2CXFzS7HqTBTRcRW/y9Exf1WRmxcFf2agndW8DkmOZNpF53ODX/tqI7iU27Xgp2ZA/xxuy/Nmq6znHirXzTjKuCdgjLIIZpTf25kuXRDsNZPcgxBJ5CzOFAfs41y3cqtFz7xkNM5lnszTQA6fd1ExTdb4gVj5WYVpCa7wGZc5WJdwLHcsOZl4Bb8tTDFv1KR3n4Uz7bbKSnS2csR/GJkCki6CarNKZetPbB+MsegrX7QiqV1AqORbgVmL93xAsaKATbU7Vw9uI8PmM9yzA7d8/4oNy3ltsy6t7UnrOJ9zaCljgUFiit62QzaA4uBVYS2hcsVPV7gpyB6Jus/clQ3Ikb1n6on+Y6rrn1nPtWRew9XWAFEas+sr1cUL+UoXg+B8Y4wNhSZTO19LxdrcHfpag0nXF/QPm6HKRW5zYpntor2pt0DFZlnEWs9GznUI+t+4AzB+XDMnBbqq67M5BoUMUWio07sMXWNzRreezn9FTs9rcnRP18i2+cDUCx9RpJj1ue2GlYISKUSZDls9QN1j0OR2N5cfbbLfT0afljZUw2LUP+uRT0v1RPu7R03Py7dPi8XvOmeRE5u4I4T9sDySbNOOz6Z8R1rTc794wN7XhC/Qz80qOJd+388af3U3k8U+dxS+48aYklTPzeG9Yf2vWsv5IpwbqdOJS8Yl1zp3evlmgXwG3581p7SkIefv0FceDuhn2S4gPDrdw1bmPrMZXR/EPOF//6F3ma/k9ekHf+L7vl4kn4S5zfyev73/7LZ2tra2tra2tra2tra2tra2tra2tra+nfSLweAmaPg6HJaagkioMcoCDIAI9tRELIcqTGx3gRHAjhSEHEG5lFOvwJfDuQJ9hRsak2uNA9yKALZbGK9C1ym4KO5eoB/sNWSqdjWG+6uTsuVjpxyH0+SiJfg10zMJ3h9/gJh7qyIY+8nmQ8Yr4LcBWdSPbPmyympGFkZiA+4JniUudDkvk05VYlLx4pQnO5yZPZDLtUZis71JsdfIPe1l5Muovpro9ZCcNJTbr7MFxamHt3WSRoZL52XaFY5k2c5mPU7zAkbuj/l8KYf+JyFfR1zQTKjHN2tC+xxUnZqMgcjVi+xulW5LqBVL7GAsXtnjj9o/RP3JudivnCCaR2r4GDMmOWyln1U7mzrAl6GnKJQx+gfGhpIlaGa92JzFe28OpjtUrSud11TE6xNeu3XMrTeeyhIXpXu7OV8bqTH7Wonk8irIp8Nj8nMS6zwFHDDDA+52c3koNVwAYrZrvju9LNAprF6lW9lwd8K/U7kXpYMN8FRM8Fg5lP79fxGmuOZ6se2dg8uKFo86jouLKpnmJAbPAYrZhhMXd0mhO/9US7yJ6s7NysaWZBZ15c07XU75FxvArTEE8LxfhDjxUzTXruuimM2gllx3YEx3kZ6c/WTZ9Pzn3Xdhr7uXlC1HL/e9LPXUFU4YMd5O+MpR7vYdWfGxKYGUmhdUfNe756Zmp2wwTKAp5+4PTAbWO8QD+x6qqI8J/7oxPjU+8FORW77g7yMnC/imoqiPj+xNMIaXC+sDTmU1zCMG2kdG98109A7lo0cg7s7GTSYgUNbkfAJj98UfR16j2tDnnhTPDtzxXcneXwjXn8nmIL+rjj/dvyFPC7yv/wfxPMf9P/5f6W7y8XfDni95Dxfr8itra2tra2tra2tra2tra2tra2tra2tP5F+OQCsfs4g+wnzYjChNVpMrD0wa4p2NqqP9ZIzT5ZDcv6BtU+5JqOAbIFHqAjaI+VuveRuY16kCdwkifVymKJY2LSzYMUoUJiCORGEJY6r/9Qm9gPQNKp/F/UQWxrmD2wOwUQ7SILIJOKgeTl202itAwWOHXIkNIFua4fAVYJ5CiQyy3VcbBoX4J2XwJWZ4ljT5MiNFzYoKOwCVTPI7HIyLlOhOdmaXKwxIU915raKx54Tb6fir0m59zLLATnkMDQE9ivW2zJIDq3lWntPjE/SV3FndaeWs9RUGlxm2lOAdQbkk4wDcyMd5gy5f0MgsFkSZ3Xcrm1gTZDKBdi6OemO5yGXddZeytWFCxyKxGZU5G0CY5AO7Tx0X9PBs4YM/tm5qsGGo1y41dNrBfWrN1ahwqtwtwijIzelu+CaHQJ7OXWcSFZMOKzr1v2aQ52z2YEY+Jy044HbxNuDH4OYzag9UoA+r4oen7dRmOrU1fl8E5SOS+7XLCc5SWaUw7iuxQ8sJw1nmhzKFkNRzKQc/YwC5V3XEuq9thlymFa3sVUcelL93zxo9WfuDc+sXuOPWtMa9rATxhP8INOYDFp23Yv+0PEi9Y9bAe2sGO4o4Ky9YDjmAuj6hsHqozYS60oqSA4irzLsG+aK7A6rVV89xubYFCCdqKc4/dC+zySb461ixOE9JONgTcMREbMSBmZ1P3vF3CtmvfsaGuiKZv/6HdoQ0A2w/iAqvj27FTytvX8e6uSeqceRilAHDSych+7RmGSrWHbrcpIvt3dMjAn9uGOuIfCw+73nXusZisi3mXh7MPoH+fxDHcQxbxDu5zf82/+L4+N/oR2dnlGJEIZ1/bzdgwlbW1tbW1tbW1tbW1tbW1tbW1tbW1tbfx79cgDY2wFziJllQZY5q/VSQKd54O3BvJ6EdfJYPY8DpiNKU07NArAMkyPPDPgQPARoB5ku0OLl3F3QLpI5J41RcdRdsdPLrYdDJMMCYasV7byiiptcsgPIS9Dx7k99gT2w8UVyMlvCnAKWdmIj8F4Ry03OV3UTq+NTzrmOvJ5B+qE0YRS9nFGRyt4FX9wEA8uZbO4Clj+4PPWPQJfNAl6uf6e6f5OoGGdgTMHcUDeuNSfcK/q1XJuhuGJzI0c5VZsBC8K6wHVUrHYgd2mdYxYMtXZgGXKEuzpUc16C15ZEGhnBjKie2gtvHa//jOXibYLWRK1rhnqYQWDOBM1Jq75YCnxPdRJ7h2Y0N6yfguaxYrZnxUoLxJkfeDlRFZ8c1W+b5U6VC1ow0PXZFenMHAX0FcedXIrX9a7I4Eyx6SxQmnKge8V4zyFgrtkHOUrpHbOkeZe721bkdhOEbHrWNDzBG9SZFzjv5YBu2ifAVN467ifrbkEQHKrOvq3p+R7CaKbOX6vPvPt6NZiwADZTfcIaRIiKKlYXLDN1LzFyPjVgwcTJ6uh+3UDbUs7m9K7jlwN+NqfNQYZi4WNOWuuku6KVs5zGMUgrMF3x8da7AGt2PCYxLoxnRWBrWCK5BIVrSIKYzJTrP3PoeI9T+3GG3MYoTt1QFzlWHdD17rMcd69xekKoA92skVP7Llyh1y2nwPfxoBFyz0Y5vo+z9p8c/WmpWGoawXq/AXZh/kH2+tr1XS7sFXdvldBgtc+ihh7sWe8Lh/6hYRBrio6OL8HeabfL3fMi56WBhKgUAEuYzxr6+aY9d/1BtqNSBMB7p12/l6v9xNsnzO9w/Cfm878wv//+r/uLaGtra2tra2tra2tra2tra2tra2tra+v/j/rlAPCcFwvweH/Qyv3o56Oiep1mcgtmPwVKrqGYYh7Q1efpGYCiTkkqfvUQWJjXOyY2TDGu3QSes7pDMaJiW30uzlFRrkZBsYrOXXHKqM81LQq6AnkIZMaBMwmbBYp7Aa5GuFOIjIjEWpS7s3qQZ8r11k88Ax+X4l6tnJaDgneBmaKZLV0g0X7sdO3lHC74XS7NvH+v6zXQMb3AGU1xuPCO+vWKdCUF89wFFafLFZgJtIpcVsdp42SMVzlGFxDUmqbJOUoI9jKvO+rW2wNfTlVgxqik3yYDZkUrRwRjPkmO2jvlJDWjA6MhoB4TzgJvXX3EK9bb/BCwDjl2zZN8peYFzg/xwxHko7HiuK3WSZtk6lrMUHxxReCS5fpdV1EAdLlry/moNe/aR+OrorNH7fVezliIWetnTdA4q+O4f2NGPUNm2FFu8RG00+kWgtwL5DrlVjcYUycTNRhA7VVz7m5eq77fikB3PwhzxT5nksxyq6eGLyz1jJZrvmFEptzfBitcWm7+1/t6SA1mWJ3THNwbQISfyAK8fsjdTiNul7IGILId1Zt76TmPWvDW3o7jWRHq3bSkCU6DGALBfqIm6QE43rxcx+X+9wM/vJzvRmSoY9i7AGiOeg8l7gc5ghyD8A6zenyb08xoTVHSVjHvmY3l71ZntMEhoKpe3No744ldLzgOJcxjxDVovWmQgSTvoQLDesOyE5FkXpVOIIjbyh1fN0hDAeYamPCDiAuzwepxlts9seODjC91N0f1cS+XdEz8+MAImmvIIjLw49RrqCLjianYazMCw/uJJbR2YO1BzonNC3NnWmBcmF0c7QMzcE8yazgog/Dz//LfPVtbW1tbW1tbW1tbW1tbW1tbW1tbW1v/Vvr1ALCpS7L5E59f2CGQor5S9YsagiW5IlctBJ3yC7KR/UORzX5hs9xkrs5WGRY75EsAZb5IX/2yXp24gzs6GsFJi3w7f81veJXmAmwgV2peGId4F8so5xTOkJE4B2SD8Ycik18v4ngUf5UbOTLJa8hwuFyH6bibOkPd5PKdKXdvZsU4DwEy78AlKIXxTkKVq1QQb8UFh+J1TTGxitw9q/fYkLW4yf3sBeS8YbNiaJOymiIwxQ8OZKuY3RA8W/HHNobciRTkRmuk+zSxpthta+r9bAQxk2xdoDKjeozl6J0ZXHMoWno+af64wTmAMehhzDQyHL4PwbSUzXfGi3Z83OuQ7YNmoUjlJoCY47vO01ZPbyswV9SQISfyHLhlObflJk/ZdfW9fhSIVP9v5lUQuMn5bEk6FZ17CaiHk1wCeNVvHFlR0d4hkvSDES+5S9fa54s7z7u6fO2GrmB5KFLZEGy1ikNebnVvuh+mCOJgQT97d2EX3LTMN8grx3NSXcnlVjZawfzlMG3Cmzlq//UarpjldP3S564eYBMUvrfz3fv90IxHBBEvImtgIAu8Z8g5bXpecmrDWjO8NSLVtWsgODwvOc77h84xJo2TNMFJNxQFvcYiTJg2p6KoLb4wGtnKATu+1GldTu3ZD5p1GIF7kFcI3l7LIV1u/FYO6ExmhBy0OfUcR3Xl0uWKPQxeTz3izeqyL/Jwsp3MDG3TZvVKcaXEvy71hFeXdOaE10vH6WelGryUllDgna8XftSgR3xhHNyTMnZi/dSaE3D9Dv2b1j4p53HS2wdeDvCRFzajYr0Vl4938vl3/PyNJLB84OeDmE5eLyxeJIn7WQMAv5FjKOL6Nbi+nsT5l3/B3z5bW1tbW1tbW1tbW1tbW1tbW1tbW1tb/zb65QCwHJshYNk77gKAbimwggGdmYJGSg/uZC/YMS58OumCbZGpeuB2Igej4pAzm+BYE1CzcQl2HBUR7CuiVpGxaeXSrW5O4R8EaglmplxyOZVMq7BqbM5yMc7qo7TqOL2gter8nfp9NqYVcJupHs8V/Rp5n2vcsbnoawY+g2wNb029unOqM7OOSXc5bUNdvepSHbpGb+XytP8ve3/UJEmSJOmBn4iqmntkVvcMlvYecA/3/38ZiA5LABYzVZnhZqoi98BiHo2nQ0/uzPailIeKqjIzwsNMTdWyaT5h5rqr29FajugwwUwOWKdcwnbHHoecn065SF18vA91p8asWOKKnyaxBdlqDaq32MjqhK1o6xVYPwSkp2BtZhDXSXrDVv3ckCP4ui5iTWw8BdzQelmud0dtLPVJ0wXqaE2Rtd4EGK+XonEJzCZ33ywV6avC34d+Wd/nbWiPZAi+u9FsYC6YmKtAJ4+CqKcgqRU5z1budLl7I+TSNEsYH4oSXrdre5JhAnXedD2pNU5glpNbbmjXOfJR+0Rrn3bo9+lvaK3/rvtz0/6nQLVlOZIhWRidzNd7OGDdLnKWPq4PUNsvq0ByrwhojyxIWNHWlFO3bLdJQcPMcvpqQEEp1V2RwetFhBHuet7lzW3dFc1N8dFcpA0EZ02u8kwirro/XTfzwi25r4gxBH8BG4oaTkKO2XIlk6EKb69nGIGHv+PY76OZqVh3Wsfu3up00hD8tSB7I64X6Q+5qCP0vOcFzfCCz3l9wW8NnHR9bV0hVtczhqLZ+4DhxHViebBiVk/1hB+/448H1h+KgufJPH/q/N/7pGnoIJb6mjGdmZiT1R54f7DmT7wPBGwvLIcGFKpvPDP1eY9v4A8sLxxXkkGrYZgarGn9GzlfeP+GHYcGLDJZ/gTTm1Ddx4FxYPysd3DCeZHtA+8d86lrDMiPf4br8+/+K2hra2tra2tra2tra2tra2tra2tra2vr31t/OgBsoR5cN6MdD5pBG4egQtlM332q7nh0rAu0xDL1XOYqx1gT1A1Xd2V/gpdT0RRxKmDBVxcuclIyz3IXPlgGrSJP5fp0oiKMDSPSCNPnujXkEtWfpyuiljDc5UIkZvWsyknL40mmKQ43TqZ1uqvj1emQLwiI6xPzg5wnNvwrFhYnOvhMvBX8bo0oMJVpBYQLNpt6Yq0dglXhAuN3LLGFYnt9yNUcs2JgBd8FZVPO6QjFaIerGjYvzI5yFyM36PXCuzpQLRNiiF2iqFtzdSdbpnpMI/T9nngmfsc0FyDD1Z2bDNY6Oa+fzNmx4wPLqfjv/gDkyr6BvvcHK4W/s8ktaV4g1AbLQweu3Jzr7q+NciX2inKuyumIRZjBeakT1juHtdqDXd20raEo7Klnl+WEzttJHgXdC+LhvOOxb+e3hQBp3q5tJ60rWthc7kzrGEFEKrrckMu4uqUZR7m9qc8W6E9OQMMHpMF86ed6vXqqkznvmGm0flEWd7v3S9b3mOO5cBre7CuiuPqok/jaS35HSydpQ3tOv6Is0GCjnMqAZ0ULq5t4ZfXmhsMKmjfMGq1VvzLryzlcgwX23hd3pHgILKZhrWELRUpbDSzEwjMVB129uHZvgJy6/qYYY7eE0Yi58PZNn5lRsc2HoK6B3/e2klg/BT3dK4Je5z4wPBtxLSyn3MbdNcRwO/2zHPMWFQk+tQ8zBNu7k36Q1yc2vpHxUg+vD/3btAatOdkfxDzJ6wfWHkomKBdyvH4o6aA52RuekzYGYU/W+YfWfBy4m6LP836GSa7AmrqBNQCz8JTzP2alvI+mr+tPbNwu+EbGidlBnP8nfnzHRn9Hzdv4Rp7/Qvz4g2gH4/kbTuL+IOMifv6vrOMv5PX6t/w1tLW1tbW1tbW1tbW1tbW1tbW1tbW1tfXvqj8dAO4tcBvYOPCYtMegOEVBMLnGzF2JwxUra5fikWOC21Rf5A2oGpDHl+uS+19W0byTdBNoS/4v8C1zYnRWCNi0Q7HACUS8cH9glqwVNAuBlOoAFeSielv9C2xSsdSia1jI5Zhmb0g3M2komjehAIq9Y4CtomIxaGTFMzfcQlGtkbgNyBc2HFYj3VnrUcbmSVoDZgHOiormpWvzIcAXitqWm1QdzDfIEvwriM4SJKq4Z7MmRyOoO/fOiM77UYaiq28n8pUVFyxYqi7iOwY4WJHEDT9XkOkEF9eaLLrWIr5ihdWAnOXSHBV67UQU3F7A8fGOFRasbkRMBlRHbNbX69fMKUD6eJBUNK8ZNMfbwM1wmiLJqW5auyOoA7gED91qDzf1pTrAwqIJ6C0NCGRc2g/eBXnjdkg3FK+c5aLuRAqKypHesLbIKbCb3rDrEqBvguDdr/eARK6r9mXnTbfvc/B2595SV7O3csLn/VwMM+jcvb/wHoR49wg3oBN+0W4oHNqvZPUmh2C33c7jrPuk+m5NDvNGsJYi4zu9+qNdkc7LiHYUoNV9pJptdSYtK4K7QHg54PVHC+9P0pa6nkH7zYzwgS0BYRtNrvpoGoZo+uzMhY97b9Sz9OpcHkd5VlcB5YT2qHOAorjXRayg2ZMIwd+0oLUDXi/ACZ8kzrr+BdoDtwLQvWPeaM8DX1M9uh0yBtrwA+JFtoP58xNvP2kfv8kL3UeFWXci9NKxXIqGfv7GmhPmJ208seYVp41cxOMD49TZOL7JgXwnCxToTl/qIB5PZQwkGgiIFxZ3moArtj2ues41YPL4C+vzB20sQeBQ9LqPJ+23/0QP7VFrTd3K6yT7E9ZJUxzD1tbW1tbW1tbW1tbW1tbW1tbW1tbW1j+U/nQAuFnH+4EfjZZDsCUX4Q0w9cL2g1hTQLVgjqpRG9EbcOFrCiZ4FJAbggkmIAbI5duAcqvdjtw3kPGKYvW7Z9PePaJrJbQnRaVI6wRyGnquimbtAnBrkraKp4V+/g3YojpdXUDLuyv2FWOmYnU9wWnq5s3EsiKYK8l2yTKHRSh91nu5jIHHs6Jrb3Bncuy2wYpUpOw4yLyIWY7CrJ7erDhXkR4E/Kxgy8Bs4v3JWhdcWhe6IqidUSDQIJ0wI9eJYwXmreBurX0ruByGunAX3rqe/e2UvqIimdV/O/NiLUV0WwS0goDdyXXK4Vy9t90gUv2ssZY6Rrs6WFnrDbS9dSIvWvLlOMfI64I5yTF4RxjnwrILVGVULLM6mLVmaM1opOtewOQMLTiYvZ6/9XJE59c+9yY4HFNu89ZJWsVRCyardxoignVVjHnV9KaNAtQdi4XTFZlbUNacN2zLWqe3C5lyxFt1Cc+zgKxrUOKtxO/YcGrNkvoZpljk6upWDLrh6UTWsfJRZ+he6/yKIb8BLq4hgeqHvp+3KZeaZRNLY61FWzrb3e6+5wvsQTavx/E3ztnUp7UaRGApslj91XI4+9K+weVwtYpJtghdo0H2Rc75NSBRLm5bs6K30fepzBdLdRAb4MchaJmpfxtw/LXeb0uduDY0fIC6utfyiuEeijhvizaUluCuwYvWD+Aiw4AHFheMJFPvhnRnkuQVNO+YLVpXd7lZqEe6qY88W9f+tA7xkum4PchYeH9gBGYfJBNbl15YK2o4wbGKMscazQfuiVuS10msiqTvN6RueoeQxDplHG+DfHwjr9+5O8lNEQK4dTx+wPqA6wfmT+K6yOOvcL1Yn//yd/zts/XfW5n5dqb/9/yM/5G11/DXtdfw17XX8Ne113Dr79XeM7+uvYa/rr2Gv669hr+uvYZb/xbtffPr2mv469pr+Ovaa/jr2mv4H68/HwBucvy2dLw1wZkIPI1J4tEFU0PQrmVizQgmOZ0OXGHV+9tJS9K7YlwJFHWb5Hq9oaNFOW+z+kHvXtrmihKuntL0RbanonBbFkAKfbZ3fVksljte3cC2QkAjDOeOWc4COgNrRetMduVAUbtmrWCn0fIifWAmIGNx4Tdn81advI1lE2fgd/TyLAAVU9DZwFaIX9JoGLlcvaAY1lvF9CLHacXiKm52KmI3k+hdLO4GtWFE63hrctfaIQD9jtoOmjuJE3NiPb/WgZSD126PaQoMX5OcwSoHtFzHoTjrnCyCmUmy5MZ1BN2iXLZtqIO1gHVYdTaz6LaI0WEuaE9y/YG74W609iDyQcRJw2ldfcR4VoS4w7q4O5PhBX5wU9fbxW03LNeiY3d0Mal1c5cLdcrtzL1frAu8lhvUVrm073juddXPSu15jBlL99pdABEE4MpVaTkFBZthLnhmGaQfBUQXZp30vzkfAEzMnLQ7MhqBZ2/lzHXSDK+OXzP+Zs2rUzpBEdN+M+Li56OuzwoA39fs3BHP9xN7f25BREtIohzmQaSzsj7L6x0SgsKkqdc7Umvno9a7QHLWsMe6avDDv4YclmK+7Xjqut/LkHoWc+l8eXUMe00RhNztt/s5ZugYpNcwxtJHjKHBAVOPcVTneZajNahnbYKdikB/Cb5e4Ic6qHtzukUNpywao36OEgGiriPPT6z3mjd4AEGcn3oP9vGOsLdac28dIol16Z3yeJDZyfmpwRHX6bUaAYjWyLWwmHqljQ+saS1iTvz5F5yFV6+3WeLP7+qwvv6A8SSvT8Vu90OjE6bBF2sD2j+T89I71TQAkNf/oV5mTr0vj4HlX/HXD3z9IPu3v/NvoK3/nvpv8T8O/+z/A3Ov4a9rr+Gva6/hr2uv4dbfq71nfl17DX9dew1/XXsNf117Dbf+Ldr75te11/DXtdfw17XX8Ne11/A/Xn8+ADw67opybQVm0xrJpS9wk3POhzo3c2LnxL2Rw8nrwtORBzdoURCqiSRmXHLmVlevek9/wvimX8cp4OKoG9QEddLArMmxZqbEXIIMWKSsgnY7GWvSASctFHHqii72duBxEb0LNmeSl3o96UPfW25JuQhPpqUgrQ2aQ0MQPBI8g7bAvMDplHM3DYHQGYKbZkQ4dMXuKpI4qgd4kStpvSB0VidrrFoDx0zOU2tLcbS341WPBEdObbx/wTqXm8/qGYQPQbbMApCDsFX9p6GIWIyIBj4E31p7V+ZmyMl8XovpCCZz4FQX6wx8PMtFe4NTOccjQ67pcnq3pn5Yy6VeU7IcnqF95b3g9QIW1lHP6lQPs2yQcks7Vo7Sgpa59LPv+GfvWHxBTblhb8ewXKyC/U3OUHPFIsdVPbbP2g+OuZVDVT9nxdReDgrevycD5Hi9P1fe8Bp2MD17c7k749I1h1Vn8PUFZSvCl4oT1x6/I5/L+Vw/J8rVbtberl6L1DK9Ab/x7ppOfZ9519BDc/TKS8iLtAPLFxkL80d9g5GcmDWaNVZ1E6cN8Ee5hLMgrMvxOmfB5VWpAbUEGbgtzA9dM7UGPWsoo+Fj1LOutVwJs0hwq71spn1uBZY9653jmMnRT2hvKwY8oZULPBXlbmF4e5KWcrzX6ySVs87iJOeSw9vBHwfd1Y/eWlOkdgvBfle0vQY7EjeI3nUO48TbQaxL7vPxJK+fSgQAIi6l0M+rgLcJBCeKsW+daBDXhbkcxXestpkpdh4NSpjrnRHrwo7vGtbB1TOcnbzXzJ3oR/VIA+2b1tVdQxF4RYU3/PEh5/D8pFnH/Dc8Tu0XfxDXD8w6fXywrt/JNv7Ov4G2tra2tra2tra2tra2tra2tra2tra2/v31pwPAtAFNDa7TF2XsFDgrM5wXFIkMwgbjeQjwRYNhRLxITPG+Nsl+6M8M6K06TxuY4nqxLug1OvizoFkWWKus2uWEJRlGt8QQ6IqCQESS5qxrwdFZGWAvvB9lGBTM9jjf8cf1wwVly80olyACga0RJsdmhiBz4ATnV9QsBVpyVqSuM+enIppzkdGJtXBPzIbWE0d9paY4Wq9o4FT07e3olCMUvLW6znJUoqjZrKhs81VRsw1bl9zGrTpf16pIV8DmuwNXtDCw1rG4n0sQJNhFUlHUC4TYgzCYNFZvci5eF3QTXAynjYcc1dVhiqnHuAGR1btrho/j3dOclhhPReTiAuOySOtW094uVcsmaEYIdrfqdp0LG4FXdDb9g5yBddO1V9eryNmn9mZ0uRqRq1WwXz3Baa6IWz/KRcq7OppUxDm4BiMMMiYrg5wFVmOpBxgXyM6ge3Vpe8dyYv4g1ylHLAeC5ffnywGKFXgjgZcuwGtfpYBq2ZLray/SujpvQzHC+qyKPI8Eq/uNKed0JkRBURRbrIED1/7yb3K9g9zfUR3V1vBQTPXK29S7lF6ecq8aiS+TqRmH8K+zzYAOKy7aemFNoFYQuha7Uecsv2rBW/WIyyZPNkVuG67IZ6dizWvLN/T17uWWtYqsHzqDXkMpTY7oIPDm5DzJrkGDWBooyXIaGynjcW+aG8HwGh6J5eS68GMQs6mDGLnb7fENppPzE+/f1N3sDuODvH4U4H3U93QsA49GH654aIxYibdGtkZcn9SUQfUoo97rOjNGQgF07eklB3r/pj9P3ae1htmDdf6gHR/qgPdO5lXudCsgrz1p40MDJOcPzE7s/IM8/kkDACvgaDSH8f0/w/z8v/s3z9bW1tbW1tbW1tbW1tbW1tbW1tbW1tZ/mP50ANidims9oT0EQO9o17s7FXX6ugl6ykRmNE88G+SD+eOT1TrTBh3F3LKa3Hm4QM4yQQa/IQ5fzsSI4kBe4GliVyMtiAj8EOTydrPMlHuyLYiTcC+ncNLc345EsqBadYgKk7g6REU7BQgb5AxdynyVUxfMkinbMdYEjGYuOgZ2qbc3EeRLA5+AIFLzLKeoYK8cr3JPqr9Yjmgr2mh9lJNXsba6GHWSpik22ELXfHcx367dFKkXkJmpvl0ca+qUte4wqydV9lr1KpvAuzp1B1ku6YhFGqw1iWspCvZ4kleALfx40rzTCJbruTmGZwCznLcCi2aO0yFfmDUBytYgXnIFv1MKUg7MNN1DnFh3Qa55w/LquU0jzDCcRgoMxh3hK/DpDFZ6uXPLbfo25Aa4kXTuuGWLijfPJZ6Y+Y7mTpNrWGjUvrp0IwQgh9zA7omvLBd9CEBm3lW6Za0GbYz6firSOJbuf/2to3nKZWx3THQ5eu+u39v9e4NenIz5hneg7mOBaTn4MyfmB2nXVwdyhp4HA6soYrPbYV5ObgLLpFfXMwQ5nfBVCQGQnhUbnmQzfZ65zsAEa09WfuIz8MezziWku87HSnVWk3BNQdhscttGYHbdLy2d/xAMZgigWoZiyr1j2cs5Dcb8gtH+VDp2ubkTw8Y3sBoEyQt7PKEJkHs5wd0abvpviynIak2d4K+pAYX20HCIO7YuzAbLLjLV/UumXMc+yFjk+oGNpwZxMJ2l9dJ700a9w6zu8YO8Psl0RTv7URM6S/eQhnUr17pc+Rk/wb/VoIJcxGiXgD9wLycwaDCkPeD6Q4MLVnutAY/fuD7/YJ6/s8aTfvyGJ7SHBnji+kHHykG9tbW1tbW1tbW1tbW1tbW1tbW1tbW19Y+lPx0AllJAa33ipnhWi/WOLcY6xlWusBuwyTnbMHo31ujqllxT0b3e8Idj64Q2yky5BLV6qs8z+pcz0YEscIULVMWJ9Q+yyxUYHkTK5ZoZsALzxgJ6ytOYFEh2V3SvO1xTjrk1SU5glE0VwAqQlUvSXGz19Skn7h2Fmxd5JovA+iBwWms4JwC+gtY6sRxvoRpWe+Hl0PsyMlrBOsPCMS9XdBNYFDyrjmKStA5r4r1Vv6siXAWbjfZ4CnhFPZtW7t9cuDeiFewxh2MQ16w1KijYHLtO0vzLlahwWiJgxkWm6RndEHY8hdLXifsDs3g7E63ikd2q/xZBTDmuW7lLC3S6elDVe2tytGYU9FYUNJ+f5Mf3AtifpHda7wWoermnT8EyWW7BP8j8KTNx7wL+64X6dI/qlK5YZpKcL/AH6VZudUGsjFP31UY5bIF4qQO13NpJwngUDH6J/7dOK9f6Hd98u1IhFb99A/e4kHP71LONJZe0jdoHVl281SXLLICcimm22x3s5dykCHc56s3IVFd0Ul24NdxwO3uBcoiCReB+xzy79todzY4VrM9yz0MyiRWEd5zqbo5yrGfWr2t/NkUQWxNkTkIDIXQ5uLPO/rpjt2utbGHhimNeq7qFXWd2LmwUjCfJCMwHrPV2E7+HTEDvNr7eYcTCLLGmiPv0ZLWm66eipWvQgPUJHOBPneMMxWWjdIOs2PCvnw2wsHGQnz+w3it22rD2wbpOucITWHUGretnnBP3syCvBgCclEu5PbA4NVDRPgTrPbHRcWv17EKOfet6ry49iy+Yb/gxsCyneYTewXGph/12iZti79MMe/wVe6agcc2bsE45uZ/f8LXgev2df/dsbW1tbW1tbW1tbW1tbW1tbW1tbW1t/fvrTweAxYxMlao+aH8LR3As1heISiuIBaIWDbdQT+4D8nNxtUHMFx5OTrnl5LJLcgW0Tq6XIkcJrFfv60JwYxb4s4I6awqKZAjGXi+5iduAdVV1ajKvReuOuxcQq97UCBlj8yoo3OWOs6a4Uk84urpm7UOdmeOBe1R/bgg4Md6QJGeSOeW6K0dduy4ijGxL/aKY7jUDpxUMLChqJrdkxRG7F7zKkNNzJnSveOApF2zrX/dUYOaOpY50rFV0MXI+G+pP9Va9yzcYxyEmYXIcWiyiupX1AYpQDowrLvI8BfNpgkidcm/rWcLCzAmy7vN2Rsr9aipWJfHaOladyJUvboOIEzPBu4jFWhotIBY8/nJnDIMH6ejPAPIqgNkr7vY+vhdpg2wC5RZGtkHOr6EBuTgFTVdv9ftGOLAEQOkFSqsD2C3JcK5WDtdYkA/tj2a6F4fRekHxXm5dqxjqJWiIl7t7vs8gBXHVHez6Gpr2nLXaPYrWdsrlSdx+ZH1thqCr1X7LG0BX5DWKH8cbdsdO51SE+A2EvcNCENgMLCoVwJXObk5GgzzJuOprGiuXnmto3dzuCYusJGidpSioai2UgC7CWPu/lVP4hHFADr66m6vj2oy8krxC52I8wA2L6x1jbXFV5Pu9BgWvoyKSU7+fMXU/bZRfXWfSujqSA7Doijy3+zqSWCduLpe8N5111GcdprMaa2kgJCtmvh9ELmy+oA88NZziXkA4Lu2peRLtAHsQeWGvH4pt9y+nvNy7chNrqETv5JyLMKU15Pyj1rZzZ3bnmjUE07Cjkgusrr8fBXONsE4kGk64I+bdyTyx9oQ46f4UBK6OdTt+Y82Tttb/zb95tra2tra2tra2tra2tra2tra2tra2tv7j9KcDwGlGSyGqdH+7CwWUKuoWRRjT2ld8bRaETKex6Abx0fHXJApOQZBWICayAGpUF6ngpxyfJmfjQr8X+hk2uuKDK6ZYX5AwL2iKULU2BGhyCkyR0B+CY+Xc7LaINVmrYpetIEX1eLoP8pA71SPUq9oqozqDaI5NyC5Al6F7yHWDTFjmpIcAXaZAUAGmyCDNaTahN9qV1Zna5FaMJZjJHd0cb0eome7DTBDRKg74dgLfjj4B5Syjpr3jg1kTwtSnXKgwCqplXGQ0goNcfwB61jMXM4MVIcBmLpB7PGC9cEKdue+9svD+qGEBKxP3kLMytcf0k430geVZYFSO0YxGxE/SB+kH7i/iLJdn8gX/MmF+Mi3pdgBJ5KS1LpibVlC9HJvz4r5CwS5Qv3GSZ0VyuyKpw5OcnwVnp3qe3eWmTKt/L2IIqNkdpTyeOivVGd3aA9XQ3oBVvb2WQXp1EMdV5yIE6O4O3FhAr2GLF9AFAWm1yije3ETh7T6X3HuVcv0W+PQv66vZ8XZpv/uCs3Kpl2KuMTluwwzCcK6KJobMF9Ye5W41GZ69C1pixJxkLtw7rXXcHc5JNg1lWBprliM/dNazHLveHO8d5pRZfHwrKKw9Q3US64w49K69NbrQa2ZFVes9kbZqdWqQJUNO3yaQ/Ibk7ahhhRr0QO8Ru93UyNBr/kGsE5oTKoFmrROLibUH0XTuPAM3uYIzgxWX3OGR0A7cKtp+BempPULXnnVB5JZOxCRNcNl8YGvqXdIfOg7Xq3p8B7EuuYdrf7l30ie5Eh8Pua/TsPmSy/rxrdZ/Qn/gebveJ1i9+9zxNojlMKeS003DIsyfeEuadSwHrR+C5xUXnXf/+NbW1tbW1tbW1tbW1tbW1tbW1tbW1tY/kP50ABgKCKb6W5NVsMgQLEOuWBNoEf8tuJQJOOaNbs5KlwOtPZlAz1DScFPMqiBegd7uVIqsQJYfWMEZc6t46IBu0Byv/l7vzkROyipzrTjkTrorRpUlYLpSbtgMhh/YWqyCy5iTTT2XDQSEMjEvSIUrJdgfAGSfQLAWhE+ZaTFyfsJ4kN6ImOplTblzIwuc3usGWB54dZd6Q1DOjYwB68Ry0dpgxRR06QKdGbMgHQXDg0h9jYBhvt3chHpG1bW8CGbF7brgF1Zw1Mg85TS2gysmnslcVDztJza+y+UcBSl54Jm1buXKtsSykUxFBv8NmBS/ndAMQxG+6vld3F22WEVtxyLWT1be3bxZUHQJtvcHnEEbLuBWYDnnLCfqgfehn3kvePKO/yYhbckRaUZEOblbQ32pJ3cnshzoof0awcJYof5nuagN608NOphjtmg+CtDf7u2/cdIb0IxcXg7dApExeUPZiv2+Y9eJWR3c9zOeLAad5LZsZy7BdhwsBKZvR2qmPtO8BjYaFlMucxTJrRB31wCGeYHhFByNhecd/1zu/NbVf9s6bhqCiLiqd1hR3kkSp6KAfU1Yps7rKMjdNbwRa2LtKbh9yVXrfWhGIzQ8khm1x3XeSHQP7dBZYwmur1WDD2g4Y57QKpaa1L70e7gEyFPPuM6PRaj7ORLvgcW9b49ylzfdozcNCbRB3M/ItYa6H3UBZzixXvh11ZBBOe59QHxWB3OXEzidmD8xHzUoUg7k6ofONtTPHEoI0IuDGoC5u3o71iqd4fyJjaOuNeB6aeDg8dTzc4M59WyXAHGsxI5vEBctkshTAyePD72/rh8aiulDPz4W1r+RceHP73ga8/UTb4+/6++era2tra2tra2tra2tra2tra2tra2trf8I/fkAcAAItKQZaZ1mFb+b5bp1OdUgsaavURdpg7wwazSCw5xogzVfYIu5Ft072R4CJZHqmfQU4HX0uVndoelYmCKBaYqqdWPQUIg06ueMJBKwB3c/pqXJyZuTlYmn4nGVqGuYNfp40nMR6cB8fw+elW7dIQ2zXs7MFL+rXtHE6C3KNau43ex/JfIkEsyeNEvdjntFIFduK/Zer7Sj4pOzYnvvP+61FIt2VK8st3O6QGC5LwVv4+0uxE5FN88Lax8kC49JlPU1oxEVAwxy5cZcApz+5Q5eNyQtCC4Xrum5mdHjdrcaFKQ1K9esNd6doxUJTCsQFQu6YbXfyKXPLcArk3iy7vubcobTbkfrJBnY8UHkiS/I1gr+f/WtRoFt2pDjc17ad5j4eXsojpj7uVy8o5Jdble7gWkB3CSJDO2bWNWf3Hj37q5LDs32UctkFcNNwdlyzy/Fc2MPdb/e/cBLHb92R6Xn7URtb7CbWa5r0/oL1i51IdedZBTs5N4fs9YHPcu4N5piuy0rht0N8hBMbxU77Ya3A66fOI0ouJjrRUTIDZyX7vXuncUrxhudX0uU/BxEBSwTCefnu3473Yi4sHSyVedwLrLcyx5eHbfwNSwwdO8VYUwNHFgk1p+ggGuIILwGBbzruZsJpnor47Rge4Siyr1/YJlEvvAuyKxJlQL0qUGIrPvLTPj5E55PaI1YV7nwG/Tjqze60hJuEK0UA/s6x3ZHfss1ndReM6t3xZN1/oEfRw1jqFfd7Ki9plhyy6WO4BqyIIHjt1o/1zNrD13b9akUhNa1N9FwgiKyA/OjjMmDtRacn/ic5PkDvv/nAuOPGpR44Y8PbN2EfWtra2tra2tra2tra2tra2tra2tra+sfR38+AJyn3Jb5FZmamQIjeYEdBQSuNwwURBxv0JEmWOEk/RiEJdfnTzmH0wS7rEOfgqIMwSozDMW65uunoFF/6PtQRO+wjmcWLJZnMbxhlkzLIpYASc5LoGwYK5PminMlHeMS0LKDFousyGWTzVewpTnwgYUAtlyYYC3LZaeeUutdXZ644GmOgnarImWbALA5GZNgQH5qnWdoTcZSn631v3FoWl1PfEHJ0HVkhPqNIwtq83ZbWjkXbx5s62eB0lofIOaSm3B9yi1dMcD6vIuJulxzXXKxxsTGh9YVqgLVirU63Q9BpVSUbdlrBX/vDudyvypetgkclRvS2hNWRS4bcpKCMndD/cW0ApY3MI4F85Jr+fiuKOZWrspyTGLVsxwX5PHleC7Ql1EQ3HlDaLeKKrfqSc7buXsvqJVrt8DvvCOXbzexy52ciZcbWzHdta/+xpWre7vqGbvgcDlUqaGFjIt3XHNBUysw7FnXktXtazVUcJ3YGDUUMOWCb6OeSbm3bYEtwda8+4ZD7mgb+p6soYVIaGDtiVni5qxYZBjWP2pow2HNgqha4zQ5ZVeC0zGHVlHNGOqpnXKzmh362Rg2urpoY2nLRLmjK34402AupV83DYqYVSR7hjps+VonWkFi4+2sNoK4LmhPgfGsKPh16ue4o9SDu5scLC/B8Pr6SOd23GuNtcfz/AnjA+j67+Mp0GuLmEnzGgpI015ziPMU6PaGH9/JkLM8Xj91vf2pAY55aQClddY15X5vDW9GXj9qUGKATTK7HPvVw30P12hI4Kp7uiOjqy/9emGjBjdw1vkH9AetXPxRUfDr9ZPr+gP79hs+Hnh3HZPr1OCJm/bu1tbW1tbW1tbW1tbW1tbW1tbW1tbW1j+Y/nwA+O4ctSjIVQ7CNGhPcT3U+3nH0ua7kfQrfhgazqJbMJvT/MFVcMYTslWs8w2LLLm7TS0XDMWPAnA7O9civMvQqSxhsiKdIwKLgtbXhN7x3nGaemrbo34WlM1Zcc3mmFccMXIdZy6B4lhfDtt1/Q3Ec5QTLSBnuaqDs8vAOSfkJMywUBSxWdJaU8IvRqymrs9M0lM9oXdkcIF2i/UF/ux23qp3F+sEcm7KeNsEww1yXawZSju2QeSCzx/Y8STXwnqXIzJRPHSucnzD8sG6PnGXo9fc1JXsB0wAxXff0dZJ0NrQGiZf7tuChtonyrbOQO7x9IKuQ1umO8wk/FDschQwjYAVFd97aE/OExuKy72jbH18x9pDfbhQMcqrgKhp7WIpBtu64HNemIOnIB8u+Gje1EkcwWpdHdW0As2rAHXH8xLXXVN7MhOzcsl7l8k2J5HQfRRElVMzb6j9NucOOTHvXmNvXy5eo9bQ6uxBZOBtoObf++yh/uuYb+AJTVDUq1+4HOPaw9Wfm/k3js8ky1tvuWrPUecarY/5fSnFwbugIXLfpsfbIU/Fq+daup5mMtq60yJ1rnqj2YfSoBNiBe6Uk1hvlows07uR1fdtGXL3WpKzzl8qGpyCtSTleK9u8XJhG01niySba3umYu8jqzPauoZYzLVm3vXZCTlf6tvNVc53r2Wq4QV/wDy/3ontqOEPvU/s8R3mq359CFr7AxsVid8OAe/UGc7jGzZP8vqsva84crNB+BTcJgWu+1Px51QfuSsRoKWeS9Qwg6FfZxjhNcRz/8xMbAZYYOMg7Bv5+gPGBw0vIK/74vkdPz4q4QDi+olZx9sDc+NOod7a2tra2tra2tra2tra2tra2tra2tr6R9KfDgA3b6y1cG9yGHr1lcqKCCysFSyNxbu7tR0QlwBZql/VMJp1xvokR7JeS7G+1vAVEKd6U62cuaQiXHNhBctkCBVstkbFFncWgdtgpUCW4+outgY9FbGLYW54e767TMvaxxuIEeQysFMQj6N+L+QKjax7TyKm4FSOcnrq97O7zJSeGHf8KvcdYXnidkAuWleXcWaTwc6iIlvviOEuyJRXOTaTuBbWmwCrXeUYrMhoBrYmyQvrHxjBWqlrsV5maCevSb5eZJ5YHPo9F/yTW1mR0BEXaaMg+CmYei14DGwusKC7K9Y3Kja7IUB/g8I7Brk9tCdS8b2il+W0NIf+1ADBvKqPWK7kWBdEslgF8Bw4YTo8jtpbKHq8QzPHayDgPVDwjktWLLPqktVvqofVqrJaXc+kYplzXeV+vfd3RfKySNczzHKKmre/AblBTj1fz7pA5PJUWroV2C0Aa/fnTtK69om73MTea43QGTGdLbnrNX9AhoYbOOuc9HLPLg1SuJXTmAJ+9x6zr3+8YWigQj8zyvFtpEe5/lvtoeqytZtaL979xiHHbJ5nOZsbeD2LtaAfcp+C4PeSA9zcsVhi5uXylhMXuJYioMuVbuOp+xijgHg50Wl63PMiW4fedf0xK2bZNFBx95O3AyOxWETFGhPrPsr1DurcsFxh6FZ7M+SSXovkxNoHrE8NQ9Qzy4rlptWADCHAf8mF75g6k4GcU2nO1jALrPUaIDANPuQkUyDcxkPvtOulBIJ2yPnuR3H8xooTW0vH71Q/sI+D3up+rcPxxJmKn47FOk8lGPQP0hQloHeHILnNs97rjXW+yPkHjO+KZPeBDyVBuCmJwB/fYf4Bj3+G+ZP1x+9/719BW1tbW1tbW1tbW1tbW1tbW1tbW1tbW//u+tMBYCNwQu7OctveTliRlqN6Wv8mjrf5G+oJst3QWBG1fRysddF60tIUdXzJ3SsXZcIYZCwsJ4y7xzMqsvZ2wznZO87CEpYZMeVcpBXQQ32l1fqp2Nk2sKUo1Ew5AsmzQJ6T1vRzU9BFwLBAV7lF5aY1Ik3Qiuq9LWicZqylaFa5/obcqnc0bl2z4qALKHVBTOYiWsMTOXrDBFpaYq1XB+1LcdPWwZNgwQrcrncsdMSq/tBBzqlo16auYtpTUCxM4DenUoep/2YQq6KtbRHzxKwTaYrhnop0vqG0u9GsyTHr/f28c03tCUzO3TfUvnhDWTXjkuuT9Ka4Yn+UUzix9mTZxbvzlknOFLhNtE/uGN8wrCeWF5a9GPTQet89seFYP2B+yr0a5a9VubB+7qw93QbppvuISWb1sHrFTotic81PATUUrZsBSSdQDHELw81pftQww/rqj+UGzoNcKVjuXSC6ya37Ff0sx7IRBbGTbo2VsJg0O/Db6ZxyYSZZztSCoxn6bLt7o4F86BlkQlRs9FJfclbk+L3G2hgK7OkAAQAASURBVL/BWlNub1JQ1occoe3BWifxnhS5l2kVHGw11GCap9DEAK1AuVXMtnWDOQlXnHxEwJQLFU8yps52VuY5Ou/ujTia9vkdV13XkubktSpZvUBrLEWet+fbRe+43ldW5y2uGmqYpB1lSlcEuz9/0/2b437o2a9Twynmendk1x7yRiB4bvd79F5TayxbeF56Bvd6L9O7yLVuloFn0NpB9kFcJ+QiVsD6QUzFqHt7MK8JXFh7oorhTsZirhc8/1Jx9kaun8Rc2POb3P/zJ9YP7Pok4gX9G/gsEG3qBrZWEdQLd4f1ImfQOpWYMHUd4y+wPon5Yo2//Jv+Htra2tra2tra2tra2tra2tra2tra2tr699SfLsByAd67+mTXEgD0cuUpC1kg9oZ85op4VbaxHJEVV2yhns1uTnen9QHcYKuR9iDXVbBNzs30ilhm3Pm3+sf7u2MUUm41C/z4JldtBubxBVFu0EZ+uXW9YaMLprkhvhSsOJnAcicqAjXMiTSCJSdqRFXtpgCZOZEpl9461bmbRkw5D/XzXF2mHnhrWpNMPBfePzAMd8E7W6vSqeU8tvEEZkEkF1DKqDjceAN3tZwaUeBQrtUk4yK8QFKWc9GA9hB4rXtgzdtSSnPkNiQJ68R1Ys3x5pidGJNmXuhf69dAvajrqmvvpFXccjks7Y5jRu5f/eyKz45QdDH3QEDILWpyy9rdV9urQ/i6AK2JHLgTf0dN8+UWpnplQ/fVYsnR6nfHMoqgTit3bkJ3jKkoXW+1z6cGFqg+2FysDDLKsTyn3OJxqSfWrGKD490hS+vl4EQ/x+UMz7hhueAjVoC5BhGgC4LnkrsVK/ez60yZlXM1akBi1WfWx+WqQYgCohX1XfZtrRkChuR6r6mAvmsNyhWcPtA8TAjANzlV3Q0z7e/Wnvj40J8V5LT+xHrXqZkv4vrJev0kXxdxvvQuaBos8HD924HrBfOCPrSOkVq7FeSsLlvRUSLXHcZenbOBrevLrT0e9eWJhTqGwyuC3RQhn/MlSB61J7njmLsWM/WesqZnZeUoJxIbB9YOen/g8aJTewDD4sQzsP4gVyh23psc3A28q5fa7d5j6lyP6wd5/sDc6N7orib0Zh0fg7z+tWKc9WzTJhmXAHHr5Djwpm71db0IP2Dp2a/XT9ZaZH+QfmiowzpcPwgDHv9UkfDf6rXc8RUYS5C4GVy/Kw68y0Fv66S1jq8fQBIE13UpDntra2tra2tra2tra2tra2tra2tra2vrH0x/OgcwAGm4d+ztLqVg2SoIrH/SCjpaAoIaeC/nXNT3ABYMb4RDeMevrLjmG3y0gm4uV2ou2ngqJjmzImNdX2dGZNJMUKj5Jddq60QY83xh7SCQ23DlUkSwKXqVFGQWY3Rdc0wwQbaskN64QfacX649gOr5NQL6U7e+lj67uT5jnoo/9q61rCWLPrBLrkCPs5bSMDO8N/UUv6/JCFKdyXfmcTtY1yQt1aPrcmummaJ2vdCsJxwdS1c/ahfMl1WxKTY2Ug5db/XMs+D3RTKEoo4nutPE/Si3ZKMVjMJWRVHL9VoLJEc0yEEIFU8cAmq9gT3I6xJcu43k1wI/wTszgiCI80XlcNdVhNaHTs7AveN+4Hli+YFxVT9zL2s5KM77hbcD8ye5XpBLz8dWxSvXNWZiObHWWRQUnqn1Td1LRHLNn4r65SBt1vVpHc0g5imw7wLFbku9qu2G4Fedl1Nnp3Vde1Y8NIAdd4a4hh1WwUyzL1d+dVK/e2ijwLnVWYQ6O10gOxQNfjuDbV4Yd/R2r51f3cB26Fn2u2+2YR3WeeEt6yqz4HLSvVe6t4ZGFtWtvS5FepvVEMPttHZo5SS+4bw1vOtcRxtaU0fg1IFQf7j2kNV19TcAx1JRzCFnv6mAmWQpXjlqeIT7+0xu5vmT7B/c5cYB5Lxk0o27VzqhP+odFRXHniR3XPcNsfU+sfbQzzy1py0WOZ7EecLrD8x13b13GN/J+YN6pakP3B6C9Ne/1rAIZMph7z6w45+Zr3/F3YkoB/1Ksle8+VI/esSpM5tRwyQG46H3YN7uc/DRyc8k7cCy0x4aWJnzhVPdxV7x9W2wrDNf/0qci3b8hI/f6M/v2Hzp5yRY/1727q2tra2tra2tra2tra2tra2tra2tra1/LP3pAHBmuWtBUMNMAIzblSm3pXpjb2ehYJlRLsVy31rKLYwZljBaZ+ViAh6XonjDwA8C9QBba1gEKy458voB/iiH6aL3TsRJchRwVuRsILewjVE/3oiK0G1332/1nOpLHctT9/k3zmIcIqqj9nbNLjkBvVyg+jPF7ZIL705GQrrcxu81AJjqTLZFy9uFq8hh7w+5KJuXE/WCHHIxRtmIF2QLfVwo+jmjwHmAMyvWWn2/a626JyOvSWQKQrVDPbvrLPf1Iszk6luTsCEAzO1wFOAmJ3FNtSz3h2CaNyxOuQELBZp3gfW19DUFkqDWz0zXYF7AtVWvrWFp5OiKIL4HDDLhKOemAa+fAtctYTlG0LzSiXPoe5ADV9HPA4sT9fkG2U1xy5h6X9eU8/F2ikfTZ90AMeoa3GtAQIB1ZYAJ1FukXLLXCY/n290d3gi/QW4BR6+4Z1Kf1w5Y/X2v7+5YC8HZ1vWsktqzLpiWNyW0+4gWHJyYK344MSUg13nMe29bgfG6ntYasSbcwwQRb2e/7jlI63jvFX9ce9dr31vDyt0aEXKPN+3jQO+FyCgnceqctFHcuqKiC2ZnNqy7hhZI2qhO4jnFzKdpSCMTi+r7jUV4wV3jDYXlrhXgzbuj2VwA0wuy3075FYpLVpGu3m+JOpR1wDU88R6U0DVnmhzO6DMo9zBjwDRsfTKOb4T9xpo/SLRWMZw1ne5dqQCtBk6sEden3rU2aM8PrPZYXr8Loo/vchbnrEjzhp0/BX/zD/zxF617XCSLtXR4rCXkQcsgPv/An3+FqAj8fpBpCnl4/FXvNav+8HVqmGZ94n3oHdeUaJD+IONfyfUi8y/0xz8pXnt8kOtkvX6njW/cx2Bra2tra2tra2tra2tra2tra2tra2vrH0l/OgD8Br4px+gywAa+Llp7FFx4cEe8ssq5uJL0U7GyKetiWnUDp2PmNEu8TSwa0LH4KdAUl8BJNuAivVcEa6of2E9FDlsQTNIUTUurjkwzWjqr+kAVgxvyJ15TTs485PxEoEYGUaPHZBrQG56TRC5gWVgDs4E1gb0iQlga1uR6tZQ7NOELZuEwX9UnSkVoqytZnb6KWL4jWaHLPYzBOuXaY+j7WtZnV+etFfDOpOHVz9lhfiHnvF5kAb9sD3Wgzk+BdBRxrc8SbM509ZR6B0a5sY01/4DokE2MPxK3JTBFU8wwF/gQCK64as0PfDmVs4A71shYEGfFPtfPsoL+LGJNwg/dS1wwtUZ5w/vPE8bAW8MJuo0yh3ddw52/nYK/6oMerPMqcGuCeeu6d3z1yk4MI8OJORU5vZZcsT6IdTHnxYwo+CiwmNdP6EOO5C6A2rzV4yowiM7NO9I8q6cWOUc1PLC+HJY3dI4o3/kNxcuJHqFe5lwovvn+YznujXIlm6tj+o7jNueOgo51yv0byNmaAvtv53DeLH7JsNy7jlEzOf/voQVPiIovB3BwG/SWrEuuctZL8D8mNjVAsTyhNe3h1spBvco967w/0AVBIxbmhlMx3ekwFGuc4XJzI+Br45Cr2VIQ0+X2/nLvy09uOcGfelYV+00uwlxnJxeRUw74bALtcXFHjed54UNObbnwFWPvx8DO1JeNRk5B7bXqubQHST3fNKWRN/UMQ9L6gbviy6GR/sFKde6qgxtynfTjg+kPujfFdOeJWXVp5yIyWTOULD5ODcT0J329sFSsfsyXAD7xdjdrQCbIVsMa9o14/cR74O3QqWlBe37Qju/0j2+KAo8k4iVn8/gNP0YNH2xtbW1tbW1tbW1tbW1tbW1tbW1tbW39Y+lPB4DdOpELyyb3qDktkXvw7hKVHbWIowvuJXKNlhMXHzAvwazbVZgvug2iG+ssSNkP9caS5PVSn2ZWVG2zMhnLUek5iSXX5R0XHeWYxI0ei4WzDEG8NqANYiVu6lRNyy8Hqilu2chyQTacxhKuoZmcj3LYxVfPcS5yVvcm5d681Q1bKRjpFWmb6hy+HYZ4E8SKKbdmA0UwK4DavGNrCRpbq+jfRgIRVznzBhmO9UFmEDEhOtEGrEuA3A3WD4G/TPXUVvxvFsANc6J6kQ05KAP1oL7jbUm8PzGSbqOc0Cg+GcqCGgKYGQK7VoDNBOsyE0Nx3fgd4dtIVzRt0sj2FOBfV3W2rnKlmlync8JoGjJotR7Vi4q8mIJu6QXuG5HzvX6WIQgZWV//BUq9YpTVQ41AJNp/eXfnehc7TjluFUd9CFKmwKy3pkjyrD1gAt/EJSe4O/hBrrreNTFrGpa4466tCap6k4M4V0U+1znqvaJ1TQMAdKxrYEKdtpeA65IbVg5Zh3ViNLI/YJ4aBPADx4lyKt8ObIU8z69zn0sQeC2dzesibWBH+0qBz6WzbtA85VqdF+lWcDa1J00ge6VAo4Vc245ho9NoGEHg6urOSTsOuWVz6X6tlVtWgyaWk7RyA1PsnQX+eJuNtZ+a+nITfV9LHFMX75pqTPahPbbU8U0fOrtuZBhrLqylnmeCxcRS+9HM9GjqLFgk3ofOenfNe7BY6ax10Xzi/QPPWd3rHWNWxHqTU9cP3IK4jJhLewzD3BiPg7RO5mBNDcokQXy+iKW+av/2P7FSgzVuiuJvBrFuR/UJPMi49H5qhwD7+tTzbAf5eBLrJ/n6V3yM97CF55SzOS/WdWHjg1jQe6vI/fULfxttbW1tbW1tbW1tbW1tbW1tbW1tbW1t/fvoTweA1/rEw6A57g/c8x17KjhUcbtZMcE29I0GAlgd4lNfczuA7+hknO5CO+GKfX07Gh2BwiUHohy+rijSUJw0bjTr5e1dBY6tXIOmeFQgGYJ7aQWZC6RSkNI6xOsds9yg3KqJWdL8jkWNv3HeOjY+UEkxX5GzMSGn3LVxlWPUMOT0veNtVV5aa3g7QV3w3ArEZGvqCzWDNrBc1QvayNcf0AW+3I/6fsX7CuSAtaw46CHH4xLEVCeuF2Su+zYj8yrAF4LIuH6mBWteeHc5OHvD1qKZYUd1wrajIonljGxOdcAqVjptYJbkvDDriq21Xu7sVVxIUDVSwwARJrDnKOa3j+oPPrVmrcC/O2lGRNLi0h7iqdkDm3IjksQsCB5ezs9ZTtCAcr9aAnER3iAm4YP0J6wXuT71yFaSrdNMexcSZpD5Uix1xe6CQ1cscMvq2nX7mzNg5TwWANVXWrlzTefJW3XYUmtl7whlOczVIW1+78uB+ahe2qW9aAbR4HbVor2NKYrcLLHR9OsC/rqns6D/vAkq2Km9sho5X9j4UAR4pgD1Cqw1wd07Gv1oWC6anWQ7CDNFBZtj4WQvB3RcRGpvWe/QGp5J9lbd2pcA5Xjo3me5qB3B6lVAmTu6HF0TUfs/lXodS19m1VWd2u/eP9RTjAYj0hqWVikC+p7MlOP/fk9h2OMbtpZA6P2Ou146t2lyXadit60fNBYxXzQWRq9u7ySXMRM8X/Q+MJbi0B0N0Kx6/usHaUa2QYQ6rZt1wk1n3LrcwwWMef0kcTKDRWd9/k64M779prO0Ul3iTPrxnYwkrj+w40Pn8Pd/YfauWG6b9b4Cjt/IlRqQyYtInR8/X9C/sVJd0J6DNhp2/ZSbeut/GGV+pUz89/yM/5G11/DXtdfw17XX8Ne113Dr79XeM7+uvYa/rr2Gv669hr+uvYZb/xbtffPr2mv469pr+Ovaa/jr2mv4H68/HQBu7SmHHwF5Vc1uezsD71JHs4bZ4B1zayhylkXaIQdj64oajZA7TwSLZob7g7ST9AbzVCyqp4CGPwVrMsGDtgR/vUBqSxPw8qS54/7kBszOortcbKsAzsqFe9N9EAI2dkB+IrKxcJqgCwh60yAKcmVBszYKIqWiZuN2hppALoru9dblFvSEcMGkVBRveisH7wMbDTcjY6n/1hVjbJmAfg4Ihkc5FK01PATfV4SgaybpJndruVhlr7Tq5l1antYgn+T8ATlZ3ZUOjD5Lz1XRsD6GOpePJ2TQPQth3v2yjq35hutrfur3zdVPClgE1r8JIod6o82rd9YKiK9JWifmSbaOAe6dHMbidptbDQkYzPnlEn13Ug+89ZozCGxN0p20iuN2dQanNTmfb8dyu+OXTdvCnYwTqP7YcuRmG5j8pKivN8muaGzFeKeuZRy4Q8Nrjez9f4qZ/urEFjC+3dh63urdvYcilgCsd26XfeYndy+3RZ07b+XWvnOgU9flFXee6OcUkCx6quU3OdCbG7GCbE+5j1Pxx7q/V925EX7gmQK21jQUYVbR36PczlprW7Ogpj57riDSgZM8633RmoY+uqK6Y6kb2VLnC29fsdWk1sLQta0phG4I9Hq/bcg6OxH1fWhYYb30M8tlHy5IbSGYvqbuwU2A2ai95OMLTE91gcss3rAVciXXvs1MWJcer+mcc35i/YE9f6Othc9PwgfXPMnRlSR+Xe/BE2t67vPzjxoiccV9r6Xgg/GE1Yh5vV3HmkdZOvv9Q276dKJ94tmYP/8VO74RU2kH6/zErGOP7ySNZYre9+sTR88gyvGuLRVQ72D6AeuTfP1k2YAW+GjYmrQ+IE68DyUNeKO38W//y2jrP1z/Lf7H4Z/9f2DuNfx17TX8de01/HXtNdz6e7X3zK9rr+Gva6/hr2uv4a9rr+HWv0V73/y69hr+uvYa/rr2Gv669hr+x+tPB4AFehdJe8fMyqlpcpZmvus5Eyr+Wa5IQbPryyEb51eULIIVd8dod0HLhVy2EV8QGQJmQhesChcoCwS+5DhUTKyVgxVQN+2cmBnZnYwXmY3IRqwLb4rfJYSib2AFlLvvwvwQtCbKUbewJsdjqzjkVRG09PZeH4+FHw9iVtxvetWzTgFD64JJBJYNzyjX7pDj74bT8ikLdHpXZ655cfcgTQA7VkULQ1XDLvEvb9AERnMFrB9gT31u6jPoH2ReeCyWL7mdo+BWGpjRrAm6xxToIwouo+hpm+UMDWAJ2hX0zJwCvlbO1ZwFViH9Ae0J109FdBs4jWgDiylXY6y673aTyuqadrlB3Yi1aG7lbi63d9TTsOqB7p2cJ+aj9swsyCqHNmZYB4tykpc71Mr5na0LvqdcnrcJ19Ykp8EoAFnX6J40TPG9mNaLO8b5hp5OzoWc9OUmpqBvNlSiC7JBF/iMq4YhTMMFuNa0uoTTtUfk7D5qeGKSeT87nWmdbeo5Np2HhFhn+ZGbXOEUhJ9XQUDD2kPwe70I/6C1ct7WAIiBXPREnUv1dHsC3fF8qA86puKu4wKHFQty4Ymup0G8fsrZXNHkcMdp3xg+y2VvEIY3dH8Y2FR0c2QlAwhmWiY2J2kaYLCuTvCIBXPVe0Th7xZyOyuO2bFYOtMV7U2C13srs2C9ecH8KAjcoT2wOL/Ojg+yBZkXzcfbzR3e1MXrQeNJXC8NxtiFzdT7oh3leq8Bk9t17I1wU5S2Npi6oFE0sxz4j4K4OoOMD8xdz8Y0xGLtgzz/gOOJHU/u2HINlVSU/3XijwH+5MofrM9/geMbXCccDT/+JyUzxKXrGc/KV9ja2tra2tra2tra2tra2tra2tra2tr6x9KfDgCnC/DFPKsS9SEIYvEGvQJXQ07airDFXSAsDpJLPatef2b9DbBIVyNwexJjEq+fpB9ETtw6Njo55T62uaB9F1icC+um3s+MG/mS6YKi5BvsmlXicpRDscBnhhcooUBQwUVrFaurzzTPv+kSVXdq6+rPTE88LsFw42+gp0C0u0N10lIOVfcFcarbNxPrD7D57qSVQzW/wN4ysjlmtb5LkMxocuraIAkyTiLkUnQz2jjKTWi6LjdyNrhOoh3kenFH+xpNrDGznquz4kXmKNifctUy5PqMCX4Ib5pcr4Kv5aDt5cDMpeu1XmBwEtcntIeeUWtyhFdXsfqQEye5YlY/s2lNrcDlqh5gKxjnQ72yFroP+3J8hhueTiKoBq2+RyusWNv8AsyuzlSile3TwZZgc0Cav+PEjcl1nZoV6F1xu9kEoB/PilnOihp/KgrdJ+AFBG/HcHXrWgCtYnxdQJzQ13i9epICjFGQe8nNXGBYz6BBXjqDrcuRG6diyXFFVafc4F4udzIUN1xgG4vao7d7fFZ8dYM+aAThTvpTXFrGZHHXLgf+e7YobuAtN7unXP/Zu+LMF3X/DfhJriwg3cnrRfjAu9PydkQLhGv/Cnyrijyxdl+vzrBqnU+tNTq7VKRyGoqJrz7stE7GS+exbNFZe2xZk4sVkwva23sgQ9XM9jZUQ5ar3SEbuS6sr4K2h6Kza/iD1rHzpDfDrLMQvI01YJ3EvGjjG218YxGkT5g/iPXC+lNO6ynH8+IH6/wDO77jqcGBjEmysLhgBS1OOL7RSNwGOX/KoWvqI3eDQLCexwfZmpIGPGuAp5PXCeOQYzuW3nP9yfr5v8IM+Os/4ccHYEQa5g+ueakf2OLf9PfQ1tbW1tbW1tbW1tbW1tbW1tbW1tbW1r+n/nQAOFZFJJdzK/NF2FPuV2syKtoD9akWeAM5aO+uyCxgV9+Td39mFnglcXP6cFYcxCpHXCLYkClA5F0szpaiTucLH89yCzuRSaR6MyPPdwcwKCa3ty5QnEk13MoISVZ3q+s+sOrjrbjdOLEwQZ/2qG7giob1hjdDkb1Z0cFaLuN2rCIYOLzg7SE+5QZ3FHRojWyVexr0e7HUhXv3hJqcend3aoYRTEj1pDqOmeMmsG2jlfP3IsPJ9iDXJzlP3ad5gUfBR48XK5biqv0bRjk/LQhb2ErCU9HgFcXsvTqGHd0f2hekVa8zQMgdHmD9KSBYwPjutg1z1lzQkrUW8CSuCx8GbZDrLCfnJQAVkDMwl3OS6oUGuZ4puBcszBaWvaKJIW9o/3bcrjLeFgC/44x7wpWYz+qFhmbOYrLWS/DXn9xDEImcjhUAjqV6phUJ7NgMdTej2F5FiFv9ue6zrNmCsrHIN/wVPH6D6YrjDRIbz5rWmG9XrHUXVLfA/FH72iA66bJHy2F9lTs0CApUavcUxD7lQkdOUiJIlzvcm+tyMquDt1fEO+/hC8wLzKrbN9eC0RWbfseaW5JxKmmglXM16z3h6vfVnrnN8VFfsyoWGvUGp/Y6rZIIYpKt13rITf7u6UUuWstUl65d9U7zGjJ5YSzCGuajwscX4Z3WGrkETLkuEiPWS5HVrZOmaHPHBZRXdVN7g+h/MywC9vgref2Op6K9Jw7DyDW07q5IZouF+QHjN3KdxJy0XgMrmVj/AB6YOzFf5Hlih79dvSLV39TZzAnnH3LykuRa2FPu3d7URRwJeQbuC1+XXN9xkf4gP3/HesOef5V7nsD/8p+xPGmP37QHzk9mZMXhG76mkgK2tra2tra2tra2tra2tra2tra2tra2/sHk//+/5P9pqp7fbpAXbk+5WqkeYOU4y3kb5bwztcNicjNiDq4o0rw/szlpVk5Ow8zp5rQ+1LPLhPgJY6hn0uWWi+tHOe0S+qNgmcmVihF5MQtQhbJdiQLQbo1mB3fwLfT6b8q1bBUP3DHU0eqZ6gwNuVKbCXiae0HaxMLlSK6UaDlk7yhYBIhzYQUW/VhvUzBvJ3WBGHcWEEvdnKt1sg8BGhu4N3FmN2aWE3Se73hg740WoRhWAvOm2GtqvWPVczBiTmLNiveulfCBjyfu32mYPl90El9ic14dyG4BvTpovZORAr5efbR3bPEN4pe6iu91viOItfynwLU7Vu5UHHgO9Rpfp+5zhVy70bH1iVvHW2N4o5kcmgKPBf1u+GhN3avckeW347b2aT+0Nlkx4IacuStq3dBnucBlXJPgiR3fKSKvfdMUF24Vw5325A3ErQmQNjnPvbqRaa24bsC63aF1TupnGjV0EOq2ltG8vftnSapDt4C2f5OL2f19RslUz62Z+rqtKya6IqCxGnBohjfDLfE466ED+YlZw9EgReQi5tLQR0Zdm33991zve8AW1pJcPxQB3Zzmjdac1h23fINcHLJVnHgfurZo6svOJKbcunI4P+Vkbh1rpnj24yEHdK53rPiXGsyrXOYXHhdCrhesF94OLE/Udx5y7LaPWruUG9Yc0nE3Mk8iJmt9kjwEkq8XrKv6g7Xfs3WtAVODEIQGBMyxnJWqkLgHx2gM63gqJj5yMa+fxAoiX4LW40l6Y14Xsa6KeXZa71g7sMdv0D8gp452THK+gIvWDE8jbRQ0rxjp+an4cNSlzrzqnd/AD9a8FKcORGs6Hj/+lbg+ifPEm9M/flOSgj2IldjjIcfz+ACH6/z8v/F3ztbW1tbW1tbW1tbW1tbW1tbW1tbW1tZ/rP50DmDWJ3JUdugD4yUYY4rIVYd01j+QLLniMhXP3AbkWXY3AcBMxRYrdlTgk+oOHc1IDsGXbLDAh5PZifNTYHCdiqudSYyj4FiAHcQ6xdnolURtmB9EXDiKih0+yExF2mL675iCoNbUdyqLMTKtdmyAk1iTy1fwTWBQTkYq6nooJtcM1s9auwP6IK8LepORsyWxfsqFqvJNEifiesNZawMz9Q3jAzJYlzp0g1XQWjHZZtD7gVkj4oLxkBs1uvjjCiKTpAmwWiOaIB6xaKx6lornjlxyZWYSeWEr6cchwDq+67lFYjkUcev+BlvqHgXy1NpY173MT3Ip6lZxzvHltm2H3Jzu5PWSmzE+tT6maGLaQ5+lolpsfNCaY6n5hK9+W7lJuffo3wLuu7e4NcUBNyryOclVTt/qdtbXCpAJAgcRi1gvwoZcpucpV+0M0ibeqlM2BeW0L6sD2OTgtHK/33HBuAss9lF9tdf7uuWkTTms56Uocu9yhZczXA7Qij23rq8p6McNjX28+6WN0DWY47YIc9waltXlXJeX6yLMFMmcYDh+u62XepnNBumNWPHVRWsps3//gs9KXQ/FQ9fAR/qsiRoB55WKb47Q/semIHSzekVkcXQNaJCzYL8iyu12BQPWPmr4wMolXI7mZrBa3YtinckkcmL9tzJXDzwWs5zbFuWeXVcB9lCnd8bbVU+iWOvWcEyu+XV9uf5B19EOaAlTbu07RcCOh9zk8dI5smTFwnIpdr41IgOWY3Zp+IIkm+L5747lMPClmH0/nuT1O+vzDyJ79QgPiJOIRX88IZNGktcnF4n3B+ZBrn/F+29EBHF+qn799YKPbyQd+jfSg2VOTr2zWEvDDeunhnVyaT+PbySLeH2q73lra2tra2tra2tra2tra2tra2tra2vrH0x/PgDcFI0MghbZB6D4YLtdjTEL1AmcKZLZ3hBMmcjS22GZdxfkHWtbnaS5aCvIBtE6MX8SS7G+Np5wfQrGFBDNdYI50RVP29pRTtSFZTIJ3AeRRjh4Bm4Dy4mcyzdA6sClyOWZgot0bCWtuYD3OuXEXYa1Aj8WMA5yXlqPmeDVRdo+BLvXxFojRyczSHn/bvMyrJAjMy7CXMDNR7lkl8BkLuZa6pptT5gvQR53jI7HxNuh59Jc8a0heJtxKj45Id3ImITDMpeT8/zBcsP6E7NLwDmpJt6g9QPvLrjvHa/uZFiYtXp2hkXIXVyR0hlfsc93/G2CoBDrKwY3pv5tJrDsnVxnOS2r//ZAX7zKbdtdnc6Z2FowDPwg40eB4lYuYiPnzwLND950M+QQJpoAMEs/1w2LVkML5Ui1O+I7WHGBD1o6M0PPf5X1u6K8exsY0Pyp/mirfmMbBV2NWItcl2KNUzQ4Z8HgClzWtVrt8yVXs3dFKVM/U6dKQBLtJ8skbemcrUt72weZobWq/mPPiju3Qcal/aaMa3JCmGD9HYvtrs+LeEF/yOibE5spuEyB2rkq3rvii7M2+h2rjZFr1rWZUqnN8PkFSvVRJnd6LCJOWAMbh6qlC/5S/ccW/naaeybp9v4sy9DepfZEf8oN6x1ike6CsG+QPiGD5o+6hnKNp75Wb8OTuE5oD9wca47FVKdyXUdGwnoJSlfPdLMXUBB/yvHtxzcyddoYDzhf+Gi0PGpGIsnzd52fpvfcKte224CjEfOz3M7qffald1m2gT3/WdHymmoh5sTbAO94JrF+B1zR1d7I1og58OuF24X379hjcIxeLuDBmlOwezwFnucP3Kci422wXr9D7+T1Az8e5PzJuj5pv/3T3/f3z9bW1tbW1tbW1tbW1tbW1tbW1tbW1tZ/gP58APiGvzjYJajhTjMTnMgpWJlx0zxFr9LfXaR3FysVyxsIFKhvF0G0AAg8nTZc0cTe5GKbE1oTyPH2FRudqLvXqkfWnGmBV2Su4NPJoiJpIwlzsJPmVmB6QbpAnA25gu3C5wVDAJHMgpsGq6J2/e7OlSPXesOyYT0Et2LJ7YsXw1t3Ci8ZFS9bIJgZuOv78h0vvbD81DVmEuHq5V2B26K1RxlcE9LwMfRscqHu3SCXQ7xYBuRBMJnzE6yzlquLFSPHN9Y8aVd161KOx1hQ0LcZAs+Lgql31LeDG7FOATWWHn9Oddsa0ARZM+T8TlfnKIGc0d3h+lk9zFMga17EdWFjAKHnc760Xn2UI3VpTzW5li0vsKeuO28n66UhhrhqreTINe8kjqXAuDXBeXt3rgqms+TwtQjoj+qVTmZcuvfWBTLdoTfsdhK3hnEVTNaaG4lnxQK3LKe4w/mpGOhYECYHb9Q5spufPrT/YureMgugp1ysvL6GKwgsG+Slfu7qvn3D5nIk57ygNVprupc7kjsmRpO7N6Zc5q5o6WxNv5eGuyLe5fRvQqUxyawqZv+KNqdi39XdW0B4HILV5Rr33sj1wqMcud7BG2v+JLPTx4H53wDZO8A9G9YazlTXcQRRED0MLJ215ApvD73C042Yn3LDc8iBbkvXNwPaKDe5XMN5/hQw7XL0RgTZD8zldPU1tYfWgqasdGORDOL6iR/f9DPWp54vAWOQ08nXT71rrOKuh/ZEwyjDMis/yDTy83et9fFRAwqnYpyP7+Trh97FnBo68KTRWDnLIT0VHt6HoHVOLC+WDdo4MB/qB89G//gO5w+y/xXrHRsPWjuAPzCCXMnMxZontMZayfXzX+nfnIhXRa//Jgf25ye0Tvv4Kx7bAby1tbW1tbW1tbW1tbW1tbW1tbW1tfWPpz8fAL7dey5A6uYVFWxFeRTF+gY91ssxacCoPxOkNFvlPHTIS7DKyv1oidEhL5o7QSfSChiZej9X4KP6gKeAKhbkCnJ0JTibnLVOYkwyZTMNc8UGc+H+qOtefwMxs6AbYM7iwqbRaLpPg2BgrhhgS9Tzy8L8WcBMENy8YoMpdzGCZ4HuIQvS6UMFGBeAtYqb7QVQHMvGykuuYlccdDeBIcFMRTzfMCwyWWuRtkh7EBkowXcRCXOtusWnQNV1Yu0ht976iYcTOQXIcZygmwsK58Rs0AytQT8K/AsImzWBOwvSuqqfDUVSu8uFal690J30rO9P0g/BTcp5bI1sieNkTkH1TEFWc7guvHf1sHqrbZoVP11NwA6EHKP0Qc6pjt5Aa9ucuBaWis8V23W5mTPlvKzrTVJx3yBnc4aczPMlCOwo4tmSsEa7IV9WRLMbbgvLS3zaGxTITPOCgkuRz3k7kgvoRkHxuACrvte7b7fOnx0YOlNGaDDjPqsmAGtNjnLFDDf8+KY9f80aakjBzwwNL6xVML0iyMkalFBwuzX1epsPRT+763pGdRPzN/sc3mcZqMjwctwWFHevgYH8geWjQPhkJTAOohus0N68hwxM3bkZU3vSf9KsC9Sb02iknfRjQDittfewR7j6gTNqkCLk0rV+VJTxfPcqpx818+FkLsIf2mf33rY7D19O5yx7v7nj7VE92UPPLy4NLUQlCSTar/daeVPPb1wkitZuz7+QMVmtMecJc2IPpTNE3CkFSZy/Y23QBjTvrFj6eVGvOisI761qnX/i4wObq97xreKkwcY3etOwBXOBGeP5T5w//gtOx9bBhQY9sj+YJHn+oD8O+l/+Z+AFKN56zUkDOdC3tra2tra2tra2tra2tra2tra2tra2/sH0JwTAgak9F0j13PJ4OxDthlC3q+/uHeX/Co4AgRe/oZCg6A09Cl/K/SrKhltjmak/FpNj2JKcrtLXNQv4JcyzHKcHZLAcHMPdiQjxXW8EzoI7hFkgt+DeHUudHJgt4o6Hva87ChIDaZfikL0ckU1wzK5TMc0R1Xc8izML4EZrgnArsOOA5UTMIqXrKz46IC7FIHuBmdZHXfNXBHJmKk44jGASM+QyTmPlIlPu23n+lHPWB55O2om7449nASHA/wrzopm6XNvxwHPokdkoaORYBh5LzyPL2Zmh3t1IORSDsjsvzA5F4BrqlHZ/Rz0TFdGbwcKxULy4YqSdeP1B9lax32BR0b/HePdJywFboN2a4m3RetMDC4H4HONrz61ZncGKRr7dr2ZAhMyVOb8c50m50gVsYyUxFRtNO7QW10la4ON79Qg7aSFAWstBRUjrCBhpVzngowYGvGLC6zxN7Q3j0v25Ye8hAv1WclXf7ofcyLJW13OI+owDtyBaV4w2BXIfHS6DWdHqNuAQ2G8G0XStnm+CqM+32tOMrz1QPdL2po3vjHPuWOSvNACTy71c2feghLdO928aAIhFnIo2jzWJubAxyFld4j7I6yfZOr6A4wNyseYLa4/q+E7cDz3XY8C8o8XlxlbE8yonsqKoW1MMN+YwPxX/3hS9jUd1O6diqa16jr2x5gtvo7qZ0d7IhR2HOp4JdU/nVUsh8G32UQ54oP2m9+DjCTHIpVh7vUsbPgxvzvV6kVe5f83IdUF/aMiiPcj4yXr9JNt3DZRUVDbzfF9Xrhc8P/CCwWaX3NzXybKOPz6U9GCLyMbyoPXO8e2fef2f/19sfrK8cy6ImMTxz3LsP7+R8cK7uqpZP7D21B6ODYC3tra2tra2tra2tra2tra2tra2trb+8fSnA8DNu1xyTDIHjQ5MJtDSBb68Ab3ihxFFy0ZkFrj9ipbNAmzy6J6Cy7nUf0nBtkD/HQtLUz+mN8Wknp+YNzJd/5yXIAjlppty1zmNiJBLMQU4m6ptiTkRQ26YnWBPQd4bIjLL/dnqm5w8P7Eh53BmOf3WhdlTUbtz6Tq8Yq5Nd2TZgEnEDc8KjPchx2FOwbS4odkld+NaWE7cG62NSlpOEkXtCgJqjWOlOn3PSz24eRJhJIq/XvMih/qIPQFTTLSnYXbSxgexltB4A/OBd0XgWuQb/vsNsbwp8noJuGb146aBW2DWCZaeD0ALqLmAJGtIwGGqF1frCc2NZFRP8Kk/807+/Hy7zbP6dT1rHduBZwog5uKOE7fR8TvxGblqw10Rw7iie9ckl6J6jSZoW7HgDtVju1AE9cJbY60lyLgmDDk6ZTlP7Y+VOJ1mU/2zfhQgDDJvN3nTOpphdMWB10BFrLP2TLnfMwXLy7WZS456RZz3+rUX1L3I1B4w+3LP6wxd5FL/cPqoe56w5FilrtPG/bQNiyVHMIn1/gaOlgZxkdaJ6xKYnHLvej/09QmsF9nqbCqzWxD17o0uS7VZkiStunI1cZDlkC3XbJ6kN9a85Lx/fgcLRSHfQyBW7w/XkEXGS+cTBPyjKWw6Q3v77diVY5+18NYFSc3Iq1IKDtfnue77/vp3R3Vc5HoRdFifgsCt11CEIrdzvYgMrB26nvnCjm9keAFcyGy4nzVsEpg3mvdKR7id0gvH6M+Deb7IP/4P7PEXsn8DVg3rBG4HC3Wh55zE64f2Y/uA54DrRbQH3h8QU73JaxHrpTSD45Drd1j1aSutYXmT8//xz6yf/zvXj/+FT/srYR3aYBwfeq8BEZ/Y4zs5Xf3ROMvyV/462tra2tra2tra2tra2tra2tra2tra2vp30Z8OAK+oqFnv72hfMvFyb6aVQ5bXO7o463vMy7WbihOW4zOEryomWWDowQ1G0xrRBIjtnKQb1gvOAfQDvGEzCi5WjCypxN05wZ0VRuakmcktOX+oH7R13MtJ27uiZn2Q61RncQDva1Ykbvaha1+z4LPRCPAnQdBSEdFcF/QmwNVgvU5F3bZBxJJLclkF/zZYLwBBQARXLVx8bHQ8h64R4PoUNDNXvW1emBsRk5lBXIoKzlhELmKlYmbpBY3ArOHdCnzJ5ZgrIT7pt/sTw9oDt8TvOG834vqpaNxI3OS+hhtFVuSzKTpYEdhyfGYYdi1wI7O/XaSJIpAzLjlhb5d4uW3dDyIDRgO+QV6CVy6InpHqiLa7Z9YgR+3FKEBuWC51/fZGnnJ/WveKcm5g6qN206BBcGoPrHx36lp7QF6smOR5MXPq/m6g2VaRZqd3p9vEGahr+Im9PecV2c0qqG7a1z7q/Dzk6A3FKVtUfHSajKbV30xT9+4dmU1FC2OB+YENQXKbl5y0ON661uY26kfSvdfZvq/LqiL4hpZR/cFGXnJDW0ywRqTDJQjuOBELd7lzvXcNYvRRfdwVqT1PAd07itpQVHMCuBzH3ur8RUVS6+cyOus1iYQ1DtLUQ26OADmLnC+sD2hOZsH1ipn31gs0O7EuWjswS8HxWORaigV31J+bE8YH1ocgamUHREFj86FUgVx4+8bKsyKe1Q+e109svbDHX+oMPTCb1VN9EB6KX3c50u3xjfcASpwCxRGQvfaWCQwvwXQPo/UPlg0yL9w07ZCmc4Ub7fkb1x//Qqw/CH/U+++h2PkI/PmtHPfBmn/IWf34C0zg8w/47S+s63dsGa0l6/yD+P6f5Vweg/79rxjG+lxKNmidmD+JjgY3IulT8dkxF+uP/0Ox41tbW1tbW1tbW1tbW1tbW1tbW1tbW1v/YPrTAeBs7U57xpZhLdSnm0FQkDAXScOzy2l4O+syiqPGXf3Ju7d2JcEqyCiAm6TctOvE2yGXLEESFdOsr7HI4ixOViSzFXDjeAjeNjnqFoqXtXKoyY0siGnZBDMMdX1eV8Ung/dyRWKYVVx1zHI5O2FyJXs6tBuqVvxuImjXO7HU2wpGvuq/I6BFAUa5Fc0bTT5I9SzPpD3kJraVMD7IuBQ3bUb6wbWucgAvkov0wVpTLkdD7l0PWhj4xKLTmnpJFcusXk/iwqxrzb3gu1OuWiuA9o3khfUHJlO0ImTHICPwMRRZPb5BNqxdxPlSl2wK0Fqc4A9ynZAn4JCTjHKhmpMGKyZrngKdyH2JD8UfW+LWqpY5sFgCuF2PiEzMO07IhW6DnJ+s+QIfes6GXKw5cXp18wroqv80lNCci2SR2cnUUMGyRvahftyZ2rvZgY6jmHA50ld1NgfWuiKtl3qUKx9YvljvGgTIJjes63upnmgrN2iuKZhvdVxQlLBgpaKoLdSRLOcpOofzKoc+clW3LjiYyD3sXs85CixXPHOG3NUGeTjxOrHWtG5r0sfBvE4NbeTSniI1vDB/Qn+CC7gaSdqs9dcrNCLAgiCwmDrLy0hfmHd9D4mPoes5X1WzW6D6epFuZHNBVW8aDikQrz0uZ72ZketTUd3umD3I64WPJ1ADAuOod4HJjU/Ccehs1jBIRmBuBbpPIi+8fWAOLXRdrdd6tg9YGlzAu6DwOmsPV0z5ul+sVsC8BjZmYq74dLrjNMH/S8+G8RA4J/D+IFYj10UbD1hLruGsHujjgDlo49D5s6bX2Og6g5HEPMkJ7ft/0vUegzh/Yn/8V6I12vim5zH+wrpO+vMDT6MfDz78wZn/ws8//jfGX/6ZNh7qWb4urD2I9UPv2Tbg8cSO53/bv6C2tra2tra2tra2tra2tra2tra2tra2/hvozweA8/alGbQgzBQXertIY+r3MLlSrbFyQkXceizSGi0Bi/q+LDDict2mHKGRS45Ie8rBZ0lLZ+UsWFrpsTErCtn0eTf4sybokcC0ip/N6vDtipR+R5AW9EqTC88ajEPsLa6vSGHvAkkZZH+Q61N1ueZgq76u/Ktesa9AxhTIWk6uJadsuyNjA65V67DKiShHZxsP3AtgrVnRq05eL613JqugUYQLDKHe36DuY524dTKTZkn2jrthcWCsd0wvC61RG4r7zRdmD7wifK0lccnl7VYR1RH1nA3rDzm06cT5SfqAmKx1kvOqftd6RpnAgqg1oCmitznwYMWLpNylBT2B6l1NQaRyUSeJzQtaI/1RseJTrslc5PpB+kFauWmtvU3oa35i1mn9wHqDOeXidLTHEtJ6dQsbGS99Dkk0rSno5wkQAvMF9iCawHvMF63J+Sr2Kier2YCmOF91V9cAg3nFF8+CyoKICZonqP1bzbIaqGBh1r86g+eLrPsnDfrAzrO6k/U1YQazgOY6iTTtizH0PZEC6XdEd51JQg5anHKRCupbO4jrD8wfeO860+lEuvbQfb0g52qzeh7VcJw6/8mQo7kNxQbnhVtTMrUPOXvNacdQNPcKOeTjHjLRetm6FE2eS/HmaAjD1gUMuD6x8U3QvY0aRKj94a2MtnLg+t3VW8MJ6vB1ueBJsIm3R51hwFq9W1wu9QzoDtHJ66egbfsG8yeM+vqubuBcL6AXoDdoB5wvcIH127adVi5qT7nSQ/nmrfeak/ipe5lRgwEn5g9aV6y9HOs/wJ54U/y8rZP0g/bxxCI1bONBe/6F+ce/4MeBHd/knPeE83clHRwH3hrP63ficbB+D/j8L/j4f5GvyXUF41uSj296/8WFP37D8/y1v5C2tra2tra2tra2tra2tra2tra2tra2/h30pwPAAXimmAYF77IAVUzSFVGcWY5cHANFxFrFA+dirkVvQw5bLkEYc8E3a8S63cIui2OYYIh3qrhXsGY52YbgjBnuD4GPinE2mqBau2A62RrhS1G06xS7aePtsMwU0NZ/BzYOfR9L0COrz7QVRO2HnJw0uZqXgF3EhWUjb3CXpp5YC3KVbTMSYskBnQlNoKx5V7JxOwSuCvrI7VjRwTzIXAXJYZ2filmt+Gi5MNU52qzjBbXgVS7FA+Mq9v5JP54wGmuqw7m5VR9xXWManAJahhE56cdD8M2GYnNbVzRwANYF+VdB1+OptU5g3e7GB4LYS27v9qFvMIGuXC+tnQGPb3rmnpg7nkErlzAY9KgI45K5wGbrcjXCG+5pDynCGzvUubui9qnszLcbPdtXz27eVucIFhB5kmlknBVjnIpB9iYXeExa61p3f2jGYF2A00YnV5a7WHDVYmovmRztFq1cs6mfgRc4T+2ZWNg74rcLpMbUGXOBf7OHhg+svqYPWNUx3OWOV5Q0WHtyD0hYuyOtBT193PtOQx6ZCSG3baaiq80cG9/V72oATX3dR8FJ5DzONcvpjhy2te/dmnpzMzD/pn3THuS69DmtC+zTaE85RyNmOcOrm3lZ4dGzOq15O/7dDFs1rNIqfryANnRFhLdeLuuFcRFhWpfq8uauCW5671hcipnvT8H+pa5ea4bHlKM9q/M4FF+tAQqgd2gf5Osn1ut59EF+ntj9rtMNEN6xNWH9AY/v8PgL1qf29vkDj5fc0RW1nz7Uef75B9NSkdPVf57rZwHcg7CHnnH7wHKy5sIe95lcGgZpHebCP77T2gObn3JaB/jxHf7430i+0R5/oX/7DXsE2f5nPv/lfyE+fzI/vtG+/VX3e8l1ntdJo+Lht7a2tra2tra2tra2tra2tra2tra2tv7B9Of7/14XhINWjs6snsmoPlbBFGeVVXgWVOPLGRsB3hT12xxLOSwh1bta7kz0qYp+dUGUyOo5xYAuKJwFxNYFTa5jPCs5V47GXBVpizpMzULQORQ5i1cUsHVyXQWl5C5lAHFgpgjezCWsnRN157p6NLPiaC3JFPzKm5GvKfCRt0M4y/k6BaBDoLeZnJGYbsHcBXRS0FXQe5L+IAxiBivkpBYwFeRZ1mgZcmlWd62TMv6NRU6BRvMh1+h14kMR2JYhQNSeAvJ39603WjcB9eXlgEy4CtaHHNtu/cvZGQn9wDLxJXCoFOmJe2ddq0BgAXgUge00Fk/IckN6quN4LiwW7g8wuablTq5nCxhdJzOrM9ef765gQcnX+zl5QeSIcsd21zONisPmdrwXLDQj4iKtoohbvPd0zokfD0VOt4avheULt4/q/UVO9dY14BA6I9kdI2TW7U39s4mu11pB51N7nNB61MBDmvqpldqcmHnFCg+B+5j6nJnwGDqfLpDvVnfXD8IVg+5m2v9LbvdcDTt6kXvKWa+fR9X1Zmh4AYcWDc0rpBz2d3e262zf7c5vSJwoqtqbQKzpHHK7TwH6Q+5m4st9bAHVvZsxiXkpNWBNIibNBwzBbprhrmhwRXE/BG5d66invBTV7Q0s1FFs7Q3Y0wLL6nb2To5nOaPvPl/tK29HdXk7a154a1j7IN2xXPrccZTb1tXj27rAswXEwo8P8vW7jsXjew2pdOjfKy5e0c5vt3J/Ehm0eOHju7qJcfUdj2/gg4gXvmYNrTzkx86l+25aj7g+yXZUn/uF2QHzE2MJYjfH4iQr+ttGx7wRz38mrv+KtU5/fnDM5Nu3xP3/w5yBfxz4/APig5WGXxPrDz2f68ff/3fQ1tbW1tbW1tbW1tbW1tbW1tbW1tbW1r+z/nQAuBUUc+9YLLnmTFjKgGAJZmWqo9KHolfdkdexgTcBxpiCdBnlbBRA5QashBy7a7Fi4j5wS9Kc5Y2yviqqdQz1wsYSaJ2nIIPJzUsrQGxD8b3lOMz7/9IEz7pj5oJYofha8pTjbxWUMlcUcBrNE8NJBotFW3JGZjN977y4sZelYowTgR68y43LwkbDEKPWV/f3umRB55iKqI6EXC8iF5GNiIuoe4j1CVCgemI5KoY6sOOJ9Se2flaUtNzazat7eAXe5WTVnxuOIq/T5Aa2leU8VRdwrpCD9u4gNfUlxym4lTEVuZ0T+qH43t7Jc4IvyFNAMoC7e9YamRetO5lPIia2lgYJ7CA5yfVJukN13naMlRNvhzqTY75dm+++acoti7qErVv98h4KaHLa3i7gcnurkjoEXLMBjZgngSBcXgnzhR2/yf1p0HLSmuP21P4HAcPHU1s8FUOdCyy+os9lq7Qy+pYr2A5FBLe8Gbb+LAVV9eyuio2mnNbqsk0+1V3dDj1XXC51IF3eY5FgxTFnb5gfYIpqF/80PQOSPF/4UJw2KypSOmWMnbXWiQAiVBR0nZu8auhAzlbPrKGFB159u+SqOGwKYquH2yzeewNrum4z9dVG4Mdf9OdtYusAL3zsh5BzBGkX1p54Csbjcv9moth3KgI+l85cqK+ZNQtoJ4kp8aDeXZlN8NuWBlnWJSC+LugPvVvOz3d0u9LaTbMfXnH0Bswf0Cta2ZwY39TvHZcg95rQuxIJcmJLQwgZE29De3cZ8fpDgwKPp+Kural12QeE3OyRhtlUR3d/4CPJ8yQ98fGoXmQjPn8nXj9oz2/4+FZJB4nZwvpQD/Nw9X3HX5ifJ3H+oD9/Y1jHnsnr80XOk6QTrz/w4zvr+sl4/BMxL+ZrR0BvbW1tbW1tbW1tbW1tbW1tbW1tbW394+lPB4AJMCZOL8C0Ktp0kBgyrxZx6yZnbwY5L7wZ0OREzSgQS3GXlEvSHLPOuuEHIVBrVn3Aps7UoMBS9V1G6OubAIr1owCqfoAihRccXaBsnYqCNQdvFblc36viVsw7LaqLdCkSmqAipwWrsiy+YRX93ATZYkHGqQjsOw53qpOUWiNPdQdbznInJhZRIPsC5HjNS5B64tAE3+J8CYCtixVZbtIlNyJBy0+sfdfiVq+s54XRSR80gsgUpPQsd3WrflUgn3L3FggUoE31fvau2Npm5PwD2nc5EjEBpLWwcShCO8oNDqQV6J9Tzso4BbUyyXhBPgRJ109sDCJNYDlD15Eu13n/ID9/p42HwCgFwqzhNtShjAs6Z1Y/bcO7ldu34oxjav8sK8A/C8yXG7kGHDL1+/pvOdIjAq5P8MAisY9vciHPSeuCs7TAyilv1kg7uEclMhTl7L0rkjmu2lf6nqzu3rSOuph1dgROIT31rOikrYo9rgjpVk7fDKx9lLs6IeRY1gNWhLZAMViG3KrW5JYNdQXHWuBBrEVE0KwRMwSDYxY87gQn7q3OTqu67FFr6TBPcjx0/d6ULJ6Kzjb3ioZPnW+rgQlPsFEDBJU2gJ6P3Y5sEh+H3OFR5uYBGV5n46L1h6KlueiGrilCncOtw/lZbvLxTinI62elCrwwVyfxvRfNKop7XXI5O1hTDDox8a5Ycesdo7HWRa6rlkIx2DRFdOMPvRsArrPisi8Zi0cjPz/1XvJGXifZUg7gNgT6PSEU/25mhJVb+zz173mVe1+Rz5kLPn/o54+PgtmLzE5//hOsT+L6qUj5ZfD8IHHW5/9Oe3wjVsDnT2IcipH3Q87mfugacdb5k/H8C6yLcCMqaj1ut/7jL9qOM+Sk39ra2tra2tra2tra2tra2tra2tra2voH058PADsYD4EY+tuRamliorI3yuV3R56iaNmIKCewAGhEVoytenBTJZqEVcyzPzAucikSWE7NBX3QLAgz1bgy5aIzrz7PKLB1KL7UmiBiHwJHGBbGXalrVJdqJJmTsFZOwFcB5ztat7p6E927JRGr/kw/w1xx1mlRazHJaO9Ia+bFMR7gh8AhhvenIobXpWjspWvO60W2wZoncUdmrxd5XUQawZS72B5Ym1ieWE7UR/x4xwabHQJ1GZAn3h/kfOHW8TRFNpu6feUqlRPVQICopWJqUUQ0seDxJK9POP5KXorXzqmfl3Eq+vo65Vik6Udfk7ChFuHmelRYgf5OcpG2WE7FAQu+Zs4CpvGG53z8BfKzXLMC2XJTypXr7UFaJ+enAJ4pRtvmCXkqarp/wJKb2VJRxmkUXExoQ5HVNxSMS+ZW79hw9Uyfq4BrDQ0MQeu0gt4ZApq5sP6twOFSJ2/oeaR3Qf8aiiCWNiZTzl0ORTLH7fgFrpdcpfVLWvtyekeqP7oGKnTvNYhxLhgP3FsNGTgsfY3NqTNRUdOCykvR1Lm0jv1QMPrdLzxfSnj2DwF3Cw0D9HpHRML1E45nvRPyfc70+hRU1XBFYk2OdouKnG6tYGdoH1Rsu+B80saHridTqe9ZVx6htAAf5JxkPwh/sFZqvy2BWmPVsMiFZfVXczva0WBGnIqvrq5wUwE6Ob86cjOW3OMFsO04Ku489fkVkx/zJ96e5Z5P4FJEuzft/dcP2vENUIdwjr8S83e8uc76SsySrOdvpghvpvaOteqaPv9VfcIoUcDX1N72A/8+oDdi6b1m10/64xvmSeLEj99J+wYfH7jL/c3nhdkPpTi0QfMONojzEz8OaEEfTvIA+07G7xzjA/MHc06u//O/6NleCT3I/sH6/Bf82//03+7vpq1/d2VFwP/3/oz/kbXX8Ne11/DXtdfw17XXcOvv1d4zv669hr+uvYa/rr2Gv669hlv/Fu198+vaa/jr2mv469pr+Ovaa/gfrz8dADZr5ZJTP67dQIQ73tgq6lg9o9MoSGyYp6Cnd+xvI2tnYD6JSEWxOuVarRjXdMINNxcknqccd5nqy313tra3+zhd/ZQZ5QLO+mzLAmYASy7A+nGZBRhTQFi9nGWYnKdAi99516siYgduiwwnvRG5RKHMBcPu6N0bSOfEYuK0d0wz07AhICkIajAXcwYh3xwZJtiMKfaYiglm0T0KIh0Fp1dBoQLjealv1Mo9mAJFng2LE0UNA77kXiRhpkC0Nzlo3eSCnFFr3snjQ7DUnZjl5lxGcMDrE+gFlYO41O0s86Qc4ekpt2gYOdVvHONBNsjzwnvTnrCDyE/y+h17/idxYA+CQa9YcU8Al8PQhp6NNzl84wVZELpie5PErpeie+PE/Em69mLmp4D33ddsDfxB5sV6/fHV5WwdO47aPOpyNoy4LnCj96E1pzpu10+5LivqWq7g0C+9FcweBVIbFiGo7o93xLoWcNVz1D7EEHT16qy12xUqRzJd15i5sHKkO0FcBb+bBjXCoF1TcNvsDZbl3tfPs3UJLHrTffcBK/HupHXiVc5ZR7HamfqaGu4wah+mOp2xiZy3RlgNIERqTKOgdlw/BfX9IXf0epE+1LWMqRfYDLpi0906yyroPR1aFuzuhBnBhSX0+UkbT12LDbnPQ++wbI2MhduEueD+ujsu+zrBD7x5udZDQx01YJLLKhr9pXNhiVknacT1O/RvOlvzoo+jkg/UDRznDwH0NrSv7Ul+/iFI3A/W9ROj4wEW830GeUeJA/078fN/g+N7ObUbzZ30rnO9yj2NwXhiBKzQgMTjn5XwkJNcgbUD/+v/m5i/a49bJxu4G3n9JKYpNjy99qMzL62bWWKv/6pubftGNuf/x959x8lVlY8f/5xz752Z7emQAAFCCVV6kxYIvUn/UgRBEARUwJ9iQ7oBLIiIgkhv0gQUYgWpAkYEpIh0khAgpGfbzNx7z/P749wdd7Ilu9lNAuF5v177SmbnzLllzsydnec8zyGOSUwRk2/0Gf3qE2MwPhx+2j9g6jkcOD2HA6fncOD0HKr+0jEzcHoOB07P4cDpORw4PYdqSei4GTg9hwOn53Dg9BwOnJ7DZc8uvskKRkoYa3wZaJOCEawYH5jBl2Y1NkLE4QxYsvVlA+gotywGUmN8Rq0NkCiHCwxEkV9P01pfy9X6EEVWQzkL0BqwOZKSL78aGMGanA/Ups6v3ZmtE+ySks9A7Fg71OIDRB1lpVN82V9jcYhfS1dSv24nDpfGPrPQBljry+VC4gN0iV9HVsSQpjbL2vRBb7HGB12y4LgNLEYcYWAJowBjfUAIk822MFnWp8lyo02WjRpFSOpwiSBpDEniM0BtAlIkDCyBMX7frM8mNiYrUZsk2Xq01gcFbZBlZSY+CG3EJ3D7xWaRMMuMJvWDOoBUij4TM/FZpZL6zE1JHJIW/RqukiLEGFvGhoEviRyChAWCnMUYIXWpfz7D0P8bmKz8rC97La6UPee2Ei8Va0nTNqzJ1h21od+prJw3Tghs5Etom8CXZO5Y6znISi1byfbJZM9NFiSM8pgw758jApyxOBdnwVfjz5VL6FgfGuOyDFGDifJZ39k6rs7510RWklfiGMGvpSyU/1cqOYgwQe5/kyOCCGsMQRD64G1WXthmGc3gIAx8BqwrZcsWC8amYBM/v8DiM70le235yHg2pjr6Df2vXZapnMsRGL/fNp/HRMYHdV2MCXKIzSOJz6g1LisfTkcJ4Sz7Oo39etPOb9eGPhvepH5tWJvP+WVxg+y8h36ejA+k+wCqz1I3vmx34I8zCCw2ymOjGr9etY0gKPhgeFiLDSNMABIGBGFIYARjHDbw49YlJT9WSH258zTGii+9bAJ/XsXFuNThggKxCUgcOMmeTxv696Ao8q8bMbgkhjDnA5k2y1jGZ0ObbLwa53wJ5KjQsYg3xvqguA1zWWls/DkOC7igNnu/MBDmSdM2f55ThwmCrGKC88FZ47BBCLkCkiTELQtIJfTltKMcFAp+QkAaY8hKq4MvNZ+rx5Xace0xkjgg8fuarXVsTeT336dw+zLoUs5Kqye+lHwQEgQWklZfrtpmZbJthEsTxBZIis0+ExxDYANfUj6sIS63k5QXIiYizDUSBhaTNGOjCEkW+td+qgFgpZRSSimllFJKKaWUUh8/n7oMYIICYHBk5YPxmW8458sZd6zZGvgAqfOL4QKpL4mcBTd9lLMEKWAtFsEawYmtlCF2zmWPdb6crwmRNCawoV/3M0mx1mSZtiHO4IMhLs1KHsdZlCwCV8ZQyDIOA4xJK8E8wQdNnaRYl61MHOADfiYBG2KCAJMFjsSVIWfAZYFCSUmd8xmbET4zNAtaW2ew1mXrDKcYE2UBF3zgLnVIFlTxa3GmOCc+UzEuZZmiUbaPqS+hm7gsqCQ+iE6aBckNBHmMiXGJA+vAZQFBsgxVF/m1ltPEl4YtFHyOcVr058X6DFRfWrbRB5aCbA3WwGJsPssIzfnsVRG/fZvzwUG/+CtB6oOILvFrGYsNfRZ0lo2NtGYTAmyWGemyrNYky1C2uDTM1kk1/lxFdVAuY/I+EIjEpC7xgcAwj5EyJCUkwk9ISJJsLOayEsk+mGucQ5zPgjZhQMc60RjBhD6TmzjNssezYyLESEKAwbmSz3jM1tI1Yn3g18WQq/FBTQxGQiDBBjWYpOiz1sFnfxshTYt+rd0gW/tYwuz40yxJNgvo2gjSGBsW/HgzAda5LHcyyc5vkK3FGiFp+X9ZuMQ+M9QYjEt9Vm8Y/m9SgPgMUOtK/rm0vsww5aIv350KJvIZ1SD+kMUHXIHs9Wl8UD4Ms6Bxmo21GKmU5s5epwSIK2GCvF+P1uEnAHRkVaeuknUv+DW3TRBisdg0wWGz0s0Gm/UraVa62fjxIqRYE2LCvC8zX26HyFcM8CXk8z5YLIAUifIFXOKwUYTJ1pomKfqxEuSxWQlqXDnbJ+ezZl22pnVY8BnTadk/jwBhhItLWQDYl1iXoCNIbvDLmTuECJF8NgEC/6aQy5O0zcOEBYJ8HeJirLU+4z6sQaxf+zsIIz/5IzSI8+8bhuw9zyWYMMKYOj9Wi/NJ2sqEtWDEEpBVbbAGMREmKZEmbYityybFiA88u4RAHKk1BEGEce0+I9pmgVuXYAu1pOV2X6a/YLPAdYzk6n2QPChgpejfn8pgKJHQ5IPxVtcAVkoppZRSSimllFJKKfXx8+kLADvns3cJkKRIGOWzwJqtCr4aOgJrkEXr/P/Fh8DI1py1oS+Hm5oI17G+qYAxoV/v1logyMoo+9LOjtTfH5ispGyIFcFYS5K0+4A0AibAWpPFD3OkSRkT5nBiCIwPY4s4nwGYrdnqsnV8A/Hp8AaywFiES2O/fmkYYeKEILI44zOfiX02LKn44LLxgU1j/PEaa7Ks3xTjDBInPsPThD4IHISkaYpYiyPBgQ+Igc8oDCIfLHcCJg9S7pT56RtJGGBdjLgEY3MI7WBqfBA5CAnoCKyWsbkC1sVgwBJl64dafITGn3NjrA8iOZfddtkSyFnGbJjHZ0QLRFmAnNAnzEoMxmDFYUL/3Dob+HVTsT4b1aU+oBYFvmy2pD7LMs2CoDagI0GwI0hPFOKSNnAhLkmweR9QD03qSwdbm5WVzdZwDfI+MCs+cxOytWZN8L/geZYcTJr6gLkYv0awS6isoSwpDkuK4CREykVMWPBlfhOHCyw2LPiAMhaH8ZMTJPD7biOENAt8+uCfD9ZmGdGpYKxfH9cQQSA+iO2y0s7i1/sVF0BoIBJfijx1flxJjAlzWCs4E/my2iaEJMYGASaIcMR+vec0zcoeGx+Ut6EfU2nq9z+MfIDPhJjAtxUEBF/iXLI1knFAzp/DVPxjnUMCA6mf3OGzZrPnNvWZsWLDbMSaSjCYrNx2x+vEVlavdb6ygKQQ+lLffr3qrOJwEvtdCUKsBEjeZ8CSZuvzikOkQCqJ7yOI/BCIEx/sF0jj7H0mxQets/V6jfhgc1Zr3QeajfWZ0knJB9A7qgmQZlUGfNCacjvGFioTRMQYP5HBmGyugV9T3E88CZByGyafrRHtEiSsQSTBuqz4tw0wQZkw8BNj0iQhAT95xoSYMIS4CHEJG+URk8NIinUpJjCkYR3i2rM1qYW07APgJgoJghqcKyOE2CBCjEDs13J2xTbSKEeQi3BxkSCfh8CXlPZrTadI6LPQ03K7HxrGlwNHEsBiTYnAWNJyC/nCUOKFbxMVahAR0nL7kl2HlFJKKaWUUkoppZRSSqml6FMXAE5TEBv7oGgQYrBZJl7WIPCBN3E+MGit/V8JZkmw1pI4B+IzglNxPvstibOAnI8FIQ6XlZ1FfIljV44hsARBiDgfIEQMgZT9kqMCLshnqxH7ILOTFJOtVWzDAg6HkRLOZcFnv9ApzjlsmJX3JUUI/Pq8REiSVipJUy5Clr3oEufjVs4fn1+TFB+USRKwcRZLDXz2pfMBF6yBxGblhm0WIEp94DcWnDgfKLWhP3Y6SkRbCPzayzhfdhdjfUZ1WoQ09Ou9dgSOyftyr1isOKwxWWZv5Ne3NSEmTX12bOCzo105xUbZWsoifu1gm5UUdmklMzCN2312tOl4/rJAamD9+rth5J/jKMv0tCHiEp95awQf1E99WWmyDF+XlVvOMislK2ksSZKVRU79urBhwVd7LtQQGvFZ6C7xwcNcjc+ytKEvuStJFnDM1qsVH2TFCmTrHyM+aGno2BeXZWiHWdC1jP+PX5PaBT6zVNpbgGz/bMEHspMUGwaYtJwF1LPs6bSMhHkCY/16sfiywBJan3GZJr78sHNgo2x8+4kDkpT9utmAZJnMkqT/y/QNI39exSCpy0oyW9I08aWCbcfzSeU5sLm8D9Zi/HNnJCsBnAWnbVYOu2NGBqnPaLUBuNiP/yCHzYL4Yn1lchMEfpKEZJMHjGQZpm1+DHRMhkgTJClhwpps/dg0Kz+eTeoQi5jUl1ZOffaxiPWZ6ybLuk5jHyaO8n5yiHGYNAtWYzFOCIwhjSyB5CtrWbskhih7/RlIkzIS5fw5N1GlXHOQK/hz40+cD6pGPtPcBHlfmjv1Q9lnE5f92AnzIAEmKIAVP0kgLkFc8uMz8OvrEpcwUQ5L4gPw7UXI5cGGWBOALeBECLIAsklTXxJdAghzOBFS5xCbEjiLDfL+/Sgp+nXGwwgJ8pCW/VtHTQOuvABXFp8xbkICWwMS41xAWNMIyUJIBVdu8UHyQiPOCFIsYWoaSdvbMJJgcwEujgnyNZi0iIlqkSjFSYDPes9K0rsYYoMNLc7msCYhaFwDSUu4UkygGcBKKaWUUkoppZRSSimlPoY+dQFgsVn2rxG/lig+M9Jk2V44h5ggK5HrkDTpyOHDmgghK5GKD376iAV+LeBsHV4b5Hy1WLLgmImx5CAX0RGgtfj1PwOLLzlqC5hsDV+L8ZmjBjA5v0YtWTlia8EWkKQZ8NmegY18gFGSLPvY75bD4JKyzw7MyhWLZOt+2tAHEK0vm+wDbwGBNRhCCEOfBUu2Hq/xWa+SuiywZ7Mgb3Y2xCKpzyiUNPZr2aZZ0NIlPmswyvG/SLsAPiCOpBD4QI6QZpmjCdb60sCQICaHYHxWrwuQpIwYCKJcVmbYIi7Ahg7JAm4mNFngXnxmI5JlVBpMkCeNi74Utwl9hmyaVIKsBnBplg2aJDiTZCWVA0TEB63F+fLcEgMh2BTnUlIRbBb4F/FrpUoSY/N5Amx2LvHrH4vxEwECi6HsA6ku8WWAbR6Rkl9j1kS4jrV1JfbnNshXxh6JwzmfFe5L6GYlcp0DCZGkDRdkgeQ09VmgYYqQIlFdZc1YEwRYQxYo9QF6E5jsnEWVrEuSsn9VZMNfCDGJL0/u6wO7rKyx/d9rIy1n59eX/BV8GWefqS2QZCV14xgbhBhrAItNgdCXRaZcwob5rJQzPsDcUUY9G/uQBfGz0uVgcDGYsOAnA2TnxmL8Ptls3W8nSJz6iRWVdX8tErdDmMcYX/raiMNgkbCQrVMtOHwGNy7FWp917CQAU8SQlVR3ZTCBr8YsRQBsGGYlxBN/QNZn2GLEZ0qnKVZcViLbr/WNFSj6agAEgV/PttgM+QYMCQQGa/P+ZRb4ALlInL3+4iyLVyB1vhS6S4HUv7bD0I9pkwLZBAMb+OC+DcCVsYElzd5LLC6btBEgxkFa8hnn1laC/mmcYK1AvkCQJlhnSNpbSNISUtNEEBb8XBJjCcI6xOZwxSKWbIJK4EtzuzTG5YeAtGPzNX5/0gTnEoJCLSZwIBFp2oLkGjBhjM3lkERIy0Vs24Js4k+EJILJ1/v4tw0RV8YEOWyu1o/fpJ0oDP0EA0Li4hxMzZDsfSnA5muI43bINw3kcqSUUkoppZRSSimllFJKLRWfugCwX/ox9evxSlbO1/jsOJKSL7FKttanGFxWotg5wUkZS0fmahaBSlKcMRjjfMlg68vLOuuQpOxTjsOabBnWLLs4zbJ/jYDN+8w9ibFWCCUiSVMMzgehO9ZxtcbHMp3DECJRwZdu9j1m++yzU8VGWAIsIUIJcQZjfYloYwzYFEyEieohLvps2sASZGVRbZBiAkFMLivl7NfSlMD6wFFSxIT5LLNWEAeSxj6ACDgTZhnIJUxqkSDwCbBZ8NeIX1dZsvWAfTaiD64al0AUYhJfijsIAkgCrAWX+jK4xlr/vFmLxCkmslnpYbLAU+CzfZ3PuBWsj5smiQ9oGjBhSGDrfeao+HVbXS7CSoIEHaWL/f46rF8j2fqSvL7kNj571VhcsezjsOIQmwcTECdt2NCPJQl9eV6bpphcmJVDdmDKPoBo81lWrfNBa0wWOHVI6jNuRbL1gKVj3WSfFS1OfMA7V4NJHZIUs9LPgQ822qw/W+tLVieJz4k1qV9b2CWYMPAlz13qg82u7Mv7hnk/XqRj/eUUciEuLfusUCuQmqzEuM967ci29RnXiT+UbD1pXJpl5bossG/BmCyYHmfnBHAWVy75LF5rcVZ8wDUu+/Vzg+zYjfhxZyKMZAFmm/dDA59dLwZc6icV2NBmAfIAl2VKBzbvM9utf65c4tc1NoH1Y9FmayrjsmC7RbC+vHEY+nGcjT1HSJrGCIFfB9wYX85cfPFtYwOszeGcD5RbG/hJAkmcJfxHYMqVcuSkko3ngm9DFgwWHxT1ZaIt2BzO+BLHxgYQJwQ1PiBvnfXlnQmwLpvIYUNw7UgQ+UkPQYBLBYxf49m4sn/OgshvI8u2xfj3NCdlTFjw62mnsa8eIAK5Gh8sd75UuSC+vHMQ+ZLjuTwmyIHEmHweKTlcsZVEHGFNLU4Ek4I1Aaa+EdrnAYbUWT/+skkItibwz3U2AcWE9VgjUC77MtE2hw0KGMkjScm/zxeG4NpnYWuG+NdlNp5dqZ1AsokfTghcirMpLo0xaTsmKOCknTRxRDYHroQVg4tbIaqBVEtAK6WUUkoppZRSSimllPr4+dQFgEWyNWLTMjYoENicD2iQ+pK34oMzCTHWGCyBX9tXYh8AC3M+UOWyLNcwyEocZ2VjTYDpCGqRh9AHZl1cBnymqLEBIg5JBSs+q9dIEYgIbN63F4skZYIgxBH6ssrG+HKtlMGFpK7o1++UjnLVIcZl63WSlcLF+HUt0xI28BltJg2wURa4CyNfYjUIMQg2ND5YayzGuGzNUnzZXueDvYEJsoCtQ5zx5yEpYmzo1+YMfJ/YyK8fm1og8cElE4D159pnNJpsGWCfR2rSrIxuCBLkIG73ZZlN5ANJzmdfmmzdXZEiZNnZviNf+pkgyOKlPuglxuLrLlsf9E0TXzIY47OCkxImVwCXrZscBn5tU+f8uqnZmqBis+Bcx3PYEWzFkBqDs8YHVU0NabkNE/iS2SaMEFcCyfmgr4PQhv/L2CUrhxxka9iWfTleayzOFX05Zxv6iQdGkCAijVv9+TQBJk0RK0ha8mtIS5KF222lnDkIzqRZYDTng41iwDlMAIGNMHGzL2uMz1g1xmTBPz8GSFIkTpBcDpv49Vmd8xn1JAnGhkjiMBGVbF/SUhaMjjAkfkJBYPyxiwFJEZNliifZOrXZ+rpBkPMBzzT1I8QYv76uM9lzGSBxqw9052qy7fmsXgDijjLToQ/eBaEfk74zjIjPaJbUx8nDqLK2MAgSF7H5el+6OVuf2aVljM37gGWWI+onThiflW2CbJ1mAZfHOd/eBL6MuDH4tWpJEIlxYeT3p9SGJfYlvyXJJn5EmCDIynEb/5pJY8JcnjQu+3GdFrH5gs+4tv65cmmKC3wmvYlq/HwVi8+ajYvZUua+TLUk7YjJ+3EprmOWjJ9AEYbZ68MXpja5GkhjrBE/sSIMIWnDhHkfHw8LuHIJi/WvLyu+KroxSKkFZyzG5rG5eiKTI04T4mIzErcS1Q/1JbOz51BsASkt9Os4Y/25yEriYwwiHe9Bkk14cCRtMaYujzWCScpIEFSGQlrT4N9jYl/FQQyYfD1JaTZRUOOrCGTHbnL1pHERyq2kAiasJSkWMSFY6ypzGTrmNiillFJKKaWUUkoppZRSHyd28U1WLCKxzwaUALJ1a63Bl/U1PrPSGQEB5yxOgDT2JWaDPE5iv46lNRjns/RMIr5PbBYQ8sE2IoOUfWlVohyWKMuG/N+PSJoFwAqVcsGGgMgaH2RyvnyqS7MMROdwYvz6oRKSlh0uSREJSNOiD1a7joCJDxhaImxQwFpLEOR9qVURrEgW5Palr41LfSJxtpaxIaAjbmSsxWalsCulakODkxLiSqSpX0PXBw8DMDkfqCXEhFnwisSXiE1SAuvXATVhti5qFtyyUY0/kzYgtEKUyxPZgMglhCbw6wALPoBrxWcPpwaX+IiMcanPsDVkGbyh/z1goshnQGZllw1gksSv42vCbE1Zsn1KMYnDOIe1QRb8w6+NG+WAFFy2/jEB1gQ+viYCiV9U1Qd92/06okEOydXiyn5d38CG+AkBxpd9xvp1VJ0DZxAb4kQQa7FhDUFH1mIQZssJ+/WdjS1gsvVwLQEmqsuyH31GrxH8eRd8VqMJcWmaTXQwvqSx8UFzl5YhV+uzgDvOoU/hzZZKFp9NGuYIyCY6xLEfFY5srWrjg9KuYxJE7NeUzTK2/ah3BKlPREdScIYA8VnvAEmMier86y0p+jLmWVlomyY+IGx8hi1pirMRYgv+eGyWVZwUs9dWtt00zgJ8fh1qE/ggv4my82Q6apn7bFMbRBgLJlfwYyrIZWvrZutJk5XHxvr3jiw73GTZvx2Vzo0JfMZpGGFM6idyZEFanGCCAiaJSeMiEuT8+0DqEJOHXJ3Pyk1TH9DOgsZBmMOGOYKoBgMEgX/NmTCflW7Gr3EssS+fHIa+QoETP6ECi4lyWTWAEBPV+sB3RwZ2mmXox0WkXESMn5RgwygrV55DyiW/LnmQyyYw+NLhWB8UTsstSKkZxJfRJlcHUS0EOb9usBFCHJG12Hw9iaklbl1AmiTErugnhpgQkx9CUNNEx9rX1lpMEBDkchib+BL7gMRtuFIZUz+EIIwICLC5GqyAjfIYEkyQ9+/ZofW3bZRlZteStM2GpBWDw0a1fsxEjZAfTlA7DJtNapGyQ4otSFCANEXKpUG4KimllFJKKaWUUkoppZRSg8uIiCzvnVBKKaWUUkoppZRSSimllFJKKTVwn7oMYKWUUkoppZRSSimllFJKKaWUWlFpAFgppZRSSimllFJKKaWUUkoppVYQGgBWSimllFJKKaWUUkoppZRSSqkVhAaAlVJKKaWUUkoppZRSSimllFJqBaEBYKWUUkoppZRSSimllFJKKaWUWkFoAFgppZRSSimllFJKKaWUUkoppVYQGgBWSimllFJKKaWUUkoppZRSSqkVhAaAlVJKKaWUUkoppZRSSimllFJqBaEBYKWUUkoppZRSSimllFJKKaWUWkFoAFgppZRSSimllFJKKaWUUkoppVYQGgBWSimllFJKKaWUUkoppZRSSqkVhAaAlVJKKaWUUkoppZRSSimllFJqBaEBYKWUUkoppZRSSimllFJKKaWUWkFoAFgppZRSSimllFJKKaWUUkoppVYQGgBWSimllFJKKaWUUkoppZRSSqkVhAaAlVJKKaWUUkoppZRSSimllFJqBaEBYKWUUkoppZRSSimllFJKKaWUWkFoAFgppZRSSimllFJKKaWUUkoppVYQGgBWSimllFJKKaWUUkoppZRSSqkVhAaAlVJKKaWUUkoppZRSSimllFJqBaEBYKWUUkoppZRSSimllFJKKaWUWkFoAFgppZRSSimllFJKKaWUUkoppVYQGgBWSimllFJKKaWUUkoppZRSSqkVhAaAlVJKKaWUUkoppZRSSimllFJqBaEBYKWUUkoppZRSSimllFJKKaWUWkFoAFgppZRSSimllFJKKaWUUkoppVYQGgBWSimllFJKKaWUUkoppZRSSqkVhAaAlVJKKaWUUkoppZRSSimllFJqBaEBYKWUUkoppZRSSimllFJKKaWUWkFoAFgppZRSSimllFJKKaWUUkoppVYQGgBWSimllFJKKaWUUkoppZRSSqkVhAaAlVJKKaWUUkoppZRSSimllFJqBaEBYKXUoJk3bx5Tpkzh2muv5eijj2b06NHccssty3u3lFJKKaWUUkoppZRSSimlPjXC5b0DSqkVw/jx43n99de7/D5JkuWwN0oppZRSSimllFJKKaWUUp9OGgBWSg2KCy+8kP/+97/ceuutvPHGG5Xf19XVLce9Ukp9kt1yyy0ce+yx/XpMfX09Y8aMYfTo0Wy33XYccMABbL311gRBMKB9eeaZZ7jjjjv4z3/+w3/+8x9mzpxJY2MjQ4cOZZNNNmH77bdn4sSJbLzxxljb/wIrb775JpMnT+bPf/4zb7/9NjNnzqS1tZURI0YwYsSIyvHstNNO7LDDDuRyuQEdj1o+/vjHP7LPPvt0+f1WW23FE088QT6f73NfEydO5G9/+1uvbXbddVcefvjhLr8XEf76179y//338+qrr/Kf//yHefPm0dTUxPDhw9lss83Yaaed2HXXXVl33XUxxlQeO336dMaOHdvn/QTI5XKV1+VGG23EAQccwMSJE6mpqelXP0qpvtNrqF5DVzSvvvoqG2ywQZffr7HGGvzrX/9i2LBhfe7rhBNO4Prrr++1zbhx43jrrbe6vW9Jx3SSJNTV1VEul/u8r2EYsvLKKzNmzBjGjx/Pfvvtx5577klTU1Of+1BKKaWUUp9ORkRkee+EUmrFMWXKFLbZZpvK7QceeID99ttvOe6RUuqT6s033+Suu+7io48+4q677uKDDz6out9ay8SJE9l6661paGjgo48+4r333uPtt9/m2WefrbQbM2YMZ599NieeeCJRFPVrHz744AO+9KUvMXnyZIwx7LLLLmy77ba0tbXxyiuv8M9//pP58+dX2h933HHccMMNfe7/pZde4vzzz+e3v/0tAKutthp77LEHa665JnEcM2vWLJ599ln+9a9/kaYpAEOGDOHAAw9k1113ZZ999mH48OH9OqalZf78+QRBQENDw1Lpf8aMGTjnqKmpoaamhnw+T6lUYsGCBV1+0jTlyCOP7LGvhQsXMnfuXAqFQqW/jt939DF//vzK/3fffXdWWWWVQTmGSy65hLvvvpuZM2dW3felL32Ja665ps99/fGPf+SRRx7hoYce4vnnn6+6b8stt2S33XZjl112YY899qi67/XXX+cLX/gCzzzzDLlcjt12242tttqK2bNn8/LLL/PPf/6Ttra2SvvzzjuPc889t3K7VCpx+eWXs2DBAiZPnsyLL77YZd822WQTdtttN0aOHMn8+fOZMWMG7733Hk899RSlUgmAmpoaTjvtNL7zne/060v75aGlpYU4jhk6dOjy3pV+c84xf/58yuUyw4cP7/d7oPrk0muoXkN7k6Yps2fPZvjw4YTh4OUFpGlKqVSitrZ20Prs0NLSwrnnnsv999/P22+/XXXfnnvuyeTJk/s8WeGpp57iwQcf5O9//zuPP/541X3jx49n//33Z5tttuHQQw+tum8wxvQVV1zBrFmzeOSRR/j73//eZd/WWWcd9t57b0aPHk1ra2vlGvrMM8/Q3NwM+KDwF77wBc477zxWXXXVPh3z8lIsFmlpaWH48OFVE8oGqr29nZtvvpm99tqLsWPH9tq3iNDc3MysWbN47rnn2HTTTVlnnXW6tIvjmGnTplU+G9bU1BBFEc3NzV0+Gy5YsID11luPLbfcctCOSSmllFJq0IlSSg2iBQsWCFD5eeihh5b3LimlVgDTpk2TXC5X9f5y++2399h+6tSpcvHFF8sqq6xSab/22mvL448/3udtTp8+XcaNGyeArLrqqvLoo492aTNnzhw55phjKtvYdttt+9R3mqZy0UUXibVWAFlzzTXlgQcekCRJum0/c+ZM+e53vyv5fL7qHFxxxRV9Pp6lYd68eXLuuedWnefVVltNvve978n8+fMHdVubb7551bH39rPNNtv02tcVV1zR574AefXVVwf1WObOnSuf/exnu2zn2muv7Xdfzjk54YQTKn2cdNJJ4pzrtu2LL74ow4cPF0A23HBDefHFF7u0mTZtmuy9996V/o444oget52mqWy00UZVx3DyySf3uP358+fL9ddfL1tttVWlfVNTk1x11VU9PmZ5aW9vl5/85Cey9tprV/Z15MiRcvrpp8uMGTOW2X6Uy2XZf//9BZC77rqrT4+ZOnWqfOtb35LNNttMgiCo7H8QBLLddtvJgw8+uJT3Wn3c6DVUr6EiIsViUa688krZfPPNJQxDASQMQxk/frx88YtflH//+99L3LdzTiZPniwbbbSR3HzzzYO41121tbXJwQcf3OUaevbZZy9RfxdeeGGlj7333rvHcbQ0xvQ+++xTdQz77ruvlMvlbtu2tbXJnXfeKRMnTqy0LxQKcv7550uapv0/8KUoSRK59tpr5TOf+YwYYwSQxsZG+eIXvyivvfbaoGzjzTff7Ndnuc4/F1xwQbd9vvjii/3q56qrrhqUY1FKKaWUWlo0AKyUGlRJklT9UfTII48s711SSq0gdtxxx8p7Sy6XkziOF/uYefPmVX0RF4ahXH311Yt9nHNOdt1118pjnnvuuV7bn3rqqQLIiBEjFtt3sVis+sLviCOOkObm5sU+TkTkhRdekDFjxlQe+5WvfKVPj1saXnrpJVlrrbUEkPXWW08mTZokV111lWy66aYCyOqrry4vvfTSoG1v2LBhff5C7utf/3qvfX3961/vc1/Dhg1bKsHJd999tyo4B0g+n5d//vOf/e7rueeeq/TR0xf4xWJR1ltvvUrQdfr06T3255yrjNEtt9yy121///vfrzqGJ554YrH7mySJXHrppVUBqRNPPFGKxeJiH7ssTJs2TbbccstKMObss8+WX//617LLLrsIIMOHD19mn29OP/30yjn63e9+12vbNE1l0qRJEkWR5HI5Oeyww+Tqq6+Wm2++Wc4880ypra2t9PX5z3/+Yxd0V0uXXkM/3dfQt956SzbbbDMBZNy4cXL++efLddddJ2eeeaasttpqlUki559/fr/fG5555hmZMGFC5bzecccdg7bfPWlubpYhQ4Z0uWb//ve/73dfc+fOXez77NIa09ddd13V/t9yyy2L3V/nnNxwww3S2NhYedx+++0nCxYsWOxjl4V58+bJXnvtVblefv3rX5frr7++ErSvra2VO++8c8DbefLJJ5co+Gut7fEz0O9///t+9dXdRDqllFJKqY8TDQArpQadBoCVUktD52yPurq6fj320ksvrXpvuvjii3tt/7e//a3S9uSTT15s/y0tLbLGGmsIIPPmzeuxnXNOjjrqqErfxxxzTI+ZJj156qmnJIoiAWTPPffs12MHy/Tp02XllVcWQDbddNOqL9/b29srXzKPHj1aPvjggwFvr6O6RBiG8s1vflNOOOEEOeigg2TnnXeWjTfeWDbaaCPZaqutZKeddpI999xzsdeejrF00EEHyamnnipHHHGE7LHHHrLlllvK+uuvL5tttpl89rOfld12202+973vDXj/ezJ27NguXyaOHTtWZs2a1a9+Zs6cWXn87Nmzu21z/fXX93n8i4i8//770tjYKE1NTb0GAxbNpu5PAPuxxx6rysjbZ599+v16GGwLFiyQDTbYQMBneHUev2mayr777iuA1NfXD2pwpju333571bn94x//2Gv7iy66SMBnKv/jH//ocv+rr75aFQT+5S9/ubR2XX0M6TXU+zReQ2fOnFnJXN1ggw1k5syZVfe3tLRUgpuAXHjhhYvt0zknf/nLX6oe1/Fz7733Dnif+2KnnXbqsu3GxkZ5/fXX+91XTU2NAPLss892e//SGtOLBhzvvvvuPu/zf/7zn6rJcZtvvrm0tLT0+fFLQ7lcrkyWqq+v71JBpSMwHgTBgCuF3XPPPVXnbqWVVpJ1111Xxo8f3+1PRwWU/fbbr8c+Oz7TbLHFFvK1r31NjjnmGNl3331lu+22kw022EA22WQT2XbbbWWXXXaRz33uc8v9M4tSSiml1OJoAFgpNeg0AKyUWhqOPfbYJf7y2jlXlUkHyH333ddj+6997WuVdrfddlufttHxBXlvAbALLrig0u/WW2+9xF8cXXnllQK+7OWy5pyT7bffvnIcTz31VJc2U6ZMqdzfWwnhvuooyTdu3LgB9yXyv3LS/SlnujSsvvrq3WaU7Lbbbv0aG3EcVx7b0+MOOOCASpu///3vfer3lFNOEaDXgHTnwHJ/A8AiIr/97W8r5SEBOfPMM/v1+MHWObjUXYnc999/v5K5/dnPfnapld186aWXqoK10PuyGs8880xlv372s5/12O6ss86q9LfJJpsshT1XH1d6Df2fT9s19PDDD6/099e//rXbNu+//77U1dVV2r3wwgs97v9dd93V67IMDzzwwID3uS923nnnbre/0UYb9TsQ2lGG+8033+z2/qU1pjsHlvsbABbxExoKhULl8YcccshyLQf93e9+t7IvkyZN6nJ/e3t7JWi95pprSmtr6xJv64c//KEAMmrUqG5fR4vabbfdBJD777+/xzYdFWJ6KhGtlFJKKfVJY1FKKaWU+gQwxgzosT/84Q9ZZ511Kr/7/Oc/z3vvvddt+7fffrvy/yiK+rSNY489li996UsMGzas2/unTZvGD37wg8r+/PrXvyYIgr4eQpUvf/nLjB07lqlTp1IqlZaojyV199138/e//x2ATTfdlO22265Lmy233JKNN94YgDvuuIPHH398QNucOnUqAKuvvvqA+lla/Q3U+uuvX3X7oYce4uyzz+7z48MwrPy/pzG1JGP61FNP5dRTT+31tTeQ1yXAwQcfzBe+8IXK7Z/+9Kfcf//9A+pzSf3jH//g9ttvB2DkyJEccsghXdqMHj2affbZB4CnnnqKW2+9ddD3Y8GCBRx88MGsttpqrLTSSpXf9/Z+cdNNN5GmKeBffz3ZYostKv9/4403BmFv1SeFXkP/59N0Df3Xv/7FXXfdBcBaa63Frrvu2m270aNHc+SRR1Zun3/++T32+eMf/5gxY8Zwxx13MG3aNH7/+99X3Z/L5ZZ4f5fEuuuui7X/+2rr5Zdf5sQTT0RE+txHx3V0MK+hixvTMPBr6Hbbbce3v/3tyu3f/va3XHHFFQPqc0lNnTqVn/zkJ4A/nyeeeGKXNoVCgaOPPhqAd955hx//+MdLvL3XXnsNgDvvvLPb11Fn//3vf3nooYcYPXo0++67b4/tPm6fD5VSSimlBkoDwEoppZT6VMjlclVfNLW2tvL9739/sY/785//3Kf+V155Za655hrGjRvX7f3nnHNO5Yvm3Xffnc985jN96rc7QRBwyimn4Jyr+lJyWZg0aVLl//vvv3+3bYwxfO5zn6vcvvrqqwe0zXfffRcYnC/kmpubmTNnDkEQMGbMmAH3NxjuvfdevvOd71T97pJLLuG+++5bKtvr65jeaKON+MUvfsHw4cOXyn50+MEPfkBdXV3l9llnnUUcx0t1m925+OKLK//fa6+9egxiDObYXpSIcNxxx/HOO+9w2223USgU+vS4jveWfD7f63vLvHnzKv/vLSih1KL0Gjo4lvU19Lrrrqv8f8cdd6wKlC6q8/48+OCDtLW1dbtvzzzzDA888AD/93//x2qrrca2225b1aavAdLB8rOf/Yyrrrqq6nd33HEHP/vZz5bK9gZrTA+Wb3zjG6yyyiqV2xdccEHVe/2y8tOf/rTyGt1uu+0YOXJkt+06j+1rrrmmMnmpv1577TU233xzJkyYsNi2v/zlLwE4/vjjqybNLWowP28qpZRSSn0caABYLXXNzc3LexdobW1d3rugljIRYfLkybz33nuUy+XlvTtdiAgtLS3LezeU+tTbf//9qzKYbrrpJl555ZUu7dZee+2qNgPNYJ01axa33HJL5fbxxx8/oP7AZzDdc889y/RLqpdffpl///vfldu9ZVxssskmlf/fe++9A/oycjC/kOvI7lh11VV7/RJwWcrlclx00UUceOCBVb//whe+UMlwGajOY/pHP/oR//3vfwel38EwZswYjjrqqMrtN954g2uvvXaZ7sPcuXP5wx/+ULnd17H99NNP8+qrrw7aflxyySXcf//9XHDBBVXZuotz8cUX85Of/ITHHnuM+vr6Hts98MADlf/vsssuA9pX9emj19CBWR7X0Keffrry/8VNeurIOgaI45gnn3yy23aLZq0uOplkoFmt/ZXL5TjppJM4/fTTq37/jW98Y8Bjr8Ngj+nBVFdXx6mnnlq5PW/ePC655JJlug9JkvCb3/ymcruvY3vGjBn89a9/XaJtXnDBBX36rNDS0sJNN90EwBe/+MVe22oAWCmllFIrGg0Aq0HX2trK008/zc9//nN23XVXhgwZwvTp0wEfBLvtttvYeuutaWhoYMKECT1+sZmmKbfddhv77bcfK6+8MrlcjpVXXpntttuOc845h/fff7/bxznneOutt/jd737HeeedxxZbbMGIESOqMknmz5/P5ZdfznbbbceIESMYNmwYu+22G3/5y1+69Ddv3jx+9rOfscceezBmzBhyuRyrrLIKX/7yl5k7d26fz8uCBQu4+uqr2WuvvVhjjTXI5/OMGDGCjTfemFNPPZVHH320xzJVF154IcaYHn/WWmutLo+55ZZbemzfXTkm8LNov/a1r7HjjjsyatQoGhoa2GijjTjyyCN57LHH+nysy0N7ezv77bcfq622Gvl8vtfz1fnn4Ycf7rXft956i7POOouNNtqI2tpaGhsbWWuttTjqqKO47777enzORIT333+fyZMn8+1vf5s111yzarbzggULOP3001ljjTUYNWoUp59+OkmS9LgfAxk/Sqn/McZUBZpEpFLytbPDDz+88v8kSdhvv/245557lvh1NnnyZJxzldt9yVZYnCFDhnDIIYdQW1s74L76atGM1PXWW6/Htp2/4CuVSjz11FNLvN3B/ELu4/jlnrUWay033XRTVTno5uZmDjrooEGZTNd5TC9cuJBddtnlY3Vt7/y6BJZKaeXeTJ48ueqzYm9je8MNN6zKovvb3/42KPvw5JNPcvbZZ7Pjjjty1lln9euxo0aN4utf/zrbbLNNj21uu+02Jk+eDEBDQ0OfsjeV6kyvoQOzrK+hzjlef/31yu3FBYBXX331qhLIL730Up+2s6SluAdLx/vxj370o6qxkaYphx9+eI/fG/THYI/pwXbEEUdU3b7tttuqXjNL29NPP81HH31Uud3b2B4xYkTVWFzc3+M92WWXXdhss80W2+7WW29l4cKFTJw4sdvvTTq0tLQwZ84crLVVGdVKKaWUUp9oy2PhYbXi+u53vyvWWgGqfh566CFJ01ROPPHELvd99rOf7dLPv//9b9lwww0FkDXWWEP+3//7f3LRRRfJEUccIUEQCCA1NTVy8803d3nsfvvt12UbgLz99tsiInLffffJyJEju20DyLXXXisiImmayk9+8hMZMmRIj20333xzKZVKiz0vt956q4waNUoA2WWXXeR73/uenH/++XL44YdLoVCo9Lf33nvLtGnTujz+lVdeke985zsyevToLvuw5557ym233dblMdOmTZMf/OAHlfMIyLBhw+SMM86Qp59+uqrtwoUL5YgjjhBjjKy11lpy5JFHyvnnny9f/epXpbGxsfL4733ve4s9VhGp2r9HHnmkT48ZqPfff7+yzdraWvnKV74il112mfz0pz+t+tloo40q7dZee22J47jb/pIkke9+97sSRZGEYSgHHXSQXHTRRfLNb35T1l133UofO+20k8yZM6fL41dbbbUuz9Wqq64qIiJTp06VNddcs8v911xzTbf7MtDxo9SK4gtf+EJlvNfV1S1xP//617+qXnvrr79+t+0OPfTQLq/TXXbZRf7xj3/0e5sHHXRQpY/VV199ifd9eTvggAOqzsf8+fN7bNva2lrV9rzzzlvi7W655ZYCyMMPPywiInEcy+zZs+X999/v8X28J1deeaUAcswxx4iIiHNOFi5cKO+99560tLQs8T4uidVXX10Aeeeddyq/e+2116Spqanq3B122GHinOu1r462PUmSRLbeeusuY/rggw+WV199dYmP4YYbbqjq75///OcS9ZMkSdVxG2Pkgw8+WOL96q+vfe1rVcfxwgsv9Nq+82fJ4447bsDbnz9/vqy++urS2Ngo7777buX3HWNkoJ+pHnzwQamrqxNARowYscTPk/rk0mvo8resr6EffvhhVR8/+clPFvuYzn/7fuc73+nztpbH338777xzl+199NFHMnbs2C7fNyzub/burseLGswx3eGRRx6p6u/uu+9e4r423njjqr4Gsl/9ddlll1Vt+/777++1/VZbbVVpO2HChKW2X865yt//d9xxR69tX375ZQFktdVWqzy2ra1NZsyYIfPmzVvs5zCllFJKqY8jDQCrQfXss8/KpEmTZNNNN636A+APf/iDfO973xNAhg8fXnXfOuusU9XHQw89JLW1tZWAY5qmVfdPmTJFoiiqPP6hhx6quv/555+Xq6++uvJlccfPq6++KpMmTRJAtt12W7n11lvl+eefl7vvvlvWWmutSrvGxkaZOXOmHH744ZWg2o033ij//Oc/5ZlnnpEvf/nLXY6tNxdeeKEAksvl5He/+12X+2fMmCE77bRTpb+xY8fKW2+91W1fc+bMkVVXXbVq++VyudftP/nkk5U/ZLr7InXOnDmVL4QPPPDALl+kT506teo5u+eee3rdnsjy+QKg4w+2mpoaeemll7pt8+yzz1ZNUPjjH//YbbtyuSz7779/5fl4/fXXu9x/2GGHVfrZeeedu/xBeMstt8gZZ5xR9WX2qFGjpLm5WcaPHy/GmC6TC37wgx902ZfBHD9KfdIN1pfXpVKp6joCyPvvv9+l3cKFC+Wzn/1sly/7Oq4jv/rVr6S5ublP2+z8ZeTOO++8xPu+vHW+Bllru1yjO3POVSZtAXLQQQct8XZHjBghgGy55ZaVCTGdA4VbbbWVXHbZZdLe3r7Yvr7xjW9Uggjjx4+v2kdAxowZIyeffLK88cYbS7y/fdXTF84PPvigGGOq9utHP/pRr311tOvNjBkzqiYxdT6Hu+22m9x2221SLBb7dQyDFQAW+d+X+R0/t99++xL31V877LBD1banTp3aa/vOnx0322yzAW//6KOPFkBuvfXWqt8PNAA8e/ZsOfXUUwWQMAzl9NNP73bimlrx6TV0+VvW19A4jqueq69+9auLfcyYMWMq7U866aQ+b2t5/P3XXQBYxE9S6DxJFpDTTjut1776EgAezDHdYTADwJ1f44BMmjRpifvqr89//vNV237sscd6bT9x4sRK26FDhy61/XrssccE/HdQi/t88+CDD1a+D9p88827jKH6+no5+OCDK5MRlVJKKaU+CTQArJaKhQsXVgW+jj32WDHGyFe+8hWJ41iuvvpqMcaItbYqe7Vz1stpp53W4yzLM844o9L3Flts0W2bDz/8sOrL03333VestXLhhRdKkiRVbf/zn/9Uta2vr5fRo0d3++HeOVcVXD7llFN6PA/33HNPpd2FF17Y6/nqnKm72Wab9ZjRdO2111b9IfL444/32K+IyBVXXCGA3Hvvvd3ef8opp1T6uvrqq7tt88Mf/rDSZtNNN+11eyLL5wuAe++9VwC56KKLur2/XC5XTUz4/Oc/32NfHV+U1tfXVzLHFzV79uyqYPIDDzzQbbubb7650qa2tlYOO+wwqa2tlYceekiam5tljz32qAQiPvzww6rHLo3xo9Qn2WB9eS0i8pnPfKbqvWrKlCndtiuVSnLuuedKGIbdfuHX1NQk3/zmN2X69Ok9bss5J7lcrvKYAw88cED73hctLS3yzjvvDOhn0WzYNE2rrpXDhg1b7H50nuiy4447LtGxNDc3CyBrrbWWnHLKKfLjH/9YbrrpJrnuuuvka1/7mqy00kqVbay55po9TgLqcOihh0pDQ4N87nOfk4suukh+9atfye233y7nnXeebL/99pW+wjCUn/70p0s146O3L5x/8IMfVI01a6387W9/67GvjnaLs3Dhwsp1rruflVdeWS688MI+BwkHMwC8aBbuD3/4wy5tisXigMd2d1l3a6yxRtW2Fy5c2Ou+dv5M0ZEttKRuv/12AeTQQw/tct+SBoB/9atfyU477VT1WWXIkCHyxS9+UX7/+99rJtOnkF5D+25Fuoaut956Ve9Vi/4d3JlzrlIpAJAzzjijz9tZHn//9RQAFvEVlBYdb91VD+vQlwCwyOCM6c4GMwC8aBbuqaee2qVNkiQDHtuzZ8/u0u+ECROqtv3iiy/2uq8HHnhgVfvexuVAdEzaPvPMMxfb9sorr5QwDGWnnXaS733ve3LllVfKb37zG/nxj39cNfm747uEvkw6VEoppZRa3jQArJaazTffvOpD8u677171wX769Oldgmt77rmngM927O4Piw4dX5R1/Lz33nvdtuvIGAKfGfroo4/22OcWW2xRabv22mtXld9b1DnnnFNp29Ms9NbW1qrygN3NjO/sL3/5S9Ux/eIXv+i2XVtbmwwbNqzS7ktf+lKv/W655ZYyfvz4HmeYdy4Vde6553bb5rXXXqvat8WVZFweXwBMmjRJjDEyY8aMbu+/5JJLKvs0YsQImTVrVrftnnrqqUq7b3zjG71us3MW1YknnthtmxdffLHLFwO33HJL5f40TeX555/vMlt8aY0fpT7JBvPL6913373qNXPffff12v7f//63HHrooV2yRTt+Ghoa5IYbbug2qDJ79uyqtkcdddSA9n1xPvjgg16XL+jrz8knn1zV74IFC6ruX2uttRa7L52znTbeeOMlOp7W1lZ58803e7x/5syZsttuu1W2M2TIkF4rIbz66qs9Vs/oWP6h83FedtllS7TffdHbF87OOTnkkEOq9mXkyJE9fqnc0aavHn/88cokpO5+VlppJXnwwQcX289gBoAXDXqffvrpVfe3t7d3CdQuyc+ee+7ZZdtDhw6t3B8EwWIDpJ0zhhsaGpb4mN99911pamqSYcOGdZkIJrLkAeADDjhARo0aJaNHj+42ULHzzjvLK6+8ssT7rT559BraNyvaNfS0006r2m53FYV62sdLL720z9vp/LiPQwBYROTrX/961X4VCgV5/vnnu23b1wBwh4GM6c4GMwB82223VfX1uc99rkub7bbbbsBje/z48V363WyzzaraLC4AvmjG8NKoTDFjxozK9a8v17t3331XFixY0OP9f/nLX6oqox144IE6mUoppZRSH3shSi0l9fX1lf/ncjmuvvpqgiCo/G7VVVetav/000/z5z//GYDNNtuM5uZmmpubu+27paWl6vY777zDKqus0qVdXV0ds2fPBuDLX/4yO++8c4/7u8Yaa/Cvf/0LgF133ZXVV1+9x7ajR4+u/H/+/Pndtrn55puZNWsWANtss03VY7qz2267sc466/DGG28A8Itf/IJTTz21S7uamhpOOOEEfvSjHwFw5513cvnll1NbW9ul7ZQpU3j22We56qqrsNZ2u90DDjiAl19+mZVXXpmjjjqq2zarrbZa1e1XXnmFlVdeudfjWdYOPvhgNtlkE8aMGdPlvjfeeIPzzjuvcvtnP/sZI0aM6Lafzu222GIL3n333W7bJUlCoVCo3H7nnXe6bdf5dQD+eT766KMrt621bLrppl0et7TGj1LKGzJkSNXtcrnca/vPfOYz3H333UybNo2rrrqK6667rvIaBWhubub444/noYce4qabbqq63sVxXNXXvHnzBn4AvRg2bBi33nprl+32R6FQYOLEiVW/W7hwYdXtoUOHLrYfEan8v729fYn2pba2lrXWWqvH+0eNGsXtt9/OuHHjaGlpYf78+Rx//PE8+uijGGO6tF9vvfV67Mtay9e//nWmTJnCnXfeCcBZZ53Fvvvuy7rrrrtE+7+kjDHceOON/Pe//+WVV14BYNasWRx66KE89thj5PP5AfW/44478uc//5lXX32VX/ziF9x8881Vn7tmzpzJfvvtx7e//W0mTZrU7bkcbIt7XebzeX71q1/R1ta2xNsIgqDL2Ibq8T106NDFHu9gjO00TTn22GNZsGABN998MyuttNIS9dOd3/3ud5X/O+d49dVX+fWvf83VV19NqVTiscceY/vtt+fvf/87G2ywwaBtV3066DW0dx+na+hZZ53FbbfdVvmb9ZRTTmHDDTfs9ro6Y8aMqtu9XXs/CS699FL+/e9/8/DDDwNQLBY5+OCDefbZZxk2bNiA+h7ImF5a+vK6vPTSS5kzZ86AttPddyr9Hd+dxzYs+fjuzTXXXEOSJGy//fZ9us719v0PwO67785FF13EKaecAsD999/PjTfeyPHHHz8o+6uUUkoptVQs3/izWpF1Xsdt7733Xmz7RWfo9uenp3VpO2dNXHfddb1uv/Os+C984Qu9tu2c7bLGGmt026ZzVs3isnQ7nHnmmVXH1VMW9FtvvVVVQqxzRmlnxxxzjAwZMqRLCbJFzZs3b7FllzpnHd9xxx29tu18DMtqBnhPnHOyyy67VI3Fnmbqzpkzp8dZ3Iv72Wijjbrt85133qlqd+edd/Zpv5fm+FHqk2ows5c61nnv+HniiSf69fhisSjXX399Vfnhjp9zzjmnqm25XK66fzDWC10e5s2bV3UcfSlH2fn89LRkw2D5zne+U7V/f//735e4r//+979VffX1fbi/+pJx9MYbb3TJRls0s0yk/xnAi2pubpbLLrtMGhoauozp66+/vsfHDWYG8C9/+cuqvnpb/mCwdV66pC8lnbfZZptK++HDhy/RNidNmiSA7LXXXj1+NhnoGsCLevjhh6s+Q372s5/VDKZPCb2GLl/L8xr6yCOPVFU5GDJkiJx77rny5JNPymuvvSZPP/20nH322TJ69OhKG2utzJ07t8/bWB5//y0uA1jEZ5AvWjli77337lIdq78ZwIvqz5jubDAzgP/whz9U9XXCCScscV/9tckmm1Rtu7f1rUVE/u///q+qfX/XTl6ccrlcGc833HDDoPVbKpWq1iRfZ5119BqqlFJKqY+17lMClRpkm2yyyWLb/O1vf6v8/6CDDuK+++7r08/kyZPZZ599Ftt/TxmwAyWLzF7t+N3f//73yu3hw4f3qa8111yz6vbrr7/ebbtx48ax9957V27fcMMNXdrMmjWLO++8k5NOOom6urpetztkyJAus5LL5TJTpkzh8ssv5+CDD2bu3LmV+5bGDN2l5frrr+eRRx4BfEb41Vdf3WNWzxNPPEGappXbd955Z5/HYUem2OL05bWwtMePUoqqLA1gsVn2i8rn8xx//PH897//Zb/99qu675JLLuHDDz+s3I6iqCrT5OWXX/5EvY92aGpq6jUrqztJklT+35dsp4HYcccdq24/8MADS9zX+PHjGTVqVFVf3V3vl4W1116b3/zmN1XXrl/96lfdXvsHor6+njPPPJPXXnuNrbfeuuq+s846a7EZfoNhoK/LgehcGWRZjO1nn32Wc845h/r6+l4/mwy2XXfdlWOOOaZy+6mnnqpkmCvVV3oN7b/leQ2dMGECL774Il/5ylcYOXIk8+fP5/zzz2eHHXZg/Pjx7Ljjjjz77LM0NjZWHrPtttsu9ev2sjB8+HDuv/9+ampqKr/74x//yPnnnz+o2+nPmF5aPi7XUFj8+O48tsMwXOz3Ff1177338sEHH1BbW8thhx02aP3mcjm23Xbbyu033nhD/+ZWSiml1MealoBWy0RfyhS+//77lf83NTVx4IEHLsU9WroWLlxIa2tr5faiZYB7smgZ69dff53tttuu27annXYaf/jDHwAfPH/33XdZY401Kvdfe+21pGnKaaed1uf9bm5u5sEHH+Tuu+/mT3/6E+3t7TQ0NLDjjjtSX1/fpfT2x92HH37IN77xjcrtiy++mLFjx/bYvvMYBF96eXGloPqrL6+FZTF+lPq06/wlWRAES/wl2ZAhQ7j99tvZdttt+c9//gP4CTS/+93vOPnkkyvttttuOyZPngz4L8X+8Y9/MGHChCU/gOXAGMOIESOYOXMmUP3lXU86t+mp9P5gWXQSzGuvvTbg/j766CPAX0+am5urvhxflvbaay8uvvhivv3tb1d+d8opp7DJJpuw+eabD+q2Ro8ezb333ssWW2xRea5nz57NI488wp577jmo21rUol9e93bNHmwjRozgrbfeApbN2D7uuONIkoSvfvWriEivS050+PDDDyvtrLWsuuqqSzTB8aCDDuLmm2+u3H766afZaKON+t2P+vTSa2j/Le9r6KqrrsrPf/5zrrjiCt544w0++OADWltbGT16NGuvvTbOuaqldE466aQBbe/jZJNNNuH666/nyCOPrPzuggsuYKuttuoSrB2ovo7ppWF5X0M7S5Kk1797Fx3bgz0J6vLLLwfgc5/73KAHl7v7vDl+/PhB3YZSSiml1GDRDGD1sdF5Ld1FA3GfNIsGSju+QF6cRQN4va1xt+eee1b98XHjjTdW/p8kCVdddRUHH3xwn/7wS5KEn//854wdO5ajjjqKKVOm8I1vfIMpU6Ywd+5cJk+e3Ocs1I8LEeHLX/5yZVztuOOOi10Td9H1nJfXOFwW40epT7PW1taq2foTJ07sdh31vmpoaKh80dRh0eDjLrvsUnX7N7/5zRJvb3nqXMWg83qx3XHOVb0PDXagclGLXu86T6T5OPQ3UGeddRaHH3545XapVOLAAw/kgw8+GPRtrbLKKlx44YVVvxtoQL0vXnjhhcr/OyagLSuLju3FZXx3Hg9LMrY33HBDwE9OW3PNNXv86bwm55FHHln5/eqrr84//vGPfm8Xun55PXv27CXqR3066TV0yX0crqHGGNZdd1123nln9tlnHzbbbDMaGhq49dZbK9tbffXVOeqoowZlex8XRxxxBGeddVbV7z7/+c8vlQoIfRnTS0PnayjQpyppg2XRKleLG98DvYb25plnnuGZZ54BWCrj+OP2+VAppZRSqjeaAaw+NsaOHcubb74JwPTp05fz3gxM5zJl0PfjGTJkSNXtlVZaqce2QRBwyimnVP6QvemmmzjnnHOw1vLggw8yffp07rjjjsVuc/78+ey2227861//Avxs6O985zuE4Sf77eHaa6/ld7/7HeAzym+55ZYuZa4Xtegfc9OnT18uGbTLYvwo9Wn26KOPVpWz7RxUA5gxYwatra2su+66fe5zl112IQiCShn5RUvfHXbYYXz729+uZDzcfvvt/OhHP1pqGaWtra1dMkH6a8SIEV0qEEyYMIG//OUvAEydOhUR6TFrY/r06VXnoXPJvKVhzpw5VbdXXnnlQevPGMPIkSMH1N9AGWO4/vrrefXVV3nppZcAf47322+/qmU0uvP2228ThmG/soH22GOPqtt9KVc6EAsWLODpp5+u3P7c5z5HoVDo0q5UKg046D1kyJAu18wJEyZwzTXXVLYxc+bMHsdQHMdVGbtLMrZvvPFGTjjhhMVO1jrppJMqr+ULL7ywkqm79tprL3HWbhRFVbc/aZP81PKl19C++SRdQ9va2rjssssqt88///wu7xMrgkmTJvHCCy9UnoMFCxawzz778Mwzz/Sakb00xvRgc87x5z//uXJ7hx126DI5GCBN0wF/11JfX98l43fRjPx33323189hHd/7wOCP7Y7g+9ChQ7t8lhkMg/15UymllFJqafpkR3jUCmXttdeu/CHw6quv8vbbbzNu3LjlvFdLpqamhnXWWYc33ngD8LNQ0zRdbABy0QzULbfcstf2X/ziF/n+979PqVTi3Xff5dFHH2XXXXflyiuvZMstt1xs8LJcLnPggQdWgr/f/OY3+f73v7+Yo/v4e/311znjjDMqt6+55po+lXJee+21q25Pnjy5y5day8KyGj9KfVrdfvvtlf83NTVx0EEHVd1/5pln8sgjjzBjxgxyuVyf+gzDkOHDh1cy9hd9P+mosNBRdrWlpYVJkyZxySWXDORQuvXhhx+y/vrrd3lP6K+TTz6Zq6++uup3nbOwisUiH374YY+lPztniDU2Ni7196S333676vZAS9p27m/99df/WEyMqqur4/7772errbZi7ty5ADz33HPstNNOvT7uiCOOoFwud8kO6k3nNZCh65gebHfddVfli3KA448/vkubYrHI+uuv32O55L7ac889+dOf/lT1u5133rnq9jvvvNPjl7rvvvtuJVhgjFmiUrQ1NTV9+mK68+eZHXbYYVDK3naUoO2wtJ9btWLRa2jffJKuod/97ncr17yDDz6YY489dqlta3kKgoDf/OY3bLXVVpXjnTZtGhMnTux1jd6lMaYH28MPP1w1aeGLX/xit+123HHHqslWS2L8+PH897//rfrdlltuSV1dXSUb9p133ukxsFssFpk6dWrl9sSJEwe0P51Nnz6de+65B/ATR/r6fPXHop83N9hgg0HfhlJKKaXUYFn+32QplRk/fnzVl3E333wz55133oD6XFz5vqXZdt99963MPv3ggw94+OGHF/tFX+c/pDbYYIPFZuoMHz6cI444gptuugmAq666ihEjRvDwww9z6623LnYtnTvuuIPHHnuscvub3/xmr+37YtFz05/zOhjiOOboo4+uZNQcf/zxPQZxH3nkEc4991zuu+8+hg8fzjrrrFN1/29/+1t+8Ytf9HkN3sG0LMaPUp80g/F+8uyzz1Z9eX3ZZZd1ybqvr69n9uzZ/OEPf+jzevTt7e1VX7x192XWOeecw3333Vcpi/eTn/yEo48+mo033ngJjsSX7//KV77C+PHjOfPMMyu/HzZsGLfeeuuAsk0KhUK3x7DNNtuw4YYbVkomvvXWW3368vrYY4+lpqZmifenLxYNbg5kXb85c+Ywbdq0yu39999/ifvqTUcQsS9rQXYYN24cd9xxB3vttRfOOQBefPHFXh9TX1/PI488wgsvvMCmm27ap+10zhAqFApsv/323bYbjNdlS0sL55xzTuX2Mcccw6677tqlXT6f51e/+tWAljgIgqDbsT1mzBj23Xffyjqjb731Vo8T6TqP7X333ZfVVlttifdneZgyZUrl/8OGDVumpbbV8qPX0Gp6DfUef/xxfvaznwE+0P7rX/+63+uxLq+//5bkGjps2DDuv/9+tt1228q15NVXX+31MUtrTMPgnKs0Tav+jt9pp534whe+0G3bSy+9tEsGa38tOmEKfGWJ4447jl/84heAH9s9eeuttyrHvdFGGw1qxa0rr7yyMpms85rPg0VEqj5vbrPNNlp1SymllFIfb6LUUrLNNtsIIIB861vfWmz7p59+utIekFVWWUUWLFgwoH0YOXJkpb+rr76617ZHHHFEpe2RRx7Za9urrrqq0nb06NHdtnnrrbckCIJKu8MPP3yx+3vkkUdW2t94442LbS8iMmXKlC7nbfTo0VIqlRb72FNPPbXyuOHDh/fadrXVVqu0veqqq3ps19bWVrU/f/zjH/t0HIPlu9/9bmXba6+9tjQ3N3fbzjknO++8szQ0NEiappXf77nnnlX7f9lllw1of1599dWq/l599dU+PW5ZjR+lPkkOPvjgyhivq6vr9+Nnzpwp6623XqWPvffeW5xzXdp99atfFUB23HFHieO4T30/++yzlX732GOPHtvdddddVe8Ja6yxhnz44Yf9Ppa2tjY54IADZNy4cTJt2rR+P34gbrjhhsr+n3feeT22mzBhggAShmGf3/u68/zzz8sZZ5whv//973tskySJrLPOOpX92n///bttt2DBArnwwgvlRz/6kcyfP7/H/i688MJKX4VCQd57770l3v/e1NTUCCDPPvtsvx/74x//uGosdfx0Z//99698vuluzHfnnnvuqfR50kkn9djuiiuuqNr+P//5z34dRxzHcvjhh1d9jpk9e3a/+hgsjzzySGU/jjvuuB7bHXfccZV2Dz300FLdp9VXX72yrUceeaTXti0tLVIul3ttk6apbLLJJn16DasVi15D/+fTdA3tzZQpU2TEiBECyJgxY+T1119fon7mzp1b9bw8+OCDg7yn3Vt//fUFkHvuuaffj110LHX8vPPOO13aLs0x/fvf/75q+3fffXe/jsM5J1//+tcrj29oaJDXXnutX30MljfffFOstQLIhAkTemx33nnnVfb32muvHbTtNzc3y5AhQyrjOUmSPj/WOSfXX3+9fOtb35I33nijx3ZPPPFE1fP129/+djB2XSmllFJqqdEAsFpqxo8fX/lgfPLJJy+2vXNO9t1336oP1EcffXSvX1QuXLhQDj30UHnuuee67S+Kokpfl156aa/b77zt/fbbr9e2l156aaVtfX19j+3OOeecquP585//3GPb//73vxKGoQCy3Xbb9fmPSxGRrbbaqmo7F154YZ8e94Mf/KDymCAIZOHChd22WzQ4f8kll/TY5wcffFDV9o477ujzcQzU448/XvmjMwxDmTJlSo9t//jHPwogO+20U9Xvn3766arAa6FQkBdffLHX7d51111y2mmndXvfoufu6aef7vPxLKvxo9QnxY477lh5PURRVDV5Y3FeeeUV2XDDDave59va2rpt2/mLqZNOOmmxATPnnOy9994CyNChQ+Xtt9/utf1ll10mxpjKNj7zmc/Im2++2edj+cc//iGbbrqprLnmmjJ16tQ+P26wlEol2XjjjQWQNddcU4rFYpc2nb/4/P73v99jX4888oisu+66MmTIEPn2t7/d5VwnSVIVAOvpWn7rrbdW2jQ2Nvb45efZZ59dabf11lt3e92bPXu2jBo1arHbTJJETjzxRGlsbJRNN91U/vWvf/V4nN2ZM2dOZRu/+93v+vVYET/uOk/86S0A3DlgOWnSpMX2XS6X5TOf+UzlOZ4zZ06Pbb///e9Xbf/JJ5/s8zHMnj1bDj300Mpj11prrcW+fpYm51xlIlhjY6PMmjWrS5sZM2ZILpcTQI455pge+3rxxRdl8803l4aGBjn++OMXG5jtSeex2Nukuuuvv17q6uqkqamp18kSV155ZaW/jTfeWNrb25dov9Qnj15DvU/TNbQ3f/nLX6Surq4SSF/S4K+If7/rfB3oy0RU55ycffbZMnToUFlvvfXkT3/6U7+2GcexDB06VAD52c9+tkT7/Z3vfKdPAeClOaavu+66qu3feuutfd7/lpYWOe200yqPHTlyZL8/iwy2k08+WQCx1nY7caGtra0ySX+nnXbq8X1o2rRpsvPOO0tdXZ0ccMABfUoM+MUvflE5F1//+tf7td8PPfRQ5bEjRoyQl19+uUubJElkjz32qLTbZ599+vWaU0oppZRaHjQArJaK6dOnV4JRgKy77rp9moE5c+ZMWXfddav+CDr22GOltbW1S9uXXnpJNttsMznmmGO6DXY9/vjjVf30FtRNkkRWWmmlStsxY8b0+qXIAQccUNX3v//9727bLZrVUldXJ7///e+7/KHw7LPPVjKXxo0bJzNnzuxx293pPJM8n8/LRx991KfHdf5DB5DTTjut6rhLpZKce+65Vc8lIF/5yleqjrGz3/zmN1VtTz311H4dy5KaN29eVZCgtyD1m2++WXm+/9//+39d7v/pT39adQzDhw+XRx99tEu7crksP/jBD2TEiBHyzDPPdLutSy65pKqviy66qM/HtKzGj1KfBNOnT68EXTp+HnjggV4f45yTF154Qb72ta9V3sfCMJRvfOMbvQZj/vCHP1Rt55BDDun2iyARn1F6+umnV77kW1yGXof77rtPmpqaqoKWv/zlL7u93on4rL1nnnlGTjjhBDHGyJFHHtnn9/ql4fXXX5fGxkYB5Iwzzqh6X5o2bZqsueaaAj7rpbsvtzuMHTu26lwvev6cc10+Fyya6fPAAw9UxkZtbW2vk2UmTZpU1dfnPve5quverFmzKoFPQL70pS/1+OXe3XffXdVXX6tvdDj33HMrj9177737lanSobW1VTbddNPFBoB/+ctfVrU56aST5N133+227YcfflipirLaaqvJSy+91OP20zSVjTbaqKrv7q6ri5o+fbr86Ec/kuHDh1cet//++8sHH3zQtwNfimbNmlUZl4cddljVczpv3jzZcsstBZBNN91U5s6d22M/O+20U9V5ueGGG/q9L6+88kpVH71N8OtceadQKHTJKm9vb5eLL764MlFu/fXX/1icb7Vs6DX003kN7U6SJPLzn/+8MlH64IMPlnnz5g3oeDoHITuurYvzz3/+s+oxhUKhX89J579/P/OZz/R6nnqSJEklUNvx010AeGmO6UUnwB966KGLnZwxa9Ysueqqq6r+9t1+++2XW+ZvZ+3t7bL55psLIDvssEPVRLtisVg53rFjx/b4OURE5Nhjj606L+eee26v203TtOrzYn8rqzz55JNV21tzzTWrJoElSSLHHHNM5f4tt9yy10oySimllFIfF0ZkGS/QqVZoP//5z/nwww+58847u6z7svXWW3PIIYcwbtw4Dj300B77+OijjzjmmGP4y1/+UvndyJEjOfzww1lttdWYO3cuU6ZM4fnnn+eCCy7gtNNOIwiCStvf/e53TJkyhV//+tdV6+4AHHrooUyYMIGTTjqJKIqYNm0a99xzD/fddx9PPvlkVds999yTAw88kOOOO45CoUCpVOK6667jiSee4I477qhqO3bsWE488UR22223LmvYpGnKhRdeyKWXXkqxWARg2223Zccdd6Suro4XX3yR+++/H+ccBx98MNdccw3Dhw/vw9n+n/b2dlZddVXmzp3LCSecwLXXXtunx4kIJ510UlX7bbfdlokTJ/Lee+8xefJk4jjm//2//8ef/vQnnnrqKcCvg3Taaafx/PPPM2zYMG688UYuv/xy3n//fW666SYWLFhQ6S8IAo444gg22GAD/u///o+11lqrX8fWV1/4whe4+eabAWhsbOScc86pGhfgz9Prr7/OHXfcUXkuHnrooW7XZLrppps444wzmD9/fuV3u+++O9tvvz3WWqZOncqDDz7IBhtswK9+9auq9YPfeust7r77bt566y1uvfXWyrbAr4901FFHsdVWW7HDDjuwySab9Hpcy2L8KPVx1fFa+uijj7jzzjt5//33q+43xrD99tuzxRZbMHToUOrq6igWiyxYsIA33niDF154galTpwJ+7dADDjiACy+8kPHjx/e63fnz5zN06NAu29phhx1YZ511WH311SmXy7zxxhs8+uijfPTRR+yyyy5cd911rLnmmn0+vrlz5/LDH/6QK664gvb2dgCGDh3K7rvvztprr019fT0zZ87kww8/5Mknn2TGjBlsvPHGXHzxxey777593s7S8sQTT/D5z3+eadOmsf322zNx4kRmz57NLbfcQnNzM0ceeSTXX389hUKhxz7Gjh1btdbsI488woQJE6raTJ06lS9+8Yv87W9/A/x15eijj2b8+PG88MIL3HvvvaRpyuabb84NN9zAZz7zmR63lyQJkyZN4sILL6ysGbj11luz7777Mn/+fG6//XZmzpzJkCFDuOiiizj11FN7XAvx3nvv5ZBDDqn63YMPPtjrc/PMM88wefJkHnvsMZ544omq+9Zbbz32339/tt56614/Jy3q3XffZcstt2TOnDkYYyrrAnf24osvdrnehGHIhAkTGDduHGPHjmXhwoW88cYbPPTQQ7S0tHDQQQfxq1/9ihEjRlQ9rlQq8bOf/YyFCxcyefLkLmsvdxzLLrvswvDhw2lsbCRJEpqbm5k6dSovvfQSL7/8cmX9v+23355zzjlnsevcL0svv/wyRx55JC+//DKbbLIJ++yzD+VymZtvvplZs2ax2267ceedd3ZZ+7SznXbaqeo5vuGGGzjuuOMWu+233nqL++67j2nTpnHHHXdUfZYNw5B9992XDTfckOOOO67qs8ezzz7LscceW1nLsq6ujiOPPJJx48YxY8YMHnjgAaZNm0YURXz5y1/m4osvpq6ubgnOjvqk0GuoXkM7c87x5z//me9///v861//Yr311uOCCy7g0EMP7feavx3XgZaWFh599NEu1zPw68TuvPPOjBgxgq9+9atd7n/uuefYYostqn535ZVXctppp/W43f/85z/cc889/OMf/+APf/hD1X2rrroqhxxyCBtvvDEnnHBCn49l3rx5bL311rz55puA/8wxduzYqjaDPaZ//vOfM3v2bB599FEef/zxLvePHTuWPfbYg1GjRtHU1ISI0NLSwvTp03n55Zf597//XfkMs+mmm3LWWWdxxBFH9Pt5XFree+89jjrqKJ544gnWXnttPve5z5HP57n99tt599132Xzzzfntb3/LGmus0WMfxx57LLfcckvl9rnnnst5553XY/sHH3yQ/fffH4B11lmH1157rd/n49577+XLX/5y5bq76qqr8vnPf55cLsf999/Piy++SBAEnHTSSfzkJz9ZautyK6WUUkoNquUaflYrlDiOu8ws7+4nl8sttjytc07++te/yoknnijjx4+XIUOGSC6Xk7Fjx8qECRPk8ssv73G9p3Hjxi12H6ZPny4ivlze4tp2lBR7++23F9v285//fI/HNHXqVJk0aZJsv/32MmbMGImiSIYNGyYbb7yxnH766f1eM29Rl19+uYwePVreeuutfj0ujmO54447ZOLEibLqqqtKLpeT0aNHy3bbbSdXXHFFZQ3dV155Rbbffnupq6uTlVdeWSZOnCiXX365FItFmTZt2mLPDSA333zzgI6xJ+3t7ZX1q/rzU19f3+ts8VmzZsmPfvQjmTBhgowZM0ZyuZwMGTJENtpoIznxxBPl4Ycf7jYz7Oabb+7T9k844YQ+H+PSHj9KfRz19bW06E9NTY2svPLKss0228jJJ58sN954Y79n6U+fPl3++c9/yi233CJnnnmmTJw4UVZffXVpamqSMAxllVVWke23316++tWv9poh2RfNzc3y+9//Xk499VTZbrvtZK211pKGhgYpFAqy9tpryy677CLf/OY3e6w2sTwtWLBAvv3tb8tnP/tZaWhokJEjR8qBBx4of/nLX/r0+L/97W+yzjrrSFNTU6/lK9M0lb/97W9y4oknyoYbbih1dXVSX18v66yzjhx33HHywAMP9KsM37vvviuTJk2Sz372s7LSSitJLpeTMWPGyO677y4//elP+5QNlaap/PjHP65a8uLyyy/v9TFf/OIXFzt+x40b1+fj6PDss8/K+PHj5cADD+xxX99++2155pln5Nprr5XTTjtNdtxxR1l11VWloaFBoiiS1VdfXXbeeWf59re/3Wu5yr5e87v7/DdixAjZaKON5Oijj5bLLrus1+yf5a1UKsmkSZNkwoQJMnToUBk6dKjsvvvuctddd/VprP373/+WTTfdVOrr6+W4447rcwnovr7v3XLLLV0eWy6X5c4775Rjjz1W1ltvPWlsbKwa2xdffLG8//77/T4X6pNJr6F6De1s9913F2ut7L777nLrrbcOaLmY/lwHevvb/4YbbpAtttii0vaMM87odbsXXHDBgLbXk7ffflu22mor2XzzzXvMvh2sMd3X70wW/QnDUIYNGybjx4+Xww47TC6++GL5z3/+06/jXJbSNJVf/vKXsscee8hKK60kDQ0NssMOO8g111zTp+dn2rRpstNOO0ltba3sv//+iy0Bfcghh1TO1TnnnLPE+z1//ny59tprZa+99pLVV19doiiSkSNHylZbbSVnn312r+sDK6WUUkp9HGkGsFJKKaWUUgNw++23c/TRR3PVVVfx5S9/eXnvjlJKKVXlueeeY8yYMay88srLe1e6eOqpp9h+++351re+xSWXXLK8d0cppZRSSqkVRri8d0AppZRSSqlPso4yydtss81y3hOllFKqq80333x570KP9BqqlFJKKaXU0qEZwEoppZRSSi2h5uZmtt12W0aNGsXDDz+MtXZ575JSSin1iRDHMXvttRczZszgueeeo7a2dnnvklJKKaWUUisM/YZKKaWUUkqpJfDCCy+w7bbbsnDhQm688UYN/iqllFJ99PbbbzNx4kSee+45brvtNg3+KqWUUkopNci0BLRSapmaM2cOzc3Ng9KXMYbVV199UPpSSiml+uO0007j17/+NYcffjhXXHEFw4YNW967pJRSSn0iXHrppZx99tnssccevPjii6y22mrLe5eUUkoppZRa4WgJaKXUMrXvvvvyhz/8YdD6++ijjxg5cuSg9aeUUkr1xWuvvcbIkSM18KuUUkr109SpU8nlcowePXp574pSSimllFIrLA0AK6WWqeeff56pU6cOSl/WWvbff3+MMYPSn1JKKaWUUkoppZRSSiml1CedBoCVUkoppZRSSimllFJKKaWUUmoFYZf3DiillFJKKaWUUkoppZRSSimllBocGgBWSimllFJKKaWUUkoppZRSSqkVhAaAlVJKKaWUUkoppZRSSimllFJqBaEBYKWUUkoppZRSSimllFJKKaWUWkFoAFgppZRSSimllFJKKaWUUkoppVYQGgBWSimllFJKKaWUUkoppZRSSqkVhAaAlVJKKaWUUkoppZRSSimllFJqBaEBYKWUUkoppZRSSimllFJKKaWUWkGEy3sHlrUxhVqMK0O+gM2lmDREwhqI50OLQ2pTTL4O2mIQAIEEwto68vlWQluPsTHGBGBz0FzE1BUQ1womAHIgMcYasHWIa8eYAOMEonpw88HUQVrE5BuROAYbYEySPT7AWJA0BhGMCBAAKWIMRDXgUkwag7FgBEGg3Aa5RsD57QMmaoQkwSVzwAQEhSakVIZciCGHKS3ARLXYJIEghMAixiEOknIr7UlKWwwFcQwdNpycBcIISDDk/PaTVsg1QNwGUS2kbRDkEAeUY0xgSV1MkkIszj8mbiVXOxKKswjqRpG2LQALQZjDtC0gaByJsYJgwUZYa5FyK2IixAiIP18iCWJCpNiGKTRC3ApBHjERrjwHKSWYME+KRZwDC3GpGakZBiZEXAuuNBeowZkaoIRxghXBhjmsyRMEITZNCfMhLhWstZggh81FkJagXMTUNiFOICliwgicQ5ISgkGwmCDCBBEYhysWMaGFcsnvc1qGqAClhRBEfpCmMYR5/3iJMSKYIAcIIg5sBO3NGBtigoAgyiFBSFpOwFrElTBBDSIxGIMUW/14yBUQ147NDQVXBkBcCkLWPkUkxRgD2GxbBaS8EDEWweLK7QT5Gii3Y3KNuLQNnICxCAkuhSBfAALS9oV+PAYRIg5xCVJoJGlfgEgCthYTlzEI+bp6bNqOxWKjWkzgsCbABgUMFlwRTEhgLSIxYkNsYME5ICTIR2AMLi4BIWm5iMnV4UrN/rkOQiRJMAaQBFwJk2vAOIMkCwlrhiOlZmy+BoljxFiMDcBYXFyEXB7nYlzsSB2IlMGGSNIOYQ1RlCcSR5QrEBpHUChgwhw4IS2XcHE7xgYEuYJ/HsslxFpMWEDSlLjYRkxCnDhcXCYxAaViiZK1FEtFygtbcKWFhPVN0LyA0AVEw0ZiSIhqG3Et7xHkm4gaViJd+AFgCPI1BDUj/PPX9gFh1ERQM4QgV4vMfxNbvzI2jPz+tH6Ak5CwcTSubRaSCmIAG5CWS0hqMPkAUxiOpC1IqR3COkwujyFC2lqhrhZpnYmpWQnXMhtMERfU4FJH0LgKafNHiAmwYY40aSdtm4OtH0Pa+hFh/ShccT42rMWRQppijUOcwaQlCANsoQ5JUyDBpSE2iIg/mk44egxJy1xs4xqkc2cQhI5c/SjSYjthPockJdK2eQTDxuFmTYX6BoLaYVgRXHE+0lbCmTaC4auSfvA+QV0TprYeaZmGcf4YiWqR5rmY+iZIYkx9nuSjD4nGjOeRKX9fyletj5d58+Yt711QSim1ghg6dOjy3oVlSq+hSimlBsun7RoKeh1VSik1eD5N19FPXQCYUjsOCKJWaI+QQpMPwpUM4lJce4CNwAQCpRDqLbSUCQqGMGryKdMmBDGQFqG2BmMEEw7P8qkNGIsxDpEEkxsJSTPIQgw1YBqyHcn5QEJQBmoxNu8DaZQgDTAm9P2HBmsDxFlsoQ5XbPeBRwJIW5FcDpOGmHyTD+Iah43qsXGJlBRxJUJbA/kAYwypEYzNYcoljC1gjMGFjqhmKFJegIki4rgFW2jCNH+Ajep9YFiKmKARycKa2ADSGJNrAOMQMSApYkKMBFBqgZp6JI5JDTgbYGweKbaSs7UEgYF8EyQlwnwBV2rGpA4T5SDwAUZCwaQC5XZEUrAWY0MQ8YFLm/e3iX3Q1QmQkroiNmpEAoMLAlyxGfJNSNqKhHkkXohzDhM2YsImoA7aW7BBhDVgrMOS9+Fb47D19ZhSkbBQg8RtmDAASSEuYvJ5H7hN25AkAQRjIrD+9yYpAwKSIHEZm6v1x5qrB1JwMUiUBc9bfdA7afPnN6wBCQBBSDAiWAlxiZ80QG0jErcgJkAkxAQW59ogThBKPtAZ1PjnKEl8QDMt+G1GdUjrbMjVI8VWhAhxMSbMIy6GNAEcYh1pUsbWDIPyQkwYIc6RpoJJS9hcDVJsBWwWB45w5ZKfzBCEmCgEgVRi0sQicQkxeUQibBASYMiFeUIDJt+EwWACizGCNdafI0mwUc6P28BAGuHiNghrIRDExaRlByK4xCEUsWEBSdv9sQY5JE1waYyJctigQFoqEzgBAzasASvYmib/motqIW5BAuufT5vDOT/GnTVgDCQWggDKBiMhJCUI8j4AbyBtX4iJ6iB1PpCab0DidtK4jI0MJlfw58glpEmRNG4ncRCnMSUxFNvnUYyFpK1I2jAUE4XkqCcMQqJRq2LnzyNIHTYUopxFmlZGym1E0kbUMBJpno01BmMTTF2Tf1tKFhKYhNA4JDcEY8CEBQgKuDDCpEWciZCGVYnnf4i0zEXCyE9qyA8jbZ6FlXaoHQoUIIhIi0UknocpDIG2VpIkIEzLJBgkGIITQUihbR4SWKTYAq4GokZS244ptUBYIC0VkfwQXPt8kJAgiqC8ANu0KiZuw6UlrMkhxmCDAJPPUZw/A1eTJ1kwh7Q5IYg/JKjLkzohCULi5mZSibB1I6DQRNL6EWldPZTm+f5Sh1vYTjh2HUgSvw/DViJeMIfAGAgaCWobiZtnEUYFpG4YactMBEdUGE0wZAzSNn+ZXLaUUkoppZRSSimllFJKqf741AWATeMwpL0ZCnXg2oAyBK2Ag0IBWxNhTOyzMtMUUxKkwUHcDKYJwhCMgLVgDCYMcKV52HwO4wASHxgNmwCHJEWMc5AbBmnqs4YlhbCIkRoICkiyEBM1QakZbA1icj54FwYEUa0PforDJgkmCHDOgrRCR7DVQIBDUoeN8lB2YByBMz5TMarBkkdcSmCs379AQAzGCBDhyguRUhtIASnFkC8RBHVIWztJoQ4X1iMBGGMhMeBKPhBGAuUihDkkaYXQZ55KAEbKOAQT5nCtCzBBlvlcMxRp/wCbG4JLI0wIQaEWymkWGCsiFh/klAQXAi6EIEDKbSB5cAkiRcgXfKDOlXFJGQkC0riMI0RMjItDXJqCaceViogJcS5AnMO1zoHIEdQYgrAdmx+FScuYqIYwCDBxKzZ12NSBAST1md3G+uMPLYS1SNwKLvHBawNiHCb2wXyXljAOJIh8H8b5zF1SJE18UNOlENbgyq1IXAKTBY6jGj924nZMvtYHkV3RB5dzBUjKSAJp6CBZgD9pIKQYk8PEMZgUk6tF0jKSgglCJIkxlBEbgJQR67MqfXZ1Oct8TxARJE2xUS2StJEmZYKolrTcho0KuDTFlFOScjO2dhQmTTCSQBjhSDEEkBRJUovDJ+u6pA0b5QlCQ84KoZQIohBjA4xzhGHe74dLMNb48YNF0hhxYMT6cRvls/2LfXa3ZIFZSX3WszgkLSJhDeKKuLSMkEOSFv/6CupIkxKhDbC5Gp/BHhZwqcMEDsIa0lLRnwsbIGJJkwRJikhQAy5GSil+xkDZZ0inbRhTAyaHEENSxka5jgPHJWWcJBhTh1gHLiaN24lLCbEJKbkyrS0LKOcaaJ/XjNQ04trbyeVyhFGOIB8SxAvJCYQjh/nXRrmETToC962YFD9xIogxkmJdidAlmPoh0BoTSAnjAqgfgpRaffA8KSK5WlIMrrgQYyySb0RyDX5Sh80BkNQMIS21YqMIG7eRiiEtp0g8D5tvIC01YwpNFFuafSa5FcrtHxLYENcWYOpHERfnATFB1I7LF0ja2wnE4dqbsYUa4tY5mNo8OUaQLIyJcvOhnOAoE7g8kswjKIyCtnnErTHBkCZob8UMqyNuL5IsLGOLMXEimLpakvJCTMs0bM0Q0jghaByOIcC1tBCYkGDocJLW+bhiCUzZj+tcDDKfMBiGSxNM7XDSpIiJcjiEoGEI5QVzCWvqMO0ty+jKpZRSSimllFJKKaWUUkr13acuAGxJsPV1QGuWYekgrQPafLAtBNqKkDOQ92WCbdoIQYIEJoszCWItIvVYMZioCXHWlz2WGGtzYBLA+gzR9hI++hfjo4HOl01OfUlkgyWIcqSuwWe0uiDL2BOsCX2Gci7vg46JI0AIckMwofWBp1I7JpdDkgRLirP4ALUrY8KCD0KlMQCmUIeJYxJJsFEBEoeJAkxaIs0VEEkI80NITJkwVyAvKQmGtL0VCX35UxvW+qxf57NbRRwmyCOpyzKDi9hCA5KUSdN2YsnhcJi4RGgFmwsx8XCMsdi8wUh2aoIYUzPMl1KOIiQIkHIJG9WRluZj8Fm1UmqDXADlEpQtJt+IS1qR0G/HhZEPEBuLxK04KSPW4lw7Jj8USmWMKWMKIcQ+aGjzdQQyD6jBShtBOAQbNmLiIrgiNpf3gcawHiOlLMhfi6QJpMaf35wv/0wcQ74OaVsAAT7IG4aIrfVBSRGfxW0cRDW+hDcOaodiktgH01tmYyTGpO2Qr/EZueIwhToQg0tKfkhFeVxrC7amwWe8hgZbaPDPS5RNJEiL2YSGFoiGQLGIkywzVvCTEgBJUohCTFKEfA0mMRDgn0eX+EC+S32mexRi4jKJizE1Q3CujCQJYb4OJyUwIWmS4owlJYVyGQp1vpy1iwnFEokjqG3yJaeNybK5waRZ+WxJfOlrY32GeVJGJACXIiYAK/ia0wVfKt1kgWCLLw1uLK7chgRZljYROINJY0xo/bGEecRaICsnHQSkaXtWstsggCT+trEWZ3PgSth8I65lLtQMhbgF8hG2puF/kyvydRgEcc6Xc7cdx5hDxCBxG46AJHGUXEopCGlNEhYuWIjURQS1BaxrwzbkCV1C6GKiIEdYqCeMAkyYh7CBNCn547EWk8thJMVgMfUrQ3E+kXEEQYgxMaZhOFJq9c9fGOISS9q+kBhBSu2kBDjXnlUwsKRRk59sUm7FBSA1eeKWWUirz7o25JAAUtuEa2tBio6g3Ey5rZmgaTgyfy6JCbD5BpJ57xG0OVKX4NrmYcgRjBqGtKYkzpGWWgmSZqgfgSWibdZHEIYkcxcixTassQRRGVcqEgwpYmwNYg0UE0zsg/iSxBBGlF0JmtuIGocCDUg8F+s+gkKepGU2NqrBNg5FXJm0NQbbionqcaWYJHXYQh6xEXGxFUMJcnlMfgTWQNA0ChOE0P6e334QLIOrllJKKaWUUkoppZRSSinVP5+6ADCxQGggtj5bMDDQHiNRDYYEpAZIoS2CXBmKFpN3SFqDq8ljTYK4CKQNkTySGAwJJrBZUKoWsSGSOAhDTBIjJudLOgMQgWmHRHw2cQqEdaQlkDDEpCmkZYyxBIUGDGUfvJIQMSk2EoIojwlrsyzSMpKvARNgTBmMYCSFGNK0nahuJR+cDCykWaC2VCK01peMxvkSr8ZhXXZ+Ikve1WDzAeVSQty2gLS+3gd6wxxi0qxEsMUlCSZf79eUTWOfqevEx9XjFBcUkLiNMNcAxfnY3BAM4pcvNsZnWIY12CBESgZJixgnGOtwsV//2MVtmFwTUmqGsIAEic8ujYYgcTviyjgsNldL2t6CwyAu8esl5xsQY0nbWoAaSMq4dD42MAQ1o6AWaC/6AFriiKK8DwqW2jFh5J83GwARxiZ+zCCQxD6IL36tZoK8D+iTQmh9kDdX4zOmgyyQWGrx6z3jfBZvmviy364dCDFRrV9jOY2hUOvHQr4RIw5stvZvWkIkwBAiSQkbRn4dVhthIvHlmGvrfQnusA7Sdh+4DkIkbsEG9UhUQEpFH2xP23wWevvCbNJAjIkK/vylBucEl2W7iwmxBrB1uDj28VYp++2UF2LCWpwIaZwCMZIrkMa+lLVQxNiI0JSIcnkiQqzNEQR5v9ZwWibI2yzjNucz2Z3zAXZJMVENYnMYYyq/A8FEeSi241zRB4JzdVBuwwQOcSEEWZa7GKCMzecxqV+nO8rn/ESBxPh+JQGxiFhIwRCQkmLCWtKkDSHwgeG45M9PvsZnhIcGG9X6oHfcipEAY2swYnBpGYIclErYXAFJSpCUkKiGOCnRLtBWhrbiHMqt8zFphC21kDMRlpBcQz22fSG5Qh1RoYANI5xzgEWKzYT1I5H2hZhAsLnhSNuHBDaPrRuFqSlgivMxLvDVCfK1PpO5fQ7ORKS2jlL7DBLKuGgYKSVcqQ2xESQxKe3+uXGtpCaHRHlcMU/SPhfqR4A4pLUMYYQsmO0nwpTng4F0YTOycAF25HBcaT4maIQ4Idc01JfrLrcSFudBmxAOG0bsajELW7B5i7S1YiwE9UPh/Y+gISJqqieZ/SFRUMC0xJhChCslBFhcsZkwl/cVGwo5wiEjiWd/iLHzMLVD/WsuqgUDLo1xSYCLfcl/WxNhTR02akJcO0FuCGl5gQ8sF3I4FxMkgk3n4MIarJT8EF1pHZCUtHn+MrlsKaWUUkoppZRSSimllFL98ekLAEuNX6+zaKBQhLAJahJIW5AowrgYcviAJvhytkGJIC0iSZ7Y5H2QMMUHf3B+TdO47EsUBz4Aa0wZg/XroYZBloFb54N5pi4LrKQ++9WIX5/U4UvEBiBJQhrHft3bQHxmrxOCMI8JIpxLsQguLmKieiSN/XqeqUDqcBhs2AiIz2xMHSYqgI0xQYLJ1frAZS6AJPZBjdBCGvkAYGkeuAIuwJfvNbks6Bn4tXbjFPIRUjKYqICjDVNoQtIiBD5YnFrB2QhcBNZgonpsPu8zUrFgcv65sO1gQ4JCI86VIBVcYKHYiik0Ii71QW1jkXIrJghx5TJEeaTQSBqXSZ0QhJYkSZC8X6eWJAUbIWlKkrYSRX6NVxvWYgVIYoIoxOZrwaYYY/z+2RCTtmBSv0yrDySCMQE4g0sSn2VrgHIJKZcxtY0+KGl9oEmSEoQFSMtIuR1JUh+cdCXI1/os1tSvAWxCH1TE+fLG4lyWseswNkDiYpYJa5ByljkchFC2SOCygG05yyj2ExskzPtKzi6ApN1ntIdNfr9M4M9pXMQlRf/4sOBLLrfNwdaPQsolnBg/scDlkbSEmFJWytlgJEVsQBov9CWPbY4wCJC4jAsiEOMnAJSKGIoEhRqCuEiIEFmwYYEgl0eSJFsru8ZPJrAGTOQHnXS8Zg2kJUwQIWm5kqGKCTAiCBZTaMCVS5i0DGGeNC1jogLGCRK3Yk0WlI+dX5tZ8E8uiQ/ch34yBEGIKbdBGJEU23EUfXDf+DWFDUBYhyRtmKDWl6AutyO5Bl++2+ZwJsSkDilnmdcmREyRAIs4IcGRpI7W9iItLS20p5ZkzjxsTR35oQWk9UOimloiE5KvqcHSQtQ03J/zuISxkS9DHtYQ1g4B69ejDvIhkmvEkmJIMWGEE8GlbRhTi4tLpGGekgsptzfjgkaKaUJanEdqQtK0RNLcAibA5mtIo3Zf8cAlftxIKzZXj2svEdDs39vKJWyD9eM3jAlLOaQ2wgZg6xsxrUVfjr6hBvfRh4S19dgxa5POehuKrdgRozBpG/mVVsHFU7GmjBQs1DX6cvErD8fU10FxHrmRK2FrmpC2BYhrx9kIQ4mwqc5P3qkvgGnBhEMJmoZDIcSERSSuwzhLmrQSRAYJC7jmFiRvsXVNSOscktIMyA0hlRQKw3CuhaBQg4n9Osnp7LkENW2UpQ5LgonnI1FCVDNimVy2lFJKKaWUUkoppZRSSqn++PQFgIM2aG2HQr1fw1XKkBhMawC0+2BkIcLkaxEslNuQlgQzcojPwHSxL/Pqyj4LVdrBDgETQtqGSIiIw9oQkdiX3aUeseJLQ6cx5HKYxGFc6MtGJ60Q5RDEr3FqC2DbfKZtsdWvH2t9UFMCRxKXs2BjiBDikhhcEYnyPqjrQFxKmG/ygWwjSCSYKPRlhGuHYkyEi9uyIFjsS+naHIRZdqPN+aBbLofYPAmGJIGcafdlhXN5n72alpEk9usUR75cMJEhTWLSFMQmWGOwCNaG2HLZZ2VGtVBqxuaHIuVmiAzOWCQu+szdOPHrNNsAE9ZkGb21CB2ljw0udaTOkZSK2HyBcmmBz+h2Fpe2Iq4ZpB4hJEgjsDFIOzbXiLV1IEVsKcXkHIG1QIQkCcY6TFSLEbKM0mYMiQ8+Bnk/DsI6vx5snGBqmsCGQACkWSnw1JcGxoLNYcLUB22l5IO8pSK2pj4r29wI5RgjDjERIP54EaS9hH9CLYLP4DY28AH/IIE0xaWJD4qKX+8XF/vnL4kxuQgptkG5HZOrQeI2n3GOX6fXBsaPH3KQJkhYR1r2Y0EIMGGIi0uV7Ne01Ab5AmmxmSBXD4Ck7dggR9pWxKVFpKEJlzqkdT5GHEGujnxaJrAQ5BswcQnCBImtHzcu9gHgcoxJIQitD74LfixY4ycNlCQrCw0mKxEtCBKFPis6NDhJsGSZzOLX/zVBHmyAczEGf+5CE2Iw2FwtrtSGiRpwpWaMtYhLEFdCogBSnw1tCnkwtUjS6rNpxfiguKSYfKN/XLkdyeX82shBhOTyGBP4EtImwVAizedpj8u0zP+IlnlziG0IUiBqqCHK12OTIjbfQD4XEYUhYQCmfmWsS7LXYz3SNgdy9QT5ArbUQlAYDulHmLSEaRiBa53nX/NJgtSOIo5bSYrziOOU2LUSS0hx9mzIx8RpQOIakbkfgXOYoSORuTNJ21JMfeTLUCeBX0865whNAUM9ttyGC0KCpnqCxCASEphaqC1h6iIoJtiCQGKxYYhtaMSVWpGCIYxKuNoCjlbC+nrSeSWiXEg6fBgmn/MvI8qkaUoQBbi4GWfL2KAJV2onNBbJ1WJrhlKeM52gdjRpy1vkm9YibvMTOaShFuIWLI1IbRtu/kLCUSNIF8zFBm2YGost1OJa5iPNLSAJudFNlD98D9vQSNA4Aml9H9dWRurBNg0hmf8hZkgtLjX+LbM9IVitYRlevJRSSimllFJKKaWUUkqpvvn0BYCjGFe2GNOOoQbKZSQXQN5g2oCyD9hJexGiCOrz0ByTSglrHCEGKCFBzmcDivElnQMLzmJtLUgZE+R8NmyY80HWdCHkOjJ0U59xTAMYA2GtLyWM8eWHScBEGEn8Oqe5HE4sJlfw2acBuLTk16CMfJaxDfJAAGFIENUCCZIKEsc+GJaPEHGIS7BBDSJlpGPxXRdjbK0PDuZqoViEQg5Xmo9IvQ8Cx3MgPxJMHjHGByGTFJP323LG+bhNlEOCCFcu4YIcNi5jxBEa6zOQRXx2orF+7VWTQi7nA7+26IO7JgSaQQo+Uzou+2A8BmxImpQxBl+eOARxJZzx2aREeSRxSDjcZ75KCi7xpYIDwVLj14tNF5DLDcGEEIQBpm0mYmv9esiBgEt84NG1YXI1fv3WXA3SvgAT1CBJO8QlTFjAhBGStkE5RkLA1AAGExR8MEtAEoG4FaJ6JG7HRDl8/W/J1oTO+2xSSXBJAkmKiaIsMzXOgp0WCP2+ucRnXJfbsrLSJgvaCpRbITJ+7Wgb+PMmghiDEPi1b3M50nIbLiogroSpnN46XNIORDjjs1hT5whyBdK4HSf40tWp81nEUR1OFpJKDLkCaRL6ctKJEJg8UT5PEIWEgWBTg7EGUyiAhBgc1vgxj0uxYQEkwViwIj7j28T4bPHIT6LIgrlSakZMDWKcPzdOEOswLiGVBLGhz3B2KTYf+TWo+f/s/WuTHDmWrY09ewNw94jITLIu3T0XSfb+/38lyaRzZrqnqsjMjHB3AHvrw0ayx/RF7wy758xRYVnRmCQzIzwAeESZPXutFY5qUcX7GfcBFstjDzxl7Hgg642+v0dUtgt4hloDLIpHzHpPEZndTkRz9PyK4w6uBud9OJjP6PNenul9Z7eT1/vO4/2gb58o1qEfJHsn1cJy2Vg8kfJCKgWVjMgSUd6akWSky4/QHwgdxUjJ4fYJb3vE2z+9UB//Rk8vNDUqF47jK4/7/4smC77eqF9+Q7eT9PITRQv+lMl5R55/wHOCr7+gj3cSoOKk5wW/V/Ja0U9/hPYV2S5IvyO+IuVnVBp2NtL2mZ7/DL6Rnq9Y/ULSjv/0B6g7sMMPn+iPTpKOfnpGOCg/fsYfb3h7jX52TejyI/gvMRBQViQJchzkyxO0O6kkUj0wD3d92l6wxxfyp3+gHf+CeCJtT9QfCtQTEcPfvsDyGb8/sMev6I9/QjtYvaOf/0D79V9R7/i9kX/4mfpvv6A/3pA//AG+BvRPP3xG9AV7/PZf8KE1NTU1NTU1NTU1NTU1NTU1NTU1NTU19R/T7w8APySck/kHeH+HpzK6WPfgcesGbyc8EZ2SrydeDX97h3JDfIPrHZfPSP8F8nNEldoF7wKlgu/hAkWAjYBAN6x3NG3hZpRbRDMvBTlfQZ/C3dqJuOQFOAVZP+NJ8KODBMC18wR3rB+kQe4sJbRHV2xPGvHHteLd0fVKSgWrB4YHaLUecHhZUJWAPm10pUrHW0XkCfYHdCeVF7w7rBlqw9WhnrBFj6rKhzs6AHmvJ2kpiDXScg1QtlS81XAze0fKFd/fYPsUJ7GOrmKxiEFWg+444Wb1RLhZpUV1rlkAQInvs34OR6lG72q+Iu6IdSRn5Hgg5RnxhuRMaju6XBBxPH1ClisiB+QL4g3fdyg1AN9xIusluneFgMtY9AC3Bm3E/eZLnKcarlws3L4kCbf58sRfe4ItqmzPeE4/w6nrmYj5XS58gHxJGfoZvchqKNFh6ibhMrVjDCRYuGF9dN/aiWyfsOMNWg+XdTuxkiPK2Rq2n6Tbisuoym2Ct3dMC94NSQvWGr4/okf2fIO80dsxOnkjttnrAf6GpmdUhFKgrBupt1hDi45dlRWyBtLvByZpwOqCk/Gk36Keo+929Cx7iftKwDUirn0AaVfBDZyEqyAGXRzJC3045tUMV5CcYkCj1nCwlw3bR6S4VdwUzyPyvB0xuOEGdiC6IlJwTqCN+0Ww1mnaKakgOLZ/BRa0LHjKNIT3+87bv/1C7YZkIbeGtAcpHeT8mVISizXy8xNyVnQZgwZpRfQCGLY/SJcrIlckZ9QqeMXLgvVKPw9qb9yPk1p/ZT8brSk9L5z/ekdeVtLxlfX5M/76G+v1goqin38g54Tvb6SnJ/TlE0JFEPJyQdKF9v4/yObIyzPiBavv4Xw/G7pdsPsv5Kd/wLuhuiJPP2L3L2i5IK2jlx/wx5eI+N4W9PYnaA809TinyzO9/gXKiq4v9Le/IHWn6EZPB6KOOaTnH+B8p9//THn5Z/rbb+Ryo7/+iiwv+O6Y/gZ6wV9/xfUJ1Svt9V/QT/+E7b9iX7/gqSPXBUzodcfOHdk2tIz3tNsFl43008/0+xu63DB7RZ5W6r/9PylPf8L2L/81n1tTfxO5e3TP/y9+jP+dNdfw+zXX8Ps11/D7Nddw6j+qeWa+X3MNv19zDb9fcw2/X3MNp/4zmufm+zXX8Ps11/D7Ndfw+zXX8L9evz8AfCvRlfvra7gr84bUHeoa8PbW4/ftE7SOnzu6PUHpeFnpnLhksir4M6KXiH3GQNuIXC5EbO+BLD+EUzaniGRdMpgiOcffdwMyJImu1/IJEaBLwExX/P1rAMLjiL5PM9CCINhSkFbDNdwbRkLtPYCoN1wcSQv9PBA/R+/uGcZYTQF6c0H6HrHOvsO6RT/tukXnrN8RWUk5R/fpcgnQqAS4c0OWJ7CKl+jh1euniLtud3RZkKTQHemGLGN9FFhKAKzeAnImxY/XcIVqGj24SreKLitsSt8dLwvt/oZJR9Yf6MceNLAL9DujoDc6d9MV6Q1dCjkpZpmSgXZGhHePyG8k1lBqdMDq9SVApBNO7nbEdQ1XMfmGSIB58gXKFbEakcHrFuuBx1CBAlwCmGp05YqWiML2E6jI9RlWIyXFH28B4vcdKQEVISHqATsbUB9IXnF/h+E4F12Q9Rb7YwFWRXK4VeloLtS6h2MWRxqwXnCrWLdwZvcDzUsYWJcCvQf8LyUc664BsFv063ouca+0ii7PZFXSkinuKJWcl3jM7RJnQk4wxyUjkpGcAvRbw12wR8NTwvqBaoaUAhCyYz1H5PU4uwFob4h63A+NWCsBvOGkb3HpWko8v1kME7Q9+l01ge1IPWMdW4teZrN47nbEHqQbdhruB1JW/HhFri9xdv0EVrydeFpgeUYMXIRunfv9ja+/faW1HUUDiktB80qRhe3phWXZ0P5A1wuyXvBmAEh/hHPaczh8W8R5p1Lw2jAzGidnOznPN3Yyj+oc/+N/4urItpB85XK74vdXtutGfrrh2kjJUDGKVpJ2KIbkHd1eoLyQRBEMrwfLpz9i77+hVKw7kq9QEp4eoI5eN1QX+vEVnv4RpCG6kZ4+41/+Eu8B2xIDEb2EE9waSA5g7l+Q5YKuV7x+RXOB9hW3NyRtiB1Qv4L+Md7L0ydUEn59IeWVtpdwW6sh9aA8/UhD6V/+38hyC3h/HCS9wvWBrD8jUun7K+IJmmH2QLcXpCT8fKd/+X+g1x/is0LeST/9ERFov37F+4mp/td8bk39TfS3+J/D3/v/YM41/H7NNfx+zTX8fs01nPqPap6Z79dcw+/XXMPv11zD79dcw6n/jOa5+X7NNfx+zTX8fs01/H7NNfyv1+8PAFuCRx9/cHj9AmTIPdyZfsJXwvF3HJAzvi5oO2EhAIPcAENSROuKaICmZY3eVQ1gIusV7wf4iVsGdrymgB7Nw+VpFr2yGHr5BBa9qNINUsZ7I9BWAqnhyMxrQFgcWsN7R4tBP0kEpHPvQEIvG9DDTdl35PJEr+8YguYrto/oW9vRvEVPau/QO33E8IKDvYPeApIq4ehdt3C/SkKsY+c7nhesNmTZkHqSrp8Q75CuAdnSJYAkDWRF9IzXV8Jliyx4i7X1vuMdRAzZFrx3umSq79ANSwk7vqC6RqT2+gS1Q1oQVSQt0BakJNROxBzSiJ2toJeneH3Ha+yrGcOCjZACJCYNKOwDfhK9xxiIagBI19jDj1hoBcm3iDNOG24nTh5R1sQZyZeIv/5YSye6n/MSZ6+EU1xKifjqfkYcuC7xPEK4nAXEFFLBPfprJS/QDFlWbB8dzXnF71+Qi+J5pZ8PZHum73dYCv18RK9tXtBUMBKCfVhs40xpgF404R2ccJnrUsAaab1StkI+Hqh3JAm5bGgueDNUEmTBRem9ogRUFjfMWsBgDKMjuoIkrIUTWMolHLlS8NHDjUVPtPceEeLk0ZU9zllzRDqmH3HtHiDPPAB4LnE2m4x+5WD9BuEOFmJfU4H2inuK81At0sHTEr3IraLliVQWbH+FfoYjPinddnaHx/sDNFGWF6SfAcrFKSmzZCeLkb1GT3SruOYYUCiXuMesI0WRXqBbXKV1TBLnfudhzmnC/uV/cD4UPv2B9Okn9MsvZAT1Rvn5j3A0cn+nrDnA+fkVzU+kTZCUoETIPe0ANBLEfbzPJUi3G94qqaw4hvWGlgt2nuj1OQYxUo49Pd7Q9SnuiaJIu6PXP9DtS0SY9/cYhLjdkB3oDd2uuN3h+I18+79gj7+APpPWn/D7XxCucLwjyxNarlB3pN/x/k7CYfkBMcce7/TjK2l9HsMlD0g/Y49XJCVSvuD1DXFByxou99/eke2CHQfSD7xlMMH2im4brhf8jGtPP/4cTvZ3/3t+Wk1NTU1NTU1NTU1NTU1NTU1NTU1NTU39p/T7sy/ZEd2uarBKxA4/JegKOUUHcM4Bf6UiBnruGA1/NJALSvSrOgFuUUXEkX5GN66skDecMY2g2+gDFTALJxoO/SvObxEn2x4jtvkI4OOMPtw2fm4HxoRDuUYEc2/hPt22gIu6xPVIw9wQGoJCTwEV5Ya50dsD0zzcrZ1+vtP2g3Z22uFUdw6XcBZaw187IqDSwrFqBLgmhUu3XHEaXjZ673jaEK+oguYVJCMDHPtacK84NVyqgFuP3t91C6iYF1wEM6WXC8f5TveFvZ3U1vH1iVYfEVfMFTseOD1AsFW0XMJJ2Q901eCm3tB1Q9QjslclnMsCsr4ENFVHth/R5RnouNdwj1JjQIDRp9uOiGfWFD21diIfccDlGm7ttgesxSOq+dgHfKyxhqnEQ/dwlHu3eC4/wA50XZGyIfkSABeFtEVMdLcAoj3cxq5rnAU3zA68HSPOWXHJmBuYY5poxyMiss+OWYpo5y7h5k0FP09IK16PGA44WzjFsfgeDOsPvD7CKWsg4uSUKUsm00nrBd2u5O0loCKGllushXdwQdNGynmsc0OWFU8J14SUG6ISrng13DtWK07BPIYZJOWAi+Zx7+gaMdx4OPwxdNlIqSC94v3AbY/7gzi/4hGHTT8CpvuAoDio4scR620VZ8XaHn3LyxWrLdbpPJBex2yARU9tLnH9ttMR9urU99cRyf2OysGyrayLsEplXTZyLuHSXp/xfkaiQE5oKrE+5YJkRWmkpQDKcTx4WOHL45XfXr/w+vrO/bHEIMX5G9uW2HJllVcuCS7auf3wifVpI6+J7Yc/Uq4/knIipRJvi72hLOFOp0cUu7XoI/eIIndaxGi3HR0JABFxHkMRaVkQ75ECQAevyPaMuKO6UH7453BYU9CnW8Dtz3+C9hYO3+OB3H5GVNDyGb38FOkK5Ua6/BRd1+2B19/AnbR8RmSLZIP7v6HbhqwXFAVPaH5B8ic43kjbQr5esK9fkK6k25+Q2qH+gjx/QuRGenrB3dCXH8IBf7nQz1+w/Tf613fs/guk0QX9+fPf65Nqampqampqampqampqampqampqampq6j+t3x8A7gc8XuFyhXJBtiukLbpsvxi8H3C9RNztuuC9Yb1FJ+9awAYQ9Dc0JxAZjtYP2NuBjpABhf4IaOsG8oRowBKRhKQb4gFAEAZUzUhaER+GU1KAKQN6jwhiczhOaAdeK9oEqxbOzFRoR8ObB3juHW8H1ndYR4xxWlAkwGBesS44GRPoZlhTeuu03mhe0W0h6S3c0/SA0R/wswZ4pBsmid4dsYaeJ8kl1iUVPOXhgl7CwSgl3NaiAXNG1LLXiqVMN6fXGpnu+kStO5ZW7KwjurnFcycPQLW8BFxFEI01wxxpNaBbd7QfcBykZSUtK9L3gHfdIwJWVkBQrwh5/EpggnYdgLZD+oiMdtwlYoPRGCoQH3vtuGvEFUuJKOG0BVBLCalvEaFrjugVR2Jg4KPjtjc4K173+BoJB6wJkq7x2iEGGtoZjth6onqNc6drxDOXBasHre3RRduNjscQRN3je91GN3HGXei94VLwfMHLGq/jjN5nsw7phpDIy0ZSJbuzbgsLhpqT1i3AZT/p/RHL1s64znTFvOLe6SR6fQxHa3RUU+94r/F85wnpgssluq/9xG3H6x03izM9zpBoDtc0AHlETSuRlW1o2cJx3y0GDnDwHjC3nbhmrO6YgbcjBjA0Y6pYPcLBnRao97hfvUeHsbeAxpqx4YYPQJ1wvVKbUl+/0tsv6PFKXgvL9TMrlWKN/PREuj2j6xUptwFR13BAxzPE8+JI66TLZ6xXzgbvFL6+feVebzz+7Qv1/ML69APJYCOxdGP96Z9Yth9Yc6FoItMpzz+idJI7+fqMljU6cI8DyhN+vI9zN95j+o4dO36ObvMU6QOaFrTc0LyG611yvE95nC85a7h/60FK4eDu1nA87oP6QPOK7b+Gi315AVmQy08gT/jZcBkR3u2ENeH2wJNgntHyGat3pCj0O/r8D9DekbYjJSOpIFKhfYXzjfzpp7g9+5304z+Qn38Eu2Nfv2JvlbI9A69ogvT0j/jxiNfr4ObYl1fk6RmWn+jNOb/+hlw+/V0+pqampqampqampqampqampqampqampqa+R7+/COh3C8YmFdkbfrnA2+i/zW/h7CwR14wm+OkJeRxoy6SLI3IJt2oawMoN0RqRv60isgIZTw6ciG7DAVsh6+i8HEGzKSGthVNUEpAgNygrnDVAk3nEPbcTtjUAUT/4cCmKCu4N3dYAnyR0BfMBQdtON0PVAjo3j35Ry3hraAa0IrJgXqMvuB24Zs7jHTszi0BqNXiUCjBicX0A6fagWxqOXEFVUW9IKqANP2vA1SVHV2pSkBU7f8PLBTSuxaXT6xmRsvtXTPtws+boP142LEmA7vIcQNQNSQearlDf0ctLQNLtBbGTtFzAMqRK7ookjeVHkDVAZsQyPwL6+R5QV3zEfUvEMqui3LDzC7IMADvikWV5ij7abnFNqlAK7jb21gPu2z3ci/1E0oamFnCNNeC+niNudwByxoCBptHbbEjZAmzWipQNP86Iz/4gxxKR1c4Z5ycphsd1rz+C5oievjwjbQ+H47kHpMfDidujn1k0RadtO0c/NEi7k7bP0F8p6Up6ekGsE8bqhGQNR6dL/J63cMSa4SnF2jSPGGeAtGAO0gfQ1ogPdknAAMeS42w54AlJBW+PeC2uAUjtQMi4jYEECYe7YaTyFG5NFE+CdMftQNKGdcO8R4S5rmhr4eDuJ1KW6L/tRzjLlwJdIxk6L0jKWDtgewbvuB3hYO4nlCsd4TzesPpGufwRtZN1ubFoZvEH+vR5OLwN6S2GQwxcDeoZAw0upOU6ItgTJsahC+9vv7K/N879AduN5VrIrbM8L+g//iPp+EJZC/npR1T/iNx/RTQHn16e6I+vWD1J6xXVG61+Qcik2wu+bPS3X/HjxJIgI1ae+ojhFCmRUnB5Gme+4T1BCmifri9Yewfr6HLF6oFQETHoRziat2fYf4Ojo+snvO/ocgnX9+OO54L3B2m7hUu+tohNT2NqabnA8YjBi/PEz1/R2zNpjfeFfHmh339D0ifk8x/HWnb6fkDZSZcbIkY/3kg//hFSDMRI2rD7X2I44+2V9HShff0z6fOPWPuCpg0/K5qvyBOw//r3+6yampqampqampqampqampqampqampqa+k/q9weANfpq6S2g3GUN12VrsCe4VfDR0+sLvH/F7o4+38JMmA33L6jecA5US0Ao6+GMc8AeiOeoz80yYoQVuoNch4PQ4bQAqdJB19G5ekJVXAp0x9UjTlYHCDzvARSrhcvQJLpLU0It4crogBW6HQFuUsHKLdxyGFoDRrqD1R6wOC9wPsAV7x3r0HzHTyWVBU0dqSesl3DZScHrAynPeIfeT9wMVSWrIl0hX8EqqOB0RFasvSMpoppNF6w6ohmX6BP1nPBeaRadq06J+GJR/Ijrg0e4kNMWvbJtRFz3DnLE/m4viBtqHVWQ9Rk9diRtQZC8g2fAUBVMNByikvAkAVFbw6Uh+RrdujIcmbqGk7qfSFqGo3TECOe4Xq91JEYvUBtYw1Wil9g9umrbCb4E8Fxf8McvYzhAw9U4+oC970hKiHtEhKcAeWgGfUQEdKtxbRYQHZXY19Zge4IzYz0iff1syKb4WZES4F5SQfzAZXQcny0ioS0iznHFj3dyeqG4g95I3klpCRf1dkVHV6xLRzSj5QnRgvUz/t7inpN0wRhA1juiTrc9BgZcEHG8W7huaeMaRkTy6MYWF0hpRBSPHmkcFfmwziN0RBLYCXQcDae3g0uitROz6KL23qAs2PEGJXqFGa50KLgYvh9oUUTX2BPJA+7XcVYUpCBlodU39qNzNiBfSdsz5TxZvVHSQiqfI055xLg7gHVcJBIJ+h1drtE/rR20UE3Yv37lvRrHe8XbiehJthRrLV/J/WC5XNDUyUVQe8SwwfaCP36NM1MP0vaC1QduEVSfrz/F+0Gr6PJC/mHF3t9wVbRckST0/R1vb+j1Z9wE64afDzgO8tOP+PEVuYzo+2qkp094fUez4q2i20/0xxekKCkV2D7j3kcaQYX8DI9f4xZb4p5T2WAFlzueE2Ip3g9bxeovpMsnaA/0+n/QjzMGaVxIVuIeO99QLogodv/K8sMf6PdfsOMLJmtA78s1hgDaO1Kew6F+7nBJ6HIhbYIflXT9I/76C7J07PEbaXuhv/3lv+yja2pqampqampqampqampqampqampqaur/rH5/EdAvL8g1IT9+ghfAHwHVegN1sAWOB6QMJKgd2TZYHJKh0kAv4ZZ0YLkBGrHMfbh2vY04YA23sO1QiL5e0QDPeRl/XqBXRMLtit4CdomCGLKtcP4W3PPs4Uo+BdjxHp2cRkfc6f1BbwNG2RGQ8zzRVKJXttcBSCLKV5JiVKKnNuKMrVcco9krXTck76Qi5OUJWRbE9lgbqwHAvGH9gawXsDPgr53RoWp3vB7hhq1vuPUAzgh2PsLQyYm1k9Y7HadrwdpJ54pvP4RDUyyih3PB62t0z3qGsyLNol/UaoAyUZCG+kEqBfEH4hpOXgzKEq7atIW7uD2wj2ht0YCDrYbTMWXQLeC0GWYn5Es4W2V0zWqB+gjXbBquXauxv6b4+cClj+e8xs/xEXt94OkSsJETlpdwRG8/4L3i9LGPhqN0UTxpuHV9wfsDyob1M+Bd73jKkAvmhvWTdt5p3TG32Is0eqLb2CP/cA6DewrQi8HlBssSUbp5RVojp8yyFVI/KNcnkhbEGlI2VHP0IIsCKXCsQ2+PWOd+jojmjouBV7o3zIzeevQSW8es0q1i/Y7bGYZo5dteed/DrS3RrUw/IOforM0l9k8TLgkfHdVmFXNwLREdbtGX7fWIezdtMfBxPnAknP2aI5a71dhPSbCuuBvmFb08YV6xNCC9eVxLj47hxsbRGr11BKW4sUpHU0I1YLX3Cv2B14osa3Rf9z5ivteA1yJYPznPytv7G6+//sL+26/gji4XFtlZpLKWK8sqLIuSFfJ2Q/uJeMfPE1eNQQAHvEVXsUrEOrfHGHh4wPEOxys4pJefyLdPiFbcznjNNaK33R1rFSkFLWV0VzupXPH9N/SyxXloFS03vJ5IzsCBv/+Cn2+IpvgA6gpndF+TMunpM2KPOCv2gHYg3pF2R9prfJ89oiN7vUak/e2GJkWSQfsV6ht5fUL2jj3+J16/jM7qRrq+kLafKRRyEez9z9j5Fk7j+/8dTYn88jPp+jO0nXxbo/N4OdEryPM/Ynujv9/pj6//ZR9dU1NTU1NTU1NTU1NTU1NTU1NTU1NTU/9n9fsDwPKA9RneHiDP0DI8dSgBpdjf4U4An5TCxdka6iviFbcUENTuAcp6HXBPR+SxQ7kO5+BCLHF0t0bfKmAjWlhzRCrnADOSSrC43hERRDR6ND1HlHBZoBmiiXz9I1oWkmakg1vCVaCf9B5xxN4bsmzgB15PvCc0XzBxRBXJK947rrfomxUCeulG8xMzI+szqxZyWdDlgpiFg5cR2esCUvC6I41vnblsT7i3qOK1GvHDfccGhGsO3TqQwzlpjh1fcKKnllyw48TPtzBQe4J6IvkJ4UC2CylnRBoiDr2h5UpCKbqQXEh5IXGJjtLzCFgb/kTAIlq7PIV7GIn9rw8EQ/oDoY+u4Y9I5tGXiwUssxOnjVjsHBDY67+Lh65xFuSK5BEJjcZjpxyvKUfkMUeNv0vXcLrmJcCjAek5Ym4NsDSe5wynsWR6a3hZsVxo5wPLl3D6Sgqgfx6Qrzh9sF6FLhHD64RD/NvwwjGGHxyxTnKhuFOysKwLCSHdniLmOwuiQkoL2k80rehw57ob1jtmSpdCF48ZCYReR8dvq7EbkjFXTGM4wVu4cpECfcC/nOOeLOEwdW8xtJEWxBPiDbUW92zfUTw6iOsZsdJlQZLi6YLJEutStgDGXjGPAQj3gtsZ/cJJA5SmgtQa8c/5BrXGHvQDlQXpRkqZvDzj7aQeD5oZ3Qtid7I6i3XK7Zm8PiMyYsc14WnDtETvcu8Rad7jLHi7Y6rsj4OvX165v+2cFBTI/aTwYFl/ZhNnXZzt+jPZOioS0c7bC+KgtIiwXj9Bzlit8d6QlnBHe8eP17i/vMVgwnnEQErf48zVDq1FZ/J50M0QIrpbtmdonTRixUUTdMfud3T7HLeCdbye5OtPsN2AfQzRGGn7hPqCtIO0PUcsfj3Q7XN0EJ+/RGR5rVDfIrLcBLhEb3H3uHZVVD6RLn8Ee0f6Qfr8RLr8iCyf0NsLfvwC5zuqPYYbTCjlxrp+RvoZbv9eYz1eX+H1HX8caL5EJ7gm9PGVdPmEXq7oevm7flxNTU1NTU1NTU1NTU1NTU1NTU1NTU1N/Wf0uwPA/gAXwXOHxw4ovAm8HXDZ4GKwyFiZB7Qyunvv8LBwzOpzRDmzRIQr0ReLMIDR6HFNSrhOfwZJGAZSIUWEqQwaHABz9Pq2PUCkRcdrdLHmiIeuO2DoskJStGRaPfG8YMcDrx4XYYTr2HdSWRkJ0vF8+SNlOGN24Jy4HViLnk13w3ul+zN0SEclZUGo0duaMtDxGvDOrdHzNVzNJRx+lIzbI6Begt6P4RhsSEoYAfzchd4rnQIp48tnrO704xf8PLB6x8uK16/DZSjf+pbleI1YZCX6RbWjZSXRyGUhJUWtIeuGaIrtyWsA3dZivR0UjX+3hjCcl3kBMywysoEOFl2wjuNtD0CpS6x17wFv+3CUbs9x2PJ1pBG3+Bnfw5Vd1nBPWx/xxA4yXNs2gK0WBEXKU0DZZSWVFaRixyuuikvD+oGULWJrrdLNMas070hZ0HIBtdERHB25vi647bFnvePu4RJ1h9rRspHaSbJKyUIpQlkKJQspR7x3OKojrto4sTEw4dbp3mjeMQHDYu9ZsHoEaBYPUPsB1K0hHvDTVQawXSPKufHXKOa8xrqKAiXWURPu4Za2DyBLCtie14jkBuxs2HkOBy+49wHrT3x/YK0BgoiFm1kzXne8hlNakiD9TsJIyxprl67xGBhSIprYATOjPd6x+5/BO/lyI62FtG7h9Lc9Ht/A2jGcuGd0cqcF2gP3Tu/G3jr3+xt7daxX0uOdnJSyLBQSl89/oFw/kZKStickOeJnxF0vl3A7t+iD9v7A8w2j0+sj1hjB2o6Jg+14ex+AvePHjp9tuJoNb3u4rHFSLnHe9i/09h570CtIR7dLDMRsMXQg/UC353CBp5X8/COaLmh7EIMGB+n5hphg54HVNzStCALLM/ryf0VlAXXS05/Cha+C968gwyXe7/j9lwDQ63Pce8nRvERkdzNERgd3ytj7n/HjDb2OKO52IOWJtP1ASiucD9Lnz6Tnz6S1IPdfkOOAryf+9pfovAYkff7bf0hNTU1NTU1NTU1NTU1NTU1NTU1NTU1Nfad+dwC4t4rXr7gecL1A6vD8GdYNrgfsG+QcoLYZXF+ig3K5wO2F+ENE9uoAUOQ1XGiuUMNtS27DpdkJdykB/YgIZU8Oeg7wlQIm4hEJvURMr1sdTmLie1pD823UCTdMR0z08LTKR/yuBgjOt5eAiiikjF6viEVPbPSYGrL8OKCY40lxVXpu2GOHLuSiJFVYXwK8pS0iplM4NI1wE6MSfcBVcMngGemO6AbD1WuaMe/0ekcEuhhdlL5/oZ0nzor1d8gvmFzAN6Q8I+sTmKEpgS1IishfXNDyTJJMKhvSzlirVKILth/AiFdOGtywtvj59RYw1c9wxOoS8chJIzY4rxG1nNaIXc6KR3lsAMHLc0Q09x3XFNBSF2D0EdsZzl5rEYlNRuQSrtd0DedwWcPJul4DdlqHs0Y8szsua8Qd9x4Rx27hEF0KuNOOhnkHOtYM7ydeNvrjDq701jBRaO+w3wOG9gPqCf4ISG9H9PKuV1guiHVK65T1wro9UbYbmhMqET2NjihkaXheMXfMBHOhtkp3B9kQuQaEtYp7G9feo4PZw4UsKQegNINyGZHIEePtdQ/4vl4g32J4oUXEsnRHxCJiuPc4/fkCyxZdruWCex2wUgNspxww2iJW291x14gnT4m0PqO2I2XF7Qy65x2SA2cMD+QF+ommJVzJOiLDiT5p6xad0JpoHl3Revsx1o7oX8bzuL4BZHXBJUV3cC6wrHjZqK1zb8L7/cGxf40hh17Jz8+UXFkvF9aXTyRNpKefkG6oZJbnf0S6Q+v0Zlh5wpLQW6UPmKvLJRzqliN6OmXsPGNARQu+73hvuB+Yg9lbxOSbIxpDExx3/Gx4+YSbjvcgD5esCJoXUKEfX3F7oOuKiEWktmTM3qN/ebth9Q3RG6453nvXZ3S7gp/h1PWOtQNdPiOuaNmgO7r+hHojPb2gXkjP/wS5x3thMVRuaF4jLtpORDZEb0hZSNc/ok+fAKXXO36+k64/0d//Fbn8iD7/M+oGbUcpyCNhb3d4uqHrJ6R/wdu/UH744e/3YTU1NTU1NTU1NTU1NTU1NTU1NTU1NTX1n9TvDgC7OP1hYBug4YblhGuB/TkqMk8F7aO3t+LUiKMVBXWMDihuFW9fR3eljkjgEfXMiIEeMbb4iZQUHb9hHRtf5/h2Gf20eXQPmwMRhQoO/U66FDRJVAc3g/MI2CuVlJy0baRcEFlJeUPSgprBcaKk6BRtB1qumIfb1+zE2h2yjzhghZrwywb3d9L1SloK0KMn1Doi47FodAWzk+6Kq9PV6P2I6F5NWAunqfUTtxRxs37imiLiWSUieNtXevuN/lYjAtnPgG+1IRSk76ApnIU4kgrqjkpH3UhSYuWXawBY94jR9uGubh5OSAk3I71HN7ALRsP7jrUD746dNRyiQzIAHprDsSsSjmCPvmXSGsD2w+lrHXTFvQeozCtSLnxzitdHxDerwrGPWGkP5yc7Lmc4da3GcybFjyP+XAJU99bQ9RnvxBq70cmoRzeui4Rjc8BJWh+/LNy3lx+jQzpvEWWsUMQp25WchZxyuK7Fh9s1Rfe1VXDBjwHbNYcz2mp8H4T7OlXE43skBbAOAp9xDLfoQAYJcN3O6OH14c62NuBxrI1/63C2iFnvZ3QAf/RqD6AbZ2nHTcPV288AtzmPvmyLtfF4aSKKW8QRe1oiVtpldCSvAaw70bmby4D+D769dXonrc+49Oiubjv2uMOIX1f3iEnOt3iNSXHRcENLQnMepyz2zHrjrAfv7++8f/2Vvj/Q8oninSUr22Vj2X5Ee0c1YsJFBYri5ztCQpcbYjuGIcsTsv6I9/foLHYbTniPPuV8RbcnJAuuK379CV83up3Y4x2O36CFy9naA447aKLXHbYNkTgjpguS1oh1TgXajj/eIF0wi/dKPlzG+zvkT6Pr+aCrYu0+7u1wDPv+Szj4dYFzx9sdEYkz4yeyRQy5tAfUHTte415LG9IeyPpzvG/XO7p8hvWCrNfYu/1XJBHu8ce/oMsSW5AcTVd4+zNiB/bbLzHA0t7If7iRboWcLuh6JX/6A6I/YHX/m3wuTU1NTU1NTU1NTU1NTU1NTU1NTU1NTf0t9bsDwFQbib4Zfz/D3dkVjgfeX7HtCX58wqrC7uBf4LXB439CjT5e/XDCuiAaoNfNwyGZt4C6qQwA3COmWDJiHZoPB2sOkDlcu6QFWguIJqDrFQhIoevy7WsRBw6kPyCBkEkuICPGWAVdF2S9IaeFYzIvASEJF6aPDlJ3jQhjN8g3uhnuCfMd4wvCSUKRNACbaHQGu0VvrHecTK9nROmeHSPjaaH1Hq5pwNMSkcFZse4YG+0DxNWKywWjQBf08oR4Ad+Dk9sJ/R7Atr2jCqksKK+kouhSooM5l+juxRB3pFzQdUVFw/mX1gHeNRym1nCX0fN6C9ene4D5nICEH/cBdlM4J9NTnB9GPy+KrrfoUpVwu5LDAep4nANJ0YHqHg7Y8gQ4PqKJEQkn9sf1WY8IaKvjcVMw6yRxTS32S5d1OGg93NiuaNIYRtBwCqMrLgm5foaS4vHTiJ4WRVpHupDEWHEKxrJuaNZw4rYjfn30RbdHnFNOJMfAg2pC8ohszgnUMD9wE0gBUAUCFAvgFZErpEv090oa17SDbriu8b2acd3iOnod99SGyHBmSwq3vORY237+NUZdiGELt7HEgtcjzpozeoN7wFjbw01uFWTB2xk9v98c+4akxLfYc1lAFgRHNSKsnXDYmnjsfdlwawNog+RELhmRgrczXLBpQcstzoB3uh3UtrMfB/dq7K9f4zj2k00z2/rE5ekzWQW9PSHJEO8k79AqenkOx23f8W2jW/QU92Oni+LXPwVot4a3DssNsxZDCwh6/Sf08odwKS8X9PYT3H6AcoHlE/L8c8Sxp4RuN/L2RNIU0ct2htO279H7rYQTe30a0fMJrzVgcAuor8sNSSv9PCJOvXd0e8FrReodWT5jqUQMfrkiZYGcoudbM0nAzzuy/RQu3Sxw3FGJKG7csPoa73NJSdcbnHd0ewYu+HHHzweSn8iXz+TrZ+Q8KE//gLhHPPTtQn76DHqBnkjbDa+/4v0O5xvL9RNyvP7NP6Kmpqampqampqampqampqampqampqamvlf5//e3/P+ZLk8Rq7vcIQvwFKD2VNgUzR3uBxLpsbhsSO54vuFyImTcHaGACeSMWw3IZYQ7sXaC/FSwFedEllu4Yi9LuFrLBY4H5CV6K5fncBNLQii4nCiCbBfEE5o9YJgFSDQDP05k2XAsAIwY4Wr+cK96XGOJeGnrbfT2Vqyd4WIVC5BWj4CdrdIocCzotpBoqDksJVx4qvH91NHv2vF2kPJC7zuyrVhrWBe8V6yesGXsfEBJeD2gPOH7CfmCf0Aaz8FnpYMpIkuA6/3XcDFKJ5Un3CtKg/yC1AonaCokOt5B5QQa4gsgAdnd8JzABmz0FPvkAclUM5qvuCvqO+4bXjJwxVtHdLhQ7Q5LCiejW0D9vODtHmdArwF+h/tZfLhn/Yjo5OGWJJUB3lu4U62N2O8bIh3JOoB0G/vdAo6aRcK0EJHd/cBTxs8zAHvvEcldLpAk+oFNICc8FaQ1RFewL0jNaBbSUsg8R6yuBQD3fkLevrlvRXMAeFE0ZbzvyPIU8da0gOYI3lvEaSNxHa7jNewRkT0GCMJ6G1AV6wG6vccN5AkXH05rQ/LoCm7RxevGANnLgK8xROEIotH7jGZMBHFDNNy2fED01mLsJV/w+gDJaB7dyBIuYdTR5Qn3MxzBIkhOqApuJyIXQLF64imgMb1i3kCv8b6ShotfBUkJN8HtQHzcq0gA0x6pAZ6v1FbZ7+/0EQddto1cT/IiEXHujd53aAlZXgaoj6ERaQ2TRG8tgLgW7HwNgLvfkXzFNSO94RxoWegtHPaGkbLg3rDjHmtSrqT16eNdBDdDygv2/hc0HwHSrUVk9vkAaZjXeB/bX0EykjP2uJOuz9j5iqQnzE84G4qj2zP93PEk9NrQvscAS/lTnIHzN1wXpJ9IvUecOUYarnXPSspPGMD1irdHDKjcf0We/oBe/oA9viD9gdSM+4ksP8QZbm9IupCyYud79Bwr0B6kvEXPuhRod9K2YfdXyFfy9Ue8ZPpf/gWRt4gtn5qampqampqampqampqampqampqamvpvpt+dA1g8gyb8MFwjEpZk8CxIA0kXxAyxa/SK9gNWJWVHS/Tgdu8B9o59uHwlAJyuEbObdLgsM5Q8nJ3RAxxAzyLiWADp4QI9digXdNlQaSSP8l8nYec9oGH3bw6+nFbScNFZk3hNrQdg6w3aPbpOy4iUbhU/KpSVXh943fG8RaRqCYhs2ukcmEafbNZCyhnSinu4Ft16wEwT0I1uAaBMbMQWQ6sVrs+0ZnSUXhuOD8dhRBrT3uIx8xUj4dLBj4CN7IgqYtF9m5YbyRuqnZwLNIvHgeHMTWCKLguSL0h+IWyg8dpluSFdArR+xDl/9J16DhfiiAI3egDQZjCc2hGNfYnfNeKMvT7+6jwdbl8XC7cqDss2oCKQriPqOwegOvcAnL3GmcHx7hElvVxgudINrB7RGbzcsGOn24l7onej1wPvB5IkQG1OuOeIQc5rONJFoqe1dTRlFKcsmaxPlGyst2dKyuF0tYpwIOLDmR7AU1Tj33oLGGsWLlwfrxkJR269Q90H2I3ljQjnHv3KLVzjCAE9+ziDacUlXLQuObp1+xGuXCweRxRJKYAuedxDA5DjRL9ygGdHAvh2AhD3MZyR4ix4KuARZY6d4WoXGTC5RV+wG0iNCHGNfYvjtJDSNSK2U8G8IZrxbpgqLhttv9Pu77GvqcT1tR4OYU9Y2jBJMTjhikmmy8LRjPf9ZH/s2LmzXC5sCuu6jhjpin2A73qgOKKF3jutPqKCvHesN8wcS9eA09bprcea5E+0VrHzNbrQXWmtYa1SjzutHhHPLY61O71WrDf6ccfORyRtLwvWaux/WK2R7Uo/T3x5Qcyhx9CA14O83GJvGlg7kPUFusR90Do4KOHAtl7R5Tlgcn2glz/C8Uq3ilw+xXk5jn8HqTPoSFhwR0XQ8oQsn2B/A02RpNA7hqLlE+y/Qn2g6YKIYPUBdkTKwvFv8HgbwwMdOf4nfvyC71/QciHlBc3P473kjOdcXv5eH1VTU1NTU1NTU1NTU1NTU1NTU1NTU1NT/2n97hzAIgfaob8nSAl57LB1/DAoGTkecD/gs4M5UlfktsB7h9uvIJ8CaNFhXYEWUEokoJSm+HXu4ZS1ERk8gE+YdHO4OUlIt+ii7B0kYY+34ZBUNGsAipyghhO3944uT3GtIiPGVPD6CJee9ujQ7Rn3B7JeoVfcGpYUlQzk6NdlRE63Susn7geWEtYWBKWUNb7fKpwRXQ0BBM2cjkF1KFfsCBDqPaKi7dzj2pYlukZZ8LMGiOtnQLfeMHtHuCD5GtGvAJ4QvcD5Z7R8ItlBvv0Q0atyg5RwF9L6jLSOogEHt0tEzWYZ7tQVLURnrYLIDTcbPaKCpsuI6u7DuSuwjO5m2+PPNjqJbXTNQuwzNhypLeKOiU7k6HZO4dYu4RRlRO+KRPSyCwMsn38FmRViIiCcyWm94HbiWgKwZ0H1gll05ErKtONElxSdqwimgp+GpIbvB7Ld8PaGHo20biQlInnLEq5IXcHe4jrPI+LLrUdHLEe4nL+9DmKo4Vv3LRFz7D32ToDlKc61R5evSxkR2B5O515xb7F/sdOIesDqfkYfsQrIhki4Zq0daO9QRuS1SLi7JZzwpPG4NpyY+YJzDogbrnKX2MeI5S4RM20dz4V+HmhZUQ0XM+oDAkvcT9aiV7pHHLguF7wLtNHP7I71HZMNE6X3RnOPgYa24yi2XjE7I3q6HbhAKjfcjd4rR++8v7+yH5WchFWcNSlJhLxttPdfMV3HHjQkXXCcvr/jyxNev4QLPd+w+xd8gFaTC+39X+K9KRtJE7K+UB+/oO2MWO/9C+6CZo3kgXqii6Oa6fVtQPlwu/fzjqRCr5W8rjFQUd8wzXFdXun9RNM6eryJTvN6xvtV7+E0/3TBHl8xO+I8sKDlQj/eMOvh2FdDeMbLLQYRJIZYZH1CrEYKQquoOlY7Uhp+nOgKenvG3n+LDuxlgxgvwHucPVHGwMIdffo5UhhMofyIa0JF8f3f0MvPqO74aWB33MHOf0Wykn/4p0h97/e/6efT1NTU1NTU1NTU1NTU1NTU1NTU1NTU1N9CvzsHcKoVKMi2IY8DthVun2BdYN3gfAQEPAct28Ih2FfH2cL0Bpg5sqyAIskDCOEB/4b7VEbMreThcNU0nHOj9zfz7Wckl+EIlfh5OuIJEVApaEoYhq4XpCSsh7MwHLALeEbSOvp6M6iheUHEApYBkldai55O8hquTH0BSyTJaHqhm+C6hKlVQdNw0uYSUCctQLglzXNE20rBVcK9eu5IKeH8FIn+XC8DeO+QcjhbNYfr1W+IF7QfpOUKxM9ryeTyzLJdEV1inbWghAs4qaMKUoZTNNkA30dE9Npw+hrRSctwCmNIXsMBaA0VG5G9GUoJqEeOF58WKNEHG8ZQC5gGsP0YDmLz2LcsEVm9lHA8nkfAYK/RyWsESFVFynU4UDXynPMazmOzcG97G67cPNyjDc+X0Zl8hrtUVuiONcMF+mF4j732oyJlRaxTlivFG8U7ebmi9IB9Iojo+CXI+jTisePvB6WODlYS9IpoijWmRwz6h8MVj7UyEG/xmGmLffMeazC6jt11wFmGo7xBO5FyY9h2xxrHWku6BETEce9IeyBigY+ljJ5di9hrzcNxnOM5UsFT9AhHjHG4j7EeMM807gO30d3tMdxhLV6/tzgL1tD8FPdlbxFZTbjTW6sBfQXa8cDzFnB/xIR7ucZz9wrd4msReq+09mCvzn0/uZ8HXaAsV3JSdLnE/eA53NBpjbWSBYTRdh3Oaco1ksbN8OXKWRtnfXDaSZNMPTutN47HOxWjlReOulMRqijH/pVjP6j7g3p26uNBa51uHauNbp3eH5gLXVecTsNpfmJ4rCcW0e/IiCeP7mQ771hv6PYS7yG9R6eyKF4fpHzDj9dIW0DjfOiKpM8RF5/GsIQYcvsxhheWC94aqgVD0NunuGfWhX4eEXWflziHRJIA9QgAX66oLIgr+vQnXFf8OLHzgV4+x3ve9Rm5/SHORsro5SXuy7qjqcQQgZ0gjfT0p7/Hx9TU1NTU1NTU1NTU1NTU1NTU1NTU1NTUd+l35wAmr8itk46CyBJxsndHksDDBzzdoWzhbs0Jzjfk+g/Bba0hOaO6g0VvJnVAM5NvsCu6VD8iatvoOk14rch2Q7AAhH0fUcUnkBHPeFakjV5Xd6SfQIJlHX2iLfp+RdFcAoDhA2YxXKUJ1gtSj7gOE3onnKl5jeeUHVEwqzgN5ILYA/NM0kIWQ8sVtCEs8doMSBbAru0R6Wsf4C5h7EgdXbeu0BpuR8DSviGt4t3RyzP++A3NTwFoZMBDz+iSUeskVVTAkxAA+BouWpSUgOaQJH6u3AKGb5+AHmHAzfBkiEVXMf1ErCLLFXFB0hL9oe09IqVdocXPQoI0YKIIkgb0aedwTyvYI8DTco2fVcIl64ZrOBYDvitkjbPWh6O4nuHePN/R9RIQko/uZsV7C7cvFtdgjV5ruL01QXsg6wqmeAsnLpaQskI90aTkflKW59g/NSSteItuXr+8gB2QtgCT6gM8R6S0pOWbIVmsQVrjWPca0Ox8h3RD8iWusXfcGzIgqpvh7R0Rw2UJZ7Z3pCx4e4yI9P7vXnP00cZ0heLYeKwNZMX76J8n9orgAAEAAElEQVROH0MU/a+R0ZJwGb3OVv/q4G46XMI5uoml4g6tngF1lxVB6McjXMzNkRyQVtxHDLUMiA2kBQuKTHew2mntoLFirdPrHRmdwNHru0QP9PlAckHsQNIWnbfHnbNXDjLvLfqDr+lKzkLO27gX4oxqeY6eWq9QPuF9D8fz8hS93Gmh9o7bjq4vmO7Y4w6lINdPsJz09z8j+Rk/f0PSM3ZUjC9QbviWsccrXRt6+5F2PtDjiH1HoR3oktB8ov4pUgEe8bNatvie8x2XFTvecSWGB/a3cY4UMcfMEBU4D1wX3PeIipaE4GiKQRMXwj3tFbEFa2esx/aEtx3rJ7q94GTs7V+QvGDnHX3+I7RzwOcA+9L22AcVdNmw+6+QLqTrc3RFH3fM7wjXGFx5vI5+7AX/+huSzjjX6xMcvyLLH7D+hugGj9+Q649/l4+pqampqampqampqampqampqampqampqe/R784BzO2O3BuiDpcLXK4gb2BbODKXBkuG+2P08wKn4D06a1VAzRGuAWf9Ef2wyzXcojIcvGk4UMsS8I8SLkAhukfPE7rFv0sfYNWjR9Z3JCtmHkbIZYFkpLwg5kir0Y/bCfcmQFJUwg2ctk+k7QI0XBphwg3W73QoCdo7eBr+TUHShvVXWrrB/Qv5ckHZwy2a1uGOHD22Dt1q9IKmjPRHuCZbi+hmXWB/xVUxq5g75h3ZtrGGPa6fhKqh1xWVTipC1kTJF8T2AK90tNYAutbCVa0ZOoikgHSjj1SsRZSvdbRc0bKgklHNpBR7E87Yj2jnYLFabtGRTA/YnDR6adsdbyekWzxHWnE10EvESNuBfOyv9Ohc7j323x1kADRTBIm+4LRCf8S+04bZtSJLCXhWK/140OwIgF8SIoZ5w1VwSbgbjYQuA76mBUkJLYVknUQj46SyIJrQ9Sl6UpUAcBqOZ7c6nLEN/IjzojrizAN0hqO3o2khi5CXDVUdPdBngH0+tkDw5rFuduLieL7Gmc854G23cPV+uOD1EpDQ21jL4TLWMURhffRPr4iWcEbLgrVYW9ElBiXaI9zBJFCQ5cIHXFZV3E+8WQDtARkjZrpFPLVVyFvARl2HkzwH9JYlOnttOIlRjNiTfp4Y0PcHdPDhnPZ24rV96wmXtKGawSveOkft3Fvi7f2On5WFk5x2su9o8rg/EogdaJJwzeZl3MuXcOjWndYbZz0wybR6Uts7npfoLq9HnF9ZoDzjxx3O6AomLfgxhkNE4foDvn2iG/h2paVEdaNlpV9fqJcf2M83jvsbjZWqSzifz0fE1KcLfrzHmcpPeL1jskbHscW1el6wumO94mVBthvWHuHibzu6Po+u8vdwu2sJUCxg6uF21gUpn+LM9EfcA/URrujzHgMQySGDnw+oJ6ls0S3c4/u83cEbVu9QD9LtH9HLhviJXv8QKRDnIwZknv6EiKLrRnr5R+hfkL6jWdAf/gmO3/7en1hTU1NTU1NTU1NTU1NTU1NTU1NTU1NT/2H9/gCwrAFSrk/hxkwPKAR4ooKtI/p5hZsh7YSn53AYwohUNlzu4WwtP4eb13r8vCYiErcHiLIeYFR6ALBUwi2aE6ThuDTHyxoxo9KiW5YANZo3zDrGiJG1PZ7/PCPWNCuqgg4Q6iKIeICrugfUzltETFt0o/p+4O6YBSBzV8yV5hHXTM9IP0nlCfbX4X5Mo6+zDbfnibceQBmIrtvjr2AwXaIHtrdwy9kjgKKdsGTEG/nyRGoH2TtLXlGBvG2IVXIGLUusa87hRixL9LtaxOnGmoeTWjwcrN4CZArD9coZ8LgkzB2RhKljqWAeUcAqCclrAD/zcEPnEq/BariH233wxDI6ZY9wFOs11qMfuOThGCbWSYCzBfTzOppIEy4jYttOJC9xHkSxVGh1x1L+FgdtZ8dbRaSEK7g1endcEr1XXNNw7zpqDVUoKVFIJI8hBCEB60hXHrHUvUevsXu8C0iKuG6Ic2h1nKccjl+Mbo2WFno7kbQNx7uBebh8HUw7hkffLhnaAfWBuEWscn3E4EROESndW8BjIjYYjw5l97gvwoXc4vxgmIwhibQChtsZ53hEB8dSDje3COSE9RF7Lh7wEUfzBes73RquG61VvNu3YQUvG+5xxgRDicGLbkY3j+rsflLN6VZj/xVYyrefdavhPC1rvD7rWK8cbedeO+9fv9De35HjjWX7IYCoLrhsODo6mmsMCOSI1PbzHesNtisdoe132rmHkz0Jfu7hNJcN6oEcd2R/I5UriQPOO5wPdMkxEHB/R8478nEfP97x44TlBs8/4+UFW1/ibF//ROvvnLbTXDhbp7vRH2+YdFq9Q9kwu8f7VV6jp1gLvT2iG9xrJA74uJ8l4a1jZhEx7SdsN7wsuO3gJ5ISQv5rXHkpmJ/QK3l9QjSjt5+wfuDnO3KeSDP0umG9wv4V+oHXO3r9Ab3coJ7h8r4UpO5YO+htR8WRckPKht5+Ql1I1xfgDFd6eSbd/gHOtwDs77/9rT+hpv6O8niz+V/+GP87a67h92uu4fdrruH3a67h1H9U88x8v+Yafr/mGn6/5hp+v+YaTv1nNM/N92uu4fdrruH3a67h92uu4X+9fn8R0OcJ6QVe/wzr5wCJqUCVcAFzwGmw3qAVSDtqPXpcvWHcoptXE/gesNYlAFerfCsJ7h7AK2WoBtnBTkRWxCwcka1Hd621gMVAygkzjTpbiSJea3dy2QImEy5DWa6jr9aHS7NgLng/6GaoOk4hbImjU1UBHx2nPTpzvf2KpB/p3nBd6M2Ry0bWgMjkEpA3CBPeK5autP0V9KMD1pB1w88jwPbjHdYrnGe4lFUQX5HzBN2Q/IT0E3UnrTdUFAqIWTj4ziPgqyyxZmn0/LoFiOpnRO3WO1I9emqthUNUU0Q1WzhOHY3YbO8Rh523b1DNNbqKxTuaEr21gKLiEfuLgmbEanTc9mP0DJ/h9EwXxM5whzbiGqWHG1UveN0jhleWAY0ZfbQeAKrk2Bo/wZyUr/S8RTTt4y2im8VxDZDqbugyuqJrx1OJc1QPckqoN1JakHUDr3HOPGKPJSU+enHpLUB3WiLu2RkwuH5zAEsqMfTggngP0KppxDQPuJqWsY7REexmf3WLuwSIZDinzwNZNjwXxM+435KNPtgBnrUHdG0WHcMEEBaREQv9AN3wFu5wOpAknMcfDuRaISVE/t19+eE2TgmRjtWdlNeIJJbR10vHbEeSxvN2i2GGD8CO4uuC1Y6d73QpnPs94Hjt4aTVCzwekeadbygDevc7zoaZc6DcH+88Hm+YZMpSWDWheSWh0B740vBlo5uR0oaIkM43jHC3e3sd8dRL9Bh7RbWCK+KN5IavGTs3kjDOcid/+r/R33+NyHYpse5rxGS33iJWWn3EKDc851jfxz2GIMoNpNAfdwBEE1YKUjZSPUEXrHWob+j6GY4dyRfEU0Dr3iBt+P0r0hpsT2MY4AL1C2KKbJ9wiEEBVbzu0RW+XOB4JbnEPSMKyxbDH2WNM15WxKNn2OsbUn6CNdPqO0kalq9w3lEVZHuJOHM/+ehzpt4jPWB5ifNznCAVtJCW5/ifixT3ko/789vZnfrfQvLR4f6/+DH+d9Zcw+/XXMPv11zD79dcw6n/qOaZ+X7NNfx+zTX8fs01/H7NNZz6z2iem+/XXMPv11zD79dcw+/XXMP/ev3+AHC7waXCI0EiHLL+FY4Vnglw24+Awaq4hpNXjwqXz6gAMqJkWUfPaxquzzJchxoRoprx/gguKAvIA6EMl6wPiAyokFpHloK4kVTCDSnRKSypgA7IZaBlxPAa4R4WReTEagAJT4XuLcBifsK90a0HcF5v+L1iLtCdbhmVhPevdBbgDbSQcwqYqBETzXlCTrh7RPL6AIFNQPJwjfaAt1i4qH28plYDlsqO5AWRTkpG1oz2ANPOFfdHsDjJsTathltVc4C4dkeWl7EmBtsF6T0AXmv48oxzIjbcjdYhLxHZWw98uUB7DBCVQVdEG17P0ZM8AC4Zb8NdTOw1siB5xNC2M8Di6HsNSL1E7DEguhCkaAnHrfUASjUGCdwMzXlA107/6FV2QVTx+ggwai3ifN0xO5ByCWdpswDKtZIAzZmcMtJTQHRR/GxwfY7YaY8zQmsBWSWP/t9r/LuuAf9EYm20hENzgEP3DEiAQ/OR9/zRb008fm1QxkBAOwAJ57ZnHIUy3pjdYnAgRc+vigKG1QPNW3yNIzlHbHZ9xP2Vows4bsZxL+tfgXa4hAXJOb5mGfdPAOJveykK4tRu0TfdTqKjtiMpuqdFx5mOmxp3HfdTHh3CLeqFDTxFHzNlQ/KC76/DfZwwOsGOXzDgNOG+P3h8+Q1fLyzZWGQhF0H6A5GFtF7iPmoNp2NlixhozSQZ+0vDuocrfX2C4yuK4Cmjx44OKEte8fPXgLApkcsFT4V2vMU9tt3iepNQ5Ix+4+Hd7t7wo2Gqcf5aRUhI3rBzh+0ZP99xO5G00h/3WHttMeTST2gP8vaM9B0jY/Uklyt+W2ntTqoP5DzwyzXWdn+gm0QMtDian6F+hWUd99qO6QWOe2wtDdMaa5UK1B3KFV0u2PmOne+k9YJbipj03jFv4zZv0Cq6bBGfrglrHdmW6DM+DmTdYijAGt5GzPtxICh6e4mYaeb/cExNTU1NTU1NTU1NTU1NTU1NTU1NTf330+8PAMsJ9RaQNwH3r7BtkDuUS8CtnoheYKAuoAndVsTvOE+oa0AsFRxHfMSnZo1Y3A8o7Ao4spagRdwCZnofXFEjFDitwBE8zZVwETspp4hQHhGobgemmZIKImDy4ZLMw33swa3oEZ9LAgTrBueOl0vATmT06O4IgknFXEEyVp3FIK9KUo0oYjyAbmuQVpp33DVieOt7QKh2D9dvO6A8Ia0j9IjMLmt0LjclpQV6R13QrEAO1yEdXS+In+G41DX2Kj3hbUfWNda87eEy9ROREpyVFs5jr1DvyPoCVGRJdBc4G05F2TAGNNSE2BHOQU0gikuNTlqR6E7uBAj0c7i6Y2vcO1KuAcfPRzwG43s1HK1uAcwwD15aHxGr2w4kbziG9dFFTThXvYxIZhE0J9zT+NmGLhdad6wdIB1hg/6OJCevz+E2TYKkOHO6XcI9S47ocC2B9ryGKbwfsY51x7eApMIFbwEHrbVYp7QOZjpcvfDNnSuS8fMOyyX6ez/c0WlDcOiGSw9ArnnEMKe4Dk3hvrUaLuNyiehwUUQ28IYosFxHrHfAcmcAXQl3NhauYdEF9zOgcd7wGu5TrIcrmIqTAUMQxPqIkl7C7btcw0mvBXGBVPD6BkQ8uKccEdyA5YQdD3zZoA7n87bG0IBkKDkGA9oduXxGtHDuX3hUeDx+oTVjvSZWKiqG6I1woSopKWoNl4JJCqh6HOj6ROkVo+M53P6dO8tSsHKD/Q1dL3EftLivPSecK1gl60KShqwbSUc89fYU8y91DKuoY8c7JKfoEvHfH+fAWwDQcqGfD+iV1u70fkJ6jghpyXirwAOzK1putPMR+1Ou1P0LrTtyecIenZ4hlRXfv6Drz/j+b3gyQFC9xnlJSyQBtBPKFSMjrnh/4FLCbJ7XONcSDmpJaySHp2W8r42hiryQ0xpJCccbknNEgdevYAlfrjgP8u0H7Dhg3dBqoJn+278g29NwyV8j1r89SJfPf6cPqqmpqampqampqampqampqampqampqan/vH5/ABjCxbgCi8K6wFMOJ2v9GiB1fYI2oE5SdK+k9YLIimEkRlwu63DyegDeZqOLVAKGuX375b0jywb1jH8rI8LWK54SmgqSFiAgl58HUlIYLfvoFxZH0wVvjkvDFbxFj3C3Fu7a7phFz6asN/ATayfDxhrxv3ScNJyWN7pZuB4dhCt51XCjArKsw41MuEbpEUFcLjBgqqqOqOUcTlCIKNWUSang3fAk4SBMirQTXRZAIwa53aOPM20D8CXEIroYdShrQEctwDB1egmwJAlrLTpS3aPfGQ/ofRyxlt7BE6Y9XKx5QTWAXnTgRpR2UPUTABEffc7Ct55c69FBqgGlvL7j/QzgODpv3SMqPFy3A5xqgpIDUPUGOdNbI6VMrw90faLv4dQNh63FEpqARP8zvcU+NpBcUDtI60ZSQe0MpzEezuYBamk7pAWzE+nH+Lcasb6s8XxpuH6l4ITb2sXDbfxhbrQe0FuW8ftwNZcSMePeYp0s/KPyAWkZkc5KDD18RFDXc5wdHX/voLf4yd4ROcPV3R3ShpQN72NwISzzY61qxPKKQspI14j07WkMcnjEl6cUQxKESzgc+X1EYAuc93Bad9CUY5hBJeC3jG5wL5gZrbXRARxRybhFv7Yb9IqUjLWI0pblCdLCedw5T+dRH/T0Qnl2st0Dgm9PoImEkPIaYD19dAZX+hlDE4Ih3pHlitUDwci3l3j9JeKJNSlsn+F4G0A34fojTkXsRBWgkW5P+ONXoKPXF7xtAYEduP0Dvr/FvojgOeNnxbLGfqmRLhdolbwsWPoUwwyt43mlNRvR6B0/9xHXDZ4ueH3QJCMPcGvYfcevf6Cflbw4nlZUErSOuqHSEANJ0f8sbHjfcVGSljhyjwdy+RE/3tGnn/D7n+n1AboEmLcHlhLQSaXE9SDI8hSO7Xqn728gK/p8o7/tOCXcv/sD6xXRhuQbnA19/jHuecDvb3D9w/d/Hk1NTU1NTU1NTU1NTU1NTU1NTU1NTU39jfX7A8DSYf8KzxtIg6UgfkTXrdXojmWNfsd6oJen6D8FsgiilwCyH52q2ACIhaBzCbIiogGopIzkWY/IX3o4WXtF84r0j7hkj2tziWDdNLqCvSPDNSx5AFBraApIax+QToyU1jAve0N8CdfnuUdkdVnj935EVylndHimjPQH6AVrnbQUkn8lXf8Z0egJjhrRcONFL7FAFjj6iObtSI7XS3K0JKQ6KW+4OIgFE7+Ey1M/3KLtDukJzVe83gdE78g6un/LgLxILLWG05PRhYzqcPL2b8BRdA04qRuooeaYKKYfEDschoYjqSBew3jdTz6c1+4Snba9Rifx+oy3HoDe3v/qFP+A0mmNbuTzHhArR6QwabhuiT5caw9kWUcEtdJFcVechJQFr19xdHTvgkt87dLw/YgzkBbUG5oTZXtG7IwzF9g1gKvVeAwhem9LGU7y0UmLIkuG3gP01n3AWA+ITwwxiEUXsluNzunacCzcvL0FZM+3b5BPXAKc/3+d43gxo0faBfLlr/cKEbEcLvMcZ1QY3bwBp10EdPnraxTB2x1SCZe163CejrhuHc5Rr+P+if3BDdFCWpVeH4go5hVTISsR480Jy4ZYQ9MaAwi20Funm0eceK9xjlL0zbJc8B6R4WaC2TtIREJb3WnnO4cstLqzFKVkIdkFloSII3ZgItCPGMRgBTtH5LvD9oS5kZYVUUXlMhzO0V8sSfERHS0AtxHPLB7RyGx414hux5AWbn+RiI2mLMh2hVrDwfwSjmK8Rgz58zOQsfMVzQmuL3C84ZroxwMnYwtgnYbhaaX3r5A2LC9Yu6O1YuUF21/xArI+4WT6+Q7pQtu/IGmjHw+whm4gZmi+RcoBoOQINUDDcX7+hmVB2n30mu9Iucb7Qlrwt3/FlxuSniFdcT/C7V0rviZoJ63tyNMHxHV0ueH1gLbj9RW5/QN2/4V0+wlsx9uIvV7/gL78H9E3PjU1NTU1NTU1NTU1NTU1NTU1NTU1NfXfTL8/ALw8hfs2J7AvsH0CM9x3eAU+3SBV6Gk4Er8iqWHWcL0iJsMVmBA/IWeoEYMLOnprdcTANoQAtZ4DAlK2EYmriJ0BfJpBWUb/akKsfYNfsm6oKCZEt2sPl7HVEUs8YLOkFbNOyplwoo4uVm9I2jDRgHK6Yt5xM8wOrC+07nR3ePsK2wXNF5J34IJ3CxerCJwRbd09ul+lGbqAmgRsloBo0qO7OOWN3nd0WdEagFVIAWgQJN3CQZ0TntaAdWUj3I8DssczxVp7xvs7kMI9nVKkAaeI9g3IqJg3cirh8OsdMwNP0A+UHH+26IM1gJRxqaOTWQJaQ7hJLbqOvT+Q/BQs0yKyOlzPJWKy3cOBrTqKyHfo4ea2NmKeraPrihOQ2esRLlQzJCl2BPC1pHhzyBk/j4ibzQsqCbVGSispE85b69EX3OOxQL+Ba28nyBFx2tYCjlqLc9pTxFKXa/yZNK5bIxq7Rzz5t07jDmYNUsJtD7jdHohfoHZkXRGPaGF03Duq4xwbThrO6BPREuBbAuyLeFyXSnQVS+IbzI2diHsHwyUhYrguw1HdY/BgAO94zjwiy89wNttwV6NAG+d1wY8jenrzCueJlCWGIMooGbYaEd5kHKe7RBeugPcYbACgnkg3nAYkNH/C9gOzCmWl9guGUgSSvaH+jORx/TXc1bpsMdigGckJ6ohO7xXpHsA+pXB7a6Iv64gYbwG21y3OihCueFnwVvExnCFlod/fQTOaPAZeRk+59bFXacXbA5eMbk8jojyc/WKOSELqF8QvyHIBc7QY1nZYPtH2ryxPL+AN44aXW0SElxe8R3z0ef6CekX6gWXDzwoo1ht+WSJBQJxuCxx/QeUSEeG9Qn4g4uhyhXPHagyESD/h+jmc0Q7qB1I6vjzHazjveE5wvqHrZ/A7nHcMgbShZkgz4EDSlZ6chCCXP0RP+PYZrzu6bPTjtxiA2L/g2zPykXgwNTU1NTU1NTU1NTU1NTU1NTU1NTU19d9Ivz8AzAPSNdyP+QmRDZMOfIblXxFOqAlZVsQE5xXyBZUtQJ84nYNExkVREyRdIqrZwwWIAW10wKY1Io7zGpG27mBCWlf82GG9IK0C4ex1EooOh6wEwOpHuGRtOHgNBoVC5YJYQ0oJp1y34GbSiVjjAgSIs3MHMniLx9IX3Gp0hVrDSyMrZE3h5kzgreNqAcxKwvoZj2H7SP31iHteCrQHlCegoekJCDgrCJJGLLAHLJZ+AgFLoy8XUMNTgv7A04LIgsgZAM47IgWvLXqOywr1CLfuso0e5uE6JSKxtSrGORKVU3QYbzekdVwE6y0AeT/Cyes+IpzD+ays3xiklCt2DpjaGmicBRdws3DY5jXibvsB6YIf94DMI6Y4IB8RzWsVt46XLcCwZLh8pnsAWn97BSq06P9NYmh3NK1oWhGpw017xds7go542xyuZYn1DCZZg1WqR1z18UBSGWD433XqWkDtj1jsiN0mBhrsiH5hkVjztI4I5g7LhlsL4JquA6AbmISLHcJJqhveD9z6OKtLOI8ZEdw+1tQMrCHSIF2IgxZ7RWq45bi36hlMN5e4RgkHNxpnwCUN16sgOoYPvOHeSGnD8zviCS8LcjygG1o2RProIT7AHaNhPU6CeY2vtYy9iQhs8x73v8ZLZ1lwc/rROPudlFZKekLVB5R+IL6O167xvMlhf49hkvUp7uu0IMlQU0Qd0RxbKzkc07WP1ynDa35i5hgSjmV1pB8oBV+e6O2IYYxSgokfJ+6Oi8W+OvG6c/Rk0x2OikK4xc9XUm+RDCCClksAfeuUcoW14PtXWD6FI37JpOVCP16xlNDbH5H9Nc7n8gISTue+/4UO2HIDLfTHV7yvmP0rPDpcb8RchtFNqElBChGeDdJldAJX/PFOxknbj3j/Gu5sqyDRgY6WcWYEbw3bd6Rk6I70V2S9xD26vweQzhtewLWQ1k+QF/rb/6DXTNKPCIapqampqampqampqampqampqampqamp/z76HQLgJWCRnohkrH8Np2x6ILfPaCr41y+IF0QtYlb9GnAg9+FavUdXrHsALBlASPxbzCw9HJwBtQZIifLR0YkaEdB0Cxfv8YiOVnFoHzHLOcCZlOgy9fGcarBu4An1TvdO9tGraQFzAjT3iEQ1C3ex9XCXesN4i8ffXxFd6PcHdEHKnVQ+I8uCa0BBRKKDVVf62SLiuBueovNWkiP06G8t4a7VdcPOV0wJp3O6IKN/VrwNp+fofC0FV4EmiLQBHiUgs/cAlymHc3pZYx0suo/j69F/qzpcpgFlu9QBBQO4SsoBXum4NUSvES/cztFzGx24OOHKTiCBvsLBSnS+uh9QPVyQH7G6KfqDrXfQhKpgqigZVQtHb290YXQWZ1QTTqW1jmEDDAbER0CsormQcopO6GVDEUT6cEDX0UG9omnBbB9OWcXrI6K2/Qy4Wy5Q36KjtwTwl7zg3uKeqPcAgozzO1y13vpwbIOYjEhkAanhvpWAq97O6F8Wh/oIRyljgOEDeAsBDrXEdfUznpsesbo53N/ihqQUYLG3uG/SGvMMuow97viAgHy43XMGHSBcx2tHAxyKxHmRDdhxCbe8tRNpsUamoKO/WeQMd6wbhtPrPeLSywb9HUpCzgZZ8b0H+M+fsP3Psf7pSvUO/Z0l3Sjq6Dgv3gyWp3Cu9x5x8X0nBjUytBMhI95I5YKY4akjrhEVLeGQFwfygh/v4ajWgtU7LuG29VSw+hZAtQ/XM4XeHrGukvHj64iDTngXnD26lodj3wySjgGGuqO60uygpBvJxlrlNCLGV6g7Ui4BeMVheYo+37JgOOlyw9dtPP+Jy4msT6jcyHQoK9YOunX8esGPd/r1iq4r/f0XXG/I8QuWV9BEF4khDrtBf0fWF7h8pvZX2v6FnON8CIL1HRWii1wTdryi63PA4HKB+gpLxo93BI9I8LYDhmaJlH5v4QIvPyLJ8f3Lf8WH1tTU1NTU1NTU1NTU1NTU1NTU1NTU1NR/SL8/ANw7XBqcBVdFNOGPd8gGW8LuO3q5Dvj1ihwF+h13w58TZgcqwyWZEkjE/Iq3cFTmBBWgIgS0JK8DWg2nJUrvjuKon6S0wrrSRZE2+mzdIl64twDGIkR55jl6d8N52M8Tz4X2AbI+omRNIoK4SFzjh3NXF7zeMb0GiJKngC33A1wRCul6GXA1Yqs7CnTMdiR7xLCSw/lZD4QtYG25xp/zSjsPpENKJSA40bkpyxUw1AnYhSOkgIQ5un1dottVfI81df8WtSytQwp3pqflW1ywuwdAPB7Rnzw6dNHhKPU+QPCA47IARL+ydtzG3owIYvGGe4997x20jx7ahJMwjGSxPn4+YLvgWHSPasZ6J+UNMKx1OhWVLVzGaYmIbu8YCc85ekf7cKtmSNsW265KMoP1Ghz2OAM+5jJc2ClAop9RceyKiIV786MztUdUuefRFWwRM+6J6H0taTg+Az5CdPG6jeEGYbTvhsQbWBkDAieYxr87wBndyhhowVwCbuYV+om1Rio5zm/x4bqW6G4O+2lEcTOGIERiYAMJwIjHfxJADgdpR9x3HtcoeR33zSXORnugugT0ljh3EsXcJNUYmtD07bxAQrVgdkbctxhdBa87Xi4Bga3F+XcLJ3e6xeLoJYZBvEFrJH0ib1ekP+KeGsdPXWNfxuMEBE/4MiLiMyQKmjJiFc8r3W2A7jiHHDtyecEpWKv4IhHt7g72oHvGzfHza3z/co2u7yqYH+j2TK+G9VdUVxChnfdw9uoW0eKA9R7R87rAZYm4bL1FdDUnygpmMeCRl7jXmkevNcPNXa5oreiyAoLtXwHFS+ypLi94f+B+ognKj89YV9Cf6bIifmC3P4Tb+Xyn1hPLJ2l9ovcYeHA7wN9hueLVR+DAhpyvKE4Xj1GOvGHHHayTSMA7qXkM+2iBy4Yfd9J6GQM7GTv+jOiA8VKC1VsZEepTU1NTU1NTU1NTU1NTU1NTU1NTU1NT/72k/6sv4L9clXC3WXR5QibdPiNtg2rImvCiAYSXH2Fd4Jrh5ROerjgVcfvmLjTXcKe2O9gb9Ib7iWsCPSNaNaWAItkDmAnx9zlAWBfoHwAtJSgF7AhY2Rq0Ax3PKbkEbDuGAzOBqOF24suGWo8e4r7jecPqiR2j/7UonHe6K1AiHlVOzB/wWZHPN7K2YKDnAXaA6IBKC3Ya3sNpTFkQclxjUeiguUTfsfcAc+aILKgSHb6aAuZ+dBz7Ge5Bt3CcUsb69HiNusEHiPpYNzrQB0gfr6sN12/bI15aRydw2/FGAHEiElsIN7Foxv2BtyP6YjWAYDDIjmtE7MrH38k1Hsc7YpCWC24n3QJ6iURkrqkFbLcT743ejnAZp4SL473hCP28Y5qj6xVFauyZUMEiUljo0cXsDdXoVZYsEd8sFiDTDVoNp27tAwyDpGt00DoR8dvPgN59dCvnGAyQJIBFr/VIa8YdPIM6krYYZOgd0YRYR8oFEY990RxrWwL8ebOIzNU1epzbe4DZdkYHs2as7ri3v0JlO0e/7BHr4+Fw9dYRySOe+sR7xXvFrPPXfuA6BjHW2EczRi4333qhVSPeGOJs9jjXaCblKxkj54LUe8DmvmPYuIeh7Ue4ZfMFzhpQ2AxwqG28Fzh2fvkGTd2VlApluyJYvFbbwwGdFrw94KyIXFESqstwbEfvbtaA0wJ0SdR6p+7vnE053u/U86TKQj0Puiu1P2jHAys3emu0ZgFfywuwYb1hbae1Bz0rvXd6O7G0Ypppx6/U/R4DDP0e73P9JB8Heuxo/YpQo/O2NfrrL9TjHWPFrYzEgmW894yObRO87dgYnhAxpB7Qj1Hp3NHthttJWjZSWcjlSlmvJLmQRMgpsYiRxVlyYtXGertyWZ31+jOp31nXJ5I/SNef4P0r/v41nO1no9eD1ivn8RumF7optZ50NXz7zFkfnG+/8Xj/gpUL9f6Gj6j43vfoUUZwvWC9YtsTkhWVjp//glxe/g4fUlNTU1NTU1NTU1NTU1NTU1NTU1NTU1Pfp9+fA1gEfEVoiFWod3pK6O2Km4cD+CaI30f08gWRhtIRCqoJ8UGfjAEXFcot3JaSwx2G4q2OSNx9gKkL0AMY14qkiPoNQNZhyeEuNsMkR2yzdCQv0BsqBTXoQT0BQdMSPbpJA3yaRW9qMsgL9rgHOMMxVRAjlQvdKmqFJgXHQVZE3snPT9HbermAGyaCq4ZrWMAp4fwTwN7CaGo1ImTdcA04JnRSiRhbZ/SyehqLphFde74NEMsAsUtE26ZwCQeJHpHDPXp50QCCgo+45hFHnDekteGO7LEP0hDCVYzHz/mIiI6Y3zI6bwknpp2QcsQk1xpAUfIAvILrcGeOsQlzj6faNsQd7+N1agL7eOaEpwKWMGt4LlgdYJ0A8qIeQL2eKBZdtb3BWgKMkr85mClbQN2+I3mL6OVS4kxaH+CtAR77mIazl7H+KeMiSLoNF3BCeh2O4Ybk4bR2GfA7or1jXUeEuXj0HMu/O3PD5S2aoI/hBW/h/JUYgBAdfb7uESeNx7rpOINpGT3EFq9HJSCy+AD+HvHEI7Zbco7HFhn7LONX3J8+OoolX0Z/bNyrLiv0hpQlrj0Tz5dGd/KHQxvBPGEiWDNQArQni73rhGu7/BhgmRTgundMoKQlnMat4mWB/cNBT0TKL1e0pHB2txMRRVXH2XA6sYbdBKvHiFvP4E7f78OJ3CNeWRboAdI9XbB6j3tEBNYn/F7x1vAS7nC3E5oiZSPlHyBtJJFwo58rnA8y0cms2w30E/QDEUHWTyOyO2MkVGzEa3sA4HXE5JNI5Qmvb5BOyGVEoRuaL9DvSK1oit5lygXFsTHQkZToze4Vt4Lp6HpOibT+M+mo8brtJNk7zmfaywu9n7C8YMcZRyHfIBW8Hfj5K8iGble6t4hwLp/Andai27juD1KreP+Klhf88YpuG6Irfp5QNO6ffMXO+9/mc2lqampqampqampqampqampqampqamrqb6jfHwC+Jfj6Ff/0M/04SetzuHddEf+K6BKuytNwbUhpsB/0zVDCTWgmqOZw66UcEbd6A/wbFPbhPhTVAGlp9OnqgL2kAGuax99rRKuWEX+LQD2QspJYcI+v6S3gmgQApvcAtFpQDM8lEFg3rLdw1ybFPXpkMUZUasP3A7lEjDPLRrZKJiEGAb/OEa1cMF3w+oDk46kjf1elIL1hbuCJhCD/H/b+bteSJcvSw745zcx9rb13xDmZXd0SCLRYJERIJKEbCbrSE0i60ivoWo+ht9AjSICeQpcCBAJkVzdUmaxmtlhd1Zknz4nYey13szmnLqatHcnmT7HynKrK7LCRiBN/e6/lbm7uC5HfHGMUwe8dri8gBvUK9HSg+plQ0keCNnGEDbQS1vPvImN7EUHKlr223jM22BLSJBhMKEiQEDM6WBAMRJ7yeyPAzowrZkwI7Ag+Y58tI11DElZOp3CUPB6Jc0YK32cnrKUr1j1dsS0BqM246KSEJ26dYCdk4N3wERlBvF2I3pG2Ied9AtOBloR5Ek5gCIpqRkjngmvCL7+Ttu9ImK0TXvpI17KnI1Ue+1EUbU/pjo6e7nSvCZ39nHHFzJjnfByIJ/iTGb1MrQlGxx3KU/6s003c9nT3lh0pQnjHLb5cOynv0db4AC0JXmW+ttt0H595D4oSkf3Q6SSPCfkrInPwQrbcV78L7wWmpX/edxth53TenvDuTE8gHBLpHPcTkY2QMd3TRzp9RyfcGGPgZH9zjDuyPU+3+ZmO/rB0Q9tIp7dsuBrhn2aMOUSpUCSTAMaA5tllrZHRxSqUouBHOua3lvevVOK8ExSCdLXH+ZrDE+HEcc+96pb3Bp0YBx4F60bxjnBCKejlhfH6G2AQ5qhuCZ1LQzXQbUdk7tF9zx8YRM0BFHfiIK+Beg5ueOB+xwe06zNx3mHbcxjALTuyx30eb+RAyvFbROt0j1+IMzu+43RkuxL9FRz0ciX6SGd9EUQeHd4V2baE0XGHl3+EHa+Uj/80Y9dbvpdJYQj4+D5j5FXw269h/0dw/Ja43wm5zMjuCub0t0/I9Qk+/4YRV2r9gLYciInToBW8/xsk/gT0Db08wX0B4KWlpaWlpaWlpaWlpaWlpaWlpaWlpT88fXUAWI4gLi8okQ5BKWj9QMQnNDry/ASxwV5mPvaA60b1mgAuJGGu1IzkLdOhKT4dmEzH4kba2BL6SLsQfkdLISzQbcso5Jgxta0ggwRXAmBI2yjbjoQj7YKTHabCmJG26ZoUegK9d3A2UDEsSHC1b5lSS2BakDDcBqN9wIdnN+z4TNVK0UK5briM7NR1J2z27foFYjp7i4PrdBN6dpeapSsT0Os1e2THAS0BC2VmDItkjDQkkOZMMB6kyw5P6FM2JCQhYwSiSlByzbRkjLC0dG4qRNT316SciLTsJdWS1ydIQNXHhMoHoekclTKjjCXSvU3LeOToCdy15PGVNuG3o21PcO8dH4ZuV5yYUGvD48SGA5WQdGRjirSKhFG05l4yQ0Vge0HUEVV83NJ1fo6M2NaS7krNayxS83y0JmCdx5Hu6qfpzB0ZDS6zh1kUylN2RT+isNEv8dcwHbDZ3/seCS3M79/y9TUjtJn9yFHa7LfNtVbV9OC6JZT32WtdHl3YjzhuEpri6Yp/OOuZMN0f0cEJiENK7iPpX95zHNNlTA4G9DeEazp/pcye4MjjkXmN48z78bzlwIFUGHdEExwTQbQNO+6493T/FoV4JmwgtaT7uicID8meYOwVajq7iz7lvVAELOAc4DfQ54xCds8U6hiIK7pfUa2I2wS+s1vYHVSyJztmL3c/8z5RwY7vUS5IlXcQLuJoVbzfsseWjKvW6zPj9obsz+keHq/TuT6m2z0QE0RH3ntlRynZhS2GXF+I84ccNBmWz76aiQBuc73rBY7PUF8y9twEqVe839MFHyDHPe+lyzO0gXuAv6I2O8H3bbrYC+E9hzWUHGSxE7Ec2ND6hGwN1SvSnrB7drV7P9EwyuUZ76+z87zSpSFVCf05cdwQqUQ5iOMGuuN2IH6Hdp2/d/ztN2jZCTpqIJd/D60N+/xros7Pg6WlpaWlpaWlpaWlpaWlpaWlpaWlpaU/MH11HcBRA2mN6K/o0zMSCW9VrkDGHnP/YUJcI3tmJcGcMB2FB0kuBbB0SMaEvkGCLh/pPHPJL40ACh5bghMXoLwDPUKQuudBekJIlUpG5iZAU3ckBhqClgullOky1OSb5y1hswvugVgnyoa4Atm7qVqyf3gcqDgeB5QN/XywtR0tmm+J4qLpnDXJ3lR9uJgL9HRgumVfLUE6QkURs+nczWhmooDu2Q8sSsglI4b1AmgCPtE0cL5HSO9IbDNaN5BaAEnHrQwoLaGY5dokiCwT9sp0jgahim5bHsMj+pl0zlKvINfpmh15XUefncL5vkjL6y7pbBQ3GGe6LjFww0KgTVdmPxnumBbcK6EXQiXPFwBD7E4RR1TQUlECilC3Qm0XSi0UmWC3zhjqfp9dxXUaXefxBemc1i2vDzLdmWO6ewcSgZQJ0UUn7PZ0P0pJgBod7MxIZH3OvUshJGON0yWsuU5M8CUlIWap6WanZGR1OGpjdmV7Rjuj2SH8DnRjQv2S62L2Huec8E+yz9nPHIAoLe+N0vLr3NLZWy+I1OmCDtB9Dhfo/Hm6iNHZ07zloEQ/Z5R7PgKlFlRzH+JGnPdMeI4C1BwamEMJcTxAPIRu+PGAxB+x/hm3V/RygbYTliAXM7R+mLHVgjSlaEW3C/XyIa+LeT4nxokEqDlVW/ZAj1v+mU7XOz2d43XLyGQPiA0/skdZtwu6XYl+4mZ4d9Adbfvs9871juOecec+HdH9yGvhs8c6zgTmkftd9JKpAK2m6dojnbnjgNrAOu6Bq+PHaz4HxomMTpwHcvmIl0KYw3D8yPPyUGyc6Qo2x88TqRfcO+YDuOD3G/QT7zfcJJ8J1uEcRD+yM3i70D58QytBK8p2/YZWKq0VLpeP1PuN4gOtj0GOmtH+NYdUYpzE8Wvi+Df4eeM8B8fbJ4YURijDBqPf8Bg4FYv+U300LS0tLS0tLS0tLS0tLS0tLS0tLS0tLf1k+uocwPSBVCO8Ev0t+1Blh/4J9m+AO1xegB+ABqNBLSAnMAh5otQPCcJEEy49oCgBdU/HWpDxvtFhTzemiKR7Ltp00J75HjI7gVsBn3HKFKiVmDGtGSutCSG3PaNy/cIkzvmjkFG2kbHEMTJy2PuZHAzFouDmxH4lohJxB/uEhiNS0+0mE9BouhejlIRexRIQ231CtQnXyiWhtc6+3jJjhUMzAtfuSCkE6eiMeJsRwyNhX53nUfYEy9ORGnbMiOwTKRc43+a5NsKOdF6rZ5+pOVIuhH+aXbIAgkQCTKETuhHDp5v0AkWRKCQEnLC/vAAO40jQxeweJnt10YrbjehO2Z8YR8LgqNuDLxOlZVy2zKhod0QMUSGkUMpHpAjSz9mdKoR1LIJSIjun2xP4Lftqfe6B6eQNP5D6lOA3BtKu83qUGc+cseLRrhAzBnwCSx6OaBtzfiGjdRFJl7fNmHI7ZpTvdB/PPuA8Sc/L4POEH73HMtdRHoC0JQQWZndzTNd2eQf6uaYCbAnkQhCd9wQTdnrPvcHsJJYJ+lVyTiPuIC2Br7T/xqCCSDqssU6U6fAlo4yjbhkdDmQRsJB3qGEm2P2Gu6eLPxTGILZ9gt0DKNmvPY4cAqGC7pS4o55Q1s2QlrHZ0RrYDRmRXdAaed8xEMvhEmkbhUEpe55jK9jd8tqE4xZou2SfLwPqE/TP+OjI1ghTYhhaDNeCjz6vR6AqlO0ZszdgULcrjO8zGV3KvBpBnGe6Y4kvzndRJIwoms+yuhHnjbCBs+WACo6PTmjLe1tk9kmPHJAYPecLLv8oIW6/zejxAe0Jv/8wr22gRXH39/vUmX3W7UqcbxmtHpIDOZpdyeKWLt3tgo6BShBbI+IFl44fgWrF28Y4fsCjIpcPhJ4J0WtB2od0JJ/fwcd/AscrIRW7/wZvT3BmpHXbPiC3z3ltl5aWlpaWlpaWlpaWlpaWlpaWlpaWlv7A9NU5gAlLCLWnE1LFUUYCHpwEUR34AJ6wKAHXBfSCIxlRPCxfzvmd7tWEojHO6aK1Gc+7oSUjeyVGsmINkD0jbb0DlqnEBKGVIMGdMxjnmSxuZFSueTDcMB8MH9kxypj9qeB2JGyRBgjhQeBozV5UQ0B3RnSCDeJKfWqU8kRCbvJ8+pExwnbPmGgZ073swAP0CrQGDLRsgM/1OBJCz5jjmJ3B6fCcUBCgNiJmR+zD2Rj5+sGMJo50+BJkz6jPP/OT945YLeA9wXtoAj+742GM0XNdmU5fuSZosvt8Dc/fx3QFRsyY4xkvbeT+sOkyDUG25/nbno5fEQxwzQ7gMIFwCgNlUBSqbEg/s7fXR0bk+kD3p3Qr24w+joyYJjQ7e2NkR7I4Zk6UnYjsfg1iQuETiQPZLrmG2mb3bnt3OGek9iOGe67XsOm+bbNDd+T3BSAVqTsinkBYKiKPuGzJuHIG8bhmdk6n+DXvlxkRneDYs78ZIcZ478mOGPN+qXn+45hu8gm36wXeYbfPP//ifgZLZ2ytCXujJ6wU0n1K5DkVQVRybRjzXnREc2AjI7N9MuYT84FZx3E8jGjbTAe4z3PoycLHHATRS8YE1ydK+wiS7ngp2SsddsBxg6Ezfnngx0lQshO3Vsq+06pSt4puipTHNmg5TAK5Z8YtQS25x+R6xXonuiHbM+6DcQ7MIZ6/xdkzpjwCZKBbmy570KePyFbg/JRrL/kczGdGDk/EecPvnwjv77epnwc+jnS9kxHVPiPo09IruAij37B+4l5wBx9vmN9ziMKOjIc+P83rGRndTUFKI8YdP+/YGJk0UCuhAbVidiaYdcPqFT8HUq5Yf81jk0LojoaiEhQL6vWZcn2maWNrhf36kY1B6Te0/0B9+jniHd0a8vRPkP4G49OMFh/4b35LlI1A6PdXzuN7rH748Z9JS0tLS0tLS0tLS0tLS0tLS0tLS0tLSz+xvj4A3EvynzHhT01QJa0hdodIwJMRuQ2agL+RS9WAyDjYdwDlX6JrR09IKBXqhowZzeuD8KDIjvv82nNMyEZ2w7Y6Y5AbRTR7aD0IlzRDQroupVAQdMI0DYHjTDBZsr9YAKk75XJNh+c4CE5GBBGOyJauvKFwKvxwUsrP0JiOP7OMBR5B0LB4xAu3PMeisM1eWUBGp5aKlo3wmHHKGTn7gLnZAZuAOLtKtzw2rYgF4hPsecbMZlQzE/aRrt3SoF3Sqak1r88EY0LL85bn6UpuCddtRnVHndBTiJD3SG6CjKW2hzP2TPDIvDYihEoC0trw6AQ7roU+HAuF/TpdpidM92StlYe3uG2Nsj2hAq09pavYIqOT3R5vhGAZtesyQbWg0lAmzLU+QWD2PD+6kUOylxf2BIP2KO+1dxNtDhnM2PEHMCeQtuXXhSPRE74R2Z+ss2/Zsr9YsOniLhkvjkG58ngTUU2Ht01X9jgz8lgzvlkEtGQEOnMe4MttZOmAfQBELI95duDyHifd8tr1Y0YUQ7SnmfJc57BAn7MYZ0LnekkYLPp+zd/7kidETfe240enm+Ju0HIIIMajx7jla4wOMWOoGUjbc22OO/24EeYZLz27kGOcSE3IL1vN8/GBlkItuc7KmHHu8EgFiAiC7NUOu+H9TnjBoyYUFRhx4gK0HfczB1TKFe8TOoeilxdwpZwO3dDSKMg8xg3ZPyJPL8jzC3L9Bq4fshf47RNxPwgXKFfCbrmmUgnrSH1CSqYRmBs+zoT5ZMR3jJGQFr440cdIaF42wg23k9CnBNLtSoTgPnAzLIJQIfqBHTesG9aN4QH7R9wVPwZhYL0zCKI0Rji0CzHOjMLfnpHLB7QUincKJ/vTz9m0onZHW6Pu39C0oNEpZ6dGh+P73PvnD8jTz5Hn5/mM6oQ0on7LGG9/+8+gpaWlpaWlpaWlpaWlpaWlpaWlpaWlpb9jfXUAWJ/SoSkyCPucQLBqxivfHPzh5h0QNxIQXWYc7kD9nnBQM7I2POOCEU2IO6Nv0+42I4R9IDKgBqozMlpIJ6jUhH6hRAwcQwhK3dJ5KQWVisSMhfUBdiDiFD8JHaRzOeOhSy2UdsmeY0s4Qq2IviA+iLhTZOD2iWgGdqPswdYy4pVSJ+QRXALXkUBuGIV47wgGgd4p24UigowTxh2dfkAPJ8okfCqzL9Shv02IGbNvdSQAn9ZCqTU5HXmNhEiYCHlc+HSUTnDqTNdpTJfoifuNkC2jpXW6iR+9sVpIuHhmDLUqlEqcM/J49uiGpMM47MzY5+i4KKMbXoRhAx+vCbXcYBzIyL7dEgnBVQ3ZLjMSNwg/0KIztrbgFoQJcR7Uumd3sh0TbhraKloEimVcNZEuVpmbhzLd1DqdmNO9LBPCI3NfXhK4E7Nf9wHu7X1/vk8jlPIOKB8/RAKRkpHYkjHlIelcFwKZ+yUesc0xO2nT6p4dxFrTgYwh2pDSvnQ/a8t7knx9kW26rxtEz/cpe4JkKblHNbuRBSHGgY0jr//vDmZEnpO4p5Pa7/n3ZZtrmNfERfNaS8O8E1qwPvt4baDkUIT3G+FOhE7/rU+EnK5Z23ZoDd0bPj4nVPcxncYlq6ndYHSkNkpps2/XcXPMz6zbRrDzxKVhNibQloww9xNXxXDcOvRB9OzB9mF4/zyjzU80jOInVU7qnn3K6gdiJ2X29T7WXLePaL3m95UNvX6LXD/A5RmePiL7C9DyHlRF6nVGPHc4P6F1y2sl6eL3ONIFPu9rs2P2gY/3ezfKho9bPoP7La+53RPczqmXqA2KZIpAOB4C1By0OT/DtuH9IDSw4xWPksB4dNzeiHGbafAKw9Dnjxm7PQZid1Q36vZCrQ0VZ3v+GVUM8ZNSn9le/j2KXtCAsj/ncy4ccUdqIz5//5N9Ni0tLS0tLS0tLS0tLS0tLS0tLS0tLS39VPrqAHA6zZ6Q7WOCk7KDT6/mPma36ASU7NDv6ZhkgrPYp5UxQai2fQLR6XT1kfHHAqgQqqCK6j77WTVjoOtOEUsYOjxjdcPTARkZd5uGyuwmpV5nFDDQsp/TGRTSbewC7glgXIRA8/xiAEoIuBtGY4Tg3nA/QZxSntG2I6pIeHYA97fsST2OdC5O+JQg/OFYna5bArZrrltN0CgyQY8dCfXGjOZlAnIb2S0cPiOgJxBWgTDEYwKkyP7bCScjPP/MDYljgsCNiNnhG4FYScchlpfFj4SJwrweLWOUzYjxllDr8pTnHQmLQ5SoOxmH65jNyO1WMn52dASl7M9oZI+s7lfqng7Vx/VkHGAH7o7qlj3QUikxe0trQUtAUfTynJBze85rZqSj2gVidvH6+SVuPHp2z9o9zy0sr6Fmf3R27vaM334MHtQ5nECCbx6O04cbVh6WYZk/5d57d2KLAIaUp/x1JDANhIjcT6IZr56R04OHwzgzzueeDJ/3ExMaTtd93qQZyzz3RI4RePb1+oyCLts8xMiXCk04DLOPOAFz7h2fL517OLwTUnDvedw0rI90oz7imkvMvdaz21qVoCR0lbxu8YDJBOYH5iOPSSro81wHmfA6QKZT34VGsOEoRtjJqE+cCK/nyR3htIGZ4zYwi3mcEP0AjLJted+HExguJW/L0NlJXdJlnpZidLsglwvgqARSGzLheg4AGPqA1X4koN+fkXpFteRgw/4hY7YZuQ8j3cSis0OadN6mU33PDl67EfqU96cEmM/u4DMTC9rL+5CG+4mXRrQnPBJwhx957g6Mni7jCe6jVNj2TCSozxl/P3o60MPg8oL1Azt+yGs77iiK7h9RbZRtp7YrGndUOjkG5JTrE9vzz9gvV1oJtutHyv2OnHe0PVPKC9gnsHs+g5b+aJTJD//wr/HHrLWGP15rDX+81hr+eK01XPrbau2ZH6+1hj9eaw1/vNYa/nitNVz6fbT2zY/XWsMfr7WGP15rDX+81hr+/av+zV/y75i2jwktLZCnbxI26h30A/ABZJDLoiA94ZecEFeEkm40YbrdwMMRFLFHbG12u2IDWsnXijMhR8z4Zx+IOI5SHLxtCfRKRSU7dkPAzSjSyFjq7KX1cUNFUC34jAN2SxCqWWCaTrlhk9OWhMFxZPcxNf/udsL+BPIDdX9J+NEuCeS0Yv1AWiWKIAFFHbZLum3LNmE2CXNHdqdiY4IuQcqHXAvVCexmPyvp+sxO4BnrKxPsGkjNXlnqDlQk7iCNmMbX5E9HXhc/wM4EUGYJpyQInV2iUt5jnLEjHw6a4DXM8jqFvLsaM746HyChgOyEFIYZVirRj9m7OsAcpVLshCJo5HkRHfGBbs8wzhl9vX9hqwTFT7xkF7K2C9gtQam2jDGeEeExgTa15d4y8rViAjjKl67fWhE5Ez5ry/MjcmjgEV08rz/6gLjZs5pgOZ3eae7OoYWIDlInsM39F2G5ZjKd4W65TjNuWLbsvE238Tn303z9ek2wj0+gN6ORJRIwa833GRPuRcx7bQ4HCHmtY+S9FJbQV3VCbd7vLzSHJN7h8iP+WTSjo6W+c24IQgR3J1Sx8waU3E+6kb3IJ1p3QBNEl2u6/6vifnL2gy5BHQNHqLXlIYeB5jOC9kxYxj1rUUQVRzhHpx+vGE7YSbmfCR/9jSKSx0vGVDt5n7kKHpru5fMtBy5QRn+DsqH1gmnGMKOFGoFsT0ifzwGp8x4j+7IZhNbk+84cNJh7YfZSS1yIkk76kBnXjaT7106EjegnYdndbP1O1CsSRswofC7fEPffQn8DKYhbDlocn+bwQsaYc96hXojxRoSgdU9obP4eQ44EnGcOqew5kJA9zwX6nWAj2oUYN1QdtqeZbgDSajqB573v5Ur4PeG55/4Od8LvaAm2jx9zi3nHVBLkMzKWfumPRrln/+Ff449Zaw1/vNYa/nitNfzxWmu49LfV2jM/XmsNf7zWGv54rTX88VpruPT7aO2bH6+1hj9eaw1/vNYa/nitNfz711cHgPUBJDmg1wRHVYEf0PIRjwrxGXxPANIS9nh0lBPh+R0SAgkTa/r5RCVdlQJ0yw5cAqxgrpRwJEBoM5Z0I2IkzI2ByoRetAmEtwxU1oShRBAWRG3pYMMJHxmvi8AY0JJqeUTCjnYlhmEeCZhqhX4S14wRVp7YSjqeXQUNI6RSSiNoqAZBJ9yQqhMOApbuZkOoVYH55w8nsxbC7gg74SNBXQyk7l9cnw/wO13X2TE74UrNaxOyvfepJuy9EBUSYO7Zxzodz9QtYZluIBnnHNISUvqEXmEZO0uFqITshGbHqyOEpmP04Ux2t+yDjYy2lgjEjEpBtoq4494Q0YSuAbpfUBHYLukmtozojWGIOmGCa8YGO6BRYWTXa7pqLZ2NGMFct5C5JunajJJ9zCKSIG+C1uhGVJ1u3+lg10hnqExXL5qvoduMbSbjykXntSmk4x2yH1e/OD8fQD1m17BM0OqBTPd3DkIEUvbpzNa59jMOXKaje+6l7O/VhM04VEmI7eeE8zP6Wafznm1y3enqfo9+9glsZ1T4e3T1jIOWHAoI3dJNKwrjTmjFfALWYbk3VOHsua/YIW6IGFIued7meZ9KodsbQy8Jwv0zHjlEIi5ICHLZUDsTbsaJ2qCUdMX24+BAOV8/c97v0J4p8R3jg3GtwuXylDHXWnKgYdzmtRDiHJTLBRsnHg7blbjdc9gBRWulv96hCNYuFK20dkXGMa/XM3H/YeZA1Bl5nVA9feAxr/8cihBHyxN2voFmdL2II7Tp/q5IFGR7zmhzLQni7XwfSKBtyOVnRH/N+3v2Wkd7JsYNaYU4M/I9ncUl11yUoMO2T9e+QJQ5VDEj4e0AueQ+PA/CHC07VM21Y8zhh4LEkY+I8kxw5Jq64nLBbGQqw+WZ6G+EGBFGUcXsjg5B25+A7thyAC8tLS0tLS0tLS0tLS0tLS0tLS0tLf0B6uuLgB6fwN+AlpCnNpAnoCREwXiPIq4CskMo6hshLSNitaYD+OEUNs84WevpCuw9YVwY3N8m6ASXBK1SNJ2wbhnf2+8Z8OojHZthMAYq09kY5HH5fXanVph9r4Kj0oCMpI0wot/ROGdk8mDYgbnhuk9I2VHZwO5oaxSJdCtKQmIR8uvjxOMkek9gPXyeu09HpcB5TpeoIbol/J0dyBIyI5olIZ89AqMn6A0yPthmrK8+YK1nLHf0PIdHn6tL9rjqpIYRSEmwI5pu4rCRx2YxO5CPjPy1ToRl7LVuOIKNgzFuDB9YZKPreySwCzYG0bNnldFnqrdQPOCyoypI+DxOR0pJwF0KzokgKAYyUHo6MEPhsqEquAduHW+VKDrdqiMHD8IIV2L0BGJS3vtiiQRj5hl3m2szvy589hsn5M24bSPGmQG3YYR7ul3tIMZtRotXQi549IxJDs+e1dmV6z5marNN9226Y8ONhLbTTRo9nZy1vrtriYwtzuvjs7eYBP+RLmIp25cuYi3ZLYtmDHjM3mESbIs/znmk+9l6Dh4wnc0P13LZp6t7vq5bDiNET8BvHUqbe4aMBH/EkruDfSLGkR3EdUOGZ6R5bYg5lIL1O+P+wxxc6Eh5SkdsUQSjtA1FUA+qRIZtbxu40c8+3b8npwS+bUS9M/TEjpNhQnfBHrHpg+z2dpvbPwcOpF3BTsLuGRVv6V71+4T85vjtjXE/6BGEtunAj/d7TsYB51s6jfuB24z8Js85zlfczuntDhDPa+Q+11QR9UwniBwWCBf8vBEjIX5Yz/WUHH4gRh77eXuH/GEZiy91usolnb5iRz5fpGSXsM1jx5E9nb3argnII1Ct1HZBWz5zbbzOe0ISLDtQGrJpguNxQ9oHCk7bn9DLUz7ey5b37TiR8xNVG7MwAM5PbB8+/rSfT0tLS0tLS0tLS0tLS0tLS0tLS0tLS0s/gb4+APxwKMqRwE3voBljCzLjT6/TcdgS7tl9ug8bwu9AYkgghibstAnwIAGQOdJ0OiZPpNYEsJp/FiIJvkoDFzRAS4NacQEbx4Q8CWtElVLrow10RgGT0NU72i7JZh+evbIldHXH7OGiTdjnNBiDNmye6jWBXrkkCBVJCCgKpSCqX85tOFoaynR8xoxdZULKCdyiTHgT6S6V8lizkcfot4zGnecSj07SR9etzd7YR7drnZHND+g4I6TFFZEtgVJpCWUtAVlYQuGQgodl12uke9lFMB+4K0Oc0U/GecOOz8iYscftgmi6KXGnjISBOjtXXUDrTp0xze5nQtEBNiOQa90pZZ8OZydGx9zRtiGjp5Mxci9FzHjsMNwso3lLyXOIngC+NMx6XrMgBw5iZLx1mXBW5oCBSrqkHwDWjRADbbgHUS7EhKvh6XgNhJA6ne0T8DGd2mZf4KkyI4Kn+zYGjwjxhHk9r5fU2a9sxCNa2s/8PbP32rPbGe/p+g3LX0O6hD0HF4iY7ux5HDH3OTIjpGes+OyMxcZ0FrcJGWefsjuOMALMDzwknbTnwG0Qw4j2ITuE48y9W/cJ3A3dCzTF+onJc75/3BE873F32Hak1BwaKBtqhu4XpFw4+8n9fuMw5bBBeFBrpcmGlmfQwI7f0MfBEDA7GP4GMaOfR4d9w+zEteWfnelmjtqI80gn8naB/WP2go/B6AMTxc477jfYLsjlW0KC0B3qC+xXsBsx7um2JVMJot8JO5FyQfqJlB1pFyh1xrM3RPI5RQTYW97aZHKAmeCjZ2S9aEa5OxNGZ1y8FIEihB9fYr3HkQMMHkQ/8eGwPyNtSwexgD6Op+ZzQC/P2SPtQak7WrZ0pNPnff2UCQLn7T0qXkpNwD8MbS3vzXFPd/71JYcA2oVSd0rbKEjWFy8tLS0tLS0tLS0tLS0tLS0tLS0tLS39gemri4CmVDiOBEF6ko7L7PbED7h34ulKxKfs1H0ASjfQF4KKeroUs+s0e3elPCGcINt0ck4yoBeKgI8g9EBUppv2yMhinbG4NqA03A0RyejlmA7TOvtK9fIeZ5yHNR225jwidaVuYIoiyU/tjs3OTvoADkI2wn+LboVaBRVFa5m+XMFt9sKODm3PLuPh6cBDkKpoKem83C/v65PuZU03aU+n5KPbVUTSuRrOwzktVDLKuSbonTHGUmeHbZmRwDxiu2cXbMhc//n7cb5fpowIDmhl9uFKcsSStNapCZDIbl3XiiLEkQ5BLRfEE2LLAPVjQkvQsoG8obXhCH4eiAi1tIyN9cjk4SpZXVs2zIN3ZD9mBHbvlKpIBIOgREYL018JBu5OaRekKOYNxHBP0F4YIEJhDiTEPcGnz/jjsAnNmb8mo4GjEwTukr2yqphlfDYOhXR8is447YeTOhIcS3tGGDNm2wgMMcvhhcf6zz7UBL+PuPDtvV+Yh+ub2Uksv9PxG56wFU0oOPt7010aE+JCyBy2iJ7nPW+Dh+tZ5jZ4vz9F8gvC00VcSsLx/hnYcijACtidqBtxv2Xb7jjRprmHpGFnz/tsRsi7Gw6c/UZcfkZRQWSnac1e234SM0VAwvNeYODhnO6cFpzhHOMt7837HblWiEIpDfwVKS/E/Y3RrngVtGw5vNCu+PkD0RNUe4nscz4NJDuA/fg1Ii/ZV6wN6oafr6gJZxFKvYIbpaSTX/Z/QswhDGlXRDeC84tTtlXi/IS4Ia0BO0oheu4/3S45DfF47oUhes2hB+sJ/S9X/O0TpRQo15xJIJDy9Dtu4LcJz7c5rLLlYAN9gmXNuPtSod+yQ3oYctnBB7pdERu55j4QnU7h7TmfneWaz62I6SgfyP6MEmADad9kPPVx5utdP8D9Fd0+4LXC7TPheT3l+efgrz/q42hpaWlpaWlpaWlpaWlpaWlpaWlpaWnp70JfHwDmCtXSNfj6GV5+BpzTvTjg6Qr+NpGdZo+vFyycGoFPR6u6AvYOmsKOdK/FmH2q6QjFBROQsuE2EN2RMLKHcsJRO6HsMM6MNJ49rBI940qTwsCMXkVjuuNmj2rJPl/w6bQVxCeELk74w517SzAXDn6nHc+0D9uMbgbiIGI6R8vsnB0HqKO6z0hhRWz2c04jMj6Bun+JMRZ5WOM0uZ930Avit9nLGnlMYQgbSILGR78sHDN+e55vPM43HbOYp4PaM/43lHQf28DphFwywRghxBP6hUGNjO+dLkjc0a0l0K5XJIJShIgdkztIRduGjXt2PJPwl/GGYNBeMLcE+KLoVmffsKIuGfEsIJSEUjA7pyVNq6UwwhKmPTh3qdi4Ie0pI3Zjgm4RwgNXR6Qgcb7DTRudKOmWFhnJu62gmtDSu+F19g6PjjQl+BLzazhFgmDuNwmEnoBWss859/ueMK/sRHjCTg+kbHktBMLzmucxkk5yJL8vAN3Tncm0UAv5fp79s+/HlXb2aeq9Y6qoFFTrHFWY+8LzPsj3mRHkkCD8ESftgZTZRzvBdIyD8JKRyOOWYDdmB7AE3gfugXi6UnNL50CCA310rG2IOMUjebYaWhthdyIU8wLqiCp+75xmnDa4H5+xHkT/jPJEffmQ5zPOTN32mh3g/XsQxcszYh07btmDWzaid6zfE2w/+pA9Y6Gplei/zUcJt+mMPYgDIipeleEnore5F2fagfd8dpSGlOcZdJDrrO0J+ifwbQ50ZDS8REfkkr26+wuMAymKasV7uozD3lD9kN3XcUxT8Q70GTcuiA6gQ7siUvD+efYApztdSs2Vly1dxNHRep0DOC+Id6QfaAhwJjA+X2F/Sed675TrR0Kmq113pF7ymW1n9k4XBcu+5bLvCfwvH4h+y2df2yh7nT3i5d2kvLS0tLS0tLS0tLS0tLS0tLS0tLS0tPSHpK8PAI9jukUdntOBmp2qZMJxbRANeJrOSvvy9ZFgy/yG1Ev2uUpCwXQ2OrgiocQ50o1YA+mdaPsEuwPvgWhLZ2QpeChCICITxs7uVZ+xu/E7Pwe4DyRKxlHXffayPiyw00wbkT2gNjAkgYfdZ6StwrnRnr5Nx2fdMSxBccy+UZ/OYzeQlvHTWokRaC1Af+/3BdKpN6GvWEC78ogcTrKZaxjIjKaeMBnJ7wuAgZQnws6EvzKJqDTwz1CfEvz6BJDjIKjpfnz0keqO9cCKIaIzbnZHQpA+4ZIlGK9quArqRsie0a+FCZQqwgUZQoRSSq6z6JZOZ5uu3VCwnpC21DxfP4l6wS0js9PxGmjdgcBHIBiqmo7aMGycCchroFIYh6XT1g68tARUaMZgG4QfSCkJG/1MmDYOIIi2MTxBpZ33fI/ZISyl4cO+nMvseg07GZSEYVUpork3Qr44iqcDHKlfQH1E9v1Op3C8dwFnhHjojA2fna3xDm2ng1fq3EIT4PqYwwGkM1YEJxgR6SovOyXS2SsWqEq67plR4T6jqHUDma5sUSgz1lwrduZ9ZxZESRd/jCA4IJTwA0PzdrOA0amyI22HOHB7xesHDg+CQglPuC9kbzcjhxzYMDPMDM43Rh+cY3D6K+OAIgVp3xI1O7wRx0+jlCDKjo0jHa7h0O94rUSrxHkQNfclBIw3KC+zh9hyUEGe0/VLoDNSXK7/GMaNsBOr15yz6CeOIpwI4L1TdqHGQCM7fSOy+Zey5QCDp5s8hOkIhzh7wvuwvBfrht/fMjZbCmEgsweb8wfcBbnss0O7wv0O0on6lPu837LP+HjN8992Ypw5dFECqNA+ZBgAG0WV0JLDJOdnHjHPGUXtubzbTpwjn/Ele97D08ku5YKPAx+ecd8uRO9zEKggdaOUmtfJIodpaiASP8GH0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tJPq68PANcgeIL4jOjHBI+3G+w7tJpdq92QOh2+kgAsVAj/LcgTtB2PBCvCjJgVT+cjgegEae0ZifE73aPZPyriEyq3/LOa7uCMfs3+XKQkqJCEETwcvX4iUic8m9AsSPckpLstOjaCGAfQwD/h1lCc0Ct+vKK2U1tDJYgC9Aky7EzYrG2abreEbNGzY7OMBMNmCf3kEZPtwAHs6dClQtwRLXmMWqd7uSIxz0+zO1gk3c0h5UsUtHyBiulIfkrwJPW9uVrKNvtrOzG7jc1OjISD0WrCW0knoKgS4oj0jFBuT7nW3vEZa+zeEqSNjpaCqxEuM/nXcfGMin3ANx8ZP/wA95YgPcrsmq3TTeodJwiveD+RtqE218U6Hg3lJLTh48SkwJiwapwJg7cnLPyLK/gMVAaKZhI4GTNMgNzvsD9nLfVxUvaK9VsuuVSk3xOou6X7PG4T9jvQEl4HuDshJcPQAyRmp3IwXeYjI7t9vEd2xzizz1XqvDc0YWyGbc9o6JrrUwrZ6zvmPpjuX63pXD7fGOaMkh2+Kpp+ZUu3e6VQPIG2KhNIPvqhFREhInAfOJFduBG5X0rF+jmjj08slHDBDFyyj1nPN3j+mNHscUfkCY9XzvMNjzd0/zklBoJmD3T07I4VJcaBuTGkMMaREPt8xaNQ9gsagagh9EwQkEZ5epn7Pq9FSA5EaCl4ONHz3GPMwPb9hTi+y3UrDT9+O527BSkX4vicxvzZOy7bS0LVfsfrRpxv8xmTwxthOaQR+0bzPh39ioTNvuUnJI68d9wo442oM1a57tjxClIJmxHLUWbCQSUYSHmGy7dEfyXOV4oFcnkmWjqyUZ0QeSBtI5MP8j6N8Rm2Z+K8p4teLPcXZ24ZAt1mj3G/IyPvsxiWAwwwI8cHjOkW358yWl8EOX9AZcbP2x0te56zzN7xENzeUCq6PyGUOUCwtLS0tLS0tLS0tLS0tLS0tLS0tLS09Ielrw4A+w3kWkA+foGtrcDt1/D8M+LthjztCGVGHhvIFY+RLtEHJw1QaQksZ7QsRDoySfcp4oR1ZN+nMzEduaUmsBVRLPNvCRyNkoDMLE2zpSAjncSZ6esZZVsb4AlQZ+1uOtEKLo55ITiJQQI22Qk+E/qERBCvn5DtSpnwDhvTvZnnJkVgpOtPvCO2pfM0IiOq3eEcxJYOYSk111ESvkWtELNfmW26eCdkKo8eYENohHt2pYqkKxrLxGfxGZn9cHfW7JadHbg+DJEgkIS34QgbEY5uBW6GyMMN2QkGWl4wPyZ4rtN4nOA5Iih1I0QwCgzD/QRaOiYFVEvyzOlApWx5TM50DXZEQLYKdhKlgRnGSCBWN+I4Zir2yC5inxC43/EioCMdi/WSADjyGrNfwAbRJ6AvG9gbPhpRs4PY/cwYbkZGdo9b1rKKEz3QsmEj471ldEox3DohitKStZ0HIjXjj9XT9RkkvCYogHvBcSRIt2rY7Gd9dGVX3m+UiDkEkdHYuSmVECW0JtQPn8B/5L1Q0u0eFpgL5p4GcSlYHyA+A6A7owSjG3Xf0Zj3c6nTYZzXK84Ts44FCYAHhJ+5d4Yl2I/BcMUke3+LKB53lAOd7nfsxOXE9YXx+Q1pLxQc8Tdq/VnGWmsjvBLijH7QkTSM/vY7Yr8gUtGYsBXnEYEunIQaop7DGFoxAymGRGTs+Hliw9JML0C8UeIJq1fK6IjusH2DnyfKyLQCKmJ3KgJ2ELrnPWwG1Ql1OO9QLvlsCMHPO4ETrbEJ+SwLkOqzw/rIx1GMTC14rKudeB4YQYeq+HEQ3omHY18HdXb5agD7RvR7guk4Eet5rbcXYvSMTi81I6DbFal5j3tPEItuxPacfx+e8dOtEZpd4SHkvU/JKvFHR3jPIYXScujGjgP253TJ6wZ1RqfbCVT8eAPOHJiJE6KmA//y8af9gFpaWlpaWlpaWlpaWlpaWlpaWlpaWlr6CfTVAWC57oClcTUMuCTcLAYx0MsNaOnodEHkToghekmmFZbwBJuOR2bfqSSA8JF/h2RkqdZ08Iamu00sY5uHE1mQCmR/KCLpcJVC2Mio4sg+Vh7uyDpdqyIQRtARJojWjH62cSNGIC3dpYSj4YhOMPNyoZYP+bqlJc0mIDawIyNWI4+9tOt07HrGpj5A7lYAxb1TWsnv0wp+ZMeqeHZp1rTrSlLEXCzs3fUrJR13eT1mpLA+nNTTWSwVomeM74w2DrHp6FTwoGiDCCqzq7Vl7KsHDOtElHz74ch2RUVn52zg4VDSxSrvHcQKeklgdJ4zsrtn/LKCXj7ifuLngRehaXbFShhmGbkbJ8AE3CHpOjRLmH07Mya7vyHXD+k6Ps+MpWW6GqMnYJfI97WOlIZEmS7ISvQxYX12v0p7Io43fLtMV/QFxmdAMH3KmGgTgg33kdekd5ya8DkEmXAMFVQVGWM6KGsC6LLn9a75Gi5ODZlgnRkJncebe6y+GzApF8JHdseqz47gQIpidkOiIh44jgFD81hj3BOOPjqHtWXE9HSXOkFMWJ2u7IAihA1Mha4NP99gKDEyZtoj8IBuNygX+vc34psduf9Aefo2t/vzP8JtoLohWhnnK2cIFNAiVJ9RzlpznV2hB12Fexx57H3A1TIxYKtIPAYkTpDsuxVRJLaE/wJNBRMDq4Tu+HmDGEj7wDjeQDqlfEPvr9T9Be9/hfgdbU9p5A7Hw9NRfFbCz9zvfubz5+27fH5tH0EN7p9ACnK9Ev1OnHckjJNAEVpraEhGl29PiB3Uus0UgiCOgbaMKo9aiTC833PwIQzdGnZ+B2P/Ygr37A4O6dmbHYAWVJToR/6Zarqqo+ezynNIQGrLtT3f8tf9RJ+/hfMNuiAlI/dFFTad+/UZjnQ8635F24644+eZjvp6JY7fIJcNoeDnke77WlDdibij2wdCIoMJju8p+8tP9tm0tLS0tLS0tLS0tLS0tLS0tLS0tLS09FPpqwPAzg31hh8d3RUQQs6Er7c7enkGNjL+Wan6xLDvs8dVZ2yxBaqXGQtKuhi1EGKo1oyFrhuMRz+kJDysDUYQw2Z0NNMx6YhPGJb5x/leKB4DLQ1xnx2sTHCdYFRLTdetCKGztzQ2QoxgAmAEkct00Q4ozzM+teFjJLiuG6KRMHwM2NLpJ9pmpLXjohQhI5i1gnm+vyVwiwfs0xlbXZhQFZKYzf5XmS/i0xkq+XVISdBYGtg9j/dxvhHpZgXcI6NqpUAFsRNpV3Qc8Firdp0OS8CDro73A+mdog1VyYhiEVQcOw6sVpTpEIyepBeyD7iU7GseN0RaQmMfeClQdtyOjPQlo3adgiuUqKg7PtdVaiPsnucYkdfOJnA9I02yMvdDSYe0+3QsCkjxmUruKPuM1VbEaoKpMXLN+5l7TU+IhsqAMaObCaJOsF0qcb/lfhNNd7JJwv7ZN02/I5cXZAw8BtiMyKYirSExsksZychl618iy+uFOV6QW3fGeo8Zif4A7tEPXBoSX3pVhzvep9NdWu7dWsAcjyMjvWkwnOEdqrHVgs7u4PCBjcER4OPEJ8d2F2J8j8kLx+h4vCEuxFMFC+T6nE583XJvoEQMLIJu2Q2rwHY5aBZo+4iMY8Z/O7zd8mSfpit8GPL0s4T5BIrm8tQXVEo6nV9vSHG0NaRtmDslgpBp8Y/IOQ0bCeDHD5jUvC+8o9vP8HEg1tFSif4DJZ7yGuxPcEpGMM9oaUrJgYKY9/fzNzkMEIFcXohxZuoB2RFet52wWz6atg05H0MT6WambfnsEgEp6ZjVhmxB9JjDNT+f/cGa3cZ+5F7VGZ29f4RxJ/qJ1Iyhp7Q5HEMmDIRPp/WF6G+4KXLPoZUaHdme01FcNri/ARekFZDnCddzqERky0EDO5DtCRmDIJD9BT8+I7Lhds578ED3l4xvHx3sQLePyNOfZNz30tLS0tLS0tLS0tLSP6Dk9muk3/Cnf5wDv0tLS0tLS0tLS0t8hQAYuRByQ7YJR3TMSGLNHmDR2fl4gF4RV5BLOjpdvvTXCoQfIHu6GyPBR4QTIUg/EBzYCc4EvjYSZPVB1JYsQsjXq5WIjkglyH7TBGiz6xdPUOPTguqRQLVMvCaKERMsbUCHI89NJfByyajko0OraV6W7CaOyNcOO/M8zEFjxlhnvG+YzNjYGfcckeenJb+ntISZWtKBayMhoh9Qruk8LPt0Z8p0VW9E9HT4ar6+BIRmv2kE08UquDlWNHtHKYQ5WgfqV0Q3tOy5XhMsBgFRURHcbsRQvM94WVO6GV47RUqC8sjv8zwAQgv0g7BAto/Q70TboG15PP1Ejju6XRKmd6Bqxj4Px82gKXFmZC+jI6JELQh7ruMYee7DoD0RciLMrlzrSHlCrKdbVgK3QVgndANt+Dld62XH/UzntXewCePDEuqVijGQMSN+fUC3CRedEulYJ0pCbTfoZ7rVEUQreh4JlbdL9qNKQu9ar3B0uFwTGPeO1IqrZHyvp9tWEHo/Msq41hnPm8BU0ARrJa2hKgUxwyUIt3cwmU7q3HPiQhRFzHIYIIIx91AtlYJjERxu2O2Gz/j0wQH6jFE472/E/Q7XJ6LcaJdv4HZH6pVpjU5mKBuG437gHui1UI6gxY72M3t8b+nCFhHi+Zn6/ffsrwW7NvzpKd3rPR3RLgqa94L5gQ5Hnz4kuA7P58j4nHvCbvPZ0tKVam+gT5kEEHMwox9o3TO6enRoe/bxjs/okYMYlDnQIdOFvn0Au6WTXATZ9lzrcRKjwH4FvQJBCDgFrS/EOPDaMhJ7xAT6A23P2PFKaLq1GSexb3h/ReqGu6NtJ84fkHJB3d8j37E+e70bxJn3lFb8/oq0p5wX6YGqZhB5fSKOT7jf0cvPCD8IfWbcDqQ6xKCYo6UR9orIcz7njgN5viLWmQHcyOxzJyyj1jV7i10C3Z7QkrHjcf6QvejhiFfonyGuGV++tLS0tPS3V+Swl7z9Nft/8f/Avv33Of/n/4f8M5H/oe9cWlpaWlpamor5edr+5f+L/c/+n9z+t/8X7N/73/wDH9XS0tLS0tIfhx6fow+tf4su/buorw4Ay3lHymXGC2f/rsQErHJAfHwHchlSfE7nWXa5ilREHKeANAoktInspg0sHZlU8E9E3RCzBDDEjFv2+euRQBFF3BOGPiJ+M0M33YJmaJnwV3xGA3uCYNkTXmiB0fGAcMm38I6HodTszXSHPY9XjTQ6PzpbR3bPhkS+lg20Zoct4UjZiNET5ErL95R57mdH9uuExpLRrbMzGJUE5AYw3kHw4/wyxjrPRTCibMTs9PV53EQhfKATOmnc0FpBKiIGLR2E4IgnhMcyvDYdhpLHFoaUHR8dq/re/yr0dJbiyNmJVgkVZLsiw9KFrY/XSgepuKP7NaG82dxcFUcIPyjasHPk+peWMwZSvrgZxy17fNsG4yDsRC7XdJ56IfonlOzH1ZKgXsMJngFL13Z0ou74mL3G3tM1OV2y4Q/Ia1gEBUPazugz5psGQzEM9UDTHozFdI9LmQ5qEvIS2RGMU0s6Wbm/UeuOmWHjyE7W6eYWC0Q8IX6+Oz4GYQ/gPBfj4SK3AoCVMd3z8Q6L8Rsx0hkvYkQUxAYRMs8XIpxxDmLLLl8bt3kNIs3WWjl6zyji8oTffkC2DakXOA6Iju47SfMlbxAfhPwA40pooP3gMpTy/Ew57ugZSB283T7xL88bIcq///SMauFFL/Ta6Lc752VPN7Zs+dK3V2g1O7qLZMQ0Dv49REkI7dnhm88Xn87/Af6W0d4ieX3dsVEg7iAb4gN0OvoJxghKOeHygt9fKSWgCYyS94YFMRxGn9cq+7+jPgBwx+pA6xUJ0LNnQLcUpCgxFPot9+C89+TpW2Kks110I+6vOWSiF/zo2cOulxwWqWV28vYJtiVd/XWfsNaRUpDIvaRFCWnQfjad69kNbCbQfV6/G3J5Ro4O/Q5VkS2bo2XbEXuUuf/OsMm457BGqRnpj2WfcmmEVSwUrQ3dC3H/DfiZAHxpaWlp6W8luX1H+av/jMv/+//G+Sf/Cd/96f+e/+pff4f92Z/xT//pP0VVuV6v6x/fS0tLS0tL/wMyM263G7/61a+I+I/4n/3v/q/oduUSsT5Dl5aWlpaW/gb9Nz9HY/1bdOnfWX11ANjbNJtxh/IE9kbgKBcoH8A/gRc0CoIhXGkinPFG+HVG43bwkjHHFTwE1ccb2Iw0jXw95D1aN4GopZOxCOEbEgmiXYSilzTXztDcrKIVoOdrRLpnkS07eGecLj4jhIV0+9o9gYZmN24CsD7dk09IfEbFgZc8vrIllCv1HcqJJ3RO112BOCnbBnbj3S0qno7HrRJCQrsK4ITsiL8CJc93RgVTNmbx6wy7nnHQQcJzIqdvpOJmDLuhViko6gOdUDa2Z4Ix4Wv2+IrWefgzShrB2VARtN/wkm5qRsc8KNsVQxA/E0D7SSmaEMw0E6A1Y5Cl1ATsZQPrhHdctwTlBNSW8csY7lC2msMBuiWsFcGjQ3yJ5UYGqlciFNVI13EoISflsk+3eSPaBe83YtuIbtORKXkZNGOk0T0joO0TKi+YdbQIo9/y73TDx4lKBy3TaFqyG1Y2fLyl+xnSSUxk1PLR0aoZ96slBxUQLO4JDVWI1tCzp/s5lBg3OPODUpsiorjNmHS3BLqQx932dKF6djaLSg4j1Evu937OtSr5bSLpsI/sYZWace3hB3pWolbG22fQCz5OYpxEHPS4MDgzvdor4h20EsdB2S8ztr1llLUfJPV+hmNQxODzd5TnbyjXJ8rn31IOpXz6zOfLt/zV99/zb457nlMY/+zzDxTgf/kCL7c798sV8aC3bxh+T1f800ue83HApRF+m5HY+xyKUIYfiFZ8TKAuDQ6DS8xBjIwfj/5GMND6Aes/ULQguhPtmTg+Qbxh8UTdnpD9hbA3ZOTwS4zvkfIMdsyY7gnlref+mn9qpaDnHWlXLBy2HbEx996jA/xC+ElEJcxw70h9yuGaywUYSN1R3TIa2z1hdakzdaHPXvWewy+1EeMgRKjlKaOXccTueWySfdA57OHT7d6AC3101AZlf4JzIPVLRLoGqBZEBhbyCDT4Mkjj/h7rj5QZSX1Fo2fIgAqyf5hJDLcf+Ym0tLS09PVIf/tLtv/8/87xH/0f0d/8gn/9n/yf+dfbn/LdX38H+g28vfHP//k/p5TCn/7pn/LNN9+sf3gvLS0tLS1NRcS7U+nTp0/8+te/5rvvvnv/+z/7i7f5GarrM3RpaWlpaenf0t/0Obr+Lbr076q+OgCcbl4luCOxYTSEE/g1yP8kHXTth4x9lo/ZtRqAPBFxJ0tnr0BHNeGBiEzgCFK2BBvEdNbeCb1CN4R0Aqcj1hO1DEAy3jafQRNMhWXsaxha99kZrLP6tySWkZLQRBTGQH1COKkZtWxBSEZLMxTe7nCRdGa+PPHeQUx2IFMEeoeqM77ZEwBNl3BEzDhnBzknrCL7OL0nhPaR0bGR7kyRjBWG6Y4uCURldh/j6e7L82/ZBWuGbxc4D0rb0TGobUP8TNPx5XlC1ImsfALl0hLISH3/O3kAnRiotISw3qd72Qgvs5t5IFWhXFHA7MDHmUBpeLq/tU7ztgMFl0ckd0Z3CxkpWxDoA21bHsc483tqg+OkbBfCHHA0HKuKDEPrNtdaELlCzf7XsIFsDTfFbOCWHb7RA7MjI6H9zPO1k4iOO7n/9CnXX0ZC8nHDtWXMd6m4v1Lq07ujFPse5ANmN+Jm1GsOPXTvhN0pUvFIJ65juN9p99mlHSUHBMRzi5RCHA5YOoPPMQcRynSDFsSNGCOdvcGXWPVxJpgzn+nqLSPJbRClpAN4K+l2P+/z2hgRQiBwvOI4oUrvlfP+PSM092NtRLyi2rIrVki3qPiMVd7yXrj9FpWgtSvS4Hi7cy+CROEvfvsJD8def5MRwv+WDPiz11f+4w+Vb+Qjt88/wIcX/PXAn+bQRjew2+yTFsrhyMsO9j1Rvkk3Nxsuv0bLNxOYj3z+OBCfkfIniCY01ghMr7g5GtmXHCSQxR27f86+W1r2/7YnGM8zmno6smOmE+j+xdnujiOMbcv72p3wjHwXH6jUjGZ+OLrf3cqVsI77oJb5rCiKbjucP8wI+QphyPWZuH/OIRFkPvMgtqf57MuEBd2fYZzIA9JH9j0rgZ8nUQbU7A0ex4leFNEgRicQRIWoleDMuHzd4LilE1g1UwW05BCE1vkcVxivSFXCsqtaVfMZ3V5+zKfR0tLS0lej2+3GYS/wp/8n/qu/vOH6v8Zvjr9+99/6WjPjl7/8JX/6p3/Kz372s3+Ao11aWlpaWvrD0e124zgOIoJf/epXuPv7j39b6zN0aWlpaWnpv6n1Obr0tevrA8DxSvANIt9A3FBJIEJ8mF+wofozKiWdqkBIoNpwFAunIglty46kF3PGM0/LaNh05BZggpg+wQTMKODs1sVjuorzvZSWBjwzpBY0bH5NzS7MqnMCxRN6RgAVouPa8GEZeWoQpWITfuIDnl/AXhHuqH6TwKvs6Wi2SEe0WIInO9OtSk2nr7Z0uhIk3mrT+Xfmr4mErcMINabNjpAyvz4dyxKRgK4UhAljvANlrm86ExlO9U7RF2TfKarzfcBcmJm/wGS5EWCWkFMioenDrWsHMN47adkvwOxkbQO8IdomxJqgiZpACCVinqNNaK1AveZ19uzsLbUQprgoYmfuBz/TsR2aDmapRBkzzruny9WNWp+I/jmheACloJPDoeDdoRSCkaBcCjEcC5/x5Y9ocCfKt4Qb2i54nGgt+DmQEEqtYCdKBbmDN4ru0xm+gThhFwhBdYdnoNSM/sZBLpgPyuxizvjdJyIckxx2yAvSiCrpnMSBiss5I6rBoyN+IGzEkXHf2ioyv16K4/S8V8o178H+9jvuzDqdmcxhhIKURsjAj4OQwvDPRPnIed4YCLFd4fUN9obc3tLFuVVkv4CdlNKyp7ZU7PaZ8nxBcTYu1OOklwu/fP2BO/9W5O9/B/x9yCL4558G/6ne+bBL7odd6bIx7DfwpvDxG0QaVZ14md3dsU8Q/QZSKOWJIDDeKO2bjGoWgfIBiZ5DFf07Iip1e861ihPxDb3+Y+J4w+3ItbKT2HfivKFiyHYBqcTxW4KKbs8Z52znvPcFGEQ3TPIJNSJo/goMat0AyYGG4jhM8BwQI2G0kG72MXJ7TNM/fiLbBzjfEJ7z+aEVfVxnBjo7kXFH2oZ4QN2QIRQEOz8hzx+h35DLC3b/lK8vgeuOmeYauSHtBeiEZ/8vVGJ4RlUz0PaEn69Ie0bKE+5nvm695LDCuKPtA+5vhOewii4AvLS0tPQ36na78ed//uccx/E/+nvcnb/4i79ARNb09dLS0tLSV6v1Gbq0tLS0tPT7a32OLi090pC/Jp2VuP8bgs94tpgilOnqNYiTEMEkprtMszdycklEErYhGAP3jOSlXkCUaC1jTMs+XZbbF8cpOuNKY3btMnszFVHSgdqmo03rdArPCGm36U7LvlvBpwu4EaUkOJX6JcZVs+M1PBF11DtRFC8X6uVPEgLSmEXH0C7TKSpIGJQLkN0xRSvqPd1wUnINHnG+Wue5Rcbz1pLnNTzPFwGfkcKqEDIjnkuC2YhMGz7esN7BoNSdLTr1cqGUQqnlfe0nPkY0nXrvkbG1EZIdv4FkZ7EZCXBLOnnD0kVsIJK9xlCIQfZ5ujGsM8YgJCOzI9KdStF0GNsdSroK85pEQkQdUG4Zf+wZOQsll6c2aBsyOkUK6obWC6UoRQa1COW6TddkIOaodLQWVAt1u2T/qUe6OQdgb9TtiVKY8eOCyJZ7NiwjeaeDU4om5JWMx0UatG9ygGC6oiNOwo501FpCap0u8BF3RJ9ykIAbgTAiO19dL4Q/eqljGq8H9DvDXzP2WWSODeRAgAPmynDHFLwo7jdcNL/GHdiJmNcmZLphdbrbDe+fZif1jNfuN9yD4Z17v3E7ldvbG/31dSZOZ7+w1rw2Ug3Z5r15f5tx65qx3NcL4getPLMXxezkX/zw3X8b/v6PUI/gP/v+O/71fVBeD66y0Yag/RleLhTdgIrzmkkEAZwK43OuJz2vl93z78JBlRF3gkJ4UFSQ9gHw3HtlIyLvOQmn1IrooxvX8/5zy3htF9h3ePpHyNO32Ut8+RnUfaYI9HSH78/EOLDR8VI5vWO6YUMwH5gWzAfj/hlzwWxkwsG251xMEWRv+WsEKS8Zae0ntBfEO1JbJuX7yKEYsj/48VwWkTwv75Ttmq3SWojzwO+fZ8KA5nWsF2K8EjKIUvN8imdEdX/DXdOoTCfqM7SneZ9fCetgd6LfiHoh3JDtitYPs19cMp1he4bz9W+9J5aWlpa+Jv0+/+B+6DF9/Vd/9Ve/1/cvLS0tLS39MWt9hi4tLS0tLf3+Wp+jS0uprw8Aj/t03E7nY8R0vR4w3iaEUyQEuAMGfkPsLWNVBWBCQQ6cM51tPoCAPsBLxoPyBYLKdNLlj4xUxWNChyDIeGdsgAciZXbm6nQWk9A0LH92f3fJEbNHs5DxqpcPUJ8SbtFxD4QXMEduNwqWPEwl41U1ey3ToVnABlVBERRHVVDRhJ1BQmKRd/drAjvJY7MxY5HzHAh7j5cVLoT47PtMoB02I19Lozo0hVaU1nZq25LJhTKDhHGR7PsMQxwEReLxFtlJqpH+vtCSyE4C0R0ps7tT83iRPuF7JXquq2hDpBChhGdkcbSWXa01ITtlzyhjS/AsIngHs0pQMw7aDSEooagwj8kp206pG3XbE25frqgEWjdEjIIn2Cp1Att0e7uWCVgNCUfbjrjhFIo2VBuq+R7IOYF5OjK1POfrTXfzo+c54jb3+6MHtSCtQhHs9RUbdzxuIC17cAlCnjB7xamYH0BnWGQksKST3c0YAaHPODk55WEQhtmR5yVl/lAUSdYpORhgKOaeXbJ+TpAtEy5PMIjO+yCw446Zc553DhsMhzDBfcBlhz6QfkeeFDkCvT4h5WWuZ0eedhSnSMHfbijKFoP9PNDbnX/+Ztx/xCPHI/gXbzf++e3G9vbKJYx926mx4bdXit8zBtpmBPP1Ka9feQa/z5j1Z7R8mPe/UKXM1PkgrKPSEpTPuHEXZ1iH8yBsEOUZN8fHSOd7yRjwGCdxv83k54wuxzpy/QDXb5HnnyGXK9I2KJeEs1iC/DHo4+C4vdKpHP3OKBVzI8xQ3ROmljbnN3bEPF3e4mhtlAjUBbVBFUFjzKur+T1FUdkSBEcgEohuCYwJtExnfvmA9yPnc/ocLJn9wKIkPO43qDthkefdj4x+JxB36D3vEzPcRkZA133ONIxMM5jDPOGeX+t/+6GApaWlpa9FY4zf+x/cD7k7v/rVr/jlL39J7/0nPLqlpaWlpaU/XK3P0KWlpaWlpd9f63N0aemLvj4ALE8QzyRyvU77pE1QeVDqhRKku1SuCWPLC4U9QaR7QstQiBdKeZrOVhKg4ETMuNVHz23Ew6YJ5QXCUUoCX1HUg0LGJ79fEpmA+AHsRCd8FsJ6QtfpKhYKUsp8/xknPYGo25HxvhTi9TOUPDe1jqiBRQJQLM/JBZndwxRBomZnr+oETo9jLO+xye7jC8CWmn9fZk+xz37RGWKbfbwZY8wDKktQi1L3Sr1eM2K4VKiXBI3y8P1CWAKuTHntidfrc8IgyDU7j4x2jZgATb7EbIcjoqi0jJP1QTqIt/n6kQ7t0rJvNPbslhXNDttHbGwXQhpoI/o9IZxuFClIEbTtIDYrSgOtlbo9oaUitSTocwc2AkUQyvaC7k/o5RmPjLPu3hkq2XEbIOHpGk+vZLp0oxOPoQJVRD/O9U0gb2TM8vs+9NzvIRPQTrCazCwYfiCXC6Et44mLZncy6YrmGKAXBg1zx0vByOMNcv9QdkCw0bHoRFjGFkdC10dPrJAgsNYdEUUkgVz+bw5VuCVsHz3BnQuhO25OtzcO4O7O3Q/G/Q2jE3tD90BaR1ugW0PLM1w0I9JrS+DMTpFnwm+EH5R9p+E039hL4ze3Gzf7aSDfD+78F6+vlPvJ5fVGHUYVRSKo5Wd5jd9+yGeJPJOx6deE8/S8zhPaS7mipSAeeESumTLXV7Pr1t8IyftAEGS7YhFzMERzHW1uh3HDz1d8ZNdzQuKa0fOAjAPZruka750YHbOTIcpwod9ujPuBRaGPA6sb3QZjDEw2fBz5TFIhxpHDFdoIbdk5judAh+7ofsktHpY9yVi62L1ndH7NZ4xuFS0tu7NryWeOTgf8OJDt23wGjBkDPwyipPO/bNB2qDsSB+9d7v2e4HcmMMSMgM571ZB2pVw+Zrf78TmHTpb+aBT/A5Htf5+v8cestYY/Xl/LGkYE33333U82Lf36+sqf//mfc57nV7OGf5daa7j0t9XaMz9eaw1/vL6WNVyfoX/YWmu49Pto7Zsfr7WGP15fyxquz9E/bK01/PvX1weA/Q2apFOsRLpAKcAG8nNAEN5AHNWKltmhS6GIItKIcgUc4ZxYUiZMMrKHNXOVQ0tGrPoAMUQSQGYE8gPkeTp0Rab78gFwNSGFJBxB6ozatenAzR7UtMgmJAk3/DwId3ycCZfLBcSJMLwq5bJTpCTs1R1KTAii6Qj2nt3EWlGp7ynOQvYA445IJfzItGHv080LSa48XcBa8tc644dpX3pxp4M3RBLGolQp6YQlCDeCgtkx+4EnQA7PYw6fTtwEqUUkD7PsSClEnX2+kg5saQr9zGv+6F/2jrRLQrduCaVHz2huG8QYmI/sMXag1hmHy4S3YzoM+3vkdLpzbxNuTYerTqCePlciPAFYjHQyk67q905oVVyEIYXjuNGD/HHcodbpDPY8lvCE/zGoWidMTDe3RIBMcGX3GYE9BwNqAn+p2eosciHM8HjLuHC54KUQUkECN8dQ4ClB4p7u2TiNMUa6s21g/gozCjgjoee10+yITkfl5Yub05wqdfo9QVUz+lyCIjI7YUt2HpeCF8G00umcYdzt5JTCaXfOc2CmxCjI5xsajtLQUISe95cAegV9y3smAvHP6bT3gjjUMFrv7MP5y+9/4P/7E8Hfh34A/sX9oOyNy7ZRt4JiiN9y6OL6MZ83RSHOGdF+Zrc3JZ3kPqOe7URbpYTP+4oJ+zvIN0j9Od2/w/wk/ERko7b9ywBAVcJveZ08M+7j4Qi3O9hBjDfCbunk9QN5OIelgjvhHVfFx41oO95zOMWGM/qdKBtuHdM9h2faM142HMM9cE/Xt2vNOPCtzfvrpOzZsx0eSJ39v+F5XNOFXBBUMqFAPFCpyGW6u/0NrY3o+Tyhbe/3DTbSzTw7pNGZRqDM+yuHD8I60d+QUvL5oprPkW1H2nUOtyz9sein6G352rtf1hr+eH0NaxgR/PVf/zW/+tWvftLXfX195Re/+MVPMn39h76Gf9f6Gvbh0k+rtWd+vNYa/nh9DWu4PkP/8PU17MOln15r3/x4rTX88foa1nB9jv7h62vYh39o+voA8NNlwh9PV52/kkCyoKLJW7RmjKoPYEM143NFFYkxnYwZxYsbznRX0iZAkHS7PnpiIfs6LXt/37eoZKSv6owWjUiXmk7Q8XANy6Oo9wGAC1LaBGTTTRuSjt8YYCe449YJ3Xi4KOXtRpGNIiRcNkf6QPyea6I1o11VEhRKJKgUmYbVeAe7MiGSe7z3674fX9XpXPbsCLYBEdmXXGZP8fCElXi6WEsBaShKEaWqUKJQJuBULRl9rI6WhuAUadl/WnLNpTTwgWvBRHEXomTHqgTp+JvrGDFw71CVaCWdkuYZ/Vxb9hv3kdHQfYDdZl+zEOOesGivhBYcxXDCjJBK0HDrFKkQ0+07ZkeuJbwWqfOazeuteY0lHHfDRLM/N8DPOxGBozk8EJ4O1phAvbRMUwYaA2XMhmjJr0O+OLGj49EZo0NI8q+kr2j5SMQbWiRjnu3TBLwzcpn73H9nQsl+x97e6GZ0HK/fMhi4RsK1CXBzmGF2V4unQbxV9HIlRKe7khnNPWFfvYAWhjudgUljiNIJToTDnB7BcCHuE9i9vqEvz+jlafZVC8ITci9wvML9+xkX/Cd5XbSCb3g44sImyo6z6xu/Hsafj47/HTyCfrDOb/vgyp12f0XJezH8BwiB8wQZuda6YdzR4jOV4JIwUiHiRil7OmIjKLrjKgwf2amM5fna9+A5TBGqWAjWPa9NzAh55qDAuOPm6eYfJwyb1cNBeMyBiNzLHunm9XFkhPV004dD9MEjEvy9X/u8YeMk9IKH4HZgMbBx5PUdJ3Ye9ONIx79HnmvdMnigVNhyWAHveXwCGpbnYDekdwRBi6IUFCWKgjhiPZ/f85jE8hjzvXxG4W9o3fMZVy9Iu6BaM3mgn9kNfP+c0flSkP3p72CHLC0tLf1xKiJwd37961/zq1/9Cvef/lP09fWVX//612vid2lpaWnp3ymtz9ClpaWlpaXfX+tzdGnpv1/1H/oA/t712w4fX6C8AR8ndKoQN0IyGhoqwuxMVcVDUC24REagohOEVSDQCYSh52vNzlKJPV+jbgmCS0l32aM/tzYYjqtnB2+U35lgsHwtpgN4xrhStvyzGPn+D7es3/EeCTDDCJuwzkqaQMeBfPiIpscTaVfETmgJLKNPd586Kp6x1FrzWM0Iah5zKekuLVvCl3Eicv3iyrV7unCBBI+SLmMZxBAsEvg1AkVRvRLRMTynEcJA0mVXNKDtyMjIWilzHUqbbtyCqGb0cEg6oAUCZfiZ0FsUFUlI75rOPxGktgm3+gSBka5LPB2/KBTJvxuD8BlXXRICU3ciBjLSsUzdMClUiQTnbgxPA6fI/E9rcNyQ7ZLfoxnjjLTpdE7IPvA0IJrlkEDvUC4QIwcLSksHd5wJ2FSmsxHQ7L4t+oS55/f753ThKukclR00AWORSHd3LQQFsXRdEgWjoW06N+N1uq8B2YCBXJ7xz7/FukGF087sct2uGS8e+X7vHdVhuEvuBdL5GnQslNqeiPE697bNTlpL568HvX/GfBB+Yr1Ba7nl7HN2LV++gS3yGu0Xwm4gswd7ayA7xD3XzO7AFfFOu1xRu7PtF9r9Tgzj//PDjXvc+Lv8OP/lp+95OirPlyufBLxUkGvuBzYYymM+R+UjqjksIG1H7JWiT/McRrrT/YQYtO0D4/5XRJT8/v1/mt3m5kQxrAeqG26f0FGR7VsYB8RniArtCbHpMo6aEPd3pttiV0IVxkGMz0SZADTIeyTGTHy3HBqwe75Gy/slekE4iT4Q6ej2DT7uyN6I8NmdrXjZEtiWObwR+fwMn/3YdoBsmJ3I5YLcPqOXZzjTGU0paH30THu6l9uH3B/ihF9yqQnizFh59idiZE+w7E8EjtocjOkGcUMuV3h0Zo87+vTx73CXLC0tLf1x6X6/84tf/ILjOP5O/1H8l3/5l1yvV7799tu/s/dYWlpaWlr6+9T6DF1aWlpaWvr9tT5Hl5b++/X1AeDzgM9OfPOMu1K4AHfQJxL+GUoksJrO28KEvaKzQvXRueookW5OnY7XKZHsZk14O3tXy54O3yYQI52HRdP1aUFp85t19gPDdOdldK/MWOFww8MSaDr5PsPQbcP6SZyB6E4RwWy6nLc3RPZsmy0N8QlHquaa1ABGwt3Q2RUb2b0pJWHqdCSHBIk2eY8+fhxjRj4rEukczDhVIVRxDYygjIHsO6KO1oo56bhGCI/sAFaZXZxCKeme1rJNV3HgUuexCDGyNzahccPHiXjHe+DygMK55rgRbYK2ChDpVsQIe0XGU0JTrXnpOKezcUt41O+zb1SJfoNzoNenHCQQYYyM0I4ohGZ3cmbGZtcrbZ+AdQ4CjCPBcPgEUoIddxhzA4iT0bw9p5ekTPB/g/1DgjCzXC8xAsWlIMwY5ocDF8lrJpLOyPoB3ChlAz9ADaETsqXjOm3ieAThrxmd7ABvwDPIHS3PyPOG+Q/YvUE9kKKElUwwtjeK7qCzUzrLbMHBJT3KMS3xw44JfTOOuEefbmdwCmZjOlEbdvsMXRE2St2RqySv1xc83oDs88Z3OA7Yr/Nmuqbb2kYyye2CWmez79kC/tWn7/itK7e/h0EuC/hn5+A/fi48EdzMGMVwKZhW1A2RO8SVUioyHbyl/IzQ54S0+wXGHYlHVPzsMtcP6TgvCuygL+DfwziJMnurQxl+UMsFqdd0RLuj2pBGduH6LV9XWw6x9LkP6k6cr6DP788GANEgzPOZ4Nn3XVrN4Ybo+byqgHWk1tzyfiKixDjnM0kQPO8LYlaOa8bfq+Z51UZsz7h1xjiQXgnrlKdvCTkyhKHtGfMuhjRF5CkHU463vO9KRslHv83EhtyHEYF4xvNHP4lwNAytW8Zwh2WkuyrCCf2n6RNZWlpa+mOWu/Ov/tW/4vvvv//Jepb+pvf7i7/4CwC++eabFf+0tLS0tPRHq/UZurS0tLS09PtrfY4uLf3N+voioL99gq3C0We88Q30AshkcCMdjm7ZcxqKuWCe8cqiEDKyMnM6GsOEpCaXjAWVCrqlqzQ9oPkjsmMyfgd2JlXLmNIgOynFHrDM8++15OtMxzEEImW64uZ5ScFdkFoSULY9YbEIjqI8I1ESANftS8y0C2gg0kAFvWyUcEIaCdISBkutCWSlpDNwxqaK1hllTTpj2WbU8ATe5Nok20w3cS2KiqIT8GnZcw088AgcYQzDevYjO4GLJzQsSpR0JotKxr2KUEt9X+nqQWvPaG1I3fEoGQURgygtfz1sRipnFLOPQXDFSyVKw73j/U6MIKRh5jgb4zhxL7iNTM8tOx4V64bdbziC6YzjdmFYcAYMc/w8ISbMmueMlBkDvRM+6GF5nkLCunaBWnNpmX3UNtIFrAmH34cVyiX3xuwanqW30D4g5TId6D2vK0Ac+euQOUygM9b8xG3MmO8z3bNUiAORp7lnZ6Rzfc4I7+0ZyoXoyrjdOe8d44nBjuF8gcmR+zhkOmxzf0U/cQpHVG4m9OF0g/N+cB43fIZaR9+Rl59DD8oFdGuoZHQ2MVDZEb2g9QKccH3Ke6af8Pl7EKFuV8r5iQ3hSnDpF/7l96/816dzG+OnfuL896oH/Offfc/tfnA9jeIBFlkNzpFrS6FoQ+oTbf8WpVPmniAeQD1ge5rXsVP1ModCBtjrdGC/JNCNPrutNzRm9HHk72nX3DdN4eVbePomf7x8hO0CdU+n8XCkbflM6G85fFKfERuoCmKGYMhlR6XQdKOg1MsV8RwSyAj7bZ6DgJ2UfUMlEwpUKi6FqDuhGy5KaCXqNX8/nH57w6Nh540oF/w8KaWhUtI17ECfveFlJ8aB1Evefz7e0wbYn/J56Y7sH8A7cdwARS4fyKgDkP2anxESiAby6FNeWlpa+orl7vzLf/kv+au/+qu/l39wPzTG4Je//CXff//939t7Li0tLS0t/ZRan6FLS0tLS0u/v9bn6NLS/zh9fQ7g6/NMalbgRvCc/8d/ZnoS5TpdlzOed0bsRgw0NtyFIi0dpVhGKLsTY7y7PCPSSRskyE1H2VzqogkYtKYjVLeENWUjzmPCPnkvCpbpdGW6WOXhMo6MRcZrQpza0h0XOqOYe/LngNCGx4b6J5RvQYJg5FuIIFIz1nXCGJGkyqEVsT4B44wZLi0jVCP7aDO6OnhE91Ky8zbXYOAYoZXROyZQbFBqS8NqqYQfuOdrRWRv6wjLpGwV7PT3a5Dne4IKEpFdxtIoJR3YKoEOYzQy1rjAadPdO6NsHcl4YpS4d6JqAkwKMsF2dp1ewO54dER2wjsyHNeaAP88IDyjwfsNsUFpW/YA93SRS3EcEJf8vqIUcUQUHwdFhSgNcUfE6Vro5hAbYa+EFuToUCVdmOPRAZ2d1fQzlz5AMCgTiGvJflaXjEcmsMiBhnTyzr5mEtSbnKg8ISgiAV4mXA+aCKYFfGAUSim4dVSvjOk4FYSiio3vZyf1TkhBzDAzRE+UbYZ8HwiGas0qbj7jsmEe+O0zpppDC2+vIILvO9xAtM8e40rUS/b8vnVU35CnS7rjcdw/p+M1Wt5DcUtYbIE/PUG80vzKdn2hjR/4/gj+1duNt/EPA/Isgj/7/rf8L372M57YeLPPjLoRaqgK+C2vi0qeo6drXOqGv1/DkftBdbrED6Qp0d/AX2Z0ewP9Bj5/gg8bSMHMUB+5To+fVeE8c5CkbIgKYZ0IScj89pfIZc+IeSuUciX8QOlZ9bw9of2A8oQi4Hfq5SMxDpBCKYr0V5CKbFfyno45EOKoaMJlEWKcjFFmYv6RblxVVGpCX5kDFJLPHxEIHIk7+AWpjbCesetVidGRvebajZEzNW0j4piOecnbom5Iye4QxoG0S3Z++wntkg7/4zUhNgsALy0tfZ2KCL777jv+8i//ktvt9g9yDO7Of/lf/pf86Z/+6Zq+XlpaWlr6o9H6DF1aWlpaWvr9tT5Hl5b+dvrqALBYEFs6zvAr+CC2SvCWnZUiCTsmqIsHcJVtwrWEDB5C3AeuG5UAVczSfVmkPdDMdKIFlCDMJqzLOFORSPBMxg1r2zM6ekaRIpFQ2UcCwLAJWx8/RfbZaiAYgRNxZmevkz2zANJwvyOMCZFmnGpp+UKl5ntEAT+RtlFmNy3ygIWzk9T6dK+O7OIM/51469uMqB5QLjiBheIRDEC6kYnSW8ZsowQ1ubP37F8OTeciQeiW5yAl4Y1mj6wYiGZsciH7d8MD3Rq1FqRLnmLd0PsNrxvWjS6GSnmH59727MQ1QUsD78lWCTI6d0ckAbLWjfCOSiHGgXp2R9sjVlohbK5FpDtQxkmRhiAJfS0y9jsGRGCUjKudseOmhUAJzhww6D1d4TadlxHp7nbPDOEJrs3u1LZPQFrQIqgZXgruY5psBRtOOrSZncBX8BNlwn8GUKC0hHcqiAhqI6Oj2xVxJ0RRdWqQvtz6DW6ODofrt7jfIW5YL0gtKBd63CnlBYv0aRdzqgeUC8Mj3ciSwwPYHW+zw3Y4tCCGwzng0qCPXNPW4LLl7MEjzMAL3N7g6QMcn4FO3a5s2sBBqGznZ0Qu/Nff3/ivx3hPW/+HUo/gn333G/7Tn33gKRr3Goz956hbOnftcYTplvbpsFUELy2fZaPPZ8WJq6L6gtVLRimr5n3cKrx8IG3DAloIm88EHXN9CqEC40RciFlBLgC1EO1jxsF7xrNL3VBp1Bjo/g0agdLR1ggc2kdEMwY6IojtGzg/g0beZ+fI+1acJpLxzm5EH5m+YJ9zAEWvRH/FNSi+f+kYh+z1LoLohmj9EildW7roRydGRt/7sBlKsOfz0iOfL1pyX8WZcForjHs+i+t0rYsj6Iy0noMp8ffnGF9aWlr6Q9F5nvzVX/0Vf/3Xf53DMv+AMjN++ctf8h/+h/8hHz9+XP/wXlpaWlr6g9b6DF1aWlpaWvr9tT5Hl5b+9vrqAHAcJ7CnQ1SdUEXefpOxyNsTDngELe6gL4jODt8wRBSVhrslvNhnj6QIFoBWlNnTamdGpj7oSQiPHuHwNqFFgl+KIuOcsa6zA1MmcBUFLemIc95jS0X3dAq7YVISttjseZ19q44TsoN9nwDkEORnOh3O6RiU0YlIeKYEGhXxBHfvoIggAqRIQuJHxHN4OpK1JRSRkrHEkRDd3TLOeQJEoVBaRYsStRATIIYKEZUYNyiSb6sFhqW7zw20Ev1M950kMEYr7kY5O0jguqHhSLtQfKAB+vQN4R2Xg60oQWCnpeO5ZJz1wPN6Scn42wAi8n10S7BalBhK8S9DAT67SSNA3TDLfmKhJmjShoWj3dERoEanoNVQ3eE40T2QkdDXtKWz2LLnVNwhLvMylIz7Hh2Ykb2e0F8e7nIp083ewA+0tnlVmVHdhsbAKdn7WzIe+T1OXCTheH1C7JxOYxIGU3C/ATUHHkgHfaVCKF0OytPPJ+A+iJvB9ecEPZ3wcsHcYHyC7ZtMpy4zGt0H3DpyvVD4hEWljA3Msf6W+/D5G+Tyklt7AxCklOyoHr8F/ZjXpTzDlp2zWo1WXtAxKNapRdgQzBt/9vl7Pv89xj3/TbKA//y7z/ynLy98uH3Pm37LkHR+uxxo0VxbmOA2QaZenvC34D2unYK6462g4TifoO+zx1fzmvsb4h3VnbDfAtd8xiF4HCgbD9e8RMYf42SPc92zd/jpBfpBwdlqQ0Ozqxin1G+yEj3/k33gbcfNsKJw/YY4P+f1qkrVQtsuGX0vs0t9nkuCYplx/YpKwUfPe6KfeZ9Afp+f+UzTlkC7O9IK0i7pLI6B28jObbtndH9riI0v0zraECmE3dB2IWKkq30cSBRivCEeRN0Q3Qj76j5Cl5aWvnIdx8EvfvEL3t7e/qEP5V3uzi9+8Qv+g//gP+Dbb7/9hz6cpaWlpaWl/06tz9ClpaWlpaXfX+tzdGnp99NX9/9ey0WgG9IEEUfiM1ye8X5D/ZigNhjyTPGOciLyDCiOUGLMiOVPUF9gdtaGe0JIKnBmBHD1dO/phKlhiE0QPDt0oUPsGaXslrBME7omSO4ARClAxkynS5mEz9YJ0hWYEDZfP8h44BEduzm8vqIve1YGW0BNB16UCmYJd7YdiY4ycM841jzGSLDrZULnM9mgTvCss0NWJMG2NjyMEdmziw1UlVoDKZKxyiMjrWfxMmBI2fAwIgINz6pa1XR4ygNEG5DR1F6BUEIGTGgu1nO5h02HoqI42hS2DbdOrdcE8Q//tReiVegGW0YJY0aYMCpwKi4QtaIixBC8XFAt6H12idYNnY5JcXDP2FyPR4/xnajPOCclGuJ3AAqGlgv0k2Ed954ORU93tWiuqZYte3ltRgNrwc4j3Z5S0hUamRBcPI8nOX/FzgOJQLXhMfeIM13FuW7aNnx2seY+m/tTa4LncDT2Gced/suiH2CcILCVC47T7aCUb/Fnw93g/hmXOuNyM6paouc6SksHNoE8fcDdEXmiiAAdP95gB44O/ZhA0ZE76NPTjKse5BdNSokl+LZPtPKBLRz9/Ea77LRu/P9ud359nHz+u3rA/AhZBP/s0yf+Vy87H8K4oxzhWAThMzq+FHxkFHIwED/TFd474NAu2PGZ6LeMEG9/Qvjn2UduCcapRHSIgdRnwgMxB7kj9ZId0woSJePejbwfxPn/s/dvPbZkWXYe+M25ltne293PLTIyIjMiMlm3rCRFUmQ3m80WRT20gBZIqNEChZZeBD33D9Av0E/gQ0P9VKCaBBr9wCJRAgQSEMQmW0WJpSbIKhVFsqoyI5J5ieu5+mVvM1tzzn6Ya/uJZFVe49wi3D4gLn7ct7lts2VruZ+xxhhScy5UD8KhDILWQhVlqPW611vGgoQQy5wbLWpG3JdooBu8CB6BjjskDCkbxKd00euQqQZLpgJERO8bL5TIBAFf9jCUTEwYukMe7eN3l5tqvCcohCMxI7UitRAmRGsEFbHAveW0WQa47tQ+yfhn6fOc54YXasUlMuJ/mdDNyUscMSsrKysvjojggw8+4NGjR6/UL9xHPh3Bdfv2bVT1ZZ/SysrKysoKsK6hKysrKysrn4V1HV1Z+WzcOAFYlwlkTFGLEyQGghFGBwaIGWRDuNNkofiWIpGORSk0WhcsM7q2xJzRo2lnTVFZU4T1xdPNGwUpwDASc0uXaJQURiwddtlNW3oMNSkwCET0yN0gxelSr923GVacAkm4pYO4LYQt6baU7NZlFLhwpJ70O655DaYJxiGFvlJ6tHJ2CF+HFnSxMeXX7N7NvOp0AkczRIYuJKYQ7jTMwVzS5ReO6oBiSPSIawagx0m3q9TH818gmrG8Yl0orLinc1XcuzNV0CW7c1NZFGSZEQT3OUVFF1i8v4WKSEViSRej1BRbuwjtbikwdud1iEJxihS8ODJucVuAQtSSDtxlodUCw46oA+qBeCMo+JIe7KDgRTAZiKU7opcFDldwegv2B+rWMW/pdg7JOOTl6LxNsVXtgLr1vuaKLzNaNmDZs+x9zJSyzd7TYZdj2UvfcOBo+oXzPeuAhFFKupTDrPfEdkeujHktCj2Su0cyR/ZA09J9SR3BHWfOPmrZAGQvMAu+GYlJiSIETil3wC6ZVRlYEJwBB6ksIkQ8IebsupVa8/ncbNP5XPdw2MLYgO1T17JujhnFMO+hGNvWGP1AXa4YT065mmY+uFz4XszPa2p5JizAP7uYuDM/5J0T5e7uHvtSmedLms8w3MVQzPfU4RSZI5970ikvpSLjiCyOuWcE8vYOLPfpNzPjlG3GCKqckVneByg7GEdkmcHOc4qZegS7KCwNtGWH7nKgjjuk1u6aDbT2BIQyQsyoB2xrbvQoI0H0TQlBqWfZCzyO3bXe8vmXQoThRG4Gccsuaw/KZqDUku7icgsPx6YnaB1wGjreRqwR0YXqMmf8ilRCh5xbD1c5VoYTNAJvc85b5vle5svcbNI3G7g5GoLWXbqAteTrHUJbdgOvrKysfMG5uLjgwYMHfPzxxy/7VH4sZsa3v/1tTk5OeOONN7h3794aw7WysrKy8lJZ19CVlZWVlZWfn3UdXVn57Nw4AVg2Z2mTjOzTzE7ehXIUlLTy1E24AdXeQ9vNsGlLRXWk2Yxq9E7fDUoKltK7e/EFofTDGTRJEaQWmBfcCyqRorFbfq1k7HL0cwPtXcQto3qPjtzwFFY0HbxZrjtA6zGxWfQKNBxFhjuITtC2MO6Alp2eFFKIHbPPUmu6lscBtyk7PqF3Eks/dsn3F+065loi0pVaKhaw+AxakVA0gkKgdezibw8mDkcIXLeIL5nd3zK+1pshUpAWmC5dSE7HbtDFbyHvZbuCzQYhCBnyfmrt97Y7DTUjYFOJF5SglIFjzCxL9vr2u0mgGbsthbI9wUXQUtIlXDb9HgV1k/G6HgtGS8FNKibgkpHT0qKfr1MoCDN2cpZCa0BbGhEFv3oAuxNgAkmXsQAR2fkqknpnid6RTMFiQYfu9lUhvNEiMsIZJ6TCuEOXKft8uyytpRI9Ojusb1po2Y2cBdbd6WvanY+7p32yvcOY7oYPGh4Flz7ufO5RxYLqLVy99ypfYW4UTpEQRKU7WbM7eSjC4reQEbi6QjaCNk+H/WYHc8B2h24EYp/9uMdnclnAjKHAEI2xVcQm7u/3PJwaV2bMxz7vVxwHHs4Tj2a4e2XUUvj6rRNK2dJsyWhxazSZGaRkX28Ze/f3gjDQuMy4dsuxh55e31ehx3t7w6RQyU0M+ew3YjxBdZdzjRw3CwQxXRJthnFHqaf5KPmSm0fGbY4/KblZpkFsRkCJZco68zr0MTTmfKXdiR5HcbnmxhvLOSsi8lmTBlXQYUTLgEifK5cD9PlcZJObGFR7jL5kssFYgJKxzm3u32PpMfmJDFtwJyLSAT3NmYwQgWx2+DwhgyJ1zG5hCaLcQnRIh/PKysrKFwwzo7XGo0ePePz4MYfDgWVZXvZp/VREBJeXl7z33nt8/PHHjOPIW2+9xTiO6y/gKysrKyvPnXUNXVlZWVlZ+flZ19GVlWfPjROAU13qzkFb0slq3UXKHjjJz9EjU81x3VB8Sj01GsgGF0sjrWwQO0AFYeyxo54dlKJE6YJI1BQqxk26FGUEn3ENyuzIeOyaHTkqfRG9mxLyY6mIL9cfp4CadrQQgdYyylgKaoLbTCwLNNLhXLbpqIsA8z75eD9Oit5FlSiesaxSwBuh5boX9xhPHV3ATZdxuoQjwMJoBnGYkEEAowwDKuT3C08nLo4OQ4qO2gVlAh9TIhWMkJr1y9fCXVw7UGOeES2EjphkXG0seyIOyDDQvGVUcgQFR6ogNqNSQRWXQKULwxRUhuwLlSDbUIXwpQvfFeudw3nNlAgj6rb32PZbXnuEbGu0TWX2IKYUr8WDeuw91nQLewQ+bpBwXBzdKBj4cJvYX8HO8djDFLApoBtkuaKVIXt+7ZDXZJ5QEQqS1yK92nm/bEai4pFdxYGA7XF2OYTCM1Vc093tWsH3wNhjvZd02Hrr/a5bWruCuunCbaNJ5L1yA9uDbPv+A0HcUWacFIq5vCB2ZzAUDBDf9+sKzgGRIaN+T0fsPOAkG4elNShjCtnjiJbeQ6xzPscysKkjW5zzaeGDaeLBIWO2jzHqnzcCeDinwPjxfs87uxPubUY2Z7eZ2WIODUuHqo5EXBE2pMs7vEd4Z385ZQsyI8tVipdlINoB8Zbx7tbAp971DegO6vE4lm7g8RSmq35rI2OlRQi7JGKb6QZjd4d7jmVsIkpN92wde8J+gxjJSTPj2+P4nttESD4nsTQYszddFVTr0wj8thCtIeMmY+GlEq1hGhQp0ASGMTdC5A4Woh1gOEWWILzle1WFNhMiiAVRsluaEqBjdiCXgZjT/StacRG87bOP3F696JmVlZWVn4eInIkfPXrEgwcPePTo0cs9oc9IRHBxkYUPDx484M033+S1115jt9utv3yvrKysrDxT1jV0ZWVlZWXl52ddR1dWni83TwB276LdAXSbf1Z46qwVhTgHOQVRzCcGMUS2KYZoOoWfJg9Ld4R2p6SQxwkjpTjN3l/NHkusi3NjBc8+TiCjRdNCmeKq9y5NTdevlO689B7B7AdEB6CmWEshYk6hIxzI7lAXy47QoWZ/pmavJ75A3aXzuGjviM3LIWSccArR9FjmFIPC6aLvAtQ8H58JHWlhtCj44ap3vjpFjCoj8mkRF1JYiXRHq2RMdogi6tAWtGSsdIRQJXcASR3ABfWGb0/w5QrxA6WMKXZ7EKrEksKflxEQmni6+WyibLJXuYim+7o1tCiiFaF14TRFNYZjtG528EozZOjCZwCb2j8997jkdGN7kRRB/QJEUK24NqSbDjUcxTAJQgNc857t7ubVD8E2hYgr/GqB3SkWT+BiIk4VbCAu97AtKdItF/h4hxAHu4LhDtqjaTWciDlFMZ/R0oU3ghi3WFj2uUZuJch07Yz3FlWKFJodT7xmvHQoaBAetPDcIOAL+BWUW/0WO/jUB1RLUVlvw/aAhyEX59hpoZQzKqVvPxj79T5H9oHIKSJC4QnWFPGGnmzSGc4JYpfASOEU84UWV/z+kwsOzfE4SopfHL63v+Kj6cDdiwveuHWbzW7H7I6rEFWAAfcAMSin6ZKmx5wL1GGLYCAzUDEZKTQkKpCbPDLKfu4vk97TnXOZSIEhS67DDdExN07IiDfDVSju+NL6RpcUr3MTh6JuGT89X30qSpx0zduBXioMLfB2RdQKy1W6hHen0PZ4NHRIN7OUbW6ioA+5UlOYjiAwShmBEXEjWmSMdTuQk07pGyD6nGoFIuPbUzTPWH23BYaKHGPpteaX2z7nLLN/8zatrKysfK6ICA6HA2bG9773Pfb7fSayfMH48MMPefDgAbdv3+aNN95Yf/leWVlZWfnMrGvoysrKysrKz8+6jq6svBhungCc9s50qGGk0jHkJ+pZFzQrGcOrwIiFEEwoYyY0h1Akumb8VGgS8Tx4TcdkXl5NQSY+1S/rrUf0HjN9BfOgqGY0bgCkKIdovuxaYI78mnqSYg8pQIh2kcYCmpFC2kyogQ9wOCe2AnqScdSeXbBCUDa3UDdUAveGlprXKS3O3bWbTtjwCdHsnj128ToFc2e2Jbt9BWRzQpn3FB3y/OouRWDJdlhX0pmr/YZUgXnOaFe4dhRL7zKu45hO7TJCa5QIpGwJm3AFNUM2W8yyMxT3a4EZqWALYbDMc0bflk06/cKRFpRqiAQiA4pkl6kvGR0rGVcrEims1uOV64HUotkf7J4xuAwZYyuVqOlUldkJWsZ/N0OpKVgTKJabEkRAHAtBx9PsQj1LkViWLXq2yzjk/SUxKiETYadgI5CXhz2ghtsFlFPEF4SKaUF8QskOU7GFohtoDevdyNnBOqPDCd4a4YF1h3g6v8Ei+vNjmHnfbKD9vZ9143zvRdV0sysjInsCJariIURphJ9iWtOpjSNR0DCcAU4y/lr2l7AZKaeKSkU5Q3s0sYpSRDhcXfH9R4/wz0nE82dhducjdz559JBvEJye3qbZBc0hdAN+Ts5lWygjdn4f3dXU4MctddjlJoFjJPuyx3BUc94JDGlbogiUnG8wR0qk83cYUBzVDcox7l4In6DscLOMhBYnpCF12597Jayh44hszzKOufeXC0LUgVgmoKQY7IHUIVurB6XUAQ0IGbPrWCSfD+sirY6ZVl03+QzOE9EUGbYEU24McbKzV2vOsdYQrTmeh4xEj5D+IC24bvBosDSKSi4N0wGPjF8PGXC++GNuZWXli0tE8OTJE959913sBmxoWZaF+/fv8/DhQ37hF36BO3fuoMfdjysrKysrKz8D6xq6rqErKysrKz8/6zq6rqMrL46bJwB3Ay1SUyDEu3t2QqRHlGqPX+7Roh6BS0H8kqGcpRNOglJSAJMuB6KaerDNGalcFJrhJcVOpHbBd6AX4WLhXWfpMbqkcCyQAiXWM1mPQrODDinWKbgbUSpuDjERmjqzm6WbTTYEC1IdlZM8J1tSJOkdw05DxHDS8SlkfzFCF0OEoy6LbsFmaJ4GYFFaGVjmfbrh2oyOW2o0pAQ6bLswnqKg6JAu36PQHRlTe3TwYTMiDdjk62rNiFfdpOtZIIahu3ILISlEuR8QDcImYrPL6OsKxQWiYT5nz2kzwhvWAkoXgorSFk2Bt6QLWCW6w7tHZsuc91kUWuR/VdLl6pEuWAFCWNxZZiOsC6fLJR4Gw0nG0I4nGWFLoGj2jXq7jsmOAC8FUWHobsOyvZ2bDZrTTvI+enuU46BsACM8YBzBGk5uQojZQS577/OA2IxQwWdMDmCXlHKHIinEh00Z+9w7UqNUSgx4WO9edgJ76pS0JxC3MsabjJyGvoGgl8EKipZTWsywN2QrxOZL1+NfF+9jeELZM+gJLnOKhAFxANlmBHaWyWYE8U4qV/srvv/o4RfS8fvj8Ah+/9EjTi6uePvsjN32gElhqafpgO8bJ/TkLvgl6IFoQgwbpJxBm9AqeAmkzbD0eOWS7n4RzWvtjrj3zxWkjjBNSAWd9lBHpAwUhDDL+RPpafEDtJZdvHXo4+Hp/CUR+XwvU+8gXnJzRBlQLbg1pI6UOlKCfF5V83ytR+zbTJDdwUUgWkOHMaOoe3d6zslLOttrjnNfrlAdMsp8ucqY6zLkPCR9fhbFy0jQsGmPh+C+4KosRo9pP3t5g2BlZWXlM/L48WPefffdL+Qu6x+Hu/Pee++x3W756le/yu3bt9dfvldWVlZWfibWNXRdQ1dWVlZWfn7WdXRdR1deHDdQAN5DbDMWOXqEscpTYTiiR4COvTvS0y0rQwqU0ft20yuHYIgMIEZzp4qkgNr7VxFByga3OYVPW/I4XTTWHjeaDl/pfbhkROqxd5cuwERkDC9OWEadpibnEDMhFdwJa4Rfplh7mODinGITcvdOdu4WgZKRp0i9jptGAo8FZUiXXZV0tmpGAuMGtBTHVXGpzO2AsSUWexr9WwANRCpHg694umWFjA5WLXm8UvA2pcB+/GLd9CRtIVQhFKGlWBNLXjnNeyEKtIUybCGEWoZ059k+z8X7axEsHMGRsssOXzLmFRkhDgSKt0BI4RkztFS8LXktzFDNrlsZTwiboU39nA1CCYHW5nQat6VfZwgXxJd0f8tAzI0oA+5LCnEccN8gCB5dYrXApTskVaDNSMnQ8HRin/bhmSG4og3qLkU7JPuex5Fo53BlcLIj2kyUAr3jGNnh3vBCdhFrQd2o8nTxaZHu6MzyBcqAm6FaELmFhQIZMd3V2T6eLceYVhxFRJDTE+LyMU4jBiXUWVhQPwUcxQmm1JEtkNOzNKNfXsKQguEoQW2BLwvfffjgxom/RzyCi2XiXz2cuLtR3ryljNtTvCiNdGbLOICfQruAHi0fWvG4oMQZWk6hOswtn/ExXfUx7/sGlJ5EEALtgJUKdUhn/jBQakEpqcuLEF2gT5d4y+cUpYx9g4Acx50QywzDmM8TDmQHtxSltD7PEDmVWlAKiEufn/t8Mc1EuU3YOWxug82E51iLItB697UJDAWspat43BEo2CGjnt2JeoLtL9HtGVFHfD7QlgVEMZQ2PYG6w2bDHHx6iMb6Q9rKysrnDzPj/Pyc73znOzfuF+4j7s7V1RXf+ta3uHPnDl/+8pc5OzujlPKTX7yysrKycmNZ19B1DV1ZWVlZ+flZ19F1HV158dw4AdijoATY1CN3o5eNj/nf6IKHT10E7kIWjRAlm2+PgtMejzEjeiU7fI2g0mNFu/AYbUmHsGmv41TCJkS3SEuXm2gQ3UGaL86eVvHognFGMKeaGP1rAi0FE+9a7kDMVz3K9yTPqTZgSafapj49dnRBuUJ461HT6TrNb1VTlNGMF043dI+1VsWlMDejSYXlMoXyKmgrVNV0s2qFsHT6UfrHvYJZKnAAGREdsh/UHBElRIgep6ySLuNjR7No9vri3cmqcq1LIkLVTYqOJ2d4W4hakaioOcWX7rAecK1EEaKm8B6Hln2npTu15wXccM/47miAFmx/hYwneJuJecK9EeMJPs8ZXasldU/rfdLNcgyJpKNVCzEferR2Hl+8YL7F24JqwyRjs1VLilQyYO1AAaQMfT+AZLy2G0hgKBFGccOioaI53LSAvo6VqxSylgoY6Eh0wTZiythsARVJcatWCoKY4xbUMhJhvWPVM6JX8n6ELbjnWM/7enR8zxlDLYKGYHTn5+lttB2gFMxA5AQTqLJFfMQIKA1RR5cp47dPTtEINlpQW3jv4UOuloWbKf3+YR5NzuPpE84253zp9IQ7Y7DIFpPS4+dPEGYkNH3npUfBa+Tmlk1/bsyhnma8uzUYBBl6x/nhkmgZte4tKMOISlAFtOQmgLA9UnpE/LFXGPJZEHIDgpS+maZHQNcteMv5yzxduzr0BP3a97k4mKOlIBpo3RDtQNQR7JAd3u5I3eRGA1VoKTATSgyS85wb4Ln5R52YZxhOcBbMZmJQlukSOzi+7GllRxweEsNpvp/Lczwu8LiN6G24uv/ybvrKysrKz8E8z7z77rtcXl72n39XHj9+zJMnTzg9PeXLX/4y9+7dWzuZVlZWVlb+EOsa+odZ19CVlZWVlZ+WdR39w6zr6MqL4MYJwEKB/SWc3erRqKdAS8dtz2VWGXERYJ8x0RHQegduKRhOCQE2IMKy7BnKmAInQZOGhuLkBZbu3M0PtHfH0h13KRaHZ3dvlMieVtVMTO2bPyJIcTTsugs47a/RxVvNPlbdwJBCqy97rPei6nZLCa6jqo+WZ0EpGFIqEk5hSKFXCn78+oj+9YJowWPBAmzuIo8BtaAItQRVlWgLOlTEU1hGvEerHjs4LeNmIT/2FHmESIG8i5FCgbDs4Y0geNoLkK7UdHCLluxddk9Xbx0pw6Yrw5palCvBCBpE9IhbChGKbXZ5HipdmLJ0a6PpYF0mVDeY1nQl24S3BlrwueFtBhz12oXSnhCNZjz30dktkuK7BhJQdAR3VDRdstSM4Q7LruRwrDlxjP22RkURrQSW/cee3cFVBEplcCW0ZC8yELX0bmVFtkO+t6LQCqGB7w/E5hSGkq72WBDbE2yA3JywsBB+xVDPEA+0BPgBZYf7AdUzHCMVXXJcyBmEYG7dTT9B1Ow/LieECKqX3cWuWJvzOVFFvDBIo8hIVcUx3IPz/YFPri45tOVZTw2fewI4nyYupomTYeCd2xNiQa1KULBYiDYh8hrCjvCrPkgb6ADHvudGRobvtl24TecwdQcsvRMYLBolcj6UMiLzjNQt1lp2AA8bsCWF3WWBOmLThOiY841kBH9IZOJAESwajuSmD2uElDQTR+AWKE5hzEjyeSYYiZjQOnSn/QIuSN1kgMIy4+Szgi3IsCOWPe5zvt1hwNqCtYWG59uf9tnsOzdicNwEDg9RKeB7igoqhsgGGU5f2v1eWVlZ+WlprdFa4+HDhzx48IDD4fCyT+mVIyK4uLjg8vKSjz76iLfffpthGNhsNusv4CsrKys3mHUN/cmsa+jKysrKyo9iXUd/Mus6uvK8kbhhWy5+8av3IGYod1OsqrWrq6TLVEtGM2dQKBEH1AWXIbt4ezdsRuiSwpY7qFPFKWRXbfayClUHNBpSKxrRI4G9d+EuoFvUDB1HZGmUcUCRjEgOR0rJSOeSvbfXImhbehx0wdxYCMwgJGizsbSJvTXaw0eEBeO9E85KZdje6nHMigzp1FUM1QFVpZReImzZMSzexVfNyOhAaeG0EGyfk7aoo6WiIQx16MbqjFFWa12Mya5lCaMU6cLSgFh3yHojQpHar3yQ/9/yPI51wdiMR6Rbt7W8nlIRCcKW7qLO3uQUmCxfixKtpUgvLZ3Nli5fQvL6Ihy7bznm7/sCUjDP+OomJSNp3TFb8BZ5f2KCskHCCHdk2EI7pLipKWuLZNAtbjjxVLCXowi2AW09bnfOLmrrsrgKblegZ3khwvIaqALW+4x7BLn0SPPo0dtFCetxujrg7Rx0mwJ/QJA9qu5d7IsD2R3dF5kSaADUbuQMBh3TtRm5ncEIzDwF7mOUd0xg8lRElIyJVlUKhTDHZCGOjnSyc3ZoDTy4Lcr9ZSYkeDJd8HB+btPCFxYB3jw7RcvAG2e73KhQC+i2u+z1aWQz9LmsIqUg45hjKCyj7HXIDuuaLnwdCmMdqEhu1ghDdMDtAGWHeI45GTYpPNcBt0DahGql1nQnZ2dwf84ssCBjqK3l82zez7GPv2GbEdThfTuIUMqYGyOYwAIdNlAVP1zlHKmez0Ld4M1xaXgo5o02H7ClEd6I6POGeI9tBxkKtEO6nXdnKIU2PaIMI9Kcf/QH334Zt/al8fDhw5d9Cj83EfGZf3h+Fsf4PLNew8/Oi7qG7s5HH32Eu3N5ecmTJ08+0/e8iYgIb7zxBrVW3nzzzes/+yLwqjzL9+7d+0yv/7yxrqHr/L9ew8/GuoZ+fljX0Od/jJu2hsK6jq5rwHoNPyvrOvr5YV1Hn/8xbtI6euMcwLAhyjZFYN2kkEDLf/SUFHaDokoho3VFMrbYcZSCi6EhLFwAp11sK4QUnEDLNh1zRTJGWUCkR+9KBVm6EzhjS9HSHXZDH7yextVwJFJMk3COsc+EQhm7QJcR1VIKLFNGqJqly5PATwSRLWJ7pN5Kga5KiipaUvyt2Z+bwuKxc9iACmXIc/MgpKRbTgu2TEjJrk3VAS2K+IT0aypHYV1Lfyh7DHIEHr3DsyhSpYvtmu9Rs19UyzE+1olIF7Ag6RD1BjIQQ0Z1h5Z8bRlSkJSS3y/bY3s1cReVJfK9WopVUsbuMC4QS17T6BOISEYli6OqNPfsDRYjzDPtuDWkBCJjxt46eZ7W457FQAc0lhSGi2bfqNQURgvQLM3I2rpgHNlX2makDBSRFN/Lrm8AaCwy5feTM4g9yB3C06mLDhBOGUe0LTnuhgHagooi/TilbnoM8Ag2Y/S4bTmBCNwc55JYtrhWVBphQnBgiYz+DUBkQGJBVXApYBf9PqWTPV2mluNNRmrutkCronKCh9HaVXZWo/jgbOWEf/3kER9fXT3n+eCLTQAfXFwCcP/ygkGEt89O8ZrP5K6me9xlA3gKsZEx39mPLfksCoQ7oZVYZnRQbDYmW5ilIAilDsRySURD+sYIHTYZEQ8wLTCMFKmoN1pzcEPrJuObfSHKSLQFb1c5Ts0AIcqIHy4pNecT1ZKRzmi6h4V0IZcN1MCXiQghhhFvDY/cfBHLHg+w1vJxnRdCWiY9tIbWQDggjJTNFrEZrZXYZBdHkbxutd5B6oC06eXc2JWfi2fxw/IX5Qfun5f1Gn52XsQ1NDO++93vcv/+GlP/WYgIPvzwQwA+/vhjxnHkrbfeAvIenJ6efm7H8/osr/ysrGPms7New8/OuoZ+fljX0Od/jJXPF+u4+eys1/Czs66jnx/WdfT5H+MmcfMEYE1hMcLT2aYA1lXCyIjiEFoIRYSQDUGjiFElo5VVtNfnnqT7UQHvPawaiEPKgIVwy1hh0xR+ozshiZ7C7MigKXSUwB2KKhGSMb9H4TeeRjHLtVs1BRQJhcVhMSLzdPP4NkG9A/uHFN2iu5oPiE1ARZBrgRmxbgLUFDClZhevZSxshGBVWErFrKVYUkaKlkyiNqNq9gZLTUEoFdwe8+oCWEZIUzIiNhyNjFHW7vaNsG72q+lGDCGiXYuUlJLuaMuY6igbQgvugeqA29S/d3ejyohYdt0yDL2PNK+lSKXUjGTOqOwBlZrO2DYdj9IvfV7ZFgbLki7sqKhIltZLheq5EQDt4yidhK3NCJHnanO/Jukixh3p/19lyWN5RWsBSgrAbtl1iuLzjKAMesJiSxepb4NbjhWfISRd0lOeZwrrgoii3SUtGDUiNywA1DEnA5vz3rcFVzC2BLkJIOKKiJFQ70K+oDhVDEVRVRYPIjZdpO/33sjxZi2HRFXKuE0nZZsRC0TPMBxXGGLH+08e82AVf58pU2tMwL96+AjI7ST3TnZUabx1+3WMJ1gUkBni5Kkru/X5CiHGE2xaqDJghwNwQHWTc0nJvnCNmdCGuCDTAhbHRwEZGrrdoMucz3GtDG1CdQvRkGXOeU13hChRj5tcIuObVRB33CciHNeBUnJLAdZ7wYcB04I3w9WJttDCsWYYBXcjrh4AG2TcoR6UmjHy5fQ2WjbZMV42CEvOQS03OGgG1KOl5saH3fjC7+PKysrKjyMi+N73vrf+wv2MmeeZeZ75vd/7PSB/4XzttdeotfLWW2+hx+SYlZWVlZXPLesa+nxY19CVlZWVm8G6jj4f1nV05bNy4wRgxxFN52EKqgKUjPo1z+pS5hS60PzLfu1uzXCIK7Axu1q1EOIULVgYTrrm5ggGBqRE79GFoMcPH3Oj+/dCwXAqDpEdvxEV8ezMfOrI7WXAUlP0O0YVW3Zn0lo6R7v+G9bF1/0BrWdofYzW20i01KDHbXeVdrHWus35WqCewFNodlFchbY0zDJ6VUulbCrS0kmtBCrZgSyi/fyyN5mYOWYSp86e4qiIETqAL+mQPvbcSr8+tqTwW7Yp+KrQFXIgI7VdAjFHpBJuKNLdvF0QDU9RvLuQpYvCEAi9w1gkxdrIKGYhkCFFJfEAMxyhWSM8CLN0YLvle4GMdc4XEM2y91bI9+0NQ0Etu55RiAUTyfusWyIOLG1AFDxmdFGKVmgL1q+325K3RirFQeoCMWesNIbHAdcd4X3MtgWKYZdX6O6E0IrFgugWTLEyd+HMqUMKzpCXWWqhBKhs+/kHIbdwM0KGru8q7hMLwtDHTEZ/d9F4WQieEGwQPUmxu1bAkdagZOdzSCB1pLTHbH3klD3vHS6fw9O/8mkCeHC1B6BdfcDXXrtL3RQO7YJlWYAKdYA4ZApA7HL+Mmd59CiF4d0uwwLiMUO9h4en0MtjhBHisict3CaWJ5RlQ5mvMi7cDT29g/mEjA5tZigDtZQ+LpfcQCMjjvVpzxERAsdkAGvo5ixj9+sWi4a1GfesJjdzbDJs2WeH8XxAixLjLYoO1HEAc4qAbN9E7CL7xOuOaPm8ax2IajBdItvbmcwgwHKBlNsv6e6trKys/GjOz89f9il84YmI67/YmOeZP/bH/hillJd8VisrKysrn5V1DX3+rGvoysrKyheXdR19/qzr6MrPyo0TgO18j+xGtDpStkg4JcDKkLHHIhDZy2rhDNoLaKV3ZZaTLgQLyIL4HsoppWhGHTPg+LW45wjFlZCFqGNGTteaTlArMGRcsYehASIbnvaoRv4/PBV8ZUiR1rsw3L+M3m9Jm8EWDDJm+OJxvu71IQVoFMYxo5B7PG/4nL2XQe+rdZAthONC9v36gvUoaFXQOiIUtKT7T7WAaLqqu1swr5n38y4p3uabye7e7syN6MKOKuINGUZi6aK3eLqKC7iXruoIIpn1LhK4ZMSxzjOUmiJrqViks1tCQZ2immJ8CfCanaWhuPbI2+iqkUqK6PT7rhV3ITB82WdCtCssh+zWHTc0c6K17jouRGScrklJxzjdtYxhdsgxVnPMaAQq2YlbRZhDKJ+KTQbB29KvaaGUgtMY9FaKZ3RBXGp3KS95mVURKcjpJjc4hBLe8LCMaF4MIWC5wuQ0BXER1HpUtFQKDuLUIcV9lyAoNJsBJcqOcKeVoMZ4Xf+7RBAcEL2FkNeaUiglo76lD+kg0kWOU8opOxYeXjZme7bP/cqP50EEejXx9nCXobyGMeHW5zk9zY0cF4+zM73mpoBytgUyM4A47eLwBbq5A7FFraHlFLe5D+Mhn01ViJGQPe4NEGRZ0CLpzm9TirACKpWQhhIgA6L5zIecwDRRtFL63OhFsUWwlp3Wtr8kYiCsUcoGqdLrshXZnGR8vc3o6V1oU0bjxyYH5mKw7KEMhBlStzBmWD1aKFpyvrX20u7ZysrKyr9JRPDJJ5/0TTwrL4qHDx8iInz9619ff/FeWVlZ+ZyyrqEvh3UNXVlZWflisK6jL4d1HV35abhxAnCTgn78EL93Gx0vKbJD1BlVCRfcBS9KtD3oFvN0f9Y4gERGjAagnkJBdxKLlN5tmvHSjmMW1FJSBCadpJm3G0/drKZAw0vJZOJjr6WkABg4lN5VrMNTZ2u3Fgcl3ZllTKetAmWT4qM5jAO0S9CzFFdYMkLaPXs3RdOUXCT7bUNSrBPSZefQrOEWGbE6jBTJGNQS1mOqo1+LjI0W0YwABrBDF0qOYmZGL6cY3F83bFJkkcg4aaJHWZO1yceU6gBRzfhly3NGtHeO9uNliS5GpKtXQQhUhnSn9vjuiIyI1n4K4Y3oYisRPW07I5ZDKuZz3lvvcdTtCrcFlwFvPXrZIt3JYTQ/EJpjo8UM1+OmO66LIJ7jqQDUE/A9+MQoI4Qiw9hjsJ1uTSciaG2BcZOvD6iqtO6QVpwxKiaB2QxskGMUhAcyN2TYI5xi8wVRd8AB2tA7pSuuoBxAR1pkB6x2l7xqirllCMIPeOS4izhhEZDYU3wAEUS3uX/Bp+uNC+7GUPLz2c2sFBmo7YA5fHhxxXtPVvfvy+CT/RW3q3P3zpdgvEOTmns4wrIGfBjA9hRxZHuKx5xRyWHIcA+WmTLcRsm48LJ9DTyj4vEDMp7h+0dIK1DBQ2C5wocRrdI70A0pJ/mceDrtJRoxT4jUHMsayDRRMOr2FGhYGYjDAbMFm/eIniIuFGkMu23vWb9C7tyF5ZKwA1IqZbdDAS+9W3zcEMs+x+awSbe8XxGLpzu/TWgdexKDEqw/2K6srLwa7Pd7Hjx4wAcffPCyT+VG8uDBA05PT3njjTde9qmsrKysrPyMrGvoy2VdQ1dWVlY+36zr6MtlXUdXfhI3TgDGFvxsCw5+2WDb8KFSXagSFAUVo5UdRGBCOhrJi1WuO6YlhUAPJDLaWCJF4SoppEIQolhY77Q1NCpihx6tmt2sWINhxOcZrdvufCUjhtEUQiQ7dNP960BLVymNoBKqxEKqpQVoBn4Bhz3cPqWWXTpKQ0Fbqp4q6cZUPhXZrNm9SUuzbXShhoAyIGieXin52rAuGJd0Nav2PtouDpuDS/Z7esYrUwWJAK0EjvuMeBAOMlbEAid7dTXye6GOhEDrUdliuHm+72PktGjv+G0oBQ9FI+9BOo2z85YQQvI6Wne9hshTIZujA1sJN1waYXNeX9UU948isSjhS0bf1oKH5GvKGbRzYHvt4j2ayKUUins39I49KlpgGHOUHQX06PdbvF/X9lQNXxbMG6WOYMd7GEAhlNxwIAvKhobleBRHd2f5dRqUXXZYe9tCVNAJaHAZ+G7Xh0P2JWe3MlgEAINukKJEa1jJXtXu5826a8kHJ2wmIqOmZRh7Kna39zoMOJvWsGnm9x494tBWQe1l8oOrA6+fPGHgVo7NSMG/haUrPGaUbQq8VKhnEBMSLZ/RmtHf6B00jLALZPMacWgIQRlPiN77XMoAxZAQZD6gopSSSfbYkptcbCJUei55xshjgRRF6g7XwDzHYcwL1mYoQhlAd7eQdp6CsWQcvUgQdUu450YYLfjSiAjCG2KW83ruxsk5s+6I5QAuyHALRGjLHi1jdzCvrKysvDzMjP1+z3vvvcc0TS/7dG40H3744XUX08rKysrKq8+6hr46rGvoysrKyuePdR19dVjX0ZUfx40bFdLOe0fsQIyVdvUELbeJMmO7W1QNCsJQKvhCiwlHwWu22BYFPAVMyFjbCFyOCceCaMnP+0KEYO7ZU0sQNMDB0l0XkeKuNCOkIK54ESRaCiElo3dB07VKENFSxAvBqWCOKhnRHGC24Ecxsypatym8GrDZpqDooLIhvDuCJUU793TQiiiG0PB070ogpUf5ypjvT6RXFB+7XTNGOl2yQ55v7/g9OliR0vt2l3T8HvuHuyMXqUSx7BPWkgIMgZhk/OwAMk29O3jK6GwdwJYUHVWRGK77d/McKu5zaqmQ17/3PwcZTR30COg4jpQU6IOgtZYtzX6Vccfh3WhccVqKyoCEZPevPQC5mzHayLXLV727oY+iu5LCrs0pOJWjM7pHNB97k3XELSOgjw5czCgqqETKrnWDm2HmGeftE2X4EhILxSGqYK3lNZWSgnwZ0qF5ctbjfns/8clJv1dCsMfakPHcaB+vehz6lCpUOcHmKcdfd6lLBCH5/STO8MMjyrAlJDBRpDU2BJf7ifevLvhkv/6g8CpwMHh8BfdOnauYCTXQoBo5RsdbCA7lNOcFIIYtsiww7vJZV0HqQLRDOoPdkHFEcGLc5GPulm55D2QoudPEFmS4jXjraQZbmK4AgVrAZ6JUQkdCCw5Em7ApN55I2SLjBmLBxSixIPUUiTn3dAy7jMcX7ZsR0oUfEqBbpO0Jl3T+0+euWLLrWDNGJbzl5pw2pWM+4kddypWVlZXnSkTw+PFj7t+/z6NHj1726ayQ/UsPHjxYd16vrKysvOKsa+irx7qGrqysrHx+WNfRV491HV35cdw4Abjc2cC8wbgAOyNmsPFATOkg81tOKY1BTxGBKjsCo8UB8w0WTqFfuEiXmCPpUoPshuXY37oBPGOGIzApiAhxFAAhHWfDCNFSUJSGmFBrul7T9UYKsN6FxjJ2/bKrzh74sqTgTKSbLQS/Arl9Dy2ah6kKpSDWUjDRAMtYZGIhQnFxwAk0tUg30JZx0Q6hKYC7R/ZxRmS3Md6te0eBMXoMtHK0voakHIoeXcEAXbA8CsZtQUR7jHSPzA6HUhEsPzdUHM/44XlBxKBHZRML1A2xLIjW7PjF8lap5rGiR2vLkHHSR9U30iUYEdntG47hNDfcWjqPg4x7zozY7oJeQAqLHzcFvAYhiApFK+p5/ShHZyF5XdpVqqhHh3Vb8r9plwUxGASaZc+vFsxnagToBpEGpmgpRHzKwSzRXY8OCEU9Hc6lAoZES7G21O7c3FBEcrya4UpuMnBHMJBNDu3DVQrTZcJiQzQhBqhlYKgVpdJ8BtsDW6RswC4Iie489rzEAVtRLvdXfOvBg9yssPLK8OHhwJe3W0YRJrw76/u4HDbZlzuk6z68EZ4O+kJkBPxQgdxgIJAbLIbSn0mFzZaYD7lRoPf+yvaM8Akv6XovuiO0EMMtAMIcjwFDsekcb0G0pc8NBdlsKX6J6A4QMMeKUcqIxqbHm6dzWLUQlvOlDKd9Q5AR2p9RD3AQKflngPiBjHE3Ak2xW0rfqLGysrLyYokIHj58yHe+852+cW/lVeHBgwfcu3ePYRhe9qmsrKysrPwRrGvoq8u6hq6srKy8+qzr6KvLuo6u/Chu3N9el4PgBeo4Ej4SZcgY3OkJPkzo4wOx2eLbhrBnGM4opTJwimPQxY4lGlUrguABmCEIqqV3r5LCSRiUAcxxDVwCYcyIUxEkNAWSLoyl4EnGpJaxn3XvgFWlElBGmvUu4OguS5mPhtF0xtkMI6g54o0ybFNENIPW0HGHmIP07+GC1wFYUmSxRpjC3KAEUragKcBGKOIN0XTuiihh3cks3gXg7o67Fl0lBaQS1/3FHGNZIY+lY4rYbuig6TIu41Ph2FNkls2IzBPiIFp6VHaPbDUnxDOSuH/bjFN+6rD9dCR0EClm9xhoBKwL+KElhfC50ZoTHj0Ou0cyA4Rh4T1eVlGEiAWiUdn23mGBAmUcsaURUlLs0h3FvJ/XgYiKhVM1Y7o9AjHF2kIZNvk+PNJ1LuRgiQV3haIZR6ulC8Dbpz3MbogWhiJgSmjFw6gRtO4kT4elQnG0O81RQbiV4r1dEOMJHgaaEbquSjSniCHDQG2GihD1Dik0CzM7YAaEEhNDjNR54nuPn/Bwv1/F31eQR8vCxeNH3Lqzo5Uti0HoQtFbGYsfis4zZkF4UKk41u+lZkx7gdAK7ZAH9Z4AsEzADotAwtF6Asse1Q3uTiyGqlI0YJkzBV0Fn2fC2vVci7UUYdsVbHfd9TvA4YqoBRelkDH1CrkBQ9PJq1JwDUQHwvpGlQCpuz7vDv2ZlPxewybP3xeo6XIWC4g9srv30u7TysrKzWSeZ77//e/z+PHj9RfuV5DLy0uePHnCa6+91uswVlZWVlZeFdY19NVmXUNXVlZWXm3WdfTVZl1HV34UN04A1i3IFCBbXIVYZmITsJuRoRLTAdtDIOhmy7wEahPDsENlQG3pcc+ava/XWmYQKBYNLUpQU+vEce1uV3GaGaVU1B2RIZ2otqRTrpQuFmpXcR0JvXbEikDd7XBR5PKC8EinrAVuKZaKNVgOsD/AyUhcCWV7CbIltCIeqFa0VGhzj4Pu3b8YbkEToGUUqgxj154lReKA8AVqwUS7ECqUo6XZe2dtBO6erwuQKteOaYlA6pBCYx1SdPHuJg7P+Ofw7P4tKZJHGKgSBNasd4JqOqmb9VjpIMqQ18IDZCYizyvdsY2gO/iO/b5I9tq2lsbcsBQ8AffsgG7WcFK0TUVqgXo7hSB3BhlxBKV9Kua69xwHqBa0FKwtKWa1K5o3KCd4NAYGLAyLGdiw+ARss19ZnKjDtbO3DEO+Txq6Saezu+Om+X2Rp13BR9dx9J0BFJzImFtNl2atG5aYgZpx0jF2J/dICswBtkdkmzXUPiFyhsuSrm82NHNEgiKgRVAXGimepSDulFC2ApdXE989v+BymV/A077y8/Iv58a/Nznz7gmt3gI5w+wCuAU6skwXUHeIGLbMfa+HYDKhwxk2z2ALWkrfiNA7u3FYznOzhy1ou8gNH8sTXColLpFYUB26Oz03foT1jTQ1O7J122PKIzeQcDA42WJ1yPlgHPB5j4xnaCkUzcSCaA1RQ+vQH5Vjb3nvnpYUtfNrzyEU2gEpO0LmHlNfetpAn0NXVlZWXgDHndYffvghV1dXL/t0Vn4M3//+97l37976S/fKysrKK8K6hn5+WNfQlZWVlVePdR39/LCuoyt/FDdOAKbcRXYXQKPohtidZA/u9i5cBd42yHZD6CW+b8jmBL98QpwIpRSGkg7Lol14PbpZS82OXQ08zZOgjoZmN2yW9KbD1DKSV8TygZRCVAWptNaoZbiOTe72WCDQYSTc0yWMkl5VAXGiFCTkWszl5BTsQGFG6+0uuGSksm623W2saKl4OCEFj5ai7rTPrz8mOguAQSiqguJAJZYlRRmfMzRaajpKkXTyljHPW3t09aeiZBXNOFjIPs4gBVUBKSUFGNL5m+9R0nVKAeLaLR2aPaKpUgPRHbhaerBzIfr7I6y7fnvKawiBYWSHMHUkfEnTcjjeMuq6ued1lxHiAHoKtO6+VkQKhUixqIvbgnehNd3SLWrGcofSXEDPwBqBYD5D2UF7Aj5l72ooqgMhQo2Ul0UFVX3aexx5z/I69vtuLTcPSHeIWwOpmDfcljRj6wxRWRxEegx4iXRNSoZi4/suEheQCrX3BespiKME4YaUIY3XYagWBtF0Vy5BqLANpfgJ+2nPu5cXXMxPBbOTWqkq/MWvvcPJMKJaOLSFf/idf92/Ijif5qe1zCsvjIngX7vz1fIGS6nMWjCPjE4OAR0o4ajsEamwOUGtZXS5N8TP0XoHouFMGVUuNYXhUkAaMOA2g8xYa6ic0pgRcaoMPTa9d5YXBQpaC+AZMDDt0XEDywy1pJjb5ypxCBzzPUO9hUpBfEZKRsu79cQEEaIMT/vGS0Zch2UfeX582VMQRqJd9e5fz57jef8yb9PKysoNICI4Pz/n/fff5+Li4vrPH9tjHOedd96hlOwpX5aF73//+7y+fZ0ihWVZXtZp32haa3zwwQd85StfQY+JMSsrKysrL5wftYaO8wOKL3zj3f+a8zhg7/x7yDf+E/7Z93+fL22/tK6hL5F1DV1ZWVl5dfiR62hrlAi+cf9jancCz6XwB6+9nq8DplqPgsLKC2RdR1f+KG6eAKwCfsYxa1kEqEpZKsgj5NZdol0StYtebYGx4pcP8fGMNo5oEUYtSFWKZ19quGe3bxSEwEVwC4SJqjuAdBwzAIZHCnnEgIalABFTul4VUsno/bwiPb7ZMzp1iR4b7XgoEfn5YwRwFCE8YMkHXSP/YlDU0/nrQMxA4F4ynrpKd6h20Y+jM24CGVAptN45GxSKGiGGUlKIlMAjujiSAkw6SZfslvWlR1orWgaUjGDFGqJDipilO1eLZl9oGTLWuSOisCwEQogRthCuPfa4QmQHMuS1AdLxG/TY5Rn3fp7RMF8Q14yrLQOCXU+OhUqUhk1XPUa6Rzkz0jORswt4OEvR17N7uEhFbMno6phxm3G1HB8IDSd0BPYpYssGPfYl61leMx3w9oQiG4jet1yyw1QJREp/HxMiglv2poZoj70WxIVmeZ9dhbCZiEKwEOeXyPYOao7XHFeiID0Ke4w9FgMlSPFeK94s9zeoZMxHyXtq5PjwXj/sCjoUigWDG9EWPrm84geXF7T+Q0FV5Y9/6TX+zOtf4nYVSt1SxjE3F6jwzTfeoESOhf/p++/zux9+yIOrVWh7kQTwry6uWOQRX7l1i6aK6Ibwj1B9Lbut1ZDxTj7bdQCUqgOxXKL1Xs5FiyO7L+WmkioIA+ENlYpXAQaEkv3mxXIMDrucp6PkfKq5+UNKj7yvIyxL3xcT6cgdNunGbQvhDamtx6Bbf7Y0henNCbjk95OKiBHLlPMr6bKPY1e71Hz9sOmR/RW0O+HbAZrmZpOVlZWV50BEYGZ8/PHHfPjhh7SWtQ5TTPzm5W/yDy/+Ied+Dh/84df+p7/0n3Jvc4+/+Npf5OLRBdM0veCzv9lEBO+//z4RwVtvvbXuvl5ZWVl5wfyba6j1NbTYnnc++Ht87Qf/LcP8iH909lUuTv8M4/2ZP/fJ/4f3y2/zl37pP+F/3Z7zpdd+dV1DXwLrGrqysrLy8vmR62gE7zx+xNceP2JjBnQfBvn3iG92kTgQ3rt3j/dv3WJfh1UIfoGs6+jKH4VE3KwSzl986ysoJVt1i2DeelGspnigA3AgSAesnz+G7QCtpcP35AQISh0oRal16BHOhYYj5mlWi3SJUSqDppu3mfc0XgMdAGcoFS2BlAFFoVaGohSV1AK1ImRctOgAzEhEOjo9xYplnpkjiNYwMy6tsZ9nIhqDn3Oy+RK77QmqQqlK0Q3M+3TvquAoTYOYDhkvHYJE6wKsAZVSwI8OUxkoNKSmcBMcnXNOFMl+WSpFA3WDUhEClYIKlDJQiqRzkLiOkUYshRgX0L4+iHRxPB2lPk3Zxds7gaP0z9sC7lhktHNECq7R+2wjwNpMlEq0SHdga72HWIDaHdm9X1eUJZzlcGDuVbrY9Knu3+641e76JV3hQvaNRq3Zo+zgVXFzmk8pll9dIsMWkUDriNJwqdkpfXQ229ydz4GUDYXIj0t3QlvDlRyvywSimOe1Dx0Qn3CHWC6Icpri8NUlbE/yHhZNFzZOcEC8IPuJenqLjVwiccpkDjhFhE0phEqOX9Eetx00C6LfXxFhHEaGCIbmfHD+iPcvLjmYUVW5u93w5778OtvNhteqMkahakHrQKmKDBukKtIMVUFEKWPhapn5l+9/yH//7nssa8fEC0WAb9y5w+3TU6ZSCAwZK1o2iAaipTvPPeOZbSHmhdB000fpc+syg9Y+Xq96J28F+uYXbxnjXvueJO8JAXGV5vbhBKY90TeOSBWCnD/ELVMDSsmI6QjEnTLuUBU2RRilIpoJBPm0QkiPgY/Ic2iHjIpHere454aSUjNdoRMB2D7dyXbFP/idf/Gib8tL5eHDhy/7FFZWXnkigv1+T0RQSmG73f7Mr//www/55JNPmKaJgx/4wfID/vbjv83kEx/bxz/xGILwztk7/Mdf+4/5xvAN7KGtPU0vGBHha1/7Gl/+8pdf9qm8sty7d+9ln8ILZV1DV1Z+Ms96DS3tiluX3+FX3/01is2cNJBylzj7P/HxsOMNbv+hY3wiV9w+e4PLX/xlPt5s+d6jR+sa+oJZ19CfzE1bQ2FdR1dWfhqe+Trqzq1p4lfvf0xx52RZ+GklxUOtfHR6xh+89iV8daO+UNZ19Cdzk9bRG+cALiG4OMKMUNG4wklxlHGD+3EHS0bVyp07RBzwEXjsGaPsgc2GF8G3oEUZS2Xwo4P2WAycwqaHp1NWQEWIqCmaiuCeoqfEQpQd4oZjiAxojxoNzf5fYklBUAuiFazh1jIG2QysYRFYd7/KvKDbM6pqOlRlTBebLDAO17HIhhDNCFHEWtc2KxHRA5gdc0Ate20HwV1RA0pGH5sH2IzqgJohQ0Wx7ADleC2i9+FWQiPfC12AFYgW+WXFEakEXYCxhaBAm6AMiLc0CNaCLVPeT2+gAxHt+CqAjF2GFE2dfL/h+QvcULOr1ww0EHUKI4jjc0Ygq0CRhqFPO5rdAMu6ZrqQJYpgCIWohfAUtjyCpTXcW0Yme4PR8z7gKe6HMYTmfSG7lqXsqGpQShrCWyPqiFl2/obkPffWMgg8cpxFuwI9wXwiYkRjk3HNAX6ypcQFjHfSDTnU3ol6BvsLyqBszw/E9pQPDuc8OOzxCDalcGezARqBcpfC3dMTJObsRY1bKdqXSp0X5nbgO+dX/KD3Qgjwv33rq/zKa/coOINUBjdKKKpKKSXHdBFEBKlPBWVvC6c68uffepuz3Qm/88H7fOv+/Uz9XnnuBPDh1RWvbTZEUZbtLUT26Fj75pAUWwnLyHPrYiqWG2BiBJuzi3qZkFpgOMkNKL1TGjlDypZY9k97dbUgugNGJIRA8Jpzh1gjFgP13GhTtTvkF4Qx46Jjj9lMlRHREdGKiIMLoY4ve4IB0e11bDRoJjC4d9fwBsEIb0837bQZkUKwASnpVl5ZWVn5FPM888EHH/DgwQPMjM1mw+3bT/9y+c6dO9y6detHvn6/3/PJJ5/wySefAFmx8Hce/x3+8dU//pnOIwi+e/Fd/tq/+Gv88u1f5s/u/ix/+d5f5uL84ie/eOWZEBHcv3+fu3fvMgzDyz6dlZWVlVeeZ72Gihu/+u5f5+0P/z4Mb8PJ/wHu/LmesAZv/IjjvB4ncH7B+Du/za07dzk9O+P7X36DJ+fnz/Ddrvw41jV0ZWVl5Wfnma+jEfzqJx/z9vmTn+t8tq3x9cePGFvj/du3ebA7IVZH6gthXUdXPs2NcwD/yltfyY5eFVSUNFwqFg1EsB5HGmEZD8pCyBYMPK6I2WA8gWWBMkGkaFtPzqilUDEEw6MSaHZFFkWhi8slI46jIZ5OtCqCaqB11w2vQinCqJvUkUtG40qAaEHrSFsM90ZrB1oIyzwT84HJjCuH+fEjhInNnS9zUgbGzRYpSq0jahNRKk5GSLsH0RY8BNGGcIwx7g477U7X1qAOGZ9KPBVEpcByRSkDEqAlu13zfCUdeZLiXhl2aBhBpLtZyWNpHkf714YIEXYtdOb3MKgZdRzWcBPMA4+WYpNk9HUAzRqqlSDSMWwHXErGJQsZkS3ZHyylULtLWzQ7hmNJGXmJiWVaMr746H71hmihlpqCbhhRR/ACJUVL84YLLC1SfL66Qk/P8KsL9PQMoaG6oR6vr1sXono0uQ6IVoo6ERNSz2jzQtieCMVFsGVPFIjY5vv3CfoIFDzF+2IpmOkmRWeboQRh50h9HW8PEW4xLI1tOPHwEe+JcBU/eoezAqWL9v2i8DT0IxeZ1qeVqsqff+sNfun1N9AIagRDGahSqRgagY4V2Z110Tefx1TrnfQwgSIUAY/GDz78kL/1rfd4svYyvTBuDwPfeO0uces2Hg6bLficG1O0IOGEbAibMwfcF0IL0RbCPGOb25TzRd3kOIyWYvC0R3a3iP15HksUKbUPKenP3CEdtwgSjs9XyGZADgucnCARqDrRyAHqgqiy3WwZ6kjFkbJBwgiHZjNIrgHuc7qJNfuF0fH6eYzI3vaYLvt8p7lpxCF0QTf3+Pv/+H94iXfmxbPuul5Z+dFM08S7777L5eXlj/waVf2xXTzHuC2AySd+/fGv8z9f/c+f2tr286Gi/Ifv/If8+c2/w/b+juHkMx1u5Wfg9PSUX/qlX2Icx5d9Kq8cN2nXNaxr6MrKj+NZr6HF9nzzW7/GVx9/Fzn5CzD+SlbE/ByECPtf+AUefvkN/uDJ+doP/AJZ19AfzU1bQ2FdR1dWfhzPfB1155uffMxXz5/81I7fH0cT4clmyz9/802mugqSL4p1Hf3R3KR19MY5gFW6tiQFFQHfZ6SxKEsE+Tf72bNaUUwq6tnfK35KbBdACal4jDBNxOlIO3+In2zw4ZRBR6ooHumcDQejuz7DKB7p+tXALOOMxbPXFh1S4GwNH6FI9P7ZkcCRUnBfiLB0vzlkVnXvqJTAp332Ee/uog6lRo+R7oIlgCjeFrw5IYbbArolLI3CKbyQnbLdlUoI0lK8FZEUuG1J9yaa/7SFot3BiSKSgmoRyZ7ZiB6zGrgtKBtCFsQrUngaMxwQTYgCuBIlkCEFTBdwT/e04yk6Sc1rghARhIDFAiGENdrxPZjBuMkMfHfQQOkdy56ub9wRlFCB2CDSMl52sRStVNCjEN6joLOhN2O5LYLmPaY7Uorl7JRA0dNbiHhG1Koi0eOz43DtqCy1ZJOxTYTlJgF8odlMhKYb0gx0xA2Cy4zPZaLoSHFDiubxZAsl+4aD6NHkAyG3iMUItkg4W0Aj+BbB1U/YE+KQsbnXX9Zv2B961oQ/99ZX+ZXXX0dE0XBKUYoIuilopLtZt2PGCtOl5C7ME54Ocsn7FktDGrx9csb/9Re/xv/65Jzf+uBj1jCu58+TZeH3Hz7mmzrC7oR22MMme5tFC2FLTq7mvb9XMmrcexqCVmABKsTcI5dn+idhmTP+WxXxsUe3Z3x7PsOGSMsNHgI6bp86jd0Ja+j2BNc556s69g7rgoqmwbd4niekGFwUln12AXueYywHoPX31PKcdYDxDNpVxvALXdC+7O9hZWVlBZZl+Ym/cAO4+08VIyki2F3jt97/rWdyfh7Of/Pd/4b/bvn7/Ip+k7/c/ipf/YXTvhFv5XlyeXnJt7/9bX75l3953X29srKy8kfwrNdQ9ZlvvPs38i+t7/7nucHzMyARnLz7Ltsf/ID6J/8UH23u8eFHH32mY678dKxr6MrKyspP5tmvo8437j878RegRvDaYc+//cH7fHh2i+/eubu6gV8A6zq6AumVumEIIZ7CrwfIrouboETv8C1UJsSDSgUVqgZjUUrZUDSQUSmDU04zXlRPbhMU2hLM5sxF0WGgDkOKjdZjRVN9zn7JEESCopIu0mnBlxkicMk+1xTxShc3lWie4htkjLQWLBURYtjk66RAdbQWtPdeZn2s96jeLR6BW0YJe1jvop1TbJkmgt57XEs6W1t3xOGAZn+wtYxslRQzVQulDkjpQs4wZExyGfu5jKh0N3AdEFIcRkpGWbvhZngINhsR2c+Z0cYzbgvugs2O9esQIYTRnb6S7tjI/thwx0VpFJBeOq9jVzCdUiq11hQZvYHWFB9VoDTMJ7xN2Y8bCuOmbxwY0R6JTVEYRgijhWEmtIAWMyYVp3SR2cCvUvzFKPUUkd5/7AtaN7331nrMs0EZCBHchGW+ornTYqbZ0q+LEPMEbBAdKXqSMd3FqWXoMdOAnmbMlUv2s5oTXonH5+hc2D1+jBB8++KCq2f0lKkI/8477/Crr3+ZUkYqQlGhloJuNpRa0VqpQ6HUkSpCHUfqdkPZbCibAR3HdMOr5tePI9QBrzvePDnj//iVr/Lvrl0GL4wn88y/uv8JcpjQFsQc+Ox4y97pH87ljtxwsjmBUhCm3ik+55yhI5QBjYaOG8IXZNgSPoHMOYf43P+ypvSxrBnlTEl38bzAuEuxuQgtjBi3uRlCABaU3qc+9s0vZQshaCyILwg9laAqGhlZr2459/khnfjWcjNL3RA+I8shUwA2t3JDyMrKyo1nmia+9a1v/cRfuH9aRAS/5/xX//q/eibH+zT74YL/Rf8Jf3P+Nf6nT/4JNywI6KVxeXnJt771LeZ53Ti0srKy8mme9RqqPvPH/+D/wdtPHiN3/upnFn9/6NjTxBu/89v80v6Kt+/efWbHXfnxrGvoysrKyo/m2a+jzh//+CPefvLsxN9Pc2ea+Mb9T/jFhw/SVLXy3FnX0ZUbJwArwkBJsewowImBP06rrtJ7drtjVSwFX1FEgoGgIoyqVBVqGaiHKaOeAYlLfDln3l8xLxNOUCWdaOmqLZg7TtC8dfdpiiUZ4+tYawT5den0/dScKPTzzGnYw7r7N/A2YSJQBeopJa6ocY4OpffUCqjTaDjWReV0u4JnVCuSsctu+U2bA5Zx0B7pHsW6EJwx2RqOFOeYFN3TlCmZdp0fB2itSCnooCjR+4Etd/yU0p2tC26O+4z5ktHU7umkNaO1/uexXJ9HqGMBzSbcr/BYMDMMMFtIG3MAjpagilJrodDydktGVZdSKNrPmQEiHc4hQdhCbcEgkkmw4dCAuiOkYhYEQovA3PDI+FtwSsyIO6ojtQwMuqFI9v+mv9qokZHQoSl2hTvNjdmMJRpNFGsNbxPhRsSB8AVVoeCU/h6UyqBDjlfNOOl8/zMRC2xG/Ooy77sK9XDJUCvvPn7MZXs2YlZV5S+88w6/8qUvUSBFPqVvEpAU95DcMDAM1FL6tU8xuAwDOgyU/nWYZ5x4KZTNiN46gVu3iWHDX3jzTf6zP/Y13tr+fHFeKz8bT+aJ3/vkA3Se0OkKmybsasKaY9MloUOOe6dHQO/RuiGa9RrwgJggJqSM/dFMZ314g+7eDaKngNvTyU96nEKt3V3cUhuOlsdaFuKw7/HSoJtTiDnnZW+IzSgZyZ/OYcmNLLGgJV38qhk9rUpu9pEeQO5Tj+cfYBiRuILpMjfsrKys3Gh+2t3WPy2qylvvvMXvTL/D9y6/90yO+YcQeP/Wt/lby/+Lv/P7/x0fX6xxei+C4+7rNTp0ZWVlJXnWa2ixPb/67f+ar15MyK2/kjVSzxhpjdN/9k/55fe+zR//hV/g9PT0mX+PlT/MuoaurKys/GGe+Trqzq/e/4SvXpw/F/H3iAC/8PAh/7vvf487h/1z/E4rR9Z19GZz4wRgWFKQ7N2V2R07QrkNGIMI6oaw6VGmkUKgR6qYCCIV1YFCoUhlONkx1B4pDcAOHObFmM0xdbQIVXu/KaTIelQabAEBE5728dqML5a9mpGu2+AolkQ6iiPQ0h2rAiGeJjwRdGioDFQ9RXSTDjZzIiJdtM1wHUG8CzMZ16wYKkO6ZMvYI46jC+MKtHTTeqBSKShFjKLbjPOtQ4p7UhDPrszsEB5IJbtHTYQT2UJMtJaR160RVNwWzB0zSSE3FLNG8+iSqeMI3hrWjEbFPUAHjAF3w6JHV0cDHK3pshYdUYVBlVIqBSjRKKWgHkh47gdQIeqGkBEQSt0gpaWr2TLyOEohvOFtzthnA7M9bp7C0rwgEcAGKSODVoqkO7YIDN3dKigNxxUsKk0KczPaNNGWC5oLfnUF84yUU1QKhU2PUs6Iakqh6CbvjY7dJd7HrzshJyna95hs/AI52bI9O+GjCC57x8NnRYB/9+tf50985StoUVQkxX6COm7QWimiabJWRYeRUitlqMhw7JTOXuSnUdCRDuYiyDBSxhE9PYPTE3S35a17X+I/+vrX+Op290zew8qP53ye+f0PPkCniWLBYk67usRDc14xx3Ug2lXuP7CcPwJge9b/rKVrX4IoNZMH5gZykrPCMhNa8TZnFLpWYsn/p015IkMFLDuFqVC39IcbiS7M1k3OARFQxkx/0IKO25wTNDKJII4bXBa0CFJq/rDbFkodMr0gFC3ptk9h2iHWH5w+TzwLp+NNd0uu1/CHiQg++OCDZ/YLN8DXvvY1/snhn/A3fv9vPLNj/igM4x+e/Lf838//Gu/N732h7s2rynH39cv+xXt9lld+VtYx89lZr+EP88zX0DC++a1f4+2P/r/I7f8z6ObZHPdHMNy/z5u//U/5EwSnJyfP9XutJOsauvJ5Zh03n531Gv4wz34dDb75yceZoPFsjvhjUYK704E//cH73D7sVzfwC2BdR28uN08AlqHvBO1usgjwi0wilk0KltCFSulKcR9UoYBmjHM0BMlIX81e00FHxrphrIWKIBH4MjMdFpqniFWqUkoXnvEUNWUgItJdhhAh+JxRyC2MEJ6er/ce17Sr0ZXfdOpK6WKHoPuFUYwybjKCVYTwwFTSNWsLEk5YRg2LW8arlpLvSbpj+eiEXaa09OpRo7N0tUqADN39TMZhi6B1k0JwGRDSHRxu2eFJJSj5vqSmcOTgi+MtMGsp/uDpgvYpL7k7HpqafGQ/s9mcx3QnJDAc4yj2VrTsqDKi5unEFut6uWW8ax1QHboLWkALUVJ49nnKjmMyGlak5DUehozGFqFZS7E6KuYNi/T14k45OUHLQCkDo+TDpiFUcXTYpI4vioXRPJiXhaUttHmPqeatZsixZkY5OaOS+xa0SrqpEdATQLAg3eCQkdpdAz6OHX9yiV8d0I0ibNhutkQ07h8Oz+7xEuHrd++QDuOCfCoaXEuPcq4FVUGrUOqI1JpOXzQ3GURBImPZJboYt93AuEGHiopm4/S4RU5uE+PIycmO/+jtt/jF01O2evOmtRfNOfAHjx6j0yVDa/hhYjlMLFcTzUmXvgtRR9xadpi3JSePzWm6b5tlfPQ84brNZIR2wKaJsPNeAXzcyCDXe0d8WXKzyJK90DFP2W/tlnHsdZvzijUsUn8OHdJdH31O95Zff+2U93QY9w0ftOl63qQ1hAWRIJYDYrkRQ+qAjLde2j1Y+dmRZ9Av8yyO8XlmvYY/zOFw4P79+8/seCKC7IS/+6//LhbPZmPWT/6m8Ngf82v3f43v+/fzZ9SV58qrEMG1PssrPyvrmPnsrNfwh3nma2g4rz/4/yFn/wHw/NcyAYZPPuHO7/4v/MnDnnv2ybqGvgDWNXTl88o6bj476zX8YZ75Ogq8fnn5QsTfT7M148988D5furpieEbmpJUfzbqO3kxunlJy3CBwLS4AepqRxXLMMAawLp4J10Kw94jo2oXAchSSAQ9E0k0mBKUqYxmoKqiAmTNHYECphVpGREcCp2kKl+GGh7NY/tejYc3T+SsgERRIB5rW7KUNQDQjU5cJ84aHUXanVO1xqamY4nUkZEjRRFJQDQQ88mP1jGNWBW3ZccsCNFBBpCFauT6hnu1cRdHosdlkTKpEujhDe1+xeJ6jlHToekuH79KICEJLunqjpdDr3REcivfzdE9ndLinQ9odp+D060chFgMvSDgVMhq59HtTd6h0kVE0RWB6J3GPUA6URSpLW3DP+Gd63PUxLlxkAXFchDDHliUFLzS7SjWyY1pyg0C+M4E6gDhWKtaW7hhutFBmsxSTbcG8EViKpGXIeOTbd6hpbmRQZSAjw128f78GYYRGbhooPXebRrTzdIxvt2ANoVAOC1tvPNhfYM9w18yvvPYa4ybFf4ogNd2+WgeKpkteA4qmi55akFpTuCsln61a81oi+fFQkFrQMuTjK+kq17GimwHZbpHtjtNbJ/zVX/pj/AfvvE1dF4Lnzvmy8K3HD6jLxLDfd2dvbuJobaFJwecJd3v6z3zIjSgxYe2AS8FsxorgRD73tmB6J/u90Zyz2kTUkahjbsBw69O35RjyiVgOMF/lfOJOC2fxhcWd2Y1FwYoQmpthPATRAjZnH3epOQ+Wmg5+Hfr/l5yDgh6974imA1hKfan3YGVl5eUREXz88cfYM/wl9d69e/zuo9/ltx/89jM75k/LuZ/zNx//TS5vX5IlGivPkzWCa2Vl5SbzPNbQNz/5RxS9B5tv9E38LwZx5/bv/Uv+7Hv/gF/eXnwqFW7lebGuoSsrKzed57KOXpxTjs6LF8zGjP/NBz/gT3z0Ieov5xxuEus6evO4eQKwfOotS0aPpvOTLphZF3eH1H5D8+MshewRunRRFbp1GOLQjyO5C0EUPfYEawVzYjF8mWiRbt1B8vPiGf3skAKsdBGvNWxZaG1KRyR0x+oxjbq73jgKHoKHIxwoTNThbgpxAUEjFKLNeGtobLN/WCp4d9e5EK2B2aecxumKTbE7EHfEFkDRWtNVWwqiBdWCilNKRTS6C7cQ0VKs1Yx8dQscw2PGgxR1l5mgpQAULcUiAg/rmr0RoinuECm+hqVo7gau6ZDWjMRO3da70y+QCCqeccQiiA59EESKv/0j80ZrCx6OhQAVj2OUchDmtJafM2+0aDQOuJYUr6OhZuhSKOYMpLCcyr3gqjRrTNMTZpx5OdAIbL7AWgMOZG+wIKJU8hijHsVsRSWju7XmPbkeh+GojIT3qO1lznt5adjVIc/hxGEYGE5PuX95yYdXz6b398ibt84YSs345si4axVBmuW4iXTGCxm3jWUXtdT+GgA870ktyHaLjpv+Oc/oYI90BiMwFPRkS9mdUE5uIcPIL9++zV9588urE/gFcL7At548QXxicMs5qfdj+zLTlkZrM80i/2kNO0wYFbMJ88iI5yV7vU0qzohbsPiCtT3WDix2yI0j0yGfxzpAqfhy3PixIMOOaEIsU27c8IZ5MFu66tviLPNCs8j4+3EkzPEwfFmgZHw8KFJr35DTcsNB3RI+o1r7nNZdxLH+Bc/Kyk3lo48+4uOPP36mx9yd7Pi9J7/3TI/5s/Dh/kP+y3/xX/LX7/91Lu3ZxVqv/NG8CruvV1ZWVl4Gz2MNvXP++xQ5hfraMz3uT4UoJf4k7/yrD/iVO3dWJ/ALYF1DV1ZWbjLPZR09HCgvOVb3y1eX/OkPP1idwC+AdR29WdxMhcQbMHXBMFtlke7kcsjIUU/XpGQv5LVQLKU7YA09Cm8ElFO6ikxRZShKlaCWSlWl6oEiAhb43Gh2wIsipaTpscwE6UxDKohgBNZmrDkenqdQChky3P/R0kXtSPEzQBGqKlrHHs1sEEfTsyFDRdSBgoghtIyfVkdqIcKJo+BbakYfu6YYXguMAzpWijiiI0Kg/TwE6Q5NkGFIsTYEA6x5d/4uOBVv3nV0J8JxM2KegXTcmTUsoruhs/c3lBR/Ra6jt4lANVCfM2IYQ4dtRm6XgSJCLSncHN2+sCBS8vx1yI5SccwcWyZongufd+cvQYhmJzGBeXRx3glOM7Y6zinTlFHamw11KNRhi9SBkIL5zBywmGO6SyHKuvjEBjlMKFBYUHWqQq2V0u+5hGV0LgsQKCP4xHV0skrG7Zr1cewgG7j1Wo6PmNBpQH2PIty/unr2z5bqtZiuZqiWHgsOSEFKRcchBWDLGHHpAruIoqEUrdTNlnpyStnu0O0WHQZENDcB1Jpitkf2TKsi44BuclxKUX75S6/zf3nnbf7krbMXHl9y0zhfFt6dl7zfZn36XPr82udNT5d/hOHRN9kMW/Ap+6ltIWRHuIOdQ8zggeuOzEAQwoxAcQx0BGswbvI1x+7oYcw5u4xEy2fLo7KgtDZ3F/I+e7Fnw9uC1C2Q86SKwGzplF8u0ChEWzJuvWzBD5kWUUZ03IGvPyitrNxElmXhk08+eebHtTB+4zu/8cyP+7NwsAO/e/hd/tbl30LOcmPhyvNj3X29srJy03heaygAun0+x/1pkIJOjbf/xT/n3/7+3+X127uXdy43hHUNXVlZuYk813X0JSOkCPxn3v8Bbz15vPYCP2fWdfTmcAMF4KM6ue09vD1q1q1/3MVOp9tse8RzZBcskGJV76gVBvAFle4+BdyPD05DIlCCcXOLOhR0KN2ZarRpT2spVhS9RQ1PgZLspnTPiOEWhXmesVBaW3pwMemqjTkFahnyVGVDKTs2my8hZZNipxhRJIXBiNSLrWXMc+mdl0UyvrqMRGgXfTPqOKIhpTtFr4VXpXhBtWa3LU5BoWh28XqjAeZOk4rNC2500Tcy3lnAYyGiO3vDiCzoJY7uunC8GdavWQQslq5o77EQxaw7RAtqjnoeQ4sgKilOa4qOKmPv8h1IUbTlEIjApNCK5LVSTYFRBdWacru1/j0rEYE5hI5pwJ2mFFtPzqi1MFZFVQgxPGA2py1Ga5YOYk9BCzEkFnQc0du3qMMpdbjFIAUdNkgpR+tv33zg0PuPWxii2+sxfBTfWZYcY/MeLNKtONaMDT8Z0UW4PFxxeE47qkQELYoO6TIvOIrm2FaOJcbpMrfICPNIx++43XJvc8prJ7c5PT1DtxtkTLdnJo8LMZTcoNDF/zDvfz4gm1P05ATZ7vjq3Xv8+++8zZ+6dee5vM+Vp1wswbcfP6YgqO3xeJw93j6laOsGpWLtivBL3JzwCqEESz6H7rg56BnHnvZSyvUcJVrQUinU7Og9bnSIBYZN7xhvmWSwGGjNxAWbaW1mmWac3FwTNmO+4DriEdn9vbS+SaTlM1PPkM0WqRvwBSmCyJiidgCh6WJfWVm5cTx+/JjD4fCyT+O58k/P/yn/xe/9F/yPV//jyz6VLzzr7uuVlZWbxPNbQwvc+ivP4bg/41lcXfHaky/xb/3+/5sv39687NP5wrOuoSsrKzeNm/C76N3pwJ/4+CPefvL4ZZ/KF551Hb0Z3EABON1kHPt+ERDvGcC941da/nkcP18gJEWB1n7oOE87XhrYRYoYlBSUXa+7ZYVKiewZLtpjpd2JZrRpIsrQI6NTXFUKgRGqhB0wgSWcZgu+TF0gNaCm2IfjEb37VtAwJKY0BY9bQkYw747gAA0k6LG8XeiWgHnKyOKwdMZ6Q6+7jVNEFneKGFrzvRQBkd7FK6ULtIotRpsOeLMUq3v3r4dk7Kpb9tZGS/E3wENTJJZ+jcz7NQjchXbYEy26+DOnKDQcXcikkF2yk1gDpGWkcvchdkFcUTRFJyyF5R4vjWX7rA4FGbpI6eCWInRIpdnC0houuRPJbYGyoLKjSKFEIN3NvbTGskx4C7x3k8IB/JBx2XKK1g0qwqAlHeOlYhLpLlyegJHO33DonaMagZDnky5gT/e4HXufgRlivo/b/Ty/MER3bErl8f7Z/7BQVdmNG+hCndqCtgW1ln3Lov256JstaoF6nIIyPn24u+NLv/hV3v6ld3j9jS+z2Wb8c7jhc+sdwEKUAVclCkQdsj9aCjEoMm7TEVxShP9Lb7zGn751e3UCP2cu5pn3Hn6MtscMcgrzhNsE5OYJWWZKuYV5SYcw9J7sEY8ZwSjaY+f7xgyzA1j+EGI24bYHW3Lu69FqIgqtb+Yp5HwY83VPerSWLl7NiP+oG7xUolRCDFuW7jI2BEfLgEqfI1tDVXv0vaN1ROsGiSX/7AauoCsrK/DgwYNnfkwR4XF7/Eo5bi2M33j8G/zmxW++Uuf1RWTdfb2ysnJTeB5rqNrE0M7hFfmNT8othsdv880/+Nu8cWv4yS9Y+Uysa+jKyspN4rmso+5Z6/YKIcA37n/C248frU7g58y6jn7xuXl/fa2kg8v36aMV73G5pCtUIicWkWuB4RiviyoyjgAp9KlgaV3EYwuyQ0IoUgmi//6Rl1i6BClERkRrSaFXhKCxHPa4aEYZa0Ejo6ldCt6UNqfo2GzGfOo9uiW/hRRCCw2H2DMoaC1IKSl9Oima2NLfk6SgWMaMw9aBbnmGOiBagdKdmmM6W6N212a68lSUUhTtr2sCFku6dWfDAqxNGIVmU55CLBmV3K+Ii15Lsx5OuBPHa0pGurobmGDmGbs9bpEi6DCiuknhp1SKZq9vFaVqUIWMBq47RAvXkdki/dfCQIYdlBHDWdyZbe5m74Cl9XtYQAzVAQ+nRY/k9oa74N5ABGWHmiE2Xx8/33MB0ikoLBQ8I6nriEpBinZh2hF3wqBFIGXMsVNOu2rd+kaEyCN7dIO6AQvEp3qGSnc3n92CzRnoCUUHuHQ09jQPpvZsu38BXj894ZtvvNn3VigqFSWQuknTvQJFrzulo1aoIyGaEd9OdsPevcvpL36d22+9yWa3y7Fg/XkaR2Ic02kOGStcK6aCS8aD+zEKuCrUAR22/KWvfIVvnp098/e88sNczI3H+w07r+iyUPUWEKh79ouLoLqlmdHceu+1UspZCrdiYIdMKiibLuYGlB3Ckpsd/BjJz7VzOELzeY2KDJvcCOFTOt+xfCbqgIVhbekbUJacR4/ueqQfK7vFoXenW4O6yY0FOOKZ0CB1zDlxZWXlRnF1dcU0Tc/8uLvdjr/34O9xvpw/82N/FuaY+fXHv85vXf1WbpRbeW5cXl7y0Ucfrdd5ZWXlC8vzWkPPrr7Dmx//D8/8uJ8FKbfZPPoy3/zWr2cc9Dq3P1fWNXRlZeUm8NzW0XnmzYuLZ37cz0qN4JuffMxb50/WdfQ5s66jX2xungB8LS6WdPRadNG3q8AivVc3HWHZYWlAgF2gZpRS8HCUoEQ7BjIDQVTNLsos4yUiOzFRzy7UsskoUYVSB6oIg2SM9GKNxY2IhaLZW+vRWLDsnt1fMe8nzCTjlRFakN3BWBdLKkUroj1WWWvGrUaDIcUKsZbuZwVqxj/TpEcutxQ30taLmMGyhzhQ3JBBuiO4x2aL4HWLe9DMsTankOsLrTWaNdyyMzcTtzPG2H1O0c8WJATVgSi1O3QDukNXJPCY0VozihryPI9C61CoOIKhKoi1jE0WQTiKqyUFyDKkgEPrcd5BuNBCaBF4C2iGlDGrdgnQjBd2X9KY6GRfsUOoUgoUzim1MNRKHQZK2eAELumkJiZEU9QVASmbFPmLUsXR7nBeMBZaXqvoxt8ofRxqjqMIWgRRhu6qddCT/BrvfdZd6E7huVJmR0yot87YoVhbmP15OHkk+4q1ZMdv7kTo17FCOOrpsEYraBft8HTOzzPL5RWXV5c0mRk2hTrUdGh7j3mWvnGgZB913hsDLb0buhJSUh/cnCCbLYwZS/4XX7/Hv3VrFYGfN+9fXXE1P2a7yXvefMFrxUpgPiN1REsjzJBaIBZUh74xJwPl0UK01ju6R6Chekop98gHoQvD0XLY20LsH2RPsGimGFg6zUOFIDCbcXOaO/O8Z5mnnjwwY9a6GznXBhGl6IBKPx+bM6nAl35egvghe4FXVlZuFPv9/sbFIznOrz/+dX7z8jfXXwifMx999BGPH69RZysrK19Mnuca+mp4f38YKXcZH73Fn/oH/zfeef/vrn95/ZxZ19CVlZUvOjdtHYWULr75yce8s3YCP3fWdfSLy80TgL2LvTJ0YbakNTHkWjtLR7Ci3qOGIR1iepIiFqAE1IpIRUsXjGMPbulg1WPP7JD/jUCGmqIdBUUpXQQUVWodoBlusBi4KKXuqML1McMNk4FZlOhuNVtmXBwzw8NQ6rXIHT06N6ylJgiIFqKmAC0RiIyIt3Sg1sLRuJwiaYrYDBVKinoVzTBoAcoG74Lk3BpLc9o8YZLCi0S6nVUAlRTxAjwMvLtZy0izJfVnTSdoitCRgmYZ0VIRFVwrYQsqgrqmrhjdWy099lgVyVLfLvaWp+I+AAFlSPeeN5ovzC27ebODGJCGkOJVnk7DLF3CbrlnILRQIGO1ZcNQKzoMVB0R0lVuNgETkEJ5OngBGTISG6W5EWXsp3bsmxbMDTfHsb61QAl3LIIiI+qGogQKfoBlgsPj3i/d+pgOiBlpj9HDzKBGZeGT59YVEUhYCvA175mU7n7PwuTr3l4iu6CjWUZXk2J5Oxx4/P7HfPfbP+CD+485WMvO6AiiSF43s4x8Jj8OUaLkGI2IPJ57ut1rTXFxGNBh4H//+mt84/T0lf3B5ouARfAHj2cOh0cM7QptT1ABiymnJpsyUQBBtOajLoFIRWpFSs0IZllSHO4x7UKDMiND770utW+uGAlf0O1riLecs8sGhpF8ECVjoAOs5cYUDyV0wNuUXdzRkLpF3NBakc1JjrDoGxYCYr7KRIQ6QhQ0oJR1JK2s3CTcnfv37z+34w/66sZELrHwG09+g9+6+q01Dvo54u585zvf4dGjR6vYvrKy8oXiea6hgeKvaDKP6CmVr/ON9/6fvPXhf/9v/N3EyrNkXUNXVla+yDzfdfRpOOqrSIngG/c/WZ3Az5l1Hf3icvMEYCRF4P7/gqfLS2t3WBqUnPpcFK1D+kijt+BK72AVRdpRmkshWPQEKNehz8cY48ymJUUwyZ43Ccs4XAItJR2h41EIhKU1TFrGBWuh4HikE9IOM8tyoLU5hbTZWVrLrtxlJmzBvREMGRVsC+LpbBbvTtgy5Ax/dGzWo0itSO39uwDDFsqYgmh96i4WHfFoGNCWmWVpGEGI9DjiQIt0sVypCFKHFM7dUySMrkqXkj2f3gXQUlOQL8PTDmV3ihakjvn9xw2lSPZ6hnfhMzK+OqILwt3FfbzXXSwuKOpGoFg47g7tQCwLWCO8EWoQSki6gxuVoPZgZ6VITdEcQ+vQnc0pUmV0dLoaRbeUctLjsy3jtiPoSi943i9EwQRiyu5fNwLDAywkhWJAtSDihBjISPEe/ywDjGf5PaYDLHukHahly3j6BuPpjq0MLEvl/DntFvvw/IJ//uFHWaMtxw0Htcc+a4ppoukOvv7FN/qVSNd3hHM4v+TBdz7gwbe+x9WH91kOUwrikvcjzHLjQ+Q9pxm0JQVza/gy40vAsuTmhmFE64BsR3S74y+++RV+9dat53INVpLmxu89mtm3yv+fvT+LlXRLz/PA5/vW+v+I2Htn5plrOodVJEWqTJGiZqotuEm2VGCrKQlWU4IbkGxd9F33jQHdEoIBwQ3YF75pQI2mgDbQEKzRkCVLdtO0JNoWB7GKLJA1sMY8Q51TZz457CEi/rXW9/XFtyLy1HhqyMy9d+7/OUiczL1jR6x/XDviXe/7jrZCvZDkAKWCNbIuGbIjPvXY5wlHorfbDGGIOOe0QJLF4xgRT8AaxhGkgl4DajiGkeiVlopkCTG4i/+yXIHkuL7raTwWwTEkj6Q87FOgaQWphXsLNxo6LtC0isoAsbie02LfTzwzM3M1ODk54eQBRWOdnZ3xF5/8i9wYbzyQ578fFC/849v/mN84/Y35DeEDpNbK888/P6++npmZeaR4kHPo8dEP8dVnfgbKSw/k+b8vJMHyJ0ks+eiX/9986LVfmT+8foDMc+jMzMyjygOdRxcLvnr9+gN57vtFcuejb77Bh2Yn8ANlnkcfTa6cAKw7d4U3sHAXhgLl8TVRug2UXTetiRFiAKj0nkh6FC/grYSg5ex/BhJ4xVMICNB6JCmEdVW7e1T3r518F99rqFVsc4ark4dV9MXqiJeC1UIrpWue3t2/3pOCpXf6ZqgVSt0nKotojzyNOGHR1oW4hqdEqC8Zn7bxb9tnHgOOdKdmyLopYntdqLWGoNIMsxiLWXR74jXOMndS/FS4nvsexp2kGZUQjandnY3vjwEW+01a3cs20mK7FA2NV3vXZ3cei3h/nnAEi/ZY6B5FTHcG710sLbqMDY3NnRqttu42VayFsGsiJNVw+dLQNJBliRBiPRCR2gjZneS9pncXz+zWhW/tYvdBfG/Xu0wGhQwMkhjk3oIFlxhvc0fIiEXc9n5XbXtZuxmaMkPOJEBSQnXL6Xrii7dvP7BVXQ6sy4T2+O2IGY/y34jPLngrcc2JomiP3I1YbbeGlUpdn3F6esrxyTHbsw2tTMQVFBHQ7mDdle3uUAuUEj+fMj4M+JDwZvjUF0BInP+IYqL8scdv8KNHR7MT+AFiDi/euUUtjdTCLWt2NyLyRZHeaY2UEFlVY4FFzhGTzya6zH1EdAXq8TNyGAtqSiyocI/7qVvpl0qLa5WEN4+kAWtQ1zAM4JXWJtwmVEcU7fOCwLB41wINiQUnxD1Hc0JqpEIILRauWPs2e2BmZuZRwd25e/cuzz///AMVPsV6Gs0FptH453f/OZ8unz7voTzS7FZfP6gPeWZmZmYeFg9lDhVlGq7h6995MM///TL+AAzvR73xI8//f/ng6/9zfPY080CY59CZmZlHiYczjwpTyhf9rSgKsxP4ITDPo48eV04Adt/1qS5AhdQdiQAYqAxdlFKyJJILKhlVQd0wrz3BVsPdqgnU0F1Qr9AFxp07sYt35P41CWEyLbpztYsgmsAqKpmMoppRm0LEVSetVqRxoIlSXWmlYVPBGoQcLRhbGEfSMPTO1N5dPGRkGLvw2wW3Jrhpj2XWELVLCX0yDyFWq0Ape2etqIa415217kIzo1kLYa/Z3glrHv3ETZQmTvXSnZ4SY3WL/WohGot2IVzDFR0Dkfi7DOGSTmMIvCKoK2Ih+KKyd5zGzg7RVLwCNRx7uz5gDTFYNEWssPcuYEl97ojjaynTRKjtjGa1dyyHSBX51wZNyRpO4BCLlpAH3BXNy724nxGSCIMODGkgpYz6BF5iu/v2yrAg5SNyXqFCdC17F8ZcaQZ1J8h7pZnhMvZ/b8JpKxVZjmQU2VbYbEitsWzCS3dvUx5I9+89fv3m87TaI8h3x6/LcWjCqkVEswguHufYbuFEa1irtFap05paJmoLIb7WQquli/I1To9meK1xyFOKbmAcGzI+jrBcIKkvtHCL8yc86GjO/NTj11nplbsFPlSqGXdKYVnWZDIpPYXZFiggY4/iP4p7hkT/dso5espliZfTWNwiHn3krcT92awvoO61dwABAABJREFUtKh9IcUYq+tr6eXZXaCd1uEQLxVMoyM6XYO8IOUFmkfULW4RpYQTPS8i5lkUMGRxEBvjDuOI5BXepjj3fF5CMDNzVXjxxRepfc55ULz+2uv8iSf/xAN9jfvB5BOflc9SpJz3UB5paq28/fbbs9t6Zmbm0vMw5tAXnvsFimygvvlAX+d7ZvEHASHbho9++e8yTrOz5kEyz6EzMzOPEg9lHn3sccol+Iw0dyfw2OaFVA+SeR59tLj4V/Z9xsVQHUg0kuYQD2nhGFVFKSG67aVKR7Ewx0qPS+YUUce62CueEUlkpJt7cwgU3XHoDXx/E3Wom1jx6Q2XFgKu9WhhzWhSkioprdDhCCmbMAYnZVCBtqG12oUwp7USrlAPwSKckdFrSc6gK9AWTmQhOjGtdLE2xLnQWxPuFRfDPeGSIlKVElGnXfyVlLtgI0xWsKmEoOcVQ/C667Ltgrk5SMLcQgj0EKwR6eK703YrYLOCVUS07wchq0TnrUaHsbqjQ+49sx7dxVhERmuKWOuUQBcgCRHI4eMNkVgcF1AdSZKgtthWEbwYrVZqK7hCk0W4CiVhFMQr0hrqjSEVRAeSDAiCtKm7vBtZE5KENGR0zOScUIU0OBkjy4JBhr7AAIRGppIxkjckR0cqmlCViKUO62t3fZ/RWsGtAmuSLNBxJHlCZaCt78Iqo3lgcOfVky3FHvxN290pVIYUcdku3QVsIZJHhHP0+Ib73mNBgRsuvdO3FrxNcX57xZrhpeKt4aX1v9fe2azxJw4s9H5qyYKOiXS0QhdDv26BnHA1qibKMHA4XMyuqEeJ19dnvLqtLK2wSpmcF8BEOPa3RDzBgJqjyZG6xd1RHWnmeL1L0hHjNM6TVogbYk8oUOI6EI/rfhig1ejCHpd7l64nhdIXXphHfLsbaUih9bYGU8XbFD/r9MLvvkBFNBITtBHXpkdYxMzMzCONmfHKK69QyoMXO5MkfuqJn3rgr3M/+K03fouqD/ZDiBl46623+MpXvkKbP+CYmZm5hDzMOdRR1smhvv7AX+t7Yvnj7Jasizur7RvnO54rwDyHzszMXHYe6jwqwnoYHvjr3A8EWD2EfXLVmefRR4cr9/G1ioQAQEMd1BUjRcOrZjyaftFdbGg3e4art4tVcoS7RdepO0INsViVpN6rfaNj1jF85zAT3aca4w3PKVyQ3vt2JYFP4V4VC6eoKmlYkrtwqmKMeWTMI6REpdLc7jl50R4n1J263cVG8S5OKmIlOmb3gqziXsKZWx0rW4zSRccaz2U7AdlC7IToJG62j7PGrLuDrY/DwbSLKR7jc0E1dN5dP3A8XwieItGvnPu+VGsIhqSMeCO7oGa9Tzd1d6/0/yJWWfpxiuMSz+3mQMItxEe8hiCbhhiqRgxxc8E8Y2VNndaxWV2gTDoikkgKg6boBCVitIVwOjsgOmCtdvG5i+bWyOpkB82JtAhXc8IZJDF2x2/YFyOKG02xcMCkX6kKXvB6Cr6Esw3URuKgO6KNdnIG5vhqBM+MZrxx9y6vnp09lCgPc+ffvvA8y2vLLrZGL6sMY79GBAmbfE9e931/805w8+Z9hVF3eHvbfz9OIekubInHtRLnshik7vBOCVkekA5WsBjje7VFbHlKmDkN5Q89dnG7Fh8lXj3d8NqdOyxbYZQl4ot+3A18IqXD+DikTeAtFnkI5GFFXjyBeCMNj8NwEPeaZl3ktbi/1G2kFaSIfXcn0hMEGML9j8E+D79N0Ss+DKg65Di3ZMiIphCZ8xDCrxEOYne81XAZawZJeF6c1y6dmZl5SLz66qu8/vrrD2Xlq7tzeHbID1//4Qf+Wt8vjvMZ+8x5D+NK8Oabb/LKK6/EIrl5BfbMzMwl4qHOoZp54bm/2hu3LuC9Ulaw/Pfirxg/+JV/cs4DuhrMc+jMzMxl5qHOoyK88NgTD/x17gcC/OCtd857GFeCeR59NLhyAnDyCEuW3tfa3FFdYEjExKpEWLBEuufu3BbJ7Pt7U/SJquSI3VUJRywF94a1CUNp3gUKpQuV3v+eu4ARfaZY7WKz9ZeIlRWaM4MYqYshqUxAQlYHyOGSlD1EbFKIYCfHME0Rbdwqbu+K4LUW8aceTl1SBttGFHQpWHO8NGgTboJtK80aNnlE7co9p6R7i/G69x5OASvIMISQnIYQWlp3ynnvu7US6axod9U6JKFvdbifW8QeAyFqa/9ZyX3/ebhjxRCr9yK0e5RzV3nCWdrVe3fH9qJ0+ICb9e0y61HFu58Oz3eTBSYpupJ30mnzEHS1i94usZneMFXMhhDRTTEP126MsQA9RhshkZGdcK6Ey1q7i7l3SXuN/d/KhqmtUdf+vQSeaLVBKlBPaGwx1jQpcH2kbbcoIwMNaZVbm839u4C+A+6cnXGy2SAmoe/ZbiHCrkfae4yvY61itcam71y9tcBUoMV+Q4AUkdGeFB803N9Diq+7hFOzd2y7hws9BP1eZd3d0zJVKA03wdvEqEtWmh7q/rmqfPXklE++9jrbszMWtiHXDZIOoK3D2esCniEtYVgiXtDdQoEo0gYqkse4n8oYfbxpSdyUM2y3UKe4eWss5qFMeJmi/1sl+oeHEfIQz1kr1IbkEd/1/o4r2NWfI3H/LNtYd0CKUPNySptbpGdmHmm22y23bt16qK/59Pg0Tw9PP9TX/F5wnE/e/FX09u3zHsqV4M033+RTn/oUd+7MkaEzMzOXg/OYQ88+9L9jfe0usaj6gqEjDB/e/3Oc7rDYvn2OA7o6zHPozMzMZeQ85tFtTmzT5fiMdGyNxQOOxZ4J5nn08nPlBGDBSXQjroZE2LrL1XqPb0iJ0f2b2JXLChFVKqE9eO9StZAvGylcipJwGTFVxKXHj4ZATBr2woQL2O6NiaSIGLaImSaP4WYV66/qpJRIaSAPA0w1HGkpM+TM0GOS87UbkIdwBKvgmsIt6R7uXa8hqrmAZtwHXBIG3VFLPLZ52PubhbCpuYtoiRA/FHSIx6bYnuju3Yl6hjdoCFWg1Ubbi+mJZhb7yS2eq+9DEYk/7HpxBXZHywsw9g5jiT7XHtschyfGIzKwi072Xaw00SdsFJqXEHOIvtjmEanRyhazittE89KPjcd54pA0IqkHURKpOw1buKR3UbReaNb3895vKyRNpJz6YoAuOrn3bSPOq/1ZGb3EzRvVjOYJGLB2tndHYsB2A8NjEfFNoUkCNtGFulQWxcjb23zp7IyHK//CS6+/yYsvvYy0CcdDJBOJeG4Er60LtA18FwLeLzOLrmWvBa+GicZ57N7FeovjZzXc5xYOYp9qF4EjGr1NW+p6S1mvaSZ4XuI6dEd/dHSrLnjfwYr3r1YPeQ9dXYoZN2+9zWZSksNgDZUVzSZMhEYsqHBrfZGKhbvXPc7/eATkVXT8psRukYukhPfIfXpfuCvIuArBF4EWUfAR/z/QaqWIhstfU8SO1xrnYZvCCWwVhhFS/pq4cSPRtg/76pqZmXlYTNPEzZs32W63D/V1T09P+XNP/7moqLjgvDK9zPObL5/3MK4MtVZefPFFbt++PcdwzczMXGjObQ7dVE5O34Kz336or/u9cP30Jo/d/ex5D+PKMM+hMzMzl4nzmkfvLlfcWS4f6mt+r1yftjy2Xp/3MK4M8zx6ublyAjBERCgu0Go4a6khtIpiZoSWG2KTiWK73kfZSVUeEaC0bhNWVEdUl4glEkqW7nKULlCIRIp097R5j5SOIuG0F6XwhlhFco9ztkZaLlE3hmEgqaBSkVJhapASwziyGkbGxYimkIwbCXfBmoUjuYWjFmnhfKtdMHNCFJYcImYaQZw0DvvoXdussVrepdKFo1MR8jDuRVLE8f66lRoitFc8JdymcPO2ClSaOy1yl0l5QFWAhksNV/WuQlk8ntyjs1MIkTh2qSI7UU+1pwJX8Boyv2RgBE/h7jXBzbuIWGk4zXeRsB5aogutFbAN1DUwISmTKeSkEWM8pP5aCimR07CPKu5DiZjolCPGOy1RGRALR7JYRfMQY+2iOXQnM4L1Bup9XrhveuQx0WEqwGoVL9Ic5FpUqpYDqMJiiqjzL54YJ+fUifDy2++Ey3mn0YrG8eriWbMQ/+/FObe+AEC7OxjMHK81xF4kOoA3W9rUaNspuoBrdw17iwjg7kz31kIEri1cyOwc/RWyxpqFx2+Qn34MXV6OjotHhWLGF95+i5vvvI2UM0Yr5Bb3UqtntGp4khBj8xjx70khL8AznnedvQUZl+HMVYU6RQewtHDdtxb93g4+ZKCi/R6avOJ1i9mIyUiRTK0Vy5nmLc5PAfIiBOHWcE3YVLBS8J4kEPftmZmZR43tdsuXv/xlzs7OzuX1P5A+0FNMLjZ3joS3r1/8cT5K1Fq5efMmX/rSl5im6byHMzMzM/MNnPcceuv6H8TbBe0BnjlX5jl0ZmbmMnDu8+hy9VAqBGcuH/M8enm5egKw9NuYvsuVKOG8lO4MS5ZJ0Pt5S49Y9i7Qae+e7fHKOSNm3RFsiFhETJsiXrte6t2hWkMEpXWnaurjKP2vjogjsnOEerjgdvHBKZFVGfKIpoyqIKUAjTQuWWlmkVYkHXAypTVsu8Gq7WOXzSIilyTxWoRrGROkGpqkJxP3hl7pvcmSsTqF5t2jsFUg07+fI5o4PNaGt4JLd/dKw6eKtYbheBOoBXff71bZdSxL6mWchMAa1uAQeFzuOa+994fui2H717qjFO/CILuu4z4u0XA9u8f+KRHT3RRcEq1vK3oUMU0mZI2OX0HiGEgit+iQznnErCEoWQeyDpFYnHI/DyrYtI96Fs0hstPFdw0nMVJjUYFmIGHNUPcQsrz3Oq9rqMuyhu0Z2AmsDuN7uoVcWahzmDM379zmtJ7fipxPv/4G680WsRLnQm24e5e2vRtxLRZXCIhZCHi1hcO+O3Udx1LGc46FBBJxwN4sBGC7930c2BSkFNSlx0FDqwVrJRZAmCHNe190nBvLlN9ja2buNw6cFLh56wSak0tBW90vCqjNIimA2q913iW2Cu7hCo6E5oSkBaRFXC+l9wJrjjjoUmKxTKuQF+iw6HXThnmhTltaLdS6pZZKdaGJ0iQi2mUce9d0i3ulZrz0qOl51dvMzCOHmXHz5s1ze8MNsL275S89+5fO7fW/G/T4ePeL4cxDwt05OTnh5s2blHNa6DczMzPzzbgIc+irz/wM2/YKlIsvAo/THfB5QenDZJ5DZ2ZmLjIXYh69dp3tJfmcdGx1fi/6kJnn0cvJ1ROAiajPiEEewBWR1IXHndu0Iiqoxof9Itp9GHJPLN3F9rr0+ONuM5Rw+0ZatEAeiIRgjzZLaUgtPbq4j6VVxFMIv5rjNTyEPx3GcDV2MVQUsjg5N5IaOiTEHBUlpcQ4KLmLzE6lekScNhdqaTTvfZatRSSug+YFkiQctyg6rFD1cKmqIsMCRDBNGBWkC3lpQFJCpCG5/z8N4AWXIUS/1qAJaBddykQzx1T2YnzzRnMPaVC0i9Kxr0Uljg0SjtfuIJXeMywYov34SBeze1R3xFzXcASKgnkIwN4oZhRzmhXMtnhVSrEYr1fEDUlLUh7ChTMuSImI4E7E8cs9glqFlBM6DNFbKv24opBHGIYQ+pNGP605rXnUQkcA9676N5zK3WluNsH6LjvZ1FZLfDgMx+8igSx60vQE+ToLOeCIyroU1ufcg7BtlY9/5UWk1h4LXmJO9tgVagYtYpy9GeKC7o737vBpurfYwnssrwhijrQes91dw7EaIR7rpSC1IlOBaSJapsOx6TqGa5iMlUbZFv7IU8+Qdw7rmYfKSSl8+c4x7WxDmjZMLlTb0MopJgusNJpVLI3RJW010gvI4QiuAmmIRIXkyDDAOPZ77RCiMBb3hmHArWKWMEmYC1PdUMwibr1OtO0JrW6ptdJqjUUD/aR1iQIBR2i+RfJAzvN5c5nw+/DG4H48x2XmUd+H7s6dO3dYn3OUlJggdy/H/eWLX/j/0dq8+vc8OD095ctf/vL39Mb7Ub+WZ+4/8znz/fOo78OLMofWfMgLH/wYrH/nwn8o/EMv/SPU5jn0PJjn0JmHzXzefP886vvwwsyjqrzw+OPnOobvlB+69U6Yp2YeOvM8erm4ggLwrlO29YjjbYhFvQMSM9zX0BxprbsEow8WAN8SPZThNsVqd2jKvp8W0Yit3QmxIjg1BOHWO4HFw2FsJVy+XsBTCI8ezxmdqN117C3GaOEcTTKieYkOCzQPaGshPqqQsjBYherR32uNWrchpJhRykQtU4ij1C7+GuTueFOQ1AVNIVzN0kLA3OdAO4KgTvTiCtAMKHjr+6pVsN7jSbh3d6Kpu1McbCduS/T+qqQQdnt0tu/CkGVnCh4QDNzCFb1TFXeCj1sItr6zFisuXUSn4l7ZlsK0nbBaMFdKA/OCSwsxXxMiRrIJ8Sk6gMVRJ+K5q3dNKCOthiApcs912OOsSUPfNu8uRul9ppWUUlx93ZXqVihtorQtbltQxXWBLw5xNuAlFh2Yx3PL4p7IzJJRYSXO8a0TvnDnDhdhHfHpZqKuN3ittNa6EzdixUUgaUJEwuUrHi7fnCElJCniDZmm+FM9nLu17a9V34m23noEOTCkEAGH2L/SDKm9N1YbnnrMdt3QtoVyckI5Oz2/nTTDybTl+fUZtr6LrE+pd04pp1uslnAC9/uE7SLRW3feWgl3fT2LiPnaXd4ikUbQSpwTKe7Lu8UGxSfKtKZuzqJRWBy3SvWGpSGeeurdvi0WsYi1eO56Bq2Q0ookCcmLc9xzM98tch8WetyP57jMPMr70N25desWL7zwwoV4M/Ez7/sZfvDwB897GO/J7/ww1Cv4buKicHp6ys2bN7/rCK5H+VqeeTDM58z3z6O8Dy/UHCrCdnycWr4Adud8x/KenP/vG1eZeQ6deZjM5833z6O8Dy/cPJoy9YLuq5mLwzyPXh6u3kc2boSAm0IUVIGcQ6SLolxUlt3h2yOQuygYxaSEqIj1OF664GD34qUb+G7XWgMDRboRTXDNYAq7Dsl9V3DvA5beK5xTf954DbeIK5ZhgQ4jogk1Q9MYjkkD6hQicBIGiejj0hrFnOpOsUprRjGhlho9m1ZDKNMcMccO0npetLQQfd0xa7Fd3nCX7pKGpJksiTSMkFK47lS7e44enx29vJIGBA1HaHNq9XD2OeCOuCM9Alq8IfQIbOFdsc9Df1wIgUK4mndCOdJ7ft3609b4u2SsC62tTtTtllK2FBfqznaK9u1RUhpIaQWqqHUnsgm0dcRn551bWbvYTJwnOsY2J+1ubuvxsf1xOsa/e3qs60B1jwXKDbyd9e+3iNFmQPKNEOpjiLGfu+49amPlhfX6Dl8urfcanz9fvHuXt+4e42WKc267pa63WOuxzzlFjHnfdqtTHCftCwI8hH5ttS9w8BB7NaK4DfBWkdZIAuqtC+eK1dad0xbneC34VGA79djg/mcY+nk9c56cmnH7zMnbbSxaSQOllhBrDWrZhjs+Dbjk7gaPtIGIVI/rnRJiv3v0CMf9QkMMTjl+kTbHa/Snuwpetz0uPmGukSiQR0RjMY/kjLeIKEcjCl4VxCtpFoBnZh4JdqutX3rpJeyCdHvX08rPve/nSJLOeyjflo996GMMOpz3MK40JycnvPPOO+f/YdHMzMyV5CLOoW8+9ac5Xj6Gn33i4riAbQ2bT3/Nl1wvR8Tmo8w8h87MzJw3F3IePTrieLG48J+VOrMIeN7M8+jl4OoJwLRw7Eru8aD0COcu2NEjnXPqRtddD+0AMhIiY3eX0sJQnOTenlQghfwbglUNnbcLTu4GPuDaRWi60KoSEaY5Q1aEglfr8dPWf7iGqxQQnKQS0c9UsgqSRzQPSJmQpKSDQ5YqDKKIRX9lbc5kx5gbrRWqCVZr9AIDtG0I2eqxLRIxxk7Cm1G9dV+u94vbSSmTJJFTRqeIz97vIw3rrmgmRRMx+BTinFUMqNs1XqcQfq11bc73YjRdjJfuEt6XxLqDa+xTHcLRrDn2lXSXt8Ufx6g421KxplSUZo3WxcFwcscCAPEW3dAuDAhp1xW9zzAeQnSq7xLIowIamOJ5tAu/lPi6LiKKOoXD2ayiOffHOEKCashmC1NEc/tmgzTr21lD7II4D20LKTECKxfOzrZ88c76woi/O37llVe4c3qGWaVKJPa2Ou195CHmxjXl5rFP606wl30X9W4iETROTW9odxPv9r2QoTneGjZt8DqBGSqKmEAzzCXOyWHsrvLEJ2/dvnD77SryGpX1cIjWFv3OdaIdbyinp2zXp5QyMU0T1cB06DHPxHVkEdlu2s+HRVxDpBw33929o2zxViOaXRVqAW/UVuIDmO1pLDKQnlhgNdbgpDEWyOgCzSOiY/S4t/ONWp+Zmbk/3Lp1i5s3b9IuWK/3j/uP8/Mf+PnzHsa35Yn1E32unjlPXn31Ve7evXvew5iZmbmCXNQ59LM/8n/j1G6C3T7voQQ+QXn5a770pQ//dUznBaXnzTyHzszMnCcXdh59+n2cDuN5D+Pb8qUnn8RmJ+i5M8+jF5+r94mNQigEPU7ZGpQS37CINgZ6ZHB3dXrrTrISX3OPmNpd7C92r7t3t0uV7grtUb0eN3Izw9zw5rgrbddX6w7e+0s9h3BBd7w6iFkXoOmOUgs9UTRclDmRlwMpJWRIYUbVTB5XrIaBZU4kdVQaoofRSeyGecXNMXUcw4dFDCWN0YcLUFvEO5dCa4ZZCKFuDSEhGKpKEiWpkJ1orV2uEA1BW7OiKYWzWmL7vDVaK5hkKtCwiHLVLrrT45N3VlmRcP+5d9FX8J2TU/oqKQ+xt/9Aj5JWrDlTNcrUmMqWWibMPSaK1IVY83v/t51TXPqh9BhXqyH+KsAEBimNiOyiqBOatUdnJ1QzDLmLnIZrnDMmeq9j1MLBSj2m1WOahNtRD1bh9t45a7r7PBYDDIzAcrPh7GzDzbt3L0Ts89dze5r4/K1biDkNqEOiuWPN7jnKRZHUj0PveEYVz0PEpwu49DhwFVQFddsvspDWoEzQCloLst3GMdxuYTNBqdAszulFjnhoPNy/Ymz9Iu65q4cB76zPeiy6Y9sJW67wxRIbRyYzirX4o0JtG1wXWJliMY87whCCbfVY2CEKQ45rttW4fXfHL6VCsVicQaJZw4clUreoT9FTrYq0bSxUGBZdFC7x7zz01ICZmZnLyi5q66WXXrqQK1bLVPj3j/59nj189ryH8k1JJJJfbIfyVcHMeP311y+Ma2BmZubR56LPoevVB3npT/9ntPbpEF8vGCaZMhzN7ycuAPMcOjMzcx5c+Hl0HHn12rUL6wI2hJLSPI9eAOZ59OJz9QRgWXVxsQIlhIOdqAbd6dtFWRWi2zfF4z1kSjRBkzCE6qJHR+/6SOlO0R4PjcXPaQ6BTnssafPor9xFG+/7hA0v0QG7d9JKjiGnFK/vHmIo0V0LgmgmuzNoZlxcIy2PUG8IBcnCuFgxipBREr2zly2oUIluTKsNr4a3EpHKzcKRad2Fqhm3TKkVY+eMVpQcu0udPI7kIaESEdEAqgPibS90S5LeZSswFQzDmmBY9PW6drdr7fumC7De+jEgBN939f+GY2+X8NTFdFHcG41GSYlSJ6bmFLMQnFsXYMVBEimNjCmheRFR1dqjvkk075HfaezHMA4FeQFp59KN88fNwzHdb3xSDW8TmOK1UtAuBDeaSwhWIrB4HDt4Ah+v41px6T3BMoXreBdVjjGqsGpwCnz57l3aRZ2Rgd954w0+/9bbqEEzpwyZakYr4b6OUyEc1k50AnsLYTxivL3HrQtuFr8YiYJKRHCXLZSGmKO2i2EXaNqPVUS9mxK9wYsDGEbwymsnZ7x8MncAXxTOpon1YgHXRnTIpJRRUTjdxP2orWmuTJszSj3F6NevZpzS710Fw2ORyfoELxPeKl5KHHsdYqGIOgwR7xxrABzygIwrclqR8hAib+7istVYpJDGd3UQb897l83MzHwf3L59m+eff/7CrbZ+N4vTBX/zh/4mHzn6yHkP5Rv4A4s/wI8vf/y8hzHTOT4+5s6di953OTMz86hwGebQr04HvDZMsP7UeQ/lG7h148d584k/ed7DmOnMc+jMzMzD5jLMoy8+9jhfvXb9vIfxTbm1WvHmweF5D2OmM8+jF5urJwC3GgKu5PgDvau19/hqdM7Gv7urQeReDDQDUO85U70LmV56nC/3ul6deJzTnaU78bKGwGvWhViLyGknoknHxb2fSRJCg/ZDtROMcdwKqgkVJ2k4kjWFuJvMwhhbQVyRITMMmaVqbC6O6CKEUneKO6UarUyYCF5bd152YVsVkiBUmgpmJYQ5QKSF8xjZj0fzQBZQmVD1Ht8cDmtxi3jnpJg6bVuw7RovLcTALmrv47Yl9Z/px0s8hFZ6Z/DOFdzHEyJiCDRmRqmVWhutFIwQXc0lROxmqFcGFbI6giESYnWSHH2gIqScIi429XNh1znco6Kj+3gI0bI2JAvQO5ZTLCRwuovYe79vd7QKldZ6D63kiP12wE/jHJODLjjFuTQYLNy5u3mbFy6o8/fdOPBrr7zC77/5Fu4R+21Ww/3dInrbcFxif7gQLmGz3RkWoq+37v7u6zLEICmSxh4ZnfAsyKAwCDKG+1MOD5DViOeEJwnXu4BV53Q7C3gXjeROOlkjwwCnJ3B2gq6WiDXsbIuXNVSjcUBpTnPFpYH3e3dfWIImXBNWtjBt+2KAqc96A6DhEqZBBmrB2zrWFvQEB8HDva9xT5DU1wPZRCQ0XOCVFzMzM98Sd+edd97hxRdfvJCrrb8eva38pz/4n144J/Bj6bFYwDYzMzMzc2W4XHOo8IUf/Bu8fG1BW16cvl0HNosnuxFhZmZmZuYqcanmURG+8NTTvHz9xoVyAjuwyXl2/87MfIdcvd84E92Z2/r/d8ISoGN8fed43Qu8xKf+mrq4G7HAO6FOxff6bDiBu5grGcjxtdbdxHShmXAlxnOP4QjWECSlVkhKMzBJeFgXY6xNAIu+29S7cZvEB3C7p3ND1SMeerkk5xwJyaLIcsEij/2hGtsqEt2pnmgOrVZMwFMO0btWqLULIdGpGoJdL1/16LDVkIBRcYacSEBOizBUa7zhCpFzQLtrT4chBGfJtN0+2Dmoudet7Pv+X42uWKTXDFuIqgBpxAlxt7pQUSoJE6WWida6SG8tFgKIQB5wQuTdx07jZLWIcdYBsBDRJfY9QgjBi7Er7CVczerR89v7f2XXM22Ki9PEMW9xrMUwWsTMWsQl4A2h90cPS0RW+/GETVgZXBh8zcl2y4t3p0vTXVvM+Lcvv8SX3r6NScJUcVHMHKsNqxPuHucVgmic22YtosiNneqLmCE19ptIxlPCFFortGL4piCti8fjAoaEp7h2nYzV7ngX5bNvv3W+O2bmGzjdriFH8gE5wXKF5AF3Qw6OkHoWi2nWW8p2y7ZMlM023L9pgOEAAC8bvPeVWI7oeW8FrzVcv1MNN7AkxPu9RnK/fxiWciQPmIMskTxG3LgbogOSx3s98jMzM5cGd+fOnTu8+OKLF3q19bsxM7gFf/OH/+aFEYEV5c9f//PnPYyZr+PWrVsX/4OkmZmZS8tlnENbWvH5H/w/8/KzP3S+H15vPg37BevKzR/4v5znaGa+CfMcOjMz86C5lPOoKp9/6mlevXbtvIeyx4GbTzx53sOY+TrmefTicvUE4J1ja+/KbbB3lkLsEmPfBYt3t2/0BQvhXsUbSoiEO6ewiPXn7b3AokAXHUMxjufZxRRrYm/f9KnHHddwstpESjkcaqmLu61bH3WAtIxuWKH3Bb87dlpQc5RGEielaOpFHUkZWQiLITPsrG6uwAR+F6OFPm2Ol4L7BJqQ5GRRcsqYGbVZ18lbCMXq+DggmpC0Ql1JYiQFdUe9YHXCrMc4u4X7VSR8wd567HQX5qVHTLv2aGzBrfTNU9xriLMC3ibMDbPKVCa2ZmzrhurRF1qnRt2uMRNqK/35WriHVRl0jKZfzWQdGGSAFk7l/blQtt0dLlC6gEyCGsdEtMda5wWSckTOSriAKRvadtvTwLtzuAlaHeyMIoJ76anjZyE8I/18MbANSER8Zxq1KS/evo1dspuqufP5N99kWp9iKrgIZo02bWjbik8TVro45+EIBvBaoRR8qvi2QndlCoJbDbe39s5sDQe44ZhoLGTwEHxD+JvCIV4bd89OL42AfpU4nQrkHEJvM0BpFGS5RHKC5RGaNqTRwLe02ijNKLXgU8HrFNeOSXRDk/pl7D3iP0cP8C4+um1iHjALIdhiEUzcbsPh722L1ym6x83jVtsKOi7Pe3fNzMx8l2w2G55//vlL10/j7lybrvGnnvxT5z0UAJ7OT5O5OG6qmeDu3bvzm+6ZmZkHxmWeQ19bLjn78EfORwRux7D+JDsBeL36AKbzHHrRmOfQmZmZB82lnUdFePXoOpNeDBlp3c1kMxeLeR69uFyMK/ehYl3wlO7QHEJUdQ83LK27fTOahi7CCTIkNHe3mA6h4/ru+9rNxP4uh7GEuGASsc7QHb8Wwi+6N7mG01j3zy06htNXvBtAW/ycWGjVmkIjlXc5f92gbdn14gqgkkkkEkpWJ+mImCOlIYslaUwM9OeXAdK1HjMcrl8R0FbRNJDHRcQ2u5K6w9lEe39rCJ7i0iOUNURM0f61hKGoCilnrHmPmS7s+pUdxZPSrHLvXtFFcs2x7Trg7ojGTnavIea5YA6TVYoJdZowK5RWaKXSyrZHRPfxaialgazC0HuSkeiPlSR9P3fHN/S+2RzHqUX8Nil6QXGgWow5J8BCaHKPGNracBFcFWsVsxA93QxJA83CtRz9zg5pFRdlj4mO4tqRLIZa64nieunE3x2vHB/zqzdfoGy30edrjjXDqtGmCdv1tdaKtBYLCnI4MbEasd79MbRwB7vLvUUDNVz7cSUrtRRa3el7hjWHFkLei7dvUy7JirurhaPbCd/WWBRStsjmNtgp3jaoNDQfkYYbEbWeElZPKCIUem+05p4M0BfdWMPzEOcWjqQBTSPUKeLuyxbyEmsbmjdMUtxHpjUkR8Yx3Od5AcMQf1QRuZzX4czMVaa1dunecO84Pj7mYwcf4+c+9HOxUOwc+amDn+IwzZ1LMzMzM1eJSz2Hrtd8/vDo4X9g7BVu/yMoL++/9Mr7/xxluJidijMzMzMzD47LPI/eOjjgc0+/j3YBhNdXrt+gpHTew5iZuTRcPQFYQSkoLYQ+urtXI94YK12UNdxqxDObdTFuZ0wtIS5IRVo4WZEuJLfu8NwJAzsXqLV4DaMLwQ03iUjgXcyveDxWrMdQ9+hnTV2c3g3A4+90x3Hz7mQeQsBtBRLIMKBqEVGdlKwJodJtkUhakFZLxqwoqXcUK07p3aoOwxLNipB2WnfooSnve2xbnfDeJQwg3roeF7G7QiWJ9q5ljZhkSdAaYtFf7F7CCbqLpMa7drPbVt+LfG51L75GRGulukfPb53CvVcH2mZDm1rEubqH4y9yoxFvqHjX9+P1nBIiOkazinnECNv+XBEcw5LCkEKktQKaQ6OvJeKybYrjpRFVbZ5CpMRBnULFEFwFzwKqaF6QZIuaY7YJgVO6kCwVtS1CIzfnlVu3HuAF8uC5efs2n3jxK7Rpwq12AXhLa4bVirUJ6k4E7lHd3RAeZ4LEz6jg6lgt+PoMP9ti24nqRhPFrEbEdPMQ3V26a10odeK41vPeFTPfBEfY+AhskcOMjCM6HoFlsC121qgyUm0iMSI0MMFqRKLXFnHi1eL/0c+9gGkTDvC6jWtPLeLFh1UIxoBpotqWqWxo1uL2M01RO54H8IpYCeElDYjP59DMzGXj5Zdffu8HXWA2xxv++jN/nT/N/+HcxrCSFe8f3n9urz/zrXF3Tk9Pz3sYMzMzjyiXfQ59Z7PhSx/+QWwYHt6LTi/C9ML+nyUfcnrw3MN7/ZnvmHkOnZmZedBc9nn0jcNDnn/8iXMdQ1HldBzPdQwz35x5Hr24XDkBWHQRokDOiFukNBOxstGzG729Yh6m3C4SezNgiseRI97XHdfUI59BRVG8O0jHEBp33bUaguc9MVciUlrCNYpK1zj744fono1Y6O5Q7t2ye0ewW4i99G7hFK408hiC9L7D2FCBpELSjAwLBIcWMdZJlSENcTLseok1IU1QjT+i0b2aJN3rTsb247eeZS2aIqJao59VFCSNgJMYAUE1btSSxui8TQMqC5pnmnkchl2ktd8Tbd1b9zb3XUI8tmJUoJUJswp1E/2fJCqVZo1JBurOJSrvWiUkscniQhoWkMaIdPZE8xYuZU8xBFVQiT5j647UYeju4N7J3E3e7MepNIvj4C1E69xdidUKSZRRYdBETtdJkkn5EDyF4xAY3ICB5Ik7mzWbR8C1+vl3bvHanWOsxvnszfFasW3DN/VeFLQ5YoaSek+z9a5guivY8O60xlusbUDiT3fiNyu0VrFpCgEPWJfCG2fr890JM98Ud+fYDE9L0KOIrd/WuJ+kx2G5gFqws3UspHAgHYBDq2eUdkKtBbcuBjcLl28rIQQ3waeKTT1VYBfn7vH/Zl0YFsOHBYjg0xSx795XanqNmPx6OVduzsxcVW7dusV6ffnv/Xdv3+X/9AM/d26v/5HVR/jo4qPn9voz3xoz45133jnvYczMzDyCPBJzqAivr1bcHRcP5/XKq3D7HwBl/6X1jR/l7cf+yMN5/ZnvinkOnZmZeZA8KvPoa0fXuLN4SPPoN2GTM2+vDs7t9We+NfM8enG5egJwnbqrt6/63Ilp1sK9mXrUr3YDp7d4fCKciKKI1Ogffffus3cJsUY8l7doeWlGuIC7mCnh5BWviAxdpE0xBlmAOioKVCSl/v8c4rBZj8L13kWZwrmcu+AbWwk4bhrREibgiriRUiIlAckRgd2I3tpFIg8LVNLe9eZkqAVxIydFpffUmBN+2nh+cwMyWI26Yyt7AzRuPTraUI1eYqGRrN7rNW4F0bY3/Lp5d9jpvWjfbgF1qxGxbOH8bOZUSbRasbqLBHYaTmkFs97l6XQBJ+KpNXURtxlqSh6UnRlYBCSnEB01IYOG8OyKpFUcB6+ggo4rRLrQ7vEYN8N1xKtTpw3dqoz3mmBHMSuAkXJmTGEo1pzjOHsOAd0KyXyfFC7TXQobHgXJ6aQUfvWlF2mtdk2tL2aYtthmg01rrLVwyLshXuLa3Wzx9QbbNmy7oW0mcAkHZ1ZcpQvn4e52qz362UBSiPCt8tpmOu9dMPNtmMqELQ9gewZ2CgcjkiBJY0gDCUWWB72RPce9AoVJMFNaW+M64OaUekZpa6oIzQxLQ+/njl9OfAqR2KYttj3BPUMa43lrwyXcwS7AsAIdYQox2Ln8izFmZq4S0zRd2sitdzNNE+qFjz52PiLsX3v2r8WirJkLydnZGdM0/54zMzNzf3mU5tDPP/74w+kCtg3Y8f6fDrz6h//vPYFs5iIyz6EzMzMPikdlHt0MA597+plzeW0H3jqYa4guMvM8ejG5cgKw9XhiZ0ts/rvil7vYCoa49vhiQBqChiAaLbd7J+o+PjqPeE64CKIawq6GoId2oXWnMDrxD0ldDI5nkmEJPkWHqTfEFZUQT6PDtkV8Mtajhm0fk+w7h6l1h3AoniGkagZxpFWSK1mF5AY5+m5FwuGbBmXMyqD0yOOGpGHvCo6u9+gwbrXRzGh4j0ruDuDdbpExNlEzIobqgAAJIYuiKZFyQjJICnG3UakIxRq7Wtf9znGLfd6dv2jG88DkRqmVVmK/NYfmipnjJhTzmGC9gJ2BOtknxHoXsyguLbp5a4/qJlynorHvrDZUE1JbF99buIRF8Vr7cVEwwdsWkxD8a+KeYxmluZG8kuyUnEZGHRlyQlKKTtLuNq8+gSjJuqOcEfET8uoGWb5xldVClSfHkYPL1n/gjrhiAiKODCmiy+kOeXe8tFhgYBYxvP2WZa3EqS5D1CSL0eiR4L5zXIez2GoIyZ6AYeDzd4/53Lwi6UIzlUJzRxZjRKDLEkyjv9e25CQMAiOG+l2gxDW4PAS9TvUU9wU0IthFMDNqLdS2pXij+UTDsEEjw6BMuMc13Wwbom8rtKl0AVj3/dGeuHfdz8zMXArsnS12t3zD13POHBwcMDzMOMj7wJOLJ/mJJ37iob5mIvGxax9jPJkjty4y85vumZmZh8VJO+Gl6SVut9vnPZTvinbtOtOHP/JgX8Qdjn95/0+TxPPP/RVe2c4fXF9k5jl0ZmbmYXJZ59GHsojq6zDg+ccfjwjqeSHVhWWeRy8m+bwH8LARyUS/r0ecrwvuae/o3FXrmmp02YqjSAgP7tzTfaMfUnCoXQQWi7hgdbCCaI6+WrNwjfn2Xvyw+bvE4BChZScMVw9HryrSutgpwt5CKjkcyR4RyS66d85qJkRfd8Rqf93+vKJISiRveE7QwEjx+lZ7ua+Qk9JadAVbqzQZUC+odIezt3DgJqXWiqiSqN0d3XZPg7Xe3doiZ1msdkFUwAxVxTTjbkiyiGa1ihXB8oBadBz7vvu4RQyzC7SCudOq4bXQSkHygNV1j+W2cNtK71CmgRygRP8vknvKtPbndEQHUA+x1iwiulPsS7OKqsZxbjXStVOOvl8lnIheEMmoOEzRH+wmuFdaa/vXS/kIHUeSGJozSMLa9t4iAWuIJlSGfrpVEkuSCYkt/WhyPWf+wOERTyxGnj26zq1pw1ubM0QzViufOz7mVrn3YfdFW2d2MhVeOb7DB68f4SnHYofcu6U9nJnS4ly1cYxY8twiQUuI47oTiOnnCWnf1xxfE7y23qdsfOGd23x6Fn8vBdkg5QUQcfopK4rGNScei2uAJCNijeZbnCWQQQo+NWpSSIZaw2P5CaID0iaaTuTxMbw0VFtP11d0oD8fFIGEodXRfi8Sq8igiCQ8zSLIzMxlwM2ZfuVVqt3Gf8RpNN6ob/Abp7/Barni2vVrnJ2dcXJ6sv+Znzr4qa/puU27KoILQimFs/XZQ3s9Qfjpo5/mz1/78xdqP8x8c7wvDp2P1czMzP0kkrbuzaGv19f54vaLPDc8x4fHD+8fd9Hn0MmM33t8zbTa8iNfzTx5rKj3z1HuF9vPQX0ViPekL33wL3DzB/6jeQHpJWCeQ2dmZh4Uj8o8us0D7yxXPLF5OJHWDrz02OPcfPzJWfy9BMzz6MXjygnAqHQ9NIU4yhTu3qpdXA2RV3tvr1uJ/ls0NKTWRTubQHL0kIogNoXjtwsJiOFUVCNu1M266BgO09B8M7v+4NBFuytVFdoGkZHo+o0O3egBHrqI7L2D0iGHcBr6cEK1hpjs9G7hyr5DOGRJskrEX6cREwsjsXT35TCQUo2OXU2YCM0UzRLxyM1IChWJ/syUuy6dMe9uXwslfeekxsE1IR49rhnBxPEUjjswkmbMwsm5k/BE7+0zd6G2hhHO5WZg1rA6heO31vg5N2rrgq0rSsNwBh0QGqZDJMZaxExHq7N2l18IvknozumESQUZYhw1xG/GHA7gEpHcEQutIXi7hwvYwtFbWgPWpHQNUUdXS3IyJB1GdHgaoFXcS3SP2hrhgF3PaKqFtDgge0PcefbggJ987DorSVwbB4YhkUU5XCz58GM3cJtwBn7s8cdYO1iZ0OT81q27bGtDHF7fblm3xqjKs4cHoInXz844Ld/ojnpQrFvl7dMTPnhwQBUYty1c1xLHOrpZFZmiD5gxIp49RXS3T5Xdab7vixZnv0gBxesGkiIYn337Dp9+++2Htn0z3x9v3L3Ns6MhchCu7r72w8koLeL6WyUvbtC2Z2QdqZtNd3ov8MFxEn52B18qQz6g1N4lrgmVA2rZYLYmW4bFAVJPyfUa5IHttKaJkPMCdSeVE3RxSOoJAdQNwiVz3c/MXFHal475zJufZv3BxGfWL/HP7vwzmjfWvoZT4JtMDZ84+wS5V18Iwl+4/hc40Oga+sj4EY7SEWtb8+XtlwF4bnyOG+nGw9okaq38wrO/wK/f+XVubW890NcShI9d+xgfu/ax+U3cJeHll1/mox+de5pnZmbuD5+59RmOj4/53fXvfu0c2vlK+QpfKV/Z//syzKEH1fnss40vfLCQTPjJ5wdunH2tOJsMnn07fffCsG1h/UnwLY7y/HO/wPPP/cIs/l4S5jl0ZmbmQfHb699+JObRkhLHi8VDEYAdeP7xJ2bn7yVinkcvHldOAA6zoAINl/iFHK+QF9AmwJHdL+aSu4AKXU3tOpOjaDhvLVyf+6JWemyv925fr4hkxFoYdsmx11sDjQhc3/2oeRQPa4JWQjjuNzfx7pwNhbFHFQumEm5Ja6SUIuq2xobKsBOOG7jGGxfpm0JGU48yRmmqSO/2dTEkLxCL17TmVMIJnVxD8sgZbQ0Xx7vo5hYuVxMPZ7RouDaNvYNZosgXSSN4RdURSdGHjGPWaNWo1shp6F3LvXNYlGIWUc0ieGvUtsWs4WkJXnDJNKs4CWsVTdI1QYEe3p00Ad1FLSlEfEK01RrO4aQJEcN1QFr0F6MZ6hTO7LzESwnntGbEoxXZLYRIE8fc+/kGyGF3mS/jPPQUCw9852weqbYOsXiaSJqRvEXSAaoDadqwkMIPHD7FYzlxbTGwWCxYdMe2IiQzRBV0AUlZLA9ZVGE7TSiVP/vcDXTakh1eL8bWKsuU+YGDFeTEKyen3FpvaXWLSeLfvf02d2p9EJfhnloN9R7TrEpqLRZUqESnLwnxiGb3ZphnrBnNwoGP08+/ON98H0QSvn1DeKcWPvXGG7x89/hcYkpmvjeiv3ygSomEdXOSOi6Kygg0RGPphqQByZm0XEItuKS9696PjkDG6DLnBNoC1yVKOPtTOsTbFsFp3vDtmlY2tLql5iVpOZGGA7Jm1BoJRy2jmpG2Pe/dNDMz8x64O5/QT/H/+OD/i2Gx5Pj2MRvfvOfPbXzzNdlWf//239///YfGH+JIj1j7mi9uvwjAh4cP7990/5n3/xl+qP1QLC55gCxkEckID4hBBj6YP8ifvfZn+bHlj5FkXvRyWWht7qifmZn5/nF3Pv7mx/nPP/mfk2vm2B6dOXT5+f8RefxDTAdHgPObH/3GuMLU4Lm3Uk9qg8dPlZ948VsnAH32ucJb1xr4hG7+ID/qf5Vbbx7z1uN/HNcr99HbpWWeQ2dmZu43r69f57/64n/FJ29/8oHPo4Lw8zd+nqfz0/d3I74OE7mnZzwAmggn44IXHn+ctw4O9/rIzMVnnkcvHlfvt1AlXJfuXYRdABoCLUD4u0IpbgVSwrz2VZ8hPIpoiMPWeidp9NOK597pa4SHuIYAKgqphNhoHs5T7/HIIUGHNd7p4wrh1DGaCzlLxA6n7uDtyleYjz2SpFXCEWfdrSr0x1q8nmp3KkdMrruhJDQp0zShKmHRzxkpW0gDoobIQJsK1ipFBzQJqqGdVklI7vHT4nshVTw6cFWGiKhO3QHt3aupue8vQYjYX7GGUlGDak4rhaaEE7f3d05WaMWwKfqb3S3qeAmXs9UN5nG8kg6IDBFb7RL7yXdTkwNTOLAb4Er1RibF9lvtsdoNKRs0jfcE/hR9z9LqvouYNMA0YWJxaHcx3gLNThlYxZiSkwZBmyE6Ymaxzzdbaus3SKswXo9IarlGFiFv1hyOyvVhxfVxxWrILMeBQRpZcnQqe3QWS4+8lnHAsOiM1gXOgoSTvLHA+ZHlguSO5kUIYcAPHx5SViusTbRmfHC15BO33ub3757QHpBy+ptvvM7jywUffeppWo7V1bEQQWKBtDuu0t3wPaI9j0jpHa0CEI536T3RIglJimjld9+4w++8/hrms/R72RAgidBU8DIh4wrV3fUbfbw6xnWUhgUQcfSI4JLR5PHQdBBu/NKw5ZP4dAzqUW2eF9HtyxHulaRHuJ3gplTLVE9wfMowbtEUkeyalWFYkHcx9TMzMxeaT7z5Cf6zL/xtzvIZtOP78pw3p5vf8LUXy4tRUQB85sXP8FR6ip85+hn++MEfZ5AH0y/8+muv82H9MG9/MwvzfeBnj36Wj1372H71+czMzMzM1eITb36Cv/WJv8VZvX+VAxdlDn3+ub9Cal8Bbn/Lx7QEL7zv3oeYz3vjdz/yrROzTHfvTxOPv/PTfOHWB+DJ+zbkmZmZmZlLyMsnL/O3PvG3+NLdL92X53uveRTg1foqP3v0sw90Hr35xBOsauH9Jyfv/eDvkuefqbyxepKT9PQs/M7M3Aeu3ic6buEG1RxOVBQXw70QLsIF4QLugpHVnppsER9tHvGjkrtIlzDfIvQ4aR96F3A8Hdb7axmAhqQRrxM9xRiX6KeVrFhpIVgqgIawkQbA8ZQiHnf3/sPB1LHmaBKcjDskHfbfp/VCY909Z/+7O+GGDeFW6c5KnxDTGJMVQgyvDN7wNNAmoy6doTuJRQzb9e2iaBfZvMdai4Ka4YxgEy459rN0gV0U9RoSehqQWvex23UybBxQDx9nIzqF67ShWXQsIy0c1Q5uDcNwgeSQunc44rad1IX6OBYpxu8eY02LeHcnhng4wEXDpUuPuI7/e/zdw90bCwQyeAEMZaS1EnG1ZGopuC8QN4ZhIC/G6C0V8Fbx1mJbasEMGql3OwvYMSojapXV4oAbQ+LGkDg6XLFUYQCyD2hfgiBuqCY0hSiPKOaARfy1iZIQxrxgYY0hD2RV8hCx2A2o24msIYI3V55dOu87WvK+N2/xibff5k699+b3Ws4k0X4ehNtWusH8Tz7zQfKYGcYF5hN1vWUQYRwGzBzMGBGGIcFi5PCxx7h+eMhUCrSG1LoXx9/dIe3iFDeSC5YSrdb+BjvvY9tViJ7rnNE08id+4IM8fbjkX73wEtO8AulSUVrj7skxSxw7UKRkSlaGpOAbdotAkqa4r1VHrCEp7rXoAq9rsBrnUhJSTvhwA6lT9E07uBsMvRO8Oj4c9sUlhlmkCpRtBalIGknmtPVddLGMuWBmZubCsm1b/vmL//y+fnD9ndC88Xp9nX90+x/xSnmFnz76aZ5MT+7jk8dxREQ4rae8s7nXSy8iPPvcsz2p5JtzXI75O5/5O7HIC1jbg4vd+q2z3+JWu8VfvvGXWenqgb3OzP1nmibefvttnnrqqfMeyszMzCXli3e+yN/+nb99YefQr0dEePbZZ0npG+dQxzm1YxzntfIVXirPA2Dpu0y7kvjY4Dvh+Nrb1Dzx5FvPkmxO0LhMzHPozMzM/eL1s9fvq/j7nfJGfeObzqOOU/PUkxS/Huetp1+JSsPvkNc+2DicIkFjNQk/9YXFd/yz6nBtLfvkz7srZ8rOb3x0y1vXDfc3WK3X8zx6CZnn0YvH1ROA970r3U0rDXXAe6Sz98hk1e5S3bl+U/+/4K2LxRqPF4YuNmr0vQxjCHwQjjSPVlt3Q7yFW1cT3iZUQrgVy73/NHfHbneSGrRSwuHmEc9sJWKIrQmKYqUi42JnsN1Juz3CuaHDshuCwwErgGuKGOlpIueR6g2rGWdC8xDCiA6IQBoy6o2pJlqrSB5Rd7JkJg9TZvK4YSMpHLcphxEaRbyiKYE7Tfr43PpIFKGhVjAXkowUQuQ1B7fY/61VWmtU7/2u0oXn/nfDMIRBxxBydxHUuwzmvhK3263JKFW6KNxKREa7kXZdybvjIL0bOo1ddMq9rze2jd5NLClH/LZVCrEAQEmINNJiQRoFzQommAmeDCuNViqtTiEEe5yTKqByhJqxYOB6ztxQ4dpywUEeGQWyV5SIfo4o7oygJAEZBkQcJyO1QXUmIGOsNDGqhrN4yNHrrCOpFURTiK8ykCRiu7NmfuqJJ/ixgwP+xRtvs1ThYFzwR556nKPlksVqybg8ZJEgq9GK8c7dM85q4/HFyMQRb45blkcDN554HFkX7GzLYjUwLobYhpzJw8CwAS8JGXI/brv9G4fODUo1rItz0wSuOa6xvt+SgqaBNMS5e7Ra8b6DI45S5p9+8UvU2Ql8aXDAt1tEKyY30LLFFUyWqKaIz2+GZ417mUqcw649Ql+R4QDKhGh3/9c1nhdIWsYrJEXaBDlBNTw3lOgiJ1e0bAnPfMwb7kYtTq3HJOPeQqGZS4G7f9/9pffjOS4zl2kfujt/9/f/Lv/Lq//LA3+tbzkGnH97+m/5+NnH+Y+f+I+53W5z3I555plnSCnxxdMv8mtv/NrX/tDr5zPWb8btdpuPn32cP/r0H+XH2o/1pJeZy4C7M03fGGf67u9flmt55mIwnzPfP5dpH5obv/yVX+b2dPuBv9a34pvNoSd2wn/4E/8hv/nmb+4fV88+Qjv74VhEfevO1zzHRz50ynMfOMMwvrD9dCwrzzyUT8HqUDjO72BqvO/1D8d7lJlLwTyHzjwI5vPm++cy7UN357X1a/zix3/xoYu/+zF83Tz67DPP0HLhzo03Mf02Iu93sXu2wMnh/gV54ZnvfNHYWOHHXxqQHpT6qQ8X6k7nFYDdPNp45vWPoPM8emmY59GLx9UTgLULoG2LkMFb/Ntb9I7Se2w9ooxddyJUBdt1/QpC/dpo5X1TbQ6RF5BhRFrE8LoJklJvJfV4nZQBD7G0lhAea8ROIwkQXBpJHcFwFKsF0+5Ww7ue3XtsW8RMa1Jqq0Aj5WWIufRoakkRA90Ku55UciI1p+UevYsjmmk4UgwdBEkDozrTVCjuJDdEd9usGIZ6i5u0RQSrUdHuzlNVvFXEFSVh4r0rwFFJeBqRYRv7azIsKaVWNGdaM4oTwjcWsdLEtrRWcBXUnSEtcQsnKt6dvtrjtqlExnYGrzQUzcsQo1sDWnQpj0ukRcey91jw5LLvXAbHm+MafbNeK+pCEemOa92fCykJOS/JC3r3MRGvXLy/7rZHzUIUN1egkD2R04LBlWsoN8Q5Ghcc5gVLVbI4SRdof21JCVBcQt5Xs95vrDBGDDcOWZRxSCw0kbyQcg4ztguYo+bIfv/FOTcQx04Wmb/4vmew7PjikHTjiNXRAQcHK5bLFcsxMWrGS2V595j16oCj932Qabtm87kvYNMGrcY4JtxHBjHytqDNyQJ5iFuR7KLQZbfPCYFPB0iCpXCDN3fqOGJu++0TETSncAFrQsUQEr5Q/tiHPsi6NP7F898YlTJzcdHRGVZPUKaGjxtcD6ko2SpKgkbc6zSTBRhTd40rTBMMGuIuHveGPEa3d2n7COhYZ9Detegg/q5pgY8Lcqs0jd73Vms4+NM12j5SfuaycD9+Obzqv2Bepn342vo1/vVX//VDea33Yutb/t47f4/iJTI37k8S9UPjn77+T/nIRz7C6nh2AT8qXKZreeZiMJ8z3z+XaR/+k5v/hP/2+f/2obzWe/H1c+hvfvw3v0aY9rbC24py549S7/5hKM/wgfetuXZYOPiRVznenmNli8D66C5HesDpV9+783HmcnCZruWZi8N83nz/XKZ9+Nr6NX7xt36RL9794kN5vW/Hbh59qjzGn/uxP8NROnzvH/pe+C537TTA7/zwt65V2D3n6eEd3nnyqzz11rPf+9hmLhSX6Vp+VLh6yydqxa2FQCgR9+sIUOJDf4+4WXrMcVeWkGi9Raz2qFtBcrgMMcGqRb+vRiwp7uEcdQg1TqMHt8cyqylqXTQsDdAQaFPvlHXHU8YV3Pp4asVIIWgMI+hA24l9rXancTQSg0PzEB6thaAnuTtjCUFNFIYE1t24otA0HK1WeydxoxEuWHMnp4TWSpsmioHXd0naIiGaqmJeMDeaV7Au3rqjXfTu/mZENQRA8Ygv7hHNTZRWtlQ3moFVp5QWAjhKwTFrIIq40zy2utJFHOuCrtPF1f51b9ERimCthvjrEYttu8jFrPHw7lGmTkQJrvbjaaE5ajxPM+tRshG1jAvJYq9orn27BEepNdyrTFsojWY1FhFgCI3sS9QryZ1DhceGxI2DBUerZY9+jjjpcVCGJAwKizGxWA4slwPjOJJEUFPEKiklchIGgZwzKSfSOJCXS1IeUG9xrEkoHs5g6MckkVCSGSOJxxcDNxYjRyqsrJAcsns8tyhZlcVy5PozT/O+Z57isYPEkRpPDcoNlHGqJGCRlFydXBqDVQZ3BhcGN0ZVxpxZJGWhwiIrS4QlxB9xliosNXGgyoEIByKsVFm6sxRhoQMLjFEGRmChymox8hPvf5r3HRw8kNvKzIPh7jZiYdLpGT4dYSXj5rTWF8xgIeJaXPle1lit0RGSo8fbm2PVIvZfU3fag+cBkuO14bVEKoIojAt0HFEdEBTJC7KAemNYLsh5QU6CeiXnOYZmZuYi8s7mHX7x47/IW5u3znsoeza+6YULl483N2/y37/53zMuxvMeysx3wZ07dyjlPT5UmZmZmfk6Xl+/zi+//Ms0vzhz1rvn0K93JUtao+M7LJ7+Vzz37/0zfv5nv8pf/0sv8Jc/9gqr5TmKvx3D+Ozw22zHB1fZMHP/mefQmZmZ75Xde9GLIP7u2PiGl+++xudeu3n5Up26CDzPo5eLeR69WFw9B7BbOHnFewds97ymFdLjhVWlxxSH8CreEMmQxogL7cIoLYRCEQnHrvT4aJf+/DWE5hw9saJj767NvX9XwDTE4l03LQ4UaE7ScCVDC0FSnLQTfSWikWObepQxGROBMnUH2xgdxBoir3t3oraGpIzbhHiGFA7QQZStldjs1Dtel6u+SkCj71JhEKWgtGp4K9GjTMPyiIdEjdAwBHVo1uI53EHCHRu9sV2ItRBoxRW1Shah1kojUyfDEzQL1621FP273Y2rophXskS3LIyYb0mqmJ2hsuydw9LFn9qFIHnXseoLAprhvo3jjMY6AJVwS2cFiYhsE8fEoy/UaozFwcxxKmoWolVSUhpBEp4XtLNNnFe2QX2LphXVbK9NqyWSnyDTxLhccT0lrh8MHC0OWOEM4mQSyZzUDJGMaiOPIzqM+7Rqq4aZYa3SFDSNZEo4Y0XRlFDJSGnRc1wmxJyMoEPCk+KaMFdq2eLijJKQQTEqrSltO0Ha4io0ElYbTRMMmWEc0VoY7lTYFp567DG2wyl1W2hnE1hBiRjqhJJziuhmFGHXwyyQMtId5LtIbzOhOfRm47iGNOMq9xYx0BdgtIlwyQ94znzgxnX+yPue4Zeff+Eh3Ghm7gentSJtg2p3+7cJWyvcOMTESCjqDZO4ntyVKITuC1s04dYQSYgLXjeIZiSPsZ5jivh3akFWS3xzgsgSAC8ncX9IC2RxhNdNrAvStL8Xy2X7xXlm5orw8Tc/zhfvXJw33I8CH3/n4/yZoz/Dh/jQeQ9l5jvk7OyM1hrDMJz3UGZmZi4R/8Un/4tLO4f+6Aev8+MfvvPeD3zI1GHi9PA247TcLTGfueDMc+jMzMz3ykV+L/qJlz7FE4c3+KGnfuC8h/JdMc+jl495Hr1YXDkHsAwL2PX3knDJeNrdPFo3/WoIpoCg4BrGz/auVbBpCTvBCutCbRcfXLpnUWDMIUZpfFv2tyoDqzGm3DtPzfauYRcwMcQMzSOkEG277hljaSXEzSbx83nAREKClQWaE0LronM4md1DMGPX1xsZwEh3He+6hqPvuO22iNaFchFFNTOOB6gKDEOPgA5Xr4uH89bovbrg1gjfc+rR2l0A9hZu6pRAB1QiTps8hgzujalWtuuJMp1QbYOIIxpxx46E4L3vHo6eWwVQIaVVdDfTn7e7uWP/CZBjX6ShnxO5P753BWMhAA8Z0BA7a6N56oerxXZKwlsNR7IBoqQspDzAsAIybT1RS4HpFFrB0hFNFKQh2hhkSeIUdESWN1imzLXliqO0YIkzaGYYFuRRySmRNZFUogtVcnQBSyJ5OBUVUDNkswWTvrhBsalg0xSuR2s9Qrlv+ziiSVALv7t4jWhoUdI4MIzCOK4YBQZJaKnYptE2W+qmYGcb/GSNnJ6hpxtsvSWZcbQYuX54wLXVkoOUGVCSR9S05oRqXEkisf9FQqBOoiQgizAMw97BrCqkZiQRssCAMBrhJDYjm5PM0WpocaRUtDSSOz/9A89xkK/eupfLzFtTYbG4DsMCxgW+WOBm1FLDN28O6UbcAzX1RRmxKAUaJEGSAFs0L/FWcN/EPTQlZOidwGYxP9SGT9sQk/MBWMXbFAXT0wbaGeKO6ojKxXFGzMzM3OO/e+G/O+8hPHJUr/zKnV9hbfPK68vEG2+8cd5DmJmZuURs6objcsl6Cjoqyh/6wI+e9zC+JXdvvIXp/N7hMjHPoTMzM98LF/m9aLXKv/7Cb3DzrZcunRN4nkcvH/M8enG4cgKwt+j4xUIMVBGkaRdRZS8UiuZwgKadGFjw1MIdi4eo6g3SyC5aWKSGp7WLp3iLTl8iYllaCxEBegR1hn0Hrew7fCNK2vEWr+e7PmAEp3axGKgtop+7uEt3SZIGyAkrpbuYG+zdat4jrglRULQbJmOMSRxVjX5jUnT94qQe74w7UisiTkpOQsFKODFbA1OaO+YW8as9mtkdXBqKkVKIeCIj4oJgJFFE6X8cd9i2xqZsmOqaYqCecDdSq9AMI4Q+9ym6Xt1wLzQJ919kTFvfLwOx07pTW+jjbUjX5wd1SIr0rmRS7s+To7t3M2HFMYNaG6WF6xpTSquIKkmFISfywQEpJ6RO1M2aMq3DkWoZdAUegrHLgkyO/l19HE0HLNOKa77luioHObFQZRgTKRGiZ0qkMZOGBZrjuHqL80KykvNI1kTOAylntBakVqgFk4gi3xmPSRk5OESWi9j2lFH1iEqfYj8nIJWCTtGHPKgyeCJPEzJN1M2GNk0hgOMRm2uOVENrYwBW48jh0SHXjg64tjpgmUeSpLj+SN35W9kto1C3OFdEGFIi9etRgWSNhEf8tDsZY1BhUCFrOIuzh0iszZBW45ytzo1x4P/6E3+Io3kF0qVhXWHQiZQ2cf8cBCuFtpmYqsV57I1m0OpEaw3aBnTEasNb6//v1zVDLOhRjT9WYuFKm3BR3B3PIzIcIqqQFlA20SmsY/xpLdIg5MpNoTMzF54v3/kyb2/ePu9hPJL87u3f5b9+57/mpJ2c91BmvkPOzs7OewgzMzOXiF9++Zf5/J3Pn/cwHklaqrz+/udpOschXhbmOXRmZua75TK8F92ULf/T7/9bXnjnlfMeynfFPI9ePuZ59OJw9T69FsLR6SDJwAXvAqr13t2dY1RSjn7UNIQrEUWGnWt2t+tqRAin1OOcCVEhKdBFJnfYia9WQ+hVQWwK9ytOuE5TxDGjIVh2EbbVgptCmxDb5fx6PFbHrpil3gPcncRuGL2PWBegEqJHj42ObsveCUx3Y6qimtA8dCdvPFIgIq1TxPF6zoAhMpCykqUhmu8JKnR3bhfaHQlRxYTmsndWq3g8DzmMvymh45KE7D3F1QrVQ9CtItHzKaC6E3mNnI72L62qKPuG4d7Z2wiVW3uvs4QwrKkfG0PzEM5t68dLvbsJwWqjkillorlTWumdwxJdRFZQzd0tLaQsaM54yjQ3Sq39mHivETaaQxEJB7SVPqYIQV5gHI4LVllZIBH7XBydPBy+4iRxskJOGbWCmMeCgGIxCgHNmdxPKZWIGjdxSkqxRxwGEZaaI47brfdCD/ig+wUSSeP/mGPbLW5OiuBnZLPBN1vK2Zpps6Ga4epI3gn6gqbEkDLL5cjh0QFHRyuODhYcDIkksd27RQ57sZboi5ZxiO0AtDZkKkgp4QA2SJrIKZNVSZpJEkHqe6E8S7iGRUgpkyTz0See4D/56I9yOPe3XgrOSuEYQe5s4PYtOCsRv57iPt5qTxyQuI9qWtBqpCuQFlAjLt8lY+UMyRlNy7j3W4+BVwlX/CZEYMyiF3g6i3tqWsQimrSAtOyLaSSuu5mZmQtDs8avvvqrvLZ+7byH8sjyxe0XWT+2Js9pGpeC9XrNrVu3znsYMzMzl4CTcsI/+NI/OO9hfM+YGx9/6ffOexjfGoH1wQlvvf/leQ69JMxz6MzMzHfDZXovWq3yrz73a5fLCdzn0dff/yJN63mPZuY7YJ5HLw5XTwA26b24jlfF9i7VFkJUFwzFLAQBQhjACJdwDYGNJPeEYBVoPYZYu2O4tRARzcIJLClEYob++NpFWwcdQr30AXYuNSNiS5UuHgviQ4h81rp7VUJMtAnbRR33Pl1a+IWdsNWKVaB0vbOPCUAVyUMXSiOeObXYftxDhjWLn9/V7HYxUHuEs+bE4I7ULeIGreAu4QAWYT+XeIveVnfofcKpi6CqA9L7l7Hot/XmtLYF34I66k7WHO3CHk5irEvUEq7tiOz2vj/pLuDUndC7bG1HzMhKPIcbrRVsF/ksDq3iVkLwrZVaCxWhtu4otrYzUlME3B1FGBYJXSxAlNaMWuJYtWY4PYrbt1RXvG3C+Vu3UEo8Z6usRDkcD1mmkUESGSFL7+gt8RjVTBIjKaQ0hGjeLNy+ZQoh1SNePI9O0ji2ZkKpE6VuYBhZPXmDa8+9j8Vj13AHKxUrhm0bbiV+TjWckGbQGt5KOCCtO4XXW+rJKduzLduzDdv1llJLvzSEpEoeMmMeGBcjB0eHHF4/5PDoiNW4JJmFS9PBfCfya5w7OJYUzyGve7NwbOdEGodwOauiSbv4G37i+JqEc5jo6RZ2keOZH3vyKf7Gj/4IyzSLwBcdc6dVZ1iNyGoBtoFyG87OsDrhm1uYFagnuDeaeDjd6wa8RAd7ZNFHXH2reJ3wsukLcCTuu66Q+7251b6YJe8X3AiC2wnajpGcICdYLM5578zMzLybk3rCf/Ol/+a8h/HI83du/h3uXL8TiTEzFxozo9Z6eT7YmZmZOTeaN97YXO6ovhffeYW3Tm5d6HteOdxw9NxynkMvAfMcOjMz891w2d6LbuqW//lzv8av3/wdTrdnPcnz4t/v1qtjXn//83Mc9CVgnkcvDlfvt07tUc/dBRoGRAVajwtu7GOCc1f4xEFbr/hN93p48wBZ7z3Gd+JgF1i9C8UM/d+9k5JMV6K6Y7eFUIGFc1gaJIc0It5FZQe3iqtHxPNqhQ1jXEQ6IHXbhewuSBMRupJ3LmTCadu/BxXxiN2NsTtI7mJs6inJHmKxKKKx3bqr01VFRMkOWRdoUvK4CqemDiAeSdv0cXvD94J0d2CTkGHs1t3oJlYRdMhoTrFfZBm7dRfr3Cay78anSNLec9yFeQ9hVtppz5OW7vptfeANIRyBFUeskdOCnAZ0HGEYwu0nSq2V2iZKLbTaQgRSYXRnTEKW3ujsjqiTVyPpYIXkgbY9o6xPqZstrVTMC2i4DhmWDOokXYRgPhzEAXInqXKocCjGQoQhC0NWhsWyu3mN5Io2R11IFj+TREkSvcM5D+Q8Rq9uNaR57N6UYNpS15U2VfRghT/5OHb9sO/HjJeGbSe8GuohnKqDthb/N8LVHZneSG1oLWit+HZLPT1lOj1jOttSm2FJkcWIrhboYiCNmbxcsDo64uDaEYeHS1bLBWMe4jTE94sUvFZ8s8WOz6inG6xWRCDlFG7eIZOykpKGy5edGT6jWUl5QJPuI6XjpIt9IUPmR598kg9fu/ag7jQz95FXT+8iy0NYrmCxjO7uoyV+9y6WDmhWcRZxn7GK4/3mQyyW8drjmg0Abz0K3w1vFVcJoZgMq0MYV73vXJDkiJwgVtC0iJ7ulKLLPM8C8MzMzNXj9nSb//Jz/yXbvD3vocx8B7z66qtRjzAzMzPzbXjh+IVL/wHdnfUx/+NnfxVzO++hfEu2vuH30r8jHVy9j+IuI/McOjMz8ygztcInX/4M//C3/wV/77f+Gfn2EamM5z2sb4/AenXCZnF63iOZ+Q6Y59GLwdX7rbNNIA3xEDBFFCGBZqTH5CKGqMRj8C5+LoAu1KYh/g5dXOxdvuLdYdzFXUnhHtOuGOsixM8udka/cII8hjihuzhmhTKBhQuTLvwaYNrji61AEwzDTbFmNHe8WYgbFFyHvUAZ4pd04VRAxnvRu0aIGbv3ewKkiG1uoriGS1IRhOiQ3T2fOYimcFjqiMquhzWiqs0FM8d6DHSkYG/iTZlEr3FYTyuK9F0gKNIFdYE0kF0YNCGSEM0kFcTtnru4BzCju2NxGAIyunf/7UR6H1Y4Iarv+kNFFakl9p9NmAnmQrWGn551ETz6aMMRO7IT+1ODISdSin+37UTZVlqtXZiKflqh9zmb05qRBMw3fZ8rYmcsfM1KYZESWYW8GEmrRXT+rgbS4ZK0yIRPVtEspCSEITGRh4G8zOTVgjwoWTUWERj7GGoHTBM2bSmvvc3mxa9S756g2pCk/TgZipA0ReS0pnDVqpM076OzkyohmzmpVigFnyq23lBOzmjbQisTNhXa1KDGeZZSYhwHlgdLDg9XHCyXjENCPXq6vZW9G7nVhpXa3b/eFy4Yaoa6oymh1lO7AVVBJRzAiiEpoTmub3HCaS5Czom/9tEf5cPXju7/fWbmvtKsIdsJ2Wxhs4WDVS+ETjAsYvGLRIx7pDEMWNolK8QiHLeIdCcpOiyR4QA3x72FAz8P0BqKxyIY4r6rwxJdPIUMCRkyMuRYqKP9PjwzM3Nh+Jcv/UuazW8uHgbH5ZhP+idZrOaFMBedWitmF1cMmZmZuRj845v/mOqXP1LxZHPK77/2pQstAhcm6gfPWB2uznsoM+/BPIfOzMx8p1zm96JnZcPx5oTnv/A2z774kzz+9nNoSz0t9AIi8Mb7XmSznEXgi848j14MrpwALLpAyaHPeu/MdaLfF8KRaRZRy7sY0LqND/qdEBx2wmTdwvYMfAqxF0BrV6IkBALNXSS27kRrXYisXajoYqq3d/XngpNwL5hPuCSaWa+w1fhZM4RtuNRsG5HMhNuWtgE84pS7+zVE4RQCdu8BjtcltifrvbFYCHsijnijWenbHA5md6M6NPp2WuuTQu2dr0JOwjgMZHFcE2YeHbPdlueesFpxsz4cDXenG5oTqjAsxnjdacKooNZNvV3MJo5biNHdxb3bl9JFb6y7gLt9GYVa7h1DTbgqtBId0NOWMlWm7ZpSJtwUWR0wijGMCfGCqkbHLU6yxnKxYFwsEJS23VI2G6hrVDJZE0mlu57DpW3VolO3FbwkvDUYBmQSFixYiTAgaOqiNoKbx9hFMQQTxRSaCAwjsloiiyGOGYqqknImD5mcc0Rte+yPEI+FutmwOVmzvnOC1YJIgh7RHcnZIf6qgi4zaTmQVys0x3MpTlZlyJkxJYacSVkQb7Ft6zX1+JR6sqFONcT6lLqNXElDZrFYcHBwwOHBgtXhIcNyiQ4ZM7BqWHdzmoQ7GDfCd+19MQMhBJvdcwB39zp199id479fDsQ1rMPI44eH/LH3PXP1boSXDAdeW5+Rh36N1wynW1it8LMTXHqcPwPYFsywso34fs2R5mAT5g7lDPcJbIO0ig4LJCnqFUkO0xZp23D2Z4G6jtvLcNAj+EssJhgPEL2ovw3PzFxNPnvrs1HnMPPAcZx/+OI/5DPTZ857KDPvgbvzyiuvnPcwZmZmZh4KzY3/9Uu/xZsn75z3UL4tX7bPsj66e97DmHkP5jl0ZmbmO+Wyvxd1nH+x+Sc8f/slPvTyj/MHvvAf8NitD4UQfAFpuXJydCs+K565sMzz6MXgyukeQgvHl0VnbjSG0gUljYjiPHRlqQtHOsBOaKzehVt6xPMYDlPq/hV2Tkt6HS+tse/+TQmasvPTihO9wpJC2O3yr6SQN836WFrFJdyReAYGrEqP902kRcTdprxE8gE6HtwbC4aI7YXT3R/3+q7VPN3t3PdHqHWKmOC1R0t7OKdVYuzRiwm7sl3p7l2RcMhpGkgaTlbtIq274ZqJ1uWCt4ZqRncObFWSRJyvGHuxMLYl9qd7o/Zd7AJu/TjheGu0UAtB+nGTcHjvY6L79os5apG47TiGxiFzp5iHRqyQhugWDid4wvKAtwru5OUB6UARNRyhFQuHr67iOHrsHyeBLEOHli3qEzAhqeI03J3x8IiD1QGr5UGIxgY+Tfh2g5eGTwXfVLyWiGGuLdYRGCGwI/vjhHjENyclDQM5Z3ISkkR0rSWlIdFvbC22FQfXcHNLpJBrGkjjMuKUhwFyjslVtF8iIQSHu9aQqeC1xHjMYNrSNlvadou3Eu5ike5EFjQlhmEIN3BOLIaRIeV+NhLO9lbw2ntZu+y/O28dIubau/M3h1NbkBDZpV+33lDfCcjEuQZIUv6D557jZ5577vu5rcw8BMK8u4oYaCuw3UB1XASotLqhrI8pzXAvuAi1FopXWi2YN5IM4CmuIY9FDtiESEWaoToiKSNpRMcVmlaR6DD1RUAp9b5gR5rH32dmZi4Em7Zh2+ZI4oeJufGv7/5rHn/68fMeysx70FqbV17PzMx8Sz71zqf4vbd/77yHcd8wdz7+4u+xrdN5D+Vb4jjv3HiNG8/MlUQXnXkOnZmZuSoYxv+0+ec0NxbTIc9+5Sf5yM0/xY1bH7yQbuA7N97k9mNvnPcwZt6DeR49f66cAOxE3HHEh477vlx3wbEQL80hRw8std5zyaahi8HCzkW4Fxl3T96I75mDlHCteu21wt1xLAY54Wy72Nx6H2W/m+qAM+Ai6DDG8+YF6rn35caLadcy6cJrqIuESEHvvy2l1w4vuos09UhUwHP/+V1ZpvXnVqQ1Mhovl3O4Y+lidO+bVe1xwWEXjX2TcswJrfcgCxEfHL7UEGHN+n7IXbzxLiSCijIoaBIkjyE2p4FsXcwz66qvhGDuBUu9s9nC7bt3PouBTT2mNcRNiP7hXhSME2Kpp4FmjVoqtdXoI85jRCArkDJaa0Rnh/2ZIS/JR0vIIw7UqWB1C8S+CYXace9/n0oXhA9DEMZRPcCnEICzCiuUxZDJNNQqWhpSBVpFWkNTIqWBlCUctyiynfBtiKRWC605bbvFWsXdYt1BTlEr3SPNrVVKfzzVwGIBg+QUr6Gpd+zGH925071i3TmdUkKHAc0J8Ya0CiZ4abSzNW3aYi3iddt2SyklbvgOqEb8tyZkHEiLJeNiZLkYGYdMzglNiovg1bEy4a2fN13Exe4dc0mKjEN0Xgv7fS/uXdDuZ6CkbgbPYdJHGVLmjz7zPh5bzDGWF5njzYazu2+h2y3IBg7GuB5XB7ivwBf4MGKSsFpwT3H+2ERzMM24GD6OyGIJ4yruWTog+QgWGRlAFgtIGYj7h4wj5IiEljwgq2sRy0/ti09mZmbOG3fnE29+gn/3xr8776FcOb5898t8oXyBYRjOeygz34Y7d+5wfHx83sOYmZm5gLg7d7Z3uD3dPu+h3FdeePtlPvXK5x9sr/H3+dS37C1eWd2c59ALzjyHzszMvBdfOfkKXzn5ynkP477wUnuB36+f6umQwuHZEzz7lT/MjTsfuP8i8Pf7fAKnR7co+eIu+JqZ59GLwJUTgMNAmHrUc+kiIkDCJXX3YBearNyLb94JAl4j8nj3ub959Pj2eF60xymHvQyGZf9ZuvqcYMjdZRv9lJKHcJntOivrFDm27pgCvo340l3cqO2ifEF0iDFJd8h2J6y1hlPjOdPY+4RrOEQBakU09SHl7oJO4QJORMSxSPS+9n0QXs+2C9QlCYh0R6Yq7hV3x607nuluYXF0WJB12AuJEQMdbt7mjvXYYdWEqqPDyKADyc8QVfaJE3kEWgjXGk7kBF1szrEtedcDnGLb07K/pMXxEQdrpOGAlDJmlTpN1DrR3HA3khpZIUs4fzXFHsALtEJOoKsU50Kt1K3QSolzQXpPrSiugkmLcGoRaEard2KcdoQ45DySEQYXRneGVhmGA4ZxQVqtkOSoO2m5JC9GctaIdV4MpOWALmKbrRptmqjrSj3dUreVVrsTF4njKYrWiGe2zQS1hNieFElx/quAJkVTDlethqsWM2xTodbu+o0+6uhEVsgpBO/NFjtb0zYbWi00a9hmTTs5o6zX1NYwHBeoojQFGRJ5tWI8XLI4PGBcLOJcMcPM+rkSiwCc+KACa2gDwcIoLulr480hFgT0P7I7lrv4cB1igUNSPnLjkB997LHv6ZYy8/BoG4dWgEOYBPQOHN/FreBs8RRR71Uct210bveFK45TreI1+qRpW1xBxwEZNOKc1RFfI77ukfEOmTjH86IvKJl6L7rgw9WbQmdmLiJfPfsqv/T7v3Tew7iSTDbxq2/9Kvkgn/dQZt6D7Xb7YIWQmZmZS8lXz77KL33u0ZxDf/eV3+fVuw/QGXQf1oK+M7zBndVbc4TlBWeeQ2dmZr4dL568yPPHz5/3MO4LE1v+P2f/T16xl/ZfU0986Cs/wWO377MT+D7Mo9vlmvXqeJ5HLzjzPHq+XL1Pa1yi13HvPA/XrFiNOFDvjlwkYoPRXlu7i58dIPc+3Cw9DrqBR38qi1X0RGKQM7IrgLfeW7sTakVDvK01xqMNJ4cbE+LnZIBiuL4rltm7yKgjpBrx0jqElF99t0W4KFL7GLs7VpJ2PTtck77ryyV34awBXWkVxTG8tYgM1hB8o4cYRLwbOXdZvA23hqv3eF1BvKKawgGbFLeKpAVijlmNn5HdvvF+s3aQjIqiWkAPwCrJE43We45B5F2x2bto5yFD2YboKxuQw9g3u+hgJY6L9ddpE64OZMwKZiEgppRJahFC7Ia6hnlYE9SJrEoaEppGvEyUMlGKY0Tfryh43ca55E7zQpIe/22GeuyDdLphcf2AZT4g+4ZDh5UIC00MY2YYB5JHTHUaRnQc49BL7sdSEBW8JVptuEk3c1sc1lawJFjdYh7fU3dSSuHMFZBR0eVIUqXt+k0JIV52x1IkhPoG3hwxQbOgeejnI7HooeximndOb7BaaQieMu4bxA0VJXGAJMfKlqrKMGRSTgyMES/ujuFYqbRaMZG9ST4WHfSYb/dIeaafCz2iHBomvS3Ybd8ZDB5uYJEQ9MmIGy2P/JUf+RFOt1s+c/v293WLmXlwvAk8NyywsoWFwhlwMOJU2uaEpCM+HiEZCo1sW2AI57sBOaGSoE2YZDTl6CFXA1Lck80gNSQpsIzKdttA64tK2gbRSIrQPJ7n7piZudLU3hH/97/09/k3X/03vHD8wnkP6cry66//Oo8Nj/EL13+Bu3fnPsOLyquvvspTTz3Vf4+amZm5yrg71esjP4euy4Zf/uz/xv/xx/73vP/60xfy/leYuPX0a7yPZzm+O7tjLirzHDozM7PD3Gje+O03f5vffOM3AXj17NVzHtX95cxP+d+2/4r/aPU34jM0INnAB1/+CUyMuzdeuy/i7f3iradeJrXM4dmN8x7KzLdgnkfPl6snAIuHYTYRzlAr8XVrEYPrFipn1vjezuGrqaufXYCdSo+QthAZyxSC1LYQxbHR8xvCsXaBN6Jy47mm7r5NeJUQlb2EsKvgtYUjTWqPys0ojssSkqCimCe8beNnajiTJSmK0rz3ElvaxzrH+C2cwTqE01l7vy59O63di1pGkbTrgxWg4K64OGKOpCH6lCXF2LtgGo676DNuKiGIN4uXly5Iax8PITq69P0FQImYbiuIZlwqzSUEct+Gq9lBrIT4vu8ILl3sroQg2EKgd+JrSbt6nEhJcTO8OdUb1WqPj/YQfzXHz/f/exOohqZMyooOi+jhbU7bVooIyboo3YCmVNtidhemJXrgJHUSwpCX6NpYPnaNoxGuHRwwyIJhmrielVGF7D2lWhzSLsq2YZrwtOvpVXJShqRgFUuQ8iIEYIvtM3OMTKsTbX8NZDRpXAsa4rZr9DPT3eCqIS5Ld/VaqVhpuHnvdqbHmCtetngxvFqcg0NChgRimE2xiKEZWHQ719NTBMiLEe1itZsxDomkSh4y43IZCxCA6WxDq9sQuCUi22NxRj9nJYT1SCMPgV+dWHTghvROaNGIf/e+0CE6jHsiuiYOlwv+5Ps/wBfv3mWauwkuJuLkA6PeAciQN+E8P72NHD6Gn66RVLvTf4HREC9UhKQj2q8hkRFNqUfkb/EEsrweCzeGESH3WHOJiPk8hvtXR5BVXPteelP1zMzMw+QLd77Am+s3+Ydf/oe8ePwid8odzOd79nnzG2/+Bj/31M+hqnO/z8zMzMwFpVrlE29+gne27/BLv/9L3J5uP/Jz6Ol0xr/8zL/h5//Qz/KBG8+c93C+KVtZ89bRK6xOHpvn0JmZmZkLyu3tbT5z6zN8+p1P8z+89D+waRvWbX3ew3pg/K/Tr2A0/urqP2GUqMxLlnnupZ/kleeU249/9ZxHeA9PxvG1d1itj1BP7/0DMzNXjKsnAEOPK7Au+kbsJ2q9g7d32tYQQBlT9NkioXNaxPiiY1eOGpiG+9QAySAVMYmo0ZyRZnsh2W3T3bx57wgFoKU4Gg6ChkjboiPYqqNqiDqqOd6ktYIqNO3uXRF0WCBmwBQCWV50Z28fvwqORNQ14AjigsuuL3W3bwANASTEVUOIWGfpfcERgbp7c+KxrzQh9Nhs6Z27FtGrZhYx12z69///7P1prGbbed+J/Z5nrb33+55Tp6Zbd+I8iKKo2bLj9oSk03bacKO7E3Q6cACj00Y+BUE+NBAgn1owYItwo2HkS9ztttM2YrVMp2U5bhlpW6IUTbQkUyIpipREicMleeehbk1nevdeaz1PPjzr1L1qS+Jweeucy9o/oFiXp+q879p7r71Xnfe//v//gNIQ1XCr0sfpcaqaOpKE3BqLR2RvVsV9iuhnCxE7nNdd3YP+tRB/sOjyRbowfHZwOcd1E6GaUXotLeIkySE+13AlI4Z5CIaaRoZR0QEkj7RWaMuO6nE2m5+QfA93pVFwSaR0gG4zG4yN7diXLdvkjNdGxu2WzZTZbCeyD+juiE1rpB6J7CKYaVxTNcQUmiO1xPXaTOhmg2+26DShh4fReUsIrq222J+QNOZMqZg3PK78/WprWwpoxIYDEZk8jKRxQIYBt9ov9TEpKTqOSCYE2tLw6jG/8uu6p81gNvARBkWRs+pqfAkxWlqD7QYRqLvej5yFlJRhHPv1ip1B5ZhwoxMR7eoNHcaI5pXofBZNeA0Hs+QUtdhNely4oO5xz2qiKWjvEI5kaYGs/PEnn+Bnn3maZ4+Pv7lny8qbihncO6mMly/hh8fIZj++qJt4pk1TzAe9BH6EsYWUyD2+3RFq3THkLdYaag10Qtocm1TckGa4RIKBNesJBgOaN1BLbGaQARk2UHfnfUpWVh4KmjcOl0P+3uf+Hr/+yq/z0ulL5z2klf8Jt+fb/PK9X+bfnv5t5tP5vIez8gfQWuPWrVvcuHHjvIeysrLyAHn9GnpnucOvvvSrNG9f+xu/jdiVmZ/+3C/xFz90MZ3AjnP30k024wHsLtbYVoJ1DV1ZeXg5Kkf8/PM/z08/89N85tZnzns4DwzD+KXlZ8kM/Efbv8Ig0Vevnnnbs9+Li3P3ygsXxgl8fOkO5c7jTPPeeQ9l5Q9gXUfPl4dPADZB/BDaHmTvbtLu6qXbAVUjWpkzZdAjFZoCMnaXbI+JFo3YW0nd9etQQ1QVTSEk5i70WonXdwNvYZJVRwbtcbYSXZbWO1Wlu5LTiJUFGyaSthCtNGH0Pls3hOhsJYNb615a6XHHlfsxzy0cu46HMN17VuXM/atnsdXeHZUtHMgkRCJaOsRsoBYkDYDhvoBMXQoOMd2J4zYdqHXBeldxakCq0SpsZ5G93O8PNiRElurhZK4VS9H2qx6CdOQsE4K7h4iDnX3g2MV76Yq6O6SpnydHzHAS5jXeS/y+1uhC/16LX2YhMicjDYIkg7yHm2O7hdIMa0DZodMmjtsbzTIpKQljQ+MamYNpn8t5YJthHEYGFVQTqRiuDUkjg3gIorXi3tDqaHNgDKf0UhDJaBaYF5o5nJxgxbBlJg29Y3ocSan3VS/AkHocdTx0jZhvzYVaHe1ObGkSexjEGDShmqmaqF3X1+0YkeWi2OmCLxH7LMSc9b6DXESQlKNzOCfSOKBifQ5JuK+XirFDxxEvM0stsLdFNxM6OoMYXgotJRgzNA3hW+KxJeYRBZ409H8P17lrdFKHGC6oeLicG7gKDLH5wIl+ZBUNx3dSkjn/h+/6IH//t3+Hl3aruHfRcGBpMNWGnRxHf7qcwDRh5R5SBsyMlAdgBHZge1QcaIx5D7eZUo5RHxHd9Hu9R8XnDV5KbPYRgWGL7477ppzWd0w4jNaDE9Yd+isrbzZP3XuKX3j+F/jIFz/CYst5D2flj+Cffvmf8tgHHuN7Nt/Dbl1DLxzuzunpt69LYWVl5Q/mJ7/yk/zXv/1fP/Rr6NF8wkd/91/xn/zJ/01UDl0wDu0u73pngmfGdQ29gKxr6MrKw8lT957iP//1/5znT57/tk/M+MP4+eWnKCz877f/R3L/PDbZwDue/gF4J9y9dnHir196/Cs88eJ7GZfteQ9l5X/Cuo6eLw+dAKw46Oa12GCtUHOPf176n/WIWa/cFxuT9txoi5pckVAihjHijpvf77KN6OWISgZHau8XTkSEsrUQzM4E49ZgGpG5QhrCiIwhriHIqqJkIHpcPSm0BZWEdfdpqFsRVa0qeBf6UguB2TWEW6cLuBKxu5LCeeucRUEDKuGCxXE7swR3UVuU5oa1ECqVFmPTKeJ3z5y8ADTcKuYDZr1DVizeLcpcUVlQzXG8xNeaOVYKoiO5NppmEhVx6eeW11zc3iOu5ezcxzk2bzSE3MDcSQLiDZfcq2MbpVW8nYDu9xhrxxxSlO6GgN1mVJUkkJIiKYdwWgu1NYo3nEPQLS5KCYWVnEdyq2wFro5brm8mrkwTe0nYqDKkRE4glvBae1x0XJuqgp1WTHuF8zAhyRCG2HPgDcqZFiX4YlizOPMlHMAkCZe2OpocTQlNNeZfa5hFPHctBc2ZrBa9xCqIKmncMh1cYjrYZ6mOyR3K7ijmSvOIe64NcYv5KRpx1X1HGAo6DvG+1mCZw32eDfPcZ8kSbmWViPpujTIvaFKG3tWac2KSGJNp3DteW59m3q+bRpG8eY+aNjyHg1g8YrTdNe4bJERgwi3smqJPufcLp5x48mCfH3jkOj/z3PNrwO8FxJqBn0SMedJIUvAJNOPcgWFLO7mFXrqGNO3P9RSPSWYQxQWsNpouaDNEMkKNhANJsTGmNmhLiMzWYlPBMOLaIvI/j7jp1xzvysrKN8dJPeGrh1/lr3/qr/Pc8XPnPZyVr4PmjZ968af4wff+4Prh9QWl1oqZobquXysr386craH/1W//V3z+7ucfevH3jOP5hE88/Vn++Du/l6QXLyLyOfky33n1h9i9uK6hF5F1DV1ZeThwd54/eZ7//kv/PR9/+ePfdv2+3yiO86+Xj/G/mv4DHk9P3v+6euLtz30fNS8cH7x6jiPsCJRx5mj/DteWzYXc7PWws66j58dDJwBH520OkZdwmd4XeskhFNSFKHLtUcGthjAMyKiwLLhMkJaINU4D4bYNwYDcu4Wtu4JrOHZxB60R5ZtSf9/cXbz0aGKPWNseO40Ayy46KCV6Tr12h61BmoaIMs2pp0l3l6sLSRU/E5Kd3n2a6dbeEDZcXhcT/ZoTtvs9uzDWBWYJEUVVe5RwF9JoiAzRyavTfUdxxCoP3YkJUPGm2KCYOSINs4jmjT9PWB6wehrnzRZa7ymuJEZRQn0vr+su7pHDVMLqOtLcaC6AULqoLFZIdoqPB+FKttbjsPcillsy4or26GrxhteIy0aVtIm+Z4eIfZ4L81KxYohte7RxQxpsbGYvD2yGkf2sXBtHrm5GDrKy1YEsjiYhaYasPY44UUvMw7orpDOX73aKrt0zpysgtSGaez/zmSCacLdwTLtER7W8Fn8sOaNLwc7cuhIX2lXwPIRzVyyu9TChly+hjz3KdLCP7hZOSkXuHeK70/sxzKQQbmUYkdT3VHgfa2tQClIb5Bx1z5rw6t3lDuQzgVaQcUQ2E1KNOheiYjihw4Zx39EhU+aFujjWlhC+k+Ap4c3BWnQEtxr3FhJV0zjogIv3rt+MYbGZIKUQkVNGxFFXkgtpGPnz73oPv/zSyxzX+i19/Ky8ce7tduyn68iBIq0hLUeX70GBvctgGbF4DpG2RGHwACqU0lBVNA+oEM+Xft97a0ipaB5jA0/fpiAyROR/6jHkqpj2yHxZtwi8lXD3Nxz59614jbcyb/Y59NgBx6+89Cv89DM/zS+88Atv6L1WHjxfvPdFfqv9Fu/L76PVhyti9K3ArVu3ePzxx9lut+vzcOUbYl1D3zjrGnr+mBu/9pXf5L2PvJNHL10/7+H8G9yxWyyPHJNvJuq6hl441jV05Y2wrqNvnAe1jv7Si7/Ej3zqR5jbWmtzRmHh7x7/3/k/7f9feSw9cf/rqQ1cu/UOji/dujCfj9259hJX7j5KsodP8rrorOvo+fHw3Q3aY5HPHLPedx2Ih4tVPIRSSSGSGV3ZKvHh/7LFySH2ooRATMQNa0J8BO3O2Tz2flLv7lUHn5DRCSGzu4pl6PHQGnHUotFL7DkEyWFASCTrf47jouiYurgaUoUkCSdy6gJysu4y7u+NRg/wWXSpWPQZp9y7gPn9Bl4kuojP3JYOiCEIScKNaWaoCJL8/vkQDaFWuntavYbrzr27oOOVvWlcAgiHrTesRqy1pISUREKpbqCC+xJijHm4rqWL2WeOYLmE2dKjpxuk3g1sinsNp68ZpS7hYtUUwqAnxCAnRacRmlNr6W5gIacc4yXTlsKyO6GczlgDT4IeHyPTRCZxZUpcTZe5nJy9zR5jVqbk7CVlO0xsuhtZJSEi0TU6RPysm2LLDq/gqTvPiyNWer9uey1aeRwRJaJpu9vVa4lo6lqjy1S627VZCK1ZUWKns9cSWwWqwQSqObp/ayEBozn7buwNE1UzJ+PIsVl0+Y6C6IimPrdVQ2SW1O8r8NowS/gy416QXulrBuIJpOGiWHZSifmhKUcXr3t0ryYlJUGHhDDi5iH2DgO+1HCtN8PNe/w0vft6QJLizdEhvbZxIk5Y3Ntmcddkhaw96T3mcBuNzWT8u297kn/29DNvxlNo5Y2itcfye98gM8DdHRxse1qDwFziGaAbYAY2MCRMwNsRzpY0jDRrxNPcyTrF86k1mPZAanSUlx3I1OP+Ndz4KIyXzvEkrHyjfCv+cfiw/wPzzTyHc5v53Tu/y9/9nb/LU4dPcVJP3vB7rZwPP/bFH+Nvfe/f4vYLt897KCt/COvzcOUbZZ0zb5x1Db04/MpTn+J//f1/4byH8Qfyu8un+eDjf4yj59aYxIvK+jxc+WZY580b50Guo6v4+2/ynD3Nb9dP86j+xd93Hi/fe4Jp9yXm7dE5ju41XJxbjzzPo6+867yHsvKHsD4PHzwPnwBcDdLYxZ5wh4Yd0RApvcs0hTuwnvXNCig4OUTIlCPGOY+4lRC+yhIxs+Ihwnbhl7pEFS2tC5fanWnRZxqCWXcjiwFDj7RdkCyRRu2CDgJewnM7DISA/FqHKTjWKqoZsdq7axsMm/gza4ik7g62HiV81iFMdyefieFEJPZZ5LWkLpwZ0vt6VcA1Rxy0CNIiZlXduxBew+EsORzCHu8jDTw5bnO8bg0xsOWMizL0PuNZGg1jGjJUo4nRGiQxJI9QZkjWz4PFeFWorYXburuhQ/msYDOeN5EaLYqJYVbAHKmObkdSd2C7RyR2FmdIGREwM+rJKcWc5TiEbzYTtCNoxqVpj8uiXB+Eq9PEpUnYDgMDRnLI1hhLJWch5xGxhrhQywxp7LHgjaSKakFaRVoKUVYTKgNaFxRF8oi0hkqIm9EXLXjK4V42x1rDagOXEI3deyev4KqILfdFUBeLzQoVUnZ0GsiPXkPf+QTt+hXme4fYzR6BvRlIeYjY5zH6n/GzLQYGLrh3QTg55Nh0ICJgFUcwa6hlTASrYCmjZlAWZBxBQ5A193hPG0LkFfrmAEXGhPf+5SSxBULc4r7ViMGWs3usz1+RcA1Hp3Lcc543kFOI4dZwtxD9c+K7HrnO22++ynMn6wcoF43Do3tcvXIF2y3I1Wvw6k3Y28LpMQA2bUjDFALw0kC2Ifa7RTKDHtBt+3h3xZtuqa2ShxHPQ3+OE/PIFK8l7jUn4qDLabzmysrKG6JY4TOvfoZ//tV/zs8///PnPZyVbwG3drf42J2P8Se2f4Ld6RpjedG4efMm73znO9cfmldWvg1wdz5181PrGvoNcrpcXHF156c8Nz3FE3vv4/Tk4o7zYWVdQ1dWvr1wd2ab+dHP/yg/9oUfO+/hXHj+P6cf4UCu8CfGP33/a8kyN155H8+98zNciNRlgZO9Q3bTMdO8t0ZBXzDWdfR8ePgEYBVgiQ/w84S3BXHrHbkK1C7O0gXZKXqCdUSkhtCI3xdCpSnkHjMsQkSNEn/PQtx1H3qfL6GA6hROXhdoJRyLorhEj6snwrB2FsWcFCP0WZMRNYj/cby7QLGKiuBWwST6dPPUReYzoVki4lp63LW07qI1/Cz++UwU1y6a+RTxwKoIC/ef5hZjM6vAEGHMrhgN9R5XJJmzGOmoTe7O6GXBpymOXSKy2EvBUyZNG7Q1kkObEm7COIItCw3FXbpleOxq8tl7DTiGponmpV/D6INFHdOR5InWSu/O9XCLiqNjImsGAZ8XzKTHJo9ITlhdaE3YVaMVw+cCB3EN93eNK0m4nmeuZOHq9ip704YpK9M4MuDQQnxMQM4DqZ9rN8NLo2mhdk1ykEYeFbyLvymhm4mUEqoDChGZbQYydmG+X280HMVZQqBvIVR7d9ViQOkxt57JbtTmtN2ONoU3WJJCTixL4d5zLyFffYHlzj3mo2M0KSklJA+IN1RAdIhzjHdxV/FWu6hWkXEKVy9EX2o1bJ6hFVyFpuFoVje0NaTM4BljiCk25Ig3rxqu8JSgdwa4N8QET4q0GhsQkPuid/eZ35+z3ntgxWv8AyAPvRc4NgUoFRcniZCHxGMHB/wn738fP/rFL/L8+gH2hWKW2OjganjdIZPClOHUYWqQJ7zWSAwYuiPYGhyfwt6IMKGa8TrjUkjNQDeYV9wGbCmoWETo5wEZtjGX2gzDRGThb6GsH8qsrLwRXt29yoc/9WE+e/uz6y7rbyMM4x9+6R/yvj/2Pm7IDU7WjVQXiqOji7E7f2Vl5Y3x6u5VPv7yx/nbv/W3Oarrff2NMNeF2yd3ubZ35byH8gfyqr7Md7z7u5GvyrqGXjDWNXRl5duLT978JH/jk3+De+XeeQ/lLcHCzMeXj/Hdw/ezJ/v3v371ztu59cjTnO7fOb/BvY46LLz45FM88cL72Mz7X/sbVh4Y6zp6Pjx89qWkPc7Yoe7CPZtS9JpmuqDWT4sCubtzbel/pOGoVIVau8u3AoJoQqTH9J6Jblm6+Nri71qL16N1EbdH9ApId/VGLLMgGvHOpAStYh5OzhA+DdBwhHrEMkvq7skseI6o37M4Z/Ue/2wN9xBlI/P5TEN83VTw1gUTp+dbd9E40UfUz5GjGnsIzAy3Hv+LhRDd/7ZIDvEuZ5CCjDkkOYtMYG+Ge8KssJweM9dGtYhAFhGSFFIaGMRJ2p3bZ+NCMbP+Ukaj9HMDpNzjYBPqitNo4ph1MbQZmjI5C0rBW6USgqKmBFaoZaGaU3Y7zB3fHcI2unj3cG6MI0/cuM6Tl67z2JXHubq9xEEa2JKYUKZhYtyMIeDSXazmuEOrC6UWdruZ3VKwWkkkhmHDMGRySohFl647iCZ0GJBhQIaMWkVqQ81iXjSLGOgaHckyDuiQwYy2FHxZwkE7aIjKU4o/r0ZdalxuTbSlML98k8OnnuXOV17g5NY9WikR+ZyAWrph3cmEeK55BFdsXvDSYj52wRrr3cJW8Va6G7zHLlujLQulVNzjvID0yPTewaEJGcYQz8/czuZhcsehNXrVMZJDKH7t0Sav+y3c73K2x2EY0JQQ1YggTzn6YVNG+/+/Mk38b9/xNq4Nwzf4oFl5UzHDD++heQOHJ5C3sDhMY1SB0/DT4+ibjm+IjQr7l8B36HIa0dFpIuVLkC4Rz+SEt1OMEsnyOmHLjLUFbMHrDp+PkZ7GwMWoOFlZectRrfLy6ct8+FMf5hM3P7GKv9+GVK/8xFd/ghvvvME0Tec9nJXX4e6RZLOysvKW5XA55MOf+jD/xaf/i1X8/SY4nI/56q3nznsYfyiO8ZT9Lu9933vWNfSCsa6hKyvfHhwuh/z1T/51PvypD3N7uU07MxetfE0+Uz/Jj5/8v37f19SVR19534X6jKzlyktPfIVlWA09F4l1HT0fHj4B2Fq4O8eMDDlstZ767zXE1pwQzXF2vEIe+5/3WGW6COkNl/S6GOkaZbbe4nWE19y40jtd8xjCr+YQIVMiJXDJeOmCcBeYpTUk957hBOY53oscv5z77stu+QyxLGVcEu6lO46J95XeByyK22vH4tKjqc/irru4ixCdq96f4GfvBT3K+rUF0jXjOC5dtHMLh6tbdO2edRzL0J3P/b2cPvYa7tBSsFqwpeDWz0PakIaETiNJnQFHzMFOQHII7yqvi7OOuGxYQicmxLxqBW8FlyXe+/go3KApRVS1KzllUtYwigu0Uinm1J3FudhmNCUuYzyuI49evsqj+1seGSeujRsOcmKjyiiKiqMqJI1u3Zg+IUCXWlnqws6U01JYlgJA1pG8mWIcoiQXtEk3O5eoh84DKedwzuYhNhxIjy5H8D6fdRzRcUBybHDwHhMtWWIKDSOSYuMCGl5Zk3B1704WTg6P2B0dU+uCtYVWGzYbNlekOFsSV7ZbDvYvMWpGK0hRVCd0mtDN2O+H7oZPKbqMhxwbI0Riw0KrtFKopWDV8GqIN8Rb9PSKxG05JIas3Rk9ICnH6w/x36La77v4HjOP+WS1O6AdSgh3kgdEBVVBVXvKu0a0tQoqMQdUnWvDyIcOLn/rn0Ur3zQFON4fwDNcOoBpC63B4d14TpVT5MoVfJ7DtWsViOeupivINMEwRse67UAKyNLXgCE20zACxLO0nGCl4cM2nOO14WcbZVZWVr4h3J0ff+rH+cs/+5f5xM1PnPdwVt5EfuPV3+Bv/ObfwPcu0CcBK+x2O27evHnew1hZWfkmuT3f5oc/8cPrGvoGef7uy5RWznsYfyi32svcTbe4du3aeQ9l5XWsa+jKylsbd+ejz36UH/7ED/Ozz/0sr86vnveQ3pJ8rv4WT9cv/76v7R89wt7J1fMZ0B9CHRaOL93p6ZArF4F1HT0fHj4BWBMybHoGfLg8hdYdsBLCLCnMsRJ9vJhFnHGeIvbTWgioOSHSRdHUna+t9W5bIuKY3N2PEmKkOZQuSCldOHaStxDF6J2piS4cp/7+A4hhWIgULFBmXBRHQ5DtXaxmjovh9HGY4NK7baOstZtnc4+HDuEwBNl4KLpHpLAk7e5k7d2+9HMXfasJUA9x2/sjNdKGc7SsutHqQjuLoZYCVrjvmCaEvlCbLaJb3bvBWcMdbPF+OmR0GJFhIA9TiDRWqe6YN6rH+QtSRHr3PthmhptiOnThcUfaP0A000phaYZZA2tIsx6XnRGU1BppT7u7e8OBwI0sPLoZeWSz4eq0x8HBlu12w7S3x7jN5GlC0TiORt9A0LCyUEuhNtiZcArM6rScEE2kJCSNGOIkRspKmjJKRTz1DQURp30mXIobKgnN4bQO1zNIGvp5FiQrXgq2W7AmeDOoNRzae1vY22Kj0ppRF6PWSlsK1BnbneLHO9g1xJw0jIxX99l//7s4+NAH2HzHu9nceIS9q9fYv36NcW9EVZBmiAsiY9xforgS7s1S4nyfbV6wRquN1kIsFgRNuXdvO5ISaRjIm4lpu2HajozbiTSMEZtuLeZea7jV2HzQwo3stUf5mvWOb0GGIRzi9537hDvaYgKLCGnIpHFE8sD3X7nMNqVv/fNo5ZvHGpIdmXdwfAx7HmLwdhsbTKjo3j7MC15qPJ/aEc4c0dCaadZw2QM2eIuEhNaO8DLTbIeXk9g4YRmvjrvEM7fNuBu27hJdWfmGqFb5x1/6x/yD3/0H6y7rh4RPv/ppXtQXyfnha525yLivH4KsrLwVuTPf4Uc+9SN86uanznsob3meuvk0v/ylT573MP5QKpV/8JlPcpe9dQ29YKxr6MrKW5Ozn0X/y0//l+s6+ga547f4O8d/i2fqV+5/LbeRq7ffgdjFMkrcu/wqlup5D2Pldazr6IPnoROAhd4HWayLsDlcg6TodpQhBMrauls49/jY2gXdFv2zaYqI4hTCaAjHQwjFSXAqaO4usbG/R+uxyl1cjcZSXAciTlnBu1utWgh4kXML3sL9aUt3IjvkDNQuMJ91/YZgZqSIlB7y6yKkvcc+G9JF2170G+P0PrYW8dJnYm5EO9cQMaEfqyMOiEQQtRlWa3clR5dqRDJDbQ0zweoS513G1zpp0ehATjkEubMo6pS70N6gNhwNg7Ok3j2rDHmP7I0sSkPi3PcY6nAbd9FaMs0rZhJisp+QUPI0MIxj93MbohadtpLjfC7L/Xhsd0PF2afySEo8Nk1c32w5mEa2mw3D2GObx4E8bUg5XLleanQGFwMTmjeKG4s3Tq1wqlCmqV/bfnqbk8YRnbboOKEKKQ8kFSgVXxbEU489jg5dVBEF6UKn73bYMofobnZf1BQP8dXnAi3crkK8rpVGM6PhWFuQukPKDi0FFSFvMnl/SzrYolcv41cus1BIhye8I214x7vfw9773oUcbPBaoXlsIBgT5BCjbTFaM1p1anFKi3tQUsYNWjOsi8IiIK1hzTAESUoaNgzbLdP+lnHakIYxIqFVaEAzp5ljvesZISKr57in0zig0yacyOnMUd8jtM16nHSPgdZwWOuQuTJkvv/KxeyIelg5PTG8LpjNeFaYGwxDf56MMB9H53jegkZMPGSkJVo7ppbT2GQAnO2K8TzhNlKb0MhUGeL2acc4hs2n+LzEM8xA8t75nYCVlbcY7s4//fI/5e/+zt9lseW8h7PyAPnvnvrveOTGI+c9jJXXce/ePVpbN2GsrLyVOFwO+Wuf/Gv8+iu/ft5D+bbh6dvP88rRrQv7QeT+5Xv8ynM3uXHjxnkPZeV1rGvoyspbj/Vn0W89t/wm/2z3j3/fGnr91XcxLBfrc7KaF+5dXp3eF4l1HX3wPIQCsCNW+pE7YKFr6hJuW2qPUu4RxVa64Em318auEXHCkdknrJuD9ujjZcFFIQ09Kvns/VoXUVMXajVqgek9p9Yi0pQuzJ3FSEtE+iYT3HO8btogaUAkoRqCswuINcS196zWPqb4WpLcnc7+Wmy1hHDs3noggkastbWuDVtE66II9lpUtt/3+yIp4o5zHuMkyQguuDu1GmWZqa2EwKYJMKTNEfMrCRGHpIjm19zAQgjuWUIoroKVBT/rsE0telqnkTEnNsAgPbc59dhskRCpiShqUw0hVEeGvEFFaLXE+dQU4n/4lqPtWBP0c6s2cyCZt+vE20a4kZQrm5H9pIw4aiGwYg61IXVBa8OXBaslRPEEDaE47Fpl0YwrEed8X7x1RBRNHjHU1iKOeBxJeYi9Bgh0p6y3GnOv1nCfa0aw2COwzOH0FYlNAJsJthF9i6Ru/O7R4QiOYt5ivKWSxBi2W4btSN6O5M2IDglNip+cMj/9HOX521x54kk++Jf/fd77F/80m70RP5pjXm4mZBx6OS8R7V37hgUhHOzzEhsDFITWe4ItzqMAKUffb6lhOsci9VmVaUjkIcNmgHGiZzaDgbcQwqkFaRVFSNsxfk0hpifrc7tvKFB3FCICehzCcZwymhSdJq6OI2mN/L0w7ABbCrJ/HeoRVIN7d6EWvBmWR9wWfNlFyr9M8bxLI03GvnkHhBOgRcc2CtM+TJuYSwKo0kyxUvr3KEa4heNZs7Ky8vXwsRc/xt/73N9b45ceQk7bKbfb7fh30sqF4PDwcP2he2XlLcSru1f5a5/8a/zGzd8476F8W3Fvd8S//O1foNnFfB4+8eiOX336JW6e+rqGXiDWNXRl5a3H+rPom8Pv1d/mo/M/x/y1TtcnX/jQOY7oD0CgDDMma+/sRWFdRx88D9+n13LW89vFUJeeepxD3H29e/QsorY1cI2eXsINSu07hvKZ6NQdhGkMEc4MbxKvmUaggg5dgBh6B65jQ0Q8W2m4KJISmjJGj5l2h1rCj+uGvi6GNjxr/QeBpt2uGwK3tohZjsxdQHtfpYTA6b3zNjpjPd7rLAJaHBdB3CMuV1IXjukiuCNEP2+cvHCRhgBaI2qaOHetd96atYjnJQRkHSZUMiI53M6lIhp5yRHJ3Q+QBGP0uqpmfG54dUSG6L3NCc1KHjdMIgwaHcOSMuqNTI9t9VPw6H/NeoDmMVyt9Ddyo7Wzc26oKkMawqdtwr5OPK6ZJ7cTN7ZbLu9dYivCYI4Wg6VgpzPt5JhaKy6KdT24udFUqcDicFoLu1aoKTprlTNx3rro2SPDrXZDtPT44hoxz+PY48NBmp3tO8B2DZsX2jxjtWJLoZVG65sM7GyaqmCqmDtuglulWaNaoyIYGj3D2z3yJkeXcO4ueXesNdrxjmU3Ux+5inzoPeyeOODo9JiTW8e4Z2QakSEENDOjzgttFz3QqqBjig5ub1Bi3CGqGVZ2tNJjwyWc6K1GrHN08yqaBvI4Mkwb0jBFB/DQneWtQu2/SgVJyDCiY+793rG5QrPGHHKgb8aI149YaB2GEN41ITnzfY/e4Mo4fKufSCtvAPVNzJHF8e0+DA3mGbZbJG2QPCLjiMp+JC4c38X8hKQDSRUrh/2mOI3ncm2xEYcKdYZyiNaGbi6Rptg4EU+7sW8QWv/BsrLy9XBUjvhnX/5nFLu4XXsrbx4vnb7Er5/8OsO6hq6srKx8wyxt4cOf+jCfeGXt/H0zOJpP+J0Xv/j7Pry+SDz+jhf5lReMYRzPeygrKysrb0nWn0XfPCqFn9z9OL+4fJTmUee3d3yV/cOLlf50eHCLmlfn98rDy8MnAJPDWVp77HFOXfSr3fy5hKvLGm4F1zHEXgea4Uj8rsSH/0sNISB3cdgKjuBp7O5dxb3iVUIgLoXIDu35tNZ6bHTu3a0Sjk7L98VfhilECu1ir7V4je4cxg3RhmjmtXhnD1EMXuv5PVNVpfcSSwO6oKxDd112x/D9PmB5LXL6LCYXulgsPbI5YqrdLcy61u7HSEsCyQM0wXMGWcAUlYTbAl6RPCCphWm3ViSNiNWIsAZoc1wXkR6vLRHvi4djFlBNaBamATZijOpkiV7d7vNGRFBpIA2xufe+ahxfu4X7Cc0qCYdkODPujczMIzSenEYe2w5cm/bY08yEMCQhTwMqjluhFaPtCrVUmhgFoyDMZpyUxlFZOLXGknMX8JUkjnYHrmfFZcDCihobFFqLLmSku7UroOgwsre/x/WDyxwcXCGNE14qbWm0atSlUueFOhdqLdRSWMrCcnJKKZVWC8WMUgul7iitUpcdvuyQskQkufaIcHO8FawYtluoS7zmcucO937jt3jxf/gFjn/pd7hyKuxt9klkrDptMeoc8dIyZPL+RN5M5HEiDRH9bR4OX2+GWURDL3OhzoXWyuvmXzh1U06kLOiQSWNmGDM6KCTpDn9HWgv3ryZ0yEjWmJOlhhEfCZE7dcHXLPZHJEXGIYz63YguoiQcTcq/8/a3v0nPpZVvhpIbLEewdyU25uyNEY1vhp+cxnNuitQB3GCzB+0Ua6f9ObSFdAl8jI0+4ng9grIDO8LTFrOKWO9srxWrBZESm2LK2iOysvK1eHX3Kj/86z/MJ29e3J69lTefj3zxI0w3pvMexsrrODo6Ou8hrKysfB189tZn+eytz573ML5tMTf+1Zd+nU8/+7kLGQX99sdP+Ie/9hzjlcfOeygrr2NdQ1dW3jr8N7/z36w/i76JNCo/fvqj/P/m/xF3J7eJq3feduG6gF+98dx5D2Hldazr6IPl4ROAhxSxyIkuiiqu4M1xGoaHm1K6M9gM9xCJvS1EVG7DWzghXROUJZy+zbuDuPT/v4v36iJadAqn1/p6rXUBdcZkwZfaBdaGdkkwIpO9Pzj7w1MTryvoDSeu9j7TPIbrTSPO2CXkz1C0XvcDjRleW4jTVkJYNOt1sfK6H356XPSZIub0qGxBvKGE6Nbtk1jvFG5WaZxF+Uo/J4I3wcWwuqAqJCqRRNx7dyWFOO6G5BSuWKLj1b1hLaKOve7idyHEbq9oElIWhjPXpsj96ynso2qIVjJCkkQWJ2sjSQK5BOyRNRzYOmxCkG4nXNYNj+aBR/cnrkwb9obMpMKgQpqGEB8HxVPGVCneWEpj3s3sysyJLRzXwnGpHBdjlkQT7YLumbZuOInqmepOKyWc0y281E0Eay2c4hZiuly+xPi+93L5Bz/E9e95L/uPXSUNAyqC9k5grxU7PaUdn9KOT7CTis1LxJQb+FJpxzPlpFJOZ2wxqNHv7GaReO4h5J9dt3o605aFdrqj3LzN0TM3OT6tPPKhd/HH/pc/xPu+7wNsr15GmsNSkKWiqqRBSaLo0KPR3fFa8ea01qhlodVKWxbKbscy76il9cbsHt+eNNzbOiAImhM6bdCk0CpeG5T+ns3DMT0MEd3b5z0a4nkahoh31ohRF01I6s52CEGwtehg1kQW4R17e3zo6tVv+WNp5Zvj3slRzI0hR7/7UYPdcXSp7+1jR8fQ+vN9OQXdA9kLhz2GkGODSd6EGz2PIPvgGU2XyXnTn18L1KXPDwc1vIG33XmfgpWVC42789lbn11/4F5hsYWfePYn2FzenPdQVjqvvrp2Ya2sXHS+cPcL/MinfoTZ5vMeyrc15s7Hv/JpPvPc72J28ZzAc3X+yW/d4dq1a+c9lJXOuoaurLw1+MLdL/DLL/7yeQ/j2x6j8c93/4SfW/4lzRtXb7+doWzPe1ivIbDbHHN46dYaA35BWNfRB0s+7wE8cDz6bAE8awhCIrgJ0Pt83cIRqxp9pJq6A9VxDDx1M22NTt74pi7KVrD0ug7h6OJ1HHTo1bsLpAmxCsMQkbWecG8kCQHKI7Q3en6pyLCJflzXEFt7dLH3jtv7TlYRUOd+tL076BTH5l2MpXdbnrl7NYUgot2l3DuPXfprn7nnCJHsvpAs2l/PcBHMQ6wMlypIXWjWehR27yQWw21D6w5XSY1kFTWNt88jJh6Cs0Yssg/Sndb5/pYF1wkoSDNchvvdwuGmNpSGW6XUcHZrUoTKkC4xJCVvJtwNbc5Aw2xLciOliJV2osN50zbc0MSjly5xeZrYjBPZK5IG3FrEFDfBcbw5dZmZ60JBqa2wiOAl4sCrJCyn3vebiEvkSLWISTanLDPahHy2byApahmbd+Q8kgTUC66O1MJslaNWwwW93TIc7GPDgHmj2YDvTrHTfs2xqDpOY1xb1RD8TbFWUYkIc1EBTSE0O3FsLlgDK61vUnBsJ5RxYbcdOH7XDU5+4D2MV68w7MFw9yZyfI+UY15ZWRDvrvJmuIXwe7ahgu7qtqwkFdSMkjIpjaTNGPeiACnOHwJUj+mJYLXhJzN+sou+3zEin30z4inc9CI5HMEqpCFH12+rVOmx7vQuYCIKmh5rnkUYsmBF2cuZv/TE2/jSvXssF/DDgYcNGSeEIeK+h97hvdmDe6dwdQvDQCunaJ6QKeE2x73b49YNQ1z689xhOUFzPIvcM7YUVIeoDiAjso3Y/90RbCd8SV9jhCsrDzeO83d+5++c9zBWLgg/9fxP8QM3foC36dsu5AfsDyPua6/kyspFxd35mWd/hlfn9QOyB0Gzxse+9Os4zg+8/UNf37Px7GOZN5G9TePtj5/w8acP+Y//2Heid++ua+gFYV1DV1YuNus6+mApLPyT0x/F3fnzm3+Px1/4IM+8+zf+6HXyAayjZ+9jqfHKY8+wf3wF8fWzvIvAuo4+OB4+B7A3nG3ELNcaDsRiOIpZjxcuS3zdKu5zuL+8hYhZ631x1iV1MSycxKQUqqnymiCZzspavcdKC3iPd/YcApZswg08JCyFQ5YkmESf8Flvbqh8FoLxWV/xmVvRe+zymRBNF2fv9/5CV8sQDOopKCFoM4SrzfrrWb3/dyPWWl7Tt9uM+/2CXuLj3RB/l1LDybksNDSSs1vETXuKbt6zbltvDq54dbw1TCrQwpzdIGkmi0Brvf+2QN3FaW0LYMiwiWhil26Ijr5mEYnY3jSR00DKAyKOyEjWTFJBlIgA7nG/WT2043GDWoJlJplwLSUe2Y5cGVJ0DCMMecOQE0mFtDRkt0N2C76b8dMd7XTm9PiEw51xunPmRSlmkBM5NYYebu1WwlnujplF7+0cvb1xdjVcjaVEtPPcnbe10Uqj3LnH6Zef5fB3vsjhU8+w3LkDZqScyCmhaYjXsBYB0wopZdKYyEnQNKDqSFbUK2qOtjlc3WLhku/Gc6uOzTNWFoyIGEfA68Ly6qvc+8RneO6f/Axf/sj/yO1Pfx6OdmQXRhLjtL3f1as5IarhyB0zPiZ8ANeGCTR3GhLTZJ6pJ8fUsvQprJhmGEdkzEjOMbh5RuaCmJNzIm8mdG9C9jZISnHPt76JoQvfpIQOE+PeHtM0hPsX4h4/C+cWUI0+ZBUlJyVrYjMIf+6xx7+VT6WVb5JdWTg5vIOXHX7vCL90JSLz9/ZhPgUE0gRygs8ztNP7UfbmLWLpc8wRIDYaIDRX/Kyz3Wv0/o4Z2g5pNd7DiQ0nKysrfyBzm/l7n/t7vHL6ynkPZeUC8WNf/DGuPnb1vIexQsRu3b1797yHsbKy8ofwE1/+CX7iqZ8472E8dPzrL/8Gn372d76+TuAH8Jnl/l7jP/qLz3JnOeL/9pOf5+qNNQr6IrCuoSsrF591HX3wOM7/sPt/8zO7/y/7h4+wf3z9j/6GB6X99fdxMW5ff/EBvenKH8W6jj5YHrpPr90cVwsRUXOvF3XwgpFCXBWQtuA4SgKVED29vdYBDOHotYZ4wbtzN+yJFakGuoFiEWucottXpIUp1wWSdzHVwHrvLh5VlGKoDPcjoRG6eNoduqrd1FlAMuJGbGDprl4ivhYd70dFoyGMYoYMQ5wPNMTV3i8bymh3+bqDLRgD4oaY46rRMXz2VvEi1FojopgQdilLRGZLjq5lJRymKhFn7dEriygmiojTDNQson4lg8+oQPPuTPUloqFT7r244BrObW/RHWx2CjohVMiK6EheFqo1BEU0hE2ximsXECWh2kgkNGfQArOyR+G6CtfzwJ4qG9UQftNZB/EAWlAD03CRp3EkO2QrCIansPKKQh6VjCOkHjY+3HdtS5vvO7xVMyoa4rQIWAmRu1ZMJfpsq2O1MtcTytzQzUQaBaYRbYZbRuZTpDlqBWRAJfeNCxkGQVLCFyFZw9NESkLuu29EFHVDnRBPrUvSPfqWcQgBFyhHR9w92nH04j3G65cZblxCpsx0sMcgUJfKfLqj1kIrM14iCjzcwAWW2CjhvgAJd8VEuiheaPOMHGwY0oCJ4jnjKnE9HbQOpP39iAxXQVMGDUe21+gCBsHdEAlBfKqNabNHHffQcWBYFqy2+xsoxA3FURGSZoY04HaKjOGQ/tDVK/zm7Vu8Oq9xbOeJu6ObfdSgUkj3ZjzFphF0A8spbBXzhuwlhCvEg2uh2UCuhnkhSe7P6SGezxqd3IigmsDmmKe6iWh/SdCOkOmR8z4FKysXlueOn+MjX/zIeQ9j5YLx4umLfPLok3zX9F3M6xp6rpgZrbXzHsbKysofwCunr/DRZz9K9XreQ3noqNb411/5NB98/H3sjRcjvnJ/W/n+77rDp39rYv/gCsd3b69r6DmzrqErKxebdR09PwoL/2z3EUYZ+cFXv5fj/VsPTuj9Wggc79/l4N4jjGWtJjpP1nX0wfLQCcBWF6D2+NvoITX33sFake54VRkwD7evVu+xyCm6bM/Mr+Y92jkEU/OIshWP1xV6x692x6x7CMVpCkdvEijhrpVhCAevJPKQ8WWHDA5WI56UFO+VxxCK/SwnQXt8qUWCdVbu9wPr61zA1cOBK45Yw1PqL6FdxG3dCVfiOHvUNTLcf/8zITp6dyX6M22hidLM8FLiNfMQ7l4/e8IbmGIkRIdwIONQDRlHqHOP951CkO4Rw7igkqJXGMNTguLh6rRwaN+Pt5bcRfkM0kLot4a2irlH1ytjnEMZ47itYtVotSEmaFas7MATGxeua+bGJnN5zGw3W/KYyDkhZhiCtIpKwqeIdMZmQBlloKVwNi/itJSQNIWfVwZEBHVB84C1GmJrHlGbycnJ4hH1rIZqBhRrce10zKimcDTnjOO0WmhHhaoO3RlrzWi7OdzXwwbRHsvde6pdFffaO5cbKUFWQXNG04gM6bUuXI85rrlHhXuK2GY3mjVsnmmLg5yQT4/ZO9pj2p8YDy4xbgaGXYF7J8jpEe5Ga3N0Gbcz4T5ctmd7F6pX1BoMI2oD1QQbBvYODkAzO4zWiF3ZPoRrvHf94pWzaHRTYl4mRaz3VgM6bTh4/FHefvUyN49nXjw9xocRdjPeLDYs9K5rUUXUSTmTZC/m/TDwuMDVcVgF4AtAGkekLqRLV/F5iau82YuUg8uX4fAupITLAWYLSSdIl+KxZKe4J5IakrZ4PYZhLzay9M09pD0k7SPWIGewIe51PYiO95WVlX+DF05e4G9++m+e9zBWLiDNGx+7+THedeVdJNborZWVlZU/iL/56b/J7935vfMexkNLs8bP/u4v8xe+689eCBFYBP7cH3+FO3dH/ttfe5n/+D3jKgCvrKys/BGs6+j5Yhi/tvwrvu/eDyIuuFycz87KOFPzsgrAKw8VD10EtCnRj1rB247WdlirWO84bdawVqh2gnmlITQreKtdXBJcpLuGKw6EVzCEugg8qLiEOOdqISbV6JCkeUQvW4uXU0NSuBlJQ2i3WPSWnsUr64DXQjsLx7cQuc66WCOt2buAexbfHJ3DvXQYqPcFPU8RdyvuYB4dmJrCHXw2JST3+N+Id3arr/UHQ4jZGC4aIqOD5yHG3Fr0HN8XvrkvRHtrWKvhpAagxThNURO09e9zwAVByF20PnPrilhsXTAJJ3ddXrMiJ434bk+YOSYCqqQmiDTMGl4KZhH7fT82m3AFS2mkWjiwhetJuDoNbKeRPAzkPKIOSkZdUWtILVAbLAU1IwuM48R2HNnPyn5TNibkajEHzBBbUElk8YhiFkh5JGkipYSKollJaQix1yUcqT2SOAloyjFvzjqYW8OWip3O1HvHlHvH1JMZLwZ9bkO4ZxGLDQG7Eu7YZv1SRyy0DAlN3Wkscv9aSTNE4sNac6EsC2W3sByf0uYFWBCfqfMppRVMAFHGzT77+/tstgfosI3zboAlJA2xYUBjDjqGNaPOhTpX6rKjloINI/uP3+DRdzzJ/tXLgGCusSlgyuHMnUbIEZluLrRmcV+jmIZb3JPCwR7tO96Dfd93sf+26+RW8XnBa0NKRRrQHLFwQKce2T2mgZwSSZU0ZP6txx67MJvYHmbunh5HX/hcojd7jo0P5gbJYW8PtpegNvz0hNaO8XbaN7v4a73m7tAqPu/wcgLl3v3oftGEDANSF7zu8KTRe356fN6Hv7Jy4TA3fv75n19/4F75Q/mNV3+Du+Ma93QRuHnz5tolubJygTA3fvH5X1zX0AvA07ef59k7L8bnPheAlOCHvvcWv/qVV7nD/nkPZ4V1DV1Zuah8/OWPr+voBeBL7fP8o6P/lsu3nzzvofwb3L1yMwxiK+fKuo4+OB46BzAlHL9Cw0QjKvZ+DHS3nqcRCGHMxUJOdSNL7wcVwVUwi7hj0RbOQEk9Nlnw7LhX1Ife0xvOVHTEl95NWR3Eokay+n2RtHVnppkCjexC67HIzY3sChJi9H1nLhkRxc/cu9IdjxaORkk54pMlhIvXaoLjPUTS/UjmeL10P15aoQshFj95WMO1u3ARqlvEPTt4a12cBVLuTuccEcKukD0iiInXkmYwTWSZuB+dnSJGGxQXwpFJ17KFEONr7WPPEe1rDZd+/cypIriMcQzu/TUNvFA8oaWAV9RhGgdcHbEQIffNuGqNK7myRcl5QyLMfpIgaUINRIZ47dowq71XNpGsMTKANVQLySqLGLV7nyUlkhWkKpYyZhFZq8OIJEXy8Lo48ThwcVARNCV0HNEh/o6YQ3VqhoZhZ65ma/1ahIiunrFauzm8hZtYU7y3RO8pKaM5k3qU8pnIb9ZQL6Hj+9DjuxttV2nNEHHS/iXSpGhqeCvYySlVE2rK5auXaAfXSMfHGM9jpUWitLXo2bboQHZrIdw1g+Y0Ztoi1N3MXBfatWtsH30EffY57NVbsVmDcLr7EA5pmHAKbSnU5jRraK0R6etgtdFu3qV89rPcfvE6u8WxYUSz9PeOdADcIgIbI5/NXwBVBGjNeOeVy9/KJ9PKN8muFGTvEr47iWeNKOw05vB8C4brsdHj7ilcvYpzArbgYrhsyAjmoPUY8oQ1Qzw6xNOwB3XBd6eQ4tpbW+JZb8oF+TxoZeVC8bEXPsY/+N1/cN7DWLng/N7p7/Gn+FM9m2PlvDg5Obkw4sbKygoclkM+/BsfZtd25z2UFeBXnvok77/xLpJcjMSKJx/bMR3c4tTfzaXzHszKuoaurFxA5jbz0Wc+ymE5PO+hrABfWr7AzZtHbK6MtLyc93DuM08n5z2EFdZ19EHy0DmAcRDN0IW8ZEJGyFi4cQWyW3j+JPTxlDJJuvs25d5RC3jDNLprazNKq5RWKBjVDPdMI8RBV8XNukiVusjcQpi0hssYnaUpYpfxFB2+Z0W7aQzxrhlmJQTW2ndJeHcQe7iLOXPymuFeEDIi3cfoHh+2eRezvQuzHlNBROL97awXuCFId8kpZ7HTIQ6GWxmvEcFsRBy2O2hEBEfHbRwjYtGx7BW36FV2q/gyY2VB2pkbuEKTHq3Na6K2KVIr4n0M1hAJAVDcIt64dbHdG9rm7pbugnYDbAPNQoSsNUTwSdFhQhWGVrlsxvVh4NKwzyBEHDG9b1Rz9MzmLsZOU8wPFG0WLsRS0OZkV7LGfBpcyYsjJRzSFmeVZE4yR1uLiGi60GxArfEg1ISmRBqGEJ81hRN4Gtm7cZXr73qMR594lL3LV9Ac8zOSrkc0JyQldICUEylpiMwCqKCicc17T7EOQ3QP5yHM5ZG1HHNeU4itOeGaadaj04fxtfvBwGqlLIXFDH3yUZ78ge/iA9/7nbz9ve/l6nvfzeaJR5Dt2Ke24MXwueK7BZsLXmv0IeO0VinLwu7Vuxy99Art3h24c0g5OqWe7MIp3AxTgTSEA1yFMPF6jAfpXdSKl0o7PeXk1WPuPnuL41du0U5PsVLxWuOec4/NBR7XIrsxZCUPA0khm5NVuZxH/q0bawfseeNEqoFfuoIpsJkQPUQuXYFlgnkGvwdboBTEt8AWdIpwBquxP8XjGa2S0RT3mpghw9QTBiSeRWmLLXP/nlW4WFl5PSf1hJ/86k+y2MX54W7lYvJzL/4cj6xr6IWg1rUbbWXlovCPvvCPmNsa7XtROFlO+cxzF8dFlpPzgx+6zT/+9As8cuPGeQ9nhXUNXVm5aPz93/37/OxzP3vew1jp7DjlH7/0Y9za3Trvofw+Wq7cu3LzvIexwrqOPigePgFYBiTl14mgIBoCapYNOW0REbIIWZWkEZUcEc0ZfIjXcEVTjr+TNMTkKFfFdcCaUWyhmoZAXBbcNcRgWu8c9oilFY1fmkO0tRKCqdcQWr2EOdktEqCxvkMihFB3i69Bt8h24dUJYVeFMzE3lDqJY9L0ukTn7ij2frySQ1DT/NprdQccmiL62p3iYEs4h126aGsAY3+/EFVcu1v2zBms1qOjvV+GcC2nPJI1k1TDdWkW7tSmiNXouE1KGsIFS2uo9nN530USUczkAYYRuvCNL/H3zLCz/tsk90U/UCYzLqtzkDMbVRKCLAWpDSkNr9EPK81RU7Jm8t6G4WBLHpTkhjSQWnsk9MSomakZozvZWwilolQRmmqcLg15KVy+IHkIp7ZZCLAKadygw9h1eEGGDJcPkLc/SXrfO5kevx6CryaSSri+rR+fCTIquhnRaYts4jy6yn2nsYvGOHJ3k/vZaUl4GjFRjHDrtlppLWLJI+G8UOZGOdmxHB4z3zlkd7Jj1kR+4hEu/7Hv4V1/6gfYe/IxyIrtWuwxWBwWg1J71HXfhDAmGDKGU5bC8d17vHLzFi/cPWHnFr3Hux1tV2jzgtUaTv0k93u3He/C+YjmHMch4GWmHB+x3LmH3jshnS74UqHUfiF65LWcxT8LySDjJJREIyNsElwfxzfhIbXyjeDu3D4+RCvI/gbGLUyXwZx0+QpkQ9JVqAnZHSGlIDTEd4AgqpgONHofuy09qj/3zTkC44hLOMTdKi4p0hp0FYDfSnwrdhY+7LsTv9bxf/KVT/KJVz7xgEaz8lbmpJ3w2bufPe9hPPSYGS+88MI39b0P+/PwYWNdQ984X+v4v3r4VX7lpV9ZIwkvEObOF1/5CsfzxXEKfeA9R3znh55hXH8OPXfWNXTlG2FdR9846zr61uSe3+Fzr3zhYs1fcWpe1rlyzqzr6IPjoYuATurhPEVAWvSRcpa+LEAJgc08OnIlhc7qLURZzb3T1vvfp8uOEYucpIup1qJ31AvVHFQwgWTg0tAuOGqteHZYjpE09BTqLsZqOHdNGkkN73294RI+u3StC7zR9dqtamc6LzD28YVTNqKhvf+ZIFgIHGi4fbvzMb4lIoDDQ9nFYgMw3KEsjUrFrYT716W7gbsQXE8iDltzuJT17Gydqc4VM1BxLCkaL9KjnR2lYeKILfgwQFlCjI4g1nCp0rC6AI6nAeoOxg200t8jhZ3WGqQteHTMahKwRlscyQnRGXVlTxMHQ2abnEGFnBWdBiSFOO6lgBkDGR23lEFprVDnU3wcQyJc5n49HM3ExoKxkRB0d0qxDRUwVcgJyYLMRJwzqc85C9NzdRIVnTbhdu5x3zjoboZXbjPfO6Xh7I5PoFSSGyTpqd1CWxqeGsiIJ2IelBDCvSyIZHIeyOMepBSuXO2x2S4xjrOIb6DVRiv93ikFtx2WBtAa2f0Oqhm/fcjxq3d4cW5kVcpyRLl5E44q47CheXRye3KQqUd7F1p1rFhcQxXcCq0Zt7/0VTjZoRlqK5gqRgjkVhVN4W73nFDLJJ96O7ej7lgLRdtzQvb2yI89yvbSJfzwHv70cZjgU7ic4/6PbuQkZ/HSsRlByHhqqGf+zGNP8muv3uLmvO7UP090ntFyC5MBHxUpgljB/RKaRny+i24HZLgc95cVOKlwCaxEVLpzgqaDcNfnjKTeqV4Vt2PwgnmLaoBhQL2RNtvzPvSVb4CzNfu8X+OtzB91/ObGs8fPPsDRrLyVOaknPM3TvG96H/O6hr4ledifhw8b6xr6xvlax//Vo6/y9NHTD2g0K18vLx3e5Lk7L/GBx95zIeawCNx4/Ca/+vIxH5ymdQ19i3IR5tLKg2VdR9846zr61uXzL3+Z73nyOznYXJwO+3uXX+Xg3iOMZXPeQ1n5JnjYn4ffKA+dAIw4SsWT4h6hu+HGjchi0YRbDaEXuhOXcK2iYEvEFHtD0og06U7ehliLMGlzJG9IbhgNTxlzw7y7XnFEG0kEryHqCo63Gq5NncAMEcFbAZki2rQ5Kg3xTFNIJNwWRLoz10NojnsgBEjRHtvsvCb+YrxOzQ192+21403h3hUTHO3xz11c6+J4qy3irUsJZ6UtkZfr0Sfr7TT0co24aKNHN8smnLzuIeamhLc5hLzNtgvOGiKwhUtXDFQF09SjgROknpOcc48fjphiF4XegYtXkiaaN4zuzKWRJIeg54abUEtBXBm8sMnCniiTZoZhJE8TSRKq0nuMQSVx5ZED9h5/lFvHC8cvvRSHjoSjOAvNFc2CqeF5IOmAVkPGhgLeFupmnzYO5O6alnkh2p5zPz0VUY042jGjeegbFRKalXF/n708UXeNerJD6oxKwpKhGcQj7hl32ryLiGPONjRoCK6SGIaBa1eukrZ7FE1kab3KWkhLwpYFXwRr4byN/t4FTeFIjk0DFu5dQNOADtGzfPqFZ3jx8Gdoj36CAmxK4d2Pv50X9q9wcucllnu3aXO42R2H5rh49FGbhevYGm7HnLx6C8wZL++Rs8Q90R3kYo5riLWuiuTMkDOpVRRFrKGl4q3FOJOi6kjZYXdvY6WhmwmS0kqLvRJ56CK6IWm4v7gkBW+FpsrlrbNJF6MT6mFm506bNtjtBX1kgtHwfAWVjJUFtoLkvXD3+iZi7vdHqAVf7iFyFR2ugBdS2oQ73iy6gymYD9BmdLoCcoKb08TJZud96CsrF4ZqlY988SPnPYyVtxCfvPVJvvvgu7nClfMeykPN0dERu92OzWb98GNl5bxwd6qtEXgXlV/84seZhpF3X3/7eQ8FgM2m8v7vuIl++fJ5D+WhZ11DV1YuBtUqH33mo+c9jJU/hHu7I37qd36R/+D7/jybYTrv4QBg2nBdP9M7b9Z19MHw0AnAkvL93l0xIta4tXCpth6TrAkRi/jktA1xEwmDbc5goL2HVqSESKlTRDtLdMNi0jtWB3BBm4fTtvf9mjcqkLz15teIg5ZaEBlQDBFHrOLZcJ8QdUgjatG7W2t0EWuW6M+V6Mr13p8qMr7WwUu77/rFIt7UrYLQnc5nlmEPERjpscThDL7vLnbH3KgY5opXh/u6skQctYdPNVzKEq45TZg3ktToyk1b8OgI9DSCOk0giYaA59x3W2vO4apOIa7Hmw33xx8XJIWmmzK0SkpjxAI70RHcajilu3s7jieuoVnF68LIzJT3GdzJomhOvVNX0KSIelyXa5ep734PSxb89rMw997YnNAx4YPCrnQ3bDisPQ2kNjPkjLcFq047mWm1YimRmuG9H9pKCbevCCkP6JiRMSOqEc0tDsMABweka9cZHezeCZzcQ06OqXfuhiiaJMTZ0Xu/dY451OIa0irs7ZPe8y7G976H/XGg3rqDlsqy2zHfu4dzAlZpPuL1OCIWXJA0kKaE5nCYu9WIsE4DKoIkIaniXrh5+w6H1dh/99v53h/6Ph67vM+l332Kz33iHu3u7ftOc08TjErWCJaO0xeOb3dodeZ0d0LdCKOPiIdQ7L22OkuO+SwgWaNTOWsI+O64LuSlMpCYdGI/X8KzYHmCcURajTvxrBI79c5pDIYxoqStxDxPI7U0aIX/9Dvex//zC1/i5dPTN/XZtfKHs0CkBFzZ4O7R23t0hF+50uO/D6CexKaftkQEvQwwH+ObK4ifojIgdgps8FrxssNsQXWD5y2SL+O+A8nxjMfxuZzzka+srKy8dfnSvS/xkfoR/rMn/rPVwXSOzPNMKWX9oXtl5RypXvk7v/N3znsYK38Ic134vZee4m1XHmdIF+MjtC8un+O73/5DTM+sLuDzZF1DV1YuBubGb976zfMexsofQSRqvMj7H333eQ/lPi8/9lUef/G9qwv4HFnX0QfDxfjX6wPEu+ArmkO4TOH4FSccpRj4FA5YLKKeNXbliqdwCqcQWoUuhvWIaNFwzJpHPLQQIqMRzmJJQLWI4639TwTUnXLmiPWGiKGS0VK7OKtIPUVTCu0WQUmogms4JLM4kjQcsqLQiKho7+MknMYRGZ1DcEO6q7FxP8xa49giJrVxFvcc4q/hJKoZzQWrBVRxn+N7EepZhytGkjgXAJH1rBH7y0j29tq40jY6kJdCFSMx9ffqwnQ3LYsYmrQPqyDqOAm3Fn2/PUJYeg9yyiN4pZ11ON+PnqZH+hIRr64Ylax7DBJ9zoigZ3HYEq5U0RRa+NGO+oUvs0uJUhd0zKTUI4wBHQR3QWqFpYI5XpaIhNbEIBNWodTG7I2CkjVREUoSRAWaoeMWcoKccElxHax2jX4mT8dcunqd8eAKOk7cfmXh5OiQZhEXrnY2pyL23LwhknFfQlxPDfdGPTnm+ObL6P4lhs0G30bUtByHGE9K+HIW7ywIDdUB1RRdze54yrifNQJrzIGcIgJ9dw/D2B/ezv73fpD87nfzzusHvPKlp3jh1i1ojdbd75IUSTXizsWhtIgYd8droe1O4VCwrZGTYkulWUPHSq0jwzSSc0aToKm7wxHEnHT5gOngOpduXOfgYMsld27fvI2YkZLipmDxuo7hNeKldbOHboaor/aMp9jMADNVEu9+9DH+zwcH/D8++Ru8utt96x9aK18XahUpC9UqcuUyTBNWF1QMqsMMsnc5YuLnGQaFvUsoGfwSqiNpnPC2QGu4O5ouY+U2QkYl5qJZIW+uhsN+3S24srKy8oZ4bnmOL45f5L32XmpZ3W/nRevr3hqltbJyPrg7u7b+HHGR+fzLX2Zv3PLn3v8nznsoABiNZ/RL/Jn3/QW+8qWvsizLeQ/poWVdQ1dWVla+Pn7zuc/x7utvJ1+EzVQCy7TjpSe+zBMvvI+hXgxn8sPIuo6++eh5D+CBU2v07IqEo1IVSVM4JQUYN8igSBpAx+623CC0Lk4NIZamcBuS+0OrEeKtG+J+X/xFNYQoJSKI0z7qmZQnUh7IU3RIqmYURyVEq2aN0mYWc6w2Wk20xWi7St0VyjKzLDOlVopVZk80DNMMMoZI7YVQTkF0g2juDtIzubePUzQslNLtk9316w7eulAqCWtOwShmXQwsnBXDukeMMQ5JjBxmYaTtUO3yq2v8gu60HsBSiJpWwvXZDOsR027STboSUc+ANCOr9N7jbj32FL2cNZzAcYEaXk7x7tIkTTGgs5hmOROE4zrllEgQommzEExrxXcn+Fxou0adK14da43ZjBMVfNrCdoNsBjRP8XoeYjG1xmuZ947k3vusQlIYxNGl4bsZ251SzSmlsFSPb+3idTOhVaOWQiuVVkv02F6/zvjk4+y/4wbj1QMcpR7vsNIi0jsNkEOMFRf8dMFOZnxusJQQbufG8twtTp96Ebt5yFV39k2wXcOjhhhrDZYF1UzabNBpGy76yB2PjROtwa7AUuJ4G1ittHnGlhOQhaVV7jz3DPXlr6JT4vHveR/To9fQzRbJE9SK1IpaQ4nO5KSZlBIqjraKLAt+OlOXylIb1Q1rznJyynx0zHJ0TNntqMWozWjWN2/gJIQ9nXl0cA6uXiE/cZ1LV7ZcvXrAwcElxmEgKyR3cmlM7kz7l8iXL5G2G4btlnF/y7gZyapoqQya2ezv8f7HH+P/8n3fzaPT+g+G8+LFw3uktiAKdTfjw4QtR1gVrIZr29VgGmHvEpIz4jOwkPKIjop6Q0hIGtC8h6owTNdJWmIOba8wHDyG4tELPqzx3ysrZ3zm1meY2+pAWfnGOK7H/O3P/21uvPMG07qGnhvPPrv2d6+srKx8Lb7y6rPcOr5z3sO4z127xWf5dd7z/neva+g5sq6hKyvnz/qz6FuD5+++zC9+8deitvGCsIw7XnzyKZZh3Yh3Xqzr6JvPBdhy8WBpraB5vztSE7QdYhJxrylFTHPqLlr3cOyWUxhCQFQSTg1xrUcxIznUXTQ6eE1BKuIWAqdXRAY8Swhl0F2eNfTWnFFP3T2ZoDVMokfWMWqxiGNWugPUSK1E968fIN6wrJgLooUsKWKLu7tYURDD3RAkonMxRFK4ZnUACeEajJB8u/tWpTuBwUVp8yluipcaIqk46UxgdMh5QGyJnlq8dydL/O4WUc4yhPDsLeKZLY6FlPCUaRRICRUjkrh7B3FSJEk48zxhtcY1SBlo97uAwfGza+HWHcQe5z5p/C4JRGneQsxxJ2MR/YyHkK8DogNIjXNQFzwPmDXaMpOsoTmEc3cFK1AqUhfUBdcBcojAZ05kALMaHm5bGDzhrZJNwU7jOorQUkbMqc1wn2lEPLaah6AuoGNi3GYONhMtD7wiMYecOF4763WWuA4ic/yparimTXCMJs58aY/licdYHruC3z3ES4lrXB1vjgyZnFPvyK1YqXircSxpjPOfu4NXIkbc54gd12GE3SnLF57i2cOZ+d1vww4G7tx8BSkLozvqTnV6XHbGRTAUlYoywNzCITwMMYdOjmjjCOOADIpXY6kV0y7wG1ALTBOSB1JOyNWr6Lvew/Y972AvO4cvvsTJ/j75XZmNwq4uESvuhbTdY3twQNXoIzA3tDvDrdXopUYYLh+Q97dYKbz72jXedXDAK2sE17ngouh+Rn3CdkfYYKS8wZdDyJeiq/z0FBMlDQOxM2bbneyG1hlNqXdQG2d7giSNoAdxz/SNQJjG/pPq53vQKysXiI+98LHVvbTyTbHYwo8/8+P8h/v/4RpjeU6YGbVWhmE476GsrKysXFjunN7jX/z2L/Dvf9+/w9Xtxejffa58he+69ANst9t1DT0n1jV0ZeX8WX8WfevwhZe/zA+983u4tnflvIcSdCfwMp6uUdDnxLqOvvk8dAIwZKzuEI3oYTn7IN8rMHQdtztEzaLP1XqUsoDkFG5f8e42VSgFUsQq4yA5Q7Uz9SDEAg2RkTSEINgaDGN8vzqiGgJpbZDCP6yWMRFcQ5Q965TFKlUyiKKlQZthaeQc3auWMkhC3dAcfcKphaCRJPfoZXDp/cAazjfOIo/v9wX3//YKorhkGhrCopcQmU27mHomeMcxq2r09abUBcgRyoJQkeSIpR5CbbCZQretEcfsdUaS4t5oDiTDJKMeovVZRCvDiNSwHTvWz/vSj0mgOioDjS7gpsxZjzESx6Ue8cAAw7hh3AzkcURFkWZ4m3uvrJKyohq9sObOOG7I00Rzx5dCMkAUm7bRIWtElOzuFC8V8YjV9iq4xeaCHHXNjFJJeUTSgHtc67Y0fGkYRsqC64RIIw0jkhIn1riDkzYT7bGrDDcvI88lfEfETlt77Xcizrm1fp2t0hgwGt4K7d49lmef5ejeHbwYupuZzCkCsyZ0VGRINDesNZqCt4j6Vo97QVKO62kW75lSnMckiDjl+IgXn3me2y/cYuMLxXaoKns3blDLzO7wmFZnrDUs8tLDOU3Dk3ZXe3cmG4hUVBVX7x3BlWZx3/ngDC2jJtgGPE8wL+zu3uLWS8qpJg5fvs3xyRFuC7vTHa05DBndbNlcPmD/yiWW0jhVhdMTpBrmTtst2G6HjAPD/h4iYObky9fQzbrz+ryobhwen7CvCd+bsJMF31bYHiCzY/MpsrdBRLHdXXyzQWUCHRAU8SWSBkSgOXK2uaMZEBHutNqN/AmRAab1HycrKysr3wo+/vLH+d5HvpfrXD/voTyUlFJ4+eWXefvb337eQ1lZeSjpW3hX3gLcOb3Hv/itX+A7H38vP/TO74naqHPEcX5790nexnec6zgeZtY1dGVlZeXrp1rjE09/lr/wwT+7Rv6uAOs6+iB46ARgsxpOMRdknnsMaEKqIBlcQRh6XnPERNPjfMUbtAWRsYuihCgwjCEwukSqsNf+e/xy9XAXlxLuYrq1rEdRY/HanhKaFTdBxPA8IK3gDCAJr6e4OS2NcTACVu+AbgCntEZyaDV6XzEn5RqONQHNmZQMdSOnIYRZEdQL5iFy9rLd+H6PDlTSSC+exVXudxW7DtGJnMfov5WEiPaO1BIn0ywG2uY4YBlCvPUascwkZGkkteiQXU7v9/6aFySP4UCVhZYU2gLkGLvVfoJ71HIagd7f2vtvpTUGUYp3xzcQij9A7wbO4ZLOogzDQB4HlBiDuKFDJk2JNGRoYG1BNZHHkUubCRyONbHLGZt3MU9ovcPWMG/RVezgxcEMbQsqieSFPE4M7gxiJBqKot1d7tA3JAy4N1RifpQ7hxw99SIvHxZOHn0eF+fuKzepS8VaBQSvFVSx3hlsePQD93nZpFHdo892nplv3eWwwriZmG48AuLUO3dJd+6EkJ0StRWKZMwLScCrI2VGk5Kk4SkjqW+MaBFB7i3hRWm1UeeZHYnDlBku77P36AHjNMDhMWqGnRji4LXizbDi+Nxd1ErfDJHwnHENYTni2AVpjToXdrXhmxHZ32fajORpIm827G0m7O4xt5fCqQtHdw85OT5GW2GpldaMlDJpu0GvXobLl9GTI4YjoUnCrFCPT7HdAmNGxgFvLWLGc45k8Tfz4bXyNakFhk2l1BzufE/Y4QneFC7vkyw2irhlkH2witkRbZio44bBEjKECOzWkLbEY3w+26mxRWh4qcApurkgOxZXVlZW3uI8f/I8T2+e5vp2FYDPi3meaa2R0lpvsLLyoPnRz/8o95Z75z2Mla+TWyd3+PiXP83h7ogPPfEdPHH50XMdz0v1eaRObDk413E8zKxr6MrKysrXz5de+SpPXn6M73nyA6sIvAKs6+ibzUMnAFcEdTjravXWwq3oIbqJCl4XJGs4ccURCgwDKhm3OZzAHpHRtRWkZTzRH1oKaiHuEiJWRC0bkhNSC94FK4aEVwkHbTJEK0LE3NqyQyWEJW8OCdwEzxtUFWoDq7hucU24N2iOewrHa7MQ/1pBqyJUTCdMQWiUcR+RiE3WIZOM7haOcyMe4rCkhPeeXBPi+GrDJeM5U+cdUirujruhYjFeIcYnhBAu4dATqYg4RoJySiYjXmm9m1htRtOAyA58wWuP5lYHA18a5NRfswvUDdwVJ8RysV04PtsuInu9IdZQBNeG5YgsRnKIlNbImhltIbcBFUGHHG5fF8Qbqqnr4tEPTB6Q7cD+k4+x2b9EOjyiPvsc88lxuGqBthRsKWEur45YQzSRcMgjkHAxNCfGYYxOYFFUDdEJxJFqCJAcVD0c6G74fMrurtB2heNX7iA0TpZTltPeAWwGZEgNUY1rKAlkxtwxF5obVSRcuxJC8NLmiH0+WmDZUe+d4K0hQ8YltPYQkBXDok/bLYS1QcHONg8ArSCMiDleG07D2oI1QzVDm1naCb6ZqEul7nZIi6hl9UpbGtQSHcCqPZJ8iJByb9iy0BaQPOBDinu1Vawu7OqETBvydmK4fIBcOkCuX4PdKfdu3eMwKZph50YpvW84aYh/KWGlsjs+ppY+h1sL52+p+DTEHoJScRFkGtA8IHXhf/He9/HJ556/H/e98mC5A1xXIe3v00524dxPjueJJtBOjuFAkf1L4E6bZ2Sb0XaC2hbqCZkNiuLLDmvW14IBcKgnWFJgQBjweY0YWllZWflWceORG8hpVH2sPHhu377NE088wd7e3nkPZWXloeOoHK0u4LcYjvPbL3yBL73yNH/+g3+G9zzyjnP7ENtoXH3kKsuxrWvoObGuoSsr50fzRvV63sNY+Qao1vhXX/oEWRMffPx9F0IEPrx8i/3jK2EUW3ngrOvom8tDJwBrUrT1DFmUMyEYKyEWmkeXbJEQnDZbpMwwRg9ukg3uLbp0caJtt+I6YCIkIpI43KoSgp0NuBYkOS4TQneuAmAhLnhD6M7eFs7X6Clu4apthuQhXLpmMCTwFL2WrUSq8bTB60wyBRpojnBkd1yEhGPNwz277JAzaXe3o+YR5gWZJtQrmjJqhHvYjdriAe0UzJ1qTtudUj2O2Xc72mYiW4lOYYv+4Gbg0vt3e/Rw9Gca1CMs7ZM0eoLTWYS2JkQs3MIInLmJ6xznrbUexRpuUChgQmuO+w7VTC0LoCQWDMUl0cRfu9YuXVVNYInWFnSckJQQAymGJIuxIBFrXBWr0X3sJjRR2pVLyI1HSAq8lGnNqMcntFZig0EYV7Ec+wJAEB0jQrZVPO2hqoxDYswJHVK4SF0i7tqBVknDiCbpzmfBPYEXyukhpYRQVWuJeHPXOOY64zUjY4tBZPCSaW2mSqYAJhrdvsOIi2Onp9jREbU55j2ObMwxz6yhaUMeF5omqDNeLZztMsSUsyXOnxPRz6rRy+tgtWD1LCa8UVtjXgqWZ1wcmUbywV4YzPWEaicgFgZoiVj28Gw7zWKTgzXw5RiZBiyl3lmt+FLY3byFtgbXr2PXZnw+pp4u7A5P0XFguxnZjhmpjeKGaMZUWcypR8foyQmtVpbTHbUsEce+t+n3FPgY7+eaerx74v3XLqMiqwB8jry4LDyxLNQ6InKEbC6DGPn4HrZ/FT+6h+WK1j2YBKkjxU5ATzESrZ2gvYtaU0bbEZL2ENeYc/WYlC5HZ/lyet6Hu7JyIbgz3+Hpo6fPexgrb3H29/dhfayeK8888wwf/OAHz3sYKysrK28ZdnXmpz/3S/y7H/qf874b7zy/cWyOEdbuwvNkXUNXVs6HL9z9Av/y6X953sNY+QapVvm5z/8qzY3vefID5z0cdptjvH/svnI+rOvom8dDJwBn5LVuXk0R+1wrJO3ir4IPOCUSnkvBZUBqpaXcBdR4idRAdAoHpUf4sDigTiaFS9BCa3QdEBxPgjcPF6MkVCX6cn3sknDqr99CnGzWx9nFYUrXrKO7V2j4MES/rGa8i2BuHu7iOpMSoT6qhDPZc4iTHi5Ys97Faw1MMKvQq0xltt5vrLTlFHejVA/x0BwXxQinsqBUSXHArYRAbU5kBfe+YSDJjMo+MhyQVNE0oKaolDimMQM9RlslxHgz0Iz0aG7X6GJWUcwSJEVr7Piys/xtg0bCzDBJqC04OZzAEl3GqYbrO0kmpS6wS4IhhfBIgzTiLkiLeOw0DGhyfLfDX7zJfLLj6PYhy9FpxBZXixhvDQE9+qZzxCVj/WvQSkKrhLEVSMNEHntHc2td3A+3sKaMKqDR09yvWJht60KrS2wSSBlJSgKaOF5rpGYnxXuEdwRNG0UUkdjKQK14UkwSLSfIMU/Be3T4QB62+LilSmM5OaQeE/PMGlXCUaxOdEPj0YlcluiMRqEsPfYcXDOYkaaJy9euUvcmEONgGMmSOd7tuHPrVQ5vFbyVEOJV4l5Cw+luFWkVSb3C2ypkRTXubcPZHZ1ANfzeEXUcY/OCO/mRq+i16xy87YAbRyf4q3c5qcZsEQdti2FmEUVdGy4C49DfX2jS5yaOkGKDQO+GXTlfdq2xu3ePdOVR/AgYPbrdBZIKvt3DZYZhEzHryw5cqcmirj0BOOqQRsV8D6kV1RHNG1Q24DNKw1M534NdWbkgPH/yPJ+8+cnzHsbKysob5OTkhDt37nD16tXzHsrKysrKW4ZqjZ/7/K8Af5r3PvLOc3EyfWX5PO/me9HVuXRurGvoysr5YG6rA/gtirnxr770CQThu554P3oBnMAr58e6jr55PHQC8JkQKVnANcRfF+LT/gQpYb0/V0mIVVAPwZXodkUSkGgUVAdC3rL7scnhXK0RjZtGXAxr0REs6uFi1AEvCzKNeFlIOkQPb9boNW0CBqYDLhHjG5XDIVBGzy2Rje4g2kXdPOB1ib+TGkbEFWMeYx8T4HixEI3xcB47IRxTQ6gtuxCUS7h+G1C7e7a6YN7jhfwY2OCa41xJd+xK9NAmETJdlJN+nnVL0hxx127huJYFGRIy9L5j633BteA6gC/Ry0nvLs4DnDmGe0y15BQCr0UctGFQG812mOzTdoewvRaqqTbAaA7JC6Pk6Hc2BQvh2nslMuL3xXyVcOiKQNLEsNnAtKW2I9rpDlpFsHDyDrmbzKNP1FqN+HHRiBW2ircaP6RJ7zeu2l8/nMeeBfTsxzhFJCO5x3G3hi0FqwVaCJGSJOKarUacMY7UQq2G10ITxURoIhRN5JzxzQgqEWfuHpHRqhFvnBPTMPDYpcu8/ZHrsLfHMyenPPtCwXbHWE7YZkNddngtKBFxrUKI9s2wYrE5Io3oEIKzZCFdu0b+zvczve0xpnmmkbj+5Nu4dmnLKy+/xO5zn+f05JhyYhEbLSk6uc+uewvXftzWtcdROzpGLDQ9kbq0guygltKN90azhraZrG9jfNs7efI738Xulbs8/dVnOH71NvO80MqCu8dY0xBueMBaw1xwj5h1FVCR+EHf7U18eK18PZg7bDeMw0zZ38N3pzAN2HgJKTtEcn9+zLBMoLU/T3e0UvA0Ic3x6ZRWlcEzDEqzGZU9NGd8PmTMWySt0SQrKysrK98+mBnL0v/9s34As7KysvJ1syszP/f5X+Wv/qm3k+VbvCm4V2qtXGzWNXRlZWXlG6e0ws9/4VcRge96/P1vzvNzXUffEqzr6JvHwycAawYS3uZuSI3IZmkCWRAXWGr0y+qZoCNQSzhB1aJrVz26TVWjqxVBk4J7dP6aIzogGt3C5IhUbmev152gGAgheqbkQOlCVoi6qZtZz4QuGjgG3kJ80glsIaWB6j2+eghHoqaMDdGt6+4hUGuOPtOsIZi2yCi22rqdtO8YzVOPcc5YLWhbSJKoEi7qJBEg3XzgzOBJc1SVpAmRAVxImtEk0aHbSoxj2gckYp57F68QMdfuCfEGZCi9Ccl2mHfXrRseTc7AgLc5nMEavcRqDSTiplUSzQRkL45tDJGckyPYCsgWRGleaDTMJryNvcI2+pTvu5C77Vvo8cYIcmmf+dJ+OHBPj9Gc0M0mDM9JQ9zXcNfavENyhqVEfLUJtBobB6xCc+wUWm0wTKRphHGM9ck8eohFGDVjw0Cxii0zmMXcGyJGO+K2Y3OAhOWRXvFLg4iKFmdGwq2NU1EWM4bmYAuWBtKQ8Gli2Ntj/M4PsPkTP8Tw3neQdofs/dbnSK88jywRM95GpdWFpnH+HWHU3psqiiYJl3XKIeImIdKsHXvpJY6P7mEk9OpVDh8tqGwppdJOTpGkaEpYi+5lIXq2RRoybeJ6toK0HmeuGdKAZEHdad5oLjR1RGrc7oDPC/Mr90g1MW32uXf1Hej+gNdC3Z2yHJ9STncgoOOI6BKivoTr10kRWz0MuMRmARWJ58HKufPK8QnvzQMyZeowYod349m43YekSGvIYuj+gNmMs4XDu3D5AHyH6wQ6It4o1pC5kQZoJ69ieUDHDY4i0yoAr6ysrKx8e/Hiiy9y/fp1cn74fkxcWVlZeSPMZeFTT/8Wf/I9P/CtfeH1M9C3DOsaurKysvKN4+780hd/DXPju5/4wLde/FvX0bcM6zr65vDwnc1Wu/tTcHIE6SYLQelMuA2Fr8cOSziD8YhIdgMdwUNw9DYjojTV6Ev1grbo0I143QYoSUIoTqqYWRd1I2LXU7hzHe2iQwlRWRXxFt2Tor17tKE64mkIsZUGOoYk5b1ml4SKImYkcVwG3BuaRiIH1UnufbwOaAiFaaLbkBE3SOCtYnnLUlJE+OoU0dEOqJKahvjoNRy7KhGdrI5YCJCCIxp9ume9xXiNDl4b+rmMiGevDfclun1bwVMOITorVs+K4TJuSz+WHplcBR0Ut4IMI7Is9wXQ3CrmUHUvhPPtNr639j7kvAe24DRMNeKlidJe77HVgiMphFbpDvFydMLJU1+BPGDjSLp6meSGe8OT9FhswbUCiliJGOOU41yiqDeGvCV7Qczw4jSWOI9nGwXMkDRysD/x6KPXOUkjL71yK86lWGwykBDL3UPsd2uYtbie1SOq2QWrhZYytRXqmDF3ZHeKacJEaWLhZvVwovuUOdkdc/fTn6B99tPsjk958aWb3DstDI/eQDHa4SFLGWjFQ9BOig0JzyOI4a6oSp83fYNETmhptFePOLl1QtOEvnQHffFVdvsbTufCcvc49HcVarWIqbbUI7SFpIK1FnshhhzO91LDyS4Rs61lweaZVgo6DnEfpoR7o5bK6a3b5M/+Hvn5FygIh3cOKaczbSl4a4g4qcb5qPMMQ+5O9IKrIj3iXPNAEpimif/ZO97Grzz97AN6oK38QRQzjhwu5wGz09gcsNnrPdEzlB2+dx0wklvc69tNuIPTFVQsQhNkBHXarVfwKxtkmsil4EczLRkyjl9jJCsrKysrXy8//8LP81cf/avcu33vvIfyUFNK4datWzz22GPnPZSVlZWVtxSO88WbX+U7H3svV7YHD9S94mIcX7rDweH1B/aeK/8m6xq6svLg6TmDdAvRyluU0iof/+Jn+JPTn+X42isPXLQ9W0cvHV7r5qOV82BdR98cHj4BWAAcUgaLCFmhi5Vau8ArXeQVulUx/r5qLxtNIQYnDzenSghvLQG9RNJC9HQESRJO29SdudoF5VBYe9drFxx6N7C3RlKLTtrE/f5eHbfxINJ4GIlHXDA0vPfj3vcg9t0S903EquEktkhiRl/rJoYKkiO6WRQQVBRSjohrHdmpImUB6THTacQlOonVu+PTCjJu4zzVEoKZnf3ukAbceh+wSHTBlhY6dGt4inhqFKgWIjxDOGeHIXppjehdJSKGPSmk7nIexriGwwh1jjhmzeGg7ef3rCe5phBFwxs70MxprdBqdLxG/XIOI/QY4raniKc2wE922DSR8sBQFobW0DxyOjUaNcbUIr44YsYVGcbolW0NaYXkjZwgpz3ElojxXgqG4vMCaQjn6SMHTN/9QaZ3vZPyyivovbtw7HEOzn6wPKs+XhpWDJ93eMxu5Gxjgzco0VWdTk7Qcewx26n32ipiDVxjE8GtexyfNthe4kgyu8Mj7pwcUqVg20yrzny6UOaC9QhpRLEK5guDCoM03DMaWcm4xWYKSUp1KAa+WLiUdzvyuIHkWIsYcVICX8IdrYYyQkqoKpjReix3Gkaolbo4YpU0jJzdaT7PtFLRpKSsSBpCyDXn5K5hc8WwiIneHcf5SgrTQNvfogZ2dIzvZkS7FTwP3VmvNBTTiGT/T3/g+8nN+aXnnvuWP75Wvn5OSuHRPEaywLUtJspy51V8u8Gna6hkKCdIhqQN2T+gLbfJcrX3rC80m8lsqdceBTFyTrFBZiOYtbhHV1ZWuDRc4ontE7x4+uJ5D2XlLcwnbn6Cv/KBv8KNdIObN2+e93Aeag4PD9cfuldWVla+CW4d3+Gjn/sY/7sf+vce6Pu6OjxZuDwecO/Vwwf63iu/n3UNXVl5sHzgygf4S+/8S/yLZ/7FeQ9l5Q1i5shXr3HNN9y+/swDFYFdnVcefRoX4+DeI6sIfI6s6+i3nocwr7THJ5cWcbCW6GpOOE0d0BQCUHfv0hoAooKkjKhBHrrDN0cnsI7h4AXMCtWNZk41qOYh7prd77yNX93d6aEt5yxka2SH/P9n78+Dbduysz7wN8aca+29zzn33P7e13fZvFS2ykxE0hgEBiHTSWAK2ZRNE2BHABVFlKMKKlzYYFc4CmNc/9hUFI6qIoQrwAZCIAxIIlMFSMjqMlNCqVRK2by+v31zztl7rzXnGPXHmPvcl2T38r377nnv3fUpXqTuObuZa6259zx7/+b3fQ2WNcIb3aypdY0STsskgqo2lqpomh3GE8fdA5LF7zKqqf1MyDm1+7dqXu3JkkmpI4vQdQuyZrpuTp8yXRZy3yF9H+NYbKEJ0jziiuPnHZozklL8u58FfNXcIHUAN7eKS/Qve1m1WGfFvOIlIrndrDlaC+4j7oVSIkIa8QDGLXLX64ibxH3Gig2luXsV0T5Aj0QnryRtO3GbK9sBU8blAYM542rASsXLiJWC1THG5xWTcNBaqVSEsYwMV69i12+wvdNz6l0Ps/OuR+hPHUe7PnagVfBhhDJEtLUXxEdEBE2ZtJiTZj1p1pFmM9JsTkqKjiWSrGNXAGZOvbnH8PIrLF++TF0OiDdnr8XmAase/9l4CNklJVJScqdkFZIqiZEOZ1YL/VjpxjWdO1mE5KBjRZYH6N4N/Pp1hhvX2du7ztWDK9xY38BWN+HadeoLr1BeuQxXrqLrgTQWdLXCVweU5YrVamA1VJbFWVVjXQvrWhhsZFivGVYDw3qgjANm0dk8loFhWNGbsdP35JQQUVLfk3OH5r5dQ5BouG4Oc8Kh3RznTnTz5pxIKSGaEDesesRje23p3o77wHpYMg4rsIKqknGSV2Q94FeuUq9exVdDbDjY/Adtrjq1Fso4UHG62Yx/59GHIwZ70pHpYLVi2N+nky7eiwS67R10uURXS2y4hKnQdSfpU2LWd8y64ySx9meekLoTWHa6uTCfJdRXqCxR7UjzbVy6Iz7KSZPeGnpo5yF+4/nfeNTDmPQ217Iu+eFnfpgz587EJq9JR6b9/X329/ePehiTJk2a9LbU1eV1nrj07B1/3gv2Ii8efzI2LE86Mk1r6KRJd1ZZM7M0O+phTLoNWrHkJ1c/zvkXH+ehZz5Gv76ztWuuztWTr0S94qQj07SO3n7dfQ5gwi2L6GFXqJTo3kU7QBHV6NnVAIVoi0TWgpDCUekS+FxofbTenJgWsROqlDKSm3PY22Om1CEWkBO4FRMMEccsGVJEzgaYLhFTjYQbmNbju+kuJp7fxQNcQ8AvmsvWA14HBI3IZ0ldgEVJMQaJEl8RwVMPLdYawjEqZJIoSVbUTvGxEewUsdUiCmox3BqgjbFAN2uke4GPq3B/loDqth7CCbr5gs/ivLkVVMIZXckt5ndEaf29NuLahds07oi5xcOUNSIZFQMLh3IU+o6oZswLjlJdqTisltF1XAo232ItULJgOeFdwoBqFfVZgECLHmXp5y1GOJy0sj2nu+de+q1t0kuv4GMD0HUNa0MqLUZcsTKG6zhnpLZYbKI3V7tZOGhTjuueZu0Kg9xYcfDsS4wXb7JaramrdUQ808B3Hanm0ZVbK6SEzLtWWqww1tjU0PWoFWYaL32pA8mEnKzNY8OpuClaBF0VOCj4lRuYxFzRYcBrjZ1ZIuRxRNu1wdt4JbqPyzhiCKXLqDsphas6iZALpCyk3AWYTiBdRk8fg0fupXdYP/cK5dK1MOOr4G5sXmVWHcYS7lyrMRdEw/nedTCbkXFsHKk5osfFHUvpcCOGK9HNLB6O5xbFriipXV83xfD2eveICE8tNr05mksZYq5nJ6tyemebP/G+x/kfv/glVm0DyaQ7q6EWrl2+yD1nzjJmp3om7ZxAukoxENmBPMczoNto6kjbCVuPuNV4j6CjyBK3QrWK0CNSED+AuoXJdG0nTZo06Xbqk89/kge3H+T3PfL7ePrpp6M2ZdId1yZ6a2tr645GmE6aNGnSO0FjLawuwSl5iP2dy6znd+5LzGvpEg8++C6uP7c3raFHpGkNnTRp0qTXr58dfpLfMvwOHr3+HmarHZ595LN3dB0teeDC+ac5e+FhkqU79ryTbmlaR2+/7j4AbCXinUWjn9QEpCLMoOxDN8cp0a9qgIyA4lIRn4G3Xt+k4RgWbzHFgOTgX7JxDVeKKZmKSI6gZa+oyCFsihhoQzTjHhBVRPAG0KKCtkFS6VoEdW3gV8K9G79s/b2JWz8JcIzm6BO2iquEk9njuDaAUHIX5mfziHbegEgbowdVhexKKQXzAM7heG4AxEG84HTtnGpEC9eC6KxBSSLiuDYXdtWIa/boQsYbWDfDzEipggmqHYiSRBDWOJWUlWqGonEOLXp2jYiTFoA6xpnwGunb1UFqRAmLtHNVQJaY7DBYYVVHqgUodu2pZUDWK+gzuRradQFX2/uPJaV4Zdy7TloPlL0DqBbx2ZJADckp2LWNeLsq7kbMmnD7aq3ozFpHriKpR7uIYVZJzFXZKsqwNOxgjR8sAQv3ax0CfONg5RBQStaIJi8j4LgHoE0ksgewlKTk3JNzioRzElYlrpuBZkXKKhy0Ep3TEvnOba4AKWGlAfJZHy55EayC1TFm4rhC8gyrhtURM8dyQXMm95D7HnPQWrGrNzgYB4bUM+4fUM0ivrptjPCxItURq0jOMQ9tpFpCyohQoJ+Fwz0llNannCQWjuqYt4xv2rzNCe8c8fb6yIoa4fzW8Ny7GS4Jz21ThxAR380xjihiXby+uxkfv+8eqhX+8VPPsqqVG6XcrnexSa9RL1nFL13gzOkz+EyhHKC6TS81OrFthJqig1oK6qC9xn4glrj0JN+O25mimkFPUIdLaO6QOh71IU6aNGnSO0rmxt/60t8iaeJj5z5GuVowM8Zxer+907pw4QKqyr333js5sidNepP1hx77Q3zq+U+xV/aOeiiTboNOymm+d/XvceqF06z7fUr+1rUxN3df4drJFxj71Rt67rWv+EL/Gd539oMsn9/Ht7amNfQINK2hkyZNmvT6ZBjX7Ro4zNfHeOjpj/PMo59hmB3cmQEI7G9fh7PPcurKvaglcp3S/+60pnX09uruA8BdCjhrJbpssYjS1QHJs+ZibYDQW0cvBjoLx2qODl3x5q6tCcmGaweSSF7DLSgG5Fd1DXPoELYRaMBN8Gaa3EQWx/8vTrh6JQfMFGm3aS5UotNXNUVnq3aIE45Er2EURvEk0f2Lgwoizf2YNp29DjlArLqAGk6Kx0oNgtcRISE5On499w14OXj0FUvrQUYbaESjJ9kdH5aQeyi1PV8GC7fpLbhNAOOUkOpoSoh2INGD7G7hzE4zpIwgmSQG1SMWO/KiUYcikCU1CiwBRgGhUqzEOIeK94s43/sHMFuz6pW1w+iBtZNUvO9af3OGTpGckM05rRUfBsrVG+x3PXXbcc/k+VZEUlfwBrzdR0wkIq1LwRBqNcQGzCvWBWwUiZhqzRnNHZoTs2PH2Dl7npNnz2MpYU89TXnqAB+i41dmC5ImdByoSABgMZAcPaXFGngNB3ingtQR0TkiTjdP5CyQO6gVGyQ6iHG0AWzpM7jhJSKPk8phFDNJqNphXcSipwZarZthNVPNAyjLZv5FMzEe/L+4Y4yoBZzV9YgdDCTdbEBoHnn3cK57e31p9CpjJea8Kp5y7GtQ0C4z72ek4hR3RkZIipnFOTFHSo1TVUesRr+rU1vXQwD5Tcx2TSnc/XUAy1gtlFIRlzAEdxoR2gKaFE0dnzhzml938hQv7O/zs5ev8M9feJFpH/ad1ctm2ME+Z9xZzzKeO9QLSTuyzhDitVHLgJMgSZv3O7EJoNbYQCNz3Eps3JmfQPsu3uYnvW3k7m949+DteIy3s77Z8f/6c7+eH3nuR1jX9R0e1aR3mgYb+Btf+BucW5zj9zz4e1iulnxs+2N0+10L3Ll7X4N3Wi+//DLuzv333/815/1ufz+82zStoW9c3+z471ncQ9LJZfJOUSc9J+UUALNhm9mw/S3vs3VwglOXH+bZRz7LcnH9DfUeHvg+v7L8//HRX1ozH7Y4+PjHudz38cu7+DV4pzWtoZNerWkdfeOaPovePfqHq7/Dd3bfBcBsvcPDT3+cZx757J2FwDvX2N+5RjfM2b15iuPXzoVp7VW3mfTmalpHb5/uPgBcAjwhFvRJCGDWHF5kwuE5Omhzt4qE81Y0XIObiGijuTtb3K/2uEKqFZeMqRIf4xqRstYpnBxTB0uohOMTb7HUHvDM8XBSeouWDktrxCs3KKyaEa/Nnblxs6ZwSzZTreaupU03t7HQcm+lPfatKGiRAJgRde9xzNUxoXWnerh7N4B7TJBqg2bNDe0GdC16ucT/aosHhq8C4XHcEanqHhHcQqRgqypoOgTfwgypK1zmkMLZ7KQWGa3gI5Jy/Dsl8IJ2i7jULlBHqOEkZnWDOkuw2oPckWbbkNeYzlhLZqCx6xYDHo5PAgKnxMat7B5O3roeqVdvslMc2dnFd09ED/TBGjOjWg2vriQqa9wT5jXitV0RzVC9OcEt+ppzXOMswk6fOXPiGGdOHWNQuHp5i715hw3LiBN3iQ0NDtrNcNVDl7G74VYjIrkK2QpdlxHdBs04haxCTrMA7KXG9VbFvIAqKSuSKl41oCnRPZ26DrBw/tJRa8SVq6Rw3rphEr5sa65pFUFTBz0xBwi3sa0TZvux+SBnqlSSjNE9LYRj3tpmib5DqoNVvBRqKbgkrNZwzktCXdCuY7a7zU5OrPfX7N0smEF1oYqEi101oq1VUSfir92b6zcmrLRgdSnxHiCiiA+IGWaCJKGf93T9DE2CVsP311AKeWsHmc14z8kTPHb+HL/7kQfZXmxzz6lT/JXP/gI/98STt/sdbtLX0bXlkjNbO/TSsTQAJ6VEms+Q9Qq1AgYmjsoCNDYH0be3tHUBv46mnXjPKIaUAdk6dsRHNunb0e344/Bu/wPzmx3/d539LuZpPn3onnTbdGF5gR/80g8C8KnZp9CinF2c5U+970+xdX2L9cE01+6Erl69yrlz5+g38KDpbn8/vNs0raFvXN/s+FWUB7Yf4Ppw/Q6OaNKbpct2gX85/DN+++zfec33EYR+XPDw0x/nS+/9SSy/sfSo9bkd/t53XOTKs1/h9/34czzw5FVyco7/5b/E88sle3uT2/xOaFpDJ200raNvXNNn0btHr27gFYT5apdjN85x+czTdw68tucZZyuudC8zX57ixNX7ARhm17D0rdM9Jr1xTevo7dFd56EOqNf6cRuedY84Y/OIc8Ws/aqdHpfoB/Z0C6oVwxXc1g3AEQ5JHPcIncWNWkY8NTdhUiQTsAlBxHDCFRoxyC06ubUCiwRo9g2Kckckk3JP0llAMZHo9E0enNgtHI2AqIFVpEUFQ2PJh7skJHpR1VG3+P8PnZXthGk4mR1wiyhh3OIcqYFJA5gWD17ak3tzWbvSil/jcXO4i5XmRM4B3USF1PekTtGckNwjlPjftIWkBNrHseQc58RKGzOHEcWalaQpIrfLCKVAiR5aV8AP8PkioHfugQNMRlyEYrAulWFwTFJsDNCE1OZ8Xa+wccTHgbo6oK7W+FgQK2yfOs6Zj36Ys7/uwyzOnwRxxtUB48EBZb2iWqHWglWoXrEWWa0CSVsEd9lAYA8Ha1Ly8eOceOBBzj/+OPm9jzGcOYXMFFFBF/PmMA+HLYQrPGK1wa1GbLEDFZII/axnNp8z294iZyWnDjVQN1KtJJQsStZMkg5FA7ZWjTnpjkrXALCS+i6uGbVV5GpsTiCRzMhmsaeivZoCAhsJI1VHakWGAV+toxd6tcT296gH+5iP1C5TZnNKEmpqbvWcoMugKTYBkPANsPV4DQRozsj2LvnsORZnTjLvEj1K505nTqoBoNF4hZRilNEoY43/ilFLxWqD6wgJ6AR6UXpVZirMFLrc0fcd/awnSdyu395hfvIE8+05i9mMnS7x2AMP8r7f+Tv46B/7D/kf//Jf4Lve/a7b/yY36Ws0mPH0tatw8wa9V1x6PM2wscSusU7R+YzkyxbSPuLDirJc4lXx1OPdLvQ95Ix20f8s05a/SZMOJSJ0OkUjTXpzdGV9hUv1Er+696v8+c/8eX6m/gyL7cVRD+uu0DAMPPHEEwzD9CXHpElvlrJm/si7/8hRD2PSbdKW7PCu9Pjrum8e55y68uBXf/v9OtSvt/gdsz/BH33o/8rJ3/afsPw9/xHL9/1Wbv79H+XhEyfY2dl5Y08w6TVpWkMnTbozypr53ge+96iHMelN1D0vPc6Ja/e94fXx25bDmYuPcvLKQ6hn1DP9+iRa+zs/lrtQ0zp6e3TXAWCTLtik01y/LeJZNm5TcFN8LHgZGvgsEQMsJRzEY+v9dYHiAZVzt3mCiOkVax3BIJUAo264FZI7arUBtfbcZEDwWuPxIOJn0QDUbkCAW6WiGi5ZaV+2Co6kFK7hRIvI7TfEFzSgIR7OYlJ0ooI0mKsBEj0FzHVvJ6lFNXsJWK0bt3L0zJKaq1gMasA0bIw3wdwFLavEGGxszuDoFw5j8xjMW9tt0IB4BCAVK0AJ2Jua+9gawL5VgIzkjHQSiE4CZoOjqTk2q5GsInRILci6QnbwHk8R0VxsnwMvLEthGJdYLRH7jUfEdQU72KfuL2EoyDCiyyWKU0/ucjAXbl55hf0XXmS4cIV6Y42vamRSH4xwcICMhoxjgCMRkigpNWNxrbhJQNiUIXXM5nMeeOAc9953lv76igs//3lufOX56FRWRTqFroM0ix5bBHOnjmMA1XEEMxJOlxOz2Yx+pmRVkhvq9VZkcc6kLpHmHalPJBW0Fnwo+GrEVwVByUJ0BgNaHRkd9egqFpWIaa7hyNb5grxY0M16Ukpx+WoN13CnLea6AeXc39rB0/ZUWB0o6xXDamBdYV2FsVaqEBC4T3iSw5huEYnNAFbx5QF+/SpcvYrsL+k00ydhlhJ9UnoVkldkA4KTttfFZuNHvAS8xnuB1EJ2Iwt0Welzx6JLzLuOPqeIvh5HdFiRF3O64zvkLpGr0ZWRWerYWiw4Paw4eXCDR47t8t/8vt/LPN99QQxHoYNx4On9PXx9QKqVslqzLiM+64EW795vo6kgNsC4xtd7lIM9xmoUhGFcU5bXMXGY7Rxuzpk0aRJkyfyZ9/+Zox7GpLtAjvN3nvo7PL94fuoDukM6ODjgiSeeYL2eXBWTJk2a9K20kC0eTI+8rvsKwj0vv49zr7z79X+x7LB74zzz9S3Ia/c8xPCJ72H46Hdhf/f/zcNbW9Maeoc0raGTJr35UlE+fPrDRz2MSW+i1DP3P/dhTl554HWtj2ns2b12D/hrN3IsDo5zz0vv4/zL7/0qA4h6YrY+yZQDfWc0raNvXHfdX3zJAyxJc9qis0aaNp22AUNNJCCuO+6C2xofR1wKLSsWxlWLj/bm/JWIgx7XERvqDp7j/SBrg7EBUSsW7ljX5s5trtrGOKMlVXAqpUG9aPVNiKd4w3KLCOj23OSMKEhKaNejm47gaojV9vThXEMUoUafpW1isb3FUENMjdZLLCk6aUUiMzwFZKO5hsNVTYBXGvwtBbxr7ulGeHUepDOnON6Uw0WXw7Eb1M+jn5juEPJKHRoYNMDwKuHI9RadLB6dzFZxKwHbq0PqcU2I5ojLbucjd304+PygdREvsGWh6py1K/tWWBUYx4KNI9J1QfE3UdmpQ3JzG89nqHYMz7zA5Z//HC/9wpfYf/4Cth7RpKRFJuXoIU4SYDd1HaopXKqqJNEYXz+HfgbHjiNbC2TWke+/j8VHPoa+93EOtre5sbdiLAPkHK7XjUu87WowPFyNw4iPFYqFczUJfZ/p+o5koOMYLnTCra4uwdNTF9G4zbCtLX5ZzVAzclJyn8m5Q0ZHhhLHNp/TzbuIixaJfmuJWHFR0FkXMP7QnRxOeTRuoymhnZC61tlsFVZL6sGKulxShzVlObJaLVmtCuuxMtZCNYvXnFvMOWm9xOZ4aXNhjNdI7hId0FWL/0Tpc47+ZAHNPanrSLNZc6MHBBdNpBTHnPoZqevRLpNnmX4xo1/MSCJwsIL9AyT1pL6P19y6kKvT555uNmNrMeP8fWfZ+cAHqA89TH/mOFsTAL5jWpbC81evosNlkq9QV+p6TS1rvILOdpG+R+YLZGsX3TrNiFOHfca9awwHA+v1AWU06mrAhjcWzTZp0jtJIsIszY56GJPuElWv/NDTP0Rheh++Uzo4OODJJ5+cdl9PmvQm6YMnP8gHTn7gqIcx6S0gceXshfdw5uJjpPJtOoxcOHX5Ic6/9N6v+VVmxfmtZzn/EWN+8AxpAsB3TNMaOmnSm68Pnvwgnzj3iaMexqQ3UeqJe198Pyeu3k8qry19LJWOU5ce5pGnfx0PPfNRzr3ybsS+ObjVmtjaO8VDT3+csxffFSmv/6ZcvroTeNKbqmkdfWO668iDb74ocg8QadEnCwJljH7fMgIKXcLGMSBuc+OCo9UanARSwEjG6AsWoQHWBiytUByy5XDgojEG6SI1Whyx6E0N1+u8OYAdvFIRzGkdvxy6g0WM6PRN4erNqfXtChDQWdxxqwGfNbdI5gZf3fDWF+xmSOoCulkNwJ3icTYRwi4NELog3sWGmcjARmjdq94AMtZ6fwegRny2jSA91HD2htNXG3SPXln3AqniZKhL0Ixoh5DwOiJJcVHIglQC8IkAFa+AV1xSPKcMAXctXMquOUCgJTQ5Khn2RioJZAbzHdwrg6/YtwUH7my50qsF5E/aosFnAdjF0C6h/QySs7y+z/rKAbUUjJG0vYXWkTqskC6hKTYcmIAXpxZHvKJ9h3aKSsaIePGyXGLriCjXm/t0F1+BxRZ+ZWA1FOo4YHXAqmHm4aiFmFt1hFrbRoAOrI0zdXQCWRQpY/TkCljrM6ZEDLekILcqgnYWjuqWTk4SpM+oeqSAiyOLDlJ0AVut4B3uFu70HPPbAaygSWNTg3n0VI9DXC9xVA0ZDa8W81FBLLfqaoU6YnUZUc04ojkcxCIN4G7ieCtuSpU2Z7MiiwWUAVknUt+hOIpQrWAi5KRUHLNCxWMuW3P5m5NSRvtMShqdy8QGDUMCjleDcYCxQJdJCN26xMtIE1kNdYXtHfT9H+T6o+9GXnyGi5/+HNc+92v8ufe9l//uC7/GlWkRuyNaFnj+mvHQuUQtA4PUeD9gRRJH+4QXwxgpw4Cg2GpAJdH1irNF3b+Cz05E9/ukSZMmTToSPbv/LI985yO89MxLjON41MO5K7TZff3YY48xm00bPiZNup06NT/Fqdmpox7GpLeI1JV7XnofZy49wsWzT3L11HNYqt/w9mLKqSsPkscZZy+8+2uqarb1Co/P/iUz2Ue6GScOPs3j5/5tvni5m9bQO6RpDZ006c3Vqfkp/tPv/E/5wz/+hxltel97pypZxwPPfYT1/CaXTz/DldPPtUTRr5aYcuryQ5y59CjdsDhcF8+98h5y6VnP9rl28gVqvjVXZstj7OydZufmWY7dPPP1we+tZ6Bfn2KYXcHVbvdhTvo6mtbR16+7DgCHqTUgoZcAltULCcFwlIBm4iVuXB20RJxyCfdvhIGGQ1WGIaKOBajeumnTV7mJ8UrxAUgkMmIRPyzmAScN2EBJ2h/13mKYXbBakNw6aTfdt5v4465rbluJI5AUidbuuHg74ADYItGVGpHMhBu5tt7Y5vZFnWhsPbQiU80pNrKJNlBV6ljjX1Lbcc6grFonbYPqpg0KWwBo13BkdtKgoh3e1hmbZXRGI94xvjJAngVAJkeHclnjklB1zFa4J4xN17GCJhSFlLFSwwlLRHlrA9WK4Ftn4rqv9pA0hxFkvsJqZT12rFNi5plSapwWKjlv1pXYAODjiA2OmUb08CyhOWLGKyOae5CKu2Nm+HrA1wOgJDGSasBSBLHooK57hmtGt3boz92HfuhjyP0PcHpnh/7T/5KDpw+oY8FqxSo4A64Zt2Wc39yhjOHOLoKIklRJMc0g9zElyogN7bxr23RgY3OX13YOaTkBghvhagXQTFosIoJacgBQ6zAfcBugdkgSkNSmQEU8hWM5NX+7aos4j3kQTmsjSXuOTeR3AlIf7uGhxjWtBXXFZjNclaQR/+zjiI1DW6QV6RJYpR6sYCiknJG5kHKljCnguaa4PiJUF8axtK5vwXOHzGfkTqFKOOYVRk+Mw4oRoQeyF8SNbhg4pT27x89iVIY6UKwyjitqKQy/8gWuf/kJri6F61cuszo44L2nT/G+E7v89IVLt/e9btI31ME48Oylizxw+jyIUkyQcaB6Ja0G3DLmgtd1xO1vbcccKCDq2DCP9xL6oz6USZMmTbprtSxL/vazf5vftfhd05fXd1Cb3dfvete76PtpHZw06XZpf9znoBwc9TAm3QZdsYv81PDP+S2z3/GGHkcQunHBvS++n1NXHsLF2N+6ypXTzx7e5sS1+zh28yy4MF8da19wO3O5jjbzwL3dr3IivcRMb80vsZFTyyfY3f4gl69Na+id0rSGTpr05mq33+UHHvsB/vZX/vZRD2XSG9BJ/eYb4gRhvtrlvhc+yKnLD1G6NS/f+2uYVobZAVoz973wAU5cvf9rNkQJwunLj+A4py4/9FXwNpWefly8pjEKQrIetRlVllMa9B3StI6+Pt11ANjE2/4NpbribNyToGSKF7JsQC6QZgE4a0GwjcU3+KakiGAuA2gK8ysSAMxyQDVpnbl5jhgUMaiGqCIW0FYlU70BQzRAsiRUwo1oKQDhoRNTJRzBqQ/ASrhzww1MOGHRAM0pt581ICypuXNrAFk4BGBi1mBw9AZ748deC24lfMleA1amDTBOeG2wW8K1HCdzM7V6oD2XDe0BN8XIEdscDtLcHMzEedNFgO7qQHMDe8U9RdyzOJ4Ur4lqFcOjn1YbkksCKcWQxgoUVBro8wCMqmBi6KwCiVmGeT5DP8t4VipQq1PXaywlUqcxTgnnM+uKM7ZO5QQZVKVxbUOlw3JFkuPrETfHhjXu4Srt+kTXKZp7fFw3J7RjpSCdkhczEKFcukCtB7zyuS+wvnwRHw0bK0aJTuY8a0bouK5hFs+AIZ2QzOgcOowkjucerwXTjPo6NgxoRj26c4lpjObUXNiCW8UGC3exC+SEzPqYS5Lw5hZXj+sqxBwVs7gG0gcwTwWXjGZBuz6ux8EqnN04mruI/RZB1MMx7MS11NQi1AUbAtLKao1lQfp5uOJdEXKY3FPCPFFKwYd1OK49NjAkVdIsrqWLoO70DsmFvZki40DqO/a6TFVH3eID9GwW4ywV1k4tI1mFLErXzThx8iTv+cDjnHr4fsbBuHDxAhdfepFy8SK2WrO8cpXVKKzX4TZ3MZjP+Xcff5yfvXgparcn3REdrNc8f+kC951c4LqgSKXzGcWjNzw2HiiumVRuIHWGJwdXdD4HMXx27KgPY9Kkt4zcPSL5J026QzKMf/DUP+ATH/8E/Y3pw9+d1Gb39fTBe9Kk2yN35zMXP8NnL332qIcy6TaoULjuV+M7Fnnj3wjHF93xuWO+3OX05Ye/5vftSx/uyV9iJnuc775C4lbC1NcbRnf9K7xrt+cy9zB9c33nNK2hkya9eRps4Ms3vnzUw5j0BvX983/vNa2fgrBYHcdXzrtvnqXkNddOvkC/3mb3xvmvgb//5n3n69fznV6st0rl/u5XqA5f4SzTOnrnNK2j377uOgAcblHDs4JnpDFHcw8YIxxGu2JGxUiiuA+INpiZcnTN2uZlD2qth1dpfbSriD6ujdp6ic5Tdzw1h60bPoKlikq4Vy1Fh6mXSkkaPcRjxVTxrsHcvAjHr4DXGvARb/250R0clDtF7HRzEkfscouRPoTF0ly4gOqtDwbeonuTRFz1ulKsxGO2Lt4AuCUAqGnAEhck9y1OOtybbtHz6q6QA85FB7BDmjfncAfSIXWIiODcxpsVUY844xog17WDcdPfm6jUBpUVcyFvjm1ctkjqGnA1dVBHyD1aC+IjVg0xJWdhWzPbXcdiPqMTxbwwSqbXgPBOizAuI2YF0Z7UzSJmWPPGVH3Yo+x1CHA+DNTRqMOAk5C+Q9XJi1lzJAtWBSsbN2785wh7zz3NUz+yT2cdz770Musr12DeocnwogH1NQWYHUrMwVLxFOc5QLeSvZJmPTlHnHUZFfV1uJXN0dmMNOvDtVsqbm1ydxlpPcNkYBzQlCJyPCVIglXHLeaY5C5ctikAbtyX1mftqHbYaAGcs8acnXfUdcFKRZOgJFIXGypcE0YAaBxUE6lr/dnVYn5Iig0btR664yXFa8P2D1gvFVYrkhBdxininLe6njTfYsw9dnKXrbPHOb2zxbBaU4aRY4s5L7z4CldeeJEkHWmR6FJHL8reOHJd4ppldxLC7MxJdj76Ufpf/3FkSxmee4GDl55lef0mw7pQhkJZjwxjparAPEM3o5pzDPjDjz/OP/zSlxk2r8dJb7oOhhUvXKncf6LHusxYD0jdsfa6HHH36F2XrXidpBxR4cMSbB0bdiZNmgRA8cL/8Kv/w1EPY9JdpPfkY5xJM37x6b/HDzz0p3nu0gE2raF3TFME16RJt0/TGvrO04+v/iln9TzH5QTvye8ntUqvN6qv92V2ZsVuusAj/Wfp5QCV17YWCnDsxhd4H9f5Eu/BuD1jnPStNa2hkybdfl1dX+Wv/uu/ys9f+PmjHsqkO6zN2tiVOWcvvutNfa6F3OC9858ks6Zvzt/M/dM6eoc1raPfnu46ACxi4TosCSjgpW3SEGqDmlYLnjpAcStU14CLjJFmbCXii73ttBTFaKA1hVMVzah7RBvrDIaK5IzLGujxseBJm2vRqATwkig9xa0GyE3gdQw36bjCZtuk2jp6PQBbjN8JIt0g8ThCzoemYYTYPeMEOG0/dH1Vb68EpIu3zei5dVEoQzgTqwMlYB8JskMJt494wT1vng2IOOkYWoxC1bDUwTAGPJQa/a5FA95pRAaTRkTGiN6WOQlFxJEuYSZUc9AcjlorePu/5E5F0bpCUgOX7mH1LQToFkjmVBFEZmQKXjLHdM19ec7WvGcuMPORBFSvFFOqZapXdBn9tKlfxDXI2ri6tz7luA4+NPBbC2VVsGqtjzcfAkoxwslMaee5gipJElui9IMxvnyV5y7sMRoshz28DgGJU0bckGGFS2lubo85OxZsdEQquevoJIchmgCoaEaTIgzR+auG9BnpcsD9zSYGAUkJSYLSYWLUlNE+kWczTMNdSznAS0SBS86o6ubFhnQdgmBWw/ku7brjqHnEZqe49tXjXOqsR7sA6q6CONjgERUtgm36frsUTnrC+WybuSaO5jky7yiLGetaEXNm1fA+41tz5mfPcPL0eY71Heube1wrFVuu2e86JGWO33+e7d0d0qVrpK4jzRd0WzMWJ3aZnzgNLz7H8OwaGwd6h9z35D6jN65w8xc+zWq54toLF7jyymWWN64zlpFS1hQXSpeweY/0mepOWY+YV37jffcwDAP/y1NPUycX3R3Tchx54eoVHjx7nnF/j7KI5ADGEelXJNuFridVYNgDjsWmGHpYro549JMmvXX0qec/xSvLV456GJPuAs1F+U92v4Pvnp/n/rwFwEpexO57P8+/8MLkRL+DmiK4Jk26PZrW0HeelhzwNw/+Oory0e7X8ztnv5cH0yN0cjvfK43EyLtnP8Op/PzregQB7ucFVqc/zDNXxmkNvYOa1tBJk26vfvnKL/PTr/z0UQ9j0hvU+/IHOaf3HvUwvq7mcoPH5z/Bll7/qp/fzwsYwpd5Dz4ZRe6YpnX0teuuA8AqPWYFWEd3aepa7+nGqWt46tofvgGE3WtANxfYdAWbbspgUQS0FevWIe6bA1C6pXAlogGb6Q6fh1pxE6TLeF1B7QmGGtDSfR3gUnvA8Bw7GkQcqAFLZfPG4uE4NmsO0i5iei36Z8OdGW5YyV3EREuKXtVWChzwN7ptNUVcsm8e7zD+OiHJyZqo1RDtUXXcFBOJ3S4aHbLhZlU05UMIKGrQJyDh4xiwvI+o4+j5rYjOG0dOJG0xwkKLl1ZcKk50u7oCZS/u4xEjba4kF1xTAEcqnko4dJPG6bU1oytKJs2cfPoE8sBDLHLH1oULzFcjuXlXRzO6sibljqRGyn2Mt9YA6DqL8+lCy8kOR7m3qOaNwXo2C9BaRsQFrXGO3CvUEel6kiQePHeGdz/8AAcn7+Hpa1e59uxzlPUyIp8lnN9CQsYSLushemkjDjq6el0TmpWcM50kshg5JTR1wf9F0JxJY8VzDpAqcW42ceaSOyRn8nxOtzVHgTo4yY1Fv8XKnevXrlCH0tzGSlJDc8RGxwYDDXM24GZIcwGrx+tJVdF+gUhGSg3orLnNmdis4QBdxqq3LuGIt1YhNkBYxavHpgkngH8/wxdbuCpluUJxZlsLdmZzbPc43bmznH3fY+ycvR+vxqkvPcn+M8+xd/kS87MnOXH/NrU6dnCAjQXrBqps4ySWF19h//INzJxju9vsnr8Xm8052Ntj+dRLlF97ASuF/fUBB8slw3CAGxQsXP19gq7DXLBhTSm1we/Eb73/Hl7Z3+enX7lwe9/4Jn1TLcvIc5de4eTONvNxpKqSZz3SHcPHq2SZIz0w9uQ6kiRRWGGlHPXQJ016y+iL177IaFOH3KQ3V989P8cf3HqI3z4/37rYQ7MLn+X+B0+zPGRoZq4AAQAASURBVH2aS5cuHeEI7z5tdl+fP3+ekydP3pa400mT7jZNa+g7V4bx2fFn+dz4Wd6fP8If3/ozbOvObXjkiHt+uP9FlDf+meSRvZ9nuf1RXtm7DUOb9Jo1raGTJt0emRu/cuVXjnoYk26Dzum9HNPdox7G12gh13jf/CdY6I2v+Z0AD/I8++zwAvff+cHdxZrW0demuw4Ajz4EYJVMULPodIzOXwsgunHUijZHKg0QA60z2FrsrLhjFNIoAWY9+kup4ep0PFyWeETW9gJlDTXgKMnxMsYYpEDV5i7WQ+AcUck5IBk1Hrs5hpFNzG5qztDUYGm4cmmOVCTgsEp8CIH2++YKdiIK2hVUFKVFNpuRLCKxq0VUszgRh2oF0XYe1UmaIhobBVu3vmFwCXCdUkf1MVy/ZtB1DRZLdM9qA8xKuI27iHI2TSRrfcheIsa5RkxxkoS5EWCdAPibKmKPiGAQdNYhZg2Ga/y+DNHXK0LdL4zPPs86JRYGOfV0EkDR3Cnm9NXwJBEF7QVDo5fWY+6YJDRFXLKL4pqAIdzRqYvp5OF61VKhlIhGrgVRRRdzZvec5dj3/g52P/Gb2BlXPP/D/4jy5AorQ0SUp9xgeY35qgm0RkQyEs+bEpTok86pkrue3HVozpDbhgQ3xIWUE1VifrvVgMApo12m63pS35G2tpnNelLukK5n9+RxTt5zlus39lj9wueoqwO8Ct5iz1PebALw4OE1NjtQHR9rAP3cx/wjoTm1uR3XLoAu4bDWiEsXLxEpPUpzJktz/hYg4za2fmfHEKRUxuWKEaNP0J06TXfvvSxO7jIe7FMWcw7ue4Ctx99DyrB1/iTzdz/E8VdeId13Dj11jP1f+gL7Fy+zurlH2T8gXz+Aa1s4yvL6Dfr5nPkjjzLbnbN/4SLji89TVrGNwqywHlaMpWBUPHd46sNRnTNWolO7tqpxUSX3c0iJ3/7ww3zx+g0uryZ36Z3UchxZXr3GYjbn5PHjqPR0VJifiI5xTaSdbRgdG5eozLA8/WExadKkSXdCCvyBrQf5C8c/wLZ+7ccXcWP7+X/OA2d/Ezf6OcMwgZQ7qYODA5566ikuXrzIfffdx87OzvThe9KkSZNepZGRXyqf4b/b/7/xb/X/Nr+5/+3oG4iFTozc232RJG8c/gqQ15d5dP0z3OAjLFm84cec9No1raGTJr1xFSv8yHM/ctTDmHQb9Lnxs/z2+r3clx486qEcauP8/Xrwd6OAwM9xhVPTOnqHNa2j31p3HQAOskSz5EdsrZmgSbDkJBrQE6Flz7Y7BkgM12Lr2fWKS4dQqZpIPmIYSboWjWyQUjhDqdGFO5QArjlDKQHcrIB2ePHgmKbhAK7rcGOOI6oLAvMqEogrons1SnYFiYjclBH3ZrlU3Mfo6GXTA5NaSHNzaHqDwbTuWWnQmei7RAS1kaSJMc2hlgDXBpLmCDVcqdq1/tUUVEtm8ZzFIJXoqpUc519zixlusdkNZuLS+pUdEUMs425I0ojEdcelD0CuHa5rvKwhn4jHtQF0gdUap9AJF684ojPwBqyrIlIROlwr+JqybxysBrZnM2SWSCmRtcNVqGZUh2Ij2WcYFucY8NFa5DfNCV3CseqCmKG5C5fv6Mh6wHN8yNPUoS3OWbIgswVp3uMIN578Ci/tX2XvxsDVrzxH2V9i4wCdIi2m2xo4dAFPjksX0eVWA857AHmVDkkJUsYkIH+thpkhOWK+MaglNjLorENnC1Lf0e1ssbV7gq3ZnK1ZT7d7jPXONn5si3WC4eZ1NAuz7W1Kt8YGgQbq46UiEXNdHcY1eEZFw3EshLNdw30MoF3GS4toHyqSE5IKnjKGtpDzcFZ7KVR3tOtjQwAOkqjjmlJGwBAppHQGPbYNx45RO2VvvWS4chV7/iL9hYt0H3wfxz78EfKj96H3n6Hffw+Ma+y5Z7j2lecZ9g4YV2vWuUOPHadqxpdLvBgLVXbVSGNl+cpl1tdvhvM7JQpQxKlZYs7mhOeMpUytI2ZOqUY1D4d03163peP+48f40x94P/+vL3yBC1PE8B3Xcr1iuDRyfHvNye1E7jPMjsdr2aMDnnEP7Xp0ODjq4U6aNGnSXaHv33qQ//zEh8iHyTdfK7GB06/8JO8//9v41Wtz1uv1HRzhJIC9vT2efPJJzp07x9mzZ8n57vuoOWnSpEnfTE/XJ3h2+RSX7CLHZJffNvteEunb+qIyMfLe2U8xl2/8RfTr0Tb7fIRf4nN8iAO2uFXvNelOaFpDJ02aNAmu+RWWfoC7vwUgnrOQGzw+/5csXsOaO62jR6tpHf3GuuvOhEofLkErm8BjEKOYoarRxVpLiw6mweCArJhB7jAbD+OfkRIORzGKJqKHdySJBPA0jVhoTQFOXcDHFq3cel9TBxbORawe9phCijhmBHVaXPMmQhpuQWlvgLVFS9MQroBI65xtxx9xvK3vd3NchEN5c0huAXU1OaoNUJqQShiXEUANt7G5WiMi2gAsgQ+QZuF4Fg0XcopeY6xGMrQJllLEHyfBS4Jizf1bcU24VKAGTNcG+jygq1NAFLMDSIsGzWdxexWwfci7OF2YSG0NOcfYxxXUgiJxPKaMYqxRvFNSP0O7TE4ZTUJxxddjdA4PI7UGBHIK6kNAZTFcFUspwHNSdDaj4pRhiHHmHqgRv6zewGcG0YhMFsf2V1z/8ouU525wMK45uHqZOqxbf7HH+VKN6ViGdk4Fr4aVMWK1uxkpCSlLXNk6UlocNxbwEQ847bnHR4dSMFPMJYzYOeGzHo7v0N1/H+n4LmV9wPKli6y//AQ3x5G95UgphbyYIQnGrke84l6x2kLF1dt8niHFGvyPsUiKWHSrrcs3x3mkGFYNG0ZQg2x46nEzaikBw6uBpOhWdouwbi+YasDiAsONFSu/irogZNKwYrkcWN3cB2DcHzjYd87vFc5fuEB34jjkDr14CXnuAl1x+vkMVvvh6t67zrhMMA6c6ec8dv+9bN9zPy898SUOLl+mIHjfYeoUlErE0Gz6HxyQEvPO26YNySm6o9uGEtHoTX7w1Gn+9Affz//zl7/AxdfoBFYR7tvd5cLeHkOtr/EdcdLXU7XKlZvXqGXOY6fPYes1YmN7q0xIv4XUTJ6fOeqhTpo0adI7VluSeG+3y184/gEeyzvfFP5uJBinL/4U7z/zG/nCtS3Ww/At73P4fFtbrFYrbNPdMel1qZTCiy++yHK55LHHHjvq4UyaNGnSW06G8WPrH0YQ/tXw4/z++Q/w8e43vOYvurf0CifSC9zu78UF2GGfD/M5fomPsGTrNd93WkNvj6Y1dNKkSZPg/3Pw3/OJ7rfwke7jnNZzRxAJ7WzJNe7pvsTJ9DwzfW3mj9e/jjrHuMkBW9S7D9XdVk3r6NfXXTerzAQXpyI4PUIJSFW9RS5HF6dSEVcgNdgq7cv/0jpfhwY3gdSH+3Rzu9ZvSiLAXQrIF+Wshqf2BVbr53WLWGdEoeS4Xx0DPBdaJ6tGdLJYwNAWj+zeIqZb9+wh6TWaE1jbc7e+WQJsu3lYZB02nxxUwlnqKMUq5OYsVo3YY5EYQ7W2E6cDH9sht3hnJ47XDCc6e0U0opxruIAdp0pt+F1ajG8K6GgRFSxJYVhDmsXjiCPF8VQBi+5NM1R3W0/sGCC4DhEbbAvMjeSlHWtux70O2CYJFcdrwVMKp2rKSO7QxZw068n9jO2+xzWxtxzw5R42jtEhrY504XL2ahEvXeOIdNaRtmdIl/DViKyWEdOdMgHwHZGCEMDaNePu1FJRX3Kw5xys1pT1inJwEy9rNHWIFdwMI2Gj4YMdAmHziJ0mZ1SFlDS6qRHcFF8PLZq4RgR1l5ozHIQaUea1UNeC2BLGiozGsgp+c0lnI8P+mv2bS+pokIUqAgR0JXdoXTUHsuDuAZIB0YgQdzUgxTxqncNuBbeCdH10+eXWPV0dG9a45IgV13it1GEZTnlNSE54NZyEJwk4rLGxQNrnzuHGHrpaodeuUrRr0eGFnBLjbM1KhNGMdPEKu+fPw71n0XtOkc6eYn4sM997hf76VaxUvByQUqI/tsvxT/wGtt7/MLxygb1LVxjMKZ1SYx9DjANvmyzC5Z4sHNySOyBhtYSX37Q1cCfIoEnRGTzYZ/7sdy348uUrfP7CK3zhyrXY7PGq97Pjsxnf/dDD0GX6Rc8nHnyI//4n/xVPXb7yBt8pJ0F0bZ9ZbLP2wtoKReaYVoQRdyN120c9xEmTJk16R0mBMzrjTx57N+fSnO9Z3PttP4bYwKmLP8UHt9/NpePv4+pauHnza0sNu67j/LnYyCOaOHPmLF/60pfY25sKEG+HhmF4i+zcnzTpra/0BqKAJ7195Tgv24v8w9Xf4WPdJ7i1bf8ba0cv8d7ZT912+PtVz8EBH+ZzXOMklzjNFU59zW1mrHmYZ1m/fJPrP/Ms9/zW7+a5hx+e1tDbpGkNnTRp0t2sy3aRH1n/A35k/Q94PH+Q+/SBw98d15N87+z70NewOfjbl9PLkvu7z3M2P0mW11cr9O2soxCbmO/jJX6Bj3GNE2/kACY1DcNwWHX5pv7R9DbRXQeAg6hGT6/bOpyzkd8bDNVGhOjVFYxwaLJJRW6dwWNz27YIY7e4QR3DZUq4Q4XU+nwTIqUBHonHrA16eoA5R8Mn6CNIHw7H1IOvEZ+RBJJ7OGtz6y7WAKtuftjLewtUb7p/G3g22u3HgKyam6u0dQd7jfF5dBxjQh3DWVxtxOoIVsIJCZgVJCtSDOl6rAZgDgDbgzTnbmrPr82tLKnFSKfm4qTFJY+QOlxGPGscj2gcg0ZnsHcz3MdWjaygEsfi1o5haF3HY3MBV4pANsFtAO9hXIJsodJhSUg0YOcjxQtVDBPFZzP0xC757HlcFb10meHFNToOaEpIVkwccEQdPJHckQzaRx+xrwdsVaLzVhP4iNWIhpZOkSzh/o5SaXws1GIwrHESZbWmjjUOVWvbCNDBesDH4fCcmrSNDS4BDxWUhFTDdcA903zfsekAwBJe1gEozcJ5XitWK6Uk6lip68K4v2aVLyG5p1iljuGCFNs42mtETw+1wehwrUvb7BCTMZzHG8BrY8FrAWvx2LUCA97nw00MoiC5C6A8FKzU5tyPXuaU4/XoCNWNOhoVGFXxvqfvMwmQ1UDSDrWEbRzEXhlrpcORg2usxm382C7z02eQ0bn5xSe4efMqN194ieXVG3TtNW4iSOroj21hqytc+aWr3HzmIlcuX2VlhaEawwilWGwiEQ7d91k15kbOVI++ZkXoPHaAV3csZyRnkghSR8iJh4/t8O5HH+X7uoiI3r9ymWtXrmNZsa4HTSzmPeSOnAVbjYi9GhFPeiNajWvcR87vnGBlhWUZWJqw0jlGbfHjkyZNAnj02KNkyRR/4110k+4+ndaeD/Yn+XX9Kb5/60FOaPeGvvQUr5zc+yLHl8+wOv4e1g8+8jW3SeNNdl7+IQDW5z7GiinV4XZqvV6zt7fHsWPHjnookya95fXHH//jfPL5T3JjvL2RvpPeHhp8zSv2IvemB77FLY3T6ZnX7ER6IzrGPsfY515eovK1X7IrTkfBzlTKb8lc+vxPYydOQJo2M9wOrddrlhef4ngfO9vr7Di2OHvEo5o06a2r17KBZtLbU18sn+eLfP7w34py3a7yhxb/AZ30t+15tvUyZ/LTnMtPkFm/YWb4WtfRSW+OhoPr+Of/Hv0D34mnOWX3kbsaBN99AFgiftVaFzAyA1uBtYhcCZjnNuLNNSrVWj/urd7goGCb3lwP8Ehp4FVBOwJeZiC6WSEcuy4Vq4ZKYoPIDgGzeIOoAAUVQ7vWWZv65va1TZYzm67iDRQVN3yTIp1yO55w+waQFsjhQsW8AWrCWei11R87qGA24qqUdaHWSvVCreHuRLoWfZ1gtOYw3uyqCDAoqW/nrB1gczsLfvh8cRwpoLbFKRNRnIqrI9R4fBFcNsDa0CxYgZQ8UoOlQ8saY4Ram1taw40KGCkCs2UR5y1BEsNdMbzFH0OpxrqMrIdCPliTr17BzRmu3aCs1ugwRjS0CMaI1xFVRVWRrkMb8K7rAS8CtSL9jOROWR3E/EkKXR/zTTcwX9ofLIoVx9wgZwSFusK0R9VwKxjhIg9ntmM1thuAoZoCLBN9vxTFsyApuqtNBLzEtSYHZFdaHHNsajAHStlwaQo56rDLEFsVRNnEorsZth7w4vgGhucUYzMHM6wOLY683S8RmwBy6wo2wcrmmhFx6Z3EnC8NhpoEqYf4QliBnPEx3OC1GqMKQ1KYZXQ+JwF5MWfr1GlmWzvUYR/Z32PYWzEexIfmbmuOPnovWx9/N91DD7G+eJ2Dz13g+pee5saFi5T1ASikpIimcHXvL9n/1WdZVuPa6oD9MrCuxjCuGatTRdHsZNUW3+7kLqLSvVSKG6VGP7iqBBh3iVNKxV0RF0QzKpCObbM4fZzOKtkLWXIkrfczitV4GWtmkZy0UPL0ofu2KKvy3e96F48d36GKMA6wKpUDW3OQdtjPMNSpX3LSpI1UI+WAaQ/KpG9DCeF/s/0Qv3dxPx+dfe3O6DcqrSu2rvwyW1d++Zvebv7ST+NpDkzJDrdDIsKDDz7IsZ0dpCwPf+5p9qrPBpMmTdpIv84Xg5PuHt3w6/yN/f87f3b7z3M+3fcNb9ex5p7uS3dwZJCpZL5xvZDmRH9mm+35s9Rnn4FHp7jFNyoR4dHTHfe88inScB2AOjvBuPsYq/t+87SWTpr0deTTh9C7RobxL4Yfo5Oe75//AFm6N/RoHWse6n+RE+mlN2WD1bdaRyfdfimVx/1XOT1egKeewyUxHn+M1b2/mbo4e1eC4LsOALf22PYH0yYeOUE0dgYLVUGsowKJ5rRVmtOU9uWmt/v0UFfNkapgFfOBJDOQAowE1ezD3esZvKKSwUElYo/NDXUaWAOqRB+oCymBak+4jDduWmvRsg1Oo5A28BXwiLqOx7LmHq2H4K7ZJyP6WjYO2ojWjcNrYHgMKOxI8Df3iMF2ieNPKSBzaV/6agOPXhoUj+emtA42T3jScCl6G7PXiKNOCakaEdllFbfNm8cnZmtdB5yuLYLbBXGnwyk+3oq8xuJc06K9G1RGQbK2mOhws+bG07eA2bqgOmDrm4w39lleuoprwobl4VyogNaC1wH1hPQaXLrLoJk6DG1cAX9FBS8jpDlWl6QGfV28/ZESGwkkd8H0s8Iwoh4Ry1U6KhIR2s3Ja1bCtCtKLRUzQ1M+vKbRKQyeKp4SJAnwLnFtzbw5xlt0uTqpa4AWiQ7eRuRFauuftuhrVtr5rRHVbBbHJ3FNwyUe19xKjZjmFJBfsPZaam+2FpsX3ISyHGIjAECMNuaHdIhbQNEKLhLTcKjUOlLdKMDgTqmO3Dig7C/RxRb5zGns+DY+61hdT4xdxpNTa0GqMa4H1iSuHazoXn6Bq7/2FZ7/lS+xvHKZutzHbMRQUI/jtsK4dm4crFhXZ7XeZyiFUiqlVKompFOyCGZOdqcXQz26ocdxZBwLozt5Pkdzi1a36HX22uLFUwon8FDwC1co125g7pRhJKnEHMqZhFJGI+0cY+vEKbKNaHfXva3fdvUp8Xvf+27ec8+9sfmkrKlZGboZ26s9DurIlgp7vjjqoU6a9JbQ1fVVfvz5H2e0yRU/6bXr8W6X37W4lz+18y7SEX+RKW6k5QXg0SMdxztBqsojjzzCiRMnyDefZfvJfxRJP8Dq/HcxnnycunX+iEc5adJbSz/4pR+c3L93uV62F/l/7P83/Ont/yP3pQe/5ve9HPDe2b9C36Juod3veoA5J5m2x74xqSrvPjfn/sv/gjTeitNO62voxV9gdulzHDz8vQynP3CEo5w06a2nX7n6Kwx1OOphTLqD+tT6HzP4mh9Y/PHXVaWxpVc4k57hvu4LCHY3MsF3pBKFD/AFznLx8Gfilf7al0kHr7D/rj94V34WvetIQcCjBt2E6J/dvFE4NDqFqUeHqrVYW6OBUm0QrfXq6khEOfstZy7hKvVqqOTYz6veuk9BTAIqlhGkw1q3R7gk8+FOBMGQfk74QFsP8AbeSoObGwdpc306KeKXhTYmom9VwYO0hdPUx3AnO7fALa+KzXANQJmsdZW2Y9cWi72x0VqDiingnmuL8bXN+ApIj2uNMddXnXvV5upVZGNbzrm5qqXl/Frr7I37uUmcF13gdWzH0+HmuMziOb3t/EoJtSE6cHtrILyDUqODN83IMrBgzZZmTohxQoVjMrKQRDKHlZE0k/ouILUB1ai1hruz79FZdOqiUGvBy4hoR+5S62r21mtb8RKOVVONWSI54GY3Qxv0jMhocFegw+uIrVaIRmy0a8JRai2Mtm6HNUNzjr0KKbe9DbPg+jk2J3i1ODWuSK24CpRw5orH5gc6jecRDt21uplnojiCNnevjyW6hcca0ekpxzU2x8uADSsgxYYKcSTF7glv59A3MebV8WEIIL1qTuCcIhLcHW8R35IFkR6vBasV9xEjUUwYRag4UgtqI9gM8yX1+lWWdWAwWF+/GYB1PaIqeBbs+h4H//rzXH3hZW6QuXjlBvuraziOVmvXLp7fXKilMlphXSrr9Yr1WCjumEfjdTD29vpIIB7dvqnryLPZYce1utPnTM49BcekbZRIEW1O2kSlgw0DwzA2x3WFBNpttzQDRb2y8Mr53kjScXZ7m1+7XW+Yd6nOb2/zoXvuRbsu1gyNzuZZglnfMVteY2aZ2XJ11EOdNOlIVazwQ0/9ED/67I/y5M0nj3o4k94GOq0zdjTzJ3Ye4zfNznJf3jrqIR2qu/YVZukEe7yRXeSTFosFJ06coLv5DNtP/RO03lorFy//LP2VL+DdDuszH4GTv/UIRzpp0tHr+nCd//aX/lt++VukFEy6O/SKvcQvjD/HPXr/YbehUJnJHu+a/Sy76cIRj/AbS1TpcsdblE+/bbSdK/df/pdfBX83EgAvLJ778fhecFpDJ93lurC8wD9/4Z/zEy/9BC8dvMSyLr/1nSa9Y+Q4Pzl8ik46vm/+A/Qye033Swws9Drvnf0r5rr/Jo/y21PPGjZGsUmvSzvscZaLX/cMpuEG20/8Q/Yf/f3Uxd1V/3TXAWDxjQuyNogL+BLYCgDTnLUuhhtUlBTYi+oRzhSTqAIjlYRKuBNxBYlkd7ExQBrNGSsFR5FaAIUyYlZbMnJAzQDR8UIXyQiFlBTVjIgFHCa6VX0DpdUDntbm4E3N/Sq0WOtw5HptfbWSA7qSmlPWQVIcU3PjHvbvGvH7QnS/WnP/0rVwjSjTdvNbzl+awzj14bA1aeOZRTSzWEDCzftZbbA2d3hZQTeHWnE6vFjAbG8YeDRcEl4GfOMktRFkFr7uTe9sCidroiLdDLHmlnbHa0EoqAo7rDiGclyVYzlxbL7NInf0KnTempk1kbqE5ERVwawESKVFFbceYK8ElCwFq07KguU5ah4QsEaHrFu4V22s0d2cPFzCKeKz3QyrhqO4xHGbh9NVfNOHF07TkZFBMpaVnLqoW86Cq2Di0VkrgpYR1y4e22v8jBFqzHfXBqdF0GGAPpzIkeQd8d+aItaZAnW9bnHdDuMQ81YznlJEdtc231IXGwNyitOExDFvzt8G8EtEWoOBabiGR8PTGJA4SWwMkAxZ8MGwAtWFglNqvLY0R2SyJo246VqoN5asbi7BnFoqCUjqpG5GSoorLFcHPPfKBWxtHKz2sTogSZghaDejJMGolOoMpTCslwwFSi1UhNquk6c4D6m5lakR31Rnc8r2Nv3OAsaBnBJ9PydvzdFakNUQ59lqOLNLpQBem+u5xbpHFLYhNQHriDzXTNrZpX/P46QH7iFfu8S///Hv5F995Ynb+r55N+m+nR3+8Afex3yxhUSwOUbBUoc55OJ0sktXoZ/c1m8redtsddSP8XbW5vjdnS9f/zI/9tyP8UNP/dAUuTXpW0qB/2D7Ub5v6wEe73YB3nKvJS0HPHD/GS4/c/2oh/K21dbWFo8++ijd3nNsP/m/fBX83SgNN2C4Qdp/Ed4zfXn9dtG0hr5xvXoNBfjR536UTz7/SX7h0i8c8cgmvZX0T1c/xCk5zW+a/TZ29BJn85Pck+9s7PPr1f3338+laQ193dra2uLx7cvoxZvf9HZa12w986PTGvo207SOvnFt1s/BBv7uE3+Xf/LMP+Hl5ctHPKpJRynD+NT6n7DnN/lE91t4X/4g8PU+Z8bcOZWe42x+klPpuTs80temd/MEFzh31MN4m8o5xk0+yK9801vpcINjX/zbjCfeA2f+5B0a29Hrrvv22t1oRaPtJwJpEe5QbxCqVpSMeUVFwjkbnkukEqBYBFigG0eu7YPugAcsUjGEHjBMFPXwBQfwtdYFTItKDresHMJfR7WQUkfGW2RxgCCxNn6t4WZ1e1UMNNwiqxvXbHMIt3+7AsVbD2uMXTYuYjYRwNa6eDkEgzFwCdewNdBqJUAvGiAw9eCbqOfWlewpXNQNcArNQS0jSEZyuK+jfjbBsAIP7OK14p6hHIBmzApoj1lEI4tJOGip1Nqc3NUiatj20LQb/c2HoNnJGNvArlaOa89uVnZn22x3mXlOdCmjklApUCOWW7PgGjG+pVq4fH3EbYaNI6aGJEO8QXkXKANeUgBXazHIUiFFF7O74TVtrlR0vyK4Vdwdq4UK1LFQ1wFwsdq6dpXilYpSRQI+2oiQSC5Iqg2Or/GuR3GkDuHqNovXQEqotWtk3ozgCv0snM7e4H6D5nWsoIp5ADEwUu7QWR+wFSL6ubY+4751VUvc343oHa5t3qrGODbu3qThbp0pMoKNY4BeBGp0b4s75gO1GGZCoVJrdE+nriOJo5pRdyhrFEFsFfsgEDpxNCdS15Pnc7ouNmjU/QP2VytKNcowUtzoju0wO7EDXeLg+h7D/sC4OmA0oXj8kbF5xRrgKWFdh0i7thaOf+97ak4MIsgwhBv/9ClksY2UASsJXWyTlyuGmzeQtgGgOphVVCKJwDBUJDaEuKPiJA93eakVufAcdf8Kyxsrys2v3S086bXp/u0t/uhHP8bJ3V1Sn+NLOqBWxdwwMnmIzspkB3Tzt45zbdK31u34sHw3f+De6AtXv8APPfVDfPrCp7k2XDvq4Ux6i+uszvjY7BR/bvd93JcW5Kmz7h2rra0t3vWud7G1evEbwt9Xa3o3fXtpWkPfuESEm8NNfvXar/LXf+Wv8+L+iww2xVVO+mo5Bulf8NHFNbKMdPL2CVXeeuEngI8wvcN/+9pazPnA9kV2L336NZ296Qy//TSto29cLx28xM9f/Hn+p6/8T7x88PK0CXnSoX5m+Al+afwM27LDDyx+N7959rGv+v3p/Cwn0/N0siLLFFXxTtQxbvIRPsf8W5RRbN5F+2tffvMH9RbSXQeAg135YcwyvgL61qvbemkVTFsvp1dMZkAhaRdgEwKuSheOR5GAj9aeQzsq+2Qx3FuTsG9uP+INCgMoER+r2ocTt8uohCM4dQ0AV4s8+2rhqswJyYpYc/1KA8G0eOZN/PMGCmuLsm59uQ7Nmdvcqxtnb54heAA9BUPxKrhYS8nexCtb/OHRLcBKQFxJAQ5rQOIAYeuI8mUW8BpafHFzCCOHj+dDc9Z6uBzdBKsVSQl0BlTqpldXIurabQggKe36qca5kIT6Tuu8zQhrhJGFzjkOnO632d2ZcSz1LJKzyB2zLtNpam5hELpI+04Z9YIh+Hok1dhxVzXj1iC7hwMYGYGIU44I3xqd0EicS0nQ9UgZ27k0fCxYUrxmkAFHqMNAHSu1FIzWyWyGCZiF61jc0NSjtWA41WFdw9VupWApkdsmA5VM8gFBG6D1cFgTsdybzQjuFaeGe1Wa61zBSwkIKxrzR2Pepk6RpPGSqjXilUmI5tZf3a5pNbzUuJ4iiLRe6aqxuUKaS1wF6hjR1L1CCVheDSglzoERXbhWw+GsmS53aJLDmG1xostPlOTNxZ06tNMAwP0MXcwBwm1cxnbeRkYXyrzHTh9Hjh3Drl9n79pVymjUClX9sL74Vh84iOYA7VhsqBCFpHhKVBGGEu8bspghDvXmDSrKXGAha1ZWSSZYqQ0C13jvSLEBRfGY4rkn4WRVkkBSQSUh1jMUYRhHdhcLvvP8Wf71K7f6DiZ9a91/7Bh/9IPv59yZ06S+Q1XBRkyUZJnihteKeYeakbwjTx+9J91FGurAT7z0E/y1X/prrL4F2Jk0CeC4dvy1Ux/j47PTRz2U16wuKcePH+f69cnB9O1oa2uLxx57lK3VS2w/9U+/JfydNOlu01AHfvLln+SfPffP+LkLP3fUw5n0FlaH8qd2Hmahb79NvR0DJ3aPce3G22/sR6mtxZzv2LnG7sWfa1vMJ02a9G/qxf0X+Yuf/os8cWNKu5v09bX2A+5JwvdsP8W7uktHPZzXpZ6BM1ziEmePeihvI4Xz90P88reEv3ez7j4A7A02WesfZfYqNzANpOYAvWKodpgXOgSrjmqHREYy2DoeSzVcvKkCHXhBdQFkLPyeFHdUrUHbFA5eybhDl/oAerk5W0nh8MMR7RCVgG9JkJwRqzAYLhGv2+yVzW3rt44jpYhc3rieG3yVHEHW7gYmzSh8q8/XpXX0WonU5w3c80QUkNL+/4DfWRImCdsAV8mBLfOspUi3DlWruGtzq/ZQDiLSWcDHJV4lxmvNhSzKpkSmmoH2uI0BmnEibBuoq+gLdshi4GtEdkAyKkYis5t6znQdJ/vMiZ0FW33HzBOdVbIqXRlJYpHs3aKqJQuqc0AQCxQrNuI5IdIFrIMWd23YUAOatqBwF8VFWkz2GCAVha5DcozXCZerNEDqY4mYaSt4N4/440K4Qs0xNbp5z2znJNUVDtZ4WVNHx8uSYg1Iu2AW90kJcupiuotFDLlUSAkRw23EPGKTfb3GUmxocCF6aF2gyy2hPJE85uWm99rN8eoBxG2EWrEutdeFtHMhmAqiKZzlm/u2+eG14J5wj25el9TAeDjQAxZHz6559AhLUjQlctvMYeZQrUHmhAJJI747oqi7cBp7xQ6WmCS8jq0q26LHV5UVCjf2WV/fp9zYpyxL9GFLAGsjotVV4vUVGyZa3LsIQjia4/gjHrqY4cXR/WXsF9FEt7VFd+oUKStbFy+RZcUNEqtxjdYRUo+017OkhKaIuE4OyUaydLgb6iN+cI39AxhWA6lWfv258/zqxcusbbMrZdI304MnTvAf/8Z/i5MnjpH6eI/39p6qZU3VLiLF12vEHE0JkW3Epp2Dk+4OPbf3HH/pM3+JZ/eeZbTxqIcz6W2g8zrnvz71UT7WnzrqoXxb2nnpJzh9z/dz8+ZNbFpDX5O+2vn7j9A6ffCeNOnVmtbQSd+O3tft0r9N0zK6ssd95Ulu6D3TGvoadYybfMfWdY5d+oUJ/k6a9A303N5z/MVP/0Wevvn0UQ9l0ltUC0n8X45/kO9Z3Mu2vn1RV6ZyLy9zlZPUuxDZvR69Vufv3a67cDa1OOSkLdI4BbQCpMXwIiVu1/pw1QFRFI0/yqzGYygBj6222FwFBsAbOFSSj5hDaj27ngWptUUsh9tPGJCUEBsh96g7XerIKaEYSfoGl/Ih3EO0wbtwVYbbt8U0S4PBm8NVbRC4gWtpv2jAdwPpxEZIOaJ4JdyiVsPBqpKx2np2ia5jrCL9AigNaKeAXzpipIjstYJrHyARsLEEq7ahRVDXlv9MixJ2XFsMtuUGdp0kFZfaTJcB1GlXLay6OQCNCOoJlRWqQkfPMYWzfcfprQUnZz3bfWZWjWSFjBFXNpEI0AaOV0Gqo9IisJu7NonieYaaUbVCN4Oui98T5zm6dg3GdQBcEngliUDKsYFgA9lbF7OZhUvYMq7hCvcsDUo65kI1h60FcuoEspijqxW5DPh6oJYR99hskJoDNuaKRVdtHUnEXNAablPt21ypjqjHfPKEldbVvNkkkLTNqVsdz24eAFy0PQ8RX22GiobD3C3c3laiMtgqIo5IQlxxRtwMqodL2gvkDjQ2C1iJqHQza97auJ2nDk0WHc04KW26vCuiKXqAVaPrWAAJ0GwlXh8iimHgA9aAblWhdhnvFAPGm/us1msY181In/BSG5yOyHZTj80TOYXjWeOxI0IcNHWHbmDTcCinMsY1yIluZ4s6n2EUZg89yLFHEnzlS6yfvhlR2UREt3bxXpAwssfbjmpCk+Iotl5ThoFaCqthYL0a+ODxk5xbLHhuf/+2vXO+U/XI+bP82e/5nRyfL9AcKQ9u1nrQnZoS2jrQRcBspFpFTCPWf9Kkd7DWdc0PfvEH+amXf4pn9p456uFMepvohHb816c+yq97Gzl/N0rrq5yvz/DyrOdgOblYv5UWiwWPPfZYwN+n/+kEfydNepXWdc3LBy/zlz/zl3ny5pNHPZxJbwM9mrf5a6c+9rb98lpwztfneY5j3GRx1MN5y+sYN/gwv8zi8vT3xqRJ/6bcnavrq/z9J//+9Fl00jfVTJQ/f/z9fP/WA++ImPRzXOAZHuYGu0c9lLe8NuvoBH+/td6ef1m+EbmDaQAu7aMb1ltXqY2gfUAdAZWAd6n1lUqDTBG1LNzaoPfq/l1aX+0QNbgKWQhXqEkYXMmoONQRSbNwEaYOcNQgdz1JBCUcyEk3Ubu1uWPl1tNtKn9fHfdM6zI2i2hnze1nfngKZNM+6+DSYp9zx6E7unXRVrfohk0popdrcxQnRWosyoaiMgMsorNRrI7gGQq4GF5WuPR4XYfL9nCshuQeMUWyATNQw4siqQ8nrCbEZggNGAuoByREEkgf58UO8NQDHQnnGMIxM473Hadmc06ps62JuQtdUlJKaA0AjEbHqkgKaNt6nyPyOLWNABWdzZGUcM1U6zBNAS9VkA7w5vT1hNWCuMQCNJ+j8z6A9mqJr4fW56y4tr5YTXgP1QQbMjaUcP1CRF5TwxG6t09dDZgZWivZjJwT4gmhNvero32KQ3HB6kgdC6REdaIzeR2PqiokzaDh1MUkHL2UiK2moqqHINzKgJugKmjXtx7c1u+bOlylxXnX6ORwC/BvGRdDMUwCqJnLYQcx2gzs1fBSwjUsgqWMice8KY54aTHpHv/b4s0zGt3bKUGfD/uV0djMUItRxhKOes3xWzFcMkUSZWvBenuLKoLs7ePLg9blHTDZVdsrSG699jQhKYc7XyOSWlXJSEBoldbhnQ5hdrwxOOXGdZbjmnr2HHWRqcsVI5B7pa4E1mvoO5KMZJHD2OcMpNwhSaILeVizKpWhOuOwYiwjhvFHHnuMv/WlL/HKeloIv5EeOXuG/8Mf+L0cXxzDMdQFSmqbONrmjzQL57pKvO8g+MFNPLee60mT3qH6zMXP8KnnP8WPPfdjU7/SpNesMzqL2Oe3mfN3I7HC1rM/zvvO/Vt8wXdZraYvZb+RDp2/65deU+fvpEl3kzZr6Cef/yR1UyE1adK30Pcu7uO+vHXUw3hD0vVVPsQv8kt8mH12jno4b1kd48bkWJo06Zvo5y78HP/lZ/9LDsrB9Fl00jdUh/KfH/8Qv/8dAn83+iCfn9bRb6FpHf32dNcB4ArBQpu7DhsPHbBoT9pE9zbw6Zu+VQCJ3xkexk6z5uRs4FTl0OkprlQpqPSYl3C0emm9odK6dmn/Dmex5ITamqyKao+IklJuFcMtOtoNSnMgC9BnzAq6uZRebt1OaE7g9ibYzLsYuJdwzjbnrWx+LyAq4eh04gc+BiiPQthwKWNY7uJhazheXTadws2bWeKLICsHhOO4hOtXuhadK0hqXZupv+W8RiFnkIoP4ZamFkQT3i2QMpA0HbpOEcE9XMfZocc5Ic6pLnOy7zmeM7tZ2ZLKXJROlE6F1BybYms23cmSGgAFlBifWWkwT9GU8VkPCr1HHPHgBHQbR7yOyGg4Y+tq7gKWO/hY8OUaKy282gtIH7AzKSYBzqs1B6x7/EwMkiOyFTXL7ZxIHZDidBKAUaohVmPjgggpt/jpOiKW8A7cw83rMQmo7njfRdcsoN42LtTolHZxpOujl1ccb53VVMNSH25JV6wOuCui1piYYSaYG1Kbo5IBqlBLbY8VoC3i2B0nY60H2qtjJIoZVTbTXUiznqyCVINSYt6WmAPad8z6jpRmrIyILd84un2kFMOK4Iyxb0AU7ZU0m5NVGYbKcHCVsQyoQVdjo4NL16Zlg/lm4eR2b9HP3pzv0QOsbqhuuryJ95M6xvwVR8yx1UBZDfjemnFvhSVhNY6slku060lOuOoRdCwkMXIXSQLaJegytVaGMrBcr1iPxuhGNaM6IMa5nTl/7F3v4m898QQXJgj8NXrw5En+3O/4nZzKW9RaIrjdvQUkRGe5tbQH8dhY4KwhKXl7F7FKLXfdEjrpLpC78zOv/Ax/5V//Fa4PUw/qpG9PH+tPvS2dv6+W4Jy89LN84Mxv4Fd8h9V6OOohveW0WCxuxT4/9U8m+DtpUtO0hk56vfp3tx7kPzr27qMexhuWAFss+Qif45f4CPtsH/WQ3nKavrSeNOkb68rqCn/lX/8Vfu3ar7FfpkS7Sd9YW5L4Px1/P79/64Fm4HtnaFpHv7WmdfTb11337bVKagbU3GAm4Qi2PUSPBWyitn5WJ20cnABYwGBpUNUa0NUB6A5hsOPNNdgB0nqAEzDinqkQkHgTU5vTYSJw1nnAI9rzSrgypdYWV92inFPGkzRYkXArASdpvasiAVtT4hbdJeKga4Os5lGzG2ZXDq3PNId0ixDGFaMECMbB5LDTxWkZ2S5RRVwJqOcVK8uIqE5bLTV6PBybJkG6FguUc0QWV0AzroZYxoc1IptI6lkDiLWNq5BUqOQIBnZI3Zy+jhzPibNJOLPY4mQ3Y7tLbHWZXpWO5qLUjCaQnJCqAe8FkIwXI1HpRampZ7SEuLFY9KR+i5U7dVzTd5k069kfR7i5x2AerteO6KJN2iqnxwDxlqLnF6GWdVxLLZDj52YlOm+L40kj2ZqI6Zaua5sGIhaaYU2qlVQKYoJYCUilHalTJGVUEmLxeOZE522cKMRGpFtEFHdO4bZ1sDGil7Ea87NLYOFEjojriCB39c3+AaCEC3qMMWgfjnNzx4aA+oLi1JiTnsJAKzXmURLIPTYWrDaTvht1AzTXKzR3dF2iS0anMyJlOgOG2xrSjHR8l3TqDH2fqVeusrpxA8UxKqUYY404dteAt2nekU6dRE+eYlSoByvGS5ex9UjSlgxQBcvEZoV2/sPx23ZSiCLuqHt0DqdMktgoIMkQzQixoUJKRbyG+9gqxZ1aBuRgP5q/W1y14qTZDO0MNUNrIomTIbpnc8JEGA0O1gPLdaGUGm5plQbrO0QSZ493/LF3v5v/7xNP8MrkYjrUgydO8B//5t/M7mKLulrGBgsSqpHE4JIirpyIgscNrY51HeqOuCLjOjavTJr0DpK78/MXf57/6hf/K/bGvaMezqS3mQT43+8+ftTDuC0SGzlx4af4jnO/lV+9sTM5gV+lra2tiH1ev8z2U/8YLcujHtKkSW8Zffrip6c1dNK3rRPa831bDzCTdNRDuW3aYsmH+Ryf48PTl9ev0jFuTnGVkyZ9A11bX+M/+/R/xuevfv6ohzLpLa7vnp/j+7Ye4Hvm976jnL+v1q119ENtHX1nHue3J5/W0depuw4AGzWAjEa3LO4BVn2O68b2atEp23pJk4TTNahTjQjZYu1+lehFBRrgtAZtk2g4yDa3SxnMyS23WZwWMds6eL2ieRGQyQ1NffQLu+GpD9ej+aGrEYu+WUkZXAMyiQTkq+1Y/NVx1RKQtY4BoSSih8U94K908VweEdDiBXUPty0w1iHu7xUvI/QzKANuXTiKTTBbQ53hFIoJ7oLWFWoNYKYtNI9Ah6SKm0Ddxz36gzGLtlftXgWknYDyinslgGK4YDMBb82NpLClPSezcKafcapTdvvEVt8xUydLIgt0fY/kHthUJ+eAn2WklnBqzk6e4NSpU1jquXrtOtSBM+eOs7N7kkvXl1y9eBl2tpCtGf3+HqUUjIFiJZyntM5bDwiISEDDvoP1iJUaDtiyhhrnNxzlSuo1zrk4NtaI0fYKWbCUcBvxYUSdBn5pfbeCausydkd8jHmlitRwpbqU6PvtZog4nmc4FZcOF6emcJYa4dBWHDWPKGvGNv+6GH8N+Iw4XizA9Ghou0ZWx9hAoIqK4hZz0rzQkp/jPiVBLZiVcD5HQy9IwlLGc8DmrkvMFzPmsy0QZb3apw4VZ4bn6MKtNy+zzplxtaR6pXrBPTE4lNS6eFGYz6hbC0o3I49r1usV+9f3GFYrOh8Ri80b8dKIiHQRQbvm6ob2HhFR8UkjKj7lHJsKVCL23QXKgA4jXS3kfhZ3FWUwo9bWgdyc7AlDVdEcEeU5Z5I7Kk4Shy66uMtYWC3XrFZrSvVw4COxmaV1QGtSQLln5xh/4uEH+Gtf/MrUWAs8eOI4f+a3/EbOnjiB2IhZRatAVvChbbaQ2MzhhmZF8gKrFRkD6FtzVItMUUST3ll66eAl/ovP/BfTbutJr0t/ZPsRHnibR1e+WoJz6uL/yvtP/3p+cbWYwucI+Pueh+5hsf8kW89+anL+Tpr0Kq3Kik8+/8kJ/k76tnRWZ/zVUx/jY2/T6oRvpm0OePy08YuXmdZQYGsx5zu2rk2dv5MmfQN97srnJvg76Zvq3rTgN8zO8H8+/gG29Z2PtLY54MP8Mj/LJ/AJAHOMm5Pz93Xqnf9q+TckolgZwFft31tAjf7TesvN2igKjlHMyGjEA0tuzk04LLP1MSKMLR1GOoNiUl/l0o0eV/HmglVFcosdlh4RJ0t0A4frs2swJ6Kow8RrEY3sEhGl0pyrJkiUC8f4zYJji0ZnrB4WlnJYGiyEy+3wDURuwW/CJQlO1yd8XGPN+YsGvva0cSODueLlIDperQJjPI5A0jl4RCiH61eAjOQOH9Zx3nwTX61I8ogS9ngOsXDiuRe8ndOwnnYBxFo0b86ZzivHpOMExvGc2UkdW11illNEPvczcj8jzWdxHqqF+1oTfnCAj0PMgV7Iu8fpz5+H3NFXo2pCH30EPb7F7OULyLXr1CuXseswWFyHlCzO6WwLd2ccK2Yjos0tWgfcMlYHalKsOGJG0hTQSUCTknOO625QxZD1itEqPoBlx8aRXBoobz3RqjFHRCTgv1n0VqsiuUc7AxJevQFpbZuHFCec3maOjSV6fs0ZbcTHIWKHU4e2GaRqh67lTaWVm2PmIBUZPcYmuXUI05zbFWueXNt0qZoAFZFwRhuGScZUocsITlanF6Xf2mJ+4gSL47vUcWC8EuOE6Go1c9Y3VyDCUEaKV7wahlMq1JzxFFC2E2A9MtYb0CnDaCxv7h32CmsCldzi4Q0Scd60HU9K4WR3J6nS5Rkq0SMdbvJNjzSgie7YgvnWnJyEmcMid1y7uc/161eRJGiXyaIkFZIIyZ2clJyFlAJIioCXQhmN9XLJal0YhxGTBBL9z6iTJLXzCtLF/D6bzvGJM9f42UuX7uoP3/fv7vInv+tj3HP8GDlFkkE4wm91s4l5gOD2/uJWMI33DKsHSFmTnOguT+XoDmbSpDdBP/z0D0/wd9Lr0r1pwR/cepAs76xkBPHC8Us/y73yHbzo9xz1cI5UW1tbvOuh+zjxwo/R3XzmqIczadJbTn/zi3+TTz7/yaMexqS3kY5Lx1899TG+621enfDNdPzyZ7iP9/IC93E3u5e2FnO+Y+caxy794lEPZdKkt6z+56/8z0c9hElvUW1J4n+7/Sjfs7iH9/cnjno4d1RzVtzHS3f5OhrO3w/x+Qn+vk7ddQAYl+ag7do/NeAUcghKRbqI7WWkS7PWCRvuRWHjqh0hzRovDTdpwFhIh65ebc7UDtOIKY4Xa4oIURNEIWVBMXLydr+ECmiyiBLWFICi6xpUapAJbrlkGwx2LzS6u7G30qzBEf9sFbrUHsegAbqAQhXQ1v/qSFK0FJIIVcLRaGJ4XUfkc13jqcdtiNhgMap0KBYRuxpR0imlcEOmTaRRwofxFiDLAcClrMNt2W1hXpFRAiDqCBa9zNUM9Qa1u3kcW10jsy1mrhzHOJ0zx7OzlTp6zSTXON+1YEujrpbElVRSdpBEHUZ8rAFRNYFVZj4yz3NW27vcuHyRg698mVGcG3sj9cZNfLXCaqXguFVUE7P5giRQa8HLimGs1GEfy4qnTFJnpsKY5qzqgKaO1PXkWR/uUQdRQccBl0SRAZdMtUJxwwcHi7hhFSVJR2o9tEDEPTeXO+IRB92FA1dFcc+4FdhEhcdOAVwEzQI6x1YH+GgUF6pLwPsa8cU59aRS0Zyg9WGHY1huzTu51aNtZoeQ163grrgmijuWBMEacNUGwWcBsdcFGUayO53AbJbpFgvSsS20z5TVEhvGANYiMfdqYUwRj1xLAO3qQrUSmxYsoX0m754g7WyTZh1LgT1J1IMlWgd0/yAOwzabNyRAn6aYc6nNSVFUEpqclBJJQVMHuWvnofWDJ0XzHNmewzzDfE7dPU5Rxb7yBH7FoBZcBc99c4/HBg1JMzT3JE3hnB5HhtWS1XJktb+kvLrfOylCxMkDeDdDveKiqDuzLvMffuhDPPfpT/P83t3pSrhvZ4c/9vGPc/bYbkSKpwzqlHGNmaGiiCQoq+jUViXlDhvXUA+wseBjgWKI5Ijj1u6oD2vSpNum5/ae46de/qmjHsakt6n+8okP83i3e9TDeFOUMN69fZNr63s4GI96NEejra0t3v3QPZx84cfIE/ydNOnr6vNXJtfSpNcmAb5nfi///s4j/Lp3oPP31UoYj/NFrrPLHseOejhHomPc5DsW1zh28ReRKZNr0qSvq+f2nuPacO2ohzHpLaZT2vPb5+f5c7vv47h2pHfYZuPXomkdnZy/t0N3HwA2ot9Ww0VKc8qCga1BEqqGq4LPokZXelwqWGltpi3S+dBKV5tbN8CaoeGA8ETygjCQ8gwpA9JFXHP0sBY09811mCJm97BTsiJV8NRD0og/tjiATV9w3Cz6gFHAvEVbN+MiAlJvHbd7c8+Gw/nQVWgesdZIxD9DRPdK65IF1IQkQ3QBV8E1hSt4DKdpbfHMgqPa+pWlR8yRPEesAF38vIRjE03Nodxip12bP9mjXzn3UGrUEbdOYoiIbTHBhuiGhURy2EY4npTd+ZztRcdcM1kFrQU8UccRccVEAwC7UTTOfZwaDeitimvH0Pew06PrOfmysHrlOkuDsQzM3PG+Z1UNHQckK9p3pMWc2SxjJPzyNayMlPWADYb3FT11mv7USTox/OJluH6DLnf0sz6u6TjCekRIuBQyRk2gr5puKpDF6fqenFMAcZNw4ZYa11NAJEfvrCqSAlRtju3Q5e6Adg3WDogrSVJsYhDH0YgJJ4CzuqFEvLl0Hd6BmlNLiRjiboGb4wp1HKkVrDkpTSI62twYVTEqSRN9UsiK5HhtyTiQbESskrWjy80da0baO2C8ecBy74BysIz+524e43dnrAOjCVUVxKkeTnS6TJ7N2J4vWCx6lp2yXO5zcGOPul4xjgVfD6QaLnvXTVA7Deamw80SSPRzizqaApAfvm7dAiKKIpsND2XE9yrDvjJ0S2aXrzIMI3t7qwbBw/3tgJTo/c0pkbKTSCjhBrdhZFgOrFdrRnNcCqQUmyYSqHaxkUISrglPGa8VTUqaLZgBf+D97+fvfP7zFDNu3CV9hr0qv+uRh3jPufMcn/Xx/lsdTR6v3eWaWg2v+6AZFaXLmU6cMpszAF7WyHoZaQ0ptTUktQ04kya9/WVu/MRLP8Hz+88f9VAmvc0kwO9e3MeH+hPv2P4lgH7vWd7Nki+mD1Glo5S7IwFCVXnggQc4vt1z8vl/Njl/J036Bvrpl3+ap24+ddTDmPQW17Zk3tsd43+3+zgf7U/Sv4M6f7+ZFOcxnuJLvJeStij17oCgqsojp3vuvfi/srhyd3z2njTp9Wj6LDrp39RpnfG7F/fx+7fuv+scv19Pt9bR92Akho0R8R2uROG9fJlTXJng7xvU3QeARQJMegAuMztMRcYSKUU3sHhwV0OoviZJAtbALNCpzlqXbgLyra5dyS2auEZnqszid1YidVd7oIAYgkfnpzgp5Yh0BiIcN0dMtCYYC3SNEEVZbDsOwXNCrOAWkMo9ukpd43EoJWKrpZWu5ujZxRwUrLb+39riTluksreI5OCEETeck1I9U3LCyyqOq/Uoi/a4jQFGLP4t0iM6NuexRgSygCeBcY14HKOXjGolJYneVzfwFGN0j2NfC0JCxBqMFIzmTM09XTUWWdhZbDOfd/SLLbrZnITjY8FWK2xogF4qnmaYOVIqWSva9aROA/4JMAyUKwfo3kjZ30f6hC86xuUa2erpT+7GKb16g2HP0PkWOsvI9jaWc0Qn7/fk+Tz6YKtQk0Ss7PomboKMhZRyxP5aiWtrAfBEoFqcD6zEudYMVlGvpNSRVNDcoaKxf2EYEB9a3zCkTtDU///Z+7NY27IsPQ/7xphzrb336W4fN7rMjOy7qswqVpE0m5JByqIoskqiAJIPhi3IBtxBLwQNmYApW+aLbT7IpE1SgES/6M16MECYtiyJYCM2xUbVsJqszKzKPtobtz/N3nutOecYfhjz3EgTRWVlVUbeiLjrByLixj1n7z3X2qs5Z//j/36USKlmTUhKFBkiYS0tOoR7ApoqiJUw7XvqN+EkNwbpHbcipJzQnNCOMG9Aa5EWNhEajnnDRLCWqQ28RSqYlDu42ml5RMYV5ATzHp8KVibUrIdnw8DPg6LrDRxuqAeH2DRRrNFai2Nr2hLgcWXfGlPfH+oGwwg4eb1hfOE51s/f4ngAvXfK47fO2D46xZqjbiRapNS7aSyanyR+DSdJpPrjyVOcrxJJalcHUr/EOJpy0OCt4Q61xXFr+4nSE/mlVbyniAXHVGCzRkXIQyKrxjlhjrdGbZW5FEqtuHsY62nVU9R9gAFBNdbjkmCUGOJIgbb/sY9/lC9+5GUeGPzVv/ff8Or9+z+Mq+5TU1bl3/rUp/jciy+S1PA2U2el7qBZjQGDlFF3ikHFOd6MXLtyjcOjI07PHnH33kN2+5lWDUlbfExxTa0TvQtg0aL3vc7LOf/Zr/9nT3sZi96HOpLM//7qF56JDqZb3OXq4Tc5O/wEX364Zv8BH6QSEV555RVut9cZX/tV8tl3nvaSFi16T2pqE//lq//lUqGw6L9T//rmBX7m4GV+avVc1AY9Y7rFXW5xj8fHX+DLj9ZccMgHGWWpND6zucftR99A+WD/vLBo0e9Uy++ii75bnx1O+EvXf5IX0+YDPWD8/Sruo3fZsuGX+cIH/D7qKMbn+DWe4+4Hdit/mPrgf1rzL0gljBoz8Ky98zfMEUlD4I6NbpBJfFkvUblDGJTNSFkieUsOA9XDWI7v83f6eXOCucb/m0Vy2ECSo3kIU04jUagQqVzvHb30dOclrdkB2iWpOVK77lEEbqnji8FpiPeO13yZeOtJSOnJT+2v0cLMcg8jXJLgFolDWph4rTnugrliruAz3tqTAGn0uTYQoiuZM8jXoRYkr8DneL1iuHazmLEjqg0Jfi6tzqRRETcsJbwVsBLYVR2ROpNFqWZUKWQdunHdGIbMQR5YA1kH1KJfN9C/0Q/bUKxd9n7uI4WdEyQhCdEJLAZDYh5GLnJiGkdKa5hXTA5wIu0quz3eCmWasDRAq9jFjD+6ILlhAlYtjP3BES0oipxekOo6DOdLDLOBTXPv5gVNKRK2JtRpwmvFGXAruDviHodsaaAVyWPHk0tsDxnJHXU+ZGTM5MMjrl+/wdHhERe7ysN795l256SkpJTQlPFxxKY9pIbsdyjhvee0ZkyK5jCAn2CqNY7xyzy51UYrhSJQWqO40sywZhgaSdXLAQPJtBSGfqsW+8bivEkCSZUsjqYUx6s1/Oyc+WJLrY06zz253Ki1Ul2YxNiiVIycE7JaB35cQJLiu4l2dsZuNXJ+vmOaCwZEM7GR0oiKRTd3SjgeCfU8dJ+vJ/RVkJwDs62xPmuXAeE+qFFrnLtcntvRs+xmTJcDz97isT2N7yL4kOK5hzgGcPDm1FKZ5xJmukXqVAgEtKQhzPye2Mci6S7icU2i95vnjOaE58ztcc2//0d/mr/4//5/8frjh+/S1fbp6nfduskf+PCHuXnjeuybfp2r1Zn3OwZRVtdOWN26yWadYa5Mec3BJz/MtQ+9xKYZ9tWvcfHlr1PfeJ1apnjLhgHPQyD5h9XT3sxFi35guiSALFr0W9WRZP7clc+zeUYSTAKMp9/k+vlrfP7m7+VLfsJ++mBOIt+4cYMXr6443n2VzZs/i/izkXhetOi3o6lN/Oydn33ay1j0HtUr+ZD/07Uf52P5iINnYFjqX6b48Na5+uiX+CIb/jlfZMvhU17VuyHn9oHxEb7N8cU3lw+tFy36LWr5XXQRwKeHE/6v1383L+TN017Ke06X95NDdnyRX/5A30df5A0+wnc4YLvcR39AeuZ+AjUCiSxJcRrBRu5IZxHcG6Z0hLNjVBIp0rKWELMwvrynM2lhsFoBaX2a00EGQKFOSM6oAavY3WHO5EAdq0U/rgMivQ81dzRvdJuSUxhErcZzDx1dnQRK6whrj3CwEmur0Ssq+TKxqB2zLKDdcLpM2FqN7dch8L3EfnAMs+gRdQqtBV64+1a4R88smojsYU8+D9cirZtaR6UOeJ0gazfMwuSWnJ7sQwfSsEKs0FTDBLeKd3MTn/uLNlQGWt1h4piuGFtjpHEwrlgLZDESkGuY2q303maL91hzjrXkFKlOyThGbRVpjpaG1srZo4dIkug39gZm1ApeZpqFbVct/r5pT1k7tORISggtMLMCqhkxhc0Gv34dGZXhNNPubKFF+lMJXHMaE0dHG0Qy9jgxT1toLVLpQJLAJPuwgloDSasS5nkboIGk3MOpiuYVq+snjJ/6MEe3b3OlGeO33+Lut76NzTskKzJXOD+F1pPiQyKZ40nJKxhSJo+r/h4T5rxmJIVhL7VirVLmmb0JpRlu0zt9ud6QFsc7pJji2hkFRTKgAwOOqpCTRipTiSStOd4qzRqY0OocwxtDwvZh/s7u7LwyS+CbhyRPunklr5Cs2G7P/FajiXK+3VH2O5pX1I0sgnrpKPYWePjLCQcHJaEqMayQx5ij0Mvebe8pe4/hBQ+T3NwjpS2K5BXuM7IvgUB3D3y1xXmHXLY4h3EvkqIbuDbafs+83zNtd9QWj0MHJEkMj6TckdSAxNCDWIskeVzscGvQZpAxtrEVbhwf82f+0P+Av/S3/yveODt99y66T0FffO42f+LTn2ZYj+8QDST2i0ui1kZr0Sku60w6OWLwftylFeX0HNvt2O5n/PiQ8dYtbLulquLjgKWesk7Phumx6IOvv/3636YuBs+i70MZ4X979Uf4mYOXn/ZSfugSK1y9+7N89rmf4suPD9nvP1gm8PVr1/jozRXH3/ybpKWLbdGi76nm7WkvYdF7VK/kQ/7y9Z/k48Oz2dn3L9NB//D6l/nRD1iCybnNHT63/TJp6fpdtOi3rOU+ugjgk/mY/8v1n1jM39+CPuj30U/z68t99AesZ84ARkaQitBwE8L8rZFoFKE1niTFRAW19KR7Vh2aVFRX0GrHv1ZIAzAgeDdlciRTNcdrdYMW9J30pIeZFLjn3ikqHS2aNLpvm4VRad+V3pWx93x29KhqmL6tEChqj4RwzxNHItFwr7GeZmEk6xBuYkScwSx6dlughgNK65hn3GZqC4PUS32CfUYzzRpJPVKHQnS5poRoA4t0pNcpQpzWe4GlIuPYEdIZJMxNpMSN3yXMWiLhLCnhVWEwvBREDNUxDDY3xI2sK1aqjEMi54H14RFXjlZYXnN+doE/2OOpRkfxuAos7pD7e+20OmMlTG7TAS52lItdJ1pXZFijueOjS4ltkzDjXASvBadFQjYNSPMw2BVUw2iW0jt6HzygiVOnGWlzGIqaA6FNxo+uYM/dIicY5B76+BGp7EmAi5Kl47KJ3WQevcuizphSN+xTDCQMA8M4spGB8XxiHk9pCsXmeM9qhcmouwnbz1izvn2XPcJCFmF9cMjBZg0IUynMO+K4sujraAZzrcyzUa0iLuQUKVn1zonWeK+xGmabQyXea/OGjDk6dXNCNaNymTyHy1S9u8d75A3JFTQGEpo7RZTizjgk0mpFGvo5KRImckoUVWpSbDVQtwkvM+oVlejilnzZ/ds7Xl361wVJq0iKe0M89dQ1Maih0s+tFsMXEsYvQ47TpZ7jc4NSUGt4SjQMk0Ry65h1hSodmd7w5vg0UfZ7pt2WMreo8k455ihSQpIEQlpGLgcpSNG1LYBkwcXiHHLinApnHSs7bh2u+F/97t/NX/un/5i3Lrbv0kX3h6ebBwd89sZ1/vinP8OYHNJA84rrOgz3lJ6YwGW/Y//Q2KxHyCPD6pBNaczf+HZ0QreJa2SuDAc8uDry4OCC2XaYBu68OVgtT3uTFy36gegX7/1i1C8sWvRb0KFk/vzVH+GPb1562kt5ahI3rr/9D/n8rd/Hl7jygcBBr1Yrrh6u+MjJzNE3/iu0nD3tJS1a9L7Qf/Jr/wmzzU97GYveQ/rx8RpXdOR/ffJZXhmOnvZy3pM6ZMsX+WV+iS9wwft/H23YcpN7fIKvLx9aL1r0fWq5jy4CeCkf8OH8QUy0vjta7qOLvh89ewYwvS8VB5NIJGqkUJVeVevRDawm5NQN4BYmbtLL9G1P7fYOSJVIwCYnsLEqYcJIRlqNzlAMoaJyAG6oJ8S0p3Qjaug+ITaGsaWOtwmRVZjAqXvAwW7G6QhrtKc2c6T87HJpYUQ7GgaT0JPKPQ1cHVd6+jfhXnGNNLOLRPo3oMc0c6zU6Ay2OcxvaxFSTr1zlctktESHL4Gjdo80ImRoLZDaOFJnSAarMRDCrYHXPrjSurlnYYYD3hTtaUZ3D4PNCQNZBQ3QMHk9kp+/hV6/is4TqUyggg4Dopk0JiQPaEq4JFpzpMyIZHRMuAtznWO/XJqPk6MtAXNsh2ikKr1EgrjOQIIMqRDHjCTwRBozw+ERgzn7iy22m2jNcCtIGsJAdkMM3Cvt4WNqnWG9wffRE5xzwmrFsejgTWvUG06iWRwHYx44XI/ouGJbnTadk1YDh7eus7pxiy0rzl99G3/8iP1uj5UKLngL49DzpUGboM6R7E0j+eQaB7dvc3z1KtUa7d4dqlWszHirWOkY5qnhdSJLJOizDh0TrYiM0VOtGWcNwMqcao1qEmn11RCI5WhujnW1ihnQcehWa0cgK/iADIb2fl1pRsqZlDPjuGKT4oQp5piVSGqTaAhzm6JD2A2VjOqADhIDDE8GNkByQsbA/UqKr4mDuKMe553k9E4iuFaYC1lhvd4gKdPMacWwOiO10scicPF+bmjg3JEwiFUYZSClzN631FIo1cKcUUPTAYghmt8hBtAHRbq5q2kgpehcDsh06vvMY9hjBhPDvXH75JD/2Rd+lP/0n/8yd3a7H/gV94ehg2HgD73yCl944TYvHB/1ARPAnUQMi1iL4xn2OE5tjVoy+9Mz5uNjDj90g/VqQ3v7bS4KPP/yc7zy8jVSdR598w6/9hu/ztuPHoMZlldIbUhbDOBFixY9e/rUcMxPb1565juZxBtX7/0TPnvrD/BlP2A/vT8/uEop8fzNa9ziLle2v8rwraXrd9Gi70fb+v4folz0g5EAf3j9PP+Ha1/gqo5PeznveR2w4wv8Cr/EF96nGEsnU/kI3+EWdzli6QFftOi3o+U+ugjg4BmpFfpBarmPLvqt6pkzgL0jm+Wyb/OJyafR2+oSiNSsT7o2wxkW3inZnUFyx6sOeO/o1Y7ilXihMGWlJ1mRjuYdwzTNEoZpumxQ7V3CMoRZ3M0LN4PUwpkW6Yhn7wFfCWSvAHndQ8YdmSwd5euOWO0mY/+6da6tRz+umEHOkV6UHNvnLbqF3QJXK/QuZO3JXo1EHQlpGq+vHQcsA2672HFlxnWI5PAwBIb1MtWsQ6CpqyGp465Nu3Fa8f5n71TtMF8NJBDC7ltUDwJHLH2/G3hp2MNT6rTHilHPLuIxSk9rDggJaxW3GvuAhI45No8EKdFqodkebwmZZ8SH6Msdwtg1L0DHc6sgVdECMggM3YDKgo0brJveKY94qngt8f5KIISNinSUdnNn7sljTw1drcg4Nu+jLTaPqBqSD6J+2iqNhN+4xvj8C6yGRHnrLuXBzOrgiBduP8eHnnuOe6fGt956yMXjPU0DFaxuqCgpDZRifV9GCl4lk9Yb0vEJ9fCQSY16cUadChBmqFmjtUKdpzCnh0zWzKBOTinS8CoR6iYDRpLoHK4eXb6lNZpkamsUiyEA6Uk0a9K7c2skZy32WXTrRkpZVwNpcrI3xpQYVyMnt65z5epVshgP7j3i/PEZNs3YvKVRmXczrQTePeWEJIMUKVokhZGaB0iKp3d6uuk47/BZUwyPqAZi2GuYsicrhqMDxoM12irt8Rm1CqVfYzDD5hi6iOf1QKirIENCT47Rw0PY77GdUgImHuefdpRxGqB3FeM94UukpskJWW/ieG8F6aF6l4RJoNBdwFuNS8WQefHGTf7nX/gR/u+/8iu8uX3/pJiurtf8rhde5I9/9nOs1ys0GaQV2BTDFa1GB7ULropLwtMB4oYPgXCf5pm9wXTxCM7us9tO+O3bjP/av4rcegEBTr79Ja6d3uX+vXu03YTtH+FkrH2wsJ+LFi1a9L30qXzM//Hajz/z5u+lxCrX7/x9PvfcH+TXTk/eV0ngYRi4dvWEV1YPObj3t0n7+1wWUixatGjRou9ff3j9PP/n6z/OevkQ+7esA7b8GL/0vkswrdjzHG/zcb7RR8wXLVq0aNFvVxnhz1757NNexvtSy3100W9Fz5wBrG79sLJukLb4EMsdcyIJaAXxjIthCsm68eKEMeT65PniR/sWSV9vkRQexjCw/NKsGQMpi6IY0mbScBB/JnpjRQMdrW6RFFbFSu8HtcDjxkcyHus2QMLYFffetcoT0zH4pBam8tAN02axdInUAqKRX+5pXvGEtT0QiFNHexewgpSAQlvpvb0V1dj2wD5nRBpCik5hC+POLPC2CIHNzg51RDQKbeUy1l+A3DpOO4w+SQTjOClWJugJYyF886pr3IzEIaM7Wio6NLzM1N2OnUeK1OZIqnqrkEfaNGEayW7vaG5VQTSFUdsqLoqJAGPghleRrPWDDZ4lTOa9BwLWHUxJPkEaAmU8ZMzAWsPOzqn79M5+tkbWjEkLL36awlhM0bmrEghpdgUrBeoMXkk5Ya6Ie09/xugAgEnCa0F8wldXkIM1cpbJL95m/bt+jJNPf5LD83PKz614/UuV80f3aTPosGI4HtBaKQ+dUra4CzKM5HFkPDgi1Up743UuSsUa1IjAhqHmRmuG1YK6kHUgJ+l1z4rmHEhqa4E6TyuODzZcPdiwNeH++Rlst/g84XOjONFxK4mccvRAm8d70AzNkeKWISGquCbUp5iBYKDlgXztKunlF5HrV2j7LToXZLeL49GFZoKvFcHJ8xTrTNLPhdznPMLc9d7rLG4IQ6TMs6J5QIXeAUzvw45zT8ccCfppji7rcUMywwvUFgasCCSVSDyrou4knOSC7LeUaaJNE9NUqK1GglkCqR3nTEZaw1UxDDVH9DKF72iZO8La4rhTQZLgjLiV6PFGcJujdngceOHmDf4XX/wiv/LGG/zNV19ntvcecuPqes1mNXDr6Jg//dkfIa9W3L5ygifFxftQR/SWiwdCHAu8trkE5UAkUt5u1NqYtzt2r77O7tFDXBXJGzYtwT/7efyTr2AmPPzGNzifLZLgdUCaIeZoXT7cWfT+16/c/xV+4d4vPO1lLHofKCH8Gwcv8XI+eNpLeU9JcK7d+yf8eLrKm7d+H9+5v4uff99jGoYgs6zZ89Lztzh68x9wcOGku28txu+iRYsW/QD0xw9eWszf71NCJJh+jF/iHjf5Gh+nvQc/plyxJ2Gs2PNxvkGmcMSSWly0aNGiH5QeW+F2Wvp/v18t99FFvxW9946Id1kq0k+Eqad5HXdFcUQHwKmAUiMs6j29Gy4Q0ubAJ/dUr7uhpoh0ZOyQ3umvTSlShN6RtSJIyqRL206k96wq0FCTSPJdpno14Sp4897J+k5ql2RPTCevtSdbuSRSB9paFL/sCm6O14YNl0a1weVaZQjss0X6sXmkQ91KTzb2D4Uun4uOmr3E37aK5AxzxVMkkD3lMFxTDsM6pTCO2wx6mYrW2Kd5xWUiObDVe/AwdGi1m93a3wbF6xS9t6IkM1ZZ2CCsREjWkFJo+z3Fu7HfYntai/cQm5A8IihJE2nIDJsVslpTq8F+G2heN1QcGTNpldHNUSzDPPaRN8wLtRjenBEja7y7bjUGBWqltkZriqahd0dH2pPWsNIN9st9SIr/rlf4OOBnZ+HheSRPxQg0NQP0xPalUV9L4/zuffL9B+web7FilMc7tm++xaOjgVkGtoMim8x4nvBxZD2uOUiJi/3Edt3wxw9ptcLmgNXJIZujDWKJsuso8QGkVdpcAm1sjichD6voOXYjZe2m6hgDDviT1Pbw3DU2n/w4xy88x8HZBbtf+Qr1tfMwQpth7hSNNLx7jZ5iM5SE5gEfJPbPoLhm3AyrHinYjl+33cT82h22d+5T5omz7Y5aLRK0SRGL7mg1Q/oQRGCYA84cvcUeRvt3/X0ko4fooO4t2WhPtTcDBsQdaiXvC1c1s9ls2A4DO+m9uxqUAUkJySnQ8RL94skqqYDXiVkytRXKXKnmuA5IIvZTvyYJ1pP0GcRI5Cdo8yErglKInm56/69IDHZAmMfouqO+K8M48vyw4vpmw2qV+X/8xrcwf/ofCF9bb/h9r3yEvF7z+z/zaT7+6U9y9dYNDluj3n/Iw/MtZxdnWJmpnjEXTOZAx5ujkrvxG8egmeF1H2axrplrY7vbMiRhdft5PvqhF7l6fIQ+ntn+06+yvbjPm/fe4tHZGTauQJy0XsV7bvq9N2DRe0bu/jtOLf4gnuO9ptNyymk5fdrLWPQ+0A1d8e8efexpL+M9KbHCgd3lo6f/kNX4Ar++f67fb5+uxnHkxo0bADzPWxywZXX3l5BvB15L3p/U6kVPQcs99DfXd86/wzfPvvm0l7HoPaIP1tH9w9WGPS/zGonGl/lM/8Tsae5RZxxGnh8ekbd3uM1bHBB1Scv7vOi3o+U++ptruY8uAqg4f/X0q/zfbvzup72U963ei/fRNXte5E2A5T76lPXMGcBhpEbCMxDNiYRGwtDA3UgSnZluhnoOnLL4k+SrSIqEr2SUDJQw+0R78rYiq/U7GGgzSAOqPfEqqRu/CcGfpNEMI9GTvNZN3Qag4cF6T/6mnhiWDGUOfPOlUWNAbZFMxCL598SAjsdH52tCW+/YldIxz6mbmvH6DnhtEbctsY5AzkqYqFqRdBLbYpGu9VZBh0Ar1xZrcu+p6Qrk2C9lhnEdH45Z6euUnloeInFshqc11AtEV3h9CL4BEZpdAIckFVY4mwyr1YqcldRNZy8zZk5rDWseSOk64+qIGKvVmtWVE9LRFfIgYUCen5NqjtZUz0gzfLMmbTbx9pdIdqsIabXCBNxmqpee7wargd92n8N8bpE0dqthGgpYTydaa2ha42qBN04W2PAy0aY9bbuLx+WRpNLf11U/mBNiDXB8mqgPKruLDapKMwMq0917vPVzv8b0a99gMrjYXZDXI0e3X8A3K8ajNSfXjlmdT2y//DXOH97FvZLywObogKs3rqEysD27YJ4mWpnwvUVuW1MkIon0slgN81RzN/sdLjEO5pFY3k6Uu3c4354xXcyUszOoYbpmqzhCq0YVwvh1iQELjbS8FMAmvGaMQHhHyjwhFLBG2+6YktJ0pM4ztU1Ud8gZcaGSqF6j/zeP0R+tGj2+SE/LDmEMh70eJ523Xgk9RGcz2gcyHPGOck6JPKw5fP421z71MU4Oj7j3jW9w/o1v0eaJJoJrv34UAzy6p4lkziAgKNUqpcxUc0xSYOA9en/FuxGcFHKCPJLGxLDekNdr0jiyOdqQcC4enXF+dgGtQGnx3mhPtwtxLerb1EtzyXbCf+8jH+fTN27xt159nfvTxHYufPvx4x/ghfh7a1DllStX+NM/8RN89KXnGa5eYXX9BsfPP8fJyQknmxNWL1WOXnuDr//GrzHNe5TWr9cZl9KTwAZiqK6wfl11EchrrBbqvGPOmfLCS+Q/+Pu58lN/gM2wwvcXnP/dv8PXv/Iq8/Y8ftnKGZE1Ok9xngyr770hi94z+kH8svxB+4V70aLfqq7pyP/u6o+Sll/X/qUSIE0PeJkHXDmufHu+wc5XlHTIbrf74a5FhMPDQz704nOc+Cmb1/4OaXoYP68vWvTb0HIP/c316vmrfPv82097GYveA/q9q5v8/tWtp72M97UEeIE3ucojvskr7DhgWt1iN5Uf6jqUxgmnfKp+g+O2RVimpRb9zrXcR39zLffRRZd6+vGT979+s/toJXHO8Q91HU/uo/wGJ5z9UF970W+uZ84AVheSNAyJBKkbaCO5YF6pl0hgQOkmCZG0TIB2o7d6JUuLrl7JkMP8jV7SAS8FzSvwPeTDMIckAzOIBuYZ+hXu0vS0ng7VeN6Ol8UCHx3J4t6rmgQx7ym+6DL23vkbbOYw32iCpww1koJCRzKnFN2qxOO9zbiPmFci0iiRNE39uTTMb1ygOOKKDFcQa0gOTLUnwSvRG2x9vzGClzC0hUBpo7HPWkOGFbQJvPepClBmRHMPHkukqM0hHcZ2OIieBI5aR1JyxkFYDYm8PkBXA6qXky4S20cNDw/CjD84JN28iR6uEE3U/YzvZyix1tRrkk0T7PeBZNZIbgbyWzF1NOcwlatTzWlUDO2J6xoJaonTzC9NdTcaYKIwaN8nQzyPZbQ4RykeV/OGmmscI+OIr8cwkfd7tGmY2Qhqsa9aqTSJdLI0R+SMx8U5lxzfe+WAwxvHDJsEPjOfGY/Oz9menrF9cA/bVyRl8pUT0q3nkCvH5DSw2Wzg3n12+32kfsdVdM3uQcqElBLGpDjsp77PCVT1kLAUvdTtfMfZ1+8w5Uw1mKdzABKOr0Z8nqNbuvdUaxqJGuFIyYoocon59cjhojkMeVVEharGbA1jj/X0q3Q0chZQM3a1BRo4RZrfpad/JQXmWSNprPGicWz3Hm23KNV1JZKlbogmRCIZL1dO4IXn2FOYvvNN7r/+OtvTM2oT2rhCakVJZNHoYDYhJSHnRMoZzChloloE9d0NkRj4CDw1SNZI+2ZF1yvSwSHDaiSvNwyHG8aV4nONxDPWryMt3g8IE/jyOuHdXG+Bq/QhkfMJr1y/zp/99GfYqPLw9DE/+41v8dV79/lvXn8Nd6hm79oPaGNS/u1PfYqf/uxn0JMrpM0KUMa5sGmFW1dOePmlKwzjEdPmkHrvHt9psGdPKwVBae6Qx0iRswbAau3X7H7NaoWW1pRaKLsddudV8lvfgs0x2699nde/8S327viVGwxSsfNz9tOMiJCGFabP3C100QdM7k5dDKFF/x0S4E8cfIg/fvASv3d182kv530hAU7OvsqPAvXgNvvhOnfXL3IqV7n/8FEwRN5FwoaI8OLzz/GhzQXjnb/D+Pjr79prLVq0aNGi0EYSB8vvBr9jXaIsP8+XcWA7fJyH88yDq7+Lew8fQy9Se3fkKMbH+CYf4dvxe+PiSCxatGjRD0VfLaf82vyYz41XnvZS3tf67vsowMTIPW5yyjFv8gJA+Bbvyr30X7iPvguvsOi3p2fvJ9TUu1+dMFyjSBXrR2Xy/qGMdNNVLvGyDZXUcc2JrB2D7N1QbSkQv3r5WAnUsa/iaSqoNpIOYeB44FiDHK3gJXC5rfYe4QFFEcKsFu99wBrrExO89/hCJHVdcu/wKt0wXcf31Bb/JdEp0jE5Jt57fSMxbN560rgjmjsi120Gd8QHtE2AI8MIl/29SBi+LT5E9la7SyXgYbziBtZNbm0wrKPbVjxMYK/gDXGFNAbltkVq1hs4pRvXOdDUVrph3OK1JZC38bIez+EWa+kpWRnGd/qWi+MPH1IfJ0wyUvZhCmVFhwFNQkopzM46gTdMxliDZsDwqWDbc6hgc8VE2JeKmJAIHLEoT44H89aTwEITpZqFod4nATyvQBPp1k1WH32Fk+vX0Tff5q1f/TXK6WO8bPHdHlSigzYraoab4SmhYoj0Y9uiLTZ54G4nCUT4eg96T5juK3NxqAVtxvl2x+7iFKch44jNe+a33+bi7IKcwliezs6Zp4k6zTSJdLo3i37fy6EFwKUnJXNGcwpj1p3aKq1UylTYmcW+ccEFUg4T3JtjfUghSQxpqEggpbXvypafHLMxwNDx7Egc/9VoraGeI6ENMchwdIhtNrRWob2NXAhSKwxDT9em2KeSYwbCG2IKQ09eX+K9kwR+WoR+8MUru+C1UR485Px8CyhzKVzMe2qpmDfMeto5SQxvSCKJkqUHesVoDq0UajOsxTkuGvh4RN4x13OGlNBh4GAYOF6tyetDajrAysR0/piynyKhjICOQUDAUJGOfI+165M27DCih3HDlSvHPL/ZcCjCzcMjbh+d8ODilD85fYFpmvmbv/7rPNjueLy74Gun57/jS7MAn799iysHB/w7X/gCN8fMeHAESbAy0VZr5OiYcvU5yme/iH/4ZRBhfPmcW9Pb3P3Hp5RpF+hwFEsKySGv8DpDrYgO+HQBIkEF0AHFaW1mroWLdMjdh2dcffMOD37hV7nzje9QxsRApdU97fQxPhuMByCO6g830bVo0Q9azRv/8a/9x097GYvegzqQxE+tn+PD+ZD/5fGnGJ/UgCz6fpS3dzjiDof6GxjK7uaP0TY3efPNN9nla0zpmPPz3/k9FODKlSvkJHz0cMfhvb9Nfute/P6waNGiRYsWvQ8lwOH51zkAXnh8B8Moxx/hO/sT5lLYHnyE8+3+B/BKzk3uMVD4JF8jU5cPrRctWrToh6w3247X25bPsRjAP0itmHmJN3gB4RPEYPA3eYWJNRMjj7j2A3iV5T76XtezZwBLwYAsiXBmG+aOAWrnmByQdAgT2AuXkbvIBCc6YzlwsZep1TaFGVwbMkjgb/1y1woJQYZLczLslh5Di8Rgyngr0Zfr4Vg96c/F0ex47aayh9nljZ4MNlxyNJeqYl4Rz5FOtDA/7ZL2CmFS6dCxtbWvtaOZ+/PFk2t4k+6ojgxu4IWacu8aVvBI6j5Zk8ffiwpetx373PeT1eiP1YxI7x4eMojGvlZFSsMpSF7jpUZnq6WwpnrasLZTXNbQLkAOMZSaPBK1tWKlYaJY28faWiC20zDGu6Ep8Lki2L6nZVMLo1GNQRLrnElpoCDM8542K7SCm1HMqG2mVsP2W6xW3LQjtDN1qkw0ch5IlOiPzRnX6J7uMGjq3DAxNAWC/EmvsipmRp0vmLfg+y0QvdCtFiwPMCpDa2F8eoCnYw9c2nhOWmcOVms2m0OqDlxstzRx2pDYi6NmmBsixq7O7Pd7WglD0KeKsWNfFBsbq5xwGmW7pUyFUgtNYjgBL1AD3Xx53IhG97SIoIOQkiIMtGkP85ZmHqRvB1UnpQGRHKhrzaiVeD7hSbe1aLye0I1Y876tLfDP6qiHmQlgJlTiPHEzzMEvLpD9BaXCfLol9fRNJHsjPS9mgVWmp4Z7GF7QwLVrnLMuFscxgY/GG14dF8XLzG5fmQTMCq0VLE7I2EZzmoMOOXDlOaGqpJziCFGj7ZTWSpxaGucNIjHAkgK77TgMGT08Ynz+RU5uP8dzx4dMZxe8+p1X2Z3uqLspSAGxM2N/dXS1ahwvIkEXcI9jMEni4GDNi8/d5JMfeon15pCL19/m69/8OnPd48PIfhz5H37xC9TaeLQ951unW37j/l3+yRtvMtXK1L73B87H4/gEYfSvf/yjXD084rMv3ubayQlXcuIAYRgG2BxQszK50O7eYd5PPLbGxd0PkdaZR6/e4zu/9m2miz25FlQHZmnocIDVCcEC/dwqyBjDIa3E+dYca5VmsL9zh8df/hKvPXiDh9uZ89fvs390QUuCXGyRVrBKR/GfBf58MUQWvc+lovz0h3+av/6Vv/60l7LoPaY/e/JZ/vThRz6QqLmnIbFKAo7u/jMArgAtXWeyIy5k5u7Vn+Tew8dUXUe1xfdQzrk/b+Gl2zcZh8xzpz9PKnvyq9/pA6GLFi1atGjR+18CJJtJwHD2dT5DDHnv/QEXTDziKm/wAo1E+54fcTrjdyGdP843WDFxnQe9+mnRokWLFj0NjSir5TO2d02Ko0Rw71N8DYCZgVNOlvvoM6BnzgA2I/rLVFDvRqpIN0cPSZICz6wCPr6DSzaP5GrKvds0jOCIGOZIrl5impMiWVFrgZAloNJqhqShP39gm8UdqYZqxluLD9p06GngOVK/KDIO8VOuxYdI3nuDgZ5eHPqHPWFmxT+RzjQiRSnmeE/MSk/vhnEo0UkrHRUrGghhupkL5Bwoaa8zrc2EGa6BvvbKpTlutDB+G2H8+uU2RPcxpeLZA6UsCbdd7L+Onw1D+tLczLjtw2Q36WnRTXxfOuw9u90wbVDrTJsVyxnGEZFIMFudEcmMooybNZZXlGbU3RavE26CpxEVZTzasHnhRdLJMfr4Ebz5NjUpvrmKZaWdXcDDx5GOHiLhKrWS5kg7q3vgg7Ew25thYjQXzGckr/As1OZYbQwKPlXIinjFvVHuP2LWASsDOY1sTta0nWIlRXdyUcwV06F3SccxbP39l5TJ166yevE2V29di/fh9fucvfEa7dFjpmFA1yv04ABNK0wE2e+QUntYOZKnboUhrVhvBopndtOWRqPhmFWklkjQmoBVjI4qbo54QVVYsebm+hjywN3hgDJX3AsNJ2mYD4kGrSLVkVoR74atagwxNAUtPf1KDBP0/UtKSNJu+zrqFoMNNNwds0jUWquYVVprTPsdtUS1tavTxz86fl3Cc9aEJnly0RDsSUe1A1x+8Jp7d3BT3CekhbmeiUhvszBChYqLxEBAmzEDE8fpvcJDZlgdkpNQL877QEF3f3GwGUnrPswgaBo6Il3ZuPOx6yfc+MjHGK7fot27y8P7ZzzgEcZFJFUJlLRf9plb0Ae8lY6y1ugGt8b68JhrL3+IF3/qpzj6kU+gByv0F3+Rq6d3Ob94jO0mNnlgSgO70bmxXnPtWuWLH36Rf/tHP8+X377Ll+7dC0NdEvnwkMNbz3O8WTM9vs/9e/ehGX/0Ex8j054cxw7k1QEQwySaElmEpEaSEeY9u1LZz86d3a/Tvvom14DH28c8unhAFuf67ZugA/cfP+J8N8f1pO5jDEcdaR3B33vKEevp7Up59IjTr36Ng+1HqNeO2dw44JpNPNxOzNt99Eu3iqWET3s85RgYWLTofSwV5WMnH3vay1j0HtMn8zF/cP3cYv6+y0r7BxzwgA1w8+H/B0tr7h5+jsenp4Ay3fxCp868I51PWT3+DZ5/7gVUlHz2HcY3/17/6rsHxVy0aNGiRYveSxJgs3uDDXCD+3yMb/CAa9znxnd908B08wtI2zM++BIQAOmP8i20fx4mfVR60aJFixY9Xf2+9U3+4Oq5p72MZ0ojhZvc/5ffR38TLffR96eeOQOY3r7rrUWglkj+uQ7AFtVDrDWS6DsfupiE0WY1/k48cME9QahZIBmY9nReYKA9g+gqEprS+eqSiS5dIr2pgqRVmCUaBrJ4637qZcI41kr1SBom7725AnlEpGOORbBwo2JtHmnYQFeHeYUq7gUnda+1oTpCNx/pSUh6mlIkRXKxI7BjDyZcNQxAa09qjCmRcHYq6CoKTPNhN4FXYZYIYcqgvZsz9qdKwpMACTfrqWGLjtWeADYKKmPv0Y0kpkqgsV2UljV6ZMeO5025o4Qz63HN+sY1hmtXmOcZe3hG2+0QD2Ne1ZEU+296fJ/06C51VxEV8pUTvDrT+Rm230NSdDPgrjBVdJ7JntDkjCmTUkIl49rfdwfajKdNvA45dkFp8U9KYWanhGjCcmKngtgWqiGq6GqN2ASt9d44xYthvQPYCdPemsUhM8/Ybsa2M9Ubte6otSKlYA5aK2k/42VPKdDKDgizzF3QVWJzdMTRi7dZnRzhp4/xx4/CmKVf4Ptklgi45p74rsiQSWNmdf0GVz7zWa599GMMo2Bf+w0u/tsz5kdTDC2kVSCXccwcpaGtxbHkYNair1biXyIJcu+Ebq13dEd62juuXARUBtRTx7Yr1gpWJqoLczOmqcSTauoDD9H/qz1pjERfMNV7Yj8SNqjE+V17ijbKdCM5C5AHpDXyas0qa6R/t62noy0GTqw/Vhxqxaynj4chhjTcaXOch4qES01CcqCedVyRxwyaaA6razd4+Ys/wUd+z08yfuj5OBduXuFz2y2pNr72aqFsz7DWwqjW1DHQkcI2El4bWCFJYhwHDlYjmyQMuzPk7AFsBR48JpWZcXWAXb2GHZ+QLs5pd+9CEVoTLCl5teKLH93wxY+9Ahrm7/HHPsyLP/Kj3Lp+jYtvfouv/ON/yuN7DzArgWG2sOCbO94aZTLm7NSVsrKEFhjVIA+4JiYzyu6UO9OOt5vRyh5fr1h95BW4dow9fBs/fxzd1JJwXcUFqnW0fatIq0ibkbwBbYiBWXRHnw3CePs5Dq5d4eqjhwzffpXzh2fs54k2T4i1oMrXGW+LAbxo0aIPlhThj2xe4KV88LSX8szocogttR3Pn/48z9N/dLx48KTq5cn3tpnkD+GNX//hL3TRokWLFi16Dypod85NHnCTB0/+3l1p51GHoNxfPqBetGjRovewBEGXAeSnon/ZfXTRB0fPnAEc/beQespQep+mWkGGSJdq7wlGojs2i+A4TRX1OVKSyXpiMDCiGJEwcw8/SQTVIVK+FFQ3fSbiu9CoomHIBKc2DDQRPPWvd6w0YognXGqkH1ugZ0UF9xLGc8fQRirWwkNzB83RiduD+K65pxg96myRnqjskGj32C6RMGcp3ZhOYKWb3hnx6JsNkxZA8GSRlG5E96a0dzpzzUBrdKpq9I+KBGZae79q9Axb/NlarMtiG10y3va4tm5AFvCEo4g6SYU0rqMWWBWhgkVn83jlOsOtW+STDbSCPZ5ge0FqDU3xHkhW3IT5wSlVFIYV6XBkHDIrd5o6bVgx7WciM2pYL5hVh7RK5NXIarUi9+OskWhWIlmJdGx4vO1ZgTGjLTp3ASR1Y3DaM995G3l0CptDPAvpYAW7bXQiD0P36B1vkdymG7O443OjbWemXeX0bMb2F0x3H2DbffQSU3sy+Z1UbLyFjqeBPAwcXb/OtRdfJB8fMF+cs337PrXUOK5F8GqRkh1GEKW5Qakogq5G0uEh49UThuzo9g77B4WzN+6EgQ4dO1yhRZ+yasIksMTau3IjQaqIOJKHQDNrmP9iEudbEtwESxnm0ruzBfWGpjA3zY3kkRD3ar1Tu3cXywDzHoaMp1W8P0g/Ng2SoCkhkpDeGS4IK3ekOmUqYIVhUNJqg6wP0XHA5z02daO7dvM3GouR1Yh0QziJRk9yGlBz2jxRt/s4rpP281eRPCDi5CExrsagtaeR65/4BJ/6I/99hhde6qewIIcbNs/f4uO317z1N97i8X7b066Kdty6WZybBpgIQ86sxpH1kDkcR4bdlvnhGXZ3j6ZGe/yIdnFGpjG0yvTwLmU/hXGvCcPR1JHuHZWtKZHGkWFcM8575Jtfw779FmtGdgdHlOkcyUqbe29TqVhrVM/Mosy1slZh8JFklTElBBhQTBuosi07Wkq4zZS3vsn2rcZ0VtmfT3g39sPdbXGet0i5ey3IsI4hEathtNcZnwv1wUO4egcGSMfXuPq5Y1b377G7d4/t2Y7tfs88DHjdQ1oMkkWLFn2wdKKZ/8nxx5/2Mp55CZB3d5/2MhYtWrRo0aL3rQQj795+2stYtGjRokWLFi16qnrmDGB54pQlUupJP5zGHrFNoJG199w+6TX9rvTrZYKvdaSxOCQCD+sWOFyR6MeV3k0qK9yt95MKLt69mp4a1ieOL64dR117R6UA5pA8kMJpFear9ueqc6R6W4nXlxQ9pH6JqY4kpVMjuWuXfbGx9EiodoMyjUC5dBKj45VL9HIKhKzyxPhlyNBA8hqbpnhYa9CNNKDvE+Oy5hYdn5jGQjfV3EjWQHIAcacZVwmTSgItre60dIDYvieAM1TAZvKYg1KfV+hqhaaEaia5oOMaHzfU88f46SO8NJoFantzuGFMiVmUaZ6p+y2tOaKJcV1ZrROHJ9eR6zeYW4E33qA9fkTdbal9F4g5mpW0PmDYDKwON6RxFcnGiwvaviIuSN9u0UBT5ySgI1J3iBpI9JKKCrQ91py9gbYC6tSpYPOMueKlUjXW6a2iSZGcemetgDa8VKa372APH1GmmXnaReIRQ+qaMWWyKiVv2O+3uDV8LrAeGW5d4+ATH2F9+wX87ILtG2+y286RUKZFKLU1JI+k1YCokEqlbi/iOIwYLu6V6fSchxc7duc7Lu7d673UNbqZqyKpwThG2tYE9YJ66+dHQ/IGzYpkiwGKuffoiqKmIAmThODR5SsJGZQ0pDCAcZwBgCZGdQLFjgXiXHrqN6/CANfe850TkteRnO/Go5BIQ2K9OeDWyQmjJh7cf8hue87h0QkvX73C4dF1zsvE3bfvcNYuUIvrgvRkvQ5D9P2mRBIhizIMAzomCtaNeUV8hmqQ+4SBVWRc46tVnL2aGJ+7yXN/6PexefH2O5hOd7g4Y/7qP+fb/+0vsz87Rd3wnoa/fG/ELK5lNc7xtDlgHDObcWBQsP2e829+i+Gt11lNE6dv32P/4BQxRXNDpOHkSLqLI5K5TPWrjHGdNUMM2qML7M4pw7VrnHx8w62Dgelbb3B+ajBPcZVq8T55myltZpLElBqzD6xaQYqRcGS1ZrjxHM0cL3tmM+p2ou4nyrZSzWjS8DzE9csSXnZxDa0F5oJIRvMK0wFs7l3AE4wbqHvaxZ79249oR1fg2g2GoyPS4ZrVrRucnG85f/MOp2/fYa5rSh5+MDemRYsWLXqP6GP5mMtihUWLFi1atGjR95YAH89HT3sZixYtWrRo0ftSy3100aJ3V8+cAawiVDOQOdKbHKNWSRxFUsx7QtcvacVKa5UskRaOaG03Jh2k1TBwJSEk8Al8IIzTGSzMZLEKaQgzSHKYrpICu2xw2UNMi85e/BKRTOQGi+NeQQZc6AjXFslYI1Kyvco0TO14fvEa3bBmuGbc524EBZrYvaNhiXQcdEi2ZMS9o6sdu8RStwTezWmL3lsvJfZJ7fFWq+EuI5cLCrOZ3q9c5zCbrQIZhoSJAi22a8hQwih010BCC5G41aHvL4UxVts8Hh/7IEFx9PCQ9WaF5oGym5gebbk09DXBsNmwuXGD4coxMhXKa9/BzuP1WCf01g3kheephyvs9AG7B485v/OQ6fEFdb/FdI1ojbSoRiKaatRacRPafkvdV6yUOO7qjFlPeWdwCUPOTbBiaI5uXPEcfc4SSe66b7hVWim0YhQaRmJAEWukbnwq3s10h+aYV0opFJlobnibSTm6T/PhAZtbt1idXGHeb5lfexW///hJ724TZb57l9O799k/3nN2ekZ1j2MVD/MwD2iywDZLxscBqWt8dqw1bLenPThjf1HZmrPbT0zbM5qEaavSSOMQ6dreM03KiK37eROpdXFD0wpJA+6tI5SFPGSGcU0TpZRdJJ+zRDJbFe94ZxMJ8rokkmZSMvSyn7qbptKj2e/g4HsaPk7LOHclo0MmHx8y3L7NcPsWBxjzqKTTDR/60Ef5iZ/4FAcvvsij77zJl/7ZL7D7yhltdxFo5xRmc06JJN0ATkpOmSzRfdwcWpkwb3irfQ6jxXXGFWqFswt0fcB6cLTC9p9/hV1dc/D8R+BgxGnYt1/j7s9/lde/+g3qVHpqOQY4kH5d097VXGsYo1ZR38T/i1Idzu5fBMXdC3ObqdaHP9oeR3rfcoNuAItbP8e9DwiskOYk4PCFWxx95GUe3XmD6ZtTpJ9xqsVjkD4kUg1rlSIDszTKMFAnQ6ugbUBJyKO3ycWY54KfbvE24aLxXqdon4ghGosuZaPj7SMFbG0Xx3oj8OuqMG8Rz3EU1B3l4hG7u4ckKu36DdK1q9jmBF0njnNlPNxwfrblfHv+g7w9LVr0VJQlkyTRLn/+WfRM63909FGGfwE7vGjRokWLfnN96sqn+OzVz/LlR19+2ktZ9BSVEf7HRx972stYtGjRokWL3pda7qOLFr27euYMYDz6JvExcLbQjaAaphA9ddsRu1E/Gj24NOmo5TmwrEkRxkjo9aSq5A3Uhg4jYhUZUnSHek8RiiB0jPGT12hhnHgJY1O6QSsSSVmn9+pm8Aot0qeuYcCGZQvkHB2x0r+vd5MG3vcSEd1Tvb0zVjSHOSIWJqZknED5hi9meDjTSI3eVbOekq6lb3vryd+MlRnNGWs9ZSkGCO69z1iA1QFPioOzkUQRVawZVmcgcNDeDHSFW4mUaxCX0ScI28Afz+2Cra3ZXmw5dGF1eAibNXZ0hLQG2xm3hpUaJeVjoGpNoe23lLMddd/C1BTFHcrpY7b7HdtqzLMxbc+Yd3ts2iMeCU116V24BrsdVp06V5qAiwT+uRRwQVwiRe4NNwUveK2BCh7WuJXob0XQNDAOAnnNXGbKXLGccDWaCWaOMKMoksOot7kFGtmJHuns8d5pDd8L4rgaRmS9wgYo8xn703Pq+R5z71XFM9Prdzi9M6B5RRMwDVPNW3THCoIOiZwy4+aAPA7MtXCxn3AJQ5xxha1W1EGpF1vm/Tll2lEAz4KmDSkHQt0vg0bWcFUg4zb140cRM5Toim3JSZsjVifHDOsV0/k587SjNae50sYMJLwZzboZ3SrNIumvKuSUsNoioZ80jOdwRSPl7jm6l71E+jfn6Op2w+eG7yemszOm5uxq5fATH+bmH/lXWX3io0gpHG1W3HjzNV7/1jeYHxm0CkOO60UCHZSsiYyTvJGc6MA2w2rFPFLArgQiXWKwYpUGrly7zsFLLzMeHZLTCI8veP3nvsTR+jvk0djtH3LvzTe58+ZbnO+3T1K/jncUe4tOaU94MtIwPOkDbrUiKYPP1NKwqdAQ8qDIkKN72RtKIlUnt8og8oQ4ENeT4Ql6Ho0u6fzSC9RrJ7SLC8qbD7i4qNRxhQwDUgPJLLWQzGgIuOJ1pmhmLo1ZBS2VXCpiGalh2Npui80Fco60b5ZITaNx3jfB5wlvDbNIjpsOWItBgZgBSNAq7vEclhKO4dPMfOce07ZQ3npEGwVZr5Ar1+H4hPTcBtpEWh+/G3epRYt+qPrJWz/J77/9+/kHb/2Dp72URYsWLVq06H2lW5tb3N7cXgzgRYsWLVq0aNGi36Y+P15ltQwhL1r0rumZM4BLNTyFSZUIhC/UnlzdE7skYnLJw/BEo6c0EqQ5+n9Nw/Qcckc+R/KSQcKINYt+SWbExuifvMS0iiMpxfd3fK08we3l7j/317OGp3fSij3+2w3p6PKNaG7uWOruXqPd0KrdRYrEcrxMx0NfXlw98MuX3yCywmioaqBrERIZS4Jaw3Xk8mU9RZ+ppwGwMIqshXmmK1qb0DSGQZOG2GcS+0Y0hXnpTjKD1vHTWfHSYNiAOZqG6E8VoXnfNt0jbYfKMUXhvDUepczGjLE0hoePSds9jIFoHQ432DxDc8bVipQH2sWeMu2Zp4Z4I6UUZuHU/0mZVuZIZtaCUMmrVRhGksPcHVI3d8FbicQyfX9Y7SZj77xVeMLCdqWJ4GnTMcS5DxsocrhGj49IKrSzLaUUaFMkHIcUPb4ugblOkCzw3mbxfoumMA1TjgGCMYxqdUOaBRb6/JzZEvtpos0lkqMtZiCaw6QVtYSshr7uSKFqa6ScGQ83rI+PWV2/iqVEvX8fPX+MlBzGfKnY9oyaMq15dBeL4vOMSyLJhHpCWEUPrXvgyb3F+eiGSELTQF5lhpxpSfEmpPWGtFqhl5j2nBEvfZChIWKBS7eG1YIhuICR8Kxom5EcxrK0iiRBrHcNC6haR7qHMSyi0XesArUyPzjl/Owccaet1jRTpl//KmcP7uHjIY8f3ePeg/vMc42+6OaoVyQllBLGthqSleSJrBKI81qgNqSfx5c4eklKvnLM4cc+zM3PfpqbN66hbWZucHFWudhecPrgAeX8gvnhfc7OHnNRpug4tkgQexIQj3MakJRxUVKtsd1AzSP79RovO2R7Qa6GDCMqRkoa2+8ZN2NQhzQyppEdxm7eY8RghhE9w+IVzGmnZ+xef5M3WuPhnbeYzx8juwlKpIAlK0rG5gntQyKC4JIpdaJmxXXAW0PmLaKV1oy23yFThTSiQ/SWIxnriHJzRVC87SJ577mfkzloDR1ZjfXzU4gBmQY+F1wvqOeO7hJlnpk1w+E9xvUApTA1oQ7P3C100QdQSRPXVtee9jIWvQf0xfEaPzFef9rLWLRo0aL3lf7Ux/8U//Ctf0j1+rSXsmjRokWLFr2v9Plrn+ennv+pZRj5Gde/sXmRI10q1hYterf0zH16rUN88C+toJ5AhneSv7qK/9rczaeO9lUHS0AJ87ISBs5l76XXSIWmjHqk9bR35yKrsFUl8LKSAmsr1pOPTqwnhZEYScwe++29uFE2O0ai0+kdvhaUXLlM8KYn5q73/t1AshIJZAzo/cV4fE2GwA5LRzPLZWdqTw6bIGnVTfIS22eVliTSuTIEXrUCCEw1PGVXRA/B92HoeA10M4HwFZEw2/MASQGhthppZFV82oUBrAJtDrOU0lHLGildWUM+xKRSZcXZ3HhA4SBnVmkmj4rYitGEnBpZhZRHWG3wccRVKNuZup/xOveQdsLdkTZHArNOuAQrVnMkuFUTPgxYa7TiWG1hWA0Z7fhrb5FedgwdUlQ5a0I0kcfE+nBk9hWPT0+Zz8+hNtyn6GlNCZsLdrHFzanbHTZX2r5iOSO9MjpZRqwyjCvWm2NkWDFPE/N+j3jryWQ6SrkiMpA0kfOADpnqRm2N2vY4hrlgtMBRrxLuQmszzK0fu4Kao8NI2qxJV4/hyhVMnfrwIbu37lJ3HYkthtRClhFQir9zPHlHqPtqxIc1nnLsZzdME5bAW0XSENs6JHQc0WHESkEMsIbNE7MIzSLe7GKIRq9xz2XHuSDajWUjMumOifTEqgdeHen9y3G+y+U5iUeiVCrgpGGNINg8sZ0jya/uTA8e82oVHnztTWT7mPr4MffuPeTi7CzOpTFhVnCU1BrZDRkFLZCysMpjrIEwaHHrwwCxDh8ycu0afnzEbrfn7ftnpDYzn56y289YUrxBKRecbR9T5l0MiHh08Lr1/nGl9yR3rLwmJI9INcwadb9luztj2hWSw3oYEDGSDP36GFQCMWfYrEiHhwzrFeuLC+zezL5cpvK9D3ok2jSx/far3H/7bXJrTKVyNcHB+oB7Wnh4Uak2IZKwNKBoILCB5sbMikkzAw1pcR1yl0j11uitdt/jx8eRFh9jqETI0febYhu9NsxnzGoMAhDnqOR1XGtVocw4ibrdUlqirdeYgqvTxCj7C6bzM9K6dxynVRjrixZ9APTvff7f497+Hj9752ef9lIWPUVd05HrafW0l7Fo0aJF7yt98son+T3P/Z7lHrpo0aJFixZ9n7q6usp/8Lv+A/7Cz/+F5T66aNGiRe+Snj0D2AVXIWnqaOQS5ohbT8mGYanSzUA0EK7egIyoQO5miCcireiIFyQfBD5ZpQdsI9kX5kkk+sQlzGUk+idbDfPWWhjNSP///vcAGGgLPG1r0ccLIAn36I7Fh967C5c9xi4Cl0m3JxPJYVh4wJzDCOp46Pj+jt1162HiSEeLe+/7DEwutT4xqL3OmKfoC/Ya63VBvCK5d/a6QS3xPasBqdYRxRrrE8faHAnaZnhyqA2LeB5JDnB7jKYNYoXsAmKYK6aNnSuPk3PYJjYlM04FkR1CQ9YjerAhHRyFyX3+mHa+p8wFqzWM3RRr8JTAx977GvtLUyQJc1oxZEAG9rXh5pECzgMkRzTh5pAE2+8hXRrqxD5er9Dbt/CjDTJt0Toh2zNcBSsNVcElUNWlBKrXrfZEcWCxRUdkM6JuqK1IV49IV66gSWmn52gr+K6/1ynQ2pc9xXk9RoJZM22/xZPF2hVIFbdId9o849IC6ZtGJEv02PZuYhGB7Uzb3WPaz+zPd8z7HWYNpZIOjlhfucLxlRNQxR49ZNoq3gpYf7yFee6t4a1gaejGn8QAhUZXsyqoeaCRS/S4am2wnTBJtP4eumbyMHJw5QoyjJzv98z7hmh0FkNBTKAZrTTMGhnCgFYi0S3e/+kdwNYCAZ0yab1i2Kxwd+o0Y80joVtG9vfept69z73qSGtI2TNNF5gHZtqtgQvNjSKOpAF1YxwHVsdHbI6Po/P3kWHuZGukYrQSQwQ+N8pbb7M93eKrN0iaUGBGaCmuM14nSnOKtcCJu8V5h/Rjp0Ya1iUSsl7DzM0DsuodxCSszsioJK/kQckHazbjJoYFyozkMdDOWRlHZX2yYjUK2/NzpnLejfc4p92Vtp+wajwuhbxZs/nIK5x89uM8f3LI8Ze/yvwrX+HstMXQiLfA5ucRE8ccijWm6gxuqASaW9sUWPicSOuMDkdYMkSj6/yy01lxzK1fRwue13HNsYqXXXQPK3EtgthvkiP5b45dXMTlWDJSJ7QWrMG8raBOzpW8OfxB3JYWLXrq2uQNf+zDf4xfvPeL7NruaS9n0aJFixYtet9ondb8+R//8/yZn/0z/Mbpbzzt5Sx6Cvp3jz/OlSW5tGjRokW/LW3yhj//43+ev/hLf5G//+bff9rLWfRD1kYS13UZQl606N3UM2cACx37rEOk7er+/w9/jDbEBzRJNzw761gSMvZUmYQRRg5zWPIQ5qYbWcIsJBGmGR5Gi1t/no4aJYfx3D1a915w2x/jzmXhbfxBApFrl2lf9zBbrIGugbk/fohO3ie6fCIIt6P3GaeG2zvptSBLZ6D2ZLJGt61rR7J6mMAi3ToWXPM73cS14BL9m+6gvfu3x2YDmY12809xVST1X5Lq3FG3Ga8F0RQ9oEyormg0oOF6gtrcDelIMydNiGZqamwl8cgzm2aMdUZnRXviETP8Yn95BET6sxVoJbZJh8BLtxqmpKcnRrpKJo8j146PePH2TcpqzasPtzx64w38/AHUboylhGSNXtLUt9eik9nN0Wmm3r2PPFD2tVB3JV7CBRlXPVWuJHUkjeGbF4vnt/gaXmHufat5A/s9bkZ1oU57bC6IFRjWiHiY17pGVUh5IGv0tVZJYeRrRihADBPYJaraYogh4agXdBjpYVlMnWIFL8J8tqXud7SpY3bVY7DhYE09GOL0kX6cuvR9HccTU2CtHce9YK3h1ocHANWMjisk5TBx3ZEhI4cr0uoApj0yEynn1Qp54Rby0kuM3hjefJu5GeY7nAGpikqJTXDD3QNVTOqIdMWt4bnvFwFPCln7ax5ioth+F7W6l/3ZtVJroRlh9LYKpcA8o7UhpWFe+9BHQ11RCZNSxg1y5YS0yugEHJ+AKLU2Upsila5x7kuZmS4uqOasD1ccHB2zzgPb7ZZ5N2GtxPZ0ZPVl0j7mJHrS2cIkdS6pAkEl0JQAwUSwlMOkH49oN2/Sbt3AEtgbrzO/fYFODV2tUK3o6gAZj7C6hWEFusfbDDrGeV0mGEZcoMaFFzlISBbqboeQGddrdJuhelwTE6DRgxyU+0YxqEkwr3F8dGLCMAys1yMmA9PlIE1H5UsakFVGW0Pnixj88RQzKzLGFSwNsJ9xpqA5pBGre0gHeMqBXG+NtF4HGCJl8lSoVoMUnSAfLh3Aiz44+lde+Fd48PkH/OVf+csxPLHomZIi/LHNi097GYsWLVr0vtTxeMzPvPIzyz30GZQAz+mavHQXLlq0aNFvW8fjMX/ux/4cxQr/+M4/ftrLWfRD1MfyEX/0YPk9dNGid1PPnAGMgnYkrLQwZ58kZ6lhTDkd+Rxmw5NOXfNApkru5qAHPtlzx6N6mMluYb4RqVy5NHEhjBcN5DImYRRaxz2rICa9B7V35XbMM976MrtZLfJOig97xzCmI5Y7RvUy8Rt/TPG83l87bNzoiUVxiS5cLPOkH1h7GpkU5qQ3mtduRGfw70q5XlrDKh0/HChhh45HLpDXse9S7uZ4N4HFI6WYFJtqhIibB65Wozc3ieHeu5Rt7mFpRWmQRgrCmTtjnch1E+HnCtb2tH1iSIKmREoD2s1syYlVXjGsBiYT2sUOaedh5tEQyeiQWT13nVs/+WO88LnPMLtw+gu/wvm9N3BNMCiS11jrBlUx1ARW+qSX1N3w2qhbpXqliNPKjCHR19tNLV2tYHMAIngpGDFQINZQtfD5p4onxZlpJVGTRo+tZiS1sLg1I9LeMcSGET1Yo5sBc4FTx+ca6zLDa/T0WhzWuDspp0hVSsIRrM4UM3SuiAveKnU3YxbdzZJiXzDN1Dv3mO4/piLspwlzR7KQkpJQJF2ilx0rkQ5He0c03SBNMRQgQ/S/Xp4y7hJzCeMKHSZ8cqwUyr3H6DxTNTPtZ7wUxPswhYahryooM3p5TreGTI5seoLeY0jCVSPtj0ZS+eICS0Mkdbv5i2oYqrXFtaE1pBaklTjP3QJj7e9AqXPKZBGSZqTsaY8eMR9dYVgl8mYg7ztlQAXGVXQH5xxo41VGjlb4yQntYMRKo3mh0Z5g4xWJlL3qk5kWV+m0gNZx0I3LhHMMpsg7aHrV8P4Fyu6M7RtnzLtKexio8vWQGLOjJIZ94cq6IJ6RgxOmUqlTPDs91S0I0ipE+JvdN15jbgluXmM9KMcHGx4/8N51Hl3aYdJqoL4lUR2qCj6sors9JdK4xhNoa9j5jmIdO90/cNO0BpnRPCC+BjIy77E24S2uVt4aJMU5wKddGPqtYloxBdMYetCkyLjBamF0KPROYoR68fi3cxdatOg9q5/58M+QJPFXfvWvsG/7p72cRT9EJeD3rG4+7WUsWrRo0ftWyz302dSnhhP+rYMPPe1lLFq0aNH7XsfDMf/hT/yH/JVf/Sv83N2f487uztNe0qJFixZ9IPTsGcASVoxZCxS05I4tjjQcHsldzMOEufRPvQX2OI/xZ2lITd3ImiEN3ezrCNtOkUW7YevazTgP0w0QSdDsu/7+0ozuSFK6Gey90pMRpPTnkzDh/MmGhYHmpaeDNUwUEiItnhsLRLFof43UH9k6BvoSIV0RUixFpSduK8kNVCL9Nox4m3ASrtbx1j3JnCSeKw3QKiaESSsCNkHLfa2GyIQOQ3TuFsfr3B8/A9CY0KpoCmB1EgK9LWs0R4pYPFLBZpWtJFJzRAvFnFIbV5Kx0UTLiSHHPreUUIc8HnBy8ypXT47ZlcTdO2+zA1qZoATGWcY1Mq6pec35/btcPL7g7NuvwX7HMCg6rGkKPhneHE2XAwFyGTjuBr1AKzRvgZhtAlajH1gaIqtIQu73tFrfSZUmAXKYlRrHkhLDAa01yrRDPJDKUhqiRnREG4KiZoFQrk7dVtq8p+4qVgvWKtYMM8NMsd55LJemNIAPWJmhGUKJ91NXkTSX1o18RTVQ6Ehsf3WoreCt4NWicxVHxkBTxxDCO8hwWvmu0zQF5rpMvaMY2lxwAcsjdT8BYK1h7jR3ain4eUa0Br5deWKeu/XhhtoQHWKAoGPRRYaOPE/vJPWtxj6PEl1aa/GcbpGkdY9+6957KwjJI+Uc7OI5EsXtnRB8Sk5WIWdFVbC5UmSPasYZEYN5v4tEcakBMXYJI78ZNjV8NyEXW8gJl0S1buDqk4Ms3g9Sfx8VuzR8NWOtdiw9iPfBDtU+EKGYSAzIzIVpXzEzUi1Y2TOmAcVJSVkdH3Hl5Rd47jOfQa+ccO3ePcZf+CW+8p3Xudid4ZI6Wr9fWywQ49UapzSOV5kr44bDow05jRS5AJXYZ2kV17gWx7WjNBJttUE2h+R1IotegqYZcmJ6+Ji51RigUUWyMa7WKMbsqzj+zMh5DW3XsecJTyO0OVDgAjIe4V7iHpFHdHPIOAjVG3ghaWMsBSuVZk7dLh3Aiz5YSpr46Q//NMUKf+1Lf41i5Xs/aNEHQv/mwcsc67P3a8GiRYsW/aC03EOfTZ3IwEbT9/7GRYsWLVr0PXWQD/hzP/bn+Oqjr/L33vh7/Odf/8+pTyoNF30QdSDL76CLFr3beubOMrMJo/fwXuKYvRuirjiO5ITQu1Fbg7wOBC8zVItuVRm6GTv2nuCZlFaoGknCwIokbu8XFkByGKt4oF17RC+Svd0s0TGSaN2UNrv8Ng/z9BLpnIYwc2OjApsMTxK10rcnEr/ecbCX+OnazaLL/mGLRKPXMI1FEO8oXw8jTNMIKeKh2gpqYB5IYrl8ukv/WrQb2wbeSKJhTOsKhjAzxa3jrUHFEYd6GT00CWSzzmQ2kCW6Z7F43tS3u03IKOCJlMPsbsA5jtWJad6zTxMXI1wbDzgB1s1gpWQP412OVgwf/TBXP/Iyt8cV+dW3uPPr32B6+23GuiEPI/O4wXaV81/4VSQlHm1ntg/voe4MJ1cYDw+Zm7E/u6D5FnBch97jrOHvS+ozBoZ7jq5cFUxWcYxJ9NMCmFsYm1gYwpIhGS6CDgn1hJZ9YLcR6jwhHqlaGZSswjCMNBKlTJHIdKftdzCuqHML87fssRbmZkNpYbGDpkhKWxxD1vZh/opEojUnZFAcQ33AS3SppjREp2zO0fM8JHQWpM6INrAwWCXctzgmhEiDO4jPiAnqAlai81UD22ul4NMecsZ2W+qcMaDMU+CvEcw8umE90sXqQOsDFSKYDkh2dI7kvCGYx76h7CGvnpyTkc4HPGgBrXRTuVZUMzJk0hAmLtZQTR2Z7oGTHjsG2eP1ZRjQpGgeQCwCwvTry3aH7/fQGvN+T21QDJrVOE9Ki3mC5LQGc7ugDQOaIx2tGt3Q5orXKY5rIdKttaHSYoAkpzCBvcWACilw+H6ZuC2ogwUSABOYYifG9WPekyyzOjaG568zfvEL6I99AdkcMZ495uPXrlL+/j/kK1/6EpNZhwikGHTxMOF9rtTtxH6qqCb2/dLH5SBO3iAWRrvqAGWiNajDKijy6wFZr0jjyHByBV8dMFw8Jk9fpZxtYd4j60N0HBiPDxlxhnROqzO26kbwuI6rqApeJqQVXKLjHa+QBmxc4cdr/PlbsMrIw8fYvuA2kMgM5/di+KUPqixa9EGSiPAnXvkTjDryl37lLy0fYD8j+tRwwijLB9iLFi1a9DvRcg999vS/ufK5p72ERYsWLfrA6dNXP80nrnyCw+GQv/7lv/6EO7nog6flPrpo0buvZ84AFhOSCCbeE2olnI48hqGoStTXFiLWaVD3yNjTmfnSFNVASA8R8VRd9RSjIyk9Qb4GJjbQy9EpLN0Ek8AF6/hOAlkHIEws6J2kRCpOdOypzBLmonfcqUjvhoXo7+2pX7rp7Na7MXs3sbbY3u8OrqUxUNUuKLFeFe/04N4dmjRM51KRPOLzhJPDGPLpHYy2aEc8x4eIkf7tPcJiSDV8kI7kNZI33Fe41TCMRbBWaC7A0DHGGZBAb2uN/ahj9O42R4Z1R2FHj3I156zB3mFfGtNU8KPAC/tmjbYZUiaRmHeV6dFjLp6/xTxmLFdIleHkkOPnrnJ47Srz3tk+PqO68ECUfZtgBZKP4MoVbFDYT+ggsMp4M1wa5oGAjo5VB/NAN2siDQdYLWhpyHYXplciUs2XxdBmkCth2AvkDRCGno4r1A3Rhps+QWrrkEjHx+jBCppTHxi23+I+hOllBZcO7c59iCElWm2doBvGq9jUByJqT2prmM95RHIYhBi9A3skJ+Hw6Jg8btg3o9SZVnbRPasJTZmcho4690id64CqgNQ4xvIhSQIDrCLk3L/uQqvbSNmn2DvReStUoHqjXWLLPYxy9474xcA0zkcBTQMqc+9oElwaLpGelxRDG9oKYgmsn6uaQeO/MibyOHK0OWAzjmybMZd9oKVFaG6BiTbHaw1qgEcauzlIrZi1wJBjkQ6mUETwOmNm/bwHt4qmER2EfP2EvDmm7ifKfouOyvrokGG9iWC9CNM0M1ufjLxEnGtHvIvjrfY0bh840AGXAgReG0kIkeh2Sd2o1o7LNyoSVb1zZXNWWJ/OHRefkJPrpJdeYHWyJmti7sh8Un9dJYzWaWJ68IhTa0yt8vjePVpp0f+tncDgDW8NUcE6tr6pUkUDdm0Nb4adPyJdnCJnW6R5XB+HQHknTRwcHXJ8vKGdH2CilAdzILopgbwvM6JrzGq8/6121ILhJpgGVaDOTjWo4pgVxBp5EKhKY/UDvDstWvTekYryxz78x3g0P+I//fJ/+rSXs+hd1kfzEf/a5oWnvYxFixYt+kBouYc+O7oiAwcLPWPRokWL3hUlSfzJj/1Jvnn6Tf7W63/raS9n0buk4bKCctGiRe+anrmfVsVzIEIvcceq0cPLZVpTwhxrYUqQiC7KsuvdqgK1R11z7knXFsleVTTnnmjTbrr2BF0U1vYq0ktUtBGmbQqTxRrejVkRv7SQexdpx0LLJSr3suAz4V7ewT274NQnjw0zmN6NGX+rknuHr3ScsHQcrKJ4dNJqdNIG3tZie92iq9NKoInbPvp/U+wHs/ZdlcqCW+kpaH+S8ow61tTN5YR5JolGT+lc6NDe2A+eqTUww6ToUlUJcz3SlCMiBZWeVDWwNnPZdzqjVGushoHiSkUp5kyl4g2yV9ps3P/112hvPmBUON0V9nVHPjmkiDI1oyShrRP17Jz6+JT99pyyn3Fz7M5MU6FJT9A6YQCb4aLdg49kqAw5dk7KkfKtFZqRxgG1gmaFJLgkbD/H41KK53QNc7FMeM4wRK9wj6l309Axc9o0wzRHsneawvBrM8JB9Ayj6FAxzXH8+YCZ0EywNpGZiT7s6H2WnJ8c25Iy7h7vNQqWyOsVq5eeZ/XiC6wHJT14xNmrb2IXrRvIDRnDzLXigU3W6C1WD/R5c43zcjWQx5GUMoMK2aHOBRnHmGVQj2EEizS1xdZjQ4qkeI7eWcyfHENCiUR9P4xVRpTau6uH3pFbgaEfoH1gITkyjrAeo4u4J6MPTg55/pOfZnO44e4br/HorbcjjZ8TXmZsMtxLTw87UhpeaxjTKaFJA8DugrdejouRUUYEV2FOOfp6hwFOjrBxTZ1nSm3Y6gCuHKMnxwwHh4gY9WKLzHNsS52BuKZE8vwSbx/nONaHQ+hDHf36IN0UF0DajLlgLYZQLlPO/txN+NDLjJ95hc2nX0aOVh3t3mh332b7+l1SqVG/LX14BYDebdwK5fyCx9sLDsrE6ANrFfYO4gnxhnq8UebSKQQD5o3qSmtCa0atF7R2wNHg3Nys2I4r9ttzrDXII3J0iL74MnrtGH34ED3bIo8iAe0IXnagsSYhhlaCWt+7oNNAa4l5gjw61Y1iTsOjh3hzDbVKsyUt936S98GIp/0c7xepKC8dvPS0l7Hoh6CfOXiJW2n9tJexaNGi97CWe+j3JxXlD9z+A/wX3/kveO3itae9nEXvkv7k4Yf5UDp42stYtGjR+0DLffS3p/Nyzt994+8+7WUsWrRo0ftaz5wBHN6Qo5K6CZLDZGh7VBOJSL6hCVIGL6gIrumdXtxhBbXBGP2+eon5NXA1EMXNkRT4Z/Fu4noYIrjGP6rhy2rDPZC6Ihk8OopFE2LWu4F7GtM6phdDwmbpCV/pnnDvU700XvHYRm9h4qWMWOmPDYNRzAPPq6lX/kr/oUJABckpTL8y0SxMN7wBK5yO8ZUUSUnbR2+wOrRuJmn0sdIMyRswxW1Gx3WQrZ1A3Q4Z5vmJced2jqcBPAEJszM0H0WHqzouDcnrnqaukNbR51l7+tJglUbWyVl5IcsKtYakAQwaYK1ycXrKvN2hbpgGWKRuJ/z+Gbuk0S9rjTpXmhm1zTQn0tZeA/WskfJ1CyM/EpaRdBZTIm+uOEqpxjxN1FZJrSBphUpCqWiFWva9jjnyoQY0d5oZLiNZO0qay0GGSOS6CF6Fer6jagqj3iVSrymT1it0XNF6EtNqoZlQWwuzuM4dtQ1pXJPGkaRx7CiJNIxIEqoZbV9wyeTNhtXBIZsG6c7b7GplNwWaWg5WkYAtDZMUxnSZkWqkYUBVYzxCUvT8lkpajeScGMYx0r/dvEY8jqNLMrMKXlt0Aw+bSBmrRn9333WSMlgJrDuKiKNupKw0g1YrLiOtNVIeUTdcNcxiHQOZPKzChM8jrpBu3mL9yod4/njkZHIetyGOBWu0ybBmcU7PBlOJ64SDD2McWxL4+eqNZM7gjmbIwxo9PGBztOZEMsPDx9x/8JBijXK+xbZbAFpPIdfzxNQc3xeGpPhcIunrQmseBnTv+hb3bvDGeS1p7L3fAjgm0vH0vcdbwDXHpSNpVCGvBVOPFPA0s7t7l+nhQ9LN8zjvd+fU195CEa5evUbaX3C23VIszqc4VQrWDPQCO7rC4Yef59rRARd3HnD/7gPO5om5gmuKk1MaJMUsBg7KNDFlYWCNq6PDSPvUJ/Erx6x//p8ijx5gzVFz7GLP/OYdtg8eUk/vs390inkMU5AFsSHeY2vRB83Q+9wNHQ7jPlEm9vsdZhmvlVkTPoxo70VWK5CXScX3k34Qvyw/a79wL3o29IfXzz/tJSxatOg9ruUe+v3rzu4Od/d3n/YyFr1LuqUr/p2jjz1zx/WiRYt+e1ruo9+/Hk2P+I9+6T9aOoA/wPpTBx/mw/nwaS9j0aIPvJ5BA1hI4tAqpH7zbDXQoymDVeQJmhmCXSqIC1GN5mHqYEirpBTJXxySaO/e9SfmS7i/GfcWaF23SOlh8WXC4ABBuiHhHWXslx2ybhHcm0uY0r374DIjLJd/8glhgxB9pRDGtPQ1iEhgTp8kki8Rt4F+JSektUh8BjeW/kLQBPcwI90q1gi861yQNOBigcRGuewVxk9BrnQzWsKwNI99vF4H7jdlqs1YbTgGzfEWCUWzSCTGvm4MwzHiCYYhOmXFoezwNAZMtzXcU0/cJjKNIwpXW+IoG2tzDvPA9aNj0sEhW1Gms3PKxQWtljC/AUxoLpQ5/i42NtbviZ4c7cbUPGG909nKhDloElQyOmSSK+I9ZW6OJ6daoc4T3hxphqwaaXNEXsUx0LbnSC24KxC9rq1WvBmSxqh1binW0moYVxpp4xgU6MeOteiiTiPD4RHDuKKVSu3bXGulSmYue4oqpkpqhuZMWg0kTWgaEHPy+gDdrHBRdDcjpYI4+eoRm9u3ODq6QtsX9vcfMJ9foFJ7khSsOFb3tHlG3VGcPAyMh0eYCG17ATWObUnCerPieFzBZEzNMFkhOfpw02qFKpg13D0StDji0SMd567EMMVlAlejf1ksumxVGpozDWgdB+8phhxcwDyhOaEd5xU44grDCimVG9r48I/9blbjim/9o78Ld16L96TWbliXnrLV6KiW9CRBH2nbfr6nhKhEp/I4kK5fJ189Yr3K3LhyTMuJu2/fpZUp0rCqkA3xRDNnLg1KIx8esckDGXisQtEc6+jbr5K6GV4DcZ9SRz7HOS49PX55AogL4mEIK3NUiQ8juFG3W/bnOx4/OGf8J7/MC195k9X1IzgYycPAlQ+9xLxew+kDzJ3z7Y5a5359cpwJDlbo7WuUD73IlAeuHSZu3zzk9bdOee2ttyhTIKjF+/VLG5hTSmNfjVz2WF6R6o79gzdIb8/443OG9SEzO8wN316we21PzSPztGeaowsdsxgSSAmnYW2H6xBp/LzG2z7OGzc4fUTdnuHDmkak/J09iVVcKyVd8hUWLfpAqljhv37tv37ay1j0LusPrm4t6d9FixYt+gHL3Tktp0xtetpLWfQuaSOJqzo+7WUsWrRo0QdS7s4/u/vP+Ed3/tHTXsqid1HX02pBQC9a9EPQM2gARyLNxVHvjp8DJAwnOR1pGjhiSQNeW6TcvCdmRYLenBSXgpJJMnTPt3fRqjxBn/YoHeCY+xMsNGmFtcBHGy3Svd6/1k3TSNoqtBJr6MjfUP8ajntDdAUdufskdicJvezl9f5fCfzuE4MlAWkV6x16F6Zox1uDu2DejVkP09GHAeaC6Bo0Q9n2JPDlGkDSSZjsrrEtqmHm6KXXFOjbpCN1NCg1Us5JoE6R8hMCca0j7obqgLpCMWQc8OyRaE3R4ezF8TqTHE7GkVvjihvDwMlaOTg+4tpLL3D9U6+QNof4G/fZ/cbXqVtHakE1IxaGuzIDGUVIeSCpMKw3eFKKCKU22jxBlp6Odqo2ioObo2pkIF8mdFuJY8aV5grDQLKJrMqQR9aHa8bDA2qtyDzBPAGKzRUrgUlWk0gdusZ7kojBAp9RGWI4oPf7ujWohmZhsxo4Oj4kHa7ZXuxpDx9itVCbMklhcmd2AzOSKGoGU4PkkAwdVoFBvzjHTGllDoNZA9vcBJoaPhiNSJoPeYOqsLcpkvGl97omJZ9cY3PjKuPhIfNc2O/3cbyNA3LtGvLSy8jxAQOOP3xIuXMPnXeIKCnFQIbVGn6eShjwGGaBMQ6rXqN/elDcnVYLMXEhgQ8363juhqf8xNSPuYnU/x8MCyR5i/2zevEF8u/5g9xjy/4X/jn3v/QqqThZE3qwYl9myr53N2uOBHhrkQp3R7sBm3JiEMgeCV1rlTrtqX7MpCNtnGA1oCm6Zg2iJ7nEOd8oyLDGViPcPEFuXmPcTYyvvo6fCsmhWaUSncRcdmhboKnRjslO0lH1EnQD877/CGy5KiYdG+1Km/dszy84Oz5C9xfoPHKzrlinQ/Lhhpdv3eTQlG/tdmzXO4btLvqQbcLzACb4bmb+zuvMp+fsP/VZ5JM/xpFvGYavku69jY1Dv7y1eCeLQauYFaY5g4yM0wSPL6h74eLaCfXaSfQrv3URQxhlprVESTWGVlqcU0kzXnZ9YCDhegA+BYKfiqQ10qYYnCABA206pwLmgTT31QG6PsBUUVsmURd9cPWVR1/h5+/9/NNexqJ3WZ8eTjjR4WkvY9GiRYs+UPrVh7/KX/3Vv/q0l7HoXdS/f+XzyyjookWLFr1LWu6jH3zdTmv+p0cff9rLWLTomdAzaAAnuMSjao5UnYdhmbr5QVoDYbSJCDL2blBxaA5qYVIayDCGAShhHosCXhFWgcHthmsgkgEfA7UqipuFfatEwpbS08JhVAmpG7Y5sLQYIqkjnjUsKu+IVTJwiYEeEZ8BRz0wwKJj7+ON3ksViXXmFWK1m9d9rZdpPSVMIr80gQ1zDVQ0gmlDveOIpad/fQ+MT/pHpdVI24lHwtotEqsiJAkTqmGoWSCOW2fQYsAes4GUNmgOTK1LhTxCDVPIZYAae8CKYd4Qb6wdrqlwcz1wdbPicLNifXSIrlfUh6fMbz5k+9Y9yukZrZZI8jkdlZ2RfID2ZHRabTg82HB47Qp1GDnfzdjD+4hXJEt02OqE6wovjWoVUaHNe8rle+MNFYntcCE3IROo44OTE45u3ECy0E7PaLViU8G8YtZ6+jOj2kgCSbshVy16cKtBLrgk1EtnWwfKezi5wvjKRxiuXcPOHlDuP6C1ig2J2Y1da+wQrEHuxxoI1BbGHQmj4K1gKIbRWseAl8p8/5TtvuKbR5hbpFLXA3K46STwiu8MNBDEqoIOQHKszbTdjlYb1gp117C7D9mWRjpYIWbU3Z6y3aMOaTUiqrTaaNWordK8p3dTmK1NvHe6EjamlT5U0dHCZgiZpC08UDRSwKVFotocV8E8IdbQpng/17xW9t95nTv/z/8vWZRqM5srGb3xEiaClYn53iN8moBI/Urv0FaPy4e6I9ZIcwkiANqx9MZ0usW2r7JOTtkXdtst3lqcdxZvi8YjojfcjDYV5rt3kfsP8GKMkrj13A2Ojo842xfevvc2+/PTfl2QSCrH9Er8nZXvSvt7dHxLimGN3kOuSWMOpTZolXm75eLBA1Qs0M7ZuTU2Dm6/QLp5hRs3r0Ap+NkFE0rTgUZcW3zIYEbbbjk1pXztq9x48DrlYuLB/UdsTHj+4IRThwfTBUxhzprmOLfaGT6NzHPFW2W727N6fISZM+/2zHOBPJKsoXkMBLlAkjC7xRTymlonTDW+XiMZ7bUBFRnWpGEgC6TkWN5gZctcBXxF2lZkPkU2R2HQL1r0AdQ3z77JX/i5v8C+7Z/2Uha9i7quI//mwctPexmLFi1a9IFSscLf+Obf+P+x96+xsm3peR72fN8Yc86qtda+nkufc/pKsqmmeGmSIgVFtmw5ggU7cSBbUmAngH4Y+RMERJzEQWDEERXLkA0HQaxE/mMHhmBBkRHFEuBLLEqUSAkSo1s3W2RTJE83m30/l332fV2qas4xxvflxzdqndbdpLt785w9X+Dg7L1WrapRs8actWu93/u8PFmePOulrPoWaUK5qcNzh2NdtWrVqm+13J1ffPyL/OFP/+H1ffR9rlsysAnU6qpVq77Fev4MYFswX0BOuhkcnblJezo2TfE1cRQJc1ENNCMSxiWSAzPrC1h8XTuWOVLDEyLRwRoxQwGpuEViMjDDkQyN1GELVi4K1EhyttZJsQmRBgT2WD26gU2k9wELPVoa60tTmG6eos9VAVIk/MQ7UlUCiysShrd0vKyEqU3OeDmAZ5zoLTYEE4CCSqCCRR2TBGqoD4Gy7qlHXyoiteOjl96nHGYLaYNoxQw0D+ANY4ljnRXKgnkBzsgigQZuM+SGyEkgu1Xi+LRDmGFth9cwxLXN3Ji2vHgycHebOdtkppSRyyvKvnBhymzOfjngtpBUyKqk5JEATpBydIIKmXSyIX3wA3D7Frrfw8VFT0eHMUuLjlvXBkNFKtFDfDRv/YDkIdKmSyJ7ZcjKME6c3L3NyWuvMJyeUHYX1LLgS5hT7tH0bAYiCykNpJRQrXiJHuUw9Rq0BO4YC5qmeBm2G2QQ7OGb7B++w7wvzLs95k5ZKodSOZixaAJpJMkIjoyb3t0cpqDXgoniYpjCMcPuovhh5mDQdvvoc1YlTxmvjSpC2c9hHrrFa02jHRbKo6e0nLuR1/qxcurlJdRKGwcyCWl7kETejsg0AEKbl8BXm1FVe0aXmFswD4NTHG/ekcth9Lp4R3EbmjI6KlYO3dxOtBT9vNJT+t6Tv0YNo88zdtjzlCfYnVuML72AqMEy4/PMfH7JcnmJtxKNzyokl270dlR1cdRb9DJnoVeDIylhtXBYjKUUaquUUsiSGcbE3ozZLAZBrCImtHLAzw2/yrSbN9m8+hq3vvvDnL5yh9wa41feIF8+QnYpjF2kp3BrTwHnGBRpBRji+LQa+1inbgIT6HFq7IU00srM7uklXgrt8Tny8C43h4mTT34Sbp3Ay6/ywrBlOjvBfuEXePPteyzVadYCr5wHvFXKsqe9PWMPzlGrtNu3uPvDP8iLL55x48132H/2l9jPhzChRTErtGLYvIsBh3qIfXyxA23RjyyQPK4NkgTdniKtIk2wVnu3uqJ5wFp0MqORABc3tDUSBW2GjAmxGZERaYZXp867wKBzoJmg45qaW/X+k7vz02/8NO8c3nnWS1n1LdYkiY/ms2e9jFWrVq16X+n1J6/zl974S896Gau+hfrd21f5ofHOs17GqlWrVr0v9R/83H+wfhZ9DvS/v/W9K0lj1apvk55DA9hIuiU6eJfovJSESsMMkrQwLBlAW1yMcoIayVKG+IW/JoGmkeT1d/szO7M40rkS2GdoYaymsHild/6imWMXcHwn0LNY7Z2hfRLGu1HbO33ja2FcR6rX+tdqGMQokiMpLB4oWqV2szcFJtc63rnMyLgNQ8UsTL0aHcjeLNKtVgMBHbYfKsKQhEaikmjVsaN57Q3KAZXeoaxhOKkemf7faKYHYjc6Ugfcl47KNZAN2aG5kWzu6UeP9DA1jlfOgSoWgSJ4i9dukzbc2W64MwycDhOjKJnoaEYze2tUK5CVLFtSxzVHQtk7Rjh6hwXFL69YvvQ1yG+ylMrcjFYqxSoVoVijVMccpAh5CUS3Q0/UlkiT5oFUG8MwMp2M3D47Zfvyy1gamB/cZ3f/CfPlJY6hQ47O0WYwL7FH00BSA4vX113jdcub6HWlIOZoAk0aaOGrmcNVAaB6xUqltUatHa/bKhnBzZFNjmGDZNEDm1PfM72/2D0M6VZpGDRB1XBXmufY2xLngaVwNlvrZpxGp2x0J0NZGskkTPtacXKYwNaoh8K8FJoQOyxlLGXcFgynzIVqlSra9127xqXL8Zgf8emiuBeuneCebo+y34obVIn8fIodFrdphsmM6IAwwaBIVlC4q5UfvnVKnba8fvGUq8sdfv6Yw/kldjggraIOKtE/nMYRUYeWSBtBNaEduY0mdBiiR7w1nNibxYGzM/JLL5JPT5CrHcvXvk7bv4u3piebyyBIaqR2xfLGmxy++jUuLmaePDnnajmEQe69K7qb4cHl7l3nWaNLWRIu+d0eYw00dFzCotPYUxAKSjPsyRP85l3GD97i4tZL3Hxwn/TAkbyBu3c4/b7v4qXH97h49ICnZnG8nY4oN6gzljO7peBpYNpmbKhcXl5w+eAhbSkkzdAx3pa2mPWBgdYgbaEVmgUpwIdGHjaRvB6iT5qc41y5WiIl7oLXircSlxmN65AYSHN02HZMd1x/XQd8mZFaSS6RGlZANrhVvK29X6vef3o8P+YnvvYTz3oZq74N+l/d+G7WxqVVq1at+ubJ3fnjr//x/rl51ftRQmArdU3/rlq1atU3Xf/1V/5r3tq99ayXsepbrH928zI/ON5ZSRqrVn2b9NwZwI0FJHe/9ohnJhKKaSL6dwfCrbUwr0x6Qjd3BHRHLws4RuKILfbOc+4dwqodqRzWbkgxGtKTnU4YnmEWdjNLMm5zJDA9Epfay4rNWnT6HtdtSzwOhugUCc6Oho419MSfpjB/UzyGe0V0BN1EGtktuj6l9522BXTE6MaQ90Qygosi3kgpTNemgnoOM7f143dd4h54afdI2PlwgojSqIgMCBrYZgjTu3c0azfPNQX0VlKYWGHq9UPdGm6l43kvcRK6LJzcOuMM52TM5JTRpGxOT5lOTvGUOCwLfv4Ym2cEIQ8TWVJPOvOu6dljpd5gvjow50QDqjWWeWHGqR7JRjNHxUhuaIquaGsJq0sY8yQUyIMwbEemV19l/OAHGE8m9veecnnvEfP5BW3Z4UN6F03eLH5+2KIdoyzjGKjscUKsoB0j7CbRkQzoMAZe241qgQM3l8AuLw1wBhzJmQGh5hzdyiqRqE4StdWSYhYBDxRvWWgIrTZUhsCUt+htlp5mtwrLrmHVqIeF1sJIxWJ/uDZImTSO6OQsVqHMkcwVyFTUFa9931rD7ICZRd9wgqYD1tHjIilw3WJkRgZNjKrM48S8zDTvpq/14D2EGVljcMLcMXOs/zmeK0gTXEEGhRw92CcvvcwHf+dv59aP/ib27zzG/6ufot67R7uaaYc9lDnOwaTIdkTGTQyQyBSGcGswL9hyiO0+TaScEE1ISojVGEIwx6cM8yXL7oLD/kDzionQ6Ols1SA5t0Y732PtKcMC43bi0Cq7ZU+Z99fXBMUQ6Sa4WST9vbOl0xRfE+8GugcGXFN0B6uHSdyT0N4WzGGxPXN9wpP7X0LrFSd54uRkYrMdkEcX3E1n1Bc/wBuPL3h88YQmDfOGaR9QacQ5MgqHN+5z9eACT8LFLpLZYxLUR6o3mhsNjeuuxPXOaycyLDtcMmIVzWOg8muDekkrlbbMWMoxNGKALEEqKDOUq0Dk64CKhNGfMs2j81zySEJIbsjJCeIVtRav0Wb6Jr47rVr1G0P/5Zf/S97ZrxPX73d973CL3za9uH7oXrVq1apvoj51/1N87unnnvUyVn0L9S9tP8iP3fzEs17GqlWrVr3v5O48ODygWHnWS1n1LdYdHTnV586SWrXqmem5O9tMT8K0Eu0JOOmGC0SJb8R0j325eOCGhS1hzgIpeldzjo5YSUMkWv2Y7E3dZOgmHop3tCwSt7duXOKp928eDdOBiNzlPjkcaVSXnthDaGj/OQvjCMEFlNbXHQaRu4aP2Dpe+phSrh7GMBYJP4t8Meq49c5e0+uu0OYFc8W9dgOthR+eEljvO7UaRqDXuG/vSd484nUOE4kWydp6QKdNPE8XvDm2HHoiM3pbJZ/S6j4aT4cNrg4yQXLcPBKadcH7cW+A6kg63TCJs0mZ3Aijetow3bnJ6Z1bmGTq46fIbk9qgrp3fHc3zf3YuSuRDnQwL7jm6M4FyrxjNqdYdLdSCoqQ1MnDgEoku80rDQcGkqbw62RAaqE9fUJpC7VULi8q82FPKzGc4LXhudFaw/BInbvhJpFiNACP9ZFwERRHJTPmkc040oYNhzrj+wPVG+aRXPSUYMqklhFvZFdqLRQVWjtgTpisOaOqqBPPRZV2KFi1mBcQwYkOZtdICJuGyVoPJV4fazRruCveKhJxZTQJuh2Q7Ri9q8EZxqcNOiZ0msiipFI4lcw0nrBz57A/p9WF5tDMMBlisACgGWkYmO7e5dZLL3FrO7HMlbfeepPdk6e4tUAB009JEZIYtRGmdRrxssBmg1uDJHia8JwwYs/pdkv+jteY7Zy3/9Jf461ffovdm2/xgk6c3DljLoUnbWFXZ6oVmtOvLUZSic7mUrDDglUnbTfoNGEpvYvcFkWGETns8fMdFahJWVrFWqDemxtUx7ORayNnQW6coB/5EPbRD6NSSV//KnKuMEe6G1F8Wfp533uerRIDHRJoZqthArv26+Cxi8M49o5fT19IxhIsV4UnX3gT+9ojLrdbzl58ie/6LT8KL7+KvPZxbn38EwxffJ3lU59m//lzDvsd6Ab3jpZvMQxDNaBysbtkZzFAMQwDN27e4Oaw4eJwxePzc2CJy2A+ASqet6gbkk8jva3aB2+CbmAy0OYdphNYpZUalAM9nlNOdKZXUhqQVnAbaCxRkeww5AnNyiiOJw1aARam+bgmgFetWvXe1MeHG7yWT571MlatWrXqfaW/++jvclkun/UyVn2LdEsGfs/Jhxiuh91XrVq1atU3S2/t3uLPfPHPPOtlrPoWayuJH7uxDlKtWvXt1HNnAKeA9oJtOtpWSORIO/oxIpgCiYsj44A0C0boQHTMkpAsYY5dT6wE2hhfwKT7udZNYMHcSMfeXrFuFnun0so1996PuGjJiNdIu7kgHuZPmDCR2BURNI2BpvYw1xADL4in3kXsgULFOj460LPSU6PBPo3UJ9bwKPrFNJC77hlvrRu/R0Pb4o9OdACLRpLwmPxN2g2d3I+nBgKXHKaxG7QZSQMwhAGYJvzaQJbot/UFyHg7IJZxKWG6NOsEW8VZoldZAnmsEg+f0oimIY5jHmCcMKI3tR0OsMyILYE6ThNujUYNi7paGDzuCEKzgkt01LZWKEujAM2jMzVpZlAnka57dK/TuAOAhAGcNJLNDvV8x+6qkK2GoaQK0zZMcBlpy4Hq8i762gW3ijVHs0aHs9P3SqCGh9NTbrzyCqd37lDqQnvzDcrVHmtC0waknpaNrmb1yKWHrycgCauVVhrFIqvumnC3wIEbyGYbZmYzpC2xf5pDyqhIoHpbpS0FF6GJ0JIiraEOKQUCWdzwQ6HVEklf6UhkE6TMaN5w+sJLfOdHP8iNuy/wxjuP+NrrvwxXjrnRgCYe+9ZrpNRzhtMNdrZh3k402fXn5mGqm/c9nwgaeI7u2RrnZ5jrcZ/NnYT3qu6GVqVc7Xn8qdfJPzeyq87ZOPFbP/kj6MdeJr94m93VOfbzf4f957+MXyxhanu+NjqZF2gWwyDTFhlS4If7mR94dkGkkDSGOlqL3mxxJ3vvYQbafAAf0dZoSKCQnzzAN5mrPLA7BPJ9GgbEheqGN8HNeqc4kfTXdB2LdjRSsxLnINYX12rsA+h4/BFPiljFvLHfX1IOC1fWOPkdv52T3/FbkFt34vW8/2XSvbfw+49IBmoZWwqSFeuYaZJAq4iPWKcWpHFg+MDL5I98DEZhfPPrpP0uzF8vcc3qBAWLK0ynfHtQEdoYvdxu+LDFaov/HKwsWFt6T/AQ59J8GX3ODm3Z40nRnGEYSFbIklADbwtsRnw8RZJGWn/VqlWr3mMaUX7PyYee9TJWrVq1atWq94wE+PHbP8Bv37z0rJeyatWqVe9LNW9c1atnvYxV32JtJHErflm+atWqb5OeOwNYyJgBSUkQpgs10MUpRfotCdHb21NykqB3ntJTkSIefxaNn5cBkW56eonEsAdq92jumHsga1XwFl2jTvSTimhPBB8TeaWbvIRB6HNPER8NoxYpYCvfYEID5r23tLuDmqD22xzxt6nf/mgAeWCGvQXq19oBkkSC01uYoYB7DcOm1egItgVL2rHPjnQDEI6JY0ek4sMAVLAwmSNhHQhYpwTO2ONnvF7gusWaY7ohkfG2R6Yz3BzfXyHkMK/wgNsuOzSf0d0d1MduMhfEB7xUyqPH8PgJS1lYdjM+L73vNmNuYXy601qltd7DXLpRlsGsYCJ4bQiZ1JZIKiNkhOyCDilwzCw9BTzDmFGXTuAerxPaLoqfbZheuM3pZsPVxZ6rd+7TFqfVRrGGkVFxdBxRF3wmXicdOx66IU2QDDqN5Jsn6K0T2gDL1Y661P6aEXupCpolkuja8dQqaFKSR2I3wtWNhlPriNohjHZvcLKBQTFrYdi1Frjd1MPlKSHWoidWwyxHHJYComhOJBWGcWDMU6CXa4k+VhGoJVLCPpLv3uTOb/lBPvAD38NZhoef/nk0K6h1s1qjS9tLGJfutENheeM+V/efUkWYW2NerqAa0koQ080ijS6gksmpEUMIHb0eIOjAHNclUvEMiBao55SrPU+3N/neH/wk3/+7/mnyD/0Q5/e+zhc/8ze5//oXefTlr1OuDpGodYflgC8F7w3IkhMyJHTKpDGTVPvTyUhtUGbUlZwGVJ3WFErFMNydxgSl4bUirb+O1vClsru44OF8CLNSEi+/8gFu3rjFYWk8ePstLp8+7fs+SAXuLUxaJAgGrUTC1TttwHq/uDu0BffAgDNK7MN+DRSHJpV6NrGjcvnml7n59tfwe09Zvv4Gh6uFcTsxuVBQDMPqHL3gPT0eNIIlOqy1obXg5+fwldc5LJXziwUrDaVBHgILTt8HKtGpjEV6O+cYMNAc+8sc8zCr3cLsN/d3H7sVVAeSBmrf3TFXNCVk2uBe8OWA1AJpg/dBE/KADOs/WletWvXeUxbh+4fbz3oZq1atWrVq1XtGAnxyvPOsl7Fq1apVq1a9p/Vv3PwEW1nDFKtWfTv13BnAjYahJC89nUvvx81hbOYMtUKawtjVBEjgWZF+e470YgwnXRuzPTEnI2GCHpN1RxO4N8y21u+rRXLRe+qY6DMN5HP8vB/NZ+1dkx5mbCRDI7UZAKKOWB6GcOPoPaIY1w5dX4fQO1HRjlMueM6B9Q13HG+1PzYdUd3dYyeSvkg8pkeyVzN48zDjzK6NXu9meQSUU5hP3cCL5HALc3uu3dmegAHT2tcOKmeRTEzSUbQOaaCVh6hscd2EgZUE1L+h07XhVmn7HXOrkf2uC7VGTFhzvE7NG7VW2jyHMWTAoGFo9n2j+d1jkczJIlgZ+hCAoBotzUJDzJCUUJlAM5JyJLG174ZqyJCQ7RabRpa2UMpMKzNLq+znhWaRGE45Awla6QnvitQSjwFoHkljZtqckj1T7j1kWRq7y3OW/YK1iidB8oS4RQI5EUZrHsJMtBJp51pIqqAZVwnkdTfVRSMdbEvFzVCjm90FGfK7rzlh0vqkuA2B0x5GdDmQhpFhe8rJrVucnJ4wLwvLMkdKE49E7qCkIZOyMpQZf3Cfp/vCk6/fo81znD8p9W5aQc16iF5xFWpb2O8bs2iktu243/p55h4JeJFI2eZAobsffciOVRfHXZFakdLidVQlnU28/M/8KB/+/f8K+dYtDl/8HJ/7ib/KvS9+kcdPHrLfX+K1oiYdD59giOQ1vV9WVOI8OJrlDl7jmN4YTtgMGy7MuTo8xbyhXpFqYQTXhSYJHyfStIkEqjV8f8WyFEjK9PLLnH3Pd6MfuIPVPXztPlvJ5OmMfa7MyxXWIt1NLceTOgY5joMjkro5bHG9Uo3rRR5iP/RrSgw0GHijPnjMw5/663zpb/4yd9KIjRVOAmVfrDJMynAkxXuLyYpWYyBFEtJqJxoAdaGeX3J1riDGIhlqY5MyQ5pYVDmUQz/HLWL/R0M45RjgMIc8YvM+hm9ajT0quePTB7A9WYWkI5mKuEAeSL2nHTc8DzGMoS1e1xQ96Z57p/CqVatWvcd0S8frf9+sWrVq1apVq/7J+r0nH+FuWutfVq1atepbpSyZs3zGZV2rFN6v+s3DLX7n5gPxu8RVq1Z92/Tc/fZaSSTpidRu2FZbGEhhLCKIOsLSU3AWyVUVSGE6YYbnFL4Sx0Rh9L5yTAf3ZN21W9y7NoPCXDt2GSL51h/XrVdsWv/FnBBs6tL/duwurv1+K6pDpH57f7GkbiR3o1gEGFKYO8FIDcPFO066NVwDB2vdSjbAJXcz+Gjs9eU04uetxTGpDZYS91FrmC8KPigqjjftx1QDVV0V2Q4kzWE+o4FgFsfbvqOgD2Eki4AtuGa8VLTjot1bTx9uMJvR1Dvs+tcX3bC4UKrTmtO80NwxMaoMNLUwZbPiZMrhQJvn6KmV3sdaK+JhZlpriAxhukokNhk34R9Z4LfdDSkFGbekvI25geGkI7njNfcWiUNxQ4rTHj1lfvyUOjd2hz2HtjDXyr4ZkpTNIIgOYdC3hoij1lCLVLBoIk0Dm7MbnNy5g2xOOFxcsN9fsr+4pAnRwewW6GfoSPB30dzVD8hSETHSMAX5VxUXoZZKckOGjA8DtRZaqYi1MNzpzyePYQa7YhZJYrfoMI7XOIxCHRLjC7fZvvISw8lEfXIFT85jOMAhDRPjmBi3G8ZhC5d7zt94m/MHT3j6tbdppfaQd8Wk4aqo5o4fb0jSPpjhGD3FqoFgFpUI+YoGZr1j1ZM1TB2Xfinsndo4SF3AMpIG1CppOiO/eBefEk8/8zMMjxY+97m3eP3+19k/fUi72iFWydOETGOkry3u02uJa4dEAltadClT9qCZtN1y9uHX+MB3fJSbtwYefeUtvvb65ylPSqTFidubZoz4ey0zeLyu2gqSocoAF+foL7/O9OUNogNnNzZ812/7JMPNF/jSF17nC7/wi9T9HKd0Gjn2dcuwjQRtW6Ad+gnvgdB2Ax0R9zDSk3ayQO/dtYQdDlwu9/nC48eMY2LYTtzYDmy80PaBv55UIUGpUFt0a7vGQIc7qFng4i2GUBbNYd5SuXX7Jt/70de4e/YCD58e+PybX+XB1SN8XiKJTAw6XBv6nVwQneRgKhgZ6gym4DtSmkgijONAEoUsDJuBjSawxGG+BFd8yEhykjlTGmAYo5v5OJCzatWqVe8h/diN37ROXa9atWrVqlW/Bn04nzCt752rVq1a9S3TKyev8Pu/8/fzJz7/J571UlZ9i3RLB15Km2e9jFWrnjs9dwawuGBiYViJomRSInC/uulo09zNGoU09n7gY8+uxs97QqwgeQwTjHbd++tHE1EEd333wXtq+JiyczS6eDl6zz1lTMKlm7y9+zeSdtHriQipp92wbrq6hdFhHRvdIo3qBO7WRYCKaOCRvRokegJ3wKxjeN0DleqBV77uFqb1lGXrCWXDvRvc2ruOVREZQEr07raFY0Aw0NGGj96TvxXVKR7XGuQUvrZrR25H6jrMS0OmKZC8aaKZIXVGkmCygW70NBxK46oVLsqBXbrNDW8MOtFEcc1Uc5qDtkpzcBdqrZgLKSkJQeX4mkWaWXLuxnv0Jov0RLhIzAcU74HqEfWO8W6GeIkXwiqQ0JTIaSBvJxgzS3X2l08pzTjUhf3hkv3sLGJM2xO0Bkbbq+EIWlsYiJstkpQ8juTTU/KLd6P7dikshyuW+UBLiSYSXaYu0Xes0U8c2y8SqNIcUUXTgI4DjlJrpR4KTaIXV1RpS6HVhpeZpEPsxc2GtNkynZ0ybje02jicn2MSCF33Gvhlj/PEaqPOB5aLS+xix9Xjp8zn59FzmzdMm8zp6U2mWzc5uXObIQlPHz3lwVv3mA9Pe/evUEVo7tFn60sMYJDiHM4ZkpByDBZYrfgSr7VIoIK9Y4iFRkoD7k7BsJQxEUw9jlk/pVUcGSdcoV3tefjzX+Lnv3CftBx4dP6Iw9U57eISGQbS2Rkkx5rjjUiGHwcd6nEvRJ+zN48u3Zs30RdvkT/yEvWG8PTJOU+eXlHMe9o5x4CEVFwzIJg7S2s0c7I4A9a7wDN+dQkVJJ9w55Pfy83v+zjLuGf+6ltcXeww6425omGa089Nt0g+S+w7jmSCpLhZJMUlRZq6LjEwkRUxjRSyJ7wt7KUwl0SqBw7nziTGOE4M6oynp6gbaR6pywwpsU2JYTrjshYO9RAJ/utEskBW8ssvc/d/9Hu4/YPfyzRkXnn6mP1P/3me/PVPUeqI1UiHS9sjOdK9bhWzGtu9HDByHw6YwPaIjuRhYGBhPNnEi33nJTi7iXslPT1Ha8E0R/h+mtic3WU7grSF3eWBYisCetX7T7/3O34vP/G1n+De/t6zXsqqb5Gib36dul61atWqb7bW99BVq1atWrXq1ycR4fd9x+/j5x/+PD/38Oee9XJWfZMlwP/65iee9TJWrXou9dwZwE0UldQTsoLbAXwEqZAMJUeXb4o0GJTomBRBPLpnU8ok511DTXJP5XY+tPu72GfKNxi7HSl6TPZ29G38WOCNRSX+3LpBQ8ceqyKt9eRujv5JEcQLdiRP9/7LCB5LeLfi0eUp0p+nhOEqxP/RSNi50LrB7NBTlf2+3Pvta5hZqXcja44e4XSClD3iiYhZ0hOvYSRLCkMX7cfI6YjZFofGIrGHJNwucRKY4yQkjXEfBg1F6i4OVTLMEz33B2YkSZjC4salNi69cdMqg6dAtjalOhg1zFXxWGMpJLfo282ZlB3Sjeg9tY4JRwkUtwbat+8n6UlSzwnBUWnQBJGG1Rpo8KSkzUg+OyPdvIlsB+phjz0+pyyF5VAo5lQrtKSIQbIKVoia1haP4Y6MA5qVlIQ0DAzu6KMn1AfOfFhY6kJb9lixSEDmBDog9OSyxCvsCF4OHQhupKS9pdYwFywrlYSp9r5VxaikNHTk9UAeR07u3uHO7Zt4Spw/vegY8dgzWmucN5q7qZ4o51dc7We8GfN+ph32uCTSzVtsXn6R0xdvIpsJq439YUc937O7ugqkt4TJby1SsZoyqhIJWffYn0SyO52eME4n8bXdyHx+AcsSuOXWE8E9xexuVM24lTCBl4Km/G7aO9HPH/B5T318jwcXD/FWaWXBd1eBC1fiNevnm/brhHqFZshS0FIRC2QyaexdzMZ8/ynny5fwYcSscTnvaSIwDtG1bI1kSkNobcE8RSc2MQCSUkIxaDXQ9IcLlqfK5S/+Hc6+8nku8gkXS+Fi/5RhHPCW+3nnHcFtcU4fkfiqiMu7SPc+3CJxAHEvMGziGooF3UAEZ0bzFoNIjCPM4pygnI4DQxZyS6CNYXPCyQsv8NGPfzenL77IW1/9Kl/45b/LYddTtyrgBWSDLQf88Zvw9ogrPPnSW7z5pbcozcMUT5H49gZY/Kw3wyTTDk9pLjFsUmI4RdKWPCjDoAzDDVScykB7+DbjbkerlXLYYeLoMCDbU4abtxhevg15oD5+TNsX/HD4Fr1TrVr17HR7vE3W5+6fh8+VfnL/Jv/i9lXGNcm0atWqVd9U3R5vM6XpWS9j1apVq1atek/qznSHP/Qjf4h/59P/Dp999NlnvZxV30QlhJd1Tf+uWvUs9Nz9hi8pYdaKREJVUzd/N4HrbaV7fdK7UofAozqoOq5K0hSVmD0xK9oIQ7d/DUCGMFGhM5XLdUI3DONj36b2dG/vCz6aovT0KxIJR2u9y7SnUa3joiUjJt3wWNAaHaY0j1StaUeqGm5HBLXi3o1Ac7AF64lAc+uGb+prJB4HibSy+rU52zBMPHo8UcgVZIykbEdqi/TkdAqzWNIJXqNz2Nzi9gy4zbFmK7Q5wVbAlzDFaZhvSEnxFjhXFemGZrteHyIoymKVCz/hqTk3FmWgMVIRDaystehW9hxmdAK0GWkzkMYBTdqx1wYovsxIHpGkSE5AjlRmWxAqmlJ8T4w8bBjHDZISS23UspCmET3ZQlba4Qp7cGC+uOSwL9T5iiISjzTcYOgGZWoHXIc4RnYgkZBhQvIQJvW4IZ+cMp5u0SGxXC0sV1eUMlNLpVbBvJGIl0bGMQxMM9waZpVWeh+1OuKOSqKZ4VoprhTnuiNW6F20OpBwhs3EcOuM0w+9xvblF6iHAzy9wOYlUOC1kvMQwwIp9zS80faV/S6KYJsH0pkkUHf41SPm+QKqkJZGEeHQKqVVPGWsGeaVpjGYoJLCrOx906IJFycNG+7evMFLt2/TJHHv/kMezQul1uh41ojIB+7bSWpkT5QCtVaSCYMEJjwMc8E8wMuUhtmOqimuI7UG0nkTvdjmoN4YUmZC0ZwwyyStuCo29ASwEEn4lGni0VN7DodhQFPCtWHDCGbokMnWaCpYWagW1xEfhzBfNdEkB40Z6dRxoy4z+6Vxz5SnUtC2Z0NmPL3BpcPh6jKOhxMo9+P5faQfmMSx9SOQXsO8FnDZXKeZ8ViDa7/upYSUOYzSWjCcWWE7bRh8ps2VJs74wm3u/jO/jZs//AMMh5mT+19nAyyusQ9rpMytgT14zJs//Wn0b36OWzbzzu6SB4dz6jGZbBanq2ikgTXRasVoQb+3uK65gywHUhrIaeqd10rZX1HTCc0ytD2eGjJt8DIHDh6ntYXDG18nm7LsFubDEgj4VatWrXqP6VPzQ4o74xoCXrVq1apvun7s+36Mf+tv/VvPehmrVq1atWrVe1Ivbl7k3/2t/y5/4Kf+wNoH/D7S/+LGd63451WrnpGeOwNYDKCbUu6knBEr4K33+fbfhvkxtevQwvg1ydHtCmCdVNz7QuNnvJuR16zn+L50U4Vediqtd7D220ok8ZAUCNbeHSwC4ob23tIjkjkpCLl33kYSOZDKqScQw8wRMmgkJjGHNPbvedQbUyJJiAdi1q23ER+XVgKLbXM3rY+mcyCStVW098V6jXQslEg21uhkdavRHWoGuu1p5MBau2r0xPZjF3hWh23q3GhoHqnnpAVrqRu/I/gVyKYbzktPGQcuWXTA64IdMlUWalOGIZGGnvoccwCAW4207TSG8TuOaM7xUtUwgEUETk/IObPdnjImoUriaiksu9o9+IwmQacz8q2bpJtnuEJ6coldXuLutMMBn2fK3GilUduCS4OTkzDu3MhEp7IVQUp0PXsTVDKaFUVIKTOOE3mYSNMASSnzzOHyKXWeadVotbefCoFgTo4X8CQIGS/WE9FxbMUFTUPsdh2oS2VpMzPEgAGQWmMySFojbSqJlEeoC/XxA/bne5aLK9wCVy7fcCzjVEq4O0NK5DxQzNkvM36In/HitPPCPBopj+g0xJ4/1MANe5+j8EgUH7uyqX1PMsTAwTiSX7rL+L2fYHz5NjLvmVjQp4/wvSH9vBZzvMX5KiZoH7xwwIfe+U0Yho5EvXcrgTpuDRNQ0UjETtvouE6JJMomZc5ubDm9fRdcuTx/jC0LXhfq4RD14GEp4nKcG5EwM+tCShOKxCBKUnRMZLb4fIjUscZ54Q5mQmkFkgbOO1nvMY7Xs8479g8X2rTh1gfucPulF0gtIV/+CrUcsFLxVuGIeXahA+avh1QisRz/iRmeon8cjwR92M3g7mjeQllAM+LgNGzc0oaB5fQWV1NCLh5THjxBCpSvfo3Dg3fYPbrk8cWe6dYNtqLY5Y4qADmw3cvC/Phtvvz0MSkpS91TJK5X3uy649wpnSTQAvU97wNV3xpYCdS5Efj0fIJao+52FIsksw4J8oBN8T2ZBqTN+NUlbZ6ZJbH3nojXTnBYtep9pkM7xPDRqvettv29fdWqVatWffP1k1//yWe9hFXfZL2SNvyuzSvPehmrVq1a9b6Xu3NZL/nJr/0kh7YS195POpFMWmuIVq16JnruDGDz0qHBguQUBir52ghz6YncNARaWFpHono3Y7lO6OIehuIxwatHvHFnMh9/f+pHAzi6YN9NCns3xpZ3U8E6doR0RSKbChiacjeiIhUMih5/Qeu1p36BPIVB62HyUsMcIkmsjzBlXRs0xb31FKBce9Z+RALK2E2gHG53FBXjNESEJBo9sR6mmQM+V6BEOjKlSA2L9+cmuBk6DN1p827iHSJ5TcNlBD+A70DvgBjKGNjllKL3NRm0HAlhUY4mvLfGiHHTjJeGxB0xzihMOjKOA0PK8ZpjWIPqIMnI4qSkqFV8rrjKu53Mg5JOThhu3WD7wdeYbt7CasPeeJP6pR2tjWECjxMiCb/aUy8u8NaY59aTzY1SoZWK5QxjQs/OArvcDFlm1IkuXDSM6bxBvKIZMoH4zePE9uZNzm7dIp+cUEplOT9nfnLOsrui23WQw6ClGkqLdK+Duvc970iT632vwxhBWjfcG02U4sIsCXHIrTK4MagyiJLzgA6RSl8ePaFpZt7taHVGxxHByMNATglDqXXBrTLcuMXJK3dJZ2ccLnfMX/4KfjDGPDFJjs5nBB/ANwlnCGwzYBg6jIxTJqMspYYh6jFIEBtX8VqoDx5y+MXPcf7FkbI4F08e0+aKDiMmFgal0PezozEPQpJMxXBzWovhEMfwWpAsaBWEuSOhDc0TOuZeFKzoMDCdnnL22qvc+cCL3PEGj3eMKLv5wLy/ZDDBWmVWwTzHc+sp4xh2UGRZQAMtLmZo8xjGyIrbgLeCV0VKoTngC4bS8oDUiiaP1/LK8f0OTk9It+9ST+/E8MHFBbU5ebvF6iW11/1aq5E4VkUkR6ewKO4lzuOIXfdu64wQCWbvx1Ek4/V47se5LiJQGq0szE/epC0VuzhAOcDcePJ4R9tu8I2ypIIeCrksbJJQfKS1GcwxzbBc0riipECTe4v1SYt9i/eBk0QfqJGgE7QWuH5RRBJ5s2EYhKSRkl6a05L2a8CCakbqjNNIw4ZxOmXyRmnO7GCi2HKB5NMjuGHVqveNLssl//7f+fd5a/fWs17Kqm+h/o2bn2C74p9XrVq16puu15+8zs/e/9lnvYxV32SdycDH8umzXsaqVatWve/1+pPX+YOf+oM8PDzEWIeSV61ateqboefOABYJ1HGzRkoZa2GQCUMYY2ki0m2BLhbt6TGPRK6mFKaGdtNRBMgds9ydlIi09qRrGGVhrirHBtZAN9d+m45YPRpZxxxu7ykOryr1tYfjoNofS3KgolW67yzx8M3e7S3tHcKROjbMDHfrva7dNBTtZm+6TuheG6vieJsDRd0akofuBTUmEs2c0ua4j3GEmnAq0MIMsjCDo9u4dmKzx2NpTyY3w9sFrjeABfxGJH/J0a/qFh550TC1RTtS+xDHwCoDzh1JvJyFlzeZO5sNNzcjUx4YN1PgkNOAtSXMoBSY6ySCdoSsmWHeENFIeJrAXKi7wnzvAfboMeWwsL/YYe5I7sjdWjB1bKl4aTiOiWBpQ/OZJhUbh0hndiRysoY0RTzRWoEmmNXAQasiMqI6IMlI48hwdsL0wm02d26h00R9+IRSFkqr2DDQvAZFmwGpCzokEkrOyjBuA0eM4CUMwjQlqAsy9J5jGbB5R2ut75ESCWlNjGlgFGfcbElZosJ5aczzOZITtSyAoEkYT7dsb9wkTxPLfuZwtcNFOHn5ZU5vnWHzgfOHj6iLMdx5iRsffpW7t2/jh4WLew+x3QVpMZpDPcxhsuURzk6Rkw3aGvL0KeYaSc/lAIP3+u2Byp6L8oCaMybCoVYokfaWY1LUO9K4FBgSiqI1kOOtNdqYIzQPqIGUiuYhjERrqEZC+zgcISq9OteQ/RXT5QmvvfoSNz72YepeuH/vEV++9wZezmkupKuZw35Hq44te7z2BHIVjnD4GDRJqMe1SpMEtvp4jZl3LK4YCZrh6ngeaWZgTqKGAT8Xlrffoc2V4e5dbm433LjzEo+GU54Mj9lfPOawu4r+cOv93tJi6KP3WOMVdAj0uXWz1xsiFmlqd1x7UFh6IveY1rdKvTqw2ydSC2TyMG1o28ScjNJ2pDmR2oKbk5OjKTGmkdoytRjm0FqY5OopUsnNYpjFohPaRYNI4IAVpBkMA6ox7CDSUKmoB4q+LjuqDNgwIMMY+5oENuOeIv174yZyssGHgXR1gT56SlsqSMY6Vn7VqveT/tgv/DF+5u2fedbLWPUtVj4OO65atWrVqm+aXn/8Oj/+qR/nyfLkWS9l1apVq1ates/p+D56/3D/WS9l1TdZH0on/E9OPvisl7Fq1XOr588AdsFTQpthTbqRswC9v7aVnviNbsgwPqL39bpv1ummbkbc4s9Jw0M9JoPj0YAW6dueePUjZxbr5vGx87cbrhYG9XUvsHiYp3DdyyneAv+sgfATLMytY0dwqwjRAXtEUlstcX/jGKhlJ8xAm2MdZtePJzqARRI2ej713aRzTy6Lv3soAh28odY5nqMS5pEBuXcAy4A0h47vFWr08JaKiKAIhdxxrQ55QrXiOGKX4Nuezo0kotg+7qsvIOXEWXVe4opXb7zCi0PiZDphGhI5dait5sD4tt5pKxpZau+J355wxBLmLYxgr8hVxQ4z5dFTRBT3SvOGSdjtgkfXcp2Pf8OEQFxbpdSCE0lbkRSDBGZ9RmCJJLcZnhWpoBjiYUCrRI9vygM5ZbwZy+UeHl9weHpOPSy4GNUrpVZqNaCRVFGBYXuD6WTDeLLtXac76jJ3JPeIDCCiYTyXQqsNt4oulYFCSidsVJmkMY5jhMk1xSiDC7UZvhTMIgU6bLds7tzh9OZNxmFgznvcoWmk6efLCw5PrzhcXEJShhfvcOvVu9y+fQdKQ063HM7Pafsd7ek5fjDMC2bQrq5ouwO1Ga0dO5oNMY3zTlPguIeBzemWF09vYinjF0+4KIfocvbovRbvOOgkiCqOknwklQXx3pHtBTUlZUFx1Gofgih4HgKjboZagVnx2rDF8dMz0sdewX70f8DwyocYNfHhdx6iP/O32b31Fvevzjm/+DqtlcB1S5j0JxJJ/bkUmkf/dMoZSZmcR4RKFiFtRuTygFl0B1cPCLMkQXLGljmw1rTe+W34APbqbcpv+h7ufPd3cVuE8vSSd77wFd744q/w9a98mf3lLsxbFbwukcxHYtCi1Z64H+M/HDkOtkhGKLGf8VhXn/fwtgBER7AFpCBttujZKYcpjnO6fIIcFC17kgppPCFNA1mFbBNFK14KptpNYKPOM6qKeYn7bjNuiqeES8JqIKtVEqoal/PxBo7SyoFydY6RsQSivd/ZQIYhZng8oSb44UCjYfMV9WqmmWBlvh6o8fzcvYW+p+Xu/71Nr2/GffxGlLvzuSef4zMPPvOsl7LqW6wPpRM+Mdx81stYtWrVe0zre+g/Xq8/eZ0/+Ok/yDuHd571UlatWrVq1W9Are+j/3it76Pvb20k8bKu/b+rVj0rPX+/vRYN82sYukEwIGkL1iKZaSUwpyKR/vUcBnCTSMhJT/3ZMY3buzqp0LSbxD2lmkbgiEsNszc6I/1dZHTEFkGk/08jxdsO8bhHRJ96rFEUyakbsCmSdnkIA1cEciRrPTleLExPq7hHF6+VFqlJC/PMdegGrXX8Ne8mmB0ghekpGawiaeh46xq9p25YGpA69/UChKHqtR8rtUhIA+QUx8ynOHRJqIcDLkZKZ1R79/gmiOfmwLSBsuC2j+MrI9iMpAExZ2qVuyc3eDGf8MI0cTomRpRkgnrDa8HTFM9LBR0iiYs5UiLFKCnQ4FUJ/HBsGHCPztq6wxCsLqgrmgeSNKBFgtwX3HOY1Jrw1qi1YvOCbKZYKxIp1LYDCWNNdcCHIQy3PKClomKBbTYlb0bGzZY8TbRd4eLRBVZmovp0prbKslTmWnCHjJE0M2xGTu/cYnPnNjoIh8sd9emC1YY3gXIF44S3inuKrt1qKDDkRCIxJWFSYcojedhcDzZYbSAHrLXokC2GTiNsRtLZKfn2TYZxgM1EScLu4WOWe4/YzTOH3Y7iFdtuaUApUEwRdZxCPX9MvbiiXlxFQlYds0JZGnMjkpdD7udojirojmnXnJju3uGFT343H/2O19jOlS+8/lW+/CuNK6thMpvhJLxVUh6AeM4pCTlN4A1JA2qOYKj0QRErYYSmHCgaT2iNcynqajNNE/vLhcuvPmL87j36igJKeuEuH/ynfxgpn+DkFz/H/Z96k3muuBn55JSbt29z684JpcGje/epl1fgRhoUzYmUMqJKcqA1bEzUQfEW50cdE1UCI66tkWuJc3V0hJE2F+YHj9Df3ODuKdy9yciW1z7yKvUUnlydU2qlLTNewTVDDmxyPOcE3k12FG8zpBHxGh3gFhxtqwverwNuf0+jOF6X8E2bUS93LHuhSZwPao5UZxgGplYZlo7Mr42cN8iYInXrjjWnJokebx+CZtBGmhtuC66GyRDXuTyg7JDNCbI5pcxXVNtSLYNqJJi9D12Q8eUKxlPEA/3drgz2A2JgnmLQQXJcb60FEnzVe0bfjA/L79cP3J97+jn+7U/92zw4PHjWS1n1LdaH8moAr1q16teu9T30H61qlb/85l/mnf36S+tVq1atWvUP1/o++o/W+j66atWqVd9aPX8GsI6BJ+6IUm9LGIF4T/9Gik28ISZ4sp4YFdS/oQs3ae8IjgyuIGF0eu//zcfJlhEgOiqlI2hTht7j2yN61/jm6+JgHRA1UkqItY54DuMhOor73dfjfShuBbEJkYpVxwwicyeBNsbAJXoyjwnjo9qMpE1HPluk+nrXJwSC+Tr2i/c+Te2dDJHgvX4ugEvthvEmjCOnm8AS+GpyHOc0Br53LoFmpoEsoCdxXKWFUVqvejL6DKz09PSEN2dUOEuZO+rcyZkTr0yeyFrj+HjD3LBRw7QaRtJmCHO5NYQD6nFMUxp7MlRjAEASVgvFBZ9hOexYDoGgHpqRrUQFbDJoCzBfm9bujpsgeYqUcZkRSaiHOacqmOl1lNrbgLiR8oRaJbmjSUnbLdONDeN2QylOuarYUjCcZo2lwFydVhtZhDyOTCennN29zY1XX0anieXyknJ5Rd3twhhz+qDCEltOMhhoUjLC5LHnpzEz5sQ4bWJewR2aYbWnoGuDFgaqiGBXVyz3H7JbKu1ki7lTDwfaslAudiyHA3U5UK1hJfbj1bLA197AikWquUai2Fyw1LAW+6yZR08uDWngeXi3frvjysUdGRTRkblAI2PTELhvkUiLHs9dDxS6WEE1hQk8DFgNjHpDyEKgho9IdWKP44q74bXGnMQwQK04M23eM59sWF6+cY3RJiXSK6/B7oLd5d/h6mpPqZCGkXzrNvqBD1AHONx/wHxYaMtM1oTWGjhwSiSdm6EGWYXp9BRXwZtjtVHLPkzKntBvXlHPWK3U3czFvce88ZnXuXv2Aqff/RqDwfLOOfPTC3LObLZbZo89dT0YAtfXODxd27k6TJ1qENxnY8C9RQLegXpAdBOmsBVUiGGUehH9vTVHuj/F/IxYRW2glRrHdDpDrOJpJKuSB0U7otraQhtPsTrTZMDMabXS3HGZ8LrHZKK1BbanNCaMRJsPlP2BlkYYcz8XW1y9yx4ZbwSKO5W4drlAXXAteJ5wBGm7uHZJ7Ke1kmbV+0HNG3/y839yNX+fAwnwL5986FkvY9WqVaveV3q6POXPfvHPPutlrFq1atWqVe85NW/82S/9Wf70r/7pZ72UVd9C/SvrZ9BVq56pnj8DmMAnm0Eehuj8bTV6Ncfo0iQ5LIYPjrDBqYg6RsURkoNICuwsgojhrfXk8NGZ7Qk2ejFmEF0jGUoGLx3zTHTYepiqxx8Vd5Io0moYdUuYjpJz3F91kNY7f1PEKHPCy4HWHDsmfjvq1lzD/PUShhWZcKZaIGL1NMxXuMZgRxC49q8fO4kHRCJZLBKesOE0EZIKVhYs5UhOHxPTOkSy2paOw7aOb+5p6O5mi8z4bobtreubhd/cU9Uej+0CXh8j6SaKclr33B3ucDdnTlWYxpExJ5IkUMNaxjzS1+4Fr07bN/AdQkJxBoUk+frvWEXyEH5/GrDqUDNNoEmktqUsYEZOAm2Ovt5xg1jB+oCBuiEdOw2CTBMyTiSV6HTWIZKLi+Edo5tSRr2h4mgWdJyQ7dTTuntMnSZOa05tUJc9VCfh5GFiPNmyffkFTl5+keHsjHK55/DoKW23Q9MQ+yURpmaZcWoghKcBbZlUCxOGDJlxM3X0swYW3AVbKrUutBqd0uJEel4dP8wsjy+xfeEwJLxWlqVQl5m6HMKI13fTlEOp6NXCPBu7eaYcDgiOphgisOaYCU2dpq2nSweaWU/yCqqCu6Hi0GC595gHF5/Fpw3eKg8PVxz2u56EJzqnrUYC3T1Q2DjJG740nITVhSqRQk+SY99pYMSBeO2bBuc4ZdwSroqZ0a6uePLzv8Qv3K9830uv8OJ3f4T8Q98FN844vPMmb335ixx2l3Hu5QFb9uzf+ApLFa52l5RSUVHSuCEPCdUYsaAaYpWkGRlaGKoYNi9UGqUJhnVs9IR5o1kDU3yZMYRHX/wqv/B0z8Wtm5xl50LgksZ04xQj8N8t3GNqM6r1ZH9HUovVgBJIpw8Q6HhFIr2fh56MHa6JCoGUb0EsSCe4GS5xLdWOdI7XPPD8nN2MXurL2infjrqjQ8ZJSFN0OcCQI318HGqxGEJpaYhzZNjS6oEmA63OmFV8PAGrvRd4QRlhWYABqEjuQzA69uuT4Msl2ioxSNPi2p1HIPVu91Wr3tv69P1P8zfu/Y1nvYxV3wYJ8CPjC896GatWrVr1vtGDwwP+yGf+CIstz3opq1atWrVq1XtOn77/af6TX/pPMF+n69/P+tHphfdtgn3VqveCnjsDuFlB3UkayVyxBup400jn+gJsIt2lCREnpQHBAvtMN316hyuqHevc/VQa7t49zW6wdky0HPNz3nt1341+9u7gEl25HJHQ3pO3KR43aTdCBaTiJhjeU2qpp5CH6Phtcd/uHfksQzzukexs8zV6mlbDp27Wk80Nd+1mdsFdr9NumiTMKARaQTShHh2azeLOkySsFHzIkRDEu3e8DRC2KZnoGqVUsAZU3KCMMGjDaiVpQtIQh0sMksGhhZGeTvF2yTjc4UwztyflNCWGUdGUwyxPimqkptUyZg2rhpUFlwXDsZRJCo4ypm52O5G0riDtAhu31Fqjr7XGMZayi5S4DD0NbUgWJDW8WYQjbUYIUzlS0xK3tYrmDToGErrudpgZrgkdhtiTniLZ2Zy6FJYn51TZU5YDdSnUVmnNaWXBWyETmOBhVPI4Iklpc2G3e8hysWPZzeSkDNsTzIyCU3YHWiuBf04SyUuJHmRJA3mcupkouCjuiVbD/LUWZnrWRMo5OorHkWGzQVKitsZyOGCt0JxIDGuO3ltRWhLkxhnbj3yQGx94CWrj8le/xHz1BFpDk0AaMK/U5iytUdwooh2lHR20kgRkE721rqTWsDJzeUjMdYn91iqII2TUKkbre05j/sItzGES1IJqi55ZEZRMMiMlxVwQM5JEz3Okj3tfcK2YOCIbyrLQHj7kzfKrcHvLx1+7xSvJ8c9+ls/++b/MF3/p85T5QJpGaJXD+SWz5OukuqqQhg0yZDxpYLbrHJhyjWS66khWY7CRWitTG7Ak8cunJLScSL0P3Ogdt+VAuxSelMrrjx8zbibynYl8umF0oe73iAjT7ZvouGV//ph63vBa+7CK4fTrj1vvSI/hjRgsiRQw3pA8olKv+9TteG31ODexgmjgsS31znG3uEYIWHPIG6QjwD1vmJeC2yGSuaYYhdqgtEZtYK5Y3WMp06rjCVotsceJVL86cU6aozoiy4LXhqcMy4LkAZdtdEX7gjVDdYvZHNfhNIIV2rKDfBrXr1Wr3sNq1vjTv/qnqb7u5VWrVq1aterXInfnMw8+w2cefOZZL2XVqlWrVq16z2n9LPr86MLq+7rDetWq3+h67gxgEWiWGJKiaJi6OgRquAmkTZiWk5J70laykghEsNM7atMAtSfgOoKZY4K3o5zdDfGOCqUjZHtvLtfp4RwJYxKBFgWV433ScdHgFsaPe4ne2pzwRnedJdK9tXQDuH+JFF+7Nm/ig2qwchOBa3a8Y6slDz3SO/SHb6BjN6zD1BEqqqcoFRkmHKgtDDK/vl/vh8LC+FGBsWO2zaLzVgVtPTlqCW/7SMzqKbQdzhiJ5raE8V0PYGegPXGoIyllTt25Pd3kxjCxkUQuKdKGk8CQo7s1O1TrncQJqQvIEJ2pDnNzmoQBO+CILfG6lSUw18sSidV6AFMSlTxsGbySVCKprWGc0o6+ekJ0iw5jN9QbkgfykBi2W4bNCeRErQ1ZCrIssccERKJfGBWQkbZbOOz3gaMGmi00FF8WRBM5ZTRPgRPeJPI4YPvC/uoBXgrmkM9Omc7uoCdbllJoT57Cbh/7R2If2TjCckBSYKm1Y8mtNcBpy0wrCzZXsITqREqFvNmQNwPDyQl52gQye7+nWcXcaR54akuZ5kq1Rk1CPjvBx8x8+YRydeDq4oLDMuMuJOvo7lZoDWYzZow2TCR3cgLNkTJ1HBeHlOKcE+FkUF4YRyyd8KgcAild5tibyxxGd+9BdsnxnN1R8RimkGiFrRaneZaZNJyApJ5WHTsmeEZk2ynJ3URX8LpHdMfV/A73/n8/ye5x462v3+fL97/K4XCFqIbJaQ1PAnIIfLVIT6cq2vdGu7rErZGmLWkzRgW2ZPLx3CehXKIupNnxBjom0nZCVWCpcc0gBjigspRLFg7kdxIbVYo4nJxy8sFXGRTmi0tsaZG8Tb3TmjgfrRYYpxhUcXBaN6cFNYUhk3IiTyOSBtr+Ci9x7qM1kO54N1kbguIp8NZtLjDPVDfIIzIkjIm6zHiJhDIpo6LUeaGaUcqMyQB5xHXALQx6r0sMvojFQEgrcbTEUdFe155gEFyG3mVsiF8ireF5EyZ1AmwA87geoTEs4IL5kfiwatV7Uz/38Of4pce/9KyXserbJAf+xnyf35c/8qyXsmrVqlXveVWv/Me/9B8/62Ws+jboiS38YnnK94+3n/VSVq1atep9o/Wz6POjP/j45/i/v/Cj6/voqlXPSM+dAYwpSQW3hkZxaaTRJAXud1CoFTzjApr12jxNAi4Jt94fjKEeRurR/HVfQBJu/WtKpA0lOkOl1TA0jynapIgp4hJGSseOXk/FOECgVb15mCVWsNIiTedcr8VFEHPMHffoJjUqImMk86rFk2jgWhHVoJwO8fg90gzSUdAiYVZ7N0oi4oymhvaeUfIAzLgNeDvgEmlKcg6DJSneGiIZTY4tFdExzBTtKWIUyWfY5Tvo2QleDdEKPvRj10AnoKA6YLYgDptBOUO5MY1sRcjJSAny5pQ0DOQ8IOKox3FI7ggVz91odcHNqaUwC3hLbDQziqAiQfvNQ/TjlgMUJVEQqQzDSJaRlKw3IOfrLl8VxauF+ZvDGHdTNI/odkO6fYvNdgNm7HYLDCOS9qSO9FURRO36+bsZlQQWicSKYaqoaJiFOpCGjI4ZGQLdXfYHxCwM52liPJnI0xRdvI/OWR4+os01hgiGAfOGAVUS1gxJipVCbQ1SgjZHqrzE3hAqKpmURnLKZJ1gqdT5grostFYjd7rMmEPTnthUpwBLq5QnF+hcWWjMB+Pq/CmtGJKhNUNKxeYDRYziE0XBvGJDwhWSZlLqidTU87zjhtNXX+MTP/C9vPYd30US48s//0t84XOfo5YHYewPI2LEc96cAKknUsPUUyJ9b6XQzKiaaJ4wa9HbjKONXj6ccDFM+gCIBhrZpdAePebBL1T2aYuVhav5grkcAt0s3rtzBfE4lmAkVbI4Y8qMKniJ+3YFU3raPCNZEYw8hHmtNsfPJqFZQ3IieSCxK2C24HkE6USAbrAXzbgqeTsxTGOkaS+umJ9cMuK88vIrbE5OmZcDF7sDu/2eUpa4btYDbt6x2BkxidSvO5pHJGe8LjQzrF9LpMUxw4i9nsZIOGv0onuZe0dz4PgF8HagtJ7SJVLYXq5iv5aCMeLtAFZjrybt15VOK9CEeKSYVSLZjwRmW/IUQzUeqfCgIxiehzC+3XEHL3PvyibOZx2Bhsvz9xa66v2juc38ua/+OXZ196yXsurbJAf+7NVX+eR4h+/MZ+g6gb1q1apVv279mS/+GR7Pj5/1MlZ9G/TAZv63jz7N/+Puj/J96y+vV61ateq/t7588WX+8M/+4fWz6HOie3bgv929wSeGmwzX4blVq1Z9u/Tc/fa6OgwqHZccCFOhBfo3TZHq6z2jIgVvJXp3NYUxQPRUinukBu3Ym1u7iZF7Cra8m+I9Gs3QH9Pi/kh4x9Q6PU2sYxBWvYVRGgWriEivzK2BYaUeC3I5JmuRRPPAnUbf7Qwo3iLBS0qREKZgnklHRyNc6m5iOy6KmHUT2hCJ9K6SUCzwqUgYx+KQJ1pZOiY2+lgRwWWB2tA04vUAZESneG7dtIMMamEont2k2QIIWTZxbDWDL0hK3X82NMHglTMGbo0Dp5qYVMnjlpSNYZoYspBSBqtYbag3THq/rxxDrxUvhVoWCommAyVH9++QHPEUhrq0ntaEjKMi5FZJm7NIa6eEDBlxR83YbDYM4wbPiWIWSOBS8DzAkCErerZlyBsaTznsrpAcuONgcQN0ZHeXu9GsUd1oKFJLJGDdIrntQClYM4w9riPD6Qn5zk22J6foMGK1Ml/t2T19Spl3uCSYJsCwmqhlporgSUgiVJfAYJcZUUGSI02iN9X35GFkvLFhc3qKijAfZso+0rbugrUZa07DMHGqJ4odWJoEynl/xdV84NBa4LnLDCSkdbSzK0wTqRwwaYxWabsDvlmwtsEQzIWWRpIlkjrD7TOm7/kw+kMfZXzhBvPTPe1Dd0lvn2FPHmD9jEHBNOHaO7QriB3PpYbogGmitQP4iDKirQANSVOA3VUD642BG9Jx5+ZxrnorlKtLdn4Re9cKriAaoxnXMyIygsCYJrZ5IJ9t0SnD5QXt8kBrLfasVdziWAVOPBD1mRFOTkmlkacSyGsVSBOOkRantgRtQYmu4tZmxMLErNqHWuZCc8dw0q3bvPo938GHf9PHORlPefLVL3HvC19A7z3hsFRKnWOrVqN5o1oY0schEi8Vm3c0Upw7or0HXcI4TyO4hhGugdA3dySlGJYB3CrSwCxS/9EpHgavdyJ/kyGuawzxHHwmWUY6qhkc2iGus9NZDK0MA1J7z7kVrEaq2DXF4wzbOOcIbLhLw4dNv0Qqthzw5YC1Awxnv853olWrnq3cnT/++h/nL77xF5/1UlZ9m/XZ8oR/9Z2/yr92+jH+tdOP8pF8uhrBq1atWvVr1L3dPX7qjZ+ieXvWS1n1bdK9duDffPSz/N/u/gi/ebjVf8Wzvn+uWrVq1a9V7s5PfPUneLI8edZLWfVt1J+6+hILxu89+fD6Prpq1bdZz50BnHI3CrBuMDoyBAtY3MPEkIRmDYPGB6SVMB40Iz0p2czQ3HtytRuVtQSy2Qz8G7py0c4FTpE066kxV0ea9uRoiy5TiZ9xljAwNHcMdawtAsIJcoIW7cPeDMMijUfGKdFL2k3huP8wnPEaGOaeVhXizdcxhAlsQTRHx2pPtQoWmOyIx6GawAuiA9KRxeJh8Kgb3g0taxLJ3ebIuA3atjiqLTCwbmGu71tHaxdwo+0acloZkgIZdcE9hU/tkBqcTQO3pXJTZrY6kZOQFFQHdFDGkxPGlHATlt2Othxibd3EibyoMChUau+BXSBtaVk6WtiQppRyCCStG6KK1oIMY0d50zt/DRGYXrjLzQ+9yu0X7qKSuTw/58m9RxwevEM7zLS909KOZRpog9G6yesmePU4Bt2Il56KNJ8xb4FSbobTUEmIDKjGa+witNqAJfbelBGFPGQsKW2ZmZ+cc/XoCcvhgBMoY3CsLTRPFISSFNIQifdiiFeSgKRE6slSbQ0ZtwxnJ+TbN0knm3iNq2EcsOtfhDieBLNEdaOWSIN6qajVSIlqnBOCd8O+RG9zT9QmVTydkq1RfUO1QmsNsxmj0fJAk0LNI5oUf3rJk7/7Re698YRDGtgfdty/uODR5TlzrXjrvRNJIylvHnhxHRFKx8IXzMLEbZJptcTQhcaaBAtDvKOYMQIbbQa14KpYS0BFau1GraBZemI+xilcwkBWnJOsfOfLH+ADr36Qq7rw1S9+ifl8T3OFzTYGLVLC3LA641Y7epyOjVZIgVfOmpA2B0IboaZEOj2LdD5KWyrm3WDt1wTDWFrwy+X0hOmVV5g+8h0MJxn2l5AGbt16kZvb2zzZH3h4/22WveJa8MXjubWGeZi9Xg40JHD0bYnrSRLEar8OWVzzWgS4MUNIca2pBU8ZN+u9y30QpbagEDTDZMA1zgPXThwwBQYCXa+IZxg2uOzQYRtDNDajBM3ALdK9MiSoM+QJ6v46aezLjJmjU0aG00gYWyCgSRtMjnj8Vavee3p7/zZ/+c2//KyXseoZqeD8v66+xH+7f4N/fvMK//z2Vb4jn/Fq3j7rpa1atWrVb3i5O3/kM3+Ezz/9/LNeyqpvs95se/6XD/4WW03862ffxXfmGAZd30NXrVq16r+71s+iz6cc+P9cfYU/v3vzH3gfPeoTw01eSNOzWeCqVe9jPX8GMES6l4q4hBnoHmnDjmRWUSDMBkHBMubW8cxhneEtTE89Jmi7ueOB7/Xj1/o0i5sj3nr0NIPXI1G5366nby2SnbQc5vQxYexxnzJomFatEVXCrX/bAkdqBRPphnLgqd38XYxz5CZ7AjkeW60GjhnvoeWKpDGsazWgJ56paArTlzREAtKjR5imQMJ8CZNMnJQmxBueM9BoBp5yJBhVenjZkXGDupHc8ea07SbMKxNSCgR0pE4bQmIzDNygcXuz5eaQ2Q4jw5DQNCIZNCXydmIzbQIDK1CXBWt7KEsY+amh1fAKgxM422ETxh5Cs0IrDWShLoXaQ9dqFdEpOqAhjCNJtKUiOWPdKLRxwqcBrwUZIG1yGEn7Sn26Y3+xJw2J2hq2VKws1NLQnNAk3WQMTK9oxluhlYp7QVVJmsiDhAEmYGXGShj5SAQs27wwP71Cn15Rd3sOl1fMh30MBuR0nRBtDao7zcNE1mq4FdwzIgaSA6HbFjRnUlY0J7BKu7iiXF5gnijzjLUlulQBH7aYlb5fDfXKIEIaBswSklK34XM3sQHGSJDjkaqXhAKqmeQwiNKAhmAGdS5YW0Bn0ESbCxeXM1955zEpJYo4tR1YlkIrSx900OjqxRCPX+KIN0DR5rgNNDtQBBZ6yr3OqChJY68qEghh4ud6djh6Yavh9QrJI5oCfS55wrQifiQFaBicIuSbN3nhkz/AR/+pH+bVV1+jvfkmy3zgXhoobUetLYYMzPFaESww6HlAsTBdW792SMJdOkK5oCmGOZI41TVeo9owHKyScrrGM9MimaxLobz5NXY4b9+6iaTE7ZMtH/+h30y68wKPv/J1fvavPqacPyXVnt6VhHusKY5nv74s+46LdyRNHSPvR9c3OnppYa6KQNlDmhCdsHYVvb7m+Nw7mFPCtQ/CtAriCAm3mWt0fjV0SJD69TNvYs+bxblrtZv2EmSBcIERVdpwGtdWKugA7tg80yvNsdYCp82mD9+UX8/b0KpVz1Tnyzk//qkf5+392896KauesR7bwn+x+yr/xe6rfN9wi4/lM/6npx/he4abAIyijLJ2na9atWrVUebGX33rr/Kr57/6rJey6hnp3AvnrfB/efqL1187voce9a+ffScfyid/z88JwkkfDF61atWq51Xmxl/42l9YP4s+x/qHvY8e9SPjXV5J//CBqv/52cf4rvwPp/BtJZH+PrS0ubProY1REuOKnl71HOu5M4CxnpAcpkD34oEj1h4v7QYKrXaTz7EhhSkoRhKluaOargnPWI/SSkdCq8RfpScMu78rnsKw7Uaw24LoAD11jBmuCXcjYNOJQM5G4jZ+xmOtDVwcM8MZogPYPLp+6YZ2f0reGp4SSY1Ir0XfKM3eRZ+KIL5E0hfv65TAGqcUSbkoRY515y20Bdcx+lJt7innY6o4+n+F+LBjDSTl+J6MyNGKawecEUkZbQfEJVKfXqhEx637FMfAhOSVG0PmribujgM3tltGzaRhAiqeJiyPyDgy3LhBzgOMA4sZzQW3hGSJBCALUgspKa5bnNRTwkJrRnVo9UDLI14aqRvykk96v6iFidritTRTDg+e8nQ/s7zxDtTK4VBYrMCgpJTIg5Jlw1Ib5TBjrVLqPrpXq5FJuA6R1CyB+TacpvF667FzNw/R+Yt2c7DhpLDwrWHzHM95v2DmtKVSl30kPyV347VhVKpJ4MtKYeymfhJFBRRFbEGaI2kgpUyaht5FbZT9gWLRP22tdAhwDFWYVLwEtjjhkVK1EthlOaLQ+4ZJCpQIhvZN5J7opau4hWGrRKoZF0zCvLZhEwMS1pCyQxyWGthqd2itIK2hpfTKbIn78+PIg+LqICPmSxiz5rg4qTRA0DgYVG+UaQOSyJLjHE8KLc676IcNA1zNYwZDPHqniT5ozKMnu9QwFtNL5O0pqics7zzmcP+cj330NT740Q/y1QcPufflL1J3C0qDLFgJA9SMjj2OgQ7XTXSQ92w37lB2YTj3dK87mCqmgrRGW/ZBMfDoeRZp+GLs58pbFzOPt1te+KFP8tHf9S8w3L0Jy1PKl77McnXFyXZiGLYcDgdst8OYIu3bSmC1rQROexiiH7gn8t1b4Kxz7oawhGkt3XTVjLc5Blau8fW5Y+QTnqbeDR64fNGOl5YUP5e3kfRNuaeMrRvFhjACCYYtooZYjbXgYCk6inPgqVuzTqxOeJkRSTHA4hlfrnAGzNZ/QK567+lT9z/Frzz9lWe9jFW/wfSL5Sm/WJ7yl/Zvkfovp//ZzQf4HdNL/8Btb+nI79y8vP4Se9WqVc+dHh4e8u995t9jtvlZL2XVbyAd30OP+qnDW1GZ9Q26IQM/dvMT/Fo+PXzncMYPjHe+SatctWrVqmevh4eH/Klf+VPPehmrfoPqZ5dH/8jvfePn1L9ff+DsO/lI+nsHr3Ze+Y/OP0fD+ec2H+BfPvkw/9TmH/xsu2rV86DnzgB2SYg3xC1QtsMUKcusiBdwRSUFfVnC0MGPPZVC60myQJQGQvrYaynW020SqVOOiNUjAprWezC9Pw5xe3eg9l7iuAn0zk+XuN804M0QdGvAAAEAAElEQVRBKrQweMxaD9EuvffXQBW3Rtw0DEsXDzMMAqeqHWecFJEwwaQbYYJ1Q6gnj0XwVlDJHfcsMAyoGKIpDOcSdrVII0uYXDWeKF4rSMZx1CXSd0pgts1x076eGglEccQzrT1FdcRbmIVeZzQLJ6LcSsLdzSm3tlu2mw3aDXea0axQZ6jLFMjpMTNwxng6Uy8vYZPRYBoTDaFKqjOCRMJaUpiDkqBUWnUKDVVnQNG8RXMjyRDpT9U4dp6w0ij1nIvdU3ZPT6AUWnVkGsmDICdb8s1T0jiGOfywYBczdZ5pVfGcIHfTXAPhbUQq10pB80QeEik5KUX6M17AGRmmqEJNYe26VVpr1KVg5YBIJiWQYUurBcQCoWwNa4aKM6bo0T2mvMUC66sIMo6kPJA3gVo2N2wptH0kfrEWKPE8BJK3nxOaMyq9c7VB6x3WJh6mrwiYYK3GQEbybg57kIJbi1OkHgcpoo9apKOGB+mGquDDGAMeZQfeUcuq3XQs0a1t4O2AkTEEn/cwTLEPZY5zygqqiexxPkqraBXyNCFO9BqnSGdHujWBenTJSnQKy3GghI47txLnsHukRq0iSw3CwNMLDp//Eo9rIX3kg2w+8jGm3/xd+P034W+do9PEmDI+F2y/Q9oOq4btZ0gancIqGCX6dn2JdYjGaWwN1PAGqop6xTxhKYfB2nvIJQDYeNvjFQ57qNvE6bay+/Lf4eSzlatf+Rpf+OLnsVsvMNy5ydRm2tvvkGvBq15fY71jpjVPMWSjkRh/lzJgBJ3A0DRhqkidA4NfC2BIjrSutSXSw7VBK1Cj/9hbDZO3HdH7x2NPGMnH9G/ZBdXBK9SKqKDp2JsuPenbwqin4tXDSJeJ5iUuVz6AJKwcwBPuDinO+1Wr3kv61P1P8R9+9j+Mf9usWvUP0Yxx3B5/fv8mf37/5j9wmxNJfP94+x97P9833OJ/dvoxAF5Mm3XietWqVe957eue/+zz/9lq/q76J+rg9g98beeNP/Tk539N9/MB3fDR4ZTfvXmV3719dcVirlq16j2v//wL//n6Prrq16Vv/Jz69+v/efGPH3D/c/s3+ZnDff5Pt7+ff3H7GroOMq96zvTcGcCtOimNYcxqCbMgwNARss1CU4cBrJYwJ6whpSE5Iw5CxUjhq6gjrh0jSk8SE4hTiS7JuELJdUI2/t/NhyMmumNzsdrxqIrRAoFaG5AwAXcFq2Fm9b5Na0sYIN5xrEiYcj2RqDpEetdqdAojEbxTB5ujy7f34oIjDjpsIr137L6VguQNIvYuHpqKi9KOCcTaEFHw0htmM40WT88T3nZIGpC86QfbIQvpmHYlkYcR21+Bn/QU7CU6nGHNmZg4mzK3UubWduDs9IRpHAL9Wxu1LlAriwuHfWN/tQMadanUwwGRRD7NDJttmFSXiXa5w6yQ0wYdRzyPgS0uhdo7g82McE8DMSuewmj1ATen4TRzmhtmhiVByxIGFB5/ZsLrhrTfxwDBoeLFIho+bPFyiWgKQ1A1sM450WajmMYMQkrX28mlRejUGq5jVFRvBdGMmdD2FV8OuGfSdEK6eYaPE15nuDjQ9hfxnNqCDglVifPCSuC5e/JSRNBxQocc+38YMGtYLbQqlFI7dlrwlBBpCEKSoScwvSdC4yxIOuIdI+2aAl2+LPGciK+J9L3aaqTrESRHCh/R6JJ1R6Ypzp9lF6a3S5wfKLiG8ZgnvO5xD2OclLDW+39xfHMS56wq3hqmRHreILpkBzQN0JZI9msGN5oItBmGE/I4xm2PxqCVMKAFhCF6rjXOe28Vb440QceBfHJCunuTevOMdzY32F9dsv25v05+8JRHbzzhrSeP0TpzMmYU4Wo26q4E0hgHmQJnbFe4JxoShqhKpLE1uqClVUQ0sPEuSCk0cZoMOE5yi+S9ZPCCL3tknmnzjnd++heQn/kVTmvjcb3gfHQGGu3BJXsGGCZu34TD4lxdXVIPO0wF9BTqHsHwpHHNsfYudYCGTqeBqlbFNePtEIMYSWO94wnUFAnmcRsmrSjiLS61bY7EbotrWwTLDWyHqUJLSJrwtqAMoB7DO22Oa/a4wUsYyG6GDye4FcwiiYw0rHr4+OUqaAdJ8OEsXtNhNYBXvXdUrPAXvvYXuCgXz3opq97j2nnjb88P/7G3+dvzQ/7E5RcB+FdPP8q/tP0gPzjeWVPDq1atek/K3flrb/01/puv/DfPeimrniPdswP35gOfmh/yJy+/yB994Uf57nxjfS9dtWrVe07uzpcuvsTffOdvPuulrHpOde6FH3/882QRfvfm1fW9dNVzpefOANbUwlxIGgaFpghRliWMIOlYUgPPA+YNFcEkRSctguQN6o6l1HshF0iJLEcUqUWiMA3RoXpkQHtgoSM1dpwKdcJtPgQGWgY66xlkhNaNYI//3Fvv+/2G5K6m68dFh0CeenTBSsphWh6dw9557NIR1hJGizjR8ypj3N5qJGUb3fg99tJmxBVV7+TqhGeH0jHW2HX6UmzuSc1+DBCaKdiC1oLmEQBD8XGL1j3mjTScYMxh0qVTmjlD3jCkhbMBbkynnIwDm2lgs9niOnDYXVFlou6ekIbKQYTUGuXpgC2V5bCLvs/NiGxHrHo3yUG3N9HTDfn0Bl4X6u4KM0MlepcbBBZavad0BRcP49mctuzD8MqZ1I3KZgrlKpDHHdutNNwzrRTqsuBEr/KQR+TsLGqoXXA5djQLpha4YFHECl4TNg54JRK2vkRyc8wkzWGCliXmC/LAeHqDfLpFcmbZH5jPr8JAdEFKJCtzdlLfJ+4pEpg1dlfabBlPNsg0YpJoh0vaYtTLK5oPNI8cmUc0llSVNG1RCZM24tgVyIH4TYoOObpcPUYULCdEB2w+RH9uow9JeOxNJM5Xj2OhTQOdvhiaHc+bSNYuh0huE2l8EcHnQwwxxCgEro6ooRYJazRD6ufiMHQD18JDNkOkInlE0o3oDU6CkSMlnjfYtKEmRWqJ/eQKYjH0oBrnR2txn63h1a/nRCQP0ektzuHxQx4+fMyj2rBDQUthaTOyHdi+9BptVMrje9S20FTxcehdy/18bgX3SP57rb1PWUBaXAO8RmI8TUDClgXTRkMxEZpksjvJaj93BaioJA7nj3hDFa0HfBxhSbTdI8omM9045e6rH+TmrYnDkx3p629TcqaUwuGww1x7KjcDBdGh75eMkGPwpDXEhUYMQ4iE6e9Wexc1sRYFZEBzRipxjmgYv14tUuTuqDgmCbFI/Yo40g4wnqJpjOGNskAeEdfeW30SGHXpe4KO9VbFRWi2j0t13uJtweyym8l/L2Jm1arfyCpW+Jm3f+ZZL2PVc6Tjv3T/31df4S/u3+aP3v0Rfni6+0zXtGrVqlW/Hs1t5k98/k8862Wsek7lwFfbjv/Nw0/zH73wW/n4cONZL2nVqlWrfk2a28yPf+rHeePqjWe9lFXPsRaM//Pjz1JuO//j7WurCbzqudFzZwBDQxkQSYgWaDVScjgkh3YACzMPl0gJmlz32XorOANtieSZENhbNQui6DFRrNbNXwBDvPVv6ru3kZ5Wo0KaIqXr3rG+huaMlRJpWRt7z2sLrC3p2hCLRKgT8cXWkbyONyelbqKJ4D4gHv2hERn1b0gpR9pZrBtGmiPNmx1J0cWrLrhbT3mCpAlzQzWhLDQ88LG6xb1gZQEZsCMKO6KIKBkfJroDjdcCS4OckVpxPZBloi47LE+YV0SEzbjlLAmn48B2c8LZeMJ2u6W4siwL5jtchbrMVHXm1miivdO1kKaJKg0/HKilUfcL1kok+coezmtPpLbo1a2BsW11pmUhi5AdpNRIdTqoNyRv8GnEp4m82ZCys1xeUR7uwwBUgTzgrVFrQeZKOyx4E0gTebNl3ExYrZSl0OZd73O2MJYNJDsqCdLYX3Mn5Yk8ZIbtKToM0BqlY4jz5oTNJrN96WVsu+FwcU45v6QcDpjV6AGujZQd9YTikSyf50g6p4E0juQbp6TtFNjmq0vKVRy71gTzhR4oxf24vmO/cHRYe8ogA6KCMOKtgi04scdIiuQhMMSi6NI7ayVBTh3vHW/IXumo38CTqzdoGulWb5j3IQXRHupPUOduktLXkKIvO4+4l3BiTcKYrHM/vh0prGMkmI94d0kxV5GhpYyJ4uWAH5ZI+nqLdLKkSFGbBUWgBRLba4tkclbICdJAnQ19fI7LFfve/e2eSclRd6bZkAf3SJ6ozfDtCWmTaQ6tWE9JC40Uhq50tHVbeqo/UPaiY3R3p0izIsRxdAN3Wh8MEQRNfX0umCYoV7gOCAVdgkZQgZzOGG/fob54i0UHZHJe+OBrbMaRR+/c4/7b71AOB7zOgaPOI16uEM1oyohHBy85+ti1EaZq2YMO/XkESl7SGBtAWr9GJRTBZcRd0Dz162nH6afxGg3d6iHQ7SS87SGfwngjuojLITq32xWkLV53yHQSyfoyg7bo+Z7O8Fr7rI3iFmlpWZGmq95D+itv/hWWtjzrZax6TvXQZv4Pjz7D//Xub+GH1iTwqlWr3mOqXnn4TyAfrFr1rdbX247/3aNP88fu/la+Yzh71stZtWrVqv/O+uk3f5q3dm8962WsWsWlV/7o01/md29fZWT9TLrq+dBzZwBr2qAC0rqh442ApSYgIdnDRGgtDGEyZgU1xVMKTrxZN0SMZkQHp3gnOr9rpFybuZLCOCWBz7hsAhMrI8fO4Ph7x0GbRUdr6+jhFgZb9P2m6+4+qzV6eLFuFINqRpPSnN7fy3V6MvC19E5OjXSjK6oKDGH6ZsLAgjBaJEENw0VSOgaZcUA9ejwjhKiIJkxOMO9dnMMYSWNTRBxrGmsoC5JHmrd4TB3wlBFqoHZlAAxdGk4FMZIdOLGJs/GU0+2Gl2/f4js++BE2d27xzmIc3niL3fnTMKElnn81oqfTGt4aMi+Uw45iEslVs0jdFsWbYVPqPbGNOu+pJZKyngbAqQZlbuALeTuQtxNnd2+yuXmbg8Hl08eRID40yuVCdVAXpDmmFZsXag3TzRzQikwb9GTDMG1ICPN+z641bN7hnqMrViDlCT0magV0s2U4O2G6eZt8sgWr0XG8a+g0cvLiS9z6wAtsb9/hcHXJ4clj2hzGq4vFfh1GNHl0UbsjaBiX1RE59qoW2h5aM5bdJbUJtRaaO+YtulsRNI9h1h6PK6lv54zkhqBhXCthfFeD3GJooYUJqWnENzdg6IattTB3c6Zbu5HADs5vNxEd6gI6kcQjbavHNLPjOmGlRle1CWRB+tCDa+yzOHGjT1sETBNCC1xy768V1TCHVRCp8TovB2pKaIJhCEMyzFXvfbLRC+wI4oJY673HI07CPPLlda40UZBEVueuJk5li21OePXmwHd+/Dux0xN+9eE97r/5JoeLPXa4QsaBtvTH2oxYLXiKHmlaxpY9opnGsQs8IyVSzckV6gGX3NHzgpyesjmd2Gwm2tJYLq7w2uK41wVJYGWORLQkbCnsv/oW41sP8ds3SB/6IKcff4XkRt5dMGxGsibcT6g2Uys02ULzSGVrXGuc/vp3pLPnbcfbN2Q6CQO+HJB80ukMHVNuC4r26w2oZMjRDSxufSAAJG9xTVhbUB3hsMNzDMOQNkFsqP06PJ7gpcbeEY1Eea3Xpno77ABBxtsgNa67q1a9B/RkfsJPfv0nqV6f9VJWPce6Zwf+zUc/y//3A/9DTuW5+wiyatWq97D+09f/U3Z196yXsWoVX65X/JXDPT6WT9dhqlWrVr1n9HMPf279LLrqN4zu24E//OSz/B9vfT9nun4uXfX+13O3yyOpV7qJ1N4NwIqGsesWiUR3hIyJAANWw7xqHvm3MBDkGmVrRzPVAr1MN8OiC7j1pG0YymFuJESiEzTQyRX3FGa0RPpOcNwirud+7FEN7Kt7YIWrexjNmtGUaNa6mXxM3KboUZWIJ4uHsSeEOafSE4ISmFhhBCwSzy5hbvduWnfvCOhAEjfriUVRnNJRroWkYe66ZoIhLVhZ8DSGEZ3HQOVqGOOOoTl6WRGQaQPlgJ5swpT0xujCCYltUm6//BIf+60/zMc/+QMMN27iX/k67zy9QEUjPI3izXEpYTK3AqWbONYiPd0MtEaqWRMybsNsd8Gk0ciYOlIXBuiJyolq/3/27jvMjep6+Pj33pmRtrrbYAMGm2II3fRqsMGEUEMLBIcWCARCSQiEEnpoKfCjhRJKQu8QCCQQeg2992obY1xw2yZpZu55/7havStvt9deY87nefbxrnU1c0caaaQ5c87xATCskOlXhR02AKr6ENTlCOcFFObOJcnncangkgKpK/ggU6GAMcUsZyNI6Ms1U4gxQR6bzRBV12ArsyRJQj6Jcc6WslT/f7A9JAgCwiggCkMClyLz5pI2NhDnEpw4gqoqsn2rydZUgovJ1zdQyBcgyWOsIWgO2johsPggOQGSxD6j0aQg4GJHkuaAAqk40tT3O05ESIwPyNogwgqExpe4xhmcS3zJ8igoXlyQgk2RQqHY4zgo9VTGptjAIjT3dw2w4Pdpl2Cb++eaoHixg8/+xfosW2MNRFl/WzH46yOaBozgSH0mfuD780rSCGEFkhYAH3QUbLEkug9EBkHgM08DQAw2tD4YbhwmCBHJYPINgMUleTBZXzLcBv5iC1fsHx5l/MUbxdejSIqxUry+I4E48f1ofYSfIFPFoKGDWGf02gwdOYq0opqK2oBwQF/SuXPJPPYMZuZMoj4GyVgkl0NcCIWCfz+yYJI84LOQnfE9nn1P4mzxshHx/Z6NJbQRgaQUBNKqSmz/Pri+1SRYpKERkgSTj5E4hxMHBcGmCRaLsQ7JCUmhwNwooiEtUG0cwcxp5OY1MK8pJqqopt+ADNZYZn07i/rGXPFClmIWuK3w/ceN9ZnBYRYxFf65NOCS5hLOBrEWI4nvJRxmfbY1/vG2RJgkX7x4w/c0R4QgExUv8DE+O9iCiSJw1j8WYQbjCogkxUzw1O+DQbHftIvBZnxZcsn5ahBRBhfnsa4RCbNIqCdd1HfDZ/M+4/WZr/f2NJRititwW8OXHFgzkkirKCilvgM+n/c5z3/zfG9PQ6mSWxq+4ICakb7olVJKLeE+n/c5b8x8o7enoVSJAx5o/IoQwyn91iJr9Iiqlm7fvwCwK/igUSbCBOKDmZHvRWviFHG+V64Q+vLMgfisPfFZdT7K4nyPTIPvKVqsC+uKQaeAwJdlRoAUI/4El1gDEmDE9/2UYgasOIexWZ+laLLFYEQx4Cs+cCvGBzNEfD9dgWKf32JgzDcF9gE2ExR7gDqfVet8UMOKYGwFRgpY63vy2sD6Uqyh76tqisV7cWmxT3Kx9LUt9gUVH5g0gUEkKWYPCgQ+6FIKYqfp/w+COyGNsj6oXkyMljQGF/jHL02LQXLny+46B4nD2gxiIHAZqm1KZRRRUVlD1TLLkFl+eaS2kkIhTzx7HmEuRxRkcBU1mFwOQuOzU13s12tDJE5KWdUm9MEjZ0KwIQ6HixtIxVAoCKn4jOTIGoIggyPrH5dYSBAoxORn1pFpiJFgKvlCgTgfI/lGjMRYkylmk2aIS1mpxQC3CCSpf44kwLmUwAnkcj7gbw0mjHBxXMxiND4LHevLb1uLNb5HadzQRNrYRJqkOCcQBgRBhGvK0TRjNnFDE/NmzSFf30janPBIxu8LsfP7ZmogLRQzXSOorMI5h0sLOOeK5W5T4jQlSSEWSI0BLEEaEwQZbBrjs2gD3yNXHJKPS32qjcUHRBEf/LW+d7UJMz4z1xUzcK3fv40DU1GFxRWDcSkOh4jP1vb7un/d2cD3khUMSOJLHVsf8DSRxUSV/nWCweEgzvkyvsUgnxHfF5hiUN73XM74KgHF+DXGP7bEBf8aNhZwuBQSl/reyXGBUEJf0hhHSopNmisDJH75YQRpwWfq43tmS+gzjiuHL8sy48YwYNUVqKwcgCsUMFM/pem5//HlJ1OYXDeDmBQHfj921pfdzviLP0xqiSk+p+IwQYAr5IoXoTgII0wQEBhHGFhMUEEQF7C5BuJCDvfNN7jpliZjkYoKP8bis1ydz8iW1AdZXRD4TG0LsUt9L+wmR94IRmLMoAH0WWkF+q6wHDbfRNM779PYWF98TAOfsZ3NkgGMDSnYSl8qOqhAJME5X0LcUMzez1QVn6uCD3oXL9gxYca//2ayxRL4gg2C4pNmSYv9zf1FLKHPDg5C/9pzrtiTPCxmizt/kUKmBkRwBL4HuDWIMzjx5dgl058kP8cHrp0sysOVUj1CRHjjW/3CrZYMKcJl8z5kragvm1YM7u3pKKVUp2bnZzOtaVpvT0MppZT6zhERnvj6CT2OqiXSfY2TyYvjzH7rUGE1CKyWXt+7ALAPhFRAWuyfa4JSCWchgdD32i1lzSbGB4GswRof8BSXYG1EKr4Msg+aFoNRPgXVB39FMIHxgYnmbF8p9t4V53v4FgNJxbguEPusQXyJU4wpZhBGPlAmKSmpD4S5xCcyxwUfxDYBgWnODgawvmSwrcAYH/6y4gMjPgcuAHz/U+MrroJLscXSrMb5YKPEsc+gNBGSAqQ440D8Ulziij2HnX980sRvvwl8oLfYT1jSvN8ua5DAQSKARQKLJDkkaQ64WSQshqLTmIiAjCRErhLjLLmp05nx0pvwzic4l1KXzxNFUDmkHzLT4SSFtIAYh9iMDxQG4nv7RiHG5TFBFUQRpJDGOZ8kKeIDWYUUDASkhNkaXCA4cbhCQmp9UEyMI8k10pAkhNY/30YcJgp9AC6JcWFAUgwaG5NgbYXPqjTW73NpgtgU1xSQa5pOGvqgZGrCYllkH8InTUAsYeCw4gP3JohIi32Kk9iVer7aYu/ZQkMDSWOOproGmubNIU6LAXGBwAYEBoJaH+QMwpDAWogiUheTJA5pzJEWYpwDsSHORsRpTCKGFL9/BjbAiA+lig2AsLivFrO+iXzpZCO+721Q7Fvt08+LQTwp9vQtBkqlORuo+Hgivt9yYLEYnANrYp+NbopZv67gs9qDCN+/uPkiCl8mGkkRHMYEfs8PAmwY+P0z8Y+n2GK2PkAB33e7GKDHhBD7TF9xAVLI+az3tImUiDhJfK/mMAMuIUiND/angjPFiw3SYva8K/YFT4uBbeNLbkucUJg6nXlvvM3sGdNxmQr6pBWETQHJvJCJTfXMrWvAGMHi9weDkMlGmEw1SZqSFmK/f6Sx7z0dBthMhX89SAw2InAOm1p/4QeCyfiMW5smxETEzpFIConDWYdJE5wBkoRQUoyzOBJsWIFLcgRhiC1WTpDiWBsaKkOLCaAwdRKF6bOpnz7Dl1QvVgwgjDB9arG1fYiSBPftHOIk9X2oJSETZulbVYlINbOTJuLU+X3bVPqLXYoXFpA2gs3g2607TNTc5zzEWIMVQUzxPTrN+7LcroBxqc+QDjPYMPLvUxgIIlySK7YAyOCStNhD3CFESFrn987mHumBZq+pJZ8gPDL5kd6ehlIlDpjh8sVjuVZSUEoppbpjVprnroaJ7FuzUm9PRSmlOqTfRdWSTICHm6aQNZYT+65JtZaDVkup79+e7ZwPFkgTJlMNoe9zalLny6gaIDA+SCE+OGvwgVyXphhbzJBt7lGZCM5YbAgBrhgw8sEfDD5jjBSIgBATOF9q2hRLAQv4xqgJxVq3ftm+/mxp2kKKQ3BiSZOEVHwp1tBYCLNY3wTTZ5faTLFMMz7QaX0vTMRBGGCLpW5NEPjHI1MMgifF35sb/Rrjs5KjLEiMuMQHuq3vcywu8eOcD2Y7KGaO+oxg4/L+5jhGjC+VjQlKpZaJxJeoJQtBFvC9OyWwPpM59RnSVhJM5B/aNNdEw/RZfN2YMK9/HyoH1RD2rSFblSHK57HGB96LRY6L1b0F62JcGPnq3FEWGwX+mY3zuDjBFQNb2JBMJouxBhdAKgaXFHCFGJHEt6eNiuV0jSWx4hOgTeiD/GJIkybS1BFjycdNmKSAMRXYCiBOMSb1Afigwme+SkycOlKT+D7LUeQDZRi/71hBUotLBFssyS34dbkkRkgJK7ME1TXF+0KhMU9aKFDI5UlyKY4CEGEDIagIiKKQqLYGW1lBUFVZLI2bUJg9h8KMWcS5JtIUhBDnDIlrInX+GgSMf04CExIYihcQ+MCuSDHD0/h66UKKDSkGcX1PY2xY6rMtLkUk+f+ZwNYUS5FbbFgM1BZLX+MgDJzv0yqJz1xOKL5GI//6yfgS0JIkxf6y4v+2gV93c0DZhn7/jqr9hQtJDoLI/x6kxQrUsc9UjixQ4bNqwZcITkEkizNCKsXMURNiwggRIRTfk9YQ+FLxBkySYlyCCSNfKr75IhFCjDiSufP45t1PkcaUdXYYy4DVV4VsBfLZ59j7viCelWIdWBNgbUCQibCVWaqMJYxj6sVQ5wwpfh0+QJ5gcEgSEtiAUAJsWoDGRn/BiE0JK6sIrSV0QhjHxElCmuRwTQXi1OFMBpMWfKZ6WIUNKjFSAElxeV9S3sb1kK1GbEiapBSmzqLh2wbSNCWXFmhyMWkhRmwWTDFYH+eROXMoFHtj+567QlA9kMErj2DNEctRlcKbb73PpK+nIC4oPX8uzmGyWYzzGeBSaPB9tWOf+Ywkvve4CIEtZtBnqjDG4eKC38+iLDjBxfPAZpGkARPV+H0gE4HEfr8G0jjv96+gwpfuNiCFJsjW9vghSi06PRFs+q4GrIqfApRaYpw3510G2AybZwd/J19TSn3ffJ+PoUotaWKEh5qmsH3lUAYG2d6ejlKqC/Q4qtSSSYB7GycTi3BW/3W0TZFaKn3/AsDFDFRMFWLwfUvDqJjBa33ArZAUY7HWB+RIMGKK2YQ+k9C5lICw2C/Vl3ZO0xgbmGKpXt+j16/S+hKw4kuKFqOxiLR8U/EdfoWwWCY5LfYG9j2AEV8WOo1TX37XhP6/xJV63/pMyIwPeoLPZA6yvgcqlsD4KVkAZzGBD8YVU5Z9gC51EDgfXCvG3giKpXeLQUmcFLOCQySJfW/fNMaKz2SWMCARQWIHJKWMaOMSnzGXFvxjFgTFrEoHScH3ojVZcAVMJoA8PuobZElw5E2GvEvJJXnqGueQSJ6k0Ehm1hziJCHfVMDlcn5+JvDZqIQ+mJcKIokPyllfRliczxaWMPKBSzFkKjNk+/XFVlZRyMfk586DpIDNVPp5W19+N40CnPV9ZZN8E6nJlPrMJjYgISBJBCHCBBmcEwq5BhJjCKRAYC2WEMFngYtzPuNafCBewixxkselDuMEJPXPsQ2Qgu/Ji7E+EBqFkPX7YtoUk+QLpLmENJ8nDSwusBBk/TrFZ+OaTBZJY9L6hHTuXIhT0jgmX99EAUeSJKQYUhypS/zjaQ0ujouvCesT4/FZ6f5iAT9vl+CDuJICvt801mCtKW6rz4YXcbjU+f67Seoz1OPU74dB5LdN/MUIkvoLMQiK5aRNiIQWmw2xptpn96YFDIZUIC4GlwmAIAP458YXT08w1gdqkRiTFhAT+n3aBKULNAirEJcvjg984C9Jiy/VYmUASbGpL+HuCnliBBOFxQsQ/PqMCMbZYmZ9WCyLTvFClNj/EhlcoYlcU8DsOMe8WV8x4NWpuKk5PvrgI2ZMnYjEBV+W2MVIpgJjIqKaGiqXGURVdZbK2XORL7+irk5wBZ8ZC/he3mEFJs35Xrk5weVzBBXVUFGJiQxhtgLrUoLYEeQgBuLE4NIcIj5bNpUEZ4XAOCTOEQSVxZLmKU5CLBYX+x7ZST5HXa6BRiypS0jDCOcMzuUxRnA5h8sXikHfYvAXwWSyVAxfjgHbbU+0wjKYOXOonT2dzLSvyRhH/2wljTbDLBvhksbiW6nBRFXFLN/Yv07IIEkBYzO4uMGXqEdwaR4TZf37sPg+3c5ZDAlOMpDLQRD4i1gKeZxAGhhcVFsspR0icT2SxjgbYF2PHpzUItYTX5a/i1+4DYZfrPELzn797N6eilIl9ZJwwqzXOb//+oypXKa3p6OU6sT39Riq1JLqzcJsfjPrNf40YDRDgoreno5SqhPf5+Po8tXLawlotcR7qOkrQmM4se8PqLFRb09HqR71vQsA26DSZwcGPruXNPABTwFrfclnn8SbRYqBU0lAAkeA9eWLxSDW+ICSFOO0xoDxpZQp9pf0vXv5//8nzgdYTYikhWLw1RV7hNpif98EoVh33sWlILKYYoZlGBaDLhAYgyEhMAKugHOBj6mSFsse+4xmI77ksImyPmCSOp89V8xQxqSQiN8OfLYi1mcn+2w6XwZVrEWczwoW5x9Dh0OIsS72twPOGGyakLpihrP4uYgxvl9nmkJYiRRyQMYHx4tJo/6xE//4uQIEWQrOkcPR6ByNJkMlKZk0wOSbkDgmCkIScRTimLSpESsGsRFCCBasDTBpgtgIKPigbGh9AN44XCI+gB1FBH1rMBW+D6nkcpD6nqJBlAGJwKUkabFkcZqQJqkv303syz5nomKQ0xCYoBhwN1ibQhL7/UkiHAbnfGlil1hcsZ8zifMBt8JcxEZYwQc2A59dagK/TzlcsWSxxTiD5CBprCNJDWlaIHXF58YGSGD9fuX8xQpSKJCkkKTig+9xghiDE0dBEgpiiElJgMQEYAOszWAkxmV8z9UQCCKLDbI+QzvM+vK9icOZGNIYEJyEOBP6/rvNpcF9/NM/HyaBuNjnNQjBRpgwxJoAkyQ+M9eExYzNENucXYxgKysJMxmi0JCpqKAyisiGEY1NOeZOn0EcF0iTGJcmEOeLwV8ftiYF0mLGuRhMRRaxse/5mgbFUsuCRD5wTAq4yAdvi69X64SMMYS2ABXVENcjSUKcFpAwJLIBUQBIgAWCYvnw4gJ8ljupzxwvZgSn+RzzvpzCG7MTvjEhaTKXmSbGVGTIRJDm8z4IHARIRRWuKosYS1zXRD6XYKurCQsxhdQVS0GHvhpBkkBTAdc4D5wvj4zB/4t/v7LGYomwIViXYjP+IoUkH5M4QxpUkqYFRCIkiGi+uMO6BGMMUmj0JbglJM03+Mxc48t2p0lKGhokEX+xh8mQpikEPvhqbOhL8+caKEybRv6V58m/EzDjm3l8M2Ua4cDBLDtqZZZdYVkaZ8wk/+qb1M+sAwFjUpCUIMogthIp1Pme32kKYbGvtQkR50tAkzT5ixWczwQ3gBTqMVEVTnwJ/TSp86/VQh6JLBJUIgI2yUPgMNkqIMDFuR45Lim1KBljGFgxEGuKxxqllhB1kvD7OW9xvlmPLTQTWCm1hFqxZkXW6r8W785+t7enolSZ1wuz+O2s17h4wIb0sxkCPY4qpZYw1lhOXf9Uzn79bN789s3eno5S7XL4nsAAp/VbWzOB1VLlexcAds4Vy/WmvjdvFCFpExAiZMCkWBv5AK2ziE18yVKb+uRYDCYtYH06H2IDfFfUYiYuqQ9yBoEPiogDUp8kiSkGwIrlX40rVoEVH0AWW/z/tFgSOsClMcb43qoOg3EJobW+56gIJk3xUaZMMZ5cLCVtUmyQ8RlqRjDOYRIfLPEZnM4HtYrloSUIfR/MMPDZicaC88EeH3m2SJIiNvTBqjDjew8bf7uIwRBhyWElJTUOCQJ/exyDKz7GQVIMEqXFYG8BQotxWR8IlLzPWhYQk/o+uQiFoJKGfAN1UqAizZAJY2y2AhMKhbSAM44kX4C44Evu2gImrAQbFMtuhz4jUhJEAkh9b+U0SUlTX+47BNysJmLbRJxPiF0esRCEWYwk2EzWx7Kd9Y9JIY+kSTEL1uDCECM+m9WKJUoTjI19j9HEApU+Czf1udROEpwTkrRAYjK+lHQgRDbEJSBiCSQlCDIENkKSRsRFuDQo9ksOMEHk+8q6nM9MFuvL1UYZf7GCcT4Qlk/AOVIf5ccFvkwwNsVlApxzJHFKPnE0xXny1pAYny4eYjFp3l/44FKsK2DDCqJCHpM1GCcEgQ8COmsxUQZxxexkl5Kmxu8DJMXYZ+jLbyfFoKwz2GwFNgx9snxQfD1g/Xa6YoAw9VnQQZjBZEJMFBKElso+NfRbbjlqBgwgcEI4cxaFOMXU15E0NvhXp0CaphiX+mCgpJhM5PvzGuPnYwxpIS725U18cBrxr7809hclIMXy1gUCE1JhBBNkcSZPapsvvbCkcQLZEItgXYqJLKQ5f5GIDXwfW5P6JGITFLOeQ0jyuHkzqWtqpKmiAiosQcYSiME6iw0islGWMMzgCkJh6izqvp1LGEWkcZ58QwNJLodxDikkSKHelx1PEkiSYhn7AMLA95BuavD7UD4msP51GRgfJLfGJ+XHQYBFSEhJUt/vPLUGJ0IoFkxEZAzOCUEY4ApNEPoArCR5xFaQ2thn2NrIB7wLeQIb4kQwQaaYMY7PEp7bxMRXP2S2tYjL0ZitoHKFFQiX7UvD3HnMnjiVfH2TL9MfhEix77VJHbgYlxpIm3yGfq4eYyuQ1JdKJ6okTXL+Qhbw/bVdvvj+71/WLskhNuOXXxniCvU4chgbkpL3GcdxHmfCYhq3Uku+tQeszbbDtuXxKY/39lSUKjPHFTh+1muc0HdNtswOZtmwsrenpJRSZQZVDmLscmP5YM4HpJL29nSUKvNGYTY7T3uS/WtG8Mva1TQIrJRa4gyqHMQfNvoDv3/l9xoEVku8fzZOJhHHqf3W1p7AaqnxPdyTfc9VI5W+ZG8xU5bAIsT+5D4WZ6Ni/1VbDIpEEAi4tJihYEo9QTG+p62TxI83PjvXB3hTn4lKc+XY5pK5+H/F+gBecyDDN5f1ASJJSQWCwJGK8VmHJkWMDwaZ4lg/Bd+D1gDGBBiyvl8ofo4mW4EV5wNpxgeSfXps4ONyNkBs8QutCXwJahv5yJkBCQxScIgUiqVzwRnxfXZFfEpyMTvSmhCaChgJfBZ1YCHvIBNixCA29IEXCf1jmhgfuC4F2IwPwEeZYnakUEhjGgXmxgmVYYa+EVRGEGUraUp9eWVHQmiL2ZZhRGAFa0AKMUIGyReDeyaBNCFxQpLU42wlYRRRkfHPfS4R8iK42BdBNibAmcCXJxafxUvs+7mGWev7h4rz+0Ua45I8QeD/L8hU+uc2cNg4TxgGkC8UM01BwiwS1+PCEMlaogEDqKzMEBYS8rProaGumBntgKxP2E58ANkGCdaFIAku8OXAxVqoCLFR1j/zcQ4p+ExN49PccZL4jOYgQKz1QWgHhUKevEspiKUgfl8NggisRYIIcSmu4Mtq+x66FolzmKga4jwWSxRlIY79BQCp84HuNPZ9tq3fr5yLi/tVijVZbCbyGec29b2kU58pa6MAk8n4rPk0xSAE2QyZmgpMEJASEFRWUDFgIH0HD2Fg375EzhHFKbn+A7GZDLlsRJzLkcYJJk59IFDSYvDcZ/rinN8vA4sJHDYKfO9jfAAbk0JzQDB1EEUYYwhtiHUxZCJEDEmakOBw1vpgZyElDR0mjAhc6nslG+t7ZzvnX19BsU9xMePZAMalSNJInIsxSYAY8cHhigr69hvImoMH06/fIBqM4ePp05k391viXMFfDJIIlWLJpo6YChoLDb6XcRD4hPGwwr++jEWMxRQSrMSYTOTLO0uCDSJs8cIOnyHbiA0MAZbAOVJJSWPr9yUbkFrBGPHveS7x72epgTT12crGl5AWSZG0uK1iEOtwsWAwGGOwhXoIKpEkRz4tMDOIMGkek2/EvS/M+eRL6vNNzIrzFFLne5Kb4kUXgKP4/NoQEucvgLAVOOczu43I/y83bQKMS3CpL78uaaEY6Hd+DkZI843FeVb4C3AkxoY1/mKBQh1CgEvyi/6QpVQPCG3I3iP35rmpz5F3ut+qJUujpJw1521GZwbwfwM2oL/2M1RKLWF2Gr4Tt316GzNyM3p7Kkq1Ui8J19V9yruFORxRuyrrZvprVQ2l1BKlT6YPe4zYg/dmv0fs4t6ejlLtcsCDTVOIjOWkvmtRaYPenpJSC+37FwA2YMj6LD6XQpLzpZIlLva79f1ajaFYLrTYZ0EcxhYDps0Jivi+vv6OvjytcYIJQn/ftFjWtxj0FUlIXOiDkhQDvyR+mc0ddotBXQfFMtKh70dpw2KAtFg61jcK9tm/koCxGAJMGGLEYUR8RrKAjbI+Kdj4/r3+/sVSBkmhmJUXY4zP/sW6YnauQYwgSeKD1zYs9lX1czHFJsHOxQQ2wrmcDyq5tBhYTiB2EBrIVvngV5pCkhQD4QkUAiS0iEt9VnAQYaTgs4yDCCTBEJCmefJBRIPJkKvOYkeuSLTsELJhSGH6HNIp3xTLCCcENiAMQ4KgAhPnkBSccbhCjAkDnyUYVJAYIY1qfJZr/75Efftgooh0Xj356TOQnPEx/HweE9Zgosj3es3nMJJi4xQT+V6yPvRnixnhkd+WbLa47wg2SQltQGCBqAKHIFLwGaJhpY/F9+mHralGshmMNILxAdTU+X3N4DPOkSYCW4lxOUyU8VmzYQXGJRgXY4MMIjFiomKyucNGGcJMNaXjljRnssaIE1zsS2QbcQShJSOQWofF91E1TnD5mMgItjnwaZv3gwI2yGACQxBYrLUkxpDYjO+xKkGxxzE+2Ca+z67NVkCYhcgUM/J9RqgtZsn7kt0pNixetFBdRThwIGFVButSaEowmQpsRRZTncUO6UsAVIfQP80TzfDXHjRiiG0eF6S4JECSvA+IOgNJzr8Wi320g0wWYxNc6DBpBoljHA4TFyCTxYXWzz2MsGnBl6p2BglCbBQQJilOxGcCp4ARXGiJ4xiiEEvxNWxDrDV+u6wtVhwQTGB9IBd/8QNpSEoMlZVULjeM5caOZdn116M6CBjw9RSyL77C9K+r+LZ+Hv3ThMohK1G1zEAqBvShvv5bPn7nLb754FNc/TwIM9hMxgesjSBhBhHBpQmGAoHxmds2l8O6HEasL++dCbBBQBRVkgqkcY5EQlyuyV8gESeYTJUvaW6DYhDY71eEFpfE/n0jyhQvqgET5zCpYG0WsT5ILoIv+00KNsQmeV/6PIF8XR3TA4txMWnx4hspFDBhMRAcZvzjaDL+AhabFi/uyfq+5cbv4778tGBc6Psbh1VAZTGI7Mugp01zSRAIKhCb+ACwv4aCpNCAsVnSNPC9gjVTTX2HrNJnFTYcsiHPf/N8b09FqTa9XpjFcdrPUCm1BKoIKjhno3M449UztI+hWiIlCM/nZ/BhPI9LBm7Iupn+vT0lpZQqs/XQrVlnwDq8NvO13p6KUp26t3EyBXGc2X8dskaDwOq77fsXAHYGsanPOgsMhNUYEiCC0GfsumIgkoJgQp8lmQo+g9YJRlIkqkBi8UFhfHalv8bSImJwpD47thT8BedCwPjlG4uT4n3FACnNxURtsfeuiIClWEYa377UWdI0D0lMkKkkkBCD7+lpxGAoloYWwQa2WLE28AFuY3z5V0OxJLH4wGuQwTgfzJViaWkoZiynMSbI+HKyATgDJvEBZsFn/okJfHapibAu9cmdYaZ41WlSDFo6JPWZt5Kt9oHDwAf4EFcMJsYQGKSQ971gkyaECiSMIZsldpAzQr2x1MeO+ln15NICDXPriOMCoUl9CeAwi8VhTbEPb0UlKY5cGJGkCVJIkdD5MtrFLEyTxEhDAy6wJLHDBiFkAkzqCKIAGxoyUbFMt40gABf4jFuHkCZCYp0vLWtSTCKliwxM6svbmspKH3zKZPw2uyrfYzTIkDqHq6vDNNYXs0kdaSHFpg5EfGllB6QJQRj5QG5U5YO/JsQledJCTOoEsj4Q7SjgAjASYSMfxDM2xYnFOd97WoIAm4kIgtBnNRcMgUspiCMWEBdgY4M1kAkz2GI2rEnySGBwJvK9nwMf0LTG+NLj2Sw2TElyKWnifK/jgg9ykg2L/X4BU0BSXyo8TfOIMf56hsj6TH0cYmNspsr3Zm2oJ4kzfh/GQF0djS5hVi5BZs6luk8fUiPYTJagTz/C1JBJYqykOGOJDaQ2i0tTcIKIRfB9rY31ZYCNFYxYJD/Pl0PHZ+cbSfFPvBCG4rc1U8yQRggqa5FUSJMmkkKMCywpAaQJqfHPnTghzGQJwAdFEXABxYa8QICQ+otDEl9a3vdnDnxgedrX8JYg/Qdj+vdnme22Z7Bz5OdOI1vVD1tRSaGQY8aXbzHz85nUzZzn+0FnKwirqnzpdgcuLRQvOcEHWwVIBZfPY2OHlQJBFGGqKjFRpc+UlpTQZHHFctOpRL6SQFgBpAgWZyxpanBEiEmQ5t7gYiGfhzDyAfQgJHX+AgdyjdhsBeIKvl95VFUqjW+swWKQJIczxR7MYpBCE2Izfs5BJcT54j4R+McrScEmSLEwgcQFpNhj2r8PNeFslb/gJ1/nL6gpNEKYxYWVSJogxuKM7xmf5JtwocWYDCYt+As4XB4XZBbJYUqpRSETZDh5vZM58aUTeX/2+709HaXa9HphFq/nZ7FD5VDNXlJKLTGMMfyg/w8YPWg0/578796ejlLt+tbl+fW3r3LRgA1YLzugt6ejlFIl1liGVA7p7Wko1WUPNU0haywH1IxkRFij30/Vd9b3LwBsDUQhZDO+P61zxZLH4nv9prGvziwGQoskeYJspQ/yuWIQGN931YT+ChBxgpD6zEbBlzdOi5m9tpjpagySGF8tWnweb2qKub7OQSD45NoQrCFNEt+f1KcR4iMiBpHY9y7OVGKtzy42xvjlGAPEGBthMRjjs5Z9GWMDhL7faVrw8zQBRgJIm/sRF8tTW+uDJIFFEuOTnCOLpD5DubncqiEECgRBhe+JGkQ4l2JMSGgcLvHZwT6K3Vz2NoNxSbEHqvGPT5wHWwx+FbOwTWiQJIBMAKnv2+qSRhqcZdaseXydy5Nkq8hmMlhrieKYwESEkRSzUA2hDei3zDAq+vUjHwbMnjOPuimTfQ5m4LMDTWCx4hBnifOFYtDbEeAIMhacL1EcVNdgaqp8UCyfYJoaSQpZ0nweyTWQBhFpoQCJ+/9B8SRBnPPPRRQVg+8WcTHESTG715X6TMcNDYjNkpqEAHwvaRsgLvUBNOdLBFvrA+vYSl9W16U48f1nU0CSBCfFrEsJCQKfNS7OkSYxaSo+8CsxNggxaZ7AZrARWAkxBV+2XEhxxgd/g9ASRiHG5X2ms0SkzhGYCDE+kO/EQhpgLYTWFPtT++xdR0oaWFwcY01SLM8b4MRi08SXRRb/+nQIFEsfWyxiYlwqSKGAq28iicBmK31Z80wWN6+JpHEKjdOmUVVbi63MIi6lEMekcYytqiCqyJA0NSENjbhGV2yzHfvexILP3A78Pk9zyeWgAombwPp+yZKkgO9/HEYhQWWlLxkveZ+tbI3PFsaXJBYnJM767GojuERIIgOxBeN8QN7Y4kvPl6HGGCSI/PtKkmBCAQwuydMwdSZTKqcQhlmWq6xkwMyYsLoGM2woFYMGQtpI7tX3mPj0C3zz2SfMbGpibtM8XBRgs7W4uBFbDNI6EYjziPPvH5IUcE05JE0Jowiq+0AmxIZZf9GCtUgcY2yKsRZrA2x1LTaJfVa7AcIMaT5PGgXEsfOlt43vGW4Ci0sKGFfAZGtwUuw9njhEIEhSf1FIGEGa+AtqTIjJNRaL2INJ02L1hBaX29jQX5BjI5wTSAtIGJGGGXApkhaQ2PcVx4T+9ygLpgJESPMNpHEdNlMDWF8xIKjE2RgXN+CoxOFIbJUv728FHCRJHucakbDPojtWKbUI9Mn0YafhO/HB7A+aLwFRaolz2bwP2b5yKHqdtVJqSXPMWscwJz+HF6e/2NtTUapdM1ye42e9zkUDN2DtqB9WT1grpZYQP1/95zz61aOkkvb2VJTqlAD3NE4mMpbf9V2TED2equ+m718AmDpI+0LBFTPOckhUhUgBY1OQBHEBWB+kIltFmqbFbEXjA1YWXJpijcE53+NWxPeUtEGIM6kvdYyBgi8vaxHSyGJdgjMWa3xWpM+sNZCGPmFUfFatry7gigHZyP9KAhL5zF6/9GLgtxh4xpeBDozzwUNnfInoUs9in8AKIWJ8diVhCHHBZ/4Wg40G6/vWFvv/Oud7sJbygosJySa0IAEmzPgs57TgA9IYAheCjTFBhMRzIOrrA7lWIEkxURYKeYiqIGsh30hzfWJxgjERuHxxXZHPzotCnHM0NsVMd5a4MId+2QpqbYbIpAShIQwiQqCyqpZBKyzPoFVHkNZUMXv6tzC33mfQBmDCLEEQFksxg7gcibMIFkkSbFrwAfEgIEwTQieQayLNF0gKBSROMakD54OkkqZI0oQJKrASAr7PswkCjA0JIkNgfV/oVASXFJBiKWAjEJiUKIgwxmGd/9sEWR94K9QV+7EG2MBgrcVY4y8IsM3lwAMkThHng8qpARFLEBkIIp/NiCNJUp/9mjpMFBVLOAuha/KtqdM8ID6wjCN1KSY0hFGxnDaRz+oUcIHvjZyKYJ3DWn/hhFiLDQPCbCUBAYUEkkIOkoLfFYsl1P1FCcVsZHH+goy4gMmExRbZvl83GEhS0gBECj7XNFsgzOax2QypNcQ2Q1MY0Vg3j8AGfn8OAwjDYpA78cFeDDYIfHAwoXSxg5EQaWqAbHXxeWt+7iJIY3/Vho0IAkOQiQhDf+EAziFJE1JRicQ5rHNQSEqlv4PIkJqANE0Q4xAJcWlCGkY+wOmbOvuLNILIX4jhEkTE96pNU4x1UHC4pkbmffEFE7+Zzrd9+jFwQF/6Dx9G9qsvqZk5j/xnU/lg0mSmNM0j11SHK+R99r2xfnlBxj/wYeT3vTTGOoeJi4+NS7EVFZANIRvhnH/tm8D6XuVBJSQFJHZIIY91jdgo9Bn32QzGWJwFCbLEdXPJ22ryqSPONSBSgCiLi+dhczlMEOFcow/MFkvWk8lgkgTI+0zbJIexWQSHcTHGRD6jmgTJVkCcI7D+whkbFbfTgBSaIFNJGqeYMIuEgmAhbkQKef8QmxhJE5wLcGEtLs5hgypcPkdq8iRiSZMU5+ogmyEl9BcASA4ISK3FUYF1hUV4rFJq0dhkyCa+pYAGgNUSakraxC0NX3BAzcjenopSSpWpjqpZZ+A6GgBWS7zpLseRM1/mh1XDOK3f2r09HaWUAmBgxUB+vc6vueLdK2hKm3p7Okp1yX+appITx55Vw1k3008zgdV3zvcuAOxMLc4GmDSHIYOEVRhygPNZqHGx120x4GiDAIkFQoOhABLgjA/8pGEA+J6giCAYH+hx4oPHSQqBQ8TiCo0gWVIjEASkhRxkspCAz3yNfdYrQBgWuwCHvsew8Vm8IqbYO9cRFP8PE+GjtPhAkfOlWA3FMtZp4IPX+CxTKYBICjZbzEzG98okgiT2f4uDGFySx9kQcb5cLFIAky1lKvrmtIHPpEzxZZxT8W+EJsbaEGfEZ/1K6IOVruDLSOMDyobEl9GOKiE2iDRgggofHE5j3181SaCY0QcRBRLq4kasCwgcZKImqqM+WGMJowzZ2lqWWWUEw36wChUDl2H2N98yb+LX5KZ/U+yRa31J2zSBOPEBXHGkNi2Ws45Acr50dmQwlX2QqlrEONL6OuKmBIdBbELqfGlhF+cwpoIgCLACzvmevSSJv7AgqsBFEZIKSZISFxyOFGdDTFSJj0kmOOd8f1bnM32NOIyt8M9RkmAk9Y+PyfpeyoEFHGmakjohxZG4hNRmcC6FOMYSkLoYE1ococ+2LQatcQKkvpew+H1OkpjQpTh8BrJzzpfQTZqQwBb3BYOkCSkpiQg2yvjHU2Kwlb7vsM2ACGGa80HNQjG7vpjT47PPm18vAVDw+3jBFV9XeZw4rMn4zEsMYhKIIpxYkhhsmi+WTm7CGENsA4Iwi80GPks447DkMc75fr5pithif+/AQJjBxKHPSJcMkuZ9KeNCzr8mJfHBw9QHxINMFRZLUMhjTIikMdgKTOqzsHEGa52/0iL0wWabxARBRBLnfZnrMEsaRJDkCawlCAKfqS+CjfM+C9wanPWvJWvASEja0Eg+18TsbI66ubP5dkYV2U8+p8YFmKTAvKSROpOSpAm+9XNzqXv/2hST8c8lKVjnL7QAQHyJ60wlEvly16krlogn9ReS4Py+Mq8O4yymkAMMrpAndQ5jDEFoCYIsLmnChSERAa5+Ng5Lkhb7gpsKn/1rnC+77QRSIbUF/3wUGiGswCZA7DBR8bKT2GFt6oPo1kBTPT6v3BHYLDZf8OWtxeGc4PIFHIEPbocVSL7RB6Gz1ZCvxxnrS0XbAJemuCTBuhwuypLEeRLJ4sIE5wxpPoeEzmfixzEk32Krh2CjLEFGe1Sq754+UR+2XW5bHp/yeG9PRak2pQgNLuntaSilVJs2X2Zz7v3iXmbkZvT2VJTq0DyJmaMXrCqlliCBCRiUHUSgPVXVd8gcV+D+xsk83jSVCwasz9YVy/T2lJTqFiMimgKilFJKKaWUUkoppZRSSimllFJLAdvbE1BKKaWUUkoppZRSSimllFJKKdUzNACslFJKKaWUUkoppZRSSimllFJLCQ0AK6WUUkoppZRSSimllFJKKaXUUkIDwEoppZRSSimllFJKKaWUUkoptZTQALBSSimllFJKKaWUUkoppZRSSi0lNACslFJKKaWUUkoppZRSSimllFJLCQ0AK6WUUkoppZRSSimllFJKKaXUUkIDwEoppZRSSimllFJKKaWUUkoptZTQALBSSimllFJKKaWUUkoppZRSSi0lNACslFJKKaWUUkoppZRSSimllFJLCQ0AK6WUUkoppZRSSimllFJKKaXUUkIDwEoppZRSSimllFJKKaWUUkoptZTQALBSSimllFJKKaWUUkoppZRSSi0lNACslFJKKaWUUkoppZRSSimllFJLCQ0AK6WUUkoppZRSSimllFJKKaXUUkIDwEoppZRSSimllFJKKaWUUkoptZTQALBSSimllFJKKaWUUkoppZRSSi0lNACslFJKKaWUUkoppZRSSimllFJLCQ0AK6WUUkoppZRSSimllFJKKaXUUkIDwEoppZRSSimllFJKKaWUUkoptZTQALBSSimllFJKKaWUUkoppZRSSi0lNACslFJKKaWUUkoppZRSSimllFJLCQ0AK6WUUkoppZRSSimllFJKKaXUUkIDwEoppZRSSimllFJKKaWUUkoptZTQALBSSimllFJKKaWUUkoppZRSSi0lNACslFJKKaWUUkoppZRSSimllFJLCQ0AK6WUUkoppZRSSimllFJKKaXUUkIDwEoppZRSSimllFJKKaWUUkoptZTQALBSSimllFJKKaWUUkoppZRSSi0lNACslFJKKaWUUkoppZRSSimllFJLCQ0AK6WUUkoppZRSSimllFJKKaXUUkIDwEoppZRSSimllFJKKaWUUkoptZTQALBSarGZPXs2L7/8Mtdeey37778/Q4cO5aabblrk6xURZsyYwfPPP8/FF1/MzjvvTP/+/fniiy8W+bqVUkoppZRSSimllFJKKaUWp7C3J6CU+n4YNWoUH3/8cav/T5Jkka735ZdfZvz48cydO7fVbcaYRbpupZRSSimllFJKKaWUUkqpxU0zgJVSi8U555zDWWedxaqrrlr2/9XV1Yt0vWussQZHH300Bx98MFEUld1WVVW1SNetlFJKKaWUUkoppZRSSim1uGkGsFJqsdhnn30A+OEPf8gmm2xS+v9FHYStra3lnHPOAXzG7/XXX7/Y1q2UWjg33XQTBxxwQLfuU1NTw7Bhwxg6dCibbbYZu+66KxtvvDFBECzUXP73v/9x++238/777/P+++8zbdo0+vTpQ//+/Vl33XXZYostGDduHGuvvTbWdv/6uk8//ZSHHnqIRx55hM8//5xp06bR0NDAoEGDGDRoUGl7tt56a7bccksymcxCbY/qHVdeeSVHHnlkq//fbbfduPfee7u176y88sp8/vnnHY455JBDuO6661r9f5Ik3HfffTz66KN88MEHvP/++zQ2NtKnTx+WWWYZNthgA8aMGcPYsWNZccUVy+777LPPsvXWW3d5ngCVlZUMGzaMYcOGsf7667Pbbrux1VZbtbowSynVc/QYqsfQpc0HH3zAD37wg1b/v9JKK/Haa68xYMCALi/r5z//edn3wraMHDmSzz77rM3bFnSfTpKE6upqCoVCl+cahiHLLrssw4YNY9SoUey8887ssMMO9O3bt8vLUEoppZRS309GRKS3J6GU+v6YN29e2ZfVxx57jHHjxi2WdV900UUcf/zxpb+TJFnoE1pKqUXn008/5c4772T69OnceeedTJ06tex2ay3jxo1j4403pra2lunTp/PVV1/x+eef8+qrr5bGDRs2jN///vcceuih3Q44TZ06lcMOO4yHHnoIYwzbbrstm266KY2Njbz33nu88sorzJkzpzT+oIMO4oYbbujy8t955x3OOuss7rnnHgBWWGEFxo8fz4gRI4jjmBkzZvDqq6/y2muvkaYpAP369WP33Xdn7Nix/OhHP2LgwIHd2qZFZc6cOQRBQG1t7SJZ/pQpU3DOUVlZSWVlJdlslnw+z9y5c1v9pGnKfvvt163l19fXkyQJ/fr1WyTzB3jvvfe45JJLuP3226mrqyu77dxzz+WUU07p8rJuv/12XnrpJR566CE++eSTstu23XZbttxyS370ox+x6aablt32yiuvcMABB/Dhhx9SU1PD9ttvz3rrrcdXX31V2qfjOC6N//vf/86BBx5Y+nvGjBlce+21zJo1i3vvvbfNIPQWW2zBlltuyYABA5g5cyZTpkxh4sSJ/O9//yvbj0844QSOPfbYRV4NZGEt6n27p1x11VWMGzeOkSNHdvj5RkRobGxkxowZvP322yy33HJssMEGi3GmanHQY6geQzuSpikzZ85k4MCBhOHC5QWICHV1deTzeQYNGrTI2uzU19dzxhlncP/997c69uywww489NBDXf5u98ILL/Cvf/2L559/nmeeeabstlGjRrHLLruwySabsNdee5Xd1hP79KWXXsqMGTN48sknef7551vNbdVVV2XHHXdk6NChNDQ0MGXKFL766iv+97//lT47hGHIgQceyJlnnsnyyy/fpW3uLblcjvr6egYOHNij+0ZTUxM33ngjP/zhDxk+fHiHy27eR2fMmMHrr7/Oeuut16oyWXsKhQJz5sxh8ODB2kJKKaWUUt89opRSi1GSJAKUfp588snFtu6//e1vZetWSn13TJo0STKZTNlr+NZbb213/MSJE+X888+X5ZZbrjR+lVVWkWeeeabL65w8ebKMHDlSAFl++eXlqaeeajXm22+/lZ/97GeldWy66aZdWnaapvKHP/xBrLUCyIgRI+TBBx+UJEnaHD9t2jQ55ZRTJJvNlj0Gl156aZe3Z1GYPXu2nHHGGWWP8worrCCnnnqqzJkzp0fXNXr06LJt7+hnk0026XR5aZrKrbfeKmPHji2b/6BBg2TzzTeXf/zjH+0+Hwvrq6++klVXXbVszsYYeeSRR7q9rDiOZfz48aXlnHfeee2Offzxx6WiokIA2XLLLeXLL79sNea9996TjTfeuLS8k046qd3l1dfXy+DBg8u2o6P1T5s2TS677DIZNWpUafzQoUPl3nvv7d5GLwaLc9/uyJw5c2STTTYRQF5++eV2xxUKhS6/Pub/OeSQQxbb9qjeocdQPYaKiORyObn88stl9OjREoahABKGoYwaNUoOOeQQeeutt7q8LOecPPHEE7L33ntLv379SvOvra2VDTbYQK666irJ5XI9vg0iIo2NjbLHHnu0ei/7/e9/v0DLO+ecc0rL2HHHHdvdjxbFPv2jH/2obBt22mknKRQKbY5tbGyUO+64Q8aNG1caX1FRIWeddZakadr9DV+EkiSRa6+9VtZZZx0xxgggffr0kUMOOUQ++uijHlnHp59+usDHvbPPPrvDZb/44ouyxx57yCqrrCJBEAgg1dXVsu6668qZZ54pc+fO7ZFtUEoppZRa1DQCopRa7HorAHzDDTdoAFip77Ctttqq9PrNZDISx3Gn95k9e3bZibgwDOWqq67q9H7OORk7dmzpPq+//nqH44888shS8LAzuVyu7ITfvvvuK3V1dZ3eT0TkzTfflGHDhpXu+6tf/apL91sU3nnnHVl55ZUFkNVXX13OO+88ufLKK2W99dYTQFZccUV55513emx9AwYM6PKJvd/85jcdLuvjjz8uzbN///5y/PHHy/XXXy+XXnqp7L///qWTlWuttZbMmDGjx7ahpeeee67VvAcMGCBffPFFt5d17733lpbR3knJmTNnyqBBgwSQlVZaSebNm9fu8vL5vKy77roCyF577dXhulu+vgCZPHlyp/NtamqS448/vvQ4A3LGGWcsMSewF/e+3R7nnOy+++6lx6ij4MxXX321wCfCX3zxxUW+Lar36TH0+30M/eyzz2T99dcXQEaOHClnnXWWXHfddfLrX/9aVlhhBQEkCAI566yzxDnX4bJmz55dCsCutdZactppp8n1118vV1xxhWy99dalx3fYsGFtBkl7Ql1dXVngufnngQce6PayZs2aVbr/P//5zzbHLKp9+rrrriub/0033dTpfJ1zcsMNN0ifPn1K99t5552XmKDk7Nmz5Yc//KEAMnDgQPnNb34j119/fWmfqaqqkjvuuGOh19PW56iu/Fhr2/2s0tjYKHvttVfp9XDggQfK5ZdfLtddd50ce+yxpcd8wIAB8vzzzy/0NiillFJKLWoaAVFKLXYaAFZKLYiW2R7V1dXduu+FF15Y9vo///zzOxz/xBNPlMYefvjhnS6/vr5eVlppJQFk9uzZ7Y5zzslPf/rT0rJ/9rOfdTvL9IUXXpAoigSQHXbYoVv37SmTJ0+WZZddVgBZb731yk6+NzU1lU4yDx06VKZOnbrQ65s7d27ppOsJJ5wgP//5z+XHP/6xjBkzRtZee21Za621ZKONNpKtt95adthhhw6PLfPmzZM11lhDAFljjTVk+vTprcY88sgjpeDk9ttvv0gygZ1zbZ6YHD16tDQ2NnZrWS+99JIAUlNT0+6Y008/vbSO2267rdNlvv3222KtlXXXXbfDcb/5zW/K5t+dgPltt91Wdt/DDz+808DDora49+2OnH/++WWPzwcffNDu2FdffbXVxQSrrbaajBo1qs2f5m1ca621ev0xV4uHHkO97+MxdNq0aaXM1R/84Acybdq0stvr6+tLwU1AzjnnnHaXFcdxKQP1tNNOa3XhTpqm8qtf/aq0rMrKyg7fuxZGy2Bz80+fPn3k448/7vayKisrBZBXX321zdsX1T79wAMPlM3/rrvu6vKc33///bKL40aPHi319fVdvv+iUCgUZNttty19Jpn/uW8OjAdBII899thCrevuu+8ue+yWWWaZDo97AwcOLAXL2+KckwkTJgj4zOpXXnml1Zgvv/yydBHJsGHD5JtvvlmobVBKKaWUWtQ0AqKUWuw0AKyUWhAHHHDAAp+8ds7JscceW/YecN9997U7/phjjimNu+WWW7q0juYT5G2dMGp29tlnl5a78cYbL3Bg8fLLLxfwZS8XN+ecbLHFFqXteOGFF1qNefnll0u377vvvgu9zrffflvAZy0trBNPPLE0t0cffbTdcXvvvXdpXGeZPguqveyUgw8+uFtBuU8++UTAl6RszzrrrFNa/pQpU7q03B133FGqq6s7nEvLwHJ3A8AiIhdffHHZ/S+55JJu3b8n9ca+3Z7HHnusVN62+efTTz9td/ydd95ZCrY88MADne4/hx56qADyf//3fz09dbWE0mPo//d9O4bus88+peX997//bXPM119/LdXV1aVxb775ZpvjTjnlFAFfPaO9qg3z5s0rBbjBlzVeFMaMGdPmMXSttdbqdiC0uQx3e++zi2qfbhlY7m4AWMRf0NDc2gGQPffcs1eraTTvH9B2S4impqZS0HrEiBHS0NCwwOv64x//KIAMGTKkzdfR/LbbbjsB5P7772/z9ocffrg091NOOaXd5VxxxRWlcRdddNECz18ppZRSanGwKKWUUkp9BxhjFuq+f/zjH1l11VVL/zdhwgS++uqrNsd//vnnpd+jKOrSOg444AAOO+wwBgwY0ObtkyZN4txzzy3N529/+xtBEHR1E8occcQRDB8+nIkTJ5LP5xdoGQvqrrvu4vnnnwdgvfXWY7PNNms1ZsMNN2TttdcG4Pbbb+eZZ55ZqHVOnDgRgBVXXHGhlgPw9NNPl37fZJNN2h236aabln5/6aWXFnq9HfnBD35Q9vcNN9zANddc0+X7h2EI0OH+tCD79O9+9zsOO+ww4jhud8zCvC4Bjj32WLbZZpvS37/+9a957bXXFmqZC6o39u22TJo0iX333Zetttqq7P87en4/+ugjAP7617+yyy67dPi8zJ49m1tuuYVsNsvPfvaznpm0WuLpMfT/+z4dQ1977TXuvPNOAFZeeWXGjh3b5rihQ4ey3377lf4+66yzWo1J05SrrroKgLlz5zJz5sw2l1VbW1u2nv/85z/MnTt3gbehM6utthrW/v9TW++++y6HHnooItLlZXR2HF0U+zQs/DF0s80246STTir9fc8993DppZcu1DIX1MSJE/nLX/4C+Mfz0EMPbTWmoqKC/fffH4AvvviCP//5zwu8vubj3h133NHm66ilDz/8kMcee4yhQ4ey0047tTlmSfx8qJRSSim1sDQArHpdXV1db0+BhoaG3p6C+p54/fXXOeKII1h55ZUZNWoU1113XbtjRWSJeH0otbTIZDJlJ5oaGho47bTTOr3fI4880qXlL7vsslxzzTWMHDmyzdtPP/300onm7bffnnXWWadLy21LEAT88pe/xDlXdlJycTjvvPNKv++yyy5tjjHGsNtuu5X+bj5hvKC+/PJLoGcCwO+8807p91wu1+64WbNmlX53zi30ejvyyiuv8NOf/rTs/4455phFdmKxq/v0mDFjuPjii8lkMotkHuD3lYsvvrh0Etw5x29/+9tunbjvKb2xb88vn8+z1157EccxN954Y5fv99FHHzFkyJBW+1FbbrjhBpqamthzzz07DEwo1ZIeQ3vG4n6fafldY6uttioLlM6v5Xz+9a9/0djYWHb7e++9Vzo2Oud466232l3WqFGjSr+naVo6ji8Kl1xyCVdeeWXZ/91+++1ccskli2R9PbVP95Tf/va3LLfccqW/zz77bGbPnr1I19mWiy++uPQa3WyzzRg8eHCb41ru29dccw1pmi7Q+j766CNGjx5ddhFZe/76178CcPDBB5eC/fNbEj8fKqWUUkotLA0Aq8WuoaGBF198kcsuu4yxY8fSr18/Jk+eDPiA1y233MLGG29MbW0t22yzTenKzvmlacott9zCzjvvzLLLLksmk2HZZZdls8024/TTT+frr79u837OOT777DP++c9/cuaZZ7LBBhswaNCgsuyWOXPm8H//939sttlmDBo0iAEDBrDddtvx6KOPtlre7NmzueSSSxg/fjzDhg0jk8mw3HLLccQRR5R9OejM3Llzueqqq/jhD3/ISiutRDabZdCgQay99toceeSRPPXUU+2eDD3nnHMwxrT7s/LKK7e6z0033dTu+Lau1gX/JeuYY45hq622YsiQIdTW1rLWWmux3377lV0x+13yzTff8Lvf/Y5tttmGZZZZhpqaGtZff33OOOOMdq9qb0uSJFx77bXsueee/OAHP6CiooKhQ4cybtw4rr76auI45pJLLmGjjTbi6quv5vPPP+fjjz/mwgsvLFvOzJkz+e9//8vZZ5/NmmuuyXrrrVe6LZfLccYZZ7D66qvTv39/DjjgAA0QK9VNu+yyS1kG0z/+8Q/ee++9VuNWWWWVsjELm+U3Y8YMbrrpptLfBx988EItD3wG0913390jQdGuevfdd8tO+HaUcbHuuuuWfr/33nsX6mRkTwaAl1122dLvd9xxR5tjnHP8+9//BnymT3sn6XtKRUUF1113HRtvvHHp/wqFAnvttRfTp0/vkXW03KdPOeUUpk2b1iPL7Qnrrbce2223Xenvp556iv/85z+LdQ69tW/P75hjjuGVV17hyiuvZPjw4V2+39FHH829997babDeOVcKlLT3eU+p9ugxdOH0xvvMiy++WPp92LBhHY5tzjoGiOOY5557ruz2FVdckREjRgCw/vrrd5glOX/wb1Fe1JPJZPjFL37BscceW/b/v/3tb3usSkNP79M9qbq6miOPPLL09+zZs7ngggsW6xySJOG2224r/d3VfXvKlCn897//XaB1nn322Vx77bWdjquvr+cf//gHAIcccki747ry+RDg4YcfLv2+1157dWWqSimllFK9pzfrT6vvn1NOOaVVPzNAHnvsMUnTtNSPrOXP5ptv3mo5b731lqy55poCyEorrSTHH3+8/OEPf5B9991XgiAo9UC78cYbW9135513brNX0Oeffy4iIvfdd58MHjy43b581157rYiIpGkqf/nLX6Rfv37tjh09erTk8/lOH5ebb75ZhgwZIoBsu+22cuqpp8pZZ50l++yzT1lPnx133FEmTZrU6v7vvfeenHzyyTJ06NBWc9hhhx3a7FM0adIkOffcc0uPIyADBgyQ4447Tl588cWysfPmzZN9991XjDGy8sory3777SdnnXWWHH300dKnT5/S/U899dROt1VkyekBfNxxx0lVVZVsueWWctxxx8lZZ50lW2+9den22tpaueOOOzpd7qOPPiqjRo0SQNZbbz357W9/KyeffLJsv/32pWWtsMIKMnDgQLn77rvls88+kwMPPFCWXXZZuemmm0rL2Wqrrdrcj+I4ltmzZ8sGG2zQ6raO+hMptbQ58MADF7h/YUtnnHFGp6+jF154oWxMbW2t3HXXXd3qy9rS/O8/U6dOXeD596aW/RdbHjvb8vHHH5eN/de//rXA691zzz0FkOuvv36Bl9HsyCOPLM2pqqpKXnvttVZjWvZ3u+aaaxZ6ne1pXkezyZMnyzLLLFP2uG277bYSx3GHy/niiy8EkBVXXLHdMeedd17ZcldbbTV5++23F3ob5n89dbcHcLP5XyM//elPF3pu3dFb+3ZLt956qwCy//77l/6v5Xq++OKLhV7Hv//9bwFklVVWWeD3M/XdpMfQ3re432fSNJWqqqrSMi6//PJOxzd/lwbkz3/+c6sxhUJB3nrrrU6fy/n3k+725O2K5h7Azd8nC4WCbLPNNmXrXWaZZbrU937FFVfs8H22p/fpZk8++WTZcrvbA7jZZ599Vrac5ZZbbrH2An7mmWfK1t/Z57Vhw4aVxv72t79dpHO78sorBZBx48Z1OO7OO+8s24arr7661Zg33nhDwjAsfU7R46hSSimllnQaAFaL1auvvirnnXeerLfeemUfrh9++GE59dRTBZCBAweW3bbqqquWLeOxxx4rfZE99dRTW32xefnllyWKotL9H3vssbLb33jjDbnqqqtkww03LFvPBx98UDo5uummm8rNN98sb7zxhtx1112y8sorl8b16dNHpk2bJvvss4+AD8r+/e9/l1deeUX+97//yRFHHNFq2zpyzjnnCCCZTEb++c9/trp9ypQpZUHJ4cOHy2effdbmsr799ltZfvnly9ZfKBQ6XP9zzz0n4AOUbZ1M+fbbb2XjjTcWQHbfffdWJ6EnTpxY9pzdfffdHa5PZMkJAK+44oryn//8p9W4M888s2zcDTfc0O4y//Wvf5VOlEyYMKHV4/PPf/5TKisrBZAoitoMNLQce/LJJ8uyyy5btv66urrSPjBgwICy2w477LAFfjyU+q7pqZPXr732WtnraI011mhz3F577VU2rjkY99JLL3V7nT/+8Y/L3nu+q3bdddeyx2POnDntjm1oaCgbe+aZZy7wepuP2Y8//riIiMRxLDNnzpSvv/660+Do/CZOnCj9+/cvzauqqkr+/ve/l26/7rrrSu/rZ5xxxgLPuSua59DSs88+W/Y5pisnR7sSAK6rqyud4G7+sdbKoYceKpMnT17gbeipAPDMmTPLltO3b98uXUTXU3pr32725ZdfSt++fWX48OFl6265np4IADdfCHnBBRcs9LLUd4seQ3vf4n6f+eabb8qW8Ze//KXT+7S8uPnkk0/u9jqbtXzONt100wVeTkfmDwCLiEyfPl2GDx9ett2bb755p8eTzgLAIj27TzfrqQCwiMjaa69dtqyFmVd3XXTRRWXrvv/++zscv9FGG5XGbrPNNotsXs45WWuttQSQ22+/vcOxSZKUzns0//z6178u7Tsvv/xyKVFgxx13lIaGhkU2b6WUUkqpnqIBYNUr5s2bJ3379i19sD7ggAPEGCO/+tWvJI5jueqqq8QYI9basuzVjz76qHS/o446qt0rLo877rjSsjfYYIM2x3zzzTdijCmN22mnncRaK+ecc44kSVI29v333y8bW1NTI0OHDi2diG7JOVcWXP7lL3/Z7uNw9913l8adc845HT5eLTN1119//XZPeF977bVlX1qeeeaZdpcrInLppZcKIPfee2+bt//yl78sLeuqq65qc8wf//jH0pj11luvw/WJLDkB4JdffrndsS0zxDKZjHz44YetxsybN6+UuR0EgXz11VdtLuv4448vLWuVVVaRpqamDuc5/xXUBx10UOm1UCgUSifwBgwYIO+99173HgSlvsN66uR1Pp9vFWD7+uuvW42bN2+ebL755q1O9jWfzLz66qulrq6uS+tseTJyzJgxCzz33tbyIiNrbYfZJc65skyiH//4xwu83kGDBgkgG264Yel9t/nHGCMbbbSRXHTRRZ2+vzZ7/vnnpba2tmw5P/rRj+TEE08UQIYMGdLmRVk9rXnd82vOVmn5c+edd7a7nK4EgEV8xZD5Hz9AwjCU3XffXR544IFWn4E601MBYBFpFaB+4YUXFnhZ3dVb+7aIP+m89dZbizFGnn766bLbWj4eCxsA/vzzz8UYI2EYfmczKNWC02No71vc7zNxHJc9V0cffXSn92mZmfmLX/yi2+sU8Rcctaxi9eCDDy7QcjrTVgBYxF+k0HL9zecOOtKVAHBP7tPNejIA3PI1Dsh55523wMvqrgkTJpSte/5j2fzGjRtXGtu/f/9FNq+nn35awCcZ5HK5TsdPmzZNVl999bJtWWONNeRPf/qTVFVVSSaTkbPPPnuxZlcrpZRSSi0MDQCrXjN69OiyD9bbb7992UnHyZMntyqLtcMOO5QCcjNnzmx32c0l9Jp/2gvMNZ9QBl8y+qmnnmp3mS3L766yyiry5Zdftjv29NNP7/QkRUNDQ1mp6bZOnLT06KOPlm3TFVdc0ea4xsbGsizRzjJEN9xwQxk1alS7X2JaXkncXibURx99VDa3zk4qthzbmwHgjrLGvvrqq1J5J0B+8pOfdLi8ESNGtLusTz75pGy9nZVwmzdvXquTCi0vEHDOybvvvivffvttF7ZaqaVHT528FhFZZ511yl5j7V0Qks/n5Ywzzih7P2j507dvXznhhBM6zKB0zkkmkyndZ/fdd1+ouXdFfX29fPHFFwv1M3+5xjRNyy6GGjBgQKfzaJlJtNVWWy3QttTV1QkgK6+8svzyl7+UP//5z/KPf/xDrrvuOjnmmGPKSiaPGDFC3nnnnS4t97PPPpPNNtus1XO68cYby9y5cxdort3VvM75OedatcWorq5u96KfrgaARfzJzbaymJp/Ro4cKZdcckmXy3X2ZAB4/uy4toLeS9O+3ay5Ak1bmd4tH4+FDQCfcMIJCxxIUt99egztuqXpfaZlMGuFFVbo8CIf55xUV1eXxh933HELtM6Wpa4nTJiwyMrkthcAFvEtlubf39pqD9WsKwFgkZ7Zp1vqyQDw/Fm4Rx55ZKsxSZIs9L7d1nmY+Utvd9ZiYvfddy8b392Lz7pq7733FvCZvF1VV1cnP//5z1s9t3rxtVJKKaW+izQArHpNy7LGmUym3bLGzVr23dlkk006/FJyzTXXlH1Yf/bZZ9tcZstMk86+FDT3H4TOr4Zumbmz7rrrdjpmk0026XB5Iv4L+aqrrlq6zw9+8IN2xzaf4ANfsrq98kQvvfSSAHLllVe2u6xTTz1VjDEydOhQ+eijj9oc09jYWPZ4z192e34tx/ZmALgzP/nJT0pjKyoqWp3IOfbYY0u3b7bZZu0uJ47jTvtpteScK5vn6quv3mkpb6W+D3ry5HXLHt2A3HfffR2Of+utt2SvvfYqey23/KmtrZUbbrihzZOc85e2XdS9TadOndphf/qu/hx++OFly507d27Z7SuvvHKnc2mZ7bT22msv0PY0NDTIp59+2u7t06ZNk+222660nn79+nX6maJZkiRy0EEHtdr2MWPGdDmQvDA6Oh7lcjnZdNNNy+Y1atSoNoPT3QkAN/vnP//Zavktf1ZZZRV58cUXO11OTwaADzvssLJlXXzxxWW3L237tojIK6+8ImEYyiqrrNLm57WW81qYAHDLCwQ7a0+ilk56DO2ape195qijjipbb0fVLeaf44UXXtjt9X3yySel7NvNNttMGhsbF2jeXdFRAFhE5De/+U3Z9lRUVMgbb7zR5tiuBoCbLcw+3VJPBoBvueWWsmXtttturca0deFbd39GjRrVarnrr79+2ZjOAuDzZwwvigubp0yZUgrUL0jg9rrrrmu17SuttJI88MADPT5XpZRSSqlFJUSpXmKMKf0+btw4Ro4c2eH4u+++u/T7Sy+9xIgRI7q8rmnTpnU6Zq211urw9pqamtLv+Xy+w7EVFRWl3+fOndvmmPvuu6/0+zrrrNPp/Iwx7Lzzzlx88cUAvP/++3z77bcMHDiw1dgjjjiCP//5z4gI8+bN495772XChAmtxl1++eX069ePn/3sZ+2u9w9/+AO//e1vqa2tJQiCNsdUVlYyYMAAZs2aBcDMmTM73Z7vgg022IA77rgDgFwux+uvv85WW21Vuj1JktLvuVyu3eWEYciAAQOYMWMGACLS4XpbvjYADj74YKIo6vb8lVLt69evX9nfhUKhw/HrrLMOd911F5MmTeLKK6/kuuuuK72mAerq6jj44IN57LHH+Mc//lH2fhnHcdmyZs+evfAb0IEBAwZw8803t1pvd1RUVDBu3Liy/5s3b17Z3/379+90OS3f75qamhZoLlVVVay88srt3j5kyBBuvfVWRo4cSX19PXPmzOHggw/mqaeeavV+2tK0adM45phjuPPOO/nxj39MTU0NN910EwBPP/006623Hqeeeiqnn356u8e/RSmbzXLPPfew4YYbMnXqVAA++ugjDjroIO65554Ot60rdt11V3bddVdeeeUVLrvsMu68886yzzeffvopW2yxBZdeeilHHXXUQq2rqzp7XS5t+3ZDQwP7778/SZJw7bXXUlVVtUDL6Yo77riDWbNmscIKKzB+/PhFth71/aDH0I4tSe8zJ554Irfccgtz5swB4Je//CVrrrlmm8fVKVOmlP3d0bG3LbNmzWLnnXcml8ux2Wab8Z///IfKysoFmndPuPDCC3nrrbd4/PHHAf99bY899uDVV19lwIABC7XshdmnF5WuvC4vvPBCvv3224Vaz5gxY1r9X3f37/m/Dy/o/t2Ra665hiRJ2GKLLfjBD37Q5fsVCgX+8Ic/8Mc//pE11liDPfbYg4svvpjGxka+/PJLdt11V3bffXduuOGGVo+5UkoppdSSRgPAaomw7rrrdjrmiSeeKP3+4x//mAMOOKBLy85kMmy77badjrPWdml53dVWsE9EeP7550t/txXEbcv8Qe+PP/6YzTbbrNW4kSNHsuOOO/Lwww8DcMMNN7QKAM+YMYM77riD4447jurq6g7X29YXm0KhwJtvvskLL7zAM888Uwr+wqL5Atcb5r8oofkEfLNRo0aVfv/mm28QkTZPyDc1NZWdEFhllVW6NY+uvD6UUt0z/2t12LBhXbrf8OHDOf/88znzzDO59dZbOfnkk8suMrrllltYeeWVOeuss0r/N/97/DfffLMQM+9cJpNhp5126vHltrwQCujSSd2WF8r07du3x+fUbPDgwRx99NGcf/75ADzzzDO8+OKLbL755m2Of+qpp9h3332ZNm0aZ5xxBqeffjrWWo488kgOPPBAPv74Y9I05eyzz+aNN97gvvvu65Ug8LBhw7jnnnsYM2ZMKRhx3333ceGFF3LSSSf1yDo22mgjbrzxRi6++GL+7//+jz/+8Y+lk8bOOY4++mhWX331VsGMRaGz1+XStm//5je/4eOPP+aII45o84R6T7riiisAOOSQQ3plX1ZLFz2Gdl9vvc8MHz6c++67jz322IPZs2fz9ddfs+GGG3Lsscey/fbbM3jwYGbNmsVDDz3EddddV7qftZaxY8d2eT0NDQ3stttufPTRR4wZM4YHH3yQ2traBZpzTwnDkDvuuIMNN9yQL7/8EoAvvviCCRMm8K9//atHvv8vyD69qHTlddnyYuae1N39u+W+DT3/GTGOY6655hoADj300C7fb+LEieyzzz68/PLL7Lzzztxyyy306dOHww8/nF/+8pc89NBDANx///288847PPvsswwdOrRH566UUkop1ZMWTcRLqW7KZrOdjvn6669Lv/ft25fdd9+9Sz8/+tGPevXK47bMmzePhoaG0t/zf2Fqz3LLLVf298cff9zu2JbZOk888UTpS2+za6+9ljRNu5XVU1dXx2233cYee+xBv3792GSTTTj99NPJ5/Nd3obvkiFDhpT9PX8W7k9+8pNSts7UqVN577332lzO+++/X/q9pqaG7bffvlvz6MrrQynVPS0vygC6ffImm81y8MEH8+GHH7LzzjuX3XbBBReUnaCOoqgs0+Tdd9/9Tl4o07dv3w6zstrS8gRfV7KdFsb8JzUffPDBNsc9/vjj7LDDDkybNo0//OEPnHnmmaWTwJtuuimvvfYae++9d9lyzjvvvEU38U5sttlmpeBds1NPPZXHHnusR9czcOBAzjnnHN55552yrC8R4fjjj+/RdbVnYV+XC6o39u1//vOfXHPNNSy//PJceOGF3b5/d7z00ku8+uqrABx44IGLdF3q+0GPod3Xm8fQbbbZhrfffptf/epXDB48mDlz5nDWWWex5ZZbMmrUKLbaaiteffVV+vTpU7rPpptu2uV1zps3jx/+8Ic899xz7Ljjjvz73//u9eBvs4EDB3L//feXnQ/497//3eMB2e7s04tKbx1DAQYNGlT2d2f7d8t9OwzDTi9I7657772XqVOnUlVVVfaZriNfffUVY8aM4eWXX2b8+PH885//LL0mVlhhBR544IGy4/Vnn33GhAkTOq3upZRSSinVmzQArL4zmstWQXkw+Luovr6+7O/p06d36X7zB4AbGxvbHbvDDjuUZQz//e9/L/2eJAlXXnkle+yxB8OHD+90vUmScNlllzF8+HB++tOf8vLLL/Pb3/6Wl19+uXTFeFezmL9L5i8dPv+X6Oayo81XW1900UWtvgCKCKeffnrp73PPPbfHv+Aqpbqv5UmyIAgW+CRZv379uPXWW8tKyxUKBf75z3+WjWtZrSGOY1566aUFWl9vMsaUneCbP3ujLS3HzH9ysKfNXyXjo48+ajVm5syZ7LPPPhQKBbbaaqs2s2hramq45ZZbyjJe//SnP3V4zF3UDjvsMI444ojS38459t13XyZOnNjj61pttdW47777ysoRv/XWW20+nj1t/pPXXfmM0hMW977d1NTEz3/+cwBOPvlkZs2axZdfftnmT0tfffVV6f+nTJnS5ZPO//d//wf496HutFBRqj16DO2+3j6GLr/88lx22WVMmzaNjz76iKeeeoqHHnqI119/nVmzZnH77bczefLk0vhf/OIXXVrunDlzGD9+PM899xx77bVXq2DrkmDdddfl+uuvL/u/s88+m3/96189vq6u7tOLQm8dQ6H1/tnZ/j3/vr2wbS3m13zc22233br03VtEmDBhAhMnTmTgwIH8/e9/b5Uhbq3lxBNP5Iwzzij93xNPPMELL7zQo3NXSimllOpJGgBW3xktv8C0/HL6XTR/z6Gubs/8pZiXWWaZdscGQcAvf/nL0t//+Mc/cM4B8K9//YvJkydz3HHHdbrOOXPmsOmmm3LMMccwZ84czj77bL788kvOPvtsNtpoI8Jw6a0k3/J5CYKgrORzs912262UTXPDDTew995789///pcPP/yQJ554gt13352HH34Yay2nnHIKRx999GKbv1KqbQ0NDWUVFMaNG7dQvTdra2tLJ5qazR8sm78VwW233bbA6+tNLUvS19XVdTjWOVcWNB09evQimxe0PtHZstJGs7/85S+llgVnnnlmu6Vwoyji2muvLd1eV1fX4xm33XXJJZewxRZblP7+9ttv+fGPf9zqorKesPbaa7f6jLCoA8Aiwptvvln6e7XVVut2y4SFsTj37crKylKW9VFHHcWIESPa/Wlpq622Kv3/iiuu2KXPj5MnT+auu+4C4Kc//Wm35qlUW/QYuuCWhGOoMYbVVluNMWPG8KMf/Yj111+f2tpabr755tL6VlxxxS69X8yZM4dx48bx0ksvcdBBB3HbbbeRyWR6ZJ49bd999+XEE08s+78JEya0W8FpYXRln14UWh5DAX70ox8t8nU2m79lUWf7d8vPaD39+fB///sf//vf/4CuH/ceffRRnn76aQCOPPLIDi9qOf3001l11VVLf99zzz0LMVullFJKqUVr6Y3cqKXOKquswqeffgrABx98wOeff96qR+t3RWVlJauuuiqffPIJ4L+kpGnaaU+2llnQABtuuGGH4w855BBOO+008vk8X375JU899RRjx47l8ssvZ8MNN2yzf3BLhUKB3Xffnddeew2AE044gdNOO62TrVt6fP7556Xft9566zbLoD399NPcdNNN7LLLLuRyOe65556yL4FhGLLbbrtx7rnnsuaaay6WeSulOvbUU0+VepwC7LPPPmW3T5kyhYaGBlZbbbUuL3PbbbclCALSNAVal77be++9Oemkk0oZD7feeit/+tOfysot9qSGhoZWmSDdNWjQoFbl/bfZZhseffRRwPdJa6/3OfjAU8vHYdNNN12o+XTm22+/Lft72WWXbTXmvvvuA3zP4G222abD5a200kqMHz+ef//730D5MaE3ZDIZ7r77bjbYYINSJZQ33niDn/zkJ1x00UUd3veDDz5gwIABHV44Nr/x48eXlb7uSrnShfH+++/z1Vdflf7eZ5992ty3lpZ9+9FHH+WFF14gn893OO7HP/5x6ferr7661J5inXXW6VJ21xVXXEGaplhru1wGU6mO6DG0a5aE95muamxsLDuOnHXWWa1a38wvl8ux++678/rrr3PggQdy3XXX9UhP3UXpvPPO48033yw9B3PnzuVHP/oR//vf/zrMWF0U+3RPc87xyCOPlP7ecsstW1UPA0jTdKEvpq+pqWmV8Tv/Z6ovv/yyzc9hzZrP60DP79vNwff+/fszfvz4Lt2n+fMhtH5Pm5+1lkMPPZTf/e53QO9/PlRKKaWU6ogGgNV3xqhRo/jPf/5T+vvGG2/kzDPPXKhldqdfS0+P3WmnnUpfTqZOncrjjz/e6ReUDz/8sPT7D37wg05P/A0cOJB9992Xf/zjHwBceeWVDBo0iMcff5ybb76501JLt99+e+lKWPAB4IXVVonkJVGhUChlzAAceuihbY475ZRTSNOU8847j7XWWovp06fz5ZdfMmvWLEaMGMHIkSM7PYGilFq8br311tLvffv2LQuwAPz617/mySefZMqUKV3OZAnDkIEDB5ZK+s+fudhcQv/GG28EfCuA8847jwsuuGBhNqVN33zzDWussUari4a66/DDD+eqq64q+7+WWVi5XI5vvvmm3SyJlhliffr06fSipYU1/wm4tdZaq9WYL774AvDlortyonqNNdYoBYCXhJKWyy67LPfeey9bb711KQDz8MMPlwVO2zJmzBg233xz7r///i6vqznQ2GxRZ+O2fF2GYciECRNajVma9u2+ffuy4447dus+48ePZ6WVVury+IaGBq655hoAtttuu25dAKBUe/QY2jVLwvtMV51yyimlY+gee+zBAQcc0OH4NE2ZMGECTz/9ND/84Q/529/+1u4xNZ/Ps99++3H22We3eVxenIIg4LbbbmOjjTYqbe+kSZMYN25chz16F8U+3dMef/zxsosWDjnkkDbHbbXVVrz44osLta5Ro0aVnZcAf2F6dXV1KbP3iy++aDewm8vlylpYtGy5sbAmT57M3XffDfgLR7r6fDV/PgRKFTo6ssYaa5R+XxI+HyqllFJKtUcDwKrX5HK5Nn9vz7777ssll1xS+vvaa6/lN7/5zUJd+d3U1FT6vbMMjJZXurf8vS0tl9Xe2KOPPprLLrusdFXwdddd12kAuOVVvfOXsGrPUUcdVQoA33333bz44osMHTq0S1kgLftrDRw4kMGDB7c7trm8NHT8fM5/W2ePe2+55557Sj2AR48ezb777ttqTENDQ+kxaj4hO2TIkFYnzbtr/seoK68Ppb4PeuKCkVdffbXs5PVFF13Uqix/TU0NM2fO5OGHH2b33Xfv0nKbmprKTry1dTLr9NNP57777iuVxfvLX/7C/vvvz9prr70AW+L7p/3qV79i1KhR/PrXvy79/4ABA7j55psXKtukoqKizW3YZJNNWHPNNUslEz/77LMunbw+4IADFvkJsvlLH+68886txtTU1DBr1qwul01ueQzfZJNNFmp+82uZbZQkSZdbKmyyySZcddVVZSd333777Q7vU1NTw0MPPcT06dO7fIxqmSE0dOjQspOdLfXE63Ly5MlcfPHFpb9PPvnkNtsufF/37QV14403Mnv2bAD222+/Xp6N6m16DC2nx1DvmWeeKX3HHj58OH/72986vUj4jDPO4J577mHYsGHcdtttHV7s+swzz3Dfffdx0kkn9ei84f8fR7vST7nZgAEDuP/++9l0001LJa8/+OCDDu+zqPZp6JnXZZqmZRdqb7311qUWRfO78MILW1VM6a4xY8a0+r8oijjooIO44oorAL9vt+ezzz4rbfdaa63VaVWy7rj88stL51e6c9xrma1fX1/f6ettUX4+VEoppZTqUaJULxk1apQAAsjhhx/e6XjnnOy0006l+wCy//77i3Ou3fvMmzdP9tprL3n99dfbXF4URaVlXXjhhR2uv+W6d9555w7HXnjhhaWxNTU17Y47/fTTy7bnkUceaXfshx9+KGEYCiCbbbaZxHHc4Rxa2mijjcrWc84553Tpfueee27pPkEQyLx589oc9+KLL5Yt/4ILLmh3mVOnTi0be/vtt3d5OxbW9ddfX7bu2bNntzlu7ty5ssYaawgg/fr1kw8//LDNcS+//HJpWQ888ECPzXP+x+i2227rsWUr9V22xx57lF4X1dXV3b7/tGnTZPXVVy8tY8cdd2zzGHL00UcLIFtttVWX32tfffXV0nLHjx/f7rg777yz7PW90koryTfffNPtbWlsbJRdd91VRo4cKZMmTer2/RfGDTfcUJr/mWee2e64bbbZRgAJw1A++OCDBV7fG2+8Iccdd1yH77NJksiqq65amtcuu+zS5ritt966dEybPn16p+veaqutBJAhQ4ZIoVBY4G1oy7Rp00rznTlzZrfv/6tf/apsXwJkxRVXbHPs2muvLYCcfPLJXV7+n//859JyzzvvvHbH/eY3vymbw4wZM7q1HfX19aXHGZB1111Xcrlct5bRUxb3vt0VLR/bL774osv3S9O09Fk7m83KnDlzFt0k1XeCHkP/v+/TMbQjL7/8sgwaNEgAGTZsmHz88cdduk8QBALIHXfc0en4n/zkJwIskse5+fva3Xff3e37zr8vdfQ+uyj36QceeKBs/XfddVe3tsM5V3Ycrq2tlY8++qhby+gpn376qVhrBZBtttmm3XFnnnlmab7XXnttj62/rq5O+vXrV9qfkyTp8n1bnpe59957Ox1/2mmnlca/9dZbCzNtpZRSSqlFSgPAqldMnjy5FMwEZLXVVuvSB/Rp06bJaqutVvYl6YADDpCGhoZWY9955x1Zf/315Wc/+1mbX9SeeeaZsuV0FNRNkkSWWWaZ0thhw4ZJmqbtjt91113Llt3el4I4jmWfffYpOxnzwAMPtDqZ8uqrr5ZObI8cOVKmTZvW7rrb0vJEQzab7dJJbxGRxx57rGw7jjrqqLLtzufzcsYZZ5Q9l4D86le/KtvGlm677baysUceeWS3tmVhXH755WXr3njjjWXu3LllYyZNmiSbb765ADJw4EB59tln213etGnTSidAamtr5aSTTpKrr75axo4dK6NGjZJRo0bJ6quvLhtttJFst9128pOf/EQuueQSmThxYofzvP3228vmeeihh/bI9iv1XdcySBRFUYfvw/N77733ZM011yx7z29sbGxzbMsTU7/4xS86vNBIxJ9823HHHQWQ/v37y+eff97h+IsuukiMMaV1rLPOOvLpp592eVteeuklWW+99WTEiBGdvp8sCvl8vhRQHDFiRJvBupYnPk877bR2l/Xkk0/KaqutJv369ZOTTjqp1WOdJImsuOKKpWW1d7HWzTffXBrTp0+fdk9+Xn311aVxP/vZzzp8bl977bXS83T11Ve3ur2urk523313qampkS233LJbwbnm5S/MycNCoVAKaHcWAG4OJAByyy23dLrs2bNny9ChQwWQTTbZpMOA7M9+9rOyOXz11Vdd3oZJkyaVbcPGG2+8QMHwnrI49+2uaGxsLHtsuxMEeuihh0r322OPPbq9brX00WOo9306hnbk0Ucflerq6lIgvSvBX+ecjB49WgCpqqqSpqamDsfefvvtYoyRMAwln8+3uv33v/+99O/fX1ZffXX5z3/+0+W5i/jvmP379xdALrnkkm7dt9nJJ5/cpQDwotynr7vuurL133zzzV2ef319vRx11FGl+w4ePFhee+21Lt9/UTj88MMFEGttm8esxsZGGTx4sACy9dZbt/s+NGnSJBkzZoxUV1fLrrvu2uo7e1uuuOKK0mPxm9/8plvzfv/990vB6zXWWKPdC99F/MXiyy+/vACy3377dWs9SimllFKLmwaA1WJ16aWXyimnnCIrr7xyqy9bG2+8sVx44YWdXvU6bdo0GT9+fNl9Bw8eLEcddZRccMEFcuKJJ8o222wjffv2lUsuuaRVYPn++++XU045pfTFo+XPXnvtJZdffnkpy2fixInyl7/8RbbccstWY3fYYQe58sorS198c7mcXHHFFbLvvvu2Gjt8+HA5++yz5YUXXmi1PUmSyBlnnCEVFRWl8ZtuuqmccMIJcuaZZ8oee+xR+jKyxx57LNCJ0cbGRhkwYIAA8vOf/7zL93POyaGHHlq2LZtuuqmceuqpcuCBB8qgQYOkb9++cvbZZ5eCpuCznn/3u9/J+PHjZd9995VcLicXXHCBHHPMMdK3b9+y5QVBIPvvv7+ce+653Tp5syCaM7P79u1bejxWWGEFOfnkk+XMM8+UPffcUzKZjAAyduzYTk9AiYiceOKJrZ7vzn4qKipaZVNNnz5dLrjgAjn66KNLVy43/xhjZM8995RLLrlEnnvuuUX18Ci1RJs8eXLp9dn88+CDD3Z4H+ecvPnmm3LMMceULlQJw1B++9vfdpjN+fDDD5etZ88995R33323zbFz586VY489tnSS78knn+zS9tx3331l74d9+vSRv/71r21e0CTis/n+97//yc9//nMxxsh+++3X5Yt5FoWPP/5Y+vTpI4Acd9xxZSdDJ02aJCNGjBDwWS8dBQ+HDx9e9ljP//g551pd+DV/ps+DDz5Y2jeqqqo6rKYRx3HZ8erkk09uM4jxwgsvyAorrCCAHHLIIW2e7P3Tn/5UNq8NNtigyyff0zSVgw46qHTfww8/fIEChN98803pJGRHAeCWx6ooiuR3v/tdu/vPZ599JuPGjRNA1lprrQ4DuvX19a0+T1122WWdzvvjjz+W0047rRR8AOSggw7q8ITr4rK49u2uaBnEBeSmm27q8n232267dl8z6vtHj6Hfz2NoW5Ikkcsuu6xUCWuPPfZotyrS/F5//fXSujKZTOmC17Z+hgwZUhq79tprt1rWK6+80ur7UXeek5YXOK+zzjoLVDkiSZJSoLb5p60A8KLcp+evcLbXXnt1enHGjBkz5Morryy7QG6LLbbotczflpqamkoXCWy55ZZlx/VcLlfa3uHDh8uXX37Z7nIOOOCAssfljDPO6HC9aZqWfV589dVXuz33lp+VdtxxR5kyZUqrMZMmTZKxY8cK+IolXQlMK6WUUkr1JiPSA01HlOqCJEmorq7utH9uJpOhoaGhw154IsLjjz/OHXfcwbPPPsu0adNobGxk2WWXZeTIkey+++7su+++LLPMMq3uu/LKK/P55593OIfJkyez/PLLc8MNN5T12GvLp59+ysorr8wXX3zByJEjOxw7YcIEbrrppjZvmzRpErfccgsPPfQQX3zxBTNmzKC2tpbllluOsWPHMmHCBDbccMMOl9+RSy65hAsvvJDnnnuu03m2lCQJ99xzD3/729/46KOPmD59OgMHDmSllVZiv/324+CDD6ampob333+fX/ziF7z55pvU1tay5pprsssuu3DEEUcwffp0hg8f3um6brzxRn72s58t8DZ25u9//zt//OMfefDBB1lmmWW4+uqruemmm5g8eTINDQ0st9xyjBkzhv3335+xY8d22v8KfO/jn/zkJ7z44os0NDQwZ86cLs/nqquu4vDDDwfg2WefZeutt+70PuPGjeOxxx7r8jqU+i777LPPuOuuu5g+fTp33HEHX3/9ddntxhi22GILNthgA/r37091dTW5XI65c+fyySef8OabbzJx4kQAstksu+66K+ecc06bvUVbmjNnDv3792+1ri233JJVV12VFVdckUKhwCeffMJTTz3F9OnT2XbbbbnuuusYMWJEl7dv1qxZ/PGPf+TSSy8t9aTv378/22+/Paussgo1NTVMmzaNb775hueee44pU6aw9tprc/7557PTTjt1eT2LyrPPPsuECROYNGkSW2yxBePGjWPmzJncdNNN1NXVsd9++3H99ddTUVHR7jKGDx9e1mv2ySefZJtttikbM3HiRA455BCeeOIJAIIgYP/992fUqFG8+eab3HvvvaRpyujRo7nhhhtYZ511Opz3nDlzOOCAA3jwwQcBWG655dhpp51YaaWVmDVrFk8//TSvvPIKFRUVnHbaaZx88sltHg8uuugijj/++LL/e/fdd1lzzTXbXfejjz7KE088wSOPPNKqZ/FGG23EdtttxzbbbMP48eM73IaWXn31Vbbcckvy+TwjRoxo8zPOAw88wG677Vb2f1VVVYwZM4YRI0aw/PLLM2PGDD755BMeffRRRISDDjqIiy++mOrq6rL7zZgxg+uvv57Zs2dzzz338Omnn7Za3+jRo9liiy3o378/tbW1FAoF5s2bx2effcbbb79d6m0ZBAHbb789Z511FhtvvHGXt3lRW1z7dlveeOMN/vvf//LZZ59xyy230NDQULqtqqqKXXbZhdVWW41f/epX7fZzfuedd0qvg9raWqZNm7bE9ilWi44eQ/UY2pJzjkceeYTTTjuN1157jdVXX52zzz6bvfbaq0vfeQDOPfdcfv/733drO6Ht78Cvv/46G2ywQdn/XX755Rx11FHtLuf999/n7rvv5qWXXuLhhx8uu2355Zdnzz33ZO211+bnP/95l+c2e/ZsNt5449KxbOLEia2+t/b0Pn3ZZZcxc+ZMnnrqKZ555plWtw8fPpzx48czZMgQ+vbti4hQX1/P5MmTeffdd3nrrbdKfY/XW289TjzxRPbdd98uP4+L2ldffcVPf/pTnn32WVZZZRV22203stkst956K19++SWjR4/mnnvuYaWVVmp3GQcccEDZPnPGGWdw5plntjv+X//6F7vssgsAq666Kh999FG3Hw/nHKeffjrnn38+zjkqKyv58Y9/zBprrEEul+Ott97i4YcfxjnHfvvtx9VXX01tbW231qGUUkoptdj1avhZKaW+w5xzcsIJJ8iyyy7b5pXzcRxLXV2dTJ8+XV599VU599xzZeDAgWVZWAvSt0yp74sbb7yx2xn2gFRWVsqyyy4rm2yyiRx++OHy97//vdv9LydPniyvvPKK3HTTTfLrX/9axo0bJyuuuKL07dtXwjCU5ZZbTrbYYgs5+uij5Z133lmo7ayrq5MHHnhAjjzySNlss81k5ZVXltraWqmoqJBVVllFtt12WznhhBOWyB5jc+fOlZNOOkk233xzqa2tlcGDB8vuu+8ujz76aJfu/8QTT8iqq64qffv27bB8ZZqm8sQTT8ihhx4qa665plRXV0tNTY2suuqqctBBB8mDDz7YrQxa55w8+eST8otf/ELGjBkjK6ywgkRRJMsss4xsvvnmcs4553RayripqUl+//vfl2Vg3X///R3epzlrpKOfsWPHdnk7mj388MMyfPhwOfbYY9u8PY5j+fTTT+XZZ5+Vv/71r3LYYYfJpptuKsOGDZPq6mrJZDKy8sory7hx4+QPf/hDh8em+VtodPUnm83KkCFDZP3115eDDz5YrrzyyiX6GLi49u35nX322V16PDtqUXH88ceXxh1wwAFdWq9a+ugxVI+hLW2//fZirZXtt99ebr755i73sm3pyCOPXKB96k9/+lOby7vhhhtkgw02KI077rjjOlx/V94fM5lMt7ft888/l4022khGjx7dbvZtT+3TcRy3ysbvyk8YhjJgwAAZNWqU7L333nL++efL+++/363tXJzSNJW//vWvMn78eFlmmWWktrZWttxyS7nmmmu69Pw0t4ioqqqSXXbZpdNM2z333LP0WJ1++ukLNfcPP/xQfve738kOO+wgq666qmSzWenfv7+svfbactxxx/V6mW2llFJKqe7QDGCllFpAf/7znznhhBM455xzunw1/MSJE9l9991LmV/33HMPe+yxxyKcpVJKqUVJRDj//PM59dRT+fe//80Pf/jD3p6SUkopVeb1119n2LBhLLvssr09lVZeeOEFtthiC373u99xwQUX9PZ0lFJKKaWUWmrY3p6AUkp9F02aNImTTz4ZgE033bTL91txxRV58sknS+Wivvjii0UyP6WUUouHMYZBgwYBLFFljJVSSqlmo0ePXiKDv0DpGLrJJpv08kyUUkoppZRaumgAWCmlFsDf/va3Uu+ltnpNd6Rfv36svvrqgO9RpJRS6rvr66+/5vTTT+eAAw5gwIABvT0dpZRS6jsjjmN++ctfMmrUKHbYYYfeno5SSimllFJLlbC3J6CUUi19++231NXV9ciyjDGsuOKKPbKs+b399tul37tbST9NU7744gsGDhzI2LFje3pqSimlFgMR4emnn2bChAkMHjyYSy+9tLenpJRSSn1nfP755xx00EG88847PPbYY1RVVfX2lJRSSimllFqqaABYKbVEOeCAA3j44Yd7bHnTp09n8ODBPba8ZsaY0u/PPfcc66yzTpfve8MNNzBz5kwuu+wyampqenxuSimlFr2dd96ZJ598ksMOO4wLLriAysrK3p6SUkop9Z1w4YUX8vvf/57x48fz9ttvs8IKK/T2lJRSSimllFrqGOlu6ppSSi1Cb7zxBhMnTuyRZVlr2WWXXcqCtT3lzDPP5KyzzgJgyJAhPP3006Wyzh256667mDBhAocddhiXXXbZIpmbUkqpRe+dd95h5MiRVFdX9/ZUlFJKqe+UiRMnkslkGDp0aG9PRSmllFJKqaWWBoCVUmoBTJkyhTXXXJO5c+cCMHToUG644QbGjx/fKqibz+d5+OGHufbaa3niiSe46KKLOOKIIzT4q5RSSimllFJKKaWUUkqpHqcBYKWUWkBPP/00e++9NzNmzCj93+jRo9l2223p168fU6ZMYfLkybzwwgvk83kOPvhgTjrpJJZffvlenLVSSimllFJKKaWUUkoppZZmGgBWSqmF0NjYyJ133sktt9zC559/ztdff40xhr59+zJy5EjWX399xo4dyw9/+EOqqqp6e7pKKaWUUkoppZRSSimllFrKaQBYKaWUUkoppZRSSimllFJKKaWWEra3J6CUUkoppZRSSimllFJKKaWUUqpnaABYKaWUUkoppZRSSimllFJKKaWWEhoAVkoppZRSSimllFJKKaWUUkqppYQGgJVSSimllFJKKaWUUkoppZRSaimhAWCllFJKKaWUUkoppZRSSimllFpKaABYKaWUUkoppZRSSimllFJKKaWWEmFvT2BxG1ZRhXEFyFZgMykmDZGwEuI5UO+QqhSTrYbGGARAIIGwqppstoHQ1mBsjDEB2AzU5TDVFYhrABMAGZAYYw3YasQ1YUyAcQJRDbg5YKohzWGyfZA4BhtgTFK8f4CxIGkMIhgRIABSxBiIKsGlmDQGY8EIgkChETJ9AOfXD5ioDyQJLvkWTEBQ0RfJFyATYshg8nMxURU2SSAIIbCIcYiDpNBAU5LSGEOFOPoPGEjGAmEEJBgyfv1JA2RqIW6EqArSRggyiAMKMSawpC4mSSEW5+8TN5CpGgy5GQTVQ0gb54KFIMxgGucS9BmMsYJgwUZYa5FCA2IixAiIf7xEEsSESK4RU9EH4gYIsoiJcIVvkXyCCbOkWMQ5sBDn65DKAWBCxNXj8rOASpypBPIYJ1gRbJjBmixBEGLTlDAb4lLBWosJMthMBGkeCjlMVV/ECSQ5TBiBc0iSRzAIFhNEmCAC43C5HCa0UMj7OacFiCogPw+CyO+kaQxh1t9fYowIJsgAgogDG0FTHcaGmCAgiDJIEJIWErAWcXlMUIlIDMYguQa/P2QqENeEzfQHVwBAXApCcXyKSIoxBrDFdVUghXmIsQgWV2giyFZCoQmT6YNLG8EJGIuQ4FIIshVAQNo0z++PQYSIQ1yCVPQhaZqLSAK2ChMXMAjZ6hps2oTFYqMqTOCwJsAGFRgsuByYkMBaRGLEhtjAgnNASJCNwBhcnAdC0kIOk6nG5ev8cx2ESJJgDCAJuDwmU4txBknmEVYORPJ12GwlEseIsRgbgLG4OAeZLM7FuNiROhApgA2RpAnCSqIoSySOKFNBaBxBRQUmzIAT0kIeFzdhbECQqfDPYyGPWIsJK5A0Jc41EpMQJw4XF0hMQD6XJ28tuXyOwrx6XH4eYU1fqJtL6AKiAYMxJERVfXD1XxFk+xLVLkM6bypgCLKVBJWD/PPXOJUw6ktQ2Y8gU4XM+RRbsyw2jPx8GqbiJCTsMxTXOANJBTGADUgLeSQ1mGyAqRiIpPVIvgnCakwmiyFCGhugugppmIapXAZXPxNMDhdU4lJH0Gc50rrpiAmwYYY0aSJt/BZbM4y0YTphzRBcbg42rMKRQppijUOcwaR5CANsRTWSpkCCS0NsEBFPn0w4dBhJ/Sxsn5VIZ00hCB2ZmiGkuSbCbAZJ8qSNswkGjMTNmAg1tQRVA7AiuNwcpDGPM40EA5cnnfo1QXVfTFUNUj8J4/w2ElUhdbMwNX0hiTE1WZLp3xANG8WTLz+/iI9aS5bZs2f39hSUUkotJfr379/bU1is9BiqlFKqp3zfjqGgx1GllFI95/t0HP3eBYDJN+GAIGqApgip6OuDcHmDuBTXFGAjMIFAPoQaC/UFggpDGPX1KdMmBDGQ5qCqEmMEEw4s5lMbMBZjHCIJJjMYkjqQeRgqwdQWJ5LxgYSgAFRhbNYH0shDGmBM6JcfGqwNEGexFdW4XJMPPBJA2oBkMpg0xGT7+iCucdioBhvnSUkRlye0lZANMMaQGsHYDKaQx9gKjDG40BFV9kcKczFRRBzXYyv6YuqmYqMaHxiWHCbogxTDmtgA0hiTqQXjEDEgKWJCjASQr4fKGiSOSQ04G2BsFsk1kLFVBIGBbF9I8oTZCly+DpM6TJSBwAcYCQWTChSaEEnBWowNQcQHLm3W/03sg65OgJTU5bBRHyQwuCDA5eog2xdJG5Awi8TzcM5hwj6YsC9QDU312CDCGjDWYcn68K1x2JoaTD5HWFGJxI2YMABJIc5hslkfuE0bkSQBBGMisP7/TVIABCRB4gI2U+W3NVMDpOBikKgYPG/wQe+k0T++YSVIAAhCghHBSohL/EUDVPVB4nrEBIiEmMDiXCPECULeBzqDSv8cJYkPaKYVfp1RNdIwEzI1SK4BIUJcjAmziIshTQCHWEeaFLCVA6AwDxNGiHOkqWDSPDZTieQaAFuMA0e4Qt5fzBCEmCgEgVRi0sQicR4xWUQibBASYMiEWUIDJtsXg+H/sfdvTY4kSZYm+DGLiF4AmJm7xyWrqnuGlmh3/v8vmiGamb5kZWaEu5sZoBcRYd4HVo/st+1Kr8ye3tBD5BR+MQAKEVEgiD4+50hSRBwVjTXyhpYhzm0S6AWrD8gXSI5bpe8G7lgznBXNE96XeK9pwHvDekXKgKaJvu0kcxDQPIM6Or/EPVcuUN/xpLGfOmAWZ9xUQASaQkqwC+IZ2gZpDAAv0JdXpFyhW4DU8QmvC73uaBFkmGKNrNHbSq8LzaD2yubCunxmrU57rPSnj0jJDNzIKVN+/o/ol8+kbmh2yqD4yz/h+4PiD8rTT/jbX1ARRBtyfYmPpfZKkkYWw4cPiIDkCdKE5YL0FZOCP/1H6pc/4u+/4rnEUMP4if72Z9QXuHwEJkiFvq54/YxMH+Bxp7VE7jsNwdMHzB2nw+MznhRf38FmKM90XZDtHfJE31Z8/IAtX8AzqRTYv6Iv/xGpD6xvqAy4CJoSMg6sX/4zNo+0r7/Q3xqp/pF0HenmtJSpb290L+j1R5heaPc/0a832D7H83XDXhfy//q/QWtxDZ/+QP36C0kE0jPp8kx9+zO5TPj1E/39X3GMMv0z6cO/4I8v/5CvrVOnTp06derUqVOnTp06derUqVOnTp06derfot8dAJbnT/jyBtMV7AHskO6AwTShc0Gkhiuzd2Rz/MmgvoG8QM4gDqogguSEbZ/RcUAMoAUYzS+A4W1FzGD4BL2Ha9g75BXxGdKEt1ekvMD2BjrjMgS8y4lULgE/3dDWkJQwU/A7fIOtAgnDu6FlhN1AjGQSTsUyo4y4dZJoXF9ycEHEgYLtr/j2AJ/wrcK4kdIVfyy06YrlG55ARKEJ2BYgjAb7CnnA2x1yOE89gfiO4UgesPtXJB3O5/kjvvxXdPiA9YJkSNMF9n6AsRVXAnJ6wzJgGVLC9wf4CNZwX2GcAtTZjrUdT4led4yMS8VqxnoHWbBtxSVjlnAz7P4LFCPNQsoLOv6M9B0pMzklpN7Rbmg3EMB7OLtF4/1nhXzB6x2sBbwWcDGkBsy3viEGnko8h1g4d+l4bwE1rUOesf2O1w3kAMdljrNTF2S8BES2NeDyMEHb8QY9G7SvxKKB0xEZkFpBOjJc8L7jHSRlvFWEHdcEvuMarspwV++H873h7njvaLng7UFvO6lc6PsDLRPWO7J32v6GXn5GekO8QS4YHSFBW2ldMcKsa+2BlpGUhUGd7BupZEQTYkbOY1yHNUQlzg+K94obiGuc2zIe11fD3e0HmPUermc3vK94nnFbsb7jDHh7j/srXeltI2tChzkc7HnCuiHJIM/0bY210IS70lvD24qnGaziWycmBvZwSPcHIjPIgFOh7WgZvr1xrO2YN0SuuBpYpdeFujWqZDbbub9/ZR+eWD6/4fMztiwMw0AuA2nMpPrK4JB/+hT3xr6h7Ru4vyOdGJxIFfGO2ka2htw+wL2SfEMswe0Dvt0DnrcVHy50BFtfEVF8fMaHpxjq0AGANn+gb3e0FLQ+6C70veP1Mzo+0bc3ZHphfX8LJ7k6+/JHkmbskZDbz9T1M1BJZcHGibYsJDdseUOnmXr/BbmMDPxIe62U4QvsDWMn2Yi3z6TpZ3h8pt4r6cMLLHfk05W6rLTXHV0rtTlyvdD2V+T9/0LnD/TaSM8/ICTs/Z0kmfTxB9r9C7ZuIHuc66GCfyGnT1hvyOUHeluRMmA46ekD+9dfyfMVWd7/Qd9cp06dOnXq1KlTp06dOnXq1KlTp06dOnXq1H+/fncAWGno7QrcD4elQb8Cj4BtGXisMAiMEROs/RlSw5McnMlxVdxvqAtSXnDTiD32iuoA0gANh+iyEfSvEjTQIja5RySyoKQy0O0pHK2WDseeo5LDoTyMAR2bkXDS8AHJGuBpW5BhwFtD6ZgSgNp2JE8BoXoFQKYrUivNG1omaIaUhPSNPky4N/L4gSY7eZgYvdMQ+nLHc8Sfar6E69fC3epuSBrxboczeEWnJ7zt9L5QfcAwpG5kdXTISP0BEUVHQfxYmlSR+VNEKZeCp4TvG1qu9O0LQrhqfXvAkGDfYFdkfMbaHc/xOpZLAGJRvN4x33FVzBZk/AjbjsiOTBlqQEMdryT/DMyoP0j5A5qfkbqCregwBmjMN8S3A/Jf8N6gS6zvEPHP1ArjFX98hURA3pxxvQSUdA8XtxiUOSK8Mbh8RFoNmP7+F8Qr0hcY53DkuiHTFVywtsWRKiN2f0fnp3C8ZkGnp9iXcgwS9PUYaHiH8gHWFfPDGevEUALgrUPJSFthnJEmkIh9tBYg33o43UtG6k6ziswfMNvx1sjjFfMNJNNbx0TpdNh3mK4RZ22V7EpxI11eInJa5HBzg/QjPttbRF+LhsO87bgnsI5LAnUic3qKqHQ5QLAS0eCi2P7A0+HSpoAJ0iuSNd5LHnFV4IiTTonelyOyW3DAW/xZVDEdwDZ0fMbef4X5I9R3GAs6P/11uGK8IjhuFnHu+u09DrgLXh8YidaMzTpbytxb4/XrK34tpMuE2gN9GsnWyFYpaSBPN3JJSB4hP9HbFu9HFRkGxDuCIrd/gvULRYyUMiIVefoB3+6xfzljTenLKxXHt4VOwmw5EgyUXl5i2GS/Ywl8Hqnvf8bv4boWBjxB1xfs8Y6vRtrf2B9vpJcf8C+/0iSh4xPt838iPYxuDXt8RhhIP3/C751mRt/upPYGtx9RCo8//wlypv36iq8PVJRUdmxbSR9WRGdcBdaG1ID43irkwm4bvD0ozx+BJ7z+itqfYBpp739By4w+f8Rtp98r6B0pN2yrtG7oNOJaqOsdYYNhRMYfUYH08jOSMiz/KV4/pX/At9apU6dOnTp16tSpU6dOnTp16tSpU6dOnTr1b9PvDgBTHbJA1XALJoGl4mVGaOAz0OFRYNhhVWQ0vM/YPKLScCvgD9xHvAlCQ5IeUOqCa8abQc5Iq7gMEekMQAFZoHm4iTuQr/QNPGekd+g7IkqanhD2gFeecelocVIZkXw5XKQ7Ps4gCZEdxBHvUKH3hXL9Q8DJpNAPULttZNWIjMYi4lUMtWN9ijLajI6JfWvUx1f67RagNw+49CMiWLHWkPEWnbK9hlPXPLh67Via8PogD0+wfkGHDwge9cUi4bDMM5oyvgneV8QcUcNq9B9bfSDDC769QZ7w1MJdWj7gdcFtx1B0uNCXdwzBrUVf8viEi9If78AMbcf6FzQJaf4ZLsCyBkBrRiljQMFtQXKJfdMEFERbnBkcWg2I79HVTBoD6NMha0DeYQ7HdDpA4vYefc9YuHh7i9hvW4CMlEt0LPcK0yXOwviMuIEe3b99wz0hZLxtaC7Rw6oFKR5xzJdbRHDnK/QlwHXKeH1H0w0vE76tAdv7I1zoy+sxNFCRMsX6dcHMscPt7pJRAfSK1Rq81fd4nf0VyRfMnV47UPFhoteIsnZWRAtZNsowUsioDqQ0Rtdw30mjHo7bIZzsZgHYvSNlxnVARH77O3CkjLAumK0Bgocr7A8kGW4Z0uFydwF2dByRHj3dZRxiUKBJPK83cMVdoYOQ6HQkX+jtgZMCDNct1mecwxGeBS2XgN71jnhCdEZcsL5DGmDb0GHC2wZtw8tMbRuLw2OHx/oL+/0L0gu6vTNIQckMTzd0eWWYrpRpQnPBzADF1zfy7Sd8eUWSo8MP+OOPJB3R68/IPCHrF8RSpBOMl3AyL79gUuh6ZVv+M40dK5/obNj2wLVAq3SW2Bu702XAy4itI235FW4/ght+3yEX/OtfYhBm/wIC/fUNf/2K/vQDtn1B0jPUxvDyMeK69zt5/QwPJ3/6RLUL8vqOjoo/7ohCun2E//IneCqUlxvtL3+kpAl5r8hUsK2RUGx9Iw9jJDZMA/nDT9S//BHRz8jlY9xz5QIC1ivWElYj8l/ngsoVLS+4LaThA33/GmB5GjCrpOZo/wXLM+pbHNE//G/gnf725R/ytXXq1KlTp06dOnXq1KlTp06dOnXq1KlTp079W/T7A8A+R1/nKjCtkF9gbtDf8VIQqzAQQBMizjZtpL7ibaTKGJCwE/AHi07TukdEcQoAK7IjaPSh5nQ4cK8B8+R6gJUe7lfx6Cc1IiI2gbdGrzV6b5OHs9eclEckFcw6imN1RcoN7zX6PLtDNwxB8zPg4WzshpQJtCKpIcMlwOWQoNWAGlmhlwCA22ewCUtEfK8MB/RM0bVbO4wF3wQpE8YDmV7wvkIKWNzVMS1gBVSQckPHMRypKMgQe6ELaCZNz5ht0B1LCusdmZ5x6wG1RfH9jqSM7TuUEZ+e6XWnm5Oy0lrDx+ippXXQgvdO63dKiY5XzRfUgVZJJaPjBbQjInF9mpH+jvSoaQ2QCCIJTLDWwmUrwL7h+45cngNKaoAmbxvkCfqO7wveesBJ22C8hIu1Rwew5ICKWMQbu9nh2DVEE17Xwwkr+H44h1OGXfFkB7DdD0dxDDZ4HiPJ2RK0JRzt+SWuS1KsaV2xtsbj8xSRy49f0NvP+L5hLjFYYCPeN1y2I8pZEO+4Jnp9jchjHcgp4XXHUgGXGADYVoSVNM2kupJxioLmiTSMeGtHV/YcwwQqICUOnX+7ZwX6hqSC9/03hyqSEHccRaYnbN+QvkMe6X1HyoSY4/WOygHlq0U3sxObSwtwn2MYgpSR/QG50NYFYw24L9EpLAD5ircHki4RQb0v+PAU8d06YJKRbvh+OK8l47KSUNychtG6cV9W3t/fWbrSfvmMzlfGjxN+/yNlvlAkM84zyjvl5YdY87ohWiKGPM/kywfQ6KNOY8aHZ5SO0JFcMHesPxC5YHWj55HNMvvyhqVn1t7o62e6ZHrfaG/vIAkdZ3pZIvHAWpwbv6PDDVs2Em/x2bZv6JPG+c2VvA34paAJ9PaM3NeIo3+asT/9kXy5of/y/6H/+f+A9Y7++DPSH4x/+A9Y/T9R2fFJ4foccfH/9ANyu8L6meGnP6DzC/74ituCaUHYyC/XGN65TSDvSP5IevkBpozkFa9XxJTe7qQieJ6wt3d8VPT6gt9/oW3/GYYPdO8wfcLsnTTNSI2e5P6XX0nzg92vKA2pX/DSKPOP/5CvrVOnTp06derUqVOnTp06derUqVOnTp06derfot8fAE4PuC8w3aLD1XdogtwTsASMnAoyXnAU9gf+3pCfPoQD02rEvNoeLlRfQD+AZOgP3DPuhmrGvUbsLjdcPaKhe4VhQJohliM2ut2hDDgeHac6gT7Cabveoz9WA2p6MlrdD9iYcTLWKtiKlzGgroFbJ48vAbLF8eJIyREjfPmISMHq44BgNaJ0dYB8uBt1COg2DLiONITWYJAlYoWHMdyrfcdbjZ7iEnHBFKG3Su/g2lARFEc1o/sersxyge0NHT/i+xsUwUTxuoZzt7boadaE5Plw9F5wvkUfC9aNbkbbVnSc2Lev4eg2xfodtzfwG04m9QJawRd0eEb1Cr6iW0cGI6kCBW8NUUPKBXEOR+kbQgv4mMY4B/kafbC1IfMLaAYS0I8o8B7RwCjogOQe0Na3gLzbis63I7b5GfaKuOFSAI/3i+PLRmyo4oSDWzQF8E8Nesd6Cyjq0feL1di/VpGh4OsD9gUZZrw+wnFO9PRqkjg/DNAbnq/0Pc6Ck5Ccsbr95n7t2wPGib6+kYYbAN4XNA30x4r1FX96wbrh9y+IG2m4MvadpJDGJ6RukBteNc6N1QDAe0U6pKwB3504CyoxNLD5EQsNckREO46XHK7oLJg3lMPJ7NH/K2kETZhVhFi7LBlB0OGCbQ+kPGHbG6KKW8Ntw0uCHm5omUaQC97u4aZ1CSjuHRmf43H7gg9DdCOngg8jIikipKUhbPRxZKk771/+xPvnX6iawSfK00wZb2hb0fGJcSiUnMkJ5PZPqLXjfrzhj19guJHGCd3eSdMP0P+E9A15+hG7f457vjX88jO13mnrZ2rtVLtTPbP+5S8wVmpPNHvGf/0TmCEff8J//Vf6oyO3EjHULUWf9GBkmRBu6P7AUia93EhNcM8kucBlQ64F1oZODk3RnNGnZ2y745OQy4ZdJow7+Xajf94oQ6b/8AkZh7iN2Om9k0rC6humO5pesG0hi+LDBZ0/sv/yf5Mu/0x//98ZX/7f1EcMcvjTBeo7yjN+eWBfXsk//0j/+iuaHsis6HTB3r/gb+/gjeGfX9j/+J/Qp2fS84/4/b9gjx2/gb58oH35I/LhgnWJj8ylkf6Xp3/gl9epU6dOnTp16tSpU6dOnTp16tSpU6dOnTr136ffHwAuFdsVkQVhhn3HhwSjIA9gD2DnywqlwG2Et0r3DRUjI8CGpyHcgC4R6ZwUTFG9gO9IGsINm4eArP0Vhm8O3R6OY55ABPIlooSRiB+mgRTEW/ScDgPmigxTuE8TWN+ig7KEy1jTCCTImVQuQMO747UGDBsL7oZbQ9OM+45/K9+1iugl4OBwgXWFacC2L7jfAgLXX2D8CWTERQJCto6M8VomFtymDHgq2L5haUDrjriRRcOB7B7uRNHoXpUOwxDgV9eAu5KBN/ApnNJ1DxiPgGZ62xEh4okzuG2YhJuUMuLN8PxDOF+9g7WICk6OMkdfbP/KMHxAMqSckMe/4nqJPuTkYC3Aoz2QYY7+1mHGl69ImvG2QN2QPCG54P0Be8UzIDMgSJoCZjl4c6h3KDe8LkgZiPxvPzqhx3CTesNag9aRUg5naj1gpwI5rs1aOK73xxErLQe0ddjvUCS6ozXFurnjIjgpum+Hgb4/sDLhtiG/Le8VawtQMAkXazcjDRO9LpgT0dXdwkVcrpi/0r3CMNFbjjjp5iQZKeNIKpmcHO2CqCDTBJ4RDJU481hH8wTeEAV1D8e3VMItXmKI4oC5vr3hMuNisTbmuBpije4N1xwOZ+voWKKDmnBUiyre97gPsFgeW/CUsW1Bxit9vUdUtgt4hloDLIpHzHpPEZnddkRz9PyK4w6uBvvjcDDv0ec9PNH7ymo7b4+V5b7RpxeKdegbye6kWhjmicETKQ+kUlDJiAwR5a0ZSUaaP0FfEDqKkZLD9QVva8Tb356pyy/09ExTozKzba8sj/9MkwEfr9SvX9BpJz3/QNGC3zI5r8jTRzwneP0VXe4kQMVJTwP+qOSxoi8/Q3tFphnpD8RHpPyISsP2Rpo+0POfwSfS0wWrX0na8R9+groCK3x8oS+dJB19eULYKJ8+4Ms73t6in10TOnwC/zUGAsqIJEG2jTzfoD1IJZHqhnm469P0jC1fyS//RNv+FfFEmm7UjwXqjojh719h+IA/Fmz5jH76A9rB6gP98BPt859Q7/ijkT/+SP3lV/TTFfnpJ3gN6J8+fkD0GVu+/AO+tE6dOnXq1KlTp06dOnXq1KlTp06dOnXq1Kl/m35/AHiRcE7mj3C/w60cXaxr8LhxgvcdbkSn5NuOV8Pf71CuiE9weeDyAem/Qn6KqFKb8S5QKvgaLlAEmAgIdMV6R9MUbka5RjTzUJD9DfQW7tZOxCUPwC7I+AFPgm8dJACu7Tu4Y30jHeTOUkJ7dMX2pBF/XCveHR0vpFSwumF4gFbrAYeHAVUJ6NOOrlTpeKuI3GBdoDupPOPdYcxQG64OdYcpelRVvrmjA5D3upOGglgjDZcAZUPFWw03s3ekXPD1HaaXOIn16CoWixhkNeiOE25WT4SbVVpU55oFAJT4Oev74SjV6F3NF8QdsY7kjGwLUp4Qb0jOpLaiw4yI4+kFGS6IbJBnxBu+rlBqAL5tR8Y5uneFgMtY9AC3Bu2I+81znKcarlws3L4kCbf5cOOvPcEWVbZ7vKbv4dT1TMT8DjPfQL6kDH2PXmQ1lOgwdZNwmdp2DCRYuGH96L61HZlesO0dWg+XdduxkiPK2Rq27qTriMtRldsEb3dMC94NSQPWGr4u0SO7v0Oe6G07OnkjttnrBv6OpidUhFKgjBOpt1hDi45dlRGyBtLvGybpgNUFJ+NJf4t6jr7bo2fZS9xXAq4Rce0HkHYV3MBJuApi0MWRPNAPx7ya4QqSUwxo1BoO9jJh6xEpbhU3xfMRed62GNxwA9sQHREpODvQjvtFsNZp2impIDi2vgIDWgY8ZRrC/bHy/suv1G5IFnJrSFtIaSPnD5SSGKyRn27IXtHhGDRII6IzYNi6kOYLIhckZ9QqeMXLgPVK3zdqbzy2nVo/s+6N1pSeB/Y/PZDnkbS9Mj59wN++MF5mVBT98JGcE76+k2439PkFoSIIeZiRNNPu/5Vsjjw/IV6weg/n+97QacYev5Jv/4R3Q3VEbp+wx1e0zEjr6PwRX75GxPc0oNc/QFvQ1OOcDk/0+hcoIzo+09//gtSVohM9bYg65pCePsJ+pz/+THn+D/T3L+Rypb99RoZnfHVMv4DO+NtnXG+oXmhv/4q+/Au2fsZev+KpI5cBTOh1xfYVmSa0HJ9p1xmXifTDj/THOzpcMXtDbiP1l/+bcvsDtn79x3xvnfp3kbtH9/z/4Of4n1nnGn6/zjX8fp1r+P061/DUv1Xnmfl+nWv4/TrX8Pt1ruH361zDU3+LznPz/TrX8Pt1ruH361zD79e5hv94/f4A8LVEV+7nt3BX5gmpK9Qx4O21x3+nF2gd31d0ukHpeBnp7Lhksir4E6JzxD5joO2IXC5EbO+GDB/DKZtTRLIOGUyRnOPvuwEZkkTXa3lBBOgSMNMVv78GINy26Ps0Ay0Igg0FaTVcw71hJNTuAUS94eJIGuj7hvh+9O7uYYzVFKA3F6SvEevsK4xT9NOOU3TO+gORkZRzdJ8Oc4BGJcCdGzLcwCpeoodXLy8Rd90e6DAgSaE70g0ZjvVRYCgBsHoLyJkU397CFarp6MFVulV0GGFS+up4GWiPd0w6Mn6kb2vQwC7QHxwFvdG5my5Ib+hQyEkxy5QMtD0ivHtEfiOxhlKjA1YvzwEinXByty2u63AVk6+IBJgnz1AuiNWIDB6nWA88hgoUYA5gqtGVK1oiCtt3oCKXJxiNlBRf3gPErytSAipCQtQDdjagLkgecb/D4TgXHZDxGvtjAVZFcrhV6Wgu1LqGYxZHGjDOuFWsWziz+4bmIQysQ4HeA/6XEo511wDYLfp1PZe4V1pFhyeyKmnIFHeUSs5DPOc0x5mQHcxxyYhkJKcA/dZwF2xpeEpY31DNkFIAQlas54i8Ps5uANoroh73QyPWSgBvOOm3uHQtJV7fLIYJ2hr9rprAVqTusY6tRS+zWbx222IP0hXbDfcNKSO+vSGX5zi7vgMj3nY8DTA8IQYuQrfO4/HO65dXWltRNKC4FDSPFBmYbs8Mw4T2BR1nZJzxZgBIX8I57Tkcvi3ivFMpeG2YGY2dve3s+zsrmaU623/9I66OTAPJR+brBX+8MV0m8u2KayMlQ8UoWknaoRiSV3R6hvJMEkUwvG4MLz9j9y8oFeuO5AuUhKcF1NHLhOpA317h9s8gDdGJdPuAf/1LfAZMQwxE9BJOcGsgOYC5f0WGGR0veH1Fc4H2its7kibENqivoD/HZ3l6QSXhl2dSHmlrCbe1GlI3yu0TDaV//S/IcA14v20kvcBlQcYfEan09Q3xBM0wW9DpGSkJ3+/0r/8XevkY3xVyJ/3wMyLQPr/ifcdU/zHfW6f+XfTv8T+Hv/f/wTzX8Pt1ruH361zD79e5hqf+rTrPzPfrXMPv17mG369zDb9f5xqe+lt0npvv17mG369zDb9f5xp+v841/Mfr9weALcHSjz84vH0FMuQe7kzf4ZVw/G0b5IyPA9p2GAjAIFfAkBTRuiIaoGkYo3dVA5jIeMH7Br7jloEVrymgR/NweZpFryyGzi9g0Ysq3SBlvDcCbSWQGo7MPAaExaE1vHe0GPSdREA69w4kdJ6AHm7KviLzjV7vGILmC7Ye0be2onmKntTeoXf6EcMLDnYHvQYkVcLRO07hfpWEWMf2O54HrDZkmJC6ky4viHdIl4BsaQ4gSQMZEd3j/ZVw2SID3mJtva94BxFDpgHvnS6Z6it0w1LCtq+ojhGpPd6gdkgDooqkAdqAlITajphDOmJnK+h8i/e3vcW+mnFYsBFSgMSkAYX9gJ9E7zEGohoA0jX28FsstILka8QZpwm3HScfUdbEGclzxF9/W0snup/zEGevhFNcSon46r5HHLgO8TpCuJwFxBRSwT36ayUP0AwZRmw9OprziD++IrPieaTvCzI90dcHDIW+L9Frmwc0FYyEYN8stnGmNEAvmvAOTrjMdShgjTReKFMhbwvqHUlCLhOaC94MlQRZcFF6rygBlcUNsxYwGMPoiI4gCWvhBJYyhyNXCn70cGPRE+29R4Q4+ejKPs5Zc0Q6pt/i2j1AnnkA8FzibDY5+pWD9RuEO1iIfU0F2hvuKc5DtUgHT0P0IreKlhupDNj6Bn0PR3xSuq2sDst9AU2U4Rnpe4BycUrKDNnJYmSv0RPdKq45BhTKHPeYdaQo0gt0i6u0jkliXx8s5uwmrF//K/ui8PIT6eUH9OuvZAT1RvnxZ9gaud8pYw5wvr+i+UaaBEkJSoTc0zZAI0Hcj8+5BOl6xVsllRHHsN7QMmP7jl6eYhAj5djT7R0db3FPFEXaA738RLevEWHe7zEIcb0iK9AbOl1we8D2hXz9j9jyF9An0vgD/vgLwgW2OzLc0HKBuiL9gfc7CYfhI2KOLXf69koan47hkgXSj9jyhqREyjNe3xEXtIzhcv9yR6YZ2zakb3jLYIKtFZ0mXGd8j2tPn34MJ/vd/57fVqdOnTp16tSpU6dOnTp16tSpU6dOnTp16tTfpN+ffcm26HZVg1EidviWoCvkFB3AOQf8lYoY6L5iNHxpIDNK9Ks6AW5RRcSRvkc3royQJ5xjGkGnow9UwCycaDj0V5wvESfbliO2eQvg4xx9uO143AocEw7lEhHMvYX7dJoCLuoQ1yMNc0NoCAo9BVSUK+ZGbwum+XC3dvp+p60bbe+0zanubC7hLLSGv3VEQKWFY9UIcE0Kl2654DS8TPTe8TQhXlEFzSNIRg5w7GPBveLUcKkCbj16f8cpoGIecBHMlF5mtv1O94G17dTW8fFGq0vEFXPBtgWnBwi2ipY5nJR9Q0cNbuoNHSdEPSJ7VcK5LCDjc0BTdWT6hA5PQMe9hnuUGgMCHH26bYt4Zk3RU2s78i0OuFzCrd3WgLV4RDVv6wEfa6xhKvHUPRzl3i1eyzewDR1HpExIngPgopCmiInuFkC0h9vYdYyz4IbZhrftiHNWXDLmBuaYJtq2RET23jFLEe3cJdy8qeD7DmnE6xbDAXsLpzgWP4NhfcHrEk5ZAxEnp0wZMplOGmd0upCn54CKGFqusRbewQVNEynnY50bMox4SrgmpFwRlXDFq+HesVpxCuYxzCApB1w0j3tHx4jhxsPhj6HDREoF6RXvG25r3B/E+RWPOGz6FjDdDwiKgyq+bbHeVnFGrK3RtzxcsNpinfYN6fWYDbDoqc0lrt9WOsJanXp/OyK576hsDNPIOAijVMZhIucSLu3xCe97JArkhKYS61NmJCtKIw0FULZtYbHC1+WNL29feXu781iGGKTYvzBNiSlXRnljTjBr5/rxhfE2kcfE9PFnyuUTKSdSKvGx2BvKEO50ekSxW4s+co8ocqdFjHZb0SMBICLOYygiDQPiPVIA6OAVmZ4Qd1QHysf/EA5rCnq7Btz+8Ado7+Hw3Rbk+iOigpYP6PxDpCuUK2n+Ibqu24LXL+BOGj4gMkWyweMXdJqQcUZR8ITmZyS/wPZOmgbyZcZevyJdSdc/ILVD/RV5ekHkSro9427o88dwwM8zff8VW7/QX+/Y41dIRxf0hw9/r2+qU6dOnTp16tSpU6dOnTp16tSpU6dOnTp16m/W7w8A9w2WN5gvUGZkukCaosv2q8F9g8sccbfjgPeG9RadvGMBO4Cgv6M5gcjhaP0GezvQETKg0JeAtm4gN0QDlogkJF0RDwCCcEDVjKQR8cNwSgowZUDvEUFsDtsObcNrRZtg1cKZmQpta3jzAM+9423D+grjEWOcBhQJMJhHrAtOxgS6GdaU3jqtN5pXdBpIeg33ND1g9Df4WQM80g2TRO+OWEP3neQS65IKnvLhgh7CwSgl3NaiAXOOqGWvFUuZbk6vNTLd9UatK5ZGbK9HdHOL104egGp4DriKIBprhjnSakC37mjfYNtIw0gaRqSvAe+6RwSsjICgXhHy8SuBCdr1ALQd0rfIaMddIjYYjaEC8WOvHXeNuGIpESWcpgBqKSH1PSJ0zRG94EgMDHzruO0N9orXNX6PhAPWBEmXeO8QAw1tD0ds3VG9xLnTMeKZy4DVjdbW6KLtRsdjCKKu8bNuRzdxxl3oveFS8DzjZYz3sUfvs1mHdEVI5GEiqZLdGaeBAUPNSeMU4LLv9L7EsrU9rjNdMK+4dzqJXpfD0Rod1dQH3mu83r5DmnGZo/vad9xWvD5wszjTxxkSzeGaBiAfUdNKZGUbWqZw3HeLgQMcvAfMbTuuGasrZuBtiwEMzZgqVrdwcKcB6iPuV+/RYewtoLFm7HDDB6BOuF6oTalvr/T2K7q9kcfCcPnASKVYI99upOsTOl6Qcj0g6hgO6HiFeF0caZ00f8B6ZW9wp/D6/sqjXll++UrdvzLePpIMJhJDN8Yf/oVh+siYC0UTmU55+oTSSe7kyxNaxujA3TYoN3y7H+fu+IzpK7at+H50m6dIH9A0oOWK5jFc75Ljc8rjfMlew/1bN1IKB3e3huNxH9QFzSO2fg4X+/AMMiDzDyA3fG+4HBHebYcx4bbgSTDPaPmA1QdSFPoDffonaHekrUjJSCqIVGivsL+TX36I27M/SJ/+ifz0CeyBvb5i75UyPQFvaIJ0+2d8W+L9Org59vUNuT3B8AO9OfvrF2R++bt8TZ06derUqVOnTp06derUqVOnTp06derUqVPfo99fBPTdgrFJRdaGzzO8H/23+T2cnSXimtEEP9yQZUNbJs2OyBxu1XQAKzdEa0T+torICGQ8ObAjOh0O2ApZj87LI2g2JaS1cIpKAhLkBmWEvQZoMo+457bDNAYg6hvfXIqigntDpzHAJwkdwfyAoG2lm6FqAZ2bR7+oZbw1NANaERkwr9EX3DZcM/t2x/bMIJBaDR6lAhyxuH4A6bbQLR2OXEFVUW9IKqAN32vA1SFHV2pSkBHbv+BlBo1rcen0ukek7PqKaT/crDn6j4cJSxKguzwFEHVD0oamC9Q7Oj8HJJ2eEdtJwwyWIVVyVyRpLD+CjAEyI5Z5Cejna0Bd8SPuWyKWWRXliu1fkeEAsEc8sgy36KPtFtekCqXgbsfeesB9e4R7se9ImtDUAq4xBtzX/YjbPQA5x4CBpqO32ZAyBdisFSkTvu0Rn/2NHEtEVjt7nJ+kGB7XPX4CzRE9PT8hbQ2H474GpMfDidujn1k0Radt249+aJD2IE0foL9R0oV0e0asE8bqhGQNR6dL/DdP4Yg1w1OKtWkeMc4AacAcpB9AWyM+2CUBBziWHGfLAU9IKnhb4r24BiC1DSHjdgwkSDjcDSOVW7g1UTwJ0h23DUkT1g3zHhHmOqKthYO770gZov+2b+EsHwp0jWToPCApY22D6Qm847aFg7nvUC50hH17x+o7Zf4ZtZ1xuDJoZvAFvX04HN6G9BbDIQauBnWPgQYX0nA5ItgTJsamA/f3z6z3xr4uMF0ZLoXcOsPTgP7zP5O2r5SxkG+fUP0ZeXxGNAefHm705RWrO2m8oHql1a8ImXR9xoeJ/v4Z33YsCXLEylOXGE6REikF8+048w3vCVJA+3R5xtodrKPDBasbQkXEoG/haJ6eYP0CW0fHF7yv6DCH63t54LngfSFN13DJ1xax6emYWhpm2JYYvNh3fP+MXp9IY3wu5PmZ/viCpBfkw8/HWnb6ukFZSfMVEaNv76RPP0OKgRhJE/b4SwxnvL+RbjPt9c+kD5+w9hVNE75XNF+QG7B+/vt9V506derUqVOnTp06derUqVOnTp06derUqVN/o35/AFijr5beAsrNY7guW4M1wbWCHz29PsD9FXs4+nQNM2E23L+iesXZUC0BoayHM84BWxDPUZ+b5YgRVugOcjkchA67BUiVDjoenas7VMWlQHdcPeJk9QCB+yOAYrVwGZpEd2lKqCVcOTpghW5bgJtUsHINtxyG1oCR7mC1ByzOA+wLuOK9Yx2ar/iupDKgqSN1h3EOl50UvC5IecI79L7jZqgqWRXpCvkCVkEFpyMyYu2OpIhqNh2w6ohmXKJP1HPCe6VZdK46JeKLRfEtrg+WcCGnKXpl2xFx3TvIFvs7PSNuqHVUQcYndFuRNAVB8g6eAUNVMNFwiErCkwREbQ2XhuRLdOvK4cjUMZzUfUfScDhKjxjhHNfrtR6J0QPUBtZwlegldo+u2raDDwE8x2d8+fUYDtBwNR59wN5XJCXEPSLCU4A8NIMuEQHdalybBURHJfa1NZhusGesR6Sv7w2ZFN8rUgLcSyqIb7gcHcd7i0hoi4hzXPHtTk7PFHfQK8k7KQ3hop4u6NEV69IRzWi5IVqwvsffW9xzkmaMA8h6R9TptsbAgAsijncL1y3tuIYjIvnoxhYXSOmIKD56pHFU5Jt1HqEjksB2oONoOL0dXBKt7ZhFF7X3BmXAtnco0SvM4UqHgovh64YWRXSMPZF8wP16nBUFKUgZaPWddevsDcgX0vRE2XdGb5Q0kMqHiFM+YtwdwDouEokE/YEOl+if1g5aqCasr6/cq7HdK952RHeypVhreSX3jWGe0dTJRVBbYthgesaXz3Fm6kaanrG64BZB9fnyQ3wetIoOz+SPI3Z/x1XRckGS0Nc73t7Ry4+4CdYN3xfYNvLtE769IvMRfV+NdHvB6x3NireKTj/Ql69IUVIqMH3AvR9pBBXyEyyf4xYb4p5TmWAElweeE2IpPg9bxeqvpPkF2oJe/l/0bY9BGheSlbjH9neUGRHFHq8MH3+iP37Ftq+YjAG950sMAbQ7Up7Cob6vMCd0mEmT4FslXX7G335Fho4tX0jTM/39L/+wr65Tp06dOnXq1KlTp06dOnXq1KlTp06dOnXqv1e/vwjo52fkkpBPL/AM+BJQrTdQBxtgWyBlIEHtyDTB4JAMlQY6h1vSgeEKaMQy98O16+2IA9ZwC9sKhejrFQ3wnIfjzwP0iki4XdFrwC5REEOmEfYvwT33Hq7kXYAV79HJaXTEnd4XejtglG0BOfcdTSV6ZXs9AElE+UpSjEr01EacsfWKYzR7o+uE5JVUhDzckGFAbI21sRoAzBvWF2ScwfaAv7ZHh6o98LqFG7a+49YDOCPYvoShkx1rO613Ok7XgrWdzgWfPoZDUyyih3PB61t0z3qGvSLNol/UaoAyUZCG+kYqBfEFcQ0nLwZlCFdtmsJd3BbsW7S2aMDBVsPpmDLoFHDaDLMd8hzOVjm6ZrVAXcI1mw7XrtXYX1N8X3Dpx2te4nF8i73e8DQHbGSH4Tkc0dNHvFecfuyj4ShdFE8abl0f8L5AmbC+B7zrHU8ZcsHcsL7T9getO+YWe5GOnuh27JF/cw6DewrQi8F8hWGIKN08Iq2RU2aYCqlvlMuNpAWxhpQJ1Rw9yKJAChzr0NsS69z3I6K542Lgle4NM6O3Hr3E1jGrdKtYf+C2hyFa+W2vvK/h1pboVqZvkHN01uYS+6cJl4QfHdVmFXNwLREdbtGX7XWLezdNMfCxLzgSzn7NEcvdauynJBhH3A3zis43zCuWDkhvHtfSo2O4MbG1Rm8dQSlujNLRlFANWO29Ql/wWpFhjO7r3o+Y7zHgtQjWd/a98n5/5+3zr6xfPoM7OswMsjJIZSwXhlEYBiUr5OmK9h3xju87rhqDAA54i65ilYh1bssx8LDAdoftDRzS8w/k6wuiFbc93nON6G13x1pFSkFLObqrnVQu+PoFnac4D62i5YrXHckZ2PD7r/j+jmiKL6CusEf3NSmTbh8QW+Ks2AJtQ7wj7YG0t/g5W6Ije7xEpP31iiZFkkH7DPWdPN6QtWPLH/H69eisbqTLM2n6kUIhF8Huf8b293AaP/5PNCXy84+ky4/QVvJ1jM7jYUcvIE//jK2Nfn/Ql9d/2FfXqVOnTp06derUqVOnTp06derUqVOnTp069d+r3x8AlgXGJ3hfQJ6gZbh1KAGlWO/wIIBPSuHibA31EfGKWwoIao8AZb0ecE+PyGOHcjmcgwOxxNHdGn2rgB3RwpojUjkHmJFUgsX1joggotGj6TmihMsAzRBN5MvPaBlImpEObglXgb7Te8QRe2/IMIFveN3xntA8Y+KIKpJHvHdcr9E3KwT00onmO2ZG1idGLeQyoMOMmIWDlyOy1wWk4HVFGr915jLdcG9RxWs14of7ih0Qrjl060AO56Q5tn3FiZ5acsG2Hd/fw0DtCeqO5BvChkwzKWdEGiIOvaHlQkIpOpBcSHkgMUdH6b4FrA1/ImARrV1u4R5GYv/rgmBIXxD60TX8LZL56MvFApbZjtOOWOwcENjrfxMPXeMsyAXJRyQ0Gs+dcrynHJHHbDX+Ll3C6ZqHAI8GpKeIuTXA0vE6eziNJdNbw8uI5ULbFyzP4fSVFEB/3yBfcPrBehW6RAyvEw7x34YXtmP4wRHrJBeKOyULwziQENL1FjHfWRAVUhrQvqNpRA93rrthvWOmdCl08ZiRQOj16PhtNXZDMuaKaQwneAtXLlKgH/Av57gnSzhM3VsMbaQB8YR4Q63FPdtXFI8O4rpHrHQZkKR4mjEZYl3KFMDYK+YxAOFecNujXzhpgNJUkFoj/jlfodbYg76hMiDdSCmThye87dRtoZnRvSD2IKszWKdcn8jjEyJH7LgmPE2Yluhd7j0izXucBW8PTJV12Xj9+sbjfWWnoEDuO4WFYfyRSZxxcKbLj2TrqEhEO0/PiIPSIsJ6fIGcsVrjsyEN4Y72jm9vcX95i8GEfYuBlL7GmasdWovO5H2jmyFEdLdMT9A66YgVF03QHXs80OlD3ArW8bqTLz/AdAXWY4jGSNML6gPSNtL0FLH4dUOnD9FBvP8akeW1Qn2PyHITYI7e4u5x7aqovJDmn8HuSN9IH26k+RMyvKDXZ3z7FfY7qj2GG0wo5co4fkD6Hm7/XmM93t7g7Y4vG5rn6ATXhC6vpPkFnS/oOP9dv65OnTp16tSpU6dOnTp16tSpU6dOnTp16tSpv0W/OwDsC7gInjssK6DwLvC+wTzBbDDIsTILtHJ09z5gsXDM6lNEOTNEhCvRF4twAKOjxzUp4Tr9ESRhGEiFFBGmctDgAJhHr29bA0RadLxGF2uOeOi6AoYOIyRFS6bVHc8Dti149bgII1zHvpLKyJEgHa+Xv6UMZ8w2nB23DWvRs+lueK90f4IOaaukLAg1eltTBjpeA965NXq+hKu5hMOPknFbAuol6H07HIMNSQkjgJ+70HulUyBlfPiA1ZW+/YrvG1YfeBnx+nq4DOW3vmXZ3iIWWYl+Ue1oGUk0chlISVFryDghmmJ78hhAt7VYbwdF49+tIRzOyzyAGRYZ2UAHiy5Yx/G2BqDUIda694C3/XCUTk9x2PLlSCNu8Rhfw5VdxnBPWz/iiR3kcG3bAWy1IChSbgFlh5FURpCKbW+4Ki4N6xtSpoittUo3x6zSvCNlQMsMakdHcHTk+jjgtsae9Y67h0vUHWpHy0RqO8kqJQulCGUolCykHPHe4aiOuGpjx46BCbdO90bzjgkYFnvPgNUtQLN4gNpvQN0a4gE/XeUAtmNEOTf+GsWcx1hXUaDEOmrCPdzS9g3IkgK25zEiuQHbG7bvh4MX3PsB63d8XbDWAEHEws2sGa8rXsMpLUmQ/iBhpGGMtUuXeA4MKRFN7ICZ0ZY79vgzeCfPV9JYSOMUTn9b4/kNrG2HE3ePTu40QFtw7/RurK3zeLyzVsd6JS13clLKMFBIzB9+olxeSElJ0w1JjvgecdfDHG7nFn3Q3hc8XzE6vS6xxgjWVkwcbMXb/QDsHd9WfG+Hq9nwtobLGiflEudt/Upv99iDXkE6Os0xEDPF0IH0DZ2ewgWeRvLTJzTNaFuIQYON9HRFTLB9w+o7mkYEgeEJff5fUBlAnXT7Q7jwVfD+CnK4xPsDf/waAHp8insvOZqHiOxuhsjRwZ0ydv8zvr2jlyOKu21IuZGmj6Q0wr6QPnwgPX0gjQV5/IpsG7zu+PtfovMakPTh3/9L6tSpU6dOnTp16tSpU6dOnTp16tSpU6dOnfpO/e4AcG8Vr6+4bnCZIXV4+gDjBJcN1glyDlDbDC7P0UE5zHB9Jv4Qkb16ACjyGC40V6jhtiW3w6XZCXcpAf2ICGVPDrof4CsFTMQjEnqImF63ejiJiZ9pDc3Xo064YXrERB+eVvkWv6sBgvP1OaAiCimjlwti0RMbPaaGDJ8OKOZ4UlyVnhu2rNCFXJSkCuNzgLc0RcR0CoemEW5iVKIPuAouGTwj3RGd4HD1mmbMO70+EIEuRhelr19p+44zYv0O+RmTGXxCyhMy3sAMTQlsQFJE/uKClieSZFKZkLbHWqUSXbB9A4545aTBDWuLx4/XgKm+hyNWh4hHThqxwXmMqOU0RuxyVjzKYwMIzk8R0dxXXFNASx2Ao4/Y9nD2WotIbDIic7he0yWcw2UMJ+t4CdhpHfYa8czuuIwRd9x7RBy7hUN0KOBO2xrmHehYM7zveJnoywNc6a1hotDusD4ChvYN6g6+BKS3LXp5xwsMM2Kd0jplnBmnG2W6ojmhEtHT6BGFLA3PI+aOmWAu1Fbp7iATIpeAsFZxb8e19+hg9nAhS8oBKM2gzEckcsR4e10Dvo8z5GsML7SIWJbuiFhEDPcepz/PMEzR5Vpm3OsBKzXAdsoBoy1itd0dd4148pRI4xNqK1JG3Page94hObDH8EAeoO9oGsKVrEdkONEnbd2iE1oTzaMrWq+fYu2I/mU8H9d3AFkdcEnRHZwLDCNeJmrrPJpwfyxs62sMOfRKfnqi5Mo4z4zPLyRNpNsPSDdUMsPTPyPdoXV6M6zcsCT0VukHzNVhDoe65YieThnb9xhQ0YKvK94b7hvmYPYeMfnmiMbQBNsD3xteXnDT4zPIwyUrguYBVOjbK24LOo6IWERqS8bsHv3L0xWr74hecc3x2Ts+odMFfA+nrnesbejwAXFFywTd0fEH1Bvp9ox6IT39C+Qen4XFULmieYy4aNsRmRC9ImUgXX5Gby+A0usD3++kyw/0+5+Q+RP69B9QN2grSkGWhL0/4HZFxxekf8Xbv1I+fvz7fVmdOnXq1KlTp06dOnXq1KlTp06dOnXq1KlTf6N+dwDYxemLgU2AhhuWHS4F1qeoyNwVtB+9vRWnRhytKKhjdEBxq3h7Pbor9YgEPqKeOWKgjxhbfEdKio7fsI4dv8/x43L00+aje9gciChUcOgP0lzQJFEd3Az2LWCvVFJy0jSRckFkJOUJSQNqBtuOkqJTtG1ouWAebl+zHWsPyH7EASvUhM8TPO6ky4U0FKBHT6h1RI7notEVzHa6K65OV6P3LaJ7NWEtnKbWd9xSxM36jmuKiGeViOBtr/T2hf5eIwLZ94BvtSEUpK+gKZyFOJIK6o5KR91IUmLlh0sAWPeI0fbDXd08nJASbkZ6j25gF4yG9xVrG94d22s4RA/JAfDQHI5dkXAEe/Qtk8YAtt+cvtZBR9x7gMo8ImXmN6d4XSK+WRW29YiV9nB+suKyh1PXarxmUnzb4s8lQHVvDR2f8E6ssRudjHp047pIODYPOEnrxy8L9+38KTqk8xRRxgpFnDJdyFnIKYfrWvxwu6bovrYKLvh2wHbN4Yy2Gj8H4b5OFfH4GUkBrIPAZxzDLTqQQQJctz16eP1wZ1s74HGsjf/W4WwRs9736AD+1qt9AN04SytuGq7evge4zfnoy7ZYG4+3JqK4RRyxpyFipV2OjuQxgHUnOndzOaD/wm8fnd5J4xMuPbqr24otDzji19U9YpLzNd5jUlw03NCS0JyPUxZ7Zr2x1437/c799TN9XdDyQvHOkJVpnhimT2jvqEZMuKhAUXy/IyR0uCK2Yhgy3JDxE97v0VnsdjjhPfqU8wWdbkgWXEf88gM+TnTbseUO2xdo4XK2tsD2AE30usI0IRJnxHRA0hixzqlAW/HlHdKMWXxW8s1lvN4hvxxdzxtdFWuP494Ox7Cvv4aDXwfYV7w9EJE4M74jU8SQS1ugrtj2FvdampC2IOOP8bldH+jwAcYZGS+xd+tnJBHu8eVf0WGILUiOpgu8/xmxDfvyawywtHfyT1fStZDTjI4X8stPiH7E6vrv8r106tSpU6dOnTp16tSpU6dOnTp16tSpU6dO/XvqdweAqXYk+mb8voe7sytsC97fsOkGn25YVVgd/Cu8NVj+CDX6ePWbE9YF0QC9bh4OyTwF1E3lAMA9YoolI9ah+eFgzQEyD9cuaYDWAqIJ6HgBAlLoOPz2exEHNqQvkEDIJBeQI8ZYBR0HZLwiu4VjMg8BIQkXph8dpO4aEcZukK90M9wT5ivGV4SdhCLpAGyi0RnsFr2x3nEyve4Rpbt3jIyngdZ7uKYBT0NEBmfFumNMtG8grlZcZowCXdD5hngBX4OT2w79EcC23VGFVAaUN1JRdCjRwZxLdPdiiDtSZnQcUdFw/qXxAO8aDlNruMvR83oN16d7gPmcgIRvjwPspnBOplucH45+XhQdr9GlKuF2JYcD1PE4B5KiA9U9HLDlBjh+RBMjEk7sb9dnPSKgrR7Pm4JZJ4lrarFfOoyHg9bDje2KJo1hBA2nMDrikpDLBygpnj8d0dOiSOtIF5IYI07BGMYJzRpO3LbFr2990W2Jc8qO5Bh4UE1IPiKbcwI1zDfcBFIAVIEAxQJ4ReQCaY7+XknHNa2gE65j/KxmXKe4jl6Pe2pC5HBmSwq3vORY277/NUZdiGELt2OJBa9bnDXn6A3uAWNtDTe5VZABb3v0/P7m2DckJX6LPZcBZEBwVCPC2gmHrYnH3pcJt3YAbZCcyCUjUvC2hws2DWi5xhnwTreN2lbWbeNRjfXtNY5j35k0M4035tsHsgp6vSHJEO8k79AqOj+F47av+DTRLXqK+7bSRfHLHwK0W8Nbh+GKWYuhBQS9/As6/xQu5WFGrz/A9SOUGYYX5OnHiGNPCZ2u5OlG0hTRy7aH07av0futhBN7vB3R8wmvNWBwC6ivwxVJI33fIk69d3R6xmtF6gMZPmCpRAx+uSBlgJyi51szScD3BzL9EC7dLLA9UIkobtyw+hafc0lJlyvsD3R6AmZ8e+D7guQbef5AvnxA9o1y+yfEPeKhrzP59gF0hp5I0xWvn/H+gP2d4fKCbG//7l9Rp06dOnXq1KlTp06dOnXq1KlTp06dOnXq1Pcq///+kf8/03yLWN3hAVmAW4DaXWFSNHd4bEikx+IyIbnj+YrLjpBxd4QCJpAzbjUglxHuxNoJ8lPBRpwdGa7hip2HcLWWGbYF8hC9lcNTuIklIRRcdhRBphnxhGYPGGYBEs3Atx0ZJhwLACNGuJq/uVc9rrFEvLT1dvT2Vqzt4WIVC5BWt4CdrdIosA3oNJBoqDkMJVx4qvHz1KPfteNtI+WB3ldkGrHWsC54r1jdYcrYvkBJeN2g3PB1hzzj3yCN5+Cz0sEUkSHA9fo5XIzSSeWGe0VpkJ+RWmEHTYVExzuo7EBDfAAkILsbnhPYARs9xT55QDLVjOYL7or6ivuElwxc8NYRPVyo9oAhhZPRLaB+HvD2iDOglwC/h/tZ/HDP+hbRyYdbklQO8N7CnWrtiP2+ItKRrAeQbsd+t4CjZpEwLURkd9/wlPF9D8Dee0RylxmSRD+wCeSEp4K0hugI9hWpGc1CGgqZp4jVtQDg3nfI02/uW9EcAF4UTRnvKzLcIt6aFtAcwXuLOG0krsP1eA9rRGQfAwRhvQ2oivUA3d7jBvKEix9Oa0Py0RXcoovXjQNkDwd8jSEKRxCN3mc0YyKIG6LhtuUbRG8txl7yjNcFJKP56EaWcAmjjg433PdwBIsgOaEquO2IzIBidcdTQGN6xbyBXuJzJR0ufhUkJdwEtw3x415FApj2SA3wfKG2yvq404846DJN5LqTB4mIc2/0vkJLyPB8gPoYGpHWMEn01gKIa8H2twC46wPJF1wz0hvOhpaB3sJhbxgpC+4N2x6xJuVCGm/fPkVwM6Q8Y/e/oHkLkG4tIrP3BaRhXuNzbH0DyUjO2PIgXZ6w/Q1JN8x32BuKo9MTfV/xJPTa0L7GAEv5Q5yB/QuuA9J3pD4izhwjHa51z0rKNwzgcsHbEgMqj8/I7Sd0/glbviJ9QWrGfUeGj3GG2zuSZlJWbL9Hz7ECbSHlKXrWpUB7kKYJe7xBvpAvn/CS6X/5V0TeI7b81KlTp06dOnXq1KlTp06dOnXq1KlTp06d+n+YfncOYPEMmvDNcI1IWJLBkyANJM2IGWKX6BXtG4xKyo6W6MHt3gPsbevh8pUAcDpGzG7Sw2WZoeTD2Rk9wAH0LCKOBZAeLtBthTKjw4RKI3mU/zoJ2x8BDbv/5uDLaSQdLjprEu+p9QBsvUF7RNdpOSKlW8W3CmWk1wWvK56niFQtAZFNO50N0+iTzVpIOUMacQ/XolsPmGkCOtEtAJSJHbHF0GqFyxOtGR2l14bjh+MwIo1p7/Gc+YKRcOngW8BGVkQVsei+TcOV5A3VTs4FmsXzwOHMTWCKDgOSZyQ/EzbQeO8yXJEuAVq/xTl/6zv1HC7EIwrc6AFAm8Hh1I5o7Dn+qxFn7HX5q/P0cPu6WLhVcRimAyoC6XJEfecAVPsagLPXODM43j2ipIcZhgvdwOoWncHDFdtWuu24J3o3et3wviFJAtTmhHuOGOQ8hiNdJHpaW0dTRnHKkMl6o2RjvD5RUg6nq1WEDRE/nOkBPEU1/q23gLFm4cL14z0j4citD6jrAXZjeSPCuUe/cgvXOEJAz36cwTTiEi5alxzdun0LVy4WzyOKpBRAl3zcQwcgx4l+5QDPjgTw7QQg7sdwRoqz4KmAR5Q5toerXeSAyS36gt1AakSIa+xbHKeBlC4RsZ0K5g3RjHfDVHGZaOuD9rjHvqYS19d6OIQ9YWnCJMXghCsmmS4DWzPu6866rNi+Mswzk8I4jkeMdMW+ge+6oTiihd47rS5RQd471htmjqVLwGnr9NZjTfILrVVsf4sudFdaa1ir1O1Bq1vEc4tj7UGvFeuNvj2wfYmk7WHAWo39D6s1Ml3o+44Pz4g59Bga8LqRh2vsTQNrGzI+Q5e4D1oHByUc2NYrOjwFTK4LOv8M2xvdKjK/xHnZtv8GUmfQI2HBHRVByw0ZXmB9B02RpNA7hqLlBdbPUBc0zYgIVhewLVIWtl9geT+GBzqy/RHffsXXr2iZSXlA89PxWbLHaw7Pf6+vqlOnTp06derUqVOnTp06derUqVOnTp06depv1u/OASyyoR36PUFKyLLC1PHNoGRkW+CxwQcHc6SOyHWAe4frZ5CXAFp0GEegBZQSCSilKX7tazhl7YgMPoBPmHRzuDlJSLfoouwdJGHL++GQVDRrAIqcoIYTt/eODre4VpEjxlTwuoRLT3t06PaM+4KMF+gVt4YlRSUDOfp1OSKnW6X1HfcNSwlrA4JSyhg/bxX2iK6GAIJmTsegOpQLtgUI9R5R0bavcW3DEF2jDPheA8T1PaBbb5jdEWYkXyL6FcATojPsf0bLC8k28vVjRK/KFVLCXUjjE9I6igYcnOaIms1yuFNHtBCdtQoiV9zs6BEVNM1HVHc/nLsCw9HdbGv82Y5OYju6ZiH2GTscqS3ijolO5Oh2TuHWLuEU5YjeFYnoZRcOsLz/FWRWiImAcCanccZtx7UEYM+C6oxZdORKyrRtR4cUnasIpoLvhqSGrxsyXfH2jm6NNE4kJSJ5yxCuSB3B3uM69y3iy61HRyxbuJx/ex/EUMNv3bdEzLH32DsBhluca48uX5dyRGB7OJ17xb3F/sVOI+oBq/sefcQqIBMi4Zq1tqG9Qzkir0XC3S3hhCcdz2uHEzPPOPsBccNV7hL7GLHcJWKmreO50PcNLSOq4WJG/YDAEveTteiV7hEHrsOMd4F29DO7Y33FZMJE6b3R3GOgoa04io0XzPaInm4bLpDKFXej98rWO/f7G+tWyUkYxRmTkkTI00S7f8Z0PPagIWnGcfp6x4cbXr+GCz1fscdX/ACtJjPt/q/x2ZSNpAkZn6nLr2jbI9Z7/Yq7oFkjeaDu6OCoZnp9P6B8uN37/kBSoddKHscYqKjvmOa4Lq/0vqNpPHq8iU7zusfnVe/hNH+ZseUVsy3OAwNaZvr2jlkPx74awhNerjGIIDHEIuMNsRopCK2i6ljtSGn4tqMj6PUJu3+JDuxhghgvwHucPVGOgYUHevsxUhhMoXzCNaGi+PoLOv+I6orvBvbAHWz/E5KV/PFfIvW9P/5dv59OnTp16tSpU6dOnTp16tSpU6dOnTp16tSpfw/97hzAqVagINOELBtMI1xfYBxgnGBfAgLuBy2bwiHYR8eZwvQGmDkyjIAiyQMI4QH/DvepHDG3kg+Hq6bDOXf0/mZ+e4zkcjhCJR5PRzwhAioFTQnD0HFGSsJ6OAvDATuAZySNR19vBjU0D4hYwDJA8khr0dNJHsOVqc9giSQZTc90E1yHMLUqaDqctLkE1EkDEG5J8xzRtlJwlXCv7itSSjg/RaI/18sBvFdIOZytmsP16lfEC9o30nAB4vFaMrk8MUwXRIdYZy0o4QJO6qiClMMpmuwA31tE9Nrh9DWik5bDKYwheQwHoDVU7IjszVBKQD1yvPk0QIk+2DCGWsA0gOlTOIjNY9+yRGT1UMLxuG8Bg71GJ68RIFUVKZfDgaqR55zHcB6bhXvb2+HKzYd7tOF5PjqT93CXygjdsWa4QN8M77HXvlWkjIh1ynCheKN4Jw8XlB6wTwQRPX4JMt6OeOz4+4NSRwcrCXpFNMUa0yMG/ZvDFY+1MhBv8Zxpin3zHmtwdB276wFnORzlDdqOlCuHbfdY41hrSXNARBz3jrQFEQt8LOXo2bWIvdZ8OI5zvEYqeIoe4YgxDvcx1gPmmcZ94HZ0d3sMd1iL9+8tzoI1NN/ivuwtIqsJd3prNaCvQNsWPE8B94+YcC+XeO1eoVv8XoTeK60trNV5rDuPfaMLlOFCTooOc9wPnsMNncZYKxlAONquwzlNuUTSuBk+XNhrY68Lu+00ydS903pjW+5UjFae2epKRaiibOsr27pR14W6d+qy0FqnW8dqo1un9wVzoeuI02k4zXcMj/XEIvodOeLJozvZ9gfWGzo9x2dI79GpLIrXhZSv+PYWaQtonA8dkfQh4uLTMSwhhlw/xfDCMOOtoVowBL2+xD0zDvR9i6j7PMQ5JJIEqFsA+HJBZUBc0dsfcB3xbcf2BZ0/xGfe5Qm5/hRnI2V0fo77sq5oKjFEYDtII93+8Pf4mjp16tSpU6dOnTp16tSpU6dOnTp16tSpU6e+S787BzB5RK6dtBVEhoiTfTiSBBY/4OkKZQp3a06wvyOXfwpuaw3JGdUVLHozqQc0M/kNdkWX6reI2nZ0nSa8VmS6IlgAwr4eUcU7kBHPeFakHb2u7kjfgQTDePSJtuj7FUVzCQCGHzCLw1WaYJyRusV1mNA74UzNY7ymrIiCWcVpIDNiC+aZpIUshpYLaEMY4r0ZkCyAXVsj0te+gbuEsSL16Lp1hdZw2wKW9glpFe+Ozk/48gXNtwA0csBDz+iQUeskVVTAkxAA+BIuWpSUgOaQJB5XrgHDpxegRxhwMzwZYtFVTN8Rq8hwQVyQNER/aLtHpLQrtHgsJEgHTBRB0gF92n64pxVsCfA0XOKxSrhk3XANx2LAd4Wscdb64Siue7g39zs6zgEh+dbdrHhv4fbF4hqs0WsNt7cmaAsyjmCKt3DiYgkpI9QdTUruO2V4iv1TQ9KIt+jm9fkZbIM0BZhUP8BzREpLGn4zJIs1SGMc614Dmu13SFckz3GNvePekAOiuhne7ogYLkM4s70jZcDbckSk9//mPUcfbUxXKI4dzzWBjHg/+qfTtyGK/tfIaEm4HL3OVv/q4G56uIRzdBNLxR1a3QPqDiOC0LclXMzNkRyQVtyPGGo5IDaQBiwoMt3Baqe1jcaItU6vD+ToBI5e3yF6oPcFyQWxDUlTdN5uD/Ze2cjcW/QHX9KFnIWcp+NeiDOq5Sl6ar1CecH7Go7n4Ra93Gmg9o7bio7PmK7Y8oBSkMsLDDv9/mckP+H7FyQ9YVvF+Arlik8ZW97o2tDrJ9q+oNsW+45C29AhoXlH/SVSAZZ4rJYpfma/4zJi2x1XYnhgfT/OkSLmmBmiAvuG64D7GlHRkhAcTTFo4kK4p70iNmBtj/WYbnhbsb6j0zNOxt7/FckDtj/Qp5+h7Qd8DrAvbY19UEGHCXt8hjSTLk/RFb09MH8gXGJwZXk7+rEH/PULkvY41+MNts/I8BPW3xGdYPmCXD79Xb6mTp06derUqVOnTp06derUqVOnTp06derUqe/R784BzPWBPBqiDvMM8wXkHWwKR+bQYMjwWI5+XmAXvEdnrQqoOcIl4Kwv0Q87XMItKoeDNx0O1DIE/KOEC1CI7tF9h27x79IPsOrRI+srkhUzDyPkMEAyUh4Qc6TV6MfthHsTICkq4QZO0wtpmoGGSyNMuMH6nQ4lQbuDp8O/KUiasP5GS1d4fCXPM8oabtE0Hu7Io8fWoVuNXtCUkb6Ea7K1iG7WAdY3XBWzirlj3pFpOtawx/WTUDX0MqLSSUXImih5RmwN8EpHaw2gay1c1Zqhg0gKSHf0kYq1iPK1jpYLWgZUMqqZlGJvwhn7Ldo5WKyWa3Qk0wM2J41e2vbA2w7pGq+RRlwNdI4YaduQb/srPTqXe4/9dwc5AJopgkRfcBqhL7HvtMPsWpGhBDyrlb4tNNsC4JeEiGHecBVcEu5GI6HDAV/TgKSElkKyTqKRcVIZEE3oeIueVCUAnIbj2a0eztgGvsV5UT3izAN0hqO3o2kgi5CHCVU9eqD3APt82wLBm8e62Y6L4/kSZz7ngLfdwtX7zQWvc0BCb8daHi5jPYYorB/90yOiJZzRMmAt1lZ0iEGJtoQ7mAQKMsx8g8uqivuONwugfUDGiJluEU9tFfIUsFHHw0meA3rLEJ29djiJUYzYk77vGNDXBTr44Zz2tuO1/dYTLmlCNYNXvHW22nm0xPv9ge+VgZ2cVrKvaPK4PxKIbWiScM3m4biX53Do1pXWG3vdMMm0ulPbHc9DdJfXLc6vDFCe8O0Be3QFkwZ8O4ZDROHyEZ9e6AY+XWgpUd1oWemXZ+r8kXV/Z3u80xipOoTzeV8ipj7N+HaPM5VveH1gMkbHscW1eh6wumK94mVApivWlnDxtxUdn46u8nu43bUEKBYw9XA764CUlzgzfYl7oC7hit4fMQCRHDL4vkDdSWWKbuEeP+ftAd6w+oC6ka7/jM4T4jt6+SlSIPYlBmRuf0BE0XEiPf8z9K9IX9Es6Md/ge3L3/sb69SpU6dOnTp16tSpU6dOnTp16tSpU6dOnfo36/cHgGUMkHK5hRszLVAI8EQFG4/o5xGuhrQdbk/hMIQjUtlweYSztfwYbl7r8XhNRCRuDxBlPcCo9ABgqYRbNCdIh+PSHC9jxIxKi25ZAtRonjDrGEeMrK3x+vsesaZZURX0AKEugogHuKprQO08RcS0RTeqrxvujlkAMnfFXGkecc30jPSdVG6wvh3ux3T0dbbD7bnjrQdQBqLrdvsrGExz9MD2Fm45WwIo2g5DRryR5xupbWTvDHlEBfI0IVbJGbQMsa45hxuxDNHvahGnG2seTmrxcLB6C5ApHK5X9oDHJWHuiCRMHUsF84gCVklIHgP4mYcbOpd4D1bDPdweB08sR6fsFo5ivcR69A2XfDiGiXUSYG8B/bweTaQJlyNi23YkD3EeRLFUaHXFUv4tDtr2jreKSAlXcGv07rgkeq+4psO966g1VKGkRCGRPIYQhASMR7ryEUvde/Qau8engKSI64Y4h1aP85TD8YvRrdHSQG87kqbD8W5gHi5fB9OO4dG3S4a2QV0Qt4hVrksMTuQUkdK9BTwmYoPx6FB2j/siXMgtzg+GyTEkkUbAcNvjHB/RwbGUh5tbBHLC+hF7Lh7wEUfzjPWVbg3XidYq3u23YQUvE+5xxgRDicGLbkY3j+rsvlPN6VZj/xUYym+PdavhPC1jvD/rWK9sbeVRO/fXr7T7HdneGaaPAUR1wGXC0aOjucaAQI5Ibd/vWG8wXegIbX3Q9jWc7EnwfQ2nuUxQN2R7IOs7qVxIbLA/YF/QIcdAwOOO7A/k23283PFth+EKTz/i5Rkbn+NsX/5A63d2W2ku7K3T3ejLOyadVh9QJswe8XmVx+gp1kJvS3SDe43EAT/uZ0l465hZREz7DtMVLwNuK/iOpISQ/xpXXgrmO/RKHm+IZvT6A9Y3fL8j+440Qy8T1iusr9A3vD7Qy0d0vkLdw+U9F6SuWNvobUXFkXJFyoRef0BdSJdnYA9XenkiXf8J9vcA7Pcv/97fUKf+jvL4sPkf/hz/M+tcw+/XuYbfr3MNv1/nGp76t+o8M9+vcw2/X+cafr/ONfx+nWt46m/ReW6+X+cafr/ONfx+nWv4/TrX8B+v318E9L5Deoa3P8P4IUBiKlAlXMBssBuMV2gF0opajx5XbxjX6ObVBL4GrHUJwNUqv5UEdw/glTJUg+xgOyIjYhaOyNaju9ZawGIg5YSZRp2tRBGvtQe5TAGTCZehDJejr9YPl2bBXPC+0c1QdZxC2BKPTlUF/Og47dGZ6+0zkj7RveE60Jsj80TWgMjkEpA3CBPeK5YutPUN9FsHrCHjhO9bgO3lDuMF9j1cyiqIj8i+g05IviF9R91J4xUVhQJiFg6+fQv4KkOsWTp6ft0CRPU9onbrA6kePbXWwiGqKaKaLRynjkZstveIw87Tb1DNNbqKxTuaEr21gKLiEfuLgmbEanTc9u3oGd7D6ZlmxPZwhzbiGqWHG1VnvK4RwyvDAY05+mg9AFTJsTW+gzkpX+h5imja5T2im8VxDZDqbuhwdEXXjqcS56hu5JRQb6Q0IOMEXuOcecQeS0p868WltwDdaYi4Z+eAwfU3B7CkEkMPLoj3AK2ajpjmA66m4VjH6Ah2s7+6xV0CRHI4p/cNGSY8F8T3uN+SHX2wB3jWHtC1WXQME0BYRI5Y6AV0wlu4w+lAknAef3Mg1wopIfLf3Jff3MYpIdKxupLyGJHEcvT10jFbkaTxut1imOEbYEfxccBqx/Y7XQr7+gg4Xns4aXWGZYk073xFOaB3f+BMmDkbymO5syzvmGTKUBg1oXkkodAWfGj4MNHNSGlCREj7O0a42729HfHUQ/QYe0W1givijeSGjxnbJ5JwnOVOfvlf6ffPEdkuJdZ9jJjs1lvESqsfMcoNzznWd3nEEES5ghT68gBANGGlIGUi1R10wFqH+o6OH2BbkTwjngJa9wZpwh+vSGsw3Y5hgBnqV8QUmV5wiEEBVbyu0RU+zLC9kVzinhGFYYrhjzLGGS8j4tEz7PUdKT/AmGn1TpKG5QvsD1QFmZ4jztx3vvU5Ux+RHjA8x/nZdpAKWkjDU/zPRYp7yY/787eze+p/Csm3Dvf/wc/xP7PONfx+nWv4/TrX8Pt1ruGpf6vOM/P9Otfw+3Wu4ffrXMPv17mGp/4Wnefm+3Wu4ffrXMPv17mG369zDf/x+v0B4HaFucKSIBEOWX+FbYQnAtz2LWCwKq7h5NWtwvwBFUCOKFnGo+c1Ha7PcrgONSJENeN9CS4oA8iCUA6XrB8QGVAhtY4MBXEjqYQbUqJTWFIBPSCXgZYjhtcI97AoIjtWA0h4KnRvARbzDfdGtx7Aebzij4q5QHe6ZVQS3l/pDMA7aCHnFDBRIyaafYeccPeI5PUDBDYByYdrtAe8xcJF7cd7ajVgqaxIHhDppGRkzWgPMO1ccF+CxUmOtWk13KqaA8S1BzI8H2tiMM1I7wHwWsOHJ5wdscPdaB3yEJG9dcOHGdpygKgMOiLa8LofPckHwCXj7XAXE3uNDEg+YmjbHmDx6HsNSD1E7DEgOhCkaAjHrfUASjUGCdwMzfmArp3+rVfZBVHF6xJg1FrE+bpjtiFlDmdpswDKtZIAzZmcMtJTQHRRfG9weYrYaY8zQmsBWSUf/b+X+HcdA/6JxNpoCYfmAQ7dMyABDs2PvOdv/dbE89cG5RgIaBsg4dz2jKNQjg9mtxgcSNHzq6KAYXVD8xS/x5GcIza7LnF/5egCjpvxuJf1r0A7XMKC5By/ZzjunwDEv+2lKIhTu0XfdNuJjtqOpOieFj3OdNzUuOtxP+WjQ7hFvbCBp+hjpkxIHvD17XAfJ4xOsONnDNhNeKwLy9cv+DgzZGOQgVwE6QsiA2mc4z5qDadjZYoYaM0kOfaXhnUPV/p4g+0VRfCU0W1FDyhLHvH9c0DYlMhlxlOhbe9xj03XuN4kFNmj3/jwbndv+NYw1Th/rSIkJE/YvsL0hO933HYkjfTlEWuvLYZc+g5tIU9PSF8xMlZ3crng15HWHqS6IPuGz5dY23VBJ4kYaHE0P0F9hWE87rUV0xm2R2wtDdMaa5UK1BXKBR1mbL9j+500zriliEnvHfN23OYNWkWHKeLTNWGtI9MQfcbbhoxTDAVYw9sR875tCIpenyNmmvN/OE6dOnXq1KlTp06dOnXq1KlTp06dOnXq1P/z9PsDwLJDvQbkTcDjFaYJcocyB9zqiegFBuoAmtBpRPyBc0NdA2Kp4DjiR3xq1ojF/QaFXQFHxhK0iGvATO8HV9QIBU4jsAVPcyVcxE7KKSKUjwhUtw3TTEkFETD55pLMh/vYg1vRIz6XBAjWDfYVL3PATuTo0V0RBJOKuYJkrDqDQR6VpBpRxHgA3dYgjTTvuGvE8NZ7QKj2CNdv26DckNYRekRmlzE6l5uS0gC9oy5oViCH65COjjPiezgudYy9Sje8rcg4xpq3NVymviNSgrPSwnnsFeoDGZ+BigyJ7gJ7w6koE8YBDTUhtoVzUBOI4lKjk1YkupM7AQJ9P1zdsTXuHSmXgOP7Es/B8bMajla3AGaYBy+tS8Tqtg3JE45h/eiiJpyrXo5IZhE0J9zT8diGDjOtO9Y2kI4wQb8jycnjU7hNkyApzpxOc7hnyREdriXQntcwhfct1rGu+BSQVJjxFnDQWot1SuPBTA9XL/zmzhXJ+P6AYY7+3m/u6DQhOHTDpQcg13zEMKe4Dk3hvrUaLuMyR3S4KCITeEMUGC5HrHfAcucAuhLubCxcw6ID7ntA4zzhNdynWA9XMBUnA4YgiPUjSnoIt+9wCSe9FsQFUsHrOxDx4J5yRHADlhO2LfgwQT2cz9MYQwOSoeQYDGgPZP6AaGFfv7JUWJZfac0YL4mRiooheiVcqEpKilrDpWCSAqpuGzreKL1idDyH27/zYBgKVq6wvqPjHPdBi/vac8K5gFWyDiRpyDiR9Iinnm4x/1KPYRV1bLtDcooOEf/97Rx4CwBaZvq+QK+09qD3HdJTREhLxlsFFswuaLnS9iX2p1yo61dad2S+YUunZ0hlxNev6Pgjvv6CJwME1UuclzREEkDboVwwMuKK9wWXEmbzPMa5lnBQSxojOTwNx+faMVSRB3IaIylhe0dyjijw+gqW8OGCs5CvH7Ftg3FCq4Fm+pd/Rabb4ZK/RKx/W0jzh7/TF9WpU6dOnTp16tSpU6dOnTp16tSpU6dOnTr1t+v3B4AhXIwjMCiMA9xyOFnra4DU8QbtgDpJ0bWSxhmREcNIHHG5jIeT1wPwNju6SCVgmNtvv7x3ZJig7vFv5Yiw9YqnhKaCpAEIyOX7hpQURst+9AuLo2nGm+PScAVv0SPcrYW7tjtm0bMp4xV8x9rOYWON+F86Tjqclle6WbgeHYQLedRwowIyjIcbmXCN0iOCuMxwwFRVPaKWczhBIaJUUyalgnfDk4SDMCnSdnT4/7L3f72SbNmWJ/Sbc61l5u57R5yTee+t6u6i4NYLAsEDqFvNA/1HoIZqhFTPfFf6pYWQQPAB6IdLqVoXELfrZuaJiL3dzdaac/Iwl+/IbgmaypNVlbfOGpmhiNixt7vZsmXmOvrNMcYGaMYgj/fs4yyXCfgK4hldjAa0PaGjNmCaOqMlWJKCj5EdqRHZ70wk9D6OXMswiIKrpYu1bqgm0MsO3IzSTqp+AiASs89Z+OjJdcsOUk0oFf2NsDOB4+y8jcio8HTdTnCqBVpNQGUDasXGoJSK9Tu6v2KPdOqmw9ZzCV1Asv8ZG3kdB0htqB+U/UJRQf1MpzGRzuYJahkPKBvuJ2LH/Leesb7s+X5lun6lEaTbOiTSbfw0N7ol9JZt/j5dza1lzHiMXCdP/6g8IS0z0lnJoYdnBHU/597R+fUAfcmfNEPkTFe3BZQL0i6EzcGFtMzPteoZyysKpSKmGelrZQ5yRMaXl5JDEqRLOB35NiOwBc73dFobaKk5zKCS8FtmN3g03J0xxuwAzqhkwrNfOxysI63iI6O0ZXuFsnEe75xncO93rHymfQqqvycEv7yCFgpCqXuC9fLsDO7YmUMTgiNhyHbD+4Hg1JfPef4t44m1KFx+hOPbBLqF0F8TdMRPVAEG5eWVuP8WMPT2mRiXhMABvPxbxONbXhcRolbi7HjVvF7qlOsVRqduG15+yGGGYUTdGcNnNLoR52PGdUOUK9HvDKnIHcIH/v4gbn+BnZ26BVF2VAoMQ8NRGYiDlOx/Fi6EPQhRirbccvc7cv01cbyhr39GvP9XWL+Dbgnm/Y6XAhiltTweBNle07Hd37HHN5Ad/fSCfXsQtHT/Pu64dUQHUl/gHOinX+c9D8T7N7j9xc//PFpaWlpaWlpaWlpaWlpaWlpaWlpaWlr6I+uXB4DF4PEFPl1ABmwNiSO7br1ndyx79jv2A72+Zv8pUEUQvSaQfXaq4hMgNpLOFaiKiCagkjaTZyMjf7F0slpH647YMy458thCMli3zK7gMGS6hqVOAOoDLQlp/QnpxCllT/NyDCS2dH2ej4ysbnv+bkd2lXJmh2epiN1Br/gwytYo8YVy+weIZk9w1oimGy97iQWqwGEzmteQmudLCbQVpAelXggJEE8mfk2Xpz7douMdyitab0R/nxDdkH12/7YJeZFcak2nJ7MLGdXp5LUP4Ci6J5zUC6ijHrgork+InQ5DJ5DSkOhpvLaTp/M6QrLT1np2Eu+fiGEJ6P3tu1P8CaXLnt3I53tCrJqRwpTpuiX7cH3ckW2fEdSKiRKhBAVpG9G/EOjs3oWQ/HPIIB5H7oGyoTHQWmiXT4ifuecSuyZw9Z6vIWTvbWvTST47aVFkq2CWoLc/JoyNhPjkEIN4diGH9+yc7oPA081rIyF7ffmAfBKS4Py/sY/zZGaPdAjU6/d7hYxYTpd5zT0qzG7ehNMhArp9P0cRYrxDaemyDp3O0xnXrdM5Gn3eP3l9CEe0UXbF+h0RxaPjKlQlY7w5YbsgPtCy5wCCb9gwzCPjxK3nPirZN8t2JSwjw90F9zeQjIT2/mCcbxyyMfqDrSmtCsWvsBVEAvEDFwE7chCDHfycke8Bl1c8nLLtiCoq1+lwzv5iKUrM6GgBeJnxzBIZjcyFMM3odhwZ6fYXydho2oZcbtB7Opg/p6OY6BlD/ukTUPHzK1oL3D7D8Y3Qgh13gopvgBsDJ8qO2RcoF7xu+HhHe8fbZ/zxlWgg+ytBxc43KFfG4yekXLDjDj7QC4g7Wl8y5QBQaoYaoOk4P3+HV0HG++w1fyDtls+FshHf/obYXpDyCcqNiCPd3r0Te4FxMsYDeX1C3EC3F6IfMB5E/4q8/Fv4+28oL38G/iDGjL3e/wL9/JfZN760tLS0tLS0tLS0tLS0tLS0tLS0tLT0J6ZfHgDeXtN9Wwv4T3D5AdyJeMBX4IcXKB2sTEfiF6QM3AehN8RlugILEifUCj1jcEFnb63OGNiBkKA2akJA2mVG4iriZwKf4dC22b9aEB8f8Ev2CyqKC9ntauky9j5jiSdslrLjbpRaSSfq7GKNgZQLLppQTnc8jHDH/cBtY1hgEfDtC1yuaL1SwoArYZ4uVhE4M9raIrtfZTi6gbokbJaEaGLZXVzqBbMHuu1oT8AqlAQ0CFJe0kFdC1H2hHXtQrofJ2TPd8q1jkrYG1DSPV1KpgGXjPZNyKh4DGpp6fAzw90hCtiBUvPvnn2wDlAqIX12MktCa0g3qWfXcdgdqa/JMj0jq9P13DImOyId2KqziPwBlm5uHzPm2Q3dd4KEzNGPdKG6I0XxI4GvFyVGQK3EeWTcbN1QKagPStkplXTeumVfsOVrgX6A6xgnyJFx2j4SjvrIfWolY6nbLf9OmcetGY1tGU/+0Wls4D6gFMIfCbfHHYkrdEP2HYmMFkbnvaM697ETlOmMPhFtCb4lwb5I5HGpZFexFD5gbl6JvHdwQgoiTug2HdWWgwcTeOd71hlZfqaz2ae7GgXG3K8bcRzZ01t3OE+kbTkE0WbJsPeM8KYSBBaSXbgCYTnYAEA/EXOCARS0/oA/Dtw7tJ1uVxylCRT/hsYnpM7j7+mu1u2Sgw1akVqgz+h064hFAvtS0u2tBdv2GTE+Emzvl9wrQrriZSNGJ+ZwhrQNe38DrWiJHHiZPeVu81qVnRh3Qip6eZ0R5ensFw9ECtJ/QuKKbFfwQJvj4wHbD4zHF7bXzxAD54VoLxkR3j4TlvHR5/kbNDpiB16dODuguA3iumWCgATmGxz/HJVrRoRbh3pHJNDtBucD7zkQInbC7cd0RgdoHEgzYvuU53C+E7XA+Q3df4R4h/MdR6BcUHdkOHAg5YaVoCDI9S+yJ/zyI9Ef6HbBjt/lAMTjJ+LyCXkmHiwtLS0tLS0tLS0tLS0tLS0tLS0tLS39CemXB4C5Q7ml+7G+InLBxYAfYfsbhBN6QbYdcSH4CvWKyiVBnwTGQaESoqgLUq4Z1RzpAsSBMTtgy54Rx3XPSNsIcKHsO3E8YL8iowPp7A0Kik6HrCTAsiNdsj4dvA6TQqFyRXwgraVTzjy5mRgZa9yABHF+PoAKMfK19DPhPbtCfRBtUBWqlnRzFohhhHoCs1ZwO/M1/DFTfyPjnrcG4w7tFRhoeQUSzgqClBkLHAmLxU4gYWn25QLqRClgd6JsiGyInAngwhBpRB/Zc9x26Ee6dbfL7GGerlMyElu74pwzUblkh/HlBRlGiOA2EpDbkU7eiBnhnM5nZf9gkNJu+Dlh6higuRdCINzTYVv3jLu1A8qVON4TMs+Y4oR8ZDSvd8KNaJcEw1Lh+iMWCWjj21egw8j+3yKOWqBlR8uOSJ9u2hsx3hB0xtvWdC1LrmcyyZ6sUiPjqo87UtoEw7/XqesJtZ+x2Bm7TQ40+JH9wiK55mWfEcwG24XwkcC13CZAd3BJFzukk1QvhB2E29yrWzqPmRHcMdfUHXwgMqBcyY2W14oyCK95b/UzmW5teYySDm4090BIma5XQXQOH8QgYlDKhahvSBSibchxB3O0XRCx2UN8QATOwC13gkfPP2ub1yYjsD0s73/NU2fbCA/sGJz2Tik7rbyiGhNK35HY57lrvm8JeLzlMMn+mvd12ZDiqCuigWjNSys1HdPd5nnK9JqfuAeOpGNZA7EDpRHbKzaOHMZoLZn4cRIRhHhe1yDPu2ZPNhZwdBTSLX5+pdjIZAARtF0T6LvR2g32Rjy+wPZDOuK3Stmu2PEVLwV9+XvI42vuz+0zSDqd7fHPMcC3F9CG3b8QtuP+N3A3uL2QcxmOudCLgjQyPBvEZHYCd+L+RiUol18T9iXd2d5BsgMdbXPPCDEG/nggrYIFYl+R/Zr36OMtgXS9EA1CG2X/AeqGfft/Yb1S9BnBsLS0tLS0tLS0tLS0tLS0tLS0tLS0tPSno18gAN4SFumJSMXtSzplyx15+REtjfjyExINUc+Y1bglHKg2Xavv2RUbkQBLJhCS+IiZxdLBmVBrgpQsH52dqBkBjXm6eI97drRKwHjGLNcEZ9KyyzTme6rDfoEoaBgWRo3Zq+kJcxI0W0aiuqe72C3dpTFwvuXrP74iumHvdzBB2jul/YhsG6EJBRHJDlbdsXNkxLE5UbLzVkogWPa3tnTX6n7Bz6+4kk7nckVm/6zEmE7P2fnaGqECQxAZEzxKQuawBJelpnN623MdPLuP88+z/1Z1ukwTypr0CQUTuEqpCV4xwgeit4wXHufsuc0OXIJ0ZReQRF/pYCU7XyMO6JEuyGesbsn+YDcDLagKropSUfV09NrAhNlZXFEtBJ0xDMcnGEyIj4B4R2uj1JKd0NsFRRCx6YDus4N6R8uG+2M6ZZXo94zajjPhbrtC/5YdvS2Bv9SNiJH3RH9PIMjcv9NVG8OmYxvEZUYiC0hP960kXI1xZv+yBPR7OkqZAwxP4C0kONSWx2VnvjeWsbo13d8SjpSSYNFG3jdlz3kG3eY1NmJCQJ5u91pBJwjXee5ogkOR3C9yAR6EpFvex4mMXCNX0NnfLHKmOzYcJ7D+nnHp7QL2Bq0g54CqxMMS/Ncf8Md/letfbvQwsDe28kLTQOd+ieGwvaZz3Szj4u1BDmpUGCdCRWJQ2hVxJ4ohoRkVLemQlwDqRhxv6ajWhvd3QtJtG6Xh/VsCVZuuZxo27rmuUonjy4yDLoQJwSO7lqdj3x2KzgGG/kB1Z/hBKy8Un2tVy4wY36E/kHZNwCsB22v2+bYNJyjXF2K/zPc/CTmR/RWVFyoGbcfHgbkRtytxvGG3G7rv2NtvCH1Bjt/gdQctmEgOcfgL2Buyf4brj3T7ynj8RK25PwTB7YEK2UWuBT++ovunhMHtCv0rbJU43hAiI8HHA3C0Sqb0x0gXePs1UoJ4/PSv4kNraWlpaWlpaWlpaWlpaWlpaWlpaWlp6V9IvzwAbAbXAWcjVBEtxP0NqsOl4O8P9Hqb8OsrcjSwdyKc+FRwP1CZLslSQDLmV2Kko7IW6AAdIaEldZ/QajotUcwCJdA4KWWHfcdEkTH7bMMzXthGAmMRsjzznL276Ty08yRqYzxB1jNK1iUjiJvkMT6du7oR/R3XW4IoeU3Y8n5AKEKj3K4TrmZstaGA4f5AamQMKzWdn/1AuCSsbbf8e90ZdI1VugABAABJREFU54EYlNISgpOdm7LdAEeDhF0EQklIWLPbNyS7XSUeuaYRH1HLMgxKujOjbB9xwRGRAPG4Z3/y7NBFp6M0bILgCcdlA8h+ZTXC57WZEcQSgwjL624GarOHthAUHKd4rk+cd7hcCTy7R7XiZpR6ARwfhtFRuaTLuGwZ0R2GU4has3fUplu1Qrlc8rKrUtxhvyWHPc6Ej7VNF3ZJkBhnVhyHIuLp3nx2plpGlUedXcGeMeNRyN7XVqbjM+EjZBdv+BxuEGb7bkpigLc5IHCCa/57AJzZrYyDNjwk4WbdwU58DEqruX9bTNe1ZHdz2k8zips5BCGSAxtIAkYi/y8J5AiQceR9F3mMUvd531xzb4w7qltCb8l9J1nMTVHNoQktH/sFCqoN9zPjvsUxFaI/iHZNCOwj9394OrnLSy6OXnMYJAaMQdFX6uWG2D3vqbn9NDSvy3ydhOCF2GZEfIVCQ0tFvBN1x8In6M59yPFArp8JGj46sUlGu0eA37GohAdxfsnv327Z9d0FjwO9fMK64/YV1R1EGOd7Onv1ktHigJtl9LxucN0yLltfMrqaE2UH9xzwqFveayOy15rp5m43tHd02wHBH18AJVpeU90+E3Yn4kQLtF9/wk1B/xyTHYkDf/mLdDufb/R+4vWk7K+Y5cBD+AHxBtuN6DEDBy7I+RUlMIkc5agX/HgHNwoFeKOMyGEfbXC9EMc7Zb/OgZ2KH/8VohPGS0tW721GqC8tLS0tLS0tLS0tLS0tLS0tLS0tLS39aUn/dR/Av3J10t3m2eUJlfLyIzIu0B3ZC9E0gfD2a9g3uFX4/ANRbgQdCf9wF3poulPHO/g3sEHESWgBPTNatZSEIjUSmAn59ZogzATsCdBKgdbAj4SVY8A40PmeUlvCtmM6MAuIOuEnsV1Qt+whtgdRL3g/8WP2vzaF8x0LBVrGo8qJxx1+VOTHF6qOZKDnAX6A6IRKG346Yek0pm0INY+xKRhobdl3HJZgzgORDVWyw1dLwtxnx3Gc6R4MT8cpba6P5TnqBZ4g6rluGGATpM/zGtP1Ox4ZL62zE3g8iEECcTISW0g3sWgl4k6MI/tiNYFgMkgjNCN25fk1ueXrhCEOZbsSfmKe0EskI3NdPWG7n4QNbBzpMi6FkCBsEAh2vuNas+sVRXpeM6GDZ6SwYNnFHAPV7FWWKhnfLJ4gMxxGT6dutwmGQcotO2iDjPi1M6G3zW7lmoMBUgTw7LWeac1EQFTQQMolBxnMEC2IG9KuiEReF625ti3BXwzPyFzds8d5vCWYHWd2MGvF+4OI8R0q+zn7ZY9cn0iHawxDpM546pOwTljH3fjeD9znIMae19GdmcvNRy+0asYbQ+5Ny32NVkq9UXFqbUh/T9hsDxyf9zCMx5Fu2XqFsycUdgcC+pjPgsDPnz6gaYRSSqNdbgie5+qPdECXjRh3ODsiN5SC6jYd29m7WzXhtAAmhd7f6Y83zqEcb+/086TLRj8PLJRud8Zxx9sLNgZjeMLX9hm44Dbw8WCMO1YVM8PGiZcd18o4fkt/vOcAg73nc85O6nGgxwPtXxB6dt6OgX39Df14w9kJbzOxYJvPntmx7UKMBz6HJ0Qc6QfYMSudDb28EH5StgulbdR2o+03ilwpItRS2MSpEmy1sOtgf7lx3YP99ucUe2ffXylxp9z+DN6+EG9f0tl+DqwfDOucx+9wvWKu9H5i6sTlR85+5/z2O+5vP+HtSn//RsyoeLNH9igjhF5x6/jlFamKihHn/xu5fv6X8CG1tLS0tLS0tLS0tLS0tLS0tLS0tLS09PP0y3MAi0DsCAPxDv0dKwV9uREe6QB+ESTeZ/TyFZGBYggN1YLEpE/OhIsK7SXdllLTHYYSo89I3McEU1fAEhj3jpSM+k1AZrDVdBe741IztlkMqRvYQKWhDpbUExC0bNmjWzTBp3v2phaHuuH39wRnBK4K4pR2xbyj3hjSCAJkR+SN+uk1e1uvVwjHRQjVdA0LBC2dfwL4tzSaes8I2XBCE44JRmkZYxvMXtYoc9E0o2vPbxPEMkHsltG2JV3CSaJn5LBlLy+aQFCIGdc844jrBRljuiMtr4MMhHQVE/lzMSOiM+a3zc5b0onpJ5SaMcm9J1CUOgGvEDrdmXNswiPyrS4XJIKweZ5awJ/vXIjSwAvug6gN7xOsk0BeNBKo9xPFs6vWBuwtwSj1w8FMuyTUtQdSLxm93FruSbcJ3gYQeR3LdPYy179UQgQpL9MFXBDr0zE8kDqd1iETfme0d67rjDCXyJ5j+b09N13eogVsDi/ESOev5ACE6Ozzjcg4aSLXTeceLNvsIfY8H5WEyBIT+EfGE8/Ybqk1X1tkXmeZv/L+jNlRLPU6+2PzXg3ZwQbStjz2Sr5fmd3JT4c2gkfBRfDhoCRoL57XzkjXdvt1gmVKgmszXKCVLZ3GoxNtg8fTQU9Gym83tJV0do8TEUVV594IjFxDc8H7MePWK0Rgj/fpRLaMV5YNLEF6lCve3/MeEYH9lXjvxBhES3d4+AlDkXah1F9BuVBE0o1+7nDeqWQns15eQH8AOxARZP9hRnZXnIKKz3jtSAC8z5h8CqW9Ev0blBNqm1HojtYr2DvSO1qyd5l2RQl8DnQUJXuzrRPecJ1dz6VQ9n9AOXqet58UfyP4kfH5M2YnbJ/x48ytUF+gNGIcxPlbkAt6uWExMsK5/QARjJHdxv1xp4xO2Be0fSbuX9HLBdGdOE9omvdPveHn+x/nc2lpaWlpaWlpaWlpaWlpaWlpaWlpaWnpj6hfHgB+KfDlC/HDn2PHSdk/pXs3FIkviG7pqjyd0IG0AY8DuzhKugndBdWabr1SM+JWX4D4gMIx3YeimiCtzD5dnbCXkmBN6/y6ZrRqm/G3CPQDaTuFjYj8MzYSrkkCYMwS0GpDcaK2RGDmuI101xYlIntkcWZU6iAeB3LNGGe2C9U7lYI4JPw6Z7Ryw3Uj+h1KzLfO/F2VhtjAwyEKBUGK4I8O11cQg3oFejpQ/Uwo6SNBmzjCBloJ6/lvkbG9iCBly15b7xkbbAlpEgwmFCRIiBkdLAgGIrf82QiwM+OKGRMCO4LP2GfLSNeQhJXTKRwlj0finJHCj9kJa+mKdU9XbEsAajMuOinhiVsn2AkZeDd8REYQbxeid6RtyPmYwHSgJWGehBMYgqKaEdK54Jrwyx+k7TsSZuuElz7StezpSJXnfhRF2y3d0dHTne41obOfM66YGfOcjwPxBH8yo5epNcHoeEC55e863cRtT3dv2ZEihHfc4vu1k/IRbY0P0JLgVeZru0338Zn3oCgR2Q+dTvKYkL8iMgcvZMt99fvwXmBa+ud9txF2TuftCR/O9ATCIZHOcT8R2QgZ0z19pNN3dMKNMQZO9jfHeCDby3Sbn+noD0s3tI10esuGqxH+dcaYQ5QKRTIJYAxonl3WGhldrEIpCn6kY35ref9KJc4HQSFIV3ucbzk8EU4cj9yrbnlv0Ilx4FGwbhTvCCeUgl5eGW+/AQZhjuqW0Lk0VAPddkTmHt33/IVB1BxAcScO8hqo5+CGB+4PfEC7vhDnA7Y9hwHcsiN7PObxRg6kHL9DtE73+IU4s+M7Tke2K9HfwEEvV6KPdNYXQeTZ4V2RbUsYHQ94/TPseKN8/ocZu97yvUwKQ8DHTxkjr4Lf/xb2P4Pjd8TjQchlRnZXMKe/f0WuN/j2G0ZcqfUT2nIgJk6DVvD+z5H4c9B39HKDxwLAS0tLS0tLS0tLS0tLS0tLS0tLS0tLf3r6xQFgOYK4vKJEOgSloPUTEV/R6MjLDWKDvcx87AHXjeo1AVxIwlypGclbpkNTfDowmY7FjbSxJfSRdiH8gZZCWKDbllHIMWNqW0EGCa4EwJC2UbYdCUfaBSc7TIUxI23TNSn0BHof4GygYliQ4GrfMqWWwLQgYbgNRvuED89u2PGNqpWihXLdcBnZqetO2Ozb9QvEdPYWB9fpJvTsLjVLVyag12v2yI4DWgIWyswYFskYaUggzZlgPEiXHZ7Qp2xISELGCESVoOSaackYYWnp3FSIqB+vSTkRadlLqiWvT5CAqo8JlQ9C0zkqZUYZS6R7m5bxyNETuGvJ4yttwm9H257g3js+DN2uODGh1obHiQ0HKiHpyMYUaRUJo2jNvWSGisD2iqgjqvi4p+v8HBmxrSXdlZrXWKTm+WhNwDqPI93Vt+nMHRkNLrOHWRTKLbuin1HY6Pf4a5gO2Ozv/YiEFubPb/n6mhHazH7kKG322+Zaq2p6cN0SyvvstS7PLuxnHDcJTfF0xT+d9UyY7s/o4ATEISX3kfTv7zmO6TImBwP6O8I1nb9SZk9w5PHIvMZx5v143nPgQCqMB6IJjokg2oYdD9x7un+LQrwQNpBa0n3dE4SHZE8w9gY1nd1Fb3kvFAELOAf4HfQlo5DdM4U6BuKK7ldUK+I2ge/sFnYHlezJjtnL3c+8T1Sw4yeUC1LlA4SLOFoV7/fssSXjqvX6wri/I/tLuofH23Suj+l2D8QE0ZH3XtlRSnZhiyHXV+L8koMmw/LZVzMRwG2ud73A8Q3qa8aemyD1ivdHuuAD5HjkvXR5gTZwD/A31GYn+L5NF3shvOewhpKDLHYilgMbWm/I1lC9Iu2GPbKr3fuJhlEuL3h/m53nlS4NqUror4njjkglykEcd9AdtwPxB7Tr/Lvj779By07QUQO5/Dtobdi3vyXq/DxYWlpaWlpaWlpaWlpaWlpaWlpaWlpa+hPTL64DOGogrRH9Db29IJHwVuUKZOwxjy8T4hrZMysJ5oTpKDxIcimApUMyJvQNEnT5SOeZS35rBFDw2BKcuADlA+gRgtQ9D9ITQqpUMjI3AZq6IzHQELRcKKVMl6Em3zzvCZtdcA/EOlE2xBXI3k3Vkv3D40DF8TigbOi3g63taNF8SxQXTeesSfam6tPFXKCnA9Mt+2oJ0hEqiphN525GMxMFdM9+YFFCLhkxrBdAE/CJpoHzI0J6R2Kb0bqB1AJIOm5lQGkJxSzXJkFkmbBXpnM0CFV02/IYntHPpHOWegW5TtfsyOs6+uwUzvdFWl53SWejuME403WJgRsWAm26MvvJcMe04F4JvRAqeb4AGGIPijiigpaKElCEuhVqu1BqocgEu3XGUPfH7Cqu0+g6jy9I57RueX2Q6c4c0907kAikTIguOmG3p/tRSgLU6GBnRiLrS+5dCiEZa5wuYc11YoIvKQkxS003OyUjq8NRG7Mr2zPaGc0O4Q+gGxPql1wXs48454R/kn3OfuYARGl5b5SW3+eWzt56QaROF3SA7nO4QOfv00WMzp7mLQcl+jmj3PMRKLWgmvsQN+J8ZMJzFKDm0MAcSojjCeIhdMOPJyT+jPVvuL2hlwu0nbAEuZih9dOMrRakKUUrul2ol095XczzOTFOJEDNqdqyB3rc82s6Xe/0dI7XLSOTPSA2/MgeZd0u6HYl+omb4d1Bd7Tts9871zuOR8ad+3RE9yOvhc8e6zgTmEfud9FLpgK0mqZrj3TmjgNqA+u4B66OH2/5HBgnMjpxHsjlM14KYQ7D8SPPy0OxcaYr2Bw/T6RecO+YD+CCP+7QT7zfcZN8JliHcxD9yM7g7UL79AOtBK0o2/UHWqm0VrhcPlMfd4oPtD4HOWpG+9ccUolxEsffEsc/x8875zk43r8ypDBCGTYY/Y7HwKlY9D/WR9PS0tLS0tLS0tLS0tLS0tLS0tLS0tLSH02/OAcwfSDVCK9Ef88+VNmhf4X9B+ABl1fgC9BgNKgF5AQGITdK/ZQgTDTh0hOKElD3dKwFGe8bHfZ0Y4pIuueiTQftme8hsxO4FfAZp0yBWokZ05qx0poQctszKtcvTOKcvwoZZRsZSxwjI4e9n8nBUCwKbk7sVyIqEQ+wr2g4IjXdbjIBjaZ7MUpJ6FUsAbE9JlSbcK1cElrr7OstM1Y4NCNw7YGUQpCOzoj3GTE8EvbVeR5lT7A8Halhx4zIPpFygfN9nmsj7EjntXr2mZoj5UL419klCyBIJMAUOqEbMXy6SS9QFIlCQsAJ+8sr4DCOBF3M7mGyVxetuN2J7pT9xjgSBkfdnnyZKC3jsmVGRbsjYogKIYVSPiNFkH7O7lQhrGMRlBLZOd1u4Pfsq/W5B6aTN/xA6i3BbwykXef1KDOeOWPFo10hZgz4BJY8HdE25vxCRusiki5vmzHldswo3+k+nn3AeZKel8HnCT97j2WuozwBaUsILMzu5piu7fIB9HNNBdgSyIUgOu8JJuz0nnuD2UksE/Sr5JxGPEBaAl9p/7VBBZF0WGOdKNPhS0YZR90yOhzIImAh71DDTLDHHXdPF38ojEFs+wS7B1CyX3scOQRCBd0p8UA9oaybIS1js6M1sDsyIrugNfK+YyCWwyXSNgqDUvY8x1awh+W1Ccct0HbJPl8G1Bv0b/joyNYIU2IYWgzXgo8+r0egKpTtBbN3YFC3K4yfMhldyrwaQZxnumOJ7853USSMKJrPsroR552wgbPlgAqOj05oy3tbZPZJjxyQGD3nCy5/lhC332f0+IB2wx9f5rUNtCju/nGfOrPPul2J8z2j1UNyIEezK1nc0qW7XdAxUAlia0S84tLxI1CteNsYxxc8KnL5ROiZEL0WpH1KR/L5W/j89+B4I6Rij9/g7QZnRlq37RNy/5bXdmlpaWlpaWlpaWlpaWlpaWlpaWlpaelPTL84BzBhCaH2dEKqOMpIwIOTIKoDn8ATFiXguoBecCQjioflyzm/172aUDTGOV20NuN5N7RkZK/ESFasAbJnpK13wDKVmCC0EiS4cwbjPJPFjYzKNQ+GG+aD4SM7RhmzPxXcjoQt0gAhPAgcrdmLagjozohOsEFcqbdGKTcScpPn04+MEbZHxkTLmO5lB56gV6A1YKBlA3yux5EQesYcx+wMTofnhIIAtRExO2KfzsbI1w9mNHGkw5cge0Z9fs1PPjpitYD3BO+hCfzsgYcxRs91ZTp95ZqgyR7zNTz/HtMVGDFjjme8tJH7w6bLNATZXuZfezp+RTDANTuAwwTCKQyUQVGosiH9zN5eHxmR6wPdb+lWthl9HBkxTWh29sbIjmRxzJwoOxHZ/RrEhMInEgeyXXINtc3u3fbhcM5I7WcM91yvYdN922aH7sifC0AqUndEPIGwVESecdmSceUM4nnN7JxO8WveLzMiOsGxZ38zQozx0ZMdMeb9UvP8xzHd5BNu1wt8wG6fX//ufgZLZ2ytCXujJ6wU0n1K5DkVQVRybRjzXnREc2AjI7N9MuYT84FZx3E8jGjbTAd4zHPoycLHHATRS8YE1xulfQZJd7yU7JUOO+C4w9AZvzzw4yQo2YlbK2XfaVWpW0U3RcpzG7QcJoHcM+OeoJbcY3K9Yr0T3ZDtBffBOAfmEC8/4uwZUx4BMtCtTZc96O0zshU4v+baSz4H85mRwxNx3vHHV8L7x23q54GPI13vZES1zwj6tPQKLsLod6yfuBfcwcc75o8corAj46HPr/N6RkZ3U5DSiPHAzwc2RiYN1EpoQK2YnQlm3bB6xc+BlCvW3/LYpBC6o6GoBMWCen2hXF9o2thaYb9+ZmNQ+h3tX6i3XyPe0a0ht7+H9HcYX2e0+MB/8zuibARCf7xxHj9h9dPP/0xaWlpaWlpaWlpaWlpaWlpaWlpaWlpa+iPrlweAe0n+Myb8qQmqpDXEHhAJeDIit0ET8HdyqRoQGQf7AaD8e3Tt6AkJpULdkDGjeX0QHhTZcZ/fe44J2chu2FZnDHKjiGYPrQfhkmZISNelFAqCTpimIXCcCSZL9hcLIHWnXK7p8BwHwcmIIMIR2dKVNxROhS8npfwKjen4M8tY4BEEDYtnvHDLcywK2+yVBWR0aqlo2QiPGaeckbNPmJsdsAmIs6t0y2PTilggPsGeZ8xsRjUzYR/p2i0N2iWdmlrz+kwwJrQ8b3mZruSWcN1mVHfUCT2FCPmI5CbIWGp7OmPPBI/MayNCqCQgrQ2PTrDjWujDsVDYr9NlesJ0T9ZaeXqL29Yo2w0VaO2WrmKLjE52e74RgmXUrssE1YJKQ5kw1/oEgdnz/OxGDsleXtgTDNqzvNc+TLQ5ZDBjx5/AnEDalt8XjkRP+EZkf7LOvmXL/mLBpou7ZLw4BuXK801ENR3eNl3Z48zIY834ZhHQkhHozHmA77eRpQP2CRCxPObZgctHnHTLa9ePGVEM0W4z5bnOYYE+ZzHOhM71kjBY9OOaf/QlT4ia7m3Hj043xd2g5RBAjGePccvXGB1ixlAzkLbn2hwP+nEnzDNeenYhxziRmpBftprn4wMthVpynZUx49zhmQoQEQTZqx12x/uD8IJHTSgqMOLEBWg77mcOqJQr3id0DkUvr+BKOR26oaVRkHmMG7J/Rm6vyMsrcv0Brp+yF/j9K/E4CBcoV8LuuaZSCetIvSEl0wjMDR9nwnwy4jvGSEgL353oYyQ0LxvhhttJ6C2BdLsSIbgP3AyLIFSIfmDHHeuGdWN4wP4Zd8WPQRhY7wyCKI0RDu1CjDOj8LcX5PIJLYXincLJfvs1m1bUHmhr1P0HmhY0OuXs1Ohw/JR7//yC3H6NvLzMZ1QnpBH1R8Z4/xf/DFpaWlpaWlpaWlpaWlpaWlpaWlpaWlr6l6xfHADWWzo0RQZh3xIIVs145buDP928A+JOAqLLjMMdqD8SDmpG1oZnXDCiCXFn9G3a3WaEsA9EBtRAdUZGC+kElZrQL5SIgWMIQalbOi+loFKRmLGwPsAORJziJ6GDdC5nPHSphdIu2XNsCUeoFdFXxAcRD4oM3L4SzcDulD3YWka8UuqEPIJL4DoSyA2jEB8dwSDQO2W7UESQccJ4oNMP6OFEmYRPZfaFOvT3CTFj9q2OBODTWii1Jqcjr5EQCRMhjwufjtIJTp3pOo3pEj1xvxOyZbS0TjfxszdWCwkXz4yhVoVSiXNGHs8e3ZB0GIedGfscHRdldMOLMGzg4y2hlhuMAxnZt1siIbiqIdtlRuIG4QdadMbWFtyCMCHOg1r37E62Y8JNQ1tFi0CxjKsm0sUqc/NQpptapxNzupdlQnhk7stLAndi9us+wb197M+PaYRSPgDl85dIIFIyElsypjwknetCIHO/xDO2OWYnbVrds4NYazqQMUQbUtr37mdteU+Sry+yTfd1g+j5PmVPkCwl96hmN7IgxDiwceT1//3BjMhzEvd0Uvsj/71scw3zmrhoXmtpmHdCC9ZnH68NlByK8H4n3InQ6b/1iZDTNWvbDq2he8PHt4TqPqbTuGQ1tRuMjtRGKW327TpujvmZddsIdp64NMzGBNqSEeZ+4qoYjluHPoiePdg+DO/fZrT5iYZR/KTKSd2zT1n9QOykzL7e55rr9hmt1/y5sqHXH5HrJ7i8wO0zsr8CLe9BVaReZ8Rzh/MrWre8VpIufo8jXeDzvjY7Zh/4+Lh3o2z4uOczuN/zmtsjwe2ceonaoEimCITjIUDNQZvzG2wb3g9CAzve8CgJjEfH7Z0Y95kGrzAMffmcsdtjIPZAdaNur9TaUHG2l19RxRA/KfWF7fXfoegFDSj7Sz7nwhF3pDbi209/tM+mpaWlpaWlpaWlpaWlpaWlpaWlpaWlpT+WfnEAOJ1mN2T7nOCk7ODTq7mP2S06ASU79Ec6JpngLPZpZUwQqm2fQHQ6XX1k/LEAKoQqqKK6z35WzRjoulPEEoYOz1jd8HRARsbdpqEyu0mp1xkFDLTs53QGhXQbu4B7AhgXIdA8vxiAEgLuhtEYIbg33E8Qp5QXtO2IKhKeHcD9PXtSjyOdixM+JQh/Olan65aA7ZrrVhM0ikzQY0dCvTGjeZmA3EZ2C4fPCOgJhFUgDPGYACmy/3bCyQjPr7khcUwQuBExO3wjECvpOMTysviRMFGY16NljLIZMd4Tal1ued6RsDhEibqTcbiO2YzcbiXjZ0dHUMr+gkb2yOp+pe7pUH1eT8YBduDuqG7ZAy2VErO3tBa0BBRFLy8JObeXvGZGOqpdIGYXr5/f48ajZ/esPfLcwvIaavZHZ+duz/jt5+BBncMJJPjm6Th9umHlaRmW+VvuvQ8ntghgSLnlnyOBaSBE5H4SzXj1jJwePB3GmXE+92T4vJ+Y0HC67vMmzVjmuSdyjMCzr9dnFHTZ5iFGvlRowmGYfcQJmHPv+Hzp3MPhnZCCe8/jpmF9pBv1GddcYu61nt3WqgQloavkdYsnTCYwPzAfeUxSQV/mOsiE1wEynfouNIINRzHCTka9cSK8nScPhNMGZo7bwCzmcUL0AzDKtuV9H05guJS8LUNnJ3VJl3laitHtglwugKMSSG3IhOs5AGDoE1b7kYB+f0HqFdWSgw37p4zZZuQ+jHQTi84OadJ5m071PTt47U7oLe9PCTCf3cFnJha0148hDfcTL41oNzwScIcfee4OjJ4u4wnuo1TY9kwkqC8Zfz96OtDD4PKK9QM7vuS1HQ8URffPqDbKtlPbFY0HKp0cA3LK9cb28iv2y5VWgu36mfJ4IOcDbS+U8gr2FeyRz6ClvzPK5Id//a/xd1lrDX++1hr+fK01/Plaa7j0L6q1Z36+1hr+fK01/Plaa/jztdZw6Q/R2jc/X2sNf77WGv58rTX8+Vpr+K9e9b/9W/4N0/Y5oaUFcvshYaM+QD8Bn0AGuSwK0hN+yQlxRSjpRhOm2w08HEERe8bWZrcrNqCVfK04E3LEjH/2gYjjKMXB25ZAr1RUsmM3BNyMIo2Mpc5eWh93VATVgs84YLcEoZoFpumUGzY5bUkYHEd2H1Pz3+4n7DeQL9T9NeFHuySQ04r1A2mVKIIEFHXYLum2LduE2STMHdmdio0JugQpn3ItVCewm/2spOszO4FnrK9MsGsgNXtlqTtQkXiANGIaX5M/HXld/AA7E0CZJZySIHR2iUr5iHHGjnw4aILXMMvrFPLhasz46nyAhAKyE1IYZlipRD9m7+oAc5RKsROKoJHnRXTEB7q9wDhn9PX+na0SFD/xkl3I2i5g9wSl2jLGeEaExwTa1JZ7y8jXigngKN+7fmtF5Ez4rC3Pj8ihgWd08bz+6BPiZs9qguV0eqe5O4cWIjpIncA291+E5ZrJdIa75TrNuGHZsvM23cbn3E/z9es1wT4+gd6MRpZIwKw132dMuBcx77U5HCDktY6R91JYQl/VCbX5uL/QHJL4gMvP+GfRjI6W+sG5IQgR3J1Qxc47UHI/6Ub2Ip9o3QFNEF2u6f6vivvJ2Q+6BHUMHKHWloccBprPCNoLYRn3rEURVRzhHJ1+vGE4YSflcSZ89HeKSB4vGVPt5H3mKnhoupfP9xy4QBn9HcqG1gumGcOMFmoEst2QPp8DUuc9RvZlMwityfedOWgw98LspZa4ECWd9CEzrhtJ96+dCBvRT8Kyu9n6g6hXJIyYUfhcfiAev4P+DlIQtxy0OL7O4YWMMed8QL0Q450IQeue0Nj8I4YcCTjPHFLZcyAhe54L9AfBRrQLMe6oOmy3mW4A0mo6gee97+VK+CPhuef+DnfCH2gJts+fc4t5x1QS5DMyln7p74xyz/7rf42/y1pr+PO11vDna63hz9daw6V/Ua098/O11vDna63hz9daw5+vtYZLf4jWvvn5Wmv487XW8OdrreHP11rDf/X6xQFgfQJJDug1wVFV4AtaPuNRIb6B7wlAWsIej45yIrx8QEIgYWJNP5+opKtSgG7ZgUuAFcyVEo4ECG3Gkm5EjIS5MVCZ0Is2gfCWgcqaMJQIwoKoLR1sOOEj43URGANaUi2PSNjRrsQwzCMBU63QT+KaMcLKja2k49lV0DBCKqU0goZqEHTCDak64SBg6W42hFoVmF9/Opm1EPZA2AkfCepiIHX/7vp8gt/pus6O2QlXal6bkO2jTzVh74WokABzzz7W6XimbgnLdAPJOOeQlpDSJ/QKy9hZKkQlZCc0O14dITQdo09nsrtlH2xktLVEIGZUCrJVxB33hogmdA3Q/YKKwHZJN7FlRG8MQ9QJE1wzNtgBjQoju17TVWvpbMQI5rqFzDVJ12aU7GMWkQR5E7RGN6LqdPtOB7tGOkNlunrRfA3dZmwzGVcuOq9NIR3vkP24+t35+QTqMbuGZYJWD2S6v3MQIpCyT2e2zrWfceAyHd1zL2V/ryZsxqFKQmw/J5yf0c86nfdsk+tOV/dH9LNPYDujwj+iq2cctORQQOiWblpRGA9CK+YTsA7LvaEKZ899xQ5xR8SQcsnzNs/7VArd3hl6SRDu3/DIIRJxQUKQy4bamXAzTtQGpaQrth8HB8r59o3z8YD2QonfMj4Z1ypcLreMudaSAw3jPq+FEOegXC7YOPFw2K7E/ZHDDihaK/3tAUWwdqFopbUrMo55vV6Ix5eZA1Fn5HVC9fSBx7z+cyhCHC037HwHzeh6EUdo0/1dkSjI9pLR5loSxNv5MZBA25DLr4j+lvf37LWO9kKMO9IKcWbkezqLS665KEGHbZ+ufYEoc6hiRsLbAXLJfXgehDladqiaa8eYww8FiSMfEeWF4Mg1dcXlgtnIVIbLC9HfCTEijKKK2QMdgrY/B92x5QBeWlpaWlpaWlpaWlpaWlpaWlpaWlr6E9QvLwJ6fAV/B1pCntpAbkBJiILxEUVcBWSHUNQ3QlpGxGpNB/DTKWyecbLW0xXYe8K4MHi8T9AJLglapWg6Yd0yvrc/MuDVRzo2w2AMVKazMcjj8sfsTq0w+14FR6UBGUkbYUR/oHHOyOTBsANzw3WfkLKjsoE90NYoEulWlITEIuT3x4nHSfSewHr4PHefjkqB85wuUUN0S/g7O5AlZEY0S0I+ewZGT9AbZHywzVhffcJaz1ju6HkOzz5Xl+xx1UkNI5CSYEc03cRhI4/NYnYgHxn5a50Iy9hr3XAEGwdj3Bk+sMhG149IYBdsDKJnzyqjz1RvoXjAZUdVkPB5nI6UkoC7FJwTQVAMZKD0dGCGwmVDVXAP3DreKlF0ulVHDh6EEa7E6AnEpHz0xRIJxswz7jbXZn5f+Ow3TsibcdtGjDMDbsMI93S72kGM+4wWr4Rc8OgZkxyePauzK9d9zNRmm+7bdMeGGwltp5s0ejo5a/1w1xIZW5zXx2dvMQn+I13EUrbvXcRaslsWzRjwmL3DJNgWf57zSPez9Rw8YDqbn67lsk9X93xdtxxGiJ6A3zqUNvcMGQn+jCV3B/tKjCM7iOuGDM9I89oQcygF6w/G48scXOhIuaUjtiiCUdqGIqgHVSLDtrcN3Ohnn+7fk1MC3zaiPhh6YsfJMKG7YM/Y9EF2e7vN7Z8DB9KuYCdhj4yKt3Sv+mNCfnP8/s54HPQIQtt04MfHPSfjgPM9ncb9wG1GfpPnHOcbbuf0dgeI5zVyn2uqiHqmE0QOC4QLft6JkRA/rOd6Sg4/ECOP/bx/QP6wjMWXOl3lkk5fsSOfL1KyS9jmsePIns5ebdcE5BGoVmq7oC2fuTbe5j0hCZYdKA3ZNMHxuCPtEwWn7Tf0csvHe9nyvh0ncn6lamMWBsD5le3T5z/u59PS0tLS0tLS0tLS0tLS0tLS0tLS0tLSH0G/PAD8dCjKkcBNH6AZYwsy40+v03HYEu7ZY7oPG8LvQWJIIIYm7LQJ8CABkDnSdDomT6TWBLCaXwuRBF+lgQsaoKVBrbiAjWNCnoQ1okqp9dkGOqOASejqHW2XZLNPz17ZErq6Y/Z00SbscxqMQRs2T/WaQK9cEoSKJAQUhVIQ1e/nNhwtDWU6PmPGrjIh5QRuUSa8iXSXSnmu2chj9HtG485ziWcn6bPr1mZv7LPbtc7I5id0nBHS4orIlkCptISyloAsLKFwSMHDsus10r3sIpgP3JUhzugn47xjxzdkzNjjdkE03ZS4U0bCQJ2dqy6gdafOmGb3M6HoAJsRyLXulLJPh7MTo2PuaNuQ0dPJGLmXImY8dhhultG8peQ5RE8AXxpmPa9ZkAMHMTLeukw4K3PAQCVd0k8A60aIgTbcgygXYsLV8HS8BkJInc72CfiYTm2z7/BUmRHB030bg2eEeMK8ntdL6uxXNuIZLe1n/p3Ze+3Z7Yz3dP2G5Z8hXcKegwtETHf2PI6Y+xyZEdIzVnx2xmJjOovbhIyzT9kdRxgB5gcekk7ac+A2iGFE+5QdwnHm3q37BO6G7gWaYv3E5CXfPx4Inve4O2w7UmoODZQNNUP3C1IunP3k8bhzmHLYIDyotdJkQ8sLaGDHb+jjYAiYHQx/h5jRz6PDvmF24trya2e6maM24jzSibxdYP+cveBjMPrARLHzgfsdtgty+ZGQIHSH+gr7FexOjEe6bclUgugPwk6kXJB+ImVH2gVKnfHsDZF8ThEB9p63NpkcYCb46BlZL5pR7s6E0RkXL0WgCOHH91jvceQAgwfRT3w47C9I29JBLKDP46n5HNDLS/ZIe1DqjpYtHen0eV/fMkHgvH9ExUupCfiHoa3lvTke6c6/vuYQQLtQ6k5pGwXJ+uKlpaWlpaWlpaWlpaWlpaWlpaWlpaWlPzH94iKgKRWOI0GQnqTjMrs98QMenbhdifianbpPQOkG+kpQUU+XYnadZu+ulBvCCbJNJ+ckA3qhCPgIQg9EZbppj4ws1hmLawNKw90QkYxejukwrbOvVC8fccZ5WNNha84zUlfqBqYokvzUHtjs7KQP4CBkI/x36FaoVVBRtJbpyxXcZi/s6ND27DIeng48BKmKlpLOy/3ysT7pXtZ0k/Z0Sj67XUUknavhPJ3TQiWjnGuC3hljLHV22JYZCcwztnt2wYbM9Z9/H+fHZcqI4IBWZh+uJEcsSWudmgCJ7NZ1rShCHOkQ1HJBPCG2DFA/JrQELRvIO1objuDngYhQS8vYWI9MHq6S1bVlwzz4QPZjRmD3TqmKRDAISmS0MP2NYODulHZBimLeQAz3BO2FASIU5kBCPBJ8+ow/DpvQnPlnMho4OkHgLtkrq4pZxmfjUEjHp+iM0346qSPBsbQXhDFjto3AELMcXniu/+xDTfD7jAvfPvqFebq+mZ3E8nsdv+EJW9GEgrO/N92lMSEuhMxhi+h53vM2eLqeZW6Dj/tTJL8hPF3EpSQc79+ALYcCrIA9iLoRj3u27Y4TbZp7SBp29rzPZoS8u+HA2e/E5VcUFUR2mtbste0nMVMEJDzvBQYezunOacEZzjHe8958PJBrhSiU0sDfkPJKPN4Z7YpXQcuWwwvtip9fiJ6g2ktkn/NpINkB7MffIvKafcXaoG74+YaacBah1Cu4UUo6+WX/e8QcwpB2RXQjOL87ZVslzq+IG9IasKMUouf+0+2S0xDP514YotccerCe0P9yxd+/UkqBcs2ZBAIpt99zA79PeL7NYZUtBxvoEyxrxt2XCv2eHdLDkMsOPtDtitjINfeB6HQKby/57CzXfG5FTEf5QPYXlAAbSPsh46mPM1/v+gkeb+j2Ca8V7t8Iz+spL78Gf/tZH0dLS0tLS0tLS0tLS0tLS0tLS0tLS0tL/zL0ywPAXKFaugbfvsHrr4BzuhcH3K7g7xPZafb4esHCqRH4dLSqK2AfoCnsSPdajNmnmo5QXDABKRtuA9EdCSN7KCcctRPKDuPMSOPZwyrRM640KQzM6FU0pjtu9qiW7PMFn05bQXxC6OKEP9259wRz4eAP2vFC+7TN6GYgDiKmc7TMztlxgDqq+4wUVsRmP+c0IuMTqPv3GGORpzVOk/t5B70gfp+9rJHHFIawgSRofPbLwjHjt+f5xvN80zGLeTqoPeN/Q0n3sQ2cTsglE4wRQjyhXxjUyPje6YLEHd1aAu16RSIoRYjYMXmAVLRt2HhkxzMJfxnvCAbtFXNLgC+KbnX2DSvqkhHPAkJJKAWzc1rStFoKIyxh2pNzl4qNO9JuGbEbE3SLEB64OiIFifMDbtroREm3tMhI3m0F1YSW3g2vs3d4dKQpwfeYX8MpEgRzv0kg9AS0kn3Oud/3hHllJ8ITdnogZctrIRCe1zyPkXSSI/lzAeie7kymhVrI9/Psn/04rrSzT1PvA1NFpaBa56jC3Bee90G+z4wghwThzzhpD6TMPtoJpmMchJeMRB73BLsxO4Al8D5wD8TTlZpbOgcSHOijY21DxCkeybPV0NoIexChmBdQR1TxR+c047TB4/iG9SD6N5Qb9fVTns84M3Xba3aA959AFC8viHXsuGcPbtmI3rH+SLD97EP2jIWmVqL/Lh8l3Kcz9iAOiKh4VYafiN7nXpxpB97z2VEaUl5m0EGus7Yb9K/g2xzoyGh4iY7IJXt191cYB1IU1Yr3dBmHvaP6Kbuv45im4h3oM25cEB1Ah3ZFpOD92+wBTne6lJorL1u6iKOj9ToHcF4R70g/0BDgTGB8vsH+ms713inXz4RMV7vuSL3kM9vO7J0uCpZ9y2XfE/hfPhH9ns++tlH2OnvEy4dJeWlpaWlpaWlpaWlpaWlpaWlpaWlpaelPSb88ADyO6RZ1eEkHanaqkgnHtUE04Dadlfb9+yPBlvkdqZfsc5WEgulsdHBFQolzpBuxBtI70fYJdgfeA9GWzshS8FCEQEQmjJ3dqz5jd+P3fg9wH0iUjKOu++xlfVpgp5k2IntAbWBIAg97zEhbhXOj3X5Mx2fdMSxBccy+UZ/OYzeQlvHTWokRaC1A/+j3BdKpN6GvWEC78owcTrKZaxjIjKaeMBnJnwuAgZQbYWfCX5lEVBr4N6i3BL8+AeQ4CGq6H599pLpjPbBiiOiMm92REKRPuGQJxqsaroK6EbJn9GthAqWKcEGGEKGUkussuqXT2aZrNxSsJ6QtNc/XT6JecMvI7HS8Blp3IPARCIaqpqM2DBtnAvIaqBTGYem0tQMvLQEVmjHYBuEHUkrCRj8Tpo0DCKJtDE9Qaecj32N2CEtp+LDv5zK7XsNOBiVhWFWKaO6NkO+O4ukAR+p3UB+Rfb/TKRwfXcAZIR46Y8NnZ2t8QNvp4JU6t9AEuD7mcADpjBXBCUZEusrLTol09ooFqpKue2ZUuM8oat1ApitbFMqMNdeKnXnfmQVR0sUfIwgOCCX8wNC83SxgdKrsSNshDtze8PqJw4OgUMIT7gvZ283IIQc2zAwzg/Od0QfnGJz+xjigSEHaj0TNDm/E8dMoJYiyY+NIh2s49AdeK9EqcR5EzX0JAeMdyuvsIbYcVJCXdP0S6IwUl+tfwLgTdmL1mnMW/cRRhBMBvHfKLtQYaGSnb0Q2/1K2HGDwdJOHMB3hEGdPeB+W92Ld8Md7xmZLIQxk9mBzfsFdkMs+O7QrPB4gnai33Of9nn3Gx1ue/7YT48yhixJAhfYpwwDYKKqElhwmOb/xjHnOKGrP5d124hz5jC/Z8x6eTnYpF3wc+PCM+3Yhep+DQAWpG6XUvE4WOUxTA5H4I3woLS0tLS0tLS0tLS0tLS0tLS0tLS0tLf1x9csDwDUIbhDfEP2c4PF+h32HVrNrtRtSp8NXEoCFCuG/A7lB2/FIsCLMiFnxdD4SiE6Q1l6QGL/XPZr9oyI+oXLLr9V0B2f0a/bnIiVBhSSM4Ono9ROROuHZhGZBuich3W3RsRHEOIAG/hW3huKEXvHjDbWd2hoqQRSgT5BhZ8JmbdN0uyVki54dm2UkGDZL6CfPmGwHDmBPhy4V4oFoyWPUOt3LFYl5fprdwSLpbg4p36Og5TtUTEfyLcGT1I/mainb7K/txOw2NjsxEg5GqwlvJZ2AokqII9IzQrndcq294zPW2L0lSBsdLQVXI1xm8q/j4hkV+4RvPjJ++AnuLUF6lNk1W6eb1DtOEF7xfiJtQ22ui3U8GspJaMPHiUmBMWHVOBMGbzcs/Lsr+AxUBopmEjgZM0yAPB6wv2Qt9XFS9or1ey65VKQ/Eqi7pfs87hP2O9ASXge4OyElw9ADJGancjBd5iMju318RHbHOLPPVeq8NzRhbIZtz2jomutTCtnrO+Y+mO5frelcPt8Z5oySHb4qmn5lS7d7pVA8gbYqE0g++6EVESEicB84kV24EblfSsX6OaOPTyyUcMEMXLKPWc93ePmc0ezxQOSGxxvn+Y7HO7r/mhIDQbMHOnp2x4oS48DcGFIY40iIfb7hUSj7BY1A1BB6JghIo9xe577PaxGSAxFaCh5O9Dz3GDOwfX8ljt/mupWGH7+bzt2ClAtxfEtj/uwdl+01oWp/4HUjzvf5jMnhjbAc0oh9o3mfjn5Fwmbf8g2JI+8dN8p4J+qMVa47dryBVMJmxHKUmXBQCQZSXuDyI9HfiPONYoFcXoiWjmxUJ0QeSNvI5IO8T2N8g+2FOB/pohfL/cWZW4ZAt9lj3B/IyPsshuUAA8zI8QFjusX3W0briyDnF1Rm/Lw90LLnOcvsHQ/B7R2lovsNocwBgqWlpaWlpaWlpaWlpaWlpaWlpaWlpaU/Lf3iALDfQa4F5PN32NoK3P8WXn5FvN+R245QZuSxgVzxGOkSfXLSAJWWwHJGy0KkI5N0nyJOWEf2fToT05FbagJbEcUy/5bA0SgJyMzSNFsKMtJJnJm+nlG2tQGeAHXW7qYTreDimBeCkxgkYJOd4BuhNySCePuKbFfKhHfYmO7NPDcpAiNdf+IdsS2dpxEZUe0O5yC2dAhLqbmOkvAtaoWY/cps08U7IVN59gAbQiPcsytVJF3RWCY+i8/I7Ke7s2a37OzA9WGIBIEkvA1H2IhwdCtwN0SebshOMNDyivkxwXOdxuMEzxFBqRshglFgGO4n0NIxKaBakmdOByply2NypmuwIwKyVbCTKA3MMEYCsboRxzFTsUd2EfuEwP2BFwEd6VislwTAkdeY/QI2iD4BfdnA3vHRiJodxO5nxnAzMrJ73LOWVZzogZYNGxnvLaNTiuHWCVGUlqztPBCpGX+snq7PIOE1QQHcC44jQbpVw2Y/67Mru/Jxo0TMIYiMxs5NqYQooTWhfvgE/iPvhZJu97DAXDD3NIhLwfoA8RkA3RklGN2o+47GvJ9LnQ7jvF5xnph1LEgAPCD8zL0zLMF+DIYrJtn7W0TxeKAc6HS/YycuJ66vjG/vSHul4Ii/U+uvMtZaG+GVEGf0g46kYfR3vyX2CyIVjQlbcZ4R6MJJqCHqOYyhFTOQYkhExo6fJzYszfQCxDslbli9UkZHdIftB/w8UUamFVARe1ARsIPQPe9hM6hOqMP5gHLJZ0MIfj4InGiNTchnWYBUnx3WRz6OYmRqwXNd7cTzwAg6VMWPg/BOPB37Oqizy1cD2DeiPxJMx4lYz2u9vRKjZ3R6qRkB3a5IzXvce4JYdCO2l/z38Iyfbo3Q7AoPIe99SlaJPzvCew4plJZDN3YcsL+kS143qDM63U6g4sc7cObATJwQNR34l89/3A+opaWlpaWlpaWlpaWlpaWlpaWlpaWlpT+CfnEAWK47YGlcDQMuCTeLQQz0cgdaOjpdEHkQYohekmmFJTzBpuOR2XcqCSB85L8hGVmqNR28oeluE8vY5uFEFqQC2R+KSDpcpRA2Mqo4so+VpzuyTteqCIQRdIQJojWjn23ciRFIS3cp4Wg4ohPMvF6o5VO+bmlJswmIDezIiNXIYy/tOh27nrGpT5C7FUBx75RW8ue0gh/ZsSqeXZo17bqSFDEXC/tw/UpJx11ejxkprE8n9XQWS4XoGeM7o41DbDo6FTwo2iCCyuxqbRn76gHDOhEl3344sl1R0dk5G3g4lHSxykcHsYJeEhid54zs7hm/rKCXz7if+HngRWiaXbEShllG7sYJMAF3SLoOzRJm38+Mye7vyPVTuo7PM2Npma7G6AnYJfJ9rSOlIVGmC7ISfUxYn92v0m7E8Y5vl+mKvsD4Bgimt4yJNiHYcB95TXrHqQmfQ5AJx1BBVZExpoOyJoAue17vmq/h4tSQCdaZkdB5vLnH6ocBk3IhfGR3rPrsCA6kKGZ3JCrigeMYMDSPNcYj4eizc1hbRkxPd6kTxITV6coOKELYwFTo2vDzHYYSI2OmPQIP6HaHcqH/dCd+2JHHF8rtx9zuL3+G20B1Q7QyzjfOECigRag+o5y15jq7Qg+6Co848tj7gKtlYsBWkXgOSJwg2XcrokhsCf8FmgomBlYJ3fHzDjGQ9olxvIN0SvmB3t+o+yve/wbxB9puaeQOx8PTUXxWws/c737m8+f9t/n82j6DGjy+ghTkeiX6gzgfSBgngSK01tCQjC7fbogd1LrNFIIgjoG2jCqPWokwvD9y8CEM3Rp2/hbG/t0U7tkdHNKzNzsALago0Y/8mmq6qqPns8pzSEBqy7U93/PP/URffoTzHbogJSP3RRU2nfv1BY50POt+RduOuOPnmY76eiWO3yCXDaHg55Hu+1pQ3Yl4oNsnQiKDCY6fKPvrH+2zaWlpaWlpaWlpaWlpaWlpaWlpaWlpaemPpV8cAHbuqDf86OiugBByJny9P9DLC7CR8c9K1RvDfsoeV52xxRaoXmYsKOli1EKIoVozFrpuMJ79kJLwsDYYQQyb0dFMx6QjPmFY5h/ne6F4DLQ0xH12sDLBdYJRLTVdtyKEzt7S2AgxggmAEUQu00U7oLzM+NSGj5Hgum6IRsLwMWBLp59om5HWjotShIxg1grm+f6WwC2esE9nbHVhQlVIYjb7X2W+iE9nqOT3ISVBY2lgjzze5/lGpJsVcI+MqpUCFcROpF3RccBzrdp1OiwBD7o63g+kd4o2VCUjikVQcew4sFpRpkMwepJeyD7gUrKvedwRaQmNfeClQNlxOzLSl4zadQquUKKi7vhcV6mNsEeeY0ReO5vA9Yw0ycrcDyUd0u7TsSggxWcquaPsM1ZbEasJpsbINe9n7jU9IRoqA8aMbiaIOsF2qcTjnvtNNN3JJgn7Z980/YFcXpEx8BhgMyKbirSGxMguZSQjl61/jyyvF+Z4QW7dGes9ZiT6E7hHP3BpSHzvVR3ueJ9Od2m5d2sBczyOjPSmwXCGd6jGVgs6u4PDBzYGR4CPE58c212I8RMmrxyj4/GOuBC3ChbI9SWd+Lrl3kCJGFgE3bIbVoHtctAs0PYZGceM/3Z4v+fJ3qYrfBhy+1XCfAJFc3nqKyolnc5vd6Q42hrSNsydEkHItPhH5JyGjQTw4wsmNe8L7+j2K3wciHW0VKJ/ocQtr8F+g1MygnlGS1NKDhTEvL9ffshhgAjk8kqMM1MPyI7wuu2E3fPRtG3I+RyaSDczbctnlwhIScesNmQLosccrvn17A/W7Db2I/eqzujs/TOMB9FPpGYMPaXN4RgyYSB8Oq0vRH/HTZFHDq3U6Mj2ko7issHjHbggrYC8TLieQyUiWw4a2IFsN2QMgkD2V/z4hsiG2znvwQPdXzO+fXSwA90+I7c/z7jvpaWlpaWlpaWlpaWlpaWlpaWlpaWlpT8x/eIAMHIh5I5sE47omJHEmj3AorPz8QC9Iq4gl3R0unzvrxUIP0D2dDdGgo8IJ0KQfiA4sBOcCXxtJMjqg6gtWYSQr1crER2RSpD9pgnQZtcvnqDGpwXVI4FqmXhNFCMmWNqADkeem0rg5ZJRyUeHVtO8LNlNHJGvHXbmeZiDxoyxznjfMJmxsTPuOSLPT0v+TGkJM7WkA9dGQkQ/oFzTeVj26c6U6areiOjp8NV8fQkIzX7TCKaLVXBzrGj2jlIIc7QO1K+IbmjZc70mWAwCoqIiuN2JoXif8bKmdDO8doqUBOWRP+d5AIQW6AdhgWyfoT+ItkHb8nj6iRwPdLskTO9A1Yx9Ho6bQVPizMheRkdEiVoQ9lzHMfLch0G7EXIizK5c60i5IdbTLSuB2yCsE7qBNvycrvWy436m89o72ITxYQn1SsUYyJgRvz6g24SLTol0rBMlobYb9DPd6giiFT2PhMrbJftRJaF3rVc4OlyuCYx7R2rFVTK+19NtKwi9HxllXOuM501gKmiCtZLWUJWCmOEShNsHmEwnde45cSGKImY5DBDBmHuolkrBsQgON+x+x2d8+uAAfcEonI934vGA640od9rlB7g/kHplWqOTGcqG4bgfuAd6LZQjaLGj/cwe37shszPcX16oP/3E/lawa8Nvt3Sv93REuyho3gvmBzocvX1KcB2ez5HxLfeE3eezpaUr1d5Bb5kEEHMwox9o3TO6enRoe/bxjm/okYMYlDnQIdOFvn0Cu6eTXATZ9lzrcRKjwH4FvQJBCDgFra/EOPDaMhJ7xAT6A20v2PFGaLq1GSexb3h/Q+qGu6NtJ84vSLmg7h+R71ifvd4N4sx7Siv+eEPaLedFeqCqGUReb8TxFfcHevkV4QehL4z7gVSHGBRztDTC3hB5yefccSAvV8Q6M4AbmX3uhGXUumZvsUug2w0tGTse55fsRQ9HvEL/BnHN+PKlpaWlpZ+liPiv/V1E/r9859LS0tLS0tLva32GLi0tLS0t/eFan6NLvwT94gCwnA+kXGa8cPbvSkzAKgfE5w8glyHF53SeZZerSEXEcQpIo0BCm8hu2sDSkUkF/0rUDTFLAEPMuGWffx4JFFHEPWHoM+I3M3TTLWiGlgl/xWc0sCcIlj3hhRYYHQ8Il3wL73gYSs3eTHfY83jVSKPzs7N1ZPdsSORr2UBrdtgSjpSNGD1BrrR8T5nnfnZkv05oLBndOjuDUUlAbgDjAwQ/zy9jrPNcBCPKRsxOX5/HTRTCBzqhk8YdrRWkImLQ0kEIjnhCeCzDa9NhKHlsYUjZ8dGxqh/9r0JPZymOnJ1olVBBtisyLF3Y+nytdJCKO7pfE8qbzc1VcYTwg6INO0euf2k5YyDlu5tx3LPHt20wDsJO5HJN56kXon9FyX5cLQnqNZzgBbB0bUcn6o6P2WvsPV2T0yUb/oS8hkVQMKTtjD5jvmkwFMNQDzTtwVhM97iU6aAmIS+RHcE4taSTlcc7te6YGTaO7GSdbm6xQMQT4ue742MQ9gTOczGeLnIrAFgZ0z0fH7AYvxMjnfEiRkRBbBAh83whwhnnILbs8rVxn9cg0mytlaP3jCIuN/z+Bdk2pF7gOCA6uu8kzZe8QXwQ8gXGldBA+8FlKOXlhXI80DPYr8KPP974d//jf4Jy8n/9P/2f6Zcrf/ubr/Ta6PcH52VPN7Zs+dL3N2g1O7qLZMQ0Dv4TREkI7dnhm88Xn87/Af6e0d4ieX3dsVEgHiAb4gN0OvoJxghKOeHyij/eKCWgCYyS94YFMRxGn9cq+7+jPgFwx+pA6xUJ0LNnQLcUpCgxFPo99+C89+T2IzHS2S66EY+3HDLRC3707GHXSw6L1DI7efsE25Ku/rpPWOtIKUjkXtKihDRov5rO9ewGNhPoPq/fHbm8IEeH/oCqyJbN0bLtiD3L3H9v2GQ8clij1Iz0x7JPuTTCKhaK1obuhXj8BvxMAL60tLS09AfLzLjf7/z1X/81EcE//If/EFXler2u//heWlpaWlr6/6H1Gbq0tLS0tPSHa32OLv1S9IsDwN6m2YwHlBvYO4GjXKB8Av8KXtAoCIZwpYlwxjvh1xmN28FLxhxX8BBUn29gM9I08vWQj2jdBKKWTsYihG9IJIh2EYpe0lw7Q3OzilaAnq8R6Z5FtuzgnXG6+IwQFtLta48EGprduAnA+nRP3pD4hooDr3l8ZUsoV+oHlBNP6JyuuwJxUrYN7M6HW1Q8I3+3SggJ7SqAE7Ij/gaUPN8ZFUzZmMWvM+x6xkEHCc+JnL6Ripsx7I5apaCoD3RC2dheCMaEr9njK1rn4c8oaQRnQ0XQfsdLuqkZHfOgbFcMQfxMAO0npWhCMNNMgNaMQZZSE7CXDawT3nHdEpQTUFvGL2O4Q9lqDgfolrBWBI8O8T2WGxmoXolQVCNdx6GEnJTLPt3mjWgXvN+JbSO6TUem5GXQjJFG94yAtq+ovGLW0SKMfs9/0w0fJyodtEyjacluWNnw8Z7uZ0gnMZFRy0dHq2bcr5YcVECweCQ0VCFaQ8+e7udQYtzhzA9KbYqI4jZj0t0S6EIed9vTherZ2SwqOYxQL7nf+znXquSPiaTDPrKHVWrGtYcf6FmJWhnv30Av+DiJcRJx0OPC4Mz0aq+Id9BKHAdlv8zY9pZR1n6Q1PsFjkERg2+/pbz8QLneKG+/Yx+N8v7Ov/0/+Hf5R//9/y7/4N/+t5HLC4w3/vE/+Secx4P/y3/2n/Ff/j//S+77BfGgtx8Y/khX/O01z/k44NIIv89I7H0ORSjDD0QrPiZQlwaHwSXmIEbGj0d/Jxho/YT1LxQtiO5EeyGOrxDvWNyo2w3ZXwl7R0YOv8T4CSkvYMeM6Z5Q3nrur/lVKwU9H0i7YuGw7YiNufeeHeAXwk8iKmGGe0fqLYdrLhdgIHVHdctobPeE1aXO1IU+e9V7Dr/URoyDEKGWW0Yv44g98tgk+6Bz2MOn270BF/roqA3KfoNzIPV7RLoGqBZEBhbyDDT4Pkjj/hHrj5QZSX1Fo2fIgAqyf5pJDPc/xsfS0tLS0i9GEfExZf3161f+9m//lt/+9rcf//5f/Bf/BaUU/vIv/5Iffvhh/Yf30tLS0tLS1PoMXVpaWlpa+sO1PkeXfqn6xQHgdPMqwQOJDaMhnMDfgvz9dNC1Lxn7LJ+zazUAuRHxIEtnr0BHNeGBiEzgCFK2BBvEdNY+CL1CnxGxZUZGmydqGYBkvG0+gyaYCsvY1zC07rMzWGf1b0ksIyWhiSiMgfqEcFIzatmCkIyWZii8P+Ai6cx8vfHRQUx2IFMEeoeqM77ZEwBNl3BEzDhnBzknrCL7OL0nhPaR0bGR7kyRjBWG6Y4uCURldh/j6e7L82/ZBWuGbxc4D0rb0TGobUP8TNPx5WVC1ImsfALl0hLISP34N3kCnRiotISw3qd72Qgvs5t5IFWhXFHA7MDHmUBpeLq/tU7ztgMFl2ckd0Z3CxkpWxDoA21bHsc482dqg+OkbBfCHHA0HKuKDEPrNtdaELlCzf7XsIFsDTfFbOCWHb7RA7MjI6H9zPO1k4iOO7n/9JbrLyMh+bjj2jLmu1Tc3yj19uEoxX4C+YTZnbgb9ZpDD907YQ+KVDzSiesY7g/aY3ZpR8kBAfHcIqUQhwOWzuBzzEGEMt2gBXEjxkhnb/A9Vn2cCebMZ7p6y0hyG0Qp6QDeSrrdz8e8NkaEEAgcbzhOqNJ75Xz8xAjN/VgbEW+otuyKFdItKj5jlbe8F+6/QyVo7Yo0+NXthR9//We08g/49/+j/4TaCmUrSMs+bHOD9gkdJ5frjf/Zf/KPqf/H/z3/7P/xt9y/fYFPr/jbgd/m0EY3sPvskxbK4cjrDvYTUX5INzcbLn+Llh8mMB/5/HEgviHlzxFNaKwRmF5xczSyLzlIIIs79viWfbe07P9tNxgvM5p6OrJjphPo/t3Z7o4jjG3L+9qd8Ix8Fx+o1Ixmfjq6P9zKlbCO+6CW+awoim47nF9mhHyFMOT6Qjy+5ZAIMp95ENttPvsyYUH3Fxgn8oT0kX3PSuDnSZQBNXuDx3GiF0U0iNEJBFEhaiU4My5fNzju6QRWzVQBLTkEoXU+xxXGG1KVsOyqVtV8RrfXP+an09LS0tK/sbrf7xzHQUTw13/917j7x6//psyMf/pP/yl/+Zd/ya9+9at/DUe7tLS0tLT0p6P1Gbq0tLS0tPSHa32OLv3S9csDwPFG8AMiP0DcUUkgQnya37Ch+isqJZ2qQEig2nAUC6ciCW3LjqQXc8YzT8to2HTkFmCCmD7BBMwo4OzWxWO6ivO9lJYGPDOkFjRsfk/NLsyqcwLFE3pGABWi49rwYRl5ahClYhN+4gNeXsHeEB6o/pDAq+zpaLZIR7RYgic7061KTaevtnS6EmREbJvOvzP/TCRsHUaoMW12hJT5/elYlogEdKUgTBjjHShzfdOZyHCqd4q+IvtOUZ3vA+bCzPwFJsuNALOEnBIJTZ9uXTuA8dFJy34BZidrG+AN0TYh1gRN1ARCKBHzHG1CawXqNa+zZ2dvqYUwxUURO3M/+JmO7dB0MEslyphx3j1drm7UeiP6t4TiAZSCTg6HgneHUghGgnIpxHAsfMaXP6PBnSg/Em5ou+BxorXg50BCKLWCnSgV5AHeKLpPZ/gG4oRdIATVHV6AUjP6Gwe5YD4os4s543dvRDgmOeyQF6QRVdI5iQMVl3NGVINHR/xA2Igj4761VWR+vxTH6XmvlGveg/3999yZdTozmcMIBSmNkIEfByGF4d+I8pnzvDMQYrvC2zvsDbm/p4tzq8h+ATsppWVPbanY/Rvl5YLibFyox8mv//5/h//wf/GP+fRnPyIl7+uIYIQgg+wtjgSaUXOv19vG//Q/+k/R/8N/zj/7v//fcj/sSpeNYb+Bd4XPPyDSqOrE6+zujn2C6HeQQik3gsB4p7QfMqpZBMonJHoOVfTfElGp20uuVZyIb+j1L4jjHbcj18pOYt+J846KIdsFpBLH7wgqur1knLOd896XPNdumOQTakTQ/A0Y1LoBkgMNxXGY4DkgRsJoId3sY+T2mKZ//ES2T3C+I7zk80Mr+rzODHR2IuOOtA3xgLohQygIdn5FXj5DvyOXV+zxNV9fAtcdM801ckPaK9AJz/5fqMTwjKpmoO2Gn29Ie0HKDfczX7declhhPND2Cfd3wnNYRRcAXlpaWvpv1f1+56/+6q84juP/759xd/7ZP/tniMiavl5aWlpa+sVqfYYuLS0tLS394Vqfo0tLzzTkX5LOSjz+OcE3PFtMEcp09RrESYhgEtNdptkbObkkIgnbEIyBe0byUi8gSrSWMaZlny7L7bvjFJ1xpTG7dpm9mYoo6UBt09GmdTqFZ4S023SnZd+t4NMF3IhSEpxK/R7jqtnxGp6IOuqDKIqXC/Xy5wkBacyiY2iX6RQVJAzKBchO36IV9Z5uOCm5Bs84X63z3CLjeWvJ8xqe54uAz0hhVQiZEc8lwWxEpg0f71jvYFDqzhaderlQSqHU8rH2Ex8jmk69j8jY2gjJjt9AsrPYjAS4JZ28YekiNhDJXmMoxCD7PN0Y1hljEJKR2RHpTqVoOoztASVdhXlNIiGiDij3jD/2jJyFkstTG7QNGZ0iBXVD64VSlCKDWoRy3aZrMhBzVDpaC6qFul2y/9Qj3ZwDsHfqdqMUZvy4ILLlng3LSN7p4JSiCXkl43GRBu2HHCCYruiIk7AjHbWWkFqnC3zEA9FbDhJwJxBGZOer64XwZy91TOP1gP5g+FvGPovMsYEcCHDAXBnumIIXxf2Oi+b3uAM7EfPahEw3rE53u+H96+yknvHa/Y57MLzz6Hfup3J/f6e/vc3E6ewX1prXRqoh27w3H+8zbl0zlvt6QfyglRf2ovz9P/s1/8v/9T/mV3//71GbUi4FvdwQO7juV15bpW4ZWS39gQYJ/KNzu+38e//hf8z/6H/8P+EvtHKVjTYE7S/weqHoBlSct0wiCOBUGN9yPel5veyR/xYOqox4EBTCg6KCtE+A594rGxF5z0k4pVZEn924nvefW8Zru8C+w+3PkNuP2Ut8+RXUfaYI9HSH7y/EOLDR8VI5vWO6YUMwH5gWzAfj8Q1zwWxkwsG251xMEWRv+WcEKa8Zae0ntFfEO1JbJuX7yKEYsj/4+VwWkTwv75Ttmq3SWojzwB/fZsKA5nWsF2K8ETKIUvN8imdEdX/HXdOoTCfqC7TbvM+vhHWwB9HvRL0Qbsh2Reun2S8umc6wvcD59od/Fi0tLS39AvSH/Af3U8/p67/5m7/5g35+aWlpaWnp77LWZ+jS0tLS0tIfrvU5urSU+uUB4PGYjtvpfIyYrtcDxvuEcIqEAA/AwO+IvWesqgBMKMiBc6azzQcQ0Ad4yXhQvkNQmU66/JWRqnhM6BAEGe+MDfBApMzOXJ3OYhKahuXv7h8uOWL2aBYyXvXyCeot4RYd90B4BXPkfqdgycNUMl5Vs9cyHZoFbFAVFEFxVAUVTdgZJCQW+XC/JrCTPDYbMxY5z4Gwj3hZ4UKIz77PBNphM/K1NKpDU2hFaW2nti2ZXCgzSBgXyb7PMMRBUCSeb5GdpBrp7wstGORx646U2d2pebxIn/C9Ej3XVbQhUohQwjOyOFrLrtaakJ2yZ5SxJXgWEbyDWSWoGQfthhCUUFSYx+SUbafUjbrtCbcvV1QCrRsiRsETbJU6gW26vV3LBKyGhKNtR9xwCkUbqg3VfA/knMA8HZlaXvL1prv52fMccZ/7/dmDOuOMi2Bvb9h44HEHadmDSxByw+wNp2J+AJ1hkZHAkk52N2MEhL7g5OSUh0EYZkeel5T5S1EkWafkYIChmHt2yfo5QbZMuDzBIDrvg8COB2bOeT44bDAcwgT3AZcdeoJZuSlyBHq9IeV1rmdHbjuKU6Tg73cUZYvBfh68BvwH/9v/Ha+//nUCeQy1BxpO2S5sr1cury9UPxAaKhcKjvSRa+pOu73yP/yf/wf8+//4f8OfqXIJY992amz4/Y3ij4yBthnBfL3l9Ssv4I8Zs/6Clk/z/heqlJk6H4R1VFqC8hk37uIM63AehA2ivODm+BjpfC8ZAx7jJB73mfyc0eVYR66f4Poj8vIr5HJF2gblknAWS5A/Bn0cHPc3OpWjPxilYm6EGap7wtTS5vzGjpiny1scrY0SgbqgNqgiaIx5dTV/pigqW4LgCEQC0S2BMYGW6cwvn/B+5HxOn4Mlsx9YlITH/Q51JyzyvPuR0e8E4g69531ihtvICOi6z5mGkWkGc5gn3PN73f5IH0xLS0tL/+ZpjPEH/wf3U+7OX//1X/NP/+k/pff+Rzy6paWlpaWlP12tz9ClpaWlpaU/XOtzdGnpu355AFhuEC8kcr1O+6RNUHlQ6oUSpLtUrgljyyuFPUGke0LLUIhXSrlNZysJUHAiZtzqs+c24mnThPIK4Sglga8o6kEh45M/LolMQPwEdqITPgthPaHrdBULJWNpY2arRiSkiJHRrzqdrm/foOS5qXVEDSwSgGJ5Ti7I7B6mCBI1O3tVJ3B6HmP5iE12H98BttT89zJ7in32i84Q2+zjzRhjnlBZglqUulfq9ZoRw6VCvSRolKfvF8IScGXKa0+8Xl8SBkGu2XlktGvEBGjyPWY7HBFFpWWcrA/SQbzN1490aJeWfaOxZ7esaHbYPmNjuxDSQBvRHwnhdKNIQYqgbQexWVEaaK3U7YaWitSSoM8d2AgUQSjbK7rf0MsLHhln3b0zVLLjNpgxw88M3UiXbnTiOVSgiujnub4J5I2MWf7Yh577PWQC2glWk5kFww/kciG0ZTxx0exOJl3RHAP0wqBh7ngpGHm8Qe4fyg4INjoWnQjL2OJI6PrsiRUSBNa6I6KIJJDL/82hCreE7aMnuHMhdMfN6fbOATzcefjBeLxjdGJv6B5I62gLdGtoeYGLZkR6bQmc2SnyQvid8IOy7zSc5huX0vjv/aN/xOfXK4wD1Bl20n1LMF1e+Ha/85sh3C0wdzrGEGHowGtGsPdx4LLxq3/nL/n3/lf/Kb8qjcvbnTqMKopEUMuv8hq/f8lnibyQsenXBMn0vM4T2ku5oqUgHnhErpky11ez69bfCcn7QBBku2IRczBEcx1tbodxx883fGTXc0LimtHzgIwD2a7pGu+dGB2zkyHKcKHf74zHgUWhjwOrG90GYwxMNnwc+UxSIcaRwxXaCG3ZOY7nQIfu6H7JLR6WPclYuti9Z3R+zWeMbhUtLbuza8lnjk4H/DiQ7cd8BowZAz8MoqTzv2zQdqg7EgcfXe79keB3JjDEjIDOe9WQdqVcPme3+/Eth06W/s4oZm3Av+7X+LustYY/X7+UNYwIfvvb3/7RpqXf3t74q7/6K87z/MWs4b9MrTVc+hfV2jM/X2sNf75+KWu4PkP/tLXWcOkP0do3P19rDX++filruD5H/7S11vBfvX55ANjfoUk6xUqkC5QCbCC/BgThHcRRrWiZHboUiigijShXwBHOiSVlwiQje1gzVzm0ZMSqDxBDJAFkRiA/QZ6nQ1dkui+fAFcTUkjCEaTOqF2bDtzsQU2LbEKScMPPg3DHx5lwuVxAnAjDq1IuO0VKwl7docSEIJqOYO/ZTawVlfqR4ixkDzDuiFTCj0wb9j7dvJDkytMFrCX/rDN+mPa9F3c6eEMkYSxKlZJOWIJwIyiYHbMfeALk8Dzm8OnETZBaRPIwy46UQtTZ5yvpwJam0M+85s/+Ze9IuyR065ZQevSM5rZBjIH5yB5jB2qdcbhMeDumw7B/RE6nO/c+4dZ0uOoE6ulzJcITgMVIJzPpqv7ohFbFRRhSOI47PchfxwNqnc5gz2MJT/gfg6p1wsR0c0sEyARX9pgR2HMwoCbwl5qtziIXwoz/D3v/9m1Zcp13Yr85I2Ktvc85mVlVuBIk7hAgkqCaoiSatKQWu9XdcveLx7D9P/rBw8PPfvDodo8huy251bS6JTXZQAEgAeJSrEtW5jl77xUx5/TDjJ0Fm6QoAoW6INc3BlCZlefsvXasWBF18hff93k8ZFy4HPBSCKkggZtjKHCTIHFN92xsxhgj3dk2ML+HGQWckdDz3ml2RKej8vCem9OcKnX6PUFVM/pcgiIyO2FLdh6XghfBtNLpbGGcbWOTwmZntm1gpsQoyPMTGo7S0FCEns+XAHoEfchnJgLx5+m094I41DBa7xws+PrXvsbf/8//GaoZdW7bCe8ZKT7Ohp/vsft75OE+D3Ecb1AxdGmU9UCtitaW8JINiROf+NXP8bv/2X/Ka594jcOyUJd0FYuf8tDF8XGuN0UhthnRvmW3NyWd5D6jnm1DW6WEz+eKCfs7yBOkvkb3tzHfCN8QWahtfe8AQFXCT3mfPDPu4+oItzPYhRgPhJ3SyesX5OoclgruhHdcFR8noq14z8MpNpzRz0RZcOuYrnl4pt3iZcEx3AP3dH271owDX9p8vjbKmj3b4YHU2f8bntc1XcgFQSUTCsQDlYocprvbH9DaiJ7rCW158dxgI93Ms0ManWkEyny+8vBBWCf6A1JKri+quY4sK9KO83DLro+L3o/elpe9+2Ufw59fL8MYRgRvvPEG3//+99/X172/v+f1119/X05ff9TH8Betl2Ee7np/tc+Zn1/7GP78ehnGcN9DP/p6Gebhrvdf+7z5+bWP4c+vl2EM9330o6+XYR5+1PTyAeCbw4Q/nq46vyeBZEFFk7dozRhVH8CCasbniioSYzoZM4oXN5zprqRNgCDpdr32xEL2dVr2/r6YopKRvqozWjQiXWo6QcfVNSzXot4rAC5IaROQTTdtSDp+Y4Bt4I5bJ3Th6qKUhxNFFoqQcNkyqlb8nGOiNaNdVRIUSiSoFJmG1XgBdmVCJPd40a/74vqqTueyZ0ewDYjIvuQye4qHJ6zE08VaCkhDUYooVYUShTIBp2rJ6GN1tDQEp0jL/tOSYy6lgQ9cCyaKuxAlO1YlSMffHMeIgXuHqkQr6ZQ0z+jn2rLfuI+Mhu4D7DT7moUY54RFayW04CiGE2aEVIKGW6dIhZhu3zE7ci3htUid92zeb817LOG4Gyaa/bkBvp2JCBzNwwPh6WCNCdRLyzRloDFQxmyIlvw65D0ndnQ8OmN0CEn+lfQVLY+JeECLZMyzPZuAd0Yuc57zb0so2c/YwwPdjI7j9RUGA9dIuDYBbh5mmN3V4mkQbxU9HAnR6a5kRnNP2FcPoIXhTmdg0hiidIIN4WJOj2C4EOcJ7O4f0Lvb7OedndTCDXIucLmH89MZF/zJvC9awRc8HHFhEWXFuV0ufPU3fovf+oM/QK8dtrUl6K8Ftoc8zCGeHc0qHBanVkW44GPDpeDjgdi2fM59wBg0UT7z6V/l61/7Bjecaed7lHwWw9+FENg2kJFjrQvGGS0+UwkOCSMVIk6UsqYjNoKiK67C8JGdylh+XnsKnocpQhULwbrnvYkZIc88KDDOuHm6+ccGw2b1cBAe80BEzmWPdPP6uGSE9XTTh0P0wTUS/EW/9nbCxkboAQ/B7YLFwMYl7+/YsO1Cv1zS8e+Rn7UuGTxQKix5WAHveX0CGpafwU5I7wiCFkWziZkoCuKI9Vy/5zWJ5TXme/mMwl/QuuYaVw9IO6BaM3mgb9kNfH6e0flSkPXmfd+idu3atevjqojA3XnzzTf5/ve/j1/39/dR9/f3vPnmm/uJ3127du3a9UulfQ/dtWvXrl27fnbt++iuXX+16od9AR+43unw+A7KA/B4QqcKcSIko6GhIszOVFU8BNWCS2QEKjpBWAUCnUAYer7W7CyVWPM16pIguJR0l137c2uD4bh6dvBG+akTDJavxXQAzxhXypL/Lka+/9Ut62e8RwLMMMImrLOSJtBxQR49RtPjibQjYhu0BJbRp7tPHRVPR6PWvFYzgprXXEq6S8uS8GVsiBzfc+XaOV24QIJHSZexDGIIFgn8GoGiqB6J6BiepxHCQNJlVzSgrcjIyFopcxxKm27cgqhm9HBIOqAFAmX4ltBbFBVJSO+azj8RpLYJt/oEgZGuSzwdvygUyT8bg/AZV10SAlNXIgYy0rFMXTApVIkE524MTwOnyPy/1uByQpZDfo9mjDPSptM5IfvA04BolvCwdygHiJEHC0pLB3dsCdhUprMR0Oy+LXqDuU/4+DxduEo6R2UFTcBYJNLdXQtBQSxdl0TBaGibzs24n+5rQBZgIIdb/Pk7WDeosNmWXa7LMePFI9/vRUd1GO6Sc4F0vgYdC6W2G2Lcz7lts5PW0vnrQe/PMR+Eb1hv0FpOOXueXcuHJ7BE3qP1QNgJZPZgLw1khTjnmNkZOCLeaYcjameW9UA7n/nMk1f5/f/i/8Dx0au4CJtB+DJhYUXcszO3FGKtaAQWMEbgcU6wvG1oGYhnR7GPdMC7CzKMaI2v/MbXefcnP+D1P/kezwS8VJBjzgcWGMr1fI7KY1TzsIC0FbF7it7MzzDSne4bxKAtjxjnnxBR8vvXz2a3uTlRDOuB6oLbM3RUZHkl463jOUSFdoPYdBlHTYj7U6fbYlVCFcaFGM+JMgFokM9IjJn4bnlowM75Gi2fl+gFYSP6QKSjyxN8nJG1EeGzO1vxsiSwLfPwRuT6GT77se0CsmC2IYcDcnqOHm5hS2c0paD12jPt6V5uj3J+iBN+yKEmEtKHw3pDjOwJlvWGwFGbB2O6QZyQwzHXtYAYZ/Tm8c+7I+3atWvXL43O5zOvv/46l8vlF/pD8Y9+9COOxyOvvPLKL+w9du3atWvXrg9S+x66a9euXbt2/eza99Fdu/5qvXwAeLvAcyee3OKuFA7AGfSGhH+GEgmspvO2MGGv6KxQvXauOkqkm1On43VKJLtZE97O3tWypsO3CcRI52HRdH1aUNr8Zp39wDDdeRndKzNWONzwsASaTr7PMHRZsL4RWyC6UkQwmy7n5QGRNdtmS0N8wpGqOSY1gJFwN3R2xUbG10pJmDodySFBok1eRB9frzEjnxWJdA5mnKoQqrgGRlDGQNYVUUdrxZx0XCOER3YAq8wuTqGUdE9rWaarOHCp81qEGNkbm9C44WNDvOM9cLlC4Rxz3Ig2QVsFiHQrYoTdI+MmoanWvHVs09m4JDzq59k3qkQ/wTbQ400eJBBhjIzQjiiEZndyZsZm1yttnYB1HgQYlwTD4RNICXY5w5gTQJyM5u15eknKBP8nWB8lCDPL8RIjUFwKwoxhvjpwkbxnIumMrI/AjVIW8AuoIXRClnRcp00cjyD8PqOTHeABuAU5o+UWuV0wfxc7N6gXpChhJROM7YGiK+jslM4yW3BwSY9yTEv8sMuEvhlH3KNPtzM4BbMxnagNOz2HrggLpa7IUZLX6x0eD0D2eeMrXC6wHufDdEy3tY1kkssBtc5iT7ktld/+B7/D5770DR594lX83Bk2EDNUlxmxLIQP9LhQcFw8/8wGHor7A6JHRDPcO58vh+0eliPUQmxnaA09Hvg7f/BPiX/+3/LtH/6QBzNGMVwKphV1Q+QMcaSUikwHbymvEnqbkHY9wDgjcY2Kn13m+igd50WBFfQO/CmMjSiztzqU4RdqOSD1mODaHdWGNLIL10/5utryEEuf86CuxHYPevtibQAQDcI81wTPvu/Sah5uiJ7rVQWsI7XmlPcNESXGNtckQfB8LohZOa4Zf6+an6s2YrnFrTPGBemVsE65eYWQS4YwtDVj3sWQpojc5MGUy0M+dyWj5KOfZmJDzsOIQDzj+aNvRDgahtYlY7jDMtJdFWGD/v70iezatWvXx1nuzg9+8AOePn36vvUs/XXv973vfQ+AJ0+e7PFPu3bt2rXrY6t9D921a9euXbt+du376K5df71evgjoV25gqXDpM974BHog4Q4EIx2ObtlzGoq5YJ7xyqIQMrIyczoaw4SkJoeMBZUKuqSrND2g+b/Ijsn4KdiZVC1jSoPspBS7wjLPP9eSrzMdxxCIlOmKm59LSjoMa0lA2daExSI4inKLREkAXJf3YqZdQAORBiroYaGEE9JIkJYwWGpNICslnYEzNlW0zihr0hnLMqOGJ/AmxybZZrqJa1FUFJ2AT8uaY+CBR+AIYxjWsx/ZCVwSuEVRoqQzWVQy7lWEWuqLka4etHab/at1xaNkFEQMorT89bAZqZxRzD4GwREvlSgN9473MzGCkIaZ4yyMy4Z7wW1kem5Z8ahYN+x8whFMZxy3C8OCLWCY49sGMWHW/MxImTHQK+GDHpafU0hY1w5Qaw4ts4/aRrqANeHwi8MK5ZBzY3YNz9JbaI+QcpgO9J73FSAu+euQeZhAZ6z5htuYMd9bumepEBdEbuacnZHO9TYjvJdbKAeiK+N0Zjt3jBsGK4bzHkyOnMch0yWe8yv6hlO4ROVkQh9ON9jOF7bLCZ+h1tFX5O416EE5gC4NlYzOJgYqK6IHtB6ADY43+cz0DZ4/BRHqcqRsz1gQjgR3fsfv/94/4eu/8Zscb27xMRDtLIdbai2s6w1LrUit6OGG5fZIe/IkY4XXFamC6oJGyWc3JBPhma7/6ZxWz/kump3j7eaOv/sP/wlf/tznOW5G8QCLrAbnkmNLoWhD6g1tfQWlU+acIK5APWC5mfexU/UwD4UMsPvpwL5LoBt9dlsvaMzo48jf0445b5rC3Stw8yT/d/cYlgPUNUH4cKQtuSb0hzx8Um8RG6gKYoZgyGFFpdB0oaDUwxHxPCSQEfbL/AwCtlHWBZVMKFCpuBSiroQuuCihlajH/P1w+ukBj4ZtJ6Ic8G2jlIZKSdewA332hpeVGBekHvL58/EibYD1JtdLd2R9BN6JywlQ5PAonzEFWY+5R0ggGsi1T3nXrl27XmK5O3/yJ3/CT37ykw/kB+6rxhh85zvf4enTpx/Ye+7atWvXrl3vp/Y9dNeuXbt27frZte+ju3b9h+nlcwAfb2dSswIngtv8i//M9CTKcbouZzzvjNiNGGgsuAtFsg8ULCOU3YkxXrg8I9JJGyTITUfZHOqiCRi0piNUl4Q1ZSG2y4R92cELJEyGmToqCbpguosVvCbEqS3dcaEzirknfw4IbXgsqD9DeQUkCEa+hQgiNWNdJ4wRSaocWhHrEzDOmOHSMkI1so82o6uDa3QvJTtvcwwGjhFaGb1jAsUGpbY0rJZK+AX3fK2I7G0dYZmUrYJt/uIe5OfdQAWJyC5jaZSSDmyVQIcxGhlrXGCz6e6dUbaOZDwxSpw7UTUBJgWZYDu7Tg9gZzw6IivhHRmOa02Av10gsgPW+wmxQWlL9gD3dJFLcRwQl/y+ohRxRBQfF4oKURrijojTtdDNIRbC7gktyKVDlXRhjmsHdHZW07cc+gDBoEwgriX7WV0yHpnAIg80pJN39jWToN5kQ+UGQbPb1suE60ETwbSAD4xCKQW3juqRMR2nglBUsfF0dlKvhCQMNTNEN5RlhnxfEAzVmlXcPMdlwTzw03NMNQ8tPNyDCL6ucALRPnuMK1EP2fP70FF9QG4OE6o67s/T8Rotn6E4JSy2wG9uIO5pfmQ53rGMZ/zmr/9H/P2/9w9Yj484jRNooYiikh3by3LD8aZxflfwWBjjgo1cH4QFxow5D0OGEk2J7SGfu1KhDGRpRNrcs186FNvO1KVQK/zd3/89pCjf/tPv8TCeM+pCqKEq4Ke8Lyr5GT1d41IX/MU9HDkfVKdL/II0JfoD+N2Mbm+gT+D5M3i0gBTMDPWR43T9pypsWx4kKQuiQlgnQhIyP/wIOawZMW+FUo6EX1B6Vj0vN2i/QLlBEfAz9fCYGBeQQimK9PuM016O85mOeSDEUdGEyyLE2BijzMT8S7pxVVGpCX1lHqCQXH9EIHAkzuAHpDbCesauVyVGR9aaYzdGnqlpCxGX6ZiXfCzqgpTsDmFckHbIzm/foB3S4X+5T4jNDoB37dr1cioiePvtt/nRj37E6XT6UK7B3fnud7/Ll770pf309a5du3bt+tho30N37dq1a9eun137Prpr199MLx0AFgtiSccZfgQfxFIJHrKzUiRhxwR1cQWusky4lpDBQ4jzwHWhEqCKWbovi7QrmplOtIAShNmEdRlnKhIJnsm4YW1rRkfPKFIkEir7SAAYNmHr9R+RfbYaCEbgRGzZ2etkzyyANNzPCGNCpBmnWlq+UKn5HlHAN6QtlNlNi1xh4ewktT7dqyO7OMN/Kt76NCOqB5QDTmCheAQDkG5kovSSMdsoQU3u7D37l0PTuUgQuuRnkJLwRrNHVgxEMza5kP274YEujVoL0iU/Yl3Q8wmvC9aNLoZKeQHPva3ZiWuClgbek60SZHTuikgCZK0L4R2VQowL6tkdbddYaSUhn+c9krYiY6NIQ5CEvhYZ+x0DIjBKxtXO2HHTQqAEWx4w6D1d4TadlxHp7nYHu3YWF8zO1LZOQFrQIqgZXgruY5psZYLLZYK+NmHwhjLhPwMoUFrCOxVEBLWR0dHtmB24oqg6NUhfbn2Cm6PD4fgK7meIE9YLUgvKgR5nSrnDIn3axZzqAeXA8Eg3suThAeyMt9lhOxxaEMNhG3Bo0EeOaWtwWPLswTXMwAucHuDmEVyeA526HFm0gYNQWbbnvPr4U3zzt3+H/+x/+8+4u73l9f/5T9HlSJGVWgT1webOxYw4lzxXEY5wxuUxsZ3RsuLjBO0OGfeZGK+VoZqdsWudc2pD6wF3xbaHdCNrTSe3KUsT/u7v/g4eF77zre9xrsFYX0Pd0rlr1zz4dEv7dNgqgpeWa9noc63YcFVU77B6yChl1XyOW4W7R6RtWEALYXNN0DHHpxAqMDbEhZgV5AIZYd0eZxy8Zzy71AWVRo2Brk/QCJSOtkbg0B4jmjHQEUEsT2B7Dhr5nG0jn1txmkjGO7sRfSQot+d5AEWPRL/HNSi+vtcxDtnrXQTRBdH6XqR0bemiH50YGX3vw2YowZrrpUeuL1pyXsWWcForjHOuxXW61sURdEZaz4MpMd6nnWnXrl27Pj7ato2f/OQnvPHGG3lY5kOUmfGd73yHr3zlKzx+/Hj/wXvXrl27dn2kte+hu3bt2rVr18+ufR/dtetvrpcOAMdlA9Z0iKoTqsjDWxmLvNzggEfQ4gx6h+js8A1DpjPQ3RJerLNHUgQLQCvK7Gm1LSNTr/QkhGuPcHib0CLBL0WRsc1Y19mBKRO4ioKWdMQ5L2JLRdd0CrthUhK22Ox5nX2rjhOygj1NAHIR5FWdDud0DMroRCQ8UwKNiniCuxegiCACpEhC4mvEc3g6krUlFJGSscSREN3dMs55AkShUFpFixK1EBMghgoRlRgnKJJvqwWGpbvPDbQSfUv3nSQwRivuRtk6SOC6oOFIO1B8oAF684TwjsuFpShBYJul47lknPXA835JyfjbACLyfXRJsFqUGErx9w4F+OwmjQB1wyz7iYWaoEkbFo52R0eAGp2CVkN1hcuGrpHOUTZMWzqLLXtOxR3iMG9Dybjv0YEZ2esJ/eXqLpcy3ewN/ILWNu8qM6rb0Bg4JXt/S8Yjv4gTF0k4Xm8Q26bTOL2uUHA/ATUPPJAO+kqFULpcKDevTcB9IU4Gx9cIejrh5YC5wXgGy5NMpy4zGt0HnDpyPFB4hkWljAXMsf6Q8/D2CXK4y6m9AAhSSnZUj3dAH+d9KbewZOesVqOVO3QMinVqERaEJ48+yT/8Z/8lr3zyMT+83yjnxgNKyIKG4VFwBA2DMTAdaF2hdxgye7sLqnlIQ8TyMMfxETHOcLjFF094mtnd83AJGd98uof1DvGBHo5EdFqB3/0n/xXV/69899v/Mw/6CkPS+e1yQYvm2MIEtwky9XCDPwQv4topqDveChqO8wz6Ont8Ne+5PyDeUV0Jewc45hqH4HFBWbi65iUy/hgne5zrmr3DN3fQLxScpTY0NLuKcUp9kpXo+X/ZB95W3AwrCscnxPY871dVqhbacsj4bJld6vOzJCiWGdevqBR89Hwm+pbPCeT3+ZZrmrYE2t2RVpB2SGdxDNxGdm7bOaP7W0NsvHdaRxsihbAT2g5EjHS1jwsShRgPiAdRF0QXwl66LXTXrl0vuS6XC6+//joPDw8f9qW8kLvz+uuv8+Uvf5lXXnnlw76cXbt27dq16y/Vvofu2rVr165dP7v2fXTXrp9NL93fXstBoBvSBBFH4nkCm35C/TJBbTDkluIdZUPkFlAcocSYEcvPoN7B7KwN94SQVGDLCODq6d7TCVPDEJsgeHboQodYM0rZLWGZJnRNkNwBiFKAjJlOlzIJn60TpCswIWy+fpDxwCM6dnK4v0fv1qwMtoCaDrwoFcwS7iwrEh1l4J5xrHmNkWDXy4TOW7JBneBZZ4esSIJtbXgYI7JnFxuoKrUGUiRjlUdGWs/iZcCQsuBhRAQanlW1qunwlCuINiCjqb0CoYQMmNBcrOdwD5sORUVxtCksC26dWo8J4q/+ay9Eq9ANlowSxowwYVRg00z5rRUVIYbg5YBqQc+zS7Qu6HRMioN7xuZ6XHuMz0S9xdko0RA/A1AwtBygbwzruPd0KHq6q0VzTLUs2ctrMxpYC7Zd0u0pJV2hkQnBxfN6kvNXbLsgEag2POYccaarOMdN24LPLtacZ3N+ak3wHI7GOuO4039Z9BGMDQSWcsBxul0o5RX81nA3OD/Hpc643Iyqlug5jtLSgU0gN49wd0RuKCJAxy8PsAKXDv0ygaIjZ9CbmxlXPcgvmpQSS/Btz2jlEUs4+vyBdlg5WPDrv/3b/NqX/xavfOKTGZN+H3i8jbpCdFoZtLowpCE0LJS+dVQXiAu6HvOf7YC4o61RakfcGUWxzSgtqK3StwfMs+vW7QwMRA/TSXrJ2acQpwtyc0dV5bf/0T+k+sYP3nqHd4dxCcciCJ/R8aXgI6OQg4H4lq7w3vPztwN2eU70U0aIt08S/nz2kVuCcSoRHWIg9ZbwQMxBzkg9pDNZQaJk3LuRz4M4UnMtVA/CoTRBa6GK0mp90estS0FCiL7lQYuaEfclBuiKF8Ej0OWIhCFlRfySLnptmWrQMxUgImbfeKFEJgh4P0ErmZjQpkMenfP3mIdqfCYohCOxIbUitRAmxBgEFbHAfeSyWVquy6WC3GT8s8x1zvPAC7XiEhnx3y/oevN+b1G7du3a9ZFURPCjH/2Id9555yP1A/dVPx3B9fjxY1T1w76kXbt27dq1C9j30F27du3atevn0b6P7tr18+mlA8DaLyBLQi1ukGgECywONIgNZCXcGdIpfqBIpGNRCoMxgWVG15bYMno07awJlTUhrHdPN2+UjIdtC7GNdIlGSTBi6bDLbtrywikoVwdqzMjdIOF0qS/ctxlWnIAk3NJBPDphPd2Wkt26LALPHak3845rjsHlAktL0FfKjFbODuEXoQUTNiZ+ze7dzKtOJ3AMQ6RNkJgg3BmYg7mkyy8c1YZiSMyIaxow46TH7EzN/wNRHNKxKkJIxT2dq+I+namC9uzOTbIoSN8QBPctoaILdJ8foSJSkejpYpSasHVCaHdLwDid1yEKxSlS8OLIcsCtA4WoJR24vTNqgXYkakM9EB8EBe/pwQ4KXgSTRvTpiO4dzg9w+whOZ+rBMR/pdg7JOOR+dd4mbFU7o26zr7nifUPLCpY9yz7nTCmH7D1tx5zLXuaBA0fTL5yfWRsSRinpUg6z2RM7Hbmy5FgUZiT3jGSO7IFmpPuSuoA7zpZ91LICZC8wHV8X4qJEEQKnlCdg92yqNDqC03CQShch4l1iy65bqTWfz/WQzud6gvMBlgEc3nMt63rNKIbtBMU4jMHiZ2p/YLm55bOf/jRf+uLX+eLvfDNBPk54BTX80jE7gS6ENqwP5NDwDm4tgXvv4As2NoQNEc2oZyphBYmOX864KD4ChmcauG1EE0QbskW6o5dbwi5QD8g4o8fbHOuxcfPKp/jd//J/z9Mf/xl/9G//Fd/93g+518K23TN8g/YKhmJ+orZbZIt87kmnvJSKLAvSHXPPCOTDE+hvzns4Mk7ZNoygyh2Z5X2GcoRlQfoG9iyXmMuMYBeFPkBHduj2M3U5IrVO12ygc1wpC8SGesCh5kGPshDEPJQQlHqXvcDLMl3rI59/KUQYTuRhELfssvagrI1SS7qLyyM8HLu8i9aGM9DlMWKDiAmqy5bxK1IJbbm2nh9yrrQbNAIfW65b5vlZtvs8bDIPG7g5GoLWY7qAteT3O4SO7AbetWvXrl9yPX/+nLfeeos33njjw76Uf6/MjNdff52bmxs+/elP8+qrr+4xXLt27dq160PVvofu2rVr165dP7v2fXTXrp9fLx0AlvUubZKRfZrZydspV6CklffchCuozh7aaYZNWyqqC8M2VGN2+q4oCSxldvfiHaHMlzMYkhCkFtg67gWVSGjsll8rGbsc89pAZxfxyKjeqyM3PMGKpoM3y3UbjBkTm0WvwMBRpD1B9ALjAMsRGNnpSSFB7JJ9llrTtbw03C7Z8Qmzk1jma5f8fDFexFxLRLpSS8UCumfPqYSiERQCrcuEvzOYOBwhcD0g3jO7f2R8rQ9DpCAjMO0TJKdjN5jwW8h7OR5gXRGCkJb3U+u8t9NpqBkBmyReUIJSGteYWXr2+s67SaAZuy2FcrjBRdBS0iVc1nmPgrpmvK5HxxgJ3KRiAi4ZOS0j5vU6hYKwYTd3CVoDRh9EFPzhLTjeABeQdBkLEJGdryLJO0vMjmQKFh1t0+2rQvhgRGSEM05IheWI9kv2+U4sraUSMzo7bB5aGNmNnAXW0+lrOp2Px/f6ZGeHMdMNH4yMTZY573ybUcWC6iNcffYqP2BuFG6REERlOlmzO7kVofsjZAEeHpBV0OHpsF+PsAUcjugqEKfsx70+k72DGa1Ai8GjcsvdYeHLX/wGv/r1X+f2yROOj57kl4+eBujumFs6VSkw8jNHO9DCwR2NE6IN27acE96pVSmtsVm6bkMhygHsjLQD3jtEIByIOsACqRVXw88nZKnT0TsIN2R0KI5QMMv49rtP/xr/0Suv8oXffMrD/Tv86//3v+Dth8rFekaL22DIRpOSfb1lmd3fHaExuM+4dsu5h96+uK/CjPf2gUmhkocYrr3Esdygesy1Rq6HBYK43BNjg+VIqbf5KHnPwyPLIeeflDwsMyDWJZ/0fsk689rmHFpyvdLpRI8rXK558MZyzYqIfNZkQBW0LWhpGblNgX6GuZ6LrHmIQXXG6EsmGywFKBnrPLb5Hn3G5M89oR3AnYhIB/Rly2SECGQ94tsFaYrUJbuFJYjyCNGWDuddu3bt+iWTmTHG4J133uHp06ecz2d67x/2Zf0HKSK4v7/nu9/9Lm+88QbLsvC5z32OZVn2H8B37dq1a9cvXPseumvXrl27dv3s2vfRXbvef710ADjp0nQOWk8nq00XKSfgJv+MGZlqjutK8Uvy1BggKy6WRlpZETtDBWGZsaOeHZSiRJlAJGqCimVNl6Is4BuuQdkcWa5dswtX0hcxuykhfy8V8f7i9wlQ044WIjBGRhlLQU1w24jeYZAO53JIR10EmM/Fx+frJPQuqkTxjGWVkpBKy4te3Gs8dUyAmy7jdAlHgIUxDOJ8QZoARmkNFfL9wtOJS8bnMuY9EAUCXxKRCkZITbgW11L3eOFAjW1DtBC6YJJxtdFPRJyR1hg+0rkZQcGRKohtqFRQxSVQmWCYgkrLvlAJsg1VCO8TfFdsdg7nmCkRRtTD7LGdt7zOCNkxGGtl8yAuCa/Fg3rtPdZ0C3sEvqxIOC6OrgoG3h4Tpwc4Oh4nuASsBXRF+gOjZDwxds4x2S6oCAXJsUivdt4v25CoeGRXcSBgJ5xjTqHwTBXXdHe7VvATsMxY754OWx+z3/XAGA9Q1wluB0Mi75Ub2AnkMM8fSMYks+EkKOb+OXG8g1YwQPw0xxWcMyIto35vF+xZwE02DssYUJYE2cuCltlDrFs+x9JY68JBgq988cv8rW98g1/7wpfQKkg3rJ/TIa3pSs8+3Xw2VRWXI2GnvN+bY8MosuB+n9BdleEBtWGlMUbFz8+hBTHIaxk9uaJOl75FxjOP86wCDjwK6kYUnWO6EpHjRFvf6/mmoFX57Oe/QBmf5atf+hr/07/+Q771v/wxbzyc2ThgDgNLh6ouRDwQ1tLlPQF2AlGDcgDZkP6Q8LI0YpwRHxnvbgP8Mru+AT1Cvb6O5XUtt3B5mLc2MlZahLB7Ig6ZbrBMd7jnXMYuRKnpnq3LTNgfEAu5aGZ8e1yf8HEhJJ+T6AOW7E1XBdX8dSYPdGIMZFkzFl4qMQamQZECQ6AteRAiT7BkP3O7RXoQPvKzajqvQwSxIEp2S1MCdMkO5NKILd2/ohUXwccp+8jtoxc9s2vXrl0/iyJyJX7nnXd46623eOeddz7cC/o5FRE8f/4cgLfeeovPfOYzvPbaaxyPx/2H7127du3a9b5q30N37dq1a9eun137Prpr1y9WLx8Adp/Q7gx6yH9XeM9ZKwrxDCRjWc0vNDFEDglDNJ3C7yUPy3SETqfkBD0JcSRdgybppnRPNyCSYMOzjxPIaNG0UCZc9dmlqen6lTKdlz4jmP2MaANqwloKEVuCjnAgu0NdLCFUq9mfqdnriXeox3QeF50dsTkcQsYJJ4hmxjInDApnQt8O1Lwe3whdGGGMKPj5YXa+OkWMKgvy0xAXEqxEuqNVMiY7RBF1GB0tGSsdIVTJE0BSG7igPvDDDd4fED9TypKw24NQJXr263pZAGGIp5vPLpQ1e5WLaLqvx0CLIloRxgSnCdVo12jd7OCVYUib4DOAtc4/3mZccrqxvUhCUH8OIqhWXAcyTYcajmKYBKEBrnnPjq/k6IdgayHiAX/ocLzF4l14fiFuFawR9yc4lIR0/Tm+PCHEwR6gPUFnNK2GE7ElFPMNLRO8EcRywMKyzzXyKEGma2e8t6hSpDDseuE146VD0ynrwQjPAwLewR+gPJq32MEvc0KNhMr6GA5nPAx5/gy7LZRyR6XM4wfLHO9nyCkQuUVEKLyLDUV8oDdrOsO5QeweWCjc8ulPvMrdEvz+7/1jnjx5zLK0dOfXed+qsi6FYp2zLozoUJcEwTFQCUI9namxASvUSim3KIHRKbVh2wm7PBC6gYG2ku5W0RwXUUQGiOE150m0AxKXTAY4Lhkt3AdSFewC7Sajj8dIQOog5ZJp4H3LPul64Bu/9dt84Stf5Yff+i7/7tt/xE+eP+PijqsQVYCGe4AYlNt0STNjzgVqOyAYyAZUTBYKA4mai2DM5963+W0ye7pzLRMp0LLkOtwQXfLghCz4MFyF4vOzLSts6Y7NQxyKumX89PbwU1HipGvezsxSYRiBjweiVugP6RI+3sI44THQlm5mKYc8RHFdVUpNMB1BYJSyAAviRozIGOtxzrVayzwAMddUKxAZ357QPGP13Tq0ilxj6bXml9sp1yx7z0m8a9euXR9HRQTn8xkz4/vf/z6n0ykTWX7J9OMf/5i33nqLx48f8+lPf3r/4XvXrl27dv3c2vfQXbt27dq162fXvo/u2vXB6OUDwGnvTIcaRpKOln9Q7ybQrGQMrwILFkJwQVkyoTmEIjGZcbx4aRHPF6/pmMzh1QQy8VP9sj5mRO8101cwD4pqRuMGQEI5RPPbXgDmyK+pNwl7SAAhOiGNBYyMkU0wa+ANzs+Ig4DeZBy1ZxesEJT1EeqGSuA+0FJznNLiPF276YQNvyCa3bPXLl6nYO5s1rPbV0DWG8p2omjL66vHhMCS7bCupDNX5w2pAtuW0a7wwlEss8u4Lks6tcsCY1AikHIg7IIrqBmyHjDLzlDcXwBmpIKlO7NvW0bfljWdfuHICEo1RAKRhiLZZeo9o2Ml42pFIsFqvY7cDKQWzf5g94zBpWWMrVSiplNVNicYGf89DKUmsCZQLA8lSNI/C0GX2+xCvUtILP2A3h0zDvl0TyxKyIWwW7AFyOHhRPba2nMot4h3hIppQfyCkh2mYp2iK4yBzW7k7GDd0HaDj0F4YNMhns5vsIj5/BhmPg8b6Pzsd9M4P3tRNd3syoLIiUCJqngIUQbht5jWdGrjSBQ0DKfBTcZfy+ke1oVyq6hUlDt0RhOrKFWEL//Kr/FP/+M/4LguL+LBfaTjeS2NhRO0R9SWBzWyl1vxUPIgyBG2M8odbTlQBaRsOMbFnTE8DynMgxK1VDwEa7dAR8stvj0HCUpNl34g6EwXCI88HIBS3Gayu0D3rNNe0lEupSLuCCfCF0QPhAuDlskC24lVgi/91m/ya1/9Cv/y//XP+c6P3+DS32U4hK7gz+ZadoCyYM/eRI81GfxyoLZjHhK4RrL3E4ajmutOYMg4EEWg5HqDOVIinb+toTiqK8o17l4Iv0A54mYZCS1OyEDqYT73SthAlwU53GUc8+wvF4SojegXoCQM9kBqy9bqppTa0ICQJbuORfL5sAlpdUk3d13zGdwuxFCkHQgueTDEyc5erbnG2kC05nxuGYkeIfNB6riueAzog6KSW8PljEfGr4c0nF++/zDdtWvXy6OI4N133+U73/nOrCD45VbvnTfffJO3336bL33pSzx58gS9nn7ctWvXrl27/gba99B9D921a9euXT+79n1030d3fXB6+QDwNNAiNQEhPt2zF0RmRKnO+OUZLeoRuBTE72nlLp1wEpSSAEwmDkQ1ebBtGalcFIbhJWEnUifwbcwiXCx8cpYZo0uCY4EElNjMZL2CZgdtCesU3I0oFTeHuFxNiLhZutlkJehIdVRu8pqsJySZHcPOQMRw0vEpZH8xwoQhwpXLogewDYanAViUURp9O6UbbmzocqDGQEqg7TDBeEJB0ZYu3yvojoypvTr4sC0dlKz5fbVmxKuu6XoWiNamK7cQkiDK/YxoEHYh1mNGX1coLhAD8y17TocRPrCRUE4CKMromoC3pAtYJabDe0Zmy5b3WRRG5D9V0uXqkS5YAULo7vTNCJvgtN/jYdBuMoZ2uckIWwJFs2/Ux4uY7AjwUhAV2nQblsPjPGwwnHGT99HHOzkPygpYgsZlARs4eQghNge5n73PDbENoYJvmJzB7inlCUUSxIddMvZ5dqRGqZRoeNiEq05g7zkl7V2IRxnjTUZOwzxAMMtgBUXLLSM2OBlyEGL9xIv5r93nHL6gnGh6g8uWkDAgziCHjMBOu21GEB+l8pVf+zz/9B//Y7QVAsvr7p2oNedPqVg5ELLQLxeiVsb2LlIOFL9wbAuPHj/BnsGolbIoUgvuivVB2GDU2SM7zoR0RB8hdqbUgcagNfBQehxwQA/HPDzgim6XOW7ZkSxFwQytK96fI4dbiEsOla7p8q+3FPJAQlhHlwPYiViUqiXjo0vwD37/n/D1d37Ev/03f8Sf/OS7XKLQ62064OfBCb15Bfwe9EwMIdqKlDsYF7QKXgIZG/QZr1zS3S+iOdbuGU8tmmC3LnC5IBX0coK6IKVREMIs109kpsU3GCO7eGub8+G99Usi8vnul9lB3PNwRGmoFtwGUhdKXSiR64Oo5vXajNi3jSC7g4tAjIG2JaOoZ3d6rsk9ne0157n3B1RbRpn3h4y5Li3XIZnrsyheFoKBXU54CO4dV6UbM6b97n3bmnbt2rXrg9bTp0/5zne+80t5yvrfJ3fnu9/9LofDgV/5lV/h8ePH+w/fu3bt2rXrb6R9D9330F27du3a9bNr30f3fXTXB6eXEACfIA4ZixwzwljlPTAcMSNAl9kd6emWlZaAMmbfbnrlEAyRBmIMd6pIAtTZv4oIUlbctgSf1qcbMKGxzrjRdPjK7MMlI1KvvbtMABORMbw4YRl1mkzOITZCKrgTNgi/T1h7vsDzZxS7IK88yc7dIlAy8hSpL+KmkcCjo7R02VVJZ6tmJDBuwEg4ropLZRtnjAPR7b3o3wJoIFK5GnzF0y0rZHSwasnXKwUflwTs1y/WdSZpC6EZrSuMhDXRc+Q074UoMDqlHSCEWlq68+yU1+LzexEsHMGRcswOXzLmFVkgzgSKj0iHaM04Zy0VHz3HwgzV7LqV5YawDcZlXrNBKCEwxpZO49HnOEO4pDPUByKN2AZRGu49QRxn3FcEwWMiVgtcpkNSBcaGlAwNTyf27Zye6XoVHVCPCe2Q7HteFmI8gweDmyMxNqIUmB3HyBH3gReyi1izo7bKe5vPiISRmeULlIaboVoQeYSFAhkxPensnM+Wc0wrjiIiyO0Ncf8UZxBNCXU6HfVbwFGc4JIc2QK5vUsz+v09tASGiwS32vjq536N3/+D/5jleMd2eUDsRNhAyiHvfXsFYRDLgcvlTLBhW2AeNCxf93AgSofbA3kMQTC7MOoNIoo6aN/QZYXLieAR7pFLAkGUFRPHoxN6kwDdjLYekdOFLoHVSlyegzZUG2FKVMt72kr2yXKbz7FHOpXtGZSFtt4QFgwtlHJE2oaNga6NOoRXX3mNf/yP/mN+/ObX+aNvfYvvv/EmD74xEkUjSwO/hfEcZrR8aMXjOSXu0HIL1WEb+Ywv6aqP7TQPoMwkghAYZ6xUqC2d+a1RakG5xmALMQF9usRHPqcoZZkHBOQ674ToG7Qln6cJyUUCKUoZc50hcim1oBQQl7k+z/XishHlMWHPYH0MthGecy2KwHSCh+VYYyNdxcuRQMHOGfXsTtQb7HSPHu6IuuDbmdE7iGIo4/Iu1CO2Gebgl7fR2P8jbdeuXR8/mRnPnj3je9/73kv3A/dV7s7DwwPf/va3efLkCZ/61Ke4u7ujlPLXf/OuXbt27Xppte+h+x66a9euXbt+du376L6P7vrg9dIBYI+SoMcuM3I3Ztn4kv+MCTz8MiHwBFkMQpRsvr26cU94LBnRK9nhawSVGSs6wWOMng5h01nHqYRdED0gI11uokFMB2l+c/a0yov42IxgTpoY82sCLQUTnyy3EdvDjPK9yWuqA+jpVFvre68dEyhXCB8zajpdp/lWNaGMZrxwuqFnrLUqLoVtGEMq9PsE5VXQUaiq6WbVCmHp9KPM388KZqnAGWRBtGU/qDkiSogQM05ZJV3G145m0ez1xaeTVeUFl0SEenVR3tzhYzpBo6LmFO/TYd1wrUQRoiZ4j/PIvtMyndpbBzfcM747BqAFOz0gyw0+NmK74D6I5Qbftoyu1ZLc02af9LCcQyLpaNVCbOcZrZ2vL14wP+Cjozowydhs1ZKQSho2zhRASpvnASTjtd1AAiNjhIsbFgMVzemmBfSTWHlIkNUrWV67EBPYRlwyNltARRJu1UpBEHPcgloWImx2rCaoFMn7EdZxz7me9/Xq+N4yhloEDcGYzs/bx+g4QymYgcgNJlDlgPiCEVAGoo72S8Zv39yiEaxaePXmhv/0D/4TPvnpT6MC2zaIPggW6noH2sAGZWmUOGMsmJ+z71eNUupMAICHbXC+nCCUWhttWZEojOhsY8O3gKjo2SAqHo7ZOecFK6VV3ALXQtWRa0jfqJJzc0SgkUHBYYOg5oGKsRHrLWaD0IUigkZH24LSKbVCabSlID4o0UALflFGfw4lx1CWAzE2fuWLX+Gr3/wNnr75lH/13/8L/u2//R/Y5IBJmfHzNwgbEpq+8zKj4DXycMs6nxtzqLcgni7bJsiMzuZ8T4yMWvcRlLagElQBLXkIIOyElBkRf+0VhnwWhDyAIGUeppkR0PUAPnL9Mk/XrraZoF/nORcHc7QURAOtKzHORF3Aztnh7Y7UNQ8aqMJIwEwo0STXObecn1FBndg2aDc4HbONaEq/3GNnx/uJUY7E+W2i3ebnuX+Gx3M8HiP6GB7efP82p127du36ALRtG9/5zne4v7+f//276+nTp7z77rvc3t7yqU99ildffXXvZNq1a9euXX9B+x76F7Xvobt27dq16z9U+z76F7Xvo7s+CL10AFgocLqHu0czGvUWGOm4nbnMKgsuApwyJjoCxuzALQXDKSHACiL0fqKVJQEnwZCBhuLkAMt07uZvdHbHMh13CYvDs7s3SmRPq2omps7DHxEkHA3j2gWc9teY8Fazj1VXaAm3vJ+wyNM0ejhQghdR1VfLs6AZN1sqEk6hJeiVgl+/PmJ+vSBa8OhYgG0T8hhQC4pQS1BVidHRVhFPsIz4jFa9dnBaxs1C/t4T8ki2p6YZevYFE5Y9vBEZ83u9lzKvTfO68JEOaDeoC6WtkwxrsihXggU0iJgRtxQiFFuPeR0qE0xZurXRdLD2C6orpjVdyXbBx0gotw18bICjXiconQnRaMZzX53dIgnfNZCAogu4o6LpkqVmDHdYdiWHY8OJa+y3DSqKaCWw7D/27A6uIlAqzZXQkr3IQNQyu5UVObT8bEVhFEIDP52J9RZaSVd79HTTsgJ5OKHTCX+g1TvEAy0BfkY54n5G9Q7HSKJLzgu5gxDMbbrpLwlTqUS5IURQvZ8udsXGls+JKuKFJoMiC1WVdW3cHm752le+wpe+8jVee+0TGRvdT4ink13bSpmOUuqCjo4vK9YHxSPHwOfc142Igl02SlE0jKEZgbzEQtkuHF0Y0fEoiHdMakLC+SyKBoXs7iZWqIPaDng3dFnTFV8C7WdkfYSZQz9lBLIJyobEQJcjxJaPdBEKgcpC+Akft0ir+NiwsWHbBeM4O5fz2UEko+b74LNf+BL/+Sc/y2/87W/y3/13/zeePghPn72DUbDoxLgg8hrCkfCHOUlHQvNr3/MgI8OPhwlu0zlMPQJ9dgKDxaBErodSFmTbkHrAxsgO4LaC9QS7vUNdsMsF0SXXG8kI/pDIxIEiWAwcyUMfNggpaSaOwC1QnMKSkeTbRrAQcUFrm077Di5IXTNAoW84+axgHWlHop9w3/LjtoaNjo3OwPPjX07Z7LsNojluAue3USngJ4oKKobIirTb92dj2rVr165foMYYjDF4++23eeuttzifzx/2JX3kFBE8f/6c+/t7fvKTn/Crv/qrtNZY13X/AXzXrl27XmLte+hfr30P3bVr165df5X2ffSv176P7vpFS+IlO3Lx5V95FWKD8krCqlonXWVClZLRzBkUSsQZdcGlwez+zUdPiExZnd/nVHEK2VWbvaxC1YbGQGpFI2YksM8u3A56QM3QZUF6uhYVyYjkcKSUdA6W7L19AUFHn3HQBXOjE5hBSDA2o48LJxuMt98hLFheveGuVNrh0YxjVqSlU1cxVBuqSimzRNiyY1h8wlfNyOhAGeGMEOyUi7aoo6WiIbTaprE6Y5TVxoQx2bUsYZQiEyw1xKZD1gcRitQ58kH+euR1XOuCsQ2PSLfuGDmeUl/0paaLOnuTEzBZfi9KjJGQXkY6my1dvoTk+CJcu2+55u97BymYZ3z1kJKRtO6YdXxE3p+4QFmRMMIdaQcY54SbmlhbJINuccOJ94C9XCHYCjpm3O6WXdQ2sbgKbg+gdzkQYTkGqoDNPuMZQS4z0jxm9HZRwmacrjZ8PAM9JOAPCLJH1X3CvjiT3dFzkymBBkCdRs6g6ZKuzcjjDEYk4JTrNUkCX5P3IKJkTLSqUiiEOSY9XbExwaw7bQzw4NXS+Hu/97+mVOG1T9zyuS/9OtE7PkZGModS14Xt/h0iCq0cEXW2fkZKpUigx5vsoO2dMZ3zwwZayJ7kmt3PiOZzejhSpeHne0pV+vkes5xf5v4i/hwq4htSbyBOOA3sQrQjEkFrDdyxAbE9R2RJiHnJsc8S3W2uQbPrW4+UUlnXhkRwGR093sJ2wXr28oZ13ITop+zJLQUuF7h9xOHxyuNHd8RmPLz7jBELvm38T//mD7k/PfBv/t2/xvolO4T1MF32+l5kM9e1rCKlIMuScygMRBBt2WFd04WvrbDURkXysEYYog23M5RjgnkCaWuC59pwC2RcUK3Umu7k7Ayez5kFFmQMtY18ns3nNc751w4ZQR0+j4MIpSx5MIILWKAt762fH3KNVM9noa74cFwGHor5YGzn7Hv2QcRcN8RnbDtIKzDO6XY+3qEUxuUdSluQ4fw/vvX6+7Y/fRz09ttvf9iX8DMrIn7u/3h+P17j46x9DH9+fVBj6O785Cc/wd25v7/n3Xff/bne82WUiPDpT3+aWiuf+cxnXvy7XwZ9VJ7lV1999ef6/o+b9j10X//3Mfz5tO+hHx/te+gv/jVetj0U9n103wP2Mfx5te+jHx/t++gv/jVepn30JQTAn83uzRjAipDxzul6vSXjhZ1FZ8/v1ZknNf2yUnAxNJTuz9NBbOlCLUrCW5F0zBWliSI4Wg/T9dqAjsgCYgiKiqKqiCi1SPbUzthjLe09UBMZvxvTkRsTVdvojKKMywU3Z5jRvXN2Z7u8i8jKypm79RH18DidxebI0qgYWlcIQcMnnJ0gVGpCJ88pEkK+lxZGvyCRsFFV0aKIX6jlBnFDajqnM/615EMpCt4TblORUlGZsH0CHeo6IeG0PpsRUtK1Srpz3QdISxjnF6IckPCMS47IkREl6Xy2oYY7jOt1zX5UndcV+R5ET+gZcwGZIDUkIezwGeWLYd0JDew8neOS18q1vkBzHCMyblmiEx5EUXwM/BrnXUrC/GC6MOdl4xlhK21ex5hdrEpEp3PJmN1yB3GC8mTG2/Z8nXDKki5YhHSKjo5eo58j8pCCTwBvW0Y/S0ZBE4Gb49wTcUA071X+4RnVI1XmR5eGR8cDXArY83w/L3nPVWbf84KGUfO0Rb6NFDyMMR4SjDqUOPPK4VX+i//kD/jKV79BN6NoUFUwrwwfGAI2WKrSLXDfMs1YG24DrYqIo7ri7lg/p0M4gu45hlFuUHFqAWkN5YyPNfti/UI93HDZBqOfXrhkJWa0tSx5bmQ5Its9IdONrHmQoGh22brPOdkvoIrNwwsvDlZ4T0esAJEO1bYsiDg2OqprQt9ySAgfGzYCt206c6dLva4sj17h8dqgnzndP8dFMBM8FB/Pef7wwP39M/7wX/0PeGlEKD/5yQ9wBi4r4BPEthkdntHlWso1x5nQPJCiLbu5tQgiBUEotRG2ETEQWebXrTO+PecUbZkV4SP7zz3XH/GRY1IWvHd83GdXt1mOX10mlM/4aNWSwJZ8jbqs1LIgJQ8SRL8Qml3hPnIN8Ons9wAbAxvgWydkQBQYJ6QWRPL6SzsgtqF1IbRQSqFIri3uhtSGjAv/zb/+Nz/vtvSx0sf5h+5du14WmRl/+qd/yptv7jH175eWZWFZFj73uc8B+cP37e3tL80P4R+WXqYfumHfQ3ft+jho30Pff+176C9GL9seCvs+umvXx0H7Pvr+a99HfzF6mfbRly4COp2sEOEJZBXAZlRwJBgOYYRQRAhZCQZFLHs9A1R01ufepPtRAZ89rBqIA4lECLeMFTYF6dNtXOZ7Ae5I04SHJXCHokqEZMxvOFfweY1ilhduVc/o1FDoDt2IRB35+naB+gROb1P0gB5rLg52ASqC5PciE0ZDIuzscg1JB2pCZ8Gq0EvFbMAYaFkoWjKJ2oyq2RssNYFQ0sEZ8+oCWEZIUzIiNhyNjFHW6faNsGn2qwmOQqbrcgHboJSEw5Yx1VFWQgvugWrD7TLfe7pRZUEsu25pbXKsHEuRSqkJVTMqu6FS0xk7LtdXmUOfIzvCoPd0YUdFRbK0XipUh5i9vTGhkjhjbAiR12rbHJN0EeMJQs2dKj1fyytaC1CQ0ihu2XWK4tuGoDS9oVufjtvH4JZzxTcISdh1yeuUyD5lEUWnS1owagQTm0NdcjGwLe/96LiCcSBo4EHEAxELoXkIwUNQnCqGkocYugcRawLT6703cr7ZyClRlbIc0kk5NsQC0TsMxxVaHPn93/tdvvL1X2eMMzYuaKnIeoOcTkSsxMg+6q1bAm0PXEjX8ayvvTrNvZ9wVhorKyeKOCxHTOBQlZvHj3h49oxuB9bDgvRgmCE6KC3wYTmXa6WYE7HmHNGb6UI/ImZoHLKjepyy6FoqRMdDZiy7YGMARjiYD0SURckDF+UGLROWi4DeQJxp7QAj45HD++y4FYJ05aMtD02I0Gql0tjCCFmopdB9IK3x6PETHj16xP/mv/piXkt3Xv/+t7ncv8F//4f/js3ewaKAbBA377myx1yvEGK5wS6dKg07n8nDAGuuJSX7wjU2Qgfiglw6WFwfBaQN9LCifR7yqJU2LqgeIAbSt1zX9EiIElVgHuyQdiRUEHfcL0Q4ro1S8kgBNnvBW8O04MNwdWJ0Rjg2DKPgbsTDW8CKLEfUg1IzRr7cPkZLHkKRsiL0XINGR1XygA+R81Eaclzej11p165du943RQTf//739x+432dt28a2bfzxH/8xkD90v/baa9Ra+dznPpeHmnbt2rVr18da+x76i9G+h+7atWvXy6F9H/3FaN9Hd/28eukAsOOIKio3E6gKUNJJZ57VpWwJutD8y36t6WQMh3gAW7KrVQshTtGCheFk/+wWQaMhJWaPLgQzfviaGz3fCwXDqfh0dxoRFfHszMxr9BfQEKnvOXQh423LjEPW4Mp/wyZ8PZ3ReofWp2h9jMSYzsUELlEmrDVJaPUCUF/AEzS7KK7C6AOzdOpqqZS1ImMDXXOcpqtVROf1xYRgW/762n484aiIEdrSBSk1u1FnpzHmGdGsC5RDEj1NdzYlI3g1ApdAzBGphBuKTDfvBKLhCcUjr0smFIZAmB3GIglrI6OYhUBaQiXxALN0ANtIF6+lmzXcppOcjHXObyCGZe+tkJ/bB4aC2nRTK0THRPI+64GIM320NEnHhnalTNeuzfF263lrpFIcpHaIbZqODY8zrkfC55wdHYph9w/o8YbQikVH9ACmWNkmOHNqS+AM07xcCyVA5TCvPwh5hJsR0ibfVdwvdIQ250xGf09o3DvBuwRrwlL3BPs4MgaU7HwOCaQulPGUgy/ccuKzn/wEp9OW7nqg+xm53OBbhxIUslPaI+9X3qyY7vgxu6uFqAXfLO99OKI3RH8AhOgPnGKBAd2c86Yc1TkWIWwwtgZjIJavL549z1IWyjhTqmNjQ+odEs9hEVwcX9f3znf81GEAEUfDkG0gx0f4w9tovc3oZNmQdge+4ZJd1BGaCeVFETnD6DiDkEacn0N7ggwjpIMPvA96BJ2KHR7jo+dhke4whKK3+OVtoh6ADS3G3/rql6jla7xy8yn+23/+X3MfnfN4Tu8dqFAbxDkTDeLIJNf0d95JMHw8IgbEU1p9FQ9P0MtThAXiHnRF/DHR36X0lbI9ZFy4G3r7BPMLsqTjvZVGLWXOy54HaGTBsbnsOSJC4Jg0sIGudxm7Xw9YDGxsuGc1uZljF8P6KTuMtzNalFgeUbRRlwbmyesPn0Hsebqf65EY+bxrbUQ1uNwjh8cQno7t/hwpj9+HXWnXrl273l89e/bsw76EX3pFxIu/2Ni2jS9+8YuUUj7kq9q1a9euXT+v9j30F699D921a9euX17t++gvXvs+uutvqpcOANuzE3Jc0OrIjA4uAVYayHTeRfayWjhNZwGtzK7McjNBsIB0xE9QbilFM3+chuMv4J4jFFdCOlGX7K6sNZ2gVqCVjBQNywhbWXmvRzXy1/Ae8JWWkNYnGJ5fxuy3ZGxgHQNCKjx/mt/3yZYAGoVlyd5TzXje8C17L4PZV+sgBwjHhez79Y55vq8qaF0QClrS/aeacb9yBb9cu19n/KuU96Jgr9HH05kbMcGOakbBtoXoE3qLp6u4gHuZVEcQyax3kcDFoDV026DUhKylYpHObgkFdYpqwvgS4DXBWyiu12jtSY1UEqIz77tW3IXA8H7KhGhX6Ofs1l1Whnm6UkXSmRwZp2tS0jHOdC1jmJ1zjtWcMxqBSnbiVhG2EMo1NlkLIPjoc0wzitYZNH2U8IwJxKVOl3LPYVbNeN7bNQ84hBI+8LCMaO6WoLQ/YHKbQFwEtY6KpkMaB3FqS7jvEgSFYRugRDkS7owS1Fhe1P/2CIIzoo8Q6ow/L5QyQeyc0kGkixynlFtu1fjG136bY0tQDD3dx+VAH4GzIFgeWoh8PqQciHiAviXIJ03eogdsm1HvophvnLQycHRcsBntvm1n+sUS6A/HitK95YsQqDqy3OT9lkN2+wJhRinKughFK0MqfduoBByOeHTCE+QSAxWl1YUYBhI0FM2GZ3R5JcfE8/CB+kBKEBzo2z0yTtBeIeJC2EYPRaJTcMQ6LEcYA7OOXTouSqjnPfUAsps3CKQ4ccl5XA6vsawrv/H3fwcOhf/mv/6/Y7FgZJw8IRlzf11LaoWahwLK3SGnGQJxO+Hwc3R9AnFAbaDlFrdtTuOWz6YqxELIKePcEaT3dD9LwLgkhBVQqYQMlJjR1PnMh9zA5ULRSplroxfFumAjO63tdE9EI2xQyopUmXXZmm5yEdQ29PYVGBeoc+0Pg27QT1AaYYbUAyzZT44WipZcb238HLvRrl27dr2/igj+/M//fB7i2fVB6e2330ZE+MIXvrD/4L1r165dH1Pte+iHo30P3bVr165fDu376IejfR/d9R+ilw4ADynoG2/jrz5Gl3uKHBF1FlXCBXfBixLjBHrAPN2fNc4gkRGjAagnKJhOYpEyu03JaFIcs6CWkhCYdJJm3m6852a12VNbSiYT+wCyexOfkc5lTXCs7T1n67QWByXdmWVJp60CZU34aA5Lg3EPejeB1nQFuiPus+MXKEIQGT0dNl/bMYdhA7fIiNW2UCRjUEvYjKkOrp3BITL7iycYsfMEJVeYmdHLCYPn97U1IYtExkkTM8oasGSwyBXqacYvGzNmV7NzdMzXIx3WRiQMVBKwSUt36ozvjsiIaJ2XED6ICVuJmGnbGbEcUjHf8t76jKMeD7h1XBo+ZvSyZe8zYQw/E5pzY8QGL+bNdFwXQTznUwGoN+An8AuLLBCKtGXGYDvTmk5EMEaHZc3vD6iqjOmQVpwlKiaB2QasyDUKwiPdp+2EcIttz4l6BM4wWvYfU3EF5Qy6MCI7YHW65FUT5pYWhJ/xyHkXcUMXkDhRPHuLRQ95fsEvLw4uuBut5J8HgCtFGnWc+cwnP81XP/9rfP03fxtsEP0E24a7IDcHwh4mKJcJqJVtnEAbGgPhkNC7HKhFORwaPs5cdEb6Bvi4zAkVSBSKF9YRlKqch1Brwu2NlnC8ZGx404UuswN65MEFpxO6gjkNGKPjM4I7TpcZTd0hFsCIbR58OBwJzujxDrZLwngR8AfKckdZBN9kjm/JQw7Lo3T4j0rYCWk32DjDckPd7tP97BdqfUT1DR465iBaMNmQWojNkNYIv+DaCK9gAz+fcH3C1772W/zke9/mX3/7e7A8YUjNMxyzN9dbA8sIbTnc4rHNcTWkvQp9o7THKBkXXg6vgWdUPH5Gljv89A4yClQyGrs/4G1Bq0BtiBhSbvI5mb3REoPYLojUnMsayOVCwaiHW2BgpRHncwLw7YToLeJCkUE7HnLt8QfkySvQ7wk7I6VSjkcU8FJzzVpWop9ybrY13fL+QHRPd/64oHWZSQxKsP+H7a5duz4aOp1OvPXWW/zoRz/6sC/lpdRbb73F7e0tn/70pz/sS9m1a9euXX9D7Xvoh6t9D921a9euj7f2ffTD1b6P7vrr9NIBYKzjdwdw8PsBh4G3SnWhSlAUVIxRjhCBCeloJAervOjXlgSBHkhktLFEQuEqCVIhCFEsbHbaGhoVsfOMVs1uVmxAW/BtQ+thOl9JRyKaIESyQzfdvw6MdJUyCCqhSnQSbhVgGPhzOJ/g8S21HNNRGgo6knqqzFhcfiqyWbN7k5Fm25ig5gUM07y8UvJ7wyYwLulqVp19tBMOm4MLxJjAM6AKEgFaCRz3DfEgHGSpiAVO9upq5HuhjoTAmFHZYrh5fu5r5LTo7PgdKAUPRSPvQTqNs/OWEEJiOjnHhLzyHsjm6sBWwg2XQdiW46uacP8KiUUJ7xl9Wwsekt9T7mA8Aw4vXLxXE7mUQnGfht5lRkULtCVn2RWgx7zf4nNcx3s0vHfMB6UuYNd7GEAhlDxwIB1lZWA5H8UTOkaABuWYHdY+DhAV9AIMuA/8eJzTIfuSs1sZLDKSuemKFCXGwEr2qk4/b9ZdSz44YRsRGTUtbZmp2NPN7tBwHqvyqU98kt/7g3/G3U0+SxYN/Dm6HrNDNnoOi3e0HajViFjAzjnvSqWstxRtmG3I0hB12trQ4dio9LERotk7LWv2TseF82jZE2yFy6hYFHxcEEn4rrLS+xkw1COdpC5ESxhZfBAoHoEeH2GjQ3ekHZGuM7b8SKhnHLQ04jKmA99zftuJECPIqO91PdK3B3pU1KfT2TqV4KIFcSgSCcLrms7sGkRVajlQzp1YDsRINzviuG3UeoR+BlbQYGwXYlmR2Kil8LWvfYPvfuePMYucm5HAf4Tle8WGko7rQoV6B3FBYuRnqRn9jT5Bwwh7jqyvEeeM5S7LDTF7n0tpUAwJQbYzKkopybmxdH5jF0Jl5pKnIxsLpChSj7gG5jkPY+vY2KAIpYEeHyHjWQJjyTh6kSDqgXDPgzBa8D6ICMIHYpbrep7GyTWzHol+BhekPQIRRj+hZZkO5l27du368GRmnE4nvvvd73K5XD7sy3mp9eMf//hFF9OuXbt27froa99DPzra99Bdu3bt+vhp30c/Otr30V3/Pr10s0LGs9kR24ilMh7eRctjomzY8RFVg4LQSgXvjLhkj6fXbLEtCngCTMhY2whcrgnHgmjJP/dOhGDu2VNLEAzAwdJdF5FwN7s8C+KKF0FiJAgpGb0Lmq5VgoiREC8Ep4I5qmREc4BZx68wsypaDwleDVgPCRQdVFbCpyNYEtq5p4NWRDGEgad7VwIpM8pXlvx8IrOi+NrtmjHS6ZJteb2z4/fqYEVKOnOjp+P32j88HblIJYpln7CWBDAEYpLxsw3kcpndwZeMztYG1hM6qiLRuPbv5jVU3LdkqZDjP/ufg4ymDmYEdFxnSgL6IBhjZEuzP2Tccfg0GleckVAZkJDs/rW3QF7JGG3khctXfbqhr9BdSQhoWwKncnVGz4jma2+yLrhlBPTVgYsZRQWVSOxaV9wMM884b79Q2icyJtghqmBj5JhKSSBfWjo0b+5m3O/sJ765mfdKCE7YaBnPjc75qtepT6lClRtsu+T8my51iSAk30/iDj+/Q2kHQgITRcZgBb7yha/y5V/9DL/6xS+i68qIAd0yZntUTBdQg3HBI+eQjAtbVIhLHmjoGy7H5KkY5p3mFQnYBoyhmF0YIxImulJ8UJYFpWLmDGu4Dy7dkHDCR0YFe36OtVbCAisZtZ0J3Z2o+QyHLrRxoveM+y5NWFvFa2E7PSXaEUfxy1PQQA/HnNqScBttaDkgOLUKy0ExU2pprEDfBhSd68SCcKHUgotN7l9xr5zPRoWMJC9C90HMTuMgMLsQ/UKoEwZehXI4MuIBGRcev/pJvvL53+TffvuPeIiNUAMN6rxWlkcIDuU21wUg2gHpM4baex4sqY0Y53QGuyHLguAJm4Xp7tU8QNNKnjSxjrTHiI+ZZnCAS/Y1Uwv4RpRK6EJomW7sC3bZXji/ZVkhOi5GiY7UWyS2PNPRjhmPLzoPI8i8vwF6QMaJcEnnP3Ptip5dx5oxKuEjD+eMSzrm48WCsWvXrl0fqCKCp0+f8uabb/LOO+982Jezi+xfeuutt/aT17t27dr1Ede+h370tO+hu3bt2vXx0b6PfvS076O7/n166QBwebLCtmI8B7sjNrDlTFzSQeaPnFIGTW8RgSpHAmPEGfMVC6cwBy7SJeZIgiXIbliu/a0r4BkzHIFJQUSIKwCEdJy1JZ2BWnEZiGUULerT9UYCWJ+gsSyTX07q7IH3nsCZSDdbCP4A8vhVtGi+TFUoBbF0H4oGMycWohOhuDjg6Wh05ucYGRftEJoA3D2yjzMiu43xad27AsaYMdDK1foakjgUvbqCASawvALj0ZEZ75vvTcLhUrP7VRRpFcczfnjriBjMqGyiQ12J3hGt2fFLQrJQzdeKGa0tLeOkr9Q30iUYEdntG47hDDfcRjqPg4x7zozY6YLuIIXu10MBr0EIotkNq57jR7k6C8lxGQ9JUa8O69Hzn2mXBTFoAsOy51cL5hs1AnRFZIApWgoRP+VglpiuRweEop4O51IBQ2IkrC11OjdXikjOVzNcyUMG7ggGsubUPj8kmC4XLFZiCNGglkarFaUyfAM7AQekrGDPCYnpPPYc4oCDKN/40hf53d/7R4QZ3Zzl4YSsB4gzxEDbSi0ZBu7MPulyfAGho5/nbVgInH65oFWoGkTpOcc3QYvlwQaVBN463eu64LElyBUh4gwxD2+MB0w/AT4QES5mhAUi2c1LuwWFLgXfTpRDMHDMejr3RTA1xihYKYT3dJBOh28+v1cH/4GIBQ1F2BBfGC7pKFbDbCOiYjghDfcLERuUI/SeTncfFLmjbhvhQbcOF5AYSF0RDWTUPAQiBfeYS1TL3m1d6GNAaXz+y1/gh3/6XUbfuODTWT/nZVuzL7el6z58ZM+xZMx9iCCtAnnAQCAPWLQyn0mF9UBs5zwoMHt/5XCX0dQlXe9Fj4QWoj3KR9Mcj4ah2OUZPoIYfa4NBVkPFL9H9JjrrzlWjFIWNNYZb57OYdVCWK6X0m7ngSBLh3bJVAccREr+O0D8nKt+GIEm7JYyD2rs2rVr1weriODtt9/me9/73jy4t+ujorfeeotXX32V1tqHfSm7du3atesv0b6HfnS176G7du3a9dHXvo9+dLXvo7v+Kr10f3tdzoIXqMtC+EKUljG4l3fxdkGfnon1gB8GwonW7iil0rjFsYx+FaHHoGpFEDwAMwRBtczuVRKchEFpYI5r4BIIS0aciiCREbFXMJbAM3tGpSzzqmcHrCqVgLIwbHYBx3RZynY1jKYzzjZYQM0RH5R2SIhoBmOgyxExB5nv4YLXBvSELDYI07RQlkDKATQBbIROaJTOXRElbDqZxScAnu64F9BVEiCVeNFfzDWWFfK1dEmI7YY2TRBXlvfAsSdklnVBtgsyO05juoQJB3NCPCOJ59tmnPJ7DtufjoQOImH2jIFGwCbADy0JwrfBGE54zDjsGckMEIaFz3hZRZF0dMagcpi9wwIFyrJgfRBSZg/rkWI+r+uckC+cqhnT7RGIKTY6pa35OTx7ZDOKPMG9u0LRjKPVMgHw4b0eZjdEC60ImBJa8TBqBGM6ydNhqVAcnU5zVBAeJby358Ryg4eBZoSuqxLDKZLdsnUYKkLUJyRoFjaOQHbmlrjQYuGTy8Lf/+3/FZ//0udZ68KmTvOO2hml4usKo8zo73Tkvognt4GUJZ+b+bBkY3RQ3IAFC+N83mi6INqIfsno6tAE8dIQyd5kJRCtCTWpeW/CkKtzXYPwYHg6yTU2xqiMOBFeMDWUBe0Vs+lcn/ehW52wOl36gRJakNG5JsmHxUwVuIDeAMqwDTtXejTifEEDNCKd0Yw8aBHzGIqNhLHbA+XRq9wtwnZ+oHtnaMV1Qc0yorzMAwy6Et1hqSAlu4q1ZTe3GE8+82k+99oneP5n32LEgW4Q2in6CCKwUHTbMMuxqVQcy9QBNGPaC4RWGOc5B2cCQL8ARywCCUfrDfQTqivuTnRDVSka0LdMQVfBt42w8WKtxUZC2PEAh+N0/TY4PxC14KIUEvQr5H3XdPKqFFwj54bNgyoBUo9z3W3zmZR8r7bm9XuHmi5nsYA4IcdXf+49adeuXbv+Jtq2jR/84Ac8ffp0/4H7I6j7+3veffddXnvttVmHsWvXrl27Pira99CPtvY9dNeuXbs+2tr30Y+29n1011+llw4A6wHkEiAHXIXoG7EGHDekVeJyxk4QCLoe2HqgdqG1IyoNtT7jnjWhyguWGQSKxUCLEtRknTiu0+0qzjCjlIq6I9LSiWoTCJUyYaFOiutI6AtHrAjU4xEXRe6fEx7plLXALWGp2MiOz9MZbhbiQSiHe5ADoRXxQLWipcLYZhz0hGsYbsEQYGQUqrRlsufsPfUg3Yy1YKIThArlamn22Vkbgbvn9wXZmzod0xKB1JagsbaELj7dxOEJ0WJ2o5aE5BEGqhljO2x2gmo6qYfNWOkgSsux8ADZiMjrSnfsIJgOvmu/L5JwcIx0k4Yl8ATcswN62MBJaJtEqkN9nCDInSYLjqCMn4q5nj3HAaoFLQUbPWHWeGD4gHKDx6DRsDAsNmCl+wU4ZL+yOFHbC2dvaS0/JwNd0+ns7rjpdJTKe13BV9dxzJMBFJzImFtNl2atKz02oGacdCzTyb2QgDnATogcsobaL4jc4dLT9c3KMEckKAJaBHVhkPAsgbhTQrkpyte//A2++Zvf5LVPfJJQIBpqz6aTNztdIxwp2YUcCrZ13DakPYbLQ4JGkeyjFgMfVC3UAlqDsVlG+3IhRBnWQe6AjVKPOZZ6oJihMbBlJSJjvmWMachWxDeu/dCSMwKNoNSF0R+IckBtUJeV4hvSDnmgxBohRlkW+uk0a6UrjuDbCbFAa0PMsifYDF0snbOWbuUqxiIFt85yvMO3e8yzidyCdPIWw3yDseBkP/O9LLhs9JKB83hAWyljYDPCW6UidczDG1m6G4si9kCIMCT46j/8J/zZ//F1tvYuoz4CucPsOfAIdKFfnkM9ImJY3+ZZD8HkgrY7bNvAOlrKPIgwO7tx6M/ysId1dDzPAx/9XVwqJe6R6Ki26U7Pgx9h8yBNzXmhhxlTHnmAhLPBzQGrLdeDpeVYL3doKRTNxIIYA1FDa5uPyrW3vOf6JQm182ufZcT4OCPlSMg2Y+rLTBuYa+iuXbt2fQC6nrT+8Y9/zMPDw4d9Obv+PfrBD37Aq6++uv/QvWvXrl0fEe176MdH+x66a9euXR897fvox0f7PrrrL9NLB4ApryDH58Cg6Eocb7IH9/AKPAQ+VuSwEnqPnway3uD37xI3QimFVtJhWXSC16ubtcx4VQ08zZOgnrGuIViW9KbD1DKSV8TygZRCVAWpjDGopb2ITZ72WCDQthDuaXxESa+qgDhRChLyAuZycwt2prCh9fEELhmprOthuo0VLRUPJ6TgMRLqXk759ddEZwEwCEVVUByoRO8JZXzL0Gip6ShF0slblrxundHVPxUlq2jGwUL2cQYJVAWklAQwpPM3P6Ok65QCxAu3dGj2iCalBmI6cLXMYOdCzM9H2HT9zpTXEALDyA5h6kJ4T9NyOD4y6nq457jLkvHEeguM6b5WRAqFdJgy4bbgE7SmW3pEzVjuUIYL6B3YIJCEeOUI413wS/auhqLaCBFqJF4WFVT1vd7jyHuW4zjvu408PCDTIW4DpGI+MnrYIXSDqHQHkRkDXiJdk5Kh2PhpQuKSDtY6+4L1FsRRgnBDSkvjdRiqhSaa7sqefbuHUIrf8PnPfZa/8zt/j0994jMUFQKl39/TOfGTf/eHYE7RSj0sPP7il1F3ynIglopbjkd4TKANPix7lYsQZck47LiQqcBB2BkrNzDOFDniItkDXVfEO8GK+5mhR2RsiB4Q34htI2pDtMxnzDMGuh4Iu2Cl4Z4x5VXytJuZs23PCD1A35CilNYYw+b4WfY8e+R9G56QWSTd8+MBX24pfcN8I+RIkL3ZHoo/3OPeicj5EJZO8LgY5orbQI8H6vnCVmsegKgH6A8ZT94Dj4KUkc+AJNRkGKKvECbEtuDjgdgGLgu1wZf/7t+h//Gf0kXYtGAeGZ0cAtoo4aic0jm83qA2MrrcB+LP0PoEYuBcMqpcaoLhUkAG0HDbQDZsDFRuGWyIOFXajE2fneVFgYLWAuT4xeWELiv0DWpJmDvXKnEIHPMTrT5CpSCe9wa3fN8Zox+lvdc3XjLiOiz7yPP39zMFYSHGw+z+9ew53k7v3960a9euXX+JIoJnz57xwx/+kOfPn7/49+OUZ7Tu6if41v/zLQC+/o8+wbvnPwdguSmE2IdyzS+7xhj86Ec/4rOf/Sx6TYzZtWvXrl0fuP6qPbS1hojw+V/5FerI1KzROz/4wQ/w4zET33r/sC77pda+h+7atWvXR0d/1T66jEGJ4G+9+QZ1OoG3UvjWa5/M7wMutV6Bwq4PUPs+uusv08sHgFXA77jGx4oAVSm9gryDPHqFGPdEndBrdFgqfv82vtwxlgUtwqIFqUrx7EsN93QuRkGIBE4WCBeqHgHScUwDLMFOWDogwxJAxGXG3kKSjNnPO92OmGd0ao8ZG+14KBH559cI4CiSsKzng65RABD1dP46EBsQuJeMp64yHaoT+nF1xl1AGiqFMTtng0JRI8RQSoJICTxiwpEEMOkk7dkt631GWita2ozebWAD0ZYQs0znatHsCy0tY52nRBR6JxBCjLBOuM7Y4wqRHciQYwOk4zeYsctbdp9GEDEw74hrxtWWhmAvFsdCJcrALg8zRnpGObMwM5GzC7jdJfT17B4uUhHrGV0dG24brpbzA2HghC7AKSG2rBnv6yOhsAZow8e7FFkhZt9yyQ5TJZBrh2tcEJEJSCPBnpOHC1wYlvfZVQjbiCgEnXh2jxyeoOZ4zXklCjKjsJc4YdEoQcJ7rfiwPN+gkjEfJe+pkfPDZ/2wK2grFAuaG7frwtf/9jf5xt/+29ysKz4ujLPzxp99l3de/w6yPSC6UJYlDxc8E05vvonGoJTG3Re+wOGVT1AefyLfgAIxkJqHBPAxn9ONQaW4Qq2EG8HCEhdKEYalc9hsy/eLjdAFl0A9EB0UAWkHlIxL1ipUWblcSMgcPvu1I8G5BF4WbBg+lKgjjek+oTqRztJogCDekaIJIXU6u92JZcleWjY8Svb7DsfV83OoIKbzeRLoZ0waKk5bGzEuL6B1QbIjuo98HbkC4QVxS8e95SEMaQ00P39Vw0oDe04pjXDj1775u1CP/H/+x3/DUEF0JfwnqL6W3dZqyPIkn+3aAKVqI/o9Wl/Ntag7cvxEHiqpgtAIH6hUvArQEEqOWUnntrRjrtNRcj3VPPwhZUbe1wV6n+diIh25bU037uiEj3Q46yGhro+5luWBHlzy/aQiYkS/5PpKuuzj2tUuNb+/rTOyv4JOJ/w4w9A8bLJr165dvwBFBGbGG2+8wY9//GPMEuZah3e+De98S2bK/tvM0y/84f/lrRe//tVff0Rv73L7Oafd7j97f5CKCH74wx8SEXzuc5/bT1/v2rVr1wesv2oPVVU+9alP8S/P/5Jvjlf43OsPLN/9zovv+xJw/vo38LrwZ5cLPzoeuVwuH86HeEm176G7du3a9eHrL+yj87BUieDXnr7D55++wzr31hcVd8BnJiQOhO+++io/fPSIU237D6MfoPZ9dNdfppcPALujlGzVLYr5QFzSwXt8gmq6voLpNDw9hUPLv/w/nwlVbARbbZSi1NrAEsCagpilWW1sEE6UFZjcmZgxyAbaGA6tBFECYfbraiHNpJFgN/SFa8+9gm/IjDKGdIRmbG3ahcMcD8PcYSkv+ki1Tgg1XYf0GakajmvBiPxeH/k1nk5cAsIEJ3tuYRDSsou1LulE5DKdck4UwSOoZaFIoK7ZSSv5WioJMbVIrv8vgHd+jaAwsq9XJPtpY8ZIR5H8M/fs4nXJKlSdDt6IBOKU2QorEwQH4Y4NI0olzLM51kh3jKejGTGQQoF5PkDRWmHM39vlve5fDyi3vNjqdKEgGdsMhESCV88eVjdn+AWiEA/vIu0AEmhNSO3U7JSejC/0kMBOgtpWCpFO6Gskrg9QcL3GQyvmgYcR0hC/JJTtz4lyS8gCp3s43MBaCBVCp1O5DugBpwvcPqJJZ2HhE69+klKU7XLm2bvv5vfYhPQWRCkM03Sf+0DEqGWhdWMN4de/+Vt85dd/k7u7R8S28fydP+db/+pfAoK9+5QWStWCVEFGgrYQxfuM+47g2fe/z/bmmyyPX+H4+S+jVXPOIMS109kHDEcOd1jNAw5Rb9EJqmO63eVq6g0jdMLXMXA84S6KVqXpAaRnZLEOijTCTtTSGL7hCKBYBHFNAzi0CSkrQjplQ1csDIkNqQeKKOKGFyW2LZMGbEPLitCTb0euIXo4onYmakFRIraZrJ7jH7Ugl2ew3GWHsQ20DJb1iATU5lh8iq1fCLunLit2HogcCU6wfoIYPQ+d1JUag1aPDH0gwgjbKOuRr37jNzldNv7HP/4jQjaiPkKWQEtBdLr45ZjAu87n0NMhHN5hyTmWkQgC6y3SHxA3itT8wDOOHBdKPeR/NXZBygpxTzhIu4HLiWAgPZAqhK4Z7W4jY+VLIWpFosDYKMsxD5nMPmqpKzJd9wl4I4GzzkMko2dfNpLR2HOdQGuulfNpjwDwTDiwPQJ6165df1ERwel0IiIopXA4HP7G3//jH/+YP//zP+dyueThtK0hzx/zvX/xFv05vPej9v+/8t//4N+9C0D9X4S7X4VPfTP/m2PXB6cf//jHLMvCpz71qQ/7Unbt2rXrY6NfxB56c3vD+fGZ//Pr/ycevXXhf/f083x9PGXlL7728Y//CICv1cqvHo/82Re/zJ/c3+O7i+YD1b6H7tq1a9fPpvd7Hy3uvHK58PU336C4c9P7X/qTqPz//Dr4yttv8bln7/KT2zu+9don9n30A9a+j+76ab10fxVUQnBxhA2hovGAc5NdtcuK+/UES/7Fvjx5QsQZX4CnnjHKHthmeBH8AFqUpVSaXx2012Lg7Mf1SOic/E6IqAlwRXC3jPeNTpRjAiIMkYbOqNHQ7P8lOkggWl5AH7eRMchmYAOLwKb7VbaOHu6oqulQlSUhriSYucYiG0KMhMxiI011Wv+/7P3Zk2VZdt6J/dbae597r3tE5FiVVZVAjagJAAGQBDgBTQwcmzSJpm6KJjM9601metE/IZPpSW/So0xPaplJMpO12HzoNrRIEFSTDQIcAAKVNaEys3KOzAj3e8/eay09rH09CiRBDJUxZMb5yqIywsP93jPvcP+t7/vS6YggeBoa1bLXtgnuihpQMvrYPMBWVBtq2WeqWHaAcj4WMftwK6HpoMxIaEnoOSI/rTgidULcAOsEBcYJSkM8nZZRC9ZPeT59JHSKcf4qgIxdhoyPdnJ/w9PF2iqEIGaggahTWEAcXzMCWQWKZH/qTUezG2BZ14wnIBJNiE8haiE8Xd0eQR8D95GRyT5gOcN0R3AkjBaa54UE21IOVDUoJfn4GERdMMvO35A85z5GBoFHXmcxrkAvMD8RsaCxy7jmAL/YU+IeLM+kG7LV2Yl6C67vUZqy/+DInU98mp/5uZ/jC5/9DK0t3Lu65tXvfpNzN/L3fu9bvPnW6+kiLysht/M4aqGY88Kzt/jiV77O57/yNcruFuP0Ad/617/JG9/+JgWnSaWVSglFVSml5DVd5oBCTYgqIvjorPdgfHCfMGf/0kvUw23KPrupLWYsb9mhAaX3jDnH8XCGp6PXx+xntpgu3hNBI1BEIruiA2ysrK2k81yEPjp4o6riPvu/A6BQApaaQwpQMVtppRIEbgKuqFyC30UicPGMHi773Ee3/FrrmA+QPSqBtEJrgqNE2yHR6UdB01YPy21KnIj9bWLMZ40qSHYx18sDZsfs5Y2CtAN1EeIKnJFgU64zgYCg7W4hcaSEUaswjpFO5VaR3SWf/+KX+d63v8Xbo9P3txG5RpfKmViLe0J1F7BImIrlMYwJScsC/YTUAu0C1usZcnACuYWUPdGvuenV1YLoAViQEALBaz47xAbRDdTz+FWdDvmOsGRcdFxjtlJlQXRBtCLiOTSijvdrgobo/iY2GjSHXtzn4MwOwXLAYA7tMFZECsEuB1ba4cNeojZt2vQR17quvP7667zzzjuYGbvdjjt37tz8/TPPPMPt27f/0K+/vr7mrbfe4q23Mso5HN793crVq4X333zg9v3jalwL7/0e2BGe+Xxw8cn5T7JND10Rwdtvv82zzz5La+1xb86mTZs2PfH6sNdQgB99+WX+xfU/57/+H/4v/L3+Zf6e/Zn8OcsfIRmDiw8+4Mf+1W9y+eKLfPflH+X9H4i/3PRwta2hmzZt2vQn14e9jkoEX3nrTV7+4P0/1fbsx+Czd99jGYPX7tzhncPFNLBtetja1tFNPyiJiPijP+3jox/7zKeyo1cFFSWrZBWLASLYjCONsIwHpROyBwOPK2I1WC6gdygniIS29eIWtRQqhmB4VALNrsii2SbqM8K2KBHpPA6giqAaaD0gkr2mpQiL7pIjF0FV0kirBa0LoxvugzGOjBD6uhLrkZMZVw7r3fcQTuye+QQXpbHs9khRal1QOxGl4mSEtHsQo+MhiA6Ec4xxwux0yCmMjNsVDyAeAFEp0K8opSEBWir1ZnslI5Il4V5pBzSMIFCt84eQszRZCjo/N0TSiThBZ76HQc2o47CBm0zX67gBduEJf4cNVBPGhQduR1xKxiULGZEt2R8spVA1O5ez+zWInhi5x4l+6gz32Rk6o2S1UEtNoBtG1AW8QEloaT5wgT7SfexXV+jlLfzqHnp5C2GguqOej6/b/JnujCbXhmilqBNxQuotxtoJuyZCcRGsXxMFIva5/36CeQUKnvC+WAIz3SV0thVKEPYBUl/Ex7sIt2l9sA/nuRB+5e//L3jpR75InN7PjuviefxnZ7B1o4/r3NY44rIjKDmk4I5Kpx0+gcQVoQvf+o1f561XX8/7JIJWGlUqFUMj0KUih1sT+ub9mLTeIX3PKEIRaE2ppXH7Z/4SuhTcnZMNzIRdEWoRqhYigm5jdicrNo5I5H0XM+YZFoKRgxShWD9S2j4hIyuUW0jkMVVVRIRhJyBo+1vQ7yO7A7pm/He/vkspO4jOak54mfHteb27zEvdJV3xKoTZvKwmBD/fK3VGmsuOGPcThnq+TkTg61XGmo+OeoGl0S5v8exzF9Rwxkina18HKgutOOP+CY+VEoPjcK6HUvYLl7scNhAq969Wrt5754GxfbcDcd76/mv82q/9Y96JwMNht880AiFhbXhuq62ZA+6d0Ny+MM/Y5nHK50Xd5XUYI2Hw6Ro53CauP8jXEkVKndsg8547Zh8wgoTj6xWya8ixw8UFEoGqE2e3vguiyn63p9WFiiNlh4QRDsNWkFwD3FeCOsG1Z//5vB8jsrc9Tvfn805z2MEhtKO75/hvf/3/+1DXrCdN77777uPehE2bnlidTie++c1vcv/+/T/0c1T1P9nFc47bgmwR+P6/FN7/FvxJwe9/TFKC/fPw6Z8L2sUP/XKb/pi6vLzki1/8IsuyPO5NeeL03HPPPe5NeKTa1tBNm/5wfdhrqKry+Wee4bO/89tcX70LNrjN7k+1bSHCqI23f+zH+N2rK051+0Hqo9K2hv7hetrWUNjW0U2b/lP6sNfR4s5X33qTT3/w/ofwnSgMEd7f7fnXL720raOPUNs6+ofraVpHnzoPgE4IgxRUlBInhEERnc4+T2grhUpD5UBzoahS5JKyP1BUUakQt+FkxLIwPniXdf2ANQSXhaqVWdtKOJg7MV2t6kYNpSpoREb1+uy1VZ1gx3FIUOQGkbCaUnDv2T2ZdbeZSeqzo1IUP50SXOyfRR2KRAJB0QSWkJ9nhq+DsOyqzQhpsgK4yMw7zez+cCFCYFi6WyOykdZGgkE0fw2jRMY9nKNURQpFCkUVmR2qROCzw9fD0z0IhMrNLsWQNBG6pqu31YS5DuaCueGM2W+bgD0BmaexMjruhtvKQHCXhMlakbLcwEElI7zFRx7rMSaLlXQBitzEYaczUNCasHvapediOHBfsXCGB73n+xsQty4JUfTyNiKBhOfxEDI+W3TudEZ0pNH4xOiDMaCvnW4rPZSO0M1wXTBbML+P+Zp9vJoB56Uoqo7IHkqZ2zfdqrEQchvvlvDYnT3w7MUlv/xf/M/55Mufx+0aV4hScByVlttcgnqxY3d5yf72bZb9LfaH2+wOd1iWHctuRy23Cal4KN/517/J2997PQEuSimNIoLuCros6G6HHg7oUilLo7SGLi3/ri3U/Z5yOCBLIxDWq5WrN97gnX/233H/ld9FPFhEqSrThZpeTjt39dbdPEc1I4OHIK6El3ThxxkyD0qtGRM8I6bFHZX9dIH3POfZck2LQVuW7FJoB3yC0TUKwwzlXIq8Zvy2lEwccEnw2fvsDbcc6iiKlkJpC7XNcyjZD65lT1WniKHiaF2gNUwKqwdWlnQCtzafNUbYFaJQW0F3BZpQ95VlWYjdRTrMDxcshwNix4wS32kOZKgylj1Rsqc8xuD5Z+7wl//SL/JMFDQqfrzGpWTigdY8Kjp7y3W6+m2kXVqYMc8KtHSOa7k59qDQV6RmBLdKxu/nzT8SJlt2+cZYcR/oskc0O8UzyrujusuBE3T2cbdMHBBNKI0T1iE6UnZo28/49DqHVWpGWfuYQxk+UxeAZXZ9Sw5msFwA0628adOmTUDv/Y/8hhvAPYd0/rBfN/DX4I3f/PDgL0CYcP2m8OqvCe/8u/kY3vTQdf/+fV555RV633rjN23atOk/pg97DRURPvvC83zud/4t9YP3uW3lTw1/IR1Qra986t/+G37qjTf41J3bmYy26aFrW0M3bdq06Y/Wh72OqjtffvvDg78ANYLnj9f81Ouv8dn33t3W0UekbR3dBE8hAAYhxMGvJ5w4ZGdlgBKIOUULlRPiQaWCClWDpSil7CgayKKU5pTLjBfVizsEhdGD1Zy1KNoatbWERBMspLNRJvwRRIJyhlenjvc14ahIRrRGul8jMqI0huPzIRlkzLCFgC5E2+XXSYHqaC3pXJyR1GVGoFL2Cawso4Q9DK0LygohxCnjcdPxW9LZOqYjjhlDW1oCnqggCTNVC6W2jI9VRVrLmOSyzG1ZMuJWBKkNId22SMkoa7eE0iHYakTYDViLseLWcRdsdWweh4iE1un0lXTHRvYchzsuyqCAzNJ5XRKau1NKpdaaMdU+EgyLJsgqA/MTPk6Il+xiXnZzcGBBZyQ2RaEtEMYIw0wYASNWTCpOAQ0UA79CxBGMUi+z41jTLal1ly5vsRnznB3NIYKb0NcrhjsjVob1eVyEWE/ADtGFohcZ012cWmZvNYBephPVhVjTkRleibsfoGvhcPcuLzxzh7/2n//P+MSP/hjhllHaFjlsYIXuneGdbgUbgnVljMC5wC2yH9kNTiO7WO2ab//L3+Ctb3+LUhoVoahQS0F3O0qtaK3UVih1oYpQl4W631F2O8puQuAyhy9qRZcFasPrgeM773L3X/8W9373tymaddellowWJ7IzV2MCwUFbLtGW0DnP9T6dv30QZZ9wT2cvdcS8jlb6uM5BA5nN4bbiuqePwL1gxzmI0U+gF3g4rntE92hr2ddrp4xbB0IDl4Bluvu15XXga0JizevafGBaMRvAQngwzDAf2QMcRtGMkQ4f9HGXfrpmeOC9coxL7vfC9QrX91eu715xvRauTTmNSgckVsZ65ChwCuW4DrqtgOHiDG1QK+x2UA+8+Mln+c9+6Vd4btmhI4g18NXxce5U/sF/vAUiFdldzAGEE1Im/I2a92FpaAx02RHekbYn/ASy5jPE1/w8yryWNaOcKekuXjsshxxqKMIII5Y9RJmspKMoRSVd5j7SRRyCRke8I8xUgqoTthd0DoHgxzw/Zyd53RG+Iv2YkHh3OwH1pk2bnnqdTie+8Y1v/JHfcP9x5Qbf/xfC3W/ChwV/f1DHd4U3f0t4+98K2/fdj0b379/nG9/4Buu6DQ5t2rRp0w/qw15DRYTPfe5zfHpd0Q8++FBe8wd15/qKH/+Xv8EX3n2HbRF9NNrW0E2bNm36w/Vhr6PqztfefIOX3//w4O8P6pnTiS+//da2jj5CbevopqcOACtCoyQsOwM4MfC7aYVQZs+uIAoilsBXsiu0EVSERdN1WEujHk/pGgYk7uP9A9brK9Z+wgmqgGq6CpGCueMEw8d0nyYsyRhfx8YgyM+LpJsPnonC3M58DHvYdP8GPk6YCFSBekmJK2p8gLYye2oF1BkMHJtQ2RHO7rgJp2VGEkfAcMAyDtoDohHYBMEZk63hSHHOSdEzTZmSadf55wCtFSkFbYoSsx/YMv+/lOz5tI6bp5PWe0ZTu+MhuBljzI9Hv9mOUMcChp1wv8KjY5bOW7NOurrTLq0lqKLUWiiMPN2SUdWlFIrObaZBaPYPSxDWqSNoIpkEGw4DqAdCKmZBIIyIBIaR8bfglFjTTaoLtTSa7iiS/b/przZqZCR0aMKucGe4sZrRYzBEsTHwcZqA9kh4R1WmWzT3Qak0bXm9asZJ5/6v6RrfLfjV/TzvKtTjfV547jn+xl/76/zIZ15mX5WdDgqR59KuUBFEJYFwzMhtBeKYxuVh9LES3iGcCHjt3/4W7772akY8x0CVOSQgCfeQHBhojVrKPPYJg0traGuU+XmYp0OzFMpuQW9fwO07WG2897u/zev/3T+iv/sWEoMixoKjQwgrMJy2XLDbKYcS7JYFUaNwBAa0gujsbqag40jxI7UVJMcK0h2/WyhqtLrPCGLPaHHFifBkn0pGKVfBap23u+bwAyPjhwkYeb3jPY9fX1GpKGB9xaxmDW0f834cGRGvBdFddtIS4E7zoEjgpeGxEuI4xuqF07179Ku7uJ04uXCyE71fY95xW7FxjQXYyCvR1yPu1/h+T4wjYtcwByuoFe/G88/d5i//lZ/nmVrQ0xV2OmFXJ2w4drpPaMvr3pkR0Ndo3RHDZg14QJwgTkhZ5q1pEGPul0Bd8rg7D55DQOYue0JpGwnyC0iMfK3eieP1jJcG3V1CrPlc9oHYipKR/JnpLznIEh0tOquU87ypksM+MgPI/TTj+Ru0BYkrON3PgZ1NmzY91frjTlv/caWqPHfxCT74rvAw4O+NQnjnd+A7/61w/fbDe5tND7RNX2/atGnTH9TDWEN/9Ed+hE+/+w4Xv/kvH8oqKuQPxz//7rv8dDi3tyrDR6JtDd20adOm/1Af9jpa3PnK22/x6XsfPMzvRBHg8+++y89+7/d55nj9EN9p01nbOvp066kDwNATSM7uyuyOXaDcAYwmgroh7BKyRiQI9EiKiSBSUW0UCkUq7eJAqwW9KTI/gMPajdUcU0eLZOTzPOQhyg1psIwZNeFBH6+tGdEru4xzzlDZCUsiHcURaJmOVSHhT5Ddlm2g0qh6ieguHWzm2R/qgo2MEEZ8ghlFpKAYKi1dsmWZ8bkxwbgCI920HqjUjPUVo+gehRmTK6gUxLMrMzuEGzd52JCgMFuIiTGIMfAxCCpuHXPHTBLkhmI20t0I8+sEHwMbxqBmV602jJaxyzO6Oh16jtZ0WYsuqEJTpZSaadcxKKWgntHMYuAqRN1lfC9CqTukjHQ123SZlkL4wMeKRTAMzK5x8wRLa5+RFjukLLQZC64y+2ynu1VQBo4rWFSGFNZhjNOJ0e8xXPCrK1hXpFyiUijsZpTyDpEBpVB0l+dGl+kSn9evOyEXCe3D8uN+D7nYs799yZ/9yz/PJz/9MujAGXgk2Cz1gJT9PDaW5wooAa1UWtQEm02ooTACaQde+53f4u1Xfx8tgook7Ceoyw6tlSKaJmtVtC2UWimtIu3cKZ29yDI7mvMaFKQI0hbKsqCXt+DyAm+N471r3vj1X+P05ptc7vbcunOH0pTWGruqPHvnNrcOO5a2UKpSy4IoaN2jS3ZSS9tRNKO3IwKRXTrXyy67pSkMwKQiMaAqRsa9h+WgSFhMN/SAfkw3a5zSXewrGiOvHdGMKd8dgEC0Em2fXbQxo6mtJ7QvFTCkNlrdpbM9AmRBZCCXtwg1yrInRtCvnd6PxOkKxhH3Qj+tGBW3YwLfcDwMM81hFB+4X8NyiUSOgyyh1EJeW/TsM18OoIUXXniG/+zn/gLP1kaxoJszru5nj/KwHODQRoyrnD+wfH4EwP7W/NhIvC6R+xhCrAPkIp8KfSW04mPF7URoJXr+nnHKZ0jLY0PdARXqnnlzIzHBbN3lMyACypLpD1rQZZ/PBI1MIojzgEtHiyCl5j92R6fUlukFoWhJt32C6RkRvekjo/gQpks/jNf4KGs7hn9QEcHrr7/+oX3DDfCjP/qjvPob45HEM4cLx3cyEvr67W0A+1HoPH39uL/x3u7lTX9SbdfMD6/tGP5BPaw19OX797j4H/8FMh5uUo8SvPjNV/jpb77CneP1tog+Am1r6KaPsrbr5ofXdgz/oD70dTSCr771Ji+/f/ehwt+zlODZ05E/8/pr2zr6iLSto0+vnj4ALA2kcO76TUB2L5OIZZfAErjpp5RzaTAJgjl3Q47ZceuIKkWEpgtL3bHUQkWQCLyvnI6d4QmxSlVKOfdfZlesSyMi0l1Gdu36mlHII4wQHmyvzx7XtKsxyW86daVM2CHodWcRoyy7jGAVITwwlXTNWkfCs1uzNMQt41VLyX2S6Vg+O2H7KV2LemZ0NjtSA6RN93P2aKoIWncJgktDSHdwuBEeBJWg5H7Nrlhz8O74CMxGwh88XdB+ykPujofOqlzFpGK25mu6ExIYjnGGvRUtB6osqHk6scUmL7eMd60N1TZd0AJaiJLg2dcTWEfIaFiRkse4NSYdY9hIWB01Y3sj3ZS4Uy4u0NIopbFI3mwaQhVH2y45vigWxvBg7Z0+OmO9xjQ7qYOW15oZ5eIWlZxb0Jq9xIqAXgCCBekGh4zUngz4fO34+/fxqyO6U4Qd+92ez7z4Al//6k+idcEl8J54PUSpVWjLRcIwgWW5RS3Z1VtV0LZDRaerMlCt6FK59+b3SYdxyY7lGQ2uZUY514KqoFUodUFqTacvOruQCxIZyy4xYdx+B8sObXW+p2QP7MUdYlnoZnz/1/8Zb732Nm/fWxnlgloay6HSNK+d49pZY0Cbna8YsnbwQPyaUiUB/7JHGVwslUMNdu2CC4dFd7QKi4LOePZQR4tTSjqERUpGbMfIzm+fwwIciGj57ChB9COEpstV6821prqAXeNoDhf0qzyPUWa3r4FZDhb0Ux7nCApCiUBLZS+NS4S23KLWZbprV3yccN1lb7fsswd77WCBHA2xQHo+E0V6uts7yDqIcZ3V6UnOefHzn+cXf/mv8dxSaGPgxxP9eKJfnRhOuvRdiLrgNkCWdAaLwO4y3bfDMj56PeG6Txg9jtjpRNgHD/rN5zPuDEO89xwW6Ub0QayndFa7ZRx73edzxcYE3qQzWct8dsqM47fs79ZMQMiHfw58ME43z83sBO+I5HkTy0EMqQ1Zbn/4a9SmhyaRH/7bmA/jNT7K2o7hH9TxeOTttz9c++zr/+o7vPY7b3yor/lHaRyF7/0T4f7r2eiw6eHqSYjg2u7lTX9SbdfMD6/tGP5BfdhrqIjw3OHA8s1vPrJeQQH2Zvz066/xvOVQ+aaHq20N3fRR1Xbd/PDajuEf1Ie+jgIv3r//SODvD+q8jr5wdUWbHcSbHp62dfTp1NMHgM/fC9zABUAvM7JYzhnGADbh2YzgE0l4o0z4VxKI4jNlNxBJN5kQlKosZYIyATNnjcDIrtJ0IS4EztAEl+GGh9Mt/+sxsJGuyxAm6CEdaFqzlzZIRyGB9xPmAw+jHC6pOuNSk5jidSGkJTSRhGKBgEf+WT3jmFVBR3bc0oEBKuk41MrNBs1s5yqKnjtUyZhUiXRxZidrupODwKWkQ9dHOnz7ICIILenqjZGg16cjOBSf2+kzujbc0yHtjlNw5vGjEN3ACxJOhYxGLvPc1AMqEzKKJgBkdhLPCOVA6VLpo2f/rcwIZewmLlykgzguQphjvSfwQkEqaGTHtOSAQO6ZQG0gjpWKjT4dw4MRymqWMNl69rxiCUlLy3jkO89Q09xIU6WRkeEuPt9vTCAZOTRQZu42gxgfpGN8n723QqEcOxc4P/UTP06rQl12pOfxBAFld4cwz55oG4hDaZWlCloq5h0jMrbXenY773bcff1VTMj9LYLUdPtqbRRNl7wGFE0XPbUgtSa4KyXvrVrzWCL551aQWtDS8vaVdJXrUtFdQ/Z7ZH/AxHn1V/8bXv+1X0U945a7Fe5343i8ZphBCG2cWEQoUnIYQJWiF4Q00EqpFQhGOBY2b/1OH0f66qxmGCWv4TGwccRCEG1oE6h7Ak24yQWYTbdrIUYCW7TOqGSf/eBB0AipCUvLftaFtzRsjxPDTkR0RHaEGVp3eX2Loii1VG4dFg6Xl7RW0qXdhEw8XhGUwopGz8EBJN3OZY/7YPQTXhtEYCsZRV/2yO6AUlDPDmSVhpQ9L3zyk/ziL/8Kz7VCu76ezt4c4hijM6Tg6wl3e/BrPeYgSpywccSlYLZiRXAi73vrmD6T/d5oPrPGiagLUZccwHCbj2/La8hPCdXXq3yeuDPC6d7p7qxudAUrQmgOw3gIoiWPjQSUms/BUtPBr23+vuQzKJjR+45oOoCl1IewSG3atOmjoIjgzTffxD7Eb1KfuXOH3/h//Z+4fu8fzvSXRyc7Cd/7x8rr/1zwrd78oWuL4Nq0adPTrIexhj733HMs77xNe+vND+01/7jamfHTb77B1999J79n2vRQta2hmzZtetr1MNbRl+59QHnE34OetTPjz77+Kl9/4/vbOvoItK2jT5+ePgAsP7DLktGj6fxkAjObcDfBC6H55yyFnBG6TKgK0zoMcZyvIzmFIIqee4K1gjnRDe8nRqRbt0n+vXhGPzskgJUJ8cbAemeMUzoiYTpWz2nU0/XGGXgIHo5wpHCitmcTxAUEg1CIseJjoLHP/mGp4NNd50KMAWY/4DROV2zC7kDcEeuAorWmq7YURAuqBRWnlIpoTBduIWIkrNWMfHULHMNjxYOEun0lGAmAYiQsIvCwyeyNmPG3CZKFCEto7gau6ZDWjMRObuvT6RdIBDV9lbPTts2LIBL+zj+ZD8boeDgWAlQ8zlHKQZgzRv6d+WDEYHDEtSS8joGaob1QzGkkWE5yL7gqwwan0/usOGs/MghsvYeNARzJ3mBBRKnkayx6htmKSkZ3a81zcnMdhqOypOs0HPqa5/K+YVfH3IYLh9Zol5f81I9/na98/SdwLayjM2LgAl73eR2GM/yEWcchI4znufBhCeUYE9weEBGu33873bWSwwkqGQMtw/K6iXTGCxm3jWUXtdSaXwOA5zmpBdnv0WU3/84zOtgjncEItIJe7CmHC8rFbaQtXL36XV791f+GsZ4oYqhnDDYiFGmU5TaOELJHpFA1r1/GFeGd4dCtczToDj3guhtoupfD5zU81oyGNggpVA2aVMSPSARqAxHP/m03iI7sEjAX0emYV9BAVbNbmiPUhqohY01YvJ7wOA8XtHSmhoL5HLyoN3MqvZ+4GoPTOhh2xMWg3c7zpjWh7iq4XYEPpFQ8TgmsfT4TT2s6+N1gjIxmpkJtCXav78++YOOZZz/BL/7yX+eFW3uaWz6TZj+295XRB2OsDIv8NQZ2PGFUzE6YR0Y89+z1Nqk4C25B9549xeNIt3w/Ox3zfqwNSsX7efCjI+1ADCH6KQc3fGAerJau+tGdvnaGRcbfL0sOOYThvUPJ+HhQpNY5kDNy4KDuCV9RrfOZNl3EsU2cbdr0tOqNN97gzTc/3B8yX15eoqr4+B45fPbode9VeO3XZXMCPwI9CdPXmzZt2vQ49FDW0IsL2nvvfaiv+SdRubripTff4M98//XNwfQItK2hmzZtepr1MNbRZ45HymOO1f3E1f1tHX1E2tbRp0tPHwCG/ME9pwkMs1UWmU4uh4wcnc48yV7IG1AsZTpgDT2DNwLKJZMiU1RpRakS1FKpqlQ9UkTAAj/DmaJIKWl6LCtBOtOQCiIYgY0VG46H5yaUQoYMz19aJtSOhJ8BilBV0brMaGaDOJueDWkVUQcKIoYwMn5aHakl+0nPwLfUjD52TRheCywNXSpFHNEFIdC5HYJMhyZIawlrQzDAhk/nb8ep+PDJ0Z0Ix82IdQXScWc2sIjphs7e31AS/orcRG8TgWqgvmbEMIa2fQK/0igi1JLg5uz2hY5Iye3Xlh2l4pg51k8wPBc+n85fghDNTmIC85hw3gkuM7Y6PqCcThmlvdtRW6G2PVIbIQXzlTWgm2N6SBBlEz6xQ44nFCh0VJ2qUGulzHMuYRmdSwcCZQE/cROdrJJxu2bzOnaQHdx+Pq+POKGnhvo1dy4u+crXfjzhVy3Y6Hk+XLOHua8MN8IrHkGUHcOcYSM/XguhhZCKLreQuiRsLXoD09UM1TJjwQEpSKno0hIAW8aIywTsIoqGUrRSd3vqxSVlf0D3e7S17L8VJWpNmO2RPdOqyNLQXV6XqHDvrbf4/V/9R7zzu/+GMQanDqGF3leuh3MMYUyXvQCijgvZId1XxHwOcuymgX462qctqsjI3msPlnrBrhZuX+64ffvAUgo1Pymd52WZvbIyXcxBRM9nz+g4JePLPSGsWqe4oPUiITma8d4iiAbCCkXwAiaSUcQiRN1zPJ5w7xgnzBN2nmOUg5rXcGt5HFFcl0wCUMlo6qa4QCxLduOKQQ2QI64B9RJiJWTgw+kePP/Sy/zC3/gb3NntUbP5+Ozz+Tqfm54u/wjDYw7ZtD34KfuprRNyINzBPoBYwQPXw2yeFsIstxkDXbJrednl15y7o9uSz+yyECPvLY9KRxljnS7k6+zFXg0fHal7mM9JFYHV0inf76FRiNEzbr3swY+ZFlEWdDmAb/9Q2rTpaVTvnbfeeushvXqh7n+WXOgeh4R7rwm//4+F917hkXQRP83apq83bdr0tOmhraER7F75xof/un8CCfnD659+7VU+8/7drc/wIWtbQzdt2vQ06uF+L/p4ta2jj1bbOvr06CkEwGc6uZ8/1ZpRs27zzxN2OtNmOyOeI7tggYRVs6NWaOAdlek+JeNiUyOdgATL7ja1FbSV6Uw1xumaMRJWFL1NjYROQnZTumfE8IjCuq5YKGP0GVxMumpjTUAtLTdVdpRyYLd7ASm7hJ1iRJEEgxHJi21kzHOZnZdFMr66LETohL4ZdRwxkDLzAG/Aq1K8ZO8roDgFhaITZg0GYO4MqdjacWNC38h4ZwGPTsR09oYliBIStGVONT4Mm8csIt2ZY5zwGQtRzKZDtKDmqOdraBFEJeG0JnRUWWaXbyOh6MhLIAKTwiiSx0o1AaMKqjVxu435npWIwBxClzTgnk4JWy9uUWthqYqqEGJ4wGrO6MYYlg5iT6CFGBIdXRb0zm1qu6S22zQpaNshpZytv3P4wGH2H48wRPc31/AZvtNntPB6DRbpVlxqxoZfLGgXvvi5z/LsM7eRsqfURhGn7XaUuqfEitiaDtN5/ERqxpWHEHFJRCVCYfZXM9KhGZGR2loUbekyLziK5rWtnEuM02VukRHmkY7fZb/nud0lz1/c4fLyFrrfIUu6PTN5XIhWckBhwv8wnx9vyO4SvbhA9geu3v+Ab//6P+X1f/1b1N0Fl23Prd3CRa1cXN7icoFdrewvLhI614vcVllo7UDVoDanFEMl3aNunbrcYlFoy4IykENFNVhNWY89I4tVKVIpCBrpOk+SbLnPviTMLy13Y3TQoEhQwymSwxe+dqRdUFp2c6sPVApqK3ibndpAXBPW8VoxCaIdCNcZhz7ms0tmokHFRXMQZhzzGIbDEHDBRJEqSCvosoO6AxPEBqUVdH+BlAUXo+12SBgvvPASv/TLv8wLt26hdo3H3ezx9rO72KBUbFwRfj+jxb1CKEHP+9Azchy9xbmnvZRy84wSLWipFGp29J4HHaJD282O8ZFJBtOx7f3EsJUxVvppxcnhmrAV847rgkdk93cfc0hk5D1TbyG7PVJ34B0pgsiSUDuA0AnoN23a9LTp7t27HI/HD/11BaFe/FXK7mcfe6fN8R3h+/+jcPebj3Uzngpt09ebNm16mvSw1tAnSc+ejnz9zTd4+f27j3tTPvba1tBNmzY9bdrW0U0fprZ19OnQUwiA003Gue8XAfGZATw7fmVwA0tuXL+SUGCMP/A6evMDugF2LyEGJYGy6023rFApkT3DRWestDsxjHE6EaXNyOiEq0ohMEKVsCMm0MMZ1vF+moDUgJqwD8cjZvetoGFInNIUvOwJWcB8OoIDNDKC1vqcqJlu5/WUkcVh6Yz1kf2ulASM07VZxNCa+5I9pLOLV8oEtIp1Y5yO+LCE1bP710PSeeqWvbUxEv4GeGhCYpnHyHweg8BdGMdrYsSEP2tCoXZ2IZMgu2QnsQbIyEjl6UOcQDw7U4kBWILlGS+NZfustoK0CSkd3BJCh1SGdfoYuOQkkluH0lE5UKRQIpDp5u5j0PsJH5FOWnfgCH7MuGy5ROsOFaFpScd4SYiHr9DfByNhYTjMzlGNQMjtSRewp3vczr3PwAqxvo3b27l9YYge2JXKlz77+Yy8LYW+diwGgwaquBnDI49tzK/zE9Gvc8jAV9xPMMFi8U4RwAPrBhPUqXV09IxCZkJ6twfDFrVAPT+CMj69PXvghS98mpe/+CO8+MlPsNtn/HO44euYHcBClIarEgWituyPlkI0RZZ9OoJLOrdf+xe/znu/+68RVnRXkVip3rGx0t25Oq2c3NI5a84YR9Zw3CJjtMcJoRBjJTyvrXSDDyAoYyAxWE1Y+zVoOuGz9ZqZIGATAAs+rvHCdJ82wDIKOjx7ZdshgXYFdjuGXWGjz/ujpDPePfu1bQU6QUs3uQdhFSmHdNCO07yXNN3IMYCOzmtfNKZTuVBrwnrZtZkaYITM58LFBaqeX6/5fGvtgopDv0LqgU988lP84i/+Es/uB00uM7p6Zoi6DaSvlHIb85IO4XkuRRY81ryWdMbOz8EMs+PcRzA74XYNNo9nKfMlFMYc5ink8zDWm570GCNdvJoR/1F3eKlEqYQY1vt0GRuCo6WhMp+RY6CqM/re0bpkPHb0/NhTuIJu2rQJ3nnnnQ/9NUUkB7KWrz12+HujEN74TeHdb2zD1w9b2/T1pk2bnhY9rDW01seVnPEflwBffvstXr773raIPmRta+imTZueJj2MdVTds9btCdK2jj46bevox19P34+vlXRw+XX6aMUf1KxJm5HPs9d3AoZzvC6qyLIAJOhTwdK6iMce5ICEUKQm+pHzG6arI/8/MiJaS4JeEYJBP17johllrAWNjKZ2KfhQxprQcdiK+Wn26JZ8C8k43oFDXNMUtBaklESfTkIT63OfZLoPl3QBamNanqG27BSlTKfmks7WqNO1ma48FaUURefXDQGLnm7d1bAAGyeMwrBTbkL0jEqeR8RFb9Csh99E0VqcIX0kJDbBzDN2e9lnlG5bUN0l+CmVotnrW0XTuSnZryr1kP2u58hskRlRHEg7QFkwnO7Oaus0ewf0Mc9hxuCqNjycETOS2wfugvsAEZQDaobYevP6uc8FSKeg0Cl4RlLXBZWCzMhkEUfcCYMRGRucQOtyUusxBxEiX9ljGtQN6BA/8A1vme7mW7dhdwv0gqIN7jsa13z6pU/x7HMvIO0W0Y1YT1gXvHsCr2y2RcsOcKScXaS7hOx+HykVZvd0Av3gdO8e999+e85WKCoVJZC6YzJIKHrTKR21Ql0I0Yz4drIb9tlnufzCZ7nzmZfYHQ55Ldi8n5aFWJZ0mpOxzlErppL9xSL4OQq4KtRGD+Wbv/ZPeO3f/luGgRTBGTiCRaWv2TmdbzGosVK1oghoI8gBkKKNsr8gOKFUquxAGj2MYz+y9pXejVg7ZkZYx9xwCuYBo+dAgezTtTw7xTXI8x1x059cwigRaK3Z7axCq5do2REe2PnebZfEsOxs9k6RYOcru4CCUXSZbuwdRR2tipRA6LOXesbHi2B1hxPUUojh+OmauDohPt22UTOG2c+9zTMiWmYHcSm8+OKL/Jmv/TwHr2jvVL0NBOqe/eIiqO4ZZgy32XutlHJr9jQbWLqSpewmzA0oh9xmP2Uywbl7dzqHIzTv16hI2+UghJ/S+Y7lPVEbFpZx55HA3y2fsek4lvlauY95Fw/CBtRdDhbg2Y0sitQln4mbNm16qnR1dcXp9OEX5B4OB55/flY2PEEKE974DeHut7bvux+27t+/zxtvvJEDiZs2bdr0MdRDXUOfe/5Df90fVjWCr77zNi/eufO4N+Vjr20N3bRp09Ogh7WO3lpXXrp370N/3R9WNYKvvvUmn/ng/e2b0YesbR39eOvpA8A3cLGko9diQt9JgUVmr246wrLD0oAAu4eaUUrBwxOUxTgHMgNBVM0uyizjJSI7MVHPLtSyyyhRhVIbVYQmGSPdbdDdiOgUzd5aj0HHsnv2+or1+oSZMP17jCC7g7EJSypFa/aVRmQMq2WMMy1hhdhI97MCNeOfGTIjl0fCjbT1ImbQryGOFDekyXQEz9hsEbzucQ+GOTbWBLneGWMwbOCWnbmZuJ0xxu5rQj/rSAiqjSh1OnQDpkNXJPBY0VozihpyO8+gtRVqojxUM6ZWZjywcIarJQFkaQlwOEfiBuHCCGFE4CNg2Iy3Jc+5Zrywe09jopN9xU7G/BYofECphVYrtTVKSZDmItP5ecru1rKkI7zsEvIXpYqj0+HcMTojj1VM42+UeR1qXkcRjAiitOmqddCL/ByffdYTdCd4rpTVERPq7VscUD75/PPcfvElsJ7HQ/I6dbvC7YrsGJ7wq+wSYEYQpeLuSBQkImOh3ROkDcMihwNES3b85iTCPI4TZHo6rNEKOqEdns75daXfv+L+1X2GrLRdobaaDm2fMc8yBwdK9lHnuUnXcXZDV0JK8sHdBbLbw6KMGHzr1/97XvvNf851KNendNHmvp7dsAkPl4tbXFwG+72k+1NaOmFbwccRYyBLsLuAtoMYa/Y5j5XQ7KjNx8h+ng+bAyUNxJEmiDq17SkEZblAFQqB647wK2otVAncVpBAq7IsoPScC6izC1zytcMNV9C64Lqw9iNRd3mM+3VCYlvxaJl8LheEHrAxo77DqZCJBsMYfWW0A7QgRrrtI9KNnwMbJWPcQ2C5k9f5ukI3vvD5L/GFH3mR/S7P+fAZTV0C8xWpC1oGYRMiR0e1zcGcDJRHCzHG7OhegIHqJaU8l89vJhiOkZe9deL6nQTUopliYOk0DxWCwGzFzRnurOs1fT3N5IE13dwis/e5JITXhsrcHlszqcD73C5B/DjP8aZNm54mXV9fP33xSJEQOO4dHveWfOz1xhtvcPfuFnW2adOmj6eexjVU3Pna91/nkxsEfuja1tBNmzZ93PUw19EnJIPqP5ACX33rTX5k6wR+6NrW0Y+vnj4A7BP2SptgtqQLLuSGnaUjWFGfUcOQDjG9SIgFKAG1IlLRMoFxXINbOlj13DPb8r8RSKsJ7SgoSpkQUFSptcEw3KAbuCilHqjCzWuGGyaNVZSYbjXrKy6OmeFhKPUGcseMzg0byQQB0ULUBNAJ8RbERzpQa+FsXE5ImhCbVqEk1KtohkELUHb4BJLrGPThjPWESYIXiXQ7qwAqCfECPAx8ulnLwrCe/FnTCZoQOhJolgUtFVHBtRLWURHUNbliTG+1zNhjVSRLfSfsLQ/gPgCRvas1HZTDO+vIbt7sIAZkICS8ys0ZmKVL2GeFa2ihQMZqy45WK9oaVZcEc56RtXDiBgBKmcnhLSOxUYYbUZa5aee+aUnnqDmOzdECJdyxCIosqBuKEij4EfoJjndnv/SY13RArMi4ix5XmhqHGnzpK18lbBD9lLHRcY2Whnjg5HnyesC85/sa4J6Oajxf229s8/gY+NpR3VGVBPA1z5mU6X7PwmTOvb1EdkHHsNwGEpaP45G7r73Jd195ldffvsvRRnZGRxBF8riZZeQz+ecQJUpeoxGRr+eebvdaEy62xgC+9c//Ga//q3/J6BmprFIodcdSFgpB+KBHZR2Kd6daR8KwGfsdBB6NTmOMno7+UpC6hwCPipcCNYHmTZc2NfuiVyMMJOZxZjrle8csMO+sXlnlwDDPuPOohBt9vaJbR0TRSOhNz4hytZL3gqeTfh2dmM82NyF85P56oH3NZ9IYyb6l4WOArbPTW4gRqBSQSpQDzGcJ5kgoRRoY2eNr9xCf57dA3d/iZ//S3+KLL79AG9foeB8VsDjlo8lOmSiAJHQP0rUtFakVKTUjmKUnHJ4x7cKAsiJt9l6XOocrFsI7un8e8ZHP7LKDtuTD3CRjoANs5GCKhxLaMiLbLIdN6h5xQ2tFdhd5hcUcWAiI9SoTEeoCUdCAUp7UfyJv2rTpYcjdefvttx/a6z/J07Zhwv1vLzxz57nHvSkfa7k73/72t3nvvfee6Oth06ZNm/6kethr6Ajn/92+8dBe/08rAfavvcpXXn+NF5999nFvzsda2xq6adOmj7Me5jp6xiFPqkoEX377rc0J/JC1raMfXz19ABhJCDx/L3i6vHTGmIZByUefi6K1pY80ZguuzA5WUWSc0VyCYNELoNyEPp9jjDObloRgkh01EpZxuARaSjpClzMIhD4GJiPjgrVQcDzSCWnHld6PjLEmSFs94ZQq0VfCOu6DoGVUsHXE09ksPp2wJZ2AnB2b9QypFamzfxeg7aEsCUTrA3ex6ILHwIDRV3ofGJFRtFHmfsmE5UpFkNoSnHvGCieIz23A1ulgZoKddEzedCi7U7Qgdcn3X3aUItnrGT7BZ2R89XSnTtr04FxPWFxQ1I1AsXDcHcaR6B1sJCxTg1BC0h08qAR1BjsrRWpCcwytbTqbE1JldHS6GkX3lHIx47Mt47YjmKQ3nZXWE6qaQJyy+9eNwPAAC0lQDKgWRJwQA1koPuOfpcFyK9/jdIR+jYwjtexZLj/JcnlgL40f/dSX+ORLL+dQgGb8tcsFQQUFwXBZYMbgxjjifZ33zZpm3Rh4nHA7Zhe1CLRGvf08ly99Kmu05TxwUGfssyZME0138A2Uj3kk0vUd4Rw/uM87336dd77x+1x9/2368ZRAXPJ8hFkOPkSec4bB6AnMbeB9xXtA7znc0Ba0NmS/YKXynX/+P/DON/4d6B6koDWo+4XQE26D0U8cr06cYseqjc6MKjfLmGpz1q5cnzrHrhz7mL22nvtmYwL2jCqOKDnUYB0vkbMmIpkOQCF8ZHdvUbQ0Smv4UgidDmQPwgtjaL6WtjznIZkMHnkPF1mINYdBYMyo5I4sS77nssMlndwSJyQMbRPKS1A12BXm9S9YH8CClsh7sjbo6WbXMFQ930MKYQqtQb2gNuVw5xa/8Nf/S770+S+z+AGNTpELlAFuVN3TaiCxztjnlUAyDtsdoWWcc9khxfPzWJAowDUsC8gAvZ37qiWHYmrJAY4qCYMn/Jf9IaOqbRDjfn4uQuBIXSi13aRAYx0ZnQeDG4YuO7QcyAeo5/1cdjf9xJs2bXo6dO/ePe49pGisq6urh/qD8Q9Dr/72B/z+PwnuXG4Q+GFqjME3v/nNbfp606ZNHys97DX03Xff4+d/8r94KK//YWj32qt8/dXvbU7gh6xtDd20adPHVQ9zHf1gt+PVJ3x9KhF87c03eHlzAj9Ubevox1NPHQBWbfmbMPB0FyaBivyYKNMGCrOb1sVJGDAjUt0JZhQvENYTaAU3XwMJfaIkQACbkaSQ1lWd7lG9ee8S5/heR33gxytCg9oO2RerC9E7PjrW+2SeMd2/MZOCZXb61nT49XGTqCyiM/I044RFbYI4yz5SzT7jWE/5Z7/JPAYCmU7NxLolY3tDGGMkUDHHPbfFPbs9iZFXWQQlvypdz/MIE0HRmm5DIjt9ZXbYnj/L87iJjRtsI5b7pWgyXp1dn9N5LBLzddIRLDpjoWcUMdMZ7GcQadll7Gju7mrYsOk2Vdym+1OEcnbCYmhpVNkjJKwHMlIboUZQYtb0nuOZwyf41gm7L/Lvzr3LE8RWoEmhyYOBhZDcXotAqIhn3PbNoTrNsnZ3tFRarVnzWgqqJz77mc/yS3/r71KaZHezrxNgVdzPTtwF0cv8vSwzDtfyvMt8XIQCeY1JKLVdUPc7VFaWWillurB1/hKd8dmdsJ73nCiKzshdMl7YDe+DcX3F/fv3+eDeB5yujlhfyTtIJjgFn67siNmt23t+falEa0Qr2bO7zgEIyesfUboH3/4X/z/e+J1/hY1r+jG4vjoxrgfheb2JCEUccfL6nK56CUfdqXaizsdCkUrDkOg0stM3SXFGFOuMEi4Cqgek+43TPtFyzXvB0/0e3onTcd56M8aYgqNoXVDNLzZRooE2RSQIhD6O9HGNe5/wcjp3CYrBbqcclkP2j5eSDuU5iOGlEdqwcU20JSG2r/jaoQ8URQ/PoPP40HsOr9Qd1DIjyQOpBRWj1h2/8J/9VT77qc9QLN2y7u+DBCI633s6fcUyCh1P93gRhGN2mceC6AE08mvkMgdqeg5UROTzNLzPW8XyXqUQFpk04AbjOiF1DMxWwldUl9wvbXkTtd0PDGhIDpzMZ47WgoxMhRAsB1f8PGCyadOmj7Migvfff59vfvObD3USdozOcnhyusWXYhzqAKDribcuvsd/fff/wSu/8RafuLNF4D9MnaevH9YPeTZt2rTpUenRraGD1g75c4wnUEJC4C//3v91W0MfsrY1dNOmTR8nPZJ1VIS1VJ50rKqwOYEfgbZ19OOnpw4AR5z7VHegQpmORCCjVKVNKKVUKZQQVCqqgobjMWaCraa7VQuoJ1AjMu44Zo60B4wJ76jzY5Jgsuymc3VCEC3gA5VKRTOK1deEuBqUw4GyNEyUEYp1w9eOGySOFpwTLAultdmZOruLW0XaMsHvBG4mhOuMZc5I3+g9oVNtCas1Ic/ZWSuqqAgynbURgrljbgn2zG+csB7ZT2yimAQj+nR6Sm5reB5XT2gsOkG4pis6N0Ty99ISNJUlAa8IGop4wrGM2uVBX0EkNJUYpEUyXbvpSE0YLFoyVjhmF7CUuXbk+fVSMRGGXWE+ZsdyQqrMv3YwpWo6gRMW7aE2IhSt+xu4XxGKCE0brTRKqWis6c78gf2VtqPUW9R6QIXsWo4JxkIxz2irc/epuROyzD8f02krA9kvVBQ5DTgeKWZchPILv/DzHPZ7mhSKNsRXSjtw2C0cVNm3hVoaVZ1KoAy01HQ41za57y7dkLJQlgNlOSCtIKUiKLc/9TL7ugck+2Fv/L15bfjwCZeFkMhr7Dw4YYbbwGww1uvsobUE8WN0bPQJ5UdeHubEyB9MRynZDUzgrRLLAvsdUuagRXheP+lBp7vznX/6q4yr+xkxHoLoHtU9UhZKAdGgtkJRR+azoopQ65IAsO6AgS67BIGl4WWP1JqO1KURohkAIIprEHSiJhyOQUYRS8F7xharNop3dhHs256G0/aXeS44obbmlSzzXnQSgs45AYvGYE+UXbrbo+exnykHEdC1ZZy61BzciE7QWA1Ow+kUaIrUJZ3JskxH+nyvUvHaiKJQ96CB6jHdtZFdytiAcWR/cZuvfuXrXIwjlUopL+J+AvocMGggt/KZIdm/XWrNnnLZE/1+DrdIZB+59Xw+u89BizEHKXJYgdFnefYEtOt1OsT7ANfc53Ib6o5SdwnUI0E/vacTve4y5lkUcGR3cV48YFmQeiBszWsvtgjoTZueFn37299mzDXnYen7b3yfn/hrn3yo7/En0c984l3+y698h5966U1+7Yv/N/6rn/rf82uf+3/yfy//G772b/4PLLGlIDxMjTF4++23t/itTZs2feT1KNbQ1157jesXX6R/6lMP9X1+WO1f+Yf82Pv/hNba496Uj7W2NXTTpk0fJz2KdfRbzz5H1ycfE9XpBF5sM2Q8TG3r6MdLT/6d/SErxBOyYBStCQ+ncw1VlOz81BtUGSie5liZccncRzTwCXslKiIlfXwBqjUBxXQchkHcPEQDxjHdxmGEWAJcn9HCWtGiFFVKOaDtFtKPaQwuSlMBO2I2JggLzHq6QiOBRTojs9eSWkEPoJZOZCE7Mb1PWJtwLnlrIWIQ4kQUQkpGqtLTKTrhr5Q6gY2wek+HoAgRIztkx7nLdgJzD5CChyeMigTWiEz4HljMB3dV8JE9p0UpKlSV7LxVEC1oBNrq7JlNxyF4RkZryVjrUkB308EKNX28CYklY3hVF4oUGJb7KkJ0x8ZgWCcUTHbpKpSC05EYiBkaRisd0UaRhiCIrdPlbVQtSBFKq+hSqbWgCqUFFafKjiZtDhhk9HJlUHFKWEJEMrZb9dzhGuBjur6vMOsZwcw1RXboslCioNKw6/fhkPD2ohR+9s//ZQ63nsFtQD0QGKGV2vYgHZfOaDtKFUp0QoMIIfyU15OnQ1c9HbdCh7HiCKNnhy2e1+TtZ2/TSsZlh0wXsCckzwjn7PFN933kQEE4IbPTd3TC1ry+Y+DmRB+EGdFt/n7MzmbNX3liYfZTSxV0KZRbB3TX5n0L1EKoM7Sw1srp+orSaoJzQPyU160oHh2PFbeVYMFiTBd5dlWbOMLA+wlnnR2zJ1yzOzd7w+scGJgudmkwAq0LMuOTiY42pYhT5URtt/O5QWBSGDGw0XEEU6GPga9HPGD1wNYrYhjQCR1QjLK7zFvdAz/3m5vT1zWPsfd8BgjQDkjdEesRc2M53GIRpS0gTWERWPbEuMrnQNmDn4h2ILQDR6IccgxlxlsPfA6HBJ/7sS/yF//iX+FOgUOp1LoD1nlMTmQ8Qct+4hLIOBERqC6YBzHep+iCcz+vE+t5v8tMKFBmFHnkfd9aAmgfsOxvXLpRFPocvPDI+PZwSivJes1gHYSt+bXBdHHPARXRTExQm/dm5FDEpk2bPtZyd773ve/Re38k79fX0yN5nz+WBJ7ZdX7+M2/zd59tIJlAca3w/nf/ew4fvPK4t/Bjr7feeovvfve7s95h06ZNmz5aetRr6Lqu+OWtJ9rBJMD+X/yf2dsHj3tTPvba1tBNmzZ91PUo19EQ4fojMpwkwOER/dviada2jn589NT9+FolHW8yo1o1FKdkw6tWIpt+0XNs6DR7pqt3wiq5RYRn12kEwkhYrErRmNW+2TEbOHF2mInepBoTRtSSLsiYMUVSINZ0r4qnU1SV0vbUCU5VnKUuLHWBUhgMLPyBkxdNuMx06k4XGz0mnNQET24Zu4tDKBE9nbkjJtDqEzqOfC0/A2RP2AnZSWx+E2eN+3QH+9yOANcJUyK3LwTV5LznfuB8vQSeItmvXOexVDcET4dpGDUEdZ99umW6e2X+T2e0bvzAecnXznjj7Gl1sxnNC6W03FTNGHAl5MoAAQAASURBVGILwaPi/ZqxXuduTUBZdEGkUBSaluwEJWO0hXQ6ByDaErRKmX3PgbhRNagBWgtll67mQtCksEzHb9oXM4obLTk44DLvVE235rgPsYerIwyjcDEd0Y7duwIP4rBAVBZ3/txP/Qw//pM/PU9TZz0d6SOg7dNta+kyVwELZ0Tgo6M1Ya2UShVhQTPGOwwtu+lKHRkILhmlrqVx8fLn2N/eT9iavazSlnmPCJI2+Zm8Hjf9zWfgFhZzwmg6vMNu/j4vIZlQVfLzrOe1nHnNCXtLQfYXlIsD7Jb8u2EZW14K7kEP+N6/+peIdZruKFpAQKzjrowejDUwG+AncKNoSRevCNgpAa8CkcMMER0fJ4gViSMyHehhp3THAlp2CT5VwTM+XkoFM8yzl/q0rlyvK4bi/ThjnGs+s9wQSadw0RmR3vIZEh4UzwEFwsH6PHbLvJcN/Drf1wdaNAdcEEpZwDqlVMpw/HhNHK+Rq9OMKZ8duHac/cJGnK5xq4RrumYB+hGXXZ6fYYgHX/naT/NzP/lTXOIsskdiN8+7Q6yUksAaWyEshzwEajtQd88jYZT2HLSLfNaYT8jr+XwZp0wrKBn7HkGmJwjpZmY+ls55+LZmr3hrGald89qSVhEtCZlrS/DrpIM4IruzR59gvxB192EtTZs2bXpC9dprr/H973//kUy+RgTX3OXZTz1Z0ZCK8Ff983zZn4eAb9fK/+dyzxe++1897k17KvTmm2/yve99Lwe4tgnsTZs2fYT0qNfQ119/nePXf3ymij250qs3+dx7/+Rxb8ZToW0N3bRp00dZj3QdFeFbzz7/0N/nw5AAX3j3nce9GU+FtnX046En+1/GD0ElMixZZl+rRaC6w8/9niozYjXTPc/Xtkjlpr+3ZJ+oSs3YXZV0xNKJMNxWHE3HoKdDLUFlzN/XCTCyzxQfEzb7fIucrNBaaeKUCUNKX4GCHC6Qyz2lRgIhSkKwex/Auma0sc0+03MEr1vGn0Y6dSk1XXwm2StsQXQDWwkX/DQwN3yNjNqVB510EZbbGzF7OCWBUmsJkst0idh0ysXsu/We6azodNUGFGHudbqfLWOPgYTaOr9W6jx+ke5YccTHgwjtGeU8KU86Sye9jwj8BkqnD9h87pf7jCo+f3V6vk12uEwgeJ4htkigqxN6h+RuhuGquLeE6K54pGs3t7EDM0YboVCRMzhX0mWt08U8u6Rj5PG3fmS1azR0/l2BKNgwKB3GPYwTzjUmHe4s2OmEstAwXrx9m89/7kuINHQYSpvR0/uEYuGEC8Nbxoo7mHvGCbuAHlCdDsl6AWXJKPCywO42aENU0sEuni7kQsb4uiTf8/MgwrlHOmaMb+A28DFy18+u3tFh7Qkvz2W5JSOjoyjRNN3freTHQ9KpOTu2I9KFnkB/VllP97SsA7oRLoStnK46/d5dCoOyVNpyoNY2rx0yIaDsEVkQ3WNuyaVH3jPioCZAm+dWZ5d35McCSkC2OhdgQFEigih13vfZf+1uGI3hDlVRyYSAEMmv9TETB5woFfqglLwGsyTZ2LUdbbejjk7RSt3dRsTQFkgTdH8nZ1nqgrTdhPdQMcq+UTRjlilQD7dopeY5M897/BylXm/NY6RoOaQzfj0hpxOMTrWRcelEuvJ3C1/60hf5n/69f8CPfeYz7PxIHUekXIBd53UYAlHTYdz2SHT0PCiQRdrAQOqSz1NZso+37PP4SIXTCcaaD2/NYR76SvQ1o6k1u6ylLVBbvuYYMAypC3Hu/V0OcK4/R/L52U85d0DJUPN+H3sQPL9p06aPoU6nE+++++4jfc/9HWV358n75/nn41n+j/3v8OV4ni/1zt//4B6t1i3C8hHpzTff5Ld+67e4e/fu496UTZs2bfpj6XGsob33R+Y2/mEkBIfX/tm2hj4ibWvopk2bPop6HOvoqRZOpTzS9/zTajFj95BjsTeltnX0o68n7ydMD1lCUJhGXE2EYdPl6rPHN1Fidv8WzuWyCTuYvZsSni/oiS+Nkk47KYQsuCoS052XpbvpTptgIgScaaGXkhHDnjHT1CXdrOLzXYNSCqU0amuwjnSklZo/fJsxyfX2M1BbOoJVCC0JjyLSvRsj4U7I7ANt2T8K01FLfq5F2vvNE2xqnRCtkPBDQVt+bsn9ySnbM9RzwsAQhoANw25gesHc8ziF52vNYygi+YtzL+4EX7OnFJZ0fookgJyxzXl6cntEGufo5DjHSpN9wk7HoifMIftiLTJSw/oJ90H4ikWf5ybyOgkompHUTZRCmU5DS5f0OYo2egLCGHATPCUULZRa5jDAhE4Rc9/I6+rmqsxeYgtjuGNRgIbb1Y07EgdOR2jPZsQ3HZMCHLMLda/suvPCYvzK3/7bPPPJl9BakKpUDVpRSklXukwnregCvoJbRqKXA5jObuaBmeNhFHVKreArWhIYlurUHZTlAtFKaXvUMxI7iIRkIgkCpys0Aa1BnEPA523m2bUcoxMjo5hDSwJTT6gbPo+7TeewO7GOCYEzhtrWE+P6RL++xlyIuie0TUd/dnSr7ji9/z53X38TgCqDqgatIX7E7QTe5xV/Aul5SUoQnv7+JnlMbsYEZJ/DElrmsEfDvU/3f01naQziPJSRWeQJdlUo4iwKy8VlOv1nIkGIPXD/ln1eY8sCKhkgoIG2hrZClXSuluUw+38lh0K6geWgC6F53foRbQsqThxPBMs0XVvC/f0FstQcvJBKaCN0N59hii57YM2K4WXGsteCSOAj8hm2HBA3RISL27f4yz//C3z58z/BoS00N1QOmK+4SHLs84BJmVH64zSfSzM2GoN6yI7fUjgPuUjJbQoj74PwdPUuhwS+CFhGwWf8f8PGoIumy19Lxo6PkdehrXm+fEBboNQ/EDfuFOx0/GOuPJs2bfqoaV1XXnnlFU6nRxvJfP/+fU7r9SN9zz+uXo47/AP7CXYh7CO4ff+bPPv+bz/uzXpqNMbg29/+Nu+9994Ww7Vp06YnWo9zDb13OrF+/guP9H3/NLrYN24flse9GU+NtjV006ZNHyU9rnX0/f2Bu/snK43qD9Od9cSz10/m980fR23r6EdbTx0AhowIJQRspLOW7P9UUdw9zXaRsCm7QGfvo5xRVWQEKDZtworqguoe8UJBqTJdjtPdh0g676anLWakdBYJlxsoRRjiA6kzztmNst+j4bTWKCqoDKQPWA1KoS0Lh7aw7Ba0JDI2ChGCm6cj2ZguTUvn25jALEgoLDUhZllAgrK0m+hdP17jo/8ApUtHpyLUttxAUiSI+b6DkRA6BlEK4Wu6eW0AA4vAMneZUhuq2ZMakg5JzhXKEvnikZ2dQkLiPKSKnKGe6kwFHhOwTUcgC0RJd68L4TEh4sAILM6RsJEsMQSzDn6EcQ2sGYFMpxbNGONW5nsplEIt7SaqeG5KxkSXmjHeZY9KQzwdyeIDrS23dUJzmE5mBJ8N1Dd54XGckcdkh6kAh0O+iQXI7axU7RcwhN0Kn3juRf763/1f8uwLn0iX5ISNXgoSAxXNWGQymnrRwuFwYF8btbTsMJbsldXZYZuh2o5FEOJ4fx93x7szxozO9o4NZ3f5DBoBZ0YrmudrwjPzhP8P4pxtDgDodAeDexBjJOxFsgP4eMJWw05rdgGP6RoOywjg6UwPs4TAI13NwdnRP9JdW0Cfe4b6iWexQ2EtO45r5+p4mlPjZcJ6wcmkgGEd88B8ELIwerCidBuYNMw7w4+5r5auVZdgRMJz944zE5lVcNXsWrYBFER3uCqjNFBB67y+XYmomAuulZC89Fx0QvAKJMT00ZGS93n4ubt7gdkhPNYrhiVAd+sMF07d6eNEH9ktPtaOheLjOp3ypRDa6C74eh+3E9av53lRPDQHImoj2iXUPV52sBTOXeAREMsl9M5ut+Pnf+kX+Zt/4+/w3K6weKdaPkt9XGHDiSIJY+uS8e9Foe4gKlHPnb0dWfbpzFWFsWYHsFi67s2y3zsgWgUGOp+hJQYxTrgvuCx0qYwx8FqxsLw+Bai7BMJmhBZ87XjvxEwSyOf2pk2bPm46nU584xvf4Orq6rG8/4/9wp385+MTqL9vP8GX4nkI4bX3XkLvvfq4N+mp0hiDV155hd/7vd9jXdfHvTmbNm3a9B/oca+hH1xdMZ555rG8959E9ff/Ke2Vf/S4N+Op0raGbtq06aOgx72Ovrs/3FiaNm36QW3r6EdXTx8AlvkY0x9wJUo6L2U6w4pXCsx+3j4jlmMCOp3dszNeuVbEfTqCHRHPiGnXjEWF6cBtCSZVSdjVSMgkIH3+NhAJRM6O0EgX3Dk+uBSqKq0uaKmoCtI7YJRlz0Eru3KgaCOodDP8dMSH38Quu2dELkXyvUjXMi7IcLTITCaeDb0ye5Ol4mNN5j2jsFUy2FYle17TiijZe2ydkOnuFSPWgZvhBGECoxMRN4dVzh3LUjIy+gxY0xo8najywHkdsz/0phh2fmw6SokJBjl3Hc/tEk3Xc0Qen54x3aYQUrC5r+gt0AVcqOoz+lbyHEihWjoya11wNwSlaqNqy8TiUud1MNJZO6OeRWtCdiZ813QSI2P2yWZUsJtPgNrn/hlcj6TLcg2nK/B7cLjMv9MT1MFOgzu7Hb/41/82z7/0mXQ/Rr+JYHZTpFS0zgGBMbCy0L1zvL7iZINVdvRY8/rXiodNx/bAxjoz/0vCOAQvFwR7goy89jAuP/15lrog3vNaGEZETLQd04jrOVwhIO4J8IZNZzLTGR54qUStCSIlwWqYJwD2B39PAMeO9I6GzDhosNFx6zkA4Y5YzL7ovDb6/Wu8r+lcl+UGIjepIBkv7BGEZ0/1uRPccUJ3qCw0iRz6wFA7zU7aE1hHZTf7xgOpLfffLPugRXPYQ8B8YNYZY2W9GtiAIN3kWhakFkJqxosD7kb0SHdqN0AZo2PrYPSV3nsOtJyz7CP7uKXuIRQbQkTF+wnKjlouUa3QFJZy03Eeo2eEciFdsOvstybjorXIjEpfckhDglKEUgpaWg5xrMd5b3eCPTpOfOZTn+EXf/Fvcnt/Qe0dtXEzFDDMMymAMe91fgC2ChHpCk7XdUHKDsou75c+e4G1Zhx07zksYwPqDm27WTfteHTGesJGZ4wTow9GCCaKSUa0y7LMrukZ162V6DNqept627TpYyd355VXXnls33ADvPf+u5Tlyfy2u6J8tzX+d/r3+Yfv/W953X/2cW/SU6eI4N69e7zyyisfiajTTZs2PT16EtbQt99+m/svfQq7c+exbcMfquvfAHsLyJ9/tPL0/TjucWtbQzdt2vQk60lYR1+7fYdTqX/0Jz4BWmycIcWmR6RtHf1o6in8F2dGfWYMcoNQRMqEE2e36UBUUM0f9ovobHqUB7D0HNt7jnBl2gwl3b6ZFi1QG5kQHNlmKYaMPqOL57bYQKIk+NWa7xEJ/rRNIDVhqChUCWrN6FltBfFARSmlsDSlTsgcDEZkxKmFMLphMfsszTISN0DrDimSjlsUbQdUI12qqkjbZYysFpwBMkFeaUhJp2jGvhpSsmM2pCX0s4ydRSd06SvmgavcwHgLS1cpQohOKJ3HWlTy3CDpeJ0OUpk9w4IjOs+PTJg9o7oz5nqkI1AUPBIAh9Hd6R6Yd9xPxFB699zeGEg4UvaUcx/ssqMUMoK7kOevzghqFUotaGvZWyrzvKJQF2jp6Mzu13Qh2+yPTbLFufo3wd50mruvcP0+Z2zqh306LOU27ArIbiZNr1DvsJMLbjH40he+zHMvPj8BJ7AUpC0JQOOEUYmxUrxTVKmeQNplnw5TQELSba7t5lyAoZLOWItIB6oo6A4koZvF/NiycOdHfgQZY8aCd87VuCqg7mAZ4xzmSAh6Pt/n06flwbBFzFheEcQDsRmzPV3DOY2Qnxu9I2Mga4d1JVum07EZuqRrmIp3o586r//2b2M2sl8YA9llX/Z0pZ+d3VJKAkZJl6mWXQJuLUSsiFSEwnDF6pKdyUy7bt3NSOPsoz7fHzeR59ER3SGmcDwBK3XZ0RTabodKRoxjpxnhDFUKRQfKSLAbCeW7KuakOzhJ8XxOke5XVSiRDuPz40ZI53CtebVpxtKHCCHQ7USop7u3FbQtue11ydNgHR/3sqe51gcJB3WXvbq7Cxg2HcxGyB6L4MWXP8sv/srf5kIKZT2yhjD8iPX7uOzwbpgPvCzZJe0j0wuoeUyHQGn5fiWQ1mBZ5rO2JRTG89nQWjqfveBS8BDWcaS7Z9z6WLHTPWycGGNgY+Q9My/akCwQCASLE1IbtW4dwB8lxYfwjcGH8RofZX3cj2FEcPfuXa4fc5SUNnj+q0/ucboW4Vefuctd9jz30ldn5/umR6379+/zjW9840/1jffH/V7e9OFru2Z+eH3cj+GTsoaaGa+99x5Rn8B+XXs/v6eb+vRnPrOtoY9J2xq66VFru25+eH3cj+GTso4OVb713HOPdRv+uPriu++keWrTI9e2jn609BT+a/PcKZtgBz8lLJodkLgTcQ2WLj3O8bLnAIQ4kT2UE6r4mA7NGWUrCXzCH7h9ExyMBMI2O4El0mHpPcFQdIiS4DHyNbMTdbqOw3IbPZ2jRRa07tG2Q2tDzRI+qlCq0HzAiOzvdWOMU4IUd3pfGX1NOMqY8NehTsebJuzS1uYuecYBl4yvhrNjWtAge3EFMAc6YfNY2ch4Ws+OVAm/gaYRQY90MeYbpkNVpSTYndHZcQ5DlrMpuGUob3i6os9U8Qx8whPYxtlarA9cnQwiBqfeWU8rPjoeSjfw6IRYwnwtiDjFVyTW7ACWQIOM5x4xmVBFbCSQFHngOjyDw9LmvsV0McrsMx2UMjtipys1vNNtTdDmJ1AldEfsLgmOCQjReT0kpLyBzOxZFA4SfOVHvsRf+pW/SauC0Getc8XHNaF7xjBGCGPGUYc0PK7z+isQraFiN270GoOmhSKVglPajhKgYYg7pV2AHWe39JjHYIFxn1IbdcY4m9l04masuAgULYhIunzP4LBWKAUpioQh65q/RqRzd9jNvRrnWOywGUEOtJIQsOXxFXNkzN5YNaLMmO1xxE6dfu8e/eoe3k+4BYyBx4rZoI+ekda+5pCInaOhl3xPhfCeMdMRUPZ4yATjc5hBBIk+BwsCqIzTimuFOjLieU52iARNBrtlRxVHsTnckAA8B1UqIgPpx3S7t0wIoECwIqwwDJZb+XXkkMv5HnM/ERGI+zyH6ZT2EIYd0+nrleIJTQ3P51I94McrGCdK3edCW3YQTowjyEKYEGvHu+OnKzhew1gJW9Mt6w5Uwge0XV4zpyte/OSn+KW//Xe43QS5vs+4e59+/4SPnk7g+ZzwcyS6Teet93TXj6uMmB/T5S0CUWa0PdlTLnIzbNBjpa/XjONVNgpLED4YYXhp+dLr7Pa1HGIRt3ztcQXWKeVAkYLU3Ye1MG16BBL54YH9h/EaH2V9nI9hRPDuu+/yrW9967F/MyECP/KTt7nzySe3G/D3n/139PJoO6k2/Ye6f/8+r7zyyp84guvjfC9vejjarpkfXh/nY/gkraEAvXfu/eRPbhGWm/6T2tbQTY9S23Xzw+vjfAyfqHVUhFOpjCf0WG16crStox8dPX0AOLL3NHt3bToHa0K6LMpFZT8dvjMCeULBLCYloSI+43iZwMFvHK0YxPnQuoGDzuhYCSG0giucOyRvHJaS2yWS7t9a5uvme4RnXLG0XTrwtKDuGQ8roA6MNSFwEZpk9HE3o3swIug+MHO6C6OP7Nn0kaBMa8YcB4jNvGixhL4RGTl7E50r0yUNRWu6EdsCpSQYUp3uOWZ8dvbySmkImo5QC8aIdPYFEIFEIDMCWsIQZgS2zHPnDrT5eQkChXQ1n0E5Mnt+w+fLjvy9ZEdttxUbK+N0ovcTPYRxtp2ic3+UUhqlHEAV9elEdgG7zvjsenYr64TN5HWiS+5z0Wmv9BkfOz9Pl/zzTI8NbYyI5IMGYVfz7y3dtDSkPpOgPjcxj/Pk3osaF3S+8oUf5S/+zb9DEcNG9iw7Be9XCdHw2f06CO9EvY1JyWZlUeqyR0Vu2oe1XWRvbQhFQGUh2X5BdUGi4+tA1SlaKASqBQ1DtXD75c+yv7gk+prX3OnEuD7hNmOfa8kY87nvPtY8TzqBZSToVxtzwCEmeM0obgfCBmKW2xc2wbniw6Zz2nN/RyfWDqd1xgbPX63NcYYJSkdC7JCS/+ia3cWt7Fmqst8fOCxC08pSF+oE5nmaKiJG0Z7PCrEHTnaOMDIeue32qA0sBCMBM+yQcFR3GWfcg3VIdtPaOvt2Zx9vCLRL0IweD1uRdpmPMBcYg6o5z7FrytICbft8oklLt7YyQb/m0AWFsHTF0irigzgD1FZYdjuWZYeqoQzUT6hWxA284lKmy1lzEKVdpuNWjBg93fj17KYtlFgpdYeUSvETn/7RH+VLX/pp6umUQyul0UdPWOsw+inNzKURUqcbvM3BhQXI+z2jsGfse5nPVzRhcKl5Tj2Ikf3poUKM04yLL3hoQve6IJrDPFJruqnHmokI5HNPYlA2ALxp08dC52nr73znOxmd/wTo/vEDXv6pi8e9GX+ovvj2T/NpeZNP1H/3uDflqde9e/d45513Hv8PizZt2vRU6klcQ9977z0+WHb0z7z8REPgiCfjeD3N2tbQTZs2PW49ievom7du8cFu90SvocC0jG16nNrW0Y+Gnj4AjE1HXJ3xoMwI5wnsmJHOtUyj67mHtoEsJGSc7lIsDcVFHhxJJeNVYQKrkZx3AqcIh2iETgjNBK0qGWFaK1RF6MTwGT/t84tHukoBITKiV5TCoGrGsWptSF+RopSLS/YqNNEENVIYFqz+AR6efaOePbAR86FppwTZGrkvkjHGQSHMGWHTlxvz5g5KqRQp1FLRNeOzb46Rzn5QTQep4hBrwjkfODBO18RYE/z6jNudDuNk6gnjZbqEb0piZ69phIO2dDTr7GmV6fL2/BU4g+DUB27KQDG3jP4dfTq5cwBAwjICN4SGUM5d0TcZxi2h0/gBQJ4V0MCar6MT/NLz47pLIFjS4ew+0Frn5wRCgeHI8QRrRnPH8YiYz/0cD+KiZAE/QSkswCGEr3zuy/yVX/pbVM3jnl2sFY+V0LxuhZGR4hG41NnJm87MUIjoaD8ifoQY+BBMG0ZluDGkZEyuH2FpRNkTmoDYmH3OdZndx4bqwkt/9i9wcesO7oMhmdhrY73xkSfMzXsqPPKYjjOwl5su6vNCIujs0zV0uonPx16oYEGY4euRmK5TFU2nq3kCThVoy3SVF37kZ36WfStUlYSW59jptMIjdcHEsXZARIn1SBBYnIjIaOlMDl/zeZG0PbfD0kXsNLxA0OfzpqO6AxsUGqUUJAKrlTGODMAwRu9zH3aoCBorzC7qHHaYsHzeZwCuBRvXeKkM9owxCBXMDa8N94zADjK6O+xI2HVes+sVYSvdhZM21mOnr1fZ/10PuX+e94eMkU7iZaG4pUM6joidKCIQ6f4X1ZvrQiuo9glcR94PKiDOz/zCn+eLX/8ZdFj2O48V++BIv3+f0/V9el9Z15Xh4NpmzDN5H3lGtrvO62GX91DGbstNwgD9RNjIaHZVGB3CGNZzMOd0P4cMZCYW+MgZnLLkgIzu0LogumSPu40fbjnatGnTE6F3332XV155BXvCer3H4T2W20/eN1J/vn+er7z5szzL+9z7/j9/Yn5Q8TTrtdde4/3333/cm7Fp06anUE/qGvqtV1/l7Z/5GfziyRym8otP8N332dbQJ0DbGrpp06bHqSd1Hf03n3iJ++3JTaQC+L0XXsA3J+hj17aOPvl6+gCwQhKCGafsBr3nX3h6AYEZGTxdnWHTSdbzYxEZU3uO/cUfdPeeD2lm7z6I6o18kLs7Hk5YEKHYua82IiNde0eiJrhgOl4DxH0CaKaj1JMniqaLshbqfoKkVtLBqZW6HDi0xr4WisZ0K15mJ3E4HoPwwDUInGi73JSyZGwtZKSsOfSOmeOeIDTcEEqCHVWKKEWFGmSP6P6AaAJtrYqWks5qyf0LM8w6LnUCL88oV53QnRmffLbKiqT7L2JC3+wnTZI4v3GKhL3zC2aUtOIWrMPpq7H2E6OveEQuFGWCWI8H//WzU1zmqYzcLhsJfxVgBYdSFkTOUdQlu1UFRAqqNR2VJR2toXnNuOiDjlFPByvjA2x8gEm6HfXikG5vnf1F032ewwAt4e/xxFc+93l+7q/8PKILg8pQJWi4O7Ze5yUU5DmMknB2DhS4ebqxXfG+ZkSzQcieIAcDhIHECkSCx3Ig+hEPkNFz4MBGQnJPeJ/7JRye/yQvfOHHKAgGjFbSmWz+wFEuipR5HiTPG6pEbTcdtCEzDlwFVUHDb4YsxAz6CtbR0ZHTKc/h6QTHFfoA87ymdzXjoQloDa3K7rCnaLq5VSul7NHolAjUB+qk6/P8DKgJkUsAPtDo81K1vP+0oaSzVmVJYK3LjHs2iBwoyAERz8dGrIBjtmJ2IkrJeONIAO4EZiPdwDL/rudASXglxnUOZeg+ob2R//UjHp4R133Ne3rCUrRAXxHZU0LAO1EE751+fQ1maAglGrF27HSVIxzaiCg4RrQdkAMNvqYbGCoekc7a7kRUOA3iOIAGXnBp2Ij8s1a0HtgfnuOn/sxP0zSjzv204vsDsdvjy8LqTnfLXzojq3WH93XGUQdCS2A7Igc7RKHVvGdt5ON7On7pA7rncAYFcyPaHhknNNY836qInXJQoe0mFO755zr7sTdt2vSR1Tlq6zvf+c4TObGqO+fH//NnuPXCk/WN9//q/l/lc+/9BP0TP8HxpZ993Juzifz+4vvf//4GEjZt2vTI9KSvoafTibfeu8vxa1/P762eBE1DQQDrZ/4Cx0/89OPeok1sa+imTZsej570dfR6WXjt9u0n1gXsCL2U7edyT4C2dfTJ19MHgOUw4eIAeoKDM1SD6fSdUFaF7PYt+fmRmBItYJJQTXczOvrcR8qkbTMeGs+v05pYUmcsqUVCnHO08U2fsBM9O2BvnLRSc5NLyfePSBhKdteCIFqpERlNu7tN2d/Kjk86UoVld2ARoaIUZmcvJ1BhkN2YPowYTljPSGXzdGSeCaJWwit9DJyzM1pRah4uDeqyUFvJKGHNY6LakLAb0C1FZpetwNpxHDfJiGJZpuu6z2Puk7lHQpuYjjs5U838lY69+SEmTBclwjCMXgp9rKwWdPcEzjYBrARIoZSFpRS07jKqWmfUNwWLGfldlnkO81Rk7O3ZpZvXT3ikY3o++GR49qC6EmPQ0QmCDQtJYCUCu+fwi+eJ5Q6hGUOclHNN1/E5qhxnUeFg8KWvfpmf/6W/zn53kf3EuiDrMY+plnSQS/YBQ5mQM9DiM8FciRj4uaM48loObRgrRXO3g3ydRiAMYhjiK6ol47DP9w454KBFQI3B4MWvfpkXPvs51ME86K0y3LGe7uu8FNJhHYmPM3LXZ5QvMePWhXC/iWZGJSO4+wm6IR6on2PYBWw6Y2fUuyvZG7y7gLZADJ75xKd56Ue/gC67ec0apQS1wm63p9VGrYUqgWK4KqMeiHIxHagZCVxaDgEUDUop1FrS3KrzGvUxEfiMkpdl3sMlO4QRTHMoIhBCEpJqaWnC15r913qYNeaB7JZ0yWsOG5QQanSaOFUFjU4rsFxcoOL5Wmh++TgldC6FQkEZNIHSdiiFpRZ2HuxqJg2UXYJaW+9DeA5PoPMW7FALerlLN65OwF5mfPuyhwqyO+AY3k8ZZ1934EegpZnZBi+/8Dyf/9rX4faCtkopFRWF+8d8Htk1Fsp6vKKP+zjz/tVK0Oezq+Pz/eP6HtFXwgbRe557bTkoogEt450zfS2gNmQ5UMuBUltC3jrhso/cp7L8QAfx1n+5adNHWe+99x7f/OY3n7hp6x/Utb/HF37h8Lg34z8i4eILL/Du2KLwnxR98MEH3L1793FvxqZNm54SfRTW0O9///t875lnufpzfz6HuR+3Lv4CXP4CSOGNn/5fb8/sJ0jbGrpp06ZHrY/COvrtZ5/j1dt3Hvdm/Ef17uHAmxeXj3szNk1t6+iTrfq4N+CRy8YEFDMSFDL6+ewGLgswOPfwwgSXzuycZP59zb+L2d07QexNdOwEl2hMOOw/0Pc7EhxZp9T5ubWlG60tuT1Bfk0tCRrqdH8IN68TPrInWBykZvSu6jQte/pXB4gqtEKToLhzgtxf3SXsS3SCjKD6ii4VGca5e1iCBHCz99K04N5nPDWz97RgIztgcUNrywZWWdP9yBmye7o2KUjRjGw9dWAlZMFrSUcls+x2gucEyDVjYyWdgxGzF1bqA1AIswMg44E9gm6Wkc89wZDd2IY1XY4y47RnfHJ2txYKdcJNodSScbHn7t1z5/A5GrrMa8ott2tXiTFwkzyfY4LNs4vYz45WRXxglqHXSMJ0cSDuT1B4kcBpBie3gL0EX/rCZ/grf+WXaAIejsgOLWQfK3VGTzeQgXg6xeFBZHUtMvtnF8IC1xVRIeoFPgcALHd2Dj0MxsiodJ+Q3cJBDsi5ozo8e4tln9HCBKMLn/rxn8QpvP3qt9JF6iNrkS27rh0hJEG+R6TL/GYCThL6nocBmLHi4kRRxJecMpIFamRMs8sE+ILUHYIT63S7x0gQPIJlaQw7zXjxQZGWbuEQOhlFTVnSJV1Kuosjr79uge5qOl/7oIQxEIhTDjR43peo4H0+R0qje4fRqXWPuBBFiRBUHNHsrcZGdmV7R0Vpu4U4rbndktHcjBNhgSxtxngXYlxD21EkIa0UvenRvnFXR8XFcRdUazqO6zL7pgdeBhEFWQdSat7zBl2Ewp5yOiKtPjgnZzhvTHAfSKt5T/QZR+4rlIpax0uDcYXUSwhHtac7WQwtStNKuXcNh0u4fy8N4Yc94oYfT8ih5h1eL+iWwwEqhngFMiKbtcPukMekn9BQaJpDNwpEA9IlLKWkq3t0QhxZDrMVIMcRKEpEB1fk3AywJjznPHCwadOmj5Se9Gnrf19H3uWZLwp3X4GbgcXHqKLOCxcnXv76S3z/3S3qadOmTZueJn3U1tDf//3fh09/mi9eXlI/+ODxbowo3P4b+MUlqz/+9XzTpk2bNj16faTWURH+3YufIER4+f27T8B3oqkAjrVu7t9Nm/6YegLGIB+xJq9NsAtJlGzC2iU//iAz9wGEkpIQTBRi4kzRNJ9JcDNQGpJvEkxIXPNjNt3ESEIT0pWYr72kI1g1o53HgKKYJ8yLtC7mtlpC1IgxO4sVMUkH6fnlwlGNjIfe76m1JlAURfY7dnWZn6oPoLQFEQULsDHSDFpqQu8xYIyMMdbsVHWCkFm+OoFueiEVlaDVQgFq2c2a0pw1CABp6HTtaWv4jHa28zE4O6iRmys0bvp/NbtikVkz7A+mectCIHgII4SBMii4KKOvmPUZ8WwJYUWgtht3603sNEFVnyw/Xa0S83My4zh7n3fLJOw9Xc064fHs/5Vzz7QrIYFJ4GF5rsVxLGNmfXb/hKVDF5C2R+Rwsz1pE1ZaCI1rvvS5z/PLv/z3uLj1CYa2CVBHRiGXfV5TkR3FeBDSGLZiHrgJ5s7pdJWHodbsZh6GWUlw6Ccgu149IrfKYxqfa0ZDy4w5Lsvsp/WM/ladDvY8Du5Gqbd4+Sf+HC98+nO4FFyVEMU98GH4WImIvK5IeAuaMdFmM7U94ae4IyOPm0glSsEVzDrWnTh2xCZIXXbQSsZ+iRJUfKTjPbTy2a/9OEMq5jZ7mguhlRBlDDLqeF2JMfLRcDTcCuIj97Vfo9oSMHrFQjGTPM5SMtK8pxtVNGFqNaeeUwV0gCoeI6OxPbBxjfkgSiW8EwFjPSaE7j5d606UjNMOhLg+4i4YQljFhhNDiT5rbt0Iz+izOLt0ZyqBi+CMjHQme8UpCywN0RyrGKcjUKi7jLQPn8+OdoAiuZ2jJ/CtcyDDO5TD7Dh/MCCRQHqXEL/tkZq9XO4G9YIvfeHzlCWHIagF9gekNiIcubiFjKtMS7g+0U8nTn2lH0/p/i0NWr5e9CMx+0q8ZvR8WCfGyPO/jjkgUZCYzxqp8/nheEnIHR4ge6QuGTcenm7sujzokd+0adNHRhHB3bt3+fa3v/1ET1v/oLTBSz8TPPP5x70lqWd3nX/wtW/z3pvffdybsunf07vvvvvk/yBp06ZNH1l9FNdQd+e7r77Kd776dexJiLL0a44/+T/he++tj3tLNv172tbQTZs2PWx9FNdRU+V3XvwEr92+/bg35UYBvPL8C497Mzb9e9rW0SdXTx8APju2ggcw8GyEFshD4tx0wRLTcRoTXjhCRuUqCQnTBZyOxHzd2QssShZ1+oQLE+KeY4pnZ2puzzodjmM6NFdKqQlVyoS7Nt3E2qDssxtWuIlafRA7LahnZG2RoJRs6kUjHX07YdcqTc75vgqsEO8nBPI8TtE7EStoQUpQRaml4u4M88nJLUGxBrG07DYtBzSUIp4RwhFodHysCXliTIgiM554vo5bOl2FCdrJbfORTmXvc/cytljm/oet2XPqg7WvnNw5jSMjsi90rMY4XeMuDOvz9dI9LKq0s0NZK1UbTRpYIOEProV+yp5nBPoEyBQYeU5EZ6x13SGlZuSsCDCgH7HTaaaBT+ewCToC/IouQkSfqeNXCZ7PbnI8Y3IlI74rxieee4lf/pVfoVVHCMruNoeLW+x3lVr2GRccazqxQ1FtFBWkZPQt5YCKJFCrhf8/e/8WbGt23fdhvzHGnN9aa+9z6TsuBAgQIEWIV1EULzJly1KcOBVJ5SRVslJJFFf0lKe8xI8sPdEPcqnkSqXslOlyXBVfJEq0YymhZEiiSEkUSRskQZDEhQCBBtDd6Hv3OX3O2Xut75tzjDyMufZpXoDGpbvPPn2+f9Xus3vvtdY3v3nda/3H//8vGhRVZHsFKxtEGpSCYEQ/oO2Q/ROpshUkx4uOS8FjpvuCC7SYk/xt5xfkZWYdGWLGw+/9dqoErkKI4N7p855+aMQ848sg5yIIGYru1mBZUsF7aMBR2Zqq46CPscl1Fqn3xUWzkCEG4dsa0eckTFtnu9sO0l7B90g44Y3CgoijsiByQLUhRTEJymaibCqGU8qGolA0QBZiOw1huaU61AwpBSm5pyS9bfSlQam0cNCK0kbRg6UNO1tAhjO80gVaW/JeA6I32PfcZpY51foGKgt1s0GLoNNVRBU3SRIaRbSlU4FACadEINKztqVs0HBEas4THC0VsQmYsLpl026hLOhU8/7KsFKeZ1gUfEHMM5N8PkeWBS2WhL3nvhSSVtLUzSgYELyfDeX9BFZ4/3vei06bJHq7A0pnQbZbpBhsr6C2xyaHONBbZ+nO0hZiXog2D9cGyYIIbCzjyIIWKZkDfLSP7vs8B9yTCPYsgsnt1nLv6QeizakI98itti/otP3WzqMVK1a87djv9zz55JP3XT6NKHzHT5zwHT/80D1tx2tz4Reeehc3P/gd9OlyZROvgNdee219071ixYq3DPfrGRoRvNgaN3/kx+65WijiOc5lf9/14YOA9QxdsWLFW4379hwV4dkr15gvQ5wCcD7EZCsuF9Zz9PLicqzctxU+CE8ZCs2apGpEKtXoQ+1bUKvjDYIg1dAy1GKaVtARx9/rEBPH6xTGkuSCy137Zh2E3lENdxE93LNN47VFp1T6SgwBaM/niSdXrTasl1+n/A2HfrSnTRNklcIx4bNoYDohHsjSkc0Wm4zKeH2pYFeT3IhU/YqA9oZapUybzBsNxYbC2UVHfmsSnhKSBLlokpii42eGkxbEVgreI1W/beGYrxwoYZrKx4u9Ii6UikiSZRGRylKEiJZkXggeMHtjcaHNM+4LS1/oS6Mv2S/BaK8WzCpFhTpykpHMjxUb/stlKL4hP3XVYfndPdtlmQua8kjPNpfMd42jffG8QOuECKGK94Z7kp7hjlile6qW00I6kpw9jknvZHDtRBFHvSMB22nLhF3kyiqOS8ls56KoLqimnS/j34gZ0woRqHTo+5EnW+kOoZm1GpYHe7ijm5O0ZLbNyDt1zIxJD1itY/qm0lNFmAyKGioVK4VN2XJagt3GONk2Jl249u5HeN/3fn9aZXv2lXfHm9PnOa2AeyNaQ3oqkFNRmjm6+HLxGHqqgyPkbtFAS9V+rmSlLUs6bves/vYe0JPIe+8HP8R0chVfFlwqTRRvM0tb8NChMC+AUup2rO8Jl04XpfnC4srcG94WYh65ujLWgWdWsgOtOb2loXbZbNKyORQPSYWxFPCcz1p3mDSEPSGWRDqAnaZN+txx08ywrtss1NBtWp23Bm3OqdoX+sHzD0tNy2NoqfqNtCpXqaMdTvdIIlwMi0D6GUqgJtTNFpHtcKAPesuihFiWLAQphm1OUCsjUntKhaw4YYKbEZFFBxENk7Srx9NuGjVcC97OURw9HIhDy6KQ5YDsb4DfIfoelY6WK1i9jpRcO95us4iwMHKjtQxngFF0450oNecWgVhFbYI2E61n7nrZ4n1Pj46L5T4yn4MFMmVms5QN1Jpfqoisf9isWHG/ofd+373hPuL8cIerHz7kn05vIyJg78HNRfnok+/lOR7lK6entPu0H1esWLFixTeH+/kMvXXrFk++8gr793/7vVEB99fgxt+ly2/ylYd+5L5Rfq1YsWLFijcP9/M5+urJCZ95/F30S0C8PnPtOou9zW+KV6y4j/HgEcBKqtjojKDV/NK0N8aXQcqmGhCZkhgeNrQpTF2SXJCG9FSyZj6wjKxWHaphBkE8GCjVkcs6slhd0hL4aPMrSdAgIy84hvWz2iCnjw0YuawMxXGPoWSuSeD2BQykVlQ9LapNKWoIjSGLRGyD7bZMRdFBKIISLGCKWEDdokUzs3fE5oqCWkkCjqC3eZA7ec8SffBxabsrNEw0LatFOWbs0jvigakRsaQS9GhJfcwsvbjXuCD5Mms2BgkfhDdaBK11eptTvdcqfb+nzz3tXCNS8ZesHhKZ/SvCxfWCJUl0nO4NDx8JuMe5IgSOW2YqEz3ni5bk6NuSdtk+D9vbtKr2SPIr51mw0NKyWYUokhmmZYPJAfXAfZ8EpwwiWRrqB4RO6cGf/pE/BULaW/eFWA7MhzPuzMFhvo3jeLRBmGdOdYTSPQl3EdKaF7nb3qPauadaU6NBb0SULHhQkLohONBEICoumq7MpNI9bck3Qzmd87yH5lzskVm/Hlx54l08/r7voM8z4W0QwAd6d7w1vM/QjiTwsOoegvCcCZLPUSE0knw9PyPODvhhpoXTJXOG3SMLDjyJ4lStC7UWrj78SKrTaVgI6oDV7HIqxTYUUUS3hPcszIiOtrQdF42hkE0VuKKoC9LmfN2jmt07Ekuu09ZzDfSFqoZGz3UKSeiXCiyoFoocjdVTxZ1bygGKI2XCdNQcmBPWUXE0yOeWngS91lyDlpbd6JVU3tqU+1E7EPs5CVAbObtOriXbQl9Q3yMcYLPBl1Rjm6dbgdAp046iqZCWNqc6v2wQ2yIRRJyBGVYcmxSjITFnTnC1Mf/2qSBWwYB3vfs7gANyWpBpQqcr4AX8gJ91mkw0nzEmhA4ueJvpEbSeduLN899UY29g3qcCvB1y7amnvXjdJWEMuBrND8zLnu49t595TlK71LxnX1KhbxWJ9iYcSitWrHg78fTTT9/rJnxL2Pc7fOjPbph2b/0b3pfONnzm5Wt86uXr/N+/uOH/+eQpL5xtaarcqav69zIiIrhz5869bsaKFSveobjfz9BXb93iC9/2PuYPffjtu2h0OP9NePX/Dee/weGxj7Df79++66/4urGeoStWrHircb+foy+cnvLkw4/c0zYsqtxZnaguJdZz9PLigSOARTdJCpSChKdLM2krmzm7mdsrHinKHSRxdAfmfBwl7X0jCLVh+QwqihJDQTol0XjMrtUkPO+SuZLEkKRqFJXBcY7H17RhHT6wSQSOTNULRXB4kr2MbGFLVRplSkL6IsPYUQFTwbQgdYMQ0FM9aqpUGyTUMZdYDemCan6JZvaqiXGRnYxftN+Hl7WopdpQQaQk3ztIMmMCBNXcqMWmzLy1isqGHoXukcNwtLSOu6Rt2g5n+0KSsg1JTWMD+jLj3qDtM/8To9Ho3pml0o4q0ddLZyRvWUKwusns0xAkjB49Vcph2QRVUMk8Yx+K1DosbRmZzEPkzUU7NfNlI4iepHUZqsTmCybKpFDVKHYNk4KVUwgbSlKo4UDFwvjwd3yYRx7/IDLt0HDAcj6Hg894N3pXJLJfxdO2OYXhSVhGNEKCiEPGsorhOhHeqNqZZEapGB2RSAIyueysg2iZbQs15180eu/MXVBNJXPvnnOwbOjdma2yDNUrOnHt297P5vQq3nI+Rw+iNfzQiX27awXtgbij2Mhp9pEVzFAFOzGU1kTP2gYkv4YSv/tC7w2f50FawsnpKU+87wOjnww9kqSlgp1g6hR1TGPUazhCx0pBxprIOO8FpYJOGc9d0vo5tECpaaPdHLUNUmquuwikpT27qqKSWbtYFqOoCla3I1N7rM8iqPa0aVZDwjG1nLxWUC2oWKpcpyljpm03aksO0FvmLB8zzyWJ4dBAp3LRLkQwy2ldtBIyETKl1Xmbc65PW3R7NRX+mpbKeMe94+NIiT7jbU/mWj+E0HGfQbbZb2H4vKQi1yrOlPbTEdTdFb77e76HsC3olbStP7TcT+xh2G6gLfjZeRZSBGAnENDbGUu/PSyzBxncPVW+fUkiuAsxN3wergJHO/fIf7sPYlicqBsQSZvr3kahBPlYD6Ldn5WbK1Y8qHj11Vc5Pz+/1834liACJ+/uTNff+v3nCzev8vNffg+/+NS7uXLzO6iH65SN8cf+zCO89tprb/n1V3zjcHdeeeWVe92MFStWvAPxTjhDAV6+cYNX3v2et++Cd/4l3Pi7sDxNTFe49cf/ynqGXlKsZ+iKFSveSrwjzlERnrtylZubzT1rwr4UXt6d3LPrr/jqWM/Ry4sHjwBu81D11vzB0XrHe6ohbVj96hBwRh/ZlOSnbqKItMwffX33+euI2PR8TWILhm1wG6reQRCLItEQqRzzP/EOsgENVNK6V8zGvyXJYfdhhRsji9JSuVwG4Zt3CQThmtYSLhCapJEZZgJS0gK7k7m1G6PUTZJIQ/UWFGgLEk4xRWVkJXuQetp8fQ8HhoWtg/pyIYAmfFhHO6qZSyx0zBsXucZ9QbRfCH7DYyjs9K6175CAhre0WPZUfnYPmhi9tcxP7Z2IoBMsfcF9ZHkGg8BJe2q1QeJ2R10pVTmKgUVAiiXpqIZUTeI5FLHdyMNtoIJOO0QG0R75mHAndCJa0OY9Q6pMjJjgQHFfAMdKYbIUQmopOc5RkkD3BfO4cAqX+TWuXq/srhSmClNRqs4UTXvrVOAOclZG7moE0fejuCBJxKJCpWdxQnRoB6CjvdMWp/dA6jbJeF8IP+C+jD7wVBUfh0cdi5bLw7a4Z+Ys0fB2Tkiht2De32Y5pNVJd8F2pzzx/T+cNsYOqT5eYD7g+z0+n+O9p0I+HIkl1+7+QJzv8UPHD3v6foaQVHAWJVQGcZ7q7vA2rJ8dxJKE7413vf9DlDDEBkmrAbGkIp1U2bs4S5sxP4A0wmfSvL2AG3hQzNJNYNiDe++ED4t3yU7K8QAtQ/2ugW63QNwdA1845lzj0KPQArCJjtEp9AiSh98gvkCxkRdtSE9iV3zGI+gy0+NA7/tRdBK5L/UlbZXbee4jS0eFzIr2Q9o2+7FwpBNWCRtFJVpG0UiMmOOjdfkh780qTENNa4UwIUShMAjmdFlQU3RKi2jxyPVsBYqm6tzh8Ycf5tpjT8DhDPwOnEyIgUmnWk1l9PZkJLKXnIwozIK70vs5oZXwYGlnLP2cJkJ3x62OfO784yTmJIl9PuCH22n7bVO+buuEpDo4BKg70AnmJIPjYs9dsWLF/YB5nu9by63XY55nHv3et3//+cq13+PDP/II195/722/Vnx1nJ2dMc/zvW7GihUr3mF4J52hX3zhBdrboWDqN2D/20A6ix2+96/wij3+1l93xTeN9QxdsWLFW4V3yjm6r5XPPP7EPbl2AC+dnN6Ta6/4+rCeo5cTDxwB7MOeODiQt/86++VBtoIjocO+GJCOoEmIZrrnhRKVo310mYhihAiimsSuJqGXxAlcMIxB/o/YIIPzlaRuIeYkn6IjoagkeZoZtj0JJHxY9/qFTXIcFaY+FMKDwJFknkAC6Q0Lpahg4VAy71YkFb5WdZCKDMvjjli9UAVn1ntmGPfW6e50YlglDwXwsVtkylvUgoinhS1gCEUUNcOKIQXEktztNBrC4p1jrOtF54Rnnw/lL1qIUpnDWVqjL9lvPaCH4h6EC4tHHrCxgJ+BBiXmVMYOi+qQntm8bVh1k6pT0ew7bx1VQ1of5HtPlbAo0doYFwUXoh9wScK/GXcVyyg9Blnqdyg2MelELYZYEpE61OYtZhDFfCjKmZC4TdldR9kxzzOH2elUlh7sz+5w58WvMN+6nWS/91TaRkPKNNS+PecSkrbIWGathoBM9OVA63PmUWgdxHlm3SKdkCStvbckOXuntz3hQddClJMhfl4IURpJYC7tQI8FIrBpm3ba3jFTTEBCcSGVxtXSLpqhkI8glp4FBu5pwzu2LO8je1ZqxiSL00lVcMRRcZ3KYm9JJIdB2W75rj/1r/HB7/tBvOS6iFgyg7c3lI7KwqYE1TbYtCWmLWITZbtDdCinScWxkq9RygbTwJAxjj2tylWwzW4o7oeClrSnBs32HtXuoqhMiJZskzohDfoeNx/ZsxNI2mKncjUIM8LApRDu9KXD0rDoqGdxRkgjasXpRG/0oZ6nZq52iKWSuCYxTalprb2cgZ+DeFpHl5IFD73lmMnCsawjVeNZOKMiqMhQXEtaitvI4L3I8i70OBCyIGS+NSqICU88+hjXrl1DNnm/KltwzfxeP1BMqAITjsZrwJJrcHsKeo0WlvsCmhbsIrg7rS20fmCJTo+ZjuNV08NgmYnINd39kKRvX+jzMghgvciPDuPuul+xYsV9jVIKL/lL3Og37nVTviGUSdleKW/rNf/U7sd5+DuDZ5999m297opvDOub7hUrVrxdKKVwcnJCrfVeN+Ubgm+3tMcee2svEgGHz8PyDADtiR/gyff8JZ599rm39rorviWsZ+iKFSveTtzut/ny/OX77r1ovPFD3nQ48OTDD6cF9SXIIV7xR2M9Ry8n3t5Pji4BRAqZ7xtJNoQQYReKzmO0rqtmlq0EiiTxEMFd3rePrNBIe1TVJE1jqAl9SSLHB1GrU1qxHu2HPV5HBicJLUdiuCUBg2oq+/RoOT0kpFJSkRxpkRyiF8pZLan0JALxNq47XlcUMcOiE8Wggw9NI96SzFChmNJ7ZgV7b3SpaCxJ6CBDsRi4Ka01RDPXM9XR/fgyeB/ZrT19lsXbIEQF3FFVXAsRjpinNas3fBG81CSvgiSXh1IydOQi9yWVjs2JttCXBSk1VafDKtuD7Dc1oIOcoGT+L1KyX1THa8bIuk01bXdPi27LvnRvqGqOc2/J/1nJ/FIllYixIFJQCZizyjdciGGRfLyelSvoNGGShBpieD/cLRLwjqilNTCQab9bzIUid/DW6RHsX73Jc5/7DPOtV7nz4ktcffhhrj72GJjirfPQhz/M7uFHCTnOoY74sOzujtMxBCmZrRqRpLhLgXYHWsdKxXuOaZLekmpQPBXw7ohsRrHEHsHyzW45IWRBoxF9QaarlGJ4P1qGK7qZOHnkUe68+gJhJQnqMrKlI5WZ0nOu+jSlLXnpcBTbq8GRIGbME0ZRxQXxLkTrWQBB8J7v+uO8/wd/CMRYeuZHi+3G+p+Gkj5p96UtLFGheSprqdBnekzAgkRh9o5TIBbUHSk7vJ3j3ggU7Quqhg6dcLQl21NtrH9P1Xb0tFp3AV/wEHoY0fdpOR6BjoxkZKj+Q9Oi3DrLfsanHRaaStmm1JIK+u6ZN57FAUFsriN+SNV4P6AxCl82W1yHIrae0KJTRAgmWA4EG1QK3s/BTqHtUa2EOHikYrlnznRIg7BcU23JTObQYcMsSezrnpANuGQdjJ9BuUrQEKtMOmFlA6SdvhVF0VxzEllcA5hMiPckk9mSkuOFmDvNFMxR7yQ9n5nW0me6zpTpIWLpqPbhrq9oZbweLAKGoy3QsReJN6QqIkbYmjuyYsX9hnQJ6bzQXuBX7vwKu+2Oj9/5OLu+4wPTBy4e92MnP8a767sv/t+OUQSXBNvryiMfKnzlt96eLPJH3nfCd/3Pdrx6+8W35XorvjXEKA69THN2xYoV9y8iIuOR/ogz9Oq1q5ydnXH7zu2Lx1/2M3RZFl65dp0npoIsy8XPJUB589oZd/4Z1BPOf/T/ylPXf4RnXlvdg+4HrGfoihUr3ir8wXP0+fY8nzt8jvfX999X70UPpfLKdscj+7fH0jqALz/0MF94+NGV/L0PsJ6jlw8PHAGMyuBDLQkf5lT3Nh3kapK8OnJ7w5fMv0WRgOiDtPMZpGQOqQjiqe6TQSQgTtAGQSKE+yAdU2GanG/hmB+cvOhQpapC3yOSRBuRGbqZA1wHiRwjgzKgJHGa/LCh2pJMHoo6onGRIZy0JEVHoKtNuHgKiWWoL2vFrGXGrhouQndFi6TlbHdMoTFUiFYGL13wGGpfTyb9qKROB1hDIkmvguAShKXiDhzTkhbBedfD/flun0UIrXecVC53J3NH25yK39byeeG0PgjbSOrNCarWzCLVmo6xPix6gTJIv2O2swlDOW24NJA67JKT/GYqqQBe0pI7baE1Ce9hH5xZz8HSO3CO2VVEA91tKeaInab1r9W0vo0ls0f9HOGEY86otQXbnFCiQ1947vO/y+d//deZPDDv1GrsquFnr3L7mTPCZ4LKnWe+xKIVX2asBO/9Ez9OqYagbB99HDWnU9g/+yzQqQ89Qt0ZEp0u2RcSaW2MbTJzWKe8fzIjOxSEBR06VvOFsjmlxZL5vpbrzDF6KCGGi6YKuE5sr17jzovP0QSmQ0/VteRYp8JVkTnzgJnS4jksrbtjbhyn+UVetIyw4uiAEm0PppjC+7/3T/Lej3wvfT5PYrG1tDQuluu3bIjmSIm8RTrS5yTxRYl2Gw3FRPE+igh8T7VdKpJ1ynVeJiQq0vLDhAhS0U2nmxHtDOwa7g2zDdEPI2O60mNGXAHPuehBWIz1NJTCAP0AbPF+IHqumUIjosKshO1obQafskhkmaGCiqEqhAsSaRMfElid8OapeGVi6VmEEGo5zyXwuY3c6WkUe9Shku2AoXLIPUgF9mcw7RDZDfX9KKNxzX6XLDoQ2QJOD8+MX1FYZuidP/PDP8Tf+bmfBU5S1T1qP4KSdLqlartsrtMPZxSdaPs9YUDdEDUIjDi7SWyVWk5Y2sgSV0PlhLbscT+neIHNCdLuUNpVKJXDfE4XoZQNGoEtt9HNKTYcAmij4GHFihX3BT756ie5desWnzj/BH//5t+nR+c8zuHO3cc8tTx18f2vnf0aZURfCMJfvPYXOdHMGvrg9EGu2BXO/ZzPHz4PwPun93Pdrr9t99Nao16taBG8vbU12I98244/9m/tuHHnpbf0OivePDz99NN85CMfudfNWLFixTsAL56/yK+/9Ot84slP8JA89IfP0Jf/8HPuhzP05pUT/uWfWTj42cXPP/Ci8YEX/vBHZFOD97xqWWj/NdAlePrRdDOD4LFv+9+z/NC/x1duHnjuuee5N5qpFd8o1jN0xYoVbxV+/fzXf/85OvDU8tR99V50MePWZvO2EMABPPnwI6vy9z7Ceo5ePjxwBHCKBRXohKQKjmhQNmnReszlhCQp9KjSHeSLDqUtmqSQp+rzIqiVYdsbI9s3WhIb3pMwo2Sv98wCldCR68nI9B1EbV+SOB6bm8RQzo580rQqFlwl1ZLeMbO0um15o1KPxHGH0HzDIuNWKKgNK2OUrpp5nAQhjpRNqhERvAepZxQsNCmPUtDe0154kG7hqXL1obZMKbClMHoomDML1BGbIBqqgYhlHjKBe6c3p3mnWB1ZyyNzWJTFjypIIXqn9QPunbAtDCvf7o3A8N5Qk8EJpkGxEJgaMFTUYknik6SttlQOmxoiTmhFeuYXowXanMrssiWWJZXTWpJMw5PgCscl8Igx3wA5HSrTbc7DSCI2rZo7oRPNz5MsnmdMC1IOiJ2gWrF5z0YWXvvKa9z54seoONNmw0Zr5jMjmDuiCrpJYlsCb2lqTus885sfQ+cDJeDKu7+NutkQ7uyffxaKMT30GFG39OWMLsLjP/ijbE7yXlPr7mBpUx6eKkkpRnSht07gI256qNhpmfEcSm9nhGySDPVAtUCfMUkKrbeOq2K9Z0GFSmb6YshQqEZ3PArePVWtg/2NY7WDHGnoXIdCUOrE7rHHef/3/yAPP/4E3SWt0E3ofUbqCSZOsR0R0GOPdoU6Ia5ZRGBJgNOGurUtIIGKEJ7W5RGeGcxHdXk/WgNnQUFfzkeeN5m57Y6i6Nh+QifSst1H0cOMIbhakt8l1cXi8yBcJ5QD3SY6qYY1HKVTOEf0Ci32SZiHo3UC9VQIS4AudCoqG0QWdLJhHS54NWiBtBmZFKEPu/qFiCWJVz/PPbMtafG82SELuHQU8LLlaJOd7gI2VP/D7lsDupBlJJ3O2OvaGcJMqFIJ8EpjSYd1D0yDGFbZ0BHN0g2xipSCbbfQFkLsQnUfV66ATJllzm3oG0K3KDFI+FOiHxCGsuFwTl/29HaglS22nbF6QtGCescI1AuqBemHb/IkWrFixduFiOBjL36M/+Dj/wGlFW75Lfaxf8Pn7WP/+z6n/ds3/vbF9x+aPsQVvcJ5nPO5w+cA+ED9wMWb7p9490/wof6hLC55C/HBH3qIlz/zKrdffmsslsSCzXV4z485N+68NP5eWnE/oPdVZbZixYpvHXOf+anf+Ck+/vLHedwef1vOUEH4C9f/Ao+XtzYrt7uz3zn7151tn3lf4zPv+8POGnWB972c7+XMhR//7IR5fk5za+d8/DvyHHaFLz+eBPC32Qf5Pv4SLz77Eq+99tp6ht5HWM/QFStWvNl4/vx5/tbn/hYfv/Hxd8w56iJ3+Yy3AF2E29OGLz78MC+dnF7wIysuP9Zz9PLhgSOAk53ITNIkYTeAJkELkPquZIr7Amb4yLGEJB5FdCjY+sgkzXxaicLwMiU1xC0JUFGwQR55pPI0hj1yUtApjQ9Gu5I4DZweQimStsM2FLyD+UrxcaSTrMogZoZadVSdEp7XS+ZlKAktSSEMNWWe51QFRhClIMsBrCLqiFT6nNmvi1bUBNXkTpsYUob9tMQFkSqRGbgqNdWHNhTQ402Pahn9lZm0UpIgVxrq0Dzoy0JXUok78jtnX+iL43PmN0d4xvGSKmdvezxyvEwrInXkkEr2UxyPpgDmVGB3IDTtbrG8f2/DVrsjyx616S7Bb5n3LD0V0mhJBe884zII0KONt0D3O1R22SYLrAraHdEJd88+3x9ofWyQ3mC6BhGIXKWIUPbnnE7KtbrDDnt2tbCdtlTpFCmZqRyZWSzD8lqmiuMgOceDDUZg0dkQ6I2Xhq3uhqu14kB/9SUWd6LPRHdeunWLa9/1nZx+4Lsghjq3H/L7egrtQDRPVb13whfMdnjPOY1t8bbHakE8KNpoGmPOOyHBI9/93fj5bW4//zy9ZFV1FiLIsJ0OQmWo4YdFe5mQZWS0CkAq3mXkRIsYYopo4/EPfTePf+QHUCuZXUzgtiHajDDRveHR8T4RanQxpC9Uq6gVcKGqIxY0KcyHQ9pGy7BtF8vcaQ9E0rJcPInvURswSGpBLsjtgnQfJHMQVshs28xJFhe8W/6MgsSBCMFMcYPoDbMpFeBVOCx7GoWjQ70EiC9J6AqZNzxskt0q5h2Rzdjzctz6YljZIv0cUUVLAy+jEMWy9mV7NfdEH/sKS+4npSIx9h2foThaNmPDjVTJbzag+WGKe0eioVREGr4M330FsU2WmtgJ+KsYQlMhlhmZdqge12/m8eqU68jqJufB2OdCCmqRD7WTVOMvHd8+Ssy3QCOjzcsms325QkTD9ArhtwlXmhdaGNy6Q50OqKUluxal1g3laFO/YsWKS41fe/HX+Ou/9tc5a2dv/OCvE1+Yv/CHfval5UsZUQB88kuf5DF7jH/zyr/JD5/8MFXemmzE559/jg/++MP8zs+9NQTwI38MHv1I0PR8FS2tWLFixQOI33z5N/nUq58C4MX+5kQAvNEZCvBse5Y/d+XPvaVn6HPPPkd5YgtX31i9tFR48t3jw8yAJ991lyROB6Q//JzzFw98/tUvrMTvihUrVjzgePr20/z1X/vr/N5rv/emvN5lOUe/8Mgj7NrCu2/ffuMHf4N48onGC7tHuW2Pr8TvihVvAh48Ajg81aBaUomKEuKpbCNANiSxM/5Q9zZck49EV6T9qJRB0hkeB4RhJx11ZAHny+Ejv5YKdMQmos0MF+MkV+hIUXzpSVgqgCaxYRUIwmwQcMf7ANdIss2EGASQab34Pf1IrBxfc3yfMsxh+5xknEfBh/1siCVJjhE0anTCKn122jaoQ0ks4vgxbxdFx5ubGLbWoqDuBBP4TEjJfj5mn4qi0ZJCt4q0dmG73WbHp4pGKk87mSnc5j3dI9snPRXVAeEdxwkBCzLb9jgIGtgg6nMsLNsfkW21DfTMjpVIBbhoqnQZFtf5b+T3kereLBDI/FdwlInel7SrpdCWhYgNEk6tlbKZMrdUksSL3vNe2oI7dGxkOwv4LVQm1Bu7zQnXq3G9GldOd2xVqECJio4SBAnPrFlLUh7RtDH2tL92UQxhKhs23qmlUlQptQ4FJrTDTNEkwXsofTnn1ic/wXzzNlc//J3U64+NfleW2zchDPo+7z2SFHUJvvjL/4ouQZ22dN/TDzM1gu1mQ+tpKb0RoRSDTaVst1x74lHmZYHekdYuyPHXZ0iHBEs4FoKb0VsbBHC5sG1XIXOuS0FtYrnxMi9/+hM8/pEfYqqGCsxEOnn3LEoQVVw6uBEYqNBbo/uCoyw6oSN7tx8JvyDnC+TciLRfFqsYnkrYcpznjtQd0JA+5r9mMUIPB3JdhAAuWEC1Sh85166KS6Uvc85PNZzGTLCEIDpx/HMuMJpsUylc0jo5VaoHos9QN2NvsySF+56ICe8ds6HwjZxTUStxWHK5NkfqjA5Lb0EI32ehSESSyT3w3lLdn5sf4T7ynXNPdNLiXlBiWYippuI4Drk2YuQ4987pw0/wkQ99J5/4rd/ATxRZCktRqinEnmMRiGmOGS3bIZZ7Lboh2jl4y7lkghUj6vVUN5exd4RDHZngLYh6OopLHPd0FVgODSTvzTzo56+hm+1Idl6xYsVlxaEf+Adf+gdvKvn79aBH5/n2PH/3xt/lmeUZ/uyVP8uj9uhFBs40TRfft2g8e/Zs/rmiyo/+sR+llrtv0m/ON3ltfu2PvI6H81//8n/N+7Y/AsB5vcWvfvv/d8QhJKa241/70v86Izm+BlycX/ng/4fZsiL9Tz79v+DO84/zxI++B847vha83FeY55mXX36Zxx577F43ZcWKFfcpPnfzc/zUb/wUB3/7HW9eaC98XWfo6yEivO9978OOrkvAIfbM8fvb/9zyFF9engTA7Q+rfd8Qkh8dvBFeu/oSSznw6Evvw3yNjbmfsJ6hK1aseLPw/Nnzbyr5+/Xiq52jQdDKPJwU/yCClx5/JiMNv048997O6ZzFyLtZ+LHPbt7gGXehAVfP5cL587VdMJfgVz5y4KVrTsQL7M7P13P0PsR6jl4+PHgE8NHemaGmlZ5cTAxL5xiWyapDpXpU/dr4V4g+yGLNxwt1kI0KfoA6JcEHqUiLTLWNcCR6qnXViD6jMpR7Xkb+aRmK3aEkdejLkgq3SHtmX9KG2LugKL40ZNocBbZHandYOHe0bocgOEknAULT8pV5ppSJFh1vhWBGS01iRCsiYLWg0Zmb0XtDyoRGUKQwR4oyLXLDRiwVt1ZSCI2m2s8srXBltC98tCQtZtUXPASTiYUkeVNomP3fe6P3TouR7yqDeB7fO44jVJ0GMTcsqI8ezDLGfKhKC0qTQQr3JS2jw7FjVvJxHGRkQ9s0SKcy8nrz3lKJnffr0QlvLCTZpRgiHdtssEnQouCCuxDm+NLpS6O3OYngyDmpAipXUHc2VK6VwnUVrm43nJSJSaBESwthZFhxFwTFBKRWRIKgIK1DC2ag4OzUmFRTWVxL5jrrhPUFUUvyVSo2MoAbwfylz/Pck7/HI3/6z3K4c4tlWbj5+d+liLDZ7djsTpgUigZ9cXjtDofWOdlMBMrL84HtlYruTpBzxw+NvqvYJu3SpS1sTnbUPcRiSD0Sgcf+zaELh6U5Psi5eYbQkmts9JspqFWs5twlAn/5JW598te48kM/gtaK9oUezj6E1gFxVAqujvZGuJE9FpnP6z2zqqMjJkS3MR8OgxDt+SmAN4S09kYjCzBKRXrmiBOF6OdQknzNXG6BWFBvyOYEDUXJ+xBvHBB8cdQctCGS49V7qn7Titryw/5OugzosImPTnhHtBI0VBTth6ESr6N3O142sNxGXBDdgR/wPue1NjUV/cO1IDyyLdjIyJZhHQ2UDYXToXzvud8J6HYH/ZDK70HkgxLFUi3NIMGp0G8jMmEaFFEe3myQ5QyX6+hyIBRctqha2ud3J0pmSqOSczh0KJcVqSewzGk5jUA7J8oGsW3u4aZIn3NMmhOlo2QWOaWhy4GhmSe3UKctQWu3MOduodCK+wIR8Ud+YPh2v8b9jPupDyOC/+zT/xn//Nl//pZf66u2geCX7vwSHzv7GH/1kb/KjX6DW/0WTzzxxMUH1LeWW/x3z/93uUeK8u+e/rtsbXvxGr/x0m/wW6/81le/SAH+xD/+/T97ffcG/MxDn/76Gz2e+48+8tMA/OOn3sW//53/PvXVuqqY7iNEBPP81ZXh99NaXnE5sM6Zbx33Ux96OB996qPcmG+85df6aviDZ6ia8ue//c/zxBNP0KXzM5//Gc5f/QF8efhoCwWv3uR7Pvwajz6cpO8L7Vle8ud+/wsX3pZPwVpduFVewbXzxPMfRN+gEGvF5cF6hq54K7DOm28d91MfRgTPnT/HT37sJ9928veiDX/gHH3fE0/Qy8LN6y/i+jVI3m+gew7A7dOLC/LFJ77+wuupwfd9uSLDKPW3P7DQjjyvAKzn6P2K9Ry9fHjwCGAdBGg/IBSInv8/LG6DkWMbaWUceiShhiWqkkQw7fdbK3NMqi1J8gJSp7R6lU64IJa2zvnYnupRMr9T2pLEY0vbacQAIaRjGgiexrltwXWo1YjBZ48c254202pK6w3oWNkmmcuwphZL29a+cMxJpRjWg16G9S6BaMls0cXRKohVJg3meWGJwMIRPd6z4jgaPTdpTwtWp6FDnaeqRG9IKIrhkmmtQhJYYRNSD9lfs+OmLK2hpdC7swRJfOMQPu7HUnGrgkZQbUt4DOJvKH112G3ThiKmQDQ6ipZtktG9A0lYxbRFeiSpNWzBLeQiczlJwSA082ajNTSERWQorvViLpgJpWwpG0b2MXif6UuM6x6G1SxkcHMDFkoYxTbUUK6iXJfgyrThtGzYqlIkMN2g49qZLauEJL2v7iPfWGFKG24CiihTNTZqWCxYKanuDMlcXk8b4iNxKjgVQUURZm786i8x01jKBrt+hd2VE05Odmy3O7aTMWkhlsb2tVuc70648q73Mh/O2X/ms/i8R5szTUbERBWnHBa0B0Wg1NyK5GiFLsc+Jwk+rWCCW6rBewRtmvDwi/sTEbSkylfUUHEEIzaKv/Yqr37mM5x+5LtBCm3YliMd90gFvi85XfxAp2HTNYQ9YWN9iVLIzOvmjuVqpyOY7Qi/k5bb6FDQR849TdtvxOmyGYUPfRCoSZKKbtGIjMqNYM7qkbSV1oYUS1vldkirZLdcbzhim3QqMGFSR9VpzVNN7VmUET09zJ3MiQ7peFugTEPVvaVH9ht9TgW5NzLxeyiwc/ZDa6AdkyD6ntArIFCqolZZlgUxBRSNBRGnc7ShPiBlk2NrFdoejY5LWt/H0pGTVNVLKLZ16tVHWOZOTHtCT2ko5di2Tu51WigCTDZU4wrzDFWT3CVybyhTZnsv/cICOpnw/rqig/xebUNMG0pvdM3c995aKvjtKv3CUn7F/YI344/DB/0PzPupD587f45/9pV/9rZc641wiAP/1Sv/FUss6blx649+nIfzdz7/d76xF3+j7vxmu3s87/nz5/mbv/c3+Q+/5z/klede+SZfbMVlw/20lldcDqxz5lvH/dSHP/uFn+W/ffK/fVuu9UY4nqEI/Nztn4NPgeM8d/YcfbkKUfFX/iKPbj7Aux8/52p9gecPl6RgSeDO6U1eefQrPPbS++51a1a8Sbif1vKKy4N13nzruJ/68Lnz5/jJ/+kn+dxrn3tbrve1cDxHH1se4t/6np/gip2+8ZO+GXyDXTtX+I0PL1/7Qes5+o7E/bSW3yl48Ajg1lKBG6nSk6PqlwVC0ZDMcAkGYRhJ8EZm+ooLWObXUmqq7Lrgr1OgpY0qIyuTQeYONWuk/av66ProQ7mnSdBaGbbCnSiVkEaMa9JaWqgWI21Sne5L/q63C2WsH62PeyAlhg01mT3KCArVmirBauBDjSuaFqgqw9I329GZKB44QTGjtUbvjZg24IGXYdMsgnkHmy4stSMC83qhKFRLQpdBNaNKibTc7mbDYtDoovTlQDPFHbwHy9LxSCveRs9riSIR9JFp3HAqqQgMzc0gVdxDtRiZwxoI0RsXJB1pZ20ARUlWdthIt3kQkDqIdydciSJEl7QF1iQJXRTcMXfEMktVbTOsmJXWFrxHjpd3ungWEfQsDLDYopxjMXGqwkNmXN9Wrmy2w/rZKXWiaCAhqIOVoxoTAs08ZhdCGmaWGcw9sFKwYlipFDGUJOA8QDCUhSKCcyyitmEjvTBhFJxalKYCvmABJYIqSS4XVXQ7YdvHubbbUU+Ms8V5rCqHWalzwzYFM0XnTnHHcIoaJYZafWRmj1Ykgf+6vyJcIudGaFqg+ygjEAF39JixS+ZXy1HNP034zZc4f+UxuP5oij/HNRkZ0gj0GGpaz3HETlPVHzNWT9JOWVKdK0UyV1qGA4AWOhVa2hlr+LB6ntIKGUULOOkOEN5RDZCCqlCqUlDODzNdQbQi/QyzE4qCtZm2nKNlQ4inotj3hAaBZ36wNlSmLOjAKAQuRrAnNC3Dc322cd8970W3Q2Xt0DqULb0fCN/nfQh02aDVcJ9R2aR9tewQGc4GS6d7uiVoNHoIjmVedsuCEZoM9fQ50TtCzWKQcCTOkuR2p3kWUrzv2z7M9U/+HofnnqNzHS+GlqDHgtYJYoGyhXbAraDLOW6b3LNLqrbpkq9fRja6B9CIUhELYu455sfCmlrQ3iAU7z2VzW0P3tHtJrliHG8LWu+q9FasWHF58Mr+FX7yYz/JS/uX7nVTLrCP/b1uwjeNl/Yv8cuv/TI/sPkB5sNbkze84s3HzZs3efzxx6n1rcn9WrFixTsTz58/z0ef/ig9Lk/UyT72EPDM2TO/7+daMyLhz//rn+H73ntJSN8/iPHh9dXXHmUz7+51a1Z8nVjP0BUrVnyzOL4XvQzk7xH72PP0a8/xmee+wA9/+/fdX+TZeo7el1jP0cuFB08/f7QzlaHw9ZaKUtuRDEgMzknI7rGRC2tI2SU5dbQh7j2FpSTpITJ+nlLMJB5j5MZKIDql2k/KePlBug6SOfn4QUb3JDhlkJM4IJGZwEfF6NF+NFLdBwUXgWWG3lCrSWJpEpcR5H96H21dEM92S0Ad5GWEgxZMFd3uUsUrhoqgKtSiqCVJGsuS/7aOR6FdUHeZPRsRww4WwgM81bEimpa0YhekqoSi7hRJsrt3aLPTcbqnGtsxWhxJW9Bh41xkEN1MaQutivvZ+NlQAR+vJWWorPvo+5H9051YDsRQ8qooasPmtihoIdRxCVyDaD6svoe614PwhrqjAWaK2ZQKzHJCn3sqf/2AxhnFxtgP4aFiWNxGDudMHlwT5dpJ5cruhJ3CJEHFKB7Y4pgUTIUyTUy7LZuTLdNuusj3NU81rdpEsWEPLYqaoTalEjwMlo4sjYJQqzFtK9PJjrI7QauhtTBttuxOd5xuKldU2R1m6tmB2B/oZwf8bE+/c44fFmoEm7awuXmLk8PCYw89xGMPXeUUoZ7N6P58jPMgjothClWVglOipzOXFapN1DoxmTCZMmlhQpkINjhbEbZW2NbCbrNho4UJp0ZQ+ox53lcpheINee020mY2dYNER9xRqag3atliAiaCWQHpCHtMOiZKLOd0cbptCcnc7dk7zYOQPqydHWyT9sNlApTe93QRQobqeKhyYUk7dfVUhlNBHZuUUpVNsVyDBtI7vhxwJAsijurTHiNzuhGt4bIlEDanVzndbpm2O0oRVAuqJfOh7SQVulay+EMqono03E6HAdVU6sqUKlmSFJVlEM8IHjJUyrm+PdKIPVRxn4mYQScIT2WxBBRFJUBrEuBS0okhV/O4Vh9LNnjk3R9gV8g9qC/E4Ry/dY7bLnNJRIfzwAG1SkjN4g4tqc/WoSwXQ0KI5ZDtKVOqmue0f4+WWd/R9qlwDojldmZchyObK1DrKKQxxASb6kXu+YoVKy4XPvbix/jczcvzhvt+R4/Of/57/zlP9ifvdVNWfAM4Ozuj98tD4KxYseL+wN/4+N+4787Q915/971uwtdEqzN3Tm/k5wYr7gusZ+iKFSu+WVzm96K/9uXf5smXn7rXzfiGsZ6j9x/Wc/Ry4YEjgKVuSGWh5gf/Ugg7Vr70kZU7iFDIPM/Q3GJeP3FtS9r9en7pkTBOslHY5vdTSdI1HVGTLAaS1E3iUcrIPE1m50KU6uJJUJUJrCOWKk9itKUvSU52yeeXikuq7pANWgyhD4vXJJkjJJVuDBIqPYARS0XwMWs484778Y7oEYPoVlQL03SCqkCtwwJ6GMQONV+68trI8e044Niw1o5BpPckxM0yk1R0kK1T0uDRmVvjcD6zzLdpvkckhoVtaohd5O69RObcKoCmLa+IjY4/Pk5G/w3CXUta0Q4FZz5+ZAXjSdLXAiihQm+dHjaGq+d9ihG9pQrcAVGsCFYq1B1Q6OczbVlgvgN9we0KXTRJRu1U2WLcAZ2Q7XW2Vri63XHFNmwJqhZq3VAmpZhR1DCVoZgtmQUshgVo5LipO7I/gA/Feig+L/g8Ey0trtNCedz7NKEmqEsa/0ZLa2hRbKrUSZimHZNAFUOXhu87fX+g7Rf8bE/cPkfunKF39vj5AXPnymbi2ukJV3dbTqxQUSzSalqLoZorKQsjLBWkUjBJA+IiQq2VMhTMqoJ1x0QoAhVhcqgRVPckyD3Q5ugSyNLQpWe+8/NPQ3gqh7UjJTNrnQnvqYQXzRwBcc1lgydhGRBhEG3sEzLWSqd7o4dlEYAm0e7e6d0IKhILJp4FD0fb+EgluvfAqfQFDodU1Wof89wq7o73ZZjHF7x3Ym70vuC6zSIWjOipShVVRJUlGvOyp3kn1PDe8GXB+x5fGrQ9SNpCY8NOmgWdNiBgTpK1CExTKp6VzC3vPZXLo+BFdSLEsvijNwhLt4QAMKT7xRxMa+8YymKHOABC2IR7yz0qHAnHVPmBP/mjbDbXoG5g2hCbDeFOWxqOZH62Xc89UG30h6bNPj0dG0yAA1q2RF+I2OceaobUkQnsnudD68R8IEKhnIA3os/pADDvoZ8hEahOqKx/zKxYcdmwb3t+9gs/e6+b8Y5Di8Y/uflPOPfze92UFd8AXnjhhXvdhBUrVtxH2Lc9t5avklNwifGbz3zqXjfhDfHa9ZdwXd873E9Yz9AVK1Z8M/jvv/jf3+smfFU0b/yzz/4KX3jpy8MN9f7Beo7ef1jP0cuDB44Ajt7v2jOLpGVs10GiygVRKFpSCWxHMnAhrA/haSSpGml3PEInEWmZnzvIU6Jnpi+DWOo9SQRIIkQKXGTQykWGb3iABtH7UA2OPGCEoA2ymLRq7e2C3E2CU5PQLIYvy1DKdtCadrUXtrokISOasbqRbTQJVDVV0lhm/RKYCOE9yeLWEAnMAht5pyHjPlzpEXg4ESNXl+TJQjqKY5YknsiEhCA4Jpou2EpmCwcceme/7JnbOYuDhhHhWG/QHSeJvog5s17DiVjoYhe212n9nDa92WlDiSiM9vZUyQJVA2zkropdKCSRkgrN/YwvgTu01lm6AwVcWXpDVDEVajHKyQlWDGkzbX/OMp9ntqoX0B1EEsYhGwol83f1YdRO2NqOq3HgmionxdioUifDLNWpxQybClY3aMlxTdLNkaKUMqWtcqlYKWhbkNagLbgo4Z61BgBWkJNTZLvJe7eStsStEXP2swG2LOictslVlRpGmWdknmn7PX2ekwAnEDSzhJujrVOB3TRxeuWUq1dOuLo7YVsmbKjKBcsiikGsKqDhOVdEqGbYWI8KmPfM341IpS9OVaGqUFQpIpRIkli7I73lnG2BLTP87u/S9+eEbJJMlixEcHId4H0UFBztofVCSe496G3BfdhPa7oGZBRxS8vpdiC84d0hzvKlPOjdCRl7i7csPlnmsb4q4nfS3hontOBtTtW+t1y/tknrYV+gbul+oPmBtpwxe3DwJdX28x2WljnATtoYByWzxssJKqNwxAPIXHIaFDnFeJ3ltgE+Q2spgDfLvvE+VO6BREf1BCmKypT9HTNWNljdJoEc+yRvcbDI9nsDq8MK+7hWx3xoc9rP905E8NBDj1F1xmyf+2cVfFno+5m5ec7j6HSH3uasMOt70Alvneh9/DvWNXndrDw47l9B9JkYkQBRJqSeIqpgG1j2mSmsU371joys5BUrVlwufPTpj/K7N3/3XjfjHYlP3PgE/6r/K0p58BJk7lecnZ3d6yasWLHiPsL9eoY+9cpXeOn25c6p79Z4/t1P0vUN8g5XXBqsZ+iKFSu+UXz+5ud5ef/yvW7G18R+OfCPP/1LfPGVZ974wZcI6zl6/2E9Ry8PHrxPr4VUdAaIOYSkLasIHkFYqj0RScLEG2I1VYkoUo+q2WPXtbQSNsvc0CBJBVNg+JzHhf9yEh99ybxgn1P9ShIxqGWeL5qE5SBhe1sIV+gz4prEjUc+VqfBmNnIAR5K4kjyJ+2cN2l3CoMoTmWzlJEJzFBjqqJqaKlDyZuPFEgLa7N8nVJgZKxaUYr0tFo9EioMde4g2gNJUsWFHnKhrE51oSOkJXYxQ6cthlxoipsvtEhCt70un1b1SPI6xa5cXFovrGwHkZXSbZLl1sFtSRLDg7wjHC01lds+xktjqAnBW6dRWJaZHsHSF3o4gdCHwlq1DLW0YEXQUggr9HCW1saYJNMcOD1gEUkFtC+jTYoibHBOpw27omwQCoYtgc6RCl8JTIKiaZOsviAeWRCwjOxiAS2FMqaUikIELsFilj0SUEXYasm040hLXWolql4USJjmv3jghwPhgdExHNnvif2B5eyceb+nuRMaSDkS+oKaUa2w3U6cXjnhypUdV042nFTDJO/7WORwQdYSqBVkqnkfgLaOzAuyLKkAdjA1ipW0vNaSFs68jigvkqrhYeusYujNl4jPfILiM1sJtlapGpgZYhXXLd6doI0CDM11JfmBd85wJ+jD8LzjWKZai6ZrALl2YUp7dN0SuhkK1ySLccl8aas4TvfAxzpW73TvSGuoGh4LvZ3jyyHtlr2xpFM8arsspAjFZKJMVzAT1LbpGhAQLEQ/zzkvkTnAtUJfMt9cAtShvy77WiuEIpJ52blnKFLsYnwpJ4hkQ8T3hMSwqU4lvknPfGGpEE50IdSyXz0t0WP8Lp9gI8NZkXoKfeaRhx7mQx/+LuTmHm68CmdLZpWPe+ttOA5I7qNqG3obtu62SUtnEUIKvpwhpaC2zb3fh2W/Sqri90kC4060hZjPsm22uSDhse0oppFcdytWrLg06N4vVe7vOxG/fOOXeeXKKysJfJ/g/PycV1999V43Y8WKFfcBbi+3+Tu/93fudTO+Kdw63OFzL34p3a0uKwTOT27z/Lu/RNd2r1uz4uvAeoauWLHiG0H3zi8++4s8d/7cvW7KG6J54+c/86/uLyXweo7ed1jP0cuDB48Adhm5uEE0xS9Uqj2JqEEYimeubZDEQPI8Am1kyprcJYI1sziRQeIqg5kZubQxFIWW6rN8fBuk7SBaVCFqEjBi4KRtqTLIY0GiJsnnI3/YJclEn/Gj1bE3wKGnXjhIWa14A5ZBPo02wcj6rIMoTXtm633kw0bSsO75/GPM7iADdVg4azFqBNIOSHhmdYakAliEi7MkOoGPw6WDJlGX5HMdakIZuaSF6EHvh7SI1UAjKFroDHtYG/3EyHQexFESrYOMH8rCVEIfvbUDcaco+Rrhaad7tHyWgN4IX5LwbY3WFhpC60NR7P0opGYRiAgUoW4M3WxAlN6dtuRY9e4Ew4o7DrRQou9T+dsOsCz5mr2xE+V0OmVrE1UsM2wlKAi65GNUCyaOKZjVJM27p9p3mZNIjbQXL1NgmmPrLixtZml7qBO7R69z9f3vYvPQ1SQWl4Yvjh864Us+b1gK456qzH4k/IZS+PxAu32Hw9mBw9mew/mBpS1jaQimSqmFqVSmzcTJlVNOr51yeuUKu2mLuadKM8DjSPLryNkN3JQoSapG91RsF8OmmipnzUzqJH9TT5w/k1QOQ6ryOVqOF+TGyxw+/j9xmGf2Ad0K4jPEgnfHY8Y96Pvb9HagSyf8PJe8bRCpcGGhrQg157FtBiEtmZNtRkTafKs0xHvOP605b82QHkg0pqmyMaFuwMoE4qhViihVRxZ5gMgE0Sk2ITWzo4saRXK9NhG8N8QkLdEVNCL7dLmTltg+IyJZczJdSfo6FqTW3BvGl9QNWBbJBC3dCS7y0SP3n94IDoQZWiyXurRR2EASwLRRoRFZGCDDEz8CrQWrp5RSUlmsBaONAgdhs53YnVyl7iZktwHfw3IDzs5SJb1/NVXF7TYRnS6RSve2h1iglGGt72lX3xvRZmLZjwIcyX03FMrYm/toL+Wi4EYQwm+j/RZSLDOfN5s3/4xasWLFN43b7Tb/ze/9N/e6Ge9ovDq/yn/0uf+Im9dupmPMiksNd6e1dv98sLNixYp7hh6dF/b3r1Xfb3z5d/jks5+79Pvd+e4Wz7/7ydXG8j7AeoauWLHiG8H99l503w7808/8K375C7/BncPZcPK8/Pvdeo7eP1jP0cuDB698X4cqNKWXQxCqqci7sPO0VIiajkzXzPlMZa4Bw0a6jOxY90Hs+V1r1Rj2wCYQZRAqxwlf8vWPVsXO3de38fqa6l4JSSJ5qAZDA6gwKR5JVGIVaYdUvQ4rZXpa6EoZBEWQtqoqI4Oz52sPu9mLHFiG4rClnW8Mm1sZCmK94L3T5rcEqTDGEdtBBF0rPqLZHdLu9vhELJWU0TJvt9rdbGUt6CCENCJtV2VLeE+ipidxXTAihtWwxcg5jovXVRHod8CujhbEIOE175vML20ExZ1iw562DALfBXymDTvZ1geRHoKoUMMvCgA8PWwRDcp2g20H+Xt+h2VueBvkJm2oDgPqltoDj00S5vUk7WUjMFVOFU7F2YhQi1BFKbUiy5LEd1S0R9rlHsluJ+/B0lY8IBWlzZEeOZ3M8PlAQ1Ih+vCOePRhfLtBbt1BKPgyE9EGoT0UvAF4H3bE0IkxDzUVqtKRMOJwoBHMkgSfnCbxa7Vm1m8EdEGiUKeKT41azjEVltbzUCCyeMCdaEkw+6ERqjkPRsGBaEnbYcg+NSWWnnp2K2m1HIr7ULWGZ1605H1FLXDrFvONl5Hr15B6gvaZrmNuhhFlk+OrmY9drCKmRCy4GbIsOaY+Y+KEN4Y2Ppc/2S9pnbxkDYKkCl2k5nSULSFpNd27IBwIN8yD3oIuHeY9dnIFpk0Sj57FIKYHbA7YppJdbAIis4wFhI5WQdygdRTNPU8rhhM9oGwIFtyEwJBSsZgzu7i3JGpFkVoJj1EIsmCahLzHQthEH24FWTgiIBuWquDniEZa4ZcNLJkPrOpQwVuDBlRFfdjV24R4I/oh/wBV44d/7Cf49Je/wH5/BkuHucFuS9x4Db92je4NtU2ucx/FHi5ZAOJ395ccGCN6ZIFN5PoM0ySKEdjVLHAIEE1FO/024rvcT4fTPhF399cVK1aseIBwY77B3/j03+CvPvRX+a7Nd93r5qx4Azz77LM8/PDDq2p7xYoVXxNfvPXF+/oDuiD4pc//Gq03/sT7vgcZzmeXDgLnu9vsN3c4Ob92r1uz4g2wnqErVqx4J2PuCx9/+pP87vOfp1jl//zH/o9wZaHX+V437atjPUfvK6zn6OXAg1e632eQjoQmSSWKYEl+DpvcJEnSUlUIRA1kQxK/S2bsMkgFgeTRh3LUZRAPNgjeGJm7kUTpkYTVMvKFDcqUjIIe7ZgVljlJt5GXGRo44Drsi32BLjhOuOLd6RFEH8QOC3FUGR7tp0VG/K2ATHetd50LdeHFPVnaNndRQlPVpwhCZsgeX88DRC0VljqhcsxhTatqD8m81GEDnS7Y+1ROSuYap/S0oQyyMQRFBlkjYJUSkipISfLPVJDwu5z60XZXj2NxOogfvUvID5I+6o7Asi2QSkdVpC3Zfz7jLngIzTtx52wQ65lHm4rYJNtwxzrUYpjl//fDzHJo9Nbo3ggyn1Zg9HfmwZqAx370uSJ+xibO2SlszCgqlM2E7TaZ+bur2OkW25RhPaxoSTI3BYlGqZWyLZTdhlKVoopEjvHRhjoA1ySDl+deZv+lr9Beu41qHwTnsMVGMLW0nFZLVa0GdiwYCE91L1AIrDVYFmJu+Pme5fYZ/bDQlxmfF/rcoeU8MzOmqbI92XJ6uuNku2WqlrUOQ2V8VCP31vGlDfVvIJEK8GMOrZqhPly7AVVBJRXAiiNmaMn1LUEqzUVQE/jsJ/Fbr+G9Zaavp42zqWIaFNugQImO1CnnVDjEQpiMeaJjHhvdC/1woPcD7mkjjVTchfBcJ6kczjEJn+ku9KXR+5wFA8tC8z2BpWW0TjQniwgiM4MxIJzmC0sPojVCjN5nWjujL2e0ZaE3yeIFOll8scHF6JTMG24L/XBIu3hNEjqWTkQ5Ct3ztZd5FDLUdEVQxWUo5kdlQBaPCCI+igIComafm42yhAPUKYsBRKDs0JrEbW+Hi0ILF+hDmR/ulHC23ZH9AfYHONldWEZTN+niIGnjnm4MFbejs4IAlXAb/K+idYvUE8KDiJ4K/JLErxKoGkLuu1q36OYxpBpSC1JLFgfp2IdXrFhxafBzX/45uq+VwG8HbrVb/NLyS2x2ayHMZUdrLQviVqxYseJr4O994e/R4v62VOze+dUnP86nn/v8vW7K14bAC+/6EvvtnXvdkhVvgPUMXbFixdeL+/m96Nmy59b+Nk9+9mXe96Uf5OGX3492G26hlxDrOXrfYD1HLwceOAJYdINSkp+NkZkbZL4vpCLTPa2Wjzag7ZAf9AdJOByJyXaAwxnEPJTBgLbBREkSBFoGSZz2ualEDYg2iIrXKVQv8nMhsFQaxpzEjvuIsNV8rjvCARFF/ZCWzKRKmL4HIu2UBaAPUtiSwL6oho1xMYeid9viSeyJBBKd7su451QORzgtUgl6oSgOgDYyX4ViwlQrRYJQwz0yY3YociMMb41wH81RRMDCUy2qUDdTXneecRqoD/vYQWaT43ZXktfv9qUM0puhyk6faUBTjXgcQzVCFfqSGdDzgWVuzIdzlmUmXJHdCZM4dTIkFlRHFiqBeWe72TBtNghKPxxY9nto56gUihqmMtTJByIcbz5UyguxGNE71IrMwoYNOxEqw0J4ZBlHSlFBNDNWRXGFLgJ1QnZbZFNzzFBUFSuFUgullLTaviDq8t7bfs/+9jnnN2/jbUlF9rDoTufsJH9VQbcF21bKboeWfC0lKKrUUpjMqKVgRZDoeW/n57Rbd2i397Q5s2+z6CHvw2phs9lwcnLC6cmG3ekpdbtFa0lhfPMkQAlcsqqbcGRk1GYxA0kEj8xZJZefENCOjx150MflQK5hrRMajrz8KvhhkJTH5RFpoy1BSlQDlkPm+TKN9VOII1HbF8IUl5YZ2TaNjGkHP4DUUfAwrLRZgPMsRPBj1uzFjE3yO4LwJQsw+p5oLR3ew4eituARacUdHaQNu/OJbqe4N5yWxRniWcgybNmPtvQOhHY0Gto7Sie8ocMOHgmi2Cg42ecaE8u2AcqUxSrdh/rXEd3lPuMHQCniTNiwobcswqEgveU+YwWVzEv3ssHpWUdjE1FPoe7QKvzYn/k3KHWs8VbgzgF2O+LsNiHDzp+a13XPvOTWcw8uqer3CFjOiJjB90hvaN0gpmg0xALmA9IPQ+Er0M5ze6knw4J/yWKC6STVzStWrLg0+NSrn8o4hxVvC37ntd/h7z3/9+hxf37Q8aAgInjmmWfudTNWrFix4m1BD+ezL3yB83l/r5vyNdFL4/aVV4dv2orLivUMXbFixdeL+/29aBD8//Y/y5M3vsy3Pf19fOdn/3UeevXbkgi+hFjP0fsD6zl6OfDgEcD0VHx5ZuZmYiiDUNIkaEodzNIgjrRyzLxM5nMwTyFJ9PiRJMorHJWWjDjetDge2b9m0JWjnjb5pWEHHYNoYtiToriPtvRGSKojiQJUvMmw9zVsUzMHtWyRcoJOJ3fbgicBNIjT41dEe101z1A7j/5Itk4RF6L5UOalclol2565mIw2j3sJSatpU9Qqpqlk1UHSRqTldKYuL0TvqBb0qMBWxcRSz+tckIV5L9mfEZ02ujgEwsc4EUTvaUcbJOl2tO3Wcgwwvrh/8UAdpDPSTDWHLILFIzliBauZLZxKcMNLJXpLW+DtCXaiaRWL0BdPha/uchwj+ycwkG3y0HJAYwZmxBpBJyKYTq9wsjthtz1J0tgh5pk47FOVOS/EvhFtAY/MqG7pcOweqcwc44TEyHhVrFZKKRQTTDJL1k3pSOYbex+WxQGhgzAEC3IMpy1WKlorlJKHq+hYIkkEp7rWkXkh2pLtcYf5QN8f6IcD0ZdUF4sMJbKgZtRaUw1cjE2dqFbGbCSV7X0h2shlHbT/cd4GIG3kAqugJZXagiTJLmPdRk/VafggeXPrE1N44RnimWdyftIyAzirQXJlqKHlFBxcFA1F+3KhgpbIx6pV1Ep+6YRR0Mj5ZwLSI5W/amAOkkS94Oi0zefhF5ngKqA6ZYYwgkrJQgL3ocU31LZoOFo3mcmtoFKyP+uWqoIpiG1TES1glvNVdUGnCZtOsOkEKYJqh+21XDOW61HEoJwQLR0GXCyVww69N5xOWKGL0/1ufnL0PpbbsfDDMr/Y2yglUXprmd8bPR0EOO4t2V9STiAmpJ7yyCOPstteh+0uCenDHlokoU2jtz3L+S2W7kQshAitLSzR6G3Bo2NSISzX0FAm4zMiDemO6pQZ0Tah0w61XTo6zKMI6MKKPpAe+f2KFSsuBX77ld/mt17+rXvdjAcKHs4/v/XP+cXbv3ivm7LiDdB7XyuvV6xY8VXxTjtDn77xHL/wuV+99JbWN6+/yI2H7t/c5QcF6xm6YsWKBwWO84/3/4AezmY+5X1P/SAf/MKPcv3V915KNfB6jt4fWM/Re48HjgBOrmZ4xdo0SF7P3FE8yUvPbExEobW7KlmrgwweWbbhd0nG44t38nceIEuqVqOl++pRcSyZNxscBtncX2elCmglqIQIWqd83bJBoyQhmsGnF3m8DOI12UVGjrDm/y9LtqlshorUhiUqEGU8fyh/8fHaivROQfNyQ2kYDDJaFBNBddgFp1w0+8ZKngkjrxch7YNTlzoyj330QxnkTQwiEVSUqqAmSJmSbLZK8UHmHTNGQ5IwH3msab+dat8L5bM4+DxsWpPchLS2zc7rBEmWhlW6d9rSaL1lHnGZ0gJZAStoayDTKAKAWraUK1soEwG0ecHbAci+SYIwiBjfz8vgFE+TECZQPSHmJICLCjuUTS0UOuoNXTrSBHpDekfNMKtYkVTcoshhJg7LUIgu9B5p69sbEZ6kXzEswIaluffGMh6f/sJZwCAlM2tNDasFK/mlR3V6NHwop80MHfm+Eh3pDTwthPvZOX0+4D3tdfvhwLIsueEHoMM2WQ2ZKrbZMm0mtpuJqRZKMdSUECFaZDbxyPI9krj43TEXU2SqyLF4Y/S9RAxCe8xAsSEGL0mwopml/OqrxP6Ae9Cj5d5QthfW8CGealtN+23RzbCHn1ALrBpUCOYL5Tw0PJbcL3ym9yQ6o5ScuhTSjn4oxL0Roli0pL8F1KYk1rUkCeuBx4KUSkhgVXDN9eu9Z3YyZ4TC3GeWrjQPPAo+cs59WejNclm0RrQ9PjsSG3zM7ehzEqRtJhbHG4Tt6N6J5Tz3UAdpBwRLsl+mkVfuIKeETLjnPOvYcDnIqopYsiJf1dHwtL/Gcd/jyz6J535AlttInGFb5SN/8nv40HsfRQ8HkD2cTHnN3QkRO4gNUSdcDG8LEZbzx2d6gGshxIlpQjZbmHaDbK9IuQKbkkLtzWZkaef+IdMEJS2hpVRkdzXHijaKT1asWHGvERHcPNzkxnzjXjflgYPj/Ob5b/Jqe/VeN2XF18DNmze5devWvW7GihUrLiHeqWfoszef57nXXnxrSeBv9aUF7lx5laVc4qzFFesZumLFijfEU7ef4qnbT93rZrwp+HL/Ip9uvz3cIYXTs0d431M/wPWb73nzSeD1HH0gsJ6j9x4PHAGcAkIbVs/LIBEBjBAb6sFBNPly1775SAhES8vj4+f+HqnoG/a86LBTTnkZ1O14LoN9NqhlqGwzn1JKTZXZMbOyzYNICVyBOKR96dFu1I9WviBas00yFLJDCeu9Z/6o1aFSTmvXOA55a4jaaFIZKujM6RQjLY5FMvd19EFqPfvRUDdVjTIUmapENCKC8KF4ZqiFJdC6oWi9IBIhBkfX6RFJrjHUlhponahasThDVLlwnCgT0JO41lQLGgyyueS9lGMOsOW923Zc0nN8JMNNrZ5glsRYm2dam+nhRDimTlEokspftewBYoG+UAx0ZzkXWqMdhL4sORdk5NRK5qq69DSnFoHu9HYz2+lXkIBSJgpCDWGKoPZGrSfUaYPtdogFGoFtt5TNRCmats6bim0rusl79ub0eaadN9qdA+3Q6G0ocZEcT1G0pT2z72doS5LtpmmBrKkSVdOhZE0yWzTV5L5v0IZFsAiZl62IKhRLwnt/wM/O6fv9UIp2fH9Ov33Gcn5O6x0nCIEmSleQapTdjul0y+b0hGmzybninlm6AREyVLr5QQXe0U4qaJVUqr7e3hyyIGB8yXEsj/bhWrPAwRS9cwN/9aVUNovm2m8zgqeFecSo+TjQ+z6Vrhcy5IClwX6BqEmme9q955oSQhSKEtHx1vBlyaKT3kYudGZFIzX14mWDHde+9vxdP5B27lkAIdHpAuKaFu8OKoaWHbosbKxiUhBfUF+yiECn3MdkKJH7IXOPvRPiiHSIGV8ayCbtp7WTavWFIo4VQcs2a1asplV4LMiyH4Ufjso54h3aAfdOixg71pT9JaQKuZShJvfRjkBDMJmQaZt7AY7s99x68VVO7cooVjiFWUBvwq3X0iqbA2Fp9d4kCD9k347ClSBo3jLTuDv0A6GgU0Wqpp2zBhLnSJwPy/iAQs7xshkFJfPIRReiPnhH6IoVlxFfOfsKP/2Zn77XzXhg8fTyNJ87fO7SK60edBwOh3WMVqxY8YfwTj1Dz5cD//CTv8izr72FyqA3oRb0sD3nfHdrtbC85FjP0BUrVnwtfOn2l3jy1pP3uhlvCmYO/L/O/h8841+++JmG8W1PfT8P3XiTlcDrOfrAYD1H7y3KvW7A246QzHW8UJ6nala8pR1oDEUukrbB6IitPdrPVigjD7fIsIPuEJmfymaXOZE4lJIkCAxFcJJwGcGqSd62lu3RnorAlg0T76kuXpKAurBljkEy6gTW0l5aa1L5LY53RIgibbRxqGPFdPDZqZqMY14uZRBnHRhMqyiBE72nZbAm4Zs5xCAyMkCPpHR0wjuhMex1BYmGqqUC1pTwhtgG8Ug1YvQUI/vIGh3210hBRVFdQE/AGxZGp4+c42Ere7TNPlo71wLLIUlf2YOcZt8crYOVHBcf1+kzoQEU3Bfck0A0K5h6qjDD0dAUD6tBmymqWDXUJmKZWZaZZQmczPsVhWiHnEsR9FgwGfbf7mhkH9idPZtrJ2zLCSX2nAbsRNioUadCnSoWaVNtdUKnKYdeyhhLQVSIbvTWCZch5vYc1r7gJng74JG/0wjMLJW5AjIpup0wVfox35Qk4uU4liJJ1HeIHogLWgQtdcxHsuhhOdo0H5Xe4K3REcIKEXskHBXFOEEs8OVAU6XWghWjMqUFcARO4EtLm2CRC5F8Fh0Mm++IdHlmzIVhUQ4dl5EWHM4xMxgi1cAig8AtSDi9TPDk79E14NH3JFlaCxoLwSavgw4B+ZTFFMP+mMjiEY1Uy4d03AVxQ6vgbR5r1NNRANJK2zshiorfzSmmD7Kx4HPHpeA+gzqG5lq2LPoQnag+5nZkAUrdbpg2FTkciFIp3YnY0Pr5BbEdS+aUC4H3QLebvJ/W0LrFfMY5Bxyxmn3eF8Kz3EKsZl8ei0O84cMRQcqE9HahRkYKYZuxn4FtgFkITVK2u6O6GfnemvvwJklqNQOvoI0eC/2O84M//uN87FOf4bAcYKNwBpxMBI2+v43pRExXkAILneIHoKby3YFiqGQOsUvadYc7osOiWmvOXetpD842I9t9T7Ltmccsmk4RWo550CtWrHi70UZG/N/+vb/NL3zlF/jirS/e6yY90Pj585/nz737z3Hn1p173ZQVXwXPPvssjz322Pg7asWKFQ8yIoIW7R1/hp4vez76qX/J//J7/g3efe3xS7v/vfTY01gvnJ5dv9dNWfFVsJ6hK1asOMLD6dH59Rd/nV994VcBePbs2XvcqjcXZ3GHf3n4ef7K7t/Lz9AA88p7n/5+XJzXrj/3ppC3bxbWc/TyYz1H7y0ePAJYIgWzRipDfcmfe09lWXiSKWWoAI8KX7VBsgwCdl6GhfTI7FzSMpXDQgbHZs5vEsc6CN60ys3Xmof61ogmSSrHksSuQrSeijQZykAtKEHIFkxQUTyM6Id8TkvVoZiiKD0CaJl1PGyds/2eymCtqbrTka/LuE/vd62WUcSOebACLEQoIYF4DHKoDyL2aLPspOIu84y7ShLi3fPyMghpHe0hSceQ0V8ALMNidkG0ENLoIUmQx2EQYCC+JPl+kRG8DLK7wdFytsdwt245JpHEsZmmXW4PWnSat2EfHUn+ahmWtflvdIHmqBWsKFo3mcPbg35oLCKYD1K6A11pfsD9NZi36ElgGhhCLVv03Nk+dJUrE1w9OaHKhjrPXCvKpEKJ4VItAXa0su24GmHHnF6lmFJNk4QzsLJJAtjz/twDp9DbTL9YAwW1JBNDk9wOzXxmhhpcNcllGapeXxq+dMJjZDszbMyVWA7E4kTznIPVkGognuTl4qm49Mx2bnfuIEDZTOggq8OdqRqmSqmFabvNAgRgPtvT2yEJbknL9izOGHNWklhPYWkS/Bpk0UE4MjKhRUEi0vZ7ZF3rcBNH08a4v/AScf0RuihSJiIa3Ze0Fdcxf6JlvrICTAQzeCE4pGIYQWNYjHtmLocf7h5yEdnXyLGaYbgICN4X1CstMh8boJ8f0KtXIBZEFJUxp6moH3AVVDphmv00C9KdeZmxumGzq7SWmdXQEHFsEOxhFQnBNEnS1gOiUaYtLWaQmkUeIYgMcpQg+oJEZgkjgobiw1Y7X7mCBeIjCD32IFfoS0PcUTFclOiHzAIPgbYfdTIT4TNEjmOX3K+IAq1TTpzDqwAFyj6V53duIKcPEXfOEWtD6b/B6UgsNATTCR1rSGRKgnlZUglsINtrWbhRJ4QybM0lLebLlOpfnUB2ufZjGUnVK1aseDvx2Zuf5cXzF/mZz/8MX7r1JW4uN/FY82TuNV46vMR/8fR/wb9z9d9ho5t73ZwVK1asWPFHoHnj1178NV45vMJPf/qnuTHfeMefoXfmM37uk7/AX/jeP8d7rj9xr5vzRyLMuXX1FXbnV9CwN37CihUrVqx423HjcINPvvpJfueV3+Effvkfsu97zvv5vW7WW4Z/Mf8TnM5f3v2fmCTf35kX3v/lH+SZ9ys3Hv7KPW7hXazn6IoVXxsPHgEMgzzxQfqm7SfqgyAZmbYtCVAmS1tTJHlOTxtfdBrMUQfXVJ86IAWkIS5pNVoK0v2CSA7fDzVvuVCEAtAtRyNA0CRpe2YEewtUHdFAteSbtL6gCl2HelcErRvEHZiTICuboewd7VchkLS6BgJBQgg55qUe+4bMPo0jueoIaessIy84LVCPbxYj+0oNYdhmy8jc9bRedU+LV9iP31d0qB2jQ/4w82E9oGsgJpTemSP1kUWViE1aP/uRFDIu2D0YP0vyB88sX2QQw8ebKyXHTYTmzjJiaZFIy9yIVFNLJIkZSRiqTdRJ0QpSJnpf6POeFtmbPc6wOCFCk9QSw+wquitscba+51R27CyYHp6Ydju2m8J2t6FERfe32faODUvkEMFdc0zVEVfogbQlx2u7QbdbYrtDNxv01q3MvCUJ19561ieY5pxZGh6dyJFPAa2Dzwto2oYDaZlcJ2yqSK2EtzHUdzBTdJqQMoi+pRMtcn6V12VPu8PBISaoiqZ+NmsE5iSjpXfYbRGBth/5yEUwU+o0jfFK0nS5Q6rRSYt2jY7WKVWjIsNS2IiWCmYplrHYXYZduKCRRCZqdAUdGcLpLC1QFL/5Ku38nLhyDVonyhbkQG8NtQ3RnJBOKRtUlJBOd0NqTcW9TUg7z8IAImsa7G52dUTPfaYLTKMNqhAdJVXuF8UUg9i27TYLNyKt1rNAA6LPqciekhiNUKJ1Yis037O0wEWIILN1j4UTtiNiBmyoos/xssvhiqAv2SfMM6HpMCBK5uduJuSwT/69z3RNAvmYpd49raidBm3UepiAG1ELcTjLnHBvqGwyC9sB2ihAEeKYmyzQ25wFChGoBZtpy3d/1w/yG5/5FHHrDrI9zb7Vbe5pm03OB70CcRtnB2aUYd8eCK3tqWWH9456B90g/ZBFKuFId0LSwcD76HeraNlCW7KYQSpSt9D2X/+5s2LFim8aPTq35lv89Kd/mo+9+DGeP3/+XjdpxR+A4/zK7V/hT+/+NO+f3n+vm7Pij0DvnVdeeYXHHnvsXjdlxYoVbyNef4bemG/wK8//Cj36Gz/xHYT9cuCjn/4X/Nt//PIqge9cucFy411sDif3uikr/gisZ+iKFQ8ubi+3+YWv/AIffeqj/NYrv3Wvm/O2wXH+xfxPKVT+t7v/A1UqABqF9z79fYQEN68/e2mUwOs5ermxnqP3Fg8eAeyCxC3oJ1COdqrLhQIvCSJNa2WOzGCkKzQLyDRUssMmWjRtb8WG6jegJakqakkklkH0+pKvHw7RUySrgVQddraSWZY+MlVlqJJtwpcZrxtMe5JWajgjzzYcITNbKRDeh5ZWht1x48LmuadiN4gkpkfOqhzVv3q0rY6hqBz5oxgipFLXB7HVFsQq4EkoyWZQwUmmJ7njuFZam/GRVWwdsJapwn607OUiP9iRJFlapJK5NdzSflYjCelkpEjCPZLEwQ/HQSaJyMGoR6T37NEK2J3A8Gh5raOi8qgGTU/w/HJPktkcq4KYQzkhPPD9zNId78CyRzfboe7sdC+YKYazpfMwhaubU66Vyq7AVCeqCqqGLU5oR2yiSiQh2hoRHW2B9gCmVErPCyIFLQKHOQm3szN8cXw+YHVkTE8TZiOLdgZqKnl75Kbr5HzrIbQW6FBiS5esYRCnqqFaaGq0wevrbkrLclH8fCbmtH0Wcs7GqCAXEcRKZg4Xw6aaVsfCUDM7MTecPTpNxHJgbguc7NDtBp2CKk4sC90MpgJdk/iW3LbEI63ATZP/j1SdhyZRmmS4oBJJInYIFahZfBBkPrKKpuLbFPOgf/bTxIc/QnvoGuoV0Wk4pPfMdtaCjYIIZFh+B0QphBWkF1yP6dQN0YKI05Gcb4c9TLvMtrXha42DBRaG94VgQqyA7DGrRJuzbyPXYMx7KJW5LZRiSWRKpbtyODjCjuBAX5S+9HTHjqzB0IAeMkjlVOTOkQR9UOnekFphe5JKV8++wsmsYg062V9igwRtc36QEgK6hf1tmGrur/QsBukt71fTdtljATZgINGTHC9D/dslyX2rqG3w+WZaykfw+MPXkdbxszuZny5nsNngy2vIUnF3rNRcM+zBT2gE0JnKCeEHluUOGhOi27HWh1V82RLLksU+IlB3xP7OKMrpo2IiYPJhnPDOVkysWHFZ8Pe/+Pf5Tz75nzD7fK+bsuIN8F+++l/y1x75a7y7vvteN2XFH0BEcH7+zlUprFix4o/GeoYmbh/O+Ief/AX+V5dYCfz8u77Iu5/7DqZ5d6+bsuIPYD1DV6x4MPGF177AT37sJ/nK2Vfe8Y4ZXw2/MP8PLMz873Z/jTI+jzWvvO/LPwjvh5sPXx776/UcvbxYz9F7iweOAFYiCYqjbbA2aGXYP8/jd8NiNoZ9qQxLZxmqVyPJ1LlDndLuuMdFlm1aL6dVMgTSRr6wkSo970mYHQnj3mEzIYcGI3MTHAlNQlYVpQCZ4xqm0Oe0UR3q02S30qpaNVV/vXesJ8EcmsRtMAhcSdtdsVTuBUcraEAlVbAE4UdJ8CC1RenheE+iUunZNt2k/e5RyQtAJ7zhUXEfGbLiebUMc0VlRrXk/ZI/6x74siA6UVqna8FoqTRkqFGPKu4YFtdy7PvsY49ORygdPAKTQTJJGdGxnaU3op+Bng4b68ADLEN3k8DuB1QVEzDTJOVC6G2h9c4SneAW6I4QZUmGlVImSm/sBB6adjyy3XB9s+HEhK0q1YxiIG5Ea8MuOsemqeDnDdcR4Vw3iDlC5qZKdFiOXJQQs+Pds+eXVABjkiptTeWkmqHWcv71jnvac7dlQUuhqGcusSbxZtOOzdUrbK6eMrfA5QbL/nbOlR5p99yOhGiS+CKRRFouNHSqeV3vMB9SfV4cjzJmyZxqZZW0+u6d5TCjptSR1VqKsZFsk2uunWh9TLOhkg3NIHmPYTXtREkFsUTaaEdorhskSWBSLRxqmac8eFgrhp2dsbzyHFy/jvQDRhA6ZX5zmaBuqdFxU7ydYfWENh/G2mpEzRxcBNSFCBl1Hw3RStSS6y+WUbiw5M895yVqhKR63aTSmyd1LHqhfncaM4XY7FiiU8sEdFSGtt8yXzxiyb6LtKP3doByOpTIY11Tsw+FUTexJcbeJ6rEsciizan01cz7TeXyMoo/ThDaWIsdahbRSJ2QSBWvaVpDRx8FFr4HSVV3RkYLihBdkN6JoqidpuW1Poy2c7QKm7ql2JzjaZpOCrFJYpkbUHf0s1fQKw8jXce+brlNcgDJe/XW6Tqj3REpCC0dDsTy3lqHPifJ7D37vk6pil7mQVYfbetXrFjxZuOsnfGlW1/iP/7kf8xnb372gf/g+n7Bi+1FPnH+CZ4oT1wU+K24PGit4e6ormOzYsU7HV+89UX+5if+5nqGvg7ny4H/4VP/nP/Nn/i3eWh37V435/dDYJkO3D69wcPzFrkskqoVF1jP0BUrHgxEBF85+wo/8/mf4X984X98x+X7fqMIgl+d/yX/881f4l32noufaxjf9sz308rMnasv38MWDqzn6KXHeo7eOzxwBHAGd5YkIEiV6QXRS0mioM1kkOuwCu4tiWFAJk1rVNmAzSmps0qqbZMwoIxsYR+q4JaKXSJAW1r5mo3rlqHiZVgTRyrfhu00Asz7zKCUzDmNNhS2DrapaWVabLhJD5VrCKZKHInkYGSfFoa0N4mNkNfZRN9Vwg695yDGBsEsSaKo6rASHkQaHZGamby6uVAUJ79UhxIToBFd8aq4ById97Tmzd8bXirezodt7kwfOcUNYxIl2fflddnFw3KYRkpdJ3p4KhwRlkEqiy+YnxPT1VQlex922Cdpyy0FiWO+qiZZ3NIuG1Vsm3nPAWn7fFg4zA1fHPHdsDbuSIetHzgplW2dOC3Kw9PEQ9uJq0XZaaVIoCaYFig67IiNtuQ8bPsFO6p8d5vM2j0qXQFpPVWlJogcCVEjwlMxHZIZ1XLX/lhKQecFP6p1JQc6VIhSU10qnmNdN+i1K+gTj7O5eoruZ86Whrx2i9ifX9gwY0ncSp0QGzUVMdraOyxL2iKXknHPakSLoXIHypGgFWSakO0GaU47LGTEsKF1y3QaaC0sh5k2B97nJL5NCDOiB3jPjODecm0haT9MgFZCYhCcBcezmMAsSWQriAQaioVgdWL5ynPw7d+Jb6ZRPOEo4FqB4OAHoCDUNAOInsUUvee87z37Q3O9ee+pgEcJq7C0QVwP0tEkFelhCIbHnONcNyB3cm2Wq+B7oi14PSGWM+iVWM6Zd1ehHSgKtc+5Z/U7oFt8SYtvDg41lfA6rKFDLEnOrjCdYCxJVvdUu4akCl+tInbMdEz79KwyWVCpd1W0R5WsTZnFK+Q9SToK9LaMApOG6Wa4EYwMYAK8Zb+VE7zfAabjxMKtAMZ3f/i7+NivfzvPzF9Eekd6ySzfqwucXAMviOc+hO2g3QQqqLAsHVVFS0WF3F/Guo/ekaUlye869jfP7GOdRqx4H8UIwzJf1gzg+wkR8S1b/r0Zr3E/463uw8gKOH75+V/mo099lF989he/pWutuDf4p7f+KT9x+hNcsSv3uikr/gBeeeUV3vWud7Hb7db9cMU3hPUM/dbxdp6hv3vjd/n5Z36ep+489S1d752IO/M5S2/3uhlfFTcefp7rNx/H/MH7qO6yYz1DV3wrWM/Rbx1v1zn6L577F/zUb/wUh374Ix/3IGJh5j+987f4v5z+33jC7jo9Wa88/Mr7uHPllUvz+dh6jl5erOfovcODtxp02CIfFbMxqg4kUsUqkUSpJGmRolYFlvzwf94RlCR7UZIgJu2G1ZCYQIdytkwjnzSGejUgNsgUJJE5VMVShz10KuoQTcImShKStSIY5uP3BCGKTjbI1aQqkoCKJAq7g/lQGY9ro5kDfLQuFc88YysjC5jfL+BFMov4qLYMQBxBMEk1pnuSSWJx0R+iSdTKUE9rtFTdRQwVdL5ydM0hgFTYRsdb2lqLGbIYhtLCQYWIOckYj1RdyyCzj4pguYL7PKynO1hkX3oSbWjmhS4ts1NDLYnBMMShmKKbCXrQ2jLUwEKxku2l0OeFeX/Gcn7AO4QJeucOstlQMK5vjIfsGtcsONmeMBVlY8GJKbu6YTvUyCqGiGTWaE372XDF533a9dpQni+B+DLydftda+VpQpS0ph1q12hLWlO3llmmMtSu3ZNoLYpiAEkiIllMsAHVktm/bcGAyYPTcE7qhqaFs2nijntm+U6C6JTZrpIFC6ggYmNdQbSOuxHzIVWoI9LXHSQMpBOieAlsyfmhVjKLNyKzV00xE7QawkR4JNlbKzG3VNx2JzyG/TQj+7oipkQPtNrdwonssFzb7rlqikLR4fSec7hPjjWnfe4zlO/9QVQKTsfVkHCiH4gwiAnRBrTM1W5ZrBD9MHKWLXOC6WAVaUeLbMWbY9sNxExI2ntL87QRn8/yHmJB2tiHsFR+e8/I21KZJHOie9mgbaGZUVGKbGAo62PsXQKELiA7Ak+7eMhilh6EOQWBdkhyvgVhhdh3XGakDkvr3sbaFtC0dg477iMdDc1odXFCFJsbtAY1reKRAv0wYssVtykzpJfztGnX3BtQQboRvRGlDkJdUZzQkoU0nZxUww6bm3u4uhtuDQKHJfcA3QIHYAvVcIHotwl2WJ3o3sndPCi6yf2pd9icgLTMKF/2IJth96+pxkdhWsmN+wlvxh+HD/ofmG9lHx76gc/c+Az/6af+U75w6wuctbNv+Vor7g0ajX/02j/iLz/8l+91U1b8/9n7tyfLsuu8D/2NMedca+/MuvUVaNyIi3gnTYmiLcmyZctWSOETPj6P5/Uo/Hj+BoceLIYiFCfCL46Q7DiyFQyJOqHLkaUTthiQZIqiRIoSAJIArwCJSwON7kZXV1dXZebea805xzgPY2Z2M0SQIAh0ZqPWF1GoRlbl3nPdK/dvfN/3dbTdDzf9QbWdM394bc/Qm6Of+/yn+L/9B3/uupfxu8rFefDMV3nutQ9d91I2fR1t98NN34y28+YPr3fyObrB339fL9mL/Gr7JZ7Tv/A79uOdR+9lPv42y/7sGlf3lrbn6M3Xdj985/XkAeAW7jRGxHE42cINLFJHl2kKd2C77JsVUHByQMiUI8Y5T7jVAF91jZhZ8YCwA/zS1qiipQ9wqcOZdlkc2q/eP1zJI47VVyRLpFG7oEXAA+SkUgiA/FaHKTjWG6oZsTa6azuUXfyZdUTScAfbiBK+7BBmuJMvYTgRiX0ZeS1pgLMRQ0twdNcccdAiSI+YVXUfILyFw1lyOIQ93kc6eHLclnjdFjCw54BVZfQZL9LpGHPJ0IwuRu+QxJA8QV0g2dgPFutVofUebuvhhg7y2cAWPO8iNVoUE8OsgjnSHN1PpOHAdo9I7CxOSRkRMDPaxYFqznoe4JvdDP0MunFrPuGOKE8X4d48c2sW9qVQMJJDts5UGzkLOU+IdcSFVhdI04gF7yRVVCvSG9JTQFlNqBS0rSiK5AnpHZWAm9EXLXjK4V624ThtHVwCGruPTl7BVRFbryCoi8WwQoOUHZ0L+bmn0A++l/70XZZHj7H7IwJ7V0i5BIybBtTzyxEDAxfcBxBODjmGDkQk4pERzDpqGRPBGljKqBnUFZmmgKiM6O6kiJWAvMIYDlBkSvjoX04SIxDhpI3Ya2EMP7hfnb8i4RqOTuW45jzvIKeA4dZxtwH9E+vD+9iD19FnnkVdog/bGylPKOFcNauohLc+BiMSoonqjpFwF9Qizlyzoq1h4vRdisjhsd/UQeYJ1YpnQfOMaGPSRO85gDgVgLzbIbbQS6YKWHeyO+oNl5meErmBSBmufx9d3jkGSGwAViSczVMBVkQrWRPmRqdiTFj26BfH4h6mJXrUU3Scu0t0d9NQAdEdSTvS1ugN1gTTFPsN4p7Z+4gM74gtSErotAv3uFZAEa+ozrh3xC7QlHHpMVjjnR/86Id5+f4r2HFF7j0Fr9+Hkz0czgGweUcqcwDgtYPsA/a7RTKD3mbY9vHhijfd03ojl2lA55GUkMYQSYs47ZilUaQe4jU3bdr0h1K1yqdf/zT/+Ev/mJ/+6k9f93I2fYv0G8tv8OL6Ih8sH9x+OLthun//Ph/84HZcNm36TpC786n7n9qeoX9AXayHmxtBKHBx8pjjfM68nGwRljdM2zN006bvLLlHwt9PfPYn+Fuf+1vXvZwbr//v4Se5LXf5selPXX0tWebZ1z7KSx/8NDfikbU9R2+0tufo9ejJA8AqwBof4OcZ72u4+jQ+5Ic24CwDyM7RE6wTIi1AI34FQqUr5BEzLEJEjRJ/z0aMqpfR50sQUJ0DxLhAr+H2E8Ulelw9EYa1yyjmFPhFFUwm1CD+x/HhAsVaxLpaC0CDQ54HZL4EzRJARkbctfThorWANWGDHMB7QDOfw7WoirBydTe3WJtZA0qEMbtidNR7/B3JXMZIR23ycEavKz7Pse0SkcVeK54yad6hvZMc+pxwE6YJbF3paETFOgHxpcd2AUgZzsaZ7nUcw+iDRR3TieSJ3uvozvVwi4qjUyLr6GVdVsxkxCZPSE5YW+ldODajV8OXCrfjGJ4eO3eT8HReuJuFe/t7nMw75qzM00TBR2KukICcC2nsazfDa6drpQ0mWaSTJwUf8DcldDeTUkK1oBCR2WYg0wDz43ij4SjOEoC+B6j24arFgDpibj2T3Wjd6ccjfQ5vsCSFnFjXyqOXXkW+9DLrw0csZ+doUlJKSC6I9wH8Suzj4Xk103Bt9haAc5rD1QvRl9oMWxboFVehazia1Q3tHakLeMYocYqVHPHmTcMVnhKMzoCAg4Injc5dTShyBb2Hz/zqnPXRAyve4h8AuYxe4BgKUBouThIhl0RejcMv/Tvu/Id/Erlzm06KXt6izJpoa8U8xVpEMVsihtpbRIzLHPvFc0RTq4Z7VSzgIgFPRRMu0bXrFrHa6obVxpInbG24ZMQhMdz1FrcTmiGeY0DFhObGThtzmlGprOb0VNDRT+4QrurIp8bnPaQM5mQqZTdh64r7DhHDU8HRuG2i4EsMGvQx+OFGyoLJnqQTULHeoRSSZEQi1traEabT+DPxGDrQW2Q/0vuKq0YvsWkMrfhZ/D0LFzXTPIZsDFriA+//CGI/j6vh7YjMCnOGg8PcIc94a5EYUIYj2DqcH+BkQpjDId0WXCqpW8Rle8OtYGtFxSJCPxek7ONc6kvEaOOg+3Aub9q06ZvW68fX+fFP/TifeeMz25T1d5ge9Af8jdf/Bv/tM/8tH5q26eubpLOzmzGdv2nTpj+cXj++zi987Rf4H3/lf+Ssbdf1H0QPzh/yq698jh9+3/de91J+V7Wy8soLn+e9L3+U3XJ63cvZ9DZtz9BNm76z9Mn7n+S//+R/z6P66LqX8q7QysIvrD/LD5T/gBN56/l07+H7efDMixxOH17f4t6m7Tl6c7U9R69HTx4AThqRpOrQjgEBEuGQHJW/l52TQdsGRLQ1QOtwWyKCtxGP3Ib/T0cEruQRWeyQB3jwS/dvH87VHq/VR0RvCbATkOGyX9WD1aYErWJJotNUbADqwEHhIBZIJfpVRxerwIiwDjhLCpcjomH2deGyZhgZMbjAVd+vWUBWdLxnGmHTY1vcUc3hpTRD6GGoxq56gAUHyUgyhIzXFZnitHOLbXELp6BbZT30gIoSEcgBj1ckFdSW2F1RssqgnwGhJeJ1u9jYHynA1gDE6uDS6eKXvBLc0FzICkrFe3gyJWlMA1ul1Y6709YjhuPHx7DfI8Ae59lp4pl7e545OeXWPHGSCrMETC0opWS8ONYM7W3ULV/2oa7UVjlaZxVhopMkUUoBH/24Fl26ruEs1aRXncpqDfdYLxL7090iyjsVZArwZ7XS13oFw6Vo9Lb2dcC5TpNGLhFj3tfK8rX7LKYBw62DWEQ+J6DVcWycrNGRbCr0WrHlEG5dBDT6n90Mv3QA90s3+OiItk5fVyqjs9rDSRuR6TGNhyakTOQWsLyLRNS1jY7f3pEcTm3Jo1+bcQ1fDiwIgL/lRBeQUqJDmDGgkDLuDU05orBTRi4O3P/5f8ntH/vTpKffgxDrPapjzSHNmC+4O70bvbfoP6ZDCihNytENbuFYjo7khjOBa0S2SwyDSCpIziQ30BkbnbwRsU1sl0q85rqSpMf3SI97mTveFqp3qig23KrWBZO4eEzGfkMxq7jFMEpNGUzpa8R4UyPm3Epsg2iP6ON1wSl4O+J5wgk3b7N1dDEbqoL1JbYtddSJLvS+4Doj9RybEm6d7mDewimuGbWO+4y4oX1BU8TfW9mDXWC7mTRPTMsRm/fw+AL2e1gd5gnWA+SOH87xdBprH/ckTm+BPUKrhZu57AJc2+U9OeH9gKcUZ4XO2HqIe5k43o7QGzoVhILfjIqTTZvelXq8PubHP/XjfOL+J657KZu+TXpkj/iMfoaPlI/Qa7/u5WwacnfMjJTSdS9l06ZN36S2Z+gfTo5zWI+YO3pDHSg9N/wDK+Ur96hLve7lbBranqGbNn1n6PH6mP/hM/8Dv3j/F3ljfeO6l/Ou0qfbJ/m7F3+T/8fp//Pqa+rKc699lBdPPnUzXMDEc/TV936RF776Maa6u+7lbBranqPXoxuYefNtlvWAKVNGSg4I62n83gIe5YRoHgypQZ7Gn49YZUZ8tHdc0ttipFuU2XqP1xHecuMOoBmvZeE+HXA3JcL9VwetFUFUkd4DaNEhgXmO9yLHL+fKfTksnwHLUsYl4V6H45h4Xxl9wKK4vbUtLgPoXsZdXwJwITpXLynHFTnlLZg95JpxHJcB7dwC/LlF1+5lx7GU4Xwe7+WMtbdwh9aKtYqtdbghFUk7UknoPJHUKThiDnYRcFlTuEyv4qwjLhvW4MRASplmFe8VlzXe+/ws3KApRVS1KzllUh6OR4FeG9WcdrTYF/uMpsQdjPfoxHN37vHc6Z5nppmnph23c2KnyiSKiqMqJI1u3Th9HDOntsbaVo6mHGplXUe8r07k3RzrECW5oF2G2blGPXQupJzDOZtLRDzLiC5Hop9WFZ2mAFU5Olt9xERLljiFyoSkMbig4Q41CaB+vFi5eHzG8eyc1lasr/TWscWwpSHV2ZO4u99z+/QWk2a0gVRFdUbnGd1N43oYbviUosu45BiMEBmduY1eK61WrBneDPGOeI+BA5G4LEuiZB3O6IKkHK9f4r9FB/wdAxo24LdZGw5ohxox6pILooKqoKoj5V0j2lrDIasCqo6dn7O+/BJJVhKNJAoe4FWlUfZzxF1rDFlIVlKeKaWgtHFfmOPPp+g5ljJDamgZx64dEMm4CGormhJSEt6PSNohvgRAt07r0Ec3rnhFvNNqJ1uENYsL6A7piaQlbj2E01pSQvKdcMxrQjQAuxZF1RFJmDZMBUsF5oyqgK2DpxdkPkF2E6QJLfvooW6O9IqIo7mM+2aPoQUfMeXYuP/OSLkTxynvkTQhOgGClJMYIBFFE6AZy6e0dqCtDe8XCJXnnn+eP/af/sfgGW7dhnkf4P3xm3Gfqgfk7l18WcK1ay3eXzOa7iLzDGWKjnU7glSQdTwDCpImYIp7myS8XmC142UfzvHWw7F8Qz8w2rTpJsvd+fhXPs5/94n/bvvg+gnQP3/1n/NTD34q/m246UboeDxy//79617Gpk2bvkm9sbyxPUO/Bfrki5/hsN7sNJ+vyhfRO9vz8yZpe4Zu2vTu1tt/Fv1nL/0zXl9ev+4lvSv16+1XeLF94Xd87fTsGU4u7l3Pgr6OWlk5v/VwpENuugnanqPXoycPAGtCym5kwCewOpyrA45qBlK47iT6eMMJmyNSWfdXLjdyQmRA0RTOV+/DPduIiGPClYdJwEhzqANIKQMcO8l7QDFGZ2pigONLJ264Yg0LSMEKdYnoWHQ4cgdztohYdcY6THB5mzv00hipecRDBzgMIBs3xfigTqNvVUbMtKZwpca7BLwG1G04iP2qTtgYzmA3elvplzHUUsEql45pIUDfsLBGdKt77BZVvBtu8X5aMlompBRymQPSWKO5Y95pYZUcBzpFnOzog+1muCmmZYDHI+n0NqKZXitrN8w6WEe6jbjsjKCk3kkno7NUdtwWeDYLz+0mntntuDefcPv2nv1+x3xywrTP5HlG0diOzhgg6FhdabXSOhxNOACLOj2HwzclIWnEECcxUlbSnFEa4mkMFESc9iW4FDdUEpoD8DEcwZLK2M8BJb1W7LhiXfBu0Fp0Qp/s4WSPTUrvRluN1lq4htuCHQ/4+RGOHTEnlYnp3imnH/sQt7//u9n9ke9i9+wznNx7itOnn2I6mVAVpBvigsgU15douNPNYi02hik0XMa99XDQWkcQNI1oYxxJiVQKeTcz73fM+4lpP5PKFJ5063Hu9Y5bi+GDvuK1421E+ZqNjm9BSgkX7eV5KIAMx717RHaXTJomJBfOX/wCflhRKbgX3DtTSqRk0bdsRCS3Bex0lRhOIAZLvC94byiGOIxicNCCWgfCsZ/NUHa0uuLWUJnBKu5CIlOkoCxxD3CPmPJm4ejPkFMin+7Y37qL5oLSSSKIpADbyUkcwS4QHYMQxOu7GdYb1hJeK6jSNUHeQ9nFvvXoPBdraClxcrdDRDFrHGPFoa0R9Y7T3WM2hViv+jGGQvoaIFskHLqS4h7VewBnAy13QDJdZswqVhP9eMD6CtaQ7MhyhPNzOPGAwft9DJjQ0JNTWFa8trg/9TOcJaKhNdOt43IC7PAeCQm9n+F1odsRrxcxOGEZbx77STSOpxvmm6Nt06Y/iJo1/s5v/x3+6i/9VT51/1PXvZxN74Ac5xfOf4FzO7/upWx6mzYgv2nTu1MPl4f85U/95e0Z+i1Qd+Nff/6TLG297qX8nnr51hfJ+ckL7rvJ2p6hmza9O7X9LPqt00N/wF87/3/x5fbFq6/lPnHvjQ8gdrOMEo/uvI6ldt3L2PQ2bc/Rd15PHAAWRoRztQFh84iMTdHtKCUAZevDrZZHfEEbQLdH/2yaIwY3BRgNcFwC7CTBaaB5uMSm8R49APCIX5VoLMW1AAH0GE4/mgXAsxH37D3cn7YOJ7JDzkAbgPmy6zc+bDMSwnBbpgS9Re+tB0yVAW2DFHqs08faunEZEhtm3h7AScfpopmI0wUkXIdqhrU2XMnRpWpmdKD1jplgbY39LtMlJQY0OpBTDiA3upWjl7THe7eOowGRJI3uWaXkE7J3siidAG6RsctwGw9oLZnuDTMJmOwXJJQ8F8o0DT+3IWojfjo6UVnXAVsjXlrFOaXxTEo8P888vdtze57Y73aUaUcpmTwV8rwj5XB2em3RGVwNTOjeqW6s3jlY5aBQ53kc27F7u5OmCZ336DSHsTQXkgrUhq8rMiK+VcLZiSqiIAN0+vGIrUtAd7MrqCke8NWXCj3crkK8rtVON6PjWF+RdkTqEa0VFSHvMvl0T7q9R+/dwe/eYaWSHl/wgbTjA9/1YU4++iHk9i7i0bvHAMGUIAeMttUiKrk5rTq1xzUoKeMGvRs2oLAISO9YN2xEc6eyo+z3zKd7pnlHKhOIRJ8w0M3p5tjoeo6I9gpLXNNpKui8CydyunTUW0RDm0WcNBox0BoOay2ZevaYx1/8PF0ybo1OYcGotdPXircVRv+11YDC7m2A+hTnZe9x/qV9HCcH+op0Q/MJaMdVaUloZjSHjsb+EI0hEx0uecnhcNaJBlTJGBVspa+N2lo4X3usKXqYIyq9Y3TZ0WrHPCHMiCS8zKhVZCrjnlHJzZC2gCVIBTDwNfaZOdJXNO8QFdzjfmJuWN5FB7I3qCuWJswvDbOK2wH3FH3Ry6NIDGgrvl7EuZoKTg03MGs4pzMwnQaMNuPD7/kguyzRvZwVlugejvvJBMt5xMvnPajFrZeM9ETv57R6iCED4HIqxvOM20TrQifTpMTl089xLCLOl3XAfpB88i14Km3a9GTI3fkHX/gH/E+/9j+x2s3+sHXTt1av99f5Nxf/5rqXseltevToEb1vQ0ybNr2b9Hh9zF/65F/i37327657Kd8x+uzXvsCXHrx0oz+IPM+PsHvbv5tukrZn6KZN7z5tP4t+6/XA7/MPj3/ndzxDn379Q5T1Zn1O1vLKozub0/smaXuOvvN6AgGwI1bHljsw3HO6htuWNqKUR0Sx1QE8GfbamBoRJxyZ44SNHtsRfbyuuGgAE9HxGnDV+xt24ejOdAbwStH36hkYYO4yRloi0jeZ4J7jddNudH+Gs89pUelrHXEdXaej69jja0nycDr7W7HVEuDYvY9ABI1Ya+uDDV/Gt2q49C6jsv3K7xudueLkHDGuAXijA7k1o64LrV920CbAkL5EzK9EtyZJEc1vuYGFAO5ZAhQ3weqKy+iwTT2qUOeJKSd2QJGR25xGbLZIQDYiito0eotFJ0reoSL0VmN/agr4H75lEj6ikWPfqi3clsz7deZ9EzyblLu7idOkTDhqAVgxh9aRtqKt4+uKtRpQPEFHqA7H3lg1RwVszm+Dt46Ioskjhtp6xBFPEymXmDVAYDhlvbc491r064Zr2WJGYF3C6SsSQG83wz6ib5E0jN8jOhzBUcx7rLc2khhlv6fsJ/J+Iu8mtEQPsV8cWF58ifrVN7j73hf43v/7f81H/sKfYncy4WdLnJe7OWCixvG0VrE2BhaEcLAvawwGKAh99ARb7EcBUo6+39rCdI5F6rMqc0nRW7wrMM2MzOZglD1AOK0ivaEIaT/FrzlgehpuXcZAgbpH7bcKOpVwHKeMJkXnmeX8MfV4Tusr3sKxbCa4JsgZwXHRq+2wXum9YcT160np/Ui3ilXDKeFcL0r3hVZXGkJbLzB3fOlgFSXOge5Qa8VGOkGrB6qtWJzkMfAhBV9TwH8SMp+SUtxPIhmgjO7tPI55w+UCl45Y3It0RGZHp/kYerFjuLkZoFcyiEV8c5lGLHePWPxW4/6WUmxHFhAD7ZE2oBbHMTm5TOS8D8ewFgYVBy2Y7+jtQK8L/XARx5QasfCufOCDf4SCIqdPQzuDZvDoTWgV74blCbcVX4/xtjLH66eJLtMY3gHhAhhx1SjMpzDv4lwSwgltitU6vkcxRp96euIeoZs2fdP62Vd+lv/51//nLX7pCdX9dp/qW4fhTdHjx4+3H7o3bXoX6fXj6/ylT/4lfvH+L173Ur7j9HOf/yT3z29u/+PqC2f5UXzWsOlGaHuGbtr07tP2s+i3R7/ZfpWPL/8Yc7v62gsvf/81ruh3kUAtCyb2+//dTe+ItufoO68n79Nruez5HTDUZaQe54C7b3ePXkbU9g6u0dNLuEG5jArKl9BpOAjTFBDODO8Sr5kmoL0FOFIZHbiOlYh4ttoDHqWEpowxYqbdodXw47qhbyvJDs/a+EGg67DrBuDWHjHLkblLwBcZIAaLbRmOvYhcvuzk9XAEiyDuEZcraYBjBgT36BQdQDVWIQOAtohxJfZdH523Zj0iZEckrpYZlYxIDrdzbYhGXnJEco8NJMEUva6qGV969I1Kie7UnNCs5GnHLELR6BiWlFHvZEZsqx/Ao/816200B7TKjDdyo/fLfW6oKiWV8GmbcKoz79HMC/uZZ/d77pzcYi9CMUerwVqxw0K/OKe1hotigwd3N7pGG+zqcGiVY6+0FJ21yiWctwE9R2S4tWGIlhFf3CLmeZpGfDjhHo3lY8eOLSt9WbDWsLXSa6ePIQO7PE1VMNWAcybhaLVOs05DMDR6hvcn5F2OLuE8XPLuWO/08yPrcaE9cw/5/g9zfO9tzg7nXDw4xz0j84SUAGhmRltW+jF6oFVBpxQd3N6hxroDqhlWj/Q6YsMlnOi9RaxzdPMqmgp5mijzjlTm6Lctw1neG7TxqzaQFJByyqPfO4YrNGucQw6MYYx4/YiF1lICvGtCcubw1S9jh4sYyJAObRmmeQtHuUoA47JD8w6XDHoCvSE6oxKdsuI9OoNt3Fd6xdoF4okkwk4Tc96TcyETr403xGrU5uYZFWEqO1IH3CkWnn1VhZ0g6qQy47LS8YhaxjBbxvBADLL4csT6HMMDrQZEtg694TohZY5r2wMCByEfQygiMUhAj9vMGK6IaHcbEeCAlBhIWcaAgiuS9oiH85u8Q3Mhjz5r0g5pNa5h0eE0ju1XJQZD1NCcUN/FObI6vj+F0mFZYL+P7uQ8IdOEymkkLpy/ifkFSQtJFauPx0VxiG1qPQZxaNAWqI/R1tHdLdIcgxNxt5vGgND2D5ZNm74RndUz/uEX/iHVNgD4pOrfXvxbHrQH172MTZs2bXrXae0rP/6pH+cTr22dv98OnS0X/NSv/gyvnT24sU7gw51HUG7m2jZt2rTppmv7WfTbp0blHx3/Lj+zfpzuUed3cn6P08fPXPfSfoce335Ay5vze9OTqycPAJMDILQRe5zTgH5tmD/XcHVZx63iOgXsdaAbjsTvSnz4vzYGlQk4bBVH8DQN967i3vAmAYhrJbJDRz6t9REbnUd3q4RDz/IV/KXMASl0wF7r8RrDOYwboh3RzFvxzh5QDN7q+b2kqjJ6iSW6R4GA0+bDEdwDxAy4exU5fRmTCwMWy4hsjphqdwuzrvWrGGlJILlAFzxnkBVMUUm4rQG2ckFSD9Nua+EqtBYwCiLK1kZfaZLYvwbgAYcA1YRmYS6wE2NSJ0v06g6fNyKCSgfpiC2j91Vj+/oD3C/o1kg4JMNZcO9kFp6h88I88fy+8NR8wolmZoSShDwXVBy3Sq9GP1ZabXQxKkZFWMy4qJ2zunKwzprzAPhKEkeHA9ez4lKwsKLGgELv0YWMDLd2AxQtEyenJzx9+w63b98lTTNeG33t9Ga0tdGWlbZUWqu0WlnrynpxoNZGb5VqRm2V2o7U3mjrEV+PSF0DJuqICDcPUFkNO660NV5zffiQR7/4K7zyv/0Lzv/lr3H3IJzsTklkrDl9NdoS8dJSMvl0Ju9m8jSTSkR/m4fD17thFtHQ61JpS6X3+rbzL5y6KSdSFrRk0pQpU0aLQpLh8HfkEmhqQktGssY5WVsYTJGA3GkAX7PBMxWZSoDGYUQX0eEIFx7/5q+RUg53bGSYk2xFJWKnI366xcCDO2Zg3nFbQDzeSyL+XfNw+JOAPZIS0tuVuz9JR8sJniWuIbmMrG7gK66JPgY3ugj0ODd6FZbqLJfnAHNci56jrxugr7EfplM05XD2orgWXMq4B1ZEMpL34eQuu3C90vG2gDluDemGW0b0FqlMEQmdEiSPKu00xWk0zSRVso5bXU5jgKGj+9OIYHcbELoitoKtES2v+arrPKLMG6rCh77rfbCewcndGMw5mSIa3wy/OMR9bh7Hyg12J9APWD+M+9Ae0i3wKQZ9xPF2BvUIdoanPWYNMY+nZWtYq4jUGIqpW4/Ipk3fiP76r/11Pnn/k9e9jE3XKMf5R2/+o+texqa36ezs7LqXsGnTpm9An3nwGT7z4DPXvYzvaL15fMz//is/fWOdwA/tdcp7NwfwTdL2DN206d2j7WfRb686jb97+An++fK/4+7kPnPv4ftuXBfw68++dN1L2PQ2bc/Rd1ZPHgAuKWKREwOKKq7g3XE6hoebUoYz2Cy6Ks3wvhKxqR3v4YR0TVDXcNR1Hw7iOv7/Md5rQLToFE5v9fVaHwB1wWTF1zYAa0cHEozIZB83znHz1MTbCnrDiasDDuUpXG8accYugT+DaL1tatQMb9FT6lYDLJqNulh52/TriIu+JGLOiMoOV6AOdDTsk9joFO7W6FxG+crYJ4J3wcWwtqIqJBqRRDx6dyUFHHcLQDRAuavg3rEeUcfejvG7ELDbG5qElIVy6doUuTqewimqhmgjIyRJZHGydpIkkFvACVnDga1lF0C6X3BHdzyXC8+dztydd5yUzKxCUSHNJeBjUTxlTJXqnbV2luPCsS5c2Mp5q5zXxnk1Fkl00QF0L9m64SSaZ5o7vdZwTvfwUncRrPdwilvAdLlzi+mjH+HOH/1+nv7Bj3D6/D1SKRHHOzqBvTXscKCfH+jnF9hFw5Y1YsoNfG3084V60aiHBVsNWvQ7u1kYRT1A/uVxa4eFvq70w5F6/w3Ovnyf80Pjme//EH/sz/4oH/3h72Z/7w7SHdaKrA1VJRUliaJlRKO7463h3em90+pKb42+rtTjkXU50mofjdlx5UnScG9rQZBwgc47NGm4VluHOt6zezimS4no3nHeowHPUykR76wRoy6akDSc7RBAsPfoYNZEFqG+cZ/zL//26PiN67OL0H30F5vjLWKbpR4GRJ1QcsBNUdx6AMgxjBC15MPGrQn3Fe8r5hr92d3pBs17RHSbRge0N4QW8ele6SUSBnalMemCeCVLIuc4H8LxLEgqqKa474mAtxH/3SLWel3BE9073Va8H99WrT1h1vAk4Etsi8SghqiRtKPSItZ8yoh6RGpLRZMisoASkfNJx5BFw9cj2Ip7XMug0Dt2WEYMc3QQmyiuCSdjrfID3/PdcW6UHP3uZx2O5+ALnJxiZ+fQx/19PYQjW07CYY8h5Bgwybtwo+cJ5BQ8o+kOOe/G/WuFto7zw0EN7+D9+K17Nm3a9B0od+eTr32Sf/3Kv77upWy6Afri+kU+dfGpG+uwetL0+utbF9amTTddn3vzc/zlT/1lFluueynf8Tpbzvknv/ovOFvOr3spv6t+M/0yy+2bubYnUdszdNOmd4c+9+bntp9F3wEZnX98/Hv8n+s/oXvn3hvvp9T9dS/rLQkcd+c8vvVgiwG/Idqeo++s8nUv4B2XR58tgGcNICSCmwCjz9ctHLGq0UeqaThQHcfA0zDTtujkjW8ahKSBpbd1CPsALA5aRvXuCmlGrEEpEcnqCfdOkgBQHqG90fNLQ8ouYlhdR09muHl9dNxeOVlFQJ2raHt30Dm2zQeMZXRbXrp7NQUQ0eFSHp3HLuO1L91zyHA+jpul6Hg9w0UwD1gZLlWQttKtjyjs0UkshtuOPhyukjrJGmoab5+n6DQdUFvE8CLDaZ2vRhZcZ6CG+1DKVbdwuKkNpePWqC2c3ZoUoVHSLUpS8m7G3dDuFDpme5IbKUWstBMdzru+41lNPHfrFnfmmd00k70hqeDWI6a4C47j3WnrwtJWKkrrlVUErxEH3iRhOY2+3zT8o440i5hkc+q6oF3Il3MDSVHL2HIk54kkoF5xdaRVFmuc9RaMbL+n3D7FSnTLdiv48YAdxjHHouo4TXFsVQP4m2K9oRIR5qISINJknNYRo20drPYxpODYUajTynFfOP/Qs1z8yIeZ7t2lnEB58z5y/oiU47yyuiI+XOXdcAvwezlQwXB1W1aSCmpGTZmUJtJuimtRgBT7DwGaDxOuYK3jFwt+cYy+3ykin3034Snc9CLh3FUVUskBJnujyYh1JxzGSkRBM2LNswglC1aVVitvfuoXmZ97P5QJb+GSjXtH9MNKyWO9GXxFGPcShOj4XhGZsWbQU7j3MdAdoLhXcskxiLGe48QAirgjfUWnfcR3N6eQaDXT22N82pF9wtJM0syksX+My/5rgEzSCettJBq0q2vcmTAVXBoumY6QyAGp1xqxYzqBd9wy3RK6m4Z7e8VpNC+gO9zXUcNdAr56juNtLRINWod2BC1omqCeY93wvMO8oxRMDfY7lExfH4FEV7amHSYnqDppN8X+beNeynD5PjrAvT2UQq8HNM/InHBb4todceuGRbexjHvleoHmuBe5Z2ytqBaQBmRE9hH7fzyD/Yyvb0Xyb9q06d+X4/zVX/6rvL5s/7jfBAc/8Hcf/l1+YPcD7GR33cvZRAxpbL2SmzbdTLk7//Qr/3R7hr6DenQ847de+xI/8v7v/8bujZcfy3y75WCp4y+scH56GUS16Zq1PUM3bbrZ2p6j76wqK3/v8BO4O//l7v/Ce17+Xr78Xb/4ez8n3+Hn6GvPf5nT87uIb5/l3QRtz9F3Tk8gAO44+wC6o68W9xHfbAGX6hp9kPSIKPbRVeqj4/bSNy0JMRuuVb+Kjg6CNKBxygEnhgPYyQGB3ON3A2QHVqEkTAXFIAlm4WR1bwOejg7fftnnm956T+/RA3zpdOQSFDMctRBvpggWEa5lHuBrQrQH41UNSKMByh19KxpaCLeczlfO3Ph4N+Dv2loEU7ca+2+1ETfdIBWk24iNbrgnRFI4QKXjaqgb6jngtWZEhNb76B+ueGtolnAoakHKDl8u8MtobPeI8k0RFZvSTHYBBaMhTGTNJPXBrguk6CfOGhGvadqhHawtJBOeSoln9hN3S4qOYYScd0iC3gwODfOIgbVu+OFIb5WDOUcSNuJ+RToyT+TUUSL+uV3GhXv05PZa6T3TSyLlNI6VQq24RJ8yqnjO4Yh++IjDsWGv3EdKobcVzEg5oQY+3OViPQJ+FUQznojeaS04K5IVXVfUBWVBdR/HN41DLGDNsbpi3SApaXzd28r6+us8+sSneem3vsQbp3uOxwXOjrHvSfR5Tx8A1K3jPWKVPUmclt7C7SlluIJGd/Cy0C6UliCPgQzTHDC/R58za4NlQZaKmI946ILspogtlnCOe1dI41rV0b2cCzkl0jGOmfQ2otYBBBWGc7agrZFTfL3byvlnf5VbP/THsXYWQBGLmGbrkRhwOVAmPu4v8a8aQ9EecdjeO8wz2IJLJk1TOOW1gFhc+6VEskAPOJ3ne9TeIkpeEjIXpBrILqAmjmgi68RSH2PmEak8wLv1Gv3brYdDXxU3QUp0/4abNnp/C+MeMKLyqWv0OqcphlGmPUKHfkRzRMg7Hcng/SJuP2mP+Io5Y4DDEQlndu8LKgmViknAc005vk87SROa7+K1gt5GvSF0fL3AUfp0i/e993187IX38ptf/iIcFnjqbtw/ThSWw7hHziAX+KJIGgMubpj2GKy5vH/CFajvLpGiYHHvFQSZMtRjAOcyj3+oPnmP0E2bvlEtfeF//c3/ldcOr133UjbdIK2+8vHHH+e/ufvfXPdSnnidnZ3x5ptvcu/eveteyqZNm34X/f0v/H3+/uf//nUv44nTv/nCL+Lu/MgHvn8MBf8eeqc+sxzv87J9mR9+/v2cvXLxDr3xpq+n7Rm6adPN1/YcfeflOP/b8f+DYfxZ/fOcnj/N+a0HX/8b3uHnqIvxxtOv8Mzr73+H3njT19P2HH1n9cR9eu3muFp0YGoe9aIOXjFSuHEFpK8EFk6ggrsMeDw6gCEgpHXEKz6cu0GQG9KGo68O6JSi21ekhynXJToyPRy02OjdxQffNVTKVSQ0Qrh0Lx26qsPUWcMZ5xZg79LVS8TXhmPvMl42+kcxQ0qJ/YEi1PF9zrDYcZVNbCtGifhac1w1OoYv3ypehNZaRBTj4VKuEWMbADiguMpYg40uW4v3M1FEIuZWzSLqVzL4ggp0H85UXyMaOuXRi8uAvx3v0R1sdghATYOsiE7kdaVZDxCrATbFGq45NkISqp1EQnMGrbAoJ1SeVuHpXDhRZadKyYmULjuIC2hFjXBOWjhPs0O2GqA9hZVXFPIU6FdII2y8XLm2pS9XDm/VjIqSpohzxmq4QVvDVKLPtjnWGku7oC4d3c2kSWCe0MtO1uWAdEetghRUcjjLJUMRJCV8FZJ1PM2kJOQxfSOiA8gTLngLoqkj+papwBQgu56d8ebZkbNXHjE9fYfy7C1kzsy3TygCbW0shyOtVXpd8BpR4AElawwKJA3XKAl3xUTeguLLgtzeUS4jgAcAFzSSx1shnZ5GZLgKmgI0eveIhG5xfvuIYBbrzK0z705o0wk6Fcq6Yq1fOfnFLcYfREiaKangdkCmcEj3116lP36InuziFGqGqUOL6HLM4noWBTpmK/SOlBOk7McwSEQ3Ix3V0+hApoHM+LpAVkxL3KsE8rzHutPaMe5VRDx270cknZC6I9kpXsGFhDPtErnMtLVwdvEY1YkkFZmE3jwCBZKjKfZdUaXLKVYP0cELkDKqo1dXPHq03VAVaB3PGc3TWwkDtqI6YxbJBF6PIAVkRTRRckZsIblgYqh7RFFbi3QEJFIWesNKHDtNJ9DPSLqj6QVihhI93ie376GmNCrp0YKnHoM5uoP1AHvFvCMnCeHueP2VboXcDPNKkjzu0yXuzyqjc1hQTWBLnKe6i2h/SdDPkPmZb9OTatOmd79eOn+Jn/ytn7zuZWy6YXKcXz3+Kn/i5E/wnvKe617OEy0zo/d+3cvYtGnT76LXDq/x8a98nDYGjTe9c2rW+Tdf/CW+9z0f5WS6QfGVRMTmy/sv8uz8QZZliwW/Tm3P0E2bbra25+j1qbLyD48/ySQTf/T1H+L89ME7B3p/Pwmcn77J7UfPMNUtkeo6tT1H31k9cQDY2gq0EX8bPaTmPjpYG0I44lQK5g3H0OYjFjmFQ+/S3WfD2TeAqXlE2YrH6wqj41cD7DJAh6Q5XI9JoIYDVEqBHrA4l4yvR6REZGq44VO8V54CLPllToKO+FKLFNo8ol59xCiLxK/mw33riHU8DZei6IC4w+XnNbZzRF0j5er9L0F09O5K9GfaShelm+G1xmvmAq4DbhOvZYqREC0Rd8uAZdMEbRnxvnNAsRExjAsqKXqFMTwlqI6kNGBReyveWvKA8hFZ6+a4dbQ3zD26XpliH8oU220Na0ZvHTFBs2L1CJ7YufC0Zp7dZe5Mmf1uT54SOYfr2xCkN1QSPkekM7YAyiSFnhzvnVWcnhKS5vDzSkFEwm2bC9ZbwNY8obaQk5PFI+pZLcAbivU4djplVBMijuSM4/RW6WeVpg5TRnLGutGPC+6Olx2iI5Z79FS7ajizJYF0UoKsguaMpgkp6a0u3NF1q3lEhXuK2GY3unVsWeirg1yQD+ecnJ0wn85Mt28x7QrlWOHRBXI4w93ofYku434J7sNlezm70Lyh1qFMqBWaCVYKJ7dvg2aOGL2DuYGXqPQeXb944zIa3ZQ4L5MiNnqrAZ133H7Pc7z/3h3uny+8cjjHywTHBe8WAwuj61pUEXVSziQ5ifO+FOx4QNYL8p27uDd6Gud6Ah8pAI5AdSg5XL1cpgAkaCs+78cAgEbvbXd0OomBFFHUohPcLGLOe+sB891juKGNztySKCm2LYni5iTtXML01jvkHG5kTbhVPJUxDBIPW3FD8ozZgvfLxAKDnBCrcR/LO4R6FUdtLYZWhIz3hplDPyLTCaozqpEG4C7RnSsaznY32mqxszTRrUVncy5xZ7CG5IJ1RY4WUfmquDsdxcou7lOSMYmhC1FIt+7hyxpHeXcC7QB37sDjNyElXG5jtpJ0hnQrbkt2wD2R1JC0x9s5lJMYZBnDPaQTJJ2GUzhnsBLXut4G3bpDNm363fTyxcv8lV/6K9e9jE03VF9rX+Nhf7gB4E2bNm36Ovorv/RX+M2Hv3ndy3hi1a3zz37jX/Pnvu9P3zgIfD+9Qkmn7Di97qVs2rRp043V9hy9XhnGv13/FT/86I8iLrjcnM/O6rTQ8roB4E1PlH6fTJvvPJkS/agNvB/p/Yj1ho2O024d65VmF5g3OkK3GlDEGyC4yHANNxwIr2CAuvA2RIemM9zGTsRAkyPOVQMSeQPUAo6MzlmHcGmmzFW8sha81eH3C6dcwKQBSo34PaexxvF1jQjcsKu2K6DnKUWyrzuYRwemphG1PE4JySP+N+Kd3UYENW9BwXCTBpiJROuAXN57OPmuwDdXINp7x3oLOAZAj3WaoiZoH9/ngAuCkAe0vnTriliMLpiEO7Ktb1mRk0astCfMHBMBVVIXRDpmHa81oBoa0H3k9Yo1pHZSq9y2laeTcG8u7OeJXAo5T0RSdEZdUetIq9FnulbUjCwwTTP7aeI0K6dd2ZmQm8U5YIbYikoii5NTRA2nHHHfKSVUFM1KSiVgr0s4UkckcRIiKjfJcI1b7Ne1YYeF9uic+uicdrHgNWK3g/OFexaxGAg41nDHdhuHWtBUkJLQNJzGIlfHSrohEj0J5kJdV+pxZT0/0JcVWBFfaMuB2ismgCjT7pTT01N2+9to2Y9YccASkkoMDGicg45h3WhLpS2Nth5ptWJl4vQ9z/LcB17g9N4dQDDXGAqYczhz5wlyxlHMhd4trmsU03CLe1K4fUL/Ix/Gfvj7OH3f0+Te8GXFW0dqQzrQI05cHVLgfaY0IqNVSSWzvPjFAIEerm4loTojKKolfpU5jm8KcA8Vd8PS6B93RjS6jTThSk6Nk9MT9rs0zgFHMYp6RHnnaaRUB0BNkuMImWPA0RLr2rFpYnXhcHHgeLwAi2vP+uhzNsN8Hc7wjrdKs5GSYIaX0UksGTOwdozfDboo5hq9393pfYVmMcRh0Fuj10bva9wLu9FaozZhXQ7x5+tK70c6CRvd20nSiBavMSzSlwDQHm/s7Thc03GcKYUf+oEfoEw7ZKnRm73E4IO5QXI4OYH9rXArHy7o/RzvhzHs4m/1mrtDb/hyxOsF1EdcRveLJqSUgO7tiCeN3vPD+Tf3INq06TtY5sZPf/Wntx+4N/2e+lfn/yru05uuVffv34/Ejk2bNt0ImRs/89Wf2Z6hN0AvvvFVvvLwlVFRdHNkdO4+e+e6l7GJ7Rm6adNN1S987Re25+gN0G/3z/K3z/7f3Hnjheteyr+nN+/eH1V9m65T23P0ndMT5wCmhuNX6KN30t8WAz2s52kCAoy5WOBUN7IEnkUEVwnHmyui0ckrMro2TfDsuDfUy+jTDWcqOuHrARiuXBngp/kVJO3DmWkW8bHZhT5ikbsb2RUkYPSVM5ccvaqX7l0ZjkcLR6Ok6L0UCXARblkG9O3RxzsimeP10lW8dMCmAZxTdIW6DhcuQnOLuGcnek1zLIGUh9M5R4SwK2SPCGLitaQbzDNZZq6is1PEaAd8IhyZDJYtBIxvbaw9R7SvdVzG8TOnieAyxTb46GfGwCvVE1oreEMd5qng6ogFhDw145517ubGHiXnHYkw+0XtcnTsipR47dYxa9ErWxLJOhPRB6taSdZYJVqIDZCUSFaRpljKmEVkrZYJSYrk8rY48dhwcVARNCV0mtASf0fMoTktQ8ewS1ez9XEsAqKrZ6y1YQ7v4SbWFO8t0XtKymjOpBGlfAn5zTrqNTi+lxHf3enHRu+GiJNOb5FmRVPHe8UuDjRNqCl37t2i336KdH6O8VWs9kiUto5bw80DyFsPcNcNutNZ6KvQjgtLW+lPPcX+uWfQr7yEvf4ghjUYvcglHNIw41T6Wmnd6dbR1iLS18Fap99/k/qZz/DGK09zXB0rE5plvPfov/bRSY2RL89fANWowu4G54/pjOvAJZznonGSmoM0hIz4+J2OkSN6W6NzPCYXJhRDdKxBo8u7HzKGRpw5QrWAmm6CWQ8HKx6R4aqYE4C3GZVO9tEFXU5QW0hph7fjAM8W7mEtuGTUG/gKZQ+9YesRYYeJojjSY3YFq6RyMgZNdAy0SDjxR281pjg17m0egy1YQ7iMkJ+R1PAc92GVBNM+riFfY8akr1edxhF5HxHUMiWkrfH+ksCcZ559llQKHB7GvUYUjhrn8PIAytMx6PHmAe7dw7kAi9d32ZGRqH5u55BnrMe6U5pjW9uKHw+Q4thbX+Neb8oN+zxo06YboZ99+Wf5X37jf7nuZWy64fpK/cp1L2ETcHFxcePgxqZNT7Ie18f8+C/+OMd+vO6lbAJ+7vOf5GPPfiiGVG+QPqef4YN8/8i32nRd2p6hmzbdPC194eNf/jiP6+PrXsom4LfXz3H//hm7uxM9r9e9nCst88V1L2ET23P0ndQT5wDGQTTDAHnJAs1kLNy4AtktPH9hySOlTJLhvk15dNQC3jGN7trWjdobtVcqRjPDPdMJOHgFfMSJN5+AHmDSOi5TdJamiF3GU3T4XhbtpingXTfMagDWNqYkfDiIPdzFXDp5zXCvAZ9k+Bjd4wcFHzDbB5j1OBWit7QEOCV6jwUZLjnlMnY64GC4lQPGRJ8yOiC4RkTwZcQt1sN5OpzUbtGr7NbwdcHqivRLN3CDLiNam7egtinSGuJjDdYRCQAobhFv3Ads9472ZbilB9DugO2gW0DI1gKCzxpOTYXSG3fMeLoUbpVTihBxxMSQgGqOntk8YOw8x/mBot3ChVgr2p3sStY4n4oreXWkhkPaYq+SzEnmaO8REc0AzQa0FjdCTWhKpFICPmsKJ/A8cfLsPZ7+0PM8997nOLlzF81xfkbS9YTmhKSEFkg5kZIGZBZABRWNYz56irWU6B7OZSQW93CJXzolVfGccM10G9HpZXrrejCw1qhrZTVDX3iOF37k+/juH/oe3v+Rj3DvI9/F7r3PIPtpnNqCV8OXhh9XbKl4a9GHjNN7o64rx9ff5OzV1+iPHsLDx9SzA+3iGE7hbphKgMakuAph4vVYDxLnpyteG/1w4OL1c978ygPOX3tAPxyw2vDW4ppzj+ECj2OR3ShZyaWQFLI5WRVZK+2Lnx/V2dO4DjNox9sS17N30Bxue5yUCyodb453wWsbcx8yBlEEmrAeKpoSc+lAjzR20egkH5HvvUeXiRRFZNxLpCO2MpVGzj0Aq/eIbL78fCBN0W0rEn3LdaH3RkfprY+u30Ktx7heexwLVY/vrdHTK95AbPSfz8Olu4Afru4ZThud4R3N0+i5rng/xveagB+AdQwDCDAjMuOWsG6YROS0zifjNjBjxKCANCe5cuvOXfzWXUyB3YzoY+TWXVhnWBbwR7AHakV8D+xB5whnsBbzKR73aJWMprjWxAwp80gYkLgXpT22LuN7tg9dNm16uy7aBf/oS/+I1W7OD3ebbqYe98f83PnPXfcyNgGtbd1omzbdFP3tz/1tlr51u94UXawHPv3SzXORSRbuPHP7upexie0ZumnTTdPf+I2/wT976Z9d9zI2DR058Hde/Vs8OD647qX8DvXceHT3/nUvYxPbc/Sd0pMHgKUgKb8NgoJoANQsO3LaIyJkEbJqMBX3EdGcwUu8hiuacvydpAGTo1wV14J1o9pKMw1AXNeALdYibtVtRERLxCiL4sPl5lYDmHoL0Oo1zMlukQCNjQmJAKHuFl+DYZEd4NUJsKvCJcwNUiexTZrelug8HMU+tldyADXNb73WcMChKaKv3akOtoZz2GVAWwOYxvsFVHEdbtlLZ7DaiI72cRjCtZzyRNZMUg0XpVm4U7tGF+iUkaSkEi5Yekd17MurCdiIYiYXKBMM8I2v8ffMsEsHYZIr6AfKbMYddW7nzE6VhCBrRVpHasdbH7G9jpqSNZNPdpTbe3JRkhvSQVobkdAzk2bmbkzuZO8BSkVpInTV2F0aeClcviC5hFPbLACsQpp2aJkGhxekZLhzG3n/C6SPfpD5PU8H8NVEUgnXt43tM0EmRXcTOu+RXexHV7lyGrtorCMPN7lf7paEpwkTDfBmFhG+PWLJI+G8UpdOvTiyPj5nefiY48WRRRP5vc9w54/9IB/6kz/CyQvPQ1bs2GPGYHVYDWobUddjCGFKUDKGU9fK+ZuPeO3+A15+84KjW/QeH4/0Y6UvK9ZaOPXT2B4VHB/gfEJzju0Q8LpQz89YHz5CH12QDiu+NqjD1Soj8lou45+FZJDxEfTcx9BIh+WI6m7EPkuY/fGItk454K5mXFL8MgtgbIKKo2UHeESTu1JEyF4RBE8yAHKASr+MjCbAe88l7hXNWc8u6AYgpJSwPlOb0K2DK611rEV0t3kGJkQmsCPmRjfBdI5zBiHpjlR2qEoMFLTz6CVOChlSLiSd0LRDJQPLOC8N0oRoQF+xKU4iIdzAtkDewxT3YfIJxhyXvwg6ncSQQ8pXoFt8ivtla9jlkIfu0DyDTpR55k/+8T+FNpDTHUx7mO+AOenOXciGpHvQEnI8Q2pF6Igf416oimmhM/rYbR3O5jyGcwSmCZdwiLs1XFKkNegGgN9N+lZMFj7p04m/3/Z/8rVP8onXPvEOrWbTu1mdzhv9jSf+mrpumRkvv/zyN/W927F7srQ9Q//w+v22/0uPv8TPvfpzWyThDZK581uvfZHz5WY5hVaOfJHPXvcynnhtz9BNfxBtz9E/vLbn6LtTj/whv/7a527W+StOy+t2rlyztufoO6cnLgI6qYfzlBHZyqXzdYAnaoAM83DtyaXjrgeU1czIwh1/n4EdIxY5yYCp1qN31CvNIibVBJKBS0cHcNQWUais50gqI4V6wFgN565JJ6nho683XMKXh64PwBtdr8Oqdsl5gWmsL5yyEQ3t488EIRx84ertV87H+JaIAA4P5YDFBhAQq66dRsOthvvXZbiBBwhuFxGHrTnckXq5ty6pc8MMVBxLisaLjGhnR+mYOGIrXko4D93Ga1i4VOlYWwHHU4F2hGkHvY73SAGlrEPag0fHrCYB6/TVkZwQXVBXTjRxu2T2ySkq5KzoXJAUcNxrBTMKGZ321KL0XmnLAZ+mQITrMo6Ho5kYLJg6CUGPB6rtaBBRvzkhWZCFiHMmjXPOwu3YnERD5124nUfcNw56XOC1N1geHeg4x/MLqI3kBklGarfQ146nDjLhiTgP6uh5rSsimZwLeTqBlMKVqyM22yXWcRnxDfTW6XVcO7XidsRSAW2R3e+gmvE3HnP++kNeWTpZlbqeUe/fh7PGVHZ0j05uTw4yj2jvSm+OVYtjqIJbpXfjjd/+Elwc0QytV0wVIwC5NUVTuNs9J9QyyefRzu2oe8BDdzwn5OSE/Pxz7G/dwh8/wl88DxN8CpdzXP/RjZzkMl46hhGEjKeOeqa99BLyoY/AfodLrEdSGeyyxtdavI7TcSH6rHPC64qWk9jmqqQZug+46BbgtTao8Y+S6IDOAUutk3O4/GsP+Cu2kpjoYui8I9VGygXzhZUYJMEXtExoj/7bfjkkgCH9gKYdptE5XRjJAiLYvEPqGVLu4nmKP5kCTPf1DHpci7jEvIX1GC4ob917nXAOqxjoHpH6tntrOIwlJ1jORy+kwHwa10ldwI/gayQUpBnJE1IXxDppOUMfP8Ck4JMiVRCruN9C04Qvb6L7gpQ7Izq6wkWDW2A1otKdCzTdDnd9jqhuvEFT3M7BK+Y9qgFKQb2Tdvtv8Mmz6Sbo8pl93a/xbtbvtf3mxlfOt1jfTd+4fv785/kTJ3+C58vz172UTd+EnvT74ZOm7Rn6h9fvt/1fOvsSL569+A6tZtM3qlcf3+elh6/y3c9/+Eadw6dP78mPE23t172UTd+EbtK5tOmd0fYc/cNre46+e/XZr32BH3zhe7i9O73upVzp0Z3Xuf3oGaa6u+6lbPom9KTfD/+geuIAMOIoDU+Ke4Tuhhs3IotFE24tQC8MJy7hWkXB1ogp9o6kCekynLwdsR5h0uZI3pHcMDqeMuYW/Z0+vKraSSJ4C6grON5buOJ0jr5LEbxXkDmiTbuj0hHPdIVEwm1FZDhzPUBzXAMBIEVHbLPzFvzFeBvNDQbj9tb2pnDvigmOjvjnAdcGHO+tR7x1reGstDXycj36ZL0fgpdrxEUbI7pZduHkdQ+YmxLelwB5u/0AzhoQ2MKlKwaqgmka0cAJ0shJznnED0dMsYvC6MDFG0kT3XuAuQ5CJ0kOoDf6VFutiCvFK7ssnIgya6aUiTzPJEmojl5XB5XE3Wduc/Ke53hwvnL+6qux6Ug4irPQXdEsmBqeC0kL2gyZOgp4X2m7U/pUyMM1LctKtD3nsXsaohpxtFNGcxmDCgnNynR6ykmeacdOuzgibUElYcnQHI5LSbH/+3KMiOOrCGEN4CqJUgpP3b1H2p9QNZGljyprIa0JW1d8FayH8zb6e9dwf5ZpDA1YuHcBTQUt0bN8+NyXeeXxP6U/9wkqsKuV73rP+3n59C4XD19lffQGfQk3u+PQHRePPmqzcB1bx+2ci9cfgDnTnRNylrgmhoNczHENWOuqSM6UnEm9oShiHa0N7z3WmTSAYz1ib76B1Y7u5ujerT1mJXIZEH1A3fFwSQreK10V2jHinr2MCOEW7l/GJeaAdHpf6b2T8oz4iBZOOc4bMXQaLlcVvJxETPKa41pcV3Clq2AUkIpOO4ROz3t6XWA34d7preNrR4ugvoILzSDNM9IW+jHhrdLbgpkEyBRGt3HAalKBtoA0sDyutwTVoBui4N5jEKVVVCfQMs6nPrqMNe5daaKooC70lKLTeNzLosN4jXP/crqgLvHaTFAPsLsF3tG0w9s5rvu4p0AA2ARC5gPvfx/PvO/9vPzll9FnZpgMz3dRyVhdYS9IPgl3r+8CIp9O0Cq+PkLkHlrugldS2oU73mJ7oWJeoC/ofBfkAjeni5PNvoUPp02b3t1q1vjJ3/rJ617GpneRDn5g8S3q9Lp1dnbG8Xhkt9s+/Ni06brk7jTbIvBuqv7di5/mjzz3XTfqw8aX7It8QGZmTq57KU+0tmfopk03Q80aH//yx697GZu+jh4dz/ipX/sZ/q8//F+yK/N1LwcA047r9pnedWt7jr4zeuIAsKR81bsrRsQa9x4u1T5ikjUhYhGfnPYBN5Ew2OZw4OnooQ0XW0J0Hg6/6IbFZHSsFnBBu4fTdvT9mncakLyP5teIg5ZWESkohogj1vBsuM/I6OBUi97d1qKLWLNEf65EV66P/lSRiasOXvqV6xeLeFO3NqJZ/W2WYQ8IjIxY4nAGX7mL3TE3Goa54s3hiitLxFF7+FTDpSzhmtOEeSdJi67ctA83H+BpAnW6QBINgOdcua0153BVp4Dr8Wblav1xQFIw3ZShN1KaIhbYCbjVWzilh3s7tieOoVnD28rEwpxPKe5kUTSn0akraFJEPY7LU3do3/Vh1iz4G1+BZfTG5oROCS8KxzrcsOGw9lRIfaHkjPcVa06/WOitYSmRuuGjH9pqDbevSHTGThmZMqIa0dziUArcvk166mkmB3t0ARePkItz2sM3A4omCTg7+ei3znEO9TiG9AYnp6QPf4jpIx/mdCq0Bw/R2liPR5ZHj3AuwBrdpwBwHvtOUiHNKXpdGbAaD/grgiQhqeJeuf/GQx434/S73s8P/egP8/ydU279xuf59U88or/5xpXT3NMMk5I1gqVj94UT1B16WzgcL2g7YfIJ8QDFPmqrs0RssAhI1uhUzhoA3x3Xlbw2ColZZ07zLTwLlmeYJqS3uBIvK7HT6JzGoEwRJW01zvM00WqHXum/9mn6D/0InNyKAQx7y+lqHv3DIilc5zK6r9txvOcRSdMw8YfT3usBSQXry9X1K5JwFLcOVqOXtnXUjdwMLw1HSHlif6uwz86yGq3s6cuCm0fv9eU+yvtwf3vHxRETyLdiwMU9XK6ueF/CpS4a6/UYIJE8hmFGfzRex9cEGW5zsRhWcK9QbuHreRyo1sGOuOQYmnAL4DxNI32go2mi+wq0uCW5QZmBGHARd7wZrglLE7efeS93nnqGlx/FOSplhrMz/O7dEf99G9pFDP30NSLopcByju/uIn5ApSB2AHZ4a3g9YraiusPzHsl3cD+C5LjH4/hSvx2PqU2bNm16YvSTb/wkf/GZv8jzeXMBX5eWZaHWuv3QvWnTNap546/92l+77mVs+jp6ePGIn/7cv+HP/JH/iJJuzkdoX3v+S7znlY9s7qVr1PYM3bTpZsjc+OUHv3zdy9j0eygSNV7hY89913Uv5Urbc/T6tT1H3xk9cR3AnhR6DxiW4hciCIKIxS8vYGl0/3q4AXHUNb6WomM0XGuCAOI9OkBFCTAsAaBGXLFoCqCoguaOCmQs4JU7zZzejdYrrS/x/2ulN6N36MsB643WKs2M3hVXsKQ0i6hkT+NwioIlnIR7OEBhOI29D0fv6NYVjTjrywbdy17glMYei7hnH72jjtIMugvWGqjivjDegeYSoFssnJKXedIWIKs7NKYrSA2GpAnXPb5WWq/RmWyj1fitWl8Eiw5SNCCYDDhofWyzQasBnrqR8hRuQbk8EuN7nfHeHhGvHt22WU8oEn3OyDiW4++6+IhHVvzsSPvcFzh+9kvUxwd0yuGwHP27WgoyT9HRi4YZuwZYU02UPFN0R24dlpV6sVCb0VyoGr3AvVuA8ZwiLlhSHIfe8Nqx84V8ds4th7u373Ln2afI+x3VjG6G9QBk9Mse6eiDNdfRK5tje7zTLs45v/81Dm++CbsdfucUdmWc5zr+ng2gLQgd1YRqIqmO3uIc50IQ9piOzgH/2vERh8cPocDpD30v+T/903zwP/4Rnn3qaXLZkXJBRNGcSdNMmgO2pl0hlYyOSWtvlX48UB9fcDwcqbWyHo4cz85Zzs85nh9Ya6WbY0h08OYCKY5LvnOb+SMf485/+KM8+6Pfz7Pve4osjpiRUrjOxSEnJQukZqg5eXfCdLqnTIl53jPdOqXsZnJJJEkUYPelz+PHy26ohLngpDg/PWLNEylSAsa5RLera9SbYeYRYWwd0Rz3Drsk2p2UWgyc6ASEq5bOcIlnSk6IQot2YnqacXOyr+GsbS2c0L3H9V5yOFwl1ov5qAsf16wY5Cn2PRb3BbVIT0gzaKaj9Gp4X/G2Yr1jC/TewsndV7pBbZ0wy6bRvx7ObiSTygk6zShTrKc7Xi9wzzF80y6gr3A8jPO54ya4TqgWtDWyC7fnmbQcsLMzvBs+z1hbcdYAugdA78Twx7JEN/bJrYjV1luoTqTd0xHdXRfcHU13sHaOtIq2BWrF6tm4v49kgE2bNgFw0S62HpZNf2C90l7hb77+N1lkcwJfp3rv2/W7adM1yt059uN1L2PT15Hj/MYrv8Wjw+PrXspbEljnI6++9wvUvD1Dr1PbM3TTpk2bvjH98ku/Tus3JPFke47eGG3P0W+/njgATGvRsysSjkpVJM3hcBNg2iFFI8ZVp+G2jLhVSXrV70kKJx15TIB2IjbZbYDj4TAdcEaUiCBOp6hnUp5JuZDn6JBUzSiOSkQad+vUvrCaY63TW6KvRj822rFS14V1XaitUa2xeKJjmGaQKQCTVwY5RXSHaB4O0svu4rFO0XDmyYBNw/UbwPESBiWsOxWj2iUMjB7RMAZHjDEOSYwcZmGkHwN6A+oav2A4rQO0Yw2shuuzBwjrLSBPmHQlop4JsJt1uA4vrcc+YFoLJ3AcoI7XAz5cmqR5gPAR0zxAPeM45ZRIgNql+zEAtx8v8KXSj522NLw51juLGRcq+LyH/Q7ZFTSHQzGitxO0Fq9lPjqSR++zCkmhiKNrx48LdjzQzANqNo9v9UZ3p5vQm9FqpddGbzV6bJ9+mumF93D6gWeZ7t0OOH9+xGqPSO9UIMcwgrjghxW7WPClw1oDDC+d9aUHHD7/Cnb/MffcOTXBjh2PGmKsd1hXVDNpt0PnfbjoI3c8QGHvcKyw1tjeDtYafVmw9QJkZe2Nhy99mfa1L6Fz4j0/+FHm555Cd3skz9Aa0hpqHSU6k5NmUkqoONobsq74YaGtjbV1mgfsXi8OLGfnrGfn1OORVo3WjW7xgUrgWOFEF54rzu17d8nvfZpbd/fcu3eb27dvMZVCVkju5NqZ3ZlPb5Hv3CLtd5T9nul0z7SbyKpobRTN7E5P2CvsP/uryOK4BPiPAYtdOO7Fo9pZ3h7BzlsOdiL+XSQilTUrkqEUpZRM3p+Qy0TOu4hg7o3ex4BESXgTrC1Y6yzNuWiVlgq1KzbFPasj4QSn470PV73gusOq0/vhKgmhe8d1DCBoAZS6OrXmuC4HUKYLtXeaK6aKpxyOd5NhzE9I2mO2jhR6x3yJ+GwNZ78RyQYmMaTQZQ6nv7cAvioR2Z0jQcFao7cVL4JNShPoXvlT/9GPka0GBD8ueJmx9QxrgrVwbbsazBOc3EJyRnwB1hgWmRT1jpCQVNB8gqpQ5qdJWuMc2t+l3H4+jm4SKJeDMps2bfqJz/4Ej+qj617GpnehXm2v8tqt15jnmxEH9iTqK1/Z+rs3bdq06feSAz/16/+SB+cPr3spv0PrdOTx+7dn6HVqe4Zu2nT9+vSDT7P0DeLddH31za/xM7/1b6O28YZonY688sLnWcs2iHdd2p6j337dnPyad0i9VzSfhltUE/RjxJ8mRVIK92oajlJ3aIbXQ8SPuqMknBZwbUQxIznoLsMZbArSELcAnN4QKXiWAGWAqEBvwVtzRj0N92SC3gOGuOIYrYb7TpSApW6kXodj8TbiHcuKuSBayRIuOxVQdxQFMdwtnM4jclckDcBSQOwq3jWQr78V7zpuzC5KXw64KV5bQFJx0iVgdMi5ILZG5C0+upPDjYlbQCcpwwXYI57ZYltICU+ZToWUUDEiiXt0ECdFUjgq1VM4kM3Ge/SrLuCY0R3Hwm0YjT32/XCAB41TuveAOe5kLKKf8QD5WhAtowu1Q1vxXDDr9HUhWUdzGbG+4UqmNqStqAuuBXJAYAbMBTBrCEqyleIJ741sCnaI4yhCTxkxp3XDfaET8dhqPpzmoFNi2mdu72Z6LrwmcQ45sb122esscRxElvhTvYwSFhyji7PcOmF97/Osz9/F33yM1xrHuDnewy2acxoduQ2rDe8ttiVNsf9zjmMkESPuS8SOa5ngeGD93Of5yuOF5bveh90uPLz/GlJXJnfUneaMuOyAiIai0lAKLD0AaSlxDl2c0acJpoKUcNCurWE6AL8BrcI8I7mQckLu3UM/9GH2H/4AJ9l5/MqrXJyekj+U2Skc2xqx4l5J+xP2t2/TNPoIzA0dznDrLXqpEcqd2+TTPVYr5eyM+vh1enlmdHpH/LdoJAfERESJ8wRBNEcM8zSDGmoRId99xY8BvYsmelsxyThOYkVNwr/uDTqkacLrSpUJmpGqMbnTvFFyIklh1UpOipmPqaoY8FDNYEdkYtzzYjgiQPASHcAS0epq6+jBXnFTrDesH5FUYsBhdKObrbikOOdUkG4074gIvUWE8igIx9dDpLHjMXyBxxBLMPNIZLDodO6SYt10XB1fFvpwKKs4U5m49/xdXntcseMZVoyUd/j6OOKtNeGHAyZKKiXuS7IfTnZD24KmNDqoo+tYIM5vvR3XzBgEwjTmT9o2obZp06W635wf4ja9u2QYf/23/jp/8Zm/yMfyx657OU+kzIzWGqWU617Kpk2bNt1YvXHxJv/Hr/4L/usf/i+4t79z3csJCZyVhzwubzAtWx/wdWh7hm7adP362Zd/dkvSeJfoc1/7Aj/6wR/kqZO7172U0HACr9Nhi4K+Jm3P0W+/njgADBlrR0QdSfuAtYkAuZTBceUqUtiLDohro1s0hdtXfLhNFWqFlCMm1UFyhmaX9CBggQZkJJURc9qhTPH96ohqANLWIYV/WC1jIrgGlL3slMUaTTKIorUHqFk7OUf3qqUMklA3NEefcOoBNJLkiK4GXEY/sIbzjavI48u+4PHf3gIKSUS+mof7VxDUdMDUS+Ad26wasImUBoCcoK4IDUmOWBoh1Aa7ObhtizhmbwuSIqq4O5AMk4x6QGvv69X+kxa2Y8fGfl/HNgk0R6XQGQA3ZS57jJHYLnVBLPZHmXZMu0KeJlQU6RYdqEJE+GZFNXphzZ1p2pHnme6Or5VkgCg276ND1oDe8eMBrw3xFGC0CW4xXJBHEvYkjZSngGkex7qvHV87hpGy4Doj0kllQlLiwjoPcdJupj9/j3L/DvJSwo+A+YC843eiW7n3y7jsRqdgdLxX+qNHrF/5CmePHuLV0OPCbE4VWDShkyIl0d2w3ukK3iPeXD2uBUk5jqdZvGdKsR+TIOLU8zNe+fJXeePlB+x8pdoRVeXk2WdpdeH4+JzelogRlgS0AIv0iDdPacT/jkRxaahqwECH3hrd4rrz4pSeURNsB55nWFaObz7gwavKQROPv/YG5xdnuK0cD8dw1JaM7vbs7tzm9O4t1to5qMLhAmmGudOPK3Y8IlOhnJ4gAmZOvvMUst8hHkMVkssw++ZYN4bTrtIBIup5xEJjuM54F9xXjBXZ7Wl1jTjr2iBHl3cup9CPeJlxP8br2hHd3yGrk7OT50LuRqsrbTU6HbGIAo+ucEeJHnT3BTEhTdF9rutjchp9vF6hGzKVGBgpJb6W54hbL1PcZ8wQqwHQeSveutJQBe8VSTO0BZkKKnEfkXKKiIeb2CqedxHVnkBGD6/IFAMWpUBbUNnhmlBPqOZYc9pxemviR7/vh/jnP/vv8JMZu1jxfYP9bWRxbDkgJztEFDu+ie92qMxxrFDE10gaEIHucfxE8G7AZTdyG0b+hEiBefvHyaZNmzZB/LPx/fde4ENPvQDA6xcP+eyrX3irCuT30Xk753F9/ET+ZHITVGvla1/7Gu9///uveymbNj2R+kbvlZuuXw8Pj/g/fuVf8F/94H/Gvf2d+NnhmlWprL4wsQHg69D2DN20adOmb1zNOp948TP8ue/90zfiGbrp+rU9R7/9euI+ZjFruGiAv2UZMaAJaYJkcAWhjLzmcO8x4nzFO/QVkWlAUQIKlCkAo0sku3obv8cvVw93ca3hLmZYy0YUNRav7SmhWXGLPmLPBekVp4AkvB1wc3oK1xsC1h6C7gCn9k5y6M1iXeak3MKxJkTHajLUjZzKpcUO9RoptTKopUSMcZC2AW8Zrk6V+Lp3XAtOhzzhvYEkRBRPCfMaO9Oip5e+xAZLCXjrLWKZScjaSWq4d3w9jPcH84rkKRyostKTRhcoOdZujase38vOXHQAsVi/9E4RpfpwfANB/CFaixVyuKSzKKUU8lSif9VB3NCSSXMilQwdrK+oJvI0cWs3g8O5Jo45Y8sxzhNG12s1zPvoegWvDmZoX1FJJK/kaaa4U8RIdBRFh7vcYQwkFNw7KnF+1IePOfv8K3ztceXiua/i4rz52n3a2rDeAAmopoo5WF8wnG52aZKmS6e5Y95hWVgevMnjBtNuZn72GRCnPXyT9PBhgOyUaL1SJWNeSQLeHKkLmpQkHU8ZSWMwokcEufeEV6W3TlsWjiQep0y5c8rJc7eZ5gKPz1Ez7MIQJzpbu2HV8WW4qJUxDJHwnHGV0ac7erx7py2VY+v4bkJOT5l3E3meybsdJ7sZe/OcN9bKwYWzNx9zcX6O9sraGr0bKWXSfofeuwN37qAXZ5QzoUvCrNLOD9hxhSkjU4ko5dohZ5LE2dfzhGuOgQ9RRAwjRTQ2gtQDMt9ClgV2JTpqXVCVuN5VyNqJ0POEieK2oB2mtMPV6W04+HHcKyYzuVbYzZDnuG7aBWtzViyGUMTwdcHm27AupFJwSYjPuHpc6qJYnsAvHw0dpnB4U1eYPHqBTeK+IB7wV3WEBgjohC9vgJ7gWrB2iMEOIgKdJpjm+Lo6bgW3uGeJRDpCXJcKegqaMSpKxdXREn3prgnrK96OaOBs7p4+w61ZeaPF8AuesMcXeFe4c0qyGBRxyyCnYA2zM3qZadOOYgkpAYHdOtIjutqXy0mNfURo1wYc0N0NmVjctGnTpmvQ++4+j4jw1Ydf4y/8wJ/hg0+9wDySGVpv/PEP/hA/9Wv/kgcXD693oZu+IS3LQu+dlLZ6g02b3mn9xGd/gkfrVqPwbtGDi4f8g1/6KT727If4Tz72Y5S0DYU+6dqeoZs2bdr0jeu3X/sSL9x5nh984bs3CLwJ2J6j3249cQC4IajDZVer9x5uRdcBMgRvK5I1nLjiCBVKQSXjtgSk8IiMbr0iPeNpwA8U1ALuEhAropYNyQlpFR/A6rK7E0mQDNGGEDG3th5RCbDk3SGBm+B5h6pC62AN1z2uCfcO3XFP4XjtFvCvV7QpQsN0Dg5Ep06niERsspZMMoZbOPZNwCsJcD0ciybE9rWOS8Zzpi1HpDbcHXdDxWK9QqxPCBAu0NwQaYg4RoJ6IJMRb/TRTay2oKkgcoyo2TaiudXBwNcevaROdM+ah0nZFSdgudgxHJ/9GJG93hHrKIJrD8BFjyjaXsE6WTOTreReUBG05HD7uiDeUU2Di0c/MLkg+8LpC8+zO71FenxG+8pLLBfn4aoF+lqxtYa5vHk4PTWRGBCNhIuhOTGVKTqBRVE1ROfojW0WGNBB1cOB7oYvB45vCv1YOX/tIULnYj2wHkYHsBmQIXVEL/toE8iCuWMudDeaSLh2JUDw2peIfT5bYT3SHl3gvSMl4xKsPQCyYlj0absFWCsaYNCjF5peEaZwnraI7rW+Yt0iergvrP0C3820tdGOR6RH1LJ6o68dWo0OYNURSV4ipNw7tq70FSQXvKS4VnvD2sqxzci8I+9nyp3byK3byNNPwfHAowePeJwUzXB0o9bRN5w04F9KWG0cz89pdZzDvYfztzZ8LjFDUFtEPM8FzQVpK9Obj1lO7sT1iCNi0JXWjqScAMWSIl4jOX7EHqMlwH1bYToF6SR1mDP1cBHzJShr3qGyxvWGI2UHhxg4cTOUSCpAnG6N5sS1ljJuR6oL1leKrVgVyDOa8rhuO71dxCCL15ECIHFMtdBtHa3Zjttj0H0AVFck7VAd8fZe0ZwjalokwG9fIKfoR/YaEdLeY0jDK0hG0wwyHLhuWF3xfBLXucxYu8BN6VS8CyqG0SL23FZEdnz0B36AZz/9KR4dzukXx3DuJ8fzTBfoF+dwW5HTW+BOXxZkn9F+gdoe2gWZHYri6xHrNp4FJY5Tu8CSAgWh4MsWMbRp06YnUyLCn//+P8O+zPz2/Rd5393nr+AvQE6Zp0/v8V/94H/G/+8z/5xHx7Pf9zV/4fwX+KHdD5Hlifvx5EbojTfe4L3vfS8nJ5uDbNOmd1pn9WxzAb/LdKwLv/ry5zhfDvzgC9/Nh5/5wLV+iP34zgNOz+/GIO2md1zbM3TTputT907zdt3L2PQHULPOv/rtT5A18b3v+eiNgMDbc/R6tT1Hv7164j5h0aRoHxmyA2eEW7YGLLRwoVElgNNuj9QFpoAbSXa49+jSxYm23RZONxESEUkcblUJYGcF14okx2VGGM5VACzggneE8cFZD+dr9BT3cNV2Q3IJl64ZlASeotey10g1nnd4W0gWUbFo9IaaOy5CwrE+yjXXI3KJdo9HWp5gWZF5Rr0FFDLCPexG63GDdirmTjOnHw80j23245G+m8lWowvUoj+4G7iM/t0RPRz9mQbtDEunJI2e4HQZoa0RRYuMns5LN3FbYr/1PqJYww0KFUzo3XE/opppdQWUxIqhuCS6+FvH2mVQ1QSW6H1FpxlJKXhTNSRZrAWJWOOmWIvuYzehi9Lv3kKefYakwKuZ3o12fkHvNQYMwriK5ZgLiO7XKSJke8PTCarKVBJTTmhJwd1cIu7agd5IZUKTDOez4J7AK/XwmFoDVLVWI97cNba5LXjLyNRjERm8ZnpfaJKpgIlGt2+ZcHHscMDOzmg9HKGOw5TjPLOOph15WumaoC14s3C2S4lTztbYf05EP6tGL6+DtYq1y5jwTuudZa1YXnBxZJ7It0/CYK4XNLsAsTBAy+imRcJlazHkYB18PUfmgqU0OqsVXyvH+w/Q3uHpp7GnFnw5px1Wjo8P6FTY7yb2U0Zap7ohmjFVVnPa2Tl6cUFvjfVwjChmEfxkN64p8CnezzWNePfEdHyM+EK3hJmRR9e0Wpx3gkY3tDtS5ogw1x1JiUGFaR/3j+60tdMTAVLLhLvRvKL1GNdYXcYgiqEVPBveV3I/QE8syxGzjNARyZhAGa5d5n1AWTfssCK7DDqBreHs7TUcv9YwNWTK6H43IqEdZ4oBj35E8gmSLqc81ugqP7kN9IhrlwrzDnHAFpCM9zim7or3Gm533WG+YhgyzXQf0e3eSTGhEvdHT2gWZL1A04zkPdYPdIve5f/kP/9zfPkf/T1amxA5Q3Z3QIx8/gg7vYefPcJyQ9sJzIK0iWoXoAeMRO8X6Oii1pTRfoakE8Q1zrl2Tkp3orN8PXybnlSbNr279HB5yItnL173MjZdg5Imvuf5j3zdP3/q5C4feeaD/PJLv/77vtaL64sbALlmffnLX+Z7v/d7r3sZmzZt2vSu0RcffIWvPHyZP//9f4aPPvvBa1vHcXeOj48LNl2Ptmfopk3Xo8+9+Tn+yYv/5LqXsekPqGaN//OzP0934wdf+O7rXs72HL0B2p6j3z49cQA4I29182qK2OfWIOmAvwpecGokPNeKS0Fao6c8AGq8ROogOoeD0iP+VRxQJ5PCJWjDUKclnHNJ8B5toNGVK9GX69P4yCuN1+8BJ7uNdV66Kupg1tHdK3S8lOiX1YwPCObm4S5uCykR9FElnMmeA056uGDNRhevdTDBrMEc7yaLjX5jpa8H3I3aPOChOS6KEU5lQWmSYoN7DUAdubIRGy0xRZNkQeUUKbdJqmgqqCkqNbZpysCI0VYJGG8BgGREc7tGF7OKYpYgKdpi4ssu87cNOgHiTBJqK04OJ/DoIE0tXN9JMikNwC4JSgrwSIc04S5Ij3jsVAqaHD8e8Vfus1wcOXvjMevZIWKLm0WMtwZAj77pHHHJ2Pga9JrQJmFsBVKZydPoaO59wP1wC2vKqAIjanccsTDbtpXe1hgSSBlJSgL66FbtK5AUHxHeETRtVFFEYpSB1vCkmCR6TpDjPAUf0eGFXPb4tKdJZ714TDsnzjPrNAlHsTrRDY1HJ3JdozMaDaDYifNXM5iR5pk7T92jncwgxu0ykSVzfjzy8MHrPH5Qoz9WU7jzJZyw7oluDekNSeFMxqInVzWubcM5nl1AM/zRGW2aYnjBnfzMPfSpp7n9vts8e3aBv/4mF81YLOKgbTXMLKKoW8dFYCrj/YUu49zEEVIMCDgR+WxCUgmX97hf6HTZJ9sRzYh4RD7nt7rCVVIcX0BljltUq7g46j3i61vHZIpjKxK94wRs17yP2OS0C5hfTiPtYMQY4x2ZFOkyrqEBkXOJe0Aihl4YQyLThFCwekHqHUkTohnSKZESsCJ6K85nq7h1rB7wvI8eZG90N7ApHN9umE7g/aqjm0kg3YZ2gSsjvr2iPqP5NmYtdnOtcW4rMQjBjOyju9c9ALdOiaTKc8/e43vf8x4+87WH+BkweXS7CyQVfH+CywJlF+B5PYIrLVnUtSfivHdIk2J+grSG6oTmHSo78AWl46l+S55Lmza92/XVi6/yyfufvO5lbHoH5e584kuf5k9/7Mco6ff+ceJPfuSPclEPfO5rX3xnFrfpm9bFxQUPHz7k3r17172UTZs2bXrXqFnn//zsz7HL/znvu/ee617OpmvS9gzdtOl6ZG6bA/hdKnPjX/32JxCE73vvx9Ab4ATedH3anqPfPj1xAPgSREqW6KVsLYClhoOPlLDRn6skxFr0VPpw1dIDEJLoVFQLgbfsKjY5nKstonHThIthPTqCRaMHVLXgdUXmCa8rSUv08GaNXtMuRHVnwSVifKNyOABlgBIiG90JMOOO54K3Nf5O6hgRV4x5rH0avaHVAhrj4Tx2AhzTAtTWYwDlGq7fDrThnm0umFs4E/0c2EXnqbXR31tBooc2iZAZUE7GftY9SXPEXftlZO2KlISU0Xdsoy+41eGYXMMxyOguzgUuHcMjplpyCsBrEQdtGLROtyMmp/TjY9g/FdRUO2B0h+SVSXL0O5uCBbj2UYmM+BXMVwmHrki4XspuB/Oe1s/ohyP0hmDh5C15mMyjT9R6CyAnGrHC1vDeUHR05q7QdLx+OI89C6iOAApFJCN5xHH3jq0Va+HKFFK4bkvGrUWcMY60SmuGt0oXxUToIlRN5Jzx3QQqEWfuHpHRqhFvnBNzKTx/6w7vf+ZpODnhyxcHvvJyxY7nWE7Ybkdbj3irKBFxrUJA+25YtRiOSBNaAjhLFtJTT5G/52PM73ueeVnoJJ5+4X08dWvPa197leOvf5bDxTn1wiI2WlJ0zV4e914HsAS3NuKoHZ0iFpqRSF17RY7Qah3Ge6NbR/tC1vcxve+DvPA9H+L42pu8+KUvc/76GyzLSq8RtSxZYjggxVGw3jEX3CNmXQVUZJyfDmmHjgJwyWlcS1NEHzMs4aL0VmMgQIAUAFisAxnf7enWMCng50hWciqICL0D7YjoDrEDNSmqGe+VNJ/iKXq2lUsyLpg3REqAWQGhoVnABT05xdYztDiJE2p38BohCcyUSeOeGMXcAa09xzVNDL5ET29C5+FaDws42QzyDtFwfqcULloXx+dp3LsGXPeEtBXKDKNXGol4bFGJARUUWkUL45pxej9H3EmSQXakBHeffZbpjVeppyf48QBzwaZbSD0iksf9Y4F1Bm3jfnqk14qnGemOzwcCn53pAAEAAElEQVR6U4pnKEq3BZWTiLdeHjPlPZK2aJJNmzY9ufqVlz9Ls86f/Z4/RdKvH5VVUuHOfOsdXNmmb1ZmxrqOf/9sH8Bs2rRp0zesY134jVd/m/fefQ6Vb3F85Ahb2nSztT1DN23atOkPrtorP/25n0cEvu89H/v23D+35+i7Qttz9NunJw8AawYS3pdhSI3IZukCWRAXWFv0TupwriLQajhB1SICVT26TTUghSABLNyj89cc0YJodAuTI1K5X77ecIJiIAT0TMmBOkBWQN00zKyXoIseLrlw0SmqM9hKSoXmI766hCNRU8ZKdOu6ewBqzdFnmjWAaQ8gZa0PO+n4YSXPI8Y5Y62ifSVJokm4qJNEgHT3wqXBk+6oKkkTIgVcSJrRJNGh22usYx4OQjEuu3iFiLl2T4gHBKOOIEA7Yj5ct254NDkDBe9LOIM1eonVOkjETaskugnISWzbFJCcizPYC8g+QJxXOh2zGe/TqLCNPuUrF/KwfY961YCYt05Zbp2GA/dwjuaE7nZheE4acF/DXWvLEckZ1hrx1SbQWwwOWIPu2AF661Bm0jwNByZxLnlHRZg0Y6VQrWHrAmZx7pWI0Y647RgOkLA8Mip+6RBR0eIsSLi1cRrKakbpDrZiqZBKwueZcnLC9D3fze7HfpTykQ+Qjo85+ZVfJ732VWSNmPE+Kb2tdI397wiTjt5UUTRJuKxTDsiZhEizduzVVzk/exTw8N49Hj9XUdlTa6NfHJCkaEpYj+5lQQgQ3pF5F8ezV6SPOHPNkAqSBXWne6e70NURaQzDLL6sLK89IrXEvDvl0b3/P3v/FmxZlp3nYd8Yc8619j7nZGZlVmXduxt9Y4PoRuNGASBokqYgEoQZJC3TgnUJSxF6kvyq8IPECIatsB9kyZYiHI6w5JCtF4qWIyDaYbpJEQiKABkQQVwI9A19q+7qrq5bZlVezzl7r7XmHMMPY+6sbqIv1V1VmV3I9UdXdGaec/aee6251jx7f/P//2fR44LXhbrfMZ/tWHb77t4dEJ0D6neHrJMitrqUgJkaEBhVsjp26PYVxTQRnvm4/tz75gvJuO9AB1LKZKk03wTorAskJ+dEdch5g2C0ZYlUgBQbPdyMJA5USI7ZQp12JGsgTnKnutKq4ZJi00GNe5s2wvE9T5glvHaDubXoDWbBZUJFSanEvbAtiNXoJybAe7OE5BTmZc+0eYYSmxXcHM+KtBnJJ5jtYwNOv81Ij/JGCqINbEb0CGHCDptAWkXyGPep/rq8Wr9HJ0S3uDdam8F2kDZ8/Cf+BJ/70pe4Ozi1DNjd23Fv3B5DUqQ1ZDb0uGA24Wzh7m24eAF8j+sIOiDeWKwhUyMVaOevY7mgwwZHkXEFwKtWrXq49flXnyOp8qNP/zCPHj/yLd+o1VY5WyPz3zV65ZVXuHLlCjk/fG8TV61ateqt6EvXv8pPvudjPHJ08e194PUz0HeN1jV01apVq753uTu//qV/irnxI09++O2Hf+s6+q7Ruo6+M3r4jmar3f0pODmCdJMFUDqA2yB8PXZYwhmM9z5K626+AI7eJkSUphruP1/QFh26Ea8brrUkAYqTKmbWoW5AIk/hznW0Q4cloLIq4i26J0V792hDdcBTCdhKAx0CSQVvCpgminQ45FJwb2gaiBzUAEMxXgc0QGEa6TZkxA0SeKtY3jIvKSJ8dYzoaAdUSU0DPnoNWKMS0cnqiAWAFBzR6NM99BbjAayw0o9lRDx7bbjP0e3bFjzlANFZsXr48DDjNvfX0iOTq6BFcVuQMiDzfA+A5lYxh6pHAc632/jZ2qNu8xHYjNMw1YiXJkp7vZMqwSNmt+QA6eYsp+ecf/l5yAUbBtIjF0luuDc8SY/FFlwrEDG5otEtSu8oVm+UvCX7gpjhi9OY4zgeNgqYIWngwvHI1atXOE8Dr16/EcdSLDYZSMBy94D9bi1gmTlUDxDngtWFljK1LdQhY+7IfodpwkRpYuFm9XCi+5g5359x+/d+m/ap32N/tuOVV1/jzm6hXH0MxWh37zIvhbZ4AO2kWEl4HkAMd0VV+rzpGyRyQpdGe/2U8xvnNE3oq7fQV15nf7xhNy3Mt8+Cv6tQq0VMtaUeoR0xy9Za7IUoOZzvSw0ne4eMuszYNNGWBR1KXIcp4d6oS2V34yb5U58nv/QyC8LdW3dZdhNtXiKuWZxU43jUaYKSu9N3wVWRHnGuuQTUVmW4e5N24QpLj22WtMG9dme3gjUUi7h238S8b42qGWiQGyqF4+NjpM6c20A1IzLNF0THfn+aOyguMZ/Go96X20g5k6eF2SvVUt/QIHF/6bHThkRPdJtBC1oGVBLa7tK8QHVUY16SBmDAUolrTEt0QKPE/xYkHeHLHimHeWiw7EAEm3YwJKROeDnC69zTpiXOl0/IEpsfzBbwgtgMsgMSPlfIKWKsdUvzBdpEkgIoboqLx/WXMkebI/74R3+Cf/a5T2G2i2O/Oeo90RMse/zoCmAkt7jWt5twB6dLqFiEJsgA6rQb1/FLG2QcycuCn060ZMhwiOZftWrVqodTDnzm5S/yxWvP85EnPtA3an2z9nXiC9e+8l0fa/aZT+4+yU8d/dQ7MNJVb1bLsnDjxg0ef/zxBz2UVatWrXpXaW4Ln/jsP+QvffTPcWl74b4+t4txdnKLk7uXv+VavOr+aF1DV626/+o5g3QL0ap3qZZW+c0vfZKfHv8UZ5ev33dou66jPxha19F3Rg8fABYAh5TBIkJW6LBSawe80iGv0K2K8f2qPVI1BQxOHm5OlQAeLRE05OAAtogmThJO29SdudqBchDW3vXagUPvBvbWSGoBjBL3+nt12MaNSONmJB5xwdDw3o97L3Co75a4ZyJWDSexRRIz+kY3MVSQHNHNooBEdFHKEXGtA3tVZJlBesx0GnCJTmL17vi0BRm2cZzqEsDMDv/v4Vy03gcsEl2wSwsO3RqeIp4aBaoFhKeEc7aU6KU1oneViBj2pJC6y7kMcQ7LAHWKOGbN4aDtx/fQk1xTh2I0oNDMaW2h1eh4jfrlHEboIeC2p9QdkuDne2wcSblQlpnSGpoHdmOjUWNMLeKLA3QpUobolW0NaQvJGzlBTkcBvNzxecFQfJohlXCePnqB8Uc+wvje97Bcv47euQ1nHsfgsDPqUH08N2wxfNrjMbuRw8YGb7BEV3U6P0eHocdsp95rqxFD7BqbCG7c4WzXYHvCqWT2d0+5dX6XKgu2zbTqTLuZZVqwHiGNKFbBfKaoUKThnlGVe/3UWEOSUh0WA58tXMr7PXnYhJu1RXwxKYHP4Y5WQxkiMlkVzGg9ljuVAWqlzh4u1TJwuNJ8mmhLRZOSsiI9TtnNOb9t2FQxLGKi92dxvJLCWGjHW9TATs/w/RRxxALk0p31SkMxheSOvvgCdrUhV5+I7t42o6lEugAAFc/H0ZMtCnXCRElDRB+3OpBHjd5nn0lpoNYp2qs3RxQRqicmUea7c8z3YQAMzSO5jKSc8UWxpTGMhVGVtgj75RxPEg5YEZpKT3ZuwIzLprvnBYYxbjBuvXe34TqAl7h3Zesx7ec0z4wsNDFESxynLDCe4G3B8iZSAVKLe4B4xD3rEBNXEq4TkLDF+oYREC0Ro98WJAksUw9tyKAbGgZeEaJbWdMQCQpU3vPse/jslz4fyQKXt5go863X8e0GHy+jkmE5RzIkbcjxBdp8kyyPED3rM80mMlvq5asgRs7h3JaNYNbiGl21ahUn5YQnt0/yyu6VBz2UVQ9Ic1v41Euff0uPUal8Ln2OP3nxTzLfWe+vD1J3795d33SvWrVq1fehG2e3+NqNF/nY0x+5r/GFrs71q1/jUr6M3bxvT7vqW2hdQ1etur/68KUP84vv+UU+8cInHvRQVr1FmTny1ctc9g03r7xwXyHwYR11MS7ceXSFwA9Q6zr69uvhA8BYuF4X6RAyBZQgoG1w2RSuNzGcIbpZNQX4IQPWwSlAjj5XjbjTMA4vmEQBqYkgEsBNzcJZ27szDwDzEImqItACvnmHZQeHrnj00EqQicB6kvprAFyR7iSkPyeSOsAhumE72EUdFcXdokvWQWRAiecVB8kbxBqiCbeIxK5SWISAvOUoxjqM/aUYkJFDl7JqwETJHKA2tHCiWkAg6oJ7QHdH488VwAMSQwfucaxaJXqZ5VDlHE5P2hIgSSy6e62BRvywSI4oYAlWRYueYkdJ4lSL2Otlf8Z8fMKyn7GUcAFTCZdkCge1SUHqEs+RE1YX7OZNxqVy/PhFxqcf5+75THvlJaa7d3H3MI3PC9QaoN1BfEFEwiVeSoDKlGMTggu+HJys/fwRi2C7e8r8yqvsrt+k7WbEu7PXHBPBm2Me8y+SxhVRjf0DiejjVSW1hUImtYosiaQzediSVAOSLw2v52jd4/PMvASwPk/CMu2x/SlME+1GpZJgd466IzWgvyelasFzouVMTUpWI7mTXEkCaXJS84iEFu7N56XOGHAyFtIwcOZGq0oahtjTpxpgNrKxw9mJ900bgnq4cp3ezZsS1o+RuGFN+tRsqORwTvvMNPcLwSPGPFvMS5scn/a0zs4h3K30W4HQj3/rUcaqAaZf/Crt0uXeq634MkUEOBpR1mbh5pdweWsayCWDNYZxYLtRNhvDlsLoM6MWxlJYEC4cCWrOq6/BMg/43CGwV0RHWlswEcx3eKTSM/tAXU5ZasNTAHNtEh3jVJCEtIrJDPkYW84CqJIxBROHdt5j5BPW9nG/8TN8qehmw1Idb47bHLHxDj4c420J524acEm4Tdz7Lc6X7mR3vDpSauw/8R4Hny9gOK4FbTuEfo+zCfIQcf2HTRDSkxW8Qtvz2IUNz1y6yNduNpYho3WhHJ9Qz+7ixbB0G0nHjOUymT06FJpfgmgPxxFSeQTzPaUIiYTVPUhF9FLEQK8bTFetAuC9J+/lTz7xJ/nbz//tBz2UVe9y/fZrv82WLX9t89fw9Sb7wHR2dsbZ2RnHx8cPeiirVq1a9a7T77/4OT729Efu+/O6OrcvX+f41mOxQX/VA9G6hq5adX+VNTOm8UEPY9XboD07fn3/q/zrL/3bXLh7lVee+hzzeH7fnt/VuXn5VU7uXvkGE8+q+611HX379RAC4ACRiN7rCpUa3btoxImKavTsavSNoj0SWStCCkelSzguhd5H692JaRE7oUqtC7k7h70/Zkol4PIhmuIQEwxBaiRDisjZgCQ1Yqo5ANze43sPUcTzuwSYCYylKN1l26mVSu4w2MP9aAcoS4ATNByRaYAeax1ArCFkkihJ9rSi+CLxWlPEVosoaMTK0lIcq6VCGTuY2eLLPmBYjUhsm+Zwgmp/Y2Jx3NwqKuGMbuQe87ug9P5eW3At4TaNH8Tc4mHqFMBXLOJx09B7jRdUM+YVR2muNBz2u+g6rhXbHDEJ1CxYTnhJGNCsoT4ifbzijgybHiMcYF+ON5Qnn2I4Oia9/Cq+1IiXbhNMhjR6jLhidQmAmDPSeiw20ZurZeygP8d5T2M/wyB39px/7WWW63fZ7yfavnekIrgZ3haaeXTlthabGDaFXowLSwtXaxlQq4wal760mWRCTtbnseE03BStgu4rnFf8xp3YLCCGzjPeWuzMEiEvC9rPDX2BFInu47osGEItAeJTCld1EiFXSFlIuZBUSQmkZPTRC/BDTzE4TC+8Sn3tVpjxNWKFD1eZNYelhjvXWswF0XC+lwLjSMaxZaHliB4XdyzFHBWJymvJcW2oKNKj2BUl9fPrphjer3ePiPDUY9O7o7nWOeZ6drLGpoT0xc9SP/jDsUnCa3Q+pwG04F7RPJJsQfJAGTYUGpYzKNR54syjS7jtF0QHdjuj2cR011A9Yn96B2oLA3vvRm7Tjj2OJWGZKpIHMKGJ9vOXwSrIgKSGLLXHsSuSRoomqieapoivr+Ei1zTgbLA2c7g1xhwg+nmJ7mCfW/Dc0hMT6hS3oGEb14MkpBKbPSRHzLtFBLybRgCDN0gX8OVu9IEbqDgiikvCZInb59JdwZp7V3jGfI/kI2S8xPHgfOiJx3nl1VdZdEfzTDp5BCmNaiByAnmDZ0CP0VRIxwmbYkwNBwpVdr3ruCEMiFTEz6EdYdK+z3Vo1apVq1Z9O33qzqf4y0/9ZcZbY9SmrLrvOkRvHR0d3VcH26pVq1atemu6Ji9y/MQpV6+9j2Tpu//Aqrdd6xq6atWqVd+//sn86/zp+ed5/+0PM+5P+NoP/Q7T5uy+PX/NM9eeeH5dRx+g1nX07dfDty3QKlC6c9YCnkkDGaHuuju0hivNUneoSXyPZ7Do1o2O2+5cNItI1YNRQTTcsTSqGW4LWLhfa2s0COeqHHKYvTtlw1koEnHBtNbx34E0l/7n1oN9gzkpkBCSOUkSWaIDOGHx71oCuqGoZlQTKo6KoCoRizsWZMgkcVIaAnSmgNMqTkpCVg2npwcc9j6WcFWDWCX6eRUXxeuC7c/xacGXhreATV6ncPC1cOpx+Lu1AOvNsGrRb2wN1QIpxetQEBopa/By1zifFj27RqOnRGNtiX/3RtJKDLLR2hLnRxIRfb3DBGar7NtCsxowWgvNnTrtqRZRzp5yREgHeceSUr2xnN5muXmTenoOzeL4SwpOmBPa+12943z3cBk2lLYYNreYixZuRskDOihlTIybgeOiXKiK7gw7m/DzHcwtIHKbEQloqT1iXJOiOSGa+1x03BdwJ5EYfGYQZ0jKOIyMObNJMKbEQGZoRlkaxZxc90idkVpJy0KySjIjm1FoDCkxYBRzhqEwlpExKUViPJhhyx4HrBl1Xlh2E/N+x7SfmeeFxZzFobbGfPMO51/8Kre/8jLT7VOaWbjoU8xVzJHFkdrQnNFhQHMCLf14VESEJILmTC4DJRdyGdBxg4hiJFozbGn4UgNCasQiiwqSFU0JSeBZ8JzwJFjKWMlYzjSBpRnzstCmPW2aaHPFqiF5JN+9TXrus8jpbVJLpGFD1hzx3t5j0x00bVFa35/h1P0pTQdsrky7iXlpLMvENJ0x7845neDO6TlLjU0ero63iEsWHXEKnjLD5gJH2w3FK9nnN+C8CjkpSCGlHvWdDLWKaENz7SAcTA7JCBXp/dAqHvNKAgyrKrS5u/kjycDdIvbeW08usHARS0F6HzLWsHIEKeM1QRoRTxgR9U0ZaVYRXzCbQAyhxsWtBdIQ40CBDJZxMlYr7o3mzod+8mf4Ez/yxznyREoK9RzVY4a0YZCBbHEvaIBJxd3QQUkpUXQha2OUY7IJyZRMJukj+LJD2oJO0zu0UK1atWrVw6tb8y3+ky/9J9y+cJuaKqWUBz2kh1LXrl3jpZdeWiH8qlX3QX/tA3+Nk3zyoIex6m3S3emMz7363IN5coGz49tcv/o1dJB1DX1AWtfQVatWrfr+ZBi37RY4bKYLvPf5n2KYju7fAL5hHZ3LnpqW+/fcq+5pXUffXj18DuCSAohZjS5bLKJ0dQ4nm9DhoAQE9g44dQzHao4OXfHurm0JyREJjSSSt3ALSkQiv9E1zD2HsC1ATh1meucyfi8KGrrzMLKYA5bIIau5u1ABUUU1RWerls6TBfcWIAaNvk89OIcFke5+TIfOXoes0fPpAmo4KR4rpYAtbUFISI6OX88DXpfuKI6+Yuk9yGgHjWj0JLvj8y7iWmvrzxcgvZOcOC9Cjw9OSPOAb1oC+vS4alHF0xgxzJJJYtACZIsT8b0OVSBLh7Thj+5P0ahWY5xzw4dtHO+zcxgn9oMyOSweWDtJw4fS+5szFEVyijjtDuh9nqk373BWBtqx457JmyPcBG8B9LwZ7hHL617xWjGE1gyxGfOGFXBviGQkaYDNXNCcGC9c4OTqE1y++gSWEvaV56lfOcfncGfKuCVpQpc5tgZoivknOXpKa8Bra+EALypIWxDdIOKUTSJngVygNWyWgJQ4qhavecgRy1sj8jipIEljWiahacFK9OqmJIgIVkasZZo53qFszD+/F4ftBtUdY0FN4nxOC3Y+kzSuHek93biHc9379aV9o4QF8EMVT+GgFQUtmc0wkqpT3VlYIClmFsfEAiKLgbQFa9Hv6rTe9RBx8RGzbbSUwt3f5nCbtkqtsWkhLiclHdKok0bv72vXsFeu0Y6O4Nkfwp55BjwhOuBtB6Ikrz0uPmOyB03dzZ3i/LUZWwxzj/lUa497jo0E1eM8SIuebBkHVB0fEstSMRxNFpXgYrQ2Yy2gqbvjQ8amHbWAEPc5m2vEv0uCFJsW4jlzuNYl4spFFJZ93KcyyMlx3A9qpAg4LZzsAswTkpa+nyXOg7AgOqJD6yB7QWSEdgpsEcIxL9KTFVKK409C8ojTomPblthEsjmB5W6AXKAMx/zkz/5J5OiIf/RP/gnTmPFcUK8kLWQdA043p9UZJ15vzPsT1Oj3N0Vkg1vAcTaPoEOJ2/yqd43c/S3vHnw7HuPdrO/0+n/68Z/mEy98gqmtGyNWvXVd213jb3z2b/BzT/wc7xnfw/ndc/7syZ9F6b97rJ1M90WvvPIK7s4zzzzzh679h/1++LBpXUPfur7T639y+yRJV5fJHxVtfMuHb/8kl8tFbl75evzjfZ76Zye3UE08UZ9lkXNevfbq/R3AqnUNXfVNWtfRt671vejDo7+9/6/58fIvADBOJ7zv+Z/iqz/0O/cvDlpiHT07uUWZN1y8e4VLtx7/5ljoh/dSvG9a19G3Tw8fAK6951esF8kSwMyWgJKZiM9dHLTHe4pETKrovZ5PxwIE54gCpk2gA66QWsMlR98qcI9IWcQfkxxTB0v3ok27ha9DFcHpDlzv0dIC0CJeuUNh1Yx49PTKPTdriv5fp6dFl5423XuOe18wIv2x34iCFgmAGU5m77G1jglYO4CwA4g2WHp/suTwI0snepQevVzpVsc4vvBNIDxed0SqukcE9yGlVlV7B248nzAibY/LBlJEYjupR0Yr+IKkHH9PCbyiZRun2iV6glt3fu/v0MYE+1PIhTQeQ54wHZkkM9PZdc+69R7LS8o9ytnDFe4B4Nq00G7e5aQ6cnIRv/gIZgv1fMLMaNaiS1gSjQn3hHmLeO3e3Rwls4aooSmjOc5xFuFkyDz2yAUeu3KBWeHm60ecbgo27yJO3KU7nUHLiKvecxm7G24tIpKbkK1SSkb0uEfnVrIKOY0B2GuL862KeQVVUlYkNbxpQFMEVSWVAljvvi60FnHlKglNCe0d04pF7LKEO1RTgYGYAwjeKjYlzM5i80HONGkkWShZyEK4VK1vlhgK0jxc9bXSao1o4NZwEZCEuqClMF485iQnprOJ07sVM2guNBFcPI5VXEzRWUuL7maVe9sGpAerS417gIgiPkePrwmShGEzUIYRTYI2w88mqJW8PUHGEW8Vf+VryOsvc7I95qnHHuOrTzzN9d3EQkBxnXcBy1PGW6XkSCFoTVAdY/NHd9bjcU02N/xejDpAJVmLeWGZNu0xClorKQ3UpSItd4DeSKkEBKYgAuOQsN3EnHNsxKDBUOKe4Rax125Is4D6yZC0QZKzLVtqrSw+I0NBXDA3DI10gHIh7mFIROkPG9jfRrJGT3INt7iUzRv3PQOT1mOmHbUF0YItM1Zvd9dwT1RgQZbz2KyiG4RwJZsJH3jf+/n0H3yOG9PELmg2KSXSZkSmPWq1P5ejso2YbgyGfkubKvhtNJ3EPaNauOKPLrwdq9Kq+6S345fDh/0XzO/0+v+Fq/8Cm7RZ33Svelv1G6/+BhCr8W+c/QZPpi3/2w/+q9ysH+L0fPeAR/dw6ObNmzz++OMMw/BN//6w3w8fNq1r6FvXd3r9Ksqzx89ye759H0e06p3SsVzgx3f/I/xF4+q1D3F+dIsbj34VANPG/ujOO/r8Zdnynq/+OGl/gZvM5FLYzI9z9X0nnGx3vPDarXUNvU9a19BVB63r6FvX+l704ZF/w58FYbO/yIU7j/P6Y8/fP/Dan2cZ99wor7DZXeGRm88AMI+3sDTfp4E83FrX0bdHD10EdEC93o/b8WwAEA1YURuY9S/1w+MSkaOe3oBq1ToTmTqAIxySOO4afaputLrgqbsJkyKZgE0cXG3So1OdiHmOuGehw55DpzCEc1IyKQ8kHQOKiUSnb/LgxG49mhpELWKVAz/GY8jhcXqGsSZEHXWLP99zVvYDpuFkdogo64Nt0/pxNOkAM6KLo1yTANpWAxxF8Ws8bg53sdKdyDmgm6iQhoFUenRxHhCiw1TSEZIS6BCvJec4Jlb7mOkQXNGsJE2I5ABltUKNHtpIrj7HN9uA3nkAzjFZcBGqwVQb8+yYpNgYoClgV3V82mPLgi8zbX9O20/4UhGrHF+5xGM/8XGu/omPs33iMoiz7M9Zzs+p055mldYq1qB5w3pktQokVSQXvB4gsIeDNSn50iUeefY9PPGRj5D/2AeYH7uCjIqooNtNd5iHwxbCFY5bP009VtqBBkmEYRwYNxvG4yNyVnIqqIG6kVojoWRRsmaSlHDaNIemMSfdUSkdACtpKHHOaL0iV3sbbboXE537laZ9/qlYxJM3j+j0ecb3U/RC73fY2Snt/AzzhVYyddxQk9BSd6vnBCWDptgEQMIPwNbjGgjQnJHji+Srj7N97DKbkhhQijvFnNQCQKNxhdRq1MWoS4v/qtFqw1qH6wgJKAKDKIMqowqjQsmFYSgM40CS+L7h+ITN5UfYHG/YjiNHSbh4fMyjP/UTPPOX/iL/4o99iCtjoS4zvsR9RMzxaWKkcPnoApfGEu5sjJSMsRjjkHqE84KbM6SB0vYwHCHujElIEh2+oo6L07wx2Uw1x9OAaZyRRqO2GU+Op0K1xLSEQ928BVRfzvF51zd5KL7sMPGAqw5Gw+rM1CpzXWgG1pTWnLbUmIN1T8SQg3nCphqx8Gkbdzibwg2cUp8oBbEZljkSGWpDPMWeHR1h2MR9NOWI1h6OkHwS96F8REqNlIce8Q/HFy7w53/+L3DJncEbLgOeRmypsWusKLoZSb7rIe0LPu+pux3eFE8DXi7CMEDOaIn+Z1m3/K1adU8iQtE1ZnDVOyPHeb29zmfmr/MfPvdf8sT4AifH9zEK7CHWPM8899xzzPP6IceqVe+Usmb+tQ/9aw96GKveZqknxvmYy7ee4YPP/RwffO7neP+Xf4ZnXvhRnnnhRzk6vYyYfvMn3W9RZd7wvud/iuPzKyQruDnLZKhnXn9+z+uff40Pb2+ta+h90rqGrlp1f5Q18wvP/sKDHsaqd1BPvvwRHrn19Nu6Zr4pOTx2/f1cvvFe1DPqmWG6jLbh/o/lIdS6jr49eugAsEkJNul012+PeJaD25TosF0qXucOPmvEAEsNB/HiAUBdoHpA5VwOTxAxvRJwFAFpBBh1w62S3FHrPb7en5voAPbW4vEg4mfRANRuQIBbpaEaLlnpH7YKjqQUruFEd/gNB+ILGtCQ3u1LkvgP6TA3ulXdU0CUIDXEFDHwGrBaD27lFG7Ae324Bi1gGhYORXIJWtaIMdjSncEOKXdj8xLMW/v3EG+AhACk0StcA/am7j62DrDlDVAtOSMlnIXx2sOpq6k7NpuFM5ISHbFTg+zgA54iornaGede2dXKvOywViP2G4+I6wZ2fkY728FckXlBdzsUp12+yPlGuHvjVc5efIn52g3anQnft8ikPl/g/BxZDFmWAEciJFFS6sbi1nCTgLApQyqMmw3PPvs4Tz19leH2nmv/9NPc+dLX8RaRx1IUSoE0Rk8tgrnTliWA6rKAGQmn5MQ4jgyjklVJbqi3NyKLcyaVRNoU0pBIKmir+Fzx/YLvK4KSBXJOKKAt+njVFZUU/bnmERueMrrZkrdbyjiQUurO1ejDlqI95roD5Ty8sYNHDj3OM3XaM+9npgZTE5bWaEJA4CG6eQ8x3dHhG7HovjvHb9+EmzeRsx1FM0OS6DlOyqBC8t6jjPT53P+fe1Mt3Lt1Rlolu5EFSlaGXNiWxKYUhpwi+npZ0HlP3m4ol07IJZGbUerCmApH2y2Pznsun9/hEeCP20KK0HgYB/Jmw3aTuXTliEsXR0b1OGfZI1lgnmNjiVXQDXYvGl7BJ9wL+1qwso3rsYGbsMyGLdav6U3Epqv1jm8hSVyP7vF4SQUpGRli40VsQqlkFClHaDoiDSeoDhELb9CmGXchlWPE9jgVlxGf4znFBfWGSIFyhLCgqaBpIOdMSpk0Xoy5kHJEzg8jMmxAIEklSUN8JumA5hHxhtoUsDspUkakxKYNdbBphy0L5olHL234i7/4P+HKdiC1Rt1PTHXBxyHuNTkhwzGaaofPEz6dUs9PWZpREeZlou5uR5/xeHJvc86qVasgS+bf/ZF/90EPY9VDoE/ON/nPnvt/8NHNi+i6D+e+6Pz8nOeee45pWl0Vq1atWvVWlNvAlRvv5cqN9/L+r/w0P/zZn+fqtQ9x6dZTaHtrMeDDdMT7nv8TbHYXv+33OImLr//2uobeR61r6KpV77xUlI8/+vEHPYxV76DUM8+88HEu33j2+wKvaRm4eOtJ8De/+G3PL/Hkyz/ME6/8sW8ygKgnxukyaw70/dG6jr51PXQR0Kn3u0ZsMAFJbQGWgIZmoBmzcN2pa0ARmzrIJByvBix7PA/h2kvebyKO1AlPQ3f15ohATnqvk9ZFMVtInsP1d8hr7sA46lEDzjqNRgpoLKk7Z8MljNs9gOzegY05EA7YsLwCde4Rxp20hmUW8RqPY/IGKCb1aGzlXqQ0oBnUGtlhSRLNujZ3OGTx+G7gC3gO520Z4zH04DbefDN0vtfX2ruG+79Ljn5SkSWiqtvheQLWe0sRRy05AL56HBNbcGv0TF/IA67RD6wex1I9kYtSZab5OdgGGLHdTdrxRSZXzqyyr4VRKskEOdp0il8itjkVJEcHXdKMamH+6ou8/votpsk4e/UaNi1oUthmZK7YEo5uTYn2Bl8M2CURA+1DCYfhhUsBsRzyM0+z/bGfRN/3Xs4/+XnunO5Z6gw5HM4uirNEB65F9LMt3V3ZDl3QEVNchkwZCskqtiyEpzfc6uoSUyBFrLNQYzoLOBrni+jVlZyiH3ap0FrEmG82qDdaXaIyu0/RiBgHLQWmg1NZ4vx5dF6Le8yBQ3fwEs519jtaK7HI9mtpSUu4PguoxEYM90OZsN3bWID1rtxqsMRmi1wSOjW0RYSxipKSUF1wIa5ZcVwCYotVWJzmHXaroqWQUu/vlhhvbIIQON/DMiN5IA0ZsYbMldw84HZSjrYjTzx9lZOPfpT9fo9/8Tnk5jkcXcYNlmVH2W6wOrPfCzuMeVmorTvwZcCbBZg2I6UhHLJ508+PMhwJm5JoplAj9cBywdoU1w8TmMU5SgnKQPRFG6INLdvoYfYajvKkSBpgmWga3dEqDclbxCaqKKrRHy2H3Qx7IyWHPILv34hPt0wCLEXcOG7kUkg+0KrHNJOE+BSO31QAQbRFUgJCq4Zz1t3/SlumntOsMX+nmTbP+KafG4sLzvMRjz6+4Rf+xb/AJ37lv+XWZOBb2jT1FIIBHS8CE3hB8gZ1Z7+7i8xntH04h0V2oBt0mdG0/rK3atVBIsKYxgc9jFV/hJQR/s2TD/BT4xXuWuU/vf0HtP6byy8dPcv29d8n8yeZWefd/dD5+Tlf/vKX+eAHP/iHIrhWrVr11vWxyx/jo5c/ymdufuZBD2XVfZJaRoEnX/kIjnN2fANLCy8//QfYoY4MMK1Yan/4AVzINe7HV15/L5duP8lm/+3hr2A8kb9IYY+sa+h91bqGrlr1zutjlz/Gzzz+M/zmtd980ENZ9Q5JPfHUSz+Ci3P34jVaXr7rz6RauHTraS7ffIbt+SWuPfElrj/+JVy/PUXWltjsLvGer/04w7L91t/kgnjUC65657Wuo29NDx0Admr/g0eUbBCH+K8u0e9bF0ChJGzpQLO7ccEDIB16eZNEdO8Sv5CLEMCz99xileqQLYcDF40xSInUaHHEojc1XK+b7gB28EZDMKd3/HLPHSxiRKdvCldvTr1vV4AcoNo9gGjWILhWwVOMyw3vfcFuFuDE2j2gGqA234sQdglAqC6Il866ex8ovXvVuxMR672/M9AiPrvDK1o4e8PpqwEIPXpl3SukhpOh7UAzoiX6QtuCJI2uzyxIIwCfCNDwBnjDJcVzyhzgx8Kl7JoDBFpCk6OS4XShkUBG2Jzg3ph9z5ltOXfnyJVBLaKgU4egaURSQsXQktBhhOTsbp8x3Tin1YqxkI6P0LbQ5j1SUsBgBBPw6rTq4V4cCloUlYwR8eJ1t8OmiCjXu2eU66/C9gi/MbOfK22ZsTZjzTDzcNRCzK22BIjTDnOtjzMVikAWReoCksKs3fuMqRHDLSk2CKgIWiwc1T2dnCTIkFH1SAEXR7YFOjS21sAL7hbu9Bzz2wGsoknDRW4ePdXLHOdLHFVDFsObxXxUEMu9ulqhLVjbHdA0ojkcxCIkTeFopc8FU5r0OZsV2W6hzsiUSENB+4fHzSomQk5KwzGrNAIAR7ZxOGZTyuiQSUmjW5fYoGEIKoo2i6jipULJJIQy1b7vI5E1NpJwfIL+yMe4/f4PIS99leu/9Ule/b3PcLLbM//kz0DasKSCiGLNqSpkHYAZZ4FyiSIW5162tHYeiQQNzMOhq1Jo52fUDUzzzCID3q8L84wlQWgYLY4txGOoBFzezYgnrM79mBuy3UYcvRteG5J3mAiqEaWgUgIKZ7DlLMBuGQFF245m9Q1nflM8J5jugvYY6X3FUoSIu+1j/niinZ/C0QkulZIH3DUio32OPual4WVASsHQHhW9C+isIz5XRAZYdhEfL1ug8vhjT/CX/6V/lb/3j/8B12/fZpYW9wP2JHF0SHg1jIU6zwiK7WdUEmVQnCPa2Q18fCS631etWrVq1duuj5dH+MWjp/nXj9+PiuDu/IXtU/e+nvua/3E+xaf4GBObBzfYh0iH3dcf+MAHGMcVGqxa9XbqyuYKV8YrD3oYqx6QBOHk7FEc58Kdx7/pa3cvXuf05LU/9DNl2fDY9fd/wyN8582pT+XP8UT+AgBpXUPvu9Y1dNWqd1ZXNlf493/83+df+dV/hcW+Oxhc9e5UssKzL/wY0+Yurz/6VW48+kI3tH2zxJQrr7+Xx157P2Xe3lsjH3/1w+Q6MI1n3Lr84jdB5HF3gZPTRzm5e5ULdx+Les9vK2GYrjCPN3C1t/tlrvoWWtfR718PHQCOtNSAhF4DWDavJATDUQKahTvWoTloDadwraBOhIGWcADOc0QdC9C8d9OmDja7FdIb1WcgkQhnINnDrZui15IDlDxQGe8xzC5Yq0junbSH7ttD/HEpMS6XeAUSblpx77tQnCBxjkh0pR6czEjEGovq4cAEuCXxDVZkmjnVFg7RBqpKW1o3NLf+Okeo3el3gOqmHQqHqxrXcGQW6VDR7n2vs3QX8kgn3jG+OgfQsQXI0aFcJ1wSqo7ZHveEceg6VtCEopAyVhve4bxbRTuoVgQ/eizO+/4USRtYQDZ7rDWmpTClxOiZWlscFho5H9aV2ADgy4LNjplG9PCY0Bwx440FzQNIw90xM3ya8WkGlCRGUg1YiiAWHdTt1HDN6NEJw+NPoz/6k8gzz/LoyQnDb/1Dzp8/py0Vaw1r4My4Ztx2cXxzQbuj3asgoiRVUkwzyENMibpgcz/u2jcd2BJA2Fs/hvSYbQljuPXzrpm03UYEteQAoFYwn8MZ3gqSpLvT+895Csdy8ogsV+0R5zEPpBQQi5hfeCPyOwFpCLf73OKctoq6YuOIq5I04p99WbBl7ou0IiWBNdr5HuZKyhnZCCk36pICnmuK8yNCc2FZau/6FjwXZDOSi0ILeIrC4oll3rMgDED2irhR5pkrOnDx0lWMxtxmqjWWZU+rlfkzn+X2F5/j5k64feN19ufn5PmMduM6+tRTJI9O4H0WmkGbF7zNSFsQr8iQKZZZ3Gh26A2PKPlK7sdqDPd36xsX5nM0DYgtuGoA+N6fjNfeeTshqj2NYIIMnrb96y2en/TGzrYc90mT6B5H9qS26/ewgqkEdKY/Z0qIN0SNrJWaYg6REqYDvkxYio5vnfZQTpDNiPjUN5UsqGSaG0KO+ydCytvYEuAAW+Ac9YYNKSCxKLbZICmRy4DUytKcRy5u+fmf/ln+3m/8E14/v0s1QZaZ5o20n3HLmAvepojbPzqOOVBB1LE5YrRh3XW2atWqVd9N70lHbPpGRsP5cj39tqldF6XwI8Ml/veXf5zH0xsfSIsI5Vt8sP0It7nMTV7hSdYIrvujdff1qlXvjM6WM87r+YMexqoHLDmYE75BF+88wcU7T3zfj3mkN/jg8E840luH1jOEdQ19EFrX0FWr3lldHC7ySx/4Jf7ml/7mgx7Kqregy/qdN8QJwmZ/kadf/BhXXn8vtUy88tTnMG3M4znaMk+/+FEeufnMH9ocJQiPvv5DOM6V19/7TfA21eHbO36/xRiSDaiNNNmty+h90rqOfn966ACwiff9G0pzxWn3PoRSMtUrWQ4gF0hjAM7WQYn0eGEBkw416hxxpPdA2QyWA6pJ78zNG8SgikGzgC0W0DbARgeGaIBkSWiPo7WkPQaXuKGohCP4Xsy0dAjT3ZreA/KsBZB1wsEsHt+jHk7fHhF9AGBi1mFwQCXv/Nhbxa2GL9lbwMp0AMYJbx12S7iW42AeptZAFJFqdwR3x6m0OI7WuoM0dwczcdx0G6C7OdDdwN5w7xHY4nhSvCWaNQxHULJ2JJci/lWcDoArKh30eQBGVTAxdGxAYsywyY8xjBnPSgNac9o0YSmRSkRTxzk1mBrO0juVU6R9q3SubagULDckOT4tuDk2T7iHq7QMiVIUzQHAwgntWK1IUfJ2BBHqa9do7ZxXP/lZptev44thS8Oo0cmcx26EjvMan7FmwJAiJDOKQ8FI4nge8FYxzahPsWFAM+rRnUtMYzSn7sIW3Bo2W7iLXSAnZBxiLknCu1tcPc6rEHNUzOIcyBDAPFVcMpoFLUOcj/N9OLtxNJeI/RZB1MMx7MS51IhadhVsDkgr+wnLggybcMW7BiA08JQwT9Ra8XkKx7X32G1V0hjn0kVQdwaH5MLpqMgyk4bCack0ddQNxWAcY5y1weS0upBVyKKUMvLI5ct8+KMf4cr7nmGZjWvXr3H95Zeo169j+4ndjZvsF2Gawm3uYrDZIF/7Mv7se7AGy2LM586ZncVmBW8kibk472YYRtRmUo+sN6+YCupK0kQTYWkJiiJ+houScSiZVmd8cSgJrOI+I+UIt/NwuqYxNpM0gVzBR6w1kk5EnvQAaQgHtHXA384RjqgWbnCRQq0z7jHXXTMuC80MTVtaq5HK3A7954qRYuNHm2JTi+0jwt37eW7dXawJb3skHeFJabZEFDkVdEFSwtuEVKHVFptHRNGU4xYkQHJkUR578ml+8c/+aT7x33+C188WqjSKj1SP3vCA6YprJtU7SBt71L+imw2I4eOFt2NZWrXqj4SaNX7t5V970MNY9QOmHx8u83+68lM8prFDt+L8P8+e52ab+b+fPofj/NnNE/z4cBmAj5RL/Nz4WCTtvEl9kC/3D69X3S8ddl+vb7xXrXp75O789vXf5nde+50HPZRVfyQUH+QIxrPlM1zNX2ajp9/yO9c19P5rXUNXrXrnNNvMF+988UEPY9Vb1F/d/C/e1PtBQdjuL+F750N3r1LzxK3LLzJMx1y888R3TMYQhM30/XymF2us0nimfIbm8CWushLg+6d1Hf3e9dAB4HCLGp4VPEc/LBGh6mIBdnu0K2Y0jCQaoEQ7zEwZtxqQid7lah5dwUrvo91H9HHr1NYr7uGI9dQdtm74ApYaKtYBTHTLem3UpJhEh6mp4qXD3LwNSCOE0y8dnMGOJIn+YAVIETvth35i7aZe+wZYLPf6XVG9tyM0Iq8JkCoFmxrVajxmNM3G41kNAGrRFeouSB56nLS/USks0flK7v3JWNhR06Y7hwtIQdocDsV86PFVRD3ijFuAXNcCy3nv9kw0WofKirmQD69t2XX3YAu4mgq0BfKAtop4j7g1JWfhWDPHpbDdjBRRzCuLZAYNCO/0COO6YFYRHUhljJhhzQdTdQfE4G0OcD7PtMWil5SEDAVVJ2/H7kgWrAlWD27c+M8RTl94nq984oxiha+9/ArTjVuwKWgyvGpAfU0BZucac7A2PMVxDtCtZG+kcSDncIXWRVGfwq1sjo4jaRzCtVsbbn1ylxzxv1jcLZYZTSkix1P09lpz3GKOSS7hsk0BcONn4+2n46gWbLEAzlljzm4KbapYbWgSlEQqsaHCNWEEgMZBNZFKgF6axfyQFBs2WrvnjpcU14adnTPtFPZ7koCaRYdwUo7KQNocseQBu3yRo6uXePTkiHk/UeeFC9sNL770KjdefIkkhbRNlFQYRDldFm5LnLPsTkIYH7vMyU/8BMNP/xRypMwvvMj5y19jd/su81Spc6VOC/PSaCqwyVDGcNhPE+0PPgXv+zCuhdbmHkm/jeTxVDBruBS0VZy4ntwbLafg8G5QKz41CjtMEq0aIgPuE4sPuPW+2yW2TCBjbNpoKW4RCEKB1AJ4i+PZcQnflVpE5cddzXAiPn5MAjqyeMJaONzdariMNaFeY8OKLnEeUSxuYEidKanQfMFtwfUCtNMAvxKbKtwzkmu/fw6wm5BRIW2QNOK14cs+NuzYjMuW6C+fAaHVPW4nuGls2hm3qAtXr1zmL/2P/yJ/9x/+Q147P2Np56RyoV+XC+4evetyFNdJyhEVPu/Aptiws2rVKgCaN/7ptX/6oIex6gdMf7xc4uo3OHkLwv/y5AM0d37p5H3gcEELx/r9vyXZsOeH+Txf4MOxoWjVfdEawbVq1dun6pX//A/+8wc9jFVvk2afeM2ucTV9/67d71dK5UK6xvvKP6PInkHO+U6foa9r6IPRuoauWvX26+Z0k//o9/6j9T3pQ6gD6C11w9XrH3xHn2srd/hjm18nMzF052/mmXUdvc9a19HvTQ8dABaxcB3WRDjaat+kEc2YoFireCocIEZzDbjIEmnGViO+2HtErShGB60pnKpoRt0j2lhHmBuSMy4TMOBLxZN216LRCOAlUXqKWwtAk8DbEm7SZY+Nx6TWO3o9AFuM3wki3SHxskDO90zDSMTnxbfpvX90/YbeXglIF7fN6Ll1Uagz5nSYXQP2kSB79IcC4hX3fHg2IOKkY2gxClXDUoF5CXgoLSJqa+/v1HAUkhZElojelg0JRcSRkjCTiL7VHI5a6zAMJ7nTULTtkdTBpXtYfSsBugWSOU0EkZFMxWvmgk48nTccbQY2AqMvJOID7WpKs0zzhu6inzYN2zgHWTtX996nHOfB5w5+W6XuK9as9/Hme4BSjABs1H6cG6iSJHEkyjAbyys3eeHaKYvBbj7F2xyQOGXEDZn3uNTu5vaYs0vFFkekkUuhSA5DNAFQ0YwmRZij81cNGTJScsD9wyYGAUkJSYJSMDFayuiQyOOIabhrqefR++qG5IyqHi42pBQEwayF8136ecfRHkUsKc598ziWOg5oCaDuKoiDzR5R0SLYoe+3pDdiiy2u3sibdjRvkE2hbkem1hBzxmb4kPGjDZurj3H50Se4MBSmu6fcqg3bTZyVgqTMpWee4PjiCem1W6RSSJst5Whk+8hFNo88Ci+9wPy1CVtmBoc8DOQho3ducPd3f4v9bs+tF69x49XX2d25zVIXap2oLtSSsM2ADJnmTp0WlrYwfe15ZFrI7/9hNJVwudo+HM/dbR9R6IpJwX0Bd5KEIz5tNmA7Uoq5Fi70jLvRjLjWtV/74mhK0Q9sFZMBUaVKQ22Ke0Gr3YEbsfO6OYmNGm2H5IzVClpIqSAar8WXGbPlcMkj1nu7aWg5wuoe0wGb7gTIxdEO0t0E89iE0lDq3GDZoWNm2Iy4HiP1HCXhZRf9wzkcE542iGa8TshwIeLA6xD32zQiNncwXPD5lLTdQNvjVrn62OP8xT/zZ/nEP/7HvH79Beo2kgNYFmTYk+wilIHUgPkUuBCbYhhgt397FqZVq/4I6Fdf/NU1unLVH9L/MF3nC8sdfiifMMgbm2aSCE+mNxev9d0kwDO8iCF8kQ/j6+ac+6Y1gmvVqrdHv/L1X+HV3asPehir3ibd8hv8X8/+j/w7x/8el/VRsrzzH7splYvpFZ7MX+Ry+vp3hL7fqHUNfXBa19BVq95eferGp/iNV3/jQQ9j1VvUD+eP8bg+9aCH8S21kTt8ZPNrHOntb/r3dR19MFrX0Tevhw4AqwyYVWCK7tJUeu/pwalreCoBPToQdm8B3Tz6WAxHLaALROAt2ot12xw/mwNQukXUalDCCpR7z0NruAlSMt720AaCoQaocZ8CXOoAGJ5jR4OIAy1g6b0P0zwcx2bdQVoipteifzbcmQFiJJeIiZYUrsFeChzwN7ptAw55OEFVOABiSEhysiZaM0QHVB23cPQZKb7/cMw04lcPEFDUYEhAwpclYPkQUccBtxqim86RE0l7jLDQ46UVlxbOQmvBPetp/IxHjLS5klxwTQEcaXiqwb2SxuG1icUVJZNGJz/6CPLse9nmwtG1a2z2S8Tm4ixmlDqRciGpkfIQ422tQ7VwUboLPSc7HOXeo5oPButxDNBaF8QFbXGM3Bu0BSkDSRLvefwxPvS+Zzm//CTP37rJra+9QJ12Efks4fwWErLUcFnP0UsbcdDR1eua0KzknCmSyGLklNBUgv+LoDmTlobnHCBV4tgc4swlFyRn8mZDOdqgQJud5MZ2OGLvzu1bN2hz7W5jJamhOWKjY4OBhjkbcDOku4DV43pSVXTYIpKR2gIaau5zJjZrOEDJWPPeJRzx1irEBghrePPYNOEE5BxGfHuEq1J3exRnPNpyMm6wi5coj1/l6g9/gJOrz+DNuPKFL3P21Rc4ff01Nlcv88gzx7Tm2Pk5tlSszDQ5xknsrr/K2et3MHMuXDzm4hNPYeOG89NTdl95mfq5F7FaOZvOOd/tmOdz3KBi4eofEpSCuWDzRK2tw++Ev/g8thlJ7/0AZZtpO4eypeDoNFFpEVvd95/UZUbGDclnkC3kDZYUkyNqivj2Vo1qsXHBLCLAhdp7oxs2JxZp0Q89DrTqNE1g0n9xiW5obxN5GFBJoNtIMlChzrvofpbYgOJ5xOouNm+khtmMu6JtgiXSF1xLRMdLxlN0C1s7jfumzx0OW/Sty4jN5+TNCdR9wN5yAtSIdLbYUBDu3xpX7XwW98BDH7RkrN6FsoVxi0vC+4YYz0dcfewyf+Uv/CK/83u/zWe++AUWII8DUi7gy02ybJABWAZyW0iSqOwDgq9atQqAz9/6PIstD3oYq37A9Hw949+4/o/5+c1T/I1HfpSjt+D0/U4S4D18nTNOeJFn3pHnWPWtddh9/cQTT3D58uXvKb571apVoXUN/aOnr9tX+Q/v/q/5meFP88H8xwB4Sp/lh/Lb7UxyHk1f5anyeU70NVTsu//IP6d1DX1wWtfQVaveHpkbn7nxmQc9jFVvgx7Xp7igFx/0MP6QtnKLH978Glu984e+tq6jD07rOvrm9NAB4MXnAKySCWoWnY7R+WsBRA+OWtHuSKUDYqB3BluPnRV3jEpaJMCsR38pLVydjofLEo/I2kGgTtACjpIcr0uMQSo07e5ivQecIyo5BySjxWN3xzByiNlN3RmaOiwNVy7dkYoEHFYJrAL96wcIQkRBu4KKoj1iFjOSRSR2s4hqFifiUK0i2o+jOklTRGOjYFMcOwWXANepx7yS4nEppcNiie5Z7YBZCbdxiShn00Sy3ofsNWKcW8QUJ0mYGwHWCYB/qCL2iAgGQceCmHUYrvH1OkdfrwjtrLJ87etMKbE1yGmgSABFc6eaMzTDU0Ax94qh0UvrMXdMEpoiLtlFcU3AHO7oVGI6ebhetbaI6yWclqKKbjeMT17lwi/8PBd/5uc4WfZ8/f/9/6F+eY/VDthS7rC8xXzVBNoiIhmJ500JavRJ59TIZSCXguYMuW9IcENcSDnRJOa3WwsInDJaMqUMpKGQjo4Zx4GUC1IGLl6+xOUnr3L7zin73/0kbX+ON8F77HnKh00AHjy8xWYHmuNLC6Cfh5h/JDSnPrfj3AXQJRzWGnHp4jUipRfpzmTpzt8K5IgO7uDdEKQ2lt2eBWNIUK48SnnqKbaXL7Kcn1G3G86ffpajj3yYlOHoictsPvReLr36Kunpx9ErFzj7/c9ydv119ndPqWfn5NvncOsIR9ndvsOw2bD5ofczXtxwdu06y0tfp+699+NWpnnPUitGw3PB0xCO6u6etT7NDRBV8rAJZ/y1a7THnsDKFiEjS2NqM60ZzXsCgcc9qspIcshlG527jLTZaYNSpzNa2KJxHZA2QbWITl7iXgaOlkR2kFpRb3gZkLb02PQeFZ0L7Gdc91gakLbDVaA6slRsiE0lKkamUOstrICkEWlLv24GtFSMKc63WsSOW1y/VTaY3wuhJkulDRdIQyKJMahheWRZJhhGctngNtPaHC51zXg+iusEokN6UXyew4mexvDIly1iNba8bDYoEYX+5GNX+Qt/6k/z0Q9/mN/41O/z1VdvUGiweSQ6xjWRTo5hcWzZoTJief3FYtWqVau+m/ZufGL3Iv/myQf4keHSO/Y88cb7BW5whR1vj7t41ZvT+fk5X/nKV7h+/TpPP/00Jycn65vvVatWPfSamfhH86/yj+ZfBeCyPMrj6Q1X058Z/iXenz/ERblEke/duSI0ni6f5T3lk98X+P3mx1rX0AeldQ1dteqtq1rlEy984kEPY9XboE8uv8Ofa7/A0+k9D3oo93Rw/n4r+HvQuo4+OK3r6HfXQweAgyzRnW0RW2smaBIsOYkO9ETo2bP9BwMkhmux9+x6692YjaaJ5AuGkaT0aGSDlMIZSosu3Ll211mGWgO4WUSpevXgmKbhAG5TuDGXBdUtgXkVCcQV0b0aJbuCRERuyoh7t1wq7kt09HJ4Q5B6SHN3aHqHJfTuWenQmei7RAS1haSJJW2g1QDXBpI2CC1cqVp6/2oKqiVjPGc1SDW6aiXH8dfcY4Z7bHaHmbj0fmVHxBCL+FpJSnMPSClDAHItuE54nSA/Eo9rc3SmthaH0AkXrziiI3gH1k0RaQgF1wY+Uc+M8/3M8TgiYyKlRNaCq9AswFu1hewjhsUxBnyxHvlNd0LXzuYEMUNzCZfv4sg04zn6ADQVtMc5SxZk3JI2A45w58tf4uWzm5zembn5pReoZztsmaFoRNviWAeHLuApOlqt1XDBmqMeQF6lIClBytG5akZrEQ8sOWK+MWg1NjLoWNBxSxoK5eSIo4uPcDRuOBoHysULTCfH+IUjpgTz3dtoFsbjY2qZsFmgg/q4VCRirpvDMoFnVDQcx0I42zXcxwBaMl57RPvckJyQVPGUMbSHnIez2muluaNliA0BOEiiLRO1LoAhUknpMfTCMVy4QCvK6bRjvnET+/p1hmvXKR/7YS58/MfI738afeYxhrMPwzJhL3yVW1/6OvPpOct+YsoFvXCJphnf7fBqbFW5qEZaGrtXX2e6fTec3ylRgSpOyxJzNic8ZyxlWlswc2ozmnk4pId+3dYC0w779O/hP/qT+PEFvC7knFGxAMc+o7pB2kwqCeZzTDeIGyktHOWBYcy02dEs0JySC1CxtMHqjGwK3uY4rmbkpEg5IhXHKdgy0dpCziOeN9DmuNe0htsuXPhLxHOTN+CxAcV8IfmMpAHxhKFo8u4QnwL+I8CCiJIEkIYm8B7Lr7pFkmCnexjjOhLdYu0UPTpB54a1HV4VvDvI2xL3EKtRiZ2IfxtGfL/DFSRnkhtM55ALPi8wXMJ8j47HCEZO8Oyli/yln/5Zfue5r/CZL/we81JhvBTXskcHPMspWgZ0XuNuV61aterNyIH/9M4f8H977Gff0ec55owf4/f5JD/KOUe8UU2y6n7o9PSUL3/5yzz++ONcvXqVnB++t5qrVq1a9e1001/nZn393t8/Xz+DAH9+/Mv8zzb/+vf4YaXzTPk07ymffNNxz99N6xr6YLWuoatWrVoVNQo7P8fdfwAgnrOVO3xk8w/ZyreHvwet6+iD1bqOfns9dEdCZQiXoNVD4DFIRKSq9r7KVnt0MB0GB2TFDHLBbLkX/4zUcDiKUTURPbwLSSSAp2nEQmsKcOoCvvRo5d77mgpYOBexdq/HFFLEMSOo0+OaDxHS8AaU9g5Ye7Q0HeEKiPTO2f76I4639/0eXlePeT28JLeAupoc1Q4oTUg1jMsIoIbb0l2tERFtAJbA54hjbRYuavMA4dbAWiRDm2C9g5QkAX+qdfdvwzXh0oAWMF076POArk4FUczOIW07NB/j+1XAziBfxClhIrUJco6xL3toFUXi9ZiyiDGheFHSMKIlk1NGk1Bd8WmJzuF5obWAQE5FfQ6oLIarYikFeE6KjiMNp85zjDMPQIv4ZfUOPjOIRmSyOHa25/YXX6K+cIfzZeL85uu0eer9xR7HSyMC2Orcj6ngzaJHdVmQMpKSkLLEmW0LtcdxYwEf8YDTngd8cagVM8VcwoidEz4OcOmE8szTpEsXqdM5u5evM33xOe4uC6e7hVoreTsiCZYyIN5wb1jroeLqfT6PSLUO/2MskiIW3Vrv8s1xHKmGNcPmBdQgG54G3IxWa8DwZiApupXdIvbXK6YaULPCfGfP3m+iLgiZNO/Z7Wb2d88AWM5mzs+cJ04rT1y7RnnkEuSCXn8NeeEapTrDZoT9Wbi6T2+z7BIsM48NGz7wzFMcP/kMLz/3Bc5ff52K4EPB1KkojYihOfQ/ON1l2zdeCBKQW/zehhLR6E3WpeGf+mfIx34MO94yaqK2hnuNeSSCS0ZtwSVAK6KwP6MdjbR5T5JMyrk796FNM02GiH4n980acxw/S7gusF/C7asZHQteayQWaA0Hc7Puhj2Jnm6Pa1xY8BobI8x2mJZII6h7HEOHI6RGf7F4w5uALrTFUB1o+zPMh4D87QxlC+UIccdpuE8Yik0NbxOtOa6Qt5uIUtctTsO8kURRLXG9IMgmgQsO9xzRskx4PY97bxKqSUS1TzuaGtuLl/m5j1/kycce5dd+7R9i04TY0m+VCRmOkJbJm8fehlVp1apVqx4OPf02df5+Jwlwwhkf55P8Pj/GjqM3/bNHR0fs93vs0N2x6vtSrZWXXnqJ3W7HBz7wgQc9nFWrVq36AVbUHf3u8k/4lzf/GvImPygWGs+UT/Ns+fTbBn/jcdc19EFrXUNXrVq1Cv7L8/8zP1P+ND9WfopH9fEHEAntHMktnixf4HL6OqO+OfPH97+OOhe4yzlHtIcP1b2tWtfRb62HblaZCS5OIyJJowvTw6XoQlg5E0pDXIHUYav0D/9r73ydO9wE0hDu08P39X5TEgHuUkC+KO40PPXe3t7P6xaxzohCzfFzbQlAU+mdrBrRyWIBQ3s8snuPmO7ds/feMxjdCdzBUO/kdQJsu3lYZB0O7xpUwlnqKNUa5O4sVo3YY5EYQ7O+E6eAL/0l93hnJ16vGU509opoRDkfOj9xmrT+Bkd6jG8K6GgRFSxJYZ4gjfE44kh1PDUgnJCYoXqx98QuAYLbHLHBtsXcSF77a839dU8B2ySh4nireErhVE0ZyQXdbkjjQB5GjocB18TpbsZ3p9iyRIe0OlLC5ezNIl66xSvSsZCOR6QkfL8g+13EdKdMAHxHpCIEsHbNuDutNtR3nJ865/uJOu2p53fxOqGpIFZxM4yELYbPdg8Im0fsNDmjKqSk0U2N4Kb4NPdo4hYR1CV1Z3i8gRR3rFXaJIjtYGnIYuya4Hd3FFuYzybO7u5oi0EWmggQ0JVc0LbvDmTB3QMkA6IRIe5qQO9e7Z3DbhW3ipQBFYk54I43x+YJlxyx4hrXSpt34ZTXhOSEN8NJeJKAwxobCw4JWPOdU3S/R2/dpGrpDtZKTollnNiLsJiRrt/g4hNPwFNX0SevkK5eYXMhszl9leH2Taw2vJ6TUmK4cJFLP/OzHP3I++DVa5y+doPZnFqUFvsYYhx432QRLvdk4eCWXICEtRpeftPewJ0ggybtt5gZ+/LnKY9cpp5cYN5c6O7yjLcJc6Huz5Frr5LHLToWuPE67cIFWtngZOpcqbUi5iAD3hq+zJCjj9xqRXJGMSCjeYtoQmyhpRS90nWHqOJ1xnNsLoiNLgLDEdJOEfVwI6cx/t0qmiIVwGWLi0QMdB7Ruo9NDBLnTYaMcxTO/2XfswomyEdYuwsmMb/rLpz/bYG0ia52IlLfVRAGNI9Qz+L8m6PqmDlShri9zzsoj8D0GjJsYhPIXGl+DipIOsLzAFR8f8bli4/y+NEJO1uYrEZMtbYA3m6kcvyW1qNVq1atehj0hG74ty98kL96dP9ivE445+N8kltc5jUe5QZX/tD3jEy8j68BIKI8cvVH+fRrW07Pzu7bOP8oa55jo+I9NiES70lWrVr1h5QOm89XPZQ6tbt8uv4zPl5+6k18t/N0+YO31fn7z2tdQx+8vmkNFeUdO9mrVq1a9QOo1+06n5j+Wz4x/bd8JH+Mp/XZe1+7pJf5hfGvoO/I+wpnkB3PlE9zNX+ZLMv39Sjf8zqK8TQv87v8JLd45K28gFVd99bRdQ0FHkIAHEQ1enrdpnDORn5vMFRbEKJXVzoUOXDKbjPs0PMAer27aCWgbc5E8HBFSAFsPSFSO+CReMzWoacHmHM0fIK+gAxhVUsD+IT4SBJI7gFYcu8u1gCrbn6vl/cNUH3o/u3g2ejfvwRk1dxdpb072FuMz6PjGBPaEs7iZgvWloA5hNvSrCJZkWpIGbAWgDkA7ADSnbupP792t7KkHiOduouTHpe8QCq4LHjWeD2i8Ro0OoO9jLgvvRpZQSVei1t/DTPRdbx0F3CjCmQT3GbwAZYdyBEqBUtCogM7X6heaWKYKD6O6CMXyVefwFXR115nfmlClxlNCcmKiQOOqIMnkjuSQYfoI/Zpxva1R9gm8AVrPdK2KJIlHIhRKo0vlVYN5gknUfcTbWnxUrX1jQAFpjkgXj+mJn1jg0vAQwUlIc1wnXHPdN93bDoAsITXKQClWTjPW8Nao9ZEWxptqixnE/v8GpIHqjXaEi5IsYOjvUX09Nw6jA7XuvTNDjEZw3l8ALy2VLxVoiRW8NaAGR/yvU0MoiC5BFCeK1Zbd+5HL3PKcT06QnOjLUYDFlV8GBiGTAJkP5O0oJawg4PYG0trFBw5v8V+OcYvXGTz6GPI4tz9/HPcvXuTuy++zO7mHUq/xk0ESYXhwhG2v8GN37/J3a9e58brN9lbZW7GvECtFptIhHvu+6wacyNnmkdfsyIUD+98c8dyjphikYgvzhFFXpaZk3nHaAtnN17n1s07WBKsjLGdwMNZnxfBzKhzg3SOWcQ7u0nEyZvi1rAajljRipehJxHsEBJCOPabLUBFSZiMvae6dy1TQGqPcb+Da7/n6YjXhWYR+W7zEp3lKD5N3em+YD5HZDQjtHN87vOkzZhXZDhBbAYmhEgC0JyIyvIe3X92Az8+pskRagWzPdDQNMa9XRNmu7hl1wbLKXJ0HNdPO0PShdigMZ3iFvO47SpSRrzFLwmWTzhOjR969klu3LjD3iq7OrMzYa8bjNbjx1etWrVq1XfS/+7yj/Ez42P3PcLrAmdc4IyneJnGH/6AQHEKNf7i4F97icRPAPd7h/kfTc3nt/FP/S0e4RYA9cL7OH/vn49qiVWrVn2T/q2P/Fv8/a//fe4s3z1ecNUfPe3Z8ZX6xTcBgI33lE/yTPnMO/5Z5rqGPlh94xo6Pf6TzI98GNtefdDDWrXqB1ZvNkFh1btPn6+f5vN8+t7fFeW23eSvbf8Nigxv2/Mc6+s8lp/n8fwcmektr7Pf0zq66m3XfH4b//T/i+HZH8fThnrxhx5qEPzwAWCJ+FXrXcDICLYH6xG5EjDPbelgA6RZ78d9ozc46MehN9cDPFI7eFXQQsDLDEQ3K4Rj16VhzVBJHBDZPcAs3iEqQEXF0NI7a9PQ3b52yHLm0FV8gKLihh9SpFPuryfcvgGkJSJe8YhmFrsHtt1brz92UMFswVWpU6W1RvNKa+HuREqPvk6wWHcYH3ZVBFCRNHzDTv8eVW3hvT48X7yOFFDb4pCJaMS+qgdAWg6R1QdgbWgWrEJKHqnBUtA6YSzQWndLa8AvwEgRmC3bOG4JkhjuiuE9/hhqM6a6MM2VfD6Rb97AzZlv3aHuJ3ReIhpaBGPB24KqoqoR3duBd5tmvAq0hgwjyZ26P4/5kxQO4E0PMF/6LyyKVcfcIOcAcm2P6YCq4VYxwkUezmzHWmw3AEM1BVgm+n6pimdBUnRXmwh4jXNNDsiu9Djm2NRgDtR64NJUctRh1zm2KohyiEV3M2ya8er4AYbncI66OZhhbe5x5P3nErEJIPeuYBOsHs4ZEZdeJOZ87TDUJEg9xAfICuSML+EGb81YVJiTwpjRzYZERAQfXXmU8eiENp8hZ6fMp3uW84jvKEcb9P1PcfRTH6K8971M129z/slr3P7C89y5dp06nYNCShrO2JLwsx1nf/A1ds24tT/nrM5MzZiXiaU5TRTNTlbt8e1OLiAueG1UN2qLfnBVCTDuEoeUhrsiLohmVCBdOEYfvQTW0NPMOI6RtD4ElDdPiGa2yUlb5Y4q7so4RgT9bg6XupFxLWiK547gAAmHtWwADydtq4gM0a/tArXCIHgqYGBeAUd9QWSM+2SQVqwtWJv7RpiR2GyjEXOdRqgzpBOknt9Lh/dWYy55A9e+gUQCXu93IILlDZQLeJuQfBHZzDgWt2NpsWmGGtDXFU8p7u00JB+BNNQcKSdIm8jjMVbPMXfk6DjGpRlLA74/RXP0Vr/2/PPo6W0uj8oyw742zm3iPJ1wlmFu09u0MK1atWrVH129Xn+aG7nyaH7hgTx/ppFp3/X7xOthu9yqtyil8RH/Ax6t1+7923DzD3BRdu/5F+P3zzQ+wBGuWvWDJf0WHwyuerj0u8tv8rPDn+GJ9PS3/LpgPFU+yzPl0+ihu+s+aF1D77/++TV0+9I/Znj90ywXP8D+6T8V76vXRI1Vq75JEai/6mGQYfz389+jyMBf3fwSWcpberTCxHuHf8Yj6eU3HfX8vejNrqOr3j7dW0eXa/CVF3BJLJc+wP6pP0XbXn0oQfBDB4B7e2z/hekQj5wgGjuDY6ggVmhAojttle40pUc9e/+ZAdq+O1IVrGE+k2QMlxwLQTWHcPd6Bm+oBDRRidhjc0OdDtaAJtEH6kJKoDoQLuODm9Z6tGyH0yikA3wFPKKu7xVfakK03QN33T4Z0ddycNBGtG68vA6Gl4DCjgR/cwevAYZokFJA5tp7ibWDR68disdzU+c4AZ7wpBFJ633M3iKOOiWkaURk1318bz48PjFb2xRwuvUIbhfEnYJTfXkj8hqLY02P9u5QGQXJ2mOiw82aO08/Asapojpj012WO2fsXrsZbsJ5d28uNEBbxduMekIGDS5dMmimzXMfV8BfUcFrxNZa25E69HXx/ktKbCSQXILpZ4V5QT0ilpsUGhIR2v1tlVkN064orTbMDE353jmNTmHw1AKGJQnwLnFuzbw7xnt0uTqpdECLRAdvJ/IirfdPW/Q1K/34tohqNovXJ3FOwyUe59xqi5jmFJBfsH4t9ZutxeYFN6Hu5tgIABCjjfkhBXELKNqIOGEHnxutLTQ3KjC7U5sjd86pZzt0e0R+7FHs0jE+Fva3E0vJeHJaq0gzlmlmInHrfE955UVufu5LfP0zX2B343Xa7gyzJfp1NTpnscoyOXfO90zN2U9nzLVSa6PWRtOEFCWLYOZkdwYx1KMbelkWlqWyuJM3GzT3aHWLXmdvPV48pXACzxW/doN66w7mTp0XkkrMoZxJKHUx0skFjh65QraF0zSAjtGf6x38myFD6vHLW1Jy1Bs0DUeyVdAtko5IdgvPQ3cqG5oUm2tMU9UIDiibHmHuEcFsTpMNSEOGAXFI2aAmtAhCobngHnDZpPR5MuBSUDuLzStlG7HsNJAcUfc6wv407iftFC+XkJIRq5BARZE84ub4dIqlAm1GtNDqEhtoGPD5DioZkYaaYW2hLRPpyCAZXjb3+sqxhVtf+gx+PnPh5CJeJ1pW5jJyvD/lvC0cqXDq73yf5apV7wbdnG7ylbtfedDDWPUDqjO7gvLygx7GqvukROWjfJarXP9DXxtufIbh5udomytMV38CLv+5BzDCVat+8PRffeG/Wt2/D7lesZf4v5z9H/h3jv89nk7/fGWC82z5FM+WT8Vm/VV/ZPXt1tA03UKv/y7ja5/k/H2/wPzoRx/QCFet+sHUZ25+hrnND3oYq+6jfmX6/zL7xC9t/63vq0rjSG/wWPoqT5fPItjDyAT/SOpbraPijeHWF0nnr3L2wX+ZdvTEAxzhg9FDB4ADHnXoJgT4ONwoHDqdwtSjQ9V6rK3RQal2iNZ7dXUhopz9DWcu4Sr1Zqjk2M+r3rtPQaxHlNYFpIQLTaS7JPO9nQiCIcOG8IH2HuADvJUONw8O0u76dBJI62P0e9HPouBB2sJp6ku4k503wC3fEJvhGoAyWe8q7a9deyz2wUZrHSqmgHvhBnSww/gqyIBrBzrtG469anf1KnKwLefcXdXSc36td/bGz7lJHBfdRheoG0IJ+CMdSHl/U5QSanN04A7WQXiB2qKDN41kmdkycaSZR8R4RIULsrCVRDKHvZE0k4YSkNqAZrTWwt05DOgYnbootFbxuiBayCX1rmbvvbYNr+FYNdWYJZIDbpYR7dAzIqPBXYHoPLX9HtGIjXZNOEprlcWm/rJGNOfYq5By39swBtfPsTnBm8WhcUVaw1Wgx+qKx+YHisbzCPfctXqYZ6I4gnZ3ry81uoWXFtHpKcc5NsfrjM17IMWGCnEkxe4J78fQDzHmzfF5DiC9707gnCIS3B3vEd+SJZyprWKt4b5gJKoJiwgNR1pFbQEbMd/Rbt9k12Zmg+n23QCs04Kq4Fmw26ec/96nufniK9whc/3GHc72t3AcbdbPXTy/udBqY7HKVBvTtGdaKtUd82i8Dsber49EQFgSqRTyON7ruFZ3hpzJeaDimPSNEimizUmHqHSweWael+64bgE9y3FPM1DUG1tvPDEYSQo3bGIZL2LTjmqtu+4zyY26NCgzbQl3fxPFFkAzsswgd2gimEUMclyz/f6w7GHIoAWpNWKZDbwakrY9Ul5RSYjvI+R6u4W2IKYkaVhOAX/F8eUUygYVQ/JxxD5r7gkIEQsvOV6niEHbRVz+soc8xocf84JZg8FwCSc0JjGfskWsNBb37mq4z6TjkdoWqjk+XO7R54aPI2KniM8st27j5xPbXGLN0OhsHhOMQ2Hc3WK0zLjbvz0L06pV71JVq/zyV36Zv/u1v8uX7375QQ9n1Q+YFPifH7+Pnz/+LI/o7kEP501pYILD5rxV35dOOOUq17/lERQAb+TddfLX/j78sRUAr3p4Va3y8vnL/Bd/8F/wqRufetDDWfUDoFftZX53+U2e1GfudRsOcsbV/GWeKZ/+gYe/6xr61vXd19DK9oVfjc8FL/+Z+zy6Vat+sHRtd41/8OI/4Nde/jVePn+ZXXt3vN9Y9fbIcX59/hWKFP7K5pcY5M2lCyVmtnqbPzb+Izb6g9Vbv66jb13faR1N8x2On/vbnL3/L9O2j933sT1IPXQAWPzgguxxow74DjgKANOdtS6GGzSUFNiL5hHOFJOoAQuNFADDJR5PItldbAmQRnfGSsVRpFVAoXZwIQFmhR5v3C90kYxQSUlRzYhYwGGiW9UPUFo94GnrDt7U3a9Cj7UOR6633lcrOaArqTtlHSTFa+pu3Hv9u0Z8vRLdr9bdv5QerhFl2m7+hvOX7jBOQzhsTfp4xohmFguodLiftf4mJhe87qFsoDWcglcLmO0dAy8Bebz2+FclnkPG8HUfemdTOFkTDSkjYt0t7R5dplRUhRP2XEC5pMqFnLiwOWabC4MKxXszsyZSSUhONBXMaoBUiHPQe4CjhrXhtWLNSTlia9U8IGCLDlm3cK/a0qK7OXm4hJPei721ZjiKS7xu83C6ipfenxdO04WFWTKWlZxK1C1nwVUw8eisFUHrgmuJx/YW/8YCLea7a4fTIug8wxBO5EjyjvhvTRHrTIU2TT2u22GZY95qxlOKyO7W51sqsTEgpzhMSLzmw/E7AH6JSOvIF9ZwDS+GpyUgcZLYGCAZsuCzYRWaCxWntri2NEdksiaNuOlWaXd27O/uote2NhKQ1EllJCXFFXb7c1549Ro2Gef7M6zNSBJGBC0jNQlGozZnrpV52jFXqK3SEFo/T57iOKTuVqZFXHUbN9TjY4aTLSwzOSWGYUM+2qCtIvs5jrO1cGbXRgW8dddzj3WPKGxDWgKmiDzXTDq5yPDhj5CefZJ86zVOPvMFbh5fYK47mktA1bRBfQ9Fo0fbBWbDc3T9guJ6iIPOeJ1hdwbpBEqB5NBS3zwTPdzeJK67NCKbY6SeRq23ONJ7OGxuOOEO17whtQ34Pu5BPTEhYPKAUuNepY6mY6SeQx7AK5KO4r53SCpoC972NElo2UBbUA+XsqSEDDluHXlL258Cu9j8kRZsb+h4EUWRzRHMd2IDQ5tQgXr3lLOvPsdmc4REsDlGxVLBHHJ1ilykNBjKQ7eEvqvlfbPVg36Md7MOr9/d+eLtL/L3Xvh7/PJXfnmN3Fr1LfU/PXoP/8Glj5Lk3fNhzId4jms8/qCH8S6Vc4G7fIzPPOiBrHoHtK6hb13rGrrqzej/t/9lJt/zV4/ez/vzCY/nLzLI7l3hTFrX0LeiN7+Gaps4+urfhQ+vAPjdpHUdfevybvaZbea/ee6/4e989e/wyu6VBzyqVQ9ShvEr09/h1O/yM+VP88P5YwDf4jqJuXMlvcDV/GWupAdTTfTdtK6jb0Vvbh3V+Q4XPv83WR75MDz2b9+nsT14PXSfXrsbQV2/oZs2bcMd6h1CtYaSMW+oSMCS8FwijQDFIsAWPThy7Qz0BDxgkYohDIBhoqiHLziAr/UuYHpUcrhl5R78dVQrKRUy3iOLAwSJ9fFrC6Dh9g0x0PAGWT24ZrtDuP/dFajee1hj7HJwEXOIALbexcs9MBgD7zDJOmi1GqAXDRCYBvBD1HPvSvYULuoOOIXuoJYFJCM53NdRP5tg3oMHdvHWcM9Qz6Of0yrogFlEI4tJOGhptNad3M0iathO0XQx+pvvgWYnYxwDF7VxSQcuZuXieMxxyWxyoqSMSkKlQotYbs2Ca8T41mbh8vUFtxFbFkwNSYZ4h/IuUGe8pgCu1mOQpUGKLmZ3w1s6nKnofkVwa7g71ioNaEulTQFwsda7dpXqjYaGWxMPgEUiuSCpdTg+4WVAcaTN4eo2i2sgJdT6OTLvRnCFYQyns3e436F5WxqoYh5ADIyUCzoOAVshIn1b7zMeele1xM+7Eb3Drc9b1RjHwd2bNBynoyIL2LIE6EWgRfe2uGM+06phJlQarUX3dCqFJI5qRj1iiRVBbB/7IBCKOJoTqQzkzYZSYoNGOzvnbL+nNqPOC9WNcuGE8ZETKInz26fMZzPL/pzFhOrxS8bhijXAU8JKQaSfWwvHvw8DLSdmEWSew43/6BVke4zUGasJ3R6Td3vmu3eQvgGgOZg1VCKJwDBUJDaEuKPiJA93eW0NufYC7ewGuzt76p1T5CnCLT8MyDJR3Vn2U+/mNSTlcOH6Ai2gOSmF+xyPjQqlgCxIPcfzgKQNSEVc41ykcDvncYTlNI7DUtHUkDyG09wNX2bqcIIuO/Do5bUWf9aUIr3AY85GIn0kB8TcmxE7j6hocozRI7bcPKHbk7hfHqL1yUjq97v+5kC2R1ireFOkgY4a18ayj130qa8BCPXmDXZfe4EyHJGGHB/SAa0p5tGjnGdICMnOKZujt7okrbqPejveLD/Mb7gP+uzNz/LLX/llfuvab3FrvvWgh7PqB1SPaOGvHr2HtHbUPTS6wF1+jE+yYXrQQ1n1DmhdQ9+6RIS7813+s0//Z+sauurbyjGu5E/yJzYLR/rQfVz30Op7XUMf7rvpu1PrOvrW9fL5y/zT6/+Uv/Wlv8Ur56+sG6hW3dP/MP8av7/8Nsdywi9tf5E/Nf7kN3390fw1LqevU2RPlvqARrnqndSbXUcPd9Hh1hff+UH9AOnh+41S6LHFh6jjPTD0Xt3eS6tg2ns5vWEyApWkJUAFBFyVEo5HkYCP1p9DC40zshjuvUnYD9+/4B0KAygRH6s6hBO3ZFTCEZxKB8DNIs++Wbgqc0KyItYhh3QQTI9nPsQ/H6Cw9ijr3pfr0J253b16cPbmEcED6CkYGi4/sZ6SfYhXtvjFo2zBakBcSQEOW0DiAGFTRPkyBryGHl/cHcL3II3hc3fWergc3QRrDUm9A5RGO/TqSkRdu80BJKWfP9U4FpJQP+mdtxlhQljY6oZLwKPDMRdPRi6kgW1ytrkwlkzR1N3CIJRI+04Z9Yoh+LSQWuy4a5px65DdwwGMLEBAr4jwbdEJjcSxlARlQOrSj6XhS8WS4i2DzDhCm2fa0mi1YvROZjNMwCxcx+KGpgFtFcNpDlMLV7vViqVE7psMVDLJZwTtgNbDYd17Vg+bEdwbTgv3qnTXuYLXGhBWAvzFJRKdwZI0LqnWIl6ZiOGN/up+TpvhtcX5FEGk90o3jc0V0l3iKuHsVIFBoQYsbwbUcIeaEV241sLhrJmSC5rkXsy2OIgbSZTk3cWdClo0APAwotsNQLiN69KP28LiQt0M2KOXkAsXsNu3Ob11k7oYrUFTv1dfzOESAkRzgHYsNlSIQlI8JZoIc437hmxHxKHdvUND2QhsZWJvjWSC1dYhcIt7R4oNKIrHFM8DCSerBrdUichlG5irMC8LWKO99gpy6SpZE036JgpxmOae4H6EpIo2xUsGmYik7xTRzZKo40XEG773uC5kiYjlNHb07aSSYa5xzUhFSwEZMCSSrFmwlMAXajVE55hnNkTqAZsA6QJQwZaA6s0jVhrDKdCmAMN5iPuKDrDZhOu77hBLeM64K9KW+H9NYVTfn8WGl6LQ9rhsafM5pkNsdPGG5y12PlG/+hzDZksaCqoKtmCiJMtUN7w1zAtqRvJCXt96r3qINLeZX3v51/iPf/8/Zt/W+PNV314J4X/zyI/xk+OVBz2U71kDM4/xGq9x9UEP5V2k2G39o3xqhb+rVn0bzW3m11/5df67F/47fvPabz7o4az6AVZB+V9d+Mi7Ev6ua+j3o3UNXbXqzeils5f467/113nuznMPeiirfkA1+TlPJuHPH3+FD5bXHvRwvi+t6+j3o3UdfTN69/1W+VblHTZZ7x9l/AY3MB2khpsMMVQL5pWCYM1RLeEacwGb4rFUw8WbGlDAK6pbIGPh96S6o2od2qZw8ErGHUoaAujl7mwlhcMPR7QgKgHfkiA5I9YivlUiXrfbK7vb1t94HSlF5PLB9dzhq+QIsnY3MOlG4Tf6fF2EQz+yeXcBtxqPT+o1yYmecUuWhEnCDsBVcmDLPPYU6d6hag137W7VAep5RDoL+LLDm8R4rbuQRYl4WmhmoANuS4BmnAjbBto++oIdshj4hMgJSEbFSGQupoHHSuHykHnkZMvRUBg9UayRVSl1IYlFsnePqpYsqG4AQSxQrNiC54RICVgHPe7asLkFNO1B4S6Ki/SY7CVAKgqlIDnG64TLVTog9aVGzLRVvGzCnVgJV6g5pkbZDIwnl2mucD7hdaItjtcd1TqQdsEsfiYlyKnEdBeLGHJp4aYUw23BPGKTfZqwFBsaXIgeWhcouSeUJ5LHvDz0Xrs53sI1ii3RqVpSvy6kHwvBVBBN4Sw//GyfH94q7gn36OZ1SR2MhwM9YHH07JpHj7AkRVMi980cZg7NOmROKJA04rsjirqE09gbdr7DJOFt6VXZFj2+quxRuHPGdPuMeueMuqvhlpUA1kZEq6vE9RUbJnrcuwhCOJrj9QfCrGZ4dfQs4rtEE+XoiHLlCikrR9dfI8ueOyT2y4S2BdIQ0JOINdYUEdfJIdlCloK7ob7g57c4O4d5P7OcnyHXXkYuXcKXFNeJCioF1wC91D1eNMB1ytgyxQYUHRFvWFLE+7kfxx43D0KcG2/7ANKuWHKMAfcCxNwjbi2IDH3+T6jF9RBfSMgQiQHiCRkKCTAzKhn3HerRa21yHE/OHOegRS+zloSaknWkLnew6jBexqloLnjz2AzQwNseNhfwvCWZ0oYNosd422Fm6O42vPQK46XLpCHu8d7vqVonmpaIFJ8CRGtKiBwjtu4cXPVw6IXTF/gbv/03+Nrp11hsedDDWfUDro+WS/zs+O7s1Mk0nuIVbnKZ9hC+Tfp+tDp/V636zlrX0FXfi364XGR4l6ZnrGvo9651DV216rvrhdMX+Ou/9dd5/u7zD3ooq35AtZXEf3DpY/z57VMcvws3UB20rqPfu9Z19M3pIZxNPR40aY80TgGtAOkxvEiN7+t9uOqAaHRG4uGmRToIjR7LiM0NSAHewaGSfMEcUu/Z9SxIaz1iufXI1Tl6K22BPKDulFTIKaEYSYYOl/I9uIdoh3fhqgy3b49plg6DDy9XtUPgDq6lf6ED3wOkE1sg5Yji7aDFWutuvoy13rNLdB1jDRm2QO1AOwX80gUjRUysVVyHAImALTVYtc09grr1/Gd6lLDj2mOwLXew6yRpuLRuugygTj9rYdXNAWhEUE+o7FEVCgMXFK4OhUePtlweB46HzNiMZJWMEWc2kQjQBo43QZqj0iOwu7s2ieJ5RM1o2qCM0ZHaWhwXt961a7BMAXBJ4I0kAinHBoIDZO9dzGYWLmHLuIYr3LN0KOmYC80cjrbIlUeQ7Qbd78l1xqeZVqPXNeGk7oCNuWLRVdsWEjEXtIXbVIc+V5oj6jGfPGG1dzUfNgkk7XPqjY5nNw8ALtqfh4ivNkNFA/K5hdvbajgxrfV+2IS44iy4GTQPl7RXyAU0NgtYjah0M+ve2vg+TwVNEVWsOCkdurwboil6gFWj61gACdBsNa4PEcUw8BnrQLep0ErGi2LAcveM/TTBMnUjfcJr63A6IttNAzCSUzieNR47IsRBU7nnBjYNh3KqS5yDnCgnR7TNiFEZ3/seLvxQgi99gen5uxGVTUR0a4l7QcLIHrcd1YQm5f/P3p/FWrZl6XnYN8aca629TxfNvXG7zKzsszoyq2NjllUSKJlUWzIgQC+GDdmQBRggbMuWDT6QBklYAE0YsGTTECAIICRADwL0wkeBAiRIKpAmq+8rq7Kyu3nz3rjRxzln773WnHMMP4x5TiSFqqzMysyKeyPWD9wmTrP33GuvtWac84/v/x3F5pm6LLRaOSwL82Fhvv8Yuf0GnN6IY2p6HU3NtO3XTYKU8HrAyRHhbQX36Db2w4L0TmmhITLG3MvhMTJu8OWCYpu4V6BgYQyLN3xIiGwinl0MlopMW6iXIGMMIRhQZ3w4opUW33d1TeiI2YzGBAppyDTRnjhwirQ5znFtmGa8nUDdQ209jECiT9wKst1ipY9ajGfY4T75xptIM9qyIDKwqY+pZxs0R8qDm/UedKelhPYOdBEwKzRriGnE+q9a9QJrbjP/6Rf+U37uvZ/jqxdffd7LWfUh0CfzMf/P2z/5of7B+zXe56t8nKecPe+lfOB1ylM+v05br1r1B2rdQ1d9p1r30JdL6x66atUfLnfn0fyI//JL/+W6j676lppE+b/e+BH+50cffSFi0td99NvXuo9++/rw/s3yjyt3MA2DS8confTeVWoFdAxTR0AlzLvU+0qlm0zhXMh1/OuzBPH+Ac2YLVGDq1FxKYQpJgZGRsWhFSRNQRGmoOfUIA8jSQQlCOSkV1G7rdOx8uzprip/vznumd5lbBbRzpr7x/z6EMhV+6yDS499zgPXdHTvom1uYe6kFNHLrRPFKfo03T0sVJkAi+hsFGsFPEMFFwuTSUa8zUHZXq/VkDwipkg2YAI1vCqSxiBhNSE29cjYFu+Nh0mIJJAxjovt8DQCAwnnFOHUjBvjwO1pw211jjWxcWFISkoJbWEAo9GxKpLCtO29zxF5nPogQEOnDZISrplmA6YpzEsVZAC8k76esFYRl9iANht0M4ahfdjj89L7nBXX3herCR+hmWBLxpYa1C9E5DUtiNCLS9phCXKxNbIZOacgKWmdfnV0TPFSXLBWaKVCSjQnOpPneFRVIWkGlWtC05vj1IitpqGq10a41QU3QVXQYew9uL3fNw24So/zbtHJ4RbGv2VcDMUwCUPNXK47iNEOsDfDaw1qWARLGROP86Y64rXHpHv8t8ebZzS6t1OCMV/3K6MxzNCqUUsNol5zfFYMl0yVRD3aMh8f0USQi0t8v+td3mEmu2q/guTZtacJSTnofI1IalUlI2FCq/QO73RtZseNwalPn7AvM+3Oa7Rtpu0PFCCPSjsIzDOMA0kKWeQ69jkDKQ9IkuhCXmYOtbE0pywHSi0YRvn1X2H8sZ9Ez06gNWToVLcmpA+OeNmDxVCGpk2Q3+WCfjLHZdokktp7ckDExG+xVJG0QWoD2wcFnQyXDU5BrMYghMZ5YC5QQMYJSSU6xHUMatpqJARIJ8vd4lZdStybqpI0hiE05aDusyIaHcMiwOYsusjHG1jZI83Q5YBJhvEGfvE+TkTs02a8FvSw53i8wHJmONEwjmvqQxx9+CNNQa6rxH0HwXfneO4916tWvaD6hXu/wH/99f+a/+rt/2rtV1r1betf3L7FW/nD34/+p/gNfpXPc8nJ817KB1anPF2nrVet+kO07qGr/jha99CXR+seumrVt9Y/fv8f87d+8W+xq7t1H131h2pA+b/d+NP87Ati/l5p3Uf/aK376Heml84AbhBeaKfrsHJNwKIj6Sq6txufftW3CiDxOcMD7DTrJGc3TlWuSU9xpUlFZcS8BtHqtfeGXpko9D8HWSw5oTaTVVGN6NSUcq8Y7tHRblA7gSzAmDGr6NVb6fXZ1wmdBO43wQ7vYuBeg5zt5K1cfV5ANAyfgGwFvIRRHoWwQSljWB7iYVsQry5XncKdzazREWh1RxDHNahfGXp0riCpd22m8Rl5jULOIA1fgpamVUQTPmyRupA0XVOniOAe1HF2GHFuinN7yNwaR27kzFlWjqSxEWUQZVAhdWJTbOaqO1lSN0ABJdZnVruZp2jK+DSCwugRR7w4YbqVgreCFMMpvat5CLPcwUvF9zNWe3i1V5AxzM6kmIRx3qwTsO7xMTFIjshR1Cz3YyJtQaozSBiM0gyxFgaXCCn3+OlWoiN1APegeT1OApo7Pg7RNQuo98GFFp3SLo4MY/TyincjL6KWLY1BS7pibem9q9Y9McNMMDekdaKSBZrQauuPFUZbxLE7TsZ6D7Q3x0hUM5pcne5CmkayCtIMao3ztsY5oOPANA6kNHEwIrb8iujuHbRWJczJFOe9jkqaNmRVlqWx7B5R6oIaDC0GHcKUBLSb+WZBcrv36Gfv5Hv0AKsbqldd3vTY4hLnrzhijh0W6mHBL2bKxQFLwqEUDvs9OowkJ6h6BC2VJEYeIklAhwRDprXGUhf284G5GMWNZkZz4voql8y/9I/Z/NSfg+2212173PvqHCS6apyGeULc0Sz4dIw3wyZBJPqcqQ0rBzRvkOEI/BJJGVqBtusfa0HJJqHNO9AjZDiG+pgrx9zHAZF+//IG4zGSBTvUWNu0RduC2x5JEypbPEObL1AxaD0eXYb43LLHyh4nRyJBHvBa8PkC8hGtLDAK7o2qkNRBz3BT/DBzvLtE2gDqEdzu3gMSorPcetqDeAwWODMkJR+fIdZo9aXbQle9BHJ3/tHdf8Tf/pW/zZPlyfNezqoPkf6No4/xvz39zPNexnctAY7Y82P8Gr/Kj3HJ8fNe0gdO6w/cq1b9wVr30FV/XK176MujdQ9dteoP18PDQ/72r/xtfufx73BZL5/3clZ9gHUkif/LjR/hZ48+2gG+F0PrPvpHa91Hv3O9dL+9VkkdQM3dzCSIYLtA9DTMJlrvZ3XSFcEJgIUZLN1UtW7o6gIM12aw450aHADpPcAJKLhnGoRJfBVTm9N1InDWTZhH9OeVoDKltR5X3aOcU8aTdLMi4VbDnKT3roqE2ZoSz9xdIg66dZPVPGp2A3blGn2mE9I9QhhXjBpGMA4W/bIQxyjiryWqiBth6nnD6j4iqtNRT40u12vTJMiwjTXlHHGuDdCMqyGW8WVG5CqSeuoGYuvrqiQVWo+uFYc0bBhb4UZO3EnCq9sjbg0Tx0PiaMiMqgx0ilIzmkByQlp0pAYymvFqJBqjKC2NFEuIG9vtSBqPOLjTysw4ZNI0clkKnF+wmAf1OhBdtEl75XQJI95S9PwitDrHe6kVcnzcrEbnbXU8aSRbEzHdMgx9aCBioVlmUmukWhELglLcEB1IgyIpoxKkp1cPqFvCgCQNiBVk2EYUd05B2zpYiehlrMX5OSSwIJEj4joiyF39an4AqEFBl1iDjkGcmzu2hKkvKE6Lc9JT2IHS4jxKAnnESsVah/TdaFeG5nxA88AwJIZkDDoRKdMZMNxmSBPpxhnp9quMY6Y9fMTh6VMUx2jUapQWceyuYd6mzUC6fQu9dZui0HYHyv0H2FzCKHSDJlgmhhX68Q/it09SiIZx6mEgppRJEoMCkgzRHMysg9SIOUbDrKzutLogu8to/u5x1YqTpgkdDDVDWyKJkyG6Z3PCRCgGu3lhP1dqbUFLq3SzfgiavRjzr/wS40/8NLKJ117MEWsMy4xMR0gWJI1oj+2WtEXaAWlzGPUSAwLSo9qditYdMt7AaJC3kEdSU+AyYqJlg1BR5nifXGP4wKcw+70Pg+x3kQCfUyQBqJAUWh2DTE4nnS4+ptYnIBvEDJeKL+dgBW8J1EgoeCHlDWgKanzIyHKgomQ5CtId4OISef892pDgUGPAgoRq7zGWFHHlRBQ8bmhzbBhQd8QVKXMMr6xa9QLJ3fkn9/4J//4v//tclIvnvZxVHyLdkIF//eijTJKe91K+Zzpiz+f5NX6NP91/8H5xfrHwx5dzyvkatbVq1R+in7/38+seuuo71k0d1z30pdC6h65a9a30eH7MX//5v85vPPqN572UVR9w/XOb1/jXjz7KX9q8+UKRv9+sdR/9g7Tuo39cvXQGsNHCkNHMNeaqCXyD6xX2atEp23tJkwTpGq5T60Sc9e9rRC8qQcP12FoXIYkGQXb1dSmDRdtmUGX0iFnpf25o3obJ5IamMfqF3fA0BvVofk01YtE3K+nKYAmjynuv61WU6rO4agmTtZUwoSSih8U9zF8ZevRqRECLV9Q9aFugtCW+3xteC4wT1AW3IQwdE8xmaBNOpZrgLmg7oNYNzHSE5gIMSGq4CbRL3KM/GLNoe9XhmwzpTi264t4IQzEo2EyYt+ZGUjjSkVtZeHWcuD0oZ2PiaByY1MmSyALDOCJ5BK6qk3OYn7XQapCa062b3L59G0sjjx4/gbbw6ms3ODm7xf0nex7dewAnR8jRxHh5Qa0VY6FaDfKU3nnrYQIiEqbhOMBcsNqCgK0ztDi+QZQradQ45uJYaRGj7Q2yYClFT+tSUKcbv/S+W0G1dxm7I17ivFKNOFzNuNTo+x0mRBzPE05E77o4LQVZagShrThqHlHWlH7+DbH+FuYz4ni1MKaLof09slZigEAVFY3+WXfMKz35Ob6nJmgV6zHAFg29IAlLGc9hNg9DYrOd2ExHIMp8uKQtDWfCc3ThtvMHzDlTDnuaN5pX3BOLQ029ixeFzUQ72lKHiVxm5vnA5ZMLlsOBwQtiMbwRl0ZEpIsIOnSqG/o9IqLik0ZUfMo5hgpUIvbdBeqCLoWhVfI4xbeKspjRWu9A7iR7wlBVNEdEec6Z5I6Kk7qhaa1SS+WwnzkcZmrzIPCRGGbpHdCaFFB8nmk//9+S/vxfQMYNuVVkOkWpcV+qMylt0TGhImQXDupUkfgcDiqkYROx5s0RPYYW56DnGBIQB6HidkHanMXoixjJDJuOEDv0Jucxzosh4p9VFPWGuEbPuM0xZOMKNgexrSPkDa4jaj0VoO5hPEJ1G8avV7wamgpsN3htMdBBxLebjFgtcJjJ998hpwExw6yhTSLi2pc+bBHUsLuhWZG8xVpDShj6Ns/9fVyjiFa9WHp39y5/8xf+5jptveo71o9Pt/iJ8fbzXsb3XMfs+Dy/zv+PP4+vP3Rzyvk6bb1q1R+iQz3wD77+D1bzd9V3pDs68Xdu/yQ/ue6hL7zWPXTVqm+tX3v4a6v5u+pb6s205X8yvcpfvfGjHOuLb2mt++g/rXUf/ePrxb9a/kcSUawu4If+5yOgRf9pe0azdhcFx6hmZDTigSV3chOuy2y9RISxpetIZ1BM2jdRutHjKt4pWFUk99hhGRFxskQ3cFCfQzdzIoo6IF6LaGTvpJ50ctUEiXLhWL9Ffyai0Rmr14Wl/b9Bu7p00zWOxDPzmyAFwRnGhJcZ6+QvGoaKpysaGcwVr7voeLUGlHgcgaQb8IhQDupXgBxRrcscx82v4qsVSR5Rwh7PIRYknnvF+zEN9HQIQ6xH8+acGbxxKgM3MW7kzEkaOBoSU04R+TxO5HEibaY4Ds2CvtaE73Z4WeIcGIV8doPx9dchD4zNaJrQT34CvXHE9N77yOMntIcPsCewWLwPKVkc0+kId6eUhllBtNOibcEtY22hJcWqI2YkTWE6CWhScs7xvhs0MWQ+UKzhC1h2rBRy7UZ574lWjXNERML8N4vealUkj+hgQMKbd0Na+/CQ4gTpbeZYqdHza06xgpclYofTgPYzSNWuqWW/ugTMMXOQhhSPtUnuHcJ0crthncm1qy5VE6AhEmS0YZhkTDXoTZyszijKeHTE5uZNtjfOaGWhPIx1QnS1mjnz+QFEWGqheosoY5zaoOWMpzBlBwHmQmlPYVCWYuzPL657hTWBShj3YJCI46b99aQU5qU7SZUhT6hEj3TQ5NLvI4AmhtMtm6MNOQmTwzYPPD6/5MmTR0gSdMhkUZIKSYTkTk5KzkJKYUiKgNdKLca833OYK2UpmCSQ6H9GnSSpH1eQIc5vrOHv3sc/+iYqU5jW+QjqJTaegnrQt2J4nTEv0e+bJtyWuCdlJaUBN9C8IXkBFUpdwhjOqXeBTzEQgkEaoGU0Cd4Ez2PvDs9xi80jmlLEeHunub0HBWj0XnsrmPXIamuYdFJ3OosUB28gA00SMm1odYe3GdcpaHUy7gnXhj+8T37ykGlM5BRJBkGEXxWTE/dMX+L5HNwqpnHPsLZD6kzqa5dUv5PtZ9WqD7z+/lf+/mr+rvqOlRH+7ZPPvFDRW9+sDQfe4l3e4S1e3snrmLb+0/zG+gP3qlV/iP7eF/4e/+Dr/+B5L2PVh0QJ4aYO/J3bP8mfnV553sv5vmndQ2HdQ1et+vb0X3zxv3jeS1j1AdWRJP4Xx5/kL23f4EfGm897OX+iWvdRWPfR714vnQGMSydoh/5HDXMKuTZKRYaI7aUwpKl3wga9KFxRtQXS1P3SoEnDjIV0TfVqJ1MHTCOmOC7WFBGiJohCyoJi5OT9+xIqoMkiSlhTGBTD0E2lbjLBM0q2m8FhvHTzSfvXul+bzliDIfXHMegGXXBsDdDe/+pIUrRWkghNgmg0MbzNEfncZjyNuC0RGyxGkwHFImJXI0o6pRQ0ZLqKNEr4Up4ZZDkMcKlz0JbDEeYNKRIGohaw6GVuZqh3U3vYxGtrEWU7uXID45WcuZGdozQwaia5xvFuFdsb7bAn3kklZQdJtKXgpYWJ2g2zyQubvOFwfMbTB/fYffH3KOI8vSi0p+f44YC1RsVxa6gmps2WJNBaxeuBpTTacollxVMmqTOpUNKGQ1vQNJCGkTyNQY86iApaFlwSVRZcMs0q1Q1fHCzihlWUJAOp99ACEffcKXfEIw56CAJXRXHPuFW4igqPSQFcBM0CusEOO7wY1YXmEuZ9i/jinEZSbWhO0PuwgxiWZ+edPOvRNrNrk9et4q64Jqo7lgTBuuGq3QSfwsSeK7IUsjuDwDRlhu2WdHqEjpl62GNLCcNaJM69Vikp4pFbDUO7udCsxtCCJXTM5LObpJNj0jSwF7iQRNvt0bagl7t4GXY1vCE9mjjFOZf6OSmKSkKTk1IiKWgaIA/9OPR+8KTRm3u8gU2GzYZ2doOqin3x9/GHvddWwxwNejwGNCRNaB5JmoKcLoXlsOewLxwu99Rv7vdOihBx8gA+TKg3XDTiqZvBvXc5vHoLthGbbqWAJFwSXg8YQhuO8XaAZUFS9BpfdX23GhH46IC0QvOCm2AN8Ey1TEoTYguGR2d2Wch5ZGkHrDgwYLZD8yaOV12wRnSeJ0XqeaynGZ6nGATRHOnjVqHUMIVzC9Nbo4e41cvoRs5TxJST8bqHdIItj8Mntkx57z2GQdGjDUPKoE4tM2aGikZsdj1Ep7YqKQ9YmaHtsFLxUqEaIjniuHX4bnajVas+UHr74m1+7r2fe97LWPUh1L+0fYvPj7ee9zK+b0oYP8gXeMIZF5w+7+U8F63T1qtWfWute+iq70QC/Dunn+F/c/Jpti9Q7PMfpHUPXffQVau+Hb198TaPl8fPexmrPmC6rSN/cfM6/4ezH+KGDiR5+WrY1n103Ue/F3r5DGAj+m01KFI6KQsGNoMkVA3X6KtsBklGXFp0UiIoPdL5Ov2zdVo3jDVDyaLgieQVYSHlCakLMkRcc/SwVjSPnTpMEbN73SnZkCZ4GiFpxB9bvICrvuD4sugDRgHzHm3dwUUEpD173e6dng3C+ZoqtKD/IvK2G4ne+01TDrPRhCRLdAE3wTUFFVyCNG09nllwVHu/soyIOZI3iFVgiI/XIDbR1AnlHjvt2vlkj37lPEJtUUfcO4khIrbFBFuiGxYSyeEY4UZSzjYbjrcDG81kFbRV8EQrJWJmRcMAdqNqHPs4NBqmtyquA8s4wsmIzhvyA+Fw9wl7g1IXJnd8HDk0Q8uCZEXHgbTdME0ZI+EPHmO1UOcFWwwfG3r7FcbbtxjE8HsP4MlThjwwTmO8p6XAXBASLpWM0RLoN51uKpDFGcaRnFMY4iZB4dYW76eASI7eWVUkhVF19dquKXcHdOhm7YK4kiTFEIM4jkZMOPSoXkOJeHMZBnwANafVGjHEwxY3xxVaKbRGdLE6mER0tLlRVDEaSRNjUsiK5Li2pCwkK4g1sg4MudOxZqSLHeV8x/5iR93to/952MT63SltoZjQVEGc5kGiM2TyNHG82bLdjuwHZb+/ZPf0gjYfKKXi80JqQdm7XgW1083cdD0sgUQ/t6ijKQzy6+vWLUxEUeRq4KEW/KKxXCrLsGd68IhlKVxcHLoJHvS3A1Kj9zenRMpOIqEEDW5LYdkvzIeZYo5LhZRiaCKB6hCDFJJwTXjKeGtoUtK0RQF5/ITDZkSZ4vrzhJcDMm7wVqjMuC9Y3gCRJCBtwfMY9wzVII09417D4GfsEecZZ8FtjzNiegz2FNKI2R5LQ1zXSJiz2nvRLcU9jAOO4TLiGJQFCPNe8tRjoA8oGbcDIoamARk2oENQwktE3ksa8eGoz7QM7L72O/hcyCKQj6A5mjyu3f1Ma4a3S9CMijLkzCBOnTYsgNcZmfeR1pBS30NSH8BZterDL3Pjv3v3v+Prl19/3ktZ9SHTG2nDv3XyKdILSv9eSXE+xZf5XT6LkViuhj9fcCUqn+P3uM3D9QfuVau+hX7p/i+te+iqP1LHkvnccMpfOftBfmK8xfiCm79XWvfQdQ9dtepbaf1ZdNX/WK/oxL+8fYufPfrIS0f8/kFa99F1H/1u9fIZwCJhTHoYXGZ2nYqMJVKKbmDx8F0NoflMkgTMwBTWqU69SzcB+VnXruQeTdyiM1Wm+JzVSN3VEagghuDR+SlOSjkinSG6M8kRE60JSoWhO0RRFttfh+A5IVZxC5PKPbpKXeNxqDViq6WXrubo2cUcFKz1/t/W4057pLL3iOTwCSNuOCeleabmIAbxbsq6IzriVsIYsfizyIho6eSxRgSygCeBMiMer9FrRrWRkoAkzA08xRrd47XPgpAQsW5GCkYnU/PI0IxtFk62x2w2A+P2iGHakHC8VOxwwJZu0EvD04SZI7WRtaHDSBo0zD8BloX6cIdeFOrlJTImfDtQ9jNyNDLeOotD+ugpy4WhmyN0ysjxMZZzRCdfjuTNJvpgm9CSRKzsfI6bIKWSUo7YX6vx3loYeCLQLI4HVum5u2AN9UZKA0kFzQMqGvMLy4L40s04SIOgaUQJSjVrQlKiyBCEtbToEO4ENFUQK2Had+o34SQ3BukdtyKknNCc0B5h3oDWghY2ERqOecNEsJYj3bcFFUzKPbjaaXlExglyguWAzwUrM2rW4dkw8POg6GYLx1vq0TE2zxRrtNbi3Jp3RPC4cmiNuR8PveqZxcmbLeObr7F54w6nA+j9pzx575zd46dYc9SNRAtKvZvGovma+DW8/3K7D4toiutVgqR2dSD1W4yjKUcavDXcobY4b+0wUzqRX1rFO0UsOKYC20308A6JrBrXhDneGrVVllIoteLuYaynqVPUfYABQTXW45JgDGNWU0TbJ5s5/tpX0Ju3Odx5g+YNxLBqEcXcGhxmJE/Rlexz7/0uyDiCTvh8jg9brFw+m0Rph37d5v4eG9J2oIlWdkE2pwmhonqM2w73Ap7wcUDc8KXgrmhSTBVvh54UYAhj71b2iKH3ExBhHEeMiueJVgdYzqOvd/8AGc/QtuP8y19hudiT1HAR6qLUPTSrMWCQMupOsbCbT7cjt27c4vjkhKfnj7l3/xH7wxIEdNrhY4p7ap37+bBq1YdfF+WC/+x3/7PnvYxVHzJ9NB3xH9z+KX5wOHveS/kT0R3ucYd77Njya3yeS455cWO4HMX4EX6L17j3wr7KVau+F5rbzC/e+8XnvYxVH3D9i9s3+dmjj/Iz02svbGXCt9K6h65ateoP0/qz6Kpv1g8PZ/wHt/8Mb6VtJESuAtZ9dNV3p5fOAFYJo8YMPGvv/A1zRNIQccdGN8gkPq1XUblDGJTNSFk6tZbDQPUwluPr/Fk/b06w1PizWZDDBpIczUOYchpEoUJQud47eul051VaswO0q6TmoHbdowjcUo8vBqch3jte8xjEceskpHTyU/tztDCz3MMIlyS4BXFICxOvNcddMFfMFXzBW7sGSKPPtYEQXcmcQ74NtSB5Al/i+Yrh2s1ixh5RbUjk59LqQhoVccNSwlsBKxG7qiNSF7Io1YwqhaxDN64bw5A5ygMbIOuAWvTrRvRv9MM2FGtXvZ+HoLBzgiQkITqBxWBILMPIZU7M40hpDfOKyRFO0K6yP+CtUOY5yMZWscsFf3xJcsMErFoY+4MjWlAUeXpJqpswnK9imA1sXno3L2hKQdiaUOcZrxVnwK3g7oh7nLKlgVYkjz2eXOL1kJHco86HjIyZfHzC7duvcHJ8wuW+8uj+A+b9BSkpKSU0ZXwcsfkAqSGHPUp47zltGJOiOQzg65hqjXP8iie32milUARKaxRXmhnWDEODVL0aMJBMS2Hot2pxbCyumySQVMniYUoS3a9+fsFyuaPWRl2WTi43aq1UF2YxdigVI+fog00pzHRJiu9n2vk5+2nk4mLPvBQMiGZiI6URFYtu7pRwPAj1PHSfrxP6KkjOEbOtsT5rV4BwH9SoNa5drq7t6Fl2M2brNyNv8b2dxncRfEjx2EOcAzh4c2qpLEvpVblBnQoRAS1pCDO/E/tYkO4iHvcker95zmhOeEpoa9x8/JQnx2cseYM4WJ3RzRni0zeRrQNME1J3PWHA8bzBKcjmGGpF5ksYNp2ITt0kfooMiqQNtpzHUAJhymvKeDrF6lM0b8nbLRsRll30L4s3tLRIGRi3pDHj+0NcKmVGh8ygGxgiBaAsF/h4jPuApBFQrFTKvbeZ3/0qJpCGHJ3CbtTqLIc9gyjTrTOmO6+y3WRYKnPecPTZH+DWxz7Cthn2hS9y+du/T/3GO9Qyx1s2DHgeIpJ/mL7HO9SqVc9H7+3eiyGOVau+A/305g4/NN543sv4E9PVD57H7Pkxfo1f4cfYcfxc1/T9kfMW3+DjfI0jdusP3KtW/RGa28w/vPsPn/cyVn1ANaH8vTt/gc/kU470pfv127XWPXTVqlXfSldplKtebv3gcMb/+/af5c28fd5L+cBp3UdXfTd66f4GakQksiTFaUQ2co90FsG9YUqPcHaMSiIFLWsJMQvjyzudSQuD1QpI69OcDjIACnVGckYNmOJwhzmTI+pYLfpxHRDpfai5R/NGtyk5hUHUajz20KOrk0BpPcLaAw5WYm01ekUlXxGL2mOWBbQbTleErdV4/TpEfC9xHBzDLHpEnUJrES/cfSvco2cWTQR72Mnn4VbQuqn1qNQBrzNk7YZZmNyS0/UxdCANE2KFphomuNWInnUPE7k/vspAq3tMHNOJsTVGGkfjxEYgi5GAXMPUbqX3Nlu8x5pzrCWnoDolOlFrq0hztDS0Vs4fP0KSRL+xNzCjVvCy0Cxsu2rx8aadsnZoyZGUEFrEzAqoZsQUtlv89m1kVIanmXZ3By3oTyXimtOYODnZIpKxJ4ll3kFrQaUDSSIm2YcpDLiUQCXM8zZAA0m5w6mK5onp9hnj536Ak9df50Yzxq++x72vfBVb9khWZKlw8RRaJ8WHRDLHk5InGFImj1N/jwlzXnP0trohtWKtUpaFgwmlGW7zs75cb0iL8x1STHHtjYIiGdCBAUdVyEmDylSCpDXHW6VZAxNaXWJ4Y0jYIczfxZ29VxaJ+OYhyXU3r+QJyYrtDyzvNZooF7s95bCneUXdyCKolx7F3iIe/mrCwUFJqEoMK+Qx5ij0qnfbO2XvMbzgYZKbe1DaokHU+oIcSkSgu0d8tcV1h1y1OIdxL5KiG7g22uHAcjgw7/bUFt+HDr2jNwjfiKQGJIYexFqQ5HGzw61BW0DGeI2t4K1w/OA+5eYJNk6QN2TJyOaIdnmBt10QvWN06lLn6PfWDPUc0U10MA8DMhyhPoOX+B7b4wXMxz4TkxAFk7guvO4iRX9UBjvgqqg6SQbQFre1ZSZtbkes9LTB/YAPE54TlRmZodEwNngpiOyxlmBotItL6ttfImUlXSUaSBwXl0StjdaiU1w2mXR2wuD9vEsT5ekFtt+zOyz46THjnTvYbkdVxccBS52yvu41X7Xqw63//Pf+c4qV572MVR8i/eXtm/x7Zz/8vJfx3HTUf/D+Nf70CzZ97bzOXX6Q3yWxDoWsWvXtqHl73ktY9QHVJ/Ix/8vjT/JDw9lLE/f87WjdQ1etWvXNWvfRVQCfzaf8v27/1Gr+fhta99FV36leOgMYGUFqmBAmhPlbg2gUoTWuSTFRQS1dd8+qQ5OK6gSt9vjXCmkABgTvpkwOMlVzPFc3aEGf0ZMeZlLEPfdOUenRokmj+7ZZGJX2TfSujL3ns0ePqobp2woRRe1BCHeeOIhEw73GepqFkaxDuImBOINZ9Oy2iBqOUFrHPOO2UFsYpF7qdewzmmnWSOpBHQrR5ZoSog0s6Eivc0Cc1nuBpUac7FVkrIS5iZTY+F3CrCUIZ0kJrwqDdaPHUB3DYHND3Mg6MakyDomcBzbHJ9w4mbC84eL8En94wFONjuJxiljcayrQaXXBSpjcpgNc7imX+55oXZFhg+YeH11KvDbx3lkqeC04LQjZNCDNw2BXUA2jWUrv6H34kCZOnRekLWEoao4IbTJ+cgN77Q45wSD30SePSeVAAlyULD0umzhM5tG7LOqMKXXDPsVAwjAwjCNbGRgvZpbxKU2h2BLvWa0wG3U/Y4cFa9Zf31WPsJBF2Bwdc7TdAMJcCsueOK8s+jqawVIry2JUq4gLOQUlq95zojXea6yG2eZQiffavCFjjk7dnFDNqFyR53BF1bt7vEfekFxBYyChuVNEKe6MQyJNE2no16RImMgpUVSpSbFpoO4SXhbUKyrRxS35qvu3d7y69M8LkqYgxb0hnjp1TQxqqPRrq8XwhYTxy5DjcqkX+NKgFNQanhINwySR3HrMsUKVHpne8Ob4PFMOB+b9jrK0qPJOOeYoUkKSRIS0jFwNUpCia1sAyYKLxTXkxDUVzjpW9thhYfjK+ywf/Rh6dhOlUlvCk4INPRkgotM1jxE33yruGZcFpOF6jPoSPdS+g1ogn8RgRZ2hzUg+Qq1gkoAloqZ1pM3CoRzIKJQDnpd+X91iuaHuWHWwJfrNJWPkGFqxA64ZOdqSxKBC3T2Gh3fh3a8zjQJpoHnFdROGe0rXJnA57Dk8MrabEfLIMB2zLY3lS1+NTug2c4vMjeGIhzdHHh5dstge04g7bw5WV8Ns1apVL5/+1e1H+Bs3/zTbl5hkAjhmx4/xa/wqn+eSk+e9nO9aW3a8yn0+w++vP3CvWvUd6D/+rf+YxZbnvYxVHyD9xHiLGzry7539MJ8YPvz7w/dD6x66atWqK6376CqAj+QjfiC/iETr90frPrrqO9FL+Jub3peKg0kQiRoUqtKraj26gdWEnLoB3MLETXpF33Zqt3dAqgQBm5yIjVUJE0Yy0mp0hmLRgSlH4IZ6Qkw7pRuoofuM2BjGljreZkSmMIFT94Ajuzk6OtX7a3IYclB+drW0MKKjyzN6VYNU7jRwdVzp9G/CveIaNLOLBP0bocc0c6zU6Ay2btJYC0g59c5VrshoAY8JV6fhHjQiZGgtIrVxpC6QDKYxIoRbC7NJAFo39yzMcMCbop1mdPcw2JwwkFXQCBomb0byG3fQ2zfRZSaVGVTQYUA0k8aE5AFNCZdEa46UBZGMjgl3YalLHJcr83F2tHXzihzH0FoQj5LxugAJMqRCnDOSwBNpzAzHJwzmHC532H6mNcOtIGkIA9kNMXCvtEdPqHWBzRY/RE9wzgmrFceigzdtUG84iWZxHox54HgzouPErjptviBNA8d3bjO9cocdExdvv48/ecxhfwh60wVvYRx6vjJoE9QlyN40ks9ucfT665zevEm1Rrt/l2oVKwveKlZ6DPPc8DqTJQj6rEOPiVZExuip1oyzAWAyp1qjmgStPg0RsRzNzbGuVjEDehy61dojkBV8QAZDe7+uNCPlTMqZcZzYprhgijlmJUhtEg1haXN0CLuhklEd0EFigOF6YIPonR0j7ldSfE4cxB31uO4kp2dEcK2wFLLCZrNFUqaZ04phdUFqpY9F4OL92tCIc0fCIFZhlIGUMgffUUuhVIt4VjU0HYEEiXudGEAfFOnmrqaBlKJzOUKmUz9mHsMeC5gY7o3UFtIXv4D84OexbfQTJ4WWcnRi1x1UII/omDFSDLnYIXq+e3JCFE8fIwmk1Rh5yYKnIwShLQd0OgXvZnQtQZ/LJsj8zYDVmdYqohUdj/HSkDaH6Txu+nujpCzocISZo1LRnClf+yr66C7Ml6TNCB7pAYkYFrFmPSL6gOPU1qglc3h6znJ6yvHHXmEzbWnvv89lgTc++hqf+OgtUnUef/kuv/V7v8v7j5+AGZYnpDakrQbwqlWrXh4J0WH4127+qZfe/L3SEXs+z6/zq3z+QxrB5WQqH+dr3OEeJ1w+7wWtWvWh067unvcSVn1AJMA/v3mDv3nr89zU8Xkv5wOvdQ9dtWoVrPvoqtDRmpTxHWvdR1d9u3rpfnvjPbJZrvo2r00+jd5Wl4hIzXrdtRnOsPCsZHcByT1edcB7R6/2KF6JJwpTVjrJivRo3jFM0yxhmKarBtXeJSxDmMUOuIdpllo40yI94tk74CsR2StA3nTIuEcmS4/ydQ9qTyRMFx3j41dRviaIWRg9rQWB7Es3mK/oYceF3oWsnezVIOpISNN4fu1xwDLgto8DVxZchyCHhyFiWK+oZh0imroaknrctWk3Tive/997qnaYrwaSuum0Q/Uo4oh7xygGXhr26Cl1PmDFqOeX8T1KpzUHhIS1iluNY0BCxxwvjwQp0Wqh2QFvCVkWxIfoyx3C2DUvQI/nVkGqogVkEBjGWHMWbNxi3fROecRTxWuJ91ciQtioSI/Sbu4snTz21NBpIuPYcoi22Dyiakg+ivppqzQS/sotxjfeZBoS5b17lIcL09EJb77+Gh977TXuPzW+8t4jLp8caBpRweqGipLSQCnWj2VQ8CqZtNmSTs+ox8fMatTLc+pcgDBDzRqtFeoyhzk9ZLJmBnVy6kahSkDdZMBIEp3D1aPLt7RGk0xtjWIxBCC9i9Ka9O7cGuSsxTGLbt2glHUaSLOTvTGmxDiNnN25zY2bN8liPLz/mIsn59i8YMuORmXZL7QS8e4pJyQZpKBokRRGah4gKZ6e9XTT47zDZ00xPKIaEcNew5Q9mxhOjhiPNmirtCfn1CqUfo/BDFti6CIe1yNCXQUZEnp2ih4fw+GA7ZUSYeJx/WmPMk4D9K5ivBO+BDVNTshmG+d7K0iH6l0SJhGF7kIneUGGTKqN+tu/Cn/qJ/Bpou4PkPprThmXglMiPSAf9RSBhLD0+9FVUsKIi8VwhNS4hyhhwDMivbdbh2NUGyoZE8jDQFJYdlB9hzCT8hnN5qCpbYKyYIMhdYfmKa75eQ/nj2nv3UUFUjI4vQk2x3BFq9FB7YKr4pLCkHbDh4hwn5eFg8F8+RjOH7DfzfjrrzP+pX8BufMmApx99Te59fQeD+7fp+1n7PAYJ2Nt/h7vUKtWrVr1wdWrOvF/v/XjbNYfzv8pHbHjx/nVD9309cSB13ifT/OlPh63atWqVau+G/3zmzf4f9z+iXWf/A607qGrVq1atSoj/J9vvLz1Qt+N1n101bejl84AVrd+Wlk3SFuYHO6YEySgFcQzLoYpJOvGixPGkOv148Vf7VuQvt6CFB7GMLD8yqwZI1IWjQ7NtpCGo/h/ojdWNKKj1S1IYVWs9H5Qi3jcsKM91m2EwSKCuPeuVa5Nx8gntTCVh26YNoulC7FW0eCXO80rnrB2ACLi1NHeBawgJUKhrfTe3opqvPaIfc6INITU41nDuDOLeFuEiM3ODnVENApt5QrrL0BuPU47jD5JRMZxUqzM0AljIXzzqhvcjMQxoztaKjo0vCzU/Z69B0VqS5CqYV6NtHnGumHlPZpbVRBNYdS2iotiIsAYccNTkLV+tMWzhMl88IiAdQdTks+QhogyHjJmYK1h5xfUQ3p2nK2RNWPSwouf5zAWU3TuqkSENPuClQJ1Aa+knDBXxL3TnzE6ANGt6rUgPuPTDeRog5xn8luvs/nJH+fsBz/L8cUF5Rcm3vnNysXjB7QFdJgYTge0Vsojp5Qd7oIMI3kcGY9OSLXSvvEOl6ViDWogsGGoudGaYbWgLmQdyEl63bOiOUcktbWIOk8Tp0dbbh5t2Znw4OIcdjt8mfGlUZzouJVEThH16+bxHjRDc1DcMiREFdeE+hwzEAy0PJBv3SR99C3k9g3aYYcuBdnv43x0oZngG0Vw8jLHOpP0ayH3OY8wd733OosbwhCUeVY0D6jQO4Dpfdhx7emYg6Cfl+iyHrckM7xAbWHAikBSCeJZFXUn4SQX5LCjzDNtnpnnQm01DFSJSO24ZjLSGq6KYag5olcUvqNl6aasxXmngiTBGXEr0eON4LZE7fA44POB9pu/jE1b2sd/ENXoDHecKomsmdoWlAXHkbIPU38ckerYOMTAyrJA1ljbskdyBld0c4RQuyHccNnS6j4odolu52V/iU83MC20/UWYzz4EwZxAy0JKQt4/4PjeUxZxfFnw4y0u3oc6ordcPCLEsYjXNpdIORAJktiNWhvLbs/+7XfYP36EqyJ5y7Yl+Ce/iH/2E5gJj770JS4WCxK8DkgzxByt6y93Vn349esPfp1fuv9Lz3sZqz7gOpLE//7sh5jQP/qLXzIJMX394/wq93mVL/Jp2gfwR6yJAwlj4sCn+RKZwgkrcbFq1apV3yv9q0cfWc3f71DrHrpq1apVqwCeWOH1tPb/fqda99FV344+eGfE91kq0i+EudO8jruieNBpONF4WQMW9U7vhguEtCXikzvV626oKSI9MnZIz/prUwqK0HtkrQiSMunKthPpPasKNNQkSL4rqlcTroI3752sz6hdkl2bTl5rJ1vh+vdyGsaPX3UFN8drw4Yro9rgaq0yROyzBf3YPOhQt9LJxk5CXz0WPWr2Kv621TB5loqnIJA95TBcUw7DOqUwjtsCekVFaxzTPHFFJEds9QFccfEwja0/txAfr3P03oqSzJiysEWYREjWkFJohwPFu7Hf4vW0Fu8hNiN5RFCSJtKQGbYTMm2o1eCwi2heN1QcGTNpyuj2JJZhHsfIG+aFWgxvzoiRNd5dtxqDArVSW6M1RdPQu6OD9qQ1rHSD/eoYkuK/mwkfB/z8PDw8D/JUjIimZogeVcnXRn0tjYt7D8gPHrJ/ssOKUZ7s2b37Ho9PBhYZ2A2KbDPjRcLHkc244SglLg8zu03Dnzyi1QrbI6azY7YnW8QSZd+jxIeI921LiWhjczwJeZii59iNlLWbqmMMOODX1Pbw2i22n/00p2++xtH5Jftf/x3q1y/CCG2GuVM0aHj3Gj3FZigJzQM+SByfQXHNuFl0xLoFQG+O7WeWr99ld/cBZZk53+2p1YKgTYpYdEerGdKHICKGOcKZo7fYw2j/po8HGT1EB3VvyUY71d4MGBB3qJV8KNzUzHa7ZTcM7KX37mqkDEhKSE4RHS/RL56skgp4nVkkU1uhLJVqjusQ0craY9avjOAeq40YiXwdbT5kRVAK0dNN7/8VicEOCPMY3fSo78owjuhhZnnykPTOl+ATnwAarRTMHI6OYJig7pG8CWM1j3E+SutDJxXPmZQy7oqJxrXbdmjekEpDhom5NfA5EhnyhCSnlR2MGtecbkEW2ryDQ0X3l6RpZGwzZ6+9ws1XP8Hxxyr1wSMeXew4vzzHykL1jLlgskR0vDkquRu/cQ6aGV4PYRbrhqU2dvsdQxKm19/gkx97i5unJ+iThd0//gK7ywe8e/89Hp+fY+ME4qTNFO+5rUbIh0nu0Qn+vB/jg6an5SlPy9PnvYxVH2D9xc3r/OzRR/mfbd544c7/76W2HPgoXyfR+G1+qP9t/3keL2fDgbd4F4DXeY8j9vCcV7Xqw6l1D/2D9bWLr/Hl8y8/72Ws+oDoxTq7/2S17qGrXnSt++gfrHUfXQVQcf6/T7/A/+eVP/u8l/Kh1bqPrvpWeukM4DBSg/CMiOZEQoMwNHA3kkRnppuhniNOWfyafBVJQfhKRslACbNP9NoEkWnzLAbaDNKAaideJXXjNyH4NY1mGIlO8lo3dRuAhgfrnfxNnRiWDGWJ+OYrk9aA2oJMxIL8uzag4/uj8zWhrXfsSukxz6mbmvH8DnhtgduWWEdEzkqYqFqRdBavxYKujWjYIaKVa4s1uXdqugI5jktZYNyEGWWlr1M6tTwEcWyGpw3US0QnvD4C34IIzS6BY5IKE842wzRN5Kykbjp7WTBzWmtY84iUrguujogxTRumG2ekkxvkQcKAvLgg1RytqZ6RZvh2Q9pu4+0vQXarCGmaMAG3heql891gNeK33Zcwn1uQxm41TEMB63SitYamDa4W8cbJwkwrM20+0Hb7+L48klT6+zr1kzkh1gDH55n6sLK/3KKqNDOgMt+7z3u/8FvMv/UlZoPL/SV5M3Ly+pv4dmI82XB265TpYmb321/k4tE93CspD2xPjrj5yi1UBnbnlyzzTCszfrDgtjUFEUnQy2I1zFPN3ex3uIpxMA9ieTdT7t3lYnfOfLlQzs+hhumaLSKDWzWqEMavSwxYaNDyUgCb8ZoxIsI7KPOEUMAabbdnTkrTkbos1DZT3SFnxIVKonqN/t88Rn+0avT4Ip2WHcIYDns9LjpvvRJ6iM5mtA9kOOI9yjkl8rDh+I3XufW5T3F2fML9L32Jiy99hbbMNBFc+/2jGODRPQ0Mw8AgICjVKqUsVHNMUsTAe/T+incjOCnkBHkkjYlhsyVvNqRxZHuyJeFcPj7n4vwSWoHS4r3RTrcLcS/qr4neQ57tDHv6EH7jEfLmxxm2W2oxLO8gT5gOqCmkE0QaLJd4crzGcSQNeL2EtOlG8T46wBvUVtGkeHUWv0TSTRIBubeS8WGM2ZK6i3Pm8pz86BE5w7C9wfTqq5y+8RpnZ2ecbc+YPlI5+fo3+P3f+y3m5YDS+v26x1aL9nu1oTph/b7qIpA3WC3UZc+SM+XNj5D/mZ/mxs/8T9kOE3645OK//W/4/d95m2V3ET9s5YzIBl3muE6Gq2tx1YdB34sfll+0H7hXrfrDNKH86HiT/9PZD/GZ4ZQTHZ73kj4UEuBN3uUmj/kyn2DPEZXEBad/outQGmc85XP8Hmec/4k+96oXU+se+gfr7Yu3+erFV5/3MlZ9APTnp1f56enO817Gh1rrHrrqRda6j/7BWvfRVVfy572AF0DrPrrqD9NLZwCrC0kahgRB6gbaSC6YV+pVJDCgdJOEIC0ToN3orV7J0qKrVzLkMH+jl3TAS4meSj9APg5zSDKwgGjEPEO/w12ZntbpUI3H7fGyWMRHB1nce1WTIOad4osuY++dv5HNHOYbTfCUoQYpKPRI5pSiW5X4fm8L7iPmlUAaJci+1B9Lw/zGBYojrshwA7GG5Iip9iR4JXqDrR83RvAShrYQUdpoHLMWNCBtBu99qgKUBdHcwWMJitoc0nG8DgfRs4ij1pGUnHEQpiGRN0foNKB6Neki8fqo4eFBmPFHx6RXX0WPJ0QT9bDghwVKrDX1mmTTBIdDRDJrkJsR+a2YOppzmMrVqeY0KoZ24roGQS1xmfmVqe5Gg6AjB+3HZIjHsYwW5yTF99W8peYa58g44psxTOTDAW0aZjaCWhyrVipNgk6W5oic86Q4F5Lja28ccfzKKcM2gS8s58bjiwt2T8/ZPbyPHSqSMvnGGenOa8iNU3Ia2G63cP8B+8MhqN9xiq7ZA0iZkVLCmBSHw9yPORFVPSQsRS91u9hz/vt3mXOmGizzBQAJx6cx4nyJeF1wNI1EjXBQsiKKXMX8uvaO2RyGvCqiQlVjsYZxwDr9Kj0aOQuoGfvagmBNQfO7dPpXUsQ8a5DGGk8a53bv0XaLUl1Xgix1QzQhEmS83DiDN1/jQGH+2pd58M477J6eU5vQxgmpFSWRRaOD2SRijXMi5QxmlDJTLUB9d0MkBj4inhoka9C+WdHNRDo6ZphG8mbLcLxlnBRfahDPWL+PtHg/IEzgq/uEd3O9RRy7DwnyKdOQObWZ7b7QrPL4/mN2w5bLW6/j9TEyjnGd64CkKZIFEMwVSCSPnvC67BBXGCsiGWvxcXdHfYHWh0pEUM9h2LaF7b17bB/dR87OSNsJUMalsG2FOzfO+OhHbjCMJ8zbY+r9+3ytwYEDrRQEpblDHoMiZwOA1drv2f2e1QotbSi1UPZ77O7b5Pe+AttTdl/8fd750lc4uOM3XmGQil1ccJgXRIQ0TJi+dFvoqhdM7k61+ryXseoDqP/d2ef4t08+/UL+oun7rasYrh/ltwGYGbnPqzzllHd5EyD+rvh9mX12FONTfJmP89V1unrVqlWr/oS0lcTR+rPBd611D121atWql1NfKE/5reUJPzLeeN5L+VBr3UdX/UF6+f6Gmnr3qxOGaxSpYv2sTC5B00o3XeUqXrahknpccyJrj0H2bqi2FBG/evW9ElHHPsXDVFBtJB3CwPGIY43kaAUvEZfbau8RHlAUIcxq8d4HrLE+McF7jy8EqeuSwxykdMN0E19TW/yXRE+Rjl/oifde3yCGzVsnjXtEc4/IdVvAHfEBbTPgyDDCVX8vEkZQi18ie6vdpRLwMF5xA+smtzYYNoH9iYcJ7BW8hVGUxki5bUHNegOndOM6RzS1lW4Yt3huicjbeFqPx3CLtXRKVoaR677l4vijR9QnCZOMlEOYQlnRYUCTkFIKs7PO4A2TMdagGTB8LtjuAirYUjERDqUiJiQijliU6/PBvHUSWGiiVLNufAE4nifQRLrzKtMnP8HZ7dvou+/z3m/8FuXpE7zs8P0BVKKDNitqhpvhKaFiiPRz26ItNnnE3c4SEeGbA+h9YX6gLMWhFrQZF7s9+8unOA0ZR2w5sLz/Ppfnl+QUxvJ8fsEyz9R5oUnQ6d4s+n2vhhYAl05K5ozmFMasO7VVWqmUubA3i2PjggukHCa4N8f6kEKSGNJQkYiU1n4oW74+Z2OAocezI3H+V6O1hnoOQhtikOHkGNtuaa1Cex+5FKRWGIZO16Y4ppJjBsIbYgpDJ6+v4r2TRPy0CP3ki2d2wWujPHzExcUOUJZSuFwO1FIxb5h12jlJDG9IIomSpQO9YjSHVgq1GdbiGheN+HhEnpnrOUNK6DBwNAycThvy5piajrAyM188oRzmIJQR0DESEDBUpEe+x9r1ug07jOhh3HLjxilvbLcci7DbH9ien/Pk4hGb80fM80J59Q5NFJ+O4dYG9wk/XMBQkDTENdIqaiO+uYHUPa69w9sLyAYV7fivoRS4uCBp4tb9r7NthXx6AkmwMtOmDXJySrn5GuWHfwz/gY+CCONHL7gzv8+9f/SUMu8jOhzFkkJyyBNeF6gV0QGfL0EkUgF0QHFaW1hq4TIdc+/ROTffvcvDX/oN7n7pa5QxMVBp9UB7+gRfDMYjEEd1/73bm1ateg5q3viPfus/et7LWPUB08fS0Rr3/D3UxMJH+AZvInyG3wfgy3yCmQ0zI4+59T14FudV7jNQ+CxfJFPXH7hXrVq1atWHXuseumrVqlUvh95te95pO36E1QD+XmrdR1fBy2gAS8GALIlwZhvmjgFqF5gckXQIE9gLV8hdMMGJnrEccbFX1GqbwwyuDRkk4m/96tAKCUGGK3My7JaOoQUxmDLeSvTlejhW1/25OJodr91U9jC7vNHJYMMlR3OpKuYV8Rx0ooX5aVdprxAmlQ49trb2tfZo5v548eAa3qQ7qiODG3ihpty7hhU8SN3rNXl8XFTwuuuxz/04WY3+WM0RB2sNhgyicaxVkdJwCpI3eKnR2WoprCmLHt/anuKygXYJcoyh1ORB1NaKlYaJYu0Qa2sRsZ2GMd4NTRGfK4IdOi2bWhiNagyS2ORMSgMFYVkOtEWhFdyMYkZtC7UadthhteKmPUI7U+fKTCPngUSJ/ticcY3u6R4GTV0aJoamiCC/7lVWxcyoyyXLDvywA6IXutWC5QFGZWgtjE+P4Ok4Alc2npM2maNpw3Z7TNWBy92OJk4bEgdx1AxzQ8TY14XD4UArYQj6XDH2HIpiY2PKCadRdjvKXCi10CSGE/ACNaKbr84b0eieFhF0EFJShIE2H2DZ0cwj6dtB1UlpQCRH1LVm1Eo8nnDdbS0azyd0I9a8v9YW8c/qqIeZCWAmVOI6cTPMwS8vkcMlpcLydEfqselB9gY9L2YRq0ynhjsML2jEtWtcsy4W5zE99tgbXh0XxcvC/lCZBcwKrZWgYt3iNZrTHHSIrlxyQlVJOcUZokbbK62VuLQ0rhtEYoAlRey24zBk9PiE8Y23OHv9NV47PWY+v+Ttr73N/umeup8jKSAOZhyvHl2tGueLSKQLuMc5mCRxdLThrdde5bMf+wib7TGX77zP73/591nqgWqAZfKDe9TaqDj24AZ+dAy3Xo0od2Kt2Z1FDVVD3DESLgPOgvqCMuKulHe+iNdCqjPT8TGbKbNlYBgG2B5RszK70O7dZTnMPLHG5b2PkTaZx2/f52u/9VXmywO5FlQHFmnocITVGcEi+rlVkDGGQ1qJ66051irN4HD3Lk9++zf5+sNv8Gi3cPHOAw6PL2lJkMsd0gpW6VH85xF/ft2LvmrVh1Mqyr/2A/8a/8nv/CfPeymrPkD6C5s7fGI4ed7LeOGkOEoMS36OLwKwMPCUMx5zk2/wJo1E+yN/PHNGlus/fZovMTFzm4e9tmLVqlWrVq16sbTuoatWrVr1YmtEmdbfsX3ftO6jL7deOgPYDFLv+VTvRqpIN0ePSZIinlkFfHwWl2we5GrKvds0jOBADHOQq1cxzUmRrKi1iJAlQqXVDElDf/yIbRZ3pBqqGW8taAsdOg28BPWLIuMQKKOBWO8w9k7guiE6dPo3zKz4J+hMIyhKMcc7MSud3g3jUKKTVnpUrGhECNPNXCDniJL2utDaQpjhGtHXXrkyx40Wxm8jjF+/eg3RfUypePaIUpaE2z6OX4+fDUP6ytzMuB3CZDfptOg2vi4d957dbpg2qHWhLYrlDOOISBDMVhdEMqMo43aD5YnSjLrf4XXGTfA0oqKMJ1u2b75FOjtFnzyGd9+nJsW3N7GstPNLePQk6OghCFeplbQE7azuER+MhdneDBOjuWC+IHnCs1CbY7UxKPhcISviFfdGefCYRQesDOQ0sj3b0PaKlRTdyUUxV0yH3iUd57D1919SJt+6yfTW69y8cyveh3cecP6Nr9MeP2EeBnQzoUdHaJowEeSwR0rtsHKQp26FIU1stgPFM/t5R6PRcMwqUksQtCZgFaNHFTdHvKAqTGx4dXMKeeDecERZKu6FhpM0zIdEg1aR6kitiHfDVjWGGJqClk6/EsME/fiSEpK0276OusVgAw13xyyIWmsVs0prjfmwp5aotnZ1+vhHj1+X8Jw1oUmubxqCXXdUO0COW6fk3h3cFPcZaWGuZwLpbRY92ELFRWIgoC2YgYnj9F7hITNMx+Qk1MuLPlDQ3V8cbEHSpg8zCJqGHpGubN351O0zXvn4pxhu36Hdv8ejB+c85DHGZZCqRJS0X/WZW6QPeCs9ylqjG9wam+NTbn30Y7z1Mz/DyZ/6DHo0ob/8y9x8eo+LyyfYfmabB+Y0sB+dYg0rO/zJHn/0Pu34NnZ2itiCuLI9OuXk7Jgb29sczs+5/+A+5bBD7r6D1yUI/H4v080REMMkmhJZhKRGkhGWA/tSOSzO3f3v0r7wLreAJ7snPL58SBbn9uuvgg48ePKYi/0S95N6iDEcdaT1CP7eU45Yp7cr5fFjnn7hixztPk69dcr2lSNu2cyj3cyyO0S/dKtYSvh8wFOOgYFVqz7EUlE+dfap572MVateWo0UXuUBr/CAT/ElHnKLB7zyLb9HMT7JV9D+d3npY16rVq1atWrVy6R1D121atWqF0d/YfMq/8z02vNexkuldR99efTSGcD09l1vLYBagvxzHYAdqsdYayTRHvVLmCUSxiuaw0CRge7AoFkgGZh2Oi9ioD2D6BSEpvR8dclEly5Bb6ogaQr6UMNAFm/dT70ijGOtVA/SMHnvzRXIIyI95lgECzcq1uZBw0Z0dZhXqOJecFL3WhuqI3TzkU5C0mlKkRTkYo/AjiOYcNUwAK1d1xhTgnB2KugUBab5uJvAU5glQpgyaO/mjOOpkvAkQMLNOjVs0bHaCWCjoDL2Ht0gMVUiGttFaVmjR3bs8bwp9yjhzGbcsHnlFsOtGyzLgj06p+33iIcxr+pIiuM3P3lAenyPuq+ICvnGGV6d+eIcOxwgKbodcFeYK7osZE9ocsaUSSmhknHt77sDbcHTNp6HHIegtPgnpTCzU0I0YTmxV0FsB9UQVXTaIDZDC2MTFC+G9Q5gJ0x7axanzLJg+wXbLVRv1Lqn1oqUgjloraTDgpcDpUAreyDMMndBp8T25ISTt15nOjvBnz7BnzwOY5Z+g++TWSLgmjvxXZEhk8bMdPsVbvzQD3Prk59iGAX74u9x+fPnLI/nGFpIU0Qu45g5SkNbi3PJwaxFX63Ev0QS5N4J3Vrv6A562ntcuQioDKinHtuuWCtYmakuLM2Y5xIPqqkPPET/r3bSGIm+YKp3Yh/EStDIaUBqp2ijTDfIWYA8IK2Rpw1T1qB/d63T0RYDJ9a/VxxqxazTx8MQQxrutCWuQ0XCpSYhOaKedZzIYwZNNIfp1it89Md+io//uT/D+LE34lp49QY/stuRauOLbxfK7hxrLYxqTT0GOihsI+G1gRWSJMZx4Gga2SZh2J8j5w9hJ/DwCaksjNMRdvMWdnpGuryg3bsHRWhNInIZSO0CHu9AE/n4mNM3TnjrT32SO7dvcfnlr9DufpUnjx9iQ8J0E1HpJJo73hplNpbs1EmZLKEFRjXIA66J2Yyyf8rdec/7zWjlgG8mpo9/Am6dYo/exy+eRDe1JFynuEG1Hm3fKtIq0hYkb0EbYmAW3dHngzC+/hpHt25w8/Ejhq++zcWjcw7LTFtmxFqkytcFb6sBvGrVqhdLJ5L5t07WoYA/aUV2hvMqD3mVh897OatWrVq1atWHRuseumrVqlUffgmCrhVEz0XrPvri66UzgKP/FlKnDKX3aaoVZAi6VHtPMBLdsVkEx2mqqC9BSSbrxGDEiGIEYeYefpIIqkNQvhRUt30moj1bjGgYMpFTGwaaCJ7653usNGKIJ1xq0I8tomdFBfcSxnOPoQ0q1sJDcwfN0YnbQXzX3ClGjzpbpBOVPbbVPV6XSJizlG5MJ7DSTe8g9kR6NLMDCJ4sSOlGdG9Ke9aZawZao1NVo39UJGKmtferRs+wxf9bi3VZvEaXjLcDrq0bkAU84SiiTlIhjZuoBVZFqGDR2TzeuM1w5w75bAutYE9m2F2SWkNTvAeSFTdhefiUKgrDRDoeGYfM5E5Tpw0T82EhmFHDesGsOqQpkaeRaZrI/TxrJJqVICuRHhseb3tWYMxoi85dAEndGJwPLHffRx4/he0xnoV0NMF+F53Iw9A9esdbkNt0YxZ3fGm03cK8rzw9X7DDJfO9h9ju0M222snkZ1RsvIWOp4E8DJzcvs2tt94inx6xXF6we/8BtdQ4r0XwakHJDiOI0tygVBRBp5F0fMx484whO7q7y+Fh4fwbd8NAhx47XKFFn7JqwiRiibV35eLeTV1H8hDRzBrmv5jE9ZYEN8FShqX07mxBvaEpzE1zI3kQ4l6td2r37mIZYDnAkPE0xfuD9HPTIAmaEiIJ6Z3hgjC5I9UpcwErDIOSpi2yOUbHAV8O2NyN7trN32gsRqYR6YZwEo2e5DSg5rRlpu4OcV4n7devInlAxMlDYpzGSGtPI7c/8xk+95f/OYY3P9IvYUGOt2zfuMOnX9/w3t9/jyeHXaddFe1x62ZxbRpgIgw5M40jmyFzPI4M+x3Lo3Ps3gFNjfbkMe3ynExjaJX50T3KYQ7jXhOGo6lHuveobE2JNI4M44ZxOSBf/iL21ffYMLI/OqHMF0hW2hLnBKVirVE9s4iy1MpGhcFHklXGlBBgQDFtoMqu7Gkp4bZQ3vsyu/ca83nlcDHj3dgPd7fFdd6CcvdakGETQyJWw2ivC74U6sNHcPMuDJBOb3HzR06ZHtxnf/8+u/M9u8OBZRjweoB09D3amVatWrXqg6FBlNfT5nkvY9WqVatWrVq1atWqVatWrVq1atV3qZfOAJZrpyyRUif9cBoHxLYRjay95/a61/Sb6Ncrgq/1SGNxSEQ8rFvE4YpEP670blKZcLfeTyq4ePdqOjWs144vrj2OuvaOSgHMIXlECqcpzFftj1WXoHpbieeXFD2kfhVTHSSlU4Pctau+2Fh6EKrdoEwjUK6cxOh45Sp6OUWErHJt/DJkaCB5g81zfFtr0I00oB8T46rmFh2vTWOhm2puJGsgOQJx5wVXCZNKIlpa3WnpCLFDJ4AzVMAW8pgjpT5P6DShKaGaSS7ouMHHLfXiCf70MV4azSJqe3u8ZUyJRZR5WaiHHa05oolxU5k2ieOz28jtV1hagW98g/bkMXW/o/ZDIOZoVtLmiGE7MB1vSeOENccvL2mHirgg/XWLRjR1TgI6InWPqIFEL6moQDtgzTkYaCugTp0LtiyYK14qVWOd3iqaFMmpd9YKaMNLZX7/LvboMWVeWOZ9EI8YUjeMKZNVKXnL4bDDreFLgc3IcOcWR5/5OJvX38TPL9l94132uyUIZVpAqa0heSRNA6JCKpW6u4zzMDBc3Cvz0wseXe7ZX+y5vH+/91LX6GauiqQG4xi0rQnqBfXWr4+G5C2aFckWAxRL79EVRU1BEiYJwaPLVxIyKGlIYQDjOAMATYzqRBQ7FhHn0qnfPIUBrr3nOyckb4Kc78ajkEhDYrM94s7ZGaMmHj54xH53wfHJGR+9eYPjk9tclJl779/lvF2iFvcF6WS9DkP0/aZEEiGLMgwDOiYK1o15RXyBapD7hIFVZNzg0xRXrybG117ltb/4F9i+9XqsE+J+dnnO8oVf4as//2sczp+ibnin4a/eGzGLe1mNazxtjxjHzHYcGBTscODiy19heO8dpnnm6fv3OTx8ipiiuSHScHKQ7uKIZK6ofpUx7rNmiEF7fIndfcpw6xZnn95y52hg/so3uHhqsMxxl2rxPnlbKG1hlsScGosPTK0gxUg4Mm0YXnmNZo6XA4sZdTdTDzNlV6lmNGl4HuL+ZQkv+7iH1gJLQSSjecJ0AFt6F/AM4xbqgXZ54PD+Y9rJDbj1CsPJCel4w3TnFc4udly8e5en799lqRtKHr43G9OqVatWfUD0fzz7IUbW7qVVq1atWrVq1apVq1atWvX9lwCfzifPexmrVr2weukMYBWhmoEsQW9yilolcRKkmHdC16/SipXWKlmCFg60thuTDtJqGLiSEBL4DD4QxukCFmayWIU0hBkkOUxXSRG7bHDVQ0yLzl78KiKZ4AaL415BBlzoEa4tyFgjKNleZRqmdjy+eI1uWDNcM+5LN4Iimti9R8MSdBz0kGzJiHuPrnbsKpa6JfBuTlv03nopcUxqx1uthruMXC0ozGZ6v3Jdwmy2CmQYEiYKtHhdQ4YSRqG7RiS0EMStDv14KYyx2ubx/XEMEhRHj4/ZbCc0D5T9zPx4x5WhrwmG7ZbtK68w3DhF5kL5+tewi3g+Ngm98wry5hvU4wl7+pD9wydc3H3E/OSSethFdK3WoEW1d5hWo9aKm9AOO+qhYqXEeVcXzDrlncElDDk3wYqhObpxxXP0OUuQ3PXQcKu0UmjFKDSMxIAi1kjd+FS8m+kOzTGvlFIoMtPc8LaQcnSf5uMjtnfuMJ3dYDnsWL7+Nv7gyXXvbhNluXePp/cecHhy4PzpOdU9zlU8zMM8oMkitlkyPg5I3eCLY61h+wPt4TmHy8rOnP1hZt6d0yRMW5VGGoega3vPNCkjtunXTVDr4oamCUkD7q1HKAt5yAzjhiZKKfsgn7MEma2K93hnE4nkdUkkzaRk6FU/dTdNpaPZz+LgOw0fl2Vcu5LRIZNPjxlef53h9TscYSyjkp5u+djHPslP/dTnOHrrLR5/7V1+85/8EvvfOaftLyPaOYXZnFMiSTeAk5JTJkt0HzeHVmbMG95qn8NocZ9xhVrh/BLdHLEZHK2w+5XfYV83HL3xcTgacRr21a9z7xe/wDtf+BJ1Lp1ajgEOpN/XtHc11xrGqFXUt/FnUarD+YPLSHH3wtIWqvXhj3bAkd633KAbwOLWr3HvAwIT0pwEHL95h5OPf5THd7/B/OU56GecavE9SB8SqYa1SpGBRRplGKizoVXQNqAk5PH75GIsS8Gf7vA246LxXqdon4ghGosuZaPH2wcFbG0f53oj4tdVYdkhnuMsqHvK5WP2945JVNrtV0i3bmLbM3STOM2V8XjLxfmOi93F93J7WrXquShLJkmiXf39Z9VLrTMdng0VrVq1atWqb6nP3fgcP3zzh/ntx7/9vJey6jkqIfwbRx973stYtWrVqlWrPpTKCP+rtYZo1arvm146Axj3IM58jDhb6EZQDVOITt32iN2oH40eXJr0qOUlYlmTIoxB6HVSVfIWakOHEbGKDCm6Q71ThCIIPcb4+jlaGCdewtiUbtCKBCnr9F7dDF6hBX3qGgZsWLZAztERK/3rejdpxPteRUR3qrd3xormMEfEwsSUjBNRvuGLGR7ONFKjd9WsU9K19NfeOvmbsbKgOWOtU5ZigODe+4wFmI64Lg7ORhJFVLFmWF2AiIP2ZqATbiUo10hcRq8jbCP+eGmX7GzD7nLHsQvT8TFsN9jJCdIa7BbcGlZqlJSPEVVrCu2wo5zvqYcWpqYo7lCePmF32LOrxrIY8+6cZX/A5gPiQWiqS+/CNdjvserUpdIEXCTin0sBF8QlKHJvuCl4wWuNqOBhg1uJ/lYETQPjIJA3LGWhLBXLCVejmWDmCAuKIjmMeltaRCM70SOdPd47reF7QZxXw4hsJmyAspxzeHpBvThg7r2qeGF+5y5P7w5onmgCpmGqeYvuWEHQIZFTZtwekceBpRYuDzMuYYgzTtg0UQelXu5YDheUeU8BPAuatqQcEep+9Xtma7gqkHGb+/mjiBmK4+a05KTtCdPZKcNmYr64YJn3tOY0V9qYgYQ3o1k3o1ulWZD+qkJOCastCP2kYTyHKxqUu+foXvYS9G/O0dXthi8NP8zM5+fMzdnXyvFnfoBX//K/wPSZTyKlcLKdeOXdr/POV77E8tigVRhy3C8S6KBkTWSc5I3kRAe2GVYr5kEBuxIR6RKDFVMauHHrNkcf+SjjyTE5jfDkknd+4Tc52XyNPBr7wyPuv/sud999j4vD7pr6dbxHsbfolPaEJyMNw3UfcKsVSRl8oZaGzYWGkAdFhhzdy95QEqk6uVUGkevEgbifDNfR82h0SeePvEm9dUa7vKS8+5DLy0odJ2QYkBqRzFILyYyGgCteF4pmltJYVNBSyaUilpEahq3td9hSIOegfbMENY3Gdd8EX2a8NcyCHDcdsBaDAjEDkKBV3OMxLCUcw+eF5e595l2hvPeYNgqymZAbt+H0jPTaFtpM2px+P3apVav+RPVn7vwZfvr1n+Z/eO9/eN5LWbVq1apVqz5UurO9w+vb11cD+CWXAp8fbz3vZaxatWrVqlUfSv3oeJNJ1hSqVau+X3rpDOBSDU9hUiUiwhdqJ1cPxCEJTC55GJ5o9JQGQZqj/9c0TM8h98jnIC8ZJIxYs+iXZEFsjP7JK6JCHEkpvr7H13b+l5h7Ab96Pmt4ekYrdvy3G9LR5Rtobu6x1N29RruhVbuLFMRyPE2Ph766uXrEL199gciE0VDViK5FSGQsCWoN15Grp/UUfaaeBsDCKLIW5plOtDajaQyDJg1xzCSOjWgK89KdZAatx09nxUuDYQvmaBqiP1WE5v216QFpe1ROKQoXrfE4ZbZmjKUxPHpC2h1gjIjW4XiLLQs0Z5wmUh5olwfKfGCZG+KNlFKYhXP/J2VaWYLMrAWhkqcpDCPJYe4OqZu74K0EsUw/Hla7ydg7bxWus7BdaSJ42vYY4tyHDRQ53qCnJyQV2vmOUgq0OQjHIUWPr0vEXCdIFvHeZvF+i6YwDVOOAYIxjGp1Q5pFLPTFBYslDvNMW0qQoy1mIJrDrBW1hEwDV0mQ4oq2RsqZ8XjL5vSU6fZNLCXqgwfoxROk5DDmS8V259SUac2ju1gUXxZcEklm1BPCFD207hFP7i2uRzdEEpoG8pQZcqYlxZuQNlvSNKFXMe05I176IENDxCIu3RpWC4bgAkbCs6JtQXIYy9IqkgSx3jUsoGo90j2MYRGNvmMVqJXl4VMuzi8Qd9q0oZky/+4XOH94Hx+PefL4PvcfPmBZavRFN0e9IimhlDC21ZCsJE9klYg4rwVqQ/p1fBVHL0nJN045/tQP8OoP/yCvvnILbQtLg8vzyuXukqcPH1IuLlkePeD8/AmXZY6OYwuC2JOAeFzTgKSMi5JqjdcN1Dxy2Gzwskd2l+RqyDCiYqSk8fo942YM6pBGxjSyx9gvB4wYzDCiZ1i8gjnt6Tn7d97lG63x6O57LBdPkP0MJShgyYqSsWVG+5CIILhkSp2pWXEd8NaQZYdopTWjHfbIXCGN6BC95UjGekS5uSIo3vZB3nvu12SOtIYeWY3161OIAZkGvhRcL6kXju4TZVlYNMPxfcbNAKUwN6EOL90WuuoFVNLErWn9peUq+LHxFj813n7ey1i1atWqD5X+zU//m/zcez9H9fq8l7Jq1apVq1Z9qPSjt36Un3njZ9Zh5Jdc//L2LU50rVhbter7pZfut9c6xC/+pRXUE8jwjPzVKf5rSzeferSvOlgCSpiXlTBwrnovvQYVmjLqQetp785FprBVJeJlJUWsrVgnH51YTwojMUjMjv32Xtwomx2D6HR6h69FSq5cEbzp2tz13r8bkawEgYwBvb8Yj8/JELHD0qOZ5aoztZPDJkiaukle4vVZpSUJOleGiFetAAJzDU/ZFdFj8EMYOl4jupmI8BWRMNvzAEkBobYaNLIqPu/DAFaBtoRZSulRyxqUrmwgH2NSqTJxvjQeUjjKmSkt5FERmxhNyKmRVUh5hGmLjyOuQtkt1MOC16VD2gl3R9oSBGadcYmsWM1BcKsmfBiw1mjFsdrCsBoy2uOvvQW97Bg6pKhy1oRoIo+JzfHI4hNPnj5lubiA2nCfo6c1JWwp2OUON6fu9thSaYeK5Yz0yuhkGbHKME5stqfIMLHMM8vhgHjrZDI9SrkiMpA0kfOADpnqRm2N2g44hrlgtIijnhLuQmsLLK2fu4Kao8NI2m5IN0/hxg1MnfroEfv37lH3PRJbDKmFLCOgFH92PnmPUPdpxIcNnnIcZzdME5bAW0XSEK91SOg4osOIlYIYYA1bZhYRmgXe7GKIRq9x57LjWhDtxrIRTLpjIp1Y9YhXR3r/clzvcnVN4kGUSgWcNGwQBFtmdkuQ/OrO/PAJb1fh4RffRXZPqE+ecP/+Iy7Pz+NaGhNmBUdJrZHdkFHQAikLUx5jDYRBi1sfBoh1+JCRW7fw0xP2+wPvPzgntYXl6VP2hwVLijco5ZLz3RPKso8BEY8OXrfeP670nuQeK68JySNSDbNGPezY7c+Z94XksBkGRIwkQ78/RiqBmDNsJ9LxMcNmYnN5id1fOJQrKt/7oEeizTO7r77Ng/ffJ7fGXCo3ExxtjrivhUeXlWozIglLA4pGBDbQ3FiYmDUz0JAW9yF3Caq3Rm+1+wE/PQ1afIyhEiFH32+K1+i1Yb5gVmMQgLhGJW/iXqsKZcFJ1N2O0hJts8EUXJ0mRjlcMl+ckza94zhNYayvWvUC6K/86F/h/uE+//DuP3zeS1n1HHVLR26n6XkvY9WqVas+VPrsjc/y5177c+seumrVqlWrVn2Hujnd5K//5F/nb/3i31r30VWrVq36PunlM4BdcBWSph6NXMIcceuUbBiWKt0MRCPC1RuQERXI3QzxRNCKjnhB8lHEJ6t0wDbIvjBPgugTlzCXkeifbDXMW2thNCP9z/3jABhoi3ja1qKPF0AS7tEdiw+9dxeueoxdBK5It+uJ5DAsPMKcwwjq8dDx9T12163DxEFHi3vv+4yYXGq9Nqi9Lpin6Av2Gut1QbwiuXf2ukEt8TXTgFTrEcUa6xPH2hIEbTM8OdSGBZ5HkiPcnqBpi1ghu4AY5oppY+/Kk+Qct5ltyYxzQWSP0JDNiB5tSUcnYXJfPKFdHChLwWoNYzfFGjwl8LH3vsbx0hQkYU4TQwZk4FAbbh4UcB4gOaIJN4ck2OEA6cpQJ47xZkJfv4OfbJF5h9YZ2Z3jKlhpqAouEVVdSkT1utVOFEcstuiIbEfUDbWJdPOEdOMGmpT29AJtBd/39zpFtPZVT3HejEEwa6YddniyWLsCqeIWdKctCy4tIn3TiGSJHtveTSwisFto+/vMh4XDxZ7lsMesoVTS0QmbGzc4vXEGqtjjR8w7xVsB699vYZ57a3grWBq68ScxQKHR1awKah7RyCV6XLU22M2YJFp/D10zeRg5unEDGUYuDgeWQ0M0OouhICbQjFYaZo0MYUArQXSL9396B7C1iIBOmbSZGLYT7k6dF6x5ELpl5HD/feq9B9yvjrSGlAPzfIl5xEy7NXChuVHEkTSgbozjwHR6wvb0NDp/HxvmTrZGKkYrMUTgS6O89z67pzt8+gZJEwosCC3FfcbrTGlOsRZx4m5x3SH93KlBw7oEIes1zNw8IFPvICZhdUFGJXklD0o+2rAdtzEsUBYkjxHtnJVxVDZnE9Mo7C4umMtFN97jmnZX2mHGqvGkFPJ2w/bjn+Dshz/NG2fHnP72F1h+/Xc4f9piaMRbxObnERPHHIo15uoMbqhENLe2OWLhcyJtMjqcYMkQja7zq05nxTG3fh8teN7EPccqXvbRPazEvQjiuEkO8t8cu7yM27FkpM5oLViDZVdBnZwreXv8vdiWVq167trmLf/KD/wr/PL9X2bf9s97OauegwT4SNo+72WsWrVq1YdOm7Thr/3EX+Pf/Yf/Lr/39Pee93JWPQf9r08/zY2VXFq1atWqP5a2ectf+4m/xt/51b/Df//uf/+8l7PqT1hbSdzWdQh51arvp146A1josc86BG1XD/9U/DHaEB/QJN3w7FnHkpCxU2USRhg5zGHJQ5ibbmQJs5BEmGZ4GC1u/XF61Cg5jOfu0br3gtv+Pe5cFd7G/0hE5NoV7eseZos10A2w9O8fopP3WlcPBOF29D7j1HB7Rq9FsnQGaieTNbptXXskq4cJLNKtY8E1P+smrgWX6N90B+3dvx2bjchstJt/iqsiqf+QVJcedZvxWhBN0QPKjOpEowEN1zPUlm5IB82cNCGaqamxk8Rjz2ybMdYFXRTtxCNm+OXh6gwI+rMVaCVekw4RL91qmJKero10lUweR26dnvDW669Spg1vP9rx+BvfwC8eQu3GWEpI1uglTf31WnQyuzk6L9R7D5CHyqEW6r7EU7gg49SpciWpI2kM37xYPL7F5/AKS+9bzVs4HHAzqgt1PmBLQazAsEHEw7zWDapCygNZo6+1SgojXzNCAWKYwK6iqi2GGBKOekGHkQ7LYuoUK3gRlvMd9bCnzT1mVz0GG4421KMhLh/p56lLP9ZxPjFHrLXjuBesNdz68ACgmtFxQlIOE9cdGTJyPJGmI5gPyEJQztOEvHkH+chHGL0xvPs+SzPM9zgDUhWVEi/BDXePqGJSj0hX3Bqe+3ER8KSQtT/nMSaKHfZRq3vVn10rtRaaEUZvq1AKLAtaG1Ia5rUPfTTUFZUwKWXcIjfOSFNGZ+D0DESptZHaHFS6xrUvZWG+vKSaszmeODo5ZZMHdrsdy37GWonX0yOrr0j7mJPopLOFSepcpQpEKoGmBAgmgqUcJv14Qnv1VdqdV7AE9o13WN6/ROeGThOqFZ2OkPEEqzsYJtAD3hbQMa7rMsMw4gI1brzIUUKyUPd7hMy42aC7DNXjnpgAjR7kSLlvFIOaBPMa50dPTBiGgc1mxGRgvhqk6VH5kgZkymhr6HIZgz+eYmZFxriDpQEOC84caQ5pxOoB0hGeckSut0babCIYImXyXKhWIyk6QT5eO4BXvTj6Z9/8Z3n4ow/5D3/9P4zhiVUvlTLCv3P62ee9jFWrVq36UOp0POVnP/Gz6x76kupVnchrd+GqVatW/bF1Op7yV3/8r1Ks8I/u/qPnvZxVf4L6VD7hXzp663kvY9WqF1ovnQGMgvZIWGlhzl6Ts9Qwppwe+Rxmw3WnrnlEpkru5qBHfLLnHo/qYSa7hflGULlyZeJCGC8akcuYhFFoPe5ZBTHpPai9K7fHPOOtL7Ob1SLPKD7smWFMj1juMapXxG/8b4rH9f7cYeNGTyyKS3ThYpnrfmDtNDIpzElvNK/diM7g30S5XlnDKj1+OKKEHXo8coG8iWOXcjfHuwksHpRiUmyuARE3j7hajd7cJIZ771K2pcPSitIgjRSEc3fGOpPrNuDnCtYOtENiSIKmREoD2s1syYkpTwzTwGxCu9wj7SLMPBoiGR0y02u3ufNnfpw3f+SHWFx4+ku/zsX9b+CaYFAkb7DWDapiqAlMet1L6m54bdSdUr1SxGllwZDo6+2mlk4TbI9ABC8FIwYKxBqqFj7/XPGkOAutJGrS6LHVjKQWFrdmRNozQ2wY0aMNuh0wF3jq+FJjXWZ4jZ5ei9MadyflFFSlJBzB6kIxQ5eKuOCtUvcLZtHdLCmOBfNCvXuf+cETKsJhnjF3JAspKQlF0lX0smMl6HC0d0TTDdIUQwEyRP/r1SXjLjGXME7oMOOzY6VQ7j9Bl4Wqmfmw4KUg3ocpNAx9VUFZ0KtrujVkdmTbCXqPIQlXDdofDVL58hJLQ5C63fxFNQzV2uLe0BpSC9JKXOduEWPtz0Kpc8pkEZJmpBxojx+znNxgmBJ5O5APPWVABcYpuoNzjmjjKSMnE352RjsasdJoXmi069h4RYKyV72eaXGVnhbQehx044pwjsEUeRZNrxrev0DZn7P7xjnLvtIeRVT5ZkiM2VESw6FwY1MQz8jRGXOp1DkenU51C4K0CgF/s//S11lagldvsRmU06MtTx567zqPLu0waTWiviVRHaoKPkzR3Z4SadzgCbQ17GJPsR473X/hpmkDsqB5QHwDZGQ5YG3GW9ytvDVIinOEz/sw9FvFtGIKpjH0oEmRcYvVwuhQ6J3ECPXyyR9nF1q16gOrn/2BnyVJ4u/+xt/l0A7PezmrVq1atWrVh0brHvpy6lQyH0lHz3sZq1atWvWh1+lwyt/4qb/B3/2Nv8sv3PsF7u7vPu8lrVq1atULoZfPAJawYsxaREHL/5+9f4u1bcvO87Cvtd7HGHOutfbal3M/deG1WBQlkZRESREkR7ACP0SWBclSEAQwgsRIHgIheUiA6MGSYdqG4RfBebABXwABfvADAQlQDEg0KFF0HMmQRbLEW5GnqGJdz6X22fd1mXOO0XtvLQ+tz3UqgC2JdJ3aPHuPHzg4e6/LnH2O0ceYe62//d+fO7Y40nB4JHcxDxPm6J96C+xxHuPP0pCaupG1QBq62dcRtp0ii3bD1rWbcR6mGyCSoNm3ffxoRnckKd0M9l7pyQhS+uNJmHB+88LCQPPS08EaJgoJkRaPjQWiWLQ/R+rf2ToG+oiQrggplqLSE7eV5AYqkX4bRrzNOAlX63jrnmROEo+VBmgVE8KkFQGboeW+VkNkRochOneL43Xp378A0JjRqmgKYHUSAr0tGzRHilg8UsFmlZ0kUnNEC8WcUhu3k7HVRMuJIccxt5RQhzyecP7qHe6c32JfEg/uf8geaGWGEhhnGTfIuKHmDVePHnD97JrLr78Lhz3DoOiwoSn4bHhzNB0HAuQYOO4GvUArNG+BmG0CVqMfWBoiUyQhDwdarR+lSpMAOcxKjb2kxHBAa40y7xEPpLKUhqgRHdGGoKhZIJSrU3eVthyo+4rVgrWKNcPMMFOsdx7L0ZQG8AErCzRDKHE+dYqkubRu5CuqgUJH4vVXh9oK3gpeLTpXcWQMNHUMIXyEDKeVb7tMU2Cuy9w7iqEtBRewPFIPMwDWGuZOc6eWgl9lRGvg25Ub89ytDzfUhugQAwQdiy4ydOR5+iipbzWOeZTo0lqLx3SLJK179Fv33ltBSB4p52AXL5Eobh+F4FNysgo5K6qCLZUiB1QzzogYLId9JIpLDYixSxj5zbC54fsZud5BTrgkqnUDV282WZwPUj+Pih0NX81Yqx1LD+J9sEO1D0QoJhIDMkthPlTMjFQLVg6MaUBxUlKmW2fc/vRbvP7DP4zePufuw4eMX/hl3vnGe1zvL3FJHa3f7y0WiPFqjQsat6bM7XHL6dmWnEaKXINKHLM0xT2uxb52lEaiTVtke0reJLLoETTNkBPzk2csrcYAjSqSjXHaoBiLT7H/zMh5A23fsecJTyO0JVDgAjKe4V7iPSKP6PaUcRCqN/BC0sZYClYqzZy6WzuAV71YSpr4M5/9MxQr/Cdf/E8oVv7537TqhdCfPfk0t/Tl+7Fg1apVq75TWt9DX059Np/yJ7dvPO9lrFq1atULoZN8wl/+8b/Ml55+if/2/f+Wn/qtn6LeVBquehF1IuvPoKtWfdx66a4ysxmj9/AecczeDVFXHEdyQujdqK1B3gSClwWqRbeqDN2MHXtP8EJKE6pGkjCwIonb+4UFkBzGKh5o1x7Ri2RvN0t0jCRaN6XNjl/mYZ4ekc5pCDM3XlRgk+EmUSv99UTi1zsO9oifrt0sOvYPWyQavYZpLIJ4R/l6GGGaRkgRD9VWUAPzQBLL8eGO/rVoN7YNvJFEw5jWCYYwM8Wt461BxRGHeowemgSyWRcyW8gS3bNYPG7qr7vNyCjgiZTD7G7AFY7VmXk5cEgz1yPcHU84BzbNYFKyh/EuZxPD932WO9/zad4YJ/I3v8X93/wK84cfMtYteRhZxi22r1x94deQlHi6W9g9eYi6M5zfZjw9ZWnG4fKa5jvAcR16j7OGvy+pzxgY7jm6clUwmWKPSfTTAphbGJtYGMKSIRkugg4J9YSWQ2C3EeoyIx6pWhmUrMIwjDQSpcyRyHSnHfYwTtSlhflbDlgLc7OhtLDYQVMkpS32kLVDmL8ikWjNCRkUx1Af8BJdqikN0Smbc/Q8DwldBKkLog0sDFYJ9y32hBBpcAfxBTFBXcBKdL5qYHutFHw+QM7YfkddMgaUZQ78NYKZRzesR7pYHWh9oEIE0wHJji6RnDcE8zg2lAPk6eaajHQ+4EELaKWbyrWimpEhk4YwcbGGaurIdA+c9NgxyB7PL8OAJkXzAGIREKbfX3Z7/HCA1lgOB2qDYtCsxnVSWswTJKc1WNo1bRjQHOlo1eiGNle8zrGvhUi31oZKiwGSnMIE9hYDKqTA4fsxcVtQBwskACYwx0GM+8dyIFlmumUMb95j/LEfRX/8R5HtGePlM37g7h3Kf/cPeOeLX2Q26xCBFIMuHia8L5W6mznMFdXEod/6OA7i5C1iYbSrDlBmWoM6TEGR3wzIZiKNI8P5bXw6Ybh+Rp6/RLncwXJANqfoODDeOmXEGdIVrS7Y1I3gcRN3URW8zEgruETHO14hDdg44bc2+JuvwZSRJ8+wQ8FtIJEZrh7G8EsfVFm16kWSiPDnvvfPMerIf/Sr/9H6C+yXQHd15E+ffIpR0vNeyqpVq1Z9orW+h758+iPTq897CatWrVr1wunzdz7PD97+QU6HU/6L3/gvbriTq148/T9v/8jzXsKqVS+8XjoDWExIIph4T6iVcDryGIaiKlFfW4hYp0E9IGNPZ+ajKaqBkB4i4qk69RSjIyndIF8DExvo5egUlm6CSeCCdfwogawDECYW9E5SIhUnOvZUZglz0TvuVKR3w0L09/bUL910duvdmL2bWFu83m8PrqUxUNUuKLFeFe/04N4dmjRM51KRPOLLjJPDGPL5I4y2aEc8xy8RI/3be4TFkGr4IB3JayRvuE+41TCMRbBWaC7A0DHGGZBAb2uN46hj9O42R4ZNR2FHj3I157LBweFQGvNc8LPAC/t2g7YFUiaRWPaV+ekzrt98jWXMWK6QKsP5Kbdev8Pp3TssB2f37JLqwmNRDm2GCSSfwe3b2KBwmNFBYMp4M1wa5oGAjo5VB/NAN2siDSdYLWhpyG4fplciUs3HYmgzyJUw7AXyFghDT8cJdUO04aY3SG0dEunWLfRkgubUx4YddrgPYXpZwaVDu3MfYkiJVlsn6IbxKjb3gYjak9oa5nMekRwGIUbvwB7JSTg9u0UetxyaUepCK/vontWEpkxOQ0ede6TOdUBVQGrssXxKksAAqwg598+70OouUvYpjk503goVqN5oR2y5h1Hu3hG/GJjG9SigaUBl6R1NgkvDJdLzkmJoQ1tBLIH1a1UzaPxfxkQeR862J2zHkV0zlnIItLQIzS0w0eZ4rUEN8EhjNwepFbMWGHIs0sEUigheF8ysX/fgVtE0ooOQ752Tt7eoh5ly2KGjsjk7ZdhsI1gvwjwvLNYnI4+Ic+2Id3G81Z7G7QMHOuBSgMBrIwkhEt0uqRvV2nH5RkWiqnepbC8Lm4ul4+ITcn6P9Km3mM43ZE0sHZlP6s+rhNE6z8yPn3JhjblVnj18SCst+r+1Exi84a0hKljH1jdVqmjArq3hzbCrp6TrC+RyhzSP++MQKO+kiZOzU27d2tKuTjBRyuMlEN2UQN6XBdENZjXOf6sdtWC4CaZBFaiLUw2qOGYFsUYeBKrSmL6D706rVv3ukYrypz/7p3m6POU//43//HkvZ9XHrLfTdv0F9qpVq1Z9h7S+h748ui0Df/H0s897GatWrVr1QipJ4i9+/1/kqxdf5e++93ef93JWfUwajhWUq1at+tj08hnAngMResQdq0YPL8e0poQ51sKUIBFdlGXfu1UFao+65tyTri2Svapozj3Rpt107Qm6KKztVaRHVLQRpm0Kk8Ua3o1ZET9ayL2LtGOh5YjKPRZ8JtzLR7hnF5x6871hBtO7MeOjKrl3+ErHCUvHwSqKRyetRidt4G0tXq9bdHVaCTRxO0T/b4rjYNa+rVJZcCs9Be03Kc+oY03dXE6YZ5Jo9JQuhQ7tjePgmVoDM0yKLlWVMNcjTTkiUlDpSVUDawvHvtMFpVpjGgaKKxWlmDOXijfIXmmL8eg336V98JhR4WJfONQ9+fyUIsrcjJKEtknUyyvqswsOuyvKYcHNsfsLTYUmPUHrhAFshot2Dz6SoTLkODgpR8q3VmhGGgfUCpoVkuCSsMMS35dSPKZrmItlxnOGIXqFe0y9m4aOmdPmBeYlkr3zHIZfWxBOomcYRYeKaY795wNmQjPB2kxmIfqwo/dZcr7Z25Iy7h7nGgVL5M3E9Kk3md5+i82gpMdPufzmB9h16wZyQ8Ywc614YJM1eovVA33eXOO6nAbyOJJSZlAhO9SlIOMYswzqMYxgkaa2ePXYkCIpnqN3FvObPSSUSNT3bawyotTeXT30jtwKDH2D9oGF5Mg4wmaMLuKejD45P+XNz32e7emWB++/y9NvfRhp/JzwsmCz4V56etiR0vBaw5hOCU0aAHYXvPVyXIyMMiK4CkvK0dc7DHB+ho0b6rJQasOmE7h9Cz2/xXByiohRr3fIssRrqQsQ95RInh/x9nGNY304hD7U0e8P0k1xAaQtmAvWYgjlmHL211+Fz3ya8Ye/l+3nP42cTR3t3mgPPmT33gNSqVG/LX14BYDebdwK5eqaZ7trTsrM6AMbFQ4O4gnxhnqcKHPpFIIB80Z1pTWhNaPWa1o74WxwXt1O7MaJw+4Kaw3yiJydom9/Gr17C33yBL3cIU8jAe0IXvagsSYhhlaCWt+7oNNAa4llhjw61Y1iTsOjh3h7F7VKszUt90mS98GI5/0YnxSpKJ86+dTzXsaq74L+V9s3n/cSVq1a9btc63vob08qyh9/84/zd77xd3j3+t3nvZxVH5P+4uln+cza/7tq1ap/Aa3vo78zXZUrfu79n3vey1i1atWqT7ReOgM4vCFHJXUTJIfJ0A6oJhKRfEMTpAxeUBFc00e9uMMEtcEY/b56xPwauBqI4uZICvyzeDdxPQwRXOM/1fBlteEeSF2RDB4dxaIJMevdwD2NaR3TiyFhs/SEr3RPuPepHo1XPF6jtzDxUkas9O8Ng1HMA8+rqVf+Sv9HhYAKklOYfmWmWZhueAMmnI7xlRRJSTtEb7A6tG4mafSx0gzJWzDFbUHHTZCtnUDdDhmW5ca4c7vC0wCegITZJZrPosNVHZeG5E1PU1dIm+jzrD19aTClkU1yJi9kmVBrSBrAoAHWKtcXFyy7PeqGaYBF6m7GH12yTxr9staoS6WZUdtCcyJt7TVQzxopX7cw8iNhGUlnMSXy5oqjlGos80xtldQKkiZUEkpFK9Ry6HXMkQ81oLnTzHAZydpR0hwHGSKR6yJ4FerVnqopjHqXSL2mTNpM6DjRehLTaqGZUFsLs7guHbUNadyQxpGksXeURBpGJAnVjHYouGTydst0csq2Qbr/Ifta2c+BppaTKRKwpWGSwpguC1KNNAyoaoxHSIqe31JJ00jOiWEcI/3bzWvEYx8dycwqeG3RDTxsI2WsGv3d/dBJymAlsO4oIo66kbLSDFqtuIy01kh5RN1w1TCLdQxk8jCFCZ9HXCG9+hqb7/0Mb94aOZ+dZ22IvWCNNhvWLK7pxWAucZ9w8GGMvSWBn6/eSOYM7miGPGzQ0xO2ZxvOJTM8ecajx08o1ihXO2y3A6D1FHK9SszN8UNhSIovJZK+LrTmYUD3rm9x7wZvXNeSxt77LYBjIh1P33u8BVxz3DqSRhXyRjD1SAHPC/sHD5ifPCG9ehXX/f6K+u63UIQ7d+6SDtdc7nYUi+spLpWCNQO9xs5uc/rZN7l7dsL1/cc8evCYy2VmqeCa4uKUBkkxi4GDMs/MWRjY4OroMNJ+6HP47VtsfvF/QJ4+xpqj5tj1geWD++weP6FePOLw9ALzGKYgC2JDnGNr0QfN0PvcDR1O432izBwOe8wyXiuLJnwY0d6LrFYgr5OKnyR9J35Yftl+4F714uuNtOFPb1ejf9WqVf9sre+hv33dHe9ye7y9GsAvqF7Tif/92fe/dPt61apVvzOt76O/fT2dn/LXfvmvrR3AL7D+Nyef5bP59HkvY9WqF14voQEsJHFoFVJ/82w10KMpg1XkBs0MwS4VxIWoRvMwdTCkVVKK5C8OSbR37/qN+RLub8a9BVrXLVJ6WHyaMDhAkG5IeEcZ+7FD1i2Ce0sJU7p3HxwzwnL8k88IW4ToK4UwpqWvQUQCc3qTSD4ibgP9Sk5Ia5H4DG4s/YmgCe5hRrpVrBF416UgacDFAomNcuwVxi9AbnczWsKwNI9jvNkE7jdlqi1YbTgGzfEWCUWzSCTGsW4Mwy3EEwxDdMqKQ9njaQyYbmu4p564TWQaZxTutMRZNjbmnOaBe2e3SCen7ESZL68o19e0WsL8BjChuVCW+Fi82Fi/J3pytBtTy4z1TmcrM+agSVDJ6JBJroj3lLk5npxqhbrMeHOkGTI10vaMPMUeaLsrpBbcFYhe11Yr3gxJY9Q6txRraTWMK420cQwK9L1jLbqo08hwesYwTrRSqf0111qpklnKgaKKqZKaoTmTpoGkCU0DYk7enKDbCRdF9wtSKoiT75yxfeM1zs5u0w6Fw6PHLFfXqNSeJAUrjtUDbVlQdxQnDwPj6RkmQttdQ429LUnYbCdujRPMxtwMkwnJ0YebpglVMGu4eyRoccSjRzquXYlhimMCV6N/WSy6bFUamjMNaB0H7ymGHFzAPKE5oRq3x8ARVxgmpFRe0cZnf/wPM40TX/uHPwf3341zUms3rEtP2Wp0VEu6SdBH2rZf7ykhKtGpPA6ke/fId87YTJlXbt+i5cSDDx/QyhxpWFXIhniimbOUBqWRT8/Y5oEMPFOhaI519NevkroZXgNxn1JHPsc1Lj09frwAxAXxMISVJarEhxHcqLsdh6s9zx5fMf6jX+Gtdz5guncGJyN5GLj9mU+xbDZw8Rhz52q3p9al358cZ4aTCX3jLuUzbzPngbuniTdePeW9b13w7re+RZkDQS3e71/awJxSGodq5HLA8kSqew6P3yd9uODPrhg2pyzsMTd8d83+3QM1jyzzgXmJLnTMYkggJZyGtT2uQ6Tx8wZvh7hu3ODiKXV3iQ8bGpHydw4kprhXSjryFVateiFVrPAz7/7M817Gqo9Zf2b7KT6V1/TSqlWrVn0n5e784wf/mC8++eLzXsqqj0lZlNs6Pu9lrFq1atULqeP76D+8/w+f91JWfYy6l6YVAb1q1XdBL6EBHIk0F0e9O34OkDCc5HSkaeCIJQ14bZFy856YFQl6c1JcCkomydA9395Fq3KDPu1ROsAx9xssNGnCWuCjjRbpXu+f66ZpJG0VWok1dORvqH8Ox70hOkFH7t7E7iShx15e7/+XwO/eGCwJSFOsd+hdmKIdbw3ugnk3Zj1MRx8GWAqiG9AMZdeTwMc1gKTzMNld47WohpmjR68p0LdJR+poUGqknJNAnSPlJwTiWkfcDdUBdYViyDjg2SPRmqLD2YvjdSE5nI8jr40TrwwD5xvl5NYZdz/1Fvd+6HtJ21P8/Ufs/+lvUXeO1IJqRiwMd2UBMoqQ8kBSYdhs8aQUEUpttGWGLD0d7VRtFAc3R9XIQD4mdFuJPeNKc4VhINlMVmXII5vTDePpCbVWZJlhmQHFloqVwCSrSaQOXeOcJGKwwBdUhhgO6P2+bg2qoVnYTgNnt05Jpxt21wfakydYLdSmzFKY3VncwIwkiprB3CA5JEOHKTDo11eYKa0sYTBrYJubQFPDB6MRSfMhb1EVDjZHMr70Xtek5PO7bF+5w3h6yrIUDodD7LdxQO7eRT71aeTWCQOOP3lCuf8QXfaIKCnFQIbVGn6eShjwGGaBMQ6rXqN/elDcnVYLMXEhgQ8363juhqd8Y+rH3ETqfwfDAkne4vhMb79F/iN/gofsOHzhl3j0xW+SipM1oScTh7JQDr27WXMkwFuLVLg72g3YlBODQPZI6Fqr1PlA9VvMOtLGGaYBTdE1axA9ySWu+UZBhg02jfDqOfLqXcb9zPjN9/ALITk0q1Sik5hjh7YFmhrtmOwkHVUvQTcw78ePwJarYtKx0a605cDu6prLW2fo4RpdRl6tE5t0Sj7d8unXXuXUlK/t9+w2e4bdPvqQbcbzACb4fmH5xnssF1ccfuj3IJ/7cc58xzB8ifTwQ2wc+u2txZksBq1iVpiXDDIyzjM8u6YehOu759S759Gv/K3rGMIoC60lSqoxtNLimkqa8bLvAwMJ1xPwORD8VCRtkDbH4AQJGGjzFRUwD6S5Tyfo5gRTRW2dRF314uqdp+/wiw9/8XkvY9WqVatWrfrE6dee/Br/8a/9x897Gas+Rv2+4c46Crpq1apVH5PW99EXX2+kDf/m2Q8872WsWvVS6CU0gBMc8aiaI1XnYVimbn6QNkAYbSKCjL0bVByag1qYlAYyjGEASpjHooBXhCkwuN1wDUQy4GOgVkVxs7BvlUjYUnpaOIwqIXXDNgeWFkMkdcSzhkXlHbFKBo4Y6BHxBXDUAwMsOvY+3ui9VJFYZ54Qq9287ms9pvWUMIn8aAIb5hqoaATThnrHEUtP//oBGG/6R6XVSNuJR8LaLRKrIiQJE6phqFkgjltn0GLAAbOBlLZoDkytS4U8Qg1TyGWAGkfAimHeEG9sHO6q8Opm4M524nQ7sTk7RTcT9ckFywdP2H3rIeXiklZLJPmcjsrOSD5BezI6TVtOT7ac3r1NHUau9gv25BHiFckSHbY64zrhpVGtIiq05UA5nhtvqEi8DhdyEzKBOj45P+fslVeQLLSLS1qt2Fwwr5i1nv7MqDaSQNJuyFWLHtxqkAsuCfXS2daB8h7ObzN+7/cw3L2LXT6mPHpMaxUbEosb+9bYI1iD3PcaCNQWxh0Jo+CtYCiG0VrHgJfK8uiC3aHi26eYW6RSNwNyuu0k8IrvDTQQxKqCDkByrC20/Z5WG9YKdd+wB0/YlUY6mRAz6v5A2R1QhzSNiCqtNlo1aqs07+ndFGZrE++droSNaaUPVXS0sBlCJmkLDxSNFHBpkag2x1UwT4g1tCnerzWvlcM33uP+3/xpsijVFra3M/rKpzARrMwsD5/i8wxE6ld6h7Z63D7UHbFGWkoQAdCOpTfmix22+yab5JRDYb/b4a3FdWdxWjS+I3rDzWhzYXnwAHn0GC/GKInXXn+Fs1tnXB4KHz78kMPVRb8vSCSVY3olPmbl29L+Hh3fkmJYo/eQa9KYQ6kNWmXZ7bh+/BgVC7Rzdl4bGydvvEV69TavvHobSsEvr5lRmg404t7iQwYz2m7HhSnly1/ilcfvUa5nHj96ytaEN0/OuXB4PF/DHOasaY5rq13i88iyVLxVdvsD07MzzJxlf2BZCuSRZA3NYyDIBZKE2S2mkDfUOmOq8fkayWivDajIsCENA1kgJcfyFis7lirgE2lXkeUC2Z6FQb9q1Quor15+lZ/8hZ/k0A7PeymrPkbd05E/e/Lp572MVatWrXqhVKzwt776t3i6PH3eS1n1Mep/e/o9H9UPrVq1atWq74jcnS8++SI/+Qs/ub6PvuC6LQObQK2uWrXqY9bLZwDbgvkCctLN4OjMTdrTsWmKj4mjSJiLaqAZkTAukRyYWV/A4uPascyRGp4QiQ7WiBkKSMUtEpOBGY5kaKQOW7ByUaBGkrO1TopNiDQgsMfq0Q1sIr0PWOjR0lhfmsJ08xR9rgqQIuEn3pGqElhckTC8peNlJUxtcsbLATzjRG+xIZgAFFQCFSzqmCRQQ30IlHVPPfpSEakdH730PuUwW0gbRCtmoHkAbxhLHOusUBbMC3BGFgk0cJshN0ROAtmtEsenHcIMazu8hiGubebWtOXVk4F728zZJjOljFxdU/aFS1Nmc/bLAbeFpEJWJSWPBHCClKMTVMikkw3pU2/Andvofg+Xlz0dHcYsLTpuXRsMFalED/HRvPUDkodImy6J7JUhK8M4cXLvDidvv8lwekLZXVLLgi9hTrlH07MZiCykNJBSQrXiJXqUw9Rr0BK4YyxomuI0bDfIINij99k/+pB5X5h3e8ydslQOpXIwY9EE0kiSERwZN727OUxBrwUTxcUwhWOG3UXxw8zBoO320eesSp4yXhtVhLKfwzx0i3NNox0WyuNntJy7kdf6sXLq1RXUShsHMglpe5BE3o7INABCm5fAV5tRVXtGl5hbMA+DUxxv3pHLYfS6eEdxG5oyOipWDt3cTrQU/bzSU/rek79GDaPPM3bY84yn2N3bjK+9gqjBMuPzzHxxxXJ1hbcSjc8qJJdu9HZUdXHUW/QyZ6FXgyMpYbVwWIylFGqrlFLIkhnGxN6M2SwGQawiJrRywC8Mv86083M2b73N7c99htM375JbY/z6e+Srx8guhbGL9BRu7SngHIMirQBDHJ9WYx/r1E1gAj1Ojb2QRlqZ2T27wkuhPblAHt3jfJg4+dEfhdsn8PpbvDJsmc5OsF/9Vd7/1n2W6jRrgVfOA94qZdnTvjVjDy9Qq7Q7t7n3B36MV18949b7H7L/lV9nPx/ChBbFrNCKYfMuBhzqIfbx5Q60RT+yQPK4N0gSdHuKtIo0wVrt3eqK5gFr0cmMRgJc3NDWSBS0GTImxGZERqQZXp067wKDzoFmgh7TyqtWvUByd/7+e3+fDw8fPu+lrPqYNUnie/LZ817GqlWrVr1QeufpO/y99/7e817Gqo9R35/PeDNvnvcyVq1ateqF1H/4S//h+rPoS6D/x+0fWUkaq1Z9l/QSGsBG0i3RwbtE56UkVBpmkKSFYckA2uJmlBPUSJYyxC/8NQk0jSSvf9Sf2ZnFkc6VwD5DC2M1hcUrvfMXzRy7gOMzgZ7Fau8M7ZMw3o3a3ukbHwvjOlK91j9WwyBGkRxJYfFA0Sq1m70pMLnW8c5lRsZtGCpmYerV6ED2ZpFutRoI6LD9UBGGJDQSlUSrjh3Na29QDqj0DmUNw0n1yPT/djM9ELvRkTrgvnRUroFsyA7NjWRzTz96pIepcbxyDlSxCBTBW5y7Tdpwd7vh7jBwOkyMomSioxnN7K1RrUBWsmxJHdccCWXvGOHoHRYUv7pm+eo3Ib/PUipzM1qpFKtUhGKNUh1zkCLkJRDdDj1RWyJNmgdSbQzDyHQycufslO3rr2NpYH74gN2Dp8xXVziGDjk6R5vBvMQeTQNJDSzOr7vGecub6HWlIOZoAk0aaOHrmcN1AaB6xUqltUatHa/bKhnBzZFNjmGDZNEDm1PfM72/2D0M6VZpGDRB1XBXmufY2xLXgaVwNlvrZpxGp2x0J0NZGskkTPtacXKYwNaoh8K8FJoQOyxlLGXcFgynzIVqlSra9127waXL8Zgf8emiuBdunOCebo+y34obVIn8fIodFl/TDJMZ0QFhgkGRrKBwTyt/4PYpddryzuUzrq92+MUTDhdX2OGAtIo6qET/cBpHRB1aIm0E1YR25Daa0GGIHvHWcGJvFgfOzsivvUo+PUGudyzffJe2/whvTU82l0GQ1EjtmuW99zl845tcXs48fXrB9XIIg9x7V3Q3w4PL3bvOs0aXsiRc8kc9xhpo6LiFRaexpyAUlGbY06f4+T3GT93m8vZrnD98QHroSN7Avbuc/t4f4LUn97l8/JBnZnG8nY4oN6gzljO7peBpYNpmbKhcXV1y9fARbSkkzdAx3pa2mPWBgdYgbaEVmgUpwIdGHjaRvB6iT5qc41q5XiIl7oLXircStxmN+5AYSHN02HZMd9x/XQd8mZFaSS6RGlZANrhVvK29X6tePD2Zn/DT3/zp572MVd8F/V9ufY61cWnVqlWrvnNyd/76O3+9/9y86kWUAP/y5o11gGrVqlWrPgb911//r/lg98HzXsaqj1n/y83r/Nh4N36HvGrVqo9dL50B3FhAcvdrj3hmIqGYJqJ/dyDcWgvzyqQndHNHQHf0soBjJI7YYu88594hrNqRymHthhSjIT3Z6YThGWZhN7Mk4zZHAtMjcam9rNisRafvcd22xPNgiE6R4Oxo6FhDT/xpCvM3xXO4V0RH0E2kkd2i61N632lbQEeMbgx5TyQjuCjijZTCdG0qqOcwc1s/fjcl7oGXdo+EnQ8niCiNisiAoIFthjC9e0ezdvNcU0BvJYWJFaZeP9St4VY6nvcKJ6HLwsntM85wTsZMThlNyub0lOnkFE+Jw7LgF0+weUYQ8jCRJfWkMx+Znj1W6g3m6wNzTjSgWmOZF2ac6pFsNHNUjOSGpuiKtpawuoQxT0KBPAjDdmR66y3GT73BeDKxv/+Mq/uPmS8uacsOH9JHaPJm8f3DFu0YZRnHQGWPE2IF7RhhN4mOZECHMfDablQLHLi5BHZ5aYAz4EjODAg15+hWVolEdZKorZYUswh4oHjLQkNotaEyBKa8RW+z9DS7VVh2DatGPSy0FkYqFvvDtUHKpHFEJ2exCmWOZK5ApqKueO371hpmB8ws+oYTNB2wjh4XSYHrFiMzMmhiVGUeJ+Zlpnk3fa0H7yHMyBqDE+aOmWP9z/FaQZrgCjIo5OjBPnntdT71J/8Yt3/ih9h/+AT/f/8s9f592vVMO+yhzHENJkW2IzJuYoBEpjCEW4N5wZZDbPdpIuWEaEJSQqzGEII5PmWYr1h2lxz2B5pXTIRGT2erBsm5NdrFHmvPGBYYtxOHVtkte8q8v7knKIZIN8HNIunvnS2dpviYeDfQPTDgmqI7WD1M4p6E9rZgDovtmetTnj74KlqvOckTJycTm+2APL7kXjqjvvoG7z255MnlU5o0zBumfUClEdfIKBzee8D1w0s8CZe7SGaPSVAfqd5objQ07rsS9zuvnciw7HDJiFU0j4HKrw3qFa1U2jJjKcfQiAGyBKmgzFCuA5GvAyoSRn/KNI/Oc8kjCSG5IScniFfUWpyjzfQdfHdatep3h/7W1/4WH+7XiesXXT8y3OaPTq+uP3SvWrVq1XdQP//g5/nSsy8972Ws+hj1r24/xV86//zzXsaqVatWvXBydx4eHlKsPO+lrPqYdVdHTvWls6RWrXpueumuNtOTMK1EewJOuuECUeIbMd1jXy4euGFhS5izQIre1ZyjI1bSEIlWPyZ7UzcZuomH4h0ti8TXWzcu8dT7N4+G6UBE7nKfHI40qktP7CE0tH+fhXGE4AJK6+sOg8hdw0dsHS99TClXD2MYi4SfRb4Yddx6Z6/pTVdo84K54l67gdbCD08JrPedWg0j0Gs8tvckbx7xOoeJRItkbT2g0yZepwveHFsOPZEZva2ST2l1H42nwwZXB5kgOW4eCc264P24N0B1JJ1umMTZpExuhFE9bZjunnN69zYmmfrkGbLbk5qg7h3f3U1zP3buSqQDHcwLrjm6c4Ey75jNKRbdrZSCIiR18jCgEslu80rDgYGkKfw6GZBaaM+eUtpCLZWry8p82NNKDCd4bXhutNYwPFLnbrhJpBgNwGN9JFwExVHJjHlkM460YcOhzvj+QPWGeSQXPSWYMqllxBvZlVoLRYXWDpgTJmvOqCrqxGtRpR0KVi3mBURwooPZNRLCpmGy1kOJ82ONZg13xVtFIq6MJkG3A7Ido3c1OMP4tEHHhE4TWZRUCqeSmcYTdu4c9he0utAcmhkmQwwWADQjDQPTvXvcfu01bm8nlrnywQfvs3v6DLcWKGD6JSlCEqM2wrROI14W2Gxwa5AETxOeE0bsOd1uyd/3NrNd8K2/9//lg9/4gN37H/CKTpzcPWMuhadtYVdnqhWa0+8tRlKJzuZSsMOCVSdtN+g0YSl9hNwWRYYROezxix0VqElZWsVaoN6bG1THs5FrI2dBbp2gn/009j2fQaWS3v0GcqEwR7obUXxZ+nXfe56tEgMdEmhmq2ECu/b74LGLwzj2jt9MX0jGEizXhadffh/75mOutlvOXn2NH/iDPwGvv4W8/YPc/sHPM3zlHZaf/wX2v3nBYb8D3eDe0fIthmGoBlQud1fsLAYohmHg1vktzocNl4drnlxcAEvcBvMJUPG8Rd2QfBrpbdU+eBN0A5OBNu8wncAqrdSgHOjxmnKiM72S0oC0gttAY4mKZIchT2hWRnE8adAKsDDNxzUBvGrVqk+eFOFf2b7F2/nkeS9l1apVq14o/drjX+OqXD3vZaz6mHRbBv7syacZbobdV61atWrVd0of7D7gb3zlbzzvZaz6mLWVxF+6tQ5SrVr13dRLZwCngPaCbTraVkjkSDv6MSKYAomLI+OANAtG6EB0zJKQLGGO3UysBNoYX8Ck+7nWTWDB3EjH3l6xbhZ7p9LKDffej7hoyYjXSLu5IB7mT5gwkdgVETSNgab2MNcQAy+Ip95F7IFCxTo+OtCz0lOjwT6N1CfW8Cj6xTSQu+4Zb60bv0dD2+KPTnQAi0aS8Jj8TdoNndyPpwYClxymsRu0GUkDMIQBmCb8xkCW6Lf1Bch4OyCWcSlhujTrBFvFWaJXWQJ5rBJPn9KIpiGOYx5gnDCiN7UdDrDMiC2BOk4Tbo1GDYu6Whg87ghCs4JLdNS2VihLowDNozM1aWZQJ5FuenRv0rgDgIQBnDSSzQ71YsfuupCthqGkCtM2THAZacuB6vIR+toFt4o1R7NGh7PT90qghofTU269+Sand+9S6kJ7/z3K9R5rQtMGpJ6Wja5m9cilh68nIAmrlVYaxSKr7ppwt8CBG8hmG2ZmM6QtsX+aQ8qoSKB6W6UtBRehidCSIq2hDikFAlnc8EOh1RJJX+lIZBOkzGjecPrKa3z/93yKW/de4b0PH/PNd34Drh1zowFNPPat10ip5wynG+xsw7ydaLLrr83DVDfvez4RNPAc3bM1rs8w1+MxmzsJ71XdDa1Kud7z5OffIf/SyK46Z+PEH/7RP4R+7+vkV++wu77AfvmfsP/Nr+GXS5janm+MTuYFmsUwyLRFhhT44X7lB55dECkkjaGO1qI3W9zJ3nuYgTYfwEe0NRoSKOSnD/FN5joP7A6BfJ+GAXGhuuFNcLPeKU4k/TXdxKIdjdSsxDWI9cW1GvsAOh5/xJMiVjFv7PdXlMPCtTVO/sQf4+RPkJXwOAABAABJREFU/EHk9t04nw++Rrr/Af7gMclALWNLQbJiHTNNEmgV8RHr1II0DgxvvE7+7PfCKIzvv0va78L89RL3rE5QsLjDdMq3BxWhjdHL7YYPW6y2+M/ByoK1pfcED3EtzVfR5+zQlj2eFM0ZhoFkhSwJNfC2wGbEx1MkaaT1V61ateoTpo0o/7vT733ey1i1atWqVas+MRLgr975/fyxzWvPeymrVq1a9UKqeeO6Xj/vZaz6mLWRxO34ZfmqVau+S3rpDGAhYwYkJUGYLtRAF6cU6bckRG9vT8lJgt55Sk9Finj8WTS+XwZEuunpJRLDHqjdo7lj7oGsVcFbdI060U8qoj0RfEzklW7yEgahzz1FfDSMWqSArXybCQ2Y997S7g5qgtq/5oi/Tf3rjwaQB2bYW6B+rR0gSSQ4vYUZCrjXMGxajY5gW7CkHfvsSDcA4Zg4dkQqPgxABQuTORLWgYB1SuCMPb7H6yWuW6w5phsSGW97ZDrDzfH9NUIO8woPuO2yQ/MZ3d1Bfewmc0F8wEulPH4CT56ylIVlN+Pz0vtuM+YWxqc7rVVa6z3MpRtlGcwKJoLXhpBJbYmkMkJGyC7okALHzNJTwDOMGXXpBO7xJqHtovjZhumVO5xuNlxf7rn+8AFtcVptFGsYGRVHxxF1wWfiPOnY8dANaYJk0Gkkn5+gt09oAyzXO+pS+zkj9lIVNEsk0bXjqVXQpCSPxG6EqxsNp9YRtUMY7d7gZAODYtbCsGstcLuph8tTQqxFT6yGWY44LAVE0ZxIKgzjwJinQC/XEn2sIlBLpIR9JN875+4f/DHe+P0/zFmGR7/wy2hWUOtmtUaXtpcwLt1ph8Ly3gOuHzyjijC3xrxcQzWklSCmm0UaXUAlk1MjhhA6ej1A0IE5rkuk4hkQLVAvKNd7nm3P+ZEf+1F+35/64+Qf/3Eu7r/LV77wj3jwzld4/LV3KdeHSNS6w3LAl4L3BmTJCRkSOmXSmEmq/eVkpDYoM+pKTgOqTmsKpWIY7k5jgtLwWpHWz6M1fKnsLi95NB/CrJTE62++wfmt2xyWxsNvfcDVs2d93wepwL2FSYsEwaCVSLh6pw1Y7xd3h7bgHhhwRol92O+B4tCkUs8mdlSu3v8a59/6Jn7/Gcu773G4Xhi3E5MLBcUwrM7RC97T40EjWKLDWhtaC35xAV9/h8NSubhcsNJQGuQhsOD0faASncpYpLdzjgEDzbG/zDEPs9otzH5z/+i5W0F1IGmg9t0dc0VTQqYN7gVfDkgtkDZ4HzQhD8iw/qN11apVnzz96HiXvKKfV61atWrVqn9hCfH+uWrVqlWrVq36nev/dv55trKGKVat+m7qpTOAGw1DSV56Opfej5vD2MwZaoU0hbGrCZDAsyL96znSizGcdGPM9sScjIQJekzWHU3g3jDbWn+sFslF76ljos80kM/x/X40n7V3TXqYsZEMjdRmAIg6YnkYwo2j94hi3Dh0fR1C70RFO0654DkH1jfccbzV/tx0RHV3j51I+iLxnB7JXs3gzcOMM7sxer2b5RFQTmE+dQMvksMtzO25dmd7AgZMa187qJxFMjFJR9E6pIFWHqGyxXUTBlYSUP+2TteGW6Xtd8ytRva7LtQaMWHNcZ6aN2qttHkOY8iAQcPQ7PtG80fHIpmTRbAy9CEAQTVamoWGmCEpoTKBZiTlSGJr3w3VkCEh2y02jSxtoZSZVmaWVtnPC80iMZxyBhK00hPeFaklngPQPJLGzLQ5JXum3H/EsjR2Vxcs+wVrFU+C5AlxiwRyIozWPISZaCXSzrWQVEEzrhLI626qi0Y62JaKm6FGN7sLMuSPzjlh0vqkuA2B0x5GdDmQhpFhe8rJ7ducnJ4wLwvLMkdKE49E7qCkIZOyMpQZf/iAZ/vC03fv0+Y5rp+UejetoGY9RK+4CrUt7PeNWTRS23bcb/06c48EvEikbHOg0N2PPmTHqovjrkitSGlxHlVJZxOv/0s/wWf+wp8j377N4Stf4ks//d9x/ytf4cnTR+z3V3itqEnHwycYInlN75cVlbgOjma5g9c4preGEzbDhktzrg/PMG+oV6RaGMF1oUnCx4k0bSKBag3fX7MsBZIyvf46Zz/8OfSNu1jdwzcfsJVMns7Y58q8XGMt0t3UcryoY5DjODgiqZvDFvcr1bhf5CH2Q7+nxECDgTfqwyc8+tn/nq/+o9/gbhqxscJJoOyLVYZJGY6keG8xWdFqDKRIQlrtRAOgLtSLK64vFMRYJENtbFJmSBOLKody6Ne4Rez/aAinHAMc5pBHbN7H8E2rsUcld3z6ALYnq5B0JFMRF8gDqfe044bnIYYxtMV5TdGT7rl3Cq9atWrVJ0z/2smnmdYfuletWrVq1ap/Yd2SIYhuq1atWrXqY1GWzFk+46quVQovqn7PcJs/uXkjfpe4atWq75peut9eK4kkPZHaDdtqCwMpjEUEUUdYegrOIrmqAilMJ8zwnMJX4pgojN5Xjungnqy7cYt712ZQmGvHLkMk3/rzuvWKTevGoxBs6tL/duwurv1xK6pDpH57f7GkbiR3o1gEGFKYO8FIDcPFO066NVwDB2vdSjbAJXcz+Gjs9eU04vutxTGpDZYSj1FrmC8KPigqjjftx1QDVV0V2Q4kzWE+o4FgFsfbvqOgD2Eki4AtuGa8VLTjot1bTx9uMJvR1Dvs+scX3bC4UKrTmtO80NwxMaoMNLUwZbPiZMrhQJvn6KmV3sdaK+JhZlpriAxhukokNhk34R9Z4LfdDSkFGbekvI25geGkI7njnHuLxKG4IcVpj58xP3lGnRu7w55DW5hrZd8MScpmEESHMOhbQ8RRa6hFKlg0kaaBzdktTu7eRTYnHC4v2e+v2F9e0YToYHYL9DN0JPhHaO7qB2SpiBhpmIL8q4qLUEsluSFDxoeBWgutVMRaGO7015PHMINdMYsksVt0GMc5DqNQh8T4yh22b77GcDJRn17D04sYDnBIw8Q4JsbthnHYwtWei/e+xcXDpzz75rdopfaQd8Wk4aqo5o4fb0jSPpjhGD3FqoFgFpUI+YoGZr1j1ZM1TB2Xfivsndo4SF3AMpIG1CppOiO/eg+fEs++8A8YHi986Usf8M6Dd9k/e0S73iFWydOETGOkry0e02uJe4dEAltadClT9qCZtN1y9pm3eeP7vofz2wOPv/4B33znNylPS6TFia83zRjx91pm8Div2gqSocoAlxfob7zD9LUNogNntzb8wB/9UYbzV/jql9/hy7/6Rep+jks6jRz7umXYRoK2LdAO/YL3QGi7gY6IexjpSTtZoPfuWsIOB66WB3z5yRPGMTFsJ25tBzZeaPvAX0+qkKBUqC26tV1joMMd1Cxw8RZDKIvmMG+p3L5zzo98z9vcO3uFR88O/Ob73+Dh9WN8XiKJTAw63Bj6nVwQneRgKhgZ6gym4DtSmkgijONAEoUsDJuBjSawxGG+Ald8yEhykjlTGmAYo5v5OJCzatWqVatWrVq1atWqF1b/h1vfz2vHofxVq1atWvUd15snb/IXvv8v8F/+5n/5vJey6mPSbR14LW2e9zJWrXrp9NIZwOKCiYVhJYqSSYnA/eqmo01zN2sU0tj7gY89uxrf7wmxguQxTDDaTe+vH01EEdz1oyfvqeFjys7R6OLl6D33lDEJl27y9u7fSNpFrycipJ52w7rp6hZGh3VsdIs0qhO4WxcBKqKBR/ZqkOgJ3AGzjuF1D1SqB175pluY1lOWrSeUDfducGvvOlZFZAAp0bvbFo4BwUBHGz56T/5WVKd4XmuQU/jarh25HanrMC8NmaZA8qaJZobUGUmCyQa60dNwKI3rVrgsB3bpDre8MehEE8U1U81pDtoqzcFdqLViLqSkJASV4zmLNLPk3I336E0W6YlwkZgPKN4D1SPqHePdDPESJ8IqkNCUyGkgbycYM0t19lfPKM041IX94Yr97CxiTNsTtAZG26vhCFpbGIibLZKUPI7k01Pyq/ei+3YpLIdrlvlAS4kmEl2mLtF3rNFPHNsvEqjSHFFF04COA45Sa6UeCk2iF1dUaUuh1YaXmaRD7MXNhrTZMp2dMm43tNo4XFxgEghd9xr4ZY/rxGqjzgeWyyvscsf1k2fMFxfRc5s3TJvM6ek50+1zTu7eYUjCs8fPePjBfebDs979K1QRmnv02foSAxikuIZzhiSkHIMFViu+xLkWCVSwdwyx0EhpwN0pGJYyJoKpxzHrl7SKI+OEK7TrPY9++av88pcfkJYDjy8ec7i+oF1eIcNAOjuD5FhzvBHJ8OOgQz3uhehz9ubRpXt+jr56m/zZ16i3hGdPL3j67Jpi3tPOOQYkpOKaAcHcWVqjmZPFGbDeBZ7x6yuoIPmEuz/6I5z/3h9kGffM3/iA68sdZr0xVzRMc/q16RbJZ4l9x5FMkBQ3i6S4pEhT1yUGJrIippFC9oS3hb0U5pJI9cDhwpnEGMeJQZ3x9BR1I80jdZkhJbYpMUxnXNXCoR4iwX+TSBbISn79de79r/8sd37sR5iGzJvPnrD/+/8NT//7n6fUEauRDpe2R3Kke90qZjW2ezlg5D4cMIHtER3Jw8DAwniyiZN99zU4O8e9kp5doLVgmiN8P01szu6xHUHawu7qQLEVAb3qxdOf/74/z09/86e5v7//vJeyatWqVatWfaK0voe+uJI+1Lxq1apVqz4eiQj/+vf96/zyo1/mlx790vNezqrvsAT4v55//nkvY9Wql1IvnQHcRFFJPSEruB3AR5AKyVBydPmmSINBiY5JEcSjezalTHI+MtQk91Ru50O7f4R9pnybsduRosdkb0ffxrcF3lhU4s+tGzR07LEq0lpP7ubonxRBvGBH8nTvv4zgsYR3Kx5dniL9dUoYrkL8H42EnQutG8wOPVXZH8u9f30NMyv1bmTN0SOcTpCyRzwRMUt64jWMZElh6KL9GDkdMdvi0Fgk9pCE2xVOAnOchKQxHsOgoUjdxaFKhnmi5/7AjCQJU1jcuNLGlTfOrTJ4CmRrU6qDUcNcFY81lkJyi77dnEnZId2K3lPrmHCUQHFroH37fpKeJPWcEByVBk0QaVitgQZPStqM5LMz0vk5sh2ohz325IKyFJZDoZhTrdCSIgbJKlghalpbPIc7Mg5oVlIS0jAwuKOPn1IfOvNhYakLbdljxSIBmRPogNCTyxJn2BG8HDoQ3EhJe0utYS5YVioJU+19q4pRSWnoyOuBPI6c3LvL3TvneEpcPLvsGPHYM1prXDeau6meKBfXXO9nvBnzfqYd9rgk0vltNq+/yumr58hmwmpjf9hRL/bsrq8D6S1h8luLVKymjKpEQtY99ieR7E6nJ4zTSXxsNzJfXMKyBG659URwTzG7G1UzbiVM4KWgKX+U9k706wd83lOf3Ofh5SO8VVpZ8N114MKVOGf9etN+n1Cv0AxZCloqYoFMJo29i9mYHzzjYvkqPoyYNa7mPU0ExiG6lq2RTGkIrS2Yp+jEJgZAUkooBq0Gmv5wyfJMufriP+Hs67/JZT7hcilc7p8xjAPecr/uvCO4La7pIxJfFXH5COneh1skDiDuBYZN3EOxoBuI4Mxo3mIQiXGEWZwTlNNxYMhCbgm0MWxOOHnlFb7nBz/H6auv8sE3vsGXf+PXOOx66lYFvIBssOWAP3kfvjXiCk+/+gHvf/UDSvMwxVMkvr0BFt/rzTDJtMMzmksMm5QYTpG0JQ/KMCjDcAsVpzLQHn2Lcbej1Uo57DBxdBiQ7SnD+W2G1+9AHqhPntD2BT8cPqZ3qlWrnp/ujHfI+tL98/Cl0ZFNs2rVqlWrvvO6M95hSmtKdNWqVatWrfqd6O50l3/7D/3b/Du/8O/wK49/5XkvZ9V3UAnhdV3Tv6tWPQ+9dL/hS0qYtSKRUNXUzd9N4Hpb6V6f9K7UIfCoDqqOq5I0RSVmT8yKNsLQ7R8DkCFMVOhM5XKT0A3D+Ni3qT3d2/uCj6YoPf2KRMLRWu8y7WlU67hoyYhJNzwWtEaHKc0jVWvakaqG2xFBrbh3I9AcbMF6ItDcuuGb+hqJ50Eirax+Y842DBOPHk8UcgUZIynbkdoiPTmdwiyWdILX6Bw2t/h6BtzmWLMV2pxgK+BLmOI0zDekpHgLnKuKdEOz3awPERRlscqln/DMnFuLMtAYqYgGVtZadCt7DjM6AdqMtBlI44Am7dhrAxRfZiSPSFIkJyBHKrMtCBVNKT4nRh42jOMGSYmlNmpZSNOInmwhK+1wjT08MF9ecdgX6nxNEYlnGm4xdIMytQOuQxwjO5BIyDAheQiTetyQT04ZT7fokFiuF5bra0qZqaVSq2DeSMSpkXEMA9MMt4ZZpZXeR62OuKOSaGa4VoorxbnpiBV6F60OJJxhMzHcPuP002+zff0V6uEAzy6xeQkUeK3kPMSwQMo9DW+0fWW/iyLY5oF0JgnUHX79mHm+hCqkpVFEOLRKaRVPGWuGeaVpDCaopDAre9+0aMLFScOGe+e3eO3OHZok7j94xON5odQaHc8aEfnAfTtJjeyJUqDWSjJhkMCEh2EumAd4mdIw21E1xX2k1kA6b6IX2xzUG0PKTCiaE2aZpBVXxYaeABYiCZ8yTTx6ai/gMAxoSrg2bBjBDB0y2RpNBSsL1eI+4uMQ5qsmmuSgMSOdOm7UZWa/NO6b8kwK2vZsyIynt7hyOFxfxfFwAuV+vL6P9AOTOLZ+BNJrmNcCLpubNDMea3Dt972UkDKHUVoLhjMrbKcNg8+0udLEGV+5w71/6Y9y/gd+P8Nh5uTBu2yAxTX2YY2UuTWwh094/+//AvqPvsRtm/lwd8XDwwX1mEw2i8tVNNLAmmi1YrSg31vc19xBlgMpDeQ09c5rpeyvqemEZhnaHk8NmTZ4mQMHj9PawuG9d8mmLLuF+bAEAn7VqlWrPkH6g+M9/pXtW897GatWrVr1wuov/d6/xF/+H/7y817GqlWrVq1a9YnUq5tX+Xf/8L/Lv/Gz/8baB/wC6d+89QMr/nnVquekl84AFgPoppQ7KWfECnjrfb7dwPVjatehhfFrkqPbFcA6qbj3hcb3eDcjb1jP8Xnppgq97FRa72DtXyuRxENSIFh7PkMExA3tvaVHJHNSEHLvvI0kciCVU08ghpkjZNBITGIOaeyf86g3pkSSEA/ErFtvIz4urQQW2+ZuWh9N58iOaKto74v1GulYKJFsrNHJ6lajO9QMdNvTyIG1dtXoie3HLvCsDtvUudHQPFLPSQvWUjd+R/BrkE03nJeeMg5csuiA1wU7ZKos1KYMQyINPfU55gAAtxpp22kM43cc0ZzjVNUwgEUETk/IObPdnjImoUrieiksu9o9+IwmQacz8u1z0vkZrpCeXmFXV7g77XDA55kyN1pp1Lbg0uDkJIw7NzLRqWxFkBJdz94ElYxmRRFSyozjRB4m0jRAUso8c7h6Rp1nWjVa7e2nQiCYk+MFPAlCxov1RHQcW3FB0xC7XQfqUlnazAwxYACk1pgMktZIm0oi5RHqQn3ykP3FnuXyGrfAlcu3Hcu4lBLuzpASOQ8Uc/bLjB/ie7w47aIwj0bKIzoNsecPNXDD3ucoPBLFx65sat+TDDFwMI7k1+4x/sjnGV+/g8x7Jhb02WN8b0i/rsUcb3G9ignaBy8c8KF3fhOGoSNR791KoI5bwwRUNBKx0zY6rlMiibJJmbNbW07v3ANXri6eYMuC14V6OEQ9eFiKuBznRiTMzLqQ0oQiMYiSFB0TmS0+HyJ1rHFduIOZUFqBpIHzTtZ7jON81nnH/tFCmzbcfuMud157hdQS8rWvU8sBKxVvFY6YZxc6YP5mSCUSy/GfmOEp+sfxSNCH3QzujuYtlAU0Iw5Ow8YtbRhYTm9zPSXk8gnl4VOkQPnGNzk8/JDd4yueXO6Zbt9iK4pd7agCkAPbvSzMT77F1549ISVlqXuKxP3Km910nDulkwRaoL7nfaDqWwMrgTo3Ap+eT1Br1N2OYpFk1iFBHrApPifTgLQZv76izTOzJPbeE/HaCQ6rVr1gOrRDDB+teiE1iDJJ+ud/4apVq1at+h3pZ979mee9hFXfYb2ZNvypzZvPexmrVq1a9cLL3bmqV/zMN3+GQ1uJay+STiST1iqFVauei146A9i8dGiwIDmFgUq+McJceiI3DYEWltaRqN7NWG4SuriHoXhM8OoRb9yZzMffn/rRAI4u2I+Swt6NseWjVLCOHSFdkcimAoam3I2oSAWDosdf0HrtqV8gT2HQepi81DCHSBLrI0xZ1wZNcW89BSg3nrUffzEoYzeBcrjdUVSM0xARkmj0xHqYZg74XIES6ciUIjUs3l+b4GboMHSnzbuJd4jkNQ2XEfwAvgO9C2IoY2CXU4re12TQciSERTma8N4aI8a5Ga8NibtinFGYdGQcB4aU45xjWIPqIMnI4qSkqFV8rrjKR53Mg5JOThhu32L7qbeZzm9jtWHvvU/96o7WxjCBxwmRhF/vqZeXeGvMc+vJ5kap0ErFcoYxoWdngV1uhiwz6kQXLhrGdN4gXtEMmUD85nFie37O2e3b5JMTSqksFxfMTy9Ydtd0uw5yGLRUQ2mR7nVQ977nHWlys+91GCNI64Z7o4lSXJglIQ65VQY3BlUGUXIe0CFS6cvjpzTNzLsdrc7oOCIYeRjIKWEotS64VYZbtzl58x7p7IzD1Y75a1/HD8aYJybJ0fmM4AP4JuEMgW0GDEOHkXHKZJSl1DBEPQYJYuMqXgv14SMOX/wSF18ZKYtz+fQJba7oMGJiYVAKfT87GvMgJMlUDDentRgOcQyvBcmCVkGYOxLa0DyhY+5FwYoOA9PpKWdvv8XdN17lrjd4smNE2c0H5v0VgwnWKrMK5jleW08Zx7CDIssCGmhxMUObxzBGVtwGvBW8KlIKzQFfMJSWB6RWNHmcy2vH9zs4PSHduUc9vRvDB5eX1Obk7RarV9Re92utRuJYFZEcncKiuJe4jiN23butM0IkmL0fR5GM1+O1H9e6iEBptLIwP32ftlTs8gDlAHPj6ZMdbbvBN8qSCnoo5LKwSULxkdZmMMc0w3JF45qSAk3uLdYnLfYt3gdOEn2gRoJO0Frg+kURSeTNhmEQkkZKemlOS9rvAQuqGakzTiMNG8bplMkbpTmzg4liyyWST4/ghlWrXhhdlSv+g3/yH/DB7oPnvZRVq1atWrXqE6d3nr7DLz74xee9jFXfYZ3JwPfm0+e9jFWrVq164fXO03f4Kz//V3h0eISxDiWvWrVq1XdCL50BLBKo42aNlDLWwiAThjDG0kSk2wJdLNrTYx6JXE0pTA3tpqMIkDtmuTspEWntSdcwysJcVY4NrIFurv1rOmL1aGQdc7i9pzi8qtTXHo6Dan8uyYGKVum+s8TTN/uot7R3CEfq2DAz3K33unbTULSbvekmoXtjrIrjbQ4UdWtIHroX1JhINHNKm+MxxhFqwqlACzPIwgyObuPaic0ez6U9mdwMb5e43gIW8FuR/CVHv6pbeORFw9QW7UjtQxwDqww4dyXxehZe32Tubjacb0amPDBupsAhpwFrS5hBKTDXSQTtCFkzw7whopHwNIG5UHeF+f5D7PETymFhf7nD3JHckbu1YOrYUvHScBwTwdKG5jNNKjYOkc7sSORkDWmKeKK1Ak0wq4GDVkVkRHVAkpHGkeHshOmVO2zu3kanifroKaUslFaxYaB5DYo2A1IXdEgklJyVYdwGjhjBSxiEaUpQF2ToPccyYPOO1lrfIyUS0poY08AozrjZkrJEhfPSmOcLJCdqWQBBkzCebtneOidPE8t+5nC9w0U4ef11Tm+fYfOBi0ePqYsx3H2NW595i3t37uCHhcv7j7DdJWkxmkM9zGGy5RHOTpGTDdoa8uwZ5hpJz+UAg/f67YHKnsvykJozJsKhViiR9pZjUtQ70rgUGBKKojWQ46012pgjNA+ogZSK5iGMRGuoRkL7OBwhKr0615D9NdPVCW+/9Rq3vvcz1L3w4P5jvnb/Pbxc0FxI1zOH/Y5WHVv2eO0J5Coc4fAxaJJQj3uVJgls9fEeM+9YXDESNMPV8TzSzMCcRA0Dfi4s3/qQNleGe/c43264dfc1Hg+nPB2esL98wmF3Hf3h1vu9pcXQR++xxivoEOhz62avN0Qs0tTuuPagsPRE7jGtb5V6fWC3T6QWyORh2tC2iTkZpe1IcyK1BTcnJ0dTYkwjtWVqMcyhtTDJ1VOkkpvFMItFJ7SLBpHAAStIMxgGVGPYQaShUlEPFH1ddlQZsGFAhjH2NQlsxj1F+vfWOXKywYeBdH2JPn5GWypIxjpWftWqF0m/dfFb/INv/YPnvYxVH5ME+D/f+sHnvYxVq1ateiH1zpN3+Ks//1d5ujx93ktZtWrVqlWrPnE6vo8+ODx43ktZ9R3Wp9MJf+bkU897GatWvbR6+QxgFzwltBnWpBs5C9D7a1vpid/ohgzjI3pfb/pmnW7qZsQt/pw0PNRjMjieDWiRvu2JVz9yZrFuHh87f7vhamFQ3/QCi4d5Cje9nOIt8M8aeF7Bwtw6dgS3ihAdsEcktdUSjzeOgVp2wgy0OdZhdvN8ogNYJGGj51M/Sjr35LL4R4ci0MEbap3jNSphHhmQewewDEhz6PheoUYPb6mICIpQyB3X6pAnVCuOI3YFvu3p3Egiiu3jsfoCUk6cVec1rnnr1pu8OiROphOmIZFTh9pqDoxv6522opGl9p747QlHLGHewgj2ilxX7DBTHj9DRHGvNG+YhN0ueHQt1/n4N0wIxLVVSi04kbQVSTFIYNZnBJZIcpvhWZEKiiEeBrRK9PimPJBTxpuxXO3hySWHZxfUw4KLUb1SaqVWAxpJFRUYtreYTjaMJ9vedbqjLnNHco/IACIaxnMptNpwq+hSGSikdMJGlUka4zhGmFxTjDK4UJvhS8EsUqDDdsvm7l1Oz88Zh4E573GHppGmn68uOTy75nB5BUkZXr3L7bfucefOXSgNOd1yuLig7Xe0Zxf4wTAvmEG7vqbtDtRmtHbsaDbENK47TYHjHgY2p1tePT3HUsYvn3JZDtHl7NF7Ld5x0EkQVRwl+UgqC+K9I9sLakrKguKo1T4EUfA8BEbdDLUCs+K1YYvjp2ek730T+4n/BcObn2bUxGc+fIT+g3/M7oMPeHB9wcXlu7RWAtctYdKfSCT151JoHv3TKWckZXIeESpZhLQZkasDZtEdXD0gzJIEyRlb5sBa03rnt+ED2Ft3KD/0w9z93A9wR4Ty7IoPv/x13vvKP+Xdr3+N/dUuzFsVvC6RzEdi0KLVnrgf4z8cOQ62SEYosZ/xWFef9/C2AERHsAWkIG226NkphymOc7p6ihwULXuSCmk8IU0DWYVsE0UrXgqm2k1go84zqop5icduM26Kp4RLwmogq1USqhq38/EWjtLKgXJ9gZGxBKK939lAhiFmeDyhJvjhQKNh8zX1eqaZYGW+Gajx/NK9hX6i5e4f1Tw8x8f43Sh350tPv8S/94V/73kvZdXHKAE+k9YE06pVq377Wt9D/9l65+k7/JVf+Ct8ePjweS9l1apVq1b9LtT6PvrP1vo++mJrI4nXde3/XbXqeenl++21aJhfw9ANggFJW7AWyUwrgTkVifSv5zCAm0RCTnrqz45p3N7VSYWm3STuKdU0Akdcapi90RnpHyGjI7YIIv1/GinedojnPeKY1WONokhO3YBNkbTLQxi4IpAjWevJ8WJhelrFPbp4rbRITVqYZ65DN2it46/5KMHsAClMT8lgFUlDx1vX6D11w9KA1LmvFyAMVa/9WKlFQhogpzhmPsWhS0I9HHAxUjqj2kfHN0G8NgemDZQFt30cXxnBZiQNiDlTq9w7ucWr+YRXponTMTGiJBPUG14LnqZ4XSroEElczJESKUZJgQavSuCHY8OAe3TW1h2GYHVBXdE8kKQBLRLkvuCew6TWhLdGrRWbF2QzxVqRSKG2HUgYa6oDPgxhuOUBLRUVC2yzKXkzMm625Gmi7QqXjy+xMhPVpzO1VZalMteCO2SMpJlhM3J69zabu3fQQThc7ajPFqw2vAmUaxgnvFXcU3TtVkOBIScSiSkJkwpTHsnD5mawwWoDOWCtRYdsMXQaYTOSzk7Jd84ZxgE2EyUJu0dPWO4/ZjfPHHY7ildsu6UBpUAxRdRxCvXiCfXymnp5HQlZdcwKZWnMjUheDrlfozmqoDumXXNiuneXV370c3zP973Ndq58+Z1v8LV/2ri2GiazGU7CWyXlAYjXnJKQ0wTekDSg5giGSh8UsRJGaMqBovGE1riWoq420zSxv1q4+sZjxs/t0TcVUNIr9/jUH/8DSPk8J1/8Eg9+9n3mueJm5JNTzu/c4fbdE0qDx/cfUK+uwY00KJoTKWVEleRAa9iYqIPiLa6POiaqBEZcWyPXEtfq6AgjbS7MDx+jv6fBvVO4d87Ilrc/+xb1FJ5eX1BqpS0zXsE1Qw5scrzmBN5NdhRvM6QR8Rod4BYcbasL3u8Dbv9/jeJ4XcI3bUa92rHshSZxPag5Up1hGJhaZVg6Mr82ct4gY4rUrTvWnJokerx9CJpBG2luuC24GiZD3OfygLJDNifI5pQyX1NtS7UMqpFg9j50QcaXaxhPEQ/0d7s22A+IgXmKQQfJcb+1FkjwVZ8YfSd+WH5Rf+C+rtf8Wz//b63T1qtWrVq16n9U63vo/7SqVX7u/Z/jw/36S+tVq1atWvU/rvV99H9a6/voqlWrVn28evkMYB0DT9wRpd6WMALxnv6NFJt4Q0zwZD0xKqh/Wxdu0t4RHBlcQcLo9N7/m4+TLSNAdFRKR9CmDL3Ht0f0bvDNN8XBOiBqpJQQax3xHMZDdBT3h6/Hx1DcCmITIhWrjhlE5k4CbYyBS/RkHhPGR7UZSZuOfLZI9fWuTwgE803sF+99mto7GSLBe/NaAJfaDeNNGEdON4El8NXkOM5pDHzvXALNTANZQE/iuEoLo7Re92T0GVjp6ekJb86ocJYyd9W5mzMnXpk8kbXG8fGGuWGjhmk1jKTNEOZyawgH1OOYpjT2ZKjGAIAkrBaKCz7DctixHAJBPTQjW4kK2GTQFmC+Ma3dHTdB8hQp4zIjklAPc05VMNObKLW3AXEj5Qm1SnJHk5K2W6ZbG8bthlKccl2xpWA4zRpLgbk6rTayCHkcmU5OObt3h1tvvY5OE8vVFeXqmrrbhTHm9EGFJbacZDDQpGSEyWPPT2NmzIlx2sS8gjs0w2pPQdcGLQxUEcGur1kePGK3VNrJFnOnHg60ZaFc7lgOB+pyoFrDSuzH62WBb76HFYtUc41EsblgqWEt9lkzj55cGtLA8/BR/XbHlYs7MiiiI3OBRsamIXDfIpEWPV67Hih0sYJqChN4GLAaGPWGkIVADR+R6sQexxV3w2uNOYlhgFpxZtq8Zz7ZsLx+6wajTUqkN9+G3SW7q3/C9fWeUiENI/n2HfSNN6gDHB48ZD4stGUma0JrDRw4JZLOzVCDrMJ0eoqr4M2x2qhlHyZlT+g3r6hnrFbqbuby/hPe+8I73Dt7hdPPvc1gsHx4wfzskpwzm+2W2WNP3QyGwM09Dk83dq4OU6caBPfZGHBvkYB3oB4Q3YQpbAUVYhilXkZ/b82R7k8xPyNWURtopcYxnc4Qq3gayarkQdGOqLa20MZTrM40GTBzWq00d1wmvO4xmWhtge0pjQkj0eYDZX+gpRHG3K/FFnfvskfGW4HiTiXuXS5QF1wLniccQdou7l0S+2mtpFn1Iqh5429/42/zaH70vJey6mPWn9q8yW0d/vlfuGrVqlWr/oX1bHnG3/zK33zey1i1atWqVas+cWre+Jtf/Zv81G/91PNeyqqPUX/u5NPPewmrVr3UevkMYAKfbAZ5GKLzt9Xo1RyjS5PksBg+OMIGpyLqGBVHSA4iKbCzCCKGt9aTw0dntifY6MWYQXSNZCgZvHTMM9Fh62GqHr9V3EmiSKth1C1hOkrO8XjVQVrv/E0Ro8wJLwdac+yY+O2oW3MN89dLGFZkwplqgYjV0zBf4QaDHUHg2j9+7CQeEIlksUh4wobTREgqWFmwlCM5fUxM6xDJals6Dts6vrmnobubLTLjuxm2t2++LPzmnqr2eG4X8PoESecoymndc2+4y72cOVVhGkfGnEiSQA1rGfNIX7sXvDpt38B3CAnFGRSS5Ju/YxXJQ/j9acCqQ800gSaR2paygBk5CbQ5+nrHDWIF6wMG6oZ07DQIMk3IOJFUotNZh0guLoZ3jG5KGfWGiqNZ0HFCtlNP6+4xdZo4rTm1QV32UJ2Ek4eJ8WTL9vVXOHn9VYazM8rVnsPjZ7TdDk1D7JdEmJplxqmBEJ4GtGVSLUwYMmTGzdTRzxpYcBdsqdS60Gp0SosT6Xl1/DCzPLnC9oXDkPBaWZZCXWbqcggjXj9KUw6lotcL82zs5plyOCA4mmKIwJpjJjR1mraeLh1oZj3JK6gK7oaKQ4Pl/hMeXv4KPm3wVnl0uOaw3/UkPNE5bTUS6O6BwsZJ3vCl4SSsLlSJFHqSHPtOAyMOxLlvGpzjlHFLuCpmRru+5ukv/zq/+qDye197k1c/91nyj/8A3Drj8OH7fPC1r3DYXcW1lwds2bN/7+ssVbjeXVFKRUVJ44Y8JFRjxIJqiFWSZmRoYahi2LxQaZQmGNax0RPmjWYNTPFlxhAef+Ub/OqzPZe3zznLzqXAFY3p1ilG4L9buMfUZlTryf6OpBarASWQTh8g0PGKRHo/Dz0ZO9wQFQIp34JYkE5wM1ziXqod6RznPPD8nJ1HL/VV7ZRvR93RIeMkpCm6HGDIkT4+DrVYDKG0NMQ1Mmxp9UCTgVZnzCo+noDV3gu8oIywLMAAVCT3IRgd+/1J8OUKbZUYpGlx784jkHq3+6pVn2z9woNf4D/79f8M83Wi4UXX7xvvcKIv4T/9V61atepj0sPDQ/79L/z7LLY876WsWrVq1apVnzitP4u+HPqJ6ZUXNsG+atUnQS/db4GaFdSdpJHMFWugjjeNdK4vwCbSXZoQcVIaECywz3TTp3e4otqxzt1PpeHu3dPsBmvHRMsxP+e9V/ej6GfvDi7RlcsRCe09eZvieZN2I1RAKm6C4T2llnoKeYiO3xaP7d6RzzLE8x7JzjbfoKdpNXzqZj3Z3HDXbmYX3PUm7aZJwoxCoBVEE+rRodksHjxJwkrBhxwJQbx7x9sAYZuSia5RSgVrQMUNygiDNqxWkiYkDXG4xCAZHFoY6ekUb1eMw13ONHNnUk5TYhgVTTnM8qSoRmpaLWPWsGpYWXBZMBxLmaTgKGPqZrcTSesK0i6xcUutNfpaaxxjKbtIicvQ09CGZEFSw5tFONJmhDCVIzUt8bVW0bxBx0BC190OM8M1ocMQe9JTJDubU5fC8vSCKnvKcqAuhdoqrTmtLHgrZAITPIxKHkckKW0u7HaPWC53LLuZnJRhe4KZUXDK7kBrJfDPSSJ5KdGDLGkgj1M3EwUXxT3Rapi/1sJMz5pIOUdH8TgybDZIStTWWA4HrBWaE4lhzdF7K0pLgtw6Y/vZT3HrjdegNq5+66vM10+hNTQJpAHzSm3O0hrFjSLaUdrRQStJQDbRW+tKag0rM1eHxFyX2G+tgjhCRq1itL7nNOYv3MIcJkEtqLbomRVBySQzUlLMBTEjSfQ8R/q49wXXiokjsqEsC+3RI94vvwV3tvzg27d5Mzn+K7/Cr/w3P8dXfv03KfOBNI3QKoeLK2bJN0l1VSENG2TIeNLAbNc5MOUayXTVkazGYCO1VqY2YEnil09JaDmReh+40Ttuy4F2JTwtlXeePGHcTOS7E/l0w+hC3e8REaY75+i4ZX/xhHrR8Fr7sIrh9PuPW+9Ij+GNGCyJFDDekDyiUm/61O14b/W4NrGCaOCxLfXOcbe4RwhYc8gbpCPAPW+Yl4LbIZK5phiF2qC0Rm1grljdYynTquMJWi2xx4lUvzpxTZqjOiLLgteGpwzLguQBl210RfuCNUN1i9kc9+E0ghXasoN8GvevVas+wWrW+Knf+imqr3t51apVq1at+u3I3fnCwy/whYdfeN5LWfUxq2LsvXEiL92vz1atWrXqY9P6s+jLo0urL3SH9apVv9v10v0LVgSaJYakKBqmrg6BGm4CaROm5aTknrSVrCQCEez0jto0QO0JuI5g5pjg7Shnd0O8o0LpCNnem8tNejhHwphEoEVB5fiYdFw0uIXx416itzYnvNFdZ4l0by3dAO4fIsXHbsyb+EE1WLmJwDU73rHVkoce6R360zfQsRvWYeoIFdVTlIoMEw7UFgaZ3zyu90NhYfyowNgx22bReauCtp4ctYS3fSRm9RTaDmeMRHNbwviuB7Az0J441JGUMqfu3JnOuTVMbCSRS4q04SQw5OhuzQ7VeidxQuoCMkRnqsPcnCZhwA44Ykuct7IE5npZIrFaD2BKopKHLYNXkkoktTWMU9rRV0+IbtFh7IZ6Q/JAHhLDdsuwOYGcqLUhS0GWJfaYgEj0C6MCMtJ2C4f9PnDUQLOFhuLLgmgip4zmKXDCm0QeB2xf2F8/xEvBHPLZKdPZXfRky1IK7ekz2O1j/0jsIxtHWA5ICiy1diy5tQY4bZlpZcHmCpZQnUipkDcb8mZgODkhT5tAZu/3NKuYO80DT20p01yp1qhJyGcn+JiZr55Srg9cX15yWGbchWQd3d0KrcFsxozRhonkTk6gOVKmjuPikFJccyKcDMor44ilEx6XQyClyxx7c5nD6O49yC45XrM7Kh7DFBKtsNXiMs8yk4YTkNTTqmPHBM+IbDsluZvoCl73iO64nj/k/j/8GXZPGh+8+4CvPfgGh8M1ohompzU8Ccgh8NUiPZ2qaN8b7foKt0aatqTNGBXYksnHa5+EcoW6kGbHG+iYSNsJVYGlxj2DGOCAylKuWDiQP0xsVCnicHLKyafeYlCYL6+wpUXyNvVOa+J6tFpgnGJQxcFp3ZwW1BSGTMqJPI1IGmj7a7zEtY/WQLrj3WRt0SmcAm/d5gLzTHWDPCJDwpioy4yXSCiTMipKnReqGaXMmAyQR1wH3MKg97rE4ItYDIS0EkdLHBXtde0JBsFl6F3GhvgV0hqeN2FSJ8AGMI/7ERrDAi6YH4kPq1Z9MvVLj36JX3/y6897Gau+S/ql5QnXVjldU8CrVq1a9T9b1Sv/6a//p897Gau+C/paveanrr/O//HWDzzvpaxatWrVC6P1Z9GXR3/lyS/x/3rlJ/h9453nvZRVq15KvXy/ATIlqeDW0CgujTSapMD9Dgq1gmdcQLPemKdJwCXh1vuDMdTDSD2av+4LSMKtf0yJtKFEZ6i0GobmMUWbFDFFXMJI6djRm6kYBwi0qjcPs8QKVlqk6ZybtbgIYo654x7dpEZFZIxkXrV4EQ1cK6IalNMhnr9HmkE6ClokzGrvRklEnNHU0N4zSh6AGbcBbwdcIk1JzmGwJMVbQySjybGlIjqGmaI9RYwi+Qy7+hA9O8GrIVrBh37sGugEFFQHzBbEYTMoZyi3ppGtCDkZKUHenJKGgZwHRBz1OA7JHaHiuRutLrg5tRRmAW+JjWZGEVQkaL95iH7ccoCiJAoilWEYyTKSkvUG5HzT5auieLUwf3MY426K5hHdbkh3brPZbsCM3W6BYUTSntSRviqCqN28fjejksAikVgxTBUVDbNQB9KQ0TEjQ6C7y/6AmIXhPE2MJxN5mqKL9/EFy6PHtLnGEMEwYN4woErCmiFJsVKorUFK0OZIlZfYG0JFJZPSSE6ZrBMslTpfUpeF1mrkTpcZc2jaE5vqFGBplfL0Ep0rC435YFxfPKMVQzK0Zkip2HygiFF8oiiYV2xIuELSTEo9kZp6nnfccPrW23z+9/8Ib3/fD5DE+Nov/zpf/tKXqOVhGPvDiBjxmjcnQOqJ1DD1lEjfWyk0M6ommifMWvQ242ijlw8nXAyTPgCigUZ2KbTHT3j4q5V92mJl4Xq+ZC6HQDeL9+5cQTyOJRhJlSzOmDKjCl7isV3BlJ42z0hWBCMPYV6rzfG9SWjWkJxIHkjsCpgteB5BOhGgG+xFM65K3k4M0xhp2str5qdXjDhvvv4mm5NT5uXA5e7Abr+nlCXum/WAm3csdkZMIvXrjuYRyRmvC80M6/cSaXHMMGKvpzESzhq96F7m3tEcOH4BvB0orad0iRS2l+vYr6VgjHg7gNXYq0n7faXTCjQhHilmlUj2I4HZljzFUI1HKjzoCIbnIYxvd9zBy9y7sonrWUeg4WsKYNUnWHOb+Tvf+Dvs6u55L2XVd0n/n8N9/mm55Menu897KatWrVr1idff+Mrf4Mn85HkvY9V3Sf/V9Vf5I9Mr/N71l9erVq1a9T9bX7v8Gj/5iz+5/iz6kui+Hfjbu/f4/HDOcBOeW7Vq1XdLL91vr6vDoNJxyYEwFVqgf9MUqb7eMypS8Faid1dTGANET6W4R2rQjr25tZsYuadgy0cp3qPRDP05LR6PhHdMrdPTxDoGYdVbGKVRsIqI9MrcGhhW6rEgl2OyFkk0D9xp9N3OgOItErykFAlhCuaZdHQ0wqXuJrbjoohZN6ENkUjvKgnFAp+KhHEsDnmilaVjYqOPFRFcFqgNTSNeD0BGdIrX1k07yKAWhuLZOc0WQMiyiWOrGXxBUur+s6EJBq+cMXB7HDjVxKRKHrekbAzTxJCFlDJYxWpDvWHS+33lGHqteCnUslBINB0oObp/h+SIpzDUpfW0JmQcFSG3StqcRVo7JWTIiDtqxmazYRg3eE4Us0ACl4LnAYYMWdGzLUPe0HjGYXeN5MAdB4sboCO7u9yNZo3qRkORWiIB6xbJbQdKwZph7HEdGU5PyHfP2Z6cosOI1cp8vWf37Bll3uGSYJoAw2qilpkqgichiVBdAoNdZkQFSY40id5U35OHkfHWhs3pKSrCfJgp+0jbugvWZqw5DcPEqZ4odmBpEijn/TXX84FDa4HnLjOQkNbRzq4wTaRywKQxWqXtDvhmwdoGQzAXWhpJlkjqDHfOmH74M+iPfw/jK7eYn+1pn75H+tYZ9vQh1q8YFEwTrr1Du4LY8VpqiA6YJlo7gI8oI9oK0JA0BdhdNbDeGLghHXduHteqt0K5vmLnl7F3reAKojGacTMjIiMIjGlimwfy2RadMlxd0q4OtNZiz1rFLY5V4MQDUZ8Z4eSUVBp5KoG8VoE04RhpcWpL0BaU6CpubUYsTMyqfahlLjR3DCfdvsNbP/x9fOaHfpCT8ZSn3/gq97/8ZfT+Uw5LpdQ5tmo1mjeqhSF9HCLxUrF5RyPFtSPae9AljPM0gmsY4RoIfXNHUophGcCtIg3MIvUfneJh8Hon8jcZ4r7GEK/BZ5JlpKOawaEd4j47ncXQyjAgtfecW8FqpIpdUzzPsI1rjsCGuzR82PRbpGLLAV8OWDvAcPY7fCdater5yt356+/8df7ue3/3eS9l1XdRDvzlJ1/gz5x8mv/T2Q8yivZ/oq4orlWrVq367ej+7j4/+97P0rw976Ws+i7pfjvwf3/8i/y1e3+I3zPcXt8/V61atep3KHfnp7/x0zxdnj7vpaz6Luq/uv4qC8afP/nM+j66atV3WS+dAZxyNwqwbjA6MgQLWNzDxJCEZg2DxgeklTAeNCM9KdnM0Nx7crUblbUEstkM/Nu6ctHOBU6RNOupMVdHmvbkaIsuU4nvcZYwMDR3DHWsLQLCCXKCFu3D3gzDIo1HxinRS9pN4Xj8MJzxGhjmnlYV4s3XMYQJbEE0R8dqT7UKFpjsiMehmsALogPSkcXiYfCoG94NLWsSyd3myLgN2rY4qi0wsG5hru9bR2sXcKPtGnJaGZICGXXBPYVP7ZAanE0Dd6RyLjNbnchJSAqqAzoo48kJY0q4CctuR1sOsbZu4kReVBgUKrX3wC6QtrQsHS1sSFNKOQSS1g1RRWtBhrGjvOmdv4YITK/c4/zTb3HnlXuoZK4uLnh6/zGHhx/SDjNt77S0Y5kG2mC0bvK6CV49jkE34qWnIs1nzFuglJvhNFQSIgOqcY5dhFYbsMTemzKikIeMJaUtM/PTC64fP2U5HHACZQyOtYXmiYJQkkIaIvFeDPFKEpCUSD1Zqq0h45bh7IR855x0solzXA3jgN38IsTxJJglqhu1RBrUS0WtRkpU45oQvBv2JXqbe6I2qeLplGyN6huqFVprmM0YjZYHmhRqHtGk+LMrnv7aV7j/3lMOaWB/2PHg8pLHVxfMteKt904kjaT8/4+9946zpCrz/9/nnKobuicxA0PO0UAQWBREQVgTJkzI8jMrX3TdxZwwoK6irhFZV1cx7QooAoorupjABCogIkkBCcIwTA4d7r1Vdc7z++Opvk5P7En0MPO8X695TVffuqdO1T1V1bc+5/N5kmi8uG/gKOtY+JKUVMSNLiNWpU668NonR1JBvI5iJqGx0SlBVSLek2IAKlxV1UKtw2eudszrdApxKiB7hIHMs8/sHdlx510ZqQr+dvc99JZ3iOKh1daJFiGQJJGqHpKqOnqcOjbaQ9B45cwHXOxphDaOKgTC4BR15+OJRUWSWmCtrwmJRBE1v9wNDtDcaSeae+xNPpBBZxhCzvTp2zOtPYOlnS6LFjxE0fGIL5FCdN9iJImKvVJ2iTiNo4+FXk+Cw6Wqvg4lveZFNXCTEo6g15qqREKGpFTXXa4nolRRUwhiIrkc8XoeiK8TB5IHcjS63uMkg7yFuFF83tZJNKmHR9MMJKm71+UBqh5kTag6faexFD1SEnwzw+WD6jBOGgFNaJHcWDy+YTzyeKjzEFc9eNVkd8OYBB6MHb48dCffG7mf7UKDN087iKk+55CGuYINwzAmgojw4T98mDuW3THZXTEeZh6MHc5Y+DvaPvDKKfuyT6aTQT1wRHOWuZoMwzAmgH0X3TYR4OKR+/i/0QdXuY+OcWA+jVmhOTkdNIytmG1PAAZ191LhxKkYKKJuwzqS2TsPqNjg8JAykqQ6nlmlMySq6OnHHLS1uCMa3ytjv6tns0gSnMTaepqBVGOJyvV6tfs2qbOTmKk4PeYwFm3T5V5FqxjRUsKxfjlpHGkqSc7VgrLGU0uSv8c4q2+ydiDrtn2qNI4ZqU3LFS40VLr2Cagdz1T4oKIvIVcHpGgdYaIHAkkKFcmcEEITJxHJMiASE0jI1MHoXW1eFlyjhZdEEEGiENstFa+SIwSNgFbXacQRaOU5U4nMaLWZlme08wZ5HvChgcvAh0DWbtJqtjQG1kFVFKTYgbJQIT9EfJWQCnJB42zzlgp7OGIqiWUEV1AVJVVtuvapwvmm1oAGFY5cIBYVLstItVCYGk2kmSNVicshtDIVkjoV1bJROkMdQh6oYiQVFaksqMqIzwI+uFpk1Jhe5zMklsSyQqTEe0/wgSx3KoA5SGWPVKqQj1ODZewV9JaN4JeNUI126A6P0Ot2dGJAFvoO0RihEiGKisi+SkgqEclwLoHLNEI3FvgsI2QenwVIFXFohHJ4iCSBstcjxUJrqQKSt0mprMdrwktF7hwhz0kp4EKoZfisFrEBGuogR9RV7wIe8D4jCOTOE4GIIyWoeiUpFuB74AOxVzI03OO++UsIIVA6oYpdiqIklkU90cFrrV4STvQhjpMIeHwUJOXE1KV0UFC73Kse3nmC17HqcRohjL6v9g5rXdgqIdUILmvgg0afu6xJ8hVOxpICvAqczpFNm8asQw5mz2Mex84770J88EGKXpd5IaeMo1RV1EkGSZCqwpE0Bj3L8SQVXWN97XABEVdHKJf4oJM5ghMq8foZVZGEQKoIWejHMxPVmeyLkvLB+xlFeGj6NFwIzBhos99hjyJsN4sl9z3ADb9cQrl8GaGq3bsuIKJ90uNZX1+KTh0XL7jQrGPkZUz11Rq9RBVXnYOyA6GJ801SHNG6vkmQXl2DOQTE1xNhYgVOcAQk9ehH51cJnwcI9fUza+mYT0nP3VTVor3TZAFVgXHeE/NBvbZSgc9BhNTrUZc0J8Wocdq06sk35YbchgxjUlleLOd9172PhzoPTXZXjElCgPmpy/zU5XWLfs90n3NsczYALxrcg4PyaXgc7XoCk2EYhqEkSfxy7i/56/K/TnZXjEliuZQsjyUfX3Zr/3cO+MfWTjScfgd65ZR92C0bGPc+h2PA7quGYWzjJElcef+V9l10G2Z199ExjmjMZKfQXu37/mnKXuybrT6Fr+0CYaVJWEmE0dq00XCBhk3SMrZhtjkBmFQ7JPOmRvciGkfsa3tpLaAQq1rkE1IeVBR0ieA8UQTvQz/hmVRbaV0dCe2dLrraYVjru06CCra1ECypwPkcatcxKSE+IJLQsOmARs6q41bfI9rXCOKElBJCrjWAk2itX2pBu94liREJgeAT6l7TeqPE9PfoU+dwUqjTF6n76TTWOAR1ymlRZO131oZYIL6h9VJTr3Y5j7mKtf6vQ7/spAguZPqaa+DGpLjYRWjgQoaPXZw4dX1KSYXWuBVp6jFIjiAVU/OMmT4ws5Eztd2m4TNC3gQqJDRJWQPXaJBPnUqW5dDIKVIiikNSwGVOHYAUuKokBI/4NkKoXcKOGBOVQKy6xKyBlJFQC/IuG6jriyYVUaN+lil5uguXsazTo5gzH6qKbrekSCXknhACWe7JXIuiipTdHilWlFVHa69WiYyA+FydmqXGfCeE6PXz9mM1d7Nca/7ia3EwIgSV8FMk9Xq6z52ClIRYVFRFR52fLquF10iiokpO48vKkkYt6gfn8Q48HpcKXBRcyAkhIzRz3W5KlJ0uZdL60ymWdQiwTqpIrkJKjS0OiLpUU6mxy24sCr0eMMEDpRpD60EkEqiLriJJBVuPupoRR3IqXqe8pRMkUsSVoziBotLYahGIscTFiC/LumS20/ZkbMqDR7yAa5CkUGE2CeKEUEbA4fVgUEmkbLbABTKX6TkePEQ977Q+rArgPonOwXCidafRetAk0TrZZaXCYtiBrD2I9wMU85fQXbCcvfbchV333JW/LVzEvHvvphot8ETIHKlUATQl6thjndAhvqU1yGtvNyJQjqrgXLt7RSB5T/IOFyOx6GiKgWidZ+ciUiQ6vYq5Qz2WtNvMOuwQ9jzh6eQzp0GxjPKeeylGRhhoN8nzNt1ulzQ6SqKpbt9Yaqx2KjVOO8+1PnDtyBeJGmedZbUg7FS0drXo6jMk9nTCSj++Pqtj5AMSmnVtcI3Ld76Ol3ZB35e11ekbstplnGqhOOFoAAHyNs4nXKq0LwikoDWKM42njjHVidUBKXs4F3QCi2RIMYKQk5L9AWk88rhuwXXcuezOye6GsQWxLJVc0ZkDwE87cwnOMdM3ed3U/Ve7/vHtHZnuGw9nFw3DMLYIfvHgL/jIjR+hSMVkd8XYghDgJ92/ixk/687VklkrMNXlvGHagazvt4cnt3Zku2D3XMMwtg4WdRdxwZ0XTHY3jC2UG4rFa3xt7Hvq6njplH3YI4yfeDUqFect/wsR4fjWjjxvYHeOae2wSftrGI8UtjkBWFzAScRJ0ijbvKkuy8zjpATxeBc0fdmpoIOM1al0xNpJphGlGiE9VtfSpdrd5tR1yljE6lgENLGugyn1dtD1RYCqrkusq0Bd81OcthtyJAq4CqIKPCnF2kRb1HV/E3iPpIiuqoKlOFExDDRO1ddxxsHjnIpgrhbCHKkWhGrnsXNILPEuq+OeHeQ53iWcDyo4lypXOxfJnIpcle4oUlXgMgTBi1P3nUdjtpMgydf9qdSB6AQnGTEuw/sGElUslKqHzxwDzjM9OGa2BpnebtNutfC14E5MxFRS9aAqmho53cjImUJjsEc1PAytDK+ZxmiFUE+oejicOqxdUHHQBSgrYiWURLwXcjw+a+OzSHC5uj+912MngVRGymo5Q6PLGF02AGVJrATXbJDlDjfQJps2SGg0VBxeVJKGelS9HrHySBYgq0VzrxHeCXXlprLEZ02yPBCCEIK6P/UD7OHyppZCDSrtSqqIMVIVJans4lxGCODyNrEqwSWNUE6RFBPeCY2gdXTHXN4uaayvx+EaDUKWk7U0ajlJIhUlsaOOX1LUKPEs10je+pzwWYZ3dc3VCLGuYZ2cqOjrHCRHipVOyAhSi8OiScEx6ilSjU2k0HrUztVRw7mrBVWH5A2d4FGOgtRRy97XomOptbUTSOySyEg4pNeBvKnj0PX0nEol3gcy0fPRxQpfObJmEydoXeOg7mx1twbworVkndYUdmMTSqjjzlOp57CIukZThSsqTRhYNkT3jntYUpWEPXaltcdeNB+1L7LgQfjdcnyzSSNkSK8kdUZxcZRUJVKnB8FrTWHvSJRab1cK7YfzehqnCD4hEbz3eKlIEkghU4G1rkPuNAAbiR2kgm4HqnZgsF0xeu+NDPypYuTO+7nr7jtI02eRbzeNZuwRH5pPVpVI5fvXWKljpn3W1Ek2Xh3jf08ZSGg6QcKHJsl7XNXTGPyqBBIuU7duioW6h6sIsYRK6x9LrFTkjWPR+2PHHhWSx9y/5aimOkgFVYXzDh/G6qa72ukbVainQipRId01iVLq5UpycIFUdkECIgJBz3vDeCRx3YLr+PSfPq1/2xjGauiRQGA0jvLepTetdp3HjsxgwK/5+vec9m4c1ZzVX94+tGzGtWEYj3g6VYdL77nUxF9jnXQlrfK7UYm8fw331bXxmHw6U33Ou6Y/hn3zqZuie4ZhGJPGhXddSC/1JrsbxiOQse+pq+NLQ2uf4P7DzoP8uruA98x4LM9o74K3NA5jG2ObE4BjJYTQUGHWlyoWaDC0mmwzR/QCOaSqVHEiRVwZcVmGE3BUJILqKl5w4usYUWonMRpx6rSWpF6hXN8hq//X4sNYTHQdm0uq6nhUTyJqBGoVgUByIOIhVSpm1fU2UyxUAJE6jhWnolztSPQ+V/duqrSmME6Nd14g9bSWb10XFwQn4POWuvfGat+6Epe1cC79PR6aCnGeOOZArCLOeZCyrjCbEYm6exKQOIoLOS5r1QdbIHOEMbcrgSxvkDojIAO1C3YYn08hRaFJkynNjOkhY3o7Z8rgAM1GrtG/VaSqCqgqCnF0O5HOyCgQqYqKqtvFuUA2mJG32ipSDQfi8CgplWShhW80kKyhscVlSVXXDE4poeqpRsw6CSq0So4kISLEJERJpJRIweHLQgUoRH+miVQtQqejEwi6FVImtYbnbaQcxvmggqD3GuucBWIvUSavcxBC6A8ncVFNpykivqElqtsO5zNScsROhRRdRDJCc4AwbQrSaCJVD4a6xM6Q7lMs8HnAe6fnRSo1nrt2Xjrn8I0mPs90/Oc5KUVSVRIrR1lWdey0Q0LAuYjDEVxeOzCldoTqWRB8A6ljpMUHjS4vCt0n9HfO1WM1Vuqux+EydeHjvNaSFcE1m3r+FKMqeovT8wMP4lV4zJpI1UFEhXFCIMW6/i+CtAb0nPUeiZHkUfd8Aq0lm+NDDrFQZ7/PQBLROYg9yAfIGg1dd0wYTKUK0A4cuda59nreS6yQKLjo8I2cbGCAMHMa1bQpzG9NpTMyTPuP15AtXMbiOUuZu3QJvuox0MjwOEZ6iWq01EhjBFxT44zTCCKBiFNB1Dt1Y3utBe1ihXNeY+PF4cqS6ITocgQhSFLnvctASqTo4Ho9Ym+U+T+/GffrOxmsIkuqIZY3hJxIXDhMhxzyJjOmQbcQRkaGqbqjJO/AD0LVwZGQ4PWak+LfUweI+OagRlV7j/gMiV2diBG89rcxAFVQB3OjrSKt8ziJeqmNPXXsRr22qbE8QRoleQ8x4EITiQWeHLzo5J3Y02t2o4WUKiBLSkg+gKSSlNSJjIukSlTHL0c07SA4JJ+in2luArDxyKFMJVfefyVD5dBkd8V4hHNLuXStr1/XWzTO93TG1P15/dQDACz60jCMRyQiwq/m/oo/Lf7TZHfF2Ma4tVwGwJmLruPFg3ty2pS9bVKVYRiPOESEe4bu4bfzfzvZXTG2UZZLyfuW3ETmHE9t7WzfS41tim1OAPYhqrgQvAoUPqiJsixUCHJ1LGkCyXKSRLxzJBe0Ji0Ol7XwIqQQ6rqQBYRA5saiSJM6CkOuNVTHMqBFY6HVNTY2K1RQtbmrMdAup856BteAWAvBov9EYl3vdwXnrg/97eJzjTwVrQXrQqai5ZhyWNc8FldHWDsVWpygdV5dQ9dPlTplI7XwO1aXNsOJx3upk6sDkgmUdYw1qe++dKlXOzXrY4AjJg+pwFclPtMoo4RHGm181SFJJOQDJHoq0oVBYhLyrEUeCqbkMLU5yEAjp9XMabXaiM/pjo5QuSbV6FJCXtF1jhAj5bKcVFQU3VGt99lq4NoNUiW1SA6+PQ0/2CIbnIpUBdXoCCklvNO6yxE0FtpL7dJ1iBMVnpMQi44KXllGqIXKmDyUIxp5XMd2eyIiGbEsqYoCQesq51kDN2WKlqEWh7ixGs2O5JPGBTuPSyVSBVIjRyrUYSuFOjcbGcFnKoKWhc4vyHIag1PJBtu4LKPodOktH1EBURyuVGdllgmhHiciQR2YlY6u0GrTGGjhmg2SC8TuMLFIVMMjRMmJoj4yUWssofKEZhvvVKRVO3YFZBrxGzw+z7SWq+gUhZQFnM9Jva7Wz43UkyRExyZOz1fRY+Gj1+j0IuEzQbKWOmuLrjq3UTe+cw7pdXUSg06FQLzgfMIndVjjMwj1uZjntYCbVENOCecqXNbAhalaNzg4Epm6xLMWqdmiCh5XlTqexINLOunBez0/YtQ2Y0Qq6c8TcVmuNb2d0F2yiEWLlrC4iqRuiS9LitjDtXPaO+xCbHjKJfOoYkH0Hmnkda3l+nyOJSLq/JeqquspO3BRrwFSqWM8NIFAKgqSj0Q8yTmiy8hECKmqz10HVHgX6C5fzBzv8VUXaTSgCMTRxZStjObUQWbuvCvTpjfpLh0lPPAQZZZRliXd7ihJfO3KzYAS5/N6vGQ4Mp14EiNOHBGdDOGciv6SqroWNdoXD7gcn2W4Cj1HvAq/UiV1kYvgnZBcwCV1/TonuNiFxiA+NHTyRllA1sCJr+tWD2iMuqvHBHWst/eIc8TU0Ut11kZiQUrDtZg8PmLGMLZkylTy64d+PdndMLYBxiZ9jXH+0F/5Sech3j/jYB7XnDlZ3TIMw9hgerHHN+74xmR3w9iG+Vsc5dPLb2dR6vHW6Y+e7O4YhmGsF73Y433XvY85I3MmuyvGNkxB4uwlf6KcIZzU3sVEYGObYZsTgCHiyXEu4HwJsVKXHAJBIHYhqZiHOHUJJtevZyuxRMiJhTrPHBp761PSRNExR7FPtfgLkHAS6xf939dxtVuNCkJTXboidaxvwmcZqSzVLZsadZ3XqLG2hL4gpo5QQe2LsY7kFSQKIdQimnOI5DjR+qFqGZUVXMrqdnapFox8pm7eTHBBa/F6cYik2uUJLjRJkvA+4CmIiMbH+jYiJakswOWksShstSLiyZC8Sa1AI1UJRYQsw1UV4rtkrklVjJKyJkkqnHO0Gm2mBMdgI6fdGmBKY4B2u00pnqIoSDKKeEdV9Ki80IuR6Hxd07UkNJtULiLdLlUZqToFKZbq5Cs7sLyqHalR6+pWGmMbqx4xc2TOkQm4slJXp4CXiMtaSLOBNJtkrRYhE4rhEcpFHRUAvYMsR2KkqkpcryJ2CyQ6CE2yVptGq0mqKsqiJPZG63rOSYXlBC4TvAsQGvVnLoSsSZZn5O1BfJ5DjJR1DHHWGqDVymjvMJvUbtEdWk65fJiy2yWlSusAV5GQCV4CHlFnea+nTueQExoNsqmDhHZTY5tHhilH9NjF6EhSUBtKERnr31h9Ya1hLSEDl+O8w9FAYgWpQNAxRvC4LNcYYufxRV2z1gXIQh3vrTdkqaijfjWe3EuE6NXdKpEk9SQF52tTf4CqV4uk1H0IWi87ayBSqhKbnAqTVa8+vnWksG+og3ks3t0FnVeRQQwZyXmk7CLdQp2+EtWd7IK6qFPSFIGokdhSRXUmZx6yACGn6iX8kuWIG6FT1/4WyQhB8CI0ewm3cB5BAlVMSHuA0MqIArFMtUvaEQkq6Lo62joWtatfo+ydb2jt7qBuVhx6HCWBCLGeGOJw+FD3TxzJByhHEJ/jKPGFphFUQBam0JixHdX20yl8jmsKs3bdhVajweL581jw0HzKbhepehpHnTWQcgTnM3zIcKI1eMm0HruPqKhadsDn9X5olLwLDR0ALtbXqIDHIa6BiMNnzfp6Wsfph0Y/GjpWXY1uJyCxA9kgNKZqLeKyqzW34wiENlKN4poD6qwve+Cj1vluTkGqqp5r45Gkbmlns++NRxBXP3g1RbTYSuPhpyRxVzXE2xf/gU/MPJzDGtvZl23DMB5RVFKxqLdosrthbOMIcHV3Hi8Y2IO98ymT3R3DMIwJ8/MHf87c0bmT3Q3DYFgqPrPsdp7a3pkG9p3U2DbY5gRgH1p4By7Wgo5ENCw1AAGXiYoIMaogTEZKJT55JATNiU+pFkQSMaE1OJ3Uic5/F1L6Yq4LKpwSQHqIa2lMrGtQ24Lr5ToOOiWt0Rrr6OGoApvW+w392n2pqrQOL6kWisH7DB88UdBY3kjfPanxtdQ1Ob26G8XjvQdyFX0zVMACFVpcgEoFFxfCmJEZAbxoHU81IXqcDyQ3QJK6FmfeUKdx8jgnpOi1D2WByxpEibpNnyMhw1Fp1K7LgYQvIkIFLhFSl4HUZEpjkMF2i9kzprP3rnvQ2m4684tEd85cRpcvUxHa6f5XCa3TmSISI65XUHZHKZNT52pK6rotPRITqRnqOrGRqtehKtUpKyEHhCpB2YsgBVk7J2s3mTJzGq1pM+gmGF62RB3E3Ug5XFAJeHG4KCRfkXoFVaWiWxLAV7hmCz/QIm+2CDh6nQ6jMZJ6o4hkWivWQcia+DFHrQPfapNPGaA5bQbZQBtSpTWORyO+2WBg+x2YvuMs2jO2ozsyTHfpEmJPhVdxScdr3sAH0VrUIji8CpeV4NxYXdWS2IEYE8XoMFV0VFVJFCFJ1NqtOHzWULF27LgS6uGc4bKIw6tw7VHhu0qQRZ20EFWE9KGBtKZCXgu2Kaq4m2XU0q46sDXntxYRBaoCfJPgRN22fszNLIhvkspKa1UnB5nD1ZMexOs40xNX62k7B8kHHFHjkuv6tc57FYe9w7lKP+eiSxUCPkCeqyCp4qrU9WS1LrDgcOJwKdZ1jxsIgSTqL696FdF5cIHMCzN9YNC1Sa0Bdp6Ws89++5AGB/jronksePBBukMdUncE18iJRb2tVoNUlUjQOtLEjFR0cD4jMlYLPMOV6moO4qHqIi6ro+cdbnCQ1mCTVqtJLCLF0AhSRT3uVYELkMqeOqJdIBUlnb/NpTF3ETJjKmG3XRncbyeCJLLRIfJWg8wHRAaoUo+qgujaEEVd2V6vNUL9+deRzpK163j7iGsOqABfdnHZQJ3OUMeUpwKPr6834F0GmdYGdpLqCQHgsjbiAykWeN+A7iiS6WQYQksTG6r6OtwYQMpKx47z6iivqr6oHrujgMM1ZoCr9LprGI8AlvaW8uMHfkwl1WR3xdiGmZe6vGXxDfxgx6cw6La5ryCGYTyCOf/P5zNajU52NwyDe6sRru7OY69s0CZTGYbxiOGPi/5o30WNLYYFqcsHl/6Jd09/LFO8fS81tn62uVGuTr2yFpHi3w2wzquwK0kdiSI4MpJzQE6qVLyKov43FRBcP8o2jYmpSaOXqcUwrQUca6etCsoqbgSc05qgGp1cIRJUjHbqvnMIktSuJzJWR1VjX0U0VrgSUaHZZ/gQiCnWYvKY4zZoHVWn9mQnKuw5VJzzrnYIOo2JdTSApI5ncSpu17VpRaSOgNZI4phqx6LzCGUd5VoSvIq74jM0Q9qRygIJDRWis4ZG5XoVxoWEz7QuKw5cswVlFz/QUlFSIg1xDBBoB8+M2Tuw1z88jv0OOZh86jTkvgeYv2wI77yap/FIFMSVKjLHEspaxElR3dMxga/U1ewDrtFWsV0cyUUiGckLrirIoXZUNqmSCmB4oTFjAL/LTBiYRhjqki0PFMuWUfV6pCikqiCmQkWmosC52uXsBMk0rpmixIUevtkgH5yCbzepqopeVZKS77tU/y62Z4QQyPJAnmWEFJHly4ijI5TdiiSJMDBAc/ogzSltSCW94RGKXgFVD+cdYUy0TULwqEhOQKpSHY0ugkAqE1XsAgVREjFqveNKhMqpIOtDjhfInEZckxwpVRpZnod6ckEEH5GiqGsch35NZXzEB48wVt814EHHdKrwY/VzXagnO6j7F68uW+cd5E19rRZ/VdF04IREVCd+0Pq8Uo1C1kJiAajoKPg6El2FyBCCOk8DIA6feRXDXcKFDJEGrjcCeFLVA9fUyHAfdLJFquuH5w2dvFGfjyIR56We31FBWWk9WlX4CY0Btt95ew45/GB23udAYmuQ1tRANnM6cdkyGj/9JW7hQvJpDml4pNtFUgZFodcjD67qAepCTk5rPGtN4mY9bUS03rPzZD4nSKQQiANt/HbTSNMHqfDIyChUFa5XImWXJAkKwccKj8f5hHSFqihYlueMxIJBlwgL59FdPsLyTkneGmTGzAbeeRYvWszwaLeeyFK7wH1L6487r87grIm4ln6WDlI1FuHsEO9xUmkt4aypbmv0eHtyXNWrJ29oTXNECI28nuDj1B3sweU5JK/HImvgUoFIVTvBo47BUNebTiX4hsaSS1fTIPIGqezh0yiSNZHMHroYjwz+uvyv/GHhHya7G4bBklRwycjfeMXUfSa7K4ZhGBPi7uV385uHfjPZ3TCMPheM3MPLp+yjoVeGYRhbOHcvv5sbF9442d0wjD4J+P7oA2Q4zprxWJrO7qjG1s22JwCnQkWjRo4LomJmrrVoXRmRpLVyhUzjmYOoa0/UVacqS9IamQ6tKVrnwqZadAoEjWVGgIgTjQkV70ACTrTup9QOWEkJ55vqUnTNWoyoBV9R4VacihkiWk9XoK7zWwtjWhRYBTYX6hqgSV21SUUNL4LzLZwUeK81eX3wGsWaaV1VV4f3kmJdJ7mOvvZ1XVBRYdIFh0hVuwcFgooufRE7xr+L4EmIeVNF9doYLbGEFPT4xViL5Eljd1OCKuF9A3EQUoNBH2nnOa32FAZ23JHGbrshU9sURY9yyXKybpc8NEitKbhuFzKn7tRU6nZ9hpRV31XtMhWPksvAZyQSqRwhiqMohCjqSM69I4QGiaYel1KoEChKeguHaIyUSJhLrygoeyXSG8VJiXeN2k3aoOy7UmuBWwSqqJ+RBFKKhCTQ7arg7x0uy0llWbsYnbrQ8Rq/7T3eaY3ScqRDHO0Qq0hKAlkghJzU6dJZsIRypMPyxUvpDY8SxwyPNHQslEnHZnQQi9rpmkN7gJQSKRaklOq420gZI1WEUiA6B3hCLAmhgY8l6qINWiNXEtIr+3WqnUcFUUTFX6+1q13WUGduqh24Xse3S+BaA3hSLcZFEgkRdWvrWNfzzgetJSs4kEqjjr0Kni73uLyt5wmORIKyqzG+tcjnROsCU4vyWnO5oSkBtX6N02NLWeg57DyQSBGqFLV2clmQSaaRxiQiEV+NJQNU2n6WQyzUqY/WzJZMHcftPXZixxOPY+b+u9NuzyQVBW7uXXR+/VvuvXMO9w8toCSSQMdx8hq73dDJHy56SurPVBIuBFLRrSehJMhyXAgEl8iCx4UWoSzw3RHKokt66CHSfE/HeaTV0nU86nJN6siWqCJrCkGd2h7KFLUWdifRc4KTErf9TKbttTvTd98V3+vQufk2RkeH62Ma1LHdbNIAnM8ofFujokMLkYqUNELcUbv3GwP1Z1Wo6F1P2HFZQ6+/jWYdgS/4EOoPzRPr+uY6iSVTd3DI9NxLqa5JntVu8aSTFBpTQIRE0Brg3iHJkUTj2KWxHVVvqQrXacUql4axZSIi3LjIvnAbWwYR4crOgzx9YGd2Cu3J7o5hGMY6WdJbwrzOvMnuhmH06aTIzcUSDmvOnOyuGIZhrBUR4ecP/tzuo8YWyXdH76cniQ/MOISWNxHY2HrZ5gRgFUJaEOv6uS70I5yFCjKttdt3zVZORSDv8E4FT0kV3udE0RhkFU1rMUotqCr+iuCCU2FizO0rde1dSVrDtxaSal0XKNU1iEac4lztIMxVKJNIJKoQlio1MpeFitguENyYOxjAa2Swb+Gcyl9eVBhRD1wAtP6p08RVSBFfR7O6pGKjlKU6KF2ORIBIcglEW0lVqmsOJz0+sdL9d0GF3rqesMSe7pd3SEhQCeCR4JGqi1RjgptHslqKjiU5gYZU5KmNS57u3Pks+N0f4eY7SSky1OuR59CePQNZmEgSIRaIS4hvqFAYRGv75hku9XBhAPIcIsSyqyZJERWyiggOApGsOYUUhCSJVFREr6KYuETVHWWkqsi8ft5OEi7PVICrSlIWqGrR2LkK71vqqnRex1ysEB9JnUC3M5+YqSgZXVbHIquET6xAPFlIeFHh3oWcWNcprsrUr/nq69qzxcgI1WiXztAIneVLKWMtiAsEHwgOwlQVOUOWEbyHPCemkqpKyGiXWJSkBOIzks8pY0kljoiOz+ADTlRKFR+ArB6rteubXKOTnWjd21DXrVb7eS3iSV3TtxZKZayman08Ea23HDweR0rgXaludFe7flOhrvaQo/WLxyZRaEw0EhESzgUd+SHgs6Djs9LjKb526wMUaN3tWqDHZVCq01dSQIquut5jh0hOWVVaqzlrQKoI0anYH4Xk6skGsXbPp7oueKyFbaeR21JWFHPns/zGP7FkwXxSo8W02CLrBKrlGfd1hlk2NIJzgkfHg0NoNHNcY5AqRmJR6viIpdaezgK+0dLzQUrwOSElfPQ68QPBNdRx62NFSU6ZEpVEqBLJJ1ysSA6oKjKJuORJVPisRaq6hCzD18kJUq/rM0c787gAxdy/UcxfwvD8BRqpXicGkOW4aVPxU6eRVxVp0VLKKmodaqloZE2mD7QRGWRJ1aGMSce2a+tkl3piAXEUfAMtt55w+Vid8wznHV4EcfU1OvY0ljsVuBTVIZ018Fmu1ykchJxUdesSAA1SFesa4gkhR+KQjs6xGunBagAbWz6CcOX9V052Nwyjz83lUq7rLeLZ7V0tvtIwDMMw1pPlUvK90Qd4dGMGDWffRwzD2HKx76LGlowAP+zMoek875j+GAYtDtrYStn2RnZKKhZIB9cYhEzrnLqYNEbVAcGpSCEqzjpUyE0x4nztkB2rUVkJyXl8BoFUC0Yq/uBQxxgRyIEMF5JGTbs6ClhAC6NW1Fm32rbmz/a7LUQSQhJPrCqiaBRr5jxkTbwWwVR3qW/UMc2o0Om1FiaSIAv4OurWhaDHo1GL4FX981ihX+fUlZw3QUokVSp0e61zLKnS9ZKK2Qlq56g6gl3q6ctliTiNysaFftQyuWhELU0ITUBrd0rw6mSO6pD2UuFyPbSx22Fk/mIeHK1Yvt002ttPIZs+heZAg7zXwzsV3uuQ4zrdW/CpJGW5pnPnTXwe9JMte6SyItXCFj6j0WjivCMFiOJIVUEqSkQqLU+b13G6zlN5UQO0y1TkF0esOsSYKPH0yg6uKnCuhW8BZcS5qAJ8aKnzVUrKmIiu0jrLea5CGU7HjhckelIl+DqSW9BtpapEiGTtJmFwSv1eKEZ7xKKg6PaoupFEAeT4IIRWIM8z8qlT8O0WYaBdR+NWFEuWUixYTNntECMIGSk5qtQhJp2DgNPPJLiM4KgnEKiwK1I7PJ3mpQsRn1GLuFrTGJ/162xLiohUf3cCe1dHkXt8Vgu1dfQ1CbKQtE6rVOpcrqjP0VzPn4ZGQEtV1fVlRZd90G2PCco+0/GdD+rEhaoLIdefQ6wTqEt1KuceaKmrFjQiOIJIk+SEKLVz1GW4LEdEyERr0jqCRsU7cFXEpQqX5RoVPzZJhAwniWrZch665S5kNHLI009g5kH7Q7OF/PVu/HfvoVwc8Qm8C3gfCI0c324y4DxZWTIsjqHkiOg2VCCvcCSkygg+kEnAxwJGR3XCiI9k7QEy78mSkJUlZVURqy6pU1DGRHINXCzUqZ4N4EMbJwVIJPU0Ut6Xw9AcRHxGrCLF3MWMLBohxkg3FnRSSSxKxDfB1WJ92UOWLqWoa2NrzV0hDM5ih3335jF778pAhD/edBt/e3AOkkL/80tlF9ds4pI6wKUY0brapTqfkUprj4sQfO2gbwzgXCKVhY6zvAlJSOVy8E2kGsHlU3QMNHKQUsc1EMuejq/Q0uhuB1J0oDl1k9+ijM2HThzZOLFpU7QxGdR/BRjGFsM5S29hpm/yxNYOk90VwzAmwLZ8DzWMLZFLR//G/zdlL/bPp012VwzDmAB2HzWMLRMBLhu9n1KED253CLlNrDK2QrY9Abh2oOIGEIfWLc3y2sHrVXArqlqL9SrIUeHE1W5CdRKmFAlkdb1UjXaOscQHV0f1ao1e3aTXCFjRSNFajUVkxYuKVvgVsjomOda1gbUGMKKx0LGMGr/rMv2VpH7tW3VCNlT0BHUyh6bWQMUTnHbJAySPCyrG1ZZlFehigpBUXKu1N0IdvVuLkiSpXcEZUpVa2zeWeFEns2SBSgQpE1D1HdEuVeqYi4UesxBqV2WCqtBatK4JqcA1AvRQ1Tc0qUj0XINeinSrHkOjS6mkR1WM0li8lLKq6HUKUrer/XNB3ahkKuZFQaRSUc5rjLAkdQtLlqtwKY5Gu0FzxnR8e4CiV9JbthyqAt9oa7+9xu/GPJC81pWteh2ia/TrzFY+UBGoKkHIcaFBSkLRHaFyjiAFwXs8GYK6wCUldVyLCvGSNSmrHikmXBKQqJ+xD0ihNXlxXoXQPIOmjsXYKal6BbFbEXs9YvCk4CE0dZuiblzXaCKxJA5XxGXLoIzEsqQ33KEgUVUVEUckEVOlx9M7UlnW54RXYzzqStfJAtrvVKEirkRA603jHd67el/VDS+SSDFp/d0qqkO9jDoOQ677JjoZQaJOxCDUcdIuQzKPb2Z4N6ju3ljgcESBshaXCUBoAPrZaHh6hfMq1CIlLhaIy3RMu9CfoEE2gKRevX5Q4a+K9alaJwNIxEeNcE9FjxLB5Vk9AUG350RwydfO+qyORaeeiFLqD7kjFR26ncCSssvyxQ8w8/q5pLld/nL7X1gw9z6kLDSWOJVIo4VzOfmUKbR33J6BwSbtJcuQex9gaEhIhTpjAa3lnbVwsau1crtC6nUJrUFotXG5I2u28CkSykToQgmUlSPFLiLqlo1SkbwQXELKLiG060jzSJIMjyeVWiO76nUZ6o4wiiemipjlpORIqYdzQuomUq+oRd9a/EVwjSatPXZl5j8+lXz3HXFLlzJ1yXwa8x6k4RLbNduM+gaLfU6qRutLqcPlA7XLt9TzhAZSFTjfIJUjGlGPkGIPlzf1Oixapzslj6MiSQO6XQhBJ7EUPZJADI6UT62jtDOkHEZiSfIBnzbpzcnYzGyKL8uPxC/cDsf/e9T/40N/+NBkd8Uw+gxLxfdH7+fwxna0baa1YWzxbKv3UMPYkvnw0lv4xMzDmR1ak90VwzDWwbZ8H91tcDeLgDa2eK7oPEDmHO+Y/mim+Hyyu2MYm5Rt7omLD211BwZ19xKDCp4C3mvks5p4m0gtnEoFEhIBr/HF4hDvVFCSWqd1DpxGKVPXl9Tavfz9d5JUYHUZEotafE11jVBf1/etEOrc+VT2RWRxtcMyy2rRBYJzOCqCE0gFKQXVVIl17LE6mp1o5LDLmyqYxKTuudqhjItQie4H6lbEqztZ3XQagyreI0ldwZL0GCYSQolPpb4OJOfwsSKm2uEs2hdxTut1xghZGym6QEPF8do0qsdO9PilAkKTIiW6JEZTYtQ1aBNpxIDrdZCyJA8ZlSSKsiR2RvHiEJ8jZODB+4CLFeJzoFBRNvMqwLtEqkQF7DwnTJ+Ca2kdUul2IWpN0ZA3QHJIkSrWkcWxIlZR47spNfa5kdcipyO4UAvuDu8jVKWOJ8lJOFLSaOJUeVJdz5kqqeBWLEN8jhdU2AzqLnVBx1Qi1ZHFHpcc0oVqdIgqOmIsiKn+bHxAgtdxlXSyghQFVYQqiorvZYU4R5JEIRWFOEoiFVC5AD7gfQMnJamhNVczIOQeH5rq0M6aGt9bJZIrIZaAkCQjuUzr745Fg6v+qZ+Hq6Cs67yGDHyOyzK8C7iqUmeuy2rHZoYfcxcj+HabrNEgzxyNVot2ntPMckY7XZbNX0BZFsSqJMUKyl4t/qpsTQRi7TgXh2s1EV9qzdcY6qhlQXIVjolAylW8rc9Xn4SGc2S+gNYglMNIVVHGAskych/IAyABD4Q6PrxuQF3uRHWO147g2Ouy/N453Lik4iGXEatlLHQlrtWgkUPs9VQEDgFpDZAGmojzlEMdet0KPzhIVpQUMdVR0JmmEVQVdArS6HJIGo+MQ/9Hr1feeTw5PgOfIr6hkxSqXkmVHDG0ibFAJEdCztjkDp8qnHNIMaoR3JIReyPqzHUa2x2rSMwcUolO9nANYowQVHx1PtNo/u4Ixbx59K77Db2bAwseWs5Dc+aRzdqBnQ7cl51234nRBQvpXf9HhhcOgYBzESQS8gbi20gxpDW/Y4SsrmvtMiRpBDRVRycrJHWCO0CKYVw+QBKN0I/VkJ6rRQ/JPRLaiICvehASrjkABFLZ3ST3JcPYnDjnmNWahXf1vcYwthB+2HmQQhIvHNyDJzZ3eMQ+1DIMY+tmzyl78tjtHsstS26Z7K4Yxjj+UCzmLYuv5xntXfinwb0Jdh81DGMLwzvPex73Hj70hw/xx0V/nOzuGMYaSWhNYID3zTjYnMDGVsU2JwCnlOq43qi1efMciR0gQ2iAi3ifq0CbPOIrjSz1Uc2xOFws8GrnQ3xAq6LWTlyiipwhqCgiCYhqksTVAlgd/+pSnQIrKiCLr38f60joQIolzmlt1YTDpYrMe605KoKLEVWZGrWeXEdJu4gPDXWoOcGlhKtULFEHZ1JRq46HlpBpHcwsqDvReUgq9qjy7JEqIj5TsSpraO1hp6+LOBw5ni5eItElJAR9vSwh1cc4VLVIFGuxt4DM41JThUDpqWtZQFzUOrkIRWgz0hthSApasUEjK/HNFi4TiliQXKLqFVAWGrnrC1zWBh/q2O1MHZFSIRIgam3lWEVi1LjvDEiLO5S+Q9mrKFMP8RCyJk4qfKOpWnbyekyKHhKr2gXrSFmGE3WzevHkscL5UmuMVh5oqws3qpc6SUVKQhULKtfQKOkg5D4jVSDiCRIJoUHwOVKNIiknxVDXSw64kGtd2dRVZ7J4javNGzpZwSUVwnoVpERUlZ8UNCYYH0mNQEqJqoz0qkSn7NHzjsqpXTzD42JPJz6kiE8FPmuRFz1c0+GSEIKKgMl7XN5AUu1OTpEYnY4Bqlr7zDR+u6pF2eTwzRY+y9QsH+rzAa/7mWqBMKoLOmQNXCPD5Rkh87SnTWHGrrsyZeZMQhKyhYspyogbHqIaHdGzUyDGiEtRxUCJuEau9Xmd0/44RyzKui5vpeI0oudfLHVSAlLHWxcEl9FyggtNkusR/djUC08sK2hmeASfIi73ELs6ScQHrWPropqIXahdzxlUPdLyhQx1Rum0WtDyhIYniMMnjw85zbxJljVIhVDMXczQomVkeU4se/RGRqi6XVxKSFEhxbDGjlcVVFUdYx8gC1pDujOiY6hXEryel8GpSO6dmvLLEPAIFZEqar3z6B1JhEw8uJzcOVISQhZIRQcyFWCl6iG+RfSlOmx9roJ30SP4jCSCC43aMY66hJd1uO/6P7PEeyR1GW22aO++O9lO0xlZtpwl982lN9zRmP6QIXXdaxcTpJIUHcSOOvS7wzjfQqJGpZO3iVVXJ7KA1tdOvfr6r6d1qrqIb2j77YxUDJPo4nxGpKeO47JHcllt4zaMLZ+DZx7MU3Z5Cj+b87PJ7ophjOOn3Ye4preAt09/DMc2d2CnrD3ZXTIMwxjH9u3tOWHXE7h96e1EiZPdHcMYx03FUm4tlrE0lbx+6gEmAhuGscWxfXt7PvwPH+a9173XRGBji+fy0fupJPGeGQdbTWBjq2EbHMlac9VJWyN7a6cswSOU+nAfT/J5XX/V16JIDkEgxdqh4Po1QXFa0zZJpes7deeqwBvVicpYcuxYZC76v3gV8MaEDC0uqwKRRKJACIkoTl2HLiJOxSBXr6td0Bq0DnAu4GhqvVC0j67ZwktSIc2pkKz22KC6nA+Ir7/QuqAR1D5X5cyBBIcUCZGijs6F5ETr7IqoJbl2R3qXQafASVAXdfDQS9DIcOIQn6nwIpke08qpcN0X2JwK8HmjdkcKRSwZFVhWVrSzBtNzaOeQN9t0osYrJyoyX7sts5zgBe9AihKhgfRqcc9VECuqJFTVMMm3yfKcVkM/+24l9ERIpYYgOxdILmg8saiLl1LruWZNr/VDJem4iCWp6hGC/i402vrZhoQve2RZgF5RO01BsiZSDpOyDGl68pkzabcbZEVFb8kwjAzVzugENNWwXamA7EOFTxlIRQoaBy7eQyvD50395MsuUqhT06nNnSSVOppDQLxXETpBUfTopUghnkJ0rIaQg/dIyJEUSYXGamsNXY+UXVw+CGUPjyfPm1CWOgEgJhW6Y6l1tr2Oq5TKelxFvGviG7k6zn3UWtJRnbI+D7hGQ13zMeIQQrNBY0oLFwKRQGi3aM2cxfQdZjNr+nTylMjLSHe7WfhGg24zp+x2iWWFK6MKgRJr8VydvqSk4zJ4XEj4PGjtY1TAxkUYEwRjgjzHOUfmM3wqoZEj4qhiRUUiea9iZxGJWcJlOSFFrZXsvNbOTknPr1DXKa4dzw5wKSLVKGW3xFUBcaLicKvF9BmzeMwOOzBjxvaMOMcd8+ezfNkiym6hk0EqoS2eZkyUtBgtRrSWcQhqGM9aen45jziPKyq8lLhGrvHOUuFDjq8ndqhDdhQfHAFPSIkokVh6HUs+EL3gnOg1L1V6PYsOYlS3stMIaZGIxHpfxSE+kUrB4XDO4YthCG2k6tKLBQtDjos9XG+UdJuw9M57Ge51WFz2KGLSmuSunnQBJOrP12dQJZ0A4VukpM5uJ/L3uGkXcKkiRY1fl1jUQn/SPjgh9kbrfrZ0Ao6U+GyKThYohhACqept/luWYWwCMp/x4n1ezK/n/ppesnFrbFmMSuSDS//E4Y2ZfHbmEWwXmpPdJcMwjHE8a49ncdFdF7Ggu2Cyu2IYq1AhfGXoLm4plvK6qftzaGM7S9UwDGOLYlpjGi/Y+wXcuuRWylROdncMY40k4H87c8id513TH0vbh8nukmFsNNueAOzA0VQXX4pQdTUqWcq63q3Wa3WOOi60rrMgCedrwXTMoIjW9dU3ajytS4ILmb431rG+tegrUlGlTEVJauGXStscq7Bbi7oJ6hjpTOtR+qwWSOvoWC0UrO5fqcB5HAGXZThJOBF1JAv4vKmmYKf1e/X9dZRBVdSuvBLn1P2LT7U71yFOkKpS8dpndV1V7YuriwSnVBJ8TkpdFZVSrIXlCsoEmYPmgIpfMUJV1UJ4BUVAMo+kqK7gkOOkUJdxyEEqHIEYe/RCzohr0B1s4vfZk3yn2TSzjGL+UuKch+oY4YrgA1mWEUILV3aRCMklUlHisqAuwdCickLMp6jLdbvp5NOn4fKcuHyY3vwFSNepht/r4bIpuDzXWq+9Lk4ivoy4XGvJqvTna0d4rvvSbNZjR/BVJPOB4IG8RUIQKdQhmrVVi582Az9lEGk2cDIKTgXUmHSsOdRxjnQIvo1LXVzeUNds1sKlCpdKfGggUiIur83mCZ83yBqD9O9bMuZkLZEkpFIjsp0kQuZpCESf8GgdVZeE1CvJneDHhE8/Ng4KfGjggiMEj/eeyjkq39AaqxLqGseo2CZaZ9c3W5A1IXe1I18dob52yWtkd8Rn9aSFwQGyWbPIBhr4FKFT4RotfKuJG2ziZ08nAIMZbBd75At07sEojtL3SCGSqoBUPRVEk4Oqq+diXUc7NJo4X5GyhIsNpCxJJFxZQKNJyrz2PcvxsdCo6uSQkOHzQFZFkog6gSPghJR5yrKEPMNTn8M+w3un++V9nTgguOBVyEUnPxAzIiW027R33YVdTziBnR53GIMhMPPBOTSvvY75Dw6waHg528WK9uy9GNhxFq2Z0xgeXsQdN9/EQ7ffRRpeDlkD32ioYO0EyRqICClWOAqCU+e273bxqYsTr/HejYAPgTxvEwVi2aWSjNTt6ASJssI1BjTS3IdaBNZxReZJVanXjbxRT6oBV3ZxUfC+iXgVyUXQ2G8i+Axf9TT6vILe0BDzg8elklhPvpGiwGW1EJw19Di6hk5g8bGe3NPUuuVOx7jGTwsuZVrfOBsA2rWIrDHosbOMCoHQQnylArDOoaAqRnC+SYxBawWbU814BLHftP04cvaR/Oah30x2VwxjtfyhWMybFt9g9QwNw9jiaIUW//YP/8bZ159tdQyNLZIK4Te9Bfy5XM65s47k0MZ2k90lwzCMcTx55ydzyMxDuGHhDZPdFcNYJ5eN3k8hiQ9sdwhNZyKw8chm2xOAk0N8VNdZcJAN4qiAHDJ17KZaiKQQXKYuySiogzYJTiKSt5BSVBRG3ZU6x9Ij4khEdcf2xV9IKQOctu88Ser3igMiY2Givq69KyLgqWOk0fKlyRNjD6qS0GgTJMOhNT2dOBx1NLQIPvg6sTaowO2cxr866khiUeE1NHBJxVypo6WhdizHEhcaGicbIDlwlQrMgjr/xAV1l7ocn6KaO7NGPeu0qkXLhER13kpzUIXDoAIfkmoxsYTgkKKntWCrDkILyUpoNikTdJ0w7DzDZWJ48TDdWDCybIiyLMhc1AjgrIkn4V1dh7fVJpLoZjlVrJAiIlnSGO3ahemqEhkZIQVPVSZ8yKARcDER8oDPHI28jun2OQRIQR23CSFWQuWTRsu6iKukP8nARY23de22ik+Nhu5zGtAao6FBTIk0NIQbHa7dpIlYRHxMIKLRygmIFSHLVcjNB1T8dRmp6hGLkpgEmipEJwpSACc5PlcRz/lIEk9KWntaQsA3ckLI1NVcOEKKFJIoBSQFfOnwDhpZA1+7YV3VQ4IjuVxrPwcVNL1zGj3ebOKzSNWNxCppreNCRU6aWV3vF3AFEjUqPMYe4pzOZ8i9OvVJiC/xjQGtzToyTFU2dAzjYGiI0VSxuFshC5cxOG0a0Qm+0SRMm0EWHY2qxEskOU/pIPomKUZIgohH0LrWzmsMsPOCE4/0lmscOurOdxLRD17IMtF9bdQOaYTQnopEIVYdqqIkBU8kQKyITj87SULWaBJARVEEUqAuyAsEhKiTQyqNltf6zEGF5XkPwk2CbLcDbrvt2PEfn8oOKdFbNo/mwAx8q01RdFlw700svHshQwuXaz3oZotsYECj2xOkWNRTTlCxVYAopF4PXya8FIQ8xw20cXlbndISyVyTVMdNR8k1SSBrARHBk5wnRkciR1yFjNUGFw+9HmS5CughIyad4EB3FN9sIanQeuX5QD8a33mHxyFVl+TqGszikKKD+Ib2ObSh7NVjIujxqiL4CqmDCaQskLrGtF6HOiQ/oBN+ekM6oaYYhaxJytpIrBDnSU5rxle9DinzONfAxUIncKQeKTQ2y23KMDYHjdDg3Ye9m3f87h3ctuS2ye6OYayWPxSLec+SP/KlWY8395JhGFsMzjkevd2jOXz7w/nR/T+a7O4YxhpZlHq8adH1HNmcxXtnPJbp3r6vGIaxZeCdZ3Z79mR3wzAmzBWdOTSd5+VT9mHvbIp9PzUesWx7ArB3kGfQbGh92pTqyGPRWr+x1HRmcZB5pOoRmm0V+VItAqN1V12mM0AkCUJUZ6Og8caxdvb62unqHFI5TYsW9fFGV3t9U4IgqLk2A++IVaX1SdVGiCoiDpFSaxc32niv7mLnnLbjHFDifI7H4Zy6ljXG2AGZ1juNhfbTBZwEiGP1iOt4au9VJAkeqZyanHOPRHUoj8WtOjKgIISW1kQNOSlFnMvIXCJV6g5WFXss9raBS1VdA9Xp8Sl74Gvxq3Zhu8whVYBGgKh1W1M1ykjyLF68nAe7ParmAM1GA+89eVkSXE6WS+1CdWQ+MGPHXWjNmEEvCyxZupyhOferBzOoO9AFj5eEJE/ZK2rROxFIhIaHpBHFYXAKbsqAimK9CtcZpSqaxF4P6Y4QQ04sCqjS30XxqkJS0s8iz2vx3SOphLKq3b2pX2e6HBlBfJPoKgJoLWkfkBRVQEsaEey9Cuv4tsbqpkgSrT8bAakqktSuS8kIQV3jkhKxKolRVPiVEh8yXOwRfAOfg5cMV2hsuRBJTsXfkHmyPMOlnjqdJSemRHA54lTIT+IhBryHzLu6PrW6dxORGDypLPGuquN5A0k8PlYaiyx6fiYE6uhjj0dcSYqCFAVpuEOVg2+2Nda80SQt71CNzmF03jwGpk7Ft5tIihRlSSxL/ECLvNWg6nSQkVHSaKrLbJdam1hQ53bQMc9Y5HJoIWUHvNZLlioCWv84yzNCu62R8dJTt7J36hZGI4klCVXy6q52QqqEKndQenBJBXnn61NPY6hxDgm5XleqCpcJ4EhVj5G5C5nTnkOWNdm13WbmwpJscApul51pbT8L4ijd62/lvl9cw0N/vZOFnQ7LOstJecA3p5LKUXwt0iYRKHtI0uuHVAWp00ViJMtzGJwGjQyfNXXSgvdIWeJ8xHmP9wE/OBVflepqd0DWIPZ6xDxQlkmjt53WDHfBk6oClwpccwpJ6trjVUIEQhV1UkiWQ6x0Qo3LcN3ROsQeXIx1esIK0218phNyfE5KArFAspyYNSBFJBZIqXXFcZn+nDfBtUCE2BshlkP4xhTAa2JAaJN8SSpHSLRJJCo/oPH+XiBBVfVIaRTJpm2+e5VhbAamNabxrD2exe1Lbh+bAmIYWxy3Fcv4bW8hR7d2mOyuGIZhjOPMx57J0t5Srp1/7WR3xTDWyMLU4/86D9KRin/f7nAGrIahYRhbCK856DX8+IEfEyVOdlcMY50IcOno/eTO887pjyHDBGDjkck2+JfgEMTpUKTacdZF8gFECpyPIBWSAngVqWgOEGOs3YpOBSsPKUa8c6SkNW5FtKakDxnJRY06xkGh8bIeIeYenyqS83inrkh11jqImRpGRV21mi6QakE21x+pQHJ19mrrtfBbC89oDHRwScXD5DQiul+zWA2skCFO3ZVkGZSFOn9rsdHhtW5tXf83Ja3B2vcF14Zkl3mQgMsa6nKOhQrSOELKwJe4kCPlUsinq5DrBaqIy5tQ9CAfgKaH3ihj+cSSBOdySL16W7m68/KMlBKjnZL5yVMWS5nRbDHVN8hdJGSOLORkQHtgKtvvvhvb7783ccoAS+YvgmXD6qAN4LImIWR1FDNI6lIlj+CRqsLHQgXxEMhiRZYEuh1ir6AqCqSMuJggqUgqMSJVBxdaeMkArfPsQsD5jJA7gte60FGEVBVIHQXsBIKL5CHHuYRPuuxCU4W3YqiuxxrwweG9x3mnEwL8WBx4QMqIJBWVowMRT8gdhFzdjCSqKqr7NSZcntcRzkKWOlqaOvYAUWGZREwRlzmyvI7TJldXp0AKWhs5iuBTwnudOCHe47NA1mwTCBQVVEUXqkKHYh2hrpMSajeyJJ2QURa4RlaXyNZ63eCgisQAIoV6TZsFWbOHbzaI3lH6Bp0sZ3RoOcEHHc9ZgCyrRe5KxV4cPgQVByv6kx2cZEhnBJqD9ec29tnlEEudteFzQnCERk6W6cQBUkKqDtJqI2UXnxIUVT/6O+SO6AIxVohLiGSkWBGzXAVOLeqskzRCrhMxUoWIaK3aGHE+QZFInVGW33MP9z00n0XTZjBr5nS222MXmg/cy5SFy+n9dS63/+1+5nSW0+0MkYqeuu+d1/ZCQw98luvYiyU+JVxZH5sU8a0WNDNo5qSk574LXmuVhzZUBVImpOjh0yg+z9Rx32zgnCd5kNCkHFpGzw/Si4myO4JIAXmTVC7Hd7u4kJPSqAqzdWQ9jQauqoCeOm2rLs43ERIulTiXq6OaCmm2oOwSvE6c8Xm9nw6k6ECjTSwjLmsimSB4KEeRoqeH2JVIrEgpkLKppLKLDwOkXpfoelTiiVUkpSFoNohkOgFAukAgek+ihU/FZrxXGcbm4Uk7PYlv/fVb3D98/2R3xTBWy3Ip+WOxxARgwzC2OAbzQQ6ZdYgJwMYjgl905/OJZbdx9naHTHZXDMMwAJjVmsWbD3kzn7/l83RiZ7K7YxgT4v86c+lK4oUDe3BoY4Y5gY1HHNucAJzcVJIPuNjF0UCyARxdIKkLtaxr3daCow8BKQUyh6MACSSnwk/MAqA1QRFBcCr0JFHxuIoQEiKeVIyCNIlOIARi0YVGEypQ52uprleALKurAGdaY9ipi1fE1bVzE6H+HS5HVVpUKEoaxeqoY6xjUPEadZlKASIRfLN2JqO1MsmhKnVZEpSQqh7JZ0jSuFikANfsOxW1OG1QJ2VEY5yj6IXQlXifkZyo61cyFStToTHSqKDsqDRGO29D6RAZwYWWisOx1PqqVQW1ow9yCiqGylF8CoQEjbzDYD4N7zxZ3qA5dSo77rc3uzx6P1qzdmTJQ4tYft+DdOc/VNfI9RppGysoKxVwJRF9rOOsc5CuRmfnDteehgxMRVwiDg9RdioSDvEVMWm0cCq7ONcihIAXSElr9lJVOrEgb5HyHIlCVUXKIpGIJJ/h8jaqSVaklLQ+a1Knr5OE8y39jKoKJ1GPj2tqLeXggUSMkZiESKJKFdE3SClCWeIJxFTiMk8iU7dtLVqTBIhaS1h0zElVkqVIQh3IKSWN0K06SPD1WHBIrIhEKhF83tDjKSX4ttYd9g0QIYtdFTWL2l2PCv3qPh87XwJQ6BgvUn1e9UiS8K6hzksc4irIc5J4qhJ87NXRyR2cc5Q+ELImvhnUJdxIeHq4lLSeb4yIr+t7BwdZA1dm6kiXBhJ7GmVcdPWclErFw6iCeGgM4PGEoodzGRJL8C1cVBc2yeF90pkWmYrNvioJIacqexpznTWJIYeqR/CeEII69UXwZU9d4N6RvJ5L3oGTjDgySq/bYUmzy9CyJSxaMEDzzruZkgKuKlhejTLkIlWs0NLPY1H3em6Ka+hnSQSfdKIFAKIR1402kmvcdUx1RDxRJ5KQdKwsH8Iljyu6gCMVPWJKOOcImSeEJqnqkLKMnEAaXkLCU8W6LrhrqfvXJY3dTgJRiL7Qz6MYhayFr4Ay4fJ62kmZ8D6qiO4ddIZRX3ki+Ca+V2i8tSRSElKvIBFU3M5aSG9URejmIPSGSc5rVLQPpBhJVYVPXVLepCp7VNIkZRUpOWKvi2RJnfhlCdUi/OBsfN4kNKxGpfHIY0ZzBiftfhL/dft/TXZXDMMwDOMRxzE7HsNl91zGgu6Cye6KYayT3/QW8OdiGQc1pk92VwzDMAgusH1ze4LVVDUeQSxNBd8bvZ+fdebysZmP48mtHSe7S4axXjgRsQxAwzAMwzAMwzAMwzAMwzAMwzAMwzCMrQA/2R0wDMMwDMMwDMMwDMMwDMMwDMMwDMMwNg0mABuGYRiGYRiGYRiGYRiGYRiGYRiGYWwlmABsGIZhGIZhGIZhGIZhGIZhGIZhGIaxlWACsGEYhmEYhmEYhmEYhmEYhmEYhmEYxlaCCcCGYRiGYRiGYRiGYRiGYRiGYRiGYRhbCSYAG4ZhGIZhGIZhGIZhGIZhGIZhGIZhbCWYAGwYhmEYhmEYhmEYhmEYhmEYhmEYhrGVYAKwYRiGYRiGYRiGYRiGYRiGYRiGYRjGVoIJwIZhGIZhGIZhGIZhGIZhGIZhGIZhGFsJJgAbhmEYhmEYhmEYhmEYhmEYhmEYhmFsJZgAbBiGYRiGYRiGYRiGYRiGYRiGYRiGsZVgArBhGIZhGIZhGIZhGIZhGIZhGIZhGMZWggnAhmEYhmEYhmEYhmEYhmEYhmEYhmEYWwkmABuGYRiGYRiGYRiGYRiGYRiGYRiGYWwlmABsGIZhGIZhGIZhGIZhGIZhGIZhGIaxlWACsGEYhmEYhmEYhmEYhmEYhmEYhmEYxlaCCcCGYRiGYRiGYRiGYRiGYRiGYRiGYRhbCSYAG4ZhGIZhGIZhGIZhGIZhGIZhGIZhbCWYAGwYhmEYhmEYhmEYhmEYhmEYhmEYhrGVYAKwYRiGYRiGYRiGYRiGYRiGYRiGYRjGVoIJwIZhGIZhGIZhGIZhGIZhGIZhGIZhGFsJJgAbhmEYhmEYhmEYhmEYhmEYhmEYhmFsJZgAbBiGYRiGYRiGYRiGYRiGYRiGYRiGsZVgArBhGIZhGIZhGIZhGIZhGIZhGIZhGMZWggnAhmEYhmEYhmEYhmEYhmEYhmEYhmEYWwkmABuGYRiGYRiGYRiGYRiGYRiGYRiGYWwlmABsGIZhGIZhGIZhGIZhGIZhGIZhGIaxlWACsGEYhmEYhmEYhmEYhmEYhmEYhmEYxlaCCcCGYRiGYRiGYRiGYRiGYRiGYRiGYRhbCSYAG4ZhGIZhGIZhGIZhGIZhGIZhGIZhbCWYAGwYhmEYhmEYhmEYhmEYhmEYhmEYhrGVYAKwYRiGYRiGYRiGYRiGYRiGYRiGYRjGVkI22R0wDGPDEBHmzp3LnXfeyfXXX8/Pf/5zrrnmGv7whz+w9957T3b3DMMwDMMwDMMwDMMwDMMwDMMwjEnABGDDeARy55138rjHPY6RkZHJ7ophGIZhGIZhGIZhGIZhGIZhGIaxBWER0IbxCGSfffbhwx/+MG9729uYMWPGuNcGBwcnp1MbyQ9+8AP22msv9t57b371q19NdncMw9gC+J//+R+cc+v1b+rUqRx44IEcf/zxvPvd7+baa68lxrjRffntb3/Lm970Jp72tKex2267kec5s2bNYr/99uOFL3whn/70p7nppptIKW1Q+3fddRfnnnsuJ510EgcddBDbbbcdjUaDXXbZhUMOOYSnP/3pfOADH+DnP/85RVFs9P4Yk8Ptt9++2nG79957s3jx4vVq6zWvec06z4d99913je/f0DFdVRXNZnO9zss8z9l99915/OMfz8tf/nIuvvhili1btkHH0DCMiWH3ULuHbm184QtfWO24Pfnkk9d77Oy7777rPB9e85rXrPa9VVXxne98h9NPP51jjz2WmTNn0mq1mD17NgcffDCvfOUr+drXvsZ99923ynt/9atfrfd5OTAwwH777ceTn/xk3vjGN/Lzn/+csiw36BgahmEYhmEY2xZORGSyO2EYxobz7//+77zzne/sLw8NDTFlypRJ7NH6Mzo6yvbbb0+n0wFg//3354477pjkXhmGMdncddddXHzxxcyfP5+LL76YuXPnjnvde8+JJ57IUUcdxdSpU5k/fz4PPPAAd999N9dff31/vV122YX3vve9vPa1ryXP8/Xqw9y5czn99NO54oorcM7xlKc8hSc84QmMjo5y6623ct1117F06dL++mMP/SbKzTffzAc/+EEuvfRSAHbffXee9rSnsffee1OWJQsWLOD666/nhhtu6D+EnzFjBieffDInnHACJ510ErNmzVqvfdpcLF26lBACU6dOndR+LFy4kO23336t63Q6HebOnUu73e7/CyGwfPlyli1bxrJly1i6dGn/56OOOoqDDjpoo/s2PDzM2Wefzfe+9z3uvvvuca89/elP54orriCEMKG2rrnmGn7wgx/wm9/8hl/+8pfjXjvwwAN5znOew+Mf/3he9KIXjXttU4zpz33ucyxYsICrrrqK3/zmN6v0bf/99+eZz3wmO++8MyMjI8yZM4cHHniA3/72twwNDQGQZRmveMUr+MAHPsBuu+02oX2eLLrdLsPDw8yaNQvn3CZvf/HixQwNDdFut2m1WrTbbVJK/fG38ph80YtexLRp0ybUtoiwfPlyRkZGmDVrFs1mc5P339gysXuo3UPXhoiwYMECZsyYQaPRWK/3bs5r1tq49dZbOffcc/nWt77Vv5eM8ZGPfISzzjprwm1961vf4ne/+x1XXHEFd95557jXnvKUp3Dsscdy0kkn8YQnPGHca9dddx0vf/nL+fOf/8yUKVN46lOfymGHHcYDDzzQH9MrirNf//rXecUrXtFfXrBgAeeffz6LFy/msssuW+VvAYAnPvGJfWF54cKFzJkzh/vuu4/f/va348bx29/+dt74xjdu8RPAN9fYvvXWW7nzzjt54hOfyA477LDWdVNKLF26lAcffJAbb7yRk08+eUL9iTGyePFiRIRZs2ZN+G80wzAMwzCMLQYxDOMRzeWXXy5A/19VVZPdpfWm0+nI4OBgfx/22muvye6SYRhbGH/729+k0WiMu95deOGFa1z/vvvuk49+9KOy66679tffb7/95Je//OWEt3n//ffLPvvsI4DstttucvXVV6+yzqJFi+RlL3tZfxtPeMITJtR2jFE+/OEPi/deANl7773lf//3f9d4DZ83b56cddZZ0mw2xx2Dz33ucxPen83BkiVL5Oyzzx53nHfffXd5z3veI0uXLn3Y+jFv3jz59Kc/LUcccYTkeS7Lli1b6/rf//73xx3Hdf370Y9+tEn7Ozo6Ki94wQtW2c573/veDWrv3/7t3/ptPPOZz1zjONocY/qkk04atw/PetazpCiK1a47Ojoq3/72t+XEE0/sr99qteSDH/ygxBjXf8c3I1VVyfnnny+HHHKIOOcEkGnTpsmrX/1q+ctf/rJJt/WWt7xlwmMxhCBDQ0NrbW/BggXyb//2b3LMMcesct183OMeJ1//+tclpbRJ98HYsrF7qN1DRUQWLlwob3/72+WII46QKVOmCCDee9lnn33klFNOkZtuumlC7Wzqa9b68sADD8j+++8/bjvOObnyyivXu62yLOVpT3tav51zzjlnjev+7Gc/k1arJYAce+yxcu+9966yzq233ipHHXVUv713vetda2xveHhYdthhh3H7sbbtz5s3T8477zw58MAD++vvvPPOctlll63fTj8MPBxj+/zzz1+vv+VW/Le2a9ltt90m//qv/yqPetSj+tcYQBqNhvzjP/6jXHPNNZuk/4ZhGIZhGA8HJgAbxiOcn/70p+O+zDxSueSSS2THHXeUgw46yL5UGYaxWp70pCeNewhTluU637NkyZJxD5ezLJMvfvGL63xfSklOOOGE/nv+8Ic/rHX9f/7nfxZAtt9++3W23e12x4lmp5566oQfjv7xj3+UXXbZpf/ef/mXf5nQ+zYHN998s+y7774CyEEHHSTnnHOOfOELX5DDDjtMANlzzz3l5ptv3qx9WLhwobzzne+UdrstWZbJGWecIX/+85/X+b7Pfe5zE35I6JyTJUuWbPK+Dw0NyYwZM1bZ3ve///31bmvx4sX9919++eWrXWdzjemvfOUr4/r/P//zP+vsb0pJvva1r8m0adP673v2s5+9TuH+4WLJkiXyjGc8QwCZNWuWvOUtb5GvfvWrfdF+YGBAvv3tb2+y7a1uMsCa/h1++OFrbeurX/2qDA4OivdeTjrpJDnvvPPkm9/8prznPe+RmTNn9ts54YQTpNPpbLJ9MLZ87B66bd9Dzz///P4196ijjpKPfOQj8s1vflM++MEPyuGHH94/Ju985zvX2damvGZtKL/+9a9X2dbMmTPlnnvuWe+2Lrvssn4ba7oPLVy4ULbffnsBnay8fPnyNbbX6/Xk0EMPFUBe9KIXrXXbK55fgNx///3r7G+n05G3vvWt/clJgJx99tlbzESqh2tsf/jDH94g8ffAAw9c7SSoXq8nZ555pjjnZMqUKfLKV75Szj//fPna174mp59+uoQQ+m2cddZZG91/wzAMwzCMh4NHrlpkGIaIiFx11VVbhQBsGIaxLlZ84Dg4OLhe7/34xz8+7lr50Y9+dK3r//znP++ve8YZZ6yz/eHhYdlrr70EWKtYmFKS0047rd/2y172svVObrjmmmskz3MB5OlPf/p6vXdTcf/998tOO+0kgBx22GHjHr53Oh153OMeJ6DOlLlz526WPlx22WWy3XbbCSBPfOIT5ZZbbpnwe8fcSyeccIL8y7/8i5x22mnyjGc8Q4466ih51KMeJYcddpgcffTRcsIJJ8hrX/vazdJ/EZEnP/nJqzyYnDZtmtxxxx3r3Va73RZArr/++tW+vrnG9Mpu6u985zsT7vNtt902TpQ8/PDDZXh4eMLv3xwURSFPecpTBJApU6bI7bffPu71MaEqhCA//elPN8k2x8SXV7ziFfK6171OTjnlFPnHf/xHOfzww+XRj360HH744XLsscfKU5/61LU6Fr/5zW/2Beof/OAHq7w+b9482XnnnfvH+x3veMcm6b/xyMDuocq2eA+98sor+07Gd7zjHasIhUVRyCte8YoJT+TZVNesjSGltEbBeXR0dL3a+t3vfte/5q+J97///f1tXHTRRets809/+pN47+XQQw9d63oru6kXLFgw4X5fdNFF4957xhlnTHq6w8M5tv/lX/6lv+/ee9l9993lwAMPXOO/sb+TPvnJT662vde+9rUCyL777rvav8OuvvrqcaL7D3/4w43qv2EYhmEYxsOBqUWG8QjHBGDDMLYVXv7yl2/ww+uUkrzxjW8cd7387ne/u8b1zzzzzP56F1xwwYS2MfaA/LrrrlvjOh/60If67R511FEbHNv/H//xHwIae/lwk1KSJz7xif39WF1qw+9///v+66eeeuom3X5VVfKv//qv49rv9Xrr1caYEPLf//3fm7Rv68txxx232gfYj33sY9dbCB2LWbzrrrtW+/rmGtMrCj3rKwCLqBgzFqkJyAtf+MJJdTGdddZZ/b6sLoqz0+n0Reu9995bRkZGNnqbY+397W9/2+A27rnnnn6k65vf/OY1rvef//mf/f2bPn36pIsFxsOH3UP/zrZ0D40xyo477iiA7LHHHmt0fi9cuLAfkX3IIYestc1Ncc3aFKzu/gnIq171qvW6tt15550CGlW+Jg455JB++3PmzJlQu8985jNlcHBwrX1ZUVheXwFYROQzn/nMuPefe+656/X+TcnDPbbHkgDWFMe9IsPDwzJ9+nTJ81zmz5+/yuuXXHJJv19rSlIRETnllFP66z3vec/bqP4bhmEYhmE8HHgMwzAMwzAeATjnNuq9//7v/87+++/f/91LX/pSHnjggdWuf/fdd/d/zvN8Qtt4+ctfzumnn87MmTNX+/rf/vY3PvKRj/T78+Uvf5kQwkR3YRyve93r2GOPPbjvvvvo9Xob1MaG8p3vfIff/OY3ABx22GEcffTRq6xz5JFHcvDBBwPwrW99i1/+8pebZNsxRl71qldx3nnnAXDmmWdywQUX0Gg01qud++67D4A999xzk/RrYznggAPw/u9/lt9yyy289rWvRUQm3EaWZQBrHFObY0zDxp2XAEcffTTvete7+suXXnopn/vc5zaqzQ3lvvvu41Of+hSgx/O1r33tKuu0Wi3+v//v/wPgnnvu4ZOf/ORGbXNoaIjFixeTZRm77LLLBrdz8cUXMzw8DOj5tyaOOOKI/s/Lli1j4cKFG7xN45GF3UP/zrZ0D73jjjuYN28eoOf/2L1iZWbNmsUBBxwA6D1o7HqyMpvqmrUpefSjHz1u+Wtf+xpf+tKXJvz+dd0/YcPG9Dvf+U5OP/10yrJc4zobew994xvfyPHHH99ffvOb38wNN9ywUW1uKA/32P7LX/7CzJkz+e53v7vOv+cuuOACli1bxsknn8wOO+ywyutf+cpXxvVxTax4D73zzjs3oNeGYRiGYRgPLyYAG4ZhGIaxTdBoNMaJNSMjI7zvfe9b5/uuvPLKCbW/00478aUvfYl99tlnta+///3v7z9ofupTn8ohhxwyoXZXRwiB17/+9aSUxj2UfDg455xz+j8/5znPWe06zjme97zn9Ze/+MUvbpJtv/71r+d//ud/AHj1q1/NZz/72XHC6US59957gS1HAD733HP5whe+MO533/rWtzj33HM3y/Y21ZjeVLztbW9j11137S9/6EMfYsmSJZt1m6vjM5/5TP8cPfroo1f7kBgYN7a/9KUvEWPc4G2OTUbYbbfdNljMAsaJWGt7eL3icfXeM3369A3eprFtYffQTcPDfQ+9+eab+z93u901riciLF68GICU0hrX21TXrE3Jddddx2mnnTbud2eeeSa/+93vNsv2JjqmjzvuOD7zmc+s9yS19cE5x2c+85m+kJxS4m1ve9t6TSDbVDycY7vX63HPPffwyle+ku23336t64oI//Ef/wHA6aefvsb2AHbddVd23nnnNba14j10bZPjDMMwDMMwthRMADaMbZCqqtY6E3lFUkprfViwKVm4cCEXXHABF1988cOyvdUxOjo6KV+YDcN4eHjOc54zzsH0jW98g1tvvXWV9fbbb79x62ysg3XBggV94RLgVa961Ua1B+pguuSSSx5WEfOWW27hpptu6i+vzt0xxqGHHtr/+bLLLttoQe+iiy7iy1/+MgB77LHHuAee68PQ0BCLFi0ihDBOdJxMGo0G/+///T/e+MY3jvv92972tk3mnt7UY3pTMjg4yD//8z/3l5csWcLHPvaxh7UPVVVx0UUX9ZcnOrbnzJnDT37ykw3e7qaajPDmN7+Zc889lx//+Md9F9/q+N///d/+z0984hM3qzBhbH3YPXTjmIx76E477dT/+aqrrmL+/PmrXe/mm29mzpw5ADzzmc9kypQpq11vS5tABZrM8JWvfIWjjjqq/7uiKHjRi160xv1dX1Yc02eddVbfVb0lcNhhh/GP//iP/eWrr76a//u//3tY+/Bwj20R4dJLL+Utb3nLOtf99a9/zc0338xee+3FiSeeuNp1vvzlL/OpT32KH//4x2v82zKlxBVXXNFffspTnrLe/TYMwzAMw3i4MQHY2KaYN28en/70pzn++OPZbbfdaDQa7LTTThx++OG8/e1vX++4pPvuu4/3ve99HH744cyYMYMpU6bwqEc9imc84xmcf/75jIyMTKidW2+9lbe85S085jGPYcqUKcyYMYODDz6Y5z3veXz729+mKIoN2V0Avvvd7+KcG/cvz3Oe/exnr3b9xzzmMePWDSHQbre59tpr17qdGCPf/va3eeYzn8nee+9Nq9Vi77335klPehIve9nLOPfcc7nxxhtX2e+zzjqL5z//+Rx44IHMnj2bl770pfznf/7nhPYtxshll13GaaedxgEHHMDg4CDTpk3joIMO4pRTTuHiiy9eZ6zbggUL+PnPf85nP/tZTjrpJGbMmDHuIVVKiUsuuYRTTz2V3XffnVarxR577MGzn/1srrnmmgn10zCMLQfn3DiXiIhw4YUXrrLeKaec0v+5qiqe/exnc8kll2zwBJErrrhinKNmxbi+DWXGjBm88IUvZGBgYKPbmijf/e53xy0fdNBBa1x3xQd8vV5vo66Z9913H2eccUZ/+Stf+QrTpk3b4LZAXR5risJ8uBlzMX/iE58YNzZijJxyyik8+OCDG72NTT2mNzWnnnrquOULLrhgrS60Tc211147TihY29jefvvtx0Wf/uxnP9vg7W4qMWXKlCmceeaZPPWpT13jOr/4xS/6TvM8z8e5tQxjItg9dOOYjHvoEUcc0XdIdrtdXvKSl6wyubeqKl73utcBMHv2bP7rv/5rje1tiQKw955Wq8Wll17Kjjvu2P/9Aw88wKmnnkpVVRu9jRXH9P3338+Tn/zkce7qyWZlB/Q3v/nNh3X7D/fYbrVanHzyyROayPf5z38egNe85jVrTI3ZZ599eMtb3rJKnPiKfPSjH+1/5rvssgtvetOb1rvfhmEYhmEYDzcmABvbBCLCZz7zGfbff3/e/va3M3XqVF772tfy7ne/m3/4h3/g5ptv5pOf/CRHHnkkL3/5y/vxV2sixsj73/9+DjjgAD784Q8TQuCMM87gjDPOIITAlVdeyemnn86ee+65VlG50+nwute9jkMOOYTPfe5zzJw5kze96U287GUvY2hoiO9///uceuqpHHTQQdxzzz0btO9PeMIT+MhHPsJjHvOYcb9fkzD6zne+k1e/+tWrPBRfm5A6f/58jjjiCE499VSuvvpqTjjhBM4++2yOO+44/vCHP/DNb36TN73pTavM0P3rX//KF77wBb73ve9xxx13rNdDoeuvv54jjjiCF77whdx4440897nP5b3vfS//9E//xJIlS/jOd77DS17yEg499FB+/etfr7aNr33ta8yePZsTTzyRN7/5zfzoRz+iLMt+FNzNN9/MMcccw4tf/GK+/e1v88ADD9Dr9bj//vu54ooreNKTnsR///d/T7jPhmFsGTz3uc8dt7zyQytQ58KLXvSi/vLQ0BAvfvGLOfHEE/n973+/3tv8/ve/3/95zz33HOfIeSRx/fXXj1teW/zdyg/lVn7v+vDhD3+YoaEhAE488cRxTpf1ZeX6vyLCyMgIc+bMYWhoaFIF0TzPufjii9ljjz36v5s3bx4vfvGLN2oyGGz6Mb2p2Wefffp1AUGdtRszZtaX9RnbMH58b0w/Vx6PKSWWLVvGnDlzNmkCy29/+1tOPfVUUkoMDAxw+eWXc+yxx26y9o1tB7uHbjiTcQ8dGBjg7LPP7i9fffXVHH300f36pSMjIzz/+c/n2muvZfbs2Xz/+99n9913X2N7D9c1a0PYbbfduOSSS8bV6L3qqqt497vfvdFt/+u//us40fuOO+7gsMMO4/TTT19jLeyHk5Ujl6+44oqN/rthfZisvw/Xxdy5c7n00kvx3m9wcoCI8NWvfpX3v//9gP698qtf/coioA3DMAzDeGQghrGVE2OU008/XQDZbrvt5Nprr11lndtvv10e+9jHCiCAHHbYYbJgwYLVttfr9eRZz3qWABJCkP/5n/8Z93q325Vjjz2239aMGTNk6dKlq7SzZMkSOfLIIwWQgYEB+clPfjLu9aVLl8p+++3Xb2ffffeVGOMq7Vx11VX9ddZ2Sg8PD8uMGTP66x133HFrXFdE5KMf/ei4dq+66qo1Ho/DDz9cAJk2bZrcdttt416/6667ZNdddxVAnvGMZ6zy/pSS/OIXv5A8zyfct//7v/+TgYEBAeSss85a5bh0Oh15wxve0G+v0WjIFVdcsUo7c+fOlQsuuEBOPfXUcfv6hS98QX7xi1/ItGnTZNq0aXLmmWfKj370I7n++uvlIx/5iIQQ+uvOnj1byrJca38Nw9g0vOIVr+ife4ODgxvcTq/XG3fNAeTBBx9cZb3ly5fLMcccM269sX9PeMIT5L/+679kaGhoQtvcY489JnyN25LZbbfd+vvhvV/tfWmMlNK46+Xzn//8DdrmfffdN+7z+s53vtNvf968ebJo0aK19mNl/uM//kMA2XHHHeXggw9eZSzMmjVLXvrSl8oNN9ywQf1dH4477rjV3mNvuOEGabVa4/r1hje8Ya1t7bnnngLIPffcs8Z1NuWYHmPlv0PGPp8NYcVzHJBzzjlng9taX1760peO2/YvfvGLta5/4okn9tfdbrvtNni7L3rRiwSQAw88UPbee29xzo3rxz777CNvfetbV3uNmghDQ0Ny9tln98/Fl73sZXL//fdvcH+NRy52D518JuMeOtbWW9/61nGfQbvdlrPPPluOOuooAeSEE06QOXPmrLOtzX3NWh/W9P33C1/4wipj7uKLL15jO/fcc48Asueee651e7feeqvMnj17lbazLJOTTz5Zvv/970tVVeu1D2efffa4ttb0DGIijP0dMPbvmmuu2eC21pfJGtvr4oMf/KAA8uxnP3uD3n///ffLC17wgv4zmw9+8IMyMjKyiXtpGIZhGIax+TAB2Njq+dSnPtX/crGyWLsic+bMkZ122qm/7nOe8xxJKa2y3mtf+9r+Ov/5n/+52rYuvPDCcV++vvvd7457vaoqefrTn95//Yc//OFq2znnnHPGtXPjjTeuss5EBWARkWc84xkTfnjy29/+dly7axKAf/GLX/TXefWrX73adX70ox8JIM973vPWuL2nPe1pE+rb3XffLVOnThVATjzxxNV+RiL6xXLlB1333nvvGtc9+OCD++s++clPllarJSeccILcd999q6z/xS9+cdyxWd2kAsMwNj2b6uG1iMghhxwy7jz+/e9/v9r1er2enH322ZJl2WofYk+fPl3e/va3r1VQSSlJo9Hov+fkk0/eqL5PhOHhYbnnnns26t/w8PC4NmOM4x70zpw5c539WHHi0ZOe9KQN2pf3ve99/TZarZZ8/OMfl2OOOWacQBpCkH/4h3+Qs88+W+bPn7/W9t72trdJq9WSpz3tafKBD3xA/vM//1MuuugiOeecc8bdJwF5+9vfvl7i8vqyJgFYROSb3/zmKuPtv//7v9fY1kQEYJFNM6ZXZFMKwJ/+9KfHtfXP//zPq6xTVdVGj+2FCxeu0u7xxx8/btt/+tOf1trXk08+edz66/vQf4wjjzxSZs2aJaeeeqp89KMflfPPP1+++c1vyllnnTXuOjUwMCDf/va3J9zuJZdcIs94xjPGXXva7bb80z/9k1x44YU2eW0bxO6hE2druoeuyEUXXSTTp09f5XP42Mc+tsbvVCuzua5ZG8Kavv+mlMZ9Zx8b87feeutq25moACwiMm/evL4Ivrp/++yzj5x77rmrfP5rYlMKwM997nPHtbU60XtrHduroygK2WWXXQSQyy+/fL3e+5GPfKQ/WX/s30477SRveMMb5Oqrr94s/TUMwzAMw9gcmABsbNU88MAD/QcGU6dOlW63u9b1v/SlL437I39l1+jPf/7z/muzZ89eY3v333+/eO/76/7gBz8Y9/pXv/rV/muHHXbYGr9wX3vtteP6c/PNN6+yzvoIwKeddtqERFYRkb/85S/j2l2TAPyJT3yiv85rXvOa1a5TlqXssMMO8pKXvGSj+zbmvgbkW9/61lr3YcGCBX2xGJAXv/jFa1z3xS9+8bj9/ehHP7rGh/6jo6PjhIeLLrporf0wDGPTsCkfXj/1qU8dd86vPFFnZW666SZ50YteNM6xsOK/qVOnyte+9rXVXs8XLlw4bt3TTjtto/q+LubOnTvuwdqG/jvjjDPGtbts2bJxr++7777r7MuKjpCDDz54g/bniCOOGLfdI488Uj75yU/KhRdeKOeff7687nWvG3etnzZtmnz/+99fY3t33nmnjI6OrvH1Cy64YNw1/swzz9ygfk+EtQnAIiJvectbxu17q9Va7WQwkYkLwGNszJhekU0pAF9wwQXj2lrdxLGjjz56o8f2gQceuEq7j3vc48atsy4BfGXH8KJFizZon2+99dY1/r3R6/VWce1deumlE2r3X/7lX2SHHXaQXXbZZZx4Nvbv0EMPfVjdYcbkY/fQibG13UNX5r777uunM439y/Nc3vWud01ItNxc16wNYWwbq6Pb7coTnvCEVa79y5YtW2Xd9RGAx7j88stXaX/Ff/vtt9+EJglvSgF4LPVs7N9nPvOZca9v7WN7ZS6++GIBZJdddlnvSU+HHXaY7LjjjrLjjjuOe64z9u8FL3iBpWkYhmEYhvGIYHyRT8PYyjjvvPP6tW9OOukkms3mWtd/2ctexlve8haGh4cB+PznP89JJ53Uf33F+kmvfe1r19jebrvtxic/+Ul+/OMf85znPGdcGyklPvjBD/aX//mf/xnn3GrbefzjH8873/lObrnlFl760pfy2Mc+dh17vHZWrIe0LhqNxoTWW7EG1//93/+xcOFCtt9++3HrZFnGL3/5y7Ue/4n07fbbb+eKK67or//MZz5zretvv/32nHrqqXz5y18G4LLLLuPBBx9kl112WWXdgYGB/s+Pe9zjeOc737nGz6XdbrP//vtz8803A7BkyZJ19t0wjC2LGTNmjFteV520Qw45hO985zv87W9/4wtf+AJf+cpXWLBgQf/1oaEhXvWqV/HTn/6Ub3zjG4QQ+q+VZTmurc19zZg5cybf/OY3V9nu+tBqtTjxxBPH/W758uXjlrfbbrt1tiMr1NPtdDrr3Y8FCxZwww039JfPOeecVWr5veY1r+Hd7343z3zmM7nttttYvnw5L3zhC/nVr37F4x//+FXa3G+//da6zdNOO40//elPfPzjHwfgc5/7HCeffDJPecpT1rv/G8vHP/5xbrrpJn72s58B0O12ecELXsD111+/0bXnNmZMby4mcl5+/OMfZ9GiRRu1neOOO26V363v+F5xbMOGjW+ARz/60Wt8rdFo8IlPfIJf//rX/O53vwPg1a9+Nccff/w6P//zzjuP8847r9/Xe+65h69+9at8/vOfZ+nSpdx0000cf/zxXHnllRx//PEb1Hdj28XuoWtnS7mHrsxPfvIT3vCGN7BgwQLe//73c8kll3DbbbdRliUf+9jHuOCCC7jgggt40pOetMY2Ntc1a1PTbDa59NJLOfLII5k7dy4Af/nLX3jlK1/JpZdeusbveRPluc99Ls997nO57rrrOO+887j44ovp9Xr91++66y6e+MQn8rnPfY43vOENG7WtibKu83JrHtur4/Of/zwAr3rVq8iy9Xv0eeONN/Z/jjFyww038MUvfpGvf/3riAiXXXYZN9xwA7/97W8fsTXJDcMwDMPYRphM9dkwNjcHHHBAf5bmRz7ykQm95/nPf37/PVOmTOnPRH/ooYfGRRutrY7Q2vj9738/bvbomiLTJsr6OIBXnPm/Lgfw2EzosX9rcifdcccd42bFHnbYYRsUiTyRvq0Yib3//vtPqN3LL7983H5ccskl69z+y172snW2u2JNs4mOLcMwNo5N6V465ZRTxl0bfvWrX63X+7vdrnz1q1+VHXfccRVXwPvf//5x6xZFMe71xz3ucRvV98liyZIl4/ZjIpF9Kx6fI444Yr23uXISxmWXXbbGde+4445x7rJjjjlmvbc3xqJFi2RwcLDf1lOf+tQNbmttrMsBLKLut7322mvccXjmM5+5igNrfR3AK7M+Y3pFNqUD+Ic//OG4ttaULLI5OPTQQ8dte13R3y95yUvGrb++tZPXhyuvvHLctjbm745bbrlFBgYG+m3ttdde60zIMbYO7B46uUzGPXSMqqrkXe96lwCy44479t3/RVHIJz/5yXE1nbMskwsvvHCDtyWyaa9Za2Mi33+vueaaVWpWf/SjHx23zoY4gFdm4cKF8t73vneVxAXnnPz0pz9d4/s2pQP4He94x7i21lb+alMymWN7Tdxyyy399v/6179usna//vWvj9vXzZ1IYBiGYRiGsbF4DGMrZcGCBdxxxx395VmzZk3ofXvvvXf/5+HhYR566CEAfvWrX42bqbrHHntsUL9+8YtfjFve0Ha2FPbff3/OPPPM/vIf//hHjj76aJ785CfzjW98g263u8m29etf/7r/84Z8nsC4MbEmvF/3pXFFx/K6XA+GYWx5rOg8Ath5553X6/3NZpNXvepV/PnPf+bZz372uNc+9rGP9e8doNeLFZ0vt9xyy2ZzO2xOpk+fvlZX1uqoqqr/80QcISsz5toZY9ddd13juvvvvz/Petaz+svXXHMNixcvXu9tgrpkHvOYx/SXr7rqqn46yMPNrFmz+N73vke73e7/7kc/+tG4NJFNwfqM6c3Fxp6XG8PK6SXrGt8rju0syxgcHNws/QI49thjxy3/7//+7wa39ZjHPIa3ve1t/eV7772Xn/zkJxvcnrFtYvfQ9Wcy7qEAIsLrX/96PvaxjzEwMMAvfvELjj76aECP7Vvf+lauvfZadtttt/42X/Oa1/DnP/95g7YHm/aatbEcffTRfSfoGO95z3v46U9/ukm3M2vWLP7t3/6Nm2++mX333bf/exHhrW996ybd1pqYrHvoZI3ttfEf//EfgCZ+7LPPPpus3Ze//OU8+clP7i9ffPHFG/y3pmEYhmEYxsOBCcDGVsuDDz44bnnKlCkTet/KD5fHBMOV21tdjPD69iuEwA477LBB7WxJfOpTn+IDH/jAOOH0V7/6Fa985SvZc889+djHPjbuS96GsuKx29jP0zCMbZsVH5KFEDb4IdmMGTO48MILx0UiFkXB5ZdfPm69sYetoA/GxqIRH0k458aJZBO5rq+4zsoC20RY+SH/9OnT17r+P/3TP41b3pgH2CtOIKqqirvvvnuD29pYDj30UL761a+O+92HPvQhfvCDH2zybU10TG8OVn54/XBOklt5fK5rfK88tjc2TnRtDAwMsOOOO/aX//KXv2xUe89//vPHLV977bUb1Z6x7WH30PVnMu6hAN/4xjf65XA++9nPcuCBB66yzhFHHMEVV1zR/37V6XT41Kc+tUHbg01/zdpYTj/9dF73utf1l1NKnHrqqdx3332bfFsHHHAA3/3ud8eVF7rpppselmMwWffQyRrba2Lx4sV84xvfALSsx6bEOTfuHlpV1bhSJYZhGIZhGFsaJgAbWy0rO3Xmz58/ofetLBiOjo4CWptqRTa0/tyK7cQYV6mZ80jEe8/ZZ5/NX/7yF84444xxtX7nz5/Pu9/9bo4//vhVvpSuLyt+phP9PLfbbjtarVZ/eezzNAxj22VkZGTcZJATTzxx3IO69WXq1Kl89rOfHfe7lR/0rVw/9qKLLtrg7U0mhx56aP/nle+LK5NSGnfNPfzww9d7eyunPazr2r9yfd8HHnhgvbc5xsoPTkdGRja4rU3Bqaeeyjve8Y5xv3vpS1/Krbfeusm3NZExvTn44x//OG75pJNO2uzbHGPFsQ3rHt8rjocNGdvry4rjcWPH4srpKAsXLtyo9oxtC7uHbjgP9z00xsh73/teQK8hr3nNa9a47iGHHMK73vWu/vKll1663ttbkU15zdoUnHvuuTzxiU/sLy9atIjnP//5myXd4+CDD+ZNb3rTuN9t7nuoiIy7hx5wwAGr/E20OXm4x/ba+NKXvkSn0yHLMl74whdu0rbB7qGGYRiGYTyyyCa7A4axuVgxKgzg/vvvn9D7ZsyYMW55bPbyytFEd999N4cddth692t17TwcDw4fDvbbbz+++MUv8qEPfYgvfvGLnHfeef0vRL/5zW84/fTT+e53v7vBLpkVP9OJfp7OOWbMmNGPkltxNrphGNsmV1999bjo9lNOOWXc63PmzGFkZIQDDjhgwm0+5SlPIYRAjBFYNf7uxS9+Me9617v6jocLL7yQT3ziE0ybNm1Dd2OtjIyMbPSkm+23336VtIXjjz+eH//4xwDcd999iMgar+n333//uOPwhCc8YYP6sCIrR0KvzIoxybDqQ7r1YeWJXjvttNMGt7WpOOecc/jjH//Y/wyWLVvGSSedxG9/+9u1Om42x5je1KSUuPLKK/vLxx577Gojv2OME/4bYE1MmTJllbF1/PHHj1u+99571/qZ33XXXf2fN2Rsry8rjseNHYsrlrGAiZfVMAywe+hE2RLuoddddx1z5swB9Biuq8zN6aef3heMlyxZwtKlS1f5bjxRNuU1a1PQaDS45JJLOOKII/qpUjfeeCMveclL+PSnP73W995+++3MnDlzvb5HPu1pT+Occ87pL2/ue+htt902btLbKaecstqxtbWM7TVRlmU//vkZz3jGZrm/2T3UMAzDMIxHEiYAG1st++67LwMDA/3Zpb/5zW8m9L6lS5f2f261Wv1IspVn0P71r3/doH6trp1HqgAsIsybN4/Zs2ePe6Awe/Zs3v/+9/P617+eV7/61f2Iyssvv5wbbriBI488coO2d+ihh/L73/8e0IcSt99+O4961KPW+b4VP9MN3bZhGFsPF154Yf/n6dOnrxKH+uY3v5mrrrqKOXPm0Gg0JtRmlmXMmjWr71Bd+Vq/xx57cNppp/Hf//3fgCYanHPOOXzsYx/bmF1ZLQ899BCPetSjxl37NoQzzjiDL37xi+N+t6ILq9vt8tBDD60x+nNFh9i0adM26Pp74IEHkud5/0HhugTglcs1rC7qcqKsGPk8bdq0fn3EySSEwEUXXcQ//MM/9Pv3t7/9jRNPPHGtNXo3x5je1PzsZz8b91D61a9+9WrXe9KTnrTRkcUHHnjgKvHgRx55JIODg32n2j333LPGh9LdbndcdOiJJ564Uf1ZF1VVjdveYx/72I1qb968eeOWH06XmPHIx+6hE2NLuIfec889/Z9XrEu7JmbPns3MmTNZvHgxIYRVhK6JsqmvWZuKnXbaicsuu4wnP/nJ/UkMP/zhD9eZFnLcccdxzDHH8L3vfW/C25o9e/a45c19nV3xvMyyjJe+9KWrrLM1je01cckll/QnPWzq+Ocx7B5qGIZhGMYjCROAja2WRqPB0572tP4Xtd///vf85S9/WefD4BUfCJ5wwgn9+OBjjjmGLMv6M88vueQS3va2t623m/W4444bt3zJJZfw4he/eL3aWBERWWV5TX3Ksr+f8uuKu1q53RVn+o+xbNkydt55Z77+9a/zile8YpXXd9hhB771rW9xwAEH9B/KX3/99av9krfi9lbe9hjPetaz+jWsAP5/9t483LKsrO//vO9ae59zb1V3090gdNvQMkMYNKC0yKhAR1BaEkDEoA+JA0/iiD9AVCRojEYTMaAB5HFMO6KIDxoURRxIjAScUCEIjczQ0NBT1b3n7L3W+/7+eNc5VUVX9QxFda3P0/V01b377LOHtfe+93zX9/u99NJLj5lZfTze9773sVqtgIiqftzjHnfc5U70nifipi7f6XRuObfGdffWt771mA/JXvziF18nMeLgwYNcccUVvO51r+NJT3rSjVrv/v7+MeLV8QShF77whbzmNa/ZRuP9+I//OP/6X/9rHvCAB9yMPYkPWL/1W7+Ve9/73jz72c/efv2cc87hl37pl26R22S5XB53Hy666CLud7/7bWOHL7vsshv1Ad/Xf/3XX8ede2M4ePAgD33oQ/mzP/szIFyZ18fRH+Le7373u9nusHmej4lWfvzjH3+zPwi/PjY/U9yYvrwN55xzDr/927/NF3/xF28nub3jHe+43td8usY03DrXZa2V5z73udt/P/KRjzzuzxUAP/qjP3qzazg2fOrPYhCOnmc+85n89//+34Hrn+h32WWXbff7/ve//zH9pJ8O/uEf/mHrjAR44hOfeIvWt5lMBzGp4Jaur3Nq0J+hx3K6PEM33Nio483vfPe///05cODATX5PuPXvWcfj6OdmKeWY33Ovj4suuohXvOIVx0wyetvb3na9rzl48CD/83/+Tz72sY9dR9g9EUcnVZx33nknnLR8a1yXH/jAB/iJn/iJ7b+/53u+57ifedyWxvbxcPdtnPzu7i6XXHLJrbLeT+XoZ+gDH/hA7na3u31a3qfT6XQ6nU7nVsE7ndswf/qnf+rA9s/znve8G3zNQx/60O3yf/zHf3zM9772a7/2mPW9/vWvv8H1XXPNNV5KOeZrX/IlX7Jdh4j4O97xjhtcz9VXX+1mdp2vv+51rztmm/b390+4jhe96EXb5c4888zjrm/Dm9/85mPW+7M/+7PXWWZ/f98B/5f/8l9e77Y/6UlP2q7npS996XGXeepTn7pd5qKLLjruMqUUv8c97rFd7oILLvD1en297/3TP/3T2+Wf+cxnnnC5pz3tadvlvuZrvuZ61+nuftFFF22Xf/7zn3+Dy3c6nVvOv/pX/2p73R04cOAmv/7yyy/3+9znPtt1PP7xjz/uffDbvu3bHPBHPOIRPs/zjVr3W9/61u16L7744hMu96pXveqYe+vnfd7n+Uc/+tGbvC97e3t+ySWX+N3udjd///vff5Nff0v4+Z//+e32v+hFLzrhco9+9KMd8JzzjXrOnYhXvOIV2/e7xz3ucb3Prq//+q+/3ufNer32//bf/pu/6EUv8g9/+MMnXM+ll156zHl661vferO3//q4733v64D/5m/+5k1+7aeOpc2ff/qnf7rOsp/OMf3a1772mPf/jd/4jZu0H2bm3/Vd37V9/RlnnOHvfOc7b9I6bi3e/e53u6o64I9+9KNPuNzRP0/9zM/8zM1+v4985CP+vd/7vf6yl73MV6vVCZf7hm/4hu373fGOd/RDhw4dd7nVauV7e3s3+L6XXHLJjfrZqHPboj9Dj3C6PEPf8573bN/rkksuucHlL7vssu3y3/Vd33Wd79/a96xbwuWXX759jyuuuOImv/5bv/Vbr/P8vPDCC4+77AMe8AAH/Hu+53tu9Pr/63/9r9v1/vAP//AJlzv6+Qf4xz/+8Zu0H4cOHfJHPOIR29d//ud//vWem08nn+mfDz+V//2///f2/Z/+9Kff5NdfffXVXmu93mUOHz7sd7zjHbfv8wu/8As3d3M7nU6n0+l0PiN0Abhzm+foD4PHcfS3ve1tJ1z2j/7oj7bLfvVXf/V1vv/2t7/dl8vldpkHP/jB1yu4fvSjH/V73vOe/lu/9VsnfB/An/jEJ17vLxvveMc7/E53upO/5S1vuc73fvVXf/WYdV3fhyCvfvWrj1n27W9/+3GX+/jHP+4Pf/jDj1n2Pve5z3WWNzMHXFX9r//6r0/4vve73/2263nzm9983GUe97jHbZf5Z//sn51wXW94wxtcRLbLfu/3fu8Jl93b2/N73eteDvi5557r73vf+0647OMf//jtOp/whCeccLkNmw/tAf93/+7f3eDynU7nlnP0B1zDMNzghzRH8w//8A/H3Iu+8iu/8oRCydHizjd/8zdfr+DoHvfCzT3k7LPP9ve85z3Xu/yLX/ziY+5jD3zgA/3d7373jd6XN7/5zf4FX/AFfte73vV672ufLtbr9fbD0Lve9a7H/aDx6A/zv//7v/+E6/rjP/5jv9e97uW3u93t/PnPf/5xj/V6vfa73vWu2/W9+tWvPu66rrjiCl8sFg74ve51r+Nu18/8zM9s13P3u9/dP/ShD11nmb29Pb///e9/g/d4M/MXvOAFfvbZZ/t97nMf//3f//0T7ufxmOfZzz77bAf8JS95yU167Ybv+Z7vuVEC8KdzTP/sz/7sMe//S7/0Szd6+w8dOuTf8i3fsn3tHe5wB//Lv/zLG/36TwfPetaztj/bHO+D6b29Pb/DHe7ggD/ykY884X3o/e9/vz/qUY/yAwcO+CWXXOJXX331dZZ5xjOecczPgtM0XWeZd7/73T6O43a5X/u1Xzvu+/3e7/2en3POOb6zs+OveMUrTrh/v/u7v7td1/nnn+8f+9jHTrhs57ZFf4YGp9szdDO5WVX9D/7gD653277jO77DAb/d7W7nH/nIR67z/VvznnXttdf6k570JD948KA//OEPP+6z6/r4y7/8y+17/O3f/u1Neq27+zRN/shHPvJGCcAbwRLwX/7lX77BdV955ZV+3nnnOcTE5usTZL/u677umG344Ac/eKP34f3vf/8x+/CQhzzkZonhtxaf6bH9qRw9ofy1r33tTdr2H/qhH/JxHP1Od7qT/8Vf/MUJl3vOc56zfY+LL774Rm1Xp9PpdDqdzsmkC8Cd2zzXXnvtMb8Y3fGOd/Q///M/v85yf/iHf7idzfmQhzzEDx8+fNz1/fzP//wxHzpcfPHFfvnll19nuT/6oz/yCy+88Lizp93df+AHfuCYX/a+7uu+zq+99tpjljEzf9WrXuXnnnvuCT8c3nxQufnzqle96oTHYp7nYxy0j3rUo67zi9lrXvMav+CCC/xbv/Vb/cwzz7zOB8t3vvOd/TWvec12+Y0gftFFF/knP/nJ62z/T/zET2xf+1Vf9VXH3a5rrrnGDx48uF0u53zcY7rhJS95yTHb9IM/+IPX+QDiIx/5iD/hCU9wwHd2dvzP/uzPTri+1Wrl55xzznZ955577vU6iy+//HJPKW2Xv+9979t/+et0Ps184AMfOOYDRcB/53d+53pfY2b+N3/zN/7t3/7tnnPe3l+e85znHPdDyw2fmqzw5Cc/2f/+7//+uMteffXV2w9Mzz777OskR5yI17zmNX7WWWdt3+PMM8/0l73sZSd89tRa/S/+4i/8G77hG1xE/OlPf/pJFW7+8R//cfuM+M7v/M5j7oHvf//7t4LtxRdffL0ffN7lLnc55lif6Pi99a1v9QMHDjjg55xzznUcuXt7e/7lX/7lDuEgPdGkpF/5lV855v2+6Iu+6BgR4/Dhw/7Yxz52+/0nPOEJJ3wevOUtbzlmXcvl8iadk6OdMg984ANvlmOnlHLMBKYTCcCfzjH9FV/xFces+ylPecoNCksf//jH/eUvf7lfeOGF29c97GEPO2nO36PZ39/3Bz3oQQ74wx/+cL/mmmu231utVtv9vctd7uLvfe97T7ieoycgAv4f/sN/uM4y3/zN33zMMt/2bd92zLX07ne/2+985ztvv/9DP/RDJ3y/o5NMROQ698dSir/yla/03d1dB/y88867VZ1Xnc9u+jP09H2GvvWtb/WdnZ3t7zh/+Id/eJ1lVquV/9iP/ZiLiC8WixMKxbfmPeu//Jf/csy6HvzgB9/o36dqrf7MZz5z+9pnPetZN+t3sY9+9KN+wQUX3KAA/LznPW+7zDAM/t3f/d0nHD+XXXaZP+Yxj3HA73//+1+voHvo0KHthKLNn5/8yZ+8we3+x3/8R//+7//+7c9FEGkORz+vThaf6Z8PN7z3ve/dJnicffbZN5gS9qkc7eq9/e1vf53n+1VXXeXPfvazt8s84hGPuM5nN51Op9PpdDqfjYh7L7Ls3PbZ39/nuc99Li9/+csxMwAe+9jH8pCHPARV5a1vfSu///u/j6ryTd/0Tbz4xS9md3f3hOv7rd/6LZ71rGdxxRVXAHDGGWfw5Cc/mXvd615ceeWVvPGNb+Rv//ZveeELX8j3fd/3oarHXc/LX/5ynve85207mW5/+9vzlKc8hQsvvJDLL7+c3/u93+ODH/wgL37xi/nmb/7m7esuu+wyfv3Xf51/+Id/4Nd//deP6Vg666yzeOYzn8l5553Hd37nd7JYLI55zze84Q185Vd+Jev1GoC73/3uPO1pT2N/f583vOEN3O52t+PlL38597vf/Xjc4x7HG97wBiC6j77wC7+Qiy66iG/8xm/kHve4BxBdQldeeSUQnb+Pe9zjuPe9783+/j6vf/3r+eu//msAnva0p/EzP/Mzx3RRvf71r+dNb3oTv/Zrv3adrr373ve+PPWpT+VBD3oQX/VVX3WdY/ebv/mbfPu3fzsf+chHALjrXe/KE57wBM477zwuu+wyXvOa13DVVVfxwAc+kEsvvZQHPvCB11nHW97yFv7oj/6IV73qVdvt3HDRRRfx1Kc+lac97WlccMEFQPRZvfa1r+UXf/EXeec733nM8o985CN54hOfyFOf+lQuvPDC67xXp9O56Vx22WX8xm/8Bh/72Mf49V//9W2X+AYR4WEPexgPfvCDOfvsszlw4ACr1Yqrr76ad73rXfzN3/wN73vf+wBYLBZccskl/Mf/+B9vsAv+qquu4uyzz77Oez384Q/nnve8JxdeeCHTNPGud72LP/mTP+FjH/sYX/qlX8rP/uzPcte73vVG798nP/lJfuzHfoyXvvSl7O/vA3D22WfzuMc9jnvc4x4cPHiQyy+/nI9+9KP8r//1v/jQhz7EAx7wAH7kR36Er/iKr7jR7/Pp4k1vehPPeMYzeP/738/DHvYwHvOYx3DFFVdw6aWXcu211/L0pz+dn/u5n2O5XJ5wHXe5y12O6cn74z/+Yx796Ecfd9n/83/+D1/7tV/Le9/7XlSViy++mAc84AG4O7/zO7/DO9/5Tu52t7vxq7/6qzzkIQ857jrcnZ/+6Z/mOc95DocPHwbiefOUpzyFeZ551atexXve8x6WyyXPe97zeOELX0hK6bjr+qu/+ise/OAHH/O1n/qpn+JbvuVbTri/b3/72/nN3/xN3vzmN/O6173umO9dcMEFPPnJT+YBD3gA3/AN33DCdXwqV155JQ95yEN497vfDcD73vc+7nKXuxyzzK09pn/yJ3+SK664gj/5kz/Z9jMfzV3uchcuvvhiPudzPoezzjoLd+fQoUN84AMf4O///u/527/9221/4xd8wRfwvOc9j6/5mq9BRG70fn86+eAHP8jXfu3X8qY3vYl73OMefNVXfRWLxYJf+ZVf4b3vfS8PetCDePWrX83nfd7nnXAdX//1X8+ll166/fd/+A//gRe96EXHLLO3t8d3f/d381M/9VPbrz32sY/lUY96FB/+8If5lV/5Fa6++mrOO+88XvrSl/KUpzzlhO932WWX8YxnPIO/+Iu/AKLT+Ku/+qu5733vyxVXXMHrX/963vGOdyAiPP3pT+clL3kJt7/97W/eAeqcEvRnaH+GbvjTP/1TnvGMZ/DBD34QgEc84hE89KEP5cwzz+T9738/v/Vbv8UVV1zBPe95Ty699FIuuuii477frXnPevGLX8z/9//9f8d87e///u+53/3ud8LX/MEf/AFvfOMbef3rX8/f/M3fHPO9L/qiL+Kxj30sj370o7n44otPuI5P5a1vfSsPf/jDWa/X3PWud+U973nPdZZ57Wtfe53fR3d3d3nUox7FXe96Vy644AI+/vGP8653vYs/+IM/wN155jOfyU/8xE9cp0f54x//OD/3cz/HlVdeyatf/erts/toHvSgB/Gwhz2Ms88+mzPOOINpmrjmmmu47LLLeNvb3rbt0E0p8bjHPY4f+IEfOOHPPSeDz/TPhwDPec5z+PEf/3EAvumbvolXvvKVN2mbX//61/ON3/iN22vk3HPP5elPfzrnn38+//RP/8RrXvMarrjiCnZ3d3n+85/P85//fIZhuEnv0el0Op1Op3NSOJnqc6fzmeYd73iHv+AFL/Av/MIv9Dve8Y6ec/bb3/72/uAHP9if//zn+//7f//vRq/rmmuu8Ze85CX+uMc9zs8//3wfhsHPPPNMv+997+vPfvazTzjT/VO5/PLL/Yd/+If9EY94xHabzj77bP/8z/98f8ELXnBcd8n/+B//4zrO3OP9OVGn1Tve8Q5/5jOf6Xe+8519GAa//e1v749//OP9V3/1V4+Zpfu85z3Pzz//fP/pn/7pE870P//884/73pt9eO5zn+t/93d/d9zXftmXfdkN7sOXfdmXnfDYXXvttf7KV77Sn/CEJ/jnfd7n+XK59IMHD/rd7353/7qv+zp/zWtec70upH/7b//tDb7/pZdeul3+hS984Q0uf0u6ADudzrHc2Hvdp/7Z2dnxO93pTn7RRRf5s571LP+FX/gFv+qqq27Se3/gAx/wt7zlLX7ppZf6s5/9bH/MYx7jF154oZ911lmec/bP/dzP9Yc97GH+bd/2bSe8x91Yrr32Wn/ta1/r//7f/3t/6EMf6ne/+939jDPO8OVy6fe4xz38S7/0S/25z33uzYo4/HRz9dVX+/Of/3z/ki/5Ej/jjDP8Dne4gz/pSU+6wZjJDW984xv9nve8p5911lk3KuLv0KFD/opXvMIvueQSv+997+vL5dLvdKc7+SMe8Qh/2ctedqPdGB/96Ef9JS95iT/60Y/2z/3cz/VhGLbr+U//6T8dNxr6ePz8z/+8P/jBD96Ove/8zu+83uV/8Ad/8AbH7ziON7o3c8N73vMe/6Iv+iJ/0IMedMLn3q01pud5vo6T8Mb8yTn7Oeec4/e+9739qU99qv/Ij/zICasoPhuotfrLXvYyv/jii/2Od7yjn3HGGf7whz/cX/nKV96o87OJ5tzd3fUnPvGJx42A3vD2t7/dv+/7vs+/8Au/0M8991xfLBZ+5zvf2S+55BJ/5StfeaN6fTfb/Lu/+7v+rGc9y+9///v72Wef7cMw+B3veEd/xCMe4d///d/v73rXu270Meic2vRnaH+GHs21117rr3jFK/wpT3mKP+hBD/Lb3e52vrOz4xdeeKE/5SlP8V/6pV+6Xmf30dwa96z9/X1/wQtecIzT87d/+7ev9zW39HfHE/G6173O73KXu/h3fMd3HPf78zz7u9/9bn/Tm97kL3vZy/ybvumb/Iu/+Iv9/PPP9wMHDvg4jn73u9/dH/OYx/gP/dAPXW8l05/92Z/drOtysVj453zO5/g//+f/3P/Nv/k3/vKXv/xm9V9/pvhMju1SyjFO6je+8Y03a5sPHTrkP/dzP+dPfepT/R73uIcfOHDAl8ul3/nOd/YnPvGJ/pKXvOQ6iWedTqfT6XQ6n+10B3Cn0+l0Op1Op3ML+PM//3Me9rCH8d3f/d385//8n0/25nQ6nU6nc0rg7vzIj/wI3/d938fv/d7v8eVf/uUne5M6nU6n0+l0Op3bDMfPpe10Op1Op9PpdDo3ik2U7oliMzudTqfT6VwXEdk+Qz+bYow7nU6n0+l0Op3bAt0B3Ol0Op1Op9Pp3EzmeebLv/zL+dCHPsRf/dVfsbu7e7I3qdPpdDqdU4IPf/jDPOhBD+Jf/It/wS/+4i+e7M3pdDqdTqfT6XRuU3QHcKfT6XQ6nU6nczN4z3vew2Me8xj+6q/+il/+5V/u4m+n0+l0OjcCd+dP/uRPeMhDHsId7nAHXvrSl57sTep0Op1Op9PpdG5zdAG40+l0Op1Op9O5ifzoj/4o9773vTnjjDN429vexoMf/OCTvUmdTqfT6ZwSfOVXfiVPeMITePKTn8z//b//l7POOutkb1Kn0+l0Op1Op3Obo0dAdzqdTqfT6XQ6N5H3ve99jOPIeeedd7I3pdPpdDqdU4q/+7u/4253uxsHDhw42ZvS6XQ6nU6n0+ncZukCcKfT6XQ6nU6n0+l0Op1Op9PpdDqdTqfT6dxG6BHQnU6n0+l0Op1Op9PpdDqdTqfT6XQ6nU6ncxuhC8CdTqfT6XQ6nU6n0+l0Op1Op9PpdDqdTqdzG6ELwJ1Op9PpdDqdTqfT6XQ6nU6n0+l0Op1Op3MboQvAnU6n0+l0Op1Op9PpdDqdTqfT6XQ6nU6ncxuhC8CdTqfT6XQ6nU6n0+l0Op1Op9PpdDqdTqdzG6ELwJ1Op9PpdDqdTqfT6XQ6nU6n0+l0Op1Op3MboQvAnU6n0+l0Op1Op9PpdDqdTqfT6XQ6nU6ncxuhC8CdTqfT6XQ6nU6n0+l0Op1Op9PpdDqdTqdzG6ELwJ1Op9PpdDqdTqfT6XQ6nU6n0+l0Op1Op3MboQvAnU6n0+l0Op1Op9PpdDqdTqfT6XQ6nU6ncxshn+wN+EzzNV/5VbgL5hOiGasFSQNJQKyACtUcR0niqCgmirshorhV3EGHBRi4xXpcBXVHNCEiiAMC4JgVDMXLjKYEOJghOSNpBDd8XoMqJhm3AijiFc0DYhVRiXV7ZRgWDGlgGAfyuCBrIomQUgYMZ0AU0AR1xmslvgDujvuMqAKK41iZMVHEDCSBCFZWoBkQBEFVEM2IKEKNg2kFcERH3CpmFVwQQFXRrCggKWPuWJ1xFCGR8uZYensHi+0FxA1JCXfDXXB3rBRqmZGUEXdcHFQRHNURrzOiA+IFkYwoccxSJim4tRMiguNMe/vMZpgVBI19w2I/JGMIuGHziml9iKk66yKUqZDqxLDcQRxEHAWGcYkCaRyhFiSPsU91RtKAWaFMheqZeX0YF9CkpKwoGR0yag6A5hznXgbysESZEYGUBkQAc0QFUkLTAin7aMqIDnFeyoQMI6oKvlnnDngBr4Ah7qhkNA+oJnSI/adU0NzOsYAobgXXjJeCu8d60Pi7tLdIGVHFa8WtghkuiuQBt4JKLCiiSEqQMkwzqnHMEo5qjnFmM0i7NYlgopR5Qqyws9xh58AZuFfKeoWboknRYQQMqiGA5ERsnscYU43zpQkXmKaJeZpICRbjgpQyqiCSMPPYJ3GUhOYEGLVMzHMhdq9C2688DOSc0JRj3I9j7H8pVBfKvI7XSey3YKQ2dgVp49wxUaxWEEEFXAWXgTKtsFpjDKtSy4rq4MXiXEhcX6QEVskijDmTcwYvVBJTWbNaT1TPyLwmJ2VcLFEpIImyXrPav5a9aY0tD3Lg7HM4//Znc+dzz+C8gyNnLgayKDqv456Ql1itmBviM5oXcT3juAvkAa8FJ2FesHmK8Wkz4pA0oeJIGlGNeytWsTKh4w6a2vFJI4JDGtr1lhBxKOs2lhtpgCSg0u57bay7Qh7j78Mivu9xn0IU3OL/OUNdgw5Q53YvkrjHpTEGuVu7p1uMX82xnvs88NZ5OJ0iXHnllSd7EzqdTueU4J1v/wcO7e0TP7x1jsdjH/vYk70Jn1H6M7TT6XRuHO9+97u5+uqrT/ZmfFZzuj1DoT9HO51O58bSn6M3zOn0HD3tBGDDQ0AwB6uIOZIqoLiOIfypI14hJUQz6gYGkhTTjM1rsIIj4FAtBF1PitQpxB0RQi0Q3BVwJGVcCFFCBa+gYojXEEAA1YyVCZEmlnkFJ0Tc9n7mYKpUK6Q5wc4AXhEBzQusTrE/gEsGptj5jWBnihsgBRhABTHH8fiQyiwEFwjRA0LocOKPDjiAphB76hp0jG2OXcVVII2YrVAU9znW7QX3itXcjlEIWGiIpG6gSRBr0rMXfCPSaGrCTdNxEDDHfQKbQyyXEEHFKugCzEJUp+IWQrBTMYRqFa+OJsHqhEgIRz6EkOYOPozYWilWKWVGvZBzJokg6sCAUMBmkITUCUqJdalDmXEJ8UqTUEsNXTUlEEXYiPZCrQWyghlJFHzGGSAtoezhWuNY2QTWhC43ZNjBy5oYpIKo4GYYitDW08Qt0RAz3RxXDzFMNAZVSm0dQCVEMavthHoIaF5D//UKwzKEtjoBjtcSAmYba5I2437EplWMSG2XBYIkaUI3iI5b8d41te32EJ8lxLiswjBkJGewjGgNQVHapAoHGQYUATaiYgkhfHNcBKpDKQV3SHlERUJslByrEXCz2GeVmBThGmKmOKbt2hRIQ0Y0UcXxWkPwdontl5g4oRU0JdzAyhwirs+oJFQFF8HcqWUO4VkTZhWpgrVJEnUucc3PBZvbZJQyYTJgTTBP4oyaGbPGelUpxVnNM9NU8FIZhkTe2SXlhGLtehIUQ9ICE5j298i7Bzi0KhxaVQ7vZnZlSRaPJ4ZbiNYW9yZSoppRS8GIyTNqxDJ1ijFbHStrVBTFYtIBIfYrDlvxO8dxrgAa9zVNSC0ksRi/qsQNQqDMtBspWBN+dWyi7i6UvTZDwUPMrdI+iPcmGKcm7jrIAmqFcRfKCkrb9rlAFkgtMCONQIZ5Ap9u6SOp0+l0OrdRhis/DouDJ3szOp1Op9M55bjgggv6B9edTqfT6dxM+nO0czSnnQAsPgPN9ehg4xgiki5wDA9rHe4FrwPoRB4S3sQsrDTxJxysIo6Kx99dEG/iWx6wOmMWYhFeEdcmANdwwQ0LcMfMQuDNCbEVOi7wMjXBIYQ5SQksHLSlFqQkZBwwr1iZkZwodU0SoHpsv4TAEcJWE0yawOd1boK0xfvTviXhKlRJsU9KE1TCiRtGzhLiHLFaR5pLuYmThIhsZR0u6yY44iDuxwSPh2DrzfUsuDi1WrioN2K5KIiHm1OaKNzczaShCZgVxxAXSBnziiJ4nXCpIAkjxEQ3x7ziIu04DfG1JsQyr8KxbIqrUMjM8wqvhiCkITNkARmaaKUhUmHhIBQDnxAGWB7Apn0k76BpRMshNOfQFqnNhb5GagiUIVgrmpv72koce4lJAC4JyctwmJc5HJcsQjj1GudaR6j7cUKHXXDB3MAmVJpDXVMTmEPDM69oKXEOvUBOQBxTb2PBN0JaSmDSzplDasKwN9FUBMkL2FwXQrzGPbYRRZujXjWc5zHuCFFRE15mJI9QBa+FhJLHHVJOUCtmFg5MtRiEZjGOquGaEakIFs5fJLbFC25Cba9XjJTiWIvFxITYhliXQGyjG9Yc7l4KmKMpkcYliGNIONWxcNFTmms1EgbMLdzoHiKxuZNySxaohnlpzvwUYm5zjYsKSS008+qYG1oLJiGzzhjuBa2VneXAmBNpHKkWjv552mc1FUoxZF6z3D1I1thnd6PWuSUOGNWN2QUzsOJM+4fZ319weG9gfWCkjIrlAWRAqHiZ2vEyqmdqnbA57guignmMZ93cMzSuURULTR3DN2K4GaBtYkS4+N1CFBYDiEkCOrT7mLXJMJpgHOPfNsc6dIzvpYG4yY7NDewxUUFzm+jQHL1pCKF3XeJCwJuzN0FdxTpUoTY3fR6AFONNanPDdzqdTqdzXT7nn30+V77nPSd7MzqdTqfT6XQ6nU6n0+mcppx+ArBudlnb5/3h/g1R1kjimCao8dk/eQg33hCv87pG8xDOSze0RTijgqqGQNkESyQ1wdeaIBSRyN5yc605D1EFbbHDdR3C1UZMpolj1lzAOuB1wkQwTdQhBFF8CE3ECIfkvEY8hZglgjtYLeHM9bZ5zcmHjtCiXWNzcjtWKYSbFpuMhxiYxMHAZBPbHCIdtP1s4lfowI7UdRyjvAjHtYboFGJuDTHXpKnJ1iKhQzjX3PbXQ2x3L+34KqYD6o5mwWUJXjEPzdjN8DKhsnGxakTSiuB1xsywUnFXfFqBJGrdD1Gu1DgnPlGmwlSEucxkM1JKIRri6JCoVvA0hNBe54iTXuxEpHeN4xVjzpDqpDRg4pT5MDqc0YbekjpPEYHsjucWUasR7StuiI4Rgd0OuasgJFyUiKRVmFp0rYT72t3xOsXxqhO6EcVEQ7xKA15quJ9Tjnhum1tStqNCi4AOsR+8ibyEK9ZqOIe1hhjczkvEi3uI/S1KW9MQ4qAREy7yAk1CSosQuduYcZtRGfCUwimcElIrSY0hh4M5JivEeJQWcyzi7ZqOGGJIbQJBm/jgIQpanbE2wSCnTEopjk9ztXtY40PH3rpzK4ZHNLQ5vnGfakRMW7WIjc8DUq1NlGjj2SQmBbgfcau6YZZRn3GbYVgipTRBMmOltAkNQoqwAMiK1ErRwuyZWipCZkjKcrlgsVyiKlSUup5Zl5n1NOMyIuVa0jAiYnFMJGKnrVRm24j8C9yuQWu4dMt6zf5qxd56xd68w6qODAp5c16l4uIYiTLPsW8Qk2FKRdIQFyK0azYDEadsYcGOMeeG6bKJ7RJR4e04aVqE8LuJJLdwWIO1WOc2FkkxHjW1aAALUTdlWsZ4jEOb2k09BH7qOv7t3lIP4p6FtZv/8swmHO9DXrbXt/eR2F+GA7fGY6nT6XQ6t0GGcUT3D2M7/VnR6XQ6nc5NQaySVnvU5e7J3pROp9PpdE45cs6ceeaZXHPNNSd7UzqfBZx2AjAe8buhH5Xor53XWAax6CeVWsErKQ24zZgXtEZnpIq2mFFHcsaA5CDjGMKI1xZNHNHOSUJEdQvXmTf3ZZgWK1BxzyHy1CleKwVNEZcsKq3HdAphTQyInlLzylwdZsDX4dr0iSRDiBm1OWWjcDYiWUvrEx6XOOFmpNbW7+tHuSWb6FdrREVvxDwRJA8R32tzdJcaES8sgIaLUFybkNJE4DQ0l16K+GxvjkgDfA5nXQk3NZqbcDm2nuEW26oDMk/4MMS+iIZAWmZkGPG5RStXB8kR2Twu4pCp44TACBqxwWUP15FqBXKcD5eI1mYdYnl1YZ72ER1w2wuhSjM6jCRVfHSstA7ncSec1TI2h60jm+7W5rwWHPFCSotwX5eKEdI5SLhXcay2nl+bEJ+R4UBMPLCC01zReQgXsLbju9iJyN1ikIcQ4492NW5crR7h0OKODANuU4jeYiFm14q0mGysKc61ttjvCFj2KMDGCJcsaYzo8hSudG3CcXSoDuG+bJ3Asum3Ro90P8fJa2JtPSKguiAakcaohxu5ljbhQMNNKikMnV6b3lzC4Z8XbT0VJ7XvtbhvK+i4E8d841rWjLe4bKuGaxOzh2UIlh73hTgmetTxJTqqq1GTkFMi5wGRitc25j3c9Kotetyi21g1o3UO4dcmpIQzutaCWaG2THUjMc8rylRQUUav5GFkyEIeo/O7irKa1qxKZVpPuBnCOiZ64NEbXB31dUtQzuQklNW1FCasVkSM5EKZC+v9PQ6tlly7t8vh5cgijdDGj7jgOsRxMke9InmJa8ZYISXuXUeo0d9dDZUU40ZHWk59zB3A4vsOogPqFr3m2qLNrYK1yTXF28SOo+4P5lA3k1KsibQaIjzSnLyl3Uva39cTDDu0zPl4rU0wLlrUdILFGPeUtIgo6LqK9eedEPc7nU6n0zkOqsKuOIfazw2dTqfT6XRuHONyhzvc6Tw++skr+zO00+l0Op2bSM6Zc845h0OHDkWKZue05vQTgNPYNDELh655uMpq9GhKGsLJmcZwxqGQx7hYBNSbg9IjXrZaRN8mHyKqVWTbHxtarTShF9RDBK4lROJNh60mAakt6dgiCrYKiodYLOHMdVJzXwrV5qj2XR5AqodIYikiaWu4GNUVSog6iDbBJkRfkxYXXEPcxAW3KfYrpTDKEeKeeYtnxqleoUbHsTSHX7ioo/vTNz3DtTQhGcwiWlhqRLm2At84RilH7G8NCRS0xR4P+Fxa5KojklCbsaSxzuaMNZubsDaEc9vb9rS43TpvRLsVkpZYXUPepQKmmWKFstrD8xKf17jOMUZsjVdjLmtqDRdiWt4OtdKE/RBC87DDNF8LOrZY4Yiy1Ty2yGYHmrNQFRkS0tzPbo67YvOE1QkfBBWjouHA9YznZUQI1xKvlxxOZ6wdy3CZmxvqGzFTo9NZBSkhLopEnLSKoDKGIEn0DYtED7RbCZGN2Ga3iLf2jWhWSxxLGSARy7XuaEkjMiyw1oddNxHPaQghchO7W2dkWISbHAnXsUYUcsQHj/jGMe8RB615aOKrYm6t0tWbozWhadh25Ya4HddNTJ7QjUaL1YqVcsRtmoc20aFNsqjR6+0e/bmkAdMRNSCmRrSu7jZBwvyIy9g9opdN43qCcLJaRVJ0ZbtVZFigtcWum7XOcW0ibVxFXudICDAoXplLE6NLZcxLxhTRyjoOJI0JGcWddXFW++vQQOeC5xwxy5JQDRHaSnQzSxLyYqROK6Y8Mk8TVh1FSKqUMrHeO8yhQ0sOnbFib1qwO4awmiTSCcIBbqjFmDBP2LSOCRzi+LRuaQeG5oGUQnT1lGK81Dk6gZWIBdfN2VJIC9QnUGuTUjT6sauFo9da9DebcUj7nsTrWz82pCOR894mQpQ5YqB1DBewza1DuLZx3rqNtcU+19peW6MPuKUqIC09otPpdDqd4zDs7HLu7W/P3pVXY7o42ZvT6XQ6nc4pg6hy7h1uzyeuvobZ/YZf0Ol0Op1O5xjOOeccLr/8cvb390/2pnROMqedAOxOuPws4putrrdxpQbU/Wub2TSH67HO0a8qKeJ5ad2paRndu4SII6UgqQkGScPlSG6RyEqtFkJeEyiiFjjEGdeM2EzS1CJgo+Ny08/q5ts+03C9ZUQSZpUyT21G5MahC8k8HJh5iL5esxBUveJk8CniidWQ5hQmjbg016qmkKc1hG5lEaJbE8SrOWohtG3ddDaHu1oSACYF23SqkvB5iuUHwp1stnXjRpxwc+4JOLU5tTX0Fffmom4xv7W5f62iacSzMrfZLCEiN4e3Oz5NpCFE5GoFyNR5wspMdahlpspALZVaPATlZCAjTgmjYdlDk6LZo5fW451UIu5X3bFECN7SRKqcSClhZQ+XBVZnxBUZd2Ks1IgVRgdMZqoO6LQfoq3sYkTnrDKEEItEmrAVEMNShlJJKcV4FqCC6hgORrcYfxKx45ozKjkitz16ed2imzY6TyUcmRYTIdya61u0Sf+pjYF4I9nEIUPrtp0xolsWayNXokNZLGKDReIXORHQvEC9NLdvi/D1cJKyiUUH1EKe1RzCsM3N8dmimtGNuB7CuLjEuUohAG6c7IZHH28pcUXmIbYbQqT3dg15iK3x1xCFqzcnqxiaEoZiXilWUY2YdtgIuhVq9F5v7hc+T4RCqRGd3iaImG0c2TFBBE14rZRaKe7MppQYpCzSwDIlhmUcB2TEqG2+g7M/rZgn8GmK3u6kMdnBIbV0guSGjkvwgrbJK/tlZn+aqdNEbhMpmAuqS4or+0W45vBhrtrJjBr3tlEgDcsYS/MUYyYtsLn9QLGZ2OAJ8YT6hM8VtCCaUDEShuYWp976kVWbI1zGGEPDEhnGOHbSYrSTR6KBrdp7tRt7amNiWMLQos6rN5FW2nIxOSM6wzc3C23zHRZQmrM3aVxMZYr7Uo4JE8zWtqVNAlKJa73T6XQ6nRNw7j3vwyf+7I85NHQBuNPpdDqdm8LijLM4eznwsf3pZG9Kp9PpdDqnJBdccAHvete7TvZmdE4yp58AbM3FCNjWade6fMsEhKPSq4HkcH254nUOMSuN7eVOEsWtNrGp4h4CbHTsVlzDaSjqJJzq4UyTFiOraQAULxMQImdibIKWUOsEnkLEctm6i0ljRKJ6QVwi+bTEvjAuwy2ahxCNm8t5Y7kVkVadaVBbzyotJrf1qrq0qDovIfqZN5didMq65hBgsXD2WmkieiWkto0TNWMlBHZvDuGIgG4drUlDjKwhXofTlNZvS/SB4nHOoImEFdHW8enh0XZSRFs3schsbu7VEWyFWetctoqZh/u3CcThAt9vYqZiZYWbgsaaXTVcs9QQVtMyzmlzSYs6aVzgpYTrMSQsNCmiEp2/tEOjCbwgNEetO+bN/SuJ6kpyCSG+OTa9xrH1OkX8ODRBmCMClo5InWJygZVwmkq4NcPdPSMsmk4a3aludqTPt7ZOVGnR5cma4zHiunEQ4viG4Fiac35ErIbLVLU5rf2Ii1JBXdBhhDqjpOh3Nke0jQGfQxzOGdk4hW3T7SrhhFYJh7Jv9DtB0k5zMTvIEedvynkrKIavN+KKFaOatWMTY0tkc021WG1iTMS4a8fASoyfdnw0J7xEt7NtrkUAwu3rkqjzvO3wRRO1zC0+O23FeTOL12sKUdwdo8SkjmrM04RXGFNmsRwZE6TxIKJCKTPmRq1GMWUqK+oUYmUYs0PsDtN5OHq1TSARFTwtmGthf7Vitb8XacZeycMCUcHmq1AHrwPTXDhUlGsnOLh2DiwNTxG9bpueayd6nc3aRJWMVkfNScxoGqLLFydJuIxFo0dbiFSFuF5SRCqnEUqNuGXiPhInp90Hto5eYRvJLUO4esXaH4VxF6ZDR+45bYIJeYTN9JrcYsfFYBzj/jociMjyut/GYYudhlh2WMbX5tpE406n0+l0ToAod7j3fTn0wQ+1ioJOp9PpdDo3BhHhnAvvxhX/+I89vrLT6XQ6nZuIiJBSQlX7c/Q057QTgENDDbdpyDyOeOuFzRmxcAS6WYsmDYHJU8atuRt1EYLgppe29bdSZ1w0hKdhATY1d2B0XCYHsxBqVPRIBLWGEOoimM/omPFayChYCH8uYzg2JQRXA9xmxIWa2zYIIURaJYujHu8jmsLlSzjbRAe8rptQMmwFu5AuE1YLKjlckV6b4y7iaWm9sC4WMbreolhdkI3L2D2igLVGx6lIHBeXEFbyGC5kE4TW1+kh2kjKEVss1oQebc5UEJlxBSsRKUud8NScm1KgCqYS+1RmGOM8UiuehhDxXbBaw31sgs8rREdkvhbxECxrhTLtAZDyiKQ4l6pHXS5e0TQgSTEXavXo0k1LsOhTzXlJykusrGBYxDlDkMUBZFrDet4eb/PmgE6KlhU+LHCTEGlJR8ZKEiQtESu4zbjlEH41N4dkjokF8364LwdFfESpR471Rmwr64h9FuJ9NEV8b3Prbhyx0X9cwFusbrsWNo5s8qId23AVI45oJkl09CZNWwEtRFeakB/XSkxkaB3ZXiNm2SuqY5vEEJ3NIZSGuC3irWfZIma59XNve3nF43WEW3zj5qX1A6ecQ4CUENw3bmYA32p9m1jgOO+uEvstIbCiGbHYDx0WqBleHSslzlkawkFcClUTVtbgTmo9ykLCy0xceU4lune9VHLKLJaJMSs6ZJIK5BCTqxtzcdbTmqnEZIhh2Inrtzn6RTNKJQuMWcATlhfUsmY22F/PTPsrxJxBlTRk8rgTHv+8IK1mpKyYD8PeYuDQMrO/HJlklySJtJ7iOiXi0bVFkkfoNmgWEhKif4vjVhXScKQrXBBUvI1fQsBdLNsYS82p21y37uG43dyDFJBlRN97CQdwas5fs+jrNQWJuPGtmJx18xAAcoz52tzuTfCnTvEe89wio/e293pSap3WHmJ+7bPRTyW8TYI62es4lenH8JbTj+Et51Q6hiLCwbNux4HLP8bh0n536HROQU6l6+6zlX4Mbzn9GN5yTrVjuLu7y9lnn80nrriiP0M7pzSn2rX32Ug/hrecfgxvOafaMdw+Rz/xic/I+3U+OzntBODoxFQgYzbDRrx1gIp4OP3MK+7aOmpjloSrID41V2VE4JKHcMBiyLAMYdRpbtZwcTbFK1JDSW09EaeqHOlcrU2cRCq4oSlH7KwO1FLwaQ9ZhPOR6iH0ecHMKTKgZY/Bd6hJkGmNDUJSb8IMuJeI/A01FW/uuehnrdGJSesL9ubOSxGfHLqvRqR068UUTdFNmkI0jMjd1n+szdGrqTlWLTqIUZQQ5iRKlcM57Yqohzs6KXiLwJbm0KtTRBPLjGnrpSWhRBS0yAhUvLb9U4mIbRWcEjphKSHiWGm1rRXPIz7XFldbkZzRaT+EQVJE56pGF7JN0bmquyGyDQvEjJwHSpqoxSGViJP1glVnGJQkI4oyTyHeJWC2iqtiZYZhh7o6hHjEBLsqliPiV00xE1IaQ/AmJgGQl9Ff6nEcQuRvglQeQgiMgYhq9PmGQ1jZCGqShnD/ysbt67g3J3lztYsk3KOzeNNTK9bOWRNXo663/V0iBlw1ocQkBKHF6BJu24370qH15TZxWDcR5BIOd/UQrd0RYj+k1uiIbgJiRJjPR8Yt4eYU2vjGMS+UuVCjPJicNdyuVkLUJfbRbI6JC8JWVAYHWzfHdm7dv/N2MoihqGrsWxrxsqJaDRf6SPRUO1iplDLh7lSFrDGuay2YjpR5FabplBkVFmMi5YSpUszC5GorzCpTcdbrmTKtEIcsRq3rmEDhMeEhp0wWYbF7kNxiqqvDVArrqWLzRPYpguo1IUO7H4qg4wItE2k9U9fOai9zzaHE1aNy5gCL5YKRiJFWUVKSbZ+0ipJFyDmHIL+d2DGgPqGLsU0WqMgmpWBYNoG1TWJood0hyNYmBJf4vyv43G5otQmyyxCHZYgJOmWGeT/WuRFsNYWj12j3Ewdfh5tXCBF4aGPUNcbU4mB8r6zi3uhtEkQpR2LOdw7e2o+ozqeRW+MH7NP5Fx3ox/DWoB/DW86pdgzH3QOcc3CXvauu3U4263RONU616+6zkX4Mbzn9GN5yTrVjKCLsljWfhP4M7ZzSnGrX3mcj/RjecvoxvOWcasdQRDjvvPO48soruwv4NOa0E4Al5xCs8BYBLGFOFI3Y2o2gK4TrctMLioNlTBVtUcKu4YZVJCJlmRFPrdczhJ/orq0RcQpAApQqBbFKauKq6EjySinhTEUytU7RMepNmMljaHobVxwpBK8WKStpjJ5hxqg0rRPVWnerKlbidZo0XI91CocmKURFD9dkiC25icUR3ixGiDXWXHHajsXmx3DRcDu7hOCbRkQ2RZvRHYw3R+u8RlMTLbE41jlDXeHqzXE6gwzIxsHrFddMmaNzmWrhqq1l6xI1qzgjEJG+R+KTZ5yKb8XoOLd1XuGawWZSHrH1HpoESwnKRMLIMqCquO7AvMKlUL1QPDOYkwaF6uE8zRmzSkpje68Js8QwjFArJoZZxD8nMkUFq/sYrfc55xAV25ghL5obG1yH5mqccEq4YPOIEPsfx3PRBNaKDIsmnBLimLdYbqInN0qDHUkLrK5bZLa2yRGKbxOhj3KoN9duW2tMgJAYH96ilMmLrTM8Iofb4fYm6qWMbCYR+CZq3MDbZIJ2rYnm2D4NR2kMowlJTTR03wrGsY42Dt2aW13xnMChesUkRSR1VkSkxYR76wiusUcp4rLdKl4d0ZjkEFHXpd0zrJlF22vLjKfcNO0aZvlSIKWIpfbWpV2ii9vmNaaJmsOpXMyodQ8XZaiV5eAsxp1WUytM08xcjQrUasxlxlzxeYUSTlbLGfWZlBekMjenuuHDDiZK8YLrwFQKczUwI3uML9EhhFGGuM9ZdOymlEkUqIX14cNcOw5cs1xw+OABzlwYKSeGvAyRt86xr4uRnDLJC0kightvE2dSRtMCJMW5FW/iq8Qkke25a05cjwkcISCnGAAWk0Cwggy7LdXAQcZ2/tvkhTrHusSAMcZlykBujt3aYjg9xOTUXMJWoMQ9bDtxJw+Qd4gY6OjKZoprLG7lp90jtNPpdDo3g9vf9e5c/hd/zrQ8cLI3pdPpdDqdU4r+DO10Op1O5+YzjiPnnnsuH//4x0/2pnROEqfdp9fRFGpQ1qGhasbVsTKFMNGcva6LEIZqCExePQRKWeC1iYiEGGFSYVw0gTTcpwwZtUKtU/SxWntfShOglWrRG5vG3XDnCuSN2CEescmSmwAbAuim11VraUKWY3NBMwhOcY2+Xs0kBxdrsboVNwFzqlloKq54NUSiMxjzbbQzHJlhGRJubZrz0LYv3s/NmggSQq6khIpGRK6Ek9QraB6wMuEqVKtQ430kh0ArNjeRBnALZ6cV3HMslxatwze6a0UFqTUEOTM8J0wXYJv+1YgRthpinfkalTGcwykjc0Es4l+TVyQlLCeqClV38XkVAqoJaRhD4B4PYnWiVo9ly5q8OIhaISWlmuOlYL4mLRaYO7XMDONIHjLODjat8VLRQZD9dUud3W/iYiExhP5kBWfEx534u1UkhQjl1iJvXZtoSQhnaQhnsgFqqKQQvs2QNFDLFC50iy5iNJzwEYXrzTGd2cRbe4llrAly4hyJpOYo8dciHleag1zMoocXjz7XrZMz3MOSh3DybqN9M15r9AI3QU2h9fjSIqFbrys0QbBuhiEuQ/zbY8KBu+OpTRIQjdhvo0Ujx/XhZrFtdWqhxb6Nc/YSYrirHnGlag6xdysag9tMNVATVJowbBXSEMJrqQzqaEpkjbsPw5IqGj2+tWIe7cOLrCwWmTxkpHVJu4RXXq1STKjTjJqRqSFWDssmymcSFpHbRPyyupBax/a6TBSBebWHzXOLRN+kGxR03Ilr31rnt4RwrhTSXJgw9g8f5toDO+yt15TdAcm7pGGMmGcBFSePOyQByTstHjli58VqjLVh0YTVRXTv1kgLoMzRK01EbOMFJ2PTPrXd53LKaGr3nZRahLOHYGvN/SztprUZb9uY/nZ9WLubWYuhH3KIuYMecb3rAKtrt0Izc43jYTOMBwiReYpxkRcxWafT6XQ6nRvCQVf70D+87nQ6nU7nJiLs7uwwdQtwp9PpdDo3GRHh4MGDXQA+jTntBODoewxnmgxjE6ZaN6oYZhunpEE1XCCRw/xlJeJFN+KJ1yaCRXSzbvpNIzwZZAzRwj3ELrNww6mGs9IJJ61FO2wioTmBVMRDkCSlcB8KqDThrjmT3QkxzdgKxFYKhYS44Dmh1RCZUB1ChDEQr6i1Ds5xbE65EJ1D7BpwDXFXNMWXJbX453Ycpbl5N+5LszAupxT7aREJLKWiFLxKuDKtOVB1wMzQeQ4HoE9ARqQt0xybYSJ0vFqLes7hiAbCwdq6Vq2GA7PWFkvsEddK9NSKh8gZUb6CJCEPKYRBhhY7DKVWbK6k4UyShutZVVu3cwJdUkthZk0aM6lUMkZa7MJ8GNeB6gV1w0nUuVKLkRaZIQvuI+7rcAprCremG541hC8L8StExhZP3kTZiK9N7dgXIB8RpzzGtuQY09J6cKGNNZFwFJcJlzkcryaIliMOzNyEUUkRTd1G8iaGNyLDo2fXnZjQIMT5FEdQxGNiQLh3BzRFT24MmehvFQdJI17XLZp8E3/RxqEbQnTFhhNfEM0hCEtbj7ONaw+hObdrtrbrTZpb18JN3rqFJbU+4Y2zX2jC8dHHu4nITZzcxEtLbS7glPG6xvC4drc9whNm1hzdBUuO6ILc3M4yjNExPreoaYcxJQYVclY0D+H0dUMlIzipRVyrCMtRERmoteCeMKuQU0wMgLjXACklkg4gxjQbUxXmeR+bDh+JSjfYNHC7SHPra3MxO2l5kFESxQ4xrfeYh4HV/h7TtKJygDQuyDmTLa6PPCxa12+biKDLcOIa8X9pTu2NmO9tdJlh5tRSIsWAzYSAiTJVSpW2zhwTGkRRT0iZED/SGa2iMWFHmxsbx+uEy+YeNbftG2LceIUWLx7Z2wOs13FXWe+HK7j1xKND3DtsavdI2kSTIcTgTqfT6XRuAEnK7T/3Aj70yavwxfJkb06n0+l0OqcMmhLn3uk8rn7v+/oztNPpdDqdm8HBgwfZ3d1lb2/vZG9K5yRw2gnAWRKuRCSsWXSXOjA0IVY1hEfi7+IWIkBtQnESoBJiSQhdmkOwcB05olr5Vrjy9lpPrX8VQam4gqQQerwWXCs+HIgeXJUWS9p6evHoPXVFRfC0wMockbOScN1ESQ/hUiU6h03DCWm2jl5WDXcfTUiTjatys+8k3GfEmhN1jr5NTdJEFrbir2kK4a4JwV5LiK8ASRALQYm8wL1s6z0lhcgm2pZVb/GqjlG2xzZ6hQuiGXNrAnJpccUhWoZLNSGlRblKbsfLmsNxjvdpSqNrCheqCLJYUOepRVdHhHOZViFMpzjOogukrpEc/aPuTi1rJhFsrrDOjENCpZIS1LnidaamjGSh2kSZWpz0MJKTUBWMzDAaU5kQosPYyxTn0QVS3rqV0ejjdVJ0RlcL4d9jXEjadDfnECo9BM9wSg/NDWnt2IeYLNBE5aGpvCGmhRi6GR+pnYNwvDIsEASb94HWvSoV6owQAu2mbzr0Pom/pwylic4bwZYakea1oDkdiXwWjW7fFgMdPb8JRVoccIi7MiziOnEL4dZKE3ijUzg6ommTE9pkC6mopBarHnHoogmb5yb+ekyGSBlKO49GOKAJh37aTLhII9g+6EC1ipepTeTQiGWuc6Rxb0Xn5hT1GXVnxElZSEMO9zkWkw9ckKzotEK1RbzrgNpMWi5QiTks1cBmwzVcwDGJwkgpRXQ3zjxV1uuJ2cHmGTFBkkVUuEo4nVNE33udcBJojmtHMz4MjAfOpOhAlZF1UQ5PztqUKhrnIoPK0NzpEutLGmNWCPF3eWDrAGcqofF7hVrCJT/PlFrjum7XqpVKMaOsJzQvcFmjJQRkUZBaQgC2iqqS8hjXvRTIbXIOIzavgIxSkTSQkqBDxPyHe1hav3ib5FDWeDqITYdJaYgnpLSJJLZuD5FFiMLlqAkxnU6n0+lcDyLK8sAB5Ior+qOj0+l0Op2byGJ3l3G5ZN0fop1Op9Pp3GTGcSTn004G7DROuzO/1RwlhfA4jOF0dbA0NL0oNUE0HL0OIQr5RKgXIQgIZSscySaCdNMtubE2WlNbCZfsEZHNYxu2vaJODcU11ueVTU+qaNo65xzFNs41keZQFdzC7UtKACFKTU1gItyJWRMqNEdpOeLqleaetBb/7IKLRzQrKRycMrTXEh21oUCFKOtEJLPmcJCaIxJioBOOUXOPOG28dSODp4iuhdwqXJs7sInkXqLr1pqbNTp+Wz9oExEhOodjkRAZpYne4dKOzk9JIYQmV5IXJKXocK5QZaTWmQglTlDXIZJt9m3YJatg1TGBpCNlLsw1sUfBTBhHQZpz1MwjBrkWUl6EGOgjqJJVcdvB54kqQh6WqO6jteA5+oalRVxDcypKxk3QOiOL3RBMbQ5npFkIjJqbf9UjaresQ6SCFum7EXabGKkhMIt7c/K2XmDNIbQSx3zjAt9E6IbrfWjnqLlqh0UIciqtE1rRnFucs+MuMZliO64lXuctBryNwRCOW8OwaIjc1HZdaZxuD5FQRFp/7abT+Yhz3823ExVo11YbpLg7pfX4YjXGyGY80xKE64xVo5q182kt0jxRDXLadA+3zmaU6tqOVThRzWp8rRTchKH1dydJ5FFJmhAVzAqVFjsvik0zFKfmMWLnvTKoMgwZ0Uz1Gi5hvE22aBMlUKBiGtdLtcp6WjOv9kLYtejMlloAQ4127SXcPKKsc/Qrey1x3xFIUhnUqPOaed7hcFUOVeXwPJFMsKSkvECrk7KTtxNKSkxE0LgfkQaYV3F+atxfzGZqMWqdKdXb8R+wUrFi1LlQ64ziVBlQiwk0MXYqkqJDWAVqnVtSgSDVkLwTfcbk1istSJlxIOeEioeSLgObvl+vE3Ve40mao/8Q+AB1bk7xhAwD3iaZMJe4V9yoJ0+n0+l0TncO3vE8Dlx1DdeupzZZtNPpdDqdzo1h53bnsNy9nPWhw/0Z2ul0Op3OzWAYhpO9CZ2TxGknALtLS9VNTVCM2GKRRN641MYB9xoCggxUsxDHJKJEGXe2wtM2ztk8YpNbHCxWmpMxBEmv4eptZksivjhjcw3Xp0Y0rZfoo/VamvBWQ6xzbxG/i4ii9ohJlhbz6yokjNCuU4hYArYuIZR5iXjkxQ6aQjiTauigiJTmuIx+XxGJPlRp25uGSD3VOAZupYkqEdcMRKVsbmKPEKIz4aR1qeFyHnKLhib2xyqSE7o5ORJiYURAe4tsTUgT4MSEJOHi9aRgBU1DdPJabV2jtF5cD1FahiMieTV8GPEsIOH+NhImymzCPM9gShKNXmBpxzEnJKWoCC2VNGR8tUdxpaz3mNhBUossXoyYK3W9RpdLxKIv2NyxUkhDZliMWJ2peSTX6E6d9w/HMbDSLLTL+L+FCCjDEmzeiqaaxrZsxXO4hH3eBxkQGTjiot7MHShHup1Fto5M8xIduN7ij7HWq9uiuNFwy7bxtzlPm/jeEOBiokQIwC1iWUKQdI+JEarhTKXOLX43Jgzo5pqKrF0ER/OwddeLtvHhBnmIruAm6rp4uE3LhHvaTlxwjhJ9rcaY8iZymzeHf+sFtrK9vtxr+6NNtI8oc6vRKR2irmGmaBOBS5kotWCuaOvUrVaptTAb4EodIlJ9FCcnIbUJHdVDZI6e5IJ4uIXneWaa93FRBlUYpe03WC0xSaFtdyQDJBCL8UqirPdYuzLtx0QGtYIkJdHis8VRiZ5gadec5AGzOe5ZOsR4IfqJsyjqE/N6n0N7h7nq2kOcNQgyKotxgc77qDt5zAxDJukEkmJdPiMaY9brDNKiq92opVDnieqC1XBee5mp8xora6w6tdmdxQqaNNzXeETjp4KOY7y3hlMc2ucB8wpxiXuoji3Oex/HEKuYENHgORzn+IQZVItJNajA8qwQxMuKTU2AlPh3bPsaV2H3Fj6TOp1Op3N6kMYF4+GrIe+c7E3pdDqdTueUY7j6E5FC1ul0Op1O5yZz/vnn84lPfOJkb0bnJHDaCcBI9L/i0aPqtM7U1oOqItv4W1LEmEop4dA1wSzje2s8g+QxRIdSm5DieJ0jzrYWDA+x1UPgdSeiR0WAECt0o72lIYSIJu7GNyzErNpEQQgRzko4WrV1Fzfnm2uOWFYBHZrIqzg2d6YAAQAASURBVKlFPS9Cpa0RXy26wK1is6FZmm+zdaFKiG4iEuttvapGc3Jqi+yttjWOUltnqkTscxUjkZoG57i3yNjWWasS6/Ey42RSDkHN3DFLCJWUZRt9nUiQB/AZ10SdV+iwaPvvpEVqxuEBtzkcqICPC7ysQ3wfd8JBWWfMoViluFNXK6oMeFmFQzfncEungVKcMhWGnBgjzxgfwnkp80wtlZoH5n0nLxfN5QjVHJnWiCsxROaIrtXoaB3GAZudeY73VIvYaNKIKM2dGtHOMXlgRPOyOcRTc1zvtC7S6D3WYSeOh5WIqcVCOPVWpryNLW9u2diYEOZoEeeeWnxuc1fTHL9aWpT0ZiCGoCpGuI/bONM0xrfdohuYjavcYrJFHrfRue71SO+uEQJfG4OSU+v8bZMc2riKiRiCW0RBo821u/keTTP30pzv4fr13MRmkbYeWkw2MTGCGLvWIsbNjFrbNa0Zs/i7zRO1Ktr6bGup1LrplG3C9DS3jmShrNeUGudAFxqx2JLCVRwHBNzQNDLPK9ZzZTUbVuMaISeqZ1KOSQnqFc3tLLSJJrSJGq5KmVasS2W93scdBnWyV4ZxSc6tzxsJEVYHNC+oqz1UB8wNIb4ec1QEyRktBZkqdb1mtbfPoUPXcu0opINnsp7XEU+tQrYdRk+IrZGYOhDR3dJiod2jJ1kAix7j6kOY08WgEt3KZaZWp5Z527NOcbTU5pCfQA4irJDJyEmxJKg57mU7oSS1+6xE0TeiYxP4I2Ggziu8zOFgp7Zbr0HdRy03wXwEFnEpieBzoRYPh/K0wvPYBeBOp9Pp3GgWZ54Fe70/vtPpdDqdm8rn3P8LuOL/vfNkb0an0+l0Op3OKcVpJwBrbqKJt3Bir03M8XC6SnR4ihDimmiLpi2oZixriLs1on6tTCFK5kVzw1pU2gpEHG1pIuawFXSRsTniNsJTiEC2cVrOLcYYgTRQywwOqo5riEVuERXtm25bhGqpRUaHyIS0OGXRqNaUkSQSMdhZ27cLquFe9eYuDWdtiu22eXPkWhyvNAdoc/e6bqOjRbQJ3fF9KCFcukbctE3RL7pdR0TwqjuaEtqOj+SQjrAjsbwyDuG+nObmLo3OUyeOm6ShCXxNxE/hunUMb7HcpkaZZ6onzCvztKYalHmFMIbAlgFdorYmqQEzM8pUnGG5ZBwMMUHZCUdxUmpZobIgFSVnxQVKFWotpDxjlqhWkJriWKuTFkuyrBnWEq7QlDChdfrGeEDT1tWIHBEpIVycYZrVJvBHrzLz1KKXWy9sjU5lkYzbujkzWxkzhDisLdLYSnTDYqgMEbNt8W+hjaet0Brx5KHxVnRYtDFLE529abVNfNYWJW627TcW0TZGW/S3t/5djwjt2FWPY+DWBOt8VJR667NOQ0wkCIsszQYe17ISDuoIIm/vtXEEh2PXvVJxsNjW6lO4hgVcB8SdJBWTEIzrXDBpEdC1NIExhQaM4ynHa6f9cOuXxMQ6nMW7OyRrzmwzUhPGixmH12tWqzVWIozcUqZaIumMewj6ktp14eGe95itQrVCmQvTesU8x6SRPAxksYiBz4KJxSQQFHIm5QXiDosF1rq+3S0EcEDate8poqpLWTOt99nb3+HQasEoe8yqYBNJYEBJ6zkmoNSCEg79jRPfRaK3vHWHe13F/XXj1sbD4VzWOEN0Ym9SGkjRQ47GWLSY+KKsMc/RbywFmvAsRznZRSNyW7WN/dU+phkrDr5CLaLJrcbkFy8Tmp1kILJqznHD8jImAEwr3Eo4w2Vzf+x0Op1O54Y59x734SNve1vvAe50Op1O5yaShjEqrHoEdKfT6XQ6NxkRQVWxprd0Th9OOwFYVHFpAtSwQGoNp69VcA1hqczRl9mEzuhFjehh8YhczTkcp0bFTDFXXAyXhKmimqCW9sNpE85Q3FP0cGpGpL2fZowSmqiG63jb8SsphF8rmAxQQyxRkfbhUbgWQ/htztQasa6aR6xWpBRkSKSsoCHMeS1oziEmewVyOFTd0ZQRdURah2tz4EHeCtkbAde9RpSvEgJNcy1amSP2WYCUSJpDMG19sE5tzuaEK5gbmgdSO+auAzatMVKrcg0ZSNMSdyPlvHUXG8C0hmEEwgnt5uHmdEVc8DJjppRSqJ6odYpa22mFe946YkMoGkiMaJK27oLNlXWupJ0Fy8EYhoQyI+vKei6YC7U4eTEw5riplqlSa6W6xOEqBU8JK8pCCkNWlrtnsp4qadyhTGvAWhR3iXPZOk5DZE9tQoI3gdVbhLJt3bmSF2z6ft0txm1zOaIjm07cGNts3enIIs6pFUiLGFNWou+0ucLFpuY8lRAeHUSa+EmMyY2blyb+SkpbwTIuwDa5IS1aHHSLP2/rbXJ/uC1DvkcsOrY363ABq3PsH7md86MmWPhGRK/hyLU2sSLpUf8ubXwbjuPV22QCmlu9RVc7IcaKtL7kOBa1zAhrdDPhAMOImHFL4fBVgZwXVDOmaQ9jF8seoqyE475SWU8z+5MxrQpeKkrBJFGLgxSSFwYsurfTLmbRH661uZVR9tcr5qrYtIeg5JRIGufAUmKeK3MTX6lrFqOzbPHaBhEXj+FuWJuk4imBV8q8x7SeKDIxrXdZ7e2z2tll5Yeji7hMyLCDrK+KySWa23Xk5CwtupyIbnePiTLSOpNF4r7ThNfoJE8x/t0RXca9KuW4LzX3rq/2QMNRramikhEpqOToofZwzotGF3VKM6oDmjM+rzGZohfYLca1DtjqcLjZyx6aB8qwS5LoKC9ljfl+TNqZ9mOo5gPNPd/pdDqdzo1DaiFf80nms8492ZvS6XQ6nc4pRX+GdjqdTqdz88k5c6c73YkPf/jDJ3tTOp9hTjsBGI8O0Y3YJQDVwimGtIjmzcLalrEmUoUL0WsTmLSSUPJAiy4emac1SMWWYzhZ3VADjeLb5oYj3HZWWt/vjFgJMybRP0rO4U42UK/hxmQTbSvhXqxzCLKScZugrkP0tYIXQy16bNUq5gWvTvWM2oyoYB69mVKMlGv0gmpqsbrNFV3LNiqYOiOeWl1swmq4MK0WXAVzGLKT0oDkhEnC5nV48uqEZm3apIew6LQu2hE338b4ah4iQreJflYqSROJVuG6EQebmLQNJa6OU6iu0RksCj6hqriHI7eWcHtGv2ilEpHAg+Y4jqUiY0LzgpQSpJFp9TFcBS/KtFIkOzu7Cw6kMxnyPof3YVUmik2ojOQhk7Iwz5VS1mhdINXwlEPkTEKSBYtxYGehlAM7TBXmaa/V6woiC7TObZxlpE7Re7qJcTZDKEgeth3PkaErEfOtMVEhHLi2FYXRcJD6NopckU0ksrQ8ZSstgnpoy7YY3tRipS0mK2heoITILLX1ptJ6d2nuS/OYmLDp9PVw2op6iP9uEROchq2wjTTBXBRqjTBq2X4rRO80tmjyunVouko7Fh7ichOpo9/XoYRDH6QlYzdBufrWhapuza3u0YGbYnSFUTmTRfBaMFVsmpBxxMXjnmKbfTYWyRDNVFPW07pNVqjU9T55COF5Nmcy2F/PlGmF1IK2fuU6r6kejcw1DdT9fWzcxWRNFiHnRHLBRJjWE9NkeJ2Q6hGdLtFbXLxgs1HmQpnXeDvOJs5sM0lqm8txZAw4M5Ji8omLUnSJDQdxW1OmiTLP0X28P0dSvVe8hGs4UgsSqhkkobqK635eI2mIseQRN76ZAHJkMkJ0rcvQYrHzgNoqEhhqRdb7iA5tMsUaUUWpqCbSsEAwkpQYb20ig09rNNWYOzDE/cKNuEdKuOepK2ACBLUVmhcRwV0PR+/5ZjKKC5T9iPVPCeq0jZfudDqdTudG4/3Z0el0Op3OTSWNC86+8G587KqrT/amdDqdTqdzyrFxAHdOP05DAViOxI5Kc9vKptdWQpyS6EEVov8zInSbG1FSCLBNrEBD8MhmTdgbKKVSV4VSLcShHJ28qkSXbRM+Y92CJG8RtYBkNEWks3s4K0kJMY0eW0lNaAvHr5uFmIaE7kXEMkcncUTemqQQhJ240FWRjUBaDHWPdF+hxeR6tHdWo4XiomrhwtPNthFxwSJ4iZBgcYveTMBEIyK1RQxbi1gVYhtCQ064NiFeUxzSFqFsm6jjjSOZjFGRLIhXXAd8vWpO54SXAsPiiJhuFdMENcR5d6dYZd7ESku03FqdEA23t44jNlXMoh9aJSGSyOMOZb3C64APsC6CrWZ2FsrOmTsMg3LNXmG1miimMIco6Tpg4kzrfWS5y6ALzGLczaWSs7Lc3eWgOut5Yn99gNriazWPhAuY5pKMCGUZF9Ejizd9c9OPq0DruM0Z6gRUPC1aBLK38apIzrjlFu/tW7FfWrR02HRTO89HHPA0M7sMixCglXBYhucyjOESwrOKhDgd5b5oqG603N7tuRW3iL124r217YN4jC+juZ1zi1M3PMeolE2kr9Bcty0a29ukALd2rVmb1NGOAW2Q+iYevanLJByNvt9NjrvEtSntuqG0SG3PlGEZvcB5iHVPM5oHsjhDlhBPSyWJMfnIXB2bCyu3mCBRnbLax+cprpshU+dVSxOIGG8lJpFUg6katp7xDK4LZrfoJZ9WZA2xXsbc+qFH3IxaY0zWssbSMo63ZmyemOaYHBGB64YMu3HPW69jLPlMscpUCm4TSQY8H6DqGCIw0bMrecSsUkq4skkD0FIV2vmM07tqEz6ORIOLKlqP9BjrYhfWJe5TdW5x5O0aaG7sJIq6xQQEjzhpqYWkwtA6tmV5ME6zD4hNIEJSCTd3HqDMrZN6wMoUExFSOKIjMl7x6TARzy/gJe6Tw4COB+OaKhMq/UP8TqfT6XQ6nU6n0/l0oykxHjwDugDc6XQ6nU6nc6M5/QTg5vzSJvaEdNminq1F6aYRFbB2eMRLxKpKinjURPSICk0gcJCMiJBy9Lk6Si0TVp3ZhemafWSRyItwqol5LFfmEESluTvrHN2eVnEFr0dH/4aL1EsIabLpLgY0RTexW4ltLHO4JFukbuxf7K9HOWv0l+KYDG07UkRUW4iQbk7NKfo7m0PSXUKIE40I6+qYW8TSqlAr0MRyLy2Sd55D3BkW4VSW2C73EOuSDtH3quHWtNJiYF1DzGp1q14M93Dzeol+5jSEndpTwusan6b4ujjuU7iTq2GywKZVLFsL1SpeostZxSjFWAwwjiGqUx0ypCwslrt4mcALViZSypRpzZ4sMFeWB8/gzHFGrrqKdZEQVCmIC9WcWgsySrh4PTp9Z49zWtOMCVRVRGpEDrc+XBkWIZSLInkJPrUI3Nx6ewtSHZHoUqascEZILQrcW5+1buKTBayNCEkx4cDmWFfIsE0njV4dsdrieNsQby5erx4CndlWJPVaMaSN5dyGXAiv0Ws8IMlCqHff7pe2bd2IzqFna4typrmBaxuzIYZSQiDcxKDbVuiN3XS3Fv9MG/ccdb3E222jqQHJY3OmWothJ8ZYjFpgRnSI/myBXAtZlEkS81yiPzdldFgwZBhTdE5XF1LKyGJkFKXMM6u5sreeKCtC4J7XhFjaxFMdIiIeSJJbPDvhkJ/2qZqYGCmbOO52O6LOcZHk6Hp2ydS5UMmUMoU4To1JBFlAKl4rk7d4b1WS7W1TALRMVDfmYtR5HadznrA6U2qhFsVE8dyi7t2h1nD61zYem/saGaJ/12ZUxoiElphcoj6TckZ8M3ZigolS4/5iFinhOOpKivk2pMWCnBKSliEii5NTJiVBNBICBEeHHVQPtGM1HXEgq9LCrmOCSJ1xz1SPe6cOA3lxEOqMrdeIptimlFuyAqShOZ07nU6n0+l0Op1Op9PpdDqdTqfT+SzjtPz0WjS12NgaomuLwxWPTlVvebOy6b+lNrehkEjRX4vgJs01PEdGrOfWdRn/H4YRH51cjUF2mOc1dW+FEf2iVQVNCbeCpuhuFWoIcM2VKNKclyLgCTHDW+9qONMshJIWvSpA0gVGwj1EUvNKEqXajBJdxClnqBVr4mDoeC36ViwEskGbc69Epy6C2kZQrvgQcdXh9HXchGpN9Ekj+BqnHROJ2GWamLwVMs1BDPcccaoCpBC+JOXYXouuYU1DdAEnja5UDRGL5szGCugYDtBhgdeKLkOsjSjijLqFyGMhtGraDeF43IVaycslaeMYngtZYRgSZblL2bsW1QHPGeY1VZS9OZKFd0bljLPPIh9asZodq4XETFZhPSvr/cPIIHgaYNwJwSkp8+F9JnEOr9ZUGVo8eA6TN94mJRQ0L1FdhBLrFUHQNIZIazUE27QMV2uNSQzhAHdaeviRiOWQ/WMMpeYWtujC9ib4xjUR4zREUI9z1ZzeG7ext/G3EXw9RWdzrK/1AZvilOhXHUYo07bbWDcR1RIdwiKOCnF9VT/iHt1EVrdr0xHMoyM5OoZjLsRmosJmn6t5i45WkmZEHLEZQdu1G33Argrk6MzGWh1xdP7S3LRs3P8poThZQmwupYIZOYfg62pxb0gRb5zzErNCqRFD7qVgxbAoPiaJhVDs3u5NA7WsQ1h3qG4UoNSCzxPqThZhyCGIklK7thQv0XvtdRWR70isSwfUK6qC2xyO3VqYZaCsDiHjMqKlkzLkcNQWM0opce14wXAoU8Q/G/hwJjpm1Dc94xkZl2iLXQfBVVFVkiTEFyEukxEx0rBEgZyHNknBQp93g6wxAWW9jrFSK+pGGhbh9N09gEpF23hTTUga2TZHu6N5JCWPZdrklnl9CLF2Tqd9bF7jHnH4kZRuCOvwtC92kSQkzTFJxUq4hMWAiP/XcfdWfz51Op1O57ZLGkbOOO8CPrm/Ptmb0ul0Op1Op9PpdDqdTuc2zuknAKdFi86tR4TVjWNRh4hXNmv9sy0CWSPiNVJitQlhJTpSqW3FGqJli5bdOFwj1tkYF8qwOIDVOTQ6d+ZVYZoPw2KJDnMTYZ3sjjqhLKZEKxANcQgJrZkZ14GmthG2xYR7wbVgIlDC4auamx6mmIFmx1svbjiMC5pbvyvRpWpikUBsYCohtLXYZ0kpHJnV8JYf70ZEEzfXnvg6+jzZuD1D1HWz1rmqpFRa7WxESYsbkkIARAbwGVxi7zQchXUTne0zEeganZ6k3Do9LY5BlehSTmOL/51RCcd1XixDXDq8ApqAXWYsV2CBLjJSZ7RUvAieMzkN+PKM6F22CmmBNdFrvV5RSmLnwA47B0d0ZaxWUGYjJUNLCGnrtBPi9bRHHneiVxRjXYX13gqjkPMC1RhDqqkJguAUIKMSTlbBm/AVDmhBwslbJponNGKcvYQ4Tgr3tabm5K1tOY/IXjHc5ibElRCTN7ZamvBKCM8im/MqTfgtrcPXEJdtzC/VsZRaJHNur0+xrFdS23Z02MYFS+sRFhFIG8E6nMyxTxJbUeYWia5xHdY5xrp7izKX5j7OzbkOc5nbterNhRou6siyji5iFWmR53FrdEkRb64hxleJrusq4RxFIA+KoKQc/djGiOQW2a0VV2VdnNU8YTqQslJtHbHKKTGkHO5VPK4fESwvqV6Y5oLNa+YKpdQQ3B0KBju7kRKwibJ3i15dzdQyY5KxUjBzxiQsUqQIlFIoteJphPWKIecQN2OKB647FHfm1R6OkFRDSK4l3PN1ovhISUptEwQ8JzQtUCDJiPoUx1EETZmUE6oDSgi/SZWsxNckPPPRT12jnzeN+LTCJaKtdQi3uCYlDSNpGBANwVc0R1R30jZPISYSpJzQ3MRfhMpAmUbmuo+ZRdV1WeGiuElMUsgjyed2HwtXdpKEUtBxiHv/ZtKDtok3nU6n0+ncSDRnds+9A5/84AdP9qZ0Op1Op9PpdDqdTqfTuY1z+gnA4uG0M4ueWYuu0XDbavt/xKB6DTdZCFQlOmjNmtg2IBIuYJcmwuAIrTtWE26GeQhtIQUXcsrRYysJG415TkwTTHsrCgl3py6ElEMYEauoKEoTZa3F17qEk1aaU1KbU7i5cxXBh128TuF4EwuBxTdO3BoRwZtY4OaqLG4IGbWI+Y1UXmkRwLT3KxGbas2tB0CI5i6OcqSjNhyb2iKL59bjGb29TVEGiR5QTxlDoNYQe7TFassArlQr0dlpJZyM7rgMmDp140xWQSSEGhHHq4EuoBxGUHTIVFFq2aeQmpivmAsVZXZDyWQVxmW4itdWI+47Ca5LrM5oCqHTS4vVlsyhw2sWo7DcyQzjyHpPWa32qWMIctN6DRLnsc4zyoRpZl0q1TwihlsU8qbDViRELm2OVZEB8U1EdvRDU2rrgrZtnPMm6zh0QUNyBka8Rh+qbJy+RLQym+hmaox3whm7+bs017NLnL9w2bZzv41Bb45hp3UTR4eyeUUsooa1iZzRHRzXkm+dvRDO9tLim705omN5JyKnhYhG3wrYXtt7Ea81QkS19nekRWA75kIxYy4Fk4hp9yaYi0LKA1krwyDkFNe9bO4NGCaxDqsTtomQlnC5bnqQdQiBXgVMElM1pjmcuSllvK5JKqiM5DywyErOGdWIpXY3SspYUdwUnyayxAXokkLc1cx6mrEkLETDla25TV6ZY7lq1BbtrjnFZIGUsXmfucwUCyd+ziOZ2iKbjWJzdFmTGdqkFKsFbceiVmc2Yb2eyZZIXlt/bo0u4ySgC1QMyWPEPYuQ2j7mFPe3nJo4HLEJ7TwKyXN0h8sSWS6hzqiO0UWtbcKClbgeLcaaqEbSNzUirRPReyxjiMoI81wp5pRpjkjtCjZbCMeqaI2eY60T+BBx4oAxszHPi5foFFbFF2eipdwKD6VOp9PpnE7o3iGkzHgeTvamdDqdTqdzStGfoZ1Op9Pp3HzOOussPvaxjzFN08nelM5nkNNPAK5EVKp6dH9OrYPTiW7b1OKhBdBNtHAITxr+tiaMaUTnoggtflhyE38ViITmEDZmXDNK9L5qjihZMSGPZ7LcqZQpU+ZCMWdar5gmx8TJ44BIRDgn0xDQTEJM8xKOzrTAbRWuySboNgUR14iYRlM4lL0JvqWiydE04vMU7sE0bHs7QxcPd7FbQUiYKFLm5ny2ptlJE+MEKzX03DwCFjGwXuN4b3qDN7G1rs1DaiRVNknD4k3EVdAUUcgh6EUQsZkhsunxbN21Nc5dOEWtdYBaOLfdmlN5wJUQ/6aJ2Zy5NjFaE15nqirr/TXuieViBEmMywU7dY2wps6VahWzSiJtu3ojAneNi7A/Kass7OyOLM8YGcfKuHLk0MThaqzWMyXD4FMT9QvzNEUUsxXEJ1Jehrs3KnejJ9dKCJxNsxdoTl5tCc21OW9TjEvZdPGGMGkbB20eEHOszs2Ju3EwtnVREcLxjjSHuVmIoMMiBNs6sen13SpjwKYjWmh9sKm9NkW0uDPjKSMp+ntDOLZwOWtqkwa8TRbwdgGx3e84z81V34RptxpiYdJwJmuOyQ61Ih79wOHGDwdrNVossoWb3MEsnMHVnCozJgmRgqR1dM6KISnHdZgG1J2kCamGDOFm3gjuqinGhAgulVqFOs2IQdJEtYqKM2hMkxgWC4ac2tgHcsSi27QPVsgYulhS54lBM8Uq81Qo7pR5DbKDzIWUElpLOGCLbUVu6swwnoG4MdXCXDPrmpqLujIkZZTo/DYRzJUyzaS8ZHnG7VgOcZ8p5UwOH7qaldVYf3XqNFEs7mlUR7QAjmWNe2DOeKmQBkws3NleqB7nV7yJ5qKb21WMlzyiOGnYieWIaHPzSC4wq3E/2aR9S2rx8a33WgqYgmRsPeHi1LlSirXwBsPndr/QhHhBTVqU9gTDDtQJtVYHIC05gXW42VNGSNRp6v7fTqfT6dxktLTqDfqH151Op9Pp3BT6M7TT6XQ6nZvPcrkkpXTDC3ZuU5x+ArB4i3/W6EpVIvJWFFdpMthGeMohLG4cmU1oc4AaYo/TYo/LmnA0hjDnKiHaRbhpxKtKi981b8ItgCNZGRgZkmKSWe4smaaJ/dXEfHifUis1hUswj4mUFE3hvBsQRKatZu2atjGlQPPihjC6cXt6+6q54WVCRbHqKFPEBpeCDgOlruM17qh4OPOI3tcQcCCJYBJOXHFDqoEWyDkc1nVG04BqbX22hKKXBA37Hu6GpnBUxx8FE9wLNozN+SiYScRTz+3vG4dvqPebettw5FrT3suMmVBtjtjtMrOea+ttrXg1PA+kRIhdojCvmUWQ0alZyCmxs1iAC3v7ayop4n8dKGtMhCQ08d+p88w118zkcWC5GNk50xjGg+Sr97h2VZgmqNlQShOXZhJOkkTKC9QrKS1aFHE7NhbuSFrcbQi0MbY2LmC8udk1Qy241tbxa/H15rZ2NZDWESyEkL2NEk/t/+08WUVUUXdIOc7xsECstIkOCSgx3rYOdGJZDWdqOM0tYsObaI7k2Ac2kbrN0Q7bHl9vjvy4SjSiva25nmXz8hZTbZu1hdu36XbR72qOWQlXuIdDPbuHmKfRZaxuW3HdvFLNmCYobky1OUebszkPys5yyU6CwS36tJubNjqLheIVc2UuhVrmEOY1ot01ZyQnNOdw6reUAIYBL2uqWXRni5ETVAzygApIccQz4sJcHCszc3V8XKLN/WpWmedCLRMDoF7wahErPcckgURlSMJiMSKasFIxjVjz5BO7g7JYDOQUncSuK3SxjASCPDDg7OTMGVkYhhizItIioMNtLF5bMEFzfdeYyWE4RqFaTJ5IGuK9uiNpEa+RiJ3Gm8PWJfqcS6FuzmdeREyzEPHjeNxvxfG0bMLvhNe2/7ZJBEgw7cUkiTygVtCcoYLZGiEmRsh0CNWMDkOL804ttjxBdbAJoTuAO51Op9PpdDqdTqfT6XQ6nU6n89nHaSkAR9VtRDLH12iuyohg3kQl+9bZaM1lFq8X3bg/m+NPQTQfJSKHEqmtm5QUr5UwtxKttkecjm6OuODDArHKMAzkLIxjDrdqFabVirK/z7weWCFIUvIikasxjANZo4NVCaHQ/Yjo7CljZR3Cb24CnZUQdkSpDkJtjk8gEf25Et2yQgiv4QwOMVCa2AqgqcVfm0NO4USuFfcCCL4REVOI0waoh4OYlCNqWy1EGCRiViXE9YjRdqxGt6/Zxu2bsHlGUmLbR5sUm6Ib1nzGfKCYUdwoJtR1iLW1FHw9kURBY3vcK6gwjEO4CSnUtVFUyMsFCWVnR7Bq7O9XzCOO2qvhKNXAmMi6iK5hm1mvhP21k9PM7u4uB26fkWsOcc3emvV6L6Jr84iKRvzzMIazVHNEZefcnJHNjWsWrvQULnOvE1Q7qgd3AmsCsJV2LOO4bCObLbZXJKKmY4KDIOTmwI0+XEhtvkCI4l4LahGjLLT3a7ngTrjhRdo6VOIa2Uw6cGs9wx4ObquxmZKOutYAImpYNDdRVAjtON43Ji9YjDNAxFsnt22v1yYhNzOpRfy1CGOKUWLu4QjOLeK3bYNZdEFbi3hXg1wqWuOaSm6oGevi7O1XDh1esVwMLEdlZxhYLBYMKYWQ3Hp2qwleK+YtZrh4q9GO3mzRGN+eR7Do13Uypa5igkEam2A9bduYNSmZEStzuyYr7k4pM4jhmijrCXNFqsPODhUwErVU6rzGzckCSR3DwlWdRqyGc3m5s8tyzKQU96vqlfU0cXi9x5QW7JgzDsoZS+XsAwcYtU0MqDXuQSmTUsJFY5KJptY57phqiLHVKQrM4a5OmsPx75UkNVICthHfMWmmlplaa1xr7sQKHCMm8qi0yRGSovu4TG08tKjo9V70nrf7l3iNyGfNSFnFZAF3xFdx7/aMzAWZ1i3iWtFhhLIfzmCMlPTWeS51PiNsahNO9jpOZfoxvOX0Y3jLOZWPYZ1nDn3so6DjZ/y9O51bwql83X220I/hLacfw1vOqXwMt5/PdTqnIKfytffZQj+Gt5x+DG85p/ox7M/S05PTTgB2YxvRTPVw/aHNnRvO4FprGCYlYkS3ApQmsDk6Li2csZIi/jnEPIDo/qXGa1RzCGzqSIqOTnL0awIh2vgEQ27OXUJg08yQhYSxIGPLkTLPFJx5f0UpE2U9sL52zX4eSOOCNAhZJfpEMXLrxoyu3BRxuWYRo0pqQtqMiMShMEXEQwhv0b9oRTWTUz4iILZ1CoRj11tXqx7VyZnHcK02N7JbxWVEU4r3EA039CbGGsFLJaVESptO2YgODnevYbQo6Txic8Qmm9fmCh2hrCMFWqCYUOaJijNPE3WaKaWEEDzvIS4RmzvsMM+FyQd8mvCUGHGyZpJETK5YRccFMlUO7i6QNHD42quppTbRVEK4FI1obcl4NVRmoDLPxif2ryGJo6mig8BMRBfPK2RcIDZFlLg2J3VKMQ7cIhU55xDeqS0aWSPi2mpzwxqSltDkvoixbbHPLVp3G/MssQ7RsbnarfUKt37dsLijpHaOwSXESkkR2x0x49JeG0KviLUIZt1u1/ZxZiWEac24W3RnI9toZVyaezfGCroRdjdrqGHplvag1LZuTeEG3TjAEczBrERXb7umQxcctvHBiGOi4a4vpQn4iq2nIyKxRb+wiWKtr3gUI0thNVUOr1ZcqzAsF+wuJw4sRhZDQh2sRrS1VNs6qTcnw9zBNvvexHnNWN1sRwKf8HY+RFM4ha20yHIhp+jJrXWO01smSCN13qfMBQEGFVQzXiPFwOqEl+h4MFXWZmAZyhrNitaZMQ+MixFJEaM+mXFovebKq67h8OFrWRwU5OAuQ8rsnnk2B3YXjE44wtv5jDEYY6ZWi/PQ7qvRQV7DQKs5vOau0XdeZlRmVFv8/GZMWjh4aV3EVguWFth8OHy/TkwE0BT3CSecwVWxGoK8QLjiS/Rwa8poncgu4Wz2EOvdLCbrlIjTdskIA0kGpK6ROZy/LhbbOe7c7GdR5zPPrfED9un8iw70Y3hr0I/hLedUPoZWZq7Z28fPPnhS3r/TubmcytfdZwv9GN5y+jG85ZzKx7CuV1z+4Q/jB848Ke/f6dwSTuVr77OFfgxvOf0Y3nJO9WP4iU98gtVqddLev3NyOO0E4E3qbIgUBWR5JE1Wh+YMmwlRmIiLbjHI7jWcqdbilfMixE5tMdCS8BpRqCQNIcsJF58KR2UU02zI4X6VHPpy60D15rwUFZJqrNMdlYGMscgHqFYwlDJl5nnC3agrj57TYYFmZZWcbGtSGhBpolYtuCmax3BoeiGljEuIY6ohuISQY0jdxC+3XlNLLV26iZNYdM0qLQ67xfeW1lkrIRSrNjcrFfUW04o0EbNF5EoInJoG3GqIu0g4FKuFU7fMR4mIhtWpic+Ldg4z03qPUoxiE9UEK4bVmYoyzyucgTElQvweyOpUN+b9wp4pZblgrHss8sBiXDIMGUsZHxZkWXNAM5Qd9lZrqimUcGhKTti8huyhlXtpDlohOdTq7O1PzNMad0e1RRL7IqJmdUAlhTAtHq5DcUQ8xN/2fBCsicQhtolbdOJqRG43VRdU8TIheSCCeFtE7saB7jWc07L5Woy7iGRWlCZeboVcaaJY+3dzxMe1MG4nAbBxZG9ioWPWRRP0cgj7HkKut45gcUNUMU8tYry93iUmXtQSwrVsvq4h61nFN5HX5jhNAHSJCGkZwkC6HfvRb70R+2r1uHbM23LRl7zZNtccgq0k1GdUKi6CZicVZ3+a2ZsKh67ZI+8s2BkzizGxzAMDhSSGNlF/exwkY+JQC6oRC+8ulFKbC7mFWYvE+RVFk8Z1pxm3SlaQcWCejOpC9Yq5U6uhmsi+ZrE8gyTNHY2FCF8jFtwQkF2kzuTWvzxoYsjh9p+tMq9npuLsH14xXXsIqzNuM2ITeTEwLHcYxoxaxHxLGtr5B5sjytowqns4kKVNytCBakYtU4juTchFlOgENzQv8JZCoLa5V7bzArgpVqdIlfcQ803CoY/X5v4m4s/jLoKkAZ9WqEDKC3JK1Hlu649zgGbE5vi3tU7rnEl1jegC6ozUmPCjCcR6b0an0+l0bhquyrbGo9PpdDqdzo3iSPJVf4Z2Op1Op3NzqO2z1c7pxWknAEvKIUqZR/yaVySNYTT0QrPgtuRaC4FL24f8biGqIU38shAYHCRrc7tuFGZBNJzC4SbO7b0EJGKRw5WpTTNrolyKbtRtTG8amqMy3LhCQl2RusYQUlKGIbUI5IzNIaqU6kzrymwzRVbIuCTl1CKowyEpJFSW8fckmCuUig4ZaI5Nbw5XCSEs4U2MC9HQNCFZ41h5HDuRgeh8rdGjvHFhSgiDohoubDdUHPIYjlSRtjyx32XeHEo8KUxzxC67Yy5Ua9HCFuJ2FWGeVsxzpcyFUifMnFKdUgqTFeo0MaRFbENaAkdF8qYQteuqcDhMuniZ8VqhrinVyZIYMhw48wxyEuYaN8+KMM/riLuttfXftoheNKKBBRbDgJSJuXr0SI8jqoToqyG6qoSAK+7osIiJAb6JZw6Bzd3CQa0jXtZNYAzBXNxjbLb+abcSIr0T56Sty8VbV27eCqvu1hynxPbrEF9LGS8FanOVQzh34wg2l/mmRLc2d25uMcdNpEZDVFPCkWqti7v1xLKJcA5La7sWY5y4tO7pNn42rlaIeHaXDGoRde6CafR3h4gXLlTHYuw08dclUeuMOSFS2kZcbMenOUqx2iLPLcKlc0ZqYZEFMci1sJ4r+9dMHDJHlzsshsTOIOwsBhZaSeLR3b25Bj3OU21JAe5QDOpm0ogqqgquyCaOXZQ0JKyGWz+ZoVmZTSKK3IylOENSUjrYku7j/uWiuBR0XMYkAatochRCMKaSk1NcmNYzxRQM1GYGJgbbp3g7r6KkYQR1ijTHv+Y4phZpAxWlWsV1pJZVXIuSqdWp8xqz6FkuDsWiY7m6bbuuVVe0wmSkTEgamxs4xqfZPl4mag3x/sg5tJZ2XkOspR1LETSNqGaSrRlkxkTRskbHZcw3KBMy7iJzi4q2GHfUkaSQWo+51imeF+NBqOtb6cnU6XQ6nU6n0+l0Op1Op9PpdDqdzq3HaScA43P7v23FNLxFPKMtcraGYLtx/zbB2NGIgG5RtrL5fmrCl0r4LN1QFEiQQ6SKSFvHab2rzaUpquEWrr4VecIgbK2POERoOUpcQ0DSAqkTrgM65Bbhm9A8As4wCsvFiNVKmdZUq9jkVAETJw05HKgiSJkRDWdzVcVNwikqIWaHQ25GUKqEM3rTB+uq4b4kxE4lNWGwCZBN/A1drwmShCAjOmzyhsPVqkNL/N0I6dJOkUCJiGXDMStxjLxC/Bf9oMWZyxyirRVqNUqZKQ7rMjPPBS2OSgjTIUITons1JCtj668tDmUyrqnOVK5h58ABxJx9FyYK4zhGN7AVxBPoyGpPWE9zdL5KxNravMZ1oDb3qaiSUxMXaWKrhONRvIYwqAmlOX43kxDMmntWW6ey4K0vNxzRvnV3+rxCTMMZKxFbHt3Mm27gmNygm/hhseZKj+tCOCK64iXGrWhzHIdIqU3EFQfHWvdtm6hg4GIhIJuyEYg359Xdtn3DbjXeehuTfCQGw7fjwLeO3OjgbsevhmDt0oRbhDZFoTl8a6uPrbhrxEKbYwhWosfZzLcx4iFisxWdcZqrOAR3R0DG2AdVrM6kIbPQyiDOWIzDtbJ36BqudrhGjMXuDsshsTMoO4sdxlwZUiZJdCBHRLtQ3bBawoVqFvcIj95sz2NEN4u2fYx7hkkB3SHNhV1RhpQYB8dQ9qaJMtVwa+cRyoR4IQ/LFsMuIZiWEjp6gpU5+6uZ9XoPSyM5ZxZ5BC2wOANfHwrhmOiInquxWs+ox/XuRx3fuhFky4SbU+d9PC0i/rn1Is+lMNXKer1mKiWE3FpgWLZ7Y26TQhSR9Vasxir+/7P3b2uWZNl1HvjPuZaZbXePQx7qiMJBBLrBAxrkRzZJtNhSv4Hu9BS61LWkGz0OPz1Fi+om0IJ4ECkBBAoFoAp1zENEuPveZmutOftiTtuRIAtAZUYBVZlhA18hMz3c97a9bJlZRPxzjNEv6fKNfnArJYdqciOPLe9FEUsuODoN6nzDxMC7xCCNGHK5ZG+2I3ZGRLDtMbeyoqVRJIC8WI+7WDnhl0sO7hw6dOjQoUOHDh06dOjQob9OiQhap5/1YRw6dOjQoUOHDn2u9PYBYMmuUdJ1SDKDUoO1asbg7lm3YXVMSBsQFAMpI1xvNALMGpQS0b5oRKEm+LTW0uUo5A9nn6e+/tndOFwrMlocY4Jl94RhQoBg3eOSazjiiiI+IRiuczgWhWDM08Q8aXRxCvRh0SW8bjhOmZxahImBSgBZHy36fEc4H00USsTEilp8n0h8bXeDRtBz/FMdmMIBPVpywUFJJ7T7wKiEr9oRKWgJKHx1hKqgGN0+AYJF0N0lSjgFzT1ATc8Y3xFtrc0GqwvbeqabsLaOjcZNrZSpUusU59kGUhIaSsVFqCWcvn0MmjuPjxvrtvHsyS3zPDEaPJ4bWoSqldMszJNyWm7Y1om+bTgTLoq1mT42Win00dn6oEvuPE83NAG2Sqlo1QBepeb+9ADjli7f7Am+QlS3jEiOcxZO8ZJu3AGlEDZNi/NR6m5wz17ehOB5LXi6UXPKICJ/tSTADxeulwDJqiXe03fL/OvoYtFyBdOika0s+NUJzA6fPQCwlHCQUkqmSsd+8gRsKiVBr2dHryaY9U/EAofj1T0dvtk1bDjmHR8jnKcu0YfrEh2yw7LHO+8NV7dxOH53xieyDzDEZ5B6QtwoZcJKQ7RT6sKpbTyuK5cO55cP3LtRl4nTqfFkmbg7LdzMC3MViqWbG6jTjG2PuOawhMTnY++1zfuGS7jlFTjVwjQX6hSu++ZwXht9BKgf40JJLD7Nt0y1MtWFgtG3C4/WOW+DBwZSb8AG1Sx6lXujz9GFjQh6egc93aLTLeadbWs8Jgz2seHWAvya5Rr317vKBW/3dDPGGDQT1vVMa4NtXRl9MIZFl/flIQYORCI1QRRVj2tE8544LnhZYNuucfEqULWgRdDpLq6D9shYHxApoIXRV6iFsa05T1HwcUHrKa6PdUOmBfdKX1/hUtAyZ5R3jTj35S7OyXbBvX/mR9GhQ4cOHTp06NChQ4cOHfrJVJYTX/rbv8F3vvOdn/WhHDp06NChQ4cOfW709gFgSEAaUcvX/tMRsEeoOAUbGxHDK1fQJgiCoTWjkb1H560kRBoJra6utYTIOr12HGcULe6vncN4uDEzhlq0Zjy0Zx+qB1AbGfFL9lvucbljZHww4Uwk+kaLTgGw6gnnggBFK3NGRQ8X2hi0Bl0HugRoKSJU19fuu7I7ZQM8++iYSEI5je7gdNpFNLEg6qglrLX47K4zEJGsmGW96xSxx+m8jeXce2kLWgUzR4bh3qKPOKGTWHxuYWAMfHR6X7l05/F8ofURMNYMd6Vk/HNVTXCeblTPCOR0QItZQDMR1KEUZbSN88MGKHenhYLwuA4eHh85LxM3qkxAXSrzMkePslTwmdFWhgnb1ng4P9Ks473j7kzpqBYfiE4BGS2g174euKNlBtsikVw198gOn/aI5i3es1QYW0aHg+h8jVjeO2WRfA0Ge1dvmF8jqty1snfwuscwAMrVyetasJHnTSTBsUVXcamgBdn3uesnhiqyk9Z6rLlIfEazeJ0xcC3xPZEjjdjANXpePa8BY3+dBL4uWLp0fVjEPdveBzzwYdFJex0gKAHvRJFa8K3jvjvLld11fD3mHYjn+4rsyQAJR2Vmkkqxzlwqp+KszbhsnYe1cVmFF5eXvKoTdzcLT29mntzeclKY55ni6Q6fIpLddhjtI/l6DCuUUqmlMEl0gosG4I7I5UYzpbdz7p0L7jUGOaZKKZVSJP6nBXGlWqWsA7eJSWCuhane4Ga0kR2+owcgl477QvfB49a4Pz/SERgecc82GL3TPYZqrufIHZMSveXmtLbS26CtZ0ZvuKcTvEzI6OHsd49hADNcBVMF2UAVydERJL6HMiG9x7zNNMf9ak9LqAu1nuI1bcOaA6e87reIUHegP6TD3pERjvE+NK45OVNEkTJT6kyRBtawdsH1tWP90KFDhw4d+qtUlxPv/drf5vsffPizPpRDhw4dOnTocyXJQehDhw4dOnTo0KFDP7neSgAspZD05uoFRhTxhjPwIZlAHEDAPQCtCNkNmdHMlIgxJqFm9tYGgODqYtM9XrcEWMIkjcWSMMmROqULeACKlL38NmzJIuBq0ZGbsExL9g4L8fMaTmKlRnSvOlAARescMdGA1wmvShmdycu1M7i3wfCIiPa5UEuJ49iLg7MjFq3BuqVEunWpDB9gPUCJZJ+tCkpE9Yo4ortzUREN16tKRBFLAuxahDLP2OiMqWbHcsb8asFGdBcXH2iZAggLDDnRzh9z6Y3zCn1tMDqTLLhuFAedZ6alUEpBag0P6XSCYWip+NjoQxljo5SZIh7OQ6lIWSi2MVbYtPDs2Yl37mbGjxovHi68uH9gWhaWWpjnhQro3vs6zQgSlcpDGRfDtk5ZTgGb5xvE4nMy8pQlBBfb4g86NaKeIR2jCcyBSNvGkHTJ4gZ1gtEzann/pnJ1Fe/dzgE7A3A6ubfx6HnWkpHkuzP7dSRz7Lu8TjLiWRmxt7Qk0I1hgCtAzevu2q0tBSlTXHPp+DX2XuHsMiadxxbObsuo8atLWRVrW0DhdPzuHbCORFy4Z+xzmeJ1+hpub4ihChG8TBETPfrrWF/3iBUuUwBQ9+zozbj263qNvAdIdFoDQqUshfl84bRMXNaNh7XxcH/m48fK/any5MmFZ7cnnrlwmiaqN4pOSIkBgh2e7hHZpRSmIpR5AQvQuncZw2BQ2foWX8eRUtDdhS+n3CMTiNPNacOpdeHdMphqRTNe2wRau6CtsY3o8h0ePbvmzmUdvHy8cKqFu6JI71hfGS4Mg7FdGO6MkX28wJASMdA6xfkaDXSJvVlmxFu4w23gxD3QR4v9bNkPrhPmI+5jZYnu6+yullJAd4f4nq4Q4zIUYsBmTOkOH/h0i7dH8A3KDaM/xN6TAS6RJoAickNRj4GI0eLI7BGxwRgO8/yTP3gOHTp06NAhEerp5md9FIcOHTp06NDnTnv616FDhw4dOnTo0+t4jr69egsBcMbPWoIelQRhEbEa7bcD28FC2DNBOq9dgCOgYd3jagPSiDUQiz7f/FlXzbdNV6EFpBAN529mTF/dfuLxfiKaLslwpAXgqFcgZD36f3cwa9YR16tbWAjHKOyHXYJt+A5lJqQomhG/qs5k4xpHixAuWfd0nYb70RICqgqjt4B6RkashiFyeAsnqihSCRBcSjhMRV+/vgouMDTPA4OuhVIsvn9YuE5xvBRGc3y9RCQsTpnimC7boNmgDwefmNpLas0+5VoDThlQlwD6NbtxfQ1grYKUGjHB6RYe40K7xn93pFRqUdQ6o21czhWRQceiE3VbOZ87931Dbm6oBaoY8zwz1QkthW6D+7XRthWpFfENdQ3XrAQClZLnNB3KlHSqA1ByYIDcu/Nrp68Q0dBjTeia328dSsRRBwiM2GaRHDDQ6Kj27HQWXWIIQQLiWl4y4dy117Hl2BXaIhZR4KIRy923TIaWdBY71z5oTQjtLd3OEUPtHhDVCWB57cLOf5qFQ98AHzsclYyCLtH7TOxvs9fQFJ2yl3Z3BKdL2EZctQmTPaPWg42H8xm3XH/BtTJ6o/cex5KfzzN7WkoMMCgEVK8Z1zwplcKNGlU25rLwOOCydT7+4GMeHhbun6w8u7vhdiqc5hsmjTj0iCWPQYoiEu3GqozRsfFJl3Yc89adbbtgIrhtsdZtw4ZdoSre6d0YJpQ6c5oKpcT6bH2wjU5rQj9v6XY21t7YesM07jttu/DwsPKRVs5i6NhyUKVGH+8YEWWdxxVu4BJ7qa9InRGmHG7QiLwXy33dMR+Yznjbok9bCzocLz3XRRE3hIZKOMvFPa5b6zk4EJHNRfbAc8FLxduIvdYv4ZofK24WSQGRxZ5O5gbUawezjEu44/uA0fCxYmawbZ/xOXTo0KFDh95G9d753ve+97M+jEOHDh06dOhzp+MZeujQoUOHDn12Hc/Rt1dvIQAur6NzpRKgYMZ6T9gUkbL7rwXSqWANKRHhHD23NXtOwdt4DZHTDYmUjKjJyNkEVpFJa5+Iv3XwEhAN8nWyjDhhm5cdfQHDr7DNevawApgjBfZYaHNHy5JQOWx4UpdwdtoI4Cg1Xc07zEp3Y51RHBWhFmWqAQ2NEo7cbgG4fNDGiL7aBN5SlSKFPrZIZx0TRQjXryXkSfdnY9Dd2Xp0ljqecdKVu5uJ06QBHFsDHOt2dU27FkwFpDLU8daYS0W9U6cZ1BAmTAptrsh6gRFu7NF6uJyzb7ZIQPNaZ8oYGFP0yY5B750+Ci5GKwtujTIZ89opGEUmpHWqVFSdVicuj2fOIiBG1ZV5PlGmCRuN1gxzRQVUKloqIuV1bK1nf7OBjwrTBIw4b6Vmz3TATKWH83z07Pxt4UiVEUBegsi7jXCIS8WtgeyuxdxzOgUI9oRjMr+O4dWCmyesDUemSIn9FVMPuCTYT5dwwNR0GEvJ/RzDD/F+BdE5nMBxIeZ7+NVp7Bm97JLQd3f17rC2GyYWa7NHkbtFRLQHOHcbDANzw8ywK/Qlwfi+lp4gPB3zPsJtLTmk4SMGN6JsGmTCR8NcGd5xFMa4dtZSZ2i5fp5wXpxyuuV2EaZtY7rA/bqy3j+yXjbuzxtPn5x4dnLu5onTvKACRWOvlBJg0iRc/8Z+L4n1bK3TWmckyHSpOQQgmEjs46LgBeuxT8o8x/DEcHrbGL2jPijTCZjSEd9Z1zN9W/FTzRh82Prg5asHzj7Q9ohMNyhrDG1IwHzrDdcZ384xuFCWuKPZQEtcx4Vws3tG478ewuj58UoA93qCMjEyKlvGQHxgakiZw+ub7uyY23BUzpjOsYYaSQYuEY/uEsDeRsM8IrTNNkRusct9Xk894+aBYUixWPN+ZliHencA4EOHDh069Knk7owx/upvPHTo0KFDhw79OR3P0EOHDh06dOiz63iOvr166wBwAC5HGOHCtegj1YwP9XQIikhym3ThlgRDEnG2ohpgyzL+eQde2bEqGf8sWsB7xCBHPnN0zu59wqWmk3KHzR4vFf8PdtAD7FHVED2r6g0Y6VozbHiAFbH40ZHHTSNyheN1dIfcBBTEG2hB64Sbhh9ZiK5j1YTdmjh6QEkXsnkAWgvXs+HIUKx0VJThBe2dWiZKhZqRyGWPtG4Nejhu2R4Z5tDDhXx/WbifJCKUifTsglN3kEyh9IFoRcyZBLQUihljmqCE63j0hvToEBWUeb5BxDF3rCvbGJSqVF3DZVmEqoDcxEDA6OFmRHFxmlYul41OQYpRqzFNBR1Gsw1EUB2cBzycV85tY7p7wlyyu9fHFYAVLEAwHdUT6h7O32mJSOiMag5ADzI8o4iz89c6EK7w3XEumtG5CKrRn3rt/1UF5qvbNyKZ9RNdwntcc3a3Cq+dwSjoHJB2d+0SAw5iuQf3LZsDDtdQiR2MEqAvosTltbveAyqDIW7pvidhnoXr3SKOOTqnLaOB473C+B7Q2WV308cwgVmc64i5sIy6yE7rMYAK2gFH3aNrWOeEq/GB0pRP0YIud/HaCiNd+N63GLqQwnDB+siH6gpaA0x3QUqJrwuIGrMahrNtg1etc3585PHuhmdPbnl62ridZuZpok5TvGbcwHJIoOb9SBl9TYA7EvyO6GYuoB5XbW+dixtVJshBiu3xzNZzjfqG4UylIKxIWfAygysiEz7NGA4eSQjmsLaV3hvSNrQZ6iMGTTTvfUxIH4hX1FZKDWe/SoseXzH2XmXzAOxmBrog3tFpiXuCxL12tBb3or7FYE1bI0q+dEqdEIThjntH3NCiEUNe5uhl9+xGtk5FEKu4KWO7YDpF7bqFM5i+Yi54F1y3nMkpYBs+BiYRW81oP6Un06FDhw4dOnTo0KFDhw4dOnTo0KFDhw4dOvTT01sHgNkdf0QvpO3dtpr9pDpl/+ZI2Jqetx2wlb3flPj10ZEyIRkDvcOz6AiNKNJrh+XePWyW36LpprR8zYAdiqFlxiyiceUanStY1gjLDoWl8rrjNXo6GSOiUZXsKc1fz4hTkQrpuPUMvRbvGU2dwFrr1RHsaKyCZ1i11nDG6XQFcxQNd7ANjMJIMFgwfFLMe0QWS0cp6FSZ55miG5MJpgu9DWyqdDOGO5fzhfPm4fpLJ/JchUmFuXTmaULdKJ7nYzlhU2WMObpCS2WMDR2DNjpgFI3obhsZsy3grvS149OCMaiqqBqqSq0TdXmCGLgWRh/MKmy909Oaqu5oKUx1xlrDxspSFuRUWb1h6xmvBZnC1e1myKSolOh8FUEo2ZGc51fDOetjXKEVqjlkoNA7PkX/NGUOMIxnhHi6aG0fVHjdWxsZxVNC1Y4Uvc4ZeEbn7hDXZQqYljHM4fBNd6x7xlgDjADWCleIrCVBrOWAAgiWdcAaED5dv5rd0PjApVyvT0TDtWsxXBBxyyPnGPTqKsZew91rF/AeBZ0/78TnjWGLHIDwkgD7dRR7vnJGku9dwGRs9hRDAd6ROjPmKYCrLQEFLQDssB69ueUm+4oN1wXBGTIYXtGToFLQyyNKY7VB35yP1gvndePy7AnPTp2708JsTsXjPuM9u7UHYj2i0UeP7u50jxedUB0o0F0jznh0msyMy4VaJ8SNvgXkxOK16nSDlgJjva4/DKzAQEAXdLqh1JneIqrZrTOJUqxT3JEI5A5XuwQcjYGSCVHHfWO45HBBD6ev7bH0AymnGFXRgOU2GqgwlHBTm2VafMd1wccF8RE/YwMzMBvIcofKHHHNl3uciCmX6ZYiSh0NrQMfzugd2/vS2wWdl3B3X+5jP4rmtRTHTLnFDLw/IP3y6Z9Bhw4dOnTo0KFDhw4dOnTo0KFDhw4dOnTo0F+z3j4ATEQli9foxfQkqln7ydjCVQoBwQAhQZuPdPEWVCSiSykgwT+Nkr2XaaeX3XWbXaFmCb/IDsuCMMKRK47qFN+vFRsdkYKKRtenZIxzVlpquindR0CtUgK+WkJAOuwdqTpB38Itl6sQ3sNwf4oI4gVIl+EeH5t9vSol4J8EzIaOUFAHUwl3s0fEKmYRVwzYDnFolG7YpBiCVShu8bpIuGBLoapgPujdMRPmudKr0LvTt854fMRKpRWweWa4sMyFea5M1RCdsS4YC71FzKv0zoaj04z7hpcZUaUqWA+nICV6aYs6qgUZHTOhy4aoMp8Wpikgb/GJ20moW2ftMLwzpGBeIiJZK6YTo6/UMqFPn2DbI5Qp4pJ7Q6c54X/EbIsLOhpST9H3agl9iySS3J3gDZcpnMSlwBixffei3tFBI16bEW7JfT/tcDd6fHcoa3i7IGVKMEoOLoTL/eraJeCa6r4HY3Ah9nPEoZsN1GIvipZwdu5duhgiERlu5qjYa3c7Es5PEXBNl6+kK1ky+jl7ewl3M1xfNocnPHpvPVzwe+RzwFfPTmALF20ULQf49I5oSddsz/UD1Wlfjfi8EPHvsoPiiF3X7NRF0yGbEdU2FLNPRBhnX6/3FZOKFWWMxqSFSqfWQnm8sHljc+H88gXbtvJ494TnT255cuvc1Eo1pwgUL9FX7tG33VrHLRzlZVpQi2GG9fxAa7lyImzd2NpjxI4zsJFd5qqxpzDE98jojUt31rWxXe5pvVNF6MDZBqU1Jgx0sFRlrjARAyeqOXgggnuPuHaHZsZw6H3DbMP6lg73gPg6zYi1vEcZLoLZeL2/bSCqlIz3NtVw4kpB+wDifcxLdP22h4zrrozRod8jpaHzCRkXSplAK2O9j8GEWgOwj467YKPQ24r1c/SOl4jAphaGdWxs4Vg+dOjQoUOHfkK9ePHiZ30Ihw4dOnTo0OdSxzP00KFDhw4d+uw6nqNvr94+ACxk9GxJ2Bl/ga9FAxLsrl0AUYpOuLdwze7RsUo4Km2PhpYAqkI43zz6SAMYK1qUP8cJpIBqAAzbI3Cn7MUMR6qZpQEvXiO6PBOoWXS3yjTjtiUIjs5KRTKel2R4exzqFMdT4tdUF2gtui4FlHBfmpEx2OkI1SXdkAkWszd19IAxbhYQbQQcd4mYYy01gBzCaCuZbswYlXkIpSqagLMQ5tZSCsUV1YKPhpyexjqOjo2CyA1uxnBl64PejYut9HlmKhOThCtVBWpZ6GPQfaFdXkVfrRbcjG6NWgqlaETdWjir3QyplVLjnI5h9G6cHy9staD0+BmpSBmcJI5TM/K2lQXpK60W1u3CujXqdEJlAt/7eSPeW0dH51tEhFJnok7aIx5b023LHim+R3/H/nPzAL8ZjQsaIHvfuw5Spvg1j+ECIeLKw8HqV5fuFbomdgun7w5thb3bWrLXNwYjMhZ6L5AeuT/Mo/824a/ANarZZSRsjRhhpeRHyvcfnzy2eFnYnaF+dfde96Vr7FPJyGspmHWGJQi2ET3PCRANx2t0OHuCanOw1vEcXHAU+kBKiz0pEv/TElB6d/zL7qze44nTOax6/dzhwK/YaHit4WKWKY5xKCqVIo76HXV0pjpxuZy5NOeCsl7OvNw2tssjlydPeXp7w800UYswTTNiW1yLFokGBSjTgkgMXqyt0YZDrdjW8TojbcVHCze8xXmQcqJdLugyo7aCKsOM7XJhbQ0bg9FW+uhQlHVtiMGMofNMk4nOyFvajOq+j6JveRi00Wld2NZHhkxYawnfowfa64R5R7qjY7vu5QD4kVBgMmJQooA0oJywdsaG40WRugQUHoMxGkZE1IezvTLcwvE+VqRGzLNt97FX6wkfZ2CLQQe5xF7IWGjrMzpVZDsj5YQ/vgz3sg28LG/wMDp06NChQ2+b7u7umOeZ7S3skL+9vf1ZH8KhQ4cOHfoc63iGHjp06NChQ59dx3P07dVbCIDTwecWsaAARfF0RWKCZeyzStgrNbt5r52l6eQNh2XBiXhh9+jPVVVsDMws4BGWEbJy7SINx6bhPTuG6xIuTHGGjeyfDSem7tHORESwaAlnnDvi4SJ2iY5W8454dBB7t4wVLvn+JWEzAbRKRXqLPllRrI/o1oRrhHVEze6xwj2OWwqUhH8JnBG9OkL3/lpHAla6RYRsbyCFjUZxQUd+dodSTxQZqEgAWCISW3GYBK0TqjWjkCvTdqG7sm6N7fzIVgOOzaeZQhzHcGfrg20bqEOZ5gR9lU6lemdWRWQwJFyZw8LHWMSoYmgVWosY6oGz9YbOC1pnZHRGvQHA28bwQttGGnGzMxq7RjJDdIjCQOqMAuojYL6HEzMyfKcAiRKubh8d0ZprPKBUctognbLZW12yjzYhXLhuBfE8p0buFZBBnEdJkDpiGEBKQP1wlHscq05x/Br7Nj3tGecsiFocn/UEzTEM4QhinkMGgiZUDoib/a8jXcR7L7Fk1HN2/cbiSsA2B1fBeuPqTEZwKYzec+CA6Hc2y/5drgMR4ZbXhMkJwW3gpuz4fJjhPbtoRSnTjKpjDLCevc1KTce6QPYZc3XMUypaX8N5F8XFwkWqE2Uq2OiM1lEtVOtMU409V4xyuSBjcNkGD72zduPcjLsFbqaZZe5UAS0LaoNSSqy3KOaDbTjbZUO0oJ6O6G60PuitQQ5IyHSD9Yap0Nct95FHjHUbyHZBLWK2yxjYaAyLPlxTZfO4R20KGwWZTogKYzTautK3xrZttOF0l+xHvo/9JBW2NY75csEIR7y4Rad0mfF2xr3hOmOjo3jEOGuFSjhwJRIYnA3rjT5GDqi06BUWxSQc8FIEP9/HPWy5wVsOA9QFa5e4FsgIbC2YlhhGEI2hjanAeIWPDe+XiJqen/20nkyHDh06dOgt0O3tLb/2a7/GH/7hH36u/uAdw4Hw7rvvcnd395d+74cffsh77733576mqrz//vvX1zl06NChQ4c+rY5n6PEMPXTo0KFDn13Hc/TtfY6+dQBYZA4Hb+SzYi6oa0InCdOlW/acJthid+mGX1USVIW7LHtyVfHWMNnhW/awjgZFs5s13YspxyOyNyOefYwEVaQLLo+pBFi+JtCKRS9wmXBVGLyOiB4CJfp5xSWin0XAp3R8RswrWlBt189JQmIfnb2jVXgddR3SBMOvHaGkQzPWKroyXXM9x4jeYguA6Q7eG8MEM8062xkfA22PaI21ri3cwHjLY95htCNUfMDQ6J+ty4y2xhgDt8q5XQKCz5Vhxnq5MNqWHb6K+qBMUzgFzenamKfKNFVqC1corgwfFCmIN6ZScHbHtsClMdww23CN1woX9hYuQ++UuSIjBggCuNdYNVmQ0dEyR/dsqREvvMeHC69hqI/oTc4oZtGSMdv+em1EYOxRzencNTKluMaQg5Hfm65KCyCZNtvYq2UC76/7d81ghCMa67HXxoZJDDi4RH+qlEIxjdcvU27Pca0cdq0xELD3EBcNuIxHhyySrvYRHbGSwxm2O6YzBDtyqLFBRkH3iAjOwGqHBJ0toLDO4b51ord17/41vzqjAwwHQJd0vRcpWO8xxOFgW6P5Skfo2wUbBrVSi8eeESiqFC0UDfd5yU5w3JE9NtgddEZKgGGVEu9lAxswSmGeZrbLmWWqTFunrhuXrbO9esGLtvKows2TZ9zNF26miWUaVAVkiXrcAaNtDHsd+wwevb2lo/0SzunRGL5l6sGGSKH4iC7h4eikLFXBK72tXPoj1jvj4myXGxSn60y1DS+DB4PBCd3usTHoBr13+uURY6Jd7gPKi2JtRWpMXQXUd7CWDneL6HJRkAuMFjHy/T6GHkoMZ1AEu/8Y6xuDEvHhOD48I7g75kYfZ9DT6yjyOsV9aH2ASwOZGQ8fIPNNdCmfH+HmKf3j7yPzCaMgVRGd8/4Wsdt2+RjrF+Tp17HHDz/7w+jQoUOHDr2V+jz8wfvZs2fXPyB/7WtfY1ki8aKUck37+Iv0/vvvX9NxDh06dOjQoZ+mjmfooUOHDh069Nl1PEffTr11ADgggwcAy17J3R0oUgKy+UCRdFB6gsoAbOIgpQQYc/uEuzI6Yvf9JaJRpfua9wYoJZ2BMsBBE+IJzvDrN15BtKb70rOX1TPil7Kgu5tSe0Az0XQoD9xGdBlrRdSRpIKeEDicnYrXcKaKCEphjBEw2Ft0XmY/qGplPzxJ56+lkzjWLyA1VRmm9N7DhZygOrpnBZF4FTfJruSNMAd7OKKBLiDF8vP1+Lw+AEVqjbhpEQpOUUWlhHMyndGP2+Dlh48MEXy7oJ5xwwSQi3UNUN26Yd6ZZ2GaKzMQkd4nBoBXpG8IA0m3rhFO5YDbGVFMAK2BhPMwoTfpkg0nbwwU7FAw3NyCjwHTEnB2CFYz0tkzMhrAOq75754DCJbDA3GkGcFd8OHR1/uJ74+k4oJvLdy+hJM8zbb5Gpowv4RbGxJGR0RyWIc1r4lyHTqIpOrobJWMXQ4Xea57urw9O2YZhuf1ZHkZ7nHSng5Od9IBn251iGNArq5MM8N8YP7aDYwWfJDwd1xd6CQmthzEMHO894j6TeAu7gHgpynd9Q6jo+5UC9Q8itIHnC8bj35BpokiTi3KNC1UdaZpUFSpOFpLfGbviGTHtweglSJomfFaqKNjNqjTE6alUR/umaTzQOGhnLisK6sWGg+sYjy5u+Nu6czLTHOnqlLKSLe0Z2bBiPXUCcWZi6JzZT23uM9tG+oZZ61K8RIx6mqU0wmfF2S7sA3D6qCvK+eXLwFjWiZ6O8NccHMetnMA8t4zyUAwM7o5o8N2fonpEvcoexG7dfScEuhIN6gK/YIsT2E8QjnltZVu9G0Nd/J4YPSNsZ4D9PuISP0R94rhYOtDDMfImhdBxNeXMjPOr6AKyB22deT8Arm5Y5wfKS50Znj1EuZbtAP2EJt0qsjlAZeO6wk5v8S3x7/0aXPo0KFDhw79ON3e3vLee+/xve9978f++l/0h1bfU04+pUTkL/3Dcq2Vb3zjG9f/fv78+V/5h+u/SKWUz/Rzhw4dOnTo0E+i4xl66NChQ4cOfXb9Vc/R7Af9T78uhAHsU+p4jv7s9fYBYIeIX06Yml/co0XFPf4p9dpxixP9vEiCqoDI4gEwNLtlA6iNdCuGw3AHeTuwwsPJ6R6OVi1TuHlFElhVIk46gKmbJWgLyBVZrxaduQS4NtWIZbWEYOj1c6lmRHQ6mfc+WcFeg7xhsPfxlokxwhE88Ov1rjugSwAO5MWYAFP3yFtDfUKkYm6MbuF0bHb9TXj8/4inJvs+rWg4NVvHSkF6x0gHaBB4lI6aRwyvB/wtJdbYVaEopS4spfDYGpfLhm5nynyLWHSIxgEIohUVQwl36LaFc3GehNu7hVor7bwyOtiImGTzgkqcS0/3uLM7twNAjd4joriWCO/2Hg7akeB+NKQoKnEu9v9DA9xZliUrhHNWEqCPNfaSVtjdu5pZzvMNtHOsnyiiaSX2kQ52C6hLj6hxMbCGjXCA+x4nLumQVcsfqTBGDADkdXF1yZtFTK/AsB59zumkl925q1zdy+Y7SB5pRM+uWEuCLPtAQDjLLS3EOyR05zqsgRbsGl0d4Nk9unADBGdncMaP75eje0al53XjrhiOeQ/36HV6g3ztEdjYB5b9x1WVKsZcZlobtCFsw3gcF1wulGmmTo2pCFOtTEUpItQiFNmi41qiF1zIqHMThILUBWlndJmp+pR5WVjqmWVtPHjn7JX18cJZB5spr84P3NzccVOE080TJulMpcRQAZbp4IOSIfbzzRNqb9T5GX0YrW+ozExamSdFpiWcu+2Ce8TRT9PMzd07jMdHuk6cLcznJ84RTW/CcrqBbWVc1rhf1QJeGN3o7cylbZwfjSYPOBvMc6Zvd9w70sMJ7NLR5TmMC3iLLTsc9x6zB8SAx2grfb1APUH7IIZtWkSre53CuX7zDOoT7PxD5PIAekJuTvSMMbf7j5Apo+ova3Y2T7THl8jpS3i74C+/hU13lPkG9yncyd3g7r3Yg9sr/HL/mR5Dhw4dOnTo0Ne//nXWdeWjjz66fu329pbb21veffddnjx58p/8zOVy4Yc//OGnfq/b21vef//9v/R7ROSYlD506NChQ58LHc/QQ4cOHTp06LPrxz1HZb2g24Xy+Aq9nP/c99+89z7Ll75Kf/rOp36v4zn6s9fbB4AZAcM0eh2Rgmck6w6CS9baRg+rBmhzECyAY6nXeFcyXjf2qKUDc2TksQVcy67TAMM9HbZ2fW/cskc0//saG7z/esTeBkyLY44YakBq9GZqxRnhbO3RmSoIrgXVev1ZkXCoWh8JIg2ZSwIuR10wBmYR3dwl4m0pFs7nhNIigpSaPxcgRUrBh1DEkeqMYWidov93703OOFapuofy4p5RyW4xSTIaTAtilu7jgviguFGnBSUcvCqK1ImBhfvPDMSiG3g907dGGYNpNKgzbh2dl3CxWgdVVDNaeTTcYFuF0V5Sb24RDC2gNzP0jSK6vwU2PPpyHdSVgdDJ4y/RF+tSoFZ825BaEdfoyZUSe0CIWGbIbt+Z3SEaRnHHNP5bJWLCI4V8iuO3Dq5AxuaaR+ezTvl5PuG49nD0RhwyuFRc9zjkjCtX+YSrN3t5JcCoWUd3UFtnvNYYXCDcuMNGuKRzSELLlI7mAP2eEb8BacP9vDunGa97kiW7bGObJNDtI5y9aEDrESA3YK+BeTrKe0RC7zA48p8T1ltec+mkF706lSOeO14XeX0szsjTYXmviB0LQhEos3LygNndFrZu9G5sa+PMwEsNkC/GNN8wVWeuylwrS61MOfyhUq/Xs04LjB5xw3Wi1pllXVnmyuNl5V4G5wb98YHHomzNWOfCnTs3tWLLLaUPtMwog1oKpYAMx4swutPGYOvxeU9z4bQoLgtm6QQu4QwfCC6Gag4HtIY5nP0BPzWKGJsX2jCeTMqyZF/wrCz1BhmDdRMet8rNVHhc73l5Nh5efYS5YA/3sZ6quK34dIP6jPdGHxd0usPXgeuKD0XIPeTOMKeoU5enMd1VHmNwodxenfPYBTk9hekWccfWj9GawxbDQC/IdAOTYo8/QE7vxjnvF+R0i/tXwFbs8hD3pLIgS/SI0zfs/DFch4gOHTp06NChTydV5ctf/jKPj488efKEL33pS8zzzDzPf+HP3N7e8iu/8it/g0d56NChQ4cO/fzpeIYeOnTo0KFDn137c/Tlhx/A/UvKwwukd3T0/+R7b957n1/8rf+C6eb2Z3Ckh34aegsBcLgmRTWjoDN+VvaYWEAXUEMlWmcxQ8RRKa9drOk6RATJnl8XxTLG2K1nZHJJV+7u8gUZI02t6aBl/2e6Nv+j3lOR7CFmJPCNV7yCYMCkIHtcb63IGPl50j2rJXpVPSCd1oKOjEIWRa+9x4Mi0R3rHq5nVAIY7nHY+0DGlYdF4Kxnx6sYqMbriQG9BZgsFcPSgSx57AHeFUHKFA5CmwIizpUilaIS/apaIqRYIsq6E8mwmMMAFcMEzm3lvDXauvJkPoVD0AT3yjCh1gDhboZpQd3RaUZsYHmuxrqFE9ocJg0HtIz4rKrR91smrF+waaZvG0ME1NFyi23ngEyu+zRBnBPveFG8X6DevY4tdg9rZQJMlfqJ/umB53mP9c7jSGc45gkto/9W8HDv+gjwqdnjS74c/vpn8Gt8M/tNXl47xXGwYa8BpSjDO24BLTXB/h6F7hkXbhmrHE722N+Sx2HWo9M14R/DQV9HqbsYbgH6wr1reI/vNbPk0zs+LgEPPSKHTcJB7Lvz3sfVxe5737Xv6xCAvKBQK3g4lcMRHZ7zGKrYh0NKuLLJn40rD6kT7nC7ONY7NpzWnC6F1hvNYHu4cLYWl+dcmZeZ01RZqrJMC0WEYgOV6FN2M7Q407JQaqHOlWWaWNR5nBbuHx5yj19oy8zl0rm5veF2OKdaWDBmBKbowJX5BG4MM1pr9N6Y6NTlGVLi2rO+YmIMr9i2EqbrwRiNPjp9bAwzsMq5rUy3J8pUaY+PtEmYykxFOFVhmgsVpdbBk5uZ+emJ9sOXjEfolwvW7vGb98Ecmxd8a7EnH14xujHYYGtoRlobECEMgq2vqKf3scuZcXNHEYO65P1mgE7Qz+G2nyaot3Ff3SreL8iyoHfvY+cfxDm9ucWLxFDG2PDtHllOyN378Xp7z/t6D6fn+HbGR0f0BKe7T/n8OfSzVDzD3myi8KfxGp9nHWv45jrW8M31RVrDJ0+e8Bu/8RsAf6PH80Vaw0N/Mzr2zJvrWMM317GGb64v0hoez9BDnycd++bNdazhm+tYwzfXF2ENg0E463e/zdOPf8Dl5Qv+sqP52t//xz9V+PtFWMPPm946ABz9lNHz6+xhyfk1EjZZCySpA7n21ypFwwEpCYUtN5ru8c4IvveRJoRyi3cFj8jk3UWoJZyz+etSF3y0cCxKAeLXg12NvBB312O4MgNij+glzr5T3K6dnj5GABZNiCxTuDRFA9qJJezTa9y1iATj9OjdjJ4UZ4yGSIBQqQqUgIa2xsJeybBEDHa6KFUl4qVlik9j4YJFs2/ZDCWPz3uC4gUVo9Ya75kQTmrFemcToa0XBgpjYyqFUidWnMfeefHwwHldkfURmwLOSRXmMgfMGY5O4b40KqM3PDg3uGImuK2IOCIV7xExbdaxvuFaY02ss7WGScNsIFRkXJAp1sfGBbwEuPpkNLcb6Jxp3j06axGKzrmfwnFO4epSjVmBGq5czU7U3S09CPg1zbmvPeKns8M6nMKewD1iq8nBh/08YT3WKYcAYi/F7tu7rB0LBzQFsy06moV0ye8uc/DR092boHlP8jZPM70hvWXEc+zBGJio14dQdPemQ9fCFe+WQxKjJ6j3dOwWrDcMi45fSxd1KdeBCbdPRrH36Gn2/fqyTwB1Yg9endgRBR+O0jyF2SEtr+8epHUZSsWtstzG3raRbtputA6tGetQ1vuNCysyVZZ5Y54Ks8I8TUwasdEiAaCZCrXecFuEZancrCuLdu43eDhfWDus/UJz5/Fy5rScOFXl9u4py2WlCNROOPrXC1vvMTNRb9nOGyKG+YXeV3oP57j6YNK4KC7byv36wGU9x9xAKaAztjnFG9PodC9Mz+6QOtGr8rCdUVXMGqsZr+5XPn4UztsZmW8p5cSg0otCa3Fvkw5tw9aG3D1lXD5iPJyRqeJjZaigchMdx8vG6BdUB8hNJCD0dk1foHeQDqOAPcY1d3qCP7xANkdOz5DTu9AuCKcYQqgg9Qaa45cHmDIivU5xZ7u5C/fvaFBK9Bbb8ZuNz5N+Gr85fNt/g3ms4ZvrWMM31xdpDX9Wx/FFWsNDfzM69syb61jDN9exhm+uL9IaHs/QQ58nHfvmzXWs4ZvrWMM31+d9Ddv5kccf/oAf/B//hvb4kLzgx2t++oznv/grLM/f+akew+d9DT+PeusAcMBfriA2G0jT0ZuAVyM62R3UHFwT3qZp0gzHUJ0yjrSnm/DP95iOnrGz7gEBPSJ6fY9NTicsGRctqjia3G3kARf2aNy9e9WR6Cylp4MyfcRFI05ZCDibn1GlBC7T6GnNQlWusbsa3y+Wx1gKOnq6gEd0vHpBs0NVuuZhDSjl6lTeXZVOCZfn9bgV0YBK9on3tdERqQEHa0GGoNKpk6JlouxOYZ0whObOpQ+20bHeGaPj7qxFweCxNR7PFy4vX2DbRhXBhmNaAoLWmTrNoBMj3dKK4eq4QfeBqCO6YK2nw3ullDkiwzUAenwExWzDVLERrm1xp9YlIpGrIT5lZHPCdBXEFEMCIrtgZLQxgiZ8dCLO2F0ivhuITGiPrmPz18mzCdH5JHDdz40UfLSIKk836+74vZqvRcORzBTA1cPtK+lWR/do6YB5IpG0G4cjmAhCdgBrASRduumOFw1nsRZcBuYRoR4cNV3ysnddj+jz3bt+3cONS8mBigx9tuyFBURKONRV4+vDAoCXKUC25dCEJZLOHmt3x3XC+xb73Cwip0UiCloVkRKQe49/9hFrXSviPc5t9h3LdV8TMeOZDlBKRJ1PdbAwAYVhTmtbREW3wWVdOQtIrdS6sczKzXKKHmHVa9Q2pVBvKje1UEphWRtLgccBj+cHLq1zedzYtsbjNPE4nNM8sZTCNA3URvQoW1zThmBtwylgjdFX2tbDQcsI6Dqc7XymPd4jUilTyX0KM87JjdMMdZkQjN4urL2wOmyXR1ZTHl58SOsbdMMvF2R5BsBo54D/rTNKDNHI5cJoPXqCfSDLjA9H7t5Bp2dgG1pvGOMMMie0N7xv8e9FA9BaA3LQpCw4inrB6wnvD9C26PEdjo4NpnQNT7fY9hEyTUjfuMaAq4AV8A2zgbaP4e7r0C9v/kw6dOjQoUOHDh06dOjQoUOHDh06dOjQoZ+yPP9+/aNv/gEvv/3HnD/64C/9fq2VL/+9v8/TX/gl5tsj+fCLoLcOALv3mGwwQ6REnHJ20+4duG4lHYsjHamFssfxWrooRfMCsis43h2F7oaZJywOSCXZ3+qUAF0WvbtalLSCxvcnRJKdOYW9M721imt0mqpoplYHjfG9hxfScSkMrcjoCBbwMh2dAcLirbRW3DoiQqkBirtnp65O4ZS0AKReSjolyZhcxxKOBTgEEGz0dIFKOk0JSGZCVQ3YLK/BZXT8OqVWNKGbaoEiuMEQp43OujXO68pYt3gfa3Rzujtt3LP1iG6mb9TrueyoLrmuHazE5zTDdmCnNR2uhTE62BqgleB4Y9tgW9Fpye/ZGEBrA7PoUpU6UcQpZaKNNfYXgvQLyE3CyIxjFs1o4nAFx75ZAoC54WXBzdOVKAHJGOCxhuwdp1Kua88O8LXgPRyVSMAraxtS5tfdpxnPjIe7HfIc57GoFnz03biex5jx5w7mPWPC063rAmKojOuaudv1PDsS1xIe7sxh4d7eo5qlQPb7OqQrOBzpMaIxPuEI3q+13IPisDuDPdc1f3Y/mH3Xk3HQnsMYEYvtiOUHjXpqTDTW38d1iOIK2EWRjAYX33IiJAYAVAJs2+jX19dpietOJYYdcKoak1Zu5sJTh7Z11jZYW+eydR4ucC+P1NOJpRaWqTKLUUoJl7oI8+mGOk2clpnzeuG+Go+r89CddV1pW2NtgweF5eaGucDsxrycUO/xGVtjtMYQCRe8dYaRIL4ymGF05jJz++QZtDOMM6VWludfoqpTpwIa8dxbv7D2wcOa7uT8TK0N/NWHnE63lFKR7QHDo9uZiOG3sYVRfTujT9/Fz2dkeoLc3iFq5AQLdXqK+RnrDdoDQ05025imBSiwJggeeQ3YR7h8KQZtpCGnGzhfQDYoN0gxfNtgforbBbEOpxPez6Cn67mmNxDD2xax0PVJut4ffxqPpUOHDh069AXX+cMP2B7vAViePOP0zrs/4yM6dOjQoUOHPh/65DN01+n5uyxPn/2MjujQoUOHDh36+VM7P/L4wQ//k6+7GT/43/8Vfb1c0z5/nOanz3j69V/kS7/+9yI59XDZfmH01gHg3e1LxixjDVXCJekeUNUdFQsXmDiKJoQL2CvZ1xuQTMIdlsBr2Ig46ITLZXdsSsC+4EmCym6j3N2DuzM4ulyLTrD7kxNSCxkzK6TbMmOAcbw7IgGawn0YvayimhAvXb8oQo/vvZr8M5pYJBx05pRpZrR2jbI274gX9t7ViBiW6ElOtylujOzLDDCmYBuUOSB02SOs8+1KuIqLKlI0I3UdL4WO4M3o1hnDaMNpW6O3ho8NB/oYtNFpQ6Kvd0CxQSnhqLYyXx3YcnVgK7VUpmVBpNC3jW10Wu+4K9YviHq6mGN9nYbgEY+L0PtgOPhYQabra6torisJCP2143x4csTspK0DvIaZ18M9bL7XBfes9jXEDCkB6V0EjVxokljjKEhN4NkQLQFVyRhpjeGGAPa5/lJwGzEcIJIDBAlIzSNe2Yk9ZIaLBXguJUFcdNTKNF/d3z4GVkpcG6OlA1ivsNUserfFWvxTsvO3j+xfDfDrEsdoZhmhbjlwQLhXRQNko5iPcHqKXocwYkBBXrvSRdk7XHM6A4jBAjQjvvP8IpnwnFdGpLMHSB5m8d+arnrC+S4SrmfGoHhD6pxdz6ASe0iSLKvs959KzJM46sbszrTM3LnT10f6EC4dLpeNs8NZBVWnnm6ZCkw4tUjct04Lp0LA4POZuRrn1jlfVtZLp7nTto6IMc8LUzPmqVC8I6MzXDAD7w0tc+z9ywOlnLiZ4ObJidNyG+HzPuKeV2r0eee9zuqJtXXuHx94+Xjm4X7lkUIfgvX16sK2dsbWDZ0mUOhthSpot4i7vlzQJ+/GMMNyC/UW90foM/gWqQrzbYDjbUXnW8w2fEyYtxh4sEekzOjtu3HdbK9gexmd1afsq6gT2Ahntc7R52sbMt1Ab+jtu9gqcLkQheb706MiS4X6TvSVr/fYZft0D6BDhw4dOvSFl7sz1gv9cuEH//5fg8Pl5cf0cwwNTbd3nJ6/w7u/+us8+erXf8ZHe+jQoUOHDv18yXpntMar736b++9+5889Q3d99Tf/4QGADx06dOjQW62+rbz6s2/z6jt/Gv+9Xrh8/OGnfp2ynPjSr/89nnztF45n6xdUbyEA9k90eQ4oU0Yjx69KEbQE+JUdpO1uUN//aQHJdosvYO7YcEbPDlbJzlEtCbwE1YhCxnZbbEQJ++4OlYhrpkScKkhCQU1QB+Kf6CclPwMBxcwIUOwBaxRJJhYweu97FS35c37tLo4WWs/+X9nDguOzGdgwVBXXcD9HNy+YWHRhSsRXq3VcHBUJ9/AOJCXAe0FeO6ORiOoVja5gFIMErdDNMYM2Or11xnaOr7dB94ibHVKhG3U0puUGy0jgPggoOUZGG9erM9sAamGqhWk+UZtzfnhg64bKHLBVA0D2Nugm+BgUbbHk5N7QKfZNiYjwWLWaa2pIrUhZsLbidcL7wBm5NhKO1+w1Vq14LZhqnqtK7LKIEZc4ubEXNYHwMPYO3KDBPcA8E4w1zneseDT/pus44Ozegx2DAXtHbrhyNZ3kdo1ZNjd0ePRga8EkOlcjJjl6dWV3Jmd3LnjEKUvBKfkeFo5bHbnvPPtaY41dIqZ5ZNzzNQ46u4t9dLCIs3b2y29c9+o+SOFEx/V+DYfr3dInn5w4nbnIEr3fFp9XRa9rHxDYKWg4P32EEzSpoPWMt6YyrCPdMjI6+o515JVUKjI2ikj0aKvGW7tH/LkPSpmoegc+uJPCMGPbOpd14zKcdt7YdiBfhCpGKZUioFWRuXIqz9HLPdOycL5/wdadtZ3pGI/ucDZqURQJnm/OooXbqpyWirpTdGGalTpPcWwlY7bdcLvFGZgPxmisvXFunZePj7z48AMeXjzQ5yXOh5Q4px7nu4/BXCf8/IBPhj+scFvoYmArevs+ZbrBxiX6gO2SveRn3C6gjjVHRseHw+ndiB6vCpcPkPkpUt6Le7soLvE9rGcoMC7nCFufn2If/xCRBZnv4PIBeIdyA9rAG7o8jyGQ7SP08hC32+kGpug1975F7HSZPtXz59ChQ4cOffHk7rz4028xtpVT7bwz3/PH/+b3+fg8/dgJ6/b4gNaJu6987WdwtIcOHTp06NDPnx5++H0uLz4C4PzBj3j5Z3/6F7qUyrxweue9v8nDO3To0KFDh35u5Db46I/+kA+/+ftsr15+5td55z/7Ncq88OW/839DSjkcv19gvXUAOGApGcXsCAP3kk5FA1dEnaLgnsujjongI72/paDm0XN77bNtmGvAUfeMmE6HbxJPFxBPt1vGyUZXJWnYTMhL9p1qiVhaiGPdLaJm165YbIAMVErGBFtC5oDLAuHc3EGixNcVZYyBZewvNig1IoUlY3Yl3ZiIREq1SzpFExZqxA57xvkKROem7w5IZXhApt3JugNpJCKSQx7Rs2oMi//tMcDYwFpnbBdGbxhCz/5fXKnWES2UeUbUkOK4VnQIw6MLNpKyhWGCuNC3LfDdcqJkDPbtzcIyHO8Lo5/ZTFjXS/T7mmG9YxrwXDSifE0yJlhnxA2tM5QKEt2yOgfcFtV04ObJrIq5YT4zsl93jIaIXfeip2N8dMNLQ0sCW+8w9BojzT4MICWjnV87Yh1HbAS4t71Tmog3Jvucd5jtMcQQ/zJikCBPj3uLPU5FHHQHw8Q5dPfdohtdyKowIqY735DoICaO1drrIQgpMDquEQPto8eQgUXUuTn5OXMf2h4tnvsfXv/81TAfoD1c8/vPvh5awA0tGX+9d2v7DL679z3qfj1j3lWgCELNFIDY1+aRCBAO+YyOzvhn98HQmeHpIO0t3jejuUWVkoMZUiqCUnygGhHoERkvzHOh1oVbM8yENhrDJ7btQnehtcYKV1c8GEMqos40z+ik6OUVnYnWO80afUDBmUx4+vwdnpbG3ZMnTHV3sBfEOiYTJh5r7nKNzzYbbOuZSzdebsbHH/+IV69ecGkDc4PxCoYgdUFax6uAL4yHR+z5DaaCy4Q+u8WsMZ2exLqaI2OL1AA6bj3vjwX3DbwiumGPH6PPv4GMC2W6QYuit1+Je6RKONS9Q98i0WA+xUCOvsS7Rsfx3XPYNnwS5PareHsR99S6gLe4Dy8ndP4GbOdIiNgeAxhbxMzjHb25/dRPoEOHDh069PmXu7O+esH9H/4bno3v8aX+fR5XoaohtfNkuuXjxx8/PV3mha/+5j88/oB96NChQ4feWo1t4/LiI77/b/83wGmPj4xt/St/rswL3/gn/4y7L3/1r/8gDx06dOjQoZ8juTuv/uxP+eH/+b+zvvj4M7+OqPLlv/ObvP/rfzf+Dv/QF15vHwC+/l2LZeSsJ5qq1zhXHwNzDeOvw0iHa6TfBvCyhKC+9+8mONUEoaIBUDX7f9FydRi6Jvp0RygEjAtIOyydkSroGKjsobSa7shAaQ7XvNr0BAeksBHgWANui9RwwqEBkD28iy4Fl+xVdRJSB3RUnKIejlRL/6VkzPUge2wl4oZH9iQ71x5gEcVLZYyBZDR1KQlCSaeyg2c69h4L7a0BEt29CasHgo4GI2C7bec4RneKVkqNzyca69zJDmVdgDUAsHWwjteIezVR3E/hPC0VFaMsTxAfSFEGN2yXR6ZHobaN87bR6gkbDSwclMJC6/0aERxwPfqPIeKt2WF5AvL4lB1GADArMNqGTDPQ0FIRj/5jv8ZoD4YrPqI/10e/buFwj/crjEeXgKESrnD3gLtOxFNHhLJEtHTGmgdk75hlD3AhI8szDh1H64QPiy5WD4CsktdQrv11DbRERK95wteMFfd4b1xgZKyzaziY3ZHhsUyRiR17wxoWWdhX17LvkFj2B1SAQ/e+L0pGqRvUiiMJgoMOq0gMI2SXbly3+1pUSrq3yThr43XfN56x1D5watwnGFeuf4WkZrjk4IFFd3M0CMfAhqviA9oISO19A41/SpkyKtrD7O2OlopWpXj0ZUNhLjNGwdqFgdINzDyGOqoy2iPl9ITeB9IviCm0FVqjTCeeLyfeefaMu5Mw1VukhmM71rViIy5OGYYjuA8s/Ov0vnLeBi9W56OXj7x8+YptGLo8RxdHxmCc/wRfL3i7wElg+TJ++xQbnfnuGVDw7KWmXVAKpg7tAdaBPHmPtn2MtAe8THBvCNnNfXoayffDYFvzuCWHYMJN7OaINXxUpBLX+d1X8PsXMFZkeg/GR9AeYbnDxwC7ZK+v4NsWYL5UON0CBeoS79UeY6CivYLlnZ/0yXPo0KFDh74Acne2+1e8+Nbvc/ej/5Vfur3n0irffbjlhw9/9VCQThPf+Kf/z+Mvrg8dOnTo0Fsnz78TefntP+HFn3yL++//2af6+eMZeujQoUOH3la18yMf/sHv8dEf/YdIo/yMuv3SV3jnV36V57/8t46B5LdIbx0A9h322CDw4oIUR3SgaXeXT8AldmfhHkU7NB2DDdcZx7DsJjUbFNJ5GxmzAX/3WFzj6mp1KdG36f0K+gJ0BazV18bFAFpm4US0185Y8RJwDbKrtQQUHoO9z9cJVzM41BogNJ29ooWScE2yx1Q0QJxm4rCoJ/TrSKkUKQHTSo2Y2/LJzlVPV+8IhqYa8awMXOsVNls6CQMGGzaMIhoMLqtmmWpGARvUAr1CX18DcfVwN8pgWDh8tVRgYOsDli5AGxuUE+yx3IBOJ0oRtMJ8mrmdC09v7ljKROvOR/cPnPuFZoOhAQPVOqWeYKwUEVyV0g2mhWER3yuqeG9Y35A6oeLZjxx9wKo1IKYPXKeAicB+1oeHY7eLURDK3mk7OiPd427xOUQMhqT7NxdVCj42oICmw9cMkf0yHzBi3cOR7KjXODcyokd1f82y53QrY4+fHlv8bJ1wH9l9rKgTwFRLxjL3iO7VANreRw5e/Ee9xD7SdSyId/CaLt8BJgGl8yKwvF7N4+HkY4v9LuQ1muHOCeB9muP43RMIxxCD7NenRyd1XA8195VAja+Jxv4TLXE9aYBoMQmHNzHUYWEvD/AMsbF1CkfrsGuKQDiaO6KnONfW8T6ukdQ2Oi4FGwFa0cLwHBTpLY817lFKXCTROV2pUphGpBr0caaZ08oJMcfplNOJ2gOuL6Xw9GbhvacLt09uEdvy/Ptrh/aeRqAR5+4DxugYSlPjvG18dL/y0Xnw8PiK/vAxOt0hMsIZKxNyeo6PR1jeB39BLhSsF/R0Q5kWvGcHdTWwuHZ9hqITRe+RywN2usX8THn+LAYccGR5QhkrjHtUb5DmyDwH2O1gFMS2SF8oQEvHbq0wTbFHyhnmW3j8GNeGTM/Bzsh8wl/9CFnu8HFBZEVYwolcYuBCuuGtw+1Xwf7qKfVDhw4dOvTFkPXG9//N73L76vf4z04/YH5qfHxZ+L0fvc+wv3pyuiwnvvGP/3PuvvzV4w/bhw4dOnTorZL1xuMHP+T7//ZfsT28igHcT6HjGXro0KFDh95WtccHvv0v/2fOH33wRq9z++Wv8ou/9V9Q5+WndGSHPi966wAwAGN3A0ZUaiG6LxXJ2GRHpFwnFKN6ZI9nTiCrS7oBPeN1Iw5ZNOkpJR2UA/HdMRvOQxe59pe6lHCcebhDhYh4DhugvIa1BERM/IUUxUXiN78W7kQhYnMp+XXJ1/Ikq3mcZiNgWAJp0YRjEhG/phlN7QrZdRokpUQHsRkywi0bLsfd6ZqeSVXGHkctYB6gzMzis0p0G4/LClKoCTdLRlWbAZqOSzy6hK2HO9TDiUydcRt0FEsgb73H+XAPwObhTHWtuKe7WQJY+uiYzozWsGVmenbL+195HrHW3/uQl+2RV9vGdjGsncPNraAyIxJuTgkbbOwdjeGBiDLeo8RrQt50UTsBKzF8bBGprBLdsaIBqmqlJCxHFS0JZNNmKkQMcsR0a4D4vga0k3CUh+M5o87dkbZhdQ636Q5nEcRLAPbMQfZ9v5QaTmIihtjFAuSahXu1NbyWAL/iGRE9kLiCru54YY9BD7C9RzdjsefNRsSPq8bre4/jGBGbbCMgNuLx8S27gmM1Y40+0aV9LZdOIOzDX0etI+B7fDSxlj3WR3Wkg1xR1/h3i2vCR+7jHHDwEg5ZHyvXOAGN60T2cvF8fSSd4PtnlJuMA2/UIZhGN7KZ5d6OSHYblu3DEntbNWFyDAuMEU5VkR4uau+MMRji9CGM7pnI7VRV5HTHtG0U2bi9fYebBZbbJxTZ7z+7Uzk/izvoHEMU4oxxZriw9Qfu187HH9/zcSs8Pj7SR4OtU+5mOL+MARsd6HTCp1v2WH179QhPZuT0TvQgW3TtyibIREBgUbwb3D6BvnF69+v4tAQQlxk8osMt4/C93FHEI/BAPaKde0fN8W277gNdFqCDLMiy4A8voa3IPOF1Dlisc/zs4ti0IL2hteLmFDW8KIYH/LVHpCzRGTwePvNj6NChQ4cOfT7k7tx/9ztcvv1/8uXz/8G7txcAPr4s/IcfvfcTwV+tE9/4x/85T7769b/uwz106NChQ4d+brQ/Qz/61h9w/71P5/jddTxDDx06dOjQ26p+Ob8x/NU68dXf/Ic8+dovHPD3LdVbB4DFM39YMg4WEDoiU0bFJgzbu4Kzi1Q845RJB6V7wD1LF6M7qhF1qxkjujuJdzcwlABobrD3mmoNICfh4BPRqzvWbeD0AD9uaJ3CZef16io284S9CXFVXscNJ/j1MZCS8bvsv24ReZ3fr1ri+EmXY5lwX+lFsd6RASWjaCPeONcIEv6VBFjhUjYPX3GkTu8dyQNc4+1FKRnvrNMUn8UD3o2+wYhY1zbCUbv3te4x2Hvv62ugCUw3WF8ZLvS2RXyvjehq1oXiYfDUouH2TnDX18GHHz7wMAqyCPdb58FGRFgnnATLztaIvh4IqjU/b6yGO3jbrqAWPPaNecZjfyL3WqNDNqJ5BzLHPoiu2ITwvVM8wdYYO0GG0eK4SoEezl+s4/SElgmUyQJ3F+g9Y8YriOUe7Ph4HU8d8cudvRfY6xSdthDvUebXQH4EhA8gvb6Oo85BA7fXEdIoMXywD1JoIFwHfIw9nBkbefwZw+4J6kmnbwDgmtC3gIyMII/rFCkJ2PMz+7gOcVi6yc2gQ+xHNPuGOypKKYqqUupEEaFIXrt5rcb9ITuuqfH1ssBo4YhOgLoPd8B+LAmHdYfVETcf3csWQx4lBhR8dLxUDI81yAEQF8+eY4voZ3dGizjz3rf4LNYy3pvsLYfmQkF4uhSePP8y87Svn2bkd4lz6TlMgODjEudAhO5O15lVjVfbxgcfveTFubO2M5hR+hne/VrG3KcbfB8YkILZQHXGlpHgf0W9omVC6xJr7CtSTzAaPP8K5sqoK6UsmK8YFXrD7IKUBTULt+7yBOyRYjX6ky3i8YcYPH0XqRPW7tERoFxLByrjdIuOFo71+RTvP1eY3sf7I3L7Dr69gu0cTutS4h6C0MaK3LwH5YT1c8TIHzp06NChL7RefffbfOd3/hd+4e4jfBK+/eIp3331JKofrsUcf7GurqWvfO1v4GgPHTp06NChnx/tz9BP6/jddTxDDx06dOjQ26rt4Z7v/Pa/eCP4+8nn6JGg8fbqrQPASVoDXCERBVsr1IILiHi4cBkJOAOkXoHs7sYTT7DZcUuXY41YZffdN+tpUNyjfjPCeRhhvMtI3HRSQkIul3x/rh27JoqMHjGkGcuMxTFEFGw4NCn1dfcq4URGNPipxjGh6eYUyxrYgrmh5kgpGfscPb7d1qiX1XAKB+BKJ67lwVs4Nj1hsRkMN9yF0Vo68RwsQJg6CEapBU2gZ+S3mDHMMDP6aAwL2Ogq0KPbVrzHuekDSkmw2WE0unW6BThWmXAbyBj4FACwacBmN6dYx8dAlxlj8PLlKx4uG+dXL2mv7pHRENfYH6OhCK7JMdM5q6IJ3/McXNcjI5jrAls4iKXUdC+XcMt6QHQjIpGoE+RwgcZiYChiPSC7GVKmBHW8hsJX6JkgTycYWzjGa0UxhArWYs+jaJlhXALoWoJSDxArFv2z4X4lXj8jo0Hwkni7b6jW+Jkxcr+l/VVLAGUaeMWlxuewDiUipc3I/l6L8+f70MLecr075QMsh9F8i1hmJ+CoOeoWrNV3222CcrcAqQmUzQbDHHNhjE73iA43F8zOcSxakFJRgVokIs7dM9Y9YrlVhaoBi6V1iu5OfEsXsF3j3sUSppcE/B7HJ1IyQjsBsHjAZp2R0SnZox171TCE4SOjp8OpLUUjWlw8rpuxYXVh9AvdhLGeKa48PRWeLRPzcrpe25bJAKONBOzpkhaJSHWJa3jbNs5b4+Wrez66NF5eBn0MzDZ4eEAnQxgBYCWc1FjcK4wRaeQ4Op3otsXay0B9QvojUhQVoY4LTDOUO9geI/5bjXVbo5d6NHQfnhhxHejNc1SeM9fKJI3qFZlvwpFOxQScJ7BdCOC94hSmmwW/3KNFkdMNNEfLiuhTTBt6WuDmhn5+Ae0BxykaQFuWBVnuomvZnc/21xiHDh06dOjzIHfn1Xe/zXd/97fxMfjOy2ef7gVEeO9Xf53nv/y3uHn3vb+egzx06NChQ4d+DvUfP0M/tY5n6KFDhw4dektlY/DB7/97Xn7nT1hfvvhsL3I8Rw99Qm8fAN6dt1KzlrTgFi5OzLAaTlgh4G70e6Yr2BxrHS8B6cxHVmaGg1c1oFRmtUacsAAyBcRij/J1bDhS53QTQv4AEBDWPTC0eQAagYiOFsX2OGQ0u3+dMqXbeFg6lCPm+Vrm63vTLDA8XK06wo2Ih2FSgm6KloDBWiKOeF+TUjMOO9bHGVeQHubRQTdjjIw93oHcGAhQi4Y7GgkopAGdBKEKCeYCIm2eTt/syWVkzDaGl3KNi7V0HdsYmKQrEo+OTlX05hTQ1oXRG7oWenO8NKxWRlHW7Z7+6pFNCq01/HymerhRrW9xHl2voFTLnPHDseZaTxGHbY5IReebgK3mSJEAv0XCPWobTkXEEp5GNPLwnpW1whCnTKdghbaiOqWj1wJ22ied3HvUdsFbgzLFPvIAoDoMiiLWMtY8YKtbCff56BG1nD2/ohHkHJHZuWOyp9k1AKZYRBsr4bIVIZzDCcOT/mXXdfxaQOrUGFeHadp32XenOOkizm7sazyxYvoaXOYL5a9v4Yr3hOHi11/br3c3x9F06iq1VIrv8eiK+YQNY4zGaAH3VosxDJN0dEsNh60MSp3RAlPRcMarMomgEs5nFUe0posYZI/xzms93MSabt28NVynNDTN+wHUwwFMxEV7RJf30WNIwgYdofcRkc2tYy3ioNWcd+9ueb4YdZ6vSQFxVQ6cQdGZMTaYTviwuLegjL6ymvFwvvDxiwc+fPWS+4sF8LSG+IX65I5SlnDeisTnkHBGK4IVQ4lrvi4n1scfMOQZbX1JkZnp9AQFyulp3EP39IJa474oA5tO+DZiAEQl3MXFKdOXUYVJJ+bTDaebU/avR0e5tRXr0bXOKT/bmrusVPzuGeqZjHDzJbS36AjuCy4WvdBP38X9Xaxv4RhvD5TpKdgG5iiD+uT4TdShQ4cOfRHl7tx/7ztv5Fp679d+na/+5j86Jq0PHTp06NBbpeMZeujQoUOHDn16uTvnD37Iiz/9Fh/90R+80Wsdz9FDn9TbB4AlYG8EEFvGAO+9qhH3GsxX060XbmDcuWIqgz0yVyRep8gexZvANd4sjIA+EuB5QlzJiFzf6RCmNYGVJSiK78UMs3YFgFgJVyuEG1ccxBnZWSoS/a7mAdGUegWV1htQ0HQRyh4vi4T7luhBjhLeALNFFPMW79c9ujoT/17jaekMg25ED+/olDqlkzk+Z1Go15hpiTRcJddYKKWgY1BV6FXp53OaOh0ZFiC+TNjYcC9YW+mi9O0ScMyV1h8ZLvi2AspkDdVbiihVnGleKGWiqDLVmSIDL8qlbzxeBtvaAuR5pxahjwEWx06ZwCzBukdfsZFRxQHDvSzY2AKCQcDHdIDu4eKxtD1MstWj09QdH77PGSAG3jasTohtBO8uCAUbLWFkgZKdvSM6oZnmAKe7U9cdtwYyIUIAT6BbHpfWjFke4B10ymvAYyjCR3jXS4DP3ViuaEBjJIYAND4bNsItbRF9bb4PUGTMcO4q9t5sIaH0SMdsrAUJ1320gLMGeMT4uhR85H5kv+aiBzfmK3YX8oC6JEQemKbjmAre0rHt6Q+OAY5SC5M4ER2d6yAFqRPuEWttBqMZW9toGzRPyJh93KKRG6B1omhD3dBSstI74pi1xjUQbuISfdqyw/RPwP2MeY8jdEZv9GEMv2Cj4RRGb7HENiJW3JxRKmW98Ox0w7NTYa4ROe1YupABoqM6bonZOY0xfNCss7bG/Xnl45ev+OjVPQ/rlv7uAjRUnOn2OVNVWB8RA739StzP2opvjxRKuMSLo9OCLM9ooyPzO4w608otrkopgsgcMfTjgqnDfIu1B+o4M8oSScvr90Fu0KlSl2dUnFKUqSpLKVSVHIZQXGd8ihQH3/fA8i5jfYGXitQTvj2ko37CdU9CmAkAb0iUkaP1hMmMMMXe6BGTThG0Ht0Zhw4dOvRFk7vz6s++zZ/97r/8zK6l9/8vf4cv/93fPP7AfejQoUOH3iodz9BDhw4dOnTos+nxRz/g27/9PzPW9bO/yPEcPfRj9PYBYNd0wY3oAL46Fg2pmsDIA0p9wonobgnN4je1dm0uTYC0Oxwt3Yl7bC4JWDX7chPuXIGw9XiFkShWwpUo5gGoRBjDURkJmQwbgkgL967HZwkHZ00AwjXS1ayF0zAjTD0Bc3FwJryHEzDgarhetWgCqR2MhbvS3HZ8jMFrt62nu9I64tGtXCSBn0MpgtYJFUFVUc3+5VLis2cHK8XSQDoz9S2gz4iOYysF61t2LhvbMLp3+raF26+eMHOsD6ydKXWBuaJaAi7L7n7eKNOJqvG6qzvnyyOt2RV8lmlGJN7Hx4bUmzi+WvHtDO7M8w0+Gq1U+nZGSsWs5Tm2dHfv/bVxwy2lIqoMM2oJ5zUSsdgu4+qatihORglA6NbRMcJ1Kwk3CVAsTriN3dOFHF+MXjqyu7RSamV4x9wY3XHvFHVUJWK0/RMR41znJDByH6vivYW7UjRM5aI5PBAuZ9mjxn1EP/Ye5zzSzbu73EUT+mZ0eMJeKxDwzfPaGPEzGu7qcApbAE/v+VrpfvY9yjpd+WWKflZJGH6NuR753w2RKXtzYy8K4ZZmdEQ14sm15Pmp+Tkcr5UZD74/As6aRc+wDYnu5Ga0sbL37ZKfV+Y5hi+QiJvXiJQW0XCwZgy97hHRqnjf4nzafuyW58RxjFEq22j0LrCuLKXwfJl4djtzmpVSow96tIxuJ/e6VPCe/c2DYZ1hg0tzHu4f+fCD7/Px44XHLvTzPXpzQ1VDilDrM6ZZAjx7g/nJ9XPo6cSoFb/cI7e34ZgVQ08narvg0xzdxZcHfJpRj5/ZI80l47LdIhZd2gNSJzj9IsWM+RTvVRTmZeJ2mbhZanZi96tBPG7FgnumPNSKyTuYrbgCp6cJgWfMsyu9Ltj5RfQsU3AduAneLzGQ01uAegyd7vBx+ezPokOHDh069HOncC39GX/2u/9frLW/+gd+jN771f8rX/mNf5BDpocOHTp06NDboeMZeujQoUOHDn16uTuPH/yQ7/zO//Jm8JfjOXrox+utA8BSpuhXlREdmlIBz25XuzoUSY/h7p713fhLMGSGB6x0C4hGiY5WDQefaDj3XveSZjeqJz6VEu5ZJ7okew+AYg5q6O4UdonY292MbCP7VcsVmHg3vOzW0QDVWi3hW4K0EsenYgyBMTxck8RrigfIFRR10rmo0Rc8dudyQhVr4TS0iNpVqehY0TJBFWSEw1dK/IAK6DRF3KtquFkl4pyRAG3RXzyFK9oGqhNFVmopbFf3KDhKt8aQiHkWCq4T3nrEY/c1m48DSg9znAIyBawvE+7GsIZPlcfHR/raAkj7iHjlAj6d4hxrAEaIHuQ6n/DeqQWWm1vOW+dxu0RUNh3NdQ536oS4RdRyDhRoKVfAubvP6R77h4gONwcbawAn0rkpmrA/IoYlAfseD45bQC4p4NGhfH0fGuIFQwPatzUGEGo4xeW61WvEddc5QOoYeRlY5JsTUcrIiB5i82yqFpSRscae7xu9tZrdvfswRLi+LWBds4h31ohZ9hE/t7uDMcdzICLWJfe771/KNdijkyWPD9KxHL8e19kOkMkZDEek53tGr2yw6YkoFI5jd+8ZQd6zNzvIuPcRbtC8rrUUFEUYUBwpjheQOpPWY1yndDQ7jC3AoqX7XhwzyahnAV/jM2kA2+jNdrROEUksSl8brXX62GB0lmnm9ubE3Vy5WyqzGOqC6Jx7ZMPLHJHZyxTXx7blfWLQzmceh/Hi/syLi/HxuGUVRy7fp57eQbhQfUK9Mj15F7WAx0wndJ7DyS/RSV7nG3w+RR+1bdDuow97qqgUtg7bdmH4YMgSe3Ka8d6o8x3jfA/Tk9jf9QlVJhRjWiLKWsbGpM5pWTjd3lG1IOJQ5xiI6ZaR1zXc3CXTGE43sEZcfAzm1HDx1wUbDxiFoTOjr2ksH8AK10uhp3t8ieukbz/BU+fQoUOHDn1e9Oq73+bPfuf/EzUwn1LLs+c8/YVf4kt/+zeOP3AfOnTo0KG3Tscz9NChQ4cOHfp06pcLDz/8Pt/7178Tf0f7Bnr/1/8uX/67f/94jh76T/TWAeCKhyHP9RqrHIAv3XEZu4pmn2lG6RowEhA6Fl2vkt+m0YSLxtd8GNdyT8nXtehPpVTcW4CvvVvUPTnPgBox0i7hhAwnZ2bvil4jpMHDkagV0Yq1FZdwdPoIxyJi4VH2AX1kXHQ6Ot3wkb2t1pFpCielGWOAqaIert48yjhOC6hm+d9FlCJOPZ32quFYjwRZogMtUzp/FdV0xLrj6WAWDVy7c/KKstTKmBf6ucW7XTtco7OWbUsAP6i1sFmHvsX6k1AnnY24o+qoVCY3phYg8GFrbG1DxJnrTKkL2/keo1Ah3Ixlir7eMqVrc4/dFmRSVGbkXK7gXa7Af48jTofqaLEd6oy0jBJPJ62KR0RxrqeqAVNAQmp29TbAGTkkIEVRk+sQgxAdwqKfiE726IOW0UArZoK5BvTqW2zHMsfQAbtR2VFrCV8Vxicmd8sUQHZs8XnrFGZk6xlBnjDVIiLXyxQOZCfcnC4wostVLNy17K5ajbhgPD31u5N571qW3ItCON0l9sK1KzjhuWt00PpoEa2dcev7afG8TrGWQxgKY81JjISIqgHas2/YMo5Z9jXx6Mf2MQIYW7u6gylTguccAGkt/90QchAAYmgkz73sPeMlnMku0ZPto2cUNeGy1Rlv8bkGI6LWS6GIc3v3lFMVbtQ5VWUuxDCCk8kFQrm7AyrDagB3IgVgdMcwLq58fH/Phx9/zIMpYyi1TOh7v4h6AX2O2jnc/MQATWFQbp6HQ97jXCjxea4DMDJBXWJAxCsqhsrAV2frRqud2gWxRplv495XlxiMGH6NGi91zu5woZZCrZVanOKdIgoimHTELX4eZYwN93BQUxV3paP0EY77gSDbiqtHD/Q2MJmx9oj0jTqfopN4sozsX5DTDTKdkHbOruxDhw4dOvR512iNhx9+n+/+7r/8bH9x/fwdfum3/kvmJ0//Go7u0KFDhw4d+vnV8Qw9dOjQoUOHPp3cjI++9Ye8+NM/4vzBj97otZbn7/Dlv/ObPPnaL6Cl/JSO8NAXSW8dACZjlRGJql0lwMXewQtQwgHoGUPru4vRyRhWkGIR0ZwOOz7h/qVEFyfpKHbfy1Pj3/fOV3cPIErEPSOabDZA4ute0E/AaDOc+E2149FHqRJO5gR+iIG1dNpqxNvuTk4IB/HY0rFpeKkZx5sENmN0bQdn/hpWiwtF438iJWOdQUqN+NbRg8eVCU0jbC0lGF7RAN2kQ9M04ZpcnawuCjIoVZiqsJWClMKw6JQNoBtuYfqKlAmxQbEW582cYoU+VqwbLht6uqEgLGY8U2EqlY/d2XpPmFoDKk2VMW6w0bAxEGsYI6AVlu7hOMbWGtpnRo/OXdMSrvI6I6PFeSjhbjYbqJRgYd1i/TFG75RaswZaMrpYKF4jmtgsYLO3AJXWEZ3xki5fd6BTtGAJuWP/zsjIuGxzuoBIj33m0D2crNEj6xQNyCiUAKbdMp46XLNoDagpBPDSKQYBWt8nIGBElLjI60hoxv4aJaAtPRy6Q2IPx7QEsZ09YHGuk/M6xprdEZyu4H1jeXYwSzHMNY41dld0QZtFVLuT6xWdsGgOQOR1754w2XvA931fxpWerxvXlkuJa1M1bh4JyCMC3RCLdYjrJmKew4E/g10CyOc6Rsp6ywEUi/sIBqzRlS0l193RPP+eHeUugqgwi7DMC6elsmCc6sQkg1JLDD7UElHx4shyE922Q+m90bPjeRuN+0vjg4eND16tPD5sDIlI+VoLWjIWvhREZ1Qqs1rcmxx0LqgY3iMRQIj7lbmDb3EvqEvsMRFgoswTN/2MP1xo9w9sT28p8y0qyrAV6w0fgtkat05KJAiUW1R6bvM7pifPKPOM9zX2wDRjYzAGDDMGhTE67oZtG0alO/RXZ3yaYl+uF6TkAMdo1PkGffoluNwHoM/PqmWCpaDLbTrkBea37xF66NChQ18kuTsv/vRbfPTN/8D5ow+49gh8Cp2ev8sv/ef/L6bbu7+GIzx06NChQ4d+ftUeH/j2b/+L4xl66NChQ4cO/YS6fPwRL7/zJ/zo9//9Z3p2flLHc/TQT6K372+v9zhaUVw9UpMTykQ/qL52JhLsJ5pIE8gq4BGTXESjP9U8oY6lYy97Rx1cLKDR7nQc6bb0kVBsBDhOsOgjQWw63UQ0XJGSjkAtmI2IcybcqJKu25CEU9NGMrM87oy4hRERthafWVTD2eZ+de5ee2v3CGEpqIKKomjGvMrrSAF31CPWGq35M0IYq2scWykJf9JNqbuzMaJZ3fq1y5V0IYOi0wxrY/TGGBYTpS4UBCsB6qSUgMzmmA+kFiDcj1OpnER4ivDuPPH+86eMpzMf/uBj5OVKxBcH0FZAizJ6RBN7UCxkKoiUAM0qNIQ2BmUo3p1S5jB5E9HXPs3QWoL/ks7B6MelhHPSRwJFj1hqFUvgHudTS8WGYdsFnWZcwxUrvSNaUN3d2eSQQKFvawLlGsB8DKyPANfaKHXG4uQzRkYy733CKOIxxOA74PTYC7J38e7DDC6YOGqvXfPitvvEA9yOvv9XfsY4JxGpHF2+WEJUG+nKD9jqotnZu/dxW7jZy5RO4nATR5yzYL1Hh+v1Go++6ugFzghnd0Zeb4wRjmO3ALj78IQ5EN3aESudn9oIEK9TuIUVsMI1zp2YrhKR2J8SHb2iEUccr5+R2rmGIhpdwHnLibVLl/TubmZ330uc/7xOoKK9U0vhphZupspJYa4zVeMa1VLRjNoWsTyW1x3KMBi9cWnGi/uX/Oi+89HLe9b1AqeFsl5wNVRLRLwT97gqQq1Qs2McndK5H+cuuqCj21u7x73UBzLdILbm/cFiv9w85cSEm2AovS4xjDAsh3EsdqbHkIJIuvGloMsNUjVM5ShoOJl7jz700Qe9rRgaEfF9YJdzdK6bU3B0a8gs6M1zGCvl5hT9y6XEYMnNTZy3donT4o7Op3CUS8BmuT2m1A8dOnTo86rt8YGPv/WH/Oj3/v0nhsh+cmmtLM/e4Rv/5J8df+A+dOjQoUNvlbbHB1788Td58e0/Znv18lP//PEMPXTo0KfR9x6/R7PGN19+k5t6wz/9yj/9WR/SoUOfWqM1vv9vf5eHH3yP9vjwxq+3PH+HX/x//JfHc/TQX6m3DwATUaGiGdsrCV/d0mnr8Zf+qolLBNsyBldeuwmjn7NEFGzv4chD/1wvqTgMS5ejSEJRSadrRNOO4ag7fo2htnDXqmbn5A6dHQjgqDYoZUKqRASuFoqUfO90N0t0h8oOakvN6Odw9WqpCCNB7u5A1IS0r9cFFzT7Ncv+6wkFIxbZroBNCHgt7K+Tx+KOt4bXGo7nhF4QUbY2wIa9BmPu2NgiAXYYwzp9u9BlgtYRrfFZZYrO1WF4nbHtjFFinbSio/GkPuX9onzl9sTXvvwl/L07/uBy4dGEUiTids0pOuX5KRmfPBCPzyCA1nAZKgHCR9/wvlHcGUJEFu8xxtkZTKZ+ixTI6OoxIr5ZCP45xqCUguY6Wg/3phLrPtqFPi7osoRje6yx57SitcJomBGOaJkiitgtIKx5Ro0LaqDWw6k6WkYsRy9wbz1jezXjfSP+WHZwKQHxYkAirwERRsJkyTUS76BT/LKniziHIiSvAZeC9TVd37kRfOBW499ti/Mnmo5gzShtISKGY6336HRH4/N7zysv4qBH75gWXEq6lj1fH7w3vBjee94P9i5fSYdsRlxLxheX6KiOr3sctvj1nqAZG83epzxGOKCLhAtaYx1Usstb0i2/x1FjAZ1tu8bSW7q1wTCN8+Q9ormlDGpx7paJJxVOVZiKUGqJvZSOf5GA+rjhGkMgsaVjAGAdzsePZ77/8QMvHjvb+QKjU2sFCbd5LYpM0e1ctVAkovOlKlqXgMnbJe5bd+/F/cQGo52xIhl9HntRRl5PdY7zV4Wp3ODnM2cKrTnqGxOKLDewvkT6lhHyhtLAoNPRVuIcrCtb77gLY+tYHwFoh+G2IihFCqgy6S3KmXJzQ5nfQcaKTkv2APcclsnY/Iwdl1Lh5sTI4QR8pCs7erFlnt/0gXTob1C+J2v8jF/j8yR357E/8s+/+c/5r37lv+K95b1jDd9Qxz58c73p5x9t48M/+D0+/OZ/YKyXz/QaosrX/9Fv8ewbv/yZj+NnqWMfHvq0OvbMm+tYwzfXsYZvrjf9/O7O+YMf8af/8v99PEN/xq9x6POlY998Nrk7/+6jf8d//zv/PR+sH/CLd7/I//hP/sc3er23bQ0/qWMfvrk+y+d3d9yM7/5vv83Lb//xT+U4lufv8kufU/h77MO/eb19ALhocNwEIyLlClgj4lXARjh/jYyQHa/jb10zrjmB0e4UFgLGOgFb3LDsZCXhWlSJ7m7QwdUpjGQX6aAg6TQuWAt4Ij2cmVqEIoJMS0SuSgEtaMb4ekJbEcVrwXoDFBVN2BQuSZPd8RvRxgHBShgZM7U3+kMtYKBqIr6EHlootUbv596xmrHakGm97jtrx9wwU8Qso33Bs4cXiThad7CxhQtWhOGDbb2wrRvr1ti80FpEw6qGa9rNo8sWpfXBMKGrMM6vuK23vF+Nr0/Kl07KL339OTe/+gv8/otXfPdbL2ltoCjmUNygxBpoOj29rwGr3cJNLWA6gw20OMYUUG9akL5ixJ7ZBwTCBbsx1JjKhKQz07yHu1UrjB6u4YwG1zIzPACnaqHUgo0Faxt2uSC1pvN35LkK+BhO5YCzNtYAdAmhJXtz3SfMHfOGezhVPd2/dVqyTzd6ZSNeeAoHqeSeJSCwpyucHcpCxChrdBGH43cEeBSNyGYJ12r0OCf09JEGz3ARu23xddHob2ZcO5QdwbyBl+jgdtIFTKxjDhmMMfKh6lgpGZdOuOp9jziPY7CejnXrefHGXoQ8FikBf1Ho6SK2FntBI65cJLqChyg6eg55KDKdYkBhjHDa5z3ER4d6ylhoYGw5fLEDd3AP57SUCcSjOnzbAmaPjmllGcKTWXknu36neUr4O4WDdZpQdYQCI68tlGadbkZz42Ft/PD+zA8//JgXD422rigDqqDTCZlA+0r1Tq03ESkv6YavJQcGIlpaqyDT04TmJRzr1vF+Dng+RgwHTBV6w3WOe9fYQKFOhXpZ6ZvR5olalVIKvV0QqSgNISH4MCi39NbZekfXQRGhSKG6MEl0jReViH6XEjHYJYYbxG4RBqInnAWzcIB78Rx0yQEFzQ52yShtAeqEd0eKxIzMfIOP7af4cDr0162fxm8O36bfYH77/tv8i+//C/6nb/5PfO/8Pf7ZV/8Z75/ef+PXfZvW8Mfp2Idvrs/6+d2d84c/4ke/9++4/96ffeb3P737Hu/92t/m2Td++XN7Lo59eOjT6tgzb65jDd9cxxq+ud7k8/f1wnd/97d5/OAHjO2z/TlIp4mv/YN/fDxDP6ef/dBn17FvPp2+ff9tPlg/4J9/85/zrz/416xj5R+8/w/4b3/zv+VvPftbn/l136Y1/HE69uGb67PA3xd/+i1++O//zU/F9avTxNf+/v+dJ1/9Berp9Mav97PQsQ//5vXWAWAxx5Xs2C2vgWmCIe/O3rfLHrG7x52mo1axAABFAq6ZBWyRdPslCBRx8B6vn9GjropoQrTsCEYKhejOFSEdc+lINkdEUcjo3zw2QEngtDsLr1G0li69JaJo06EYfcSCeI9ey6K4l4B5JQC0CJRS0TiQQNOqCUMDXouEUy7eSxOgGx74Oh3OMd2ClOiy3R10CYqDv4fzsrUNs4imHX2jmdPN2baVtq5szdi2C9v5kTYI0DYFvBttAyTjk8P9dzLj69r45bs73j9Vnt48wZ7f8s2HC3/wrR+yPj5SMCxhY8QdByBXi95WSg0gadFrijsu4dwuCf4YnfnmltFnLpdzRifX11BI4xhdNCCUJ8hKqOzZP2xmuBZKxnCbCdIbWmbKNEUfqjn0jqlkj/IULt4SDtpwjtaIM/ceHcvp9nU3rM6YTOHIHD2mGwCTHhBVBSixjkXjNUZA6XCuJphNZ/trVzqIDGSQLloiplhyECDdlUjjGpXs8XndWnZn1+jMToevZ/y22whXtedb9VhLx8E8Pr97glLBvGBjy2EMx3YgTDxww7mZ3d6eUJoY2oAE6ZLx4ZYdwdm9G8MeW7iZq+DbmbjQw0kfGekRHS3TiGNsLUBuwmBKzc7giIOOoYwppyRa/rfGNcqW/x79xj5WqghPb2a+clN559kdSyUTAgRVp8x6HbwoZYprWITu4BYR6pc+ePHwkg8eNn740UtePpxxaxTpIBElXuqgzk8RfY6OgfgaPcKAeBy/Lu/GcIFHx7Hs5yJTCEo9gS5QBO+d3h5AK1JnFI+O61xfcWcpEXHezy8Zz99HbYP5FikLqGMuUKe4Hm0g6wXoeLmFeeJ0N3Nze8MyJZx3iwh9ndBSssu3YL4w2gVRDRDdySGgGaxBmTCJYYHoac/7hEsMLSh4a0id0GXG25t1dRw69POobp0/uf8T/of/3//An9z/CQC/9eV/zFdvv/ozPrJDhz6b3Aw34+M/+SO+96//Vz5L3DPEUOfTX/hFfuEf/RZap5/yUR46dOjQoUM/f9pdSy/+9I948Sff4vFHP/jMr/XsG7/M81/5VZ5+7Rd+ikd46NChL5LcnT++/2P+u9/5765/Fi1S+G9+47/hv/5b//UBfQ59rmRj8Oq73+Z7/+p3sN7f+PWO5+ihz6q3DgCjntCV+N/uDIQEQJ5du+Fq5BP9p8FmnSLEX/xkBW5E5Xq8XIJE2SHS3qUrBS3xazt0NjO01oge9usBXV3C19haEVQ03jfsgewwrZSIubXxGq5eu1vFUK2oB0hLiyFG9Lu6Kyryuke4Tqh4OHtLTTcrIB4MPC3CYYCuCeH86rSEEbHEgA/J3uKB5R8a9tdyyK8NzIVhTrusNHdGb7TW2fqg9YYPY/RzOG+1sPQVt4yVto6MHaJu1DpxKs6XvfPLtwvvP33OaZ7xqfBHHzzyp+cHHj5+oJpEL7EopcSHHEQUdwDvSh8tzkudcdvCVTicrW9MWqiq6awclJqwaGzRYVpKAlaFdNTacDToJtI7KgXzcLb6tqI3N+GcJc55R9J9WfBlpp/P8bNDcDp4x3XBh1BKwazhdEQqw1tEWZuALni/MFpHS/QHSwVpW7pgwbUgRrrg/erkpChGD8gVBcngirc19nCZwSyAmNo1Bjl6nDuqFc9hB+8jAKPGvnUj3JUW0d9oxdoGEp70GCgQGBueYDv6bwcmJaKsfWDblttUrgDafO8Fbvulm47/vF61Ys1BW2zwbUsHewWNjmbbvz/XJSKbBakFoSPTEiB9qrAaPjakdVhmGB1NU73I2C/5vHx67N2iuc49gwAkOfAaUNhjwKCKMqtyMynPnzzl+ZMTdzcz87zEIIpED7aXErHue9S8SFwX0rCts60bD63x4WXjBx9tfPTqnvN6RgXKPCPFkHIXjl4BrSXuIdOJorf8/9n786jLrrO+E/88z977nHvftyZVSaXZIzK2sc1g44EhgThA0p2hCU2HXukQaJIO5BfS9OrO6gScpJPgzCsJAQxp3EDMkIQQG4zA2JjBA8ITniVLsuZZVarxHe695+y9n98fz75vScGWSqW3VJLqftcyot577z77nLOHe8/3+X6/Ku2a1tIKI0Zf84Ig0wONjK+IuFsCGgkKtY7UNtcar+rra8UzkbWAGCEok1oYZA2zgIl6Vnb2YoZqEMcBCT0alTiJKAkNRkjQ9YEuJboIYoKG3i36cYUyJhASYk7+S1P7Gp0fSyNCBIS61lEXG0gXMJIXPrjPOtUKTNYh9gh5pwhihRWeK9jO2/zrT/9rPvDgB5iVGQAH4xpv/pK/TNftv8C9W2GFJ4/NIw9x8q7b2Xz4wZ2YjCcLUWXPlVdz2Ze+gm7vPjSE89DTFVZY4bkC286Uu7eQfYlw9dqF7s4KK5wzyjiy+dADHLnpU+TZdvtNdG7Y/7wXcuVXvna1h66wwgqPizs37uQHP/KDPLDtbj2ddvzNV/xN/uzz/uyK/F3hGY/ZieMMWxs7/z52y00sNk49pf0ToFvfy54rr+Lwl33Fah9d4Zxw0RHAS2dat1luqlhpxG2u1Kbmsx1CQ5r9Ky0jUjHTpvp1skvITYEJLO14pZGM2KOOqa7kpLbPtTxNrLknu+oWmgUw0tTHIJyxqxaVxkmZc9CiiHpuqNsBG8iyD4Yuba5bJq/guaxYdUKukXIuQNSm4KUpS2lkyaNJ8iXzbU0B7ESLASVnJwLFrYytjJgESik7KttaRrdzrVDKgmIw5EwuhXEcyKWSxwEb5jvWu1ILyTJhMkVwhZKVQo2h2Qor0xg5TOTqYBzcM2W6tkZcX2MjKg8eO83mUAk5E0KkmiAUQsv6rGOmamrK1eok1fKSaWCSlDFFFvNtihSCKrlWFosBUSGIq5ZNqiuEW8axWKTWQkWcVN8pJnBSyTRi44IqgqXOrXvFRdl5mJO6nhQjRCUvRmoIBBQpRqluf20Uj5mVZu0s7GQFVx/caBmpY1MiB2nK4hmMmdpIYB8b6qUFVjz2dzkmy4iUJSPa8rPzov3/rpKWWlsRRFP1Uv1EdjKRBatjm0MVE6OWjJhnZ++oclvesElyO2cbPBN49HMzrTsK4FrGZmuOqzQ5o/SptSLalMmlQh78Hog5sVxHJE68oKAOwOCZr1Ug16Uc389t5745uU3oPOO3DkiKO9dEtKl5l8LiACWP/v6x5SgHxaqvDZQBk4hQCTGRQkTLgMZIr5H1FFifdKxNe6Zra/RdIHU9IXSE0BTCeSQjFMtImjipHgKlFko1FlQ2xgUPbcw4enyDze1txlzQRq3HqEhah9Ahati43VwFBJE5EtZcNR0jZrHNyYLUglRrTtpO/KpArQuQ7PfdlnbzwQn7UpEQvN/j3IlqCiAE7eiC95/YFMe1EqyiKTpJ22zwFUH7fagmYsDbpkJaIwZBY+9ruvvN+7gsTvzGbg2pg6/t2vv9QbwYp2YUoaR1LM+QOmAhutIe0BohTLxAYZnvvMIKzxHM85wf/eyP8p773vPYF8IEPfTKC9OpFVZ4AtScKeMIwLi9ySM33/iY17ePH/VIlXOEqHL4y76cg1/y0tVDpxVWWOFxUWrh+nuu54N3vZ8/89nX8bprXk94lmacrnBxoeaRMp5RJtUy8vBnPkEdx6ek+AVIa+tc8apXs3bZ4dVD6xVWWOFxcdfGXbzpo2/aIX8Brli7gj/zvD9D0NX6scIfhc0Llh+fXJWpP0t8zOeqYdv5C7521sc2oyzm5MWCIzd+EgwWG6d2xeb50Uhr61zz+q9jsv+SXW13hYsLFx8BbBWKIFEbUQLVtJGfBhZoNBbOtjpRa1oRYsv/bSrGZQ7wUoOo0ckLjU1x6ISRxLhjJ224WtRJWycbVdyGVJqlM9asl1NCzR8+WVNneqYwjag8k70r2hEo0HJt3QK49S80K2mXHoMmt4E2V88i4tmay2zjZXYrgjU1s1lTFZot9bJNlenWvbrMYm3K2KXFsRW3GS7VM5HrOFARap5j1SgGuQzkcfT3FLDFgiCKxI5cM5DQ2uy33amYSE9t9q1WjCiZwzFybaccDD2TviOu9wxJObkY2BoFzZUQO8CJQzFtolalDAs0eZaqqpPjpRYnxDTRTSJ9nLJ18ji5ZELsialnzBVRJaSOshgxFElrhDwHU6oq1LmTzM2+lpCwce5jpJoT8NkwzRQT1AoaIoWIjZku+YKPGGV0gjmgxHFBFigETCLUgYpSUUojUVUDsdkRmwq1KMTo4ycE6lgo44hGa1bVPgyCBnYqH2puilx2xrqPEc4QfWVsY5emEG3FA228iIbWnNuHC0I1nILMY7NgbzbhtPxqcXvvYsUVpuKW1rUUjODjHLBxoIrPZ7ckplmuZ84Q7uLzcxyApsyvAuPg87o24jq3rGUfxE5AS2M2JWB55v1UaaT1sroj7Fg5u+23OVG4GNrr42Ms5QW3aY4WmPY9nVT6KCRV1Jw4DTEwnSSmfc9k0tH3idQl0mTdHafV52TNHcy3PUNXR1SVmjNjHliYcuL0aR4+ucWxk1sMsxnVRgIjVueE6QG/4bGjjE2NHNehLJxQDQp5y9XRVp0spzr5qxBjQpae93mBirbCiaVlfkEIaOzcGr2bYsN2U/92UBfIdL+ryqMQsrQ1ekFMHdonJLit89JCXlXQdKCp9c3dCLQnSyJrIMRJI5XxfoeOJdnuRT3Lv0mz9F/21W28BUNDQlNPrYUyzHeyryVOALcoJwSkXykiV3huYFEW/MtP/Ut+6/7f+iOvveay16yIrxXOGnVzhO2CHj5/eURmxsm7b6fmzOzEcU7fd/ejX9y146xddjn7r30BB57/otUcWGGFFZ4Q77z7nfzIZ36ESuV16boL3Z0VngWwXMmfPgHli+9d4eX70fXdjx3YfOgBFpunAdg68jCbDz/w2Dfswn7are/lmtd/Hf2+A6t9dIUVVnhc3H76dt700Tdx/9b9j/n79738+wiyIn9XABvanvmo/SnfdIp6ZPa4n4uvPoTufew+aovK+KGjpNccIrxsP+Hy6RMef3by+GOKoqwUjt782SYgPA/iEBEuecGLueRF19HvO7D77a9wUeGiI4CxiolSalOelYAHQYZm19wmrlW3qN2xZXalMIjnSQb1LFMJbm8b1N/XFLRIhKY4UxGqpmavOzaVrStCBWu2y4I2kkxEGlHmxxRcqWulqeyskbMtD7UZ3+6IfG1JOFej4A/KVBQxJ0WWucOeUby0m4ZlhrA9xiIbTCqV0D7fPmvFP2dgVSg2ttcedQmtUjDKWMh5dJVrrZRSKHnEEGrNlOJEMVadVo5CLYVMoZYMY/ZrkHqgtmxdQ2MH44hiHIgdV611HFqb0pdCnPRIDGyZcmp7RslCaEpLs4qhBIyYJpRaGSQiedyxC1ZtebhAtsJYjH37O/ppYmOzkiWTgiK1UpqlYJxMKMMCbCTEjlq3m/pQsDyyzEitplgdd0hbMaAuYHDb8aquxEXw62aVLka317aBXKFWc8vfPMBg1AhBlGSFkgsEoZZKqEZUIbAgD0JRxUa3TXbxs5O3FXNiuvp8qJR2Ldp4sUJjSbFlYUQZfc5oI9tsQDRBzYgVP10zt8Re2i+3POzlONuxnG6q9OqhrF5IUEqbihkzoYpQzSjVLbA991mwXJxctOrXDfy6at8GsVtuq4FaQMs2EhJSvKhA1FWsSoI87uT0UowaE0WCq+stO2m9sz5wptBDAqrSVPFgmtqsdCU+5j1TjBQTfVKmUZh2U7ouEhW0Lgs4qiu5jUZYKho7QkyE1BFEkCBoSG6NTiEaSKnMT21gqcPUmOXK8dmCh48e5/TmjFILMCAUihgWpyxypVrGFpmSM1UMCDsFA2aZXISksCwuQZVgIzLZh44GtglWCP2arw11wIbNlicuhNT5GBqzuxqE6LbjEkE6L7jp1qlljjF4jUq318lj8wxyKxmzBdB7YUktjYS3tn4rJY/MZwa1klJHVHVyejkWcWJcoyEpwTDDJHiBTlsbaxmhrdkwIqroZA0rlZqHne90y8IXoTvnrWiFFZ5JeOvNb+W997/3C772xqvfuPrRvQKAx10cX+z8ux6ZM370kce+Z6wwGrJ2ZszInkT3xy6HL/LsV9YjNRnj9hbbRx/m5D13PEFHYH765Pn5oQ2Evmft0GVc+ZWvJfbnj8heYYUVnhsoVvjVu36Vn7zpJ72AEvivh3+bb/m6v8hqBVlhieUeWk+PjB942P9YoT4y3/mt/IUgnzyOJN35t169RvqKg2deX4/Imj/WK8OCcfb4D8JP33c3mw8/wLC15RFM5wESAodf/ir2XnUt3fqe83KMFVZY4bmDuzbu4oc+8kOPUf4CrMd19qQ9qwKSixCWK3ZioB5fMP7BUf9bNezo4gk++UeRP3rsi762/fH7OLbxUWT9iemxvJh7POPTgD1XXMXhl7/KC6hUn/gDK6zwBLjoCGAJERNBSqNO1S1qDXOyq1kyu9TUrZoptqOaxKAZkLZAS0NUGumr7WGU/0+D7thNq0FtObYKO1moSPXWYud5vEvb0hCB0hSiODkaPGPVrVWLE4smVCvN5rnZlIKTw9ZUuFbc0qDJO8WcPAZrGawG5nay0qxOneAoZxTB1rJaGwFsLNXATf1nO2yhE3sGpVZKMUodm1Vt8b/V0ZW+LTu4lgJWCU2ZmUMkjzNKGSGPLiYU9U2/ZFfeVc9pVoy9Url6refwninrkzV0sUBQhiGzNRYWpaA0Za/rF0GEYCMxOIluZIZ5piIESQQZqHWEIpgGNrfmpH2F0CWUOcUMKdmv6Tg022NFglBKRftI0IhpoQ5ub7wkCa1WTBLLS+cDAqC4e3dVJ1PTBAkdYx0ZF5mgnvcrCjUbpcCYC0OuSMh0k46+LJigiDVr7jK2TOcAdcAsOQVoBRElUFDpXVlphUhAcgaEbNbyUYWAQR1bdm+zD69u9by0soaIVCfcpOIZroCVEbQ2Ha42Erg0y2eXdFteuFV4y/aFZmVeM1WUUuYUols9j8XbrDj52uZhzQWrTa0ZJ9Q8AEpUI0WYmtAJxKbyJPocM20Ww3GCjYF86gQmEOMEjb3nIJt4nxkwAqYJoxWIqKFR6fqOkCKaOkRCI3F7UEVLQamkfkJIHTFADEKQ4MUN+LlUa9bgtaMsCz1sOUebDTwg2Zrltq8VOQQohXFzm+26xUzg5FA4dnqbze3N9kxhoFgmVyjDgKQ9kOeYBmoZKIvRr3nqqeOMPA7k+RzW92JmhGb1DkpNisxHhrLA8gYhrmGL2Y7VvI3bUBdot4bUTUJcQ2IkDgvCdF9zDR9cXZ23vRgku2p8EqoTt2XRCg5c8W01eNFOv8+vezlj8S3F51jGWCBuDx4DQdXdCSq+9oYCZM8oV79miLb/GiYtM73VJdSanYAWkJAoZfDs8lqRkLwAYIUVnsUwM+7auIsbHrqhOTw8Fi/e92Ket+fisK+sVh9zDRRdPWxoMDPqw3Pyp0+QP3H87D6z8Sjb5YfnzG7f+KLv1eevMz+4xYObn36qXX3KCF3Pta/7eqaHLlvd/xVWWOGssDFs8JYb38JQz5Bpj6ST2L5V8dRFC1sWOeNOYA/PGD998qz30Mc0dXx4zDe0+vCc/PEz7ejz1wmXT9ncd4KN2YNsHXnoKXb+qWH98BUceN6L2Hft81f76AorrPCEMDP+v5v/vz9C/gJ89eGv5lUHX3UBerXChYacPkp8279gM/8FKufHec8wNvedYDFuwMnzcohzwvrhK7j6NW8gdP2F7soKzyFcfASw4qSputrObUXdmdcso2FpxSzUWsDUVWBLd2SxnQxMNDYCrzF4dZnta6hGgrhCrVa3eQ7iWadOCi+zUf144IsP6g8dAaqJk75La+hG2NIseIVG9FZzNS+6o9INCDUmxOqO4MKaenGZZezklSLm7YgHuDals5O5Ioo2paZnrLoFrJ+T7ZC9nhe8VHoaxdxmeZkBaq1tTzgWlOovNfK4ilAFqhnDMFDN3BbW1PsouKo5dk76pSnUkWkKXK4TDk961qd76JKTrAUoY2aYVyqKSjMwtkKthpSCTnpEBSUSNTKvIywGtEto7IkSycMMCTCbj/DIJlICIUbPLV6MxC547mkj/ESTZ/6W6jm7taKpUMboBGKz/tZG9tH4/oog5vnMpVTESlNZu/11rpmxxdJ2lokhOvkeA5Ln1CwMGwNGJVtFUgdBkZoJFomqqCjJBqiwyIVq5mO4U89SlUAaM1Iri1optXoubLVmBVzcJdyGVoRQ/YG5RreCNrcql5qR5XiLE0quQDlTDKGhqaKbMjOYW2YIPh+Lk8GuFBYKlXHM1NErvcQEa0QcIVKrekqvKbbILWN3gKG4Cj0qmpS+TwRVkkAUQ/sey3OyVQwhdKDTNeZlpCwyIkJQV9d63xSRiaucGUFcnVtroWoh9krqImEy8TldFAnR52CtaAioxmb57oSkxIhSvIAiZ0Ij46nF55wERF3NXcZCTT6/NaRWSKKYCFYyNbvS/sRs4MiisDmMzLPbgpc8p863sdhj81OE2KNUV3WLYgFSMCxWLFbKZC9pUbFuDnVGSB1aC9JHn3+TNaxWYs0UKiIVGzKl9Rk60ESl9+KKrU1EO0yUmDdRM1ccm0AekCgEgT6BTg6gltE4RWLvinKJSNAdQkbq6IU37qPvNuO1YiOMZY6NYGlK1wXQQtBmYR06LzawZmNfihfgaKCOCyT2rs4nuqP40vVB3fZbrOWFF5+b1c49V3KFFZ4JKFb4+x/7+9y7de8XfP3aPddy9frVT3Ovnj7cfvp2js/9IeoND9/A+x98/85r3/aib+NrL/9anr/3+ReqexcU4aFPIgsnbcd8DYt3L7CN/ASfOjfUu7ewh7fQKyM1np9jnA3iZMrVr/3aFfm7wgornDVOLk7yzz/1zx9D/q5wcUM2HiDd9yGmN/xLyoEXsvjK74H3vofZib98Xo5X796i3r3F6SvuZnvvqfNyjLPFniuu4uqv/hpCWrkkrbDCCk+MapW33/l2PvTwh/7Ia4Lwgj0vWH0nvwghGw+w57f/FiF+jF5v4XT+n1nUL+N8UFhrW/sZJjPm0y2Px7vAWO2jK5wvXHQE8JJYcnvfukOYesajk6lgWHBlHhRUlBASEpZkZ3Ul8VKVZ9XzLKXuKIHFg1RdFGsFs6bOTRF5tGWd7tCzgDhJ3KpFg0ijXh+14TV3ZppSWTG3RLbif2/qRiuN6NVm4bvTkiK12ftCI1CW59CUnSKYVJovtCsQm+PAGTWikUtpxK6T0SpOmhmcsYtuSmUnop1UDiFiFCrZs4pFKHmkZCGXTFkMWBlRSW4vXIsTjrWCJpBKLZVE4aAql08S+/pEl5SUItIl6mKgzEdyrliKO8pu56pzU2oLpbg9dhcCNUbysKAESKknCBSbYGUOdWCxvUWKfm/VhBEh5wUKhBCcyK3ZSdgsSAoEBYuJPAyUnBELrYjA/Aa0MWUotQ5oKx6QCmksLtwFNGc3+S4jmkcnFqUSqxCqNcVlwKqS55vk1DFPPXMVct4i9BP6vqMPiV7Vs3AVLEAo0AskAlLmFBUYi5PtdcQoFDpEklt0W0HLCHFKtkItA2bRv5iZF0JUMoQp5NyyjkewCKqteGBZZOFKXmOpCvYCDbNCLVDKSJWIjVDHplaVQA0dlTmUsRHQXpzBZOKq92GAcQYGJVe2h0DJU+oksC9GglRiZ+h0Hd3cpAyDW6f3gTjpsZwpY0EWmRSDn7eqqz4VkAmiXgQiiwV1GMinK7pvL9r1TtDGiHrINKgXBzhp2/K0xW2fNURXMauvOSEkz/c1o1SoolgeKUWoOXumt4JEkNBR8oIiylBGjs9nHDm9xcnFyEKgDrM2LtxyPagS9hwkhjYfUk+tzaJ8OiVXn7cRCOsJoQdb976bIXkGIaDaYTpSVJHpIaoYNQ+tsCFTwwSo1DGTEXItmLjDQJ1tO/HbHAAEIWQjaCWt7SWmDq9PSW7fHCJUI5gh6pnCZtZIci8sgNTWK8/VNul8bSvmeck7R6peLJBd+e0DpPjaFwJQIPSUccEyC72KYbU5PtTiBQAWKaX4uF5hhWcpihXede+7eHD7wQvdlfMCM2NWZu373RkcnR/l5279OQA+c/wzPDT7wkqZn7zpJ/n1u3+dH/7qH+YFey+ehw+yfYzpB/8p6a7fQWdOji/qlzEf/g7GPuD8KNr6+TrT7T1s7T35Ra2izyckBK5+7deyfunhp//gK6ywwrMSszzjn3/yn/P7D//+he7KBcG8zCm1cGo4xU/f8tM7++11+6/jz73gzzEN04tm71xCto+x513fT3zwDwHQzQdJ991AtsN08loGewnna5Pbc/oStvecvmAPsNcvv5KrXv361UPrFVZY4awxyzPeduvbWNQ/auu7J+3h21/07RegVxcOxQrzPOfzpz6PivLifS/eeW0ap+7u+BzHf7uP9vpZLk1/j1n9Gjbzt+7qPioI/XyNw/e/kFMHH+bUwSMX5HfoEqt9dIXziYuOAM55dItVY4cQpJG+0qhSE/MMXlNX7TV1rBmeDamRMzm4Roi9OyDX5hGNK2prcdmwLu2LW2auS4mX7/QsYcDtmUUpJlg1VGuzXeVRFrCwpHS9AW8faW0sM0pd1uuq0rBzsCY9bGSqCFabuhZrxHRwG1pCIzfc/pSdTHOjok29Kq1P1jzptVnYGtWMWp0kzibknCl5aGSwK5aLKMM4UCuUYuRS3dpXarPXdtLYmspPUvLTyAMqkb0Yh7VySd8z6SJJk9suI9S6YBwKpWQ0dTTxshNP4NbFzdZXoxKj0NsEanGrahkJUQlAqa7yZMxk00aGZaIIY65URlR6Qmhq0DFT1MdUjIloniXrOcfFNdQSKIyoBaQWggaQiGolmpAkEMpAWFoc15FiRpaOEgplmLf8YbcFrgvPjRU8nzqOIyGDinIqLzg9GLK9IEx7+jghmqF5m2mKxMkeEtDp6CRdVToBNbfrNkmYDZhEz7Q1J+BqGajLMd3UzbQ8ZEu95+mKNNti9fFUm7p9hyzOruQltOzdNtbEgNhsnp2gqzFRrFCbehzAxhGCK3GlFmoVJz3ziFRXJbs4f84sL6hlDdbXsCDIbEbqp4TUYXlG3t6k1HW3qw5QZwtKCIQ6Ntvm4PMqZ6zrMfFNWfsJIpFcC2VuWN2mW+vQvm/FGqDaN9Lb3I5YlBgigqulFUFTQDW4NXTs0aBYqeQxk6N61nYulFBQza60rQOLYWBrPnB0ts2DW3NOzreZEaEMBANiwCzTp3WCuDW3SMVqZcgD1aLPW+s8l3rcIk0SnbrKO8syq1yg2+ckbLOfVjHPpDLDtGsW0ea2zDFRR2WsRpU1aoXaktEkKiwGJCbI22hIxK5nurZO17LRMTx7d8yIermLSHIyuxbo17w4JgQvgIgB0QgY2q8R+0RUJShtfTIfi9F8rJiv5dqOJRKoNft7tYOm5JBW9CDFhyMheb57GbDzlJu1wgrnG2bGr9z5K/z4jT9OtgunuDxf+NDDH+LE4gQ/c8vPcGp4rBqmUlmUs8sOunfrXv7uR/4ub37tmx/zA/y5Ctl4gD3v+luEBz/2mN++ndzIFf1fYyP/BTbKd9D8/ncdlxy9ipxGFtOt89L+42H90sOsHTz0tB93hRVWePbiZ2/52ec8+Vtq4Xcf+N0v+F3hN+75DW45eQuGMS/znb//zgO/w3++/T/zj776H/HKg698Ort7QXFmD/3DP/JalCMc6n6YR4Y3MdqXnpfj97N1+vnahdlDL7+Sa177dYSUnvZjr7DCCs9OnBpO8U8/8U85OZz8gq9/z0u/hz3p4sgQ//CRD3NicYIHtx/kP932nyhWAAjLCDgR/tcv/V/Z1+3jqrWreNWh56AtthXS59/F5JM/+0f2UZHKWvggvd7IqfydzMrrMfbu2qEFYf/xy9Ea2NpzimG6vWttny1W++gK5xsXHQF8Jo7FLYdF1bNsRcFKI1MjVgr+uD8isV2msszEdWUreO5rtZbjK+p2qlY98xf8fY+u0lGDbM3WuJGcKkg1AupqOKH9yNJGTNBUuZ6hSjNSdpLUVbl+7EYyl6bSXT6+M2nOzM7iioam0j3DCy/JZAJIlaZCbsSOUyBg5uRbKe1c/P+4whNX8jbr4LEWxjGTx5GxFCwP7fDljHUtlZKLq4BzdfVgNWwcmzLZQFPrZyPlo2KSmGjkUJ1xSVyjD0pEURXP69SI5UItY1NPS7uni2ZdLGCZasmV2c3qOkawrmdcDORxBCIhgFmgloJJoeRCbJnPqeuwYcE4JnKpxBgIKVHGTB0Lecgw6YhRSGtrIDPyMDq5aebXIwRUKkolhEgQJZWR0O6j5jmaIohSx+2WY6yIBGyYo2aYZDQmyrBJCAHVnlo2SQSSFVIwTuU5W1koecEizRnjlESglJFscxYxMVWhC4qEZidevQDBiltam1ZKyRQJbWwPSIxOquV5y8kWV+Q2JamfqivDXX0+4nrfljXdrNMFt4Eei7kSl4pGRbuenAfKWME6bJi5Krwp1iUmz2KO4tdzmFOHGVpPgfUwn0GKENZAlWE2cNKg7tnjNuw2eHVVSGgp1DxCbArdYJT5FiVOiKG4Xbi44t5ypUr17OeY0KCkoNRxgeWBoQxAQDpc0R31jPpXXT+lKqhFQowEdZthTYkYIyF2nveMkYeBxaAMY6bW6qr2MkNypCBsDQMPnDzNvSe2ODkU5jl7gUuZw2QvUgY0uKJdxBARShkZxzlF1txaO3TUsSKSid2EuLRcF/ExxjKyWgjFzhSNdBNEGllq2bNyTQidjwFVdbv3vXtcrS2ROm77nJx636Q76BbZ/ZQYI0LFSnYC3GojaL0oxuro4yr2Z6zJRaDfg4Tg658YVtz+W2Pvc15oGdgF0eBktlUv5qFlL8vSXrplLcc1P6cyElTbMh5cwY7b2ut07Vy3ohVWuKDYztu8/c63P+fI32qV99z3Hn7kMz/CVt6dB6APbD/Amz76Jv7hq/8h1+65lmmc7kq7zziYke75IPHBj/2Rl7xucc6++EtAx0b587jzwu4i1MilDz6PI1ffwdifHUm/G9hz+ZVc9eo3ILrK61xhhRWeGGbGjSdu5L33v/dCd+W8INfM0flR/v1N/57ji+N85vhndh5Gnw2KFY4tjvH/fOz/4U1f9Sau23/dc/8h/uPsoUsEOcGh7p9xbPg754UEVgtc+uDzeOSKe5wEfppUTKKBgy96yeqh9QorrHDWMDNuOXkLNzx8wxd8PWliX9r3nHWR2Bg22MpbvPPud3LTiZu49eStbObNx/3Mj934YwAc7A/yz173z3jpgZc+HV192tB/6m1MP/BP2jPjL4wgJziYfoRt/Sinxr9K4RC75U4lCPtOXsbaxoGnfR9d/hZd7aMrnE9cdAQwtTjBIMmVirU6YdBUjBVDQ3JSUNX/bcUJyWZfK3gusKv7QiODm0oY2VEMK7LkwZx0dI9mRG1nIRF1VbEmbbm8TlCE0LJSGxFr0FTL3l8JfussNztjCW5fLcWtZKvt2BxjTvpUszNksSpmZec9TngFjNIIUWnkcyPqlgpknLR0G2VpRB8YIyZCLpUxV4Zx4YTVOFKqEzqmLiVWEaSYq/tKdrJlh5ARNHZOyodmyV0L5AXWdVAyMUUOAJfGKXtioouR0HWEFNEUqFQkeeanhN7JKgHT5ES7WLN3rdQQXKlNQMjE2GEYdRjJYyalDo2CZahlgUqgWELFCBhddNXgmAsGpCh0kx6Zz8lVGMeCVSEFJXVuqTxkoI6E0Ih8lFgziYFYjcDoSscUscU241gpBCoBGTcI2qEolgJlMYMihJhIa3sgD6447SdYLqgZASFR6Mhs5J6xLKhRKH2E2LFYzBmHBZspEGPfODUnykVdlS5lRHJTvVsGCiEkGDOmhlSB+YB1E1dwjwayaGOzw6I62SuKDHOqgJlSCJ7HKk78Fqtoim0MeDZvqXjmqgmEDurcNfDVvDjD3OrYxtKGkAJTCD2kBDEiceIFC7kwzOeclohOAzIJTKVAbH7YwwIMtFSiCjn2WIgYoeUV+5jXLqASsHF0O+Hq2cNBjGJGXQgjM6RXuumauwzEgFZDNaBBiBix7wiiTtwLnsUcO2JUQoyI4qRozNjGBkPNjDUz1gpjZpYzRza3uOfIEY7NB+bj6OrV+TYWUpvzPr/NjGAFq4VcjUyE3IozrMPGSpwkkipB1de2mtE2/yuGkv3aG0i/12tExsHJ4v4AItmV3CW7R7W5ylc0eHGJBCyuA0Lopm7XHFydG+SMPb3ErpG9FRAsD140EmJT6C4dD3SH1LUyej50CDu24F7ZUlp+eVOha4JuguR5K8QJMFaqgaq2IzpZjinWL0negqqhw4CGlveuqy9oKzz7YGb86t2/+kVzf5+tMDPee997+Ref/Be7Tmzfv3U/f+39f403Xv1G/s5X/B2SpufcA4l49/tYe98/fNz3iGT2xZ9F5Rjb5RsYz4OVZSyJ9Y1LONk99LT86F4/fAVXv/ZrV1ZbK6ywwlnjphM38aaPvonji+MXuivnBW+/8+285ca3NN+ec8fR+VF+4IYf4LWHX8s/ePU/eE6TwGezhwJEOcql3Zt5ZPih87aHXvbQ8zl65V0sJttPyz56+Su/gj1XXHX+D7TCCis8p/CWG9/yRV872B/k+Xuf/zT25umBmfEb9/4Gv3nPb/Lp459eepI+KRxfHOc9972Hl+x/yXPGElq2Hqa75dcel/x9NKZ6A9P+w5zIf4Pt8s3s5mb3dO+jq9+iKzxduOgIYEObLa0vtCKhZeNKsw9VsIzKkjhxtZ+4KLGpIJoONwRKy7JEWiKvmRMtHorrC7IVCF0jFOqj1L/4YtJICM+GbbbEqi0zt6mAl/aluDrT83BDUzDXnbNrobz+HpzgNVEkKCrR1brWLHd33gMS2vFNdo7Hzo8+QRoxjJl/qtlIG+K2vM3WeayFRcu7raVQa6WOc1f2aiNhxFxRh7XsV2t2twtXvQbPSa64AtGiImHd75ONrJtyqY3s6zq6ACkqIQY0BkJ0VS6jobkStemlrWmBrTQCyYlHasTEbZOX7twh9aSSGYeRbIbGpiwuTnxXWi5rySQN9F3EZiNlNCiV2HWkvkeGOQVhzJlaIMVI6Cd0MjCUNbexrSMJCBUCBUWREty+WIQq0RWa6jm0QZQUAxKVYbsHGcDpSbTrsTrHgistiQkbZgTEs4KHQi8DpzUwH+cUEqoTJzhFKGVw0rAsx1rByogRfB6UAaN4wULLZA0SCVKQrvcRWPwztRZKXarUt5ESCajbZAuUMbtVuArSLHVVQfoOxW3ESy0Uc9tuK6OPRgk+aUp1RaYJRMVGo9SWUVsyGtbc6nzStRFcXBkvrvgfF9uckj2ILjCr9FXQmlFTKAN0PdJN0KhOLFugLmZo9nGmEtyaO3rhhRWwYqBC6CZUUUodqTlh2eehSiWkCUEjASOKkkRJXWpjNxFUiTH4fA2dryGhIFGpY8diKzNbLBCMYnDs1Cb3b2xxbJ6ZjYU6bhGcsqVq8MxgMlID1UbGmkGTz7W4BmSkm0CuxF5IAVI/hTJgZbutM31bK/12ivl9MAMWXgwgXSPYcRtmKwvPmRYnbwk92nVuzZ0miARXUwe3lkOgWCVQqbm4aruMXuxiBlVapnpBxO3bxUZM3TVAbbmcmseq10zNBYsFQtxZL02Ktyd4frMIqCHSXBtMfIxrQIhUMaRayySOPr9a/rBIQJ8b37dXuMhw04mb+KXbf+lCd2PX8Yljn+Bff+ZfnzdVs2H89v2/za2nbuWvvOSv8Cev/pPPHRJ43Ka/6b8g4xOrpkVgb/w11sLvtwfYu69i2nfiUsA4efCIO+ecJ+y5/Eques3XrH5wr7DCCmeNsY781v2/9Zwlf08sTvAb9/zGUyZ/lzCMDx/5MG/+xJv5rpd8F1+y/0t2LC2fM3gSeyhAkOOPIoF3fw8NJXLZg8/nwWtvo6Sze5h+LpAQOPzyL+eSF1733Pk+tMIKK5x3jHXk5z//849bjPzw7GFuPnkz1+2/7mns2fnFWEd+8bZf5G23vo3xLInOL4Z33PkOokS+56XfQx/6XerhBYIZ6+/+P4kPffysP+JbTuFA/CmEzFb50+xmRJHvoy/g6BV3s1g7f7EKq9+iKzyduPgeXzfFq9VGBFAaqVsbMdwIDpwQVXHCFhE06E6W5A7Jas48VMSjhHHlL0JT4Va3Ga4tQLLZRAtC0ICaWxvbTu2P+vGXobXOWjbSsqlkW3yqWX2UXV17/07fWv+cuYDSlL5LC2mr7DDQIi03t7b223ubPSq1epYwToSG1oqJUepIKZU8jCyGgXGoTvoWz3attbh9suHquzJQS25W1BCCIpaxPKK4Fa4rqwUsNuV0bLnFRpKOAyJcMlljIoEgS4WzX1MxJyFFjNQr0Qa0XUsRJ1FVA9oyP82qE5/mUm1ryu2YOkLqqCVTspOVxOjXpJH943zOWApBhenaOikmag2Mg1uFx9QTRIghUlDmY6YYaEpM+kgXhS5EYisUMFyNXHOmjDNKs14Wq6hVoggi0ceDQAowmUzpo5BiIooQUk9UIYoQNRCppCikfsK079mvhUMlc0kd6fJAmS0YEYoqFieYGYVAoVLKSLFIrYVcK7ksKDkzDgOLWtnOI5vjnNM1c3oxcLpkTg9zTuaBY4s5j8zmHCvCyVzZGAsbeeTUYs7maMyJjLVSqF4cAJgIqFLMGGsljwN1HP11c8JRrCK0rNlaPEM4F5CJD9sCStfGS8YzlwtmGStuNY0KlJFhMefExhYnZzO2S6aaoLUgVRETYrdGislJYNyq3Mwg9IgNqBWCZGKAlBKx74gKSZSOQmeunq1jRmohaiCpkkolWaUPyqTv6VNHn3omkwlre/cyWd9DlyYkDUSJhBCJXU+3tkaXOvJiwWwsnJzPObq5zantTYbZhuchF/Nc5unUyWk8S9nyQJ1vU6tQtjd9TTKDYYBSCAopGDGIX8S+h7iOhY6SF9TqKmerLaM79a0ApFDzgJWK2eh9sOI5uWkKkz2utl3qai22uHLPCLeCr025tPuX3ZZ5nINERCZIWkeme6FbR7p1LxioubVXqaOR8wBkRCpBIOAFCgJO/i69/zW1zGmQ0GHmNt6E3psjYKX6ml6LL8PafPhrpRZpxLSg/XT1sGOFZx2qVd5681vP+sG1mXlMwzMcZsbGsMF2Pr95PYZxz+Y9/KtP/Sv+7Wf+LQ/PHj6vx3taYEZ45Ga6z//6k/pYkOMc6v4ZSW6Bc6hefzwIyr4Th9l/4vBuN72D9csu56rXvIHYP8sfmqywwgpPKx6ZP8Kv3PkrT/i+bJnfuf93zn+HdhGzPOMf/+E/5o6NO3a97d9/6Pf53g98L79+z68z1rHFVT0HYMb0hn/xjNpDAbRE9pw+eN72UBAOv/xVHLrupe352AorrLDC2eGR+SP8h1v+w1MmQZ9NqFb5L7f/F3765p/elfMuVvhPt/8nfvqWn35SEQ3POJgR734f4chnzunjKnMOxJ9iLfw27FLh2hKhRC596Hn0s/Xzspeufouu8HTjovu2JlERmqVzswFWzqhDQxQ0ChKDq2KD50UquvNfV+O6ktGzeENLyXViVIK46myZyQuN+MWtYOsyo7eiIbnVq50J5HU1bvucyVJ23IjRRkA7A+yklAQnZXaOt1Q3A6GphrXJ9xRgmeHZ3tSYa8GJZg3BLaE1oCG6Gq71yf9XPRe0VLdPrq4K1lqxMlLGBdWWxKWh4va20qjjIIIGQURRq25/i3nWbUiuPVYwVT83y5hlVGAPhYMCe2IgpYjG5IRRI0WtLvOEM10/oRcnrIMI2jKbd8bCkihXoeRCLtWVpdUIITmBm3oohZoz5OzW2aW4FTIwjplhdPVq3wX6FMBgyJUigkhBxUhiSC0MuTCU6nbRXUfXd4QuYZMJRcxJtBgx8zxZzZkoiYQRq5EAmc/QxYL1yYS9e/ew58AhJusHSZNDdJN1UuxJIZJUSJM9JI30MTDpJ0y6dfZ3Ew7qjEsp7Csz0tY2dTYjl+LEPa7gNcSJ0zw64Ramrhot6mMm9JgEyjiQbWTMhbEU5vOB+XxGLkoeMqUaNmxT27ioNTsxGRNCdGVpN3Gb4pwZ5wvKYqRWpeaKjdnHQS3+PymQJn4va7vn4+BjME0hdW5TbRVKwXLesXrHMkJw0tEKQ66c2NjkxLBgpkbpA5I6RCKSR1QLXVI0uIW4xfWm8k/NGWANFSWoNKJ9SiyZVIweZZILYVxAKWhMdCky6SPT2DERZSLiBHDXMel6+r5nMp0ymU7p+44uBif3gZgiqY9oFDZmMx4+dZpjY2a+GIlxykS9UMOquS2zVWRc+MJRcPI1dGi/jsQOyYXQdWjqSGrE2HuObhX/7hQ8Ozl0ewj9XkKYNMcEbdd3dLeEburvXbjKuuaMVfF5mTNC9A6IQKeY1qaaHl3lv8w7J0OceIZ3jGjqPWNaI1ZLs513pwE1wyxThjl1nLW1Un2sh0CfEt2SkFdphSU7aeatOKDZ65fmbiDB17Y49fFSK+RCHUvLFM6+bsceTVOEuuMkscIKzxa878H38aljnzrr99/w8A18/JGzr8a9ULjt9G386Gd/9Gk73qzMeMdd7+CHPvJD3PDQDS33/dmLtff/43P6nFtZ/jBJbt3lHnnJ4v7jhzlw7Ipd/9Etquy79gXEfrK7Da+wwgrPaRyZHeGffOKfnJU6tljh48ee+fvno3HTiZv45LFPnrf2ixX+3Wf+Hd/9e9/NXRt3nbfjPJ3QU3fR3fbuc/rss3kPveLLv4qDL9599fIKK6zw3MfZRgzcefrO5wRJXKzwH279D7z15reek+Xz4+GXbv8l3nLjW561v0Vl8yH2vOtvootT596GZC6JP8F6eBe7veHFkrjsgec7CbyLWP0WXeFC4KKzgJZaXGlouO1vVUTFFa5WUW3KuNqUv/h/Qgw7SlOqE8m1LrnTgkr0t1fcurc2gledMpZGcu4c+1EqXRHBtNk6y9LLtDG0hv89BLeKpmBL+9Vad95riJNb4sSztA9Ls6LGqhOk0ghlalM62xllsWtoWyyttAzWumP9upMbLAGayrTU4grZHWqluCquZL9OZGIXsDy6Ra/aTm4nqt6+uqJWbGTpjG21nYF764IGEpUDGtmfAn0MRKuELjpxbK50FsFJayABa2JsU9mWpngWaYbJFdWmZF4eC6PWSohO1keFmiK5unW2mLW8VDx/ttlkj2OllkIIisZAp9lzezNOEmJQMwEnl60UqiRCMCedMKK4DbZaRHMBjdTZaTR13m5tSnVRUFf7TiYRlUDoe8L+FyKsMX/oc2xvHnEbXQpGwoZNqkaqVWIXKcVIHKRfzFnD2LbM1mgsrJCTuoK2jmiKnsnabMZF/H7Rx2Yj3sapSCNnxRXTxaih83xrywSNiLXxXdzO2Ek/txYXnfjosUrNPqesZFQj5BFDUUnUpRo9tOMbWFm0SOLxjCoUoGwh2oF4prPUDLZwIlkVqa5QR9coBqe3ZtQxc3DvOutixOJzRzWgUSH2zoH30e3dy4hp8uIHTQgVjUoIHQT18zRzRTOeX1yHAUuBNOnoY6JLPTEEYlS0S6R+QuxbQYi4ErXUwjg68Sh18Ctmxun5gmObM2bjiNmISgTJdJOebJ4ZHSjUqFCcbJXpHkTNLawNiNKUuUbsIprWqKVS8iZqa27RbYbG6O/reqidF7CUEXdSGDxTd1i4QtYyatGvuYBYbEYECcsLlo4DUCEPWOigDG6xX9THfRtbUj3D2qQVyGh1VXBzL5A4bcrkAnVAY6CfrNH3idBNialHtGJ5gaCoeKa7acswl0w1Pw4tGkCttvHZYeOMWj0/mZqp0hwWYoRasAJ1fHZ+0V7h4sSiLHj7nW9/UhbJYx35L3f8F77y0FcS9Jll2bg1bpEt8zO3/AwffvjDHJkfedr7cOupW/m7H/m7/KXr/hLf8eLvYG/a++xyBrBCd8uvEY5//pybCHKCQ+mfc2z8O4x2HTv78C5AEPaduAyTysaBY1Qtu9L8pS99JQee/6Kn3tAKK6xw0cDM+IOH/+BJFVHlmhnrSNJ0Hnv21DHLM246cRNv/sSbz1uMwhJDHbhn8x7e9NE38YbL38BffslfZm/a++zMMbRC97m3o5sPnnMTz9Y99JIXveTZ9X1nhRVWeEbgk498kk8f+/RZvffX7v41vutLv4vUPbP30C8GM2Mzb3L93dfztlvfdl6UusUKv3zHL9OH/ln5W3TyqZ+FYfMptyMysD/+NEZiVr4WY/cI21Ajlz58LY9cfi/DZBvbhXii1W/RFS4ELjoC2L8KGwQnfZHiJLCAxtCUtsEJCXCiT4LnQIITtCk6GUwjByTSonNbTG5thGw7YgwEqU11DFbNtcLanI537Enbw1UxhLpDFiPa7G8NcCtk/4yh6qRENXPZrDZiV5YEqqvcdnKCawHMLY2bYnipnBUUCc0Ke8lPI80C1RXQEKi1IBWiCjlE8ji2dj2v2KotWVzEaW9sKTSu2c99niEmEKXm3DYpbZas2e9SbTbWoUOoTKlcEgPrXSTFQCigMaEpOUFtfs4apOXeZvZ3CnsnPDQYizIisaPWilT30ZZGQC9tXU1bJrQoGiIJwUJHzaOr/ao6GdbsgD1TOFDMKPMFGhMhKDFWpOIWysOiFRMYoXiGabCCDiNCIJSRhMKYkTxHa1OpxoSKEUMidP0ZW9yQSJNEp54be8mk57Xf8rUcOnwNN759wcfvNuYbx6hVqKbUuA/Lharidt1aqTWSYqHLp1iTfayLsT0WZmNiiAtKSgxUECVoRIurYVkK1TUiO8rN1AhRdUV6Uhek1+w251SkmyJl9OttpanCg98/rW6tbc2avFSo3l8k7BC7IfQUZohln2ca3R68zDjjfD4HeoSIlS0kt/EuCy+iWBgySVi/Bub9QSO1FDbmc2qeY3v2s7dLdE09rmJo7KhRCdOOGJVy2rD5gpqUkCIaIyl1dH1C++RzkwglYwYFoc4HTAMjECaByTTQpY5ufQ8xRlLfkdbWnXBueeK5ZMIwUqks5orVwtZYOLW9xTyP1FJctYsAvRPWwwKJAYYB6fZQyhyJvVuuN8JeKZhUJHRgBY173KLdjCq9E54mTpiW6nO4LK2UDeoCMCRMkOqKeBu3W4EFMLa84OYqQAgwLiB0XhyC324ZZpB6LBe/blS3568DpAkVo1bPPKa4or4oaNfjlu0JUk+IgTSZ0u3ZR0qt2MAyYsmV69KKBUxcYZwX1LGgadKWSl9bl8pfCQlJSiiFWgaqteKdkNwJoBYvkHmGEWIrrPB4+OQjn+RzJz73pD93y8lbdr1a+anins17+Psf/fvct3WfW0lewP4Zxi98/hd4513v5Lu/9Lv5hqu+gUOTQxesP08GsvkQa7/1t5E6nvkSew6IeoRLu3/M6fw/s13+GMae3esjwv7jl7P31CGOXnkPi+lTy2Hq1vew76prnlUPR1ZYYYULj3mZ81Of+6kn9ZkbHrqBjx/9OK+7/HXnqVdPDWbG7z7wu1x/z/V88pFPnnfy99G4d+te7rvjPt55t++df+zKP8bV61c/bcffDcjmQ0w+9pP+j13YQx8Z3rTrmcCrPXSFFVZ4piDXzG/d/1ucGE6c1fuLFT517FN8/ZVff557tru48/SdfPr4pzGMn7v15zg2P3ZWiudzxX/7W3R/t583Xv3GZ/w6rSfuIN3xW2e2zqewjwKoLLgk/hjr4Xc4OvyzXejhGcTccfn9L2S+tsUjV9xDDedO5q/20RUuFC5CAljBPOfTVa7BzZtNmsrTCVWfjG4HvcztdelnI09xC2Y0tH+bx+eGRgjUlnnZ5rS0fFNrBLRbh7qdtH/aq16dZF4SvwETVw+KNatbCTidZGdIZvH2TZqlqSzJ4iXMFcYGRnAL3dBIkkojngUTwwjN2tTzjq2p9Xb+W2tjdoxafLPBvC8mbmUQg1DMZcPVImXwnGC3cw6IBtcdloJnqwK4FbeLagsaOmqQxjcV+lrYHxJ7VUghoqKu/G2qUiYdBDvTh76njJmoyt71yEnJDLOm6DYw1XZrIlAhKFKqE++lINIBRggQU2C0ipXB75kE6jjDQvK/2QT32FVKzpQMQgEVJzlToGaDbEgpBKnEWogSkDy68nw2Q60gUkmixBiQFJA8J4qR+h7EsFwIk0jas0bsnCA+dOl+Xvw1L2PfC17Gqdl93Hv9jJNHAuP2SVfjItQ8UoYFNQjjvFClUrpECgcptZBKZqrCQoWhjAy5MFdYoMw1gs0JXe9FDCG4Yr4CwVq/KmUcGGNHzUYd54Q2dbQqSHbyTMBqdgttDW6frkYIQrUA4+hKY6luMSypKYyzW5KLq8ktRKplTBOSRicXa5szywIMWcdUIMyQcInPjWDYbBNZ2w+2DcWQoDsWwLMCJ+bbSOg5oFOiVaKuuTMAgoyel6uTyLiYU+eei7y8NpoCKSoh9KRugrYqv7Io5Jypw0Axo4oge/egfUdIkTSd0E/XCF2PxtDGphBKdvV7LWjcYj6OnNjaYnMY3GWgZp+XVjCUUgTiFGFEuz1evGIL0Eho5KwAQZNfoxD9WKpQxDO7m013JTSN+6MzypdrVySkic/9kilktDvgpGgdocwpGt0CHbeAdycDWS5YSOxdpk+A6HOyNHt8iRNsmTG8zPDGqIxt6Q1OWEtwxS7a7qP3B1omuxVX/lbDqjYb54ppouYFNQ9ufd2y4L14pTlAiHgRRnDHhdKKRNBWqCC+FqywwrMBN5+8mTd/4s0M9cmr1k8Pp3nbrW/jf7nuf6EL3Xno3dljnuf89C0/zQce+gD3b91/QfvyaBjG6fE0P/LZH+Gdd7+Tb7n2W/j6K76eS/pL2JN2jwzdbUw/8mO+lsJTVgUFOcGB+BZ6/RQnxu/fdRI4lMSlDz6PR664h8XauT/A3nPl1fT79u9a31ZYYYWLA//59v/M5vjkVCqVyts+/zZefdmrifrMeeyyNW5x58ad/MRNP8GtJ29lURcXpB+GMS9zfuKmn+D6u69nX7eP73zJd3LN+jVcsXbFM145vdt76KHun7Cor+Lk+NdXe+gKK6zwnMOR2RF+677fOuv3Z8u88+538prLXsM0Ts9jz54aFmXBg9sP8mOf/TG28hbHF8d5cPvcnSHOBY/+Ldppx9vvfDt//eV/nUP9Ia5cv5LwTHtuZZV0x28RTtxx5m+7wIeKVBK3sx7exVb5ZmD3zltQptt7OfTQtRy74t5zIoHTdI1rXv/1q310hQuCZ84vkacJS2JFW3ajibgFdJPjijUiwd/lSq9lvm70vMhaC2L+uiyJUWv2vGaYGGKgIm5X25S2zh870Sqqrnqsj1o0rDZaN2BLq+ml4q4pZBtb44TFkoyR0N6jruQN2j5f/IzFWh56k0lWg+DkjmeqNlpahFIKqkpQb1taQrI0Us1cO0iulTwWz401ASs7BJEKaJec9LSKSFPQWQaDUjIaA0uuheLZurWOIB0aJsCIEJ34K7AWlH2q9BjB3KpV+95tmceCdS2FOQTImdD3xJIZtgfqyU1iDUQTLChVnCS3ceHZpdUwCQjNQlgCJc9RiYgKMSUEIY+Vmls2aOyceEYhz328qCs+JTRj3bGikglkYnHyXGshmRFqJtSm+rVMKJkQE0qg6yP9pEMlIYvObYKDUPs1SJk06UldJPQTVCuzofKZP7yNK48XxmHkmpe+CM3GVlDybIMigVIiJQjFIIZEWQyU0bAukYtQ8khXFkxsJJMpoSeLMB8zM4wFgcVsoKgwApUO0epsegho6Kmx87zdcWzW557V6vbO5spdiYgmoLDM6PUsYCPGgKWOOmRUk9tEL4sLrIApIgnULY0ZK5azF3PkCmTPr13MkW4NUoIYQNaRargsNcBk6sSprrOTY0tjqw22T26BTgjBuKTvCaLE2JGiq+CtZB8CUSmjIbmgzHdWmLB3D93alL6PxDjx3PBpgZIZR2NRXElex5Fa3Iq8i5EuJR9rIaLRiy80F6oIKY9UgRPzgRPzkWF0ctwdAJJnbmtHzHOMSCkZYkQYfZ3SzrnXOnphgQTQNaCgcUpdZG8DUIk+V/OsEbUTdxyoTrBiGRV1O3MRqggq/U5BDLT1qI6YCTVkL6zRiVt7R992rDRr+zI26+fs46EsqN2UWkb/t7irgYSAEbG41uwE3EKcAtXMs6VLoarbZ0vwogOT2JwFoBTPIjZTqglSC9WMsMxk1+W3zlYMJJyhv0NCyjKDxtp6vvs2PiucP5gti7subBtPN8yMzXGTk8PJc/p8tszP3vqzfN0VX8d1+697eq+hLb9ZwMeOfox33/du3nPfe57S8c837ty4k5+86Sf5yZt+kq+5/Gt4yf6XMI1T/qcX/U87FpfPhHEYjt5EvOcDu2g26V9Vp/r7SFqwXf4Es/r1LL95P8WibqDlMD30PI5eeTeL6faT/ryGyGUve6X351k4l3cTF+t6uMK542IeMw9sPcD7H3z/OSl4bjt1Gx89+lHecPkbLvg1NDPe/+D7efd97+aDD33wKfVjt3Hv1r2wBf/3h/9vBOFbX/it7Ev7eMHeF/CNV33jY977TBiH52MPjXKMoL+Lpm2Oj38bwx2P4DzsoZPtJ93gvqufx+Ev+wrvz7N0Lu8WLvRcXuHZiYt53JgZP3vrzzIv8yd+86Pw4SMf5sdu/DH+z1f9n6joM+YaWvuN+unjn+bd976b6++5/im1t5sY6sBnT3yW7//97ydK5Ntf/O302gPw517w5zjUu1PVU7kGT/UapluvZ3rDvzrnzz8eVGYciD+JEdkuf5Ld3EcBptt7ufSh53H0irux8OS+F172slfS7zvg/XmWzuXdwjNlLl9MuOgI4BCCq23NdpSzAqgaqopoAKsEUVfKSnCSUaCUpuoFJ41bHqWJ4hm2bseqVRCtzR66os3O1QXBzWJZmooMeQyJ3CSS/h5VrGWn+v/v5NOZ5Yt2XP+c20ZbUwo3QovqltOiO8eVaDvHMhrJHNpxTVt2p7TM4aVmeUl2G+MwksfCmEfPmrXi3I+4krioE3PeMScJRWNT0Hrvy9JOttliL622oWAmFAnY6D6yKcCBENgfI9MukYK4SjK0czDD5nNMFSu4JW3y7NYUhJJhTxko2jO3TA2RalBCxHBbYze7Fco4UptaupJdwaoBVSFNppQ8UscM2Ulwt8du92+ZZ1wrkeAqXytQKzlnyIVQja5kUhBidcVxUHHb6qDEFOim63TJPDs37UHzgPY9VYJb3QYlxODZwKlDg3Lsnvs5fXrggMy4+orDdBvbPHzPwJbASKWEwDisUeanyRRKDeRiVDFSF8gpUBZCNaOUQNVALQNrk8SYM6PBYCPjYCxUmNvIds0MKWIaKH2gijTx5Egw0BTRzklGoCl0a8up7qg2sEx7crWnq8PFZmAt51cKbrEe3dK3qUetGjYukJKbCnTaMmY33O44JaCNwWqNrBdERtDSMnoTbM+Q6dQzjqugIWJdzzhfcLLrSF2mW+uZdIFJH+n6nlCEvLnhVtpBqKMgZUStoHmEcUDGhCYlSCKpEqMSdEItxrgYWeRM3pozpJ61PWs+DsRJ5dAlNEZXL4+ZUCtVtjixPeeB09tsjSP0PSyG5lxg7mYQDLOWKZ3WEdQLFjS5DbdVpO+QMrS51taKWpDgHgRinl8uaerDuoxYnWNFkJrR1LvTfJz4GlQGbJxDt+4FAU3NT+j9HoYOs9HvpWWk1mZu0HkGsFkzVyjYkBEGiL0T/LUiqW8W0smLO0TcvaDZMBP8PLCKNit9W9r1W6XUjFrFHd8D1RQbBy9C2BlT5tfazP9emt27aFtfWuGHeqa75QFTQSRR8pP7AbPChcVufDl8Nn7BNIz/93P/71Nu59985t/wD1/zDzk8PfyU2jmba6jzE+i4SS5zbr/jHbzl9M18bvbQk1ZfXWjc8PAN3PDwDagov3rXryII33j1N/K6w68jSOBll7zsnCqyn/I4rIV0228STt/31Nr5AhCBafgYvX4WzZtslT+FFy7uDkJJrG8cYDGZte+ZZ49LX/YKNKbWz2ffXN5NXKzr4Qrnjot1zJgZHzn6EW47fds5fX5WZrzr3nfxFYe+YlcUTOd6DR/cfpCPHPkIb7nxLczK7Cn343zCMN5+59sBmIbpjvX2d7/0u7l8ejl7015etO/cs/Oe6XvoRD/M5d3/j43ybedlD73swRe0QqqzVwJrTOx//ovQ5nb3bJzLu4mLdT1c4anhYh43N564kfc/+P5z+uz7Hngf3/Hi7+DaPdc+I67hPZv38IEHP8D1d1/PxrjB6fH0U+7T+UK2zH+87T/u/PvX7/l1Ou144b4X8hdf/Bc52B/k2j3XPul2n+o1THf9nkcQnSeIFA7Ef48w7vo+KgiT7T1c+tDzOHXwCMNZFlTtv/YF7L36eTvX7tk6l3cLz4S5fLHhoiOAlwSvleLxpS0jF1uSANVJpqBQWxbu0iZ4GZuLNiJqmZfbcoVRMGkkVm4ksuwIbz3nV91OVpb1J96oFdyCWl3pK3amqoilIlmA4mTjY1S/JmDZea7qyl1dPlBsxzYrrlAVt6petifBz02kZRJXXN0HiOoyutYjNcVc0YdR63jGLlVCU1KyQ6o7Ye6KYjPZURMj1uyuvc+2JMGXFrNWsaG4crP1d2qF/UFZT4Gu64kRVwwDkiY+6XOB7QFSRXrPcg6xI+hAH2Bf7EjTjpMlslUyBPFzKuKW1tUJehdZe5Yt5teq2hyJyUlyDUj0AgGr2a9HLX7eZSQKhOrmuQGD+TajCeNsgc5ndJMJnRgTSXSqBCIxKhoCUSup70lrU9+enM0G7V1RPkn061NiEAQfZyH1aAqE+TaycRK6wv6rLkHTtQzzDboYWSxGFlYYQyAHYWSTMW8SslsRmypFhRojeZxRpaeWTM0dFShWySRqP8UkkOenWYTAdlhje75giwWLAnNVxmpO+AGxnzQiTn18xOjD3XIrLgiekVsDRSIyDlRVJ4jBxzWGmKLqClQTL0SwCBILkgc8+7nlCIeDEJq62DwzFgMLgImr8psNuorA+gQtA0JyVb2Jq7uBvD3nhFViDPR9z1QiXTdh0idqF8ibp7H1vdh8Tj592k3ADXQYYKORr+sgXSIkzxTWKND3DKWwuTWnbMwY0gajJro0IXaTtqy4Ih8NmAgbsxn3Hz/JsY0tspmvTdVAE7VmEKWOBRMvchFxRWxtGdWhSwQMiYkq6udYCxpC0zJkJ2Ulk4o0onUdM6OWSuyUWkZKnqG+cGJWfZx2e3ydaGQ8LTuattZCpNaBEHtKEGqtBBtRvDjCOWMBG1vueXNe6Nba3JRWaGKEEKEMfow6+o2VgHaucJZa3P1AAcTtwjV5wUBbw50nLkgQpF0/UVcJay2IdrjOHd8ndoqF/DppUAg9NQ/UuvrCscIzHx948APcvXH3U27nxhM38g8+9g9482vfzMH+4C707L9BddeHyUN/QHfyVsbZUf7z5p38u9M38+RovmceqlUe2H4AgJ///M/z85//eaJEvumab+LbX/TtXLPnGjrtdhTC5xuyOM3kE289r8dQmXMgvpUjsyNU+bOsxwNnvps+Rew5dQitgY39x85axdTv28/eK1d5SyussMKTw1hHfv7zP/+U2vi9B36PS7pL+P5XfP/TZgVdrDDWkU8d+xTve+B9fO7k57j99O1Py7F3E7My4/5tj3z44Y//MACXTS7jdYdfx8sveTl/8po/SZT4tFpsn+89VASiPMwBeStmHdv1TwC79/0glMilD13L9vppTl76EKZPrGCaHjzE3iuu2rU+rLDCChcPqlV+5a5fYTs/efcegNPjaX75jl/mf3/l//60/Vb6bzGUgQ8+9EE+dvRjfPLYJ7lva/cLgJ4OHJ0fBeD+7fv54EMf5Nr1a/nyQ1/OVx/+at5w+Rue9v30fGL5W/R87KOCsLa9j+n2Ho5f9gCb+48/7u/RfVc/jyu+4qsJ6ZkdbbHCcxvPjZn9ZFAL4Io54FHq39iIzIIh1OzZoNJSMMVwwoLgZGkMSyYBpL1Pm3p2maErfgBzma0/+FpmCTelnOfiVpY2zK5HXhK8jRwpzuJKTMsAWz8XsWZB6jnGtb3fmpoRWbbofVyK88CtXtl5COaqVaDZRxum/pCuoqhYI5SMXAolZ2ptVsimrqKT0Gx6OUMKy7ij7Kx10dS2zjIL6sfI45m+mbkiMLWs2LIgZmNvF9gXlUnfEwJuBWuVOlZERtTaeYQI2RWApk4cJjUnuUolKmgv5FOVzUWhNPKZeiZD1WoBNRTDNGK1UIvBOGtkHGcsvnVJXRckG9EKXa2EOiIkaq6URUZKIW2PBKmsUdnTTVjreyYpkLoOFQgaCVoJKaChQ0Qpi0XLRHWFcOwi/XTq9QC1IrjKWdMEXSwIbDIW4ej9Jxk3Npl0iemVlzFsLdjc2mY2nzFqYDRlKMpYTlM1Qp8oBoVCWbiWOY+CdRErC4p25LzAgkJMEPYxZmMfwiIJszxny0Y2csemZWZsQzxAwb/saYxQ3O5ZpCIh+dZrxedATn6NbYFodGJTQMKEOmwDc7Trmtrdx7iaebGEOqHYqjkILFX8I2cqEpw6kDoiIbiNtBQnfHWC2IDUVpagBZUIFCgVmxVOqRIlEPfBRH0sJYSUXDms+/ZRUOabW1QNBIMolWAV2Z4j44iu73GFd4yELtHT03U98+05nN5ktECOHSlEagxQO4pAobI1LLjv6HHuO3qSxTj38RIDWMdQqqtVg69dGpNn/aoiZY6IElJCFeJkr+dui18rVb8/FFwsPelgBGwOaeLrjpnfv5qRmNAQ/Xrbcr0qxEaiqkbPEAZ2VN+GE8ESXNVbMhp8TaptXaXUHesOmeyF0tbPUttrvh46H94eUNgIaQ1pf0cMi60opGa/rxKbennRXB+0WdELZmMrBjAneYMX01Rze3okIGXhRS4SlzU4rhrukhcdZK8sXGGFZzK2xi3efd+7d03tc+OJG/mhj/wQ/+DV/4Ar1q7YlTaphXTqNiYPfZg7N+/l9sUjZCv821M3c6wunvXk7xdDtsy77n0Xv/fA7xE18l1f+l284fAbuGbPNef92NM/+JcwnttDmCcDkYEX7/mvfPDoTRwfvoGvOvRNxF3IdBSE9Y1LmGzt5ehVd52VHfTVr/1a+r37nvKxV1hhhYsLv3jbL3J0dvQpt/Ord/0qYx35W6/8W0zCZBd69oVxcnGSjz/ycW49dSvvvPudjGW8YBm/5wtH50e5/p7r+c17f5O33PQWXn/49fx3z/vveM1lr3lajv907qGXpB9H8sBW+dOcVbXTWSLmjr2nDpHGjkcuv5caH/83zeWv+qpdO/YKK6xwceGO03fw4SMffkptXH/P9VyxdgVXTK/gDVe84bzuo4/Goiy44eEb+Llbf457Nu9hqMPTctynC/du3cu9W/fynvveQxc63njVG/mma76JVx161Xk7pmwdRc+Dg8YXPNZ53EfBxWMHj16FmLJx4JEv2Hy3Zy9Xveb1aLj46LcVnlm4+EagNoXZkrwNjcjQZof8KHWCLAnCRha702pjdGuzN+YM+aTCjmXyjs0z5tallEa4LkmpxijU0lTH+GdUgLijpAWjjgVCI8YqLJnlJV0sKtSqjdD0c6s062mX+UIdXOXWVMeugGu33wwnthun22yYpRRCTK6Mbuo5qiv/XGApmFRMarNMFs/htNKUsa4erNWguk00tbbCGyfa3fE6gyScffIMX1ICgz5E9ofAWuyIVDR0fnyJ7dp2TTFZCWu9qxPnA3Vzhoh4RK0KlcgwG+hH45IINhROzQeyVLeczbkRQ7j6T4BG7qguz2WgVsUoaCnEEIl1JEggaCGMlVBBCT4WRKixp9RtbNoTiOxJkX3797A+ndCLESfTNm6kbQjWLHkjpjCWTBlcvZ3UCAoaWoZubYUJtaDDgJSKhJ6yGKnDAjG3E16/dErYMyEcO8GwSGQNDGYMUrGYsBDIZpQ8klOijJWS/P5YDdiQGeejc/dBoF/zqNYyMEhlXSbsqwMHqWxVOC372UJZLEbGbMjESCH4OZbiVumAosTgiteRiln098SI1YWrWbsOyyNU8fOuC6qJj5nR0IITreqmHqGN5ypOHoriBGQuTipiKBVSbBmvAdFLkXLSxdaG27ebOZFcC2xtsRECUSM9ShpGYoh05iRvmkb0wH7Wp2vMt+fkkvHDKikGoiqaB2w2YrlHiWivpEmipzDOK5IHyuYWQx8hJaJGcgzM5nOOHDvOnQ8e4cT2NoYSoiC5YFEIkhCdgBRK6NEYCdXnn9VKSD0hBEL0fGon3cXJbvG1RtSw1FwOUkJarq219U3MM3BVXKWvKk6A1gWEDgnRC2DqAsxV1BaCc8AtV9lCh5WKpuTuAVYo+HyR5rxAmriSv+b2xamtlQF3NbARqeqEd9zr6xgKUSkaGGogxkgU9XtbRwjJs4sBK6Mv/6ItWrg4mZuLj7EYoVZqHpGuR7THbERqU4uL+Po0ehGRALILVn4rrHA+ccfGHbue83fjiRv5wY/8IN941TfyHV/yHaRzIPRyzZxanGB6//tYjBv8yH2/yVgLd+ct7ilnb0n4XMCszKDAj372R/m1vb/G11/x9fyFF/4F+tCzJ+3Z9eOFIzeS7vjtXf75+8UR1fiay27mdx8aueUUfOn+3SGBAUKNXPrQ85hPtzh+2f1fNIdp//NeSLe2+9dyhRVWeG7j4e2H+c17f3PpU/WUUKlcf8/19KHne1/+vfShf8ptDmVgY9wA4Bdu+wXu27yPjXGDG0/c+JTbfjYgW2Zz3OS997+XDz38IV5x8BVct/86vu2F38Yl/SXnx/GhDMjs5NO2h4qM7I8/jRGZl9dT2b1CJkGYbu/j0MPX8sgV93zhPVSES174Jas9dIUVVjgn5Jq5/p7rOTWcekrtjHXkJ276CQC+6tKv4q+85K/wlZd+5W508Qse6/RwmptP3swv3/HLfPyRj+/K94BnMoY6MNSBX737V3n/Q+/nKw59BX/jy/4GB/uD5/Rb//EQjt1MeuAju9rm42G5j070o5zKf5VsV+9u+ygHjl2BibG5/9gfIYEPXfcyf7a8wgoXGBcfAYw5sSuN2F0qbZ2dJAR1RWptCkP/CAAqqdk4u82pq16bdbC51bPnojTf5CWJK81qGFf+urNtcQmpqpO6rU+2tLM1IASsZKpAaO+XpvQzaxa54sSzxkC04opRabmVO226/bIrK13ptlTr0eyel8pnzFAVt4xetlPE85HFmsLZ7Ymtuip42axZxdBm+1yo1Si4hbBfjkBtlrwiukMwuU122VFHmwSkViRE9lplvyg9mRjXsaTOpasiXedqPatuX5vOqKnjtHPiNgdXa0vEZnNsGFiXglGxPDATYVEWfgua/beVBdAWaAGaKjXWjEgHRYl5TiwjqRaCRJIKwZyQjzES+wBFqAJVehAl1DmTqKylxLQL9CERU4f2nd8nPGtURs+jttCsbKsrItXaPWwEqqsfpRHdGc2FuhBkXJBSoEogzwqh6whMmHQTUkzkEOhKYQCsT1Qp5NEoOZBLJsdCLtlNrDPUEEm1UGtBokAv1BIoudAVo2jEypxsygHtOSiBrfmCk8XYqCPZCnU6JaVE0B6tI0kjqe+I0wk1L7Dt0kjDETFFQgJVzNqc8xlIkQh5QEv1rOduigwzFEjTSLd2gLJ9mjKbQ4pQ1Qsx0hoiCdEZDAMqPSYGdUS1ILruObp1gVr1daLOEYlIWoN5ZlO2OA6k/etMQiJKItVMQuj3r6MqzE9vMDu5wWLMrUjCqFZZnNqglorsW8diQoMSTIihJ4WRYsJ46hTzMZOKMRWoMXLq5CnuvvdB7n/kOMM4QB0xc5U+mlB1ZXupjcwWQWJHyQMyXXOFeVurvLagOglqFSlGCAmTkRr8OmvNWHTl+07esmUnxNs3GUPchrmforHDhgHqAGmKmjjBW7KvgzWztNCvdSTEzklcFaJ69jEa/R7VoY31ViBT2po3giZBUmo1NBmxwZX+EgGlijBuzxhQz5428ezxVtThvVZES3N16BCKF1vE5OQ4uLpZ3OVAVf0ap9DWyOzriGXArf/DLtrIrLDC+cCP3/jj56Xd207fxu2nb+fY4hhXr13NwclB3nj1G5/wc9t5m9+45zc4OjvKL93xSzuuDuU5/qP6bHHXxl3cvXE3v3jbL/KKg6/gv3/ef8+3XPMtu/cQ2yrdre9Etx7enfZ22uVxC6o7rXzTlZ/nxHA/nzm1zQv3fus5ZR9/IcTcsb6RCCXyyBX3UMNjVUyaOvZf+wJ3s1hhhRVWeBLYLts79v27hbff+XY2x01esv8l/PGr/jiHp4fPqZ1PPvJJ3nv/e/n1e34dcMvnixmbeZMPHfkQHz7yYd5x5zv4zpd8J990zTdxaHJoV48T7/8o6bZ37WqbT7SHqsy5JP4oObyDY+PfJdvzdvXw0+29XPbQ87/gHtqt7+WKL3/NKj5hhRVWOCfct3Uf77jzHbva5scf+ThXrl3JKw++ctftim88cSO/ff9v844734Hhz/IuNpxYnOB3H/hd3v/g+/m2F34bb7z6jbzskpdd6G49Ps5iH53oR4npQY6NP7jr+6haUwIDGweO7fy933+A9cuvXO2hKzwjcNE9DRFRqtIsR93qF9QJEjFqrc4NA54h6nmhZgLBs3+tNtKS4iRwaYq54MJgVZzAFEUl7BC1RiN/m3EwLWcWQNTJ5Gq1CY6XFtNuUWqqrsDEbZJbQy5GXlqYqjQ6IlDMMzvdibXliboXtSvfWh5rLQXVsEM+i5kreEPHUrALRrWCYZSlxasoGjxn1JV4rmJ2oggncXFCVWp20kQSO1nIITTi28+potTs5IxEV172BntEWAtGDNH7WWoTXRcnf1UQIhKj35LsucWx75o6OmA503Ugkx6dzZFaPf9URhahY54Loyhjyw8tBPKwaEpjRXV0tWpKpMleQg3IxkCohSCBqELsAjFNkGELCQHt+kaCBaoGSqkEenqrpDwS6YmpI4gR2mZVQ0IpWK3I6ISUZiOUAtUJrDosCGniOdOqiJmPT2fFqYsFi9NbLQcWJknZv38vUjtOLUbmtiDHyGJYEBB0bUoVI48L8mIkmzEOA6VUaq1YEMpYKH2H5eJ51HGCJShDIZeJqzB1SqmVEhLr44J9AQ6MC06PxumamQ2Z0Qzre7r1dfb2PX03IZfC3CopFMyCu5uLOMlp1QsPUgd5oDK6jW81lIJEI1hA1/bCsEUwRccZSEWnB3bUooI0G/XshG6szRa9w6IixekH0Q4NvZOboggR7TpEXaWqw5ztLeNEJ6ztW2fa9YT5QDCDXAkxsNZP6C7pWJzaZr49YMPIKArbMyeAQySsF8K60k06gkHXCNzZYmTj1Glmi4H5fEGdTnngxEnuevBhNra3WpFEwHKhqkFoed2pJ+TSXAl8/NRhRPve56Oojx0bqOOIxTb3Uu/FBKkn1FbwgVLMGjGLt2mKJMFKbY4FQJeQLjWL5gyanICuoNK5GlhrcxPwpSeEBAW/xuq29jSLbqkLqNLcAyqapu4EkAdMBcsRo1CCoqlvquPWH9ytodaRXEZKVKIu17tlxri4GrgUCIIG9XW8ZqytS1aa44PhjgW1NJtx9fxv6VqfWja3KBJWpNUKz1ycGk4xy7tj/fyFYBhvv/PtAEzChP942398ws/kmrlj447z1qfnAgyjWOFTxz7F5058jv96x3/l+77s+7hu33Xs7fY+xcaN7pZ37k5HH42z+E2rAof6bb78wC/x6ZOJ5+350yTtdunwwmR7D4cevpZjl9+78wA7dB1Xvfr1rB/eJbvyFVZY4aLCz9z8M7vepmG8+7538+773s077noHa3GN/+1l/9tZEZUbwwY/ftOPY2YcmR3h5HBy1/v3bIdhbOZN3nLTW7j+nuv5lmu+ha+54ms4PDm8C3toZfLhf9OKSXcRZ7GHihhJ7uVQejPHxr+/qwqmx9tDL3/lV+zacVZYYYWLD0dmR86LevZd97yLW07ewiX9JXzfy7+PaZxy9fqTWxfNjHs27yFb5pOPfJJ33fsujs2PcWxx7Ik/fBGgWOGX7vglfvv+3+YrL/1K/o9X/h+78ltUNx/anQ4+Gme1j0KS+87LPupdEA48ciWYuB20Cvuufh7d2vquHmeFFc4VFx0BjAS3OM0Z1YAkJ4WbBzPVDDVrZC9nyN+lBbKoV2+YNYVidUIzRlei1qZOleD5oEBtbSPmJGcjWmSHfH5U95b5wAEnN3RpC+0khvPF6nm74oS0x/ouVcyuNHYiNjTi1VXJJkIQzzbeyR8muyIQa6pfV9M5MaIta9ZQUYpCziMlZydHzTzPM7uiz1TIjaSqObecUFcNo7Ft/KWRK9mvY4jYOLiFby2I1ka8GxOFPSHQxUiITvhINs/6TWnnmoqqW/0WQfNIDIKmBDl7jiiBYALByGWkmisbU5eoIZAjlBipdUnCC2MwFsPgisnQgSh7unVedu2L2DPZw+fvNrZmJ4ldR7CCdo1EUqOaQUieN6tKlcC4PYNS6EJPNFANzitpRFs9gdgyOVWc9M8ZQUgamgV4RSw6qRlo5Jxg2Ym2KtXJ7WGGhYgCsQtce91h9r7g+Rz5xIT7b7+H7a1tSBOiRGTaQ1BqWWOMm4xWKV1PLm6Fa6WShzk1RMrmKRDQLmFBGG2kDAGJHdYsq3Mt1NBTkjCUjr01cHCxxela2aiR+TgS966xdvlB9k17ZqfnlKPHkTpiUbE48aICMazUlrccMAmEmtEQkLUemWd0XkiT6NdL11zNuZgTrUK3hqih1dWatOIJ0eQqWTUgYsPCM4HVdv7med5f7//qAAEAAElEQVQB0ejWv7XZbFOQWWGWOo5NN4mX9aS1RFoYMhYYjRhg7ZJ91C4y25qzKMZiPqekgE0SYwU5vY1MetLalDTpiesTn9+bcxYnN5hvbrFRCpshcPsjJ3jo+AlKNawuCNIjESBQ8bEm1ee/qmKmlJIhdjs27ARFqud2W0xYnnuxQC1eCGMVFXNlLU7Umk6cfBUwRmysXoTS1j5JPTJWz8yuGcJk6R+PBCVM9zqRbh21EVBe9LJAdK0pu0sjmTOigVo9t1lSRMyzVSz63LOuh9ABBSG14gpfJ6W6pT0CdVhQY/C8YoNqbf1sNtZodGtoVV+n6sIzn9t5aUxQ5kuzBqjuaMAylxpAjVoV1UTNz61MtRWeW3jvfe/lzo07n5ZjzcucW0/d+rQc62LCUAduOXULP3DDD/C6w6/jm6/5Zv7E1X8CRc+pkjje/T5kcfo89PTscUm3zWX9r/DJ4x2vPvTNzMomt53++GPe8+K9X8l63P+kzlEQplt7Wd84wEaz39p3zQvYc8XVq6rrFVZY4Zzw0Ow8PKR8FO7b8gy8/+tD/9d5Pc7Fins27+Gnbv4pfurmn+Lrrvg6/t5X/T0mYXLue4IZ4fT9u9vJJ4mk97eH1z/YHl7vzv623EMvfeh5HL3ybqRTrn7N16yUSyussMJTwltvfut5abdSue30bQB89H0f5VB/iG++5pufdBvvvOudHsfz30BRf0a+cqni2OIY773/vczLfBf20cr0D/717nbwSWK5j26XP85G+R/Zcf/cBagplzxyJSaV2eEtDl33DFdOr3BR4aIjgMWKOw13jQiSJTFRXMmIZ18WlFCtkQQVp4Nre91J3mWmrzRlsAqNrHVyWNQVqksiwmpFZXmMxjA0NXILRW00r8KytrS6yk9b7i+Wm6bN26k8yn55mUvc1MQSAmJCtbKjHC51+TPBmjJZyNVJ4qVlrag2JbKgCqBO/tRCxdxmeqmuqwVRpdTqKlVVV9eaUVpspoo2fXBFJGLRbYvJ2Qnt1EMZ0S62U6wEUdZQpmIEU6Q4WWQSkShICm5jW8XVebURWUFQjTBkyK7mlvUeVKnzARUljBlqIU26RgopFiPSspgtBHKZUBYLt+EOQlDlBddcy1d929dSLznEqV84xYnbihOSpSIolgdqiGgjrGopYIXa9cRi2DAniLhqMzclZXClt4q0RFOhaGj2yxnFiGs9Mo0UAWvkeSU29aOio9/7qgHVQGWOhoC2TOkjJzcJNTN9/iUclsL20dMcL4XZWCEFNLqNd1RIpVAnkEulLBbUMZMFio7UsheCECaJCsRxDVvv/V4k/1s1ow4LSu7IY2ZNlGES2DufszUObGhiWGTPaV5bI6wluvU1JAiWCybVx1ctFBRJ2kg+oZaCWCVqoEtrBO2wccBKy3oNijTLby0+5rWRfxIEyeYWwCk0e2EDmWA2byQgUAPadU5q1oJYABsQ7QghEfoOcmHjxBZWBbv0AIc62FuFKIKkjtB3TLpASpHF1oI5mVlVxpAoqowUZLEgLAYIgT5FQgrE9QmTPLB9couNzQ0e3J5zz7ETbC9GalkAXkShGt32u2Yk9qgU6CJWCtnwdaAVc3jhilINSs6ema0RDdELFfC1o9TRVf2IFxrEiMZ1F+yPI7UDhhl1GDA1RCPYwte6kDwbuinGxUaInSusc0W7KeQtiB2ia0ipmA1eMFPYIV+lWeMrPRIq0ndUCU4sV6gyojG5VXqpTiDXgRAT1Inb1YuQcyIHIYZ4ppDFzMlmFcjNcl4jUqMX02jLabfittK1ICF4xnnNvhaLW1oLFUkTSslOsK+wwjMQYx25d+veC92NFXYRHz7yYT557JP8wud/ge/7su/jlQdfyVpcO/sGhi36z70dGS98xvJ1e49xZP6b/P7DN3Fs0bGo2495/c6Nz3Lt+kt5wd5XcKi/8qzbFYT9xw+ztfck/eGDXPayV6weXK+wwgrnhIe3H2brGbBerrA7+OBDH+Qv/c5f4juv+07+/Av+/DntDXrqLigXvvgz6T0cSv+YY+PfI9s1u9buUgm8Zpdw8KtfsSJ/V1hhhaeEp3MfPbY4xn+8/YndqJ4IV+thXt+/iu9e+/PclO/gP22/i0+Mt6yIYJ57++g++QWCHOFU/qsY011rWxAueeQqDr/uCudHVljhGYKLjgBeZv9KbSpZwx/+LwnWpp5Vw+1ul4pg9xPeyclFmiVqg1IJGpraFsAaoerEsGpwIkLMlZ16hlAGz+m10vJ3xW1+qC2r09xu1KOFdSfD2JqNtGjAUCrVbZzrMpe4KfIk8GhTfKvNrrnZPjsRXGnmzZ5/7JSxZ2JaoZq4PfBYyGPG7a8FzE20qxlUt4K2kiEEzwPVjKQOyblda6hFEFUs9UgZnDxt+aU0Ej2hrInRWYBhoOB5mxqzH7OYE01F/RwITgCquIxWAqSmkA3tPVbQoKQuISPUoRH/CayMiAmdAgRyzlhK6KRDukjqE9NrD/Hwg9ts3nSccmzOpOsBQWzwe4KSpmus7ZmiqWexvc04V3LOjFIZyNTRsDFDKVip1NxydqqA+jVVEeg76jiiWdEQkep23VaVSoE6a2MtUDUijXSvtUCGEJPn3Wnl4fuPszHcQRhnrEVh7+GDUCtbmyPVjGwLahByHxgXI2ZQykjtEmVckCfqds8hYcOIxuh9Sz2yFqBTais6oIClSBkzpRiFypA7JlpZGwJ7CszzyLixTZVEYXQr9hCpi7kr59HmdiyUCpqU2ndNKSpOVEtEOyUPc7TYTu2zxM6zgcHJTBWw6lbvUZ0IDtGtfHF1uJn6fN/5AuNkppPzuLd7GTCpUAKGUIbC1saCI7JB2buGTTv2aYSYyAgpJaYHOlKa0W0JKRc2qzAEoWokZ2O2NcdypUwnxFS9YGOSCL0ym894eGuLE/OZ32/3PW9FEG5NL8HnimBoCNSS/f5DswD39c1qpdZMSC1zN/SNaDWonlNtVdHUgRm1DFg2TINbJ6tna4d+DbTz5a8VjWAViT2YZ4+LCVRfg7ygpiBSsTjxtuqyinKZk5587QmCFJDp3lbUYZiouzVUMBWqVaRkCkYdB7euDmuufs8DhOQK/ur23hKSr2l54eunz1ZMI7R1x9fx7E4HIfo4CpE6LmAcnJvWztfhVtAjcerKYkpbV1dY4ZmHzXGTd951Hqx+V7igWJQFd2zcwd/+0N/m9Ydfz599/p/lqy/7aiZx8oSfDcdvI33++qehl0+MpJVvuPx2Hlk8yK/e+3IW9bE/uhd1m9s2Ps69W5/jqrUv4csPfgN9ODuyW0vkMv0S9n31S4n9E1+XFVZYYYUvhA8+9MFVIdVzDI/MH+HHb/pxPn3803znS76Tq9evJmk6uw/XQv+ZX0Rnx89vJ88SrmD6Jy0T+ArgLM/jCSAIl4eXMrlyd60xV1hhhYsLZsbvPPA7z5p9NBL47vX/gW/sX8N18fkAXB4O8TXdl/O7i4/y01u/wp3lwjpAPBPwVPZR2XiQPdd/7zNmHxWprId3A4FT+bsaCbw7RU8qSuyfglJ6hRXOAy4+ApjmsyuuQtWgZ6yTmw2vxkA1PNvTA0A985GWWylLBe8yv1dZ5vlK0+eCKz13iKW6JKnM43vNc29Z6o5NMKnuNGqtDVFX8bV/W2n5vC1TeEnwWrNo9hxYwShOdEizlxbP/TVxA1WacbS727ZsYjNX8IkiZFBXLJsEqhmGMObMOIyUMnr/rGC4XTPidtO1FCpO1pSacQ9Zt6p1ZaJbzVqJrQ3aNaQRwE4a9QrrtTLpJ3STnlCNOlsgkwSpIEPrA27RrJNIiB1SMhoFiYFcjDpWJ6hzpQwZ7TpCioTFQJktGLfneDez2zl3XeuHK4y1VsSgl0A+seDE8AA1G5OoWAhYdgveqJHQG2uTngOXX0IIkfkxKH1iHObMJ4ktjWxtbpHHTJ6PiMxg0myjU4fWQFBDY1P7dgkWC1cuZ7e4NVGsBL835vbGUntX0LbxKgTqOBC6CWaQT28wGwo2mzOZRA5ccZBLLt3L815xiO3tkQfveoBhGFkgnmlqFSsDZczkqMTUUdKCDJRGMJsm0lRgbUINXgSAVSiG1URdDNRxQZFAqh1ZA6PMmMxnDAjj9ozRhJmAmrEAFhowVZj0yGKBFPM8bHN7cYmJOMwJmCtIK6SW0yrq98FV3MaOQH9ZZ1Grq/Jr9bGWOrci7wLYOtTR566NnjkbIuSCRWtWyB1VA+M4oLUQrIJUNk9mH1tpP8M0sT6O7FNBJLG2tkbsIwRFhxGlsjkUFgYjBYbRM7c1EKtBEHIpjAInZls8dOo0szz42mON7FVFYmyWx6Epvd1+26ISq1BkjsbeM8DVrZYlJFTdBWBp3+7zTlAM6XoEt1If6+iKWyph5yK2GpQUXPVaBmwc3I6dllFdx7bGRp/nZXTb7er3jupZ5owjQm6FGwsgIGNBugmq6nnPuSDd1F0VGgVOgYoQAElTRJoltboqmDhBugmxn7qcnYJoxGJb9+vynCNWM0vrfsm1LeWyY+kvoXOlvVVsMXOlPyDa+bnKmeu3wgrPRHz2+GepO0VmKzwX8aEjH+IPH/lDvmTfl/CtL/xWrlm/hlccfMUXff/a+//R09i7s8Ohbpv/4dobeed9L+f48EcJ3kWdcefmZ9gYT3Ddvldz7fqXPuEPaUFYl0tJq7ylFVZY4RyxKAtuPnnzhe7GOeGgdnzd5DAACyu8Z/bgqlzxUViUBe+9/7184KEP8I1XfSNfdelX8eWHvpwr1x7fbUJP3EH/qbc9Tb08O0S5h8PdDzArf4wT+XuB/qk3KqBXPwl3kRVWWGGFL4CTw0l+9pafvdDdOGv8qcnX8j1r30qQxyo2e+n4U5Ov5cvii3nr9tv5nflHmXPhFawXEue6j6a7fgc9/vmnqZdnBxFYD+9iLbyP4+MPMK9v2JV2wwv3El70FPOSV1hhl3EREsCuwnUVLVRRMGm0qFOyVHViRvBM2h37ZRpBaU7AAkvTZtHYbJ+1qebcNFpicA50zGfeK67y1WrscL3UlgosO1pds0oxIxDO2I+KugVqbcSxWMsNroh4hrGzQZWgxa2PTXBarLK0hDUJjcB2NbMTUTgJTLORFZzINaMssufC1uIkSXWyGVHPq6U0m1lXErui2UnnHeIbdevV0DdSGyeoqOjyflghlMJEhUmKdF0ipkBw/1a37p0b6AgpINMpkhKSEhoioakhCYpSqGHq12scXAHaJUQVtQjj4KrFKGhMxMmE2Hu+aBBXDFvOoEo4ELCJUJjTH1onbfcM2wFkABWCCKmLxK5DLCFDYaoRSWBJWWSj04AIbJzeJs/nrgA2CJ3b+Kr4NY0KIQZqilgQt3cGiIqpYZYxiRhuyy3Dwkkty83KVlytKK6cFhNoBPD8RPXc3RdcSrjiEJXK2p4paZFQVap1lMU2ORhBfURqjE6o5ooMIzYWtAvIpKf8/9n782hLsqu8F/3NuVZE7L3POdlnVlZfJZVKUqlDKvVCINFjAUYMwOb5Ymxf+/qZh/2uwf19trGvfZ99DbYxbjD3ebi7xjTGBoyxDAJEJ5CQQEIdKjVVqr4q+zzN3hGx1prvj7n2ySpVl1WVpcys3N8YOTJz79gRKyJWrIhY3/y+r2lcQVpzUmXSYWkkpbHmFQuaC820o20ieTrxQoICqSTWNDLPI3NgW4SFwqhCnnSQzQs08gi5EIMwmbTozhwZR5pugq6twfaWq10juOIUtBQotXBDAxKaavk7+nUUl5nUVi2CPWfZsrlFcR79usyCtVMYE5aS92ltsGK1liGzXSALzKcL9rSBMXUU1rGQmLSKzKaoQVcSZRqwMTNPmbEq+YtltHje9pgTJxY9950+w+nFNlnwa82UpsOJ7vqRNk7eIkJJiZLVhe9xzRWtFiniY0qoNvK51GKMVNWrIRLUlfGSPRNYJboldBa3FRff11yVu0FchVy6qV//w1DHg+Cq6Xq9S9BaM1PQGGAsFFEn81Ektj6ChloxaHVsCQKxQ8XdFyyAFqiVM5ScUAk+UlrycaVpPZs3F8Y00IaIadWBV1cGCT7maC2usOxFKRY7VzQHhWTYOHqfUEU0ItrWHPNqI+3WDE6i6xV4C13hssDP3f1zFFsRwM93jGXkE6c/wSd+9xMcnBzkiw5+Ed/9yu9mo3mcF87cX6Ca5gsHETjYzfnG6z7GZ7YO8hvHbiTZY3OYjvf3cvLYA3xm83d5xf4v4dDkSZRJCs3rDj2HrV5hhRWe79hJO/zifb94sZvxtLBH9nJ78ya+uHsTL2v2AnAofpw/OP0M/2LzQ3xkPHGRW3hpoc8977rnXbzrnndx88bNvO2at/G1138t68066836Y5bX+QnYLXa9NODmaT2z8G6i3sdm+sY6ef3M7/YyizSv3H/hGrnCCitckTgznHFhwWWCSHgM+ftIXB+P8r0bf4Zvmnw5Pz7/ed43fIQztvUFbOGlh6d1Hx22mHz4311y76Lg7qvCNvubH2Qov8Dp9CfJdghon9kKFZo3HFqJRVa45HDFzl6rxqWItSonXZ1aJBBU6wQ/VfBbFXOGkw4hQC6PyK10y2Qquauqu8/dHpXrxImQa3ZwcGVZEHSpSkNd3Wm5KnKpVsy1bUGgVKvqck4l7JQIu5kEYtlpbAkUilsHU7N2q00xuOLNliHD4ut1+1OhFPM/lilALoU0ZlI6p/4NpRBiqNavSsmFbKUqfd2GtpRS7baz2xrXDN9dpaqxm8WJnrO6jk3LlMJUI0GciFQD2ugRx12D5oEQXPnqfr+h5v+2flxCqMS4VcduQbsJFqqiWgOWMyKBrmvRrnUSrJLHYoU8FvIikVPm7LHIzvwYjUbabkbZ2cTGEcYEySjqlsklu9I4qBBVCZMGkZaYEjHgua2jsbmVyQmGrTlxZoQS0LajYBSCL9tEchMo8wXkjMRZVbI6sUoQJFdSSgyTtp7bqtIVnFjPbgWc5wtyP5DHgYfvLpw+lYhB2Hd4SrvWcGB9yr79V7H5wIM8fOphBhnIY1VaxwxdC31DQQmzCTJpXWleive54IUKqYBk0AwSjNBETJSYRqyLdAhjv2BcZIpkJiGwlkc2RJkXYzsldiSwoHgesgUn66vlekiZKELE0CJIuwbDtl+LjdtISoyed+tXoXd982NnVbnujHHx61td9Y1mSiqotCCZElrvX03jWRUqkBYUOkpJFOsoJVMsMe60zJvIsH8PeTJj7AfWLTKdTYgqcHqLiDFdXycveoaUGfOIjQGSkVJmO/Xce+Ik92/usJOLK71Tj8TOc6HVlfMhsEuwqikeKa0ENXdmzoZoi44DhqBNzbhOuRKfBekmbkVukVK8RMSWtvE1O9kskVHvQ0GcDMawoIi2u9c6MSLtxEejUrC0qNm9rkI2MyQ2SBqRbu2cc7L4GOoxwKGqfwekm7r9syUXQBdza3tTpJk4iYuPN5q9H0kRUhiIKlj0cU0onqwdxHOe81gZ4Aghu7GD1Lz17AUyomHpGO320NogYhjRreIxqMUbpo8lKlZY4WLj7q27uXf73ovdjBW+wDixOMEv3veLnO5P8zXXfw1fdd1XAf78Ex76PXT72EVu4RNjf7fg9vY+1uLAx88c4a7t/Xz+BHYh8/Dibn7j4f/CFx/5Jg50j59NGG5aR6+6cDlOK6ywwpWH33roty6rIqrr9Eb+n2vfwyE9gohwpgAYkq7iWt7G39v32/zk/Gf4se3PsW3pYjf3ksOdm3dy5yfv5N988t9w+6Hb+d7Xfu9uIdXyPjP9tf/jkpy4Bp+87uTjNM1n2EzfzE7+ShKHnlF7mzcdhsnq/WaFFVZ4dvjhT/ww6Xl2vxERXtW+mFc0L+KT6S7+8pl/zAPl+MVu1iWBR95HX3PoNdx++HZef+T1vGjPi5A0Z+3nvxs98cmL3cwnRZCzTPT9HG1/m838zZxNf5RnUlC1ehdd4VLFlUcAm6EaUNWqNvN8XClOCqoztvVhX5wY0Uq1KpWpKJ5HKVVdVgpEqfpd/xxRimXPnaVm01KVaIBY2LWNRqk20uYZvsuFdKkF9lxNJ7LK7hDkTQxgS7ZPMQ1urZpLJbzqbpelnbXs2uFWSbOrSNUzQ62U6pLthFSxQs6ZNA7kfI7cRdVJGanOv8V/W7IT2LkUV4Sak1i+YG1ncWUyUpXTlZArCJJGWgnMYqSRRLAWia7gtJQhqSt225nb+RZDgytwQwyogZhR1J1/3am3ABkJwcmrItgwwpCJUlA1VxFrPfYaCZact18MFBGS7TCMRoOQpnMkD07+5oIMAzkoLBZEOvI4UpoGC04kqShNCK48RmhU6dqGs9sLFv1I7kcKRtCIBKFYIo/eT2knFF1g44CMCQsTjIKI59eirgh3k+4MZuScsDSg1qJJ0MaVimO/IO/sYGYsQiQlaGKgjD2z9Za1W47SveII8+mAfOgUNl/UIgFDtEHoa6dzdbgqkDLFDDHBCpRxRIZECAFigNj49YJhUpXhqoQQ0XSGYkYTlLbALMC6RtbGkR0yO8DOKOzgylVb9IhACJGYFsSSUIl+bU3WXc2Z54g2ECZeALCr1Pfr2mW93jdNGqxfeCaw+jVSNAAjFgxCh7tsO7lISt6HQwfZLahzv6B0U1frDwtyUApKkobF3jV6W2NvGJkFRdcm6FAIORNVGK24qrjL0ERGjFNntrj3xFlO98kV+TEgOtmNzLXsqma/bqraXYAhoxJ838wJTqlji9myUKCSrSVVy+5lji1YEXLOXn4QWtSEPM69gCOP5GyoNlWlb+zaPNeMcGmm1UIbt4VvZjAO0K25VXQdX5e5u2gdB7yOAcHHHBNBmq72GYihwwxCEz3r3KxmYme3uBagCFpAyTQx0MaGqIZKHd+qghkNiEZKqqphiZiN3jZxMl1C4+fWfDT3cTvXfOvkltNm1R6/IefLZ3JwhSsHd569k89tfe5iN+OCYE3W+fPrf53NcoZ/vfPPOWunL3aTLnl88PgH+ejJj/IfPvUfeMn+l/DOm97J1Z/9OfZsP3Sxm/akEIGX7j3Gzeun+B8PvIhPbx7k8V66F3mbX3/4P/P6Q++gDecyfqdhndl0D/EV+5H2iSv4V1hhhRWeCr/6wK9eFjEKU2ZcH27ij619Jwf18KO+2xC4MRhBAF7Dd613vLL9Jb739O9y+jJSZX0hYRgfOP4B/vSv/WmmYcp3vfy7eNHeF1Uy+NLvDyo9e5v/wCR8gNPjn2a0m3g6CiY53BFv27vKLFxhhRWeNZaRY5cqXhCuYyI+PnqI4fmPeyrKS5sX8C/3/3V+YfFb/OziV1f5wBWG8cHjH+SDxz/IT372J9loNvjWa7+CL7/7V7jmYjfuPOC3P2Mj/BdAOJv+ME9LCdzI6l10hUsWVxwB7K7FVpVnnvO6q6NdZgFTLU5LqlmPVSFbVcKoEw6AW63uXttCFQqfUwwv84F3bykGUgjBc4YLgARX5amrhM8xwFL5K921qxXE267qPA/qls+iVX2LEyt1vVRrZSeGIrtE2FIMDJRcEBPnQErGzNWzgpHHkVzc3rnq5NzuuYCZt9fEyWC3i1ZXN4+D74OJKw+rVaqJudWqlarMrfbW9fhoFKYY06ahiVoV1dH3t8HzS4u5GrgecymGqiASKsetldQRz3btR6QYpTUsQ5kPMB8I6sHsxM5VgKGS0CpQFMPJvDwOjAU0Zee8+m1iE4h5IJblsRfSvIfQOhFZycRiudozQai21nHPBt1kSre+4MyZOTvznpSMNAxYVBQlSEZzQdMIUgne7LmllqtVboxuW1zcdtvJYHF1YvJsWo1a82ojebEgDz0hNljusdyQRClbiaFvGC1w+rRg802yZFIevZAgeJa0hBZpWqwfKhnvSl+GtJu1rG1DmLROShtOAouQ00ARw5JT1RIjzXSNvJhjCE3bYRGSGTHDxArrGAsNbOXMmVQYSiZQCCESuhlaCmoZEVcYEwvozNXlYlXRurw2a7YxUi2f8YzXKKhlKE74YYrF1lXUBSDDWIs/Qgtx6gTrkJ0obNawYUGyQtCINQ2FTbIZYxnpU2YYWvZNW6ZtizaFvBgpQyIPXlxgkw5pjV4yx/uBY/M5Jc/piritevAc7tI26G4WeTUt0EAZR4hNzcsNlex0a3jPNHcS3oqr7VVGLDZukb50KigJy3gfsuK2x+1yPBucfM2Dq/oNaGIdWwRpJm45npMfQ9ccI23rw2XXYePCx4CoPgZ4EDtqCcEzja2OV6LVnjtMvP9Qc9OLuy+YJR9GbfRxSwIhGl0MTKcNk0njLg54YYkbHyh5afUv1dpBqx3+MuvdPJcYDdVhwf+UTC2wUIxUnQ0ClpMrhFdY4RKCmZEtX+xmPGuE+sQUCFyt13F9uIk/vfbn+aHt72fLNnddT1Z4fPSl566tu7hr6y7edc+7uLUf+IEYuDblS1bBtMQkJL766ju4fnYV7z9+Pdv5sS/di7zNrz7044/6bH97lLe9+ttZe8neL1RTV1hhhechPn3m03x287MXuxlPiUDk22Z/gje0b32C73df9YFALq/gS9vC398f+Nmde/nv83tJq3vp4+K+bZ/I/1/f+7/ypqvexNWzq/mT82NcxzPRAn3h0eknOdJ+N5v5D3M2/SF8yu8pWi7QvOoAMr3ipgdXWGGFC4xL/T6qCH9n73fxongDAIONfHT89NNez9XhMH907ev5uumX8K7Fe/lX2/+ZTdtZvadWnOhPcKI/wf/5yf8f927M+KsnL5/sZJHERvhxQM//PgrokSlx9S66wiWKK+8JTxQzoVhh6dRsPJL4ZdeaWDVUFSu71sgiVU0XlgSI/wapKjIUK+IiNw1OIZhbQ1efU5bkrlnNajWrJLItZb2e5ZrTUi68a2W8Sw7L0no3uxK5JBQnhYm6aw2NCaUsf+PKZiPU76o9srmaM1cVp1AQVUKMhKokDgESgknACJSczllPF8/7LZVEUgxiUzNuPRu05OIWr5WIQ1z5W0pxBWa1f5ZidCp0QQkhuHV0GZGuRUqAYK4YzvUziZ5NO45VxdtUkl8JXYf1g5PVY4Zs2JCdkK5kmsRQ35AFtKWkgZzBykiezykpMZZMP46EkJBijMDa+oxmEpk2LRoCed4jJTNtlEY8n1my54QalbQeC6EYISjN+hrNpGM2mXB2e852X9jZ2Wboe8a2kJuISEELhNhhQ0LMUMuYKZbGyuW7ta4tVZ2IqyjFlcClV0pKiARSv/Bj3EZMCikNaHHSMA8jBWHeZ8K4gH5OWQyehds1VRFZ+6d6jm0JVdmLoY3nG4cmeL6uCGVIlchzdXnGM55LtfMNMYIUypidJCQjBCQIrWVyjKzlgfU2sk+nbNrIfExkD+f2aznPIbp63ptXyUX7vJuzlareBIgwziG2iDaUYade+MsiiXqt5uT9VZfXrZ0jhSdTyEu7aXFyfugxDaT5DnMr5FwYFwP9pGOxvsb6+oTOjDTv6Xd6BitkVWxzB8vGqb7n/lNnWSzm7J9MmXYtYyqc6Qe2rFCykW30cWsSEIQ0juShR2PndunB7ZyzCZa8OENV3Oo+tkjOaDur9tXmHGjwworQTpwkzhmT5P8WrWrqDHGG5VpAYq6qd5luw+5AkhPESFB5RCGLkaUWfZC9aAZXXEtsUJbuyz3STp1sNVcjp5T92lHPMXaS1v0CQnDLaVGhiYFuMqFpJ6gKxMaLMoY5JY3uIlCsWsMLVpL3A8te2GKx2uwXLMSq8Hblc1leZzXDo4SujssDhRUBvMKlhbGM/MtP/MuL3YxnhT3S8Df2vYItS/zDM+eUzC8ML+Zvbnw/vz78Ip9Nn2Jg4PfTRy5iSy8f3NG1/LmrDvOPHjrOjenSH7cmIfPq/fdzdLrJbx27gc9t7/OoiSfBqeFB0ir6d4UVVniWuG/7Ph7YeeBiN+Mp8Y7JN/G65i1P+P0Zg02DPbuvREIqr+A14cW8YuNh/vj6h/jBzffwvv44O8+DwrHnCr/50G8C8JE9wj+ax8viHgr+KrsRfoJZeDcnh7/AYC9/4oWj0H7Z1cRX7f/CNXCFFVZ43uJSv49+0/QruDlcu/v/Vhpe0770Ga/vgO7l26Zfw9dO3sKP7Px3/u3Oz6xI4M/DfU1kU4WNcvkcl6d1H61ov+zoF6BlK6zwzHDlEcBVFWgIUolgam6v2x9LVftWIlYMEVeJLXN0RTxHl6XSNITdLFQL/p1bB+gycbeSv9WOlZodLK5GFHNiNi+VnEv1mcCuhHF3OQCpmZilBtVnNEYne80quWGoCEXUiZml+616LmZJI4Y6qRicCVfzLNddvbE5kWnqZLVSKMWcxNlVTAffQ6lq3aULLYapQbJqS720mqr7tquKXiqk/buAMBGhUyXsfu+7IGJObhfcEttA2+hk61iQZITo9rI29FguaE5+6LsOz8l1u1vVStqporH1c4TVPOORPAzkNJLVGIsyjiNlTJBHQgCVKW3XMZlOaJoGmXRIybSzCU2MRPHKMslac1ZdG6khoLHBgqKx8XXMOnb6kc2tls2tbXbGzNgnshQ0RCfBgxJyIpRKflpxErzYuQIGS9UGPGIMUEnINBREjdQPhLahWCZlQ8fB99sEDYEibgOsOUOq+cY5wWhYJcos5SogF2w+IF1DmE5RNcigUb04Qryfk6qBWmiQAmPKSFC33TbzggJLaCm1zxqxjRhKLkYODW1QZlFYi3vZ2trhzKJnKLU4YpnPvauwd1U/1U54qfolj1AeYT/cNkiuecrdBjbs4PLwgmpTrcwFiJAWrraWsFshYlY8l1lwwjBOsOjW2ORE6RcMOWP9AlubkYbCzk7PpI1ISqRFTzYjKSQLDP3Iw1vbnNzuSVJti2MgtK7epR8oJaFiiHZuvS1hV42qAiEEJDYw9MQQyJY8BxghVyK2DgNepKGCSONcuS7zkus4VPw8i5Sq2LVqqe1qf3A7eClCWFp8Zy9KEJb5u+yqhFUCpdRKk+I25+TiGt0YgeT9RgUdM4WCaqEUJ/NthBx8nDAziJGCEEMLTQutQjehNC0D4pnRRVzBXwRNBmlRrcwFSz2o28KrZZey13x1S95my4KN4znXAUBi4203z2bOl9ED7ApXDrbH7YvdhGeECVO+bHoNf3B2Ha9tD/P+xW18+2xRn6RARNiQPXzt5J0A9NbzsfFDHC8P867+p9i2bZYOCSs8Fp9qW777qkP84EPHuCZd+pP9InDNdJNvvP5j/ObxG3j/8eufkgRuX7PnC9S6FVZY4fkIM2Oe5xe7GeeFjgkqTzwm2q531yMRgCkNN3K9XMv37X01vz3+EmftJP/47Mc4lheMq/vo4+Jyu4cCiGQixznQ/gNODn+BMdxKsZYxOondFCMAzZuPEF+54e/IK6ywwgrPc0ykJcqFzToXEfbLHv7U2jdxW/MC/s32T/OZdC89wwXdzuWKX5lN+fNHDvPOzS2+dnvnKd7oLh08+j76FxnsZTxSCWwYY5eJg9K8aB96sLt4jV1hhafAFUgA1+xZqEQRTqRVJadZJS+qoteq8tFQRPI5UVtxQlKqPS44qagqleTAFWV4lquIgeLqR3xiK+CZlp4ZDGhDTqlakLolsVtJu6JTBRDPsa20ltsDWyWmKKi4hW0pqXLHpeYdV8K2qu/c0rWSzDEi5tSviCt6Bc/R1RiwZORSnDy2RJCA5lTVnzjxbYWy1B2rZxmXQiUrsyunRSnFVcQsBYKYk1Dmys1WYIIRzZWyGqOfB9zuVkNATZxcFdCgBA0EFI3qZKkFIEA2kEiZugqblJAQ/LzUfFSAok7Eu1pVsRFsHCk5MwK9GckKWhaUZBQLFMFtjCcdk+mUkAuaB6JGQlDXWBtu7U0lpQuYlEo6BzQGghVi1zCZZaaTnrVpx+bmFpt9YnscGcdEEoE4QceBdhiQJgIBk4LYgFjjinGyW+RmXCVsBcFIpZDnCzAjSPRzlQbMImYZK9W+2IwyJFRdHe/O5xHGVJXURhl6zyNuGppJS2hqrrVBaCPSNPWa8n5XlGo/7H0hxpacMjnnSuBPsP4smo04m/m1o+IkvEFOhUghB6VtOro2EHYaTp7doc8JDR2SU41wFT8GJft1kYLnaAsQOiQolpOrSyX6/i290LXxv/Fcacm59g/DmuZc7JMOvlwZqwo5Ay1WXM2rY4+aYZYwVdJOz7yIH8M0pazNiALEsEuyWj8nD5GyNadbDIwWGeaZYdwhKwymboucIQ89SCCooFqgJDRGjzInUMZU87zBtEFCREoixOD7FDyz3IeDgsYWywOmrRcQCEhsXWGOF3/4QOAku1tF51r4EqHxAhC3Z57u/rtIRkWWdR5OTI8LkBZK7+0Inf8udvVzt043l5JXVbexLHGwVOXKqUena37s3X6BHKcMfSYPc5ae/MXc4tkLfECKE71S3RcoO2gzIwR3UCjUXHhdjsnqbSulnmc833kYII1VEX9hXxxWWOHZ4iMnP8JQLq8XzUjkDe2X8BXdOziqVyFZ+eDcS+he2TzxZGQnHa9p30Cxwpd0X8m/3fnn/M74vi9gyy8/3NE0/O8HD/AvHjr22C/PmeFcWDzL9arAmw7dTZTCrx+76VEru+GaNfbtPfeifeD+/05o38xw8GWgV+ArzgorrPCsMJaRH/r4D13sZnyBECl2PbfHb0flHt52+FX8zPx9fHI8yy8tHuTEZfYs8YXAHU3Dd111mH/40HFu+nwl8CV6DwWIcoLD7V9nvO6NfPCqb+Wjt/kzwNse3uaWzQGCMN55Ezs3vQOLk2ff5hVWWGGFKxStNLy9ex1vab+I3+h/l59d/Bq/OnzwYjfrksD7phM+OOkowNdv7zx2gUv8Pnqw/bvctfHnefDIjbufl1C443X3cv0njrDn4JzINofyVewNB57dBldY4TnAlTc7okINtGVXKVIzQaliWZebsqt0Wy7nyl+rVtBUJW+1uzVXDqpWgjkshZp1pKlWz7DkLFxlLEV263N1GeeLE75FhJJyTZ8UQlBQdRWuZc/FNavkjJPGrj4W0IjlwcljwEKDlOTKNkCIiCWM4so+nKAVLTX/OFOyK43LUgmtgFWSPCytW3NV+xXIwy4JbFbVia61rsrFwG4uq7kaV1hmLQOWiZZppEMxV5NirqAtIyxfSFRd76mKZAgihBiICDE0aNOQmkBKZ53A0mrpqy1WFq541arijg0IlFJ2CW1yJg09Y0oMZmQTQjehIZPm2xRqNqtz82gMNBr8GC0zUgUnk63UIgInGv14glZCXM2PSxAjzDraEJg0wlqf2Jz3bG3P2RkHehXGGLExEUUIref95mzQ94iqW2AvVeLBdZ5kt+XOefTzIhO3hh5AgpP9WEa0+P7EQhFX8opWtXqjYIGyMyf3PcUyTZigbajf1QKGpnHibhx2Vd1GcVWtiZ/vtoWQsbJDGoFUCChRIUxaz4cuBQmCWEaiIrkgJbs+vGlYa0d2AvSjkVQJoUXT3K3ECcuD78pSAUNrLnYl+IdK3la1PyX5ta3qymdVtGkpNvp1I9GVrTlV9fPohCYDlIDZQJXKU9Ttzd1We8BEyIOQLJMbsGmHdhOCGWJKkUCrQgdEJqxHY3sxsj0MLLKxnY3GCr0ooyVyCNiQSGLEtkVQzCKZXT97VIU89H4dZ+/TlVcHyVjIeIlCdLv32HlRSa4W91TlbwFLTqqLOnGMLW2h8WsHo+QEpkgTdvscpXgRCrVwRNzSvZCwpiXUcbfEpR1zHZNLhjRg0lJycqcCAxDUjKiF2E4IoUHIfmkb6Jic+6+qcDS6bTWhKp3rH0ueRRwaKANaRkRbXz+FkkffF6l29cvfxegFEGlArGBq6DKHe4UVLiG854H3sMiLi92Mp4WDeoRvm/5xGvGs16erPVJRJkz4n6b/C29tv5wfn/87jpeHGBmf+sdXGkT4UNfx69MJXzz/vH7yXAl/LsB6VeBlex/ivcdupCDMppGv/pJrOXp4xuwReYWfHu/nez/4N8izo0ybdb7r5d/FJEw4Oj1anX1WWGGFFZ4Yp4ZTl10R1ZNB5CxteB9gZLuBUq7BmALNcgkgUOwmxK7mGyevRKcP8YfWfo/Te6/hBx/8eT69OMZ2ujydRS44RPhU2/Ke2ZQ/enbz0QqmS/geCiCSuKc5ycdfei8l+vPWBw91nJgKLznbM9n8LOt3/Cj9oVdy5+ww+ycHmcXZhdn4CiuscMVgLCM/8ukfudjNuOhopeHtk9fzuvbl/H66i79z9oe5rzx8sZt10ZFE+D8OHmB/KZfVuyjAOEm8/6uOc3ZPecx3d77qAeABWMC67uGN07fTSseabqzeQVe4ZHDlzV7bkryFpRmqiKvOBHFVWymPmIC0cwPGbs6oVGLJiUApVeZWCmihSgt3nY2tKm/dNdmcpXDhGrokbGPASnEFKyAS3KY1RHJx+10hOMFTlYcmblErparW3AzZidGSKbiKzsztW6Vxld+yXaJaraQ933JppVvEnFAk1/Z6lqsZUFI9ZgU3DTpHjqsGikFJBcN/4xbO2W378lhJ9mpaLNG1zLWNIbsCuAmBEGM9RMtcYifhrNpNmyoaXOkoVoiqTLs1Jht7CBtTzpw5S9ncouBqzyxgQaoAvHGiSf2YS7X7LSlTTMnDSOoTfUosFJjMaDQQKBQrhKKEboqFlqwdWZSshkig5BEthsWGUjIGlFycMxPBQt3nlCv56qptNCBBCdNA2wSmQ2JtbcrO2hpbOzucnfdsjiP9QiljprEegpNSrjQuSOoJChK9AADx/ma5uA2uqu/jmFETj60W8RxkXfbpTBkTxUJVt4vbaJtnCqfs2bABt3augnmChkq01lzs5bWGIsWLCkzUiVKJWDcFegoNNka0jU4gVoWqjGm3XwbMQ2KTFzu0KDNL7JSBXhqKRjROcDltnfDXiRN1y8xeoRZ+1Mxkl8GDDbU4w+07iC2SFu4IIGu7DuyYwThSUu+q0PkAUbCxByIWDCnqIvv1FsvZSdcgiA1oNpQZQSMxRFopBG1qXwZTZdZF9s0mDDmzSCP9fMFOMrYWPTvJ2CkwF2UYE0MbyWauvpeR3BdvM8XV32hV3y7tjLMXI1CPb80jN6t26I+4DkWik6RlrISvq+ddJVyLTDT6ORG8Lwcfcwzq8VQ8p3dKTqN3FG0hLQhxtzqmKuWr0je5ets0QO7Jya3rxZS2m9DFlqYNxNARqs3+7rOUukNDDfT28U4CIVDLZwwJDT4Q+BiETigleb8A78hNwHIPy2IQ8wIWSq4Z3IYWqwUlnbd5hcsG3r+f3QP4hVjHCo/G10++hbg7Gf3MsabrvFReyd/c+D7eO7yHk3acX+7fxbZtXYBWPn+wFZSfWl/j9fMF7cVuzNNAGzI3rp/mzq0DvPYVh7j5+se+UO9Y4sPDKRhOAfAdv/wd7G338s6b3snR2VG+9vqvBfzZ50q/llfj4QpPF1dCn/kPn/oPbI2Xxz1jX3iAwEh+kvtn1A8hcqy+Lx8H/R2KXYfZPnK5GWOdcxMdHWaHyXaIG+Vl3Lz5MP/qwM28e2PgXSd/mfc88J4vxG5dFvih/Xv5ls0t1nZfFC99GLA9Wyc15+78Z9rAh5sJH9434egi8VUPHKO7+xf47a3P8qarv4S9V72RYe8LAB5z3V7q1/JzjSthPFzhwuNK6Ddmxn3b913sZlwyWNcZtzcv5fv2fQ8/O/9V/uP8v9eZ1CsXl+u7aNbA1vrep1xuq5zl3ds/TSBya/dyJjLllva23e+X1++lfi0/17gSxsNLDVccASw1w3dZsVmoBEmpObb6CNtSqUbLUsndSkgtyQGrJIZIJVNxy2itCt9dUkkqSVeVoUJBdl+2zNW6S6tnraRqFR6LgC4Vr5VAc5VehOxE7dKeVHRJwLn1rlZVrZntZlgavo6AYRKdbKmknVujFqJW9W5sEIwRIxejiLmzcqn5qGVEK2meJGEleR1xDJSxkLNhWmr+rVEsIyaurhPPKjYq8VwyQQKdZVoxNEa3rxVxArEMWDKIEQuKtR2CoaqoQauRjQP76A4doHQt7ViI7RnyXLAiWIw8Kre5GNTcYqvkU0kDuRhpyIwIY+zIk47JxhodULbnSNMxCcLa/nUma1NGMRYlY6FBGT03OdfE1RgoyRW4IWdijMQm1lOrkJIfv8qLulA5IFFRhKZrmXYdG7OWPcPAmZ2ek2e3mc/n5JydYA7BSUzxbORSQFJGoyuLy5hI/YKS3e5XgtuRu3051Zq89jtxgr0I0C+QGD2v2Py6ybl4DrJlTKXmn46oKYTyCLI0ez5uMSe3m+Bty9mdt0WJMRLESCWTm4jE6Ar23VxudbvxEChBKWbIOCDZaFBmkzU2shPfQymUoKi50tRZd9tVxIqy28dEBFN9RE2H+oUpsqtIt3bmn2VDtF64RfxamW24HXROkBLEDusXgGKhYNKgQ8ZIkArSdYSoxCbQdlO62NCp0ES3z5YQan8QCg2l7UilsNYvGAQGE/o9M+aLnkXK9BmGxcAcZbuMLEJHT6Goq+UlBC/qwAgIJWdoGlfjpkzRWMchrbnctVggFbRaoZecUHV75wKYLXN4+2qRLE6U12xcEy8WMDxD3UpCmqlnixuEZgqlr7b01ZrczLOF6/C4HOysGNZEypAQ7QhNwyRGurZlEiNNzcRWDLGlDXeoY181i9bo5zqqjw+hcTv5EFGZEChuy68RGxK5eE65ph6NLQPGkBO51Pz22qVk2UfE3DK8FsmscPngQjwcXsoPmNvjNicWJy52M542/JHpwhzX5Xre0r0dM+PN7dv4tf7d/M74fh4q91+QbTwf8PNrM47mzF84efpiN+W8EcTY13g255ktj6UI4an7zZnhDP/mjn9Dqy2/dedP8Xevfwdy+DUApNlVEC6nqYcLh+f7eLjChceV0GfsaXtQXBy8IK7zx/Yoi/x+Pj285XGXiSyI0u8WSy7/DnIvcC9BP0MuN5DKq3j0lJAvWOwqWMCXJeX1Gy/iwDjjN7d/lwfmDzxn+3W5YEeEv3PwAH/15En2lMujzywmMz708jc/9ovaMR6cRH7h6DoP3vspfn58mBvmd7DnwdM8/JDyofWr+IN7X09AyGtX786hXMm4EsbDFS48Vv3myoSIcEu8nj+7/of55ulX8EPbP8HvjJ/gWDl1sZt20XA5voue2XMAexpy4kziE/2HEJQ7ho8C8ILmxRyKR1GUA+Hwc9XUywKr8fALjyuOAGZX/VuqDeiSoMRJJgom5vmV5p9hlTBEK1nhiteScrVbBhN/ZSw1h1eqQliWArcl+bhU+GoAc7Weq96ciHIlblVQVrvqsKwQqrbFVhXHogZSKunlW5fgFrjFBKSg4ipXq+pVkKqOk3Okr1Y5ssiu9amKEMmoNEQN5JRIlhlHP1aeeSq1LfmcOtolr25nHSJ5GJzYMXNyshIornQuuwo7o6DiBE9oxBXIIlWdDYwZip773IoTixpRDTRrEzb2HWC25yBJA2l9TtpYJ22fdRLHAA1oaCAPnr+qwY9hVeJagjSfM6bMaDA2DXFjL7M9M2IeWAwjIQXatQk2jQxdgy0SecxkFM0ZUnZFsTV+jNKI5UxTMp11NCLEoCg1I3U3k9qLCaySsUtVZmwauknLJCfW1kbWpi2nzzZsnt1hpx8Zsqs6LSdX6wpIzp6/zIihlOwZzRKoxHoDZCfqQqyRqbVAYKkqTcUVleYKcarddC7mxJlCDg3kERtHLBoSzM9/Sq5mD4HYLO10MyDYmCGaZw5rQ46uwC0EJCUnwIszt6GdUHDLYS2RkhIiRhRl0k1ZK4U8n1MK3i5x9b2cYxV5VOXFrqZcnMAN0Ysvhr7yoLGOAZ4Ra1LQXVY7Qyiu3i/AdM1JbgQxxVLvYtkokAdEMxoikjNSCt2+/axtrDOZus13jNEzk1W9EMLEyeSSSHkk0xENIoW2CE0pTDRSUoG2JbUtfRpZZGF77JkT6C3Tp0wyYzRcyS9go6u2jeAksKmr+q0gKuRcqov8BCmj54h7PQoBheAFKlmCq4LV1b2UgqUETYflXMfO6kJQxnPW+OIOBFa/spIoy8IP8VMl4g4BJXbV9llpJTBRmLSBSRPoQiCE4MpqK4hM8KukDqtaav51gdj6+c0LYMRzwRVpIlLJ97br3DDcCrGZ0DURS5nNzW22tk4zDIlS889zMUq9V1ic+j0hF0hXdvXmCpcW7ty8k19/8NcvdjOeNhLpMdWbgjufLFF27f3PHyLCQTnMN06/jbe0b+eHd/4x9+W7yavCDUyEX55N+frNbW4dx+fMceupG8J5n9bt1PLhU1cD8OGPn2R91vDGVx85700NZeDLpWPPqU/CqU9iwLjvVkqccubqN1EqESwIrbarF8oVVljhksW3rt3IHm3oeJANfZjNcphzg6kRGLml+032hMfJe68QmRP0k0BktFfVgvfHTm1KKmychr8Sv51PHfoK3nPi1/l3Oz9Lz/PHKvvpwkT42Y013rG9zVvmi8viHvr7t7yK+XTtiRcQ4YFZQ3nRS3nZ2YN8eu9VfHp3Oyf44Omf5rYzI/unt7prEtBf/XqsXQeJj7BlWmGFFa5k/NL9v8Q8zy92My5JBAlcF6/i7+z9Lj4yfop3L36Ln5y/m/4KjC26HN9FP/yKNzlv87Q3UdgumwB8pP8A9BCI3NC8ABCua27icPR33EBYvYOu8JzhiiOAS3HlmStLrdoeez7rkqiskkcnOqySpAJKBo1u6SuGBsFyAVMyNQa0KmUVJyJkyTibVivm+n6VM0i1da7KN1cTOwFrBSqL5/bU4urcXT6rEmquGl6St/6nlIxirmg2qeq8wb8z8c/q8fD9D+cmX0UrYWyuAlQowSgRNHtbGT2314r7/5bi+xlCpOSEBc+FleKq5GJGSk5CSckIAW2bXdWeBEAjmo1GIZiiOaGNQDPxTNlkCJkQpqhEokSCedRtiIHJZI191x6lu+VmShGGj20z9FvsnGgY+x6N6grmqJADNmZXEKJQVZhFs6tc8wjdjG5txv7DB5iuzxjOnGXQbUyULJHNUzuE0wNNE8mTjpwKYRgp/UAZBkpMZA2MuWCLHTpV1oowKUYMgTa6FXCoObVuTV4LBKQqhIuhAVQjUZU2KJMYWe86zrYdp7d32Fwk5v1AKgWTqgiOHWXsXU1ZhDyOu7nLlBErbjlNLrUwIQHiKlIRLCU/p2O9BlSxNJLnO+RxJK6vUTTC4JkNBpRxRNKS7PProgyuhNbQwLKfM1J6I0RXq1IKxYwguEIzCCYNRvbjUYyCIUHQzu3OQ/b9msaGHEfSmNi2Qim1sGCpMKWcu0YsYzl5ZqvL6j37NzRI13kfy/kRitziNuGieGCygnbV5t2qAtggNuikoQyVGBYnztWUkCuBO1tnuu8gk4112iYQRYltQ2jb2tZQs7ydeJUyEkpB2w6lMO70SFK6IsikgWmDtA2mgQwMeWTRj/SLnvmYmM975gXmJTEXpacwGCQrpDSSbalBL2glr9GIZ/0qZt4fqgmBF1uUggqudBepSvHshPcy7zlnoOaM1/xrP15VZWwFWy4urrqXMlLMKuEcGGXExoFJFCZtZNK0TJuGtmloNBBDcBdxbfxP7SNmY91GdWioym+J65AHIFKG0TORI5TRlfntpKVtIu36hOnaDBsGH8PyXlQXnlmcsxfAJCEVI1kh55Fiueacr7DCCs8GPzX/v3lrdyvZDrI/uGXY4Xgne8K5nKR7x5fTl0dPXBYip/K1nM9b2+FwlO9Z/5t8dPwQ//f8h5nbzgXdh8sRdzcNf/aqw/zgw8d48XCRJh6exvvtrzx0M+URP/jkZ89w2y372LNRs6PN+Mhw+knX8fHxDO+wa2nEnUDa03eQzfjh3/857nh4A4B93T7+xEv+BJsHPsOrDt9C6fY/3b1aYYUVLkOcGc5cFraVN8U13j65CoBOd3jJ5D3c2b/W3x2BiW5ybfNxwnkQtMYehj0vZTxykLKnIz64Rbz37OMOzVKMW/ujvHDtnXzr7Kv5Z8N/4r3jh3l4fuVmGv7tgwcui3vo1mwPn7n5ZedF0qoI1+y96vO2I3xmo2O4d50Dnzq5+/FN9/4njmwsmF/7VixMAEgbN2DNKjt4hRWuVHzs1McYy6VNaH4uPcBv9b/HG7tXXrQ2vKJ5ES+NN/NHZu/gn23/KO/tP8xp27xo7bkYuLtp+EtHDvFf7nvg4hHAT+M+uj3duGCbzSTuHO8A4O7x06gEIpFXTF6H4kVWVzfX08izj8laYYUlrjgC2K1PnSBQrVm0nCNYbekZK24nalUJ7OOC26yisZJL1eK2/jaXVCsiEyZOeGBSVbl1vbii1ampqqQzcaUxYLlmTuKE0FKKt6tErha3ThaeG69kaSqt4LnAo5NhWm1Vq6pRDDR4JqYhlU32bEsnf6RyKLKMLQYzjIjlofKTVtWmLt9bciCiSggTSho8W1fcftZSqkbSSrGEqkDqMVNEA5JdFRxEiRlXgjaRpfBZJIL1qAQnfMUIktEsaGiIBvv37aW7+SbkqqvRIRNjRyMBbSIy9K4krufAFITRKS4rmAl5HCg5U0SQYMz2zGj272Hvkb2oRvJ8ToiRHFv67QEWhWa2hllBmwjFyPMFqR/JqWDzBSUEJ95SoY3K0Gdm2Zi0DdmMVoVIIPvRRYsSAoArJp3YwvNlCUQrhBhoJsI0RNbWJ5zZ7jm7uc32YqDv52RchW5U4t+y22YPC1dxZkDG3XzaUjKaascR79MUL1QoQ8bUidky9qTRc52LGWkxOq+nTqA72bZUvQvWj1gqaNTq0htdwS0KY08ZM9IE7+9ixK6l6TpX05v6tku1KM5O6IpGIEFOBFWaGOlCw2wcyRhzKxQRghUvNliql6NiGmsGtqvBLQRvjx9uLwww8eOgBcbRizFKRrTBlsfGUu2XgqnnLpccIPVIbCGPaB4JGgjNjCYo3d49tHvWiLMJUcyLF7qO0AY/z9lHAyOTh4Qkb3cMBmPGRiN2M+Ja5wRwN3Hb7hAolskpM6aRsR8Yd3qG2YwBYz4mdsbE1mLBVspsDT07uTBgJDJFCkUn5LzwQpQy1qxqV5TnUkCMGFsnvauVO2JOpguITjDLlfxVdg9oKRR11wTzi5iSslsnpx0vQIjRFbvBs3hLSuQ00mqgnUzoYqSLgSY2xBCIGokaXAfYtL6pUqtApFuK2GuhgdME2jZeNIJg40AeE9kKZTCGsqBkSF1HYU7JQgyCTlqa6gKQc8aCEdpMUwrFIA2Dk8E5keXpVwCusMIKj8ZUB17c/RrGXvboQ487P3lT+zuP+Sxb4Gy+ike+uT2YbuVsPkJ+nEShiUx5bfsmjoSWD6Uf59cW93HWLu0JiucaDzSRn1xf5y+dPHXJvhCYwZ1bB7hnZy+PPNfHTy74b798D9/yB24mRuV/zB/gB87+/pOu68e27+JI6PiTGy/a/SyI8BfWb+IXP/kyPvfAIUTgQ5+4g619n+OmL7qXk4dP8t/6Y/yxl/4vDKLsbfeuKrNXWOF5iHu27uEDxz5wsZvxlEhmnCgD+0rLTCON9Nw6+Y2nvR5jwvzA1zDceAMEf55N12xQ1luae88iW8PjzosGCeyTDf7a+v/Mh9Md/I3+n3CsnCJdge4aDzSRP3/kEP/qgYe5Ol+a+2/APde+gBMHjz6r9XRbwuxEIJdz7z53HZ/SMOfg3T+/+1lau2aXDO4Pvoy0cb3/Xy/Vp4wVVljhSsOvDb/D74yf4DXNS/mz69/Ghjy6aOWe/BD/due/8sar3sg7b/pG9ExPfGjrnMngBUKUyJFwgL+15zv5yPgp/tvi1/ip+S+Rr6CM4Pti4Ec31vnWza1L912UC3MffSJkMtkyIwPvn//K7ueHh6M00vGqyeuJ0hAl0siVGV20woXBpXqNPbeoI3cxV/KaeY4lLO2gXZUoUoN0qyLYCFUR7GpDsYLE4ERdUYrUxcWVxiJSLZ2dHJGq2i2V0FWp6kmqw7TgqsRHEMNOZITa8EoS1/UISjFXG6Kh5vIKu17LVnYteiQ0aCmYVmsnCa7sM9xyWAwJ6gppExJCxsipUEomjYm0VIaGUAXI2XNOzVA5115VcbtGc7K2qBClqaRTSzEotV2WFoTYoiUQGAmhw4pQhoTF1i1nU4Y0uBXtWDBJTt4FRUqmaadsvPAGuOY66BqIoE1LtgRNA7m4DbK4SlHM7b9tTJChWKIUJeeFWxtLw/TwBu1sA+kLEo3YtDRTV2QOO6OrasuIDUIKgZSNPCYkFyw00FBDmCF3gXmuZHM2SlJQpRg0ORFRcskEEloUDYqa2xWrQFHPrNamQ0Voo9I0kW5twvrawJ5Zy5mzc85sT5jPt0lkUhdJ4whibuMrig09SSPQYCGBNk6M1RzrJYG2JPnECqUfKZLJ/YLcz5EgroxNCYJiwcjittXenwKSBicESyZZQiy4PXap8vfg5QcljeRFVd5adgtn8+2bCDZaVbv7sfOyhVizpo0YA81syiwKaWuLMScSSslOO0rbVWttQ1Dnd6uFuhS3B/UaC8Es1oKI7MchRKRkiBP/nQhiCQ/B3sHCBOs911o1Vevn0VXbsSNIQyPQdi3twQPEPRtI21ZFdFXAi+zuswEMmbK9IJ3eIqeMNBFLRjuZMD10gGatdZWwqF+DoXUlahoZFz2JhlKtj0cKown9MGdnPme+PWd7E7b6kc0Cm+PAXBr6PlEyFBm8H0SlBIE0OrEvUGyoY6SgKFayW3Pr1O3rl5nP7bTa1mcsD26PnAulabw6oGYnW5zA0iY/BkrODGPGSqEVoQ3CpJnStg2tKo3ixLkaUdXt0UW8zwapxSZLi+86kNY8abL35dApcbbmqvcCZTTGYeHjmgnbGYZ+i0asOu2bF21YIWXDVNEmEiwTaGjESEFJtiIBVljh2eCaMOUfHridDZ0jsnhavw2S2R8fneu7L9zP2XKYh9MtHEtuq/T5uCG8hle2B/lj6/+Dv3jqfdyVtp/NLlz2+LE96/yhzU1eOKZnvpKnYZ/1dJFMedcDtzLPj62Ank0ipzbXuevUOj+z5/3M7fEn4F/V7ucVzT4ArgrTx3x/Mp7h4Zf9DH37Kt66vo/XvPguAFSM63aMlwO/97vfx4dt5A+98A9R2j2M+2+9ULu4wgorrPCEeOnRF9JWi/rPHr+be/ttvv3Yb/Blk6P8nf2vYvYUxFqaHiFtXE/cuo+48yAAeXKQYf9LGK6+8dGqUBXKvgn9rKG74wSy9cQqYkmFV9kL+c8H/yH/due/8kPbP/Hsd/YyxD0x8u61Gd9+9lkot57De2gOkfff/vZnvZ44Ck3/6MLXbMoiPbr/xe1zz2Xx7J0gwnDwFd7nDrwUa57EhnqFFVZY4QuEbZvza8Pv8N6TH0I+bwA2PLLuL932vZRuStnnAox4/+ZzplR9RfMibosv5Ige4BcWv8mn8z3P0ZYuLfSq/P2D+3nDYnHJvoteqPvo08Wx7M9sD2zdDcChcJRrmxsBuLm9dUUGr/C0ccURwGayS44KNT+3DvlV2FcJsWqtbE4Ul5JRMUKMTr0qWKlZmVYQKzXP1YAWM7c6FjlnpUoG01Kz7KrD8zIkGEFKqctmX5dZtYf2jGBFCAKFqmIGILga2arqs4DfsvA8zmrPLDjR62T0UsnnjLXiimKpR8JQJ/NSxorbIqeSyTlRyrLsSRGJqCQnx7XmHVNIFpzYCk5SR0YI5kTzOJBHo+TkBE2IpKEnSoMy0rQtQRTGTFkMWA5OmklE47mXDokukA4qhLZFrzqItI2fQPUM1NP3n2Dc3KLkRFlkLDaUUlC0EvOG5UyRSg6qkjGGrGyd3GaPtGg3oxVICmEywawQx0LAiFpAI2kxoMkIk4BOp5SudWI/Z1QgYqQCYy6MAm0QEtSs4IyZn0MrgkhGGnF7XhHUQHIiaPRjrkJoOyQkoghtDLRRmbQtk0nP1llhZ2eHuRVSO2PsFyQyKh2iBbHBCXZpnMSlqUSfE3dSq82EiFYl8djPXZGbM6HpiF1E1BDLkBXUMFw9TkowJqzvMVGCOQdXMKR45nORUAlnJx2ViIwZG8Zz1tRVlSvFUKtFFiKYKjlHZByIKjRNoMiEqcGgc7a3d/x6aGeeySu1r2tBsRrlXZW8EnbV8Utle1H1DF+JXjzgn3oxxdh7vnHTOSHcKvTeZisGRVFLaJwSCDStEg8eJO7dQNumEubR/8TgFsl5d3TAxpG8syCPrkqXpMRJS7d/jen+DWLbeG71LoEcyH1P7hPj2GDrGVoniEdLjCkx9A1rQRlEWKiyMFdKb+7M2R4Sm9tzNoHNPNJbIRWwGL0/9AtMFIuNE/oaKDE40U2AWNXQZfCCijQgTYshlCKu7I8dZRwwKUho3G46uPW1JSglMRYj5+Jkb2zo2oamCTQihBAIIRJD4xbhGp1Ax88t5uSsVCWuEGrxjGIi3p9MyIMrrTUqTafoVGlTw7gY6cdE7uekHCG4Jj+ESGwCqkbfG0MqlewHbRpCFD/H45WtHlzh0oGZ8a8/+a8vdjOeNl4Q13lZu++CrU/E2BseZkOP08oOx9NN9LZBHgv3fuIs801/sdx/9SFedN2X8337lb9x+re5M209IXn4fEcB/uGB/fzdYyfY90xt7Z/DWpgPnLiORX7s60oTleuvvY6ff98XcWK7Y37t/Rw48OFz35eON9/1TtQCR8KEQ9oRtPDFr/sY4BbgC8vcNW7xN0//Hh8fz7Dvuk/wqWYfN9sXcSh0dde8LPM17T5eA3DvL2HasvPAr/NzIfBVN/wBLM5Wk9orrLDCBUUQ5fYbXsHtN7ycoD53cMvhG/nPH/ofZIzfG0+xsMzsSaZz0vQw2y98J6Xbiwyb6OgFTxanlG7vE2+8DaRDM5rt4UkVTyJCJPLts6/jLe0X8U+3fpRPpM9y1q6gwioR/sn+vRzKma/dfobxEs/hPfTDL38TffvYwqenixJgsVbotn1mKTVGao0PHe44MG4zLY/tKO62ZnTH/d7cHf8Qpi3z695GiTPK5EB1yFthhRWeD/jM2c/wnvvfc7Gb8bTwRGrbP/LCP8yeZo//R4R03R7ygSntnafQ7edmDiiI8ifWvpE/MPli3jt8mP+489+5K9//1D+8zHEpv4uOoeG3b3/bBbmPPlMsmZ9j+QGO5QcAuHP4JFEaXj15EyqBiUyY6Cp+YYUnx5VHAJfEUnK3fN7UatFsVXWJuM0t2Wp+pUIR52pycZK4Knxtl78RV5YVc+WgVoKnUqp169V21gldzHwZrdbSlbDFwCq5IZWY9IoW2VXiuY11bQNSiWjbtWd2pTFuMUvdD8NJkUoJW7Vrrc13Eg/PxVWEGGMlqDzytIgiUjyTuLj9dYkB58eyk6qoqzmt5r8KlBgRadA0VEvWzNhnauxx3ZeR2EyJTSRMIgFBCzAWmAQkNkgTkDZ6vmgTMQ1OxoQA5dFdeXt7Tj8kxq1FVQ2PlK5UxbWgVemcx0xplkrsQhYlS6GfJ9KiENuITCP9VmEsybOKm+BKxsVIEc/0bfasw/oEa2LtS2Ape06oNpgZQ8qEUnOONSDZkOJWzIq4BbOqqylLRtBKRFGtvakW4MWVkCgSFTUI08AkTtk7mbC5eYZTOwPznJhTnOQ2wUTrYSpISdV4OhExPLO59p8guDS6Wj7nTEkjghAaJwc1LPvq0kbb1dSkBDlXlWihmJBLqTmygBXKWIneZORxJDQN1kZSsVqw4MUUkqu9NsGtkMEtupsGyR2aRoIqMRhdN2VWhH5IJAFR76O7/u21UGN5DD2f1uoxDywtmN3zuquS/ABkz85GoJ1CU+W6aY4NI9Z0yLjwYgY1RFs0KBHxgoQ961jX4ppZYRTBghdyaDG0FCRnJBfG7TkpF3RjRojV3nxtSrt3L2HaEWKDxkBoInE6I6pC31G2M2HWYSGxwCjaMIxz+sVZhigMOTOOia5pmMXIMCzYmE5YbO2wg7GdMpt9z6khsYkwT4UxtlWlX2AYEG2xcXRlsKpbeueCM/zBO6hkbPQCA0Sw2Pno0kywxTbZ2XfP87ZCzplsXlQSJdBpoIuBtpnSqNC0HU0zpdGABEVDQFS9MII6zroMGFHXh8vyGqnfSfBxEMyLWUbD0kjsOpq2Rc1zo4cMqSSK+DrUMiX5v9uoWC6k7G0WcRJYQ3A7+xVWuETwYFX2XC4ICF81veYpl/OnENn93/lcdSqFG5oPcTTewfF0E/c+POXU/ft3v39gc+TEPXt40Q1v4kdum/LjO3fy9898jGV6/BUFEX51OuFn12f8kbNbz+U89NPGmaHjjs2Dj3vWr7lqxvGtV3F6a40A3H7v13D7vV/zqGWWFf0GHKuf/ff3vopbb3iQV7/4Ln5o81P8q61Pn9teGbk2zrgnbXModCQr/PLiIb5scrQWHvn6pAyszY/zzQAf/9fktatJ69cz7r2FtH7NakJ7hRVWeNZ4zQ0v53U3vvJRlvMbEy80uUon/MCB17JfH1/9YUCeHmb7Bd+4S/Rau0Fuzz+/Ll+1hgz5vBRPrTS8pLmZH9z3V3jf+BHuGD/Hzy1+jTvz/ZQr4K66UOWn1tf40p05M7vAHqHPEAZsru/jsze+xOedniXmG4VPv37BwXsjYjDfU9jeV/wN+q7zW0dYeH7w+h0/CqL0h1+NxRmLo6+nZgs963ausMIKFw/zNOfMcOZiN+NZ4+j0KG+75m27xVeARyuutwy3HiQc2yHed/aCW0Lvbj8c4p2TL+NrJ1/MT87fzY/t/A8eKse5NO4uzwHqu+hfPnyQf/Tw8UvqPvr+29/OR1/6uke7pVwCOFNOAfDu7Z8G4EA4zOFwNdc0N3AwHGEZc7rCCo/EFUcAS52wLznju+8kldsX2+4gbojb8orngyX/kSvigGXepVvX1s8UJ46LoZaw0LBbDGnFcztFQKITs5KdjEKcUDS3RXX1sWJ5dCWglN0Xr4Jn+iIBpeYUV4Zrd4Jslwj2NuZcMFuSzHUfxUk/X2dGLCKSd0lkFYHgubMg5CZSitvnFissJ2Fl+axuVFV1zYW1WHn2hIboqks877dIQEPLOPZOqNChY6IpgyuBa2asSkBydpIlCsTOiZ46kGlw9fa0m9BsrD/qPG/PtymNwCRi/QIbMmVcYG1VZZt5u9Wne0v2fmCWkBAIquQhcfb0DvnUJmdPnSGnnlaVqIaFgPUjcdrQ7l1D9q6R25ZsVZVY3D47ZafUs3r2MGK7FtlmnqMbLVOCOnFmhuUCJRMsodWe3MwoVDKzHvhYiucih4a1aWTf/hkpj5w+qXRnt9nOhdOLESOQZU7KRgkBi4rkHi0Zy77/gWoDbt4XliR2TiNp8MzeuDaFyRRi640IbuurueDWyhkbMyUnLLudeh4WoBOk2jab4dbUjL5PKSFNQxlByYiCUpDG1y+14KIGU1cL3+yqXW0IZDQGomW6Rpm0E3byWJXu7BZpWC2EMARTL/LwzOuIpAUWOi+AWF5ooVq319qLkqvKPtSHwDjx42QKjUIZkFIQiwSD2DXE6Toqig0DY4xoFlIcCaUjhEjIGRYLdByQVMjzEVMhzlrCpCO0DaGbeB52cNtwZx+dDJ3MWtpZx97rDzE9cDW2OMvW6bMQ18mbp7jvwc9wduwJKHE2JYSA5oIWiP02XQhM96yzV4TFYs7BLGwuek7NF5yxzHYpjKKUds3HS22AAhKxPALZXRA0wphxiwNIlhEJiIy75Kv3Jx9LSImCuAsA4sRv09Kp0k5mtG1L00RC0xGkXjOCK3vFVfBi5iR0HYtUxS35ATGBduK235ZQWZ774C0xo/QjY4YYhKaNhAxjElIqrjK3hEghhobQKB0B6QtDMoq6NXexTHiunvpXWOF5jpkE/srel/ENs+uedDkTZX7NFzMcuA0xmN73HuLWPbsqpieDCHSyw7XtxzlyvfKyqx5rIfzJu6/GTPiWtRt4++Qq7sk7/MvNT/HB/gTp+fua/ViI8H/t28s7tnbY/0wrry8wzOCu7f0c79cf9/s+H+XO+4/s/v/z7dueCKc213n/x1/IRz97HT/z4g9ALegOCH9640X8zxsvpK0xJQX4pfmD/MjWXfxv+17OLc2jyZPlFuP2A8TtB2iPf5i0fh39Va8jrV9/yU0WrLDCCpcPbjxw7WMm71oCb5oc4s9tvPgJ3TNK6Mhr17Jzw1c+ucr3qVAVT9In4on5ef5EeGP7St7YvpJvmL6N3gb+6dZ/5F39e595Oy4TvG864aNdy+sX/cVuyi7e/aXv5OSBqy7MyuocxIkbHm3RaQa/cmSNtz+8zSyf33OTaxAKk4c/iCF0xz5E7vayuPrNfu98JOmywgorrPAFxuuOvI5b9t7yuN9ZF0nXbmDTSPPZU8h5jntPFyLClI7/afYO/sDki3nX4r38s60fZeB56kAncsndR89u7OezN730snifO5mPcTIf47PD7xOl4Zb2pewPhzgQD9NKd7Gbt8IlgiuPAKa48tSctHXy11DVSv5WkpSlVbRV1RmUSh67elagJIIEzIWCVKMbzw9GsVKwmjFMkcpjOQlilaxwktRVhkuey+1oqyV1ZZbFcrV0dvKXknYJQSd3nSguApKL5+PWfS0GFCdblpnGZflgvcwLplQ+WKtNr1RSFLI5zUxtu5jvY5FqRV1Gf0GttsqhQGwCZjCOGQsRTb0T4KZuBUyhbVtyGskG0ZS2catXNDhRX/cFC0iMaBuRpiFUBa1WYfNk2tCsrdVjYSzO7rB18gxpTGRxW18jwyhYP1CCELp21/YaXOFXTDANoEpsA6rKYnvOIiW2z24RybST1lXfCdrZlG7vGrqxRprOyCLknJwENWXUhlHFiddqrY1Cmk4Y25at+Zw8jmRLdAiqTnh7Dq33kSg40Srm5Htxgr1QXA0rToQ3k8jawX2M2wuGuStSJ8XoJtnVkjGyMyaGlMkIQTtKHliSeIgTtn6aq1UzxTNSi+esarVLtqVltrmFuoi68tecuBYRJDaUnR1yP6KhcdW4geVEGkfPZC6eiSypoK2hXUQ1+PmuinhfJjmJK07cGurnKUAY3VA9i9CESNtG+nnCHYu19olciymAGClWp4mXCnqdsCSw/YUzYFKWJQ5+XJYZ2waiCqiTwsUgRkQbZExoToQ4YTJbo9u7TpRIGDImI4OMiCjajmiMSEowX7iNdBoAIa5P3ZJb1QtIRNyCPmVKEdTUFe9pRHcKTCboS28iXPcCyFvs2+wRnWFnjrH1iw8y39yixEAJLZIKthixIRFCRNfXCKVgFCaTltlY2Ls+Y+98h80hcWo+sDlktlOiL5lRvE2G5zuXMpIsUGSsLPnottqWQNuaH+zji4uv3V0A3O5ayHRxwiQGui7Sxo7YNER1BbXW8+NFM+LHTpVQM7FFoveFYljp0dh6/ymGaoOTvlrt+BVqnrpp404QVkhJiFFoukgQZbAFqdr5mxmlFEJ1Q5Bq1T+ipJwoY6KEVaX6Cis8HQgwk8hf2PtS3rl2w5MuayiLo2+kv+oNPo4A2y/4BsLWfbQnP0F37Hcf4bDy5GhioYmPfZm8/SV31X8pR+OUo3HKa9oD/OLiAU7lgX9y9pNs2XgF6JfglCr/aWOdP3Xm7MVuCgDZhN8+8fgFAjEKt7/8CL/8O8/shdxM2Jp3vPiBt3DsBf8JgAPa8scfQf4CtKL8fw+8mvf3x1l8nkX4wjKpOos0onQS0NzTnvkMzdm7GPfdSmnWmF/zFtD2spg8WGGFKxVmxpCfOO/2UoCa8ZatwncdfMMTlruYBObXfwXDwZddoI0K+ap1wqmFOys9DexTL5j5Sxt/gpHMb/S/y4JL+xg/G2QR/v2ejUtm4vrOG1/C6X2HnvsNiXDvWssvHBVu2Rx46dmep/N2JBgybqLjJvFTP8G470X0R24nzY548fFKFbzCCpcFLof76PngrUffynfe9p1PvpAI+eAMi0o4tkM4vvOcOigd0L182/RrGGzkX27/BInnZ2xRFuGvHT7IP37oOC8fLm5fOru+l59/+zezMzt/55RLASMDow18pP8AAFfFa5nIlJd1r6HTCZFmpQy+gnHFEcCIQLbdeRgrVi2ICypa7WHFySypdssiEASq2tfpUldfEqoFM9nVm+BkWNOAJUquihPLnmcbQKQqbIsTwmKgUpWJ5vbTUhJOlvj0pqiTbi7eTbvZxWbmarj6W0Eoop53qYFiVskXXw7zHFXL2cnfoKi5HfRuZqpAKYUxJ8Y0klMml1xJ87KrcjZJTgZmc4UiUq1/K29txUnrmk9sS1WzLFOJjdAEpAitCE0j7rhdDGnUj7l2SMpo8RffKIKqEKygWTwXODZYTk4wpsyxu+5nvnmWcbGgDMWtotfXYOyxhWGLgZIHRJQyjpguCb/MmAvJCp2ABaOUkTwmZEzEIGg2FKGZNrRrM3R9Rp5MGFVJVsgmmEZyTgxBGAmMQ6oKcz8mqW0Zph0J2EyZyQgzy0zwXQ7mFsGNCNFctxhi44UKVmon8CxoP5JKLoFhGDGBrutAjCYbsU2oQlzrOdUP7PS9tye5DXlJiULZtRJXy9USXMjZGFMmp+QW1TW3tuTRCxpqLYItFe3Jae4QG8+FLa72lWGOhqYSgUYuiXEYoUDTdoRpi04btO08B9vAxsGv1aaqgHvvN6VeAyV4UYaUiIyjK6FbmJRInxsWlmpBgxczWOwoVKm6gUnjilJ8J6xkQF2xa5UMFq3FHgFshNAAEaTF8tzJ5apuFRE0OnEd20C3f4Ppgf3eRglQCrn4g5rm5OstxfOS01jttBMxRXIfaMyIJmjO6JiJsSGGEQ0Qxky/M2dhQjudwifu4qpRCWkO03W6I/uAFjY67Fjw/c/KaEYOETogjwiFqBMoIw2BpiTaPNK2kbVhYGM6st0PbM57NhdzttLItiUWZoxjImHVEcGJf8MoNtYRKwH5nD2ANl4c0LTIsEXQCbPpXiaN0nYz2hD92geaoG53LeLjkfq44N2yppSHxnPXLWMCJTSuY5fgcwVWty0BNNbChuz22dJQFgtGS5TY+nciTGYtTaP0/ciYC9mij4tWEBNi0yJa0DFBUZIJ6UpghVZY4QLh2jDjbZMj/L/2vJg1efLHT0OYX/tW+qsea7mU169lvnYUa9ZoTt1BnD90QdsZxK2pzYyvm13Hv976DPenHd69ePB5nRNsIvxe9/h2ol9o9Dnw8w+8iDPj5HG/f8MXHeFFN24y5Dt4+NSeR3330Mm9nN586jxeQbjx1Mv42M6v0ayf4e8feDXdE0xbv7579CT6sbzgr576XT4ynAbgzd1h3rl2PV8ycaWVWKY99QkM6I7/HvNr3sy490WUyX5WWGGFSxM/+LEfvNhNeEKIGW84MeflpxdPTP5qy/aNX824/yUXdNtlreEJN3oe2NAZf3vPd/KZdA9/7ewPcm++sPfsSwkPxsj9IXBNvrjPCkPT8qkXvIKxeXLVj5rxgq2B+6YN8/jsiNYHpw0PTSIHh8zRRXrqHzwOBKM9fQfNmc+AKIurXse471by7MhT/3iFFVa46LiU76Png1mc8dXXfzVrzVO/RwCUvRPKRoe1gXByjj7Dse98ICJ8++wdTKTlB7b+w/OWBH4oRh6IgZdf5FqCX3nz13Fy/+V/73ko3QfAveNdBAm8cfp2jjZP7sC2wvMXVx4BjBOIlUNzJeMyU7eSuLuBqyY1CzjvZtCVUoghIFUdJsEVZ7vEbXDr5GLmqjUqYUcllQvVutT8t5Zd6SriamSKWwFjzgaWAlbJ3+LWwphh1T6akhENaCVYd8lZII9jbQOuIs252lyXc/uYExZaJ6QUrO6rkzmFXIxkRkkDZbl9SiVM6/ZDVQLnTDEn+Ny519terFBKwqoSFo1u/1oV1yrFSV3UOZvGFb+40NJJ42GEiSHV9nhX5VwKw5CY33+MyXQf25s7HP/c5xjmC7etbSIapGaCdm4POwyUhWE2YMEzajNCSol+TIwC7ZgIOaE5I8PAtAk0IrQI7awldg3STUghMKIMpZCKkQvk4nmhqbgzbjbBhoTgFsLDaNA0jEVY2MhWKbQk2lJoLdEAbWhYW1qMp4xqIAap6nCrfcLVoSKBnKHfHp34aiNRJjCOfgxL64UKqkgIzLUnl0xJHZJHGAYs5WonvlSGJ1LO1T6c2v9GcgpggoonRtO0XoRQlbpIQMax/iaRh4LEQCRACORsDP1Ino/ELhAm0XNacyHZ0sq3KtRzRqgkXFQsVVJWnHAs5hmvmgSxEZFAbBs6hHkZyUVR9TzHUollJxKcgMWc3PMxIPi1Ykvlr3o/iy00EZMppISFPYTZAVicJW0+6NnSaQQrxBCJbUe7vk5YW4OgXnwhwVXMxbdly6zvmrEMZVddnOYLxpSJXUsYMxIjopHYtsSgNTt4m1CM2E1oxsT83vs52480asTJjLVjW8jphzl5Yovt+chgMMTAaJmshVLESWmredPBc52jBM8xNs94jk3DpInM2sBGG9jemXN2GDmbjc0COyEylEQZM6aGBSdTc05ImmPBrbNDjF7okTMSE6FdYzqZsDaZEiTSNg1NjFge0BBolnm/4IUutSBnSf6KhlqII4TqOjAOCdNCVvH2U63iVc8dXw2Eacva3g1kscbWyRMMZk7sF0GmHV3jxQAiyjh64QZN6zbUqgRNPiyZschLZfMKK6zwZDiiE7557Qa+cnr1Yyx0Px8GpPXrWFzzxaT1a59Y+SGBxdVvYjhwG9qfpj35cZozn0bT+dlUng9EhDWJfNeeF2NmfMtwisEKHxpO8cObn2J4HuqCPzTp+GDXcXt/cRVMv/LwC7hj8xCPxzpcdXjKbbfsIyi88pZ7HvP9qc0Zd91/mA9/+ga2593jrmOJQ7PMv7jq1YTJNi9pz98qtRXlT62/CIB/vfUZ3tcf5zf747yq3cf/tu8V3BB94kgAysDs3veQjn2EU93L0etejk5mK0XwCitcYriUlUuzVHjJmcWTKit3rnubk7+X4NjSSsNLmxfw9/b8v/nfzv4gD+Tjz0sby9/vWj7WtVyzc+GeRZ4Jdm55K3fdcOsTL2DGWjZefnrBK08vuG8a+dUja2xHn194pjA5X1+WJ4eYO6JNH3gv7YmPkfbczOLoG9zFqZldgC2ssMIKzwUu5fvo+eDG9Rt569G3Pr0fqZBu2Es+NKP95HG0f+6I2SCBb5l+JQvr+b+2//Pz8j4K8O/37uFtO3MeG9703MMQPvXCl3P80NWX5PPUM0UmkS3xvvmvcGi4itdOv5hOH7/QeoXnL644AvjchH21Na5WzW7lKk4EkarVcnxUri+W0dCQrdrlgmeoig8USEGKEw5FwIqiWjNxAaMgpjXD95yyjEpCidUMXl3m9y5tqDPFQt2mnLPqVTmXiauedbpULrvvs+2qkiWDs6m17ZJ3LaixjIQGydmJMna/wBnN0V2JH5njiT5qSi2LE72UdC5vtRhmriBNZfkZYAv/kUTPCgWCOhEsBdQU8uAP+dWSGcRVoKVAO63qYEUq+XPm+Ck27W62treZH3+YvJhjZm61W4ARhORNn7SwGEhbO1gQikLKRkrGMCZ6E9rtHVTcarorQhsj7XRCE8UteGOkRGVsW3qBfkgkw0lTE1IRV09ntznG3M66xI5WG/YePIBK5PSDD5P6SEpzTs/nSBJag5m48lRKRmxwLXiMrgiNjc+JV9VutoJYZkyjL199v8MyI7WJ1drYCXPRwBAiYyeYTdBslD5Rhp4yDjD2UIIr2bOvW6WBIZMlYRFUnFx1TrVDpdpUD2MlUd32POOEu4QAGP1iwTjW3NYgaAiYKDlnbExotfct4P7eam7bawnTqsaXCGNyZXKMhND6hIjgRJ4YFlvSYuHXozkJbPYIEtg809bSQAnRxwFb9vkBbWdo13j/NCGGgEVh36Ej3HbjbdiJB/n9OxdsLrYYEFcsG7R7OroDB5G2pZi5JbHixRMU0KZat2cn5bOr8M3tACi5kMvImI0wFLeDFtC2IYRAEHXFczFityBs7xAXxulTWwT1PiKzB2GxRTp9imG+s0vkp3Ek5+W45zb1lGoRLuYq+lKccA5L0tUIMdA0genGGmvZ2NMPnJkPnOpHTmfYGncozZScxmpJ3lB0xFJGmxa1hGhB2j1EBmaTdaaTCUGEIEoTA8ESyYygUtW9eXlCq8NB4/1fFY2Ni4pVaZqAjRmLXqxiVigSCQISldC4cr6MiVIg9ZlhzEynkcnGGmVrDjGgFGxMSBOYdB0iidgoQoACoyU/ZjHQqhtKi/qYtMIKKzwxXtse4B8deC179anthpz8vZbtF3zjeU/wlW4vpdtL2rgBXZygPfkx4s5DxLN3XVArLhHh1d0BAF7fHeRw6PjZnfv47eHEBdzKxcfpEDgZdLdI8gsNA44t1vjc9r4nbMFXvuVa9mw8sVJ5/8YO+279HC97wb38j/e9krsfPPh56/Kn3IfWP8dNX/SrvGzP1cD5k78Ae7XlDRNXBb+uO8gHhhP89M69/Mz2vXz/mY/zAwdf99h92z7FT/+EcNP1v8mNr7uew7ddh52nwmCFFVa4crE+Zr7ywS2aJ2HW0vQIac/Nl/xk5a3xRn78wD/gz57+e7x//OjFbs5zgl+fTXn7zvyCTLI90Sl/vLO8u+zaIe7c9Hva2piZFONEey7a4MWbA7NUeNXpBW3xsufr5olv+9wZPrpvwk4QPrZ3QnrkRs6nX5lxcMisX2B7pDCcQY9/iPb4h8nTwwyHXkF/+DWXfF9fYYUrEW+66k3c89nHFmdeLvgzL/szz/i3No0MLz5EfHib8ODWc/YeFSTwHbNvoJWWf7z17y9I0c2lhjubyFIT94WEAZ+++TZ+5c1fRwmXWRb9eb689zbnvnQX/c6Cm9oXcXNz68oS+grCFUcA51yqXXLBipOtaJ3MF89CXV48QnKVK+qkqmhV0OJKYREnc0oBwq542Ja2sparTWmg6o0rMZYxDdWSOYFGTKwqkas218B2SRCpSluB7OSai9usEqhWyS3btbIVBELEspM4lbP1rOKlFTS+3yZWlZ6ZsVq5mhkpZ1LJntcqeddKGnK1cPZ15FI8+9b8OGTLroosgmjxz83I44JSTYsREOuR2EBOIIqG6O1JI8S2km/4elpF2gihwYKgTQNRoREswvbZ05TR6MeetH0GoaBRq2JQMXUCx3Ne8Z7fRGwcSPOeEmJVPPv5Mm0ZB0PIxEnDdNIQ1zq065yQL8aYM2MxRlmqfRPJhJwSo8EwGrl4f1Fz6+h9sxkHDu7hyJED7FnfYHP/lHvvvYeH7tlmGEZKzljbsP/oHgLKzrETBINYCqEklEoCVzUkuD1xym4lLgJSUlUJu9IzRpgs851zRkthMxsLUegaNCiSDBtg2DzFuAOWRsKIW5lLQNXQSBWyJwrLQgZBxpGi2Uk08euhUq5YKfTJnFQ2o98ZsPmCZtISoqstc1FEhTKMSAhoyfWcGRCdBmyin5tijAajGKYQxVX4Ig1mhSxK1kiygUFCVaFbFdz69bS0Ii+hPlMU8UzY4DsoYYqgBCKUQpgFZO8aYsLrvvUr+aqvfAc7n/l9uh8xPnPfvdx37z0MO4ZOJ8i+PaQ2Ls8MoWZxmwgWo48vBL/JWvEiBqkiV7wQRfDjn0qp6jdBhp4QA4rC4Op53Z4j3QSdZ+LZebW0Duj0DFJ6ZLFJyQu/zkuhYFhQLI2usC8ZiriyeVncIaB5xCQQTDGi28W3E4IVGitMGmU2mbC2tcX0bOaYRDYzmBiKF3FIM0EakEYQOsQKgcykiUybQKz7GtuWgLspuGsCIEoxL3SgabAQsaCu1G2cmLCSCU1DaCMStOalF3IaSdnQtvX+aMWJc208E9yMnc05rE2IkwnNmMkCsZlgKSFNQxMV04GhZNrZhCDK4vQ2Q1W4a6M0QZF+rIU2K6xw8fEzn/sZHth54GI341F4Q3eQf7D/NewL52crbM3G0yJ/HwURyvQQi2u/FElzws7DrN3139Bx6+mv6yk3JXzT2g28fXKU7z39e3x4OMmJcnlXvD8S/2L/Xr50Z86zMYM2hHz4tlp8BuHEp5CnUGeXdoN85GXwlr/Nm08nPvBffozPf/O/9rZX0L3+NaSHfuMp26DAl73tOMdO9/zmR28h7BxDLHP04BnydR/h32/9JrdZy+uHKS9u9tA8w5xBFeH13SFuGq/hK3g9h6ywGGDSPtoGLobCa1/yWX7j917MXfec5o2n7mR2+0vYv3b97nFaYYUVVgB40eGbOLS2H8z48oe2OfIUiqK8dpTSPb1ClvOGCum6PTSfO/OsVyU1BurPrf8/+Atnvp8Hy/OriArgF9c3+Cv9hLh5/3ktb6J+v3yce5C168zf+te9IL6i+9iPEe//7ccsmw/dxuLVfwJix6HU8fX5bqan7yAY9HpucnfPOCGYwWTvYwxEb6uPMrfksHv3/eCBKWc/zx66WOFMOcn+PhHrgi87s+C6nZG1fOHfjbz1Rpw/TLjnl9H+DP2hV1KmX4CM4xVWWOG8ICK87Zq38eOf/fGL3ZRnhC+9+kt5yd6XPHMyTASbNYw37qVMIs3dZ1yY8xxARPjW6VeSLfNfF7/Cnfm+52Q7VxIMuOOWV/Ibr//qy4/8hadduX08P8jJ+TEWZYeXdK/0eNMVnve44ghgzG2JKVYHd3mEWna5jDkpWz9mSXwulbxVRXjOSFp39cQUI4i4ulfqT0txS1NbkrwGOdefuqUupSp3rX6vytJEpxR82yKuHNRz5Kt4gqsrY6nrKzXb1Ko6eNnO8oh9rJ8ZAtlAiyvkUiGTsWKklMgp13XV9YhieJart7m6VJeClZEikZQy5IRI8MNdzEkwrf8HJI+YuUW2mUIZCE1bibHlC6Lb/fppKlgQrAm+TbFK+BSGxUg5s0naGhjGgbKzgwalmCKp7Kq5TUdMlTwOiEGYdM5/jZkkoxvFqtAGJWglmUJg0kWaaYM2TkS5XTGMqZBSJgV1lWXKToxaodTvc0quJAYsCmsHJ+y7ap1rrtnH0aPXcd/xjs89dB8784EyJDJGHgvHd0a2RZAhkcT89DcdBD/nUrOB/fQ6GVuseN/IBV0eQ20QUYIUOjP2TDowI+z0bJswoFho2Hd4g421vTx89z2cevgYadG73fLOHJGFH78QydpgeQAy0k4IgKTkimCp9uLmBHCRgOUFWSYsFgOSC9b3qIEGxVByPyDZnGguI0LwjOyupfpXu5IcyJWgXwyZoZivI0Q0BFd4joW+CIvkpPNizIjWeodSrceLXyMlGaYdFgRKzewW9WsyNAiClqrsn2f6vIN2LZ8+ucUrHzrJTj9w4uaDnFmcZfvYlIRia1NyM2FnKGgeCKEhRCNY9u2L7BZoGJ7XrZRqS1Cti634Pkvwc16y99/s7Qk2oMVqQYG6gngxEPqRECA0LdESWkZIC8i9r1txNfLS2WA5EKgiqlDGqvoeIY/+dz1PYbbu56QUNA2E2NCMiYYZrRhdEzg2JE72W6QQvJhDa6GMOBUec2HWdkynnU+yW0K0I4aIYK6QTiMiG+6Ub3XcSIlYbaCVTA61jmUcoW1oRdDg9vUlZbJl0pihbT0DPSUKriwmKoyZse/ZEphMGrRpsHH08USEUgwmE2ToKUNiGEa62BCnkbSTyGmAtkNjJBq06flD+qxweePMcIZkz13u0NPFa9oD/L39r2F/ePL8uUeiP/QKLD57az+LU9KeG9l6wR9k/TM/habtZ73Ox8P+0PIDB1/LB/sT/Nz8fn5i+3OXfRW2IJyc7WfnS/4USQLN536FePevP2a5sn4V/av/5BOvSAP9bd8C0a2lmjt+Ft05/qTbLnuuZ3zBl9MA1xyFb/irf+txlzNg88ALz3eX2At8zZcY7cmE5B7Y4JfnDbc/vOC9D76X7zjxPv7g7Dq+de1GXtw8Ik/YqIVV54cj6wOH1uD377qGfnLqMQSwCNx09XE+fue1nNpc512/uMaph/853/XFX8/k6i9eqZlWWGGFXUwaf0a+cXvgQH+R7+0i5H0TwsPbyDxdEFXTi5ub+D/3fjd/8cz381A5eQHWeGkgSuQrbv6DLN78ldh97z+v31hoGW77Fi90PQ/Mv+SvP+Uy6wAchYOvJ/PoCb+d89jGI6eB3/A43ycbuXO4g1vSwCT7fbLNHyOUh5/aQtpsd47rmUAoTB7+AO3Jj7F989eTNm5c3T9XWGGFZ4VOO77uxq9jEi+AJa4I+eg61ijx2A56evGcqIGjRL597et4x/St/H/O/FN+Z/wE+XkST2TNjPGFX0P36Xd94bYZWnbe8pd5+XTfM/r9Z4ZPsFXOPuky5RI7P4XMR/sPslU2eWH7Eg6Ewys18PMcVxwB7BmqVEtaV/65rayeU8FlQ6U+mFqulrFW7ZSdtDFn4pzw0uj5p5UnLaq7pE4QoRiUnKsl77mHXkE9D9RKzeWtF5t7IjtBKm6DihkeUVw3UpuU678rs8WSNGNpIQ24SjQ/gvstnLOXxldU8q6FdEmZjJCTWzpTFcFL/2xD3LJXnFQu5vR3QclppKTk5DOgZFdDp0pKjxmTmn8s5p+Lk19CqTmbWq2x3RLbCdXoWcc1y5ZSMInkMTHMF4RZDwmsX6CWCUFceZ08szOnAZJVAjqgk8aPlgBlQRkTFoSmCTSTCW2jkAsxCmE6IbQN0jaUpoVBsGHAxkTRnhIjObuC0Sw7KZwrqYXnz4oYlMCxB87Q74DZBqe3hYcfepgTD55x4rqS5akkTpzcJpRCXAzMFYaS6RH2dh05RrqSaQpEFVcDq0/cFjNMat+Uqmwv3o+a4va50hotLbNkzFWY5+z98PAM3VmHM6fRoG4dHQT6iJSENS1FIgtrWIwjUoyGQDQIJRExQllmtQZsmEM2hEQpgvY9zZiIXeNK0bFmaKcCkpAYdtXnOSeggdRjKEWqsrofmY+JxVgIk4asAVVhiJHtlNkswnYWxqCkyRoigsmSlB9dFa912FOp6mHYzYllORbghRXiltQpCFaET3zoU/y77QCnT3D87rvZOXuWRTMjxwk5RvoS0FSgKDIMaGyIVlCNrvbPBSy5gUDN+BapVtDL3KSSMPViBK1W88XwLGgxQjEaFc+E1uwK836bEIRAJuQ5ASWUkUAhiqGmqIBkH0sChlohNLEWpCiWE6UYZchVdR+hdYJWciZohBDRlNBqgxyA2DQ023NimHI6FYYgXqyBF9moKN10jUnb0IqrrAvQBiVWxX9Jg1ueByWXREkDaD3vOSPRydliwYs2zAiqWNNVJfOOu9Uj5JIYUkGlHr/FgMZAqLamOWWGs1vkNKGNSh4GLGdCDIQEjIFUPBM8bWXGtiGIW60XjDKaF6ZYQdvzJ7dWWOFKwq3NBoeeBvkLXPBJvLx2DVsv/EamD/xmtYR+bl66bu8O8op2H4e0493zB7gjbT4n23musHe6wf7ZXvbqfr5o/2v4ppu/idLuoxehv+1bkPQ408USsen+897GeOvXXcAWPwOIMBx8+e5/3wy85tovYyef27cNaTjzCAWW9qeYPPg+AOL2A+jjHYfPgwrcdvMTq772beywMVtwanOdaVrnrvtu4m/83j/jr3Z72X/wlc9gx1ZYYYXnG1SUA7N9ABxZJNqn4MpMlDx5bpWQNm0YXnyI9pPHkfmFIaRfEm/i+/Z+D/9y+z/xvuEjjFw6RWxPF+txnVcfejV/5rY/w5HpEQgt/eHbLnaznjNEaXhR9zLooK+fDQde7u+6T4H21B3Es589t67t+9GncAh5PGias/bZn2L75m8g7X3B0/79CiussMISTWh41cFXXdB1loMzhn0T2k+fJJxaXNB1PxIHdC//YO9385H0Kf7W2R/ieDn9nG3ruYYgvPbwa9nX7iOH6+DzCOCydhXpyMuf4Nefty4rxLt/HSke2WbNGuN1b3zC5YcXfwM37X3T47pxnA9ubG55UoLXMD6y+ACDPXFfOJmPs7DzKdO6sLhrvIMH0z28ZfaVHIxHvuDbX+ELhyuOAHa2BJYMmSwVs5VorZo8Jx6r3bNbKIeq6hUKlZzMnmO6JHupCsxSstsXV9tVDcEtkHOpJOc58lfKkvhxReCuKjmXSigXt2mVJWlcJ0er3bKo/9vUVXLnVMZLqW3NNrXl26MrISkj7BJe/ncQo4lOzDKOTtSKE64l56Vz9K6SuJRUCeSqYqyH1mSpSBby0v5WCpL9WIZKqBcxJPgx0KCE6ASliqGYqxBpXbGt8ZzdajE88LRA6LB+RPqBbtaSJZOjkgS0uMpZSkZq/rE0sZKCTvFpG9AhIv1IEYgx0rUNMTaUNEeseB6zKhoUbRqkiS6aRgiDZ50WtNp/KzlDTgUtuSpIDJGIoez0I7NkjCVz6uw2i9M70CdKSu7OXc9dGgbUXOk7jsaiGJt5wZFiHOga1mJkUgpdI7tZqip1P7Wef9G6/ZrZG6nHtmHSNuxppvTjwNlhh62zPac/+TDzM1vebbrOFc/LayALsjZDmobtMbM9d0KuBdSEmAY6Cm0prny15MerFJqUyKJEDTRTIcbgttNBkSZUy2jvE7n+Ow+jW0CrUspAAooJqRT6lNkZPBM4TjoQ2OkzfTJGbRgmApOGWPOvXfmba52EIepW40rN51a3a3fb9YIub/qPyj5STCP9fIfPPfQAtn2WNCy8CKJtnWgMkazqJHz2vNhghknjqxJ1Qr0q56v3vBeZaKTkkWy4RbWAFPPrGXnEdVdN1HMhJH+YEVGEwfN/F8NuMUDAieIuGCo+VgQMFaEpmShCq0ocemIRpBhZoEw6NFcb8eUxEIFUUHGnAlMlBJh0E0TVc4S1EIeRM6Ls4IUWQZRGI7PpGm2MhNiSxoXn93YtipCkwchI07HrbVCl24KhKgR1u34zt2EW0VrzIqhGtJ0ipceK21cP40CMrjY2ccV3zk5cA5RhoMewaQc5k4cRiQ3tNCA6YMXIRehTYizQqFHGkULAxB/aQgjEeOXdQldY4bKBCHn9WrZu+SYm9/8Gk4feV4vtLjxaCfyZPbfy9bNr+Z6Tv8PHx2dvlflco9GG/ZN9fO1tX8qXHPxyro7Xsx72PHqhdg1rn585tZM4eUyl/yN5ltyssX3LdQCErXvRcRsdt5jcf85+WizvTiycL25/6Z3cdvN9/PYnXsCrzryaf7v9Lj6yOM6XPOM9WWGFFZ5PaEPDbVffct7Lmzb0hy/sxPXjbmcSGW49SHvHCfQCkMAiwkuam/n+vd/Dbwwf4kzZ4p9u/0dOl83LQsXUaMN33Pod3LB+AxvNBrcfvv1iN+miwmIHPHXhX3/k1fRHXr37/7h5z6MKzcLOw3THfve8t9ud+MiKAF5hhRUuTQRleMF+wpme5q7T7k75HGBNp7yxfSV/a8938jfO/rPL6j46DVMA/viL/ziHp4d545E30oYWznyOrbWrHrV82biGfPSLzm/lJdPc9cuQ3bHP2nXSDW99zhwjWn3q+9/rZ0/+tnciPcyOPblz2ScWH2LHHj/iyqww8vTeS5dY2Jzf3PkljjbX8UWTNxDl/FxJVri8cMXNXluualdxgkwQL9lfEizBKvfglrqCogUkOkHrimBXC2todn9rpUAI1Vh5SQov7WdrPmsoqIaqRjSiLC18i/9eoYb7ImIYCilX0trVtqJhtw3gqj1nXZd2z6XaQEtV9Vb75908VACD2LjqcEkUiiAqblVcMto0BBVSNtKSoBPx9laRsQuNaxas6ZLudUIqCKVmZlrJrg+O0e1ml+ropV1x2zgRbriar20gBN/XNIIUJECIjROZutwXJ3ejeC5pXGsYLJBwgkhycXWpeQ4w0TNwCWAmlGxYaJA2o6mBYU6go2lagqrbI5uQx4HSNOiSwG86QjcSBOJOIg4JbQLJhDFlxmyeKWu7dD1mhpTRiaipMGihESMcWMOOTSknqrVyVWebGcmqYlGElIX5IrOVF5wdE4cmLfu6lv8/e38eLVt2lXeivznX2jsizjm3z75Rk+oli0YNCAMWxgiDAZexwRQGl4sq27jKVR4PqjFuAD/cVPPKNrbHK4/xXA8/nu0azy6XKYZMZ8BgW7IBCSEhCSShPjtl3rztaSL23mvN+f6YK+KmkBJJmec2qYxPypF5z43Ye8Xea+84Eb/1fd+Oj/Sa6DR6SdVrzDFRzAwl5pO3xQnJM908s3P2HLN776dc2ef8h3+Ly9PIlTJRaqEYDBRICZ3NYv5Oidm8R+ZzylA4qJWVO5ZTLIjoBa1GdSGVQlegV6HvHR8dH400U/Jil5SbMzSl5gRXXAQjg49xfQBGwcnU6kzVqOJMwJFV9t0ZqpEGR4pRXLDUQyeo93RWyO04OIaZYFbaGgJHNTfQGK5bb13D67nSlnm06PUMVMwdmUb80lUoK9SJ+Z08UuNTXINijqeEaopI57Z4I9ZgCKTMJpk9Vky0u0YAf2tx67FwQtpCEA03vq8XgHjELa97c53oB193hNNAtztZInbaakW8oqQWHy50uqJTWPQ9SWEyKKWy44UdSXitTO6bGPfgpxILHvodfJrAK6nL7OWzMBRkXMI0UEToNLFYLOj7OerRL44q3WxByjMwx3VCch/3HwWpTuq6lnofaQyR3LA+FgLJqXXCWtd37hNpdKwUSjWqJcasaM5QY/ENODm1xTeqTOOAY+TUUc2wYYXkiJ33OlHNKCWOufcdIjANK8p6wUpKVN92ZWy11afTB6d9LtSBc5+jC/i6SJTVPV8Ompk/8u+uSwzXWvflXf76mS/imx//N9dxL89cX3zbF/O1934tb7rvTSRVdN1Nv9WnVd27L7oS3RluuwZa8uGj9Bd/45Meq+NVuqsfecpt3XPbZQBecPd5ht17OKX/EW88/fJnfXz4VlttdTy65/Sd1xZ832LaOIE/cAE9enpfMv52qShfOXsN7s6b5m/g/3f00zxSz/Mzw7/n0MMZOqfn6+Zfwb4f8vPDLx/Lfp+J1u+hX/+8r7+2cHirp6Vy4v5P+vN0+iWs7v6yz2ELt+a1stVWW20FQJeo5xb4LJEeOyQ9cXTd7lqv717Fj5/7O/xvhz/G/+fox6/TXo5H6/fRr7nva4CAwU9+P7VTz8dOPf/p70AT0wNf80yHeUN1Lt/Buc/wmHvy8+ApPjUe2SHvH9/9lM91Nz42ffDTOpXvSPewl04iCOfLJ7i7u//TbGGrZ7ueewC4ddhKA7PaInLXUc9SHU25AbvWQQoR6dqAi+PNLTgBuUEcuwZjJG0cve4RO0wDn5sA6KBNDaxG96g2gBxESlAFk3D1BjON7cn6Y6FwrZp3beb1cA26KEYlSK0GbBMNIF1r/HjdVdy2aQ32ltYh7CJUc2pxfBraGBWRiN2ttXVmuoBPEXlsHqCpgdrWgoyZgYaD0HmSqzp5QJ6krVt2EY5bN7w4lIBasu4NLY7kaFGlViwZtrPAcnSZJqLvVt2jZ7iUa0U2RnNhpgBlm/hvichtg4TS5S5ihdUpZWAaJrq+oqkjWUBrSV3E72qi84JORhFlGifM2xvXxpkNiLdYa2G4tKKcXJIXpxiL0xXISRnpsDJFHHY1PGVoc67vehTl0rBif1xx8Wjg7t05ty9mnOg7FiQQocOQzQuWcLZa67ZFkCSknMn3nIbXPY/8xCVOXn0CLu/TjRNdziR3rk7CEqeqQtejmsPNiXO6T5T5jEtlYkqx3z7vMtNEbe5lGyfSMOBFYFpCigUA2ndtgUIsYhANR7o0+7h4xuuEVcfMsBxxv+OwZFRliXDV4Cgr1vURAd3NwimKBbxzQZlha4jqhpSCaEbDIx2QVxSz2qK6BW0LLwICd23BQ5vDnlAzqCOyPAKii9dbH7d5pAkIjvZ9HGvxBo8Fx2IxhqQGMp3NG7coIGg/Q8xJ7phXHG0LUerGQexJoxfY2wIQjzkYCz+I4wqRQODhNJ5qbKO2a13FYvuS8VLpVOjriIoxThPVE51NzBlAE1UTWEE1R+x0tfZFuCAl+sInmWG1oFmZs2APwXLPfLFD14ULt7pSSkVydOiaG9Uq5oa5kdu9ubBOCoj7phFjRRXzykRHdYc6sRwHXDsswSTGaioM44SliEFnPic3Z/n6PAhC9RoR9ykBBZ8migPjFMfQPeZGKe1+VskIUit1mpgc8mLetrvVVlv9dr19vMi7xkv83vmdtwZYFGV155eAV+af+GXE63Xb1f15h/907wH+8cFHKLcg1vvdd/5u/vwX/XnOzD77COetmkTa+3qonLj/U768lrJEV59dp6X1J3lTf+IWnCVbbbXVzdLL7nggEoqA3zw542VXB3brJ98lHp1nfvncgrMHI7/rkXGT+HQjtHECv/+JY3ECryUizOj5E7t/EHfnmxdfzdicLJnMK/ILWTHyH09fx/+4/yM86I/xxnveyM8+9LMA3JMWZJSH6uF19T1t30Ovs6QlZG211VZbfb5IBDsxw3b7AMHnj9Dx+D+Lrt9H/+TuN/MV/RfzNw9+lN8sT70o9WbodH+a7/ui7+MVZ16xfR99Gkry1AaUE+kUr1t8xVP+vbvzQP9y/NN88jyhp5jpMXRgb3VL6zkHgEUUq4aLNwazjk0GcEQS1mCuiuNpDVoaWKWxm1qjT9QqVJojtcUAr12YhFutuiOlhrvMPZyzqV24DUyIrGOeNRyXqs0t1zpcdQ19GmeWcBlLylDLBgAjElDKHXKPlCm6Tpsz9xqEDmewStu3KjaNDNPINBXcK16NWseAcQ5Z4xdyN2tAOly4HkHX4dy0iG+NMRopJcSdQh8wchOrre14rzuLBZE+jkfk9kJqcNkIGDw60nvA3XYgrFSmo4GyHNg5pWQUOxyQsSKjBczRjJcxgK83KFdrvI52JEwU6aMXOONoN0N1ZBwKwzQxs0q2cFc/OZpXUiJLuBarte7fWtCuaz3O7ebqhuQZfd+zmPecOrfDqdM9O7vC/tUzXLx0Hl+tsKoRO10NZQy3qAqzMzv0ObM6X7iwv+Sxw4kLq5Hnn1pw93zGmb5nb+64JjqP2FwRooPXAe2IgTqWlPHxffq3fgiWh+g4sTvryUDuM2m2oFsNXCqFfVGqCq4pXK4VdpMg8xlpaVyo4Al2ROhnmVGEZZ+hGN0wJy2X1HFCp4k8m5G6jFVH+i7gL22Bg8Sc0E4QKjTwVs2ZSmUcw3V8NXWsuh7vOqTvSX3f5m8mVmUURFpvLa2f2mORhTSX+3rBRZz4El2xqogGZKS5y68pYtFFG+T1Ca8Fl7Z9ab3V0Nz72va/jkOv7X6Sr+138++166o51SUSCZJ5uGNFcM3Y2oWvEY1M25/n3FzeGTCqpoDZ0vrHbaK0hSEZkNzhtSCaKNVRB9fE5EKtTvVE9cogPUsPuOvTgEsiyYTW2E9KMwLNCtUE00TqexQn9XD61Olw8XYdZoVhNTCVGo5/dawaY61xbErcQYoZR+MSIZzTJXeoGVoSWQ2xiPBmOIwOboGDYSTnjFRjGlaMy4FxnBAVxlJYlUpWJXnci0Ci69gdMyWxpJ/NcJxaJmSlDCluu3WaoptbEqZxrF0cryNVO3wcqWn7BcWzSb6+xm7yNp4r+jtX38fvnd/5mR94o6SJ1d1fjnV7zC68l3T48HX5wnwmie89+QruTgt+/OihWyoO+nW3v46//Jq/zF63d7OH8ox0K1/LnhfUvXuPfbvHrVv5GG51a2o7Z66/zuyc5Mzuqc2fD7Lyob2eV18ZNh8dHptnfvauPZZZefhjV3nX//UR/uM3GPkGhtL4LDG+7DbS44fkR/fXYUfHpnU89G/Xghlf2L2U/8ep7+G/v/rDG/j7mv4sf/vsazmpHT90+d382NGDxzugJhXle179Pc/6L6231/Iz1/YYbvV0tJ03z3GpUO47SblzL5I0Dsbr9Fm05wu6l/A/n/oefmz5r/knRz/JwHgd9vS56czsDH/1dX+VV5999bN+Dj8br2URuaU6fp+Nx/DZruccAI40ZIn4Y1pM7jqKWRLW+nLDRdgYZxBeIjvYWtWv0mzEDd42MBNZsAG0oP2suc/sSV3BOJ7ztc5fkfb0uoknFgnoIxoQOpy+67E21zASjtYGVANOtzUdEqAIqw3gNauweusTdZwUHbkpRdevQakGJTpW1/xSWlSuhM04jol7A7beIFe46yL+uGCSEfVYhSsB9hyBHNHZXku8LhLUKZyhmgKypBbFKmAp4wnMJ6wI1Uskb4uisx7rMqUd924xQ86DrkZkqtgwYalGXGwZcE2QWiR3c3MbtD7iRFJBzcg5kbqeo4MlR/sHLLqe2WIHmTskRQnIh9eIqp0Kph3uinkBr7itYX4CMrvznnO3nYF5z+WDI1aPXgRxjqaJilzrOH5S9G84gZXl4UDaEXISSjH2h5HDqXBYCld3C/cuCneac3LWM6uFLnfkFB2siCOdYDnHORbwqTBdvoROI5iTxJnlBHszVJy876RlQcaJfatMDeblJKjATnZ0NietBqbizOYJz5khph+7u7vcfiphF86zLANVl6Quk3KOqZvCsU0Nf5SogjpS2sKEpGiC2hZdTEU4KnAkivQzdDbbuNqRtrACQLo4ds3FHv9v4FbW15KEA9YMkYxqO0cim+25rWcGzXEvSMptYYdhKphJgPW2IEG1RcpLxAzL2oXfALNqwmoNV3+7fsUDWoenvcFna4sUUotyJmKlkRyLKNqCDaiIduHgTx1ep/h7hdTGYev4ZmnJBhIQVEphpkruelQ1nPKSQApJcnPGd3H9p8y6K8OskqSLe6gkkJ7czZv7OI75PHfkfg7iLVLcca/knGLBSjumcV6AVNF1L3eJa9FdqLXAqmxer7rh0lISrACOLIfoAXawMsR9CwVXbCpM09UA/Dia+rhXlqmt4lEkZdJqBHdqMSRHF7DYBGWgViAlBqukpFCGMFlbiX3Z8Tkftrr+Oo5fDre/YH72WnrlE3XF3Xlxs4dyTSKMt38R05mXkvcfYv7Yr5AOH11nsxzjboQ/tvdCft/iLv7chbffEhB4lmZ83f1f96yHv7C9lo9D22O41eeq7Zy5/nrB2fs5u3MNACPCr5zboapw17Lw66fnPLrIDJ+0APEmZAiI4PNMuf8kvsjkTxwgh9MNcSGLCPfnu/ibp/4b/trVf8ChH/HB6UEOvXBWZvyZEy/hfdNVfvOY33dnacZ3v+K7OTf/TAGNt7621/Iz1/YYbvV09Pk8b9ydhw4futnDuPUlAn1ifNk5dH+k//Cl69INLCLclW7ju3e/lRfn+/kf93+Eq5+hX/Z6KUvmj7/0j/MH7v8D3LG445adw5+LPp+v5Rul7TG88XrOAWBvkZ2NrwV0BbDEk/KXAcdbF2iQFAJMakQRu2h0gLKO1w3QhDvirYtTtLluvQHetlMJ2OfuEfNrDRK7BoSx2p4XbkC3Bn3WUJAGb80CjEjAZV9vGw/XMOFARBsga12j66hq9+ZUzKk5geP5Yhbwt8HdqCAOMOdOc8+1196ceaLS3KbWQJJEfGwdcXJ00EoOx3CtAc+aI1k8nH2udu11FYMk4fxVwZPiXR/u5gazJSXoEvQZ6TI668lm5C5TdnqyVNwNnwq+mvCxYnWF9/01MI5Fh7E5kjNpFsciO3RRGMo4FqbVClYrdHcXOkVNg/8D1jqcwwFd43w2dyu5j+2LwUwps8Q4jBw8dhFJh7jA6urlBvwTSI2ealXWH+jFhXE1cmgwldJizJ3JChcnoRwIy6kwmHFXLZzImZ0e+s7pUiYJzWELYIgLyQwdB7QUcqmkhh+7nGExj65YHUkYaTQOijFROZJMzcocY56U27qeSQrWKSuV6FrNPXfcexd39crVaYWNA1Whm81IXYenhLvGNVUD5CWIYxCzCm1ufPcJE2FUYZUV7xKSA2JGZHO8Nl9fyG2xBdJi3ttcXwNXaB27Fu7zlCLO3KwtTmjOem/R6cLaTR37Etr8dInX4EbOPVnjuvM2F7Q5+sXiubnrWhe20DBxwE9rUejriGLRFk0aP193iYv2sX0Cnq83IKJo65qe2nNUouO4liE46RpApy5epzspKTnl6H4UsJwQz0hV3EtEz7X1LEhqUfNKyuG4NgpuMOsyKXdUq3gp5L4jq2JE724tBa+QUkdqcfbSutcdA0t4Xt8/QXK/iWD21l3tm8UqRI/weqFN7hpQdqgjKfUB6KshOaLGzSu1FgxFbUAs4rmt1Bbdv4rFF+vEhdJWYbpFykONRS7WYvPNazjFxfA6RrT9Vltt9Wn1aF3yY0cP8mdOvCTuibeQPO8wnXkp06kXsfPgz9FfeA+s4/aPUXemBX/z7Gv4peECbx8u8LPLRxmvazjlU+v5e8/nTfe+6abse6utttpqq99ZKspr7n/Vp/y8akDgW1Ii1Nt3qWcX9B+6hF5aHrsb+Kl0T7qd//XMX+TQlvy1/f8XxZ3ixv985TeOHf4C/MmX/0n+yAv/yPbLxq222mqrp9A/+sA/utlDePaoS9jZBWMS0hNHpPPXpxtYRXjT/MsA4a9c/fubaoUbqbOzs3znS76TTrsbvu+tttrqmp5zANg2vZvN72E1UI8kTAhHp7R4VTe8KpGpVMNdV9ttWdbOOmg0pjkZ47FBF2IbAVwNE0GtxnNSav3AFSzcb+otZla0uUB10+m5oY3u8VyLfUgDsrSO3I0bUgxNcYPduHwl4Kxo699sX3ZKisjcrJlZmqjNZSpeN7DY0HD4mVMd3AX30iBROH/RhFfHJWComGMeQAlbg5uKuwY4XYdZuVDwNeuCWghI4zAZ0uXN8STlTQyuJ4GcoTkz+zyDbKTFDIYOqRXdKfjo5DTHDgup9CCKDVNAW40IX2pBU4fOFkiOqN6UlL5TpuKUYcTHEZ0K0ndYi68ep8I0TkxToaZookVzi/9m4woXMqtlxR47wJIg2RAdA6gPA11W8ByH2MaYe7qOyQ63ZF0O1HFE3Zj1M6YyosDShcdGY9pfsqrGHfMZZ6qxax2zzph1OQDjOOG1+UxzJmkijSNiJTpxEVQzfS+onEA5JAv0Y+bSMHHFYDVOHBWhqrBQmC1mzGqHNxfmEUq/2OXOM7exayOr+YxZN6OWGjB6FvHNuNJJR111DKurATVVSdNElYR6RcyAxDQNLItTuh2k79GUN4591osfWoyyNGdnuNQdIVy+3ly33q4/yTOStmPb5rDjiBfC6U9cLw3WqkTct9WYx9UC0uY8Y9Zn+q6jlpHVODaXcbDkJKC5Q0QozeHdUp6bA11btLO0NIH1/GmLQiS85mCbKOuc+3hdmxX/glk4XwXILQ67lkguyCqkdi+IbvFMShETramDMpAa4FZJmMnmvrZeDgPhKpYUKQlSIOUckd4GWZQ069AkuEnEd9cperWTkOka4VWE2r48ye0+27X5ntYvp42VcBlri3xvLm1qCef15tYrSD9v/ecJS3Gnj9CEuD/E4SxEL7OiYu0xbTu1BlBev2JVpNvFc42/o+JJEJ3FPazWuG+k59xb6FZbfU76hwcf4pt37uPu/Dt/eV37U9hneMx1kSaO7v99rO58Pbsf/nHy8vyxbDYWy90DJO5ND/Mtu7t80869/OcnXsT3XvxVPlpu/Ars/+p3/Vc3fJ9bbbXVVls9B5SU8cVnkaOJ/rcuoMPxdxs+lXZ1wV85+V+w0Lfxm9M7+aXhiWPfx3279/GVd33lFv5utdVWW30e6uWnXv47dqteT9mpOXZihveJdHGJLq9Pwtzvm30JelL46/v/gH0/ui77eCr9l6/6L8my/d5sq61utp5zV2G4cAPKGgYW0MVkCkChGXNpDshwskktzdEZcFVVwkUoAXDELcDtOt5Z9BqsFYGUmrN1bcINZ7GTwgBIwFIXRWp8YIpo0ejqjd7SFOABjf5aEVQj5jXqQxvabh23IiXG5w1hacZqAQxNGpG4NKdh+yclpesync0oDeCoJsADeCC4e3O8hrs2dumbXmBD29gjQtedcNsJmAiUOAYRwStgU8BNr/iiC6eqJqjRBaxdQnJ0LZvIxtGJNvdv6zfeWeyxd+o0R12mu7yDHB2Ek9rn4egsNbbXp3AL9wkrhTIVbDU0x3SD2CkHlMNIOVEI4GelItOI2ry5VKGWieVUGWulevS1hrNQwyEahceIVMyNoayQ2qEVtOsa+MxoUrJnrExMxQOOW3NJUiO22SrUiUShywnReet2dQrCJQMbKoOtGKznjBmn1p3MMobzss0HB1yjw9ZEMFW0wVPNSlZlbgtUldQXUlqhy4HLk3EwVo68UpOzt7vDPCeSR/yCdB3zU3vcf9dd2OOPcwmNKOqU6LsA7N4Js509ztxzOwdPXODCbx1gxRAFNScLWNaIgy6FUitFEtp3cS0FncXcw/EphtdwdNp6oUCpSOrQvkNcmpvUm7PV0Rxdtm52rV9X4v4g4hHx7uEQR2eQYjGGmVM9rtEkidkss3dil1mfONw3VsMYMckph9c3g2qKCPLm0k+59f6u0wiaOV8I1/K6pTvcvGszuFJr3JNSIhIENG8cz7XE68i5Q0UZpxGXjj5BSokkKeLlU3Qha0oxD7w5jdsVK6JxX1n/pJ3XpAGMzWMBQe5nSMrRb+5OlwXJPdZc8NHFkEh5HWOd4kZFivuorP9p92S8da+3e5h2bbGIbxIPJMrT8ZRYR+CLN6f2OvlAlAiJXjuYPVIDoC0csTjPXd8W0Tiauk1SQjgA14uE2n0mdYgYZhKLgDb3QNscu6222urTa+WVHz34MN93+nf9jo8rJ5+PLW5SrKJmbH6Wwwf+EN3VD7N4+N8gxxDvXuprcfbI+m5yeg8zSbykO8nfPvs6/usLb+OheuM+fM/TnL28t/3yequtttpqq+sjFXyvZ3zpOfrfuogM5Ya5gWfS4/YKOn7jujiovvSOL+We3Xuuw5a32mqrrba62fqqe7/q5gJKFcr9p6i379J/4AKyKlEBd6y7UH7f/EupVP7a1X/AkuFYt/9UevXZV/PFt33x9jPoVlvdAnrOAWCrY8MDzaGL4ObR4yoKdWo9mYErxGsYelPEATsR6xpf/ns4C2kHUmhdvPFs2WDCACs07CtuqOSNA9khXGkNTAutH9XXG/VwAlv4Zl0IeLSOLbXmnttE4EasM86mG1UkoK83vBTmxsS6xzeAMA1+NX+uCnjF1tG2NNDsGv5DX5v1wploXjEb2fQgu2HVsWkKgNv6St0D+sbraHG2Hq9JvCJVoExoN0dmXcCqnOI1N/DjEsfUW1Tzbc+7i/kD9+CPPE534XG42sE0BGy2Flnd9XE6ug7JjkwSIHAa8NpAP+Cao7u0xmsWib5Xa+dC3UhJSUkQlFIqUylY7gIYSo55sIndDid5HSe0h72dGdpnJlMgR59qmUjJSLkylRrnlBbB2yJs4/Q6yY2Zpua+rAFfNebQgUEdjcEGhmLXIBZCn+ONX/HWgRxzxUhUonc4pXY6q5BmziwJWiualSRCOhoQr1wthWU1KBVyZpEzM1FmXcftZ07yvFe+mIPsnP9wx9hlbMxoSmhOeO5YnDqBGvjRGJHdqxU+BlzUpKTZDJUxnOa5Q0h0s0U4xdt8c5RqFlHRSdrrsRZrPgunb8qsY8ylHQtVQSX6rqvVcNCvJUoKy28YPzXcuSIJs7pxAKskur5nb2+X3d1dbJqw2q4TDVCtIiSJHmOzuPJy17Pp/cYQadHGsg6/DhezS0JTT04KPmIlYqVT7kg4dT2/qgUMrwGak4BVQ3TGTqco4XhOOeMW3eeSYv/SFoWYyyb6VIluX/N4rQAp9xEz7456AGVRoTpgQtcrKWWqG17jnirEvhXZ3Puk9SUjfQB2q+Ct91gFPLU0gYxJSxxY/6IobYEGsehmc9No0fTa7nHrP28anNf3UAGka/fbcBB7A9BxT2kOcM3rO38b02ZioBjW7o2q1gD8p3uX2WqrrZ6sD0z7N3sIn5VsfoZh9ho8Ldj52E+1OP6nL9VHqfZSqt2P6kdQCdfvi/Ief+/c6/mei2+/YU7gr7//63nRyRfdkH1ttdVWW2313JXvdAxfcCfpsQO6j125LkD20+6XM7wwfSFfMX8/P7185Abtdautttpqq2e7/sFv/gO+9r6vZZ7mN3UcPksMr76DdGFJ96GL12UR1ZtmX0Z/quMnlv+OXxzffvw7eJJUlDfd9ybOzM5c1/1stdVWn52ecwC4eoO+Xlp3LrjKBmSunWQB2wImKQo1HIphqFScujYiYrWgaR4wwWpDVIo/KUIWaECwYghVCkkzaDh2g+NGlG2SugELsu7+XTvcoEXfRjempMiZFbdw91VDmssPVayWcCgjSNKAPTEYUk4NMI6YCMUqYymMxcLJV6fovaT1FXu4lt1Lg7vNb6kaVZleW6eyI15xK3iBagWnw2tzWUduNQFmEtQVRftwATt4bcdNG9wSou81SXQTV4vYamsx3NVId51B7riDfrlitrMIw3bz8hkaq5JzwqlICkdxRHsXNCkwYa5YKdhc8NxTxhqgTcIzK2vXcZwYVBNd18W80ETVcDmLe3N0N/evyqY3OVVjnCZy6lHN7O6cIItQOpjqxIEVVgOtL5bmYhSqFWotsW2iI7dXwbQDt3BqS3SmDiiXi2Mejm9TbcA0M0vx2BZYvpmXayBfvYVtt3GnvkMs3KJqlSRG7gSWlSvDyOFUKVIovXCig7kYXSfI2RPoPafJsy4Ab+4h5XDbF6PWGt29VHLKEWueIm49eQ10mBTU8S4jaY5mhalAznFMqZi1rmRpGFAqKjmuAX3SNfOk69vNqbQo59pcvg0Uasqbay0WVzhKdPFae2zSjiTQZ6Wf9fQ7PcMg0XfrBhJQOjfQ6uOE1RrHXVNcG76e/7rpZvbmTsUDaM5mM+Y5MQ2VlRuiOeBrmWKsIpB7yjTEuFKPSEK7xIndEywSHB3tU71dCU7MebdNx7Ro9Hh7VbSB2HD95oC32uDq+tbTHNS1xgzqu0xuLJriiFksFEgttl3Wrtu4H0Wagja3rWzOy2YRSDFIfUSAp771abdrTuPYWDXca9w6U3oSHG53bhewghLpDr6e5xrpAtHPLtDNoI7tHjqL5IKU8TJdg79r97DZxqUdSQ5duJRt/PRvNFtttdVGR164YiOntAfgUh04bwO365wzqccRrNu7yaNsEmE8+wrwwvyxX0FXF5/Wl9cikPWdJP1Q/JnxSX8nvLg7wQ83J/CDN8AJrLJOSthqq6222urzQV6d8ec/dut9mSPxe329aw9xyA9dPXYn01PsGLdX8MX9nccKgE92J/nWB7712La31VZbbbXVraX6DBf9HpvaZ7V6bgF2hvzoPrIsx7qQSkT4qtnreV33Kk4e/GPevPo3jX4cv5IkXn321ddl21tttdXnrlvuM8P1lrW4ZFHDWEcIE3HIdi1+VTYRpIQbDVCz5uy0TVSsrwHdVAKoEpCgeci49n1bo8XNuefmESeKtl5KbzzEcYluTNyaS03x2rbcYBEa7tdw0BHbaq5R2jil1nBQrhHIutvT2y1e4nGlQmngd7VcMY2FOkWnqLOGvh5R06zhVcCxIg4mWC14sU3nsbfOUm+5164CVZq7uvUg1xJQR+cNjGe8OmYTaQ2JhIjwrU7LGcZFMauIBzwrAnX/CK8VnWVmJ3ZQjb5XqwZdhprD3e3r/mTZRO4KChWqF6bJmaZC8g6XhKfoJ805keY95C5efwn4nbscUeEa57MaqHgASAJEwbq3uGIF6lA4OUu84K47eelLH6Bm5wMf+SCXLl+iUtk/XOI2RiRui+ktU8XNUCojTjbo3UldxsnX3MYtKnxEuVIdW1WqLAHBpMe8x7LTMYF05CeB8nDDV1wSIha9wCKoGF0ymM1Imsndam2n5eJUOCgrxqlSZh2nca5+4iIX3vpr1MuXoFS6fo7MgCR0ixk6VZYXD5murpiuHqEpkxcnqMOyLcZQxAM+ooqKkmezcP96nP+U+4hOLgXDI55bQVNHFUGskiVdA5C0C3INuz3mhtPmlkgsMoC4tmpp8cgSUd5Ww4Xc98EExen3FszmO/iksCyoRRwxLSZeuoyNE9aax5Osw7fX0FqxFpcewD1hdUJcyLMZt99+O6d65dGHVqx8out7sigTU3PZtgUltSKaSDhJE3unTvPA8+4mT0s+8tFDrk7hgKYtBECiIxg8epdTR/WJlDJegSzklDZIFW8JASJYHWMuOvSzFFHkrlQrLZI+khIkS+tUd5wUkdwiSN9vkvGvAeD1uTHo+o2DOGKZo2fdnuT/zVnjiySL8cfKinZfdQLoantrqxVJKaZAai7rzaIaxbVvx16jc1wE+lksVhFtALi5gWkx1TrfuPP9ya9hq622+rR673SFn19+gm/euZ8DL/y3F9/Br4wXeMPsNv7m2dey2+2xuutLb/Ywr0mE8dyrGc+8gt2P/gTd5Q88TQhcEC495d8/kPf4O+dex//twtv5+A2Mg95qq6222urZLbs8ML3lIewTh5BvrmPpKSVCuTsWd+WPX18nsHt8YzHR8/evfvBYt500cfvi9mPd5lZbbbXVVls9pUSot+9Qzy3oP3QJvbg89vfQPd3hz5/4Liaf+Knhrce89dBkEz/6/h/lh17/Q9dl+1tttdXnpuccAHZv3bre4pPFG1poYMAbL2oRw3hFJDpZDQm+Y9Fl6WbhUCTgn5tGxWWLCvYWwRzraa5FHQvhOjZ3EuAaMCOiXwNEOgGB1Wnw1zdu4I3bbd3Dq22fWDgcAd+gbKIvVBxNsY2oQTbMoZSJ1XJgLBNjNco44XWM+FRrrkEqWHkS9GjeUZEAcGKYlQ2UpcWqxnDD0UcdG0dpHZs5Y5IRHxF1vDp1mihT67tVCeA8FsggOYBywG1vadAFLQkRWF3ZZ/f8BWw1UAEbKz5ZuJ/FIQvUdQQs7fxIiwduML5F1HpzY4tbQK2uZ35il7wzxzUF2GowPmVFu4QNE97ckSaBzqP7NwVz84qRSCLs7PS86MV38sVveCV3vPhlXLn8BO99+MNcXQ6sVgOljC3dty0McI2uZC+YKJWMudEBXTuWbjHPzNr5dhjduVyhHE0UlkzVOD1zqnXMFCwZnjs8JVKbs5gjlLZ4W1BbRw8nYIamEjHOmhHJcLji4rBitVpRxwGbOlR6HvzIQ3R1CNPpvOfUqRMsl0fhgVKwaWJcDtjRktR3pL4nW47xa7jVpRrqA6nPmAplGKlmUBXLib7rUTziuhHqNAE0cK1xvCFAIsQ8IK5BK7UB9vaDQLRxjdUW9WwVUcFriT7bLsA+btTqTA7MOk6fOYUte/avPIFPU/T0asxVq47V6JkVbRcQcX8x880YpM1rJa6b3RM7vPhVz2enTjzxiYdJXceZU3tombgwLsORnK5do6oS8PfESe593h3c8aI7Obp0AT4xJ3lF64h5iuj0tuTFrYajXCUc2CJUCdSaNz3lAtLBOgXAMiD0nZIlxbjjaiC1Ff9xRJ1kcQ2EiTdtAPzm/uW07nRvz5G2UKJtQa4lMsQyl/bnNjfpuvYsD/eu5oDMOKyd3LXEPZ3Kpq/XG4AnYsTxGjC46+P6lxT3yFqJuOfoWo5Q/wQ5tc5zB3/OvYVutdXT0t+7+n7OaM+RV94+XgDgl4Yn+EOP/SLff9uX8EU3d3ifKhFIHYcv+APsfDzRX/zNY//gLSK8pDvJ3z33ev7chbfz8Xpj4qC32mqrrba6FeVcPLrMvf1dv+Oj7OKK1T9+L/bwQfzgVv5VVIRy1x4u0H38ynXtBP6zl/8GD9ZPcMUPrt9Ottpqq6222upGSASSML7oDJ1AunD8ELiXjr9w8j+n3+/58dUvHPPWQxeHi1weLnN6dvq6bH+rrbb67HUrf2S4PgqyiSdtX+ATsCi1Hk5prjIPsCuqASPdMSuId+3GW8OpZg7SoKAbUiSgsWpz3REOWFkD3gYdrYJkvMUFrwELkgLgaXSBmjawS8RKAwGhGiCJNuN1N65sIEu4GuNTljS3oDRwySbO+Zq7zd2p04hZaaDHGqxqTjuXxscNj6bQGENKeBnDTVmh2hSRvuv4YyqqXcDx9jpUwnOqWoEOSUrWhKDYVJBe0NmsufuInlO3cGRbWofzYuOEdD3TVLl88Qqzx54AK4z7q3jpZVrTfEQzojVePgKl4GUKl3R1sHD5nji5w2JnThkraok6JayboX2HdgF/0NTanTNKQjWjKTpWN4Dew2XsqnFqtENqAH7rlP7cDr6YsbSBRy5e5pGHznP50iHDcoyI2hZL7E5zEQckLBr9y+REAfoG4zW3uSQxj8xK7Mthv4IdTUzFGEvh3HyGdR2Q4hiniP1d96hKrWs0hjXYJiIRP4wjkjixhnHNJfzE0SHDWLlSK6lbIRcuskuBYYRZQu48AY9XbH9FbRBUU4vS9Rh/Wiwi3tgdKQU1J6HIMGLiVDpcHLdKqZV+kZjlhK4E9wmrEl25yZDUYWYkVZLqJl597d53VbzEOYpo6pjVrB3s7RoRbz3ZakjOqENpEdZlVdjdWXDuthM88fiSqVgz9sZ1WqtTWo+trhdNYJvFF2btHtOcpuoRMy6qdPPov15ePWIslTTvmN+2S72yj18JVz/NBRsu7UTuZixOzck7PRceuczlhz5BrcLpvR3qEeyPY8xNkbieqeGUFSep4nXdcdvmQQOn68UkVg03yFnJucWIS+sO3yx0uXbPWccny5PuQZ7a8XSL6HVtDn9fbyvuwWFYjvtM9I63MWlErLN22OOoKnjegP9GfEEM0mzzx/V1hATUd9HoTrey2f96X3QzyDHXsAKaEJcWG2+RZNDiybfa6lbQV9z1FfyLj/wLLg4Xb/ZQPq2esIE/d/FTe4bO28CVWzlKPfUs738TYkZ3+f3XxcH0ou4EP3zutfwX53+ZJ8qKmo5/L5NNVK+k9UKYrbbaaqutbimZO+966De59/RTA2B3p/zaY9fgL4BDnSZyP7sBo3waUok4aPOIg75OEPjAj3jcbs3fgbbaaquttrp1tSor/tWD/4o/+II/eLOH8qlKyvTCM4EproMTeCFzvnfvjzP6xE8Pb+W446B//eKv895L7+XL7/ryY93uVltt9blLb/YAbrhyQAezdXwzLbrXqXWML/xbHLN7aU6vdayx4q3nM6I/o+8XrwHtgjI8CcY2VyUVvKyxJQF7aR2oAesC+gpmFXOotVKl9ZyKNHR67fHmLe7Ipk+Or0URab2YAuoSME/XpzoCaY2ETbW5GwPsWouTNTOoMeY1CHdtXcAiYBNWpziGT/rHXDFzyhSxt1bGiHV2Q2k9tUo49VJH6mZ0ndD1ORyCSSG16FeJx+qsQ/sc3a+RmB1HoRo2FWw1MK4Grpy/xOXz5zm4compDEiyZocNp3d1wyy6HbwBPiy24eMU8IhwSs9P7nD67AlO7sw5sTdntlhQXajDGIAwJySncF1izLqO+c6M3AXkDmgbk8vNseLUqVDNqKWwf3XJBz7wKL/8jt/iF3/+V/n3//pdPP6JixxcPWC1XFLrSF0fd6+tSxqqKGOpTCWcrp5yxB3XinolpUTuEqlLaOraBHMmd67WiUeXAw9ePeLicsVyKgzmTK2TeCwTU42eYadu5n0ki9cNdNa+J80y81nmxLzn9pML7j61y7lZx8yMqQqX9g954sIFrh4cMJTC0f7AEx94jOXFg3CG06C1SjioVdEWsZ37PuayR2JvxknVYZoCahNF3DZOTKslnsKBrSotpdewYliJ+WkScyrPo3PZavT+2nqBg1u7VOOcxuwKh7yss4pT2vRqr1OB4zqtkOHyhas8/pHHGKYAytocwrXEMYzpHPvyUnGLL3m8wfRYFBHwMgC7IyQOzy+5/PBlxsmok3F0acVyf6CWCauFdlGiCDnFdSQGw9FEnUYOh5HZ2ZPc/6J7OHli3l6nBmzvZojmWP+RelKaxf0CR1Pe3G1kfUAsesk1PSkeOnVPusc50vVI6lrHdDvHCEmjLzvWp7RY8dSRche9xe0f1URqxzl1mZzYPDenRJcSXVI6TfS5o+8SfT8jd5l+NmPWdfRJ6VIiJyVrJquQlfhz7sgptetkRlalE8h5Fj/XWIiSVElE5FvKHWm2Q8pzUtfFXG1LcVQh63PvLXSrW1MvPPFCdvPuzR7G09IPX30fw63SvfRp5HnO4Qu/geGO11Gv0+rlF+cT/KHxDr7+7U5Xjv/b8Z/8+E/yoasfOvbtbrXVVlttdXzaHw7ZXz21g9Wvjoy/8PFP+tm4POIdb/4/r/fQnplEKPecYHreKa5Xe8oPnfwveX66+9i3O9aR919+/7Fvd6utttrq803P1r7X4oX3XX7fzR7GUysr44vPUu/aw2bHv5h3R+d8/8k/xTfOf8+xbxvgvZfe+6T0xa222upm6Tn37fXafefeIjwDw2BmmIXZtFaLeGVLmChmU4O1EYrqvnbCWoBEt3Cb0eCRO7UUvDaQBuEuA6w6bhIA0yq1GrVUrJYAVrVsIKybx7jKFD9rY/ZKg5sha5G1AX39mlvTW41lSkHT/Bqsxb0Bp/Wg2USxbko6N06RcAT7GmiKUl1bNHE8JyGoxxhMwVxxMqiQvJIAlUySGTnJxm2qKUfE8KwPp7UKrhnKuHGXhuMvjp+XsT0unLt2tMLHkeHwkCuPPsaVxy6wvLRPWRWYpnBjm8E44SVOsLjF664FHwvqTu5miEM5WDJc3Eenid155uRizs5ijqNMk0cE8aZDOV73TJU+CV3XhwNRQFJqx7VSrZ1PN6pVpnHksfOX+M33Pci73vlBPv7gJ1gtjyjTSJ0K5tLOE7EwoExUL22RwhqExnk2TdE3XW0DAlNqQFTbQgQUc+WoOheL8cQ4cbVMrKaJoRTGyZhKYapGWV8fGlHhSDi+Y8GC4ikhmkldz6zv2Zv33HZiwV2nTnFusUPvzqqMXD64yuXVIUucgnM0jqzGCcHJ8zm5z6g4KSsym4W7tuvRlNDqqGaSRBRxb85sGNBxbK5TAauMQ2EaJzQFmJOsMe/lWmhwzPPU3NTWotdt4wxfO7bDOG/NcB7d1JK62HZSIja+OYPbUo5qlaNh4InL+1zcP6RUa4c7x7W+jlhmHZ8cx9JSdBOrKqoE/KXNGVXchby3h57YZenhdrZaOFwuOaql3Yta37Z4AN2USClDqZRxYjmsWC5XDKuBS5f3ORoKqsq8z+zMe87s7XKyn5NTYndnl8VsEc8nnLe6vn94AG+FBlMl7htyDfJGnW5zxWucAxUJiJqEJBGJLSkTadMdKefoSSagczw27gkpZWapo08JFScn6LtE13V0qaOfL5jPZ8z6nr5LzLqeWZfp+o5u3tH1mb7LdDnT5Y6cu4DHXU/OmZwSOeUAwprIIu3fGh3X2qA1ThZIeDPKN5e2t3h0Tdc1ym6rrZ4rGrwe81rj6yDNLO//aqZTL74uYxUR/tPnvZL7vvB5nFgd/7fjxQv/5Lf+SfzOutVWW2211S2p8wcX+filR5/yXu2HE5RP/RK1TuV6D+2ZS8IJXO47eewQWER4IN/H18zewHpJ/HHpsBzy5o+9mcmmY93uVltttdXnk0SEb3z+N97sYTxtLeuSsd7CqVQqTC84zfjSc3h//BC4k47v3fvjfOP89xz7++hPfPwnqLfwYu+ttnqu6DmXX+nt81F88Ki4R+SyqoZb0KMDFdNwXhYh4dG/SktYxls8aosr9YhnNiuw+WqwxTQXkCSIRIekVwunoiZkDYwMyDn6NNcuYgSrpbkSJeKdzFBRVAK8rsNWDcArSbr4ydqRp6kBKAsT8jri2sId6+uI5+ZaFXdUjLr2GjdoZV4jxrq2I7CxTod7WDXj6uReYJyo04TbRByVjHbRVotYcHJRsBrAiwQU0I5qFU+OawBmLYbkCnkeYKxOWEnUMuJeEYsuYD1YUWZHHOWL5MUMG5bYuAq35WRx0larcBFjSJ5FBOxY8XEiZ8GSMnqlTBPL5ZJ5zuwsZix6wZgxLEemYSIfLUldh3Q9LhFVncYRmZpzdg0WiQ7SOMYtWjgl3CUihA+WHI1P4NJhqyNsNQSAtIoZrWc63njdnDIV1h/thRQ9uB49stUDUqVpAksojkiL2MWpvpm1HNnE+eWKLLrpl/a+iy5gKxsnNJri/IuQgk5SqzV3KhHhq0KvGUS57XTMLbssPH605OjoEC8jnDnJbkq4Feo4MKs9nfTRRW0WQLnrAmQiASFTm4sKXRbmA+yVwtKco5Swvg8AOg1MY4fu5HBl1wQ+oSk1c2/dQHfWkcs1HPsREdyud32Sc1s24dexAKHFwSOKr53QknBbMa5WnH/ocdRgGIaIUl67+kuLaNYU5762nlmI6OkWka4SiQQRDNBcyZLo+wW7aYeDIaLmMaOsRryEkzx+FmNPGr9w5y6hXWIaVhwejaxWE8qSJ1aGrSZyl+kWPfPTJ3jpi1/AxQ98hAcvHXDbXedIA1x+dMn5YUDzjNTNqH6I13C9R3p8WzQiimqLS16PWxXxGmBYBM0zBItr3GJBTUDjSqv1bXN5vRAE8OhE7voZXZcoEyTJpCRo7hGNDu1Y3BKLKsKVHQtF1tH2EWnfUhUkEXHq62spFvQE4VdsmuLegmx6tL2s46G1nZO4jk2iJsDX6Q6aozt8q622ekYSq3RXPwZnXnazh/IZtbz396DjVbrLHzj2CK497fgvXvAFvPZfVX7g3kd49CxtEdfx6J0X3sl7L72XV515VetX32qrrbbaaq0vu/PLePDDD97sYfCWD76NxfnKfRd2Nj9LLzlD/fBlpl9+9NpXDU/SxYc+xsHFC+ydPXcDR/o01JzArkL3sSvH+D7qqDzCn9z9Zh618/zk6i3HtmWAn3rwp/jDL/zDvOz0rf97ylZbbbXVVp+7fv7hn+ebX/DNfMG5L7jZQ/kd5Tsdw0vP0f/WBXQ43u+i9nSHv3TiT3JfupOfXL2Fj9dHj2W7q7LiHU+8gy+5/Uu2n0G32uom6jnnAF7MO2ZdDrcXGTWPL/VtitjlBuCqRliy4VRR6tpxZ3btg9fm5tXaL33zn5iHW7Ni8d9m1GnArDYXb22dmg0S0fpIGyQyoLrHY9Zu441Dt0aHqcRj3Frvq8djvNnyglOsm0ehmjcncaWKb+KmY9wNRrtHNCzrjGrBTZthUlv/cMRYK4aKh9sUR3xCUiLlRFIhdYJmwa2iSUmSSF1Gc0a7AERhAewoblRJFNbAxqGEa1dqgVIRFyQZLgF+XRQWM2zWUctEPTxiurhPuXiVuj9iQ6XuryhXlthBwa6usCsj5dI+5dI+9eAIDFJSclZUBTNnGieGGk7YlHtmXSJ3mVqM4WDFdHREnSbMHVEiUhbwYWzn4slx03H+I647HOFWC2VYUg4PqQdXsdUhtcUvW11j3uYsN6dWo5hTqmG1IlYRLy2mG1wT3uKzQZBpRM3CrZgSorKJJDdJXC3w+HLFE8sl+82ZO04jpc1TM+J41kpdx2dj0SlsgpXaks4zSRN9Dlfp2VO73Hn6BGf6TK6Fo9XEpf0j9mthqCWii6cJaonryizc4lmpkmLe5ryJM1cRkmZmKpyQzGkzdoYlqVY0ZwSYlkvGoyO8AVZSF6+5UcZwUoeDNtzT7TpbRxy3ju31+Vkf/wCd0vq11yBfAwy266qME4cHA4eHq1is0eBwLXUTM+3rflldR5s3digp4K83IC0B283imnjeC57P67/gxdx9eo62KHYzo5TSup4ljk9KSOstN3PG5YqDy1e5cuUqxSpeJsZphUnM8zIUjg5GLh8eceSGLubs3HaGL/7dr+SlL7uPrLCzs+COU6c5M98hIYhkJM8QSagmdmY9OzmAL9RICpDEuic8bj8xRnlSUsEG3rY+YGn30NQ6wFWUbjZjtpgFxG33i67vyUnpszKb9/Sdtms2kfuIgu5yxD4njb9LORaepCTh+s1CyimixnO4y5MouetICtr2ryKk3MeCCongBNVWlU1LsJdYGKBu65rjrbba6hnosC750Yd++mYP47OTZo6e/3XU3Xuuz+aT8vrf+wX8zY/dx3f9nDMbj8+xe2m4xF9+21/mvZfee2zb3Gqrrbb6fJCI8FX3fNXNHgYAkxU+/M7fYPzpj2z+Of9P38mFX/wA/sTy0z7niY99hIMLT9zgkT5NiVDv3KPcvXesm036YWbp/fz3e3+C/2j+Vce+SOvvvefvcWF14Zi3utVWW2211a2i4+6/vS4Swfd67NT8uow2S+Y/3/1m/pdT38t96Y5j2eayLvmhX/0hfvGRX9y+j2611U3UcxAAL5jP5+zOZuzuLFjMe2ZdR5d6EiAm4YqsFjHGNQBcrU41KEikHku4w7y58QLQEi7fTdxsuCgDBkFYGwWTdb+rbxy/7h6u1BYbTYsMtmpYKdExjGACheZ0W7t7I/M2xt0AXcQ8V0ycyYypDExlZFkry2litVqymlYMw0AZh3DoAap9uIZTRlQaBGMDqryUcBaLIJrj3xKRqOKK+hrUKiknRMJxe418KaodmhOS++i4tRaN3KK4XYDUOoqPBhgL4gVNguTmcs4J6RPeZ2qXqcnQudAvEinFuGUxQ3ZmyKxDZom0CLeo9AmvBaojsw5y19ynHV4npnFkNawYW6x2J0qnAuqM48jq4IhpuQqYiZC6RCdCcsemCSsFM6O6PaljOoC/VQvQWyo2rijjkjoNAQrXwLDGdrEx+oBxrI4BkF1B0iaWY9Nl7B5APGdcEpR1zLe0dQq6iS6eNHPFhMdXE5eGkcNxZBgnxmFkGkamUgI2S8z12pzG697acCg3/7kEdJulzE7fc2a35/YTC04v5qg7h4dLLh0ccVQmqgplmpiOlthYMDQirEWpDqVaO7cZ6TpSv0PXdcwWC/ZOLDjbZ86Vkb3lkm4M97mUkboaAwB7bY7NBiCJDmWrNY75NDVo7rHIgDV0reHk9E+2pooI3pzvXiu1FOr62mK9RmGMyOgGD73dDbRFdCtKwiIa/Emdw6rSnMcBlqmxXYuDytn77+TOF97DfKcLR7KwiYEXJPppk7Y+4rjnTOPI8mjF0dUl01DQ9X2ltCh6F6Zx4nD/iI//1ie4dP4AMWVv5wQnbzuNarhab3veHbzhK34XL37+bS2qHcSNlDsWe3ucvPMsaZZZd0yvHcFp3cNMMzi3A6midDmTU7TnqkRUtIiSJfp9RSDlzGwxB3HGcQJ3smq83pyZzef0XSalDtFEns3o+lksBlBpcLcnZSV3M1LuI8Kbdj+gHff1va05vOM8eVukIhFDrnkDeLWB4NTmeurnpNQFDLZnwYeErba6xWXAxSsfRlaXbvZQPit5njPc/kXXqgaOWWne8aqv+wK+jXv4jl/0Y73PXBwu8lfe/ld4z8X3bOOgt9pqq61uUT12ZuBgcS3W+Z13Ps7PvvYx3vHSy9hT9I9E8tGz5L6uQj23gx1bjKUw1deh8hgn84P8dyf+BH/+5DfwvLRzbO/Uv37x1/mBt/8Al4ZL2y7DrbbaaqvPQ/3sQz/7rHkfnZ5/Cjszv27bf2G+l+/c+cZjW0y1P+3zg7/6g9v30a22uol6zgFgzUrXJ2Y7c2bzGfPFgsVixu7uLru7OyzmPV3qERcohcbXWnRsADrz2lyRYG7R01ubQ9jrBs66SXN/0iKRo1Qzun0D5hgSncPr/zWnpqu0+GXDXMKBXGv0AZcaDk1aNC1rWGebflhzo7gwTBPLsXBwuOTgaMnR0RHL5YrVamAYA/YZLUJ148xbx56Gw0/wMJdawDNp8b+aJGAw4YLV1JNSRnslZdl0DIuV1lfKphc3Dqw092D0Ck8YRYSqPTUsoM292pysXqOkGfA+413GRVonaoxPpCKLDj21Ayd3sZ0eFhk9uUBP7qI7s3DKmsWu+wQ5QZnIVlETbBoppVIswG24UKM3tJI4Ohg4unCZcnCITQNiRt8l+qRACrduqdfO66ZbtlLtWpdvLSNWp03/c/xPqc3pWWphqhNTNUxSA8PhWEcUKYasY29FqRCxtV2PEe5hMSOrkjNIc6EaMLpwaXLOH63YHwfGMsVrBiYzijtFaIsVwg3v7gGyoXVjg3u0uCrQq7LXddx+8gR3nd7l7E5PFlgdLjkcjUESYy1MU8xRXfQwn1OBYhbjxxHJpJTJXSbvLJjtLdg5tcfJE3uc7WfcocLpWtk1WIgyoyB1wqcRnyawEWzCp4E6LinjgE0rsILbiFsFc6xO1GmkFqcMA2V1RBkHaoOx7utFGBPmNdy/tk4JaJ2+JRzNos0qGgS3naeAwVFuG45mbddLrAhpEBqomihWw1WeE7ffc5bc99hhrPR3D/e+O6Tctesv7j+1Oc5LKUzjyDRN+NrRb3HfcCQc3bVSh4GDw0OOysjJO05z733nWB4ecf7qEa6Kdcp4qmPsaPH2hAM3CVjl6OoBwzL6mDUlclZ2+swsJWRt6tdYHNB1KRbY9B1JArJ2WckidDmxs+iY5ehD7uYdUFkdDZQS5cPmsY/5vKPvArKD0/UdOUc8tKQu+ppzT+4TmrvoQdcUDt8WNR/gPpy/mlq/uKQWTR2OamnnKXd9QGTtAyAjmz71CAmPc5lms+vzRrXVVs8x/czFd/Heo4du9jA+a41nX8nqri+9btvXLnH3H/tivunE/Xzhh4/3i4jHV4/zl37lL/GLj/4il4fLx7rtrbbaaqutnrkunZx4+LblJ7mRru4Vfv1FV3jrqy8w5k/94vRX/vn/fiOH+Izlux3Ti84c4xYXTPXLEbnMTvo437bzEv73O76Cv3T61Xxhfzz7effFd/Of/MJ/wo+8/0eiYmirrbbaaqvPG/3q+V+92UP47JWUctsOfh0j6b5h/pV80/yrjnWb2/fRrba6eXrOdQAndG1cA6JnFI8+yI5M7TrqzKnTEHGrBlNtzstqsepWoE7XIpOjTzL6c5OEqy86KQPCeXP6Cd66e2MAqmBWkXV0tCawEtGwGKKJcAev/Z4BI80qeAAMw1ufabQBq6YAr9WoZgy1BNgrET3cXjZ4aRHBtukp1RQuUUzD9Wg0x6nj0iCkO1Ib5UFgE7dr0U2rEast3kC2JDyFYTqgcrxOEXvScYpo3dGNyQrmhUrAXVJCzKM7ODWC3EW/rgGaMljFBmMw8NkMmfdYFowO+jkg0etZajg6hxEmQ3Z6yIpoRkvBpkKSRDVnKgODFfpSyF2mX8wwW1GnytHkTMMRtRa6vQWeM11W5p2SpDKZU0uDnDlWNkeIcgqILvqk/qZmlbSYK9Y6l6OTFOpUKKLNhQudOtrcqaJtewi+vpI94pU9dy2ynHCXSgPnQSipJFYkrlrlYCyc6DKznDZh48UFikGO/t4kLXJ7Eyu+BtFxrqU4yWDWzTjZIsWtRGfw/lQp48RBKaRFj2hBSonO2pSiW7qG81sIYB37E8QT2mW01HCC1krnMOs6VnnGSuFoGFnVEnN848yPc01tCxraQgLfgFdaFLVga6+q5pjbPuE5I5ICSKccoN0CwJpXatig2zXebiHr9TRWYmGDajs/ijTAH6XVijkRjywa3detsxgEd+PycMQjBxf5xMGyjTZ6bYWIXE+qiCSwcD9bSmip1HHC9Zrr2L0tOnGhlnWkvONTwREmcy4c7TOcP+CJ81dxMw4eu8Jv/eqHufLwJcBJonRJkZQo48RUjJSFJD3mTjfrWJxYUIeOcrCMQ42gSZnNZ3itTGY4GvBa4p652F0wX2SW+0umaaKaMQ2FcZhIuYu+9K5jNgvYG2kKre+4xZpb6zJPORy7pdK6qoWUJbqTgVpiZqe2KIX1tdO6fJmmZnW+1vsskuO8eus7hhbr3eaqxH1rq61uFf3hF/5h/s57/s7NHsbT0oDxzz78f/KDZ37Xs6MbSJTx3KuYXXgPOu0/rU2U+jLMb0dkIOuvA4bItPl77RN3/+FX853DXbzg9h1+7fyH+ejFh49l+JfGS/zg23+Q33Pb6/gLL/p2/Owr2OuON45zq6222urZpCyZTjsmmz7zg2+A3vaKS+QqPPDI7rUfCnzo3kPE4Q3vPUu2a7+HDkdHlHGke7YsThTBc6vWObZ1TjNKfQMqj5LTOzklPX909/l8zfwu/tuL7+Bt4zOPnrwyXuGf/NY/4cNXP8yZ2Rn+1Mv/FCf6EyQ5LjfzVltttdWzU/fv3c/rb389bzv/tps9lOeE7OwCf+gqsiyf+cFPQ3OZ8b17f5x9P+QXhuM7p09+H/2OF38HrzzzymfH5/+ttnqW6zkHgJs/DRfHq28ibEEjZlgVTUanPbVLOMqsFGqNDtZaoofViEhcSdGFGXAogzpJW49tA83igd2kOcdEEsY6cnYN5QQvhlLxFOM0swavAoJGZK0GhMJINTpKJeUWaxsx0tqimQ3DysQ0hWuY5hZ1a/Batbl9FZWMYLgUNJgs7om6BkwWna1Y3UBZTxlpMcnBMSvqgdciqnkNN8NFjGaQtg1SQLlagIB2o1dGU6ombA0clYB2dUJS3w5ofNgVU6ROIBmbKmVwuhzxsCaKM0GX8Jqjs1YqbgM+FiQrMstYnkVcb+7CrZw7VqUyXT3gajFkb5d06gSLnV32zpxgtlOQKyNXrlyiXD1kNo2k3QWaE4tZzyIPDMMU3balsO5ijgUHNaC2R3cvKY6RE85y2uIEVKGW6NyF+G+PhQPSHJnNsA3mcYwavAqWWjG0LTwAGye8BBgUFarX1nHrjA5LYHBY4GRbxxcrlXrt/CK4BlQUjb7hxn9xAZ33LRI33OGC4rXN+OWSw2FkonJQLeK2zdCpoGbtenGs1LiekqLdLI5FKUjNiI4BBOsO2Yzd+QLbXbAy5/Lhiv3lkkOUZTVGAydtFjs4hpeIg46oZsIVW6NLm9Q+sK9zi4WYY5I2XcOItxj2ipWRajXuFR74WE3D/Y+18xox2SLawGKLYxb5pK5w3CNSvvUEa3veO37h13lwvsMjTxxEWkAbWMo5XKjS7hztehAPMKpumDnkHK/d6qZ/2K1g04hpwuuIa2b/8YC9thzZX64QFcblkocffoxx/6DFO0szLBfMBG29v4KTUiKpUkejDBXRROoUnyqaIhq/lIJVaS79CXLPbL5g78SccbliWI3hdi9GmSoptUhmgZxj0cE0DFSDlDKzWY9gTFOlFiP3mT4r01AYliNmHvCXFJ3ew4S7o32PrKNmJLqTNdbktPtqxGvDk+4x61DvuGHGvVwTbnHf2Wxvq61uskSEl51+2c0exjPSO554B++7/D5eceYVN3son5VsdgZPM3iaABgq5veBC9UeQDgkpd8kyUfaIjnY1cybFnfT7+xyxyvv46d/49/ysWOCwAD/9om380sX3skdO3fx7S/+dr72vq9llp4l8GCrrbba6hj10tMv5evu+zre/PE33+yhAFCy89YvuMDhonJl70lQWuCD9x1S1fny95wj1/id9eDCed77cz/FF33DH7o5A34a8p2Oescu+bHDY92u+V1M9Svp0lsRGTibZnzjzr382niRwjOnzdUrb/nEWwD4mQd/hu94yXfwxrvfyAMnH3jG295qq622erbqVH+Ke3bvgfM3eyTPHdnJGXqdADDAri74A/Ov5JfHd3Pkq2Pb7vp99L2X3st3vfS7+Op7v5qT/clj2/5WW231qXrO2ZdMaA5cQcVJIgHQPJx5okIikzS6JlNOdH3HbDFjsdOzs9OxmPXMUiKrokbErKIBSWthshJdweaYldYB3P7sFfNCAN6K2xhOxA3gDVBSzSJC2FsUtBnVBCtjOGTLFHGuLpRaKHXEqjdQO1G8Rqyur6OpC6wxUrMch9uUzReNa0eubroxvTHGNtYa0delGtWcUibGWliVialFG4OjObOOt42jve5mLdf6kVukcNDsOB6jVYbJWtSt4RLxudKF+8+nuoloBYcywXKAw6OI/N3tKZ1QpjG2sUbuGnBatPXi1gCNvphDg1SSldR1dJ3Q9eGcXB0cceXiFQ6vHjIOSzQnztxxmrufdxeLvVNMU+Hw6gGrK1fxcaAXZzcbnRcoU0Rzl4KV6AIOmustxlrxaaJOE3WMbtna3IwRUWzh3HWoZaJMA2Dk3AX8sxJ/X2nHMbqFx2FFGadwDKuGGzglPHdQHcYhQK1464itjLUyeTgno+eXgPMe4zGr0UMs0oCptjvHulUYSNKOYdo4Qk/dfprbbj/D2b2IVldVVtPI/lBYmVPHMcBsdVjD4JRIWSOKvME1SQlNmb7rmO3tsnvyBKdOLDh7co/bzpzgzlO73H32NPeeO82duwtO9j2z1JMkNTd9ONS9zUc3g3HCS4kFCK0XWIiFEdLgKugmzh2L82J1ojZgm1p/bCzOWM93aQnQ4Q521eiAdqO2nubo462biGnWPSOtT9et8thDj/D+j36cy8OqLRsJx7UkbYs9mlu/JQ14A5fRZyutc5i28CEWqdC6wdxriwcvjMtDrly+wpX9q5RpwEphWq4YDpdYjddktVCn6K2WNSRtu85JSEL0I1t05gYE7jGUaYz72Lqb2asjqsx3e8Zx5ODqEcM4shoGxnFExei6jKqScwdmjKvCagWlKjnPUYThaGR1uKSWAkmZzBiWI14MqZU6TAyHR4zLFVYrqsRRFI0zK2uYLvF3Siyc0RzHBo/IZ0mtC12vua+p0QGclK0BeKutjk+Xx8t8/9u+n8eXj9/sodwQJf0QWd8JxKI45ySlvprfvjZTgN/9xBEvWjlf87Iv54Hbnse8Oz5IO3rhocOH+F/e9b/wd9/zd3n48OFnTf/VVltttdVxSSUqSW4lmcI7XnaZ82fGT/4LgY/cc8QvvfIiU7q2GPH9b/kFDi4+c5frDdO1D17HveEGgX837j0AX7e4h/l1cOmONvIP3/8P+Qu/8hf427/+tzkqR8e+j6222mqrZ4u+5YXfwm7e/cwPvAVVvHBlvHKzh/HZS4Ry+/U/1m/sX8v3nfjP2JXFsW/70nCJv/Xuv8X/9M7/iQcPHtzGQm+11XXUc88B3Lo914AHIq5U0tqm2XpnUbRGx6ZoxOlqEsg5ekCrUy3gVSkBhayOwfYa+LHm7A04FM5IRALgtX5QXztjwx4HHtBOGtQx1o5Bg+Ktt9Jb/G9EM4tHd6+JIRYetlIrU6mUUrFpjM5bDRjm6+MgEuM1w/VJjaQpBRRrkb9IRJ66poBnpVBdAg4CLh5Qu59DTq0xk7CGWutE1gVQmvs4gacGpdcwMbp/l1KZag0HrUhAYFG8FCxVxBTqyNotax6uR08dJjC5h7NU+jjOdQqgI81dWwxBkUWHzefrUwNSoj/YnGygqlRTVpK4dHkfKYZ6xNaevfMsR6uJcXmV/av7DJeP2AHSvGc39+ymwjQUigNSw11cW9R2qz+mTuE8pAFckY3rF2uQ2B0vI2WaIjK8S5s5goULXJtbtVaLjtsGK2mw29cOWHUkgU+CjBXJjjbQWqeIDo7i1gbda4kYDs3hIM0JyxmdhbtVisd41073Ku35CVUha2WumVN7TrWTjNUotXI0Thwsl6Qk5J0Z6jVWZQDa4KTQhTO2Ftyj4zppQvuenFL0TfcZnc+ZCXQIi6kwpMTeYsGpVeFomFhNI0M1hlIYCi1evC2qENk48CN2U5oBWEC7FhNfMAvIG8HYYJ6oNVyqKSVEUwB7j5jliA+2TTx7be7tAKbraOFYFuEbK3Dci0Q0FlrgrC5fomimTkO7VxD3KKJD3GV9wcb1vP7uJKZDQGETQBvM3Dyg9XI3iEuplOUSW60CxKtgwwi5OV5TF/cCveaIxaMjWdYd3ZqwMX5Ry11EwktWvIDV5vanrUqUzGzeQylcvXzAcrViHAaqRYxz7jqURNcrSTu8OrVUJCt7856dnczq4IjhaAgXdqr4lQZzBebzjNWJ1aqEO9odNKLrrUy4JJIG9EW0LbypMc9LjftW0hZeII3nyyZhIdKrW2e6gW3j3p5Vcn/StXATt3G9dMfiDl5++uW87/L7bvZQnrYeXz3Ozz38c3z7i779lj3OxyURJ+n7EBlwjw/vKo8C46c8tjfnpVcHHr5zj69/5Rv5xNXzvP+xD/OeRz9wbONxnDd/7M38q4f+FX/0gT/KH3z+H+SOxR1trLfWufh8v5ZvhLbHcKvPVc+FOdM+wT471JzApvDlv36O5MLhpYt8+G3/gVd/7Tfc0sf5Rikg8FeQ06+RMH7P/A5+cvnIddnXo0eP8mMf/TEuj5d5/t7z+YbnfcP2PfTzWNtjuNXT0XNh3ty9c/ezNhL/seVj/MDbf4C/+vq/yqn+1PFteL2w9jqdt2ZDuW4SEb5u9uV0ZH7g6v/KxPE7jv/dJ/4db33srXzrA9/K6f403/aibyNJumXn+nPhWr7e2h7DG6/nHgAGNrfHdeyn0cBXbk61AHLSHL6RgJub085Ac/ydR0RzVxPmgltPLQEewg0c7j8j4JihLe7ZEFecikq+Fj/qHnDX1jm0jteJIKRPcvs1WOw2BQxUjT5fae7D6lQ3pjJRSnP7ibTOUWn7r+DR+SoNCkVkrjRHcwIpAaRdcAtgpSgSfA43oY5jmFWtYmVFt+jJORx22mBwLQ2ua0asxDFO2sBfRbSPHl/gyGGwyqIqphL7Tu1olAKla+7ha65C6fqAv2ZQDNcA2A548WtOyMmQUkjJkEXfgLhHFFQSJM+RcUStkqsFJMxwME3I4QGSM7vzXV7x8rtIaYcr5x9luVxxeHiEXz1iYZV5TpzOMBW4Oo4U0QD0AmgNMOgV0RaR7bTobQ1oW1scuAplGJmGAVtNoEbq8rWU4tYz63gDygXIwTQlOq0xg3V/qcU2JaVwQJeK1hJu8JyQJEif0CzhNPWIKhfxdpz12r4kzol7gGJ3CwAs6y5sJ0mmF4N+hp9KlFoYx4FhGBnMuHqwJKeEKOR23lJWkkgcDzSilIeA3ykndDaDlEhdInUdqKC14l1CZh1ZlFOnTjObzSjTyODOME7sX73KpctXuHD5KodjYRCYnFiEoATcq7Vdh4K2Y2tYu2ZaRLVNDWg6Kfeb6PUAjBagtoH3an4NQGJx/wDYeKbbindfB5FFrzdAnUb88CBc2xaA10Wbmzl+vateUbw931sWd8B7bbCaWlFNNGoJKOYDkiJCWZHmMl/hddjEeuOGlyHOO+uY+3DuoqnN2wpVsMkpFbxWNEen8/o+lGKXmy5xITotNQuHh0sOjw4Zx4JNE6nP5KRoyuROyV2KeVYCnJ84seDE6R1sqozDhLlH37nO8DSj74WsylQLVrwdSwEi/txUkNSR+pjfbhVrve5u7RfyKGWPa9MKWEFQ0LbAhgDvovnafXybAP2s0nH8cngr/4J5x+IOXnb6Zc9qAAzwI+/7EYoV/tiL/xhZP79/TRWBJB/5rB77wOGEPX7IW27f4e5Td3DHiXN84X2v4F+//9/z6NXjy1ob6sA/+q1/xE89+FPMdMb9e/fzHS/5Dk73p3n+iecf236eiT7fr+Uboe0x3Opz1XNhznznS76Tn3v45ziYDm72UD47CXz4nkN2l4nXfuAMAL/25n+B1coX/P5vRNOz80v445NgfidTfS3CIf/Rzkf5d6vH2ffrF5f5C4/8AgD/8uP/cvMe+p0v/k5uv9xz9u77me3efGfcc+Favt7aHsOtno6eC/NGRLhv7z5+49Jv3OyhPC2988I7+cG3/yDf/5rv59z83DPf4LDPzr/9Ibw/wfKNP/DMt/fb5Dsd9bYd8hPXN31CRPjK2Wt4Ub6P95WPXpd9mBv/9EP/FEV588fezLn5Of70K/40Lz/98luuoui5cC1fb22P4Y3X5/c3a59O3iCoC7QuVKBFrwbEudb/SACP9ZzygF7KGr7ESmFN+do2+y66chFqLRGrWw1ziyhlb65eHJEc8a5WMAOxGv2eANpFV64qtGcQ7awBeqpR2+uRlFuUc7g2o9fXUW9uPSE6gInYa1k72VhHnQpOba/PqKWEy9ArtZZwlFppRt2IQ42eYEG8w1dDxE+448sl3ldy7uJi9IpqH7GzddoASq8lXHcNJkkSvBpDhVWBIkZNBWMWcFQk4rqlOVxpHbsoNmPT41rNgnVZAFwRbxG/4Va0WpGcoZ+hKeNemmNS8Nz6gqdKzhLR3dNEMbg6FrphYNcL9eyc23Lm5GKPg9k+yyNhWI2oQndiwaLvOTlMrMwoGKVO4aTUBtjEN65CRKEMAa09XJlWJ2otlKMV9egIcUG1Q2sNjOcxOz08wFADzEOAdUdarHb7mdcNjIwA3IpYoEgTsGzxWt0ia0xtvRrgSTHdU8AwokvYEawUxBxNulnUpjiau4CGNTqQd9w5s7vDcmfJ0cGSUWFVCpcPlyTdYUcqWg3yLNzjLb5YRMhZ8VLpspJmPa6C5xwO+xZHbg24VnV2z+1x7r47GA6OONo/ZD4VFic7uq6idWR2uOLqUDjSRLFKRXBNmBmlTCRf92M74gF+a1vhto5x17bOIhaEENewSByT6p8c64w0+Nv6t5sbOGLQlWpT65cVkvqG447Lq5A71AqG4dXJQNKINafamhcHyI1c42vOhbbuIThzxLrXMsaUY+1wiOvfyhTp5DgqzdHcyOb6khMn7kfm7RqGWISSqDXizgWhTlPcw7qMNKet0ZzAEtf44dVDVodLVsNALUaXEzkpSRM5J1JSbIoEBBFld2/G7okZdZpYXl1RakWz4mR0scu5c2c4k0cuXLjA/qpQp4jaj8GnTbS/EoteioNPIxCQmRbhLe2qCl7f4dYc21bj/kVEcMdinYbi03PvLXSrW1tffc9X8zMP/gyrenwdPTdao4386Pv/v8zSnG954I88vVXkm0UsxGKizwOQLMBLDkY+eKLn4zsdSRNndk7x2ue9mn/5nn997Pt7YvUEAA8fPcwvPf5L3LNzD19591fyXS/7LuZp3hYlbbXVVlt9/uh0f5qZzjjgWQKAAQQ+dtcRDzyyy+mDjlom3vWTP85Lv/yN7Jw6/fS3W6d4H8231peun7sE9ztwjC/pvpS/dHrJX7j0Dq530cGT30N/+RO/xAOP7PLil3wx33LuG3jRq19P7vrrPIKtttpqqxuvTju+/UXfzve//ftv9lCett7xxDv4oV/9If7a6/8aJ/oTT28jblBW7Pyb/zuz3/zn1FPPY3jVt2G3vex4B6sC+cZ8JptJz984+ef4vqt/hw+Uj123/RjGI0eP8MjRI/zXb/2v+ep7vppT/Sn+s5f/Z8zTnF77LfjbaqunoWf/N2Kfq57kolORduPQFglbo4pNPdyTa+uo5ghy9rUDssWcStkYc3ELeCKEM03AkkTMr0ffLghWo6t1KhWjRb7KGtglvDqeckBc0RgTAInUenO9QSrRfC0uukFh1YjgFYeMY5qYCgFEPHpQk0YEbuxDqO7h8GuuRNWE+xTxqC0OWlLXxhHRv5JysHQriEe0Ku5UBKaK1Th2SQOYiwjkHH3HDe5tACjSnHeZEedwHDnpmaJKLYZJIanjJs2ZHQ4+R9Cu23Spmjm1jIjnTdy1lHBoihq2MqRu6F0cR8IlaQjVGgTy0kBr9NR20lFxDseB/YMV5z9xhb1zp1jccY7d1T7LWti/fJXVasD7jtR17HXCNAKTcWhGMSBpIHxJ2DQEnEsJqRH1HPDXsNURVgo+DOiwRHKO14Sh6/hyCXvlta7cFHHZG2mLl644Gl3TwjWgK4KljoJRh5FJjiiiWNcFYG3zdw0UJUnsp/pmgQECdAopx4doJbqjifmkQBKhF2VHhHN9Zrm3y7Bcsl8nDlcDSQWZ9fQpkarhw4j3M8iKJKWTLhxSSZFZT0lp00E9lUoZSnRRi1BE2X/sMrY/4tOECQH+ijHTObt7exHLjONeOKrRf7ysMJkjmplrBZQEpHWEe0rtdlDXzbsY9dqxJK5zQRCv8do1Izlc3k7rv/VKbd3U5hX3dU+4NcdsQFBwbByYao11Dd7wqlk48MOzjktmHSXttQbsdI85ZRXPORZrqKDq1BYPLjQ2CrHopUESFWkLTWKssnGCRxx8sOvmXzaDlOMe4UGIzWJ+VSuoRWfx2l1tKGLGNE4M48BYDZ+g6zu6riPnTJczO7M51InBQZOyu7fDiZML6mQcHQ5MY0VQsiiumTvvvYsveOV9HD72CI8+8gnKMF5j7y0pIXU9Ko5VqKshjhPxd6qpOX/b8ZM215GI927JBW72pEUV8dw4EJ/Vu85WW90wverMq+i1f1YDYIBvmn0L7/7g+5jbT/BNL/7Gzx421pHuwz/H/O1/H70SH0zHV34ryy//vgDBnwf6yvNH/PTde1yY3dhf4R85eoR/9qF/xk98/Cf4s6/8s/yB5/2B7Yfvrbba6vNKnXb8mVf+Gf76r/31mz2Uz0lX9wo//7rz/L63386Zg55aJv7Nj/x93vhdf4ad02c+p23p5Y+SHn8P3Yd/lnT5oxx+/d/DTj3vOo38Rkop9sW8oTNe1n2I901XbtieXeFD9x3yoaO38G+PfoXf965X8N1f/xc5ddfdN2wMW2211VY3Srcvbue2+W2bhTDPRv3ahV/j+9/+/fzl1/xlbpvf9jk9Vy9/lO6jv8j8l/42Mu4DkK58nP5DP83q3Es2hrNno+7Pd/E3Tv45/ujF/65VXl5//etHYqHzzz70s+z1e/zpl/9pvvTOL+VE9zTh/FZbPUf1nAPAKoJZDVAln+zuxSUMkGs6snH2NfgrGrBFAr1Jc6a613hOSohdg3CqCfHo2k3rnlc3nJ6+FAyhjBPFDLOAkGaGa0UKsZrHLSpdxZvDT8NxSA2gV2uDoY6gVFfMCAgskJK0yGqJaFlzKuAuJIRaHdV1P3CARdGIrHV3mFov5qYhWEh51hyCTk4Jck+phULrVy0jlhQtGquRxBExRDKSetQaFNN11yYIDXyJcyTCgGMi0a3sTpLmOrWKtrFql9CunUcn3M6esGrh+JXo3RUPUO9T9K+ScvSdlgkQNDlORkqJOOydE9jqCC1jQLe546ljOVUOp4FLF68w1x12dveYnzrJYhw4PDhgHAZ8uWQmTpczJ+fgjLCK7tsiLfU6NeehdEiZArqVGudyGGCaEC8k01gDYO3PzGkkEaw296Fcc3umyOZeuxXtkxy8sZ3ogG3OVU+oK7kqvTmJ1mErxPOna4541QaUlXAri63XUQQmXXfSrvdZwy2q7qRa6M3ZW8y5TZSlJlZ2lVWpHC6XdClxIiU6CfAqbS6rg+SESmyzOAzmLKe2gGIsWClUc4pNuGQGW8KykmaZ1HeIO6VYc+4qWTPZDK2VMowsl0sOCkyqaD/DOsU0AGOflJRbRLs6tbl7zQy3GhHwNSBqnNeOnITc9RGF3HCpNDe9TROlFHwd3Y1gbi0xwDHNuIbD2MyoSVEUK5Xqce2nlBCLRAE8xblcfwHva6rb7hUSuHj962Vt3eSyTjXQHE5iWR9zuTbmhv5lfelLdGfTwDWbPvEnLSio0X1MJbq/m03WrLYQgcI4VSYHN6frE7nLJA13f+oyeZGZDifQzGJ3h9Nn96i1cLQslMlxyaRFR6dCN5vxBa99GS+8bZf/8O73slyOm/nu0BbpKCIthcF8PdS4Z7T0g3iwXnNLS4r7OY7VtXu+Ha+2kAZq28ZWW91aypr5Iw/8Ef7h+//hzR7KM9Lbp//Af7P3gzz80Mf5haMf4U0v+gZscQ7Sb3PMuCPLi1AHdt7yPyDLS+QH37pZzAIwe+c/xOdnWH3Jf3WsY+wufwAdb9wXyGvtFeNrHz3gXzUIfG73DHecOMfj+xeu+74d52A64J9++J/y1fd+NYu8uO773Gqrrba6URIRTvYn6bRjsulmD+dz0sFO4YP3HfDa959BXXj0/b/Bz/39v80Dr/8yXvl73xSJZU+lYR8pS2a//o/oP/QzpAvX+uV3f/LPcvBN/xu+d+fxD9q81R/dKCV2eTV/6+wb+FNP/FserssbuG9AYMnIz5z8dV51/q18w13fcmP3v9VWW211A/TKM6/ktbe9lp956Gdu9lCekd7xxDv4H/7Dn+f/edU24KSefTGr1/ypT/v4dP43mL/rR9H9hz/pfXSt2bv/d1av/7PHB4DdkcMJvXRj38vuSuf41sWb+OfLn6XeIAgMcFAOOCgH/NA7fojff9/v5y9+8V/cLkbeaqvPQc85AMzanRd/aNBkTQXSxmEpDXit+YZ7wBq31tfaYIG3bbpo6770J/08IKQ0gCCpRZIi5C7HNnOmWgl3WgNLvukQDbegtz5RF0OTtx7ZAEvSoIQTcFpquHGtNhZERQSyCtJ1WCkBl1woVPBKVid34VaUoDftA1lp2zZgBE+oKCmBkVEMchBcm4Q0jQFatANzhmHECnRdJXWZ1FzGokI1wUtBtEEszVgdUJSjlDiqhZNuzGgOyZTQJPhUm9syooejw7XitQF5BcvteIuSNCCdjQM6jbDT4zlTCthU8F6hC1AmgPQZA9R7cq14NUox3EacGcuDJftHh9xz+5z5eJr+8nkWezvMduasViuWV/bxUtjZ22Fn0cfkKYVURpbFmKpQbcBzBjuKYy2Ey9ok5kqJrmmVcJ+bV9y8OZPDWSut/9hFMGmuTovnbeCi6CYu2xuQZFoGtNOEWmWuibOLOWfP7HFi3jHTTG5AzoYRmwxJcc6lFjwpnpq7ul034Tx21ARqwauF87a5xHU1kt2Z78w53SdWHi7vaTpgNOfgaEWnzrxLVE9INVKKa9OLtUULzmqcWFZYjVPMnTbDFehThyJ0CdJMSJ0iDa56LddijyXc/rVUprEwrAYmSdGLW51JwG0gu1JnM5LHlz/mjk0TZhUMujqRcdQqLomqyiQDY0pgTj8TFjmRzALuuzFWZ1mcUleQlJpa8kCs86DWCSORiGsDEZQUC1BsYqpO8hQuehRKRV0xL7gpri32uOHb9T3OiHNZS0EkoV4RUtzLWsKArMFxc6jHvv1ahGpz0663Ka0XFxfcCqxj3l1aRy4BjGXdh14YxsI4FlSV3DdYrkKXEilnFBj2V5gri905p28/BTjDgXP7mXN0BheXhZM7C5Ia5+6/nXtfdAdPPHaBhx+7Eh3g5hsnfNclutxRxpGpRKJC9B9XSDk6rr1df9pWM6iiYiSNuWusI/gNabA8KHK+5hzeaqtbSCrKAyceuNnDeMZ6tD5E9cID+lL88ovZf887efjkw7xrdoqM80f2D+ia3X/+jn+AHD3Rers/VeJG98GfYnz5H8JO3nc8A6wj/YX3IjcJEJwsxhsfP+Rf3H+KYoWx3NhxfHT/o7z5Y2/mj77oj97Q/W611VZbXW996R1fyutvfz3//rF/f7OH8jnrN164z8s+foKTR5F48cTHPsITH/8oR5cv8cLXvYHbX/Ck3w/GA2bv/WeA033oZ8mPvj0WGP+2cOT0+LtZvOV/4OjrfvjYxytHE+n89e0t/FTNuZOv5WvmH+dHD3/zBu87lHZ6Fnecuin73mqrrba6EfruV343P//wz1OuY+f6jdB7r7yfdz12njesBgDyR3+R2TufYqG1O+JP/f2QTEfkR95Gue/LjmVscjgx+8AFZLyx30nNpOd79/44HZl/vPyJG7rvtT509UPXdfsPjh9m6b/z7ycv7F9GJ58fCWNbPTf0nAPAItKctQ1eEdBNtLkkRdYMJVyMzZnqbWXLGjpFh68+Cf7qxnnXdoSbIS7N+esBw5LgHnGoKC2etcOzYHVCyIgmzCq1WOvkrQGy2keycG82x11ap1WX2I4HtI08XtlEz0qLu9YU7llzDzObZ6ZasLZyRvGIRfWAs6rN8UiHoNHVSzh68RJvcqokmtNYDDMorTPWGCim9CLkDEkD9IhGbLXg4ci1iDFGYPDKvsPpYWCWM7mfkVtSq7vgU3zJm1RB+oBok4XDdRaxuiBkApZjhg8FSQm6DpIi1WA0bBzjdKVZHJsWZyw+J6HINMJqwKuRFk7KyuH+EcurR+xoz7xf0OWO3VN7rA4POVitWB2uyH3H7mLG7sIinteBo0OkGqUW6jTSrJ5ADaBkNeK8JeZHOC0FRTAboeS2SnrCZ3PACN+uou6twzrclUbML8fiOc1NvjHuKsxz5vadGXcs5pze6dnpMplwrkeNcsK63GB6c7SjLRL8SdG5RKQ4CiYaXc1ZA7jV6KhWr3TiLFLm9GLGHSd2GabC/nLJVCZWY2Kce1Rue1xf5gatW7agDMNENSerQJ9Qj85aQdAcbt2UOyRFj62LUNyoVqlWMdpcbtv3Gv3Pfc5o1zUruuOpx8wpLUYaWcdpO64deFwvJoZqh9QJmQpiAl1CkpPmsNMJC4PUJWbdHKvG5UtXGFwwFQ7NWLb49orgotg4BGaV6FimrU+Zyoii5FIRqxQ3zEs446uR59Ki5j0uIweR1qNrQh3HOHaz3F5Pi452mgs2vuwxj4DrrBkktWjpBoU94qKFtWuWuBtp2jwfIaKS23Xk1SjmrFYrhimc51kTWSEnJSel6zK0+0YVZzHf5czpE4gklkcTaWeH7q6T9CXx6nP38II79nj8wnkW5xY8/IEH+ej7Ps7l1di8y3Hv62Y9OycWUArDEM5nTdrgdCbnjr7vSQJWDLNCmYZwu+dEWveFl8InVTpvIHPrZfdP/pJsq61uBb3m9tfwhee+kHddeNfNHsrTluNc8kvs6gmExDtWt/O3nvhbHJUl33vxMnv7B3wuHvx8/r3kj7+F8VXf1la8PDOl5Xm6K9f3g+dn0pmx8gWXlvxiVzE3zu6cwty5vLx6Q/Zv6wVCW2211VafR1JR/sRL/wS//PgvU3+HL3JvRTnway+5zBvfdfuTfui85+d+ig+/7ZdYnDzFy3/3l/Hax//f6LSPXvjAZ2wzEaD76C+QHnk79Z7XHeNgne6hqzehTUUwv4s/tPM6/q+jB7nin7nveZ5n3HHiHF/6wi/iX777X7Ocnl7NhojwRfe+glfc9WLuPX3X09rGVltttdWzQb32nJuf47HlYzd7KM9IR6r82Ik9vmgYma+/v7anB7Vl3Kf/jf+DctcXQ54/s4GZk584uuHwdy0V5bv3voXzdomfGZ59C+Y+kx4qH+HB6SO/42M+Mr6fTnq+aP4GsnSc0FNbR/JWt7SecwDYpQHR5gn0auga2iaaoy0cjNj6Z+tk1XC+bao/szbAFtCFlKFOOGugknAJB6OuHcItzlm0i/5faL2poNIhbi12doaVEWuxymaljS2iaNdfu1mtTJJw8wCdSHOBgrhEcHNtrubUxQGgGZ4V8OjuDHegNMfx1GJlwz0YsChIlFfHRDePFTeSKtJlkjjmQqkGkihVmEqlqmPDRCpON4NMQlNGtISLtL2JCtFXWtzZ98qhwWI1kM3p6NEuIw4UC9dvVmTdfWoFJ+E1BUS3KaCegy1HfCjovMf7jJvjUiPq+mjER5BcYJYBC/CdaL23iZyU6WgAGZFzsH90lY8/9AgPvOBOTt57lguPP8qsS8znPYdXjDotGcsOCzNmsy6gay14yYhPrFpEslUaVKsgpdlAo8MVb127FiCVaRVQrRupi12SFbAUYFcytfUyC7RO2QD88UV3bDfVQhKHlOgF7lj03HfuFHfO5+wp9KLIFLHYoqA5x8ID1ehEVkWzoiJxzv3a9aJdgFhp10VgawAnJcVGSOZ0OLuq3DbvGRc9dVgyAJPBahxJsx76hAlU4jjFIgpFVOm7BOp4iUUMTKWNJ6FVUZkigtyFSYWhFIZhYpwm6ljiF6Wc6YhY8ZTnSHaSVFwTVWOhRGpj97U9lwDTUKk4Q0pUU7I7SXtEPQD0zi65z3hO1C6BwZzCmTtP0qtwcgGrw4FxnLgyVK4MhcPqrKxQtGuR52O4+J1YAOBOrUaVQppGEi0hAKFKuOpTtXYtacRNu2NPuqZ8mmDdSaxKbeCeT+oFVsyFFJc+7iXuJJJahD2bBANp4NOJiPCAohGnHisB4rnjODGMI6thxF3JXUfO2lzmKSKtk2DFMat0izmnbjvB/PQOy6uFk3u7dLOO2aGwc+4cL379KzmzmzjxiRmPPPgIH/zgYzz8yBOMpb0eSeScmO/uoqqsVivchb7P5JRxlNTP2D2xYG9vl3nX46sVB1f3OThwKhWZz0g4NlaqRtKCbdqjBXJuixSsLeDYaqtbSye6E3zD876Bd19897MW0hUKP7H65/zpne9BRJhxgtv0xZwZfpVv2z94Wl8YL37pbzG+4g9/aoz05yp3Fo+85VNcUjda2eENF5bYbTscvOQN3Hv6Tq6uDvgnb/vxmzqurbbaaqtnu+7auYsk6VkHgBF47NzA+dMDt1+efdJfHV25xNGVS/zK//FBXvPiXyOlz/73Ax2uMHvvP+Pori+KSpRnKnf06oAejM98W09Lwr18HX/91H1835Uf5sCfOj6zTx1f+4qv5P4zd+O0Beif1R6u/abyotufxx175+hSx++656XbL4i32mqrz3ud6E7w7S/+dn743T98s4fyjPWTuzvcUSrfc+kyn907wFOr/81/wfBF30W9/VVPPwranfyJA9InPvMCpuupucz4psUbeev4Tg4+g1v22abXzr+C0QceK4885WOu2CUAfu7wx+noeKB/OTu6x4v6V8R349v3+q1uMT3nAPAakm6ca1JbX+a6BRNc4+8lNbgl4G40AsI6IrrWdR+kRrlrrQ2wSvt/wFWPItAWPxt9qmHyaw2dQWkDNGvafFwIJ6bGl4zaIZqx1gns5hv3cakTNiXWVZWlrUhau4QtzbE6huu3AV+3Kfpxab2deHOORlcoqhEBXQtYxayhYEm4t95gETyFQ1TVkV7JLiQzUhFSA8ZxHEs4MctAyh0pB/jRtcXRjZwUb4fxwIwrtXJChd6dYpAQUg53speCTRWZCprDSSseUY9aDaaBOpToX50KIob2GeuiJ5fJoYbjF41uYCngqcUrV2uOXEerk80ow8TqsatMq0QZFa+FenREKROCbByNkynjODIsB2Z7u+zMBKkLotF1Fe5vh6mOG5ckOYXLMrUIS0ng0yZiWVOOjuAaMcRuCa0jiTku8QHe6xROYhSvI6zPrcVxEAsneBbh3HzB807tcffenNN9T++QJEWkdJ0QB5Nw9rrGYgKpEYOrKaEeYDKAYrjmFUNVSblrsN6gCHgljQHeE5WZCCf6zO3nTjGlxGNXDpkcVgKpVswyq1oYi0EtdKLMcmY2C9cvU6WWgpWKjwNWnJI0nMZdxlNiEmc0ZypGbW7f5EbXYodTTmRxulmCbk6nzuQB3CO+ujlj1y5PAc8ZX88JhJSUnNfR4+EutaRUYCjGlcORyQaKAOevMFdHykQvHkkAGXKBOcKgicEqR145Mmeygk8jZjOSQC0GNeLhZ30HdQozuEBOmdy6sb25nMdxwmpBck8nkXCcRAMqV2N9N0OI89q6zBFtPea1ubCjx9nX0dJRoItT1+XPtExyhLjeXWM+TmPlaLVk/P+z9+fhlmTnWSf6+9ZaEbGHM+RclTWPqpJKk63Jlo2QPEgeGuMBDDR203AxGGy4bUMzNfhC2w9TY9P4cnlo0wyPjQ3YeKCNjSVbkgcsayyVpJJqnisr53PyTHvviFhrffePb+2TVa6SasrKoXK/9WRl7r1jR6yIWGvFjni/9337RFYhOKiCJ4SA96WYwDlT4CqID4Ta08XI1nrLwUMHWFkasLQ0IgxX2XP9Tew/uIoTpRoPOHb0FE8dX2cWMxJqcyJwSj1sCJWnnfVEAs3ykPHymJBtDh7vWWVldcTSnhXG4zHMZmweP86pU4HN7W16BBcq0ITrIkKPzG3YnS9O2aVffKk8tQUWuID4qiu/iuuXrueRrS9duXox43P9p/lw92u8p34fy26Fb7/hD/Pgk08hx068pPXJ5BSDT/5LZu/4yy+9UZoZHPsYYfvJl76OcwgB3nF6gt+3l/988jF+5+FPXegmnRe42Rqu2yAND6LV0oVuzgILLPAqw2q9ynff+t386/v+9YVuyovGZJB45PAO+zdqnJ67h4/1Pf+ZvHLNy7uGFrjNlvr+00i6cIVUIsLb6zv4x3u/nr9z5v2czi3BeQ6vHtpd5tDSft549e2M6mG5V3ph7b169QredevbqUvB2aCqqfzCJnKBBRa4fCAifMO138AvP/bLr7hd7ysOEf796jJJ4P+9fobmZVy6BFj6L3+a7vZvZ/qVfwVC87zf+f1wa1PCExsXwEHj2Xh79Xr+/spf4u9u/UvW8saFbs45Q+0arq9u5VQ8TuL5iwF7eu7rPofDcW/7Wfb6/dxSv46RW2LF73nlG7zAAi8Al9/Tax+QuW2tpsI9mipyTtKSdv1NESkWz97hcizvu2KLnM8qiSkKwUKmIlLyVwt5BOV1JqlZE4srSlqZ26c6cGKpplrsqouSUtxckSq4nO27zrJYkwZyk8nRCMOsDaq5WJeqkWXZoSgxKS2JPmHLF5WhZX8qGW/KUTWiS12wmFopashCEObscKJFETi3xw6mWHRKpc64ETXb4JwTOSbjVaOQYwKXzahZIoISXSpqwJaZek4R2TtoGKoSY6SqKhwlUzlURtTPMlp1iPZmBz2dmXN326GDIW5paPvWJSO1fWVkfkpmYR0E0YBUghj7bfmrbTSlcEo456nHI8xpOtJubpKyklKiJhP7hCaz//beE1Fi1zGbtAzrhmZQMx4vgTOCVT3k7QmaIKmSSSYCdgHBF5tdI1bRiEhAxTJbc9uR/BQRh6oHbRGtkFBstMWym7Soca1nOlxqcTnig2fFC4eHFYfGA5YHDQ1QKTgRcl2ROjVlvETw3uzBU49oRtTPpeMAaCxh0yI2hkJRJZdjp33cJbidc7ic8C5Si7JUB/Z5YZoiG21iu8toiAxcoM2ZLpnCUl2m8mZXjnOoZsuy7iM5ZnTaouKhTpASMUMUU7qq1WIQvKnOc54Yie0qwqAmKLjgCV5I0UhsvBgJKrkUhFiuLznhs+IrsxB2gmUe+3JMxLJ7+2hzQq/QdhMm6thqI4MqUNMT8ASThVMFYeQzjVTMYkRnPV3bkWIyAr7vST5Y0YfDLJnTwCy6S0FHCooLnhwqU4BrJqVoWdYpmo21c4gmYlScY1d5LuJxIoh3aLK8cAkeTdkU1mIFMWX0W1RuyQlXCZYFnNWIcVchZDRl+tQzmcyYdWZjHUKgqgJVbYS2uICTQJawO1/64El9ZGdjysFr9rJ6xSrLg4rBeMxg7zVcdfUBvLMKkVNPHOfxx08ync6ogjBYXoE4JeUO8ULX9uQMdVNTjQYMl8YMg6eqAsv7VhgOGnwzYrC0TDUeorNtNre2YceTs1retTjUBdRTCPBsf+dojggLLHARY7la5ttv/Hb+6Wf/qUUCXILo6fiF6U9TUfHW5XfQ7T/DDXvfw9HTaxw+8cSLXp9o5s7HP8CBN3wnh0YvwXpRM82JOxk89bvm0HGRICh82fqMn697Jt0XVzG9miC5J2w9QXPyLnZu/EPFCaZjePQjSL9jC7mK6dXvQu0H9LlRrS2wwAKXBZw43nP1e/jVJ36Vo5OjF7o5Lxr3XbfFHQ+vMG6fPe+l7Pj8xhV82b4Xt1+imfq+/4futd9BXrn6pTWsKH/rB9cuKPk7h+B4W/gWfnhP5H9d/wChHvItb/i6Z6h2+hTpUw/AQ6eeYNa3z7veIxvHufPxzxcSuFqogBZYYIHLEqMwYim8Ogo1owg/tbJMo8pfXN/g5ZT0uMkpmjv/FRoaZm//y/BiCoRiJhzfQS78JRQwzuIrmzfxle0b+ZXZ71zo5pxTXF/dwn3tZ3eVvi8EmcxUd5jGHZ6Kj7PkVtnnD3B1uJ4rq2sACCx+FyxwYXDZPQ1xYmSGiqn5zMXWVIoU61yDqXxN0avFqtkX1S27JLHsrk/t4dLcMVa1WLLauk0n7HZXv5u/WUhk4xOM3DJmKRsRTEJVcFKUwrhiUW2ktZDx6nAZtKqM0BZHTpEk0fahakrerpA10cXEtPXMppHYxxLjWgisYnZq/J7R277yRvKkRE7JLKM14Xwo8uYEKe9aA6tm8GYVLHjjBnMF9QDNkZiUnD2qXVEkC5oTSRL0WI5pbll3jvW2Zbn2NOJJKD6rqWUdZmmbBZ21SMpknULdmKpx0KCjgFYBUsnbDQGtPJrEsoBRXN2gnRHnvnIm5s4OIZOSkAkQe8SX4x8TmiL9xjobqaeuK4KYlXUuVrp+UINCN5kwccpAVhgMapaGjfUVb/miiNBPp/Q0pNRan5sbRokaoa4lQ1jEyPjYw2xCxqHNwESI1itK9q2pv0W8KbY141LCpYiLPWMJXFUPODwasuIDA+PAzZbckmhLdLTuWpPLXO2YMtr3qHelj6gRoCp4H5hbA6smUuzQWWfrErHjjqk8RUD6liory95zYHmJzIS1yYwtMn1KZISclOCdZWL3iSg9UldojKS+J3fRhJh1ZX0KsxpXFAucBo/D1zWurkg5Emf2sLwJjlDXVH3EO7XiAJOQG2keQlG3OlM/ezvGwZU4ZRHICS1FGCJFOYw3K2Ugx8TMBWY5sdlFfNdTpUyFUJOoJVHtFoPkYpGdGTjFOSGpjbnUTozwxxT3uY9QGTFvdt9D+q61dmfLTlY1p4OsSvSCF0H7rrg4F9LVWTazwyHRig4cJvpHM8zz0FXJYrbuKnPTUym1H7nMaUXxj9D1PTvbO0y7DlBCVVOFYDbMwYokvPOl2MVI9npQ03hP10VGwyHLKwOCNzv5ja2eQ7euMmzsR1Lc3OTBex7j9NaU1aUx1x7ewyA4tk6d4tjxE2xNOrz3jEYNIIR6wHhpxLAOBC+760VBQiDETOoSOzsts7YliyOLI/U9MUZyUjsyUo4LxVYdt2sPvsACFyO+6bpv4pcf+2Xu37j/QjflJaOn5z9M/w3p9hPsb1ZBlnjwxju44uQR3EsYf29/6m52dtbQF0sAqzI49rGLjvydo87Kd2w6Pu5q1vKFstQ8f0ijK0jDg5bBJR7XbbLyhX8LuduthlegXvsCAP3qLfSrNwKObu9t5gC0wAILLPAlcN3SdXz1lV/Nzz38cxe6KS8aycGnbj/Duz5z4FmfZRz3bh7k1pVTLIX+Ra3Xrz9I9cgHad/43aXo/cXhYlD+Phsj3lG/lR/dt8ZP1mfVS1kzD558jM8euYfTO2cASDm/4GiN+048zMOnH+fdt34Ft11x0yvR8AUWWGCBix7f//rv53t++3sudDPODUT4N6srTEX4q2tnXhaZIiiDT/wLJM6YftVff2GFql2ifnANt/n8hUjnGz+w9N08lU7y6f7e87K9zX6TR7ce5caVG1/R7bxj9B7uaz93drt5nfV06gV/fztvsJ03eLJ/BDd1OPG8oXkrXjwrbi/7wsFXotkLLPCcuOwIYC1yWrNxLTcf8xxZ3K7d6/x5v4SK1PeFEKF8XhbICYIZ+2qxULZbIUWyqcWEeS6vvY935cGTFsWmva+KfccpSsJ5y/XNyllysSwrLoBkNCez/NVYyGlnhAxi5CweoZDAxboVKnyIZi/tlHYyJRUrXxByTiTnSJqN7E3zbE0jwdU7vARrK6lEiFoGaHYK0fKHSVoEzakQaELwHlyFc7nYYg/N/Tomch6gqS3WtEYwtjlxatqxN3iGvqFOGa2kKBjV1KcpGinqAtJUaGNKWKn8rlpPpGSeJiO2paqLtXXJKxXLDhUE8ZbzLK4p+bMJ9W5XjQ0zcuqIbaaPVndv6AABAABJREFUW8SmwrmIQ+mnM9Q5fOORBH07YzrpmFZTBiEwGNSo82RV0mCAc45pjvgu0rl6l1vazRUsfU0UVN3ZvhczuZ2iOLIXcgbvFVdlU7eL4LzHi+D6Dp+NyB05z6EKDq8M2bs8ZDio8VIo55x3bcjp7djoPJMYSiZ2RGczK15wRnLauCiqyRhJOSNRoO/QmI1oz5GcrXAiq6mLJSpeEwPx7Fkekb0w6Tu2Zj19TASgdkIYNDjMdjzHaI7SfSJ3nSn3nQcf8LUUm+pYDpOp6F3laZoacUJb8r6bpiI6CH3E9RGPOX57EZILxQhATKVa5g3vzcJYyJbPm+f5wGXuEMtINmW+kNqePppNsx1WK+wwsjXhNFMJNGpkcOUyXi1jNtQVLs9ISVE82QXoOrL24AbkWQfDgbXVC5J6q8GIpZiiPJBRC5lGCeSsZ/NKUi6OBhlUcCLm4ixabJStp6PJChqK4ls8hQgtxz3FssKMhGDnJ7ZMpy2zPqEihFBTB09dVYS6xovDOSPXNeeS7+zN+p3McHnE/qv2gSobmzuW8bx8gOHqqFivZU48eZzHjp2hGQ+55oZ9LO8bszoasVQljh05RiYwXhpRBQcEVlbGjJqq5LBDO2lJLrM0dITgSdOO9fUtNjYmdEkRlyAnYh+JxXYbir2/5vLa2Y/0RQbwAhcxKlfxXbd+Fz/0yR+60E15WUgkPvHkXbzvte9CEO6/5Y28+e6PsLyz+aLXVUEprXvhkH6beu0LDI5+5KIkf8F+Sn5FvZ8313v50Oz4hW7O+YG43TznXI1pD7yReu0eJJoCWACykRvV2hdYP3UnCaFavp7BVV9NGl+NhsEFavwCCyxwKeBbb/hWfvXxX2WnzCuXDASeOjDlxJ6WQ2eebS/51HSV9z/1Gr792s+/aB53+Lv/kDzaT3/rN7/wL11kyt/fj5xv4p3NtWwfvoFHgSNnjvHxRz/D0c0T5oj0EtGnyG898DHuO/EwX33T21gZjAklPqbTlqzZivwvMqRjU/r/fgLty28egfrdVyIjjywvlEsLLLDAC8d1S9fxtVd/LR888sEL3ZRzgizCf1hZZqjK957ZeHl20JpoPv1v8Mc/y+xt30e68s1os/LcC6vit1r8RUj+Aqy6Jb5j+HXc3T9IT3z+L7xMnJie4GMnPvaKEsAiwh6/j3eM/uDue5O8zVbe4In+EY70j9Lq7AWtK5f/0MinZr8LwEjGLLlVRIQ3Dd5OJSU2QkZ4WRQrL3DucdkRwKbgLcSoqtm7znNo8abaLYpHBXLq5yLg3QxIs4fFyJ4MYv/bVYOJAD6gqecsJawkMftmp7mQwPksiYVlrRYTWlwhfp24ucG0KRuzIq7kGCdQl43sQovK16yIRUv7NODcPLfTCKtdskczofL4XNYhQs6emDIxdmStzBJaMymlQmQp4iIuhELeGI/a205a3mlOaI5IEsuDZYrToSl7BVMuq21fxKMhIzGDr8l9S5ZMLirTrQxrHSzHnio4vKuAhEfQ2KF9IjiBJpCDWXOjihPTL7tspLRktfNd6HgRh4YAuZxf58kOQi6KTO+hqsmayH1vXHG0vOiUIr5rydIRUyDGOBdMI0sjxHtyO6XvI2k6oxHH0mDAcDgkDGuz740KOeJGQ4Jv8THS96kUEjjbtxCQucrQF1JOBNEeR4N2La6uUBXEW96u+kLm4nDeUQEVytB7DtYV1+xb5sDykHFVE5yz41BykjWZXbOmbNsXU5ObXXBReydFXdy1V0YzBNDYknHWbzVjoc3Ozm+K5JRQcaZqjQkpmc5BM8NBzcrSiPHmDmd2Nui7zMAHmiCIK2r5urKihL43JTKCBoc6wTc1w/GInJRu1oGaytOFAMOGanlITj3Tk1NEbbva9WgfcbkUEoSAc1CL2Tlbnq5d9J0TnBecN7Uv2dTSSgKpdjNhbYyYQj6mRCo28YippyHYPgjWj70ja2bST3FJqQFJHRoVwVM5hYy5C8Qe1CG1QnCoZFLqyepJ0lr/TZns7JjofA5Livpcxjymmk89TktWNOCd4DQhEnAuk73gXChzj2WCCx6JGUcsEmi1QpqSE5xTIvY9XdeTejvXZvtcUzeByluauThTiduEWopSRInTFj8YUI9qUh/pJh1tm4ldz9JgH8Ng7gs7Rzb49EfvJ1c1t91ygJx6TjxxijOrQ2ZnNtnpE8O9S9SVQwis7N/Dvn2rRvj0HbNZx9b2DrlqqPbtoe1mbB4/zbGTO0yKdbeLVviSouX+ammr/V0U+S6U4p6LkwxaYIE53rT/Tdyx9w4+v/75C92Ul4UjZ45zbPMUV64cIIaKD7znj/LeD/9nlnc2nv/LT8MLvkdXu0Y3J+9kcOJOpN/iUnjc+YOrr+XDs+MvfD9fBj59+tN8yw3fwiiMzsPWngcuML3mPbSH3sL44V/CT4wEf7oa+H8++Xs8lSZ808bV/P3JceLStXT776Db/4ZnrmvxYHuBBRYouGZ8DX/4hj/Mzzz4Mxe6KS8asybz0NXbHDhTl0CpZ+LodIUnJqtcO9p4UdOe9BPqe36e/vp3Qz1+/i/Myd+LTvn7dFTE/Hq+4vSn+Ehs+W9f+K0XZPX8QtClnsfXnuJn1v4Lr73iZlZHK7zuylu4l89yS/1ahvICjuF5hrtiQLh9lfj5M6RHtwGY/eRD4ITmf7gGf9vKggReYIEFXhCGYcg3XPMNfPT4Ry+9YqovgizC/726wpr3XNtHvmNrmz05v6R7RdFEdeRjhCMfZ/b272f6FT9o7z99ju0T4cQO4amtc7MDrxDe23wlP1f9Op/p77vQTXnFMHJLjNwSh/xVvKZ+PY/2D7CR1jgaX3w81UR3mCQbEx/Y/sXd92+qb2csSwzdmOurW3bfX1x3F3i5uPwI4GyWymbRXOyK55m7rmRewtkHQFrskMWBRlSzKVtViE9TzpJTUdo6Uytq2n0Ap2U5zZbE55wz9ZyW7UhRE6uRwZohypy0mZMtZuuLFIWtmj2rbYtdNbErBIvgcE6BgEpRAmYjtZNAREnZlMfkjART9hkP7PAEXDDSO6Vkn4kRJFr2zUiibNbEmoltR3aVkdpZTYkYZ2QRKoHsnqZiFrebg5ylxtOa2tlVCEZSqyZmOXGqa9k7FRrnCYBLZqUt4iwTtnKFMDbrVgnOlNnz85t6W84bla5ze1sfUJ/R3ggzLUpulyIEhy/7i3NGiuYEbYtve6qqAm+KRNVEQpHK450Q2xmzWUuOCddFZtsT2pURupyo6sDyaECKmdj3xD6TQ8B5T131li2cMfI9zSXBdbGIFpx41DvILRIaglN8EFMlR0cqduZOhaoOrDQ1y3XNnkHNwaZm/3DAaDCg8qazRk2JabmnaqpPwVTxYPuIoqm3Pq4CKdm5UoWU0D4jdYCYSX0s5xaoKitIUCscUKemCs65KIoFyQnJis+ZRhRPJvYdThVxRantPTmVc9B3qAtk70EFD3jn8MOG0EPjLRvaDRpEM32MxJ0Z7WRKO5mi2RS/LmcCghdPKgULvqoIzpk9/NOureLMljtHs3pXtVxwXG3jB7FpIqul4xbyF91N8Ia6RnMiJ3DBrJBdCKTU0yZH6jNePCFDSC118Hin+OBxvUJdW0GFF1wd0LpCC/2YyWjqraasKMyzryx3WylKXrN81tSi3pNSVzhcQb3NWU4EJ3ZsvOt3nQREBNGuEMSm9tccS7FCpk+W0Z363vqoE7wTgnfUlafy1r+dc2b9rMXmnYwTbyrzkh09m/akuGkOBm2iaxP7bhpTC6Sdns/d9TCPbk+5+uplXOM49eg2k60ddh4/zmw6geGA4bCBLPjxkOHKmFB7tOvp25aN9Q3W13eoVpcZb2wRN3c4/dhRtiIMRmOqOKFtM9MYyfPCDl9hTLyR++prU3trXCiAF7josbfZy9df8/Xcd+Y+or7y1bivFKb9jP/2+Q/zjXe8m8Orhzh14DBPXnUjtz9w1wu+2VYXmL3le0l7b+b49Dhrs7VnLTMMQ26q9xI2H2Zw/JO4dv2SIH7nuMqP+GPjG/iPO4++4tv6+ImPs9PvXBwEMFgRY7PK9i3fgcQZgxN34ifHdj/+lwffScw9Sy4gQLX9BGHnKQbHP7G7TL987bMJ4achDQ68uJyuBRZY4JKGiPBHb/qjvP+J93O6PX2hm/Oicf+124xmgTc9tPqsz9oc+K9HXsvh4SbvPfwA4xdhB109+ltIt4V+KQI4KzLpqZ7cxG13FzH5C/YASNie7fDfjvzuOSN/fz/uOf4QAHtHq9xy4CK5dj4HRITw+j34m5fIx2d0v3XWWaS/a410ZEL9nivtfn6BBRZY4HnwtkNvY6laetUQwACI8AvLS6DKLyyP+batHb5yOuPaGFl9gc+ItkR4rLL7ijXv+NG1D5I//Bned+37+PIDX04Qz63heuonNnEb7UV/Tyoi/MXxd/Lnz/zwhW7KKw4RYcXv4Y3+bXTaciat8dnZxzmT1sgvymvs2Xi4Mxttj+fe9jMA3Nq8nr1u/+4yQSpW/J6XtZ0FLj9cfgQwJWK3eDyX2M9dwldzUdKqoK5If+cWuRJMOYuaijUnVOdG0o4S0mvZm7lYJ2PkF5i1sTDflqlnEQepWOw+XWWWQ9mONcGUyRkVI6yL6LBs04gw5kplTYW3NGtVVaGP0dSXquQc6WYdsU92cybOCMCyC5IVhyPnDhK41Bflr7Hj6mtUM955XMlR801N75wpAEXR4I1YjD0uDG2/Y0KL0tKJgLdl5ja6IoILZjucc8YcpRMb0bPW9ozrjqqucDEhiMX4ZkWlQn1AfbFJ8MHUztlIHD/rIHjIGY0JDaZ2nltji1KsjAOQyclycy1j2ey8JSckZaSD0Ayg8rZMNvJtlkFjJLaOvu3QHBmNBoSmQWJi1na0s86yUINndTwg92P6mOizEUlOKpzrqara1OeaTHzrHDlZf8PVaF1U4SkbUaqZ6Mz+22PWxXVuWa0GHBiP2D+s2TscsCTKUISqiyVTWkGsIEJMkouowyVT7yacEZ7ZbLrRWM63mAp3bqPtFaaZ3NmDAxkMccHsz1FBnUcLiabu7FDRZMSrTjK5j0jq8ZrwqvgUIQmaa8v8LRnUKSaSN/K/aryRjQoyi9TLS9SjhjyNpGmErienRB9b+smENJkizrK5nfNU3hMUknqy87s/qETURuo871ZtnM+Vr6KlkEFKBvjc+jknnGI5udms3G1aEZJC7HNREtus0nU9sW3psymBE4KGGkJF1ohTNSts78yy2ddI45FQmxLX23yEmKVylorsPdpHkiZi6lB8aUdA1QoMcrL9yghS19C35CxEMet5iQkXzK5ZktnLm2reilyyJmLORpBGJWas/5KpQsCLpw6Bumqoq4APAecLeYwVq6ha5rBGYVhVuCDElGhnPSkLVVBy7tFqxI2vuw7NypMPn+S+x4+xf3XISppx4rFNhoMhtfYcP3GKGbC6Z4xvaugyg0Ft9u8JUtuyfnqD06fOMO0yg8pz6qlT5DYy255y8PBBrt87YnLiKPc/ccysrovNtXNCwJP6nt5Z/ribz7+LHMkFLgF86w3fys8+9LM8NXnqQjflZWHSz3j/Pb/N+177Lg4t7+eDb/4DPPXU/XzNzuSL3hArAtWQ6dv/MjrcR/e6PwLiOHrmKH/z43/zWQ8i3jy+ln935dfiZy882+diQiWOPzS6mvdPn2L9PGQBv9AsxPMJrZbQaonJ9e992pvKgbXPI8ke6qeTn8HNTiOa8LOzpI6fnWZw8q4vuu5u7+3kMCIP9tIeePPZIs4FFljgVYt9zT6+8bpv5N8/8O8vdFNeNNTBo4d3uOmpEUvTYLE/T8M0VTy8vZ9fOeL51mu+QO1f4ENL5y0qKp4tLBMndp9YXIj88R2qxzYu+gfWc2Q9QNIR3StE/j4dH3/0M9y8/zryefHreOmQYcDfsMTwhqUL3ZQFFljgEoYgfP01X39JXkefFyI8UVX8+L49/Djw1ZMp1/YvrOj6aPD85vhpxUBpG7a3+b/u+b8QhD8+/AbuWPqTJZLt0sBBt/dCN+G8o5aGQ+EwXzv+Fh7vH+Lh7j5OpqMve72JxGY+A8Cnpv/9GZ8NZczV1fW7rw+Fqzgcrt197ZjHWC6wwFlchgRwIXaKsvfsXFreT3lO+ZpSUSzvFO92LZ5xYirVomZVwTJws0dlbgvqjNzMiRJaa5m5KJqkWC5jDRCzGgUjlHDOcn8LSTenoX0hruckLngUs5owYq5kAuPLQylrR8zQ9mbRqjmSFFLsd5XJGUUTuKS79rGqpmA00ivsKpaN/Eio9zjBFH1kvKtwziPi6IOna1s0ZxKBOrVUjccPBvSxQ1B8cDhRCI3dPOZEUiXiIbaQBCcB5x2tKqdiZqVLDKoOh+Aqj3e+HC9TpRIKGaNqZLNiWbRdb0rWGKFq7FylRBYhi9i57XpMLJyQtiWLHYPcl2OBLSaDQA4B74XcJ0LfkYdD4mRC10a0tXNZoQxGNbUP0EWiCps7E7w4RuMhgyqwZ2VEr4mua+njlIhDpSL0EamCKb6dKaldcMXaes7yWx8RyUjlEARJM8BR+8xSPWLfoObw8ogDK2OWnFJlxSfw0w7pc1HlCng7DkYe23akrnApWdZtTGieZ6FmXIrl30DlrX/HHpVsSs5Qkq91XpOQ0TYaKSqQcyZmszCOUelSz6zraNuIxoxLCrEjiyO7SJeVRLSnGF6o6op6aORicI6qqQmDhqBKaBNVNWAwHOB8pu1bjh47ynZWs93GiiN8CDRVZqaJaSwKXrGii13Vrpaii5JZ6zhrsW4VFoUgnu+o9ziNVpLhnNnLp4hmI/NzzoU4DsSY6FMmi0OJ+Kqh8uCDFWN0vUKKeBVSs4IfJARHp4lalFodQRyOjHgrpsCJWT7XgawZ9U0RqIoVRRQ1f8aRSqGDzmY2/tSRnZJKprLEGX2x8LZGZzKelKJZnmtGMZvoQKZ2jqpp8OKoak9dV1R1wAdTyzofjPhOkFIy4tx7qsGAau+YbntK7DJBMqnvyWX8Hr7hOg7tqTn22Bqf+PTDNPuHvPv1V/HIR36PndyzcsUqOw+cpO97m8JTRFJiuDwgBE/fR2qE9dObnDyxxmQyIyPMtoR2poBntDri0O1XsLrSsL11gr6PRmqIxzU1ew/tZYnMqRPH6bu5tU8+O38vsMBFDkH4i3f8Rf72J/72hW7Ky8Z2O+GXP/dBvLNseDmwj7/qHO+cTrkmPvPB9SNV4NRVb+XW9/5zdLjvGeP19ftezzAMdwnggXje0eznB8Y3XbLk7xxvqPawLBXrvLIEcNLEv/jCv+DvvfXvvaLbOScQodv/+t2X3b47qDYepFq/Dz89he9emJV4vW4V2SqOwdHfIy5dTXvwy4jL1y+soxdY4FUKEeFrrvoaPvDEBzgxO3Ghm/Oisb7S88G3nORr7jzIyuS5HQyenKzw1HSZG5bOPO/62uR56Irv5CM/+mO007PZd9e+4c3c+s53cfXhVYYPfZI4ffMlQ/4CKHs47Pfz3Us38u+2H35Ft7U2OcPnjt7HtfXH+MrR176i21pggQUWuNC41K+jLwb/fTR82esQhD81+ha+Z/ztlxT5e7lDRLi+voXD4VrunH2EE/EpZjp9RbY11R0e7L6w+/qR7n6CnKX3bqlfxx6//7m+CsCSW2HVX35k/eWOy48AVgG1h/ha7Jrn6lbJuRA8RtiaeM8X4lfmYlsjolxR8hWrYyFZVqcaOSSFLTUV8dzK1xW36VJ/6wNF6mmkm7fJfTfTN+fdLFItlrtSVIhOnH0tRsT7sozDMScJbTnVRM5pV42bNRfL6Ll9tbVG1exjVQVyX5yQ7PhQsoRVs5G23psdMKHkIkvZPcEHh6qQfUWfbVsdMOlaXIJcjJYTgveBQGdibO9wGWSWcK5GKjESJiZybDnTtpyuA8sdhHqA6UkdRcxLxiOxt2NccnC1y+Q2In0HywPwVbHHTrgqkFMyq1nncHjLc3YeKm+kZyF7pApGLleelLIRbaqEnMnZsoqD90SJZE1UzuwaXMw4yfihEXGzNrLpprjgGQ0bhoOafXHIbHvErJ3RdR1SBbPkFUi+KhnUlj2rGsx2Vn3pA4FAxueEd46qrvEp0XjPSl2xZ9CwOqxZrgPDrPgYEe/wrijRZ52pWCtvamyB7J3l1aYIOMtpnpOFWcmV9R1TpBdr55Qhg/OVZcx6K25QTOmpUS2fNinqHUmNROxTosuZadsymcyYTlty3+FxEALKPHs6gziqQUPT1FS1JzgjZN38v5jwVcVoOOLaG29i6dA+NjfXOHrsGHL6pNm7aySljIhQO8ew8uxEBzmizmPi5DL25spwH8zS2iYPs0MG6wMl01q12DCXZeZW4kSz1k4qRWFfsqXnTgKlyEKco6493ntSjMReEQIuBLyvqJuaqgqW0ZyVHBMxtmQErx0hmPW4dxVKtNklQxJT/9qYcGZ3HhxKIIqi6ohdR04O35qlTERJzpGbqpwDiFmJXaLPmaj2t3MVdRMYB2VQBaoQEB+ofEXVBKqqIgRH8EJwHl8Hct+RooKvrKAlCL7ydNszYhstUTelMl9WhNGQ5YNjjjz0OI/fu8HR02e49U2HmTHj6PoWk6g89cBTbD+1Roo9rqpsH3JiazqjTp4DwxHdzjanj6+zs9OSY4f4QOwz3jtWlhv2jGu6Y2d44rGW40+s0cZE8ddHKke1PEA0k09VIEaoaJm3xV/clfsLLAB2M/KGfW/gzfvfzF2n77rQzXnZ6FJv0Q4AzvHDB/bxurbl/3v8FIdKfMGP713lN0Yj1gY7/N2dx3jL6MAz1uHE8Udu+Db+w33/DgT+wvJr+B/HN7xqKmX/xNIN/KONVz73eRInr/g2XgloGNDtfz3d/tfjJydw7TrNqc/gd44BiqQvbbMmmpE4oT7zANXmI2zOvpLJsasJr13FX18sUQf+VdOfFljgcsctq7fwQ2/5IX7w936Q7jy4K5xrnFnpeejqHd78wOqzVMAAiuNTa1dz7XgDLxYv1abnfkz0Wydu5PP3Pwq/bz0PfOS32Xr0bl7zbXdAu9fuoy4pJDzKPtcQxPG6q15D5QKfP3o/AG3sd13dXi5UlVnfXpJ9aYEFFljgpeCW1Vv48oNfzq898WsXuikXNTyOPzp8L98z/nZqWcTOfCm0qSVrNnfLiwi1a/iK0Xs4FY8z1Qn3tp9hJ2/R6SvnMJKIpKfFfX2+vfNLLr/q9vEHxu9l5BYOH5cTLj8CeFfGKkia5+nOlYFFAWnsVVnOWWaNyWwxYjYBJQuz5PfqPB83J3tvrtScM0RiGbuokYfZCS5nsmYkK+ICc1vop99apGykqytkrKhgxFwycrjYKCeSEUrOn1UDKqSSA2pupZaj6niaKjn14IzkMjvmYg+rWpSBxe5Wc8kbVjOclmAE1O5nlVm2JssfDgE0N2icklWY9QliV7KSxbJOfSYEj/NGXkmOeK9kqdAUkWzqyKyO1tWcnM5YkUSFI2jChwpxDu9M7azJ8n1TO8WJKZRxGXGOJIAoTjOiAdHiRJzt+LtBbURoShAF+h5Ilm3rvJ2/usGlzsR/4nAKZsfdkbyj1wwx44Y14jxZIyl5xEUkVCiOnT7B1g4iMBoOGA8G7N+7StvNiDLFklWFrBSlpQLR7JgRNIt1JRQniroKYo/H432gAsbiWHGwHByDlPFdxInHV6GQpmUcOGuvr72RtmTrSzGBZjLJiF9VGxcuFUvoWCyznR2rohTPVY2r3O7NsSq76uGsQtZkdrwp0XU93WxKm4VJ3zGLPX1MeFcRHHgXCEBVcmi9E2rvCMGshDVFHDUhOCrvCFVFVddUV+7F33YFKhX9yePsnNygm7WktiVNJuSs1EvL6DAQekHaGZmI4u0ISyCr2beLr8z6fdfisswDIvbwVxw5xUL4RlAhk8vYsqzsLK7YwWN5uq4CSrGHOlwEfKDyZq0teOo6oJJxEhgNLNPWe0dOCUWIsSOlhr5tya3D99GOg4NQ1zRNZTnHbUeOc89tLQUd1v4g1sdCFnKE5JQ6zugjtATamOi0J4sQU6bLSp8SLYAExuMBowArwwGVE1QcrvJUVUVTDwhe8CEwXF3F50jOSkyKSsANRrjU4ZzlBsdkhSIiSk49ztWoE3zVMDu2zr1PbnDs1IzBao1OtvjsJ45wYnPKTHu21jP9dAYi+ADddEa306IiXHHzChIyp0+eZmNrm5gTzgmazcr80LUHuO7QCnXqWF/f5PiJdTa2W2K2edQHT+M9cTpjczaj7U2FPVeGE+qn9Y0FFri4sbfZy3uveS93r919SWcBfzF8oa75S1cc4EdPnOKamPj4YMAjdcU1g+v5wJMf4I3730jlzt5EO3F81xXv5M9uPglA9SqySRIR7qienfe4wHMjjQ6RRofoV28BFIkThkc/Cs/xoF/SjGr9vmfQHpIjcuQI6aFl0oOb9vu/ctRffQh/+wpuvHh4s8ACrwbcvud2vvzAl/PREx+90E15SfjczRv4JFx/fMTqzrPnpcd29vLI9j5uWT7N3Weu5EPHb+L3k7wAqRS7PxeuuvlGXLtJm95zjlv/ysPLo4ic5ruXbuSR/SscvvZWAN52/RsB+Oijd3HXk1/4Uqt4Ufjk45/ja6559zlb3wILLLDAxY437nsjv/Hkb7wq70XPFb5r9M38hfF34uXSjBr7VH/urpPPh5964Kf45uu/mQODA8+/8AXAgXAFAFeF61CUe9pPM8tTHusftOfGFwiewGubNzGU8QVrwwIXBpcfASwYoZcTWdXsmsXvqua0qFnnpAlzK+Td7+8G7RqJXHJ10bnqUUu+pZFE+jQ7Wcns2kO7kvebFRxG6qkmI2JLzqhZQp99KJnFyF+VbJ7uFGKYonh1liXqsrPcUhGiQt9F+q41cjonlGxkkpoaMKdYiN+SOYs3ki8nlEI8lWNgXLmpk2WuHp0fq93KG8t/9US0CqS+IwMx9oAdB3JP9g0pD/CaqIcQgkOyqZgzllnqxFTFsY9sJTgxVYZJqMcjgutwweODlExOUzUizlS0TtBuRnbOblZzBpdw2ZuNccpo31vGaVOX06TgFOq6KCftYWpOGecE5wdGPmaFKuBVqaIQnaNSsz32Rfmdo6PvI2jA5YirLFd62keqyQzvHcOmYWXc0K8s0/aZzXZqKs0cyTgignadkXeIKVO7Fqk8QWrAkVNGvKfyjkFTsRQ8K3XD0HlcTuTYo42HKiCxR8WUrHPluwLad+QcyZi1dU4dWctNvmpRQJuCmhhJfQIfLOvXlWNeCdmB9sX2OGPFAeXylosFcd/1dF2iT8osRbpCMDqBUV0RnBQ1qVAHy+n1ChWKpkgm4IMnVI7gFZcidC3JOyZPnOLU7EH2rO7B7fQw7YmTGXnakduIXxkRVodMz2zSloxgsqnrrX8YCaxixRPWm60IQopNvBV6WF8S8UjOxYzdSAUVKQS5IFmLbYvivS/ku0N9IGrGqeUCV87jXUC8aYmzCk1dMRw0eF9ytkpBR0yJGC3DV+uKWZvJ0eNzxDthiKNxQu2rUqzhkELuq3jInSnuVXFVbe4ArRJF8A5C8ISqoY6BPitRZjgXkDaRU8KPh6wujdizPKTylalvXaCuagZNs6tIdt6TY8JXgdi1pOypx8tcf/AAeXOdp7bPkKNZZu9msAMpZcbLy9x67ZW0W+s8drqlS5krlg+xc3Kd40dOMkst3WRK12c0ZYaDilqg3Z6SfUDqwNb6Gu2ZM2wdW6OL0eyyi1vA/sMHuPaqfaw0sHF8nfX1TTY3t+l7s7f2vmJpZYjzwmx7ymxnav3Bqmp2XRAWWOBSwjde942cnJ3k3973by90U849RPhC0/C/HjzATx49zoGiBP6yw3+YL5z4b8zS7BkEMNhvr+YVuLnuNXPkacrYsQsc9INzvp0vhvXU8f/buv+8be9Vg5LprvXKM/ODn47c4w+99VlvpxsGuOkZ8lNTQCElut84Sjg+pf7Gq181xQULLHA5o/Y133L9t/Dp05+mTa98Tuy5RnZw5+1nEODmJ5cYdc+8/inCx05dy41Lazyys5ekpSj+BUC8Z3n/QW77qnfDqd8ALi41zvNDQaaImPrqdVfewkZ5ruGKQ9s7bngzqspnn7r3bBTSy0BeFJEusMAClxm+7uqv46cf+GmOTI5c6KZcdDjk9vH1zVfyZ8ffcUmSvyfTOv+9+zT/5/b5y3lOms7J9fiVxvx8vmHwNlQzN9W3c2/7WTbyGpO8fd7J4GW3yjXVjYv708sQlx8BrNiDey0Wz0WpZ7GqDiGwq/RjThrO/12I3/k40WKiJK5YPs8rYrUQppYZqlLWgxQL2bn9MqCW4is5FxK4qIbLukQUxJdM3rKOsnknRhxnjKDc3b7Mm25qzhhbUopG9JLJKZLVo7kv/smpkNem0tRiGS3zC4/zZsOL2Uzbv81q2shuBySzZFQgRyRnXFVT9R0uBBIOJ33Za8sNTimitGTv0FmHNnWxxjXFqasqs4wtxHFEONUrI42MqxlVCHgRQjR+UsThXFFfz3n6rkPrilTyUV1lSmzRuY1xRqtg2bXOm4ozC5J7y7etAhp7UxeLN0I4m5X3XF3sUYIqtSZ8ySHV0BArT+56uhTxqadOCakqshNmLaZoFWi8sHd5xGwyIaWWiRq5nnMkdopmgdyaHXk5vy5GnAsQI2D5y03wrAxq9jUVq4MBIUOatbTZCOIk7JKROUZS16FZizI8kdvWSNqo5M4IYBWHOkXqGp8bgoDEiM563MAZESaU/GUhRyXGSDZe1QoTylhSyfQ5kVKmJ9IKtDnSF6vlpUoIdWWqax9wAqFrCRihKDGS2w6pLPPaxYTLQO3IbSRqj68UBgPClXvoZ574RDbCXsAPKlJOTNa3mW3ukETsachc5SugGslUJRN4V/DLWf93z9liBwdBcdkIeyN/HZmiaHXY2MjRrKRRvJNSsCG4UJNVcA6CN8vnFCMae5q6pmlqEKHro5H880zwbMp7kUAio97amzUTpCapI0oA75HK5ieyFXwYaVmBJiRnkAyhQqsKTQ6XWpqBpx42RFUikFii6zu2BzVVr4z3rLJ3z5jKB/quQ1ygaoz8reoGh9ljOrHzlCLEGPCjIbd++e288y238Pnf/iRr928xaztSUrPG1nK8gGvffDOvv+1afu8/f4Dpds9o75DUTjh+cpvt6Yw8mdK3vamYq8BopUbbntgmJAiDpqLdmrGx0xK7jiAZL9DFTLNnmcM3XcHKcs3Gk8c58uQpzpyZ0vdl3mvGDAcVQYTZzoxZr8SSLSoCVLXNYynCJfCDc4GzUNWX/UP3XKzjQsGL511Xvov3P/F+npo8daGb84rgwbrid0ZD/j+n1rhteAiO/izX7L2dxjXPWG4e/2F/n/1Z91zn9unLKHB/v8kHZ8e+aBs2cs9/3Hl0t3bw5rDE1w8P737uEL5r6UaWJJzzvqSq/MedR/lYe35yjOfH8HyPiQs2ll1FWrr62e8vQfOtK8TPrD/j7fDGPRftfHG5z4cLvHgs+gx81ZVfxV943V/gn33un50zO+DzjU/ddoaHr9rhaz51kKVpeIYl9Kl2xCPb+/gDBx/lwa0vnh03h68qXv9138hgeYXXvfvrCZOj8EUuPy/mIe0n+y9wZ3fP7uuRDPgfR99YonoM57YfdQRnsQlHh4GZf/a6Kx/4qpvfwkOnHmO7PTfxBxeqBy3G8svH4hgu8FJwufebxjf88Nt+mL/zib+zIIGfhtvCDfzj1R/gsDvwvOf2xRKer3RfUVUeT8f4axv/lIfTk6/oti4mvNRxKOLYHw7xTv+1ADzc3bubE/xE/wibef1Lff2c4A2Dtz5nHMj5xuU+H14IXHYEcM4Z5x1ZQFxAcgIS4M0cV7PZu7pCCBbidpf4zXP7z8aUYGJEh2V8morRlLXRCDsteZKhkKkqhae1XFHvjdR5BtmcM+pNtZfzWZWxuLlKzojkPCexBSRY1phoGQQYQeycMwKXjpwtkzRnjFBkrlrGsn9RkkKOESdh1344q6ApmqKx2GJDJjtnea3izBZbBdWSj+MsmzU7Z9bDSYsdruUQJzUSNaYOsi2XU0eoGkKQkmOM2Th7D9lUl9spc0J6llNDrYmQhZAyPmXEVaVyFyN2U4KULVdXhJx7UrJ2ibhdG191ggZn5zwn6O04qZvvb0X2WqyBPeIKCS5i5KYqro/ULqAjI+ByzkgIpEFFjB2xj2jfU6nuElHbRf29NAg0wbNvZZmu70ltR1QjJhXITiEL5IgLNc5VINFsskUItWNQB0Z1YFzXjMcDqiqQ28h02jHtWnb6ZKTpfKpPik4n1ptmEaJl9YqU/Uqp9FkjoLWNSLYqaCm8YRUzIWUI1nfFF+tqqUjak+M810jIIuSciX1L7Hv6LHRdT0oZL4FRE2hWK3zdkEXoY0L7hKsFyCS1cSAp4pwpayUVVWtScpVNLS7K0MOIzOzkGdicEESoBjVV7kmzjq6dkXJCqoAThwuNWZ6nZNp16Yzsx0PJwVJsLAlnmWFBy/zhEV+borqQ9+IhZyN3LXvaiiREwWkudszJSFJf20WrKO2rakAdAiRMQS7gnbeCCxTxDSn2iJiNfBNqxDubV5zlQ6s4eiIkIdOby0GZh1RAk5BFSeJQbWEwBunxTY2vxdT4OCpvlGwvSuUblgYDlsZL1JXQTTuyOuqmMeWv94QQ8LEnlzlFq4rUOWR5iWuu2ceVVwx58sljBOdoyOwkcx3ACVJVaLHiT5MJj9/zCOsbU7x3VCTWj67TtQmfOrqYSOIt07quiLNI34E0Y5b2jKm1Z2N9QpeFZjBgXCvt1jY5Q7W0RDtNHN9a59RjJ1nf2KGLkZxBvKMe1oTlhn7WMpt1dLEUvKClCIPdggH8wtrzUsK5+HF4qf/AvHn1Zt5x6B384qO/eKGb8opg5hz/ZWnM26cz/vShd9N++feQqxH4+hnLHZkc4X+/84fZmRiRu9fX/ODKa7mj3sOJNONkmu0uezK1/PjmvbuvdzSy9iJy+x6K2zy09cAz3vulyRP88fENvLHew2uqFZbduZlLjqYpP73zyDlZ1wvBZ05/ht87/nu888p3nrdtwsU5lt1yRf3Vh87pOl9JXIzHcIGLG4s+Y+1/z1Xv4Wcf+tlLt5BKYH2l50NvOcnXfOogy9Oz15+onrvPHOJ9hx/gzXuPctf6VV90NVfcehvv+lN/jqX9B59xXhXl7v4Ben3mY6ZH0hF+avJfX1ATN/M2m7qz+9rh+PnZb+w+sLzZX8OfHH0z+90q14XDX2w1LxAtlf8E9jwInhoEWv/FFMznrv+qKh995NN8075vP2frfKFYjOWXj8UxXOCl4HLvNyLCLau38Hff+nf5c7/95y7ZQqpzidvCDfzo6l/hCv/Fi65m2nJPb/d3/2X2YT7TvzCnp+8f/3H2ueeOBVpxY24O1774Bj8NE51xT/8w/3Dr3/BoOv+/iVSVJ3ee5ODw4Hnf9ssdh/Pv39y8dve9m+vbd+3RFeVzs0/sksO/H2vp5PMqhwcyZMmtPOO9/f4QB8Phi2IeudznwwuBy44ANsWv2S0j82oBfzbXUQS8N743F+tjwdSwWvI9M5BiUYQ4U62KKUyhhMtqZZbOOCOMUjSrYTmrJvFzwlbmRO5ZC+Xda6FIIaMK4VAIZSOZLbnUiZZ82JLLOlc179pEC06VJGWZPF+57Z/iLSs4G0GuOZN1VtpaVL5zwktcMXy2HF3NGSWRstkFZy37kDOqnSl5xaPSgfQ4KbdX5Rg4cSQyKWbUB1LqSEGomobgFF9XBM3kXkkpk7RnI1WcaHuWak8dAiEnfF9UwFiuLSWfl5TRtkdDhtSjEtDalM9ZKYRdQGPcdbJ23pu9bo7FVjqTUgax4+XsU1Nex0wq5G5wDjcaE5PSbW/avtcehyNKTerNjtlLoNZIP9thG9BUMW5qhoOKveMhs5joY6IVcLm3TGQXCmlv1ssQyGJZslVw1L4Qb7NM65QcKmLX0+9MyFkIVW/kunN4cXgpBQVFJU1MuFDh6sbOa3BFfX7W/iuhpL6HnHApkXNFP2vJzooHfB1AnNk9pw5NsShp7Vj3XaKfTUk5k5P12ToM8E2F1BX1aIBUFVEVmcyIwdPHnjibIeLwXgjq8GLDUPzcfhqksmPdLI9YumovbuigEUITqBtH0pq+7eicFXwIWM6zlvE7z1iOqeQhR9SZQsw5B+qxEWWqfilj1Io5yoW3KPXnBRjeWz4z82XmYdxS2oyzHFmwzO6U8d4RSrFIytZO74yIjymTc0JE8OKoqgCSURWyZpu+UkZcsNxlHH1KlunsjCA3xwElx0TOCSdWXCFhQBZPV9T/9IqXRKWBiNKrZzwesrS0ROU9fdeTMlT1gGZO/tYNV63uQSZbHJtMrD5GG5aWVrn+5tt5x81LPPDQPTzQZepKmSa1QhBvRSWardDFSebM2gY7s46t1NGMR6S+ZzpT9u5Zot9s2Y4ZVRgOBix7oe2VXoXVPcvsX244c2pKFzNVM2C8XKHtjDYmK+aISrcTkckOcdrRp0zue1xVMV4as9RUxEnL1qynVyEhVD7gco+KL3Pr/DwubloWuPTwva/7XtbaNX7r6G9d6Ka8IvjQeMT/kTN/NQzJg72776sqbW45NT3F3/7E3+bhrYd3P3siTfifT/0e7x0e5sF+i4fj9ivaxqfSlB/bNHXTu5pDfMv4Gr5+cHi3+O2l4ucnj7OR+3PRxBeENrVM4rlRQi2wwAILXArY2+zlR972I/y1j/01Ts3Oj9vCK4E5CXzDsRF3PLyCz4IgbF3xlex8xR/mdb/3r7lv8xDT9NyPi0LdsHzgbNFLzJH/+5GfY/PMXfzq5FfozqGlYSZzJJ3Yff1kOs5vdZ/iOn8l/8feb+QadwtBr8XJi320laj8J/Du8eddUlV58OSjzPoXXgD2fJj2s+dfaIEFFljgVYbrlq7ja676Gj701IcuWxK4JvC9S9/Je+q3PSf5mzTxc9Nf56H4BJu6w4faj7/obfyNzX/2RT875PbxzvpN3FHdwjcM3knAE17gNVRVaen459v/kZ+bfuBFt+tcIZP5mQd/hi878GUXrA3nEgM3esbrd46/7jmXU1Ue7x8m8aXv+ZfdHg6GK89Z+xa49HH5EcAl49dIkgwpFWWtmIpVpJC9FMJVTRXqghG5qqaonRM9UrJ6MdWfaiiKWmOHjJTVs4riuWpXM6q+kI6KakR8VdYfIHeoC0Zo5gxi1tSZs+s0C+qEC9ZeZc5hln8VG9Ygjt4HtO+BZMdAA2g0dbEImpUUO7KK2cya3NnWpZbp65y963w5XmoqYy2qaE3JlMKUYyd+1yJVRPBhABiBRdeb8lcSkj2KmP2t9+ROyUlh0OA9uKoiZCHX4LrMTOFk37HUBoZVT6gCAY/kiFNFfDBFoYCq2bWKZlMod72pDOsaYrECL2peqRQXHPga6TuIHi0KUEJlVocpkdSUnnP7bI3Z1jUcIpWnqoB2uEu8eanwQek1mR2uZnIXURrytCX3HbHvGA0bhsMBe2Ytfc50VKRKyX0iKUiocd4bmegrgnNU3vJeq75DVZi1iTNtwoXaLGpTohKPyxnNPVk9PlSWAeuC7UJM9rMrOHzd4JySu1Jw4ALqgvX3viN3vfXt4E3xmyG1HTmDj9bvc444Lbbi2QoP+i4R+56UiuswQjUc4pdHpl53puScK+PbUcN01hKTdWw3tyt2YmNFBbIVVLgqgBcIjpw7Tj/+JBs42s1tfFZ8hEqFwXBktsY72/Ql91dIeIXoHFL+ZKsQsfkgVOYMIEZy59KffRlieV6LLmJjS8sHbu4aMCfQ865yWHeJw/JjV8qY80WRXMhlJ0X1m+KuzbtzQih20lZXYm3SbHOMOFOq55RQcbiUisW9R9TU/wJU4vCDIbhMTpm+n5FiKSxxHofigycidF1kOBqzvDQy8jcmughhuMSwsbxg7zz1eMTwlgNMnojknR0kVLz2za/njutu4Ya33EI6+SQf+uCHOdIqVezo+lJQg+6SvzZmM5sn10htIgebl6c7Hc3qMoMGdroORfDDhituuZKaxJP3HaNeGnPNa64kb22z82hLs7zMVYeWYbrNU6dnRHW4EBiMx9zwmmsYba7TrR3n9FYGFxgsjVg5tEzIkcnJCV3XkbJS1QMGlSPnitjats8W4pzrC9QCC7zyGIYh77v2fXzy5CfZiTvP/4VLEL+0NGYip/iBOGUYhhyfHOdjJz7GT9zzE0SNX5S0/MD06HluKfx2e4KPtqfo9mb+0Oial7WuT7Zr56hVCyywwAILfDHcvHIzbz/0dn718V+90E15WVhf6TmztMEXrt/iLfft4epTQ0499ghr3/EnOPh9f5zwI38PTj83yX3w+pt2/33nqTv5tSd+jfc/8f7z+jD/8XSMP336JwlU/PXVd/INzdejjFB9PjWQ4uQY3j2Ikyee8cnaZINutaIOz3TmuPf4Q/zm/R8lncPs3mmesJO3Gbulc7bOBRZYYIGLHcMw5G982d/gwc0HeWz7sQvdnPOOJRnxvy3/Wb6meTtuVwR2Fvf0D/PB9mP89ORXicWd4lzjRF7jl2Yf5ldmv8OPb/8M76zfxB9s3sJt4Uau+xKk4dF0it/r7uJfbP8sW7ooAr4QEBGur2++0M1Y4BLEZUcAzy1c1VjaQvgWK99csnaLhbExh1J8iDHmJFEyc4WzNkCmCqRkdBppqs/YKvP3BHZzRJ1DNZNVcRKKWjiaDbUrRHIuBKyL1m7xFIkl+FDanUDM2ljJhXi1tjsyIQghB7oYdkla3SWuQEmWU4wHjcXqGFM8q5Ffks1OOTlQ9Wg2RaFDzZ1YFVMpl33NcnYfiaYgdh4zccpUwYjFnBKpT8VuGcsfdoGUM23b0sxtegHRhGgEAjtd4rjvWPFQV4EgRiA6H4zwCkaY27nIOI/l/BZxtSQt5LpCH4uYz4Eb2ClPEejIudiFe4+KIH2yTGAR0GhbiL1ZRTc12YnlzdYeV1SuOUUjjFCzhY49yXvanMlpStSaPisJoXbCcNCwHCNtn0hVTZaeHBUfAs4ZgxeAgXMMnKeOHQFTWsccmeSE6yIeoSbjKodXLDO3cnYsPIizwoXQBNQHRIWlpVW8ZLZTJOaEiCMMRrafXVtswR3iHXhBvSvFBEpULWRwxLmM9xWaIMXOznFK+Ay+ahjuXaFZbiyO2zyOTWnrAz1K12XaNqIpU3kPfUc3bUtkrUebTBCHOMFlhaSkWcv09Bn6rRYkoLln1nW4qqKqPKlvqVpTwzsSzpmq045CGa9OirW72YSj85KKzNwOQIqltVMjK8U5a4M4ELOP15xtLnFmNQ7G/86LQMhzVX1RMbuSD+5CIYczbj5fiUdQqirgg9nSm5W7/UE83il4R547E/ii1K88ToOdv5zwgA8O5y1fOKeMqIKr8BWoC/h6ANnU6rFLDAYDxsMBdaiIKdJ1EV8NGDSBEAJ1qBjXDV0XOXn/UdLWNuoCzXiZA7fdyHVvuIVBHXjks/dyemOHLgudRkLweLXs4pwU0WJxjWO2NUEz1LXQzjqyD6ysLNFNp0w7wTdDDu5foZn1nFnbRAYDDl65H9mZceKxk8RqxA3XHebAWHnkoTO0XQ8uUI+W2HPlPpoBTE9ssz2boWR85WlGNdpnNk9vMm17egL1wLM8bgClnfZ2Wcg2r2UcuVi0LLDApYavuuKruGnlJj639rkL3ZRXBFmEXzv6YY516wz8gFOzUzy4+eCFbtYXRUfmH5y5mw9Mj/K3Vl/P4TC80E16wdiJO4v8nQUWWOCygojwR278I3zoyIeYpUtbxakO2ibzkTeuccXphj/wmQNMpjvo4BZu/ap3c+f/85+f9Z16NOb2P/i1bPVbfPLkJ/knn/knbPVbF6D1MFFzd/qHG7/Nrzef52+uvpUr/PX06W1A87QlFZghdAT/aZycQOTZv+Nv24k83LZQCGBV5f4Tj/A7D37inJK/AGfSaU7H44zrBQG8wAILXF6oXc133fpd/Ohnf/SSv46+GAxo+OvLf5qvG3zFsz6b5Bn3x8f4O5v/nGP59HlpT0+k18j724/w/vYj3OSv5t3N2/jO0fvYJyuICDt5ylRn/PLst/iN9mPcHy8/0n6BBV4NuOwI4KwZwe9yt4qQxZJRnc9GwoorqtviCaxzRW9RrKkrRFhml8wVj6ZcNLqm8LNc3EKYGbtZXkpRjxXyRaRQxHNb6ZI7jNhdmRbSGoyUlUJOZ8jOI5LsfQoptWu5ZKSS82L5ry6A9sUuujysK7awxsRFnBTlcizqX9GnZdEGJAupa806VyPZ10ZsoUWl3O8S5lkzksW+7xzOp9J2I+7EKSIBSYW8yo6ULKsYlJyTZfY2ATdwuFky3jsnIrDezTjqMgMvBIXaO1MnZzs2Lmc0JlMEq4KmYo1b47zivCfFHlKPJIfJQjsIAXEBl01pSeVJc0l45cka0ZiMgOt6tIvIaATDGg1GauMcXgRXBVILxNJ9VIjO43wgaqaPQu57UopkUQZVTeUDw7piOWWSEwSPc3PlthHytXgGWRkq1NnhMUvzlCJkR/CeqqqpnBA04wl48aaQzglJVSEBjZP0TcWwHnLlrdcw66ds3r1GViHUAw5efy197Dg52cZnTPXuzSodpzhvfS0mSAKJDAkGldtVtDtv+dg+NFTDEc3KGO8Er5lchWJ/7Eki5K4vKuKMiNlOx50Z2vf4rBAr6BI6S9DUSN1AXZOrmlkSXBWNeCWbjXIdIEeYKcSMj5ngPdEJoYMgQu+ElDOazO4ZNyeFcxnrUKoYoBRa5KLwdVlRX/KTcWfHjc7HRemTYlbNFEWxlAKN+b9FFHSe18suKS3i8EgpAJCi4hWcZFP/l7FuhHMAbF4RcaRiwW1DXXBNZWO9TIDiPFJ7s6p2gvcVITiSeloV6qFj1FQ0wwE5RrpZh3OepqmoqgonMDqwwupoxOTMlG42YRITfrTCoWuu5forrmRUB3Rrk88+dIQz7cxIXufIzJXWc2vsjKsCITj6rsUPBtavYmK4d0TSxNZWS3IV+w/t4eqb93P6yRNszRLX3X4T1x5a5tF7HmJtp+O6O27jjluu4Mg997F2aoOsUFc119xwPbfs28PssWM89sBTbE57fB1o6grJiem0ZzLrabOjHo85tDqgdolpl1hdWiJubHJ6c4N21wHhsruELvAqgYjwfXd8H9/7O997oZvyiuKu03dd6Ca8YGxp5Ddnx6kQfmz/W1/SOr6yOcCd3flVAf+re/4V773mvQwvIdJ6gQUWWODl4uaVm/mm676JX3jkFy50U84Zju9r+fl3H+H0R/8Rf+nMt/GFDz93VMSb3vfNTI7+On/rxK9z/5n7nzeH7nxgfg09mn6HH9s34Ro/I+nTXTUywd2NVfOb69Bz4To/onnqSbpbbwfg/hOP8MH7PlLu4c5xm9tt1qebXFef81UvsMACC1zUEBHee817uefMPa+q6+jz4duGX8P7mnfuvu6055emHyaR+Hh3N7/Xfcaep14gPJyO8MjkKX52+gH+X+NvxeP5SPcZPtl9njR3CF1ggQUuSVx2T69FvCl+MataEV9CaQWVouYrRMpcJWyk7lzAW1SuiuXJokg2O1ezGgYn2VJG52pYwUieopAQMjkbKayaLSMXzNoZs4h1YLnBuKIazJDdrhJRxKHZ2pW9MPd2FZGSUKs4LJNXUyp20NmIu2RklRaCO6eIOleUiwkfHOpsW5rU+KKk4CGlXNrZ45xDYw/eI2oksQLEZG7ZeS5EVsQFu2AkU90670peqcM1pop0xZY49mZfSzkP4pzFrKoi9GiKiHO0GU60kSWmpoatK7xzCA5pW3IWtIuoy+i0xTUZpw5xmaQZV3k7FzGaSreuER3gklrGc+whBFxRNIuqWev2Rvpq7NHtqSk1xw05+KIY9+AdSg1OkSrgczQ7X3U4heQEo22LXS8B7XpiUppQIeIYeMdIM1rXeE1kxGyli7K3VqhzxmfrE6rgVKnoaMKIZhCovcennhA8oWnwVcC7irppGI6X6WOkSx1aw+iWqxnfepjNux4gtRHXBIary4z3rbJzZoMQakKoSX1LFCDUaJrbDZ+1JteiHM/FktgFj/SWrdws7WG0NEJUcS6gA6FLCVcLGnvyrCXHiOsjlXNMpi2TrYm9JhOCRypn1sxi9sA5JUQzjmx9Oedi625kdM6Z1FsWrsaIq2tcjkhKeJSQIyarFlPIiyAW+FtoUkW12J4XcldwqIDz82KQUlDhAsSSeuUCaMnt9XP7dztObu4qoGVeUFteSuGH7FpHC+IsK9t7V9S6phrWZPPKnGdWtFjIWBaz5oj3YV5KYuMbQX1TFOm5KIMdznmqaoBIKbzoI04Cw2FDU1cg0GeHhoZBE6irkmeelW6aiI3n4KH9rD/VMhFh76F9vOltr+Oqw5a/efLhp3js2ClyjNgAECOAYff4gVBVtSnJgic4SL1lp4fUs32yZTpT9hw6yA3XLDM5cYb141tUK2P23rCPnbUznDh+mnp1Dze87mpiO+HIk6eYdB1hMGL54F6uvOMqhssNjz72KCc3dmA4YO/KCK+JaZchRGiG1NWI6685wLJrOXHyDNEJsqdCo0d3ghWWkAvpv8AClya8+OdfaIHzjo+2p/hYe4p3NAde9Hf/zPLNRJR/s/Ug/Xm6QY85Mtk4w3D/ggBeYIEFLh+ICH/spj/Gbzz5G2z2mxe6OecGYo5Fv3vgSW645x+zP17DjOVnLBJd5sT9/45/sX2ce6tnW1deaNzXb/JX1+7kpw8OqdyJ5//Cc+C6Dv7Bnb/Ka664kY89ctcrQv4CrE82Obl1GlZfkdUvsMACC1zUEBH+2M1/jM+c/gwPbT50oZtzXvBrs9/l0/09u68TmQfjExcVsaoo2zrhn23/zIVuygILLHAOcfkRwHNb1pwRCYXILZOtGqmiLiNzr31jZgrpeVYJOFf0mu2r2UQ7vNkBm0ksRt6Es5bRYso3ChGsZZm50zTqdtW0GikKXVMy6tyKOs8XNj9qI3/mamEl49HcgziSM/vjhJBzJmbLs40Cue+NKBWxzNAci4jYkVNvGatiGb8EJc8JPi322HMVtepZZbMAKZN3yWkjwBVvJLQDohaF8NzOOhqRp+A0ITh8bUQVqcM7NeLUgXOOhEd8RiIgnmk34bSMWJ22DJsOHwK1L+Ra6kCUHJwRtjhUe1QqNDl8G5GcybG3Y92D7PRk5+y0p4TWDhnUIIrLYta4fSZPO/L2FBcjbnlE8h5VKTmyRrRrVlJoEO1w3p9VaMZobtlO8FVFFjE1sUIXO2KMeM0QI97E0YSi/NRQWd5qN6NWIWQILpTz11OLp64GVF6ovVAHh8Pjg6NqKnxVUTVD9u07xOoV+9jY2uLUseP00ylnHj9F3IpsPXWSnBNVGLH3qgPsu/0m/IOPs37iOIPRgMnpddLOxDKBo5LaHnWCzglwX0MIdCnhBSpfEeohYThg5epDjFfG9JtTQt3Qx564tY1mJfaJWNT23gkxRnbWtmhjTxUCXlxRj5paVoIjixVyEB14V5Subpf0VFFSyqS2Je1MrKgiOKRTO74pU+HxOZNiBC9mBS5qfxcFsOJM/Ls7B+hugYf3wSK34eyckDNZoxG+4o3oLSSnk2daxIs31a5VcDgEPesM4ATnBD+fcwRT8ucErjLCHWfW6VqB87uFJjlm6zxz62pxVqGvoOpIWVH1BGfqYi3zRNfPSHiGgwFNZarxroukDIPBkEFjl42cFdTh1HPlTdfx5Tcd4q4PnGF9OuPgtQcZ7x0zyxEfHffc9RDrW5uQO7MMl2zTlpZjBvi6xjkhdhFfmVtBTJmVpREuJbamibC8lze+7TaG7QaffOApOl+xXFUcv/sRJus7bEfH3uURp588wWNPnODkZot4x3BpzOqeZfojJ3lsfYtjjx+nF8fyviGDgWe2HfGjxtTxTnjNm27jTTfs49HPfIE0y2js2G574qwjRiVrUW/ni+/B1wILLHBpY0sjv7jzBG+p9xGeIxfqS6EWz/ctv4aM8q+2zo/l9ayb8F8//Z/4U1/3v5yX7S2wwAILXCw4MDxA9SospupE+LcHh/yZO45w6PNXc3y6hABXjzb44I0t//66Gbt2ahchjqYJCaV6/kWfEz+2/nmOpx2Obz13/vG5xG8f+23+4FV/kMq91NYusMACC1y6ODw6zI+87Uf48G/+AP82HqV/lUfKrOsm6/FVUjS2wAILXFK47AjgjBi5p0Yo6lxZ6qUo0UylOSdg5lawqkpG8d4/7X7HCBNXCGU0GUElzpTGWpSZLhSboaLkLRayxu86cs6Wfzlfb872fSeFgJ6/n+ZCOSOZXFEeJzXiB8gaMcNYNXrYBQQjonzZ15yUFDNoIWmdMxI4JUiWhWOJtD3e18Z15WxElnOQTMEoIoj31n4nQEbLcVQtRF1W1BUr7YT5ION2SWPNmCoZitJZEM1mVxsCoapxvqKPne23E0gBFxTaKT5m+mnHuksM6wFeHNLUhBRxKdvyIUBjpKRqcdWuIROQFFHXoBqNpC05wYhHNEFMpK2ZqcUrb6rmyQxpe1MyVwE3GpHqyvpRCLhk2cBZvGXFeo+GCu1as+2tK1zMaNciEnDlvKrz9h3NpAya1LKGsqLi6DVDcNQp0kigEaWpA847UnSQE01wNHVFFc5mJ3sXqOoaV3l8qBge2Mv+N9zIeGkJffwo7XTC+qxl+8wOs84TJ60RXDmzszPhzNFjbJw5RTtr6btMN+3p+0TuelKynGWNGXJCnUd9QKPl94Y64IMjDAcsH9zPnoN70aTU+waMl5dYP7XObDql7Xp650jRcodn7RY7GxvEmIyUU0F9ICPEbDm7aa5gzwn6aGOjPjuu8J4cEzlGtG2tKKHyOBzOB1ya4tqeyitVVRFdsApvVST3eB9sG85ZFDBFqSsJ8DYuUJQE2ZT3Jpe3aGlcQHMsKl0pZgBaajWkENlGhlpud9p1C7DiETm7L66U5M8dBZxZUYua6lr80xTDZTFXBdvWPJ8YEGy+MfJXCXVdpjkhpUSMEcUzaBrq2uMQI9BVaAZD9iwPSSnRxgjiqJuam153C7dedwUb7ZTJMCCVJ/bKcgxs33uEu45scueDD9N30zKuigJ6Nw/Z/u2rhpwSWczCvG876uGQpnLsbMxw9ZjrbzrM0kB59N7jbLeR0UpDlVtOHNtmMushCGky5fEHnmRrbZMuRerxmNUrD3Ld4f20ZzZ57MhJphmGY0/lPTtnpkynLdWyg5jYc81hXvPG66naKRpGHLxqP2un19hsO7oU6WNv/Vwsu3qBBRZY4Fzjd9uTuy4JLwWH/flT42YPD4WjtDs7NOPxedvuAgsssMCFRrP+CP/78WP8nZXAmn+VEcEi/NTBActfsc6bP+8Zk5jcdJRP7h0+rRj94sRG7vmJzQf4y6u3X+imPC8+eeKTxBwXBPACCyxw2eLq8dV8jxzgxpN38/f372Nr8YxlgQUWWOCc47IjgM2ymV31nsztnAtp6cSsUneJForI1bmznsa7b4KoZf2KAN6UeVkV54ods3pkngVciFtRE8NaNrArIr2zeaK4p5HMCsztpOf5oVkxCaw7q7LVYkFtzLZZSKQeJOByJlQVdYaoiRQ91AO064pjsSNnJWkh81BTQmsgK5YfHByapZBJRmTPSWBTBpsCGS/Q9bZ3IrZsUiPQxBSVLpRs3d0I5bkU0OHEcpDxQnCBqq7IMaF9NkVj6kxFm3qcBBrtCLGnnSY2qm1qJ3gnNCL4lAjDARIc+IB6I9EsB9qVOGI7ryINuarIYr6/3oFzDRIj0iXYSqTNiWWvhgpWKvJsisQOqevC25kMNGeMAMWjNKgXstgx0ixoFRDf45sB0vckFVzfkT34SsmiqCjZOVI0E8eskEXwKVIpDMQzHA6oa7MPJ2Wcr6kqT9U0VCEU5dCcZS8kfS0QYPvMFtNJy6Rt6SSiJMvnbSry1JE0kWNi8/gZ2p2H6Ha2kKjUg0AMgdwLqY/E3pScWTP4CnGBnCI5ZSRHnHqqekg9HlMtD0GFbtpS7VkhrCzhZ1PSpifGSC79op9M2Nlp6VLJDRbw3khbBDRlUu5NVe695T67ZIUQneJ8QPzc8lh3letOHM7XuK7FpYRvO9ysJwTHoBISQosVJjgcIUWoBmQpN+S7+dsKmIV6VkFSsVPXosjHMq61EPvW7c2a2pTsxep5niO+2/09uwNfhHl2uGhGNOyOEeZLFcWB/L4qyTLMQExJL878rBUlZSUne2jjmwG+jNOUEzEmUlbqQUMdBK9qxQhJqZoBBw6sUFXC1vo24iqaqmG4VKMjx6Sdcurxk5w6vo1zEJYH1Dft4+GP3csnPv8kGxund63bLe84GDGuERVPqAKhCvTTKc45y8XOyt49Q3IbaaNw7W038PY33MgTn7+Hp55apxo1rK6O2D69zs7OlKSwNK5Jkylbswl92+HJSFZGB5dhZcDao08ybSNh6HBkplvbTKaRPkXixg6KcNUVq0jseOrRo0wqRV1Nf1zJfaadRVLOOFWbTxYO0AtcqkgdN93573jPzoTfHA2toGGBiwbNbgzAS8NOjueuMS8Anzr6CZ568Be48Q1/omTRL7DAAgu8iqHK8CP/mOqh9/OuM8f5sWnDryyN+PnlJfKr6Hrai7A2VD701pPlndEFbc8LhQLtJfIjfZZmvP+J9/OtN37rhW7KAgsssMD5hyr+6J1Ux+7if9iZkET42wf28UWD2hdY4Pchp/IU1i/6zAILfClcdk9pZNe+GbImy8x0lvGpxYvZLFTBbIodOXWImhWrktFsJKFDUe8sH5dC4hYiV0tOLyUbV8ThvJiS0Qma4i6viwhOjcplng2ai3oVtYxdLJ9UxBVVcC7LG8nnxPJ7jYCOJQvUgZoysnIePxSEhGRl1s5IPuDI+OBRPD4nYkpo7HGusgzcYjtrykJTSosLiJg1s1keV7vEaootuWSUGs+uRsblZHm4zpfcTG+vLWG32GYD2ZtFL2rqYxxZIyn1RnhlBemRWUuljib3VFKjKbAz6amrGT44cIFajfzx4oCE+sbOSxTIGXWK4I2obBqyc+WoCpp7gmJW3s7IU1cPYeCgqYx67zscdSGMfXH1tZvNTLWbEapZTU3MPG5aIYTStowET3aC2+0+DlyCujJL5K4laUTrhkqhQRhUnso7vAgxdqUtdj4dajSh6q79eM5C7hJoz/T4Ot2GqRhjOyPOZpDhqluuYuXAAR65c43OebIofTujy5vk2Q71nhr2j9CjE9J2JPaJpJaQrVVTaipiyQBWPEJoxuy54hBVXdFtdWxNNmgVRk3PZDJlujWhS8mUTg5SzvSzHkKFrxMudminuGx20r6urUgim2VyzpnsQslt7sklh9j5ARqjFQ/EDPPM1mxjUJLifI2vG4LzDDUj9Kh6OhJZILqKhJC0L/boxf5cfLGIzkboz0lhkd1xa+UYVhBRZgc7ryGYCj+XMepsbFmBh5bx5HdF/qasd/PJy8aenrV2VuZqg7mNvYL6s0p7UTQXVXmMpJRxzuN9wDu/S0z3fSSmjK8CVR3sGCcjhF094MD+ZQajip21CUmFfQcOsHd5wObWhKrPbJzZ5pEnjnFmMgOvHHv4KB/52d9k8+gaW+ub5Hb77G54jwQgKVnNQr9qApITOQtV5egmM2gCs0lLP+kZHDzAa992Eym1PPboSSZZObR3iM6mbK1vEZOwvLLEUg2bmzt0HdRe8KGiamq2njjJw09usLM+wdeCJzHdbmnbSPaeJjhS19uDruMnuXfas3VsjWnf4nKmbTv6dkrfd+VoKxB27asXWOCSQj9h9KH/jfreX+IfCHzTtVe9+pRLlzh+cOW1VC+RABYR3lTvPcct+tLYGkc+tf473PHIMpMbvhF8c163v8ACCyxwPuFO30997y/ito8B8Ja25Y1ty1CVn1xZXjy4vsBYdRV/YfnWC92MF4Sokd848hu86/C72DfYd6Gbs8ACCyxwXuGPfZql//rncNPTAHzVdMptXc99TX2BW7bApYC+TTxx9xr1KHD41tUL3ZwFFriocdkRwKomOBXVOa9ppE7ORU+ru8tJUYc6503JJ5BVyFoyOp2YWlhzsVo1hbCb0z+SEZnbSmdydngnRuzgbfls6jy8WQ6LM/JYnUeyBcAKnM3ULdsSZdci1pSCmeRcIYSkWNCWdnuzZ/bqqOvaSDFVWm0BZ3asgMNDziRfoTmRfVFCa0R0noE6X96Zva2KqTOdIydBtQbMTlrVbKMltqgLaIq44M2DGS02sA7JHeIrI5S9AJXZ7OZoStSciqX2xI53MuHwIEcaIGiPy5ASbE9aAhk/GuGrmqCWVeqSQsyomBpZNaNqpJPTEkWquSinIYuSXEZiRCdG+rAygjpYv0nRSHAfEPFmYauKpmREfU6FJMSKBEKFprxbWGAqbTt/oYihNWc7b6po6hFXU1WBPvVoG5Guo6k8Q+9pKk/wRQEesZ13Ss6enCIaKju2WgoFsuVKZ+3oMmhUkjoj+fsWFzypSaRRNLKZTNaMpoh2m+S+Q0+0pO1I381Kf85EzaZ8Fzgrdy37FSrGK6tccdXVJHpOHTnOdG2T0IyQUeLM6Q22J1Nin8jZlLopZzKJUDsa9aQ+kDvLq3aYWtXXFZJjGcNqhHMuanxNaNWQc0Lz2fOxq27LEYmRIDBshhzYv5eqqXj85BOcnrUs1Q1THK1gatliy6xiM4PHs2vlrmbNjaRdZf9ZxTWlWKFMODZ0ijx8Xghhk0oxbrbiDtidg0zhT1HNgvceH4Kp1ymq6zmr6sx+OqXE3ApatRxTzaQ+AYqvAqKmqBYUFU9MPSlnvHdUlccjeOdJmKX53n1LLK0M2Vrbpu06XKgYH95DcJ49ztNNZzzy1Gk21jfJycj2yfo2j/THaDe3iNNtyH2xz5aS0WzZ46pKVXszDph1OA8p9cQYCc7RbrX4wTLXXH+YvL7JPfc8wdp2y2CpoRZlfW2LSZsYrIxY2tPQb27SzjrqpmFQB5wPDJtA7DKtwHg0Yhx32Gk7+jbRx0xd1yiJvmuRwZCt42vMTk+JbUvSzKCpwUE/a0kplXFfcXGb3y2wwHNjGqcs/c6PUN/7iwjQKOxJeUEAX0T4snovX9EceJa7w4tBLY6ReCal+Ox84Gd3HuU71+5m5AKT694LfvHgZoEFFnj1wa09yNKv/Pld8neOCvj+9Q0iwn9aWSItSOALBkGo5aVbiO5xFY+fv8snn137LP/oM/+If/j2f/iyrv0LLLDAApcMVPHH7mTpV79/l/wFOJAy//TEKf6XKw5wf724l1jgi0NwvCt+A5ONDl+Zq+mc21hggQWejcvOXN9smSFT1KcZU/FBIYNLzu0u+fm0WM5dV2gjbhQg9mb9qkoCtKhznYhFe84zOYvCT7MWy2FXLJKLIo5kubfi5vQNBMGLwzvLxHTOvmOkkMc5b99xRUWrZ5W0TsCJ4p2RK048ThyVrxgOGkbDIYOmoq7qklnsEO+KOrDGeV/I7ZJp7HyxbXXknMi5NzU0hfwCfPBUVWVtlUwST1KI4kiqpAx9l4xEz5C6DlIGV9s6XAXqcSScr8DXZAKaFM3R/qhDZjNqVRpR6lDhQ43TBH1HP52yvT1lZzKlTYlkXtZGtve9keoxQjIL4ZzMsltjNCV3jKYcnfV0m1O6zZaYlTSuiLUnBY+G+fE2wtrVHl9XeOdwJT/a8ps9TooSvHKI9zhf4SuPryp8ELPXdoJraqQOSPBmBV4NcN5ReU8TPCEnfILGOZqmoXLOaMCs5JRIyZTGmgPaRbSPkIw4N/YqmlpWAiln4nSHPN1B+5kpUvvI6XuP8uRHH2C21ZFiJs5a4mxCnGyT2pY+JqZdT5ciUTPRV+SqKWPFBpDDWd9zjroZcvD2W9j7rjexeuVBxk2DHzdU1+2DQ0tsbrdMt2ekLpL7SGw7Up+MdFUjK6tg9sC+kNlOsEzhpsH56qwJumJkrK9sP4u6NEtNdpVxsDmbVXbwVOOG1dtv4g1/8r3c9i1fzni5xs8mDLRnRZSxZgJmnY3mXeLWSjui2SsXlb2q2aBnrODBbKcVLRG3c+topah2yxg2GawW+3fdnZ/mWdI+VIj3+OCpBzWD0ZB60NAMK5rRgOF4wHDUMBzXDEcDhqMho9GIwbChqivUOWKxjPfBEaqa4DzBh7lxPH2KdNGKXKrBYDc3WkUgNCwtLTFaHrOzNWPaQRLB1w26nVgJQ5ZXa04dO8OpE+vEmRG9ADnPmO1s0E23ILaQoxVJDEZUTYOkbD/QvKcZBFKfSraz0LXJDkeCFDMHrzvEdVcuc+Tuh3jkyAk6J6wsN3TbEzY2JhACS8sD8mTC5voO6oTQ1IR6yHjcEIIH13DddVdw262HGC0FomYySrU8ZDCu0Gi52/SJbhLZ2dpiNp3iQmA0riC2dH1v83Wo59YNuAXBscClBFWOPv4hztzzs7vFCx74sRMnubHry0T60nNnF3j5cMAfGl3DvpepoH1dtco3Da8+N416gTiWZvzU9iPUa1+gXvv8oi8tsMACrzq4U/ex9Mvfgzvz6HN+PlTlr6+t8yc2txZz4AXEVzcHS7nsS8NfWX3dOWzNC8Pda3dz1+m77L5wgQUWWODVDNWi/P1e3PbRZ318TYz8rVPri+voAl8St4c7uCHcDMDWqRmzze4Ct2iBBS5uXHYKYFMMFrVuIa7MslaKnTKAQsqoC+DUlHzz75eqTJW5f7NZ0YoI7ukXKKFkdBZC+Bk3Id4IDhUj+0SAgJoOFBFnH+V8VllJIXqLunA3p1jnqaP2HRVn7ZhXvQqoekSiqWidIFIjApVfJqdM7E0VGnMiuBoFuuTRti1tgqcF9pbIXssxVU3F9dbspb1z5BDIDAh9JqUZKdu+i3rwSk6lMifUZskMSO6NCK/8rnutpkzuo6mJs6LZ0U83aFJHg6cWwedsOaY4RAX6TC+R7VlH7VtqUUIIdg5jQnqz45bKI95I05zN/tcpkDrLG+6iZcrWFW40sL7iBCdiatU+WU5yFdBcYpuDGOmYXFHj5nLMQZMY8SeWP4wrOajOmSo1l94ZW8i5CNOt4qAKNU3dozkzCI7KWa60qhixHim5wYIEQZqhVQAoZkesiSQeLxmIpKi2bxZivWtfPZlMoeuJsTXL7eTQmEwdL2bVm2ZGgkZ15FDykLsWNJky1wHRrHeHS2NGqyPy1gRZXWLpukOsP/IEfRdJG1NiimSN5L4np0yfevqs5CzobEZGzIbbOVPEe4doKsUNQHA2fpRiw27W17WvyBLQMEBdTU4tXd+Z7XrGxkzfQTdh7b5HScdOEk5vEybb7FQBP3QsSSCoMFFhRiIiqAabN0oG9q7Wv+T1ohnVvKvsLcN513GAMveczf0tdvTFkh4pNtElE1vEiiqCD4S6xgcjwl0hcCl5ulasYfbTOSl92zKb9XjJ+ADRl+zurLvzlZKJMRNVCFUoxSUOJx40ga/Yt7rKnpWGzZ0p00nEeU9VecarS9x06zUcvGKJez7+ObZOr9P3LcTe6HiBNGnJ0kPsbJuuwoWGffv2siyJY8em9A6aYQUamU2mUFV0XSL3keDM7nqwusLq/iGnj57g6PEzTPvE6v4xQTKnz0yIoWL/gb0su8SprY7kKuqmJria5YN7WWo8Wxstd7zhtXz5zft44u67ufvkFjtbU9QHfN8zayf0bbIxmSO5a1ExhfR4FPCpZ3tzh6SK+FD6n0Oco1laep4rzgILXESIM978O/8EH8/mwwpwcx/5iWMn2HHCT+xZ5RODhpPhsvt5eFHgaj/ifcOrXvZ6RITDYYiVF56/hyc/u/MY3za6lsNHP0oaX0UaXXHetr3AAgss8IoiJ+qHP4A/88iXXMwBf3l9g+Mh8OvjSyMz99WGrx1eSXiJCmARYewCe13Nej5/D5O3+i1+6JM/xE+86yc4PDp83ra7wAILLHC+YeTvn8dNTj7n5wK8ruv4nza3+OmV5YWjxgLPwh3hzfyZ0fczlLO/sxblAgss8KVx2T3h0/KfFEKLrKCmlHTOvJXz3Kq0KBGLXo8sdlMnRXJoOb9G2mQRszbOhfxxJRc0FVJHUmGCALKpB10hI5lnShZTUSdm3Tsnm1WNQHYeo0vzXFpYMo3NnFYwUhDRXdWxKZuNdFSdk7QeF2p8hWUGp0AdMzFGvAjqHTvTltjP0JTKTOrKuhQnxfJXM7iAIyGhNptoyXiFrApBUcQI0ZRAesveFSEnjxDIOHxKRiTqXGatILWtPwgaBc2RFDu8ehqNNBIIeYL3QwTBi5bj5SH3zKaZLe+pA4RQUzdG6JAdZLOnFhfsWPYZzeXPrCO3EaqANpXlNUdTbPvajr1myF0PVSA3Dap29EUEHwKEgC99y+WSmxxqclWIx6RIcCDRdjmZDbiqkl1AtbO4WTGiODmoqwr6jkqCbctVIJCjKU/NajHtKmS9zLXgGQ1GGKac0U5JneVPO+8QLyTNaIzGaUZvqugMKSeUjPNzBW008lYEdc72QRO4ovoNzvqZQO0de644SIhw5vNPoCSmsymznHEKdRVw3lnGthP6DL0IMUWrfI4ZopH7wQm58iDFFj0XC2VvY9jF3nKjRQjLQ0YHD7JUD9kz3Es1GnNmssWTD0Y2ZlNEe6TrrVhgfcbUrzGoAqv793J6c4uNrR06X1E3MAxG/klSpiL02XKtEcGXwoy5MfRc4SsU2++nE8QUVW/WMi9ImYMyStglTUHJOs+stnlCcLvkrPeBELzlIBcS0nkjhL07mxPehREhdChTJpMd0iRa9nCamtuBmr21c45hFUhJTYm++7vasbSyxBXX7We2tc10pyXjWF0eUWlk+ZoDVEueRz77MCeOrNN1MytiYD73CJo6O1bzPHVfMdq/j5vfcAPp+FGOHFd81RAqT7vdgQuIOnJMNC6g3hOGI/buG9MeO82JU9tsTTsGSyP2rTRsn1pje2dCPRoyXm1otydM+0xVBULVMNqzyt6VId12y6FbX8Pb3vVmWDvGI4+fYGMyIwJVZbbZ3cysx0NlDgeaIjhHs7JK3VRsn1pj1vXF1jzgUMQ7QjNgMFrkXC5w6aC+/7/iNp98zs+uTAkS/KOTp/lsU/Nr4xE/vbJsv20WOC+4yg/5P/e/lRVXnZP1/U9LN/GTWw+zof05Wd8LwXru+MXJE3x/GDF++L+wffO3kYcHz9v2F1hggQVeKdT3/AKDj/34C1p2qMp3bG3zkeGAHffSiMgFXhpeE5a5rVp5Weu4rVrhvcPD/Kedx77kcsNqwFuve8NzZsLMupZPPXF3iex5YdjoNpjF2Ytt7gILLLDApYG58vdXv++Lkr9zDFX5gbUzdCL8p+Wls5FuC1z2eG14A3969H0sueVnfqAUjmbRVxZY4Llw2RHAIgIxkZ0rCluzcaVkiVoKcMkCnnOyMA+JRSXj5jdyimW56lmVsDhnarxC+jAX+Kk7mw1aMnAp25pTzKpKnls8l9xQQRF31rIVjHPDFaIlFyJzrjoURQiIs89295lC1hUraHzApd50vb4mhGjrz6YkzQpdP6TTQojI/NgYUS5ATtlygJ1tw/bb4zJIUFz6/7P358GybulZH/h737W+LzP3cOY735pHlUqzLCEBarUMNAYMlghwAN3RHUFjGwdu3NEOGjcOiHAEdNOB3U20iabbBJPbtHErBGEhMQkzCJCQhEqypCrVPNz53jPuKTO/tdb79h/vyn1uqaqkUuncc86t8z0Kxa19du7MlV9+38q98/c+z1MgjR25G86ItxpAHDArgOKayeaQe/8x0b3bzKKvuBqtVawaS6ssZcmowiArVEeSK+qNvIvnbQlrzvpkzbFmhrQlpSHcsRLAn5pgfYakcHXatgZTHxcwZnxMyCKAu9FjnWtDVWnbCbYb0mKFjwlDcbPoqDULQFkNce9gdAjo1hp1Knh1ZKiQEt4qVsFswmrFm0EDckbGjJYtYpXkFR0W0VfcDFksyMsltt4gpeJe0bQgpYRmQfICSomhhZzCye0Na1Brib7cYQiY37ZYNXrJLtYq5kbzjjglXpNmQnHH84AuevRtbdHLq72lVxQRZ3HhkHS44PjOGueQPFaOT7dMklmmzJXLl9hm4fnj29SkFDeqJcy3AdtV8CRkMknA+zGMtGSPHu0UEdqi4b4XBJrRaiG95608/uv/DVbXHuOxT3yW1s5Yr0+RszVqThpGFmNCW6H1mOfkkCbYnJ6y4QJ7S2OVBy4JJDOOvVItx/yFcO7eld3V7O38lw0h7a5Wdn284uGmJi/uRq2axZCB6+c7gaVHsPfnl7JGR++4c7gqOgzknMg5uqIlJVyEDUv8aM3pK8/TplNkeZE0ZOyk0rwgIqwWe6QhsS014stTju7o1ljtL7l87YCz4w1H109wh9X+ggvXDignp9x64TXqzTV3bp1wut7iNsWAShr6ttfwXe+kOa6ZYbHHU49f5WBQPvnqHZoO7K0WtM2G0uL5uTmXr15EpjWnNfP0s0/w+IXEc597ldvHW2QceezpS2hZc/vWCQXhwsU96tmG2zfXjIuRMSXywQFPv/VJltYo6lx6Yo/Xrr/Mix/6RT77yi0mgzyOiFfatlGNDsBjgCVM3cJyL7E9OePOnTU2LMlZST3eXXNmtbdgzHNv6qw3h2R9M3p/7VeGgV+/nfia7cTNlPihg/37sLpZAL9+8Rjv/TV+aP16JYT3Dhf4yenGr3zje6gf277GH+F9pO1t0vb2DIBnzZr1ptev5j10p+9cb/hjN27xpx67+gaubNbrJYT79y351/67y394+F5++OwFjr1+wfcE4dve/g188On3ssyLL/phs7nxwWfex/f/9A9zvD39sh/3L/zCX+DPfcef+zWtfdasWbMeStU1Bz/0h9HTV76sm2fgP7lxi4U7f/3C4QyBZ/GB/A38of3/mD35woSVVz51xDu++doDWNWsWW8OPXIAuJlDCrec4Yi1Dmm1RxoDeECmlMEKLnLeMSo7p2l34O6cm+7hvMTCubsDbqgG0HGl2wbj8cx7PG/Fm/QOXMVodyNgzwFyO097jq5e6THBAYm6zxcTBa+dVceaza0jzEyAKOudoxGxqrKLlB4CAPfnshgXLBcLWim4dYrdXb+iHf7Zzokcz3vXmyzuJFW8BTJWq6gZtohoXuvORkcCZjajKSQr/XEEo4YLtVZqOaNOW7QaY6uMKYBxEiV7Q72ShwXJDHo6r4pAnVivNxyrk1JmT5doIl6b6tjUAgqrB5sfErrKAei99hRv6cfT8dow2+JnG4QEQ4qb4piE89Os50EvVrA5C2ekD3EujRnaSF1v8c0WhiWUGq5yVQwNCD5ERHOdWkRHl4ZKYjEG6FNNpDGTxkSqoHGaIuZ3Bxegu00j/FFEcasRXw14NcwDBAt6Du6oAXtNoZaKY6RhSXOhtYblBClh1uJ17ud9yuO5A3Uxjow5c/rKEdfe9lZWb3871299js3RLVoCLiwpFzLro0I1wySuRacF7FyOJIto6JQGcmsohqcUZmm6I7u7riUN/TILoFqO15y+eIvjT7zIarFiuLDkyrWLvHq4x6Z3a+eUyKr42Qn1bI3dPmaYGsu6ZbtpnO0dcOThRN3PsF+cVpwzn6g1YyI9Mb47/SX16yvik8PQ2wIEewxy0AcvxK0PRLw+Cn7nfu8JABLnROqDFaLh9s3jyHK1ZBgGhjEzjgPjOLJYLBBPlOI8ffA0w+XGp09O+MTpKc88+y7euhj4xMfOeHFtLA+W5DFxdjqR88jlS5fZS5nt5gyzBgcjpRpnt08ozViuFiyWmWmzZX10xmmBs6HQponWeq92UnZk3HdTMxZ7g2BcffIa73znU9z47PPcOCqsDvZYjs6dUyPnSDxYHOxz8eKSO9cnLj55jbe9/ylOnn+Rk6NTzDNXH7vM4ai8+vIJZ9vCsByR1jg+XpNWCw5WmWmrPPPWp3n68gHXr695+h1X2N9s+Pl/+nO89OIr3F5P8TIksE2j1uhz1hSvkbeI5x5WK9wqRzeP2U6FlIQsu9dFGFZLlssF0to9foeaNeuNUbr+EYbn/+WXffsBWL6u/mLWG6vftHzynncODqL8rw7ewYdu3qQ+gFCsuv8UdY6xnDVr1leBfrXvoRB/Nn/nesPXbCc+shucnfWG6lAGfv/+O+7Jfe1p/pI9wqthwTc++zUM6ZdP7PjsjRdYl+0X/Lui7MkB37v8fWTJ/M31X+PMTwA4rV8+LJ41a9asN5XckenkV/UjC+A/unWbrQj/v8ODOQ76EdRK9lCUt6d38b/e+w+/KPwFsDZ/djFr1i+nRw4AswMxtutA3blj6aC3d/omDedjB8MiLVy5LqA92FV292gBbeggRKTnRQdc9d7FuvMGgu/qdKPf1HtMdId3u15cJIF4wFzo3aLxR0NQQ3pvcQe6/SlZj4h2EdzuRgGr7tzG0p3Pcv5nzQ4iR4qrMiRjMY5MOVHqDszuQm930DD1+Oyde9GjB1UVm2q4kgn4yKCoeXfKGq4encDKeZy2maHJcU9YLdRtiYhjN1ppLOrEQoQRY9AOf03IqhEVPAyxOp8CDJqSpsaUjfV6TRoGBgRpAUM1Z3zYxWxLwF5VRFN0ofZY3XheQKl4qfhmQhYLbMf+8A71upJADsBOE6zE95Mo5IynikuKcyUvoru1TBEb3X3eroU6QZkqddoiQ2ZYjeSUwr3rFj29GkMASVrE06aMdCcmCl4dSsFzxjRjdYpe6xwubK8GGSRHF6sVo3mjtnAsmwq1lICaYfDFreDTQE5KzolhMSCasFJAEodPXWVQwRd7LH/Hd7G4cpX0915kvVkznW2on3mJ9Qs3sTZRVWgpRUy4g2pG1JGUwIyWE0lAhgV5yORqtNpwqzHYoEokZWs4092gVjY37/Dyp19CqrB3mNHLK0ZVVDO6l0gNMKfVDVYLowiHq8yUlW3ObM04axVbG5Yhu7Psww5rUUrZ0jQhKdYXNuUGkqFZHCt3XPq1HBMiEGMnqKQYxDhPhetxA32AwHfu4JR6VHnfcDSittOQGFdLVssAwvsHByzHgcfe8haeeuatiMDXP7PiR3/0x3jL9/163rq/YvOXX2R7IzNZYTs55srepUMee8fTXLmwopyecvvmTW7dOKG6onlguRhY5DgrT26vmdYTzcAaPfbZ+vWiHfzu9pEhrgEgDZlLb72KqfPqjSNYLrhyIXN0/TaeEuOYqZsNy/2RzdkWl5GnnrrK5voRL372JpMpy0uXePbZx6k3rnPz5hEmcLA3ULeF7dS4cEExT1x4+jGefts1yp1Tnnz6CZ65suDjP/+LfPa5G9w+OsZEWSwUWmFbG+ZGUiX1oRvXHD3d5pzePGZ9tkGSMqS+1dbYa8fViKjS6he6AmbNerPLgb+7v8enxkfwV8QHoD1J/Nt7z7Kv9/54f9fyCb5j8Rg/un31nt/3ryQbDvFx7kmfNWvWm1zu5Od//Cv60Sdb49o8LHjf9B9ceA8X71GNQkL4lsUV/sfNFzrVft07von8Jd6zP3fzRY42x6zLhp/47P/EBS7yDeO3ft5tLukVfsvi3yYTa72i1/hLp3+eO377nqx91qxZs76atHD44zdu8aHFgo/OA1VfFUokft34XSR+pUQ94bctv5cDOURQssyfT8ya9ZXqEbx6JECuCE7vgpWdG3iXF++4aY8y9YCCaACAKAGNWOSkPVIYQIPdaMC5czq4Q6zS+s3iTsS6o9Z6V6hbv6kCrYPn6IYNE47dxbUdSIZZ2Ti3vYY9rbf1dmisgOl5J7DsbrsLnu6gTTRFZLRHJK0kIQ8DeRwoUwFvNI+YVnHDvdHaNpy2afdcwwlpZRtOQo8eXPEWHZ87CO0JN2i+BVngbnedqaa4F8wjYtqsIc3R2liYsdDGwp2BFVkg927U0QuDJsQzqEecaxpJi0waRmRcUUmIDqhP4aIecne+Kk7vOm7RzUrK4ZoVQeqEt4a54Jsttp3IY0Zai2PYISROADtv8fNLIEc/tEw1IotTQhdjAMxB4j4twKFoC4CJ4OqQKuYNN0WdHhecoTTMaxyf3jmbBIZBSYsR0X4frQXkNMNbDCfsooYlO7SdGxw8CeaJup0oDWqrVK84A5iRBlASmGDNyNnJ44I8CJLT+XmqCtNmS63G8tkr4BPYhtNWWE9bytkZrZxxNi6QQSJ+ug9g7GLU8d7x6+XusIOGgz7lBToVbAu0hlrtLvtdVy6INXR9xubFl3nxZM2A47fvsNeWDAcLvLUO+hrTyW2kFWxvRXOnnKw5NWfdjI01qjROJ0gtOnOzOqs+9DD1a7gJ4RiXcIxHF3ePw97FvkuKzWN33ffu8IDGQevl/Jef7rLvXcDh2o/D02pju5nCfE+/xFSRscCg3Di+zcXt4+yPC+Q9T/LE9B6WbSKXBV//Ne/ghZ/4WU5OhNWQ2RsWLJZLlpJYmNAaeAHNOYYKhtzhurM53TJtC8363sXEzuurOd8d/uB1YyUO0Us9sn71Ns/fOuVkMq49dhHZHLMpRhoGpMFqtcI2ldOqXHvLE6wWwkufepXbpxMyLHji2WscLhOfvnmbdSnkRcars91sGZYjbdvQ/Uu8853PsJy2vDQ5z37wWerRdZ777Msc3zqhThuGMa6jsi2YRxe8Kojk2LOSk5eRfLA+24ImBhVUekc4ER/urTG1LVbmnq5ZD7/Mjf/6E/8f/j0RDvyLT8a+mpQzUf7rSxf47DDwkXFkulsMPusN1CUd+e7lE2/IfScRHktzV/msWbNmfcVyY/zID/yqf+y6Kj++WvIzi3kPvh96Vz7gt66evmfdf4Mov33vmS8KgH/mhY/w+OE10uv6nT/26qd57tZL3Dq7w7ZOXJarPC5P8vtX/1veP3zwl32s9+YP8EcP/jM2fsZP1p/kxlHl6oVH8GO6WbNmzfolOhXhtZT4l6slnxvmffGrQSMjv2v57/I9i98W9ZT3SHXbuPHcKVef3e/1lLNmzXq9Hr0d1FrANXpPL9HrG5wmCJZ0cOuimLcOPLvLVuTc5SqinfEKWOt9shHtDBFtG4/Z4e0O5GqHQQ7dchswoi9hB1OASFfutxWV7pRtCBqI18DFAsZIAEHc4pntnLm66zfOdwHNLoa2xRq19yGbQWuVba2st1O4PwOPdlDbQTYa7mLZuZwD+JrEbYGAcglMco+1DrCMKK0atIyYIUlJEl3H7hHNbAZmFTGgGgOJpTRWAguUhTcyShZnMGccM0NekrIiaT/A72IR4HK1h+aRrJlBE9stiE/xZiOEi1kFyYpYRdoQ0ck5BgUUp22m6Aluhk8TNmV0GJHcXdxxEAOQ1hLdzIsBcsJbj5Y1I2kAYneQWiMi2Lpj2AQtE607Ta00zByGAVGjNbAgvT1CGGoD3MgOKaU+2CC0qQQxNO5Ge5eI1ZbchxmkYSK4C7RKK4Vp2jA1KLXheQhDewrwb5065sWCYbUiDxox5uaYBZAWEabTbcDiW8fc/PFf4ODyVY5eOaKdbbD1BquClYpnhQxJDamGW8NRrPb444uHjMMAZ5s+IJHi2lJBWkFLJWs44hUnxake1/S2Us4m/NXbtMWK/cuXeevXPcOY4fjmKSsGEhtefr5iZcLV8cWIL1ac3LrNSWss3GiSwRobwERYCixp7BER1NtmEYttPTncohMZWjfAp9dtNdId73GexAcUEq7huIoCfIv0PSjSAKwZrRlaG9O24mwppTFNjW0xztaF09OJYRxoz9/muVcrT+8fcvSZj3KzHKEnE0fPPsWRFI5PNjCOXHjiIslgeXiBp971JBey8umPnnBaGjJksipWKtUqm21js95ivktMiGjrmP2I/xoee9/OyRwXRmw/g7LdThzfOuPw0gWevbbHJz/yKoyZxZg52D8gWeX27TUHF/ZYZePlT7zIzZtrqguHVy5z9eohx6/d4PqN4+gtz5niCmmEBtMW3vft7+EDb3uCj//rn8f29nn5w5/m9ief49XbR2w3Z6g4YpmpGWWKQNSkmZjhMby7yL0502aiuTDqziyveG14iudV16WD/9nV8WaSu/+aPxi8F/fxIPTD9TovXLvCn7l+g6Ez4NdS4vsPw6H5D/f3+OTwurSBWfdN37N88kuETN4b/UcX3scPnj1P+RIx0K/LbrinSpvr6PoGtrxyz8+pR/lavleaj+GsX63mc+ZX1u434b9x4YD/4eCAj4zD/J56n/Q79p7lsbS8L4918/Q2f/Nf/x1+3fhdXNPHAXiS9/Ek74tP1zJ85/jdXJYvv//5mfQWAN7p70VPtvhhesOulfla/rVrPoazvhLN582Xp9176T/dW/GDB/v8w71VfOOr/Hk/ChKE71v9Ab57/F/c8/PYHV77zDGH15Ys9t5Y1DVfy792zcfw/uuRA8C+cxtKd+Nh565eJLPrwG1YN7WGw9YlkJs6PQIZaL23tEc8uxjmjjYPWGuEw1e6iw8lmcdjQ8Q7y849SwCU7kAWvBsiwx0s7rQd3OrO4YiJzuw+ugsQ13tgJdzNveEXtYpYuIRNFFrDxCgOXitjHkBhas52WrPdFLbrNXVqWKvhhO7AdBeLLCmjWbqrOY6B1YCkogGFrE0YSqslYpVtCr7tQBrIhGtaVAMAmlNrf8yyIfKJhQWVlTeWDKzEGTUxuJIVxtWKcZEZVyuGgxU6LCKyWyMuOQ0DMo7sX73E6qlrvPa5l1i/8ApeCjl34G8NGLrrsuG1gkXBrreKT1s0ZViMNDyik1vDp4oPDjlBbbgJXhX3LUJGJIGWiH6uJaKmNSOlYK3gyXGviPb4anOsbKnFaJstIk4aM5rGiHRO4H3QoLaJWhq5GTos4jk0w3ovtOOI7M7WGBzw1nAXLA3QoJUposJzolWjFGNr0WmcNF5zRbCyJeptMykLSSxOY1dME2YV94bq0AcJGq1Wbt66g+QFhyqcaqIlwVKAY1K4e701qPV155FCFcQMS7pjjuEmx5HWsFrJDoucGFVIbqiBlHiObX2CjFvSgbD32FWeeu87OXzqCn7jFntPvYPhA2/FfvHnuH39Jc6Wh7Sc2TsQVo8n1qNw86WXWeqKKuH6rAhFFMcwhwXOkoYiTA3qbhik1Uh/F40obhrnDn33oMS7Pu3dntRKj4fm87q0d0Mi7oa1Rq3l3GVbpZFqY6oTaT2ieoK5sinG9NIZHxoWjGevMWxO2X7iBp/58Ev4jZc43Ux4NY5uZPYPFrDZ8NynX2Rw5/rLt5imhqohOeNmbM62TJuC1QbUu3wXDxY/5DinfikHTZmUB5jOEBHO7hyzbcrV9zzFUdty52yCxYImimWhnhkHVy9z8cKC41tH3L69YWqNdHiVt739WS62yi88/yon2y05R4T/mBNjdtaTsXzmMZ5+51VKOeWFl25wvDzm9Podjl65wcl2gu7itVIopdLMSCngtUvGWkHSgIhTt1uqCYu9A/YG8OmMqUR/tmQBr9SWYzBkjvV7U+le/HL4Zv4F8x/u7/ELi0VPCYFJhFfyI/dr4EOn//nqiTf0vLqsC/74pQ/yQ2cvnP/b795/K984Xj7/+g9f/1d8rp3d08dNm5scfPIHOHn37w4IfA/1qF/L90LzMZz1q9Uje86IYJfeTjp+4UvepAI/txj5f126yOeGzEs5U9+Mz/VNrO95A5I0LunIRRm44+X831ayx7+7+t/wrvQ+LullRrm3Dm8R4frnTlgeDqwO35io00f2Wr6Hmo/hrK9E83nzK+uj48D/cLDPP95bcTMlTl+XtDDrza2Rke9d/X6+a/zNb+h5/PLH7/DM+y+RF79SvPRXrvla/rVrPob3X4/gJ3/W3ajh+pIOVUW6o1bkdZBDCAffrod3188ZkbvWfbiiqQNXPwfI+C7KGVwkooWxSIDdpTWLRqyoGdHR22GuJLyFK7W1hki4kc99Gu50zy/qAaFcEgGbPJzH55Gs8WQaO2dzgOXSnFLWtBbQcaMTCGyrUzYbWp0wM1qPn+3PFBDMLI6XEq7fGgBLXKkumDvmRi1Oq4q3dfycOuKCaiZlUNWAML0HuVkA1VamcA/qgLoiKiTJDGNioYnlkFhqYsiZYbFgGDNDToz7e+S9FaqKl+jmlJzRIaOLAVdnfeMObWrYOFDLBJXo5hWQ2hDbvXQ9wloK3hqaBnRMkAW3hG9LQMsE5vG1tDg/wAL09mMXgwU1QKyA5oRtJ2wzYVIxFajRv2ul0bZb3CEPI0ktIn4JN7ILAU2HjK0NL1tSSqRxRFKKoYFSwKPrWST+26xD/N2gg8UwhLlidcJqo5lTrWGiSB6j57k5Vuu5ezmnhJjHcyfHYIQbXgqiiTyOPQJb0cXAkBstrWkrxwehpSFczCI909hp00RCUU3dBdyvz+a0TSG3gKfigDiIk0QYsjCOCwYxtFbUDZXu1F8Aw8ByuceVx57g4Bu/AX38Mmw3pP1DaFs42ZLHFcsDRbYjpC1bMRRYpkzFKLJgypAckgqm0Q/cUAZA3VhYI9EoFnHfLsCgPdqb82tW+iyj6wKX7vLf7Ttu/Rru126/1mIGJY6x1Ep1MN+iKjQbkQIiBTOopTFtCzVvsZRYjQUpa9brxu2TNazPaM0QGuv1FgTWp4XbN9ZIc8wLiOHFSM2w7cS03sbP2C6iXhBRkvae9D474d4QyX2AxVjuHfD0xQvceu1FzszZnKw5vHCZ1StHvHjjBhOQJJNMmY4m0jBy7ZnLlFt3OLp+QmnAsMfTX/N+vvGbn+XFn/5pbt66Q/PKuNgjDwMXHz+knJzirfLM41cZr5/x0Y9/kpdevsG0t2I63LI+O6W2FkwdoZYtzSRgL7vebsdJpBSvWSkFGfe4+vgVDrRy69XKtmls3A5tamh23CpTmQHwrDePXIQX5uish0raf7t6I5VE+L37b+P37r/ti37f3fmjF9/PrTbx/zj+KHesfNHbvV7X9Al+6+J38ZK9wD/b/kOMRuv5L+5Q3aKiY3uLxas/zfqtv+mePqdZs2bNum8SZfNtf4T8/I+z7aPVO/3I3oqfWi6ZRPihg7276V+z7qt+4+Jxrr0B7t9vW1zjWxdX+UeblwFIZH7f6g/y7eNvuOeP9Xq1qaIv/iy891vOK4FmzZo166tBG9UvCm3WIvyFyxf5kf09bqQ3DtzNejD6QP4GfuP4PXzT8O1vOLQ7uzNxdH3D5af3ZkA4a9br9Ah+EhiA6BzWSgANpHtiehetWnTk+q6Dt8c1W50QSfFz2pt5LTpBmxDRvyjaKpH3ynmcs4igKM0dtxZdxNGA2vNj/RxI7IzBOwCsKVyE4ZT1AIqadim/8VDWO4tFemdowVo4jxXDCadmqYVSAuw5Adzc5RzCWi24G63VwFApR//s7ril3HuOe9Ux4MUwClYLrQnWIhI4QCQRpexCGjI6JJJoABdxzBSvBZumWJMksIpIwqgkdRgEWY4shgV7iyXL5ciQMzkrQ86oJPJqJC8W4UyVFDHOLkhtkCrltFJpWKmIJiqCVSN5Rd3R0mO8RWG7DmeqJnS5hGVCcu5QXWgGTCW6Bdom/pujV5Yepx2vZWNXruo7OA+4KowjXlrEYReP49+i/zktBnS5iNdju8Gt0TTRasNT6pW5jaRCSkoa4/a0CS/Wu6U7kE4KpAD1EL3VWTvgTnhbRd+1VawlBIkfEfAa8eU5J/JiICdFrGGeca+x7v7x9bDIDBrxxWm1JB8sqXbGrU/f4s4Lr3ByckIdEj6Eu9RLxbZT72DdueLjfFHNqE1kF4ZhxM7O4jhsQc/OGJozLDPJCt7AXBjGBYMo1ILkBePBRR77pg+y913fg7znLXB0CzZnMF7Aj87Y1MLiwhXe+Z7HOH75OT7xix9hOjpBTtfs55EiiUC9ShJBvWKmNBcqzgRkdzLxQXd2p3qjjUusX9veHeW7/UO0x4v360njyeIKuhv06L+juDecOFZWCnTAr7WFS7rU2DtEaaVSK9RWcM5AMmtpqAfYl7KFOsW5KFCnwsYjMcBYx7bo9fyxkzhWWzh/pZef9+eQ1MMh3DukvQ+EuDc0JcSFZ7/xfXzbOx/nR3/4DnUqnFZ4/F2PY8fH3DyruCvLvGS5SCyACxdW1Bu3ufHqbbbVYNjnwjNv57u+61t43G/wM595ibOphAN9WLBaLpiOz1gfb1hde4x3fOOzrG+f8NHPvsKd7RlijXa2pk0FTbH/1WqUFhH8SXsfuhmGoSnhrVCmiknm4pWLPPn2a/jNm7z2whZr3vdY7wM9RmtO6YMms2Y97PqaS1/DK+sv7LGb9WD1W1dPfZ4T90FIRPgtq6dxd941HPLHb36IV+xL95s/oU/xH+z/JzydnqV55bcvvo9Pto/x187+n5z4ER8ud/jBs+f53v23ApC2t5C6xvPqfj2lWbNmzbqnqk//G/z4130f/6db/5LpdZ8lblSZ5g8XH6hWkvgde89wQYc35P7/Dxe/hn+6eYXMkv/l3h/iW4bveEMe5/PlXD35cTZHV6kX33kfHm/WrFmz3nitVfg9zz7Fupx+wfcc4Vhljnn+KtShXOAP7f9R9mT/vj3ma585ZlxlDq7c25SOWbPezHrkALDtis48nK+Ch5Pt/BZOVOt69EGyyyvugIbdNJLhruHwDa9k73ptwR7pjmGR3o3J+cywR6FvdNyi7DyPbgJKOEclnMS+W2sruOaAmRruQPEOEs3wKIPFBaZaI0a5tgDWqTt3gdaMVto5hN5ltzoaLtFmHWJHrysGonddvW6G4mgnhM2c5k6rBasWcbaiSEpoVjQJauGGTeN47nJWJZysJthmS3OjlW0/1hqtw9XCkO0Oi2U4gIcF42JksVwxLIYAoN2VOIx7pCH1YlzBLKKaXRNeCuYbdgncKSV8GGi1UjYBGpdj7hzdccl49zyjEueNxWthrVvAEagOVFgoeIBya60zv9LPM/CpxjGsQKvhzh0zebWIqOMy0UoFz+RxJA1RPmpTQ1ocY1qhDiO1RcyxVWNM3XWbF8AAXvvpYYgMIA1rEQeO9ijirLiEkzvM6o6Z0KxFzLgYkKFFzHPOwjAuyUN07TrRE83U4pioMgwLhmHX5azkxYCXysnnXmZzfMbpdktJCc+KN8Omim8maAVJKSLSVdE8sLt61CBbY5mUqlDLhK0nssMwCNmB7QSeSPt7DKtMWserlsaR5aVD8mOXkWmCF6/DjVfYfOIz6OIz2PqU0xsnrL71vSze/xQn/+AG9ac31KmRx5F9VTbbQhWlphSudUmQB0SUOk00rxhCdWFAGTAygpZKzUqTBj7EcIc10NyHA6RD1ej+jlh473tNHHtzQ1qPnE4JOmwVIyLIY6OI68kNa314wyzOR7aYxHULDqWeP7b3eG6vLa433YHQuG5ElSYRwS0OYjV6l81ij9GIIA8A3NdFCiebJhQYroyceiUd7JFu32G5Ghinxu0bx4gYq8WS97/37Tw5KC9tjjg9W3Pj+ducnq7xtMeFJ9/Ct//m7+Dtb73KrZ/9FC/dPME0sVyu2B+XZBonx1s8j1y9csBrn3qJW599ldt3jrBpglJpqoz7+1xZrtgc3WZTSo8033nMY6BFdEAFrDUamXHMHF7YI9fCrZvHbFvsfTRB1VBRvCm1lOjonjXrIZeK8rve/rv4Jy/9kwe9lFm/RIMo+SFx94gI37q4yncsH+Nvnz33Bd8fGLiqj/Pv7f/veTo9C0SSw74c8PX6zTylz/DxdkTDuWlTVKKIMBx9Gt3eps0AeNasWW9WaeYHLl3m+vHsSnrYdFlHfuvq6Tfs/h9PK/7kxd/LUfsa3p3ef1/cRI7y0vQennrlJzm58LYYbJ81a9asN7kcOFblbHb4PlL6Nxe/jSX39+9Aa87NF0/ZuzSiOg8VzJoFjyAADqerBWQ1QzwFUFXOnZtBc8Nh2YN0w9GpHea6424RNYwEKNV+P2TYxfV67Vl4JRx7GkDHO+SMNOqCSD7vAhaL4NfmHXzGqmmiiLdgkN2op1HCiohiVjCcWoxtLdQaDl5vhkweHcUYrVp02op0oNuCimsKF6FVVDRcit0DjQXsNtdwa7IzCrZIse0wS0VhXCFqqJUe9yxoTiALRLqzGaW1hm3WGAkrUzcXpu5QbOAJqIgq0gRJQlJlGDPjcmQ8WJF7JLGXhmKkHDHCKLgoXsF0iojtVqNXdjkwLAdk2yJyuhRqccwraUjhvK2NhiCupDGDgFlDSo2XzcOVLQtFhhEvFa8CNkWobK0xLGCCr0/j2NWGJIVRYFwgXgOWDRmZJrw7u1XD2RodrE7rJ2NrRpUEZRNgsDqDGcNqwWK5x3J1gItSyoTQIBGwWhK1THhtEb3ce5Gxilk4zIsbtRnFYqBARPHa0NoiZluVLE7SiCiu0+Y8BjwR8cw6RiyzSCINglil3Dlic1aZWqVYpaUMRWnbCqWhGhHVPhWaCtq7ccUdcSd7Y0Vi6Y0mznYTDvFxyGRRtFSyO2nIDFbRM8Ob4Hlgub9izAv0eABdwsUF9uqak8+9yNmZY76FcYs9/zzrX/w4r37kE7TtFjFjzCN7bmw0URps8TBS9/5sNyflAbMU57JHHHBTJaMkQGpBc+4AtoaLvg8zxAcHPfbzfMrjLgBw8xjsIB7LaDEYYuHStd4V7jgqFtHyTt+b7O7UpO8wJ4i1833OXTvw7d9rhnsLNzh2vg9qh8F4T0nQ3bBIOOfPkw1653TfYHF3XvzQp9js73M6FawJ417iuEzcODll3Bs5PFjy1He8i8Ptmv/pb/4ip1NhfbamurL/xNP8W7/z3+Sd734CqZXnf/Fz3DjbkBYD+xcOWC2Fk9dOaZa5cvECy7LlMx/5LHdunTBNa9xKuNKHFQeXVyyzcHyr0czQnOM88z6Ik1JPV3BqLaRxZO/iRZZj5uj6ba7fOqVYjyAXi9dJhVoatVm4pmfNesjl7vzkaz/5oJcx65doQPi2xdUHvYwv0P/x4gd4pa35se11AC7JZb55/HW8Jb2dbx9+Q3+X+3y92J7jtt88//ovHn+c3733Vi6lN6a/cNasWbNmzbofOmrv4kn5Dp7I9xNYCDfrW7l2+hPsqslmzZo1682uLJlvuvZN/IuX/8WDXsqs+6TH9Um+YfjWYAX3Wac3t7z6qSOeeNeFOQp61iweRQCsjjQHUne+BQhTukMvRV/vLiKaXjov3vAmPQm1hat0B1h2HbL4+f91RBvOy5Tu9rdo7/2l82Ik4qRp4TIDTFJ3jtboa8WC68D5fTsdEDdwrwF3zZmmxnbaYtbYPQXxcCYb3t16jreKWTjgrBVQw3qsaZOCxlK71zAjCNrXlyR6G3YgPbp8W0SjqobBcRjv9iqjGAFRsYbVSjPFquM+hd9zOgsXoQY4DTdzOG/NC7kNmAeQTjmRkpBSuA7JipLieFpAbasFs+jUdTOkOTIElKY4Up3cQKZKshYgbNNo5STuHyLSm+5HrRVLgMUQQEQwa0QlD4pPFa8OzWDaYprifNIONZMgg0AW8IpJvG51XbGp0moA0SwanC5p9Om5YylRW2OyituA18YwFRZDIouwd7DPOz7wXkprfPbnPkxdV/B217XthnV3r4sh1l2j1ii1sq2V6tGDqtqPkQjDcsmYc5id3fAW99EsqniTV1I/n3NSkmYkR0d2OZso20prRvH4f0RQJmTb0JxICWyzpTaQxSIms/oQhJox5AV7OMO2UaYa0E6cDCRzEkpOHo/t0LwxOci4ZHXpkMXTz5C/45vh2Wu0X/h5zv7Vz9KOjxnTyMY2tPWWmz/3Gc5O12xv34r+YwwTR3NikY2FN7IKatFZLc0i7lsElRR7RVJMR6pVmggJJ7mEgx2PdPeR7m5v7LLTPaVwo4qAVUhDh6raI78jvF2a4RaueulRzO4BgU0I4OsNTPp99Yhos/P9y4jXrBd6Q2sBd+nn+M757XENSerfEL17/7u9zh2X7qI1B41BFCy6ycE4fu0Om9OJVXaQxva08srmVdbrDUuFszvOz/3wT7CaJu4cn7JtFWuOLke+9ru+hXe/7ynq7Rs898mX+PmPfY6tO/v7e+wvMtvjU7bF2H/iMteevMDpaze5df2YqTmaMliiuLJYLfDWOLpxh806Os5VBdG4nl0SOSvqNc4vTQxJ2T/cQ1vj9qu3OD07xSyirTs9p5VGKQHNNT9yb6Gz3oRynH/0wj960MuY9Uu0kMRvWj71oJfxBTrQgf/L5W/i5bbh5fpe7tQP8kT60u4qc+ND5Sd5ze5GjG+98f8+/jh/7NLXArB68V9w8u7fPce6zZo1602pj9z6CP/y5X/5oJcx64vo+/be+obx0eoDr5T3vC4B7v7pzC9zZ7rCxVd/iu2T337fH3/WrFmz7rWyZj54+YMzAH6E9Gx6G0+ntzywx7/z6pqrz+4zLOfP7WbNeuSuAjHvDMRQ8Yif7W466RDDrQCKyN3I1ohnrSADeHR1untE4u5A4c4djHQHbS/y9cgPdiTieGuFlKKCVVP/XoTriqdwm9LvVHo/b+Db/iTClWzN+r8KZo1aK9tSKXXqTrwOoCVh2204mCFATZsiIrZGh6d6i5hmlQC4ErBbrCFZozc2ReGvaF+DLFBpAUJ0wBPnkbNOB91uUYHru27hBq5468HXXs7BsTc7h61NE9YhZWrCMjtmYKpx7E3idRLtHm3DS7iHrTbKZhsx2GUKJ6Q7yoACeVywwBlc8JSju1clom2bId4iwhrHHFqJ194QNCmC4mLocggoZxUWI2YVL4LIELAtafRE19pjlyPKW5B4HrVgRcIxnDNp6P2wQCuFVltEa7eA+VIr7tHLl4HRGuKgBwvSU/uUO8dolog0r7U7sxUZBGseEcoYUltAyzqxniaqhxNZVVEP+D0kYViO5MWAasZLobVGLRELnlIiqSKtonlgGBJpTHgzagfaTYVmcY4yjEhWWK/JCHm5xErBagxZSCK6br1FP7RASuEeV28Mk6DjAC0AeR4XZDektYCs1kFzSqgIpQnDB98F734S/+xzHP/Yv+bOK69y6ekrrPZWvPriGWfHZ5weHbM53lA3G1oH+wFXE3kQFnbKiLDGEOuR4Ca4JlQittp7JLrLgJtRBZoLWXNcPy4Rtyw9btnDiR193RrRyjmfg9Vd1HNvm+4Qtvd+u3dXbt+xxM4dxRDOafo13ovAw9Uryi4NejesIq33SbeKa0R+o/16Mu8/0+9z113tdv4hS0TVp/M9BQVvAeptu0HGjCdh2hbW20L1NQmo64JL5aWXrpOa0UoJlz6wf+0KX/v+tyDbLS/94nP8/Mc+zWev30KGzMGFAwaB9XpCFgsuXd3Hpi23bhyzrcbe3opDSdy6U6kCA8b2zobT00aReL1yiuGHYo6MShpGbLulOQyLBYu9PcacmDaNo5P4d9EE1mKIIy0omzNqfV3iwKxZs2Z9lelKWnBZF+zzFC/4l4a/7s6PTf+EH9p8/+f/O/BiW5+nXuh05w1e8axZs2a9cTqrZxyVowe9jFlfRD9w9jn+0OG77zkENhc+tf12juzxe3zPX76e234dF6fPPLDHnzVr1qx7qU3b8N98/L950MuYdZ+kJL5peLADTFad5z98i2e/9grDYv7sbtajrUcOAIMiXru5LYF4uHtJAWKEmPJsFZKfu+TCPjb2KFBF6IBXe5+nBRhxocMZwKIjUmQIV55buH13UdDWwY6At3beC0qHuwFqWncjc3cd0ntlBcyc2hq1FVqr1FKji7hHlrq3gDJ5gNrdtq1iLlAKkEgiqHp0lWq6C4Cl4WmIP6ikx9V2kOQ4bpuIcLZze3LvVbZwkbqH87ftHKlhcPTuJsXBtcPy/iGhlUarW2oTaguX3ogjKdMcSm2UdSE7eEowDAweHcjdpIjVipfa+3sHvE3RB3y6IS8XjJ7IQ8azhFlyXKCLAfLQ7ydeNxdoqpRGRGpvt7Tz/lajnKxJi5E0RIdtbQ3MUBS8Bkj21IF1WGc7DqVVw6qhKfVjP4XzWcBKxZpRzbFSSK6sJJyVWYQBYZGkO4ChHp3x0oc+jZUt3sKtaLrE23S39FrD2TpNBYjXr7pQTSCFm1ctoyn6CMcxk4eIx41HBqsNWovobXHUwvWcMwwpQGabGm0Kt7SbYCnhKhEVPVWkCuNqRARsO8V1s+jnVMwq9BR2IU0NpgplQt3JWUnjcB4dTYnjJ25Ua0zdeZ4wbHvG2S9+gsMpU167yemrrzCtjyh6yOrwkHxhwfT8MeXWLXyaQBqaB1Jrvc3WGYAlK/YssXEozUju4bo1i/+q90h0QboT1lwgD5hEbzhodPhifU9Z7Dz8cS2gfb/pgw0SvdXSr58kcc6wc/N3WIrkcxAsu77xvj/hd2HuLpI+zoX+8ztmbA6agy+7R++v9ouZdh4jfZ49ttsHENAh9kNv/SEift2sYa2QvFDOCtv1lm2J+x1yglqwNMDZFqMPu2j0JOfVSJnOePFjr/Hp517glc98lvV2y+ryPlcuHrKYJs6SIhdWqBVee/46x2drhoMLvOU9T5OPrnPr6Dar5RKpldOziVLjsXMeI+Ch1QD4OFYb1YQ8jiyWSw4vrNBpw50bJ0xl6oMgEicmSiuF2o+fpkROw1f+VjRr1n3S86fPM7XpQS9j1ptMWz/g5fK+X/Y2H6sf5v+7/iuRWPJL9M83r/Jj2+t85/IxdHOLxWsfYvv4N79Ry501a9asWbPuoZTb7WkeZP5yZcFLp2/jSttCWjywdcyaNWvWrFm/Wj2tz/L1w4P/229zUjm9teXiE6s5CnrWI61HDwB7C2dm/2Xee3opWABFwgHrKZ0DFRCk90S6W48/DgjYGAPu0o16Dc7DoDsQcVoHpH43UtXCzbcDdN4KkqILONzGDdF8N7IVib5ezeEg7vC5GdRaqLWc93KqduevGepONUAsIKQFGE0InkekbVEd4rGgg+mC6CKcw61292nBdQjIpSnWIEKzipUaPmRVVAJopiGjYpgqVivigiHUKSKgzQN2aXfxmlccxerOMRxdyQ0CEsqE6B5WHB8cr4boiEytQyntgE2xbcNz/5pEq4YajCmxTJkxCyknfFLIK9KQyfuHkLsLtTl1fYaZ0cRRETQtKWmglolGgO52tqGsz0jj2F/xFMMFIrHm3WtqHdi3htUYArDWkHFE1c7fhKyD/mqO1UrpTtRRPdyw44qEhYMWIUtCUOo0cXx8Ej2/UwlnqXo3n/d+WFGqO1vvCNrBXXpQuYIrSYUhjwwKOWeSJMQa5hKdze6knGMtu6hgIVzjzbD1FOeg9sGEcYEtRpIbUiq+LeRhIK+WtE1EMNugfQigwrCMcwhHNZHV0VIiblqVYTUyLJfdwb5zRU+YCdN2oqgy5kZyod065faHPs7muTWmlc2dG9TNmqNPK5tbp5zeukO5c4LXDSIeMA/HkjCQsKTQCmMprKqxbMbGjAYRpZ0GzDi/fgWQHMMTmkbO7bYacc5OxJnHdSbsMgespwMkD6e/qJ9nMoeZV2juEbXunEc6x8UakFaIOPbod+4d4yoBdSPT+e4AyXkygYXr12LwRHqkcziQYyeIiALC7e8R8R4p0dp7xwNqy25f6s+zmCHa8Lplu9kyTdsIkteR1Aco3CyGHryh3s4dxpsbN/mpf/yTSEsc3X6FGzeuI6Ny+bErXLp8QL19O3j6ZsPNFwsnJ2c0nKuPXeLKxRWvvbzBcyYlZXuyodYSvdQKw3JEypbaDNIIGLWG238YM3sX9tnfGzi5cYfT9TqM2uxeX8c9UhZ2zQA5jwHeZ816yPV3P/d3uTXdetDLmPUmU4zzfelJaXPjn0//I4UvPlywxdju6gMwxus/x3Tl/Xjee0PWO2vWrFlvhMyNH/zsDz7oZcy6z3qtvoP2wD8qE7j1HOlspB08M9cozJo1602rZo2/89m/w7ZtH/RSZt0HCcr3LP4tlrJ60EsB4JVPHqFJOLy2nCHwrEdWD/q32vuuiG2mO3el91aCegcrVnsYdAchkpBWESIqGu66XHHDCUel0GNSe3LzefTzzi3bPDpOJb420XPQE4QrB0qotXfGpvPbxv0ou0Zd0RQxq0CzSq0T1p21AXK6mzhpRPYi/X71HOagdAg+9Bjl3rlrLczKreDaoaSBpAFvjZRyxJ6KIkBOhqWE1QIYmiHnffIQUb0mShsi7hg3kghFUnTgWu1R0Q1F8RpxtSllvG5I4iQ19sW5lJZcTXC4v2SxGhkXI4MKyYXUI3Mxp9VCrQEDG2BtIldjqcpyNTIOAU7VQQ8voiTyAHl/xBCsFKoabgvYbOM1ShHZS8rhCq4TtQll2lIl4dtT1Bo5JZKHKxUXxA3Ryq7zVVVJWeknIJQJJEeMsBte41xsrVKb08wYVBlUGQXUHEVQiR5mlfjfvp0ofgw4Xmr8t5tArTYaQnHYmnO6PsPcGfKCAXqEtZOXmXEQxiFgmfR+7FqdOlXcBZV4bcK0XbBmaMpIHrCpRfeyKi0pZTHSxoynHN3R2w2SE+P+Csyop2tanWhp0fttBc3aj0thmTKruiWVgmisKefoiCal7iivmBnVnSoa50CLfuWy2dI2le1kpNRo2zvYVChklAE2WxQniZE0wThQRVDNDMsF2RpszjCcSYw9Klvt/c8yhqu8dyWHaz52DdnFvu+SADTKdAPQDufXmBgxjKIJ0wDwYgX3RMsx7KHWz5OU2TWLawe7dLAbbt/uvOpwV7C+r/ndoRNi0kUAt4Ln3eCKID2qnb6ToemuY1ggOqvjfN6VA/tuv2Pn6g/421qhIIyDUqYW549LANPe8bybuun5ALHvddi8vnPCpz/+HNRG3ZxwNm1YXb7A3sGKqVRON4XJYLvZsimGtco4jFy9fEA5OuXGjRPcGpujI2rxvuVFb7i4UatRHdJuYAMjJ2FcLthbJsrplqObJwGGtb+eHcpbLZRWwY087kcEupxHM8ya9VDqpJzwqeNPPehlzHoTainHPDV8hBfLB7/o9wXhm4dv5yfKP/+y7i+tXyEffYZy5QP3cpmzZs2a9YbqY3c+xk+99lMPehmz7qOKL3itvoMH0f37S/VqfTfXPvvT8LXPPOilzJo1a9ZXJHfnb3zib/CXP/qXaf6FqUGzvvr09vROvn38jQ96Geey5rz40du86/BxhuWDf2+fNetB6JEDwILedfNaj08VCYCjmV2hb7hzLdy7Kp3nBlhVlR7nnHpXZnf6EV29O3dlRK12sOzWWa/hLfKbnYhZFtXzCGVEEJWOesOdGQ5JQaQDH42o6PMoWDTgTDxQfC39p/NImiZMNbpId0nO3cbmOmB1CzpgbRdLLeEgFEguEbOrAtnAnYSjCimHY7la9L2qKJqFlJTUu1FFElImREaQCdPgqeZboPeLtoi1BkhmiGYsL0h1y2ESnlwd8uSFfa7s77G3t2RIGVUhaY4/y0pBao98dkFroSKsXRgNlsPA3nLJuEikcUVOyjDucfnSNVZ7e5zVDevtMT6Vu87p6hgZ9xpfi6MeXa9SCdhZK9WMst0GINMpnLM2BZx1JXHGsNgnDQpZ8KRQHWq87u4OLRzCZlNAKRWyQE6JQYXRGoM7KkP341TSsCAPQ8TumsF23R3rhpUCOcd8QXOaRZ/wVBpFRhBjcEMRsirDYmQxDozLgZxTZ36NupmoJc7bNGbSMKLuMG2j31lyxBZrOGdlGMK1nYQm0QOt4kgzpBh5MaI45WxN225o7jSP6OI0DBGjXApDSly8dol0+xa+3UaPrmr0d9eAhj45NhlWAzoPfeDBXSnF0TIhtiG1gg8J6y5T3RvRPUE2FbVCUiXv75H3D1hvJ4bVCj3cR46OmDZnlDSwoLBfw41uu25qiU5n09yjmfsprCkmyhxccs8ZiGvorlfY+zBJgGxa78pmiGvcCuiIicTQR6txPYsGs+3OcMERDZgsqRN/b31/iOBu/7wO8nR+P94aO1uwiH5ezy/Wo9+V3kzec7nFYs/RgMYkDa6c8nns83aKvmurlanWGAyRGAbZuaJ75kHH0XQgHAC41cr69k28FUqt0XFujZPrtzizW2zPNkzTlloCOA/JWV07ZLFK3HzuBielUqZCbXehr0jsM22qTLU/bzdEMnnIJFXGg32sFU5uHbNpDffao5875HajtIiqH8Y9hmGIJIgyT7DOerj153/uz/Njr/zYg17GrC+iP3DwDhby8P4BKgJP5o/zWn0nxb/QtSsivCd/Dd8y/Dp+sf48p37yBbf5ayef4jcuHyf3ocG9z/0Ip2lFvfiO+/AMZs2aNevXpmqVH/7cD8/9v4+Q3OG4PcaRPfmglwJE8tKLx8/yzGu/QHvsA3c/W5o1a9asN4GqVb7/U9/PX/vYX5vh7yOk37r8d8jycOEmN3jtc8c8+a4LaJrfS2c9enq4rsj7ITHCgtlwF6x5GN6SnFe8+M7Bt4tGFSL+dNeG2p3BQcbk3PUX0b+C+d1ey/Mk2JzuxkB3B+95DPWOyvY2zwA7uXdrCqk/fgA10O5ubWa0VuNp7bqEvcNpjXtKmtBxpNrUHbYBrvB0DmIggQqqCd/F+iZBdUC7o9WB2np8tCuaEyml84hZ175+752nGsdEaukwGEqN2NhmHYB7j5lNCmUTQCWPuE9kMofLPZ4YBx472OPiwT7jkJDmWJ0iTlq28eMmqAgpJbQ1PC/ZbM7AE4MUlnmfMSs5j+RxIKXEtWuXeOd3fjN6ccWnfvrnOf7UTdpmwjYbvIazMDyKTq2NNrUe+WsMtYZrFiGbUTO07na02iitoYtFRCOnEXNHyoTKEABvKmiKvl12x0uIXl1Ac0ZUsdrIZgxpQdbuVAU0LeP1SfnuEIBVXDS6i1G8VdyI6OZSadZorVGtkfOAYuRBWCwXjGMi53ATYxVr0KaCl4CTQkNqj31u4QL1sM0jmmnVaHsZHQea1+hLbk6yiGPe9VurgE0T9WxDLZWWB1rK5BTx09oqasZ4sEfCwmm8VPy0YFuj7GKQEbwUWms0HIaEDhkzj27lUvAyIaUyNMNb9Fh7FsrxEa0WytlpDGuMI74a8TEx6pK2TGynDWUq4ab1SmqwFMHHGJLAwL3DZnfIuRPgDljx3qFtPdZZEcnh0PVdzHJ36VsAWHPOu7oXObO/WIEbm+2EuWPuEVpwPvTRkJSRFoMcYhZ7h0p0NStxLe8GU9xBLCD6Lg+6u4MFA3WUHiENQI1UgN4jrFbDqd7hdgxudJjaBDOn1EJzJ3kA4B3uVRztH1YY0h3LEsMV533CEseCButjmjWqRPR3Pd1we3LEwaxgrcb5NQ6YN6aTNc/94mfYHK9Zl4JbDKdodzGLgFeLa2AHyxGSOGoNzRnfbLmznTg9OcNQUsrgFW+GKJSp0moj5cywGElJoBWszQ7gWQ+n3J2P3vkoP339px/0UmZ9Cb0jH0Qs/kOshZygX6Tfd6cDPeQP7f1RPts+xV88/S+55Tc+7/sfLnf48e11fsPycQC0bVjc+Hnq4Vvjd+FZs2bNekjl7vz3n/rv+duf+dsPeimz7rM+Mz34zsLX61Z5gsNP/QxXKGwf/6YHvZxZs2bN+rK0ex/9ix/+iw96KbPuo96d3se70/se9DK+qO68vGbvwsilJ+dKolmPnh45AOznvZbhoEsewHP3QVSw3/6hvvbQZVFEdi41PYcWohruPe0xq9bCGWnRlxlkOQCEvL7/twe1Rpxs6g64ADHhOkvhypN4jEggDceadLjUmmGlR7dadAyLSLiSe08xEhCDlBDL8WWPsLVWA+SIRCyxaHcyB3hWHckpYqdRpTVDXUiJcASzi5MOxzEMES3LLhY19S5lR0i0acKbYaXhrXTDn2Jtii7Z3KFia7goY0pcVOPSkDhImeXkjNPEIMqQhJwy2QMspSQkIGtCkpKyUX2EqTL6zpmcyYsV6gGH9t/3VvIzF7nxkc9w8vJ16jTRasQa1+1E80bziIpwC1dmlnDmekq4KC0nWpmopVDTSHOjudJaoVmBQRiXK0YRsidyf846juEkzorkMUCkGdIUaRXFo3MVI6VEyomUx0iOtoj3lRZRvuEED+jf/wNtFwO+c7dHlHep2zhVBtBhIK1G0pDQPOAItdQ4P6YAsSlnsjrIHkJ0IUvOPSDd4lirICmDgLVCK41WHZKjPmJToa23cb4gtGlLnTZUN1raXWOg1dBSSOb4Zs1msyYdn5KnLalWxMBaxoeI5XUz2pjZkmhJ8d4LbBLxxUJP7vaCekZzXNxls8amCbeGNEMBPy3U5R750opy64jtnQ1ujTQVBnPIkPNAcmjrDWVbqb1z2l1prUGO/nA8+ov7GAcmHow/dpdwxO9c+liPiI4Bi+jbbuho1IWjpeI+dWicadb6ftTdv7WcR3VHsoHgzWFn7jUP4Ou1D53EkMAuhtm9xflnRnRoV3rxbWwFAmKx3/gucaADYrEOniXhBFytLXrL+1gLu4EW1RR7pbWoTO57qfc1ohL7qChYgeZU6c5kDCvOFMXbcb7vurVbpbpR7pxypgOtVcwgp9yPsSMpYsNbc2rdnPeU5xwpBZIyeRyo2y2nx2ua1XitNUdPeSt4NWopoMqwWJKTkBVqAXvI4c2sR1cfu/Mx/sRP/Ale27z2oJcy64voQDIXdXjQy/iy9LbxZ/jY9ktHeIkob8/v5g/u/+/4cyd/6vO+t/bGP9u8eg6AAYY7n4jfTR+CaM1Zs2bN+mKqVvnvPvnf8Vc/+lcf9FJm3We9XN/L1g8e9DJ+iYSXyvu5fOvnYQbAs2bNehNofh99dPW+/EEO9MKDXsasWbN+iR45AIx7RDDTY4c1YGs4+sJdJz2OFPfeg+kRw9wjnUWku8h2lKTHQ2NxW/ptohwT2cW/tnBR6C7yWbv7TrrLTnPE+FqAKRUN9yAQpa491jQ8mQQM4Ty2GkB7r2j8QwAft53jruHSe0JThhrRspoH1BUTx6yhOgSs9AZpCDDV7Dxi+Rx6u3fHcX9eMnTAlQJOdvdn8+7MbA2rpUdg7/6/IDog7rhm3GFRncsJnkiJKzpyCOzRWGi4RTMe/b8isfaUSSKkxdBdx8aYCpKcVCKCOOcFe6t9Sil4dW5/7Hk2Lx5zfOMmp7dvU+uWWkpfZ3TLhmmyoh5uaKwE8E4DLhMuiZZHKhrwuBZICc8D1ZWmGczJQ2ZMmcWYydpjyM3DnSmC1RhMSAlEF9BagOIMmgXNEU0uNEQS5jVcuOLhtKw13KAazm0kIntNBWmV2hLrcsq6OgVjsIalTFOloXitUAteDGkR+6uLsdcerxCNgQftnnXv0F9SQnPu63PMBfcEbYObUu8c4dVwEYb9FU2E0pypNaoKDUjmaNmC9S5aoByvOd0WFq2FM3kc8G2BaiANT3HdldbYNGeqFdeEacJzfBidN9H7bClRrZBsCORqBfN+3nfnvrljm8J2Wyh3TqA4OWV0GMg4E862Fqb1htxggbAl4qubcNfJLuHqdQ8Qqj3i3CXhu2uFFue7AiYRo+0Wfb0aqQLlbMOdEpHjO4e9qCMSAw+ROBDR2bQY9lAxaLs9TJDWYgDEGtB6BXB0l7vswqglusH73uYQkNjD+WyivdZ8Bznvul3dHHJCpNEsrm0dRlQ0un7x3kkeAyHBenvss0t3SguufVBENPrGW6QaeBpJ3UFdazmPtlbA3EhifS8vNFdMDfdK0l1KQ09C6O5qqzHQEUkBivZ9UXJmKpVytokhlzxGgjbhOCaNVC/4sCJnYUgpotc94qDtLt2fNeuhkLvzsTsf40/+1J+c4e9DrK8fL30eFH2YNcj6y7rdJbnch5/mZIRZs2a9eWVu/MCnf4C/9JG/hGG/8g/M+qpRdP++k4eh+/eXqvrY/5acNWvWrIdff+szf2t+H31EtUv/e1h1fH3DhcdXUe05a9YjpEcPAGvqYEKgwwjzhrhHfKpHz6WqhiNNpd+W7ui1Dodb7/D16OVt4Z51q+F2C3ttBzfd7WsRNa0pB+wARDIawagR6euG5Bxdq3L3sSWlDoXlPC54GBLT5AEuNJyPhiDegUqt4UqWWINJwqujqkhSPA0kjJQ0enetUUvAG7J0MG4Yimm4lkXlHGip7SoyE0qNztAsiNpdp7QHYPTe5SlkUEMs3KNVckTUWnSdDg2edHhyGLi2WnIwZJYpsUgwpBTu32FAk6I6kIRzFyvqWDPEjXEYyLWgy5E0Lji8dpULzzzB0Ss32B6fcHx8xtGdM8rpKW19SnVoNnXgL+D13OGdtff3luhaVhR0EfG2rUX8cFI8dXiUheZEFDLAtsDoMAo6LMJVPZVwMUum0TCf4nVBcV8gqqRB0f5BanixnVbWSE406c5rEzwBg6LLRZyP5tjUqHXLVCvr7cTx1DjFkcXIpAmpDdsWijSyDGhrDJrIGg5v4hTHmZA8xPFIQ481BlLvhhaJTu1xEa5m2wAGJaCj5oQsBwyj3Dliu5nYoBQcqY1MjWuvX39q4MmoOTHSsHHAVfs8Q9tdVuEAnhrVHMtDxD+7xYxGK4hkvMNJRJFS+nWUo5d4lwAgDlaop6fUZnhtHFy4wP7Vyxwd3eLO8THT6TYiqx0sd+dviV9lNQzC1Lbr2XVIi/C+9r2hX1FxTWjq7ncL17BHHLsC7tazAaLLufkOLju0EgheHZEWkdLSesxziuOXch9S6Y8lPbbTWnfl9xdVw60sIrEOj8EErNG8gifE+s/u9j6P/Q0Ma4bpgJRNPE838jAiAjl1/2+NtWnWWBMSQxV9LzuP2e897D1fPY5xd/56bd3VHp3LSZXqEudZ36ebGWYNN49YdOgp/YEgBMPMqOaQo2M57Z6SCN4K06bRmkUMuU2IDpHkQAfHUyENA4vliiFlaJWy3VJ72sOsWQ+TPnbnY/ynP/Gfcn1z/UEvZdYvo/cNFx/0Er5sfWb61q/4ZweUdw+H93A1s2bNmvXGydz4bz/+3/JXP/ZX5w+t3yR6/3DvXEZ32hOc2LV7dn/3Uk8PHybtH1Ae9EJmzZo161fQ9c11fuT5H5nfRx9BXdXH+E2L3/6gl/HL6vT2FIaWGQDPesT06AFgtwBK0mOcobtxO7S0oJruHb72+FPZ9VS6hPurO/eQAMpR4yqoJM4rfS36I6FDMuk8RaKzNhx5d2N0vX+t2kFv7/x1CRgrvTsz4FUHPSRgwlxf53ojHJUoLrU7jCGL4nkX0RzP9+5szg5CK9YKeA6E0iNbvVUMQV1xBSNcdIgH1CZ3VNnA83k0rBP9m15rwNVWkFa62zkgrrlj0hi98lhS3ro64MpCAv6qMigMaWQYB/KYSENGdUBc0LZFXToQCqAmZiRV0jiS8sjq4JCDq5fIi4HDi4c0a2xPzmjbRt2sqfUMI6CotBYdyHkIRy9ERLYKnoZ4bfKA5IghtjySaNFzO4ZLk5RoBkmn6HaN3FvatjF14JkQhuWCi888jhfj6NVXGNPI/vIASSPVGkjt5kxDx0QtE+vrE04NbuhGQ2k9YLlNpbskG+ZOrULZNFqteFMGBmSaEKn4sGCyiqWBPDSGYSANIDl6lsUTtAoVvEbUtI2KDgmLAOgeBy47E3w4u6fo3s15JO2NSEq0VqjHa7alsDanaAJXBoPRYJkGBm+k5EhWmntEgueMCbTu2pQeoY0q3nOOxR3tQxNoivO0xeCD9O+f88UOSuONvgbQbIY0C2jcjDQmVk9dZnn5kJM7N7H1hmZOHTKTGZtmbDSzTZVirfdiCwmleQOG7pD2uJ4lYHqkzqfoxg7La6xftbv7G7Ed+3nNuPe9AnKPXt5Fwnd3qmhEyNN7zdtdV3AMqvQ9pqcVeO+bllogjeFKFiGLoBIR4dWJc90dasNxmkXPeOyLUMxxbQwqLMYlOSmiQt6lFSABcZMiwxhx5Ow60q0fHwN7HVyWMCu71/N9WLrLvQIiidazqQXp0NcwN8yFpNGf7dajpLtDWjy6yw0hEfuGtxYd6ZKpZYqfx6P3lxTwmu4eNpA0MAyZJIoQKQbNjGYdYs+a9RBo5/z9Uz/1p2b4+5BLEX7/wdsf9DK+LF1vb2dtXznAfTwt+d69t5x/7Sjbx76pDxTNmjVr1sOlH/zsD/JXPvpXqF4f9FJmfZn6Awfv6J+b/Nr1an33Pbmfey1Ngj32PjZPP/2glzJr1qxZX1LVKkfTEX/6p/80H7n9kQe9nFn3WZnMb178DhYsH/RSZs2a9UX0yH0CoyoBsOoOeKZwxJl1t2/uMamdSEi0SUYHcPSYOrve3vw6x2j0BEvu7jNP3YW2o2OcA2OzFo5eIXooI3M0olwJOHw3elXOO0N3tNpxikOtLaCb9PWniH52393WIwa2lQ5vw8G7ix8Ope5mzlgrWHPII9ZKrEOlh/8OQA2PowWMQi06P631+6ADrn6fHpHSrUwB5FoLgJOHiJo2EM24Tagp19KKt+TGY8vM/mLJMiXGMZOHRM4jOXlEtCYlpWXA2JbxWmnbCdsdR4+OT8QZVysO3vokZUgcv/wa4hPT6THleE2ZCq2c4dVQzaj2uF0j+nh1gNTdmq7oIqFjgqThjjbrqb0V0RTAuINZEUebhusx554mLvimYIANieSNs2kDU6WZs3j6Es9+wwe4fPUyt1++wSvPvUCtDZm21DvH1OM1tUfkNonGaEtK8YSUhopDYLJw0wqMOQGZIQ9UAfdVALjWcNXOHh1Tp8qISsKSBdRUxYmo8B3UM4i+ZDdSHsEjqpjNFD3Pm4k8ZNL+ElGhbQp2fEwxZ+PGxsMVPC4WrMaBixcvcni4Rzs75fi1G2y3ExPOxf196rYxeYw4pJwjSrg2rBhVEj5kkrY4rs1Qd1J1BsssJdy9STSM0h6QPlmDAmTFFNwaDcEVkjg2FabnX6O+fIvNyQmuA20vsXY4U2FTnfW2MqVMrQVLQ4ByN/AUgx9UmifS+YcREQ0t7r07N65ZESV6EC0ivHv8OxLws+dEB+wlzjnRXRR3B8h9f/Fdp3BYjc8hrkB3UIc72ns3uPS0ABdncbgiWWOqjbrZ0GzCNfqE3Y1aG60Pc9T1FnTkYC+xPNhnHAU80VvBAaG1GEgZUkK9IaJY64kJqkR6c3ckC4h7B7k9nl61HxtnKgHqk0WUfJyTRqM7f937vhnwP9LJtR8vj25i6+5oN8QTzXoChBdarWjKSE7RR65D9w573B+Qc2ZISpLowW7mtLZLNJijTmc9PPozH/ozvHj24oNexqxfQV8/XmJPHv5fv6sPvFbfgfGVdRU/k1b8F1e+mV0zfN17iunK+9k+/i39/W3WrFmzHh7d3NzkR57/kRn+von09rzP43rvPmh++/hT/Mz6d96z+7tXessHr7C6MNxNZpo1a9ash0zuft75O9n0oJcz6z4rk/l9qz/Id47ffTdB9SHV3sURmd2/sx5BPfyfQN1jRRqpAgHtfNfT6xHRzA4i7m7Gzs7bo5Wdc8daxD8HLEAUFYmQC2+I1dd18QZoCjegBDT2gMD4+Z0GdMv5HPgKrbuThw6cA4pUa5RtoZYWva+SAmYJ0JTo/NzFRwtOwAshnMTn0bBIuAjd8DphJJyGlxJdmqmDFO9wURIqEtGpZhHlunMH2s4NeteF7Ei4MWs9P64gmANEjLF6I6HsUXhiXHJ1GDlYLViOI0OScP1qOBQ1j6SUSZoYlyOaE14KNoGfFXwbzk8dx4BIaWS5vyIj3HnxJuubt5BWaWUbsKkaUq27/wJMY9HfGiAr+lNVBMlCGgckpwB4Grd1NfDFuanTasNrRTXjq31sWp/Hjjsa4KhOWBXqnROONxE/4Q7r0w03X3yF6XTN6fXbnLzwWoAzh1a2tFrw3t1byoS5o9L7alP0w4ar3ElDANImymJIWM64OG7RW9qsYa3R3KgumApWK5tpExHXgwYYI6Cy6AiiWPOOmKPHOHkiNcMNkoMsRzxJ9NeuK36yplZn44XTUplSIvmIKGwTnNiGunbq8Qknx6dsthOWlDacsmyVixjDsCKpRNyuO6jSmmENrBrFGg3IKbE/DIx5YKzRZewe0cPN2rkDX8nhAqXFwINXZAyntk2NLad4SlQFWw1sU+K4OreBrUEZ9bynGa8xeNFav0a9O3CdJqA6xlYRDxzA0kCkr2cHLN1AMillxCPWGYjrlUgE0J4EEP+7d4TvBkx2+5TI+f4UX2o4fYnzz7x3/VqjGTgJWVeaxXWYNeGtUEp4rJM74sZ0ukGGkSHvsbe/ZP9gSc4Jbw2VRE69y9caVo00ZJLELuYejx7QlehL7/tEQO8aEF9SXHveoE1MDZobWVsPZmjnv6iZOU6O67LHRNNdx7iBphiUaOHm1R6ibm2K49ImTFJs3a1ixDUv1vu1a4lo/ZTIOQZQhqyYVZrdnftJM8SY9ZDo7z3393jh9IUHvYxZX4a+Z/kkF/Qrg6r3U8WX3G7PfNm3/7vbv3Xe//u4LvkvrnwLXzteAqCNFzl517+Dj3Mc9KxZsx4+uTt/9mf+LD9782cf9FJmfZkS4v307cPBPbvPpZzwweXf59PTt3L6kERBH15bsjzID/0H6rNmzXq09fee+3sz/H3E9FRaMfbPw37D8L185/jdD3X/bxqUp957kdXhMPf/znok9cgB4NY6zNSAZa4ZsXD8ioaz11vvO911VdLjZ3cQp3mHu51HSQL1cK95gFLX3vHrAQZ1yEjPhhYNZ21SOe/VVO9uYOiRrRrJrhLxsDsg5K2iKiSFlII6moer11vl3BWo2v9QcJIOYLU7ehXTEW+lT5FGVK254D1W2nsMLIAFZeuPH8BbtLtjxYGEeCFlwSTgI5Iwh9oKViqeMmI1jqE7+AS6QDwhMpGSck2VqzmzN2QW48DQI2WzKikpKQ2ohqtytRw5uHaJvLfi7OiEzZ07yHaLtli/quAeINdHZTttmW4fUU5OMItYZSW4nXiK7t1hQHOGnEhpCGB7eoqXOBeGxQJd7gWsirDZAHVJEJOAx0mpTLiukBwubB8VLwVkwEvDxMNVKimOcQOvAeTP7qx54eMvkPVV2Gyp6+OIOxaJ2NlWkXFEREk0UgdtkRPd4jWPhmGUiuZMlhGvGn2zKQYQmnukcdctdapUMypQXdnWxrZOeEvI4LhCHkdSTrhK7381aI7QSEMiDYo52JgDLFbwJuTDA2yR2ZyccXrcmDxRa+sdqg07K0xH24gdr6U7TcEqtDunbETwQZDcMFG0OqrCsFqSmqFrkFIQM9RgoYmDvSULBZkUb41WC2YW7lsJxzlyt99asiJpEbHmZcLcaLVRzdhk4SwJx9vKneqcuNJEEUmkJDRtmGkA72HE6tRBsINUSCPmNbqS6QMm/feMiJfvsc7S9wGPDuHzRABi81BNEROvAYFTSiSiH1pflyDQ6Tbiu37f2JNiPiV6ha1Z9Oo2wzXOFLOK+UCSLWNOTJs16xJx4gnn5HSDDonlcuTwYJ/VaoGKh3tWB3JOfSDd4xwVYUjhqHeg9QGHvqEBhntAbLfedcxuT42hhuZKbROaw0WvmqL7FzD3SGGQALvSe9FJ0vdLxVuN/uc0ICni6t3jOnOE2sLTnFO42t0sHNZWQByTjPVLRpOAVGpTajGmOoUzWPueMetNo51j/EHfx73W0XTE33/+789/cL9J9HCdPfdOr7aXAXgyLfm/XflWPtB7jh2YrnwNfg8/pP9qvZbvp+ZjOOtXq6/mc+Znb/wsv3DrFx70Mmb9KrQvmX//8D339D5VjAvpNd6/+Ke8Ut/Di+UDGH2A/wFIVLj4xApN9/YD9a/ma/l+aT6Gs74SfbWeN+7Oq5tX579Fv4r1TFrxu15X6wPwe/bfylVdAHCjXWBtP8er9V1sfb/f4uE5T9OgPPuBy6wuDPfk+vlqvZbvp+ZjeP/16H16HaZYLCyqYUw9h7/R/yp5jBDQDnMkve4Xf9EOawJk0OGFuJx3fwp0KLc7GR3MSUkiMlno/cAd3tgOMqQeF93ve+fm61G1AWvjSYw5YYuRLU4ppT9G/xlNJDxAxi46VoZw1u2cxL0v1ZGIhrUSj99lzaJDuB8kt16B3MGTEBARaYEcc0Ld7h7fWmlTwaeKNeJYUBEx1FM4gzXg9YEbV4aBgzGzHDIZD+ir8bqoRBey5sSwXLG6dMjelYvImCk0prNTJCfULCK8UzhVUaWtG7Y+RupEahV1kOT9eAsyJlIeI0Z2uSSPK1LOTNMaOT0BD1e2jstw/zZDz8+LIV4/9QDDLSJhdTEGhG6OJ8FTOA3JBq0FbKstbIQat0EF32zZro2zVvFScQqSOhhzQTCyNDRFDzE5QLfvzpXWu6hF0LRAd27lYYzzMg+gQlbBzGl1QHVDmjYkF9TBk+INpu2EimKLgbEPHLgZdZqoJfpfPSWKCsWFhlHMmYozTQU5yAxUplZZm0FOPUZ3jHMPo6nQ+rodh5zPAaY1owDHothmw2YcGFojb2u4fPf3GMcFvtxDyha1xrI2hlJJQ0ZUqMXxydC2i1ROHQKHK16q9Whux0RoaaANTk0w4Zw04+baeY3MMcLkhg4jecz4NMWgRr+Oo6s7R151a+zc+mCY5UgLILp7JeVwkLZw+rrZLigg3PVi4fJFzsEtMsTP9z5vIfaliIXWc9grSOwzSAwhAMNyibhTtlPsTh4DL0MKd/BUKq1VqgpmBbeJyY2UB2rZIjJwuJc4PLzAYjl0V7Wjw0BOOeLxu7PcRck592Mc/dV27kzu/evNkDQEeEUiZl120fJgrTGVvpd0eJ1S2qHjuM6Sob2H3U17mkK0U+Pe+3n7UfG4/9jzox+7NUPU+06W7rqnJeHWqN73nHEfSYJ5otaJaROdwZoEUd/Nycx6k+he/HL4MP6C+ZHbH+FD1z/0oJcx68vQdy+f4Pv23/qgl/GG6cm05P9+5VvPnb8A05WvZfPUd97T6Mqv1mv5fmo+hrN+tfpqPWe2bcs/eP4fcFSOHvRSZn2ZOpTMn7j0dSxf99nFvdRCz3jL8LM8nj/Ji+VruNWeYev3P8FieZA5uLy45/f71Xot30/Nx3DWV6Kv1vPm1c2rfP+nvv9BL2PWPZAAXzdcIncn779/+B6ezXssJfF4+tKVC9fy54DP8Xj+OEZiYxd4vnyQ6kvWfvH+LP5LKI/K0++/dM/gL3z1Xsv3U/MxvP969ACwNzhHKIabBczQiCOVQYEMbQKPDs7z7FgCTkUK9A5Q9LjWDnCShvOzOfRAaFTDhbeLexZNAfE0nffmqkTZqVh/rKCrdy3EOzAsHo7HpCTPSJmit9U75GL3cxEhnaTfRwfNuAXYc8ObnkNqyUM4oqFDpd3jhVuPHUCJZ9khcDycdHDtyHkkdGslOoXNgdKfB+dg0tywVhF3LqbExaQsU2LIiawDOQ2kMZOGgZ3BTzWhWbBhZHN6ht2B6fQ0HLYpI9nCkedgtSCt4KxAnJwNGTNmFVK4dfGG5ExaLtGUGFYrLjzzFtJy5OanP8XmlqCqLHRgf+8ydcisT25Ej6sDQ+q9qwal4WVCVOMcACTtzpVwo0bcdkU9nJsMOY6bCHg9d/pSC62VcOaagBrDkBlyDmjl8fhuAq30uO3ojNVagYRoOMF1uWR3ckqKfuYdFEN6T6wqlIjP9kExH6i1sp4mWhJaUqRUzJ1SKlaMcTFgtbE+20Q3rMc535phOOlsYtqWGCTwcIQma+G0TwlPGe89rtYMvJ53v6pJOKslXMmnU2M7rUmtkouxRbk4ZPb2DthbrahlCWViaIXUXeBOgtpAMjKGy5+du7ZfIj5kSAmrjXJWMRVazlR1Nih3auM1V246bDq8HAZBdefKTTQxrJ+bqFDd4/xqtV/vgDUsGD+iA9Zjuh0JFzkSEfQaDnlHI65796GGRxcvmvo+pLs761tZ7Gfanel90wER0jAwLMYe/x77iwBVjDQu8NaQoVG3G1ISpqlhNWKqfZqgNPZXK5Z7I8NygVkMfmgeSLqLfIdmRq1GSpmcI9XAzM67f0UjFQBr0cfb3b/hUO79xBb7VamGuYW7WTyS6K3S3HCL82e35/puSMZb7EU4zSKC3nDEFXXDWsSBe+9DN009KUDOt3cheqKrRWR1HlL0p0vGrVKKUa2iknrSw4D5Lk5/1qwHo2aNv/6xv34evTvr4dSA8u2Lq/znl77hTRH/DDDIhivpOW61Z+I99VfQ42nJf37x8+GvA55XPe1m1qxZsx4+vXz2Mj/0uR960MuY9WVqQPnPLn0dv23vy68o+EokEpHQ71z8JGf2MX5x8z9j64fxt8t90tVnD+aewlmzZj30atY4muYhqodVC/SLmnF/5+pZPvC6v9sgkjJ/+94zLL7CAauFrgFY6QmX84ts7ICb7S18bvqG89q1+60Lj63Yv3Tvh6lmzXqz6RH8REYjhtctoo+N89jccOY6SENcIyLVw7mGKIYgXjknn611d6bHzZRw4e5qKEWDNXlE14Z7L2KMRcIlJ2jEMZPwZr0nM2AeWEBbJe58FwWtgrRYi5AQic5ekYy5dSdwjvtoNWBn7yB2ApYoEvCL1tl2Ao/+SzGLh3Mi7vo8u7aTM3SHFHtfabgaI2K20axRS/SrutU4tkG6O6jx6Ap1WLpwKSsHi5GFCllzuOskgJ236FgVUZIkRJbUs0q7cxb9nqXQSoFtCeitGavhUtVSacOWvFig4wDmaBFIGbMO58dFuIfTwOrgAgeHFynbSiux6DyOHL7lGd7ybd/K8VR44Sd/inb7NjuHZ/T3Bsh0TWjW7sLOATUr/XbhCCaNiAasDxjZo7fTgJujKbqVdWpQBaxR3TBXXJUG+LS92yvbJOByuhsdHJ+zyjnIF02IZHRYREdyUprL+TXgxVAa2R1DGMQo48iJO1OD9VTBnDZtMWuoJ7aibFJltHTu9gyQJv0P1ehoJSWUiaQJPKE5I+NIMadNEw2QrLjlPpYxxnm9i1tXYWoDU5mgOmrG9uSMkgeeunyVyxcPKSennN05otUKU0W9E70hIcnCid2vZYtM3wCsKjSLyOeGUZtTga05Z964U4U7bWKjOZyrYSClSjw7JLrDwwUuEY/u0uFrxmvp10vrztxwYFs1TD2uIn/ddeca5n+PfmO0RoeGaPQ9957bSALo4FNeB3135wS7LuhwBAuJlJVhOECyoGbU4rTSMBWkQXWn1sJ6O9EkkxcZdWOxXKJJ0Zzx2ghjvZJ6D7G40Wql1tibUgfTZnEu+c6l213HEWu9m4VxoOHu1GYkFWopTOYkVVQyKcf5Za2nGaQOgIlzN7Zsj/1QJEIZRLC6xRzSMIbL2xqeBtwVawUZclwX1LgW+yBHJTrW86B9cCccx1aNev561t6JLbFvzpr1APWPXvxHfPjWhx/0Mmb9MnoiLfmzl7+Jrx0usdQ3z56RpfDexT/n1C7xYvkAAEbiVnuWX/rH+0KO+dNX3ssVPfn8O9GRcund92nFs2bNmvWr13/1C//VPET1JtF3LK7xfXtv5besnrqvj7und/j61Q9zo76N1+o7ObIn3/DHzKOSFw9vl+KsWbNm7fSR2x+Z30cfQl3Skd+weIz/+OL7v2hixkryeYfvG6WlnvCUfITH8yd5vnyQW/XZ++oIToNy7W33roZo1qw3sx49AKzR0+s7R2534jULx6RJj1MVoJUAO2gAl35b3CLRNPVe3p0btnnwWgPtLlk8nJdh0ks7Y955/LO7dDAbbswwZobbzy1+1gnXb0Q5C26QVWDIuEdcbWEbHsBWsR3kTQknRRemR2frzm1o3VG8i07tGcXhLMwJaT0uWhNWrMNnPe8W3cFcl4jTVQ/bc3WnFcO2UwCw7nA0MtgWlRF6TKs6HOrARTWWOZyDacjomAP0IIgR/bWaGPPIlcNL+LDg5Og2dX1Em6Z4bkMOYJMUbxKdtFMh5bNwEiYJEN7BuDdHfOhOSkGHxNPvfAtXPvA+br90nVc+9ylEFwx7K5587Gku7T2OTre4vTrgdLMli1LLxLSe8NYw7yDuvGO2wyFvqA4M40BthnsBOsjv7knZQSS1zgJTQERt4TT1AL9TKxEbvF6ThwHNY++ejt5jHQZEAqrR432tGWrCcn/F3sVDmoCmgbLdYHWDVSPlgHRtyAylYEDyiltlvUjQHC8V1REVZ6QhywWy2gsXMyDNUPd43bq7Unr3LQyItW5MFxQYIMCiGS4Jk4KfDxZEH2/w0YwNO5BYEKKH1xFSq7Qs6JiYpoqUxjiMDK6w3UANkCznDlBHJGF1CvgoQrNKqzWuPQvYV3AmMyaHyhBJARLno7kg1TCLgYs0BoB3tz7Q0ftwXWlpALNwxQuYxHMXle62j17acJM2diHHor18djewQY9HTvG198ES33WXO7jsjrX1MY0A0YvVgjwmUo71R8S0hxtcnbIulFrYbNacrQtTNYYxIymfR1DjAX5rqxHHLOl8HzB3ikW/8ZAkOqq89ucWz8dFe8S8nO+X5xMDGFO1OP/cKaUhOaLV8xCPY82xGvuPau827sfV2Tmhu8u+H5fa7HxvMxQT6fHrjquSxEkCSXKkGUii1ekccGeJqHs3aFao565kQXPsNeLWEyVmzXowMjdub2/T5vPwodVlHfm/Xv5mvnlx5UEv5SuSSuMw3eB96UcBOLOL3Fo/w+sB8CBnvG/xoxykU34pGBabGG59jHr41Rt7PWvWrDevNm3DcTl+0MuY9ctoRPnDF97Le4dDvnm8wsEDStHIUnli+CRX8vN8cvvruNOepDG+YY+3ujCyOnzj7n/WrFmz7pX+9mf+9oNewqwvonfkff7PV77pQS8DEchMvH38aZ7In+BmfZbnys4R/MZL5ySNWbOARxEAu2GaoEcVh4OrR7B23CI7MCwBIL27+3wHUlN4FeXcFcs50BCreBoiMrk75eR1UakQYE5856x93cSNRDeqIHiLCFXtzmL3COALcOWoZnIaWKYp4BIBONwtgIam/u8R/WzdWagpIJab4zTOrb6SCNoa94P3XuRW4/auqDsZxVr0EQs7A6KjXnFvtNqw7SaejiaEDDZ16N37j9MKqVtGb1xKjYNhZEwBfHQIoIVrP76AGSmPXHvLM7zrO76F4/WaT/3ETyPbhMjADiuJ6HnnqFilobTS0M2WtBwxUdwaXjcRcwvs4rZTzrTDgboPJ+2MzfoMsjBcu8CF7/46hq/9Bpaf+gyrG5/E6hodR8qt27AW0HDv7hy45wMCNSBwPjxAFwlfb2gb6/HeEU/bba+4ph6Pu0vszrhGPLeKUtqW7abRSmOQiC2G1h3jERMt0hAU0iLWIooVR1Ij7w8sLu+RhiUXDy9x+/Z11qe3SdsJhgTDHuKGqzC4M9SKGGzPCu6NQTM5K+MwsFgtGVdLxnEg5QFKgWnqAC7W7B28SrzwuBhYg+q9nzmhHuc52dE8Ym40EUzA2gDSr46kmIy4NZSKN+f0dEP93CscX78TQwNm7C+XXFwu2FuM2HrNdOcIsYpI7hHXwLhEMGx9hrlTXbF2100K/dw3YzAYU2FjQvUBF6FK6S5nhZxREZJ0kCt2t+JQd3e1g7jaHbfW3a0xdOK9q3YHE1XDrY8Kdh4yljCRXWU0ZnFcLQcyjiES6YMffaAjJaBRrGItXL6tGM3aeVR72Va26zW1FjZna6on8jiQ+5SK9z3KSDSraE7h0O/R025GKRNWGjlr9Kl7nN7utUPuDmt91yFOvA59SKW0SqkBVq01JC9Q6D3GO3AfcxsxUCPRXe6G6CKuNRXoHeaOUa3hKZM0+nybVUTjtWg4OWVyitctwhwqzZzSYuAlDwN4PYfOzSwGhFRJKeLpAdzb3f1/1qwHoLN6xl/+6F9+0MuY9cvoj1x4H980Xn7Qy7hnut2e4i7kdR7Pn+DJ/HH29QbVB16t7+Lx/CmyTOc/k9avIuUUH/YfyJpnzZo160vpH7/wj+cUjYdYCeGPXfwAv2f/bfFZykOgQba8b/HPOLGrvFLfw6v1XbwRkZbTujKtK+Pq0fu4btasWbNmfXVqpUc8PXyYQde8Vt/Jnfb0g17SrFmPjB653ygV6UDCIi7YojvSNeOi4RKLXOhAw6+DHWgACDPQFE4/7z+PhstXdu4478A4pfO43vAW7xy0nf9ikAJERH+uRt9ndyi3HumsCJ5yj3eWgFb0COKeqSq9l1g0yJmQQBsM4Y6TXfcpEo48dqBn52reAcx4Pu5xLNwDirlBo8bzFj/vAE35rotYLJ6PasQTW52AjHpAv+junEgJFrLkUBqrYSSroCmTSCQBJZNTQsaIDV5evMC1dz5LOtzn7LUb4bRedGgtGtTUHa+ADugw0mxLLROqPfLbDCs7aBpEyqaKTRNeGs//5Md59cMvc+fWLTY37pCTM603vPivPsLVzx1x84UXOX3hNcaDfSQn/KaR9hesLh7iG2e7mWi1IDTUIY1LlvsLdDGw2WwjglrjOLr3Y629r7cDKq/hfBX3+EN3GOKpWcR17+LHpXc8SxrCFdwMakMGRccFmgTVIc60WpiOTtkOI3vP7FMPF0y3oJ1NSDOGcYEkpdWCS1wbw5RIpaG1IuPIYlDGcWAcBsYhkYV4Tc9TwRWplV08sVQLp2kOEB3u62DViNyF76od+Md5m7y7y8fuLMaRFk5zyxHnqykKh9ebie1USeJoTmwOFviFFXpxhVCZzhRdV7JkdEykvGA5rKht4rROTK3h4pAHfNpAi3N6cFg6XHBnWxsTwjop5jl6kqU3+GpCiJ5oUJJAtkZpjUZA/OicTufXlbdwuibZ5SDHcfB+3UfKs57HPTvWo8INqw1LjuTchxwc03D9qve9qu8t1gzbONa2qG5wD5BpzbpDtlBrpRrUaYOZkscUrtvIn8YlIvLNWhxjzWGYx+N5tIo1SDkz5N0e1s5dynD3+Qmv/1LAK6UWttPUExiUNCzQBFmVlDKt90PndHcYxKk9Zj8mJSLeXOJQdrewS+wh8v9n78+Cbcuy8zzsG2POtfY+3W2zq8ysrKrMKhSygAJRRCMQICXK4RADtMzGfrKDtkMig5Qj/OAXh/0ivclhOxQhPzoUDDOoUMh8IEMmAZCUxAaWCIJEodAWUKg++7w3M297mr3XmnOO4Ycx97lVRPWVmScz7/oRFbh57zl7z72auc/Z//i/Hw+iw5ChVWqtDMOKMfd/l+inNiNMYolzmNyQlBEUKzO192yPaSSnjBJDJu7GgoBedJF6c/sm9XyYbdF7UcqOCPLB0BP5i1zSN87/e1/voNHxwdenn+m/yD/Bx1f/kqGbwMPJKxx8/Zc4/dhfwIf9C1n3okWLFv2bat54c/vmRS9j0XfQh9Ief37/qfeM+buTiHOU3uJAb/NE/iIvlZ/kpD1C5e3rGJxOK6d3JoZ1+kD9HLFo0aIPlv7FjX/Bl+5+6aKXseh9JBF4LH+dq+lVTtt1Xig/xcYu4e/AZ2utGm+8cMyjHzlC0/Jeuujh1kNnAIep0qKftCNhA9zsWAsDqzNccc09uduA3uEK8TXh4hGOa5iv4ec4Qpg30ZHZcay7L+WBSYxoYJ7dzlOC8XXRKRoR3kgE7xK9WH8N3nACy2qlRhepWE+9pfCP+9c7gGtP6Mar3eGJQ/ZN5lHgbsMINPrr97CtvT3AV+/Sj8karoqi5JTj8ExTpDvNe4yzgYVRLN3wPhDjKCtjygx5QC1eA0nRHAZvyiN5GNm7dJl0eMjp8cy9m8e0YpHAPMfoBmLXbYrHHxVtCSsegWZvYIEZjs7ijtP2BjLQ6sTdN29jd86Yz+4jtcCwohXn9Rdv8OaNE+zkNskn9g73KKcTVgxpFauVa08+QZLE3TdvMd27zzCs2X/sEVbXV7TqTDdvw7QJ879je5HIeFqzSGLvupvVYMioJ8wi1ZtTxq1itV9naYi0MC1MUklx/QSwHB3WDOMKXKjzTKmVzWaL3jzG763Y3N5Sp8qwNzIc7CFz7X3Nimoip8xIZZ0cTcKYMqMkMkLy8N29GPRObPGG7wYg8PMu21YtUslCmN3DLhrr/bky1Brp595rmySFIe7E9d8aqGICnhOD7ZK2RnOJXmMTpnsbbp0Vzt64Q7IK08wa2F8JY16x9+GnufrYdU5ef5W7L26YrJGykizhOvR7KgzddYZLOLU15jmcy60Z3drFTTBvJNXzme+8GqA1ytlZN7i1J7J7KXjvnXaE1jquWVLvx25Yxw6zQztr9D4HxDjj/U/irV9D+YHBatYjtjHY4B7XktUWCGsD8+jbNSfMW2+Rfk8ZHYj+Xvoggu2GP8LoV03RG973RrNGdSVnISdIIpAGmnskwd06proCO8x9N2yt0lwp1akeuPOchugWVkXFqDVoBinpA3S0Wx+g2QXnvfeVC3EZFKp5N3+JgQYNs9clEPPjMPR/l7hmEUwSLpB7h3EM8sT92ETxPJJSJvXBFnc/HyJi+UBm0QXJ3fnbX/zbzDZ/9y9etOhtkopzmG5/y3871Ld4k49xpz3NrfpRnhgefBiUj1/i4Gt/n9Pn/iKe9pa9c9GiRReu03LKf/Xl/+qil7HoO+ivHj3Hobx3P65SMQ7TbZ7Xf8Y9e4I368d4sz7LOeLrh9TNrx2TBuXokfViAi9atOg9J3fn7nSXTdtc9FIWvQ81yMzl9Dp/Iv0yb9TneKM+x7E9xttK1XC4/copAI997Gh5L130UOu9+xP1O6Sgj1rHk8o5/vjcy5Vdz22kbEn6AHkqhEklgY1+UHSvYXipnCd0HQvTpKdkZYcblTAYIk1siHosyvtjSTdZLFKknZkchoW16Aburq4BbWfwCmEiukRvsDne5t6/uQsUK7tO0e7IxRGR3HHQDqRILvcuUSUMEK+RMnIi/axEQlUSuGjvGbaecW74kBBxlCFecx7iGKQErTG2xmWF/XHNMOR+XIGUI/W7XpPGgWFYsRpXHB4csScHtDag6310tYcnpdkmOmqT47WeJxBFB5BKE4F5JufWg8L9vKUc3a1mkA10wDanVLZYmUkSBnDFsPt3KZwh0zF5LZyejGGO1YJ5ozYYHzlif2/NREETfPT5H4WDNTe+/hKaBR0EmmHzHMdeNfqC6X3UpfYkM0hOaFaaCTLP0UWq4KypDeZSKK0iZJK3MOZSdFVLN+mG/TWXrl9lNe5z54032Ny5QzndcuVHPsTjf/InufkHn+fOq1+mWfjNYYjFda8SJuiQhNGVlAdGEbIIySU6rncmbJkACWNStV87LeYU3BBTvM8uoIKrkDzFv3XMund0thh4bZAiDey742SK1D6kIdFpHVev4NJgNZLXI7TGNFc2xzOUSK2uB2Vqznpw6tkp29dmzm7e5mQz0czJHt2uCDRVXBKr9ZoBgzLFfZScTOOuGacNLA9AxatRRdGUycNA0oSYk4ZVkAUI81v7vuEueF5FitiiAzgmQGL4YscHoHeBG4a4om4YgYduOBJFtYi0/u0jjd2PSQ2x3R5Sugkbhnx0HAfS3frQRvQb02dawiB2tzgPGvuhpv4a+v5m7nHdqJBVItwtqZPN+0bprQ9d7GZZOsoZp4lSSqU2o7WOF09KFiFrXPdukZJWAIsOYjMLkzqPkQB2i/W70GqjViNpoKJrC5h+lhiqUZXAPnvrVIdEqwXriXXVGAzZ7Yd0nH0zQ3PQCHJKCEbzhnkks3e9w4sWvdv6nVu/w6/d/LWLXsaiRed6In+Za+kVtn5E82/+9UKAfPIyl/7wbzE9+hm2T/xcf+9btGjRoovR3/yjv8m2bS96GYu+o94fFA0RuJJucEnf5KnhD3hh/incla0fMfnRD/y4bs5rX7zHk3XmyjXHVlfevkUvWrRo0Q+ppY5o0Q+r3Vv848NXuZ5f4n57nNfK89y3x/lWRrDQOo0q/JPKilO7/l2f5/arp+DOox+9tCSBFz20eugMYG/RFSnS03g9zRX9rXJuUhgdsVx7urWbQ2KGiOFpZ0HtkKYN2s48DkNth/7T3gOMhLEBgli3jyUB1k3m/m/YeZJP+hqjljNSga2bNObeMdJyjpjuEeFu1oSREbZeQ5NiFn2XPXrcvWDvcWFiLZoisWc1EqY7TG1PMCKK5HT+/c1qN5/p/baRWDZ3kEBGizckBWJbgaOUuZxgnSRSfzmjWZEhkVYrJCuSlDQMXHniMR55/uOsn/swPo5c2tylntyjbiY22ykMMxesNKxYdBib9ecTmgtaW5wHDBhwjx5RUvQcy+71ewUsEqG1YtsJ1xq9vKLUrbGx446brrg75fSM+6/eZF7vc/b6W+S9NfmRAzbHGzb3z/A6U06OYbuFamF4Sh9C8IZVQ5ojangeSPsjWMVOC5Jz9E97JBtTTVAru/BhSoHXFunXgFsY72Nm/cgVLl87YrINm7v3sKSsPvEU+UeexL/6+4DTaotE72oPKRUdo2s3jQNpmsiuDOPIkCKDmrRfW8SAgWiKxKjsrkN6tbV0I60n4HWX5Nxd1wnt2PCmKZDpOYYAdt24kuLLU8pYncL0VMAkTFvt11xrvdM57tsw5St4o1qmmDPOjeNtI6dMmydqi+OvtTKohtE4ZNZPfohrj11mvnUXv3McvdqlkOfCUBq5VU5VmQXE4/iZOXkcyasU16wZdSr9Ghzi2scC4S5h1geGvSfse38tKdYR/d/x+JoE14HWMe9qjkkf3XAjSaTEVQS3uNe/qZV2N+hikSzGvQ9JxEDCebGwg5lHj7RmJKXzXuQd9plullutgYNWQdMO592N5o7Ph0g2S07QSjxWSoFbbnF+ajPyMDCOPV2boM0xtJKykvpgjktcO5qGmJEQ7SjuOCatBdlAJVLM1roZPA6xz2siqZN0QMw7irr1pHPt6PlIpPcXgplTzZGUGccV49B31xrvCXFc++DLokXvspo3/t7X/x5Tmy56KYsWnUvEWckZK86+9b8DUk5Zv/ZruA5Mj/3UYgIvWrToQvSVe1/hX938Vxe9jEUfMKk09uU+n1r/cwBO21XO/Mq3/fozu8yN8kkAGpmOTfsmuTk3vnyX/atfYfjkz+DjpXdi6YsWLVr0fetXXvoV7kx3LnoZiz4gylK4ll/hSnqNW+0ZvpUBrFSupZfPjePZ9vjy9Avct0eD8vrt5HD71TNEhUc/ctQ/01606OHSQ2cAdwcB8DAodAijknPnKjxMLMxhIVK1nX9s0E3dQJIGcLeCDN1s1XOjFsLE6fHHnv6NBK7v3C2iJ1UkDDF376ZMpM1k1yHcl+/eu0NxvNVI2WFhKEY5bxhIweXtqN1GHjRMn6qBgtUHvblhZiTESzfroif0vFd4Z5buEtLaDW8Ul0zq5rLIgJmRK3FMR8Va7pjl1A0oZXTnSnIuaWJFGGbaez9TSh1va0g10l7m6MpVDi9/CLn+BDIo1x9/koM0cueNm0wntwJr3QwrFS+1dy/njiBOtFoRhUF2hy7MQvbGMLPdIQW+N3XzsTWjFSGVbThgw0Abwuyk9/R6K3jKWCncu3GPE+7Tpg0rUW794Ss0E6hG3cy0TcFbIHWtVDDFq4eJPzdUPLpKqXiNtmjN+qD/WQzXTMqJ3IcJ0qBoSoFPVkGzIBrJ5nJ8xp0Xb3D2+i02949xa2gesHt3OP3s53jrC1/HU79OW0KHER3XyNkJoqljfzUQ38NAHsJoViGMWwM3gVKwbGhOYcx5zALgvX9VJc6p9pQl9K+Rfv/kuO4tuNIyJCgtTLbaSMMY/avdmC5JKTKEYalEt6t7mPhCXGfrSKhbcQyntEA3z6WgLmCV5h2FnqLPeEhDDCJQOTnZICWM6tZAc+ZAhZwq+ybca8ZtgTN3Goq1mTpnbG/FMGRyycwlcd73rYL40NPZcQ+LRFparGFomLJ1l50G17gPzARovQM8DEtLQ3RguwUBwA3yEObxLlHckfShjnAXgTxAKx07Td+TAvnsPVUbxO4hEsmqcf9LwlqL1LzE3ETgmTXOZYtU7G4SDyFQ8bu9BqeZ0Wo7x0vnMTOuRlLf4+o8I5rJOZFEIx3eO9ZF+j27G9QQx2uJ+7RWdFyTFbwW5lIhjWFQAyklsnSSQ3K8Fcxj7SkPga9G8TZHH3QzWisgyjhmxlUmq2ClxDFwwjw+R/cvWvTu6ot3v8hvvfVbF72MRYt+IAnO3iv/P8QK2yf+1IPR70WLFi16F2Ru/Orrv8qNzY2LXsqiD7gO0h0O+PbmiLvw9PB5AF4rz3O7PcOpXftjX9cYuX1/n4995Zcoj3+G+eong4a0aNGiRReoL9z9As0XItqit1cqxqP5he/pa0fd8Pz6n3HfHufL0y9QfO87fv2tl09p1bn82B77l8e3YbWLFr1/9NAZwLskW7gcHuYtvRfYFZUKvXzcO6aWnQms6dxUaRaJMd/1ufbkrXvv8iWB+nmyOEzHHt1De9K4Y4189/lXTz6eT6M8SKS5aDcvwyRubh252r/ZwvDtAbdIL5v11+KRsPRA7uIW/90Tz26NwOtqJOIk1mvBe+643ugtTmlXztpX2I1CTSBJqSUw02YVb62n6sJg97pFJbGXR46SsJaoefdm+DwRZZ0Kc8XzgB4O7F+9xCM/+nHWn/lx5OAAtlv2n32GvU9+GPnyH3Ljq1+iiWCthBmjYbKbG02hCkwtjpUPgTBOHa8tpYSTpb0PWhNII6HYbLRphhxdx0oCg9bPhbqH0dYML425nUTCOEOlMR+OrI/2ODhs3H+94KctEqBD4JPNdj24jmsgbpMbSVbnPc9xJOMc7c61qpLHkVajr9pai8RzCuNQJEx2P9tQDTZpwOoWU4EBbnzhReqZc/u1m7TayMOKK089xdG1y9x86QXK2THY7lpNMYDQEb8qYUYn8oMENSnwzCJRKx308o4wbkjKYU6r4OKIa0egp54ar6gkDAlzs/WhCIkkq7Ua5rL2HmwHF6F5xVtglZt5XEM96X7euy2CafwvsOkNtznmJFQxEZoKtTWKO8md+c4Jc4GVOJ4SZhK9v6Wxp8o6K/sNVtW45c5djAmllsK2zCDrSGCrdrR0JwJov28kUrWBIVdEDNGBVolEsMQ9jgmeYtDE/JwLEHuFRRMxuRvdRLLehThG1sKYtdbN58AnI9ofLCNeO4FAzvcQkW+YsROLrcqjC91bdI4HelrQXZrZGuYa16Gfb2TRSWzdLFWheQtyAU41Q1QYhkSSMO9bi67zYRgRb9FP3M3eTpA+X6tjmFXMleYgOcfjuDNVowkM49AR/E7qHe/e8e+xRzdSTiTNYCUQ0xqEBxPBcIZ+/N2MhtIsBgpMhyAwWMdJL1r0LqpY4Zdf/GVOyslFL2XRd9GAsi/LB7TfSoKxfv3XccnM1z+FD4cXvaRFixY9JLo/3+eXXvyli17GokWIOImo2frw+Ps8al/jS9Of4cyuYAzf9LW32kcY7m752OZX0O2bbD/0C71OatGiRYvefX3hzheWgeRF7wmpGFfS6zy/+mfcqJ/kjfrx7/j1d18/4/itLU9+8jJ7RyNpWD7TW/Rw6OH7qVEiXRlpwYy3+KFbdvjkHRIZkBwo3vi76NdVlTBTSL0lt6IaZi8SeWDFUfVuDkgYaCmdP4vvTGiJv5dzvLL0vkwNcy04sJEWbI4kiVRiNWpttFo4dwf76wrTbIo1a7wm0YxbIcxkIiW5m9TqpkwgpQVvQrMZldyNSIsUqEgn7vbXJT1t6i0MR4tEnCTBa5h12p/HfYW1gpmSWmW/CQf7I6shk7KieYhkdHHwiqxWgYRejayuHrL37NPI0UGsf28Pz8K9z/0mr/32H1G2G1oJjDJ4mMiW8TLRGpRpQyuFgqPkbmAayZxUB0QndGjIsAp875gZNJHWxnSyoU4TKe2Fmd3AuzGIR3pTPFC6zoTnBJ4op2fce/F1TtcjZd5Q7p+EIa7EL2oS5wTrgwjeoDoyjpHktrD7RCSSzJqhzdHzagMksNoo04RK6onduATjkhTk9BQtgbd1i27kNlXObk1BAy4TosrqYJ+nPvERVpcPuf3ma4FVTgJZ0CGh5qgO0QOrObp/m8ftgAamuXkYgWY0FaRjqekmWZMU16coLhUlhbEpoJ4QVdQUqpNU+7BBpWkcm+iOVlQS2ho0o7hSe5d3zD9oXIstDNIepCWZ9y5qkFa6OfpgwMM90dJIs0oSKKWxvXfGeLjm6pUrrLcb5jffwqeZVR5Rh1Qb4kZuDTHnriS27sxnW8SFVc6MqWEthjVioOObMfA7BH0MaChZE1Y3kQx3iZS+9WOkuaPqa8ABJGPmeKWntCskxcho827i9m7j3rcce8ouQWsPcqvy4BjTTf/AoMMOqk0LE9Tp+4z0hH9r53tVzMC0biR/M+7bVWk1ntH616asKBr3rhlDzuRxhbjFfM7OmIZvSIf1vapVmkeHr6REGgbEjVIalYFhlciq3eDV8709hkQAHVCraMdqmxnWKmgOY1kSabWOwYTWKBb7qVtgo1vbYh1zn1bf/OHMokXvtF48fpFffumXL3oZi74H/dh4mV/ce/Kil/GelXhj79VfZf3G5zh57i/R9p9Y0sCLFi16R+Xu/Ndf+a8XZOX7QI/rmh8dHi7c8UpO+fT6H3O7fZg367Pcbh/mwXiucKc9xeP2ZfZv/GvEKpun/yfL++aiRYvedZkb/+0r/+3yXrroPaXDdJtH+Rq364epjHwrhPROrRgvf/4O+5dHLj++x+XHIzksy3vqog+wHjoDWMQizek7FGv/Mw9SuIFUjm7Yc9PBJUzcXQ9pT/SKO64SqN4U6TiXjl8+t1kSatGtGbaPQeqIaKSnBR2zSA7GPyielPhPQdRRiX5L8b5GODeXcMPqHEYRgYCNRF/8u6FhctB7RjU9SCR6xrxE+tItcLHaEMKQQwXJYex4/3dhZ1JqrxG1sL6dSAzWLeeRQjesGFYLe61xZUjsJ2VImTyM5PXIzqaRpGjKYVy6U2djrrDabcStsn35ZV743S9z/OpN2mbCurGnKZKK1Qq1VMpUkCqscmLQjNIQ10AMG1RzNDmWVug8R0dpAx0a5Ei+1tZgmneeWByblMK8R1F1dBwgj4FpxrFt5eT2LXQY+muf+vXTEciS+uVhgfE2C/MYR0wRdUQHUs6M4xrJwunZKd5qpDLPBxCIBK0mfBgjBC4pOlObY9MMPp8n1FsG8dPeF+ykccX+U09w9PxTzPdOKNs5DEpV0BGVwPQO3UjTnhL17u2dX3sozFOkhNMat0BbS0d+uwSSXKEn2QP3rJrjPtDI4OdhxTiObMvM5uwkrkdLiICmRNKEJ2GskcwX17AyzbCkvXPaobRusgveKrUYKY8PkvUegxKeUiCRAbMcx69UrM2UM0MzjK2iKMP6gEurFdmEzXaLtoL4jJcKmrirmXmrFFWUIe4ZVWgFyP0+6Eh1SYTxGx3U0jusU1qBRnJfW6RQzSXw1mndO74b7jF44JpjGEUVM0N1Zyp3nL3EoIKzw8Z3IkA35wNWECjk3X2KS0/E04kBdr4/ej/buGOtIpI7Qb7FfpMG2D2Xt/59Qi2dqqCOeZj7qkPvz00M65GcUmCurcVAQZbuIfc0On19OK3WnsSPDmJ1p1ZjmisyJBRDPNLR0g3nICNoHwYwpO8vZpXm0ud+jGqANjJxbI1ITbvEoI17xboZrm54XX5AXPTu6r/4wn9x0UtY9D1KWH6J/G4SQMoxh1/9bzh57i/TDj500UtatGjRB1gvnbzEP3zpH170MhZ9D3oy7/Hj45WLXsa7qt2PDNfzy1xOr/Pi/FPcaU8ye1AyJj/ij7Z/lufX/5y9N34bRNk8+adBl4HURYsWvXv6O1/5O/z9F/7+RS9j0aI/pkt6k0/v/SNu1k/wWnmejmD9tjq7N7M5nrn96ikf+pFIBC9a9EHVQ2cAR7I1TA3paVvzQmCZwySRnooDi55T1W4EtG7WAHgkf6GnYcMMc9khkCGdo5wjuRss1mC6Soo0nXcDKx4jzC4nUowCePPe8eKdDBw2kLTAx7oFitfNMRLutT9fR872Xk+RHcLaw8Tuyd9I7YFIRphxjw3SrZF64lglhaGG8KDruFt/3RQMtUjy9pSlYjTXnv6dSWZcSonLq4HVMJKSklcjeYgUpHYDUwchrQd0HJmacfsrX+fx8QgX5eylV3n181/k/muvU05PHyCQHZyKtUYtlXlu+FwZs0SCGY1jXwtCRcf9SLqmFD26KUc3aa3U4lg9pc6FNhe8VlLOSIuuYlbr+F7pWG2NJKyfm2uNNm0eYKnhQU+vEAZ+/18rM1YMUYfVClIch/31PleuPooejGzqhu08BaJWK0kFyz2pawrjCtYjaMLr1I1lYligpzSFhqog0pDUj0kaaGczNz/7JTb3j9neu4/VgpeCzVvEIeVMGnKcZzrfmUiUigeynGqgq0jy7oYKzPHevewOnh33FB2/qtGd3H3HpAmSMhwesH/lALtzj22doYDXbrYhpJT7YEWkya21jkpOiLe4IpMgTc8T+y65Y9WVhndzM7DFux8GRDUQ1H0ww6xS5xm/7QwqZId8eY/Vk49x6fCQS3ePyS+9jMyncb+2grlzH2hnjeKHpGGIgQ2N5P/OvETj790DIR9Ia+/OauDSRcL0VTOsBg7ZrMbAQZJIOOsA3Yhs9K2lG56qcQ2xw157JLaDIz6fPw+azkdUdp3AO3S8+C6E6w/+vp/M+HOk6c/NX0mBw9eE19JfF9RacARywmqNfS6vqDUQz8OoZA3z1b0jnnU3ZbDDUMfrMIjOX9OwstVJRP/3drvFUwwrqORzs78FOx336EAXq7jGPdta6Zj7HPDw1mjWSCnHus17l3rfwwOv0I9HYPGtLebOondPn3vzc3zh7hcuehmLFr3t0nLCwdf+AeXqJ/uH2Q/dryeLFi16h9W88csv/TL3y/2LXsqiRd9VWSrPrf41p3aFW/WjvFo+haNMfsSr84/zifWvsbr5WUDYPPVvx+++ixYtWvQO68bZDf7pq/906f59Hyh9hwTsB1UisCfHfGT4bcwTN+on+U5JYIiPG6fTyqt/eJdLj6555CNHaHr4jt2iD74ewk9Y/EEyFXoqr/eTSnSK7gxbMQlC73nvbkKsIkikbK2ShgFqoEU19/QZOZDOor0D2DHbmcCcf7DlEsm981Rl7xAOevPuh/h2bn54NSSlbkzQzWvBa8Nbx8ySAi/bk5ZWK6LnNmRPZ1p/DfF/gqDitG4WmxU0j4iGiR3LzhgeXaAeRo5LJgzzbnJZwzVMKjNoTWhli80FrY3LeeTamNhXGLKS8hC+iiSS9KM6JMgjNINqzHfv8+pvfpHjr95DknD/5D5n996inrwVZqdVpDko2FwptTKdnmLTjAqklNGU0LmGSaUgGnhpSQTqNQ+0VrFCRxk7ZQrEcquVJAnMGVZrVAUdE4xjN54DSSvieE49SV4D5budwuz0CuaoWU+LR59qK04rJV5/HiKxKMJ4cImnP/08j/3pz1BOj3n9X/8u9966dW46Jw1rzB0sCUb0rYo1fCo9pdnNtG7wB7YatG4jTTqM6Gpg3mx59YUbzKcb6skGpho4bRkRLZEd1xzXtjXE4rpyN7y1QKSbQY4krwCpdLwuoA3cSlzfqoHMHgd8yOf4XZG4xmqtbO6eUM9KDDAQ5q73gYbVwV58TykdtU5PYwZeOuqtWx9IiAStJEGHSPeaapilLY6NdPNfdr3RLYw97yn+rRvT3Mg4nG4ZbtwmP5nZ2xtZrUf2c4b5jJoVs8ZQnWOrFM0P+n9V+pCG90RubDznP06o9kQ3neG96wom+nwH6UZ4w7SBDHG8VFBpmKfzNK9QQARrYZzG3mPn92bEeANbz64ruJvBSh9m2BnD3YT2c2OYbjX31ad0/kO/tIZkRcxif3TwZjQzmub4icpaDDCI00qY0uM4kHaoZxeQhnlPQKfEeX+4Ck4KnDiKiaPa+8hbZTvNmGRyVnSHDDdo1tPXu7WL9uPg7MLS3od6aJXW5zLEA7ceAXaNoRSVB4MbxHlAlNbm7/F9Z9GiH05Tm/gnr/4T7s33Lnopixa9I0rzPfTmb6DTPc4+8u/hee+il7Ro0aIPkH7pxV/i737t7170MhYt+r50oHfZH36XJ/If8dX5T3GnPcm+BnZVgNXN3wRvzNeepx0stROLFi165/Tq6av8x5/9j/nK/a9c9FIWfQ/6P13+1EUv4cIk4nx0/BxZZm7XD3PmV7/r95SpceuVU+Zt40OfuLx0Ay/6wOmhM4BTzt0PMaxF7k9zJHmb0Y2zCq3390rYI2YF2UFQO97Z6d2RsjNsa8dLW5gvEgjUiMsGejQMFelmc/R6Ng2zI4inO4M6EnAuijcLxGv/Pt0leHsHKtrRpkZgSlOmh5RxIuUWpl2k67x+g9GjAm0GUdTBJfCo8eq1d4MCtDCNveEuuAnqHWUtHQesik+F1sLYa6VgFrjZS+o8psaVPLDOIxlBNfprqQ2GIUzGJGhOuCpWCqWc0DbO9mQLCq3NtLN7tHmL1Ip4dLy22qjbiXkutO0E1kg592OWkXUKbPV2RnyIhPUwomNGx4TPEsaVSyBgh0SrQ7yWjuMmCQwZkmA0lOg8pRaa99SkSgwUQMfn9oRsi35aw8CN5k61SFVm6cnOlNFxIF8/YHjiED8+5uTFG5zd38b1pDlMMQ1zszX69VFh2xALHG4S0DQ8QB6r9sGG+P8iyurSHimPMBvqW9ie9nS0IKkn2qsgOSM5oeNIgnjuqeAtWpUp3QSUjgp3D8JxdUSjJzsQvBYdqjvDkgepUjdHxfG50EzCZtQUpmILc9BQyjzH9Y0geSDRcCpeDBdHk0YaWBXrfxfp7H4+EEwiJSsGSRVPgXaXZj3RHAhkt0qzhmORIp0KLW1od0+4NCR82qKtkjQxunN5HBhTZiiVe2XL7I4POVKzxHkS0V2Ovx+zTPxNN34lx94jAmmMWRVr6NjPsRleWqS/zRBpHd3cLVy3oAvQX9duf9j9f9EwflsQD+LvWpxTJ64P7By77LLL+ff9ojukrglp9Rv2qD5b2IdjsIrH3XGO2TcruAitxL/llHqqHMQDJ9+aYUb0f/fBmd1QTGuV2oxmvaZaBWxm3hZqbeS9IeYj6Na/7fzuCSTHoIs4kJDWMOEckY0L1QVrFR1WlLqN49n3rtxp3ecJf6zjrCs+LwbwondH/+WX/ssFW7noAy8BxrtfBBFOP/qLC9Zy0aJFb4tubm7yD174B0tiadH7UiLOKFs+vvqX3G+PcjW9+uDfMNZvfI7x9h9x+rE/Tz14Kn6PXLRo0aK3UTc3Nxfz930kAQ4e8t+jVIxnxt/l0fxVvjT922zsMvY9WGDHb21pxbj8xB6XH9tbKp0WfWD00BnA3lqYljuHAOnI526UiAMZ0Y6E3iV+JVK9IhqpPnecRDOij1QdUgrjSqPRVtzDNDYAw1OmP2l0e9Ix0FFAGZjmXTLXwnahdwNj4Mk7MlfQnKG1MGl2Sc9df3DHxUZiryOYd4+7Q7hapHmjq7WhKRC75t2UUQmDuKNZRXZdrEOYUR0DHIhp8BpGs7Xe0bmZIp3phT0xro97PLoeORyG6JRNinpFmiDjCkm7x+rpUsl47wS1zT3a6TEMkQL0sg3TPSuSBurphnJ6ymxgtSAe6VDNOYzVIc6JzwVhQHY4h+bYZo4gpHh8XUR4kbGijOS9gTpt8d696i5YNcSHWF/rqcfq0BTJkRpHE+IJ6hSJU2v9+lKaGaVWqig5KZpAcxi2+5cusT/ucfPzr3Dz915nmmaGVDk43Gc+PYWePp+3Z8xzpF2DIOxoik5dHSQqV1XRcR3XPf1azwkZRpIODGMmX9lnWI+0N7aUzQjTrgu5py01hSkYBwbRjOS4rt0EyWuwEknKNMQwAiAjYVJ6DAZY6+nxHHj0XbrSrF+DrrhaR1ZHajfrHjbPeAu093xSkOZoSuQ8UnzuOGSorZuUu+SzCCQN07EnN88T75IRn6PjO48xyKCRuJVkcZ/j/dgqKs5UK+V4w7x9k+MsHDRjSANewccBOVwxTIWDTUVpnIlz4pk5DZj2/mzoqVbtyXvtAxeKN+9mqfaOb+IaT2FEuuSOIA7sO61GR3h3PSNkWzASUPvz9M5r6WaqONRIV0tPwpo46jtMfMIw1AXXjqbfJbHNohu6X/NoN4o9vkZ2gwBmuBvNG+4xTLLrzDWLYYCsEtc9hoqiCM36QE5ekaTvYR1Z32qYv9WdpIlhSIg1ahXmpuiYyBJ7o0gQElwz5g1vhtu2p70HRIJMoBqd7YhQ54lmYWx7K7hnqBskJXIaOwFBgBoDPWmNSOvEgV3v8qJF75xeOXmF//HG/8gDlsei94N+ZvXIRS/hfavhzh9x4I3Tj/55SKuLXs6iRYve5/rPf+8/Xz60XvS+1yAT1/Mr3/LftJ5y+JW/Rz18mtOP/fv4cPgur27RokUfVL1y8gr/yW/+J8v76PtIy6cGD7SnJ3x6/Y+53Z7mK9PPY3x3Y/zs3szZ/Zm6bRw9uma1/3Cb6Ys+GHroDGBxcAvUc6Q1w0AKryNhVs/NYRGQIXfvrnefaurmLWGASKBWw7Tp5inxWLIDLHf87c7XQcPYDPRrJBB3xqz0x43wWU8L7jo8RXofKPQv6FhSogvVe9elVZD+2NKTuWXq9FnFa6V5C7Sp9+SphCGXevemdEx2tK/27LPQ05sS6Thxap3RbpabGVZrJJZJu1wjh6JcHRKH48BqyCTVjlQFbf25cLwazSqoopp3vjmUuZOwo6dV3NA0YKXiZWaeCvMcBrSqIus1eENXqzAiNUOp2Bz9ubJeIcOApAwIXgrhmDpe50BtN2MYBlJS6jhQpxodsrWBJ9TKeSeqtIZmBZsxH5EGUqdINxvgpYc+uxHmDRONjt2Uw/xNggwjZGGeJ7bbib08MoyROHZXJKduOjutGNtpS9ZE1oxiIIHgjmugYSmHSU4kdN17GtadaTsxn54it+6TVivmecJoeIpzYeJYWnUccDfVRHqiXZAhk7qpSdmhcSGQvRkv0aFKTpHWHRPiFUlQBaoZDcEgBhwwpBpp2OGQI4kr1vpAAJhnchb2H7nGOCrHN97CWiWluPYCTx7JVdVI/Dbp8xduvU+YHuJ3xEDN8dx7rbWRPEEWjIyKQ/LAF09Gc2hlplSn0tgnhiayZuYJZDIOzDkQ4aRFF/c9F5omLEXgNCG4V0TCoIzEdBiR4tbvmEA3x9FRRHPv4u17hK77vdmwOiOaewoY3Ot5l24MHABe4x6zvm8ZO9Yx6Bj9wtKHYgBsRnZvDWaBs/ZOM/iGPcp717fv0tM44hUzieS1RirZgdYi+ZuA3M9P0tgvm1VaNUj9mEM3fyMVXFuhNiPlgWFIJKC5MJeKjJlhyH1PyOfXuXmJYYC8DoO/HwcnReLbC9BorTHXXZI64aWikkjDfqzPCkjsebhDjr3FW6OWgu2GbhYteodkbvz6G7/OC8cvXPRSFn0fEuAv7D990ct430qA4e6XOfri/4fpsc8wX/80S7/hokWLfhB99s3P8nu3f++il7Ho+9RfP/rERS/hfSdxYzh+iYOv/wqnz/6FpUph0aJFP7R2v4su5u/7S0tm9ZulYlxPL7Fen3CjfpI77SmKf5f3SIc3Xzzh3htbPvzjVxn3Hjr7bNEHTA/fFZxHxFskEwk8aSR9A3H8jfF+cWFXexl/G18rPRm3IzWH0aUgOywzgUHVhJifY1rp3by9wDceeNchm3J076bAxoZx0k1l2TX1dpNHhOa76tBuXqToJzake3EtkLcWXZw7FC0u8WcBaQVN0WW5M284R1bTQ4PesbUeKU53moUZiwHJaWXCPAemtcxYa2F+e2MELu3tc7QeWA2JASHRbS7N58hkmuOtBBZ2l+LE6a5udHRua/SDiiED1M2GMhXaXLBW0NUKzdGrjGRkDOwztSe3d68zZ9JqCGMNp7U4F2bR1awuyGqFiAUGewhc7TwXarVIvKYU/bJOGK67Y9VqvDpv+FwD/aw93Uwkv4sIJi1QzXmILxdFhoyIMJ9ucUuMj1/CW+H45j3aVLC5QCK6hocBrNHcog93GDGJ67hVQ2RErOKe0SGRNIeBaw1qZT45xSqRti1xrszA3Kk4xcByRobU078aSVUHMNJ5N2xHYpc5alyHQBe7OSI1MMg5I5pQtJvdYTZ6k0CSq2DNKa3heFwXYmGXpxRDDh39TFbKfEbbRKevjgNahCyKEbhu2z2OCKhQLQYS4maNf3PNVCXQz7VF4l0ySEOUwF73QYxm0Wnt8wQOc08UW84cXTnk8v4+l0Upd+9R80itlTGnOL+lcs+cyQUzpSUjpaHfU30faH3QQ1KYwOJxyEl96CJw2njDNcUe4ob3/ch3/b7Sk+bn900MNdAqdt57Lt+woQVGWUQ77jn6yJtoJMq9d5f3oQU64jwugRZIZKzjxS2MaNNdJD0e0/r3okFQH9aoNnIKyoJZi/1E+l5AbGwuSrNGrZVmRGI9Z7LGtTZPMw1hlE5gyENsWS32QPqOGX3ogrlHN3JSUnLwjpg3AvHeCkrQHdKQyKqoJtxXQD3f07Ha8esNaxXJ+9/zW8+ii1ek7H+4X4fejsf4fjS3mb/1xb/1rj3fordHPzNe58pDjt36YSVA3rxBeum/J23vMD3yE9jqKvQKlffbvfxe03IMF32/ej9eM8UK/+ilf8RJOXnXnnPRD6+fHa/z6fHKRS/jfat8/CIHX/3/cvrcX8LTuk/xP9D78V5+r2k5hot+EL0fr5vld9H3p5bfRf+4ROAw3eY5/XXO7Apv1Od4vT7Pd7PL503l5T+4w5XH97j29MF5nef77V5+r2k5hu++HjoDeHdtaKJ3fVpHNdNTdpGYDUdv54XuMLatGysPkr4g3Txzkuee+O19qLvvV+n2LUA3bPCIBO5StS2Qp9a8BwCjz/M8fYYjJvHfZriVSIPW/lB0k8o74pXoMbVdEhACG9v7PZMokgdUAikcHb8amNddD7K3nqijG7LR/eutYBb4YZccxrDtFhKGtVBxKlkSB+KsVcka6b2UIgWctBuCRH9u/LlB6ea59ESkl558bvg04TljTIFRnifASWMmr8dIIrduGnukasXDJJdpJknqBqT24+TnSG/mGunAIaHewHpScRzRjrLdzjOTQaqFIQ+B7ZaElzl6gg2gp19riwR13iUQo/fXJLC8knP0BEsGb1Ab8+mGhHJ4tKbd33Dv5hvM04xImJHd0ySpkIeB5I7GRRbXaG14qQgVBcgtrgWvPaUeycq4Vpxhb49Lly8xzTMnx4XWKnOrzJKxVSKlIUxVjwQsYnG8LNDIklPvDNZAOechBhYUvCoMCVYrcKGVEoa4QN7dTxqJzCo9YdsCGx6BTI3GXFGk09NbM6bTgqqE4ZxzGJV9WMIg0udmIAlFyDnTUiRNk0Xy1uh4cTy6X3s6NqpyE5oTJoJqxueCEwl6aQ03p2gMiySFfLRmfxzYv3rAqlY2Z1vunJ6Rp5n1Fg6rcWzGaXO2Fv3RsaUkXPo+IdHfvOvdFZVu6oJjcS5FAzPfU/giCWtzR6f3PUL7niScD7Z4L841s+j83pEKUmLX7U3HfEdPsyOmcb+7R5jYA+0sFtaq7bpwIb5HBKGRUg4cfithFHtgmZNmhiQMOaP9ZLY602o/6Gm3OwKSMG/MZcLMY9/IjnrFqzKVRofntuQAAQAASURBVDEhDzlMfrR7zhbJenZ93PSEcevEggHtWOzmHvuHeQzPaCalhCoMmjs1uxMWJPaTqAEotDLTqiPDmrQE0t5Xejt+OHy3f8B89exVmi2dhe83/fTqOpd16eB7OyRurG/+BsO9r3D2zL9H3X8CeRv6DR/2Xxbfj/vhoovV+/Gaadb4Vzf/1bv6nIt+eC3voT+cBMgnL3P0hb/N/MhPMF/5JLZ3/cG/vw/v5fealmO46AfR+/a6WXjC7yutUP7n+08v76PfRiJwkO7yUf0t1nrCG/XjnNq17/g981nlja8fM28bjz97FHVyP/Q6Hu73gPftfvg+1kNnALPDqqaO75WGS4KU8Bopt0jx9sxt0p4E3CV5rWNmI0UmO/NNlLChdumz3YbQXeC+QTg9Idd22Nyd+dKTyD1hvKNM45VOW45wXdKAw7YWz9cTtOHrJKwJwUumm1wdZw24DpFi84ZYoKfpCToYwnc+R7vG2m3X9WvS1xtrduIlaCnhL4v19dcwycaMTI0RYT8pQyKMnKTozplJEmaNDN0s93Nsss/TuUHVphmb5p40jJS1TY1mccRzEtIwxPeJoqsB8hDdnUlQHfDW0DwSMcQRhiGClLpCveG14ak7rB7mm9S5pzLjrKpmvFa25mQR3AvJoqc1SWCaVR3XoffWhqGsrmEGi9AkYW79aVKkUSUM1NYMppnV9aukw4GTO6dMm20Y+Eq/ruJ7VZXVaiR1XLengWbQ6ilYQ6uQVLFaMUkxlIAjTHHNpQRZGa4dcPjhx9E79zjd3McmoTSnrnOkczHMhYZArYhHmhpRRJ3UY/CqLc57SqBCSglJjqmyWu0xmTHPM27OkMfoV6XGdS6R/BSRSG/2gQZapEhdBLUK5tTmqFt0O6cciXYENGOtBqoaYoCA6CBWchjSFmZrovd779DlfcAhUMH9/EuGlOMyHUeSKo0wtkVHRMME3UwVXrtNXSX2Lx+hV/ZRdcbtFlFYrVcc1MZ2mjgxuO3GPSvMrnj2SM0GRmAHV47EqnVsswdKGXoQWzK0cn4+NQ39luzXLpldj3a4x4qNa5w4fpj3hGxCXEhImKgi553ggTtumNs5ht6tEwVEKNYwD2y1utFag5TIuZv1EgMCIrFfJYdhzAwpBWoeodXowXbXcxS0OpFgx5nnGUjk5OQ8ktMAFMrcKDVS/OdDAN76PhvAelwQaqS0PfZKHHQ1IOZYrZRWKR2FraJksfP1KbUPTdApBNbT8zUSyaXgOjCMQwfdL1r0zunvfu3vsmmbi17Gou9D13Tk31k/dtHL+MApbW9z9KW/w3zlE8zXP0259NGo+Fi0aNGiRR8YLe+hP7yKr3h5/hPYpDyy+QqHb/0B9crH2Tz5C6DDH0sEL1q0aNG30z959Z+wbduLXsai70OPpz3+4lJF9F0l4nxo+CKP5Be4VZ/hpfknqay/4/fcff2MNjcuP7HP4bXVYkAuel/pofvkJNKePb8rDr5CvEU7pQBeaSQ8Gl8Bzk0QkUhsikuYkBjSInHWaIQFvLN+BfJIuFgZb7sm3Y5r3X2VCO4JdmbtzhCmRx7Z2a0Q5ifUVqnNsGbdfAtEc5hADhZtszsUL9CR1WEASU8e7yxB6chps3b+jFh/3dYgZbwjYFst1Op4c1Lqvcm62/TitVvOtKlAg3VS1u5kJPo9PRKq3rs6ydHHGUlSjVfrhk8tUpeueC2BiB1j3VaNWre0skVzIq/D2LXqYRqPqZuDYbS7BV5acqQmaY4khzSEuVZr72Hu2G1RVNa4ZKgVaj++1RAdyK2QUHSuaBoIsHTCTbqZFx2ltQGt9NQmmPb0pwp53IMmeOpXhRnaDbGzu3fZ3j1m3pY4/jvs7q7juW4RM1SFNK7RQcOsNI+042mLlHhr2Nzw7GgWzDXQzB13jgvznTPeqq9Spy2tVKw1mgoyDJHmlBTXqIKZIK4dC05HiUcPNqn3BJtFonK9IpEY9ve49Pg1trdOuDcbVjeQYgBDTLohN0e6NA84Qq0FMQ+zOQ2BvW6G19qvZ0HIPaUe5m9EU+P6P0/ce+rXQKTVkyrqhjc6vLjfx7XGfSRhYHrquOwWjy8iaAoKgLccpm0CMJonNgJWGvPtYzbHm0jyNidpJouz547nxDVXrrlwqxm3rHFcoeqMpYRqZod2xivfSAfAYg2iGcHxnPvfl3iNEulWaLF1SB9MIa6XhIInkmp8b6uQlKSRYB/HgXFQbKpMZaI6gXf2Fua4GaJKrTPTvMWrI1lJSTvVPtLn2vcRaqT5kwgpKXkYGLrJ614pjUCpu5BU0JziJZhhDnONgZicEzlntOPTW4WpRM91wrBSSCnS+do7tXf7pRNpeHOPe0MgEYM8cylUD/NdRBhUGIZ9xI2UMt4K7mF4I9FnTIuBkzoXXHr63iz21EWLFi36Bv2p1aM8P1y+6GV8YDXe/TLDva/T9h/n9GP/PrZajvWiRYu+tb5070sULxe9jEXfh5b30B9emQmVwo3y47xVP8bR/CYfOvsiV279v9g+8XOUK89h6+vf/YEWLVr00OsP7vwB1etFL2PR96GlQuH70yATj+cvs6/3eK08z+32NJwH+v64jm9NnNyZefzZIw6vrRjWD52ttuh9qofuSpWe8DtP+YpAytCMpNLTmy2gq82w2nHNYReFxSnEY0iYpI6hnqgW6V2VhCXQFik+l94LG/GyeDQzwqbqqeNu87p3fKnU+BvpSGl6r25HQRpCM0fFqRpmlZlhdm7hnpthbgVI0MLIwQrIcI6lthYGWBSAtvNEhfdXbKXSWgkTrjlWJ9IwhrHU14hZNwuj/xYrqBsrbwxNUQNKiURtHmMd7ngpkBM6JlQj4Wu19u5ljZSwRto0DFqodUutLRC8QzfPW5jdknqHrIYhKT7D3ALruz8CirbajTaL9GBKeOvNoe5h6nSUtaewd70ESjeLkFeJrBlpiTyOSNYw1lsjjRkZM1oEFaNMG8yU2tOrnjM5XOwYEFCNc9aHD2jKtI0fsFwFVY/UsjWsNaw0Wmlhmo5DoMY1hQFH6whgQ9OIDgOkMTqRzcNg1wwtjMaojK2c3T+l1ZkyVwoK44qc4zG9DxJgkRpFPNLv5hhGNQ1ctvREu4OXEolhdeR0S3nrlP2Dq6QnRu7dfA2vLZLlzZDSr6tunPuuw9cNKZWU495ys0hRu+BiHVXe70VxhIRaDEWIAylswDC8ndSD7KieDwR03nAkRCXSyikH/tfd8NIi5p6jP1h7Il6ouAwxwJAyJlAl+opnUZI3BmLoYaSR3RjzAMnYd2F/Fi5X464bd5pzLEq1MIJFEuAkt/NBjfOObtklZeM+dlkFdps4t16B5Of4a9+ltelt4hp7ieYRJR5DBYbVwLjOsLdC7jvz7NEnzW4fUForiCpp2GcYoHmNIQGvkWT3eHyVwDJrStGni6OaEI9u3lpqbDEi6A6j3TvOW607cDk5KzlH97bgeDPmaaJWI60C3Z/GfXLKZAwoePOePm64joH3thlB0QTWGlOp1NaQlEgi5JTIKVLQojF0I8MaL9vz4RcAs0otkeLP6yOGHAj0mPJYtOid0dfuf43feOM3LnoZi74PrUX5K4cfW6aB3yE1TxiZgYl8+ioHX/1vKFc/yfTIT+B5f0k1LVq06Jv0j17+R0xtuuhlLPoetbyHvj0SgcfzV7hTn2bjV7hnH+L+9BiX6w0ef/ErvP7qI+SrA9efPiANuhzvRYsWLfqAaHkf/cEkApfSGxzqm9xrH+KV8uOc2jWMb92j7Obc+Mp9VgeZvEpcf/qA9eFAyktH3KL3rh46A3hHRg2caoqOTB707QaR1iPZlgdabewQzeqCseu61W6yBmbVzTBJWI3+WnMFpXfEhinrSJhdvsMoWyChNUyLZmHwppS7IbhDRAcaWXp/p9USfZ5mNBW8tOi9hcALs0M5d5NZh0DMyg5BHanVMGFBNHfjkzBDvYYZjTPXSi0zVmbwbu7ICkEidbfD1rqHWTkk5ga+2mOYJ8aUepJU4zXkhA4ZHUY0ONVx7EuB6pGiNYcxRxLTahjiSQClzmfUWvFWAvssGmZjaZEYzYr0VKjkOL9kwnQexzDLto64dFxtvwZqw5t3X7iFMW8evqaEKW2tkIfMMKwRc5wNecxo6qlKTYzDGsaM+BYvGVnvBcLYHauNYo1ajSGvcc2Y9GvCwlqUZjR3VBIyKJ4CAWy14dOMlTD5zYmu5FQxTVAnWjdJNY3kUUnrMXqVg4mMt/7ZqOZuBhPI3bnS5pnmDRMijR1uaZi9Kv1akXOTt08tYETalR262RoiI2ZCWg2sr1zm2oc+ytPPPMvtmy9zdvcu89kZvp3ie8sMzXqamPMBiUj6hhm8Mz5dFFrFcJJ0pLobpIyqkSTM+zBHlSoevcXJSU7ggUXieGk3bTsO29sc6X13pPbe3OYBjLdIx1NbpJ+r0eoc98xoMGRqHmn9Xs8qYP28loJ6mPipwZ43hqwcjSuuu3C8nbldC7dFuW9OUcPyGM/rMXSC03HVFZPAa8fB93N8s+CRNAdEA+EeZGSNPvE0dINWUFUSQz9WGamObwyoiI7kNAUunrhmzWLPGmRgtT+QBU43J8ytIOOK3AcD8BZY8BT3ROcsgDdaM1ozSm2oCBnQnGMfrDO1OdUVUWMYlaQ59od+judpw1wqOkSqOw9rVsOApo5qLlOkykUxbbFHuKLDHikp1grzdhtDOmkgJ+J5XOlzAd087pQGjefxVvFWmactTYS8PmTIStJAVsu4ehvelRYt+tY6Lse8uX3zopex6PvQ/2zvKX5sSS69I2qe+Mr08xzbozw5/CErOeX65mXS5g1Wb3yO7RP/FvP1H8fz3kUvddGiRYsW/QBa3kPfPq3lmB9d/yqvlx/lZv0ETuJue4q77UMwKZyecvf1Mx555pBLj+0xrNJFL3nRokWLFv2QWt5HfzipOFfza1xJr3OnPcmb9VlutY9+26+fTivTaeX09sTB1RWHV1fsXxlZH35r43jRoovUQ2cAB2QYXDXwoN1UlSQdnxommUpP+iUJ03jXBykdh7xzq3qHsHgLI1ET5g0nd6OX+OZu6IordIMVN0yik9iIlKecp43DXPXeyesSGFejgAgJeg9xxzirIzQUDTosLUw76Wv3eB3uEkayxfeqRrflLunZ7WZwo5ZC2Wwp8xRrp0aab5XQNETiVzNJIA0JQSitRfcqxjCuGVMlr4fo/s2KDhlZhTkrbmg3Yr0UbOpI6/0V5BymmwAaCcNmhVoNn7akNIIHcjtwwR5o4SyRyFULE8yJx1yvSN3YdE+oVcQMsiHDGAnLlpCmUOc4CyVShYbTSkHEGMZMzgrVQQ5Q1UgyDnukQdm/cjWSuncaQ264rPAECYHthG9bJCGtoE2iw7nOMX8gKRLkrUYv8AyMK6S0TvxteGvdrFfEDcqMuWMi1HmCUgOdu9onrw6QnBk0kfMYj9EmGj01PEY63OuGVidmTbS9PRhHyEMMQezuEYmpCW+EoUi/KVA8JbzOgJM0kVNGxwEZR/LBIU/81I9x/Uef59LLV7n/+l3u3XqTO7deQ6zGIIU3pNV4xGyRUnbHaZgLUkofuLDz1Gik7rUPVVTEos9WJAy93T3qKkgaOgbZkVbRrEiOLuZGpN4jTWthOFegdZR1TpHA1TBSvUlHXRdMBMoujT/FfSlAa+gOb90HM7w1VuKMYox5xSore8ABiaPSuFaNO7VxR5T7rTEnxTSjO3S7h6PrCM2JY5aGvqf04ZIdjlhTYK81EsQuCppQiV5zTdrvO0ETICNguGn0Wffkt9VC1ehaTp6QPDCuR6xOnJxUmmSy5J7cb0jaR3YkAPqIiztmRmtBPQhkek/WiuIC1TKNSCunRO/c7sMuzahlYjvP3cROJJzVoIgYVrfgO+S7UftwTPNGBOMzeKNUobZ4/mEcGBIkjR7k+N7aI+K9Q7in6mPIIfqaVWDIkNRIkuJ7Hr630EWLFn0bHUrmPzh6bpm4fofUfOR2ewZHeWH+GRIzr5W7fGz8LHt+n71X/jmrt36P6ZGfoFz5kQUNvWjRokXvIz2bD/kPjz6+vIe+TRKBPTnmY+NnOdA7vFY+xcYv8Y1YS2vOG18/5u6NMy49usflx/cY95bfbRYtWrTo/ajlffTtk4hzLb/K5XSTYZ64UT/Bd8JCA5zemTi9M5FXyuHVFVefOiBlXQasFr1n9ND9hKc9PbdDJYfxabFJdsNQNeMSPaoBX1ZIClbDdOvJxyjKdZDWH4uOHe29swqGoXSzTnvvr0dvprsE6XiXQlZFVTsKugUuWcC6QRPI3TCsUlZEBmor8fSSejKzo1idSK/1dKCIdpMxOoAdxVvBPMzVXXPmznBuBq2EoTykTEMxK7g6eCT3VJW0yrQ6kbQjrlFokRhNbqTUMa6qgaBNPQm8M4qGFKZmjT7NtEowhPkiWDyXNqxWam207UT0nhqexjh3zZAho6swjsPV0t71m0l7A2m9jrRuHqibCbbRo4sDKaGecVr08nazD3OsWeCANSF4dI6Kgk+QoqM07R12PLUxzwWvBfFKXo2QEg0QN1YIViszGdNEccilQQOximhcK5GmHtAhISmFQd2vM58M5kZqPanrEulgc6iQHMaUGVYr9vb2kfUh+dI+lx67ymCV+f4ZbQLUuX98h83JRHNj9sbsiqGRYBUHDSPvPLWeNBK+7nHORNl178YQguKrkfHaVVbjCiuF9SPXuPToFcgVLl3mp/7iL3Lnxa/wL37lnzCf3UUdtFl0unoYrCKGi58PUDQJZLBYXKPSEceigeo2B7USqF8GINDGO/qG7QzsEnhzpYEqySXuY0kdfx6vVQRkTKQ8YLlvkc2wlPHsMSDQU7Eu0lPQCVVl6KMbnhLNCu67FG3FMWQYGNXJzQJBrLAeBw61cQnjmhl3XLjX4MQrWxk6droAcj4M4d4Nbe+9194x2i2QzBBmqltQAdI5hYDYnwRyWmHeOth6h46PIQwxQzQzJHlA2k4ZxSjTTENpLbDmYx4QglCgeQyEvgdm21qhOZhnkgpp0MBTo7Gf1UatDZVEyn04RQRzAxptnthstpgIOYGaM6zXuAiteR/AiKEaM4tudG9oymFkuzHNM9UFGTJDVoaUYhik1c4vaH2wpw/VWPQMA0FZMENSZhhHsrb4sa/VGJSxBQG9aNGi0J/be5Kn0v5FL+MDLGf3g6678+pXZ9z2uPfcn+fR8QVWesaT9ofsvfyrrN78HeZrn2L7xL/14D1+0aJFD5VeO32NL9z5wkUvY9H3qP/DpU/yTD646GV84CQCj+WvcDW9wuc2/wucP/5B9LxpvPXSCXdvnHH58T2uf/gQTbKYCIsWLVr0PtGz+ZD/57WfXt5H32YlqXxk/Bxv1Y9QWX9P31Mn4+6NDXdvbFgdZC4/tse1pw+W99RFF66HzgDGHPPWnZBu4u6SX+LnN6UgYRzR5zzcOhbUwtCz1k0XBSxMKBHMJQwWD2PPk4RHnLTjoruRRhg31hom0lOLHilDEZoJ2nHTLik6U83CdAiQM6RIw+GNZtYN3o6xRvvj0V+PB163I61VFcYhkNUdrXtuAjfCGEqKmIbP3SbEHdUV43pEVUjjipSUxhCpVE2BUkW68RuIWvEw7aRpdPu2nio1gzpBi45PHUdYj0gezxOcoo4ZtFqo20rbzKQxh4EN6FzwPAZSOg9Ijh5PSkXMUVGGYU1ar9nf30eGga2c0DBs2sZzCZBy7051IEGZcStIbbRWISlptR9h2BbmfBoGLj3yKLLe5/R4g3mlnG2wbUF1YH2wBzmxPTvDS0Wskvs5LwZWneZz4J41IXmH1R4CezxkdL2KLt/aMOuo3ZRI4mhO8XFomZEGGScr5PXI6solPv6ZH+fw0nXKxsirxObsLsP1Kwzrgfv37nP8+bcoU8fvppGqGucc+j1hWKuY9evWWsc8G1ob4gXfJbWRGGwole3ZGSQhr0asFM5efh27uWHCufojH+GSPcNHPvFxXnjh67TtKUnP8DLTauCdRUElB3q4I5qtnyN1w6uF8SyCaiR58RER4fLeEXkYuTdvKHWLVOvGcU/NdptY3BkFRJRKdAeD46pxLWpHkIvEdWGRAGVMMAT216cZrxWTHEl0793Eq/W5gR0J1xZ9wh490eqF7E5WRZPg6mQa46AcAJercebGqQm3qnMvJzaeaCrnhnsMOXincneEPd0YDu4yZmFgQr+HJQYNzBKSM7WWjpkmTGSrcXzyEK+jRhe1WDyvN2euxunZGcUaO6OdVpBhiD3Gok+8lYK3RnXBayWvMikFVjrIBkYtc/TxigYDoRHobPVI/LfCdpoxyeTVGsVJ4xidxKWF+UuY1uZQq1H7cEzSSF2X2qhuaMqMw4qsRpJ+iHZd5+a4l0Bot54E7sM0VoPWMIyZIQtJhzCIpZMhFgN40aJFwM+tHuH/cvnHyLL0/rxTGmTi6eHzvFz+BNNp440XTmnFufXKho995ikOrg68WZ9FaXy0/SZH29/i8lu/y+ZDP0+99FFsdeWiX8KiRYveRb25fZOvHX/topex6HvQz60e4c+sHrvoZXxgJQIDE8+O/5oX5p+hfZtOwzobt14+5d7NDVefPODokTWr/Yfv48JFixYtej9JgD+7fpyPDYcXvZQPpJTGM+Pv8LX5577v751OK2+8cEyZGpcf32PvaHwHVrho0femh+4nuvhcP4eRkjpqdIchDvDp+VfJzmwVP0/IAUjvJ3U8KKEmkXgDoGKewzT0bsXmROBMPUwo1XO8aAc9E+m+HW46zKPWDWNrLcxpekelCiKZ1Ps8I/02h2nogaVGWqBNzc5RrN4qbjuDaOd1Zrw+SAq7OSb0PtxCm0ukFz2Sw5qFNGQ0hWnpRLGse+BazVogdk0C8erdYE8K4xhJYC9QJUzcGpa2jMN5V7J1jC7dZAr8s2DWAtUtEnjcHtlWCsgqTHUHqTWSs6sV+4cHXPrQo0ylsX+wT97bR1cDGxWKOz7PmARC2WsJ02tufV0jnkrHLEMa+3kSJY/C6vIRhx96hMt7l9keb3jr1i020wR5hY7KcGUPTZlaKm0zoS2McHVHSFizwGLnTEqgeYyOVjwGDJKS9tekccTOJrzMeE9SRzq4d6iaBU47hymdxwFJytk8cen6Ho889zQnb97m1m+8StpCPhZO79xne7qlbie2tTIp2JBIHilYpYYZiuK06A/W6J3FDJO4dr2UHsqJPuuGMp1uaFNhXO+Rxn1uvHWb689e4tr1a8jBivwjH+VH2pb2zxKv3nyF02Mn4Wi1MCothiRQwWokbSXHcEFKgR13KzQzNCVST8WOV6/wzE/9BI/tr/n9f/U5bt4qMWjgFp3NvS8XM8SdJInmFml5USSFPSzaIp3uO8Q6QXvXHF28rWG7zmlyp8jHfeU54wImikgGqXhOtNoorTHbzP6gJIRkHv3C84Q3Y1ityGKsh8SRJGaUazj3VLnTGrdJnLnRJFK5u5Q60DuCPczhKuH5GkAkmsP9VTwNeKmoOqax1wnRKy7SDd1+r58PiHvDPVHnmc10xtlcwCVQ40mQPJzXQ9MHVcyd2gxUSOMaTcFSwDra26CWGD7JGoMeO/S8eXR/TwWqK8NqRMXOe4VxR5NEkruCiVGaU2oJJH3OeCuUacZESSkzDIkhKaoJ9W7aKngtuESXeNzkK6jb+Lf+OjQlsgpJwyRvtYL0t85lim/Roodee5L4Xx98lLUueKd3UiLOIFsg6hU0Ca06ZbJO9RGKR//vF6c/yyW9yaP1azzy4j/D969RrnyC7eM/DToue/eiRQ+Bnjl8hp+8/pP8zq3fueilLPoOUoT/1fIe+o5LxHl8+Cpv1I9zbN/ZbK+z8eYLx9y7ecbh9TWPPLMkghctWrTovao9SfxvD5+96GV8YCUCV9Nr7Ottzuza9/8ADndeO+P+m1ue/OQV9i+PaFreTxe9+3roDGBRuskaaUBSPwRRnNuRzgnpCUPxMEQjAelh3GrrJm0YtiISdOeeWjUi5Io3xMKYsYjjhd1ijuvuhu+P6doTxR7rOwdKRxLTPR5ZUpjVkgIr7SRkJWgSSqm0Op8bp9J7ZbF6bgBp2r0UQ1xozfAWqT0DWqm4R69tJAUbWpWkORKYaSCnTB52HceJQYSUwvRqPQlNJrpVk+LS/9cKnkZcR7xWmEuYwkP0iArE+WgGppG084rV0l9XDQOYBoxQpt4XmvHtRCtOWkHaX5HWa2TI5IN9NGf2hzXrS0eR+tMNiEZy1R1rgpUZm7fQDFHBM2HgzdEFq41IvaYhTOxhwGgcb8/Ye+ZRDp+5wvSqcvDWGhE4uX9KOdsypEoeBkoa0WYMSSAbzZ3qhokGdjYN4S13s857/7OVRtLAYwceNyPJkV2KESGJQBK0Y7BzzuS04vjWKXzhZe6/cJM7b93hxss3+vCAUDcnbE9OmKYtm7kxr8cYhKiB7KWbd2iK67UZrUnHlIOKxjWNhnHeAp8rZlAb1cHbGf76m7w8O5uTM+xHn+X6mBkP9lh//GP8xOXrXPnsb/H7n/tNNgLZHZsKxcBz9LBKikZpJwVqOfjoiEukkV1IQ8ci18rJ/btIXTNpn79QkNaR1gTy3VWREn2/pD6ksDN71WFIcZ+6I80jMZpTxz0LJooeHOK1ItWio9uin9dUaB4Y+GQVycM5Wr56YK5F9dzo1+ZRPzvPUCsyrhjWiXHI7Lmzb8KhNa4A19W5ZYW3UDYITXsi3LoJz27oosUgCNbRl9D6n8Xj9RmOetzzSCcWdLNfvT+uBrXAyZQys63GdlY0jWQVdBzIsjOdLXD65lirlD54knQgpd6XK6kPilSqGQaoOgHRzrRWMBmweWLenGJpZBiHXdY/EtUa14VbBR0wCmUulBaDJnnQSJO7YmmFSmPIEmDwJIG/b7uhG8U14dbO07/iCsM+Pp/RWgmDOgX+2qxhrYKuOs2hxqDKokWLHmr9R0ef4M+uH7/oZTwUeix/mRv1E/ilqzz/Zx7h9qsb7ry2/ZZfe98e53h+lFfLj/F0/X2ub36D1RufY/PUv025/Bw+Hr3Lq1+0aNG7qaurqzx18NRiAL/H9enxMj+zun7Ry3ho9NHxN/n97S9yngj4Dpo3jduvnHL39TMe+cghR9fXS0fwokWLFr3H9DeOPsE1XZKl76RWesooZ5zxAxjAXa0Yr/zBbfYujVx9cp+jR9bLYNWid1UP3U9wQpi1rYWRGtxk7djm3gdKJFpFBNQQ145WljALdmYLHp2tvTcTcvSXulOrQwYV7x2S3eCUngoOJm2syI3W88feWhhGePTidnK0dCTvLpWcRCNJTK8WFqGaQ1Xc5LzTGAxPitQSK3XpeGqjifbUXl+HRBq1WcOto6tV0HEF3XhOOZGTkocx8K2thKEGJHFSSrTmeG24ZlozKnEMch67UdfikA0Jxhzmi9Uwf6Vja1uFUmmtME8zdZrj0O1SjdPUE4+CU5FBkXVC9tewWsGQ0dUIw0ApsH+wz976iM12e97XG2jh6IYV10j8TmdYysAQJr8mRAdUjWEYODw6Io0jJ3fuMd8/4fRu5ez1+6xWA1Uql564xupgjdSJcr+gq5GjgwOyKMf3DWkVb42hgZlgJhRzxByRwJMrcf7cjNYaQxrIorS5YrkhIoExlzjvkjPMMypKHhJ5/wBNzrzZ8NbZTJuMzfEx2+P7mILnhNWZenbGthQmF6o7Wls3fiNpLSmFYdy7gIFIeXe0tzq9X1rwrDEAIZFkxh2rlXJ6xkl7izIXzu6dcPvrr3Plwx/iyU8+S3ryOh/6zCe4efMVbrzaYv4iFyiNWlpPh3aPkxi4sD700Dq9PdKcYWy2kw03/uBFbg8Dp9NZHB8No9hrH8BQpYqg6rvLnnCJu4HZA7Uu3WC2FodZ9fy+NMCSgqeelpbYNxTUpQ90GJ4ycZBy9AAnoehIXY/4OMaxbhs0KZ5HrEVSWZsCBYDBFdVMtplVaexZ41BW3PPEvZw4c6eikZqWHaWgny2LBHT0N+ceFo57RqyBGpqH6DR2cAoMA9UaOeVu7DaKGdP2lHmOdL+mSAqLeBwDa5ESbhVzp9WG5DE6e3vnegzMGNY7dd1qPAZQG7QS5m+zLbVEN/kqK6lf406LnuEWWHzRAQMqwmzRhzzs7SEqtFZpOGkYGId17JUeDOzdUIC3FinpUvBmMI4x3NDR/tFAHOefJpht4/rQVU+U766TBfe66J3ToAN7aY9N21z0UhZ9G30kH/Dn955afnl7l6TifHj4Pb5iP0/LI49+5IAPP3mfJ8bf5AX/ef7ND7QdZeuX+Mr889ysb/LE8EUeefG/o+0/xnztU8zXP4UvuLRFixYtuhANCP/L/Y9wpN8aSbzo7ZfK9z+8as1542vH3L2x4ej6iqtPHpBHXX72WbRo0aIL1vK76PtL7nB2b2Zzf2bv0siwTjz6kSPyanlPXfTO6+EzgKV7iN/4wb2FqbazWOMr5BxtjLduBjuiGWlTmE/nhgs7/mlPEIeRZ6aYBJo09WSsqkXqEMN7t2pU/5ZIIXs3nyB6OJFIIKogaey9qEBvAwZImhjITD5Fl+k5ilX68wQCOlDQ0k2LRoT3ekco3fAiDNraWjxWjfRjSomUMzkn0jiiveNXZOiGKtCc5GGs1Jxoc2XTtsw5U4FkjmQhDSsYBhg7OtYsjrOkSPm5h4FmhTrPzJuCtzn6Xs2QucDQje9xRNbr6CDNO1MtemIlh0F16egK157+GHK0gvu3uHf3Nm0Oyz3Ocz8Eu57nYBrH6/IGo5IR8v7I+MRVSEK7d4/NNFO3dzg5PkWHkayNejwx7u9RNmeUzRYdM6unrpKmQ86+fEw7nSNcaZVUG2YxLCDN8VUmq+LSoqe2Vtz3WB2uWe3vcfdGw61G6LBI75g1JA+R9FaPdaxGMGd75y6tBcapTKe07ZZGw1CaGWWemVqhjYFDbG5UA5smSImch+glHteophhSUM5TuO4t/E1VhAaupPWKo+uXqdtCOT7F20w7a2znwnS65e5b9zh88zbz8ZaPPP8cq71Djh57jLu371ObIfvGnirbeyfUbel3Yglj0RzrRqU5pJTjxFXryGiYzjZsdUPz0lP2Hql60Yjl14oMOe4t91i7KpICfe1zjXSx7m4GwwyEhmeheeRVzXuC2DtOOik6JDRltK/ayYHtNglytguTONs8sDcOpGakPJDKRBJhSBpGcjNsKrTiMB5BGsjTbVbTCSklLn/oOpc/8jSlOl9++QYvntznuM0USXj6hr1Ih/5n6YMSfV/zMGAdRTyFud37ggPhHvdl2K6NUiq1xT6gIoFztgKk89QvVjEZaXVCUo7jkTrKraeLzSq1CV620HuGW+8qNnOcSpm31Glm7/Ilksbe6yhJg0ogPalrFkMlpUw4iWFvD8UCBYqTh4Fxh6gmxXViFW/eE9kPhlRMHK/R273bBxoeSe9hFQl0q1idkKw9FQ+0OfacRYveIT1/5Xn+3Sf/Xf7hy//wopey6NvoF/ee5Im8d9HLeKh0Lb3M4/nLvFY/BQjb4Qny8HWYv9N3Ccf2GCfTdTbDJR47/Rp7m19l/cZvMj36J2mrK5Srn/yGL19+AV+0aNGid1p/9ejj/KX9py96GQ+VBrYc6luc2KPf9/fOZ5VbZ5U7r59x/elDrjyxRxqWD60XLVq06KK0/C767ulqeo277Sm+F4LGd9POCOYe3Htjw/WnD0iDcu3Jg25FLe+ri95+PXQGMBKZ3Uj7tej17P23IrsfYOMrWrhNiCbUID71VySPmBFJNjfwboB1BGtEzITWZpSMaoJWkDRECs+kY0+lI38DR9ojwt2IteiclEiDpp4cFgkjBU3Q1yspkVGGuqKUgpcWZo/7g71JolfTvYEpYXd3Y7u74i5hEKnkMHfVYEiMg5L31mgacYRSCmYeOGYVKN7Ryo5LJAEjOVjZmlAgOnYdaIprxYbcjbbEecOyRMLaa8Pnim1nSnFaa4Grrg3mOcze/TVpNZLygAjoKiNjRoYhUNUoYxq5dPkST/zkj7L/6c8wv3GDW7/+AnWz6SjeFLhs0Z5Kjtcv3UA323UnR9JPDeT+ltmgbSe8tbDIDLxsseRsEtH/3AJdPd055vaXXonr6axi20gcihvZA0lbrFGnKS6fHIaki4MMmBl1rmQtqIeJaXPFXCKdTYIhoUOk25MkaFDnSt1uwzQtNUw3m8PT9IohzAhzWnUDN15vc4/T2QoOZE9obnF/pBxmqkrvBO7oZTMgheG3t4b9kWE1kJJS7h1j1cIwnE+ZjhtzmWH/gMd/7JMcDCOPPv4E6vDKK69x/95tVOEoKYOumItxfOdW9P2p0rwylSkQvjnhKZ33V4sTaGAjksspo6rRcd1T77tyXMljwNqdGLBIGfOG50jhBlo8zg947wPu91sf2nAzxKwnhCV6sZNGChin1Xrey+sS2PWpJs6miX2BMSdSViSvAeKelUTe2yMfKeP6Co89/RxDhpf/6Ld56/WCDpn1E4dcffYaK9njmcuHvPDVr/HbX/8699LIiSqzaiSUpcen+5BHQAf8gSmcBpzdfVDj3muG64BbI+VEnYzWCqJOljDZsTD7A1Pe4jH7W0ke10FLcAIBTwOJqf5WG2jG8woXodUSpIH+uHW7pTVjdXSJsff4NockFqhoc9wUV8Vao5QZR6Ij2BulOs2F3Dt7lcDpu0dq2F1wL3FOdhhp0QfDJyK4G602qnsM+VCClACQ93CrSJ3iekq5A/8XLXpnJLL0rb1XlRH+2tHH+WtHH7/opTx0EoEnhy9w5lepPnKkb3Atv8SH7Cqv1+e/4/c6iVfKn+Bue4pPr/8xWk7Ye+1/wHXAXv81ALZP/Bzl6JkFEb1o0QdAf+UTf4V//to/56yeXfRSFv0b+sv7H+avHX18+TnnXdaoWy6lN34gA3gnq86bLxxz98YZVx7f4+jRPVb7D9/HiosWPQxa3kffu3pEV/yFZYjqXdP19BIv8NPfEMV7m+Rw6+VTgKhceOaIcT+xOhhQXX5GWvT26aH7SW3XrYrzDQk1zo1WcQ8joEViVjrO1RR26bnoDtbA0XoYqWH8JqBENLWVB127zSKNaJEwNbewYzQSdC4SCGTiuZJ25HNPKctuiW6I0PGsHdmsYe2oCLn3szYNJK5jYXZpIgWbt5cT71Ku2rtLI91nOJihGuhJN0VwVusVw2oAFVpzWk1QDfXoDJXmiM14x7+2uUaG1p1ijdkVU8ez4lmxPESi2VtHrgY2V6xGwrk0bC6U5sw+08oJUh1dHyDrAR1WpDGRhhz9wbsE99BTmFlJ6zWHTzzCYz/2HKsPPwJjZTo75u5rbzAdn/SkcOvnKpDUPs+4GzKMYZrNJbqBzUnjCnKC/RUyRz9uyhLdvG1mnhszylAmVBVVw2zCZuf0/jHDuEJyDhO1bMAqSTOpv33MDm2eEBkQid5Y9YaVyub+MUmVvF4jeYgkby3oOJLUkWSkPKKSycNARpnrFpvmwPKaP+j0FQIZbUpLCW8VTTkqTd0QiXPa3JFaA0k8RqrdWw23lDCoHfpjKruRhjoVTm+fcunKJQ6uXWOrA+Vsi7mANKxuqacz926+zs2XX+Bjzz7Do88+yYEI070t281ErRN7j13nkx/7MJxMfPnFF0mjkFYj27MtN196hWmzxXOiiaISqGAVBZvivtTc7y1Hc+79scS1tjNvJYzdZB4maIr7UWr0gwuCaOwD9LkOkT6sgYDWGADRHF3h1uIeReNY7YYLKIGYzgPNYaqNqTVWGqlj2W5hiv1C9w7wK/usrlzm6Gd/mmt/7s/BdI+rf/M1jk/uI3PFXrrNto0cfOZHOfr0h7l+9xWe+lrjqhXOSNyvxr1xZItQxXG0m7Y9897R3u4WBul53y+YGeaNhuDzllrLbteMARgTrNbowu5JY/fo49XU501ShhJGq6O4RZLbJYWpK4E+9/MBlUYtM7U1xtXIekxg0Y+tQBpSYMVbnM/qRilxjvI4kMSopVG9J6rVAp9OivsaiUEOTdDa+bAAooGaF+37fpjUtRZMIGmY3IjFgIimMHyFMLX74y1a9E5KF8z4e04C/O8On+V/f/Qj6PLB9YVo1A2fWv/T8/+efc2JPdL/y3rPe8h379nfoDO7zNfnnwbgWn6ZS/4GurmFCBy88CvU/SeoB09Srn6SevjUN1ODFi1a9L7R9dX1Xte06L2kKzryl/Y/zErSRS/loZR02tkPm2Iq28abL55w98aGw+sr9o5GLj26XtJLixZ9gHR9dZ0sD51t8L7QX9r/MB/OBxe9jEVvo+ZN47Uv3gXg0mN7XHlij/1LnTq4vLcu+iH10O3kgT6N5K1EeDcMWdFuDDnWk4G7e8usRdqRxK7nVLybCOGogNeeuhUoUzeUO0zaGkiiuaG9r9S8GypOPKamMI809yRymC5R2+mBSRbpHwZHsi+Sh+ncuE4pkzTjUjpGOuGtmx+0nvrdYVUd2/F8RTt62QBHUkJb7UFmxbBIHZMRM1IOA9rMoz/Tw4BGJLpjh4zXMFpmlE2tlJYZ89C7XON1mYMUCzx1Gmi+xZvRpobNM9UrtUwYSlpndJ0Q76ZrSt2MtN1J6Djf3p+LM21PeeurL3Lv1bus84pbr97g5PUbOA0vczfoakcpC9YTkOpTdKbG0UBTGERts0XMWB2uSZpwIOVIARYpVDemeSIlibR0AyiYjXhqqAgyKLo9D2VCUsasyDxTPWHVaV56snvEXTEX0mrFmITN6Qlych9rLVDNOZMczGbSXmZ1+ZBxbw+7D7I5i/NDRyRjmAhNwQ7X8fQnW6TGYIG0hkrGJFIyZhbAaKso0etLNVzCOLeUEI/UsqaEFaNVY24zsjfw+ONPcnp4xv4qo63y+gsvs91u2FpleustvvRrv8PB3Hj8Jz7Nwc8+yac//lGuf+53eOnLLzId7jE9donVo8KqHGPTFs4K0iCt9pG2M+IS5IFx3CfJHvXsPlJOgUBli8UghOxSnzt0c52RNCLSSAxxs6tgaYhjZcTARqMn2yUMRA2cMjbFn3M+R2JLs54sbf3xFK/RO0waYEgwz2ynmWOLQY/V3EhTDWTyuGZcHfLEUx9hWK+Q23fwz/46fveY+WxmvLyHTGHOb+7d483f+jz3m3Hv6y9wUCbWI1z2ylXJHFvh2Bv3zTkdRookmgiSYjgFifvH6K6t1cBru1ObgTqmA00b1Brp/Ln07umE7Ap8TSAlNCfOP29vFU8D3lrsPe64NZpAs4qT+h4Xe2+dZ2oxhnFgvRr7wwpJA4mekiIOTaE1o7SGUxkUkkEpjdIT4uKCm0b/dFwguPSEswTdQDBEYzAA99jnUxASmgulzKRxQHqvcoSBBd2h6T063WNr/oYagEWL3gH91U/+Vf7pq/90mbp+jygh/EdHn+A/PPr4Yv6+h/TV6ec4tkdITDy7+g0upxvn//bS/BneqM/xjR90GwM36o8C8EZ9jkTlieGLPD18HhEnn90gn91gdevz1MOnmR79SdrqMrb3gyemFi1atGgRPK5r/m/XPsNnxqsXvZSHVk8Nn+fUrnaU5Q+vMjXuvHbGXT3j1suZZ5518uUrkJaqnEWL3u9apRV//fm/zn/2e//ZRS9l0TfoT68e5a8ePXfRy3iopNLY07uc2bV35fnuv7Hh5NY2aJNZeOxjl0hZ2L88Lkbwoh9ID50BjPUUX9TvYmZhHGhMoAqgZr1gNsyLSEV2dKqF+RgGQgozySJxGCm7jgQVxayE2ZtHaJXaFDVHVVBJ5/jnuHd7Hyn0PtUdqTX+hm7OuDckBdk1SNVhfoZ5GT3C0ZVbo8czCWpGs/gaOvoaSZEsFkXcIi/hFiZgGpCcwvDq4VGbaiRs3cGiaM2akWRnumpPVu86RgN1XMw4w9gWY5UqWYeoa61hxHstGAo5EK9xLMGS0IpjZEQNGQKvK+yMJumY3yFSmuZhZNLPB8LZ3VOmjeF6gpOo9+7gm7MIQFvDaw2z0xyzguO0ee5pRaGWiouyWiVyUpIkvBhmZ2GepiHQsUyknJltorTGXAtZNI6nNbSNtFk7yjuMU2qNa8UVRNDVmoxRnUDVYkAFr5gVtlM8ZwwjKCrRgTrPM0MTZFzR3JlaAcuQBBkHqLtuacNRmgszTt3O8bzSZxA61ldoqKR+J1hHIIcRvuuoxgxLPQnvjjp4qZFgVcO2Mcjw+OOPUg+3nKwrQuOgbdG7d+H+MbbdcP/ubb76+g1sHFjlQFxc+pGn+MwnnuH2l1/lxue+zrEIm82WtB44vHTElXWlbmsYl8T5znuHXHn0oxwePc29u7c4e/33qad3z9OfIoamHK+fGUORJpgZOQmYo9pTsKp4ynjryGjpJrB0I1EFaqDjZUhAik7qWlA3yA/S+w54ctx2N2vD3Shz5WwurMYhuqXXI6rOcOWQS3/6J3jiz/48yWH60iuUtyYyYW66C77d0k63eDqj3T3mtFQ2d+6itaA4abUPUoE1uRYGF/ZlZquJTV5R00BVpZUaZr5GgtkJI5cSCPfqDZEYAnCRfq8k0IzmHEayA1LjGrGdEdq7qa0EYplAvzeLVLGQA3stQp0LZZ6oBYbVwGocQDSQ70nJSfrcglO90XrvrxtkFVIeKPPMPE+xJxt4nSnuuAyx1ZVtdGNrQvIaSfF4KmEWIyneD9xppVBIsNpDKGEau4AnYj6oD92Me3EvWO146UWL3jnt5/0lvfQe0mUd+N8cPsu4JELfc8rMHKRbPJpf+Ka//+j4Wa6kV3lx/pNM/sexzsaAMfBaeZ4nhz8k8YDsIDYz3P8aw/2vRU/wpWfZPvkLUV2QVu/0S1q0aNEPK4F1XnNSTy56JYuI99D/+7XP8FOr6xe9lIdaWQofX/1LvjL9PHfbk/ywSeCd3GA6q7zyB3f5e6v/Nz/74V/gTz/2s8jyfrlo0ftWIsJ+3r/oZSz6Bg0o/8HRcxzqcNFLeaiUZeax/FVemN8dAxjAmkNzWoFX//AOmoSDqyvG/cy1pyL9nVLUQy5a9N300BnA1vHPUbfbP8BzwrztBlAYAt1bRaInuH+hx5cEQFmF1MA1zCREv8k4DnNQwrQQwbyiIoFjTt1MjRwpIil6Jfvjugvdeeqp2TB25dz0VbxFQs3wSNNKQnTA6jaCvb5DuwppZ3oDbrHG+O9MsxppRwfNIzkl2s5s7pMlIo5YJI/jMXqraO9TDZRqh8WaB+5VoHjhtBjTSii1kTWRsUDMlhl3Rb3izdExI2MkBxsDtRnWZna2uIuiSSNtqGEGSzeB1eIcuifEBKsNr1vq6TbOQ23YZoPUCVKitTCokTB+rDXabNhUoHr0/yrIoIgPKIqsB6o36mkhqaL7h9FHqobXQqlQ1ZBtYUShBOpbziI5nFZjN62dNKw6WnjAByX3ft1WC4WMWaM1Q+ZCmyunt++ScqaZI8MKnWesVKqBeQIpMG3Z3FPKpuCtUGvt6W+wlKjTzGTGVhQdw/hSBM/xAajXnvbUblqmHKhzgFLi+rbA61oBl4amGI4QzaQUQwRmmakUTo5G8hpe/ezn2dw5oWxPsM0Zda54Uvz4Ljd+74+49+VX0JQ53EscPnbEEx95hLLfuPL8szy23uPe3bvc25wxKzz6yDXytUvw4i1OtxWdTnnmqWf51E/9LPsfeZKzO7f4jX/8JpvXjb3VyP3NlvvzJo4bgllGrCLa+h0tnYy96/IOpLX3rUG7yS1EWbBrJMLdpeOecxjgHSssvcvbVDDNWI3OZGnxw7N57EGzCFuFcRTG1boblMr84ivc/Ref5ejKEfPpTLl0APfucPfGW0x3z6AYrUGbjmE6o82VVlu/Dke2pTCL4klRSazM0XnLWgeurPbYf+QSt09PuHl3oopE0rvGMEDgCnpnsgh4pORdiCENJdbZB1Z8t2dKoOu91cDMe3RCu+TATKvhpuSUzvfO7XbD9mSLtcp6f8U4JiSlSNoOI3kYSO6AUVuluVHdURJDFvKwxkqhyABDRilIXkXXuPXUuwi6PuhDMQ0VQwlsPG2O/VUcLxNGZm7gYgxDJllH/7cCMoMpmgbIQx/sqP/Ge8OiRYs+6HpM1/w/rn2G/QVZ+Z7TJ1f/w7f9tyyV6+kl9tf3+KPtv8PWL3/Lr7uU3vgmdPS/qTTdRd/8LVZv/S5tfY3psZ9ivvZjUQewaNGi96RWuuJvPP83+E9/+z+96KUsAv6Pl57nT47v3geni769RtnyidWv8bmzv4zx9poIZ3aZX3/jt/jHb/z3/F+f+kV+4en/KW3vEdrh0lW5aNH7UU8fPM1j68d4Y/vGRS/loVdG+D9f/hQ/PS6DVBehfb1LZktlfSHPb805fmsLwO2XY7jx6pMHjHuJNCqXHtm7kHUten/oofv0OjCgvctXLExE+QbeM3KeYhXR8DY6grnh3xC1TwiNlFOYQVJp5h3aHOZppDsJc6Sbwd4qIhkxDxvUnaTSzdoHqWQkB+5ZNZK9wjcUgMs5YjqM4lhzUmEcM9ZWtLYB3xlVgS6V3nns6h0DK9GXKkoT66a4haG4e47uIYdF1qC2jhMOLG4cjo6MtegyFtXen6mUsmVCmVuhpZE2F0p1PEkkaqm4aqC5PdZnElXFrW77seyGuDkMgXpmyL2TtR9iJZKJEoY8bQ7zbZrjtQwJxGjesE2hTRPWwrj11sKwNxASlhoyRKutdswstaAnE8ZAw7ly/RFWl6/w1p23mG9vMBWmajTpCfDthCAkN9QbUhOuM9KRtGndj1EKQ8nc2X0rCo3oWjVrsN1gvkfTCh7XjWsgdFGhNhCviJ9hBmVb4vqLKHMYkq1R3CiA5Li2dh24tuubHpWWI50cV1UcA3o6PDqCA8EdUWXDW6CRhS2SE5oHfC4cv3CDLxz/S5I6dzdbWpkjjTzVODY54Zyxbc72dANJuTcMrN+4za0vvYatB/afvE567ik4UqbTDWV9SLq85on2GE/tPcMmZd4cGj/yk59k78oBqHBw+TJHH7qOjY2j9cD41h3aG2Gqj0Pm5N5dth591NGHHQj0SFfHoIUnOhpdQFuEd80Ri1SsqvRUr6ApIYPHUEX16G1u4G406XhoSZAamAQOOisF58yMZIlcK2kQtDVObx3zstzk6iMzabtl+qOvszk+4f5ZJT16jTFBe/U1Nrc3uEkYtElpObMR52wY4fo1Lj12nSNJnN29y60bN6E563Vj385oZ6eUecNGE1utFNFIN4sHQpk+AGMtkv2q59tO3PfEsdOeBDbDWgWJ5HQkoGOCxjvWflhHar1a4+z4PmfbRhpG1kcHjHkI1LwkkkDCuvnbO3lbpbqTJJGHhIphdWYuNfD9qriOtNZwTagQick+zBODMRlphqSO3lehIxQwHZm3W5oqg2YSBLJMDFGN12sNMyN5i++T6CnWvExdLlr0MOiqjktq6T0sPf8B6ltLBPblHs+v///s/WmMbdt6noc93zfGnHOtqtr93qe/555z7uVt2IidSJmiZFKyYclSJFhxGgRxAiP5EyDJj8RBftiCAwcKFMcQZDgCZBsQbEGArR8WbKojlSAUbVMkzUvy8vL23em73e/q1lpzjjG+Lz++WftcipRI3q7OPnu+QJ29T+1Vq8aaa86xqtbzve/7j7nfnuON6Q/RSfzyfD2/wuX0Dms9+r3vB8AbeXub9No/Yrj9GazbY/vcT2P95QUGL1r0PpPjHJWj817GIuCPD0/wJ1dPLrGF7yNlJp7pvsib5Q99W+/31I+pXhm98HO3P8W/YoblfWyIAay2vs7uyT+CDZe/4T24RYsWvV/1iSuf4MWLLy4A+H2gp9KaP7f33FJFdE66nN7lIN39tlUofCs6a6O799YpAKLCvYNTVvsdV57dJ2Uh98vvpove02MHgGXu3JU0d+fq7F5zR1wfunCZ3Wdh0E3hwq1nXbc6RzfPOdI6u2qtzP2zEYv88D6kizhUAXHFmiG2Q3IXgE4tXKpy5kxOAd5EZzfb3Ddp8caUpIhD1jOYjdBUSUUYuozZmsna3HfcMMIlO/tzgxdbNzv2EiL1Yd+wu9DEUXdkdvvKHOMaUNvDKUo4GklzhLQZ3gxzwa1hrtRa0ersqdKroCkip02hpoTjZO1jnZIw4vGaQQVcuxnCADVAl3iOXxTc8OYR14si3qBMc0KxQTXEc8DgLqKwz3pA3S2cvVkxn+L4mJP6AHbanObhtJRq6OCkfmB1/SLDwUU224mrH3+O1dXL3P2V+3P6raKaqFkoKZNrQSWM1ULCdyPmA2IFlUTqI3K3X60xc6btBp9GRASVgohS0+ygdMd3m4ir1oSVgrXoGe5yxseC13Ass9nhlJg9UA/QalDLRHGwlNGc5srb6Fa1FlHT5BznhnaIGTo/JjyckeHqDte5djoD/zle+mwYQRTBqLsdt+/cJXWzk90Mry1c662iNsecTw1LG7TvKdbTPLHd9egxHG0mDm/uUJ/Ynh6i6x47PuGTf/JPc/nqJS4DT55M6H7PWZZ1vX+C3dly+s59fN3H+bt/gWvP3GBNo3xlw3YaMVFcHWmO1jbDQqJXup29Aew4GREDLzNQb3HszOOc0xxf23URvWwVSDgaTvg52j2GOxqeBRo0V0YXpMzd0Gd7Tztiqk7dbOku9tROGLXS9hOlg9Ebu6yMkR8PWWAY2HrjSBLbTkipQpc4OLhMc9i8c5PtOLJ7Z6LcOabVSu9OWu2Ry45TF4pmvEuxD80d5ZISZxkFTgyS+ByZLnMUdEQhl4jDFAuHrE3zF0VkckqCZmXabTnZTkw7Y1iv6IcVzNHOmhVNQk6KeoBta5VSRopFe3nuhUTFamOqUFsJ43IDJUBzkoh7jn1yzg5wRzyhad7v52c2eqItjrckur4jzeXw3iJGXj2izn12w0cqg0Nr8/FZOoAfJbn7t/zG47fjPv4g+pVbv8Jo43ft+y36nbqqPX/16h9e+go/AFrrMSv5Iis55kp68+Hnv5lLWnDy5h0c6A6/znjjh/C8R1tdpVz55Dd/x98lPYr74aLz1aN4zkxt4j/78n/2Xft+i353/cRwnf/n1R/m4hJX+b6SiHMtvc7t+iKjH/CtR0E7p3bCf7r5D7nvd8gIP71+EgCtp2iNN6nT6dv0dz/P7sk/jOc9xid+lDPzwaOiR3E/XHT+elTPG3df3vd4H+jZtOavXv1R1ksa1bnqo/0v8eXxpzi2J857Kb9Nbs72qLA9Ktx/Z8PqoOPSk2uuPLP3vnyteVT3w0dZjx0AVlUghSt2hleksz7eFuDWwHMXwAeL25m99yOxW4DbOcJXiJ4wkQAmcgZnvHLW/RkEKLpdHXsIHVUCvZrNEbQuMFeNPuxcFQGiw/XsrsVBc5o9bCAW8C1lJdWKaD9DmBYgd74wdDbSus7AzkpAYO04i5ZWmeOr597hCDKOOGhNGsDT2tyPGYF5zWZu5nNEsBu5VS5l4cag7OVETpnUdwGwiR5QF0iyinhcBBOlEfG0JkpKiXQG25rj0xSOxNYgd1DC4WsGXsp77u1Vj2SL426G7UpYi2tB1fDUYeMWNKFW0dUQXLm1iIPdbSnTiKYuAGke0H4IV/DewOm9E+6+cpOTd+5Qtzu8VXJKAc7c0W5AagGde0u90KyhKnQZWhPSQc/q6h5ZezZ3YXcUsF5x2lRQFO8SXgvNhOZgdop5eHPz/h7d/gCn4LuY+DlzhgOIxvlYW2NCqQK5SxHVrOm92O/Uh8tbmKF/9EZri+MuhCPcreIpxXEX0BbDDTSL2O5mAaPFyKnFEENTNDH3xNaHsLBZQ1udLw/Fpxbwte9I6zXd6gKr9QE3rj/J4ckD3n33LfxB47TA3hNf4OAPfT/dhX3Shd/eKZQv7fORH/4Ih0d3OD08oWuQc2L3xjucjCPbzQ4nopgjIV3DReqGzr3NVMPmbutwsrZwmZ/1NqucHazo2v7GAIGYmAj4OcdOIxLtsbnHO/AW9zViGBlao6qyrSM9Hbls2N7fYXeNUhoujltlrHF++GbuMUtCFaG2xslYObaRSRK6deiv8/zT17h6+Qqbmze5dfvrbFB2aSRnxVLG/RRrhiJ0mrHJkG6gpY4G2DThEi51mAdB1DGL4YFWyryPVtAuXMFScTJ4DDGklBFptNoYx9g39y9dQAVaiRSCro/I56wBjDELMFsKtcXBzf16jhivFBPKPGzDnDaQxB/WpTNH14vNbndrCC0izVudo/6hTJVajaopAPTscrcZyMdrwzDHntfYE89S/pM8hOWLHh19O344/G7+gHlSTvjZ13+WYuW79j0X/Xbd0IH/19Uf4Yf7K8svFx8QicDV/ObvfcPf7/3Nf65u/yYQQ3T29i9SLzwfEdH/lNr6Bp7PJzbsG/Wo7YeLzl+P4jnz1uYtmrXf+4aLvmP648MT/OWrP7TA3/ep9tN9PjH8t7wy/Rgndi1+N/0mtJZDruXXuZq/zMfXTwBPIMAz6Xd2h56laazf/R9whOH2p7H+Erun/ygAbX0Nf593jj6K++Gi89ejeN68cfIGv/juL/KZu5/5rn7fRb9Tf3S4wSf6373KZtF3T73uuJ5f4WS6hvP+gPG9nDLIyW//5AbK2wm9/mF8uHg+C/vn6FHcDx91PXYA+Cyi+GH/L2ewdqYHLnMcsCFZo8ey1QCWD79EUHHMU7hfXaIKk9lj69Ehi2iAH/UZJoOj2MwlxUBomDguTlLCbTg7CPEWkdWckY2IoEXDkWulhgsPCUCaE1IrKgHdDMVqncFu9Gc+1ByD2jxguEhAXvcW66OF+7TaGVNFaEiXSZrn6OKIeG3mAf9q9IU2UQTnYoYnpONS7lmr0InQqZA0hfsU0JTQboaKNbqIXTpsHMOJrIqoz7cXfGp4GzFvSMq0LkNf0a7Dcx92wGYRbYxExLM1KNERSkpBv5vjk0Fz8pkb2BqGU81omvFez4yMWKmM2x3NjGkzcu/WfcZSsVJpJaKxO01IyqScSC0itzVnFAJqxQlIbRN0mVQmppMNsuekQenWQ7hs3YI71oCxzJHCdRqppYQDc+iQ3RQDA6IB1rH3AKX53ClcGZsxokjf03UB1evZXEN4plGJSPMYNQDteiQ1pNTZHR8AEPOIEBeN862FI9KY3d81nO7KRFIliOd8EOfJQSOgnlslWUJSHJ9M4rnrT3HxxnNcufoE6489x7Xnn+Tw5S/y9uufZ3u8xW7e4Yu/9HlO3q288PGneep7niGtVpwNJNrukMIOWUGXLvL8ix+C7Qlf/9xXOD7aRKv2HGEuksN1KxmakRykOQ/fJpphfsBeYrBA5uQAb5hbDEP4Q+ob9bdeEU1hGJ2HHBqCp4ie9pRiSEISeGM7GaUaJw5ZN3R5S3LFWkQmK4a3wtQiQlp9QtwotTB6oiGM7pxOlaJKd/ky/Q9/nKv/0o9x+cEdhs//BilFHLnnjmITpgmfDBcnk7C2xYsitdENa+gGTJRiRvNC7ga81bkbO+GtPpwBNfuGtISYKEByT3JAlGqNOhZSVtZ9PO+txPWRu56hS6Qce4bXhqE0qzQdcErA4RQgvVah1DGucTFS7sld9H7HwE1C5+MqYtF9nvMM4udUgTZRrTE1qGWHrtaIzC+F2kM5QXQISjAPLNjZLmwVbxGF7mkeoli06Dsgd+evfe6v8cu3fvm8l/LY6pJ2/AdXf2SJfV70B5JYIY0PSOMDhju/9Tv+vVx8EesvnN2a7TM/iacB0CVCetGib5PcnVePX+Xf/dS/y7Ztz3s5j63+6HCDv3TlB7mk/XkvZdE/R/vpPt+3+v9wtz1P9RWvTz/4+wLBvWx4tvs8AJfSu6w03nw+0P3f9/cW/OFrZnf8GgDlwocfxkWXiy9RLr34u3xhfqQcw4sWPWo6ex39i5/6i7xx+sZ5L+ex17+8eop/69L3nvcyFs16Kn+F4mveLN9PuJp+p+L99X+2YeRSeper6dtzbe3pfS6ku7/rv9WXn6TtPcl05ZPUg+eW3zcfYz12AFhktjoGqZlBlEfHZ0pAwK6zHyhn715ctmeRy2aIyuzeDSdvRJ+2AMAWbkazOsPGNn+/M6irmGegRkypRzSpeQA/q4ZS0RRu5YfR0BCg2hp2xqsJ56e6nrUPk2bnrJnNUbRTxE1HmWk8Kg9XXsAbB2l4MxDHJc+3CbTnxYJfO5hNoDW+BHno7BSiZ7PZhFljb5q4kpXL3cBeTgxDT58TKWVS6iJaVXM83qnMZucAiBG/Om+i1uL4DB2qgkoX/5/6AG+5g66b6zwDPgbHj+fUa8XGCdzRHoRwKLcpun9TDjDqbtTSqCUc0XlvjbqTSsGsUafG7v5xHFdxvBtodUdrE1ZjvXmV6S9eRBFkLGiZEBrarRBVWq3YbsRGwdqO1qDujKHbIARQ1S6Tckfe36drjVYippbtBvc5mhrDPWNm1NZQjT5mm0uR3QouGXNjV41dq7h2ZKuIr1FRUuQZU5pjchYNHlMMoml2nzueu4gen2GoJI15CZsdl3MnrMxf64CVQkVIagiVVuZrSIj7emg3NgRHk+I4w1NXee7P/iQffukTiEbMOdOInE5cuniZstti08R4521e3jVuvvEaz/zGPhf2Dxir0wm03V0e3H6Howf3yJf22BzfZnv7AbvNFvcW5k+Pj4ePFaJruxSwiBA2t/mcjAhgma9AY75G53juuE5sHhIhnodmqBWk64IhEj20NluFIx3eAuy3Qpnd06IpwDHbGIqQhMtM6qvT3KNH2RvmzlSMKopLOIixFq7k3Zbjz36Zlw9P6Fvh/r1T9tY92vWxT8kF1D0i21uLLmpPM9CM6HdaRczoMXTdk1cZvItzcf4xxoBpGmkIzR3zFt3dRLpAU7DacFdUo2+9lRFzIfc9XcrkviN1OeLczUATbdoyThMmmdwlujnBYBx3TMXAG+pG6tbh2p1GSF2cSxJwVrsunOutRO9vjJvgWMRK10aVjOchIhFSuLKlT9AdYCXeLNS0plmMRUTksyB5RXzBhLclmnfRd0YvH7+8wN9zkgI/vXqS/9XBS/xIf/W8l7PoA6bu6JWHf3egu/8lAOrBs0xXv5+2voatb5zT6hYt+mDIcf69X//3ljetz1F/bLjBf3D1RzhYnL+PhETgen4dd7ieX+H3FwftZPn2p9R0x6/Bcfy9v/uFSC77p7R7+iewLhxN9eAZvH//uZsWLXqU9fLxy/zbv/pv887mnfNeymOvfcn8ub3n2NfHDt+8byUCz3W/hVJ5vfwQF/UWnex+222e6r7Cvt7/Z98HjSTf+ZSavLlJ3tykv/cF6v5zjNf/ELa6Qtt78jv+vRe9v/T47SAKSDh3gzHKWVFrOC81Qdb3ADHzH07EG4sE7DKL/ltmSCwKKuEQSxlvDSUHrz1zF8+9vqC4RLxsa/VhNDNoRDObkxWkGWibHb+OuvAw7j/obMDrGeYlESRlqhlJoTDnoaceMUAaNne+hvNxpmHWIrogBaA+OxbWnDaF05EWx6hpRqTO1ceCipFSilhlDyieXbjSD1zNwkHuGXpl6Hu6LpO6Ds0d6g1ao7WGNYHdjoAqBrWGs7QAjXAA42jKqGr0E1tDuoT0Cl2H1zKbpFsQqOqQAvb6Gcw8i4uuDRun96DeVGgzbAXohh4D6lQ5y/RttYHt8C6jXXRA2zTipeCtoDnB0CN9h08VEQ8ASHTiQrjE/SzOWzradmScDPrEam+fpAOkgX5/HbHFu2Mcg5ywquSS6RvsvFLdMSskyyTRgPdzn7V5otnIiLKpce5lCRe1ukNzdCb6ns7mkqK72ltFJKLFRefzXAxXxZujFn3V1c5aQHzO/paIUBZH+hWEn/y9DlWVb4DzMsPX2H4E6LqBC0WQXUNyioGIGp3NFz/2PN93+0U+M+44OtlgdcK3tzkdD3nlXqZLEd0sSdE20cYjWh2Zpso790dqHamtnCX/zr/PhoNTJIYhrJT3HKytIno2+hGyVgNSimF6NhzxXnpAnKGCSMFRmhhJEylBVg3nf4lrymud46QFmqGpQ7RFZLfG/YSv1OaI4XDhixNRxJrBKqqQk9ByR1Kl6weyhEt7d/ser50WUinsjk/pVivSsIKcIAe8ttKIavK4T885YtjbiJrA2OjM0WkiiaCp48L+BVJSJGdKm3iwgamBWaNUY3RlV0YqEntlypGEUOcua3Nyn+lyJnU5kOxuF4Mo2mFWGKeJ6pCz0OWEW2PanTKWAMSdKjmvSLnjLPJcrJLSak53kLkjPSGSI8LdHCsTU2uMzbBaYHYd0ywc79JDKzD3kYs12nSKiyKiqAxIzjEgRI1Bkn/GtN+iRd+KXjl6hb/4qb/I/fGf/QvDom+vFLiqA//Hix/nibTijwzX6WS5vhd9ZyWAzINE/eHL9Icv04bL2HCV8dr3Ui88D0hEYS5Op0WLfl9q1vh7r/093tq8dd5LeSx1IJkf6a/yf7/ygwv8fQQlApn3T/WIeEVa/R2f33vzFx7+va6fwLsDrFuzfeZfnH+vXX8XV7lo0QdLzRr/8PV/uMDf94k+2l3gT6wWWPd+k4rzbPcF9vUeB+kunUznvaR/rsQq3fGrdMev0vpL1IsvsH3mJ8N4tLxmPhZ6/ACwv2f/E5GH7sboe8wRdxseNiDiSs/gFjNQda/Rx9taANvAOxE5jOPeZrekYDbHKz/sBQZQxA23GciaQQWViDANc6AgGl2UXh1xQ3KK+5bEWa+o4SQESR0iFu8PlajjdIvIXebgXfNYR0MC/pjjZyXCXuMnfgFrjVYrrVZoNR6TNXBBNGJswWlNMSZEM0lygDyHywLP7A1c7jtWOJ0k8twfrAnUa7imG6Tm0f2Z17hAqQUxQ2qJVG4cT33EKadMUol419wjCq6CWUBGWov7VcHrhDTBxhGbJjRl2E3hXDSfu0ATXsJN6Q4qEpHU3hDJ0d1bHRPI0uiHC7SUaXWLUWi7CXdISZCcaMXwk4lax4hOVkUJx270ToMkQVYdYnGOiXbIuufgQ09w5eoNvCV2khm3R9jhXerpOA8ddOQ1eJdoW2NXJk7HEV01Vl1HpxmxHa4akcBmjEARIYsG28exVua43ABnWYVuGOi6ntqgGHE8ZO5dtgBi7i3AZ/OIN7SIfI4u1DifRRqecgwdONS5uzmJoprn7lwNEDhP8jaL02544grP/9gnGabGyVt3uPDUNe587uvcv/U2V57aw1eJ5z/0DCcPNjzYbtjXzNicw82O7ekhlJFEXNsRox6g9bSW9yKoAVxiySLh8pWIW3fN4fZt86Is4tVdiAEJC3hp4lhjfizz/ZqF+xaZBxXiOk+q9KtMhyCtxUAFhvU95vGDtXdz6sAcLU3uke7MyX0GmQ1thliFmqPTuiY6WT104lYcm0GzC+jRIbbZ4UlJScl5DVnxFB8qEcNOJ+A50g9Soqlg3iMl+svFGqmPmOXhiRs8/dHn6VD21wPHb71FfXlDkUZDKXlF3m1IOGXe17wVqkDzSEpIqcNpFE/htneJY+8NpkIdt5RxpNvfp8+KlZFWK6VGd3rWTE5KzoqoxfUjIHmFcja0cxZVbvPTrlSvTOOOyYXqhnqL6zzl+GgRES8ukRbQDYjFlStzVzRe4vt5uJVrKfgyhbnoO6C/8lt/hbdOlzeuv1v6SD7gT6+f4X9z4aOxXy+gbdE56iwKMx+9Ej8LaMfu6Z9guvp9ePf7j9VctOhx1c+89jP8R5/9j7Cz/qVF3xV9f3eZH+qv8C+tn+KH+yukZYhq0XdJeXsLtrdwoL/3Ray/yHjjRwAolz+KDZfPdX2LFj1q+pnXfoa/8/LfOe9lfGB1RXv+7PrZ3/H5z5YHfGa6jyL863sfYpjdX//Lg98lBn/R+0IizpX86A1KpOkQvfMZ+rufjcHj63+Itr5OvfjCeS9t0XdQj9+716oPOzsdx2ewi571RMp7vY5ztK1LQEAN2ofQz/230WuqD/slBWsRf3rmxhQBFY3OWp9dhWd5uGfQxwlgkTQQksxI2XkIGwShtfhVVlXQDMxRterEfc8gMyKmbYbXM6CSiIj1ZnP/6FxCbI55oGRRAU+zi9Yjqjan2V16lgpckJICKBdBUgBx9UxOwiWBJ9cDlxKscibr3GEsArVBnV3UGi7PlCWcvbkDUXQaSeLUrKQy0XIcK3fBW8MdxAyweE40xeMo4eh1PIBlLUjusGbhtkwawNvSw/txrwGhSOSc6PuE5iGczt5o7nir0PWsnrzBh7/vYxw+OOXdz32RujnBdE3XD3g/UK1E77AbtRrjOLIVJUk4gFWILlPboZpR7VGfUInnqZgxPHuVJz/6UbwqNz/zZV67dw/XEW8NyZk8pDCoipNOhbKd2O0mJhVWksnSoe7x3OaO7GfwH0qriLb5WM6J2aKIJNJ64PLzT9H3K7abhk6V0+MNRw/u41bi3JCIRm6pRdVqO4uNTpCYhwcSpIzK/EzURm0tCFoTFJ0hZUSlm4Yz2Ethd/+YW197ldMnJ/Yk89T2Aa9++Yu8/eqb7K879q7s8dQnX+TFCyvevHWP/atXuKGZW196hc9//ksc3jmJyPV5XZp0BtPRU80ciY3HOQ/zMUgtrm+ViOEWHkYu4w1yDyiiFaEgLrN7Os4nrIYjvcUEWE6JbjVEz7XG4Igq4RgXoeaMpUxNCTGLNTEfv5SRnCOBQBVcUaskq9FPbG3uY9Y5iFqwMgZMtzma2gI0O4Zrw5NjEi5cM6ca4XLNidSvUK9g3bwHJjQlTCF1GRmG6EBOAUQE2N4/oq57bNxydPcIGRudRPqAmqG5o5tjracyUWYga9rHdarKtBkpUsIBrsLcQkxyi4j4/XDc2jhirQYoryMpr+gT5JSItIDYG10gzdHkwLy/z85/UaxVpnFibJVIII89R7yhJLLE/pMg9kmfz5+z58UDXIt00GIPbXa2/sfvJXTRd07NGv/1q/81Xzn8ynkv5bHRniT+6tU/zIvdwXkvZdGi3yYhfseQNrL35i/QHb7M6Yt/Hu/2zntpixa9L3X2GvqffvE/XeDvd1EXpePfvvz9/NhwjSfS6ryXs+gxVngHjDQ+YO/Nnweg3foNypWPReTlcGVJ01i06PfQ4XTI33/t7y+vo99BPZFW/F8vfe/vGDq+3XbcbiMCfE93gbwMUi36Durha+buDntv/jyWVrT9p9k++1N46uI1c9EHSo/fu9eeETXcBGqd3b8OqQtQNfdQivhZumtAXPVwhxHg1wCdAUPsyylillMXLlSrZ1cU5ERnOjtpHSi4JMzPIMP8fQjXHyLQKpaU1FpATs7qw+fIWrM5ihRal6MvlRTu5KjJxM1Q8YiVFZ37PYMtiQWwbl7DESpphh8l+nPnvl18BilqAW/pAnJXw6iI9TMzq6wQrveZS5oYUod6dBOLxPHwJuE+zBlJ4QjOw0BKaySdxVk70hqaDekyZTfNLt4dKQ+xzG6FqAewqw42BggyDyjjEQPt4xZpFpHPanPs7RgAMnfUacIbdAm61NPnjPbRJ1zGhnWJCkEKh4xcWJGtRI9qXnHh8gVWF/c4PjpGThqp62hJaQobh824Q/NAl4WsTq0TnQtdgqwl4qLdsSacvHuHW/tvsBp6Dp55gnyjI7/eoaeJ5obTEOkQGpoTfU5xn+ZMDp0qmhOKheNWINVGNqNJwqSn1IqnRq9KyjnOTXGozumDDXo9c/Hpy9y4doXpdMvnP/U5TrZHAeg9zjtXwZKGg9jmvgInHLHmSG1IF+DbBEyVNkd2hzteY6hgjoKOXmPHjk958ytvsffmPfLLt3irzzy4c5PNbsum6xlOenzccrTquHd0yubKAfqxF9DnL3Lx3mVOTo4om4JbuO+17wNeIqh2GIYa4ZC16OP2GU6K1/i/uRPYtOApg2RwDUdxymiqaGtxXZEQc2Rqc7ss5Jzo+ky/tx8A0SvuirUWPctDR17vUWtDx+3co6x4i5h3cod0OQYYWkOtkZuTjLhmcsLnPyWFE9h3E0xTPAl9H/HOtVFrw1AMp3q4/5lhMtqj2sfABx2S9WHNk7jFIMHspLYoLMZbo223HN90tMb1VXabgP0KeOxVycL5nMoOlYgaF4nz1xDKGN2+gmNlRFImJaPrevYvX2adYHN0RNkVqlVaa1RvSLem6zpS7pHUxbk423PDATxHlgfWj+EXc2qrTNNErQ10QP0EJaPdGkVIOSMpzgbVhJURzUMcGxMiDL3NG2bsMS46Jy7M/e6LFn2b9A9e/wf8tc//tfcGQxZ9x/WvrJ/mubwAtUXvf+Xj19l/+Wc4/ci/tsR0LVr0u+ioHPHXv/DXKfb+ia/9oGslib905Qf56dWTS3rGovel0vQAvfmr9Hc+y+lLfz6qFZZzddGi31XbuuUv/cZf4mtHXzvvpXygdbvt+PXpHn94uPbbPn8jrbixDFItOidp2yFHr3Dh6BW8O2C89v3snvojoB2cmfoWPdJ6DAFwCQgsMgPH6MGVhxTWw2GqM3A1mR2nAYeiL5dw9JmjOt8HjugchetzH6sIxuzwTTMsEBDy3O1JGHQ9IJIozF8RTmOZXYl+BtoCRKERG+tYpNs2wyTu26yFe1XAtcOthOu4nnWnhLOXDHgON6TIbJqrEYnrDTSctqKJ7EJzj/ssBaNDcyZruD1TlxgkcSklLnWJ9WoVXb9mSGtIbVAqqOJ0oIaK0PUD1y9eY7jyBJtppGyOKFNi52CnJ2QLq2pxBwtnntCRxKP71xUfR7zMpB4HKfNx1hlwgdLTWoDxVidaXgXATJkuC0MS+i4HDBJAEl0eAjCZ457Y3j7kzV/9Aq01xAt5f6B76lJ0hx4dIeokBbyRzfFmbFxwjJUkekBbYyqVTKMfVmSpZHOSj1Rzbn/9HTZ3t+ztDZSyZdxuZngPPlW8VDwJWAuzrYZ3MmK9jewB+wM+OqKJJB6uTklUS9RWMWt0NWKhBcPGytH9Y3anI/3dDafv3MNaY7cdsdlBqRKYUwmoS4oIbZ2jwWkzCEQjk1jCDRrdq5XaGtoa2iWoGVcwG2cg61gdMY4p00jajtD3tPEYqwXbJuom8+ZmJKOUUuHNu9x++Ta5g800hpu46zABMcdqJecOyT3kFSkJ2goQXa8qhtCYTg8Zt2MMPaQ0u10zWMG9gnRoiq5Yy5leEpKEbDWc9ynindGePPT0B2ty38W5ZkKtFUNoIiAB/OPcX6EtIqVb1+E5BQAWJ2km46RakVojpl0Fco7rP8XxExxd9YhGB7SnucdbQFO45qUaeXY/iyXEdI5zD1jvorhaPEaJx646x0NLRZrFOa8Jb5W6fYAXw7UjrRK9riNOXaLvOI0lwO/qAtoqakZq08PuafOK5YibxlN0hksi95msLW6vyiSNhlAkgST6pHRdRlKHtYYz37brSSlFR+88T2O1YbVSxh2TK808OoEVNO0jVmYXcB9R0A+7sSfwRBs3pOEgQHtrs8M6YsBbmQALSMzsLl+06Nug++N9/tGb/2iBv99l/cRwY+n6XfRISIB88kZA4Bf+LN5fOO8lLVr0vtH98T7//m/++wv8/S7r37r4SX5qgb+L3ucSQNqW/Zf/G05f/PPUS0uc6qJFv5u+cvgVfv32r5/3Mj7wumcTn58e/A4AvGjReevspzkpJ6ze/RWGu5/D08DmuT+Bp4G2/8wCgh9hPX4AGAKoSkTEAtEFO8fWIvLQSfYQnsqcfyxATqjZw7hRmTtCTWbHrUcv8MMo0eaYS7hOZ8ekqJA0QSPe7NWIfoYUAPoMGtNAuuj8PfMZimAipNk96aLvAVxxzCw+agkXXGvg02yR81hXq4ik8D9KQiiAxLqtISpYKbS5V9fdArrYHCjsBSSTc6brA8DsW3sIf3NOJPfZVRcQSmiIKCkraejI/cCFG9f4nj/xkxw8+zSHr73Jrbducv/mO4xvbBCziL2VDh0MkxnOp7PjN8SxMMHqiNeKtxrOvCwBzOasY2/gtdEgHI0KSYUkmV5gGAZy14dzMa1oXYfZKZoGOtmjlIlxV2gPTsjJAng12L5xm11SWtmhqY9Q3jn2W/qMDB3WJUruaOMEU3Tn5toYy5auy/R9R2eQS6EdH1JK47Tr0OykDkiCT4bV2dmNUaeROpb4XBba7HRMrpAUb2O42FWRJkgzPM9x3drR5s5aR0jqSC3Y6OxqZSzOyemEeKOWEem6ALjW5gGFTDqLfAZoAi2GFiLiPNyirhqOdo/+aCtxjol7XHYqcV7J3MNrBmVEWsFtRKaMeZ1jzRtCZXc6X7ctrpF72124V6nYmZ83dSBGc1BJ7O3f4OKNZ+iTQd1y4cINLl69zjgY9++8zTtf+iq77Qa3aY6AB00agNoBVVQVFSOToRVStYCkydFuD7UWUHc9hIu0WhjTm2HV565hnbuCY1yE3CH7Pa6gSDyXLuSkrHKimwesWlZqjVjv6JsNR3GQbo3e45wiZr1VmvYYiktC1NF+oOt7qIXtZhugmBR7RJvds12HCmg/EHAz+rC9pogMl4iXjt1SZjeyklZd9EST4xgN+2iO/mttFa0SLmYyst1gXqlToU6VMSWmriOljCkB4+cBlO20Y9ztmCw6zoeuI6c096EX3IWUIK/WD3t/lYaVcGe3WpgMpuLUckSSjjT0qPSzq7uhCOJn3ekewyJjQ6XDuzVYRbwhGvDczOIlQEDTKoZZrGGcDdYsWvTNy935jTu/wWfvffa8l/JY6WP5Aj/YL9FGix4dCdAdv87+K3+f04/8BTwvLoFFi85eQ3/p5i+d91IeKyWEq2mYh4QXLXr/S9vI/qv/kNMX/wz1wgvLm9iLFn2DqlX+9tf+NtWX9ze+G/rZ7dv8hf0PcVH7817KokW/q4QAwZQTLnztv8LSQLny8YiIXtKoHkk9dgBYwgb2HtANuyJy9kIn4XRD08P+x8iCtnDUEdGgMsckiztkneOYHSwxM4mZ2Spi9aELWM4Mw4CooW7vRZe6BdhRYI6D9mSI6BwAneaO0kZ76NKLruCAz46LhNvPmQFomt2zsX4/W1wL95+YoYTL1PzMZRtdxc0tOlQJZzFN8DRAHUkZhmFF6hLZ4SKNC7mjE0VbC/cwAQWlhIMwdQF/U9+zunjAcPkCh+/eYnv/kKkVtocPOHntbdrhYdTKargyvVvRhhVWLVx6BpSC5ERKguz12M6wSYAaz8EM3bw5pgnLgkVQL11W8rzOjKN1QlJm/8Z1Lj91jaOTLce3K9eevs6Vy9d48/Ytbn31i0ybgquQFFLX4zhtqtCEPN9/s0qrI1UcX63RVU8rjd3uFK2N3iMSV7seW62wvqMwQ/tpg7vhLZO7jJsgae46bYVminkMFJg7jQCCYmClUlIKN21SksT5EPHNHrXTSDjW53MiHOQSEcdewQQvW7wVIKLHXdp8O6UZ4cA2UNsFBNMMXgPszmnqNjtUH8arq0DXY3VCag0Tu6QYrsBwlei+FYlY6FqRWiJilwbaRXy474h5iYDLNMUK0OkMuPXhwALasXf9Wf74H/9pLn/8JcgjR0cnXHrqabTvaO/e5+jNy1xPHV8cd9y9dzMctZrjepsve0lK7hJqEl3YIog5qRopKUmEvN4j9xmdTf42x7gLoLmLa04En6E7IpAV7TpQIWuG1lB3+i7Rp0wqE9RKSR1i0Cwi0k0NSTkc7SI00YhFzo60jDWPYyaxf6Who794gR6PKOrSMJEYlmgxENKJ0vcD3WoFojQvc01yi15jUTzliF3PCZ+m2CdKnaF29IeLRUR4ktlxm5RkaXbwpzh3a8WaMe0K4+Y0XNV9YtwlNps1Vp02jQTUVVKf0LN4aoWU+nj+lejxnTub27iJbuWWKK3RakNyxzBcDUjuhShqTiBD9GpTIzqeCnkNuUdSnl8WYuDCxk10h7ug3YB0q3C1txId0I/fS+ii74CqV/76F/76eS/jsdONtOLZJf550SOofPIG6zd/gc0Lf/q8l7Jo0blreQ09H/3YcI0/uXryvJexaNEfSFpP2X/573H4A/87SAt4WbToTJ+6/Sl+5davnPcyHht9qRyxc+PieS9k0aLfp7SNSNngks57KYu+ST1+717LHBU894POZb5xEmugz4h+toBRM8iMaNtwlDJ3/jK7bh3C3TuDozNjsWE4FrAqRQyzqOCBJKIz1QNmBYQLV58zO/NUHzqJcSdlMM4gbkBfSxE7DU5zKK0yjRtaG+N2LtEzOgNh0fi7awA7ySncs2cNwyrR4ypptkCeHSvF1eO2OUc0MIY06MzYz4khQcLnXtAZPArQdRFBbUBtuEy03cjJzXscv3uMF0PKxHhyTNudQg3wlDIRcdt10SVbDGyYD64FxEkSsVN+FrHbOEvXxivNGqVNlOZgE71msiZULCKJU0ZSRocO3c9MVhlPT5Ahc+kTz/Hcj/wA4298ljuvfIW2mzCHlDskzY5kb6Sh4/ILzzG1xu2vvMy4qeysUCvIbkLLRLeDVAs5Z5IbOSspC5rT7AaP2HHzQisOdYSi6LB62DvqNFwE+g5XhTKR8kDOiSyE07dVzDK0cIZK12Y4m+hmEIkIkhOIzdG5ORy5IrgKYU03xOaI2xRu4v39S6wPrrAZd0xHt6MD284gMnGuWEW1w5sH2FVBpIvHRxf3J+GqnfO24/tCOIHFI2K61nCNa5z/Z7Dao344HMbm4cauCVL38LoQhNT3XPjkc1z48e8hD2u8rrh88VJAVyA/e42rz1zl0sev4XLCr/33R1hStMuklEmrAW2FZA1KodVGmwruLXqWEfqUGPZWpC5DSjTzuHZXOVzwKdPcaAalRW/3w3DXOfM9hgmUvOoZROjdyLVGrLsKtjegU8KK4bUGqM+zS19zRIJLDFrghFNfoElA2UgJiL5lcsJr/LtkRTrIwz57+2tWXReDJa1h2watkbuBPOSHSQHNJ6zOtzGbo6RBPM47UhdO6T7Tpoa0igIiTut6VhY90ew2FKtkg5NaaMNANmA3UcaJuj1BU0/Xd/R0yDB3HmeNAQYvYBmvuwC4brgrrRTKOEZCgETHuMxR/uLD3MtuzPnp0UMuPSIRlS9zN7EgWB7w4lQ6PEVqgo0bdHUhQL61eb5miY5d9K3rZ179Ge7t7p33Mh4rKfBvHnzkvJexaNE3JQG6w6+TD1+hXnxhcTEteqx1PB2zq7vzXsZjJQX+twcfJS8VCoseQUkbWb37y+ye/knQx+/t0EWL/mlVq/wXX/svaGfVg4sWLVr0u6g7/Bp7b/w8mw/9yWWI6hHU4/cTz9xlGobd8p7Ld+7dOwOziOLeMPe565O5Z9YexkKLGA+duzC7dH0Gt+BNAzho3LOkTLiNAZ9hXpojpsNCHJ/Dw3lrjskMwVQxPcO0GhG+EjGk0T3stFbZFqOMswOOcPpp6mbXMwGLvuGNIg+7YoA5nx+/SsBb83gctdFqxEuTBCuONGOUkZx6Loux13d0KaO5e9iDHGDWEBK0Fm5Za+HI3UwIhzg5XMgCiiFlG4wyZ0QVVSEPK7zTAHBNaK1Ri2MlekAxx6yFE9oreI7nDqdWY5oKkzlJK30ON7WaBVvHMVPqVNm8e5+NP2C32aKrgQcvv4Pf3XL37XdRoFnF0dmZauGidYdOGY+P2B6eUnYTtTakVtZeSKXS4XSDovsXZnuoxoHeTZgkJCuKz5w9YTPEtqak3TTHzc4gq+8xhdJ1yMGaVTOyC0kUSe09aD/HKtfRaKpIK6hmNGdUnZw1YohTh1i4fbs8UBCmsw7suSsVK3QXLvCJn/oJfuClj/LyV77CZ37509RtxcYtVjZxDaUcgwPecJ/PF00xXCDzuZdznHO1zud1xIxLiueseVxP5o2k6WGsmKpGHzJnz3OMQoiA1wLekC4ii1UzT1y+wqWtces3v8azP/q9SM7Y6Uk4qkXw0x3l7gO2x2+zPd6wOtijALkbGFYDA4ZvTmnbHQ192IutnknidCnTr2antgrVnQp4n6HLSNfjOc+JAQ6lRPS6Ci6KWpwj6pAbDAlWInQmZO1gULYIzaEmxyRH2naJ69E10gi8Fix1lPk6oDZaK1jKyNDjY2F795AxdxQUy4lmFRByN5D2VqT1GpFMtkb2Qpm2lGbk/X3296+xf3GfPsGtt9/kdLoXx5kO14TUEoZjSagL0q/RTpCTDWwLzaEhdHmIgZd576p0jNuRWp1xN1K7DmmNutuyK2BtRzo9Jmtmk5WcJQYvco+IoNpFJ3sriCZUEnqWyiDgKZNMwjGtGdQjbSFOSTQlhLnbWgDXOAdFaGXEGgH05WwkqIthmVICOtuIpvXZ1r9o0bekN0/fXOK2vsv68eE6399fOu9lLFr0TUvrKfuv/F1OX/rXqBeeXyDwosdWf+urf4ujcnTey3isJAgvdQfnvYxFi74pCc7q3f8Bscr2uT+5vH4ueuzlOK8dv3bey3isZDj//e4W//r+8+e9lEWLft8SoL/7W+CFzYf/1WWI6hHT4/dsObiXOQE6OnfxGVBZ9PGSImpZTCMKVNIM7jw6T0VAG0F2z5yzAfXC8TgDZhoiGff6novYC2gf/cAe3Zpn5ZLRExruPggHn6bZJelgjdntOzsuJWJ8WzNaq4zTyDTusNoIq2RFcwpHsvgMsD2cw2bv9QaLcmbg9LPoWlVyN/dfkhCpSDNamXAPQN2mgawjQyf0Z647Jb5PJTqIzfFxQqrNEdcJ6VKA2gTqJaKKmZ28cyenag6HsineWjB0U1oLMB0+aKU1C7crgquD54iatUZrTnWniDOJ06EBnFVQ6ZA6oSkBho2FXSmICV6dVitHb99mezyyO7yPNEN0hrXDQFLwdgbIYdxWmmToE2Id+5aQ+XFlIA09klI4Wc2xqeKWkNNNQEnz6O/tUzg7m6GtYJLQVEldh6aBZjBOlU1rtJzpVSIyeB46EJmdjBKxteazL1YUc6PVGvC3WTihk5OGFd2qY7W3pnlCh4F1t+Le/fucHh0hFh3UR2/d5LUGR5sNL3zye9jvMm+/+TYP3rmJjQUnxdRgncCjc1lbmy8tj/PGmCMjKrSK0uFdxJ4bEq5yDcDnZxcs0TOsmjDJuEV0L81md7M/dNFrSgw3LnD5Bz5MduVu17h+eIi9e5MvvHKLy9ee4fKwx63bNzm++za+fcDpvWNyUrJm1vtrDi7uk1tjnCZGCiONnBXRTAJUE7nv0JSoKlQzJiL6GlFEu+jOtTmG3MMZHHMmjmBIaySgl8SARg+0VzRl0t6K2hq7exOjJGzQuT88o5qo05aGIDM4x6KfW1QgZ6gNphqzALnDLFz95i3imVUj/l2BqUDvrNcd/bCHitCmibIdeeaFT/BDP/SD6PXLlNORg1/5J3z5yydM1iAlbN6rxBUVRc2RVuOHAGtxPVtFRcmSoVuBj7R+zV7bUrqE7SpHBWoHnkCzoM2odaLWiW3ZITmjWaEeIyLI+gKZimhHAlLXkbtEThKOfoOsjZx7kjaythiwkBiEcOmjIxunPRy2qDA6Pu0Crptg1qhEuoCQSBrOaWfeX8+i/Rct+ha0rVvuj/fPexmPnf7YcIMD7c57GYsWfUvSNrL/8t/l9KX/EfXii+e9nEWLzkW+/DD2Xdf/+uAlri69hYseYQkw3Po0iLJ95o/B8jPhosdY/+XX/stlkOq7rKfTmu/vL5/3MhYt+gNLgP7eF0Hy7AQezntJi36fegwBsM2xpcR/bP7zLB7X5wxnbwFpU/oGkEO4yMSAhHsLiIwgZjOrCoexuAeUM4toW49fUEV7zqCW6FwX7LN32B2xFvet4TzzNq9NE+o+A9JYh2m4C2udmGpjHHeUUsEbSQTRhLc2g8E8O93ANdFmwOGzg040bq94ADh3JAmtVMwMyYpZRbscEEtBktDj7A0r+i6AireIj7VpwuuZy9FJKg8/xBtJFKWhXYfmhCZFpQtgbECZoNUAMmKwt0e36mFbsNKQZqgopVZqKfgMW9FwAZsmDKF6oXjPbjrBUqL6hBPuwaSJlKPb1E3wcYdVh65D8hpxx05P8e2WJEJJM/D3huQV6FlHslBboVilYWiXWNGhu1NUBzRrwLAWQwRihnRdnCemEe2Lo6qkFvff3CApachokjhP1KgOU6uUscVpsV6RcyanjE8leqpVwrl7FrGcEi0JZvF8ttIwE8yFDkHUsSqUXaW7vMf+tcus9lakg4y+rRzff4BV4d3Xb/Hg9in54h4f/t5n2d8f2G+nVDVWuacnc/vWEdujQ2x3OHdUg1ilSXx/aoXcxYfVcIpbgF80BzxVmQcvYu4iZh2EdcpUh22b5j5dj85sifhjLw1B8U3h/udfQVNHe+0B9rnXKLVy88ERbw53yd0A0xFaTpG6hakw9D2rLrNeD+ynuIZZ9dg4RiS3JGQ+V0mJhlERajOKQ005enKTzoW9sTZJEj2/IljuaLWBNdRhSJl119ElIcfMB7VO7A4Lp6cbNpuRZhZva8U0BGSFFgMCbk7Coo889QgWQxVDj01jDEZIRftw5bt7OGWtxODCVPHc01+/xtWnr9O1ynQ6cfrgiL1rN3jpf/ZnuHLjKtRwaV9aHfP2rZc5PTqmzIZj93h80csc0fFuAmlAugwpBgISEsM1XcJqw/oh9kN3aMbJbstOiZ5omTui+wPwCRNoxWh5hWpCp0rxBnVHypncGqlpxKB7gG2pE3nYY7V/ga5Tsnj0vEuCzQbRCU09jPfwbi+uQcnh6K+ncX42x8VIeY16xTRSB7wVUhrokrPMiy/6VvXq8av847f/8XkvY9GiRY+otG3Zf+UfcPrSnw8n8KJFj5HcHVviWL7ruqb9Ev+86JGXYAw3PwUo2+d+6ryXs2jRuejdzbv847f/8fJa+l3Wv3HwIh/vlgbgRY+mBBjufhbc2Lzwp+fI3EXvdz1+AFhmiKcCMgMLFG8BgMV97oqU2dGrRKrt3IcwQ7UAxAG4HsLjh7WmjTPaqqI4lUZ7GL0sEkDQcWTu0zy7b9cUXb0m8xoDHCstXGsys6D5cbgLrcE0Fmpt4Yx0aEQHbAC4iFdm7jT2Mp3VCkdEbwvoDGXuHs5Ai69TIatQm89J1h4RxS3uYNV1rHJGcoeL0qYJpoZMFW01ABUSrt2UELe591ZIQx/OVhWG9T4XLl4mrfbYbnacPDikHh3izai7ivsW2+wwd8Rt7s1tmAu72thMOyCRNZHEwjGYE14EKyNmjUlhqj0tG0Yjpxk854ybRG+yCtJn8npguLQGd6aTFs8xhkvGSqMyBtyfna3mawpK045UC1orqV+TckJyTJT6WCN2Nmf0zHWNI30PKaEqAc0AaxOeo3fZSgnQKM62OpsyIWnumdX5XmqJc5iEeJtdwNF16ingvs2gzm0GdynjmmmmUAxrE+Yn2AS7gz0kGTooqY+BgVoqp/WYtDvh9ZNTNGXG7RGkxIVnr/L0U8/y/PcMvPyF3+L1r5+AGYqT5nNVcsIlxzkE8SKhCgjSjC4r/bBHA2rdYK3gklCBvBq4+KEbpOa88/W3mDRiuH1+/BFfLqzympWuOd5MiG/heMebJ1uGDIM3at1i3shZyOqIjYg0ssJgRrfZYLWL62QqqAjdqp+vF6OJ0sxpJKxLWBaaxTCGp7mz+2wfaS1gv4B0mVXuyK7Y1FAqfU70CuoVSokY8u3EZjcxtWgQj6j66LlFo+c4dot4/CaQJCFntxZFOiflPbxVRJSEk9Spo9MS5H5FKxO1VDQX2BwzHa9YX7rMen+AG09QhjX9y69E5PmFfSgTd24eklf7HJDZnhwzWcVsHrZwZjf9e0M0rhVaA8nQRiQPSJfo6CEntFN06GHuyW67HZN0KIXcLGK7JY47ZlALnqCWEa8jkhRjR9s20sEFpjZBddIqo2kP2RbG3bv0mtB+jSTBXbGxkDpFU4cKMJ1grvh4HxkOYn+edmRxUj9gdYbspWK1kNb7aO5w8eg+XrRo0aJFi85RWjf0975I3X9mieJa9Fjpa0df42df/9nzXsZjp6/XE0ZvDMsbfosecQnQPfgy440fxIbL572cRYu+67o/3ufrR18/72U8VvpIPuB/srcMbS569NXf+wIAm+f/5cUJ/Ajo8XuXQFIAXqvMOb4ztfWIWJboFRXmXl6bg2g9opE5A24et0Uc6gyAZ5ewnBWehq0XF5DZ5Sui0SfLWd+uR0iwz5HSwsOuYZE8fy1zpOyZdZkZq8bfHMO8YmegCAJEmQcotvZPgep+dg5W3HUGRwJzdLBIirjUbGgTXFskSCfASnTStoaZh7s1JaiFZg7jiBQnt4qmREod2Yw89HR9R5K5p1Wih1cNNCfWVw649NJTDBf22d09pntbOMmJ8eSUOu1o44Tn+CVTiB5PstD1HblO1C2MU2G9clZ9N8dog7qTNZPIlLGxKxMFpV8NAaQVpAsrttgambbRR5wyfcrUWsJtKuCkgIDVcKsB8XOmZsWz0Fr00iZrJO3ohozMx8dLRfouukYj+BsxkG6PbujIw5qUFROjThOtKdaMVsE9ulatNXaTsVWBBNlbnBcNzGpE1ZphGF4rmnjYecsck5y1Y1ivSF1HdcX7NeQBb4XWRrQKYmvKLkPdUadwWnuSOTrdqZ64XyNyWcTIyTjdFlYfe55nnn+BHfd4883XaLstZoLK7PymYqpxveBYl+euYUdc2bt6jY9+3/exOdry2pe+QHGPLmdJmBknN+/hux20Rp8yJc39wpoQa6T1AVc/8RH228TbL79BrQ1NDRcnX73ElUsX2J6ccnyyoZ+g17AYt1JIrSDa0UqjbCeqNao1TGR2aztm0WtsKeNdQPsY+lCSvwdgHeZBDMdLxTBUldX+wLULl1i5M50es9lusGlH2020aWIsjW2tTLXG9ZfmLnFXvJ7tJeFQR2o4jEXxOcYczREj30oMXZDiOR86MnMk/HxuR0S0wDgx3rrLkXT0Fy5z4cUXufqxj7Dbben3LmC3jjj8yi3G+4e8c/M2zz33HKcPDrk5TbQyhutZIrHArIQTWCKtwFzCOS0xOCEOSXIMObQNJMHdaV3DW6OIMJGYeqEf1rTSqNMWqmDixAxMxZKheR/6eW/rhLY7RvcuI3srHInhARqmPWNz2JygGK5rvE3k3Y60uoSXCjaS9i7F/jftYmBjnEhu5LIl5Z6Ehiu5ix9qplJo6SyhYdGiRY+a/v72Lf61/Q9xaYmwXPQBUX/nM1i3z+6ZP3beS1m06LumapXRxvNexmOnv7t5k//DxY/zRFoA8KJHX2l8wP7LP8PJ9/zP8bw67+UsWrToA66bbccv7G7xp9ZPo0sH+aJHWILT3/s8SAoIvAwiv6/1+D07bQqQQkDZM6ArKhEhLAElBAs45TZHHodTMZhuxZlvb4SL8Wzf9vDrBjENSKzM7sAzV+/cWxq3V1Ti3gzeA9KqmDfUFZWzzt9wIjsS5sn3ePBDdy9IdIKK0VIikcL16Q3RHC5kLXidAhCJY6oIgkkDjfhqFUCcJtFzK1HqG52jtQSUcsF2Bd9rtC7h04hUowO6vYE+KRmhS5luNZCHDnWwUiN2ulXaNOG1cvr2berxSLfeI+11rK6uuHjjCid3T7n3zjtMR8cRDa0ORHS3GiRvDDmzTor1kNSjp9WiezbiuxueleKxNlUNV2ULeEqbnd5ipH7FxRtPsPfkDbZ1orx7hDen1YI1QfsB+hznAY6ncMZ6NcQaXSNcr8nIfRd9qxZx4qJx/ISKphXrbs1wcJm9Z26wf3Wf7faEw3duYw+OkKZIEsyVstkirPAysecVK4VdPUP/G+iGGfJWmmrEkrc297AWaBrO8uTky/tcf+FDXLl6nU56hv1r+LDPya13uXvnJlcuX+WJFz/Krgpvvvwq92+/gaQJrdM8CxGuR2/RMStJwDJHtx/w1Z/9JU7Wv8Gr774dXdI54y26b1VngOYt3J8WMegmM6QGDp57kk/88A/ANHFyeIcHt95hbI5boxbjuGwRGutrF1j1PZvjEdEeTzBO8W+b195gbIaNU9R5A4lE8oLWkb6OrMeRPmc6hLLZhjM7OTY4VYRiTsWxuRc86rMDvKbckUSwnGgp4arRdd1kjnuHao61Fq7V+VhlEVZJeea5J7h0sOLW115lOjxke7JlOt1QqjGVSsHD1Zx07iSXmHZIGvdlHntJUkiCxVRG7AUAGKQcvchzrDvRFI4OmawdSbvoZK4ReV5PNpzqfS5cus7BM3Bx1bGVie7iHrUKRw8OeefWLfLlfZ5++iJX7j2gbje8vTkmSUJzJurSHfeKJkVWPWnnWNvFeaOxFyaJPTKlLvYPc4YstF6ZWmFXJmob0eEAaqPLHUUN6MjFcFVwwaySfIg9TQW6G/HYz/ZVTahH77jnHisVb46kcE1PCDJuce2QtCbtTkh5AN+ivod0PeZQqShC8oaUkdR1SJkCdE8VkaUvatE3L3fn03c+fd7LeCz1lXLEL+5u82f3nj3vpSxa9G1RuJi+wnTt+xcX06JFi76jcpxPjXeX19BFHxilzS3y0SuUq58876UsWrToA64Tr/w7938Tx/kzy+vookdcAvR3fwu8svnwnwJd3iN9v+qxA8Au4b+VOarUIVy+okgOmOPo3McbAOG9uGXDZzelzhG0iIZh1wMJir8HKHEghQPSPfo5OYtpFcfcv8HJOzNjJ8CPM8NaAqYRbjr3El/vijnvdYRCgN85flVomGuAbCLW2WuJGFkXTHqwMsdRa8RQOwGdFdQDygXwNhKKq2FWECwc0l5A1pg5ZSxINQY3cpfpuo6cE11Sun4IAKgC1ZDZ7WwoVna02ijbkd2uot0xOSfWlw649OEnWT1zkfV0HP28HofGap3vI7qRU3L2+gGfJsSNToWsEQmsGh2hq/UerS/k1LEaBrKk6BsuI6xWoAEsZbVi/3ueZX3pIidfeIXxdMtUJsYCxSakSwgJKxVSRt1RzbgbWms8iZpQDfeltLksFUXmY6DdAd3Fi1z5yPPsX7/MdHjI5v5dTu4csrt7CBh5vY/2HWXcklergJjTFhm3JDe62tiZUUyRNpJzF07jpOGybRkTQ03CSeqG0GFjZfNgy7Bfef6PfB/PPP8xRBLl+B43777F/vUnufrUC2DCc9//Am/+6q/z5c/8GrvTQpMuunwBtxgi8GJgYOWId4pzJ/WU00PE2txdO3dOx9gCOscYI+/15MbzqKDQLiXWcoGDCx3H99bkrqfWgo3HtNoQb0zHp6A7LjzzPC9930fZbk748m9+njIWjk62pNYiJjxFz7KPW+p9YXt8CqWgpeCaqOJ4aSjR8dscKsYkgqWEk2Yjf0DU1GVW6z1ypxRrlNrmrSNhtcawgVnAdxekRQw6zeimib5OWNkwHu4YTzeUVqnu1G6gSo09okb8vKQUgBehCZBiYEUkBkLMBFxxGkjCrEGpYeQ/i7lPGVqLSuLUkbqBS9euc2V/zcndO9wRp4zTvAMZlz75MZ744R9AMVb3D2F1gKw6nu06rl3rmfYSre5YHRzw/I0b3H/wgF1zDlYrmleOdyOtFsDQpFiXkJpiAEZ75mmZ6PdOHW6O5oFcnT6t2M+N7dbYeWZyw2fuXWvFx02cL8VxKmm4hkzbuTPao3JCQK0+PEZ4xbSDNoIoJoJ6AzeMPvYwjeEPawpiJFk/3KOdCiQaGVOLqHYgudDqFGf0Un+26FvUz73xc+e9hMdSBvw/HnyWq9rzE6sb572cRYu+LcrbO3SHLzPe+OE53WjRokWLvv1aXkMXfdAkOMPtz1AufQTSkg6zaNGi76wKxl968FkM58+sn12cwIseaQnQ3/sS45M/Ttt74ryXs+ifoccOAM+UM/7UPP9dwC3iRJ0AnHMHsCMP3XTuc7fnTBvmZOaAXT677dQi4Nd9fu9lBlxCfN0cBx337Ij6HP98BoJnN7EqLj5HuUrEzeI40aMblj8JB/AZeJL3wqWNhJjj6oG729xrXKZYg6Rw4Uq4TEXCPawSnxNxksVjkk7QJGhJTBOYFFQqmge8V0oZmawjN0dzJqcc7sskaDfH0uK06lBrxNmWHQ2hlgmbokdT63v9wGOZmHaFpMJURg6euMr6wiU297dsj08Z64i0TUT/Kgytp7UCZPouB3SvDbqEDOtw9JVCVqVPQtb83rElunPxipWJw5ff4Njg9HhLnbaMbWLXjCqKTRWpDepE1+3Te4t+UWSGvgR4Q0kzxG8eYD4czB05dwypZ/f6LY6//BpjadQy0lpFc+LS889y8fJltkcnHN4qrK7uszo4YHN8DDffhaJoi1jubRkpusbVortZwXJCsiBeUYlu4dSvSSmh/T55tY+bcPfXXkZePqZ64/7dd7m/OeXgiScZfrJn/0PPsH7qgEsvXmb1xj79KjNYR9M1RzJwuj2Go3fxNs0DFEIbN5iMAdVtHmjQs0GLcKgHoDzrtY4Oa597ne99+S0+/eD/y0GfODo+5cmnn+eJGy/w9vEJb776OdrhLXCnbLawt8+Tz17i4qXE7tYRYkLSPPcxQxJFDCgjPu2oJ1umbojOZG80UTwr2g+IKqVVijVaSjSJ601zN18fjinoeiDtrxFzdDeRMRpOSglPlToZXgp5rhjPEPdBRZrhdw65Y2+AKkfHx5xOldr10DsUietdwkkcDcBKUwnXq1l0egNicyy3BED3WrESAxEicwC96DyE0pCkZM2sr17iiR/8BM9eu8y7n/s8pVa2HDFNBe0TuZvw3Qm+HuDyBeTixXAUXdwjP3UZjo4Y794nffhp8uWe9VuvUZuTr+4h25E0TZho7JvTFOkHXUdmjOOYhohOZ0BsQiSGN1JOpHGid2e/z5xOE+PulKlFBLhS0W6N2YhX0P4Coo54D3mO2rcWjnNAaIgnXDNi4cTOkmmasPEkvt4LPp6iaS+i9RHce8wnpB8QHyNeXTqkFdA5uloTmgSxnmb1rKV50SMi9/kaO+f7WPT+0LFX/pvNm/zh4RrdMs2x6AOi1du/yHj9B+D3SKhY9sNFf1At58yib1S8hr7Bjw7X6JfX0EUfAOWT1+mOXqZc/vjvOUS17IeLvhm9H8+b66vrfPLyJ/nigy9+2+5z0e9Px175v93/LTpR/tT6mfNezqJF36Is6hQ+8hew9fXf89bvx/3wg67HEADzMGI5IlbP3JECzK5ejS7fufQ34DBzevNZty/gzSOeWTVOOrG5ZnfOYxWNu5h/KZL37jSiZc1JohiCWaxBLBx9HrmzOIbrDH15r4/YzcL9Jj73EWsA6Bb4lxlNmFUkDRG96/JwHUpDJUccqzpJO9xtdvcGIEYU1ei6bTguRpcTknTGd40J2KGsS0W9IrlHc0LTgJvTpopLobU5B/asq1aVYoWym7BxDNC8HZEc4C2V+DfN4Zodrh3QPXHASx95Bu3WfPWLX+P0rbeQUvCpkXKmywpkks3PC6A5Q5+xVkkidECfE/3+BXLfUXaFtttAsYh6LpWjcgoJrO0oZkwubN2oKPV0Q8o9fZdJnTKs1qRhTRtHpBU0Z9arfST1bDfH+LRDXQOEqqNdT39hTT7o2TzYsD08ppYWzmoxuosXkeLYtmGNODe3I80aPk6oJzKJPs1DDC5sWqWYkFJ0C4cL3HHtAENyh6561pcusHewz3CQyHsDT37yJS5eusRXf/OrvHv3BOpIaQ/44j/5Mlev3aFt7vLWq19hd3jK3vVLPPn938vzL30SNgOvv/IKr37mVzm6e4sybedSZgcvEcnrOl8Xca6bz3Hc1UF1doWG09VVwo09nvDmzVt07uw/c5FP/rmf5snnXuCjpyNf+jnjN3/9mLbdoQ6+23H0+Vd49c0HNOCpq9dp3qA0MGcsE5f7RD06ZHvaAJh8R+oTKaVwgZrSZUVyxsaAuaYdkjP9asVqvcYFxjJSzXF3ptMJBXJSUpdpFt/PXFl1CVVgKiSPczD1HToMtHFH3U0cvXOP2gk7a4xWA+6qYCit67Fa4zJvhjUwbbgqMg+mIAq1xfCIg4l/AzjWef/wuUZ8HjxB8B761Yo9zdjphBxcZu8FkJu3sdv3qUcb3vhvP43e3HHlxjUuXb3O/kcH6BQ2G+TgAn71Mv3+HnJ4iN8+BVP8+IjNySb2KKsk5kQCzagoKdcZwsaIi+cB0QJ0eJvC1IvFwEgW1gb7ljneFegGxBzRFckd8YPouPaG+IS1QkKRnOL8c5v3Jai2BTdyt4+r0qwgmvBuH9pIVqUNA4w7GA4wA9jgWWHzICKoU0LaiKiTVRBJZHc6r+RuRfOesjv+trwkLfru6Nvxw+HyA+YHS7+we5dX6kf4WHfxvJeyaNG3RdJGVu/8Mrtn/8V//u2W/XDRH1DLObPon9bPbt/mybTm/3xpic1d9OhLgPVbv0i59FGQf/7bpMt+uOib0fvxvLmxvsEnrywA+Lw0Yfzi7vYCgBc98hIgjffpH3yN3era7zlI9X7cDz/oevwA8BmEneOWz/pZU+4efl6sBTuZTybzhkp6r5cTwnGGh5NWZkg8M06XGQiffYIZ/qo87KWNmNcMbiRJSOrAfY4iDaj7EOS5g+t7fcVz5LTXGqBHBRVFVWkzaAlkm+J2rQToDnszScMViseHeoUM7gm32Ylss0NZZoOnGVARhYxQzWmtMaae49ZYIeQzx2fONHFsmqA5tArWUBTxiJ2OjmOl1YrXEeiQnJDaaK1htWClkrp4XtordymHhf77X2R9yRAxnBbPXwtbqXYr1I0sNsflRpezNYdqqCcSTpfXXLl+Fd3b48Hb71JPwvnr1rA8YOywYhEHXCrTuMOngqae1Pf0SUldQjrIVy+RxedYW6G/eMCF556GZmy/dkyrFUkdmqM/OA8ZNaccb6mnW6yO1DrRqpMUOoOD69e4cPki5eYtcCKG+jSCy7FAXF3Os4M2ANpUHZUWzdRtgpwhKY5j1Wi1MVVHimGbgnU77r/1Dttbt9kc3qFfO91qjytPXOOJS5e5dbjlq197g92DI3JyfFMYx0J3/Qr9as0nXrjIcy/u8/Knf4O3v/oOZoltrUzbQ3zcBuhPGZCIMG9tHqywcP+qxvpF8NTh3ezH9i3WDLcL2OER8gIMl/a49pFnOPjaBUZRmHboVKknW467gcsfeoIbz1yn73oOLh3QTja89qWXYbfBjiopR792wage3b+KI+I0ooPYuw5xyMPA6uCAS09c48L+ipMHx9y5M85d2xHBnDBys4gzLxUbR7peWa97ZKq0ZlDDNZrWa7qslC7Rjje07YZawLOQJFFrpeFUj0h4T2eOnTl5oEbTs4vjKUX/dqvz/jIPr5jNHeUWCQFEZ7l7HN95w8ByYnIh92uudCum7QTdQLp8nbLZMt4/5dXf+iLjD30fV773E7Ae8O2WzddeZmyCWqNeXLG+dMCxOyqKmFMQrE3RMw7v9aVbiceROrApnN9qiPR43cZ+YJUkAeVTMXK/Yo8de6MwlkZxQZOSxFB3miiUQhu3uHeBm21LSiu87qBNtD7BcBlNjpXTSAlIA06O/ZRIcFBJeH/m2k8wjWha0aQnlQ1aM7I+IKeEipO7xKrvGIZLJDwiyWv6DrxILfogy92ZbMJxfu32r3FnvHPeS3qstfHGf378Mn/pyg8u0VuLPhASnP7BV5iu/QC2unLey1m06Dsmd6daPe9lPNZy4Od37/I/3vsQL3QH572cRYu+ZUndzOl8j9/bpIseX/2JZ/4EP/fGz7Gpm/NeyqJFix5xDTd/ld2TP/p7plEt+u7r8fzJRmcoKxZ+V7eAKppmd3CPylk0sxPxwHOX68x/3ee4ZJ1B78PIaOLrJPOeY9d4jwdHjGj8f3QRuwcmdsLJ57MTWagIZ3HTATUVMGu4KD47h83Bfe7/VJ8Zd3rYbczs6g00rNDazLGdpIJodLM2j/5SP+sfFg3gaBGRLNbQYiRvUAqVjOuOppltgj4rkyZ2pZDdYapoaajVAM4SubgP47XrFE5HU7RX0moIN2UtAW3HCSsT2idK7Xlw95jxV75EPwyMuxNs2oLmqJJtDbeAz6JzRDI9s4UT2nw8h558sGL13A3qVOZIWgPJSN8j0jAqYylMpdKKk1zYH4YAuH2PeDgYzYTtncN4rK2RcdqucPrmTVpT6m7ua55jv1POpL7HVKmnp7Rpi007qkFxpwNYZWRlWKp4q3hr4eeeu1VFIa/XpNTRPGHbE/bKhFfDk6NnxbvmkDx6jVVpDcqu0g3Q1ond5Lx7v5C1MNaKe6XtCrJOXPmxl7g+GeXkXd7ZHvPg5B7T7h4vf/4V9i88wbMvvMh0dJt7X/si07jhhR//OC8+/xJHt+7y1d/6Am++UmjT7uximJ2ocZ2JxrlpklCMJHPEcYprSTShtVLuH/OlX/ocjPs8ceMy/a5w5dpVxi5RDgWzU7QWdHOK3L1PLQW9sKZsjpjuHsLNW5TTLXW7wzEsJQoagFyVrIp2HQx76N4euY74OKJdZrh6idXFA7wYVmc3M42sQmeNnIRMItChY8lIpZAtrsGcMw2wpLDuqLnDsqLuJDPK6QnSlNR34dInIa2FQ1oc13lwYx7C0GqA0UoLJ7/NXeM+9/yqhis6BYy0OvcQ1xJ7WWd4yexuP+D0mR36/PNcGDInK2FlRj+sGbanvPLpL3Dv8CaH7+xx97XLPFGfZffGO3ztU5/n5GRCU09//RJPXr+MeeXCE9fZbEfaboNbiz0odSga62zfEKsvjs57CwIaWfgxSFN3ZHdat0enwgplvVJOtqeM4ig5kglUArqmDl1dwj3ivPGeVk7R9UU0X410hBQx8CID2AReSZJRFRoTyR1ST2mn2LQj719BVhdQAe0U9230we+OsLxHWvf0wz6r9R5D3yNWabmhaQFGi35/+vrh13nt5DUmm/iPv/AfM9nE1CYmm857aY+9fm77Fp/sL/Jv7L+4TJEu+kAo7e4h9RRYAPCiD47u7e7xm3d/87d97m986W+cz2IWPdRr9ZR/vLvJv5n3l9fQRY+8pO1Yv/2LbD/0L533UhYt+o5qV3f80s1fAqDN70svOh+9VTfcayNX03DeS1m06FuWeCNtbtEOnj3vpSz6p/T4AeAgqPEX0XC2ag7YqWcvexbOOQ3oKYDL3L+Z5v5el4eOXAgnccQmy3vgi3A8PoyanrtPcZ/jlmUGhNGFKjiaohPVmwQoESHYqcy+RUdEMTOmOlFnx2yzOXpVBPV4TPGZufPYHdWAyUh0Y4owrymAjczrPus91lboaqVrjcGMAafrIOsesrYAmR7duF4mmjgnY8W3W1bSkRE61YisThlzCyezRy8stWGtQL9Ch0TqOhTDskQvcIu+YANsGBGF3c6YtjukbfFmSCp47uOxZ+GMf2qXw9VXJ7Q54pXUDYhC207c//IbNPPo9FVBB8VywjxTtzvGqdCKkcTJq3U4p/t1xF97ppWKn+5oJfqhkwApYcU5HbfYOEGrs1O4C+BVWnTFjhN1N9GmQkUo7rRmaEpMD464+eU3GPb2aLuAUNYa1ioKiCaGgwP2rz3BbtrhtwrtpNHKxFgjajrlBC36pUVmN8iqZ7h4kfWlAy4Ma568/BR7N25wZzxlszniZFupuy3+9bc40F9j78Iez/3oSzz/yQ/zmV/5Td55/TVOb97hM7/0OV777JuUw9vs7r+DeOPJqfJgPZAurLny0hMcb+5z+M49Wm0xOJGGcIZi4NFF7RqOW8TIOUWMrwiqGRFBzdjcu80bb7yM1atsDw9JLnSaUATzuC4Gr7DZcYowbRvTzSPq8THt5JSym6JjOinmjnSJ1GlElHcdqV+xd/kqVy5fZXN8l0M/ZBjWrEoiHQs3rl7jinbsju6xK07njpojzch7F3jyqSvsiXJ06ybb09N5mKHQmDvDu4EyVWpxWlJ0fx91R0slTTUGFlJCMFTDJV1sHvCYe5NRRZJjxaB5DALMAyyoIG4xm5L1IfcndwgewxB2llcPJkK/muPLrWFHx+zWK9b7PW1zyG7cUnc77n/1Vb58bNy+/DWm0xMe3L+Laibtr+mGK5ysEmt6DvYuoFef5O692zzY3H8YA+5WoUak+lnLNp5wN8wFp8Qu03UxFCCg047cEq2O5FZZYWRJqDUMJ88DL6qGjxuEFEkLSSNRYX1l/v4NNOG2w1JP1++hnmPQo46Q10gaaOWUREHzCqeHNhIxCEbqckRVW4/L2b6g0Aq5yxEHneIYpn797X6FWvQBkrlxf7zP3/zK3+RXb/0qb2/ePu8lLfpdVHD+1skr/IW9D3GwTKou+oBodfPXON1/9veM31q06P2qb3wNfXfzLofT4RJR+T7Vf3n6Kv/T/eeX19BFj7wEkLqNIWZdkp4WfTD0YHzAz7/98/zKzV95+LmxjXz67qfPcVWLzvSp6S4v15MFAC/6QEisMNz+NJu9p5bX0feZHj8A7MxduBbRyWfdpeksv9kegpW4eQKv4cY7ew9lhrnRtzn3CUsADj/rACbz0C78jdNU1t7rET4DwjC7jcPxKw//Lfpvw43rMEcbnz2O1ozaHJtBmxCQ18WQOjEjZUAxb2AVTXl2Ls+9opGDjZ2t3Z0kxtAqe6Wy78Y6Z/okdKIkEXR2a5pAc2NyZTsJ07ZQxh0VxVMhDRkxQ7sBEeMhobIW0Mp8drR2dH1AUtVE0oRpou7G6DOuDTvZwtDwVcVri9sKIHmOyA4IK6o0FJEW4BwndSDDBbAJ145SnHZ4HO7tMkb/qhmtKdNUKbsd2iArqOZwg6eMZCP3HdYEphGShruZROp7NDuaE2W7xaYtKSe6bk233puTsHe0cYe5U0uh1pEqComA3y60qXL87l1O9V6cgzbhOc3Qv6G55+D5J3nyYy9w+O5tTu/fpsMZNNOsUqXHEqTUhbsbGC4d8PSHn0E0ulXXn3iJD//UT7Hau8iTb73N3rjha0f3OTTYnBa+8tWbZIQPPf0kH/r4R3jh4x/lwe03OL1/yOatl9loxssWpg3JnfFzO+58/TbrPuPJ2I5buv2LrHNH223Z7Ta0Vufe3y6GDc5+qUpx/SURtMuIKCqOjhMc3WXz5S9x6/ZlLqxWPJn32aXGNm8oQ0aIblYtE2wFd2NCmepImSaqNQzFSWg/kFY9KSeyJFLuWV27wUd+8se5+PQ13vnlX2ZbG9effJar3/Nx9j/0FJph9+UvIi87ag2ZIpYcAb1yib1nnuDCpT36Hu688hZ1mhjLLr53ylRrjK546sgolgzfW5M1MYyNrJmxbNmUMeLKPUGpcb2qY6qYOV4Khs/O/7MBFI90gFqRLs8weY6EVoesiOt8rRmeoLuyx+r6BbqDNdOu43RsHN27Tbl9yHj7DqcnGxyjVePw9i1O7t8jeUXWiYNLe6yHNc+v9+h2Dl3H8KM/xv5+4tWf///xuS9sGZkj9Wf3t6pgc/857pgppgI5OqDFIZmHAzx32GmcT9kbgwprK5zURm2JahNZYVgNlK6nIlAbPu1iUEcjyr+NOyQNaFasTTSfO7BTBz4hPuKe0Cz47hTpDyAJ4rEn0ncRV28e52nqYvvuMq0boqu9S2QRfLUi+fIDzaLfXW+cvMHPv/Xz/K2v/q3F5fsI6J225f9y7zf496/+MJe0P+/lLFr0LSttb5/3EhYt+qa1vIY+WnqnbfmbJy/zv7/48fNeyqJF37L6e19gvPHDi3tp0QdCXz38Kv/Op/4dbm5uvvfe86L3nX5pvM2P9leXJI1FHwgtr6PvTz1+AJgUMNVbJDULIAlp9t6U/Bm3dcBrRAqrYBaR0OLMLrwAyT73AvucbypzhCtnPcJJmXOV5yjpiDNFfL4tM/DtZgAdkcFuAYBUApQ6GuAnRRepth6mowChKQCge8XP3HEYpIS39yKq1aP7FEkzdJ6dyc3xNpFF2ffGxWrsu7POiS4n5sDpqDr2iqniEuHSNo3oVOlbY1BlEKXvlJSU3HVI6sJxfNaTbA2bwtGqwx59P5CGAMBiFaw+hDFujVYNyoQjtBrfu+s7yB0uGRIoPfSgorCdaMXxaSQlIXU9khVqRxsLNk1ov0JoM5jy6DRmh4uSuxV9Z3FcNeFJae44GqC8GYqiXhA3pE/oqif1CS8tntec0dwzXL/CpaefZHt0woN33sV2GxyhTjsmcwoNzR0pCVIbXkfq3B+LG6phWhcv4aaUie07d7hzMrLdjngB1Y6uVYY5Prt1XTyt4qgIrTRObt6j1cq2KqnvuX3pCk9+3/chq47L33ODK+2I/tZd1l1ibw0HFy/z1L/whxguXuBSOyE1Q8ctjGMki8/HDoy6bRztJo6BLM6Fixd47sXnGZ65wd27d7j56qtsH9x/eK5hjidFRHGgjROUiWG1plt15JzRVvFdRPB6usiNH/04F64dcPi5L/Pu507YejiowVEVXJwyjVgzpnFk0kzpFM/2MKpdXUnNSeKkdcfFl57l4IUbtFt3ePDWHVZXrvHsH/1RuhtPQk6w26Im2PGOdnyEW5vP2RUyGfXwMFzaLXFw/UnG00N2uw27XaENSqVS+p7+ygH9akC2W6bdSMsDlz/xFBfVufWVr3P67ml0Z1sMLSTxh5HzYZoX3BXPc7y7tzmxQCMCWoi4ZZ9DCZzo3j5jrwguSj0eOX3jDqe+z3j7Fsd3HrCZRgrC+OCI6g1drbhy6RKDCycnJ+ymLZkVU5e4+KM/zhM//AOk1IM6rNb4yT1an9F1j5aCT4D29Oq0NjLOv2O0+YN+FddiK/P5mclJ8AZJJACwwVAaQ92hu5GG4HuXyDn2oJy6GWor1nqcCl4CPpOh7PB8Yf6+EWGvVEg9dXcSiQOWsLwmSQY1nIxmRdoOSX10tFsM1UCCZngpVKCJIkDueijjt++ladEHQru64298+W/w373z3/HO5p3zXs6iP4D+yXibL05H/Aur6+e9lEWLvmXpdER/5zNMN37ovJeyaNHvW7u647WT1/jLn/7LvHz88nkvZ9EfQP/V6ev8xHCDHxmunvdSFi36liTEa2jzZ5YUjUWPpMyN109e529//W/zqVuf4vZuGQp8v+vvnr7J/2L/BW6k1XkvZdGib1nxyrkMnLzf9PgB4BlahYV3BrKzw3MmoTOktPcAy0yK9cx9q8qMM4Lrqr7Xx2nE7TXhCiLR7iuqMyAm4I3b7BrmYddwMNq5p9ct4qG9zu7f2amsgIezU5OguaO1CbxEBDUafcGiuOscdx1doqIK6nPktM3rmGOixcnABStcFbiQEz0WDkvxOYrYMRK1GdVq9A9Xw20iJWFPMusu06dM13ekmVGphJNWcKwUvDZogkhHTityv4pqZECqYtqRBLTvMHfYbCJGulRst8O7DpeenKJqWVJC0hzpLQnzCa9TOD9TAFwErBmtFCRlbBrjGCSlAiYJSUJKkIdL8XXS8DZRa8Q9W6mIG5o6knh8TxPEwEqhtPmxmaFdQnulu7hP7hVXp5UtZZxwd6ZS2baKp0SfoqeXnDEUaRNeRjBwEawUVDWc0V7YHm6YJienjvXeQYDw3Q5vldJ2FLcAirkjmWO7yp1ygs7n/r3X3uVTJ5/hxm+8QW87jk4P2WwP8Sy0gz2qGqJrbn/tKxxPjTc++0W294+jV9YLKoLT8LOOaZlAasSkrwYuf/w5PvKv/gusrz3JtV/7TTa33qacRGz2XJIbJ4Ub3sB2lVZGUjE69khDDA/IkBAxfHtCeesNptsd21ffpB1tyLNL21PCm2HJKLUy7SaKNVqe487J8XwYpFJIKZOHREqJdnTMvf/+N9jcvs/xZsOVlz5EunIRunlbzB3SDfQpURzUhdwPHBxcYA/h+J27cPUyl5+4xjAV3v3qEbupBVCdRqYdtKEyNWM62COvBmopWJ+Zpi33TzYcHp4ylYIDpRm17mKoQb+h73vu+3UKSAeumGjASdXoVc6xZ7h4uIQ9+qDFDe16NGeyZg6eeJrLH3uRY0bq5zfUUnCHUkdandBLF9GXnuNgf0X6+itMr5+we/CAOk6sfu2fcF8Kl248TXr+Q6hAefUt7rx5N/anNoJAd+MSqz6xu3UfKS0SFNTnQZgWwyYmAbdb9GSnLqPDgGxOSKJ0vbLiKoMfMaYhgHzq8BQDOEl31NbQPoNrxE1rHwM1SfGT+7EvHFzAqDSPvYCUkbpF+gtI19F2D8jdHjaeIOkiaIfXDdIdzF3ljeiAd1otTCfHaNmxPrhActBuielZ9J7Mjf/ki/8Jf+eVv3PeS1n0Ter/ffQlfnz4SXR5w2/RIy7xhpaT9wZQFy16n2t5DX20dcdG/srhF/hbN5bX0EWPvtZv/yLlyifOexmLFv2B5O4Yxi+8/Qv85U//5SVB4xHSTdvxf7r36/yHV3+Uqzosr6OLHnn197/Mdqkjel/psQPAZgXRFB9Wv6G30uY3/N/r3oRw4dXWwNscxzxDVSHeVNEAvA//32tAhBZQ1b3NMPZsBeHMFc2gGq41c/Bw+uGGzxA6CZBzwM+zLs/ZROlCwBRnhimGZICGSo7o13lNczMubuEOrnjEI89gW1zI7lzwyjWBiynRWyO5RGxvM6wJrUyUNoVbthpgiGQ6VYauZ9V19FlJ0pNogbIloUkRc6SF69q9I3VOt1rTrXtSl5GU5uOaSGk+vknCAU3CcsWt0FpHtcpUjJqMTipZ4rjoqkMkxVPSDXPcvGPVkTqFe3h2UFtzatliradJtCunUugvXeLyk1fI6xWHd+8z3tng21NSM3Lqo5NZQQzwEXIfLuXdiGiOVON+Te57JDvj3UPu3D1hc7plPGnUWqnN2JqxMyOj4a6diLZU9zl2fETIc/d0nR2fCSRhdQdNWD9xhYs3rrE5OuLuWzexo0O6Kuw2J1QSskrRXSyV1sA0ReT5uGFz5y1ePz5CrNLqDqsjtMpRPkJzx62X75DXb2DSM91/BytjxI/Pj1s045qxVtAzNzuNVidObt/l9qc+z+X1K9x5+23s9JTOI6q4zj3WZy8CkkA6QUbB///s/WmsZVl6ngc+37fW3uece+PGmJFTZWXNE6s4UzQlirYlWbJalluCYLvdbXUDBlqAe3ADDat/yLamtgGjAf9oQYAH2ID9xwZsGIYswDIg2ZJsUiTFwWQVs+Yh5yEyM6Y7nHP2Xmt9X//41oksUgPZrKyKyoz9FosVcePec9fZe+197rnP977vfqKpUuaC9eOccmI6PeelX/86o8U5UxfSaoO2hgETxjTNlFapOE1j8kA9zqukFH0+KTEcbbh0coK7s33hdV7MI23aUaYLTl98jXsvvs61j6yR1QA5kx+/yY3rV/B5hyTl8oc+wFMffxY/v+DWt17jfJoYamVzvGK4dgl7ax0m59ko045S9/hUON/ODCmTiOvyjdvn2Dwx7baYN9pUqcVogDGhKTqszYlrR4SUNlirmBx6y8PyG33i7UGSAKLvxEGniEhXFYabl1h/6DHS41cZXs5oi327PjkhH13irDTK6QX3v/p16qVLSMqsHrvO/NZtpl3lrZfu8Kunv8rjn/oUn/7QR9iYsd8WfPMUQ7nNMMO+XeBn57RxHVHMNvVlluj/bRUsfdt9Sft9Qkg94lzKFnEYx8xmHLmYK03AJOK8s4Jd7KA63nbRJ7xeYdMe5h1oQteX8CHTdhe4JHy1RitI2kT6fy0gDUlHYEJaXaaWCV2twUeS1R63nRAJWN3myq4VdLzOyg2oeCnfldepRe897due/+hL/xH/3Qv/3cNeyqLvQF8u9/nvd6/yzx8987CXsmjRd6zVm7/KfOMHsdWVh72URYt+W/033/pv+Gsv/LWHvYxF34GW19BF7xeJFXS6h62vPeylLFr0O9Zr29f4S7/yl3jx/MUF/r4H9fn5Lv/imz/Lnzr+IP/ayScZDnVqixa9BzXc/xa7Z/7gw17Gom/TIweA3RXprlhEcG/hlOUQ+avdifttdnVJ4UQFkh/ikyMyWkQ6kO1gWHPA3iQR9cwB6sbXC0BKYeT1AJ2uGo8FQXYP0/oSYNd7R684JHmHAKt0J+ODCGoLMEkDhKwJMKy1AJ+aIkrWC0LCJbqOkzsnDo+JcCLOyiGjKId+4A7rNKGtoLUgDUQaw2pkTJnVkFgNiaQZrHemmqK1x12jUCvJYHV0jKzXqDo5C5IEckIkh8vUOtjCGXXAhplyUcBS9OBWZaqNWvaIrNA0xnE+RLaqRieoZtyNWlu4nlWxLNg0UaxFpLYqKSdojjPE51vBLip2eo7MjdQamhRSuKjVHVFBhg2kRKsF1QFdb0iDMhwnJA+U8z378x3VjDLtKTRmYHZnW1uswSE7+LAieSMNI1kVxhXisRc1b1CxgIItnMxSo9s1rdZsrg2M9/aU7cQ4F4ZWKdOOpok0DuEWp8Tu8xh2aNM5lC1owizAuItALYgZpTXY7xEUn0+RCOLt9dcroOG1xLVhDjS0u9xPb5/xjd94gZU589l95v0F66Mjji4d0Sxxd7tjLtu4wswYxjXp8grqjJdGqY3qYBhs96Rhx6AjgzVWCdabkbQZwaHs9kxTZcZpInjKcW56nPCD+mycNAykzQrdDJTzbTiSW8XmHe1iy/aVN3nt732BdGFc/vSHkM2IXL/E1euX2d2+Tc2KtsLFt17k7N6Wexc7fDuxQ7h8cszuYsZWa4a04fg4U/I96v400pybUaRAFnw/M9ddwNxSwRTThGdwc9yc1irSGiYeQwD9spckEXfeB0/ELQZE5NAVHlHScaotnMJu2HbL/uVb3PnaN3niaM3pvbvM0w4ZRtZXTsjHJ1QSu/MztheFfd2y3iRk2sftyBttt+XCb3Pn/A7bN19mrcesV9f43B/6g+xPTzl946u8/sXf4Pb9c44eu8HaK7dfu82u7KnmkU1gxFBMz9lXhZgqMNIwokOFaUaSkr2xTomBLdWcMhfycET1hucTdJWwNqNWaLtzJK/h+Cq0OVzmbjjxOewrab1BdYXpCLYnpRPqfotRSevLpHFEzGEcqbv7yL6i6yO8gqUBXV1G0gpJIwmFdnjtWPSoq1jhP/3yf8p/+/x/+7CXsug7VMG5a5HUsXQwLXqvS9oU71cWLfo+1+vb1/kfXv4faN4e9lIWfQdaXkMXvV+k5ZzxzpfYP/3TD3spixb9jmRu/Pcv/fd89f5XH/ZSFn0Hetsm/vOzb/Gnjz/KtTQ+7OUsWvS7lpYLhntfp1z75MNeyqKuRw4A68Gx2wszD4BVOmGRw79JAsDNUFpEAluNbt1DbLQcvka6r9f7fwiQG8W9h28crlwknLA5hXONd2BvuOIEUQkgZzWAxGFJ3a0cXxKuPk2KF6dZCzjs8fy0ux6D/mVotT/vgjej2YSnRFLlqME1d04E1gi5ziQ0OLfog+hoPDqF02DIKpPzQBZnNawYRKLDc8yoA8TxU5cOrBtOYnXlhJOnblJdme/cRZohw4AkRUWwNMRx6e8Zx3EkyTHTasZKpZUZ3Z0jvmPfhNaM5o6KYrV1155DbUhtvapZabVhtdHm6YHDOw8DebNBpGHFYZ6pZ1vO9m9EXLIbahUZVqhqj5pOaE6oKsNqxHOiTBOXb96EYeDiYoudnzHfP6XURsOopTLVwmwzezd2bc9EDjfukGN4QByyIszosOmRH633Tw9R9ereT2NEXR/LMZt2DUuJq5cLbTdRyp6Vw9QcKzMujjXQ1SrcsA/c5A1nH65MSdihL1ZAvOLWIl7bo+PaLfqjERD12GeqUadN9M+6RT9yKzNn9+6ytYaUCawxrDPXPvYMj127zKvPv8btO28zX+xoDT74zLNcv3rMK998ntM372C1YjgVZ95OWNtFN/U4wiqOVUFwVeZaKW5YzrAa0ZRxMUQSYkbC49wF7g/X7dsFK3MMK6xXSHjyGWTNqqy4+NZb2L3bnNw4Rs/uw+me60eXmebK/VfvcHsu7MyYcGwonEtil5SjGydsTk64+fSHuLxZ8+IXv8jzX/4KitPM2G7PmVrBLfboAYKat+7qjV+WqIOZ0w6DI3q4jwSEj7j6iIWmtXiuojFI0lpcs7VFd7AISsUq7G7f5cW//xzz828x3bvF+ek5ev1xLt94gg9dvsq9a3e4e+cOd+/d5my7Y3/3AvVGXh+REWzeQ7lg+81v8M2/NdL+6d/HzR/6MKMqJ3aTS1+eOb99i4txYHPjMiuHKyLonXvMZ9F9XisxcEDsLckSgxoCSiJtt4gVEsYIrHMi6xD3zjTSvCGtITmRRbAkNB/g6HIkJ/RBBxEDHZBxQDmJc1B3MCgiTm2VJBXNissIPkeSgoCWAcmX8LXRtCL5CGs7KKcMV55hHFIMjXhD8iP3Errot6hY4a8+91f56y/89Ye9lEXvkv7D06/xvz16hquyvOle9N5XPn+FeXEwLfo+1svnL/Pnf+XP863TpfP3/aDlNXTR+0Vp92ZUHKWl8mfR97/+1it/i//qm//Vw17GondBhvP5+S7/9OaJh72URYt+1xKbSbu3FgD8faRH7rfXwVn9nbRUAHE066HQlyhf7RPzhy5evANYwMIVq26IGa7hOiTlcEW6BaTTDne9P84BoKXUp2IFweNbmQUcFnunW9VjwXoAGjiY4xrRyNWdUivWImb68PnxXwtwKwMiFTyAxQH4dVMhgyuXxbicYE1icCN1h7IQazEqbo6WGvG76w06DAw5MaiSRVDNpDwgGmA6bI+OtBLOXlcaAc7L+RZQchpIOZHWKxhH3OK4m1WsVbzMqMJ4tObSyVWaJk5P7yF3LLo/tzuKOeaOzTOiiUYAz9pqRL0KqBiQadOMeGZYZVJyhvWISgFT1As6DAE0a4noXTFSGsjrI1QFazN5NSJDnG9djdTSAKWUGZv2THcvaLsttcxYHphLZaozE8KEsm2NbRNMhZUYJtDEyUpEPOsq4FgKp7q3hsuImccwAJCGkc0Hn+CZP/YzPPaxHwSE3eltvvk//22+9XP3WLXG4DDRiGbnjJcZmj+I/I6tIqg3rO27qTxBCvfy8aUThs0Jcyvstue4nWNuuLWIVhbtqdQJ9wxWIwI7j+AVs33syQxUmO+fc/7i6+TtDil7jpoj1bE8sL42MFzJjKvMOEb08QO4Wwp1H27hyRuqI00UNUGy0lAsgWQNoN4jj7EaAxWSUO8DEVG0S7MWscrJSd0le/T403zwR36Syz/4aWTe8sYv/CLnL+xYrTKXnnmCS9cv0+7epTz3NW7fvs1WFBsEF0emPbv7p9j5wMkzz3Ltxz7LMA58YDWzlUKpM9s33uT84h7zbgdodGPjvael34k0hQM+pfizGdQGZY6P4aCpj5pUkIT4oV88HPOGxXl2Q3UMgE/CxKNTeL/l9q3X2d97m7k560FZf+rj3PzUp3hsv2X/xou89oWv8Oq3XuL1N86RYeTqjZs8sd5w/84bvHXnLuX0lFsvvQi/mBgH5/KTTyMKmw99lKfvzpx/6fPMc8PcmDXDakXej3itsM7U4rHew01Y+/SBFCTniI0noVQyxmAGuxnSjA8bWoJERpNBjV50kRXNC5iARAICDjTHZIY8Rke6NKxV8BFnjgQHXUUEel4jClYaDBKcPR2heU1eX2a1GdisV4w0vM4YiTQs7oZHXW/u3uRvvPQ34tpb9L7Q7PZttR2LFr13JcDq7S8wP/aDD3spixb9Q7WtW/7Cr/yFBf6+j7S8hi56v2i493W0bLEFAC/6Ppa58T++8j/yV37jr1Bsqad6P6ji/HfblxcAvOg9r/H2c0yP/RA+njzspSziEQTAB4gS7lpAOuy0cLdGjLP2qNxw2QVIBZpFtCi9rxXHJQNC69HNcnBz9segte7+9Xc+LvFf+bY3SC4e8FH0QXdvROQSgCP1GGaB5o3SZqa5MU9Td8zKA6KtEr2a4TD08Cdro5WGN8Obh4ETZ61wMigbNQaDbELSjLh3aOyRKO0tYo6zkJKSxswwjKQUXcrae3wPx4UWLl5dRZSxzwWa0YpTLhrDKrE6OuboylWOrl3F00AtE61MbO/fYzo7o+0n5uJkyVz/oY+Rr16hfvFrtPP7eGusNht0nqj7PU0TTQvNhFngzB2bK6nBaHvWOjCkzDgqwyaj4wZtFeYCrZGGRB4SomM4UPd7VDOXblzj0oeepXnj/kuvUi/OkDIDQt0HTGoVyu4e1mbqXDGrNHFqmZhKZW/GrjSKOfO0w0Sxacc8ZrRpdy0b6BrxgsgY+6bFcVSbUTVEtFe8VryB3T7HPzqDOvM3v8H+1m3yekOe9gxzoc6O5iH2jitWZxKHvt4MKt1xPkTccGuAc/LBJ/ihn/m9fOT609y/c4fnfuPLvPXyi1xcOHWuAfIkkbLEY6T8wLUcXa4O0t3ykb9M2e+5++pb7O5fkFexS1Y5YXPl7nMvca7Kbr9jGDPj1Svko5F5t6W+NFHPY38XcbwZgzdSEnLOqCoJQTSxWa1J44qLeUdt3c1vFhdFa2AW8Nrqg15uRVkfr7nxk5/j6u/9UQDm577B7W99i2k/s7p2QlnDiRR2t+9y9949pvNzLCVsE1HMrc3Mzbjy9GM8/bmPkrPSXn6Fu8+/xNkbt5j2e3YXF9TWME94m3EzrCcNuIabOYZM+nXear+eewR0H0jAFa9zn2zwsGwf9ooPcOhY1hSx8SIkB8ZEun6J1ZOXGIqzO4224bY9Y3rrFdrlK8gANQ+sP/YEq/tvwquF4emnePKf/YN87LFjXvybf5M3794GKpyfcef523xx/hZPXX2b4+PClZ/8HDd/9LOst/f5pV/8X7hojonDXKFJOLLzgOcjvBTEYtjCW3Q+uw6QnZQHdL5A6ozWPansYrijVUQmWB3HORch5wGAUuN+ZV5wDDHHk+LNYgZHJkhKnbZoXiF5xGwm5QzNkLTqMzIthi8aESWuQhqU1WbgeHPEZhjIuYP6QfC2RBU+6vpPvvyfLB1L7zM1nJ+b3lw6DBctWrTou6yff+PneeHshYe9jEXvopbX0EWLFi363umiXPDvf+HfZ9/2D3spixYtWvSbpPN9xCrLXOD3hx49ACwRWxtW3ABgpEMkK0hOwWrRdwAL/fOTPujXFIvO1PjX7jh84GQ7fJ522Nz7gtH+SBYmY5xI420BejQfEp7fAcfSen9w9Ks2c+ZamKeJ2qIzFGv4IUY19WjVpCDRgynWEAuA6OKIRrSsoGxqYZ0GBlGGlEhq8fU6RCerteikNYchoUnQlMki5BSABMnxNa3E8ao9ujil7hQVWGdYJ8b1wNHlEzaXr3B0fML1p57i+OknqbWyu3+f8zt32J9fYPsJ2+4wmalHa9p2T61G2+6w3r+Z3GHIiIbb2CQh6kgtUAyvhrRGUmVcj6yOjhhUUJWA+rMhzUgiDKsjxuNjZFiFy9r2WIJ8coTtz5jOdkynW+rpOTqO5PUG9QCJeRio1miiFJ9ozajmzLUymTCXCfdGclirkqtRXfDi+LTF0kjV7ogdB6pLzA/UAFJaG0njY6pKSpm0n7h77zZHr78M1bn99j1UlVGFrUfcMUlxjShpQyMCuRliNc4lCaci/fuJgZvQTrecvvgK9zYDvoGrH7xK0R3tW69wdrrHvMXgRAXxDWk1oEnDwEx30DeDnKAZvt9Hr3BtlDqRxoGcEsMg5CFjZkzThNIQycg000plPj3FdhNJFcNochjegMEa6oqqkIYVl554gg9+7EMcZ+Gbz32N22/dRrE+yBFwW7tbXJPgYrgKerJhvHqZ+fSM819/Dk53vPz53+CNO7dxh1Qn7m53DHnAdnumiz0yjkiZaefntLnQ+pDI2e3ErS84Mn2J+y+/we03XuF0v6W2RqszrYUD3lFaTw94EDjQI5xFPFywwwpvrT92H0SRHNdw82+LotcHXb9+gMaqHRxH77anHIb83cz+1ts0j1teNsNm587bM68/cU4+u8PtF17k7iu3uH3rTWoVrqcTHjuf2b95izdeeJ3mzniy4eSJp/jcs59kc/Mp7u0Kt8/OWQ+wPh45+sRTpF/esJE1Mma26ZRhnPHzGtetJjwN+OhUq5hpxPHXEveOIaF5JEki6Ug+GsnW+3Yl4sddClZhyDGRnaUiIjRNVMDnLdQZWV2Je3KZ8RzR+mINSQnPa9p8ig6XaPMdVK7SykRCSMMGHdeMWVmv1myGzGoYSVE8DcMalxFvy5Tto6zzcs7pfPqwl7HoXVbD+du7N5ZfXi9atGjRd1k/+8bPLr2/7zMtr6GLFi1a9L3Tf/a1/4ypTQ97GYsWLVq06Ptcjx4AtgIpP4CxeEBekejM9Qcpjo5IdPYGpT3EOXdCmzRim13ecb1+u3vYFaFFDPKh0Falm3Q7jOXQC2wBTr27PAXoUbqkhLWD07Qxt0ZtlVItOokPa/UaEdLeI6ahdxtHp6siD0zI7tHHmXxmFGW0RhoGUnLCT5kietZqNy0LjBnt6dKaOoT06DM+wHGp4HVGRNHspERE8GZAE0kHVifHXLp5g6uPPc5jH/kwm099AskDfveCzdU75FG58/pr2G6PzTOe15g799++S2nOfHYO3iAnpDnaIGmjuaA4aciIGFd9Ff2f7uSsDCmTE4gFwPZaoVaSNcb1Ja4+9UFufvrD1Czcfv4FpjuVtp3Y7u6zRWhTw1qB9RGMK7h0iXw0sLlyidXJEfvdxO2XX6fsziilUmplrk6zSh5HVilc1dagejgeRQUdMzoMaAugaXOJgQFr0RXrTvKG5ZGkimSotWBvKC/90pfYPX+KJqWyw+qMIeG2jrJerBnucT5A4twTx+gA01BBLMCo5Mx8PvGNL77Cm8/fJjns6455e8F8sY9+5+okaSRPrLRxfCnTUkBNTRE/bBjUgu0n2m5PMkNzotbMtNsBQk7O5mjDarNhVO+DGWDTRK2N+eIioKAZaMSDuwLd9YlZHLeVMK4yq/VAqo2VZEbJWN2F4d899mltYaouLY6TJHy/x08Td+7seelrt5n2E+f33qCUucfEO8l6rLk5acwkddo8UfaFVj06ns/3XNzZcvtbd0iSKPsdZX9KrYaRiDbiiIz23FMArA+KmOHSh0LS0N2/MaiSUopBE7fohe7Xr0v0WLt7nGPzOLd5iFj57hompYi8b5W2n9kzYDmiv10UbTvmV5/n+XtvUc52nN25zzzPlCocf/Rz/MA/+wd54mPPcOvv/k/cvncegyM5c/X3/QiXf/KnGHPmuDSeeu113n7uq0w3bvLGV57nykc/xad+6EeQ/Y5v/Pqvc+/uKbd3DZvuoqOSJIKsI7jA0OrIvI2+7VLR9YhWI+8mhiQklGYV1xViFZtO8ZSgFVQESQnmHV4K+ITrJTw7sr2N5ATDZWgznlJEQEscF00bRBMyXkXKjnF9TC0lrk01xuMj1psVq2EdMe1W+qCOIm2mzeffndepRe8JPXfnOX7l7V952MtYtGjRon+M/EGdzaJF3096/ux5vn7/6w97GYsWLVr0j9Y7vxxctOj7Urf3t/HFX/e+UkL4o0dPP+xlLFq06H2mRw4AOwFo5QByhaCaHMy+3WHmLWBqd9Q5PR700LNrDfEgog4BEej9wm5AAjRAsbcABm4kTTxIaxbF1YHoLRX3bsN0ukUY9xbrJeJUzY3aWrj9iK9x0fjhNIhrfF8AC9Alrj1q2gP2NUdFGDUzPIDSAaojSjpBLeAZ1RquX03oMKJiiEWcqxA9rOToRhYMYUCzIsMI3nrfckaHNSkJq82Ko8tXuPHhZ9h89pPI9ZuQV8iNhjw/s//mlnKx7TXKGTCmexfctdu4OlameN55wKX3pVZF646cRtI4MOSBzdDieHI4j+HAVhy3cAZrEnQ85vKHn+GpH/k4mHHnay9w+uIb1O15wHwVvEVH9HByiaPLV7l84zFsvaJ5xaQwz4WLe/fZn55Rpkore1wG8pgY0HAL9xkAm/e4jrETU0aSI2mIpO157hHBDVyii1kgpwEFNI9I329Yw6QyX64Mmw2pjjA3uBPtzRy+H4lmDnNBPdynksKdbUlw7+duUIZLG4YhM+0qZXvK7Wkb+8FLHPMxMeYTpIPXQeD4ice4cvWE/Z1T9rsagxW1hKO11agdVoVWIgp4iNjz0pwJYb+bWA9nHG02bFYj2WKPu4O4x1WUFHQIGF4bKRtqCfXYy76fqfcuKLfuszq5xONPP45q4/y+Ms0TVmZqdZrFtWMm4TzViW0aSLtCKZV9cWqttLLDDq59EVKHr4KQ1MJJ3J3LZobNFdoFpUYke1aQNkdM+eWr3Lh2le35Kbdu7R/Ewnu/FuOaHDBr8XGPTmoRQzX81OGy1we3Ks+532fegaiHRAPcYwAgZTzC4aPXOQ24ZlqdmKogrQPlCudvvMmOO4Bw+coVnnj8Me5W4zN/4Cd5+jMfod19g2995RvsHcajY8q+8OYv/Bo3395y/Qc/w/raFWrZ8a3Pf4Xd/A2a7vjE//6f4/jGE+DGZz/xLPbWXV76uf+ZL37xN9i2OZ5vbYg1tBQcJ+mIpgrDiEhGt3tUGgkn05gBKXtkyKBX0NWAE25gn3c0HfA8ILKC4QjcMdtGjLrMkQ7AQKs7zGs/JhWdztCj65BjMCitRnw6w01pOVGT0FTJ+QhJaySPce8ve4zxXXttWrRo0aJFi95tpe0bjHe+zHzjsw97KYsW/Sbd2t7ilYtXHvYyFi1atOgfqaNX/g7nn/gXHvYyFi1a9AgpIfzk+NjDXsaiRYveZ3rkADAQv7z3b+vp9R7nfIiGfgCHO1Sh9+D2fl9R7dCVcCdGZjSaRswqD+KlHzj24u+quX8cJA2AoG7YoXvYI5o5Hk/6xD6knDBLYdi0PW4lulWJ4Or4PhLPi3D7keLjYXAWzIwDAyZpOD4F1A3x7kRsQyy9TXjrYNgF9XAjikVHqYiGW7R3DHOIVRVF10OApaRYDXeiaEV7h/CwGlldOWJ98ypyvAIMbI+fnXP3my9z+4VXKffugVckK25OKzt8q723tQIWkbYIbhEbJmkgr0fyao23GWmKikbfaqkR0W1E36cmRARdjaTVCpJy78XXOL+/4+z119mfXWBlQgdFdSAdHTFuVow3H+Pqpz/Kk888zv233ubNr7/Exe1TpvMd034X8cKbDSRhIIB5nKQO9gQ0DxG7nMdwk+aA9m2qUCrst5BHUlYShg4jeRzecTlKxDh7q9Q7b3E2z+hmTd6Eq3l/7zzgWsowDNg4YG2OvdEMFUe6I9v6YAOSwI35fEeVXj3cKlKHd1zfWSJeXBPYoY8WysWO3TTH+vf72OsarnFBoRSSG5YHvANdE6A5LQnFnLrdU/cz86BsNkck6fvanKSJnKFpjljx2L6knMLZqSBZSFePSU9ejb7hi0axRrXK/vwiXPM1nPM0x0xoAnWucP882rLrRBPFrYUbV3I8b6s0SREdjGC1O2nM8JziyTTi66Y91ApjigGJ9ZrNM49x6fpV2sszw51Mnedw57bDtRMR3UC/9on7QCL+rU8OqCSgx7PnFI5f8/g7AA2TRMwo5z6EoqA5hiRywnsHuTsRDV4a6hZR05oZn3maJ3/mn+Djg3Dr9n3GTeHO3/9V3nruq7z64muYG2U/oSj3XnudX9srT+ydj330aeZvfJU7L3yLswq6gbtf/xYfvH4zIsuvHJGuHPGB6Qd46dVvsbszhwPfDdVwRdscEc7SKlpn1IUkTpKRpI3sSvKGJ4NawzVPRWWNaMbSMcMwYK1Seiw+GGm1iiEHd8RLxPrnAS9b0uoKStx7E1N8jYOmy7A6onljd3ZOI2O+Q7ywPr4e7nx33OqDc7fo0ZO7s2u7h72MRd8FZYR/8fhDD3sZixa9KxK3+Bl40aJFixYtWvQ7lhC/F1u0aNGiRYsWLXqv65EDwIJECprm7gauAXAOsRndBRymugMAtnDh9U9wKjDE12rAV3entdIBsTz4XoYh4qj072Dd+efGgciKK6oPGHDnx9b/N3cwbNRWaT0yNji1BlSkQ00VVARXwb3HN0uKLtjWcG8BhlwQEtJKuDMP0dYKbhEnKx3QiPRoa7MH8FiTICmjOQW/khTOYpXu/IU2F6w0NAvoQNvN+FSwo0u0iz37W3cYLGGaqadbTl9+gze+/jUu3niVOk894nkKGqkDXmf8EHWL4Uk6hHFaa7TWSG2E3S6coykH15wNaw1vjnT46y16nHVYIw4Xt25z/upb1FKp0w6vc5Qp64geH7N5/DFW1y5h4tz/5ktsv/YS5xc7thfn1LlSy5467bCyQyUxrEcuXbrC0aWr7Fvj/p23sKn1WG2PWGwxEhXNK7zMCJUkEmArJ9KQScMY0LMD9T5xgPQ4P9tvmRDUnGkn1P055eKchlBFsBSdz026K5cAvtZjkdHct3fFRGPuoB02YTh43SPW3C1FvLMdYsXjmpnmGm5lDFYrRLWvL66nJI5PAe2K+QOXvati0qgeYLbNzrxt7Ld7xtXIsF4zjCNplcm1dzZrRoeMDiMyDAGAm9F2e/av3eL27Kxy4v7dO9y9d4/9fmY/V1qrtJQxyXhqMAw0a9Qyxz416071ANc6aPQRm7wzJNKPoblHr7F3yCgC2cPk3yrWGuwDTkspnL30KuWNO+x3W2otIIamFAewtA4q+41nGGOgwbxHtYObxvlJOYY18A7iFUn0vu0BLBzOlJnq0EzQnOP6VY3I6ENagQBZw81qvSO8zXDvLvWNN5k+/jGuXHLKl17lGy++xauvv8Rufx7ObHESQju/x0Uz3soz0+3XmO6+xb5c0PYTNimv/P0v8djmJk9/5APolQ0AevmInIU2TSAZHRJijnlGbU8rE9S4pmkGzRGfkdaQ+QJJGW8Rn46uEHekn6e8WeNth/e4exfBW8JTB+wicV+jgiZkPIp7XB6Q1SV8Po+YaG+I7YiXxoQNK0ptTBVSVXTa99SEhkF0hi96JNW88R988T942MtY9F2QInxmuPKwl7Fo0aJFixa956TAj69uPOxlLFq0aNH7Xre2t3jt4rWHvYxF77J+ZLzGaqluWbRo0busRw4AP+jAMo+J+Ae9HgLkYGyHaGgOf07Ru+nhzBXSA4ArTu/bjFhXcY9YZ+kuveaQD868iGTV3hoc3cJEV7B7PP4D53FERysRhWxt7iCz9TX0NVtFckQFi/Jb1higE3dUUzC5DoSiAjZgtKeMJ32nk7PzLnnQFSuIjJ2HH1zA1uOINVyPkgJKOVgx2n5Gx4G8XofLb6q4wTzv2Z6e89rXXsCee5EyN+bdnv35GeX8NjZdQJ0iMpuMJzCb0ZYhr/BSMAunpJtj1jCLOFyfC66CakR4eylQPSAZhyjuHt+dh4B9QKuNtt1H9C/d3U1huHTCybOPkzYb9vfP2N2/oDaJHt+yo7WZMk+UajDPJHOGlTBevsTVH/gYz3z6k9x96212P3/B/PaES3Q1QyP5GjVF54bVSmpOUocxoG/KOXpQk4ZJujbswXOP86Z5w9HRCfnSZS6mXcBoNwpOTQG/Usp9rxlu0Ucrog8ihREi0ta6k9vlnY9LIqLMHbyFC716j0ePGPGmUCTDZkPqe0IOoNIb7BzaENi6NZooq0ExhDJNeHWkFJoFpK/Tnmlas1HQ9Sqiw6X2ZPOE5NwHEGIvG4bPlfM79zCDvFlzcXHBdpoptVBa9P5abbiC5yGOg0DDA+j6O9eVeY9AV0VSQECMHs3cYiDB2jsJAD0xQCRFHLpFJLvXiHTeVdjrFA7jWghXbgyN0F2+3o896hFvfBjAsAbiuEbXttHBr0V8fEScC2oW4FgSnhKptbgevMU5NMNzj5kmIcMQ8dICMs99oESZz3a8/mtfwe7c4+qnPky+MbJ9/h7zfIq3OZzEOvTOcqfsz7n/mrN7401ohdqHXfBGu7jD87/+Bfb7mac/+xFWVrj35efZ7huecuwzcUjhGKdfr96PkVPxdo5PE9gEKdNqwbyRhiMSCVVHtN9ZW3Qoy4N294rpEF3UEr3Xnga8VHRIuGRaPSfnDAiuKyi7cH6nVdyP2wxiNF0x7S9IOZEraM5x35Uc95dFj6SSJP7lj/3L/JXn/srDXsqid1n/yqWPcKKP3o/HixYtWrRo0XeqjPLPbT7wsJexaNGiRe97PX/2PF+9/9WHvYxF77L+yOYpjpb3oosWLXqX9ejdVcR77+nB8asdktIjl8PJ6k53/vVYVklgBbB3nJOe3wHF3bnb7cX0nFwkHXBvh7seHae0HhNt1p2ZHQan3uFrtbOlhuFUt+DFooiVDvEcT/1zU/RXxrcOCCUqD9yLTgmoJYe4acc0YbVgOdNQpPaE1CThsCRih1UVdT+w44iQTqtwTrYAXdAwz5iH21jHNcM6Mxxtwr25HvF5Zj7bcffrL+LDGk8JqxUvMzbvoAWQd+twrXfHuhlNav/mEeFKC9dqE6i2i2hiujtznrEO5lJOaIrOVHSFlxqsLUUksqN4m8IpaDkc0CR0s0YvH1NL4/z2m2zvnVNrOKirJEqZaK1Q54K36CjVlHAr2FTYvX6HV+/8KmenF7R791BvWC24JzQr6kZyQebpUEEdx1x7RLhq9EVrwnDcGmLh3JUhIXlk9cRNPvp7fpDjkw1f+dUvcvbGq0z7PdMwUnq0tFqJLZoG8BKPUx/43aO/WUB1QPMaXa8CGLZ9DBQQznBaPE+XjFqBWsiaGDfHDJduIkOmzffCkalxQYgPjJIZhhlrTqmVdnLM5uY1ducXtOdfRqzGAAUNcw+zeW2kXSXlfeyfIaOEO1pyRlIOpmeOTzNtnqgZyvk5XJwzl0KpjVoLrdZ4XNeY74gLGwNM4zqNuQjpPdGC1xb7ISnDuEbVmbfntFIC9Ioi3nDp5+VwhWt3DNNTAcyw3m9rVrr7PlzUzgGUS49mThHz7Rbu8xzg3mtFPMUgRtI4Bpri+miVoMhGSglESUSns9eCEzHzknIMa0jvg97vSOMK7V3QYdiuULec3YXZGuf3ztD9lvu338amXTjo1RCJiHtH8JxRdWrZYs2I8mPHS6Xdu8upfZM2T5RbrzHOhTdf/ha7iz3S4hpEE5ag5UQbxnAPd5BNclhdAy/o7hQtjo7HfbDASH3IgxRDCq3MKArqKEKdZtx25PUJ7jli8Mlxbqc9DAnzkTbfR+wabnvy+gibJ9p8jqyOIlrfDGqj2swkmSQgTVitugN5mcx8T8k9uu7fjccQET5wvPyC8/2oD6QNebm2F73P9W7eDxc9Glr2zKLfiZazu+hR0HI/XPS70bJvFi1atCi03A+/93rkALC4gB4ilLvrsXdlHvpOEWit8Zt7gntcsh/ibXt/56FYt7WI6bV4fJGMSMalRq+kJqQ7gEX8AWB2yYDjrp1Fa8AaHXrEsfWo2olW5lhvCgDqrt2RG25W6TG+bj0GVQXaHHHBJFqbEVJUmEqY12ZJ1FqpKKpCUu0x2drdwilceh1MOYaksTtE6T2k4YD05kBFx4E0DOT1QBrDVW0GLopZpXaHo3i4UjGPiOJWcRomPX7YWjyf5gEiU/QvH6BdqzO1tAc9yF56z1ltAcpVYv3UAGlJHji66Y/iZY7AbzeMhmn0pVoa2J8X9md3mLdb6txgFCwNlHlPnStW9iiJZJWkEcXse6PUC+7Mb3DPM152YHOEL0tCxlX0oubonA53bkO0IDIgSdE0xHlNOSJqa4291Q5xxI6MGcE4f+0WF6JcvHWH5o2aRmYRGop6jxqWDIO8A85rQenudXN0M3LzA0/zqQ9+lJvry5zOO154+00u9pUhZ7bbe5yfnzGS2aSR/f6MOl9w6cknePZTn+HDH/ww27MzPv+Lf4/p7BxaC0BqkFJisznhSKFNJfqC37yNz42xGe5QgdadtToO0XmdlKlVzBo5967kJEjqztFqWO9ibeOAaeyH1oxWG8UMa95N9hKTDSZ9AMMxJWKCvcbchihJ4npyCafx+uZVLh0fUc7O2V/EGoXWO7cPvdexk8gjD6zTPU5dvCcDtLgHSL9nBAe2uPblAJMU1Q4cWg0nKoLkHgFvhkp0Mus4sFoNUCrTxbY7hRNJgJTiesyrfpX0mHhVkmaaG615uOM94+LkHC5zUoY2s79/j7fOd6jNEVuNknN3HFfDs0aU92okHa3QWWhnW7z1Hl9VyjwhZYvefZNX7p1GFPnujDZtw62LdnYdDueW4r8+ruIwzoasFK1zQH8cLXtav9+W7b2YO/GGjGt0WGPzFp9n0phxHSFtoO5Isoq+a5G4H9ctrteQvMbMaQlcE767h66uUoszmEfKQdrE8c0jbd6xv2iwPiKlNatxwPcX37XXqkXvvt6NHw6XHzDf3/r0cJk/drSA/UXvfy33w0X//+rd3jMnwwlXxivcn+9/x4+76PtH/+eTj3NZh4e9jEWLvqtaXkMX/W607JtFv52W96KLHhUt98PvvR45APztc6mxWRrR+PaOE5AehexByOJzu/vUe2xzJC1ruGGthtvOLdySqt09NuEe2cxy6NPFI55Zeoetdzdcdx+qvOMe7tQIR2hGh0Vg3h2+Eg400RSxtBrA2B28x6qaE12meDjkBJyAprPBzozZhVErnlex/jR0R7IHLHIP96EHwPZaMXHELICQOa6HHuJIuBUabinAXiu0uQYIkw6TvUaMbpaIkM7hnI4oXsV1iM5Rr/1/gbnE6VPFWqOWGpHPSaEUpBmq4ViOuFqJY1Dj+2oaaTiUibaruEZXq3dYWAHL/ZjPE0xzuKTnfTg0SdGVWvcMEm5MFRBXYIA6dbdlhVLxVvB6gZLQPDIeHXPt2mV0s+b0/C5tdxEQcRjAEyAPepx1HBk2lyCP1LLHbcab0poEhC+F6XzHK8+/Tp0b27O7TN7YirNPOYYJUrg0I8o5zperwrCK45M0wGMW8pU1+YM3ySdXuJQyn7QPMm4u4bbjxS8+x6uvvMnNT3yUZx+/wWtf/hovfuHLeC2cfOoDHH30Kfa/cY7X+sBxLmlEvOLzFP9dDyRNZFHavkLvUi0acN9EkPWKNKxIKw3gllLvgm2RmOw1QGVkBoeLF4+U71KwUmjmNISWEtZ5qorGXm2t3wEMqRbnPzy4qLaIZleNwYkxYt+n3Z55u6O11u8PvZv2gdM/ALtbz01X50EeeykcTL8xBUFcpx69yTGcEC50ce+OXu33pnd6vXHFPdzhiJKPVozHI36xo2yN1qdWBEjSHeRZMevxx6qsr1/l+NIx2zv3ODs7ozWntIZnhVZ7v3AAcdUavdplwkoF0egT7r3O1hySQZ2pFwVa73auJQYW0oC1Qtmek1cr0nFiwGgXM6U2vDRIguI0EbzOGC1iy73v75yRKWK3TQSrU8zimAS01TWMQzh6bYb7ryHrazBcwpL2czpSm0C9CA4+XIJ8jNUU7mrboq3gu3ukzY24F6GkzQm2u00rFRkLCUdsQxtWeBWyJKobY5uRvPyC61GWiqJoRI0ves8rI/zLxx/mZPnF9aJFixZ91/XZ65/lxx77Mf7Oa3/nYS9l0bukj+RL/OHNU6TlF3KLFi1a9F3X8l70/aXlveiiRYu+m3r0ALBaOCK99ThYRbyEizSlDmU69DxEQz9w0nX3rtUHsEfMAt6J9fhnDyCSGqnHS0vvWw0AHP3B3dPav8UB/gq93DagT4dO9u39wN6htNHXI4gFNOUQ0yyOtRrOVjKSGmLt8CUBV1tlb41tc3bSWJsyApbCTSkHODU3vFRsVpI46o5K6m/sHGuOYOFazYok7VG3CTPB9lOAocA60ArSwgVJqsgwhjPVDMcerM26K9olg5bob+2g1m2iIhgBfJOMJHGGcSRjaM64G80cqx36SQdsHUKbC600pFbcGy2tsKyYNGgJn3cwjEgtaHWST2iLSF3NaySnd2K6bYiu0tUq9lA/7zYkvB2jCEeXr3LlUx/jiU8/Szm/z/yrz7Hd75ExoSbhnrYaO2FzxKUbN7n2zNMUFe6++jLz9n7E5ZpFn60JtDNMZ2YrbM/vc+rCPZw5ZdZ1Yl2EgTUpOSnnHvus4UZ3oJTYQ6s1F/cKr37jdXbXtxw/8STPfvZTsIGX/t4vcOurL7KbC/uvfpPXn3+Z22/foU2F7d0Lvvk3foGzky9z984d9hdbxBuyWpGHAR2EYRCkVOrU8HIRPbiqZBEGVZIa0pw05HCMrzYBhBFaa5gZZkZOmWHIuM54GvGsmFu416tRS8VKxEj7kMJFTu0RyyBmCOE2F6MPGkQ3tCZFcST1Dm8XrFR298/Zt4DLXipqDdE4fugB7PZLlsPwhwRwb7WnyveYdO/RyS7d3RtdynGDiHtHzJrUcMUS9w3tMfJx34jrv90/Z3c3HNDtwUBKJBnooUeacHiTV+RVZpRwZa83a2opnO/31LlgVTF1xtyjvVOK9aK9o1zCrVxKOMb7fdNzorVKWx2hGsfXS0Hd0E0Oc28pzPst+eSE8fgGctFwfysOWJuxnWLiVDNqnfHq2LyPs28gFtdrm2dMUsTm+z6c3Aje6PcOYH0F1Q2SUz9uPXJ7PMZyivvh9i6MV2Acsek+ur6E9eQEtQvMBOwCfAXDZZw9tVVMKjIZOm3J156gzHvmJGQdGXN6916bFr3n9GOP/Rg//eRP87Nv/OzDXsqi71AbSfybVz7HP3/0zMNeyqJFixYtWvSe1P/n2o/yseHkYS9j0aJFix4JLe9F3z9a3osuWrTou61HDgCHU6/QiSKguKSAQBpOYHPrMa8SzmDpHb32jjuYDnWTCCYSsab6jhPvnf9vAXpEAwZJhyj0nltvpDQQf7GICZYU31sDlJp16Ox2MAXjEg7FyJfOOIZ5uBjBex+th2Oxxx5rfy549LlWa+zFmTVATCRaG+YOpQbsrY43w9tMs4p6IuWxQ2KQ5uh6Ff21KcCrGJBb9MYeKJl7dKY2gxIwTNwRc6Q7/8wtPudQeO+76F1OEZUbaytUdyqOJyF7Q6yQvUZUcEqIZtp+31l6dCR7Ezw3PKUAy2XCmuFuWB4wCUemWcP2F9AquTTSuCZvBpIr6gBKHkbSZhPRtfNM255jq1X8+5Bxg/UwsrlyjZqV8fiIxz/2QWzI3PnCVzh//W22d96CNiPjKmKoPXVvujCeHPPk5z7O4x98grdee5O3ni+UqdFKozWjWo8/NqP4zLYa95tw3ypnoth+YraRqUyMY2Vcrxi8ksIDTvaGukbcuCredpT9BdtyykYzsr/N3ee+yPb+fb70xa9w++4pVgqvne3JnmnlHK8FmvHWrTvcvX2OtFOERspDz5ZuqDUSgmqOiGZNNLWA2F4BJ5NYqyM5oeOIA8UdrzOtu3wpM+QMdaSNKyQ5hndzsFNqoR32jllkSrsj4xD7zsLti9H7s4EeyY4Rn9+ds5qVcRhoGPtSaBAR6GS8xHEX7z21IvRabrw61BmxhKQGaUSyIF6i4bsN4BWRvpburHfp3cZ9HMQLmLToJO7DHgGN+7BHipj3Ns+g0UEu49jjxDNYRYn7QriHK75tTNVgbgzrNevNwL7MVJGIZHdFrZDNOwhXpFUwQ5PSej+zoHE91Rmao2mIe4PHPU7HDQwZSRKQfC60/cyKY37w6U8yXbvJr92/xb272+hhr60766N/HGs97lriPm0RE08SfD4nra6iJuzOTyENZJyVGUNxhtUJ2ekR9oqTcCaaj1QGJgPzEbOJYlBJMYiiI+5C2VZkNUSvsRuiGfIKaZk0DHG+64zPMwVnopL1hJyOvxsvU4veI8qauTpefdjLWPQdaiXK/+vKD/Anjp5ZYoQWPTpyR+qWPun6j/40HSCtvjdrWvTI6U995E/xc2/8HMXKw17Kou9AH87H/NkrP8CH8vJz8aJFixZ9r5Q1k/WR+5X++07Le9FF71vp2NNt/xGyipZzNq/8HcQa5crHmK994jd9yvJe9N3To/dqIQdnW3e56cGhe4hczgFq+qdCdNi6VSAcgB55p/FvPSpZDuXBHlEcvSW4SwMcHfCsRlx0fI8e7erSXXzS+18VbZmZcPk1i8+hO5PjS7RDk9q7P5VmESmrHiDINcfX9R5QvDsKzbBqFIMZp7hjFr3CTu/Z9QBtcRwEJOHF8N7NmtYb0vEGzYIOI6rRw+tTC1g8BJQTTdG/2VqPqiZcjlOFISGZcM42w6tBmcO9KALDGH3AzbEG1YWatKfpzqhk3CIqVmtBJAOVtFqRUGqZqSVgm3XHpQtYSh0eTrSU8bbHi0AtaKuoF3S1YRiFYT2iKaEpMaxWXLp6nfV6zX7ec/rGW3hZI6WQ1mvSeg0JTp5+jGvPPkk1j4hqa9x75TZvvXyLdnaGzxVVoIbjlBbdyXp8ic0TN1hfvcT2fMfbL77G9u4pdSqU/T7gtwptNVBpXJTGqQmnosw69KEEpZmz250z1Q2pCYnGIM4wjKxIDArDak3KSrNMuXePspu498JryLDiK/mYOu3Ybe/Q6oTXmeKlu5TDaS7VqHabpImkwpCUJBZgP8GQlKvrNRsdOT2/H93JwGTObqrUuYJmRkpE/ibFNGNzeeB+9w5ZW4qBiYSjNJoJrczUWmmiNHFEvfcaVySticLYiEuPx3M0D3HNaMS/y5AD7hFRybJeI2OC0kjmjKsBEaHs9phVpF8nQjg/RaSnUsdAiZcCskYHRTWek5eGz/uIS09DrMc9OoJ7tHz8X8NUMFGkFpCMWnqnG1j6NUiNGG8riAiaM0kS47DGamGet/FcmtFKpdWKV8NEI/nAgNYibNqiD93c++BLuImdhqjH+iQ9GF5BBUkj6tFobm3GmqPjGh0z4oLNM6yF9dEKrl/jI3/ij3D9g08xvfot1r/2d9A00ErDreE1oG8yI1nt/cARRc10gU1b5v2O2Zx6dgf3iTRc5ZLD9fURJ5s146ikQUmaIkmgFVwytc2UaszNaHVgXiv14i5tuEQZE2W3o2iirtZYGmjesGmPry7hrYALaTXS3AOq502H9olp76SVIfv9u/rytOi9pz/zmT/D33zlbzLZ9LCXsuh3od+/uskfP3qG/83m6eUN96JHRuPtLyH1gvUbv4i0f/y9qx5/gHL1E//Ax8vVT2CrK9+tJS56RPTRk4/28dRF71V9LF/i/3v9J/hQPl5eRxc9MspnL5O2t35Hn+t5zXz9s//4X4IvWvS71I8/9uP8L6//LzRvD3spi34XGlD+/JUf5J9f4O+i96H2T/wENv7D3y/ms5cZ7n6Z1VtfgMiLJZ8+z+aV/+k3fd5vfS9aLn8Y2zz2XVz1+1ePHgCO8l7cIhY4QGPD0HAIMgd4EaDHtDoaQMU9Yl89YqIDJkp8jvBtEPkQ69wQiZ7RXsoaTmKJGFrpMcoeH+q9ov0hWsWIxzZrBB6KnxsTAcZUvw06Q3Rl9ijlWFsOt600ko74A1dzRsQgjcx1ZlahkmiSMQn/HDmBKWR/4NSlVEQNFSWNazTniIwVxUuh1RauY6IX13ZTOAyUcFFqxtGeYl0CJrUCkyOpO5W7Ozj4muClYbVhLpiDtRIRzSWcnI3CDKRhJLcDBHQkVXS1RtOItEIrFreU4ljdMjvsy47ZErbfgQzkNrPqDkKVNZpTgGmXGBrICc0wXL/E5sY1/O1ThjvnyPEKScK4OWK8smaaLtjfOeXNu2eYZKwabdfY73e0tseZsb6vHIeccSIaWcXY3zvl1c9/lbk492/fppQdTY05J+bWqDlTvLEtcDoXLgSKCORMat57iYG8Ctd0KRiJapV5e59ZE0MeGMwZzZHUqGXHqZ6TJOKhNa9I2sCnGE5ohtgOsUbShJqTBNxXmESXsskAc0UF1pdGrj/7DE9/+hOsk1P+/q9y/vxdZnf27kyi1ATJQWTEayOlBDSyCq4JKzPVjJYG1BqaRlw1oGZrNGu4WN9jEeuOhqs3HMqxldDUe7k1unVTgMJDrLt3V65pwueZug2neLp0xLhZYbVQJV6QrMfBS60B9pUAyBqAVFqHpWXGhxYuXQ4gund5SAer1npPdHzYktLcQDOSViABZTFib+DdCbzChx413xxPA+unn+Dm4zeob7/FW6+9TalzPOgcYNtbpe127KZ9j79OvfK2R0ub0axfZxrDJWoNfI5hC3P6BRjJAOMxtEgpECzc+Ks1qRlzbZgrnjLjeMR45xZt3nP75/4+d26fYs3jPlX6m5QagwW0gtVCkxT3iHwJ08osSpFwiR/pCY+tVjx++RLXj0bW4xD3uUR3XAu1TNT9BbUm2jhSDaoVqiXq0ar3RCt1tWL2maoj0/1b1PEy27nS6l0agpHxnVLHAaTBcEwWsPu3SB6vCayWfpZHXUmWGPD3oh7TFT+5usG/efVzXNHxYS9n0aLvqrScobu30DqxeeVvk3ZvI4cfPn4bDecvM5y//A98vL31v+J9Gnu+9hnKlY88+Dcbr0JaXh8XLXo/6yP5mP/D8Uf4J9eP83Q+etjLWbTouy9v6P4Om9d+jnz+Klq3v7Mvk8TqzV998Pd6/DTTzR9559+HY3y5hhb9LvUHnv4D/NXn/uoCgN+D+iObp/gzJx/nk/lyr4NctOj9JvlNw0863We491XGO19G57N/4HVU8G/rWgz91veibbyM5w0A5fJHma9/uj/4uAwn/zZ69AAwQcck9a5f0YgZBYK0gGpA2ajcTOEYPMQndzCM/haXr/fmUkmAotghEZpuncO6q1borj803L/miCokhdY6tMpQC2YG3hBxkjTcFVKOWWlNAZGthStTcl+7o/QIaVJ3OMYaRAzp0araoWHFaChGxckEeK0BqPydamNxJw2JtF7H8UvdLeyOtVgDOeKa7QDfII5JNUitd92+88OJIwFXzOM5aPQhU2uA+FVChoTXhs0zyZzkjSwesMpmmsDUDB0u0WpDpwtkXJH20SVszbFaMARrhVkHLubGrnWQ7o1BG1mVQQVNK0QCoIfBeo9pgqky7zP3dy8wHd1mvHKJpz71ccb1mjZAPho5v/M2+2+esjubsDKBJlJe414xLTFEUDWiZ8XxlJAUe7I5+Nxod844u7fFzJnmLfNcmV2Z8sAkSmkzsyV2kqnrTbgn8YgP14bmfDj0eKux/zTihl0HqhXMErW1iBtGmFvFpingd4M0DAxDXButEdEMrmSvDLkxbI7QcaBWY1gHAO/TEaTNivX1I4ZUufjKV7h7dsGd124xzZUZowrUrNTiNOvXkTWGVhl06JHdlVZmXKJbWlNAXDPDewSytQYpeqfFw9XrxL6kVmgthgE0wZAD4B+uMXfQFMMSeYxfVIpGLDiO1Iqdb5n2+3B99nuEaIqocqthGkbB54iE9z740VpEi8cpCcjrgtJifd3C79116/Mce4EV1jukUz58jgX/dYeeFnBpdYyOay5255TdBV5mbN4xXZwylYplwS3R5ooJcc0J0adbE+YNkR7XbREXv95sGNOa7f6CedriJuSeKkB/M2F1xktFxnU/1TU6f4c1Oh5x5cmnOKbx9guvMU+FKs6NkyukN+/y0i99ja995UuUaY6BEVXI4binVWwuWNmBrkFzJBgMgg2X8GnPhsa11RGPXVrz+Hrk2lFmMypajUbB0wq3QjOLgQlRyANepx49PgbUTRVrE01HTJTS9lhasV8/RWmV6coV5rM7kRBAxlvc42bbM++3lHlLGda0zQophfP57F14TVr0XtcyrfvekQL/yvFH+GNHH+Czw5Xl3C16JLR+/edZv/7zwIMfzb9jpeneO3/e3mLz6t998Pdy7TPY+E4PaNs8xnz9B/itvwRYtGiVVvz0kz/N337tbz/spSz6HUiAx3XNn770Ef7F4w9xJGl5HV30/pcbafcW49tfYPXWr9N/g/U7lngjf5tbOG1vsXrr1x78vZ48Szt6Mv58/CTl6if/IQ+yJCUs+odreR19b0mBm9/2Onq8RHgvehTkTtq9yfE3/xo63/+O3o+m+RTm0/jz9hbrN34BABuvUK59Kv6cN0xP/J5v+6rlPSg8ggDYrcPeg/tXrW8EedDraW6HpOUHzlpMvu3z6LArwJ2KY5J6323tEDjFj4YWQPcAkazHiaomRDzclYcI6XZwJdIjoaH1dSVmWncsKoKkgENhCux/FkNovcs4gCopRSSuHPqMBTNBU0Qluzml1ZgYkxXuBSdjpaItIq+NiKGVdaZj7gizrgG61Fo4hpNGjLMZVms4LlXAS4Bh7TT58Dzp0HCeI6o6JSQ5aRhJR2tUheZQdjtaawwpo9miF7j3kZoq1hruwm6eUBGSQ64FJY5x9DeHg7q4sJ9nph4xPWCs8sBmyIwiEWGsGnCwA0E2YxyvudG2e/bF8JQY1iccPX2Jo5MTTm/d5faXn+fuS28wnd6liYAOjEeJSzdOmGul3pnxGk5GaTUAoFVEM64DNk/hoK41opKrsS2VWaLzuIlSEVoKmJU0sdboccXoXdWKt4LmAXfB0xhb1fqGFgEZEDekTLTzGHtofYABD/etiPUpwkzrgFO9Qk6kcYRhhJRJo5JSRlVIFnvPrTHd33LvonHPoO22TNs9rUzU5kymzNaCw7YJl4xqQkVRs4gnTwMcZVQ1Bg7GAUdoPdb40I0t3mI/pdjr8UpikJQkgvQY6HCuph4LHeAf1XCxZ0WO1uSU8OrYdoeXOQYwquB6eKHQcPUDpkM07dYW6xbvQwxxb/FmvX+4RZcuCjnF3rLeS2wWUDVF97SoB7xPvZ/ZDXft6QCQxhXHz3yQj//oZ7k+Zm597XlefuFVTu/c4fTVO+xunaK0iHJPOQCyyMHoHL28gFRBzGJPDInhylUe/8wn+cSTj3P2wgv8xue/zPnFOdaiV1uGFXjDqsIwdkgNeVjF464yq+OBzcnA5fUNUoE3Xn8DGwfGGyec7k556eWvcW9/L8Cqg2nCqvTe7T2tTpgpqKNesbxCW2MzFD58/Rp5TFw9XnMlwcZhNa4YxxHxhhkBdR2m0xlpDVZHCHFvt7LDPOF5CJe3DzAoLgNtSDScOl5lnvfMkijrp6m7t3EGfMiYGyZXKWR22y0XBpMKdT6jLO7PR17HwzH/6qf+Vf7DL/2HD3spi/4RUuDHxxtcSyP/xuXPcDOtGZZfpC16hPTdfrv7Wx9/vPvl3/R3l8zm1Z+lXP4Q2w/+oaXHadEDjWnkx2/++PKL6+9z3dAVnxxO+H29NuGx5Rpe9Igobd/gym/8x2AFbe9O9c9vfc0czl5iOHsJiKo4T3/nN/17PX6S6eaPYONlbH3jXVnDovePxjTyRz/4R/mlt36J83L+sJez6LfoRDKfG6/Gn3Xg37j8GUZJy+vookdCOt1D5jM2r/5dhvvPv2uvowd9++tpmu+Tbv0SEGNa6zf/1/izKLtn/gCehkf+dfSRA8DNw/F4cOFGCHO43CLCOR3CnaNnt8MXzRmz7gQ80GHjQQRyADbtUI+Al9YCnEUBb8An70DJD0A53L9uFk5kTQGIEKo5VqMrEzTWo+GoFenfzzy+jxNwqnfpPrDtenytm8WzDVvzA1DcpFA1MyehCgyuAW9TuEUlJVzDsWcdcnmt78RWq+OqqDeYWzyn1mNnLeJrtYNzORw01YiUtgqk7mxUNGv04OaEJKU1o273tP1Esv59DusxBzFMFW8JM8MaFDGKJAYRxpxJtYZzNEMtM60WaI0VCXVjHFash8y4WQV0qwYaDnHrju+22wf4zCtkzGCNcnbG/W8Z5fbEkITtdsfF6Tnz+Tk2x+fr8cjmQ09z5aknOH3lNc7uOjqmOKfquIHPFUrDklLdMGvUCvsq7Fph0gBXeA0j+TAiAtmj/9XTECC8lnCwuoXLtDUkj3gCqzW2wiFKQXqvbC3AjOZV760WnEbKgljFLKFeyDlAaU4jOSXGPDCOA3m1IqXM4I0sgrZwiePQSqP2TsyGwThCmfH9BV4CkIa7VSJmPGfcWsT/ItScaTlFRe5qwFOiVrA6xToluljjunVoNYBrH54QlQCq2IOrXKwEbJf4enKCcYj9MVXaakQ0IWNDzfDWsDIHkBaQcRWx5S7QImg93PeC9I7oSBUAaou9XXtMNYfhkYxoRby78WWgWe09xU4aoqtbex+4u0HKyPqI9ZWrHN84YTzKcHLMjc99kuPLJ7z4pa/z8suvcL7fsxoz43rNOK7QVWM/zMz7bfSCHyLnlT540nvJy0RKTrm8Qp99gmv37zI9/zLTRcRpq/ee4KS4zYgOaAbNzjAeIWb4vrKWDR/48McZn3iKwSrPv/46t3/xVzhtyt3tfZrNtNawlLBmNBpWtnibwCI9QVwQm5GcWJ0ccf2px/uwi7JSY9zu0LMzkmTGHN2/kla0WSnF8KOEyOWIvQckXYLWsLbHazjKvWWwGVmN4Q62GR/WlGGklj1VlLZ+nFYrrUafcLNCU2W+coltFXZlx8W+sE+b7+4L1qLve6koV8eraL//Lnr4yggrSfxT68f5mfXjJIR/ZvPUAn0XLXpIEq9IOWN1+zm0XDA99kOUyx9dYqIXAfCjN36UDx5/kJcv/sGo8UUPRwIcSebZfMT/6dJHeTod8WOr6w97WYsWfc8l3pDyvUt8EiuIld/0sfHeGeO9r9NW1ylXP87uqd8LOi5upkUP9FNP/BQfPfkoX7jzhYe9lEXEe9Gn04Z/7fInuaErft/65sNe0qJFD0XjnS+Sprvki9e+p99X8N/02n3pW38NgLa6Tj1+EhD2T/9+LK+/p+t62HrkALBZRdMIWHTuekQsIzkgrRIw1yPyOUCwAjVA7bd19UqO/3V35MB0of8wZt2Z6+A1Ymg1od56D3AAovh3f/D58XNcd6WVQq3lQQR1dJj2iGgStBkz6U5NR80QyR1oF6TDNDsAYjFEM+raI1wNyYm9CVsT5tZY5YwlgjT3aGLBIor64GTuXcaKQ3f6GoJYHAdJ/ZesSaE2mlcocwfXoGlAUkY1I8OIKL3LFXyeqHuQFu5JQ0hpAAWrOzyliAhWxVqJc7MacKsRVVwK1Yxqig4DLYHVRsEpZqhkVuMYlcRDIicl50Qacif94LWCOkmjS7ZV8HkPq0JKI24Va4XWhHkO8O6eYm+NA8Yatwn3xu71N3n7rTtsL3aUswtEBGtGRXArOIlWZkobmBymOlElMbt3o7QGEJMErUd3o2geAnwKNFVsXOG1xvZNY++ZDeCXcg4o2Rx3CfBuQFr1HuwZSflBfLJqwkzxHmM+rlcMOTHkkSzOkAbGJCQ3shWSO2o1emU1MyjkpKg0rPa+VxoyDOgwkHwmdXcrqmQVtDufzRwfV1jSgLM5xfNzpXrF1XlQhj3EwIW0inhA7xioyHEtpD4ooRr90+5YqTE4oYo0I7kgecPq+CZXrj9OaVv2d15DUqZoorjQpl24Z3vEs+ohjjrc8nHN88417OFAxhveGm4dtoqAFTwNHEzFmhLRfBxR1EI3yvdZJk0ZIWKWj0+ucl1W3H/uRW6J8NinP8GHfuSzXDoeuHPvbe5d7OBkw3h8zMn165ysEudv3eGNW5U6T30wpd+emuOaUBI2N976/Ncoz79JGhvb0zOqewx8tBarc6eWcNQGlF+Rr9/k8Scex+/f4zyvufoTv4frP/Ap2J5y+d5r8Ppr3Lt/ijfDxOI8poSVLRalvbA6RnSNpj1sd3F8AIaB4eZjHD12jWGesIstui+kVhCcYcwM64FhzKgIdTjGL87J5ZCokEjekLaPjunhKAZv8hjDJ1NMsFhSWJ9gVGxYUatFKnVa0axGwoALVmaaFWpecalMTCVzodfYfdd9VYveC/rDz/xh/vqLf50v3v3iw17KIyshepRWkvix8Tp/dPM0gyjjAn0XLfq+0nD6PPnsJdrmJtPjPwbXfuZhL2nRQ9Yzl57hZ576Gf7Lb/yXD3spj7wGlD+yeYqbacW/dvJJEsJal7SbRYu+H5SmO+itX2b11q9z8aE/Sjn0Hi5aBHzu+ucWAPyQ9WRa85Orx/ix8Tp/bPM066UmYdEjLoHvOfz9xylNd0jTHRwY730tPvgH/98PdU3fSz1yAFgkOn1FpJtk9Z1oXIsI3HASBrxNSABfCQdvknDuPuhY7a7DcMRGtLEKiCpi3WGHPXDiHpyATsTABvTt0awaQMxaY66FuVZarYeVI2oBunCsFdz6VKKDucMDV2ECMt7jUcFQzUDra+/h1hJO4WaVCxIX1VglRzX1CNv4d9UOupthraHeUAl3pjiozWgaIubZSsRl93jjAEgJcUelocOKlBPDZkNejTBEV2m92GL7gtTao6vjueS86hHOBnnV+8BT9Jq2iugAQ8ItWpalDYgYRqKURm2NOm1pmlE8nIODkJOgSVEUSWM4kJNCGnEtmM2I6QMHuItiBaTWfuwUSRX1ic3Vy6xPrpFWI8UL9958k+1bt7BSuTi94Byh7nfhwpbogS3mEbuclVkTU6sUSREt3J2qmjr8TUMHiDyI/EYEWa0C2DpIqzTRvh88OqS/zfXrXrsrVsMB2btuY98lcOsxxj3eV4FhIGkipXCxJ4QBGOrM0ISUMllAcUQUzRIwGEdbCbe4g5lR/eCIFzQ5iRznNCWSVVQEl0SrjSYl9oxlIGHVaDjeakRyu0Rc+KHz18HmGTFDJcFQY8hABNEBT90Bb93bb9GprbJitTni8jOf4NO///fzxNM3OH39Jb7yv/4Kl/NIPZ949dU3OHvrVdwOgxvg4qgK1H4+rIUr2DR6cQ993jjWrxG3htcCkhBvuESM+9D3XWqVWmOwoTWDFjA/pYHh+AbPfPSTfPCHP4HaxOtffZHb25nNvOdiv+V0tw+HMmBToQyFKmDjCl1lkrXYb7WEwzmn3rXtiBpZjLbd8fZuIqkzTzvatAtXeTNgHXHSCaxVkrcA9ycr5NIA04ZkmfXxJTQr59/4Bq9+4WvU6QKf4lxa1oD7JFxXfeil9XSBnhjghrWKJaHVynRxym41YGKINbQWXJS8OQlXtBfEw3kdPdiQhtQd7iCmyNT6HESP9W9znLO8isjzw/CJDtTmJF2TV4kmSutDQE7CklNbotXKKo+sHdYnwtQO9+dFj7KyZv70J/40f+6X/tzDXsojo40kruoIwL9++VM8kdb86Hh9cfkuWvQeUPQhvkF+4W/AxxcAvAj+5If/JH/9xb++xFd+j3VJMic6kBD+7JUf4KoO/Oh4vacGLVq06PtNEr/44Oilv0l7+9fhn/i/POwlLfo+0fI6+r3Vb30v+riuuZpGPjVcfsgrW7Ro0W+n8G+V3+7T3nd65AAwB5jLocPXOgQO6Bt8NvV/N0xz9Iw+kAKKe8Ach4A/WLgBhfjaHn3s7gga7mDvyM1rgFkJ919093YwbM7cKvN+Yp62WHPULRyckomy1x4zqYJbQL8kB5DdiZNFf67ZO05nlUMncTib0QGxGRsGtu7cc1g1J4swphROSQ49xeDmePPuaCxESG3C3ElWuwNYgAo1XHhCQrOTVhtUQPNA6k5QAWQ2bC5QKuqGDj1OO2W8CV7LOz2tkrBasKbgc/TyJkXyAJYwr2RpYJVmUJsFXJYBGYcA8Qgigq424RCUBBhaNWC1HlyOIxB9vYQBPP5s8bxc45jIKnHpmZs89fGPI8dHvPr1r1NefpnmCcNo80TZT7RWw409DlRvFHcsrTAxbDV26Nf7ZFmFO1MSLmvc+/4w7wMHjioBP41YXMrhpG7doWkOZLxDM/CA8FYfDBqAYiY9MjkhSZCUY4ggD9FFK+HAlg5gve89s4bIHktjfI2GUzwOp2P0bmM89vRcqdNM3U/Rg50SkqNDVwxkSDB0qFoaQgKpuKbo4C4BeMXoceaKiXGIcZc8IvMMrcRQgBLQT+PaMxE89efpDU0ZHUYef/rD/OA/94e59uGnoDX8+S3Dtat85HM/yIkIj3/+C/zyz95ld34az38Y8e5wl4PTXjNidKc+/bogrp1u9RUXXMa4xqWvQwxUyC7RNdz7uF3ojuzYpx/4vT/Oj/+Rn2a4fYcXP/9Nbly7wXrc8vZz3+S0GWU3w3gVGRtl2mOlYvs987CmlolGQrRhotTaArgPm3BIx8kMt32JnvFqFhHXNSLoc9s+SANonqjbC2xYMd26zy5d5WNPP4sMQnvzNbb37vKNX/g13r5/ijNgavi0x2WFD+sedT3FcWm9CxnCbWtOmybasIrvxcDFHH3GY97gR0N0hc81wvpNUNe4JXrEoYfxXWDaIaWiCOolRhfGSxH9XMMp7jnH0Eoe8IMzvK9NsiD5KIB/nWgtQhukOtrOSMOaMW/YLLBpUdfnrn+OH7r+Q8vk9XdRAvzvjj/EsWQ+M1zhn9k8BfTRuuWX1YsWLVr0ntUTmyf4yz/xl/lLv/KXOPsexq0+qvpEPuGfXD/OT6xu8HtXNw9FNctr6aJF7xFp26O9N3jRIojX0X/hI/8C//nX/vOHvZT3rZb3oosWLXov69EDwP4OpDFrnYMFWBPvXb1i70DPWiEJ6vIgUte94R49qRHtDO7pQawrKC4Wcbsekc3CgKsjbggZFw9oqynApEW3sLuBOe0A+6wGNJbUXZwKMiDWOtRVyPLA7Wnt0PvbXb5BCLvjjwDE5g8+lvIKt0Jx59SMsRYGHQKieA3oKvFcsR5ZuxoimlYUMesAqRBw1RHJAVGoJFXSMKJu4Ya2hlcNkzAgNQCwtji+kgSSxnE3Q9KApoSuV5ASZqUDLul9ya27XBUSoJmhOjokmGechJpjdYZhFfHNKUGLxlsdNTqH0QB8Ru8wFsgZFcdLQQZBV2vGozXrzTFzqcxnZzgZSyvS8Zrt6TlvvXCLi/MtpTXmacvkQjEiRtYrXjM+ZHyIQQRzjT5aBEljfN8UwF50iDGENkcvcY3zKkqAqzZjpYarUhqete+3cHkiqQ8XGMKIV4vuWbcA8HKAyB5zA62heTjsYBKG6BjXg8ae8+YYhqVEGscAvN1B7w42zZQ60QTSah09q82obWZfZlrpHctjH7IYMu5C04gklpSQ7QU27XEZMPUA2C06ssU1+plT6n3H4VgXBdUN2sLlCuBufW9E/LOkASX6v3XIHH/kWT76J/4A1565CdPM+ee/zjd//ku0J44o5Yx5XMF64PLVq5R5Zt7vwsWqAq2Al7gOWneFe7hdvTWciFcPJ3MLx3JcdeF+tQo5h0uZfvwl425U4UHvdnr6SR77oU+wGlbs9wW/eYVrT1zl5O597n+hsmXFM5+4zrPbU775lS/z+q3XcWvsZ6PsT/uwiHOwK5uE03aQGHDx0qOeLY6ltRn36D9PqxW+2kCJa9vLHqt7bFjhpWFirD/9EZ75fT9B/eY3+OW/9cu8vC3cvf8GpbQw/1vfF3PDpT6A9tFdLjSgNcdaw6YJH47Im4EkgpRK3U9sR6NlZRgGsmwQtvh+D/uK133cR9Oaur8I4F0qWkFppJxI+RgRw23fkwMKDAOSBmgl9rA3tEHWES9TDBT40PdQxq1C3cW9eThGxxFT0LJ9116aFr23dWW8wr/ze/4d/uKv/EV+/favP+zlvG+UET45XObJtOH/cflTPJuPF5fvokWLFr3PJCL8xGM/wZ/9oT/LX/zVv/iwl/O+0wrl48MJHx9O+FcvfYxLOvBEerR6zxYtWrTo/SwR4ebmJoMOlEfQ2fbd0rPpmBPN/F8vf5IPpKPlveiiRYves3rkALDo4Sn3eE/nHRfvIQpaNIBsd4x6gyYBjiQSZwPW0r/GIYpJI2bZvAMoa723V3oO7IPvHM40lQ6f6S7KiNlNCDpMaE0RK33oCE7vAN0Ax4Gz3BvWKu6KeV+zewCQsIMGJKR3iyZHDQKDRxSrSWJGuGuGTJXqlcuiaI5eW5IETBlTuHlk5aT3AAA4HUlEQVTzQJJwOlMr4mPAXAgn5TyRvE9DzeWBE1g1YBfmUbU8F5j3yLDq7lPpUcwD6ICOA5q7U9vag2pbG4fOuMeAcmWGNoRLloPDL6Knk1RqUVo1GkJxJbVCUkWcfhwD0tl+ptUCOZNSQLkwhWfSaiBfPSEfbbB9obRCbZWzN9/mhV8unJ1tuffGLba7C7alsmtC9YoMA+iIsIoBg9zPo0U/rDXDVCALkgfix4mAt+YBA7EGKYMLrVVst0fSgEj0SotIwGRXvHcxm4TjPJ5DxC1jgtOjvPNAyglxAr63hpaCWiXPjZRANwHmndYfS2LXSOw1Wa3R3DulW6y3HuK5q8HFjlomyre5f2W1glaprcU5zWOH1YKo9yhxp5WKzQWXIfp0vUWH62YdjuPDsRGN66zFwIK4ISmDDuDhfB5S75sWjaiHJIgY2699hftvnlHuV9566w5HT19nKoWv/OxX2YwjadW4+uRjTGXi7m3Hd7sYTsgZkegSd81YbWjrnd/iIBlvjuZwVcc0yQGklh7/Duo9Yj1ljHBwu0OzFkMSZ/c4/3u/ymtPvE46OeaaGtOXvsF+Uj7x6R9k/NizXH7shGF/wfrKmtOfrezO3sbnQp32uBuaR8QFTQPW+6qbGXpIHWgl7i/9XoL3AQ7p8ffDEA5sH1AZoofcGtQZe/tV7n9Bufv1l7j72iuUeaLVi16j3vr9VWm1RN+uOtKHW1qZA+LXPkQyJNLxhmE9shoyuLM73VKzMK3XtKGy0iGGYNywJtAmBh1wOaWYMO/3+PnEMK4D4raJxH2ERJMxOqDbjOZv64z2hgyXsBQDOq6Zuj9FZYusjrASx8c3x2g1NCeaRTICSy/aom/TtdU1/sKP/wX+wq/8BZ6789zDXs57Wgnh08Nl/qXjD/Enjj4Y9RuLFi1atOh9KxHhB2/8ID9844f5/O3PP+zlvOclxBDVj66u86eOnuWPbZ5eHEqLFi1a9D7WH3/2j/Nff/O/5sXzFx/2Ut7Tygh/ZPMUPzxe459aP8EH8tHDXtKiRYsWfcd65AAw1tCccD9M7QTENALGiAbAcQ59wOAakcqI4aaoCNZp7qGH19Hgxr3fV4LDckiE9v6fQ8wzqf/dopc1pAGFlOgyVe1Rth3SfXtfsUd/sDu4Ce0A9pyeZe6YavzStANpeky0oDCA6oC3QitDuOjc2ZnztsNEpuJc14E1iZw0AF1edXBliCnqNWKvS8VKDUdws4h5RnpPsKNmSOtr6GTY57kbVjOoIcMxw2rFsBoYNsekIWG1MV+cB8xqgBEwmXeguSWnpRxOxWZ4j/BOYsCW1EaSzJRGdKGWQh1WEdHdKlmMtFpHxLW26AG2GffcI42jF7c1Y7p7ynTnnDIX6n6La2I/N95+/Tb7ecf5xQXn08ROMpaUpMKQA0Q+OH+txyMLcbxSdMY2BaHiedUjn+uD+Ga8b1JRTHunctIOzVeIV5InzIxmjWZzh+jhHPcDqJLehSuK10Lzgo4bUupdv24kH8nzTDIl7WY0d3eqSERf5xx7No0xPlBbREb3mGlLmVoNK9ad6YIRsdm1lQcO8NnCdZ+R7ih3ZBgjqrgU6lyxMnX3pvZrdEZzDqg9aPQNS6BMF+0u/Uy/YNCcGdaXGcdjLh9dJq833D27y/bsbS5evc1zp1/lhctv8/SHHucDH7kMU+Lttyam1UDSxvnZPSZPXHryGWw44c7rL1HnPV56HPYBkmoKJ37vF3avQMZbiWtZQDQFPE8ZvEdfe1wfDAHpkwtDq3iFapViidenysXunJvHjp5ecHvbePqf/Cme+sAHkRSPzXiJxz/yKX74rPHlL/4ad2+9HsxZMm5GSkoWJWmimuFmtLZDe4S7xrQDSKLVCcur7uwm+rzF8SSoRWy6eaOenfPWF77Jr3/lNebze0zTRdxP9jOuCadhRPx2a4U2EXMNSMRNN6N5oZWClULeXGEYEisao65BnXQ8YOOKySwGAobY4+aZuj0NJ/FKaHWiVMcKDMOacRgZ8sCQN3FaSoP9GWim6YDtZ/RoQEzI44qjyzfJKbHb3udsew7jEWYzPm0R6wMz4uhwhLap37MbrI/fxRenRe8HPbZ+jD/09B/iK3e/QvWlI/p3o58cb/AvHX+I37e+yYkOD3s5ixYtWrToe6TH1o/x7/6ef5d/+5f/7QUCfwf6ZD7hn9k8xb90/CHWkjjWR+9XPosWLVr0KOrP/eif4y/+yl/k1u7Ww17Ke04/Ml7jqo786z11ai3LsP+iRYveP3r03g2oRu+kOaIaILebuTj8+RChLAR4ay0gnYSXVqTHQXfXsB9swN4OJl84TNgeHrdHzxoWoIvDBG7AuKA1LSCbeY+orh0wB5AO72WHxR4xqhFJHdHR4Uw2zGL9atBSC0hl3X2bBsQ83JptDojlAerQCAu+sEYxIoLZjCuDsFmtkGFAm5EQOMDKuser4y2cyiKCjmP3JtOjcj1iiQ+x0Q5U69HDjuQVmhLDZmRz7QqXrl9lvHQpgPTdU8p+F1HHJWKvzQ1USerIkFEHqxVZr5H9HlpDrKKao0s4AcUjXrq1gKRuFM8BlF3x2kA8HMzaAMVqo5USW8EbuDPtE22uNHOqVQynXFwwycC2zuxKYVbFMYaUApweepQPULBHZVOtn8qCsyGGAxr4jOiApBQuSuhu34jxRQdUw40uSSMC2VK4PS3hDcy6G9xj54l77H1J0dXsEt21RMy5iaB5QDSjKsgqoWakZgG/au310gFyJa2it1eiO9l2O8QsBgkwDGeqpTvCHa8zzSqlOK2D7aoZlYZmhSGRcqI5NA+HpQ2OyTpAd993bhYOXJ8QT4gKqoZqRtGoyRbB64ykkeHSVa4/+WGe/uQP8dEfeJachde+8kW+/mu/wZ1799if3UMHZy9HzCc3ufHRZ7nyuTUVRbbn3Pn687z+1ppnnnqc6fQuP/+z9zm/PWHVgulLi25hdzwRrn4jBhwQvDY8C0kzeIUWUeoR+ewgDdforhXRgMAkknj08Z6fcu+bX+P8tUvczxltTrm84eqbz+JPf6D3j4eDePPpj/Kpmyfsz17n/puv0fIApfQu4iMuXdowYJxu90z7cxzt/dIa50jjOZFXh9sRZnPs0Rxd29I8HMDEuZ+nHW9fnEPdQ85xL6g14tildRAMphnzhtcMdRv3OMuYjpAHUnKGYWS9XpHmLVom8IHh6Jg2ZHy7Z98Mz3HtWau0acKbUcoe8wGnsEqZ9WpgPa4ZhoyKoJaAGWna76vEcExShiee4tonP81nPv1xTsaB09/4PL/4619gN8cd1/RS3Meq4a2iGvc6rYZkQeXBHX/Rogf6kx/5k7y9f5v/4hv/xcNeyntGI8pGE//U+gn+zSufW35ZvWjRokWPqK6MV/jzP/bn+bd+6d/iq/e/+rCX856RAic68H8/+RS/f32TZ/IypLho0aJFj5JEhB+49gP85Z/4y/y5X/pz3J3uPuwlfd8rIzyWVvw/L3+G37u6ybU0PuwlLVq0aNF3RY/gb9gcP4DYDl0iBlTwXo/rxbqrryNXzR3WhEvOrQMQJIAOHh+XAMFh0tXotex/Vj38W3S4imbQ3l3bY50dxy0xl0KZZ7yEUy+in7VD5RaLtN4VS/9wd7OCo2mAFtHO8ZImfe3grQW8loSXHTDgcoCHDUNozSmtsvPKhSVypbvgjLQieom1UkuD2gLCJeI4OdF5aj0CuzXIQ/CiUdCcQSLeNQ8pumBzIueRYbNhdfmE8egY1UzZbmmlUUujzTVczymhaUDHFdBotWEGrTTMSwDnlIEeYes5Il7HFTJPjDnTevC1e6NahXyMlnBq2VxwV8RrHBdaxExLxqeKUSnNmVphW425OUWFqkYzw3Ic5ySHGYAOXnXEasFqCbeoe5xbetx43UUHMB79xAqaBtxaRC33PSQaccgRbZwjQpjogRZPMShgA81AasEsYHQzi0hrPfQCxzBByglaP2etYpJoZDQdeooFKQUpJaC+5B4f5gHGhxGyoTqQWkOtYigkwapTavRlt2aU2aiHDlyPQYiUhxiIUMFEaa1GJ2ytmCoMAUV1WIXjvTasNbxVZG6kYYyhAxxNguTcAWRG15e4+aM/zk//1E+x+ugzvQfbefZ65ujaEZ//hee4/fY9pm3l9S++gN87p338PidPP83xjRvo5WOOn73JBz75IW48/gS8/gqPf/FLzHdvU6gRiu3Sz4/EVWw9Slky5oZpgHSoseusD45oN4SnoUfEHyLjM+qVVCfEoOCUuZIuZi6SknDkNPGVv/tr6FuNJ57+IJc+9hTrk1WASnWaewxpWOxvSwm9ckx+8ga63ZHmiuhIKxUv4dZFU1wvOkRUdSv43HDN6LAiRmUsBmVMcM9xTyo7zAzS0B3pK9A9VqaIm9YMQ++yrgWGBKtLAaZrQ7ySxzXjZsNmSIw5YVkpZ6e0/QXYHjm+zKCJ5hM2NWoKQF014aIRklD3jDkxpMSYBsbVSF6tUU3Rvb1zWhmo+wvKeIwPK1Bhdbzh6k//CDd/+IcZpi3l/i3k85+PPdeHMIQEudcATGek1QYrM0PaYNPFd+l1atF7WUkSn7z6yYe9jPeMfmr1GH988wH+6NHTJIS89CotWrRo0SOtxzePc2N9A+4/7JW8N/SRfIk/vHmSP3PyCVboEvW8aNGiRY+wPnP1M/x7P/nvLU7g30YC/N8uf4r/46WPMC6vnYsWLXqf65EDwG6Oy8E5G2AzTHTdJSm84xJu4a6VlIDUwVt0BAsWrj+V+Dcc612sqsK3+8JEAjZ6d/1K76mVKFXtPZkNQWjeKLVi8z4+WyIulnToJe7wNlkHu/H4SaKXVTjA5YqHDREPJEdzBQNtc7hGdRVAGA4eZswqVmbwRrboMHXJVIPmQpsrpRmtRaysJkH7fwIgKWKt99AKMqZw6Y4jaRxJwxD9tMWil3bI6DCiOQfUVQWN52ENyjThbYLsaF6Rh4F86RKaM3UqzOfn2LSj1QCYQhwvxbDmaE9PFjGk9xYbQm1Oq4bJQHWDGs/bJfVc3iE6ejWgfxOnyUipO3YO583YWbiR1ROaFeH/197d/Mp2nfl9/z5rrb2r6pxz38jLN0kkpZba/eo25MQB2oMAdgYBEnjmWYBkEgOeZpB5gOQPMYIAnmUQJEjDATJIrIbddruhtqVuSS1RJC/Jy8v7fs85VbX3WuvJ4FnnKnbbiYxWi9Lh7wMQIsV7z6lTe1cVyd96fk+hWAPLJI/CbyfTagc7jGnIHM3iPXYOU8dhgt4xKtAjt8+O5bg2ycCKRRBFjzB/3Hu9VaiVNE1YyXFwoeQx/R0hrHfDPfbTWo/A3izHtUoZmxM5/nPBuOeuKpsznoB5Jo2wLlsm2Zg67XXsdC7kTSJ3j1BvmvDlSLNEOyysyyEOVozq8lRmSJmUEymX+Jq90/oS1cotFj0nS3HdUhkTzxkvCT92/HjEfIt5JbHB8hTXzWOPq6WJcrLhjV97i/mrb8Zeaca08+WBOzdf4W9885t89NkFj58/4/6P/4T3v3ePRx8+4fT2e9y+dZNdXXl63LP51XfZNYfzc07vnrL9ZENbV6yNQJ8Jn8o4BDLF67O3CKJJsV23x+5YmyLkt97Hu0G8KyTLpOkq8J/x3pho9LXRC+A1JnJzwcg8++hj/vDcuXHnY94+/yv81l/9Vabeefbt7/LkkweUbPF8HRvYRDu/4OJex1qlLod4zTt4nmL3tVes+thPneP9LXXMV1LrsHTIhewdlmMcslgSTIXYT16w1mKfsEWtdMeiJrwu0MBtigMwXL0Hd+bNjtOTE85Od8ybjK8HlsXpBj3v6D2DTUzzjKWZpa60Di0X/GRLW2Iau5CZbWLKKQ7reCfRSDh9bbTjBY3EakalMW03bOZCev6UJ//4n/PZvUdszy/4/h/9M5alxnt8X7F5JmWjtxbXa3MCh8vY1d1j57SI/PubSHylnPDf3vpNfnu6rdPWIiLy0mW9ZF/3n/fD+IV2J838h/Mr/Jdnv8KrecPbmvgVERF+Mgn89ZtfVwD87zCR+K9vfIP/6uxXmHT4WES+AL5wAXCM3o1qXOsxjUkMJXZzktvY/zt2itLxDlBxtxEu9QjvzEaA2yLAsxz7f6/CtpRwh+YjDnYb047xAdMt8a9FxTZ2iDJCXPcImDy+Zn65k7NHwGQpAj+PGt3UY6qUdhwhNfTe4jFYI5lHzaxF8Bc/mGNjMJHueI8gd9NhlzPbnCglQr+2HKljCjTNBdtM5O6xj5cRUHrFc4lqZ6Lm1kqKutQck7VUH5cgpl0pEZXWtbHsD0ylUEqhHi+oo/7Z0kyZNmzPTtndfoU0zxz3S0zn9qeY73GP/c1UH89NxVqle44KYyOC1bIDKvRGd2j7S2reQM6k7Y48TXHdHfp6oAONiQVY8sRldfbZ6L4np5k8MULVuMb0ftX6jVvUVrtF6O9ueK8RiHbHyKOe2fCcI1AqcW18TARbW0fxt0NPOBWb5wjLjfi5sdgnPe5pK0bOmT71qLxdK77WMYHsEbqPsJgEnmesN3LK40BBJo9rlnMhTVf7X6OKOabnM1xckKaJtN2SNzN5N9PalrwcYbeDiz3lYqLansWMta1xq6cIgzOQMLx2end67bEfNxnWVpg28RqJmzlW/OYJn4k64mZYiTl6Uhx4SHk8N5746Dv3ePP2r/Da21/Cz/c8+/ADnufGl37j69z+zQ23gfbkOf/3//SAB/c+oRfn8vI5jz97RNtf0mmUJy94+t33KcsLHj/6jGW/RPjda9TI94pXi5+pRDWy41iaoXfcKz3FVHq2sd87noUxDR3vCaent7B5y/7iEr+AdrykdY9h/trifSP1uL/2T1mWA89ePKDtH7H/4T1O6Dy9/x4X+wPzvGXpznpyC9YjbX/gfG1M80TOUQOOdbwu9Lwbe37BW9Rz53mHTRtq61iPELi3jvUK05ZIYRuMWnm6R/W8dTwnOoneVmxtUArYirWOtzYmawslTZzeOOX2rRvscsJorH1DLz2m3Ul46/j+kpQ7JW9xL/jhSF0PtPVy7CKvTKc32Fpj3hTKPJMKpO0p05TAL/GU6O0wKqcLeWekzRZfDjz/+AHffr6Q90843y+svmBlA1fT7rVhvZHcaXWh9xWbzmLqe//iZ/S5JD8PPtYU/GV/jdorv3//9/9C3+c6++Z8h79z8hX+7sk7ADptLSLyS+Dn9RkK8AcP/oA/evRHf6HvdV19c77D725e429uX+N3ptv6DBUR+SXx8/wcfe/5e3xw/sFf6HtdZ3/vxjf4+zd+VZ+hIvKF8QUMgH3UPneMTCqZViOE9d7x5DE1FsuAwcqY2LwKdaPe11vDPXE1YmqpRPjI2JHqV8FcH6Fgiz9PV9XSYL1HxS4wOn+BmPaMkd1+1e88poRHdezVn7cWv84StBXLie6GkyNA6fH4zSvJEilHzWtyH6FahI7eOn2E1GZOzoUdsEvOZirMycg5sicDUslR+Tymo80c8jQC5TwqoBvuTreMdYsJwvWA1QhEySmCJK/01sibLT07/ZjgsDJlqGuN8LdsKOZsTrfc/tKb3H3nK8xnN9mf73l8/wEP72Xs0QPaMfZzunesjctdNuN/iWvjHXojUSk50YjA1WuEdJYNH3s+e0t4KnjO9DRBSkzJOG0Nq431GHXPKY/pz3Wlry0CXTLJO6w1JmnN8VFNfRX008F9weYtNs+Y+ah5jkgdjN47nqLeO5c5vp/ZmB4fFcIlX82Wx8GG1kh5HjW2cT/2BC1lUk3x91slpRx14jkOEfScyclIKVMskUuiTBuyd7L3UWGcxmOISVtIsK/09ZLkCTs9ZZoqKW2xaaZsNqwnG9rlhno4sByOHJeVljJsZ2zsrq7HJe7BHn9tPg4otHj+Wusk7zEV3MFKieljwLyTaoWWII9JdEtYXWi1crhZ8Lbn0af3+c4f/ks2b93mzrtvc3YraqXzzVPeeedNlsunbLfG+dPnLBcXtMOK98ayrhwePcXakX48p9U1DkGkdDVeTqeB55ih9qhp99EIgGXcyjigELugr6b/6Q2mLZvX7vLOb/02d25/mcefPufeD77Nw0/+jL4uJItwnNbG28EEvuB9pbGwf2zcXw5MvkI/Rvi/22G+QjvGu9nawNPLqXzvK97ihdHbApZiYns5kLcn5Mmwlmj1GAdX3ChlwuZd7PvtQCnYPPZsj9d1s0xLK2SHFvcjEO8HLSrvLUGeJk5Pd9w63XI6J1L1qCkn7r+8OSEtK3Y40C5jZ3s+S5SU8JKoS4LNTdblklQruV0wn91me7ojT1vSZku6cQp9ob84QDtieYO3I+14oJ5vWddEyRlePOFweU4/nlPdsc0tOF7gPa6TtxqHDWIzNOxeoXm8n6f5C/gR+kvsZ/EveD/N12je+P1PFQD/m+6kmf/u9u/w1+dXuK2JXxGRXyo/r8/Q7p17F/f+wt/rutFnqIjIL7ef5+fotz79lj5L/z/8yjjQLyLyRfGF+6/XNiqeGcOitBZThYyp2JS5GrTFYy8sTky0+ghr+yhMzsSvtKgdNVJMDTsR5vaV8YuwPGpezejmYxPx1V7fFBXQvUcg18auYCxqkilEZXWFFnsvHcNS1FjjHW8dLJGTQVtJTFFfWiteZsx9fKsce4sjdX75syZLLyeeS1/YTBNzdkqOMDn2v44J4uxR+ZoMy+OxjF239DaC8xzTvmb4stBINI/QOI3fZ14xZnoDO1YstbEndKHNUenrBmVbKLsdm7NTtnducHL3NuW1u2zdyGcnLMc9vq6sl+cRwh4T1I6nCa8LVuYI6x28JbxX8IJNKXbHtgjqeu/4/oiXidYbDaOcbiknW8gZp5DM2GZju66cn2fWGhOTfT3E9LUV3FeMBkx47i9rws0r3hO+Lti0JRWDUYdsxcYu2/iHOs8TjRVnAz6qplOKactIjske1y1Ctg6pjMlFosKXQu5O7xH25rlHyN0iXLXWosrXY5rdzGMCPUXAX0omZ8gdco9Qlb5izSOETkTVb6t4M3p/ga8r09kJOUWhdCmJtttQS6bvZo77PeW4snjUancm2rLGz9BqVOvaOCDBFPe1Wzz/niLct0QqEzmNifwWr83kjZILlic6hq+N/uwhl3/8xzzMO97/8BGsndfqCTxcaDsnhqxXfJuYb++o+8rxsIxq5xoV1MslsI9qcvPYmxz9yS93gKc08/IfH91jUvaqWD1S6gjie3/5uk/GqOF25lfv8upf/494/a23eNPhzr/Y8E/+54/x85WcCjVB9xYHGLy+HB7udaVePqL6gbTZYL2OgyUxZW+jfhocr0fcOtWn+L306BfHsTLeY8qMtxWvOSrkLULjPE9sSoGUqaO+2doB2vZlkN2OF/g047ngViF1uq94LzHZXaM9Ydrc4PRky62bp5yYk9ZDVJWnGaNi9RDvx+7xR2twONBLwqeJTGJOmVYP1J7wfAKbTLlxi3J2g8yox7/YU3un2oxPJ7BcjneqifV4SZpyPJ4GZSr4vCX7iq2dNRk0p++fjOC6k8oE0y18WUnZoIBvz35WH00i197vbu7yt3dvft4PQ0REfoGtfeUf/tk//Lwfxi+UicT/cOev8R9v3/i8H4qIiPyC0+fo/7///um/5KZN/O72tc/7oYiI/Fx84QLg7j0mCN1HVevVTl+HFMFZ7yNYHXXLZsTvyRHc+Kj4Td2jCtrX2B3bI5yKybHY/znGdWOq08b37A3vnZ6cZOBWgPQyzI2h3tij6RjmY6o0R0hDr5AS/ap2ujWwRLIWD7qUCGoBy7En2GuP4PqqOrqPqmA6OWeyNzpO9U5JxiYntjgFx2ofzcbxfSmngOFmtLpgOXaWjpwLcx/1wtMImtPLytgOkEsEyt5HxXCiHlfa4RjXYdNobYbW8JzZnO4oU0zPXj7f8/STh5zZRD1ccv7xp2ymzO3XX+N4eUY9HlnPz1kvn8cgZjJIjtsJXteYdqSQ0hqV37lESObQW6fTaa1GFbjFzuWcp5iSzTGZO5XCNE90h/PDQj3sYyL8KvC3FDXh1nGfIlBL+eWOZNzGBO14bnrDupHyFBPl0wbHYp+utbhOKcK4OAhgcV+0Om6pFNO8Ftf4JwcEYmI8e+wMbhZBM3mEar38JFBMBUsF84nUO6lHpXhqjZQSKc4GcJVyttpjF3ACz4n1uNBrY/XOzhLzNENb4XiAuuJrpdU69uJ6TF33Tl9jyvaqJZ0WO4TjQEKPHczeR8DbY2fxFNPYUSNtQMUSTCdn3HnrdW6fnXB8ccHj5y9Yn77g3vc/5uLO69x896u885tf5+6dLYaxf3aA9cBy7z3uf/8DXnzyiF7XqKkeU/LJEpYm8rSNx1ujKjnWfntUP9eYdo+gf0yMjh/HcWzajPeS2BMOJabDr35Fd/KNE26/+wa2mXB3Tl65xcnZjuVwybosJMbBBQwoLyf2+7LQ3KjsYVlJDil51Nh3SMnI00zrS0wlj+e74/QyDjUQvy751eR4w+uYTidhm4mTN97kq29/mfr4Ifc++JDj5QW1F+g26qEXfDql9pXeV8gTfW1xNmGtsBzJeWK7u8HNk4kbt87YbTZYh7Y3Wm90b7S+Rs3yGtc6bWdyB7eK1xbPAwvzlPG2o7XH9Ga05LT9YRyCSCRLEY63RusWteFp7FGvK61sWGunnN3Ebt3EciJPBa9H+uPn1DXjZQO2ian02uh1AY6j5j/RjgtsbvwsPpZERERERP6tihnfnF/5vB+GiIj8EvjBsx9Qe/28H8YvtGd95X/bf8Q3N6+wfbmiTUTk+vrCBcBXAZ37CE1hTNmOSUrvY0osdsDGHtuxFH7UMePQY3Mp5rHX1XqPiTlvo+aVEa7FFGv3PMLlDnQ8FboD+AgDI9DJFrWkpTfWxNhJHEFSshGw5hwDk5bo6zomE8dPYSkemXtMv6YUIU7OeI8KViAeV4tKaiPCK2tO6hGQZyqbYmxSiR95bfG89ATrcTQZz5gXqGPS12PqMAasRwB5FUozvnfv9LqOyuwSU6clw3H9Sb64HGi1420hb0/pHVp3+lI5fvoZF58+pnz3A2pb2d46486XXiW/NXO4PHI8P2f/5DkXjzb0pdF6pdcDva70WkcIGyG+5TImlola6BxhfOqVVA9UCv3ygmaG73ZYyVAM90zJme12w+G4UNcVKtAdsxWjxFRqNjIRYvY8xcEBIM0npDwCViyCVW+kvIWpAI1uBS859o+axXWxjqVphHstqoXTuLY5RSXxmNS+umd6MpJlrC0kMv7ywEAmgtMJ606qnYRTUieGG50Zp7ycNAYrE94avR7jXpqneC2lRM2dBbDWMW9sprGTel3huMLa8HWl0WnZaDitObXF/x8Xv8RLrHZgxeYpduy2mJZNKf5+Sull5bolg/F8JHOy9ZiwnxLzvOHs7qt86dfe5cu/+uuc3b2DnY4wtoMfFj76F3/C/e/9MY8ffRbt7CWRW7QX52lmc3KDt+5+mTs3b/Ds8gUfffABy/F8PL+x05Yc+269Ot3G7t/xWu19HfuASwSvYy/w1YSwpYx1uPjoEX/6T7/HN+5+hfzwUz74g2+xf/6CRKZYiQdsJb5v7XgsE47gtjd86fQ5QvGrpwTA80RKC2ku1IsLLBvJtkCiJ6OvR7onSpooU4kW+kbsnW4N8kSaN5x85S75tRMuHndo4M1oDaw6bi1qzgGmXVRlnz+nLxd4cxIzJZ+wvTFx5+4dzm7eYDNv8HVhvbhkuTzQeovqdQeYKXbE63GE2Y3eDV8PeF1I21NynsibFY6FtS1MPWNLJZ008qjhftlv4B7PczJSO5IsU5dKTXuW/ZbcE70YOVk8psMS7+X42Oed8ERMRxPtBn1d8TzRl/Vn8aEk18xFvYh7Rf41R+8s3pj1L9kiIiIiIiI/c7/34e+xb/vP+2H8wvtfLu/xatrw39z6jc/7oYiI/KX7wgXAZhZ7Ox3oNcKZMo99ueAjyIyx3xFq4rjFTt00Kl1HjDr2/MY0rF39914fg4nxDeN79op7VMcaVxO4YBbTmTGBCbiTfIPVSrKJZnXs1k0R/loEe7RRc5pSTAhbiclPB3KEft7H2OaoCbbs9Ba7Z1/WNfceP3N3YkXnRKIypcycE7ucR0Cc6c7Y79tjyrd7TIbGrCRO/UltayEmV1uPgNfAqTEVnGORck9OXSocVnpd4zkzo7tBW2LXKU5bDhyexlRgr5W02WIlk+cdm3zCfLpj2u5id21O7MqO1+5+CTYnnD97ymf3fszy5LMIw9Zj1G1PJ2ArvafYLVviiraUIO8wK1g70nqnXu4jPD49I1khzYWSYDtFLe5SZuq6HwcIDJ8S3jqFTrIMxPPu3qBsXk5lp5zJ3SEbluertnCcEtXGox7cWiUxJl6zx7RvGruXfdRpT1N8nbErltrjvpmnmD6e5gi/SdAT/djGvRnV5NmN3DuZxGRGaUcKMJdCaryciO0tHqunOqY+t7ScaNsta1tpvcO6Mi0Hdu6klCjJsU3CSqGOyfreOrWuNL/aCzsOAtSYTjUK1hMpE/XYxCGHZBEsW6pRyYthaUsa07nPP33C/uEL0jSTTzec3N5y+tYtzt5948/t+Dh58xZvfOMNfvy9lV4gzRlaJ7fYgWxl4uY33uUb/9nf4tbNG5x/97v0dOTxpxuOhwUDWj5S1yMsne4N84Sbj/KAFpPcbqMOfRw+MehWaC12CedyAs8u+fBb3+aT8n3S+afsn7xPW1tUOifINkGK4L579Nf7mKC3q7rkXmMf9DRTUiK3jq1HPJ3SDkfYbvH1SGored6SUuHQ+2gugKk3PM+x03eFvqwkFqzuufjhPe6/d5/jxTl1f457pveVbiUmvPMUASseh13ShFHwZuSSObt7kzt3bnF2umEqGW8Ly+Ul9XCkLgu9VryAH8drLSfSdkseh2ksT/Ge6fF+U7KR5lNKLvTLA/O6Uub4ulFtPdN6x+ty9UZN3uwoa6f2hvXG2sD3K62dMycn5w12eEHKhe4WjQd5orvTuo8DPYb3ZeyFTzF1L/Jv+Aff+wecr+ef98P4hfN/Hu7zz4+P+Zuq2hIREfmpVe/8wfER/4nWKIiIiPxMOPD99fnn/TBERH4uvngBsMdEqo801Qzsqo61j/Ayj3raUcHsfrX/NgLfiDlG2msWU619TGCO3Z6MsMDSmMZMZdRORyV07NaMLxErXKMyOkKu2JNLspgstdH+230Ey3bVLB2/J0cIiKex+3WMLieLCUVGCG1pTNRFmGE5j3DZR9DdSe5kYJ5n5imxLROlN+hR39x9JpkxG+QStcGO01oEcBmLQD0laFEhy7pEYj5vYnoVp+dMskKtNXa79pgcTSlCbGuOlRXvMa3XlpW2NKh7ynZHnjL5htOPNzg8fkY7jX2m25Mzzn79bfLXvg5T5ta99zj875c8e/IIK5nOFNN9eUwze8V7H5OHRrKCUelTJucd7bjHumNrw5aVlCBPR6a8IZfMbrthf3nBaoy64hbXpGwjfEqO50LKCZJjnqPy2XL8kWPnLnhMU8YoMalWeqT9sW95MqxEiBpTnFPcT/3qeneweewcTuBrDF9f7mPSOWeYywgRjbwZBwRaI5lRppkpGbkUcmukXiJoxZh2O1KKQwL1cKAd93Qn9vd6p0+ZTqLRqbVy8fQ5/uw5fnrCruQI69tKzylea97pbY1KaLMIvpuTWo97vTlYhVZjl/U0RUhvMcVpKSbHs1mEwK1DnmJjdjOOdOapkxrsH17w8If3Od19xO133iJNI2VPYMk4e/su77zzGt//w/vUfYepkE9OmczjoMTlkWff+jYHg32vvP7O27z62lscD0c2JfHi8SM+/vGPuFydahHs99Ep4JbHQZOY+h/jy7h7hNxly+bul/i1r32N2zdPuH//Mz558BH4MQ4krEYfhzgsObnM9OaktsQ0cErxvlBrTJLXBfcS9fQnp6QSryVvFbcjyRJtc4anzrzZkOO30lMcSIld37FLuk0bbMvLqfK2Vp6te9r+It4vzOPebrEzO8oMGmZRDW5lxuctm7lwenPLK2+9zlmGXKPCfq2VtjT6ukDbx/VrlW4TabPD22GE3gW803AoE949wvTjActHNjmTzrbktoF6YPURrNdRwV4SZhtyrpCMNiWmpdLcaOvKsn5GPzmjlQ3z5KQyY8fjqItu9N7icEu8beGHJ3g3mHfYlPGD/oVB/rzuOhjwb+Nc/XOLiIiI/LRWnH+0/1gBsIiIiIiI/Hv7wgXAI8Ud05CQrLzcaxqp0AiGU+ypjV2bNUZ6Ey9He6N69idBq7XG6IaOIDCNqbxRQepj57ARO1p/sssVrupdzRLufYSkEd723khOTLWZYy3hflVabVEF69DXUcNMi8pqa9BzVDT3BmVmpHZYH6Gje4ShbcVI5N6w3plSpvSFZFusOMUmcs9janjF8mbUCveYdEyGkWNqc13HUFylY7S1kUnE+uSr/bQRgjaIULsfY9+tO249AtfWSD3RRk1zPaz44XxMUR5J5YyUoK4r58/P2TaYT7acvf4q+WtvwOkG7n/Ciz/6Lv3hM27eeZVjrxyfP6H5qEc+1qjIzjN4x0tcX2eKqee1k6Yd1AXrRj9cYLYlJcPKRMnOVAr5ZfDbRqV27C/1kql9xcoMOSa4E6N62noE/eQIRbtHCH41Ld46OSeYIuBnnuOapagtT3js4M1zPFYnrmcau2IT47ma4yyCO3ao2BSTxObxOK4m1y0bNk+UaSZbTAOnZOTNlun0lGmeKbXT9gvnz55Rl0taMtaY+6a3Na5bJ4K1XOiWsKng6zr2wVr8nK3hxzruxwrzBsqYUm+dnDzuV6/kUW1tbqQU0/pxIGIi90ZaV7BCmbecnJ5iljjs9xEQl8SmO/3eZ7x//DO2d17h5M4OIOrPH75gufch6/7Iq6++ysXzCy6Ol3AA2245ffUud19/nfP9yo8++YTdK2e88+tf480371JOtvTnT/j0T+DJwwccL/aQVipjOrdGYhi7vA3zEoc66HRfcTfK66/xV/7Of8qv/Qe/A/6Ci//1/+AyLZxsb/Lis894tByotcf7hVkcGEgVmMi9/+QwSS6jIjxhPSbm+zRFbXecWCD1DclT7LEtM1iilMw8b6jrkWyZPEV1uTcjex2HFgyzQu+dZga50I4LvTupxF7d3uMgQspRaZ2sY8mYtiec3jzj9t1XONtOlBX68ZJWD3h3vK2QOlZmClEhnTbbuJfKlmYl3nu7k8qG6g1PxO7q9YinOZoISoJ+YPVGPT+nFKOc3cUy+HIR74mpUKaCb3f4eo7nCXpjsUxfK81mluWCsjmJ98jlIl5T293LyV9LE+QZ5h2pJPp6wPpVF4SI/DT+0f5jfndzNz6/RERE5Kfyp+tzfrye89Xp7PN+KCIiIiIi8kvkCxcAW8oRWo79vIxwpfeV2APcyZbH2toxWTf+2rtHxW6OX8eo6MVaTGZa7PB1PHb9jl/TGZOmgPuoDbUICj1GEWOKs499uR4BlQPkHGEVRqudUnxMGaeRR3tMKjuxI/Zq324fi0BzgtrwFvuAYzwwYdTxc0S9btRAl6gOLoVaYOmN5eBYaZRpIidIeSbnCLPmaRPhZ2+0Y6MfG7Y7xRNUy6zHhW6J1qPCOCdgBGKdaQTQTq8OtUXYRMK94qVg8wx0vLbYtzpt8LbGvuV+pNcd6/6SQ0okc6bTLW1ZaH/6Puv593nwo494eu99br91m83tMx6+f4+6LtCOUa9rCWvHqMGeosLWao3JyuZYiXnqTIHWqZ7oh4W1xvXImxlvUfdLzrB6TOuaYTnTSsFrGVPQa+zQdSP3Ea6ZYeOKYYa39eXE5bjhwDN4gss9TAWbdzFh6SuWM3mT2e1OIRm1OT1uH+p6pC8Nt0RrNb5id6wuEWCToMV0sJeZTsItY9tNHERoFc8FLwmnU7ZbXv/q29w43fLBd77Hhz/4Ia03aof1eKRjpOrk9UgumbKd6LvC0ke4nQprq6yHA2tttLW9nEJP40AGvcbzVxveKuQcofnVHmvrWF+xNJFopFRINPJ25vZXvsw7775NW+DDH33A/uIJ3TLz229x+43XuPHKHYrtOT6q1E9f8ODDBzz/5GMOx4dwAq/8+pvcfnHJg/c/5eJY2d29yZd/4y1O5i0ff+dHnD95xOFwzmYzwbJS1pXzD+7z2fsfcXj6YtTDpwhmvUWDQCpjQjvue3NGiN/pU2Hzypb+5ANefOsFL+495pNPHjDdjF3By3GJ6VPz2NtsRAVyj7p3KxYtBB7vS86otzcHz9TDEhOweaK3SusV4sgFvTZ6SqS1x3ObJygboMWUeVtxS3TrpLxlM0+0BJe90r1HnXlr8X64PYmJcpzU6nhvSEy7DdtcOL11g9OTU6a+0Ful1xqVyuvlqKPv4zACpO0N6As1xQZqWsPJWB51zO7EMRuP9waPXcS9N/qx0upEwZg2u5jBbsB0GrXc/YjRKPOWfvoq/eICtwlvR+pyQS/beD9cXsT9lTdYXenPH8V1JCabKWdR7b02zA2bdn/Jn1gi18u3Dp+NBhUFwCIiIj+tH9Vz/q/jA94tp39urY2IiIiIiMi/yxcuAKbVmETFIxDtK54KfQRultJoaLZR1xszuu4tQqj0/w5/GdXNHhXMg3eHlLmqaqav46/HRJ5HAD1W3saksIGRI9xNo/YXI3mPKTkSlLEHtjMmlSM4vvqayTzqcT3qTp2o7cUi/ANGqNahEuGjgZVMq0Qw0xrn6yXP1pkJo3tnkxNzhpJjt2gpiXmeyWfx56VMTK3T14SVjm929GkDt2/SOyzHlXpcaMs5WB+TjJBao1PG/uCO2RShaJnAje4NmkOL5za20MZe5t4Sdb+PGluMNSeOj57i+0YqzzheXPLk/n2yVzalsH98CQfj5u3b7JcDl4+fwHEheRrVzR7PFYV4Mgssx6jkHnWw3hvejdY77RhBVoeYRmwd6ho7fr1DW8jkCDJbf3mvJMBaBdvG/YHHhGWKgwU+MjybSkybl2nkwXPcD62RPCaMLXfm7czp3VukZCzHhgHdEpfnexY70OtCX3pUfBuY53EQIJGaRfWyxc5VUnx/T0CL56M3sLLj7MYdbt16Nc47bE5iUn1doua4eUyRt0YZk/W2VI6PL2gksjeSOWsqrJsNzRq9HYgfrGFMIwrwMZ0eVegdi5DewdLVay6mlxM5KqAtM5/ueP2v/Tbv/tXfgtrYntzkT77zQ5bDEy7fe49y3JP3l1z8+DOePDjn4uETnh3OOSwX5NI4PZuxFzNtbazHJYLGw56H/+o96osDT58+ZamVdV345Af3OP/wIdadw/lz6sUL6vEQwWyPGuu+riQSXjopOUxTTD97A08xCdw6lx885HtPnI9ObmHHC7x0dm4cDkcuL9eY7CVR6yHuxzZqsPOodrcce6UtAkqw2KFsTl9qBLs9rlGvIxDtFvu36fQ0xX1UNuM9KMfBhJ7oa4NesNNTTl67xfHFcy4eH+hj6Lg59DSTPDFZIvUDeXNGScbJbsfpdsNEYzNN5FppteFtpQF12dO9RCV9nkgldqzH29iELRfkMke9ddqNsyzLePydVg9gE1ilN3A3Wo294bmckOlkj2r67hnPYBYV0ZYg5ULql2SbKWWDe6G3hd5jwrlgEQan7ajaBj8u9OOLeEwG5o3eD9j29s/kY0nki+KZL/ze/hP+85Mvf94PRURE5JfK//jiR/wXp18l6xCViIiIiIj8lMzdtZJNREREREREREREREREROQa0AJDEREREREREREREREREZFrQgGwiIiIiIiIiIiIiIiIiMg1oQBYREREREREREREREREROSaUAAsIiIiIiIiIiIiIiIiInJNKAAWEREREREREREREREREbkmFACLiIiIiIiIiIiIiIiIiFwTCoBFRERERERERERERERERK4JBcAiIiIiIiIiIiIiIiIiIteEAmARERERERERERERERERkWtCAbCIiIiIiIiIiIiIiIiIyDWhAFhERERERERERERERERE5JpQACwiIiIiIiIiIiIiIiIick0oABYRERERERERERERERERuSYUAIuIiIiIiIiIiIiIiIiIXBMKgEVERERERERERERERERErgkFwCIiIiIiIiIiIiIiIiIi14QCYBERERERERERERERERGRa0IBsIiIiIiIiIiIiIiIiIjINaEAWERERERERERERERERETkmlAALCIiIiIiIiIiIiIiIiJyTSgAFhERERERERERERERERG5JhQAi4iIiIiIiIiIiIiIiIhcEwqARURERERERERERERERESuCQXAIiIiIiIiIiIiIiIiIiLXhAJgEREREREREREREREREZFrQgGwiIiIiIiIiIiIiIiIiMg1oQBYREREREREREREREREROSaUAAsIiIiIiIiIiIiIiIiInJNKAAWEREREREREREREREREbkmFACLiIiIiIiIiIiIiIiIiFwTCoBFRERERERERERERERERK4JBcAiIiIiIiIiIiIiIiIiIteEAmARERERERERERERERERkWtCAbCIiIiIiIiIiIiIiIiIyDWhAFhERERERERERERERERE5JpQACwiIiIiIiIiIiIiIiIick0oABYRERERERERERERERERuSYUAIuIiIiIiIiIiIiIiIiIXBMKgEVERERERERERERERERErgkFwCIiIiIiIiIiIiIiIiIi14QCYBERERERERERERERERGRa0IBsIiIiIiIiIiIiIiIiIjINaEAWERERERERERERERERETkmlAALCIiIiIiIiIiIiIiIiJyTSgAFhERERERERERERERERG5JhQAi4iIiIiIiIiIiIiIiIhcEwqARURERERERERERERERESuCQXAIiIiIiIiIiIiIiIiIiLXhAJgEREREREREREREREREZFrQgGwiIiIiIiIiIiIiIiIiMg1oQBYREREREREREREREREROSaUAAsIiIiIiIiIiIiIiIiInJNKAAWEREREREREREREREREbkmFACLiIiIiIiIiIiIiIiIiFwTCoBFRERERERERERERERERK4JBcAiIiIiIiIiIiIiIiIiIteEAmARERERERERERERERERkWtCAbCIiIiIiIiIiIiIiIiIyDWhAFhERERERERERERERERE5Jr4fwD8bv8s36O5RwAAAABJRU5ErkJggg==",
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[INFO][htc.no_duplicates] Found pretrained run in the local results dir at HTCModel.py:481\n",
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+ "output_type": "display_data"
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[INFO][htc.no_duplicates] Found pretrained run in the local results dir at HTCModel.py:481\n",
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[INFO][htc.no_duplicates] Found pretrained run in the local results dir at HTCModel.py:481\n",
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