Predicting trajectories of acute illness using RNA velocity of whole blood

Predicting trajectories of acute illness using RNA velocity of whole blood | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Predicting trajectories of acute illness using RNA velocity of whole blood Claire Dunican, Clare Wilson, Dominic Habgood-Coote, Suzanna Patterson, and 51 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5764288/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 May, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Transcriptomic analyses reveal the status of cells, tissues, or organisms, across states of health and disease. RNA velocity adds a temporal dimension to single cell analyses, predicting future transcriptomic and phenotypic states, based on current spliced and unspliced mRNA of each cell. We hypothesized that RNA velocity could be adapted to predict future clinical status of individuals with acute illness using their whole-blood transcriptome. We developed a method for quantitative prediction of transitions in clinical state from a single time-point sample, which we call VeloCD. This predicted transcriptomic trajectories and future infection status in influenza A and SARS-CoV-2 human challenge studies. In HIV-TB coinfected individuals, it predicted the onset of immune reconstitution inflammatory syndrome. In a multinational observational study of acutely unwell febrile children, VeloCD predicted those with greatest medical care requirements. Our results demonstrate a novel application of RNA velocity to predict the trajectory of acute illness. Health sciences/Medical research/Biomarkers/Prognostic markers Biological sciences/Biological techniques/Bioinformatics Health sciences/Medical research/Translational research Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The human blood transcriptome changes in response to illness. Many studies have identified characteristic patterns of gene expression able to distinguish between different diseases or degrees of severity of the same illness 1 – 11 . Whilst much attention has focussed on the diagnostic potential of measuring host gene expression 4 , clinicians are also frequently faced with the problem of predicting the progression of acute illness, but prognostic tests based on gene expression have received less attention 12 , 13 . Most commonly, gene expression analyses utilise mature (spliced) mRNA transcripts, enriched through PolyA selection, or gene-level expression summed across spliced and unspliced (nascent) transcripts. However, recent studies focusing on unspliced transcripts have revealed an additional layer of information about the dynamics of gene expression. These yet-to-be-spliced transcripts, quantified by intron-mapping reads 14 , provide an indication of the future spliced transcript expression 15 – 19 . Taken together, the relative abundance of spliced and unspliced transcripts indicates whether expression of each gene is increasing or decreasing. This concept was formalised through the calculation of “RNA velocity”, using measurements of spliced and unspliced transcripts at a single point in time to predict the future transcriptomic state of single cells, and by reference to the transcriptomes of other cells, to infer future phenotypic states 15 . Since its original description, RNA velocity methods have been refined and widely applied to understand dynamic and developmental relationships between cellular states across numerous biological systems 20 – 24 . We reasoned that RNA velocity analysis might be extended beyond projecting the fate of individual cells and could be adapted to predict the future clinical state of individual people with acute illness, based on a single measurement of their blood transcriptome. To test this hypothesis, we developed a new analysis tool: RNA velocity-based transition predictions of future disease states of subjects in clinical datasets (VeloCD). VeloCD makes predictions based on three core assumptions: the current spliced mRNA expression profile in blood indicates current clinical status; the RNA velocity of blood predicts future spliced mRNA expression; future spliced mRNA expression corresponds to future clinical state. To make predictions, RNA velocity is calculated, and future clinical state is defined, with reference to the transcriptomes of individuals with the clinical outcomes of interest, such as presence or absence of a disease phenotype, or mild vs. severe illness. Quantitative probabilities of transition to each reference group are then calculated. Low dimensional fate maps, like those generated for single cell transcriptomics, can then be produced to visualise the evolution of the response to human disease and relationships between different disease states. Here we test the core assumptions of VeloCD and provide evidence that it can generate meaningful prognostic information. Results RNA velocity predicts future transcriptomic and clinical states in controlled human infections To test the core assumptions underpinning VeloCD, we utilized whole-blood RNA-Seq data (Extended Data Table 1) from two controlled human infection models (CHIMs), in which healthy volunteers were intentionally inoculated with either Influenza A virus 25 or SARS-CoV-2 26 and serial blood samples were collected over the following days (Fig. 1a-d). Although VeloCD does not use serial samples to make predictions, the known timings of inoculation, first-PCR positivity, and sequential samples in these studies provide rare ground truth data against which the predictions of VeloCD can be assessed. 17 of 23 subjects in the influenza study (Fig. 1b), and 17 of 26 in the SARS-CoV-2 study became infected (Fig. 1d), as determined by PCR testing. Amongst infected volunteers there was some variation in time from inoculation to first positive PCR test, and variation in symptom severity, but none became unwell enough to require medical intervention. In those testing positive, the median day of first PCR-positivity was day 2 post-inoculation in both studies, with range of 1–4 days in the influenza virus study, and 1–2 days in the SARS-CoV-2 study. We first investigated whether the current spliced transcript expression in blood is representative of the current infection status in these two cohorts. To do this, we quantified the spliced transcript expression of all multi-exon genes with spliced and unspliced transcript expression, resulting in 21,647 spliced transcripts in the influenza study, and 23,903 spliced transcripts in the SARS-CoV-2 study. When spliced transcript expression was transformed into low-dimensional transcriptomic space using principal component analysis (PCA) 27 (Fig. 1e-f), there was some segregation of samples by both time since inoculation and infection status, with the separation of PCR-positive from PCR-negative subjects becoming more prominent from day 2 for the Influenza study and day 3 for SARS-CoV-2 study respectively. The greatest changes in gene expression appeared to occur on the day after first detection of infection by PCR. We used differential expression analysis (DEA) to identify the genes that distinguish PCR-positive and PCR-negative subjects at these time-points of greatest change in gene expression. In the influenza study, subjects were subset by the day they first tested positive, and the sample from the day after they first tested positive used for DEA. We identified 2,988 differentially expressed genes (DEGs) in day 2 samples between those who first tested positive on day 1 (n = 6) and those who remained PCR-negative throughout (n = 6) (Fig. 1g). There were 892 DEGs in day 3 samples between those who first tested positive on day 2 (n = 9) and those who remained PCR-negative throughout (n = 6) (Fig. 1g). In the SARS-CoV-2 study there were 406 DEGs on day 3 between PCR-positive subjects (n = 17) and those who remained PCR-negative throughout (n = 9; Fig. 1h). Only a single time-point was used for the SARS-CoV-2 analysis because the PCR-positive subjects had more uniform gene expression change over time. Using these sets of DEGs for PCA, there was clear segregation of PCR-positive and PCR-negative subjects in low-dimensional transcriptomic space at these time-points in both studies (Fig. 1i-j). To further elucidate differences between the PCR-positive and PCR-negative subjects for each virus, we used the maSigPro backward selection algorithm to identify spliced transcripts changing differentially over time 28 . This identified 1,563 and 2,700 genes for influenza and SARS-CoV-2 infections, respectively. Figure 1k-l show the spliced transcript expression over time of two of the top genes (ordered by multiple testing corrected p-values) identified across both analyses: Interferon Induced Protein 44-Like ( IFI44L ) and Lymphocyte antigen 6E ( LY6E ). PCA using the genes selected by maSigPro (Fig. 1m-n) shows clear temporal ordering of the samples from PCR-positive, infected, subjects, which are clearly distinct from PCR-negative samples. Taken together, these results from two independent CHIMs indicate that spliced mRNA expression profiles represent current infection status at different timepoints across the course of an acute infection, from exposure through convalescence, discriminating between infected (PCR-positive) and uninfected (PCR-negative) states. Next, we investigated whether RNA velocities could capture future gene expression and corresponding future disease states in these CHIM datasets. Although the power of RNA velocity is that it does not require serial samples to predict trajectories, it is helpful to validate VeloCD using serial samples to show that it produces accurate prediction of future transcriptomic states. We first assessed whether unspliced transcript expression of the genes selected by maSigPro follows biologically informative pattens across time, including upregulation and repression over the course of infection. Unspliced transcripts of IFI44L and Ly6E (Fig. 2a-d) showed parallel profiles of expression to their respective spliced transcripts (Fig. 1k-l). Future spliced transcript expression values are predicted as an intermediate step in the calculation of RNA velocity values 15 . For both CHIM datasets, we used day 2 samples to predict future spliced transcript expression values for all the genes identified as significant in the maSigPro analysis and correlated the predicted future spliced transcript expression with the measured spliced transcript expression on day 3 (Fig. 2e-f). The range of Pearson correlation coefficients across subjects were 0.86–0.99 (mean: 0.96) and 0.77–0.99 (mean: 0.94) in the influenza and SARS-CoV-2 datasets, respectively, indicating that this method accurately models the temporal expression of these genes up to 24 hours in the future. These genes were then used to generate fate maps with the RNA velocity of each subject at each timepoint indicating their predicted future transcriptomic state (Fig. 2g-h). The RNA velocities are highly concordant with the observed temporal progression of the transcriptome along diverging trajectories for infected and uninfected subjects. Together, these analyses illustrate that RNA velocities calculated with VeloCD predict both future transcriptomic and disease states. RNA velocity enables quantitative predictions of future disease state To move towards clinical application, we studied how VeloCD can provide quantitative predictions of transition to defined disease states. To do this, VeloCD uses reference transcriptome data from subjects who have the disease states of interest, and then determines the probability of a test subject transitioning to each group based on their RNA velocity values (Fig. 3a and b). We assessed how well VeloCD predicts future infection status in the CHIM subjects using only samples collected at day 1 post-inoculation. For the influenza study, samples taken the day after the first positive PCR result were used for the infected reference group (n = 15) and the day 3 samples from the subjects who remained PCR-negative throughout were used as the uninfected reference group (n = 6). The day 3 sample from the single infected subject who first tested positive on day 3 was also included in the infected reference (n = 16 in total, Fig. 3a). The single subject who became PCR-positive on day 4 was excluded from the reference groups because no blood samples were collected on days 4 or 5. For the SARS-CoV-2 study, the day 3 samples from PCR-positive individuals were used as the infected reference group (n = 17) and the day 3 samples from those remaining PCR-negative throughout as the uninfected reference group (n = 9, Fig. 3b). Many genes expressed in blood do not vary in expression with changes in disease status, whilst other genes may have non-monotonic expression dynamics which are poorly modelled by VeloCD 29 . Inclusion of these genes in RNA velocity calculations may mask the signal of more informative genes which are changing in response to infection. Consistent with this, we have found that VeloCD works best if it uses transcripts that have been selected to distinguish between reference disease groups, and which vary in expression in association with diverging disease trajectories (Extended Data Fig. 1). Therefore, we developed a more rigorous framework to identify genes with differential expression between the reference groups and with concordance between their RNA velocity (at day n ) and measured change in spliced transcript expression (from days n to n + 1). For the Influenza study, we then used Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify a smaller group of highly informative genes ( BAK1 , APOL3 , SLC9A3-AS1 , PLAAT4, H2AZ2 ), from which we picked the three genes ( BAK1 , APOL3 , SLC9A3-AS1 ) contributing most to the first three principal components (PCs; Extended Data Fig. 2, Extended Data Table 2). Phase portraits of spliced and unspliced transcript expression further confirmed that BAK1 and APOL3 had desirable patterns of transcript expression (Extended Data Fig. 3). BAK1 , APOL3 and SLC9A3-AS1 were input into VeloCD to calculate the probability of each subject becoming influenza PCR-positive, based on their day 1 blood sample. In each run, one test subject was “dropped-in” to VeloCD with the reference samples (Fig. 3a and 3b) (with the removal of that subject’s own sample from the reference prior to each run, to prevent bias due to sample donor identity). This process was repeated for each test subject (n = 23), across each possible number of neighboring samples (the Transition Probability Number of Neighbors (TPNN) hyperparameter, see Methods). Transition probabilities (TPs) were then calculated by VeloCD (see Methods) as the probability of each test subject transitioning to each outcome group (infected or PCR-negative). Sensitivity and specificity were then calculated by varying the probability threshold required to classify a subject as becoming infected (PCR-positive). Collectively, these steps enable the use of VeloCD to make quantitative predictions of future disease state. A TPNN value of 11 was optimal to predict infection status up to 3 days into the future (Area Under the Receiver Operating Characteristic-AUROC Curve value of 0.89; 95% Confidence Interval: 0.76-1; Fig. 3c). The range of AUROC values across TPNN (range 2–19) hyperparameter runs was 0.62–0.89 (Extended Data Fig. 4a). The process of differential expression and DISCO analysis was repeated for the SARS-CoV-2 dataset (Extended Data Fig. 5), identifying 232-genes (Extended Data Table 3). Neither LASSO regression nor selection by contributions of each gene in PCA yielded a smaller set of highly discriminatory genes, therefore all 232 genes were used in VeloCD to predict future SARS-CoV-2 PCR status. At the optimal TPNN of 6, the AUROC was 0.72 (95% CI: 0.51–0.92; Fig. 3d), predicting infection status moderately well up to 48-hours into the future. The range of AUROC values between different TPNN (range 2–25) hyperparameter runs was 0.53–0.72 (Extended Data Fig. 4b). Interestingly, applying this signature to the day 0 (post-inoculation) samples, yielded a predictive performance similar to when day 1 samples were used as the test set (optimal AUROC, 0.73, 95% CI: 0.51–0.95, TPNN: 10, full range of AUROCs: 0.52–0.73), indicating potential to predict up to 3 days into the future. Since influenza and SARS-CoV-2 are both viruses with single stranded RNA genomes 30 , 31 , which activate many common immune pathways 32 , we tested the predictor genes identified in each dataset on the other. The 3-genes selected to predict influenza infection performed moderately well in the SARS-CoV-2 dataset, with an AUROC of 0.76 (95% CI: 0.57–0.96, TPNN: 16, Extended Data Fig. 6; AUROC range 0.56–0.76 across TPPNs 2 to 25), demonstrating validation in an independent dataset. Two of the 3 genes showed clear patterns of expression change across time in both datasets with spliced transcript expression changes in the SARS-CoV-2 infected subjects lagging slightly behind those in the influenza infected subjects (Extended Data Fig. 7), consistent with the divergence between the infected and PCR-negative blood transcriptomes appearing to occur from day 3 and day 2, respectively (Fig. 1e-f). Of the 232 genes selected to predict SARS-CoV-2 infection, 216 were expressed in the Influenza dataset. These did not perform much better than chance to predict future infection status from day 1 samples in the influenza dataset (TPNN: 5, AUROC: 0.53, 95% Confidence Interval: 0.11–0.84), possibly because they were selected to identify the slightly later onset of the transcriptomic response to SARS-CoV-2 infection. If day 2 samples from the influenza study were used as the test set, AUROC values were between 0.75–0.88 (optimal performance: 0.88, 95% CI: 0.72-1, TPNN: 4) across hyperparameters. Taken together, our results in CHIMs demonstrate that it is possible to make quantitative predictions of future disease state based on RNA velocity of blood. RNA velocity predicts onset of TB-IRIS in a clinical trial Whilst the serial sampling in CHIMs is helpful for validation of the assumptions underpinning VeloCD, longitudinal samples should not be a requirement for VeloCD to make informative predictions. Therefore, we next investigated whether VeloCD could be used to predict the onset of a different disease, Tuberculosis (TB)-associated immune reconstitution inflammatory syndrome (TB-IRIS), using samples from a single time-point. TB-IRIS is a paradoxical immunopathological reaction that develops in ~ 18% of patients with HIV-associated TB shortly after initiation of anti-retroviral treatment (ART) 33 . It is characterized by systemic hyperinflammation and new or recurrent symptoms at sites of TB infection 34 . Recent studies have demonstrated that TB-IRIS has a distinct host blood transcriptomic signature with some prognostic potential 35 . We analyzed whole-blood transcriptomes of subjects (n = 95) from the placebo arm of a clinical trial 36 , in which coinfected subjects who had received TB treatment for less than 30 days were started on ART and randomized to receive either prednisone or placebo 36 (Extended Data Table 1). Using samples collected two weeks after ART initiation, which is the peak time of onset of TB-IRIS, we assembled three groups – a test group who developed TB-IRIS after the two week blood sample (range 15–37 days after ART initiation, n = 8), a reference group who had already developed TB-IRIS within the first 14 days of the trial (n = 36), and a reference group who did not develop TB-IRIS during at least 12 weeks of follow-up (no-TB-IRIS, n = 51). The test group was used to examine whether RNA velocity could indicate the future occurrence of TB-IRIS in participants who had not yet developed the syndrome at the two-week sampling time-point. PCA using spliced transcript expression of all genes demonstrated modest segregation of the TB-IRIS and no TB-IRIS patients (Fig. 4a), with those yet to develop TB-IRIS located between the two reference groups. DEA identified 5,108 DEGs between the TB-IRIS and no-TB-IRIS groups (Fig. 4b), and PCA using these DEGs produced somewhat clearer segregation based on TB-IRIS status (Fig. 4c). We then applied the DEA and DISCO analysis steps of the framework described above to this dataset (a full description is given in Extended Data Fig. 8), identifying 1,980 genes to use for prediction of TB-IRIS status (Supplementary Data File. 1). Each test subject was combined separately with the reference subjects to predict their future status using VeloCD (Fig. 4d). The transition probabilities of each test subject to the TB-IRIS group were collated across runs with equivalent hyperparameter values (TPNN: 5–50). Seven of eight (87.5%) test subjects were predicted to develop TB-IRIS (TP threshold: 30%, TPNN: 30, PCA used to construct the fate maps). Across the full range of TPNN values (5–50), using the same 0.3 probability threshold, TB-IRIS was correctly predicted in a median of 75% of test subjects (Extended Data Fig. 9). To illustrate the relationships between subjects and disease states and demonstrate the potential for VeloCD to utilize different low-dimensional embedding methods, a 3-dimensional UMAP-based RNA velocity fate map was generated using all test and reference subjects in the TB-IRIS dataset (Fig. 4e). This shows the two reference groups progressively cluster towards the ends of a continuum with velocity arrows predominantly pointing away from each other, whilst test subjects lie towards the middle with velocity arrows predominantly pointing towards the TB-IRIS group. Despite the small size of the test group, these results demonstrate the potential of using RNA velocity for prediction and exploration of disease trajectories, based on measurements at a single time-point. RNA velocity indicates changes in severity of acute febrile illness We have shown that VeloCD has potential as a prognostic tool when the timing of sampling relative to inoculation of a pathogen or initiation of treatment is controlled, but in real clinical environments, unwell individuals seek health care at different times and stages of illness. Many clinical decisions, such as admission to hospital, investigations, and medication choices, depend on perception of the likelihood that a patient’s condition will improve or deteriorate. This is particularly the case for febrile children, who are often admitted to hospital because they appear unwell and there is concern that they may deteriorate, even though the majority have self-limiting viral illnesses and recover quickly. We therefore explored whether VeloCD could indicate trajectories of illness severity in febrile children. For this analysis, we used whole-blood RNA-Seq data generated from 399 children with suspected infection, recruited to the Personalized Risk assessment in Febrile illness to Optimize Real-life Management across the European Union (PERFORM) study - a multi-country prospective observational study of children with suspected infection presenting to hospitals in Europe 37 . Children were recruited from Emergency Departments (EDs) and Pediatric Intensive Care Units (PICUs), resulting in a wide spectrum of severity ranging from mild (well enough to be sent home from ED), to moderate (admitted to hospital ward), and severe (admitted to PICU). A standardized phenotyping algorithm was applied to group children by proven or likely etiology of illness 38 . The present analysis focused on subjects with proven or suspected bacterial (n = 204) or viral illness (n = 195; probable, definitive, or “syndrome” categories of the diagnostic algorithm 38 ; Table 1). Whilst accuracy of the etiological diagnosis is expected to vary between these categories, definitive diagnosis is rarely known at the time of presentation and therefore should not be essential for predicting outcome. For each subject, whole-blood RNA-Seq data from the first sample collected at or shortly after the time of presentation was analyzed (Extended Data Table 1). PCA analysis of spliced transcript expression of all multi-exon genes with detectable spliced and unspliced transcript expression from all subjects revealed some segregation by severity with the mild and severe groups most distinct from each other and the moderate severity group positioned between them, but there was considerable overlap (Fig. 5a). Unsurprisingly, across the entire cohort, the etiology of infection (bacterial or viral) was related to disease severity, with the bacterial infections over-represented (71.4%) in the group recruited from PICU, compared to 51.1% and 33.3% of the moderate and mild illness groups, respectively (Table 1). We created a test-set for later RNA velocity prediction, comprised of 7 subjects recruited in the ED but transferred promptly to the PICU (severe-illness, all bacterial), all 184 subjects admitted from ED to the ward (moderate illness), and 20 (10 bacterial, 10 viral) randomly selected mild-illness subjects. Using the remaining mild and severe subjects we sought to minimize the impact of etiology on selection of genes for RNA velocity analysis, by performing separate DEAs between subjects with mild (n = 68) and severe (n = 26) viral infections, and between subjects with mild (n = 29) and severe (n = 65) bacterial infections, identifying 4,915 and 7,087 DEGs respectively (Fig. 5b). 2,348 of these DEGs had concordant patterns of up- or down-regulation between the bacterial and viral groups and were taken forward. PCA analysis using this subset of 2,348 genes with spliced and unspliced transcript expression revealed clearer segregation of mild and severe illness (Fig. 5c). We then used a similar gene selection framework to the preceding analyses (Extended Data Fig. 10), incorporating LASSO regression to identify a smaller set of 59 genes for quantitative prediction of future clinical state with VeloCD. We used these 59 genes to create a fate map, in which we also incorporated data from 19 healthy control subjects to illustrate their relationship to the mild and severe illness groups in transcriptomic space (Fig. 5d). This fate map illustrates diverging trajectories of severe and mild disease with the RNA velocity arrows of many mild subjects pointing towards the healthy controls, presumably indicating a trajectory towards recovery from illness. Interestingly, the mild bacterial illness subjects showed some segregation from the mild viral illness subjects, perhaps suggesting different sub-trajectories within mild illness. Using the remaining mild illness cases (n = 97) and the PICU-recruited severe cases (n = 91) as reference groups, we then evaluated the predictive performance of VeloCD using the 59 genes (Fig. 5e). VeloCD was run for each test subject (n = 211). TPs to severe illness were calculated across independent runs for each subject using TPNN values from 10–70, and the median value was used for further analysis. The mild and severe subjects in this test set had very polarized TPs (Fig. 5f). Median TP to severe illness was 0.0035 (viral, 0.0018; bacterial, 0.023) in the mild illness group and 95% (n = 19) of these subjects had TPs below 0.25. The median TP values of the severe illness group (presenting to ED but requiring immediate transfer to PICU, n = 7) was 1.0 and 85.7% (n = 6) of these subjects had TPs above 0.75, suggesting that these probabilities could potentially be used as the basis of a risk stratification system (Fig. 5f). The complete range of TP values across TPNN runs is shown for the entire test cohort in Extended Data Fig. 11. Next, we examined predictions in the subjects with moderate illness, who required admission to the hospital ward. As might be expected, there was much more variability in TPs for these subjects suggesting more variable disease trajectories. Interestingly, the TPs of the moderate viral patients (median TP value, 0.0019) were much more skewed towards zero than their bacterial counterparts (median TP value, 0.043; two-sided Kolmogorov-Smirnov test p = 0.0028; Fig. 5f). In this observational study, subjects received treatments determined by their attending physicians which would likely reduce the chances of those with moderate illness progressing to severe illness (and preclude meaningful analysis of categorical prediction). Consistent with this only 3.8% (n = 7) of the moderate severity subjects required later admission to PICU (range: 1–9 days from ward admission). However, these 7 subjects had a median TP value of 0.36 (range: 0.0–1.0), which was significantly higher than the median TP value in those not requiring later admission to PICU (n = 177, median TP value: 0.0080, range 0.0–1.0, Wilcoxon signed rank p-value 0.045). We assessed whether moderate illness subjects with higher transition probabilities had other clinical evidence consistent with a more severe trajectory of illness, based on the other available information in this dataset. Baseline clinical features of subjects in a hypothetical higher-risk group (TP to severe disease ≥ 0.25, n = 43) were not significantly different from those with lower transition probabilities (TP < 0.25, n = 141) at time of presentation (Extended Data Table 4), although odds ratios above unity indicated a tendency for these subjects to have more features of severe illness, and bacterial rather than viral infection. Mann-Whitney U tests identified significant differences in the distribution of baseline C-reactive protein (CRP; TP ≥ 0.25, n = 41, median (IQR) 60.3mg/L (16.6-166.7); TP < 0.25, n = 131, median (IQR) 13.2mg/L (4.1–49), p-value = 1.67x10 − 5 ) and maximum CRP (TP ≥ 0.25, n = 43, median (IQR) 71.9mg/L (25.1-196.7); TP < 0.25, n = 137, median (IQR) 21mg/L (6–64); p-value = 4.81x10 − 5 ; Extended Data Fig. 12) between the TP groups. There was a significant correlation between the time from ward admission to hospital discharge and median TP values (Spearman-rank ρ = 0.17, n = 179, p-value = 0.023) and the subjects with higher TP values were more likely to require surgical interventions (Odds Ratio = 4.02, p-value = 0.0052), all of which may be consistent with a more severe course of illness (Extended Data Table 4). Taken together with preceding results, these findings support the concept that RNA velocity can be used to make clinically meaningful predictions of future disease states, such as individuals most likely to recover or progress to severe illness. Discussion Predicting the trajectory of acute illness is a common challenge for clinicians, who usually adopt a cautious approach to avoid missing patients who may develop life-threatening deterioration. This inevitably results in over-treatment and unnecessary admissions to hospital for many who would have milder illness trajectories, but also sometimes missed opportunities to intervene for those who appear well at presentation but subsequently deteriorate rapidly. More precise prediction of future disease state and severity could enable better risk stratification and clinical management decisions, and better use of healthcare resources. We have shown that using RNA velocity to predict future clinical status may be a promising solution to this problem. We developed VeloCD as a new tool to predict future disease states, over a timescale of days to weeks, from whole-blood RNA-Seq data. The concept of VeloCD is based on observations across multiple diseases, that the whole-blood transcriptome reflects a patient’s current disease state (both cause and severity) at the time of sampling, and that RNA velocities indicate future transcriptomic states, which therefore represent future disease states. In many cases, small sets of genes (“signatures”) have been identified to discriminate between different current disease states 5 , 11 , 39 , 40 , which have led to the possibility of using these gene signatures in diagnostic tests 7 , 41 . Similarly, we have shown here that a gene selection framework (Extended Data Fig. 1) can be applied to identify smaller sets of genes which can be used for RNA velocity analysis to predict future disease states, across different diseases, study designs, and outcome measures. These examples illustrate potential clinical applications of RNA velocity, such as: early identification of individuals who are infected or uninfected after exposure to a pathogen, which could inform decisions about isolation or early treatment; identification of at risk individuals who will or won’t develop a disease manifestation, determining the need for preventive treatment; risk stratification based on trajectory of acute illness, which could help determine which patients can be sent home from the ED, should be admitted to hospital, or are highest risk and need urgent intervention. Other applications could include improved understanding of pathogenesis and mechanisms of progression of acute illnesses, and stratified analyses of clinical trials to determine efficacy of treatments based on risk of outcome. Although we expected that RNA velocity would only predict future disease state over a timescale of hours to a few days, it is interesting that predictions of TB-IRIS could be made days to weeks into the future, which may indicate additional utility if there is a lag between onset of a disease process and its clinical manifestation. Although we have used RNA-Seq data as the basis for predictions in the current work, the identification of small sets of informative genes for calculation of RNA velocity opens the potential for translating this approach to other detection methods. Spliced and unspliced transcripts for each gene could be measured using quantitative PCR or loop mediated isothermal amplification methods, which would allow measurement on common and emerging diagnostic platforms 41 . Further studies will be required to assess such approaches. Our study has several limitations. RNA velocity was originally developed for analyses of 1000s of single cells rather than the tens or hundreds of samples available in our analyses. Whilst we divided our data in separate reference and test sets, larger studies would allow more generalizable reference datasets, larger datasets for gene selection, and independent test and validation datasets for assessment of prediction accuracy. Although we showed some stability of performance across a range of TPNN values, our prediction of future disease states was made using selected subsets of genes and hyperparameters, which risks over-optimistic performance. It will be important for future studies to evaluate prediction accuracy across independent datasets. In our analysis of the PERFORM study, relatively few outcome measures could be used to determine whether predictions of future severity were accurate for the moderately ill subjects who were admitted from ED to the ward – a group in which we believe there may be the most to gain from accurate prediction of future severity. Future observational studies will also face the challenge that association between prediction and observed outcome is modified by treatment given to patients and might need to collect more detailed data on early changes in physiological status and medical care to evaluate trajectories of illness. This study has shown proof-of-concept that RNA velocity can be applied to whole blood samples to predict future disease states. Further work will be required to develop and validate predictive tests based on this approach. If successful, this could lead to earlier detection and treatment of diseases and better risk stratification to aid clinical management decisions. Materials & Methods Ethical Approval and Conduct of Human Studies Controlled human infection model (CHIM) studies Influenza and SARS-CoV-2 CHIM studies were performed as previously described 25 , 42 , conducted in accordance with the protocol for each study, the Consensus ethical principles derived from international guidelines including the Declaration of Helsinki and Council for International Organizations of Medical Sciences (CIOMS) International Ethical Guidelines, applicable International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) Good Clinical Practice guidelines, applicable laws and regulations. The studies were approved by UK Health Research Authority Research Ethics Committees (references: 11/LO/1836, 19/LO/1441, 20/UK/2001 and 20/UK/0002). For all studies, written informed consent was obtained. In the influenza CHIM study, healthy adult volunteers aged 18–55 with microneutralising antibody titres of 1:20 or less for the inoculating virus were enrolled and challenged with 5x10 5 tissue culture infectious dose 50 (TCID50) of influenza A/Belgium/4217/15 (H3N2; SGS, Antwerp, Belgium) by intranasal drops. Participants were quarantined for up to 10 days post-inoculation during which samples were taken, with outpatient follow-up continuing until 6 months post-inoculation. Generation of whole-blood paired-end RNA-Seq data is described in Rosenheim et al., 2023 43 . Data used in the present study originated from 23 individuals with 7 or 8 samples each taken at day 0 pre-inoculation and then day 1, 2, 3, 7, 10, 14 and 28 after inoculation with Influenza A/Belgium/4217/2015 (total n = 178 samples). Day 14 and 28 samples were not used in our analyses, leaving 138 samples from days 0–10. For the SARS-CoV-2 CHIM study, healthy adult volunteers aged 18–30 with no serological evidence of previous COVID-19 infection or vaccination were enrolled. Participants were challenged with 10 2 TCID50 of a D614G-containing pre-Alpha SARS-CoV-2 challenge virus by intranasal drops and quarantined for at least 14 days post-inoculation, with follow-up until 1-year post-inoculation. The generation of the HCIM SARS-CoV-2 whole-blood paired-end RNA-Seq data is described in Rosenheim et al., 2023 43 . Peripheral blood was collected from 35 participants on day 0 prior to inoculation and then on days 0, 1, 2, 3, 4, 5, 7, 10, 14 and 28 post-inoculation (n = 375 samples). The day 28 samples were not used in our analysis, and we also excluded all samples from two participants who sero-converted during pre-inoculation quarantine 26 , and 7 subjects with equivocal infection status based on PCR results, leaving 257 samples from 26 individuals for analysis. Subjects classified as infected (PCR positive) were required to have multiple consecutive positive PCR tests and their first day of PCR-positivity was then defined by the first positive sample from nasal or throat swab. Subjects classified as uninfected (PCR negative) were required to be negative by PCR in throat and nose swabs throughout the entire course of the study. TB-IRIS study Transcriptomic analysis was conducted on samples from the PredART trial, a phase 3, randomised, double-blinded, placebo-controlled study to evaluate if prophylactic prednisone can safely prevent TB-IRIS occurrence in high-risk patients 36 , 44 . The study was approved by the University of Cape Town Human Research Ethics Committee (HREC 136/2013). The study was conducted between August 2013 and March 2017 with 240 HIV-TB co-infected patients enrolled in 4 different TB clinics in Khayelitsha, South Africa. All participants provided informed consent for the laboratory study. Whole blood was collected in PAXgene tubes prior to commencing the randomised treatment, and at day 14 after starting treatment, from which total RNA was extracted using the PAXgene Blood RNA kit (PreAnalytiX). Total RNA sequencing library was prepared using Ovation Universal Human Blood kit (Tecan Genomics) and sequenced on an Illumina HiSeq4000 instrument using SR100 reactions with minimum 25 million reads. Only day 14 samples from the placebo group of the trial were used for the present study. One subject with sepsis and samples with less than 50% of their reads uniquely mapped to the human genome were excluded from further analysis, leaving 95 samples from 95 subjects. PERFORM The Personalised Risk assessment in Febrile illness to Optimise Real-life Management (PERFORM) study was a multi-center, prospective, observational cohort study, seeking to improve the diagnosis of febrile illness in children across Europe ( https://www.diamonds2020.eu/our-research-history/perform/ ). Ethical approval was granted to the coordinating site (London – Central Research Ethics Committee: 16/LO/1684), and by local ethics committees for each recruitment site (see Supplementary Information for details). Children (< 18 years old) presenting with fever, recent history of fever, or other features of suspected infection, who were considered ill enough by the medical team to warrant blood tests, were recruited from emergency departments, paediatric inpatient wards, and intensive care units, in 9 European countries, between August 2016 and December 2019. Full details of the study design and protocol have been published elsewhere 45 , 46 . All decisions on clinical investigation and management were made by attending clinicians in accordance with local practice. Each patient was assigned a final diagnostic classification by local study teams based on prospectively collected clinical and laboratory data and according to a standardized phenotyping algorithm 38 , 45 . Data entry and phenotyping were quality controlled by cross-site validation and automated checks within the study database 46 . For the present study, severity of illness was classified, based on level of care, as mild (well enough to be sent home from ED), moderate (admitted to hospital ward), or severe (admitted to PICU). Blood was collected for research purposes at the same time as the first clinically indicated samples (wherever possible), and otherwise within 48 hours of admission. 2.5ml whole blood was collected for RNA expression analysis in PAXgene tubes (for children below 1 year of age, 1ml blood was collected into appropriately reduced volumes of PAXgene fluid) and frozen for later analysis. Total RNA was isolated using PAXgene miRNA blood extraction kits (Qiagen), and after additional DNAse treatment (Zymo Research), was sent for ribodepletion library preparation and RNA-Seq at The Wellcome Centre for Human Genetics in Oxford, United Kingdom, using a Novaseq6000 platform at 150bp paired-end configuration, generating a raw read count of ~ 30 million reads per sample. Further details of the PERFORM RNA-Seq data generation are given in Jackson et al., 2023 47 . Ribodepletion was performed during library preparation. For the present analysis, we included subjects with RNA-Seq data whose samples were collected on presentation in the emergency department or collected in PICU (within 48 hours of presentation and > 24 hours before PICU discharge), who had complete data on disposition (dates and locations of admission and discharge), and who were assigned one of the following diagnosis phenotypes: bacterial syndrome, probable bacterial, definite bacterial, viral syndrome, probable viral, definite viral. Two subjects each had two clinical episodes (> 1 year apart), which were treated as separate events, giving a total of 399 infection episodes for analysis. We also included 19 non-infection control subjects recruited to PERFORM, predominantly from out-patient settings, who were well at the time of attendance and the reason for attendance was unlikely to affect the blood transcriptome, for example pre-surgical assessment for polydactyly or tonsillectomy. VeloCD Spliced and unspliced transcript expression are input into VeloCD alongside two column metadata (maximum: 2, sample and group columns) files (CSV file format). VeloCD first constructs low-dimensional representations of the spliced transcript expression of the genes in a dataset using PCA 48 , tSNE 49 or UMAP 50 . VeloCD then uses the spliced and unspliced transcript expression to calculate RNA velocity values, which indicate the direction and magnitude of future spliced transcript expression changes. These are then used to calculate transition probability (TP) values. UMAP and tSNE both use a Number of Neighbours (NN) hyperparameter (also called perplexity) to specify the balance between the conservation of the local and global structure of the data from high to low-dimensional transcriptomic space. VeloCD builds on the methods established for the single-cell velocity tool, velocyto 15 . It works under the null hypothesis that no gene expression change is occurring (ie. gene expression is in a steady state). RNA velocities are then calculated as a deviation from this assumption: the modelled unspliced transcript expression subtracted from the actual unspliced transcript expression 15 . Positive velocity values indicate higher expression than modelled under the steady state assumption, indicating increasing gene expression, and negative values indicate decreasing gene expression. These values are then summed across genes to give overall RNA velocity values for each sample 15 . Velocity values are embedded as arrows on the low-dimensional transcriptomic space to give RNA velocity fate maps. A full description of these steps is given in La Manno et al., 2018 15 . Velocyto and VeloCD both perform these steps, but VeloCD has been further adapted for analysis of whole-blood RNA-Seq data. This adaptation process included the following: the removal of the spliced and unspliced normalisation steps from velocyto, removal of the grid-based velocity arrow visualisation, the introduction of UMAP-based fate maps, and the inclusion of functions that take the sample-to-sample transition probabilities and calculate these across groups. All expression normalisation steps are now performed prior to the input of the spliced and unspliced transcript expression data into the algorithm (see RNA-Seq pre-processing). To embed the RNA velocity values, VeloCD and velocyto both use RNA velocity values to calculate probabilities that each sample is transitioning to each other sample 15 . A nearest neighbour search identifies the n nearest neighbours of each sample (defined by the user as the transition probability nearest neighbour (TPNN) value in VeloCD) in low-dimensional transcriptomic space. The probability values for each sample’s non-neighbours are then converted to zero and the remaining non-zero values re-scaled to sum to 1. As additional functionality only present in VeloCD, this algorithm then takes the sample-to-sample transition probabilities of each sample and sums them between (user-defined) groups, giving the sample-to-group transition probability (TP) values used to predict future disease outcomes of each subject. For prediction analysis, VeloCD uses a “drop-one-in" approach where each sample in a user-defined “test set” is iteratively added into the spliced and unspliced expression files input into VeloCD. The algorithm is then run and the TP values for the “dropped in” sample are then extracted in R and collated across TPNN runs. Low-dimensional embeddings are regenerated for each sample in a test set but remain the same across repeat analyses of the same sample using a range of TPNN values. For UMAP and tSNE the results shown were generated using a single number of neighbors (perplexity value). VeloCD is implemented as a python-based downloadable software tool that can be run from the bash command line. Several R scripts are also called by the python code. Transcriptomics RNA-Seq Pre-processing The fastq files from all RNA-Seq datasets were first quality control checked using FastQC 51 , mapped using STAR 52 and quantified using featureCounts 53 . The strandedness of the data was confirmed using the infer_experiment.py programme of the RSeQC package 54 . Mapping statistics for all datasets are summarised in Extended Data Table 1. Any samples with a percentage of uniquely mapped reads ≤ 50% were excluded. When quality control revealed evidence of rRNA contamination, genes whose biotype was labelled as rRNA, Mt_rRNA, rRNA_pseudogene and ribozyme were removed from downstream analysis. FeatureCounts was run using the “meta feature” option meaning reads were collated from all exons (per gene exon-level), introns (per gene intron-level) or features (gene) that overlapped a single gene into three separate gene-specific counts, using the human GRCh38 genome assembly fasta and GTF files from Ensembl. Prior to read mapping, the GTF file was edited to reduce the transcripts of the same gene down into the minimal non-overlapping set, with introns introduced between the new set of non-overlapping exons. This ensured a 1:1 ratio between the number of genes with one non-overlapping transcript and the number of “transcript” level features. This method also ensures that intronic sequences do not overlap any annotated exons of the same gene. When required, reads are summed for each sample across sequencing lanes. The exon-level reads were used as an approximation for the spliced transcript expression 16 , 19 . The intron-level reads were used as an approximation for the unspliced transcript expression 16 , 19 . Genes with introns overlapping any exons of another gene on the same strand were removed from downstream analysis. Similarly, genes with any introns labelled with the biotype “retained intron” were also removed. Only genes with both spliced and unspliced transcript expression were used for analysis. Spliced and unspliced transcript expression were then separately normalised for library size differences using the edgeR trimmed mean of m-values method and returned as log 2 transformed counts per million values. Principal Component Analysis Principal component analysis (PCA) was performed using the R packages: PCAtools 27 , ggplot2 55 , ggbiplot 56 , factoMineR 57 and factoExtra 58 , 59 . The latter two packages were also used to extract the percentage contribution of each gene to each PC. The scale = FALSE argument was used for the generation of all PCA plots. Differential Expression Analysis Genes with very low expression in each dataset were excluded from analysis by filtering on raw gene-level expression values to exclude genes which did not have counts of least 5 reads in at least 3 samples. Differential expression analysis (DEA) of the CHIM datasets (Influenza and SARS-CoV-2) was performed using the glmFit and glmLRT functions from the edgeR package in R. The DESeq function from the DeSeq2 package was used to perform the DEA of the TB-IRIS and PERFORM datasets 60 . Methods were chosen based on their suitability to the datasets. PCA is used to determine which clinical variables are driving the axes of variation and should be included as covariates in their respective DEA. Sex was included as a covariate in the design matrices of the CHIM and TB-IRIS datasets. Age was also included in the design matrix of the SARS-CoV-2 CHIM dataset. XIST was later removed from the list of DEGs for the TB-IRIS cohort. Gene-level expression values were required for this analysis because edgeR requires raw non-normalized expression 61 . All volcano plots were generated using the EnhancedVolcano package in R 62 . Gene Selection We developed a framework to select genes with desirable characteristics for RNA velocity-based prediction (Extended Data Fig. 1). First, DEA is used to identify genes with DE between the reference “outcome groups” of interest (eg., infected vs. PCR negative, step 1). Discordance-concordance (DISCO, step 2) analysis is then used to enhance identification of genes with concordance between calculated RNA velocity and changing expression, because it has previously been observed 63 that RNA velocity calculations can produce misleading results when there are very rapid changes in expression. This is because RNA velocity uses the expression of “extreme” samples (very low and very high expression) to model the steady state, which becomes inaccurate when there are a small number of samples with very high and/or low expression 63 . This was problematic when identifying genes that were suitable for VeloCD as many undergoing this change had nonsensical and ultimately useless RNA velocity values. By incorporating knowledge of future expression into this process the DISCO step identifies genes unaffected by this phenomenon. When serial measurements are unavailable, the unspliced minus spliced transcript expression can also be used as an approximation of the expected direction of RNA velocity. The gene selection framework then takes the intersection of the DEA and DISCO analysis as the initial gene set (step 3), which can then be reduced further using feature selection methods including LASSO regression 64 (optional, step 4) and selecting those contributing most to the top PCs in PCA (optional, step 5). MaSigPro The R package maSigPro was used to perform backward selection to identify genes that were changing across time and also had divergent expression between groups. For the influenza dataset (n = 138), genes with low spliced and unspliced transcript expression were filtered prior to this analysis (≥ 3 in ≥ 3 samples for both spliced and unspliced transcript expression). The participant IDs were included as the “Replicate” design matrix covariate in this analysis. For the SARS-CoV-2 dataset, the post-inoculation day 0 samples were assigned the value 0.5. Low expression pre-filtering was performed prior to this analysis (spliced and unspliced transcript expression ≥ 3 in ≥ 3 samples). The genes identified were then input into VeloCD for exploratory analysis (TPNN: 25). Statistical Analysis Fisher’s exact tests (fisher.test() function in R) were used to determine the significance of the relationship between clinical features and the median TPs (to the PICU) grouped by a threshold value (≥ or < 0.25). A two-sided p-value cut-off of 0.05 was used to determine significance. Pearson correlations were performed using the cor.test() function with the “pearson” method argument in R. Similarly, Spearman-rank correlations were performed using this same function but with the “spearman” argument. The wilcox.test() function was used to perform Mann-Whitney U tests. For all statistical tests, a two-sided p-value cut-off of 0.05 was used to determine significance. For the DEA, the Benjamini-Hochberg method was used to adjust for multiple testing and a threshold of 0.05 was used to determine statistical significance. MaSigPro returns Benjamini-Hochberg adjusted two-sided p-values. The ks.test() function was used to perform two-sided Kolmogorov-Smirnov tests between two distributions. Sensitivity and specificity values were calculated across equivalent TPNN runs of VeloCD, and ROC curves, AUROC and 95% confidence intervals were constructed and calculated using the roc(), rocit(), auc() and ci.auc() functions from the ROCit 65 and pROC 66 R packages. The hyperparameter run with the highest AUC was then selected as the “best performance” of the signature. Declarations Data Visualisation Raincloud plots were generated using ggthemes 67 , ggdist 68,69 and ggplot2 55 . Data availability The raw and processed RNA-Seq data (including spliced and unspliced expression) from the TB-IRIS study is available under the GEO accession number GSE274086. Raw RNA-seq data from the PERFORM study has been deposited in ArrayExpress (409 samples), with the exception of 9 samples for which permission for public sharing of sequence data was not granted by participants. Data from the SARS-CoV-2 and influenza controlled human infection studies is available through European Genome-Phenome Archive (EGA) as described previously 43 . Reasonable applications for access to data in EGA will be considered by the relevant data access committees and data may be shared after completion of a data access agreement. Code availability VeloCD is available as a downloadable software tool (including documentation) in github. It is accessible using the following link: https://github.com/DrClaireDunican/VeloCD/tree/main Acknowledgements We are grateful to the volunteers who participated in the studies described in this work. We gratefully acknowledge funding support from the following sources: the European Union’s Horizon 2020 Research and Innovation programme (grant agreement numbers 668303 and 848196); the UK MRC (MR/R008922/1 to RPJL) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement and is also part of the EDCTP2 program supported by the European Union (MR/L006529/1 to A.J.C.); the UK research and Innovation Engineering and Physical Sciences Research Council (PhD and Postdoctoral prize fellowships to CD); the NIHR Imperial Biomedical Research Centre (BRC, support to CC and CD); NIHR Biomedical Research Centre at University College London Hospitals (MN); the Wellcome Trust (207511/Z/17/Z to MN); the UK Vaccine Taskforce of the Department of Business, Energy and Industrial Strategy of Her Majesty’s Government (BEIS); NIH Centres of Excellence for Influenza Research and Surveillance (CEIRS, contract number HHSN272201400008C); Defense Advanced Research Projects Agency (grant number 140D6319C00236); Taiwan’s National Science and Technology Council (grant number 110-2923-B-006-001-MY4, to C-FS). We thank the following individuals and organizations for their invaluable contributions to development and implementation of the influenza and SARS-CoV-2 human challenge projects: Chris Woods (Duke University), SGS (Belgium) for supplying the influenza challenge agent, Andrew Catchpole (hVIVO), the NIHR Clinical Research Network staff at The Royal Bolton Hospital, Human Infection Challenge Network for Vaccine Development (HIC-Vac) and ISARIC4C Investigators (https://isaric4c.net/about/authors/). 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Compr R Archive Netw Retrieved March 2022 Table 1 Table 1: The clinical characteristics of the PERFORM study subjects (n=399) Additional Declarations Yes there is potential Competing Interest. Aubrey J. Cunnington, Claire Dunican, Myrsini Kaforou, and Mauricio Barahona, are named as inventors on a patent application filed by Imperial College London, titled "A method to predict clinical outcome", Case Number 11445. All other authors have no relevant conflicts of interest. Supplementary Files ExtendedDataFiguresandTables.docx RNAVelocitySupplementary13122024.docx Supplementary Information SupplementaryDataFile.1.xlsx Supplementary Data File 1. 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London","correspondingAuthor":false,"prefix":"","firstName":"Rachel","middleName":"","lastName":"Lai","suffix":""},{"id":399167456,"identity":"66d1e11c-f33d-4e5f-a758-89305ef35ea2","order_by":50,"name":"Graeme Meintjes","email":"","orcid":"","institution":"University of Cape Town","correspondingAuthor":false,"prefix":"","firstName":"Graeme","middleName":"","lastName":"Meintjes","suffix":""},{"id":399167457,"identity":"1dc394dc-b74e-4ff2-99e9-a87a3dab13c7","order_by":51,"name":"Christopher Chiu","email":"","orcid":"https://orcid.org/0000-0003-0914-920X","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Chiu","suffix":""},{"id":399167458,"identity":"ea0dd750-b4a0-493f-875d-f67cf0d7f455","order_by":52,"name":"Mauricio Barahona","email":"","orcid":"https://orcid.org/0000-0002-1089-5675","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Mauricio","middleName":"","lastName":"Barahona","suffix":""},{"id":399167459,"identity":"6d3ae590-f947-43a5-b5d9-8748d7bf56ce","order_by":53,"name":"Myrsini Kaforou","email":"","orcid":"https://orcid.org/0000-0001-9878-4007","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Myrsini","middleName":"","lastName":"Kaforou","suffix":""},{"id":399167393,"identity":"4c5b1d37-7d10-4956-bf8c-1e3f0f03c42a","order_by":54,"name":"Aubrey Cunnington","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIiWNgGAWjYPACCQj1wQBEMjYwPABz2QhoAcozzoBpSSCsBSLPzAPj4NPC38D8TLpwh0WefHzzscc2Bdvy+KUPN35IYLCTZ5BIS8DqogNsZtIzz0gUGx5jSzfOMbhdLNmX2CyRwJBs2CCRdgCrNQd42KR52yQSN7bxmEkDtSRuOMPYANTCnMAgkd6ATYc8ihYLoJb9ZxibfyQw1OPUYgDTMp8NqIUBZAsPYxvQlsNALdgdZniYzdgapGUDW1qaZA/QLxJnGNssEgyOG7bxPMPqfbnjzQ9v87bVJc5vPnxM4sef23n8PeyPb3yoqJbnZ08zwOp9ZrgLITTUYAPCEckg34CiZRSMglEwCkYBAgAAKx5VfIvwl/cAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1305-3529","institution":"Imperial College London","correspondingAuthor":true,"prefix":"","firstName":"Aubrey","middleName":"","lastName":"Cunnington","suffix":""}],"badges":[],"createdAt":"2025-01-04 15:10:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5764288/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5764288/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-026-71685-5","type":"published","date":"2026-05-06T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75314828,"identity":"fcc85cbf-a7a8-4a56-8a8c-f74d688f719c","added_by":"auto","created_at":"2025-02-03 09:33:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":308949,"visible":true,"origin":"","legend":"\u003cp\u003eThe blood transcriptome response to respiratory viral challenge.\u003c/p\u003e\n\u003cp\u003eAnalysis of blood RNA in subjects inoculated with influenza A virus (\u003cstrong\u003ea-b\u003c/strong\u003e,\u003cstrong\u003e e\u003c/strong\u003e,\u003cstrong\u003e g, i\u003c/strong\u003e, \u003cstrong\u003ek \u003c/strong\u003eand \u003cstrong\u003em\u003c/strong\u003e) or SARS-CoV-2 (\u003cstrong\u003ec-d\u003c/strong\u003e,\u003cstrong\u003e f\u003c/strong\u003e,\u003cstrong\u003e h\u003c/strong\u003e,\u003cstrong\u003e j\u003c/strong\u003e, \u003cstrong\u003el\u003c/strong\u003e and \u003cstrong\u003en\u003c/strong\u003e). \u003cstrong\u003ea \u003c/strong\u003eand \u003cstrong\u003ec \u003c/strong\u003e| Schematic of sequential blood draws.\u003cstrong\u003e b \u003c/strong\u003eand \u003cstrong\u003ed\u003c/strong\u003e | Characteristics of study participants. |\u003cstrong\u003e e\u003c/strong\u003eand \u003cstrong\u003ef\u003c/strong\u003e | PCA plots of the spliced transcriptome across time following influenza (n=138 samples, 21,647 genes, \u003cstrong\u003ee\u003c/strong\u003e) and SARS-CoV-2 inoculation (n=257, 23,903 genes,\u003cstrong\u003e f\u003c/strong\u003e).\u003cstrong\u003e g \u003c/strong\u003e| Volcano plots showing differentially expressed genes (DEGs) in day 2 samples between those who first tested positive for influenza at day 1 (n=6) vs those persistently negative (n=6; 2,988 DEGs, top), and in day 3 samples for those who first tested positive for influenza on day 2 (n=9) vs those persistently negative (n=6; 892 DEGs, bottom). \u003cstrong\u003eh\u003c/strong\u003e |\u003cstrong\u003e \u003c/strong\u003eVolcano plot showing DEGs in day 3 samples from those who were positive for SARS-CoV-2 (n=17) vs those persistently negative (n=9; 406 DEGs).\u003cstrong\u003e i \u003c/strong\u003eand\u003cstrong\u003e j \u003c/strong\u003e| PCA plots of the unique multi-exon DEGs identified in day 2 and 3 samples of influenza challenge\u003cstrong\u003e \u003c/strong\u003e(n=46 samples, 2,115 genes,\u003cstrong\u003e i\u003c/strong\u003e) and day 3 of SARS-CoV-2 challenge (n=26 samples, 294 genes,\u003cstrong\u003e j\u003c/strong\u003e).\u003cstrong\u003e k \u003c/strong\u003eand\u003cstrong\u003e l \u003c/strong\u003e| spliced transcript expression of two genes with greatest changes over time between infected subjects (red) and PCR-negative subjects (blue; influenza, n=138 samples; SARS-CoV-2, n=257 samples). \u003cstrong\u003em \u003c/strong\u003eand \u003cstrong\u003en \u003c/strong\u003e| PCA plots using all genes with significant changes in spliced transcript expression over time between infected and uninfected subjects (\u003cstrong\u003em\u003c/strong\u003e, influenza, n=138 samples, 1,563-genes;\u003cstrong\u003e n\u003c/strong\u003e, SARS-CoV-2, n=257 samples, 2,700 genes). The red line illustrates temporal ordering of samples, blue circle identifies samples from subjects who remained PCR-negative throughout. Day 0 (pre) and (post) indicate samples pre- and post-inoculation, respectively.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5764288/v1/e6f07fcc815a8ff9cdd0d73b.png"},{"id":75314844,"identity":"3ad90bf8-726a-4392-b00f-5fbeef4d31cc","added_by":"auto","created_at":"2025-02-03 09:33:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":161491,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the balance of unspliced and spliced transcripts in blood are predictive of future transcriptomic and disease states.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea-d\u003c/strong\u003e |\u003cstrong\u003e \u003c/strong\u003eUnspliced transcript expression of \u003cem\u003eIFI44L\u003c/em\u003e (\u003cstrong\u003ea\u003c/strong\u003e, \u003cstrong\u003ec\u003c/strong\u003e) and \u003cem\u003eLY6E\u003c/em\u003e (\u003cstrong\u003eb\u003c/strong\u003e, \u003cstrong\u003ed\u003c/strong\u003e) over time in influenza A (\u003cstrong\u003ea\u003c/strong\u003e, \u003cstrong\u003eb\u003c/strong\u003e) and SARS-CoV-2 (\u003cstrong\u003ec\u003c/strong\u003e, \u003cstrong\u003ed\u003c/strong\u003e) infections, respectively.\u003cstrong\u003e e \u003c/strong\u003eand \u003cstrong\u003ef \u003c/strong\u003e| predicted future spliced transcript expression (calculated at day 2) versus actual spliced transcript expression at day 3 for influenza (1,563-genes, \u003cstrong\u003ee\u003c/strong\u003e) and SARS-CoV-2 (2,700 genes, \u003cstrong\u003ef\u003c/strong\u003e). Genes (dots) were selected using maSigPro and are coloured by sample. \u003cstrong\u003eg\u003c/strong\u003e and\u003cstrong\u003e h\u003c/strong\u003e| RNA velocity fate maps for influenza (n=138 samples, 1,563-genes, \u003cstrong\u003eg\u003c/strong\u003e) and SARS-CoV-2 (n=257 samples, 2,700 genes, \u003cstrong\u003eh\u003c/strong\u003e) studies. RNA velocity arrows indicate predicted transcript trajectories. Transition Probability Number of Neighbor (TPNN) values: 45 (\u003cstrong\u003eg\u003c/strong\u003e) and 25 (\u003cstrong\u003eh\u003c/strong\u003e). Grey arrow indicates temporal progression of samples.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5764288/v1/abba352c969dfb2a0fd50596.png"},{"id":75316763,"identity":"dca04c83-4e4b-46ee-b2ad-f0bb9f95d574","added_by":"auto","created_at":"2025-02-03 09:49:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":94392,"visible":true,"origin":"","legend":"\u003cp\u003eThe prognostic potential of VeloCD in the Influenza A and SARS-CoV-2 challenge RNA-Seq datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eand \u003cstrong\u003eb\u003c/strong\u003e | Schematics illustrating the “drop-one-in\" approach, where one test sample (green diamond) is dropped into VeloCD alongside samples from the reference groups (red circles, blue squares). \u003cstrong\u003ec \u003c/strong\u003e| Performance of the 3-gene Influenza signature to predict future PCR status from the day 1 post-inoculation sample in the Influenza study (23 participants, generated using a TPNN of 11 and PCA-based embeddings).\u003cstrong\u003e d \u003c/strong\u003e| Performance of the 232-gene signature to predict future PCR Status from day 1 post-inoculation in the SARS-CoV-2 cohort (26 participants, generated using a TPNN of 6 and PCA-based fate maps).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5764288/v1/51eed23736c42028ab4b4900.png"},{"id":75314846,"identity":"f45dadd7-6804-4464-b8e2-9bd45ff859dc","added_by":"auto","created_at":"2025-02-03 09:33:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":122888,"visible":true,"origin":"","legend":"\u003cp\u003eThe RNA Velocity analysis of individuals at risk of TB-IRIS following initiation of anti-retroviral therapy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e | PCA plot of the spliced transcript expression of all multi-exon genes (n=95, 23,041 genes; green triangles, participants who developed TB-IRIS after the two-week blood sample, n=8; red circles, trial participants who never developed TB-IRIS, n=51; blue squares, participants who developed TB-IRIS before the two-week blood sample, n=36).\u003cstrong\u003e b\u003c/strong\u003e | Volcano plot showing the DEA for participants who developed TB-IRIS within two weeks of treatment versus those who never developed TB-IRIS (5,108 DEGs, red and blue points above the horizontal line). \u003cstrong\u003ec\u003c/strong\u003e | PCA plot of the genes from \u003cstrong\u003eb \u003c/strong\u003ewith spliced and unspliced transcript expression (3,448 genes). \u003cstrong\u003ed\u003c/strong\u003e | Schematic illustrating the “drop-one-in\" approach for the TB-IRIS dataset. \u003cstrong\u003ee\u003c/strong\u003e | UMAP-based RNA velocity fate map of all the samples in this cohort constructed using a 1,980-gene signature (TPNN: 48, Number of Neighbors (NN): 25, UMAP with three-dimensions). RNA velocity arrows represent the future transcriptomic trajectories of each participant sample point.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5764288/v1/956b6add97a472ac65ee140b.png"},{"id":75314850,"identity":"009593ae-631e-4eaa-b5f0-7b32869bd3bd","added_by":"auto","created_at":"2025-02-03 09:33:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":176277,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e | PCA plot of the spliced transcript expression of all genes (24,092) for all subjects labelled by severity (mild, n=117; moderate, n=184; severe, n=98) and diagnosis (bacterial, n=204; viral, n=195). \u003cstrong\u003eb \u003c/strong\u003e| Volcano plots showing the results of the DEA between the mild illness subjects recruited at ED vs. severe illness subjects recruited in the PICU for the bacterial (7,087 DEGs, top) and viral subjects separately (4,915 DEGs, bottom).\u003cstrong\u003e c\u003c/strong\u003e | PCA plot of the spliced transcript expression of the 1,585 multi-exon genes DE with concordant log\u003csub\u003e2\u003c/sub\u003eFC values in the analyses shown in (\u003cstrong\u003eb\u003c/strong\u003e) for the mild illness subjects recruited at ED (n=97) and the severe illness subjects recruited in PICU (n=91). \u003cstrong\u003ed\u003c/strong\u003e | UMAP-based RNA Velocity fate map (using 59 genes) for 207 subjects including healthy controls (n=19), mild illness subjects (n=97), severe illness subjects (n=91) recruited in the PICU. Two alternative disease trajectories (deterioration and recovery) are illustrated. The RNA velocity arrows represent future transcriptomic trajectories, generated with an NN of 25 and TPNN of 80.\u003cstrong\u003e e\u003c/strong\u003e | Schematic showing the “drop-one-in” approach applied to the PERFORM dataset.\u003cstrong\u003e f \u003c/strong\u003e| Median transition probabilities (to the PICU) of 20 mild illness (n=10 bacterial, n=10 viral), 7 severe illness, and 184 moderate illness (n=90 viral, n=94 bacterial) subjects, calculated across TPNN values 10 to 70 and projected into low-dimensional space using UMAP (NN:20). Background color indicates hypothetical risk categories: high (red, TP≥ 0.75 probability), moderate (TP 0.25-0.75) and low (TP≤ 0.25).\u003c/p\u003e\n\u003cp\u003eThe RNA Velocity analysis of the PERFORM cohort.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5764288/v1/049449c15711be5284da3b5d.png"},{"id":108668286,"identity":"7b2e47fa-b618-45bc-9522-1abc53d3ecf8","added_by":"auto","created_at":"2026-05-07 07:07:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1576042,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5764288/v1/df07e04e-0e20-4b96-afc2-02c33fb2604c.pdf"},{"id":75314838,"identity":"5abd1c7b-dab3-4f41-80f0-e6893fb81880","added_by":"auto","created_at":"2025-02-03 09:33:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":895243,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"ExtendedDataFiguresandTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-5764288/v1/b5a9db57d744d0cce098a04b.docx"},{"id":75316479,"identity":"87f2cb53-bf21-4324-adba-7dd96150040c","added_by":"auto","created_at":"2025-02-03 09:41:41","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":96152,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Information\u003c/p\u003e","description":"","filename":"RNAVelocitySupplementary13122024.docx","url":"https://assets-eu.researchsquare.com/files/rs-5764288/v1/6d47e55e51d6c5d7d768c806.docx"},{"id":75316764,"identity":"750f8649-551b-460f-87f8-ad5f206c2893","added_by":"auto","created_at":"2025-02-03 09:49:41","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":56188,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Data File 1.\u003c/p\u003e","description":"","filename":"SupplementaryDataFile.1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5764288/v1/464a873c9aaf6a726813cd33.xlsx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nAubrey J. Cunnington, Claire Dunican, Myrsini Kaforou, and Mauricio Barahona, are named as inventors on a patent application filed by Imperial College London, titled \"A method to predict clinical outcome\", Case Number 11445. All other authors have no relevant conflicts of interest.","formattedTitle":"Predicting trajectories of acute illness using RNA velocity of whole blood","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe human blood transcriptome changes in response to illness. Many studies have identified characteristic patterns of gene expression able to distinguish between different diseases or degrees of severity of the same illness\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Whilst much attention has focussed on the diagnostic potential of measuring host gene expression\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, clinicians are also frequently faced with the problem of predicting the progression of acute illness, but prognostic tests based on gene expression have received less attention\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMost commonly, gene expression analyses utilise mature (spliced) mRNA transcripts, enriched through PolyA selection, or gene-level expression summed across spliced and unspliced (nascent) transcripts. However, recent studies focusing on unspliced transcripts have revealed an additional layer of information about the dynamics of gene expression. These yet-to-be-spliced transcripts, quantified by intron-mapping reads\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, provide an indication of the future spliced transcript expression\u003csup\u003e\u003cspan additionalcitationids=\"CR16 CR17 CR18\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Taken together, the relative abundance of spliced and unspliced transcripts indicates whether expression of each gene is increasing or decreasing. This concept was formalised through the calculation of \u0026ldquo;RNA velocity\u0026rdquo;, using measurements of spliced and unspliced transcripts at a single point in time to predict the future transcriptomic state of single cells, and by reference to the transcriptomes of other cells, to infer future phenotypic states\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Since its original description, RNA velocity methods have been refined and widely applied to understand dynamic and developmental relationships between cellular states across numerous biological systems\u003csup\u003e\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe reasoned that RNA velocity analysis might be extended beyond projecting the fate of individual cells and could be adapted to predict the future clinical state of individual people with acute illness, based on a single measurement of their blood transcriptome. To test this hypothesis, we developed a new analysis tool: RNA velocity-based transition predictions of future disease states of subjects in clinical datasets (VeloCD). VeloCD makes predictions based on three core assumptions: the current spliced mRNA expression profile in blood indicates current clinical status; the RNA velocity of blood predicts future spliced mRNA expression; future spliced mRNA expression corresponds to future clinical state. To make predictions, RNA velocity is calculated, and future clinical state is defined, with reference to the transcriptomes of individuals with the clinical outcomes of interest, such as presence or absence of a disease phenotype, or mild vs. severe illness. Quantitative probabilities of transition to each reference group are then calculated. Low dimensional fate maps, like those generated for single cell transcriptomics, can then be produced to visualise the evolution of the response to human disease and relationships between different disease states. Here we test the core assumptions of VeloCD and provide evidence that it can generate meaningful prognostic information.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eRNA velocity predicts future transcriptomic and clinical states in controlled human infections\u003c/h2\u003e\n\u003cp\u003eTo test the core assumptions underpinning VeloCD, we utilized whole-blood RNA-Seq data (Extended Data Table\u0026nbsp;1) from two controlled human infection models (CHIMs), in which healthy volunteers were intentionally inoculated with either Influenza A virus\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e or SARS-CoV-2\u003csup\u003e26\u003c/sup\u003e and serial blood samples were collected over the following days (Fig.\u0026nbsp;1a-d). Although VeloCD does not use serial samples to make predictions, the known timings of inoculation, first-PCR positivity, and sequential samples in these studies provide rare ground truth data against which the predictions of VeloCD can be assessed.\u003c/p\u003e\n\u003cp\u003e17 of 23 subjects in the influenza study (Fig.\u0026nbsp;1b), and 17 of 26 in the SARS-CoV-2 study became infected (Fig.\u0026nbsp;1d), as determined by PCR testing. Amongst infected volunteers there was some variation in time from inoculation to first positive PCR test, and variation in symptom severity, but none became unwell enough to require medical intervention. In those testing positive, the median day of first PCR-positivity was day 2 post-inoculation in both studies, with range of 1\u0026ndash;4 days in the influenza virus study, and 1\u0026ndash;2 days in the SARS-CoV-2 study.\u003c/p\u003e\n\u003cp\u003eWe first investigated whether the current spliced transcript expression in blood is representative of the current infection status in these two cohorts. To do this, we quantified the spliced transcript expression of all multi-exon genes with spliced and unspliced transcript expression, resulting in 21,647 spliced transcripts in the influenza study, and 23,903 spliced transcripts in the SARS-CoV-2 study. When spliced transcript expression was transformed into low-dimensional transcriptomic space using principal component analysis (PCA)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;1e-f), there was some segregation of samples by both time since inoculation and infection status, with the separation of PCR-positive from PCR-negative subjects becoming more prominent from day 2 for the Influenza study and day 3 for SARS-CoV-2 study respectively. The greatest changes in gene expression appeared to occur on the day after first detection of infection by PCR. We used differential expression analysis (DEA) to identify the genes that distinguish PCR-positive and PCR-negative subjects at these time-points of greatest change in gene expression.\u003c/p\u003e\n\u003cp\u003eIn the influenza study, subjects were subset by the day they first tested positive, and the sample from the day after they first tested positive used for DEA. We identified 2,988 differentially expressed genes (DEGs) in day 2 samples between those who first tested positive on day 1 (n\u0026thinsp;=\u0026thinsp;6) and those who remained PCR-negative throughout (n\u0026thinsp;=\u0026thinsp;6) (Fig.\u0026nbsp;1g). There were 892 DEGs in day 3 samples between those who first tested positive on day 2 (n\u0026thinsp;=\u0026thinsp;9) and those who remained PCR-negative throughout (n\u0026thinsp;=\u0026thinsp;6) (Fig.\u0026nbsp;1g).\u003c/p\u003e\n\u003cp\u003eIn the SARS-CoV-2 study there were 406 DEGs on day 3 between PCR-positive subjects (n\u0026thinsp;=\u0026thinsp;17) and those who remained PCR-negative throughout (n\u0026thinsp;=\u0026thinsp;9; Fig.\u0026nbsp;1h). Only a single time-point was used for the SARS-CoV-2 analysis because the PCR-positive subjects had more uniform gene expression change over time. Using these sets of DEGs for PCA, there was clear segregation of PCR-positive and PCR-negative subjects in low-dimensional transcriptomic space at these time-points in both studies (Fig.\u0026nbsp;1i-j).\u003c/p\u003e\n\u003cp\u003eTo further elucidate differences between the PCR-positive and PCR-negative subjects for each virus, we used the maSigPro backward selection algorithm to identify spliced transcripts changing differentially over time\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. This identified 1,563 and 2,700 genes for influenza and SARS-CoV-2 infections, respectively. Figure\u0026nbsp;1k-l show the spliced transcript expression over time of two of the top genes (ordered by multiple testing corrected p-values) identified across both analyses: Interferon Induced Protein 44-Like (\u003cem\u003eIFI44L\u003c/em\u003e) and Lymphocyte antigen 6E (\u003cem\u003eLY6E\u003c/em\u003e). PCA using the genes selected by maSigPro (Fig.\u0026nbsp;1m-n) shows clear temporal ordering of the samples from PCR-positive, infected, subjects, which are clearly distinct from PCR-negative samples. Taken together, these results from two independent CHIMs indicate that spliced mRNA expression profiles represent current infection status at different timepoints across the course of an acute infection, from exposure through convalescence, discriminating between infected (PCR-positive) and uninfected (PCR-negative) states.\u003c/p\u003e\n\u003cp\u003eNext, we investigated whether RNA velocities could capture future gene expression and corresponding future disease states in these CHIM datasets. Although the power of RNA velocity is that it does not require serial samples to predict trajectories, it is helpful to validate VeloCD using serial samples to show that it produces accurate prediction of future transcriptomic states. We first assessed whether unspliced transcript expression of the genes selected by maSigPro follows biologically informative pattens across time, including upregulation and repression over the course of infection. Unspliced transcripts of \u003cem\u003eIFI44L\u003c/em\u003e and \u003cem\u003eLy6E\u003c/em\u003e (Fig.\u0026nbsp;2a-d) showed parallel profiles of expression to their respective spliced transcripts (Fig.\u0026nbsp;1k-l).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFuture spliced transcript expression values are predicted as an intermediate step in the calculation of RNA velocity values\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. For both CHIM datasets, we used day 2 samples to predict future spliced transcript expression values for all the genes identified as significant in the maSigPro analysis and correlated the predicted future spliced transcript expression with the measured spliced transcript expression on day 3 (Fig.\u0026nbsp;2e-f). The range of Pearson correlation coefficients across subjects were 0.86\u0026ndash;0.99 (mean: 0.96) and 0.77\u0026ndash;0.99 (mean: 0.94) in the influenza and SARS-CoV-2 datasets, respectively, indicating that this method accurately models the temporal expression of these genes up to 24 hours in the future. These genes were then used to generate fate maps with the RNA velocity of each subject at each timepoint indicating their predicted future transcriptomic state (Fig.\u0026nbsp;2g-h). The RNA velocities are highly concordant with the observed temporal progression of the transcriptome along diverging trajectories for infected and uninfected subjects. Together, these analyses illustrate that RNA velocities calculated with VeloCD predict both future transcriptomic and disease states.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eRNA velocity enables quantitative predictions of future disease state\u003c/h3\u003e\n\u003cp\u003eTo move towards clinical application, we studied how VeloCD can provide quantitative predictions of transition to defined disease states. To do this, VeloCD uses reference transcriptome data from subjects who have the disease states of interest, and then determines the probability of a test subject transitioning to each group based on their RNA velocity values (Fig.\u0026nbsp;3a and b). We assessed how well VeloCD predicts future infection status in the CHIM subjects using only samples collected at day 1 post-inoculation.\u003c/p\u003e\n\u003cp\u003eFor the influenza study, samples taken the day after the first positive PCR result were used for the infected reference group (n\u0026thinsp;=\u0026thinsp;15) and the day 3 samples from the subjects who remained PCR-negative throughout were used as the uninfected reference group (n\u0026thinsp;=\u0026thinsp;6). The day 3 sample from the single infected subject who first tested positive on day 3 was also included in the infected reference (n\u0026thinsp;=\u0026thinsp;16 in total, Fig.\u0026nbsp;3a). The single subject who became PCR-positive on day 4 was excluded from the reference groups because no blood samples were collected on days 4 or 5. For the SARS-CoV-2 study, the day 3 samples from PCR-positive individuals were used as the infected reference group (n\u0026thinsp;=\u0026thinsp;17) and the day 3 samples from those remaining PCR-negative throughout as the uninfected reference group (n\u0026thinsp;=\u0026thinsp;9, Fig.\u0026nbsp;3b).\u003c/p\u003e\n\u003cp\u003eMany genes expressed in blood do not vary in expression with changes in disease status, whilst other genes may have non-monotonic expression dynamics which are poorly modelled by VeloCD\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Inclusion of these genes in RNA velocity calculations may mask the signal of more informative genes which are changing in response to infection. Consistent with this, we have found that VeloCD works best if it uses transcripts that have been selected to distinguish between reference disease groups, and which vary in expression in association with diverging disease trajectories (Extended Data Fig.\u0026nbsp;1). Therefore, we developed a more rigorous framework to identify genes with differential expression between the reference groups and with concordance between their RNA velocity (at day \u003cem\u003en\u003c/em\u003e) and measured change in spliced transcript expression (from days \u003cem\u003en\u003c/em\u003e to \u003cem\u003en\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1). For the Influenza study, we then used Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify a smaller group of highly informative genes (\u003cem\u003eBAK1\u003c/em\u003e, \u003cem\u003eAPOL3\u003c/em\u003e, \u003cem\u003eSLC9A3-AS1\u003c/em\u003e, \u003cem\u003ePLAAT4, H2AZ2\u003c/em\u003e), from which we picked the three genes (\u003cem\u003eBAK1\u003c/em\u003e, \u003cem\u003eAPOL3\u003c/em\u003e, \u003cem\u003eSLC9A3-AS1\u003c/em\u003e) contributing most to the first three principal components (PCs; Extended Data Fig.\u0026nbsp;2, Extended Data Table\u0026nbsp;2). Phase portraits of spliced and unspliced transcript expression further confirmed that \u003cem\u003eBAK1\u003c/em\u003e and \u003cem\u003eAPOL3\u003c/em\u003e had desirable patterns of transcript expression (Extended Data Fig.\u0026nbsp;3).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBAK1\u003c/em\u003e, \u003cem\u003eAPOL3\u003c/em\u003e and \u003cem\u003eSLC9A3-AS1\u003c/em\u003e were input into VeloCD to calculate the probability of each subject becoming influenza PCR-positive, based on their day 1 blood sample. In each run, one test subject was \u0026ldquo;dropped-in\u0026rdquo; to VeloCD with the reference samples (Fig.\u0026nbsp;3a and 3b) (with the removal of that subject\u0026rsquo;s own sample from the reference prior to each run, to prevent bias due to sample donor identity). This process was repeated for each test subject (n\u0026thinsp;=\u0026thinsp;23), across each possible number of neighboring samples (the Transition Probability Number of Neighbors (TPNN) hyperparameter, see Methods). Transition probabilities (TPs) were then calculated by VeloCD (see Methods) as the probability of each test subject transitioning to each outcome group (infected or PCR-negative). Sensitivity and specificity were then calculated by varying the probability threshold required to classify a subject as becoming infected (PCR-positive). Collectively, these steps enable the use of VeloCD to make quantitative predictions of future disease state.\u003c/p\u003e\n\u003cp\u003eA TPNN value of 11 was optimal to predict infection status up to 3 days into the future (Area Under the Receiver Operating Characteristic-AUROC Curve value of 0.89; 95% Confidence Interval: 0.76-1; Fig.\u0026nbsp;3c). The range of AUROC values across TPNN (range 2\u0026ndash;19) hyperparameter runs was 0.62\u0026ndash;0.89 (Extended Data Fig.\u0026nbsp;4a).\u003c/p\u003e\n\u003cp\u003eThe process of differential expression and DISCO analysis was repeated for the SARS-CoV-2 dataset (Extended Data Fig.\u0026nbsp;5), identifying 232-genes (Extended Data Table\u0026nbsp;3). Neither LASSO regression nor selection by contributions of each gene in PCA yielded a smaller set of highly discriminatory genes, therefore all 232 genes were used in VeloCD to predict future SARS-CoV-2 PCR status. At the optimal TPNN of 6, the AUROC was 0.72 (95% CI: 0.51\u0026ndash;0.92; Fig.\u0026nbsp;3d), predicting infection status moderately well up to 48-hours into the future. The range of AUROC values between different TPNN (range 2\u0026ndash;25) hyperparameter runs was 0.53\u0026ndash;0.72 (Extended Data Fig.\u0026nbsp;4b). Interestingly, applying this signature to the day 0 (post-inoculation) samples, yielded a predictive performance similar to when day 1 samples were used as the test set (optimal AUROC, 0.73, 95% CI: 0.51\u0026ndash;0.95, TPNN: 10, full range of AUROCs: 0.52\u0026ndash;0.73), indicating potential to predict up to 3 days into the future.\u003c/p\u003e\n\u003cp\u003eSince influenza and SARS-CoV-2 are both viruses with single stranded RNA genomes\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, which activate many common immune pathways\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, we tested the predictor genes identified in each dataset on the other. The 3-genes selected to predict influenza infection performed moderately well in the SARS-CoV-2 dataset, with an AUROC of 0.76 (95% CI: 0.57\u0026ndash;0.96, TPNN: 16, Extended Data Fig.\u0026nbsp;6; AUROC range 0.56\u0026ndash;0.76 across TPPNs 2 to 25), demonstrating validation in an independent dataset. Two of the 3 genes showed clear patterns of expression change across time in both datasets with spliced transcript expression changes in the SARS-CoV-2 infected subjects lagging slightly behind those in the influenza infected subjects (Extended Data Fig.\u0026nbsp;7), consistent with the divergence between the infected and PCR-negative blood transcriptomes appearing to occur from day 3 and day 2, respectively (Fig.\u0026nbsp;1e-f). Of the 232 genes selected to predict SARS-CoV-2 infection, 216 were expressed in the Influenza dataset. These did not perform much better than chance to predict future infection status from day 1 samples in the influenza dataset (TPNN: 5, AUROC: 0.53, 95% Confidence Interval: 0.11\u0026ndash;0.84), possibly because they were selected to identify the slightly later onset of the transcriptomic response to SARS-CoV-2 infection. If day 2 samples from the influenza study were used as the test set, AUROC values were between 0.75\u0026ndash;0.88 (optimal performance: 0.88, 95% CI: 0.72-1, TPNN: 4) across hyperparameters.\u003c/p\u003e\n\u003cp\u003eTaken together, our results in CHIMs demonstrate that it is possible to make quantitative predictions of future disease state based on RNA velocity of blood.\u003c/p\u003e\n\u003ch3\u003eRNA velocity predicts onset of TB-IRIS in a clinical trial\u003c/h3\u003e\n\u003cp\u003eWhilst the serial sampling in CHIMs is helpful for validation of the assumptions underpinning VeloCD, longitudinal samples should not be a requirement for VeloCD to make informative predictions. Therefore, we next investigated whether VeloCD could be used to predict the onset of a different disease, Tuberculosis (TB)-associated immune reconstitution inflammatory syndrome (TB-IRIS), using samples from a single time-point.\u003c/p\u003e\n\u003cp\u003eTB-IRIS is a paradoxical immunopathological reaction that develops in ~\u0026thinsp;18% of patients with HIV-associated TB shortly after initiation of anti-retroviral treatment (ART)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. It is characterized by systemic hyperinflammation and new or recurrent symptoms at sites of TB infection\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Recent studies have demonstrated that TB-IRIS has a distinct host blood transcriptomic signature with some prognostic potential\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. We analyzed whole-blood transcriptomes of subjects (n\u0026thinsp;=\u0026thinsp;95) from the placebo arm of a clinical trial\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, in which coinfected subjects who had received TB treatment for less than 30 days were started on ART and randomized to receive either prednisone or placebo\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e (Extended Data Table\u0026nbsp;1).\u003c/p\u003e\n\u003cp\u003eUsing samples collected two weeks after ART initiation, which is the peak time of onset of TB-IRIS, we assembled three groups \u0026ndash; a test group who developed TB-IRIS after the two week blood sample (range 15\u0026ndash;37 days after ART initiation, n\u0026thinsp;=\u0026thinsp;8), a reference group who had already developed TB-IRIS within the first 14 days of the trial (n\u0026thinsp;=\u0026thinsp;36), and a reference group who did not develop TB-IRIS during at least 12 weeks of follow-up (no-TB-IRIS, n\u0026thinsp;=\u0026thinsp;51). The test group was used to examine whether RNA velocity could indicate the future occurrence of TB-IRIS in participants who had not yet developed the syndrome at the two-week sampling time-point.\u003c/p\u003e\n\u003cp\u003ePCA using spliced transcript expression of all genes demonstrated modest segregation of the TB-IRIS and no TB-IRIS patients (Fig.\u0026nbsp;4a), with those yet to develop TB-IRIS located between the two reference groups. DEA identified 5,108 DEGs between the TB-IRIS and no-TB-IRIS groups (Fig.\u0026nbsp;4b), and PCA using these DEGs produced somewhat clearer segregation based on TB-IRIS status (Fig.\u0026nbsp;4c). We then applied the DEA and DISCO analysis steps of the framework described above to this dataset (a full description is given in Extended Data Fig.\u0026nbsp;8), identifying 1,980 genes to use for prediction of TB-IRIS status (Supplementary Data File. 1). Each test subject was combined separately with the reference subjects to predict their future status using VeloCD (Fig.\u0026nbsp;4d). The transition probabilities of each test subject to the TB-IRIS group were collated across runs with equivalent hyperparameter values (TPNN: 5\u0026ndash;50). Seven of eight (87.5%) test subjects were predicted to develop TB-IRIS (TP threshold: 30%, TPNN: 30, PCA used to construct the fate maps). Across the full range of TPNN values (5\u0026ndash;50), using the same 0.3 probability threshold, TB-IRIS was correctly predicted in a median of 75% of test subjects (Extended Data Fig.\u0026nbsp;9).\u003c/p\u003e\n\u003cp\u003eTo illustrate the relationships between subjects and disease states and demonstrate the potential for VeloCD to utilize different low-dimensional embedding methods, a 3-dimensional UMAP-based RNA velocity fate map was generated using all test and reference subjects in the TB-IRIS dataset (Fig.\u0026nbsp;4e). This shows the two reference groups progressively cluster towards the ends of a continuum with velocity arrows predominantly pointing away from each other, whilst test subjects lie towards the middle with velocity arrows predominantly pointing towards the TB-IRIS group.\u003c/p\u003e\n\u003cp\u003eDespite the small size of the test group, these results demonstrate the potential of using RNA velocity for prediction and exploration of disease trajectories, based on measurements at a single time-point.\u003c/p\u003e\n\u003ch3\u003eRNA velocity indicates changes in severity of acute febrile illness\u003c/h3\u003e\n\u003cp\u003eWe have shown that VeloCD has potential as a prognostic tool when the timing of sampling relative to inoculation of a pathogen or initiation of treatment is controlled, but in real clinical environments, unwell individuals seek health care at different times and stages of illness. Many clinical decisions, such as admission to hospital, investigations, and medication choices, depend on perception of the likelihood that a patient\u0026rsquo;s condition will improve or deteriorate. This is particularly the case for febrile children, who are often admitted to hospital because they appear unwell and there is concern that they may deteriorate, even though the majority have self-limiting viral illnesses and recover quickly. We therefore explored whether VeloCD could indicate trajectories of illness severity in febrile children.\u003c/p\u003e\n\u003cp\u003eFor this analysis, we used whole-blood RNA-Seq data generated from 399 children with suspected infection, recruited to the Personalized Risk assessment in Febrile illness to Optimize Real-life Management across the European Union (PERFORM) study - a multi-country prospective observational study of children with suspected infection presenting to hospitals in Europe\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Children were recruited from Emergency Departments (EDs) and Pediatric Intensive Care Units (PICUs), resulting in a wide spectrum of severity ranging from mild (well enough to be sent home from ED), to moderate (admitted to hospital ward), and severe (admitted to PICU). A standardized phenotyping algorithm was applied to group children by proven or likely etiology of illness\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. The present analysis focused on subjects with proven or suspected bacterial (n\u0026thinsp;=\u0026thinsp;204) or viral illness (n\u0026thinsp;=\u0026thinsp;195; probable, definitive, or \u0026ldquo;syndrome\u0026rdquo; categories of the diagnostic algorithm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e; Table\u0026nbsp;1). Whilst accuracy of the etiological diagnosis is expected to vary between these categories, definitive diagnosis is rarely known at the time of presentation and therefore should not be essential for predicting outcome. For each subject, whole-blood RNA-Seq data from the first sample collected at or shortly after the time of presentation was analyzed (Extended Data Table\u0026nbsp;1).\u003c/p\u003e\n\u003cp\u003ePCA analysis of spliced transcript expression of all multi-exon genes with detectable spliced and unspliced transcript expression from all subjects revealed some segregation by severity with the mild and severe groups most distinct from each other and the moderate severity group positioned between them, but there was considerable overlap (Fig.\u0026nbsp;5a). Unsurprisingly, across the entire cohort, the etiology of infection (bacterial or viral) was related to disease severity, with the bacterial infections over-represented (71.4%) in the group recruited from PICU, compared to 51.1% and 33.3% of the moderate and mild illness groups, respectively (Table\u0026nbsp;1).\u003c/p\u003e\n\u003cp\u003eWe created a test-set for later RNA velocity prediction, comprised of 7 subjects recruited in the ED but transferred promptly to the PICU (severe-illness, all bacterial), all 184 subjects admitted from ED to the ward (moderate illness), and 20 (10 bacterial, 10 viral) randomly selected mild-illness subjects. Using the remaining mild and severe subjects we sought to minimize the impact of etiology on selection of genes for RNA velocity analysis, by performing separate DEAs between subjects with mild (n\u0026thinsp;=\u0026thinsp;68) and severe (n\u0026thinsp;=\u0026thinsp;26) viral infections, and between subjects with mild (n\u0026thinsp;=\u0026thinsp;29) and severe (n\u0026thinsp;=\u0026thinsp;65) bacterial infections, identifying 4,915 and 7,087 DEGs respectively (Fig.\u0026nbsp;5b). 2,348 of these DEGs had concordant patterns of up- or down-regulation between the bacterial and viral groups and were taken forward. PCA analysis using this subset of 2,348 genes with spliced and unspliced transcript expression revealed clearer segregation of mild and severe illness (Fig.\u0026nbsp;5c).\u003c/p\u003e\n\u003cp\u003eWe then used a similar gene selection framework to the preceding analyses (Extended Data Fig.\u0026nbsp;10), incorporating LASSO regression to identify a smaller set of 59 genes for quantitative prediction of future clinical state with VeloCD. We used these 59 genes to create a fate map, in which we also incorporated data from 19 healthy control subjects to illustrate their relationship to the mild and severe illness groups in transcriptomic space (Fig.\u0026nbsp;5d). This fate map illustrates diverging trajectories of severe and mild disease with the RNA velocity arrows of many mild subjects pointing towards the healthy controls, presumably indicating a trajectory towards recovery from illness. Interestingly, the mild bacterial illness subjects showed some segregation from the mild viral illness subjects, perhaps suggesting different sub-trajectories within mild illness.\u003c/p\u003e\n\u003cp\u003eUsing the remaining mild illness cases (n\u0026thinsp;=\u0026thinsp;97) and the PICU-recruited severe cases (n\u0026thinsp;=\u0026thinsp;91) as reference groups, we then evaluated the predictive performance of VeloCD using the 59 genes (Fig.\u0026nbsp;5e). VeloCD was run for each test subject (n\u0026thinsp;=\u0026thinsp;211). TPs to severe illness were calculated across independent runs for each subject using TPNN values from 10\u0026ndash;70, and the median value was used for further analysis. The mild and severe subjects in this test set had very polarized TPs (Fig.\u0026nbsp;5f). Median TP to severe illness was 0.0035 (viral, 0.0018; bacterial, 0.023) in the mild illness group and 95% (n\u0026thinsp;=\u0026thinsp;19) of these subjects had TPs below 0.25. The median TP values of the severe illness group (presenting to ED but requiring immediate transfer to PICU, n\u0026thinsp;=\u0026thinsp;7) was 1.0 and 85.7% (n\u0026thinsp;=\u0026thinsp;6) of these subjects had TPs above 0.75, suggesting that these probabilities could potentially be used as the basis of a risk stratification system (Fig.\u0026nbsp;5f). The complete range of TP values across TPNN runs is shown for the entire test cohort in Extended Data Fig.\u0026nbsp;11.\u003c/p\u003e\n\u003cp\u003eNext, we examined predictions in the subjects with moderate illness, who required admission to the hospital ward. As might be expected, there was much more variability in TPs for these subjects suggesting more variable disease trajectories. Interestingly, the TPs of the moderate viral patients (median TP value, 0.0019) were much more skewed towards zero than their bacterial counterparts (median TP value, 0.043; two-sided Kolmogorov-Smirnov test p\u0026thinsp;=\u0026thinsp;0.0028; Fig.\u0026nbsp;5f). In this observational study, subjects received treatments determined by their attending physicians which would likely reduce the chances of those with moderate illness progressing to severe illness (and preclude meaningful analysis of categorical prediction). Consistent with this only 3.8% (n\u0026thinsp;=\u0026thinsp;7) of the moderate severity subjects required later admission to PICU (range: 1\u0026ndash;9 days from ward admission). However, these 7 subjects had a median TP value of 0.36 (range: 0.0\u0026ndash;1.0), which was significantly higher than the median TP value in those not requiring later admission to PICU (n\u0026thinsp;=\u0026thinsp;177, median TP value: 0.0080, range 0.0\u0026ndash;1.0, Wilcoxon signed rank p-value 0.045).\u003c/p\u003e\n\u003cp\u003eWe assessed whether moderate illness subjects with higher transition probabilities had other clinical evidence consistent with a more severe trajectory of illness, based on the other available information in this dataset. Baseline clinical features of subjects in a hypothetical higher-risk group (TP to severe disease\u0026thinsp;\u0026ge;\u0026thinsp;0.25, n\u0026thinsp;=\u0026thinsp;43) were not significantly different from those with lower transition probabilities (TP\u0026thinsp;\u0026lt;\u0026thinsp;0.25, n\u0026thinsp;=\u0026thinsp;141) at time of presentation (Extended Data Table\u0026nbsp;4), although odds ratios above unity indicated a tendency for these subjects to have more features of severe illness, and bacterial rather than viral infection. Mann-Whitney U tests identified significant differences in the distribution of baseline C-reactive protein (CRP; TP\u0026thinsp;\u0026ge;\u0026thinsp;0.25, n\u0026thinsp;=\u0026thinsp;41, median (IQR) 60.3mg/L (16.6-166.7); TP\u0026thinsp;\u0026lt;\u0026thinsp;0.25, n\u0026thinsp;=\u0026thinsp;131, median (IQR) 13.2mg/L (4.1\u0026ndash;49), p-value\u0026thinsp;=\u0026thinsp;1.67x10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) and maximum CRP (TP\u0026thinsp;\u0026ge;\u0026thinsp;0.25, n\u0026thinsp;=\u0026thinsp;43, median (IQR) 71.9mg/L (25.1-196.7); TP\u0026thinsp;\u0026lt;\u0026thinsp;0.25, n\u0026thinsp;=\u0026thinsp;137, median (IQR) 21mg/L (6\u0026ndash;64); p-value\u0026thinsp;=\u0026thinsp;4.81x10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e; Extended Data Fig.\u0026nbsp;12) between the TP groups. There was a significant correlation between the time from ward admission to hospital discharge and median TP values (Spearman-rank \u0026rho;\u0026thinsp;=\u0026thinsp;0.17, n\u0026thinsp;=\u0026thinsp;179, p-value\u0026thinsp;=\u0026thinsp;0.023) and the subjects with higher TP values were more likely to require surgical interventions (Odds Ratio\u0026thinsp;=\u0026thinsp;4.02, p-value\u0026thinsp;=\u0026thinsp;0.0052), all of which may be consistent with a more severe course of illness (Extended Data Table\u0026nbsp;4).\u003c/p\u003e\n\u003cp\u003eTaken together with preceding results, these findings support the concept that RNA velocity can be used to make clinically meaningful predictions of future disease states, such as individuals most likely to recover or progress to severe illness.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePredicting the trajectory of acute illness is a common challenge for clinicians, who usually adopt a cautious approach to avoid missing patients who may develop life-threatening deterioration. This inevitably results in over-treatment and unnecessary admissions to hospital for many who would have milder illness trajectories, but also sometimes missed opportunities to intervene for those who appear well at presentation but subsequently deteriorate rapidly. More precise prediction of future disease state and severity could enable better risk stratification and clinical management decisions, and better use of healthcare resources. We have shown that using RNA velocity to predict future clinical status may be a promising solution to this problem. We developed VeloCD as a new tool to predict future disease states, over a timescale of days to weeks, from whole-blood RNA-Seq data.\u003c/p\u003e \u003cp\u003eThe concept of VeloCD is based on observations across multiple diseases, that the whole-blood transcriptome reflects a patient\u0026rsquo;s current disease state (both cause and severity) at the time of sampling, and that RNA velocities indicate future transcriptomic states, which therefore represent future disease states. In many cases, small sets of genes (\u0026ldquo;signatures\u0026rdquo;) have been identified to discriminate between different current disease states\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, which have led to the possibility of using these gene signatures in diagnostic tests\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Similarly, we have shown here that a gene selection framework (Extended Data Fig.\u0026nbsp;1) can be applied to identify smaller sets of genes which can be used for RNA velocity analysis to predict future disease states, across different diseases, study designs, and outcome measures. These examples illustrate potential clinical applications of RNA velocity, such as: early identification of individuals who are infected or uninfected after exposure to a pathogen, which could inform decisions about isolation or early treatment; identification of at risk individuals who will or won\u0026rsquo;t develop a disease manifestation, determining the need for preventive treatment; risk stratification based on trajectory of acute illness, which could help determine which patients can be sent home from the ED, should be admitted to hospital, or are highest risk and need urgent intervention.\u003c/p\u003e \u003cp\u003eOther applications could include improved understanding of pathogenesis and mechanisms of progression of acute illnesses, and stratified analyses of clinical trials to determine efficacy of treatments based on risk of outcome. Although we expected that RNA velocity would only predict future disease state over a timescale of hours to a few days, it is interesting that predictions of TB-IRIS could be made days to weeks into the future, which may indicate additional utility if there is a lag between onset of a disease process and its clinical manifestation.\u003c/p\u003e \u003cp\u003eAlthough we have used RNA-Seq data as the basis for predictions in the current work, the identification of small sets of informative genes for calculation of RNA velocity opens the potential for translating this approach to other detection methods. Spliced and unspliced transcripts for each gene could be measured using quantitative PCR or loop mediated isothermal amplification methods, which would allow measurement on common and emerging diagnostic platforms\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Further studies will be required to assess such approaches.\u003c/p\u003e \u003cp\u003eOur study has several limitations. RNA velocity was originally developed for analyses of 1000s of single cells rather than the tens or hundreds of samples available in our analyses. Whilst we divided our data in separate reference and test sets, larger studies would allow more generalizable reference datasets, larger datasets for gene selection, and independent test and validation datasets for assessment of prediction accuracy. Although we showed some stability of performance across a range of TPNN values, our prediction of future disease states was made using selected subsets of genes and hyperparameters, which risks over-optimistic performance. It will be important for future studies to evaluate prediction accuracy across independent datasets.\u003c/p\u003e \u003cp\u003eIn our analysis of the PERFORM study, relatively few outcome measures could be used to determine whether predictions of future severity were accurate for the moderately ill subjects who were admitted from ED to the ward \u0026ndash; a group in which we believe there may be the most to gain from accurate prediction of future severity. Future observational studies will also face the challenge that association between prediction and observed outcome is modified by treatment given to patients and might need to collect more detailed data on early changes in physiological status and medical care to evaluate trajectories of illness.\u003c/p\u003e \u003cp\u003eThis study has shown proof-of-concept that RNA velocity can be applied to whole blood samples to predict future disease states. Further work will be required to develop and validate predictive tests based on this approach. If successful, this could lead to earlier detection and treatment of diseases and better risk stratification to aid clinical management decisions.\u003c/p\u003e "},{"header":"Materials \u0026 Methods","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eand Conduct of Human Studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eControlled human infection model (CHIM) studies\u003c/p\u003e\n\u003cp\u003eInfluenza and SARS-CoV-2 CHIM studies were performed as previously described\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, conducted in accordance with the protocol for each study, the Consensus ethical principles derived from international guidelines including the Declaration of Helsinki and Council for International Organizations of Medical Sciences (CIOMS) International Ethical Guidelines, applicable International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) Good Clinical Practice guidelines, applicable laws and regulations. The studies were approved by UK Health Research Authority Research Ethics Committees (references: 11/LO/1836, 19/LO/1441, 20/UK/2001 and 20/UK/0002). For all studies, written informed consent was obtained.\u003c/p\u003e\n\u003cp\u003eIn the influenza CHIM study, healthy adult volunteers aged 18\u0026ndash;55 with microneutralising antibody titres of 1:20 or less for the inoculating virus were enrolled and challenged with 5x10\u003csup\u003e5\u003c/sup\u003e tissue culture infectious dose 50 (TCID50) of influenza A/Belgium/4217/15 (H3N2; SGS, Antwerp, Belgium) by intranasal drops. Participants were quarantined for up to 10 days post-inoculation during which samples were taken, with outpatient follow-up continuing until 6 months post-inoculation. Generation of whole-blood paired-end RNA-Seq data is described in Rosenheim et al., 2023\u003csup\u003e43\u003c/sup\u003e. Data used in the present study originated from 23 individuals with 7 or 8 samples each taken at day 0 pre-inoculation and then day 1, 2, 3, 7, 10, 14 and 28 after inoculation with Influenza A/Belgium/4217/2015 (total n\u0026thinsp;=\u0026thinsp;178 samples). Day 14 and 28 samples were not used in our analyses, leaving 138 samples from days 0\u0026ndash;10.\u003c/p\u003e\n\u003cp\u003eFor the SARS-CoV-2 CHIM study, healthy adult volunteers aged 18\u0026ndash;30 with no serological evidence of previous COVID-19 infection or vaccination were enrolled. Participants were challenged with 10\u003csup\u003e2\u003c/sup\u003e TCID50 of a D614G-containing pre-Alpha SARS-CoV-2 challenge virus by intranasal drops and quarantined for at least 14 days post-inoculation, with follow-up until 1-year post-inoculation. The generation of the HCIM SARS-CoV-2 whole-blood paired-end RNA-Seq data is described in Rosenheim et al., 2023\u003csup\u003e43\u003c/sup\u003e. Peripheral blood was collected from 35 participants on day 0 prior to inoculation and then on days 0, 1, 2, 3, 4, 5, 7, 10, 14 and 28 post-inoculation (n\u0026thinsp;=\u0026thinsp;375 samples). The day 28 samples were not used in our analysis, and we also excluded all samples from two participants who sero-converted during pre-inoculation quarantine\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, and 7 subjects with equivocal infection status based on PCR results, leaving 257 samples from 26 individuals for analysis. Subjects classified as infected (PCR positive) were required to have multiple consecutive positive PCR tests and their first day of PCR-positivity was then defined by the first positive sample from nasal or throat swab. Subjects classified as uninfected (PCR negative) were required to be negative by PCR in throat and nose swabs throughout the entire course of the study.\u003c/p\u003e\n\u003cp\u003eTB-IRIS study\u003c/p\u003e\n\u003cp\u003eTranscriptomic analysis was conducted on samples from the PredART trial, a phase 3, randomised, double-blinded, placebo-controlled study to evaluate if prophylactic prednisone can safely prevent TB-IRIS occurrence in high-risk patients\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The study was approved by the University of Cape Town Human Research Ethics Committee (HREC 136/2013). The study was conducted between August 2013 and March 2017 with 240 HIV-TB co-infected patients enrolled in 4 different TB clinics in Khayelitsha, South Africa. All participants provided informed consent for the laboratory study. Whole blood was collected in PAXgene tubes prior to commencing the randomised treatment, and at day 14 after starting treatment, from which total RNA was extracted using the PAXgene Blood RNA kit (PreAnalytiX). Total RNA sequencing library was prepared using Ovation Universal Human Blood kit (Tecan Genomics) and sequenced on an Illumina HiSeq4000 instrument using SR100 reactions with minimum 25\u0026nbsp;million reads. Only day 14 samples from the placebo group of the trial were used for the present study. One subject with sepsis and samples with less than 50% of their reads uniquely mapped to the human genome were excluded from further analysis, leaving 95 samples from 95 subjects.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePERFORM\u003c/h3\u003e\n\u003cp\u003eThe Personalised Risk assessment in Febrile illness to Optimise Real-life Management (PERFORM) study was a multi-center, prospective, observational cohort study, seeking to improve the diagnosis of febrile illness in children across Europe (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.diamonds2020.eu/our-research-history/perform/\u003c/span\u003e\u003c/span\u003e). Ethical approval was granted to the coordinating site (London \u0026ndash; Central Research Ethics Committee: 16/LO/1684), and by local ethics committees for each recruitment site (see Supplementary Information for details). Children (\u0026lt;\u0026thinsp;18 years old) presenting with fever, recent history of fever, or other features of suspected infection, who were considered ill enough by the medical team to warrant blood tests, were recruited from emergency departments, paediatric inpatient wards, and intensive care units, in 9 European countries, between August 2016 and December 2019. Full details of the study design and protocol have been published elsewhere\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. All decisions on clinical investigation and management were made by attending clinicians in accordance with local practice. Each patient was assigned a final diagnostic classification by local study teams based on prospectively collected clinical and laboratory data and according to a standardized phenotyping algorithm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Data entry and phenotyping were quality controlled by cross-site validation and automated checks within the study database\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. For the present study, severity of illness was classified, based on level of care, as mild (well enough to be sent home from ED), moderate (admitted to hospital ward), or severe (admitted to PICU).\u003c/p\u003e\n\u003cp\u003eBlood was collected for research purposes at the same time as the first clinically indicated samples (wherever possible), and otherwise within 48 hours of admission. 2.5ml whole blood was collected for RNA expression analysis in PAXgene tubes (for children below 1 year of age, 1ml blood was collected into appropriately reduced volumes of PAXgene fluid) and frozen for later analysis. Total RNA was isolated using PAXgene miRNA blood extraction kits (Qiagen), and after additional DNAse treatment (Zymo Research), was sent for ribodepletion library preparation and RNA-Seq at The Wellcome Centre for Human Genetics in Oxford, United Kingdom, using a Novaseq6000 platform at 150bp paired-end configuration, generating a raw read count of ~\u0026thinsp;30\u0026nbsp;million reads per sample. Further details of the PERFORM RNA-Seq data generation are given in Jackson et al., 2023\u003csup\u003e47\u003c/sup\u003e. Ribodepletion was performed during library preparation.\u003c/p\u003e\n\u003cp\u003eFor the present analysis, we included subjects with RNA-Seq data whose samples were collected on presentation in the emergency department or collected in PICU (within 48 hours of presentation and \u0026gt;\u0026thinsp;24 hours before PICU discharge), who had complete data on disposition (dates and locations of admission and discharge), and who were assigned one of the following diagnosis phenotypes: bacterial syndrome, probable bacterial, definite bacterial, viral syndrome, probable viral, definite viral. Two subjects each had two clinical episodes (\u0026gt;\u0026thinsp;1 year apart), which were treated as separate events, giving a total of 399 infection episodes for analysis. We also included 19 non-infection control subjects recruited to PERFORM, predominantly from out-patient settings, who were well at the time of attendance and the reason for attendance was unlikely to affect the blood transcriptome, for example pre-surgical assessment for polydactyly or tonsillectomy.\u003c/p\u003e\n\u003ch3\u003eVeloCD\u003c/h3\u003e\n\u003cp\u003eSpliced and unspliced transcript expression are input into VeloCD alongside two column metadata (maximum: 2, sample and group columns) files (CSV file format). VeloCD first constructs low-dimensional representations of the spliced transcript expression of the genes in a dataset using PCA\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, tSNE\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e or UMAP\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. VeloCD then uses the spliced and unspliced transcript expression to calculate RNA velocity values, which indicate the direction and magnitude of future spliced transcript expression changes. These are then used to calculate transition probability (TP) values. UMAP and tSNE both use a Number of Neighbours (NN) hyperparameter (also called perplexity) to specify the balance between the conservation of the local and global structure of the data from high to low-dimensional transcriptomic space.\u003c/p\u003e\n\u003cp\u003eVeloCD builds on the methods established for the single-cell velocity tool, velocyto\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. It works under the null hypothesis that no gene expression change is occurring (ie. gene expression is in a steady state). RNA velocities are then calculated as a deviation from this assumption: the modelled unspliced transcript expression subtracted from the actual unspliced transcript expression\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Positive velocity values indicate higher expression than modelled under the steady state assumption, indicating increasing gene expression, and negative values indicate decreasing gene expression. These values are then summed across genes to give overall RNA velocity values for each sample\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Velocity values are embedded as arrows on the low-dimensional transcriptomic space to give RNA velocity fate maps. A full description of these steps is given in La Manno et al., 2018\u003csup\u003e15\u003c/sup\u003e. Velocyto and VeloCD both perform these steps, but VeloCD has been further adapted for analysis of whole-blood RNA-Seq data. This adaptation process included the following: the removal of the spliced and unspliced normalisation steps from velocyto, removal of the grid-based velocity arrow visualisation, the introduction of UMAP-based fate maps, and the inclusion of functions that take the sample-to-sample transition probabilities and calculate these across groups. All expression normalisation steps are now performed prior to the input of the spliced and unspliced transcript expression data into the algorithm (see RNA-Seq pre-processing).\u003c/p\u003e\n\u003cp\u003eTo embed the RNA velocity values, VeloCD and velocyto both use RNA velocity values to calculate probabilities that each sample is transitioning to each other sample\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. A nearest neighbour search identifies the \u003cem\u003en\u003c/em\u003e nearest neighbours of each sample (defined by the user as the transition probability nearest neighbour (TPNN) value in VeloCD) in low-dimensional transcriptomic space. The probability values for each sample\u0026rsquo;s non-neighbours are then converted to zero and the remaining non-zero values re-scaled to sum to 1. As additional functionality only present in VeloCD, this algorithm then takes the sample-to-sample transition probabilities of each sample and sums them between (user-defined) groups, giving the sample-to-group transition probability (TP) values used to predict future disease outcomes of each subject.\u003c/p\u003e\n\u003cp\u003eFor prediction analysis, VeloCD uses a \u0026ldquo;drop-one-in\" approach where each sample in a user-defined \u0026ldquo;test set\u0026rdquo; is iteratively added into the spliced and unspliced expression files input into VeloCD. The algorithm is then run and the TP values for the \u0026ldquo;dropped in\u0026rdquo; sample are then extracted in R and collated across TPNN runs. Low-dimensional embeddings are regenerated for each sample in a test set but remain the same across repeat analyses of the same sample using a range of TPNN values. For UMAP and tSNE the results shown were generated using a single number of neighbors (perplexity value).\u003c/p\u003e\n\u003cp\u003eVeloCD is implemented as a python-based downloadable software tool that can be run from the bash command line. Several R scripts are also called by the python code.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eTranscriptomics\u003c/h2\u003e\n\u003cp\u003eRNA-Seq Pre-processing\u003c/p\u003e\n\u003cp\u003eThe fastq files from all RNA-Seq datasets were first quality control checked using FastQC\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, mapped using STAR\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e and quantified using featureCounts\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. The strandedness of the data was confirmed using the infer_experiment.py programme of the RSeQC package\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Mapping statistics for all datasets are summarised in Extended Data Table\u0026nbsp;1. Any samples with a percentage of uniquely mapped reads\u0026thinsp;\u0026le;\u0026thinsp;50% were excluded. When quality control revealed evidence of rRNA contamination, genes whose biotype was labelled as rRNA, Mt_rRNA, rRNA_pseudogene and ribozyme were removed from downstream analysis.\u003c/p\u003e\n\u003cp\u003eFeatureCounts was run using the \u0026ldquo;meta feature\u0026rdquo; option meaning reads were collated from all exons (per gene exon-level), introns (per gene intron-level) or features (gene) that overlapped a single gene into three separate gene-specific counts, using the human GRCh38 genome assembly fasta and GTF files from Ensembl. Prior to read mapping, the GTF file was edited to reduce the transcripts of the same gene down into the minimal non-overlapping set, with introns introduced between the new set of non-overlapping exons. This ensured a 1:1 ratio between the number of genes with one non-overlapping transcript and the number of \u0026ldquo;transcript\u0026rdquo; level features. This method also ensures that intronic sequences do not overlap any annotated exons of the same gene. When required, reads are summed for each sample across sequencing lanes.\u003c/p\u003e\n\u003cp\u003eThe exon-level reads were used as an approximation for the spliced transcript expression\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The intron-level reads were used as an approximation for the unspliced transcript expression\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Genes with introns overlapping any exons of another gene on the same strand were removed from downstream analysis. Similarly, genes with any introns labelled with the biotype \u0026ldquo;retained intron\u0026rdquo; were also removed. Only genes with both spliced and unspliced transcript expression were used for analysis. Spliced and unspliced transcript expression were then separately normalised for library size differences using the edgeR trimmed mean of m-values method and returned as log\u003csub\u003e2\u003c/sub\u003e transformed counts per million values.\u003c/p\u003e\n\u003cp\u003ePrincipal Component Analysis\u003c/p\u003e\n\u003cp\u003ePrincipal component analysis (PCA) was performed using the R packages: PCAtools\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, ggplot2\u003csup\u003e55\u003c/sup\u003e, ggbiplot\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, factoMineR\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e and factoExtra\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. The latter two packages were also used to extract the percentage contribution of each gene to each PC. The scale\u0026thinsp;=\u0026thinsp;FALSE argument was used for the generation of all PCA plots.\u003c/p\u003e\n\u003cp\u003eDifferential Expression Analysis\u003c/p\u003e\n\u003cp\u003eGenes with very low expression in each dataset were excluded from analysis by filtering on raw gene-level expression values to exclude genes which did not have counts of least 5 reads in at least 3 samples. Differential expression analysis (DEA) of the CHIM datasets (Influenza and SARS-CoV-2) was performed using the glmFit and glmLRT functions from the edgeR package in R. The DESeq function from the DeSeq2 package was used to perform the DEA of the TB-IRIS and PERFORM datasets\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Methods were chosen based on their suitability to the datasets. PCA is used to determine which clinical variables are driving the axes of variation and should be included as covariates in their respective DEA. Sex was included as a covariate in the design matrices of the CHIM and TB-IRIS datasets. Age was also included in the design matrix of the SARS-CoV-2 CHIM dataset. XIST was later removed from the list of DEGs for the TB-IRIS cohort. Gene-level expression values were required for this analysis because edgeR requires raw non-normalized expression\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. All volcano plots were generated using the EnhancedVolcano package in R\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eGene Selection\u003c/p\u003e\n\u003cp\u003eWe developed a framework to select genes with desirable characteristics for RNA velocity-based prediction (Extended Data Fig.\u0026nbsp;1). First, DEA is used to identify genes with DE between the reference \u0026ldquo;outcome groups\u0026rdquo; of interest (eg., infected vs. PCR negative, step 1). Discordance-concordance (DISCO, step 2) analysis is then used to enhance identification of genes with concordance between calculated RNA velocity and changing expression, because it has previously been observed\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e that RNA velocity calculations can produce misleading results when there are very rapid changes in expression. This is because RNA velocity uses the expression of \u0026ldquo;extreme\u0026rdquo; samples (very low and very high expression) to model the steady state, which becomes inaccurate when there are a small number of samples with very high and/or low expression\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. This was problematic when identifying genes that were suitable for VeloCD as many undergoing this change had nonsensical and ultimately useless RNA velocity values. By incorporating knowledge of future expression into this process the DISCO step identifies genes unaffected by this phenomenon. When serial measurements are unavailable, the unspliced minus spliced transcript expression can also be used as an approximation of the expected direction of RNA velocity. The gene selection framework then takes the intersection of the DEA and DISCO analysis as the initial gene set (step 3), which can then be reduced further using feature selection methods including LASSO regression\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e (optional, step 4) and selecting those contributing most to the top PCs in PCA (optional, step 5).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eMaSigPro\u003c/h2\u003e\n\u003cp\u003eThe R package maSigPro was used to perform backward selection to identify genes that were changing across time and also had divergent expression between groups.\u003c/p\u003e\n\u003cp\u003eFor the influenza dataset (n\u0026thinsp;=\u0026thinsp;138), genes with low spliced and unspliced transcript expression were filtered prior to this analysis (\u0026ge;\u0026thinsp;3 in \u0026ge;\u0026thinsp;3 samples for both spliced and unspliced transcript expression). The participant IDs were included as the \u0026ldquo;Replicate\u0026rdquo; design matrix covariate in this analysis.\u003c/p\u003e\n\u003cp\u003eFor the SARS-CoV-2 dataset, the post-inoculation day 0 samples were assigned the value 0.5. Low expression pre-filtering was performed prior to this analysis (spliced and unspliced transcript expression\u0026thinsp;\u0026ge;\u0026thinsp;3 in \u0026ge;\u0026thinsp;3 samples). The genes identified were then input into VeloCD for exploratory analysis (TPNN: 25).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eFisher\u0026rsquo;s exact tests (fisher.test() function in R) were used to determine the significance of the relationship between clinical features and the median TPs (to the PICU) grouped by a threshold value (\u0026ge;\u0026thinsp;or \u0026lt;\u0026thinsp;0.25). A two-sided p-value cut-off of 0.05 was used to determine significance.\u003c/p\u003e\n\u003cp\u003ePearson correlations were performed using the cor.test() function with the \u0026ldquo;pearson\u0026rdquo; method argument in R. Similarly, Spearman-rank correlations were performed using this same function but with the \u0026ldquo;spearman\u0026rdquo; argument. The wilcox.test() function was used to perform Mann-Whitney U tests. For all statistical tests, a two-sided p-value cut-off of 0.05 was used to determine significance. For the DEA, the Benjamini-Hochberg method was used to adjust for multiple testing and a threshold of 0.05 was used to determine statistical significance. MaSigPro returns Benjamini-Hochberg adjusted two-sided p-values. The ks.test() function was used to perform two-sided Kolmogorov-Smirnov tests between two distributions.\u003c/p\u003e\n\u003cp\u003eSensitivity and specificity values were calculated across equivalent TPNN runs of VeloCD, and ROC curves, AUROC and 95% confidence intervals were constructed and calculated using the roc(), rocit(), auc() and ci.auc() functions from the ROCit\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e and pROC\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e R packages. The hyperparameter run with the highest AUC was then selected as the \u0026ldquo;best performance\u0026rdquo; of the signature.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Visualisation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaincloud plots were generated using ggthemes\u003csup\u003e67\u003c/sup\u003e, ggdist\u003csup\u003e68,69\u003c/sup\u003e and ggplot2\u003csup\u003e55\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw and processed RNA-Seq data (including spliced and unspliced expression) from the TB-IRIS study is available under the GEO accession number\u0026nbsp;GSE274086. Raw RNA-seq data from the PERFORM study has been deposited in ArrayExpress (409 samples), with the exception of 9 samples for which permission for public sharing of sequence data was not granted by participants. Data from the SARS-CoV-2 and influenza controlled human infection studies is available through European Genome-Phenome Archive (EGA) as described previously\u003csup\u003e43\u003c/sup\u003e. Reasonable applications for access to data in EGA will be considered by the relevant data access committees and data may be shared after completion of a data access agreement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVeloCD is available as a downloadable software tool (including documentation) in github. It is accessible using the following link: https://github.com/DrClaireDunican/VeloCD/tree/main\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the volunteers who participated in the studies described in this work. We gratefully acknowledge funding support from the following sources: the European Union\u0026rsquo;s Horizon 2020 Research and Innovation programme (grant agreement numbers 668303 and 848196); the UK MRC (MR/R008922/1 to RPJL) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement and is also part of the EDCTP2 program supported by the European Union (MR/L006529/1 to A.J.C.); the UK research and Innovation Engineering and Physical Sciences Research Council (PhD and Postdoctoral prize fellowships to CD); the NIHR Imperial Biomedical Research Centre (BRC, support to CC and CD); NIHR Biomedical Research Centre at University College London Hospitals (MN); the Wellcome Trust (207511/Z/17/Z to MN); the UK Vaccine Taskforce of the Department of Business, Energy and Industrial Strategy of Her Majesty\u0026rsquo;s Government (BEIS); NIH Centres of Excellence for Influenza Research and Surveillance (CEIRS, contract number HHSN272201400008C); Defense Advanced Research Projects Agency (grant number 140D6319C00236); Taiwan\u0026rsquo;s National Science and Technology Council (grant number 110-2923-B-006-001-MY4, to C-FS). We thank the following individuals and organizations for their invaluable contributions to development and implementation of the influenza and SARS-CoV-2 human challenge projects: Chris Woods (Duke University), SGS (Belgium) for supplying the influenza challenge agent, Andrew Catchpole (hVIVO), the NIHR Clinical Research Network staff at The Royal Bolton Hospital, Human Infection Challenge Network for Vaccine Development (HIC-Vac) and ISARIC4C Investigators (https://isaric4c.net/about/authors/). ISARIC4C is funded by the National Institute for Health Research (NIHR; award CO-CIN-01), the Medical Research Council (MRC; grant MC_PC_19059), the NIHR Health Protection Research Unit in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE), in collaboration with Liverpool School of Tropical Medicine and the University of Oxford (NIHR award 200907), Liverpool Experimental Cancer Medicine Centre provided infrastructure support for this research (grant reference C18616/A25153) and NIHR Health Protection Research Unit in Respiratory Infections (NIHR award 200927). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, DHSC, BEIS, the US Government, or Department of Defence.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSinghania A, Wilkinson RJ, Rodrigue M, Haldar P, O\u0026rsquo;Garra A (2018) The value of transcriptomics in advancing knowledge of the immune response and diagnosis in tuberculosis. Nat Immunol 19(11):1159\u0026ndash;1168\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChaussabel D (2015) Assessment of immune status using blood transcriptomics and potential implications for global health. 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Compr R Archive Netw Retrieved March 2022\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eThe clinical characteristics of the PERFORM study subjects (n=399)\u003c/p\u003e\n\u003cp\u003e\u003cimg 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efGB6O7bKNrGfTwIS6VV+OQz4Xcae7eycP58VqzdjLm9I1llopx0dRDjf5sNq5ez94gGp0+fZNPaXZy0dBXe0UF2YhBmemqsXLaEBUtWomF4mpCETH7+OupNugj1dsVMywDvx0GcMdJE3diGNNWopbwYPwzVNDh73kP5+88Rfl1ZQnTEM/z9A5R+pfQt4WMBT4NJzcx765zX+qpSzh7dg5HlDQqqnwvrFgLuOLB7jzZh6SXUlOVwxfIENg63ScgqoqI4B9fLVpictCCxUPZ0SiQSyb8Mj1ythKj8kVVb1THV02bB1NEs32dCTkklRbFeLF+6lHXb9mN9Uovp48ewcO0RchvaaShMQWfTPP7ti/7o2N4SV2rkprUeA4To3HT0NBmVLVRkR6Kzew2D+vdj3MSpbFi3luULZjGwX2/GzljIVf+Xb2LfTjvVGYFo6+hx+3EkZeIzLY5roGFkQU5VO/XVxTioROe7ejrLCjNwvmyJvr6+MAOMjIww0NdDW9eAc9dvklH068NycxKfcUz74M9E53OKsuIx1d//TtHZ0VCO363zHNAwIi6nQvZy/gVQiE7DTauY8uM01E+eFQHqSsaPmYDNvUjqRPDmdvoQsxeuRvPEGfZvW0o/IRSNHHyUf1uUFoH+3nWMHD6SiVNnCx9bxjRFb+noHzl04hx5VY00VBdgd+wgE0YMYujIH1m0YgOrli1lzND+9BgxFf0Ld4V7NBLxwI21i+cpj+saneDwzvXMmDEdTREEFpXXEHnzPFOGDeSrb/ozY/ZMFqxYg7XTHSJDA7h44jja2vqcNDnG1tVLGDlyHAZ2TpRVFeN314G5U35kQO++jB43mfVb9+HyMJyCnDQsjQ4za9ZsduzT4Li+ljIIV7y0uhOYQMs7Vq3upk2IzsdoaOnhERxHaV4CZvpHlD0iBXVd1JbnCdGp84uiszgvlesXLNDXE/5s8Lo/X3C+Q07pr83t7iIm0AOtQ3u6ReeLUcNdJIX6onN4Hwc19Dl9Qp8tW7Zzyd2f2qbuk/JTwzlz8jj2Lj5kZKThan+KU5ec3/3iSvLnorOZIE9H1s6bxDdffcnA4ePYfkALF8/HFAmfU9BWW4TDKW3mzZjGhu0HOHFMj81rlzB7+SZueAfzWOEXI3vQe9RcbgV1vyx55GzJ6F7/ZPyiHfjHZBDocY2Ny+ezWPiEwQlTDuzawPSZ80W5dqGmroagu3YM6f8DX3/fh9lzFrJo6Q4s7W8SF+OPxvaVzJm/jCPCL420DjB35kzW7zMkPOXXFgDqEt/tBsfUDYjOqSY+xJeDu3dz8aYfda2iHUz0x+gXRWcnWSnh2Jw1Q0NTW+lXSt8y0Edby5CbQnRXvbGQn4L25np8rp1l1+6DXL7pTUp6BvERjzE+spdVq3bzNKmIzs42Ep66c3j3To7qn8BIT0OcfwAnr7DX1mj4syNFp0QikXwgj9ysmD15tLJhUwwj3bduIWOnr8AjIJzbdgYsWLSCk+ccuXX5NFNGDlWKzpz6dhqL0zm+awVf9xnN8YuKHpxG3KwUorMnW3UtSRPB34NrJkwc0ovxs9fg7PGEpLhoLlvri8C5DyMmz+XKe/R0KkSnppYOTj5holntIuqxGwf37ePSvVAqKkpwtD+GzhkhOoVIfhtNDYqVCqMJCQkRFkpomLCQYIKCQ5Q9IzUNvz40LiM+9BdFZ2FmDCa/IDqL0p5hYaguAtS71H5mK/ZJ3k636FzJuDFTMb10i0vWJ5gybhRbdGyJDA9GbcMi1uxU56bXfbT3/0SPYRMwueGnDHw9L59k8A9fM2nRJu74BZOSkoKvkzXTRvZjwPiF3PaPIi7Ui0WTRjBw2CTOOt4nLj4JT8dzLJ02im+GTMTQwZOawjTM1TfyXY++rN5xFOfbnlw0O8qIft8zeNZG/MKSSfR1Yv6Po+g9YBRqJ+zwfxZNcWUVVWWlRAcG4evlhfstFwzVdjD4+y+Zu/4QkVklNNZVYG9wkKHf/MCqbdqExOUoe/SCPS4xY/wwBo2dwYmzl7jncpk9P83im5592WJgLwLTX/KnbtGprqnDrSdR4vdOQn1usH/fAW74RFFRVsBlGwP0FaLzHXM6G+qqSI6PIkT4r9KfQ1/6c3xqxnvMr+wg6undn4nOtsZq/O87oal+GCPTM5y3MUftwD5OX7opBHAdDVVFuF21w87BhZL6DroaK7h1wRxLx7u0vLoEruRPTXNtBcE+tzi6dxNjRwzi66+/ZcT4mRy3caSksoa8aF9WTB9Dz34j0TGxwvuuCzr7fuLLr39g/aFTJCZGYqaxke/7jOTklfu0Nldz/vhB+vQciL61C3n5KRjsW0Nv4Q/r9+lxx9eXsyYaDO7Zg7EzfiIqLZsQn6tMHDOY7/uPQf/0BcIiE8nJSOHqWS369/iemUu3cOm6C/ccrZk9bgjf9hyJjYuvaA1/iW7RqSdEdERuM62NNdyxP8EeNX2CE/OpSA3F5KgWFva/1NNZTGxUhHIUkNKvhCnazaCgUNKyCmh5214oXZ1U5idjf9qYw0eOYmx6mkt2pzmydw8bd+gQmlasbIMVowbUDh7C+IwNlqdNOHzwMOecPClTTM35TJCiUyKRSD6QR27WzJwwlPWHTQmPSeK80X4mjRvHkWOn2bRyCcvX7uJJWCSPb9kydtDA10Sn8e6VfNt3DMaXFA1Zt+gcKETnDgMrkgpKsNXcxcg+fdhjcInni+llpjxl4YyxDBo3/b3mdNaoROc1zwCau0SwX1PCdVtTjmge50FQJDcummFgaU/eO0SnYohsdVU5JSUlSistLVX+XyyssrrmvbY7+CDR2dVCkK+raHCP8DA8TfZy/kV4LjpHDhmP7e3HPAv1Y/2CqUyfvZhTZ4yZM3sBR81vEBsbisGBlXw95EdOOQnR2VyCld4OvvjqW46YX3qx8FBnVSaH18zhhwGjMbvkxp0rNkwcMVwEoPvJVM2hri9MRGf3UnqOmIjehXukxz5l9+oZ9Og/jEWr1qOmdoRDe7YyY8o0Fq4+iO+TSGJ8HJk7fjSjJizjcVy+8jrdw3tjuWpvhY6WJlq6uuzbtZEfh/Vk1tKd+Md396q4Wxsy9Ivv2aZuSbnqRp0tNRgzvD/DJ0xn2669aBzaz/pVS5k8awF7jWyprP2loaYvRafrgzDlkMGmqkIuWRzjqM5J/IQ/XzlvgpHNxXeKzva2Fqor3+7PVcKf237Vn98Qnar6JyXMCyNdTWyvulNQVkVlWRFRQb7oaGjhdO8Bbg5n2Lx2DdrGlvj7P+a20xU0921jpwjqHwVFUd/8ctEWyZ+f8sIsHt934/DWlfT57huGTZzHzYehPHaxZ9b4kfQVfr11xx6Oahxmy/pVTJ40hX2aJ0TZKMXzugUjB/Rjl9YZIp4Fobl1McOmrOJuYAKlqY/ZsmIWPXsPYtnqTWgI/9q5fTOzpk1j9YadQgBm8NTzMuOH9WHcgvUEpRYq76epIh3Tw2v47vs+zJy/nAOHDivnPC6aM4MZwrfO3/Til/vTuwj2dERvvwYhad2+U5Ubg576QczsHQl7cp8z+oZYvrOnE+Ue2oo538XF3X7VbcK3ikupqWv4xT0+y/LT8XK/yZVrroQ8fYjzZVu0jlsTn5VHcpgPWurqOHs8Jq+4Qjn//JG7Izo6ujj5RQkxp7rInxwpOiUSieQDeeRmw6wfh/DTfhPRQJQT6X2JZbPGM2HSdAYP/5E9hucpKSsh5PZZRg0cyKJ16i9E54k9q/iu31hOPBed1vrKhYR2GliTXFjCOa3dDO/Ziy1HrcmpVASDXYQ9dmP2xJEM/XEWVxSi8xcbnG7RqaWty3XPpzzftSEvMZjThtoc1jXlhIkex2wdlIHi26guK+CBpyvnz9sLu8ilS5e4KH62sz/PbZ9H5L/HKpkK0Xlc59Avik5TgwMYnfu56GytysHF/hRHDS3ILH25UIXk80Y5p3PzKkYOHo2VR4gQSUVYamxizKA+TJkxhSnzV+Pqn0y+KBu6uxfz9dBJnHIWorOlgvPH9/HNV1+zU/8sJfXds7WqciLZvHASPYf+iI2jBx7XbPlx+FCmLNxBnPBLBXmJgexdN5sfRk7G4KIHGXFCdK6axrd9BrNmlxqXrlzl1i03blx3wtMniJzsfJ7dvczscWOZOHUTEWndYrKxJANbo12MGzOSdXv0uH3vPvZnDZkxtg+zFu/kaazivC6czTUZ+M+vWaMYbq8ahe5iqcXIQb0YPnkuRqfOcvPmTZycnXB2uyVEYwzNvzhf+fnwWl0hOkNfCL7smCec1NdCTfckxia6nLC/SklV93y5N1EsNuRz15nz9q/4s/hZ4c/3HvhTXPlrPtYtOrUPd8/pfL56rfcNc3bt3o1HYFJ3gqC5rphj+3dw9IQ1ZicMObxnO/sPqaGnp8uBPbtYPHcmM+etwMLeWfi9nNf5Z6ejuY5y0ZZVv9IbH/fQiYUTh/Bt7wGcvOohBKUQnWOG0WfIRDSPn8LRzQ0nJ2fcXJzxexpMY3MbKUHurJ8/ngVrd6InhNzqmeNYe9BEuWhOdYofG5ZM5rueA9i8TxOH6y64ODvj4uqKt99jioVo9b97gdGDezNr7W6iMouV91FbnIjJoVV89U0P5q3cjPWFKzi5uOHs7Mit23eITs5QvqR5N92iU//AUUJSn7+AFe2ttxOaGuqYGOphoGmI3WUv1bE36aIwJ4mbLtextjmn9CuFXbhwHjvbizwKiqbuHXvWvkl7dR63r1hx6rwTBUV5PHI8xfotB4lQfVcFBfEhaO7bg7aVixCzn4fqlKJTIpFIPpAHThZMGtGH+Zt0SMgVjWZeNLt+msPf/+Pf6DFoAnbuoXS0NeN3xYiBPXsya/l+shWisygVg62L+Nt3QzCwVyzD3ojzGU1++PuXbDhqTkpJDb5XTzJ5eB8hMOdx9oIjIY89MVbfRv8eXzJo/AwuP0kRQWobJQXZZOUV0dDyZi9FO9XpT1E7osHlu48RH9tNWx3PHt5k65oVLFq8ADUzOwor3h6k1lWVEhrwgDt3bnP7zh3c3d25c/s2brdu8yj4GSXVvy4E02OD0Tuym3OuT9661UlBRhRGWjvRsbpCWe3rojMz+gkntA5y0v4mtbIz5C9DS0MBOmsWMaDXQI47P6aho50nzmcY3f87/t///IIFGw6QVlZDWW4Mh9bP5D/7jMDoimJOZwcBN+2ZNLQvI6cuweKyG08Dn2JjeoQRg3ozeck2AuPSSA73ZtHUUfQbNJKjpvY88A/E/rQ+E4f15r/7jkXH/i41xemc1dpGz559WLldXYiuJ0Q8C+Hxo4ckZObS2t5EqIsNk4YMYviYnwhKzFPee1n6M9Q3TqdX7/7s1z9PcFAQ505qMKLPl/w4ZzNPorvP87xozPAe/2T09BXYO94nJbeIQI+rzJ00koFjpmFqe5XIqCgC/f14GvCUwpoWWpoayM3MIL+kgle2+1XRRlnSQw6qaeDoHfTiJRItNQR6ObFx1VIWLVmEpsVFSl9ZSfRVaiqLCHrqw23hw3de+PMtborA3D80kvLaX1uYpIOIJ7dR27ON20GpL0TnM5/r7N+1Hasr7lRU11BbU0VMkCd7N23i9OW7JGbkkJ+TSXJyEskpqQT73eeYqBMO6JkRm5xJ++fSXfMvTGtxAk6XrDA9a4u710P8H3pha3yU8cIXh4ybiZt/FKmivt4wbzK9+41E28yWwGcRBAcG4P80kLT8SuUcxIbiJEzVtzBy3FiGDR/JpNETOHnpLm1dnXRUpKC9cyV9evVm/T5dPB6FERYajL//U+JTi2hvbeDpbWsG/vAlExdtJDS1e/RBW30ZTpbaDOz1PTOWbuDqbR/CwiPwf/KI0PAE5XzKxoZqkpPSKK2se8uImS4C715Fc+chgpIreP7qp6uxBBdrY9YunMOcBZuxcfBWHXmTLsqKs/DzvS8E8k2lXyl9S/iYm9sdwmNSaXzb1BBR7tuaG6mrq6OhoZHa6gr8PZ0x0NPj9pNoWlvqCfd0YPPG7Vzz8Keyulb4ViWPbl9m9/ZdWLr4ie8iRadEIpH8S6AQnROF6Jy3XoPo3Eaa66qx0dlBry//waiZP/EkPkuc1YTneX3RUP7ATCE6s+raaVCJzv/+dgj6r4jOHn//J6sPm5JW0UqZEGOGalsZNrAfvfsOZP6CRaxeuYSB/XoxdOJsbgRlUV2SzY3zZ7l824/i2jercCE60xSiU71bdL7yqre+ooBLJocZP2okm7VPU1j+dtGpaBTbFStXCnH7pinS36fBS4sNQlclOmsa3iE6NXehbXlFBL2v9ngoVhR05vDuPcpVa2UD9ddBITq1VwvR2XMgBlceKIe+pUf7sGzaCP7xTV/2HnegtrGViqxI9q+dwX/1HoHOeU/l39YUpGF3QoMZk8YzdMhQRgwfztChw5ixaDW2zr7UKPa2qyzE3kSDqYpel779GTd1LstXLGfimCF8NWgC6ta3RPFqIy7Ii53rljByhAh+p85g9uxZLPtpPde9n4jy1kKYiy0TBw9i2OhVBCZ07zfZXJnHdUtdfhw5WKRPYc2mHaz/aQlD+3zF1AXb8AvvPk+xR++W5ZP57ptvGCICa9MbvmTnZYu/NWT2pHGMEmmzZs9l/tx57NcyIK2ymYK0SGxPm3LPP+LlS6IXtFKuFJ1HXhedAsWKoXaGexkr/HmXkTVl73gZJOK8X/Dnjvfw5+eic6tSdD7vba0vyeSq1UkOHFTH9uJVrjvYo6t+CDUdMyJT8oQnv05VfhqXTxsqVwKWq9d+HnRWJmNjfJAxotz37duXAQMGMnjwUCZOX4D2yUvkVTQIl6rB/aI5S6ZPZNSoscyYOYt58+ezdd9RHsUK0ai8Uit3r5xm/JCe/Pt//UOIxG3cf5apPKJos0K8nNi5egGjRo5g0vRZzJk3l9Xrd+B4P4a21haC3G0Z8MMXTFi0geCU50PeO8mOC8Zw/wYmjhvNuImTmS38asGiZZw85055XSspMY8wND5DQGw2P59G3C06jwrRGfiK6FRQnBzMwXWLGThwGjaX3tXTKa7Q2Un7W/xKYYpV2t9KZ4fSF3zv31X25l45b4u+rh7WV26TX9Xdo1yRm4ydiR6H1TS7fevyOY4e3o/eCWvisj/y/tqfECk6JRKJ5ANJCn+IubEuVg63yClvpqu9jYSA+5gcM+CsouGoUAR/LcQG3MNQWxsLexfKRaTWWlOiHAJ4WNMIj4AYcU4r4Q/voC0Cyguu3hTXizCtq4PspAguWZ7k0KEjnHO4jt8jH86Ym2BodpbQtFJSowKxMjXG0z+Spp+9vu2ksSSNm7duERKbwusdoZ0UpUZge8ZcCFYfqj7h8LaS/Ezu3XLiaWQaTW/ZFqK6LBdPd0fcH4e8Mberg6RIf5xuuBCV1j13R/LXoL21mrsXbTDQMcQjOFE5N7OuLA+3S6cxNDqBb0gqinU36sqycb5kwSE9Y9yfxnSLFyGM6sry8b19nWO6mhw8cAgDEwt8AqOpblS9WRHnVBdlcPv6ebTU1Thufo57Xt7ccLBCy8gEF79nytM6O0QwGhuM/Vkz1A8fFoHdUSzOXSY2M09co42scH9OGRpy3OQC6YptVBR0Cd/JTeGavQVaWnrYXnHlvuddzhprcdr6CgmZ3ee1NFQQ5HcHA211jhzV5ra4/8Y24fvVhTxwd8JIR5uDhw6ja2TKnQdPqWxsJPzRXUyMTQiJS30t8O2mg/qiZFxv3uJZYsaLfQ676SAvMQSr0+Zcv+f3Cfe+7FRuTn/T6TrRGSUv70GR3yV5yu9lctwAPT1DUV85k5hVLAL8nwvZxuoygh964B0Q/tb9VSV/QtrqiHv2GNuzpmioHeKAYqj0cTPcPB5TVPlyP8yWunKCfN0xNdTh8KGD6Ogbce2WN7nlL/epzU56ho2pDvsPHOaCy30Knm82K1CMDEoQfmd7+gRHDh9S+o7NJSfhV+V0CpGWHu0v6g11zGwvk1n8clpIV2cbxVmK3lhrtI8eEb58hBPmVgREZ1FfVUbAvSuYWl8iuaD6Zy9BFKTHhnLnhhvpRW/s7SmuG+B9W7Tp5jwOjFMlfiy6qC/JEp97ET1dbSGKzXH3DaKkpunFPXQpFhsqzOSuyxWO6euiZ3AMB+d7pOZ+XvtVS9EpkUgkH4hi8YCa6krlQgHtyvXLu0RA3UxNTTW1DU2q+RZdtDY3UlUpzqutVw4x6hKNZ2NdLRWVVTQ2K0LuLuXQusqKSmrrG1++iRUNTmO9OK+igvrGJtqFqK2rrVEu+qHYGDvQxx1zcxtikxQ9qj+nq6ONhoYGmkVg97P2SYja+tpa6hoaf3GRgw+lQ9xzY0M9zS3iHt7SSHZ2tCuPNzQ1v9HT0p1v9fUN77VgkeTzQRFINYjyX6Us/63KsqnwiSZR1mtqamhRjS1VlI36uhoqqqpF+Xh9ZdX2VuF7VZWUl5dTLfyq2/9eR+FTVZUV4rgoQ21tNDXWUymuVS/K2kvEvYjPLS8ro6ysnFqlLyv8oYs2hX9XVVFdXffGIjtdoszWUSl8uk74eWtrC3U1VeLe68TnvDxPuXCPuMcK4dev3r+ijqgWf1smPlPx3ZqEb7Q0VuJ58yqn7VxIz+ueh/omncKf64U/t7zNnzvbRd2g8OemTzrkTvGdGurrlatxvvkpLU31lBQXUlhYpMzzd6EQD83i2TSKPBGxpypV8meno71V6SuVwqfKRZtUXVMryuLPpUOnOE/RLirLtyjn3XW76qBAcR2Fv3S3a+KYKv0Fom1qEH6v9Mnyiu42UeXfCp9UtKXVtcInlX76Os0KH68oV3624v5aRWNakpmMs70FN9y9qPnZNJRu2oRPNoi25m3XbGsRbbpoc5s+wYJXirxSLO5VUJBPUXGZct7r22hqqKW4SPhWUbHIj19eFunPiBSdEolE8jkjquyM1ERCIhKofI+5lRKJ5M9LR2styYnRRCZmU/u8x1YikXwwVaXFRIUGk56Z80lfyEjejRSdEolE8jkjGs+29jblm9zPaZiNRCJ5C10dtLe30t7Z+fNeTIlE8rtRzKlsbWml811zKyWfHCk6JRKJRCKRSCQSiUTyyZCiUyKRSCQSiUQikUgknwwpOiUSiUQikUgkEolE8smQolMikUgkEolEIpFIJJ8MKTolEslHQ9Qhysn6io3HFf9Lkybt45nCrxQ+9ktIH5Qm7dOYwqfe1wel/0mT9rqp2qZE4RtSdEokkg+nqqqKrKwsMjMzlf9Lkybt41lGRgYlJSW0tb19Kw3Rfiv3q1ScK31QmrSPawqfUpjCxxRB9NtQiM3S0lLpf9KkvWHV1dWKfVMT09LSpOiUSCQfjqKxTUhIID4+nsTERGnSpH0kU/hVXFwceXl5vyg6CwsLpQ9Kk/YJTOFTCt8qKip6p+hU+GZ+fv6Lc992HWnS/tVM4QuKF6bCNxKFb0jRKZFIPpyWlhbq6uqkSZP2iay5ufkXh/cpjr/t76RJk/ZxTNHOKV7wvA2Fb0oflCbt56bwm6ampsSGhgYpOiUSiUQikUgkEolE8vERGlMuJCSRSCQSiUQikUgkkk+DFJ0SiUQikUgkEolEIvlkSNEpkUgkEolEIpFIJJJPhhSdEolEIpFIJBKJRCL5ZEjRKZFIJBKJRCKRSCSST4YUnRKJRCKRSCQSiUQi+WRI0SmRSCQSiUQikUgkkk+GFJ0SieQvTUtTPYV5WaSnpZKamkpKSgrpWbnUNv15q7q2lgYSo4K4fdudmLRc2jrevhH5q5TlZeLl5syt295kFlSpUt9NW0sTxYUFFJRU0KlKe0FHK1UVZZSUV4rP/tnRT05XWxP5udnieaVTWlFD+6u30NVJY00Fmemp5BYWU1VZSnjQI9zv+5CSV6Y66ffQRk5KDK7ON/Dye0pZw6+Vjy7amusoKMijtLKOjs7Xn1F7awuVZaWUVdbQ+caxfzW62psoLykgLTVNmMIHk0lNz1Dm258WEQoVpMVy/+4tnjyLp67l18tDdVk+j71v43zbg+TsIpHyy3S0NVNSkE+BKMdtP3OzTuGDpZSUldPU+sfXVR3tbRTnCx9MSyO/tJLW152QpoZqMtLTyc4pUG78nhYfzt077jyLT6P1A6qM8vw0Ht5z5uZdT1LzSlSp76atuYmifJGH4h5/RnsLVcIHS8Sxjl99Gp83ivq8tDD3RTuXnJxMWkY2FbWNqjP+nBRkJOLpfocnodHUNv96wamvKsbvzi3cbt4jMUP4TYfqwDvo6myltLiI/OJSmt84uaurg5qqCorLKmhsblOl/jF0trdSVlwgnlMqhcUK/3q9fLY01pKblU5WTg6VVZUkiXjgjvCJqJQcWt44970RbWdVYTruN0Wc4OFDXtmvxwntoo0rKcqnqKyaN2uhzo42KkQdVVahuP9feRAqpOiUSCR/aQoyYrE312XP7l3s3LGDrVu3sn33PgzNLPEJiqaqoUV15kdAVOoN9fU0N7eKylWV9juoKU3DTHMLg4aMRNvSlZLaVtWRdxPtd5dVE0czdfoa7j5NVaW+m8qiHBwszNDSMSMhp1yVqqK+mAcet7h+y5vKj5k/70lHcxVu50+xZ+cuLK/eJb/iZeDU1ljDk9uX2LdnH9bXPYl89gjtXUsZNm4GFo6PPiC0bMDb6SzjRw1lxfbDxBY2q9LfRRdVuTGcNjPC8IwDWSW1qvRuakpy8b4tgmffQCEa/tiA5s9Ge1UWd69bs3v3bnbt3Mm2rVvYtn0nmnrHcb77gILy1/PugxCO19zQQGPTrz2/X6ajpYa7542YOKI/S/cakVTwFlHzKiKATY9+wu6fZjBqxmKu3Q/51bLYWFWIyzkT9PT0CUrIV6U+pxk/L2cuu94hvbhGlfbH0dJQg6+THfv37uXY2avklrwSoLbVE+Hnxr69+zCzuUp6RjIXzdQZNXwI+47bkV/3+1VnnL8rmxaOZtL8Jbg8ilClvpvyvCwumBqjb3Sa5MLXg+jO2gL8bjlz3dmbuv+Bl2d/JBVCTNw4Z8b+XdvYsWOnaOe2sH3HbnSMTnL7YRCl1R9XfDY2NdLQ+GE+pijj7g6nGDtsCD/t1CY641d8TJCfGsr2WVOZNHkRDnfC+LXmqaulApfz1hzVPM7T6DRVajedohyHPLonfMyD1Lw32sBPTIcQc089bnBg+xaMzl4h49X6paOJ2IB7aB7ah4H5BSJiorDQ2cSIUT+ibn6D4urfme9C3iUH3mLO5DFMXbSWhzG/HifUFcRz0fI4Wsa2xGW9nkfNdeXcv+eMq6c3hVXvV76k6JRIJH9pkp/5snHhGL7r2ZeJM+axft0a5syYRP/+fZm1fBMeQXF8DElQlBHFjQs2ON7xo7z2wwKcxuo8HO2MWbtuE+dc/Kho+PU7fObpzLTe39B/0EycfBNUqe9G0YtzZO1Shg+ewHH7O1Q1v1L1V6Vjd8oINSMr8qubVIl/HJ3tDVwzOcCA779l8tJdBMVlqY6IWytMRWfTXH7oNYgj5i6kpMRx2UKbdZv34uIT8QGis47bF47R46u/M2XpJiLyfu17d1Cc8JiVi+cxcvIczt/xFwHQy7e9JRlxWB7XwsDmBrWNf7xw/zPRUhSF8aHVfPPtN4yaMIVVazexbMFshg0eyOhJc7G67kVl/YfnUWN5Ae7XrmJr60hK1q/3kv0i7Y0E3LvMjk2r0Th1iazSXxF+QnQmh3mzavogvho0HrubT361LFblJ3FKbQWjRgzikOlVimpfzYM67C202K9rTGh6qSrtj0PRw/HgyglGDOjNyKnL8Q1PfTEiorEsC0vNjXzfsx+b1E+RlZeDt8s5Nm1cj7nDbUrqf3/9F+FzmQVjv6H3qAk43A9Wpb6bnIQYDi2dw/jRkzG+5EH1K92s7RVJ2B/TRk3birKfdyX/pchPDkNt03y+Ez42cuIs1q9fx4K50xk8sB/jZy7mulcwDb/WLfgeNFTm4+N+AxsHR6LTClWpv5dWnt53YuuG9eiY2pGa/+s9b9kJ/iwdOoA+fcdh6fiUul+pNjob8jHZv4UhfYahfvIiuRX1qiMK4VeJ6/mTHNA1Izg+R5X6x6AY/fHYxZpJA75jwPjF3H0ax/MBTS01RZw33EWf779nxV5T4lPThDg/wcZN27G+4UPFr33pd9HVRpzfNYb3/ZYB42bh8ezX44TytAAObFlM/5FTsbh0i+qml2WoviyH0ye10DO3JKXw/V6MSdEpkUj+0iQ/8+anOSP4bthkdKydSExMwMfRghmj+vC3L3tw/MItXsgLETg2NTZQW1dHU8u7exfbWltobGyipbWVzq5OWpvrcbPRZXT/71i2Q5+YjEqa3xgS19LUoByG9uowtS7F37Y0K3tlno/AbG9rE9ZCZUk+yUnJlFXVvTgm/oKW5kZqa2vF37S8FtRG+t5myajBjPtxGbceJ6tS301WQiSmh3ewesUqNu1Wxys0ifbnHyRE5znzY6gds/4fEZ2K7xnrd4MZowfSZ9gUbngHq4b2dJIe7s2CcYMYOm46V32ilC8MKktySUnLoFohXLq6RP61iufY+GJYruL39o7uxlIxpKqpoZ662jqaW14V8/V4XDvFsIF9WLRhL9EFv/Y2uY2ihAB2bNwhArxV7DxiQEBslpCi3ZRkxmMlRKehjaMUnUXRGO5dyjff9WC7xjGik9IJfeLFxvlT+Md//5NV+4xJKXgZtLQ2N4nnU6vsSXm1jL+K8hk3NYpnqPDBLjraW4n1dWbOqJEMHbuMOw8jhQ+/7iOKIYgKH3x1uKqIeUS6wp8bX/hZm8IHRXlpqCknPTWFnMIy2l4ZIt19nVrqRR3wMrWDtIiHbF0ygYET5uBwL/Cd9/6ckowoLpofZuWK+azdfoQbniGvDE2t47wQnQf0FKLzQ4aN/146yI/3Y8X00fQdPBpzRz9q2rq/UU58IFvmj6P/8EmYXnlAfUsHjdWlJCenKIfhKc5SPA/F83k+7E4xXLftxRA8UY+JY4p6trH5dd+IfuTImlmDGTV9LjcePFOlvpus2EiO79zIpjUbWLfzMD4RqS+mIyhF53EdITqtKf2Li84ChejcMIt/9hzATkNbkpKTeezlxKqZw/mP//gbB0wvUPJCrKjyX9GOiPx/VzntEO1QkyjjzeIchY8pfC7qoRMrZo1m2NQlyt78FuF/r3YiP/exV9u/7nZO+Jho5zqePxuFj4ny0FBTqfSx7MISXh01+sLHGppem/6RmxzEhsnjGD1mFufdQkTZUx14By01OdgaqrNqwQK27D/KBdeHPNdNnc0VuJ0346DuqT9cdCqeQXacP9uWTOar7wZgevkeNS3d37Q4M5q9K6bx/Q8DMHa4T0NrGw3VJaSkpCqnJCiy6Xn916Zq19pELPKmfynq0NfiBCE6E/1dmDJ2CONnL8cnMkl14N0UJgdyTO8Q8xYt5Yi6Ju4BiS/aOIXoPGMmROdpKTolEolESaoQnWvmjabX+LlY3AykqqaWtKc3Wb9wKmMmzeO6aDg7RbVcX15I4MP7XLK344yFBXaXHXkWl/5agNpSX038M39uOJzD/NQpbOyvER0bg/+je2xeOY9+P3zLuKkL0D52mofPklB0fHW2NBAfHsiV8zaYnznLjZuepOSWKhuC1tpyAr1vY3fxOg8eP+WRjwe33O8Sm5BAeIAvFx0uExqXQau4TltjBRHBj7jicJ4zZyzEZ1/hSUg09aoeyighOhePHMTYCUvfS3SmRQZhdUwbq3MXsDAzQuOYFUm5quEz1Rki4D2BxglbCt4hOhXz0RTzPgsKCikqKlJZIYWFRZRVVNPa9mFNSUNJsgii5tGzzwC0rV2oEPqwq62Wh9dPMnzwYOavPUBsTrkIWsrw83DlitNN4jKL6BLBbkLYUy7Z2XHvocjTBz44XbtKWGwSZeVl4hl7cOGcDRbiWVy46kpEQhbNSnXaiMfVUwwd0JuF6/e8h+hsJT/WH40D+txwvIaB1iGMba6TWd6dX6VZCdiY6HLczpm6t4pOxQuEBspKi0WevZKH4ufComJRTuuE8PlrBMqtxTEc27ecHr37oWVmS50Q/aUFaWhsWsWIYWPRtXSkuKaF1qYGYkIDcHS4gNXZM1iLsukbEP7avDTFfOf0+Gc4X7vIKTMzrO0uEhibRnREIKc0dzCkd2/6DxrH1gNawrf9qW5qp6ujhfS4UJwun8fc/DSXHG8Rl56vfJHR3lJNuL8ndra2+Pn78/CBN9fcPIlKzCAjIQSnq+fxfBJGbVOrKFuNxIUFirRLWFqcwcruvLi/Z1TWK8pKF+mRfmxZPJ4B42e/l+gsSg3jou1xzlifxfrMaQ4fMSDyRa9mIxet9DhsaMqzjLeLzq6ONqoqyylQlBnhdy/Kj7DSsvKfzWH7rTTXFnPq4Fr69RvEZk0rMksUz6GTkPuXmTisPzOXbuRpcpEQlHXEBD/g0oXzBEYnoyjtWfHB4jna4urhQ6C/Ly6O1/GPSKa0olw848dcuWDPWVHPnrtwhafPYqlTzamLEaJz9cxBjJw2571EZ1pkKGe1j3L+4g1MDbVQN7YhSTVUsqMimYsn9DmqZ/vOns4OxbxPRR6+UY8pfi8rr6L1f2A+7e+hIOUZ6pvm8uWAkeicu0NDQwPZSaEcXDuXISMnc9bRm5rmDtGGVRIR6MfVS+eV+W974SqPQ2OobXz5krW9pZH0hAhcr13glOlJrGwvERyVQEJ0OCZq2xk1oCf9ho1h81517ng/paiqjU4hULMSI4RvXOC08LGL192ITc1RCknFlIiIJ15ccLiGh+8Tgh954uR6k6ikZFJjhS9fvoLn4zDKm7pEu9JAQlQQztcdlD521soeb+F/5XXd9XFeSgjrJ41l1OiZ7yU6myrTOWdqIOqTs9jYnuXgYV0CklSjINqruHnpNGqGZwhNzO1Oe4OujlblvM/nfvW8fCh+Limr+NnL5d9CQ1U+tgZ7RNzwHevUT4m2o0GZHvfElVnjhzJsygoexeUKYd9I6GMPrlx3JCgmjbbOFlLiQrgofOiulxdPAwJwOH+doIgUamsriHn2lGsX7UUbdxqb81fxfxZPo3JOegdJT12YPGYw42Ytey/RmR37WMRD5pidtePi6WMc0T1NelG3wGysyMXytC6GZ21IVaW9SVtrM5UiTigsLKC4uERYcaLIOyk6JRLJX5PUcF82L55A78GjWLl1P0bHj7NtzXJmzlmMsa2rCHYVwU4biU88UduxiSWLl7Bg7hzGjBnLiq2H8A5N7BaIdaV4O9uxbtkchg7qR+9ePRk0dIoQbecwO23IqBHD6Ne3L/379WP85JmYXfemrKpKBGiO7Fi7nBkzZzF71kymi/8P6Z8mOb+KmpwEDHcs5dveg5g+byGL581k1abt3HC5wTER7H31zffo2N5ExONUZgRz7Ohu5i1czKKF85kwdizzV2zhulekCC47iX989zeKzgDM9TVxvPuYxJhQNA/sw9bJlwrFYg4NOb8qOmtLMrl/8xqmJ82UIt1C2BnRyJ00P8MVN28Ky6pVZ/5ORIDvYq7BkL59WLRNn9TSJhFA5GNxZB1Dh4/hsMl1KurayIt9xKaFY+k1/EdsbwfR2VzLtVPa9P/qK36ctYAF82YzZcp0bK/fISjgMRq71rNo8SLmz5nB2DHjWbPzKE9ickQo3YqX05nfJDpzox9x5IARTyISCfFz4/ChQzjc8Vf2sFZkJ/2y6OzqICctlusO1pwyN1e+SFDmofkpzCysuPcoSCVmPn8UotNEbS09e/Vm3vI1GJmcQPPgNmbOmMXuoyeJzeoWWoWpcZwU4n3RvAUsXjSfyT+OY9qCnzh/86EImDvpFOIm0NORbauXMHzIAHr+8AODRfCrbe0sBI8V6xZNoW/vXkof7D/yRzbqWJJRUEpc0F0Obl/DjBkzmT17NtNmzGCbmjFh6SU0Vmdgqb2Bb7/5mpnicxfMn8fkJTu5cucBt+y1GNrrbyzerUe2KM+t1dmYax1myaJFop5YwJQfxzJt/irsXB7Q0tlFVqy/EJ0T3lt0FgvRaWOuy5Wbd4mPi8JU8wDHbV2obFAIgNZfFZ1t9SXcd3cRPniS06fPdJef06eVYtzhmiNZZbW/eg+/RJcQZI+dzBkl8nrykm08SykSxbaSG+ZqQtgPZbOGGZWKESFNmZxUX8dXX/yNQ+aXqRTl3/uqMRMG/p0Rk2awdJHI0ylTMHW4yzMh2k10DjB3vqjvFs7jR0U9tmorLr7htHR0kvjU9TeJztSIIE4dPcptn2fEhDxCXdRj51x9qFY4YW36r4rOuoo8vO44cUKIqxc+KOoxUxMzrjl7kF/86/MM/wwoRKfGtkX0FG3QvDU7OGl6gv3b1jNj+myOGJ8nTdUTlRcVgNHh3SxcvFiZ/+PHj2PBqs04irxuFAqxq72eIK8b7Frf7WPfffet8LEJ6J2255bzVVbPmKwc9tlvQD+l7+3XtyQ+s5SYQB+O7BCfN7Pbx6ZMmczWgzqEiDJTV5qDrfY2+vYfpKyTf1o0g6kLV+LoeQ9HK0OG9/ieJTv1iC9tFUImmbPHj7B02VIWLFzApHFjmDp3JVZOD2gV5aM4M5wNv0V0VqRhYaDJNde7JCWLekj4r+GZa1Q0ivLQ1cBth18WnW01RTy+7yb8SgivU+bK8mFxxpyT4me7q7fILKr43T7W1dJAwK1zzBo9gFGzNxKUpBiu3IW7rTYjBvZn1T4ziuu6qC0vQHvrXH4YMBSNs87UtwqxfMWEfr16MH7SFJYtW87IYTM5c9GT1LhgThkeZtnihSycP4fxY8awYOVWbvrH0tbeRlrwTaYI0Tl25nuKzugHmJw05YpHECkRj9DYuws75wfUtXTQXleElUJ0WrxbdJblp3HX9QqnTp3E1u4clpaWiVZWVlJ0SiSSvyYpEQ/ZsUI0lL17M2DgYIYOGUSfPn0YMWEmuqdVC8CIai83Poq7t27j/cCPR17u6OxYTq+Bw9l1woGa1laSgt1ZPHkUA0eMZ6+2CVdvOAnBcIWQiEiiEyLRP7iJYX17Mmv5VhxcvUnJKRKC9yHrF89k+ITZnDrnSID3TTS3LmbImMkcv+xNUXo8Jw+s5ftvv2H45LloG5/mQWAgMVH+6O/7ia+//QE921tUN3fRWJyCj+cdbt33I/CxF2baOxk8cCgrthlRWF5LSrA3S0YJ0Tl+yfuJzqhATuoc4YYIjpsaavFztGLvfk0eRGYoVAJXrU1/UXTWVxQoew0dLl/h+vXr3Lhxg+vXruJw5Tp3HwQphwV/KIn+rswZP5ShU5fxJCGbgsxYNs+fyLjJc7j+KB7FaL/sKF9l71LfUZOxdQ9Wik5HCz0GfvMNfQaPYre6DlddbhGfkkFyQhye7jfxffQYXw83Dq1fRL8hozlieZPatgYe3rFhaP9e7y86o/w4KATJk9hsWloqcThjwEGt40RkVlIjGttzJ/V+UXQW5aSI+3Hm8pWrIg9vKPPw2tUrXL7uxJNnMdT8RYblKobXmmluonfvnvQTgefgIUOFMOzLwGGj2XrIiECRf13iWVYW5+F97y4379zn8eMHWBtrMEz47cLNasQVVinfuu9YNIk+fYezZb8uNxwdlT1o3kExJCQk4GxpyMShwsdHzeK4xRWiktPJjA/mwJr5jBw7Gb2zlwgI9sNAbTMD+o9Gw8SB0uJ0zhlup4cIrgeMmsphnRM4ezwlJTVFXE+N4X3+zpI9emSWVIqyVY7PPQ/cbt8nOMQfK3F/I/v3ZcH6/SSWNZOdEMSOpQrROeu9Raf1SU3Ou7pT19ZKmI8ju/fs515wEm3i3zU7IyE6Td4pOtsbKwh47IODgwPXrnX74LVr17h8+TLuHl4UVNV/kOhUBMD5if5CBI5l6Jjp3ApIojQ3CYPt8xkxfhpmN57QoXhw9WmYaazn26//idrpK1R1wMMbZkzo91/06j+CdbvVuXDVibjkTHIzEvHyusdNz4c8feTFCY3t9Os7gHX7DCmoqCU99A5rZwnROfX9RaeJ2mFuPYilvr4Gr6un2XdYC7+oTKE48rmmGMXxC6JTMWwxwM+Li5ccuC7yT1mPXb+Gw8XLeHj7U1LxfsMG/6cpSH2G5s6l9O/1LX0HDGbYkMH0Fe3c4JE/slfHnJj0QmVZKMtIxtv9Dvd8H+P/0JOT6lsZPmwYS/abkltWRUlKINuWTeOH3oPYIHzM4bojjjcc8XsaTEZmOtctjZg+VvjYjzPQNbdXrkKdERuM5vaVDBo+ET3zizzy9xPX3cDgocPZa3qV3OxMrh7fz4DePzB43BTUdI5z/Y43uYXpOFnqM+T7HizbpS98vJnWmlwe+9zjtii//k/9sD+lwfBBg5i6dB/JxdWU5sWyaer43yQ6z+gewf7GHVo6Own2us7+Xfu5/TSRJiH67l2z5Ijh6XeKzva6MsKf+nbXy6KedhR1zg1F+bh8Fdd7D8lXDSf/vRQkB7Lzp9ki1hjHFZ9n1NaVYLh7OQMHDsPsmpdyKGtdWQ462+fRa/AINM+60tBajfv1U/Tr+b34u1Fs3avFlWt3hH/lkZeZyAMfDzy8fPB/4IXx4c0MHjSYlUdOU1RZRU74XaaPGypE59L3Ep05MQ85ZmTIRUXb2lSBp/CvXQe0CU0poLWhHLuzBhj8guisKs3hse89rly5jJvbTYV/JV69elWKTolE8tckVbmQ0Hh6DZvATi1T7ogG1/WKNasXCDE4ahLG525S3thJnRBRDz1vYnXmFMf0tdj601y+/aYfq/edIjsnA3d7fXp/35flW7VIKapUDh9VzMdsa2+no7OFm7a6jO73LWsOniSrsoMu0cB5Xz3JuBGD6D9yErsPHsXUUJM1C6fRq+9ItmnakJYcjbn6Jn74vj9bjp4RFXc17Z0dlBelCtG5hh49+2Jof1uIzk66RAUfKoI0G0tLTI/rs3vzSgb07cvspZtJKi4jKcSXZUJ0jvktolP7MFdu+inntTUWJ2GidQBDy+vk5aTidsmSoyds3ik6OzvaaWqsp6amRjk3qNtqxO/d83A6PsLQ0JqCJI6sm8OgYeOwcvLg0b3rzJgwlrmrD5CgWqkyI8KXHcsnM3jCDOzvhShFp/NZfYb2+IHpy/cQEJ+hnPfXIfK1LD8DH3cnLMUzNtTVYt2SmXzXezDrj1pSVFsurm/PsN8kOh9xYJcuikVWFIFHjhCh2moHsLjsTnpyApctjony5fLOOZ2KeTn19bXKPHstD8X/irlWf5WtVhQ9nYqFhBQ9nT9tO4CTxwNu3XTi4BbF8NpRrN9/guSccuUCUs+ePsDe+iwnjhuxf8dGIfp6MGXpVvxjk7kjAsSRfb9l9k+7CEwpV869VMyvbmltU5a3ONWczlGT1vIwTLH4VDNxDxyYOnoo/QaPYes+NUzNjrPhp4X06tGbTSKoTspQLDazi949f2DFbj1i0wpoUfh2fTlOFmqMGfg1K/YbklFSLTRYExFBjzhvbcGpk8bs3b6BEf16Mm3xenwTK8iIEwHk8h9/k+i0EaLT9oYbioF1deW52Jtoo2ZgTVZREa4OJ1E/ZkrYu4bXijKtmIP+ug8qylONKFcNH2V4dnN1IacOrWXA4FEcs3fD3+cW62aPZ8aidTxOVt1XTSqntTaLPPyOo2evUy0iZT9HM34c+E/Gzl3D3YB4GpTluZP6ymICHnpgfdaCE8cM2Ll+uQiee7BgzXYSRT2WFn6PDbMHM+K9RWcwJocP4XI/Qvh4Fw0FcRzT2I+RtSNZWYm4WZuhqWfzTtGpqMca35WHDY2fzRB3ZU/n1gV832cgP+3VxdPbF1dHB3atXczQoSM5dPwc2WWNtNVXinbEE1vhY8aGuuzesJTBAwczbclB4lMzCLxlzfDevZkwf5uoO3OV7ZxivmCrYv0C8fyiHzmzctYoRs9exe1AhWjpIOrBDeZNGs4PA0azZe9hjp84xq71i+jxXR+Wbj9GTHwc104eZmC/gSzdpEFcWh7NihW9uxpwtTJgWE9RL+wzJK5Y1NNtdcSGPOainTWmJ4w4vFeIpj49GTt1Cf7JuRTmJrBl6oTf1tOpdwTrS26iNhA+VpLORXM9ITQtSErPxNPRlqPHLQhJePucToWPNSvWeRDl4dV6WvFz3UfwsYaqQqx0dzF8QF+OmDkQ/OQe6xZMZ8SUFXiFdrfjNaVZ6O9ezMCRY9GxuakUnXdvnBIivqcQ41u5H57VPbddlP+asnwC/TywtTyDsYEeO9ctpV//AUxZo0ZKTgHZwr9mjB/2G0WnAXZuT8RvXZRnJ3Di6EFM7NxIzRB5aWeC0Vmbd87p7Ohoo7GhO05QzPUV/ycKk6JTIpH8NUl95sPquaPpNWEBNveedS9K0NGAhfouevz9S1bu0SMkOoY7V06zcsli9qjrYmNnw/GjWxjQcxCrd5iQHBfFFZMD/NBjCHt07JSL17xGVzOu1lqM7PMN6w6dplg5Ba0LJ0tNEVQPYNik2RxU1xKBqgn6+nro6ptwzeUBWSnRmBxcK4TPKPTtbgsZ001VSSp6+1bzQ6++GF+6R0V9I34u9mz5aRnrdxzC+pw9p4yOMGb4YOYsW01McQkJIQ9Y/jtEp4PbA9UCDh1E+Llw+IiW8q2knZUFhmcuvFN0NteWEhcZjJeXNw8ePOg2X1+8vR8QFJ5Add3b/+630NkiBOTpI4wbN56lW9XQV9vKtKlTUTt5narG7qeQJkTndiE6B41/LjrrcDyjw5AePVh7+BTZquXl68pzuHBKj6ULF3NA0wBbGzv09m+g/8ARrDlsLr5nKY88XhWdv3b/L0WnYgi2ouHsamvE28kWTU0dzl1y5NxpU05edKOu6W2RUScVJXmEBD7Cx8fnRR76+ngre9tjktJFoP6zkvZZohCdhnuXKed0Knobld+qqw2/q6cYN6A3Q8Yv497jcMKe3GLPxpWs3bKXM5Z2nD6pz6wx/Zm+bAsPQ8JxsDRU9uSsP3yM/LdkaZyPE7NHDGfU5PU8jVEMVWsi7NZpxo4YzJBRk9l7WJMTpqYYGRqgo6vHdbc7ZGYmYq2/VYjQ71Ezu0iVaq/AjsZKnM6oMXrQN/x0+IQI2AqV/rFv+3pWb9jBadsLnDYxYPb4IcxYvA6PyCLSYwN+l+i0ueqq7B1UbLmUFv4QjQMHue7ihq2FAfqnzhL2jtVrO4R/xEWHCx/0wtf3pQ/6eHsTGBxGWd27F2J6X7o6W3jsckbk4WiWrd6Kse4hpk6dxma1k5S3qK5elaIUnb1++LZbdIosfHDNhB8Hf8WczUeIz6tQntbZXMO9K2fZuGopG3epKacmnNQ/zLiB/Vi4ehsxRWWk/k7R6ewRqlqEqZ2Q+zc4rKHNJYfzXDA/iYHx+XeKzua6KlGPhXH/vpfIO1UePvDF674PIWGxVNV83K1GPhXdczrn8IUQfvqXvJRpXaJ83LY2ZOBXXzBh8VaehEfh63aRzauXsXn3Yazt7DDT28uPo0Yzc95eImPj8bhwjH7f9mbxNiPya38uByL9nFg+YySj5qzG81mmSOnE/5Ydk8cOpN+oKew6qMGJEycwEcJT44guF67eJzU5gQtG+xgwcBQ7tG2pqn/e0jUpRefQH3qxXu0EcYW1PPO7zb4NK1m9aTfmZ22xPn1MORx09OTZPIzPJF+InpeiM/j9RedFV5SySEicjNin6B05iJX9VS7ZnELnpO07RWdHUzXJseE8EHW0txDyDx6qyoe3D0+CIymtbvggH+tsbeCpqzXzJ49h+srd6GvsZu70iazebyKEXPdooaqiTPR3LWbAiJei8/bVk0rRuWqPDqkqvdfZVo37NSvWrVjGlt1q2Jw7j5nufsaNHsnE5QdIyMwj6/eKTtdH3QmdrcQ8voXaIXXOX3fj1JnjnLKzJ/UdorOusoTo8CCRd14EBAYSGBiYKNo7KTolEslfE0VP59p5Y+k9bg5mjg+orKmhIj8ZrW0r+fLv37BVw1gEG7fYvnQaPXqNx9bFh8SkaOyO76X/9/1Zvd2Y7OxMPC+foK8QMlMXb8EnNI7S8gqKc7Mpr6qlTVTEbjY6jOj5T+asOSwasAIaGht54HiG0YP7MEB8tu0Nd1LS04mLiSI6Opq8khpKs+I5vvcnvu8/DsNzt16sqldZnIz+/jXKRXTMrnpSKMSJ2oaF9OjRGy2LSySnJeBie4wxQwcye/kaYotLSVSIztGDlXM67zxJEVfpoqmxjtqGRmVP5psoRKeZjppSdLaoVhRUDJdxtDPnyK5NrNu6G0Or6xTXvr3Hr74sGz9PN6ysbbG3P8/58+exF0Gkja09LveeUFzxMfZe7CT2iTPL58+ilwimJk8cwaxFS3DxT3qxmqhCdO5YMYXBE0QQ8lx0WuiKQOYHNmqcJr2ie3GG9Ih7LJ4yih6DpnPpziPxHCI5fXQb/QYMZa3aGQpryoToPM9wIYIWbdhHjKKnU4iAhvo6GhSrLv6s17FbdCqG1ypE5/MwqrY4lQtnjFm3diOHDqhh5nBbiMfnR1+hq4P8zARuOl/CVgR/z/PwnJ0tNucv4hsYTvX/wB6pnwLlQkL7V/BDn74cMbGiWIihqvIirplpMKTXd4yZvYU7Pn5Y6GxgQJ8f2G10QbmYlscNS2aM6s+UxZt5FJ0kBL0d4/t/z6hpy3F6EEFFZQXlJQXklyo2J+8kyc+ROcIH+o2Yq1wJtqmxmtiHl5kyagD9h0/k5HlXIhPSSIyPISoqhpyCEmpKU7DSVQim71E3v0SZKiBua6jo7ukc8h3rjp4iJikRC/VVDBk0kO3a1kSmZnLP0ZqZowcyfdF6vKK7ReeuFRMZOGE2lz2Cuq+jWrX6bZpHITptzbSwFaKzQlVEOppq8Ha0QUdjnyhDK0VAbE3cm/voqmhvLMPP+y5WVtacs7d/4YO2NjY4ud0mp+JDh9cq6CI33p+NC2cyUjHMcdJYJs1ZhoXj45fXFqLzjPYWZU+npuVz0WnKpKHfsGCbBrHZ3aK5Nj+RfYun0PuH/ujZOpGYHIejlT6jhehcsGYHsSrRuXHOEEZOm4vjw3Dl3zU01FNb3/gWH+wWnaZqh5Wis0m1/GlLXalyesChHRvYvFEE8qZXqXy+H8Ub1FcW4ud1G0uRh/Yi/5R5aH8OK0tb3O48+PC56X8Qyp7OLfP4esBINCzdqG9spqwwA2uDffT84gtmrt7Po0f3UN+yjB4/DEHf/DIpSTHcOKvFhBHDmTFvj7KnM8TDnrH9ezJowkIcfUMpq6yitLCAosJi2tvbiRaic/H04fQdOxP7W49pb20kwseR+ROHirp1AsZ2N4iKSyQ5IYbw8HCyCiuoLMrCTncnAwePYZf+OaVQ66YRVyGKh/bqy1Yt4WMZ2ZzV2Unvb75mu84ZIpPS8XGzZ9qofoyaOpdHidlK0bl12o/K1Wsv3AxBsf6RYkG22vr6t5YPheg8q6+OzSVXnj9JRW/q09uX2LtlMzt27uCwyTkiUgpUR1+nvbaEoId3OX9O1Mu25zh/QZQRUT6sbey44upJTnHVh/mYaGNKU4PYvX4pA4ZMYNzIQUyaPh3za5483ylNIToNdi9h4Mhx6No+F51mDOzbi7V7dUlTfbH6wggObJjP1z8MR9/qBulZKTie1WH00MFMXnWIxOx8peicOWG4ciEh36jul9OKfY1r6xXi+effRCE6jx8z5Nxz0SlQ1I3OdifZsX0XG7dv5cylay8WF3qTCnHv3vdcsLGxxtnFVTE8OVH4mBSdEonkr0lisCfLp/TnP/7xDeOmzWfr1s2sXT6foQP7M2zSAhGcPiUr+RkH1s3nqy97s3rLfkxMjrF55Uz+7f/4G3NXqJNZVEV+UhB7f5rDwAED+HHGPNasW8f61Vtxuh+sXK0xzPsK88b24YtveioXZrjuGUxqTBiaO1czZPBQFq5cj5aePtqaGljYXiC7tpPqnHg0183k//zvnhw0u0a9qvatKIzn0Ma5/D//8Td07NwoKivixMGN9PrmK+asXMdJsxPs3biMr//xN8bNWECkCAhi/T2Y1uMLevWfwa3HKTRUFPDg/h28guKUwwXfJCnsEXr7tmNzzUO1ems3JelR4rPWCIEwiPUaFpSpehTfpK25nqKCHOUy7mlp6aQLQZ2WlkZKaho5+cU0vbYdye+nIicOzS1L+bf//X/l//zPfzBv/SFSSl7utZYU4sGq6YP5Z58RWLj6K4fXXjp+kC//7/+bBdv1SVTM2RXkxj1k05IpfPHtANbvPozxcSPWzJ3Ev/3XNyzcZUx+dQneNy345m//xqSlW4SQb6O+JIu7d24RGJNGo2q7iJe0khXmxaY1B3EPiH0hOhVCOS38EbtWzmbYqB9RM79K/VtFZ5cQtNXkZKeTmvpKHoqfU8XPRaUVL7ab+NxpKQxHd8dC/v3f/53BoyeyftsuNqxbzfhRQ0WgNRrNM06kZmZw6cQuBvT8ikkL12NkasLh3evo/ff/ZMikJdyLzKRQiHSjPWsY0Lc/4ybOYO3aNWzatIkzTj6UN7RTnRHMrlUz+fvf/sHo6Ys5bu8sBOYzTqlvZeyIocxavBo1bQO0tTQwtbjYvbVRVRonDy7l//m//i+26Vm8eMnSVl8uytF2fvji/2DeTl2ik5K5LO5vxMBeTJjzE/qnTnNoz3pxf//FiMmLuRNRSEqkH6un9+fvfUdz/m6gKArNhD26z+07XtS8JbIqTAzATG8PZueuUvZKEakrTsX6+AH69/iSn3YdJfEdvQhd7c0UF+YJH0xR+t5zH0wVPpmdk0v9R/LBpso8zqhv5Pu//d/8f/6//xfTl24lMLlYdVRQIZ7LvmX8+//zf7D7xAVlr63neV0GfPm/M27pdp6ldu/n2FCagd7mpfT6+mvmrdmC6Ulj9qxfxD/++z+YuGA1kQUlor52ZdHoL/h+2HiuPXhGkwj677vf4f6TyLf2/CeGPEFn21Yu3wwQPvqyHitKCcNw32p6fz+UrQctqH6HMmgToqmoIJdkRR6K/HtRjyWnkpNXSOPbfPdPSF5CEHtWTebf/1P42PhZbN+5i3VrljNq2CAGjpiI2cXbFOQmcFxtM9991YP5yzZgYmzE7nUL+faLrxg2dg3R6XlUlyRhuHcNg/r0YuyUmaxavYb1a7diae9GXUMzFWkhHFg/ny/++Q+GT5iF9UU3oiPCOaO3jxFDhzJjwUqOaOmhrS7q2NOWxOaWi/KTi/n+lfztix6sOmBGUcXz+f4NXDVV46v/9z9YskuPyIx8rp48yuDvv2Li7KUYmJxE88BWevzz3+gz4ke8YjPIzYhh2ZB+/NBzJNbOYTRXVxAofMz9YagQaT9/yI3lyRgf2sFJq6u8ugtoY3kO1kIIDxkylPnbdIl6x56jXW1CvBfliTIh6mXRtqUr6mllO5dKRnYB9W8dxfLbaK0vxVJ7J73+8Z/8r//b/4/Rs9dwPyRRdVS4V0Eah9dM5h/f9Wa/2Q3lQkJO5/T4+3/+v8xes5eEsu7v3VwSi+7en/j2297MXbmZk6dOsmftQr74x5cMnbON2Iwc0oPdGNH3G3qNnMb9qBTam8p56nOXe4+f0foW0Z4Z7omGxhHMr3qrUropzYpGa/sKBgwazN5jlmSWvv0lc0tTHYX5OSLvUsgRdVJWVlai8C8pOiUSyV+TrLhg9PavZeb0aUybMoVJEycwYeJU5fCdC673KRVBpmJYpJ/7FTatWsSs2QvZf1CdY/pqzJ25iANHLcgqaQAR4CUE+XBMfQ9zpk9i9JjRTJ+zGkfPEOXQypriDBwsDJg7bQI/TpvNqWu+VNU1KXs/TmgfYN6sGUyeMo15C5ege9KK7Op2GkqysTPYz6RZy7C44UWjSmPUlGVy1ugQU6bPwsbNj1oRbCUG3ufI1tVMmz6bXfsOc8xQh2VLFrJtvxop5RXKnss9S+axUogg/4hs0iOfcNpEH6dH8W/dOiE7MYLzp0246RWoXBXwJe2EeF1nzcpVaJheokaxX8v/IO31lXg4nGbxjHFMmr0M0/PuNLxyT9nxgejsWysa2Q04+0Urhyt5OJxhvnjWaibnyVL1dDbXlePtYs+6ZfOZtWAJ+9U0MThykDnzlnPk5GVK6ioJeezMgtnT2KNjRnp5C8nB9zluaIi3CABUO9O8QhtFicGinFjgH5P+2pDrzuZqvG9YsuqntZhfuUfjRwr+P1day5K4aK7JjOnTmSp8YNLEHxk34UcWLF+LsaUDiYr5nF0dJIf7oblvIzNmzmbj3kPoHzNk3bw5rN+lzqN4RU+EYoXYQMwN1Fg4eyqjRo5iqvBXC+cH3cNiW0W+u51nnWLxrnFT2KhrRW5ZLcVp0VgZH2XxvFlMmTaN2fMWcET/DDGZVbTU5XPdQpMpkydz4rwLFcqVYxW9iNXcczBh6bwfOXDCjtySCnLjA9BX264cXrpJ+J2+oQEbFy1ks7i/J8ll5CQLcb1vFXNWbeV+cIIItrM4f/YUJyyuo1gs803KsmO4fuEU14UorXqtiHSJuuY+O1bORd3oFBkVbx9t8IfR0UjAPQfWLJnNkDHT0TN3oLzxFYeoycLh9FGmTxHi5spdasV3DXK356d5E9h61EQ83+6eToVIDve7xYEtK5kunvGeQ4cxMtRmwfy57DyiR0pZpaiv/VDfNI+lm3bxICyO/KRgTI2Pc9XzmRDRPw9Ps+KjsD9hjOfjGFpeeXmmeCn05O5V1ixaiYHpFT58WbM/NyVZcZjr7WXOtImiLE/lxwnjmTBhIktWbeTMRWeySxSr8HYS4X+XfZsV+T+P7XsOcdzgKCuXrWD9Zl0ScrufU1ZsMJbHNFg4awrDhw1nyowlWFwQ9a5C9LfW8PDmZTYtn8uIUePZp2tJSn41eekxWJ/UYcnc2UybNkPpwwd1jInJLqW1tgSn0zrC75agc+Y65S+GLDfhdc2KBcKfDp84p6xzc0R7fezwDmaJumLTtl0YC2G8evkiVqzfRnB6PsVCgGmuW8myZZtw908TdXAo5yxMsL/p95Y6WtT71dlcPmvCNVcvXpdFii2OfNiybjV7dSxIzX/7aII/gs72VkLvXWGb8PexE6ZyxPgiaQUve9hrSvM4q7+LuYtWcOa6D41ttTy4c545M6ZyQM+czCpVuW+vJ/ThTfZvEXHCzDls33uEY9rdz3ftAWPS84vJj3vEhpULWb5pLxHZJRSmhmAr4oRLd0Uc8JY6qiApCBsbKxy9QlQpzxFxwt1LrFu9Gj3LKxTXvt90GqEx5T6dEonkr0tjbSXJcZEEBwcRHBREUGAQISHPSEnPea2Hr6WplpSEaEJCn5GRlU9FSS6RzyKIiU+noenl2+6Kohyiw8MIFNeKikumovp5r1sn1eVFRD0LISgkjIyCshebXVeVFoj0UJ488Sc0PIrcYnFMpHe0NJKVHE9gsPjM/BKejwBT7EWYkSLSg4LJEtdRpouALSctkaCgEFIzsqkoLyEmOpLo2HjqWlqpr6kkNiyUiKgESkrL8fd0w8L8NBnlzeLOfk5jbTU5GWkUKlblfGP4bXN9NYnxcSSmZQtB+vO3n38oQoxUFOcTERZMcFgEecWK+1UdE3Q/3whlvhaU19DV2U5Jbiah4vkkpOXQ+MY+q8mxoiyI66TnFFKWn0NEeATxqbkiYG2jsryAUFFO4lMzKa+swsvVgbOW9uJZvi0g6RRCtpLUlAwRRDX8LI8basuJi4khI7fooyyq9DnT2VJHTnqi0geDFD4YFCjKfAgxCSlUqPbfU9DR1kpeVqrwzxDx7DIpqygnIfwZkdHxlNY8D2q6qCsvJDbyGYHClyPEMcVQ7udlQvFMUoS/Pw0MJjY1W7lXroIa4ZuK+Y/+/k+Uzz9L+Jvi3UVHexN5GYnC1wJJE2VCMUxXgWJ/vuLcVMJCA4gT5aFF2evcRX52qrIOSczIobS0jOSoKKLEPZTXtdBYVyXKYjghETHK1ZuzIv2wtbbllv/b51i3NNaQn5NOfpG4lzfcrKO5ngwhqJJS05XbE/zP0kVtZTHREWE8DgwjPbdEpLxCW6NyVdqgwEBxrFhZ7ynqyYiQAGKS0qhtfOUZt4o6Ly1e1MXBpGVmUy7qsfCIZ0THJynrsUbhNwlRoYTHxFOYn88z35vKoa+xeTUv6tNXaaitITs1lRLh+8qVdF+hQdSJiTGxpIh64G1/+1eipbGW9OQ4QkQ5VvhYYIDi/xDik0X5eUWNdYi2RZH/QcGhog0U7UhZIfGx0UREJlHd8PI51VWVECd8LEBcJ1zhY2WiblXlYVN9DSnx0QQIP4hPyXoxr7KuuoT46AieinYuSLSxmaLuU/hYZ1uz8LEkgkPDSMrMp/XFS9BOSvKylHV1XGoWDcrb7KBQ+ERYSDAJSamifJSRlBAj6vdIKhuaaG6qJykynPDwaEqqGgkQ7ZytpRWhiYqFw36O4iVkrmjn8gpKlG3uqyj2701JiCMhOYO6V9r4P5wuxQJbIr9F/aSoF9NzikRs8rLAtjc3kZkSS2hYOJn5paKct1Mq/CtE0VaJe294pfJQfN+s1ASCQ0JJFjFOWWEhCTFRhMeliDimhSbhX1Hhwr+i4qhrbSPIxx0L01NEZbwycuEVmuoqyMrKJF/50uJ12hqrlHFCsqgLm99zVI4UnRKJRPIXQ7HASICfD87Onspl4iW/nQbR2Prcv8vt+0+pqn0+B0kieX8Swx7ifvceySUfvqjWvyKKRX6CH9zH/bY7Nf/iowUkb8ff1xe3m54UlL46eFbyfgjR6e/HDVcPymv/mAWzpOiUSCSSvxqd7cpl3StfLNgg+a10dLRTrVgav67prQtUSCS/RkNd9/Y3f/Vetk+FYjuTOsVWFdWKXjaZiZI36RLtXB3V1XWfzdY2fy46qKuvo6qm/mejnT4VUnRKJBKJRCKRSCQSieSTIUWnRCKRSCQSiUQikUg+GVJ0SiQSiUQikUgkEonkkyFFp0QikUgkEolEIpFIPhlSdEokEslnSGdHG+UlucTFxZKSmUtzq1zd8Y+ktbGWzNQUIqMSqKhpeH0LCcm/CJ3UV5eRFBdNYmIKdf/De9r+q9HW0khuehIx0VEUKrbNUaVL/kJ0dVBbWUR8fAzxKZk0tfwPbm3yL4Zie6O8zHQiI+PIKyxTpX4YUnRKJBLJZ0hbcz0JYb5cvmjDZfcnlFb/MUueS0TD2dlJTUk2vvduYmJ2nvi0fCk6/xXpbKUgI57rDhewP+dA8St7jko+MUKMNNaW4evuwhlzc0ITM+UqwX9BOtpbyUkI5NolK84536eoUq7I/kfRVF1CoK8Hp8/Y4+33TJX6YUjRKZFIJJ8h7a0t5CaE4el6Abcn8ZTVy6r7j6KzQwS8NcXEh/thae9MUkaR6ojkXwoREFeX5uLj7YH9BUdqG+R+nH8YnW20tzQSEhSAjY3ixU/WH7btg+SPo62tnbK0CHxuOuDoHUhJrRzR80fRXF9LakwAl6+6cP9xrCr1w5CiUyKRSD5TKgty8Ha5QrgQPU1ybNkfSldLNbkxD3H1CiC3rF6VKvlXo6OpnMjQRzjeC6axWQbEfywdpKXEcM3Zg8zcchHQqpIlfymqctLwv+eGf3gsjbKd+0MpSg7G08uH4LQqVcqH8btFpzjvdVOlv4liGFJbayutbW3iPFXi54K44fb2Nlpb2+Tm4BKJ5OMj6piO9vZfHJrZ1dVJxzs2vs7LTuPGxavkllSoUiS/ishzRU+lIt87FP+rTLR/4pCiPVOdp0Jx7tuq/4aKYkI8nXkQEkmVFBufN7/gYz/n9cJQX5xBgPdNfCJzaZHjO38XCv/7xTqwU+GfPz+jq6mUpLAHuHoFUlDVKoe4/8lQ1KeKZ9ve/rKefbWufV8yk5O44+xKVGySKkXy3oh8VuT/m7ndHVcons2r7WCn0s9ePBvxnGKeeHDf15ecjzSqWVz7t4vOmspikuKiCAsLFRZGSHAIkTEJ5JdU/kycFWUn4nj5PJed3cmt+LzGYjfVlPDonivWdleISMyW8wUkEsnHQVTqbS0NZKcmEhwcRHxqFvVvES6KxWpSEmJEPRtBfmk1ry1T0tlMWlI4F6/coayyVpUo+TVaGhtIj48j6OlTAoMCCQoOJiQkhKCgAOJT0qh98Rw6qS0rJPpZKNHxqdQ2vD5fr6QwH9frVwiJjhNiQw4Q+lxpaaojVfhYaNgz8oor39nOdwrhU5iTTlZuHo0tL301JzGWO6IcJBTVyIVsfiOd7S0U5abzLDSURFEHNr+Sr89prKsgPuoZzyLjKK993QdrCtJ4KmI03+Ao6uQaTn86aipLiIl8xpOnAaJ+DRJtnaKuDSY4LJLM/BJa21/1mA5am2opr6qmse1VkdRFQnwkTs53iE/OU6VJfhVFjNFcR2ZKPEGBwSSn59LS8TxXO6gsziEiLAR/1bMJEc8mWGi5kIhYcpVaTlyiowXPO27c93nIx3qt+jtEZxexQR5oH9jC0iWLWb58BcuWLmPlqtVs232Q0+euEZNeqKp82wnydmDG2EGMmfUTHs+ylamfCwXJYRxcN5defUZi7uBBXYtsUiQSyYfTVF2K/z1nzM1OcfLkCXS0dbC/7k526XPx2ElpTiLXzp/l2LHjHDM04LiJBY+fJdGiOqOjsYK44Pvc8AqlSi5g8t7UlpVw75oDx3R1MTQywtjEBBMjfbZt2cKxsxfJqRI5LBrb5PAnnDE9jpGRMQb6+lhedCYx5+UKfgX56Vy97kpMYgbt791LJvkzUZqbwo3zVsLHjDlxzABDYzP8nsXT/DMB00VlbhwnDTQxs79BblmNKh3iRFB9/bIj5XVygZPfQnNtGU/vO2Ny3BDjY8fQ0zPg8k1viquf52MXeSlROFibY2BohKG+ASctLhKfUfRCkOSkJHHb6QZh0XEv0iR/FjrJSY3E3voMWjq6GBuf4MSJE+hqqrN55yGu3fOnVuVonW2NJIY9xO60MeecPcl99eVCWw2xz/xw9fAjvVgulve+VIn2ye3qedF+HcP4hDH6BgZcdLpPWZ1i9d8OUqMeYWlugpaunvK5nDA+hobaIXYcPMq9wDiaFUqwuZybt+7g+/jjLCKk4HeJzgB3e+ZPGsLXP/Rl1oLl7Ni2mcXzpjOoXy/6DBrJloMGhKYUiCLXRU5iEKeOaWNoZkdc9uc1BKy6JAfnC2c4oqGHb5B4CC/eEkgkEsnvp7G6gsiARzx8/JSwZ6G4XTBn9141Lt8NVAZPtRV5uJw/g7rOMe54+hIa/IizJwzQN7UlIrVAeY3O1kbK8zPIKqmiVQ7DeG8UPZ1pcTEE+vsTGBhESFgY7tes2bZ+HcetblDb2klZThzWJtpoHbfA51EA/t5uHFU7iNU1d4obugOlhvoqUjOyqayuV7xUlnxm1JXlceuyFRqaBtx78JiQwMfYmOigb2ZNpMrHntPeUofXZVPmz5jCdh1z0gorVUe6KCsrJTU1SzlMTfKetDfwzO82OtpHsbniQmBAAHeu2aGupctNv1AahYs1lGVzzc4cDe3jPHwaiJ/3PTQP7sX8vCv5Fd1zqGsqq0hPS6ei4uPMN5N8TLqoqSgmKiKMJ0/8CQ0NI/DpE2xN9fhp3XZc/cJoEzG1Qhx5uFwSgmcvPy1ZxAE9K9JeHRXZ3khFUQ5ZBcUo9ZLk1+nqJDs2DDfH69z0fEhkZChODlbs27OfW09iaGztoLY8j/DQIJ6IdjDsWTj+fl6cNDjC2i37eJqQhVLutNWTlZ1DQfHz+u7D+V2iM9D9PHMmDqLn6JmcunSblJQknomgyMpYndEDe/NtzyEcNrtGkWIJ/45m8nOyyMkvouWVt8Htrc1UV1VSISqNpnfMh2lubqS2rp62diFfO9pEI19HXUOTcoWyzvZW6mpqqG/ufu+vOF5fV/vOifwtzQ3U1FSLv2986/ycrs52cb0qysvLxWc2KruWFfvgVZQUkJGVQ82rQ6s626itrqS8ooLaenGuFKMSieQ3oJhP2NLcrBSYyt/LYtBVO4S+pbNylEhG+GP0jqpz6c4T6lq6RU5axCOMdbW5etsX2a/5MWnnyd3L6Bma4huWpkyJeerOkf3bcA1IUw1pbueymQZHDU4QmSuHMv8VSA5/wDGtg5xzffrihXJpSgC62npcvv2YlhfO2UpmTAAGGofZvmEt2qfPk15Yrjoo2/7fQ21RMhfPHkfHxI7ssm6B0VRdzHlzY0wsLpFT0UTKMx8MtDS46hmuzOUuIT4eOJ5lt7oBj6NSlX8j+bxorirkipUJJ6yukFrU/aKgJCuZR74e3HC6hqHaftS0LUgpr1MeUyJd7Lej2NKrvIzSsnKa2lR1W24Cp3T2cNzahZKqn/cYl+UkYmtmiMWlO5Q3PB9P9fEz/3eJzqB7F5k7eSgDpq/iVuDLib31pZmcPLyR3t9/z+TFu4hJy6MkN4mzJ49hYX+FrIpmpZBLjwnEzvIURzWOcOCAGqan7QmJS38xpKWjtZ64UD/Mj+uwZ/8RzlhexPHyBYyMjmN93ZM6EayVZsdiYaSPmZU9d719uGBtzqED+zG1ukREco4qUIDGmlKCHt7FxECLHdu3cVBdh6uu98kref5mrIvygnTuOjugr6XO/v37OWl9hbjMMupKcnC/aoe+iSUBMenKYLA8Pw33G+fR11Zn377DmJjbk5hTojwmkUgk749iXmczDfX1lKaHcPa0OVfuBSjrkthHd1BTU8czPL37VEF7ZQY2x9RFHedAiXzj+9GoK07B4oQ+Zx1uU1LX/dIyOfwRZobauPrFUlFTT111KVfOHsPc8jypJXKI11+B8Ec30Tq8B/fAV6b9dBRjpqfOCZsblKrirrLcRC7bnOK07VXOnzXB/PwVUvJKuw9KfhfFac84fVyLk3Zur80V87lqhZa2CaEihnt09xpGeoZE5T9/xdZFacIjDuw/ipNP8IsYT/KZ0NlKcpgvWkc18QxN7B6+KWhuaqS1vZ2GyiKcLU04qnOW5DL5Yu9jU1eRh43RQUxt3Sipfn1rJ8ULnaD7zuLZ6BOWLvTMJxT6v190ThpK/2krcHoUrUoXtLcQ6GTB5GH9GDVlEUFxiYT4uTKi1z8ZPGkh/mnVIsiq5aGLNVu2bGbj1m2sXb6QcWMmsHavIc9SSpQ9lnEB99i1dj59e/ViwNBxzJu3iOnjR/Df//iOOVv0RGBQS2qIO5P69uSHvsNYsWkrPy1fQN/vv+S7PkNQM7Ynr6qVlsZqIRrPsHTOZIYMHsqIUaMYMmQIo8fPRMf0PAVVDTTVFHPR+AjTxo1h8oy5/LRmLYd1T/IkMouCuEB2LpzAv383gjPOD6kpy8XBXIcfx4xkxtz5rF67ib17tQiMy0AOrJFIJL+FusoSQp/44HrdgWP6Olg4uJBW0v2GNzvOH32NAxhZXSMpM4/S0hIi/W5xaPtGdEwuifpNeZrkA+nqaCXI8zoaR/XwCYxXpSqGPxdz39FeCH8tzl9xFGLDGDUNPe77x3y0BRUk/7NkxQVgrLkPA/PLZOQUUFZSSFLofQ5sWob+aQdKRaPeWl/KPecLGJvbEZGQiucNG07aXiIpt0R1FcnvoVEEwNetTThwRE/EWmmUiPotLzOFs3r72XFIl8dRsbhdtkbf8CSZtc8j4C4a86M4KkTnxdtPkK9+Pi/qxTN3tDFG08SOzNKXc6KfU1uSw7XTx9CQovPj09lM+GN3jhw4yJ0n0TS2vt5NVp4Th52pHsetnaht+7RdaL9fdE4WonPqcpz8olTpAiE6o+9dYOaYQYyYPIfHkXE887/L1FF9GT93NQHpVXR2tpAZH4KHlw9+T55y39WeFbN/pOeAyZxzfkxtZQGn1DfT94ceLNt6lFueD/FwvcyW5TP54uterNxvQll9HRkRXiwYPZye/Uaw38Acb19fLAzVGNL7G6av2MmjuFySn3myZu5Yeg8eh+4pewJDQ3G5bMW8CSMYPHIKDl4hpCeEsmHKcL78qjc7dS14GhZDZm6+cohtYUIQB1bP5vuh07F3f0p+9EM2zZ/Af3zZl8NGZwmLTSAlJZMaxZBdVRZIJBLJr9NFZVG2ctSEiaEGm7ds5bgQmCkF1cq6pKm2BG/nc+zds4cT5pZcvX6Dk4babFizGlMbJ/Kq5Zijj4EiELI10+WE1Q3Si14GQoqVy+872rBdPBftYyao79/JzoNa+IbE0/KJG2XJH0NrfTm+Ny+xd9duTp6x4uqVC5yxOM6axQswt3OltLGRqCf3OGV2moD4fBG7tPPQSYhOuyukF8u3Ph9GF0lhjzE8egg1TX0uXXbg0gVb9m76ib1HjQiNjuLq+dNoHTMj94W67KK5JAGtQ0e5cPMRcmfcz4kO0qOeoH94P44Po2l4Q/QoqCnO5qq5kRSdHxnFisDRwd6cNNAS9ZojBZVveI6QfKE+rmgdUccrSjWX8xPyYaJz2gqcH8eo0gVtDXhePM6YQX0YO3M1EUkZRAfeZcqofvw4fw0BaZW0tynmR4Rw5bwN+vr66B49yJwpY+k7cBSnLzqTkRrOjpWz6NNvHA6e4d09iO3VuJ3Vpn+ffizebawUnenh95k7YhDDJy/lTmiK8uNjntxh/o/9mbJ8Cx7BUXhdNmHq8N7MXXuE2IzuVQfry7I4fXQDQwb1Y7/pJZKTYjFX28K4EcOZMnc5msfO4v8sgYamFooTQzi4Zg49FKLzzlMqc+M5dXQngwcOUS6gZHTKBv+IJOoaW0VGKi8vkUgk70VbSxMlhXlkZaYSEfQQU0N9rEQdWKQa4qmY4+TvdRNrK0vOX3bGzcUJY90jnLvh9mLon+QD6GwjPfIh6ocO4/YwnOcDjro6Ggnzu4XOUQ1c7j8lNTOXrLR4HKxMMTKzISylUHWm5HOnsbacp163sbY4w7mLDng/vIuuuiaXb9wjNMiPY1qH0DOzJzW7kJz0ZC6Z63HEwJSnUSm0tMo+7w+hs62ZVBELOpyzxNrOnlte3libGnHihCXxKUm4XbFCx+Ak2S9i5C7q86PROCCej7u/nNf+GdHRUI6Psz37jxiSXFitSn0dKTo/Pi0i3wN93Dimp4u1iC1ySn+e921VOVyzPommkSVF9Z++TvudovMCcxTDa2f8hOvTBFVyJ8UZ0RzZvIgfvu/JCiEOc8oqiXx6k0kjhRBcvIGwrEoy40M5vH4xc+YvRdPYAlfHS2xdPZ9+Q0dgduEaCbGBbF4ylX4Dp+L2RHXttmpuWuowqH//l6LzmSdzhg/kx3nr8I7NUZ6WFOLBilnDmbZqG54hUXgIATxlaE+WbNYhMaf7LXZTRQ5WOhsYPKAXu43sKa+poyI/hasioFi3dC6jhPicvmgTV+8EkJ0YypH18/lhyDRs3B7R0tFOSWY8lyyM+WnxPOW5sxf9xOV7wdTL7VQkEsnvppNb506iflSHwNSX23J0L3BWTX1jC1nxQZgJYXrVzVvOZ/oItDZV4+NkxSFtUyJTclWpilXis7h6WpcdB/TJr38Z2sb6OrN772Eu3g1QpUj+Ciim9NQKH6uta6AiMwxTMwsc3X3xcLJl/ZLZrFq/FQ11dQ7s3c2KhbOZNHU2msesyMiV8zo/nC6am+upqa2lrq6Yq7YWnLS6QU5FBY9vX0RXy5CowuevgzopjPHhkLo+tx9Hir+UfC6UZidgc0Kb43Zu1Da+fUECKTo/Lh1NlTy+exVjIyNcPPypeGOP2+ekRzzkhJ4Gdi5+f0hc8ftE590LyjmdfSbMx9LxPsWFuYT5e6N3YDMDen3P8AlzuOgRTntnJ8EPrjJ+WF+mLd1EeEYRAR6XGf7DV0xfsgEP/zDig++zc+UMfug3CLNLjuTkJHJk00J69OjHPiNbYpIziQ70QU2kffNtL1btN1WJTg9mD+3PuNmr8YjIVN5ZQqA7y2YMY9KyTdwPSyDqkTNLpw5n8NhZ2Lv4UiwqsqAHLqyZNZaBQ8Zy1ukh5dWV5ObkkZaSRoTfLfasmcVX3/Rnj44tCTEhqG+Yz/eDp2Dl8pCyykpKi/PJysrC3/s225ZO4ftvvmGPkQMl1bLrQSKRvA+dtLY2U1fb+HKERFsNV07riQb3OFE53Q3uq5P5u5qruXfDBv0TZwmI+bz2O/6zUlWShdUxNYxtnckteznkqLUyh+tndVm/XY2Y/Odvhrt47HKOTdv2csUzSJUm+Zzp6uoUPvbKy+K2BryvWyl7s4PiMiktyCY04DH+T5/yVJiv512Oaexh855D3PIOoKlZrub1uxEVn4g1Vb8IujpIDX+Ikb4eN7yCaBaxY1yAOzrqh7nmEaYUmB2tDXhdsUD7uAXhyXndfyf5LEgI8eLo/r24PI6n5R3jNxWi88opQ9S1LaTo/GA6iPZ35+RJM9x8Qmh4Z13VxUO3Cxw5pIFvZHfn3afmd4nOJzdtmDGqF//x339n4LDRTJ06hXFjRzN40GAmz1qCucNtilQi7OEdWwb0+DvDpi0hKL2MhFAflv04jH5CZK5YvxnNw3uYOKI///XF1+hbXqSmqQYPh1PMGDOYnr37M2nmAtatXcfS2ZPp1X8YKw+cpLyhnpSgW4z97gv6jZ3HzRDVMvePXZg56nsGTl7IrcB4aqtysDNVZ/yoYQwfNZ7Zc+Yy6ccxDB0+jp0aZqQWlFOYEYH23l1s3bqbQ/t2MXvSGMZMXSJU/yOyYgLYtWgC//bdSM44+pIS8QRzfTW27djNQfE30yeMZtykWdjdfCp7OiUSyXvSQVFOMq6XrnHnjiePHj3E+bI16uqaXLn9UDnJv721ifT4aB54evHA14sbl2zQ1NLHxUcxqkL2c344HaJ+f8ye9Wuwvx1AtWpZeQVd7S3EBnpyVLQHuiaW3LnnxR23a6iJOl/nhC0JOZ/XftOSd9BWT3ZqNPd9HuDr6y180BYNDW1c7wdQ0/I8DOpCxEV0CGusrcDJyhBNk7PEZRapjkt+L+UluTx+8ghvHx/uuTlirK+Nmd1V0gu7/auuNIMrlsbsPyCeibsnd29eR+eoFk6e/lQ3yqHNnw0tlXhcOcO6TfsJzShTvkB4G1WFGdgbqrP7wAniS36+0JDk/elqb+DyGQ02bNnKOWdvnoUGKeOMBw/8CAmNo7ZJ1UlWl4fdCR12HjIkufiPmSX9O0QnxAXfR19tJ6uWLWXpkiXMW7CAFavXo3PsFN5PIymrfT4cooOYEC/2bF3L3qPHSCxupKGmAq8bduzevJ5N2/Zge+4cxw00WbthE1fu3EeRFQ2VxXg6XWTPlvWs37oHcwsbDA9sYciQYaw4bE5lQyOFySEcEdfYdcSQINUcm6y4QPQObWH3UUMCE7JEShdlhek4O1izc9NaFsyfxyohdE/aXCU+u0RZ+GuKUrE+psNmIWzXbtjM3oNHuXbnISVVDVTlJGFtdIQVmw9y92kMxVnxXD57nPVrVrNe3O9+NR2u3faluKr+nY4kkUgkr9NFpWhgne2sMNLVRU/fgBNmZ3B/GCTqne7Va9tbGoXwechpQ310dbUxPWWBx6MwKupeX+pc8jvpaiMnPgBz01MExuf9bPXxlsZaogN8OWN6HB0tbXR09Thrf53olDzkOkJ/EToaSAx/hKnxMXR0dDlx8jR3/UIor3n7uqidbU0Eebly47YX+WUyKP5QivJTuHLeCi1NLYwMT3Dpxm1ScopfLmQiQtGCtFiu2pxGR1sbo+MnuH7Hl8IKuYTQ50RnfQl+tx0xt3WkqObd7Vd9RRE+zpexu+BGjmKPf8nvp6NJuRDekf270NQ15PgxI/T1ddHW1sfSxoX8SlWcUZ6J69VL2Dvdo+r5HjafmN8lOutrK8nOSCMpMZGkpCQShaWmZ1Ba8eYk1S5xbgVpqcmkZeS8WLGqpaGGrLQUklMzqKmtoby0iOTkFIrKymnr6KC+rpbCAsUCG1lkZmUT/tSHA6vn0KffYNQtXWhoaadVBAWZKeK6mdnUNHar9qb6GnFfKcq06vqXhbulqY7czHTi4+NISc+k6pV5Op3tLZQX55MUH0tUTCzpWfk0tXZnQXtzIwXZGSSmpFNeXU9HRxuVpYUkxMUQFR1DZk4hjTICkUgkv5HOthaKczOIDAshMDCY+NSsF/sUK+jq7KBa1DUxz0IJDAoiMS0bOZjiI9LVSZNox3Ly8qlrflevSQf5WSmEBgcRGhFNkQx2/2J0UCMC3ahw4WMKH0x53QffpKujneqKUkrKKmiWiwh9MM1NtaQlxRH4NIDwqHjK3iE0qkvyCA8JJjw6nqoXm9ZLPhc6W5upKCmmoLhcOeXuXXS0tYr4upjCojKa2+Vong+jk8qSAhKiwwkR7VdgoMICCQgIIjI6mbqm7uG2HULjFBcVUVReRccftBrq7xKdn5LW5nqC/G5jbm7K+cvXuHH5Ans3Lad/n94sWLMX/9jsT76kr0QikUgkEolEIpFIPg5/OtHZ3qpYrt6NPVtWMnv2bObMmcuceQvYcUgHr8A43rK9j0QikUgkEolEIpFI/qT86USngub6SmKePeW2myvOrrd5GhpNWbUc2iSRSCQSiUQikUgknxt/StH5HHEvSpNIJBKJRCKRSCQSyefJn1p0SiQSiUQikUgkEonk80aKTolEIpFIJBKJRCKRfDKk6JRIJBKJRCKRSCQSySdDik6JRCKRSCQSiUQikXwyflF0NjU1UVRURH5+PoWFhdKkSZMmTZo0adKkSZMmTdp7WUFBAbW1tVRVVSXm5ua+XXRWV1eTmJhITEwM8fHx0qRJkyZNmjRp0qRJkyZN2ntZXFwcJSUlik7MxOjo6LeLzpaWFioqKigrK6O8vFyaNGnSpEmTJk2aNGnSpEl7L1PoyObmZurr6xOFrpRzOiUSiUQikUgkEolE8vERGlMuJCSRSCQSiUQikUgkkk+DFJ0SiUQikUgkEolEIvlkSNEpkUgkEolEIpFIJJJPhhSdEolEIpFIJBKJRCL5ZEjRKZFIJBKJRCKRSCSST4YUnRKJRCKRSCQSiUQi+WRI0SmRSCQSiUQikUgkkk+GFJ0SiUQikUgkEolEIvlkSNEpkUgkEolEIpFIJJJPhhSdEolEIpFIJBKJRCL5ZHyQ6GxtqqcoL5u0tCxKKmro6OpSHZFIJBKJRCKRSCQSieR3i85WUmODcLCzxPj4cfT0dDA2syAwNpXWTik8JRKJRCKRSCQSiUTSzW8XnV1dZEY/wdRAE02d41x3uYXTjUuct7fF71k8De2dqhMlEolEIpFIJBKJRPKvzm8WnV2drbhYGLJzjyZ3/WOpb2ymoa6a8tIiyqvraHujp7OjvZ3OV3WoOPyuvtCuLilYJRKJRCKRSCQSieSvxO8SnY6n9di2/QiegUmq1J/TUFlCoJ8XFy/Yc/HydQIiEmkSmrK1qYawp348CY2htqlVeW5RdhKP/fxIzCpR/i6RSCQSiUQikUgkkr8Gv1l0KobXpkX6YaC2j/2Hdbnt85Tc4opXei+7aKguwfe2M5aWllhaW3HG3ATzs7b4RaTRUFuKu4MFB9QNCErIpq25jjsOZ9HQPUFYaqHqGhKJRCKRSCQSiUQi+Svw20Wngq5W4kIeckxLjc2bt2JoZkVAZBLN7Yqxs21E+t1E7ZA6Dm6+lJSXk58Wztlj6uxRO05+VRNluUmcMtLl3I07PPJxx9L8FE73g2lTXV4ikUgkEolEIpFIJH8Nfp/oVFFZnIun60UO7tnCzoOaPIrKpK6+HldbA9au24iG7nHsbG2wPGPKvm3r2Lh1PxH5jShmbiYG3eO4rhqrN+3mnJMXFQ3v/bESiUQikUgkEolEIvlM+CDRqaCjvZX0mKcYaOzhyAlr4lMzcDijw649h7G0dcDV2YnLV65w7foNfB8HUFzXffnW4gSMD65l3KzVXPeOVKZJJBKJRCKRSCQSieSvxe8QnV10dHQopna+oK2mkOu2RmxX1yYsOp5bF06haXiGoOgM6uvrqa2ro76hgda2NhSL23a2NxJ43wmz47pCnB7E6pIb2WUNqqtJJBKJRCKRSCQSieSvwm8WnV1dDUSE+ON28x5PQyJIS0vG944jmof2cczqGsXllaQ/8+bIgQOY2lwjMSufovwsIiLDiE/PoaOlkfhgL3S1NHHx8ic60BczQz0sr3pQ3fyuzVQkEolEIpFIJBKJRPI58ptFJzQT4HMHXQ019u0/wFEtLTS1dTl77jrxGSXKVWzbm6p55H4DPW0N1DU10TfQ5+z5K4Qn5dBQno/rhTOYWl4ktaAK2hrxvGGH/nFzItLklikSiUQikUgkEolE8lfid4jOLiqK83kW9ASPu+7ccb/Lk6Aw8stqVMe76WiuIzE6lHvut3D38CQ8LpnaxlZaG2pIiosmLbuQVsVqtwLFgkQx0dHkllQrf5dIJBKJRCKRSCQSyV+D3yE6JRKJRCKRSCQSiUQieT+k6JRIJBKJRCKRSCQSySdDik6JRCKRSCQSiUQikXwypOiUSCQSiUQikUgkEsknQ4pOiUQikUgkEolEIpF8MqTolEgkEolEIpFIJBLJJ0OKTolEIpFIJBKJRCKRfDKk6JRIJBKJRCKRSCQSySdDik6JRCKRSCQSiUQikXwyflF0ip8RaXR0dEiTJk2aNGnSpEmTJk2aNGm/yRSI/xPb29vfLjpbWlqoqqqioqJCmjRp0qT9/9n7CydLlvROG/yj1mx3bc2+nVmYb3Y0kkbdklrdalKr7+2+faGYq7KSmZmZmZmZmZk5T2YexjzPekSeqsqsyroNo4LW9cfMrSrjxImI4+Hv6+/PUSaZZJJJJplkkkmmPyvZbDb0ev2s+P/NotNkMrG/v8/Ozg67u7syySSTTDLJJJNMMskkk0wyyfQnJUVHCsHJ2dnZrPj7+4fXyiSTTDLJJJNMMskkk0wyySTTn5tcmlIuJCSRSCQSiUQikUgkkg+D0JhSdEokEolEIpFIJBKJ5MMgRadEIpFIJBKJRCKRSD4YUnRKJBKJRCKRSCQSieSDIUWnRCKRSCQSiUQikUg+GFJ0SiQSiUQikUgkEonkgyFFp0QikUgkEolEIpFIPhhSdEokEolEIpFIJBKJ5IMhRadEIpFIJBKJRCKRSD4YUnRKJBKJRCKRSCQSieSD8clEp81q4Vyj4fDggAOR9kU6OT3DYpOaVyKR/HXgdF4g/Kbrr3dx2K0YTWZsDofryPsxmYwYTBbXXxfoz044PDrB6nQdeg/nJ8ccHZ9gsthcRxQuMOpOORI+9d07O8QzGTGareI0BxaTAb3y/78ylLw3GYxYrHZuzqIL8dtMmEW+XPyRPLTbbBjEtaw2h8gSO9pTDUeHh2rdpCSt3oDD+e5FLAat+PyQc51RlIM3n9stZk6OjtEaX73PN5hEeTAYzOoz2+w2dDoDNtv7y5Dk+3HYrJiNJuz263nosJo4PDzgXG92HfnjKNcxiOR4y6adF3aXHb857rCJGObs1FVG9jk4PBL3EuXgSjkxivJxdHKK1fFu2THo9Zhddmcxm0U50L+nHH9+2M0G1T60BpPryLsotnF0osH2p/4oYXdGvU7Y4FU/JhD5abOYhL+yvGWDDrRnxxyIe1hvcK9Ohw3NyQlnwr7exmE1qv7WLt6ncp7RKN6F5a/PB35oTPpzDkQdZLqhErKKMnt6fHwZw+/vi7rqCIN4Rze4yWvYrWb0oty8MiWT/kzY6RFGy7uxv2Jj2jOhE4QdK3am3ONcvM+rNqY847GwMfM7Bc0pbNnw2sas6n2Fj7/BFv8aMIvfeSJ+5x+ry15xIWIPxZeZXXWL02Hh+OiQ0zO9+vdNOG1mcc4xRut1/6dwYbdwenLI2Q3+1C7ek1HcyybyVqk/jQZhT+K+Nz3qJxGdTuHQl0b7yYiOIjQkjOjoaMIjo8gpr2F1/+SvxvFKJJIfJgbNPj0tNXQPDnP6nsDL6TDT2VhJTlkd6/sa19Gb2Vudpauzm+XtYwynB/S11JGemkx8fBzZRZXMbxy8JR6dnO6u0lBVTHJCPDHxaTT3jKEziYDNaWF5dpSi3Cwys3Np7R/jxPAmoFqdGaaju4+1Y/HcQrgdbC7Q1NTC5sGZkGl/JVxYWR7rIje/jJHF3Xef2+ngaH2K4qwsukfnMH6P5ndYtIz1d9I9NIVO1E37q3Nkx0QRERFJVGQkkeLf5t4RdOY3wbDVeMZIdxPpKYnExiaSV1DP3olW/ez0YIu2+irxWSolFQ3Mbbyp08yaLbo7OxmZXlP/NumOXfeeRGv+4w0TkuvYrFoG2mvIL6llfffs8phJy9RIL3lZacTGxZGckkXH4BT67xX2Tkyn27S3tjE+t4blSmBqN+sZ62wmK6+ctdd27GR1YYy0pHiCQkIvy0lcKo2945itSgjlZH1hkoriXNIysqhvGxDl4434Odqco7m1h+WtY/Xv04NNejvaGJ7f/JODyk+Bw6RjcXyQ4rxs4uNiSUrNoGNA5O2VINVuPmdysJ2stETi4hPJLalhZff4tQ3chNNmYmlikLYOJZ8u7egVmu0lqovSqe8ZxuBqWDs/3KCjqZqUZGF/8Qnkife/vHOZlwq6syMGOpvIzkgnp6ic0dkVXge2dj2D3W0MjM2gswibu7CxODdKa3s3O8fvCtQfItqTPXraaklLTiA6Np7MvDKmlreu+Fk7kz2tJIaEEBEZLXxkOPFJyYwurKvC42acaI926G5tZnZ9B+3pEeO9beRkpBIbE016ThGjc+vYX3/dyfbMCLkp8YSEhRMdFUlMQgqNPaNY1AYmB1srM1SVFpGSIspHRx9750bXV4X/X5sW5amLNVH2FM6EX+7tamdocvFPbwj5DLBb9UwMdJCZkkRcXDyFFfV/1J6wmxjt66J/eJIzg5nNpWmRT/kkilghISGZ2tY+jrVXGkSF7NtZmaYsL5OE+FhSs4sYX9zilcs0nu4y0N1Ieno6+cKeZlbffGY5O2Skp0PY0xRmccxu0bMwMUBLZx+aGxpdP4novLDbaMyK59ZvvuSlVzAZIjBITEqhpK6JjcPT789MiUQi+UQYzo8Y7mogNSaYL37+T3hGpbB+fO769CpO9hd7+fbLf+XHX7sxOL/tOv4uduMJ5TnJJGeVsHFk4HhzkbKcNFJEsJqZGo/bo7v4RWextPtGuJ5szZGTGI67pw+JyWmkZuTS0Dksgj8bu3MjZCXGkZieQ25mGkGhUdR3z6gtj5azTXJS4sgqqefEeOnqT3aWyUuMJLOsXYjTdyuJzxH90QYRz37PP/7ydxR1zb0JKF04LRqq0oL52//P3xCT14DuvXrOwfpMH7GREVS2jaBI8+HGYu788jf4hsaKYCZVBLZJdApRaHC1xFv1Gjpqi/B0e0FYdBwZmbkU5NexI4Jls+6A1spcYmJjyS/MJyk2ivi0IpHXypUdDDQUE5+YzsDshnotRdCMdNURFhnDwIwIuD5nxfHZ4WRrrocnX/2Un//BncG5A/XosbCN7LQ4fIIiyBSxRUSQL0/cPGkZnruhN+QSh01Pb32hKhyHZja4GjcfLo/y8vZv+Z8/+46BuU3XUSONlZl89e0tXvqFkJ6aQnJ6Lh3Ds2rP++nuLLkZiSQkpZIvxG9YaDiljV3YxDM7LaeUZyeSWlDN+tFlr4NZd0hLZT7hcRnCB+g+2xjIJMRId12FsIkUsjLTCfVz5+EzLxoG5jGpeWthqLUKX093gmMShf9KwMftOeHJJWwdXxeTb7jgQAiE5Khwiuu7hQ+60pNyYaGtOJ5/+h//BZ/4bE5dQez24jD5Oekiz7PJTIrmxeMHBMXns68V33VYGGitIUrYVGZuIUkJcUTFpbGwZ1A7PDYmu4iKjqW5bwKzqnAuWJ7qIzkmUryjPgw39PL8oHDamBvpJDoyjNDoRFG2k/B6+RQf4Q/nNg4uhadTS350IN/95g+EJ2Wo4j8zO5tJIUzf58NsxjP6mkoIj0pmdnMf7cEa9SV5JAvBmJWWgPeLJ7j5RTG6fHBpf+I5+sqyeHj7Ns99QshMTyUtK0fY2DRWm7Cx7SUKkmOFjaVRXJBJZHg0hbX9qu1YtftU5iSRklfB2sFlY5RJd0R9WQ7R8WnMbX1/I/DngtNqZLK/CW93NwLDY9R8fvHsKZGphSy7ftdNbM/2EhsVTV3nKGaLkdHe5st3lJlJbGgA9x88Ib+uhzNXDLC/MkFyuD8efmGkpSYKm32ER0gi0+uHqg0ONlcQI0R/fnExmSJWCE/OZ/VAp353rq+R+Jh4mvqm1HpY6RFdHOsiOiycxv65d3pNP4noVLrXy+ODcX/qQ3v/NGdapdtYI/5VhlbI1l6JRPJ5sre+QH5KBGHB3vzmFz/mQUDsNTH4CpsQHyVJAfzsp//Aj77zpH/2VbB6HeFvWRxoJCggkMrWIZSGd6P2lI3VVQ6OTzk92aWrNI7ffHmfys4J9TtW3T6NBfG8cPemoX+MwxNx3ukZp2dKsGqjsTifiOAEFveO2V1fIlNUFsnp5ZzqtKLyKCU8Juna8yjDEMc7KvH0ChKB++Y7Au5zwykqtaGWMr7413/gn375a8p7pkWo+wbnhUMElp34P/ma/+P/+bdE5TeifdN8fg2z/oiK7GhCREA7tam8RysNBWk8fejN1M4xZ2dnaDQn6I2myyFdonqcGWjA3/MlURlCNOwecC7qrZMjDRa7ELBTPSRHBlFe18KZ7pyxrhoRmHswf6jleHOOhPBw8itbOb0i7o9F8JQS4UeyECH7p66WeskfxaI9pCw9kp/+6H/w69teDC9cis7Tg3WGhgbVHsvTs1MWJwfwfXwH98gcNg4vA6VrOB0cbkwQFeJPRlkrh4pwcWEyntIiRM9v/vV/8T9+eY/+V6LTcURRViwvgmIYmt9Cq5aTU1FOzKpND9SmEpMQLexsjTMRXJdlxxGZlsmu+HxxsEGI0Gi6xq72uDhZn+4nMsCbgtYxIY4/zzjIYtCxu7nB1vYeZ+enzI928fzud3hEFbJ7asa0P0ekrwdeIQnMC2FxerRLX2MZjx88o7Zb/K4b7NAm8rijOhcvITgmlneuCG6nOloh1O1r/vt/+2/4RqZxor8cVaI92Wd9fYODk3POj3aoyYvht1/coWd+D/PpDkWp8aQXNbKl0bI80k6S8IFVvStqj3JBahQJuRWsH75pLDSdH9JYmo5vSByzG0efdW/zB8dhZmNplr7BEZbFO9Qc79PTUMSdb2+RVtmJVml8s+4RH+BHYEgKexodZ6caUf41mCzWK+/vKk52l8aJDfIiu6aXc5MVu/GcrY11dvaP1F7PzrpCvv3qW+KLO4RQUXqgzdQIu/EPTaBzfB2d0AmaU2FjJos6pH6krZ5g30i6hYYw6I4py0gjMiITrdXC7EATQcERtA/NYbpiS0uivEYF+5FX0ynu8fm/5PPdJdLDvXnwMpSZ1W2hk4RwzosVovEppS0jN9bVTpuOIhGjRCRksrSvlHE7B3vbrG1sqvm3szpHiOcD7vlEMbkh6jy7nobCJO4/dqN1dJGjowMmeiq4c/sO2eWtHB1ukJEq8jalDK1ex9JEP+5PguifXEWr2SQ3WYjhnHJ2jt8M29VrdihOjSY0NpulnVPX0Us+meisSgojJDCO+fU3QyIkEonkc+bs5JjlhVk2hPiMDX7GyzARXLmGyL3iwqaIuGr8vX0JDvDgXmC8CD4ve7beRhnaWZIWS2hMugjSjlxHr+Jkf7qJh488qXaJztWJbsJ9Pckob1Z75q7PKbVRkpVJVFQOShVgOtdQnZ1BVmYJs0prfnIStZ2XPXpX0R6tkxzsQXZVF8eu1s/PEiEQthdGiIuMwD/QF/eXj6jrGeeNVBOB6vYy+UmRxEWF8d3tp6RVdHBiuuE3OS/YWRol1MeDivZR9Eo2Ok4oSU3koVske1alX+o6Fu0+BckRBITFs6yxqwL36jnT3Q3EhwfT2T+q/r05O0BylD9DCytUFWSRll3KmhCgV3FY9Ey0FuERFMvg7NY795S8i8NmYLy7kcCAYLyELXgK8Tc6t6N+poi+q1xYjRTFBnHHJ5YZpeX+LewmHQP1BQQEhjE8v/N6GLvSuDHW30qkCJRDg7248zKIgXmX6NRvk5MUjq+w2+XDt4dkOmnMCyEpPZFlZbim7Zzu+nySsrMZml0gIz5abWC6MuJdxao7pLk4Fc+QdHZP3j/v6nPCcLpHUrgffnFFQnSa2Bqow+2FG0XNQ64zhM0Yz0jweUBcdgW75++OpDjcmCM10pe08m5OjW+GsOuPN6kryyQgwBOfl8+JjMvm8PzmfJkfbuTB/ef0zu+i3VNGbsRSLsS7IlHP1icoz0mnuH6AvqZyohJSGV/Zv/ziFdbmBwj08Ra+YATj9w7F/k+O8GlX56craHcW8X3+hODMKg61ouCerxLs4U9oQjG2t+ztJpxC2Ay0lOLuEcj8rvZGH7exOIKfhzsJRe2X8zvFd/KTQwiOzmZq/bqNKaKzo7ac4KAU5hYu7b69uoKk6FRml6bIToklv7bnnV5r8/kujcXJBMSksbL3+Y4oeMXh2iRJIZ6EJJa9nre8MlSLt9tz8qu7RW3/FqJO06yN4O8TSJmwwRtrcoeZsqwoXoQkMLV1hmV/loQwX4KSS96c79SSGOBGmPBvA2NDxKVlkyjei8LxzgYh7kLs9w7SUVdIYmouU6vX7enCYWVxoIEQf3+aB2evzbn+RKLTSk1qJC8evKSktp2ltRXm5hfYPji+cUK4RCKRfE5YTOckhj7nRWgcc1dF54Wd1ckBEagGklfWSEN5Fs9C4umeWnWdcBUnhv0FEqPDSStp4eBVQCaEle7shNWleUYGeshPjyM+p4zlPaUnzkFPcwXP3fwoqGxksLuV2ppaugYnONFe9gL015cRERBGc98oQ/09JCekUFhUTZ4IgvPFd1Z3jtRFTK4u2qYMfWouTMA3Npu5zc+3IVCzs0R+ajSxmZV0tVUTFvSC8jYRwLs+t57vU1+cSUikEPojfQR6+pJS3MSR4d1q7cJuYqy7ipdeYUKwXM6xxHxAcVIEv/j1LdLyS2hoaWNmeROTKwg9Wp0gKiCAoMhUJmfHqKuppqG1k7VdjTok7HB1jKz4YFJz85manaapqpj4uHgaaiuIFe+hZ2wRnUlZrMZ8pTdFBAob47x84UtF6wgmWQd+P8I+lid7iY+MIKNE2FhtPn4hMfRPuN7hW5yIgDnS5znBqaVs3yDmtJp9ClLiCI/JZvf1UHknm/ODwjYjyMwpore1jKceAfTOrF4GqqdCdEYF8PWteyIgy6OppYP51S2srp68me5SoqPCqGjsZGJQ2HB2Klm5eZSVFJCQXcrq3hEmZZET69Ww0cpMXyMej90YWt67Mn/u88JqMrC7ucrs+DANlYVEifLdNjKP0q6zM9rKy2dPiMmtVudfXjhsHGwsEvrsazxDM1ndf7un+UIIxk6C3N3oFoLxVW4oC/20VeUTnZBIQ2cH+bEBIgDOYP/M9X0RXJ9pDlhZnmOkt4XctCQhaqs51JmEVjmmMitJfDebvrF5OpuqyUhOpqyqlpS4WKo7RjkR11EWDrs6jPpkZ4XcOH/icxVhdfM8/R8mDub6GnB7+oT8JmUKhxPn8Rz+z5/x5XfPKamrpa2rV/jAkyvzMa9jPN6iKieBwMRitFeGTxvONWyuLDIx1ENJbjoxyZmML+2q78Vp0ZMd482t7+4RnVREU1sn00ubWJSbCB8w399GhG8QlVXNLM8NU5CTQ1JSLjXl+SSk5oj6UqsuJHR9gT0zU/31wueH0DW+yltrj312WPVHdNUX4OcXSm1bP2Ojw2TEBRMancDo0pbrrDcoQ1vHmvIIjrw+mslutXC4vcX81ARdTVXExSgNXwMoIcf6ZDshvi/JKO18LcKdDjuVyUEERsTSOTxOcV6O8I+pzC/O0ttSJf6fQW11OYkxcbT0TXNudi0O9fr9X6DbnSEuUtSFZS3sKw0VLj6J6FTndGbG8dVPf8nt+88JCg0mKDiE0qZelLUtJBKJ5HPGbDwiPuQpz4WgfCM6nSJwWaIgNYbY9DK2DzRMtJfwNCSO3puG1wqBujXdQ1RoGFVt4+hddeOF3czSZD/pMYE8f3CLL79+QHnnpBAjikc30FmTzVfffYebVwAxYUG8ePqIW/cekV7WzInBhvFojdqSTELDwkhKSaW4vIrKknyi4jPUHjhFyJaXlNAhRKlGdyl0lXuOtJXxyDua4Zmbg/dPjcNwTHuVqFAjEpnbOWVrtoewwOdUdY+qwarzwspUd704FkbT8Dx64z5BXkJ0CkF/+nbXrkAZ1tdamYZXbIYQ2q4eMOs5w+2VuL9048njxzy4d5tHL31pHFBaay84XOjF98VDfn/7KSlJUXi6P+fu/bv4hCUzs3YoKl0Lk33NolKPJDI6jpz8fMqqa0mPj6GwvInx0RHaG6upqGlkceMyuFLQne4SK66VV9HCyfeteiRBu79KUXqcEHslqogc7SjBV4jO4bl3500rC5dU5SXw5IWHEEZL3DRd72RvgfjYSGLyGzl2Da21nO9Skp1IaFw2m7uH7E418Mg9gIF5V6BnPKO3tgQf8c4ePHzE/Tt3cPMJom14AYsoJxfKEHhFkIWHEZeYSkFpBZXlxcTFxtHaM8ToYC81leU0d/arC+e8itU2ZocI931M9eAihs+0GBzvrFNXlI6v2yO++t3v8Y3LZ09z2exjOd0gLdKHWw+eUVjfzshQHyVZCfzh337G84AM1t8WnXYDgy2VwoeFsrTvmqMmBOXSaAeRYeFUdY6hO9unINaP8NhsjoWoVE8R/mpqpIPUuCDu3/6Gb+48o653HpurJWdpapC0xBgiIqNIz86jRPi7ovwskjKLmJoVQrStmfKyKsbnNzC7lIf5dI+WwmSCY7JZ3Xv/fLkfFEodtTxBfJg3XqIem9u6HKVxoRUisjCVx0+e8OTJQ+7evYNXaDxjK3s4bhibfLA2S2Z0MBkVfddWqV2fH6coLZrn97/j93+4TXJJ2+XQWoGy+mq3Irjcn/D48VPu3bnNE89AGofmVV+sNDC2VuYRHRVFTGw8JWVl4p2WkhifRFX7MJPD/dQJG2to7WP/io0tTw4T8NST6rZXCxJ9xjidbC0M4/vwG76680QI/wf827/9lvjCJk6UuThvYbMYqckOJSo1m7ndN8Na9acndFSUEOrtzne//3ee+sWo83MVpvuq8X/5iPyq/tejPJTGoqa8UPxDwxmc22BdiPqslGgilDUM0oTgbGgiMzmejOJ6Ricm6Guro6S8lsnFjddzeq2mfZJFvRecUizs6c2zfBLRqXSN16XHcO933xEWnUJVjaiEK6sYmJzniiCWSCSSzxKT4fAd0WnU7FBTkIp/SDTjqwcYDQYGGnJ4FBBL+/jSu9swiEp1fqiJ4LBA6gcWeNX+q2zPcLC1Sk97A+VFOUQEBxAYkcz48r6oOO10lsbzy1/8HO/ofLYOzzg52qIwJZTv7j6mrmdSOPULdQXcydEhxidnWJkfIzMpjsKqBhH4lpGSEEuAry8BAaFUtQyiV1uO7cwNtOH2TFQyY0uuJ/l8uLDpGWgqJzAgiJqeWXQGI5vjLQT7PBWic0DdZmF1qpfYkEBS8+rQis+151t4uXmQXNzMkdGu1N/XMJ8fUZMVS2hyLit7J+oxJe9sytL6Oh1nZxrmRzrxf3GXh14RzKwfoVns5MXtf+Pf7rgzNLcmzjlhrLuGx7e/JlrpadHbcdhtbK7MMTw6xvziIl2NFcQnZtHS2EJhZgrhIQH4+HqTmFnAwrZrkQuthryIZ+SW13AgK8H3YtMfUluYTlBoLINLRxh0p/QJG/MMimJodh3Hq30YhKVo9lYoSY/Fw8Ob0pYhdDcNsRbnHa2OEhcdRHp1J2dGpXdOT0t5FgHBkTQOLmEya1kerOT+C3/6RQCmbrNx4cBqMYlyolXLyfRAK4EvH/DQPYRZ1yIlyoJTcxMjTMzMszA3SWluGmm5ZbTVV5KaGE9IkCgH/kEU1Hao5UZhZ3WauChfijtn1F6IzxHd2bEQfD00VJWIoDIKTyG2y5v6OVfGCwv72RRBcnJUAE+ePScoLJqwiGhe3P894eklbGuuz1l2mpVFuQp44JMoPlMEqZPD1XESIgJJyK7gUGdQ5zxnR3qLWDGT/eMzdW610hOzs75IT0cjxQVZhAb4ExaTejkfU1xF2bbhYHuVocFB1QYHe1qJj0+gtrWDquJsIsLDCfDzIzQyTcSdl41sTsMJw3X5BEYlsbj17jDsHxpKI97qRC9xof74BEfRN73+esVSpY6ymA1otaL8nxwIcVjIo9t/wD8hn90rqzS/YnNB2Fi4FwWtE69HjSgc728y3NNGVVkeUWHB+AZG06k03LjEoNX8ysbOmB9uI8jjIbeeBzO/o/hNJzaTOD45wtjUHItzE1SXZJGUVUhbexsJkeGEBgbi7elLUU0nRy4b25oT5cv7GRVNfZg+86GVBs0udYXJeLp7kZlfpvYuRgf74BMaR+/kyjtzj5VRCMVxnqJuyWH96E0Dj8WgZ3FslKbaKnXRJh8fX9JLGjk2XrA4VEeA20MhOgfeTC0Q9qNMEfAJDmZgflt934fby8KehplbXGG8q444Ie4bWtsozs8mTNifj6cXUfGpTIrYR3ksu9VAXkIqYVF5LF+ZOvRJRKfS1VudHI6/VzhDE8sYjUZ1qIMy1OSGRhKJRCL5rLgqOue3L4PMsfZSfv+L/8U//eJ3wvkmEBMdyeM7v+dv//FfeBmkrPy3qzrjVyiic26wkeDQQBoGF1+LToULhwOLxawOwTvanMTv6T3iCxrRie90VqVx5+ELaoeWXWeLYHWhj5fP7pGQW82rtWiUwEunOaajupD41FxGBttISkqmrr2P/b1dmiuyCItNYXrrnAtl24ChDjwehzE4unh5gc+I7cUhnt/+Lf/t7/4ZbxEARYu89X5+l3/9yT/w9csQ6htbSQty48f/8GPuuvmTGBdLWIg3//JP/8hvvrovAvtejs+vizmLIjozYglPyWPVJTrfwWFmvLmAx89e0DI4ycF8L37PnxIUV/x6KJkS+ORGefFCBEzqan8qF0KcWNmcHyU1PpYWIYxLC3NIyS5mYmGDpfFOdauw9Kpe9Wyz/oz8mOfqtmFSdL6fqY4qvvqXH/Pjn/2WiPgUIsNDhI39jn/66S957hnLjAh4FPbXJsmICxVBbDhtfRPoze8La4ToXBkhLiaIrLoezs0ONqc7uPPbf+FHP/01/pEJQpCG4fHoG/7uH/6Z5/7h9IsgzPJ2nOIwMtSUz4N7t6gbXn29sJVix3aDhoHWahKT0+kZGSE+Jpby5n629/dpriwhKCSRrtFL4bO/Pis+D6CofUo8i3ros+OyYcaC2WREc7BNWXoYz176MfxqhW4RQp4e7jA+PMj4zAJLizPEeD4hvbRBBP5vlW3zCR01+dz3S2ZP2T/wQk9Zgi8//vu/53d3n5GUkkyQnxdf/uKf+fmvviS1qJ7908veTsW/KT7SKHzkwuQAPs/uklTUeil+XdjtdlEWZigrzCCzpJ7OhnKShOCfmJsXPnCV1Eh/EcyXXw7PN2kYbSwgMDqBha3LcvSD5cLEVF8Tof7ews4ymVzeeS04b8J6tkdNZjiPPAKYWN51HX3DhhCdsWEu0XlluMGF2nhjUfdM3VqcIFH4cJ+wJDauLErzBjvD7cXc/v23NI+tvR6KrQgkx4WRwfY6dbXxjp5uCpXh7GUtrKxvM9ygDA1NoHtyXT1/e36CJL+nVDT2fvaic7K3hqf3vyW5rJNzvQmzEOGa1SEhEp8RHJ/P3lt1hSI6C2M9VNG5cXxlVIHTqe5FrXxfrz2jszyDhw9EnTa6xMbiABF+7mReGV6r2FZpvB9+oq4dX31Vp4lr2G3qdIWshFgq61porSomKTmfrpFltqZ7yUkMI728DbO4kMNuoTA+jYjwXJY2PgPRWSVEZ6CvqKRvmNAtkUgknzPXReelYFkc7yE5OpjgsAhiY2OJCg/l/rdf8D/+4Z9wD45hace13PwrhChZE+IjIiSU6o5pbph2eIn9hIgX3xKQUMShqPlHO6txd/cRQnXedQIcLA/h6/6I5IJ6Tkyvqg47C2PdxERE0TY8x+5MK/FJqQy5gsPZ0TqCIyPpnd1F2YR7oruaJ+6RDN04//TTsrc5T05aIj5+gSJv44iJjeHl49tCVP4dXzwJpKqhi7aKQlHBhREeHaPu+xYe8JKf/OhH/OKLW6roPHW1dL/CajihuSQZ34QsEWTetIiTgpMZEeg8f+FGy9A0usMVEoMDiE4WovPVGTY9RXH+eArxMHNlYTyD0vOdl0JKXjV7R1uk56aSUtHGmRIt6bdJTEkiKL1GPdeoOyLR5xm5pU0cv7cgSBZHB0gWdhUUFEJ8fLy6P+D9777gR//0Mx6/jGVhW8PpwRr5aTFExKYKEbr7vcGywtHuDLFxYcSXtaERomh/ZZyMpCiCgoOJiUsgNjKU5/e+4n/+/Y957BXMwOKbuYevcdoYF+Xk8YPbNAgBeTUU3JrtJyU+lorOEbb2FngRGEvH5OXCYguDvQT7xFDbMq7+rSxsFRnwjPLeeXTv3OQzRPzu7vIk7t97ROvE8rVGtUts4vd34v7cg/qeSSxv9yrYdfQLIfjsRSSbynxap4GehlLCQoQfDY0gMSmB0EBP/v1nP+anv/gtScUtHF7dX9DF+eEaYS9vEZxU8XqItIr1XATGhSSk5bGwvkNbibDBpBS0rr2VqzN8SUlP4Ug8ll13SEdZGkHR6azsXDYk/jC5YH60g5iwYNILq4Tv+uNDjR3GUxrzonjqGSQE6p7r6Bt2V6eEwA8iq3oI4w3DQhUs5/sURHuKOiiIuYObtyGb7q7gztd3aJlYe7PojWB7foi0hESqWofYXR4V9htD3eCqWt/qVtoJiw6npvtyIb6V6VGCX7hT2TqsbiP2+eKkpy6DL7/4d/I65lzHBLo1gh4/EL4omuWj673KNrORinRRP2Vks/hquPoNzIt8fPD1bcraR9CfrpIS5oNfTD6vQwfDHpHeHoQl5LOpeTPn0aI7pbU0m9jUQlbWNugozCI9u54N5XVZ92guTCQivVydGuCwn4l3EkdoQiHLV+zpE4lOM2WxgXg8C2DwPas6SiQSyeeKSb9PpO9dHvhFMuNqxbOYDJyeHLG/v8/21jaba8tqL8C/33ejrG1IHclxLeRyXnC+OUVsWChZ5Z1CbCgVoJWNpQVGlF6141OOD7boqMnlwZ17FNT3qS2IJ+vTJIZ6q0NsppY32d9eoSgtkidu3rQNKfOa1Iure99lJceQkFuLxmDGsD1MeHg4FaKyVZbBb67IJCoxg4V9I3aLia6qNDwi0t5Zie5zwGq1cHZ6ysH+Hjs7O+xsb9FVk82ju1+SWFSv7pFp1Gk5OT4Sn2+zLdLyXC93v/4Wn5gcFvfO31k04sKqZ7ClhBc+UYwvXs651Z8eMjs1xsziGpoTjQi+eghyf4BHSJzao60s1NBSlIKb20uq+yY4Fvcb7azlxcMHZJQL0WK6VAoO0yk99aWEhMYysXGCzWagMCeV8KQc5tYPWRdCJDoulqyGEfV8/eE8Xs88KWkYwPDXtHP5R8ZsNHJ6fMzB3q4oAzusryxSkhHGrYdu1HaNY7LZ6a3N5eGDh+TW9mBzXKDTnavbOZxrtdjs7wa8Z8fbZMVHEZaYz75GJ+ITkyhrJ2pZ297eZmdrkfayRP7tq3uUtw+hNVnVcjIzPsDc4qK6DcFUvzL07xkegfEs7Z29tvOzg3UK0xOISc7jWGfm/HSLQL9gCmu7ORJlp72mjLCIVPqnlLmiTiGqO/B68oSeuZ33LsryKTne22ZiaIyl5S3OzzVMD3UQ7KHs4ZjMorp1lBPN4SG7O/ucn2mYHWojwu8lAXF5bN4kJES4OdPXjN/zlwytHAghcYHRoOX46PDSznd3mBU2GPL8Fs88Qphb2xfv1MHq3DiTkxMcas452l2jpjCVh/cfUNU5eWW7mQum+hqJDI+grn9B3RqqvzZPHVo7NrfC0f466dGB5BZXqaNMtOJdFScHi4C9lN2zH+7WRbbzPTKi/Xj4wof+2XV1RM65siXQqeZy6yhxzsnuBpNjY6xs7qrDa3sahR99dJvY7EoOz95dmEV7sEpJWjShqVXohf0obC4vMzY0webOIeeafTrrinF7pKxyXMWx0YLDfMbk6DhTc6vqtopzo0I8+jzjmX8cq6IsvTIPZc/mfMXG0oo50Aob21sgPSFCXZxv50DDWIuynkEyfXNKD+wFs8PtuAlh2zy88HoO8OfK8ng77ve+5ql/DOPzqxzu79BRmsLXX35NZEYVp2+N4LiwmemvVhpOUhhevBT/ynD46fEJpkU+ngpftT4/QXyID09fhqjbPSm92i0lqdy684gq4UMPDvboq83m6XNvKjrGsbgWHnCYdYx11RMWHE7b2Ko6pWWwpoBwZYj6zDb7i8PkinzPqelT53XazlaJjwkloUDUz6dvGoI+jegUGVObGkWQbyRjC+/fNF0ikUg+R5SezsSwF7wMT3zd03kTo61FPA9LYsC1rPvbOAwn5MSHEx6f61rZ0cZEdxNhnl5qT0toaAi+Pj6kFdSy/WplTauB2cFWwgI88fD2JTDAF2/fQCFYejkVga2CQwiqvpZKImMShOi5FMUXllPqy/MJDwtTA6+Y2CRa+6YxiSjCpD0gK8qbpOJGdj/XcX1vsT3dQUTQS+r7p17PRbmK3bpHoMifjMoutDfVahd21qb78HX3oK53Su2dMh1vUVeYho+XD0GBIfj7eOEbEknnmLLf22Xle7I9T15aFM/d3AkKCsLP14folAKWr+zXurs0oQ5BUnpY1RDL6WR5tJfU6AjCQsOJjIgir7SGdWXfSCFkV/qreekdTs/4yo2/RfIeLpQ9Wwvxj0hgduvSDutyo/ntb37Bo5d+xCvDrMNCCQoIIDkzn8Wdk9fB6ius+jMRyGXiHxDB1NpNc/lsbIzW8sQzhDHXyCz90ToV2VF4ezwnIDhUlBcvAsKUeVYiGHvVuiFCqfHOOhJiE+kYuxzaZ7cY6KsrIToiTJSBCNU+a9oHONHbxE85p7s6GzefmGt7SH5O7CzPkhsXLWzGm1CRr/5+voREJTI4veKah+dkuq+DBOG3goMC8Be2ERadyvjyNq+n276Fsn9jfJAbuQ1D6CzvnmQ42aEg1p/YpHwhepReTicDzSWEB3oQGBRCaHAgfn4BZJY0Xlt11qDZoCgnmdQ8IYSU0QPifRxszJGbEU+wEKKR0ZEkZhQzsXg5HHR7eZwwX08K63vFc/xwrVC/u0SU7xN+/dsv8A2OUEeNhIi6SPF1JXXtaEwWjtdnyE2MxNPbR/3Mx8uT8KRM5jeVxdRcF7qCw3JGV00uL7xC2XTN610Y6iFBXNPX20+UpRB8fH2JTc1naetYcZeibjygNCMFj5ceBIWGinrQm4DwGHqmlrG9vomD8Z46YuKTaBtZUI9YTFr622pEuQgiMjJa1HXR1HUMoTE4sBs1dFRl4x2WyMymRhXQnzMW4ZsGWyrwcX+Gh/Ax/v6+PH78iNDEbGbXD9/xZVw42J/twtsriJquSfWQ7lhZTC1d+CovQkIufaFfUBRNvRNoXY2kx9sLZMeH8uS5m/CDgXiK+i+nspn9szc9qSe7ixRmJJGeX6suWKhwuD5NZnKsqCuDRVwRTlpWIcvKokHK1mZTnYQE+VLVOY7xSkPqJxGdyjjutekxhgbGOFQnj0skEslfD3abkcmRHnpHJtG4VlR8G2Xy/e7aHJ1DY1e2YriOUzjn0dYy4eiDaBSVgDLd5Xh7laaKIlKSEkhJz6Gpa+ja/nUqDivLkwPkZaaRmp5Fx8AkuiubXSt7Py7OTTI8MXtNxOhEANdeX06a+E5rn6h0zOKGdisLg014ikCyfXz53flqnyPOC84ONkQd0s3y9uG1oVavsIkgfqC3T11Rz/SebiODZpvchGAi0kpYVvbPdBhZmhykMDuDBBHIFJRWicr93e0rNHvr1JXlk5iUTHldC1vH1/fe3N9aZ3hwiL3TK8Of7GYWxvooENcuLKtjVQgghfODdfJig4hJK2Lr6PMUG58rygqXO2uz9AkbO3Dl9dr8KCUFmSQlpZCcnCz+TRTiM478kkpW90/fDdSELW3P9REa4ENhXR+nrkDsNU6rulVPZ8/gaztWesnnx7qF/SUTl5BEYUUt8xtvDSsUodTK7CTjEzPXhspa9cf0tdWSlZFBvRCcGldD0e7SqBBrXqRUdWEwv/UMnwkm3SnjvW3kpCWRIMp+cWWDEAlXhbqT7cVpynLSiU9IoLRa2aLp/Y1yCqbzQ3WxFO+QJBZerSJ9BbP+nNnRfkbHZzG5tpjZF+KxvqKAJHGPtIxcOvpdvuwKyj6fE+Mjbz3fBVvL4vmK8sjKL1HnKiq+w2o8o722AE/fCEbnd0SMenn2DxGz/pSR3layheBT/FtycgoJIp+V4ex1bb3CPiw4TOeM9rSQnpJIQmIq1Q3tbJ98XyyvxPz9hHi/pLRzEqPVjv5wh+7GatJEPZeQkk5Nazd7V/SAskLxvHjv+ZmpxMUnUFhex5zwxddxqPXcqLCxc8ObYdcWnYb+9nqyMzOpbunnWH9pYxszA8LG/EkrbBCC66+kYeHCwvy4Um+kifeRQlldG9tX52u+hcN4RFpkoDoEdkf4RKfNxOLEIEXZ6SQKX5VbVKmuK/D2rz/dX6WmNF/4y1SqmnvVkRlXOT3eZWRomNVrNnrBxsIEJflZZBdWMrN6+X5M2iN1ixz/iESm1q5PXfkkolMikUgkl5jOtslKjiYhu5iNo4+/Kfz54RYlqVHEZlWxd6Vl8wfBhZWZ/iZCQ8Kp6xzjvevNfCAcNmXfuBaCg4JpG5r7/Jfw/0+KzaShoTiVqLg0dU7ax34LNvMZXfVFBIbGqj22P6hScGFnY3aQiJBAKtsH0V7bV/FjcMHm/AipMRFklDWrKxhL/uMxnR/RXJ5JUEwa89s39NJ9YGzmy/m9YeGxDM+/u9jRfyYWB2qJCI2koWviow/TVxrb14Q9R4cGU9I8zLnxeqUqRadEIpF8Ypanhqitb2R27WNXhhccbi1RXV3N/ObBZzmP7ENjNRzT3VxPS/fo62X1Pxam8xNGu1ppbO99vWeq5FNwwdnuPHXVNYzMrH303n7twRY9ogx2DC9gvWl84n9y7GYtY30t1LV0s/uxR785rSxNj1BX38zStuaji6EfDhccbS9TU1XB+MrmR7cxpXG1q7WJjr5xbtw96T8RDtMZXU11tHUrjTgfN6PtFj3zE/3U1Lewd/puI7oUnRKJRPKpEW5X2fNMqzd+3KDnwo7FqOdUa+DiB7xflcloRHN2junKxuUfA5vVxvmpuK/JKoPdT43TifbsDK1Oj/0jj69Uth85Pz19Z7GrHxLKApOnGg1my/VtID40ykb4Bp1W3ftX8mFR95DWaznXGbB95MJuMZs4O9d9dB//qbCa9ZxrRd3ykbeFUexJrztHZ7TcOGJDik6JRCL5wSKlzjWUFSwkko+MLHWfEJn5Hx8lz2W+fxSEpnP97/NAik6JRCKRSCQSiUQikXwwpOiUSCQSiUQikUgkEskHQ4pOiUQikUgkEolEIpF8MKTolEgkEolEIpFIJBLJB0OKTolEIpFIJBKJRCKRfDD+qOgUf8skk0wyySSTTDLJJJNMMskk05+dXJry/aLTZrOh0+ku94+TSSaZZJJJJplkkkkmmWSSSaY/IzkcDkwm0+z5+fnNotNoNLKzs8PW1hbb29syySSTTDLJJJNMMskkk0wyyfQnJUVH6vV6Tk9PZzc3N28WneJvtbdTJplkkkkmmWSSSSaZZJJJJpn+3KRoSrvdPiuSXEhIIpFIJBKJRCKRSCT/8QiNKVevlUgkEolEIpFIJBLJh0GKTolEIpFIJBKJRCKRfDCk6JRIJBKJRCKRSCQSyQdDik6JRCKRSCQSiUQikXwwpOiUSCQSiUQikUgkEskHQ4pOiUQikUgkEolEIpF8MKTolEgkEolEIpFIJBLJB0OKTolEIpFIJBKJRCKRfDCk6JRIJBKJRCKRSCQSyQfjk4nOC4cd+8WF66/3I54Ju+MCp+tviUQi+dTYLEb0RrPrr+sovs1kEp/rdej0egxGo/BhDten78chzrHY7O/4OpNRXMNkEc7adeAGLsR3Ly6un+CwWdTv3YTdbsdqFa7e6cAmzrM6/oo8rMMm8tbAH89RtYJT6xDnH6lBlE8tVis2kS/v4sQh8va9VxDXV+9x9QTnhXhvRqz2d79lt4s8t9rU66nv3HL5f8mfingfwsbMwsYMwsb0io0ZTCJf344nnBjFZ0ox/1O5EO/fZhPvQ7wQ54V4N2aTen2dTrmHUZSPN6XOKd6x1SpszGBAr1Oew4hZ3OxqObDbrRiNJtdf17GLMmAT9q6g3NMq/v5rQP3dFuV361Ufp9iiYjuKrb0PNT9d//8+7CI/FZtQ/29T8s6g+lC9yGOT2aqYmnoxi9msvpdXSXkHRpNZzUPFVi9xChs0iWe7IV8vbOL6l+c6xf1s4vkvvuf5JcJXCXt4XxZZTMIGhA2q7+ePotiNVbzn6xezimsYxPu6eg3FBl/ZmE7YmPKvUtauVnVKPae8+7eqPxWb8BOKzSof2e02LOL/f9IjfmbclO9K3piU3+36+09ByXebyE+lblJtR/FdLvsxmS1qXHC9pnNiFrZ2U94qXNZxNz+BRfgIpV4Ub0it4169708jOoXT2pgbp6SwjNGZ5fcGD5rNKepqq+gZX8b44Z9KIpFIvhfd6QEj/R0UF+aTk5NHc0cf+xqd61Ph1IVAnOrvJDctlfTMTNLT0iguq2Bl7/CaK3+bs/11RkZGWN56c97J7hodjTXk5WZTWFLB3No+1rcvcmFnf3WC+sZW5rc1Ll9qY29jkcaaCsoqqhmZXcZgfVMxHKzPMzI6ztaJCIaF6DzaXaW3b5DjM6PrjM8Tq1nHytwI9VWl5ObkUFLZwPz6PjfrZSfawzXa6uuYmF/D8n2i2mFiaWac4Yk5jnVW10GFC45F3tTWNzC+sCGEuevwFcxne3S2NNE/uYLecnmC4fyYkd5OSoqKaGzrY+vYoB5XcBiOGR8dZWZxS33PJt0pM2PD6jOabxCokndx2vTMjvWSmZFBZqaSMskurmVl+0j9/MJmZm1xmsbacnKys4X91TCxuIHtj2SvzXjCyOAQSxt7aoC9OjNIUbaw44wsMtLTyC8sYXb9gFevaXdznoqSQlJSUslMTycjp5jukTkRFF+Wg6MdUf6E/RYL2+0Tx88Mb8SP7miT/oFRNnZP1L/PD3cYGxxgYetY/ftzZmdzkbKiAlKFb8sQ7yA9PZ/eoVkRtF4Xd3b9MbMTQyLv1zgXtvG92S/82O7qHENDE2i0JuFn94XvqxL5ni6un0ZmVhaN7cOc6cQ9LowMdTaTmSryXXn3whdki/eclZVLeXULGqUBwmZibmKQitISqhpahV/df3N/p5XZcfFcs0uqzTovrKwtzzA0otz7qv1LFKzGc6YHWmnp7uVEiJyr6E526W1vJD83h4LCUoZnVjHd5ChdmLQnTAyLci5s7JVP1h3v0tfWRH5ONvlFxYzMrLz+7FCUiZoyYWNpwr4y0snKK6BrdNoldJyc7G/Q0dxAYVEpPaMznJtc709ojHNRpw6OjLJ1eKq+e624z+jIMLMrO38VwtNwssPE2Aiza7uYruqfCxsbSzPUVZWTnZVNbXOn+I1nrg/fg8PC8dYyA6KMr68v0VxdQabI00zx/ZycXHGdLPH/Apo7B7G4FObZwSZdLbXCh2ZRUdPEiohNXnFhMTA3NkB1WQk1De3Cvo6xuTLVYTWyKvTd4ISIO8zKgztYmBpldGIGk7j0JxGdikJvK0zjH//r/8lTv0R29De0RDnMVCQF8C8/+TGBmTUc3dxgL5FIJB8Fp80gKt9mgoP8CY2MJjoilCdPnpJaVMPe6aWw0OyukxHsxbdffENASDjhYWEkp2Uxu7n73qDrwqqnqSyHlIx85rY04ohTBGDTZMaF4eXtT0RUDImJifQqTvyt+tx0vkdK0H1++eU3lA2sqKJTszZNSVYq0fFJJCXEExadTPfYGjYRLTuMh5TlppKlPLP20u8eiuA5OzGasqYhtG8Fjp8TZ0db1JemEhsdIVIkbs+e4BuexOTy9rtBhE1Ha3E8//oP/0xSUTO698ZBTg5Wx0lLiKWsoZcz05sTHaZTarLD+Juf/Jrk8o7rFb/gwm5hqDabL371MwIz6jgSL8dpOaevpZy4mBiSU5KJi44ms6hB5Ovll2e6a8TxTLonV9W/rcYzBloriUlMZWp1770typI3mDXrorwGizL/HaER4URERBCZlCeCsz3189PdJQoyEvDw8Sc6NgZ/Hw88A8MZmr+54UDhwmFioqeWuMQ0Rha3cdh1VKSG8N2Xv8EnLFrYcSjxiSmMLu64xKuF9oZ8vvn2Ox6+8BTPEC7sLImG7nHMFjsGzQblhZnExsWRnBBHRGSc+GzsslHIYaC5LJPU/AqW9y6DRcPpLg1lecRllnIgRNfnWwxsdDaU8Ifff8MzTx/Cw8MIDY2jqWMUg+nSd5i0GuanhslPjeLh7W8ISisTMd73iTknp3sr4vx4Cms6hDi3sjrZgc+L+9x5+EK9R3hEJMVVHRyfi+tc6OioLSU8KFgcjyAuPoHgQB9+9+tfcs8jmp3DI5Ym+kmMjSMhSfhBYYOJGQVsngrB5HRyuDJGovhOQ9coRjVSFkHxeDfJ4l3Vd01ikY0/19iY7Obxl//MXTcfljTnrqOKKFmlNDuel57ehEVEkZSURL0o4+fGm+sQh6jnJnvqiYlJZGxJ2Jg4dqpcIzMeL09fwsIjSYiPo7F7BL0qKi8Yrsnn8Z1b3Bc2Fh0l7Dw2gfqeESw2B4bjLarz04mLSyQ1KZ7ImARqxPtT72XQCBvLJiW3lGVXw47hdI/akhySRFlYP9Krxz5HtMd7Qpj3khzpy717d0mt6ED7qu4RgnN1so/YUH+8fQKJDg/By+MlCTllHOre7zfOd5epzEkhr6KZpZUFyvOyiAgOEX4pSvi1BLxfPObX//orAhNKRT3nwHy2S0lGLF6+vqqPdXd7TnB0Bot7Opyi3lsebSUhLp7U5BTxDCLGySphaVuJXWB3aZz0+Bjq+2YxuRq754eaSBTlY2Rp59OITmX4WV1aNP/0f/u/8Lc//Y7GsXXXJ69wot2d4+kfvuS//tf/g4D0UnY/70Z4iUTyn5wL0znz431UNLQzt7zO5toSeTF+fPfgBbU90+o524vThLu7ERKVzYI4Z3tri+3tXfTmm4fiKpXE1nQPYcGBFNZ2orS/mU62KUqP4ckLLxo6R1jf2mVnc52jU+3r1kQF54WdmYFavvj5/+Lv//lnlPQtq9frrioiIjBaBM/rLM+MkxQaSmpWLecGEzN99YRFxtIytHB5EYHNpKWvLh8v/ygmFeHjOv65oT8/YW5qiLmFBbZFfvTU5nHrm29ILKjlzNXLqOK84HB5mJDn3/L/+L//n0Tk1qN9T0+nzXxKY3EygZHJQpTsuI4KxDU2Z3pxv/0L/q9/8y/EFLe97sm8RNxjY5EQt3v8zf/5X/BJreDYKILn1RHSY4LILa1me2+H3oZiAkXFvXSsR3eyIT6LIL2wlr3TNxXa7uokMcE+ZJW3cKKTrat/DM3GODHhfjwNy2R9c1O1sQ1hI3p1KLmTo60FWpsbae0dY2t7i+HuBl7c+5bA5BJ2NDcFEhec7i+QGBlEUn4t+0L0Wc83SQ30EII1mLnNXTY3N9ja2uZcb7kM7C6OKcmJ45F3CM2D08LGxTNsbnF8pleHlI235hMdF0HL4BQ7K9PkJocTlZbDkdXK7myvCPjCaOgdx/i6SAnhM9JJsJ8PNf1z7x2y9slxnlGRl8Ldp4F0T8yLPNlkY32TYyFGXg1r3ZkfJT89lkeP7vKrf/kXnvilsqm92f8pOMw6BpvLRBAdwsDsZSw43FKAj9dL8mp6L9/vxgb7B5rLfBEhquZon/W1NdbX18XxXQbbq3l061sSi9s53lmnKjuFhMxy5jZ2Ge+sJTpU5PfQphAeB1TlJBKVks/C5mXPuILueJOK3ASCotJY2Tu/fMcSrKYTqtKD+bv/9l/5/e0nLJ5cNpI4LXpq85Nxc3tJUX0nqxtb7O5ssnuoEYLw5rJ7tDlHWmQASYWNHCkCSYjQmgJxDSEoi+s6hS1vs7O1zt7hCTanMuzZSENuHF5+YUJMTrC3s+2yMZ06VHaqp5lQvxAa2wY43FsnNzGeiKhcTEJfrE31EhoSSm3nKLrXDakOpvuaCQv0o6xlCMsHVzp/AaLeWRxoJyk2ilvf/o5f/eoXhGdWivrr8mPj8RoZUT689AoV4nycnY1VaovSeeHmTknbBLYb/MaF1cBIWzl+/iF0TqxgNJk42tu9tB9hV0f7G1TlxnP72ztU9SxgtZoYrMvlyeNnok5qYlXYX1NZBg/v3iWtrJOT4wPKkgNFPVbN0uaOsNVSwkWetg5MYTKe0lCYRnhcjhD2WtcTKCOCtslOjCCvtIpzq+Pji06HKDD1mXG4f/MlP/3lV0Tn1XF+paA67SZ6qjO4e/8Bv/7Vv5KYV8nOmxFKEolE8tFR56HbrNdE2d5wEw8ePSOlokP9e3lqFL+XvqRX9GJ+T+V7FWUoSrUIgkIik5ha3RdHnEx31wmh4kNB87gI5C7Pe5cLDjYWSI8Nxt3Lkwf37lIzeCk6K/KyiY3OQwmvzdpT6rIzyUwvYXV5isy0JLWCf91y6kKzu0i0vwggmgaEgPvjz/05YD1YJMzrOdEZZexdGRZ3drQtAssEURF689sv75FS3oHGfFUwvsLJ4foUUYFeFDb0oXSEvOJ0f4syETh7uz3hm2cvyazrQXOlt8ak2aapIg93H38e3/2ChMI6Tgx2lofahHgJpWtwXD1ve26QlEh/Rle2aK0qIik5h+n1A/WzV9iMZ/TVZOETnsT48vt7xCWX7M71ER0eRExJ+7t5JYI2xUZfzQtUsBq15EcHcNcnhpn1N0PEXqEEZRPtFQQEhNA9taH2wJxtzRDj40tSUuHlSW9j2CYvNZKAhCw2NG83FDhpzAslOTOZ1RNhhQ4d3Q35JGVnM7G6QVFaHPkVre80MBg1O9TkxOMfW8DR5zrU3bxNQWYMbhEZHJlu9hOHm8tMTU3Q19NOrPdLPAPSWD+/eV6rwsnOEtlxQcTlNnKsuzTCzopkAkP96Vp4Iwzfh8N6Tm9NHl6BMSzvn2PYWyQvKZaKjkmUq51vTFCek05RwwiTvY1Ex8TRM7nKdRfoYHGyEz9vX+p7p4Qg+evwgR8SZSTH5EAjob4vePHiqRA2viyfnKqfHS70EeTnS1pJi/r3H8N5YWasqwZPUTdObGrUOvRgrpvgAF8yytu5qR/cbjoXthJBeEIec9vXGy0UG++oKyc0OJX5xV31WEd1BcnRKaxsrVCcnURaUd3r8vQK/dE6lTlxhCZms3n0GdqYiDF2FmaYmJyhvamCUK+nxGSVce4qrBtT7Xg+vUtudb9avytoD9ZJjw7GIyybc8P136twujVHXmIoMemlHIk66m2sGsUnRRMcl6vGBSbtLjGBnvjF5LJxcCkcDYerZMX44ReWwNi8EL6h3rQNTanv7XhpiJzkSBq7ehgdHiAuMoqB+ct3cpWJjirycnNomNj5+KJTmSxeK35keECYMHIvfMNi6ZradH3qRHu0SpS/B+HxCbx89oAEkelbb6ZNSSQSyWeAk4nGAp6+8KS0fUw9sjzVz/M7D3nmH0dn7wBLIsjU3lARvMJ8vEJSTDjJBXXsK8LJoaexMBk/UaF3TK4xNT7GyKgy90uZM/EmxNYfblCdl0ZobBZNTdX4ez6jsndeDaS6KguJCIhieH6dhZlJMhJTycuvoqwgjayiShY3D9UFA64unGHVa6jNjiZIBNlLO5eBxeeIw27l5OSErfVVeprKiY6Koql/+nWP0YXpjK76EkLCImjrbMHPw4fUkuYbK1unw8JUfwMvPYIYnF52HRX3MJ7QVl1EWGgUzY21+AYHkV7Z/nq+54XdxGBLGWHh0ZQ2tpEe9YzEgmoOxPtTelhTYwLJK69iZ2+b3uZq4qOjaGlpID4hgVbxrHqrXYiiq0GtnaOVQdzc/KntHOdah6rkHTYnegh8+ZgnATH0DwyxsLSG3vj+HmLd8RZJwR74iKBq7eDN8MBX6E8PKU9PICwyTZ3/paBZn8Dv+VPuPvGjb2yM2aUVzvRX7FizSbYiZJ+8pKimlcnpeQ5Ozl83SI00ZRMl7LplYJKNxSnK8tPIyMmhpraCmJQc5l/Z4NXx1CIwn+yq5eVTLybWjz7PxgedMrQ5kC8fvKCysZ2JqTn2xe9+PZBA+BSlp1fEkWi2lkkP9MLdL4W194poJ8vjPSK4fk7LxAav3mJzYTz37/6B2Lwqxiam2Ng5xPaeFrj1mUFCPV+QXNyiLsTmON+hOCWOpMwydQ7ueE8zWSnJlFY3kZUcT1FdNyfnBpH/1w1N6SFXepGSC4VY0b+/PP0wuGB1qo/khCjSC4upLkjG092PxaPLIZT9tVkEBIVS0TLEqhBJg0MjLK3vvLeHXhmuWVuUgm90DmcuW+0qTSYoKJCqjkEWF2fVayxviGu43rPdrBVCx4eHj9zJFcJ0anae3aPTSxu7sDPR2UCIdygtbQOcHKwIG8snIT6L5voy4hJSmdo4Fc8jysO1OQsGxrureOkbTt/05nvWA/iEKD28ShL/3Z7pI9r3KZEZpS7R6VRFp/vj26QVt3Lqahw+210gLfQlDz1C2Na+bWdOZke7CfEPpKxx8Ibf61RHGXi5uVPVPace0a6Pq0N2E4ubOXHNQ78waagrSsY3KJyukVkq00LIKq1iYW2L/uYqMhT7KismPy9XHSJvsdnesa+TjSnKCwoIS6udtX8K0VmTEkl4cAK11VWEhYUINV952fJk0zPZVoyHbxh1Lc3EBLgJhV7EphSdEonks8HJiXD2kQHP8YtIYW7L1QK8NUVcqBdf/O5rbt+5zcMnz0gvrmfn5IY5JMriGXMDxISGUd48qs7XvDAfUpIUyq3ff0N0SgbeHm7cvXuHl/7hDEytXFa4DgN9DcWiwhYV58waa/MDBHo+paJ3Tm15PFmboDA9nhAhilKVxTZyC6ksLSYqNoW2vlHmZ6fp7uhkan4Ng2uS24XNyFBzEY/8YhmZe9UA+LlxgWZ/mcK8TCGyX/L1V98KsVcvxN4rMSAC2LEuIgKDKG7uR3O6RZCXCCKLmjgxvxsM2UzndIjgySMqlZl1pZdZ4YLZgSbCQ0LUIV0n4n36Bweo82pOXcp2fXqQ2JAgsoQQ3T3aJT/2GfFC1Cui0+nQCjFcQnh4CLFxieoCKPmlFeSkxJNdWMP09AyTIwP0Doywd/xGAOk020S8eEF+Rdu1eaWSdzlanRFBlg9fff0t927fEUHpU9KL6tg9eTdIUObMdtXl8fTJE6o6JzDesJrQycEyiXGRROXUcnx+GbQZjlZJiwvh3377e+7dv8cDYcfxmaVsHupEKRMlTXdMU2E69259y9ff3OLe3fsERSczvrSnBnbmoxVKclMJDwsnMSmVjNwCSstLSEyIpa6jn/m5GQb6uhmdWkBreCNw1qcHCPF8RN3ICsZ3i+ynR39Ia2kGd29/y7ffit995z4B4YmMLm6pQyKvotlaIsXfXfiu7xGdFyZG2mqE0A5kfvdNY9eoOOZ+9xu+/vob7ty9xwuvIJr7JoS/uh6eOm0aWsvSefTMh75XvStOB+N9LURHhAnhH0daeha5ufmUFucRn5LN5MIKk2OjdHX3sbZz/DoQN2l2aMgTfjM2l439N0MDf4iYRPnPT4kRdVA+G0rjWW02Hi+Vns7L4bUNuRE8fXQb39AYgv28RDm4zWN3fxoGpjHeMHH6eGOenLgwUks6X48AqhQa4Mmdu/iFRhEc5CfK1C2eefjT1DeJSZlXe2GlsTiJh7e+4pvv7ohycB/fiERGlnZVYWY4WKM8O5GIyGgSEuLJzcunqKiUtMQkShp6WV6YU21seHye8ysNRosTA/g+9aZW+IPPuUd7a7qXCO/Hqug8c40QNhyukRgsBOYzbyqbe9naXKOlMpu7X/6M+y8DWH1nRIGZwa5KfJVG2PEVRdNe4+JsjYyYINwD49lQbyJiksluPN1eklvTxfmrEUIWLe0VuXgHBNE3tcTqeCdxMdHExsaSkJJOSXk5uZnppKblqgt0TY0O0NPbz/ruEa+y2Hy+S0dlEeFBsbNL9k8gOquTwwkOiGVifp2qwlQ8/YKZP7ZxfrxJapgPcTmVLK4skCQyOFIU/K3Pd96vRCL5IeFUVt8eJSnCH/eAcHWolk2NXJzCtxk5Prycb7QwPUK+8HNKUJpd1Ymy8OJVnHYrCyPNhIYGUd+/cDnEyLRHRrgHP/q7n5JY2MDi6jpTQ+0EvLiDR3g6C1tHrE50q71n5a2j2C6EEFsfIdjrOXXD85c9BQ4rh9srdLQ00dkzIETmOLlp8WSV1NBQX0dGUqxaqQQGx9A2MINFefQLG7N9zTx/HsHg+JJylc8QJ+cnWzQ11JCRHI+P+zNRmcYzIIS3wuHGHDnit6XkVKnzeGxWDX6ePqRXdr2utK9i0R5RlxNPcEoey7uXLfjbi2PqokKZxU1q8GQ6W8Y/NJicxiH0ombU7a9QmJZAUmYJxwYzRu0h+dFPSS6p59D1gs2GU6ZG+mhqbmNkYorullpiYpJpbuqgsiCT0ABvPLy8yS6uZlsZfikwajXkhDwlp6xWXOeyR1VyMzazsLGDPTbW11mcGaM4I4av//CNsLF2zq40Lph1R7RU5ODp/lIEu40c3ziv0MnR+jixMcGkChs9Nbh6s21mde6SMmdweWGGUhHc3vn6a2GTTZwovWAXDnSnJ+xsbbKyvEhLVQFuD2/jHpzImjok7YLzgy362lvo6BlkcnKcyqIskjLy6WptJDslEX9fT3yDwqjtGuHctSz1zsoUsZFeFHfPiGPqoc8KxWfpzy5/96r43cqQ8ef3vsUzKJ6l7csFW15xvLlI8h8TnZZTumoLeegVz9bJK6EnBIVOy+7WFmurK4x0NxImhPjtx170Tm28Xj1YYW9ljPggd4Licjm5ssqaVZSRJVE2GhsbGRqbYKC7jaSEOMob22muKSUiNAQPD09ikguZWblscLrQHzNYk0dgVAqLws/+ULEaT2kqzSYqLoXRFeEX7XoG63Pw8QgQ/upSdFakBPLLn/wjPtFZzK5uMDfeT1ywO7eeBTC8uK02zFxla2mchIjLKSOXIzkuKE0O42f/+DO8IjOZ29hkZqSb2IDn3HvhL66xqzaw6s+O2d7cYHV1mc6aQl4+vsNj/wTWj3SimAgbFPVBf1cLrZ19TE+OUVueS2xKDh3dPWQkRBHg7YWXVxC17SNoXW07ys4ZsR7PqWzq/96Vdj81G1M9hL8Sna98wYWdpfFeQrxf8uD+I0IjIwgJ9eXOd9/g7R3Ojv4t0Wk9p7smn6CIaIYUse46/IrZ3mpRjz4nvaJdnVagzJfeGOnA4/kL8mu70L4WnTo6qnLx8vOnd3ZT3TJOWaG2saGe8Zl5+tsbSU9KFeKzQYjgEsKDA/DxdCc+LY/5jcvpJHbzKWPtZSRFBc02Hn4C0VklgrEA3yjxQBo2pzsJ8fcjp66X6bF23F960Ty6hPZ8j4TAZ4QlSdEpkUg+PcrKbbNDrYQF+BAWLSrlubXvXe1Qf7ShDpHxDk9kbvf6suZKADc32EhwaACNg0sokuXCsEN2pA+3b79gYveV03MwWJ3Goxe+ZOXnEe7zkP/1T78gJD6Twvxc4sK8+c3Pf8LjgFj6hQB7VY9azSbORWA81FZNXGI6vX3tpCanUlbbzOzMJBW5SUQmZrF4oBc62sHiUAeej8MYHFm8vMBniLIegE6rRXNyxO7mDJGBHgTGZbO4vEJ5ahj/+A8/5qlfJKXFBWSkxfKrf/0F3z7yoKZj7HK7hSuYzw6pToshPDVfXfTArtslKegJP/rRP+MugujSwnxS40P41b//hjsi79sH+slKCuMn//TP3H0RSHl5KWlJcdz54p/58t5zcut6ONFdVvpOhw2T2czu2iw5KQkiwOmksqSAlPRcugbGGWyvIiI6isLmUfV8s/6Mgtjn5JTXsP85qo3PmPOjLWI8b+MVnsTC7mXv8dn+CqVZCXh5+1FS287he+cUCtG5MkJcdCCZIsh6JTrfRnewQW6EN498ItWGn7e5MJ/RVpHGvXt3aBpdfz1M1GoxYRHvdqK3heSkZJp7BkRwlkBhZQtTc7OU5WcTEpnKwMy2ev7+xiwJsYEUtU8JAf1+v/K54DCd0V6WyoN7T2joXri2pdOfIjqdphPaRVB83y+Zbc37ehftzA01cvcP90gr6+D8ysIwg01luD19TknL8DsBtSJKlD1Sj7aXqCvPIymnlM6mGvEe4ujsH2R2dpg44XvzSuvVuZ+YThlvKiQwOoGFzVcjH35gOK2MtRTwxc//mV9+dZ+MggpyM1PwevwtP//ZL0ktrGb7WEtlRhiPHj+hduBNA+XSWCN/+N035DUN8fZsho2FUWLCPClsm3SJTidlCcHcu/OM+r559Rzl2Fx/Fbe+vUteXT+Gt+cZWI1CeGbwzVe3aJ3cuBwZKVAEkNVuYrK/lfTUJOrb2iktyCQlt5LRsUmai5KIiE99vVDc9sIEKf5PqGjqubFX9nPhRtEpUOKG3bV52hrraOnsYqC/g6TocHxD04X/esvPiTLdVZ6jjnoaWdm7biMXBkpSo3nhEUD/7KX/UWzqYLZXbajLqGpH41qy3WHS0FiWhpd/IEOLx67rKPZl5HR3mdKcDPKKK2hqaSY2JoG+iXmmJvuIiAwn0zU1xWlXVi+uIDkhYLZs8xOJTn/vCGaWD8UPOlJXFHv27AVBQcEERaezozFg0+8Q4/uIsGQpOiUSySfmwqLO2YwJCyK1oJKlP6k13ExBnB9eITGMr1/vCVB6JFdG29ShnHXds2qPo9OioSojGn+/EFavjBZcHahWV/lLTEsjKy2aW3cf4h8YJM7z5f7tr/iH//nf+eUtN+r6Zq/Nq9laHCUxOpLqzhE2JltJSExlcOGy8p0fqyUoMoKeuV0cNitTfXU8dQtncGJF/fyvgfyEAO6/DKa9d0wE/Xk8evAAD98AAv398H5+nx/93d/yk1/+jvRSUfGdXxedVv0xjYUJBCTlsLx/hv1si7ykUB7cv4eXX6B6DbeH3/G/fvQP/Pzfv6K0ppqcnHQePXqMu6cvwaKu8nB7xs//8X/wDz//LSGZVey7ts5RsBlPaa/IISa1gPW9VTJyU0kpb+VciXMMOyQkJxCYXq2ea9KdkOr/lJzSBo6ULlXJn4GdwqhneIbGM7+rw3y+T3VhGsEhEbQPTr+16vC7HG1PERMbSmJZ2/tXD7aZaMmJ4ZFH6OutWd5mvq+KJ/e/pWZo5bXoVDhenyI7JZ782g7Wtud5GRRLx+TlEPaFwT6CvKOobbmcE767Mk50sBtl3XPvjIz4XJnvreLpw0dUdkxfWxDmT+rptGnpqS/liVsMG1eGm7/NmXhHz+8/JqGwCY1rnpndcEhJWhQvPEMYXb0cqfAuVgZaKolPzmBkboX2knSSk1PQuvabrEr3JTk9lWPhey/0R/RUZhEQlfJOr+0PhgsT423leLk94cETN2FDIfh4ufG7X/8L//Nv/g6PqGzhK3V0V6URGB5B7/ybRdHOtse4++1tsuv60L3lwnaWJkkO9ye3dhTX7lE058YKHxvC8MIbezpa7uPp/UdkVCq9bO8OfV0eruPOV9/SMLpyraxptufIFYKzsKqVjcURMpNihCC+rMd0q+2ERikr4F4u7rY2O0b4Szcqmgcx/xWKzjdcSj9ly6/Y0AASSrsxvTX8XFnErLe+kICIaPrnr/dA6/fmiArwIDguh93XdaMT7f4CQZ5uhKaWsn92aSdGzSYFycH4BkezphECUj2qYKVfXD82MZOpuXl6u2rwis9XG9AV0jJTCUwtZuVABDPC1ocaS4gJDZltPfnootNMWXwQHi+CGJ3bEkecLA3V8u2vf8x/+9HvKGkaUiejmzRrhLjdIjA+W87plEgknxTd/hrZsYE8841k4+RSXNhtNqwWq7ogiNNh5+Rgl5WVVU51Jkz6M0Y7q3n28C5JBTWcvL0fsdOBZnVUOOFQdWuAU2U1SOF+J7prCHB/TlpZFxqdkdOjLXJjfEXwFkXPxBLHR/sszM0xOzPN9PQEdSVpfPPlrwjLrmJNBG6v5m0oe9+VZCcRlVLE3qke3XofERFRNPTPYDHr6WrMIzw+maltrfDJJvrqsnkRksTE0pWtQz4bbJyf7LO0ss7JuVHtQVLmsiqry/qIQGh+4wTdqYbV5SVmpkW+TE0zIkT0t1/+nhfBiYyv7AsxfrXKFfWxRUdfYwFuAfFMrYjfbDdzuLfN8tICM2reTtHTWMjXt77FMzKVuZVNDg4PWVleZn52hqnpGfo7W/C+/2ue+EfSPbn2JoixGRjvaiA8OJKemW0s1nNyspOJTC9m40gvhMgEMQmxJFb0qqcbNav4P3enqLYH3dXuIsk7aA6FjS0vcKY3YjGcM9HfyLO7t0gqauRcBKqT7RW8eP5MBLg9l19wXmC1WrHZ3lq4x8Xp0Tqp8ZFEpJRyJOxE4exgX5SlNY7O9dgseqYG2vB6co+w1DIOhICyGM5YX1lgd/8Aq83K5two8UFePPMIZnrjWB0aqGA8P6CqIJ2IuDS2jrWcnq7j5RtCedsIJpOJgeZagkX57By5DJBXJrrxffaY9umta72GnwsGrYb11SV29o9EmTazuTBOfKA7z71CGVu6vt3S0cY88V5PeeIVz8r7RKfTymR3A95PvRh/lW92A9tb66xs7okg2sbpwTpVGZHcfuhO4+D865ElBwuDBLs/wicyncP39AorW2fERUVS0jIqfIaBnopMdfGxxY19TLpjcuL8ySwowiC+bhB+tiI9gnDhL7c0P9DtEpQhq6cnbKyvivplRvWD48PdpIS+5HdffE19/zQ6YWP7872EBHgRkVbCgfDHes0uDYXx6hDoronVa0OgFc52lylMjiAqqwGja6+S7SkhBn3ciUsXNqXUT5otanOiefTcm86pFSzC7taXV9naPcJmt7MrxFVquBf3X4Qwu325Aq6CVXtEVX4qUYnZrB+cc7YzK+w5Qth/v7r411JfORGxcXROXE7DWBjr5qWbPw1qA+1naGQu1iY6CXp5l5CUQk5fiU4RH+j15yK+MIhnt6kr2GcnhOHpF8bEhsiTd36OjZHuenwCwmgaeNWjfMl0RxnPHtwjtbTlmq+xmc+pyIji/mN36rvH0Rv0jHXW4OP+gvTiZl63BYhnWZ/uIyYsnNLGAeFjzQx3VuIWksimskWYKBMJSfHEFTawp7Xg0G7TWFpIYEj67OHHn9NppiIxRPyIEEZU0SkKpSgokd4P+OKuBzPbl8PQdEerhHncIyQxT4pOiUTySdmeH8Xv8dd8cfsROXn5FOTnkZGRTnpaOr1j85hEJbA2NUBqXATRccmkJifh7+1OUEwa0+s3947YtHtkxIQSKSqWjcNLJ6c469qCVNyevyQ2MYn4uCi8vHyobh/mys4gr9mZ78P35SOqBxdf97AoCwMNttUQHhFNz+ymqCCcXBj2Kc5JIyougaysdBLj4qlq7kcrtLBFf0JhfAAxudVsugT154UI7BeGSU1KEM+fTEa6CORD/PEOiaZzdIGbdnlxWHfxcfMgpaz99ZLzV3Fe2Fga68TLzZuWwbnXw7WuYtqfwTvAj4za3tdzgq5iFgFPdvhDEgprOb7SQ3m0PkOOEJVphU3qgjBOIXzGO5uICw8nOTWdtOQEMnKLmBFiWZlPuz3ejJtnCG1D8+8EbJLrrEz1kRrjR0x8Iukpio154B+RzuyGMvLAQXVWFL/9za/xDImlpKiQrMxMUlNTKKmsZd21Ou1VzDoNjfkpBAbGsLh12WO2MzNKdmIcYTHxoqwlE+LvhVdQNKOLl5vaq1sv5MUToYx6SM9Q5zC5ewWqi5O8CqqVZ5npbyI2Kpra7st9fK2mcxoLM9RtO7KyMklISKCguo3dUzNOm4GBxkKeu4eytHd9KP7ngmZ/VYjoVMKVDeHTUoUNit/t4Udd59iV/RAvOd5cINHHjec+Say6Fmi6ia35YaL9XlDSMY1RKfw2LZ1NFeoK1IkpaSTGROD50p2MkkYOr2yBMd1TzYsH94nPq3/ds3IVs3aX0pxkYlPz2VT2QxJ2tjUnfEhCJLFJaSL/04R/zRK++3Ll6r21aXWriJwqZQjvBw+D/3qw6+muTMPdzYc1jatcWrS0VeXh6eFGRHwKSXEx+Hh7kVvdzslb25Qo2I3HtJVn4uYXze6Zawio9YzWyjy8xbuNFHVSgqjnfLy8ya1s5dRkwWk+ojIng5DgcNKFDUeFBOLh7UdV1zim19uRXTA30Ex0dAw13RPqEZOoy5qrCwkODScjM4eE6Gh1iP3embimVUdfQyHuwbGMrxzeINI+H9YnuwjxeEi4EPZvRKeFpekBcrIzSRTxRVRoAL5CUNZ1Tah+6SZWpwaJDPAnq7z9Sj1pp6kgjgf3n1PdPeU65kLUVfurE8SH+Qif5kN8fALBgf7EpOaxtP3GfyoNamUZ4r2ll7DsWhVcaXxLjgwnMTWT7LQkElIyGZhaU3ul9xcGKMrJIbG0/+NvmXJhtzPd105zQztbB5eFWNlLa35skIGhEQyukmAWhaejoZL2vjFO3zPqRSKRSD4Gp0fb1JRkEBwUQEBAgAhSg/AXztzPz4/ajiG1tVx7uEltURYB/n4EBoWTX1antr6+r267cNjorc7FPzCUrpG51yspGjT7tFbnExrkLwLfJFr6p94z/8TB8c4yjTWVTKzsvR5ypCw3PzHST0vXAFc7AZT5TeX5aSKgi6S8oYtDRUldKC2Wvfh4uNPQP3tlw/rPi/PjLRoqCgkR+e8fEEJabhnTqzvXeleuYjMf01BbS8/oPAbrzWdpD1ZJi/QjPreWjdO35/05MZ+IezY1qiv2vVrp9ypmwzkDbRV0DU9xbnoT+m6vzNPW1MLK3pthfxcmLSMdDcRHhZMgKvCpxcsGV6Nml8rUKMITslnded8wQckrTvfXhI2lEejvS0BwmLog09orkea0MTnYJvI4GD//SxsN8PcXAbE38alZzG+/uxWJMk97ZayVIF9fqtonRUDrwHi8Q3NlIYEBfqKsBZNdWK5uc/IKZW/V4c4aYsL88fYNENfOEcHV4nXxI55ldqSPzu5+joxv7qo/2aS+PJcIIdwKKhrVOXIKxxuzZMb4E13QyPn3bAHzKVF+92hPAzHhgeJ3+xOXksXAhPjdN5iX4fSQvqY6apv6OTHf0FrmQn+yQ3l2LIEx2aztX77HpekhUuOj8BZBb0RsEo1dw5y7htW+QpknWFVexfD0ZQ/W22j3V+jpamN0fv31O1fmWs+N95KaGEtEXDLd44tC6CqjHoyqHXt6BdM3uf49+yP/8FD2kl6c6KOupoED3ZsGSatBQ39bNWHBgQSFxVLdMnDNB17HxsJIO37uL2kYXsLsmgJi1WkYaK0kMsSfoIhYatoGOX31nu0G4S8biQkNwkfUsXHCxvomRVm7ZsAOJkf7aO/qV0cgvEJ7vC1sLI/IiAiyShrYOr5s0D1YniA9OoT47Aohjt/3rJ8Hmr1V2huq6BxUVvN1HRS13cbCGBlJ0aKseqv2NzS9cmOjyysMot6szonDPypFiEOtyxYs6hzYiuomlt8zlHx3ZYqC9Hh8ffxIL6hkdffqeU70IkZpb2xkamnz9f2dNhNLoz2kxkUTE59Cr9IgrLwwu5nWUiFEs3IZ3zr/+KJTIpFIJJec7y2SFBNGSn4lu645FB8Tw+ke1dlxBMfns3H4/nlV/xlx2k0Mt5QTGBxJ88Ds6+XdPxbKkOwFdR81P2o6Rq70kkk+JmbdHqUZsUQnZQtx+fH3yLxQejlbK/D3D2V4ae+H1dstRP/CSAeB/v409I+LAPvjt3rtiwA7Mz6ChOxKDm9c5Vjyv4vuZIeKnHhCE3NZOdB8dBtz2Ix0N5QSHBRO59jKR7//p8PB8mgn4UGBVLQNv7Pt0MfgZH2SxMgQKpt7cDidUnRKJBLJp8PJRF8bRWWVTK+59pr7aDg52FygpCCPofkN18qCPyyMmi3qyouobR3i2PBxVadJe8Jgez2l1Y3sfZbDmn8giLBnf3mUooIidT/cjz2fUlkdt72mjOq2EYw3dRv+J8eiO6GzsYKy+nZ2NR97LpWV+YkBSorKmVje+6yHXP5Vo0wjWJogPy+PkaWNj25jyiikltpKapp6OP8rWBn6PxKL9pDBlip1oaX9s49dzziZHmijrKKKrVMTQmNK0SmRSCSfEmV/z4ODfY7PtMr0y4/HhV3dI3Ln4Ojaqrc/LJycKXsu7h+hf+/wsA+DxWLmYG+fkzO9DHY/McrQy/29PQ5PzrB95PGVJr2Wg90dDG/Ni/zh4MSkO2N3d1fY4MftabxwWDk7OeLwWINdGuEH5cJuU/exPjg5x/qxbcygY3//kFPt+7ZP+s+MsC/x+ze394R9XV199mPg4Gj/gKPjU3UqjBSdEolE8plw8VEVp0DcT/h81x8/bJR8+Nh5odxOxrmfF5+mHIh7uv7/g8Z58Wny/iPf84eOutq7tLGPzqcp607xvl3/FYj7S9EpkUgkEolEIpFIJJIPgxSdEolEIpFIJBKJRCL5YEjRKZFIJBKJRCKRSCSSD4YUnRKJRCKRSCQSiUQi+WBI0SmRSCQSiUQikUgkkg+GFJ0SiUQikUgkEolEIvlgfK/otNvtmEwmjEaj+q9MMskkk0wyySSTTDLJJJNMMv2pSWhMbDbbrMFguFl0KmJzZ2eHzc1Ntre3ZZJJJplkkkkmmWSSSSaZZJLpT0pbW1vodDo0Gs2s0JQ3i06Hw4HZbJY9nTLJJJNMMskkk0wyySSTTDL92UkZPWu1Wt/f0ymRSCQSiUQikUgkEsn/DkJjyoWEJBKJRCKRSCQSiUTyYZCiUyKRSCQSiUQikUgkHwwpOiUSiUQikUgkEolE8sGQolMikUgkEolEIpFIJB8MKTolEolEIpFIJBKJRPLBkKJTIpFIJBKJRCKRSCQfDCk6JRKJRCKRSCQSiUTywZCiUyKRSCQSiUQikUgkHwwpOiUSiUQikUgkEolE8sH4DxCdF9gdF67/f1rsdismkwmbzeE6IpFIJBKJRCKRSCSST8lfLDrNhlMWpkZpaaijorKK1u4Bdo/OuHC6TvgErM2PUVdTw9z6GTarmZWZESam5jk3WFxn/MdjMWlZmBhnc+8E26fW3uLVaQ83GBwcZmP3mM+jKUAikUgkEolEIpH8kPmLRKdJu09zRS6+nh54+fjh/vIZ7l4+NPRNYnZ8OtXZXZeH2+Mn1PfvYNCdU1eUSmpOORuH564z/uPR7C+R7OdNVesoOuvH/e1Oh4WjgwP2Do4vD1xYWZ/sJCoygc6ReeQ4aYlEIpFIJBKJRPKp+fNFp9NBf002D+7cJTAqnb6RCUaGemhtrmN0bhWj/dP1rw00l+Ln7kHTwCY2q5W1pRkmZ5c/aE/n4dYUft/+gcyybs7MH1d0Xmg3qSzJI6+m03VEiOCDLcbEO9k50MieTolEIpFIJBKJRPLJ+bNF54XDQmbgC2498KF7att1VFzo4oKLC+fl8FrnBRaTgZOjfXa2tzk81mB5Nc/S6cRmMWMyGjGZjOKcQ/YOjjCYrerHZqOeg709Do6OMVnfPIrVasFoNGER39WenbC7uyuue4r1yvxNRXT6e3rR2L8unuNCXMuAQXzHIR7KeeHALO5nNpkx6nXiHrvsHx5jtNhc377EeWEX19e4rq8R9zRiMBixWG3Ko7/D0fYsQfduk1PR+17RaRW/6ehgn+3tHfWaZtu7WWw1mzg+PFDPOTg+Efl1+Vw2q4nTkyN2drbVHk2j+fK4w2Zhe6IZr+f3eOgfz+ahBoPJisNuRa83qM/7CuU36c5PxW/aYXdvn3OdgTcd0k4sZpEnIp/M4hlOxDPsiXO0eqMUrRKJRCKRSCQSieR/mz9bdCrzBhtyovnmq+9IK27h3HSDgDKd0VtfQbifJw/v3+O5hz+VrYNYhGpTxNJYew3psQmUlJQRHx3Kg4ePSC2qYX5pibaafDxfPOb+oxfkVXVyorsUoxMDXSTFpVFeXUN+ZgLPnzzkyUtfqtsG0ZkuBdZAy6XobBnaxWTQ0lWdQ155PXunQoSdbVOdk05WWg5VVRX4uD3i4ePn5Fa0cqq/7Al1Oq0sjfcQG+zDg8dP8Q+NJDsjg8DwBBr6pzFY312gSBGdwffvkFt5k+h0cn6wSW1hBr4ebjy4/wA3T19KG3o40r7pfdWdbNNQloufuxt37tzBLyyGmY0d8Tw2ZgdaiQ/z5/HDBzx65kFueQsanZmjzVlSgh/z9/+//zf/r7/5MY99oukamUWzOUFydj69E0vi7uIJLiwsTPSSFB3MU5Fnjx4/IVzk49DsBlZVVZrpaqomJi6V6vpaYkMDeHDvPsFx2SxsHavXkEgkEolEIpFIJJK/lD9fdAp0eytkRPnxuy9+j0dQDF3DMxgsr77mRHe6R0tVPc2N3UxPj5MdH8yL5+50z+xis5joKEri13/3z0JcZTAwNkFlXiLfffMVv7v7nOziMjq7O8mKC+Kb7x5R1z+vXrWvqYhf/eyX3HYPor1vgMG+TpLDvbj74Bk13VPqOWOdFarobB3ZR392TGGMO17hiawenGM7WyPoztf84l9+T359NwO9HaRH+vHtN7ep7VtAka3LYx0EvHyGV2AMrT2D9LTVEex+j//ydz8nsawdg+1dCfZ9ovNsf5X8hGAev/SjpKaZocE+SjPjeHjvIRllLShyWne0RUFqBA+euJFVXE1Xdze9Pd3saTTotCcMdzXR1NzC2OgoVQVpPHn2nLLWEU7OdMz2lPPk/rd88yxQiMx5To6P2Rpr4JYQpxVdE+ozzA424eP+DN+IFHr6B+luqSXM7yWPvYIZXtoRZzgozozk7//p5wQk5jI4NEJNUQa3fvc7ItLK2T01qdeRSCQSiUQikUgkkr+Ev0h0Kpzsb1FfmsNzIXq++uYWcVllbJ8Y1c8cdhvnJ8fs7+yzu71BSVoo3/3uS7JrRrGaDHSVJPOHX35NccOQutiN6WiB0Jd3+Zdf3aVzbl8doqtZH+HpH74iOqcF5ar9TXn87rdfEZnbgME17PZkbRjPp48IjC/i2Ohgqq+OAE9PVXQazo8pjvfCNyqZtQMtVs0KQQ/ucOe+P7OHBvFtJ7uL4vsPvyYuv5XT02PKM6J49MKXzsk1dWjphfmc7sosfvKrL0ksbUNn+XNEp52xjgoe375Nbv0w5+bLZzacHZIc+hJ3/yhmN46Y6anjxZPHpAlRe6Qzq+dc2K1cXDiwi3zUnWs4ONhje2OFtqo8vvnqd4Sml3OkZMrJHHGRQQQLcXiJnY2Rem6/8KJucB6r/oi8WD+eu/szuHx0eYrDytxQE88f3iGlpBWDw0x5fiS/+PIW5b3z6rBb89kB6YFPeOIZwujK3uX3JBKJRCKRSCQSieQv4C8WnQpOm5HFySHSovz46quvSShsRGu0CLGjYbC7mfzcbHKys/F5eZ8vvviKrIphzAYdXaWpeD7zZ3hh8/I6xgMKYoN59CSYeddCsw79FiEP7hKSXMeJ+Lu3vohnT/xoG9u4PEFFS0ZkGJ4BKawcmZkZaLoiOk8oTfQlICaN9UMtpsMFYjxfEpNYiM71bcPpDkkRnsTlNbM5N0VKZAChKQVoXo98dXI0M8Djx26kVrRzdsNQ4veKTvsp9blJ3Lrvx8rZm/mVCn0NOfgHR1HXPkpzSRLunn5Mb7/bo2gxnjE13EVhfi6ZGWnqcOV/+/Vvhcgs41DvwHk8S1xEAEGpr0Snjc3RRu57+NMyusDp1hSRns+ISytWe3Jf4TjfIy3Mh6CEAvYNx1SXpPLYPYy5w8tncNrMtBVE4OUfSP/c1fyWSCQSiUQikUgkkj+P/y3R+QqbZp3E4Gf84YkHkwurDDYV4+MbSEpuKUPDI1QVpfHi4QNyKocwCdHZWZKC78tARhbXL7+v3aUgJpQXbhHMuUSn8XSN4If3CEurV0VnjxCdz5/60zF5KVRV7CekhPrjHZLGxqnVJTovh9e+LTqNB/MkeHuQlFqE3vX108MNkmP8SSrpYGtmnKRQH8JSC3ijEZ3sjvdw994TUirahKi8WXQq4riwbhD91Y8vzoToTOS7uz4si2e7Sk9tJgHB0TR0jlNfGMcLD19mdy97Od9gZbSzVt2WJiY1l76BQbobq3F/9JCIjAr2dXYcB5NEh/oRnF7p+s4V0Tm2gEaIzgj3p8SmFF0TnbbTXVJCvAhJKuLQeEJVSQbPvaJZVLtPRbaa9bQWRuIfGsygq2FAIpFIJBKJRCKRSP4S/gLRaeNwf5eNzV3OdEYuLuwcrE6RGObBPY9AhidnSA99xqOXoYyvnarfGG4q4umtb0gv6VdFZ3tRohBP3gzMramf2863yQ7358GjQGY06iGMmhX87nxLUHKtKjqHWgv5w++/JiqvHr0yf9RhZXmwgWcP7xObV4NWqKrxziq83V7SPLSnzuksivPEJ0IZXnuOYX+OKLdnRMfnor28BacHa8SGuhOT28Th3i7FiSHceeIphO2qOrzWoj2gIS+eH//8C1LKO9DfsA/n4dY0/uK3ZZZ2cqS3qUOLrerKsXbGOyp59N0t0iq6OXYtVnR+sEFMwAv8wlNY3dUw2lHFo4d3SStt5Uhrdq00q0F/sk1efBi//+4RrROXvY17cwO8uHebwKRi9rR2Lo6nifB/iVt4pmu+qU0dXnvrhSd1g3NY9Mfkxfnx+JkX3bM7KMsgXViNjHVV8/zRA/JqejE7TJRkx3H/RQgz+5dy3G7W0ZgTjKe/P/3zsqdTIpFIJBKJRCKR/OX82aLT6dTT0VBBeEgESek5VFVXkBAVzBNlJdjqHg5PTmnIj+PpUzfS88vp6+4h2t+N3/7mC9LLLofXthUm4PbQk4HZN6IzK8yX+w8DrolOXyHmApJqVNE51lXBF7/6NyGofKltbqahuowQrxe4+0UwuLClfmeouRiv5y9oconOwlgPvF0LCRn2Z4l8/oSouOuiMybEjbD0Gg7ObSyOtuP57CHPfEKpamyjua6CKN/n/OgXX5FZ043hcrnXaxzvzOH5u38XgtmbzIJSKstLKCyrYnJpkxMhMEvTo3j01IOM/FKaWprITY7kpYcfle2j6vfPDrfISw7l4ZPnpGQXUV1VQUVlOUsLi3TWlPL86SOi0wroaG8lNyGUf/vlbwhILmZfawXTHvmJQXz5h7vk1vaws3fI1kSTKjorOi8XElocayfAQ+STfxQ19c3UVpQQGuhFQHQai1vH4gwL+SmR3HsWfE10NuQEibz1o08Or5VIJBKJRCKRSCT/G/zZolPpwZsa7FS31nj86BFPnj0jICyauo4hzo2XW4qc7a9QmBnPs8ePCY/LorSikuLCfHpGVrFaTMz0NVOUU8LyzqF6vt2oEQKrnKzsCrZdY18t+gNK01KpaBlFj5O+pkqePfAgMSOLtMQI3NzcCI1LZ3R+E7urA3Jpsp+S/AIml0+xCHHb11hMSW0Lh+dGLGc71BXkUd/YzavZk/qzQxqqCqlpG0ajV3onHSxN9BAT6scLdx9SMwupLczh+XM3Spr70d8gOvVnBxREh/P4zl0ePnrM08cPefDck6LGXpR1h4zn4h6l2fh7u/NUXCc4MoHu0XmMV3JZWcG2riQHbw83Hj99RkJ6Plv7Gmy6E5rLMnEXYtk3OIri0iqK8/Kp6xriRH85HHdjdpAofw/c/GPon1jmdHuGrKIyBmcue2uV3s+VmUHSYkN5Ia7t7u5LZlElq/tnIlcVTPS01pGZX8X2mWshI6uZiW5xr/IKlnZcCxBJJBKJRCKRSCQSyV/AXyA6hTRz2LGYTOh0Ws61WoxGEw5lydkr2GwWdNpz9AYjVrsdu0gOhyKDnFyI79tsNi6cru8o+3eKz202u7py7atjyjl2u0N8w0ZrRYEQsQG0Di9iNhnQ6fWYzBaXcLrkwuG4vK5yEfWaNmziuuI3qX/bXdd7hXJcWSFWvYfzQn0us/hdep1O/DYdZ8d7dFVmCmHtRuPADDeMrlWvYbNaMYjztSIvtOfnl3kinu0VDrsVvV7LmThuMJnf/MYrKM+qf5WfJsvrvHHYlGuL6+oNWET+KPloF79T/U0K4rktJqN4Xj1Wq/JbL9R8dFxcEcjiXIvZpD6bVqvDKj5/ww15L1DesZJ3al5KJBKJRCKRSCQSyV+I0C7/+wsJfXhsNJfl8uieD62jq65j//Gsz0xSW1xJR9cgS4tzNFUV8PzBLfyiM1k9eNUzKJFIJBKJRCKRSCSSP5W/EtFpZ6izkejINIbmtl3H/uPZnJ0gMyocX28/wsND8ff1IyIxi4nlPeyyx08ikUgkEolEIpFI/mz+SkSnk9PjfZaX19Ho3t3P8j8Ki1HP1soCg73ddHR0MjI2w+7RmWtupEQikUgkEolEIpFI/lz+SkTnx+fanEiJRCKRSCQSiUQikfxFSNEpkUgkEolEIpFIJJIPhhSdEolEIpFIJBKJRCL5YEjRKZFIJBKJRCKRSCSSD4YUnRKJRCKRSCQSiUQi+WBI0SmRSCQSiUQikUgkkg/G94pO8Tc2mw2r1ar+K5NMMskkk0wyySSTTDLJJJNMf2pScDgcs2az+WbRaTQa2d3dZXNzk+3tbZlkkkkmmWSSSSaZZJJJJplk+pPS1tYWer2e09PT2bW1tZtFp6JMdTodWq1WJplkkkkmmWSSSSaZZJJJJpn+rKSMmjWZTLPn5+dyTqdEIpFIJBKJRCKRSP7jERpTLiQkkUgkEolEIpFIJJIPgxSdEolEIpFIJBKJRCL5YEjRKZFIJBKJRCKRSCSSD4YUnRKJRCKRSCQSiUQi+WBI0SmRSCQSiUQikUgkkg+GFJ0SiUQikUgkEolEIvlgSNEpkUgkEolEIpFIJJIPhhSdEolEIpFIJBKJRCL5YEjRKZFIJBKJRCKRSCSSD4YUnX/VOF3/SiSSv0aEv32PFX8C2xbPonBx8QPxK+/N+w+D8q7fj+sz5Zl+INn/uSDiHtf/PhLf84JffeR0fuRn+oS83y4+viE4Fd+n2uDHv/d/Vj5YffK97+jNZz+Y+uwt/qPL8PWrvf/ar+7rfI9fFZ9/CtHp5Pz4gJnxEfp6e+nr76Onr5fx2QVOdUbXOZKbsJh0rC/NMjTYR29fHwP9Q0xMzbJzcIzV4TpJIpH8B+DEbNSyvb7M8NAAQ2OT7BxqcLzlb60mPWtLcwwODjA9v8K5wez65P1YzQa2N9c40Rq56poN5xqOTjTo3zZmh5Wj3XVGxXOMT86hOTe4PniF8Kknh0wLnzo4MsHW4em15zTpzlmam2J0dJLtgzPsr2/qxCJ+49rqJlrXc+tPj9na2uLsU/piEXTvrS8wMtir+rn+/n4GBsQ7GBxkcmqG/TOTq9q74Ox4jynxu4dHJ258P2/jsNs42d9hZ/8I65uMwGk3cXx0xMmpHsdbgYpJf8aiyL+BwWGW1ncwX/megsNiZHttniHxjDPzq+gMFtcn4gkddo4PthgfHWV2eQO96epnVk4OdtnZ3cfmENe8sHOwu8mueDaL7Y/8kB8QZpH/K/PTDIr8nVtax2C2uT55GyfHh7ssr61xqv9+O7SLd3aws8Xu0fm1+FWpYw8ODtHeUP7PNcLGJkYYEja2vfduWTNpNSzOToiyOszKxt618uWwmNhbW2J0ZJSVzV0sVz6ziWfZ39nk4ESrBm02s47trQ1OznR88phZhIQnh9ui/A4JHzjFztHZNZ+lYDaci/czI+xjkMW1LUy2PxaMOEReHrG2uYPefCXkdDo4OT7iWHOGzX79GnariV3hi0eGBpleWEFrfGNHKhc2Dnc2GB0eYnxmnmPtVR95gf7skNnJMcaE/1Ty9XW2Cl+jEz5ve1s8i8UuHsHB6dEem7tHmG2yD+aSC1EGdpgSZb+/f4BF4QNN3xdwiveoEXm4s7eP0X6Z0zrNAbMTowwNj7K5cyj83c0F2+GwsLG+JGy9X9UHXV29jE3Mc/q6zrsQ195lYmxU+FpR3+pNruMCcV+lHtzc2BbxsFJKnRxsrwt/eijs7VMb0l+O9uyU/UOlvvrj5dFqOGVTqb/1IrawWUTcsCHyaojB4RFWN/ev+Z2raE8OGBf+tc9V3yppUNjz8MgIm3sHqs3bzUZ2VhdEHDHK+s7BtXdoFT5rT9jfkUbYlvBhFuM5W5ubIlbRX/Ovn0R0KkY91FCB54P7PH78Av/AALy8fUjILWJx++iNM5C8hZP99WkSQz15+PAR3r5BBPj54uHuSVBEHA1dw8IRW13nfjocoqDrtefoxbNcXC1tHx2nMBKTCEpPRQAnKw/Jn4nTzI2PigYAAP/0SURBVMp0Lxkp8QQGBuHn50d4fBojC9uvg02b6ZSexgqiwkLxD/AjICiUopo2TvTvt8MLu5G5kU6yhb9b2TsTMY8DvRCbS1ND5KZEk5JbxsLxlaBaCJGlsW5SE6OEvfvh4+NHen4F20da1wmiYt2coyAjiUD/APG5N1HJWYwt7amfWowaepqrSYqPIyYqisyCaubWD9XPHJZzJvuayCmuY/tYpx7T7i1TWVpMXfsA+j8aPH4YnCKAHGwpJirIE29RNwSI/A8M8OHJg9s8euHLwPKheAdOEcTPkJMWj5+vL74ib2JTMhmZ3/we4XnB7tocJfm5dI7OqYGTEvAfi4Cqu6mUmNgEajsmrzXgmU/3aCzPIzwkUOStl+prWwZnsLjOUcTLcHstsZFh+Pn4inNCKKnp4NwkfI54tzsrc+RnppKQEE9kbCJN3cPoXF8+2FqivKiA5r5xTGqQ7WBqoIWCwnImFnfUc37YODGd7dFcnkt4sKjv/P0IDImkqm3wxrLpMOyRkRDOY58whl3l/0ZEmLM00U9hXj4Ta4dqPWXUa9V3VVWUSXRyBqPz666TBUKYaLYXKcpKIjhYlAMvYWPxWYzPb6nBmPKcuuNt6koyCQ0RvsLXh+CIWLpH5lBiXYfFwNxwN9nJCcTGxhGfmM7Q9KqrDDmE7Q9SIJ5lfGlHXEkJHDU0VxdT0dDG7snbDUwfD8UO12cGSU+KIUjkv2Jn8al5TK3sq8+pYNIe0FJZSERoiPjcm8CwaJr6J743yDdpNmmrK6O4tgONwYbDbuX0+JCx/jZCw6MoFD5Ue6VhwWbWM9LTRExEGP7Czr39AimoaedAeyk8L4RQWZ7sJTk2gqBAPxFPepNWVMPG4aWP1B1v0lpbSFx8PLHRMRTXtLDl8neWs3066yooq25CY3ZwIWx2fXaE3JwCxhY21Pf3Q+bCYWNzfoQMpQwEBYn8DSKvspmto/eXS+3+GlXFBTR0DmEUJeVkd5XSnBRhvwEi+RMWk0zfxCLmtxoWFEz6HZJiwrn/4Ameoi7zeOlNXGIBy5tHl3a4t0JpXgZJCXFERcdR3dzFsfaygej8cIuGylKqmgYwqP7BycJQK3lF5cK2vscffIYojZW6MxEXTA+TlZZMTHoxh+eXZfZ9XFhNDLdUUVzZwObxuXgPy1QUpBMcEkxQgD+BoVG0irrL+E6DgZONuXEShRbzEfWtf0AAgeJdezx7yLe37lPW0odN2OjsQAeZiSKOiIsnKUXEGAubWFUHaGd2tJf8vAJm1g7UK5q0h9RVFIp30cXB2ZuGgU8iOh02K1XJ4dz76i6J6YX09PbS1tbO4MQUx0IVS96D08HyeDvuD77DOzCexrY+ujpaKcpJw+PJXb78/S1yanrQKsHOJ+RwY5bq0mxGl49FdfopEQHfwjhZ6aVsnLwK0CWSPxGHgfmJbiqqqmjt6qOpMhe3h7dwC0lh36jY2AUjQhi5P3tKZGKO8GPdZCVG8uTJc6o6xt/boni4MUNWTCApRc1CmFixi6CtuTwHX88n/OTHf883D/0Y2X0lWp2cbc8S5uOGZ3AUje1dFKZHc//eHVJLm7GKYNmmO6I4OZQnL3zIK2+ktaYIt8f38IvJ5dhoYWWyjeioYBHgNdHf005YoA/ZlS0oHWnbSyMkRIZS3Nh7KZKUOzr0NJVkERIRz6AIqj8Jwtdtr0zT29lCa2sb3b0DNFYV4/Hwa7596sfSwTlm7RFZsUE8eO5JbnkdTbWl+Lx8jFdIIhuat3pBXNh0Iq/LMoUgiGd2+0SIDQebM72kxkXz9W9/yU9+/u8kFrapeaOg9GB2VWTx9MkzkrOL6GqtI9j7BY/cgxleOVGfc32yE4+nTwhPyKS9q5vkMD/uP3xOfe8supN9moozCYpKp2tgiLLMOEIjkxhfPuDCpqWjplDkcwJjq3su8SKE6NokiZEhZBbWcGq6uQz9ULCZzmmvyhY29YT4tHx6e7pIjg7mqbsfzSMrr4XPJU6WByr5xU/+lv/vL+7QOnFFNL6F/mCJwrRYopILOTFfCNFjoL+thgBvT37503/kl989pHl4znU2GM8OKU0J46mwsaKqBtqEjb24dxef8HS2z8X3zTo6KrPEe38s3lsFXZ3N+L18gJtfFAs755zszJGeEEV8RhG9/f3kxIcQmV7E6pERx9kGZVmJxCUXsKNxBfIXdiY6qwgJC6OhZ/x7GlE+IMK3nOwsEOPrzku/CBHcd9JWXYjX8+cEJxSo+aYEuQMNRTx+8JiEzAK6u1oJFb7qoXsAPXPvaTQRIeZoRxVhoWHU9U4LUefkeGeVvPgYXj74mv/+9z/BOzafIyFG1dOF8F0a78TH7QmB0ek0NreTKez+zv2nFDUPqb0oR5vTRPk+Fz4yjvrmVkozY7l1+yHp5cKWHVbGuxsI9A+mqXeQnrYqPL1CqO2cEt+9YH64TYjZKKraR18LTO3hJplx4cTlVLF6/EMefedkd2mCuEB33APCKattpq+3i+GJWQ6vCImrOG0meusUvxZPz/S68KEaqnMSuPPgOflCDLU01eL34gHugTGMX2m8uMTJ+e4kTx4+wSs0ngbh+9tb2+kfnORUZ8GqP6GjOpfAyBS6RJ1QW5hKUHAk3eMrQqQ5GG0X5Sosmr657dex5/nuIklRoaQVVKOx/PX4U+3xHg3FecImbvPzX/yUXz8IZEMIyfciyvLR2gRRwcEU1nWjE8LyaGuWhtpKGtq66WqpIczrEbee+DOxevhOvp8e7jHYIWxc5HlnZxf9/T1E+z7md3+4T2P/tLDRWdLiIkjKLqWvv5dMEcPEZFewpTFjO1mmMDVe6LkyDrWXI0ycDosQwCUEh0XSNjT7esTGJxGddquZysRQ/L0jmZjbUudUOOwOHKLQiPuLM5zqMeX/DocNq9UqVPabx7LbbFgsIli7oZVEcSI2cb7ZLDLi6ufiWso1L+9lx2qxXBtCpQy5Ur5jtdrE8cvzrvfSOcV9L69rsdqVy71GGbusjBtX/rVZLZiVa6td+28hvnT1Gpf3UVrW3j5X+d03HFd6PEZb8Hd/SVn9CHpxDZu4nsloYGt5gmhRoH773TO6ppbfiL0r97TZxHO7Div3ePUblX+V6yjPbX/7ucXnSn5ZLGZxjbc+d31XSQ7xTpRr2GwWRjoqcX/0B0q7ZtRAVrmH8i4v8+lCfR7l/b3Kf6WnxyLureT9TT2j6v3VPLOK71/5Ba77K9e1261qOblaJhz2MwZq8/nq9y/onl5Rezsvy5dE8sdR7flKubebj2jKj+HOrSfMiUDEYTkSAdlL/CIyWNg9E2XPzuH6HNkiIAqKzWLz+IaWYKeBvpYyPHxDmNo8Ue1Rv79KY3UJJUJU+b14yuPH/gxvXzruC1GB91Sn8eRlAB3D81iFnZkOl8mLC+KRVxTrR2dszfbh/eghBQ19nJqFDVpMQlil4e7hKyqLebrF91MyEtk5Ov7/s/eX35EkWb8m+p/Nup/urDuzZtY5M4deau6irGQQM7NCGGJmZmZmZmZWSAEKKQTPNfcIQVJ1db+VldlZ9tSyVSkPDw93c7O9989Qtafl4hlSS6qY29ynoSiVyIRsVg4eDDcT7M/2CkcdRkJBPUbLB2zZr4DS0mtR7YpFzduZwU6CPdzIru3BLOr63lwfng52JOY1iABYnCPyqq08HTsR+DeP7j0YQnzP/FgnmsAAsivaOFdsyc0588NN5BeXExetwVGIx8T8ekQ8raII/hAvV8KSClgRQlexb6Md1bjavSapsBX96SEVaUG4BsYwsbglPr9kX9yXJsATv9gspseGKEyJpbhjjlPzFSez7STHp9DYPcniRC+J2kiKmwbfGq57fWUWwX02AcFRtA8v247+NtlfUxpcnAhNLGZp+9RaxxRRHhYgBEgWhgf+4HRXCJfoAP7wh3/jO7dgagfmbZ+8T19DMcH+ATSK/FWuYDEfqkFZRk4xIf6uvHR3o65vwnqy8KarEx04CGGVW9uLznSu1rHq3EQc7J2pG1pia3WOmEBXQlLK2To0iHJwwURLIc7OHmRXtbAy1YE2LoGW8S3hxywsD1UTFJfO0MQsk501RERq6RpdvgvMFCzGHVKiw4hKzP2wLfnE3JwbGKzP5fFrN1pHZtX7Vp67uTwHL08PEdhvcbCxTFyINwHR2SxuHoo6aGG2tw53BwfCUis+2OhsUuxXkobI5Fw2ThVhec3W0qwIWrMozU7kxatXBMbnsnVitYHGo21KEsNw8QhiYnVHjRN0IpiO8HXCX9jZ3aNThpvyee0g7mlyTY0TzKY94kO98fSLYGJujprqMrwicjkXcZXFbCTaJ5qikla2NucpzEwiOaOEI9N9z+qNsJMjbZX4K0K1Z8x29LfHhX5XlPMEHN0DmVzbF/5HxHMiXrPGpw89xj0n2wtCpHqTXlLPqeWKvYUhNN7OBMYUYjxXbPkFfTXpODg4U9I0/E4ZuRD2sx07Vx8quyY4t+kCqzaAw41FshJCyWgcVWNL08oQ6THxVDaPszg9Tk5iFNmV7SLWu7enSnzZW5tDUFiUWt//WdjfWKWmsISMhAQ0Qe48cgljZU9n+/R9LownNOfGERIRx/jSlmrXlBEEt1ro6tLEbFcZ3//1R2r7596rm2ocbfO3ih+zGLaIDw8gIqmIQ72RxeFGorSJdE7vCf1kFvW8jID4LCZm5hluLiMyKoG+qfW34ohz3bqII4KJSy9i29ZIIWLwzyE6z9WeTn9vITrntmxH7zGbjmksyKGuroGqsiKCfD0JikpkeGaZ7bU58tO0uLm6ESEeeHp1/66F+EoYyfGeJqJCA8TnHkQlZDG1Yh2LfH66R2djBUWlxeTn5xMbHa0Ov1HqzebcMOnxEbi4uAoDnynOKaWwMJ/eiZW7DFyeHCBNK85xdiE4Ip6OkTlUc3lxxkxfOwWl1XQ015GTFCkcjYvaqrJ+cN9re20W99bdhFa8AAdHZ8ITcukbHKa7qZK84hp2jfeVZHthjMKcPNqHpmxHbAjRuTDcTICnJ6V1w5gfvl1xp4v9Vfz4l78QK4ImnQhwFGc5P9JFYoQIisSzabSp9E/aWn8tJ7S1NlBcXUdbSwMZ8RqcXdzVwjG3cWg9R3C0tURtcSYBPh44uXiQmFHM8ra14Jv1B7TVl1NZXk1BZgbpSbEkpaXx6tkj/s//7/+H/xCFOzK9hOV9kzAIQ9RVllNTU0+W+C0PT1/r8Ji9XYY7awkP9MTF05+K5n41OLNyw/H2IiXZyXi7u+PlG0xFk3D6auv/BUszw5SWltHU3EZhRgJe4pzwuEwWlPu/OqO/pYhX3/87/9v/9r/z5+9fk5BWzv7hTw9PkEg+hsWwR11+Iu7ekezoTdwY5vF48wZNaiW6W6tpPqa5OBFHUZaH53fubNMt+p05ClKiCE0qRW/rTrOYTZyc6DCYdJQK++EoRGf/ujXgOjcekxHugl9UBst7t/bkkq7aXF7bewk7NE1PXQH2dr4MPeiVXBxpJTTQX4jGBtqF6NQmxYh6KESuqBflwjFlFxdS09xMvFZL2+i9nbvjUk+5qHe+fuJ7tiFqn5Mrwy6V2fF4Kj1H29Z8OFgaIsTHnaS8eg4Uf3ZtpLksVdh+d3rnde/3Dolnaq/OJyAoit4Z21CrmyvOjKecnBqY6W8gxNedhNxa1H5SpYelv07YThchDIc4t13vWAS8cYHuhMRmMr+2TKzna+Lz6zi67ZW8PqQsXYujRzgdne3kJ8dS2Diu2rXtsSbS0jIpraynKDud5Kxicfx9l7s03kFEsB+ZZY1YS8JvkWvWZwbwdHxJWmkXp7ed/xdHlCZH4OgUwIrBlucWPTV5KcRGxhAZGY63JprK3hnrZ+9yvk+eeD+BkSlC2FgvenN9gf70RJQDI41lKTj7eFLbaxMb5hP6qrJ46iTq2ML9EL2FwXqCvF1JK25moLsFX5c3lPctC89kxbI7QYifL+EiTpkS7zNGG0PD0DLnIvCe6a4gLiOfmqpyclOSKKhs++CaDG2laQSHhNM29vFe20/FpUHYspxYfnAOYn7rwHYU5sVzKw0BedX9TA934e8h/i3iEVvHJOajJeKDvHDzjWH/A9HkTH8Lof6BFNV02uzjjRoYHx/qMO5Oic+8CNRms6WzjlbYWZ1E4+lGsLaAk1theGWkNC0K71Ah1gdHqc2OwTM0md2T+xEOLYWJQhx7U9XaJeKEUrzDszAL8XJ5tk9MWDzFJbXUlmSRkpLNsDJq4R10G9PEh/iRll/NwYP47LfE1twg2lAfYnKbMZnPVbHxsRE8Ckqv9Gx/LR4iVmsbtI4UOFwZI0njQ2hcIfoLYURvrumuSsfDw5vansm3fc+1mY2RGp46ulLQ0MvJO/OyDzeXyIwPIbmqT4hOC0dzPWQlp1JW2UhxXh6JqbnsfGCK2e7SMFHCH6bmVatx+z8DSkfLqe6EYyUGz43lmbuGpd1j26fvo9tdICrEn6SCGg7073uNmwsjE21lOLj40Tu78b7Pf4eJ9mI8vQKo6bG+x5kBEadHx9A6vqF2Qo21laLNLKSuupzM5AShSbp4v336Rmi5RILDoumd3rAe+TzDay1Up0Ty4psfCYlMoryykrKycnrH5lQHazzZJujpd/zw3SvS8sqpKsnBy82BH1/YifNjyS8qJjMlXoibJ+owj/V9RUiYhcioJlKjoaC8iqb6alJjNITGZjO3dcyFUO2xPi/501++ISarhJbWVpaFIV2e6ifMx0VUkiByi8soLcrG8eV3/OvvvyG7bowLywWLo90kxESRkpFHc3OTOocoIDyK9rEFLKZT2rJi+eOfv8cvJJa6+lpSo4N5/Pg50VlVKHHI5bmejspM3JycCY2OJ7ewiPK6NiYnxqjMief5a1fapzatBlg4v9aiJJzc/Kjqvh/eo2ITnf4e7sJJKfOtbMdtnO7P4Wn/CO+4fDaPz8S5bSTGRpCeV0xrcyOZiVpCgmOYWDngynwghKYf//GX7wmOTKS5oZYMIRqdXr3CLyKdFds8kgXx7MX5WVRU1VCUkyqEnQvRmeUcmy7VuTaRns/47tFLYlML6OzsoKO9jdhwP/78u/+utv43dI2pLVKdVRk8+/ZPuPlrqa2pITkqlKc/PMbLw4ekjBzx/osJ8XYUgbQr1d3WFuaj7Xlyk6OJS86gtqGZalEOAgNCKGnq50IYroGWYh5983vsvSMoLqukIF3k5ZMnwjllC2F8LMR7PzEBLvy3//ePIsBIpq1nHOO7iw9IJD+BMjl+enyAipJ8osNDCAiJpGlwgWtR/rDsEevvwhuXIDpHl7GIgGZrYZwYfyf++PgNvVOr7xn25bEO4jSe5DUPfmCu0AVlSRrs3/jeiU7TqahjLi+ISim9C8IUhruqcHd0pLyxnYrcROx9YplZvg+It+b6iQgLIiy7itWZHuKFTYpLzaK0WDjm5DRycrPIyEghq7iW/aNj9nd3OH5r4Q3orSsk0NOD9snPNMT2AQtDbQS4OxOXW/NgLqVR2JUcPFxcCYtOVue8uro4k1rchOEDw6jODlcpyYwhPDGL5eP33dzaWDOBXi7E59SogcmNxUR/Yx5uzq40ds/eBSuGvSVy4kXeCgfcNzqEr91zcut7UTttVM6oL0rHwTmYrvEp2qoLCAwMp7i0jGQhPLJy88nJTCchMY2+yVWMugN2dvffCuZOt2bJ0oYRk5zPjuEDauQ3wQ26zTkivO1x8YtheHZdHR20Nt1HkPNTvn/tyYQywln8N9lehq9fCM2d/bTWFeERFEp594dF5+nKKEnRkWoP+YdmorRVpuEgRE6trYfLcrJLdVY09v5hzK5Z5yspbM52E60JQJtRRGNlIZ52DnTPH3F3SdMGCSFehMaksri1QX5aLCERMcJXlZMYF0NBcSkZIlhLzypkemUX3cEee4fHb9mF1YEGIkJCyK/r/dXnFirlf6KpgEePnpNd1cG+/kydw15fEMezx0+Iy26kv60KH3c76voX70YHXBp3yYsLxt0rhNn3RgOK+Ka+AJ+QKFoGP9ATfbKgNvwExGbdNQisznTj7eZKbF4jp7ei88asDpP3Co+nsLqewoQANPGFHD4QncP12QT4uVPW2s9wXyfhQQFkFpZQkptKbFIWxQV5aOMSqWkZQKc3sL29jf5BfHBt3qNQCNuQuAwm1j/ew/Q1MyPKX4DbC8KT8kiNj8bHywPfMCH0xxdFDGY76QHK3OXWomQCozOZWN5Xj11emBjrrsbb1Zmw2BTSk7Q42tuRkFfF9sk7Q3RvLlgfruflk+/487c/4OjsRkxiBkOzq2rPnMV8Qnd9IT4BYRSWFJOWEEdmdgH5WRkkJqaLMjWDwSBiP2FPzy7uK/fZ8Zawp8FEJaay9eFRwV8uZ4fU5kTz1DWMxY+JTqEbNic7CRbxcUXbKCZbg/bN5QVri1PUVxeTHBeFv18AhY2D6IRg/ykujTvEBroLLZDKwq61kXd3ZYrUOI3QC1rVhiUIX1YofFpaYhxZeWXCNu4JG7bD/qHurV7UxZ5qQoNDRT0cUv/+LKLz+tJCbVoM3/3Lf/DslYtQwaEEC8Na0tiNIh8Nuh0CHn/L05d+DM5ui/PN6nyJf/8f/4NX3nEi+DJwpj+iMC6QJ8+c6RCO+3B7WjgnR964BQrxOs3q0gz1+bH8y//8I5l1/cKo7BHt9YLvf3xN95x1WJv59IDCxFBeOnrT2DulTohVJr/mJvjzu9/9hZyGcU5PNkmN8OW5nTtFNe1sbG7QJO7lybd/wDMijV2Rwe2Zsfz+j4/Iru7EKAq66XiJWD8XnjoGi+DGxJoIAP1cnhMcl83s5gGXwnEqQ0HPzcKo9zXiLIReUlGzOqn6yrCNVgilYG0mS0fvzCV4IDoLq5SFFGzHbRwfLOLv9hSvuBxmlhZIDvHhjaMHdb3jbGysUlecyQ9/+h2a7CZ0R9tkR7nzx++eU1AvRJz4bbPhgKaCJJ4+f0VJxxhnlmv0J6ICC2N8dKxjZbyHME87fnT0Z35Hz8XpLiGuj/jRzpOuqS2raL66ZKK7Bl+351T1L3Eb+7WUJvLDN38lNqce0/klB6vTBDt8z7/8x1+p6BgXAdclS0P1uL95SlhSMaenevpqxf0+fkZ6cTXzy6vMT/bi8uyvvHANYmbPSF9LEX/96+8ISy1XV7O8MJ2QJ4LBH1+7US2coOLgRptKePbUh4EF63ADieTv4Ux/SE9bLdrIQF48/ZFndh40DczZWvSumBYG1dP+FfbOXmrjSVxkKK8efcN3dl4M3C0ycs9oRw1hPo40Ds+/95mwfBTFhWKv9nRagx+jboWgN8+IThFl/F7VMNZTh7+TvbADjSKYjcYxKOGtgHhvcYjwUH/80qqEndEzN9JNTnoaGblFtHV1U5CTTlJyKm0d3dQWphMWqojSbCZXd++C2+neWqLCvCkTwftnrTsi0KjMisfR1VfYmft5Yjc3FnqqMnn54/e8cvIU4tOOHx49JaWsQ9jy9+/4YGWCDK0/Cdn5HH5Axy0MNeDvqYjOWlVgXl0Y6KxOw9XekeaehTsxYTxYpSA1lIiYSDq723F+/ZS8pgH0d57zkvrSLN44+NG3tKuu/NlQXkB6WhpltS20NlSRnJRIblktfd0daMNDCQ2LpLS+jyOD9b1fn25RlZWEJjadeVvP7m+R6wsjAw3F2D9/hounHxnZ2cSIOvb9X34vhKE/c7prDtZnSYmNpLxlkIurG0Z769SezoaxD88pXBpqFGIxTPi9wbu5uw9pKkvBXhGdtp5O4+E2hSmhOAeGMrd+X8e2FwbQxoSjTc9R65DrS2cGlh+s7Hq+S1qYsshfAlsiuNhYmKQ0L5O0zGxqm9poFGUiPj6Fqvo22ptrVFEUHpNAq7JIla0B4mx1mPjocFKK67gbAPSrIfz//gopwgY8e+lIWEw8mZnpeDg+49HTV6QVt9HdWIKH82sahlfveuQtpgOKksPw9A1m4r6D1IrlhFohSoJiE4RPvs/LWy53JwnydiNA6ek8sdq75bEW3N2cSShpQ38XLJtpLs/EKyKJrLJKckUsExVfxMFdd7iwka15BPi7UNI+hEmvEzaznrTUVHLyShjo7yZTiJ+U3FLau7ooyYgT5waSmlfO2v6pzd6dUVmm/EYi3VOfv+HtczDRVcWz3/8/PHHwVn1HaVGeiO3ssfMKpXP8/aGqysicwqQQYjLKWd6zqbtrC5PiOg5CSL509MTX3Ym//PU7YnNr2Te8IzdurjEIe9nT1kRpSREZycLuv3yGk2cQvVNKb/8NehG7NteUkJGeSlFVE50tDaQmxJNdWE5fXyfJsREEhWvIr2oVcaE1hr4+O6IiN17Y02Sm7tZL+Ofg4mSHyszInxSd18JXTXZUEBKooX14xba4j5L1Z0wL358WH46Lwyt++PEludVdHxxdc8uN0BmLAzU4OTiRX9vzoJHXzMLksBo7pGXlUtfSJvxcHtr4NGHPOmhrqCQs0I9IbTKdQzMiRrLWIv1Sn7DZYWRXtKp/fxbReWk5pyo5AqcXzuSX1DE6NsrI6CjLGzuqs9cfb6IRgVxsaiX7Nk8+29uE+/MXRGQ22lT0DYPV2dg9daRueIExkUn23/8r3whRqdFEohGOPNDbhT/8xx+EeOxj92CT5HBPAiOT2LJZx+OtecLcnxOVUcuB4d77TA804uXmSEnzBIfLI3i++ZH/+OZHvPyCiI6KwtfDicc/fEuQNo/17WM6C1KEAwxjet3asqOInfrCZOxcQxhe3aKnLgeH1w70zFu7lx+i31sW4s8f7/AUNg9O2ZntxN3dk+KG/rdaC1T+hug82hoXou0x4eklTAy34PziMf/x5x/VFnmNRoOPlxs/Pv6BiIxqjndX1GEKHhHJzGzdD5/bXxrC28uZ6IIGjs0WDEc79LQ3U1RcRGZCNK9FgPftGx8hno84P9kmNsiV8IR8No7ui82syD9/jxfUCkd0W71by9PwFEFDc7/VUCkTwouTA3jlGsD02pF6TL89TWywB2HaHFaW1iiK8eJ//u7PwnmJghyhEc8QyrPv/4SLfzTDy/v0tZXh5OZO06B1Xo7CSEsh9m6eIggcFX9dMdVWzovnfoysvO/gJJK/xaXFzN7OBrMzE/R2t5GeGIVfUDiD88JWKYbdome8p1FdiCYiNoWc7CziNAF4BkUzJsrou8JysLGCIEdH2kdXfpboNOk2CHd8LkRnKdu2IExhqEuIXUdXKhrbKM3W4uAXx9yDMr49OyiCWF+C0qts88REcL69wfbhMVOD7WRlpJNXUklVcQFaEQDm5+UIYR1KTGYl67bhtAtjbeKzcArbJ963Rb8ih0vDRPq6ExiTycGZzforjnGsnSAfN2KSs+gfGVe3slBGO/iIYLd5aNG6BckDdhaGSY3wFIFmrtq4+S7vis5ri5HuumxcnVxp6p6/E536vSVyE4LE+9aK3x0Q/kGITiEY79sEzNQVpfPSwZ/BZeuwPWVBnNXVNU4ON6gtySYpPZfq2mqyUrUk5RSTlZ2Bt08o7YMzVid/fkRzcTrh2iSm1t8f+vdbwmLQ0d9cTVyUhuj4JPKLCggL8Cc4OIqx+Wm0/o788NyJChH4TE+MigBUw6NXdsQJH7Z7fCsg7pnqrUITEUxx68gHy/W7otN8vEN5uohXAsKYfSA6N2e6RZ0JJjG7mIayXNyEj+9dOn7Q07lJQog33qGJWD2cEtvss7a+wd7GPDkpyWQLAVRbXUlqciKZefmkJ2sJCYtkUrxz9d4OJ0lJiiChsJq/0TnxyVB6OAozkwkLDyM9v4zU+EiC/L0pbhoWPrgSD9c31A2u3InOS8Mu+dpgvHxEkPxue4nxgGoh8DSiXI+vvatIxXf3pt4TnSuT7eI3XNAqc6hvM+HGTFNJBv4ifimqriNP60tEQhGHD0TncH0WfiKWKWkbth64Pmd7Y42dw0NG2qtJFGWpuqaKwtxsoqMTKMhIJjIkgLyqDnTqyrk31FWX4BmeSPvYb3Nu9Uh7OY//+L/w0xbeCcSNySZePn5MaHIx+ne638/0B6TH+aDNq2L92Pqu1mf6iQnyJCQmlb7hMSbGRqjKS8TTO5DKluG3Rxu8W1mvzumry8f++WM0CYXYFitWp9Ktra2hO95TF7dKTsuhoqKCwrR4krILyRT21M8vhJrWAbWTgysDjUVZIn5PZ3Thn2tRyZ8jOi/PT+lrKiIgMIKe0c27xjTFT+oO91icm2ZooI/y/HQR3/tQ3zOF6UNd1QKLUUdhXDAuPuEMfWAFdXVLmvVNdlZnyUhMIq+onOrKUtKSksjOzxf2N5qwiFjmdk/VGOd6d4S4uHDSyxrV738e0Wmb0xkcEMP82vsOVX+0ToTjaxEc1ImCbnULU531+NrZE1/Qdjffpq8yG/tnrtQPL9HXWYLjkz/jFhBLaUkJBQWFFBaV0tDYyNrekbrMcmK4F+FJWezYPM3eyiQBTt+TVNQmjIz1mMJ0fxM+7k5CdI6zP9OH6+snvPYMJLughOKiQgoKC6mub2J6YZvTo2Pa8pNxC9Yyd9fTcE5tcQYuXhFC7KzRVpaK4ytnxjduRek9ymT9keYS3MT12/rGachLIDg6g5Gl98+9FZ0Bnh6U1A6qQ3fvuWC4IY/HP76grKWftYlGHJ4/4417EFVV5RSIwlBYVER9czuzy+tYjldJ0obiG5cjCvK9Z9hbHBbi1J24kjb29tcpz01WJ9MrAWpdVSlhXi48cwlkduNIHbKQGOpFfFoFW8e3N3PNeHcNfm7PqR1auxOdbUJ0BvgpQZW1xdCk26UkNRSPgGimV61l4Hh9ghjhpDVJBSzMLpIT7s63zx3JysmjRNy78gwVFZUMTy5wqDump6kEb98AEcBv2HpnbhhtL8LV24+CZiVguGC0oZhnz3wZlqJT8guwOtGO3YvvSS7vxnA7yU9wLgLjgyM9BzublKZHizqcysq+8T0fOtxSRbCrIy0jS39DdFoNkll/RIq/PYGxmawd3o58OKezPh8H1wB6J6boqMnitRA4ow+WhF8aaSXQy42Uyp63FudShr7lpsRTUF5HZ18v4ZFaqnqsw9yWBppxdQ5nYGRB/XtuqImY6CBKOqc+cK+/Ftf0NRTh4uhCTlXXXX4qUxYq04L4y5PXdNzOUxfszvfj8eIpPrGlGN6J0veWhCCJ9iExN48PDZZ7KDpVuyWC1Onualyc3ShvHbub03m4NkVcsDeRiXksrS6hcXlBcmEjJ7eT7C/3Kc2IxV7Y//mdtwOc+b4a4hKSqW/vE8I0k6SEePHWFdNuJDnEnqyyag7PxHXOD6krSFG36JnZ/Pg8nt8SptNDdQ84xZfnpyQQEpvPkPDVb374HX/65kfhQ30I9vPkh7/+nv/9//d/8NjeU90+592yO9dfR5QmlCIhmj4U7LwrOq/NOrqFD3/h4if88o56TEG5TpifB3k1nQx0NeLl8JKaYeFbbZ+f704S6udDUGKRNWa5w0JXdS7x6UV0dXVQlJ1AdlWT+olhb56IYHeKe+ZQpqve7E2QqNWQWFjzwaHAvxrXF+ztbKvTWkbaKwkVor9zel1d3dvbzY7i5gls7UGcHS6TEOyLT1AiJ+9m/tmRujBNqBCdw7Ztmx7ylui0TSfYEvU2yMOVkMTS+x4ai55yYWeDolLoEUKmPE2Dtyadff296GzKF8LG1ZOmd4bxnuzMkRAZSXltO70NypD3dGFDlBjExGhdujoVZ0mtt0J0VubjJURn5/iq+t3fGuM9NXi5vaa482Ee7hPm4Yh7aBJbiq16gOKvsuMDiM2uFP7KWhOaSuL59vvvKR98kIdHM3i9finyNo2tvzF9wHK8THKYJ14Bcay/M1x7Y7KT2KgY6rsHaRBxf1yQlkNxS0oPX350AInJIt5X1vG4MVEv7GlkVCpjy/9c42t/jui8Ev5wuLWUoEANXSPCBr39Wu6wnGwQ6PiIoPgSNo/u68o91xwJ/+bv5khEahnbD4XRQ67PaCnNJCGrjN6eNnIztBTUd6gfHa2OEhboQcXQijrk/nJrhNjoMDLKm9XPP5voVFav9fEMZXjmtvfvPpf0x+uEvXkujHIVu7aezsmOOtyfvyQmp8naoiZEZ095Bq8eO6qr1C1MdeDv7khUVt1dd7CSgaYzs7pK6un2rNorFywMzG3v+unuKjF+b3AO1DKxbG11uzQbqM7W8sN3P1IoDOnR1izBnk4ExuWqC+LcYj47U1fyUlphGzPjcPCOZHrlNugzUy2EqJ1zCGNCaCoV1/G5dTuT27HUyveNJmvh31+fJcrfk4gYLe4iwClt7Eb3wfKgrF7bhK+bC0VV/TbReYVet89ItxDlDi9wDkpiWRRM484EPg52hCfmcnLrDUQenxmNVoFm2iYl3INnjr602lb0uhaGvKsyB3t7R6q7J9kcbcXu1UvCUkqsra6mXUpTNDxz8mFy7QCTEJ1xIk9jkkrYOLr9jWvGOqtxt/uB0u75O9HZUpoixGwAzX3WAFERnQWJgTh5hzNpy/vj1TEilAU6RF5vbGxTlxPNC+dAppfvHb2yyu3ZucjDSwMd9QW4eHrRLAq3tZLdMNSSh6O7D/mq6LQI0VnA99+70HNXziSSn4eyyvLpyYkob9ZSrDiy3uYSHO1fCNswwtmFsmqystq11XbdXJ4x2FaNj5sbOdXdGD+wMsiMCJKjAl0p75r8wBwtAwWxwbx55UPfmtU2KPNhmgtjee3iTdOAdZjr2d4cmTFB+ESksaM7ZUXUU6fnz8kUokzdyeXSTF1hMu4eXnTP3i+NfnNlpqsml/DYFKZWtpifHsE7OIaq3nlxzjXzA20iSItkaHRJPX+kpYxQf3fqh97dmuLX4+r8mNwEIcTdA+icul907urCSF22hj9//4TCpl7rcKIbZcXJct78+COBSZWYFDvxgNPtOXKSw4hKz2HL9P4TKQuk+Aofos2qsU0LEA54eRQ/NyeCEnJtov+KobYKPFw9yK0dxnByQIHWB2efSMYWrHZqd7qLiAAvNKkl6nz2W06VhUkiwsmramfvWPiNgmQSY6NRbsViOiJDY09uRQ1HylYUujUKkqPQxGd9dPuX3wQ31tXTb6e7KsNtu2ry8fP1p6RjioPdNXraGygvK6WouISSwjzxDu34H//2O3wi01jbOXqv7O5Nd4v3EEFmWcfdPMSHNJYm8drNmZoeZbSMwgXLQw28evqCrKpOdTVnZVuIysx4PNyVRTm22V6dJszbjuBkEaipcxGFOGvMx8PLR7UVtygrRK6L39cEhwi/O8/exiw5wqdm1zSo93kkykh0hC8lvXMYRdE5VuaNhoeSWdH60UDyU6Ku4G25r0fH2zMkRQSqc732jBccbs0T6euGX2SWEGrK+IEbpkW8o+xjmlTcZv3SQ65NNFZk4q+JpWtyzXbwnsu9SQI8nPGLyRSi0xrw6g83yYsNxN7FnxHbvPWDlXE0/m7EZJRwIGx0b1UGT1+7qVt0KK/UrN9EG+aPd0gi81u3SuVGjTuqcxKJTlL2SD5kpr2aeCFcu6ZFMH99ykh9JiGxaawooz2EUCkTwtU/KpmhhfcF8m+BnaUR4sI9CRMC5Hbv6eOVXlwc7YV9K+XB4BsVZY2TqsxowuILmNuwCvf2ymS+/eavpNaImFWtyNfsTrbw+tGP4j3nsnV8yuHeDjv7h+rUszOjHr3htsH2kpneOnxd7IlILuL4gSk07K+QERdFSk6lKIsiHiwvIcIrmmPhdi+MxxRpg0hNy2XvSJTLCx2FKWHqyJHF489Qkf4TKKKzPF3Dj0JPfHROp/D5SwONQnSGUt87g9o2c3OFQX+CQcT8Vq5YnR3E2+EJ2pw6dSj66fE+m1vbnNrmMt9cmhhrLebNmzcUN7/TC21D2bd1cbSNsOAwuidX2F2ZID0xnIIma33fXhwmQuNL5dCq2hB1MNGGJlTZlq1H/fyziM6riwshOkN5/s0TISxL6Bsaoq2tla7+UQ4MZk6PNgl5KTImtfxOdE60VuPy+CmRWQ13orOrNJVn372mrGtKBGeHFCYG8+jJa+G4m5mYmKC7vYm6lh72T88wHSwSE+hEQHQKm7aCe31upLcmR50UH6bNYGhiioGOJvwcX/CHvzwiv2kasxChbSWpvHzyjIiEbIbGJ+npbKO6tpGFzQMu9CJ4SI/ljUe4COTuRaeyuMfT1z6MbJ2K59kgI9KXF2/cyS2vZ1g8b31DCyNTyrxDUahMJ7QWxPLk0Z/43UsfBj+2Kbitp9PlxSO8A2Jp7higr7uZ/Iw4XO1fY+8WqC4Zroa6QpSVp4Xz/MVzYrPKGJtU7ruFmppGtvXn3Ij8yo714l/+488Ex2YzNT1Na20J3s72+GmS2Tw4QTffh5eTnQhukxibWaCvsQTnp9/y+2fuTG8cqj2dyqIpyrCW9QeTpBZGO/F4/S3e4cmMzKyq81OahSP3FA66sccqOo3HO+TF+WHnHsK4rVdXbSHxdRIOLY0jo7L/XTuOLx7jHRpPW98o46ODlFbVMTQjnNWNWe3tcXRxp3HgXnQONOZg5yICwgaro5/vr+XJ9z+oQ4DHp1fUhgKJ5OdgON5Tt1Goqq5heGxSXf06SARYnqFalvatowMONpcZHuxneGSEFlF/gnzdCY7JYGnv/V5Ohf2lUdJigtHmN33AoBvIiwrgxXMPemyiU3HQ+ysjIhBzxCcklo7+QcpzU9RVGStsw8bOdZtkR/mJ+u9PWWOnuiJ2oI+PEE/ld0M+la2ktmb7iQgNpaShV+3h2VmeISUmUtjgAgaHhyjNSSUmvYyVLcWxXdFQnImvZyDjq+8Pg/u1ON2aIdTjDR5BWpaPHkQ4yv6aE93Cgb7A0cOfmtYuetrqCPJ04LmdBy2jq7y7+9O18YDaojQCwhMYW32/r3NO2ApP5zdEZ1TdzZ+zmE+pK0jijb0jyfnlwoY2ExPuR6DwI7M7whNdXwixXofzqzdo4rLpF2UhQxuOt28oXWNW8a7YpTP9MTV5yULwpzKh9vBcM9RcSqQmhNquIQZ6mwkPCaO1b0LtKTsQwV68JoCkvApOfsMmSxketrexRHdvHwODQzRXFeDj4UpUaj57agvLu1zSXJONnVcApT1ztmNvc3W0THp8JGHCJ9ytOPyAuuIEngs/WNl1LxYNR1tkR/rh6OyrLgLY2VCOv5c32oxKDkUxsJh0NBQl8/KVI1ml9fT3thPu74kmPoPl2030RR3U7a6TG6chQdTNnWNRx8/2qC1MJUKbTHvfsLAhpWgiIhm3Dakeay4kJCSEmu53Vvn8lVC2F5ka6WdwYIDhgS5SogJx8/Snrte6yOHluYiPyrN4/cqOxOwy+nq7hEjxJygyicmNDy++MyhiOT/fEKpabMNeH6CITh9XB7wi09m87WW5Ome2tx4PRzt1UZ/Onh7yU2LxEnWsecC6WNS2qC8BbvbCNsfR2tVLZX4SHj6+InAevFvlX2moGu+qJTQknKb+GbVBfGeuX4jOCFJzqxgR7yw7LoLcynZ1ZdTrUxG3xYYQm17E6pHtXn5jKL38HdU5ODg4kF0hYq+BPtJiA3DxC6N9xBq/PkRZf2W4MR/PgCh6J6w9m8ruEBovR147eVFR30pvVwvRAS48fuEkytEMp7vL5CdGEpNRJESsmYWxHspLi2nv6VPjW43wqc7uAeL3rCNwFC5MenFfeYSEa+mzjXSZ6q4nOjiQipZ+BnpahZ2OpLKxF4P5GtP+EglhfsSl531wasWXjDKNrTQllO/sA1nYvR2o/y4iTlgdJyjAn6zKNuuIgOtzRrobqSgtUKefKHmSGBmIo5sffWonzA3tFRkEBUfQYevJPzccUp4ayrOXznS+s/2JirBhh1uLZMaGkZKvCFdRLwxblOckERWfTvfACA2VhWgiY4R/tOb0YG22sGHhoq5ae8s/i+i8ubqip6oIu2++4S9//oYXr17y3fePhHALVUWT4URHbpSyEW0HRyar918c7iFeOOWCun5r75m4zfG2KsICImkZthbG460FCtJisX/zmpcvX6kr0pY2KksrX3JxsklRhpaMwkr2rA02KpdGHa3l2Xg6veKFnSPxmUWkJMbi7uRAUfOMOh/qUgQrjaWZeDjb8fTZc+xdPEktqGbjyMjlmYH+miKiknJZ3r4Nzs5VpxSiSWJ629rac7g+T15KNE52r8Q1XuITnsjg9O1wg2t2Jur5/k//ilNkHhsPm3Pe4kYY1zEifN7w1z//mUePn/Pq9WvcvUVBK6xienXvrW0CjIcbVAqH5mj3kmfPX+Ls4UdWST3HSs/NxR5pSRpeu/kQHRVDmI8bz1+KwCk+UwRFu2phu7ky0V1fLIy9yBt7N2LjkklLiCcyKZvlPR3m0z2K0uPIL21h98E4mrMTEdzlxfP8x0eExOeyeqisXlZLamIa/ZNWYW7WH9JYkka0EPKLtuFjpzvz5KZqSc0pF8GAyJWLM8Y6awnydhf3/4I3Di6EKfsCqUOcLhjrb0Ibn8jAzPbd8NqZAWF4xLGGvlnlAEYRLOQnhvD40XMi4grZlVumSH4mFyKQbKvJw8tF1PunQtw4u4tgsYiptfvew835QZIi/dVVk984uJGcU8rCxsHd5+9yY9xT5/u5BcWxblsh+p4zGgoz0AjbMLnzwAYIWzc33E5UkBfPnj3B0TOAsuZeTu66aW7YX5shMy6cly+e8srOWV3m/+H1lf26umuLyBZ2YnnHGgwqmzcvjnahDfO1bhcl6v6ksCGqzTuYIyMhghBl83zjA4P5K3O4OkGSJlTY7UZO39EHyl5686M9Isj149XzJzwVNs47NIbWwWnOP7ib/jXDXXX4eQepK/y9e8radC9JcVEU1nTdLYCmYD7ZoVrYUac3r3jy7I26LdP44oa4mhUlmO1rLFd7RJ89fYZXUKR6D3eLGQmBfLA2RV5OOg094xhsc2kUIVNXmqWuuOvhF0hxfS86ZSEh4dgHWioJ8AumsnX4o2XpN4Eo+8vTfYQIAffk8ROcXLyED6tjTV2x/kNcMdRdR1RKBu0f6ElTubEIYZmKr1+oujbAu/S1lIp3HEPX+EPRes3xxhyZWg1vXjzn5WtHUvIqxX3cj/czHG1SkZMgfPxLXry2J0LEBDMrDza/F/VtbaqPlNRsdSVea/m5YW9V2aMyBkd7R/xF+W3pnVQXRFLqZ0FSNGGaeGFzPk/Dz4XS6CIEnKvdC56/eEVAmJZ2UbbND1p0zPo96kszcHF8Lerga0Jj0xidE8/3kYK7Mz9MXFggCTmVHD9YsVnh6miJ1PhYkoX92tPf20BF3A61VRPg6Sxs4HM8/TW09E9jst2HklcLI20iHvTkufjcwc3fur3ag+0zjCd7NJQVkFdcz/6J1TYqo1NGhE0I8XFV7XtiVqm61Z2yIvLqeDuhfl6UNHTyzijS3xRK73B9SQZuzg68eGmHd1A0XaOz1n2O30PEsgsD+Ht4UNzQo9pAZZ/MjflRUmNCsH/1nOcilnP1CaG+e4wz8f5MIu7LigsjKq0Q/fUlq1M9RAa6qr5OqUeh0cn0jy9wefd7N5zsrlGRm0pVywCHNo1wod+ns64IT3cX3Lx9yS1vEu9ZGZ0i4sLeWgKCQihqtK6g+s+ExXBAe1UOATEZbBy+PV3jIcaTfXK1wYQL8bdq6xGd6Kkl1NtezXM7Byd1lFPPxLLNN13SXJZOYFAE7WNWW3l2uk9ldpxah5c/JHAvTSyPdZGUlqfG4dY3InTJwqR4h5E4CBsWJOxVx+Cs6l+vr4zkxIYTEZ3CvNqYLc7+HKJTwWwysLOxztLiAouLi8zPL7C8usmp0axu+K/XHWEwmO4Ml7I/0MnxMQaT2fagQtoJwXd8pMNkvjUsN5wZTlhfXWFhYYG1jS1xvTP1GkqAoj/VcWqwDS9VT7f+48Js5GBvh82tLQ7FbzSVpuFs/4bGMduKrAJlL72drQ1x3XmWVtc51OmtlUAECGajHt3JqbplghVxH+L5jo5OOLc8OKbXsSHubX5+kc2dA86UCc4K12YWe8pEQPOakrYR61Cxj3B1ec7RwQ4ry0vqMy4uLYtr7WE8+7BQPTfp2dpYVfN3ZW2TY72Sp+K+DZskaEPxikkT4neJ3c01llbWODox3D2zgrIZ9M7WGguLy+zsHYp3YuBUb8AinIXSCq3kqd5w9k6vwo2aJxtrK2xs73J2cSXy2MSpyCOz7ZkVQ2TSn1rzzeZ4lMBYf6JT90q79UXKks+He9ssLYlnXV5h9+CYCzVPb9TVf3UnJ5gfDGNUfkc5dlcmxLOa9MesiXzf2lY2N74/VyL5KZTeQaP+hI31VRbm51kV9Ucnyvqt+VCwnJvY3VoX9WterV8n+rc/f59rIfQ6CfXxorBJGaL7sDwqZfVU2DRhN94Ze6vYryNho5ZEnV/f2sX0YDl4KzcYTg6FXVgU9XidE3GfDxF2XdjUY1FX722qwrWwJ4d7W6ysiLqlLHWufnbDQH0BwUFBVHaN35rJz8LlhRnd0bGw2x/JV2FHTo72WRH2YWHJ+gy3q+Z9iOPNOXITwtEI4bjxzurgFvFbJzodBuEz3r3C+ZmeTWHPFhZXODg6fS+gvhLf3VP9wwJbwk5evGUQb0TQbLVLD5fxV46r/krk/er6JkZ18RIRPOytkhmnIULc4+Ld3qy/VYSdF35+87YOCp/+cFuL9xG+58xozet3hlc/ZGO6F60miMSiBiFcbAdtKLHJsSgHt8Pq77kRgd0Rq8L3KnVMsQXvcm48FT5+WfXLhzrhSx+WE2FPLkQ5OjrRv73IlSjDp8cHou4us7G1dxczrE90EhIUQE5ZM8afCgo+IYoNVBYiUezKwuKSuqWL5QMNOkoMpb4jcc7+4cl7owwecm05paEoA/+QKLqn1kSuPuDaou5ZfCJijNtpC7dcWczs72wKG7jE1u7hW1sMKSh7RB7tCxu5tMTa5t59fGXj6lJc+wP1+/LiTLXhyyL+ORQiRflMWeiwLDNWnd85ung/vee3ym0dXFTiwP1jLj9QBm65MB5Rkh5FiFaZP3k7LPkag4jp11aWxDWWRCx5xIXt/d0IH3RyfMSRiKmVI8r7UBbvW1Ji7aVVDo5P3ytPymKkyneUOO/hnSjx7sb6CivrG3d24vx0lxxh8yPiM5i/XVH3nwglzlb2kj7SnYp8/3jFUhpeZvtq8fMPoKZ9WB1iq9RLpWwrtnN5eZV94bvuX52wZ0KTHB4q+WitK9dXIqYXccLxqaJvPvBbN1fCPop7ETrhvhFAuUerH14WNmxrR8TZyrsVtmN5pIVgcT9FNR2c2WKazyY6vwSUl7SzMklLZy/TS0JwHe7T31yBl+NrPILj2dR/3Gn9UihBx1h3A6GedniEJbFgaw345JyuEa3xxTUyldlt2fsnkfxaKC2ybZX5RCXmsrr/8ZbLz8XZwRpFWSmk5Vfezav6Wri5umB6uFVdfKJzZP69wPWzI5z63GArCfFJNHdPfGDer+SX4PrCRFdjKdFxSUwLn/ulZbNFiNPGshwSM3KZXvn6RM/u4gg5KfHklrdxcr8Ix5eBqIOb8yPEx8ZQ0zWG6f0d7yU/xc0N69N9JMTFUtHa+9l7iZW9sePjE2jq+drt6Q1mwxFlmQmk5peL2OK9TXJ/Vc4Nx9QWZpCcXcj8g0VUf9OiU2lVW5nsJCQ4EN/AUGKjo/B2d8EvVEu3bUL6p0Rp4ZkZbEPj7YyLZyDtIgi6+LVqhXGbrPQ4wtOKWFCHAEskkl+HG04PdxgaGGDzQP/eMM/PjX5/g8nxMVa2Dj65DfwcnJtOmJsYZmZxDfMXFlDeXFpYW5hicmaBE+NP9ehJ/rPoj3eZGBtW12YQcY/t6BeAuJdzwwnTE+MsrG7e9Qh9TSjDWlcX5xgcmebkwRDYL4EbZaXe9UWGx6c51P8253L+Z1GmRs1PjzM+M4/+nR7nX5Wba9YXJpiYmfviytmn4nhriaGxcbYOPjyn+ldB2DDz6RGTY2Msb+68NTrity06le7lk31G+jqpLC2juLiUxpYOFtd3rYvxfGKUoW3LMyPUVVUwOrP83pC6T4rFxPzsFGOziyK4+W1URonkS0LpdVOG0v3EaNDPguXigsvLr90NXHMhnvP6C8t8dbXQi/Mvruftq0UZLqYMo/3CROf1pUXUwS+sB/AXRsnxc5H3l3fTkr4MlCHFyvBN2b/5n+VG2FgLls85pUlpwDm/n5L3W8FiuRDpc4p9YcPEPXzIhv3GRec9SgaZPjIv8pOhGjfhXH5qAsSnQqmFomCI9239WyKRSCQSiUQikUg+AVJ0SiQSiUQikUgkEonkkyFFp0QikUgkEolEIpFIPhlSdEokEolEIpFIJBKJ5JMhRadEIpFIJBKJRCKRSD4ZUnRKJBKJRCKRSCQSieST8TdFpzgmk0wyySSTTDLJJJNMMskkk0x/d1L4SdF5dnbGzs4OW1tbbG9vyySTTDLJJJNMMskkk0wyySTTz0qKjjQajZycnEytra19WHSen59zdHTEwcEBh4eHMskkk0wyySSTTDLJJJNMMsn0s5PZbFaE59Te3p6c0ymRSCQSiUQikUgkkl+enxxeK5FIJBKJRCKRSCQSyX8GKTolEolEIpFIJBKJRPLJkKJTIpFIJBKJRCKRSCSfDCk6JRKJRCKRSCQSiUTyyZCiUyKRSCQSiUQikUgknwwpOiUSiUQikUgkEolE8smQolMikUgkEolEIpFIJJ8MKTolEolEIpFIJBKJRPLJkKJTIpFIJBKJRCKRSCSfDCk6JRKJ5CvlxpYkPw/h/2z/kvyW+WWLgSxTnw0162X+f4n82rZWlgIrv34+vP2Ln010nulP2VxdZm52ltm5OdY2tjGYzLZP/xFu2NtcYXJykkP9BddXV5we7XN4dMzF5ZXtnP8815cX6A73ODw8wnJtO/izuUavO+JYd4Ll8u/+8q/C5cUZhwd7HJ0aubr+dMXz5trCie6QteVFZmfnWN/aVd//J/zJt7gwmzg6OkRvfL/MmfQ68dkx5guL7chPYzk3qXmm05t+4WBF8k/JjVLPD1leWmB1cxfT+d82qYpdOT46EmXIaD1we41F5Rp74hpv2zCz8ZSNtWVWVjcxmS9sR62cGU44Ojzk/PJG2MFLTo6FzTk5FfXZdsJXzpXljP2tdRYWFjnUnf4sm2IS+XlweHz3rixmIzsbqywsLYu8M7zlNq8uLezvbrG0tMKRQdR523GFqwszuqMDTg1G9bhiZxTbYDr/ebZE8stxZtCxvrLE0soa+p8VW1xzLOrN8YmeK/Wl3mA6PWJ1aZHllU2MZ+fqWbdcCru/s74q6uA6hnc+uzAbhA85EnVTvHdRl436Ew6Pj7FYL/zVcyXs2f7OploHd49OfpbtUerc4cHBnT1TbOLeztbdNd4OmZT4bpelxUW2do/EZw/yVbGdJ8fqe7wU+X1lMVttq+HMdoLk56K8g8O9XRbm51le3xT16PzO3hlFHit1Y07E7zMzM+I9LXAk/NdP2tvrS2sMfGoQb1Bwo7zHPauf29h6z05eWs7RHeywOD/H8gfq2bWwxXvb6ywur3B4YrW5t1yL7x6K+nyrK8wmg2rj9Wdfti1W6o6iMZYW5oXtWv+o7bKcn7G7vcG8op+2dv6Gj7kR9cvE7t4hZxc2Hyfs15bwcfOLS6J+3dq8t1HqjlKP50SMvrom3s/Zfayh2NfV5SU2tvcxWx7GJzciPtGzd6DjUtVeV8KuHgjdo7d++jlE5831FWPtDYS5u+Pu5ol/YAA+vgGk51WwunP0VsH5+VzTVJKEu6sLXYunIojQ01yWTW5xDRsHp7ZzfgKhFi4uLjCfn4tA7eMW0qTborY4i9zCKg5MtoM/m3NaKvIprahj6/hnGkBxXxZxX2dmIcg+uSK74WBtmtyMVKrbhj5Z5bSYThnvayE5PpqgQH/8fH3wD9ZQ0dQjgri3jcqnYm12iMK8bNoG52xH7hlqryavoJSZlQPbkZ9mZ2mSwuxk6nomuFA94w3n5nMuzi+EAf7U70zyJXEjnOrm/Bh56fGEBgcRFBJJZWOvcMY/Va5vWBrrobKqhsnlXeUibC2Ok58mrhESTLAmivKmflE3rAbfoNujs6mSRG0M8Ykp1Lb0ciqCAYXrSz09rQ1U17VxYr4WjveMxbFeSsprWNg8FFby68ZsOKK/tZqYSI2a/9rkbMYWN4RD/Xg9vDAe0NZQRV3bAIfCLJ+fHtBZX0pUeChBQcEkpBYwu7KrNijdXF8wM9pPdloS2rgEsosrWd45tPmsG+bH+qioqGZycVM9oj/aprW+nPqOERE8vN04IPlU3HC0tUR1UTbhIUGEhIaRXVLH5tHbjQdvc41ue56y0goGJpdEmCR84cYc5flphIhyFBKioaCiif1Tq98+Nxwz1NZAeoKW2LgkKkR9O7it4zcW+trrqalvZevAoB7aWJygsqKMoZk1LL9Wy+pn4upcz2hPM/ExGoKCg4mOS6F/Ykn4xo8/t+VMz2B7I1X17WwfC+FyecZYdxMJ0eIaQYFExafSO7l8l3f76zNUleUSFRVFWlYegzPiM9vlT7aXqC0vo3dsVkRcQriYdAx0NlFe08TR2a/Sp/JVYDnTMdLVSGJMFMGhIUQmZdI/tcyFqi8sdFcX4+/ohLd/IH5+PoRqIuidEu/57fbRB1yJ9zZHZWkJI/OiHgjBuTY3LHxlolrHQoWfK6ltF3XMZicvzUwPdJKeqFU/Dw4NF/W4hvV9m3gRZWR2tIdsEa9GRceI+lnP0o5O/YyrC1bGeyirrmN+c189pNtdo7G2isbOQc5+oix+ThT/Mi+eKSslQfX9IcFhZOQrPkb3lu060+0KH1VBbEQYwSEhJGUXMLG09VHBf312zFRfMyXVrRwIG3ZuPKS9roToyHACRR2NS81heHbtre8r9aa/rZbE2Ej8/YUvjcthbGZd3OQNpqMtGipKSIqLJSExk/b+KfRm64s32e6ttnMCg1rfhN4T9qCytkFopsvPIzqvLBdUJUfw7M+PCIlMpLK6HE2gJ99/+z3Bcbns/lxB9g4N+Vrs37ymbe4Y85mRbhGYlde0sS2czd9EVLC+znbaeocx/ETPxPnpLq115ep1j0x/b8E1kBrqRkBIDDNbP0MICyzGY8YG2mnq6n+nNeFTcMPh5gIVxUW09U1ishWiXxKL8YjW8ixeP3+Ck6cvyRnZFBXmER0VTWpBJSu7x7YzPy3jnVV4uTqQXt5pO3JPZWYELh7+tA2v2o78NHtrc1SX5dM+MmvtHb44pqWxke7hKcy/kZZtiZW9lUkSwv1wdPMjO6+ApKgQXrywp6yxTw2APoRZt0lGQhQJGaXsnFo43pwhOcIPe1df8gqLSdGG8PS5PRWdk2ojxlB7DeHhEeSXVom6moO/rz8d42soVmtpuIWY6GhKmwZQ/aoQsPvLk8SEh5BV1sy+/itu7b8+o6+xBGc7OyKF2CwpysXtzQs8NMnMbH64AUlx8lO99cLBh1HbNSnq7yWdtXk42tsTl5ZHQU46ji+eERyTxdqenuO1aVKF2I9OyqGhtgJNgC+Zpa3ozy64PN0iOymW+KxiVg+tPdaWs1N66otF8K0RTn39qxf9XwJnR+sUJUdi7+BMWn4hWWlxvHz+gviiZg5sDTfvcm46oiY/mXBtOlPr+1hOd8iOD8XOyZXc4lLSk6J4/uwZuTU9GM8vmB1oJjI0lLTcUlEHcwkP9KdM1E/L5RXHqyPEajQUVisNP1Yfery9TEFaNNGpeWweWIPmr5Xxrhp8XR3xj0iktCiPYHd77NzDGF/esZ3xDiLkXJvuFzFgEOWtA5xdXTPdU42PiyOBEQmUFGTj5+aAnVcEU2t7QtPrKctIIFKbSF1zPanxUQRGZog6Z+LqykxndR4R0ckMzq2pgbrSEDjZ10SkCM4rO2c4/0JHmX1RXJrobSjG09mJYE0cJeUVIuZtYHxu1dp4cKMjM9SH598/J62wguLiQsorqphf3/lor/bZ6R71xWmERKexsHPI4fo4MUEeuPkGUSSunxyrwd7ekbzaHkwWIU7OdilKSyI0NIayqmrhIyN4+eIlMXl1og6ec7A0TpLwa0npucIWl4r6GEZibqNqY40Ha2QKn5pSUK02NilcmU9oLs8lNDyKgdmtn2iA+nxYzvYpSxd2KDiS0spqclOieSNsV0hSCUdGq+26EraqriAZVycnwmOSKa+spLaxmdmVTWv8+QHWZvrRirwqaezHoD+hsyoTR0dnwhMyqCwrIMDTBe8grXgvtpFWlwbaKnLEb7gQGi3qYGkFVRWNzC3vcmE8oacyjbDIeCqqa8lKiCYkNJr+SSVevmKqp56w4HC6J+8b2Lbm+oiNEj62Y/DziM7Li3PKtMEi6A+id3gOo8nI/sY82SK4+uaJPU1Di9aA6e+ktSwFFydHWmf2lQfjTFzXYDBx+TPGdlztLpKoCSMqtYhD00+IrZsrTEYDBuPZzxoy8jZGUoJc8AuKFkHQie3YT2PYXiQ/MYzgpGwhhj+16LQ2CBj0es7O7odR/HJY6G8s4s2TR/hGZzK3tsGR7kT83in7+3vs7h9y9s5QwU/FWEclHs52pJV22I7cU5ERjpMQDa1DK7YjP82lLc/uhjkeTxHo70N8QR36T16bJF8MF6e0V2bg7OxKecc0utNTDrfnCfdxxD0sgalNWyvsA26urhgRQikwMITGvmlhsq/prszE1dGJsq55Tk71HO0tE+5lR1B8AVMzs9QWpRMvgt0dvZGDzRVyhDHPrxtne3ONknQt8elFrB7ci0ulxbK7PA3/cC19Mz+vIeWfkcPVSeJD/fAIjGf7UIdeONeR+myePH9DXn2vyNv3MR1vkpsYQmRqMctHZo5WRogMdCcgNof9oxNOdQe0FSXy0tGd6o5hxtqrSU3OoHXqgLOTbfrE+45KFCJzc5eRjjIiYuJpG5q7n3qhiP61GbSaQBJy69g7/ruHx0j+Lm4YF7bd09WFuJxqjkUdPNpbpyAhgOcOvkKIrNvOe8CNhY25HkKFcCxoGMAkhONkZznubu6klAjhqDeI+GSBjAgv7Hy1zC3O01GTT2xaAePrpxj2F6nITyIip5aTgy3q81OJTsxRRy3cFgNlmOJUXyM+3v7Ud09i/vvn5vxTcKnfEnFUAK5+UcwIYaEEuDMDjbx6/CNp5e13IvwhZyd71ArB7xuSyML6gbCjuyRFKPU4mknxt+H0hLHeWt48e052RRtbG7OERiaQX9kh4oUz+lraCXDVMDy9wtbquBD84VQ094p46X6k1un+GsVpsQRHJrFxJOvg32JlrJMwHzeikvPY3jsUMboJo9GI+fxCHfFxrV8j2s9P5GcaxyLGNoqYWPn8QojFD3PNykQPYd4ewq9Nizp2TmVGLI6uftT2TKI3GNlbnSEvIRSvkBhmNw5ELGpgeXGZpdVtEW+b0O3Mkh/rzzP7QJZ2dxnuaiBcCKW+oVkR6xuozskkJiKFA6Oe/uYyQjQxDIgyoQyxtnLD6tQAcaJ8porYTBkJ9KVhOdezOLvI8tImevHMJ4crlKVo+PapG6Pr1lGg0101eLm4kl5Yze7BMSbbuzm/sHwwZr883aGxOFltwFnY0wmxP0mQ02ui08tYEt83Gk4Zbq3B39OdjLohLJZzdmc78PNwFTa0hlURlxvFvRjFOzq/uOR4b4vUEE9KG7s4FO9+eaSZ+MhAGnuG2d1aJ0erIbWoiWObSFa4ujBRlZtAZk4ec8cXn0d0lieEEeQvxNfynu0ojLeU8+jxCwpax4Q8UYYtTlGUkUhwUBCpIsiaXbV2k1u5YmV6kOzUeELDIymubiI1LhQPV1c654/V8c5zI930DI1xZJu3t6v0SBVloQkNRhMdT1PPmCpujUdblMQF8C//5f/k//mXP+Afk0rP2ByGgx36u7ro7OqkuiSXgvJapheWmJ8YpHdwBN1dnl6yMN5LbmoCgQEBJGYXM7v+8F5vMZIW6kFgaBzzu2dqMDg50E1nWxdDg71kJmvRRGlp6p3kXNQHw/4WhbGh/P6//p/8l3/5HQGRcbQPTaPa7Wtl6EE7ybERBIdFUVTTxq7OakyvjHu0NrXSNzhMT0cT2ogwtEmZDD1oab8+NzDW3UxitIbAoFDKG7vRi+cx6ffp7e9ldGGDvZUZGhuaGJu/d9TnRh1jfc00dwygV+ariGI+L/I5TRtJcLAIfisb2bQNKXoXvTAa0b5OPHPwYc42ROJDnJtOmehrIz0hhpDgEJKyRH6u7dsqlIWFsUHa65toa28lNTGelj5RXsSDneu2aakuJFy83whtGp3Ds/fB3zuMd1Xj7eZERnmX7cg9VdmRuHkF0jW+KfLplJmxfjo6+xjs6SQnLYHwiBjq24cxmK1V5VgEGoMDvUyLIGNPBCclid781//7/+C//e4b/KPSGZhcVs+TfN0Y9pbIFk7RKzCSjbu45prGwnheOflR3zNrO3bPlemQ7AQN4fFZLO8buTnbIzM6EDefcHYfxGcthQm4BMZQ09hITUEqiUV1KD9xcbonnFKsqP891JYWk5CQSu/Eu40lN5xsjQun4kd+RQeGiy/P2f4STIg67evuSlbNsO2I4HQRnzcvCE8q5uDdrubrK1Yne/F396Sue1ztKR5qyMfTyYGK7nnrOYKzjSHcnFxJyKmkpaqAjPRchtaFsbzUM9FciFYI1u72duJjoqhq7eHsnfy9NBtoLEnFLyhC2FXrsFvJJ+JCT0WWFnefYLqn73vWFgZqeP3kGSVtw++NOFBa7ttL0ggM1DAt/IwSW5Qlh+DpL4TMxu18KguzHSU8fulJe3cX9ZXZxOeUsaK0HYs621KVQ3RWGQNt1URHaukam39vjpRuZ4XEMF/isirY0X2dIw52pzrxcndHW9BgOyL8smGfON/Xwhdms7L3fmywtTBKXIgb2SLgNV5ecyACXh9PD5KLm21niMjpeFNcw5EQbSaDI334hSdR0WKt59PDQ4T5xdLe3kVlTiLJuTVsHr2TvzfnTAn7EOTnS8voKjbXLfkQ1yaq85Px8A2lf27LdvBtLnZnCHT3IzSx1Hbkp7k+19FRk4uL8GsbasObkQgfd9yCklk5vm0cuGS6vQJnVw+aBme5eG+UmJ6eshQev/JhdmuLvpZqwgMTmZvfVj9tqSgVsW4iw2N9JMZqKG0ZFeJW/eiOS6WulqfhFR7P1PrxXTz8pXBzc/2OcLxgpCGPbx85M7R6yI2wbwVJkfiGJjC9cWQ756dZnx1GG+JPWnETlqtL1kYbsfv+R6p6pmxngFnELmnRAXjHFXOiO6E+W4tPQCTjy++PEDrcXkcr4vjqrmHVZ+7O9Km9ytX1dTQ11BATl8raBxp2lkdayMnKIq1+/POJTn+vcEZthVppNS5O1vD4tRutY6sio0bJS0skLj6RnJwMIkJC0cZnM7ep4+bKwvJYB0Fe7nh6+xOfnEpyfCzPv/uTKJBO9K2Y1AUAirTe+EWKQrlzLN6mheG2SlLiIkhJSyciNBAPEYS1Ds5xuL9NfUYkf/7X/86//uk7NCm5TMytsCtEbZDLG144eJKYmEJ1cxdTUyPkxgURGBHHqvBelxYzI62lBHh74B8YSlRkBMmZ+QzPKfOI1Ed7wL3oXDywYDnZJDvMmx9+/4i4jHwRzCTh5fSKl3YeNA8uoDs5pjpVy/f/+j/4lz9+iyY+jQFlzPzZMaNdtcTHRpOUmk56ShJhYeFkldZzYLRwqZvD1e4VP75xIz23kPT4aOxePMXJO4LpzRMsFyZ1GFpYcDBR0bFoo6NEMNrAkfma3ZUR/IP8SChrZ2N+kAB3Z2IyKu567HZWJgj3dCQqs4qDUz1zw60iT2NISE5Tx9ZrQsJIzSxj5/T9ntLZ7hocXr8iJL3KduTD7G0sUZaeQZJ498mJWrzFPWiEs1nYUSrZlciTKH78978QHJVIRkYGXUOT7G9a5/DEaLVkZeeqAWB4ZAwtgx8e4vpzRGfP9C5Xhh0KE4N4/ONTNKIyZYpn9HGx49lrV6rbR9Tek+XpfpGXXmRU9rEr7r02K5R/+1//nf/45gkRSflMCgEv+frZXRwlJsCNwKhUTu+GudwwrPTu23lR0jBoO3bLNacbE8SGh5JZ1obOcoNpe4ooP298wjLUhjeV62smWwux8wqmpr1HrftBIRoKq5porCkjLjqOisoKYiNjqWzuVRvZTEbjWyM8Li9OSAn2RJuS90GH8DXQWZMtAl4n6oce1LcrHQk+9gRGZ7Bw/HYvy5XFSH9LEc6+QgzaGtbq8uNwcXChe+6+0fDasEKgmwdRaaUMD3WTkaAV+ZhPa2MdyTGR5JZUkinsVHJOBau7R1ydn2F+MEXj5uqCmd5agv38qO+e4ivV/F8EF/pdsrRB+Acro4luhccNW3MD+Dg8Ib2qC907nW26g00y4iIJj89jTydEkSgzKUFuBIUlsmobbaYExNvTHdg9f01d9zD9Pc1EhIWQUVhBfU0FKaJMFJRWqI2vBVXKvDQD52cm2xx/K+enhzTnxxMQkcTU6n1j+9fEVGuZiKs8yW/ssx0RsZ1BR1GiP57BCcysvhvEXjLZ04SvkxuDSweqPx1vKcDbw5OS+nt7aTzepzjOF38Rdw1OTpOTkkhUXBKNzS0UKD1cCVlUFOeLeCaR0YVtddEno22eu5UbdpZHiAwLJLGs426oouR9rk6UoakRuAeGUVJaSnpiLFGx8dR3DnKq9IYILLuTBDq/4X/8+7ciFo4kJStPnQv9sUZ+/e4yRanRhCSXY1IXsrkgOzqQl/ZeNA0tqOeYT/epzo7j3//6mNK2EbXj5SH6rTkSQ71wC09jX69ne3aA6AAhpjIKaW6qIT01hSQRuxflpBOXXsj6voGzM2GLbQvnWLEwOdCEq1sAjf1zX/zCXqaDZTIi/bH31rJ1YuL8cEXtqQ2MjKewqJBkYbei41PpHJrhwx23Vwz3NOAbEExt97SoBtci33pxfvI9Ueml7OisdWRV6Cnvly9w8kvmYH9DHbbsFxRDWWkRmSlxaGITae4ZxySCEovhmPpc8S6j4oTQbCIvI43U9HTyc7NIEVqsqX+SiwsRg5jO3pojqt9boKogj4Cw7KmTzzGnszolkjePXhCfWkBXj3Dk8ZG8evGS2MwK1re3yYrwx1UE/rVCTR8e7lGbm8DzH4Q4y29ic22ZrGhPHDzC6BxZwGQ2C5Hah6/dD/z5kT09yyaMJ4fkRrriHhLL1Nah+NVLIWYWWVle4Fh3ykhXPU4vvsNXm8f+iZHzrRmiAnwJjlXmBgjHc3PD5mgXDoqQdQ5lZHZDbXnR7y+THO6OV2g0mxc37C+N4OP4QhjDZDVw0Rv06I6OODk1/QzRuUGCtwP/8l//TFnXFMeiIi2OtOL4VFTk2Fx2zHCyOkWaxg+fqCR2jpQVKG9U5xfkaY93mJbh+TVhTKfIivHnr4+FQxxaxKybxe7pX/nDcw+6pzcxGU5pLsng6TffktMwIkT2FrH+9vzo4CU+FwGaCIp0Oh3nIkjdnuvD3dOVqMIWjkUeZkZ44uKtYUzturlgpL0Ueztn6vqmRX7OECEEqHdwFP2zq+zvrJGXEMb33/xAUasoeO9kQG91Ac9fO5JQ2WM78mEMJzqWZxfZ2tpnb3NZiL4AHj16RpXII4WK+GD+7f/+d9Iqejg1CqNiMtFemoy9gxOpRbVs7R0y01OPl8NTnnlFsvyBXtWxriq8VNHZbTtyT1V2FK6egWpL+dXpNukaV/79j38hr2GAIyG018Y7cXz5FHdNKntnV6wK4+fv5Uh8YScX5+ec7wzhLfIwIr2ELZ1JLib0G2Fztp8QL3v8YjIxPbC2462V2AvnWljXazti49rCylgbmpBgqtpFfRGHjhYHCHN3w19T8KAV9pr5nhJeu/oIxzGO/niH2pJcYmJiyMwppKamgdyUeJIzC+ju76WhphRtXCLVzd0i+LU6lSvLOdXpwWiSUpnc/HktpP9stJQl4e5iT8vkfUB/c2UkI8xFiM5Upvbe7uO6MB7RUJSAtzZdHc6lBKalWeG8dnClf+V+bvnl+TbBIggOTyll7fCYib5WEmOjSE7LorisirrKQiKE8G/uHqSjpZHUhDiyCiqZXrGNzri5Yk/YiKjgIAqqe9B9gcO6vhaU4dIpGg8CAiNZ2LlVl8JnLo4Q4vmUtPI2Dm47L23sb04RHR1GbEGTdbEu8yaxnk4Eh6Wxc9/yw+5iL27PHqtzq/ePDmivLiY2JloNuCsqq6jIF+InLo3ewWEaqstIiNNSXN3Mpm0hQ6WXYrq9BJ+QSPqnft7UjX82+urzcfN0o6xtwHZEWdxLR2lmBJ4hCUw9GNmmcqmnu74ER+dwVm1zXftqU/FycaZCxCq3KENwK9P88dVEMbm2y/r8GEXZyUTGxFNcUkFjQw0JsdHkVbUy2NshPksjPjlTHbFmts3VOtpfIT46gpDEUnaPv+55tf8ZzDsLJAe78ddvv8M/TJTvxDgR37jw2s6RooY+9OYLbkwHdDVWCJEfjSY0CGf7V7xx9aVlaJ7zDyhPZWG85IgA0iv7ObOt+LQ42ISP0ysc3f0pKK+hODcDd7un/Le/PKOiY0zEo/c+VLe3SkFKLI5OXtT0TqmrFV+bdQy0VZEk7G1cQgrVdbWUC5EUHR2vzh3sbKwmPTmRzLwKFu5GyonfnRwi2EmU0cZfY52UfxzDwSZlGXE4OSj1SRnNd83x+iQB7s/59vELQoTwTE6Ixd3JAQdnH+p6poWIfifvrwx0NxTgp4mka9rasGo2HFKUEsbzF68Ij8ugtlbkYaQff/n9d7j4p3G4N0NsoDN//vYpIRFRZKTGq371jZMHFa1KY8A1hxtzFApxr42NIae4gob6GtIS40nPLGJkeFCtf0lJKbT0jKC3rTh8Ydynp6aQyMDQqQHjryw6lWWO69Ji+PZ//htPXjgSEhaGp6cvcUJ5r+8eo1sf5uW3f+G7p3ZEaBNIFyo6xM+VH378QRiMXPo6m/C1f05Byzhnd3l8TWNBHHavHeha1Ft7OpWWMSHWZpSeTlHkTg62Ge3vEQaqnowkLc8e/QXH0HirARICMEmd01nMoS022RjtxsPOQZ24fGIbj3F2vEF6jB/B2hQ29CZGajJ57uBB78zP6c16W3SaD1dJD/PFzT2cdYOt8J/rSInyVrvP5/auuBCGsiAxlOCkHG6nmnaUpPD97/+dp3auJKZnqqt7ebvY8e+ishY2D2I8msXL1ZGIzGpObN/ZXhgl0vMF2rx69g8OqMmP57W9PVEpuYzNLtmGysLOwiC+/t5oS9rQicyd6y7Fydmd8vYprs52KUkJJyAmnZ3jE5b6y/nmd7/nibiP5IxMUpNT8PVw4q/ffSsEWCvmd5YxG6wr4sVLO3Ht9+dRPkTZtmV9ZYbO9haqyooJ87Hnz3/5keKGCfXzuoxo7F940G9rhr4Uxkfr+Yo//ukb4dAjSBPlRVnA5bkoL39+7cfU2vtDBEaVhYTcncj6gACuyY0WjlOIzskdLkW5yIkPwsU3guVdW7RiOSItNgSnIK0oW2dsirwNDfAgubSHqyvxzKezBIk8TCis4/QuaJF87WzODRDu7SDqRwbGu4aGG0ZayrGz96Gkod92zMqNMs+rv07YgwDq+hdUkXm0NEi4h4dwEnlqq7+Va2a6hOh086GyfVQ9ogimudkZ5pfWmOxvRatNpKpGOF0R+MaLQDhaBFfBoREUC6Gr+u4rixBliYSLQGxUiKGvkdbyFNxcHWia2LUdEVzpSQl0IkTYrPmjt12bWbdPZWoUoUnZLO0q821vKM+O5I0QnX3L98L86mxDOHpPcV4x20blLVnYWJlndm6ezZVpspO1ZBRXUyvEZ2JCAlHCEQcEBqNNLmL7RLEZNxhFsJAQHSrsjbKYjTQKn4ozITpTI70JCIphfvv2fd+wNT9EkNtTMqo7OXor+2/YXxwkJjKAtMoOTs5ErTNvEecjRGd4Clt3HWJX7M534/D0GSXtY2rdvD43sjAzzeLyMrPj/STFxFBS1URVSa4IxmKJEfVQmW5TUNGIQbmVG+HXRmvwDgune2xere9fGwMiwHXzdKe09R3RmR4qnjv5/Z5O85E6NPmlXzybOqs/769NF/GLC+WN98PkTbo9SpN88AmLYnpb6USA4901pqbnWV5epK0yn9j4FNramkkUea+NiiI80EfEj8kMzVtjs5ODXTKj4wiPzGP34OetqfFbxLy9SJT7K/707RPKu0TcJ44dbQgh4ufAaydfhue33y67l2dMdNXh9uYxbmFJrOy+n7ers4NEh3mQ1zx2JzqVxYpGOmqJDPbDKyAUbVwcMSF+vHHzo3FwzjYi5JqtpQkyEzV4+ARQVC9E78Mu0Otz1pdmmV/ZYGF6hOLsZFLzSmhoqCY6IoJYIYqDfHxIy6tm48havtZnxoj3caC0vpOzjy+1+1nZXZ2hIDUaTy8/8qqUudDWZ95ZGsX55V/5/rUbbWNr6rH1yR4CXF7j6BPJ6tGZsGgPMB/TXpYpbFkkg4v3Q6WPthYoEILWy9uL0PBoUuI0ePn64hsv9M/2BBEeT/jjjw5CyFo7evaWhgnzcsDeM5i5XWuDjfF4l6nJSba3NmmrLSE5OZWKqkbyU5PQxscTGxlCoPjdNmVKoOVanao21lFCYoz/VPny5+jpVFaXe2JHSlYRPQNDzCysYV3NWhj3iTp++MtfeCHUe3xSEgniAbTaODILSuifmGC4oxKXx8+oH9m4H4ImaC9LwdXRURWdSk9nUZwP/tEpLIqAwnCwTF5qnAgGwsjOLxCqPA6H549wj0y2is79JeJCgolIKeBAVXc3QnSKl+nlRUZVhxBg1l8yHa2THu1HSHwqKwc62nK12PuEs7D9c1ZdfVt0mvaXydL4ExISz75tL1GL4UhUsFCCotPEyxXn7CySHRtIUEK2qGzqKdRmxvDd7/6Is4c/KanJxMcJIxufTFZhDUvrO5ztTeHh7EFqaevd/BWlpTc++A1xeVUYLi7VFuHq4hx8PT1wcfcmt6qFU+M5e8sj+Af4EFvUwrHxkvOjRUJ8PEnJKWFmcpjIID9yqnswn58x1pjOH/7wV5w8AkgX96E4WmU4dF5JBaNzK++tpLU60oLr6+e4a7I4f/ujO64tBkY669CEhhAjnim/IF8YJTd+ePSSssZJ9Zz6zGi8XQOZPbRWRLNxm2Bh8L7//gWhUXEkJiYQF6dVWzorGpRtWN5p2hZM99bh7eYo8qjdduSeyoxQYeBCGFg44fx4TV3IKSg8lfV9WwRysU+2CFbdwpOEAzS9JTovL0UhPhjHX+SZNr+Gk3fHiEi+Wg7XpkgK8SAgPIGjO192qa4UZ+cWSI2tp/4WRXTO9NUTHCI+651X50ec7c2jFXXMMzBJnbN5S391Jo7eQTS9s8XP6d4auYkxFNZ301RaSrImnuEpxRmZachPJjRQw7G48PXNFS0l8YQlZgjR+X4jzNdAnwh4Pd2dqehesh0RnG0R6uJAlBCM+++YAfPJPlXp0QQnZrGoNkxCS3Eqbg7OtE7eO+iL/QkRBLsTm1PN0YPJYDfnx/TWFRCVmElPXx+pUYFUtfWoAdlEdzWhvq50qe/iBtPGBPFRiuhskaLzE3JpOiQvKRzvwCjGV257s27UEQVuL59T0jr8Vr1SPttfHiY6MpCUinZ0yiSwKz0Z4Z74h8SyoLt1VBaWh2p5+txFDYgfui8lJqgqyCApu5z+znoRq0TRYevJ7KhMJyQqhvF9YRBuzKwN1+IdqqF7/OsUnUu9NXh6eJJVcz9txWw8IkPjQpA2m2VbwHrLjRCdbVW5vPLWsnFsne8521WhDq/Nr7kfoms43iE12JXAqBSW9x6KmiumBlqFyIyhsa2bRvEeYpOr2D4+x7IzRlpsGOllzWpenxzukRmlJTwqn72fs43eb5TLgyViAr3xFvHN5m2r+fUFw41Z2L18QU33rOqrHnJjPqWlQMsbFx+GZ9/vgFmbHSYm1IvcxlEhOt8u+QbdLovLq2wJ8dIlyoKvfwiD8ztqHdtbmSApOlTU53CaB6bfivcfcn2hp7migMTkDAYGu8lIjiW/XtkaRYiywQpCNBE09lvXVFifGUXr7UiJ8JmmL050Cnu0Ok2GNhwvkQ8N3eOc3RmbGw5XxvF1dyQqq4z9216oixOaCuN5+dqRvtWTt+3KhY6O8ixV/A0svLNi75UQ7CuLrG4equ8nJTqE+LJ2DncXheh0IlCbz9bxrdM0C1uWiYOTM81Dy2pDxC2b0z0kxsVTWd9CS3MNgUJT7ZtuuBG/nRAfTURGOevKHOtLEdu3lgg/GDBVvvYZ5nSWxYWIwDyC8bl3F1a45mC5F4dnT0lUlji/3Svy+opzs5lL8f+lsQ5cn/5AUmknRzb1Yjk/ICvGh0dCyKo9nUJ0Fmq98Y9JY3H7hMG6fF6/fEVySZt6/vHmIslhrnhEJLJ9JAyQqGjRAX6EJuRyYLZeU+np9PfwUIfk6JTBzALFwaRF+xIYk8TGqYmRumyevXSkaWDmbvNiZUN2i7Lk81tvWMFIaog7ASHaO9GZGe5HYEAsO7Zx5xeGA9K0QcK4pjG/e8nZzjwZUX74xaajt7UQdZUl42jvRGVT/92eSErvsbKy2NX1NaatMZzfOKn5d+tgt+aHiQ14RWxuBYcmYZCF8FeGfer2NkgM9eTxCwcR0C6xvzJGgBCdUfmNtn0BLdTmpxDk70tYZAwaIer6Fw7VDdKnO0p49uQNJXVd6kb0Cup9iPf0oX3xznWbpEX48Nfvn1PdO/PWZtsXokycifs/XB0jLsiVVy4hTK4cWrc06K7A5ZUdxbXWls/ajCi8nPyZObA+vLLcfZKfg3DmiUyI71hR9l09V1fbekf7quwvDBHi6YRbSIIIAu/n/phO1on0scdfk8yqCDiUMfV5CaKsBieyetvTeb5HRpIG17BEptWezhFC/N1JKum+E50+7i5EZ1ei+5i6lnx1qPPS0yJwcvVhcNkqYq7ODsiOC8RHk8jIu2JP2In18U40wSGUNY1hEvbjxnxIcUq4MO4eDG9Yg7Cr8xOyo/0IiEhmcv2+B+7SdExndT7h0ekiENPRLkSnNiCB6Tmlp++GjrJMIgMD2BWmS9kKpCI5EE1iOjNb76+i+zWwNNquLv2uSa26s4vbky04OrqTVyPqpvXQHRci/5pKkvEUgezMmnXY33R3Db4ub0gqbrWtoG5hrrMEV3dPyloG7+ZjKntNL4y0ExkeQcvgLJurcyQGedHSaw2UlYVrtCGOtI4LASzO3ZvpIyIoiMK6Pk6kTfh03JzRXJKOo6g/xS3japB1c2GgrTyNNw5+HxzWerA9Q0x0GFG5tRzqFRt/TX1eLM4eXtQMWs+/Mh7SmBuDg3eUdS9dG8p+kn1NZURFJTAhjk/11BCtjWB4zRrX9DUVEKbV0q8sSCTuY6KpEJ+QKAamrb0UXxv69VGCPFxF3JWF0SYudlaFP7R7RVpp8/v7ZF4Z6Gsow8kxhAWbmDxZGSbQy52guDx0tgq3vjCIl6MzSfkNHD9oRFa2g8pO1JJe1MjW5iaNGQmk5LVb91A/mScvJYrUwga17h/vrQhxGk5YagV7tkUXJR/AckRRegye/hEMLVjL+tXZiRD0WnWbjY6xZSwizju/uBsGgF4Z/hofoI4I+9B85d3lKVIjgkkRMZLJtguD0Bvq/2/ZWpxQF9oKi8tRF9o61++QFhNGYLSwzxs/0alzY92mQxsdR1P/NPtLw+o2OpVdc+p7P5prJCxaQ3WXdaScshCln6MH5c3DX9wq0menu+TEhRMUHsfYu0PRBRcnG0J/BIs8UbZ2suaJWbdBcXIYjq6+TO/q3xaWNyZ6movxDYugbXzV9pkQhA/i8yuLmc76IhGzetE2tY3RoCNPG4pfqFbYOusiTdemAyqztLi4CRs6u3MnbJUtJIvTtep2b1vb2/R2VOCmzcGovOLrc5JSkwlWVobfF7GMiG06KkS8EhgxNfJrD69VRGdJbCCebkEMfaBVxGzYJzvKi+d2zhTUdrF/dMzi1Ci93X3snJjRKxP/NR78+NqDChEIHBzs0d9RxbNv/5U/fPeSriWjKjrzotzxFqp7XgRZ/TW5vH79ilQhIPX6Y7rE38++/R1vArXsKC1shi3iRdDwysWP9okVzoUo2xjpxsfFVQjV5geic42UCE8hcGLYuLjhYG2KYNc3uPqE09o/ycbqIqMjw8wsb/H+DidGkgKd8QmIZkERncqKUSFe+PpEsW0TnRb9PskiwPRV9pbbtnApRG5ejD9PHTzomVxQlwE/WB4i2EPZ+yqYjpEFDg92mB4doG9kimNh1M93J3B8+YY4YaBvTasy9C/a9xkxuVWs7x4wMtTN0ISyrYMwJkVJODo4U9c9y66osN4+7mhy6u42u96YHsDx+Y/8l//2b2SVVqCztQPoNmcJ93jJGzdfVXQfHR0wLSp0b+8QR2Zle+13uWF5ohc/p+f89dEbMgurGJ+eZWF2ksbGeloHJ1mYGiQlzAM710DGFrfZXJolNdSF3//+O1EWxtWrVKcJwffGmyml9VigLNQxUJvDy+eviEgpZHl7n931BQZ6exib37Su9vsON5YTqnPi+eYv3xKVUszo5Dxzk8PkJoXw49M3FNb3qWL1dHeRrNgAfPzjWFEm2Sqc7wqjFopDcByT2yY25ocI8HZW53Qq+7ShXybEw0EEKBq6xpZ+tW1gJJ+Zm2umBhrwsHtNSGyeutJ1T2MZ7s6uQmx0vjXP08oNRhHwasOD1WBqT69UrBume2vxcHiFX1whs0sr9LeU4eLkTHF9DybbPBdl77n54TbCg4Oo7bPOW5rpqiVKCNia5j42lucoSEkgPrlQHe2gCNd4f0/hHIptQz6/Pi6E6C8SQebzV0409oyxsjBFeqSvEPxJjH/AgV9bzhhqLRfiIpR+2wrTZ0crZIv6/srOm7bBKdUexQR4EiwCnzllOwcF4bD3VmfJSYxGm1nGkfGcc/HbWTGBpOSXs7qvrGaahyYsitnNQ9U+TXVWERjgr/ZUS2vwadmcHSDY2wknYX+HpxcZ628h2EdpBKxjW/d+2T893CYnIZrgqHQ2963CZ32qmwB3R9yD45laWWeosw5fdV/nFnS3i9CI+r402k1iRBgFNb1qY/PubB8xEaEU1XexubVOSVYCcanZ7IivnOsPqMuJITgmldmfufLkPx3XJqoyY3nzwl6d/7e6PE1JWjROoo4NzGx8ICa4Zqa/mQAXZ9omN609WddnlGfF8fKNI4UNvSwvTJOfqsHZN5ze6c27tTIuDSI/izLRRCUyvnaM5cxAd0WWCNhTGJlaUhcuTIiJVvffVdhdGiQiLICMmp67eE7yIYQP6m9UF5HUxOcwv7yuroHi5+5AeFI+G0cGjjaX6GpvFfm8wM7mKrUFKbx++ZzUkiaOP7AXrnF/jbKMGPy1+ZwIe6lwqttXh0ZvbG6xNDNCmhA6zh5+tI2uqnM2F/sqePLspYhja9Ttx/b3dkSd2mRndw+D6eKuLJ2IODQlPpqE3EpOr67RiZgtPSFS2OYKllY26KxIJyYxneFFxQdcMdZbh4tnqPidpbuOoi+Da1ZHG3j+7DnRmeUcnJxy8OCZjWcWtRNmsLkUV2cX4rIrWNnYoKOmAE8nJ+Kzazl9b1nmG8b6WvDzDaJUiGz1ca8tIj5eYUHpXd7eEDFKKT4ebkQkF6MTmuZGiPi5vhohQh2JzShkcW2TvuZyPF2ciBYC8ti2JPDNpYnBplLCw2PUBVkVITvaWYO3fzgDU8sszwyh1UaTXd2F0tFp2pujPD9X2L+SKfOvv5DQOXUZWhEwxaqi4F2Umz9cnyY9LoxXoiC/fP0GRxdP0vIqhdM4Exl/rS6zHS1E4tPHT3By9SIpM5uwIG8hmAIYWBOiUwjLirRQNEk56kIyZwerZItC/eTHR7j5BhCrFb/v50FYirKQkLJw0CX9zRW8efI9T138aRueZWd6hNiwMPLqeji1tdCd6TbJTw5XVzJdF/7rWrzApbEuYoL9xL2+5MXzFwRGJNI/s/aBHjYTebFBRMamsXJk4exwjcI4DVGRqezZmuYtxiPy0qKITMpjblv8wJVF3QrA7tkjnjl6UNM7iVmIdiXg1Pi7i997zus3dnj5BVPZ0o/RciWE6hwBXn5kVXVw24e3szRGepQnmZVt7Bwe06TsBSgCW2VyuKf4bmF1C6emcyGiJ9AIp5lU1nZnPCyGPbTBnvzhmx9pHppRjykoge/SRCeRweI9PH0q7sNeXaq+uKaDk4+0IF2L51mbHRHCOpQ3L1+o33sh8s3e3Vd1MDqDnumBJrycX/PilQNBYVpiI8Nwd/ehvtM6tLClMIkQvwjmDu/V5Ll433VFGeKZxPeU69k7oNGmMDi38dE9X0/2hCHMisfuxUuePH4mvvcKR1GWCsT93wru0/0VStNjiIzJYn3f1jN7fkCB+F5AXBZze2a2lyfEPQaJytWHOi9dGIbOSiGCf/yOJ47+dAzfb78g+bqxnJ3Q21CKp+Nrngtb4OTqQVZpEzsf2Z/x0nyqLoEeHJHEzIpVGF2Ka3QLseqmXEOUTWcXDzJLG++2RFIwHKzTUJxGck45+w/qaXNlLr6ebji5uROfls/EinXF56PFPnx8/Clu7OHsnQW+vh6u2VufIzMuRN1E/NUbB3xDYoWgXHl/gQUF4Vw3ZgfVVdCLmwfRq6dcsbkwRlKYPy+FbX1jZ09QZCLDc2v3+71ZDAwL5xqfmETPjG3IkvADs8ImR4YFYe/kSIBGS6MIdpXfvTg7pSonEf/QOKZXPrJBvuQXQ90Tc7CFEB9Xnjx9jp2jG7GiLqzs6j446uVSvJ+eqhz8/UKE395UAzPlGqPd9QR6OvLsxXPsRT1OyClj8/C+J0FZTKWpopA4EUMs71pHJSjX6m0oF2XKCycnEcjFpTM0taJe82BrCW2AF6kiiLb2qH6dnO4uq3tivnr2jJfCp3r7WYdGvjus8pa9lUmSwkUMV9xmG11lvUZRuriGyPvnL17jGaAR15h8azuitdkh0hISKKvrtokHETduzJMlBIebiyOuHr4UVbdypAhMESAPK7sM+PvTNbPN7bRCyYe5vDAw1F6Nv4eTGtPaOTgTk5LL3OahOortULyzjJggIZCeibj3DW7u3mSU1LJ1bFTa5N7jxqIX9aIQVzd/ZratDTvHO/NqrKsM2X3xyh6/0FiaesfvFn6abCzk8bd/5Y/f/oirqzP2dm+EXX+OuxBQTcOLtpVyb+gRcXtcUhoDM9aFcpSeu6nBNsKDfHAR4sw3KIr2wWl1JekL3Ra1+cIWR6awsP2lDbEWQru3nGeP/srv//L9W8/s6ulPXeeMeAa4MOtoqczD09meZ8JH2Tu5kyoE9/qh4c42PWRnUfgzTSCxWeVClCtHLhlvryHQy0W1bU5CW6XmlbPyYNj61fmpiEFK8LO9/zeOLsRnFjO/dd/jrEztyUmIpaCqjUOb2D3ZWaMyMxEvNzc8PLxIz69kdVun3tdsbz25mZkUdcz++lumKHvRHAqFvba6geGtZa3fRtloeXSoj7a2Nnr7R1jd2uPiNngQAcP+5jIDvV10dPUKNb7Fzu4W62trqtpXhn/ub6+xvrWNWd2w9oajnXUGejrp6h0QKn+d3a0NNnfFNZUhkQJlf8i5yRG6+4fY2DviwmhgQ1xv9/BEbXlRuFY2TlWuuyGue6t5RJbtbij30i3utZ3x6QV0tr1B3+ZKnLeqfleZTH0tKsfe5jrr69tc2Gqqsh3M7rY4trWL0dZVeiHua35qlJ6+AVa2rcuKKyvO7qwv0i+ep629k6GxKfU+FcF+YzGyurzCzsGJ9VzBxZmB7fUlITh1WMTzHojCMdTXTWtrGyOTc3dDVixmA2vra2wdHIvzbEVY5PWOuO/p2Tl0pndasWzP3t+r3EcHQ6OT7Ozr7lojP4bucIeJkSE6Ozpo7+xieHxavTfla5cXJpbmJsVnneL4DBviHa6vrXN8Yg26lc1p11bW33NiZsOJyKcxOtrb6OzqYWZxRV1p7acwnR4yMzFCp7j3TlGOJueW7xZVUri6MItytMH65g7ntw5PBJh7O5usimMmcexc5O3G+qqa37cG12w4ZnpsiO6+ITb3fmJoiOSrw3KmZ3F6jC5RtpX6cHh62/TzPsIKMNMrHECAP6WNvUIQWo9fmMU1ZkbV+jEornH0zjUuTHq1Tu6KIPghxpMDJpVy19vPirCXimW7MBxSkxMjHG0iI0vbH3RMXw/XHO9vMihsW2d3LwtrIsD8CWOkDg9KjSIoNoNxm+hX7N2BsPGKbe3q6WNZyccHpkbpuTzaFzZpU/Et99e+tJyxvDBDV1cnE8KOGFUHoQjbITSBPmSWi6DaKHtYfg2uL8/V3v6ujnZ6BkbYFr7xoyjve2WEyEBfdZjmvq2uXYn3ubY4JepgO33D49btVB5wIz7f2d5kY0cJxG0HBcpe1rPjI6IciPK3siXiC+GTzacMNJXi7R2gNkLexhNfK6fCv48O9NDR0c30wpoQEm/76odcGI9pKc/E3T+CsaX7udR6cY2RgV7ab6/xzkqjp7oD1tY2OD590KCnxIVbK6L+99A/MqnuTKBwuDmnzpMLjc8Vxz4ec0ruuTw3sazGYaL8Dyp1yBqfqZ+JmGdtcZruThG/ifhzfErEhh+MeW+5YXN+RO0oUrctEnHk5bnRev22VmGr+1lY3XpruKvhYJepMRGbdXaKMtAhylKHqgV6+gdZFwLpts7tijq4viXiswflQ7n2yvwkvT3dTMysiFjaGuPPDbWj8fclW9jijzWCfE7OdHsiHhV+X8nX22cWMXp3z4CIN4+4lT/nxhOhVUZpF/kxKGL/A1s5/xCXymJCldl4BoQzMK+MFLjhdH+LYVE/W8X3lRj7wLaI10MUzbA4M67G0/3DE+wevT1098IkysDyMofCLt4dF9ruVPjGkcE++hW7u2+Nia/PDihIiiIjt4gtk+XXF50SiUQisWIxHVOUmUBsag5zv/R2JjcXbM0MEK3RUNrUd7cKt8SGcHcrE11q/pQ19HP6021Ufzdm/SFtlXmERyczu7Z31wgo+bJQFiLprsklMiaB/qm1X/w9qcOxk6JJLaji0NZ4KrEhAtXd5QkSYsLIq2n/xXuBby7PGO6oIVITScfEOtdfueD/UrGYTuiuLyQ4Mo6xlXcWtfkVULbdqS3KIio2hRnbfMjfBjdq/cqI1ZBR3Mih4ddvdFkaaSU2RtS/sQWlY0yKTolEIvmc7KzM0NHVxdza7i/qjJV5ixtzY3R29bPzTkulxMrVuZ7R/m66Byd/8ZVljbp9Rvq6GJ7+yBBfyReD6XiLzrZ2JubXuPglhcn1FbtrS3R3drFk2+5D8jZKr/HC5ADt3YPs2lax/aVQpjDMjA/S2T+CbTqa5LNwg/5Q1LH2FqZWNtV94X9NlLnbAz3dDE0u/uaGV19fnLE6M0xTRz/7J6ZfOQ64Ym50kJ7+IU7Pr6XolEgkki+Bc/MZxjPzLys6r644MxqwWOSwzp/i6uoSg9GE+eKXzSdl0TyTSfZs/bNgPjtT39cvOvxViM4LUbfPz2Ud/ElEPpkMei5+YVulzM89U/JfDjP4Iri0nAtbe8blryw6ld0MjKbzt6ZK/NbQGwycKztr2P7+dbjCaDjDYlu3RopOiUQikUgkEolEIpF8MqTolEgkEolEIpFIJBLJJ0OKTolEIpFIJBKJRCKRfDKk6JRIJBKJRCKRSCQSySdDik6JRCKRSCQSiUQikXwypOiUSCQSiUQikUgkEsknQ4pOiUQikUgkEolEIpF8Mn5SdJrNZg4ODtjb22N/f18mmWSSSSaZZJJJJplkkkkmmX52UvZAPj09nRKa8sOi8+zsTBWc29vb7OzsyCSTTDLJJJNMMskkk0wyySTTz05GoxGdTje1tbX18eG14u+7/8skk0wyySSTTDLJJJNMMskk089NNi0p53RKJBKJRCKRSCQSieTTIEWnRCKRSCQSiUQikUg+GVJ0SiQSiUQikUgkEonkkyFFp0QikUgkEolEIpFIPhlSdEokEolEIpFIJBKJ5JMhRadEIpFIJBKJRCKRSD4ZUnRKJBKJRCKRSCQSieSTIUWnRCKRSCQSiUQikUg+GVJ0SiQSiUQikUgkEonkkyFFp0QikUgkEolEIpFIPhn/kOi8vrzEcHrK/t4+u7u77B0ccmIwYrm8sp3x6biyXHC8v8v23hGXV5/+9yQSiUQikUgkEolE8o/zD4lO3do8JVlphISEEhEZSWR0FNrEZCrr21jbPebGdt4vxbUQl+Le1H8b9jcpT0tAk1rM4alRPSaRSCQSiUQikUgkki+Tf0B03rDa14Dzqxe8dPQgIyuL1OQE/L3dePrjI5x9NAzObvCL9EHeXLOxNEvvwAgb+zr10On2CpnhfriEJrOnM6jHJBKJRCKRSCQSiUTyZfL3i86bGxY7KnFydCOztJ6DgwP2dndYmpuhuTybl9/9EQe/WBZ2rCLxP0tnRQEabQYjKwfq34bddXKjg/GKyuTIcK4ek0gkEolEIpFIJBLJl8k/JDqXOmvw9AqipnvcdtDGjYnWgmj++PtvyGsexmwbZ3uyt05TTRlpaRmU1baycXBq/UBwpj9icrCLorxM0jKyaeoexnChDKW9YryrFpcf/8x//1+/xzkohvbBSXbXlilLjsYvJoOx4X7qK4rIyi1gaHqZc+sIXIlEIpFIJBKJRCKRfCH8/aITq+j08PCnuLnfduyek61xXB5/Q2hyFYdCPB7uLlKWm4EmXEN4eDgBwSGk5Fewsneqzv1cmxkgNyUSTVgoft5euHn6UN46yMnZGb01uTz/0//i//qv/y+P7N2p6RxhZ22FioRwnoq/U1OSSY2Pwu7ZE+w8QmgbWfjF55NKJBKJRCKRSCQSieQf5z8lOks+IDr1x2uEOvxIaGwpK5vb1BTG4+zmS3XbCNsbqzSVZ/D0+VPii9vQmS/Z21hkfHSA9a1dFsZ7ifCx56V7MNMbR5hPjyiKD8fB3Y9KITjPL68x7G6QE+LFv//lGQU1bWzv7jLaWsLT73/AJyqb00spOyUSiUQikUgkEonkS+GXF537C/i9ekRsTiOjQ32EuT7l28evSMkro6K8mLS4UP7wH7/HLTyLrSMj15ZzttaWGBkZprmmlADXl3z70onBhW31ei2FGfiHaumZ3VL/VuZ0Zob68dIjkvVD20JCl1vEB3nj4B3D6qnFekwikUgkEolEIpFIJJ+d/4ToDKC0ZcB27JYbVocbePH9Y4o6Rpka7sD9hz/zhz//QGB4OEFBAfj5+YsUQW3zIHrdIaOdDWgjNcQmJpMUF4PTi0d8/8aV4SVFdF7RkJuCd1AM3dMb6i/od9bJiw3FOyaTg1OTegx0pEWH4eIfx/LJhe2YRCKRSCQSiUQikUg+N/+46PQMoKJt2HZMHL2+4nhvjZRQdx47+DOxusfWVAc+jm+ISitAd2rg7OwMk9GIwWDk/OKS/dle/Nyc8AqNZ2nnGJNuh6bCJOzd3RmYV0TmJXXZiXgFRNE9s6n+jn53jbwYITqjM9i/2zLliNSoUFV0rkjRKZFIJBKJRCKRSCRfDP+Y6OyoUrdMSStuQC9E5NH+DsNdTUQFevDD41fkNw1wdnnN5ckKKWEevLBzo3V4CWVxWZNul7n5efZOzKwPNeH0+inByUWc3cDByjAR3i/541M7IToVkXlDY24cr8T3S9rGlJtFv7VMTmQQ7pq0B6LzkMTwQBy8o6XolEgkEolEIpFIJJIviH9AdMJabz12j77hX3/3JxydnHF1dsbFxRW/oHAqm3rRmWzzKsWllsa6iQpw5/mrl7i6e+Dr60dKfhkrhwbM+n1K0qJ5+ewp7v7BaLXRBHg48MLFh8H5DSE5YXW8A0+7p/zx0WuKmvrYXl2kNDESn+jMt3s6I0Nx8ZM9nRKJRCKRSCQSiUTyJfEPiU7j4TZ97U3k5+eSm5tHUVEJDS0dzK1ucXFlO+mW60u2lqdpqC4V5+ZTXlnH6MwiRtuJxztrtNVVkC+u0d49yOz0FOOTUxyeWAXl1YWRKWUfT/F599gcp6enbCzMMCaucXZxu2jQOQszUwyPz9n2+JRIJBKJRCKRSCQSyZfAPyQ6/1Fu/pO7mYh7s/1LIpFIJBKJRCKRSCT/DPyqolMikUgkEolEIpFIJL8tpOiUSCQSiUQikUgkEsknQ4pOiUQikUgkEolEIpF8MqTolEgkEolEIpFIJBLJJ0OKTolEIpFIJBKJRCKRfDKk6JRIJBKJRCKRSCQSySdDik6JRCKRSCQSiUQikXwypOiUSCQSiUQikUgkEsknQ4pOiUQikUgkEolEIpF8Mn5SdFosFk5OTtDpdOr/ZZJJJplkkkkmmWSSSSaZZJLp5yRFR56fn2MymabE3x8WneJDtre32djYYHNzUyaZZJJJJplkkkkmmWSSSSaZflZSdKTBYOD4+HhqbW3tw6JT/JurqyuZZJJJJplkkkkmmWSSSSaZZPq7k4LQmVOXl5dyTqdEIpFIJBKJRCKRSH55hMaUCwlJJBKJRCKRSCQSieTTIEWnRCKRSCQSiUQikUg+GVJ0SiQSiUQikUgkEonkkyFFp0QikUgkEolEIpFIPhlSdEokEolEIpFIJBKJ5JMhRadEIpFIJBKJRCKRSD4ZUnRKJBKJRCKRSCQSieSTIUWnRCKRSCQSiUQikUg+GVJ0SiQSiUQikUgkEonkk/Gri86bm2sslgsur29sR97m+upSfG5Rbsx2RCKRSP7JEHbu3HzGxeW17cDf5uryCotId5bv2noNywevcYPl3MyZ+dz29wOur7gSNvRa2FDFjl6If199xN5+rVxfWTCdmdU8+Fmofknk09WV7YC4xuWFmr8fyn3lXJPJdP+uHnAlfvtS+DGFa/EuFH8n+RzccHlxjvn85+f/1aWIP0S6KzeiXFycK9ewWP9+hwvl+qKcvY/4bXGdqytResQ1ri5FmRB1+7eE8vxnZ2f8PU99KerKlbB7tyjXMJvNXH2goil13Czs47nl/V+4EfVO+e61sHvKvy0i/z90Dcnfg/A5F2Zh9xS/9jPeqij3yju4VOqAjSuLqC/mC1G/bAc+wpWwn2ZRdhQbazSaRDkSZeDBdYRj40x8diGu/x7iM6UcWTWEcs/nb9n1f15+fgFWNdbtMyu+TXlvIg9/6gqK7TOLPFXy/C6J7yg2TrWJtvNU3yo+U+rWu1xfK/ZT/K7I+2vxHeV7754l3suvKzovLWbmRnuoa25n/fDEdtSK7mCdjpYmhqdX/65gTSKRSL4UzPpDBruayM3OJLeoiunlbS7/hr842V5maGCQhfV99W+z4Ygh2zXySqqYWtm9u8b15Tkrc2NUlRRSWlHD9NLmA1F5zfLMOAMDI5yaRbArnM3q7AR9g2McGz4UHH9lCPe1szpLTVkhWZlZNHb0c/S3nvv6nPmpEfpHpjg0CvcnrrGxMEFFcR6ZWbm0dI9wYrwX90e7G7Q1VFNQVELn0AR6EUTdcrC9wkB/P4vrO+rfBt0+I4M9TC3tCJ/2NwqB5Bfj8twg4oxeinKzyMkvon9iAfPfUB1XZ0cM9vUzvbiOEgRdmfVMDnaSlyXqcUEJI9PLWG7rmQiKtxdnqC8voaikgtGZFS4eXH5tfpLBoTF2j43ir2t21hfo6x9g80CvxGNfPQfCnjVWlZAh6mB1Yyc7aj58nBsRrG4uTNPXN8LesUE9drhpvUamco3mTrYfXOPCdMRYfxv5uXnUtXSL6+ttn4iA23jMxFA/UwsrmEVeX57pmB4fYnB8Rn2vkr+fqwsj0yJuLy3MJT09g+aeEWFXP9DgeYvFxJzI85GJGfRKe821RfihUcrE97Oy82jtHRX+6MONQRbLKZ2t9aQmp5CSkkxCfDLFZQ2sbx+pn19fnolr91FeXEh5TSNLG7t3Ivby3MSS8H/9A2OYLMrbvmFpsp/BkUkO9P+cjX/XVxcszc3SPzSO4ewn8lxBCMyj9Xn6BobZEvrq0nzK5HA3hQXZZGTnUN/Wx6H+zHbyQ27YWZmjNCOD1JQU8Y7TyRDvOT09jVTx/35Rd5QsNhxu09NcR2FhEa09o+ge+sW9TYb6e1ncOBB/3XB2ss/gQA8zy5ucP6h4v7rovL46Z6KzAvvXb8ip7sJoa0C8FpnTVpmFs5sXLSNLH+0JlUgkki8Vo26H2oJU3FzdCA3X4OPuhoevhoHJFdsZ73NzZaSuOJPEtHwWdvRcGPbF32m4urgQponAz9sNV79IBua21fOXJgdIjotFG5dIUoIWTXQC02uKoYfjtSmyUpPIq2jFJETOjQiO16f6iYmKpb5bOOKLr6HF92NcszLVR1SQD14BYURpQrF740BycT37px8TnjdCPIyQFBtFaWMfQqezONZFmJ8nvkFhhAcHims4kVveyonpErNum7K8TDQR0aSnpRASHEpd55i1kfTKQH1JDvHpeUytH6pXPzs9oKk8l9jELFZ3j9Vjkk/LzfkpvfXF+Hm6EijqYIgoDw6uPtT1z3D2EeF/dWlmtL2SmLgUBmbXxQETHdV5eLm7EhoRRZC/N07uPnSOLarCZWthlMz4GGJi40iI0xIVGUPf7KYa/F7qN8lNiSe3uI69UyUoE2VseYrs5Fjyqlo4NX241/RrYWd5jJToYNw8/YiMCMPNwZ6I5GK2jqxi8n1u0G0vkJ0QS35VO0fmSw5WxkmJDMbD24+I8GCcHR3QpJawfWIS51vorStCq40lWdTBmCgNyYW17CsNRsIGzPa3oI1NpHN0Vn1X15cmBtqqiY2JY2jxQPZ4/p2cG49or8rBXfiygFANcdpoCquaWd27F/pvc8P6TB/xsVqq2ofEO7hhZqAVf+HHfIRN1Yj36ejkQkZJg6gf79tlw/EivqLePX5hT1B4GKEh4aRllrG6pRP10szCaJew7RohiFKJCg8hLjWXeVtj7eb8KGnxWspbRkRdt3ZcrY53EBsbT33nuPr3PwsWs5HFqREKMxJ48/oFr31i2Tw6tX36YS6Mh5SmJ5BeUM2Ozsj+yhhZKVrCo2OJiRD5bm9HbFYlezqlHj3kRm1ozYyOFvUtnCjxf602Hg/7J/zHH74hv66bM8Mh9SJOidBEkpKWJnxjMBUt/eiUlp1rPZ01xcTGpTG7YW0cuDDpqClMJTEjnzlViFr51UWn1cDMEuDwhJfuoUysWp3z9lw/Qa5vcAlMYOOjxkkikUi+UK7PGe+qwt3Jgej0CqZm5xjpqsPp5VNCkwrY0n1A+Ah7vS6cYlhgAPk1nZiEJpzprcbb2Y6IjEqm5+YZ7W/A4cWPxObUsb2zS1NZNpqENEbnl5ge6yc6wJeSlhkMBj2tJelChCaKwHlLubTK2dEWxUkRhMVnMvPA+H9tmI43yBfP+crBk67hSRbmZsjTBvDjG3cahOD4EJdmHXWFCQREJDAoAtLz43UyYgNx8AhiYGya2alRkkM8eOUaQM/EEkvDbSKYiqOoeZT1pSlKU6KITi5j79DA9myPcNga8mu7OD23ivubKwuLIkgK8fehuKEf/VcuOL4E1id7CPZ2xTtUy5iogxPD3YR4vMHRP465TWu88TbXHO/ModUEEZ9by67+nK2ZXvw9XfCLSGVqfpGR3hZ8nZ7jE5PD1t4OfU1FamNPy9AcS5O9pGvDiCtu5cykZ6y1lNCwGNoGZ7gd+Xlu1NFenYt3QDiDM2t8tQO5LKci6I3GwdGdio5hFuZnqM2P59EPL8Tfox/sbb406+lrKMbLK5j+iRVht0xUZP7/2fvP98aSLT8X/I9mvs98mGfmXulqNLq6rdvSldRSm9N9+pw+p1xWWjLpvQNJkAS9995777333nsDWhCEIfBObAD0zKrq7srKrKx4n2c/mdgb2AQidqy1fmFWiHu4elPRLu4xP0Nlbhxff/eW2u5xTrWbxGmiSc2pYG1znfrSErw9I5lY3eP8ZJus+EhScivZvDf6ubM8QYoQsbGZlWgv5XT3n4wQeeM9dXi+f0dCTjkjU7OsrS6zvrXD2QfKURlpLk3TEBaXzuyuFt3BCqkR/rz1CGFgYob5uSnSo3x55+ZDy9CiaH33saBdG+DtWxchjsqZXlwQz9ASa+s76A1mdEdblGfFEZFWztzSKhPtFahD1DT0CP93pqW5NJ3QqBSWdk9vRz8N5wfkJUYQIfzfwv6vR1soI/1FKYm4vfqef/rj7/jb10GsHgjh/QEsJiOLfXUEBwRTq3SEirZ2drTN5Pgo0/PCd81PUJSk4g9/fE3H+OqTae/6izPWl5dYXFxkaXmZzc0lElQevHYJYGhuXQj6AWKiIoV/62NFPAN1+XEEJ+SwuHvC/uIg8RFqcsrauLgZ1rRaWBlvIyQomOI6EdsY7TX9CUSngpnRply+/vM3ZFV3c3V1SaMwTN+/fEfL+LrtHSZhpKdGeqmtraG9b4S9B8r8mu2VOdqbG6ipq2dyfgX9F92DL5FIPncMp1uUpIbj7hPC3MGNuDBRlhbOGw8V7WNrjnN3WIw6EWDFERKRyPS6CIjN50IghuPmGcDi6U2AZqUyLQyvsCRa2zuoKkghvarV7qwNZ1Snx5FfM8RQdwdJsbFUtw7xIBywmNmeasXPL4SqttHbQPhLY3GkjWBvD+LzWx1n4GpnFJeX34sApoaLJ7/byv7qBJGBXuTX99quz/dW4efmRE79kOM9cDjTJoJoNzLLGumsKSQru5BJW8e6gcXucmKSihkfn6QwI570oip2tA+nEirTrUszYwiOSmVu48sV/Z8F1waaitJw9fKnYXDJcdLKeEs+L755RV3f1JMpltcGHaMtJQQGhNA3vWk711gQh7tvMO0zjvqyGhisTucbpwD6h/poqMwmpbCOHaUfyXJCZ30BEZnlLE4OkKyJoryxj7Orh39pb22GiAAfMstb0d5M8frCOF8fFWLdg/CUsttyvjreQOXyLWHJxWwfP+14024ukBETSGxuI6eXJi53xgn28UQjBOKNHbvYX0bl9oaolGImpwYJiEijrnPKdm1xYopwHw3dAyP0NpWgiU1ncmXPdu0Gi/GcvvpCAv2DGF7e/9HlDhI753vLZMeH4aPO4IcnSN9gYXdJ1I+PN2XNfbZnYGeml1AvZyLTa+1vEcx1F+Pi5ERRw+Aj0Wlgd7qZt96BtE3atcB99tbmSY0JpqBr0d6pejBDXlIKlY399HW0kBQTLfzs01lF8wN1qIJVFDXe2fXPnZP9XeHT++horCc/JZwXXpGs7H94tozudJf8+FAiE7NZ3X+4dPGGnbEmvvrjd9QNzP/oWmvdvrBXIUFkVnQIAWthdqCGyPgURjbsbXhzvJHw5Fx6BweoLcwgLiWfraNHT8n1BblC8Ktj05hXRqoFn0h0itjqbBONrzNuAcJAlxcT6utKUEIhOvEkGYST7m6sICZChY+fD95CueeVN9rWBVybDaxM9ZMWr8HP2xNnp3dkFFeza5t2IZFIJJ+Gw7UpElVeBAkBeXQb1Vjor83jnZMflS2jjnM3WNEfLBOvCScxv9Y2PcxwuEi8yh+f0GRuLZrVwkhjPs4Baqpb2umsKyRMBFaDs0vMjvWRFhttW9uSFp9AXmmDsIVPAzuTbo9oYUuTsyrY/5Wubfkx+hoL8fV0o7p3xXFGYDpA4/kOVWwOq+cPwxurrRe/BnfPYAam7J9pLU3DQwjM9in7VGaF6+N5Aty9iM0oo6etjrSERIpF8LIyN055VgLpeRWUF2QTHZ/B1OrDYFfh2nTFSEspQQEBtI8syOl9H5Fr3SH5SWr8QzRMrN5MRbOwMdON15vvyKnr59FjwIV2j6KUWMKjUtk5FMGaVUdWpA9BYfEsnNxUlpHNiWZevXxPY1cfbc3lRMYk0Nw/zdx4PwWZSWQWV1KWlURydhmbR09HVHTH+1SkRhAWl8XSzpc51Xqptxovbx+ya7sdZ+DyTEtejBe+4SnMb9in3t1hYWGkU4gSV1onN20ic6GnQsR2PhTW9tvfIrjQ7pOnEXUSlczA2Agx0fGkina3vLJCc20FEZEp1NdUkRAdTcvgAldPIupr8Qz0EhEaSG7TCGdP3yB5hqXxLiKDPEkqamVClHtPVxeDY9Oc6J6ZtSOwmoWtay7EWxVza1PPDzeozE0gJCyOgckF28h1nmijoZHxDM1v2MXjDdYr1oaq+fOLbwmISqK5vcc2Sneut3fSnB1uUZoVTXhqsTi/xFBTGempGZSUlJOenCZ0QivPdeec7i6QGB5EfHo+ul9J1d+u/TYe01QQy3ceapY+uETDwsH6uBDWQeRWd9o6bxSsFgu68xO2N1dZmBqhOj9N2J8cFrYft8NHCN9YkxWJm08o3Y6OuOWJdiIjw8it6WRxadE2dTa9oIzivExSU9Lpnnh+CdFgbS4qlZrmoXnb608mOhUmO0pxevkV/8d/+kuc3P0ZmN/DYtAxUJOLr48f+RVNLK4s01CSjtt7Z3JEYW5vr5AR4cVbDxW9w1PMT48yPTfLse65xbESiUTyy7A9P4Ta9z2BMRlc3MvAON5WgZOTD8X1d0GUDavJ1gusCQ2jtHnEJjKPV0cI9/EmICL/rifSes1cdwlvPQOo6x1ne2WKpFgNEZoY+4L/rAJKhOGPTcigc2iUhcVpurt7mVvexOAQv2bDFSXxAUQlZTC/+3wv6K+d9oo0vNydaJm8E37W63PSVa4ER6cxe/AwCYNJf0pbVTqeESnMrO6LM1YqczQ4OXvQv3znlM1Xm6g8vYhMLWNxfY2GsjxCVaGkpaWTmJRKVVU5MVFRVDb3MTc3z3BfDyPjc3eJNoRL3ZoQwZsICEqFWD27n3FG8rNyebJNmsaPoGCNCKxuwk8r24sjhHi+IKOig6NHuTiOREAaHxNJVFYth2cijjDtkuDviio8le3b91rYXejB7ftvqe6dYnV1gbzkGMIjo0gSz0FGeiblpUVEa2Jp6x9ndnqKvt4ephZWuHRMN7sWz9tIfR7+oTEMz9sDuS+N4aYiPH28KG0bdJwBve6E0oxwvEOTmF6xr727xaJnsKUKD+cg5vfsnQRDDVn4untQ0Thie62gP1UEe4CwizFMCLs22FZjq7PouARbwpNCITqyUhNJyq1ibmGBidFBevqH2do/ETVn53BnQbRTNRGZ1RzIQYqfxFRvPZ4vf49XiIbwkCBcnd7y4s17Msub2FfayiNM+jNqs2KISCpibvPOzyyPtuLx6mteufgS4OXKH/7wZ1LL2oQtdLzhBuETd2f6CPNz55tvvuX7Fy9wcvWkoLoNrSJ0hRhaGOsiIjycpORE4uKSKClV6j6JlPQ8IWrnmRkfoqdvkOXN/dvcMMbzfYpSI2wjdUsnj3qdPnNMZ3tUZUXxnXv4h0XntZ6VoUZCQ8Jp7J3lZpKFxaRnbryP7BQN3q7vePnaharuaRyrPz6AlYudaQI8XIjJKOfA0UmtCP6awgxCw8JsNi8pJZXKilISY+MoqWpidmGZ4f5uhsYmObr3bOyOtxEZFk5Rfa/t9ScVnej3iAt8y//7//PvCUutsJ062V9D7f6K7165kldcRV9/P1X5ybz46o94qFOZmp0hK8qLly7+dA3PcnZx+ZvbDkAikXx+bM71E+rzDv+YTHT3UlSOtVcJIeNLySPRab02MjcoHEVYCLV9C7apSEeLg4R5eBAYUXAbLCkB73xvKW88/KjqGLed2V2bpbmpgc6eYSaGekmOj6W4qo7mujLi1MG26aChmhR6JtZsPclKBrym4hjUyRmMO5IOfWm0libj4aqITkVA2rFeX5AR6kpIdLoQnQ/7wA2nh9Rkx6BKynaMPFkoz4rg9Xt3BlbvnPu1YZsQLy/CkkrYuTBjODugv6OZ5rZOpmemqCxIIz6zkLa2JjKT43FzdcHTV0VxTQ8621xmK6cr48SrVeSK4Ono8pdzsb81lHW9qRGeBAVFsbhzE1nZRafK6zsyq4TofBDoWjlYHSFGE0JKZQfHygjB1RYxPi6EhKexc/vIWNhb7MXtu28pbZ9A0aLH2yu0NdXT0dvPxMgguckJZBRU09FSR1xkKC7vnfFTRdI+MGmfznmtY2WwCr/QCHonl2zt8kujryEfdy8PytrvROeVEJ1lol35hCUzs/pIdBqOaa/J57VPLFsn9tHh/po0fEQbqmh6KDorU/0JCI9ielvYL6uRhXFl+VUTo+NT9LfWoBGCv6mzh6LsVIJ9PHB2diElr4L1I/vazpMDpUMijvCYIvYc5yQ/zHhnJX/+7/+R79xC6B6dY3ttmbL0CP783Vvy6vuezNq4ujgmJyGI2Jwq1hwNzXh+SHNpGm7OTqhjk0hLisP9vROh8Tm2ke8H2ZzFC5P+Eu3BATs7W8xPDBIb7MnX374SosX+9xS/uTY/RkN9HUOTswx21JOSlEJRRQ2tdaUE+fni6uFJaFyWLeu7bdscwyl1halExaQxsfrr6nAwnu7+qOi0GM4ZbS0jJDhS1NMGJkeZKpnuV+YmqC7JJU4TjrurOzFphazuHt+ueX2MxXRFb3Um7918qO+ZemCndMJnDvV20NDUKnzfNFUFWSSn59PU3EpJdhJuLs54+QXYZqaeOpSvYXOY2JhwsipbbK8/regUNBcl4vRWqO8O+9Dr3uYY777+I//9b/7Ae1cPfL29cRc/5JWzG8nF9RyfnrE63kFYgCcu7j7EpuYzNr9+l8pcIpFIPgF7y2PEBboTEpXK6a0nvaa/oZB3LoFUto45ztmxmo3M9NcTEhZE3cCiTXSeb06hCfDDLyzr3jQhC2MtBbzzDqSud/L2nNlk4ux4n6bSLFJyK2isKicrPoGm9iG2N5bIT4kjMjaTS0W9CrPeVpGMOiWbsdUvU3R21Wbj7elC/fCW44zAdESstzPq+Dw2zh/6iCtRdlWpGiEmc1gWTlihoSBRBEfu9CzcBcfm0yX8PLyIzKjgwCEYlT3NjFdnLI62oomOp76lnZzkaPIq6lk/1NJalUdIkIox29oyK/qtKZJiwsipaubwA1sFSP71GM73RBsIIVAVzezmTW+7lY2ZPvycX5Df1MfZg8dAiM6lIaKjgkiv6uREqV+zlpQQD4LDE1m7jU/NbE628vKFE9V9M6L1Oc6aTejP9ultrkITm0ZHW7MIquOpaO1jZ29biK04ohJSWVGePauezbE60bYj6JlYuL3Hl8RUewXeytRYUc436M+1FCT4ERiVweLmw6DZqlfaSi6v/BNuRedYayF+Iu4rbbhbf6c73iNfoyxdiGfBsXWGsv+m0SgEyNw4OULIFJbV0FJZSFRsDpNzqywN1AvxH05ps10Anx0ekBOdSGRUAfuHP5wFVGJntLOCd9/9kZTKvtvRMfPRDD5Or/HXZLGvZC69h/78SLQ/f+Lya1g/tnuw0Y5KXN+9JLWsg3O93rZ368pwI14uLiTm1fFcfr07rGzN9RPu40yQOp3dm1nryt6TJiOX2lUKM5LJLqqmpqyUjJhYemfWWFuaJFYVTE5RHdpz8Qcsl7QUZxGtyWBs6dfV4fBTRKeyNYqS3CwoWE3v2OaDNcuW62sMV8r+qhfMj3bi7fyatLJ2jmyBwVMuj3dJCPXGLzyZuc2nideUPVRNBlGH413ExadQXVtLqRCfGXnlLG3uMNxeTUigPz2L+7aYxro3SnxcBJkVzbbPf3LR2VWTTbC/itaBDdvrndVR3F++ICShiOnFNba2tmzHzt4+p+c6m+pWMgIqe8I0VRfj6fQ97gERDM3eCzQkEonkF+Z8b4XsmCC8A9Ssnt+YUT21eXG4BWjoHLfbuFuEHVscbiVcpaK6Y8Y2emI+2yIzOhh3nzC2bmeoXNOQrcE7WEP/7P1peeLzwolEqaMZnFuntbScpNBkZhbs00s7K7KICAqwjdZciwCtPk+NOimdSSVh0RfIZE8tfp5uZFTdG2XZm8L7vQvJBQ128X0P48Uh9QXxBCRk3WY17W8owuu9M2Vd07bXStCjne/E09OLrKp2dPe8+fH2AtlJMRTUdDI/N0lKmD9tfQO2a0uDdcSGOtM6vqQ4LE6WRogOCyFfvPdYSVEs+ShYTWdUZsXi7q2iY9IRE1hNtm3a3r5yoWVo+onYO9icQBMdRnxJC1qd0iFgpCRJhXdgGINrjgDVrGOsOZeX74NEG3yY4GR1qo+k2Dga+qYZE89gXGI0Ixv2NcGDLYWExcXSty4CX/MlC90V+Kki6JtatsUyXxr7M134engQk3+XNObsaAuN1xtisirYebze3HhGd20xTm5RrDlGH3cnWvHz8iSpoOl2iYF2b5UIL1c0KcUP9vw0n+7RUJxNbHI+M3MLNGWnkFHQKQJqcfF8ifxUDSmF9bYOvJPDLZKiNIQmlMiRzp/InPBPwf7vyW2dvGs3ph1U3u/xiUxjV//wKTbojilKCiMmo5IV23IGi6ifGP7xT3+mauSe/9PO4/v2NZ6hKWw8k1zqPrrdORKFCAoIS2brQbWZ6W8sJSY2leHFZVrLK4gLSEDRpRbjBeUJIaSmZom6PhMO8IKqvHjU0SlMbT6aX/+Z81NEp1X83snOKkJD1HSMrOJIFPuEy5Md1F7fo4ovYfPwmXJXloLM9ODh4kRKcbOwh8/LQdP5HnlJGgrqupga7yc3I5rSti7btcN1YU8j/SntX7Zl49evDojXSodrm+36Jxed7ZUZ+PkE0txrX4R6frhJWqgbrz2C6Zu7EZJXHO6ucyxEp+5Sx8baim2huDIMXJsRwldfvaCwceSLNOISieTXgcV0SWdtLm++f0VO7aBte4D9pVGCPVyIyylnzxbQ3kOIEe3yMJqwUGGQu9Eqm0RaTHRWZfH2+xdkNozZeiwP1sYJcFVS1leyfy/r5dHmHOmxajLKOznVmxmsKyVCCN6RacW562nITyUiPJYzcdvr6yuy1D7EpOSwevg0ycmXwMnWHPEqX+E7wlg7EIHp9SWtRfG88wqhdWTR8a47ro3n9DTk4xoYw/iCXcwfLo8Q6fcet8AEtk8MmPVaypPD8PALo39q5Tbw0p/s01iaRVh0Kiu7J5wfrJGk8qKs2e54J7urUAd5M7iwbeskXRluJjQ4kKquSfQfCAgkPwcWW+eDy7u3qJKKOTdZOd9fISXSF8/wDBa3nwZtx6LuUmIjUScWsX9sbxvjHWW8d3pDVE6DLbnN8eYMsQGuhCQVsnVvS7eT7SWKUmNJEEH22aWe1bFmwiNCaXckUVHiG3VcIvMn10JzHtNdnkagOoGJ5acJp74Eri/3SAn3442zL2NryoikgaHmEt68dKG2ewrD4xlpVhMTXfX4OHsxsnpki+HM+j2SIgJ47RbAqDIrw6K3tdM3Tp6i/UwLu+poQBYDo+01REVoaB4WAe7pMS25iUQmlrF7YuJqc4T0uAjyanps7fZoaxpNhIq4IhFMK6Nfkh/lVMleK8Sba6CGBWHnRIUx3VXC67fOtjWZj8XNteFC2NwkgjVZ4hlXZtRYGW4r5ts//j1BicUcnAvBZ7lipD6LP/7DH9Eo66i1h0wMdtMzPMmlycTB9jpr65u2UTLFhjcXpfDu5WtSi5vQ3/bXWdiaE74zPJySum6uzGZ6qosJ91GzqbMI8XtItkZFek4Zh6eXWHT75MQFo0lS1mn/upSC8XSHiowIvnYN+/CaTquRralOgoJVVHSMoVPm14pYYmt1iY1NR0e1KMu+5lJev/iG3No+257dq7MjtAqxuGqrW3v99VRn8Pr1O2r7Zm+n6d7HpNfRV5dHeGQSs+sHXO7NkZEQRmZVk63ONmf7hPj1p2V6GyV9wfpgLeFhYZS3Dds+/8lFZ2tZCp7uvtR329ObW8zKvmYdBHo68+69K2GRUSJwUpGalcvctpaT4wNq8hIICw0lOjZBOHJ/ouIzxAN+l21QIpFIPgUnuyvkJ0bw9pUzQSGhBPv7EhKZwvji1jOdYlaMZ7ukRYcRmZTHmiORxsnuMnlJkXz/xpng0HBCAnwJjkxmcmnn9h76kz1hOzNRRSYwv3NmO3+8OUVeqgaVsI0xsdHiSKKha0Zcs2I4WiLE14es0sbbPSS/OMwGJvua8XN3wsMniPDwMLw8vSmq7+bk8pnebeHqFkba8Hbzpql/xjYaYjVdMtxaiZfTO7x8g4SfCcLDy4/y5j7O9I5OAyHgJ3sbiVSHUdYxbusYUHqaO6pzUAnnGhkXS0RUNFmFdZxeGrk2GeisysEvMIyB2fVb4Sr5OOjPD6gtSuXd2zf4BKoICQ7CNziczvFlrkxPS//qXEt9XrJorxoRRNlHvPVne5TnJPDuzVuCRBsMDg60ZcRV9qsz3Sxkuzqjo6qAsLBoUa/2+OPyaIPSrGRCQsJF+4sjMiqG6pZeFIlzfrRDdlQgManFbD2T3fbLwMryaAdqXw+cXH1EsKmyTZVNLqgXQvBp4hmFjdlBooSgL26bQOdIsqXM4Aj1d8fJ3ZtQlQpvYbtSiurYuXePvZVJUmKiSMou58TWYWcWsWMnalUIEeoIIkNVpGQoG9MrAbWZxeFmVMF+1PQvov9iN0r9mbk2sjDaJXyHK14BQbZy9XZ3Iya96PkMzELozPXV4OkVTNuQfW/kC+02VbkJOL15I2xgkKjPIN6+/h6/iCRmNrVc7MwSF+SBT2QqJ8JWzg21oAn1J0gVKmy4CjcXN2JTC26XQChciLZUnhGLJjmfOVv9ws7CEJmxYYRECN8XH4M6JoX+ySXbfpXatXEixN9Nza+63Ybn14IiOstSw/ijU4gQ/h8QnaLdHe8tERniJ9patT2JnfBv/c0lRIR42RKeRUWE4+fjTVRKAeuHypCxhersKN46eVA/YO+UvTzdIzfaj+/eejHynKZS1tNODYi/E0JFyzB6JWeBSUdPQznhISrh92KJiY4hNbuUfccykubCJOEXo+izdYaLr/WpRefq7ChtLe0sbdwrTMV4TA6Qn5FEZGQEsYmpNHYO2Ka+GK90jPc2kZoQTZQmmpySGubXd2UaeolE8hkgHNz2MtWF2WiEoU/LKWRCCM4PxThW4aS7hSAJCAoVTvpurdjh1jKVhZlERYl7ZBUyubj9wMZdnuwz3tfOwMTivfUbZjYWxijKzSAuKZXW3lHORLSrZLBT9kX2CVbTPvZlriW74dp4yfRwB0ki6NDEJFLZ1Csc8IdHNZRRsGS1P9HZVaw7pu2Z9WeMdjWSGKMRwUsS9Z1DHN/fCN2sZ3lmhI6ePvbO7sTs+fEOTbVlxMbGUlzdzMa+vRPheEf8jcgQolJLhNiQ0/p+CXQnu7TXlYlAK5KElGy6R+dtwedzKGurV0ZbCPL3o7x1hEtHp4wy66qlqpAoEYMkZeYzOL36YK2U9eqEiaF+OgcmhIi5OWllf22eyoIc4uISqWsTcYuSydFiEMF7J/5e3lS2DH3Z+4qL37o4MUBGQiyRmhiKq5rZdYwgP4dOu0VppoaAqIy7/QWvRXlN9JGeFGcLZItrWtm9N8KssLuxSGd7J7P3gmOT4YLRnmbxuXhSsouZmN+02Tv96S6VOQm2zMHLe+dftA38ubGarpgf6yEjJUH4o1jySutZ2/3QlhtW2/YkUYHepBbVcHRhn5mjiJmuxkoSYzVE2jrkqpjfsCd8M55s01ZXSXVrL1fWaw4256koSCdSiKTImHjK69vZOrifcd0qnpk9+jtbhF/cwJEcWrRjYZenB22ZbJMzsukZm7OLIkx0VWcTGBpF88jTvbI/d8zimZ4b7aWysVv4suc7bhQMwm815McRGK5hfME+S3RzcZTiHEVHRdp8WUVdKxsHNz7omsn+VkrLKples9eF4fKUoc5Gapu7OTp/JuGSaJfby9M0tfWyd3J3XdkOqquhkvjYOArK6lh2LFe5PFonNjSQuLQidh2Ldz+56PxBhPjU6y+5MpgeGQkrJuMV+kv9B4M5iUQi+XRYuLrSY7qZCvYDnO4ukqgJIzGnlO3j+4Ze3EMv7vGMkVMy8gl77Xj1kGuTEf2VwWEzLRytz5IQEWzrAd2+5yi+ZMwmA3rDj6/dsV7rGWopIygkwpapz3CvSE3i8wbDM/3iVott/7NnEUHTlf4Ks0PgmK8uGGypICgolK6xxdtU9pJfBiWBhsH444VuON+lOCOGyLgMplf2RYRxg9XWjg3mpyLRqjwHD1Jv3qHM2NKL5+DmKTnfX7clFQqLS2dlW3vv/l8utjK4tUMfRslGOj/cbtvHtqZrBN2NihBYzEZR/nfleB/F/n2g+DEaRL3bBIdAGX0T949UBVPQMCTqUjbCfwk2v3Lvmf4QyrK37to8QoT4aR2Zf/B+o7CpV3rDjz7/lmuzqPdLYcOfG5e0/mDbMwttcHXvc9rNGeIjVKQV1HB4+QW3PCHd9lbGiQwNIqeiBa1jdo+S9M6uo3683O1l6/jvc4iLtnbnePkQR8zjiFeU/amVJIohYRG0DUzfdpp/3qJTIpFIfgOM9zZTWFrO5Mqu48zPg9L7uzbVR0FhmW167v2RGokd3dEmtaWF1LQOcvQzJ/lRNrXvaKigvL6bs+em+Eo+C6zWa3YWhykqKGRgchnTD0Ze/0xEgLy1PEt5cRHdY/OYP9Rh8RvGcH5Ie30ZpXXtbP/AqOi/hGvDGcM9zRSUVrFzavwJgbfkX8vZ/hoVxQU0dPVz+UlHhqwsjnZQVFrJ1M/sWz9HrBYzQy3VFFXWsfLBqbi/BFb0Z0c0VZVS09LF4dldZ7cUnRKJRPKJUdYS7u3tsa+1r8/8uVB6p7X7OxweHmH5CaOuv1VOj7Vsbu9z/jMLwyvdOXs7O5xfXctg97PHIupqm/1D7U+aofBTUQLBi5Mj9vcPH4ykS+4jgtSLU9tOBeeXH55C+C/BbNBxeCBsq2NLFskvw+nJITt7B1xc3SW/+8WxWjjc2+Lo5OQ2E/KXjtWoY1v4nOOzuyzPvzii3I26M/Z397jQP+zokaJTIpFIPhN+zgGWGz7GPb9UZFFJJBLJz8en9j/S/30iPlDwUnRKJBKJRCKRSCQSieSjIUWnRCKRSCQSiUQikUg+GlJ0SiQSiUQikUgkEonkoyFFp0QikUgkEolEIpFIPhpSdEokEolEIpFIJBKJ5KMhRadEIpFIJBKJRCKRSD4aPyg6zWYzl5eX6HQ627/ykIc85CEPechDHvKQhzzkIQ95/JRD0ZEmkwmj0Tgt/v+86FTepGzWu7a2xsbGhjzkIQ95yEMe8pCHPOQhD3nIQx4/6VhfX+f8/BytVju9srLyvOi8vr7GYDBwdXVl+1ce8pCHPOQhD3nIQx7ykIc85CGPn3oIfanMoJ0WmlKu6ZRIJBKJRCKRSCQSyc+P0JgykZBEIpFIJBKJRCKRSD4OUnRKJBKJRCKRSCQSieSjIUWnRCKRSCQSiUQikUg+GlJ0SiQSiUQikUgkEonkoyFFp0QikUgkEolEIpFIPhpSdEokEolEIpFIJBKJ5KMhRadEIpFIJBKJRCKRSD4aUnRKJBKJRCKRSCQSieSjIUWnRCKRSCQSiUQikUg+GlJ0SiQSySdC2FtxOF58Jgg/4Pjf58VNMdnLzH48RLy+d812OK7Y+Anl/PSeP8w/7933sX835c/9M/+k5GfGavllK+AHnzHHQ/FLf6dPyYfL42OUwSOb8Ihbu/FDdfRbxPrz+oSft4x/+D43f+fLrdJ/zg9Tyt3x338Wz3/oB+9lu2g/7r9P1McvLzqt4gHeXV2kraWDhfUdx9kbLOiPdxgdGmNr/1i8kkgkkk+BFf25ltnxIeqqq6ipb2FmeZNrx9Ubri6OmRjsoaqyio7eYQ5OdY4rH0a578zUONuH51zfGmQrJ/ubLK9vcHT56K9cG1idm6BefI/Glm429o4dF26wsr+5THtjPdW1TcysbnHfkF9o9xjqbqehoZX51T1M9/7m5ek+Y+MzHJ1d2s6c7m0yMzPL7uGp7fVngcnA9sIMjbXVlJWVU1FZKcq7muqqKupbO1jdOeRob4PeFnG9vIxycb1SHNXV1dRU1zA2t4bO5LjXE8xsry8xPb/CiV4pdws7q/O0NtRS09DM9NIWpgeOyMrx/jrdbY3U1NQzPLGA/q5AMRsvWZoao7Gunv6RGU6v7n3YfM7C/ByLa3a/p784ZXFmiuWNXfEc3PPMkgfojvcY6W23tbHuwUlOLgyOK4+5Zm15lv7hUXa0F45zz6O026W5GZa3Dh8ERfqzQ5aWVzjQPn7+rRxsLdHe3EB1XaOwBaKNPaqys8Nt+jtbxbNXy/DkInrjXTs2XZ4zNz5AY0OjuLbA5b1rl+fiOZibZm37SAnKMIjvNjc7xfrOEeZPHgSZ2Fyeprm+RrSHFubFs/v4K12e7DPc2yHqp5KBsRkuDD8WRpo42FljYnqBk0uj45zAYmZtdZXVjW0Mpof3MF1dMDs2QG1VNa09Q+w/trPCRi7PjNEgbERDWxebByeOCwoW0WbX6Gptormtm837saUIebXb68zOzYv2b8Zyfc3exgpTcytc6K8cb/ptoj89ZKy/i6qqShrae1jfPeb6ceVbTOxuLNE7MMze8bloJR/GfHXO8vw0K1sH4gkQRW+6ZHFq2OZfm9q7WX/i1+Bod43OFuHXahqYnF+757uUdnPE2GA39Q1NTC1tYLy9ZsFwuse08GPbDj92drwv/NoMa6JNfc4odskWc4gyrxP+em5150nMoXB6sM1Al7A1VRW09w6xq5T9Bwv/mvPDLUYnZjnR2W3nifCXPe1NVFTVMijskc741NDoTvYY7uukvKxCtJs+tvdv2pSF7eU52psa6ewdYf/4ri1azeJ5WF9mYm4VvUGpZTMrC7PMLa7Zr38S0Xltpqssh9/9H/8J18AENk7vN2wDu5Ot+LsF0tA7KV5JJBLJJ8ByycxwM/FxUajCIwgK8MHbX0Vd7zQ38eLF4RplOUkEBgQSGhaKn68PcWkFrB2c29/wDGb9KUOtFcSn5rK6dyqMsMkmGDsaygnxcSEkNp3J/XuB2LWevqZSQkMCCQ5R4e3pQagmiamVXft16zVLkz1Eq0Pw9w8m0M8b32A1jYNztsu64y2qi3OIjYkjPiaamIQsBiaXbdcMF4d01haSmlfJ1pE9SL88WKU4N5P8imaOLu59j0+J4ZLZvnbiI8MJDAwmLCycKPF/p5d/5k/fu9DQN8PO2hxFKWqCgoNQievh4eH4e77j73/3e5JLWzh7znMLjrfnyUtPorxpwBYsz4+0E6EKIjg0jJDgQLwDwmnpn+bKJlqtrE33k6wRdR2sIjxUhY93AMWNQyJAFa7TomOsp5Gk+Fhi42JsZV5U03UrMJZHW8nIyKZjdNH22qjT0tdcQVJGHgtbR0+CeYmFo605cpMiCfD3F/UuytvHn7TCGvYvnvYiGLQrhPq78PvXXvTObDnOPoP1itHuBlKS05na0GKxWtCKIG6wq4WYMH+8QiLom1xyvNkes2xM9xIr2liQaINBvp74+qtpErbAHjBZ2F+dJj1OPJ9B4vkLCcLD24+yxm6UPgeD7kS04Srio6NISEgkUq2hqXucM1uAY2Sit5mUpDTGl3ZsQbtiI+rLc8gsLGd5+2kg/kthMesZ66gmTLSDYNGmQvy9CVRpaB9edIgLK8c7S+QmRxMQoPzuQDzF786pauX46gMNTqAVba4sL528ilZOxPtMon2vzk1RU5zFm3duJBXUCAF4V7/680MaRHkE+AUQHByCp7CB0WlFLO7Yy8YkxMxQi7CfgQGoVCF4e7iiis1gYmXfdv1wfYbSnGRh+5JEHUSQlF3M7IZdfOgO1qjOzyavrJ4T0U6tFgsbcyOkJCSL3zmH/jfaKA825slPicHPxw+VWk1UUgY9Y/PCDj6sV/3RGilRfvzPr97TM7X2QdFpNV8xP9pBYkIqo4tbmA1ndFbnExzoj0rYWn8/X1QxKYwt797eY3N+kOToMNHWhD0O8BNtP4jarkkhfK2YzvdpqsgnPj6e+NgoYhPTaB2y21WL4YLe+mLS8sqYE3ZV4fJoi5qSXDILKtg9/TyVxbXxjI7KQkIDAwlTRxDsJ2IO4X+ahS833c5+sLIxO0hqjFqUh594n5qkrDxGhCBXyuU5Lo42aCjOJLusmeMLPbsrEyRFh+Mr2mxoaJCtfAtrOrm8Z1J3V6bJStSImMJf+NQwYmOzGJ1aF/VoZHu2n7SkRBLj44iKiCKnqJaNvTPb5w6V5yY1iRpRT5e2Z+Waqb4G0jIymVnf/zSi0yKUcE1KFP/9//X/5N/8739NdsOgzTDbuWK9v5yv/u5bChsGxCuJRCL55bEKsbcyP0JrezuDo+P0tVUR6PaKtx5hrJ8ZhfE001qcjKuzC8m5FYyMjQgBKl6/eyeCri4hYJ4PujbmBolW+ZBV1cOF0YzxeFMEPalCsL7nv//lf+LPL30Y3HI4RSEodxf68XFzJkQ45N6hYRpL0nB+/R3qtDKbUddrN0kN98LVL4zqtl76O5sIdH+Le0gC60JIzvRVExkZQl17N1PjI0SH+pJSUseF2cqSTWCpqOocQXczOiF+d0dlDiGhETSP3ASXnxgR9B/vbTE5NsqQKIOxsXGmRjoIcH3Bd06B9Exvc6U7ZWlmlOHhYUZEfY2OjZGm9uYPX72kvH3kwcjvDdfGczqqsggMi6F3bofDtWniQzzwCIiis3/Y1pOs9nXDNyyJ+c0j9KebZEaH8M7Fh/qOPkaG+siKDeWtW5AIlnY43p0jJz6clMw8xqcmqCtOJygomNVjHVen++JaFIkZRawdOEbRrCZWpwcIC/ShoLaTU0W4Sm65Oj+gMieWd87uZBXViHofoSA9hvduXhS3jj0S6WZGGrL4L3/xv/Fv//YVjcP2jpXnOFoZJ1UEszGZlUL4WTAbT2kXQaqflzd/91f/hb/++hX1/VOOd8P5wbpoY964+YZRJ+p9oLMZX+e3uPpGsXBwKUTlMdU5cbx8505JTTMjw31E+r8XtiKIocUdDtbGSRRiJyW/lvHJScozogiJz2Z+54yLvXmyxHORkFHOwZne8RctLAw3ExISTElDjwj0P0ErtJjZWRwSbcyJwMgUOgeGGRS2JcTbA9/QRLbPjZj05zQVp/L6jQs5JTXidw+QEO7P9+88qBlYcNzoEWYdnbVCbKjCaRlZEiGplb21OVLCw/F8+zX//i/+L7w02bedCtcmPeOdVbg5vSU6vYS+gSHKs2L5/rtXpJa22dr19nw//i5vUcVl0jMwSGt1Ni+//57ozArOLs7pa60SgXUY/ePTjA+04+0WTFnDMOZrA8PtVaiFrWsdmr8dUbo82SUvMZLwxFxmtu+PmP420B1tUpyiwdXVm8KqBoaFvR0dn2Rtex+T+c6vWa9NTHWU8Q9/9R/4f/yXP9E4uMCHZoYrnRM5caFEiTa3c6Zjtq8Ob1dnwuMzGRgZo7GiEC83J6LSy9DqDFwbjsmOCcLFI4DK1l4Gu1uI9HXhjXsYy3tHLI/3ERGooqSikZnJYSF8I4mKK8RgtbK1MEpkaAhFdV0cXzp8qVnPYEslKiHoFOH66WcQPMV4eUhjWRkF+eUMj0/Q21Iu2t8b3vrGsOaYuXG6NU+yaGOeQoBX1LWIeplgYnqarf0jUfbPFP71JRPd1bbOmpaRec61O+THBfHeM4jihi7GRoSAjAnD1d2PrukdEddYMJ6tkxUnznkFU1jdxLCon7GRKXb2TkTbEDY5VYjQtDwGxTPRUpZJaFAQ7cOzGK90tJdlo1InMLtxeGufT/cWSY5RUVDRgM7yCUTntclITWo0ge/f883X3/DOT033ba+kgc2hKl7+8TWlzUMPRjpPD3eYnhgTwd04S+vbGB7PbZFIJJKfiacG3EhnaSLfffWWmUMdlqsdgoXTVMUXsKezm1fDyQ5laRH4hyewJALKx3ewiOC2tToXT/8Ilg/t01kv9lZpa6ihrqWBqABfXN8HM7hh7267NupoytPg7B3GyJJjZPPqkPL0KF66qZjbOmBpuAW3V2+o7p68DZoGGwrw9vKlRATmrSWJpGYlozXYrWljUSzpZZWMrWxRnp1AvAjMDh9NVzzfmiItNkIEAOUiKH9ePH9qjlaHCA/0J7uinWP9U19g0h0K5xxEbGYpW/em/9xhRbs5S5wQ4RkVHZwJz9dTkY7rq3c0j2063gO74414+wdS0znMbG89vkKYJFV0Oa4qIyUrhHm+obR9kP6edtKi1fQNT9qu7S4MkaD2YWR1i4HmCqKjk+mdXLVdu0GZStlcmEhgVArTa/aRGYmClZ3lcQLc3xGb08iRvblgPNkgSwRJPsGJnNyb67e3NCaEix9/+vMfeCnaV93AvOPKY8y0VeQQFKCib37PfuZKS58QkmWV9SRoAnH29aGh316HivhaHGrk5ct3VPdM3wZSPTU5uDo5U9E5w+rCFOF+74kt7Lidxr052oiHuxcphTUsTbYRm5hE76K9s2FvpkUImkx6h8dsIzJR4rmYXD2wXbvFdEqOInxi023i9JfGcnVGd3kaX731ZXTFXk4KI+0iEPZ2p2V0jZ2VeSIDPIhMr+LIIRJ35/pEoOxMYEy+bQrlY043poTgVxGXVcqRzcxZ2F1foKKgjJaaQtzcXVEl5rPtGI06E4I/RxMgbKaG9SO7KL8+3SJR5YWfOpl1EWz31Wby2jWAqY0bgXhFblwwnr4q+icmKK8owy+61HEN4oPiKC5tYWl+mOzUBLJLmni4osHK/GALwf4BVLX1P5jS+eVjYbi5BB93T0pbxhznnmdneYYs0V5efPdn/uAaSMvwIo8GQu1YjEz0NODr4UP/8r5ogVeirfrj6h/DzJbj2TZe0FOZKWxtKONLm+zN9+Dh6k52dbf9umB+sAmX199T0TVEU00VUep0lh2j2d1V5cSrk1jb36K6IJXotELxbDycIn+ys0hBcgQRyfnsnjz0eZ8D1+YrTs/0mG9V2BWDtdn845+cGFg5EILwmo6yDNw8/Gkb+XCn2n0udhcptv3mQrR6g2if3bh/+w0FTYO37fNoddLmByPyWtDr9cx0FOPh4UVd/1MberizQayfK82D07bXZ6sjZCWE0dDZxdzUiKgDNQ0DC/eWDdnpbygkKzuL9iXtLy86zUYDlUlqIkLjqKoQBszXC5/IDA5tvbxGNoereflPwok7RKfFbGRtfoy8lBi8PNx47+qKf6iGxq4RTu+PB0skEsnPiNVyzdWljmPtEVvLUxRlJhCqyUJ7KaIl3QK+Tk6oEko4cIge6+UR9QXxvHX3ZWhu+zZAveF4fVIEkuFEZ9dx6YhkzLcexkxVajTObwMY2LAHV8raz5SQ90LY5rJ2cCOczPQ2FeHk7EXzwCjtVTk4uYQwtrDhuA4r411EqEKIy6ulvz6HmPgIhuYXOTzcFL8hntziIsqrq4lNSGZcOLOnXFFXmIGfTzizm59uet+HMF5qhfCOxM0vUvyup9MorddXjLUU4vzei4beKdtoyGOs10amBxqFT/Gne8w+LaupIBHnl29pndy2vVbQLffh5uxKRkkDfY3ltilNSdV9jqvKuqcdYvxfE5NVJ4RLFzkJ4ZRU1XFweMhwez3xURG0dbURHxtHdesgusdBmdXI9kwXrp7B1HZP/sYC3B/Cwub8EL4ur0nKb+H4ZhBQf0BRfChvnf1ZOLMXluFsh9wkDclJ6aSnJeEXqaGiZ9Z27TGWyy1yRCyhisvmUOd4MkQwd1PsnTUZuPh6Utc7bnttFc9aV1ka33uEiLZy91ysjDajDvQmtaCe3vYG/Nze0TC6icFxI/OREGTBAYTHZTI91U98nIbS1mF29vaE0CwgJbeIytJCMlOSqemw/63H9NfkERoaZpsq/0s/FmbdCW358fzpfaBtFP+G2f5qgr3fk1nezcRAO4HeruJ3TXAz29lwskaq2h9Pvyi2n8zOtzLWVU9IYAiVzQOOcyLYvhkeu1glPMiHoNhsthyiYHt5jFAvD9TJZZzedI6ZdVRmx+MXHkdzZx/VmVEEKKOjJzcPiajH0jT8fXyoaO2muqoCX1Uy24cHHGzPoYlIpLS8muLsVNKzS1jaezqnTieC9bTIIJJyytlWeqR+I1hNWgoz4vANjaZvbJ7VpUUWl1Y4On24Rtpwuk1pTjrRkTEUFebiFhhK48Dcs4NBprNd6otS8Q5P5VjxnVwQI9qOe0AcszuOpSjWK0aaSnnz3pPG7jGGRRvxDAyjeeiuHW+vzBId6ExiQR0tiugMiaZTvPfwYJfKgnziY9Jpa6khOjqOgdm7abq3mM8YbBGCKiiSgbmt207azxbLKa0lKXzz2p/pnRPx2B/YRiVDhU0ZmpxneXGBpZV10S6ePr92rMyOdBMWEERxXT8Wi4n18Sac/vwVxS0Dt7//YmuOBJUvHhG5aI8OyI+PJCQiiZHJGVaXF1lYXuXYkfPh7HCHrAhvssvr2dg7ZKy9lvTkeMory8RzkE96oYhtHgc+gv3lEYry8lBntUybP4XorBCBV3CAhmlhzHrqC3nz8hV5Tcp0GTO7o7W8UkRn66itULbnhggPcMfFK4jqpg462hpJVAfw5p0LVV0TXD6W1BKJRPIzYLg8YUwEVtlpCfh6uuMVoKZzyjEKdn1ChsaP187e1LSPcHx8wvxYL6Eer/nbr9/SP7P6xOktjLQRE+ZBaefYM+LikhIhWJzeKaLT7kQuT7eIcHmJJqWMndO7CG60pwaf986U1rdRlpOAc2A8c6uOkVDB7vwgEWEhhObUsLs6TkZiFFGxiWRmpJGSmUNObhapqckUVbWwt7vDyvIyWzsHGO/NORpqKiHU35Pm8RXHmc8FC1sLQwS6O5FU1MzBxVOXdSbEdWKQJ0GaDFb2HieEsWNbU1SXhXt4ApOOoHq6tw7Pdy8ITcgXDvWA/Z0N2kuS+Z//59+SkFvH+uIIGnHf9wExzGweoj3cZbCtnBf/9Du8IgpYW12npy4fVVgYKWkZZKankJ5TQJ54fpIzi5maXWJna50V8b5T3V2gcHq0hsrFlfyKNk4Mz3js3ygX+6skqzx456mibWCSk5MTpgda8Hr9R/701puJIxFWXZvor80jRB3HsAjEelsr8VKpqeq3dyQ85mhhgGRNFGklbTy37LClIg0nbw/q+uxC8Opkj8pMDe+DwpnbuBvx257vIyZSRXxGIQ0V+Xi+fU/v0vFdB8flNkmh3oRqUlk5OLBNoQ8JCyczJ0cI0DhKy0pIS4wnu6CC2dUdNteWWdnYQnd11853J9rRqMPIre58tuPko3J9xWJfNS+/eUFcXg0ru0ccHwmhka7mqz99Q3xOE/1tlfh5vKN+cJkrhz0z6/YpTAzFyzeUmSf5zq5oEXXlHxFL+zN2xXI4i8rXg6C4HLYd9m5tugsfDzfiilo4vx1kuKKpNAMfdQIF1XUUxAcQmVDM4X0b2ZQrYkwPytqHbIlZEtQhxKdlkpWaQEpWIUX52cTEp9kGL44O91lcFG3z8ORuhMZwSElOnBDA6YwuP9cx92Vi2l8iIy4MZ08vYmNjCPB4z+s3b4lOyWVxW2v3adZr+huKCQsXom9wSvi9Vpx8g2gaWniUdM2OdmOe3Hi1eI5aHOvbrTTkJfD69Tsyy1s4FL5zY3GSlAg//vPvv6WspZ/m4hT81ZH0Tt2N6O1vLZIY6UN0Vg0LU2MUpUYTJQRmhmJnM0Td5hSSnpRAVlkLG1vbrK2ssL659yCh18xYN97v/agV2sH4JCvSZ4Qo4+WJHsJ9PAgRz/bplQHd7jyaUB88/YLRaDR4ubzj1VtX0vKr2brtlbuPkYHOKnyFPWwesq9R127NEuL6vbCRcQxOrwmbekx/Ywnf//6PuPglc3C4TEJYEC7ugbaOskCf93z36h3xWWWsH+htifLG2kqEj1OTkppKYkISOfkFZIvYIikpgyFRX1vrayyvrKI9093GQPrjDZpKClAFJ0yvmD+R6PT3VgvReYD5fJ0UEYi98QxjYfeUo+lm3n71jorOSSxWA3W5CTi5eNmmjxlNZsxmE4e23q/3eEdmsrjz25tzL5FIPj5XF1qGeppJTYjC1fmtEJieVLYNCcepmFIrGzPdhPk481IY5ZCIaMKChAj9+p944RbE8Pzmk5HO0fYawrzf0zK69OSa0vtbHB/mEJ32Hv2Lk1VUQgTFpJaze3Y3q2NciKMAF2eKa5opSI/mfXAi82t3AfHe0rAQnQH4p1dhMhnYX52lRgS5pTVNDA4PUpSbRmpaNp2dXbYkLe5ubgSFxwgHv4rBMeowN9hIdGQApd0zz3zXT4eSNKSjIoM3b5VkMU+FPRYD8/0NuDg5U9jYj+4DQ4eG80Nq82IJTswWwZQ92YTSk1xflMSrF98Ixx5CZHgYYf4u/Lf/8WdSSttFkGJitLUU55ffCSEUQHSMRgTE7nz1T/9AcHwpm0cGTBcHdLfWUVhcRkffAB0ttUKcxFLf1EldaZ4Iqt1w9fCmqKYVrWNOn+7siPRgZ7JKatg/l7N3blBGo+f6W/BxesOrd++J0ESjUgXy5z/8XtSPigXtFavjXaiDQ6jumMBkNtPTVo6nCLKqhRB6bonT4lADmsgwipqGn53+2Vye+kB06rQ7FKWF4RIUxvz6PdEpxGtcdDhxGXnUFmXi+sqFgRUhWhzXudolPdybwNAEdoWSOd3foq2+yvZcKGuzm8oLSEzMoK6pnfryPDxcnPD0V9Yadt9mfzVsjZEUJ4K7krpnBfLHxSrayAGlqZF89+IVXoEi0I2JweXNt7x45UR2RTe9TaV4ub6hYWRVyEA75stDilPC8fUPZfpxolDDMXUFSYTGJTG0/HQquXl/mpBb0WmvnRUhaDzdXEgoaef8NrnQFc0VWfhGpZBTXk1elCdRiug8uxOd4y15BPm7Uto+zLWIGddmhygtKqCyrpWp0QHSE2LJLq6krb2VZHUg753fo45PZ2Zt32HvrqiuzMUnMonuKXvmzd8C+t1l4v3f8T/++u9QpxUzMjoq7FYmTt9/Q3hSIbvaC7Zm+tGEhwvB3ylichPz4yJmF6KzZXRF2MinjW5zcZR4IShz6kduM32fbc+RJs69ePESP1UkkWEhuL7+ln9840VN5whVwjb7qSPom7nrnDjYXiIxyg91aiXa0wtOxXdtqCqhpKKWoaEB4ePyiY5PpU3YXWVmpLerC95+4bQPztnWeiqsTo8RKZ6nsoZeru6J0c8Jq8XMylQ/0So/vAOiGF3aE63RyuHaBN5v/8Bf//4bUgrqGR8fFbYpllffvSQup5YLe7a7O0xndNfmEhQRRZ9jRpBF2NSBlhLRjl/w1sWbyKgoVEJY/vmrl3ipc9DuTRPt/4r/+jf/RFxmCRNC3JflJvLm5QsiU0s5NlowGy4Y6WunsCCf9r5hYXPrSU9MpKC4js7GKsICvXETMUVKXjnr+yc2H2261DLYWExMePB08/4nEp0B3uGMzikFYWG6txY3JyeicypYEE7B9XsXanpmuBBBQLomiCB1CquHdw+I1aijNFnDa/dwhmfWHWclEonk50OZXqu/vEB7dMju5gpVRal4eHkJ57qKLcO4MOC7K7PUlReRJwKYpqYmclMi8QuNZnL18IkgGmqqJMTlPR1jqz8iOu0hnO50hyhXZaSzhO17a1AGu6rxeO9JTXsXVfkJOPnFMnuTzVawNTMgAnE/woUjsscAFi4vzrm4vGS4vZaMrByKK+sozs4gI7eckTHhWHKSCY3NE4G1vVd/cayDOBHwFndOfVbTkI5FoJEQ4o5fdB4bR0/Xal6e7lGeFoGLVwhjy4qzfp6r432qUjWEJeWyvKN1nLV3NIz0tpKbm0dFlTKlthg3Lx9yarpsCe/MBh1zo70U5efagp329jbUvm9JzK9j+8xeq8YrPefnF2yvzlGYkUReeQMVxQWkpmbQ3jtMV1M5EVEayjvsyWquLk7I17wnq7SKPXtKU4mDa5OB9blxKovzyS8RIqGjhRRNJKGqOEamRgh4/zV/evGeosoGxga7iIv05x+++paIjDLbdgmP63+6v4aIyBBK2kafHT18LDqNJ3vUZEfjEqBm9l7HzsZ0J7ERQSSLwKq5qgjPN050Lx7d3fNinbgQX3zVqdwM+Bn0l5ydn7GzPEFqQjLFJRVUC9uRmpFD99A4bXUlqIKD6V/ctd/naIrU5EgSi2pEsG67xS/O5dkhfR2NZGdlUt/RT1VxFuoQf8raxhnsrMHH4y11A3cjnabzHfKiQ/D1U7PqWId7y8Uh1ZlxqOOSGV8/dJy846HodIx0znbbOnZi8ls4uymEaz2NhWm2BEflDS0UJgQTIUTn0T3ROVibhb+XGxWdjnWJIpC/EGV/enZCZ1U+KalZ1NdVkZWeQW5RHVPi2VGSpyTn17Jvm8Zrpb6mFJ+IJDo/u9keHw/D7gJRPs68cvVnSIgdJbeBXtjUyswInIVIKa+uQeX1jj9/84qssiZGhfjITgzjb/7pK6LSi1gStvSxb1ubG0YT5k1+87h4jm+uWjjdW6O9oZLsgmIaGpuozEvFN0iIxKE5mktS8QuLoMeRaV1hf3OeOJUbkRk1nDgWT+t1F5zrLpkaaicjNYHSugZKCrJJFN9leGSCqpwEohIyGBO/RWFzfoykQGdKG7sfbFv02XB9xUhnnWgHnoTGpDO6sH2b9GhvaVTopG/wCItnbss+2Ha6s0Smxp/XLv5M7Vw8tHf6E9pLswhRR4m63L69pmSLVrZvKsoXcUB5He31lcRqwgjNqORoZ4oIz5e88YkSZWafonyl3SBP1PFbN28GhW1SUOzy2dkZZ/urlOdniTiikNq6ehJj4mgTPm64p4mIiHDyarvRXlqwmnSMd5aJ64HTFSufUHQOz9rXIZn0RyJ4SuH167fCGEfy9oWHbaj+UojO1KgAITrTWNfeFee1+ZKy1GjeeEQw4riHRCKRfEzWZ3pwe/c1SWWdXNwbelCytp2dX9q2PskVxjk8PovN48cRF4y21RDm5UyzMtL5wDso3Bed9qkyBiFGMsJc8deks+xISS6kKJ01ubz3DGV4boG+hnzevlPWkN71xs8NNRHk4052/fCDBf2XB0vCvsZS0djOwNgQoZpEah3TEJeGO/F8r2ZwxP56brABjbC9Zd13yVM+Pdcsjrbw/sW3FLRMoHtm/ZCSfCZEBJuqxCL2T5+bcmTHIILputxYghKzWXCMdN5ivRYO9VQEqRfMdJQSEBhMfc/4g4yHuvNTEezoOFobJ9D9PWUtA9i2+bzBeE5vQzGauHQWVufJyksnubiBQ/GVTCdrxMTHCidfY3vr1bmW9GAhOotFwCtHOp/l6vLc1sYOVqfIio9BnVzBxEg7nm//xB//9DVOLq54uTnz+7/7K/6Xf/Nv+NMrN/qmV590mCwMNRIdEUZR49BPGum0Gk8ZqM7khbNoY4t3azqnuqoJ9feipHmAsYEOfN+/pkKIr5vZ0brNUXHdD3VaxSNxq6euIJX0wloGh/oozoknv67ZduVkZ46YCF8Ku+dQZo1f74yLIE1NshBFVx89MvsBLCZOT44xXFvoaSwhKCCIQRGQLk714ufhTEH9CDe7K13sKsLAn4DwtKdru6601NpGOpMZdiSAuc8D0enoZNtdm7RPMYwrvN1jEMMJJSmRqGLSGJ6aozorEs/QlAcdNrW5Sbb12h1jDwXj/sIQsVHRQkAP2DoB0xOy6Z1VugVEUNyYQ4gmhcVdxdZaqKnMwzsike6p387AhvVsneTIYAI0aWzdzK6xGploK8Tj/XuiE1Lw9XTm6z//kXeunni6OvOnf/hr/td/9+/4/devaBmeu7dfpp3NhTHiwwLIrR15MNVV4dqkJM855+Rwh8aCZALDYpldO2C6vVT4OH8a+u6ySG8uDhP0/gVZtf3iebv7I/qDFYqzUsnMr2JxeoDMZOHjOmds7e5wrpHQqHBquu3teW16lChPF0ob+oQA/sxEp8UshJmIEQIDSMguZ2336IGIPNuaJSzAi5icSvZvkgOYz2ktTeTNW2cG1k8f+mrTKV0iVgiK0NA/v/lQkAp052ecXxpYmRwgOsSP3OYxjo82iQ3wJiy+kN3bNdJGeqpFzOHiRvOIsKn3bjTZWUVsXAp9wyN0d9biJ9quzYUJ8ZyUkogqpYSVgwulN4qhxmKiw4On6z/JSKcIrvw9wxiauROM+2uztmH93/3Vf+E//edvaBiYx3JtoC4vgRcvnchvGHa8Uzx80z22BAPBSSWs36S1k0gkkp8Jg17H3u42upvedfMFTaXpvHr1iqruWQxCgZiELbsz8pe0VGTh5uJJefsYV8+sF1kYbiNa5Ulpx9gzKdvPKYoL5d0bf/odI50Ws4HemjRevH0v7ml3muc7k8SHeBMUm8uxTs/2TC/uL74hNreOC+WeVh3FGRrcvfwZE07o1j+YddQXphCZmMXK0TEL08P4BkVQ02vf2mBhoBlPTw3Djl79wcZSVL7Kms6H2VY/Jdf6Y1qLk/jzCzdGVvefOFFl5HlMOMF3b9/a/MVdr/pTzFfKPnGivlQJTC3bxcS1yYTpXiCiCJwIP3cR3GayZNsT0IrReCdV9EcbFCWG4uqvYXL1bhRM3Iml0U40YeE2P6YXwXZqWjzRudUci7jYpF0WojOGyDy72NAdrxEkHHpeZTunN5loJLb0/SaT8U44ms9sW5N4+QTSNLrK2ckBc9NjDPT30d3dTVdnKzFqX34nRGhkRjm72qcZpLUL/SRFRZJW3Pawk8BBU1kKbz3dqHUkEhJ/lPXJLt59+y1x+fXYElWLNlaQEIGbZwijqyccbi2iCXDCQ53J5qn9pl3lKbh5+VLde5cIxXJtZr6/njBVJP1Ta5ztzZOdHE5mVZPt+tnOLJGhPlQOLNn2hzyYaicqPJTc6i4+RbJ+iwiCjca7kFDZny9MCG1VXB7nBjMn+yvEBXviGhDL/LY90cxIcyHuHl5k1w/aXj/AaqBZBK/+4bFPxKCC+WCaYB83Am2JhOw28Op0j4qUcF6+dqNn2h4v7s0PECCEQ3xuFReXeiEeC/j62zfU9dnL2nC0KL6nN/6RmbcZbxUuDkR7TY0RAX0FW1od8101RKsT6LItPtUxVJ1OWFwW60qQbLmgNDeBYCFsx5afjsp+sVj0NJak4+bqTW23PUOp6XSLnJgA2xYarf1jLMzPMDxob3PdXS1kJ6j46z/8CU1aIau7x086eo425siJUxOT23LrT40GwwMfOC7qwsf9PcnK2l29mbO1UdxfvyQ0Pg9bk7LqaSlJ5c1rd/rmt++Ej/BrbZW5RMakMLV+xNH6BInR4RS3Tdp8895EDeGaSJsdVpgb68HTxY+azs9tTaeV480ZwgP9SMwr596g/S0WvZaStGhcfULpmLB3Mp9sTos26IFngIa1k8uH9s5qYqC9Cr9gNc2DC/blBsqWKI5M9gq2uhW2zN1bxcyuzjYK2iQ0l4enL63D9r2+Lw9XbNu0uPupmdu6iykO16aELY0gp6wV3aWOvtZS3MLTOLcV6yXJKXGoMyvY0Io4Sbcj4qcCgoMTptc/xZrOYk0g7s4iuLo/V/7axGxPNX//l/+W/9v//f9HsSNd8/7qBLHhfrx970FKVj55uTlohCr3C46gd2bttmdRIpFIfi4ujvdorMglOSGe3Jw8MlLj8PH2JCq18LZHfWdlmrrKInE9h+TEaHx8vEnKqWTvA5tPn25MCgcdTkRmDZdPoshzciP9+O5rd3rWbwIlK2f7S8QL++cqnEBadhbRESH4BYbT4RiRNF9qqc6Ow8nZnaj4VLKS4/DzCyKvuuM2qFaC3bm+BlTBYdR1T9iCguPtVRGAxROu1lCQn0+yEEHpZS3sHCvBo57awnR8fcOYdUzj+Rw43VkhIciFbzyj2BSC4jHKesqqzEi++uYV7dM7D3pkH2O1GJjub8LDzY/2YeGQxbnrcy1j3a3k5+eRk5VJhCoIr8AIOseWRICifEj4qNEeinPSyBV+KDYyHC8h7mtFmd6fqnW4qezXGUVMainKLEEleO9pKCciNJzUjFyyUxNJSM2kf0EIVeFyd+Y6cHMPoqFn6pOIi88Vq8XExtIERUUFZGblkBKvwcvbl8zSRtv+ms/RVZ/LO99gKgefnxJpudgmJymakOisu9GCe9QVxvOt01sqO0ccZ4TwuTiiVrQx53euaOJTSBef9/EOILey05a19dp4SX9zCe/fviE0KoGc3Axx3YuU/EoOzhyrHUU97wt7kRAWTEZxEyeX4sEwndFeU0hYWCQZeUVkZ6QQI9rwsmOrB1v22pBQmkXA/CkeC+PVGb3N1RTmZQsbl0monxdBQjD2z9qTqVnMekY7qkUs946AUI1tCm6Qny+x6UWsa58fDFCy1wYpa1cbB578JvPeJN6ub/FUp7J5kxhFPAObM32E+7qJoFdFWmYmkapAgiMTGZyzf4/j7QUShFh39vAlJT2LuMgg/EIiaB6au03YpiSF6xZlHSTO903alzccb0yTnhBNaEQcBVnp4nORVLeOoDNaMGhXSI0KJFHJXnszjPsb4WB9mjSNCndPP9JFu0uKjcTDw5vCum7xvD9td4sD1Xzr6mPrCHruOTWdK9lrk/EMTmT/3O4btXurtDZW2uL59JRkAsRzExqXwdzNvqjGC9vIp+s7J8KEoMxKjifIx4+04hZOb+eaK/vZthEeHk5hfa9tZPPydJ8q4buCQiPIzi0gKVr8W1RjX4phFu20pQT3gCgG5u4J188CC/Pdxfzt3/wNrzyDqCwvo6Ag3+ZnSsqqmJwX/sxiYWt+iIggP1vOgaysbGLCQ/DyCRZ+fRKjuP6YeeGvQgOErarusncGCB+2MNJLcVE+2UpSs8hQfIS9LGu+2ULFytHqGAkRfnj5B5KRnS38nAoP4efKmga52c5bf3ZARUYckXGZTK7bFxAsC0EfERxCalYBhbnpaGITaBmYQdmY5Gh1hGJFu2W1/vJbpljMJoaEISstqmJ562EPklEYhuq8FDxdQ4QgXXE8wBa2VyYpyEzEz9cXX/8gEtJyGZlZxfShnWglEonkX4FJf0Z/WxVRYQF4CocbHKqmsLqR9YM7sbO7PEl+WjReHh74BYVTXNNqS7TwQa5OaK/Kw903nFnhXB9aryv6GqtE4FbK4tHDIGd7aZzsZI34Hu6EiKC2bXDmwXS788Ntqgsz8PfxxDcglPL6bg5v9jAQXJuNDLfVUdvUxe7NtF8hhHYWx8X3j0WlCiVDBMgrjt92qiQwiVYRlVHO2Sed1/cQZa/m6hLhhBu6bNOCHmPUHdPXXCkEShFrxzepTT6EFe3WHLHBPiSVNHOgiAARZA+1VhEa6GMTNzGJmQxOL9vX79o+YmJ6UBmt9sHTy5uImCRa+yefTNPamJ+gurSc8Zu9VQV67R5tVUWoVcHicyn0js7ZgiSjEDSNuXEiiE5hVu7T+RCrhY3FUdsUUw83D0JCo6hq6RVC7vlOHRFVMj3aRW5pBUMLd1NhH3It2mAu/iKO6Jx6ujRnQtRvdmEBYwsPp1ReapX9/zII8PHC208EaXWdHJ7fPWMGnVYI3mLx7IhgzVcEayX1bNzfJ9BiZHthjJLSGhY379Z7n+5tUleca5uyGpuSzdD0mk0QXV8ekyl+d0R0Gku7z2dg/tiYDOd0VOejCvDGU7SHuLR8xuY3bN/vBrN4z4BoM+EhAbbRkdTcShHXPc4gdIci9FKjVGiUzjvdQztnOd2ivKRACNIutPe2w1M6HxbGe0mICsXT00sI/3SGZ9fvpkeL50SZVp+eEIWXaJdB6jg6h6aFjbz7ppciSO5qqqW+pZ/Tm78r6mRe3Dc5JowglRAuVa0c2p4tC7N9dUIc+1PV/nC/+N8GVraFb8tVkvF4egoBFynaXT/Hio18goHNuSEyCksZW9p5ZgaPwGJgqrcOb9GGO6c3bO+50G5QXZyBn6eHaC/BZBRU2drFfZRR7sYy0VY93fHxD6Ggopn9m04cG9e29feVtc2s7Tv8sngWdjfmKcpKQhWiIiGjhDlHnoJTYe9z48OJTC5g7/TH/MMvjSjz2R5iIlS8F+UUIISionc8RfkEh2lo61vEPungmqWJPlJiInB39yA0Kp6WPmWpie0mTzjbXaYwOYIQIei3bPbqWgh1ZT16CO6ibsMihTDsHX+UcM/C2vwImYkavEVsE6zWUNc5xMm9qSFKzFFbWkrv2Dw322QbL04Zba9FEx5GuDqaxs5h4aeVZ8ZMd3UumRlZdC8d/PKiU8FsMmI0GLl+Znjben3NlV7ZIPWhI1fmfh/s77G3f8CF/rdnBiQSyS+L2WTg9PiInZ1dDo9PMT3qGlX2ED470bK7s8Oh9un159icHyY2xJ/0yk4h6O4CK8XpKHbRoNjFZxy38eqC/b1djs8uHgR9N1jEd9Uq+9AdHWN65PmFXefqUthUYVsfYkUvHMXBgbCpDhFnNV3SVp6FSojstjF7mvXPBYuyb6rhypal9DZqv4fVYhGBssFWhpbn3vAIi1EJqnMICIumfWTJFsQq06oPD4Sf2du/LZP7GA16Uc777OzucXr+NJGRgtFoRH/19LNmo/LZA45OzuzB2bWJ5ckB1P6+FNcJIf04+6BEFJGBE9EGd0Ub1J6c/+johJLd3iDK3/xMr/8NB6sTpMWFE5NZxsmjMlcynRpsU/+efl6Z7q7Uva2NPTs175oT7SH7+4dcPR4REsGwWcQwV/emq95gNlyKZ+7A1rYVlARm8wMNtqC5rLHXNlLwaRB2Q3dui7t2RXvQffD5tAg7eGSLzS5vhkI+hEVHd20hwaoImgbnH4oUZeqf0WDLiPrceMLl+ako233OdHdTZu9juBQ2Ulw/Pr3bquEGm+24UmzH03o5Pz1m/+Dodr2h7mTbltVbnZTH/A8I6C8dJVfB/t4eR8dnz9aHHSsWYccUm2v+gemqJzvLtmQ0keklbBwpz7nVti5+b3eX/UOtaC+PfZMdxcceiza3f6S1LWl5iPBfok4NpqfP3JV4Fg4O9m1bUylf3WLS2zokQ5T1+d1TP/B7Ph3KjCS9aG+H4nvviHLZFT5mVymfg0MudAb79FgHSrvcE9eP721L8iwWPZPd9QSK313TrXSQWrg2XnF8dGDzYSfnl8/GEwq29iTq//j0/Ml7rpXvqhdl+9hOWsycHB1yeHRinx0kON+dJzkqlOLqJi6t1k8jOiUSieS3yLX+jOGOWhLS8lndf5pd81OjP1yhvDCH4po2jhwZAr9ktNvzFGSmUdHYh1ZJT/sLYrzQMtBSSUaukvXx+LN7Fr5cTIz1NtmyCU9tPkzW8TlgvDyjSZm2X1LF2t5dZuUvheOteaoKsiisbhei/3mx8ckQIfDm/Chpqel0jS0/m2xK8s/HajawMNZNUkoqI0sbv/jUVt3hJg0VheSV1LJ3b/ux3wLKqGRTaTY5JU0/mFzv42Blur+Z7JxcZjeErZWiUyKRSH5ZDLpTlhbn2D26eHZU81Nyrt1jdXnZNqr028DC7tYmCyvrnFz8slOuri51rImy3to5tK3XkfxyGC5P2VhdYl2IOmVbiM8G8V2UaWrrKyvsac/4zCTZz4SFo4Md5hZWONc/N2Xz02G9NqHd22B5Y+fpvoeSfxVm4yXrq8usC3tn+IUzx16cHrOyvMbOwaeZqv5psXJ+esD03JLNx/2y1u6azdVV1kV7Uv6uFJ0SiUTyCVCm0F1blC2fPy+EH7AdvzWur6/F7/5la0P422eXmEh+IayWz7L8lSlrv4U2aGtzn5Pgt2G1TcX93L7VF4PD5v3S1a60J8Xf/pb5NO3tYX1L0SmRSCQSiUQikUgkko+GFJ0SiUQikUgkEolEIvloSNEpkUgkEolEIpFIJJKPhhSdEolEIpFIJBKJRCL5aEjRKZFIJBKJRCKRSCSSj4YUnRKJRCKRSCQSiUQi+Wj8oOgUrzEajRgMBtu/8pCHPOQhD3nIQx7ykIc85CEPefzUQ9myxWw2T19dXT0vOi8vL9na2mJ9fZ3NzU15yEMe8pCHPOQhD3nIQx7ykIc8ftKxsbHB+fk5Wq12WmjK50WnyWRCp9NxcXEhD3nIQx7ykIc85CEPechDHvKQxz/rUEY6r66upoWulGs6JRKJRCKRSCQSiUTy8yM0pkwkJJFIJBKJRCKRSCSSj4MUnRKJRCKRSCQSiUQi+WhI0SmRSCQSiUQikUgkko+GFJ0SiUQikUgkEolEIvloSNEpkUgkEolEIpFIJJKPhhSdEolEIpFIJBKJRCL5aEjRKZFIJBKJRCKRSCSSj4YUnRKJRCKRSCQSiUQi+WhI0SmRSCQSiUQikUgkko+GFJ0SiUTyOWF1/Cv5iHy6QpbV+yvA+jPW0s95L8k/CxHPOv4n+WR80iqQ9f/R+WcW8ScRnVaLmd3tVVY2ttBdmRxn7RgNF6ytLLJ1cIzp+qf9msXRQbrau9k8OHeckUgkkk+E9Zr9jSUG+noZGp3hRGd0XPgwZv0pmxsb7B6e2U/Y7rHMQG8PQxPiHpcP73F+sseEsHsj41Mcn+sdZ+2cHu2xvrrBpdGKxWRkf3uDje09YU8tjnd8WRwfHbF/eITJfO048yGsXBwfsLa+IcrT4Dhn5+TkmN2DQ66uHp6/RdTHmXafmfEROrt6GJ9b5tzw0FWaLs+Ynxyjf3CU7YMTx1k7V7oT1tfX0J5f2nz0+fE+6xvrP+nZkPzLOD3YZmSgjwFRH7vaC8fZH8LEpqij7b1DzI7Q43h/g6H+XgaHJjg8ubSfdHB1pmVmbITB4XH2Ht3/zFa/W5xeiOfJauHkaNf23OkMX2YbfIzZoGNxZpzunl5mlzcx/oSffXFyyOrKuigzezmbr+z36BH3mFvZwPTgHmb2NoV97OtjZnGDq5sKU7g2sCds3vb+IQaL1fZdtjfX2dw7crxB8i/DwsHBAYfaE8zXP2Zrr9Hub7OxtcPFAzt5zbH2kIOjY4ymH7uHQNjdnc01llfXuLinFU4ONhkZGmB8eoHze7bcarkW10RbE8+RwebvLOwKP7q5sy9e29/zJWAx6VlbmBZto4fpxdWHz/+zWDFcHLMoyuVMb/c518YLlmdFG+3uedpGLSa21xZF+xoQbW9TlN3d/S1Cv22vL4lYRTwH4jNmwzmboo52D49Faf84n0R0mo3nVBQkEpOZz+L2seOsncO9eZJiVOQ39nKq/2lfpSopCh9Xf3omNh1nJBKJ5Jfn2njJ9EAbcZEqgoOD8PMNJC2vio2DU8c7nuOase4GCgrLmV47FC+vmBlqJy5CRUhwMP6BgaTk17BxZA9sj3ZWqSnNIy5aQ3x8PPmldeyd6GzXjGd7NFSWUlrVyplNdF6xPNZDVk4BY/ObNifxJWAxGzna3WKos4HQ0Aiyyxo5cTjTD3Gp3aFZlE1RZQOHIlBROj+PhTAZ62lBHRlFUmENW4fPd1yaz7doqSkiKjKSUFUIfkHBZJY2sHt6Zbtu1GnprC0jLSkBTaSGzJxi5tb3bdcspksG22rIL6piecfu77Rbc1SU5lPT1o+UnT8zIoRZnxkmKzGaINEGg4ICiUrKZmbjULS057GK4HZrYYjsnDz6p1aUM6xO9pEaH0FwSAhBgUHEpeax4qi/cxH0tlQWkRATTXR0HLkFFazddHqbL2iuKqa0spFtrSKgrGwuTlCUl03b4AzGn9iZ/mvl8nSf1upCQkOCbDYwKDiM6rbhR+LjIVfnh7SIMiupbmPv7ArD2b4o30LCQoIJDgogMCSc6o4RdDahYq+b/Jw0okX5Jyan0Ng3gc5x+/3lCQpzc+kanUWYQKFBzxhoryMnv5St4ytbp4/kp2M26tnbWKKttpSAMA0VrQMPBOBznO0tU16QR62wbxdC7V0br4T4W6K1ppRITRSVbX2cPupIfY7TjSkig7zxj0pm5VDxfxYOtxYpFnWflBhHVHQsFQ3tHJ7Z/d/p3hq1JQVUNvVxaeuEtDI/2EJOXjEj89u29/zaMV4c0S7qIjJMJeySP0GqcEoauzn/gZ6dq/MDehtKyStv5vD8StxDK3xhPuGhwQQGBBCiiqSmdcgmPC3XZqaHe8hMSSImJoaYpDQ6BiYxKA1H2NZtYcuyMrMZnluzxRPXQnR2NFVSIPzb6t6PD/x9EtFpNGiJDXnHSx8Vo0u7jrN2tlaHcPr+96iyKjm4+Gnu+E50bjjOSCQSyS/NtQh2+wj3c8c9IJKqugaKM+J48c0LMsuabc73Oc525omPDCUlt1qIIQt7i4NEBrjh6h9FTUMTpTnxfCPukVcvHPiFjp6GMkLV0dQ0d9BSX06gjy8NAyuYzGam2iuIiIimtnvKEWBbOdlcICkihMTcKjZ/0ojP544VrRDehcIp+jp9y7/7j3+Ja3gae+c/4C8sJsY7alCrwihrG0VxcrrjbSpyUvF3esH/93//S74LiGd+xzHS/AiDdoWWxkrKaxroam8mLtyPr796QXHLGEZR7pvTXaiCVMKp19PVWkV0aCBpZS22OjlYmyJWBAh5VZ0cC4evoIjU+pIMQtRRjK/LEZifk9PteVIjA3jn4kVZbT2Vpbm8/f4bVCll7Jw8nBVgxyqE0g4FqRrUSbks7p2iO1gmIcyXd+5+VDc0U1aQxqtv/0x8fiOnugsmuqtFwBZOfmUz7Q0VRKkCyGkYFvVtYls8CxGhasqbetE5AsGzoy3KRTtWRSeztK0VgZft9JeH1UhPXT5uTu+ITCukubGOuCAPvn3tRc/U6u0I8n2s1wbmh1oJ8gugrnscg7BcA/X5uDo7EZVWQFNDDVFBnnzr5MvA3DoGET/mJMagScigq6+H3NR4fIISWNw9xyQC4Jq8FKLiMpha23P8hWsWRjvRCBGcVzvI5cMhU8kPcs3m4iQZ0WqcvvkD/+tf/A/UOXUcX35YJlhNV3RW5RASHkPHuNKBY2F3aYLU2EhefPVH/tP/9V/R5FVx9GPxvcVIW76Gv/h3/wu/e+vPrBA05stjWiuyCVQn0d7VQ1VeEkHBajpGljGbDAy3lqEKjaR3ZvP2WTvZWSBerSIppxLtD3zvXwXCj422lYn25UyoJoUm0b5iw/z57o0r9UOLXFueaWCiHOdHWgkPDqKyYwy94YqR1lLev3NCnZAl2mi9iDe8eO3sy9DSAae7q2QmRBObUkBXbw8ZcZFExGSydGTi6niLisx4IpIKWNm9GzCcH2oiKjSE/JpuDD8y6vqJROcxieGuvAtQM7Z8YxjsbK8P4/b2z6hzazgUD+W1ycjlpV4YGgPnp1o2NzY5ODp9YLxqUmMI8Ayid2rL9toiHj7t0REXlwYslmvx+UsMRiO681O2NjfY3T96tmD0ujO2tzZYF3/j8PiMm/ozXl1yfn6B0eQoGqsFgzinu9QJpW9/k9Jrrjs/xyj+jvJd9for8e8V2sN9Nje3OD7TyR42ieQL5vrqiIbCBFw8fOiePbCfNByRpvbGJUDD0KLj3D2sZiNdFVkEq6Lom1pXTtBUkICbqwfdSw6jbj4lXe2JvyaLgeERagvThKBpQgmfjReHlCVHC2M/zvLCHNlJMWQW1XGkuxO4FqOeydYi/EOiaB9ZuLVrv16u2V6dJTshnaKsJN6J4DQwNofN4+cEhZ0r7Qb5CWqiEzPYOLeXzfHuEvkZmeSlJuP0xgl3dRrTWw9n3txgeTR1d2tuiKC33xKRXM6ZsPvjrQVEpBQzu6WIeh3ttflEZRSztr5Gc2kW0Un5LO8/FLSbs0PERqhIFkLm6ksZgv7UWK/pqy/E3d2TgoYB+ymznpaiOF68cqNrfOmJH7ZaDCyPNRMcGERt74xt5HmgPhd3T1/KOmZs71GC3ZoMNS89IpicHqe1JpckETivKhMYDAe0VGWjzqpid3OJYiGC0kSAu34zVd7GNStTfQT5+1HeMsTFFzrN1qBdEgLRB7+INA4csx4PVsZwf/m1EOz1HN4MR97jQgjy0oxoQmJy2TkSwkK3TlSwD4HRGew5mvTOwiAeb1+RXFjH8vIEwVEp1LSM2K5ND4+i8opiYHyWxckuoiI0tA7O2EdmHFyd7VNflEqQSsPS3qmMxX4yBhanBklPSBZCP46vX7wlpqDhBweETnfm0QT7kil8lNY2IHrFsnj2U1MzSExK4I3TdyQUifj+/ANLGRxszg+i9nHld3/9P/GNiGdm54LjnWVh98PJbZ3mSjQh89Y4mfEJVLWMszgzQU6ShryarkezRyz01+cJMaqhcUgRwb9eFD+WEO5HQHgqcxv2JRzbSyOoPJ3wjcrh9JnZPoajNaqyYlHFZrJ9doXuZIt4lRvB8cInHdgb2OpkN4E+bkTl1LI01EpKUvJtDLMy2EhqSjZtouym+hoJU0fRM7nycNaUfp/KghQCwhJY2v3h0c5PIjpNQnQmqd1wClQzvvJUdLq/+4rIvDq0OhPajTnKSsupaWihvroYP08PQqOS6Z9eE8LTbjoU0RnoFcLQ/IHwOUbGuxtJiEthcGoVw8U+1eUV1DS20NhQSaifN/7BkTT1TnF1s8ZJiMjjvVXqynKEU/ASzsaH6OQchufWhdA0sjU7QF5BMaOza7a3Kw6os7GMjJwC1g/s6w9ORABTnl/E7OIy05NjlJaU09zWTkFmMj5e3sSkFjC3eSQCPmnuJJIvkZPteTKi/IXhjWFHf9POzbSXpfHmfQC1nVOOc3cYz3ZIi48gKrWIDa0ey9kmaRGBeAdFc3RrKqx0V6TjGhxNTVMTjWVZxOWUs6u/4mh3mcLkGMrruynNzSIlPY+pVfu0zvtcHi2g8vEnu7hZOKbnR1x/LQgfZbPLhisT16crRKn88YtKt5Xfh1gTTjU0OIScilbHGSvmaxMGo0lo+h1iVcG4hSYxual1XH+K8nfNZhNXV3qmhzqEL/Ejv7rX1qk53lZIaHwGQws7XOk2aajIJz6zgNb6KmI0MfQt7D1Z73J5vEN5Vjwh6lhWj+wjoJJ/HVbDiRAwMfiI2GJg4dBx1sLKaDMu339HYcswD1dminjp7Ij6vGRCQ2NY2lZGnU0UJQbjLwTKxO5NEGdgqb+a71570trdTVONqN+MQiY3zrjYX6G+NIuEvAraa4qIiUtlbOHpVL7T/S2yooOITitm0zFV/ktjc7QFby8fkktv2pl4zs8OSQt9T0BkBsvPzCTYmBskOsiNsq4ZFNO0NdaEn48PmeXtjnfA+ZGwkyoPQqLTGRgdICQqiWIhLq6urhjq7UYdkkBLayv5qbHkVnZydPEojLUYWRDBc3hQADX981yaZBz2U1DWSJpMJnEY0S6O4u7ibhMm+x8SjBYTcwO1ePmraB+yd9hgMdtGIU0mMzsrE/j5viU2v5J9IYA+xOXxJllCQEbHp6JRhxIVG8/45imnIk7PTQ4nubKXw1Mde5OdZKekU1HdRHFeHsnpBexfPq3b/ZVRNCH2pS4fXeB8LIRO2Z7tJ8jblbSiFo4dxac/3qIgPhRn1xBWju3TjO+zNDGAJiiQ3IoOIRSv2Vnox9fpe/Lq+7h5t/5AiHkRh7gGJjE+2EaqEPJN/QvoRPsaa68iJzuX8ppm8lLjyavu4PKZJQIjnXWE+PvQODjPD0UYn6/ozG/gWFigrYk2/ukf/4Hffe9OdWMzhVkpuLz+jreeauYd84drU6MJ8VMzurTP0linKNCX+IYls7xxwLV2Dqfvv+Fv/vSKrJIqakoL8H3/hm9fe9Azs20LBI43Z8X3CbT1bKbnFVNamEugrwfv/cPoFsJ1d6YTl3fOpJa22HpQdAdL4m98w7//j39H59iabTR1qruSt87+9IzN0t1Uzt/8z7/hjW84tXV1tsbz7Z/+ieDYPLZs89IlEsmXxu7iCNEBrgRr0jm/HU60Mtpcyrt3PpQ4Rl5usV6jXR4lJjyMvJpezoXlPd+cJNLfF391jgh9HQhnM91RzFuvEOq6h5gZaiM0JJTUnAJKi/KIjUmkorRYiJt4GruHORYi6Oz0FP3VXa+nyXBBjtqHmOQcVg6fOqZfLafLRAR5/4joNDPaVEyoOob6gUXHuXsI0RkjnLJraDJTPyA6D3dX6W5vJDs9kaCgIGIyy9hQpmsKMbo9P0h4cAAJadkU5meRnJxMbn4hWYlxwtm3cnR2wcW5ECiXV3cjzcYLBhuKCRF12TW1/ShRiuRfwtXZLhmxQQSqopnbuglqlfoZItD1OzKqOh2jL3ccH66REheFOrmEfWVt9LWW5CB3VCKG2Lh9pESwNtuF8/evqOsdZ3y0h+jwEOJSsygqzCMxPpaSsjKSoiOpaO7n4PgM3YWob73hdlTNpDumqyyNQHUCE8sPlxV9KUy0luHl401RS7/jjAhoL04oTg3BJzSRmdXHsz2MjHc14OvszfiG1lZWY835+Hp6Ud4wbH+L4PLkgLLEAAIj4hidW6QiL52IqGgKSorJSkshMS2PkvxsNPGZzK3tcqG75PTsHOO94ZiDjUk0ESriiprR/sgom+Qp2vlhXJzto2EfEp1K8qe2omRU0dlMrtx0+tyxvTSGj/frHxSdSuKn1tIMQiIT6B+doqYwjVBR1yPr4vkwntHXWIxvYBhFJSWkC/uakZVPXmYmSUnptA3Nc6XXcXp6huFeoiL9yTY5CSo0SWlsfrhv8vNGiPf5wXp83d9QWN+PzmFYjBcH1OXG4+rizcj2+aPOTTND3bX4hYTTMLBkG5RbHKzF6cX3VAk7dlNClosdytLjeOehYWlzlZKsZMIjYm1lnJqcRFZmFnk52SQkZTK7cSh82blt9qf5XnLC7dkBkiOVZSUd6H+gU+eTiE5lTWei2hWnoA+Jzj8TkVeP9vKazbEG/viHv+ddSDK7FzouzrR0VmfxzR//RHn/ok0ENmUloPIJIju/hFh1MMGRicxuHtqyGRr3J3j97T/ylXs446uHXOqEo2+t4d1X/0RyRbcIAs7pLE/j9VsXimq7OT3XcXlxzvRQC75u7whOLGFvd4vYAE/Uifm2xnawMoLnu+/4L//191Q29Yp76GjMj8MnPJ35nWM6a7P5m9/9PersWmH8dLYMeHnRfrbpPR0Ta7dOSCKRfDlszvUT6vMW/5hMLu/NaJhor8LJyYeiuj7HGTuKA1gaaSUsVEVN16xNZB4tDBDm4S6Cq8J7zsPCfG8pb939qOkct033H+yoJz09jaKyGjo7OslJiSOzsILu7jby0mJx8/AhraCa1QN7J9e12UhdTgRq4ZgnRHD3pWA9mic80OuHRef1FS1FqURGpTAw/3T9pOVoA01gwI+Kzs3lScoK0oXTd+aP//QVwbE5bDnWCCrJNmYGu8nPSiezoIzOrk6qi/OIicugb3CIkswEfL28CROvJ5fsy0CU9W8LA42EhYdS3TONQU6x/VejjJCkqj0JCopicecmpBKic3EUlfcLMirbOXoULx9sTBCjCSWhpJVjJZvw1RYxPi6EhKexcytQlbXWfXh8/w3lnZOcXemZ6m8jMz2V/NIK2ttbKcvNIC6lgP6BXgoy4vEU7TgmveA2oRSmCxa6yvFTRdA/tfxFxgF9Dfm4ebtT1j7oOCOKU3dCWVakEJ1JTK88moWuXKxcAAD/9ElEQVRhOqO7rhgn9yjWHevN+2tS8XZ9T0Wjffqsgv50n6q0QALUUUxv7HOh3aSluojklEwamtvpam8gNipKCP5uOhoriVYF4Omnorp9iHPHPNvjg3USNdGEJoiY7gsdaf54XLM/PcB7px8WnYaLEwqTQ4nJrGBl/7E9trI+N4y314dFp5L4bnagRfhENc3DC7YYvrkknTBNHOObytIHK7qTPds66syMNEpqWuhpbyItKZGckkr6ezuIFxrAyz+InMoW8Tfs3+Faf0xlXjwRsalMbd91xv6asF6bmOqpwvP9K4obB23LaxRMuiOaipLw8PBicOP84Sij+Vy0r3wRT2joW9wT9zAw01XCd9+9oLp/3PEmce/LfSqy4nnlGs6m7pKD3WWqS/JITcuiqbWDhqpSkpKTKW8Qba2xClWAP/5BETR0TXGut/uti81ZipKi0GSUC/v4YRn5SUSnMtKZGObK+6BIJh71fG1vjArR+S2ReQ020bk20oizsxvppW2OdyjraQbwc/oTuS3jNrXfWZjO67/7K/7rX/0d7qHxLN5LV6/fncDt3Ts0OXVcOKz8weok0f6viCuoZ2drk+zoIPwik5nbvsswabrYpywpDFfXYNF4jmgriSc8Jpm+8UWGO+uJjw4nOjaG1PwyZpeWSI/wJbemm3OzlebyTN44e1M/vOy4m5nhxmzcXJyoG5z79Q7vSySSD7K9MEyknwvB0Rmc34pOC8PN5bxz9qNMOIr7WM0GpvvrCAkLpG5gweYsTlZGiPDxxl+dd89OWJjuLOGNpz/VQnQqKL3Be7s77OzsMNReQ2xCBrU11RRnpZGXX0xNdQVxmigyChrs698tJlorklGnZDO6+rQH+tfKTxGdFpOOylwRcGjSmFh+ut7kp4pOZc3/zuY6C/OzNNeWExroS1JehS0boIKyPvdgb5d97QmTQ12kxcdTUFZLbUkuCan51NQrCYhUpOQUsW4Leq2sTrURqQmjvGOMKznU+a/m6mSb9OgAglQxzG/fKEYrW/ODBLl9R3ZdDycPojIrB0tDxEQGkVHdac+Yb9wjwd8NVXgKW7ex9TW7c128++4lZUJ0Km1TCZD393Y42N9hYrCDuMgYauubKMrLIEPUcX19LfFRYaTllXFou4+ezbE6/MLU9Ews3OtU+nIYaS7G08eLktZ7ovPimNI0Ff7hqcwq2bnvYdVraa3K5ZV/AtsndiE42JCNr6cn5U13ovPyeI+SBGUGSAxzO/Y2enl2xPb2Hhsr89SJGDA5PZfW5lr7zI+KaiF0E4nSxNI+Yp/dcHa4T050IpFRBew9WG8r+XF+mujUnx+REedPXF4169rH4u5HRKfVxMpoG56v/4yTj5qu4UlW5yZJDPflzXt3KtpHuHCsWTRcClssfN+JdpfGsjxS0vJEnVdQlJFEnrC51dXFhASHU9UyID4j7MD1hRCvmURrMhhb+vEMq58lFrMQ5LV4u72hqGGQm1nENyOd7q4+TOxeiFK+hxDb7aVZhKg1jCh+X0kq1FfJ2xffU907fvtey8UupelxvHSLYNcxQ+r8RLSvnT22licpKcghOSOf5sZqUlOE+Kyup1TYuRBVLINTdp1jOhBCNUf42fSCJ1uS3ecTiU4dORpfXLxDmXw00rkvROf7N29ILu/k3GhlabARNzd/Cmp6He+A9dlBVF7fUNh6Jzrf/u5/8H/+5X/jlU8E8/t3D9Xl7iTuTu6klLVyUwy7y+MkhTuTXNrI1voqKeF+qJLzWD26W+2hrNtsyInFxcmXpb1D9mdbCA+PJCO3mMK8QsrLaxgb6yY0Kp6q6nKCfIPonrD3XtYVZ+LiEULntH0LF6tJz3BLPr4+LjQMzd9Nm5NIJF8MxxuzpIR5ExgWz+GtFb2moyIDZ08VDb1zjnMOhOicG1C2/AihVlxT7MLV4RKJYQF4B8Vz3zX2lKfh6hdK6+iS44wdZR1panQUNV2jtJSVk6JOYmxKyeJtorkoVQgyFYfixhbrNS0lsaiTMxl/FPj9mvkpotNqvqQ2P0mIu1RGl55uXfNTRed9rJcHlKSF8srNl0lRnvcd/aV2k9KcNFJzKhgY6CY1TkXT5K5NqAw35hAaE0vnrH20c3mslfCIMCo6x6Xo/Bm4vjqmICUSn8AoRldu6vqa5dEWXF6+pqpr7In/PVgbRaMJIbmiXQRL4qr1kuwoXwKUKbpah0K1GlkeqOKFsqZz9KFgPNtfoTAzmZyyFoa7G0hIiKZ3ftV2rbtW1Hd0LEPbIvqw6lkdrME3VBGdi1+k6FwfbMBLCMb0qg7HGUWIaElVvScsMf925sUthmM6qgt44x3H5ond4i33iTLy8iK3+i7mOxPiIinQXbSdTDYO7gtGI2MdtcQIodndN0xLfjpJWY3snZq5PpgmK0FNakmTre2dHu6RLkRoWHQhe0e/UuHxyfhpovPqQktOfBDx2UJ02nta7vEjotNyRW9VOr//b3/B7//0Le5e3ni5OvG7v/rP/G//7j/w3k/D4ubDmSob0z3EK8tKeodpKq0gITiOvSsLxkst+dFBJKflsKcVdS3adH1hqvABaYyvPu8nPnuED1eypPt5OJMp9NGZw5ApWdgLkiJw8VKzdfpo6YzxlM6qXIIiNAwuCa0l7rEx1Ynnq+8oahnkpgb0h8tkx4Xiokrj7MF2OFZ66gpJzcqnvWeIqqw4sgvL0BuvbcsMYwLfUt7cYdNWxr1FytJjiUgrfLKv+H0+iehU9oHpq0zj+2++JbWwgXPHbzRfHNqyP379woXGoQXbGpf5vgbev/chp6rb/ibBqpIFzuNrCtsmbGq/OSuRYA8/UtKFgQ/yxTssgdlt+2in+WAKt3euJBQ13g5H7yyNEhf8lsTiRrRHB1RnRvHynSd1vY6Fz4KdxSHChUj0CsvgUNl8Xb9HrMoPp9cvCE/IpWdqm8PtZSF+3QnwcsZdvG9px+7kagpFkOkaSOu43fEoG7kONuTg4638Lik6JZIvEdPlEdW5sbx560K7EBkK1+fbJKi8CIpJZ27nUaAjTO3OTA9RqlCKG4dtazqtpguqsqN5/dqJNkf2OItun7hAN1SxWSzt3wVt+vN9avJTiEouZPtER1dpsRBPsSw41tJ0lmcKQRbEgbiv2WKmNC6AqKQM5nefCq9fK1btHGEBnvhGpn14eq2yjYNwvGERCbSPP93LWRGdUYroVCXdik6L2cDR3jbrmzvoTWYudWe2dWI3nG1PEx/mgbOfmsWtk1vRaTWd016ZK0RMAuMr+6zNDxEd4UuHo3d9vC2fsFghOucU0WlmsrPKtqazaXjli9+/8RfBaqKrOpe3b53JqHJMZ78+FW0qijduYYwsOKY23+Nod4HY6HCisqo4sk3Hs9JelsI75/cUtDligst9ihOCcA1OYH7rLvA16bS0VOQQKdr36p6Wuf5aIjRqRrbsbXCguUCInFhGdo1YDeeMNuThFxrN8NyXuae4fn+eMB9X3INiceRYZH2qB5fvX9lngolg9QEWPUPNVbg7BzHrsEuX+6JN+7njGZrEvs4uzZfG23j/9j1ZFV2c35u6tymEUHJ0FPm1fRzt79KYEUdidgsnShR8tkBeUiSpxc020Xm8t0RsVDgRmdUcnDxOJyX5Yeyi01nE0nbR+byoMOlPqcmKRp1YeJtd9Q6H6PR8JURnxa3oNBsu2d3eYG1dHEvTtNZXU1VZQWlZOcW5mbi//pq//d0/EpdTyd7xnf/THW6QGa8hVZw/vLgQbbaYCJ8Ye3KcawMlcSEkp+awq4hO4wlFqaLu45NYOv21dvdY0R+sEhvkhYfw8zNbdoG5NtlFoLc7Edl16B7vhSt8X19bOb4qNW2j9sy9Z/vLRPq8xUedxvKxvR4XBprw93AhtbIb04N1mn3Cf0VQ3TnBwfYaeTFB5BZX2a5ZdVvEB7+hvKnNtsxRuzxGZkwo8XkNXPzAnqGfRHQqhXe6NY0mwEUIzNfEZxbR3NxMfmoc71+/RJWQz/ap3ShMdlTy/cv3pN2bXrs80Y3X238gu3HUNmW2OjEcX/cQhuZ2mO5vwOnFn3EJiWdmddM2vdbp+ze2hnLTB6BsAh3h9RURWeWcXerZmusn0M0ZD99QqhpaaWtrJCUuHDfvAGr7hPi12UkrpfH+/Nf/8G9x12Sj1PeldpdU/1f8t7/4DwRnNnB0aTeoZTmJfPfKkwbH9FqL6ZK+mjTeO7+ktt++dksikXxpWFmZ7iPU2xVPv0hq6+ttC/I9vQJo6Bp9kMLfjuJElklQq4jPrmLHFinZ05eH+rri4q+hvrGJstwk3D39aeiZuL2HWX/GcLsQLMFhdE1vChtlZXW0nbiIULJyi+hoqyctIY6swgabvbnWH6Dx8yY5p5z9n7j/8a8B6+EMAR5Owt4nPJip8hArc4MNBAlxV9T0KJmTwHK4RoiHO698oxlbswsK/ckOtcKOB0WksKI9ZWtlnNqKYqpq6mlrbSMrMZL3rq7k1/cIR+8IpC0mFkdb0YQr+zd22Mr9fH+F/ORIolKKaGjtJjkmgrS8EraOdViMpzQXpxMcEiHE7tkXOfL1KTjcnCFe7c8bF2/K6pqoKhVCz8eTrOoetM88+xfHexSlxBASnsz6nl34HKxNEh3ig5NHILXNLZQXZuHt7kl56xC6G9FjvmKiq45IVRhV7Upmagva1XFRx2qSskvo6OgUbTCGjPxyW/Kiq9NDKlLVhMdlsrzzOCD/UjDQWZmN65u34pkvpKWhhqSIIBH0JjO99nTLKKXMFkc6UYmAt3Fk1bY5vXIPpePG2ckJTWo+zfVVxEcG2JZATa4eCvlj5+xwnYK0eNSxQvAfKu1Jz3BziWhPUZRV1VNfmEZCXAKdY0qwbRVxXi/qUH/ym0ceCFfJT+GavYlevv/2FSGpFeydPT/SaRFtYqSpAK9AZQsw+6DLHcJHzSjC9U+EZxSzd2oXnWdbc2TFq4lMLeTROLjAQGV6lHh+whm5t5+x4eKYjopsVOo4+ibtu0rM9jWINhtEYW07TU11RIVFUd3SbxNil4qfDQ8gIT2f819z354Q04NNpbiKthEam0ZTcxPJmlBbpu2hZdE2nvlt00PtBPsHUtTQb/MxVlFHvXV5OL9xIiohm+aWJuLUIQSGxbEg7NLNLS6ONoWfiyAmvZiNYwPXukNayjNRR0ZR39JCQ1UhanUMg5P22Vfzw62og3wobhvjh5JDfyLRqaD0eoyQFhvKuzeveCOMlIuHF4lZJSxt3U1xWh7vJTYmmfquu0WvOytTZMSraBpeRNmZoL+mmNREJavSmbjtFQONRXi5e1DW0odWqPrU+GTx/4HboeTDzQVKMiIpbugUilz5uRbmhjttC5CdnZxxcfNAFZVAS//EgyxMs13V+Lm5kSMCCpttNJ4zWJeFm9N76vvnMdreaqW7pZroOCV9vn20Q1m7NdNfT3JSDP2z67ZeN4lE8uWhbIw9M9CG2t8Lp3fv8A5QUdk6wKmSoOQZlA6pyux4gsNiGJ1TpsUKhFOYGmwnNMCTd8Ie+fgFU9U6yKmyNsWBTjiEtqoCiqs7bnsVr4WIGeioIjwkAE8ff7KKa1jZF0JMmPTN8Sa8/cV9OkZ4PNjwa8Z6uk5uWhIpBdXsfiAQUjjemhX2PYhYEXSc3YhEB9bTPQrS04jNKmdpzz51T9nOpCo7Dv/wJFaOzzjYnCYnUc17p3e8d3YhMDRS1GsPR/enEZku6G2uIq+4mvmbabqiLpcn+okLU+Hp5UtUUi6Ti/Z6PlybITUqFE3i0+8k+ddgZWN+RARjIba4ws0rgOyyBg4cSUUec32lY7yllEC/QNpHlrF39FtYmeoVz0wAb0Q79hBtsKCmneP7olV/REddJTlFtSIIt7dNZSnNVG8r0aoQPDx9SMoqZW5NWUJkYWdpCrWfFznlLZz+wPSzXzvG8wMRnObi7vyOd6KthEYlMSxsm21t+TNotxfIjA1Ek1nDvqMNG872aarIwd3FibfvXG0b4Q/PrT4IqpU6LszNpbV38jZQPhd2sSIvBX8fD3yDwqlrH0IxvdfGM7pq8ggIDGFEBOcyBvvncs3xyjQxmjjy63o4VqahP4uFvaVBArw9hcjp4qG2t7K7NkuSEDJFIvY+1tnvcSpEZ2ZsGOrkPE7sruwOo47OmiLSc/OZvacLTvc3qCrIpFbUr9ax96vhfJ/uxhIC/HzwD1KRX9XKnmMLkeneWoKCQylpvlsn/GtFWfrXUVeEn6crr4RwVIn21TO2eNsZ85ij9Tmyhc6KSM5n31FvxvNDmkuz8XZ1Fu3LCXVsCgPT9zsJrGzP9ZGdl0evY82mcu5wZ4mi7ES8fXwJDo+msWeKM73FZvcaRHv18QtjdPnplm33+YSi086l7oS1lUVmpmdY2dji4sF8YlE4V3qOtSe2VPM3mEVgd3J8iE5vtIk//fk5x8cnGGwCUjQPJaHDzhZ7RycYjUZOTk441+ntQlG5bjJyfqLl7OLywb6ZZ9oDFmZnmJldYPfw5Pb9N5gNeg4Pjzi/uHFeVoyXFxzsH6I33H1vve4crfg+esf3UbY8MOrPxffQ2t73AdsrkUi+BCzXaPe2mZudZXVzB6P5BwSFsA3rk12oVcHCSbZx5rAZVotZ3GOLWeUeGzu2LH73MRmuOD3WontkL5UEQ9sbK8wvLnN0au831ml3KE5Wo07IYnb7y1nPacNiEuVwzMnZOWbLY4t9D+M5beXZqMIiaBtffmjbRVmfCR9xfHK3xYJFnLs4O+Ho+BSTuK/VYkS7v8O88A/TMzNs7R0+7c0VnzkXnzk9v3QIFzuKSz3c3mR+bp6do1Nb4KyMCPSKAEklAqGmwQW5f/PPjpULESMsiPaztLwh4oofCmustg3tEyNDSMyuZPN2W7NrzrV7oh3PsLy2hf7RmltlxOD8VNT3xV1soqAklNrdWmd2fpH9Y/u0arNOCNSqHNse4UMz6x8UYF8KJsMlm6tLIpaaF7HUD0/nN11d0NdUird/KP3TK5gdytJ0dcmW7R5KPPY08c+ViL1ORPu8ejSl8PJcy/LivKizbS5t16xC8I+TGBVKdFalEPwf7pySfAiriJsNaLXH4nnXC/v24QdYmXJelhFNWHw6Y4+2TTEbDaLOjmyx9809LKK9nIrY+Pj07G47qRuE7dWdn3FyenpvCxSruM+V0AVHXBkf64UL1pcXWVxe5cxRz8azPfIS1UQlZTG/92VMq1aW7O1srjIzM8f2wfETrfIA8zlDLWX4BYbRNrqI0VHI1yJW2Fpdtt1jT/ilBwh/dHVxYvOr97cdUs6fnxyytLjAyvr2bZ3sLgySqAmzzdg6+ZF9wD+56JRIJJLfMlaLnrrSbJIycpndeG4K2r8CIZY2Z/qIj0ugsXcK3Q+stfjS2VuZITc1keyyes7u7eH2KTjdW6EkN5X0okrOvnQF8ivgWgSx451VxCek2KYF/txPx87qLLlpCbapfxf39s+V2DnZWSI3PZ68mjb2Tj+wNvtfivmSofY64uISGV05+tnrVvKU9ZkBEuJiqWrvx/iJO9RWJzpJTEiiuX9KyNXfJtrtZUozEsgsbuTAkW39Z8N6RU9TBQmJ6QzN/vhadSk6JRKJ5BNzfrTFxMQEq9sHP6tjvDZdsb06x+T0LGf3Zov8FlFGK7fWFhidnORISQ73CTnd22R2aoqN/S8nqdOvHdPVGRNjYyysbmG8P1T9r+XazOHOBlOT0+w7clVIHqLsQbirtM1x0TbPnq7s+9dgFvW6sjjDxMIqph8YoZP8fCgzOZbnppicW+Ts3rKQXxyrhY3FSWbmF+3bIf1GUWZOHW2v0j88xeH55c8aYyjJoxZFXc8tr3P1E/aalqJTIpFIPgPMJhNG089rfq0WCyajgetr2b9vxyLK2Ij5h6Y8/wL8/9n7Cy7JjnNdF/1Z954x7j573D3OPvueBd5rLS97mW1ZsixuNXd1MTMzMzMzMzMzZTElVGVWZuVzY87M6qrurhZZbbWseDRitGrmzJgzA7743kCLyGuzWTa17xq2Onj5/U53FnFZzEp+yzr4lSjLkIxGzN+zrbJeWbhU6psUnH9XlHRXlreZv88OnG+NVS1Tlq9aevETwmj8/vNDOY3kUrSp37R6SdEpkUgkEolEIpFIJJK3hhSdEolEIpFIJBKJRCJ5a0jRKZFIJBKJRCKRSCSSt4YUnRKJRCKRSCQSiUQieWtI0SmRSCQSiUQikUgkkreGFJ0SiUQikUgkEolEInlrfKXoFP+vbrUvgwwyyCCDDDLIIIMMMsgggwzfNiiaUujMabPZfLfoPD8/Z3t7m83NTba2tmSQQQYZZJBBBhlkkEEGGWSQ4RsFRUfqdDqOj4+n19fX7xadysGup6enyk0yyCCDDDLIIIMMMsgggwwyyPCtgtlsxmAwTB8eHr55TacyHCqDDDLIIIMMMsgggwwyyCCDDN82KHzl9FqJRCKRSCQSiUQikUj+FoTGlLvXSiQSiUQikUgkEonk7SBFp0QikUgkEolEIpFI3hpSdEokEolEIpFIJBKJ5K0hRadEIpFIJBKJRCKRSN4aUnRKJBKJRCKRSCQSieStIUWnRCKRSCQSiUQikUjeGlJ0SiQSiUQikUgkEonkrSFFp0QikUgkEolEIpFI3hpSdEokEolEIpFIJBKJ5K3xnUSn+cLA7uYGszMzTE1NMTkxwezcHAenWqz2e17mkp2NFQZHpjg9v7Rf+/ac67VotrY50V284Tk2rFdmzk4O2N7ZRW+UGloikUgkEolEIpFIfii+k+g8XJggPSYCNzcPgkNC8Pf1ISImloGZZSxCDV5ZLjEIgXhuNNnFoZbW8iweOPgzvaNVr3wXVmaHyUrPoGd8DfNXqM7L8zNGOuvIyClkbutIfQfzpQmdXofRZLLdJJFIJBKJRCKRSCSSt853EJ1WVrvrePblfZ57+FNSWkZBfh5lldXMb+yodxhPNmmrq2J2dVP9WxGdDYUpfPylJ1O7evu1b8/G4jhFhYUMTK9juXqz6jSf65joaaGgqJwlzZF6TbM0S3NbF4tChEokEolEIpFIJBKJ5O/DtxedVitLndW4uvlS0T6kRGAPV+q/ZtM5y6MNOD+8T0l1Izq9AbP5hObSDL585s/MnpZzgx6tVhl1vHmU8n2L2YzFYhH/XqLXadHq9JiVoVM7yvVzgwHTpcV+xYbZZER7dsrZmZYL06X9PYycn1+I71swXZzTXJiOu1cQFV1T6A3nmIxGEY/5lWm64nviGer1rxhJlUgkEolEIpFIJBLJN0Pos28/0rncVYO7RwDVXWP2a3YsZlZHugnzesLP/p//h/c+/Ji04ko0e5t01pXw+Lkv7YPDlBVkEREWQUFVKzvHtpHPc+0R04M9dHf0MDLcT1ZKHOFRsTT3TqC9uFLv2d9cpbupmYX1fa4HOrWH27TVlRERGkhwWDQtfWMYhHDcWp6it3eQnf0Txjprcfn8PX72b//Bpw7eFJbX0NfRSm19O7unNyOvxuNtujtb6BicwHhL7EokEolEIpFIJBKJ5LvxnUTnak8dTk8d8ItMpLOrk9b2DvrGZjk81bE1M0JCkDu//Ld/55P7zyhq7OLkZJfu+iL+8Me/EhKXTF5eHpFBnnz00WekFDVwJp54cbxJTqgXH/3+Y2LT88jOTsPH6RH3HzlT2zOtPnm6uxmvh4+oaJtRR0CPNuZIjQjGxdWTsKgY4hPiae4Z5Fh/RkdVBt7eIQxNbzE71IX/00/45X/9mife4bR2dtFSkcOT+4+p7ZvDaNO0zHdX4+fmTFpZKyapOSUSiUQikUgkEonkb+Y7iE5Y72vg2Wcf895fP8fP3xd3b18i0ktZ0pxgutAx21+L0/0vyS2v5ehMz5XljLaKFH75uz+TUNSMZv+A7Y0VQpwe8MQlkInNMyxnWyR7O/Lrn39IWdcUewd7LI604vzgY/xj8jg02kSn62f3qGifxaA9oTYnnsdPXCmq6WRLs8ve7g4np2fodUc0FSfg5OTNwPQuFwYdTfmpuLj5UdI2il6vZ2NuBL/n9whMruBQKyLHRHlGNA7O3rSPrbwy7VYikUgkEolEIpFIJN+F7yA6b0Y6/SOT6O3rpaOrm4HxWU70tp1hdxe68XnuQF33sPq3spFQc0kyH91zonfpZiOf5twIIQQ96Z7RYD7dIjvMD2fnYFbO7DvMms9Ii/LEOziJlWNYGOjE/+lz6nsX2F6fJ1yI1PDkMnZOX17jadIf0VqWipdXIMNzh+q1gdpi/IKiaZtYV/+2GM9oLkrgkVMQi5pDLo6WiA3xJTKtlMPzl+OTSCQSiUQikUgkEsl34zuJTnVNp6eypnPcfu0WVgsrY024PVamwfbZRwyFwCvN4MEzf+YODOoVhc7SGHwDfOmd3cZ4tEFuZCDBwQnsXdpewWw4JicllMCoDFaEdpy/JTqX5kfxcXUgr3oA3StHf17aRaenpyI6D9RrXeXZePmG0TSyqP4tYmd9shuP589pHZlhtL2GMD/xmzpGsM+2lUgkEolEIpFIJBLJ38h3E52dNXh4BVLfZ1tr+TIWlkcaeP7FPSo6Bu3XtDSVpPPlU1+md8/s16CtKAoff1/65jQ20RnhT2BAHJoLm4q81B2SlRhMgCI6j4To7O/A74mDEJ2L7G4uEe71HO/wDBZ2boTslcWM/ux10dlenIaLexAt42vq3wr6Yw2FSSGExyQRERpMfGo+0+u2kVGJRCKRSCQSiUQikfztfCfRudReyZMHjwiKy2BycoqJ8THGJ6bY3DvCYrWwu9SPy71P8Q6LY3JlnQvjEfWFaXx0z5WJ7VN7PNCYG4yLhyvd01sYD9dID3THwyOcrWvRqT0gJcobz+AkloQWnO1pVdd0lrXNcKHT0laWwQPxHjEZxYxNTjI61Mv0/CL7J8e0lCTi6Ghb06kw1JDPI3FvSEoxq1u7GC+vsFpMTPU18uX77/GBiLe8a5RT+8xeiUQikUgkEolEIpH87XwH0QlbI+14PLnH7//0Po+ePOXBvS947OBEfn0veosVixCZZelRfCTEnH9sOlv7Gvqaq3D1imBuT2ePBbqrUoiIimBkcRfTyTYlSVFERaWxa7RPr9UdUZwZS0xKARvHsDzST4S7B3Xdc0LcWrk401CVn4KzgwMPHj3GycWVssYOjg06+hsLCQ2JYWxuX43raHOG+CB33v/oc+Lza9k/UzYPglPNEg4f/o4P7jszsLCtXpNIJBKJRCKRSCQSyffDdxKdJu0Jy7OTdHd10d7RSXtbK53dPcyvabi0L4jUnewyOtDD0NgkpwYD2tMjVlY3MJhuNuk5OdCwsbmB9tzEldnI3vYmm5saTEJQKlgtl+ztbLGl2UMZ/DzXnrGxvMLRid6+VhQuDWcszEzQId6hp3+Yrd1DrqxXnB3vsb6+idZgH7oU1/a3Vujv7WFsdplz+688Xp/C1+FLQpIK2Ty2CVGJRCKRSCQSiUQikXw/fCfRec2VxYLZbH4RxPftn9iwir8t4p6rt37+iFU8/xKz5Ur8IPulN3B1ZVHfyWq1cLizQW1ePE+eutE0NIv4ukQikUgkEolEIpFIvkf+JtH54+WKo81JMpMiefzkCQkFDexp5WJOiUQikUgkEolEIvm++YmKzksONqaoKCuiqqmTrSPdi+m6EolEIpFIJBKJRCL5/viJis4rTBd6Tk7PMEu1KZFIJBKJRCKRSCRvjZ+o6JRIJBKJRCKRSCQSyd8DKTolEolEIpFIJBKJRPLWkKJTIpFIJBKJRCKRSCRvDSk6JRKJRCKRSCQSiUTy1pCiUyKRSCQSiUQikUgkbw0pOiUSiUQikUgkEolE8tb4StF5fn7O7u4uGo2GnZ0dGWSQQQYZZJBBBhlkkEEGGWT4RkHRkXq9ntPT0+m9vb27RefFxQUHBweIG9jf35dBBhlkkEEGGWSQQQYZZJBBhm8UFB1pMpk4Ozt7s+gU/4/FYlGD2WyWQQYZZJBBBhlkkEEGGWSQQYZvFBQdqSD+nRZ/yzWdEolEIpFIJBKJRCL5/hEaU24kJJFIJBKJRCKRSCSSt4MUnRKJRCKRSCQSiUQieWtI0SmRSCQSiUQikUgkkreGFJ0SiUQikUgkEolEInlrSNEpkUgkEolEIpFIJJK3hhSdEolEIpFIJBKJRCJ5a0jRKZFIJBKJRCKRSCSSt4YUnRKJRCKRSCQSiUQieWtI0SmRSCQSiUQikUgkkrfGDyY6jecGjo5PuTC9+jgrlksj2jOt+OxS/PX9Yjadc7C7zfbOASbzN4v9Qq9HrzdwabpApz1DqzvH8qavWq8w6M44PdVyKW6yir/PDTr1b7Plyn7Ty1yZLzna32ZjS4Ph4tJ+9QaL+Fx3dobRaOJcp2V/d5ftbfEb7OH49OzN7/O9YeXSeM7xwT4ajUZ97tbWFnv7hxhFPkkkPxVUG7K3w7ZmF925yX71ZUznOvZ2d9g/PMFiv/aVXJkxCDtzfqHEJ2yGXsvujq2ebW9ts7Ozz7mo/3dhuTRhvLRwddsGiPhODvfZEfXzQnx2myuzsK9ard3WXAlbrONMp//ebe3b4Mps4uToQKS9hsMT7WvvbL2yoD87Zkek297BsbDB9g++BpPxgjOt7rX26MpitqeV8aX0NV3o2RP5s7Ozh8H4epN5oebfLsdnevuVa6xqPuv05+pfZvF7zkT850ZpQ78L56Kt3dGI+rG7L/LBloYG3Sm74tqWqDdK/dFsK/m0y8HhIXpRv+5uhW0oeaM3GDg7OWZXqXv2oLR5O6LdPTg44PyO9s5iueRCPF/4T/YrAquFs+ND27u92q6LzxQfx1bflfIkyp9oxxWf4V1H8YMOhR+wvb2l+gBbWxpOTnVYXjJAAvEbT4+U+zQcHJ1yaf6qlFfsmOL3nWG6vJVWL+LY5kD4i6ZbcSi24PhgT3y2w6nWYL96g+Kv7Il83xfPftUMGIVPphN5bVZ8NNX2atEZLn4UNvDvjijTir1T7KP56uU8NJ1rRf3TqO3cN3OnrVyItNcK+3e7vCjt5e6OqGe7e7Z8uBXX1eWF2t7u7Im695pBvxJ1Valje6Juv1rHzOh0uhe21STK7an4DUpb+WPhStgVxb9+tWqpCG2h2Be1bhwdf+3vsoq4ToXNMVmEryDK/NnxkdpObilB1GMljsuXHmRFe3qk2r6TO+qX+cIgfJxdDo9f1x+Xwkc6PRM2QdU8V0I3aYVdNaqf/SCi0yoK7kx/J1HBEVS3DnDxUjm+5GRjmtK8MsbnN/hbX0ZxGkwi02w/Ho41CxSlRROfWsruyeuNx6tcXpzR3dxE98AE+xuzVBdnkpRTyequzn7Hy+iOtyjNSiEto4it00vMRh1jvU2UVjWyJwzzXRhOD6jJjSMgPJnZtQP7VTvWSzaXJqmpamJtdYO+hiqiI8IJj4wiNjqaqKhoGjp60f6N9UhpNC+F8/paw3GNxcTKRD/p8ZGEhkcSExNNREQkWfkVrO8e2W+SSP6BEQ7Qyf4mnU3VpKUkERufSEFFPStb+y/qjVXcc6RZpbGqiKSEeBJTsukZmcXwNR0zO6KOt7V1sLh1jNV4Rk9zJQGBQUSJOh4dFUNSUiHz67v2uwVCXJ0L53ppeoSWpgbGVw85t9sAizD4yzMjFOVlkZGdR3PfGEd6o92humJpcoDWjm7WDhR7ZGZ3bZb6hkZWNUd3N27vCJfCMZkf76YoN534+DiSM/MYnFoUjoi9lRAN6ebKLOXF+SQnJpCYlEFT5whHugvb52/AYjpjqLeDtt5R9s5u3SuawrXZIRJTM+gQeWhU0/eK/a0VWmrLSFGekZBIWW0rWwdn9rS74mR3g3Zhp9PT0iipbGB+4/BFul4crtPe2sbg5LKaH+faQ4Z62mnvG+XsDR0YktdRBMfW0jSVxQUkxMeTnJrO+NIWlyJRZ0c6SUuIJCIyhtjYOOJiY4kMCyEiLpnB+c03+hQG0Xa3t7UxNjPLQHsraaKNixTta2ycLY4IJY7YJGY29ux1yYrxXM/6oqi7rc0MTKzedCxfXbK+MCneL5e09Ewa2vrZP77ugLCyszJJQ1Mni5u29v5kZ4Pulib6p1aEY69eekexsjg7TEJsNMGhYcL/iCQyIpHmrrGXnH6j/pjJwXbyMlOIE3Yyu6Serf1T+6evYzEZRL710trey86RVr1m1J8w0d9BXoYSRwJ55U1s7NniOD87YKi7haw08VlsPLmF5Uwubb8QPvqzQ/o7xefp6WQXVTA6uyIcattnFqOW3rYm+kYm0SqV2nLB/NQQTa0dbBy82kkkMRzv0FKeRX55Nbu6a/Gh2MFlGkQ7lyDqR1J6Dm0Dk+guvspjt3K2J2yj0l4tbHChVFZhs3c3FkV7WUJSXCxJKWl0j8xwYa8E+pMdeltrSUtOID4pheqWbvavbbSoY5vL01SVFZKalk59ez+7p/b3E7Z7d3lCzdOlrUP10uneJh2iXveOznzjzsgfAqXjymQ8F37/FO0tjfROLKFX0uoWl0LsTw/3kJ+ZKvyQOFE3qoUt2Re5cjdXl3pWpgapb+vjWAj+s/11ylKTCFV8DMU+RkRQWtvAgV2MKUJ/Sdxfkp8lbGgsWQXlTCwI26m+hpUL7REDHc3kZqaTW1DB2Oz6C9Fr1B0z2ttGa/+M3SaYmRrsEj5Hr9pB+8OITiEEG7Pj+bf/4//gt391YmBpz/6JwgWbwzU8+uQpZS3D2LTxd8XKrjDuQ8NTwuDbepYP16dICfPELywDzcnXlTwL+4sDBAcGUNw8yN5iDy5f/p7/819+R0njoIj9VazM9dXy+3/6X/zn7+8zodFhNpzS01BMSk4xm0d3G93zsz0KE3x56hrM2OItx1JwebJBbUEK/jG5zE9PkRbsxaf3HhMcGUtGaopwrJJp6RlE9zdVIivbq9MM9IsC+Sa/RxjqjtJ0vvjkQ1z9QkQjn0JSYhKFpXVs7h/bb5JI/oG5PGO4vQoPTy+iE1JJT47n8f3PRd1MZ3HnRL3lZHeZvMQwnjo4Ey2crRBvNx4/c6e5f/KNsxEs50cUpYtGNa2A9UMjur1V4oK9+cNfHxCfnERKSiqZWeUsiUZFRTS2BxsL1Jdm4/L0c/7wpw8p6FpAb49/ZayH1OgIYlOyyElPwdc/lOqOCdHIC+t6tEpGfDSZxXUcXti+cLyzTE5cGKlFTRzcFl3vGIeaFcrz4ohPiCMzLRUPx8c8dPKmY3ReiDrxWy6O6WyqxD80mpzcHKKChK38/BGF9X3orj3OVxHN3fJYO1FhoVQIG6+/lUnGsx2yo135b//0S2IKWzhXorjS0Vqeh7ezO3HJ6aTEh/Po3peEpZULh+eCq4s9GovTCIuIJCcvl4ToMCIScjk0CMNqtdBdlScc6FT6ZzfUZ1wJuzraWU1IWCQ9Qoia3mXV/65gVTpOegn3dsHZw4+ktEwhLpIZmBLOmfCKFif6yU1PJEE4qelC8KUlxeHwxV/4w0dPaBpdtkfyMuaLMzqqcolMSGdodonpwT5yRd1T2tf0jCySEmJ49NEfef9zR4YXNOIbVk6E49ZaW47b0/v8+c9/JaWkm+uB8qP1UfHcaGISksnLSiUoIIj86nYUF8xyfkhJRiypBdWs7ds6oi+1+7RU5BEYmcrK7ukdvsW7wgWNFTl8+MFneAWHk5ySLPyATDr7Jl+MNF8Zz2ivzsP5+RMCRbnOzM4lI6+c5a1rsf4Kol7sLo+TEB5CYV0Xh3ozlksD7aKuuDg8IyBEiSOHrOJqYQNFHKKuzw20Eh7oS1BUPFkZSXg4PcbBM4KZrTN1oKGnoZyQkHDSc/JJjIsmNCKBGeGPKZ34q6Oton7G0tQ7iVE1C1aWp/tIiAyjoLoTvekdViR/d6zM9FTz0S/+F588cWbR7sOeaeZFGxPIMyd3klLTRF748OSpK3Xdk5y/IpCuMemFfa4tIDg8galVm8+vWRwhPsSb586exCWmkpOZQUvviLDXFlEGTqnMicfhuRMR8ckkRofw/KkDqSXtnIuKdro9T2ZcBDHCnuZnpxMSHEp2Radaxgwnu5Skx5OcUy7qmO2dL8/2qS/JIiImkcnVd3eg5GR3i562BnxdHvCnP71HVF4DZ7dU2ZXZQG9dIR7ODnj6hpCWmUV+WTnj82tvGDSyohECPC0mlNwqUb7PjayNteH80cc8d3AnRXw/KTGBmsY2joTgUkb+Fwea8HNzwtMvWPgfieJZjnj4xzK5cYbFqGe0pYjQsCjRxuUTHxFCaFQy44vCLoq6uTTaSUxYGK3DS1zYq9LqRDuxMZG0Dc78MKJTmS5amxrFF7/6Bf/6778nJKOCPWFobFywMVjBF+/fp7hpUPz1MlcWURhFuD38/irKSKr4LZguDDQXxhIRncXk4p64ZuVgfZr0SD9ConPY05nFNWWo+W6HxGQQDVFZhpqxQ/MatJvDOD74M//v//N/EhKXwZbu5e9dnmxTmxbAP//3/8Ev/+LC8PKOeE/bVDllWPvylem1ynOVEVi9qCBlqUE4eYUz/pIAh6XxbsJ9PchrGGR3bZHEIF8CE3KYXd9Fr9Op0wcujMaXejhs8SrT7e4ugDdpaPvcenVBe0U6/t7+rAj1emd6GI5oLEgWjbwn7WOLnNmfrTecv3Ha8FehPOPV57x4769yvMQ7f909tt93pRqfV1HKxtd933bP3d+X/HSxnGtZX5pjaHqZE1GftdozWguTuPfIkdKOMXGHlSFhjJ2dnChsGFanvR+sTBDo9gTvqHQW7pgdoUwHne2pJTgwmKq2EXWUY2tphNBAP/wSSzkSceiV6Zg6A5dmmwW36I/orS8hMNAbVw9nPvjdB+S1zmAzR1fUFeURGZrIyoGOvc01cmJiSEkr4/hES29DERGxKUL0bL6wGVfCwZvoqhZiOoD+6fV3ttwbL845PNgVtlRJEy1Lg804PbpPVEYZ+8KWKw7l2ckRu8IxUqZIHu4uE+p6D7fQJGY1Z/ZYXsaoFbY3M4oQIQwnVw9vfrvFxFx/A88//x3/45fvEVfajs4kbIJFz8L0NBNj86pNPzvcpj43hk/uuzK4sCmcqGHShXNU3dQu3kHPRHcdEYGezOydsL86KRzrCAqr2jm9HhEQ9uxga4XUCH8Sc8vZeTEaJnkTJ5oF0mOCcfOJYnp9H53eINpC7YtpaJcmo7oMRpm2rLRRmtUpwr2e4R9fyMreXbONLOytjhERHEBmeRv7Z0YuRZt6O47VuRH8hLCJThe+yqnSFW5kvKuCsOAg4Ww/597nn5NU1Mm1XumpSiYuKUYI1E0Mh5tU5iWI72ayqdepHdOhYbH0TCyJ+m4vccJPWJsZIlrU6bymYQzvqvC5OqQ0O5En7uGivggfRLVNeuGDmF74E3O9tfh7uBCbVyPslxaDqIvKPdf261WMuiNaK7Lx8hf5uapR83C6qxZfN1cS8+s4vI5DWeIk4rgym9lYnGZ6dpbdk1MR9xljPTU4fPkpWTVD7G+sUJqZRGZpC9vCbq6Nd5EqbGBl96Jw6NfJTY4kOa+Szf2zF/XdrNuluSITn2DhWK++ecTop4Zuf538GA/+82f/yucPnFg+tgm45vx4XJw9qGwfV+uIZmWG7Cg/vCNEO7d9t6Dbmh8hJtCL3Pp+Yf9EWTAekpMQJupxCL0TK2oZMYj6ca4sZRDt4tJAPc5CcKYUN3Ai2sEz4V/XF6bh7BrA5NoWw90tos7GMjg8q36vMiud6MgMTkT9n+xtJCQ0kvaROYzX/qmoYwtjPUQF+5FT0Sbq2DuYy1YL0531xMdE4ej0hC8++4jIzHJOX0wisLI83IKfqzOxGcVsHtjaOiW8cZnbxQFd1dn4BkUxsrwv/HULo81lOD/xoL65X/2uTtjP8/MLte7pDtaJD/bCPSie0bk11c8Z720iwNOd9NImdvY0okz4UlwrbOWpgcXBRuLD/WnpG+H0YIvS1FjiMqs4ODPc1C/TCaUZseQUFLN1/gOITrMoFNWi4of4BBHo68MTZ0/yGgfsnxrZHKriwV8fUdo89GKk0yoco4XxfvJFwUpJy6CmtVcY/5t5xoc768xOTzI+Mk5nQxV1jY2UV1Xy5PP3+PVv3sMrMJq+sRXWF+coTAknPDqboYFhKovzyc4rZnR27UWDcc3R1hKJYQFEppaxd3LB2Vq/EGYu/PH9D/DyC6C2d95+p4KVucE2ov0cef8P7/HYPZLe2S3Fo0OzvszY5Dza8+tfc6VW0vLCbLLEs+tb2slKCMQjIIapFftohsKVjrbKXNw9Q5nZEsZ1b53EkEDi8mo5MLxuwK1XJjZFvLXlxaQkJ1Nc1cja7s0opMVsZH68j4LsdJJSlWl/C+hOj5kdaMLx3vv82//+D/yjk2ntGUL/agEWorOpKA3v4DCmt18fsT0S6T86NMDC+o7dYFu5ONkX+THKorimDMsf7G0yJRy28dFhmqpKaewY5sw+nL+9Okt1aR5JSckUljewsHEtvq3ojneZnJpmeWWNmZE+CnPSySooY2Jp80WhtmFicXJQLSOpGXkiv+cxXt+gTLdYX6CpulSkTRL5pTXivWzPUObNK1PmJidnWFlYpKu5hoyMDBraBznWyilvEhuKQDQLh+d2U6UZbubxUydyG/uwXBxRkhKCh28oS8fX9dNETXY0j50DaB5csl+7wXxxSmFaNMExqcys26baLU21ExgaQFL1oPr3q1xe6NnVbLGxucloXwPPPvqC3OZptOqLXVKUkUlUZI7aYWcUDll1VgZpqcVMjXWRmBhPddvQa7budG+VpCB3m9OtfUPj9Y5xvjVJsPtzYrMq2H2lA9DGJamBjvhEJDGnuWOWiWjgt+aHCBaNaXnrEBe3Rjl3hD3KEOIxyM+Xh24epFd1cqgT6SIcF7PiPL+41cr6UC2ffPqM9sklFie6SYoIpntQ6YSAzdkBksJ9GJidpzw/g6T0Qha2Xp4ZcmXSMd5SiFuAaKPeYdH/rjDSUkqAl3B4++4etXyZS6Z6a3AWPkbnxCp39Y9eGs7oq84lwD+EwbmbzpgXmPUMN+bj5BZI/8yW6phxdcGhZoPNzS36OxsJcH9GclE718t767NDRF4nsnosaqFVR3ddPomZGQxOz5ASG0lZQ++Ltu8as35ftLEpuAeksH14lzh+BzjfIDcjGveY7JdGX14gnMv8+FD8guNY+YZt597qNMkRnqSWd3GqDj2ekC3i8A0V6XfXEighbpWlQLfryeHmLGEunxGb2yzE+zgFqfFUtE0I6wtn65OUZadRWNtLd0MpEbGJjCxsv1LProTd7SfA24OylkEMP6J1f28L68Ux3Y0lBPi6E+TvJUSmD0tHiu0ykSr8XB//GFZOr1PRyFxnMU9c/egcX3y9Dpm09DYV4+IRwPyOzRavCLvp5+tNkagLr9k84Te3FyTg4O5P5+Sq/SJszAwT6vqIis4BGqqriAwTIndxR/2ss7qCBGHrp+fHyUyMIruygyPFZt/i8mybhqJE/KJThDi+6XR4V1B8jMPtTXWd5mhXHaFez4hIK76ZgWg8FT5GFJ5CK4ws2H7316FZmiAlPICE7Ep0ahtnobMiH0e3KAYmbTNubrBwMNuPo6MDCWXNnNmTT0m34pRQQmMShRBdJS3Eg5aBCXWpwsHCINlJUdS2tNDd2UJkZBxT66/PfpzrrycnJ5v8zsUfRnRWJoYRGpxAb/8wSVFBPHbxZ3JT6Y02sT1czcOPHlPaIpwjceVSt09rZR6+wjnwDxSOQKAfLq4uRKfksLRt+3GT3dX4uTzBzSuMtLRUistKyczJ4dO//I7/+Pl/8djRl/bBRTZWlylM9OfRQ0diE4UTIBoAh8f3eeYeSOfows2CcyHQpnvqxDPdqB1cUBf478+0ERIWin9YJLERgYTF53Jqn6JmVgqDUPKRkREE+viIChlJ1+yuaEiOqS9IxclbVNAdWw/Q3FALEQGeOLt7Ehkbr64VefD5Bzz2iGJu7aaX6GhtgvQofyLSSzkXz7/YF6JTfM81IIqmrn5mpoTInp5j59C2BuL8eJ26smxCQ0NFGvnz/LmD2oO/IESisk5suKOK8BAh9AMC8fYOIl84mHsaDRMdlXz5we/453/6V556+FPV0on2VdF5fkJLSRoOojDmVzczPjHB+OQUK5viN4qPZ3tqCBKCPLumS51CpBjxw3lhIIQ4z6/tUh3g0Z5q3Jyf4eEfIcRlEuX1vRyc6pkZaCUyxB8vb1/x7iG4OrvgExrNwJyoEMLJ21kcJsTfReRtEMkpmSTGhOH47DFO4m9l1FXFes5AaxXR4SEij8IJFs6if0A4Nd2i4RGZemk4pqUiR3weSlhoME4ibYKiUpnbPBF69JzR9jLcRZz+IbGkpafh5+HIlw8dyC5t4ez8XTNNkneFnvJUHJy9aBlZxrC/RLIQbgERqRy/aHWtDDUU8vSpF+VNw/Zr11yh08wSJ8psWnETe1qb6V0Za8Hx4Uf85uPHRMclUlnXyvbRjRN6ewbD1mwPzz++R07zlF10XtFdU0ywdyCVzd10tjYSGxlDQVElWcIRyy6rZ/cOUXl5fkpjfhxe0ZlMv7qu/B3iQn/K3MwUfV0dlOSkkZCUzsD0ygsRfXmuZ2tlkeGhfhrrKogMV2zlKPrXNqwTKWU2MNxZgZNHCMPTN46N/mibiuwkgsMT6e9uxTcogJSyVg7ucKKvhO1oF07Ml0/9GF3aRr83R0ZsADHJaaJt66emWNicmHhqq8uIjBLt3cRdQumKo40x3J4rZWSQ87feAv+IsRqoyY7Hx82T+u4B8rNSSUzOoG1givM7plCf7K6QFCzak9h8NCd3Tx0/O9KQmxhDSEwW2wevj4jviXY43NuJsNQy9rX2jmPRLl2ztTBEkMdTkgrbbOvUBDNdJYSFBZBXXk9/RyOZKQmk5+RRXJBDbHoRW0d3jGgL12umtxG3Z04MLGreuPb0B+VsjcwYL375hz+LeqHs6aB02Asxb//YoJkiLFD4GNGiXop2PVmZCl9Qzoy9g/d1LMwOtBHg7Ez3/I66BOF8e1z4REGECP+ss61BjUPpZJ5/0RH9KlbmB5txfHSf0vZJdaOxiqwkQiOTaO0WdqCyhMT4eEorqkmMiqC6e9Luo7zMkWaJ7Fg/YjIr1KnyP22sTPfWEyH8paLaehpL03B39Wb+QPFPLWSHufHcRdi8tevOvAsGK9N5/09fiPSdeC199QdrlGfG4R9fZFtHK2jMjSEoIIDimkYqSnKJi0+moXMInUmpQ1f0V6Rx/6kzVT0z6v0KK6NtuHz5EfGFrQx1thHtH0heYTWDfR1kp6aQkJhNlYgrKiGT9cPXN7/BamK8r1743kG0jd6aafCucKtt35sfINL7GeGpRRzbE/R8Z0EIa6E9EjKorq5W7UpaTgmTi1tvENBWxgZa8PUNpKJ11H7NRFtxMr/9zft88ciVtKxcOgYnRL4on1k5XhvBzeERASmlL2agmoS2SAvz4KlzkGgr12nMiSRKtL3tPX1U5mcKvzyFwsJ8VXtVtds6XF/lVPg6FQV5wu8untb/EKKzIjEUP+9IVWRtTLbj+uQewWnVQlxdsT/RwCPhcJWJl7eK/yaFWHJ2eEJgbDqTswsszM3QWJLK4/v3SS5p5Uwk1lhLIR/96b946hvL7NIKGs02ayvLpEd64OjqT03zIIcnBo62l0kLdeGDj74gv66HpaVFemvy+OzTTwhMKmLP3jNyfqKhIjMWz8BIVg5sok4z2YxfcJhw3Gppq8zC0cmbvvl9dVrJ/soIPu4ewkFsoDw3DTe3ALrm9rjUHlCSHMbHj/xY2tpXN5KIDnTH0TNANJTjLC8v09NYxuOP/8BnDsHMvBCdV4y0lOPj6UVlz6R6xbi3RqK3I3/4819x8fAWoj0Qv8hkUXkW1M8v9fvMTo8xPb/M5uYGVdkxfHHvIcXNI5yKwhrjdV+IylBG55ZYXlxidXULg96A9nCT3Fh/Htx7RMvYPLsHh5hvNaoqJi3tpel8/P7vuPfEicDAQPxFWhTUdnImjMRUe7FwlD8lrrBJ7ShQ3n9vsgfXpw4kFzWirKjtqsvij7//FS6hacyLPFJ21tQsjRLo7oBXUCydQ5MiPZboay7HW+lAiMjkUG9CszjIg09+x/v3nKhpH2ZVOJVdDUIkOjzAMyQFjTAue0tDhPh4E5deyNLmjoh3jKRwP+4/D2Zy7RCT0cDi9CjTcwvqblxKj/KjR4/JrOkXNvSCvtpM/vSbn+MWkqxuOjAz0UewEJ73HHxEHr+psZP8dFHWjnXj4fyE8JRCdUrewfKI2jPpK4SbMKr2+2CivZJnzzworu+zX7FhvboUtq+T8OBg0SCMo8wQVTjaXKBMiB4fP3/8fDx5/vixcOLSmH3N6bKwMNKijnSqotPu+Z3uLNNQmk14eKQ6K6S0qoYy4eyGx6TQ0TdMT0cz6alp1DYrnT4258pqvmCopZQnHhEM3OpZfre44kCzQHZ6Mu6Oz/jze38mMqOSfcNNk6Xb36atqoggPw8+/uufcfCJZtHeMfkql4YjmstT8BBpO3ftFAsh2t9UTmhoFAPzW2iPFvEL8ietqouT652arrGYGOusw/3ZMxLy6jlSWu0rAxP9zcLJjSY6LoG8giLKq6pIjo6gsLyJ4cE+qoqzySkoZWp50x6ReO+THaLdnpNT2sjRHbNYJHYsh+TGB/G7P7xPsEjf6Jho/DyFM+TgSnlzP6fnt9zdKyNT3bU8ffiYmv45TLdGsm9zIJyh2OhQokUeHp7a9n64xnqpo7epkMfC+W0ZWhTt4utxLE304O/2RBWd5/YpexdnOzRVFxMTEUFCcjpF5ZVUlOYTEx1Nc/cg/V0t5GZlUF6vdCjdCN21mUFhQ55S0b+A/h0sBlZRZwZbKgkNChSOrC+uz58K8eFNfd845+YrtCt9eLo85S9fPCM1MZHI8BBcnBzUUfzRpa3XhbRFT5+ob46OwSzb/az9iU78XZ/wycMnRMXGER0ejIvjEzwCRRzKRlCvZIFGiP7YAA9cA+JZ3tMKKyHEycwQmclxREbGkJlbRFlpKQU5acRnFDI+OUl9ZRnpmXn0jSsbkdniMR5raClIJCAyg+W7Zkb8hNjfmCE1JozUglqOtSLP67PwcPNh5dC2d8GU0ln3+Etc/SKpbWyhobIAf6cH/PznH1PTM/VaPisbZ6VFBZJW0YtRzUArpUlhfP7Bh7j4hpKQkoSvpwvPHBzJq2oTttbMydYUgaIcPHLyoby2gdaWRiL9XfngvfeIye1gf0dDf2Mp8cIGxCsbulVWUlJSQlx0HNVtAwz3tpKfnUlJVQtb9rKlsDg+iJ+DGxUtQ+JdXu+oeldYm+gi1PMp4WnFnNoT9GB1nCDPR3z24AnBkXHER4fj5PAcd99weiZXXuqQVrk6p7elGJ/QSHWmhw0zy+OdxESG4+3jh4+HE0+fO5FW2syx0Yr5/IiqjAg+++IhkcnZNLe2U5gRz2cf/pn7TuFMLx+pa7Cz0pKIjoomLbeQmpoqMpOEAM4oYGx8ktqyfLJz8uifmH+x47SylKWzqpAw/7DpUf0PJDp9hACaXNgVhueM6uxIvnjgSOvYEnszbTh8/oyqrikuhdEvSAzG0T2YoSXbLlQKFvEDkoO9hFMRx4JGz2xPFU5PH5FZ1WO/Q8FCc2EMYcKIzK/bKsvp1hzJQrE7eoezsm/vtTzXEBnoiVNIErPbtgZgfW6AiAAvksq7XiwsV0Snu7cfRY397M4PCMPoREp5p7r9c09lGs7eUUyvrdFani0aQR9VdJr1R9TkJPDMI4oNzSFLPSU4Pnckv/56OrHIjJNtMiPc1I2EJpdt02tNOg1FGTH4hCUxb18LZtAsk+Djxr2nLqRm51NbXUlZbYu6DuKao70tpifH6e/tUn/nH3//e1GYGoVTs01OuCv3HjpR1dbD1uHtHt1LOspT8fbwY/1NHXwXZ7SWpHP/3mcEx6ZQXlFOhXBme0fn1IXjM12V+IjGJr2y42akc3aIAC9vsqva1ZHOnqYCHj19QmHzdY+LmdH6bHVNXEPftP2agpFOpZfrngPDa8dsLo/h5viY8NRS9q9bYrOOxuIknjv7iMq2RXtJAh9/8B7PPYIoKqugsjQH58ef8Iv/ep/6kRVh4pSd0HaZnhqnt6eb7JggPvjLh4Tn1Ipics6QcCwePX5CZdvEi17brpo87gtnprBjyn5FIhFFT9SFwbZaQnw8CFI6wlZtm39tC5sQoqwdi70tOq2MtVUKe+BFSUO//ZoNq9nITH89QcH+1PXPq3VE4eryEv3ZqXoswdrSHPXF6Xzx8V+FsKnl6MXadwULi6Otr4lOBUXEjA0OMDo+wcLUEKkJUeRX1lEunK9U0TiEhwQSFBBGeUMfZ8ocdGH2Z/tacHEIpX/Y1on17mEVv2uX3u4OKkoKSYgKwicwjLrukRdiw3h2wtzYIPW1VaSmxOPj7UteZQu7x69PV7w42aMqPYrglByWd5X2QRGR1Xi7uZCQX4/BfCmcpUFcPNyJFX9r7JvRKSi7Z3bWFqvT8SKTCljdOVFtjILl8pyVuSkGBofVdWctNSXEJmTQWN9EYWaKaOzDCAz0JSEjj+kNWyfjufaY3LDnZJVWsXv2+oiqxI5ph7RID/75V38WbW8ra1salmdGifZy4L5oP/uV2TF2DIcbFCUE8NwnmtX9G6fzZZQO4yGiIwNIVabj6V9Oe2X9aFqEN+7BiWzcNTopeFl03lTC87NDJkeHGJ2cFm3yCIXZKaTklNJcU05qchJREWH4BwWTK5ziXbvY3V6ZIjbSm/y2yRezqN4lrBaLetzIydERmq11Bjoa8HF8gINHMNPrh5wu9+P68K+8f9+NzpFZNJot+lqreHLvY8LSS18bbVamcLZV5/HQO56tE1sd3Rprx/neh3z0UBH6Shybasf8k88/IjS5CM11x4D5nLmRdqKDvPAJiqR/eu1F2634Fjsbiwz09zMzO0OveM+YGCFGmlqpVGYfRMUQGhxESEQKvWO22QdW4asN1ebhH5HA3BtHVf/xUQdmovxx8w1jcH5HuH67NOTH8vyZK+Nrtt2fLYYjOmqLhA/vKgRPELHREUQFefL+Fy60DM/dygcba/PDRId4kt889mLzpuL4YH73yz8QlFTI6vYOK+KejEhPPnvkTMf4mrjjitnBJiFSvHFx9yYmLomQoAAcHnxMelUfBmHyleUp06ODDI7OMDs1THlhOgmZhUIoNZMYHUl0eDj+QljlV7a9qGMbM+PEezpQ2tD9Un1917hLdO4IX9j50Qd8cO8Zle0jaITw7muuxPXJAzyUAZhXNwI0HtNRkUVAeJTQT9c6QQhL5UiTk2P1WLfZ8QHiRd588fAZjcO2XdVPthfJTY7G1dWFwPAY9cQMp2eP1MGh2W2bbtrdWKKvr4+FlWU6G6tISUqhtKKBqrxMYkTahwf7ERabqM5Esm0SfcZoewmJUX7TFes/kOj0FaJzxL6L397KGOF+7rgGRqvzyJ0futLQP4dOJ8RlhA8hMTlobrUbyg5l1VmJPHYOZWJxj7m+alXgVLfdDMUrCVuZGUZAUALjirgVHG7MkBkTIBIjn32t3agbNKTEh+IRla7ufqb0YHfVFuLu4SNEz9GLCqSZEqLT05Oc2gEs+kMRdzSeQTH0DQ4S7edGSnErp0Yj9UUpPLklOquz43nqbhOdveWxIg7xmahU1xhVYRqBq0+4EJ02Y6csfg8L8CW9uMm2a6JAu7lAXKAfERllbL6y5kPZQn5jfoKCjCSihXFNz8wg1MeRD9//gNSSWrUgbc+PkRkXjpubuyiEcbT2TaBVnTU99XkxuDp5Mnf4hkqorOksTsPDP5DBxVfXQ8B4Wwk+rk/Jrum2p5eVk4UxAr28yKpsV0c6+1qL8PL1p3HQ1uNyda6jOTuaZ97hzN0+DkKwONKEy70HtE3usjw3TIC/j7pj8IvBBusFw+3leHr70zy4THGSN59+/Bec3P3U7Z+DQ0KEMx9MakYW81sHaNYX1Ol40dHRpGZmEeHrwccff05sQYPwt/UMCtHp6RFA9+iG2lOqiOb+tnKcfPwpaBtXr0gkF/pjehpK8BXlOj69iCXNzSja3vIE8QGuBEamcNNPfkVfXT5Pnfypap+wX7PxsuiceyE6X0OU9dSg57j7xzO5drvev1l0XmM+P6S1PJe4tFyG+1vU4wvqOkY5Oz2kqTyDoMgExtZPVXuq7Ajp9ixEiM7ba9XfXS71ByQEueLgLdqRpdfXt1gtRtoLE/nSwZPGgZt24RpVdKZFEZKcI0TJmYhvk2jfx/z8P3+Ju7DrORmpxAhH6te//Q1/+eIpNV3CYRKG7+L0gMayLDw8PEkrqGHz6I5pXHY2ZodIio2lubufovxMEjOLmNs8YHOun/DIcBLLOtX7LnQn5EUI0VlSxY4ydUdyN+YDsqK9RJ56M39rAHupp4xPHztS3HKzDloRg96Oj0kobkH3RudSiM7lYaKE6EwTovP4JdFpYbq/CTdHB9LKRBv2hpHSN4nOF5h19DZWEJeYRtfQkHhWNKWN/Rwfn9BRU4pfQCxt9vXeO6tTxEX5kt/6borO17lSZ5k9efCEup4FjtdH8XVxICil3P65uOPSQFGiNw6eYcK/ebmeWs+PaK2yi057x9DBZAduDk+JyKx44XtdGk4pinHDwT2IKWXt+5WJie56gn3dCYlNZXThZtbAqxxtzlAk2n5lx+72uhL1iI/xuTWO9zdJCfchNacYdedv8S7DDUJ0Rsa/5o/8ZLi6ZLA2kw9+90v+9Mlj9Viq5IQont37C7/8xa8ITy9medc+YCGkwvrSNN29A8zNzNBXmyP8pVAGZhTB+DJrwoeLCvEgv+lGdJbECQEpbHP39E3ebU238UD4fWnlHWjtG3Icbq/S393N9MIqk0Nt+Do+orJ7kpdPZzHRL8RXcnIK7T2d5KUnklnWyt7hCeOtBQRFxNBu71zYmBsnweeZKjrf2Q27BHeJzkOhk9ydHhOcWsje9ZEVxhPqcqO5/+g5g6ItfyntL4ToLM8SwtEmOt9kUTanevF1ekBkdi0m+8J3k/6E6fEhBkYmmZsaJS8plGBRNzZt63heoBxxmZWcREllHc0tdYQIn2Jhc5+D7SXhb4cTk1/L9qmwq5d6xjtKSYz+gUXnsF10YjUy0lGLy8PPRaI+45OPHGkeXsJoPCUnNhAnRaCu2EYrFS5Od0gO9cXJL4lFzRkzPZVCdHpR3fqy6FR2hfUJEEZkzdZCHdlFZ0h0Lppj+0/VbQrHIBiP6AwW9o1c7K+SHSfEamwBZ7cWlF+LzvQKm6OwONqF6+P7eLo958FzX/rnttXrVbnxbxSdky2ZPHd4TmnziHqvwtnuGilBTjxyCWZmXRnNNdNalo2PbygdozdT3VTRGeRPrMjEg2slasdwrKEgxpcHD5/T1DuuCtXVsSY8HR4Ql1umbshkK3CXTPS3E+B4ny8fudE5pYhfMw05UTgJA7D8pg5hu+hUNhKa2rrJh2tme8rxdr5PYlGj/Yqo3KPtPH/6lIzyNvX5r4nOy3MGqjK499iJlv5bo4nCye4U1x8/cGZs7YTNpREhlJ8Sm1uNsi+DijIFIDsGRyEyB+e2aMiLwMHVl+6JVxdGK2X9mOrMaD6+/5zy5l7Vcdwc7ybAXdlhr068hxCdzYV4ibg6xLvZSoWZvpYSVXQWttumN0t+2ihnyY111xHg50t5Sx+vLvU9P9qkICEYJzc/pnftvfEWLUXJIbgFxjEw/7Ijo0yvXR1rJywoiAohSK8HMdXNim5Nk1G2mo/3c8A3LI357dujLV8tOpWtz2dH2gkPjaR1aIbtySZiE5IZmLU18lND1aIxDqd7RqMezD7WUclT13D671x3+ENzhUWky0sbwVxdkB7hxQMnP3qF46LsOq3szHeDmZGqTD5/7ExV5/Xsihsu9Yc0FiXiHZOhTsFV8q8iPwVvLze8ff3w8/HB+ck9/u3f/41f/fEvFDX2cao/V3cODvL3p65r3N5BdTe6PWUXzQSShN06ONwgNSuZxNIWTpR81m4SlxRPQFqVeu+59oA4T0V01nPw0mi25GUuqMuLE22uFyNb12Lfymx7PveVKXMd9ny2nNNVl8cDkfctwtl8g15U2d+eFs5RCDGFjRye2eut4Mp0IspHEk+dPGif2LRtIHQHXy06rern8VFR1HQOodmdw9EvmrYxWxs4299NkLf4TDjjClsLw4T5OFDWPfdiuv27hMVitm2kdY3FIMRGNg7PnGgSwvnibIu4UD8CY3NerE1W6llOrAcuATHMrt/aKFHBrKWnvhQHxzCW921ixrQzS2SgD6HJhS9sovFUQ26EC+4ijoWtQ7YXB4kOCyYxt5Z9/ZtroTIrpbE0h/jUXObXNTQXJJOcmMSZqMcKlaneJKUlcyCisJzt0laUgn9E2ou9Qn5yiHoz3dsoBGIAru6e+As75+XhzF//9Cv+5Z//FdeQNHWzy1frwqmyHjbaj+DYbNZ2X5+avL00SXJ4ABmV/S/WPXeVJOHr5UvLoH1fDsHSaIPw+56QU9v7Yu3nNdbLU9oqM3Bw9mdiZfcl27sx2UVSXDyVLaKOLQ6RFh9Fdd+Ses/ZUguhUWFUCXutsDw5TKCjM2WNAy/OlnwXuS06r/fTMh6ukiDqV3BCDisHtjJsPd+nMjOCp8/dGd8+e7lNsujpaSwSvkMk3VP2TeqUzfAu7RHamRtoxtflCYnFrS+mw95mYbiNUF9PUgrrXmzuqmAx6qjNTyM2OZfltVV6hA/hHpdrn+1oJSU9hYCUIpaV2ZqmY3pqCoW/EzLd90NMry0Vws/dKYCB6XX7VUU47VOVFsKvf/a/+G//4/dUdc+q1wcbS3jy6CGh4uXXdnbZVaZb1Obj+OwJ6ZWdKPtvjLQU4unsQnnTjXhRHKmarHAeP3WntnVQGJoL9tdm1Oky/qEZbB/Zf6p2g9hIP5yFsVk9MbIy2kGwt7I7nrKGw3aLwvZEI07iGUrPqYLuSEOKzyP+/Z//fzhFF9s36DBTmhbF/UfudCgbCekOqUiP5t7zEFa3D4SvMYaP02OcfSIZX9hk/+CQvsZK7v/5V3z4JIAlzQlXui0yogKITstn+3o0VqCIzmgvVzxCkxiaXmJ3R8O2RsPRyRkHmnXSgl148NSDkakFDo+OREEM4b3f/YqEwmq0pgsWpidY3dhhZ1uItOxo7t978mK9aHtRAl9+/CU1A/Mca3WikbbeXtOsis7G/ESeOTpR3TnM9s6Oum52d29fPQBW6S0O83zMM48QptZ22BN5VJUezn/99k8kFTerQq67KR8XDy9q+1ZscYrqsb80jPvTh3iL36RsSLC7t8v0UDMh3o54R+VxfG5id3WUp/ff55NnnrQPz7G3t8NgR4343iMC43LY1yrbojfx9MGXhCTksbixx/7+LqsrSyyt73C6t01xjD+fOfioBxgfHB6JxieBD95/n9DMGnVn5P76PJwd3YX4XX4hOrsbC3js6kleixzplMDR+jSJwR48941mdU3U3f19URaFPdrd5VSrF6XZyHCHstblEXFZtaxvbDLd14y3izOZpQ0cvzZyYeFsY5KY0GAySluF2FAaQCuajRXGRgbZUOvYFm1VuTz58gHZFR0vdpOzcSUagyYevvdXMhsmOLvdflotaJanyEyMJjGvRtSjc3Sboo6GhFHW1C3ee4vm8kz1XMI5IZAvjQbaypJxj0gRTsU72MtvNXGoWWNkbJLVrR2R5hrGemtxfv6UkOQSto6MXJweMjMxxuzKhponqzPDwl464S4E/+Qdv8lyqaO/uRgnrwhGZm3tkCJslZ0xTYrwNxvZme3kmfNzIoVze2S44lDYIl93J3zCkkUa7qtlQHmWYgfPdOdcH8VkEs5ud10xIcExTG6cCOdGR2FWEuEJ2eoh20tjHcTExZBVr2wuZeV0dwZ34TSU1PW+dhC45GWWxtoI9nEhJKWUTVFHtlanSQgQ7WJIgshn20yh8/0lsqJ9eOgWyPz2652ktzk53CQjLlI4zLlobi07OdmeVUfSn4s2benwzVOelc5nz+dfEpvX/Jro1O4uU5AeT0xKAYfnV5wcrRPoF0hBTSeavT2aq8oIjUimV+0staqbDHo6PKN7duu1zVjeBc6OdpkaH2V6doEdkfYzQ8JXcnuGR1Acc+pRGVZaipJxcXalrH1YvWesuwGXZ49IKW7iUPdqOpqZ6m3C57kLfQtCSKhF/4K6ghSclSM5hN+mxDEs2nvXZ4/JKGvl6PRYPdfcyTuElvEbO7yzu8eh8IUs9qPYlNlf4111RIRHUNe/gOVCJwRyLuFhYYzOKH7EMqlRQWQXVan7UJztrlCYGEhUmjIN+Kbz4SeFcPqUI+cUUWIymYQ9u8Jwsk1VRhiPHjxndH1X9QuN2lP2dxU7vCvaq2Uq89JweO5KjTICeceGXtqdFYpToghOKkerHBgt2F8eJMLPg6DIDObWNWwsTJAXF6gukVKOn7KINux4d58tYe8Vn6+nvhhPFycSS1rQCr/wGsPxFrmp8eoGXXsG8Z3teVLjw0kubGR5fZuhumyi45Ppm1E6W6+YHGzBxVXZTX4B81f1Rv3AKB3SgS73CU7Mv9m91qKcX5uDo5MHuVVtwj/YYbS7Hl9XR0ITC23nQb/EFaM9jfj4BlHVaZtpZb08Z3F6TN0fR/HjN5cmSYny5amjB70z22rHrvbkkD2hL5T8XZ4aFELXV53VObpys8mgUr+mehqICImgtkvRXVaG2yvxCIhkWpST7e05YuOjSS1tYU9rwXi8QlVhHv5huT/ERkIXVCaF4et5a6TTjjLN1uXzP/Hf/z+/oNy+C9Klbo/60gycnj/D0cVdndakhJTcMjbsjcRwSzH+nl5U3RrpVBT90nALbk8/55MvHlHdMc3m2jL5ScGERr0y0hkXik9CPmtbG8LJy8YzMJrNV9bWaKZEg+DlRVp5u+2C9ZyxtmLuffFQZOiUfQMdI5XKyOZzX7rn97lURzoThHiJYFlzqL7TQGMpbs8e8OWjxwRGxBAXl4jr0y9x8o9nTXPAylgr/n4BlDb3iWy8wbCzSpIQY7/6ze/54uETXIUz9PjJU+FUVrK6e6pumuAu4nn05DkRcfFEhXjy5SefkV/ZjFaI3+rMGNwdn4u088LbS5lWUsn6nq1XammsC9f7H/ORSKeCmmbOXt291qSlsyydD//wX/z5k3s4OTnx7Mkj3P1D6Jza5PLKIn5XCQ4PPhXC10k0pNHEBPnx2RdPyKnpVtOmr7VYPWrmeqRTQTnmZbSjmgAPN3URuZK+rq5uhMamMqFONb5ia2FAOO6PuP/EmeDQCAJ83Hj06BF+wskYW9pR08iq9EqXpqkdEY+fOeHp6U5geCx1ojIYDQa1HCgHSD94/EydIhAT6Mejh0+JL2pSRaeyptPT3Zf2oZdHOp19/CmSI50Sweb8MD5PP+I3732ollMfH2/c3d1wdXOjrLFL3T3WeLpLQ0EqDg8f4uDoJBo3V2LSiljevlmPfhuz4YichAhR3jNY2LKt71sY7xaNsZP6fVcRv4uzC8k5VS9thmDDNtKp7F6b98pI55XxlM76EkIj45jYtD/78pT6MuVoCB+8vLxFHY1TZxgoGkd/vE1GpBdJxY3C4XoXp3daWJ8fJDo0UKSLi0h3D9VORCRnCaGhnIesiATh2KTF4OwiPhf2RLEBvoFRdI7Mv3QcyguEU7OiTCsS8VR1jL/Ug3uNbmMUL39fUqu61I1dNNPtOHz5Ib/808cEBPjj5am8hwteolGvbhnlzL4J0PrMEKnRkeTX9r4QD8tjvSRFhODt6Y2fEB45xVVsnAjLaDlnvrsSN+8IusZsvfOSN3Nl1NLXXCHsuYPIa2fc3Fzw8A2jQz2Tz5bPmoURonwc8Y3IYPdrhgyN+hPaSzII8Atj/NY07Y2ZXoKEoPKPybbvDH03SxO9+Ls/JamojfOXRmcsDLZUEB0dR9uYbTmNMluiT9TLsCB/fL29CAyNorp9EK3ZivXiiLbyTNHmRL840P5dQ9kNuCQjWrSzj3BycRXC0Ak/4Yz2Ty5jsne4nAmhXZQezRPhmyinDLiJ+yKS8oT/c/SSP3ONsulffLAbWTXK+Y32OHYWKUqN5NnTp+IZLrg4uRCZUsia8FeuTAfEuD7g17/8Dc88fPETdtjNTbyLuw8JudUcaW2CUb+/SG56HMl5FRyo01Ks7G/MkZMajaePL76i/sYK2zxhP3pidXaYEG93Cut60F2ffSPh6uKEtpJkkY/eLBzYRoDPNhcpy0zEzdVZPZfaw8ufwtpujvV3d5VcGY/prMnFySOY5euybTWpsxsDPZ15LvxSJ2dnPLwDqesYUTel4kpPa0k+vsKeu7qKttDNk6TsV5eWXTHYVkFkdDxtI/YNNS+EfWgTPqWv8HO9/fALCKW2fQidUscMe7SWZeAdliiE0d3l8V1hfaKLMC8HItOLUZqJa/SHm+oMGieHZ0LoO+Lk6ExYnLLr/N3ny27MjKozJJMLatXZWcqu7R1VuXi5OIp0F/XT2RF3nwAq24YwKnVYaJTFsW4hNH2E7/FcCFxn/EPj1Hb09gi39mCD3Pgo0kS8m/ah2P21eXLjIoTI9RMaxo+EtHxm7B2+iojOy84mo278739kinIWzebCDJMTMxzeOmtTQeldXpwaobGujfWdm8O6L3RHTI30UlZSTFFJBZ39o+K7+hefH2rWmRqfYMt+BtA1FpOe6ZEeysvKGZ5SDjrVs740w8zcqmgg7N8WmbAwP8PsqoaZwVbiwvyF89X2Wgaen+yoR4Us3TpD0qA/ZmpmgVPt9e8Qv21lgeGRSfbOLtTegM3lBYZGZ9AabG6NWTSa85MDlJcUUFnbxNjMEovi+dNzS+pGQCXpQlhFpTK6+vIUj6sLvSgMg1SWlVJQWEiRCPn5BbT0DnNouMIsPp8RaVReUkJ9c5v4jbNMijRZ29BgsRhZmx2htryEwqJSWnuG2TvSvihElxc6ZkZF+paW0D82xcWr0w5Enu1vLNFaXyWeXSTiKKIgP4+y6jrmhFOrpNWlaLwnB7soLS6hrqmD6ek5kcdTrGv2VSF3sLvGxOQkmoPbRkOk2OUFy7Pj1FWVibiLaWjtZkU46bZ3M7M63Y2/v7dwvMvo6OyisryY6oZWljb3X5o2ZTw/ZXK4T5SRIorEO7R1D7KlHAAt7lGeoZxRWlFaRFVDm1r2ZqenWdwQDc6VWR1FGR8TeSYMmi1KKwfK2aOTU6Kh++qecslPA/3ZESN9bcKWlFBcXCxCiVoHC0UYmJh7MRVMf3rIUFcLxUXF1LV0srl/8tp0pBeIsjfaUkJAYDD1PRNqPTKc7jHUrXy/gOKyCrrstu51rGiPdhjpHWBV2L3bA2SKc7u2PM/kzCK3O571xzv0d7ZQKuLtH5u1HSVyZWRusBl3Ny/aRxfvPMvwXcCgPWR0oJtSUb8VG9bU0Y/m8OSFDVDs37JoYGsqy1T7VNvYysL63p3Tha5ReslzEkIISy1m4ZW2Q0nfS90h45MTLG7uqul7frLLoMibIpG3yk6JJcXiXQoKKCmtZGR6FYO63b+yI+qW2h7t356ueWkU7zdGXWUF9U2dwi7a2rdTYfNzo4OISS1k844jOySvYzKcMTMs2nVRFpQ2aHJp86URFt3xvmj3h5leWBdiyH7xDSguz/ZcLyH+vhTU9nBir8hK3RofGWJ6acPeJtyN9mSfqbFhFtd2RTt7606rhY2lOdEOL6K79W4mwzGj/R2iHS+na2BciCTbmhFlJ3dlQ4+Uyi50Ly9Ye2dQ7MrK7Bg1FSXkCz+gtrFD9YcuXzJwV5zsbdLVXKfWw8a2brYPb3yNVzk/26e2MBmv4AQWX3TOXXG8t0Fni4hDPKexrYftA60tH64umB4UbbmIu0jY4RIRigoLKFBsQveISDubh34ubN3crGi/d25GZ6yWS7bX52mqr6GipoG5tR11tOvq/Ij2mnw8/cIZnt9+ya/4qaPO+FhfZHR0grMLmw97qT1hYqCLYpHuxWXVDI7PYrCPYN7NFStTfYT4uFPSPobOXinNwj9fnh4SdaGIkopahqeXubgW/FcmliZHqFLsvWhrFZ//tj21caW2c9OzS/b9SRSsnAtfdGygk8qKctr6xji0b7CzPjtEXIi/EEr1aC9e8XHfMXQne8xMDDOzJGzYS68q2v1DjWjHm9W2p665kzXRlrypfpm1uzQVpeIVEse04qsLUbm/qfjyNeTnFVBeVc/EnEj3F8eKWTkW/nBHYw0FBXnCXxbt6Nr2a+2osvnd1Ng4mzs3YlepX3uron7VVlFd28jCqqhfyotZDVRnJ5KVlcfS6eXfX3QqWMUPV6YivTSF8xbK+hzxLva/7AgjbtDr0OkMXL7iGSn32r5jv3Ab8b2L83NRmC/Vz233vnzj9bPaa4oIDAhjcPGOKWbiHts73/WQG5TPlWkJ1/fd/K3+aUdUDIMeveH8loGzcrg+RYS/H3nVbdw6CeAFimA3GS/Q6/UiHZS00HMuDMGLOES6KvEazi9EbPZL1+8hvntxblC/Y7y9LuMa8V3lc6PR9OI7t1GumS9NGAwiDvF8vXi+8v+XIq6bn2ARFV55vvFFJRAppn5+dzrcYBTP1mp1GG+fqSeK4/JkBx5ebuTW9Ki7C+pFGbh4dST2GvvvV3/jq2fz2T/TGy5u3u06be54N9u1r89vyU8Fq7qmyXhxbq97N+FC1JnbJuVKGF+ljr6xnN7CeKYhIzGS2PR81uzrmpR6ppRznd5gM9pv4o5yq6CUWYvFopbfV1GWN+hFvNc29HRvnYLEcPV8Os3xmzfFeRewmC/tbYCo33esx7mxcTrORZ58LcKxme5tICQohOr2UQwvNyuC6/S9Tkfxt3gH9RnCVqn5r/6r2JvLF2VASXslvIpVsY/CBp0Lx1i51SqcronuOoICg2gdnMX4rir+dxHVH7C1da+i2m6Rlq/Wizdh0h9RL4RPeHQyY/O2DTdsPoqIw3bLG1Gedae/Ir5pEW3ja8cYCCxmpX7rMdnLsFl/QFtVPn4hscIxPH7JlrxrXNcxrWr33mzflOVNSv0wXn61S6nEtz4zQLgQAyXNfRwr25LaMd8Zh5K3FtUO63VaWx20/2sQvtB1eiubo11PtX0Zq/iuQfUDrpN5fW6IpMgwdePGG/EiuUatT7frgvj7UrQjSprf9qe+iouzAxpL0gmITGJ26/ZZ0Lf8MvuVa1R/027vzW+wjco6/rvaOcVOK3Xsup0w65XN83IICYthaP7lDa3eRa7T/C77oaDYECX9b8Tim1Dq16AQ2wHkVnXad3q3qjpCabsUX/1VbHXc5ue8qR1V3u2l9d23UOzDbQ2yOdNLTGQI9V2jQqtYfxjR+a6yvbnG7NzSnQdN/z041x4xPTXLzoEcXVMRjsXKVBfe3u7kVHW/k5srSCR/K6vTQ9Q1NjGz9nduDIW539tcoLa2lvn1l2cO/FRQBEd3SwOt3SMc6P++U4uV3uKRnnaaO4SzrXvj/sWSt46Vk51F6mtqGJlZeeOZnm8L7f4mPa0NdI4o54naL/6EMJ9rGe1tpb6thx370Sl/L5RdxBemRmhoaGVZI2cavD2uONIsU1dTzfjixt/9jMyzw226WlvoUM+UtV/8iXBp1DIz1ElVQxd7J3cf/fT2MItn99DU0q7O6hAaU4pOybvMFYeaJerqqhmYuDnIWSL5h+LqktPTE051hpve5L8H6syEMw5P37Rt9U8Dpaf98OgUw1eM3LwNlNGC46NjDLc2xpD8cJydHHNypv3q2QVvgQtR/pSzL1+epvrTwmw85+jw8JvNUPgeUaYEas9OORO2V/K2sapnUB+f6l+brfi2ubgwcHx8Jmz8T1PiXFlM7B8cifp1+ff1MbAI23aMVmffbVeKTsm7jjJNR6sVxuLcqMzqkEgkEolEIpFIJD8ipOiUSCQSiUQikUgkEslbQ4pOiUQikUgkEolEIpG8NaTolEgkEolEIpFIJBLJW0OKTolEIpFIJBKJRCKRvDWk6JRIJBKJRCKRSCQSyVtDik6JRCKRSCQSiUQikbw1vlJ0Go1GDg8P2d/f5+DgQAYZZJBBBhlkkEEGGWSQQQYZvlFQdOT5+Tk6nW5a6Mq7Radyw+7uLhqNRgYZZJBBBhlkkEEGGWSQQQYZvlVQBjJPTk6mt7e37xadFosFk8mkBuVmGWSQQQYZZJBBBhlkkEEGGWT4JkHRkQqXl5fTFxcXck2nRCKRSCQSiUQikUi+f4TGlBsJSSQSiUQikUgkEonk7SBFp0QikUgkEolEIpFI3hpSdEokEolEIpFIJBKJ5K0hRadEIpFIJBKJRCKRSN4aUnRKJBKJRCKRSCQSieStIUWnRCKRSCQSiUQikUjeGlJ0SiQSiUQikUgkEonkrSFFp0QikUgkEolEIpFI3hpSdEokEolEIpFIJBKJ5K3xg4tOs+kC7dkpZ1odxsu3/+gr8yU6rfI8PZYrq/3qt8Bq5fLShOlbvKvVeoXJZMJovOTquzzzW3BlucQonmWxXGEWv/Xy8m97ptVi4vTkmFOdHiWWqysLFxcXXJotthveMlcWM+cGPWeijJyenqIznHNl/+zrsCh5rZQtneHONLCYzSJPxG8RafX3x4pZlCOjUeTVWy4TEolEIpFIJBLJD8kPJjqtV2b2t5bpaK4hOyON9MwcmnqGOdQa7Xd8X1iF4LhSfqj61+nuCtWFGWQX1HFw9u1/quFwk7bGWuo7htGa7Be/BtP5KcN93bT3jXGiNdivfv+YTQYWp4YZmV7g6Eyn/v/Q8DgHp5f2O749F4cLpMaHkVpWj5Izx/trtDY3MbOyjfltiyWrhf2NBcrzckhLVcpIJrmFpfSPz6G7+Pq8O9KsUJ6bRH5ZuxDNr6fB9to87W3NzG4cKH0JX4lSfhTh+nX3fWMsF8yO99PWPcTmgdZ+USKRSCQSiUQi+cfjhxGdVyYWR1oJ8nDk0RNHAgOD8XB3xTM0mr7pNb4/LXPF8e4qc3PLHJ9dqFcO1qdICfPCLzSD7aNv/1Pnu0r55A8/5w9f+jC6cmi/+tUYjrcpzUogKaec7YNT+9XvGwu7y8MkRkdR1zmGVq+jOjeOqJg0Zjb09nu+PeeaUVwdPsU9NlMVnVtzfcRFhdPYP4Px8u2OEFrNF4y1lfL5n//Mc1dv8VsicX56n48/e0B6cRN36MiX2FmZJC7AieDYYiG8X+/MmB5oJT4qkpbhxa8sc1fiPfY2lphbWMdg/H6qx5VJS2NFNrHp+Uyu7NuvSiQSiUQikUgk/3j8IKLzfG+ZGD9XnroF0tQzxtbWNqsrS8zMzbN7cKyOKn0fWC2X9NVmkpxWwuKGTewdbkyTFu5DUGQW+/pv+RyzjprMUH71v/9v/uP9h+RUdX2jqZ5Xl0YO9zTs7B9+q2m53wb96S41+YkEhsczu7XPleWc/Dg/fANimFj57iNp5v1JvF2+xEOITiUWk+EMjWZLHbF92wOdFqOOnup0vvzkC6q7+tnZ0bAyN0qUvzOfP3KkZXTNfufd7KxOkRjsSlh8qRCdrw9LG7QnaLa3OdGd26/cjeFYQ1NJCmm59ULMf8Ph7a/BemXh+HAPze4++ovvPhItkUgkEolEIpG86/wAotPK2cooTz//BOfwXA5sA5C3uFEyVqOWuYlBaiorqK5rZm5V8+LTK6OBteVlljZ2MJhsr6yMSO1urbO+toXJbGRyuAePRx/ypz9/TlRiHtOLGjaXZ8lPDiMyPp/Z2QW6WhtpaO5gdfvw1pPv5mB5mIiQALx8hGgN9MEjKIHts5cFg+F4h8GuZkrLyunoH+NQiBRlHeTR/j6anUOM9nfVHu0x2tdJVUU5Te09bB+cqdcVdMcHrK9vsru3z+LMGLXV1fQOTaM1vkniXrG5MEREUCAZ5Z1olRFIi568+EACg+OZXj8Xwlekzc4WmzsH7K4v0dXSSH1TB+s7R/Y4rhFxLU7RVFdDY2snC+Md+Hg+wy8pD+UNTRd6Nje2OFJEp3r7JTsbSnwNlFdU0Tsyxal96qtVfLa1vs72jvjtIl962pupa2xjaeubjRBbRP731GTg9MyZ8Q2N/SqMtJXz+NEDUiqF6L88Z2NpmuaGWiqr6xifXeXCPgK7uzZNcpgHkUnlnBhs77S5PMPoxAyHOiMmUYb2doTotI+CK6PF28vKb6+ioqaRhdVt9LpTuhpLePTJH/nwk6fkFVUxs7yL8giT4YTp0X6qRfmsb25nZXPPHg9cnB6xvrHN3t4uq3Nj1NVU0z0wyqnBXl6sV5wdHthF5/cjZCUSiUQikUgkkneRH0R0Gk+3yIzy5aNPH5KYU83W0evrHPXHGupLsgny88HbWwg8Vyd8A8Np7J1CeUHj0RapMVFEZlayZl8Td6ndobYwlcSETA4MOppri3j/tz/nn/7l37n3yI2mnjkhVJcoSg7C0dGblPRsYiOCefr4IT7hSYwvbqvxvInO8hTcfMNpHpykqzaLh48daRlbt4tVKwfbS5TnpxHo6427uxvh4j2mNk8xaA+pyEgnJrmMzT0h3Sw6+lurCQsOws/bA2dnZ8KT8lnZsY3Grk32EuQfSFBkIsVFuXi7OPLkqTPZFR0vBN1trkw6RjrKCQgOoWN6B4t4IatJS15CEEEhicxpLBhOtqnIT8A/JIqMjEziI0J5/PAR/lGpTAoRpWA26RnrbSTYxxMPD0/CIiKJi/Lng7/8icDUQhRptL44THBAKLV9MxjFg/QH65TlpRPg64Onuysubl7kVbajE69pMespykjByz+U1KxsUuMiefLgAV4h8Yws3ojIN6GIzt7aTJwdXJm4JTqHWkp4+vQZOfWD7KxOUpQeR4CfL64uzrj5BNPYM66+6/66Ijo9iU2v5VyIfc3iIEE+rkSnFYnyccnKVB9J0ZG0Dq5zeWlgbqST5NhwvDzdcfcOoLK+i62NTQozovnlf/wL//bz3+LsEUxr/yI6IbpnBztIjFLu98TF2YWgKFGGVnfV9D+YHiAuIoKwyFgKslPx93Lh8ZPnpJU0cqoXIvfKSHt5PrHJOYwt79h+mEQikUgkEolE8g/IDyA6FSzsLI2SEOTBpx99jJt/BLWdQxzZpy5aLvW0lSQLYeFIfEYRvQODdLfVE+rtzFNnHybXD9DurOLr+IxHAcnMbR+r3zOerJMZ4ckzB0+2L0ysry0Q7efA/Ucu5JU0saE54WBrhfRwVz748FNSi+sZHhqgIjOGv/71YyKyKjl6wwY11ot94gPdCYrL5UyIip2FPrwdHxObU4tB3cjVRGNhAp8/dCC1oJrx0WEGh8fRHJ+jPdok3tuLp25xLGwcqqJzYqSfls4+piYnqC5M54tPviC3tp/zyytWhmr5/W9/xR/uu9M9OkFPewPh3s/5+LPn9MxpVNF9m4vTPRqKEgkVQnJRPE/BcnH6QnQu7FjR7q+ItHjIL/7wAUn5NYwOD1GZGc1HH39OVHadEJBXbM71E+j6BJeAeJo6uhkRaZMZ48Ov/vNn+AvRqYwfzoy38NlH98ios72r4XiLrvYOunuHmRwfIiXMl0dPnBla13IhRGOs/3P++Rd/JDyjhImxEYrTYvnyk48ITCji5I0jtzYUMd1fn83jz+9T3tLJytoqI73N+Il3dPAMY3LtiL2NBXo6WhkZHWOkrw0/l6e4BsWxeHjO8dYsaVF+JKTXsDA5QkKIKBtufjT2TappONpegcvTJ5S3L3C8N0+Y53Oee0fSKPJlaGiI6dkljo/PmBxsFfHe54ljAE1tvWztnnCh1bI4PkpXRw9j4+M0VxXg+OgBIWnVnOiN7I+0cO/P7/HBPRcaOgYY6GkRaeHKXz59RufECmaLkYrkCPE+QXRMfvU0YYlEIpFIJBKJ5MfMDyQ6bWh3V6ktSsfh4Wf85fMHJBc2cKRVRNoyfs8eEBCbz9attXgz/Q24PbtPRmUPe2tCRPn74hGTy+LOifq58XSb4qQQPDwD2bHrmdaiWMKiMplft91zuj1PYqgbj1z8md2yr3U82yDY2wWnkCTmdu5e/6iZbObh55/gFZbM8sYmYz2NeD7+jHuOwawdK9MzL2nIi+PDTx8Sn1PNzuGt6bInW6SFivcKTGd5yzad1aA9ZUOIqIX5WRrLc/jsD78hNL2aY6Fg10fr+fLLewSllqub9ygsDjfy5NMPyW+dQP/KaSXag03KUsMJDQ5HY7CLzvPbovMK/cEqSZHu3HcNYH7bvpmRbpUQH1fcItLZOTqgvTyNB/cd6Vs8sH0u2Jlu5dkXf8YnMVcVagvTXTwXorKgeZhzszLGa+XoYI/V5WVmpifJjPblo4/+SvngJrrzM9IiPfnUwZfu6S01vovDdTLD3YXYCmDh4KvXMlrN5ww1FfCXX/+GJy5exCbG4+3yHEc3X+q6xm0jzEK87e1siucvMjXUTaDLIz596kHP/J4oX0tkRnnh5ORDgJc3vqEJ9M1uvFiHO9ZVI657Ud+7wrFmjiCXJzx0DaFtcBq96SaR9UcifdNCiU4q40x/884Xei07WxssL87RWV/K008+4ZFrPHuHWk6munl+/xEhKaVo7VGtT7QJwf0lWfWD6C+NNOWn4h0UK95p03aDRCKRSCQSiUTyD8gPKjqvUTZ8SYrw4q+fPqCwqZ+t1VGefOlAccuoOk3ymv01cV+YF1Fp1WwvzBEd4ItXXAFLuzaBZ9ZpVPHlLkTnrnD0LVdmqjPD8A9KYHzBNoX0aHOGjJgAQoRY3deqsgXOd0iOC8E9Mo3pdduo6UuIJKnNDOf3v/4vPn3oQFxCEtERwXz24Xv8528/pmV0RRVARxtzZMSF4/D0GSFRCbT2j6M9N6E/3SEjLBSPoAxWt4851x7T01JFckIsSckJBHg7896vfkFkVh2n5xaWB+uFUPKioLbX9nzBxtwgAa6fk988wtntRBFo9zcoSQwjNCCSXbvoNL8iOrV7y6THBxAQly+EvT2CSw0psSH4x2WxvDxDaWo0T91ihUi6EVzW41mCPJ/gk5ArZLVNdDo8dqKwdYzzS4tIzwWqSnKJi4sjIS4Wx0ef8uEnn1I5tI5Of0pyeADeEVksbF93DGioyg7H2z+A6Z1XfsgrWC8NDDbm89Hv/ygEezg5+fmUVtQxvbSlCkfljNfFqUHyMlOJi48jOjyELz78gM+eedO/uCeE9ppId0d+/Z//yf/+j9+T1zym/oZrFNHp7+FKZcciJuOF+LsBf09XXFzdSc8vY3ZFo67dPNldISfWl9CYPLYPbFPBz3XHDHY3k56SQFJSPME+bvzl9+/z3COJgyMtRxOdeHr4UNDQo96vsLE8guszZzLqBtAaz2nKS8ErMEaKTolEIpFIJBLJPzQ/iOi0Xl2pZ2feZnu2D49nn+ESkc7S8jRuj56SVtLG6a23WZ/uI9TbicSCNvY3VkgI8cI1PJPFXdvopOl4lexoLxxfiM5LKtOD8ROic2bFNsJ4uDFDphCdioDYObZHrtskOT4Ej+h0pjdeF53G0zUCnR9z78FzEtKzKS4qpqyslKSYED57/09EZFRybJ8afGXSszTWTZSvM18IgVraKYTn8Q7ZURF4hWSzsa9lsCYb56ePSSpoYOfwhPXlMQIc7hMtxPSR9pLloUacnb2F6LwRLMr6w0C3z4XYG31NdOoPhZDLiiYsNIwN+06sr450ngnRmSFEZ1B8AXvqyKzAuEFqbBD+8TmsrC1Qnh7NfYcgtrQ3uzudrvbh9uxTdaRTFZ1TnTx/6kJZ1zSn2kMK4n157u5HZfuIekxLX3OJEFYPqBxYQas7ITksCJ+ITObtI7znR1tUZIXhGxjIjBCdVvGf2Wx+rTwoXK/pfP74Ob1zS1isyjmZ9o4Cwdb8KIGezrgGCOE2Psfp4R5lQjg7i/zvnttRhXZmpAcPHzzhwRdf4KN2KuzbRkgFquh0F7+lZUY9g1NBu79Je3Uhzx/e47lfFFMag4hnlaxob0Jj8zm0n9My1FyO45MnBMeJtNs5UNeWpgT64eSVwu7+qSo6vT39yK/rUu9XWF8axtXBWR3p1L0mOq1YLJcivJ4Okn88rsvbNXeV/69D+c7Lsdj4yriUOqT+83Jd+mnx8m9X/l9JMzU9/9Y0+Zp0vf7op5v27w5KHrytfFB8nDeiPPf6359sOXibv13U56+I+/q5Vuvd9lPybbGX52+Bre7Z/1D5ihjUe9/wuXLd/tk/el36SpvyRr66LnxbvqrO3OTpV9dt8dnfWXSKl9GfHjE/M83i6ganOgPn53p6G4p5+MWnBAsBd3B0QE64J09cAmgbXcJwfo5Bu09VbiKPHzvQPLqmjjQVJ/rz8QMXmvunOb+4YG6oAYdPf89D9wD2RP5cXZmpEqLTwdmPrqF5Ls0WDtZnSI/0JTAii+0XonODBCFEXSNTmXpFdFrNRqbbi7j3xQOyqzqFWDBiVILJxMnuKvkxXnzxxIuhmVUO9/Y5PDnGoNMy3V2Hx/OHBGeWCzG0QU5EGB6BWWwe6KhKDOXR/ec0j29xYbxgvKeaL9/7NSGpVUK8WlgaFL/DwZ2cyhvBsjzZg6/TJ3eOdF7qjuiozCIwJITxzTO1UFwZz8iJ88c/KJ55jSI6l0iL9cUvJleIbbuovFgnKcoPr6gMto5OGGjM57OPP1E36FHy5eLilLaiWH73y5/hLUSnMv45P9nBs0dOlArRube7RqjrZ3iFpzC7o8VkOBTCNZSP/voRpQNrQoSekhTsj3tIKrOb16Jzk9L0ILyF6JzbOUd7uEnf0Ajbh6+fX6ruXludjsMjB4ZW1u1Xr7Gw0FPPky/vEZ5bx+6xTj2KJ9LLgY8fudI7v8fJ9hyp4d5EJxbR394g0u8x7sqo98oeFuH0j3VX4+PiRFnzDBfaU3Z3ttFqtZydHot3jOTzh8+EeF7GIN45M9ITZ+9oITAP1XJUmx3HZ58/Jbe6T5TPC9YmOnD55FOeKNNrD844Gu/Aw9WL7OoO+/vC2uIgjkJAZ9QOqKKzIScJd/8oBhZ3MZ7uMTHUzdLWvroRkeTHg3L8zdnZmSjvr2+I9irKjs66kwN2Do7VxsB8aWJ/e52pyXFWNnbU2QNfh/nygr2tVSbGx1la3cJwPRVc2RH5eI+5mRnWt3Yw3+7AEJ+d7Gk4PDzmUpR9s8mgHuN0dHz6Vc39jwLlPN+93R1294+/tu4Y9Gfs7u2hNyodPCY0qytMTUwwLsLY2ChLws583Vm8VxYzeoMenf7l84+vzCY2V5aYnVtSl4ncfhXzhZ79vV11EzHluuHsiL39PQz2nbYlfxtKHTScnaq+wtfbTyvHor3T7B5gErb8GsWp04m2W4njKztu7JhF223Q6UQbfmv+jKjTJsMpy/MzLK1ti7J0qz6LOnh+dizy/QijeK5ypNvR/i6Hxyf/ADbfysnhLlvqbuxfXX/MRj0HuxqOT26WIKkoNupwR+SLqBff4Bgxo/AvdjRbwue6WRJ1JdJ0d2uF6dl5DkT8t/v2FLt5KNL76Mygtv8m5fuiDp4aXj/DW/JNEL782SFLoqzPL62ht5/M8FVYhe08Odxj/+iUS7XQK3rAHsfyOrpX4rCYz9lYWWBuYVnNp5vsVOqZlr1tDXq9baBFe3wgfDgNplc6dH/UqD6CkeO9bWamJlkQ7ZP+/HY6vBmDsDX7+wfC17yVpqKOmUxGTkUdMN3pa1i5NF0IO3iGySJslP2q8j3dySELs9PCT9lVfeAXWIX/I/Jw/0DYNZGnFtEOHh3ssi/qpVLPXuXvLzrFzzjeWiInIYxnDg64evng7+eNw7Nn+IbEM7akUQ3+xkwfIX7uPHdyJTg8jJAgXzzcvckobuD4QiSGSITFoSZcH3/BF/efEJmQpI5W3vvgDzx382NH/RVWpjoreHLvI7587ExL/4JwCubJjPLGLySd7SP7T9VuEBPhi2NoIpPrLx8hcqHbIyPUmXsOXvS/Ng3SzEx3GR//5RMySlvpbqglNjyEgBDlfQPwD46gbXxJVIZt0sTfzr4prO2eMd/XgNuTezxydCc5M4u4GCHUfv1fRKRVqUd7LPbVCnHtTGb5jWBZmujC89mH5DQO89qRk1dGZgea8QsIoqpnXjRo4ppJS5b4nT5+0cxuC2d0d5HkSC88hdjW3BKd8WFeuAQnsnZiEsJ0hYxob764dx/fgGBS0lJJDPfiN7/6T7wSbKJzbqKNh188paB1gjNRMOtyonlw/0vcfEPIyMzF1+0JH3/2IWWDiug8IS7AG+fARGaUDZQEBiHAi1P88VRGOreOmWwrxj8ymZGF13cONl+c0VWRwqN7j+ibX7FfvcbK0fqsOt36iwePCAqPISMlief3Puezp970zO2qGwnFB7oQmVylismFvmoef/YRTv4JzO/oGO+rU0VnRes82oM1KvKT8PcPJDQsAl8vL6KTc1nY1WE1ndFRkc4nH36Au184fROrzA53EuTxnM8fKNOtU0iNDuOLDz7msUuCKjoPx1pxcXQjs6LN/r6wutDPU1FWU6t60V6cU5cZj7NPJCOizM0PdxPo6Unz8DzqUlnJj4JL/RF9rdWEJ6TROjRjv/pmNCuz1JQW0zu5gu70gK66YsKCA/EP8MPHN5Ds0ga2j98sXs/2N6grzSXQzw8/f38SkjOY3RLOlfhse2Wa4rx0oiPCiE9Ko6137MXyBGWqeVlhMV2D05yLimw1nTDc1UBReS1rBzr7XT9O1iZ6RVvhT1pRPedfUXeMp/t0NVZTUdfCqWhQdUcbpPgL++TghKePL26uzqTlFbNx9Ob0uDjbp6WmjJiEDHrGFuxXRTk4P2OopYa0pARhPyLJLaxkRWPrwLSYjUz0tVFcWs2K/biog605qitKaO4ZU/+WfHeUDefGepqIj4ihbWDqpSU5d6HTLFBXWUptxzAGu9qzXBpEHM2ERyfS2j8hHFf18hs52RX1KTOVtLR8FrevfQbh22yv0Cjqp7LUJCIqjtrWXo71Nj9DcdqaqkpoaB+wCR3hw4z3t1FSXsnMqm35z48VZRPH3IQggpPymN6+e18MFesl0wMdFJdUMPHKaQGKb5Ad5UVUUjqL9rrzZq4YqM0l2M+DWnsdujIbmBluEXmSTLjwGTPySpha2bE5zqIOLo0PqLPU5kV+Kdl7cbxFbXkRjR2DGGTfz7dme3mCvLQ4fLy98PX1JyW7jI291wcPXiCkhWZ5mpKCfIYX1lV/UqPEkSri8PLEy9ePxNwKVvds5cd8cUJHY6Xw66NFXYohr6SWzV3bMi0lr0e6GkXdaWLVXlZ2liaoKimmfXjZluf/AFhEuzIp7FJiTCRBQkN4e3uTnFvGsn0fmzdh1h/QUCFsXEuP0Au21LgS4nVxvJcsUT+K6/o4PHuls0Xkz+b8OLkpseSVVbFzdt0OWtlbnxZpm0VERCTx8UlCS40LMWsTnqdCz9WLZ7X0DGEUj1I6lSYGWikQdXxt/5WOJcEPIDqFHhJiZLS3layMFGJiYohPTKK0qoGF9V3M15X/6pKNhXEqi3OJT4gjKTWL1u5hjrW2Xg0Fs9HA9GA7mcmJpGTm0NbZRU9nO11dfWjtjYlRJH5XYwVJicl0DC1wdHTKzEgPPQOTaBXvS0GIiuHBHtoHxtg/vYlfwSQKfk9rrXAOhlHOdnwV/YmGpppq+oQDsjw7RY1435iYWNJzixmYWFDPjLwUmTDW20t79xgn2gs1U0ZEI5kUH0N2USmdAwN0NDQyOrnEhckiGq5lWlo6mFrYsD9FNHJ7m3S31TK9usPtztNr9jcXSIoMJjqtlB1leqzVwrQQMj29yntbRTocMz7UTffgFLrrnkjzGaMD3bT3jXCsnmN5xYF4dlVRDgnx8eQXVzE2NkxzcwPdY9OqoT4STm9TQ4t4DyWvrOj2N2moLCI2No7S6ma6xe/sbG9kaecUkyjkI33ddPSNigJuS1fFOZsb76NveFgYkH3KkgKIzyoVhfPlUQOFK9FQbC5O0NzYzLbIt1dRRhaUczVL8tJITE6hsr6Vno5OOnuH2DjUY9QdMtrXTv+IXchZzhkQeVlUUs3C1gl7mjV6OztY3Djh8kLLRH8LGaIsxcYlUl7TIhzEA3sP9BWnB5vUleeRIsrh4NQGF8ZzFkQFzkhJJC0jj7Y2Ufa6emjrGkdvuOB8b52O9k4mF2/y8PR4m9bGFvVoHpPZzMrUKB29Im2Od+msL8HdN4LJ1b1/GIP5j4zSYzsz2EFciB9fvP8HfvbHz0ivvpkOfxdXhn0ayrIIjUphfuuUfWGsC9KiyMzOpbm1jZz4UB4+fEp6efudjvP56Q4l6bE4O7uRkldOU3MTnZ2d6uwJnShDFaIeRMSlCSeqm/wMIX7C45jZUc6jvaCzMpew6FSGZ9dtvf/CzM8NtRIVEkRRfR+GH2lPx5XhiJJEH/77//d/8SQo/StE5xWz/c1EhoZR3DCIWdQyzWIfDh98SGBEMtVNrdRWV9E3NCpEwl0jH0IgdLeREOjDB3/8Hf/1/j1y6wftn1lYm+4hPDhYOEbVdNWXEhMaSG5Np7ok4WhzjpSIEHLK2ti3N/Qm3R7VhamiLMQzu30i6/x3wGoxsTrVT1q0P44PPuFn//o7UkQa3ywOuQOrgc6qPFE3oukaXxH2/YrV6RF1GYjD/b/yT7/4E6llrWrHzF1cnp/SUZNHVIALv/uPX/Dh5y70ztrEk8l4QlttAYGhkVSJtkjZJDFQPKdpVOkwNTHV10hISBitw/PqbvEKm7P9xEeHkllcz9n5j1X5XDHUWsxv/+W/8bt7bgysvdkhPt5eICUqjCTR5u/eWsYjUpbx9hJ++X/9v3jvcwcm1m/OvL4L/f4Kvl/+nv/5f/3fZNXYOuf312eIDQ4iq7SWru5GUR9DScyu5VhUubP9NQqFMx2fWSacaZsvopzx3V6RTWhYPN2Tr86kknwVBpH+6bHBOLn7UlHfLNqbZJ7cv09SQR2H2rvsp8iz4x1qCpIJic0S/uGx2lGRHhuEq4c/pTWN5KYn8uTRQxLyG1Rfe2W8Q4icGAoqGmiqLCAkIIDSpkEur4SfujZBbGgwxY09HNtHxQ3K0q3sZGFT04T/d/c7/Ni40B4x0dNKeXklzS2NFKdH8+W9+8Tl1bH36pTHW0x2lhMaGk5974zq+x5uzlCQEcezhw/47a9/hW9sMduHN1rnWOiLupIMvBwf8ttf/m/ue4awvGfrTDNpD6jOyyAsLJqOni4qClLwDEgQvqqy4eglvXWFRIh2rGt8SdUIyqyTtflBooOVNlDpdHv5PX8Q0amgTCvTnp6oU472Dg4x3J6icosLZTrUzg57h0IY2IXky1jQnihTlQ44F3Eos8svLy9fmlZhMV1wfHTIqdY2rcJivhT3mG/mOot/le+YlGuvDAdbhXi7uDhXp+Hc9XSFS3W6rVnEa0Z/dqK+7+Hx2Y2AVuI3ifhFuI5fmWZ3fLjP/qGofOpUmyvMyr/i3iuRaSaTSf37GiUjL01GdcrcXe9hVnp7W8vx9QmmdWiWC1HSlDS+TgtlZNgs/l/53dc/W1xVr91+LwWjQafmi/IblDRS3udS/DblDuU91He7tdmQ6cKg3n98plN/szLFxSIMgy0vTCJdlXewxa/8PrNIf+X7xwcbwmkPobl75A2NvLLO0aw+70Ve3YFBJwTk3h6nOtu0qCsRtyKIlZ5kJQ2UfH3xbfFueq2Wc2GolHuV97PYy5VZCEklT3Z299DeMeXGdKHn8PAQ3YvPLJydHIqyd6jGp2BLX/HL70gnZfqWyahcs+WhUl4uLy1CoC5QV5xJSnETR7bzdyTvOJbLC4Y7m0hPFkLP05U/f/aM5Iqb6fB3sTzWTWSQH1kVbepshHNRbleW5lUboJSbk70ZEZcDzz3C2Xx1sFOY5bH2CjzcPUkT5UQx5Gq9VGyCqF/bs4PERodT2jks4r5iWQjK5KhY6gc2hSAaFg10CGXNg5xe3DQA56db1OUl4hOaxPz28U0d+dFgFQ2ycOTdPuNffvEbvOKK0L5huurF2Q5FyZFCeCexdKh0cF2qM2We3HOhb3LZNuXYbi+V+vs6JnoaG8lLSiLI24WP7z8lvarP9pHFwGBDLsHxOYwt7XNlPKS6LIOIdNGwa7bprMknNDqN6dW9W9Mor1gc6yIi2J/M8nbVkZJ8OywmAzNDbWQnR5GdGMZ7f/yExKJmvmqS+9H6KPERgSTmVXF0Lmy2EK6zIwOkRSWQERfIex99QXJJM7rLu2vD+dkhDaUZlIh64/70KZ8/9KBt0taxqD9eISczkhjheG8dnXNxNEtYQgLp1V1oNYtkJUarx5Xt3erYNl9oVdvvFxjB4PxXnxP+TiLaWGXJUlJMML/9+T9zzyWQkbWb3e9fwmxkoLEQ34AIGntnb9mbKw635kiP8uY3//GvfOngw8SSbbf7u7AKX6enNpf7f/kNv/rtn8mvbBVXzcyPtPLUI0odNb66MlGSnE1kqBA4m1sMd9cKwR/J6JJG1MGbJ+8vjxMXGkBSdgXav4vn+4/BaEsBri4uZFZ0YhC+76lmhYI4P+45+985a01UNJaEvQv0dKOqV5mNZ2WyOR83JydyqzrRiwbxZHuJ3Fh/HNwDGZpZoaUslYS8Smb3LzAdLFGVkURKTj2b2zs0FSUTnZzHwtbRTTkSvvrcUAuhAX4UtUy8YfrojwtlqqpO6KQzrV744MJ3PVkj2P05T0Q5H1+5u55Z9LskhvkTl17E9pnSsWNmZ3GIwtxMYmITefzFXwkQbeWmfVNMhZ31JepKc8hMi+X5w7/yuVsgCxpb/IpgVURldFad8OstbC3P4vU0kJ6RObZXx9Wz7vPLWzg13PgWF7oj6guSCQiOYXz55TP5Rfv6w4hOyffP+dG2KDhF1HcMcnRnb/27hfH8hKHBcXb2f4wO7/eH6XSfpZkpFjf21N1yJe8+ynpxpSPr8OiU6bYaHJ29SREN8BuxntNckYtPQPgd0/SvOSUn0odnzoGsv+I5X+oPyY/3JzQhg/G1Y7THh2onyzXbs/1EhodQ0i5Ep6hMyyMdZAuBVNHQS1mmaKxzq9k5e7Vjz8K8cNp93Dyo753EdGen3rvLwcYUsZFhpCaE4+nnS3h6iRASdzsaW7N9ak95Zlmz2hur5MdEaxl/+YuDcIBHOdHpbdffiIXDvUNODo8Z6qjEVaRZepV9d3GLnv76TNGQZzKmOAIXe9RV5hObUUBXSx2xUVF0z7w+kmIQ9ro4I5aAsAQ2j5VN1STfBmVt7dnJgbpO73RlEJenjkJ0NvH6nJlrrPQ1FOPvF0xzv20qvFXUY+3JKXs7+xws9ODs7EJKSRNnb5hfazYZ2d/VYNAfUZ2XynMnX1rGbecs64+WhdMWRlReDdsnF5zvCzGWlUlaUQXdtcVExKaxuv/61O1FUVfDA3zIrem6cxbTu4zxZIeKrESikjOJCHAjKDKO/jtFh9LJtUtOjC/xWSWs31ojpFyvLkgiJjGBqEAPAgKjGJ5ZtX/6OjMDjcTHRxMdGYK/lxe5xQ3iqpnZoWYeuoQzvaaMkgrRmZlPYnw+bc215KQlUt42oX7/JcxaGguTCAmPYWzjK6aGSm5hpjjeDy+/MEbXr6dPmljur+Lze8+p6Znk1T4by8UR7VXZuPhEsH2qCCELeTEBeIi/J9aup6cbme2s5KmzK+WtfdQWJaqdQ7N7RmFSF6jLyyAjt4q2xgaiImLVGWOvojtYpyApksDINPb1t0fS/wGwXgkxpyE1NpTg+NwXJ0K8hBCFmsk2vH2CqOm6Lu8WLrTHHBwcsb21RlyQM6GJxWzs3VjKc72O40NhS3dXyY735rF3KHNb++pnh6KdDY2IJiqzXvx1xfbyDAEekbR1dFKWnUS6yJONV5cEWYU4negg1N+b8vYhdLccWyk6/4FQOvB0J/tsKwvx3zByLJFIvl+Wuut56uBO8leIzsvTdYoz4wlPzGdt/+7GcHO0GS9HByLTyzG80mjr9peI8nxKQEgkRdX1xEeFERmbRGP3KIZL0aic7VOak0JIpHCu6hopzM8hNSWNstJioqITGRBO3Pb2Nusb2y9tVrS9NE1CgAtppU2cKiM/PxIuTnfITw4nKDqD+Yle0pOihOjL5vAu0SkawLHWUoJCYqjtnbddu7pguqOE9//0obq2Pjw6mtzSauY3D75GfMJ4d7UQJ643olM4YatT3YQE+ZOWV0RdeYEQHynkFhaRnZxAVnE9+0d7bG6ssX9s2+hN4cqopa+uQDjZIXTP7sq13H8Dxq1hHB8+Ib6w8c2i06KlODcZ/8gUJpZ27Bdv0K308dzBkeTiRiE6vy4zrEJ0JvLMwYuWMZvovLw4oa0mH//AYArLayjLzyQjJ4fsvDySkxKo6Rjh+GCH9fUNzs5vRJd2Y4bceFGfU4vZt++O/mPAcql0thTi7RVEx8gsDfmxBIVF0nVnp5qVvbUpQtzcKa0X4tp+VRmt7mkoxscviJ6xaepzYwjwC6F/8tU9HGwcbkwTEehNdlUzQ73NxPi5kypEvsLB2jRRog4m5xXT0FBBUloG2flFZKamiHwoZ33vhI31NTT7x7fq+BVjXdX4BYdS3D5pvyb5Sq5OSPB3VM89Xz65lgtX7Cx08/iLZxQ0Db52lvzZzjJFqZGEpddwYRJl/OqQaF8vfELSWNq77oi5ZGO8leeubuQ39jA91EZ8TBwZeaVUFOeTmphMcXEpqQnx6skO+zs7Ij/XOTy96cgxG05pK8/Cz9ePSc3p19ryHwMXBi3TI73UVRSREBlMYFQivZPL6mzGV1FmYHWXJxEUk8Lw4us2TlkOGBvwnOCEwpdE5zWG0z2yYz155BXyQnQadftU5qURFBROnRD8FQUZxKYWUFqoLCVMpW9sUbRvu6wJ30IZsbZh5fxgiVhlyV9uNRtHN6JUik6JRCL5G5hrr+HJM/evHOk8WpsgPS6c5NxaIYzsF6+xmNhaGCU20AOPwChG7ujB1e5O43nvY95//3N1VCEhNhIv1+c8fuZMff+MOkKu9EAW54oGITaO4vI6aquqyUiKJ0/8//BgBzkZqUTFJtLcN/HC6T3RLFGQEkxURgU7ag/0u4/FqGO4rUo4uDH0TinT8PbIS4kgKKWYV5NWxXJOa3EqkVHJDMzZpySJZu5wfZriwhyio6II8ffgiy8+wzc6/e4e5BdcMdJWjtNLolO4S4YzBlpqSBciMz49l/rGRiqL84iNS6F3aIz6sjziYmNIzytncX1HXfKA1cR8fwOhIcHU9E6rU84k34UrtKv9PH/whITCN490Xp1tkC0c3/DMAhbtZ3vfYOFooQuHZ8+/oeg0Upkdz1MHbyE6r0flrBxpbBsJJcYnqM5yc2Md2RlpJOcUMzg6QkFGklo/yxo62T+1OWLWsy3q8pMIjE1j/Y6NN95FlJ13V6cHiI8Io7x5WHXue0rj1Q39RlZtm2W9zBVrk124OAdT1zGqXlHiWJkcIC4yjNI222ZAXUXxBAZEMLH48pQ8BcPJHlW5SUSl5LMlbNXp2ogQLm7kljSpn1uVjYSGWklPFWkcl0RTS5ttT5CkNGrbB+hpqiQxJpqkjHwml7dezOyYH+/BLyCM1JJ29W/J12DSEOb1BM/wZNavz7oX7K4O8fT+M3Lq+9G9oiK2FydIDvcnq3pIXf6BaZVwNzd8wzJZPbwWJGa2ZttxdnEhr6GPs7NjBjvq1I2E0kT9qa9vojw/k9iENAbHhqkuyVf3hMkuq2fZvjmU1XzOSEelEEiBtE/ufu2GYD8GtCf7wqZkEeLjwscfvo+Dbwxjon7c3pz+GmUJWGmyL1FpWSzsvj5yf7y3TrS/wxtFp/Zom4xodx4K0TlrF50iVdldm6GqJJNo4T/kF5TR1dFCgjKttqqVnu4OioVdi4hOpL5zCJ2989pkPCIlLpaA2DwW7adXKEjRKZFIJH8D30R07iwMkhATSHJpAye3BjOUdcSzA21EB3njExxD9+SSMPGvo92ZwPHTD/j4oTdj6zbhdLw2ToTHYx75JLJl33FVf3rA2vomW+srtNWWERufTmtLA5nxUSQnp5KYEEVASCwdQ3O2+w83qM6JJyqxnO2DOyXbO4WyudhkTz1Ojx8QIkTm5Pwqa0s9okF2xSUonuXdg9d6gK2Xeipz4wiLSmN8+fXpjQpW4xl1udHc+/I+6eVtX3GExcui86XbRNOp2Vhj9+CI+YlBspISyCmuobEiXwjeBJJS0vH18iSjoALNqTLWY2V1qo3wyBDKO8bUTeck34VvJjovd5fJigkhPquY9dd2KP4+RKcN84WOteVlDg726G2pJiUxhcraJorzMoiMSyY1NQU/bz/qusZQ9+8z79NWlYF/bDLLX7tr67uAlZ3lMaL8XHDwDGVsYYX19XWyozxwcvempmdKOJ6vdGBZLSwONfLUK5zGPtvU5p2FUaJ8XXguHNzR5Q3W1tbIjvDC0dGdhs4REcfNEiGT7pCmohTu339MXl0X6xsb9NUX4vnsAVHxOeoMgus6e7C9zrpml/U5IfLTU8jJK6axvprI0AiyhCCNDPIlPqPoxQ65a1OjRPpGkJHTqP4t+Rou94jyeYZ3WBJrZ9f1RAiTxT4e33egoHmQV7emWJsbIirEnfwW+zIO0yYRnh5CdGawenBdYy/ZnGrFUYjOXCE69apiNLG1scru3g5zY72kJMRTUtVIVV6S2pmQmpai7vqeVVTPkbpLtImZviYigiNp7l9X9074saMcYbK1tsDk+BDtzdWE+nkRmZSrrmd9FdO5nvwoV+LTs1g7eH0X6W8nOl/ezEs5plIZWdbs7NBTW0hicjpNTfWkJycTExVPQlQoIaHhtA4vqB0AZvMF+XEphIUJAbx6E5cUnRKJRPI38E1E597SsLrZRnJxPcf2mXXK7okTvY0E+/kQnZLP3OZdIwQ2TndmCHz+gNiMCl70XxqPacmP4aOHvsxu39pUwHrFdF8L6UlJ1HUMUZedSXJMlnCCT7gy7ZES7ENWXplws4XoPFijMjOaqCQhOg/f/ZFOw8kuubFe/Mf//hkf33uMi5sHzk/v8Yt//xn/8vP/IjyjmJXr85ftWC8N1OYlEhaZyujim9dtHS/2E+brTGhyPvo3znL8CtFpx3SySVVBFsmZpfT1dpEaF0zD2LaI84q+6gxCYuPonrONZi9PtBIaHkxFx7gUnd+Zbyg6D1bJigslNrPojmOCvj/Rec3u4hBpaWkUVzbQ1VxJZGIaE3sW9VzPypRw4tIrWdoXtfDqgObydPxiUr72KIR3AxMDjXn89pf/zn/98WNc3dxwc3Xhvd/8nH/6159x3zOC4fmbXdtVhOhcHm7hmUcYTXbROVCTxZ9/9R/86o8f4erujouLE+/9+uf8yz//M49FHKMLN9MDTzcmCHj2Mf/6H7/i3iMHvLy8+PLTv/Czf/qf/Pq9T4UQ7UOrLGa3Y7k8o6ZA2L20Ano72ijLSCWzrF898WBvopGwUPEeA7PqvWtTI4T7hJGeZxsxlXwdF+o5+j7+kczsXHdUmlgdrOXBUzfq+qdfW9O5Pj9CbKgnuY1jQnQqV/SkhPrh6RfHzKa9zFuNzHVVCdHpRk3PBBe3hKt2e57y7GTS86sY7Osj2s2PkaU9dTPIhrwUIkPCmVxRjh0yM93bQFhQGC2Dm3zNkbE/SjpLE3n65Cnl7SP2KzcoorMwxl3YlixW9r9f0amirNWc6Sc6LJrWzj6ai7NJSiljcl2PeX+O8vQIYnKq1SOILJZz0VYnEhKWzcLa9aipiEKKTolEIvnuzLVV81g0tsm3ztV9Fd32LNkJUcRnVGKz9VesTfcT4e9BihCim4f61wSM5dKIXqvHqOxwrN0nO8qbsOg0ZjW26Uj6gw2KEwN57B3D6v6Ns3qyNUtOcjxZZS3s7+9TkZBMSnQBp+p0Pi3Z4UJ05hYJN0E0QlvzZMd6E5tbzf4dR0K9axjPdUyNdFNZUUZpaQmlZRWUZMbw6Yd/5k8fP6SqfZCji1dS0mqiuyKbsIgEuibtU5eFML+07xCurIVXdvee6anC21WIjsJGzs1WTBfn6PQGzC/tLCtEZ2sZjk4upFX2vC46xbP6m0uJjkpgYHqD1flhYiN96Fw8UUX+WGs+YfHxdM4q04ItTHdWExQYzP+fvb96jyNb93TRv+pcnJuzz83uvXv3aVi9Vi+aXHMWu6rMFjMzMzMzMzMzM0upBGVKKcr3jIhM2ZIse1atnmW7Zo33eeKxFRkZETngG99vwDcahxZtzpjkp6OIzj7sn7wgvuA9azqNe+SmRxOSnMvc5v0RRUV0dmL3yp6koobXolMJVnRqNGI8PbNOiX6NmYqsOF7aedH8gOi0nGsoyUogKaec1e1depuKCEtKZUloXeU2TbnxxArRubgvaqFhk4rMeILjstm6tfbpk0WU8e3VGaqqRN0rKaWktIyy8hLcX37J7z/7M6EZFazv358mfK06q56OftS0Datn9panqKuppFjco6ysjIqyfNyef8Xvf/cHooSdVCIAX16eYzIa0B1s0d/ZLOp8qbi+hOrqKhKEHfvzb/6ZJ+KevTNbXLz2XC1CeDQRFR5D8+AsO4vjFMTGUdAwLT66wiDyOTIigkZbMKm50Q78AoLJrH4zXV7yftqKk9Stu6q7phQRgVm3R0NuDM6+EYwtvb08ZH91mvSoINHWdXGmKlILzYXxODqKe3RPqbsTmI53qMqKwd07hFFFUFq/iuXCQFdNIVExSYyvbLE2M0LICx9mbZ20ncXpxISFMqFESb02M9BcjL+/Lz3zh/xyVkg/zNXVlbrjweuAy6KdahHpZm/nSGX3uO3kGy7MJqqzQghLzmD6gZFQRXRG+dkRFF/wTtGZHuXGU8/gB0WnZmeVwuQYUgsb1F0e2gszSEqtYvFQvKBhjdrcOKIyK9XdKK7OD0iMiyQ4qYiV3TedvR9VdCoNvbJ9yempCcOJcK6UBcYfgcvzU/Q6HaYz8+uC/lNQKt2ZcIaOdQbhIP7vJd+l+QytVsfpra0N7mPdxsRacd/HtUjbM5NJOE5Gcb8zkb5mNa2NxhNMohG9/BtHq1S2BFF6ntT/i7w1nZygU7ZREQ33hbIlzN/4eRLJp8B0SwWPHtsTW6yE7n8Yy5kQfzlJBESmCEfzVPhtJ7QUJfPZH74mr7aNlY0N5mdnmJicZGl9S9gTIxtTvYQHhVHfM6kG/5ntqcHL2UkNRjQ2NUlNcQZuDnbk1PVwYptHZDEfU1uYRqgQWFNbetEGn9BdmUd4cDitPcOM9rcQFRpJTcuAaj3W58YI93Ymv7YL4/mn39+o2Fply6Vzmy0zCftm2h1V17s4BSezZxA2/C0zY2Gmrw7/oFBKW6wO78XZCRN9rdTUNTI8NslITzMBHvY4C4dnbEWDWX9AY1EaIVFpLB/d7jG+ZrC5kGfPX5JY2nXPAltYmeghLjyE7LImde3S8c4CmfEhJObVMzo1S25KDIkZuawoU8rO9bSVZ+IfGMbYmvY/1PZIFK7RL/fw5OvviMyp5eEJ1AKLmeqCDHwDYxmcuh9N+JKj2TYe//CUmLwadLbFYHsbc2QlhJOUXcWROn3vhjNKUiP4/rEzDcPLtnNWLJdnDAqRGRoWSZsy1UyU2bn+JsLCwilp6mVyYoTE8GCKGroxXFo4XhkjNSqQhNxqdGe/hFIg/A/bVmZnp6fWOmg6oSjOEwd3ZeR3xbpl2T20O4vEiHqaI+qGTjilyl7Ht+9hNGgpiPHG3t6NzuFFdc3aWEcNEeGhtIl7KgFozMI/Uq49Fd+Z6qrC48VjYlKKhN9lSzfhdyhbuKREh5KSV83eySVmzRqlyr7FsblMj4/RVJBCXGImk2qUWxhqKSZQ5E3d8MPBiyRvc7Q6QpiXE07uoXQPjtJWU4SnoxPZ5a1o7+3LqHB6tEFFdiw+UbnobDsrHC0NEezpjIMQqu19g7RUFeDh4kx6cSNa040WEHVnuI0oISrzqzvVra00G7Mk+HmSW9nC9PQ4WYmxJKUXqhGjL0+PqS9Mwc8/mGWN4Z59/uVxuLNBa1UNzS3d4rdO0dlUIdr8VwTFZL6eHn4bZfnJWFMeviExdE2+3Rmm2Vsl2O0x3pE5rO29bSkNR1skhdjzjZMP0xvKyPEbzPp92oQvERQWy+jyvrot4mhLOcHB0VQJf2KkvY7kqHAq2ydEultEuzxDdHgAqWXNol1+o+0+qug8Pdyms76KlOQkEhITSUnPoKG9H807Npf9WRANwvp0L2lJcTQNTr6OqvZTuLo4FYarnLCELCYfCALyoxHvsjnZR3REKp2D1jVX97FcnQrHsZ2mth52jt4XdMDC3tIUVYW5JCQlkZ6eRnpaOmmpqSSLv4sqaljd/9tuSr46N059bQMrB6JRMBpoLC8gMbuSzcMj4VBPMTIx985NtyWSXyrLgx0EBEVS1DxkO/MQV/TUFePr4y8cKNEYXJrorcniz599wdMXdnh4uOPs6MCLV68IEQ7R3OoGW1Pd+Hp4U946rK79ujw10NNQRoCnE3b29rj5BJJX3sih3vS6Hh8sj1KYnUFt+5B1ipPlCu3BKhX5Kfh4e+HtF0hJXTf7yqinxcRYeyVungF0iwbq9v51vyjONinITlE3Jlem9TzE0fokMSG+JOaUqvs4Xp+fMt5ZQ6C3Ky9f2eHi7EpYXCr9k4tq8Ikz7S6VGTF4BMQyf3B7Sq6F6YFmISgiKG0dvWs/L4z0NlWSkV3C5Ip1OpHSNswNdxIVFIC7cMjD4zIYmVlRAz/pdpfIig0lPCaL49dOluSnY8G4NUGIbyD5omy/b6xwrqeWIH9/yloG742CXKFbHRYOVAiFQgze7NO5szKljqiFJxdzcGfO9TktFXkEBsfRp45a32DhQrdNSW4WRdVtHJxYHfBT7T7tNcX4eXngJeptan4Va6L9VRjvqCfI25eqttH3rCX+9OmuzCAxTfhA6w8vE7g0aSnLCCc0Tgi+9YfXrnaWZ5GQkMa8bQ3YQKNIMx8vGkdW79Y1wc7cIOkxEVTUvdkf+frqnKm+FlJSshmZtn3nyszK9BCJkYF4uLsTHB5P7+iC2pF3aTqkOD2KsNhkVm/WPUj+Otdm5oY6iPb34uWLl7h6eJFRWMOu5uStfFKwXBkZaCnBxcWHyU3bFnkiX2bEPSKDvHnx4gUuwj5mFteyJcTiDZenOjprCskpKGVx1zoyd2k+YaKnnvBgYVNFfYpJy2NiYVON/n20NUtqTDDhaZV39qT+paLb26IyMxE3+1fYOziIttqbpJwS5td21T3x30K04brVEXw9fSis63lrpFd/tE1WQrCwP7XsPDCrwqjbpyw7Cr+YVJb37o6U7m/MUZSVQVlDDybbTBCjZpvqwgy83V3w8A6gsKJZ6LczpeFjpqcaPz9foatm72wF+FFF58FUP/7O9jx6+oqo6Gg8nR347rvHJBbUcHRrD7qfFcs1090VOL18TGZt53sbrHdxdW6gLjuSL5550DG8YDv7H0C8y1xnJd995UhRbb/t5F0sFzqahGFOzSlmcevda8AUtmfHKMhMJSoqBDe75/zm3z7D3tVL7XHNKihhaff93/9pWJjobSIhPoWRVS0nuiPSI/x57hHNwtaBGkQgISFJOLfW8PISyd8LJp2G5eU1do/evV5QQbM+RYpoECPSitgXQlF3uMXgQB+tLa20tipHC01NTfQOj3OgNWA2allaWGRP3PcmUp2ymfz81DAtLS0Mjs1wfG9K7Ilmj82NTbT39unV7G0w2NdN78DI66iZyj6XmbEhhCYVsKP9ZUSufRAh4Le31lnf3n9whEXhymygoSiVgOAweuaU9WYWDJp9Jkb6RVo209ndz9rO4WuHSZnavLe5xsLS2lsjwIptW11dZU9zr9NPOLz7O1ts7d4NZnR9bmJxapTOji5mljat02ivz+hvKcPfz4+a7mmuH3TVJD+WK1EvVpaW1e0w3tevaT5eJys+nBAhfBbvRHe0CAdXz8rKCruH4h627DCbDKyvLLKysWsNgPKaa472d1heWRd17W7dUd5lfW2T/eO7DrhRd8DYYC9dPQOs71k7fJUp8jnJ0QRFJrG883778amj2dtkXdgevekdXfeWK+YGm4Qj6k9Bbbe6n/BthO/J0e6WSLsNTmz30B7usrS4yKGwWfcu59SgZXNV5Ne+5vVnFvEMJV/WRX4pa2dvsFyaWV+corOzk/GZZSE4rQZ1fqCBQH9/cqu7+WCO798L1xdsLc/SLtqt3sHR1+3KwyjBpyaIC/YkqaSZo5s6I+6x+foe42o+30bpHNwTtn13/+hO/ijBumYnhuno6hICbNu63dSFke76Ynx9/Wmf3BJt5v0S88tDaYd21xfp726nWbT5SiT0fe0753JYuTihLE3YOGFTJlffrKVUuFSCEm2ssLlzoC7buc/V5Tl722ssC/t1f9tFZSbC+uo6Gt3dabnK1Pehvi66+0fUTgcF7f4aWbHBhMTnCJ1x1659VNG5PdSCq4MjYSn5rK6tMdHfTriXHX/8/hU90ysfqNfvmtm+Wrxc7clr7Hk45P5f5Zr2kiQeOwbQPbZkO/cfQIjOhZ5aXj71oqrFGlb8Ppbrcw5EoVjb2OLk1l5fD3Fm0IkCts7a6jxlOUl8/tkz8mo7mF9aYnN792+8EbUFnXDillfXODaeY9QfkBMXiqNfIktbGs6OVkmOCCRWONza+62NRPJrwHLGUGctEVHRdE9/5M6Xi1NGxLvExMTRObr4q9gjcndxnIzEWLJKlTV7H9fFNOyvUpKTTFJ2MYd3pm1Kfl4sor1vVPf/q+0av9MD/8FRRVgrCfGx1HQOvxV85e8Rs+GAsvxUEjPz3nJGPzQXpmOq89NITC9g6a0tdCR/a5QO06G2ckKj4xld3vqbtzlHm/MUZiSRWlB3J6jUr5G9hX4SoqOpaOz94MGUri/PmB3pIDoiUo1ka13D+4aPKzqHW/HzCaLsdRSmawZqs/ji++c0Ds6rolOJ8LixMk9XewsNjc2MTC1w8lotXbCzucnmxg472+uMDPTQ0tbJwtruPcF6ybYQXh0tjbR39zO3tMbG+iqbuwdcXl6yMFiPj5sD+U296vTa6wsz22uL9Ha10SieOTA6c2czZwXD0Q6DPZ3iee0Mj4xTlRPLM7cQeiferO8wavcY6e8W791E38jUndEH5Xetz0/Q0tRAc3sXS5v7XF5dsTLQiJOdP+W1nawsTonPm8Tzp1UhZ/smh/t7QjTuY/oJ0wfGeupxeOlP362IcNqDLTXNGhoa6OobZvdm7ZIQv0oP48bGtipax4Z6aRXvuGyLkHm8u0JPZyttXX3qhss3SX2i1bC9voHh9FKIzkN1w2snIToXNqy9LT21eQSGhNE28/amtRLJrwGj9lDUpz5G51bvjZx8WK5OdSxOjzA8Po3h72Ezsx/D+SnLs+P0Dg5zePJxR3aPd0R7NTTI4r11M5Kfn2uThsmRQQbG5zh9cI7aB+LynOWZSYZGJznSf/rbFf2tONhcor+vn8X1j1v2lSnPgwMDzC5v2s5Ifm5M2gN1xs3s2jbmv3H7ty/K1dBAH6t7b0dt/dVxfc7s2KAar0B/OwzwB+Dy3MjK3AT9wxNC/N+f4PuRRefOSDveru5EpRezubnB9FAPqXHhhCXlsqz2PFk43pqlOCcZTw93nJQ5zUKkVrSOWjeY5YTyrHR8fEJIzc4kISaYx99/j0dADMOzW+p0DSwXzA23Exvmx8uXL/EOCCU8MhJvTxfic+swmM5YHWtWRWeBEJ1K9uj2VqnIjcffxxMXJyecXL0ob+lDd2ZNQKVHpSQ9Fgd7e3UuuxJ+29PpJV87BDAwYx3B0O1vqGsaQwP9cXN3x9M/hLKmHjQnZq7NJib6WoiLCMTVyQ5HV0+KmwY5FRm0Pd6G/RNngiLiKSzKUPeje/rCkcyKdkzqbzZTlpNKSFw207f2vnk/ZgabynjxxJO28WUhW5WZCBo6aosIC/DGWfxGBydXUvIq2dOfqYuRh1tLCA4IIi4+lbioEF48e4Z/WDxtHZ3UluXg4+HM4ydPCEvMZcm2mfpoaw2RIn8GFo6E6NSSlxAhRGfCa9G5NddLeFgICYVt6t8Sya+Ta87M52qAkY/FlRLc60KJivfx3uFjoAQ4UH73+QNTiz4klxcXXF1+3Hf4NaNEhTSbzWowjI+F5fpSOGgXv7o6qHAlyr8SPfpj/vIrYQcuRB38iN0Ov0os16LuiXL/t657F6JMXYo8ldxwpaaJUsY/JEoQUaWNfddThb37eKJzb6xLjT721Q8viYoMx+HZ93zx6BmlbTd7+VyzvzpFV3cnozNLLE0PEBPoynevfFg4VLYYMJEd6cM//PPvCU4rYGV9lZaKXJ4/+gbf6HwhEs0Yd2YIdLfDxSeUjqEpVpdmyU0M5F//8b/zKiAL3ckpGxMt+Lg7UtBsFZ1HOyv0tNcxOTPPwvQYSUEuPHfxZ2h+k6tzLSUp4UKE2VFc38XyyhK9TaU8/vwP/OZbV4bnt4Qx01FTkCwEaQB1bUPimXPU5Cfw3MGVyq4pDlfGCfNxwT4gjrmVNZbmZ5hf2eZcVMSt8Wa+/uxzvnMRAnZ6ntnxAaJ8HEW62DO0oePSck5GVBAv3cMZnLsdwOB93BWdSgeT8XifoZ52+gdHWFtdpjQrhqfPX1LUPo357JTuiiQ++82/4RGaxuT8Ej0NJbz48g/84Y9fkFHSwPj0NHWFiTz+9hvSK7rVe/ZV5eP83VNaxndU0ZmfaBOd61bRqd9dJCUsnPCoHHW7BolEIpFIJBKJRPL3z8cXnS+f8cNLZ9Iz0okM8eX508d4hSQxvbav9oJdX11wtL/L8uICU6NdxAU58dlfvqZ75ZgLZZPaKF8e23nTIcSUwomyMD/aHzu3YGYPNAzUZ+Lg5EV97/TrUPobc8P4On6PZ5gQnXoj6zbRma/MfxafKxHQdEd7rCwtMTrYQ0qoO188fknr4CTbMz34eLgQmlb2ev+2c5OG/Fh/vnnpLYTpDkdLg/jaf8cPdu6U1jTT2dZMeVYUv/vtnwhJq2R1ZohwXzeeuIQwLcSmsn2J9d0sLPfX8MOjJ8QXNWNWe0AtTHVX8+LbR5T0r2I8NwsxF4mzbwwjCzvKl34Eb0Rn69iSdeqx5VoNiKEIztnpSYrSovlOPCOqoJlT0wm91ek8e25HZduYmg/n2nWSQ5354psn9Mzsquf0uwsEujwhJK6AI6EihxvK8HzhQNvUvlV03ox02kSn6XiLorgwYiLieDh2nUQikUgkEolEIvl746OKzu3hNnVLgNSSBk6MJxgMeuYG2nF+/D2xuQ0cGM7YWZqgMCuVmNg4EhOiefX4Cz7781f0rGiF6DwlKzIMzwBFpFojsV6cHFKbF4enfzCjW3tUpPvhHxzD9OqbSK3a3WXSIjwIjMlBqz9hffxmpLOPSyGndhYnKMpMIy4+kbi4GJyfPeJP3wnROTDGeGslbl6+5N4O9HNtpqM0mWcuQaroXB1q4cVn/87v/vQVHt6+eHt64O7qzLNnbpQ19HN2YWKsux4vZ0ec3bxIyytlfuNI3eNypb+OV8+9qGy+WecKa1Pd+Dg8obB7kRPzGYVJUbj4xf6HRGfbuDVAk/5ondqyfPH7YklIiMfL4QV/FukaI8SuKjpr0vHyCqZn1Dpd+MKwQ2FqOM4egUxvWKNXmTTKRrMOQnTmsX8KI40VeL10oP0dovNUt0NZShjxsdEcKKpVIpFIJBKJRCKR/N3z8UWndwAFjX22M3Cyv0mw3Xd4RBcwNb9IRoQvXn7BtAzPs729TUtVGs9+eELn4hHn1yYhOiPwCkgWotIa5EbZwFQRnT5BIYxt7QvRGSAEaBjjS2+C1xyszxHja49PRLYQnUab6HSipHMY/fEORYmhvHLxp2toks3Ndboqs3Bwc6elb5iJlkrc3TzJvLWlydXFGdUZEXxn78fwwq4Qjg24PH9MVHop6+vrrK2tsba+wZYQwQbbVjDKd7ZXZqnKS8LF/iU+0Zls7h2yMdKC40s/ITqtm5grLE904ef8lOLeJSE6zRT8B0Xny6fedE1vcG420lSchp2jG9ll9eK9thhpr8ff3VkVnUajQRWd3t4hdI9YN5g1a7eE6IzA3SdEiE5rWGT9/gqRfo6EJhT8KNFpFEI3JyqMiPAkZKw4iUQikUgkEonk18HHFZ1DLXi6eJBS3KiOvp0aDmmuyOHRF1+RUtbOytI4bo+/wis8wypSzo/Ij/Pgs8/+QseihgvLKelhIbj5xDOxYg2qc6bboyorCldvfyb3jpnuLuP54x+Iy65GjUx/dUZbWRZf//E3uITmoDOYWB9rwsPFjpKuEXbXpgl1ecarwCTrnp3mQypSA/jLd09pHJhEuzlFkKsdL11DWT2wiq+1qT4cfviSf/vaieH5HU73Z4nydsDBK5zFXZu8ujSys7OjRrDVHxywub6lrmu06LfJi/Hly6fiu7MrbIy24vDcm9KGQev3BEtj7XjZ/fB6pDMvPhwnn2hGlw+4EGk2NjTA+Py6yEzbF97CzEBDCU9/8KBrZoszk4bUIFe+eeXN4LJVrPfV5PD9N98QkdeI0WSguyoFN3d/Ooas05bN2k3ykkJwdBPpumaNDqbfWybU6xVBsdaRzuH6MtyfvqJtYk8Vnblxodh5xzK/bs2bo40pYkLDCU8se+ciY4lEIpFIJBKJRPL3xUcVnXujHTj/8A2///NXBASH4u/jwYvnz/ANT2VpW6OKo8LEEHHuJcHRCeTnpePj/ITPP/uSruVjITpNpIUG4+oVd0d0VmZF4OjuzcTeibovVGFiKM+ePMU7IJiM7ELiIsN4+ugr/ONK0J+csjbaiJvTSwpaBzgxHFObG8+z5y8Ij0kgLSONEPcXQnQ+p2FwCmVT7776YhyfPhYCzJfElFTSkuKwe/oNf3jiwcD0ungLMyOtVXg4vMDBzYOIyCiioyLILq1l/fAE7doiJWmJBISEER8XR1hIMMl5lWwfHLM62MDLxx6U1g+ov0dhcawNj1ffUdClbBdzJsRcGI4+MUxtGtidHSA+MpSSjhl1neXDmOmvL+Hxd260Ta5zcXlGf0MhjvYvcfcNJDM3jwg/T7755jsiC5oxGQ10VSbj6upHx6BNdB5vkpsYjL04N7FmFdKK6AzxfEFgTK4qOofqSnFV1r6qovOYnNgQXnnFvBad8+K3BQeHkNc0rv4tkUgkEolEIpFI/v75qKLTtL9JU3kh4aEhhISEEhERRV5xNctbb9ZfHm4uUJ6XQWhoBCXVtfT2dtNYU8/m8SlXlgvGxN+t7UPsHVvXGV6aT5ib6Ke5rZ3dE+sWJ6eabTqE6IqPi6G0to32lkbCPV8RkVqJ1niGbm+FtuYGJm37NSn7UFbmpwuxGE1eaQ193Z3q/ZY2rVN0L091DHU2CPEaQWxiKvUtnQz1d1LX2sPmnjVEzpXZyPRQJxkpQlSGR5Cclk3X8BRG8yWXBg39zTXERIUTHhlHaU0rOxrr+x9vLVAv/p5b2lb/VtDsrtLeWMPUxhHnV5dMDvXR0jXEocFEV1Ue/t6+DK692S/zba7YWpqhtrqVld1j9TrzySFdjWXERISRnF1Ma2sXXe0dDM6scnFuZn1+VD23vm37PWd6Joe7aWrtZE9r3ePObDymp72B7oFJjCKpd5Zmaa1rYFWI/XMhjicHe2lqH0SjN3Ft1lGWEUNgWBxz+9YRYolEIpFIJBKJRPL3z0cVna+xWLi8vHzvvj2Xlxdc34Sf/QlcCRGq0WoxmW2bdFyequsbnZ59T3ZNL0br3iwPciXe6f4z70xhvb7iQlzzXsQXlL1yrh54d2WvvMv/nT10LnRUFeWSnF6A6acnjcr11RWXVz//TlXrEx1EBgeRV9XJhy9gEolEIpFIJBKJ5GPxaYjOnw0LZwdLlJcUkFlQRntXN3Ul2Xg62OHiH83ctuY9o4OfPpZzAzMzM4zOWoP9fLJYLhhsq6e8qoE12zpYiUQikUgkEolE8uvg71x0wqV+l6riHPz8AwgKCiIwIICY5BxG5jd/0YLzF8WVmd3dA450UnBKJBKJRCKRSCS/Nv7uRSeWa87NJrbXV5mcnGR5fYsT87kUnB8Uy91pyRKJRCKRSCQSieRXw9+/6LyF+H22/0kkEolEIpFIJBKJ5EPwqxKdEolEIpFIJBKJRCL5sEjRKZFIJBKJRCKRSCSSnw0pOiUSiUQikUgkEolE8rMhRadEIpFIJBKJRCKRSH42pOiUSCQSiUQikUgkEsnPxntF59XVFaenpxiNRnnIQx7ykIc85CEPechDHvKQhzx+0nF5ecn5+fm0+P/DotNkMrG1tcXa2hobGxvykIc85CEPechDHvKQhzzkIQ95/KhjfX0dg8GARqOZXllZeVh0ir8xm83ykIc85CEPechDHvKQhzzkIQ95/ORD0ZSXl5fTZ2dnck2nRCKRSCQSiUQikUj+9giNKQMJSSQSiUQikUgkEonk50GKTolEIpFIJBKJRCKR/GxI0SmRSCQSiUQikUgkkp8NKTolEolEIpFIJBKJRPKzIUWnRCKRSCQSiUQikUh+NqTolEgkEolEIpFIJBLJz4YUnRKJRCKRSCQSiUQi+dmQolMikUgkEolEIpFIJD8bUnRKJBKJRCKRSCQSieRn4xMRnde2fz8AlmuuLi+4vLgSP9527idgEd8X6SQOi5J44oTtA4lE8qvlnWZAsRc/wdAo9uU/bFIe+OL7nq3YMcUIWoQt+6VhUd79R6D+xh+JYs9vp9e1yIv3oeTV+3jf5zefKc9U2xHJ/zbvTEYljX9CGf9ReWK7p9UXsB4331H/vfX1dz9aeY61HFj9CfW/f1f8FNunoqTrT/qOyAPb/x5EvZ/t/28hPrPlwU97puR/l9f+849FXKvWMzU/xffufFXJP9t/H+DmOcp332+xP13+I+VTaSOV47aNUu2U7fP/Ha6vrmz/exu1zovjXW20+C0fT3Tqj7YZ6m2noqyEkvJKekanOTae2z79eTgRz+xqqqSmoRut8d0Jd5dLlmcmqK+qpLSklNLSEkrLxPsOTaM3yYFhieRXh+WC3ZV5VtfXMZ6/bbPOTjSMD3VTXVlOXWMrS1sHXLzH2puO95ibnWF958h2xoriEK3OTTA4OsaR/sR29g1nukNWlxfYPDrh/JaN1wk719veTENzO/Pru3eerd1bZ3p2ju1jk/jrCs3uOqMTM+hPL6wXfKqItDg+2GGwt4PqigoaWjpZ3dE80IhesbM6R3tTHRXiuu7hSbQnp7bPHuKSrdVFpudXxXUmDIfb4hmiXaqsoL65i9Xto1vPsKDX7DEh8ramqoKa+iZmV7a5vPUSl2YjCxPDNNTW0Ts8xfHtNuLCwPzcLAurO+qfZyc6FmamWFzfuXMPyY9D8SEWl9Y4FGX5vo9zdXbC/PgQtTWVVNY2MrGwztn5e9r8SyOzU1Msi7y4fZnu6JC5uQU0OoOtHFyxtzFLc101hUUllIsyppSzigrxnPpmJpa2RImCU90BQz3t1DW0MLWwwfmtYnCmP2Bc1LmtfY36t+5wj5mJCbYODerfvwQuzwysriyzuinsy8WbBDOb9CxOj9JQXUVldS2j04uc3jZOD2LhSNihqckZjnRv7Jzl8oytlUXWNkTeXb25x/mpnqW5cRrqqiivqmVgfA6j+c07WK7P2VieobmhntbuAXY1t9P1mrWFaeaXV63fuTazubbA1NwihjNZCe9zuL3OyvI8BpFWD6WO/viQ2YVFdg+P/2onw+WpgaXZSZa39jm7ZfCMoh1bWlpiW9SH2wJLs7VKZ3M9JaVWv7u8qoahqQVRP61lQfneSF8nNaJ+j4v6bb4pIuIeZ9o9pqan2TnSqaf0mn2mp2dEm3G3jf1UuTo/FWV4ltbGWtEWVQu9MSE0i9n26bsxCps4Mz/P9s4W08P9lBUVUVZeLuxUORXlFVSKe7W093Gkfb+t0R+IdrB/UG2bbnLkzHDM1FAvDQ0NjEwtYnqd4EJbibyYEzppfe9Y/CXS36BhZnqKjd0jLm9V/48jOsUjdhdHSIsJxsXFFT9fP5xdnfGLSmJ4YVspL38TFIdNr9llY30b/Yk1sw7XJ0kKcccnKJWtox8ncK+vDeTHhfDk2+9x8fAlODAA/8BQCqrbOdD9vCJZIpF8OlyYlYZgjpaKAjxe/UBURgGbx3fFoHZvhdKsRHx8fAkMDiYmPpG2oVkM79J0FjNddcWkZxYwtbJnO2lFvzNDiOszfnD2Y2J523pS2DWDED5j/R2kKjbU1Z2a4TVMNsN+vLVITVE2kdExxMZEE5WUxeDsFqpfeK6jtjib7KJqNvWKTbxmb22arKQ46rqnML3PKf/I6PY3qcxJw9fHj9DQELzdXfALF23G/KaQAVauL0zMDLYQ7u+Nn18AERERpBdVsLJ9aLvibTRbc+SmJVHZMohWq2G0o5LwiDAiwkPxdhPtUmgCI3PiGUq7ZLlgtKeO+Eh/ce9o/Hy8cPMNpmN0EdXnvTxhtLuBxLgYYmKV9I+lqLoToy1dl0ZaSU/LomNkUf373HhMb1MFiem5zG8d/WJ74j8oV+dCoGzQ01JLVJAX7gGxIn92hcNr+1xwqtsTdTQPXw9PAoKCCYuOp7Sph2PDw50PlusLFoZbSUpOp3d8iQuREcf72wx1NBIZ4s9L9yCG5tZtztcFy5NdJMeG4+ntS2hYOKEhwTi+esofv/qe4rYRTvT7tNeWEBsdRYw4ouNS6BycsX3fTE9dIal5FSLPraLTeLhJXWkeqYV1HJs+7c4fk3A8Z0b7yU6KxtXDnby6Tk7ObO8sbNlIZwPhAf4EBYUS7O+Li7sPxc39GE7f7SsZjjYpF3W7oLKFPd0pV2Yj64vTVOan4uHwivisAjTnN+lyzfbCMDnpCQSHRhAa4IOrqwe5op4ZzIr7amF+vJ/UxHiiY+KIiIoho6CSXYP1+dqtWdKSk6nvHMKkZLTIz+lhYUuTkmkfXnhv5+CvheuLM/Y2l2mrLSXI0xG/kDCWtebX4kPJA+3BDmNdTUSJ+vHCI5jWkQUhLt6deNdXZ8yPtJMQnyxs9obaSao73GW4u4WYMH/c/AJpHp4VdtZ2D6ETRhuKcXn5jGcOLoQEBRAUFkVl+wDmy0vOdPs0lecRFxcn7GwkMYlptAwtWL9qPhF1rIi03FJRx6y233S0RU1xLhn5Fezo/rp4+6gIe7Q83ktSVCi+AUEEBwfhJtr5tPwa9rRKZ/HDXJ0ZaCrJIbesloW1FTrrygnw8lZtYGhYKIF+3jz6y2e8cI1gfn3f9q0HuD6nqTCJP/35EVmVHWq+n+oP6RTlITYykviERNE+RtPaP4VRrZanDLXVkZySwczagfhbiM6TI6qLMlRfY33PKvwVPoroNB9vkhnpx+MXjiTnljM4NEJnRyv1zW0srO/9zUSnwkRnOXkFtWweWBsbzeY0aZFCOEbmcGRUT/1VLi80RLk9w8HZl+q2ASbGRhkZHWNxbftH9OBJJJK/Fw62lilKj8HH9RX/87/9J574RrK4q7V9anV2a/KScHBwIrukhsGRMSYnJ1jdOuDGL7uNYuoOloaJDA4go6ieo5M3jpnl4oSOkgR+/9//T/7pq1cMLWyq56/P9Ex01RMe5Mmjbz/nn/7hN2S3zmJU7aaF7poSwgJCae4XdqqnnQghvtJyG1THcGWsTQi2cNFwj3DzJPOJhubiVLyCEpjdeLc4+9jsrS9RmVdAZW0zo+OjwqnP58l33xOUWMS2zYlYnewl3NsJr5A4mtp7mZqaYnJ2Hs2t0ZPbXJoNtFdm4h8cTfekEJbnRubG+mjp7BH5Nk5TZR7ff/ElYSml7CsiXYjO+alBWlsaGJ+cpaupGtdn3+IelsbW0QnGrSkyY0JIzspjYnqSuqJ0/Hz9WTk2qiNf2XGRJGYUsbZva4RFc7s2PUConxcFwnnXfeqjzZ8AF7oduuuLcXN24s9/+A1/+OYVXeMbrwX7leJwirLh5uhAdFohvYMjIq8mmV1aw3T2kLNpQX+4SprIt+i0Ilb2DEolo6u2ikCnV3zxl9/xD3/8gWbhVKsdD+JJ+qNdZqbGhe8yzMTEFMMDPcQGuvLld3b0Ti2zOFhDQlwYZY2dTI10C+cxiKj0fI4vrzhaGSEyKJAyIcR0Z7buEuGQj4k67StEbMvYChfWB32CCHE+3kNUsD9ff/4X/uW3/05kfu2bWRJXenpbG8jOKqJveJyR3laifR159MpHiPY3eXQbpaNovLMaL09/2oToOBc/fW95iqK0aByffMm//tP/4LlPBAdmm8WyXLG3NktnZ7t4xgQTg13EB7ny1ffOjK0dcW7SUJ6VRERCDn0T07RXFxISGCLSdVvYumNaStIIjk1nbHFLtb8Kmt0F8lMiCYnLZkM4hp9q6n8o9Pvr1BVn4O34hD/99p/47bdPmTq6JXaujfQ1VxPi5sBnv/0N/+OP31PcOXlnROs+xzvLwv4FE5lWxpYinCxn9LdW4+fpxu9/92/89stvKOkYs9UxxdSe0pAdi5OLJ1nlTUyMjwm7L9rSnQN1idz8YAehvgGUVDYxOzVCSmQEkXGFmC3XbC2MEB4cSHFdN8c3o4Oijg22VAjh5UdN16TasfSpci3aoam+DgrzC2npGWR0ZJCshCAeff+c6q4pIbptF97CcnXJxkQnQaK9KVdsi+mUvY1VRgaHhF4ZFW3hOJ2NJfzw+Z8IiCtiV53p9DB7S6P4vfwL/6//478SU9imnluf7SMmPJSM4kYmpyYpTgkjOCmflYMT9JsTpMZEkJJX+3o01nJ9xXRvLQFCNFe1DmK2ZexHEJ3CwK+MYvfdt7hF5bN/Zjv9AJaLM3Y3V5mamGByelYdvr8xBpbLczSHhxwe6zkXhlw9d32BXqtBo9FyeXXB1toC0d7P+f6pMyXVHWztHbO7OktOfDDhMXmsCIG7ND/LzNwCh9qHnRKFy4sj4rztiM8o4fB+Z50o4Cc6cd/9I85uzZ8xneg42D/g9OJKnUdt0GnFex1zuL/D4vwSO/vKELSCaIR2N0QDNikycpoNUaHezDq4xmQwcCR+t8l4wvb6iig4kyyubgqxe9O3b0P89j1bWs2I+x+fvEnYa5EWmoNd5memmZicYm1rj/PXjdolBq14tyMtBv0xywtzTM/Msa/R/+oNr0Ryn31Rx1rqKmhqrhPC8ykuoQnMbdqEmjCdswPN+Li7kV3dYz33V7i+PKMxP5mA0BiGZtdtZwXCYK9N9xPo7YndD9/wyieE8SWr6Lw0apkSjlZ1XQ1lpbm8+PJ78ltnOFEb0UuKMjOIjspBMf2XpyfUZKWTmlLIxvoCeekJZJXUsXdyW9xYOFybJNjDhbK2YU7e5zl8NCycnZ5xYrjdYJhIj/DllVsoI0tHoqHWUpYei7NbECvae/bxQSwcbc4SF+xFRnk7B9YEvMuFgWhPB9yFIF/avrHZtxDipD4tmFfuISxsHLAy0k58eIg6FUphZ36QhHBPhte26GuqIDomhf6pNfWzGxRHuLUoEf/IZKbX3tP7LFE5O95itLeN0vJqspIicHT2onts7fVo987CEOF+HoQnF/Ee9+I115enTPcI51c4sM1D86rosVzoGWjvpLqomIKsWB6/clc/e5ejerg5T2KoLylFLeiNZ7QVhpOckcSaTqlnp3TX5RGXmc7U5haV2QkkZ1eyrbnrcxgO1lVHLiipjKN3jMh+dCynLE0PUVlRQ25+Di5uL4gvqkdvsjpGliszJ8ZTzEoiqlyyOVTFt18+ofxWR9dtdLur5CeGEJZSxoHO6ghvLkzR3lhNTXkOQd722AdEC1/RJh4eGE1bHKjju68e0zq5iX5b2LmkWCrap9RpzsbNKUoyUymsH2VupJOoiAga+yfvTO9EvNnUYBNeXr40D85x/p4Ru18DxzvrdIo2rqG2hNQ4f7555cTk/i2RcnXCUJ9og0rLyUyIU+tHccfEuztLhH862VOPp6sHvfM7ar5wpWVECKvyylpSkmNwcHOktGOUm1nUV6d6itMiiVSEzb0ZsVcX57RWlRESmMLSsnV2UGdlubC1yaztbVJVkEpMehHrB3r1sxu0O4sUpEQI25DHzvGPsQ4fh6tzM0aDkfNb09aPNybwsPuO2JxGDh4YMDs1HFKRGk5oXDoLWw9NIT5nsqMMN89A4WtsvtO/P9UfUCzaUT+Xx/zu66dkNQyq52cHa4lMSGVo1frw9dEGghNzGB4fp60yj+iEDOY3rTM3brCcaciICSVSXLeyb7V3H0F0ikbjcIUYP2e+e+FBTdeYaqTvc3F2ok7TiA3zx9HeDjt7B/HiGYwubKk9IeeaHfLSUkguamTjyPpjLoz7tFTmk51dzJHRQE1pNr/95//Kf/q//gtfPnpBRfM4K4uLlKSG4uUdTkFeAWEBXjx/8ZJYIShXd9+I2ttcCdEZ72NPYHgSk6v76HQ69HoD5otL1akbqC8jMD6HxS1lWNnKWE8tSbEJTG+J685MtFWXEh6dRHpGKiFB0dS0jXBxfsbKVD8p0SG4ODlgL35jYFgMXaMLnIl7K4VkvLuViLBEKmuryUiKxtnxFfZuPlS3DaK3DZ1cCXG+PDVAWky42vvr7uVHXkULuxpr4dDtr6rD3F5uLuIZdviExNA5tohZEesWPa0VpURGplBVW0ZUiC8vnr8kLD5X/B7NQ/ZdIvlVImyj2oGkcGbSkxDihFNQDDPrVtF5bTqiqTQdL/8QOkaXWV9bY3V1Te3QergaWTAfr5MUHUZsVjmbhzctiYX99TmykyIJT8oVIjIRv/AohmdX1E+vRKt8ZWvct+Z6cfjme3KapjCor3ZFXVGOsGux6qjl+vIC2ckpZOdVUFeWTUpWATOru+p3b3OuP6IiJYSQ1GKWdu821J8q1+f7xAf74BqYxMLeCbr1SeJD/QlJKWZ/d5u11VU2tnY5vb2Y7haKgzw90Iibm68QLfO2s+K+QvCfGk/QHh+xJuxqRHAwWWXNHOitQkBZtnF2auL4WMP6ksinuBCS8qrZ156yO9tLYoQ/JTUNaifjUEcdcZFhtHW1ER8XR3XrILcGs22ci+914uQaSF33pCp6JO9GyR8Fi1lPX10Ojk7udIysWkWn5Yr+5lI8ff0pqOtmf2eTlZVV9g6P3zkKY9IeUJkRT0hYAiu29V5KL701G86Y7Czj6SsXGoUYeUh0KgK1vSJL+CgejKwdq+/RX5NMlGiPeyYXOdpcoKowndSsbOqb6omITWF80bqm9w5XJkZay3F28GFG1N1Pshgo6SLKv8LG0hg+XsJ3KqxFZxOdb3PGTHsh3z11oqFv2io27mBhecI6O6FucJFTm4+t2DgF/d4CKZHuvPSPZO/0rp+o+E8GvZZDIVpbynKFX5PA4q6OM80mBUkxpObVsHpwzNxQJzmpSRRXNZOXmkBmSSOaW53yN+yuzgib7kFqsai77/w9vw6uhG+olr/LY1pK4/nmhSMTe2+UjlIGrDl0zXBzNc9eulLUPn5rMOMu5/pd6otScQ9N4cBgE6/Xl+IZ1uuHOqtwdH1FcfvI63p6YdKRGeONo4s/NS3jwpZvo7OtzbdcXTDYUk2Q8OO7e8c51uxTWZBPfEw6bS1V6rTqgdmd1x1Rr7kyMNhagptfhPh8+/Wo6i+BtelePOyFCKzoQvtW8bVwtDVDaKA/meUtony/PaPjcGOOSC9HIjKqObrTefuGqzMd/U0lBASFU1uejZefP1nVXepnS+OthEdFUNk5wd7+Pl3VeaTkllJZkk+a8DEa+6bU6+7TVZFBUEgE7WPL6t8fRXQqU5SWR9vwdXjCF19/r05pmVre4dxW2pTF40NNhTg52Avxk0F3/yBt9aX4utjh4hPJ0r6ek711AlydsQtJZ27bOr3NrN0gN8YXZxdfdswXrK/OE+b1gm9/sCeroI6dgxMOt1bIinTlL59/TXxOBX19veQlhvLlN49IEsZG/8CE/usrLYl+jnzz1fcERQnhKMRuVk4+ysjD+YmW+vRo/vTcm+GFDds3oKU8GbvHz+mePxYOqoH8eH/+yz/9QTh1uQyMTLG7t8/kQDP+bg54hibS2tlLT1s9UX4uvHT0pmtsSa2OnZWZ/NM//juvfENp6eqlr6eNmEAnnrx0pq53Wq38y2MdRAV6EZ9ZzMDoKG21JbjbOZBV3o5BGI+DtUl1wX2L+owGgjyFwPWKYm5dEckn5EX78z/+8XeEpuXSP9RPaU4iX3/+JTFZNcJA/LqNr0TyEObTQ+KCHXAMilUdRAXj0SYlaaHC+XxFdGI63q5OPHvyjMDYdKZWdt7uwBGmdn9+kOiQEIrq+l83JOeGQxpKMgkIjRUN4zpDDQX4hkYwNLtqveA1wmEbb8NO2KWc5hvRKRqnyR7ShJANj04gIz2NxJR0ykpKiYqMp75jgLXNTZYXF9jaPXzdM311bqK3Lhc7/zh1vc2njrLmaKKrCmd7R9JKWjFaLKxOdBEoGlW3wEgSIwOxe/mCV/ZuFNf3oHkgQN2lWUdnXTYuwv5O3qyXFeiO9uhqqiApLhx3RzuCYnJY3Hkzynl2csxATyvxsdF4ujjjHhDF+Iq1w9Fi2KGhNF00siEkp6aTIdqK9NwC8tITSRb2eWp2ie2NNZaW19Dd6mzVH60S5OhMfkUbulvBGSTv5lo4SO3CoXEQorNz1CY6hXBrLc3E0dmJwIgYogI8efbsCZ4BYXSPL2G+FYzmBs3eMokxkYSnV7CvvT+EIIRgUwGPX75bdO6tTBAb6E5AYoltTSEcr4+rncTh4h3S09JIScugoKiQpIQYSuo7WF1bZ21lifWtvTvBjZYnuoVf84IG8XtOP3GHeGVmAE+P5+8RnRb212ZJDPHENSSJuc0HRl+UNaDtNbjbezP1wOfanXkSwpx5pYrON4709eU5C1ODlOSlEOjthouzN5Udk9ZZYsK/7G2sICQwiISkVNJE2qempVNclEd0bLJqRzc31llaWuZQdyJ8KOs9jYcbVGVGERqXx7ptVOZXj/mIuvxIvr4nOt9wwUBtqfBH3y86NRvz5MaFEZfXgvGBdSY9zcXYu9wVndeiTSpPD+WbP/+Oz0Ub9/yVIzHphSzs6NQ802zMkBUfTkRkHOmpyWRkpAu/vIiM5ATh+7awvrmtBrpa27zb8Tg72o2noxc1QjzdaI5PHYNmh5LUKHWJX5+ytvx+Ml+fsT7aSnBACLVKbIZ7SWxRRqaFHXv6wpWOidUHOn8Urlib6iVcCNcCITSP1ocICQ4kpbRV/VS7u0JBRhwBIWFk5OSoa2nLyspIiYslv7SWhfVd1lcWWd3YwnQrqNfGcBPhwaEUNw2of38c0SmwXJ2zuzRBXlIYj776M988c6K0eUjteTDqN4hwf4V3eJo6inApGorL8zMGmstwfvmMwsYRodpXifDxxCUyi/kdm+jUbVEQH4irmz+74jdfimfUZIfjH5zE+Jy1Z1G7vUBSmCuPHTwZXtzj4vIS48YU3i72uIansvzAuLVVdDrx+Wdf4u4XSmREGLHxyQxMr3Bm1NGUFc+X9v6MLlqnvym0V6bh9MKO3kUtp+Ka/AQffvftK5pG59XfY9ZukhMbyCsHbwZXNOI9rtQpA2tT3XjYPSY6swqjqA89tRn86c9fkVLdjVEIaWUu++FCNy529mojeXS8S3VWNF9/+ZjChjYmZmYY6m7B5ekXPHcLZUzc++rCjFZzyO7uLkszo6SFu/OXr36gfUoJZnFKfrQvf3lkR5NSGEV66A93iPF4haMQphOqMJVIJLc5Ne4TG2R/R3SearfJDHflX/7nPxCSUsTA8BgddaXYvxJOb0Qa81tv1n4qKDZwYbiZkJBAarpn1KmAlutThoUTFh4WTn3fPCaTifH6HLzDohhZuDstU2kkFkVD81p02uz89aWZjcUJKooL1eiq42ND5KclkJxTRnNLM1nKVKZXrwgSYqtf1Hm12VWmP3XXYe8USt+YNcjNp4qy7nK8ux5/d2fhIOawZAvGMtlXz+Ov/p2/PHWloqmLsZEBcuJC+O77V5S2DAr36C5mIe7rckQjmpDN4q3pSLqjHToaSoiPCsFetDeOniH0Ti69XvagiM6+7mZioiJwcxDC1smL+t4Z2/r+awzi+z2t9RSVlNPRO0hncy0xEbHUN3eqwWKCvJ1xcvOgsLqVI5M100z6I9ID7NV1wPvvjDgluc2F6ZjW8vS7otNyRkNWFH/67b/wyi+ansFR+jrrCfZ8xSvXAEZt66LfYOFgfUzkZRBJZa1vjxBYhLPWmP8e0XlJf0MxDi/thFB8M8VXGcVRIijXV5RQUdfCwNAQVUWZxCWl09XRRm5aPO6uDngHR9I2PIPZVrY2l8aICnGjqHOGTz1G4eJk33tF5+7yJOmxITi5+tM6NPewIDnX0llbyEu3aNZtUUZvo9maJT70YdE5N95PYXYivp7OPHtmpwZhOrKNYJpPtYz1t1NUUGTt0O9sJiUxTjjTzXQ0VREe4IeDowuJWZWs2AYtrg0H9JZn4R+RzPzNkolfO6cH1OZFvFt0CrHTV13yV0Xn5sIYcWHe5NSPPBAH5ZrOxqJbotN6D2W2wf7WKrNT44yNDlGancDLp9/jEZnNtvZUfH7J/sYcDdVllFbWMzQ8QE1pPlHxabT19pObEoOHsxMe3qG09c9yZnvs6tQYkS5OlDX0vj+a9SeCMu2+Mi8ZVydXSoVw098SdDdcnxsYb6sgKCCCrtH1t4JhHW3NkxrmhldkOtvHD03dF3ZQpGVuUgwxaaUcaAxYdFMEBPiTbgskpMwwOdxeo6W+kuKySvqHRqgrySEhOZuGlnZqijNxcbDD0y+YqpZ+Tm3G0rQ2SGxUKFmVVvH60UTnDUbdEZOD7QR62vHNEyca+6fZ2xjH/rEdubV9d9Zk7K9OEBPsQUxGLdsL88QE+uEZm6dOqVA4129TmhyGp2cgeyJflH3W6nIiCAxJYnLJKp40mzOkR/sREJHJnt6WM2d7JMYE4RqRwvTG3TnJClcXGuJ9HPANjmVgZo2dnR11pPLEdKaOdDZkJvCtSwjjt3rLu2qy8XBwom9Ji8mgoyAxBDuvSOY2rO9xvDZBqJenENY5bxoqwfWphtRIT/yjlIploLehiBfPfekXAvkNh0QHBIrfkMPCohCRYS78l//+bzi7e4jf7ombizPff/ctXuL3zK7to9tdp7mqRO1xTYgN58Wjz/jjn7+mY0ZxLk0UxEbg5BHDqq2VUxZwV6b4C4EdzuDSm98kkUisPCQ6TUfrpEd48O0Pz+me3VNHNpWw53W5sXz/xJGKtlH1uhuUdenT/XXCPvlTN7SgGvbV8TYcv/+Mz7+3F4a7k46WRhICnPni0WPS8qs5uLP2/GHRqWK54kSv4/hoXw1QEhObQGtHKynJKRRUNDAx1k++aJRjUvJYOTxTG/D5gXbcX4XQP/xmqumnhtl4RGtVHj6eHsRnlrK8dfh6BHmsq4bnT74mILnodWATw8EivvY/4Budyar2rpg7O96nKjWK0KRclnfe2H2lY8+gO2Z/b5el+SlSwn3xDUtgcsU6LVlxhk5O9OyJz5WQ9pU5MdgJx7pPtA03nJ+Z0OkNbAnhUZieRH55A5UlhaSmZNDWM0hHYynhkVGUd1qnJJ2JdiQ/2oGs0irRLr09NUryNg+KzmsTVWkRfPf9U/KahlSRqGyfMdlTxavvvyG7qhv9nWIgnK2lQWIihXNV1fH2lm1/RXSaDlfIjgvC0Tv6dXTUNygxGfQYDMoa7HaSExKobGonOzlRjeg4MjZCYVYKobHKsiFr2dpZnSYu2p/81kl0n/j2He8WnVcsjPUQGeglRHU0HaMLamyLh7CcKvU5l6e+CWwevz2t/12iUxnqOTMZ0Qj7tr21SXdTGZ7ODhQ1D7/eOkXpbNdpdRxuLdFYVUhMci6tzbWkJMVT19rDsBClkYGBFFa2WDukTo8ZbSjAPzJO+Gl3o4j/avkbic61uWGiQtzJbxl/LUbe8LDovM+pYZ/G/Fi++fYpHVMbNr/ZgknYYv3JKVNDnWSmJFBS20hJYTYJ6QUMDo9RlR1PVEImY7a1n5tzYyT62gvR2fVJR2tXfpsyMJcVJzSNT6C6pO5dgeYuz/T0NxfjL0Rn7/jdLbyU+0z1N+D04iX59f331jJbMR5vkxvny29/+wfCUwvp7OykqTSJb77+Cke/CGGfNl5Hxz8znYh6pWVzfoSkuCTKKqqoKCkgPSufnuFRGivyCAoMZnRNo+aRZW+M+PhwMiqa1e9/dNF5w/JIMy4vvsUjJpulxUmcntiTXzfAbU2+tzwuHD03YnMahABdVNfzeMXms6xEmxNcGrYoSgrC2TNIFZ1X1xdUZYQSEPxGdB4pQ/IxAYTF5LOntaWicUs0CCF4RKe/Q3RaAwnFiMzYv9dJcGnS0pyXwCOnYKZsTolCR3U6jnYO9LwWnRE4+yewaBOdmpUxQjzc8RXC8XYRsJwfkyocV//oPLYNp/TUF/DqpS+j67fe63KHaH9fguNymZsbJCXMk+9e+akRqoYGBxkcHGJiaoa1rV12lmdEmoTj4eFLRXM3k1NjVOfF8PTJM1on58TNFNEZhauXMPoGq1E/PzWIhjtQNBhRDK08sPZEIvmV85DoNB9vUZIRjmdQBCu3Ys7MdlXw7OkrcoRTewchOucHmwkJCqK2b161AzM9NTh+92e+EiLTw8sLV2dHvvzDv/J//+f/yuNXvowt3p76+h7RaeNwfYb0uCiK6rtYHmshPimVbtFgK4z3lRMYEUGPsvZFvMtUTz0OjqH0j36aI51m/RGd1XlqNNjiunZ27gViWRpqJdDHg6Ry6xoUK1ekiXzyDIlnev+umDPrhUOVG4u/MtK5/VDgBSt9lcm8eOVEfc+07cxd9qea+fLzrykQDu+dxvPcQG99EVFxGSyuLZCVm6Yu4dCI17jSrxOTEEtQRrV66enJMekBDmQW17J/J8iT5F3cEZ2v13SaaS5Ow807iJbxN4G5dFtT+Lz8ioT8Jg7vaXplpDMqOpiEslaOfuJI58JQE34uL4kraHnnPquGvUUKMpJIF3m7uDaDR3AsTcPWOjbT20GAVyS1whlX2BY+TnSIC0Ud0+jfHjz8pHiX6Fwa7xa/QfhYSTlMr1r3LH0nyhT3mkJeusb8pJHO++j3lghz/YGA+FIOdbeHKmBC2cIoMVmUkRnaSjNJSkx63XlXmuRNUnoqhyJfLcYjeiqz8ItIZN5m03/1/M1GOkdFPvqQU/fjRzofYnWknidffUvt0OKdoFSmg1WKMlPJLKhmcXqAjOQYKrpmxZ1FGZpvIjgylJpuax1bnR4l3FUZ6ewT7/Lpis695Qky40IJCoulZ2wJ03v28lG2iBlrKxdiL/ytkc4ro8jD/HheOvkysrB/R2/coN1bJinMnb98/gX2zq64ublh/+Qr/n//z3/mH3/7ZzKrOzm4Xa0u9GpAtMzSJkaGusnLjKW4pVP96GhtnIhQL8oGllEm8pyvDxEdGUb2RxvptFhEA6tlfW2FnQOrEhYnme6uxf7x13jGFbC7t0OynxMOXhEMzt8IOdGYlKTz8vlzqnrn1Y1Ic6K9efTSm94Z62jc9nQnbk//zDP3APZFabu6vqQ8NRBHt2BGbNNrjzenSY/0JSgii+1j20892SAhJhC3yFSmNt6OUKiIzljPl4THZrGhu1dIr08Za8rmz188prx9TD1lMe2T4P+cz79+RN/aiRrJNi8+DHvvWOZs0QnPDXvkxwbygxDXrVM3U34szPTVYvfsMclFLSjR1HvrM/nssy9IKBV/265aaC/h+bNnpJS3oBeOU1lqKD88c2T81rub9Tourq5Zm+rC5Ycv1GAbVr18Sl1WMF99/S3N44roNJIXHY6DWzRrWusV5yY9ZUl+eASK9F+WolMiuc+pcY/ogFfYBUQzfRNI6PyUgZYyYbBd3giQcx2FycE8d/anZXhJve41liv2FwaIDA6moH4Ag2iMla0YpkeHGOjvVXsb21uaSAhw4fNvfyC9qIZ9zW3H7IqF0RZefv4tWY2T6O+ZplPtDrXFGYTGZLCyp0G72EFYeBRtE1ZnfLi9UDTGsQyvatXlC331uTj4xzI69ymu6bxiur8FH3d3Chv6VWfiPkbheGbEhuDoHSUcWGuwiv2VQVyfPyYusxzNzfYUNpTe4c6aLFwCE5iyzVI50WnY3d5S95BTMB6skSYaY3vRpgzMbKn7B+7t7qI9tbYdl6ZDWorj+fJ7R5qGFmztmcIlC8PtRIWE0jC4gNl8TFpaPJHZVRwL3/n8aJno+Ggi8lvUq03aNfydXMiraEd3M9dS8l4U0dlSloqdgyvtwys2cWMRQrAVP3c3otLKVKdHpDY9tTm8ePKMqo5xtV29jWZ/RbT/4YSnlnFwb89drg0Minrx/XMnGgZm74nOc5qKU3jx+BUNw/fXW1u5OtXRUpFLWEQ88ztHaES+O3sGU9s7q34+0dVMoF80jd1KWwwrEz34O76kYWz1zUb3nygLk724uz0lOr9GiE5rR8nZ8RrJQsD7Cl9qz/gjXMlrM6PtNbjZezG5oXmrXisz02KDHXnhG/5adF5fXqoBoo6Ob5YrXDHSUY3D00eklHZgOHvz3N2VMZJjIsmp6lJnJ7QVJRMdFcOuutfhBQXxPqRkZquRv0+PNqnKjCAkPo+1Q7mmU+V0n+qcML54Zs/4O0Rnb2WRqB/OFLSNvVN0Hm3MkRMXRnRuM8a3Ruuu6Ggo4JXTC4raRLtpKwRKMNGtzR30Jmt+Gg9WxD38+f6FJ6MrB2/KyvkJLeU5hMekirZY1LH1CRJjQoUImlCv2R2vISQyksYB6wyeubEe3J28qe2afHCN96fAuWGXqpw4vIWIHFp+IGr6fUQ9WhtrIyAgWIhrZU3nm3w4WBknVugpj7AUdu70ZF2xvTbP6NgkqyKdVxZnGB4apLurS/geHUJvJfHVl1/g4BfJxPImtmxQZxBMdlYSHBzF6OIO2q1p0hJCyK1vVz/fWxomNNiLuvFN1YbtjDYRFhxCSZM1Cu5HEZ3H28sUZ8WrG6fHp2SQmZ6Ct3DUnD2C1AhHFxfnzPXV4eHiiJd/KAUlJRRkp+Lr6UFkcr66seu1EJTjbWW8/P4RTp6BlFfXkZcaxTd/+GdeuPioazqVyFhDDbk8E9e4K0PEsxvqOov0CC98gtPY0th+6sk60eE+2AcnMnF7RNHG1ZWGaGFc//KnrwiMSiI/N5vElDSK6oT6152g2ZggwOkZz+zdSC8opbG2CIdHf+Czv3xDvxCdRoOWrKhAnrpGMLt6M23jmuXxTkJ9XHAUvzunoJii/GyCfTwIiExRo+QqhUIJ7vGH3/4ROy9/iirKKS7IxMPhJV4h8Yzb7qUGZXJ5ygt3PzJzCykuyhdpmsfc5j5H+6ukhSoReh3JK6+gurIQL/tv+P3v/0zrpFIJjWSHB/PCIZxV27495yYdxXGeOHkH0b8op9dKJPc5PdklzOsJz7zDmFQ3Q7ZyvLtGfnI4jm7uJGflkJESr/YaZlW0cfDW0IVFXdudFBFMTFoJm+9wdIYq03DxD2Zs5X5dvGJ+uInHv/8LaXXj3O4Pu744ZbijVjQMwUL0zKtLDS71G2QmxRAZn0JpST6JsVHkV7SgOVXqvJaylCBCxXus7FtnjnxSCOe/sSCWf/n33+Mfm0Z5RQVlZSUUFuRRLRq7NWU7E9HwTvY14evmSGBkAgUFuWqANc/AWAZn3hbSyrTLmYFGXF286Ri2biq+vzZFaU48qelpFBcXkxoXgZOTGwW1nWiFY32u3aa+olAIxlRKikvJSE3A3cOVhII69m4FoTnanCcnIUrkaxlake1Ke9VbXy4aXyUIQwG5mckkpGYI+7orXuSS3dkOnF38aej5tPeP+5S4MGpoLE7gyXN7WgeXX4+oKfuh1hWk4ergSFxyJrkZKcJ3cCNSlO31w7encJp0h1RmJhAQHMfS1j0H71pPf00mX373UgjFmTt5c23cIjXMm29feDO9/fYonbiCuYEWosPCKG6wBtFQNkwvTo0jKiaB4pIikuLjSSuoZl2pc1dnDLeU4uToy8zWkbAOnzZz4904OjwiPKdSrRsKexOtPH/0OX956kJNTZVaR4uKCikpLWd4ZvWBUS6LurdumKcjlb1znN4bLj7amCLC9wXfewSzeyM6hdPb11xKamIUuYrflJcphLsnvqHKPsNHr8XImW6P8txEwmJTmdsVtvXqnKWxDmKFvU3MzBN+Ui6RUQm0iDqnsLcxQ3ygB6klTcImytkGKqd7VGQE8sdHTxnbfVh0dpXl89V3r8hpHnmn6Lww7FFfmIJbQCL7+rsj0Uo71laby+OX35MvhMmN6DTrt6nIyyYmOoFCYYsTo0JwcXEhu7oT/euOhWsWR9oJF3WssKFX3El4tCLfK4vSCQiJoqCwlJSYCLKKathQosNfmuhvKcFVCKmB+Z1PNnqtVmiKIJdH/PGLb0krrqWioky0R0Xk5ZbQ2TOB/q3otNdotuYI8fchrbTxzd6kgsmeWhx++JawjCpMd36wmbr8WJxdfWgZfaDT7GAUd3c3ksus+3SqXF+wvThOXEgAeZWdnCj5YD6mqTyX0DBRH0V6Zws9F5eUybpt7WhXebrwQ8LpHLdG3//wolNwpj2graZQGAovnJydcfP0JCY5i8GppTfDwsKBmBpoIzk6VHXaPH0DyS9vvOOYXZxo6KotUDeX9QkKp6S8nHIhuMrKq7nZps10uEFZdrxIPE+q2sbZ3zugr6WKqvoutEbbRWcamhuqhIhsZ/Po7YplsZzSWlGAp4ODuum7pxDIrxxdCEpSpvbq1fVQSsGPCfHD2c2D3JIyKkuLKMwrYuXQpG6Z0tNcS54oDDuHtxonywWrs8NkJUbg5emOp08AqdmlLGwe2gznKV1VRTz7wYWMvFySE8Lx9vYiIimbCSEGX5vvS7O6hiI+Igg3V1e8/YPILKhi/cDaE7gxM0RypPjMw5uU7BxqayrIycxhflMZxTTT21BHflEDh7b1LJfnwmFtraCksk44oD+il0Ui+ZVxfqajoTKH/MoGtg5vizSLMP4LlGQnqnXa2z+U0vpODm3bbdzn+spMU1EqfoHh9E5YI1bf5vpCmYLbRmFFNUvbb8StFQt7a9PkJKbSNbnJ6S1/7kq832BPG+W1zXcicq/Nj5KdEoO3sDXZJXW2fcyu2FkYxt/DhYr2sQe29PgEuD5hpq+eAF9PnFyFrfQSYtLTQzghToRGpTM6axXklgsjE33NhAf6CTHoQUR8hhqN9+GZSUpezRMX5EFycZOwf2ZMx5s0ikbS38dNtDsehETE0dA18nr64JVJQ29zlXgPb/G5OwFh0Wo00gP9rT3sBOvzk1SXVTBhW0ekcHa8R3t1MaHi3cJjkukfm1dtuBqtODtW7WyclYHbfjTKqPP0UDu5op2dXTlQHc4bjJodmivy8PEQvoO3P5mFVawdGN6qXwpKJOS5vlr8vH2p65nh7HZES8sZyxM9pGUViLy8u8XC1fE2DRUlZAu/RHv6UKW5ZKy3jdraJjaV4W0FyzVH67MUZycR4OdLam6ZaO+tea7fXSQ3IZCg1Ao0D2wj96mxs7kohFsWDb2jr9fGHS1PkZ0QiaOTM17CV/Hy8lBnfnj6+AnbMoL+Zk+UW+j31yhIDiE4oZAt2/Z3N5xotmmszhdpXI/u3CYERRrOjrSRFB2Aq6sbXsKWZeSVqdH4b+evQaRnU0M1nSOzr8vGhfmEoe4GIsNDhSiJpL5zTAjma3XHhOHuWjzcfGhV9mP9REfAPjjnekaFaEnOymNDd1/oCIQPuzAyQIaoH8qa9qt3TY21nDPVW4+nixvtk2v39vO0MDfRT05BNgPTq6/X6F+bdcJeluAv/HsXoQGCwuOo7XizVaCVK4Z7W6mqa2bj9Z6c1+yJslmUJeqYfwAJGSWvp0trN2fIiQ8lMqWQvYd+zyeC4WiLytwEXO1f4O7lo9YlRQA6OniSlS98ddt2iLc5M2qpyIhUO1xvb4u2Mi3SNj2TjpFFkba30/1c7bxJTMpgaP5+h7aF88MlteO1pX/ydYceV6esCz1RIurj+u6bwIjHO2tU52fhJ+p5Qno+Ewtb6nnzyQFJ4YFEC82ybqvbH0V0KijBGownBnWvs2OdzrYv5ducnyqLxQ/FNQYuHgxvfIXRoBf30WIWRkkZ3by6unwjyATKcLBBr8NoOlML9LX4/PLqZh8uBfGdS3Hu8up1gb+P8rnJYECrvK84NBoNWsMJ58pelzbMyruK88ZTszKgK56j3M96Q+WdlAi1In3Vv2+jiDzrfXWY7yy4N9Jaks2LZ34MLW5hONGh1WrfGXHr/MzIseaIY62OUzUt3nBhPhWfHWMwmdTzV8q72fYcVH7bhUj/m1dTP1fSQz339vtKJBKLsBcXavTru4bcijWIxTFaveGv9KZa2F8ZIzoskLSiWg7uKz7xXcV2KFGlH3qOsmXSxfm5aOzv2kaL5YpzIVgVm3MfJRCAYi/PbGHkzfp9GgqS8Q5LZt4WCfZTRLHbSvAQ7fGxameV4+joSNhEvbD9d9uP0xNrm2A8e7+CVqL+dVTn4hccTdvworou7+Jc5J3uWNxbI2zuXTGpcHVxwYleqz5bbzA+mL+K7Ty/cZJvoayd1YtyoTeYrG2N+E3Lk/2EChFbXN+NQY6w/CSUNlZpux5ydpWAT0pAKKV8nP8VEWHSrJGVEEZ0Sr462nn7btZnXIj8ulfHRJ1T8lnxJR7Gos7aUurufcyirVbKsclWPhXhq0wRVZw21Sl//+t+Eij+g/X3v3lZJU0uzGci3UX9sNVRjagnGuHfGE8V/+vtfFJmZYx11+Ht7ScE3/QdH0ixbze+2c2ejgpK3pqMJ2odPFbq/wOBipTdAM6VfLP9fYNiGw16PVqdXthW66dHm0rkzkjCk/OF8H1gRO9Xi0W1uxeirXvYN1b2rhb1Q/n8Xht0H2XLDWW3ivC0YtbvdNRay9JD9VjJQ7UsiXw+MZ7eKgE3WITtf7iduzCb1DqmbNGifE+pY31NFQT6BdLQM/2O3/OJIOqJ0g6dGHS32jrrobRJ99t7lesL1qe7CQ7wV9uSm9FgNf9E2l6/9YOtWulSqSMP2U9R9xTbpdTv158q9VH1K95+/qWo90p6n5isHexKdP6JzgoCAkOo7Rh+vWTlo4lOyY/BSEthBk8eezOw9KbnQiKR/D1xTXdjOZm5BUy9nn7/4VDCzuemJQuHbxbTW9Pf/t5RlnssUJiVRkVjH5oPvDniueGY/uZKMvKKWd7VvmncJR8UxUFaGWsnPT2TvonldwYF+rkwHqzTVFFAdnkr2l9hx4PxaJvq4iwKqlvZOf7Aok+I0NnxHrIysugZX/q0xcgvGIs6I6+b5JRUhhc3RB37sAl9crhBfXkh+aV17Bv+PmXO5bmJ1vI8sourWNn9uLMUzwwadYuwwop6tm8ta5Ci85PmjOHWOsJD05iW0dQkkr9bzk3HrK6usrV/d5TlZ+f6Ev3xHourG9zeQPtXheWavZ0tkQabaD/wtEazycjGygrbu0fvnpom+SAoez+uLC+xub3/V0dG/9acaI9YW17kyPD2yPqvAlEHdYe7LC0tozv5sKLz6uKM/d1N1ra2OXv/tBTJ/ybKrL6NtRXWhb0zPzA6+XNyotewsrLGzgPruv+eODccsbS8wsEDWxB9MER9NisBY1dWOdQa78w2kKLzk+YK7eEeS4vr6B9cMyKRSP5eUKam3Uz3+nBYp0dJvSOs7a0lBx8K0d5+hDyXvAtrHVSWwdhOfCCUcqc899eOMhXww9fBa9UGShP4gbDZPMX2fUjUOvYraeiU8vzgFNwPiDIF/uFpu1J0SiQSiUQikUgkEonkZ0KKTolEIpFIJBKJRCKR/GxI0SmRSCQSiUQikUgkkp8NKTolEolEIpFIJBKJRPKzIUWnRCKRSCQSiUQikUh+NqTolEgkEolEIpFIJBLJz8Z7Raf4m4uLC3nIQx7ykIc85CEPechDHvKQhzx+8qFoysvLy2lxPCw6TSYTW1tbrK2tsbGxIQ95yEMe8pCHPOQhD3nIQx7ykMePOtbX1zEYDGg0mumVlZWHRadQoxiNRk5OTuQhD3nIQx7ykIc85CEPechDHvL4ScfV1RXn5+fTOp1OrumUSCQSiUQikUgkEsnfHqExZSAhiUQikUgkEolEIpH8PEjRKZFIJBKJRCKRSCSSnw0pOiUSiUQikUgkEolE8rMhRadEIpFIJBKJRCKRSH42pOiUSCQSiUQikUgkEsnPhhSdEolEIpFIJBKJRCL52ZCiUyKRSCQSiUQikUgkPxtSdEokEolEIpFIJBKJ5GdDik6JRCKRSCQSiUQikfxsSNEpkUgkvzKEnbf9T3KHD5guMgc+Ph+zHsg6+Ikg8+HD8I5k/psnv7ifzNJbfMDE+DE2TVzzcUXnpfmElYVpurs6ae/sYnJ2CZ3x3Pbpu7Cg1+zQ1dnK6MwqxnNZwiQSycfBcn3O7t4uR1qd7cy7uT4/Y397g/XdQ64s15ydaFmcnhC2rIOh8Rk0hjPble/mzKBhdnyYzo5ORiZn0Jlu7OUl+5srDPb1qnbUaL6ynVewsLu+yvbOAefi9JWwuzub4u/dA25f9Uvh+tzE4cE+h3oTF9e2k7cRzdjR7i4ajZbLq4cuuI0FzcEOKxtbaG+3PdciPfcP2D/ScHn5cLNouTxlbWmRta1DzJfW51iuLthZXWCgt4/phQ1Ob3/1+oytzQ02dg5VH8x8esLG2gq7h1r1b8lPxcLx/iYjg3309A2wtqPhr+U2XLC/s8Xy+g4XtkQ/3t9gdLCX7t5BUTc1fzUvrs6NrC1O09PVTt/QKNsHWtsnwjc53GZsqJ9hUZ+Pb9Vny/WVeM4O65s7nJ5fir8v2NtaZ3NnT5Qd20W/JMT7Hx3ts394zNU76piSThuriyxv7nNmqx/v4kR7yOrKKroTk+2MggXt0R6HhweYr9/OlcuzE5bmJugW9nNgeIL9Y6Mt7ywc7W0wPNDH6OScsJFm9ewNB9trbG4r+SCsn7Df+zubog7v/jLz4UMh8ntT2LW+nm4GRybRCNv71zgX9m17c53tPY1o72wnBecnx+zt76tt1022WoS91R3uMD02TFdXN+Mzi5yc3c0Qs/je3PgIA4OjbIk6d7tEmAzHrK+ucnxyIYTNNYbjPVbXNzk5/Wt64tPm9ETDhEiTjo52OtrbaWnpZHpujdO/UliP97fZ2NzCcHa7hb9Gd3zE3sExZqXs3+LcqGV2ckRosW5mFlY5u7j9+RWHu6sM9vcypuSL+cJ2XnB1zqGwp5vbov6ITL66OGN3e531nX3Vx7nPRxWdh1vzlOem4OPtiZePD37eXnh4+hCXWsj8hrVRVhHG+sSg40ijwXyhvN41WwvD+Hg6kFjQxL7hl+g2SSSSXzJnRgMby3PUlubgGx5LQ9+E7ZN3szE/SnFeHj2TK8L479NWlUt4SDDB4vD28SU5r4KV/XeL14P1OcpyUwn0DyQwMIiktGxmt/RcXV+zNjdKXmYqMdFRxCUkU9fej8lmzY+3FsjLyqVzYAqlDbo26xlqryUnv4TFnRuH+VPHgtlkYG1hmuqiTCKjY2kdXcV0y/xfnJ+yv7VCR305ob6BVDb1iUb3/U2aUbNBZVEOFU09HAnReSkE7c76Is3VpfiHRlPa0o/hIcdFtEsroy14OYt2qKRdfFc8x2JmcaKPnPRkoqKiSExKp7F78rUg3ZsfoCC/kI7hBfXvs5MjeporyS2pYvNQf8eJkrwfy6WZ1ZlB0hIiCRJ1IcDXh/C4VEbm1t/bkXK8MUVZYR51XeNcCkd3Y26Y1PgIgoPFPfz8CYvNYGR24533ODMc0FpdSFhQAIEB/kSJutY+NK2KSs3mIpX5GcTHxRERGUN5XRt7Oqvw1B9tquWstnUAvUk4bcLVGu9tIq+gkLH5LfWaXwLnZhNba4s0VhaJOhhFRZuwM7ed0Ndcszzaio/LC6IUP+09gwln+gNaxP1KqlrY1QrheHkuROMWvS01xIYFkltWxbEQ6rcxHG3RWJFHcKDIh8BAYhNT6Z3e4FwI4J3VGYpyM4mOjiYqLomyunZ0NufbeLRBUV42TV1DnCiOt+Wc6eFOcnPyGJ7d+hGdFr8+LswGhjtriQwPJSA4GF9fX9Lzy1nbe19H6wUzwx3kifZuUKlPQoCc6DQszoxQkBxOVFIWIyv7r22e8XiT1poCwsLCCBF10dvXn5yqVnZ1VnF7crxLR20piXExoo2LIT2nhIXNQ2s9PT+hr7WWXPFOu3pRzsSztLuL5Odk0to/genBnslfAtesTvfg4uiIvYsrvn6+eLgHUFLZIUT/uzuozdotKoWNq2rsRn92zZWwlfs76/SKNIqPjiSvspND7Zvv6/fXqS3OIDgoUNQnf/wCgqluHxBtp1KvLUJvjVKal0p0bCwJcfHqZ1qbc6FZm6G8MJ/GziHMIpkvz41M9LeQlZ3P/ObRW3b0o4lOvSgQSWEefPX1t/iGiR/R0ERLQw1J0SF8/803eISksbRtc4auTYz0NlPZ0IzWbC2i24sj+Ho5klLcxvGpekoikUg+CNfCWZ0Z7CI+JIBv//jv/Od//4rUqm7bpw9zbtynsiCF4KgUVvYMHG4vUZAaSUZOAe1d3RSlhPPD94+Jz2/gziClDdPxFrkJYdjbO6kNfnt7Oz093WwcGjEcblOYkUBEfAYdPX0UZyYSGBTJxNap2oPZXJRKaFwm44vbtrtZWB3vIiY4kKzydgwPPfATwyKc3YXhLuLCA/n2yz/xv/71d+Q1j2O8pdS2liYpTA7B6em3/Nf/6x8ISq1UheS7UEYqB5qL8Q8Ko7FvVnWAtpYmyIwJ5vnXf+E//+NvCc6u4/iBe5g0O6QHO/Of/8//D24JpRyYLJztz5OXFE5MUhqdvV0UZcbjFxjGhu6UyzM9pWmxxCZlMysaY5Xrc+ZHOgj09xbO+xDGc+ny/jiu2V+dJDHUC3tXX+GE1dNUXYqn8yuCRDlfOnjHKMzlCQ3FaYRExDG+sodud4mUMC9eufpRWtNIc10FHvZ24h45rB6+fQ9l5K6zIks4gQ6ExKbR3NpOV08P00vrmE1HtFTn4B8cIYRlD03iOp+QMGoGFsQXzcJpryZAlLOusSXOL62FdmdplISoYJJyK9CdPiTcPjUuWZsbITkmnG8+/zP/9O//RmR+LYYH3v3MsE92pBv/6f/7/8YuMo9tw8P10CLSZn6olQBvX+q6x9ROJEW8Vwlb6fzsS/7X//xvvPSL4sD85vsXoi7Vic8dRV5Fp+bS3Ga1hXPr+5i0B9QUpBMSmURDZ4+wuRmifoXSPSsEytkJ/XX5BEUk0je58toh3lufIiM2lNj0Evbf48z/Grm+OmdupBUvh+f4RyRR39JOeV4SDi9fkVbUeHcmxy2Mh4tkJYYTkZjFV2IOWQAA//RJREFUhsbEhUnHYEsF4aE+/Omf/xu/+9KOprEV29VCExys0NpQQUl1PZ3traRFePH1o2eUtY8Ku3zNTH8TQQFBlNS20tHWQHiAN1nVXegvr9id7SU+IkyIqS5uTOjVuY7q3FhCYlIYX961nvylYTEx1FLCn794SnJ+KS2tLTQ2tjIxvYLpnSOdVww1FhAYHE7zkNK5aUGzNUuh8AlePn3CP//jP+IVXcD2kVU4XZzqaCxN4+XLl6QVlNHW3EBSuBcv3f3pnhC2yqyjUunoDokRbVo/NUKcunhGMr60z7UQsy3l2YRHxjMws6reT3n+1uIYMUH+pJe2c3hy1zZ8FNF5fW6gLjuCL7/8luTSVgy33un6wigKZiFf//lLkouEoDSZ0YkESwrxwMk7gOGFLXQGI5uLowT7e5BZ1s7mnpb9vV32D5WR0Ld0NSaDlt3tbXb2DjCpyt16/vLiHKPRxNmZiaODQ440Oi7+yhQQiUQiubw8Y6izkfTULJJCA/nupRvp1e8XnXN9zaKh9KeosV+danR2eqJOEb24mbp5uk1KkDt2riGsn1hP3aCY5f76AtzcvClrG7OdtWG5Ynu6h9iYSOHgzqmntqf6yIxPpLZnlYXRHqLChKjqnUIZYLnh+mSPtrJ0vIPjmF47UgXXp8yF4ZjxjmYys3JISY3jxZNHFLWM8Gaii4XFiQEh9GLJz0nn5aOnRGRUvdeJ1O0tkxrpR0JhPZvHSuJcMy/ukZOSSnp8PE+e2hGR18DhvWnP1xd6RroacHKw5y9//F9E5lajMV2zPNhKYkQInf2j6nUb073EhXoyvrHPVG8T0RGxtAzM3un9NWr3qcqMJiQ+k8Utje2s5H1YLo30NuTiaGdHTd/y6ynWEx0Voo74UFA/aD1xj8PFfmLDA8muascsystoSymOT59TM7Ih5JSVqfZi3L2FCG2/X8+u2VkYxsvRntSiBnT3XA390RLZmVEklrVyJMoCpjVi0tJIrWrjcG2StNgocqs7hLi85WJZzLRWFeAXIMToxOonXwexnDI72kVGRjaxcbHYOTwlvqge/esp/lYUP25ioA0P55f88ff/TmBKETuGh0cHTg43KcmIIjAmh+1DvXpubWaEquIcctJj8XR5hl1ANPtnN1NkLSwNt+Dl5k5KUfO9kUkLepHWWfHRlLSMoTxRtzZOYXoyRU2TrM8OExcVRllL/718MDLcVYmnTyCd48tIN/ANSmdncXI4zx2DWL6ZFSPKQUVmDD4B4YyuG6znbiPqykBrGf4BIVR3WeuR8XiPztpicouLifR24cVzTxpVUSSwWMRX7ia6cWsU9x++JS63VthLEwOivoeLcrRvUi4/p7Eik+jcKpaWFqkvzCIuJY9NzW07bUG7OUqgry/5ot6d/RLz9OKIjposnntFs31bKL2HC/0m0UG+pORXc6QmxwXbCwMU5mSTnJ6Hu8MTQpNK2LJ1qm3N9BDi7UpoSrm40srZ8TKBnu6kF1SztDJNQkomSflt6meavVUCnELpHpxmdbafxJgYypv6OLtlvC5ODXRU5OAfGM7Q3PqdOvpRRKd2cxpv+2e4h6aytPd2gTXpD4j3t8clOJ6hiWnqheH5/X/7f/gv//0feCHUd1XHACsLI0SG+hCbVkBFSTER/p6ioQimoWMY0+v1BddotpeoLszAx8MND58QKhp7hZBVkvaS3eVxMjPyqKuvIi4ymoycanY0RutXJRKJ5B0oa0ZOhRNkNp+z0FmHg5MnyRUdtk8fQDTSDWU5+AZHMTS/Yzt5j4sDMsK8cPYIY+PeIMv5yQE50T6EJWQxtW3EeGLAaDrFauos7C4MERsdJkTYMMbzcxaH24VwSqaivpuCtEQyhGN49JbTZ2F+tBs/Nw9qOsdeTwH9VLm6uMR8eibS/JSViQ7cXz0mv3EIg+21Rdul5ofZrKybncffyYHQ1HJ2te+YCnN9wcJIO96uXrSKBtTaZl5xLtLPLPJ2c044PU4uhOXUcXBbdIrnLE50kRAdSVxGPtEBL0kQjfO+cArWxztICA+grq1Lvc/MYLv4O4iugT7SkuIpqlam4d5TK0J4rI824eQRQtPA3GvxI3k3V2daGkuTcLB3oG/22HYWdmd78BT5HpNVbsvP21jorC7A318IvNEl8fcl7aVpvHr8ir5bFe54oRMPR1fi8httZ6xcnxvpq8nB0TOE7qEZjEYjhhMj55fW/DwRjlquEE+KI7xxaMC0PUlKdg7pRRW0VOQRnZTN2q0pbTcsj3aqTmJ2ZZs69f2T5uqCc1G/zGYzy3PD+Hq9JLaw9ta6cgULyxOivCcmECuc1bhQD8LTCtnQ3utJs7E+O0iUvwulHdMYbV6v+exU1B8zR8JXTI5w56V/JHunVtFpuTRRlxeHX0gcHeObmIwnnIh8uBkwONmaJTsuklxhj4+EPdicEQ53Roqoe21U5KaSmFXKluZtv1OZ4RAd4EFWZSu614MTEv3+EhkRPrzwSGLv1rTCnjohYDw8qOqZtZ15w5XpgJKsOMLis5ncsKb11eUFZ2dnnJqMtOYlYP/Sk9r+t797fXXJhcj7jelOfNw9KWnsF2dN9NfnERyfzrLQDEbRHlYVZ5BVWkNNaR4ZaRl0jr8ZNX3DGblxgUSKurd08I524FPGtEt9cTzfugQwsbjBidHExftiFCjLBUYa8PULo6Fn+vW5yzOTWmePDnaJC3QkJEHUxwOr1pkbbMTHTZk12q7+rXJlJtnPnbDoVAbGR0hKzSAus5bzCyFgVyYI9ouhpaWVoox4sgrrWD+6N4tByMjd6W4iAn0pbu5HdyvuzocXnSIBFoeaePHsMSklDTw04+LqXDhouTF85+xDx/AE80qP8ndf8uevvyeloIKZ5Q22licIdPuBR8+dSc8rp7G6BC/nl/zwypWWUaXwXXOyO0tmYhQRMYlU1TVQkp2Kn48vubWdwsG6YHmwmn/71z9g7xtKUUk1fQOTaN7RGyeRSCQPsdRVzyt7d1IqOm1n3uZCs0phWjyRyYXCQN8NanHDgjD+isMcm139VnAc/f4iUR52BAZHkJFfhJujHS5e/pQ39mISztbl6TEV+Wn4C1GbV1RETmYGKUlplBYXEhWTxNjyFnqDAZ1OJxqON57t7opw6oLdSC5t5PgXE3BBOLXjbbi9/EGIzuHXovM22v05fB3sCU0TolP3sE2/OjfQ05iHc0AM44ubtrNv2JsbwdXR+S3Rqd1ZIjshgoikPKaWFsmLdiIut0I855xL/QZlWTEEhYWRX1hEdnoKiWlZFOekEZeczcTcmhArJ2h1+tdiRUEr8tfXwYXC6k7RJr7HqZCoWK7NDLeUYv/kO1KKWjnUGtUAFt2VqXz72e8ISSsWbuo9LrUU5ibiH53O5KoyvdnCcGsFr777lqSyTo70Ji6F79FblcEXv/lMCKWKOx0A5yZRx9LDcfENISUlichAL17ZuZCaV8Xm8Zn4rlKeivH39SMrr4TstGTSMrPJyc8jOSmBut5pDCLvdTotZ7eCeBi35shPFOVJ2IZ9ZT3ap8wtJb82N4SX5/O3RKfpYI28pCjh6OcyuTBHYbwPocn5rIs8eptzxoX99LT3ZGztVhwPG/q9BRLDXXh1S3ReGI/IjvHGxz+Q5IwCAj2ccXD2IKO0gUOhWq9FOWgpzyUgIITMvEJyszJJSkyitKSY6KhYdT29Vm9AL2yh+dY6UcP+OmVpYYQlFrJ5KAcfbjCf7FGdE8m3jxxoGVjAKMrumW6PrCg/4ZN/p3Z03udYCP/kaB/ShCg8vK/fLde05ibw6oUHdbaZOSrXVxzurNLZXEtuRoqoRz4k5lazpubFNQtjPYQH+ZGYnk1RUS7xCYmUlRaTFB9PWW0bu8dGUbeOOTHd7djpq8wkOCyW7sl125lfEMYDmgvi+M3vf8c33z3G2dWTzKJqNg4fXkt7dX5Ge0kCwbGpjC7v286+qVUnxzvEBjgQfEt0Hi6PEunrhLNfLKv7eiH4z9hZHMPjyZ957hPN0OIW7TVFBAUGkl9cRn56IvFpeZTmZxMdl07/xBLGszOOj0V9uuVbnGlXiIsKITKrko2jN/Xpg4tOy5ViZKp4KhyGnJrW14EubnNlNtFVms6XrzzpnlgUDtU++Unh+IbHML+j5/LqSjhLk7jafcsr7whG57fU3pOh1nJePn9CeF4zZ0Y93WWJolGwI72khqX1LZZG2vF3fcm3zoHMb+6yPlLFb373B8JyG4XDYOT8/EINyCGRSCQ/lrn2Gl7avV90albHSY0LIym/zjbl5TaXLI91E+zljE94IjObb0ZubjDsTeP66HN+94dvxD0qaWqoJTEqgJcvXlLUNMSpMPa6w02aakpIS8+ktrGdFnFNWmIcRdXN9LbXERMejIdPIGXNfRydWJ1E7c4yRSmhRCri7IFRmE+S6wvmhptxfY/oPNqexMfe7r2i02w4oj4/Ht/4TBa2Dm1nb7CwNdGPi4PTHdF5ZdKoo1aRUclMbuo40R8I0elAYlGtOtIprmB/Y4Ha8kLSM3Ooa2mntbmO2Igoqhs6aK0rJzrEB2//YOo6B7hZ7nKiOyTZx56skloOTn7eft+/F3R7a+QmBvPddz8QFBpJenoSnm4v+Ow3fyA5p/wt0Xml3SA7OYLIjEKW962jbrq9dfISgvlWOHSBYTGkivoS5GHPv/7LF8SKfL9dtMzGY9LDnfn9Z5/jHZpIY0srBelx2D17QUhiEZrTS0wiH/uaa8hMT6e4upHujma14yE1r4yBwR6SIoPw8vYhQ5SXXdtom+Vki/rCZAJjUlnbs04v/SWwONmHp8c90XlhoKMqX9SPBIZXtBiFk1sU50VERqmoQw/UwwsdXXVFvHKJZPUBR1pZixYf6nxXdJoOifV6wu9++1vhPOfR1NQo8jWa5y9fEJtfj/b0HIPugPbGStJT06iqa1bXqWWkJJBdWkd/V6uwnWG4e/qSV9HGjm1t26V+X/U7/SNSWNyyrbmWCI14yfbCECFuL3nywpmo+GQ1GM3LR1/x9TMHyjruTUMXbM71E+HrTFFdA/e7WK8vz2nKjntAdF6ytTJFSa4QnJ7OfPnlN4QkFbJ+aK2rSqTv6aEu8rIzySkso7u3m6LMFFJEfW7r6FQ7Xd3d3YlOzmZqafO11FruqxaiM5yG3knbmV8OFiUS7MocHW0t1NRUkhEfwZPvviUkIZvVvbcDAJ6fGilJ8CYmLYvFB2zJ8f4a0f72VtG5bxWC1+enDDSW8OrxDzh7+ZOSmkxkZDhf/e5/4RQQy/zuibBru3Q2lJEu7ltV08hAbxtxEZEU17TR0aqsAfXHzcOPopp2jm2i7uL8mLS4WAJiclm4iWEg+OCiU3EYFgfrefr0ESllDXfWc95waT6lMSeax44+DM9tiO/oKM2IISgmkU2t1bjtLI/h4+FAQm69cKCsTYNmbYyIIA8C0mowaI7IDHDi3//pX3lq70RgSAhB3q588fnnPHIKYXZ1g7XRer5//IqGkZsFsBKJRPLT+DGic0c02vExQersDu0tm6eE/O9vrhC2ydMa6Gdp5856vxv0O+M4P/qCp8J2TWwdI2w1+8vjxPk78tQzjo0jqwN7enLM1vYO26sLNFQUEpeUTWuDcIIT4iitqKOqNI/gkCiausbV642HG9RmJxKVUM724S9klodoQ+b/quic+qui8+x4n6rUSEKTc1nevb+WUojOyQGr6Myt4/BEcZ0u6KjM5NFXX+IVlsbUwopwYJvxtf8c56AYuiaWObMNURt1GnZ299lcW6QiL530/ApqyktJS0mhrLKGUuEghUfH0TSsTPNU8k1LfpQDWaVV7OkfHgmX3Oda5PMqtSW5xMfGkltUSElhBk52LqTn1bxVj853l8mMDiU+u5i11z3v4h47K+IeecTGxJGVU0BdSTp2jp6klLbarrFyZjgiIcSRL57YU9c9gfnSop4rS4vihx9e0D5rHVlQ6vTO1hbHmj3aastISUyhpr5ROMsZpAnxWVdXSXiAHxXNA6juzNUBbdUZ+MUks3wTPPEXwBvRWYdeDSR0pU6B/P6br3APTmRyfpXe1nr8HR9h5x1K9/g8Z/e2HrKcHtFalcNTnwQ2j992ku+IzjOr73dh3CfC4zHfPrajeWyDy2sLJ4drFCUF8uUPboys7KnXmU16kQ/b7G0u0y4c5qj4NJpbGshMSyBf1LPaymJCAkOpaOizlpXTY0Yb8vGPjGNuw3oPiRUl2NP63AgFmanExiVQUFZJakwwPn5B1PVbbdhtVic6CXF2oqym6U7HjcI7RadF1Cejnu3NNeZnp+lsqsLfx520olr2DNZyowxaHeztsrO3z/RgO/FxyVRUVlFWXEhGdh51tVUkxYSTkJbDrtHaUbgz2UhIVDg1trWlv1SU6Nj6ox2KU4J59tyOytbRt5ZiKKIzL9KVhIxs1mxi/TbH++tviU4Fs2h/RroaSY6PJik9k5qWRvwc7QiJTGfF1kF3ZtSxLXyLnd0tmksySU3Ppam+hpSkVPLyy6gsyCQqPJT6nin1vZSIuQVxKYSFZbGwejPqqmTzB1/TeY1ufRLPF49wDkwUxv9th+DCsEekxzNcg+JZ3hM/+GSH/ORwAqISWLVNe9heGiXIz52ssk40J9Y+jaO1YWIj/AjNrEd7uE+ilx1PnrqIxqiM2toaqqurqWtsZXhigRNlT5q+Gp48c6Nn7u2pVRKJRPJj+DGic39pRI3MnVLYgMY2KHB+qqe/pRw/bx81euXye4LIGPZmCXZ8RmJ2Da+bkssTOksS+PKpNzNbB7aTVqZ6GkhOSqG1b5LajExSYnI50okHX2tJC/YiM6cY5TWMB2tUZEYTmSREp+bXJTrNugNqsqIJFMJ8cfv+yMZt0VkvRKc17TJj/Pin//UvfPvUDn9fX5zsX/Kv//B/89/+7U/4p5Sxo7nd0J8zoWwzEJXI+Nw0OblpJBXUomj7c80K0bHRhGbVqVeenRyTFSxEZ3G1EJ22AiL5cVyesbezg044mTP99Xj7BpNfP2T78A2XB6tkJYQRk10k/Ih7Dplyj+1tdV/Nue4K/IOCKeu4uwWSsrdgQXwgLsFJLN3aZmhxuAHHH76hsGvhdeRMhe25AZKTUyivb6OruZLIxFRmjhR5c019RhQxKeUs7guxdnVIc1maEJ0prOz+9b1+PxVui051awWLgXwh/P713/6dr4QIV7aTcbJ/wb/9z//Cf/+X3+OXXKRuh3Ibi/mYjuoCXrjHsaH5caLz0nhIcpgLAbEZrL5OrisW+yr4yxePaR5fs52zsjLWKRzjRGo7hmmvyCYxIcEWsOiCwngv4WSno1HyzXREf00OfhHxzG/ctacSK2ab8Dg7N1JXmERgSATjm3fzVGF9qodwD2dK6hrfEkbvFJ1vcUlWjCf2nkEMLt61z+f6PfKSYsivaKentYr0jCRax5fVz0Y7RP0NCaZ/Xad2JmyN1hMUEU5tj7Wj9ZfOxniTGmE7raiV+3OTFNFZGOtBTHo2K4dvr1t+l+i8QXu4x+7BkWgbV4nw8ycuo1II/tvtkYXlsQ4iw6Pp6huitSib5NRylvZFBTJuUJERSVRmBUbx59XVKdlxiQRHZLOw/qY+fQTRqRgaA425sXzx+TdEpZWycfAmcY53V6nOi+frb76jsKHPurBcu0FWlD/uQVGs6azDBEqocX9vZ1KLWjiwhS88WBkkKsSboLRaTrRHVCUHYOcSwND87XDJ19Z5xxYz010VPHrsQsfMhu0ziUQi+WnMtlXz/JUryeXvDiRk2p0jNzGa2PQK9k4sWK4uWBztIsjLjby6TjXK4n1M+mNWllc51J6ooqQozh+/4BjGRGOqcLq/SGa4B3Z+CawfvXHYDlbHSYmNIr+mG41GQ1l8PIlReZyo4ftOyAz1ISvXKjqPNubJiPIkPq+aw/dsLfJJoYjOoSZcnn9HXsMQeqv5v4MiOr3tXhGSWvbOQEIXp1raKtNxDU1mZvV+SH1FdPbjbO9IaI5tpPPaxMzkCNXVVVSUl1FSUkp2ejJPPv9nvnzuTEFjP5rXaz+v2ZwdIDEijLLWUYymA1LSE9Tpf3rRwl7r1ohJiCUkq169+lS3pY4M5JS1cnx6S7lI3oFF3bbo6mZneYHxaJPkcF/8IpOZfmDE0HJ6SF5GrHC4spnbsHbwXF/evcepZpOEYHcCo1JY2tNzcWZke2OVzZ1Dzs9NjDYXY+/sTY1tpoASIKyuUNmK4BXdc7uvN8A/1W6TkxxFakENW4ca+hoKCY4T97T5eY05ccQJW7B8dMG1fp2ydPFe8TlsH/9CprgLFiZ78XB/RkyBbXqtxcT89Ch1tTWUlZVSWlpKTnoiz776LZ//YE9+Qy/ak3u/T9SpoZZKXOz8mNk5Frl6F83mDHEhTrzwi7gVSOiMpuJE3H0CqB+yzVI7P6ImN4pvXnoxahvpVNDuLZGdGEtqYQNHx8e0FCQSHxuHxvYeJUneJGekoxNVzny8TV1OLEGxWazsvy2Af60ITSAExNWdvNmc6cLfw5XY7Epsg5B3OFgZJz7EnZzKOvT3zJkiOhuzYnn5zP11ICFlFO/EoEenf6MFTjQbhPs64BEcx8zWm/y4FPqht7aAkIgkptc0LPTXEhsXTtecda/byZ5qAsJDGFzXq6JzsqWQgNBIWkcW1c9/SZybT9Eea9RZFSoXJ9Rlx/DqlTOV7WNvCfpLs0mU4TDCktKZujWl9QZFdEb52REUX8D6jei0XIv8vXWnqxN6KtJwdPOlpmeKNzGALMK3mCZDpHVGeTuHwrdoL0wjPrmcNcXcnm5SnRVDVFYVp8IQXpv3SIgNJySlmJXdN/n3UUSnwsnBKqkRPjx69JigqFRq6pporK0iOSqIZ08fE5ZWws6xLVFMx1RmRPPd9z+QUlLPyvY+a/NDeLvbkZDXyL7N69hf6iPEzwWP2BKuLsxsTrTjZvcMF99Qqhvb6WprpKq6mr7pVS6vzpjtKuPzr17R8ktcYCyRSD4JppvLefSDHbHFd6fj3eFMQ6WyViUskfldk2hNDDTmxfKbf/8zGWVVDPT30d3dRUdnJ8MTMxzrtWxMdBPg7U91xxinF0oPY6va0AeEJ9MprivITMTNxYWyjhFObYFJTnUHlGUlEBabxsK+Sd2XbrCxlIjgECobmuhoqiA6Io6mbuv6lvWZIUI8nSlu6v3lbKAtROfsQD2vHn1OVm3/W1tXKBxujePy5Af84otEO/JWSBmVa9EGjPfU4uwSQM/48j2H18LmeA8vnzzHL61StDHvGC01CDHp/wMRWeUc3NqPTH+wRmlGAuFxOewbr0R7c05TWR6hYdFU1bXSUFFAbGISzbagdwcrQuA6+1ItHAkZR+jHYMF8qmVmYpim1g46WpvITAjHxd2Php5x63TJtzinrjgTH/8o+iato2EXJ1rmxwdp7egU92gmS9zD2cOP+p5JtTzsr8+SEhVIbGYFOvOF6rfEBXvj6R9GTUuHyMc8fNxdiUwpfb2W6dpspKs6m5DwGHqnrM9ZGukgKjyczNIGOrraiY0IE07joDpKf7g4QlKYP8n5dRhs+5D/Epgb78LR4RFhORVojQ9PCTcdbZEWZI9/Yj67D15zzeJoJ0GujtSPrKiby9/maGOKCN8XfOcezK7pzfd3l8aIDPTE3T+Ups4uqouy8XRzJqmo+fW7nBuPaSjNEOIkgZEVjfDIT5nuqSMyLIii6ga62huELYyiqqlPvf5wa4GUcG8SRT4cqNPpJQqK6NQd7TLU2ybanQ6aG6oJ93NT27KxpYf3vzzVbpKTFEZkaiFL9yKbKqKzNjWc7791oNIWYVXphF2eHaG8OI/65nb1OTmp0dg5uVLW3M9NP5zy3aWJHiID/KloG8coFJF+a5KMxBgSMvLp7mwjOzWBxMx8DtRyYKE6I5LQ6BSmNn45swhu0Gt26WysoKi4lI6ODqqLc3B59YrwxDxWHljTqaTjdHsx3oFRtI9ZR35vo2x3EuL+GO/IHFaVWaQKlgt21hdoaRP5K55RWZiJm7MjyYVVanCmG5R1nfVFGQSFJzC7eSxqroXp7lpCQyLILamjuaKQpOgoGvrnxdXXGLbHiQrzI7NKidj+pm38aKJT4eRwnbqSLLxcnHj+7BnPX7zC0zeQwuoW9u418sr+ShH+Ljx38qCyc5jNtTnyMpOpahkWRsZaIo+3ZyjMSSG9zOb8Wc4Z664nzN+dly+eY+fgRFRiJqPKBunis/XJboJC4u/0jEkkEslPYXWkm/CoRMo7rHszvouBlkr8fXyp65/l6sLEQGMBj775jifPX+AibKC9/SuePn9OYFw6Myvr7Ez3EihEZ2XbiBCEwhm1mBnpqCPEW9jBly9w8w2htKHbOrXNhjKNtyQ/h+a+SWH2rZwoPfilmXh7e+LlF0R16yAao0Vot1P6m4px8w6hf3bj9SjNJ8/1pWgPBogPD6JxYEadynMf3eESqaIBzFUavHc5kJZr9lcniPBxJ7u6C82tSJbiQw6WpomPjiWnrofjdzjVRv0hFdkRFNQqm2C/uWZ1eoT8zCy6x9/EC9Btr1KZk4qXm4u6v11T17A6wn11qqO7PB2fkATGlbZJ8qMwGzW01xbg5GDHixd2+AVH0dw/pTqi72JpqIkQPz8K67u5EAX+UgiTrpp8XBztefnKHt+gKFoHpq31TbC7OkNyVAAxaaVobPuZbM4NkRQZzEtRV+2FU5ySW8Hq/psRmgvtFqX5eVQ296G17QV5fqKhr7mCAG93PEWdziptFA6dcPqEu9UvnMoAb18hlq1C95fCxtIkyUnhFDV3c/KOLUYMRztU58SRVd7A/v1RThuarUWyYv2JyqxmT3f3Gv3+KiXi+xHpBer2J2+4YnG8m9gwH9VvdHL1IbO4ll3dmw4m/c4sFSV5VAtxf6PlTw2HtNYU4evjJeyeKAc1XSIfxDOFTZkaasPb3Yu67slPfvuoD41mZ5WClDAc7F5i5+hKVFI2k0vvWZZ2fUprZS4+Qvw0Dy3eKdfXQhj11xUTGZ5Ez/TNVGgLm4vjpMUGYG/3Cjs7J/yCwqnpGBS2902+X5h0DHfWkZ5bJcrKTV5fMjfUQ1xwAM4uHsSl5TO9ZN2WzKxbI9LXi/T8GrS/wH4EZc14R20ens4vefbipSjnnqTlVbC2866lOBZOtqbw9/Ihp7yVs5sRUhsGIWIL06PJKmkW5f5GY12xNNlHsK8HL54Jv8LDl9yKRrbvbSu0uzFHaV429e0jr0deT/UHNFXkinrjhLtXIGV1XejPLMKsnTLSUoq/fwDto4vqrKobPqroVFDCnB/tb7O8tMji8ip7Bxp1YfhbKItoj/dZW1vjUKvn4uJcHY43mc5se9WJe11azxlObvdsX6E92mNlaYHFpRX2D49t+9xYuDCfcqzVcXbxQX+yRCL5O+Li7BStsElG2/Svd6HdmlejZ4Yn57F9bOT81MDO1iZLi8L2LQr7JI75hXlWN7c5ORV27cKM9lir3tdiM4lKIAWNsJfKtVu7B5jvDemYTQYMev2d0OUKpyY9m+trrG3uYDIrDqKF3eVR0mKCic0qt0Ve/aUgbPf5GTqtFtPZ+WtxfRvFsVG2RDgxKnuZPtCe2Lg6PaahMAU/4QD1TW9w+1Il/ZV7GNT9UB++x7Vol4wGLSf3rlGiqWuPdW9tMm8S126sLbOxvWcdnRbCd2txgig/L7LKmjh+h2MueRtlSp5Be8jq8hILi8vsHmq5eE9eK1gM2xSkxhIck8bMhkaUJIvtHqIOLi2zd6S9439cCj9DrztGpzfeyV/98ZHwKRZZWd9CbwtYcoMy/VPZEsdaz95weW5iZ3OdlbX119FejftLZCdFEp6QxZotYMcvhQulfuitZf/6xkDdQ9lz0WgQdchoetivE1iDqZXi6RMk6uDyneuuLy/U7+sMBq7uP8MifELNvmo/FbtmPLvrxylTo/XCFp7e6UwSNvLUyNbmmsiHTQw2m20Q4rY0M57A6HSWd3WiVEhucyXqwaFod5ZEu7O8IsrvvTL/EFvzQ8SFBpGYU8HRnbyxcKbYR9Fmnp6/qSOK/645FL66qM+LSn0WWuD+npSKXTeeiPIgnn+7OCkjoJq9HZaV5Si2gFSK8OmvyxXiNZLOkQX13C8OZT9wo46tjVUWFhZEOd9+y668hRD8dbmxhEQmMrJwdyT6+uqKE72oj0IjXd7qZTYLX8T6jEU2tnaFJnp7roiyP69W0Uv365PwObbWV1jdUPwWq2+h2V0SvkUIEaklbBzcHWH+6KJTIpFIfhWIBmRisIW42Fi6Jlc+rmNzbWKkq5b4xFT6p9d/OaOcf3OE+F4aJy0+jrLGfrTvGSX7ObgwHjPQUkFcUhpTK3sPCmjJ3xKLOtU1NTGR2s6xOz3wH4Pp/jaSk5JoHZi840T/2lC2bsrLSCCvupU97cPT4X8+LMyP95CSkEhD9+QdZ1zyH8ci2piehjLiEtIZnreut/yQGDUb5KTEUVzbag0E9yviaG2cFNGmVTT0vB7l/1AoYn9mqJ342Hh6pzc4v9dZJEWnRCKRfCDMpmMWZieZXtr8qM6NMsq6ujjN3OLSr34qmbqeaGGW8ZnFDz7SeGrUszA99f9n7y+85EjSPl3w79q9u2dnZu/Md+/H81EzVVcXl0pMSUpmZmZmZmZmZmYOjsh81twjUkpUVfe2SlKXPefYyQx3D3cPg9fenyHTcysiHW62Lkv++liMWmamJkR6z6N7j3n/zGRgZXaayel5jtQegp8u52fKvLJZhkbG2Du+uarmu0QZbadsbTQ6Mf2TT4e/NqcHm0yMDTO7vHlj1Me7RN1eZHud0ZEJtg6O328D7/vgzMzSzDhjU9Ovh/j/WJhNWlYWphmemkNjuPlsKTolEonkR0SpEPUGo2J8bUd+fJRhNsoUhfNz2bemoMSDwWjE/CMLP4vIC0aD6Sfdy/U+UIZFG0QZPHuPEa/YAbNRpL0sgjbORVkwYBa26cdESQfFFpplQrwTlCGxRtOPbONE3aoMjTf/lBtUz0XdIuLg+hDld41Sl5pEel+bTvoaKTolEolEIpFIJBKJRPLOkKJTIpFIJBKJRCKRSCTvDCk6JRKJRCKRSCQSiUTyzpCiUyKRSCQSiUQikUgk7wwpOiUSiUQikUgkEolE8s6QolMikUgkEolEIpFIJO+Mt4pO8T/imAwyyCCDDDLIIIMMMsgggwwy/NnBpinvFp06nY6NjQ1WV1dZX1+XQQYZZJBBBhlkkEEGGWSQQYYfFNbW1jg9PeXg4GBcfL5ddCobfB4dHSkXySCDDDLIIIMMMsgggwwyyCDDnxUUTanVasf39vbePqdT6RKVQQYZZJBBBhlkkEEGGWSQQYY/J9j0pFxISCKRSCQSiUQikUgk7wYpOiUSiUQikUgkEolE8s6QolMikUgkEolEIpFIJO8MKTolEolEIpFIJBKJRPLOkKJTIpFIJBKJRCKRSCTvDCk6JRKJRCKRSCQSiUTyzpCiUyKRSCQSiUQikUgk7wwpOiUSiUQikUgkEolE8s6QolMikUgkEolEIpFIJO8MKTolEonkJ8a57e9Pl/cXAzLuJQoyH8g4kEg+Gv5KhfW9iM7z8zMOD3bY2dvHYLr6OLNZz+7OFvsnGixnP+xXrs/PMDYyzu6h1nZEIpFIfizOOT454VSrs31+CxYzJ4f77BwcqzbcYtSztbbMxMQY80tr6IwW63VvwWLUsb68wMT4OAviO1rDGxt6crjD7PQUy2tbmK7d6nh3m/2DQ0xn1nvsi88HRye2sx8v5yYDezs77B0cYfmeKsOgPWZndxedqHfOLCa2V1eYFvE4JsLoyAgLy6tovy8NRP11ItJbq7ua3mcmPetL80zPznNwcvWcyaBlZ2ebE51BTXft6SG74j20hu9Pb8kPQ3t8jFarR2Tv7+GM48MDtnb2RRm5erWSrhqtVnGMbEfuxmLQcyqeaTBe9WH0Im0XZqfV8nwlfcU9tSeHIv8dCL/HIrKRmcP9XfYPj7833374nAvbs8emiFOtwWQ7djtmYfOU8qrYnqs/W9xD+IXbSvm8Fqe3YdSdsrO9ycGxxnZEcC7K9NoSU9Ozwsae2g5aORN24mBvh0NRNhXX0qQ7Fs/a4VRvsF0huQ3FTu5tbzA9MSls2xJHGr3tzNs4R3N8wO7+AXrTOefiHvviHlOT4h5zSxxqvi/OLeL6NfX6JVGXGa4VEO3JPnOijC2tboj7Xy1jupMjtrd2RD2nlO0zDnY21brB/BGXMbPRwNHRMWbL99cXZ0alrtnhRNhC5fefHu0xL+JqYnKarb0j60W3oKTX3MQE42NjjIv6UAlTU1PMzIqyJNJRjT7hv+xtrjE9PSPuZfVhLjCL8rW/t62WLyX9TXpRPsV7nOqMV657L6LTbDqltiyD1IJSFrcObUetHOzOk5kSQ2lrP8e6txuvCyqTIvF09qVrbM12RCKRSN4tSkVwsLtJb3sdcWk5tA1O2c7czc7yNDVlJXSOLnB6csBAaxUJ0REEBQcQEBxGUU0bOyd3V+on+xu01JQQGRZKQEAgSeK5sxvCeRNWfWdtjvLCbGIiI0hKzaRzcBKjzafW7K+Jc0V09I+jE/XWmf6IgbZaSsprWTv4uBvrVsd7CQ0IIq2oDt1bHAuTZo/uphrKapo4Eo6x5mCV1EBfXJ1c8fYPwMPdjbT8YhEfl5zYS6jpvbNOS301yRm59IzO2c6Ic3oNI50NZCYnEhkeQ2FZHas7x+q5c7Oe8b5misuqWdg4UI/trU5TXVFCc++4cK8kfykWk5Gj/W1GelpJi0umY0Dkedu5u9DuLlBfWUZN6wA605nwo0zqPYZ724hLSqddlBGj6rDegihoRp1GlLV5KvNyyM4uZt6WpkpjxPH2Ki0VhSTGxRIZk0hDx+BrP0Z7tENTdQl1wrc5Eo6Y8A4Z7WmmtLyKmdU99ZqPFePxKrmJwYQm5TEp7NGdCKE9OdBBSUkFY/MbtoNW9IfrZEf7Ep2cztw1v/AmZ/TV5RMc4E1tz5j1iChncyPtZGWmEBkVQXZxBdOrO9ZGiDMTS+P9FBcVM722p4p83cEa1eXFNHYq+UC9heQaZoOWqaEuUhNiCQ4OITAwmLTcMubX963xegf6gw3qK0pp6hwUAlPD7HAH6QnRBIcEERAUTGpehSg3t9/DYhL2sr+N5LhI/P39CQ6Ppbq1j0M1kc452dugtjyPxPhoouOTaeoa5EQpTwLNwSYttRVUNXSjVcXoGVO9jRSVVjO18nGVMaVzTq85YXluivLiQlE3VbJ3uYHlNoS4H+9qpKSynuWdQ7T7K9SJuAoNVdIukMj4VPomljCar9c65yxPDZEorvH19sFPxHtgUBDebq94YedEeXOPELwmFkb7yEtNJDo2jvTsIqaXtzBbCxjztvI1vril3k93tEVdRTH17X0cnL6xyu9FdBoN+8QEvOSJRyBD88oLvmF9qR+7x18QmFHO7qUXfRulsSG42LnTPrpqOyKRSCTvjvMzC4sTg2TERfDws9/yD7/+kuSKTtvZ27EYj6krzSAwJIbJ1QPhuM6SFu1DWGQsJWXlxAa68d2DJ6RXdNzaKmvU7lGWFcvL588IiU6huKSE6upq4aCdoDnapzQ7iYCwGMqqqkiNDccvKIrpHRMWg4auymwCwhPoHl+0VfQWURk3EBIQQGFtNzprzfHRYTGeUBzvy3//H/8Xz/yS0N4lOkUFPi2cjzAh1HOru0SVeM7mXA8vP/0UD99wcotKyM3JorG9i73T20T/GbMj/WTFhvLFJ7/mP37/DZnVva/PrU52E+zrQ3JWIRVFWQT5epFV1c7Z2TmHG3MkhgeSkl/Htk3g64+3RHolEBgRy9zu1R4ZyQ/jXAiJjflRClMjePX4a/7xH35BfGEjb+1DEd9pE2UhOCiMlsE5xQFiY3FKCKZoHB9/xd/922/Ue+juKA5mUZYG26pIifDkN//27/zxK3s6p9at5/RHtFbn4esbQH5JFfmpMXgHhtE4tCRe1iLEVhMB/gHU94yjtxXwpfEuIkMDyCiq/WjLoCIkB5sL+O2//H/49X1nehbvFownm7MkR4QQk1LA6sGlcibcztHWYn7xd/9PPvnuBSNLO7YTt6PfX8Ln8R/4u//1d6SWt4gj5+yvTRElRGi8KINVVcX4Cec5LquaI9MZJ7sr5CVFE5mUz+bFKASzhvqCFIJCoumc3LQek1zheHuZ8qwE/IMjRZ4uJSs1mntffU1ochGbR3codSFO+hqK8A8QZWxoFs3xJuXZcaLeC6eguISM5AjuffMNIeIeG0dXba1Sr65M9uDu+JJXXoFk5+UR4u3C4xdC+LRPYNSf0lNXiKd/GBWVVWTEh+Er/u8eWxK21sJYVw1BASE09M/ZxBDsLo4QLsRUemE9mo+ou1N7tEenENB+znb89je/5A9PfFjevbunUmkQO1yfFWXAj/SiBlWkby8MkZuVSkZeCcW5aTi/uM8z11BVgF+NiXPhjyxQU1xEfl4+hUVFlFWU4+PwgN/+4RtKmgc42JoT/koIoTHJVFZXEeXnTmx2JRvHZlGU1ihKiyMsJpPVPWt9pjQCtVZkirwTQefwrDq6QOE9ic6Du0Xnct8V0amofYvlTGSoM8wmI1qtFoPxamavSIzAw8mLzjGb6BTfMRqNmIWaVyoVi8X612w2qd/XG652915wZjGjE+eV4TWXh8ycie+bzWYRaRffOlczuHpf2xElwS3KNeI9LwejQS+eqcN0o2VBIpF8rJhNerrqygkTTqXfK0c+e2BPUnm77eztLA61EyGcorTSJnW4kObkgMWFGQ6Pj4W9MqHbmSLcwx57t3DWr3c+Cqd1tK0cFydnkkWFokxLUOyLYnMswm5tTPcSExlKUdsIemGXFgZaSIiIpqZvldWZIaJDAims7+JQ+8Z2GvZXqMyKwSc0kdnNt1RmHyznjHXXE+R8j3/8j1/iFp3Piek2yw4mIfKKksMJjogTQlyRJSZm++p4dt+BtvEFLLa4VOy8UlfcxERzaTExgcF4uDrw7VN74ex2WU9ZtPTVZhEYkyXqs11xnz1ReacRllrIxuYmXdUFBIUmMLq4rVQTVkQdNTfYSmiAL+klbRivDfOUfD9mo9K7XE10sCeJEf58+sk94gsaeVu//cnqMHHi2tjMMnaVLn+LgdGuVsK9xLEwb/749QMSRfk6vSMfaY92KU6LIjk2BMenz3jwzIPmsRX1nOZgkcy0CKJyq1nf12LemyI0LpaUinZOtubJSogkIauMreM3Q6/PTMdUFaTiExBO//TVnr+PAyH21mdIjA7kl//299xz9GFw6Y4eJSH4e+sL8PIJoa5j/LUTqnAgxGh6lCc//9//yP2Xnjd6QS9zbtEL4ZHLg89+yc9++Qdyy5vEUTPTA828dA1nemVbtYmFydmEB6ezsLbOUI8QI4Gh9Ik4vqw7tuaHiAryJSGzFP3tSf6TRmnMXF2cZ/dYq/rUep2GnAgXXji40Tq8ZLvqKpqdJeIDPYnPLmNTlDH96RHL8/PsHZ2q99CJz1mhnjx+Lu4xsmD7lhWj5pD6nGievHCnY2RJtcebM90EutsTGJvH7PySEDdhpFb3cagxcjTbQ2J4FOXNo6wtzZCfLMqYEEInl6btnZ8ZaCpKEuInmtbxj6eMbS/PkZeYRICnFx4uL/jKIYjF3bsbdJTh4h0lyfgGRtA3saxqE2VotE6vxyTiw2IS9rIxly//9A3VPTM3RtgomsYk0kdJI8UfMeh2SQj1xi88jVUhdueHG4iIiqV5dAODXsdYWyE+MemMzi0z1l5FWHAkTX0zl3SSsLdb00QE+hEv7N62rRNR1K8/vug0CdEZF+TAc68ghheui85+HJ99TUhWFftaM0ebCzQ3NtPZ00drfQVBvr7EpeYzubJr+4ZVdHq+8qFHba06Y3Gsl5yMHIamVjBo92lrEt/v7aeztY5wEQHhMSlqolyOdP3pHp2N5UQE+eHtE0BKXgVz6/siwwqHbm5YKPtaphatLZpn+mOGupsor6ph89DaUnO6t0ZzVTULy8vMzkzR1NRC78AAZXkZBPgHkllYy9r+93SNSySSjwLFQB8eHLC/d8BofSkv7N1IKG21nb0NI41l2Xj4BNE5tqweUcTNVT/ngPQQD146+bN4rfPLpD0gJ8YHv4hERtavzhdUbN7aRBcRYcGUdk6oPZmro51kJcZTUtdNaVYSsSn5rOxah3u+Rgif8Z4G3F+5Udc1fmN+24eNtWcjNjyE+Eh/Xrm5EZxcwIHu9uprXYjyIF8fUvKrrXb/TM9Ycxnf3nOje+6HTMuwsC8q/IO9fboai3F8JcR/ma1nW4jO7qpUAuKzGV9RxPsBTZV5RKXk0t5cT0xYKHV9c+ivRe/p7gq5SeH4hcSxdijH9/25KA7VsXCKlXlEB/Pd2D9+QUxeHW+rZXtqC/H18qOuc0Qte0rPyunxiTrHcHe6DXt7exIKajk23q5AFKdsb2ebo6MDtWX/uZ0H9UpPpkBzMEdacijRBXXsnIh8eDgrxEw6SXlldNYUERIZx9javnrtZSZ7Ggny8SCnquP1cPiPBd3xNpW5SWrvR6CnPT4hkXTPWP2k6+iPtsmK9iZS+G8L+2/KqfZ4h9qiFEKjIgjydMLbN5T+8UXb2ZvMDjYRExNOcKA3ni4uZObXiKMmJnrreOIawdyWdbhzWXYhcdFZtDZUk50SS05Ftzqf/TLnhiOqs2MJCo1iZO3jn9/+1+b87OxGI1xDZgCOjs7U983ajlxClKf5oQZc3LxEnTJsK2NKB8zVe9SmhvH8qbhH/7TtiBWTRtjOghjsX/kxMLOtHjtYHibC7xVhCflMz80LmxlEet0QGlFGNYt9ZCYkUlop/PHCAmJjk5nfudnstD7VRbC3D6mibN9esj889Bqd6l/srsxSkRnBd6+CX+ft2zjaXSQqyJuYjBK2Dm+zggam2st49MSR1tHF742H+d5KXFw8KGwaUq+d6qkUZTSG9qkdtYNuqrOMiNQ8GupE+UqOJ6u4Af2NyekWKtKj1J7y/lmr4P9wRWd2jXAgLKyPtfDw22/5+rkrhaVlxIcH8fTBd7gEJLByYHW+KhLD8XELZGRhTwjEQQKdX+DkHs7Ewgbmg1levXjKFw/tSMkpICspFrvHD3jm5CeevaNGpmZ3mTxxDxdnZyFIE0iIjeKVkyMewhkYnt8Q79CEg50TGeVtKJGj21vE3/kx//GLL+kaX1crrumeGlFhedI5NE6HEK9ffPYl9t6hFBTkExHgxbdf3yMivZzdo7e1w0okko+NhY4anr9wIb7kbtFpOVqjICWGoOhMFnaui0YrG9NdeDvZERCXq867vMzJzhwR7i8JCo8hv6wSf083/ENiaO6dUEWU/mRbGP5ofMWxsupq8rLSiBO2rKiwiHDFEZxaURdWUByAy2zMjhLl7UhyST3H+o9H+BhPtykRlZl3SBIj6vyfUHxjMtm/HnEqFkaaivALCKWiY8J66FyIzsZcfvmzX/Plwyd4+QeSo8zD3P1+x3O0owIHR6c3olOI9/nhFry9PEnOKaSyLI+EuFjSsvJEfRNNYk6VWpeJC0WFa/2Kwrn+mI6KHLWRs2tq+6Ne6OJ9Y9wYFKLzGdG5tXeLzrNTCrLi8QqJZ8jmAF1Gu9yD/Us74oWIuUt0XqYyO55nL9yF6LQKJJNun/riNNyFqC0oq6E4J5W4lFQycrJE/oyiuGlAnW96vQweLY2TERVIeFIBu6fv3P36q3Fu1DLQXIqnuzeNvWNUZUcI5zKU9snbGnHO2V2eIEj4WAXVLRhs0Xtu0tLfXIaXly9t/SNUZUTi6x1I99jVHrALjrfmiAoU4qGoiu72WiK8XEjOrhBnztlaHCFA2MWE7CJqqkuJjoonLSOXzJQkElLyWTs0iLg/Vxvl3nDGYFsp3gFBFLZN2o5J7sJ0uEaEpz2eQTFMrt0UQBaRJ9qLk1W7PDx3+5Bl7d6SSDdH3HyjmVh+03mkoHTybC+NESPSOCA0jsKiEmJDfPHwDaRtZFEIsQNayjJw9Q2jsqaGnOQ4EpMzyMnMIC42kZqOUfU+yujEyyhzG1MjfAmPT2H7h83a+3DQ71OdJUSnY+DdovPcxOZUpyg7vhQ19HFqa71SFnDaXJmjo6WWopw0IkJCyChpYe8t60YonOt3SQxyxz0glslVa+/q2kw/MaG+BMckU11XS2J0OFn5RaQlxJCcnsvEivXdLNcaKaZaSwn0DaCsZUj9/F5EpzKnMzbInufed4nOrwjOqmZfa2F1qIY//uE3fOMcwvz2Ltsby5RnRPDlZ19TNbCI4ibVpggl7R5ASWUdqTFBvHLzpWNkDp3BhGl7hAdffcIfHrnSMbasrtjYUJzF/c8+I6WiB61eR29NNk8ePSUhq5zVzV32djZpryvA/vkTQtMq2V6bx184g8Hx+cKpMbO/PITDwy/4+3/6NeVNfeI5OhoL4rD3jGJseZvWygx+/stf4x6dy/buHmtzE8R42/Hdk1d0Tsh5pxLJ3xJTzRU8fe5MwltE5+HyCMnRwcRmVbJ7y8TD7cVxYvzdcPIIonv6Zk/B8dY4rvc/59NP7xGRnEVGahLe7k68sHemrncao6hkNxYnyMtMJjJaCNOSSqrKy0SFEE1GYRX9vW2kJcYSFBYjHMRxtZVY4WB9juw4X0JTS9i6Nr/mQ0UZXjfSXoWffyidg7PCUdkkIzEUf2Gfb23SsxhoKkgiJDSe7skLJ8fMrnBuCvKySUhMEJWxDw8f3Mc3Op35tw41PmNACFh7B6vovEhJg/aIzvpyEuNjiE3KoKa2lrL8HCIi42nvGaS+JJuw4CASs0pYWLe9w7mBqa5q/AP8qOicwCBV51/IGccLXbx8+JSY3Lt7Os9O1kQ+CSMoOYeZzWu9/ljYm27lxfOXQnTe3dP5BgOl6VGq6GywiU5lCPze2gKV+RnEijKYkl1AY321WlbjUnPoGxogNzWW4JAwims7ObStAnp2tCoEbBy+EUks73ws83vPWZseID4siJyqbuHkntNWIPywkEj6568KCZXzM5bH2nBy8KWqZdB2UPh7MwPEhgeRXdOrLvzSmheNn28wg9M3hatZf0hNnijHoozObp2yO9dHpLfS01mrnrcYtIx11ZGSGE90bAJ1jc2UFmYRFZNAZVOXOpItXJTBWJH+E4ubamOdwvRwO15C6CYWKHNDJXehPVijNC2CZy9dqGgfRH/LHGSD5pCCxEDCk4uZ27xpjbV7qxSlhPLMzpmK1sFbV2s3nWyS6O/An778FgcnJ77+0x944hzAyLJil8/Z31ykODed+LgY0nJKaaqvIVnY3bScInr7e9WGviB1yG0vR7YVoizafYqFbgiOTGBi6+MaVWI82hTxHvpW0Xlu0jDZXoG/KDvNfQuvR0xYTDrG+1tJEIL7+aNv+ezLB+TXDwh/wXr+VoTwX1E62l6+JK2kCa2tXlJWix7tbRFxHU1sYgpF5dXUlOQRFRVPRV0zXa31RAQHEJOcIcrv8uvGncOZLlH3BZCpzr0W7/r+FhKy56V3CCOLVyeMr68M4vTsm9eic3mglmfPXhCXX2+7QijuyS5cHn9JZsOIunBEc04Sz//0Rz759CucfEIZXHhjsPSbI9g/fUxgShnHtl+2szBGqMsDIrNr2NpYJyvSh1d+UYzblLqCUWT83Cgf7B0DWNrZpyYzjMDIRAZEZCrziMKDvPHx9SM1v4z5lRXSwz1ILm7iUKRmQ0kqD586Ut49Y7ubhb6qVOxePKNKOIhvS2+JRPJx8UNE5+ZMHzERfiQU1HBlJOWZiem+VqIDvHH3C6N5QAjIW/zd441h7L76lK8euNI5tYZOr2N+uA0/x8c890lk3bbi6sHOGpOTMyzNz9BYWUhEdCINolLOSowmMTmNhNhI/INjaR+w2ibN7grlGTGExhazvnt7D+yHxJmoREc7q7F//ADvyAxmFlbYXOzC18MRB98o4ejsvV6k5QKlQi7NjCYkLImR+TdOvTJCRZmXr8y7399cJj8+kO8ePCKjov3GMLw3KKKz+IboVDgTTu/S3LSoDzaZGOohTXGKckuprxDpEBFLcko6vp5eZOSXC4GvxLUyFaSJ4LAAipuH0N/9UMlbOeNksft7Radpa560iACi0vNZ2rvuEFvYn2n7M0Vn9FXRaUN7uMvM5ATrwrfoqC8XZS5ebRAvyssgPDqB1JREtUeitnMEtWPTvENTeQpeEXFvVsL9wNlcGCbMy4GnDj50jy6wtrZORpgLL1+5UdExpjb4X0EI8tn+Gp65BlPbNa4eWp8bIszTgSdO3nRNLLG8vEx6iBsvXjpRrawsfGldDf3JHnUFCdy/94DkwgaWN/fpqcnH5dkDQoUI3T08tZbFMwNri7NMzS6zMN5PTko8aZm51NRUEhUWTlZqMuHKYkOZRSxsWRuXlsaGCPEMISXDKl4l1zlnc36U9LhgnN08KWno4lB3+3JdOpFOqVEeRGWVs7x/uUvxjI35ETJig3F186K0sYcj7c0uR4PmiNaKDDzc3EnNK6Oru4u64gxhNz2Iyyxn+8j6XM3hNtOTU2xurdNSmU98QhrFpWUUZyURk5gq6rpYvPxCaegaFXlR5CPzCXX5qYSHpTA093Et3KaIzrLvEZ0W/TE99fn4+ATTObT2etSM0nO8u7XG6HAfbc21pMVH4u0bSPvwvKgnrddcx6w/pTQ5GDsXZUeQq/N2rT2ni0zNzLK6OEVmQhwZWQVUVZSRmhhPfFKy8HNCCItKZHFfo5ZJ8+aQEKZBpBZbNdx7EZ0mwykpwc7YuQUyfm2Vsp3VYeyfPCKmoJkjwxlzvbU4OLiTU9FhuwKWJ3vxd75HbuOwKjpbc1N49Ov/5B/+8d954hnJ2ukbg6fdHMXxuSOJRU2vV7XbnB8mLvAF8cIBXF1aJD7AncDEHFFI3jhdFt0hdVlR2D9zZXZrh5WhCnVLg6zCclF55JNfUEhXV6MQognU1NXi5+5Ok3AYFbdBWRjAzsmXVtvCRufCURpoyMbN1Y6avmm1d1Yikfxt8MNEZz9x4f4k5tVwYKtr1aXh+4QN8fYkMCKZgamVOxukjjfH8Xr+gKjUMl4PADUd05QbxWcP3Zlcu2pHF4ZaSY6Pp6yhm5rMDOLDU1nfOcGs3SLW14207GLVDml3lylLjyI0TojOvQ9fdCpzyHJiPPmPf/s3HrxwJiAohEC3F/zqZ//Ov/3id8TllLNydLUKOzdrKM+KEeIukeH56z1cbzhe7CfI3Q6/mEw0d9aCd4vOC8zaA2tvV1I2HZ0iHaIDqOxbUhsTuspTCQiPpMO2aMzCSBNBIUJ0tkjR+ZfzA0Xn9oJwiAOJTM8TovP6VX890XnB4eq4cMQSyS2qpEWIz7CYeAbXNVgsOkrjg4hKLmN+T2S0sz0aSpKF6IxnYfP7tgr5EFBWJ83l97/8D377+QP8AwIJDPDj609+wb/++3/x0i+WscVrQyuF6Jzrr+O5cxD1ncow1jN6qjL59Ff/ab1HYBCB/j58+ftf8q//+r9xFPeYWH6zINHpxgT+dl/zb//1G146exAWHi78xO/4z3/+e/7w5SNKmpU5fpfKz5lOiNR04pKyaGtqpEgI/bSiLsXosj5YTYB4XkPvlFp+l8YGCfYMIjmzzvpdyRtEuq3NjZIY6ounfyh1ncM35qZfRneyT3qUF1EZQnTuXYhKi9rAkBDmi3dAGI1CCArX/lZ2Vsdwe/YFzuGZbJ9eXKSlNCWMew/taRqxrodwwf7SIPERkZTWt1NTWEysfyQrJ0b0p9ukh3qRkJLNtrJf67mWurxkQsOSGZr/uKa4/VDR2ddQgK9PEB1DK3dO1TgRNtDr5bcEJpSwceGIXEFZbX0GP5eX6jSf1Rt28oJzeusKiBa6qaW5kdz0WDLLatELB2Z1ogt/bxcqh5bVdD7bGCQqQojOkvcoOpV9sRRn6eHDJ+RVd7zOgOfGE7qqM7l3/wXlHcpeWTDdXcPLl67iB7VZLxIsjitzn74lt8na09mQEYeXnRNBweG4uroQlJDL+qE1Yxm3x3B4Zq8uMHDhUm2IAhDl+5Q4ITp3t7cojA/guZM3LcNv5hHsrUwQ7euMk1csW0ennJ0sE+bnyStHO0Ljs2gZWGBzdQYfF1eChBPn5BvHtG2vrYq8FJ7be9FoW91LEZ29tZm4OL+kVopOieRvCkV0PhGi821zOk/WxkmPCSM6rZxtxTSdWVidHiTS353YnFK2XlewbzDqtezt7nGqM6hiKzXYhQAhnGb3rMPyzIer5Md48/BVGAs7b5xVZf5nZnw0qfk1bG7vUBQVS0JELlq1aVNHepA7qRm5KHc5WJshI9KDiPRSdk7eutnEB4FOc0RvWzVpqUnExsQQHR1DZKAbn/zmF/zsd5+TXlrPhrKIy2XODLSWpBEYEkv7mFXsKQtBKSuVX9Z5k+0luDm+EOlRg9ZoQXN8yO7e4bWVZa2i087ekaTbRKd4Vk99MRFhUXSMLTE/2UtEqAfttpWhxlryCIiKolmdZnHGREclfn7+1PTMiOd8n9CR3M6F6HxC9FtE57lmk8zkCALjMplevy7urKLz+XNlVNUb0alseH54sMfB4QmWK4uhWEXn0xdut4pOs+6QIuGIxaYXMrOyTmdtLoGxySzaRrA3Zol8m1jM7LaR81Nl2GI0flGpwsn7GBxiPctTA2SkpRAbG0NUdBQxMZE8/vJ3/OcvfoVLeBoTS1enTSlOqrLYmYudF5VNyvDaM1Ym+8jJTBXfjRXlOJqYqHAefvE7/us/f4ZneLrwpw7Qa0/Y29tjd2uRxqpi4uPjiYqKJi4uGk/n5/zy3/+FL+7bUdM5ge616BQCt7eesJBwqoQfuT4xQE5kJHn11tEduvl2QoKVHtcxtfxOD7Xh6e1PSumbjg2JlaOteXKTw9Q5mtNrdzfYXWDUHlGSHEpIfAGz69bm0aONWfLig/ALT2R64+Y9ztQyts/RiYbNlSGcHn7Ki4DEN6MRzIdkRvjw2ZfPqBt8U9YMJzvkJSvTGfJY3d+nqaiQULco6xQLs47iaF/iE9PY3BfPNB1TkBJCUKTw0/c+rrGGF6Lz3tuG15q1zHRV4+cbSL2oSwzKTzw/s9Zxl+a3bi1N4ilEZ0hSmdA1wq84PRLla1/4BlZVoqz0O91Vrm7Lll3Tow6bv4G479ZsP2EB/jT0TLI6O0hyXBAFLdY1Dg5WxwgRPkZRzwJa8ejDqTZCAgPIrrRquPciOsVj2ZntwcvhId89daKguo3JyUkaynJwef6QVwHxzO9Yhz6Mt5bz6JE9KUXN6meFhZF2XJ9/RoaygpWIk8r4YGGA/OkZW6SrPo/7336FX3we67sHQnSO8uLRM8Izq17P91mf6ReZ8x5hGWUca3TM9dfj+OQxXkFx9I5OMz01QkF6DHZCyObUDdrm2pyRG+7Gr//t73klDOK8eL2TnQ1iXB/xu5//Ox6J5ezYelhLMuN58MSFWiFMFZQhYV2VKdjbPaKqe1KKTonkb4jxhlLuPXhJVEGj7cgtaLcpTo/DNyyJOWXLDuMpjfkJ/OmP31DU1Mr8/BwzMzNMTU2xKJxUje6Utak+YiNjaOqbQi9E0EhrCS4OjkQmF6nXKfbS1e4FyWWtnNgqDaP2mJrCVHUv0MGlA8zic2tJBiH+IXT0DTA51EJEYAjldd3q9atTg4S625NV2SoqmB/B9L8LTqYJ83fDNSzt6tDl15wz3lGBt18gRc3W+WQmIegn+jtoEHE/Oj7JeH87QZ722Lv60ju5geFUWbAii8iEHJaUlvLXKC28+Tx+8pzY4jbVaX3NuUXULT3EhimbnwvhKk4ebEyTEuVPQn4DwxNz5CZFqnNe5raPhaNwQnNJOj6+QQwuHKgb1kv+Es44nu/k0deiThf1/J2D5871lOUk4+kXRd+EdZuTN5jZm2rmwYOHRGZXcmRztpS9dHPTYkkrqL22MrKBwqQwvnvoKOr5edsxK8r+dEPNxQQGhdDQp6zOecZEZw1B4rPSIzc+MU58aAC5Va0ciPx6tDRCcrgf0RmlHN61QeiHxB35tDzRh1eevrTfsfWL0oMS7uZIZnEdJ7f6/eeUxPvh5ORJ/4S1R0uZoxkXE0Pn5M157vMD9XjZPSFexNtrhEO8vzZDYmQw8Zml6l6S+t1F8pV5ftHZTI4Nq/O7I2NSGLFtYt/fWIhPQBBVvVfTUQIjbSU8/+ZT/FMqmJyZY3Z2hunpKaZmF9k/ulnSFF+3pzIDV79o+iatI/2UHrjHX3xKSGopM3O2e4j6a3p+kcMTLZqtBeEzJ5NZWsfe8Y4QrSHc++4RSbllTE5N0lyeybP73+LkF8/SrtWLV/bLvdgLtEktYzDcXEaIly/1or4cHuwlMlAIofJmjrQmDPvLxId4EZWUzsFHUMQuYzjcoCjRn8+f+TCzeXP1aytmtuYH8PH2Jru6gxNFdQoBOT7QTlNDLRPT00yMDpCZEMLTF440DcyhbPjY21BITGwyvba9ho2afcrTgoVde0HT8IK45hqifB1tLZEXH0qU8EO2DzScHa+QmxJFRGIWYzOzdNSVEBAYzPDqobB8Ig815Kk7eNR0WRfxe0+iU3BmZKKnQVT0jty79y33Hzzg0dPn+IUlqpPIL/LFdF8jXl7CWajtsR2Blel+wv3sKO2YQCcMYHNuMqEBEYwqmxIbDqnJiRFC9QkF9Z3sb0wR7BtAelnL657OrcUxUsNdhJCtFYkjfq5InN6GMnxdnXj8+AnPn7/E1dufwpo2DjRvvJj+6kwef/UlUVlVqnA80x/SlBfON599Tn7jsG1D6XPqy3Lx8AmjffxieK2ohFqKCAzwomV4XopOieRviLnOelzd/cmssQq5u2irysfDw4emoUVh/jS0FCfy61/+jm8ePMLByYEXz57x8NFjfKOSmVxYZnmoCYfnDuSK+2qUfQP1RzSWZOEsHK3HTx7z0smNpOwytmyjOhR25vrJSIwT9rJLXSlTsUf7mzPkpUbxyvkVzm4eZJc2sranF/WHmaHWcl65etMyNP96M+2PDs0CqfERhKUWc3KHcd1ZGibc34u4zEK1R/FMWXmzoRh3p5cizhWb/wKvwAhaBqxbzuj218mPC+D5qyAmbHO/rJwx2lGNt48vObV9InYvYToVaVxAfFIWQ7PW4YVnxlNGRf7w93DHwcEZ35A4ukdm1O8dbsyRFhVESEQKe5f2T5X8uZxxujKA5ytX0kpb3rpP51hrBX5e3pQ09V7ZJ1LpHTtc6MHD3ZOM8pbX+72uzQ0JR9YBb2W435XMZaSmIBU3j2Daxq8KWNPRGtkpCWQJcbV9Yi2FGpGfaouFMy7KubOrJ9GpBSxsKCOjzoRvUC3yhwelTf03nbyPiMa8GEKi4hiYt251cR2TcGgLkwMJjk5lfPWWVaKFQ1ufG0dwSCRjc1bh2lGRiaODHRXdc699wguWxzqICvAmt/TNeh9nFgMjHbVERyfSN3ohIk3MDHcQ5ueOk6MDXr6hNPeMo7QhWHS7FKaEERQZz+yOLIPXGemo5OHvfsVnX9/nhZ0ddi9f8FTUPY+d/GgQcXiDcwurE+24vXKhtKVXbUgb7Kzg6z/+nk++uMdLe9s9Hj/muVsA7SNzHK9NkhjkiUdYMko/6PHOMvnJEdg/fyT8+AfcF3WiX2SSsKlv1mo52lqmIDWGnPImtcdO4XR3merCZF45OeHs7qE2OixtWnsG5wab8PXxJ/vSNL2PBdPJNrX5CTj4xrBo64y7jZODDZJCfQgV4m9N3R7tjIHmYrxfifR68owXL+3w9A2momUAjdoVaqEyS8Szgzt1fdayoj3aIj8xCBffSKYuFry7jEXP/FArERFx9L7edlJZvb2buGB/kbYO6p7DlU19GISBVeaA5sWFEhQay7htpeL3JzoF5xYzuxsrDPX10NbaxsDwOJt7R1cqA+3JIcvLq+wevDFSBu0paysL7B1r1R99uLMpPq9xalupSie+Mz0xyuLqFnqdVj23tXf42qAb9Rq21pbY2j3AbFve98xsnYDe3dlOe0c30/PLaK+1/OuODpifnWdz56KL28LpwTaz0zMcnF5I2nMO9nZYFs881tiGqwljenq4y+rKMkfiuit1nUQi+ajRHR+ytLzC9iUbdRtbc4PEh/sTmVHK3omB471NhgYH6Ghvp6Ojg3bxt7VN2MHRSfaPT9VFFRbmFtjeP37dC6YMX5qbGhHXtjE0Ps3+6dUhsRphZ9bX14WdubQSrXAEDrbXGBzopW9wRN2kW7ndwdoU2fFBBMZmsXppPvtHh1mnLgi3trX72p5f51x/SGVOPP7BYfQKQSjqOTRHu0yMDNLe1k5X7wArm+L7tsrHYjKws77M3MIKmmv1gOb4gJWVFXaup/eZkd3NTda39q8sZmQ26FiYGqOnu5uZxXUMqroXgr9NWW3Ql5LG4WtDNyV/Lhb9KcuLok6/VM/fhml3npToYILjM1ncvtqDbdYfq4vZqPewJYdBd8rq8gLLa9vXhj+Len53S5T7NY60V8ug0qC0urr2ZnEblXO0x7uMDfXR0zcgnEKrn2M+WicvOVrdq3Vq5a5ejI+Dwx1RBoXtOdHdNldMIPygsc4avLx9Ka7vvrF3oyiUHIh7KHGnscXp0f4WCwvzwte76TfpNcfCt1tmR/hxF+fOFVu3uy3eY8s2ncCKsorn6sIU3T09jM8sobeV6fnBFgJ8fUgvabrWCCFR0BztMzk4aPOLO0Q9Jf6Kuqete4BVYeduQ3+0SUaMvyp+ZreOMYhyNTY6Qqf4rlLPqfcQ9Vx7zwDru6KsGbRsri0Lf31DWEUrytzQqTFhm9tb6R0cvaYLztWVVDdWlzk40V5qjDjnRNR/I6Ke6x0YYkMIL6U6ONMfUZwaQWBEAkNLd4u2D5Vzi1GUi011BJTedLUuuoyyXc1QYwFevv40KlupiR+vEzZnanRAje/O7j5ml5X658JCnrMn/IL5hUURj9b63yJ0kJIWSyItDLc968wkxK3QN+vb4vwbS6sMkd5YEvqpq4uxyTlRZyrnztmY7iHQ25vMojqObfMo36vofI2IHGUVwb92mVcciz8XZa6P5aPaJF0ikXwMnJmNdDeXEhQcStfk8mt7p+wppti/10HdkNt28g7uspdvs3nCvl9y9PQMt1cRFhahLqjxt7+GzTkrk/0kRIaRVlLH6YWAsNU9NxzgvzrnV/aOO9ldpiAjnhhloQvZy/njIUTJSFslkaHKfL/RdzTq6O68dC6El1K+L5juayYqPIzSxm5MV0Tt3yYGIUjy0+OIFvl+duPdCIC3xeJlu6nMuy3PSiQqPp3JjTsHZUsEFyt9Xw531jUij08PNBEaEkp5W99rUWj1ra+Gs++p6NTn2P7/oSj74Cp13QUr451EiHpOWTH38lpTf3uco1UFfyiJOWWs7V/kaWsdd9nuvCuUOu4iSY26E2oKUoiIS2Z8fu11Oop88wGITolEIvkJoDnYZKC7nYGpRczv0ck06Y6ZGumjZ2CYY9t80L91LEYdM6MDtHX3snu5J/g9cLC5RF9PN5NLt2+gLnl3KPOWBnu76RycQPu61f/HR+kdmBsfprtviG11C52fAmesL0zR3t7B7Mrtw3B/LHRHW/R0dTIyLWyx7Zjkr4MyamCot5OeoTFRv7zH2BUCeHG831rGjn8aZWxzbozWrh5Wtm9fdOhHQcS77miPflG+xpSRBZfsrBSdEolE8iOiDN080Wh/hN61u7GYjOi0Gkzmn5a5V+axajSa18Pr3hdGvQ6d3nBpaJjkx8Qk8v/JqQbzj9D6fxfK9CK9Vovx0jC1nwTCIdWLMqjTvd+GH7NRj0a8w8WQeslfF4vZKGyt9sowzB+fc7SnJ6KM/ZTquTM0om7XGe4Y5v6jcI7FaBA+hu7GsHUpOiUSiUQikUgkEolE8s6QolMikUgkEolEIpFIJO8MKTolEolEIpFIJBKJRPLOkKJTIpFIJBKJRCKRSCTvDCk6JRKJRCKRSCQSiUTyzpCiUyKRSCQSiUQikUgk7wwpOiUSiUQikUgkEolE8s54q+jU6/Vsb2+zsbHB5uamDDLIIIMMMsgggwwyyCCDDDL8oKDoSGWP7MPDw/H19fXbRafBYGB3d1cVnjs7OzLIIIMMMsgggwwyyCCDDDLI8IOCoiMVTXl8fDwu/r9ddIr/EcewWCzqXxlkkEEGGWSQQQYZZJBBBhlk+KFBQfwdN5vNck6nRCKRSCQSiUQikUj++giNKRcSkkgkEolEIpFIJBLJu0GKTolEIpFIJBKJRCKRvDOk6JRIJBKJRCKRSCQSyTtDik6JRCKRSCQSiUQikbwzpOiUSCQSiUQikUgkEsk7Q4pOiUQikUgkEolEIpG8M6TolEgkEolEIpFIJBLJO0OKTolEIpFIJBKJRCKRvDOk6JRIJBKJRCKRSCQSyTvjLxKdFpMRjUaDwaRces7p8QFLq+scnhoQ9+Bod5vV9S20eqP1C29Bpz3lRKPFfGY7IJFIJBKJRCKRSCSSvxn+AtF5zu7cBBUVtYzOr4vPFoY7KnEPCKVpZBm9Xk9zfhr+kSmML25Zv3IH59pdaooyScwqYXbz1HZUIpFIJBKJRCKRSCR/K/xFonO+rQoXV29K2obUI931eTxwcKGidx6dVkdlYgR2nqH0z6yp5+9ifbieZ5/9J//rv/5Ebv2w7ahEIpFIJBKJRCKRSP5W+ItE51xLGQ4OrhQ2D6hHehsLeeLsQVX/gio6q1OjcfKJZHBW6Qm9CzOVmZE8/Op3/MsvPsE3Jodji+2URCKRSCQSiUQikUj+JviLROd8WyXOzp4Ut1h7Ovuainjm4kW1TXTWpMXwyjfqraJTtztLgJcbwdHxhIf44+TiTfv0ju2skKRGLVMjfUzNLzIxMUxRdgpxSel0DIxjOLdeYzo9YHxklOnZeWbGByjOTiU+OYOOwWn0VwSshcXJQXLSEolNSKGpexiNyXaKM/Y3l2iuLSMuLobkjFyGphbFrxSIqNjfWmZwZITRoX6qSwuobuhi98iE7miLjsYKkuJjSUzNomt4BqPFOjF1f2OJ8bEhJmfn6GmtJzUpkbzyBlb3TtTzFxg0B/Q0V5OcEEtCivLeM+hN1hfXHGzSWlNCTEwMOcU1zK3vqcclEolEIpFIJBKJ5GPivYjO87MzRhvzcHBypbp7nNnBBjydXhCZXW8VewK9Zod43xfYv3ImMimNxLgo3Bxf8uKlCwUNAxjPzzBtLxAf6MMLB1di4hJJjY/ilcMLntm7UtTQh169mYXR9lpiwoMJCgkjJMgPH79ACmq6OBXK9NykoaexlKiIUIJCQ3BxcsDNN4SuiRWhR41M9Nbg4vgMF68AIqNjKK3tZGV9n4meeuLCfQkODsbL3RUnV2+a+6dRtOxcXz2+To9w9PAjOS2dED93Htz/jpCkfJb3rHNXD9ZnyU+O5JXTK3x9fQkKDqOkto9TnZGjrQVVZPv7+xMeHo6vjx/hcSlMrexiuYggiUQikUgkEolEIvkIeC+i06LbJyPcCxffSGa3tUJhbpAY5Iq9WxDrJ9bHG0638Hn8e37zhy/IqGhjaXmFicFWwjwcuP/Ci+mdY4xCdPo++YZf/uFbssqbWFlZZqirAX+3lzx39mN8dZfDtQnCvN0JikxmfG6VzZUJUqJ9uffQga7xNcwmIyvzk4yOT7K+tU1PbR7PHz/EL7GMMyE6R9qK+OR3/4W9Xyy9Y3PsHBxzeqphbVF8Z3iAtfUNRroacXz6Nd4xOWxpTMz1VPLgk//g22fu9E4sMD81TGqEO18+eEJ17yRnQujW5sTy5PFT0kobmJqZY2F+XojZPTQn+9TmxeDi4UVebTdbO9t01hXz6sVDAtLKOdIa1PiRSCQSiUQikUgkko+B9yA6z9lfHsD+/mfcf+lOZVMXo32t+Dg85nef3qNmcEW9yqjZIszlAS4BsUJg6tVjCpOd5Ty7/y3lA0scry8Q6mKPa1A88zsXq99aGG4owuGlA6Wtw/RUpfPdV5/xzMmT/MISykrz8HR6xC9+/hvyWsbUnkmT5oDp8VF6urspyYjiyy8+wy4gVRWd412VPHzyiNyabi5vAKM/PWBucpy+3h7KC7N49M0feeYdycKujoW+WlxePiA6u5qLyFwZbsDe/gk5te3sLI4S4uWCR2Qmp9e2ijndmcPryZf88U/fEJaUI963hKSoEL769BO+do5g60Cu8iuRSCQSiUQikUg+Hn500XluMdBVk8Uffvmf/Omb+/gGhhAWEsTjb7/kP372G0LTK1WhZtZuEeX5lOjcKjbeaE5WprrxfH6fvPYZ9pZmifD1IS6nkl3txSTOc5b7W/B0dSe3toeyjFC++/pLntu7EBQYqA5Z9fP1JS4xhZHFLXY3V6jISyc0NEw9FhviyRdffoFzWCZmi56xrgpeiXvVdkxgsg1tPdldpb4kl5DgUPGdJKIjw3n4zWc4BMSwtKdlrrsKH3cnshv7sGpKC5vjnfgJoZlf18ZkRw1eHp4klLWpZy9zsDKE/ed/5E9/uod/eDgB/n74+voRGBpDflUbGt33730qkUgkEolEIpFIJB8KP7ro1ByskxDowjf37UjJKaaxuZnGpiYqirNwf/mQ506+LB7osBgPiHR/hFdYMnN7F4LyjL6GfJ7ce0zD2DonGwuEOL8S16SxuGdTpuc6OsszsLN/RU3XBK2lydg7elDXPWk9b0P8Ts6MGror0/nyqwekFdVxLPScdmOUSH9XITozMJp1jHaV4+zmQbUQnUZVQZ7RXZPLs0cPCU4tZf9Uj+5wm9RQV1yDY5jb0VhFp5sDmXU9tp5OC2sjbfh4CNFZ38HqWCf+7q/wjc1Bfym2zywW9pZH8Hj6HYExOexeWQxJvK8QvXJKp0QikUgkEolEIvmY+ItE51xzGfb2LhQ2WbdM6RFC8KGDK5V91n06q5KUfTrD6J9ZVc9fcH5uYravnmfffU1EVi07JybEM63BckpHWSrffvk1hc1jnGiPiXT5jk//dI/i+gH29/eYG+0i2MOeZ+5hrB3qMO8sEyyE6u8/f6y+y4G4ZnKwFT/n5+qWLctbR+zO9eD0/CFuwfFMLG1yeLjPytIck/OLaI53aCuM5rNvXlDe2MP+wRH9Dbl89adPeOGfrPZ0jnSUYufoTGXrOEZV8RlpKkzi8ePHJJe3ie+IZ/bU8uzbP/BY/OaFXS2zneW4OT4jparjtehcHW7B7ZUdWVVtaLV7FCcFc//+c0pbBtjd3WVnfYmZ+VUOdjbIj/XlwRN78up6OTg4YHtjncnxMRa3TzArylMikUgkEolEIpFIPhL+ItGp9HS6uHhT0mrt6exvLua5mzc1A4uq6KxNj8HZP4qhuas9nSbtIfX5sdx/+JymEdu2JJdYmezF4+Vj/OKKhGDcIdH/JQ+fPME7MAxfD2eePHrEK89AmkfmxYuDYWOWSM9XfPvwKT7+gQR4ufJIucYriJaheSziovMzHR3VuTjbPRP3eobTK0e8/IMobuhCb9SzuzBMkLsj9x48wtU7mPhwP548e4x7VA4mi46xbqWn01Pt6TSoPZ3nbM4MExvoxtfffoe7jx8REWE4PxPCNjyZpX0tcz3K8FoHMmvf9HSuj7TjI35DTnULWiEcD1amSI30V8Xry5cvcXX3Ire8g+NTPQerk8SH+fDg/n1e2jvi7OJGVGI6I0sH4k4SiUQikUgkEolE8vHwF4hOON3dYGR0nLWdQ/Xz/vYaQ2PjbBxosFgsbC7OMTI5y8GpTj1/gUWIvOWZcXoGRjjU3FyF1aA9YWZsiOGJebbFPeP87AhJyKC+s5/6ihJKKqoZm1nGaOvt0yyPE+brQ1BsGs2tbTRUlVFaWcf4/CrGS3uLmPQapkf7qCgtorComKa2LlY291TRe35mZnVmlOqKUnH/OsbGxpiammBC3MNybuFwb51RcWxz99g2P1N8x2JiY2maWvGdsspq+oYmmJ0YZ2p+iVODmdO9DSZGR1jc2Ld95xzd4S7jYyMsb2xhsg3T3d9coaOxlsKCAiqrG5he3MSsqsozdtcXaGuopkCcK6+qZWh8lpPLY3ElEolEIpFIJBKJ5CPgLxKdfzHKPMqzsxs9nNcR74DmcI1Q1weEpZeyfGzBqNdhMCprzV5wzuniGAFubkSklbAmBK9Bvebu1zcadGi1Okymm/2FJqMBnU6vzpv8oZhN1u9YLtToZd52n0vnzi1mcQ8deoPxxrPPhLjVarW3npNIJBKJRCKRSCSSj4EfV3T+GeiPN4jweEKYIig1toNXEKJzeYJgTw9VdG6eXBakEolEIpFIJBKJRCL5EPhgRadJf0RrdRFNPcMcXNoy5Q3nGPY3aayppqV7mCOdFJ0SiUQikUgkEolE8qHxwYrO8/MztKfHnCrDV+8YWnpuNqM5PUGj1XMmx59KJBKJRCKRSCQSyQfHBys6JRKJRCKRSCQSiUTy8SNFp0QikUgkEolEIpFI3hlSdEokEolEIpFIJBKJ5J0hRadEIpFIJBKJRCKRSN4ZUnRKJBKJRCKRSCQSieSdIUWnRCKRSCQSiUQikUjeGW8VnQaDgf39fXZ2dtjb25NBBhlkkEEGGWSQQQYZZJBBhh8Udnd30el0nJ6ejm9vb98uOpULtra2WF9fZ2NjQwYZZJBBBhlkkEEGGWSQQQYZfnBQNOXh4eG40JS3i07xGZPJhNFoVP/KIIMMMsgggwwyyCCDDDLIIMMPDQoWi2Vcr9fLOZ0SiUQikUgkEolEIvnrIzSmXEhIIpFIJBKJRCKRSCTvBik6JRKJRCKRSCQSiUTyzpCiUyKRSCQSiUQikUgk7wwpOiUSiUQikUgkEolE8s6QolMikUgkEolEIpFIJO8MKTolEolEIpFIJBKJRPLOkKJTIpFIJBKJRCKRSCTvDCk6JRKJRCKRSCQSiUTyzpCiUyKRSCQSiUQikUgk74wfXXSK+2MyGjAYTZzbjr3Bes6onLt5UiKRSD5ILCYjmtNTdHqj7cj3YbN15jPrpzMLeq2Gk5NTjKa3m+Azca2w1bZPNs7P0GlO0Wj1nF2zncq9jUYjZtsJs3hXk3jGx25iz8VvtojfdHddocSx+N1my/f+1jOLGYNBufZqvKrPsIjv3/IQo17H6alGpJfFduQN1vygQS/qsuuYTKbXaaykpfpcy7X0lPxglLRT8veNMmFDyf9nP9ChUNPmFv/DWoZMan67FfEFpTybRV55wzkGnVbNI6Yb6Xsu7ieuF3lTwWIW+U/Yjrtu/yGhlAmjXi9+1ylaveHOsmU0iPJxosFwS/m4DTWODQZR3m7GldlsFLZS2KxLCXN+brOZxyfCn7xpM5V8oZRBre7mO1rMShk0qflC/T0iLUzi/hIrF366RtQpJycnatBodVfsoxKHOo1GpLH1vJIf1DSynb8LNe5FeTUa9GqdqX7X9n2NuJ9Wp1fLkWJDteKz9by4Tpw/Vc/rxPffpJXRIMqYOH6zjCnparX/CmaljBneUoY/IJT8rsaJRofljtdV0keJM0VL/SDUfK7U/SLdVF9DpNtF/Ctxr9WqddFtj1Psk2L/LkedtXxZfZ7r37moT61290y8o3iuLR1E3vpxRafFpGdmqIPKuiZWdg5tR60cbC/TXF/HwMSieMHbKxCJRCL5kNAcbtPdXEdGaiqF5TVMLW9ivqOiuOBwe5Xuzg5Wto/UCmB2tJ/S/BxSU1KpaWxnY//0miE/F4bfyOLcDD0DQ6xv79uOizMWI2uzo1SVFpJfXMn43ComW+2g2NulmVF6+obYO9Grx9YXxsTnfpZ3TtTPHxtnwhnZ31hiZGiQ2bV99Lf6tOccbcxSXVpK//j8HdfYONMzPTZI98A4O8fWOFIc4MP9bfoGBhmbXUQvnJULFGd3c1ncu6yAtIwsalq62T7SvE4v/ck+g53N5OXkUtvUxeah1nZGPEqzS39fH8NTy+r12pMDRvp7GBybQf8DnXPJG8zC4Rzrbaaqppn1nWPbUSuK47OzushAdzvLu0ffWybNun16uroYn1nGcMnTsxh1TA10UVJRz5q4z03O2VqepqqsjNGZNeH8Wo/tbSzSVF1KTl4hXQOTnOrfuFZHW4t0dPSyuL6rfj7YWqNP2IPJpe0PWnieW0xsrcyJ31VORloaeSUVjM2tYbwUX4qI216dp66ikHRRPqqbRRk4uG7PrnIm7rsyM0Z3dz87h1ft0vHOCi01RbQOjKK1OdgmvZbZiUFKC/NISUkX/mQrqyL9L55hNmqZGumjMC+XkupG5kU8X5w7E3Z0rL+TobFJTgwisSx65qdH6OodZOfkBzrwf+NoT/bobq0lOSmexIQEkpKSKCyrFfn1UEgIBYsoE51kJYjzyckkJiaQkZXF6ILI/29x3XXHuwz39TA7v8DoYDd5WenExSeSJvJSelqqeE4yKbllzKxssbUwSVVxHvGJiaSmppGWnkZyShKp6Vl0DS+oAvd4b42W+kpycvPpHLSlp4LIg8db83T19LK0aa0rD3fX6RGfR0X5/mCLmBBpR+I9u1vryU5PJSu7gO7hKZHvL9cN58JeLNNaW0FaSgrltUJL7V61fTc4M7CzMktHzyALC7PUlhaRIuI1WfgsStynKGmYkCZ8j25018S76XSH7pYGqpt62NdZY+5MsYlDPRQX5FFa1cjC2p5NYIqyp9cwMzYg0mMajV4pT2Ymh/vpGxyzNRL/2D2dFgNjbaU8f/KE9LIWTm0dA2f6QxqL07BzdKN5aOF7KwiJRCJ53+gPVsmOD8Xe/hUBwaG4OTvi4BFIx/jKnZWv/nibxpJ0IhLy2No7ZHagjUBPDzy9A4gICcTu+XN8otJY2L0QK0LkrMzTUJrNk/vf8u1LTzpGl6ynhLlenuonNjiQuKQUosMCCYuIo3dqRT29tzFHZkIU2aXNHJwa1GOb80MkxUaSXdaA9chHghCCx7sbdDaW4+f6gm++e0xxm6iQb4lni/GE2qxwfv7P/05Mbi2ndzpCZ6xP9xEfGUZRbRenosLVn+4z3FlPhJ8bv/viPrFFjRxp3zij82OdhHo7Y/fKndCwYJzsnhGUmMva/ql48BHtlbmEhIYQLxyyiNBg4tLLONJZvz/UVCqcrGTaRxbVzxb9Eb1NJYRFxTE4uy7rvT+T7YU+3B7/iT/ed6N3csN68NzCwfYKHfWleDs+4cuvvqJ2eJG3yQmzSUd/UzHh0Ql0jy5eEX77q1P4vPyO//jDI3ps5eoyxqMN8mM9+OVvPiO9og+d6RzD3hw5afGEhUeSEBuFf0AopQ29VofdfEp1XiIJWSXMb1hFrOFwg5qiLEITctk4eNNI8aGh212jXrxnSHAIkZHhuDu95IWTFy1DixhtZWxlvJdIHzfsnNwIF+Xjld1L/GOyWdy+TbArCCd6bYa02AiyypvYOr5klc6NdJSl8Ml//t/4xGVxqLM6jLP9LYT6eeHuF0xUZCiOL5/hEZzM4r5OPT/YVke4eMfYuETCQ0MJj01lYU+cE3lja6aXqIhoatoHhIMtEloIlJnhDuIiwilv7L/S4PBTZXNhWNi/V9y7/wS/gGCCQ4JJySxmZnnfJthOKYgO5N4nn+MVFC7yQyCRMTH0TS3dWe+ZDaf0t1aI+imBiVkhOgfaiYsKw88/kKiYWMLEPR5+/Tl/uGdPx9gCG3Mj5KcnEhAg6rTwCGLEtU7PH/D7T74iv2mU3Y0lSjMTCI+MITE+mqDgcAob+tVnGTQHVOemkJBeyJxNdOpFGasoSCc6MYO5LWGrP0As+gMqxW9ydXIhNDKKQC8Xnj53IKe6G72tcthfHic+1BcnUb6CAwNwtLfDKzxZ5O+77cauKF8FKdFkldQzNz9DSXY6oYGBhISFERMXg4fTC/7w6z/gHZWH5lrj54TQaw/+9GseeESyILTtuVnPVE8VYaFhJArhGhYUQGR8FpPL1ga01akBEsJDqemasr3zOTP99cTERtMt0vVHF53KCxxtzeDv8IBHTv4ML+6oR9cme/B1eMqrgCQ2LrUMSyQSyQfJ+TkDdZnY270gMa+K5dU1+lqq8HR4REBCDls2kXeVc2aHW/DzdKW0dRy90cji1CjNzZ2MTy+yvrpCRXY8jx4+JrWix+r8nmkoTUkSDvRLPvv093z60Inq7hn1bmZRSbWWZ+AblqKOEFmf6SI6MoSMynZ0mmM6awqEwxvOwMwGZpszZRJCpzo/BT/hLHRPrqnHPgYswpHoryvDy8ONJ0/v8/s//IGc+iE0150cUYVtTnfjZ/cN/6//4+8Jyajm+Go9+hqTdp+q3Bh8whKFUN9SHarJwVaC3F7x4sF9fvbbzwkQ39+ztY4aT7fJiwvA7rkTtQNzrK0t01aRyqN7D6kfmGFlfoy0yAC1B2hjc4OO2gICfDyZ3j3meHOeRCFGU/Mq2Lb1OitO8PrCOJH+HqQUiOdcHJd8L0btkXDQYvj5P/8dv/nWmR6b6LToDmmuyCXQw54Xj77mZ7/6DcXd0xit2f8WzjhYnyQy2JfYrErWLjlvRs0R7aVJfPKLf+F//eo+HRPLtjMXCIeqr5H7n/yM//3z35FU1odGqK+RxkwiokNp7p9gc3GC7MQwwlPS2dbrWRlpJsg/mLqukTe92xYT0yLfBXi5U9Iu7MLbuoveI3phU9aXFlhYXGJ9fZ2hzmocHj/AM7rAJpZ1FMb6Y/fMkaqeWVE+VuisyuTBNw8pahpAe4t3adIJO1Wdj5t7AH0TC5guib6jzWnCXe7xf/63/4ZbSCIHWmv5WBjvp72jg4mFZTbWV2mqyOTRd9+S1zCi9hoXC8Efl1HG9NomI+01RIWEUtmzhOZgi6K0KMKTcphdfdP7qTtYoSw7Dp/gBGbWD14f/6kyP1CHv6c9Ycl5LIp6bW1tja3tXXS23voz3QbRXm44u4Ywubouzq+q+eFEpI+tw+sG24vjxAV5El/QwO6xHq3IS0rarayI725sMtrfjrfTMxx8YljYOcSo07CztameX1tbZ0WImdRQDx7a+TK2vMlYTzMhvsE0tvSyubFGrhBPYeEZnFrMzA61EugfQHWbqB9sQ3GVXvrRrgaCfLzIr+3C8AG28Bn0+3Q3tdLZ3seqiM/liR5i/V24b+fP/I5G7bSrzIrF3sGd4poOVleWqS/J5OnjJySUtGO80iNqw3hIb30eHj6BtI0siDTUsbe9xdrqKqsiXTe3V4V4j+b+vUeUtk1guZSAxuMNkoLd+Yf/+d947B7IvBCdpwc75EZ5kZpfwYIoXwPNJYQGeNPYM4zmeIfKjHiCo7NY3T2x9YoLu3G6SWZcCJkFJeya38tCQmaG6zP55qtvSC1TIkpLTXY0Dx8+o2HE2pJ4pjtiuKeVosJ8alt72D6ytmBZOWNtfpyaihLyC0uEszWL7rbIlkgkknfF+Rk1aX48d3Sj0+bwmg7XSAuyx9EnlMVbeizMmh1qCpJx9o1h7VBU0OKYXjiil2cTbEx34WH3FI+oIozKPIhzI2O9Qwz195CRGMrzV15Udk6p1+qP14VIjSIyr4kD1UTuUZSXKkRwhajEO4iPCKG4aRDdNfO4ONqlVhRJeTUYPkz/9gYm7YlwPCbpG+inUYhp+8f3yK0b4OTa+yvD8YpTwoVz4cannz8ivrCR/esRoCDSb2t+hGAPZ7KrOzlRVYmFjdVF+rt7aaku5+Vze4Izq9m1iUHt/oJwtuxx9Y7gQD0iKubtUV5+8QUFrUMM9LWTEBpA1+Coem5zpl84Wm70zS4K5yCLyJhUhmc31XMXWAwndJen4hoQxcDMuu2o5K2YdUz1t+Dj4cGLl8+w9wyh39aAYtafMjMxxvBgL/VVGXz17VcUKa3ud+Rz5frB+ny8PH1oH124NIfKzNRgOxG+r3B99ZyvXnrRNbl0RZDsrE2RERfO4+cOwhF8KfLRIKc6M/XZgcSlxrOidrEb6azJJjoliaH5BeF8hZFRUMfOydVGKcPRBmXpkbgFZ7B9qLEd/bC4/NsVzo1bRHg54uCfwtK2Miz2iBjPF7h4hGLtThC/fn+C519/QUJhA0e3THnfW50hJcKTqMwa9k7f9EfrjraFmMwScf+Cl08eExiRws6xtYdKr9Nemet2sD5NgP1XhKfXsTQ+RE5CFKWtE+r76tYnKE5PIruyl4HWKoKEAG0bmbtic61p3YKHqyvlbcPob8wP/Gkx3lZIUKAb1X3WeuY6xt1Zgjz8ic2osx35HkR57W8qxdHJnaGlHWFlr3PGaHslHm7uVPVMYbxljPnmdB8h3m5kVHSiNehpry4lwC+B+SVrD1uH0APRwbHMLE+TkxxJXFbFlakNCvr9ZUrSI/CNEvlViLgPDcuZMt/yciEx0V+Vyedf2zGwtM/J7pI6yiY4Lp/NY2scnWzOEu3zCjv3KLZPL+skK3vLk6RH+BGWmMfupRE7F+j3l8iKDcIvMuNaPWmgQ9SzPl6ePHr0Df4hIaro3BMCP8rdnoa+CfWq48VB0sX3q5qa6e9tJywwhM6JtRu2YqSlVB0iXTa4+j5Ep4jKk3XC3F/g4BFCYWEufq52+MbkohFvqj/aorEsj9BAP7y8PHD19FZbh9cV5SyU/uxIJ/FRoaKScMfJ0ZH0IpG5jmTvqEQi+TE5Z36gXog3f1Jzyhkdn6CpqkSIHQ8ySxs5ujSH64Kd+QFSIn2JyWt8PVzmOuNdVTi+fEF0YRvGa85PS3kaL0XFXWETnUpPZ3NZGu7+kbT0jTDcXU1SYiLpOXnkCac3Lr34hihT0OwskZsQRlBEEisHbxt4+OEg6ifbf8rImHZcnt0nu7bvyu8z6Y5oKs3B2zeQptYGvFw9iM+vZVtz8zcqw4SGO8pxfOVDtxAbVt7c7GBxDBd7R4IyqoRAsIpOs/6YhpIM/Hz9KWvvY3x8nLqiNDw8AoSwXGN7aZSkMG8SMnIYm5igsTyf8LBw6uoqCQuLpKlnUr3PVczsLPTiYO9BaeMgF9OSJHezPj1AYngwUWnFlBSn4xkQRtewdcjy+aV8sjhWy7fffUVB5+SdovP0YEuUhUgCQkVZ2H6zzsTu8iiJ0eFExiaJcp3DSxdFlM6/ab0XoqgkMx7/4GgKK6sJCXAns1LkR62ZiZZsIW78RHp2MjHQSXZqHIkZmZSUFBAam8bC1i3zqc8MjLRV4PjcicHFrVsc8w8DZYGg7fVlpsdHaGusJDIqkrKWAU7U+VtGmkszCfAVv725h0kh/mvFZ2d3P1oGp4U9s97jDWfMDLTi98qOxpHl10N0OTfQVVtIWHgYRVWVwmn2ITBMGQV3+zz0xZEOXj29R1ZNP3ub6xQlRRMem0nv6ATtdWUkxcRQUCJsY1QYeXU9t04r2BXiNynUnZjMCiFuP6qJB391hhvzeX7vDzx2dKOgtILOgXGOdW/qM8PGCK7PnvLJF0/JKyymsb2Hzf27RZz+YI2yjFjcQjM40NyM28P1GRJCvXD2i2bjWmOMgvl0j4qMaJ47ejO6ciR0gIWx9lp8XbwpLm9iSmloSE0hOjqVGmFzQyLjmNoQCuk6ZzoG24X4dVV6/ZbuXKTnQ+HMsE9VRiT3X/oxv3PM+mw33kIrZZS1c2JLDsPxFqUpYbx46cbEzsk1sXfOaG8zPkJDFdb23ir2OytzcLRzpLTdKiIvmB9qJMA/kEyhr7JTQgkMDmDqwNbTGelBZGo2g2OijivJIT4+joLCAtJSU8mr7LDd4SoHK2MUZWfhF1M6rn8folNhor2Y5w++4h/+6V955ujJwPy2iGQNXeUZeHp4kVdWx8zcHLWFKTjZKRHdytraAknBr3jq5Ed77wBD/d2MTk5yaBt2IZFIJD8a5iOyI3345pt7uAib9fje19x/4cnw4kU/2FUmuusJ83WgvHPs1vl7uyvjxAR64ugVwvDS3rUKREdNXryoeN+ITmV45ursCAkRIcI5jiEqJo78gkJyM9OIjUmgpXeEsb4OamuVBdrm3owIEWK1RTjrvkER9M3t3GiV/KA5MzHVV4/T0++E6Oy/JDrNTPc3E+oXQGnbCEcn2/i5ugvRWc/BLT0sJt0hTWVJvApJYGLxau+jUlmvjXQL8W9/RXQqbM4NCif5CV98+xhvH2+++OQTfOML2dOIqlPkh676IkJDg4mMiSctI4Oc/CKSo8NIz61keHiE7pZ66pvaWN7cs90RTg5WCXIQdVyReNfbemUlr9ErTlZGvHAsU5lZ3WGwtQB3/3D6Jq4PFTcz1VXM1/feLjr3NmeIEgI2LK2SXVvjtZI3ynOS8A+NZ2phlbXRWp45edF9MadTCMT+xmJCAkNpHJhheWmKsEAhOqsH0BrP1R6VouwUQkKC1YVS0rNzKcjPJioilMpm4bMMD9BYX0NH7zAHl3onFse68Xd5Snnf7K1zlT8E9jdXqCvOIMDDkfvffItHeDprh29+w+7iCIHiN3z5zQP8fL348o+f4hGZzc7FAh6XESKgp6EUh5feTG2+EfxKr1ZkcCDZVR3s7qyRF+FJUHgqe8c3OxcO1qdICfflhUsA46sHnJ2dMy4c7eiwECFao0hKySArM5vcrDQi4tIZnJiht6udmtpGkbYbtoWfRL7aX6M2Kwo/8XsWNm8RLD8hlsb7ifJx4fHDh7ywt+OFgzMp+ZWv5xubDxfJSQrnwaMn2Au79eLlSwJj05la2711IaztpXFSI/yJK2hDc6Mx1sJgUwnO4jmZtYO3CsHlsRYC3BwITS7lojP8ZHuRssx4wsLCiIuLITs3j5ycfOIioyioaVeFaFNdLW1dgyLfvLHfM8Kue7xwobRpAMOHvGCpRSdsTAkudi+Jya1HazAwP1CDq8Nj8mp7hDdgxaTZozY3FgdHZ3pWTq4KS6XxpqkIj4AQmoYWbtTz+sMF4kO8cBJif+3oTRwd7yyQGO5Pokjz9d0jGgtjCBCic/ZIcTkMzA40EhEeRmRkJHHJaRQUF5OaEEt8YgaD41P0tjdR39DE9OIaF+3m+qMNmkpyCfAJG5/QvyfRiW6baO+n/Pf/798TmFSqRsjh1gKB9vf5+sELMgvK6O3rU+c4ffvlpzgExjM+OUF6mDMP7Txo7x9/vTiGwkflOEkkko+cM1anegj3deWVmxdJqcLJ9HHHzsGF4kYhhtSW/6sMNJbj7/SCpsG5S31qgjOjEI/DJEYE4OzuT13X2C327BbRqaCspro4TX1tNW39Iwx2t5CWEE96TikdTdUECcfP2dUVD78wekZmrCtNnmvpayrAVxlqdm3hlA8eITqn+y+JTvXdLer8l8gAL4JictgznHN0uIyznSNRmdVsXRq2d4H+aIfy1HD84jOY23gjAK0I0Tnac0N0mvQndNcV4yYcLZ/gKNIz0vB0dlBFT78QJEpPjVF7zGh/JzV1TfQJkdlcU0JkRLwQ/s0UZaXg7yXyi4sryTklLO9aewe0x/ukB7wgraCc7ZNbnHOJirLIU31hKh7u3tT1zmAQn9srUnDyDKJjePG1g2NFiM7uku8VnTsL/USE+ZBQ0sKBxoTFcEpbZQ6uokwXNQ6I/KZlqrOABy/daB9fxGw2MNfXgJ+7K/FZlWiEl7c0O4ivpxPJJZ0c2NLvWIiljqY6mjq6GRIiszAridjkHFqE2EyJjxK/wRlXL39KGrs5tg2pW5sbJSLEjZzmMY4+0M62o91NuhorSE+KwdtD5GWPYBqEvdIZlEYXISKFo+zqaIdXYJgQe+n4uL3C1TuYvklRPq63tBkOaK7I5rFrNKv7VqG3tzZFQoi3EH9JzO0a2VuZJTnAGd/gBDYu73hwbmJjfpS0mEBh3zyp7hx9LSDPlRVpJwepqa6mt3+QztZ6oqOjKK5ppLYsjyA/P169ciYkOoORGevUiLPTPXrKM/AOiWV65WJw8E8TvU7H9sYGywvzDPe2kxjqyddfPyCrupdTJZ2F8FBW915eXmZqbICCtEjuff0lQSnF7FxqgLhgeXqAyEBXsoSo1F2br2w+2SRHpKHDK19m926xfWY9tUWJvLR3FmV+znbQysn+Gp3NddQ1dzI60k9ZfhqRCek0tbWRFBGMl6sLLi7elNb3vF7MbWliiEjnlxTWdIh3+TAb+PQnB/Q2lYmy40xARBoLytD1M4uwQ+W8sntEXn3fVdGZF4uTEJ39QnReiV3jIW1lGaLsiLp/dv2GTzHaUoSrkx0ppS3CWgqUVX93VshLDMXdV9Rp82uiutVRlhqAl58Po5u2bZ1EHTw3LspXVRUD45O0N1aSEBtHfnE1tcU5BPp64ursRFh8KuMLG+pQdrPuUPg/BUSHeo9XrL8v0Smoz4/lxTN7ylqtC2NsrQ7y9JvP+OXvv8DO0Um8uDOOQuk/dXAmsbCGw6NjlkfaCfJ2UVdHi0rKYXhq6QfvBSWRSCR/Dc51O8T6OAqHN5QB2zy9091VipPDeGjvSc+UMNjq0Tf01ZXia/dCXaH79TmLkdnhDsL8PVXx0j4yd0cD2h2i04ZSGVgMh1QVZhEnKt7ammpS4yMpbhljeXWFtAhfYtIKWFJXeDQy0FaEb2gorSPiXW5/4IfJZdFZ148i286022SGufPLX/wKj7AkamoqKcyJ44+//R3fvfSgrnMczbV9U/SH25QmhhEYl8H8xpstaKxcF51WBbA03IK7EJy+UXkcKoeE4N9dnSbQ5Ql+MZmvRaSVc9bnhkmJi6K0rlW8TzpxSZkMTQtHbaCJoNBg0qp61Cv1p4dkh74krbDs6uqdkitMd1fz+LPf8clXT8kvr6G0KA8/16f87tOvCIrKZXnz8gqpP0R0nrMz30d4qDcpFe3CMbWwM9ON3Xef8vsv7pOSX0FteT6Rfg78/DefEpGWz8j4CKHOz/ntr/6IX1QqDbU1JMUE8/mnv+OxYwgdfdOvF+1SODdr1LmEUdHx1LV3ERcRQV5FM6trq1SLsuodEEv7sHU16s3FcaLCvchuHOVI/+EWSmU7mnMsHO0sC8Hgh4NrAGOL25wsD+PlZI93VBb7Wmt529uYIdT9GT4R6bZ5n2841+3TWJbJI89YNg5PxQET5cmB/O5Xv+KlezA1DU1kpSbw5Kvf88W3T4RA70GjiBZR7hbHuogK9MDNN4Tm/skbtvaCo805SvJE2UsvormmmBghPjsGp1hdmCA6wJPU3FIMSlSLdxmozcY7LJqplS3rlyUqh2tT+Dva4eSfyvLWzVWIlSlxhfG+PHHyYmTWtor0JZam+gkLcCG7fviG6Fwd68DX9RVBiSW3DP8UderGFHGB7rgFxrB0cNM2Cs2iFDIG26pJjI+lurGJ7PREErMrWVxZp6M8lcDwGHVFeYXVqSFivZ5TWNt+bSuSDwOlIa2hNEton1fEpBezeFFmRJ6fHajD1fEp2VXdr0dCGI5FPZYagb2DB9O711blVabfFKXhGxRG39zGVb/CoiU3PkSIfZ/XCwqem3V0V2fx21/+nAcOXuQUV1AhRKTz0y/55LPPiS/tZOPg6jNON2fJSUkkq6CE2toqQkOjGZxeYmlmSPwfSHxBHdvKVkSmU4ZbC4mJ8BovWX6PorOtPA1vD18aeqyrwm0sDuL48D7+8YWs7Z2qG8nqdXoMBoO6oba6me+Zsqz9Hq3VxTg++YaXLn70jK+q35dIJJIfA8vuGM73P8c5KIUVa3ebQEdHcSp/+OIhlV3jN5aPHxbOZ6DrS2r7Z6wVgLBnKxM9okL2JTwpl8VLQ8xu8nbRKW6mzgmLiYmjoXuM3rZKImNCGLfdsqUolsCEDEZWlQMGumty8Q4KpWNy9U6H7YPkkujMqRtAGfBl3FmiKDGcRw8e8PTFS+zt7Hj+8Gv+6f/+O/73z39PdEY1W9ccFuPpLtU5MXhGpzK9al2M4g1XReeubUTNQHUOD75+QHhWg/pZQVl9M9rvOY9cfBmZfzNMV1kYpjo/mUiRruubCyRnJhFXWI+yc4P5aJnIuBh8k8vUa3Une8R5vCAtXwjcW3plJVZG2urxevGU+/cfYu/oyIsXz/jktz/j7/7+n/n8Gxe6bVvRWPmBPZ2rI4SF+xNd2MSB1sTCaDuBXo7cf/CQl/aO2InnffHJL/mff/cPfGf3ivzqemICAnh+7x7P7ezVvPbtV3/iH//+7/i3//qKzMJmTi8tBrI2O0hijBCaVc0sr0/h6BtJw8C8em6ssw0/93Aq64fUz+vi2hAfewraJvgQt4xU5lVfWZ1UCOrWwhiePHtJ2+Qy8311PPzuEcGZNbYLlHK2R0rQSx4JJ3ZEOL5XMB7TXpnPc4cwVvaOhVE9pDY3nmePH3L/0VOcnBx48uhb/vOf/y/+6X//DD/hF24e6thbHiE2xJ+g6Axm1+/aikVwZqS9qoDouBSGhCPckJdEgrCP+0dWR74k0Z345AR2hPY4O92hpTAZr5AEZtauN0L9lFB87CvyhOPtZcI9XXHyS2Fp80gVepevsIi4q8kI5YWLD0OzNxdDW5sdJjbIi7SyXrRXJq2f01qahqO9A2Xd1jJxncnOKtzs7YjJquKuMSC784OkxMWRWybK2HSv+D+Css4ZdZukg+l6AsOCKW8fVq9dGBsgwN6ewppu9B+Y6LSYdDQLwenj7UNxfc8Nm6WsCeHjbEdEShnbtvZNZT5sfIg3zv6J7GneDJFVMZ+I/J+DV3AYHROrV9LsZGWEYM9X+Eams3FsNTYWo4b2ukIeP3qkDp12cHDk2ZNH/Oa//pH/+ff/xCOfBMaXt9VrVc5NtJRlERGTwvjkJJ1t5bhGZ2HdztNEfFICvon5zCvC2XREV1UeoX7+481H71F0Npck4+HiRV2ndSGH0701kgMceaQMnx1bUkWmWXvA9uYye8caNFoNq8uLnOjMnJ4cU50RKCqgR+QJ5+OjcpwkEslHzblmk2jPF3z70J4q4dQajUa2FoUD6/aCzx68omf6Zk/nwnAb0YGuQiz1qRXAmW6flEh/XAKi6RVOm9KwZjab1HuZzZarDp4QndW5sTy2c6Ws49qCNOLCw7VxEsNDhHCp4VRUplPCAQwJ8aNl5pDjUy1FySFEpuZa9/807AnBlYBfSCxjqx/mnmV3os7prMP+0Tdk1fSqczrPLaI+OD5ib2+X7e0ddnd3WJnq5Nl39/GKzGR+6+hK75OCspdnR002Dp4RDE9fb7S0zum0e/YC//TK1z2dy8MtOD76jqcuoeqQJ6NRx0RvPY+/+SMu4aks2fZWPT8zMNRaSZB/CF1TG+gNR6QnxxCaVMCS8BZ2FweJjI4gtqhdvf5kdxYPOyfyKtptq+hKbkNZtfRASeOdbZHWe6yvLlKYFsqDF86UNwygM1xWamYmu4r58uvPye+YuFN0HuwskxgZQlBcHlv7J5iNBo4O99kRz9gRz9jdXKS5KJbP7j2ltKWHI61O5LVj9nZ22NndVfNaV3Ml9i8eEJxkW/DQ5rQbjjcpy0kkNCpFnTO1v7eIm5svxU1D6EwmOuur8PePprnX6nDPDjTj8fIZzWMr1t63D4wDESfzM4vid5+qNmpjbphwDwccPUOZ2tjnaGUIl6ff8cgliJnNY3GNQZ1n/eLbT3AJShR5/9pcyXMjI63VuIn061/cEfbyDL3mhD0Rr9vbShrvMj3aTaDTIxzdgoUY3BF28ZSiBH91VEjb5DYG1Waa1fcxib+vbeb5GXODbUSGhlLYOIRBc0xbcQqhwaLsrm9iMh2QHulNSmYuiqt+urNEYWIgwQl5rL5lUZy/eYRQ315fYX55nWO9EcOJMoQ9kycPH5NY0sahzsLp/hYLiwvsHmnUNJ7sqcPt5X38orNZvTLaw8rh+iw5cSEEp1RycqlB5tywQ0qoh7pA0NTeLa0s5zp1i5AnIn8oWwndhkW/T0mWKGOxGSzt69lbHSc+IoiM8i72j3XMtOcTHBFF85C1QWqsX9hwBy9quiaubM/z/jlnc7YXFyH0kvLL1TrbYrGIfGoUcWwS/58Jcb9LdnQAT166U987icFgpLe+gFdOTmTWDd4yR/WMvvYq3L0CqBQ28PLZsZZCHJ49IbGwgYtBFUpjgl4n6qfdPXZE+dsV9m9tcZqEYEdeODjQMrljXVFfvdaiLuAVERREUV2PeD8T3Y1F6vzQ9WMjWu0WMbERRGZXsXFkEppzmcr8bHwCksf33tdCQgqNRQm8cnClut06VltxHuZH2vBxseOJqPC9fHzx8/UkPkWZpLzH4cE2ZRlReHt7ERAcgqebC6ExqYwtyuEQEonkR0QY3fmhNoI9nHj8+AUenp442D3n0RN7cio6OLplTqdmY5qchDB8E4qFU3mOZXcOr2ff8l+//RNOnn7qRua+3p74+gWQW9nM9unlllgtFVnR3H/mREnb1ZXmzjT7VOclERSWQN+UVUApw8oKU2Nx9fAnMCQcHyGAmnqG1Yr2YG2SlHA/IhOyOfiAh/HdihCdkz01PLv3OelVPRzfISYs2iWcnr0kIquGWxfoPTMzI5x8N0cXaoUDcrXiO2d1uJPnD5/go7Qq2xaiMBtOaKnKxf7ZY144ueHl6cHL54946epPx+jSaydmbWaIxLAgEvPq1O1qzoUDPNZeT5ifr0jbQIIC/ElMz2VqU5mrY2BpoAZHcY/GvqkPfkXFDwoRd20Vydi5+dMzfnMhoYmOAj797BNylD1x78gneuFU12TF4+MbxvjSbXP5jMx05PP1U2fax63DYK+zMtOPl5sdKUpPzuu8ZmGwuYzQoGAq2ifVRialV7wuLwV/Pz+CggPxDwwVtqKZHWV1ZdMJHdXZ2DsHMLv5Ye4VuTo9SmpYCK6izPj6++Hh7oqrZwCN3WPohMOrrAjdWZ2H08unPHdyxdPLHbtnj3j+yoe2kXlRPm4mwupkL+FeDuQ2DnPbWkMnO8sk+DrgI0TridYgonWXgGdf8LP/+jWv/MIJCwnC19cXL78gUgvrOLT19mh3F8hKiiAsPotV4fSKzCLK5QDxkQHCFgYQEhpMaGQKXSPWDo/VuVFCvVxIL2u61Xb/dDAy2l2Pv48nTq4ean30ytGB8PhsFneOVeGyOd1PkojHVy5u4rw3Lk4OeARE0D+9eqNxT+FMu0uDEPwOHqGsXhqeebLYj7vIK/Z+iZzekuFNJ5vEB7zimaMHvbPX590rnDHYWk5AUIgoY8Pquxk0BzSW5+Hp7kFAQAh+Pn7ki7p0V5nTaTigrTILF78ohpSFS603+UA4Y6otlz/85td8eu8JEZHh4v398RHxGxYRR/vAgtoQvTU3SKivqzq6wM3DExeRBrHpRULY3d4PPD/aQ6hIw9SienSve7CNVKYGc/+7Z5Q0W3uA78SsoSjJBw8fL6YuJYHSQZgVE0pUcj6z29YZpmvTQ8QG+eEXKMqWsG/hMUn0ji2pczrXJzvJyUgnJr/z/WyZcsHKzAjtrR0srr0ZVnZmMbI4MUBhdioxMdEkpmTQ1NmvDn0xGbSM9TWTnhxPXFwCBWX1zK9uY7q0TLpEIpH8KAhbtTw9QklehrpQRUJqFk3do1dac68gnLKu2iJeufoysrKP+fSQ3oZKdcnx6Ng4EhMTiYmOJCYunsrmHnaVVUpeY2JhYkhd+XR27WoFrGyIP9TbzcDYPKe24UvKZthbi7OU5maRmpZJU9cIR4rTplTULeX4eilDePo/SOf2rQgBt7e5SFNdNeMLm3du+m82HtDS1ET/+LwQArfXD8oKiImhXoSnl7G0d7WF/nh7g6aGRrrH5tDaNhhXMGiPGOlrJS1ZpJVIs6z8YkbnVrl0CVvL83S2tbO0/Wbon7LP6EhXM1lpyWTnlzExZ+0J1x7uUBQXRGB0GvMbt696LLmdM7ORlbkRWtp7WL/eiyZid299hqrqCnVVzTt2KBLlxMjScKO652dRwwCaG3vWGNlbnaSmsY3VywvZXOJwb4PO9hZGZ9dFPrDlNeFKzY4O0t07wL7WljnOz9Hsr6uLS6WmJFNR18rannWo5/7qBEmhHoTn1HKku2sg4ftFc7BDT0M1KXExIu/Hq/O4BqcWrgxTNAphPdbfQUZKEtExMWTmFjEkxIjhjtYUJT6K0iLwDElheefmUFmdsJHDXS109QyL54h4OdfSWVtBQmQUcQmJJCQkEBcbQ3RcIoXV7TYbJ+67t0JvTydjs2+GFSrDF2fH+8jLziQ1I4e+sQV06upfJ+qq0y5ufnSOCif5dXfpT5FzdtbmqSzOJToykoTkVCob2ljfPXwdj9r9TTobRBoo+SAmnoKSKqaWtzDdFW3nFrWBz9PZiYreaS52X9FsL9MsbGzH4LT1wDXM2mP6Oxtp7ehm//Q2aWJRFzLq6hl8vZeyUsaO9jZpqSklLSWFoqoW1oRYVthaGCEhxIfotBJ10bAPi3N2l8cpzs8kKjqGhMQEq18QHU1ymsiroyu2vWXNLM8MC32URlRMLCXVLazuXrd9bzDsr1KeHi3KVxyzWxfXmZga7KCuvpWlt07pEW9l0jM90kVHV6fwR94ksEbUW92trUzNr75Od7NRx8JYL7kZaWRk5qn+iNU2mKjPSyI1NZ3hbc37FZ3KfjvK0IiL4SiXMWhP2dvdYf/w+MpCQco8z+PDA7ULWGNbkUoikUjeF0a9Rh0SdnSi/V4Rt7s8QXKoH5EZVexpjMI3NXKiDA1Vh+pZh+sptk0ZXmO+Zhcv7KXl2vHzM2V4menWBYF0p8fs7x9YV60VHO8skh4bSmhcGisf6TAyZV6/2WQS8XC7mLRyrsaV2XJtHtollFUu+5uK1X09q9rHrgxpVOY1qXGtfN927DKa40P29vbR6G8KBJN4NyU9rqM0BBwd7It8orGmlVnHRE8jvp4eVLYOCHF8XfBIvo+zM6VMWG71IdR8IvyFt+cT4WucbFGYEkFwZDJjC9s30lt9hrKmxG0FTGB9jjWvvOFcHfJpFmX2Ooq9ONjfR2vLO2e6Q9qr8/HwCqRvbvOD7u02G/XW4cfCRp28Zas6jbBpyvA8zfcIaEX0zwy2qJvQl7X0cXJtSw3hkwqfTxlCaxExao0Ysyhbp0c3bebh8enrtLYIu2oSaXYjKoUAOj065ED4lRfxvDk/TGJ4EFFppexd2hHhJ4uIc73wv/f3hP99cPS67riMyaDncH9PpLGSj7/fD9fsb1CSHoVvZAozW9bGNaU+U9Loen12gZL2avm1Dem8yUUZu1m+lQ4qpYxd2GelYbapPBsfn0DahpfurBPeJ+fCzhj1Wg5FHaEM3VeGlyv5e0/U31pRji6/s157otY/r7dBu4tzI3ODrQT7eJFf082pOgXBGq83p/DcgrjAYrEOYb98rZJ21jru+g3OODk64ODo5HX52loYIDLIj9L6DiFQz9+v6JRIJJKfEucmHdMD7WRkFbJwS8v+u+VMHQJTUlRM56h1xfCfOpqDDaqK86ls6mHvzdjIHwWDsjR+cw2F5TVsHvyE55G9Z5Thz1vzg+Tn5tEzOo/xDif4XXG6s0JzZTFlTf03Vvf8KWA83ae1tkTttbm+Oua7RhG9UyPdFBaUMDIvp2m9M4SgWp8fISc7m4HZ5VuHWr9LTnZXaagqpaKu/YNeGfpdoGw11dtYRn5ZI1tHmpsNMe8UC+PdTRSVlLO2eyJsrRSdEolE8qNiMRvYWl9RNzv/UXs1hHN9tKcsgHLAjT26f8IcH+6ztrF997Dod4RBpxP5YJMD4QhI3jdCeG5usL27f2vPzrtEe3wk8sEamg9sNc0fE/3pEWura6IMXuxA+ONwLmzxwa6yaNThrVt2SP6KnJnZ395ia/fwzTD0Hwnt6TEbm9scnt7dO/+3jMlwytLKOic6w48sOs3sbGyp5UtBik6JRCKRSCQSiUQikbwzpOiUSCQSiUQikUgkEsk7Q4pOiUQikUgkEolEIpG8M6TolEgkEolEIpFIJBLJO0OKTolEIpFIJBKJRCKRvDOk6JRIJBKJRCKRSCQSyTtDik6JRCKRSCQSiUQikbwz3io6TSYTx8fHHB0dySCDDDLIIIMMMsgggwwyyCDDnxUMBgM6nW5c/H+76BQn2djYYHV1lbW1NRlkkEEGGWSQQQYZZJBBBhlk+EFB0ZFarZbDw8Px9fX120WnxWJBr9er4lMGGWSQQQYZZJBBBhlkkEEGGf6coGAymcY1Go2c0ymRSCQSiUQikUgkkr8+QmPKhYQkEolEIpFIJBKJRPJukKJTIpFIJBKJRCKRSCTvDCk6JRKJRCKRSCQSiUTyzpCiUyKRSCQSiUQikUgk7wwpOiUSiUQikUgkEolE8s6QolMikUgkEolEIpFIJO8MKTolEolEIpFIJBKJRPLOkKJTIpFIJBKJRCKRSCTvDCk6JRKJRCKRSCQSiUTyznhvolM8Rw23Yz1352mJRCJ5j5xZLJhFuM1EKbbLopw3m9Wg/H+3rXuDcs3Z9evUe33/PW6zl2dnFvG9M9unq1x+1vn52c3nfgRY49hy67ufn51Z48z2+Yegxontexdpd5F+ynFrHItr7jivXmO7l7jQevy2d1OfYz1+8UzJ/38o8XgdJf8r6fPn5O2LNL4N9Zzt/+tY84SSD65e8bZ8qJy7eJby96PNB3fEmfK7lfDncDlOrnL1GRdp+zqoz7pWBgXqO9wWr+o7X1xr+/+2x0pUlHi1XIrvH5ZXrWmmflekw+X0ukiX29PahjinXHd7+bWdu1berFwtS9fzxMeIkj+V+L81L9+JNf7fFsV/HlZf5K44V5918f9b4lxc9+OLTuWF1uenqK2uZ3Ju1Xb0gjM0+2v0dvWxsrknPkkkEsmHgdmkZ3VhkvqaCopLKxkYn0NrfONYmfRaZof7KCvIp6CwiPz8fKpq61ne3rvTCCvoDjeYnJxgcX339XVG3TETA91UlhZTUdPI4sb+TXt4ZmZ/fZb2jm4WN4+4eJPj/U06Wxqorm9hZmUb86WHH2wtMTY5ydq+RhhjM3ubiwyOTnCsNdqu+LA5s+hZnh2lrkqkQUkVo1OLGExvYkYjfntPSz1lZeUifWbRm77P8TWzsjTH9Nw8szNTtNZVi3QroLi4mJKSUkpLS8X/VUyJump/a4Pe1sZL50vE31JKxLO6hyc4NYjq02JgfmyQmsoqugZEvOovPd90wrSI++mFdfWj7vSIqfFRZhfXMf9ZDoVE4cxsZGl6hO6eAXYORH5WOLewu7FMZ3MdJaUlNHUPsn+qt557GxYtE2NjzIm0MFxqrDk3G1ibnaCto4/tw1PbURtnJtYXp6kXebGktJz+sSl0JqsLpT3aZaCrhUrh54zPrl4pg7qjHYaHx1nb2lc/H+1uMT48zMrO8VvtxIfFGUc7a3R1tDG7vIHR5oxaTDqWZ8apqyijrLyakWlRPi1v/1WKT7i7vsToyAT7xye2o1a0xzuM9LUyNDWHUQh7JZ1Gejoozs+jqKhIlD+lDJZQJMphfVMXhzqD+j01XWqqaVLS7ciWNwSKA784PcbU7AIag3I/PSuLM8ImznLyA7LJT42DrVWaK8opKiyksKiQAlG3DYzPoL1kc29gMbKyMMvc/DwLCzM011+2qSVquSworWZ0egnTLXnDYtAwNdRDubiuob2frUOt7YySfmY2FqdoqBVp294jyuSx7YxSHA1sLM0yJsqhXskrIo8uC/swKdL6xPDxlKw3nKM53GO0r4vKshKq6ptZ2Nh7Xc/fzTlrIt4np+c41L6RdOeibtpeX2ZuaRONzmQ7+oYzs4nttQUWFxdEXXbJHzgzsjovynR1BS1dA+yf6GwnBEatqI+nRHlaRCdezGzQsjQ3ydiMUi/flJPvR3QKtdxSmM7v/vGfeeEeydL+pR+AyDSjjbjZeVLdMSI+SSQSyQeARcd4bwM+Xm54+foT4OvFCzsHMkvr2Tu1GujDzRUyQ725//k3uHn64O3lSUR0PKNL1xvXLmOiuTKPpLQcxhd31CMHW/PkJUXwytkVH/9AwsLCaRmYQnPNOhtOd8iJfMUX3z2ltGdRrYyOVqcpyUkhOCyCyIgwQqOT6Z5YRfURjIdU5qWRnlfB2rFyszO2lidIi4ukqm3U6oR9wBg0BzSWZuHu7Iy3fwC+XiEUlbewd2z1FjcXRkmKCMDZzQs/H0+RPk7k1rQLR/Tuam1vdZLMpDiqWzoYGx0iPSYcD3cP/AIC8Q8IwOXFQ373h2/Ir+kUDs0cpVlJuHt44ucXQKA47+Fqzx8/+ZSIrBpRGR8x0V1HdHg4ERERRIRHkFXSjMbWMDHbW09Kchptg3PqZ5P2gM76EmKTM5lelY2sfy5HGxOEuj3huxd+DExvWY/tLFGckYiPhzfBwf44OtgTGJ3B0vaRev42FPE61VdPXEIyncPzQiC+cVBPtpeI9nLgk2/s6Z95U47PDSd01xXh7eaMm3hWkMgLOcVlbGu0mDWbVBVkEh4aSkRYKCHhcUL8jlsF5bmBtqockrJLmF47UO+l21ulujCLhOxKDmy25EPHoj+kISeKP372NZnV7WhVVW2mr6kSPzc3vH0D8PN0x87BWZTBLo51d/+u050VitITyS5pZOOSwBBPYbipkIef/pzg1ALrPc6OqSvKxsfdHW8fH4KCg/H2cOWbz/7EM5dw1ncOWJwYICE6kvCIKEJCw0jMLGTzWHGyzzlcnSAxPoGq1n40JvHO5xYmBtpIjImnqWf6SuOAxMxIWxUv//Q59s7u+Pj54iXqtIrmLo4Md1urzfkRUhOTqG/pZmFG2OTYcKvNFDY7ICgYx+cP+PWn35JW1sL12xi1+6KOSsbd1Q1PT0/s7B0IjMtidkNpjDCzMjVArKjX4uNi1DKXnF3E0qa1HO0uTZCTmkRRXS86tcI7Z36wibi4FFr6Z9RrPiaMJ3v01BYTEhgobFkgrk4vcfYKpn14QcTE3RxvTJOWEE+RqPuM5jO1oXZNCPXS7GQc7V4QnVHD9sGbFhZFbCqNPvUl2bx6/h0BMcmsHtjE/JmBqd5GNQ3Do2OIDAkmpbCGrSPr91cn+khPSqS6tU8tO2dmHWM9jcQKv6d/ZlN4N1d5L6JT+YEVCaH86n/8v/mf//Qb0qq6edMIoWelp5hv/nCP3Noe8UkikUjeP2e6QyE6W8kqLKd/eIxxIVDi/J158NKFut4p9Zq1mTGCHF/g6hFGb/+wEDGjTE3PcCgc0VsRzu3WdB+hAb6kFtRypDWhO9yiNDOO5y8cyatsZmRsgsmJcdWZMl6poM3MDdXzxW/+hX/891+Q2z6rOrXdVUUE+wbT1DfOUE8HEUIgJwtBdCqE1/xAI0FBIZS3DL2u7A2aPRoKRSXvF83kyq714IeI8YTBlhLsXzoQm1FE/8gYYyMTLC6tozOY0R9tU5IWxdPnTlS3D4hzgyQGuPDA3pPWYavIu45FCIeW0lS8AyPpGFng+PiIhelJRkZGGJ+YYGR4gGgfe7769gV13RPoNCesLs5Zz4+NMy6eUZoaxB8/vUdp2zCby+Nkx4lKOTOPyekpagpT8fX2YX5fI95vg4zoUGJT81netfWYnZmEg9xHkLcbOZUtIv0/DsHxIWAxamktSeGX//x/8vPPHeie2FCPa0U+GOnvpbtnkMnJccpzE7j/zTdkVfVyor9NUZxzsrdIUmQg4Un5zG+8EadnRh0D9bn88ef/yP/4z69pn1iynTlnuL0aDycHgqLS6B0cZnJ8jPnFRbQmExOdRURGBFHe2Mn0UDeJkQFEpmWzbzKyO99HSIA/xQ3dQkTZ3C2TjqGOGiGUPakfXMD4PT2DHwLrU704fvFz/u5f/4vYokZOVIOipadFGQlQxuDIOGPCXsb4v+LeC096J5dvbVRR0nGopVyIDB8hDCZtYsGK7mCFeO/H/Pf/4/+BS3Ai+xrFIzSxsbIoyvcIo2Njqn1trsrn4ZefEZRUwfbaMpW5KUTEZ9E7Pk17dREh/kE0DK0LW3dIXX4igZHJDM+ucRHNx5sz5CaGERiV8aZsSkTinNBUlMJ3f7pPWXsvY+PjjIo6bW1r9848atadUJUVS0CYEB2zm8JmHjN7YVPHJ5gYGyHS25HPv3tBZcfolTyh5IWxtlIhjOyIyyphaHiYkswYnj55QmpJK/sH29QXJgkRmsPQ2CSdVbkE+gVT3z2LQTy3rTwT36BoRlf2uBgFqj1YJSUikMikXFb2P65uLKOIu/nxIXr7BhifnKCnsZgXD+/jGZHF0p351ERDQQJ+wZF0TigjaoSPIWxOQoQ/Dx885te/+BnuEbms7b7p7NtenaMgJQIXu4f89hf/yrdOvszZRmFo99dIj48lKCyZielZOupKcHIMpn9qHZPhiLKsBMKikhizNZgLq8nOyiTxob5Ep5WxcXD1Pd+L6DQLw1uREIbny5fc++Yez9wCaB1bsZ3Vs9pXxoPPH1NQ33dFdCpDOfq62mlt72RifhXjh2+XJRLJ3wjK0B3N6YlwKm0HBGs9NTx88pz4wkb189zoAL4u3uTWDaufv48zs57q7Hi8A8KFU2btRZnqrsXb3Znk8h71810cby2QFhUgRNYz7t37lvIeq+gsTEshPDTDOgTHqKdSfI4XlfTy4hQZSdGk5Fexe3K58j1nZ3EUf1cHihr7OP1ADev24ogq8v3jizi95RWXRtrxd3tFSHKl7QgczLbz9LuHxGbXoLvh8Z6rvZxRvq6kFDexe70bWaDbnSfSz12I3HI2Dm9pAtUfkBvrh390NhtHOmb7GokO9qNrcEw9vTnbR0yAM/2Lq3TWFRMeFkfX6KKaThcYtYc05sXiGRovKu5t21HJWzk3sSTEeqCXO/e++4ZnrkHCCVqznbyKYX8e18d/Iiy9lm3NzYyjDAed6CjHy9Ob+t5p3ozGPmd5eoj4QGeePfmWT5+40T1l9VO0QqQmhHrjE5bIxsnN0QGVab7EpcazpmZUE53VmUQlJzK+ukZxWjTx6SWs7r0Z8qmg2VsmLz4In5gi9j7wcZ6ao02KU6N5/N19njx9TEZFKweqYTRxfHyKQelBVDlnpbeMLz9/QFFTP68PX+JQ2LGsWH+CE4rZto1YUDBpj1Qn28XuAd9+/TUBEansHF+NMytG+uvzcHEPYGxpl9O1STJiwiltnVDLmWFjksLUBHKqB5jsbyY4KJCazhH05ksG4dzIaE8tbq7u1PVMCkF1mzz+6XGm3aUyOwVHl5i39qy94ZzdpRH83FzIrmzm9JYv6XaETfX3IDqzlI3jy6Mc4XRvnYxgF1x8o5jftg6zNu8tiDLointQHD0Dg+QkBpPXMWcVqzsTIq1jKavrZ7i3UwirYMraRm4MPx1pKcbXN1DkCatd/li4OX1SQ1yAK09dQhicu62uOEe/M0ughwepRXXW+Be2cmdplIa6WkrLq/ByfCREez6rO2/K0s76Mp2NtdRWlhDk+ZxHboFMr1sboJXv+ofGEJ/foX7WHm3gYxdIa/cQ4721RIRHUd0+ckWPnZu19Nbk4uPpR8fI3JW8835Ep9FAaVwwIQExlJeV4SkcLOegBHbUGDKy1lfOwy+eUChEp+oaCcdsbqyHtLgIPNzdcXZxwSsglMqmbg41cgCuRCJ5P0w3FfLMzomsGqtAnBnp4LkQOc88IqlvaWdydgndbZ6WiqggDlaICw8kKrWEdWW4i1lHfV4CPj7eNI8uM9DbTWdXL8ub1lbHC/SnO9QWpOMdGCUqkiI8nV9S0jGpVrb1BRkEeIcxPLfM9NgwKVExZGSVUlGcRmxaLjPrN4cZGk72KEkMICAhn7nNN3NkPhzO6G+twtnuFaWNvUyMDNDe2cXUwtprR7avoQB3Z0dK2matBxQMmwQ7PMU/MovVa4Lj3GJkXDiar1550jF4c+iVxXBMW2kKL+zcaB6cv+l0ie/P9Fbz9OlLKjrG1OHLa6MdxAR7kVVUxsrqKt2N5USEBtHY2kRkeARVzf03HCLFKdiYasPByZvKttFbHXPJVfZXp8hOiCIoJoOs7EQ8AsLpHlm0nVV8DB0Hezusrq4w2NlAkJ83DX1T3DbKWnO0S0lKDH5BsSxsWIfpKZzuLpCdEk9IaATlhSm8dPWl09bTOdtVTZC7KxnFVczMT9Le1sHY9BIX03e7y8X3wgNo7BtjdXaUoqwkEtIzqaqpIjgygXEhjm5g0dIvRJbDS3fGV3av9AB9SCg9Wa3V+aoTn51fTKivG+nljeye3uaLGZhszuWb+3ZUd03cMnT1nPmRToJc7anqm38df0p5H+2oJToqlPTcHJLDvAkMT2Lj8Op8T4WN6T6C3RwISa1AazJjOVwhMyaUuLRSplfWGO6oIzUhlvzyOrLiI0kraeDo5nQ2NpcniQ1wISGvhn2NHHGgYDnZJD8ulF/97onIu/X0DY6ytX8zDS5Q5gyOtBTh7BlCuxAb1zkzaGgtSeaFgyuNA9M3bOHW6gTedi+ITCt/0/BiPqI8OxZH72Dq2rqozIklPLOSmaUVxlvLSY5Ppqi4nPSkFFJzK28dHXmgNC76e4v6L5+rMvfD58xi5uRwj621VeYmeogRZSIlv5qN/Zujp84tJmbai/HwCaPxYjjxuUVd6EzBpNknxt+egJhcVrbfiM6LBQctukOyYz2E6Axgcs3ac3m8NUtURDj+Udn8/9j7C+7IkjRdF/xDM79g1po1c87cc8/tQ32abldXVRdlVXJmcEiKEDMzMzMzMzMzo4tckrMk92dsu7tYkZVVXZERUWVPLlsZvn1r7+3bzL7vew03t7eFuG8n0D+OpqYG0hOiKaru5Ox+dRH3PFoaIirEl5zaLo4MN4b3w4nOxFChgqOZWd6lr7GQF8+ekd0wJkyNGdVYHc8V0dk6ai2U27MDhPq68sbdl7KqeuqqK4kP8ealcPZVneM/PKFZIpFI3gOnB5vWXiyv4BjGV2ytjjuro4R4veE3f/iK1w4OePoEUNLQzf5jPRfC1Krm+4kODaGoYdDaKmnR7wsnH8KLb5+SlFNAoI8XTo5OQjSlMrm4aXPSZhMjHdUikA6haXiO5dk+AjycKBOiU7H9WzO9QmiGExGbRFZmBsmpWZQWlxAREUtj1xBLyyuMjY6yvLZ9vQCP2aSjty4PR79Yhu29OR8XWnrq8vnu25dExsUTHR6As/Mb6/yWtqFZa69Fb0MOHi6ONI/tWns4FCyXp6QFviEgKp3Z/buR5oXhhI7aLFxCEplesi3sc4OFAxGoRPi54h9bwNa9XikFpWeqINYX18BElndtQv7idIfG8kxCwsJIzcgS7z+dDJGPOSnxpGaVMDk7z8LMBJPTcxze6rU5PVwj6I0zeRVtPzhXSmLLt6bSbPGO4xmeXWeovRjvoCj6p67mW5pF3i3QUFVERGgQjg6OxGVXcfyOeb2HeyskxkYQnl7BvtqWJ+ZzPW1VeQSGxNA/NsvqWD2vXf3om7f1pg42lvL2xXO8gsJITI3nraMjbt5B1HYMcyYCrNOtabJS44TwjCUzI4M0URYKhHhKioumtLFb1MFFJifGmF9eR2e6ea7l8W4CXV5SP7qC4aMsBpfMDbUSFRZKSVM/G4tTxAR7kVXTLt7vQyW3szRBfJDSqZDMgn3+6h0sBobbanB38mF66+i63p5szZEcHUZaUS3rW6sUxvgI0ZnxoKfTcq6lvTKLlw5udEys2/5eCJ++5krCgkNIFIIkQ7z79PQMiovyiBKCf2h6mfn5OcbGp9g5OLkW97rDDWqyogiJy2V9/93C6m8Ji/7QOlTzd599wYtXr3F2cSEqOYfplR1udxRfcWE4oyE3ntCEAmbW7+e3BfXWPNF+LgQkFrJ+cP8dW9ha7MXlmaMQ/i2or4cUaWgSQtXJK5j2kVkWxzoIDQkjIyuTxPhE8gtLyE5PEX4uh67ROVbmpxmfVKajqK/z1nSyR4Hwq5EJqaxqPy37qjs7tpbn9LgIXBxe4RmezPT61VDWu1wY9TTmRxGWmM7kIw1b6v11ov0dCb4nOq84O9ohI9rDKjrn7KLTbNTQXSfih5Ag0rJyyE5NFOK+jLzMFGKT8xiZEvZRvPOxiRl2D28arE2nm8THhBOaXsqq6ub4BxOdFUJ0+nqGMb28z+XpBqlhbrxwCWZ+R83BTAuvvn5NRcckZhFg1WbH8crRndq+WfsVxMtbnyLY3QG3sIxHW+4lEonkvWC5RLW5TGFaNE5uPtR2j1/PtTTpT1hdnmNwcID2phqiAlz58rsXFDYPor/npZWetvmhJoKDA6i1C0YMe6SHufO//9v/TVhaEV29fTRUF4oA91v8YrJZUWnYnOkjSvxNWnEjJvEsO/O9+Lq+prR7Eltnnon1pXHKiwqormsUAnOQvLR4UvLKaWltISs5hrdCEAeFJ9I/sWJbYOjynImuehzfhtI3dqun8GPBckxPeRw//7d/w8ErlJbOHrra6/F1eYGDezDDi1t01efi6eJEy9TNsCPLpYaMEGcCotOZUd0Nio1n+9TkxIoAKJvF7UP7URuWSz3D7WW8Ee+psnv2YQ+ZxczCRDsur16SXdWDVlm11s7Z0S7dzXUUFZfR3ttPR3MtsZFxNDR1UVuaR5CPC84enhTWtHBg71HRnRySEeBIVkkNqgfNxpIrlJURh1orCfEPpKp93BrkDjRm4+4fwcD0pl20XLK/Pkt1aS6B/j48ffKM4NgsFjcPRKB8JWtu2F8bIzoqiMTydmsPl+XCxGRvI0HiHnm1veIME6tDFTx/6yNEp61xor+xmF/+/N/47KUnTd39dLU2EuXnxpOXzjQNLVnnCu6tzVFbXkx5TRN9A4NUFmUSl5RJd2c7eekJeLi+xTc40jr3+mqF5c3FcaJD3SjsnOb0YxvEJWzN3tIoCZHBRKWVcCKK6dH6NJEBHmRW297dFUqPy/byJBlxYbx186dteJ5H1ygzqumsLeCleyzr9oBVd7pLcVoMQREJQohqOd1bIyvck5CodPaP7wqVo/VxEkPd8Q5PZe/05v5G3QmjfW0U5hfQ0tFNd3sTqUnxFNQ009lcTXiQP2/eupKUXcnyltr6N5dnB/SWZ+EfnsS8KCsSgfBRR6p1IdBH6e3uoCw3kSdff463yIsle0PbbQyaI/ISA4nNrmJt/25Dq+VCz3h7OU4vXlHTO8fD6dWXbM208/b716SWtHNybXS1tFRl4OThT/vEqtAPOiYHOygW/q2pc5D+jkZSkpIoLK+hu63WOuTe2d2T2KwyVnaPrUNUzQY11QVJhMekMbn+WF/ox4tW+Iau+hISo0JwevVc+LsQ2gfnMN5qrLrCZNBSFC9+e3oWy480nByLvPxh0bktRKe7vafzxo8aNQeM9jRTUFBES3sPI72txEREUdnYQVNtBeG+bji+cSO1oIptta0v+cJ0KoRyAoFRuczfWiviw4pOj1BGr+cx1eHi5EBkVjmzA/W8ffKW2t5ZNNp969CKgIh01g5vSqkywb8sNYYXzqEMzazbj0okEsn7w2K+ZH1umIRwfzx9Q2gbmBGC5GEgK85E2UvucGueUI9XeIclMrd9d9iqshXDtLB7gSF+1A8sWFd5s2i3hOj05KuvnOhfVVv3xDILx99RksRLZz8KyspIjfLit188IbuyheGhfspzE/j2818TkFLM7M7VlgvKvmjnaE8PGeuqJSY+ibbOVpKSkimqbGRqfJiCtFhrq/WyymD9XfMD7Xg4hDAgAsSPj1Pai2L4/PffUNIxIYSybU++2Z5KXN46klneSFN1HoFC0DWO3uq1vDgmwcuJ0NgcVk/uin7DsYpKZfGQJPEOdu4OX9YcblKQGIKje6hw3g+Dq0vhlxqKEnklhMjg/I6tB/oW5+cm4ecM7K7OUpCeRL545xXF+SSnZNIxMEJPSzlhEZGUtk1Yzzdo1ORHOZFVWiWCZzll5F2sjXXg9uwrvnruKsTeEAM97SRHePLFdy9Jz29EdWwLeJTVaDWaM07UR8yP9xPh60pwcjEbj/Su7C8NER3hT0ZNtwh0L1ELIePj+D1fPnGkrLmPqeEuCpOD+HdR9tJLG1BpTHTV5fDV118TXdho3epGqetbU724Or0kKL3SnocWjMZzEQhqmBpsJykhicrmTrKSEsgpqbUurFKSk0pIdDpDc7vWp9lZnSYuxp/81klOHl306MNxcbpNZpgbv/vscyEqKhgfG6G+OIvX33+Fe0gCo/MbVttjMV8wP9ZNZKA33sHRdI0vPbothoJFf0RrVS7PfRNFsKoRBy5pLUzki99/hqcQf0OTszTXlOPx8iteOHjQOSTs7fXINgtDbZW4OjqRW9Nz3at1hSJ8TULMH6tWqa/MJy41h9bmGpIT42no6Gdc5GtkUAD55U22ofPiWUYb8/GPimduw7YSsuQG616RIu5uzBdxt8NbqjpG7d/coD87JDPOh7i8GtaP7jaeaYWgKUwKwcErUtjbu418V2wvD+L6womkwts9nWc0lqbh6BlA56Qt1leeRafXi/vtUp6XQXpOKTVl5WQnxNLQPcLIcAdhgYEUCB95ojSGXArhWpJJTHQGY0ufVi+2sp2QXqvh9ESNamuJnNggPHxD6Zu6mU5whUmvFX7ElfiM7EcXGvpzRafCpfBp58KnHas2qcxOJDu/VPjcMhLi06lt7me4vZb48GCKmgatsczluZGCuFTCw7NZWLvVEPyhRefwrG0ol1l3SE1hKq9eviIpNpyX3wunMrSITndAWqQfvqHJrOzfuPYLo4aipCheuUUyMvdD2xFIJBLJXwILG/MjxIf5E5GUzfTa3vXqh+9EOLvcGB88g6IZXbnbeq70dC4MtRASFEhN16x1/rrFsE9pShjeXoEs3/KNSz0VvH3jTmxCPJGhnvzrz3/N85evefnyBZ//7pf8X//H/5d/+t1TiluG0N9a4la1Pk1aXAyFdZ2sjDWTmJxG9/iy9buR3jIChfDpmRWiSTiUqe46nJyEMxv9CHs6RVjYXVvIq+cudM/eiMoTEaQEermSXFBNV0sNIb4e5NXfLOJ0fjyP9xsnEnJqOL2nDE1nB9TlxuKfmMXC1t282ZgeINjjDeGZdY9u9aBamSDO3xmfyHR23yESLYZjuuuKiIrLYGltgcy8dJJKGtg7u8R4uESMyMvgTNuiR3rNEWkBQjwX16I6u9sjK7lhtKUKh88/49e/+T2vHB158fwZv/q3f+L/+D//G7/7wpXe8RX7mTdc6NSUJgfynVsYE0u2FW5vs78+RlRUIEkVHdY97ZZGW3F99TW/+s1nPH/lKMrcUz7793/hP/3n/8LvhRDtmt1koLUCXzcXykWwdcXZwRJhfi54x+cLcXsT8B1vL5KXlkiWEJrLazN4BsfROGhr2BnvbCPIK5r6VluZ3V4eJyrElaKOaW513H0UHG3MEOvzml///Gd8+/wVr1+94Ovf/4b//nf/hf/5j78iq7wV/aWZtSllJIawkckFLG4fPxCDdzCd0FlTJOK4WDaVXszLY8rTw/n9b3/NZ19+h9MbR558+wX/8N/+C3/3v/4R39gCNvbtc9n0B5SlK6NN/Omfe7dIHO+qIykpha6RWVpLM0hJTGLvQBn6eUFJsjfJGWkciIe0aPfpqcjELzyRBdnT+U7mO0twdnamsEEZBXAXo+aY/IQgYjMrWb3X07klfGeQ2yui85reOWf2QNSVEDdHwpKK2L9auE2zS0lKFB4BUUxu2HqlrxjvqiU2JpGeiVlayipI8E/gWOSl0ohXHBtAalo2u0enQmBoqclPJDw6hYlPrKfzPkO16Ti9eiV8/eCDxk5rT2eiFzHpmSzdGtJ6xX9EdFo51zLZWUN4VArDY5O0FWWRllHD4p6Qjadr1Ah/GpVRjlY82KU4NzMmnsDIHBY2bhoZPqjo9HG/EZ0K++vzJPo78Jt/+xf+9z99S/3gImbzBfV5iTx95kBB45C1JU08NptT3Xi9eUlgcinrh+/YjkAikUj+QmgONqwrNnqHJrBxb2EfBaVl/eTokIOjm++WJ7pwdXhCZFoRu/fFhDC1+wsDIsgMJr+2nxNFuwh7199UjJfrG4qa7eLp8ozK1BAcPYJo7p9kd3ebqakpZmdmmJ4cp6owhe+++C0hGZWsHJxwaR9CqD1W9grMIkyInpX9I9RLHYSGRdFhHz47KoKH4MgYhleOrb1yffW5vPGPZeSjnNOpzHfrJcjVgbj8GjR2b9tTncUbJxcqWkfYX5slPtCDtyLwOLSvFjFUl8lLZ29qe6bsvuOGC+MJXTXZuAQmMLl4a+VTi4mBxiIcXzyjsm8F4yNDMqd66njz6iXJpW13V8G8xsziaCfRoWHW4ZZavRCVaXHE5FRbV1DVq+aITYghssC26rFGvYb/W2fyKts5+Tgn830UnIr6tTw3x7Qo/9Ozs4yNDpEW68dXT16TU9aO+lSP5viIw+PT64Bsf2WScE8HXCIyrufe3uZItUxiTDhhaeXWOZ16jTJEfoHp6SmmZmaZHR8Q4iaUX3/+HVkVSsBs4Gh1jNhAd0Lic9g8FeXDcs50dzXOjq9Iq+pEreySLjCLMtZalUdEVIJ16tChEKYunkHU9dtE52hnK4F+MTR126YOLU/0EPD2FY1jq48MP/ywmPQattaXrFteTIv3ovy/qSyX10+/xjsihfmNfc6VqVJx4eK9ZKCyz4/9QSwGRpQ5nY5eTG4eiYD0nMO9LeZmZ5iYmGJ+fp6+9lq8X37Nizc+9E6tYLDveXu0Nm6dH+gdlszWO+bp7SyOkxIXTU51N9pTNe3FKURHxaA6VsrBOYUJvqRkZqM0EegPN6jKjBTPnnen0eBvGYNOw75qD629IVOZG5kV5YWDqx+dY4808Igy0pgXT0hcHtNrt3ozRT6PtpVaVzuuG1m9szfnue7Uuk3Vwuo2mrNDajNCeOngYZ2jq7A63om/hzPR6WWor//QwvHGLElREaLet6AzndNVWUS4V6S1AcEonjs/OojUzAKR1xpx+0PyE4OITExh/ZEVrD9W9BoNqt0Drto9L7RH5Mf4WacbNg/PP+LT9DQVxBCSkMr4rd7FK2yi0+GPiE43nnk+Ljo350dJjAiluGmQ07MzOorSiEkoZn5XyMajJSrSI4nPq7Nup3Op2yZB2NUIkW+350h/INFpoCjSm7evve92EV9eMNdXy+/+8f/k//n/+L9E0GXrvj9cmyIh3JfnDm+ITUwlNSWZUD9PfAIj6J9bv7d3nUQikfzl2ZwZxP3p7/j550/IyMiwLtKj2KLk5GS6hqetw75WJvtF0BVBTGwCyYmJ+Apn6R4UxeDs6oNWScVxGtRbJEcGW/cHvAp0TvZWKUqNwtHhLXEJCdbV6lzeupBfIwTJIxOjVsZbefv8Gwo7Zq5X7lMW2BhorSQgMIjG4QVrb8Pl2RaZybGEhkeRlppCbFQkBVWtVkeu7F9XlhpMWHrJ9VL1Hxsm7TGd1Tm8ffuGsKhYkhLjcBb/jkorZHVfvLsLIwPN5cKvPMc3JJqEhEQ8XF1IK2pg9+ThmoUWs5GZgSbchCjtuOXAL852KIzz5Q9fvmR8V/vAsWM8pSY3ji++fUpV7+yjvd17q9NkxoYTm17OicmC2XJJX2MFYSI/4hJSSI6PJj41k8ElZbGGc3ZmO3F2DaBeWQX304mJPjwWEx1VKbx2C2BwztaLuTreR0ZCHNFxCaSmJhMW7Iu7tz+tIo8Njyw6qDvZF2IjgcDgOBa3HjYmiZLHfFcBnz9zputqKs+Fnt6mUmv58gmKJCE+Fh8PV4KjUpnbPrSXCTPTfQ1EhNgWClNqrlF3RGl6HGERUaRnpBEZFU16UQ0bykqUlzqGWspE+fZlZuvgYbn7CFHND+Pv+prUqjZrQ5B6poOnX/yWX371goKCPDIz0q32MS09k67RebQP7JeFVevqtU5U9s6heyT61B6sk+TngE9wEgeam16qyY5SXn77FSHJZdbhfPdRbGtxphBA0aks7In3K+rg8lgX0REBRCUkkZGVQnhkAq29tgapXWujlSdpJU327V8kp3ub1BVlEBYZZZ2aERsWwCunt+RVt6N+ZE9hZfXaqc4y3LxC6Bq7WRHcdLJNcXwAv/7Kield9Z2yfbw2QYy/G56RaZyZLzhcGSXE2wUndx9iY2Px8/YiIDKR4YXt67/THu9RnRMn8jaN8SVbL/faZDeJ4coopHRS01MJDY2lvX8K04WZk+1ZooP9SMopte2I8Ymwv7lCSVoKUcJnJ4tYIy4qlLfObmSXNXPwyOKEFvM5S31VePqG0zRg2zv8Nke7KwS5fItneBaruw8bVqyLIwa95nNHL6bWbUP+rzjeW6ZI2K7QmAzhb22CdXm0g0hh38Ii4kmOjSY2Jp6+6Q1rXTteHiQi1Jf8+l6Ob9X7DyI6zRfnjLbVU1lWx+q9uTTnumPqizPx8wxneHrNXsgu2FmZoiQvjeCgYIJDwknNLmR8fvWT2ERZIpF8+hzurIogJhZfPz+Cg4MJEcY2KDBQCLtA6jqHUBrbD7cWKMlKsu75FxQUSkp2AWPz6++0U2YhlJoKU/ALiqB38qoBTtlceZGKvGRh7/wJjoihvLGb/ZPHeg7MqDbmqCzOszrlK4d6aTxjdKCTqvrWW0LVbN13sDAzmdCwCArKG4XQVYbgmNldGsXPw4XKjlF0H7HqUTb+b6spFs4sUIiEELKLq4VQuNleQlnIol+I7bBg5ftwsksb2Nx/OMzIhoXjrXnigrxIKm5mT2MLos7PVPQ2VJBTWIv6ZtPGayzi3Q51NZFXXMryzvGdAOqKraVp6iurmVq5GfanPVbRWVcuAocI4pOzGBibt+5tZhI+rzEnFv+IVGYfaZ2WvBvzuYH5iW7KqhpELGEbenewPk9FbipB/qIOijKSnJlD7/gcensP2X2UvXLn+2pFcOtLfe8Mt9aEsmExsrswRG5RhbWsXeW3/uyY3pZqokUg7h8UQlpeKTOrt+b3isBrUtTB+oZWtq6GClrMHIr6Wl6QQWREODlF1Sxs7luvqVEtW4cmBqdVcHRLXH3MqHfXaKgqtb1f8d5OhXDLT43Hw9uHkNAQYSODCQzwF/8Op65ngrObPVGuOVGtkZ8sbGlCITvqh6PWtOoDuurLqGvs5FR3IxlWpvoozM2na+ThdkcKyvtsba6je3T22j6Y9KcM9zWTGB9DZEwiTb0TqDVCYJr1jHTX4unuS8uQqJePjG74W8QoyviAKOORoUH4+wcSGR1PbceAEDzv2nhElO/1SSEa3cmpar0eIm48PaCnoYosIehP9Hdln0a1QlVBphBS9fbtTC5YmuglLSFK+FE/ElLzGJlZu9W5ZEFzuEN7fSV94/PXvbDnIm8nB9uIj4kiJjGJhq5RTuxbKg63FOMXGEZ1z0Mh9jGjPd6no6qYyOAAa5wRGZtIbXvfO2IBBQumg2XCfL1IK6x7MC9cd3pIU1Uhta0DHD0yLURpfO5qKiO3oo4dZVjyLQ52lmmuq6F/fPF65WKlPg10NJAYHUZkXCqtoj4p8YP5QktPTS4+/sH0Ta2IHL3hg4hOcVfxsHp0Wj0Xj2zCe3Fu5OzkFKPp/I5DN+nPWF9dYWVlXbywHzF0QyKRSP5CWPfLOjlie3OD5eVla1paWrIm1ZHaFmyKQPP0+IDVlWWrnTp+p3O+4UAZJhYaQGph9Z2WfJPuRNi7Zda3dm/tX/eQywuTsKXKhuwX1/ZSeVatTivSfcdiRn2wy+rqzbPphciqz0vAO/Qd2xp8ZFiE0NjZXGdlbQPNI9s0KEMd97Y2WBXfnz32/S3MJg2dwjn6BkbRNiScqThmEe9Or1XenZIXD4NP68IOeh1acc6l+PdjGIR/05w99FHWIYoba2ztHmBdfNBssgZYwT6eFNd3i6Bc9rD8aVisi1totTrOL2yVRFnI5kwIlXVRB5dXVjl4ZG/H+2iP1smKDyU6Ke+ROmCxLoqhsd/jTokwn6PaFvZgdU2Il3t1XQhMrUYjyspDAXkqnm9tbY3DY3tvgyiHyqI4vt5+tE+tvXPhnY+NS6WuCDtjMJqUsM7aoaBRH1nL+KKwi1Ybubwo8mGF/ePTR1cPvhT1eaKnDi/x25sGp9HdG7qmLNJk0GvRifdoVm5ix2TQW9+vsljQY5wbRB0Vz3Z+755KQ8WeyLO1jR309r892polOyGCsKR8th/Z//BvFwtG8e4Ve7u0tGy1Wz/giqxcGrU0KntNh8fRM2sbGaDsE6kX9UerCM5beaig+CqDsKe2/LUfFJweqYQPFeXmwb6gtvqo1Zw9GGWirDyt2P71TZG39kam0/0VkiODiMssZvuxDVo/ZsS7MmhPRcyx9qPfv+L/uirSCQyOejAEWmg8a33VGcS7fqQuKr5NqWsanf56784rzhVdptEKzXb375T6u7u1zsb2nn1PcjO7q5PEhfqRkF/H3snd+vRhRKdEIpFI7Fxa9+HKzC9iau0nXjVRBMaqjXnyM1JpG5y7tTLk3w7Hu0sUZ2dQ1dTP8YNurveLSaumv6Va5H2xdXn/h2GA5KdAaWhYGe8gMyOLvsmlR8XR+0R3sEFzZSG5Fa2cvmM/0b9mlJVNa0qyKaxtZ/eR3s73iZL3c+O95Cg94pMr172ikj+fg41Z8rLSqenouxb2HwYLS6OiXmfnMTD9cLXXv1a0+8sUZWVQ3dTzk/t087mOmaEO6/64k6v7d3o5FaTolEgkkg+MSXPMyuoKW/s3G6T/JJgvOD3cY2FlHcMj+379bWBBtbvJ4srG9XCsnwqDTsv68grbu4eKM7YflXwQLEZWlpdY397D9MgIrPfJ2fEha4sLHP6IkRF/lYiyf3Kwy8LiEmrNTzuKTen5VO1ssLa584MjSiR/ApZL2wiAtc0/OtrkvWIxs7u+xMbO317eHu6us7q2fmu/05+GC6Xnc3udla29R0eyStEpkUgkEolEIpFIJJL3hhSdEolEIpFIJBKJRCJ5b0jRKZFIJBKJRCKRSCSS94YUnRKJRCKRSCQSiUQieW9I0SmRSCQSiUQikUgkkveGFJ0SiUQikUgkEolEInlvSNEpkUgkEolEIpFIJJL3xg+KzouLC3Q6HVqtViaZZJJJJplkkkkmmWSSSSaZ/qR0fn6OyWSaFv9+XHQqgnN7e5uNjQ22trZkkkkmmWSSSSaZZJJJJplkkulHpc3NTaumPD4+nl5fX39cdCqqVKPRcHp6ytnZmUwyySSTTDLJJJNMMskkk0wy/eikoNfrp09OTt49p1N8lkkmmWSSSSaZZJJJJplkkkmmPznZNaVcSEgikUgkEolEIpFIJO8HKTolEolEIpFIJBKJRPLekKJTIpFIJBKJRCKRSCTvDSk6JRKJRCKRSCQSiUTy3pCiUyKRSCQSiUQikUgk7w0pOiUSiUQikUgkEolE8t6QolMikUgkEolEIpFIJO8NKTolEolEIpFIJBKJRPLekKJTIpFIJBKJRCKRSCTvDSk6JRKJRCKRSCQSiUTy3vggotNivmB7Y5mltQ3O9Cb7URtGwxnLi3Ns7B1yfmm2H/1hZge7aW5oZX3vxH5EIpFIfkLMJraW5+hqb6VvcJS941PMFvt3j2AyallfXRLnabm8dd6F/pSDw0NODJf2IwLLJTur8/R3t9PW3k5LSzO9/YPsHJ7aTwDD2RETw/0MDE9xeGawHxWIvz07VrGyts6pwWZrD/fWWd/YQnP7Hp8wF/ozVhYXWd7Y4eIH3rnIJDRHKlbt7+LiXM/CxBhdbW3ivXbQ3NzMyOQMJ/d80n0uLy84ONjnWH3X35iNGuYnRujtG2b74CZvhJdFrzlmdXWFw1Od9dDp0R7ra2uotT98L8kPY7nQs6864FSUefO9cEF7vMfIQK+1Pu6f2N77D3Mh8mSVzd19TLcq5cX5OXt7e5xpdUrAZD96GzNnJ0fsizy/vFXpzaKOL06P09M3xObesf2ojUslzllaRXVkK0Nnx4fi8xJHmlt196PHwqn6ENXBMReXd22J5cLAyvwEnV3dzK1s37FxjyLeq+Zon9XlFU40t/PKgu7kUNQ3Fcb7GWzHYNSzvatCpzfaj9hQH2wz2NfL6NQCGuPd59vfWmVjexu9SRy/NLK3vc7q1g5/JSbxL4pJ8UmqHbTnlyI3HqLYQ9W+sIcnp++oHzdYzg3sbq6J+nCI4fzmXMvlOYeHBxwKv3n7Gmf7O4wP9dOm2OjWVjq6e5hdXhfawHaOWdjw5dkJUccGRfx/u45ZuNSfsLK6yv7xmfWIUkeXV9bYO7J9/hQx6c5YnJ9hY2fv+h28i2PVNmvCz5/qz+1HbOi1J6hUx6Le3Eg9o17D0twEXZ0ixlDetUjDY7Mcn17ZIwvHexuiPvUwPrsk8u5WXTSfc7C7ybq9/lyIPN4R9Wtte5+LR+qsyN+fXnRemM4oz08gJiOPxe1D+1EbB7tzJEQHkt/Yw4nu7st6F9UpUXi7+NE7uWk/IpFIJD8NuuNtGsqyCfT3w8/Pl6ioKLpGZ4UBftwpWExapgdaSMssYGX3xOrIjVo1M8OdJEUGEpteyOzBje0zX2ioSA/D+fVz3Lz88PZwIzIuidGlHev3p6p1qvMzSEyMJzwsgryiauGA1dbvdGf7NFcWU1zVIgJvvfXYxtwQ+Xm5tA1OWj9/6iwMtuDi8IaYrAoMP+CHtUfbNJUXUVTVhOb8gtODZWLc3uL0+g3efoF4uruSXlDK5pHW/hd3uTw3CvE/S35GCn4hMbQNzdq/EYHv6QFtVQWkJCQQERYp8jaf6dVd63fmcx39rdXkFlaysmMLjI625ykvyaOypZcf5+UkN1g4NwhBNzlArvD9oTEZTC6qbjXyXLA23U9qbBg+oj76+vgQEpvO5No+79IUFvMlm3MDZGXn0ju+hBJTXZr0rC+Mk5OaiHdwHGNLm0Je3mA2i2B7a4W60hxrvatqG8d0YTvDrDukq7GKxNhooiKjSEzLY3hmzfqduDIT3TVkF1cxu2mLf87212moKKKwtgvtPYH0saF0Giiirb4sj9DwMMpb+9AZb0qx7niHxooc/H198Bfv38c7gIrmAc4M7w4tDafCTlUUUlzdwq7aVv+UxrLexgoig3zIKqlELersDRZhM0+ZH+0mPjYK/7jsO6JjfWGSvKw0oqOiiYxJsF73WG97r/qDdYpys2nsGkZjEvklfs/MSBfZIu8HZzas9lhiwSDEyfRQF8lRwUQnJrF6elfUXwrBt704SW5GEl5hcXRPLvND/URKuVmfHSI7PYOhuQ0hmhTbaGRjYYqCzCRCo+PoGlu41UBxyXRbDb4ub3j1xoUAUZb8Q8JFeevFeHEp6tgxvc2ijgmbGxkZTrKwuQPiugqWCyMTnXXkFlUwv7lvPXa2v0GdsP/FVc0c6X7gQT9WLOJ9dFXh6OhIRkWzKLvvLqkG9TblBblUNnWjVRpWhOU6Odylt6WaqPAQcso7RDxw08C1uz4nYg8fXjm8xVvYS18fb1KyK1jbtQn0zflhirKTRV2KJS42hgrht47t+uxwdYYyca/GriGr3VT83eRAK5lZ+cxtHt+xmQofRHSajEfEBr7mmUcgo8s2x3zF1togDk8/I0gEEAdnP64VuCIhHHcnT7rHbQVOIpFIfgos52fUF6bi8saZxOwSOjq76OvpZGljRxjgx53C3uoEyRG+JBc1o9aZMJ7u0FSWg5/3G372T/+br196M7h9Y/uM2h0i33yLq7u/CN466Whvo29wmN1jjQiMNYy0V+DnH0Ztazfd9UWEBAdT1j6K+fKCuaE2Qv2Dqe+eRm8PZjVHm+SlRhMen8bK/qfb6qtgOFGRG+nC/+v//f/jZWA674wlzCbG26sJCwqhvGNChFQWtue6efXb3xOWkE1rVzftbS2MTs5w+mhP5yVjnS3EB3jz2a/+jX/6zbdk1Q3YvhIuc3m0leDAYGtA0yeCndiwQNLKWjCJQHl/dZKY4CAKaro5tvdkmUTAVF+aSVBYFKOrB+Ia1sOSH8GFRkV/SyU+7q78/le/4DdfO1p9/1XWH2/OkiLql5OrD7UtrdRXFeL04gmBySVsizrzGPrTbfJTowhPyGF2Q4gXs4GBlnrCPN/yh9/9O//w6ye0jCzeEq0mlia7SYoO46vf/55f/PLXxBW2i2DY9u3SsCgD0aEUVNTT395IdFgACbklnInA+2x3jvjwIPKq2znU2AJ5s/FM3K8SX98g+ua2sWvXj5ALVmYHSYgM4ZvPP+Nff/kLogpqObvqTbGY6KrNw9nJgdjscjo6mkkK8eT7F+5ClKw8OhLBcmlibrAVPy8/6nvHRZBs5mB9nvKcRJyff8m//vPf89o/in3jTb00nKloEiLdz+k5P//Zv/GzJ55Mb9gE/KXmgKq8NCFiUmjuHqC2OIeQwDC6Zva5MOnoFc8XHJFI/9TN8+yvTZIZH0Z0ejF7t4Lxv1U0h1s0V+bh5/qSX//8n/jVty+YOrA1Wlqx6BnpEuXa14Xf/fsv+PvffE9J59QPltuz/U1K06IIjc9lRaX4HQOj3Y2E+Hnxa2FTf/nl15R2jt2IzgsDTTnxvHrlRFxmCT3dnXR29zK3usWF8G2rE31EBYh6VFTNQF8ncaGhxKaWWxvxDjZmiAsPFuKqCdWZ7bmV0QVddcUEBgTTNrz4gyORPkY0B+vEun7Nf/ovf0dAWilH+ndJtUsG6vMJComgRfxOhcPNKfLSYnn17Dn/+i//jFd0IVuHN/k5P9qO55vv8Q5Pp6Wtnc7ODobH5zjRmjBoDinJSiEkPJ5eEXcofsvNM5KxZRXmCz0tpVmERyUwOLtuu5gQx1vLY8SG+JNe0sKB5la5EXww0ZkQ8oZXPqGMrezZj9rYXh/m7csvCcupEQ9rsrYu67Q6jCYjxwd7LMwvsL13dMv4Q01qDL5u/vRO2Xs6L43s7+6iPtNZu/61Wq1w/uecqQ9ZXJhnfWsPq/i/hzIEamVxjtnZBXb21ddxgEGnQX1yYr2GFbMZo16LRqsRlcx2ltKKozlRYzAY0Ov11mQyGtjb2mBhcZl99ePOTiKRfLpsTrUJ2+NGmhCQP8pwnuvobiwSRjuQqS2bjdHsLlFXVkhBaQG+zk44OvgzuHUT+GgPlwh2cBKCc9h+5Ab9yRZ1hQlE5zVxYB2Vtk9hbiopJQ1src5TmBpHcq5iS28bPAuTPQ1CJAVS3DDwoCXyk0EEuP1NpQS7fc8///vv8Esq5VTpuXgEw9EGOXFhRCZksH2q5JSJhYFGHF74MLVp6xX+YUz0NjZQkJZBdLgfzx1dyKjus311qaW/NpOwlCIWVUq+GWmrLSAivYi1tVUaizOISi5gSXXXB2zODonAKICk/Hr0t4aaSX4Y49E6vS1VJKdlER3ix0snIWjG1+wxwQV9dQU4v3Uhv3HIekQJblsK4/juqTOdY4sP9L3SK7I00kSAry9VXZMoHXKW81M66urJTUwkMSaQr1640Dy8wHU2mbXMDDZRkF9ATEwsLo7PSS7uuI4r6nNDScxIYv1EiRmMdFZnEZOWysL+Ps0l6UQn5rG4dXfIrXpnkWxxr8isetS6u71KHw1CbMyOdlt7BWNjY3jt+IS4onpO7KLTKGxVuL8HXmGpHNp/wr4IQF2efk1cXh0H2odWUhE4pRmRBMTksnVs6+VcmxmluiSfbBEouzs/xSEgCtWtxiCl4ay2soJskT9BXl589SaQybUD23frk2TERVHWOmEtE7qtKYpSkyhsnGB1ZoiY8BDKmvrRGOzxnMKFjuHOSjy8/ekcXxZi9G+7Ph5uL9NQWUxBTjJhgS58/sKJqf1bw54vz+hubSAvNZWooEC+fu5KccfknWHpd7CcMzfUKnylO81jK1jNtFlNT1sdGZk5hIh6/ML5tRCdo9ei02w4FeUihihhV9fvmeiLcxNt1WUEBSaxuGrryWwvKyYmNIlttdJrnkVoXAaz66o79f1IiNHM2GBiMkp+dKfWx4BZlM+eukKcvv+a3/3hc2Fratk7e8xGWDg/3SQ60IeU/GohTMWvF5JuZ2GQ4vwcktNzcH39hJDEYjYPbkb0TPTWEBrkTsfCQ1+oWp8iNDKB5KIu62fN4Tr+b0LoGpxmZaaX+GhR1xr7uT0w9dJ0RkdFNv7+YQzNrd+JMT6I6Dw3HpMU5oyDXxjjj4hOl9dfE5FXz5HuguPNecpKyqioa6SxppxgXx9Co5LpmVi+7kmwik73AEYWhdG5NDDWWUdiQiojM+uYNCpqyiuoFH/f1FBDVLA/AcGR1HeNobvyEEKZH2wuUFOSI168P76+AcQk5zAwvYLBdM7O/CD5hSWMzq1aT7/QHdPZUEZadj7r9oqo3lmivKCYWSEwZ6bGKSkupaGllZLcTAKFM4tJLWBGVIDbcz4kEsknjDCVtRkRBIVG0zUyy/rqMosr66h/YF6WZmeWgtRIQlIrOLPbn3OjgQurp72gKjUKp1e+DGzctA5qtidx+vYpkSnChiwssbN/dN1CbzzbE6ImieCkQmbW9kSwMEFubgYZhWU0lOcTE58mAt27LY0KxxvzZMSGEJ2cy6H+U5SdZjaFXQ4PDSU3PZ7A4EDC04rFb3ncfa1NdltFdm5Vm+2ACJ4nWkv53e9eU1bfxtLKGkenjw+rtXGJ3n7tmf5aXF3dSK/qtX7mUs9QUx6BMan0Tq1xfLhKdWkuCZmFtNZXEB0Zx+CSLTC6jf54l4os8exhMawc6B+IIcnjXJoMmC1mzOK999bl4OTsQefoqlVgWM7VIlCNxtM/nMEFmwhR8m5ltIU3T59Q0DyEEofdRn96RF1uMsEhsczbh7uaL88xKsPXLEZmusp49tqVxsE56/AxK5cma6OywubCCMGeDiQVtl33dHaWxhAdH8HAnCgPuyvUlWSSnJVDa2cHEVGxIra4Gmp7g0UEav0NRTg7B7O4c/RxlgfzOSYREylsLI7h6/2K2MLaa9G5OdKMu7snyeX2eibQnRyRHvIG3/AMlnbur7thYWNmkChfZ8q6prma4mww2AJq/eEKKVHuvPaPZO/WnE1FdFhHF+pPqM9J5TunACbs4kO7PU9WXCRZpa1sHqtZGu0kLz2Zouo2yrKTSM6rYOf0VoRsZ2tlkugAD7IqWq9/z98qRsUnWYXhKS2l8Xz18g0Tezf20SLKgcF6wiXjzdU8f+1GUfv4O0XnpVYIwfJM3Pzj2LmaX31pxKSMsRUMCsH/xu01xe0j172lF3o1GTE+uHqGUdc5w9rGNhp7viijePoaKwjwiaSnb5qT4wNqigtIVGxwbwvRUZG0jy5bz72D0B69jXm4B0YLvXB7SP5HjPmSZeG/woKCSBWiMSTAh/TiSrbUD+MMRb6tD9fjI7RVQ++M7aD5ggujzb/oTvZJCHpLSEIhG6qb/BxqLebty89Jq+1jfnmVg1udZEfbc0RFxxCeWm7tfFucHibIO4bW1jbyhe/NKq5j9+Se3xXPvDvTQ4QQv8XN/ZzcmgP68YrO/AbrGPyNsWY+++2v+fX3zpTX1pGVHIvD06956RrC3O6p9UXWCdEZ6BPO6OIui6PteAgl7xGYyPzqLheHs7z89kt+/sUzayBWnJOBy6unfP3Mhe6ZLVHoLBxtzpIY6ouziwdJ6VlkZ6bi7e7MG69guiZX2J5u59Wzl0LpN6HYRJ1qAY+XX/J//Ldf0DG6hlm84MnOMp6/9BDB5wwdDSX8/N9+wQvPUKqqKkmOCuHr3/8Bv5hcNg8+7eFsEonEhsV8RnKwJ56ePqRmZRMREoCzqzfpBdVsHtps032WxzqIC/OgqG0I0wOPp6UkIQSH13dFp25nUtjEZ/zyd1/g6OJGSFQCbQNT6JQI99LAdH8T/j6+ZOQVkZeZQlpGJvl5OaQmxFvnUh3u77G5ucnRiebayV4KsdpQmEJQRAITG7cWvflE0B6uk50YZm0FX10cFX4hioD4PNSPznO5YKSxmGAh7hoGl2yHzAZmhZj4w28/48vvnuLm6UV8RgGTS1vvHBZ9xWhHBW+dXW9EpyKAFwYJCw4gMT2borwsUlNSyCsoJDMpjrzyVlR7u2yJPFAdqq9Hx3CuYbCxhMCgUDqmxH1/+LaSeyhBaVtFBk5v3YXoFH5YHDsX5Toj1h//gGjmNq+CMjNb80P4vf2ejKouju7pieP9dVKESAlPLkH1YJERLSNNBTx55XJXdN5ieaoPf7dXVtF5tTjKznwvCbHhxCamUlyQR0pqCjn5+aQnx5Fb1cLG1jbbW6I8HBzfWjDRwuJIB75vXtEysY7xIy8Py9MDeHq8tIrOq8VKxltKRV0S9q2l3/pZwaA5pjgtEM+gRGaUoeS3EaJe6STwcvJkfP3wgc08FgFvfKjzA9F5zZmK8rR4vn0TKGJJlfWQxaihpSKf4KAwsvKLycvJJjk5mZKSIqKj460dFtu7ik3c5lRruL6nMq+2PD2M8EQRkN/qBfqbxnBAXX4EX94TnTcYGagp5ekr1x8UneqtBQqSIonKbuBMd7+H0UJ3c4nwbXdF56VJS2GiH5//5t/55pkTXn5B5Fc0sXeqE39hYWdhROiIQOIT0igrziM9LU34vnxy0pJJEz54S7VnrWN7+0d36tiMqGPub32o75n6o4vxfAwcrM+RER9GfG4VS7MT1nUfkgqUhpOH9eHCpKe9OJ7QuDTG7PXhNqcHm0T7OxJ8T3RO9tTy4rOf8Ydvn+Hi7kV0Si4TSxvWOerK3NmG4kyCA4MoKiunMDPZ6iuL8zKJjstgYHKOQ9UOm1s7nOpuhLDheJW46FAisypYP7gRsR9EdJqE6Ez8QdH5FeF20bk10ciXX/weh+BUVDqlle2U/oZcvvvDF5T1LljHbzdlJRDo4UdmbiHRoX4ERqawuKsWYtDC+f4kr77/nG9cw5haF4XPZGKytxHHrz8nqayLM+0Z7aUpPHvxlpLGAYzie+WcpfFOfJxf4xtXJArtDgmBnoQnFbB7akK1PIKH41P+9WefU9nUK66hoTE/Hp+obBb3Tuiqy+FXv/09EbkNGM/P0aoPKI7z48nzN7RPrNh+qEQi+aSxmFWE+77ln//t13hHJlLf0CgC3ghefP+UyPQyVI84hXERYIV6OdE8Mn9nyIkNDcXxD0Xnhe6IkaE+qqurKS3IxvvtK56+dKama5JLiwWT7pThjkYy09PILqmmp6uNgqx0UjOKGBoaID02BDd3d+IyhUDbEYGd4mcvz+hvLCQwPJLemZv5cB8/FmsQ21qZi09gJCMLO+K37JAWF4J/Qj7WkbP3udTTXJRKhPALQ/P2hessl5yqVunqaLU2DOamxYp8+5q3flGM2XtMHsfMSFv5tei8Clkuz4WIHe4mT7z39LxSOrs6qBKBUEy8cMpDwxSnx+Hh5mpb0GZx09rYaR3iO9hISEgw1T2TGK+iLcmP4lwEQ63l6XdEp/Fki9QwN/yFSFncuSoMIkBdHCHYQxGd7dfDPq/Y35ggOiqYhNIWjm6v/Kxg0TDUmP+DonNpoudadFpXQxVYzCYWJwYpyEwVwqeMls4uqkpyiI1Pprunl7K8dHw8RHmITmZ4dvV6BNTGwiiRQc4Ud00jQo2PmsXJPiE6X9wRnb0N+TiL31XWPmj9rGDQqinPCsczJEmIznt161yJl4p57RLF6u0Vn+0cbc3+sOg83aMs9Up03sSSp0fbtDVUkJqcQkVtI23NDWSkJpBTVs9gTyvxEaG4unmRV9HKzrHN1l6c7tNVkkFAuIgf7T3ef/Po96nN+wHRaTbQV13yR0Xn1tI4ieG+5NQMoXuwUJaZzsaiW6LTdg1lytrq7CitjTVUlZcRI2L777//nrCMcg61JmsdW5keoDA3g/TsfDq6OqkpLyA6NonWvmEq81LxE2UxMDSRoem16zq2MjlC6Ju3lDX0YbD3tH6cWNAdbVJdkExARBIrh1ouDteFdvIhubCSvbN7rWcCk0FLSaIP0WlZQos8rE/HqvVHRaey0m1PSxO1VRVkpyfx5tm3vPFWhsZuWuOFs4N12utLSU5Jp7K6gYHeNuIiIyipa6eztYH4EB+c3X0pqGkXus32XErnYmpcnHXY/MKt+vThRKcwJE5+EUzeM0I7G2NCdH573dO5OtyAozJ/puxmuMbGXD/ejl+R1zKOTryQjoJ0Xvzq3/i/f/YrXIIT7syd0e9O4vzageicOjT2+qBaGSfG5yXxhY3sbG2SHe0vBGMqs1s345mVFtPylFCc3wawrDqgozSBUOEgesbmGepssLZixsTHkZpXxszCImkRXuTUdnMmCrYyjOClkycNw3aBaTlnuCEHl7cO1AvH9d5fsEQiee+YL1SEeL3gd09caBpawGA0otlbpTQlnK9fe9A9tfZg4YyhpkoC3zjSMbbyo0Wn4nyU0RRKY5hBr2VxuB1/FwfcRRB3FTApc9/3VSpONSf0NNeQnJBMRXUdpcIhp+SU09TcTESArwiymjjQKBbIyEhXGUGRkXROiGe595wfKxaTjsHmEp5/+zVhqcXMrmyyO9eJr7sjDj4RzG7scT+mMZ9rRQAST1hUGuPLt3qyLGbrnH+TyDed5pSOigxevXxhG9HyzljkcdGpYL4452h/n5OzUyYGu0iLj6ewvJbq4iwS0opobOkQwW4wyZmFrNlHvKxMtRERFUp5x9jdZeglf5THROe5ZoeMaB/8g2KYv16MS+npHMT/7fdkCx+tvuOALewvDREd4S/ys4Pj+1vY/JmiU0EZAnh8dIT6+JC5sV7SkhMpqm6mKDONjJxiWkSQl5EUR1RyPtNrtqBsZ3WauGg/8tsmOPmhpZg/Ah4TnSOibrp5ulPaekt0ao4pSQ3EOyyFufW7Ys6iP6K1KpfnvglsHv3HReftN6ZsA3F0dMz+zhqt9WVExafSLMRnZmoixRX1NNWVExoo6l69vR6LZxkRee0fFc+csCMSwV9IdK7NDRMd4k5B8/gj89cfF50KFrOZC2FXleG+p/urlKSE8OW3r+md27bWd0WYqg+FzT05YV7UsfzMRHLLa6goKyQuMYOmphZyEyKIS81nZuPIes3NuTES/Rwpa+y+mWL3EWIxqmkpTubJt98Sl1fN6voGcwNteDu/xDcyicmVXes7uI1JxAf5UW7Ep2cLH/NwHZl3iU6h+7i8EL7QZESrUTPZXsz3Xz0hsaDR3vilbPt1wtHhIardLVpKMknNzKOhrorkxDQKS6qpLc0lKjyM2p5J61QHpSG2IC6F8PBsFtZuel0/iOg8N2nJjfLijUcw44vb9qM2tteGcXr5ipTyLs5MFpYGGnF29qag9mookyjAs4MEuX9HYes4WlE+FdH5+re/5J//+ec89whjZucmsFBEp4uDMymlbSLMsrG7PE5yqCPJJY3ifmukhnkTlJgjxOVNJp1rDqnPjuato7dVdKpmWwkNCyctp5CC3CIqK2uZHOsmKCKWyqoy/LwC6BbBm1JdakWQ4eQSSMeUffnmcz0jLfl4eb6hQXFc1qMSieRTxnx5RoK/E75hiSztX/WQXDDbXcE3L52o6Z16IIBG26oJdnWidXT5gcN4t+i8h1YEWkkROLgFsbh9dzGSg+UxEVQlU1TVSnd7PYkJUeJaWnQGI3V5MUKo5TCzrQR3JobbSgiIiKRLiONPRXRqT1Tkxfvyr//8j/z+6+9xdHqD45Ov+N//4+/4r//zHwlKymfp6K4Ls1zoqMtPIiIyjdGldy8apF4eJMzHmRBxDe07jfS7RecVWtWKEPtCWORXMzjQQ2p8ME0Ttn3u+uuyCImNp2t2y3ru8ngrIeHBVHSNy57OP5G7otM+p9Okpig1Ak+/cIaWbEGmCH9YGmnm7bOXVHePWc+7zf7aCFFRgSSXt3Ok+cuJziv0R1tU5iuNPyXMLE7jFxpLnTK07/yc4dZGAn2iaeyYsp67szxBTLg7he2TQnRaD320PCY614ca8HB3J63ippNAGemVEuhIaFKBCITvCRcRWHfUFPDSI46NW/sOX/EfEZ1XLI52kJaSRH3XCG0V2SQlJLCxreLcqKUw3puUjAyOlHzVHtJfnY1fRALzGz802uFviL+Q6FR68ONCvcmrHxVC7/457xad95kdquHp519SO7hkm9Nrx3K2Q3VhNqlZRUxN9JMjykRx8yinunM2R4XPjQynoW/aeu76zBjR7k6U1vc8Wl8/Fk73lq315p//1//gyycvcHR04Ok3X/AP//Pv+Lv/+a/EZlZwdM9RKT2dhXEexArR+djK9O8Snfcxa1fxePVGnFfE3h2beM7KaCdREfF09g3RWpxDSnoF0yLGMB0sUJ4ZTUx2FTrxWi+F382OTSA4QojO9Zv69EFEp7IRbF9VmlXBJxc2cLWgmUV3SF1BPF8/fUvjsG2/nvn+Bt6+9SKvutt2kmB1uo9At+8oapuw9nQ2ZycR4O5DemY+IQHeuAXHM71lM2AXqimr6EwsbuZq7a2dpTESAl+TVNLA8dE+NZkRPBOVpqHvZt+1veVhQoRI9AjJ4OBMh8WoIj7YG8eXTwhNzKVnase6V1WQmyu+Ho64hmWwvGMLaGqKsnB0DqBtwrZYgCI6Bxtz8RTXaxyal6JTIvkrwCLC19aCGDy9/GgZtY1qsBiOaMiJ4ZmwWX2zD8Xc7EALUf6uVHZPXs9dueER0WmxoD87Q2sP6hRUCwOEeLzFIzSFneObFQXNhgMqshJJSMtnXaVmvKeG8Mhgpuw6q7koltCULJvoFIK5vSKLwPBYhpcfzqX6WDEZdCzOjFu3jWlubqapuZXGknRefPcFnz97S/PABGrDvRdrOae3OleIuwTaJ2wrnCu9UGenp3caBYYb83jr8JL00g7rsCv1wS4bW7voL24HJn9EdJo1dFblER2dZF3YZGN+mOgwL9rtPaxjzXmExMTSaRWdF0x2VBEYKETp8Mo7AzbJ49wRnSM20aks7tVdk8urV47W/LFiOaMqK5JXLqGMLtxt5FY43F0gLjqMiMwq9tW3VuhU+I+Kzks9A83lhIdHM7wkYob9JZw8Q2gWz6sw3dshRGcUdW22PXPXRGwT5PqS2qGld2//85FwW3Se2OfpGfcXCPV8i4tfHLv2V7k53cObp8/JU0aC3X+BZj1DLVW4OPoLu/SwQeg/KjrV20vkpMSRWtRg7aVpKUgkNiaOU63NvpYl+ZCYlsaJ+EPj8RZ1ObEExWaz+sjQxL9J/kKic39thqzoYBKKOtA+2K/1vui0HVV6yk5Otdd7fyq94tU5UXzzvRP987aeThtmBkUdi4xOZEDY1f3VMetWRuVdtiksR7P1hESGUdtrE51z4/14vvGiqn30o27oU/ZJnZ8cpr25kdbWVqu/K8lO5tnXf+CJoydtg9Po79UnZcGg2pxwwhIzmLJvIXSbR0XnhQmtRsQYppt8WRqs5dkTYdOKmkSdvbFphxvzZMZHklXezuHREW0FqcQlV7CqtH3rN6jNjiZK2FHFBV8a9kiMjRAxRzErt+rTBxGd4rac7swS6+/MV989JSIhjZKSUtLiwnF49oyQpCJ2Tm1GYaK9gu++e01Kcav1s8LSeAeuz39FZsOItaezKj4YtzciUJvZZm6oGaenX/HaO5LR+VW0O+O8/O454VnVXFUZZahNqOsXhGaUcqo3sLswSICrE87ufhSUVlBVWU5cZCAunv409C/YV5a0UBrnzT/91//E24gstoVB1R3vkeLzlH/87/8Vv4wGDq2bD4vzsuL55qkL9UO21bOUzVJ7qlN4/eo7qntnpOiUSP5KUK9PEBfqi5tvMMVlZeSkJuD+9g1JBco2JQ97Kw+WR0mPCSImv8m++t9tzsgO9eTrr97QtWr/W/MFGxNDFOXmkltYQmVFKRGBnji4eFHXO4XJPn7XbNQw0FRMUGAYjT32XpPFYZKjQ4hJK6Giupbw4ACKapWVGS+sW4gUJkcQEZPO1v2V5z41NAuECyHvEpbO2b2Y34aFucFG/AKCKGq0LXByYdAy2llPdnYOxcLmVxTn4uH8Gp/QeOZF8Kvs/1mVHYOHfyzz+7cDUDP9QoR8//QZccUdd4Ncs7LfYBPhQUEUVHVaFwY6O1ihIEU43vgcyqrriQ0PJj2/lC3h3y6NapqL0wgIDGd68+yT6W3+WDjXHlKfH8e337+kuX/petrK0dYciRF+PH/tTG5BCQW56UIcuZJb23ctjm6jEX68KCWGoLAk1nbvCR8hWHur0/ntl8+o7pm2bfVwj/mRdpxffClEa8P1Xrg2LCyPdRETFkxGaat1YSDd2T4Z0eFEJ6RTVVNFSkIC8enFosydiNONjHdV4+zoxtia6lZQ/XEyN9ohYpo/EJxZxrHWLgjFb+iuzuXti5cExWRQVlpATLCvqFcpzG7s360vVswsjnYS7OZE48jKg5EhB+uThHk95StnfxFzPdL1e7JNXkwov/rOleHF7TvXN2n3qS5IITA8jlElKhb1c7q3nvAQf7ILiqmpLSMyLJLqln5rg4Wyg0FKqBeJ+YrtfkTg/i2iV0S9Lz//4jtGdh4O11REZ2dJDr/74jlZTcPvFJ1G9RbVuQl4Cht9pL2fj5e0VGfxzbOvyBb2+cotGk93qS0tIS01i4qqKnJSYnF540hsTg2HV+VN5PjKdC8RYSFklrVYxY7maIuizAQCwuMpq6ghOyGC5OxiFnaFHbcYGOmsxNk7nO6p9Uf3jf2Y0W7NEOThKIRdEapHth9S9rydbivCKyCa9pGHq/ce7a4Q6PwNHmGZrO7a8/P8lMmhNpLTMygpq6C8pEDoIUc8g0S9Wdy6tkOnh9tU5aUSIHzk7KZaiEczMz11hAaFkpxZTHleOvFRkTQJzaR8p9kaJyrMj8yqdg50N8/6gUSngpmN+VEyE8NxfisEn7ML7l6+pOSUsbR1o9DXJvtJTEynqcfWEqiwuzpNbko4baMrKNMehurKyErNYWFbyEph9Aabi/Hx8qa6c0go+1UyU4SBbx/iqqgfbi9RnhtLeUuPUPeKublgYbSLpOhQ3Nw88PLyISI2hbaByTtDAeaEwQrw8CC/tsuaEcrG8MNNeXi4uNE0tGjv7rfQ215PQnIuo0u2eQHmC6MQw02kpycyNL8p53RKJH81mFme6CPFajtc8fQNJLOohg3V/a0BbFyKQLmxJBMXv2jWDu/1qqCnrbKAhLgsZlVXQZwZ1fw4GXERuLi64iHsT2hknLCHY9dbrihcag9oriqlvL6DXbVNsJ7rhTPpbSVKiKCA4FAyCqtZ37HZ1pXxLsIDfMWzNt0ZpvRJot20isbsimY0D+YL2VDvzBIf7kdser51jorl3CAC0AbCA32t2594CX8Rn57H+KJtSoThRAQ8eYn4hyWzdGfFcQsLY50kJacI0T99N4g+19LfWkdhWS2L9l4bZfPslakhkiIj8PMPEvcoYHrJ1tt6uD5DamQQUYnimQz3om3JH+XCcMZoVx2JSWlMLOzeGjZ7KWKLEdLjQnn71gVvn2DrXOZ9e0P2fS5MOsbbSvH18qN9VIjX22rPorfuLxgp4oGhuY1HRifA9vIU2alxVLWNYbzd82C5FM/XSGFxuS3gVbi8YHNmgMzkGPz9fUlIy2NkTgS/4iuDepPy9HC8Y/JRnd63DR8fWyvTIqaJoaytH+2tPS8NpypaqwrwdnXBzd1DxFJpjC5svnN15uPtRbJifYnMrObg3t6DZ/trVBQkE5ddwpHxYYMBuiM6a8oJS8xl6d42MxrVElXlRdR1DFl3HVDQnezTVl9qXbwrICSckvoe9o6Ud33OtMhnbzcv6kSsabhe7fRvHKOaPhFPh8QmsnL8iOi3mJjp7bDWj7Zxpe68I5MvbWLP1cWDoRVRV++cZmZiqJ2ElDjaxxauezYvdIfUF+WI+NoZN+H3fANCySmtZ+vwtj02W9dYKSipYN7es6dsd7Q6N0p6YhSBAYHCvmYzOmurY9qDNUpTowgRPnbjQFkF99NCJ+pDaX4GVS1dHN3eGPMaM9rtaQI8vcgpb+X+DmIa9R6l2fHkV7ShutpyRQjxuZF2ggN8RIzhJuylLzFJWeKdrd0Z2aHamhd+Np+mLmHn7MZWd6IScUehdRsXZSvKyoZu1LpLYfoMjLYKm+rnL2zqrf2NBR9QdNpQJpkvzEwyODjE7OIqmnvO16TTsL9/wInmxgifm/QcHexxpjVaC432RM3B/iF6u+FTVrXa2Vhha/cAg9HA4cEhJ8oQWeu3ojCfG1EfqVCfnd3ZN/PkYIfx4UGGhsfY2H045OzcoEO1t8/J6VWLjwWj7pTdnV10hhuDqDk7Zf/gSByzP48yRE57ysHBvjDOpk+uoEskkh/Cglq1ydjQIBOzi9fzm97FykQPoT5e1s3rbY1eV5g5VR8Je3d4pxdU2bx+b3OV0eEhBodG2dx7aJssF8LOHR5xImzi7XBJEVfrCzOMjU+iOrbZLaMI1CpzkwkMVfYq27Ue+6S5FPb8+JAD9ekde34Hk4aOyhyChPhuHbfNpz3Xn7G6OMfQ4CCjEzMc3dpf1Tr8VuSFSthx070A1HDtk+6JmEuT9TmU/T7vCpdLdtaWGBu1+RXlq0shdLrri0RQFETL0JLVR0j+NJTWdJ3mxJoXeuPDOnd6tMvEyDCTM0uc/aCoN3OyO09iRCDxWeWs31rbQflOrztjT3WAVi/ijUey6dygFzHGvogndPZVie2IfFcfHnCkfrg35d7mirU8rG7u28qKCNKm+psJ9PGkemDuk5jfe27Ui5hGiaO01lW0b3MpYrSV+SmGRsaEvbo77/w+FwYNA81leHgH0ju5KALam2uZz00cHx2gErbt/j2smEU9FfGfsi2G4dbwQAWjRm0dUqu92jzVjlEZtjgzxdjULGp7r9vZ/grFGbEERKazctVAIFEMIdrTY3ZV+3fy5QZRP7S2+nGmMzxaP6442JgjNdKP2Pxadu80AFnQiWvsizqkXOPGuVlEHd5ndnKM/oFB5pbWRV7ev4FF+D1hc49P740UMbO/syrq2CjLmyqbeBJ+dLizniBfP8qbhzn/SZXOXwZlwcAjUR+OT8+4eFfDiFlPXW4sIeHxDM/v2A/aUAT58eE+h8fK39+8MJPwhWtLii8cYEz4wv2rvVRvoddrUAlbq7sX3ygabn56nImZBU6sQljkyfYiqdHBRKSVsH5vVeoPLjolEonkbwnT2SFdNUXWVSsVg/xDjvovj4XdJWWxoSRKGzuvewD+FthfmyM/PZmc8npOP/BS+ad7K5TmpZFZUs3ph30UiUAJ5ia6akhITKV/avWuePwJMBxv01SeR0JWBarTR3qU/qqxCNG/Qn5GPAW17ez91L/fcsn8eA8pCYk09Dw2117yl+DyXMdEbwNRcYmMrmw+3ojwHtGrd6grzSUlq4i1g8dHPfy1cLQ+TpqyT3djz/UUnJ8KpQF8dqid+Lh4+mY2Huw3LEWnRCKR/MRojvcZGx1mc1/pnbMf/CkQAZZqY5npmQVUJ3/djvcBlnN2NxYZm5zi8B1DLX8qTlSbzE5Ps3FnvqjkQ2I2nTI1Psbi6hbGn3h45enhHjMTo6zvqYUE+9vDYr4UdXOJsfEpDk4emTv4HjGLIHlzbZGp2QVO7y9CJvmLYtKeMDM5zuLG3iNrGrxfzoTPnZ6aZulvYjucS9YXZ5gWZVp9az7lT8GFSc/GygITc8sPRh8oSNEpkUgkHwDz5eW7h4O+R5Q9P80f4L4fBxYuxXv/0L9f2X9O+Fv7J8nHglI3LkW+/NSlQykPSrn828ZeN3/iHjDlvlab+JPf928TW1n/UHXsp7/vB8O6D7VSru2ffypEPfqh+iRFp0QikUgkEolEIpFI3htSdEokEolEIpFIJBKJ5L0hRadEIpFIJBKJRCKRSN4bUnRKJBKJRCKRSCQSieS9IUWnRCKRSCQSiUQikUjeG/dF56EUnRKJRCKRSCQSiUQi+UshNOaUSP/5SnSeig/X63aLf1uX3JVJJplkkkkmmWSSSSaZZJJJpj81KYj/zxuNxr+/Ep13NlZRTtLr9TLJJJNMMskkk0wyySSTTDLJ9CcnpSPTYDDsHx0dvbSKTuXAfZRjMskkk0wyySSTTDLJJJNMMsn0pyaFy8vLA6PR+NoqOu/3dEokEolEIpFIJBKJRPIfQYjPfSE8X0nRKZFIJBKJRCKRSCSSvzhSdEokEolEIpFIJBKJ5L0hRadEIpFIJBKJRCKRSN4bUnRKJBKJRCKRSCQSieS9IUWnRCKRSCQSiUQikUjeG1J0SiQSiUQikUgkEonkvXFHdIoP9sMSiUQikUgkEolEIpH8xxE68+D8/NzBKjplT6dEIpFIJBKJRCKRSP6SCNGpury8fHklOk/EgUv7d5K/Zv6EXm3ZEiGRSCQSiUQikUj+XITGnBf/+/sr0XkoDlzYvno3xpMjZkaHaGluoqGpkebWVgbHJlEdn9nPsHOhZ3N1gZn5Zc705/aDHzfqw11mZ6fZ3D+xH/nTMOlOWF9dZmP30C7WLBzurNLf20mjeFd9Q5McqLXWb943p4d7TM3Msr57YD8CZ/vrlOamEB6dTFffOAuLS6yub6Ezvrut4cKgZWNlibXNHcz2YxKJRCKRSCQSiUTyYxAac0qk/3wlOo9+jOjcm+wjzMuNZy8dCAgKxM/fm7dvHHH18qOorpNjne0SFu0elQUZxKfms6q6J0g/UuaGOkmMiaaud+rP6OE7Z7y3kazsfEYXd4VYO2WgpYwAb3c8vXzw9nTj9SsvKhv70V++//7D+aF2YmLjqO0asX7WnR5QkxvN6zdviM8oYbC3m+zUZHKKa9g9MVjPeQzzuZahtjoycgqZXLsRsBKJRCKRSCQSiUTyxxAac1qk//Inic7ljmpeP32Bb0QSff399HS1U16ch7+7A3/4/GvSy9pQ64xYjGdMjQ7RNzjOsdZo/+uPG9XmCv3dPcyt7/zJovNkb47k6CDiM0tQnehZHGrC+dUTvEMSqG1qo6ujjZLcEnoHp9BdvP8+Q9XGEr09PcyvbVs/H20vE/rmCXElnZyY4FJ3zEh/L8Pjs5wZfjjb1yd7iYsIJqWogdNzOeBWIpFIJBKJRCKR/Dj+DNFpYamzBncPf6q7xu3HbJxszRLr+Yzffvma9sk1q2i7ODeiNxi5tK9RdGEysLe1zszUJDPzS6i1D3vYzo72mJ+ZZn5xlaNTDZrTEzQarXVo58W5gbOTE4xGI6rtDWbEees7B1w80EEWDnbE9+I+U7MLHKg19uM3nB6prPeZnJlj90CN0vloMV9gNOgxmW5eg+7kgMU5cd7UDNuqI84f66U0mxhoLsHHP5SmoSVx4JzarDB++dvvGFjX2c65hVk8n9Go5/RMg178lpPDXeaU37y0jtb0cKir8k7mpqeYnlvkWPPwnRm1apYX5picnGJ1aw/jubiGxYzBYOLi4gKTQU13YwVPf/0bQtJrmFzeQGc8xyS+M4j7315DymzSsbG6JK41yfzKBmd6E2admtbyHHyDohle3v9TpoRKJBKJRCKRSCSSv2H+LNG53FWLu7sfZS0D9mNXmNmYaOL7339GYnkvBiEQBzsbyKtsYufEiOXynI25YbJTE/D39cbT25ec8iY2D2yCUPl+e2WagvR4fH18CI9OIr+wiPS0JBraB1H6SlUbMxQlJ1Pf2EZxXgaBPh74hcTSM7aA0a48zw1aFka6SY+PwsfHCw9vP5Kyiple2ZVOOQUAAP/0SURBVEH5cZZLE3ub81QWZhIonsPNO4jShh40JthdmaSypJC+0VWh2S453lujviyH4EAf6/MWVrWzp37Ya2s6U1GcHkl4SjGzO3pxxEBXVQZ/+OwLkktb2Tk6tZ14jYUtca+ikiJKqutprioiLMhP3COQ4rpOa0+p9SzxTvbWF6goyCDIzxdf/yDyK5rYUJ1YRb04AdXmMjUl2YQGKn/vTWJ+NSr1KUdrMxQXVDMwNs/Z4TQRPq/5r/+f/8S//OpLYtLz2dhYoamlibLGDg61ypxbC5qjHTrry4gKC8bd05OQuCymVnaVO4l32kOgly9Ftb0YL+V6UxKJRCKRSCQSieSP8x8SnaUPRCecHizh9+prQpNqUO/vkJ8WzjPvCCHENFwaNUz1NZKdV0BlVTU5SWF8//QVGeXt1l7GfSEok0K8eO3gTlpOIZWVlQS7veJ///3/xDexFEWGLY228NX/+nueOflRWFFNmRBjr77+PW88w5naUCsTEJnqrsfnzWv8g6MpKC4lPyuVN69f4hWewsKeHu3+Ormxfrh4+pNXVCKEbQnNHcNojRameqvxfOtIQe0Y2pNdStKicHTxJjO/iLKyYhpb+tg5eLgQ0KEQsQmhbuTWtHEoxKv12NY8KWGefPb7LwmMSWVwapEz3Y1gnRus4+k3v+NX3zpRVlpOdWUZMUHefPfdS3JqezAJYXe0MU9OYjhBETGUV9dSXpSNu4srSXlVqHQXnO0ukxkdxCtH8c6yciguKaaiqYuj01OWeupweO5OZnErl4Y9SjNi+MXf/wNPXIKpa+9DvTlFWJg/zuK9bJ1ecC6Ec01ePK9eOhKVmEZBSQlFZXXMrdqG5+4tTZIa6kliTilHxk9jYSiJRCKRSCQSiUTyYfnLi87DVYIcviEkrpyj3R2Kc2JxCIxldktZDdaM/uyIPdUBarWanYU+nJ98jndEhhBQ53TWZPH65StKW8fRGkycn58z3FzIN5/9Bp/4UpRBpUtjbTz5t1/iLz6rzvScm0w0F8by8uVrqjun0e2vkhruzSu3EGbW9jCJa5hMRnpr83j57DX59QPsrc0S+PprnPwSWd5SoTMYubiw9dxN9NQQ4OlBRcs0x9tzhArR+8wtksmFDXGegQvTBebL+/MxzWwtDhHq/prKth5rj6wVi4WTvTUq85N4/fRrPv/qOxKEWNw5sg23nRuo4/V3n+MYmMTu4Zn1957tLhHt+xbn0ESWtnfE7y/iybOXZFU2sb6zx+bKHNFe4pkcPemZXqGrrgjnF68obh1DJ96Fco3z8wslY5ntrsPFwYus4nblYVibGcbju+9JrxlDbbjEcrZMdLg/XvF5HGhNLI004+bwhLi8evZPddZrmcQ1Ly5tReJkZ4Hi9FAiMwrYPLUra4lEIpFIJBKJRCL5Af7iovNoYxSX774ktqAN3dkhJTlxvAmOZ37nTPzpBTurM9SWF5OZnkJkiDc/+/u/wzc2i5WDQ4oSgnH1CmH5lqA53pgg1MOBsKQilP7FpfFWXL99QkX3vHWorMJcfzXevr6UNwywNt5HgJcrkYUt6G9N9DxRevU8nAhPLmBPcyaEaiIvnr4gKCqR5r4J1EL0Kkz11hLk7Ulp4yTnuhPay7PFec/xCQynsXuIY81jCyKZ2ZjtJdDhFbXtvdwfeGoyaFmZHyM7MYQ/fPY5MdnVnIlnmx9twd3TncSK7lt/c0FDUSJOwXF09g/TmhPFP/7jP/LVs+e4urji/PYtX/zm57xwDRLfD5AeF4mrTzSqh5Name1pwM3Jh+ySLuvn7aVh3L+1iU6dckPdKjGRQfinFHOgPqS7Ih2HF06MKnn1CGf7a1TkJwjRWcT6kRSdEolEIpFIJBKJ5I/z54tODz/KWgftx2xYLgz0VqbyxZfPqBmYw2w+oSA9GuewZNYONaiWx4nxdyMkJo36tj5mJ3sJePtECMFc5re2yI3yx8Mnih3TTU/iyfYUoV6OhCQUoMz8VESn55PnNA6vcTXAc3awDv+gYKqah1ga7sLXw5W4ii5Mt1aI1ezOEe39lqCYDI7EZ4NGzfRgO0nRYbx8/oqY9FJ2TkzMDzcJ0elGYe2w9e+M2lNmhrrISojk9auXhKYUsLh7fw9PC5vzgwQ7v6KqvYd3yTHd6RFJfk48e+XN8Nw2y+MdePn5ktkwLGTrFZc0FCbhHJxIR1cv9ZkRfPPKmcauPibGRxkeHmZqcoq1zV1Od+aID/TDxS/Z+m7uc190bsz34/L1tyRXDHCmPKTWJjoDFNF5pKK9OBGHly4snjw+X/Nkb5nSrEgi0wvYUJa/lUgkEolEIpFIJJI/wp8nOjtr8PAMoLZ30n4M9CcquuuKef3913hEZbNxrBenHpCdGoVrRDpbB4eMtxbw5W8+o3pozdpLeXm8QIDTl/hEpbN5oqMuN4bvv39B09i67aKCoaY8vvrsN/jGl9nmdI614vH9M+r7lzDZO/em+2rw9Q+gsm2Uk5154gJceOkexvLuzeI9w02FOLx4RXppM4bLS3T2ns3Tgx2ywtz44qsXdI2vsjrVTpiPO4U1Q5iMBgwmm7gynB5SnODP7799QVHrqPXYbfbXp4kJcKagvoMTRUFeGjk93udAbV/wx4qJ9AAnvnvqysDMLpuTHbx9+Q1uEbnY1w1CI4RdqLsD3pGZbKr2aS9N4esnDnRObNhOsGNShgNfnFKRGsl3372mYWzN/o3IC52O8wsjcz31uL3xvRGdc3120Tl4LTqjhej0SyrkUGtgoqOcl19/SWbNwLVwNup1aHW2h1OtTpMR6U1sVhEHeik6JRKJRCKRSCQSyR/nzxCdsNxewfeff87TN57k5xdQmJ9LfHQYzo6v8QyKY2Jl1zZcVL9JUnQAz3xjWFGpWR5u5vXXXxAQk05VbS1VxSl89i9/h4NXJLtCAypDVH2dX/LMwYvsvGIamhuIDfXi17/6JWGZ1dY5nfOD9bz6ze8p75q/Fp3jnaW8efuG7JpeIfb0DLeU4/LiOQFhsRSVlFBSlIuvhwtB4r6Lu0doDlTU5OSToyxWVF1FfJgPHn7hTK8eiOvX4f3GgdzKITRHu3Q2lpFdWEhFVQ3JYX64+oTQNqpsiXIXvXqHnIRgojIrWFaGnppPGO+qJDw8nPjUDPILCshIjObls2fEZVdwrL1gfboTp+d/4LffvqGkuJKKshKig7158dqFqo4Ra+/n5uwgQW6OvHoj3kmBsphRBek5xXSOzGIyW9ieHyTcx5mXb73JFnlRUlJGYWUT+0cHLPTU8Op7Z5JyWq3PuCbu9+yXvyKqqBfrCGbNEkH+7jiGprB9dmkdPput5NcLJxIzciguLiErv4yJRVsjwMrUACHenuRUtKK3z4GVSCQSiUQikUgkkh/izxKd+/MjxIf48ezZC5ydXfB09yAwNILc0hoWNw/sZwkMBzTXlZFaXMuuUDmGM/G5LBtPVxd8A0MoqSwjJSaSwtJaDpWOR7PROpQ1OsgXT08fErMKKC7IwuW1EGq55daezp3lMTJj4hic3bnem3NtdoCc3FxaB2atn01aNUNt1UQE++Hh4YG3bwDJ2SXMrNm2/tAc7lObnYGXqyse3t7EJGfSP7EgRJzSGzhEUU4WPSOrGDVHdNUX4uvtgZu7NxHRyXQOTnNmeCi4LBd62qvy8AqIon1sVRwxsSBEdligD2/evMXF1R0fX39ySupY3Tu2/s38WBseXq44i7/JS423bt/i7RdERUsfR1rb3FHLuYH50S5iw4NwdXfH28efyMQchqZXOVd+v8iupYl+kqNDRD644entT2phLQfqU/YXx8lKzaO5c8p6rf3NWdIjoqjtnUen5LJhj8qyQrIrm9nXKBlgQbU2R0FaLD6e7rh7eBGRmMvshsjTCx3dDYV4+YXQNbWJWW7UKZFIJBKJRCKRSH4Ef5bovDQZONzbZn5ulomJCWZm59jaO7gWgddYLtFqTjk+OeNc2RNFcGnSsr40z/TMHEfiuE6rRXOm4fzCzOXFpXgg0J4esb62xu7+PsPtlbg6OJJe0midw6ncW314hN54fj1s1WTUoz45QaMz3hrKamZ/e52pSfF8c0uo7SJOQdl/8+z4gIW5aSanZthSHd9cy6DnRH2MTm8bfqvMw1xeUH7nFOs74jfeTL58wK4QxFFC6CbnVnGkMYpn1aPa2WR+doapqWmW17au9xJVnm+yvx4PP39SqrrZ3VpjdnqG1Y3dh+9RcHq0J76fYnJ6lq3dgzvzVRW06n3mZ6aYEu9190CNssDupcmI+liNVqv0EQvdeG7k+PDQum2LWbmHyOqz0xPUZ1oubq3IqyygtLIwx8Sk8ptV1ufZXx4lJSaImKxS9vU/8BIkEolEIpFIJBKJ5BZ/luh8LwiBerS1Qn9PNyPT82xtC1E73k+YrwtvPEPpn76Z5/nRYtbRW19GUkoOw/ObtwTwY5iZ6K3hrbs7CZV9NhH4kWIxGxjtrCM5NYO+mbtzSyUSiUQikUgkEonkh/h4RKeQaPsr46TFBuHs4U1YeDg+Hq64eAXS2Dd1PX/zY0en3mduZorlzT3snbvvwMLCeCeRcbEUtY79kXM/LBdGHWuLsywsr7xzZV6JRCKRSCQSiUQieYyPSHSC+cKEamuFnq5Wauvq6OwZYkd1dGfo56eAeI9civTHUIbMzszOsrp7ZB1W/LEiygTmS2Xo8yei/CUSiUQikUgkEslHw0clOq+4PDeh1xus8zz/mrGKOSFO5aI8EolEIpFIJBKJ5K+Vj1J0SiQSiUQikUgkEonkrwMpOiUSiUQikUgkEolE8t6QolMikUgkEolEIpFIJO8NKTolEolEIpFIJBKJRPLekKJTIpFIJBKJRCKRSCTvjR8UneIzJpMJo9Eok0wyySSTTDLJJJNMMskkk0x/Urq8vOTi4mLaYDA8Ljr1ej07Oztsbm6ytbUlk0wyySSTTDLJJJNMMskkk0w/Kik6UqfTcXx8PL29vf246FR6OU9OTpSTUKvVMskkk0wyySSTTDLJJJNMMsn0o5Myelar1U4fHh4+LjrFv60nySSTTDLJJJNMMskkk0wyySTTn5oUxP+nLy4u5EJCEolEIpFIJBKJRCL5yyM0ply9ViKRSCQSiUQikUgk7wcpOiUSiUQikUgkEolE8t6QolMikUgkEolEIpFIJO8NKTolEolEIpFIJBKJRPLekKJTIpFIJBKJRCKRSCTvDSk6JRKJRCKRSCQSiUTy3pCiUyKRSCQSiUQikUgk7w0pOiUSiUQikUgkEolE8t6QolMikUgkEolEIpFIJO+NDyY6NeojluZmGBsdZUSkyZk5dg5P7N/+6eyuzlBelEtFfSsr2weoVCrUGh2XFvsJP8CFUYdqd4f94xMuzPaD/wEuDTp2d3bY3j/iscudG7Ts7u6hOjxCc3rE7t4eWuMFP+JRH2C+0LMyO8Fg3zAnxj/nChKJ5C+NVr3P7NQk88ubnP+Yanmu52BfxYH6zH4A9CfKNSaYX93CdO8aF4YzludnmFtcxXDPYmtPjlHt7GIQN768OOdoX9iag2Nhi/667INRd8bW5jbHZ7oHv8wi3uf68hxj4+OsbO5hPL+0f/M4mlM1O3sH6MR5Rp2GlblZxsfGGBW+SUkbOyrOL29Zc7OBTXH9iakZ9tRa+8EbTg93mJmaZn3n0H7kCjOH+/vs7autz2yy+4rj04fXkPzHuNCfiRhjmtn5ZTSmH87/K/ZVe+wfHnNuz2qjVs38zCSzC6voz23HrjCbDKwvzjMzv8SZ/m4lNGjUqER9PtObxKdLEe8csKfa50c+xkeKhVPFron3MTw8wpioH+Oifk0IOzc5u8Dugfo6flJ+/8L0pPhuTtRPve3gH8GoPWN3eweN7ur8S/Y2lpmYnGL3HbHh2dEe0xMTLK3tiLPvYtAcMzczzeLa9gMbrPyd6uBA2E7xwJcmjg6VeOz4wTUkP4xJd8KCUj/mV9GaHot27yHkhfpQheroRNQFW6aYRB1bmJliVvgy3b1rWER8u740x8ysUseUunSDSa9hTykvWp318+nxvoirdx74yk+ZC5OOU61e2KObH3Vu1LMj6sXkxBgjIzb/tLCyzpkIBH7op1+Ka23u7Il3fM7FuYGtlSUmxsTfK2lkhMXVde6ZMYGZ/Z11odPGRKyxxpnWYD8u6tfZkbCNM9a6d8e9Wixoz9TiXgeYrF9cWPXVwdGp/esPIDot5ktG2+oIdnPFzdWT4NBQ/AL88AsKIaugkoWNffuZP46DzVkyYgN56ehEekE5za1tZGVl0jm6KAKvP1YRLBxvzVOQnUplS48QbrZjBr2eszON8oKsZ/0pXOoOaCzPJz69kM3jm0y6YnG0k+zsXNr6RpjoayIjK4eZzaMfJZDvc67fpzIjBj+3YJbV0mRKJB8Sy4WJ1ZlhMpNiCAkKIiAwjJLaDg5OflhULIx0UVFRw8zagfh0wersMNnKNYKDCQwNp6i2k4NTWzB2IgRNW305SfGxJCULu9XQxbHdGZh1x3Q11lFd18GpURGdBpbH+ygqrWJ+fe/PsjEfI4qo7KvNIyA0lrax1VuNexYONxcoy00jIiyUsPAwskpr2Ng7sn//EMOZipaacurbh6yic21ygHBXFzy9/QgICMDfz4+a9j6rU1c42V+nqSLPev1Q4bui49MZmlm3C4oLthYmKM3PJjYmhozMfHrGlq+DhqP1KaoqK+keW7QGCIazQ/ra66yNpaqTHxecS/4YFlRrc5TnpRMSEkRwSAhpIq5Y3T+1vvNHEX7+YHOG0tIyhqZXrOftrkxSlJVEsKiDwcFhZBfXsHNkq8c6Ib76mmtJS4wnNj6Jsqom9tRX+Wekt6WWGpGn20dKQHzJ5tIkFWXFDEytfMJ18JKlmQGS4iJF3fAR71WU/7AQfH08ePHWi+rOMWs531qaIDc1ibDgUKv9ik3KZHRhXQSm7/7hF0K89LfWUlXfwd6JjnPtIT1NlUSL6wcHBxEdl0zHyIwIiu0xzqWR+dFuMhKjCQoMIjQkmoqGHk71tu8PtleoLS8kPi6O+JQsmrqG0NqV5/mpitqKElr7Rjkz2kTnzHiPyPsKZtfuNxJJ3sXOygwluanW/AkSfi4zv5otUcfeicXM3prSOVTEiCgPSk7tis8lIvYODgwkQJSXjEJhq/dtDa8X+hN6W+vISE4gNi6RksoGtvfV1u+41DPS3Ux5dbOw7bZju8uTVJeX0j2+av38qWIR/+nOjhnrbiA7M536nkk0Fzd152BjlqykUFxcXfH2E/7JP4DMompWD/XvtG8mnZqxrgZKRSyiFiL2eHuBtGB/XN+64h8cgr+vL3ll1ah0N55UafTubCgnNjpc3COY+Phcxmc2xANeciZ8YE1ZEckJcSQkpdPcPcbxmVU8oTvepa2mlPruSbRGJZcvhM5poaKqjt1j3YcRnZfnJqqSwvnut18SHJlMbV0dhflZhPh68O0XX+LsG8n06u6PNs69lZni5TlT3DKMTq8ThnGMyqoqRuc2MN3KrMexcKpao7G2gs7BcbTKrz8/Y2Kom/rOEdspfypmLTVZMXz15RNq+hftB68wUZQYyKu3nrQML7A4MUBVTS2reyfcasz40Zzr9iiKD8b5hScLx3aDLJFIPgAW4fgmiAvxxsnNn/zCEtKig3ny5BXFDb1C0DxewXWH66THhZOYWcbuyTnHG1Mkhnnh6BpAYUk56QkhfPfkJeXt48K+GRhsqyE4NIKCijqqSnPx9falfUw4cXH5pcFGoiKjKG8dwdZofMn+6hRxYUFklDSiOn3YCPYpcrg2icuX/8p/+5d/J69tSrg1G+rtRfLE+3J8406aEHy1dbU0dnRbR508hsVsYqK7WgQ9odR2TStH6K7M5ttf/o6otHzKysspKy1lZHoB/bkZo/aY2rwkHF47kZBTRKVwvIFub3EPSmZZBD/6s03KM+KIik2kvr6erMQogsLj2dGI936upS43mZiEDMZX9mz3N+mY7G8mMMCfxr5JW8+L5D+E5mCN/MQwHBxdyCoqJjczgedPnxCT38DB2ePl3yRETmVuIqFxGcxsHImAa5us2CBev3Elv7iMrJRYnn7/HVlV3WgNOuYGmogQYjYjv5zq0nzCAvwo6ZjkwnzJ4cow0aFhFNV0CBFkK5nHe6sUZUQTkZQtguofEL8fNWZ2NxZpqqumsKiEquoaIaRL8HN+zr/8+5eUd4yK33VBnxCLoYHhFAkRV56fgcurZzgHJbKw9bigs5gvWJvsJdTfn4q2IWuv9EhrGW9fvyYgMkXco4AQH2deuQcxOL9lFSt7y6OE+bnh4hlAcWkZCeGBPH3mSMPgIgbtGa0V+QSHxVBa00h+RjJBwZEMragxC7E62VFOUFgs7SNzdhsJW4vDJEcFk5JXy7H2bo+a5CE69SZZccLOOnuQJ+pHZmIMz797Qkpx4zvfn069R21BEsFR6SzsHGM62SU7LhgnF09yC4tJjY/k6dNnJBe2WK+xNtlJeHgE2cVV1JTkEOTnR1nzkKhjZo7XJ4gND6GgrptDu9jRHm5SmpVESHQ622rDJ1rHRAyxNE1lcR4Oz77gZ//2c0Izqzm5pcoWBmpxdfwWJ59ACsoqRf0oo713WPh20zt+8zlrU71EBQdQ1DCA3mhiaaARhz98gZdPqBDuNZSVlNA9cDNa0mw8obksi7dvnAmLS6G0vIraqmYWlneFrTyhqyKVsKh4quvqyU4WPk7Ur4HpNaFHhT/tqCE0KIy+aaG/7PVrZ3GQ2IgQqtsHRZzyAUTnhclIuRBK3u4hjAojcsWl4YyOihy+/PXP8Y/PY+f4pnfAfGHiaF/F7t4+Gr2tkClGUHt2TFaYCPKcPGjsn0cjVLzy2sRvue6lvDg/5+LSbBW7p8fKMJcDdIabsTLKedbzxb8vxX125/tF4OjBC98EVta30egM14JQeY5D63Oobj3HQ+aFU3J9/j1BKZXXL17BeDCLv8trPEJT2dZYxL3Fc1mfVfnWwrnyrBcX6DWnHB0ecGa/x4VJLz4rwwf2UJ/ZhhMonOv2KU+JxOuNP0uyp1Mi+WBcnp/SVpGG01sXGoeFARbHLk63iPF7i0tQHBPr9lba25jPGaovIECInrZhpYHqgs7yNN46vaFhbNN6yoVml1gfR/zi8hmfnKa2MJ3konqU9mCjcNyF8eHk1Y2zublBQWocSVklbB/f9Jop0wcGarPxDY6ld2r1HY7p00HpFWkoyeDz3/6c33zxOZU9kyhW0iJEXWtZNm+F2Oj4ka3d2qNNEfgEEp1RweqBeGcWHdW5Kbi6RbL/iCdcGW3Fy9WZ0PRKbPLFIhx6D+5ODiLgnWR6tIfUqBBa+4at365OdBId6M7k9oH4dw/RYZFUtw1juGWqNcciDxNDiUjJZ23vz59iIhFYhGBpK8fV2Zm08nbroUsRQFWmh/L9K0/6px+WC4uog5sz3QT5+1LaOoJe+Ovx1mJcXNzIa7Dlo/Fsn8I4P154RjM7P0NbbS7x2WWsHInapN8T5TGNsOxq9vc2qMpJIj6lgIXtWw0dZiNzw23WBqLa7gn0f7Qx/NNAd7BJRlQAfkJILKuOxZFztjc22Nqx/3azjrGmHL746gW1PRPXjUO30QsxUp2XgK+IidasPWUa4v09cXAJZ1lte08bMx1C1LwgvbIH7fkFzSUJOHv40Tq6Zv3+aGueaN+3eAkbuTA5TFF6ogiwh1AiJfXKGDlJ8RS3zlh755KiQsmtbOVQaQiyYxFlpKexCE+/UAbmNpXA2P6N5DFGWwtwdXElt27A+tlwskdJUgBPXQIYXdx+xMdcsjwh6piXO9V989YpIVNNebgJX1kghJCC4XjH2iHj5B7E0OyaED1pJBfUsKKYxLMNanJSSMlrYEvEwM2ivkUn5bC0p5S5K8yijrUQHugr8lr4hNvTIT4VzJfMdjWRkZYmYgJvHF5+T1RmGepruWJhtDmXwAAvWkZm7cd+GNPpNo2FSfiFJzK/dya0jJnB+hJhI4PoHVu2n3WD0gi0PtaEp/Bz6aXNnN2bVnC0t0lSoCs1ncOitoNqvpcUYQOauvvZ3VwiMyacrPJOtKbbtd1AXWEqmbl5zKoNH0Z0Vggn6+sZxpio4LcxnOxTFOvD75840z1pcxDnQoxODXVah7JExiZQ3tDOniK8hEGb7K/n2R9+wX//H/+Ik2cEXX1j7Gxv0tvdzfzGnnVIx/LsKB09fQz0D1BbnE1UZAxlte0cXHUHiwrT19vF5MImKtWuKNyR/O6X/8T/+tlnhMSkMTC1zIUwQspczMnBTtKFAYuKiqO8rp1dtRC5j9gn/f4y6RFefP82gLVbQ2wnWgpwcHgjgsQ+8cnC+uIUHd197IlzLkWBmxnpoauzh6aaCgoL8umbWUOr1zI90EFOWiKRkZGk51fYhsqJOnVpkKJTIvkYON1bIjvGD++gKDY0Vw7PRHNxEq/e+lHf+9BJnOsOyEqIIDwxl1WVFot2h8zoANx9w9m9NvZm2ouTcQmMoa65hbqSTBILKjnQG4UD2KAkOZbS+j5qivNJSc5k+FZDng0LZztTBHj6kV/RjubevJlPCculnun+ZkJCwolPjMbby5GqzhHxlsX735olJTqEgOhszk7UHB0dcfrIfM9rhKtbEYGQn5sHDUK4Wl/3uRAXyXE8dQhmaf8A9enZnXn+Iw1FOLm4k9lsEyMK59p9EvxfklraSldbO5lxoTS0dWM0Gq3DphMiAoVzHyc7NZGcklpUp/caKy90LPRV4+ITTvvI0q2hwpI/FYvplMrsOFF/QuiZ2bUfvWRxoB6HJ08paR+xNlDcxqg5pqU4jcCgCGbXleHtZspSgvEOjGB0w97AazYw113Ody89aO/pprEmj8TsUmZ29OgPV2msyCEur4KB1kpiYpPoGX8YzB3tbpAa5ktcVjk7xzcNx58sFiNTPbW4vnGmuncG3aPhxwUbQ9V8881rKjvHHp1vt70wSpwIYvObRjmzdpKpRewUhJd/PIsHekxGAysz3bg6uQqB0s+pRkVGuDuhcVls2PslzLojWotTeSaEam9PO4XpSRTX9XNkMLI9009RZiqFNV00lGQRm5bH0u7DYaDrCyOE+rqTX9vFmfFepC25hdkqML39wxheuWpINbDYU8H3T95S2ztls6W3MBuOaa/JxcU7nE3rVBMzBXGBePqKOrZkn04nbPtUewWOwr5WtPWJWD2ZlMIaZvb0GHbnqc/PJDOvmo7mZqIjoq293veL04lqlXyl5y0qnUPNpzddwXJ5iVZ9wqVwOhvTPUT4OBGeVszxVeexEIw9FWl4urhQ09TF8ckphjvi7iGr08PEBvuRUdyIwfrCLmkvzebZK38qmwZQi2vcXvPAqDulNDUC35BYxmeXUR8fcaw+vR4efyxEZ2KACxWtfahF/VodEz5PxCDVjY001FUTm5DJ9snD3u7V8Q7yc3NIrR7+gKLTI5Th2Q37UTuXBqaaC/nii1fU9M0Ih29koLGU0AB/wqMTiI8VgYa3D7G5IuhSHzI31sarL3/N//xf/8Qb3xj6B8cY72kRmeJOcesoOtMl9YUxfPXN17gHRlOQk0mA+1vx+Smp5e3oRYYdb0wQ4OtGUn4Dq1t7NBbG8sVv/pV/+OUXhCVmi2dcFYbvjJ76UkIC/YmOTyAhOgJvDy/ic6qv53ncwWKipzaHp98+pWZwxRZIWAzkxPji7BnMsFLRzBoay9J54+7L0KIa0/k5OVFufP3VU/yCo8gpKGJoboXtzVVRCNJIiI0nLiYCh+ffExidwfLemXhfx1SkStEpkXxo9pbHiPFzISAyjZPrsfIWhhuLeP3Kk7LGIfuxKy45UYYJhYWSXdnFifDU2u0Zony98QrNtgopG2am2op45RFEXecA4931IkAOpbCilvrqMmKiYqkoLycmMo6atn4OdTq0Wq1wJDem/Fx/SnqwJ3Gp+axb55l9mmwtjhAfHkhGRQcjvQ3CL7ylrG3Ial/XJ7uJCPIiKD6TqoJ06zyhqPgMRuc37izEcMXluZb+liKcfSIZn7P7IaNKiPgQ/uEff4GTuydh0fE0dA1zorPlxspgE26ODgSkFKPRG6zCcntxCJfnn+MbXczc1DTVeXHi7+KobWikOCeDuIQUyotyiElIZ2R2BZ3IG61Obx19c8XxzixeTu4U1faieccwbMkfx3i2Z+259hO+fmbzKug0szU/gI/T92RUd3N8L8JRH2ySHh9FWEIBKmV0lfmY1EA3AkOSWLt27Rdsz3bg8Owldb1jDPa1Eh4abF1Doq66nOT4GApKy0iLi7DOv97eP0Gj0WAQ4uUqN01nh7QVJREQnszUqsp+9NNFmZaUHR+Ic0AcqweP99AbjnepzozghWuQiGUeigTlvU72NuHzxo2hZZUtThLMDjQRIuxgREIGdZUlRIf44BeRwtLWPobDBUKcXxKbWc61dLzQMNZSzJNn3vRNTtJYmW+d51la00BpYR4JImYrEfkTExVD6/CCdfExxUaeX80RFZzsrVCcHERkqjJS5K+gUeB9YVGTJOyub0g8S9eV6ZLd+W5efudIQdMQ2nuh6OnuMiWp0YSlV6NVBL3lkBh/b7xD0lhSaexnmdgYbxGi043Cxh6mRBmIjowhq7CC2rICkkQeFhSWiLoaT05VF/tHp6KOae/UsXPtMW1lmfj7+zG1c/JIeft02BSiM8zLwSo61dfBgIXeqhy+/eUv+PzzbwkU4ruwqtk6Qubx32pmWPhJb/9gajqn7MdMdFem8+tf/oY/fPWcsEhRT2rb2LLHBdqTTaID/PAJjKWkKI/E2BD8QqNp6B6zzqlWppg0CU0VGBlLZV09eRkppKank5ebRUpKKi2Ds+isMYjuTv3SqJaoKcwnICx7+vCjEp1mA8s9lXz7+5fUdo2yMj9IkLdw/vG5bO4fcKjaEg+ezPfff0fj2Cp6o47iuCCcnX1oGV3DYNCz0NuI2xtXituE6BQKvjo3nF/97nfE59dzcnLC9sIYcf4ufOMSyoYouCfC4QcJ0RmbVcWB7gL1xjip0X68CUm3rshmMJ2zPtGBn4cHMalFrO+J5zhYoyY/gS+/dqRuYO7RVenWZwbwf/uSoNQa8ZznmPZn8XZ5Q3hKEYd6UUTOT6gvTbOJzqVjTCYTaSFO/O6rZ1R2iEDn9Ezc22RdCWp9YZWDwyMOD/epSA/hxXMH6nuUnhMt1RkxeErRKZF8UDbn+gn2dMAnJhPtreEP4+1VODh6UlyvjG64QZn/sDLWRnhwMFWdM1aRebQ0SIibK37hBdcBmOI4FnrLeOXqQ03XBCbdES3VRcTHxpKVW0RjQwv56Qmk5ZbS299DeUEa3j6BFFa3XveqXZ4bqckKJiwpnamtdy+q8zFzsrdGcXqs8AVZ7B6fsT3TSbDPGyo7bb2Oc0PNOH7/W7549pbE1EyyM1KFXXyNg1sIvZMr1nNuo8zjayhOxCcug/lNe2v7uYbFiT5yc3JIS0kU13fl2yfPSCtr5kj4hgvDAaUpYXz15TcEx6VTVCSCWz93Pvv1L/GOLGRrX4NqbZqCrFTrAjMllTXUN9aRGBlJRV0H3W0NJEcHEhgeQ+fIzHWPgEatIsHLkezieg6sCwtI/hx0x5ukhruJoDOShZ2r92hhe3GEIA8hOivbObjX1bm/NUV0dAhxRS0cKfPRjFvEer4hMDSNnetgz8zeYi+uT7+lomuKI42OvuYqayN4Wk4BdfV1VBTmEJOYxeDwMBUFmfj6eJNeVMOGyi7ITGfMtJfiFRTBwPSnPszdYp3X6un4guyGketFem5zrlPTXpmH0ytHsio7UOse6T28OKW7vgQH53BWbgnX/eURAp1f8Ps/fI2H21s+++1v8YnORa3RY9obw83hCdF5VVz3ZVn0THZV8N0TV/oXt0SctG2dBxgTE0N+SSUNdTXkZiaTWlDFyHA/mUlx+PqHUt0yJJ7LFjedn+zRUZRKQESqELe3h21K7mDaIcz7FR4RSaydXeW7hb3VQV4/cyC3YQDNvazeXp4gJcKfrOpBjMrQEeMq4e6u+IZmsnp4JfAv2Jptw9nFhfyGPhH/HjPYXkdyXDSpWUXUNzRRVZRFbEIGAyMjlOdl4O/nb61j6/v2snOhY6i9gsBAfzqmdvmj64h+xKxNdBHq+fqB6NxemaOysIDU5GSiwwJ59ewZ/lGpLO6ePbQplxp6GgvxDYukZ3rdfvCCHZEfZcXiGikpRIb48PTpE4KTCtnTX6I/WSRU5O8vf/sVQZFxFBfl4u/twvNXbygSMYzBYkatWrU2pCqL5eWV1dHcXCfqVCLZueWMDPaSHhdGYHCotRH81D5F0KTdp6+miKigkOk+7cckOs+1TLYU8vW3b2jpGWKwIY/f/+oX/PyzbwkKCSbA34cX33/Bz/71F+TUT1uddl1WNJ5K7+Gyrat/ub8ZbzcvyjrGrD2dtYXxOHr4024f/4/5jO7yFL529GV2Ry2CmQUign1IyK2x9jacH62QkxSCV1yR7XxBR2kcv/vFP/Pbz7/DLzCIoCA/nn3zGf/7H35GfuvInTk6VxiOtyhMCOKFawhbJ6fMd5fy9q0LpS3D1oDSYjimoSITd99gRpbUGE1KT6gXHgFxTK/fGGDzuZ61+XFaGmvJz8nE+dln/Puvv6GyXVn4QkdNphSdEsmHZmt+iHBvJ/xjMm6JTgsjLRW8dvSmtHHQfsyG5cLITH+dsGv+1A8uotTe45Vhwjw88AsrsH62Ioz8jLAdL129qe4ctx7SnhyyvrrC6uo6Yz1NxMYmU11TTZkIdjMzs8jLzSYyMpqCqi6bIzKf01qeRFhKFmOryhDCTwvD8TYVWbG8cnSlpnuSs1O1CDLLcH/zlJzaDmvwOdFby3df/QrHwASWtw44Pj5kfqQFh++/ITy1DNW9zguDEHqVaVGEJOWwfDUHTZljf2FCp9OiFn+/vjhBQrArz5w86R5bsp6i3lmhqjBDBDZBxMQnk5GVjffbp0RlVLGpLOZmueRgd5vVtTVWVxapL8sjMb2IuqpKslJTyM3PJyMllmjxt73Ttukles0xeZFOZJdWsXd/+K3kR6NXb5Ee6YV/QDQL21eRr4WthSECXL8nq6brXk+nRQicIaIjRfAq6sqJsvCPcYcE77cEKesuXAd7tp4cxydPKe2wDcU26U7ZWFthfWOdyeFeEqOiqahpoqIoj9S0TAoKc4kKCyG3tI4TpTILYbQxWodXSBg9E4u3GpU+Pc41B1TnxvNC2LW5vYcLI53ub1Cek4irmzsZpU2oTh7vObQYjmityuW5T7yoO7ZVS41H6xSmxeLlG0RVUxdTEyNUl2Th5e5FddsAO6uj1nUx4nIrrXM2rZh1jLeV8e13bvQt7FgPKQ05KysrbKwv09VSQ1RMAg0tzeRlpZKeXUh+djrBwdE0iDjRiu6IkYZ8AiLjmdv49Hui3xvnKqJ9HPAJE6Lz9JboXOrn1VMnCpqHbAty3mJtbpjoEA8KWiZsW6WcbxDp6SHEUIYQnVfDCc7ZnG7ljasLeULcKAvvKduirK4ss7OzLepYlxCg8RRV1FFTmEZKRi5FJfmEhIaTXdLIgTI22yJ8al8TkcERNPevP9hO7FPicdEpLNGlEIY6HacnalQ769Z6+FQIz9SqXiHo72kAoTE6yrIIFKJzaGnHXk8tmC/P0eu11mvsbm9QnB7FsxcO1AzMc3owS6jLN/zhqSvNA1OoxTmrU/1E+Lng5BXMjNBMCmfqA5aWltnb2aCluoSU1EwqquopSEkmO6+AgswkQiMiae6bRCsMpsV4xnhHGUkx/tMV6x9sTmcoI/dE57n2iKJ4X566BDE2tUh7UTLffP9UBHJpNDXUUVxcTHl5Ba3t3axsnXApgoTS5BBrT2fvjLIioIWlPpvoLLWLzrqiBHxC4xmasxuSCzVDDVk8cw1iTrxA9e484cHeVtGpZK5WfE6LCcA9Ku96/5mqND++++ZrQmPTqa2poay0xLqyYXNLK2uq48dXnjUbGWop462DA419g+TGBeMbFMPoos0oXuptotPNJ8guOvXkid8eFpPL4patIl7ojhlqryI4KIik9BxqG5pIifHn+TMnatqnsFik6JRIPgYO16ZICnLHPzyRo+uqeElXTRYOboHUds/Yj9lQROdsfwNBwQHU9c2jGF793jxxgT54BSbdBFSCgeosYfCDaB6asx+xcaZaIzcphtKmXporKkiNSKRvcBbN2SE1uUJkBoVzJC5sFkKopTSBsOTMT1J0rs/24fT9Z/yX//5POAnb7uXpyfNvfs8//I//k59984a6oXmmepvw93QhobzD/lcK5yT6vcI9OI5p1V0xp6wfUJ0RRfD/n72/fpLkzPc1wf9qft0x27GZsd0d23vn3rPn3EN9zm1Sd0sqFVdmJTMzMzMzMzMzM2dGRgZmRDz7ukckVWWVSmqV1KV+H9lrqvDw9Ijw17/weVFZkOJGdD7Ccm8lTsKHl4l7fBMcjboLNtdW2NjaZXd9lnDP1+TXdov4cT8QmIXo7SQ+JoHBiQkKi7JFjKlkZV8kC2vjRMfGEFvYqp5p1F6QF+FEdpkiOu9lGJIfhEV3oq5c6xsUzdTGzZ63FjYmu3B78YyKztH35hUerY8SExNE+k1vnPWS7EgvAsLiWbqZsiaS2fWxBr57piwStvhAMGpPdqgtySGtoJahnmaSk2PpmJxBe3VJc3kGYXGJTB6IOrXq2RyrxyfULjofSxm+FA7WpogNdMM/sVRdbfY+Z7urFGYk4CnyGkUkqr3HH0KIzo7aAl74KKLTPsxyebCO16++I6Gw5tbelAW/MkJc8QhKYHhijDh/dxIzyzi5qQjjOf21WXz7JpjpjYeCUWkMzM1Mpaypl87aIpISE1hc20F7cUxOjA9pOXlcKJUhROdwfT4BUYrotK8uLXkEm57scA/8g+NYuPWpJrYmmnj2yt0+p/Odh3tzQdRZmA+FLZP2UYE2LSmhIs4FiXu96+hgsRpYHmjExd2Tmp4pIRjvLqI72aKmMJOM/Cp1bZZ4ryAGple40l1Sm5NIVLiy44UyWsXM7EATEcERQnRu/SpF57scrU0S4uuEZ3wx2nf2MlXsorsql6DIGIYfXeDJzs7cICGeb0gsa+PkaJUoLxcCYgvYvV2PxiSuk8Vrp7e0jq0+8H87C0OkJSZR39JOZ2cTQbHpbB1rOD/eJS4+lsicKrZP9ULMXDHeWU5SjBCdu7+E6EwKJzggjqXNu2W0lf1d2qrzefKXr0gqaebs8oqRhny+e/qSgkb7ClfvY6EsJRR394AHotPP0/dWdDYpojM0nqGbhQVMZwzWZ/HcI4xFh+iMCvMjuUCIThFzrvbmSIn0wS2qwH6+oK86RQScV5S2jjuOfBoHIiGJVVoIXL1wchJJRWUbR/bZ8lw7ROddT6eewqQAImLzWNq2O+CjtQlifF/x2iuazX37kI+BepHEPneium1S/FohOnPi8XEJkqJTIvkFMV0eUJERyRtnD4ZXHcOzjKdkRYvgGpHC1OY7Q7ZsZrane4gMCaWidUKdB2MVQaIsI4JXr10Z3nIkzdcasiK88I9KY37nbgSESa+hq6aAiPgcVg/O6CorEYlgEitrdp86UJNHqK8fh8KjK4uUVaUEEZWSxfzuI6vo/o1zfLBJY50Q1ZlZ5OXlkZObS4iPC7/55//GNx7hDCxucyIS4ZTIYAISix0ry4pbp90l1PUpIeIebb2zDJ9JdyaEeBqeUenMb9iTTLPZJPzwvfOEUOgsTeKVs6u6X9r7Hvaa4cYCnFx86Z1affD+6eYcOYnR5FULMarZJzUzkaSyVi6ViH21TUJKIoHpteq5evHsRHm8Jbe8hdPHV2SRfAoWk6jTLF69caWk3T4qQBghbaUpPH/jz+CMY7TTPY735omLDScmv4FTdUsVkTMUJvDGxY26YcdqtxYN9TkRvPaJYea+qDFdMtBSQXRsMrMb+8wN1BIZG8n0kd0Gh9pLCY2JYWjHjM2kZbq9RF1obHjuZqjbl8lkbw2ur55R0qWsu3GXyioLxtSVZhMWFc/o0juj2B7DesVgcyUuzsEsH9r9nbJg0zdf/4HwzNLb5FZ/sk2s+1Oeu0YwOrdOaUoYnr5hjG7Y/aHxZJMM4SO9Re605xCvCvrTPSryUknOqWD34JC2wmQSE5LRGew5WFWaH0npKSiLEJsv9mkpShJJcxZr+1+ej/z5sNFekiDyWVdqB5bsh0wXNOXG4OQdxvjS++LmYHWGjOgQ0ir6MKiK1EZzQRxvRJyrG1hUz7EZzqnJjsXVK5ix5f27jhwhVnoayomOSmRyZYf1mVGCnvuw6hi60leRTXRIiPjcPSWAMtJWSlCgP72LR3zJi0QrolOZ0xkl7OBWdFquMRoe7se5MNSM59uXROY3qWvUPEDEr4HWcnxDo+mcXLcveipyAYPhbnFThcmuKjzevCCvaZTLS6XjLxyfoFhm1IXVBMYzqjJjeOvmy8jSwW3dGDWHlGQkkJxVztHJIX0dFbjH5tzabVpmKkGppawdXoln5ETkK8WEBUbNTht+ZtGpbF1SFhvIN7/9muTMYnr7B+hqbVJXcXQWjswnIpllh8A6Xp8k1N2JN299qGvvZnh4hN7eHroHJsTDq3xVG/nR3jx/7krXlNKDaGOxux7nl28oUCY0C9FZnRuFk1eISAocqzqajumuTOIPL7yZ2znjbHcWfy9ndQjWiagL88Um+fF+/PbPr2noHFI3pD3amCDMx5k3HoHUtvWI7zEkvkcvfaMzaBwbsz+GWX8hKiuK/8f/8r/wP756Ts/sxq0hXOtOqSlO5uVbTwYXz1TRmRHhKio7lbkN+xT5480ZEgKdxTl+9A6MMDk5TpzvC/71X/9IaYsSVK+oSA7hzRN3FuU+nRLJL8g18yNt+L59JZx8Cu1d3TSW5eLh6qX2RL6/QIwN7f4iceEiGBc2cqhRxI6NhZEWfF1f4R6eRpfwMS1Vebi6ulHZOqQOU1GwGLVM9TURGhSs7k1nurayNNhCrAi+hSU1DA51k5ucQGpWpSrArCIpSPT3IimnjL1fyfDNhf4a3F7/hYKmAXU+rJL4tFXm4fTmLflVzfT191GWFYeTsxd13eP2c+6hTFsY7azCxSOEYcdWGtqjXQa7WqlvaqW/r58WESTdnJ8RmpgnElElwbWwu7HEUF8vA+L6DRUF+Li+Jbm4meN7WzBcnuxRq2ycHpnK+qke87We2uIs8TqRhvY+OhrKiElIoqpP6bm2cr4zgbuIcZVtIzj2t5f8SJT5m+H+Hmqsbuvsoa25mgAfNxKLWji6eH9FS42oq/ykGEKis9g5tguf3YUhQnxdcfMLp7Onl6a6MrzdXNTe7JtFpZQh64tDbcSFhVDSMCQs18rh8jAJ0WFqj/Xg4BDZafGk5BRxKD7WqDmhQSTVobEZLG5/mfOqFWyGE8pSQ/j9n14wuvlwAZPTpT7ePP0LT92VkWrTjI4MMzAwIPzRMGs7R4/sm25hYbidIJe3dM/uqrmRsuBIYogn3710pqhG2HFvN4VpcXzz1dckFTZzIvzX0nATnk6vCYhIo1v4yMrCTNxdPGkamL3d6/bacEFnTT6h4TEi79oVH6Vnor2SiNBg6prbGBrqIj4ijNK6dnW49NneKtkx/sTnVv9q9jP+XByujYp8WNiHj7CP7h4aq0qFH3Qlu6KVM8euEPfRHW9SmR2Pf2wBF3p7EDtaGiTE2xWXgCgRK7uoqyzCx9Od9OIGTh2BzmYxszTSrg5Tz6vqUntQz7bmSfL3Jr+imaHhQdLiY0nNKFIXf1JHD5ak4xcQyvLJI3McvyBWx0Qu8eYv+Mfn3a1ea9IwNyruVWOj8Et99LY3EB3oiYtnCP2Le+pe3Q+xMTnYjr9vEBVtY+r9sJqumOxvp7GpmZ6+AXrb6gj1cxO+LoLZbWXl3GtWR5oJ8HpLVHIWPcL/KSM5vET9JgrbONfZJaLJcEl3TQGhoXF0Tdgb86b6GvH1DVa3TxnoaycqJoaixgFR51Z0h4tUFeYTFFs2a/i5FxKyCbXeXZ7H89/+jq/+/ARXD0/cXFzw9vFTV4N7MNRJOPa5oQ5xY7149cYZL29fgsJiqGjqQ+dQ9Y2FKYSHxzO+rKhyG5vjvep+aI1Dc+qG3l31BcSkZDO57GihNF8w3lmGb0QKq4caNEdrpCfHioe6A6UXWFlldrq3Hpfn3/HCI5yusSWsVhOLo+1EBHrz2tlFfA8vQoQzq2kfRnMThB7FypxIBr/6t3/CIzyVrZO7VjiLcIrdLeVExiUzs6nBZDZSkx+vJorre/bhtVaTlvGeBnyE8H7r5kVMYqYIaqHCqCLoGl3FatPTUZ5NTGgiG5r7nd4SieTnRmlkGmypwlckqG9dXPESPi2vqpX98zu7v4/FqKEkXSS8UcIHrDl628Q1+lur8XJ35q0Qm97C5+VVtj2YF6U/26VdiNGsojrOHAt0GC736agvIjQ4UPiHINLzSsU1lR4XG8ciGfTxVjbXHnwwbOmLxWZRF3dLjAmiedC+T6fCxeGm8KEpuIp77+7hLvx0AGUiVpxcPSK0bcqqpmOEenlQIgS9Mp3PcLpLQ3EG7kJIKvs0enkKn5uWx8z6vqP11iz8cSMRfvbre/v6k15QLcTKwy0YNudGyU1LpbF/9jbx2VueoTA1gUB/P4JCIqlq7OREZxEhTsd4UwFeQbEMz2/eni/5kdhMzI10iFit5Axv8fD0IzG75IN7oJr1lwyLHMHfL5iB2U17K77VwGRfE2G+HjiJZ8nT14/U/Gp278Vvm/Gcjtpy0rJLRVy3i1mLYv+ttUQEB+HnF0CieHbG5+0NGkebyl6SXmQWN3Ki/XIbfq7PNilLT8AnIpPDm1YwB/vTfQSKXOX3f/lO3E8/kYD64KHYiZ8/VR1jaB5Z/OJoc460KG+Si9tuE9qtuRF1+6O3Qoy6uTjhLHKuhKxyNhyLMtmEuOisLsJb2KmzyB19hb8rqu3iXDFiB9rDZUoLsiiu7UDrOHxxtEV1aRYBgUEEBAWTXdLI2q7SwWFlaVJ8d2HvVe2j6iqdko9hZqa/mXBfT5yc36p5eXJO+d2CPu9i1jLQUoqrux8zOyd2XyrsdFJcIzTAizdOb/D08hE2JnLfw7teZotBQ2dtCdl5JSw6RugoK7GPd9cQERaKf6A/cen5jDgWyTnZniddPDeRaZW3C9h8qewsDJMWHUh6SQNqe7TCtZaxzkr8vd157eQifJuHyB3iaR+cxfiB9P9wdYa0SGUv6go011Zs13oGGkvw93IX9uWOp7s7YTFJ9E4s3Y7UsYm8pF9okyBRvy5vXXD3FPaZU8rSvUUINcfbFGekUt7QK+KY/cPPD7aoy88gMCCAoMBg8kvr2HCMGpjrqyc3O5uyvtWff8sUpY/3/Gif8f4+moVib2xsoqO7j6X1HR7fQk4kTXvr9LS30NDYwtD4DMf3krjjvS11QY0b8ae/OGNzbYPjC626ye/Z0R4bWzvifcdDKH7ixek+K2tb6IzXXBv17Gxtsnt4Yp/kLLCYrtRNhts6elndvuumP95bo6ejlYaGJvv3UPcc+jj6qwsW56bV7ViUHokblE1Yz04OWN/cQiucpU0kQcrE4K2dQ/TGO6dnuTayMj9Ja3MLAxNz6ua4uzs7nGuu1Llapwc7bKxt/mo2nJZIvmSswl7X5sZpbWpmYHQazUf2fFNsfnmwSQjFIMqb+9E7JpErq82uimu0NNuvcfnO5BTDlYbdzTWOFD94z+z1FydMjvSrPTyru/ZGNuPFEXU5sQTHpTIlxNOvAuHXdZpTNtdXOD67fDCs1XR1zvhgD03CX47OLGO4twfZu5i0h1TmxBEcn8346rG4lSLW7K4z0N0ufHwj/UMTD3owlZt9srdBf5cSi5oYmZwTdfN+0Do/OWZdxCBlpM19jrbX6O1so0+5rtrrZuVgY5HkUD/SCmtFfT6+4IrkByJi/L4QeR0tzXT3jXBw/n4P5x0WTjYniQkNIKOkif1zR31bTeysztLW1Ei3qK8T7cPGZatRy/7uNtsHZ/dNkGshPOfGh2ht62BR2JvyBFybLhlqKcPHN4S+8ZVHV7v/UrAYrtjZ2GBlc1/kRQ9zDuPlBSuzk3S0CfsQ9tPU1CT+30BTSwszqzvCFt+3FcVelXmdXv4RTCxtcpMiXYocbbivS80Ph8dnuXhnCIAy2mNpeoQm1Q7nha09/C564fe2tzc51ty3KZvasz3c3y1yzgF2T+yNRYaLXeqK0/EPTWJ+48Q+DFHycYRo3F2bt9vHwKi4zx+zMWUEwgTxId5kVncLkeKIieIaO6tztDU30TMw9k5diTo26djf3mBf5Ob3I6DVeMXshLCx9k5hY/YecquImYOtFQQHBtE6sXW7HsuXikF7wfb6qsj3j2/1h4L2/IgJEeMb6upo6+4Xok6JWx9Bf0pvXSE+QREMzG+p9qUVtjU+1EdDfb1qBztH70z9UbDoWRW6o1nUb++w8H8P6tcmcpBL1ldXOb+8mTtv50pce7Cnk87eIXaP7b3NRt0JRSnR5BWWs2+w/QKi8x7i8xzFceBj3JzrePlz8Oh3+wW+h8In3yeJRPKLo9jrp2AznVOem0JCZj6Luw+H3X3qNR5i9092rtlbGCEuKprKtmE093oCft3cvwcfwWZhfbqfhMhIqlqHubqZrCL+VvW39lfvob73Kdd/BPvf2v9tNWoY7awlMjqOobm7hFvy03D/Xn+MayFg+hsKiYtPZUTUw/2/+dhz8GEePh9Kw0JBeiyZpfWcPWjE+LVi//3vlsexqtsMpcRHUNzQzfEDcf+xv7Pzfe8/yoPvY2NleoDk2BjKmwceFcaSD6PWrePfH0PpoexvKSMsOompjX2lCm751Gs85OGzcba3THF2Csm5NVw+3nv1q0K9Z/dv4kc4WJ8jJyGK3HLHtlAq9r//vkv8mLp597utjneQkBBLz8SicvyXFZ0SiUTy987x1jJ9AwMsbv60qyZaTHq2FqcYGBzh6J1WZIkd6/UVM2ND9I/OcKwuJPPzobu8YGZkmNHJOXQf6RGXfH5M2gP6e3qZWdrA+P7kqB+P1cLB5iqDA0NsHJzfLZAiuUUZHbI6Py584CiH5w97Tj431ms964szDAyNcXDxZQ/J/Fvn6uyAgb4eZtd2bufe/lQcihg6NNjH6geG0f89Y7MY2FmZob1HyQO+f3TmT4uFpakxhkYnMFmsUnRKJBLJL47NitFofLhy6k+AzWrFZDBg+omv+2vDcq3sW2bg+meey2W12FcS/Lk/V/I4JtUGTT+xMLRxLa5pNJp/RG/O3w/KWh8GvZ5rYRM/K1YLZqPwkerClJLPjdlkxGD8qW1MXFdZeVzYr7SxD2Cz2GOcEH4/LzaMBuOtfUnRKZFIJBKJRCKRSCSSz4YUnRKJRCKRSCQSiUQi+WxI0SmRSCQSiUQikUgkks+GFJ0SiUQikUgkEolEIvlsSNEpkUgkEolEIpFIJJLPhhSdEolEIpFIJBKJRCL5bHxUdIp/Y7FYZJFFFllkkUUWWWSRRRZZZJHlBxeHppy9vr5+XHQq+4cdHR2xv7/PwcGBLLLIIossssgiiyyyyCKLLLJ8UlF0pF6v5+LiYvbw8PBx0alstHp8fIw4QRWfssgiiyyyyCKLLLLIIossssjyKUXRkWazGY1G82HRKV6rJ8kiiyyyyCKLLLLIIossssgiyw8tCh8dXiuRSCQSiUQikUgkEslfg9CYcvVaiUQikUgkEolEIpF8HqTolEgkEolEIpFIJBLJZ0OKTolEIpFIJBKJRCKRfDak6JRIJBKJRCKRSCQSyWdDik6JRCKRSCQSiUQikXw2pOiUSCQSiUQikUgkEslnQ4pOiUQikUgkEolEIpF8NqTolEgkEolEIpFIJBLJZ0OKTolEIpFIJBKJRCKRfDZ+MdFp0us4Pthjc2ODjY0tjs8uHe/8dRj1WrZW11jd3OfaYnUc/WFcaU5ZW11h71gjbpDj4DtoL05YXlTOucD6oZM+gP5Ky/nFBabrx2+1QfyGi8tL8X8DRt0VWp0eyw/8jBsuz8X3XF5h5/DMcUQikXxurGYdB7s7HJ5oHEe+DwtX2ksutXrHa+GczXr2d7c5PH3kGjYzR/u77B0c865nMBv0XF6cY7TYsFquudSco726u+6XjuHqgp2tLY5OLxxH7Bj1l+Keb4l4ssmmeH9LKdvb7O3vcX6pw/yRcGAyGjjXCJ97bb+bOs2Z+PvNR++99drI+emRen0lfm1ubnFwfIr52uI4Q9SB7pLd7R1ONTrHkTuuhP+/uLxS681sNnF+foHeKNt6f3JEKnN8sMu+sJFPzQS0Iu5eaq+4vvkDq5nDPXENET/fj8BWTg/3xfN19N6zZRW2eymuZa9Xq3hm7df9cVH8y+Di9JDt7T10pk+729fXJs7ONRjM7z/7ZpMJi/X961yL3Gh3Z4eTR+zKatKxJ3zuY7mkyWDg7EwjasImcrpr8e9zDEaz413Jj8ZqEnFoR9jH6SfamE3ktJdcaIU/FvFJRVzjcE9c4+ixa9htbHfv8M4mHdjE83N5rtSjSX2t016g0Vz8am3MZDIiNJrj1ce4FvdBw5Xefl+wKHW0zebOx23ToBO2dRPTlP9vbor4uaPGzwut9va+6kVs3BHHtYaHdmuzXqO70nCpM4oXNq6NOvV73Nazg19EdNrEjVsc7SMjJorwsAhi4uOIS0yhtrWXo3Ot46wfx/HWEjnxCcRn1t3d9B/IyswAifFRVHZMC+H6+CO8ONlHREAUVR3jItn4NHO7YXN+lPKyMoZmVh1H7rCYtPQ0V9PUPcL6+iaDHXXUdw6gMdwlND+EhdFuIsOiKWkachyRSCSfDZuVk501mqtLSElKIjk1h97RWa4MH09w9len6ejoYmHzSLyycbLruEZyIsmZOXSPzaN1JEl6IbqmhrspzMkkr6CYfvGezuRw20LsTg7209k1wIXRhsVsYG12lOa2HnaPvuyAbNJfsTQ1Qmlhrri3iaSmZ9ExNMXFld3PH6zPUp6TQExMLPEJCSQmJgo/HkNoWDj1PeNoPhDZro0aRvs76R6e5uRCiMXlKSpK8kkS9z4lPZuOwSnOr0QgdXC6M095QRoh4RHEx8USl5BEaWMXZ44Gg/ODTTqbashKz6C8uonFrSOsjhuvO92kq7ODkZk1tS70l6eMDXTRNSi+n14mwT8V2tN9BtrrSVPsJzVdxNAhTrR3dfgY2tMdWlvamFjYQIm2l8e7aiy223EWrT1jaHT2Z80kEueF8UHK8nPIzi2is/9+/VmYG+uls2eQ/VNFHFnYXZunra1NfRa+ZBt8DKP2nLH+dvKyM4S9JVBS1cja3rHj3ce5NmiYHuunuUvUi8N+lUaCw511Ghqa6B2e4uq+KLSJe7ixSG1ZEUnCttNyikT+tCz8nv1u6s4P6W9vIC8ni4KSaqYWN4UYsRu88fJY2He3sOMZURNCdFr16vftFjb3fc+E5MNojvfoba0lNSmB5JRMmjpHHvjJ97FxcbRFV0szUys7mEXVaU726GmpJSUxgaTUTBpF3nuqtT8Ptms9C5ODlAt/n5VTQGffKOeXjsZTm5mV6RFa23rZOTpXD+0L/9/Z2sLMxon6+teBlQvhhwb7eqgX+uj8ExqPt+dGae3qY1vEe93FEYOdjaSnp5CYnExheT0rO0dYbgLSLTZ2V2bIF3E1NibGHj+F34sTGi00LIr2oUksIrc5WF+iqbKEnJxcqhs62Dm8UH2lwv7GAu3i/is+ThGd+otDejpbGJpavMtPBL+I6LSYTTRkxvHNf/wOd59g0jNTcHd6zh//9A0pRXVcOBz7j2F/ZYpwD0/cg/LQ/sjrTA814+n2hoyqQXGjHQffYaK/kdffOJNZ2YvxB4rO7elOPN68IDi5GMM7f3q6OYnP62fE59WxvrlFW00BJXWt4p78uGoZ76rlzUtnkoraHEckEsnn4mJ/jcKUKN44uRETn0iIjwevnLxoEwnPhxoZzfpTSrKSSMwoYuNIh+F0i+L0SF6La8QlJRMW6MEzZx86xpYxm6+ZGe4gOjKC1MxccjOTCQiMYGTpQE1mDxYGSBEBo6iuF7UhUgTn3YVRYsT5ZU39nH/BwuZoa4W8+Cj8/ENEEE0jzN+N7166UdU5jtFs5Xh7keqCFJJUQZqunhPo/pz/3z/9G8nl7Wgf9eUWVie7iBX3p6ZzEq3mlIHmUqJF3WVnZRDm68qT52/Vz9A7/PxCXy2ur77hu7depKamkJKWTkVrn0gIlJEphzSXZhIZFUVefi6J0eHEpBRyqhP3XdRFX20B8YlpDM5tqdey6IXg7awlLDKa/uk1rt9LBiQ/FJPumKaSdN46ORMp6jEmMoinL95Q2DTE5Qd6lK+Nl3TV5hMRn8bYygHXol7q8hNxdnIiNimFyFB/nj1/TW33FOZrM+vT/SRFhZOQnEF2RgphwSG0jq2qo550x8ukxsWQX9FyK2qOthbJTY0hpaCKk5vE+VeAcp8Gmspwc3YiMDSG9OQ4XJ1fEZVexI7G4DjrXaysTfWQHBtJgfBTWmEaemF3IyJBTQ735r/9078TmFTIiWIzCkJwHu0skhLlz1t3f5EQJ+Pj6oSTVyi9s9uiPrSMtpcRGh5Jfn4eCcLmIuIzmNs8EH9sYWagmbjYGFpGl+2CX6S4i0P1RETH0Do0+8EcT/JhzIYzaoSvdXF2IToxmYggP549daK8dYgr4+MdJKarc3rqigiLShbCUAgfwyV1hcm4vHUlKi6J0EAfnopctbRlFL3JwuHqOPFC9CSnZ5OTlkRQYCgt/TNqA57ueJWsxBiyK1rZP7P3ep/vrVCUkUxcRrmIc0rzwpeNMmpzfmyQrPgw/vSH3/GVcxhbHx05ZePqdJccESMzS+o5En5GuYc56QnEpWSSkRSD04vv8I3OZvPw3evY2Fubo0TEzOTEJFLT0sjKzsLz5V/4H//ye4qbB7k836M8K5nwyDgKCgsJF3WeV9PBsd7KteGUprIcouMyWd23NwKYhU9tKs8kSsS7iaXt2/r4RUTntclIZWIIHm/96RlfUruMDzfmSYvw5U9P39I1taEOJxXfRZxtwyzO1xuMD9S5ckx3JRI0R9f6DQdrM8QFBBEUU6oOVTMa9Oj1htuW5vtcC/Gr1+nEtR++PzfSTmCAFwUNYxhNJgz69z9neqgV91ee5NUNYronOhVBfXV1hcH04eTOcrlDUogbT9/6MbF7LwAJ5zrSmMu3376gWBivgnI9k9lsvxfqPVFPVH+/wajcE/U0LNc3v8X44LdM9Tbi7upFenmX44hEIvkc2Kw6hpqKRCB+K5z0KBfaK443pglye4lvVCZLB1eOM+8hbH6+t46Q4FA1oVV6SkdbioVvdKKoZYIrnZ7T7VkCXZ8Rnl7J8uqacO7ZJOZWsKu1cLq3TFZ0CEXNM5ycikQgP0UEmBwWt++G0ys9ES1FyQRHpzCxuus4+uVxerjH+OA4m9tHGIRfP92eEaLyDe7BySwov1c4R5Pq73Wqz9RrTyhJCcHFL4z+2U3HVR5i1B5SkRVDeFI+c1un4oiZi7MTzi+v1KHJRxtTeD//Fu+IDDaOlFE4NobqivEXMaZucF4VIMoQWaNjiODKWCcp0cHUtXWLdPea6f4GIgO9mD0440Q8C0mRkRSJQH1xb2jSyd4aGdGBJOVViQTqkWdE8gOwMT/YjK+7K9HZlWhE/ZwdbpAV5cMLt1CmVvYc591D2ODhxjhRoYEiiepGI3Ti8nAzXu5uJBU2YVLyk+1lEoNccQlJY31zjR5h57Fp+UxtXqA/WqEyN4GYgibOz4/prMwlJj6TsaUdxwcITFrGumrw9Q+hU+Q8pl+D0hH2trs4TKjXG8JTSlnbPRW5nImBxiK8vHwpaBxxnPgQi+5QCBa7P5reUvyUla3FcSI9/Qj2cubff/dHAoTo3L+0C3bz1SmtpWk4O7vTMjiHVn/N2ngHnm9eEJ9fx4rIHUvS/Clu6eFS5F7rk51ERgTQOjXP+ckmxWnxJOVWc37P5iz6Y7LjgonPKGDj9NfTCPBzsSREu7eHq7CPRgzCPg42F0gL8+C1bzRzG4/1ctvYXhwjNtiHguYRLg1W1obq8HF1IbW4Ca3Zyv76HClhnrj6xTCzvi8Eaj4J2WVM72jR781TKuoxo6iF49NzhppLCI9OZnL1no3ZTIx11xEaFEjT8Pp7wzq/NA43VsiNi8Pt5StevfyWPzqHsn70cErJfZTh5eMidwgKjqR7fFEdqqxMOTk9PVFFvEl3Sk9VOr//3bc0j66IGnmIRcQ7o16Jn3oRX43C7o5EbuGPd1C8yCcO2ZjtJiYuQdzbVXUq0GhLPkGJOcxuHLA62kFceBS1HePcKSUrR2tjwq8GkV3eIuzPLlZ+MdFZlRRGgG8kU8v7N0cZbizlL9+9okxpVb46Z2F2nsWlBXq7Omhu7WX/VARkESBOd9foammgpKiIqrpmljYPbluID4RaTxQJXHB0IYvz07Q31VJcVsnIzApXN10NIrHTnh0yNthNeVkpxeVVjM+voDfbW2jmReIQGuxHTmU38+ND1FeVU1HbxKLyOXbVp4pOj9de5N8TnfrzA4Z7OygS36u2uUs44ZP3KtaOja6qTF4+f05+05jjmNJqeE5WpAcugbFMrJ+IOtOzub7C2pYyP9WGQSSPywtzLC0uMdDZSmfvILtnOnSXJ0wMdFJRVkJZZQ1jc2sYHJNMpnqb8HDzJkOKTonks6I92aAoOQTvgHCWT28anfTU5cbyyi2YtpFlx7E7ro0XIimKIyw2Qzj2cxElTihKCsXdO4T1K4f3EC65Pi8Wj9BEmts7qCvJJL28Qe25uzo/pCo9gbLGYboba0lNTKZtaMH+d7dYOF4Zws83iPKmAUTO9kWiNE5aHszTvxJBMRAnzwhGFt4X0+c7UwR6uImA18qZ/v7fORCxZGdxhDAfT6raR9A90jKpDE+K9nUnMCaLTVV0mukozyU4JIq++cP31g2Y7mkmMSKEjr5h9fXGzAApUQEMzS9RWZhFanYxK3sP59fbjFom2orxCI5j4APiWPKJXGtpLErFw0fY2/iG46CZme4KXjx5Tk3vlMg0HmLWaRgQCW5wUBjjy3ZRWpcXg2dAGP1LSkOEwKJjormQb1/70TvYT3NtHmkFNayeWrFp92ivKyQ2r4rZoXYSY+Np7Jl8bxTT0fYK8SLpTi9p4PjyQ72AXxAWE5O9tbi+fkp1z7K4y3a0e7NCXPgTEJN3LwG9Y1PYRKwQ+DllTehVk7OiuThjY2WHk5URgnw9CUrIY+fcfo9OdpZJDvImIDyDA0ejjE1/Ql5CCMGxmTR3dFOc4k394ITIxYSvWx0lJSmapoFRGutrSU5KY2bz/TUtpjvKCAiNoWHgXX8p+Tg2arMj8PYLVn2givWKqbYivn7uRsvw/O2Qy1vMl/Q3l+HmGcSSo5etKi0cL+9gBuccwtF8xUhTMW9cRV12j1BblERGRTMr5zZsZxu0leSSmV/HUP8AydGRagPEuzZ2srNIdkI4kSmlXHx0qO/fPtoLDTub+2wvzVKZE8237hGsHLz/HN9webZLZrTSkFLMzvFjPaJGVgbr+O7b18I3rqii9GMcznXi5+VDXv0AVqGZFoYaiElMVX2i5drM8lAdUeKzenu6qMzPIC2vmuOrd7yrzURpeixhMclMOxojfjnRmRxOoF8UM0uOZMGsob00na+fOdM+sYLuYBYPJ1ecvf2JiIomPbuCFSH61meHSQgPwMvTG19fb9ydX4ngEEHXqN1xnO2skBDox5+/cyUrL4v46AicXj3npbMHte2jqhO0iiA/099MfEw4fv5+OL96iZNHAK1DM+o1Vmf68fd4gatvBBkpSUSEBPDq+RNc/CLon7UHstnhdlV0FtQPqwtI6E93KM9NI8DPD/+AAFzdPYhIzmZOGd/8CLtzvfi7vMY3OpebKfFnG+O4v/yO5KJmtEJkXuzOkZ4YTmx2Fcow98ONacK9XuLhFUxkRBS5JdXMb+wyM9pNdEgQAeK3uDm/FkbrR+fYknrNmX4hjt28pOiUSD4zR2tTJAV5EBSZxtlt1LUx3FjMq1c+VLaMOo7dYEW7O09iVDgZZa2cGmzoDxeJC/LDJzSTu7TUxkRrEa99w6nr6KW/qZyg8Dhae0cY7G4lITqKqtoGkmOE+Kzv4ugRVam0cib5e5KcVcrexa8g4RUYztYIF4LQLyqL5b2Hi4eYhJBoyk/A1TtUCL7NRxv/rNc6xrqrxDnhjMysO46KvzXo2N5YZmpsiNbqUqKjk2kZmLYPr7UZqS9M4qvf/oeITRFk55cyMD5/O8XiaGWM9LhAEtKzGRkbpaE8n1ghQuprK4iJS6J/cunR+TQnm8L3v/WlomUYgxzv96MxaY/ISw7DPziG2c2b9SFsbM8P4PXmCTl1/Vy8kxFrTvcoSIkjPDaH/RPxHNkuyQr3IjAsidXbx+qanZl2Xjx7Q1PfCD2dNYSHR1Le1MtgTyuZ4u/zSispSIsjq6SR7eP316YwXBzRkBsnhFIGi2qv+heOzczcYBNvn/+FnFpl6LL9sNLAFeTyLW8DYzl+T33Y6G+vwj8oQgjzaccxcdTRmM/ZEmH+4t4L0bl7YZes20vDBHi4EZ3dgOZmyK1VT0NhGp6hCdR39tBeHi+ERjaDQ8M0lueRnJZOaVkhKRlZ4v2R9xahUdDsTAmBG0x6YfP3JuCS+2jJCHUXgj2BpZObpgYL+3NdPP/GiZK2UXTv1LvueEsIpwQCE0u5VNc30JAY7ItvSArL+zcCycTGWCtObh4UtfQy3FFJlBArVS19DLbXkZGYSF5RBQWZ6aQWNHB08X4PtenyhObidAIDQlg+ulv85kvkxiasV8c05sfxxC38I6JTGY48SkhQMEVN/eIe23MAm9XC+ekhiwtTDHS3UZSZQmJOtfBPH1+41WbRUpwSgldQLGMr9oaFtZk+osKDSCmsFrFtjKKMBLILyynMTiU9I5fBud1H7/dkaylhIRE09M+qr38R0akMGa1NjeLts7cUVjYzMztFZV4qr777Fp/ILA7Pr9DvjvDH//nv/PFtCN1jC+zuHbEhblxysDtvRbDvHp5lbW2V+TFFvL3AMzCG5UMdusN1Yn3e8A+/fUJl9wCbmxssChEZ7vOa157hDC0fihtqZGd1gam5BXWVprXpHnWOgJ8IOgdaGxtzAzh99z/52tlffM4MG+trTPY14vL8awLj8zkSyeHiWBeeTt6UNE+g013RU5uNrwh0zT1j7OzsMN5TT5C/O2HZNehM73leLNp98hKCeeniy/S+QdwTI6ON2Tx97Ub76KJ6ztH6ODGh3sIBF6qic39llJd//CeevA2mZ2SWvcNjLi8v2FpfYXRsRl1NcXF2FN/X3xCaXCR+i5WlkU483WVPp0TyudlZGCLM+w3+cdlc3SRRgsnOat68Eb6iccBxxIH1mq1pZaGvMKo6plByttOVEcI9PPCPLLrXWmxlsa+cVx5+1PdOoRUBvDQ3nUilMS4rl/LyWkrz0knKLGRgZISO1mrik9Jo7R9H61iN9dpkoCotkMjkDOZ27XMuvmSuhbioyY7ltbMXNd2TQvTdD3c2jtZmCHRzJja7ksMPzC0z689oq0zDKzadeXX+l52zwy3qyzIJ8HTmj//5n3iGZ7J14hARIjzOCJ+aEBVIQEAg7q6uvHzlQm51F2dXRnWI09RQK8nxMUTHKklSMVW1tUKIRlNc1croUD8V+Rmk5xQxvXI3NEx7tkeshzN5Zc2c6t+PF5JPQ3e2Q3qkFwFB0Szt3aQyNnaXxwj2fEJWdScn73SAnOwtEB8XQWxBE6eKcjLtkeDrQnB4Bru3XXVWDpb7cXv2LTW9M+wdHVJfnENkRARJQtiUlpdTU1FMdFwqA2OTdDbVqgve1LQPcHKzMIpRw1RrsUi0Yxiee7wh5MvCxvn+KqkRHnz95A3puWV0draSEufPf/7zf8crJJ79dx9lkcw2VWcSGJtE//y9oZEOLIezBPu4PxCda1OdeLi5kFjWweXtnHQDbVU5vPUX4nV4nsP1KSH8E4iLjSc1u5CGxjrSk2PJL6lSE+Tq4mzSMnMZml6+9atm3SFpIb4kpxZwLlXnp2M+IMr3Nd5RKWxc3jzFNg7Wh3n99A15ytzpm2pyoEx7y4gJJqNyAKMyotC0SZSnO/7hWayf3HS7XLMz14GruzsFTYMcH+3QVFlEbFQECSlZlJVXU1NeQFx8Cv1j03Q1Kot8pVDd3i9szG7UNtMVQ21lBAb50zt/rC5W9KVj1uxTkxP9cdF5rWdpoJHQ4Ajahpe4mVZrEfFoZrSLlJhAXn73F/7wp+dU9859z31RhsUO4+niTGpRI+eOi+mFoO9tqSYmOoL45FRyCopprK0gIT6ZmqZOxkYGyExOEDGshtV7o3mO53qIjginsL5Hff2LiE7rtZnGzDj++E//wrfPnNVVAN1cPQiNSbsdbqvZGODZk6+JyGviZpbLuEjeXn7zTJ3rdN+XdZYl4+TsRtPQClcHK8QHuPPcK5ZtZTiug+HmIp4/dxPJgX2upLKlwcrCnFD/HdQWp/I///M3PPWOZfPCwtrsAO5vnxGeUaOKPTsWytNChOANY3zzlJXJXrycfCnvmEajDMFy+Zp//Y8/EhART2ZmFvFh3vzmn/6R374I5fCmde4BFvqbinjz8hXFnYvoLjXkhLvhFZHG8r593LYynygpNpjItHKuTDYhOsfwfPOtSKTqOLuXR5n1WtYWZujr7qJMBMJv/vO/8zpQOAThSVfFAydFp0Ty+VFFp88bAuJz0FnvspiJDiE6nbwpa3ooOm3XRuYHGwkJDaRhcEn1aacrw0QI0RkQWXyv9d3KQn8FL9197fM+Bce764wODzE2NcfkUDcJMQlU1tbRUF2krtipCNKImKTb3gSbxUxbRSLhadlMPDrn5svhZGeJkow43D18Kavv5fydFSgtRi1DDQW8fuMiYsLCB7dKMZwfUpsVQ2hKHit7dz1PustTZsb6aayvFvcyDh8ff8oauznSKC3rNnVLq6ODXTY31pgaGyAl3Ju/fPeWDseQJbPxipX5GYaHR5mdn6OjoYK4xCyaGpooyU4XYiSWiPAQUrIKmdu0r7SoLKSSH+FMjgjYB5rboCP5gSiiMyPKSySdMSKOPhSdIUJ0Ztd2c/Lg9ioNFKPERgeRXt3NuTJKwLBLgrciOjPZuz3XLjrffvct5V3TagOR5niH8ZEhxqdnxHMwSFZCHIUVDTTXVZGSmCSSsyjCwiKpae23j1pQpsuM1uMbFkHf1PI9+/5yUfzKxvwoiRFBuLu5C58TI571CF4+eUqYyGXO301udSfU5gk/lJjKhLpS90OuHxGd69PtIodxIem+6LQZaK8UotMnlIaRVSWpZHdjicGBQRaXV4UgqVAXoKmoqKa2VBEq8ap4iUrKYGRxG6UfwGa+ojTZj9SMDHZ+HYM/fh5M+0J0vsI3MoXN+6JTCJVXT50oaB5C+46K2FwYI074ycLWKft8ZtOGEJ0izkVks6Gu8Kwg6nCuExd3N/IbB9EJx32pTIMbHmB6boHp8QFyUhLJKa6ivaGE5KRk4hNiCAiPoap1kEtlhwebUV04Kio0krahLftiel84povvF50Wg4YRIbaDgyLpn9jmpg3WajGpdtHd3kRZYS6RIYFExKUyubp/e867XJuFbZUk4eSmjJh0LL7lwKA9Y2FmgsGhEZaW5inNTiMzR4jPpmYKRWxTFtCLigwnPaeEA8cUAtPuGInxEWRXtaqvfxHRqfR01qXH8OyP34obkE5DaxtdvUNsH9wEfiunq/28fq48wCOOORhm2ityePLEk9GNhy31K8N1+Pn4UNE6imZ3ieTQEMKTqjHdW6Z3bawTd2dvskq7hWI/o6uhnLi4OFIzs8lXVtL9y1c4ByexeWZidXaQoCAfSlpnxA1yXEDQXZuFl184fXNbt6KzonOK09MNvL/+Ld8IAR2ZmCKMIYGY2BhiY1Ior+ni8rGxHYLt+SHCfdwISyxiYXYST6fXaov5pWPuqSo6Y4KIEKLz0mhlb2mUYC8XimoGuRkhd3myR6f4LbFxsaLy88jKzODVN7/BPTJd/BYzK0qPrBSdEslnRxlemxDkRlB0GnczKqwMNBby2jVAXR31PoronBOiMzhMEZ3Lqp/T7s4SF+ArEtPMB/OhxpsKeeMVRNOAfYjKDco88tqCNHIqWmitqSErLomWjiG2NlcpTo8nMiIJjRKLRVLWXpUiRGcO4+uPD/n/20ck/mvTZMSHERAcQV3XqEhC3w9XmqN1MiN98QiKv11J7zFU0ZkRQ1hqPiv7D4c7WiwWdQ6p9uKIqsxwnD0C6Zlcc7z7kOO1QV7/4fdkVvSgfeDqrWwtjJImEt6WngFKi/NIzixkfH6dhbEOIkSATqvqVc9UFnsqiHYmW4rOv4pr7SF5iaH4B8cyt30z/M7K9mwfXq++o7R1CN2DOhKic3VECMQgMmu6uVCeJ8s5GSEeBIUnsXY7StbM9nQrT0VSXT8496DR26A5pqu+lMTUAnq7W0lPiaeue4Ct7S0qchOJSEhhUcRiRShtjzfgo4rOJfGtfj2c7G0y3N/DyMQ0s+PdIukPITK5zJG73ePqhLoccU+E6JzcfL/x64HoPLfbwdbSIH7KwlC5jXc7EggB31iYiqd/GJ3T2/ZjDi52FkhLTKC2tYPyKqXBJ5PxpQ0Wp8eIDY8iJb+JM6VHWwiU8lR/kQOms3WjeyTfj/WS9GBXdXjt8ulNDZvZne3g2QtXqnsmcOxkc8uGsoJ6uJdDdIoD1nMRK0WcC00TQurGyIysj7Tw1t2D8o4xITrvLmK+PKSjtoiktHy6uvtJDQymuW+cnd0tClNiiI5OZEldPM/M3EAr0SFRtA5s/F2JzqHWUqFbIhmY2HkgKIWmU+OZMtppc2kMH+enJBQ0cfRud7SDq6NNovzcCIjNYe3gsbmhAmWO53AriSk5NDW3CEGbRVZxOaubW4x0NxAc4EPH3K7a4Gs7mCAhIYKsqhb7n/5SczqrUyII8I5kbN6+dPxDLEJ0DvBGPMAFTUOOCerXDDaX8fQvzynrnn2gvnsq0njr6knz8Cra/RUSg7xxCUrj+K6bkv6qLF46+1DXP83KRDvPv31CQmE96/snGI+XiQvywD1EBJkTE2tCdHp5vCKhuM3x2QomCuL9cfONZFZUxPJED97OfqroPDvdJsz5W/xictlQhgrYLJivr0W9WNXyIYwXe9Tmp6i9tLExobz1CmPEsZS+giI6k5WezvSKW9EZ5u1KUfXArehUlgp3efUtkTn17B6eojndJznoNd6RaayfGFkVotPLw4dMKTolks+K/mybwqQQXNz9mTt0JLw2LcWpobgHxzG0eDeEU8FmNbM+0UlESAjVHTMoa91Y9IfkJ4bwxsVHJKo3/stIaWIQ3iLIj6/dCUbrtYmJrlrCIxOZ3jigo7RUBPJkltftAmqwNo9QHx/2hROzWC3UZYUSkZzJ7L2Vbb8kzvbWKM1MIDopi+kPrsJrZX22H28XJzWw3uyr+BgmkQA3lyTjE5/FgmPuvRKcH3JNR3EMz1660NQ3rx6xvTMn82C5j9df/Zm82v4HgkZ/sklFdhLJ+TUcnmyTmpVMckU7aueAboeElAQC0qrt52qPSfB2Jqe0kZN3F2OQfDrXOhqKREx18aZhaMVx0MJAQx7PX7irq+U/rD0bxzszxMSEEVfczKnaa35NdVYEb0Xc7Jx3CCObnu6KZJ67hTK2fP/ZMzHT30JcTDx906vM9NUSHR/FzLG9B3uko4zQmGgGtsRzeH3FQncFPiHRDM6uv/M9fj1MiJzE1z+Q0o6HDWQq15c0lqUTFJvE4OL7KwkrojPEVwj+xHwhOu0jGJQVupW58u4BCbeLC11rdkiL9CMwMpmVk7u5fRb9BQ3FacSkFbFztEtRRQkBiaVqHmc1GChITCY8ukjYozJ3V0NWuC+JqbmcSJP7AVipz4rExdWbjmnHEGmrjr7KdF66BYpne+O9Z3tvZZrUqEAyq4YcQtBCVUYEzi6+dE45cl7zpYhhIpf38KN/duuecDIz2d1IXFQs3eMLLE+NEvLCj7Vje733V2QTExLE2KKwS2UF284qQoIC6Jo5+OAoly8JVXTmxvDdRxYSUnrt53trCQ6OoHNk1S7sRS08XHgPdbHDAOc/E5JcbrcvoVVMJrPIDxzniZxkQ+gjV6fX5FT3oPnA9jcXQmelRodT3TnKytwQWamRVPTaR3JdHC4RFe5N6eCKmtNcCS0XHRku4qNdg/xCotNAaYw/Hm8DH4isOyycLPfwzZ+ekVHTe9viv7cyQbSPE06+USJ4zLG2vs7S5CChnm/xC09m/UgvROcyYa5P+C///jV1vcNsbG4wP9JFiMdrPCMzxDnHTPeW8KevviWjupvN7R3Wx1p49tVv+UZU6vqZhfWZPr776n/wlVMAg1OL6pzO0Y5qnJ89ISazAo24kTODrbz59i15DaNcXV1Snx3Bs1dulDV0sb6xyebqEktLC8LxaT4cXGxGFsfaePLHf+V/+9//T0JTizm6N//oYG2c6FAvAuML1SWmleF7Pk7P1N7ac4efVXpfX3z7R5LLutnZ3VXnuDr/+Z954RvH2qmZpaE23rxyJlHu0ymRfF6sRkY6K3F79YLYjCrmF5eZ6GnE192N7IpmTt5dXUE4/IvNaeJCQ8gqaxdiQ3n/mpH2clxfPScyu46l5RWmBpvxcnUiRwiWM4eqUXpJV6cHiAsPoaR1DL3xmukOkfAGhFHf3Mf62jxlmcnEJSjzS8VXE8l4eogXCRmFbJ/dJWlfDCKZ6K0v4Os/f0tuTRfbOzvqfP211VW2tve5dKxUaDVd0lWVxTfffkdVr7LAz4dTe4tZy2BrCS4+kYwv2OPQ6cE202MDzM4vsS7iy7QSOzzfEBCVKoSpEPMiKK/MzDDYK4Lt2hrz06NkiFj23MWfwbm7lWeVlca7aosJDYtjausMo/is4rxUwhOz1HllM0OtQpzEkauuXm5DcziPl7MnpQ0DXH3kO0u+n5WJTnzcnPEOS2RuQdzriQGigj0JSi5jW8Tjdzk/2SYrIYrQhHz2T+0LbCyNtAibe0NgbBYLwgYnhroI9XYhIb/u3gImFhGTh8mIiySjuFU8a8piQz0iwQqhoqNXfX7KclOIS81iU2PDfHVGe3EyQeJZmt34Ukcb3Mcm/IqREyHuFFtYFbY4PtCq7k0cmZQn/IzdJh9yTVdTKb5BkbQMzDmO3WE+mMLb5ZWaq934qWshJHurs3nz/KVIhNuFT1ymo6YQL09PCmu7bxdcU4a1T/bUExISTvuI0rhgo6GmBP+QBEYXVpkZHyVBCJfssk51ZdPrc5Er+gaQklt3r3NB8imsC2Hi4+JEYHQmC0vLjPV3EubtTlxWhX2XiXe4PFilND2akJRKtI69cjfGmvB++1rkt7kiVi4x3COeHT93otOK2bt5dmxKZ8sIqXFRpBbWozVdc7w2TbS7J3WdIyLXXqMoNYHERJHbH4pcW/j/9spsAgICmDvQ/CpGExjPdylPC+b3L/1ZemdEzi1Ctu3ODxAYGEh524i4T0oMMbO2OM3Y8KCwz3XWVuapL8ng9ctXVHdPohNxZmGsS10TYs7hj8y6c5oK4vj2yUtahpeEtb6LshfoHg35KUTE57Ap/KnxfI28tGiS8opZWV0XsbFBxL0Q+leO1L9f6qkiLCSM2i77SK9fSHQaqU2LIsg3UjiDh0Mj7Fg4XRvESYi4wqbhO4dg0TMrgnWwrzvuXj6EhIUR6OtFUFgCveP27Qgu9lZJCvblP//wjJikWGJjI/FwdcbNJ5Q24YgUjjanSAzx4flrF8KiROBPi+X502/xUIaknovAsTSGv9tTnjl5ExsTQ2R4KC5vXhMQmcrUqn3OqbJBu7ezL0VNo2qv5un6DPGhPrxyEglKcCjhIcGkZOczsXrw6B6hN5wfLhPi/i3/2//5f1PWIa5179wjkZCmxoUQk6EYqpX9lXHhJN3tw2sdcW9XCPGkMC+evnQiOi6ZlNQMXJ7/Hn+RbG6J37I61o2Plx9ZlfZJvBKJ5PNxdbZHXUEqzi9f4+3rh6+Pt9rqvrz7+DzKa90JuYkRhMdnsbxtDyjKNWoK03n1+jU+fgH4e3up17g/OV8ZJdFWmUNMspLc2Z3BpfAlVYWpIukS/iciksTUbOEX7auyavemxXV8RZLWebd11JeE9YzO8hR+8x//ySs3X6KUOasR4QQF+ovfWcj0ot0vm7SH1OXF4eTsxcj62ceTDhHqlE3+/d08qOuZUFvWD4Ufz08Oxs/Xm5DQUAL8fPAPjaN3csk+ZOxaJDUVRXi9csY/OBh/8b6HTwDVXaO3yZTCzuI4mQmxFNb338av5dE+UqMiREAOF989grySarW13mY1sNRXjbuIhz0TKyL6Sf4aLEYNPU0leIiE1sXDF19ff4KiE9XtUB5LboxXF3QqC0f5RzC5si/SKvG4Gc5pr87FzfkNnn7++Pn7EqosOCWSs5sQbTOLZ6G6hNi4NGYdW3IYzw9oKssjNMhugzHxKbQPTKjvne1vkBbuS1JuFfvnv47xnNcmLZP9zUSEh+Hv74+3p4dIRrOYuL9H6TusiJwkQoiCvMq29+aVXR/NERbgRUhSwYNVti8PNigSedpb57fCr/ri7eVLWmEdG0d3q3Aq97c4NZbM0hYOHQnS9sKEsOdogoVPjI6KJDm7mLnNY7UOV4fq8AuMoKrLPk9e8gMQz35ndQEeTk54iLrw9xX5eFQK0yI/fmzxbavxjK66PFy8Q4XPczT8mC6E/eTj5iLyc08vfL19hI2lMbG8c5szW01XagNDfGI6I0pPpsCgOaK9Klv45wgioyOIikuhrW9K7Zy6PFoX9R1FSGweJ1d3z8+XjElzIGJaLM88o1g7/PB0kYvjTRLC/UjILedA9S/XDLaWEurromoSpffXx8dP3bLpSJ1vaVNHdDx/6UrdoF0b6S4OKE4KxskjlOkNx3Y497GZRcwcICY8ivbhBZRptMpQ9/HuZmJEXAsOCScmNp7CimbO1Tct1ObEExoRz5hjvZ5fRHRaLRY256cZH53i+Pz9VhHlZhgvjxjoH2Jl6/Bh4qBMjF2Zoba8iPT0DEoqG1ndtjsRBf3lOQtTM4yNTTM11ktRfja5RRVMiQf5VtCJn3iwMU9NaSE5Qp0PDo8xMzPB1OIKF7prtBcn4vUkUzNz9LXVkZ2ZSUlVE5v3Kvz0aJeRwVFWNsX3c1z3bH+N9oZK0tLSyCsqY3hq/nbp4g9hMV6xODlEQ1MHu6cPe0UN2lPmxPeaXrBvdKsT32tyRNyTzQNup6vahBjdWKCiJJ/cglL6xxaYnRxmdnFZTYIuTvYYGRllcf1mP1SJRPI5MVydMNDeoPqNivpWdpShXB9AmWs53qosABBCU+/k7X7D+stTcY16+zVqlWs83IJBd3HM0vQYy8IX3DVq2TjZW6e5toK8wlJGZ+0L21zrLuitTMc/Io6B+Zu9C78wrEb2V2fUFUIzMjJEySRT3Ju01GQKy+tZWreLesWfrs5O0D84ztknTOjRnW2TnxRKZEYpSwc6EVO1zI31UJyfRZqILxW1Leqm93eJlJW9tXl1cRLFzxeWVzMh7unNXs03HOxsivg2/nBZfxG7VqaGKSvMo7ymhXVxXYXzg03yY4OJTS96tCdO8sO5VrZFG+0hVzwjhWV1Qiwqe2w73nwXkUjtLvQTGuivNhKcOUYkKPtmTw60CxtMp6S6kZXdswcNyFb9BcvzsyJv2HwwjE97sktnYw25eQX0jc+rPQrKivmzAy0isfanpXcawwfWefjSUBYS2lmZprwoj9TUDKobO9k9+vjq2Ndnm5RkxhESl8H83uWDnMemP2NydJgJ4bveXfX/6myfrqZqMoXt17X0cnj20Cdenh8xMTwk8qjLu5xR6QFanaayOJ/SiloWHHmQMsqhMDGCmKRslm637JD8EK4NF0wPdpKdkU5xZT3LO0cfaeSzqQ18Yb6elHVNcukwGOUak4MddhurbGB5+65RR8Fi0LIyO8n88jr3F/XWnR/S2VRDXkEhfaOzXBmVqWwWZgabCQsOoLz746NcviQsJj2bSzP0Dk+j0X94uoiyoGhvdRaBoZEMzqwJX6XkA6u01ZcLrZRGTn4xvSMzXJlv4qKF9cUp+oXO2nTsnWoS8XNe6JEBoZ80ukdE+7WBo+1lRqYWuLi6+y7XOo14FropyM2jvrWPQ429p1qzO0d0cCDpebUcO+aQ/iKiU0F8ljreWHye48g7OCa/Wu97+Vts4uYY0Ol0GIwPB0Yo11P+Rv2/cIh6vQ69wfjgQbZjv4Zer+f62mI/XxTl//f/rfTKKp9jNL3/OY99v2uzST1fbzC8t3H4h1Dmfar3wvH6FuV7iPeUYn+p/KbH7onjfii/xXFP1fmkyjsf+J4SieTzoWyerH/EbzyGUbNLVlIUCZlFD1ruP3YN1Wco88Ydr++48WsGxzwNG4erU8SGh5BT2S4c/2ND3r4MlN9sNpnUe6L4WHu5esfX2n3ko/70MaxmZvqbRKISRl3npLoqqcVyjUH40sf8voLyPe7ij/GBELnB/h0eJs0KSmKkXNtgNCnuXQQMPdN9TQQHBdM2PCvEiPTTPxXKvVbiv8Eg7rXj2Icw6k5pKEwhKi6NiZW7uYZW8Swoz5tSX+9hU+K2iK1qRT7ELPIGNbdwPJene2sUZ8QQm1HI3kcaob5ElHt0Zy+fkD6K+6asMBoTHka5si/t/TTpNsd53H5v8ivTbeJ8h2KX18Invof4PKNB2JzIA+22alWHh4aGhVLbOfJoz5zk07Dds4/vu41KD2VzWSZB0aks7N6JS9XGFDt97Bpq/nr9STamPdqkLEfYcGIuB5cfFmdfInd6yXHgMcSbZwcrJEYEk1XWoG4VZhPPvnKfFJtRcoLrdx52+3Xt+kdFvd8idonjjyLeV2PbI0FP2ZVEqY8bH6Bs19JZnUNYdDyD06u3dSs+65cRnRKJRCJRsLE2PUxdUzMza+8vrvHXoLSSrs8OU9/QzPreqRIzJO9g0p3Q3dpES/cYR/cWn/s50GvOGO3poLmjjzPHEvOSXwIrF/vL1NfWMTq7ivGnVCJC/O6tztPS2KiOuPqVdHL+VZivjhnpbaOurY9T3c/bEKbMQx3rbaelq5+D84e9pZLPiBBAxzsr1NVWM7G0+ZP3RO6vz9HW1MDo0u73CuBfK8rihEuj3dS1tLO2b1/M7JfBhu78SMTVRnpGpu2rgjuQolMikUh+YWzXJs7Pz9Fo7w3H/AlQWpG1Qthcaq/+bgPxp6DXaTk906gLMv2cKD23F2dnXOm/3B7oXw9WNOdnXFxqb4e5/ySIZNtwdcnFxaUUnPcw6q84OT3H8Cm9oz8hNpuF81Nhcz+z2JUIhC1oNefCxq7e63X7a9ELG1Ni6P11Uf4esZkNIpadof2FY8q1Uc/FueY9+5aiUyKRSCQSiUQikUgknw0pOiUSiUQikUgkEolE8tmQolMikUgkEolEIpFIJJ8NKTolEolEIpFIJBKJRPLZkKJTIpFIJBKJRCKRSCSfDSk6JRKJRCKRSCQSiUTy2fhe0SleyyKLLLLIIossssgiiyyyyCLLDy4OTflh0WkymTg7O+Pk5ITT01NZZJFFFllkkUUWWWSRRRZZZPmkouhIRVNqtdrZw8PDx0WnXq9nf3+f3d1d9vb2ZJFFFllkkUUWWWSRRRZZZJHlk4qiI41GI+fn57Pi34+LTovFogpPnU6n/l8WWWSRRRZZZJFFFllkkUUWWT6lKDpSaEylt3PWYDDIhYQkEolEIpFIJBKJRPLTIzSmXL1WIpFIJBKJRCKRSCSfByk6JRKJRCKRSCQSiUTy2ZCiUyKRSCQSiUQikUgknw0pOiUSiUQikUgkEolE8tmQolMikUgkEolEIpFIJJ8NKTolEolEIpFIJBKJRPLZkKJTIpFIJBKJRCKRSCSfDSk6JRKJRCKRSCQSiUTy2ZCiUyKRSCQSiUQikUgkn42fXXTabFZMRgNGs9lx5H3MJvG+yYzFYhX/Vv5vcbzzA7HZ0F6ec3Ryht744c+zY+FSc86F5pJrq81x7HNgQ3N6zOHhCXqz1XFMIpH8urAK36NBZ/g+v3OH2WzC8MBPiWtoLj54jSuthkutzvHqDpvlGpPBoPox4dMxGPSYr3+kD/0bxab8Lr0OzcU5Z2dnatFe6T7Bd1sxGo2YzA/DnMWk51y9hoFPcv8itphFDDOLGHUfo04rYohWfMq72DCZxOc66sGs/NtkUv8t+Xzory65vNI7Xn0/ZlEnyvNhufcQ6IQda6+MjlcP0V9p0VxeOV7dw2ZR69tud0oeYxDP3Kf7gi8Rk/6Ki4vLR579x7GJ//TCT5kfye+UPNEqyntYr0WepsFgfsSfiXuu+YC/tIrP0OsM4hMF4jyd+Pf9Opb8eHQiDil+81OxWpQ4Z3poY8o1RJ08hmLDio29V1vCB5tEbLu+tvtyVVcI2/21YRW/86OPqrATcconodcLe3svF7AJjSVyBhETP3aZ9+1F0UyKvb0fx2xWYWPis5T8Q/F/9s+12/PPLjqvzToWJgfo6Bnk4Ox9Z311cUhfdwdTSxsc7m2If3ezsLbtePeHYbk2MdxVQ1ZBBfMbh46j7yCc2NH+Jn1dLRQX5pGTl09bzzDHmk8PVD8MM+1l+aSmFjG//37CKJFIvmz0mhOmhrupKC2morqZpbW998TJu1webjI+NsHqzon6WqdcY6iL8hJxjTpxjY19TI5rKIJlY2WGprpK6hpaWN7cv7u+zcza/AxjY1NcmqxYzAa2lmYZGZ/m/PJz+bSfH+3pPp0N5eTn5pCXV0Cu+H9b7xAnVx8OXzaLka3VeXEvJjm5tCcnyrG9jUU6musoyM+noa2XnZNL9b0PIkLk+tw0jY1trOye3CayZ3vr9LQ0Ul5Zz/jsCrqbBgSRFBzvrjEyOsbusf3aZwcbTIyPsrZ/qr6W/LSY9FqWZ8eoLi+hrLKa8YV19KZHhMo9TLoLRodHWVjd4VpUqvJ6YWqYSmHHSp3OLG9hdCROFrOR/bVF2htqqaptZmbx7j2F7ZU5xiemOToXMV48Gwc7q4yOjrJ7qnWc8etB8THbK7M011dTWFhC98A4p5cfz21s18KHCVscHJ3mTHeXtF5pzpicmGRueU3U10PxqDk9YLinnbLiYmqaO1ndPeam3d6sv2RR1FVtTRWNwoY3906FGLG/eW0Uz4Kw15HJJZEmC6lr1TMzPsL0whpX3/NMSD6MUadhYVqxjyJhY/VMLmw83hhwD/2liGvjwyxuKjFRucal0APiGmVFlFbVMbGwKerdUakiNz/YWqK9qYaqmkamFtcx3DwTwqfub64wMjzO4cmFeuhwe5mxkWF2Tn8NebUNzckuC3NTzK/vYVAc0jsYri5YmplhbnYRneVjclFxQWZxf1boG54U2uahgNcc79DV2UbP2Jzq9+5jEna1sTTDzMIyh5o7e7w26lieHqW+plK1tzWRt5gd38Ei3ludm2RoSsRAgxKPzeJ7TjA+PS+eD2F/P7voNF7RVZXJaycPGgcXHraK2YzMD9Th8taDms4x8cUHSEuKp6l/4pNbz+5zbTJQkxeFk1covZPrjqN32MRDvb00SkyQF69evSEgKBBPN2e8A6JEBWw4zvqpMZEd7MGzZ770rtiNRSKR/DrQnx/SXJaF21sX4U+C8HjrjFdADGMiIH+otdJ6raepLI+UjEKWdi+41p3SUp6Fq/NbAoND8XR3xi0wlrHlXaziIiuzo6QmxBITG0diQgwRMSksbNvFi3ZnjrzMdAqqOtAqntxqYnO6n/i4BJr6ptEJp//lY2Vjthevp7/jxRsngsIiCA7wp6iqkf3LD4evo7VZCtKSyS1r5EKcpvj/5bFOwgK8cff2IzwslLScQsYWdz/a4ms82STe34V/+Jc/0zS8qKSxaI82KEyNJyE+kYToKCIjYugcW1CvY9SeUFuURVpuBRv7Z+o1zg9WKclNI6OwCs33JGqSH4bl+orh1kr8PFzxCQjE38+T125+al0ZP9D4YxM2ONlTS1xiOoPzW8ImjfQ3FOHl/hb/4BD8vD1w9vCjb3pNbb3fX54kNzmOqOhY4mNjiYqKZ3hxX63v66t9CjJTyC9t4ODc3tCzK569nLR4Cms7uNT9enpjrEJwzg+3E+zjjqdvAGHB/rwVvi+zvIkL04dt8WBljPz0BPKFn9IYbWpP8KZIbiuyE/j9H78hKrOcM/3N39u4PN2lNDseVzdPAoOCeev0Gv+oNKa3ToVfNbAw3EpMZAQpyYlECDtOyi5n3WFrazP9ZKYmUdc7a88jbWYmu6qIT81gcG5drTPJD8N6baC3sRRfTzd8A0PE/91xdvGjc3QR0wcEkMV0xURPAzExSYyv7gkhZKK/SbmGO77CTr2Evb7xCKB1ZFkVMJr9JbJTE4mLiyc+JorwyDgGp1bV+jJp9inPyyCrpJGtI3tD3snWHEUZKWSXtglx82MUw98GxqtL1hanyIwN4LsnT0guEz7jnildm/Tsry9SX5zOsz9/jX9EBkcfNjWVs90VYVuJ6v06vN+hJmJgX2M+//ab3+ASk8+d3jdyvLtOS3UBTk//jFd4CrN79p5om6j7pZFWYqOjSUpKJDJU2FtmCUs7ir3Z2F0aJysxhnphb1cm+7MwP9RMckoyIws7P7/oFF6K5eEmvvv6z0Tn1nCiuwu4+vM9KtJDePLSlcHZTYxGAyfHh1xode85BvE91XKD8m8lGNw/T2mNbCiKx12IyIHpTcfRO7Rn+2TH+vLcyYvG9gEODw/ZWFtmdnae7d0f1gL97vd5l5vvJx4ZCqICeOMUxMCqxv7mLUo3unIdx0sHyt8pxz+GMtzs3TPsn/ch3r9fEonkr8BmYqa3Dk9XZxLz61nd2GRuqA3310+JSCtm+/yR4ZQ2K1vTPUSKRKm4oY9rkRQvDNTj6+ZEfEEj61vbzI+14/ryiXjdxM7+AW1V+cSk5jGzccjq3AgJIQGUdcyhNRjpqMolMi6F4YVth23buDo7oDQtmoikPBYcPalfMkrv5FRPDa7fPKGipZvtkzP2d7Y5Pj1XW88fw2bW0lWdR1h4NJ1Tm+q9Od6aJdLHlZC4DIZnVjgWsebg8IiLK8cwvMewWZjrKecP//L/5f/5D19R0z/PtdnE4kAdwREJdAzPodmeIT8thoSCWjQ6PStjXUSEhNE8MI/O0ftitegZbCknVATsZpFkfU9DteQHsDEzQJgQQf7R6azs77MsbCTSx5m3QUksbR87znrIxeEiSdGhpBTUc6Axszs3QKCXO8EJueweHzM3KV67vcA/oZD9g12G2sqIjE+je3KN7cUxIUAj1eRQq9Uy0V5JpBChXWOLt0PSzFcndNcX4B8SzYgQtY90XHyRHG/OCv/jiXdYEoOTC5wd7VKTm4iHdwB1/QuOsx5is1wIH5ZNcHgs/XPCT4ncZntpkghPT1xffct/++ff4JdQwOGl3V9ajZcMNhXj4uxMYU0X6zv7DDSW8Ob5C9Ir29k9XKMyO5SMsjq2dnYY76oRdhVI54wQQIZjavJTiU7KZefeyDr96TqJ4QGkFVZxeC8HlXwaO3M9BAkbC0nMY+vwjLnRXkI9X+ERnsba/uOdKUeb82TEBJFS0sbRpZHDhV6Cvd1EXCpgfe+E6eEuQjzf4BORzureKaPCxqKTc+ib2WJ7bkiIsAiyhY1dCJ86P9Ao6jiKnsml2ykVVpOW7oYSgoNCGVg4/EJtzMp8bzPR4SF8/c0f+e3v/pOI7Cq1kfSGg9UJ0iK8cXv5Nf/+L//JS69Y9j8yU8Nm0TLWXoF/YBid48sY7wWbo/UZ4v2+43/9f/1fPAvPxaERudxeIjchgreuLvz7v/0jr71Dmdixi1WtyCeKE/3JLK5ldftQXLuSqNAA2ocmMGlPaCrKICI+h82ji1stY9TskCN8ZGFFLRfWX2AhIb1mi2jfV7z1i2J6/S4I7C6PE+L+Cv/YXA4udOL+X3N8dMqlOtZbJE+ac/Z2Djg+3GN6eoqFtR2MIsvQX54xPz1BT0+P2oV75hhGZhFqvbEkEa+gGAbFg/sup0L9B739Cy7Bqawd3B9SZcOk13Gws8XW3pE6t/QGZTjA7u4eh8dn6jj0g8NjNOfHLM9PMTAwyOrW3jstPTZ0F4dMjw8zMDjM1sE2ORGBuLtHMLKpFRrcxJFIJI+Oj1hfWWRsfJbjC/sQHItJpw4H6+/tYWB4jM2947veElFNp0dH6tzQ46M9JkaHGJuaFQZp7wLf31plsL+PyfkVtPqHT6RinCsL0/T19jI+u4zmA/NVJBLJp2M436UiKxJ3n0Amtx3D6CyXlKSG8tozjB4hdt7FYtaJJC2JoIhEJlcPxQENlRmRuLn7MX3ksMtrPaUpoXhHpNIpfFxtSQYZVY0oXs6gOaY6LZ6ihlHG+rpJT4yjsmUY4/1uVZuZrck2fHxDqe0SgeFjbVFfAEqL+XBzKZ5v/FhYet+vP8bZzgIpkcEkZhejNVuxGjT01Obg7BnK1OLmJ4+k2V6eIj8phFfPvuGFuz9VvfPq3NKRplyiMsqY31Hq/Yq2phJiMkuYm5mkOCNRJMT2ROs+hxvzpMeGEZNWzIXxs4fdvw+sBjoqsnDz9KWmb+722HBjLk+fvKJxaO69urYYr5joqCAwMJjeafuz0FaWhIew49YJ+9QemxA+vRVpfOsUxJCIta21eaQV1bF+Lt7UH9IpBGV0TjVrs8Okx8dRUtfDmf6+mLGyuzpLhL83uVUdnN8bUvrFYjExM9CA25tnlLbNcKPdTtbHiAj0JzSpjMfk3OHqOClRwaQW1IhkWrnbVk5FTjfSO8rcYAue7i4EJuSze27vWTnfXycrwg/vwHg1kVW4vtglLdKHEJHcdvb1U5TsSePwtNp4c74xTlJCOM0jU/R2t5EUF0f31Lsj16wM1OYSFJlA13vvSb6PluJ4YR/+tIzZRxDaTBcM1GTw9UsPuoSwec+fWvVqY4CHmy9jGyeqGGnJj8XDw4+O0RX1FJv+nO7KTN64+tA6OEldUTKZFS2sK6n55TbtxTmk59WL3H+GnIRIShv73rEx2FubFM9WCPF5jWgfmWv4N4/Fwu7SAnOLy3S2VhLo/lLElVLut1ef7G8yOTbE4mg7sYG+vBGic0d/X3M85GRrnvwEJc4Ucai9u5DNcExdSS4Brq/49vUbvFNK0TsaRa8UjTU+yfTcNIlh7nj6BTO2ZW+0Od3fJtHPjeaBKZSrHS0Nkp0cQWOX0F8jg8SGR9760TtsjApxmpuXS8PE9s8vOm1WIx1F8Tx/7kxN16TjoIHRtnKcXrymuG1aHQZzujpGeEgsdX1T6imTPU14uwWQkJJAZFQ0BTVdQvTtMtxZQ0igD05Or3j1+i0peZUciACvzOlsKk36oOg0aA4pTvLnD396QlpJI8cOwaagFwlkZU4cAeFJrDu67xWm+xuITUyhqXucpcF2QoLDyEjPIE1pFXjxlDfugbQNz2C/ko2D9Tmy48NwdXEWyagfCWkpvP7zH3H2iGbm2CIqcIPUmGh8gyNJTIwXvyuThc0DzMYzOuuKCPTzxN3NDZe3b/ELiaZ1aB6j2oRzSWV+Fl4+YeQUFhIW5M3LF69Izimnq6ONoqxE3J2e893Lt+RVtQvjtD8CSpLaUllAsL8vbq6uOLn5kFvRzL4y90QikfxoTjZnSQ31JDAimZNbLyqSm/p8Xr3xoaZt3HHsBhv641WSo8NIKqjj8MqC8WSVxBB/vIJThXS5Y7Qxnzf+kdS1d9JeW0BYXBpjC+ssTA2RGhtJZX0rGYlJ5JU1sqt534UbL/eI8fUWQbv6QeD5ElEazXqqM/nXf/490QnpdPYOsLpz9H6ic4/F4RaCgsMpEYJcQXO8TU68HxHpZYyNjNLd3kJ7zzBHF48sCuPApNmlKDOJ5KRkKsry8A8KpLxrRh2RM9tbhY/w4U19k2wsjFCYk05KThF1ZfnExmfcDgG7z7X2VMSnDAKFX5/Z1nz0+0s+jWvdCUWpESKeRjO1fjOSyMbWbA/uL74lt3GQy3fyM+3ZIWUZCYRGpbFzpPyNjrxoH/xDE1g8vznZzNZEK0+fvqW5d5D2plIiYhNFrF9geXqYQhFvM0qqqMxLJimzjLW993t7dOeHVKZFEJ6Qy+qeola/cGxmpnrrePPsL5R3LKgJqIJud5oo79e4CJF4/shDPdJdj39AGDXtN/5QWcjEilXRD+crhAd4C9GZJ0SnvZFmZ2WMYA83ItOr0dw0oAsRU5+XhI/Iz+raOqnPDycpv4KllRUGWipISkmmsqaKtMxsyho6HfnYQ07XhgnwDSGrvNNxRPJp6MmJ9BL2EcfCTcMo1+xOtfH0mzeUiXo1vFPvRpFP1xem4BOVw7lahzpSQv2Ez0xicffGVsysjTSLnNSD0pY+ukXcjE7KEqJ0keWxbvJTkkSuW0VFQR5xqcXsnr7vq5Wcvi4/mcCgSDYeef9vHqHGrx2L3O0tDhHt50zkO6JTWThJHflo2CMvLswuOg0fFp2zo90i3w8UIn3g3ogai9rQFhYeS0lRMalpCfgkFXJltIt4q/gMe1+bmbK0ELzuiU5lPYW8aC+S8sqYX12np76MtOQkyspLyc/LoaD6cXs73piirCCf8LT62eufW3QKH8PhQjdvXz0nNrsakWuJgL5HSUoYTq4BzO7aJ7puT7TwzV9eklHTo/5Zb3UG//3//mfcRFI3ODLG4voOJ4fbTAyLB7Snl+GRYUrzU4X4dKW8cxqdXk9befIHRafSk3q4Nk1CiBd//tNXeASG09g9gkZYjM18SXtpMl//5Sl1A4v23gHrFQUJgbgFhDO5vMNIZTb/8o//gntIEgPDQ/S0l/Pqyde4+MexeqxXhavym1689qKsro3+wSEaK3P587/9A392DmNexJ1Dkai+ffpH/tv/fE5FWzfj43OcnR7QVZ+Hi5MT0WlF9Pf309HaSKi/K688ghhd2hNfRkdyuDv/7//+nyQWNTA6PEBJegR//N1/8M1LVyrqW+jr6STcz5UnL91pn9jAYrxkpL2M4LA4Gtv7mJycpLUiRyRPQeQ19mGUWY9E8qPZXRwhwteZgNgstDfjSoQnm+yo5M0bb9XpP0D4n735fqLDwihvGUMn/uR8bYxwL0/8IgvveglsVhZ6hW/xCKChd5LD9VnS4mOIjE0gKzOLnNwiKotziEvOpndkgsmJIaqq65hc3BCpgB1lAbfSBD+iU7JY/MDwpy8FZU7J3GADb16/5sUrJ9xcnPHwC6WxZwLdY9HLamagNp+IyCS6Ju09V8e7Irl1+wtOnv7ieAyhAb68ePmaiNQ8Fh4dgmmmp0YITSFm+maWmJ1sw9tb1GnHpDok+vJwndyUOKJjY0lMTCQvv5ByEYST4+Kp7xpmaWqYhtoa+sZm0d70alr0THVUExIcSuvoqogxtxmB5EeiP98hUwjGwOAYlvZuUh8bu8tjBLk/Iau6i+N32lxODpZJjIskOqeWk0uDqOp9Ev1cCA5PZ+e2c9rC/lIfrk+/o65/mrX1FfLT4gmLjCIlI5PsnByqK8uJiY6jc2iCidFBaqqrGJpeQu8Y52fVXzDakIefSNZHF3/c4oh/W9jU/CnC+yUvXQKE+Otjfm6Kitw4/vSbf8Q9OJa9hx1R4k90tNTmCv+W8OjID+vxnDo/VBWdF/aKWp/pwsvdlcSSNi71jjq1GUTuks3bgEgah6ZYn+5R53QqDUKJqRmUi3ufmRpPVkEpIxMzDHc3U9fUysrW3YKSZu0OicLuk9KKhb92HJR8P9eHxPq9wScyhfXbFhwbB6tDvP7uNXlNQ1y+ozqON+fJiQ8juaQbozLX17xLlBLnwrNYP7np8LhmZ7YDF3d3CpsH2N1YoCgrjajoGJKT09Q4pywMFi/iXtvgNNMjA9TV1NptzDGH02rU0t9UTECgH4MrJ7fx70tkUzzT4d6v3xOdt1xtkRkZyBvvj4lOI4NdVfiGRdA2Zu9RVjhYGScy0Je0omYONpcozEnAPS4P/c342lv0FCYG4Ol7JzotZj3zg41ER0cRH59AUmqWEJwV5GWkkJaRz9jsovjMFuoam1nc3L8dnWm42KW9oojQwLjZpeufW3QKbKZTEoPdcA+MY+XIwNHKBGE+wtEnFqJ1PLDb0x0isXCnoGlQfd1Xm8UfvnpCcdeM+lpFJHfKsvk7G2siERglPzOW//nbPxCR28yl9or2ypQPi04HR9srVBdm4Pb6O/707VNSi+o4vjILhzpFiOdborNqONOZMR7MEurjSXJhvXiYRSJTlsWf//xMCNwJx5WgIj2K71540TO/y8ZMH14vviGtsl9UvR2zZpdI9+d86xzCtMhtjoTo9HV5youAFI4cQwU0O7OEebzCOySezdO7ZaQXhppwev6EjKpuDEIU56QE8Ifnngys2ltNLw+mefX1v/AntzCWDuyGPNVdwfOn35JZ04/maIs4j2/53devyC6qoKmpiYqMcP7tn/4HTzzjOJGqUyL50WwvDAr/8Ab/+Bz09wTEZGcNrx8RnbZrk7DpZkJDg2joX1RF5snykLB9dwIiS+56voToXOyv4KWbD7U908ofsjo3RkNdLW2d/YwN9pKqDOmrEq9basgQ4sfPP5DYlGwGZuyJnfJZLaVxQlRlMbnx+Ly2LwZxP3SaY5HgzjDQ309bQxU+zi945eJP7+z7ybztWkdDYbIIkmlMLNsF99HWAl7f/Au//dMz8itbGBropyo3XvjKZ0RkVKJxtPiqWE0sjXYQHhJKRbvd16/Nd+Dt60dV37z6WnwKBxvLtDbW0dgxwOjIIOW5mSSlFzLQ101afKwqLgNComnvH+dKbcW0sDrWRmRkGNU9k1/04hd/K+jOtkmP8CBQxPzl/Zs6VETnOCFeQnTWdHHyIIGzcbQ+TmxMCKlVXSLOi/RHv02811shOjPZv02erRwsD+D29BsquqeFfcPe6hzN9bW0dvcyPNhPQWoSWYXVdHc0k5GUQGCAH+ExiXQPz9iTX4uO9aFafEMj6Z9aEZ/85aNMAZrsa8LPzQlXVw9i4mIJC/fj6z/8kZDwFE7e/ZGGU+oKkglLSGV87chx8I7rw9n3ROfaVDse7i4kl3feiU4MtFXn4OwTQsPIivo95sb6qa+tZWBsip7WWhKTUymvqKG1tpLoiFCCg4NIySllafdE7fGxihyqOMGX1IxMdh9L6iWPY9ojwkfkp1GpbN0O4FBG9Y3w6ukb8kW+/u56bluL48SH+1LQPIFJGcJpXCfSwwP/iGw2blebvWZ3oVOITlfyRaxUVv/eF4JIWRW5vXdQ7VgpyhTCJreE/q5G0hITCA4JIiQ2hc7hOdSFUq1GpvsaiAgNo31kG8O7jR5fEBtT3yM6LzfJiPge0Wm+oLsmn8CoGIaW98UBGxdHG+SnxBKbVsS2Rtwgy5EQjDF4pZS9P9rGqqPgHdGpoKwIPTc+QE1NNUMTwt7aG0gT9lZcWk97TTmxUeEEC+GfnF3A/Ma+mttY9OfqPN3EiKDZxt1fQHQq9Jan4OzmR2PfNOMdVXi6ulLWNX/rjN8VnT3V+bx84cvwyuHtOedHu3Q015KWmkpGegp+Hm/4jz/8ieiClk8WnSpWMxvzoySFefKXvzwlv2EUnfaUmuxYvJVW051jZjpKCAoKo0VZ2damFd8/l7eeIUytHzguAh1V+bh6hNA1vcREXw0u371kVFTW/UeiLDEcF7cIJg+sInBNE+TpRnRuozAyu4WsjDbj9eqlmgzdx3SyRWKQl5oUnVwdkp8eg0dQMtuOZ+HseAV/9+8Iyqhg/8Iuc5UeAXfXF6TX9rArkiK33/8rf/z6BT4iKQ0KDCDQz0vcYw/S8urQyOXDJZIfjdJ6GBvgQlBsBneDKW0Mt5Tw6q0/Ve8Mr1WE4OxAA8FhgTQMKcv5g2Zrimg/H/zCc+9aaW02ptqKee0ZQL1jqoGC1WLlSnNCV00BqbkVtIiEKycpifKqJkZGBshIiCI2KRedEkms18I3pRKemsv4+vvJ3pfObEclb1++IL6o2XHkDpv5iqr8RCKiM5hasc+1Pdiax/fV7/GKymHvZpUGyxnpUb58/cqH6f27rS1Ot+fwf/0n/uU/vyIuo4jmxgbiI334zX/8Bx5hSUys7N7Ok1X2eVRWQh3raSE5MZXapnZKc5Shl80MjU2SkxBGdFIGszv2hsL12U4iY8Ko6JqQovMnwKQ5IDs+SIj7OBZ2b5p6bezMD+Lr9EQkvQNoH9xmITpXhomNDiRTxMhzZcVU8wmpgW4ER6SwebvQo0iIRV09e/KKmr4Z1VYVlGGh+stTRroaiItLp7Ojjez0BIprGhkdHSErKZKYlCw2leFcNgM7Ew34hEfQO7X06xlObTWxOqNsT1NKXUs7/b1NhAcGEhlfeDvk9hbtMbXZCUQI0Tm5+X7j12Oic2OhH28hROLyW+/1dOppLsnA3S+U1vG7HlNl79urk00KM5IpFXVQUVlJUlIarX0j9Hc0Ex4cQXZlNxqlccFqoCLdn9SsdDbl7KJPx3JBcuBbAsITWTu/sYRr9he6efbMhfLOsfeG124sjBId5kVh2xRqmmk5JtbfB9+wdFYPb8SMic2JNt66uVHSOsSVY6V1s/kas+6M4Y4aEpPSaenoJicqjLK6dsYnR0mJCScuIYv1QyXqmpkfbCVGmYrWr2zhol7ii+QnEZ36M7rKcwiJiGVy60zEJ2E3hbH88z/+I2/9o6ltbqG6IEX4tT/xmyfOtPWPcH6/8j4gOm9QGhC0xxuU5WWSW1ROc0uT8KVxdA2OMtzXTmhYKJlV7UKviIoQcXiyp4KUhIDZqrVfSHSerg4TLBKsUPElUxMi8AuMZPV27ItNFZ1ObzzuiU6ReL0MYmRZGV4qsOjUVrPXTi6kFNWxsrHNaGcV7i5vVNGpudQK0ZkqRGPso6LzsWq63BjH5/k3PPNKxmAxsS6MwM8vgJqWDtLiwkjJUloHRKUYz+mtyMPNN5yxpZuVIqG1QohOrzB6ZlaZ6qvlzZ+f0D5zb4/Q60syQzx56RLO9JEiOoWT9fYmLq8JrdpUo3Srd+Lz6hmJWWW3q0kpnK5PE+r5lvj8Ri4NJ+Slxauic8vRrHR6uIifmxCd6SXsOZZqnxbC19XlBRn1veyuL+H33R8ISihShyYf7u+ys7fP0fEZVx9bsVEikXwv2sM1cmID8PANY+12XqWRmrxYddG07neGk9lEsrYy2kZ4SAg13XMYhQGaNTtkRQfh4hlyL5BYaMiOxiMwmoGFHccxgc3K6kQ30eHRDM6u0V5aRlJoCosrdlGpLJQR6uur9tZYrFaa8iMJT8lkeuvLXsFWxCjHv+7Ym+wi0MOVmLw6x5F7WAy0lKQTGZ3G8KJ9VfLT/TWSwpyJzKvn1DHfXUl6ijKjePLGm/Ft+3YLSpTYXBgW9/Et33z7La+dXfB0fcOff//v/B//x//Ob776jkohQnT3lkrcXxwiMzlBXehiZmKABPFM9Dmy2tmuIkLj4mhz9EAvj7YQFh5Kbe+sY66+5K/CdElVTrywn0C6pm9sxcZkVyWvnznROjR3KxjtCNG5PUV0TCiJZe2cqvOdTZQmB+EuEq2hjZtEy8hYUy7fvQmgf+6+HVtZmxkQ+Us89T2TTPbWEZsQzdSh3QaH2koJjYlhYFtcV+Qry/1V+IRGMiDyg19rba+OtRAkRGdWrT1ve4DpnIbiVILjUhhZuWusv0ERnSG+HgQlKgsJ2RsNDrfmiPZzF2I9i6ObcZu6Y/ITQ/ALjWd299586Wu9utVNRFwmi5srFFeWEZhUoi66ZtYp06OSCY8p5vBYK6peS160L0mp2Rw8ltRLPoCFipQgPL0CGVpxNBzYDEy1FPD8rR/dE+8vJLSzPElShD+5daPCzylHzBQmhODqESx8suM5EPYxVF8gRKc3HeMrt3uwKja6OtFDalwstZ3DzE0ME/ban5Uje8Ngb3mWEJnBjC8q9m5Wtz4KDQqmY2LXLnC/UBTRGeHzhqjMD4vOzMggnHziPiw6hd7orS8kIDKWkbUTDHotNbkxQmR+zYuXb3B18+Dl07/w3/7r/4f/47/8A1GpefbezxsU0ZkUiJdfyKOiU2lw6qsrIjY+nanZWXo7a/CKz7ttME/LSCEwpZjVA1FX1xoGm0uJCQuZ7Tz5hUSnYvQlScH89p//K7/580sS8xvuBQQbW5OtPHvqRE59v3qksyKXJ996i8RrV32tdPMnBrvyxDlAPKSraLWXDDQV8ZevviIspxGNVktLaQKuQhj2PzJ/wGi8Ym1pmpXVNeGETjg7PWWquwa3F9/hGZUvHl/xEdo9UqJD8XV7xQuXAOq7HQsfXZ3SWZwpxKM/Q/P2JfgVGkvEMWdf2sY31DleioANSCxRV57VaC5Yn+nm9R//hd+9CGBa5D+7y9P4urgSllF724qntBykhXvi5OpH2+gq5xcaISh3aCzL4PVrZxpEgmJDQ1p0MG+841h3tNSfHMzh/vorvIXDvtkgd7Knklcvv1EDqrLZbF6ku3hII5mYW+VCc8nF+an62y9+RXuHSSS/BMq2HJ11ebx5+Zr8ukGOTk7ZWRghzMeN+JwKdhxJ1C02Cycro8SEhpJf08u5GjgMdFVn4/TyBTmNo5wIn7S3OkGI11sSc6vZu3AkXeJv99fnyEqMIqO8U/g6AyP1lUQHRNE7OCvs+oD6gnSiIxPUxTwsFqOwfW9i03NZO34keHxBXBsMHGxvsr17wNnZOafHB5Slx+D01l0Vb+8hQtpIcwkh4bE0Dy+rh0xXZ7SVp+LsHUTn2Bxn5+fszvcQ6udJQHwB+6dnbInYMDQyycHZmbp119bWJhub2+KzV2koTeHP33xDfEEdJ1c3UyBs6DQ7lOUkE5csgre4zwfrkyRG+1HVv8SR8ONtFUL8JqWKBECIEpuR0ZYydYn/7pkdHAsHSv4qrEyJpNPVyYmItFIR24T9bC+TGRuIe2gGy1vv966dHq+TEh9FeGoZB+d22xhvL8flrRMJ+XUci2dhe2WG5BBPQpIKRb3eiRzN3hIlmYkkZFZyLmLo2ngbUVGhtE1Mc3FxQaNIiKNEfS+cWLHqz+mvzCAwPJGpFWWo25eP1WrmSqtR7edClL31GVKEuAiMSGR2590t4RSMtNcX4Rsae7tq6X2uD6bxc3fGJzqLnTO7XRkuj2nMixd5jAsNAzOcCp84M9BMgJcbqUX13OTIysKR69P9RIeHUtUxjklkcFWVxfiGp7N6dM72+gqpsQkkZzdwqtFj020JMetHkqi7u0lMkk9hpqcKd2Fjibk1wj5O2VycIjXUh+C4bNb3318k63xnicKkCKKzGm8Xq5nuKMXtzRsShQ89OjlhfW6cpHBfAqPTWVNEioqNy33xtxmJxKWXcHipY395gnBnL3oml0UOe0FtbgpxMQksKntVX1/RXZdPQIA/E9vnX/RogvWJToLcnhKSUsTZY6JTs0FKsA/PXCPY/pDoFDYw3F2HT2AYTUMLauPz5fkx+7vbbGxssLW9y+JIK35eb/izRzirWwc82MpbiM6cWG9cPPwY2XyYNyj7XG8oe4BHhFPWOKjuFjLcUYlbaCKbx+ecnOyQkJRAXEE9eyL3ub7coamskKCQ9NnDn30hoXtMthTwH//1f+W//PY5bZP3WvHFw7Y52cbzp87kOkRnV0Ue3z0RonPRITq5oqMqm5cvXuAZHEN5eRmxwZ78x+++IjK/We3pbClLxM0vgv7p95fFvjzfpyhVqHhPIfoiY0lMiMfT1QkP/zC6JlftJwmnOliTw1f/8H/x1DPmzpEK0dlVksUrV0V0btyKzqZSRXT60DC4JHKKC5pK0nny9JlwwnHkFxSSk5fKX/79H3nyJoRZRXSuCNHp6kp4phCdt8uoW1kY7SDEx523orIzs3JIS4zF29uD5Hxh5OpKsxckhwcKARnPmsaeiJ4czOPxRhGdBcJh20XnVG8lr199Q2xRk7il16wMt+Lh9AJXHz/SMrLJSEkku6icqbX3Wx0lEskP42R3STjpEF69ciUmPoHI0AACwhIYXXy/0UvBrNkjPS6M6NQi1h3bNp3uLqorXj9740ZcYhLRof74iUR1fPluvuL11RGdtfmEiORuYdce5I/WJshNjSZKXWAoncTEZGo77HMQDWdrhHh7k1PWLMTtz+rif3KMmjO6q4qJCI8iMSWNZHH/lA3pldEuylz8x1if6RHiLojcqrZbX328NUeCSJTc/QJJTk0nzN8bn5BohhZ21dUWSxIDeOoUwOzhu0mUlYnBGt66uVPcNn7XIm81MtJeRVR4DE390+oh/cWe2hAZHBpHRnY+kRGR6qJySgOD+fKQ6txEgsPjWDu+Hccp+SsxXOxTmZfE65evCY2KJyYqAm//YNqGl3hnhwUVveaUhrxkgoJjWdiy91AazneEmIzh1es3RAk7ihVC0lvkBf3Tq3dJmdVAT10ZkWGxDM7Zc5KrwzWKs5KJiBQ2mJVNXGwclU3dasv/5fEOebEBxKYXC+F6N3z7S+bapGV2pIv09AxSkpMJC/TBOyCK9uG7aVLvMjPQSrBfEKX1ve+JAvPhDH4eb/GJEaLTMVpLyQUPVqaErfqKfMiHOJGnBfv7EZGYy/Ta3Sgyjbi/ZVmivtPL2HJsrbIw0k1yVDgJwk9kpqcQm5xJ/8yGWof7sx34BYRR1DSkniv5dK61+1RkxvHmpTNRcYnERobg7RtK9/jSO1sG2lHiVUtFBq7+sew6GnauhW8sEdd45eRMeEws0WHBqjhqH1m4vYayaFxvfQlRUXHiuL3B8Opkm8qsGCJik8nOzSQ6Jp6Khj51LRjd2S6l4ppBEckcCIH6JbM+2UWw+zNCU4o5e6xP6FKIzhBfnrlFsv2RLVNWJgeJFCI8p6L90cVCzYfzJMeH8DYmz9ELfQ8hOnPjvHH1FKJz46HP0l0cqKt+KysJLzp00cbsCIlhISSmZZGVnkR8Yhp9Qksplz1YHKQ4L5fE4r6ff8uU+2j3lqkqSCe/spnDq/s3zsa5eK+6qo7RBbtgXJ0do7Kyxb7pqHrEpi6R3NVQRmhwEElpubS1ttLS2kz32BIGo5FlocQbWrvZfGSJ8mtlf67eBlITowgMDCQkNIKswkpml7cw29cLFtjYGm3iyR9+T0x2NbedFSYda5Mj1Da0sX0vKVmcGqGmroV5xzxP3cUJnXWlIjAFExGXTOfwKB1N9TQ29bCntaI52ae1oZ6ukXkM93Y2t1rMbC5NUpydTLAyPyImiYaOfk4de5Aqq8ANdrVT19rHmWMM9pX2kMbaUloGxm/369xbn6W2tkI4A/uCF1azkaWJXnLSYgkKCBQJahLNvSOcfOHbKEgkfxPYrEI0rgqflkFIUDBJmflMLe8KYfJ4ULBaTHRXZBMUEknPxLLdr4lrHO+uUZGfTpByjXRxjZXd202wFbTCb4z1tDEwcRfk1ZbHhXHyM5OIiEmguWeEK2V8kVnHTEcxPsHhtI8t3u31+4Wi+LC1qUEyk2Lx9w8gKiZR/a2n2g+P1tCdbJCbKMS9SDq3bobo2SwcbsxSkivuc2AwCWmFjC9uqj2O17pzRrsaKaloZO/G595iY29zjvqGRnU+522OZb5ieqSfjt5R9hx7DCp1eb6/QXVBFlGRMRTXdrB/Ym9c2JofJl7EhdT8hge+X/LXYkN7uke7iIWhQUFEJ2SIfGAe/QfG2tksBlbGmgkUz1JN15RjLplNXXivpTJf2HGgSKxyGJxZE0nZXdZm1Z0yMdRHR/+kY2Eo5aCF/bU5ynLSiYiIobq1n+NLJbbaWJ8eINDLh6qWIa5+JfuyKvPSl6f6SYqNUhcvU6YfTS1tYv5It71ub5GcxAgiUwvZuhm54cAqxEybyI9ahA1dKPNrHSi+bX9tliIh6AMDg0jPr2B569CxrYOd8+NdOpsbmBG5103DgEXY5Nxol9qTnZCSRb8qiqzYhO3X58QTHp3M+Oqvb47750fY2PEWbVWFhAYGEJeSycDk0gP7eICQFfPDbfh5eNA6vsZNx9ylsLHmqgJClGskZzE4tYzp3tz2a/0Fk/2d9A2Oc+rIc23Cxk62FynNyyAyOoaq5l6OHP52dbqXmNAAsuqGP2jvXwpne6t0NFbTOTh5uwfuAwxnDHW2Ut3QzcWD7smH6I/XqcyOIzA2g7VHGrssQjcM9HVQ3zP2/mgbm4mx3hYamtrYeTDGV/Gx+0LLNAp7X+NmZojFqGNptIf0hFjiEtPpHV1Ap9aDTR2pmpOdw+i29pcVnYqTNuiu0OmN4ms9RBFeOp0eo2PfmmuzSX2tLFF/H5PhiqODfQ6PTtEbjJjFeQaTWZ37YzYZ0esN7/2NHfG+Uc/56Qn7+/vsHxxycal78D2sZgNdpUm8cvWlVQjD2/fEta/FtXXvXFv5PGWrFvO9JMLs+H77B0fojWZ1sQmD+J7Kn1nF79eL8w3i+PtTlaxoL07Z293l4OhY/O39qrFiNBjE3xpvk0jFkerFvdQbTfZ9fATKkBOdTqfej1tEFWvOT26veyW+y3sfLZFIfjQ67YXqU07Ov3//Rc3eAvGRwSTnV7J/cSdwdJfn7O+Ja5y9fw3LtVn1m+8mdzbLtfBnx+wfHqFzbI59vrNIckQQiXnVbJ/8WnpYDJydHKs+7PDoRMSI70kwRNI61lFDWEgIFR2j6tQJOyJ4Xpyxt7cn7vPlrYC0Wa0irujVeGN5RKWrflX4bZMjNqkI/2sQf6P48ndqhSvxGQcHB2gcQ3GV/SQbSjMJCotmYv0LX034bxST/opDYYPKmgWP9b7cYUN3sUNRaiRRSTnM35vzbNRdciCejaPT8/cTMpGfqPWtbAFxH/EcXJ6fCvs/4NIxbUXp5azKTSAkJpWVnZNfUby1CTvRcXx4wO7uHqfnl9/r75R5YEMt5ULMh1DXO/3OcD5hQ8Ku7ucwdyi2Ku6r8IlnGu1791Dxibqrq3t7EdqxiBzu5OhAPAent37iYHmYsCB/Cms70H1EIEs+jkmxj31hHyfn9+ZgPo4iMMsyYwhOUKZ43G3bpdqYuMaxuMb9hlUFpbHBoNdhetfGxFOm2NiByNkvdY5h2BcH1BSmExSewPLB1SP59JeFVcRy5bcbhC08+lNsigYQMUpokI82JFvNLAy3ExrgR2XbiEME3qGIeIPQEjpFB7x3HWHfQiMpGurdOKjY25Wwt2sRK++jNAqfHh2qcdng+Cxlj86EqBDKGzqUrcF+YdH5t4oIHAdby5TlZfDiyXfE5daIhFCO/JdIJD8114z3NlNYVsH02k8710tpNFuf6qegoJSp5Z0HPQN/b+hPd2mrLae0tpnj23mYvwynO0vUV5XS0DX8YLE4yS+EiPe7S8MU5hfYe1t+UjuxsrMyR2VJIV1j83LBKIHueJPW2lLK6js5u90G5efBajEy1lFPSVUdK3tf9qJqXxKKgNxaHCM3N0+dbmL8aEPQD2d/fZaa8mJaB+ffWSxMYtIc0tdUQWFli9AxP/eaDlZmBtopKatk6/RSCFspOh9FGUs+M9xJaHAIsWnFLG0ff38LnkQikfwIzLpLdnZ2ODx9bPGNH4/12sTZ0a46yuJDQ3z/frAvorAl7rNG/8tOJ7hSRprs7aG5nccv+cWxmdjb3mL/6PSntRWl5/PshJ3d/fd6Gf5+saI5O2Zja4cr488rOhXxs7u5zdHpxRc/1eBLQ4lHB7vCxo7Pv2f0wQ9Hc3akxtAr2Yr3CFYMl2esbWxzqf/wNJTPgs0q6nyXvQN7A48UnR/AZjFzJIxjcnqe43tD3iQSiUTyhSPzEslHsP2UD8iXPs7vsyPvz98l0i7+PninmqXolEgkEolEIpFIJBLJZ0OKTolEIpFIJBKJRCKRfDak6JRIJBKJRCKRSCQSyWdDik6JRCKRSCQSiUQikXw2pOiUSCQSiUQikUgkEslnQ4pOiUQikUgkEolEIpF8Nj4qOs1mM1qtlsvLS1lkkUUWWWSRRRZZZJFFFllk+UFFaEz0ev3sxcXF46JTvMn+/r664eru7q4sssgiiyyyyCKLLLLIIosssnxSUXSkwWDg7OxsdnNz88M9nYo6FaoUjUYjiyyyyCKLLLLIIossssgiiyyfVBQdqWjKj/Z0SiQSiUQikUgkEolE8tcgNKZcSEgikUgkEolEIpFIJJ8HKTolEolEIpFIJBKJRPLZkKJTIpFIJBKJRCKRSCSfDSk6JRKJRCKRSCQSiUTy2ZCiUyKRSCQSiUQikUgknw0pOiUSiUQikUgkEolE8tmQolMikUgkEolEIpFIJJ8NKTolEolEIpFIJBKJRPLZkKJTIpFIJBKJRCKRSCSfjV9MdIrP4frajMloxGAwYLq2ON75cVivTZweH3J4fCquZXUc/SHYxH8/LTabVf2NRvEbjUaT4+jfJqI6vh/xe8zit1zp9Fxbfsw9lkh+jTxiPFaLavc/xKfYrMJfWO78oMVs+kF+493Psiq+x/S4O7eK76d+ljB8i+Uayyc5gC8I8ftMJrPjxadxLWKQ5Z5fs4n7YjI/fv9sav0+Xjc2q7if4n0F+32W7bifEyWHMIp6+ikeYcUGLcIulPzkPmaT+AyTCev9w+Kc6+tr1c6VHEZ5HsziGbp/ilXUvfEDz6HyOUq5+bfy/P26sAob/GF5j0XcT5GHOl4JRM7x4Wso7xmF73K8fIDi1yxqfd3UqcxY/jqs4mY+dquvRZwy/6B8ULEbpW4eXs1uY+aHNuZAsaMP+mKHjT323RT/e+PTlefq15W32lTbeOx3fwg1xt23L4eNPn4Nm+rbHr1j6n1V/KTyzzs/9in8MqJTfNOT3S2GurtoaW6iuaWVjp4+ZhZXOddoxU1xnPeJKIJzbqybxNhIMosqmV7aZHl5id1jjXjIHCd9BKP2jJXFOdZ2DjD+VH7fauRoZ5me7g6am5tpbW1jdGqRs0vdD3pIPjfXujNWl5ZY3tjje7W68YqV8SGq61rYOjr7m/odEsnPiVUE2vPDHQ4O99HfS4oUoXd+csj06BAd7R1MzC5zrjV8r62Yr05ZX1tj7/hCJF4mTg52GBnooaOjk9nVLa6MjyeuhisNh/vbHF7oMN/Yr0jUtBdHTI0O0jMwwtbhuXjv7htoTw9YFp91IP5GfGHOjnaYX1794Gd8Uai//YTZMXH/O7pYXNvBYP4epy7+5kDEo6XVTS50oi7F60txjyaH++js6mN5U8SFe4HEqNOwODNOd08/K1sHmO4FXN3FMWsri+wfn9hfX4rYsrLA3tmVTHp/Ykx6LZvLc3S3t9HV28/O8dl7AkSnOeNge52zK/2jyex9lDxibWmFrZ1DkUTbTzYZdeyuL9Lb2U5Xdz/ruyciTtrfOzveYbC/h6amJlpammlp72ZmZeu20dt4dc781Cg9vQOsiufEfC/AKu8tLi5zcHKhvr48O2F5YVHY8ZX6+stGJKvK75sepaOzk6nFNXSG7/EtNgvnR7ssi/t/rhV+SWC4OmNR3L9O5RoLaw/8k+XayM7agsivupicX0V3T9jbzAa2N1bZcORz1wYtW+srrG4ffu8zIHkfq8nA7uo80/OLXN67z0qj6MHWGv0ix+0ZHmf/7FLk7h+/wdcmHVtry2zuHd/6VLNRz866yJU72lQdsLZzdGtjSuOe5niP8aFeOoT9rYr37jqVhOjSC188Pabav+KL78SveE97wtLKCgenGvWI9ly8Xl5h5+hcff0lYxS+b2V+SsS4DiZnF4R9fU/jjhLTzo+YXRAa68qgHjKocWyU9vZOUber6O/Zl1nU+brwrT3dPcwsbzywL4vwiYrf3Tk8UXMOs6iDdbVODz5Ju/0iolNpteitKsL5L1/z4qUzAcHBuLu95dnz5/gGx9I9tnjr9D+Fzek+IgLccPEOoralk9bmRmJj42gamEN/m4l9CBvH69OkJ0RSUNPKqVofNi4vzjk8OlGV/I/CcEhnZSpff/MNrp5eeLi/5emT74hOL2bt4Kd46C2cnp1ycqYR3/bHc7Y5RUZKAhmlzVyZv+dKwojbCzN45eLP8MKmTKIkf3coreZGIfTGe5oJcn9FYl4JO2fam3c5XJslNykaT08fAgICcHN1JzmvRiTEN+c8gnAyAy3V5BaWsbh9yvHWIsVp8QQEhRDo742LmweF9T2caW8Ci00kXWYONxcoz47Hx9efhjERGBwGeXm4TmNlIRGRkUSLEp+ez8SqCAjKm9daWoXvLSirY+NMuZ6N/XXxndOS6RiZF/7yr/EmvzwnuysUZsSr99/f11v4Xn9qukfRmj7srS72lynNzaCypZ9LEXeOtxbISYrA08sLH09P3L2Cae2fFgmsuO/6czrrK4mJiiA2Lo6I2CQGZ1a5Vm+bmcGOerLEMzG5sqde++p8n+aaInKK6zk+/zUIir8Nrk73aSzNVOvHLyCQ0JBA2oamuVKzF/soqgMhFgtTo/HydKNnYUvUzoexWU2sT/eQnplD3+SKGttMl8e01RSqn+HjG0BERAzVraNc6pQPsTLQWcWrl895+soZX18fvALCKW8ZRK/0uhhPaKstJS46kqgI8awkZdE/saJ+FiLFGu2oIquomvnNY/XQ5cEmjeWF5FZ2cKH/2Df920d7vEVNYRre4r4p98XdzYui2m7xuz6cGOvP9qgvzaeouo2DCwOmi12qC1LVe+/r44mrhzcFdT1cGpV7b2NhpI2M1ETCIyJFrhdLZesAl+pts7E9N0pueibd43OqXdqMGgbaa8nIKmDtSC/OkPwQDlYmiXL7Fo+QSNbO7QJOKHsme5uJ8PcTftIbL28vgiKTGFva/mDnhc1iZGWyn/TUdEYWt1CtSH9KV10RAcJXe/n4EhoaSnWbUpdKtLKxszBGalQI7h7CF/v44CNsrHlgVhW3tqsTOuuEjYlcPyI8jPjULHqm19TPshivGG6tJKewgkUhVBUuDzepKyugoLyBI+1dQ+GXhkmzT53wfX4+XsK3uePh6U1WeTOnxv8/e3/Z3UiW7+uiX+y+O3DPfrHPWvvuxaupuquLK9lpZma2bJmZmZmZQWZmmWSx+bkzQkpTOrOrq7u6KrvjGSNGpkNSKDTn/MNvUny4ZZuPt2mvyiOnTLzPaBHX2KcqP0WUqbeIlV7iCKC4ppMzkYtIHecj3c0kJcQRGxtLRKyK+u5hrPLlL9maGyY1LYuRuXW5Dm9uTHQ2V5CVW8LChq2z9WP8LKLz8vyMKnUEDt86kFNSh2ZhjtGRQapLc/F6+4JvnjvRLhKg8x/YLlryk/By96auZ0ae+nm8t83k5CSb+8d/evROYDEcMTczydLalqzcz093aReNOau89UHv5J+FfoPStFA+f+FOW/8I46OD5KtC+N3nz8io6MDyIy/7DuveMmVFxdR3jdrP/Dis0m/XTDO3IpzAn7onSXQWZeDoLonOTUV0KvzDod1aojAlCi+XV/zb//ofvA1JYHnP1ol0fWFlZWqQ8uICWnqGhA8apzJXxYsXrylsFMmo/K7HSJ1eUyRER5BV1oTOesnpwTZDvV2MTEwzOdZPRpQP377xpXPcnrRaDYx31BHq58a33/yR//y335LfMY/JHnMGGquIFQlCy8A4I31dxIWGk1XcisF0xpaml+iYWCrahoV/tX3AeqqluSiVoKgMlnaO5XOfIpemA+rE73B08qCqfYhxEVOSQjxx8A6jX7Nuf9dDbi5N9DUWEhwRT/fkmhCVh1TkqHBy96OxZ5jhvm6ifFzwCIpjemWXndlBUlWJ5FZ1MTsxRE5CJMm59RwbrJxsz8qzbbIqmjiyj+xcX1iY6mskNDiY5sFZZL2i8BdxaTmhpSIHN2c38iqaGZucZnpyjM29Q6Q+k2thH/0tlYT7OvPdl7/jX/7zv6gaXuJjYwHm001yk2OISytiaVeyZytdNQUir3BHnVfJ6PgkmpkZVtb3OZd7GHSU5at45uxFfk0rU1MTjIv72NiVRkKvmR+sJkEVQ2VLDzPDvaTGR5CUV4Tu8pzT7RkSIsIpbezl1D79XZo5MdZVT3BgCN0zG3ezFj45zmivyMLD1U3YQbvwX4MUJIXx/LWH8F8LcpL6mOtLKzP9TQT6h9AxOidPR++uysLd1YPsihYmR/vIVIXywtGbnsklLMYDshNVJGeVsLyxRFVxDr5Baua2TrgwH1OVl0xCWiFzmzZBL42irmoGUUWEiXvqwix8rMIP5MJAR2ki//R//n/4o8jLF45tI/Mny6NE+XngJfziyPQ8XU3VBLg5EJZUwObB0x2sp/trFKdFE5NeyuahQT7XU1+It4ewsdxyxiYmmZmaZHVzl3Mhfs5EPefF+OPq5kdl+wjTw10khvjh6hvDqvZYjrUJoRFU1LSwsjhOelwc8cllogXCweoUqpgIiuq6ODRKZ6R2ds5gWzVhIaEiNgrhao+XnxQ3Z/Q3FArbcCc1p5y56XHyhN969daFyl7NB6Y5nzE/0ES4+N2NIgZZhCDva8jDxc2TjKJa5jUTZCaE8dZZ2Nf0Biebi2SpVWQU1jE5J3Vsq4lVZTG3f4bhcJvi9ARU2eVsHZzaO3CuWZnoRBUdTk5VB6Y/Nej6c4nOyqRwAnwiGZ/bsp8VRWM2MD3QgvN3n+MSksjyzon9FekzVva2N1nf2EJnsE2/kH6sUSj25GA3kdg5U9s1hckiDVXecCGc+Lv527Y1H1fykPHx/g4bm9siAbMNMUtI876l91+JViitx1mf6hQBy5FnnlHMLa7K7303a0CaCrC7tSHuY5NT+zSQp7g5XaMkIxqn4BTedaoYl/t4+dU3BCbki6TEdm/S7zrS7rK+vs6uMKSnhN+5xcD2pvjO9U2OTw1YzCbmO6txe+NAQFwGq9t76O/9Hqsox+2tdda3djDem9ZyfXUhXjPLc7gPtXvsaQ/kIXVpPvbF5V21X5yZORDltLa2zt7B8d2UCdMxXaW5uPuGM7q4pYhOhX8wbtBur9HZXENbSw1ezt/gHJrA/Jatd+/m8gLjqY7jkxMh6GzWcbo7R5DT14QliGBsi9cPuL46p70si+DQGPonl2UnLq2xlNaP2RD+aLiSr756Q3HzsHzmSpq2N9pPY3MdRflpPPvsa/JbNRjkr7ygNCeb2Ngc+W+L7ojqjFTSUovZ3l2lOCeZ1PxKtk/vSWCRlG3PjxDq40lt9yTGT3SgZXtuiOggH0LVpeI3XIvgds32ZBNvnr8mraztCdF/w8nuEilRfqSXtnAsktEt4ftDfV1IyG/CKuKBVD/znaU4CCFb3NDNYFM5WVkFDG+KQro4ZaatmLiUUuaXVmgWybYqI5epdXuya+f0cJPspCiikovY2H+iESj8WaxN9RAV7EtCTgOGe+thRO4i/3suzUQY7KW9sVLYQjx/+OKPlPfP23vq30fqGJgfbiQkMJD6Pqmz+4aDtXHCgwKISCrkxJ60Srz7DqzbFGTGEazOE3b9OGW6obkgEnWGmiUpAb/Q01GTQ0JWJvN7BzQUppKULgTTvfxG4nR3kXyRF0Vl1qK7952fEuaDBaKCfPGPyeTYJDmSa3aWJ/B6/R0JeXUc3M7WuEN/sCHnShHqEraPzFwdLxMT4ktwQjb7UgYr/NOasG0vh1ekFNSwJK4XEptGRcuI/PnpwRHCvGIZGp9ldqyN6Kh4esbmObNNP5A5NxzQXJIh+9n5Hb2Su/wgblifHSQmwJlvv/mat07eLB3ZOljnO0pxdnYio7pL/vtSr6U+IxIHjyAmlnbkcw85RzPYgr+nNx1TGyLPvUG3MUV0SABxmSVyp9073tnY6e4Mvq++ITa9CL1cYdeMdJTx4tsXdM8s0dXaSHREKjOzm/L72ypKSIxOEW1oj4byLKLVOSxvHT6YUn28qSFPHSF/577+T6ijXxw3mA/XSYrwJygqnbkNWwfx5twwYd7O+ETloLO87zcM2lUqMmKFL8tmT+gE4/EGqhAXwtRFLGttGmZlootgP0+isoV9DbeTpk6hd9ZWj8uD9aSn59I2tMxkdy0R0XEMaKRBqruClToCa4rT8Rf50OLOx2dyivr9mUY6kyPFj4xhcuFRAz03Up8dze+/dabd3rNvPNikqSKfAD9fPL19iE/LY3rtQCR5RsY6y/ni1//C//F//g++c/Clqb2f1aV5qsoqGJ5bl+d/Twohm19UQmVlDTnJsXIvQXRSDgvbx3KSZzjcoKa6jK6ROSHWNilJDuA//tf/4P/+p//AxT+KztE5eWqONG2koSyPYH9xHx7exCfnMrO69/Q6Af06ZVmxOAYmsn9qwmIysj7SKj4bTGFtD1bhEC8tp4z2NBEbEYKPlwfefkGUNfdzYrI3HFEVW4sTFKQn4evthZePH8W1zYxNDJMc6Mw//X//D/7f//gtQXFpDM1uyB/Rri9QkpNCoJ8Pbl5+pBVUsbpra5zHIsEqVqsoKa0gSRVDVnEFmmkN7c0tNHYNyr2Q56ZThtpqiYmQpjyLewoIoa5rxDZ1RTSsrtIc3HzCFNGp8A/Ju43PzoQtpES54x6ewOy7HvUnODtZJ8ztGdEpRew+0UclTSNLS4gkPqP0QQ+xvFGN1cyp7oBxEWgDwuLpnVyRX5Omv1gtFuH0L1jX9OL2zQvyWmfsovOKmvwswoOlEdhjea1MrlpNTl4lzXVFqDPzmVzel69zH6v+gNLkUGKyKlj7QE/1L53R9goCvNwp75i3nxGc7RDh+oZwIR52LA8dtTSlcm60DW9Pf7pHF+RzvdU5eLu40jxu86cSlwcafN28SMqpoquxnLSUdJpHNznVrtNRlk5ydjktNZXExavpHlu0f+qOKyFqehsKCAgOE8F6TfGbfxEXdJRnEuIfQJdIXjdWFplfWEZ3r3td2ozGbLZwLU3nG2/k2YvvKOufw97P+x5m/RF1eSmEhieyYk+YhpuKhUCJpqptlL2NZWbnFzm8v95S2HVGTACv3f1p6B5jbXNXjunv6KpQEZMQxcDsOid7K9SWpJOSnUNLRxsxop1MiPj5GCmfGWouxd0tWOQmf3qa2i+RzdEWvL18SLeLEQlpbWZWpCv+URksb7+fkK5rhLDxd6NmYFFeg7kl6szPx5e8ml77O6Rp6vtkhXsSEp/O0JhIsiNV5Fa0YToz0NfWQkRoEu1trWSr4yhvHX2/g0HkUitj7UQE+1PVN4fpI9PtFWwcbc+Tn5lIjIgfBZkq4ScDWDiw5ZLbk52EhwaRXtqI3mxFu7lESWo0wXHpQnS8P1vmwqiluSwTr1A1BwabrQ7XZREWFkpd7zi72+to5hY40N0FSdPxOuowH1TpBawd6jGdHtBeV4CnTwhTa1sMdzYS5h9N78CMsGEtVUUFqOIy6O0WOXVcLP33BrTecXNxymBrEZ7BcYwsfiB3/6UidQ7P9hHk7Up2eQfvNPPZ6S5lKRE4uQSxfPz+Eo7FyX4ig4OE7ugV2cE1uwsD+Di8oLhlmHdS/+xojQKhjVwDkuUR5eS4OGo6xznR6xloKiU7K5eK6kZyUlSUto48OWtkql/UR4AP9QOzHx1F/llFZ6BPJKNztl6KW66tLHRX8M0fHajtm8FoPKKpLJeE+CQq6uqpq6kgJVFS7Vmsa7WszY7g6/At//XfvyM8sYDJmQWmRKNzc3ShoGVEOJcravOi+O1nv8UzNJGm5gaR5EXz/bffEp5eyan5jOPNSQJ8XIjPqWNj74DBxjzePvsjv/76LXll9WhWt0WD36OhJJfExGQq6xuoqy4lJT5a/s6l7ZP3EwnjFpWZ4fzmi+9JTMskQzhDh++/JzAhTySDtnnxJt0OPW0N5BWVC9FbQZYoEwcnb9pHbInL9vwocaE+uApjz8wrEmKxjM6+fpZFItmUn8hXf/gt3zv7UN3Uwcr2vk1U5qSRmJJDZXU91dXFREeFkVJUy4H5iq35EV7+27/y/I0vuSVldA+OsDDUS0RAICHqAhGUbzAf7dFZX0u5EOjV1VVyr4qrbxjtE2simhvpEXUhi84FZXqtwj8uVvMRiWEuuIZ9RHRemkSCnMFbRzdqu8ffd9QiiBwujxAXFUFR/QAn9qz45uqczeVpaopzSIgKFcHEk5LWYQxP7ES7KoK/01fPbKLTPuizMt5NWlwECcmZ5OflkZyWTXlpOXGx8TR1D7O4tMTo0DDLG/v2tYjCtM+N9NXmyjNMxhbeD9afAl01Wfh4ONMybltPKXFzpSc9xEUkq5nMHz4sv8szPT1NebiHJTKzYvtMbX4Czs4eDCzfJf1Xlg1CvbyJzahEMz9DWW4akTEJFBcWolapqKqpJjk+lqKaLnlzmMmRIWYX15GXn8kXuGB1rE0kvIFUd0woU2z/Eq5PKUuL4dWz12QWFRMTGYavtx9xyfnMSh3A9re9Y2G4hm+fffNR0XmsXSUlIYbo9AohLG1Jb0N+PB5uzsSqc0iKC8fHx4fIhAwmF3dsCZVIoivTY/num6957egqr/nMLKpi3b5fw8HqGBnJccSrksnPzSE9K5OikkLSk1XyiPnq6iKT46Pyspb7G6MsT/QQ5OZAi4i3Z59ggB1vK8PT15uS9iH7GeErjSeUZ4p6Cktmds22vu6Wm3PGuxrwcfZjRghSqWjHWvLx8/Sisvlu6ZBZJ8o7JZCgaBXj8ys0VxYQHx9HfnEBOdmZZBaUUpSTiSq1kAUhSOZnpxidmuP43ojx4ZaG+Ohw4gubOdLfjawpvM+F8YCqXDWh0YkiR5+ju1oIRndfZvdtse7m3ERjSSpvHd4SEZ9MVIgvz167UN8/i+Ve58s7TrYWhKiJJiG/BYt93Vx1Rgyezk7EJKWTmJSAl5cXUQlpTCxII6HiO66vmOyuwsPpDT6h0ajjI3n94jUpZZ1YLy/Rbc6SLc4lCBsrKsgmSwijrJwSslPV5FQ0s7G5zpSwsdnFDeGL3xnTNfNjPXi7BFDfM22fKv9pcCPiyPxQA77uDpQ0DWG23/qF6ZCmwiTc3LwZ3TY88oHnDPfW4h8aRdvoqpxbLA7V4vDiJTX9k7fvvTbtUpmVhKNnnNASG9SV5hAVFU9eQSEZ6ZkU5ueRJ/xYSlYhc2ubzExOMDE9j+He+vPd+WHSo4NJK+nA/JG9IX55ovPGwnJfNd998VY0ilEWJttxefMCt8B4xmZmmJ4YJicxmP/+r/+ktNsmzppyVQQERDK5YZsnvtzfiq+HD+WdIsBfXFNfGMcX335PtlD6MtYjilRBfOUYxKr2FP3ePFFhfiTl1SLNlLk+3aAwPYYAdZnt/YKFwVrePv8e3/AkBidn0MwMkp0QwL/862cUt0+8v+utaZuqzFD+/def4RsUQkiAF9988QccxW/un14VRiPK4cKKXq9jV3vI7s4aA835fPnrX5FR3Y/ZaqYmO4GXDq7UiYB5h62ZmNcmiY+MIlskOjLXZvrrc/heBMHo1BIGx2aYneohxPMVv/nakc55LTvLMzh99nv8ogs5tvti48okscFBRGaUyhsJ3VxcoD8+4Vgcu6LxNRck8vnvviW1pFO820pfuSI6FRQsJi2qMGchOlVPik6LXktvYyk+7q6oC+rYP3liExkpAIy2EhkeRl3P3O1GQNKGCyuaYXmthr+nE98+f0N6aSta/eNrXLE03v6e6Lw+NzA/0Ut+dhal5VUMDvaJhCyZpOwy2to7KSvIJDgggMTkPHl5gxx3r8+Z6qnHyT2KgYkl24U+MdorUvF0c6RtRms/I5WlkZxIdyE6M5jVPpT9Z4ZDERtUBCXnsGif6liRG80bIfKH1u4v7dgh1MebiNRytvUWNoUfLS/MprC0gvbuXhrK84hTZ9Dd1091WTGhwp9Gq9LpHHo3xe+ak+VxVJHhFNR2c/yuohX+fC60Iu4G8j//5VeEqFKprKwgNzMFh2ffE5pUwJrWlgPYuGR+sOpPis6DDRFLhbBUV7Tbp4RCWYofn/33/8IzLJGqqnLxHcm4OrzEMzyNFUlYXl+wuTBDS0MtRYX5om6DePniOdEZ5ezLU9cvWdaMUiqS4aKyajp7e6kqzSNeJNi93d1UFIqkLjxYTrj7pxZvE/WtpQniI9xFbjN7O5LxKTHQVISHryeVnbalABJWo47KnFh8I1KYXX0kOs9P6akvEcluPJvHthkWg3Xp+Lq5UdUyLv8tYTnVUp0ZRGBULLPbhxiON+loKCc9I5eW1k762utJiIujuq2f1roKkmMjCAyOoLKljxOjTWCeHG6RGp9AeFIp2qP77UThPtLupKPtVcTExNExIa2Fv2K4MQdf7yDW9bY6kmYHVOcl8ELkxF7+wQR4OPLNM0cqOyae3AV9e2kcdVQA+Q2j9lz5htLkKH7zb/+NS0A0lbU15KapcH/7Am+RYy/tnspxcKStFMfX3+Ps5Sv8qj/fizxenV/HsTxN+5y1+THKivPIyS+jp6+H2spC4hLTaesboSo/g+iwUCJjUkRMu7OxtZlxYtzdqWgauBXAnwKS6NQIbeTl+oqS1pHb5SLSzvctpSl4enozLDTQg190pZdn2QSLuhxaPpDLdLanlOdCdNYOTtrfJK5t1lKVo+a1eyTbZisHu6s0VBaTnZNHW2cP9VWlJKekUNPaRWdLNTGRkYRFxFHbNirvzi9h2l6gLC2BuMwKTi0flpG/PNF5aWGkLoevX3jQOTBGX3UWX3z+Gb///g1hIUH4eHvj7PiaFy8dqOhckKdRVGdE4eERSP+cNG3shuWBVvy9/KjoEqJTNKr64iS8QmIZtM9R5vKU4fpsXriFML+rQ7e3QHS4v9yYdaItm/cXyFSF4B2Xf7uZUVN+JF/87r+FYTkIgRuIv78vjg4v+ObbZ9T2STsb2t53i36D8swonrkFMTI7z9raCtMjXYR6vCEgMpm5zVN52+MlkVxWFOeTkZ5MWKC7MMJ/JbdpjK2tFZLDPYkQgVT3RI0caQaICAwmuahR/luaHleiCuRX//KvvHb2wE/cY4C/F9999TUvhbgeXtgRhj+N//OXFDRP2nf5A8PyhEieQ+RgaRZGaTGeMjc+IAy5gExxT0EeDvzHv35JVokkbi2K6FRQEHxYdN5wqt2gvkQkTr4+pBXVsnP89Nrvm0tpnUsDIRHBNAwtyhsg2LiRH4Wyv7PF6vI8PS3leLm5U9LYh/FBD+LTolPm5gqzUc/JyRHzo52oVIm0draTlZFOVm4BtTWVqGMjScwsYVNyesLtLwx14O0YweCIbarpp0ZnTSZe0kjnxJ79jODqlBR/VyJUuazqHjpp64lIZNPjiEzNv91RvE4e6fRkYOVupPPatEaQpw+RaeXs2Re8SssldLojdlcnSVElUFbfRnlBOqlZOVTV1ZGSGEd4lEie9qQ1nKI+t2ZITYggt6aFwyfWtSn8QC72SY/x4j+/eEW7Ztt+8pKOogS+eu1Off+M/ZzEDxGdNxwsD5MQG0xWbRc6OVm6oTDRh6+//ILyrlnb24StjbcV8NUfv6G6d/phYie4MOxRnh7Jdy/e0Dm9dfu6ZINGg47FyQEyUtWUN3ZQlJVGek4hdXXVJCfEEq3OY2bN5kN21zQkxgdT1D4l7kU+9UkxIY10+nhTdm+k0yJEZ2lqCAHR6cxvPpp6aTmivSaPV0Fqtk9tgmakORdfaaSzZUz+W8KsO6A0yVeIzgTmdm22eSnyJ4PRyP7mMvWFmaJMi0RCXEtsrJqyskp5Z++o6FhahubkXOX08JD8+GSiYwvRHth3YVV4iIgby6NtuLz4htceIXSOaBgb7CIt2odvv3lOY/8YpwYjA3UFQoT6klXRyvbuHqsL01TlqnD1DmFQs/be9Mr1uVHiInwobJu0j+BfU5IYyh//8Iyilncj2laGRd1/++wNFR3jbMwNERbkR2xaAXNrO+xtr9FaU4i3u6ssbt9NZ5ceS2W2mFiaHiQvM5ni6gZqhPiMF+KzurqWrIRw4lLy0Nh3Vt2enyAl0FmIzl5ZH3wq3FzbRjq93d9S1DTMu6xC2mW7Pj8JDzc/ZvYNwnvdQ1oSV55DWFQc48LH3NxciDhfw9sXz6ntn7rN4a8Nu0KvqHjpEc2ufV3ohdWETm9ib11DaVEe6bkltDZWo05KpKKqVu68DgyKon9yQf7Oy4MVavMTic4sRCc9euwD/KyiMzgghqmlu6lQEkfby6hD3XAOSmB+ZYvusgy+f/mG+NxyNNMTDAwMMDIyxsLSOid6qXCuKU+NsInO2TvRGeDtbxvpFI2qsTSZwMgkRubtycjFCUON2bz2CmfhneiMuBOdpr150oXj94krvK2U+pxQnj9/QXJuFRMTEwwNDjI8PCruY1Eofcvt+94hbSRUmhmHW2SGMKU76jODcXzrQWP3DBPd9QR5uxAvEr/egSF6Witw/PpLChvHWV9fIlGUQ3hiLvKTDR6hneojXAjL1NIW+W/LyT6Fcf7CMbymuL6T8clxhoekexxnaXmbi+trNueHCHj5isouDWa7rUmiMyE0FFVeLRarcCbNZXg4uZKWV0bf0AjtlTm8/MaRzOIO6VsU0amgIHhadN5gONiitjBdOOMIajpHPropjyQ6F0daCA8Lo65vlkdLDu84PyDC9TsCE3JY0923Orvo/Pr5+6LTzsHWEtnqBPKr28R7W1GpU+ifsq2VnxAJeUhUNP3zWnmTsdm+JlxdhOgcf39d4qfAWFelSFZdRRJzJzwudcsEujiRmFOD7lFdnOlFsM5NJCQ593bX3l6RAHs5OVM/bFs/K2FcHxRi1pO00mYM99aCSWv8awuSSSusZnJinNTIANqGbSM8c/2NxAS50CGvw73GsD5JUnQYBbUdHCnza38813pKUsLw8A5m5d7S482ROp47ulHSOmg/I2ETnd89//bjonN9gri4MFIq2zkxS43kmpq8eHwDI4Rt3I1460Q7cPzma/Ibh24Tvjuu0fRW4fzmO6oHlx+sKdTvLVOSk0pGUZXIaTT4hcbSOmprX1M9HYT4x1IvRKbE7soUCRFelHTOcHrXC/XJsC18jI+XJ6nlrfYzkmDcQx3kSHRaCZuPO+DOdHTXFfHWO4GNE9vo49pok/y4lczyjtsE2nCwicrPlcikPLaO7ir++tLEYEsVcdJaz5FJWvLTSM1rZV+asnYyR35KDKnFTfKeHKdHe2TEqYhIKGH/UBGdT3ItcrzqDL7+w6/5r8++xMXNnTevnvOrf/8n/u//6/8ReXkSs6urZIR64uAayuDq3YjxvqZFfO4PZNX2YXzk4jYXJkiMCCBP5LbvBmjq06MJ8BPXmLvTADuLvbg6upBd0UZzcSrfOXhS2zNtf1XEs805Ap2/wjcul6N7m+Gdn25QkZdJRl4FczNDZKbEU9E9J3/XgaaBiJgo6ntt11mfHSfWy4Xyxr5PaqRTqE525gYJEPeeVtSCzu4fTEcb5KnC8fCPY1f/yL7OT+muzSdIiM6hJUn/XLM134/Pm2fk1/ff+jHz/qKwjWA8onIx3h+pvjqjtTKPDCE4e4fGqBSaJr+kShKO3Bg2SQx+S3lzp2xf53uLlGfEE51RKkTno2B7j59NdEq71/p5hjEytcr5xQVWq0U0qEXKMuP59jup90MoeRHgpzsrcHj9lvTiBvlxKGdnZ1jMZsxmq/zDpcSrRB2Cq6sffRqpUG9Y6m/Bx92b0o5x20hnUSLeIXEMauyN+/yY/toMnksjnTtCdO7OExHiQ2JuDSeirCz786RF++EYlMKp8Yzr6xvG2gpxeOtETnWHuA+rvKGIxWIWQs32+mNuTlcpTo/EIUCF1mSVd8c1nR6SGuaGq3conYMTFIsyePPSlcF1vfhdVjam2nD48g+kVfYLgzqhMDkcB3c/eiZXxevn4jutmIwGWUAeafoJ9vQkJquSs/NLLkwntAtx/eaNEw0iwBos9nsUZXVmb0Trs334iLIta5u6XVdkWBIGGBRMYkENpwdr5MYG8e1rfxY2D+R7mmkv58UfX5BSIAURC72l2Th7BgsBrzynU+EfF4tpn3jhcJ1C4tHYn7V3c3ZKr3DwPr4BVPfOYrRIO2JLO2NfcHl5ZfdX9xCudm9+gJiwcEoahu1J5hVGg56TY2HnF5dcSg/gXh7D1+E74nOq2XsQU65YHBM+44/fktMy/Z7otOi0NFfmE52QzuLeEceLXcSKpKtzdF7eIXe4u4wI8ffI8pE81X+wuVDu7Htv9sknwu7iCNGBXvhFZ6GT/Z+VqfZinNz9qe66m0r0jgvrqbwO1DMyWd4QTmJtsotQL0ciUkrRW844M+vpqUjH0yeQ1kHNrc+7EnU93FZFRFQi44vbHO2skBrqQ2N3D5eizqe6a4gL9aBXsyFyhUu2pnuIDAmmsm3sk90d+JfBDYNNRQT4eFDZI5LK83NRR6e0FCTgKIRo54ONnC6ZG6jgq2+/pKRv9oPTa4+0yyQmRBGdXcWBfU3ndE8N/v4BZFd1YxWx98ysY6ixgJciXreNLHAu6tQoYrTRZJLtW6fdkteavnXyYFDaoES+imgn5wZ6GoqJiU9iYm0f7dEyHoEiARb3Iz0aZKSjhbBgFc09thHVtZl+QjwdaBxb/XAn1C+YM/H74oQNegg/snZi4VzkeisTHbi9caSkafDRTA3BtZXRtlo8nQPRbNsE/rl2kehgX7zCkljRGoUdW5gfacbVxYP8un6R29iSF2nN3+JYL+o46RFQY+iOD2mRlhFkNrB1YOZid5K8lDiyKzvlkedjkVjHx4YTm1cv6vkTHEb+WyDa9dHeprzeuK9/gOGREXo7G4kNcOQPn31FRee4ECUGiuP8+P6ZCzV9whZE+z+3nDBUl87nn30ucvcRLI9i0eHGPDmqSJIKOjDbFyvP9lQS7OdDfk23OCd8remIwfpc4a+FH+2fYKihgGfPXpMhBKhkgxeiLWmGW3F69gdCU8pud3i+ujynu66EuIQUJpb3OFyfJC0xhpK2SeHDL9keryEqPpbmIdsGc/MT/fi6+MlPu7hb6/lpcHa0SUqEP55B8SIH3xb+74y5sQ5Rjt6oitqEiH4s384Y7KzGLzSK1jFbR5fhcB1VsAt+ESlMrx7KNjrd10iQtwd59YN3j4kUIndlvIOYmASa+mfR7a1RJDRLTkE5ZhFfjcJvJoU6U9PeLYvOw+VxMuPCSC5s/ehzsX8W0XklEilJdH796z8SEp1CbWMjJdKOi34evHZ4K266hiP7PGFpu980UcivXzqQVlBGW1srleXl1DT3YhINUaIw3h8HB0+6pyVRecNCTyNujq4UtY7KGwnV5Mfi6hdJ37R9Os75Eb1VyXzl4M+scHQnO7Oi0tyIzazkUHztjXGX8rRwfv/HZ2SWNbO2e4zxaJX0mABevXUlo7CS9vYWeT1JQ8cgx08tSjesU5wcwL9/9i0ZBSVUV5aTHBPCt99+T7q4ps5klHtyXnz9Ler8anp6OskSIvPX//zPJBW2YRIJ6opIgAI93uLqE0pJRS3VFSXU1TewqxeO+GiFxCBPvn7hTFnLAPvHp3KlR/u64OjhR355De0tjZSXl9E1rJGn7q3P9eL+xZcUNU/cTq89XRglwteX6MwKTHotjQVJfPnNKzKLa+jraic90pf//U+/JznfJjq7i9J57ezH0Ny6IjoV/mExG/eI8nvBK/9Iptdt65RMh2tkhLvxu999SWZVO93dXbS1NFNb30z/qIYTw/uJztnxGuqYcBJzKtk+lvzIGeO97WQmZlDb0ERDXSXx4f64+4bJa8EfTlu6kpOx57/5nIzGSU7vB/rrM6b6m+WHZldLGyZc33CpWyVFFUtSRj5NjfVkJieQUywSMMu1SBpOqcmOIjy1WF5P8ykirWXtqM7n7evXJGYVUVNVTqivJwnZFWw8sSOvtL5lur8RD/cgeuyju1fWIxqLUnB46UhqbglVZQX4e3qQInz03u3OgDcsTfXLz4HLrerAenHFtUiYqnISiE1Ko661ldzMVFTqXLaOzPLzW0daSwkKDqFnakXxm38hp7vSjswROHsEUVpdS2VxjrwhSUpRPfun92PxJZq+Mj77/HcU9sx88NnYRt0BFekqQiOTRKy3TcGz6LYoSIvGxdWT/LJaqkryCPByIyqjnL0TM+ZjaSf7THJyMmloaiJLHY+zyDnSi0Vsv7d5zfJENwnRUeRXd8mbo1iMh+QlxZKQnEVTUyNZackk55SzuCuNGAnbFwmiu0iIpzcOb0f5PilurhhsKsHTwYHw+EzZBuNCAgiKy2bR/rSAh4g8Z6KHCB8X6oeXbR0DIgXtby7DzektYQkZIs8qIyrUjxBVJvNbpyJptX1SJ9pBYXqiPHVy4+RCfMzKREclISHRlFWKOstPQ52USv+MbSdq6RnFUWGBlLRPYvhQD4SCjNRBKvSAfFgNWhryYnF45cLcjhTrblgUccfPzQEnryDKhQ0W56bhKu29EqxibuPgvXqWnn1fJ/yqd0Q6h/Y4eGncoTgtFjc3H/JKKqgszSNI2FhMaqGc45oPl1GFevLS0Y3MokpqywsJ8nwr/vaSn6l8aW8Iq5M9xMVEk1drszHjyTZlecmExyZT19hCfmo86fkVLO1LHUpmRjsr8QyMpn9266O7rP4ikTZX6qrFy8WJwEhpmmslCZEhBAvfNb15Zxv3mR3tISwgiKK6Pjn2SNN0R1rLcHnzltDYNGprK4kODSQ0JpW1w7t9I0wHQvOoolDnVrBvuuLGckxXbT6R0dFU1jVRLWJjjCjjiblV+f2awTb5cUmVQsx/ZB+hn0d0Sj1UY231+Ds68OL5K1w9PPH28ScxJYvuoUl5uuotwokdbMxRlqPG090Vdw9volSptA+JRMr+LLzehjIyswqZ3bT1lO3OjZOfkydE5ipnF9eM9tSRX1rDgv25NlwamB9uQZ1VypZIJKRGWl6aT137EHLMur6Un30TF+yNi28E7SPzojFfc7JtexyJl6e7uA8PolVp8mNWzCLpeI+zY4abC3FyfIODoyOurq74+AVRUtfBnn1b6KPdVfG7EvFwcyUsTjSg+nrS4hNo653GJH6alKwsTQ2QlhCFu3iP9MiUkuoWTqRncoqGM93fQpC3O35RakakR89cnbM1P0q2OkouKw8PH1RpeYzbp8IeSltgJybSN3W3M555d4XqkmIqhXC9EvVyvLMsgm0c7u4eRMSqqRFiWa3OobVPmppwxmxfmzzUvrT9iQZFBYW/AudWHTWl6WQIv7Jpn6pl1R/SUJDO22+/x83LBx8fb7w83HFwcic+q/z20UX3kZ4R2JCnJiQygbEFqVPsmlVh88nhQUIMueHlG0BiumTDG/Kzhh9yw+7yFGkxCbSPr99uRCRxadUz2N1CcUUdJ+/WVwi/tjQ1SHpSDL7iuhkF1SxLGzaIl442ZuVOv9LWYfSf7pPpMZ9q6agrxt/HCw9vf1LyKlkX5f701vg3IrbMEB/sQ1Z1Nwf2UWTD4RZNpdn4Cj/v7etPdmkdm1rd7TWknRs1I52UVdUIsWCfpifilFaUYUFWsvDzfiRmFjGxuCt8qmgX4p6KhU8Ni89mWSTeCn8hoqz31xcoSkvAW9SRT0AI+ZUt7Ag7fFjN0lSyAeITYumd2+T8AwHr+sLMTFcVgQHBtI0sYeukv+Zoe4mK/FTZhr18A0XiK2z94FRuBxeGAzqqcwnwdMLZ1YPg8FiqWnoeTPnj5oLx3lYqq+pEMmc7L+U+e8uT5KcnEODnT3JWMTPLO0gmZzzcoDwjkiB1CQf69zuoPhXOpVHh1mqChQ26uXuRkJrP7IZW5FBPV4Bud5ncpFCiM6rQ2nMjafR6oE1cw88LV3dvEtKLmF3fl0XFO7aE75MejdcrcjDb6RvZ/pur8gkN9BOJuIrOwRl5CuWF5VROmAOCwplaP/q0HpXxM3N9YWKyt5HszDx2T23Taa/OLfKIYZoqwpaXe/qiEnUkCc53z8d/wPUZkz21+IicdHBpD/tYtZxv1hSk4SvEpodvEBnFtazundjtWNjKmoaibGmTHA9c3dyJSkilb2IZ622MumKkr1X44jqW7UskrkWc212dJS9NRYB/AHFpBWhWduW2c7q3QokQulHqfHaOP00bk9ZajnQ3EBnsK/ue+JR8xuftGwI+wanQLYVCE0QkF7Bn309AevZ/f0sVYX7ewkY9SMoqYmZ1325HAmkq7/wQZZVVjM1v2upD+F3dwTa1ZdkEBwYSGZ9Cz/gSVnl01UhTRR7+AdFMP3pO9WN+FtEpYTbo2VhZYmZqiqmpafk5WPuH7xrb+0hTUxdmZ5gU713Z2MJgvutNlKa5HB4e3w4tX1jNHB0dYbScyT02JsMpRyc6e+EIROFJG3UciO+Ttiu/EmJN2nDjVG+SkwTbW87lB8FLzw7aP757mLBRJ+5jTtzH5BQr69viPp5YcCkhvkPa4Wt1eZFp6TdOT7O4soHpdh99G4YTLXOaaeYWVzgxmNAdHWMQovuuB0YEv70tNNNTzMzOizLSiXu0vXgpDH9rbVl8dpmj27ncN6Jh7MjXnJyaYWN777ZcLs+tHGm1tnKRz0gO5Qzd8TEn9kX8EtLah1nx+fmlVVEmenS6U/RyR8A1FqOeQ3GPt2WpoPAPiJQ86k4OORR+5fzSJgblc4cHLM3OMi3sdUqy+8lJxicmWVzdwmR52lfsLQ4TGRZEVnkTx+YLkSAZ2V5fFn5jkmnNPDsHd+vKHnNxZuFwX4te+MP7iZS0RlOnO+FY92jt0tUZu5urzGhmxXWFkBKnzuWp+Wl4h6rQiOTwQz74U+Hcomd5QdSBZk747o/vUnl5dkpreSaB4Qn0TN09m1N6zMPSnEb2/0ePdw0WZWvUi7I91T8K9NccSr56Zob1HZFkS6+J8p4baiNICFHpuWef0sYVv3T0h7vMzojcYWH57tnWjzi3GtFq9+Wp7h9u19fotcukxYaQkFHC2v6dzUijoFIs1cw9ek6nsHX9sZZFkQtMiFxgeW1L1O3jmHjN6cmxsMO7/MHGFQe7GyLGatjcFUm6/KJIykViH+jrS+PokhChn7YVXp6ZWV+aF3mPhm3th/2XxNW5SKI7quRO+c6xhdsO8cszE2vL88KXCl+lff/5nmaTQeR5J1isD+ery7misN2Flc3bfGtjbgRVZDBJRc0f3VlT4X2kuGYy6ESOfXQb62REjvsuN5XsQ/snfO3x1hJZCSHEZlewY98FV8IkbGx+dpoZcY1309vvYziRXp9haka0pf2jR7Z0I3L3YznOPdC64t4O9zZlG1vbubOxkY4aQgKCqOmY5Kmxok8FqbN6a21J5BgzbO0dy9PHP8SNeK80g8LfP4imwVn7jupSaDKxsbwgbHRGzjEeepwbeYOmY5H7P56CrD/eF/Y1y9Lq5q34l5buJUSGklLQiPG9XVUf8rOJTgUFBQUFwbVVHp1Lzy1iZl3aDO1vyTXazQXy0tTU9Uxi+pQj8Y/ihsPNWXLSUqho6kf3V97kx6TT0l5TRHp+FdvKYxp+sdxcn8mzn1JTMxiYXJZHHv+WmI625WdPZhTVc2z6QEf23y03QvRvUJqXRmFtO/tPCI+/BOkZxMNdDaSmZ6HZto1UK/ztkWbuzY20k6hOZXRx429vYyc71FcUkVVQyc7J/Wn4f/8YtOvUFWaSU9aM9vFmQ38h1+cmepsrycjMZ3r17nFlH0IRnQoKCgo/M8bjfWbn5tjcO3rU4/gTI0+Z2WFKM4/+AyOxf/9cyL3GMwurnPxVE/4bzPoTluc1bO5LM1TspxV+kdwIcTKvmWF1c4/zv/FiL8PxAYuaaXb+UTsmbq452l1nemae4/eeR/yXcWk1sL66yPza1t/Wtyq8x4XFIM8OWN0+4OxvbWMnByzMLbBhX7f9D4WQdafaTcan5zm5v3zxr4C0lEd6tNvK+u4P6khQRKeCgoLCLwThg//GovNG5HuKGpIQsU8u/78eomxFMi1dV+HTQbKHv3WdSTvgK+1EINvLX9cfyTZo/7/CL4PrK2mTor+9jf2jY4txf91ykKZf/zmIe1BEp4KCgoKCgoKCgoKCgsJPgyI6FRQUFBQUFBQUFBQUFH4yFNGpoKCgoKCgoKCgoKCg8JOhiE4FBQUFBQUFBQUFBQWFnwxFdCooKCgoKCgoKCgoKCj8ZCiiU0FBQUFBQUFBQUFBQeEn46OiU/zN2dkZVqtV/lc5lEM5lEM5lEM5lEM5lEM5lEM5lOOHHhIXFxcaoSmfFp2S2Nzf32d3d5e9vT3lUA7lUA7lUA7lUA7lUA7lUA7lUI4fdEg68vz8HJ1Op9nZ2XladErK9Pj4mMPDQ+VQDuVQDuVQDuVQDuVQDuVQDuVQjj/rkAYy9Xq9RqvVKms6FRQUFBQUFBQUFBQUFP76CI2pbCSkoKCgoKCgoKCgoKCg8NOgiE4FBQUFBQUFBQUFBQWFnwxFdCooKCgoKCgoKCgoKCj8ZCiiU0FBQUFBQUFBQUFBQeEnQxGdCgoKCgoKCgoKCgoKCj8ZiuhUUFBQUFBQUFBQUFBQ+MlQRKeCgoKCgoKCgoKCgoLCT4YiOhUUFBQUFBQUFBQUFBR+MhTRqaCgoKCgoKCgoKCgoPCT8bOLzpvra66vb+x//eWI++fq6koc1/Yzfz43N9fyNT52Xz/kPT8E6X5Fudv/+sSRfstfWPYKCn8v/BjfIPmDWyR7uv/3n83HP2/7rpuH3/l3xJ/7u6T3P/7Mxy7xYb99d52nrqnw0yKVt1Q3948/pw5+TH197CMftEFx/t13Sf/+mO/9pfNn/6YnyuFj15DysA9hK1P5Px+9hsJfgFS27+xM1MUPLWe5buz/l5A+/+P5eP2+e+3xd/498EPL+x1yGTz6zMeu8eF6ubuO7Zryf38Q4v0/h+i8wag7ZmVujsnxcSYmZ1jb2MNyfmV//cdjNhwzPTLG6OQyFxc/7nonB9uMjgyyuH7wwYBxrN1isG9IvGefqx+RXF5YTOysrzI9NSl+/yTzS6sYref2V385WI2n7GsPODWe2c98mDNR9nMTouxnlri4VISnwt8zN1ydmTCZjJxf3nOZwl9cWC3sbKwwPT3D1v4RF1d/2j/cXFo4PNRyfGoUl7jGYjKwvjjP9Mws+ydGPnSJm6tLzswGDNYL7pucdG9ba8vMLaxyarmwn7VxbjEKm9ZyYrSKqHIlfPERO9ojLv9OOouuLqzsb62i0cxyeGr6QQHRcKpjd/8Ao8Xmgy9E+e2sLTE7t8ixwfogWbm5vmB/ewPN7DxHp2bul9ql1cSRdhedXi//bTUb2dvbwXimTCD6Kbi8vMBoNGERbVwOwzdXIjZvMzsjxdUppqZsx8zMDAvLqxzpzR+0JVGxHOzvc3h0Imz2rlYvL8R3GAxYz86khMl+1sa5Wc/m8qKwsxV0pvOH7eTqnN1N0Q7nlzjUPWyHN5dn7O7ui3ZilP8260/Z3dnF/HfSTiQbOdzblG1w5/BU5Ej2Fz6EKJwzo5793V1hg8IvSafENY52JTubZfdQ9+gaN5we7TE/N8vm7uGjPO0S3ckhB8ennIvKvjwzc3iwz4HwBQp/PpfnFpFX6zi7vC/aRPmLeLUwM82UyF8nJiaY0cxwoDPY7PAD3Fxfcnp8IOpGz/nFNVfCfrWijqenplnf2v9wDBJ2bbWaMZitD+KcdL297VVmhY1pT4SN2c9LXIuYuq8VNmaw1bvZpBc+XovuB+Syv3RuRNw+3Bd+TqNha1f7QZ1ynzNRfls7+7c6Q7rGwY6wrxkN2/vHD64h5SAn2h1xfWG/B0cPX7u6EK9ti1xFL9eFFPMORDmfGGy+7E/xs4hO6ceOttYR5OyCq4s7fgF+uLp5EJ+cx9y61v6uH8f+2iyJIWGExJWIBvrjGtfsWAehwb7kN4wKI3i6MmeG2/Bx9qOwflAknX/Ko77P3sIEaZEhvHVxIyDAF3c3V8LjUplc2uEvl96ijEUqZDJbRKD8y4Ts7GAzquQMOseW7Gc+zKko+6zYCEJTipQkS+HvF+GQtxYmyU2KoqSmkf17yYxub42awmxCgoIJCQ4mKCSCipZBIfA+bodzQ11UVNUxv3mIble6RiYREcKWggMJiYyjfWgWy/lDP3NmPKKvqZzUlBS6Z3ew2F8+Mxww2NlAYkI88fEq8ioaWNXq7QH5nJGuBirrmlk9kpK7G/bWNBQXFDC+uIXIAz5pTKd7tNcWER4STHCgP2GxagZmVjn/SEy2nu7SXFNKXbuop4sbTMe7NJbliLIPIDDAn8j4dMbnNm3iUiQy4wOdpCeriFclkZZdLGLWnl143ojY0UtZeTVTS9vyGcPJHm0NFdS0DGAwffrJzi8JvXad+vIckjNLmF09sLXvqzNGu+uJDvXFxy+AoKAQggID8HR1xNkriI6pNR52wdgRSe3BxhQlxWUMTCzfthezaE8NFcXEJmWjWdt50MFwuLNEWV4qQf7+BIrviUvOYXRRxG/ps1cmRntaSVPHExMbT1ZBlbjHfdsHxZ0ujXdSLOxyfv1APnMq/EZLbQV1XeNYP3EjvDCf0NdSRVRYEAGBgYRGCP81OCtEy4d/14VJR39LnfBLHewK8cCFXr5GTFiwuIaf8IHxtAofKIlIia2lUUqLsoiKiiYlLZPusTnO7Jc/2likqqSE/knxfvH2K4uOoa4mSirqODB92mX7t+ZgY4GynFSy8vLYMtyPYWd0VRbg8/oNXsLOfH29CQmPoG96+cOdrEJa7K3MUFZUxOTyFobTfdrriogMDyFQtJMgkbfnVTazd2qxf8DG9bmR8c5qEefU1It2ZLZX4fWZnon+VjLSkuV2kFVUycyGzZ6uL89YHOmgtLKWhW27je2v0STZWNsAfyIc/7I5NzDUUUdcZDi+Pj7CvmKo7xnD+oFil7gQucJ4Vz3F1a0cGszyNfqaK4iJDMXH24ewSBXt/dM2HyoE5uLUMHmZKcTFxZKYlsPA1IJdl9xwvLVAUWERY/PriHApqlVPT3s95dVNbB/+6Y6dn0V0Xl+cU5MSzYvPvxPOPIvWzlZS48N5/t23BMZlsnX043ukdpeniPTywSs090cH+emhZrw9ncmoGnrQq3Kfif4GHJ+5kFnZ+1Fn+iHmexrwePUSp6AYWlqbRQUn8+ybP+Ibl8G6Vmd/149nZ36CmvoWZtf27Gd+HJuLEzQ0t4mA+aevo1uZQh3ij3dsNgarIjoV/v442d+krjiDIC9n/u2f/wdOoYms7J/Kr0mjF8sTfWSnJJJbUklTUwPqaH9eOniKoKB5OtkVGA/WyEiMISWvSgTcM463V2mqKqe6oYWmugrCvd/i4BXJyIJNyHBuYn64k6TYYJ59+zn/+Z+/o6BzAZM96Ez3NJMYFUWRCAKN1ZWEBYVTVNOPSdjk8eYUqrgY8ms6MNlFrOFwk/KMeKJTStk6/GG9lb9EbkQg7Wkows3JhZSCSuprqwhwe4tnuJrpjXcJ/yOuz5jsqSUsPIrG/lkuL020V+Xi4uJGdkk11eUleL59TXBsJiu7JxyuzpCRlEhSdhmdrQ2owoPJLmvj1HqB9WSDLHUcyfkVbNkTp0urgcHWCkJDIxmY/rj4VfhhXJoOGe6oJdjXiy9+/xs+f+ZO35S9U0CIx42lGdqaaqmpa6ClpZXaymJ8HL7hs+cedM9sPBCO7zg3H1KdL5LXxCymV6SRgzPxHc3EBnjy5ee/498+f03H+NJth7DVoKUsV4Wzqwdp+VVUlRUSIHIG/9gM1k707C71k5oUR05RBW0NlSJBDCWjpEYkzFec6TbJSIgkp6IZrcGWo1yIdtLbWE5QaCyTQkB/KG//5XPNaEclfh5uRKjzaawTvz3QAwf3UEYXtp4se5EQsjrVR4QQ7rVdo1hFPjXZVSVfIzo5j8baMiJFPbz1jmBieUfYlI6yjGQSkjPpGewmNz2RoOhMVg+M4lLCfitziVZlyJ1ocjFeXTA73EFseDjl7VOc/8gZcP9ImE52hSAsJtjrLZ/9+t/4/KUTs8f3xODlEZnhfrz6xoGC2hbq62tpam5hZUf7wZzZrNulviiFSFU2K3sn7K1OUZilJiO3gLb2FtITw3n1+i35jf23HS+r04PkZCTy4uvf8d//+RviitswypV6w+bMMGohvLILSunraiA+Mork3EakzFO/t0xmYrSwuQa2pU4MgWRj7bXFwtfH0C/8wKdpYldoBhrx83QnIjaZlkYhPkP9eevuT+fM5gdmFFyzPt1LXEQoZS3DmM4szPQ14O3uLvxdOm3N9UQFeOPmFcrM+qHIR7YoSBf2lZIntFkXGaoYElILhF+7Fn7vmMaidKLUuSxtH9rt+ZKp3npioyKobB+57fz5ED+L6Lw8P6MiMQwf9xCGp1bFjUtDuesUqSP5/DsHGoYWRMO9axIX5+dYLNYHQ+/SNUwmE9azh6ncvkgKVMGhhKsqMElD8edW8dmnxefV5SUWsxmL9eHrs6MdhArxVNQ8Kb77Qh6Wtp4/LJbpoVa8nXwpqHs40nktHKhZ3NefcmzTHXUEePuT3TAoz5s2neyTG+3Bb5+70DezYn+XqCARpKwWM2bx+z9Ul9IUO+k7pd8pTfWVpkN0l2bi4hlIdeeoOHcln3/H1YWt7O7ft6h3+/8E0vQ+s0X8hktxb9I6zWtx6u71u3uyPDDc0zUNmTHhhCQrI50Kf5/srS1SVZhBXm4KL7/7LY7BCcxvHcmvXV+ec7S/w/r6OjqjZD/n7CwN4/XqSyLVJewa5Lc9QJrGMtRYTFBwOK2DM3JSK03PPdXpROC95FIkUbMdBfzhi1cUt4zIn7kynTDe3UR2dhqxsaF88avPyWubxSib8yUVeTnERKVzYDlHf7BLqTqR5NRiDo72qS/NIj41j7ntE/laEtJ0meWxTvy9fGgbnsf6IUfzC+dQJDGqUD+8I9Lk3vIzUY4zHcU8e/aGvLreJ0W/8WiTHFUICdmVbOktHC4PiwDsRmhSEcdGK1aznr7KdF46eVDdMcxYezXp6dn0LOm5shwyWp9LjKjblc1dBluKiVEl0zu9dm+2yg1HO4uoo0NQ5dSwZ0+AFH481uMN+lurUSWlCeHpxrM37vRMrN8KtSsRD89FznAmjqvrSw7Wpwj1ciKxoIl9g23q5gOuztmY6SY0KICS5mEM51fcXOjprG8gMy6OyBAvfv+9E80jIi+xf8fuXC8BHq8JVWdzaDoXolVHd1UGr1+/omN+k+66dNIzkpjZPOJKCNrm0jTiMoQwEoJ0oL6A6Pg0Jpd3H8RP7co0qVFBqEs7MT6aEv+pcHG6SWJEAJ5BcXJn3MWZmYWxDhy++4bUslZ0lvfzArNuj9r8JAIi01nZ1QnFsyvERADeYSoW9nTyVPeZoRbevnhBVkUrmxsawqLVFNf3y58f7+klyCOK4ZllNheHiY2MpK5jFOO9jm/j0RaV2YmERCWzfmC2n1X4EEc7KzRVFpGTEouf12s+e+7I9L1yu9KvExcUIvxdoRwvrkVOKE1Dv/rgGsArViZ6CfXxpLpPI4tKg4hHaysrHJ6cyp/fmh8h0sMB14AktnS275odbKO0TPjVsEBe/PEbYrLqkV65ubmit7GasGAVY9Or4swFDUV5xEWlojWditdKRRtJYnxhk4t7uevOwhgpMcEkF9RwYv70Oh/OT/eETwoW9hXLoBCZUuyeG5JmXb4hWF2G4ZEekrDqdmgsVBMcnczKoQGLfo+MaG98I1MZXzmQO30mu2rxcnMmqbSdtfFuUhKTaBtfEWV3jaa7CrU6i+7JbRaFLUdGRNA4MCvq8K78LKfblAj7CopOY+NPjHb+bKKzKjlSOPl4FlbvptPOdFbzzbPXlHaOYz7dp6OlnY7OdsqKiykoqmNTa+Dm0srcWC/5manExkSjUqfSNjCB3u6ktWuzJIWG4ReWzkBfB4U5aURFx1He0MXBu4AvClm7MU99ZTEJ8SKoRMdT3dzDod7W0OcnugkP8SM5r46uploykhOITUimdWBSVKqteKZFRXs5+pAvROe76QTbyzNUFGYTEx1NYnoufZOL8vDzU8x0NRASGEpR27j896XFQG1WLG7BcUyu7MrnTg+36GyqIik+lqiYWPLKG4TDFAYqvyq4Pmd9bpzSvEyRZEaSnJbNiGaRkd4W/F9/wf/8n//MN2+9qG4Rv03qURVVuzY3RnFOuii7WNSZRYzPiWAtXUskt3Pjw1TXNNDd2UKGSEwHxhfY3VhleHiUpU1bPRkOt0WZVKNWxRMZFU1Bef3tyKxeiM6M6DDR+AsV0anwd4nFaOTo6FCIwj2SI91xj0hkdvNQfu2pZRXXxl2iPL4nXJXHpm2Z3wOkUZucpGiikvNZFQnWY66Fv1sZrsXdO4TmwVnbuXMrpydHnBr0LE514/bNC/JbZzDYRWdZdiaR4akcmM/RaXcpSUsjK6ec/q46klIy6J1Ylq9zH4v4Pbmx/iQV1rOteyIx/wSY6q4hwMuDguZJ+xmBaY0gxzeifEs5eDzMKATJ6pQQD54+tAxoZL861FiAj6sLdYN3ZXS2My4CsifJ+dV01JWQmZFD74JIqE1aRhryScwso7u9hcSEBBq7x3k8c+vy3ERbZRYBoVGMiSToAyFB4QdyaTWi151weHhAV20u7p6+dI+t3QrC+1xfmBlszMfVO1SIvP0ny/7MpKOtJIOQ0Fg09uU9N1dnHB/q5HWD4x1lvHbyonl4/nb6+f7CIDFBXiTlVQqReiN0q4mR1hK8PT3oX9qmszqZlNR4poVvuDDs01qZQ0p2Dt1Dw8THxon7XXivE/naqqOnJk/Yegyr9tkTnxr70534eHkL4dxqPyOSXtMRycFvCYzNEb/r/Z637cUxVCEeIheawCQSpgNNB/6+PmRWdNrfYRt5Sw5yI0wlcpzxIYIihehs6BOvXDPe20tkoIrOrm7KctTyzAPdu7UG7xC50txAA+FB/rSMrsrrExU+jLSnwPHRkSj3TRpLE/n2rRtT+3di4mxfQ4CbN96haWhPTjBZP95JcmU9obMmHw//GDaPnxb9luNdcsK9cfEU7f/Y1k70x4cYTGYWhjoJdXEhNrPuVnT21FcQ4h/P+KQQnddWGstLSYpLZUTog6T4GCGM5t6zd6mjsKs2Gx8Rt6fXpbWK9hc+CW7YXxwhxMeVtKJmTuxh2nKyTaEqBCe3MDbsYv0+65phEkKDyC5vF9Jc2NfKCP5OL8irH7CPGoNZu0K2KhL3kDSmB1tJSUikbXSZ86trZnpqyMnJp6qhg9IsNZnlrRieqO5RIVwD/f1oH1v+aLn+rKIz0CeCkZk1eSROWh+RqwrjOwdvhhe2MO9O8vKbb0Vjdyc+MY2i0gZW1jYZFz8syMdLXisVHxdLWIAXTi5elDf1YRZ3frq7jCrIh8++diAtL4fsrDSC/dx58fI1WaWiosxX3JwbmRloFclDCurkJGLDAnEWBlTa3IfUEbdqAnYGAABjf0lEQVSqGcDX5Tlv3IPFe7JIU6vwdXfkpaMHtb2T8vC9ZrhDFp2F9SPyhkVHW7PkpqeiSkiS11hFRUYQpVLTq1mTRwsfs9DbhLejNPUrkc72VvLS1bh5eFPZMYpJ2lBJVMP67CAF2WkkqtXERkfh5uaOSgjhA71tmsPcSAdRQb54+gYSGRlFYqKa7tFJhnraCHH6nv/9L//GcxdfyhuF4D4+YXthhKzUJNQpqaSmphIdJq1DyWJqbV94kSNKUyP5X//xOVFCyKelFTA0schQUxlevkGUtY+Kb5SmNIxRmJVOcmoyqvgoXF1dSRaJ6oEQtZateUV0KvxDcGY5ISnMFbdw1a3ofJ8rJjsqcXrrQFFjL+bHbuDmmtP1SRKiIsmp6ubIZLMZaXOE/e0V+tubKCvMISwkmJzqdvZPnwgoM904f/WMvJZ3olOIr6564sPCyCmppra6kqSkNEpKquSOosrmHta2dtnc2OBIdzeV9spqoLMyA4/IVHkE5lOkuy4HH08Xmkbt0+oEN1c6UgKcCY3PYun4oU+S1goNthbjHhjH1OKWfK6hKAk3Zw96F23rgCSuhXANEYl0XEYF4xOD5KWpSUjNp6WhlrTEeIorqslKVpFZ1MDy6ha7mxvsC8HyLu2VBMzCUKNIeAOp753G+ul1sP8iuTnX012TjYubj0102s/f52Bthkjvt0Tn1HHygeU2usMtMhNjiVYXoT1+3DNkZaK9hJdvPR6IziuLns6qQvy9fUnKLKa4IBt/LzchQms5NFnZmesWiW8k6blFNAgbzMxII7+wgLysVDLLGtnY2WF3exPt0em9BO2KeRHT/Zyd6NZsfbDD+pfMdEeFyBd8KGodtJ8RSbHhhNK0YHzDUphbf+QrRZ4z1dciBIwXY5IIEKcm24rw85JyOtvMDgnTiZYydSBBMUmMauYpzU4nISmF1o4WSgpySEovoKI4n3hVOtMrO+xphY/b0T5YH7u/NklcdCjJ5R0c/x1sJvM34eKYpuI4vnFwfSA6z4XojBDi51d/+I7wuDhSs3JF7jlry12fQL+3Qkl6HJGZtRjNTy2ovJZHNb2dHEjKrePUvqHbO1ZGOgl2ciJGiE5b1LphY2oAVajI0TML6GiuFrl+FqmiHRTnpJGaV8HG/pFsY7IvvrWlS2aH2/HwCKZZiNIfssnfLwaRFyyONOPn4UixEIwme1GfGw9oyFOJXNyH8V3DbeyzccVoXxMBwRE09i+IWHTOymg9Ti9eUi20zLvaujLsUJGZiJNXPIur8xRnpghbyqC+uYX8bGlmVy6FBfkkCb0wsbTN3s4227vaW38osTnTT3JUANnVvXxsdd3PIjqvLs6pS4/F4esXcvDu6OokNT6CVy9ekZBVw6n5AvPOKN988Xte+Ceyal/juTU/TIjba7wi0lnZswUHy9EmCf7OOHoEMbmhx3K4RpyfI//+xRvaJ+fl91yZ9smK9eW5ox+tY5vihDQNbpet3T1Meh17CwO4v32Ja0gy20L6S2LP6YX4bo9IZlZtyYdhZ4Fglxd4hon3CKNZGOsWBuJHaeskJqOB6swIXjv7UdMxzO7uLmMd1bi++Y63YRnon+gFWupvwfW7r/jsm1fyhhXPvvyM33zxgtK2ESHYJIM7R3+0y/bOrigPE9trC6hD3fjqhTvTazucGbeEwXnjIH737Vov8bvOhaCXGG8owTcoiq5JW4+9QbtKfoIfb8X7O8dm2RFBb6wpn2fff0tEdhVW8wGVmeH8039/Te3QovwZicnmMjy9A6nsHBN/3aDXHrC9tYPRYuRYu0JCgBvfvvFncH6Pq/0VMhXRqfAPgMWkRRXmjGvY06JTGvlYGO0m3NeNEFUuy7vH9lfucXXB6ngHUUIg1nbO3AYRaXRzYbKftNhg4UOe8flXryhoGET/3tSZK5bG23GSROftSKcQxPp9BjtqUMXHkZmTS3NbK+UFGcSlFtDZO0BzbTnxMTEUlNSxun1iC1JCGI131eDoFc3g1N30/k+J9oo0PN0daZu5W795c2UkJ1LUQXwGGu3DRObcdERTiRp/dTYL27Yp0hW50Ti4eDC0dldfl2e7hPl6E5VWztaJHs1YD+lJ8aSJRKuitoHmmmJiEpLpHBimo6VBiI04MvLKhZDdwbYi5IqDhRG5I6CorpcTyyeoJn6BSBvWtFdmCtHp/aTovLkwM9JchMNbV3pm1p8UpRIH2zPEi/wjsaSVo8fTb2+M4hqFvHgkOqVpbWPtlTi/+JpvXzni6vSGzz//gsyaPhHvhSGLdjfS20paUgIpGfnUNrVQXV5IfEISPX2DtNRXkJIYT1ZhObOrO7ejtBsLY8SGulLeN4fhEwyhg83FePh6iXxh2H5G+ErjKRU5sfiGp8gbPj3gwkBfYxnObjGsHdlGtwbrMvB1c6WqxTYLTMKs14ocK4jA6Dg0WwdCQEqzyjKIiU2isrKWdmF3amF3JQ09DPa0UZCRTFxiGl2jmtunIhwfrMvvCU8pR3vy6a5d/5tiOaC+IIavH4nOa9MBA531qFQJRIaHivb/ipfOntQPzDyYdvmOnaUpUmOCyKkZwnz26HURe1an+1GF++MdGi8Pgjwep1kcaifQ0VEWne/Gyq+tp0z0N5GRoiZRnU5jcyM1FSVCLCXT1DtKR105aYkJpGWXMreye7vWdGV6lHDh4yubhx50SvzSkXyOpq8Gb5fXlLSO8G6F7YWIYy2lKXh4ejO0abgVkjLXRvqED5Tspn9hXxadcz1lPBeis27wbkbQjUVLVY6a1x5RbJnMrM6Pk5+RgipRTWVNA7WVJaSlplJW387IYBdpKckkpWTTP76CxZ7r6zdnKU6OJT672uYDP8DPIjqvLy9ozEzgi3//L7577kiQCMZ+/iFkFtWyfSgPnqNbHeDNy5ekVnbLw+nSuf6mUl6/cKFlfP3B1JTxtiK8RYHXdc9g3FsiKVQ03shcIdbuerM0vfW4OHmTXd4rLnXN6ZGWkb4uqstKyEwK57e//S1vAxPZ1F2yMjOAn5cLKWU9D3pCWkSC4u0fwdDiHkuTvfg4+1PROS1PtQtz+Irf/eFrPANCiIqKIsjHlT/+7g+89IiT11Y9Zq6nCV8XFwITshkdHaW7vUkkmaG8dfGldXRBXp9iNemYmxymvqaSjNQEHL7/PV98/4bpjR1WRmrw83CXk9H3uWG4thCfgEjaRhbkM2uaYTy+/h2fff4d/sEhhIeHi0TKhf/41e8JTC7hRIj32rwEnrsGs3p6V27TLZX4+AZT1SUFgBu513JuYkjcUzm5GYm8/OPnfP61F0Mz21wJEZoVE0aQIjoV/s75mOg8M50w0lFNaIA34apMNPYdKh9zc3mOZrCB0IhgGoYWeZfuSlOHTo/2mZ+ZZFD4qNLcVLx9gmjum+ZhR/Ij0XnvNUn07m5tsLm1JfzZIMkiwa1uaqUwL1ee5ZCWrCYyJJSskkYOJbUr3P78YAdeThEMjt51On1KdFRn4OnhTOvUvU3PrvSkB7sSnpDz3kin9fSA6ow4IlPzWbFPba7Oi8PJ2YPBlTvReWXZJMTLh4jUMnaM0jWuOdzbZnNzg52NObJTEsirlAJzPskpKaSkpRERFk5cUgFb8lSyG8ybM6QIYZNX0ybK++NT0RR+GH9KdOp2FsiK8sE7OpMd3V3C/JAbDpZHSIgNJqOmixPTo1j9WHTK6cAN2/PDJIQHEBKVSNfgBKNDvULoJIiYG0ansB/bOrILtDubcif0yuwYeSKJy69ooKY4j+TUNDLSU0UcDiMpt4JVrU0E7axpSIwPoqhjitNPcJb7SEspHj7eVLTfF506yjPC8YtMY27d1rlzi+WY9pp8XgWo2bbPvBhuzMbXQ4iCFqmj24ZZ2Gp5ij/+UXHM7tiuIa0JXN/cZmtjVdh+AYmpWXR2NJMYpxL+TSTMUSIXi1czoFkXNSaS4kMteeLv6LhCtIdPrHVQeJ8PiM6HXLMy0U2g6wtcQ5PkDdcesz4/SnyED4Vtk1juCT3pcTaa4XZiQnwJikhkWNTVU9wXnfq7lFxwycHOOjv7B6zMjVGal0pWcSWN9VXExcSRLuwsKiiAlNyqWxvbmp8gOcCZiqZezB8Ymf0lIj1CaHawDm+3N/La83fzns6NhzQWJuHp7svkruGBNsKqo6s8hzBhN+Nrh7LoXOivEjrqBXUDd6Lz2rRHRZbI590i2bbvgaM/2mNtY5ud9QVqygpJzsilta2JdHWCyCEyUMfHEhqZwuSCbZbQucj/a3MTic4oQvfkaLaNn0V0SiOdtakxuL52paCsnun5Bda29kRjfNcArjheGcBRel0Uri1EW+mszhMK3Zv+pYc7Ec6KQvTz9aO6YwL9zhLJIuCHJpQ9eObVwkAj7s6+FNQMYjzcJk8dQ2BoNPnSLpPV+Ti9fo5bWDLrxxeszg4RGuRDQdPEgwpsq0jBOzCKoaVdu+gMEKJzkpPjVQKefckb1wByS6uoqiynpLSUyqoGhkfnsD4xvXamUySbgaHCCO8cq2F7ApevvyK+sJ2tg2NaSzMJ9A8gs6CMutoKogNcePnahRkhOic68vDz9KCmZ8b+6ftcMVCVh5d/JO32BHJxvAfnLz7DyTOMovIKKivKKS8vk7c5nlnewaLbpiwzEUefWPbvPWZluqVCiM5Q6vs1XJqOqCvKEPcUTGZ+MQ31lYR5uvLsZSAD01uK6FT4h+FDovPceCJvYhDk70t6ce1HF9VLonNuqJmw8BBhX/Mf3PL8wrBJqOv3hAm72tTf90gfFp3v0B1sUy5Ea1p+JTOjbSQmpdAxPMOF8MGD7SWExsQzvHTItbQhwUAr7kJ0DnyionOwtQRfTzcqu+/u/9q0RZi7M/HpFRw8mlF3JhLZ2qwEwlLybkeiOysy8XR2pW1yU/5b4mxvEh9Pb5IK6jm5t8uSNKLcU5dPYnoBg8PDZAjh0tDdJ8r2gtGOGiKDPOURNkmkmDenUceFC9HZrojOvxIfF503zI204+noQG79AJbLDyWXQnSujhEfF0Jq1RPTLh+IzgX7d1zSlB/LixdvqWyfkM9IaFfG8XjzkuDUCvbuKcazE+kRPPmoMwsYn50UiVos9X3SiNAFQ60NBAfG09JjW6+9uzqNKtqf4o7pT1J0rg42yo9gyKrptp8RvlLYWWakq/Bf+beJ/y3SWr/aQt74JrFpH31c6K/B38eH/BppzaYN4+Eu6aHuhMVnsnZwJxilRHx6sA1VXBJt3QO0FGWRlFHP2q6es61RspKiyKxok+tNf3hAdlwikXFF7Cui84fxg0Sn8LOWE9qKVLz1DGDMLkLuI43gqyL9yG8ev330l7QB5nhPPeGBfiRkFrG4/aFlKh8TnTauz49pqSoiJT2X0dF+ctISKW4a5cRgZW2okvCYWJqH5uT3bs6No/J1plyIznej4J8EN1fychp/LxdyKrtu11WaT7YpSonG3SuSjceda2dCdAotECJE58iy0E3SNaa78Hptm1X5zttZD1fITQzHJTRd+J17glG8f6itkozsfBo7+qnJV8u7BZtNZoz7C8QFulDV1iOPrl5ol6nKVgnRWfzLE53ymk51OIF+UUzMbvL+g01totPhlQt5jYP2jRlu5OlfTs9fkVM3cDssLu3+2Jivwt3Tj66pLUx7y6gCPYQTi2fLXgE31xaaCxOFQQTTMT4rEr06nn33ktyGXvnBsZaTFWJFguAWksjK0bm8ptPN9SURWTW3PSE3ovIyIj3xDklg8dgiizhvR1/K2ifR6XZJ8HMgIDZbJKAnWCwWzBZpV1wrlyLYPZVLznTUEyzEW2Hrneg8XBnC8asvSanoYmy4hxDn10I8Z3Mo7tF4sk99bpQQnU6Mr2yyMdOGj9tb4vNqMdvn5oi6E2V5Lf69ZKAyR94Cucm++cjG7KhIXF8QmlTM/tGp7R7tO/dKO9tenG5TnBrPG69Ydu81uunmcrz9wmkW4nlrfhCP168JSRROW2fAYj6lQh3Nq1f+9ExsyKIzMzqUwKQCRXQq/F0jic6EUCfhpBPuROeVmcnuOsKCgymo65cD3ke5vmRrupeYsHAqmsdug8jFxaU47ryG5XhV+ILvCE8pfrQZkSQ623D88ntypTWdj+KntE5zqK2G6OhERpc20c61k6BS0ze9Jr8+MVhNeFysPO3m8sLKSFsZTr6xDGtsr39qrE1JuyO6E5FayaUcU27YGG/G2dWLosaBhz3AggspUapMxzs2nVn7BjJzA40EuDuSXNxim+J1fc6MEOceXr7yTuC3+4/cSM+BaxdlG0fn+CK7G4ukhPrQPmibeTI/0CDahyudU9ImF1fszQ3Ia+hLm4c4VZ6b8ldBFp0VGXL9vic6RbxuKZN2HfamX8ox7Kef4mhvnoT4KGLzGjh4vG5aEp1NBTx3cKfpVnSeU58TwdffvKCkVdrrwMb2TD+O33+Ln4ixu7cbelwy2VsvRG0CnZPL7B4s4hoUQ+uobdnLdF8XYUJ0NrZPyX9vzA4R4etI7fAin+Dmmph2pgnz8SAgJodju7FoV8fxcXhDthD1J4+n3V2aGGqpxs05jEX75kmmTSHM/bwIVuVzaJ+KvrMwiLerO2mlbejujUbvLk+TrU4gt7KD/f1dmrOTScvvQCutNdDNU5gWLz7TItfbsXaVxLgookRed/DEhisKTyBEZ11+NF+9cXkgOqWdai8vL29zW/3hBgWqANwDY4UvfV887q9qyIwLF3XRg8X+TI3VsQ5iw4LIKG1Ea7z3OJYnkERnwNu3QtDUPik6p/oahQ0nyc+Tl9pbWmIs1T3z8uM7juabiYiPpr5vWn7v4uQQgS4+VLeP/6jHHf6cmPYWiQvyIjA2S4h0m71sLY4QFexLeFolhvuCUeLGzEBbOf4RMSIW2UaRT3YWiPR2ICK5iLVDW44ibagX4e9FUknbg6daHKxOkpQQT0XLEPtbKxQnhlFYWi1rjUv9Jsnhb6lo7ZA1mm5tilxVJInCjxo/8tyUn0l0WimNC8TTLZjRufd7RaRk6nCph++/fkVGTe/tboC6nSWyYwNEIPGivLGD2blZ+QHCnk6ORIkCPLBeYhSiM1yIq//nf/+e3JpGZufn6Gosxc3hBWEpZRwJIagZqOK7L78hOruK+aVlxjrL+eJ3v+J7j2jWdVeszfTx4st/5zffu1In1P3c7Ax1Bam8fPaSzPJ2uSFPDzSLAONCbv2wEHAm+iozhPhyICG7DM3iMrMTQ3T3dDO/cfDe/HQJTXsNLi9e4B2dztTEOF0tdUQFuPPdS0+GNOvsCIHn6/AMV78YZlbWmRxqw9/hD/zu828ZWt7gwqqnKCWMF68dyK1oYlxco7+rmfFZEayEQ5jrrOD185cEJ+axptULp7BLvTR99qUTOWUNaBaWmB7to6Onn7UDI1fGXQqSIvjOKYyde4+YmagvwsXNj9pBDXtLo3i9fo5LoIrJ+SVmx7rx/O4bfvU7J3qnNrkSBpES4oeX8pxOhb9zLMY9ovxe8Novkhn79Fnz8QZ5cT589e1LKnumWVtdZXFOw+S0huX1HUyPA4II2eb9BVRR4aQWNrB/KqnOMzSjg9SU1jE2NcPk2CD5ydE4OHrS+MT02vmRZp7/+nMyGibRP3jtkqWJXlQxkeTV9gqfcMWZVkN8dCTZpfVMT41Tkp2EOquYDd0F50KgNhWqCEjIYW7z0RS4T4QL4yFVOUm8fOVIeVM3Y8P9JIX5EBSfyewTv0laOzvWVY27V5jwubZ1rNIzFIuSI3j92oPa1l5G+tqI9PMkMimHlXvrcreXJslQRaPOq5Y3vTjX71OgDkWdXSh8sIbakiwhSFUs7erE95wz3V1NcFAQbaN3j91Q+Mu4MB3TXKLmxStn2odXHohO3Za0i707b7zC3x9de4T+eI+ClHjCYzLYPHi0g/SNgYH6bP74vQN1A7O302tXJjrxdXyFo1sgzT1DDA90ogrx4pvvHansnsFif7zbztI4KQkxpObXyjO5jPpdVBEi+c6vZGp6koqCbGLVuUwsS4n6JRqRPHs4eTCysv9RofxLRdo0q6kwlbciF8oobWJsdIDcxEjc/WIZW9x9Ihe6Fj6snRAPFzqmN225nrDLhuI0HBycyChrYnSon0yR7HoGxzGysHe7MaPlZJuagjSi4lPQ7Bi4OhMJdl0BwRFJdPWPMdRcijo+gaZB294ee0tDxEQEkdM4KGxWyU9+EJZ9KrNC+a2IaeN7d6JzZ1kjPyOyo2+I2akxSrISePP6FXk13U+Wrflwk8qcRALiC9Dbl5uVJgXx3dfPyK7uZGNnS+TZGqZnNCysbGAwWx8M1sz3NeP5/TNC1JXviU7tygTJwsZSihrkjYz0B2vkpsYTm1rI2ISGxqIUVGm5TK5L034vmOptwN03gq7J1Q8+U/SXivQs8O7qPBzfOJKQWcbwyAh5KbH4+IfRM7vzxADeNVODHQT5BwvhaH/k2qWZ1rJU3ggtkJJfx8TYMBkJEfJyvElpCq79EucGLSUZKmKT81gR4vTKqKWhKJnohET6Rifp76gjMjyS/jHbLsFLY13EhfpS1DKKfTD7SX4e0XlxTltROqq4NKaf3CnxGt3mBCEBEVR3T9qn1wquL9hZmSE3ORZvL3fcPTzw8fWXHfiiPak43V8XwV8lj/KlZyaKa3jj5OxCTEoeM/bHsxgONqgQyYmLszNe/iHkZqYRGhxIrKjELZGA7a7NyA9eD4lUkRgXga+XB64uHmQWN7BlX+y+ONVPjHCCNV1TouFeYz7RUi2EqZeHEy6ubvJ9pReUM799/MB43rE52U+omwOfffG1uLYzjo6OePmFUt81IS/MvbKe0l1bhJfzWzx8AlCnpZIcH05QYDjTmzvyNY635inLVuPp7iZ+oyMBwSG0D85wJgKe+XidrPgQvhFCObmkEa3eim5vjZJMFR7uLkJIuuPj40d2ST1bx8LArUc0luYQLATjwb0NSxZ6m4mPT6ZjfJELi4Hu+mI8XZ1E+fqRlibqMDIK/9BUxhdEQDnaoDwjhaSCWsyPnmuqoPD3xJn5iNyUMCJTpU2CbGtYzCc7FCdF8Mf//g3Onr7Cvrzx9HDDwcmdOOFbVp/YTOj6zEhpWhxhsSn2HuIrZgfbifL2xM3NFQ9PH0IjYmnsGUN/b426jWvW5wYJc/ehum8R0z1Hf3l2Sk9LDakZeWwc25PuSwvDXQ3ERYbg7e1LfHIOY/ObcrKuP1gnIdiTHJE0HEnbgH+S3HC4vURxRjzOLi64uXuJchW+aX5DiPUnvPDNtRAFY4T5elHcMoxedntX7IkYk5MQiYuTM+7unkQnZTG1vH377Ghpg5rJ/hYysnIYXbKvHxWxaXm6TyS50Xj6+BKVmEbH4BznQmFeWPTU5KkJikxmdu3uEWEKfxmXolwHWsoIC49lRCPqx35eYn95iozYMJKyqji2fHzIUJoRMNxYSIBI3AY0W6IF3OPGzHRvLb4hUfRMr97uKHt5ZmJqoF1+MLuToxNOoq14ic9XNvdzbLR3LolUakQkZllZ+SKZs9m+1AGxMNJBssgr/Hx9iElMp39iXsTLG3m6dmNhIt6RGewcP5om9wlhkPaHyE/DTeQ0Ui4UEpFA98Tig7V89znY0JAe7Ye6uI1j+yimXlxDyqfcXJyELbsRLHKxnvF5cY07O95aHCc/K4u6tiHkyQPCnk92VyjKSiLAzwd/kStVNfeiM54Jm7Uw0lZOYGAgA0K4fmpi42fj7IiOmiw8g8JZOLwbjTxY05CXHIOzkyNuru4EBIVSWNOKVmd6Mt+9uZRGtMvw8Ahgbs+WQ1fnJ/HVH7/g+Rtn/Pz98BDxzllqL6os5jcePt5ofXKAxJBQMss6MD34giuGO+tJz85nbMm2oeblxRlzE32oYkKFjfkTHpMsbGxBtB2hoXU71OWrCY7LYGX/8U6vnwYm3T4NZdl4iTz+rZMrwcK+Wvom5SdvPIV2dVr4whDisys4satBw/EOVbmpeAgN5OziSlhMEp3DIl69swuhPHfnB8jMyqR9WGPzrVdCf4l6zxVC1MfPX9h1LNVtI5wYpdHSC7oaSvETGmZIaIGPlevPIjpvhEg73NlkbW0Tg+mpKWi2QL20uML+sV6kVg/RH2wz3N9FY1MTPQNjD6bEnFnM7G5usbGxzfrKLF3tLbR197N58PC5V4ajXYb6umhp62JhaZXt7S22drXydFqrxSj+3mRre4f5ySFam5vpHRoXhXuX9Bn1J6yKz+0f6W8L+Mx4xMzYAI0NDbR39bG4to31A97tzKBjYXqctrYWmsTvaOvoRrO0/mCb9HOTjumRflpaWpnQzLO/t8vayip6y53xm3UHjA32iLJopHdw9MH9HGyt0NPVwdDkHEZ7azLrtIwP9YrvbKRDlMvq5r5tR76rc7S7WyytbnF2rzvSdHIg6mmdQ/sif+kh2NOj0j21MTmzIG+SsLaxg85g4ebcwt7mBuvbWnnKroLC3yvXwl62NpZZEX7m3TPKrkRCub26RG9bGy3NTbJdS3ZWV99E3+gMx8JGHiP1Ki6PthESFCjvvGgU2ZNJd8jM6AAt4rOSf5pZFH7B/v6H3GA26lieWxB+0nT7cHyJqwsruztbrG897NS7tOqZk3xaWyea5S05yFwJ4TvYWIyHXxgjC4+S7k8Qw9EO/d3touw6mV/f+2gAPDcdUpWdQHBsuhCQd4LwZG+d3o42Wjt6WN05eHANqbf5aF/El+3dB48/kUZ5VuanaGttZVyzhFly5iKcrs8MEhboR35dL/qPrHVR+POQ1oSdHOyyvLwq4s/DkRGLiK/rS4tsbB882Nb/SW6uONqcIi4sgLTCBvYeTL28EgmaVuQIK8J+zY9ykSuRhC3Q2dpMU0sHU/NrDx8VIK67vy1t5rX70H6vLCzPTtDR3s7k7Ip9qdA1i2OdhPl7U9I1ydkH16B+Gkgb/4z0ddIs8oTphY0HHQKPuRA5RVddAV4BEQzPbdz6H/kaIs9ramkXPnDzvRkCpyeHIjfZ4OTRlOjDnRV6u9rpGZzg0P54Oa1IltPipE7CEg6MT3tThSe4lp70sCnPCLz/OBSp02VtcYYO0fYbm1oZm17A8JEplRJ7K1OownxJrWjn2GThYG+T/t5umhobaRY5thQrGxqb6BQ5/bt88x1mkW+vi3vY3D16FJ+u2RG5+oawsftTZa9F/FuZn6RT2NiYZtne4XGNZqCViMAACmv7hN19LDL8srEaj5kY6RNl38Lk3CO/84grEfP76ovwDQinV9rF215MkhYY7Rfl39wqtMfGw0c0icREfyhi3Nb27ci0jWt21xfp7uygf2TyduM17caUvFFebFqpqNuP29fPIjoVFBQUFGxcnemoKkgjKTNfnhnxt+Zoe4nc5Dhya7vRPbHT9t8312zODpEYF0tl8+BH16L8GKz6QzprColTZ7G4c/RRAazw83F9IY3ElJCgSmF4Zu1vPgXaqt+npTKP2OR8tj7hUc4fxzUHG3OkJ8ZQVN/JoTxy8tfj+sLCWHeDvK62b25X6RD/mbiQZhR0VBIRp2Zixf6Yv78hVr2WhrJ8VMnZt+sh/1E4Wp8nPzme7LIWYV+PZ0z9ZdxcWhhoqSRRlUyvfb+Ij6GITgUFBYWfmYPNJfoHBljcfLgz90/OzRWHu2t09fazd2L8xxRFl2Y048MMjM1yaPjrim7jySETIwNML2492KBB4ZfHuWGPgd4eNEubH12T9FOg1+4wPtiLZu3htMJ/FKSdvNfnJ+kZGEV78u5JjH8dLi2nzM+MMTCusU3DVfjZkJahDfZ1Mys9a/6pJQ8/IdK+JmPDQ0wvPHzk4j8EQhhuL03R0Tvy/mZpfyGX1lOmJ0YZnZz/QZ22iuhUUFBQ+AVwcXnB2fnF3zbpvL7mUiR85/aNT/5REXFPlP25qIO/bvi7EteTHk+j8Gkg1dW5ON5tVvO34lLasfreo8r+Ubk4s3J59dedWnx9JdnghbKB1y+EG1G/Upy7ErHnb4nki88vrv7xBKcdqflbz864EOX/1zSFK2Fb5+L4oddURKeCgoKCgoKCgoKCgoLCT4YiOhUUFBQUFBQUFBQUFBR+MhTRqaCgoKCgoKCgoKCgoPCToYhOBQUFBQUFBQUFBQUFhZ8MRXQqKCgoKCgoKCgoKCgo/GQoolNBQUFBQUFBQUFBQUHhJ+OjolP8n6urK/m4vLxUDuVQDuVQDuVQDuVQDuVQDuVQDuX4wYeE0JMa8f+nRefZ2RlHR0ccHBxweHioHMqhHMqhHMqhHMqhHMqhHMqhHMrxgw5JR0rPy9Xr9RqtVvu06LRarezv77Ozs8Pe3p5yKIdyKIdyKIdyKIdyKIdyKIdyKMcPOnZ3dzGZTOh0ug+LTglpiq1yKIdyKIdyKIdyKIdyKIdyKIdyKMefe9g1pbKRkIKCgoKCgoKCgoKCgsJPgyI6FRQUFBQUFBQUFBQUFH4yFNGpoKCgoKCgoKCgoKCg8JOhiE4FBQUFBQUFBQUFBQWFnwxFdCooKCgoKCgoKCgoKCj8ZCiiU0FBQUFBQUFBQUFBQeEnQxGdCgoKCgoKCgoKCgoKCj8ZiuhUUFBQUFBQUFBQUFBQ+MlQRKeCgoKCgoKCgoKCgoLCT4YiOhUUFBQUFBQUFBQUFBR+Mn420XlutXJ8cMDO9jZbu3sYTFb7K78cRFlwfmbFarEgysZ+9sOY9UesLS6jPdRxYz/3NJfojw/Z3tlDb/4pf/c1Jwc7LK2scqgz2c8pKCj8tbi+uuTqvm+4vsJi1KPd25V92/bWNnv7Wkxn5/Y3fIhrzGYTJovNH9yI6+iOtOzu7mO9kk+9h/Sek8MDDo6e8DfCjRt0R+wdHHPx6MWb60uMJum7xD2Ja1jNRtkPfdxnfUrcYDHo2Nvb5+LqT/ttiYvzM04NRiznF7YTN9eYTkX5aY+4fKJgzMZTdna1om4evXh9gcWkx2y1yH9enFvR60+Fx1f4qbgRdXxxecW1iNePkWzkSLvH7p6wQau9bj+CyWgUtmF+aNMC6TqXwtZt33DDmdkg2/iWsO/dXWHrOzvyv9rDI/Qil7m234pRfyLa0AHW8/fboUFvwCLyIAmrsHv9qZ4PmPonyZnFIMp9X/i+H/arbi7PRXmdcnZx5yutZj37+/uYz963oHNxfe3+Hjqjzdbuc2Y1y/V4Iezz+uIMo9GA+fzvqXR/JkTskOzp6NQkItYPQPhRuS5EfHlnE++QcurLK8lu7ScecSPFVvH6/ZclOzScHLJ/cCRs9OEHr6/ORQwV9Szam3Rtq8mAUdjVh67/qXNmMaLV7gt/8377f4or4Sd1wsdYLu78oJSr7AvfaJBygUdI15diqOmRNruR6tR0Kucqkpu8FBrJIOzLevHDotzPIjpvxJ3ODfWgjggnODCY0LBQImJU1HeOoDP/tb5eOBtROFciEP3YNmcVBdvbUk1+aS2bR6f2sx9meaKTuIAQalsHPhg8dlZnqSjKITYqkuCAAKLjk+id0IjE8KewjDP6GksIDIumeWjBfk5BQeEvxXCwRXttCW29/RzdS3oshmO6a0sJ9w8gLDySsNAw1KmZzKxvf9QPbS1M0NbWweLWIcajPbobq0hOTCQuNobUnGKmlrcfiB/D0TatNSXEi9dj4xMpq+9k98Qsv3ZzYWRucpC8zHTS0rNo6BpEaziTX5P84tJkP60dPaweivsWfmdvfY66unpWd49/WCLxC+byzMjUYDtpSSpiY6LIKqpgcfvgo2V/ZdUx3NNGe/84x9Ybrix6xrobRfnHER0dS15JHWuibGREeFydm6SsMJtUUbZl9W3s3IsNm0vTtLS0olnZlv82nOzT29FI98gc1icSZ4Ufz/XZKeP9rVTXt7OyJdrug0q+Zn9NxNr8bNmG4pLSaOwdx2B+ZwePuRF2t05TYyOjmhXe6aSbSytjAz2UVgr72HnXji5Y1fSRkRxPUEgYMbGxoq3FEB4WRmBkvIi1c1xcX7CxMElJXibqlDSqGnvY0hrkT0vsr0xR39TJ4saB/Ldub4Pu1ib6pldEziKf+nQRZbYwMUB2WiJRolxSs4qYXtr5aPJ/fW5mdqSPprY+4ceM4swFC+P95KSKa0RHCx9YxOSyuIbt7ej2l2ltKCcpMYmCkkphbzu3/tF0uENXs6jHmUXOxLkri47xwS6aOvoxKib4I7nhaHuFupJ84kWdxsSrqesY5tDw8c7UvfUF2pqbGZtbv82JbyS7Ff61uqmdxd2nBmgumJsYpKSslnkRN99xIUTQwmgvhdmZpKRlUt/ex5HhnSC6ZmlmlKamFvZOz2VhJLWRhqZGZkXs/ORt6gEXLE8PkZ+ZSkR4OCmZeYzMr300xt2IuLg8NUBtSw+HeilfuWB+rJecNLXIUyKEjYocY3HL9mbB3sYS9ZXFpKakUFBaI+ph1/6KsK/jHZrr65ha2pQ7tK8vThns76Cte4iTH2BgP4vovL48pz4jgWeffYlfUBR5+Vl4u7ziu1eOlLSNYn3Uy/jncmE1sTI1xNDwBPonFPwP5cyst4nOsno2DvX2sx9G01uN27fPySlrfr9nWyR3UqBJDA/A0cWDeHU6KapYoiJCqenoxfKTdMec01isFuXqRHHbpP2cgoLCj8V4oqWvtZrECH9+9x//jEd0MmsHd6LjYGORJD9Xnn/9mqTUDLIyMykqrWBpV2t/x/tcGA8ozkxCnV0q/IyZQ3GNCpGsZuYUkJeVgqfTK3wi05jbOpLfb9Xt0VCQjJuTC/HiO1KS43B2ciOjrJ1TyzXbM8NkqOJIyS6gMCeLkNBo4WOm5NEW69Eqmep4skobOLbY0oDj3WXy1dGkFbeIJOJhr+YnxfUZU/1NBHi6ERafSrYIyi4ObwhPLXxQRw+5ZHmyi9jIKKrbxzi/vmSsswZfcY1oIVTSkhNxevUGVWYFe0LUG/ZWyE9PJkaVRnlJERHBIZQ09sujKFfmQ0qyU0gUSfbSvu37zkwndNYWEhGdxMzq3t/VSNbPxZX1lNmxHtJU0bz89nO+fuND//TWvQ6TG3YWx0iLCcXTM4DUjCzyi0pp6Rvj9IlRMYmrCz2dNXlEJaQyMrclktRL5sYHyU2K5eX3X/HfXzvSPbli/44rtpcnqBAJeEpqOnkFBeRkpcs5zH/85guquic52NII20sWPiCT4rwsIkViV1jXwZm4wo1FtBOR/6QV1sj2LnFmPKS1qoDw2FRWRNv5lHPkpbFOogK98QmJI0fYYICbIz5hySxuH9rf8YibK3aXJ0mMiqC0qQ/D2TVrU91EBnjjHxZDVroaH3cnvCNTWd475ubSTHNJFnEqNWXV5SSrYohOKWFbdyZlwIy0VhATl8zAjK2+pI6D8Z5GYiKiaBsT4ufvS4H8TTg92KI4NQZnVy9UyVkkx4fh4uZFQX0fpg+k61Jcqy9JF/WUyuy2jsszM3MjPeSkx/Pi8/+S7bZ9ZsP+bskGrSwL4ViYlojrm+/4t89f0zg4bX9VtAnNMClx0UIg5VFUkEVEWBj1vVOYxfcb9hYpSFORllcj2o/Ny5r1e+SnxJCaXyns7K7D59Pmhs3ZfqKD/PALDJf9TqC3Ox7BsUxuHt7zgQ/ZF/aVniC0Vk0XevOZuMYAYX5e+AdHkJudjp+7KwERajYPTVhPD6kqyBQ2pKa4rIw4YZdpuRXsm6T+JAN99cVExqeLeLZr+76bMwbbKomNiaVtaO5PxrifRXRenlspV4Xg6RJA9/AsJrOBye4qXJ7/nteBKnZ/wDSYj2E43KE8KZLoyHS2j398YxNlgdViwmC2/KBpWvODjQQ5uVJc0/Ge6Ly6OKMhO4bvvn1FfmUb+0c6jg/32VxbYmdf+xMlI9f01Ofj6ukvAqHGfk5BQeHHsrM6T0FqNNERAfz+N//Cm6B4Fnbso2CCtdlxwr28SEivZv/wBINBz6lez9nl025V8jFzffWEBIUI0TMiRI80Wqpja32dw5NT9Lp9+irVfPbH15R3TMifWRntIODtM0Jic9kR33G4v052lA9vnIOYWd+ns66G2IgkVg5O2N9YISc2hpS0Mo71p/Q1FImAkczg/F2v5pXwx5NdNXh7BzOgWf9kp4Lq95ZIjw3G2TuSuU0tJ0cHtBcn8e0LJyqEoHzKx1oNWsoyY4hIymNBJPvGvTkSI7zxCktideeQQ+021emRvHT2oWVwhtneRpLVqTSNbWM83qajOJXo5FK29o+F/28gMjqW+r4pIS7siIR6Z2mCmNAAssrbOTZ+aKRN4YdydrxOe00Bfv5BvH35PV+9cKFncuO2fs0nO5SIBNnDO4COEQ3HOr1shzq9UZ6G+x4i5dlfHSNKqqOKdg5N5+KUgZaKciK8PHFxfM5/f/WGltGF29E0edr0qYjhxyfytOzdjSUy4gJw9YthcfOA0eYsEpPjhZ1tYDjYoDJXRXR6JlsmC3O9tURExNEzsfRg6vu6ZojYYD/ym0ewfKJTQa+EoM5KCMXZM4jBuU1Ojw8YbC3l+2++Jb+hH9MT04wvjMe0VWbjK3zp9Nq+OKEjVxWKi08I/bPr6I726W4s4vn3LykRovRgf4noqARyytswn5vpa2nC1y2KsflNDnfmUcdGUlDdwZHhroPhZGeJ/KQoIlR57J8qNvhncX2JZrAZl1ffkFTcJoSJTpTzCrEBjrj6hzN39NTAzg1LE+1EhgaRU9GK1JVp0WlpLcsmSgjH11//ni++caFpdMn2dsGZ8VTkq1WEePri4/pCiM6X1PTYYh4XJ/SLNhAaKwSsEFfHIscvzowjLr9OxMBjhhpLiRJtYmjubmRU8r0Lg3UEBIZSL67zaVrUQ64sJ5SkRePiEURjz6Q8fb+/qRynVy+JzWt+0m9cmo/pqs6RO3CmNg6xmk4oTgnF1TeStuFFzIYTOqtycXrrSF7TMNszgyQnxFPdPYnOaGSooZD4hHQG5o/ZXhghLjJU2OHggwE93d482epoIpPy782qepqfTXRWqSMIC05gbs02veR0VwT7UE/cQ5PZt9huQepdGenroLy0hJKyKkYmFx4W6tUZW0tTtIiGWlRUQlvPEJtbmwy0lPPqD7/m17/6ksSMHOHIdpFl7PU5awtT1FWWUVJSQffAGMciwEhYDIcsLi4wMz3NYG8HHd39bOzp0G6ts7K0htFqK8iT/Q36O1soLS6irLKWsdnV26k4kugMdHR5UnRenlsoUfkLQ3tDY98jAXgp7mtugpEJDafGu5EG04mW2alx5leEMz3YZ3Z8nDWRjI4MdFNRWkxz5wB78lSUOw62V2lvrqektJL+vm4K0uNx8Qqipn9eBNJzOQmdnJllZmyA5uYWJhc25F5VqUdemoJSVV4syrqS9v4J+zC8QJTbztYqM5pZ5jVzdLfUUVlVh2Z5Sxblq7NjVJcVU1HbwsKG9kEv7ZnxhMnBHipKiikTn5mcW8XyeJGZgsInwunJEatLC2xuLpEQ5opHRKIQOHc9+AsT/XgLgaIu7sBg/tOzLKRRm+LUOCLiM5m/d507btidauKtkw81XVPyGU1fIwEub8mt6pH/FlehozQFZ1d/xoWvqi+vJj46C4MwM8vpEVVZ0ohrKdPj3aiT1TT0jL/nnwyHG6SEeZFZ2YHW9Jd1+v1czA40EeTtSWbVoP2MKJnjOXzevCY2o5KTxz9aJCVb88OE+npR3TWGVFuT7WX4uTtT3nXno42rA7i7epBWXE9XbTE5WQWMbIp3X54y015CYnoZQwMDZKoTKGvoRGd96N8uLHrqi1IJjFChWd2zn1X4sVyYjtkVcXlxaZnm8kzcRZLaM75mb9M3zI20EuzvLwRkmxyLpHVhH+PCYqCvNp+g4AhGF21TOG8uLGyIuLu2MEefSHZfO3vTPDLPxZN9zzeszvQT6uNFVc8sVpGjtBdFkpSWyOKBCc5P6arLR52TzeD0LKkJsdR0jGJPc26RRoZaS9PwCklh++jTHJk5XhzAz9ub+PwG+xnhg4xa4nxfE6IqZOPANrJ7n/01DSlRvmRU96O3XqFfGSDQ15vk4ubbkRvD8Rbxvs5EqvMYnxokMDqNipZR+bXp4RGi/BLo6RugqSIbdVY5W0cP97CQRkcnOioJCQigZ3bnA/Wo8CQiz57srsHD+Q0Nwxu3dlaeHoaPXxCTO++LjJsLA3Ul6YREpzA0b5vlI62D3lpbYmtnh/psFa6O/jQOzcuvSVycWdlZ22BxbpHJzlLeuIiY1z1ue9G6R2dNNhGpRSI+3QgdbKFJCKWUklr62hvJzUinXLSH9zLL82OSIwJJyi5j/6+2dO/n4oajtUnC/dxJyqnjwD7EbBKxOyvGH2fPGHb179vX3vIEqdEhpBQ2YL254XhrmmC3V2RUdnFqLxL9zjypsSF4RWUz099MalIyvZod+bXlIVG+OQXUtQ7SUJJJYk45+/rHOcIlfS1l+PsH0Te7+X493ONnEZ1XF+fUpsbg+sqNosoW5henKMkXQTkkigYhyKSZptK6z0mRXCXHRRIUGICLsyPe/uG0jyzIPRY3whBmBpqJDgnAzdUVdw9P4lJy6O3vpzZfxa//9Z/5p//3X3jj6knTwAxG0aBXpvpFYhBHcHAogQGBhIRGUtrUj8F6weHamLiWN29dPAiPjCYrv5Sx6UVqM+IJ8Y9lflsSxzeMdTQRFxwi7skfNzdnvAMiGJzd4vLqmuXh5g+KTmkB9NxAo/jN3/HWM1gIzwkOTmyB5fpML5LGJF46etE7uWqvsBsmeuoI9POltHVAiMQePL78mvDYFJJT1fi6vearb56RWlAnkhxRIiKB0q5qyIwLw9nJCXcff+JjInn74nu+fO1Fy8Q6l4YTGjMTeP7GleiICKJiVbQMTnGg3RHOOg8fLw+8vb1wd3PD0cWdjLJG0biECL6x0liejsPbt4THpZMUG4bTm5e4eQdTVlZKblYK/t5ufPftdwTEpLC0L/2uG86NR/Q3V6KKipDr0N/Pj0hx/z1S8P5Yq1RQ+IVzZjklKdwVt/AEuef1HSszvbi+fMG3Dj7kFBTT1tXHxt7xB9aU3GCUOttiI0kva0Wrt4tUYcunx1oWNJP0dbWRlRyHOr+Kld0T+WXd7hplGQkEh0XTJhKtns5GwoL8Scqr5cBgYbytgejgMOo6B+jr7kSdmEJhcbW8Pqqwupm1nQMMer1Iju8Cx4X4PS1FSfjFZaFZt3UEfmr0NRTI02Lrh9btZwQXRyT5OhGakMOK7mGmeS0S0dGuStx9I+U1RxItxSl4CN/XPbcv/y1xpV8m2EMk0pkVDPV3kKVWk13azPhQL/kpKnJLqijMTkUthP3i6jZmgx6T5S4Ru74UsaqnhtCgQFpEkqUkvH8lzvX01Obg4uZD99iabSTj5oyu+kK8A4PJKq2jV9hCaWkZ7b3DHD2RkEnoj3bJT44nMj6HncPH07DPmOoo4+VbD5qHn667s9MdyjOicfGOYv3EZsNzfRXExoRTUtvKcG8HeVmpZOflUVKUJxLGctZ2DzBJm9tY73dMXaDpb8LL0Y3Bxd1Pch3avBAn3r6+5Iu86h0WkXcUqwPwC09hbsO2ROAOaX+PDmFf7vQt7st5k6arTAhXH0obhmxvEZiEPyxO8CcoVs3w1BQZwgbVGQXMLExRU1FMbGK2+LeUhNgkhjQb6IwmDEbzrWiVvmd3cZjYyBAya/s4MX+aHWs/DzccbsyTo4ogPCaN1q4hupvLhYD3I6u4Ad39JmzHdLBKdlIYyfmVbEm9n/e5vqSjQI3zW18aBu9E5x1XrA9V88rJk6quMdupGzPjIh8OCQunprWboYEOMtOTKa2qIiM5iYLSWpZ3dSKunWI5e1i3rSKuhccmM7L0aca1W4Q0Wx5vw9/TiULRho32vrRzg5banHhcXPyY3jfa9cM7hI4aaiMwMJSazmmRWlywPtEicpSXVHZP2AbjBJenWyKnUOHsrWJuboKcZBVpwk8NT0xQWZxNTk4uxSUlci7RP7OOwWDAKOzr/netTHSTEO5LgbDbj+0d9rOIzuvLC1n8fPUfv+Klg7tQx5787jf/zRuvKOa2T8VNSeV7weqihrnFZXQmM4ujHfi7OeCbkIfh/JLD9UmChPDyCE1ieG6TszMzx0eHwtFYORZJWZb48f5+cbJYFL+J/dVxokJ8CYhUM791xOHeBjV5iTx74cjA3Ja8BsPn7ZdCoLnTNb0phO8NppN9yhND8XYLE4ml1FtzzersPLPTq1jMJmZHmvF6/RWRaVXCyZ2xMdn2wem1Mldmhjsq8XB8yR+/fk5kYhYT85tcXF2xrenl7bPvyarqxCi1hKtTeUqOh0iIZrYOhWDu5Iv/+f/DNShZ/tuk2yMj3J0Xr11FgnTImX6P0pQIXjt40CgakzQyuyecrMeLb/iPP7yifVoIY8MRFbEB/OdvvqawTjheozSNyEhzSSpvXr4mu7KDY1F+xuNdGovi+fr5G8o6J+Sd/OoK4vj1b39NTH4Lh6dGedOkV3/4V371+TcUtw5jMBnprk7n1YvvyGkckefna0Si5ePiSk5pIztHOjZmR4jydcXDP4qlo0947ZjCPzxW0wGqMGdcw1QPROfx/hJFmcJ5O7vi6vKWV0KABsdnMrOhvZcA2RHBd2Oqh5jwCKrahM3aI8D1pZXFyX7SY4Nwef09n38t7LBjAvOl7QrS7rMjraU8+/IzXjk64/TmBb/78gV1fRphq2DULtNQnkN4RCTJKWmUVtaIwJFHjCqTzv4RBvs6KS4opEUkDwenNjuU1j2NtJXz1jeWoZlV+dynRkdlOl4ezrTN3I0m3lwZyYp0E3WQwaz2YXZ0bj6mpTJNxJSM29HqqrxY3rp4MrR6N2X68mybMB9votMrWNNq6W+rJSYyHHVKOll5RTTWVRAXl0CLEDajwwOUFxdQ29zF5ru1eTdX7M0OEBMaSknDIKePRkIVfhyXFh3tlZlCdHrTPW4XnSLGthSn8eL7b3HxDSIuKlTYoSMvXzuQUdrMgdSJ+oijnTlU8VHEFTSJ1x+v+TQx0lzIi4+IzmWRbAWJRDC1qpczu41eGvZpqS6RNwxMUKdRWFZFZXkRCfEJ8oysob4uKkuLqW/rYVsI3XctYn12iKgAJ6oGFj7JTW+GmotFzuJFReew/Yw00qmjIjsa37AUZlcfJf6XJgabK0UdRbByYNs3Y6ghE193N6qa7YJDYNZpqc4IJDA6npn1PRYneshMSSAiKpb8vEJqa2uFEE2Up9xOT47SXFtBcVmtvKHXpX2/jKP9FRLjoonMqEL7aIaYwse5tOrpKEvhqz/8AQdhby++/pxvX3vQOrr8SOTY2FseEwLEk4K6RmFBD5GET0tuIo4OPjTcG+m85fqCxd4K2eakGSjvONnforYwk+iYaFTqFEoqqigvyCI5LYf27kFGB3soKiyguWvgbpaeYFbk3GERsbQOz9nPfJrciHLR9Nfi7fqakpYR3nWhSTM/mktScPf0ZmjTYPOD7xBifaCtlIDoGLo1O6Lsz+UOsZcvXlIzeG+fF7OWqmw1Dh7RbOoNTAy0kyRsRYpruYUlVJQVkZWRRkFFAxrNFNUV5ZRX1rOwruXS3jt2vDZNfqKom5x6ecbCh/hZRKc00lmXHsfrr54TK4RXQ2M9KfERuEgbY2SU/v/bO9OvRLKs3fe/dj/dr/fre9++7x26q7uqqyqzKjPNVNN5VpxnBBVHFOcBZ3HGGQXFGQdEAQVBRCaR554I0NRMKys7K7NKu/dvrWepYRBiBHHOfuLssw8zKFyDEOBL9m5vrmJiggVJlcV4/t23iGQN14nFjNmeOmaw4jGp+fBDz83plBVmIFMgxnFoqRDVQAO+//tf8R27UaqrayCpFCMtIQL/+R//Cdn4FvZ3V5GeHAlBWT2soc+r6/wUHaJsJMXlhExnAO6LM+jWNZgaH0NdRQGe/+2/EJ9VB6vtEocrox83nQwuYDze30BzdTF+/O4bPA9PwoTmABc2CxoLEpFaUIX9Eycc+hWUClIgrOngh8R3Fofx5q8/oHlkFa7QPzw3JEN8XAJ6JtZwtDmP5Li3KGjo481kEA96pSIWmMaid2EXXjv7f0pz8PxtJrYOg42/z8Q6XEECEnPLb4frOdy2fWREhiG/og0muxO9bRV4HROPUXUwZ97rtKAgLQwvEgRQ64KjAgfMOKcnh7POW47LczPaxAL85//8CxK4Cc91dSgXFuKn7/+Cvz1/jTH1x4fgCeIx43KaHzCdAT6jwev14PLyEo6zY744ybMfvkNhXRdOuIoHd+DS3dfnBiDITkfvzAY/74XfztoIs2EPM+ND6G5rRFFOJlKzSjHPpa2wtuBYp4aoQICEtCx0yPvQ3dEKQXI8coXV7L0EH7Jd2k6hVS9hRbvO2isVKoVFaOrqZ51HM0SlRchITUVaaiba+pVwcGkHrNlfm1EgOjIHs6qt0Dt5WowwAxkbHQ7FyrtRSvgdqBJEIbO4Gpun95+Au+0n6K4qQraoDjojN4ocQEd9Pl5HcKYzOKrM4XcZIGDtbJaY9U0OH+u/nNhmfYB6ZRV7u5toqiqDRNaJof5OlLPANz0jHUkpGais74WFT1UO4GJfg7LCbEjlI6FtxG/Fd3n2oekMuNAjycP/+fOfkcaul5H1yzbLEVolufjx2SsMzKy9V0U1gBPdIooLMiDpGsNZaLrNLYGLj5rOwJUd/bJyhIXHYfng7F6fdnVpx9bqMlbXt7C1pkZzbQUk0nYMy9sgFpYgOysDSakZqOschjGU5WDQrbD3koymsVU8xamHvOlMiEPb6H3T2Vadi6TsitvpVDcE3FaMyhvwKkkIQ8gIzvZJkMCZzqE7ppPdqx0VKUjOKYTWcMq3s1xl1EWVBtvbW1AOtKK4RIyR8VFIRKXIz8xEcnwcisrrmEkN/k37iQk1RULkFjXB/AmFIYkgnCXYW59Dfno8BPlCdPQOo7ulHimJCSipbsGh9cMMgv3VKeTGRqO1d+i+CWJwhUQ/ajqZMXrIdLI3Aue5DWsaNZZXNrGjXURlmRCy1m70ybuYh8hDenoaUtMEaJYrWL8W/MuG5QFkF+SjV/m0i2lyZp0znXGc6Rx8Zzq5WHygScT6vkSoDI77D7fdNkx01EGQW4jFPXbf8KazjZnOnyC/YzoDThM6q4V48TYbBpeXXwruULeBBXZ/6djXvs5mvija0OgI6itKkZ2ZjYy0NBSIZNgOxT8e0w66atm9J2mG7SNTi/4Q03nFgrJOUQ7SknKxuLILj9eLS8cJhmSV+P4nbnLxFjN3JxhskyI3JwflNfWoFhbg5bNniM2txNGhDv11RQiLLWH/8Lsn0jfYzYeQ5qchM0OEo1AhoTGZCP/45i94FZuCynIxioqKeElqG6DZs2KfmbbcrGSUN/bhxrNdciZNnMturjxsHZ3CfX6EblkNsnNyUSNtYAFGAV788Dck5zbCYmemU/PrpvMGz+UF1DNDiH39DG9TC7B3bMHOnBwx8QLMqjehmexDOgtceieD1bu41Ny452EY1RzdHntZKUd6VhZ6R5ehmxtBFFdNTKG6s66fHxM99XwhoZ55HbwsEJWXFyM6p4J/uspxujaD7JQEFhR33H/P125UpkQht7ieD8gGuInIGXlQbdv4X3suLCgvSkB8oQibhmDKzAFrmDIFMShq7ITjRIeq3Dj8+f/9wKcBVpaL2PkuREFxCRo7erFn/PC6EcRT4WHT+SH+SwsqcuKRkFkMzf791DLOdK7N9kOQxUzn7Oat6eTgSr77fF54PG6cGbRIjfgZxdI+1pi7WAcjxvOwGPTObfJtp9fjglbZjYjXL1DTqYAzdP/za5pZTzDcLkVphRSqOQWEZSJ+CRWL5QQKdk9nFZZBfWBjnYwPG3OjiAvPxuzi01xeaXqgEYlxb9EzuxfawnCbUBATgQJxE44u7z/m8nCmU8JMp5hr44LtETenLjY8GuNr70ZLfZZ1JMXFo6iuG6ehir/s5LLO3srPdSoUVmJ0Uona0my0DYzAbLFgrKcFWalJmNvk5sUE4DpchagoC/XyYb5QDfHbedB0Xl+ip6YEb9i2voUdfj8O49YcYl9+C3GTAtZ7g53MdO4uoLiQmc7u8TsPbEP8iuk83VtCaWY8UgqlLMh975cMbprQFQsKpwbaIRRLoFQtoqigEB3DczCxz8lIdxsyBKUYnQ++V+PeKkoKUyEb0SCUhPCk2JyUIyEhEdJ+ZWgLi6POLZAWxyOjuBY7x++lL3Oms1uKV8llMNiCpnN1og1JCQlo7n83N/vCakRtTiwy8suxYwrGINy14+brbq/MoqKkBN19I1Cwtq5I1IqVDT2MKyMsRslFQ88k/9k4Pz1BXaEQeQUyMp3/BC7bEdorsvBTWDzUewbW5/j49etH2usR9iYK0jsPB27YX5lEbgwznT1DH2T4fLbpDMHdU9xyhsMd9RCzuFUxKEd9rQRdwzOsXzvGaJeUmax8rJoc/EMg48ogMgvy0DMVKkr0VAn4oVseQVJsOKTdSjhCXZHLbkR7ZSGiozOgs743rsxM53g7ZzoLsMhlGbBj7C4PI/rnn9AxtnSbXus7O0CjOB+vU8pY+3jTBgbvr7W5YVTX1KKtR4G+lipU1khxfGyEgXmmnORYFk/M8df4yqxDN7uueRLZIzWdZdkQpBVj8zbdIoC5Xhm+ffYSbWOL0M0OIioyGhWybn5xZfP2EsS56UjIq2D/sB7jbSzwepWEmfXgZNe72Ex61kClICNdBFPo6dlEhwRhYZEsKBuDlTX2ZrMZp+zrRagyrX5dydx7Kspl/bjJwLkxnWnJhdg+tmJ5ohUxkW/ZPoMwn5xid2MZJalvkZ5bh1P2dz5lpPMePjtaKzPx3bPnmNsywuU4RlF2GiprpagQFaOkvA6bJu5xpx/amT7EPXuFEfXh7QdFNdWNtMws9E2tQK+ZQlzEGwibhnFxu8jYJdqrCvHji0j0q/b4kU55RTHicitweBJsuC8PV5CfFsefV4vrXfNwZd+HIOoNCis7YLI5MNBZzUxnLha2gsGZ6/wE4sJ4xBcIsREaNd1nBjYjPZqZzm44LIdoKErFyygB5pbXWWNwys75CaxnNly63L9a3IEgHjMPms7AfVPDce2yopKZzuScMqzqb4KlEMzo7S2PIZfdw12jGjh/qdHwniAn6kdkSTpwcmZHizAT3zyPxvSdOSqGtQmEPfsG+ZUtd4JqZmrnx1DIOpxR1QaOVodRUlbOz0Hn0C7IIcjPw9TaMa58HiyPd+JNXD7mNDr+90+NjflhpMZFo0w2GtrC+gLdLKIiolHTPgrve5eHM41DzSIkl7wr4rQ82onEyDf8vJQgAehV/YiNjUdT/zTcd4bJjrcWmZEs4AOj/Z01iAUJGF8IFjfh5veXCMIxsszOJevoLVvzKMwSQNan/KDQEPF53DOdt3M6/ZjukyEhKQPdU++KQRm355EQ9j0k7eOwvXefnRxrUVScjeKmwXtpeTy/YjpVo+2IfROG+v4HipiE2GN9c1lJIbomVDg0biAqvYj148EHIxszU8hMKkTPcLBgyuHmAvLSItCuXH+S6bU23RxSY6ORU9HGF+bicJh3kPn2FTP8fTh9f5T/yoHp/nZEvs2FLmQEuWJEKQlxyK/uvM3qshxqkRYdhdJa+b0UabvxAK01YojqO7G3f4BB9r2kcZLFMuyXzh3IWDAuZjEddyrPTvQoK8xHdkUHpdf+EzhMO5Cwtuzvr5JxcGde9NJQG3744RkKmxShLe/g2sbijFjIevrxfsL6bzWdvHHSKJGXzfqupR2sKOUoZ3GtciOYhbcx3w9BXg6Uuza+Tdie6kRmTgEGZ9f43z9dArDpV5GdGI288hYchuaPnx6uQ5iThuSCetic76VHXF9gerAZqcyET60Fz8/Jvgbpb19AKO3FSehBrHlnGcWs/8quksPlfdfwuC37qBWXoqFTgd2dLTQJs9DU1hP85aUR5ZlhaBsc4au1X+i1aCjLR1FN9yNMr2Wms70kA29+ikL38DzMp2aoJvqQGhWGV3HZ0OgMWB+S4bt/PEcVM4kutwtb7IP15sdv8TyhkHUMThyvTyM27Eck5on5CrKnpiNo11axb7Tg8swIGTM8P//0FmMLWriuAjhcn0VydDiikvOxojuG03mBvd0dLK5uw+Xx4nhjCslJsSip7b59wui0mdFSnI746CxsGE4xKa/Ezz8+Q13PDNzsPakUHXj97f9GVKqEmU4n9MvDiP/5Ferbhz8wndfXl9CuLKJncBQLmg0YjQaoxnuRGv0KEYlZ0Fm5SbnXUMolePnzM3zzj9ds3yl4+E7OB81kF8K/+R6DCwe3jfnCWBti4+PRNrqCS+sBJLmJ+OHnaPSxzu3UYoWOBbSv//EX/I//eobBJfY62wnahTkITyvFgSk00uhzor+pHC+ev4CkZQgGkwUmwx56pYV4wW76XqWWmXIPuhpLEZWQzkx+MDi7PDehiAXd4Zn50B4Eq5PtrkwhMf4FMiXN8LovsDTcjFc/PYe4oRN6sx2Os1NsrKxgZ1cfDBAI4oniujAiL/lnvErJu03f4kYuj/Y2MT87C93BMX8f9bVU4uVL1ibIx949DLohcI1z/QqKcrJQ1TqKE74ygAe7G1rMTC3iyHQK09Eehtqq8PKnV2hRLPJPmWf7m/DT998hXSjFxp4B+1saVOTF49sfX6BzbBHBAt8BcGvfScoKUNbAjZB6cGlY5DNH2gZmcGIyoL+1EoVl1dgyu+DzXGK0vRzxeZVY2b2TnvqEuLQcoLYkiwUrcVCqt3Ck16GpTIAYFuTP3Vki5oZr1vbNDbcgKjEPqo3genEXzBRU5CTg9dt0LGh10OtWUZGdhOTsUtZvGG+NheVoF81VQuSLpTDZL+GyGlCdl4TaNjmOLBZM9MqQy0zmCvtscClN3HIqgvRU9M9q+WVxiN9OcC6TCC9eRWJ0Xnfb55p2NSyASkJiWj40mwcw7G5AKspGeHQyplduqty+w356iJrSAn45jWPLeyNx1w7M9tbi78/eoHdm7f61u7ajpTwHPzyLhHLz4XvmjFsDt7wEReUNsLKA8NymR2aqANKuURhNRgx3tSA7vwIzK1xAeI2NhREkhEdgav3oSRYSuvbY0SzKYW1eJHqUahwbdtEnEyOcWx5veed2yZlbWLipnR5C6ttozGwHCwkF3Of8qMuL19HomVTDwO7jTikzKbHpGFnavZ1DdsWu/0hXA3JyizHLzr/30oHxVgky82uxunWIfdUQxEUF6BhZ4u/bk301CnIzUNE5/mEaNfGLXLnOoGgR49u/fssMfDd2Do+xu65CbkI4P69zUPVhDQDeqLIYn4v9jt4bfONMZ58kH89/fIvu6QeMIIs3Nyaa8C2759oUC6GNNwRwcrCBmqJsVMoGYXFe4WRjEsKiHH69ZJPxEP1ttcgpFkJncfHXfbxVjKx8IWY3nn7l8GuPA3Jm2F++eosG+SQMBj2/FmpMfBK6Z7Zwdf1+5+LHknIIKSkCyCeC1e+v3Ha0SXLwIiyGr/hrOtajo6YMsbHJmFgN1rPhCHgdGGipQk5ROTQHNnjtx+ioykeJuAo7+mNsa6aQl54MxUxwObKDFSWEWSmok0/fybb8kD/EdHKpXIP1Inz3H3/Gdz+8RFJKKiJehyEmKQMDyhW4fX7YDRsQ5bHtEZHILChDRWkhYiMikJAvwYnDy06IE7PDrUhNiEFMTBw7qakoFNdAtXnIggk3VibkfKXYl5HxGFFxS61cYmGkC8kxzChFxSM1QwABa6wae6b4pQ1OdxeQl5MBSfMgzkMPCy7tFnRLCvkR2Z0jC8yGVYhzUxD2OhIFpRUoKynhF27PEbbhzO6CYXUCWdFxaO2deNB0TjKTGhUZgZdh4UhKSkBMdBTSsopYh7lx+xTeqpvB87/+L/zf72OxvHWzoLwPa7MDSHoZgTGN4XakUz3diwyBgAWaq+wP+LDHpcoy4/yavb+0rDx+YebE2Eg8j0rBiJqZTvspequFSC6ogiE00slxZtpHR60IMVExSE5JR3p6OpJT09DcOw4Lt1g8l7LEgue07EIsbodGOh0nqCxJRkqJGJuhRZ+59NqcrFgU1LezziUAl83IjluCyIhw/qZI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RNA velocity adds a temporal dimension to single cell analyses, predicting future transcriptomic and phenotypic states, based on current spliced and unspliced mRNA of each cell. We hypothesized that RNA velocity could be adapted to predict future clinical status of individuals with acute illness using their whole-blood transcriptome. We developed a method for quantitative prediction of transitions in clinical state from a single time-point sample, which we call VeloCD. This predicted transcriptomic trajectories and future infection status in influenza A and SARS-CoV-2 human challenge studies. In HIV-TB coinfected individuals, it predicted the onset of immune reconstitution inflammatory syndrome. In a multinational observational study of acutely unwell febrile children, VeloCD predicted those with greatest medical care requirements. Our results demonstrate a novel application of RNA velocity to predict the trajectory of acute illness.\u003c/p\u003e","manuscriptTitle":"Predicting trajectories of acute illness using RNA velocity of whole blood","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-03 09:33:35","doi":"10.21203/rs.3.rs-5764288/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fce36558-faab-48e9-930a-047eb514aaba","owner":[],"postedDate":"February 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":42529569,"name":"Health sciences/Medical research/Biomarkers/Prognostic markers"},{"id":42529570,"name":"Biological sciences/Biological techniques/Bioinformatics"},{"id":42529571,"name":"Health sciences/Medical research/Translational research"}],"tags":[],"updatedAt":"2026-05-07T07:07:31+00:00","versionOfRecord":{"articleIdentity":"rs-5764288","link":"https://doi.org/10.1038/s41467-026-71685-5","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2026-05-06 04:00:00","publishedOnDateReadable":"May 6th, 2026"},"versionCreatedAt":"2025-02-03 09:33:35","video":"","vorDoi":"10.1038/s41467-026-71685-5","vorDoiUrl":"https://doi.org/10.1038/s41467-026-71685-5","workflowStages":[]},"version":"v1","identity":"rs-5764288","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5764288","identity":"rs-5764288","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}