Effects of physical activity on the incidence of stroke with polygenic risk score

Effects of physical activity on the incidence of stroke with polygenic risk score | 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 Research Article Effects of physical activity on the incidence of stroke with polygenic risk score Yun Seo Cho, Tae Yeon Kim, Kihoon Yuk, Ji Eun Lee, Joo Hee Park, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6518109/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Stroke is a disease influenced by various risk factors, including genetic factors determined by single nucleotide polymorphisms (SNPs). Polygenic risk score (PRS) calculated from multiple SNPs can reflect the varying genetic risk of stroke. Physical activity is a critical factor that influences the incidence of stroke by improving other risk factors. Therefore, we investigated the effects of physical activity on the reduction of stroke incidence across different PRS levels. Methods This study utilized epidemiological and genetic data from Korean Genome and Epidemiology Study. Through genome wide-associated study (GWAS) analysis, SNPs with high frequency in stroke patients were classified. The presence and amount of physical activity was assessed based on the weekly average exercise reported in the epidemiological survey data. All participants were classified into three groups based on PRS levels. Results Through GWAS (p < 1×10 -5 ), 28 SNPs were identified in the stroke patients for calculating PRS. The physical activity significantly reduced stroke incidence with high (p < 0.0001) and middle PRS levels (p < 0.0001). Notably, the amount of physical activity showed a difference in stroke incidence with high (p < 0.0001) and middle PRS levels (p = 0.0002). Conclusions This study investigates differences in stroke incidence due to physical activity across PRS levels calculated from stroke-related SNPs in GWAS. The reduction in stroke incidence influenced with the physical activity varied depending on PRS levels. Our findings suggest that the stroke prevention effect of physical activity may differ across PRS levels. Stroke Genome Wide Association Study Single Nucleotide Polymorphism Polygenic Risk Score Physical activity Figures Figure 1 Figure 2 Figure 3 Introduction Stroke is ranked as the second leading cause of death globally, with approximately 12.2 million cases occurring annually [ 1 ]. The increase in the number of stroke patients lead to economic issues, such as the rise in medical costs, or to social problems caused by the aftereffects of stoke [ 2 , 3 ]. Stroke occurs when oxygen and nutrients are not properly delivered to the brain, which leads to brain damage and causes various neurological damage [ 4 ]. The occurrence of stroke is influenced by diverse modifiable and non-modifiable factors [ 5 ]. Modifiable risk factors include disease-related factors such as hypertension, diabetes, obesity, and hyperlipidemia, as well as lifestyle factors such as smoking, alcohol consumption, physical activity, and diet [ 6 ]. Managing these factors plays a crucial role in preventing the occurrence of stroke, reducing recurrence after treatment, and promoting rehabilitation [ 7 ]. In contrast, non-modifiable factors are factors are unchanging and are determined from birth, such as age, sex, family history, and genetic factors [ 8 ]. Both factors not only independently influence stroke occurrence but also interact with each other, leading to a more complex impact on the risk stroke occurrence [ 9 ]. Therefore, it is essential to examine how the incidence rate varies depending on the status of various risk factors. Physical activity is known as one of the key modifiable factors in stroke prevention, which is associated with approximately an 80% reduction in stroke incidence [ 10 ]. Previous studies have suggested that physically active older men have been found to have a 4 to 5 times lower risk of hemorrhagic stroke compared to inactive individuals [ 11 ] and subjects who engage in moderate or high levels of physical activity have a 20% and 27% lower risk of stroke respectively than those who are less active [ 12 ]. Physical activity also affects other risk factors for stroke. Physical activity mainly contributes to managing of weight, and regulating metabolism, and improving stroke risk factors such as hypertension, obesity, diabetes, and hyperlipidemia [ 13 ]. Furthermore, physical activity not only affects disease-related risk factors but also helps lifestyle changes, including diet, smoking, and alcohol consumption [ 7 , 14 ]. Therefore, recent studies have been exploring the relationship between physical activity and other various factors [ 15 ]. Identifying the relationship between physical activity and other risk factors could help in developing better stroke prevention strategies. Recent research has increasingly focused on the impact of genetic factors, which are non-modifiable factors that mainly affect the risk of stroke occurrence. Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) that contribute to stroke risk, revealing significant associations between these genetic variations and stroke occurrence [ 16 ]. SNPs represent variations at a single nucleotide position, and SNPs located near genes associated with a specific disease can influence the function of those genes, potentially increasing the risk of disease [ 17 ]. One study has revealed 35 SNP loci that are strongly associated with stroke [ 18 ]. Since these SNPs can vary across regions and ethnicities [ 19 ], additional research is needed to classify stroke-associated SNPs for each population. SNPs identified through GWAS are used to calculate polygenic risk scores (PRS), which serves as an indicator of an individual's genetic risk for disease [ 20 ]. The PRS is calculated by multiplying the number of SNPs associated with the disease and the effect size of each SNP [ 21 ]. This score quantifies genetic susceptibility, with a higher PRS indicating a higher risk of disease. Calculating the individual’s PRS allows for the prediction of disease risk, enabling personalized preventive measures and encouraging lifestyle modifications [ 22 ]. Therefore, calculating the PRS for stroke can help identify an individual’s genetic susceptibility in advance and develop better prevention strategies. However, previous research primarily focused on the independent effects of genetic factors and physical activity, with limited studies examining the differential effects of physical activity on stroke risk according to genetic susceptibility. Therefore, this study aims to calculate the PRS using SNPs associated with stroke and to investigate the impact of physical activity on stroke occurrence among individuals with varying levels of genetic susceptibility. This may propose effective intervention strategies that consider both genetic predisposition and modifiable lifestyle factors. Methods Ethic declarations All experimental procedures were approved by the Institutional Review Board of Seoul National University in accordance with the standards of the Declaration of Helsinki of the World Medical Association (IRB No. E2411/004–012). Prior to the experiment, written consent and questionnaire scores (online) were obtained from all participants. All subjects were informed about the procedures and purpose of the study, as well as the potential risks of exercise protocols in both oral and written forms, and they confirmed their willingness to participate. Participants This study was conducted with the cardiovascular disease association study (CAVAS) in Korean Genome and Epidemiology Study (KoGES) cohort with 12,502 adults aged 40 to 69 living in rural areas of Korea, including Yangpyeong in Gyeonggi Province, Goryeong in Gyeongsangbuk-do, Namwon in Jeollanam-do, Wonju and Pyeongchang in Gangwon Province, and Ganghwa in Incheon City. The baseline survey was conducted from 2005 to 2011 and the follow-up data were collected from 2007 to 2016. The epidemiological and genetic data in this study were obtained on November 3, 2023. 12,502 participants were enrolled between the ages of 40 and 69. Participants without genetic data were excluded (n = 4,397). Those with missing values for stroke diagnosis, amount of physical activity, or stroke-related factors were excluded (n = 119). Additionally, participants classified as stroke patients in the baseline survey with no previous physical activity data were excluded (n = 196). Finally, the analysis was conducted on a total of 7,790 individuals (Fig. 1 ) (184 of stroke patients, 7,606 of controls). Genotyping Quality Control and Imputation The genomic DNA of each participant was isolated from the whole blood and the genotypes were identified with a Korean Biobank Array Chip (Korean Biobank, Cheong ju, Korea). The Korean Disease Control and Prevention Agency provided the data of genetic variations. Imputation for autosomal variants was conducted using IMPUTE4 with a 1000 Genome Project Phase 3 panel. Quality control was performed based on the following criteria to ensure accurate analysis and remove errors in genetic data. Missing individual call rate > 98% Missing genotyping call rate > 98% Hardy – Weinberg Equilibrium (HWE) p > 1 × 10 − 6 Minor Allele Frequency (MAF) < 0.05 Removal of non-biallelic SNPs Overall, 5,428,572 SNPs in 22 chromosomes were used for analysis. Linkage Disequilibrium (LD) analysis and LD Pruning According to previous studies, SNPs with higher linkage disequilibrium tend to be more strongly correlated, creating redundancy that make noise and biases in genetic prediction [ 23 ]. Therefore LD pruning was performed on the identified SNPs from GWAS results to identify representative SNPs for each region by removing SNPs with the lowest p-value among those with r 2 values greater than 0.5 [ 24 ]. The LD pruning was performed using PLINK software (version 2.0). Exposures Polygenic Risk Score for Stroke To calculate the PRS, SNPs with p-value less than 1 × 10 − 5 , which satisfied the suggestive threshold of statistical significance in the GWAS results, were selected. We performed LD pruning, which is a process choosing SNPs that are close together and have similar effects. The SNP with the lowest p-value was selected as the representative SNP among those located within the same gene. As a result, a total of 11 representative SNPs were used for the PRS calculation. For each participant, the PRS was calculated by summing the weighted values of these SNPs, where the weight was the number of alleles for each SNP multiplied by its log odds ratio from GWAS analysis. The PRS calculation was performed using PLINK software (version 2.0). Subsequently, participants were then divided into three groups based on their PRS values: high PRS (top 25%), middle PRS (middle 50%), and low PRS (bottom 25%) [ 25 ]. Physical activity We first divided all participants into two groups based on the presence of physical activity, using the survey question in KOGES epidemiological data “Do you engage in regular exercise that makes you sweat?”: PA X (weekly average exercise = 0 min), PA O (weekly average exercise ≠ 0 min). Then, for the PA O group, we calculated the weekly average exercise level for each individual by multiplying the responses to the surveys “How many times a week do you exercise?”, and “Average amount of exercise per session.” For the control groups, the average weekly moderate exercise amount was calculated as the mean of the weekly average exercise levels measured during the baseline and follow-up surveys, while for the patient group it was calculated as the mean of the weekly average exercise levels before the onset of the stroke. Afterward, based on the recommended weekly moderated exercise amount of 150 minutes as suggested by ACSM [ 26 ], participants were divided into two groups: low PA (0 min 150 min) [ 27 ]. Incidence of stroke Stroke incidence was measured using survey data on stroke diagnosis from the epidemiological data. The survey responses were conducted as discrete variables with “Yes” and “No” options. In this study, all participants who responded “Yes” to stroke incidence in the follow-up survey data were classified as the stroke case group. Statistical Analysis Statistical analyses were performed using GraphPad Prism V. 10.2.2 (GraphPad Software Inc., California, USA) and PLINK 2.0. All participants were divided into stroke patient and control groups. For comparison, age, sex, body mass index (BMI), hypertension, diabetes, smoking, alcohol consumption, systolic blood pressure, diastolic blood pressure, white blood cell, total cholesterol, and blood glucose levels were assessed. Continuous data such as age, BMI, systolic blood pressure, diastolic blood pressure, white blood cell, total cholesterol, and glucose were presented as means and standard deviations, while categorical data including sex, hypertension, diabetes, smoking, and alcohol consumption were expressed as counts and proportions. The significance of continuous data was verified using independent t-tests. Categorical data were statistically measured using chi-square tests. Principal component analysis (PCA) was conducted to control for population structure and background genetic variation, minimizing bias and improving the accuracy of gene-disease association results [ 28 ]. A logistic regression model was used for the GWAS analysis, with age, sex, smoking, alcohol consumption, hypertension, and 10 principal components (PCs) derived from the PCA analysis included as covariates [ 29 ]. The threshold for statistical significance in GWAS was 5×10 − 8 , with suggestive threshold of 1×10 − 5 . The PRS was calculated by summing the number of risk alleles for all SNPs and then categorized into three groups. The incidence risk of stroke associated with a single genotype was estimated using odds ratios (OR) and 95% confidence intervals (CIs). The relationship between PRS and physical activity levels with stroke risk was estimated using logistic regression. An OR greater than 1 indicates an increased risk of disease, while an OR less than 1 indicates a reduced risk. The statistical significance level (α) was set at 5%, and all data were presented as means and standard deviations. Results Baseline characteristics and confounders for GWAS The baseline characteristics of participants are summarized in Table 1. Data were obtained from 184 stroke cases and 7,606 controls. The mean age was higher in the stroke group (62.2 ± 7.6 years) than in the control group (58.3 ± 8.8 years) (p < 0.0001). The proportion of men to women showed a significant difference between the stroke group (45.6% men, 54.4% women) and the control group (37.1% men, 62.9% women) (p = 0.0173). The prevalence of hypertension was higher in the stroke group (33.2%) compared to the control group (26.3%) (p = 0.0364). Similarly, smoking prevalence was higher in the stroke group (37.0%) than in the control group (27.7%) (p = 0.0057). Additionally, alcohol consumption was more common in the stroke group (54.4%) than in the control group (46.2%) (p = 0.0283). The stroke group had higher systolic and diastolic blood pressure than the control group (p = 0.0004 and p = 0.0445, respectively). In blood markers, the white blood cell count was significantly higher in the stroke group (6.9 ± 2.2) compared to the control group (6.3 ± 1.8) (P < 0.0001). Table 1 Characteristics of participants at baseline Characteristics and stroke-related variables were observed for all participants. Statistical analysis was performed using an independent t-test for continuous variables and a chi-square test for categorical variables. Continuous variables are presented as means (standard deviation) and categorical variables are presented as number (percentage). (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001). SNPs frequently observed in stroke patients were identified through GWAS 28 Statistically significant SNPs associated with stroke between stroke patient and control were identified through GWAS [30]. All participants were divided into the stroke group (n = 184) and the control group (n = 7,606). Although no significant SNPs were identified at a threshold of 5×10 -8 , 28 SNPs were classified at a threshold of 1×10 -5 [21] (Fig. 2A,B). To confirm the characteristics of identified SNPs, we examined the chromosome, base pair, and associated genes. Significant SNPs were found on chromosome 1 (n = 6), 2 (n = 1) ,5 (n = 1), 7 (n = 3), 9 (n = 4), 10 (n = 3), 12 (n = 8), 13 (n = 1), and 18 (n = 1). Among the 28 SNPs, 1 SNP was located in both FAAP20 (Gene ID: 199990) and LOC124903823 (Gene ID: 124903823), 3 SNPs in SYT14 (Gene ID: 255928), 1 in RYR2 (Gene ID: 6262), 1 in LINC01470 (Gene ID: 101927134), 3 in DGKB (Gene ID: 1607), 3 in OR13C4 (Gene ID: 138804), 3 in LOC107987105 (Gene ID: 107987105), 8 in PPM1H (Gene ID: 57460), 1 in HS6ST3 (Gene ID: 266722), and 1 in ASXL3 (Gene ID: 80816) (Supplementary Table 1). Furthermore, in the genes where the SNPs are located, two of them were previously confirmed to be located in stroke-related genes (PPM1H, RYR2) [31, 32]. Additionally, SNPs were found in genes associated with stroke-related diseases, such as atrial fibrillation (DGKB) [33], Parkinson’s disease (ASXL3) [34], and obesity (HS6ST3) [35] (Supplementary Table 1). SNPs for PRS calculation were selected through LD pruning LD pruning can extract only the SNPs with the lowest p-values among those with significant linkage to classify SNPs for PRS calculation (Fig. 2C). As a result, 11 representative SNPs that had a significant impact on stroke were selected and used for PRS calculation (Supplementary Table 2). The PRS levels were different between stroke patients and controls The PRS levels were significantly higher in stroke patient group compared to the control group (case = 2.970, control 1.869) (Supplementary Fig. 1A). Additionally, to validate the reliability of the PRS, the cohort was expanded by including previously excluded participants (383 cases and 7,715 controls). Consequently, the PRS levels were significantly higher in the stroke patient group compared to the control group as well (case = 2.368, control = 1.870) (Supplementary Fig. 1B). The stroke incidence varies depending on the amount of physical activity across different PRS levels We confirmed the differences in stroke incidence based on the presence or absence of physical activity (Fig. 3). The stroke incidence was lower in the presence of physical activity in the high PRS (Fig. 3A, p < 0.0001, chi-square = 27.77) and middle PRS groups (Fig. 3B, p < 0.0001, chi-square = 16.81). There was no significant difference in the low PRS groups (Fig. 3C, p = 06005, chi-square = 0.2742). Regarding the amount of physical activity, stroke incidence was significantly different based on the amount of physical activity in the high (Supplementary Fig. 2A, p < 0.0001, chi-square = 28.52) and middle PRS groups (Supplementary Fig. 2B, p = 0.0002, chi-square = 16.89). However, there was no significant difference in stroke incidence in the low PRS group (Supplementary Fig. 2C, p = 0.7776, chi-square = 0.5031). The stroke incidence influenced by physical activity differs across different PRS levels We first examined the effects of various stroke-related risk factors on stroke incidence through regression analysis. For all participants, age and polygenic risk score increased the risk of stroke. While no significant effects were found for sex, hypertension, and smoking. Physical activity significantly reduced the risk of stroke (Fig. 3D) The effect of physical activity on stroke incidence was evaluated as odds ratios for each group (high PRS, middle PRS, and low PRS). At high PRS group, a trend towards a reduction in stroke risk was observed due to physical activity. At middle PRS groups, physical activity significantly reduced stroke risk. The middle PRS group was found to reduce stroke risk more than the high PRS group. No statistical significance was observed at low PRS levels (Fig. 3E). Discussion SNPs classified through GWAS with a disease are associated with the genetic risk of the condition. By calculating PRS based on identified SNPs, an individual’s genetic predisposition to disease can be quantified. Identifying a disease-specific PRS in advance provides preventive benefits by enabling individuals to adjust their lifestyle habits and respond to potential health risks more effectively. Physical activity is a key preventive factor for stroke, with its protective effect potentially varying based on an individual’s stroke risk factors. However, there is a lack of research confirming the stroke incidence influenced by physical activity according to genetic factors associated with stroke. Therefore, this study aimed to examine the differences in stroke incidence influenced by physical activity based on PRS levels. First, 28 SNPs were classified through GWAS with stroke incidence as a variable. To determine whether the classified SNPs are associated with stroke, the genes where the SNPs are located were identified. With 28 SNPs, 11 genes were identified. Among them, 1 gene was associated with stroke, and 3 genes were associated with other diseases related to stroke. RYR2 is a gene known to regulate cardiac rhythm and calcium signaling. It has been identified as being associated with atrial fibrillation, and has also been found in previous GWAS study related to stroke [ 32 ]. Additionally, DGKB, HS6ST3 and PPM1H have been identified in previous GWAS studies as being linked to diseases associated with stroke, including type 2 diabetes, obesity, and ADHD, respectively. DGKB is a gene that encodes a metabolic enzyme involved in signaling pathways and vascular function. It has been identified in previous GWAS study with type 2 diabetes, a major risk factor for stroke [ 33 ]. HS6ST3 is a gene involved in heparan sulfate metabolism. It has been found in previous GWAS with obesity and high triglyceride level[ 35 ]. PPM1H gene is involved in the production of PPM1H, a protein phosphatase associated with cell signaling[ 31 ]. It has been shown to associated with attention deficit hyperactivity disorder, a neurodegenerative disorder in previous GWAS study. This finding is consistent with previous research, which suggests that SNPs classified through GWAS for specific diseases tend to be located in genes associated with those diseases [ 36 ]. SNPs located in disease-related genes may affect the function of genes and disease incidence. Therefore, additional research is needed to confirm the impact of SNPs to the mechanisms of stroke with those genes. Nevertheless, the significance of this study lies in the fact that stroke-related SNPs were classified through GWAS using KoGES data set from a Korean population, suggesting that these SNPs may have the potential to increase the risk of stroke occurrence [ 37 ]. Second, the PRS levels of all participants were analyzed using 11 representative SNPs. Consistent with previous study [ 38 ], the results showed that the patient group had higher PRS levels compared to the control group, indicating that the PRS based on the SNPs identified in this study may predict stroke. Moreover, a validation was conducted to ensure the reliability of the PRS in predicting stroke. the results showed that patients still had higher PRS levels than the control group. This is consistent with prior research suggesting that higher PRS levels are associated with greater vulnerability to stroke [ 39 ]. These results suggest that the PRS in this study may reflect the genetic risk for stroke, and confirm that the individual’s genetic risk of stroke can be calculated with PRS. Third, the preventive effect of physical activity on stroke is well established. In this study [ 40 ], we aimed to investigate whether this effect varies across different PRS levels. A reduction in stroke incidence influenced by physical activity was significant in the middle and high PRS groups. However, no significant effect was observed in the low PRS groups. A previous study confirmed that, in the case of type 2 diabetes, the absolute risk reduction due to lifestyle improvements was significantly greater in individuals with high PRS [ 41 ]. Since physical activity is also a key lifestyle factor, it is possible that the risk reduction due to physical activity in stroke may be relatively smaller in those with low PRS. To understand the cause of this phenomenon, it is necessary to secure a sufficient number of participants with low PRS and further investigate the characteristics of individuals according to PRS levels. Nevertheless, since stroke is a condition where post-onset management is challenging and moreover, the prognosis and treatment outcomes often remain unfavorable, highlighting the importance of preventive approaches is essential [ 42 ]. Therefore, confirming that the preventive effect of physical activity on stroke may vary according to PRS levels holds significance as it can help in developing effective stroke prevention strategies. Next, we examined the differences of stroke incidence based on the amount of physical activity within each PRS level. While no significant difference was observed in the low PRS group, differences were found in the middle and high PRS groups. When examining the stroke incidence with increasing the amount of physical activity, the middle PRS group showed a decrease in stroke incidence as the amount of physical activity increased. On the other hand, in the high PRS group, stroke incidence was higher in the group with more physical activity compared to the group with less physical activity. Some previous studies suggest that excessive endurance exercise may have a negative impact on cardiovascular health [ 43 ]. Furthermore, on study revealed that excessive physical activity duration could be a risk factor for hypertension in middle-aged population [ 44 ]. Physical activity plays a critical role in stroke prevention by regulating key risk factors such as obesity and hypertension [ 45 ]. Although physical activity does not change the genetic sequence itself, it can influence proteins or gene expression [ 46 ]. This study suggests that the preventive effect of physical activity may vary depending on genetic risk, however, there is insufficient evidence to support this result. Therefore, further research is needed to explore whether excessive physical activity may increase stroke risk in the high PRS levels. Furthermore, regression analysis was conducted to examine whether the effect of physical activity only on stroke prevention differs based on PRS levels. The lack of significance in the low PRS group may be attributed to the insufficient number of stroke patients, similar to with examining stroke incidence. The reduction in stroke risk due to physical activity was greater in the middle PRS group compared to the high PRS group. Generally, the effect of improving lifestyle tends to be greater in reduce stroke risk with higher PRS levels. However, there is insufficient evidence to explain why the reduction in stroke risk due to physical activity was smaller in the high PRS group compared to the middle PRS group. Nevertheless, the significance of this study lies in identifying the differences in the effects of physical activity on stroke prevention across different PRS levels. This study has several limitations. First, the cohort used in this study consists of individuals residing in rural areas of Korea, which may have influenced the results due to lifestyle factors specific to rural living, thereby limiting their generalizability to the entire Korean population. To address this limitation, further studies incorporating data from non-rural areas and comparing them with data from other countries are necessary to provide a more comprehensive understanding of the characteristics of the Korean population. Moreover, this study has limitations due to the small sample size and the use of a single cohort. Thereby, there were limitations in the process of ensuring the predictive reliability of the PRS through validation using other cohorts, as well as in the significance of the low PRS group due to the insufficient number of patients. Therefore, to overcome these limitations, securing with additional cohorts and conducting further analytical studies are required. In conclusion, this study suggests that the stroke incidence influenced by physical activity may differ according to the PRS levels. Additionally, our results suggest that further research is needed to explore the relationships among risk factors for stroke. To the best of our knowledge, this is the first study to examine the differential impact of physical activity on genetic risk in stroke incidence. However, additional research overcoming the limitations of this study and with larger cohorts is required to provide new perspectives on stroke prevention. Declarations Acknowledgements This study was conducted with bioresources from National Biobank of Korea, the Center for Disease Control and Prevention, Republic of Korea (NBK-D03-B, NBK-D03-F01, NBK-D03-F02, NBK-D03-F03, NBK-D03-F04). Author contributions Conceptualization: [Yun Seo Cho, Hyo Youl Moon]; Methodology: [Yun Seo Cho, Tae Yeon Kim, Kihoon Yuk, Yeon Soo Kim, Kumpei Tanisawa, Hyo Youl Moon]; Formal analysis and investigation: [Yun Seo Cho, Ji Eun Lee, Joo Hee Park, Kumpei Tanisawa, Hyo Youl Moon]; Writing – original draft preparation: [Yun Seo Cho]; Writing – review and editing: [Yun Seo Cho, Tae Yeon Kim, Kihoon Yuk, Yeon Soo Kim, Min-Chul Lee, Jin Pyeong Jeon, In cheol Jeong, Hyo Youl Moon]; Supervision: [Hyo Youl Moon]. Funding We gratefully acknowledge the financial support by the National Research Foundation (NRF) of Korea (NRF-2020R1C1C1006414 and NRF-2022R1I1A4053049) and was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. RS-2022-NR070859). Data availability The datasets generated during and/or analyzed during the current study are not publicly available due to ethical restrictions regarding data protection issues and the study-specific consent text and procedure, but anonymized data are available from the corresponding author upon reasonable request. Ethical approval This study was approved by the Institutional Review Board of Seoul National University in accordance with the standards of the Declaration of Helsinki of the World Medical Association (IRB No. E2411/004-012). Consent to Participate All participants provided written informed consent prior to participation in the study. Competing interests The authors declare that they have no competing interests. References Fang, J., Z. Wang, and C.-y. Miao, Angiogenesis after ischemic stroke. Acta Pharmacologica Sinica, 2023. 44 (7): p. 1305-1321. Murphy, S.J. and D.J. Werring, Stroke: causes and clinical features. Medicine, 2020. 48 (9): p. 561-566. 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Nalls, M.A., et al., Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies. The Lancet Neurology, 2019. 18 (12): p. 1091-1102. Wang, K.-S., et al., Association of HS6ST3 gene polymorphisms with obesity and triglycerides: gene× gender interaction. Journal of genetics, 2013. 92 : p. 395-402. Shastry, B.S., SNP alleles in human disease and evolution. Journal of human genetics, 2002. 47 (11): p. 561-566. Lindgren, A., Stroke genetics: a review and update. Journal of stroke, 2014. 16 (3): p. 114. Lee, J., et al., Comparison of polygenic risk for schizophrenia between European and Korean Populations. Korean Journal of Schizophrenia Research, 2020. 23 (2): p. 65-70. Dudbridge, F., Power and predictive accuracy of polygenic risk scores. PLoS genetics, 2013. 9 (3): p. e1003348. Lee, C.D., A.R. Folsom, and S.N. Blair, Physical activity and stroke risk: a meta-analysis. Stroke, 2003. 34 (10): p. 2475-2481. Ye, Y., et al., Interactions between enhanced polygenic risk scores and lifestyle for cardiovascular disease, diabetes, and lipid levels. Circulation: Genomic and Precision Medicine, 2021. 14 (1): p. e003128. Chambers, B.R., et al., Prognosis of acute stroke. Neurology, 1987. 37 (2): p. 221-221. O'Keefe, J.H., et al. Potential adverse cardiovascular effects from excessive endurance exercise . in Mayo Clinic Proceedings . 2012. Elsevier. Zhu, Z., et al., Excessive physical activity duration may be a risk factor for hypertension in young and middle-aged populations. Medicine, 2019. 98 (18): p. e15378. Elkind, M.S. and R.L. Sacco. Stroke risk factors and stroke prevention . in Seminars in neurology . 1998. © 1998 by Thieme Medical Publishers, Inc. Booth, F.W., M.V. Chakravarthy, and E.E. Spangenburg, Exercise and gene expression: physiological regulation of the human genome through physical activity. The Journal of physiology, 2002. 543 (2): p. 399-411. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6518109","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456043479,"identity":"6abef6e9-0d81-4b66-a5cc-5198709d2f01","order_by":0,"name":"Yun Seo Cho","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"Seo","lastName":"Cho","suffix":""},{"id":456043480,"identity":"d88b7ea5-a21b-468b-9b63-91c443872d52","order_by":1,"name":"Tae Yeon Kim","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Tae","middleName":"Yeon","lastName":"Kim","suffix":""},{"id":456043481,"identity":"5b8aeb40-52f5-4eb7-97f4-8dd66b1ceed3","order_by":2,"name":"Kihoon Yuk","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Kihoon","middleName":"","lastName":"Yuk","suffix":""},{"id":456043482,"identity":"0e58e9fa-24a1-4ffa-b1b1-59ea42e04a71","order_by":3,"name":"Ji Eun Lee","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Ji","middleName":"Eun","lastName":"Lee","suffix":""},{"id":456043483,"identity":"11adc88b-33b0-49e4-8b77-a70496b8ffc4","order_by":4,"name":"Joo Hee Park","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Joo","middleName":"Hee","lastName":"Park","suffix":""},{"id":456043484,"identity":"1ec6d464-0c36-4310-893e-e623d9bc026c","order_by":5,"name":"Yeon Soo Kim","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Yeon","middleName":"Soo","lastName":"Kim","suffix":""},{"id":456043485,"identity":"491177d4-b829-4063-96ad-c8e17e922624","order_by":6,"name":"Min-Chul Lee","email":"","orcid":"","institution":"CHA University","correspondingAuthor":false,"prefix":"","firstName":"Min-Chul","middleName":"","lastName":"Lee","suffix":""},{"id":456043486,"identity":"55c73989-716e-452b-90f1-233fa904053d","order_by":7,"name":"Jin Pyeong Jeon","email":"","orcid":"","institution":"Hallym University","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"Pyeong","lastName":"Jeon","suffix":""},{"id":456043487,"identity":"fef8c005-c9a8-4883-b4b5-897c159eeeca","order_by":8,"name":"In cheol Jeong","email":"","orcid":"","institution":"Hallym University","correspondingAuthor":false,"prefix":"","firstName":"In","middleName":"cheol","lastName":"Jeong","suffix":""},{"id":456043488,"identity":"177327c6-fe4c-4ac5-a389-e969e5271d5b","order_by":9,"name":"Kumpei Tanisawa","email":"","orcid":"","institution":"Waseda University","correspondingAuthor":false,"prefix":"","firstName":"Kumpei","middleName":"","lastName":"Tanisawa","suffix":""},{"id":456043489,"identity":"f9e4e49e-9875-4bf6-820c-6db21387c5b2","order_by":10,"name":"Hyo Youl Moon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYDACHjApIccPE5AgUouFsWQDiVoqEjccIFaLfM8Z0w0/d0gkbj5/9pgEQ40dg+TsA/i1GJztMbvZe0bCeNuNvDQJhmPJDNJ8CQS08POY3eBtk5DddoPHTIKB7QCDHA8hh/XzmN382ybBuLn/DFDLPyK0MAAddhtoi+IGhhwzCca2AwzShLQYnDlWdlu2TcJY4kaOsUViXzKPZA8hh/Ukb7v5tq1Ojr//jOGND9/s5CTOEHIYA4cBgp0Aiyf8gP0BEYpGwSgYBaNgRAMAnb48k0xBPZ4AAAAASUVORK5CYII=","orcid":"","institution":"Seoul National University","correspondingAuthor":true,"prefix":"","firstName":"Hyo","middleName":"Youl","lastName":"Moon","suffix":""}],"badges":[],"createdAt":"2025-04-24 07:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6518109/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6518109/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82716197,"identity":"5a46cdbc-c5dd-47db-a6bc-949d64eff13f","added_by":"auto","created_at":"2025-05-14 12:15:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":125011,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of sample selection.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6518109/v1/32246d0f3ebcfda78df43b0e.png"},{"id":82716333,"identity":"6e3135f0-f050-44bc-9e21-a5232d236f1c","added_by":"auto","created_at":"2025-05-14 12:15:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":134898,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of significant SNPs by GWAS analysis and LD Pruning. \u003c/strong\u003e(A) Manhattan plot of GWAS results with stroke (184 cases and 7,606 controls). All SNPs analyzed through the GWAS are represented as dots, with the X-axis showing the chromosomal position of each SNP and the Y-axis representing the log-transformed p-values of each SNP. The significance threshold for GWAS (5 × 10\u003csup\u003e-8\u003c/sup\u003e) is marked with a red horizontal line, while the suggestive significance threshold (1 × 10\u003csup\u003e-5\u003c/sup\u003e) is shown in green. (B) QQ plot of the GWAS analysis for stroke. (C) Heatmap of LD analysis. The color of the cells reflects the degree of LD between SNPs. Red indicates high LD, while blue indicates low LD, with darker colors representing stronger levels. All SNPs are labeled by chromosome, base pair position, and allele. QQ; Quantile-Quantile. GWAS; Genome Wide Association Study. LD; Linkage Disequilibrium. SNP; Single Nucleotide Polymorphism.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6518109/v1/84c9ae177e6c0942ef0f88ff.png"},{"id":82714819,"identity":"9c049eb9-b040-4548-9acc-f612aaf16263","added_by":"auto","created_at":"2025-05-14 12:07:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":106571,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe stroke prevention effect of physical activity varies depending on PRS levels. \u003c/strong\u003e(A) The number of stroke patients and controls according to the physical activity within High PRS, (B) Middle PRS, and (C) Low PRS. Statistical analysis was performed using a Chi-square test. (PA X : weekly average exercise amount = 0 min, PA O : weekly average exercise amount \u0026gt; 0 min). (D) Odds ratios for stroke risk factors in the entire participants (n = 7,790). (E) Odds ratios for physical activity in 3 groups based on PRS levels, High PRS (n = 1948), Middle PRS (n = 3895), and Low PRS (n = 1947) were analyzed. (* p \u0026lt; 0.05, ** p \u0026lt; 0.01, **** p \u0026lt; 0.0001). PRS; Polygenic Risk Score. PA; Physical Activity. OR; Odds Ratio. CI; Confidence Interval.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6518109/v1/9fdd4cd5f81e5c0454560846.png"},{"id":83080001,"identity":"116324e4-7cf4-44f6-981e-3591493d2ac1","added_by":"auto","created_at":"2025-05-19 19:16:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1317448,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6518109/v1/6139613f-6a44-4bbd-bcc7-b43da153718c.pdf"},{"id":82714823,"identity":"f8accfed-f546-423b-88bf-af2952123672","added_by":"auto","created_at":"2025-05-14 12:07:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":545105,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6518109/v1/be1a8fee459797b0080e1898.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of physical activity on the incidence of stroke with polygenic risk score","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke is ranked as the second leading cause of death globally, with approximately 12.2\u0026nbsp;million cases occurring annually [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The increase in the number of stroke patients lead to economic issues, such as the rise in medical costs, or to social problems caused by the aftereffects of stoke [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Stroke occurs when oxygen and nutrients are not properly delivered to the brain, which leads to brain damage and causes various neurological damage [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe occurrence of stroke is influenced by diverse modifiable and non-modifiable factors [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Modifiable risk factors include disease-related factors such as hypertension, diabetes, obesity, and hyperlipidemia, as well as lifestyle factors such as smoking, alcohol consumption, physical activity, and diet [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Managing these factors plays a crucial role in preventing the occurrence of stroke, reducing recurrence after treatment, and promoting rehabilitation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In contrast, non-modifiable factors are factors are unchanging and are determined from birth, such as age, sex, family history, and genetic factors [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Both factors not only independently influence stroke occurrence but also interact with each other, leading to a more complex impact on the risk stroke occurrence [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, it is essential to examine how the incidence rate varies depending on the status of various risk factors.\u003c/p\u003e \u003cp\u003ePhysical activity is known as one of the key modifiable factors in stroke prevention, which is associated with approximately an 80% reduction in stroke incidence [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Previous studies have suggested that physically active older men have been found to have a 4 to 5 times lower risk of hemorrhagic stroke compared to inactive individuals [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and subjects who engage in moderate or high levels of physical activity have a 20% and 27% lower risk of stroke respectively than those who are less active [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePhysical activity also affects other risk factors for stroke. Physical activity mainly contributes to managing of weight, and regulating metabolism, and improving stroke risk factors such as hypertension, obesity, diabetes, and hyperlipidemia [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Furthermore, physical activity not only affects disease-related risk factors but also helps lifestyle changes, including diet, smoking, and alcohol consumption [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, recent studies have been exploring the relationship between physical activity and other various factors [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Identifying the relationship between physical activity and other risk factors could help in developing better stroke prevention strategies.\u003c/p\u003e \u003cp\u003eRecent research has increasingly focused on the impact of genetic factors, which are non-modifiable factors that mainly affect the risk of stroke occurrence. Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) that contribute to stroke risk, revealing significant associations between these genetic variations and stroke occurrence [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. SNPs represent variations at a single nucleotide position, and SNPs located near genes associated with a specific disease can influence the function of those genes, potentially increasing the risk of disease [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. One study has revealed 35 SNP loci that are strongly associated with stroke [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Since these SNPs can vary across regions and ethnicities [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], additional research is needed to classify stroke-associated SNPs for each population.\u003c/p\u003e \u003cp\u003eSNPs identified through GWAS are used to calculate polygenic risk scores (PRS), which serves as an indicator of an individual's genetic risk for disease [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The PRS is calculated by multiplying the number of SNPs associated with the disease and the effect size of each SNP [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This score quantifies genetic susceptibility, with a higher PRS indicating a higher risk of disease. Calculating the individual\u0026rsquo;s PRS allows for the prediction of disease risk, enabling personalized preventive measures and encouraging lifestyle modifications [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, calculating the PRS for stroke can help identify an individual\u0026rsquo;s genetic susceptibility in advance and develop better prevention strategies.\u003c/p\u003e \u003cp\u003eHowever, previous research primarily focused on the independent effects of genetic factors and physical activity, with limited studies examining the differential effects of physical activity on stroke risk according to genetic susceptibility. Therefore, this study aims to calculate the PRS using SNPs associated with stroke and to investigate the impact of physical activity on stroke occurrence among individuals with varying levels of genetic susceptibility. This may propose effective intervention strategies that consider both genetic predisposition and modifiable lifestyle factors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEthic declarations\u003c/h2\u003e \u003cp\u003e All experimental procedures were approved by the Institutional Review Board of Seoul National University in accordance with the standards of the Declaration of Helsinki of the World Medical Association (IRB No. E2411/004\u0026ndash;012). Prior to the experiment, written consent and questionnaire scores (online) were obtained from all participants. All subjects were informed about the procedures and purpose of the study, as well as the potential risks of exercise protocols in both oral and written forms, and they confirmed their willingness to participate.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThis study was conducted with the cardiovascular disease association study (CAVAS) in Korean Genome and Epidemiology Study (KoGES) cohort with 12,502 adults aged 40 to 69 living in rural areas of Korea, including Yangpyeong in Gyeonggi Province, Goryeong in Gyeongsangbuk-do, Namwon in Jeollanam-do, Wonju and Pyeongchang in Gangwon Province, and Ganghwa in Incheon City. The baseline survey was conducted from 2005 to 2011 and the follow-up data were collected from 2007 to 2016. The epidemiological and genetic data in this study were obtained on November 3, 2023.\u003c/p\u003e \u003cp\u003e12,502 participants were enrolled between the ages of 40 and 69. Participants without genetic data were excluded (n\u0026thinsp;=\u0026thinsp;4,397). Those with missing values for stroke diagnosis, amount of physical activity, or stroke-related factors were excluded (n\u0026thinsp;=\u0026thinsp;119). Additionally, participants classified as stroke patients in the baseline survey with no previous physical activity data were excluded (n\u0026thinsp;=\u0026thinsp;196). Finally, the analysis was conducted on a total of 7,790 individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (184 of stroke patients, 7,606 of controls).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eGenotyping Quality Control and Imputation\u003c/h3\u003e\n\u003cp\u003eThe genomic DNA of each participant was isolated from the whole blood and the genotypes were identified with a Korean Biobank Array Chip (Korean Biobank, Cheong ju, Korea). The Korean Disease Control and Prevention Agency provided the data of genetic variations. Imputation for autosomal variants was conducted using IMPUTE4 with a 1000 Genome Project Phase 3 panel. Quality control was performed based on the following criteria to ensure accurate analysis and remove errors in genetic data.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMissing individual call rate\u0026thinsp;\u0026gt;\u0026thinsp;98%\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMissing genotyping call rate\u0026thinsp;\u0026gt;\u0026thinsp;98%\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHardy \u0026ndash; Weinberg Equilibrium (HWE) p\u0026thinsp;\u0026gt;\u0026thinsp;1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMinor Allele Frequency (MAF)\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRemoval of non-biallelic SNPs\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eOverall, 5,428,572 SNPs in 22 chromosomes were used for analysis.\u003c/p\u003e\n\u003ch3\u003eLinkage Disequilibrium (LD) analysis and LD Pruning\u003c/h3\u003e\n\u003cp\u003eAccording to previous studies, SNPs with higher linkage disequilibrium tend to be more strongly correlated, creating redundancy that make noise and biases in genetic prediction [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore LD pruning was performed on the identified SNPs from GWAS results to identify representative SNPs for each region by removing SNPs with the lowest p-value among those with r\u003csup\u003e2\u003c/sup\u003e values greater than 0.5 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The LD pruning was performed using PLINK software (version 2.0).\u003c/p\u003e\n\u003ch3\u003eExposures\u003c/h3\u003e\n\u003cp\u003ePolygenic Risk Score for Stroke\u003c/p\u003e \u003cp\u003eTo calculate the PRS, SNPs with p-value less than 1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, which satisfied the suggestive threshold of statistical significance in the GWAS results, were selected. We performed LD pruning, which is a process choosing SNPs that are close together and have similar effects. The SNP with the lowest p-value was selected as the representative SNP among those located within the same gene. As a result, a total of 11 representative SNPs were used for the PRS calculation. For each participant, the PRS was calculated by summing the weighted values of these SNPs, where the weight was the number of alleles for each SNP multiplied by its log odds ratio from GWAS analysis. The PRS calculation was performed using PLINK software (version 2.0). Subsequently, participants were then divided into three groups based on their PRS values: high PRS (top 25%), middle PRS (middle 50%), and low PRS (bottom 25%) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003cp\u003eWe first divided all participants into two groups based on the presence of physical activity, using the survey question in KOGES epidemiological data \u0026ldquo;Do you engage in regular exercise that makes you sweat?\u0026rdquo;: PA X (weekly average exercise\u0026thinsp;=\u0026thinsp;0 min), PA O (weekly average exercise\u0026thinsp;\u0026ne;\u0026thinsp;0 min). Then, for the PA O group, we calculated the weekly average exercise level for each individual by multiplying the responses to the surveys \u0026ldquo;How many times a week do you exercise?\u0026rdquo;, and \u0026ldquo;Average amount of exercise per session.\u0026rdquo; For the control groups, the average weekly moderate exercise amount was calculated as the mean of the weekly average exercise levels measured during the baseline and follow-up surveys, while for the patient group it was calculated as the mean of the weekly average exercise levels before the onset of the stroke. Afterward, based on the recommended weekly moderated exercise amount of 150 minutes as suggested by ACSM [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], participants were divided into two groups: low PA (0 min\u0026thinsp;\u0026lt;\u0026thinsp;weekly average exercise\u0026thinsp;\u0026le;\u0026thinsp;150 min), high PA (weekly average exercise\u0026thinsp;\u0026gt;\u0026thinsp;150 min) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIncidence of stroke\u003c/p\u003e \u003cp\u003eStroke incidence was measured using survey data on stroke diagnosis from the epidemiological data. The survey responses were conducted as discrete variables with \u0026ldquo;Yes\u0026rdquo; and \u0026ldquo;No\u0026rdquo; options. In this study, all participants who responded \u0026ldquo;Yes\u0026rdquo; to stroke incidence in the follow-up survey data were classified as the stroke case group.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using GraphPad Prism V. 10.2.2 (GraphPad Software Inc., California, USA) and PLINK 2.0. All participants were divided into stroke patient and control groups. For comparison, age, sex, body mass index (BMI), hypertension, diabetes, smoking, alcohol consumption, systolic blood pressure, diastolic blood pressure, white blood cell, total cholesterol, and blood glucose levels were assessed. Continuous data such as age, BMI, systolic blood pressure, diastolic blood pressure, white blood cell, total cholesterol, and glucose were presented as means and standard deviations, while categorical data including sex, hypertension, diabetes, smoking, and alcohol consumption were expressed as counts and proportions. The significance of continuous data was verified using independent t-tests. Categorical data were statistically measured using chi-square tests. Principal component analysis (PCA) was conducted to control for population structure and background genetic variation, minimizing bias and improving the accuracy of gene-disease association results [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. A logistic regression model was used for the GWAS analysis, with age, sex, smoking, alcohol consumption, hypertension, and 10 principal components (PCs) derived from the PCA analysis included as covariates [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The threshold for statistical significance in GWAS was 5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, with suggestive threshold of 1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e. The PRS was calculated by summing the number of risk alleles for all SNPs and then categorized into three groups. The incidence risk of stroke associated with a single genotype was estimated using odds ratios (OR) and 95% confidence intervals (CIs). The relationship between PRS and physical activity levels with stroke risk was estimated using logistic regression. An OR greater than 1 indicates an increased risk of disease, while an OR less than 1 indicates a reduced risk. The statistical significance level (α) was set at 5%, and all data were presented as means and standard deviations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics and confounders for GWAS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe baseline characteristics of participants are summarized in Table 1. Data were obtained from 184 stroke cases and 7,606 controls. The mean age was higher in the stroke group (62.2 \u0026plusmn; 7.6 years) than in the control group (58.3 \u0026plusmn; 8.8 years) (p \u0026lt; 0.0001). The proportion of men to women showed a significant difference between the stroke group (45.6% men, 54.4% women) and the control group (37.1% men, 62.9% women) (p = 0.0173). The prevalence of hypertension was higher in the stroke group (33.2%) compared to the control group (26.3%) (p = 0.0364). Similarly, smoking prevalence was higher in the stroke group (37.0%) than in the control group (27.7%) (p = 0.0057). Additionally, alcohol consumption was more common in the stroke group (54.4%) than in the control group (46.2%) (p = 0.0283). The stroke group had higher systolic and diastolic blood pressure than the control group (p = 0.0004 and p = 0.0445, respectively). In blood markers, the white blood cell count was significantly higher in the stroke group (6.9 \u0026plusmn; 2.2) compared to the control group (6.3 \u0026plusmn; 1.8) (P \u0026lt; 0.0001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Characteristics of participants at baseline\u003c/strong\u003e Characteristics and stroke-related variables were observed for all participants. Statistical analysis was performed using an independent t-test for continuous variables and a chi-square test for categorical variables. Continuous variables are presented as means (standard deviation) and categorical variables are presented as number (percentage). (* p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001, **** p \u0026lt; 0.0001).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg 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xelSylUZv+v6dvzfv1ozoGdDBvRsyMU9G3BR1wb0696A2Ogg1m8/jlOH7u3q88AtHbjzmtZcdmETkhtFkJ1XzvHcchQQGxXI/Te15983tuWaAUm0SY6mtNzG4cwSDAYNh0PRJDGU4Te14+7rXXk6pMRgs+scPFpEWKiZN0ddSNMG4SxddwxQf1mAdGq0OgtMdaWIDAvg3ze05f6h7bn1qlZc1a8p/bo3oFnDcIpKKzl6vAxN0/AzGejUqh69O8Zz8GgRDqeqUa7cVY4aai2DwH1D2nHvkLbkF1nYuT/PUw86mZOd35fdoXPj4BaMf6g3a7ZmcaiWes7/wu7QcToVF/dsyEuPXEBSo3DmLz+MhoZCEREawN3Xt+GeG9py7cAkLuragPh6wRxML6a03O75/SoFTqfrNzb2/h60TopiwYoj2B06993Unruub8OvGzIpKLL+LQ+8fs+ZEpgqwN9k5KWHe3HjoBZcfmFTLuvbhMEXNqF/9wa0TY6mwmLnSGZprdfF06Hc//G+/sItV6bw4LBOVNqdbNyVg/FPvM7JfgN1RQJTl7or2XXgcGYJLZtGMrhvE9o3j/Z043A4FB1a1mNAz4YkNYzgSGaJa39V11+Ha7NWOry6Ddjsru41Vd07bDYnDqeru21RWSV2h+4p9HaHjqXSdR67u1XUqStPoNqmeTRtkqMJC/WnwmrH5u7CU/UelVKEBJlo1yKGji1j8av2I6zqwlb1Hv+OLjhCCHGmceo6nVrF8MpjF3DLFS2x23XWbsvm4NFiurSux9gRPRh5awdMfgZPEOt0uroTVnU7s/p0M9V15bpW213X9qrrttXm9HTbtVY6ubBrIhOf6MOAng0pKbd5KrQW93XbZndf76tdw6tes+p8vl0JWydF0bZ5DJFhAeiqZhc1p66IiQzk/qHtKS23s2HHCUwmAza7E0ulq/vlb13qHF7DPlzdZh01u925u+LqOtSPCaJJg1CaJIbRONH1/wG9GjKwV0MS40KwO3SaJIbx4sO9uOHSZEorbFgrndw4qDnPDO9BWLAZXVfcfk1rRtzSgYiwAHLyK7jswiY8/2AvmjYIx2Z3EhpsYtTdXbl3SHv8zUaO55YxsFcjXn6kNy2bRpGRXcb6HdncOKg5PTvWr/E5ncuUgqGXp/DYnZ2JiQxgw47j/LopE4dTcd3AZF58qDdNEkKxWO00TQxnwuN9uOXKltjsOk6n7lV+bXYdu/s7rlJVb3HVF2p2eW3WMJzWSdHExQShK4XTqWOtdHj9TpS7q3WlzeGpq/xel3anroivF8zwm9pTUGxl8+4cTH6uclt1bPVyW/37/r1yW+nuVqsU+JuNXNyjIf/5d1eSGobTqH6I5xmZ0uHai5N44v+60KB+CPsOFRAR5s8Dt3TgruvbePV4cDh0WiVF8+gdnV3nSQjFoGnY7TrLN2TQsmkkd/yr1d/eVfVMpuuK+jFBDOjViNZJUdjtTnILLBSX2IiJCORfA5MZe38P6kUF4tS9y1dlte+8SlV5slbrzl9V7myeuq0Th6dcKBrFh9I6KYoGcaEo3TWkoqrcV9EVrl6IPt2v7Q7d6zdwPl1fzgbnTWBqNBjYtOsE2/bmAjCoT2OoetqnQb9uiQCs3ZbFtr25aAaNoAATHVJi6N+tIRd2TaRNcjQBZle3LU2Dpg3Cad8ihtAgMx1b1aNXx3iiIwKY98shpn21i027cjAZDTicOqFBJjq1cp2rT5dEWjWLwuRn8PwIK6wO13glh06LxpH07ZpIlzaxhAabcLgDXKeucDgU5Va7p03C7tCpFxlE93Zx9OvagNbJ0ZhNrtcUQohzla5DgL8fD9zSkZbNoli7LZtHXvmVkS8uY/i4X5j5/W7KK+xcekEjVwVJKRxOHZNJo2WzSPp0SaBXx3iaJIa5g0rXg8LgQBMdU2Jc3YH9/ejcqh4XdU2kTVIUfkYNp66Iiw7i6v7NiIsOIjjITLvm0Zj8DMREBtExJYa46CCaJobTt3MCjeJDXd2NdUViXAjd2sVxUdcGtGseTUign1dgYHHfB1zX75qP8p1OxcBejWjRJJJvlxwkt9CCAY2khhG0bxFDUIAfjeJD6dslkR7t44mJDMLh/nclNwqnR/t4urevX2Pr1i6OkGATM77dxeMTVvD4xBU8/PKvzPg2lQqLnYPpRXz87W6cTsWQQcm0SY5m6dqj3PrkTwwft5T5yw9jsToIDzFTPzqQm69IwWZ38tIHG7h77BJWb80iqWE4l/Vtgs2u07VdLNdcnMTR46XcN24pdz61mOlf72Lv4ULiYgJxOnW+X3YYXSluu7rVn2oNOVsZDQZuGNQcgM/m7+X/xvzMvc8tYdTrKzmUUUxEmD9tmkcTGGDikt6NaNUsCn+TkTbJ0QQHmQgOcpXfRvVDia8XzAWdE2idFI1SrjGBMRGBdG5Tj4u6NaBz63pEh/tjs+ueOkVlpasSb3foOJyKwEA/2qfE0KV1LFFhAa6y7NRJjAuhZ8cELuyaSIsmERgNrvJZG6UUgy5oTNMGYXyz+CD57tbG5EYRtGseTYC/H40TwujbNYHu7eKJDvf31GF+r9x2aROLv9lASJAf9w5px5v/uZCUppGuQMfudP+EFEajxuC+rjrf5NnbGTZ6EeM/WI8CrurfFD/3gyunrggJNvHI7R1pnBDqeZgE4OensX5HNnsPFXLDpck0SQg9bwMap65IaRqJyWgkJ9/CYxNWcM+zS7j7mSVM+GgjDodOStNIkhqG43T8ViaMBo3WSVG0bxFDcJDJM17aYNBIaRJJh5QYwkLM2B06/iYjbVvEcFG3RC7q2oD2LaIJDjR55m6pdAegNvd41ohQMx1TYkhqFIFSrmteoL+RtsnRtG4Whcnk+o7tDp2IUDNd2tSjf/eGtE+JITjQ77z9Ls9E501XXk2DCouDuOhALuiUQEiQmR+WHabcYicyzJ/R/+5GVLg/H365k407TxAbFcQ9N7ZjxM3tuaxvEwb1aUyvDvGY/QykHizAaDTw+J2dufXKloSFmnnw1o5cdXFTtuzOoXeneC6/sAl5hRZ27s8nNjqI+4e25/6h7RncpzGD+jSmR/v6oMHutHwMmsYtV6ZQLzKIwhIr1w5M5uYrUri4RwMiwwLYc6iQMoudRvVDGTKoBdZKB7O+30NZuZ0WTaN45LaO/PuGdlx6QSP6dEnE5Gdg35Eid0B7/tzQhRD/rLrsyqu7K0dj7u2GrmDM26v5ec1RdF2RV2Rl3+FCrJUOFq1KJ+1YMRarg9BgMzdf3oIRN3dgyGUtGNynCe1bxOB0Kg5lFGOz6bRKimLC4xfQvEkkzRqGc99N7bjh0uZ0aROLxeJkd1oBN1/RgiGXtSAwwI+YyEA6t45l2YZjXNS1AU/c1YVGCWFcOyCJ265uibXSydptx+nTJZEHh3XkjmtbceVFTbmgUyLRkQEcyiimtNyGQdMYekUL6scEs+DXw+w5VIip2j1OAX5Gjafu6UZCXAjvfLKN9KwSDBo8M7wHN13egsAAEzdd1pxbr2rJgF6NaFA/hF0H8ikurWTUXV0YPrQDg/s05rK+Tby2Sy9ozPKNmew6kM/hzBIOZxSTk2/hzuva0LJpFC9MWc/Pa44SEmRi9N1diY0O4r9zd5OdV0FMZADrd57gy4X7yS2y0LFlPe68tjX5RVbGvbeWzJxyGsSF0LdLImUVdhasPMLQy1vQvV19fl5zlF/WZ9CsYQR7DhXw9aKDHDxa7B43Zufyi5rQNjmG+csPU1Bs/Z+7BZ46jbrqygtw/aXJ1I8JJjOnnNwCC7oO6Rkl7D1SxOotmew5VECLJpE8dU83QoPNhASZ6d6uPlv35BAeEsCExy8gpVkkF3ZtwD03tiO+XjALVh6hZdNIHry1I3df35ar+jWjf4+GNIwPJSunjJyCCowGA4P7NKFlsyiWbchgy+4c7rquDQ/f1onWzaLYlJpDToGFzq1iefSOTtz5rzYM6NWInh3qo+uwP70Qpby7XSvA32Rg9D1diY0K4s2ZWzieW4G/ycjYET24flAygSYjt1zZkmFXteTiHo2IrxfM9n15WKwOHr+zM/ffXHu5vbhHI35ed5TwEH/eeLIvx3Mr2LArm5SmkRzPLeeLBfvdvdY0LuicQKtmUSxbf4y9hwpJbhzB5Rc2Yd/hQr766QCapuF06tx8ZQr3D+3Azv15xEQFkldk5cuF+z294ZomhtGnSyL7jxSybU/uadVD/1dnSldep1Pnin5N6dM5gZ3785gwfTNFpZXkF1mpFxXIdZck49QVs3/cR3ZuOUajKyg0mYw88X+duenyFhSVWNm5Px/NoNGwfggvP3YBA3s2ZNWWTGx2nf+7rjUjb+nIlf2aMuiCxlzQOYHgIDM7D+TjcOr0796Azq1j2bjrBL+sO0afLomMe6AnLZpEsGDlEVDQpEEorzzWhz5dElixOYviEivxsSGMuLkD97uvhf27NyAi1J99R4qwVP5vQz7+V9KV16XuSnYdMBo0flmXQYXVQdPEMDq2isFa6aBt8xiSG4WTX2Rl5aYsNE2je7v6jLi5PcEBJpauO8bmXSfo3CaWJ+/uQsP4UHSnom1yNJ1a12P40HbYHToFhVbyCi10ahVL3y6JNGkQht2h07tTPPcOaYef0cCStUfZsS+XHh3iGX13VxJiQ1xPgNx93a/q3wxdKTbvOkFsdBAP396JIYObo7uyeCilCA4yMequLtx8RQq5BRUs35BBZKg/Y+/vwRUXNsFxkqeXQghxttN1RfPGEZhNRopKK9l/pJCwEDNms5HQYBN5RVbe+O8W5izYT0FRJRoaQwa34Nn7e9KyaST7DhdyNKuEft0b8OLDvejfvSF2h5PQEDMdWtbjuoHJXHtxEgeOFJFTYKFnh3hG/bsrUeH+VFjsnpYUm91JSbkNp1PRoH4IHVvV4/ZrWpHcOJys3HLyCi00bxzBiw/14ur+zSgsrmT7vlxiIgN49I7OPHxbJ8wmI3/0vF7XFbHRQbRLiaawpJK0Y0WeLsptmsfQuXUs993UjoAAP3bszyMxLoQ7r23NoD6Nsdl1yi0OcgsryC201NjyCi3ouk6Avx8hQSYC/P24+uJmXH9JMis2ZfL9L4cI8DcSHupPVEQgSkGvjvG883Q/3hnTjzuvbUV8bLDrQW94ALh7AVkrnQQF+FFYXAlAfEwQge7WMdxjUd94si/vjOnHo7d3pn2LaCxWB5pBo6jUyoH0IkKCTHRqHYu9WsvLucypK75YsJ/i0kpuuLQ501+8hAmP9+GJu7pg1OCX9Rns3J+Pck9OhHvYUXFZJXa7IjLMnw4t63HDpS3o0yWBrNwysvPKiQz3Z+yIHtx6VUuU0tm+JxcF3Pmv1u5ul0FeLZ5Go8a/BiTzxJ1daBAXwqotmaQdKya+XjDPjujBNQOSOHSsmDVbs2iSEM64B3rSu2N8jZYnpStio4PpmBJLboGFzBNlGI0amqbRvkUMXdvEcd/Q9vgZNXbsy6dRfCh339CW/j0aYHc4KbfayaulzOYWWigqtbo+hwoH78/ZwcgXl7Hw1yPuF656Bxq6rpg1bzeHM4t54JYOvDm6L0/8XxcKiqx88MVOHE4du8NJ2xYxPDSsE7sP5jNz7u7fxja6YxWlFNv25gHQu1OC14Oj84XC1VOlZZMoAEorbLRPiaFP5wSuHdCMB4d1xN9sZHdaPnvS8ql0OLFYHVisDopLba4HG61jGXJZC0x+RhwOnb5dE+nTOQF/fz+OZZfRvkU0D9/WiZjIAH7dlMGabcdp1Syax+7sTMumkdh9up/rCupFBtKhZT3at4xxtZiiCAk00b1dHN3b18ffZMRgMDBiaHvuG9KOSpuDZeuPgQaP/18X7ry29Ulb/MU/67z6VRmNGvuPFLIp9QR+fgYu6d0IgIt7NMRg0NiwI5vDmSX4mw3kFlbwxsdbGPfuOl6bsYmZc/eQV2ghLMSfuJggnLpOhdUBwL5DRdwzdgkPjF/GwaPF2O1OFK4+80ajRnZuOa/N2MzYyWt54+MtzPp+D4XFViLC/EmoF4w7LgVg2YYMho9bygMvLmfaV7sAuPnyFEKCTKjq4yCcOp1a1ePyC5uQW1DBM2+vYdx763hvznbMJiN3XdeGALPRq1+9EEKcKxSuSWIAysvtXuPElAKD5q5EmY2gQWx0IPcOaYumweTZ27jr6Z+5e+zPzJm/l+iIQIYPbUdQoAmHu4uYAt747xbuf+EXnn1nLRarg8YJodSPCWL2/H0scFeAl2/I4O6nf+ZEfoVn3FRRSSVPvraS4eOWsnDlEW69MoUWTSJZsSmTe8Yu4Z5nlzL2nbWUldu59YoUOrWsh93hrLX7bhVdVzSODyU8xExBsYXcAounBbHC4pplfvveXNf9Y/wyVmzMAKB1UjRGg8aseXsYO2ktz0xaU2N7dvJajueUYzRoKAUhQSZuv6YVlTYn//1uN5U2JwZNIzjAhJ+fAaNRo3eneA5lFLP3SCFDBrfg5Ud6Uz86yFNZd1UeXeer+mr8/f0wahohgWYAenaMx+bQWbMti54d6zPh8b50bhPnHkumcehYMQDJDcP56xZEOfPNWbCPN2duYdOuE9gdTgb0bMh9N7Xjw+cH8MKDPWnWKIK1247zzuxtAOw7lM9945ayY38eBnetzu7UmfDRJu59dgnvf7adgT0b0q9bAw5nFPPgS8u5d9xSHnp5OemZpfTr3oBBFzT2dJ/VNOjVIZ5nhnfH32zkjf9u5sMvd2KzO+nbJYHu7euz/0ghT721iufeXcunP+wlPNSf269pVf2fAe5y27RBGEGBfuQWWigssbrKGYqyCle5Xb/jOPc9t5SRLy5jzdbjALRJjkIzaHzy/d4a5fWZSWsYO2kNz0xaS36hlZyCCiZ9uo2te3LxNxt93oFLfqGV3AIL0REBdG0bR2JsMKXlNkrKXA9NIkIDeOzOzvibDIx9Zy170lw943RdYbG65gTR0DiRV45SipbNojxdgM8nrvlO/GiV5ApMu7SJ48NxFzP56f58MG4A/bo3IO1YMe99toNAfxNdWsfRISWGjq1iaBQfwrc/H6SwpJJOLWNp0TQCs8nAVf2b4XQqZn2/m9JyO6UVdiZ9spXxU9bz6rRNzPg2lcwTZfibjDSMC8VRy3jmqgci5RWuejnuhzwVFjul5TYcTp0mDcK4+YoUyitsTJy+iefeXccrUzdiszu57eqW1I8JwuEzJlb8886rwFTTNCxWB4tWuioUfbsk0iopigs6xaPrioUrj2B36hgMBjan5rBqSybNGofz/IO9eOKuLoQGuSZ20BQodwVCKVi67ih7DxeQllGM0l2v435FNGDjzhzW7cimfYsYXnioFw/f1pHgQBN2u+7uMOTuOKQUi1enU1zq+hEtW5+Bw6lIiAshMszfc3PHPb6qRZNI158V3DAomafv607PDvHoOiQ1CicmyjUeRAghzjUaGhVWV4tRUKCfJ6jypGsaDeJCCAzww+lUNG8UQWJsCPnFVr5YuB+L1UF+kZXPF+wHoG3zaMJDzZ5JTYpKrPy6MQPNABknyiguq3RdTzXNPVGHqwJkszspq7Cj67/N8Lj3UAErt2RxJLOUSrtO9w71UUoxd2kaB48V49QVC1YeZn96IX5+Btq1iPHcD05GKUV4mD8Gg4HSMhs2e7WhGprr3/7rxkzyCqxYKx0cznAFda5JlBQXdErghkubc8Ogmtv1lyYTHuaPrlwT6HRMiaFnh/rsPVzI+h3HCTD7gfuBqNIVSil+WHaYB8Yv45FXfmXn/jy6tImjV8d4LJWuz0UzuO5umoYnWHI4dHSFOwiHvYcLeeilZTz66gq++zmNmKhArurX1DX2TEFhiTtoCPPHz90d8FymALOfAT+jgU9/2MuDLy3j+ffWM3n2NpasPYoCbrmiJUMHN6fS5qC8wlX+nU7larWvVgjzCi0sXp3OkcxS8outdGsXB9WGQulKsSU1h2UbjgHQsVU9jAaD5wHAwF6NSIwLwVLpYPvefEDDz6jRspkrIDH5Gbjj2taM/nc3WruDlDbJ0QQFmrzrKgpiItwPkCx2Kiy/Le2hoaErxS/rj1FUWom10sGRTFe5NRg0DBr07nzycnvDoOaEhZhxOHXMfgZCgky1PtsxmQw8fmcXureLY878fYx4YRnvzt5Oo4RQxj/Um9joIP41oBmDLmjM9n155BRU0C6lHgBBAX60c88lonBNplNpcxIR6o9BM1Rvmj0vKB3qRQXROCEUh1Mn60QZlkonpRU2du7P47Mf9/LC++v4/pdD3HBJMu8+05+3x/Rj0lP9GHFze9KOFfHL+mOEhZjp160BKU2j6NomjqzcMpZvyCTAbGTXgXx+2ZBBXEwQz47owdP3daNeVOBv9dnf+ci1aktSVWXTAN2p07xxhOt+oCsG9mzMM8O7M7B3IxxORXREAE0beI+JFXXjvApMAYxGAys3Z1FYUkmD+qH8379a07xJBFm55azZlo3ZaEApxb8GJvPBuAE8cHMH6kUEknowj6KSylovegXFVoIDTQT6+6EZqv1mlGvq91uvTOHDcRfz7xvaEBnmz44D+ZRbXNOTe6a4cJ/X4F73SdNca8ro7kqAprmeMHpOreGZat3sZ6BZwwiSG0YQHuLPd0sO8s2ig+D+kQohxLlG0zTSjrqCvMjwAJokhmGx2rHZnVRYHCTGBfP0fd2476Z2roqx+1rodCp03V3xNWg43UGQQXM9SKy6ylptTgyaAZPRgNnsWp8Td4BoNLqCh6r3YTAA2m/X55JyG+iK4EA/TH4GjO7xYDa7Ez+jq8KtoXme8p/qXACe9+Cz30VhdzgxmwyY/IwYqi13puuKC7smcvOVKdxyRc1t6GUpRIb543RPaNmvewM0TWPl5kyKSm0YDK57SVFpJaUVrsn39h0ppMJiJyunjCOZJSgFDeJCKSiyAhAYYMTPz4DNrrsCBnewZLM7OJFXAUB6Zgkn8i3YbE42p+YAEF/PtQ6ics+26nJqn8/ZTtcVCbHBPHpHJx67owt5hVb+O3c3Y95aw/3P/8KKTa5W8F4d4wnw9/utDBo0jJqGpn77nIpLKtE0jaAgP/zNfp6xczaHjtFgwKBpGI2aZxZTo08ZLCqtZEvqCUKDzdw/tB3BQSZ03RXk4e7OmdQwnOSGEfj5aXy96AA//nrE/Z68S2jVaytdeR7ueCiw2XRMfgbXb8X9FKNqUuqLuiTUWm5vviKFoZe1IDzUFTAafB5MVdGVIjoikEsuaES5xcH0b1JZuu4o78/ZzvHcCtokR9O5VSxX9GsKQPPGkUx9YSAPDesIuMr0G6MvpFVSFHaH0zOxjtJd3UXPN7qu06ppFGaTkZz8Cka/uZp7xi5h+PO/8MD4ZfznzdXMX34Yo1EjNMRMeIiZ6PAAIsL8iQwPQNdh7pI0HA6dwX0ac90lSZhNBpasPcaxbNf6sJf0asSH4y7m0Ts6k1AvmH2HisjJrwB3L5ja/PaMzvWHqknfqo9Lr1rex2g00CA+lGYNwkmMDeGnlUf4fOF+SsrqYs1k4ev8C0wNGoczS1i7LQuTn4GbLmuB2WRk1ZYssnNda4P5m/247eqWNIoPZdb3uxny6ALemrkVu/uu7TtNuK5Xu4F6/V/h52fg3ze0JT4mmA+/2MmQRxcwZc4OV8usp/xXLRvjGqAfHuqPn1GjQ0oMZpOB3AKLa5FxTUO5T60Bx7JLAcgvsvLExJXc8uRCXv1oIys3Z/LDskNeXb2EEOJcYjC4gqNNqSfcrTdtaNUsitBgM4lxIQy7qiXXXdKce4e0IzzUTHpWKQXFVqLCA7ioWyImk4HgQBP9ezQEDfYfKaK0ws5vISicfH6R39aLDPA3EhRocl+fXTcAp9PVMqiAykoHew8XoGkaF3VrQExkIEaDgc6tY0lqGIbdrnPwaCF+xt+Or63ypWkaJWWV6Mo1dsp7DUjXEZpXxe23PxmMGrPn72Xcu+tq3Z5/fx3ZeRUYDBASbKZjq1h0XbFxx2+TDGqaRnmF3TNhX6eW9YiLCSIxNoSE2GA0DQqKrGTllFNSZiM81J+2zV0zabZrEQPAnkOFVFgd7DzgGqeX3CiCpEZhhIaYaNHU1QMov8iK7lSgQUiwK6AtLLHWWHbinKQgKMDEfUPa8cAt7bn5ihSaJLpm1w3wN3q6ipeV29FVVb8tV5AYFGjCYPytADicuuuhNhoOh87+9CIAurSOpVmDMAwGjcYJYa6JGIE9hwu8PuMla9IZ9cZqjh4v5dILGnNJr0ZYrA7SM131jqPHS3lg/DJuG/0T787ezsrNWcz/9TAVFrvXBDKaBnmehxV+BPqbagSQJ62lKJg9f1+N8lq1PffeOnLyLN6zNvv+eJTrt+rqDafRID6EqPAAGtYPJcDs+kztdiepB/PZtOsEJ/LLqbQ5PS3/VV15dfdnE+BvxGw2UlBidbXsn2d0oHVyNACHM0rYuieH9OOuCdOycspROoQGmzH7GfhxxWHGvL2GMW+v5plJa5j+TSpGg8aW3SfYd6SATq1jubp/EuUWO/N+ScNocLXK33RZC5IaRjB3yUFuemwBL364gQqrw/XgsLbP3L1qBUBIoImoMH8AUppE4m82unqzGDQysstwOHQsVgevTN3ALU8u5NnJa/l1UwYLVhzhSFbJX7q2rvhzzr9vQHNVGhaucHXnDQlyjXVZtCodh648XY4s7vGjjRPDuPyipjx2p2v9K4OmEeo+xt9kRNNc04hXZ67ab3Q1n5a5JyhIbhTBVf2a8sRdnYmJCMRkMhIabEYpRYDZiAYM7tOYUXd3ZfhN7Rg+tD26rvhh2SFKy234GTX8/DQC/I2Y/Ixs2ZPDrgN5JDeO4Im7uvCvS5L5zz3deOs/F3Fpn0Y4HHqNG4AQQpwLDAaN0jIbU+bsIONEGVdc1ITXnryQJ+/uwviHezJ8aHsqLHZmfJPK4YwSMk+UMX/FYUx+Bp68qwuP3NaJJ/+vM8NvakdxaSWfzttLSZnNE4gFmP08FWZNc113Xd0LNUCjxD0+rnOrWB67ozNx0cGe/GaTa5ybpmnYnTo/LDtEfpGV6y9JZuzwHgy/qR3PjehBTGQQS9YdZcPOE5hNRvzNRjRNc7caeV+8DQaNY9llFJdWEhURSHTkb2sEuu5FmueegwKTn+s9mPyMGDSN5RszmP5NKjO+3V1j++/cPRQUW9E0jfBQM22bR6MrxeHMkhqzVH7380GKSyu5ol8znrqnG0/d243OrWI5mF7Ept0nyDxRxrINxwgJNPHIHZ144v86c0mvRhQUW1m69igasGpLFgfSC2mVFMXY+3vw2O2duXFQMoUllSxbn+GqZGrQqH4oAIeOFaN8GtrORUaDxr4jhXz50wGUgsfu6MzY+3vy+P915vUnL+Sqi5pRXFrJ4jXp2GxOKtx1i8YJYYy8tSNtk6M8D86r6hS4H+IsX5/BoYxiOrSsx4sP9+beG9sy7oEedGhZj53781i8Kh2jQfOU3dS0AjbszOa/3+3G5Gfgkds70SQhjBWbMjmWXUqX1rE8ekdnrr8kmWeGd+f1UX3p1aE+dp+l6owGjSNZJVisDqIjAokIM7uDPA1/s+s3ZTT+9tC9aoxy1f+XbTh5uZ39w16Ky1wP7T2v524VM7vHmmqaRm5BBb9uyCAowI+Hb+vEvUPaMebe7sRGB7Fjby47D+Tz/pwdPPzych4Yv4x7xi7lPff43ZxCCw+/vJzUtHz8jAbqRQZ5vifXhFzneKGsRikI8vejQ4rrQVPasWLs7h4Rgf5+BPgbPZ+/waCx+2A+Xy3az9eLD/D14oOs234cg0GjoLiSH5Ydxs9oICE2mB378ti+L8/1nWsa1kpXI1BiXCiD+jTmsTs60aJJJAaDRniYP5rmXU4MmsaxrDIsVgcN40MZeWtH/n1DW26+PMXd2GTEz2hg3+FCVmzKJCYykIdv68gNlzbnsTs7M+mpfgy9rIV7HhepNNe182a5GC+aRnFpJV3b1sdidbA/vZApn+/A6XT9mOwOnTKLg+aNwunQMpae7eMICjSxN60QDY0d+3PZe6iQ3p0SMJuMLNuQwb7Dha4xMECP9vUJCTKxeutxtqTmYLU5SWoUTqdWsfRsXx+DwdUFTQFbduew91AhA3s3wu7Q+XVjBhd2TaR/jwYoXfHLugwmf7qdknIbcdFB9OwQz4n8Cr79+SAn8iwcz62gYf1QenWMp2+XBOrHBLFhRzbTvt5FToHPk0QhhPgL1eVyMbgr3AfTizmR71q2JKlRON3b1adBbAiHMkr49uc03v98u2dG1z1pBYSFmGkQF8qFXRLo0LIeGSfK+Wz+PmbN243SIS4mmJ4d4snKKePbn9Ow2Z2EBJm4oJNruZO5S9IoKLZiNBro2LIe0REBtE+JYeHKI4SH+JPUKJzdBwtYsvYoJj8DmgHSj5dRYbFTLyqIbu3j6NGhPv5mI8s3ZjBx+iaycsoxGY1c1C0RBSxZe5S0Y8Ve9zhN0ygrs3NR90SaNQxnyZqjHDtehtGg0bdrAwwGA79uymR3WgFGo2vG0/h6wWzdk8PqbcfxN/thNhlr3VwzVmoo3TWh1BX9mpJ2tJivfz6Axerw9LzRNDh6vAylIDE2hD5dEkhpGsXutAImzdrKhh2u7rjpWaU0TQynXfNoenSsT1ZOOZ/8sIe5Sw5hNrlam7LzKmicEEanVrF0b1efE/kWPv1hL18vOuBaR9zfxIO3diAo0MSbM7dQXGr/B+9nGtTFcjEaOByK1LR8IkP9iQz3p3PrWLq0jiU2OoiD6cV88dN+Zv+wFwwaTqeic+tYQoLMdEiJYfveXHIKLPTqEM+RzBIWrDhCpV3HaDSQU2DheE459aKCaNcihgs6JRAXE8zmXTm89vFmtu3JxezvR+fW9QgP9Wfttmx2Hczn6PEy2qfUo15UIOlZJazYnElRSSWN4sPo2yWBCzonEhpsZuWmTGZ8u9u19FG170kDLJUOLuyaSNMG4fz462Gycysw+Rno26WqvB8j7Wgxfn4aHVrWIzYqiM2pJ1i34wT+Zlf59C2zVeXWNZbZRVeKJgmhtGkew6Fjxcz/9TAGzfU5HUwvpH69YFKaRnJR94Y0iAth+9483pq1hV3u4VX5xVYKS1zLnoQE+dGzQzwHjxYzc+5uz5juawck0bNDPJ98v4fNqTmeAOmfUNfLxSiliAjz5/K+Tam0O/n25zT2pBWctC7u52cgKMDP1VIe4Od5WKahUVBspVfHeApLKvl0/l42ur9rh1NRXFZJStNI2iRF0bNDPFHhAaSmFWDQ4ODRIjal5tAxpR5xMcFs2nWCTTtPUFxmIzzMTFx0EF3bxtGiSSQ79uWhUBSX2fj+lzTyCqwcySymQf0QOrWK5aLuiSQ1imDH/lw++jqVtGPFGOuwxVSWi3HRVK3t4mcupRQTPtrE27O2EeBf++xrp0JXio4t6xER4k9uoYXdaQXgvvEq9dust22So7HZnazdfhyHQ9GmWRRZeeUcSC+iTXI0UeEB7D9SyPHccteNXUGrpEhiI4NIyyjmaFYpCkW3tnGkNInEanOwestxjEaNFo0jycgpI+1oMZ1a1yPQ7MeWPTm0aBxBm+bR5BZaWb/tOHlFVvzNBsJC/WnfPAa7w8mW3TnYHDo2m5OkhhF0bFWPyLAAjueVs2nXCbJzywkMcE1YIYQQfwfNYCZ/z38pzVpOYu8JmAJiUMo9UPEfousKS6WTpEZhtG8RQ2xUEBUWO/vTi9m2NxddV5jd4+KslQ6Cg1wzRTZOCMWpFHvTCtm2NxeD5hp7FB5qpkNKPSxWB1v35GB36gT6+9E+pR5mPwNb9+RQVmFH0zS6to0luWEEpRU2ft2YSUSImWaNIsgpqGBPWoGngl7VXbB1UjStk6MICTKTdaKMTaknyCuyEhzgh66gY8sYwoL92X0ov9YHi3a7zl3Xt2b8Q7159p21fPjlTgL8jbRvUY+IMH/2pxeSdaIczQDNGoTTqH4oGSfKOHi0yCtYOBmlFMGBJrq0iaPcYmfH/jzsdqdn3BZVY3SVonu7OFKausbdbduTS+rBAvzNBjRNw2pz0qh+KL061ick2MyBI4Ws3X4cpVw9iZRSVNp0WiVF0qllLAEBRvYfLmTdjmzP67ROimLu5KvZtDubW59chMHgGgP8z9AAnRObXkVXOvW7PoWmGf+xhy7WSgdhIWY6tYqlYXwoAf5GSkpt7D9SxM4DeWjuliKnrmjRJIIOLevhdCjW7zxOucVB+5R6lJZXsutAPg6HqyurUgprpZPGiWF0bBlDdHgg+cVWtqTmcPR4CUGBfoBGiyYR1I8O5uCxYjJOlOJ0Klo1iyIhNoT8Iou7TOi0So6kQ4sYggPNZJwoY8OObApKKgnyN9b4lBxOnXuHtOPZ+3vwxMSVzJq3h+BAP9q3iCEsxJ/dafnk5FswGCCpYQQN4kI4ll1K2jHXmranSinXkkopTaMoKa1k+748Tyu7pdJBXHQQ3dvFkRAbQlFJJVt253AgvahGXUkpiAwz07Z5PcoqbOzYn4fT6epR9/kbl9M6KYqr7p/H/vQiz7Xlb6cUOdvfwWHNI77HODTN7x8rj1WUUgQGmujYIgajn4FdB/Ip/FPrCysMmoH2KTEEB5nZtifH80Cjqv7dJjmaDikx6Ao27jxOabmddi1iyCu0known2YNwmhYP5T046UczihGKUVQoIk+XRKpFxnAseNlrN+ZTfvmMZhMBrbtzcNa6VrmKzE2hC5t4oiNDqSg2Mrm3TkczighqA7rzEq5uonPffcqWrknGDtfnbeBKe511nRdYTRotVyYFBVWB0qBQuFnNLhaU+06ZpMBs8mIpdKB0+nqhlv9iZG10oHDqTCbjZj9DOjKNUah6lxmo2v5Arvjt3NVWB0oXeHvb6TS5vR0wTWbDPibXBd6p65jsTrRNAgOdI29UQqsNgdOp45rNInrhnWyKdOFEOKvciYEplWqrsdVnes0ICDANVtvdXaH7lmDFHe+wAC/akFk1XVWIzjQdV/QFVisdldXNndepRQVFoe7aqgICjDh1BWVNqdryIV7Jtsqyn0fcHW1dL1W9es77iEkTr3mPaVK1VqpX0+6gszsMm558ifsDh1rpes4f7PR04JTaXdit7smlTmd+4Guu+59Bs11X6yt+6zrYYDrnoa75TrQ3+SV11rpxOF0LZ2m4TpX9e9CKdd3pru/M4P22/dld+g8cHMHxtzXjftf+IXvlqS5lv35x9RtYIpPOfWUaQ0C/X8rq8pd36haf9HVNRYsVicGg1ZrRbvqd1LFz6gR4P9bPqvNgcPxW/2l+jFV9RXPd6f/Vu/wLcvVOZyuJWO+eftK9h4u4Pb/LHLVXdzltnp5r7Q5XXUjP4OnO+7pcDh0rDZnrfW6qnMr97s2GjUCzX619sZ1OnUslb99jg6nTtvm0cx992p+WnmYEc8v+1Pv7087AwJT3L/bCve1MMDf1UX2z1DKdb3TlSLQ3+jVUukqX66ZzlGu7tl+Ro3Katez2sqJ97VdEWD2w2Z3fd+B/ibPUL3KSid2p+7+Xbkmswus9huoCxKY/ubPlahzRFCAazFx34sXuLpNBQeaCAkyERpkJtDfD3+TkZAgE2b3GIxA92LkvhWIqkXKqy7qBve5gt3n8vd3jSWqfq6gAD+Cg1wTWlS9blV61aXHaHBNh14VlFLtRhUSZPYcczqVECGEOBdUXY+rb75BKe4Hd775qj/x/+06+9t9weB+GFg9r6ZpBAe5rushQWYMBs1zbt+g1JM/0JXXdYz39R13gBxSyz2lisGocSSzhM/m73W1viZF4XDonuOqdyusul+d7v3AYNAICTIRFFh7UIo7T9U9LSTIRFCAd1CKe5IY133JdW/y/S40zX0PDjYREuw6l9HdYhIeYuaS3o1Yt+04S9Ye+0e7S54paiunIYHeZVXzKfcm96y2ru+kZhmkWv6q7656UIp7bHVItfpL9WOq6iuaBsEB3vUO37JcnZ9RI+1YEZ8v2Ev7FjE0bxyBw/lbua1e3j11o9Mst1X83J9bbfW6qnNXve9A/9qDUtwzt1Z9jkq5fr+X9G5ESZmN6d+k4gprzj9VDSMh7vrqn6VprmW+QoJMNbrPuq4N7jIf7PouTT7Xs9rKSdVvpuqa7OdnICjQj+DA34JSAH//qnLgylfXQanw9udLlThtJ7n+/WX+7vMLIcSZruo6eKrVxlPNdzKnc92teq3TOaY6zV3x/nT+Pt79bHuNYPCfdiovf/p5XK11S9Yd5dWPNlFabqsR1J5vTrdMn4r/9RM93bJs1DRmfr+HqV/uxM89QU5dOf1XV5j9DBw7XsaEjzaxbU8e/rU8fBJnhlP9fk81n/hnSWAqhBBCnCWMBo28AguvfrSRTak5mM3n1m1c0zQKi628NmMza7YdJ/BPtpyJM4vRaCA7r4JXpm1k5/58rxbZM52maTh1xZwf9/Lxt6n/8HhnIc4vZ8+VQQghhBCY/AxEhPpj8jN4xnmeSzRNIyzY7OpG6ZsozlomP4NrnXY/1woGZ5vAABNhIeY676kgxLlMAlMhhBBCCCGEEHVKAlMhhBBCCCGEEHXqrAxMXV1AzsaOIEIIIYQQQgghfJ2VgammaaAZXHNKyyabbLLJVgeb+xrsuS4b5Los27m1edU5ZJOtjjeva20t6bKd/Zs4OwNTF0022WSTTba63LSqP4PyTZNNtrN2M7j/L/UN2c60rao8VpVR2c6ZTdOkMyigKXV2zemnK8WTL3zK+zMWehZ7FkIIUQc0P6w5a7GVHiCk0b8wmIJB6b65hDjLaCgUFZmLUUonOHGQq5VKao2ijigFluxl6I5yghtcjqYZpTyeQ5QCf38TP30xli7tmvomn1fOvsBUV4we8xyfzZyGQTuLG3yFEOJsp2kUFRdRXlFBXL1Y/IzysFCcGxSQm58HCupFx0gvO1GnlIL8wnwcTp24mBhX93JxzlCAyezPN9/NpXOn9r7J55WzLjBVSrF9+1Z27dyJZpAfphBC1B2NWbM+4ZdfljFp0ltERIRzlt1ShKiVriteeullnE4n4557ztXbTlqoRB3RdZ0335xEXl4er77yspTHc5Cf0cjgwZcTHhHpm3ReOesCUyGEEGeOxx9/nHfffZe8vDxCQ0N9k4U4a/Xr1w+73c7q1at9k4T4x1177bUcPnyY7du3+yYJcc6QvrBCCCH+FF3XsdlsAJSUlPgmC3HWcjgcOJ1OnE4nDofDN1mIf5TT6cRut0t5FOc8CUyFEEIIIYQ4C8j4UnEuk8BUCCGEEEIIIUSdksBUCCGEEEIIIUSdksBUCCGEEEIIIUSdksBUCCGEEEIIIUSdksBUCCGEEEIIIUSdksBUCCGEEEIIIUSdksBUCCGEEEIIIUSdksBUCCGEEEIIIUSdksBUCCGEEEIIIUSdksC0FuXl5ZSXl/vu/sfouk5+fj5KKd8kD6UUubm5vruFEEL8DRwOB3l5eWRnZ3u23NzcGtfpoqIidu/ezeHDh73216ayspK0tDR27drF8ePHfZP/dtnZ2aSmppKVleWbVKujR4+SmppKfn6+b5I4w+Xk5JCamsqBAwewWCxeaeXl5ezbt489e/ZQUlLilVabwsJCdu/ezf79+7Farb7JfzmlFAUFBV6/vezsbE6cOEF+fv4fvoeysjL27t3Lnj17pOz+hTIyMk75eqCU4tChQ+zZs4fS0lLfZAAqKirYu3cvBw8exOFw+CYDkJeXR2pqKhkZGb5JXo4fP37SclFcXIzNZvPdjdVqPel7Ky4uxm63++7G6XRSWFiIruu+SeJP0pTvXfU853A4eOedd1BK8eijj2Iw/POx+w8//EBFRQU33njjSV/f6XTy8ccfk5SURP/+/X2ThRDib6frOg8//DBTp07l0KFDJCYm+mY5Zxw8eJD33nsPs9mMpmkopYiMjOTRRx/F398fgK1bt/LFF1+Qn5+PyWSiR48e3HDDDQQHB/uejtLSUj799FP27t1LWVkZQUFBXHbZZQwaNAij0eib/S/lcDhYunQpCxcupLy8nKCgIAYNGnTS166oqGDu3LmsXbsWq9VKZGQk119/PT169PDNes5wOBz0798fu93OqlWr8PPz881yVrDb7SxevJgVK1ZQVFSE0Wjk2muv5dJLL0Upxe7du5k7d66nIh8bG8uwYcNo3bq176lwOp2sW7eOn376iby8PJxOJ82aNeO22277W3/7DoeD6dOnc/DgQa/yqWkaFRUV3HvvvbRp08brmCrbt29n7ty5ZGdno+s6LVu25NFHH/XNdsZzOp1cffXVpKens3379lp/p/8Uu93O/PnzWbFiBWVlZURERDB06FC6dOnimxXc17ovv/ySbdu2UVlZSXx8PHfccQfNmjXz5ElPT2f27NkcO3YMpRTt2rXjtttuIywszJNnw4YNfP311xQXF2M2mxk4cCBXXXWVVz25qKiI9evX8+233zJmzBgaN27sSasydepU+vfvT/Pmzb3279ixgx07djBs2DCv/QDvvfceV199NQ0bNvTaX1xczEcffcQjjzxSp9/JuaT2qOc8lpaWxgsvvMBLL73EwYMHfZP/docOHWLixImYzeaTBqUARqMRg8HA+PHjycvL800WQgjxF1FKsWvXLqZOnUpZWRl2u92zVcnKyuKxxx5j8+bNdOzYkYiICJ5//nm++uorr3NVKSoqYtu2bTRu3JguXbpQWFjIyJEj2bJli2/W32WxWE6rh49Sis2bN/PII49QVFRE586dKS8v55FHHmHjxo2+2VFKMX/+fJ555hnMZjOdOnViz549PPTQQ6Snp/tmF2cQpRQ//PADzzzzDKWlpXTu3JnGjRtTVlbmSf/mm2/YunUrLVu2pE2bNvz666889dRTtbacWiwWZs2aRUZGBh07dqRx48Z88sknTJgwodbWpJNxOBwUFxfX6G3wexwOB3a7HZvNhs1mw+FwsGHDBqZNm4bT6fTNDsDevXt58skn2bx5M61bt6Zdu3ZUVlaeNL/4Y0opli5dytNPP42fnx+dO3cmNTWVhx9+mOzsbN/s6LrOp59+yptvvkn9+vVp27YtCxcu5JlnnvG03JeWlvLiiy+yYMECWrduTUJCAm+88QbTp08H92sePnyYxx57jGPHjtG5c2ecTiePPfYYa9eu9eRZv349L7/8MmPHjmXq1KlePQOcTicHDhwgKyuLRYsWkZaWRlpaGkVFRRQWFrJv3z7279/P4sWLycvL4+DBgzidTvbv38/x48dZsGABhw8f5uDBg5SUlHhad7Oyspg/fz7Z2dns37+/1pZYcZqU8PLKK6+obt26qZ49e6rx48f7JnuUl5er/Px8VVlZqZRSymazKV3XfbOpoqIiVVhY6Lu7Vk6nUz399NNq2LBhSimlKisraz2nruuqsrJS2e12dfnll6s33njDN4sQQvztnE6nGjlypDKbzSojI8M3+Zyh67qaMWOG6tmzp3I6nb7JStd19e2336rAwEB1/Phxz/4RI0ao7t27K4fD4ZVfKaUsFouyWCyev5eXl6ukpCQ1duxYr3y/R9d19fPPP6svvvjCN+mknE6nevLJJ1WbNm0878tut6sePXqoESNG+GZXNptNDRw4UA0dOtSz7/jx4yoxMVFNmTLFK++5xG63qz59+qgePXoou93um3xWyMzMVB06dFAvv/yyb5JS7vKzdOlSVVpa6tm3a9cuFR0drZYuXeqVV7nL6JIlS7zqJXPnzlVRUVEqPT3dK+/vOXbsmHrjjTe8yv+fcfvtt6sHH3zQd7dSSimr1aruuecedemll6ri4mLf5LOOw+FQl19+udfv9q9WVFSkcnJyfHd7KS0tVQMHDlT33XefZ196erpq1KiRmjRpklde5S6DKSkp6r333vPsW7NmjYqMjPSUsZ9//lnVr19f7dixw5Nn4sSJqkmTJio/P185nU41btw41blzZ1VeXq6U+/O44oor1HXXXef5++jRo9Ull1yiXnrpJRUWFqb27dvnOZ/FYlGvv/66mjRpkhowYIB6+umn1fjx49WOHTvUpk2b1JgxYzzHv/fee+rdd99VFRUV6tVXX1XvvPOOuvDCC9Xzzz+vXnjhBbV371517NgxNXbsWDVhwgTVq1cv9fbbb6uXX35ZFRQUeF5T/Dknb5I7D5WWlvLVV19x3333ceutt/Lll196nixWt3btWt5++23efvttPvroIzZu3Mhnn31GYWGhJ09RURFz5szhrbfe4s033+Szzz6r9QlkdXl5efz444/ce++9AKxatYqvv/7aNxsLFizg119/xc/Pj7vvvpvPP//8pH3phRBC/G+qxrhFRESctCeLruuEhoYSGhrq2RcbG4vZbK51/FFAQAABAQGev/v7+xMdHU1ISIhXvt+jaRobNmzgp59+8k36Q5GRkZ6uZwaDgdjY2Fr/bUopjEYj0dHRnn3BwcFER0ejaZpXXnHm0HWdxYsX43Q6eeqpp1BK1SiHmqZx8cUXe5W55s2bExER4VWfqRIUFMSAAQO8vveOHTuCu7v3qcrPz2fmzJk13s/pWL58OWvXruWhhx7yTUIpxZEjR1i8eDETJkwgLCxMWkl/h8PhYOPGjbz11lts2LDBN9lDKUVGRgY7d+7knnvu8exv1KgRl156KYsXL/bKr+s6W7ZswWq1cvPNN3v29+jRg+bNm7Ns2TKUUvz888+0bduWdu3aefJcd9112Gw2tm7dilKK77//nmHDhhEUFATuXoO33347a9eupaysDIPBwNChQ5kzZw7//ve/a5Qtk8lE9+7dOXz4MHv27GHlypWkpKQQHx9PQkICycnJLFmyhH379pGZmUmPHj0wm8306NGDPXv2cODAAVasWEG7du2oV68e4eHhdO7cmeXLl3PkyBG2bNlC7969Pe9P/Hk170LnsXXr1pGXl8fll1/OFVdcwYkTJ1izZo1XnpUrV/LII4+wefNmiouL2bJlC88//zz/+c9/PJMRWa1WJk+ezOTJkzlx4gQ5OTm8+eabTJs2zetc1VV1FXM6nfTs2ROlFPv37+fJJ5+koKDAk6+oqIinn36aPXv2ANC7d2+sVis7d+6sdjYhhBB/paqJj77++mvmz5/Prl27PF0RNU2jd+/edO/enfHjx7N69Wq+++47Vq1axfDhwzGZTL6nA6CkpIS9e/eyY8cOvvnmG2JiYrjmmmt8s9VQNUlH1cNOo9FIRUUFpaWlNSa28aVpGjfeeCOapvHee++xZs0aPvroI0pKSrjjjjt8s2Mymbj77rvZuXMnc+bMYfXq1bz11lskJiZyxRVX+GYXZ4iqLpd9+vRh69atTJkyhffff58VK1b8brfbNWvW4HQ6adGihW9SrRYtWkRCQgL16tXzTfLicDgoLS31lNHAwEDP38vKyk6rW29VHWvQoEEkJyf7JqOUYtu2bZjNZsLCwpg5cybvvvsu3333nVd9Sri6O0+bNo3XX3+d/fv3k5CQ4JvFy7Fjx7Db7V7lw+l00rx5c3JycqisrPTKf+DAAcLDw4mMjPTsU0qRnJzsmSBu9+7ddOjQodpREBMTQ3h4OOnp6VRWVnL06FE6derkSVdK0ahRIzRN49ixY2iaRseOHYmOjq51AiOj0UiXLl1QSvH444/TvHlzEhMTiYmJIT4+nsTERNq2bcsjjzyC0+mkY8eOXseMHj2aRo0a0bhxY6KioggNDaVt27ZERkbyzDPPYDQa6dChg2e+AfHnSWDqpus6n3/+OX369CE+Pp6mTZvSt29fPv30U08eq9XK888/T+PGjfn000+ZNGkS7733Hg0bNqSkpMTztHnjxo18+OGHvPTSS0yZMoUPPviABx98kLfffrvWPvhVUlNTadGiBSaTCU3TuOKKK9B1nZ9//hncP8R169ZRVFTEDTfcAO4fb3R0NLt27fI5mxBCiL9KQkIC0dHRfP3110yZMoXRo0czd+5cT3p8fDwDBw5kxowZTJkyhddff53y8nIuvfRSr/NUUUqxd+9e3n77bcaPH8+zzz7LZZddRkpKim9WL0opfv31Vz788ENmzJjB2rVr2b17Nx999BFTp05l6dKlvod40TSNzp0707x5c15//XWmTp3K66+/TtOmTencubNvdjRNY8CAARgMBl577TU+/PBDPvzwQ/r27UuDBg18s4szhFKKtLQ0CgoK+Oijj1i7di2LFi3iySef5LvvvvPNDsCuXbuYOHEiAwcOPOlkQtWtXLmSadOmcdttt3m1qPtS7hbMqVOn8tFHH/HVV1+RnZ3N9OnT+eijj5g9e/Ypt7gqpdixYwcbN25k+PDhvskeVUHPjBkzWLp0KatXr2bixIm88cYb530PM6UUWVlZzJs3j0mTJrFkyRIGDRrEjBkzvII/X0op8vLyCAwMJDAw0LNf0zTCw8Ox2Wxen61Sivz8fK9eJFWqAsiqPDExMV7pgYGBmEwmKioqKCkpQdO0GucJCAjAbDZ79UZUSqFqecihlCInJ4fAwEAeffRRrrnmGnbv3g3u+v/+/fu59dZbeeCBB8Ddqo977oDo6GgefvhhBg8eTGpqqueYffv2ccEFFzBixAiSk5PZv39/tVcUf5YEpm7Z2dksXLiQvn37cuzYMdLT0+nTpw+LFi3yTKWfnp7Ozp07eeKJJzw/Sn9/f+69916CgoI8P4gFCxZgNBqx2WzMnTuX7777jsrKSvLy8tixY4fPK7sodxeJRo0aefY1bNiQSy+91NOdVynF559/zsCBAz1PtQwGAyEhITIBkhBC/E0MBgM33HAD7777Li+88AKPP/44MTExPP74455J8lauXMnChQt56qmnuP3223nsscdo2rQpb7755kknxAgNDaVly5Z07NiRiy66iBUrVrBs2TLfbDUUFhaSmZlJRkYGJSUllJWVkZGRQWZm5h+2CFVNRpKVlcVzzz3HsGHDePrpp8nNzWXmzJm+2bFYLLz77rvExMTwn//8h9tuu42nnnqKX3/9lSVLlvhmF2eQkpISUlNTuf766/nkk0/45JNP6Ny5M8899xxFRUWefE6nkyVLljBu3DgCAgJ4/vnna+3WXaW8vJzPP/+cF154gR49ejBy5EjfLDVYrVZPGc3JycFut5OVlUVGRoZnxtxToZTik08+oWPHjr8bPJeWlnLixAkSEhL44IMP+PLLL7n//vt5//33a/SEO98UFBQwceJEHnjgAWJjY5k+fTp33XWX19CCk3E6nSctG5qm1ejer+t6jX1Vqs5zsjyapmEwGH63bFTl+SOaptGoUSNeeeUVNE3jqquu4s477wT3+3jggQe45JJLMJvNvPzyy8TFxYG7a/uLL74IwJAhQxg6dKjnmMGDBzNixAgAxowZc9JZicXp+eNv8zxRNf35ypUrefXVV5kwYQLr1q2jsLCQBQsWgHvNppCQEGJjY72ONZlM+Pv7e57SZGZmYrfbmTx5MlOnTmXatGl8++239OnT53f7n9tsNq8p6ZVS3Hbbbaxfv57jx4+Tm5vLmjVranS3MplMJ13zSQghxP8uPj6eli1b0qJFCy6++GLefvttT48WpRSvv/46iYmJPProo1x66aVcf/31PPnkk3z88ce1PpDUNI1WrVrxyCOP8PTTT/Pmm2/i5+fHs88+W+sT/yqapjF06FDeeustXn/9da6++mouuOACJk6cyJtvvsntt9/ue4iXkpISXnjhBa6//nruvPNOBg4cyB133MF1113Ha6+95jW2ULnXHnznnXd47LHHGDJkCJdccgkjR46kZcuWvPbaa7/bLVTULZPJRO/evbn44osBiIiI4OGHHyY3N9czHMhqtTJz5kzGjx9Pw4YNmTp16u925ywsLOSdd95h8uTJDBw4kLfffvt36zW4y2zbtm09ZXbUqFE0btzY04L53HPP1WgNO5nc3FwWLFjwh+W8qhvviBEjPO/v9ttvJykp6ZQe/pzLqmY3TkpKIjIyktTU1FrHFNcmMjKS8vLyGl12i4qKCA4OrhHchoeH19q1Nj8/3zNOPSwsrMYDtYqKChwOB+Hh4QQHB6Preo3Zxy0WC3a7/Xdb66vzDWCr17erB8bV8/3eMb7Lw/j+Xfw5Epi6A8I5c+YwcOBAmjVrRmhoKGFhYSQnJ9O/f38++eQTHA4HISEhWCyWGj+ygoICLBaLp2CHhYXRtGlTZsyYwQcffMCUKVP46KOPmDNnDl27dvU6toqmaURERHg9xdQ0jW7dutGwYUMWLlzI0qVLqV+/Pj179vQ6try8vNZ18oQQQvw9QkJCiImJoby8HKUUqampNbrCNm/enKCgIPbt2+e1vzbBwcHccMMNpKamnlawZzAYalSefk9JSQlHjx6tsQZphw4dKCoqqjHc5Pjx41RUVNTo4tepUyfS09NPuQum+GdpmkaLFi1qtEQZDAb8/f09rVBz585l8uTJ3Hnnnbz11ls1ulRWZ7fbmTp1Kt999x3PPPMMo0eP/lPru1Ytd3c65Rb3g5IVK1ag6zoXXXSRb7KXpKQk/Pz8vB7yKKW8GhHOV/Xr1+f9999n/PjxHDhwgLfeeotJkyaxbNkyiouLfbN7VLU6aprGgQMHPPsNBgN79uyhWbNmmM1mr/zNmzenoKCgRgv9wYMHPS3eLVu2rDFPSnZ2NhUVFaSkpBAcHExcXBzbt2/3pGuaxsGDBwkODpYhBeeY07sqnKO2bt3K+vXreeGFFxg/fjyvvvoqr776Ki+++CLPPvssmzdvZvPmzTRv3pzQ0FA+//xzz5ObnJwcvvnmG68Wy4suuojDhw+TnZ3tGSxd9dTI92lSdc2bN6/RRz0oKIgbbriBL7/8klmzZjFkyBCviTQsFgvFxcW1LiIshBDif6frOhkZGZ6JhaxWK/Pnz+fEiROeB4WdOnVi4cKF5OTkgPuB548//uipXCmlOHjwoCc9MzOTzMxMz2tkZ2ezYMECunbt6lW5q43dbsdisVBRUUFSUhIdO3bEarVisVhO2m24StVD1y+++MIz63xJSQlz584lOjqauLg4ioqK2LdvHw6Hg8TEREJDQ/nyyy895z5+/Djz588nOTn5D1vLRN3Q3PNULF++3BNEWCwW5s2bR0REBK1ataKkpITx48dz2WWXceedd1JcXExRURFFRUWesn7ixAlPd/X09HQmTpzIQw89xCWXXOKV/4/KndPpxGKxYLFYMJvNDBgwAJvNhsViwWq1nlKwqOs6CxcupGXLljUmW1LucayZmZkYDAa6deuG0Wjkq6++8gThq1atIisriwsuuMDr2PNV3759ee+99xg+fDhZWVm8+eabbNq0yTebh6ZpNGzYkLZt2zJjxgxPGdm+fTsbNmzg2muvBff1IS0tDc09nj0gIIA5c+Z4voelS5dSVFTEwIEDAbj00kvZvn27Zw3niooKvvzySxITE2nXrp2nLM+ZM8dz/czOzubzzz/nyiuvrPV66XA4TqlMiTOPcdy4ceN8d55PnE4nkydPpqysrNanf1FRUSxcuJCSkhLPj27GjBlUVFSQnZ3NL7/8ws6dOykoKOD2228nJiaGxMRENmzYwPfff095eTkHDhzgp59+Yu/evVxwwQU1nmBSrZ/8jBkzuP766726tSQkJDB58mSys7N54403vNJSU1P58ssveeKJJwgPD/fsF0KIv5tSip9++onNmzfz8MMPExYW5pvlnGCz2ZgyZQobN24kIyODZcuWMX36dPr378+///1vDAYD8fHxfP7552RlZZGfn8+KFSv46KOPuOSSS7jzzjuxWq088cQTlJaW0q1bNxYsWMDs2bPJzc1l165dzJs3j/Xr1zNu3DiaNm3q+xY8lFIsX76cRYsWsW3bNkpKStB1nc2bN7N161aKiopo3ry572Ee/v7+BAQEMHPmTCoqKsjMzGThwoV89913jBgxgr59+/LTTz8xYcIEBg0aRHx8PEVFRXzxxRdYrVYOHz7MN998w6ZNmxgzZswfTtZ0ttJ13bOkyV133XXarXt1TdM0EhMTWblyJatWrcJqtbJixQq++OILRowYQZ8+fThy5Ahvv/02zZs3Z8eOHaxfv56NGzeyfv16nE4nzZo1Y+bMmXz22WdcddVVbNy4kU8//ZRWrVqxbt06NmzYwMaNG9mwYQNhYWEn7QJcFTR+8cUXbN26lbS0NEJCQti2bRtbt25l3759pKSknHT26iqVlZWMHz+eK664gr59+3ql2e12nn32WdLS0rjwwguJjIyksLCQ2bNn43A42LVrF9OmTaNNmzY8+OCDNep6ZzqlFHPmzKG4uJj777//Ly2PzZo146qrrvL0FmzYsKFvFo+AgAACAwP573//i8Vi4fDhw8ycOZOEhAQef/xxDAYDH3zwAd988w3XXHMNkZGRlJaWMnv2bJxOJ7t27eLjjz9mwIAB3HzzzWiaRnx8PFu3bmXx4sXY7XZWrVrFvHnzePLJJ2nbtq2nLH/99dekp6dTVFTEvHnzOHToEC+++KLXjL+4u5tPnz6de++995S7+Yozx3kfmOq6zt69e7nkkks863FVZzabiYiIAPfaS926dcNkMrFu3Tr27t1LXFwc11xzDd9//z3Dhw8nPDycgIAAevfuTWZmJps2bWLfvn0YDAb69+9PUlKS70t4hIaGsmzZMiorK726WUVERPDZZ5/RrVs3brvtNq9jPvzwQwD+/e9/e+0XQoi/2/kSmGqaRmpqKmvWrGHHjh3k5+czcOBARo0aRUBAAJqm0bhxY1q0aMHWrVvZuHEjx48fZ8CAAZ48lZWV/PrrrzRq1IjOnTtjsVjYtGmTp2IeEBDAQw89RP/+/X1fvoY1a9awZcsWCgoKyM/PJz8/n5ycHHJzcwkJCanRpbg6TdPo1KkToaGhrF+/nq1bt1JWVsawYcO4++67Ue7ZXA8cOMBll11GaGgoPXv2xG63s379erZv346fnx8PPvgggwcP9j39OeNsD0xx97jq2rUru3fvZsOGDeTl5XHTTTdx9913g7vlX9d1rFYrxcXFlJSUeLb4+Hjatm3rKe+DBg2isrISo9HoWaqoaistLaVFixY0adLE9y14HDt2jJ9++on8/Hzy8vLIz88nNzeX3NxcysvL6d279x8utWG328nOzuaaa64hPj6+RtqqVauIiIigd+/enlbT0tJSVq9ezYEDB+jYsSPPPPPMWXmd+jsD0yopKSm/G5RWadOmDcHBwaxatYq9e/eSlJTEM888Q2RkJEopNm/eTFlZGZdddhmaptGlSxcqKipYvXo1hw8f5oILLuDxxx/3PIjw9/enW7du7N+/n/Xr11NUVMSwYcM8q09omkZsbCwpKSmea5bZbGbUqFG0bdvW671pmoau69hsNi6++GIZ5nYW0tR53tat3DPp/tGPXHfPGlbV2mm323E4HJ4nR+PHjyc1NbVGV92SkhKUUqfcmvndd9/x9ttvM2vWLE/33EOHDjF48GCmTp1Kv379wP2+U1NTueuuu3jjjTdqPD0UQoi/m67rPPzww0ydOpVDhw6RmJjom+WcUl5ejs1m87QanExRUREmk6lGpUjX9Rr3mqoxXad6j8B9HqWU535U9eeqe1ltvXJqY7PZPHMU+HaHq+29VlRUUFlZSXh4eI20c43D4aB///6egOdsa2HzVVRUhL+/f63l1rcaWFWeqsqRb1moLf8flYequlb1v1cvp390PLUc48v3fVYpKytD1/WzMiCt4nQ6ufrqq0lPT2f79u1nxEQ7VZMP1fa51vZdVQ0dCAkJ8dpfXVFREYGBgSd9SOF0OiktLSU8PLzG+cW5oeYv+DyjneJU01V5li1bxqpVq0hPT+fYsWMsXryYSZMmcdttt9UISnGP5zmdCsdVV11Fx44dWbJkCfn5+Wzfvp3XX3+d5ORkevfu7cmn6zoLFizg0ksvlaBUCCH+AcHBwURGRtZaua8uIiKiRlDKSSrf4eHhp3WPwH2e6hPIVP3ZaDSeVmXNbDYTGRlZIyjlJO81KCiIyMjIWtPEmS0iIuKk5bYqCK3afB9u+H7fteX/I1X5fMts1XYq/qhsn+w8ISEhtQZP4n8TGBh40s+1tu8qJCTkd4NS3OX0ZEEp7omzIiIiaj2/ODfU/isWJ7V69WpeffVVXn/9dV577TXeeOMNOnXqxEMPPeSb9U+pWi6gXbt2bNy4kQkTJrBnzx6ee+65GpWH3r1789RTT3ntE0IIIYQQQoizjQSmp0HTNP7v//6Pm266iZSUFNq2bcuIESP44IMPiIqK8s3+p0VHR9O9e3fi4uLo3r07b775Zo2p/Y1GI3369Kn1qbwQQgghhBBCnE3O+zGmQggh/pzzbYypOH+ca2NMxdntTBxjKsTfQVpMhRBCCCGEEELUKQlMhRBCCCGEEELUKQlMhRBCCCGEOAvICDxxLpPAVAghxJ9iMBg8Y+9OthSFEGcjPz8/z1ImMr5U1DWj0ehZYkfKoziXnZWTH+Xm5pKfny/rGAnxN6msrMThcBAUFCS/M3FSSileeeUV5syZw4oVK4iKipKn+eKc4HQ6ueOOO3A4HHz55Zfouu6bRYh/jNPpZOTIkWRmZjJ//nwpj+cgTdNo3Ljx767jej446wJTpRTTpk1j9uzZJ11MWQjx5xkMBioqKnA4HAQHB2MwGCTYELUyGAzs27eP48eP06NHDwIDA6XCJM4ZW7duRdd1OnfuLNdBUaeUUuzatQur1UrXrl2lPJ6DzGYz77//PklJSb5J5xXjuHHjxvnuPNNt3ryZnTt3+u4WQvwFNE1j7969HDx4kPj4ePz8/OQGKE6qoKCA0tJSEhMTMRqNUlbEOSM7OxulFHFxcb5JQvyjlFLk5OTgcDhISEjwTRbnAKPRyJVXXklUVJRv0nnlrGsxBbBarVRWVkoXQyH+Bpqm8eCDD/L999+zefNm6tWrJ8GGqJWmaTz55JPMmDGDXbt2kZCQIC2m4pzgdDq54oorsNvt/Pzzz9JDS9Qpp9PJzTffzNGjR1mzZo2Ux3NUcHDweb9G7VkZmAoh/l73338/X375JWlpaURERPgmC+HxyCOPMGXKFDIyMqhXr55vshBnJaUUF110EXa7nbVr1/omC/GPu/rqqzly5Ag7duzwTRLinCGPXIQQXnRdx+l0opTCZrP5JgvhUVVWACkr4pxSdQ1USuFwOHyThfhHOZ1OnE4nuq5LeRTnNAlMhRBCCCGEOAvIMDZxLpPAVAghhBBCCCFEnZLAVAghhBBCCCFEnZLAVAghhBBCCCFEnZLAVAhx2qxWK2VlZb67a7BYLFitVt/dJ5WTk8Py5ctleRrxj8jOzubQoUMUFRX5JnlkZGSQnp7+hxOO5ObmnnSpnLy8PA4dOkRxcbFv0u+qrKykoqLC8/e8vDzS0tLIzs72yud0OikuLqaoqMhrKykpoays7KTvq7q8vDzfXSdlsVg4fPgwBQUFvkkAlJSUeH7DVqsVi8Xim0X8jcrLyzl8+DAnTpzwTaqhsrLytL4fp9NJaWmp725wv27VZGhVLBZLjbJpt9u98tSmoKCAQ4cOkZWV9Ye/PYDS0lIOHz5MSUmJbxJKKa/3XF5eLpO1CXGGkuVihBBedF1n+PDhfP311+zdu5fY2FivdKUU3333Hbt27eKee+4hPj7eK71KcXEx77//PklJSQwZMsQ3uQalFIsXL2bUqFFs3rwZPz8/3yziDKPrOg8//DBTp07l0KFDJCYm+mY5IxUUFLBw4UJSU1MpKysjLCyMyy+/nN69e3vl+f7779m5cye6rtOsWTNuuOGGGovbFxUVsXr1atauXcvYsWPx9/f3pDmdTn755Rd+/fVXSktLCQ8P5+qrr6Zr165e56iNxWJh9uzZXHjhhTRv3pxFixaxdu1aCgsLMZlM9OzZkyuvvJLAwEByc3OZMWOG1/reVf83mUyMHDmS4OBgn1dwyc7OZsmSJWRnZ/PEE0/4JteQlZXFN998Q35+PmazmWuuuYY2bdp40tesWcOxY8e46aabUEpx5MgRFi1axLBhwwgJCfE615nM4XDQv39/7HY7q1atOmuuR7t27eK7774jLy8Pf39/LrzwQgYNGoTJZPLNit1u58MPPyQuLo4bb7zRN7kGp9PJV199RaNGjbx+KwB79+7lyy+/ZNiwYTRr1gzc5581axZHjx71+vyuu+46rzJTncPhYOnSpaxevZri4mL8/f258cYb6datm29Wj0OHDjF37lxKSkoIDg7mhhtuoGnTpp70n376ybMurVKKXbt2sWHDBm6//fZaP5czkdPp5OqrryY9PZ3t27ef92tdinOXtJgKIU6Lrut8/vnnPPfcc3z++ee+yR4//fQTY8aM4YsvvvBNOqmKigqOHj3qu1uIv4xSivXr1zNr1ix0XScxMZFdu3bx4IMPsnv3bnAvfTNt2jReffVVzGYzYWFhTJ8+nZdeesnT0uJwOPj111+ZMGECo0aNYubMmT6vBEuWLGHUqFHk5+eTmJjIzp07efjhh0lLS/PNWsO8efOYM2cOwcHB2O12li1bRmlpKQ0aNKCkpITRo0fz/fffgzsAyMzMJCMjw7NlZmYybdo0vvjii1p7IFgsFn744QdefPFFRo8ezfz5832z1OBwOHj//ff59ddfadq0KZmZmTz//POUl5eDu9X11Vdf9bRaaZqGv78/8+bN47PPPvM5m/irZWZm8uijj7J8+XLq169PSUkJTzzxBD///LNvVgBWr17NI4888rvX8erWrVvH5MmTCQwM9Npvt9t5/fXXee6557zKdnl5Oe+88w7Lly8nKyvLUzar9wKoruqh59ixYzlx4gSJiYlomkZubq5vVg+r1crEiRPZtWsXTZs2ZceOHUyYMMHTKnvkyBEmTpzo1erq7+/PrFmzWLBgQbUzCSHOCEoIIapxOp3qnnvuUZGRkerEiRO+ycrhcKihQ4equLg41bVrV1VUVOSbRZWWlqpBgwapqKgoNXToUN9kVVlZqSoqKpSu6559uq6r77//XsXHxyu73e6V3+FwqIqKCuV0Or32i7rldDrVyJEjldlsVhkZGb7JZyRd19Xq1avVli1bPPvy8vJUp06d1OjRo5VSSmVnZ6uGDRuq1157zZNn/vz5ymQyqX379imllCouLlY33XSTuvLKK9Xdd9+tmjVrpiorKz35y8rKVP/+/dU999zj2ZeTk6O6du2qnnrqKc++2uTn56vevXurr776SimllM1mU9nZ2Z50u92urr/+enXxxRfX+A1VbcePH1ddunRRn3zyiSe9uvT0dDVgwAA1dOhQddVVV6kBAwb4ZqkhNzdXde7cWW3atEkp92fQunVrtW3bNqWUUhMmTFC33nqr1+eglFILFy5UnTt39vo3nOnsdrvq06eP6tGjR43r0ZnI6XSq2bNnq5CQEHX06FGl3NfN66+/Xg0aNMg3uyooKFD/+te/VEJCQq3XaF8VFRXq+uuvVy+99JJvklq8eLFKSkpSsbGxatGiRZ79mZmZqn379uqHH35Qqlr5PJm0tDTVpk0b9frrr3vtdzgcXn+v7uDBg6pt27bq2LFjSimljhw5olq0aKEyMzOV3W5Xjz76qHr44Yd9D1MzZsxQF154oSotLfVNOiM5HA51+eWXqzZt2vzu5yHE2U5aTIUQp03XdS688ELKy8s9rTbVrVixguzsbC666CKvsTzFxcXMnz+fd999l7fffptp06Zx7Ngxr2N9paam8uGHHzJp0iQ+/PBDDh486JtFiFOmaRq9e/emU6dOnn3R0dF06dKF9PR0cHebU0qRnJzsydOyZUtMJhOVlZXg7iJ777338sknn3DjjTfWGDd34sQJDhw4wK233urZV69ePW6++WaWLVvmOY8vpRRLly5F13WuueYacL9WXFycJ4+fnx+NGzfGbDZ7tYZqmubZPv30U4KDgxk6dKgnvbqQkBDGjBnDxx9/TJ8+fU5pzF1FRQWaphEZGQlAQEAA/v7+aJrG9u3bmTdvHk899RRms9nruP79+xMREcEPP/zgtV/8tcrLy4mMjKRBgwYAGI1GkpOTa3y3uq7z8ccfo+s6V1555R+O4VRKsWfPHnbv3s0999zjlXb8+HHeeOMNbrnlFhITE73GmFqtVhwOh6eLf1XZrI2u63z33XeEhITw+OOPe6WdrNtq1djRgIAATzfx4OBgjEYjBoOB5cuXs3nzZkaPHu17KNdddx2lpaWsWLHCN0kIUYckMBVCnDZd12nYsCG33norU6dO9apkV1ZWMm3aNK688kqaNWvmqRQppdi/fz8ffvghW7ZsYffu3Xz44Yc8//zzNSpOVVJTUxk1ahSLFi1iz549zJ07l//85z+nNKmHEKeqpKSEHTt20KVLF3AHqrfccgtz5sxh7dq1rF+/nnfffZd//etfnvFzgYGBXHzxxURERNQ6EVhVBbywsNBrv5+fH8ePHz9pd0Zd11m4cCH9+/f3Gv9W1V338OHDbNiwgSNHjnDzzTdjMNS8jWdnZzN9+nRGjhx50rGRUVFRXHzxxQQEBHgCzj8SHR1NfHw8c+bMYffu3cyZM4fExERiYmJ47bXXGDJkSK1jB81mMwMHDmTRokW+SeIvomka/fr1o1mzZkyaNImtW7fy008/sWXLFu666y6vvCtWrGD27NmMGTOGmJiYGhMW+ap6WNK2bVvq1avn2W+1WnnzzTeJiorinnvuwWazecqRUgqLxUJeXh7r1q3j559/ZsuWLSedaMnpdLJs2TIuueQStm3bxn//+18++eQTdu7c6ZvVQ9M0GjZsSGBgIJ9//jm7d+9m1qxZdOrUCaUUb7/9NiNHjqx1HoSwsDB69+7NwoULfZOEEHWo5h1NCCFOgVKKW265hYyMDFauXOnZv23bNrZu3cpdd91VoxUpMjKSMWPG8Omnn/LJJ58wevRoPvnkE44cOeKVD3eA+9JLL2E2m/nss8+YOXMmM2bMYOfOnXz11Ve+2YX4U4qLi3n33XexWq3ccMMN4B6DdvPNN7N+/XpeeeUV3n77bebMmcOwYcNqTCJU2/hNgLi4ONq1a8f06dPZu3cvJ06c8AQLdrv9pMFAZWUlx44do2PHjl778/Pz+eCDD5gwYQIPPvigJ3iuzddff01QUBBXXnmlb1INpzJjb5Xg4GAeeughtm3bxqxZs1i+fDkjR45k6dKllJWVce+997Jz507Wrl3L8ePHvY7t1asXqampJ30IJf43mqbRvHlzBgwYwCuvvMJ7773neehX1fIOcPToUV566SWGDBlC9+7dsVgsf/hQQinFxo0b6d69u9f+b775huXLlzNu3DgCAgJqlGmj0UizZs1YsmQJU6ZM4ZlnnmHy5Mm1zpzrcDg4fPgweXl5fPTRR8yfP59PP/2Uxx57jNWrV/tm94iOjmbkyJH88ssvzJo1yzOOe/bs2cTGxnLdddexbds21q9f7zXztFKK3r17s3nzZq/zCSHqlgSmQog/xeFw0LRpUy655BKmTp2KUgpd15k6dSq9e/f2ai3FXXFKTk6mcePG/PLLLyxYsIDs7Gz8/f3JysryqhwZjUaOHz/OokWL6NatG2vWrGHhwoXs2rWLuLg4Fi9e7MkrxJ914MABXn/9debNm8eLL75IkyZNwN3KOX36dC6++GKGDBnCVVddxdChQ5kzZw4ZGRm+p6lVUFAQo0aNIisri2effZbXXnuN9957D9wPaE42G2hZWRnl5eU0bNjQa7/BYCAkJITo6GhSUlIoKytj7ty5NQLLsrIyZs2axS233FJjkpq/wiWXXMIrr7xCr169GDNmDC1btmTGjBmMHj2ahQsXMmXKFObMmcMbb7zh9cApLi6OkpKS01o+Spw6pRSrV69m48aNjBw5kksvvZQ77riDmJgYpk+fjlKK/Px8XnvtNRITEz3dW6u6vf4epRS5ubme7uRKKdasWcMHH3zAM888Q4sWLXA6nWia5mmh1zSNBg0aMGnSJJ566ikeeughunfvzhtvvFHrg0Wn00lhYSHbtm3jmmuu4ZNPPuHdd9/FYDDw3HPP1XjIWd2QIUN49tln6dWrFy+++CIBAQHMmzePJ598kk8++YRp06bx6aef8tZbb3mCU03TiI+PJz8/3/d0Qog69PtXIyGEOImqlqLhw4ezbt069uzZw+HDh1myZAkjRoxAKeXVmqSUYsWKFYwZM4YpU6bw2WefsX79ekwmU43KtaZpZGVloes6P//8M5MmTeK9995j8uTJAJ7ulEL8WZs3b+bZZ59ly5YtvPrqq1x11VXgLqc///wz8+bN4+WXX2bYsGHccsstvPzyy+zbt48ZM2b4nuqk+vXrx9SpU0lKSsLpdNKvXz+uuOIKYmNjTxo0Op1OnE5njXGasbGxjB49mpdeeomPPvqI1q1b88gjj3itaaqUYsOGDRw/ftzT+vt3SE5O5pprrqFp06ZMmDCBCy+8kEaNGnkC1ttvv50DBw4wdepUzzEGgwGj0fi7AYb48xwOBy+//DLx8fGMHTuWIUOGMHz4cO69917eeOMN0tLS+O9//8v06dPp1asXixcv5scff+TAgQNkZmaydOnSk65PirtlvephSkZGBg8++CAOh4PQ0FAWLFjAwoULKSsrY/Xq1Wzfvh3c45i7d+9O165d6devH+PGjePqq69m5syZNXoaaJqG0Wjk4osv5pJLLiEwMJDmzZszevRoduzY8YdzEbRt25ZrrrmGiIgIXnrpJW655RacTieTJ09m0KBB3HLLLaxatYo5c+Z4jqkau+p7/xFC1B0JTIUQf0pVC2fHjh3p0qUL06ZNY+bMmbRs2ZJevXqhlEKrNtlFSUkJY8aMoaysjCeeeIJXX32VZ5555qRj4IKDgzGZTPz73//mpZdeYvz48YwfP56pU6fWOpmFEKcqLS2Np556isDAQKZPn06/fv08aUopNm3aRJMmTahfv75nf1BQEB07dmTt2rWefVV+rytkly5deOWVV3jrrbcYNmwYq1evpmPHjjUCzyr+/v6YTKZauztWMZvN3HvvvWRlZXl1mVXu5TbatGlD48aNvY75O3z//fccPHiQRx99lGXLllG/fn1uu+02unbtyvDhw1mzZo1nYp3KykqUUl7rvIq/jsPhYOvWrfTv399rf5cuXdA0jV27dnHw4EG6dOnCr7/+ygcffMCMGTPYtWsXR44cYdasWTXGQ1dnNps946JPnDiBUor4+HimTJnC9OnT+eKLLygqKmLRokUsW7bM93CP1q1bU1JSUiMYNJlMNG3alKCgIK/9ISEhmM3mk04W5uuTTz7Bbrdz1113MX/+fLp06cLVV1/teWBS/b1ZLBYMBsMfthgLIf458msUQpw2Xdc9FQuj0cjw4cP54osv+Pzzz7nvvvu8nkRXjTsqKCggNTWVIUOG0KNHDxo0aMDWrVspLCz0mjDD6XSi6zqNGzemfv36HDlyhLZt29KpUyc6depEdHQ0YWFh1d6NEKdO13U++OAD8vLymDRpEjExMVgsFiwWC5WVlWiaRsuWLTl06JBnXVPc6yFu3LiRdu3aeZ0Pd7n1rWjjnsW2qvzb7XYWLlzItm3bGDJkiG9Wj+DgYMLCwjh06JBnX0lJCZmZmZ6/W61WFi1aRP369YmNjfXsLy8vZ/HixQwePNizr4rD4eDo0aO1TrpU/fd8qo4dO8YHH3zAyJEjiYiIwOFwYLfbPS1heXl5nplScX9+9erVIyAgoNpZxF/Fz8+PlJQUfvrpJ89kXLqus3TpUmw2G02aNGH06NG89957PProo4wZM4bRo0fTvXt3UlJSePzxx70mNqpOc08yVNU1OyUlhU8++YSxY8cyatQoT1fdyMhIbr75Zs+Y1pKSEq8HLMeOHWPBggV0794do9GIzWYjPT2dyspK/Pz8GDhwID/++KNn3dLKykoWLFhA/fr1a3Rtr82uXbv47LPPGD16NGazGV3XvQLa/Px8z71DKcXhw4c9MxgLIc4MEpgKIU6bbwvRhRdeSNOmTQkJCeHyyy/3SqvKGxERQefOnZk5cyZff/01H3/8Md9//32NyWQ0TUPXdSIiIhg+fDizZs1i4sSJfP/998yePZs33niDw4cPex0jxKmy2+2sXbuW8PBwZs+ezbvvvsuHH37IBx98wDfffIPdbueyyy6jRYsWPPfcc3zxxRd88803vPzyy2iaxh133OF7SqjlN6GUYv369UybNo158+bx0UcfeWau7dmzp1fe6kwmE61bt2bdunXgPk9aWhovv/wyn376KfPmzWPatGl8+OGHjBgxwrMUh1KKQ4cOkZGRQZ8+fXzO6pqpd9SoUWzYsME3CYPBUOP9/x6LxcKHH35I8+bNufrqq1HuiWTKysqYOXMmS5YsYf78+Vx11VWeHhHLly+nW7duJ+0hIf43fn5+PPTQQ2zYsIFJkyYxb948pk+fzqRJk7juuuto3749TZo0oX379nTr1o2uXbvSvXt3GjZsSL169Wjfvv1Ju5drmkbfvn09ZTI0NJQ2bdrQqVMnz7l69OhBSEgI7du3p2nTpiil2LJlC6+//jrfffcdX3/9NRMmTKCgoIB7770X3GO8n3zySQ4cOIDBYGDo0KE4nU5efvll5s2bx9SpU5k3bx4PPPCA10OO2hQXF/POO+9w8cUX06NHD5RSXHzxxRw8eJBvv/2WxYsXs3LlSq699lrPMStWrKBv375e5xFC1C0JTIUQp0XTNAYMGEDv3r09+wIDA/nPf/7D008/7WkR0TSNCy64gEsvvRTcE76MGzeO0NBQZs2axYYNG7jtttsYMWKEp8tk48aNueWWWzxdq+677z4eeughNm7cyIwZM/jxxx8JCAjwaiUS4nQopbj88stp0qQJ69evZ9u2bZ5t3759OJ1O4uPjmTx5MrGxsXz++ed8+umn6LrOO++8U+tyKE2aNGHo0KE1ugRWVlaydOlSPv74Y1avXs11113H2LFjvfL40jSNa665hs2bN3PixAk0TSMqKgqbzcY333zDxx9/zIYNG7jlllsYPXq012vqus7dd99d6xhsp9NZ6+RDmqbRqVMnrrjiCq/9v6e8vJyKigpGjRoF7nO0aNGCRx55hOXLl/P111/TqVMnbrvtNnD3llixYoVnHK/462maxr/+9S/Gjh1LamoqM2bMYNGiRQwaNIiXX365RtnEXV46derk1ZW9NgaDgX79+pGVlcWOHTt8kz3+9a9/ERUV5fl7QEAAu3fv5r///S+fffYZNpuNt956i65du6KUwmazUVpa6hl33KJFC1577TXy8/OZMWMGa9asYcSIEfzf//1ftVepXWFhIcHBwTz88MPg/jx69OjBXXfdxQ8//MC3337LoEGDuPrqq8Hdert9+/ZTmrlaCPHP0VRVvxsB7rETxcXFGI1GoqOjiYiI8M0ixDlN13WGDx/O119/zd69e2sNAh0OB5p7sorfUzW+rHoricViITc3l3r16hEYGIjdbsfPz8/TUup0OmvMWFpYWEhJSQnBwcHExMR4pYm6o+s6Dz/8MFOnTuXQoUOe1rszncPhwOFw1Oi+ajAYanQ1zczMRNd1GjRocNJWxZOVW9zdB8vLywkPDyc8PNw3uVY2m41hw4bRoUMHRo0a5TlvVlYWdrudqKgoQkNDfQ+r9fdWXXl5OUFBQTX+HU6nE6XUSY/zZbPZsFgstf578vPzqaio8HS9tNvtvPfee/z888988803NT7fM5XD4aB///7Y7XZWrVp1yp/NmaC8vJy8vDyCgoJO2j23yql+906nk6effprc3FwmT55cYyxo1TAM3zGbZWVlFBQUYDQaSUhIqFH2ysvLa/SasdlsZGdnExoaSmRkpFfayVRUVKCUqnEu3PU6p9NJQkICuO9BL7zwApmZmcycObPGezoTOZ1Orr76atLT09m+ffsf3nv/Kbquk56eju5e2/xkY+erS09Px2az0aBBg5O20ttsNoqKirzqH5WVlZSUlNT4voxGIyaTydOqrus6WVlZlJWVERYW5vneq3M6nZSXlxMaGlrjfKJuSWDqVlxczIIFC9i0aROFhYUo98D+J554wusJoBDnulMJTIXgLA5MzwabN2/mhRde4K233qq1BfRsUTWD66OPPsqFF17om3zGOpsD079Leno6Dz30EP/5z3/o1auXb/JZQSnFnj17GDVqFBMmTKi1B8SZ6EwMTAsLC/niiy/Ys2cPDoeDpKQkhg0bdtI6Q1lZGd988w1bt27FZrPRsGFDbrvtthrjfA8dOsSPP/6I0+nkkUceAff3tm3bNr788kuvf7vBYMBut5OSksKdd96JzWbj22+/ZcuWLZSWlmI2m7nooou48sorvYLm4uJiZs2axQMPPFBrbwJRd+TbcD/R/eCDD3j11VcpKSmhSZMmxMTEsGfPnt+dpU6Ic92ZcPMTZy5N0zw3dam4/7W6dOnC448/fk5Umh588MGzKijFXeGtImXbpXHjxrzwwgtnTat3bap6+owZM+asCUpxv+8zqWXP4XAwZcoUPvjgA+Li4khISGDatGm8+eabngnfqnM6ncyePZvXXnuN8PBwGjduzJdffsnzzz/vWe+8pKSEmTNnMm7cOMaNG1djBnS73U5xcbHXduLECd5880127doF7pbz1atXExgYSKtWrSgrK+Oxxx7jl19+AXcL/cGDBzl+/DjfffcdOTk5pKWlyTJWZxBpMXV31WrdujWvvPIKI0aM8OwvLS3FZDJ5XYSVUp79VV0QdF3H4XDU6MLwe927hDhT+baYRkdHI5cJ4auq6/Vjjz3G1KlTOXz4MHFxcVJW/iLVu8pXddE921T/N1R1GT0baJqGw+Fg4MCB2Gw2Vq9ejaZpZ837/7uca2XybPk3VJXH6667jvT0/2fvPqOiutrH738PXRAVEUUUe++99x6NmmJMuZOoMTGaWBJN7DW2BHsFxYa9V+xdsWAFKxaQ3kF6m5mznxcPnL8zaMr9uxPR7M9aLBf72mfPOHNmONfZLQQ/P7/XerNECMHjx4/p3LkzS5Ys4cMPPwRg27ZtjBw5Eh8fH6pXr250TGRkJO3ateOnn35i6NChAJw6dYoPPviAkydP0rx5cx49esTnn39OgwYNCAsLw8nJiU2bNhm1Y+rUqVOMHj2affv2UblyZbKzs8nMzNSm4eXk5NC1a1dcXV3ZvHkzwcHBeHl5YW9vz969e/n0009JT0/n22+/fenUBOmfJxPT3JXhatasyd69e7WJ8S8THh7OiRMnCAsLw9ramhYtWtCuXTuys7PZvXu3tqUFuUnt7t276dixIxUqVDBtSpIKLFVVGTZsGBs3buSnn3566Vw2Scpz4MABfH19GTdunPzDLr01VFVl1apVGAwGvvvuu7ei51p6c6mqyoYNGzAzM+PatWt/uErx3ynvs7Fy5Upu3bqldb6kp6dTq1YtZsyYwcCBA43qHzp0iO+++45Hjx5pz12v19OqVSt69erFtGnTSEhIICAggJYtW/Lzzz8TERHB9u3btXZMpaen8/HHH9OoUSN++eUX07Cmf//+FCtWjNWrV5OUlMSJEyfYsGEDfn5+dOvWjf/85z+0a9dO7rFcQMjENPfk/uyzz4iLi2PKlCm0aNEi34T7uLg4Jk6cSGhoKOXLlyclJYWgoCDmzp1L27ZtGTp0KDExMezevZtChQqxatUqPD092b17t0xMpTeKqqp89913rFq1ipIlS2JhYfGv7ymQXs7MzIykpCQyMjJwcnKS54r0VomPjwfA0dGxQA2jlP6dEhMTKV++PJcvX8bR0dE0/I8xGAz8+OOPBAYGcvjwYaPyTp060bp1a+bMmWNUPmvWLM6ePcuZM2e0mzwGg0GbF7pjxw6tvqqqDB8+nOfPn7Nt2zat/EVCCI4ePcrIkSM5f/680foGmZmZREVFkZOTQ1hYGEuXLuWHH36gc+fOAAQGBjJ16lSaNGnCgwcPmD9/vrypWoDIxDRXQEAAEyZMIDg4mA4dOtC/f39tzzVVVVm+fDnu7u4cOnSIKlWqADBo0CBiYmI4ePAggYGB9O3blxkzZtCqVSv69u3Lzz//zKeffmr6UJJUoOUN5d26dSurV6+mSJEiqCarl0oSuYnp6tWrOXr0KGvXrqV48eLyXJHeCqqqMmnSJAwGA25ubnIor/RaqarK7NmzSU9P5+bNm69czfafoNfrGTRoEAaDga1btxqV9+/fH2dnZ1auXKmV5y1iFBYWxv79+43KR44cSVBQEEePHs1XnpiY+MrENDs7m48//phy5cqxdOlSrVwIwdOnT1m+fDnPnz/Hz8+Pd999V0uUVVXl8OHDBAUFMWrUKKZPn06vXr1o2rTpC61Lr5NMTF+QnJzMhg0b2LVrF9nZ2Xz33Xd8/vnnGAwGunfvjp2dHT/88ANZWVkoisKFCxfYtGkTfn5+ODs7s3btWrZt20a1atVQVRUPDw/Th5CkAi8vMd2zZw+RkZFyeIv0u3766SeWLVtGQkLCax1eJkn/ax06dECn03Hp0iXTkCT949577z0CAwPx9/d/rUPL9Xo9X375Jebm5kZzQPV6PR9//DHOzs6sWLFCKzcYDIwYMYLo6Gj27t1rVD5y5EiCg4Pz9bz+XmIqhND2oPX29qZBgwZGsdDQUHbs2EFWVhZhYWGkpqbyn//8R9tHWafTGa39Yvq79Hq9vjO7ACpatCijRo1i27ZttG3blgkTJuDr64uqqiQmJhIaGsrSpUtZtWoVHh4e+Pn50aJFC+34AQMGEBMTg6enp7bJsyS9qYQQJCUlmRZLkkZVVbKzsyH3xp4kvS30ej0GgwF97p63kvQ6GQwGdDodQojX3nOvKAr29vakpKTkK09MTHzpdjFFixYlNTU133NPS0v7y8OShRDs2rWLqlWrUr9+faOYoiiUL1+esWPHMnXqVFasWEHZsmX56aefSE1NBciXhJr+Lr1eMjF9CVdXVxYuXIiDgwMHDx7EysoKGxsbOnfuzLJly1i4cCELFy7UEtQSJUoAcPnyZYoXL06DBg04d+6cabOS9MaR86okSfo3k9+BkpRf1apVCQsLM5q6kZmZSWhoKHXq1DGqqygKtWvXJigoiIyMDK3cYDDw+PFjGjVqZFT/j2RkZHDo0CHee++9P/x8WllZ8eWXXxIYGChvnr4hZGKaO6H89u3bPH/+HCEEer2ewMBAhBAUK1YMCwsLmjdvjp+fH4UKFaJ69epUr14dBwcHFEXBwsKC8PBwJk+ezIcffsisWbNYtGgR169fN30oSZIkSZIkSXojmZmZ0a5dO+Li4jh27Bjkjp7Zv3+/tmMFQExMDKGhoSiKQosWLcjKyjKaY3rmzBmio6Pp0aOHVpZHzd1u0ZQQgnv37hESEkKXLl1MwyQkJBAeHq79npSUxKFDh6hevbq2hYxUsMnENHd/pcmTJ+Pm5oaHhwerVq1i5syZFCtWjA8++ACAwYMH8/z5cyZOnMjmzZvZtm0b8+bN4+LFi+Tk5LB8+XIsLCz4+uuv6d69O61atWL27NnyDo0kSZIkSZL0VlAUhUaNGtGrVy/c3Nzw8vJi/fr1rF27lsGDB1OmTBkMBgObN2/Gzc0Ng8FAhQoV+Oqrr1i8eDGenp54eXmxePFiPv/8c2rUqGH6EJiZmWn7zb5ICMHly5cpW7Ysrq6u+WJ37txh1qxZbNy4kd27d7Ny5UoOHjzI2LFj5RoIbwiZmAKlS5emadOmBAYG4u3tzenTpylatCjLli3TPjANGzZk6dKl2tj2/fv3k52dTfXq1cnKykJVVWbNmoWtrS0AU6ZMoWzZsiQkJJg8miS9+fR6PVlZWWRmZmo/WVlZ+eaP/JMyMjK4ePEimZmZpiFJkiRJkv5HLCws+OWXX2jWrBn79+/n9OnTvP/++y9dX0VVVSwsLPjpp5/o3bs3R44c4ciRI3Ts2JHx48ebVkdRFNq2bUvXrl1NQwCULVuWwYMHv3SPdWdnZ633dsuWLTx79owxY8YwYMAA06pSASVX5X1BZmYmaWlpWFhY5NvHNI+qqiQkJGBubk7x4sUh9yJdCJFvArWqquh0OrmqqfRGyVuVd/fu3QQEBORbyEAIweHDh/H39zcqs7e3Z8iQIa9tGfunT5/yzjvvcPz4cSpVqmQalv4GqqoyatQoVq9eTVBQkNFectK/mxBC22Llj+aBFUR6vZ6OHTui0+nw8fHBwsLCtIok/WMMBgN9+vQhJCQEf3//l/Ymvi7x8fFYWlrm2ws0bziu6bVxYmIiiqK88jqb3O8PXjHHW1XVP1yVOD4+HlVVcXBwyPf4UsEmE1NJkoz8UWJqMBj46KOPuHz5Mu3atYPcPyIODg4sXLjwtQ2XCQgIoH79+ty5c4fq1aubhqW/wduSmAYFBREfH0/jxo0xMzPj8ePHPHv2zOiiSFEUrKyscHZ21kbSZGVlcfv2bQIDA7G2tqZ+/fpUq1YNclebvHr1ar55UpaWllhaWtKsWbM/vGnp5+dHyZIlcXFxgdzPmZ+fH0+ePCE7O5vSpUvTvn17LC0t0ev13L9/nydPnpCRkUGZMmVo0qRJvovFF8XFxXHz5k1iY2NxcHCgadOmODs7m1bL58GDBwQEBGBmZkbDhg0pX768FsvJycHPz4/atWtjZ2dHVlYW/v7+1KlTBzs7O6N2CrI3LTH19/cnOzubZs2aGZXnnTMBAQEoikKdOnXyLU6TkZHBjRs3CA0NpXDhwjRp0oSyZcsa1XmVyMhIYmNjqV+/Pjk5Ody8eZO0tDQtsbCwsKBZs2Yv7d1KSkri7t271K9fnyJFipiG80lLS+P27dvUrl1b6xh4laCgIO3maZ06dahataoWE0Jw/fp1qlevTtGiRdHr9dy4cUNbO6QgKsiJqST9L8nEVJIkI38mMf34449xcHDA09PTKGZKr9eTk5OjDXF/maysLKysrIzugGZmZmJpafnKi8H09HQsLS2xsrLSyh49ekSLFi3w9fXVkoM8eXsP/1EiIP01b0NimpmZyVdffUV0dDTe3t7Y2tqyYcMG9u/fb3TxJ3LnL40YMYIffvgBnU7H+vXr2bZtG8WLFycnJwdzc3OmTZtGw4YNCQ0NZcKECUZDy83MzAgLC8PKygpvb+/fTRofPnzIlClTmDFjBrVr10ZVVfbs2cOmTZuwsrJCURRsbGxYunQpxYoV48KFC7i7uyOEQM3d4qxt27aMHTv2pZ+/uLg45syZQ2BgIDY2NqSkpFCpUiVmzZr1yot+IQRPnjxh5syZODg4kJGRgYWFBXPnztUu6A8dOsS+fftYtGgRRYsWJT09nfHjx1O/fn2+/vpr0yYLrDchMdXpdAQGBnLp0iXWrl1LkyZNWLp0qRYXQnD8+HGWLl2Kra0tOTk55OTkMH36dG2BmpycHNzd3Tl8+DDFihUjJSUFZ2dn3Nzc8n33m8rIyGDy5MlUqVKFYcOGERoaynvvvYeLiws2NjYIIbCzs+OXX36hYsWKpoezdOlSVqxYwfbt22nYsKFpOJ/Nmzfzyy+/4OnpSfv27U3DmoiICKZPn46lpSVCCDIyMpgzZ472/XT58mUWL17M0qVLcXZ2Jjs7m19++YUiRYowbtw40+YKBJmYSv8Wv98XLkmS9BJ5q1G/ihCCK1eusGbNGjw9Pdm/f7+2EJgQggsXLvDgwQOuXLnC+vXr8fT0JDAwEJ1Ox8mTJ1m3bh2rV6/m3r17Rm3ev3+fLVu24OnpyapVq7hy5QrqC8vVm95ny8jI4MSJE3h6erJmzRrOnz8v9ySUjBw/fpwDBw4QGhqKqqooikK7du344YcfGDVqlPbTvn17nj9/TtOmTQF48uQJU6ZMoUOHDixbtoyZM2cSGxvL7Nmz0el0ODo6MmzYMEaOHKm18f3335Oenk6tWrV+t/cwJyeHBQsW4OrqSs2aNQG4du0a06ZNo3HjxsybN4+VK1fy/fffY2NjA0BgYCCNGzdm7ty5eHh48MUXX7By5cqXbl0mhGD9+vUcOXJEu7EwevRoTpw4wcaNG02ra1RVZc2aNSiKgpubG7/88gs3btzg9OnTAERHR7N8+XI6deqkJd12dnb06tULd3d3goODTVqU/ltCCIKDgxk/fjxr164lODg433dbVFQUkydPpmrVqixevBg3NzfMzMyYOHEi2dnZCCG4evUq8+fP59NPP2XlypWMHz+e8+fPs3LlSqO2XubkyZNcunSJ3r17oygKKSkpBAQEMHjwYEaNGsXIkSMZOnQoTk5OpocSFBTEypUrCQkJMdpC5FWioqJYuXIlT58+JS0tzTSsybuBExoayty5c5k9ezbR0dHs2LEDgOfPn7NgwQJatWqljQ6wtramb9++bN261WiKiiRJ/zyZmEqS9F/R6/WoqqptQP9ignj48GEmTpzIpUuXePDgAb/99hsrV65Ep9OhqiqzZ89m4sSJ7Ny5k/v377Np0yYmTJiAl5cXGzZs4OHDh+zatYsffviBqKgoyL3g2LBhAzt27ODx48ecPXuWoUOHcuPGjRee1f+Tk5PD6tWrmTVrFnfu3OH69euMGzeOgwcPmlaV/qXCw8Nxd3enR48eFC1aVLuxUblyZTp27Ej79u21n4cPH/LZZ5/RunVrhBA8ffqU5ORkJk+ejIuLCw0aNODrr7/m+vXrZGVlYWdnR5s2bejQoYPWho2NDebm5owfP/6VN3ZE7nYIV65cYcyYMZiZmZGdnc3ixYtp1qwZ06ZNo2LFijg5OdGiRQsKFSqEoij069ePn3/+mUqVKuHo6MjAgQOpU6cOZ8+eNX0IdDod69atY9iwYXTu3JlixYrRrVs3RowYwbp164w+yy/KS2Tee+89bGxscHFxoVGjRjx58gQAd3d3XF1d+fzzz42O69GjB5UrV8bLy8uoXPq/yc7OplSpUri5udGlSxejxFRVVc6dO0dcXBy//vorZcuWpUaNGsycOZPbt2/j7++PEIJ169bRrFkzBg0aRIkSJejQoQMjR47Ey8uL7Oxso8d7UUZGBp6envznP//B1dVV65m0t7fngw8+oF27dnTo0IHWrVvnm96RlZXF4sWLqVSpEhUrVsx3Q9GUXq9n5cqVODk5Ubp06ZfOO8wjhMDf31+7OVK8eHE6dOigJZxbtmwB4Pvvvzc6rlmzZrRv3/5PJeSSJP19ZGIqSdJfZm5ujr+/P0uWLGHp0qUsWbKEEydOQO6coylTptC+fXvWrVvHqlWr+Pnnn1m6dCkPHz5EURTS09N58OABgwYNYsmSJcyfP5+TJ0+ydOlSRo0axdKlS7W9gF/s8WndujWrVq1i5cqVrFmzBnNzc9atW/fCM/t/rl69yuLFixk5ciSenp54enrSpUsXZsyYIbdxksjOzmbhwoW4urry6aefotPpTKtofHx8OH78OMOHD9fKypUrR9myZTl79iwZGRnExcVx7949mjdv/tIh45mZmcyaNYuuXbu+dFhjHiEE+/fvp1GjRto8v+joaHx8fBgxYgQhISHcvHkzX++j6Rw9nU6HTqd76bBcnU5HQkJCvufRpEkTgoODiY+PNyrPoygKrq6u3LhxA51OR1xcHCEhIVSvXp1r165x+vRpJk6caHoYQgg+++wzDh48SE5OjmlY+i8oikKtWrVYtWoVbdq0MQ0jhODSpUs0atTIaEG6ypUrU7p0aW7duoXI3Xrj3XffNTquTZs2JCcn5zvH8gghCAoKIjAwkI8++kgrT0xMBCA0NJSoqKh886vz7N+/n9u3bzNhwgTMzc3/MDE9ceIEJ06cYOrUqdjY2Lzyxgm5r4uzszMPHjwgMzOT5ORkHjx4QIMGDXjy5Anbt29n8uTJ+RbEEULw8ccfc/nyZbmbgiS9RjIxlSTpLzMzMyMyMpJz585pPw8ePIDci/hnz57RuHFjfH19uXjxIkWKFCErKwtfX1/t+G7dulGvXj3Mzc1p0aIFVatWpWXLljRr1gwzMzMaNGhAjRo1tN4Yc3Nz3nvvPZ4/f865c+e4ePEixYsXJyQkxOi5KYqCqqrs3LkTOzs7ypYty8WLF/H19aVKlSrcuXOH0NBQo2Okf59du3Zx4cIFJk2ahIWFxSsvdnU6He7u7nTs2FFb9EhRFOrXr8+AAQP48ccfcXd3Z+7cuZw8eZKxY8cazX3O4+vry/379/nuu+9MQ0YMBgO3b9+mZcuWkHvB/PTpU3JycoiIiGD58uUsXLiQmTNncvDgwVde1B8+fJioqCi6d+9uGsLKyorKlStz5MgRLSHPycnBx8cHIQTPnz83PQRyP7dffvklvr6+bNy4ES8vL0qUKEGjRo2YN28egwcPplKlSjx//twoKVFy9z1MSUnJ93mV/nt58/INBsNLz99nz55RuXJlozI7Ozvs7e2Jj48nOzublJSUfAsd2dvbY29vr41WMSVy51vn9WDmSU9Px9zcnCVLlrBgwQKWLFnC48ePjY69ffs2Cxcu5Mcff6Rq1ap/eKMiKCiIuXPnMnDgQBo3bvy7vbjknmt9+/YlLCyMdevWsWnTJjIzM+nduzfz5s3j3XffpWHDhiQlJRk9dl6ib2Vlxd27d43alCTpnyMTU0mS/jKdTkePHj3YvXs3u3btYteuXYwYMQJVVQkLC8NgMLBz505WrFjBypUrWbt2LVWrVtWGdJmZmRltjm0wGLC3tzfqwTEYDNjZ2WnD05KSkliwYAFz5szRhvSGhYW9chGIkJAQcnJytOewYsUKjhw5QuPGjV95MS/9O9y8eRMPDw8mTJhAxYoVMRgMKC9ZHEvkLvZz/vx5vv32W6NYSkoKVlZW2NnZ8fDhQ8LDw3F2diY1NdWoHrnn8po1a2jXrt0fbmWUnp5OcnKy0QJekZGR2tw5FxcXunTpAsAPP/yQbyi7Tqfj0KFDzJs3j88++4xGjRoZxcldKXXYsGGcPHmShQsXsn//ftatW8fx48extbV95WdKURS6d+/O8OHDCQgIIDMzk9mzZ3P8+HHMzMz44IMP2LFjB15eXmzcuJHIyEjtWHt7e5ydnfMlKtLfQ+QOrTWdy2xmZoalpSU6nY7MzEyU3NWmX2Rubo65ufnvJo2PHz/W5j/nqVy5MkOHDqVo0aLk5OSwadMmxowZQ0REBOT2/Lu5udGxY0c++OAD9Ho9iqLk673M8/z5c9zc3KhZsybDhg3TPqevqk/uOdqsWTMmTJhASEgI8fHxTJ8+nbt37xIWFsY333zD/v378fLywsvLi6CgIO3YokWLYmtrK29cStJrJBNTSZL+KxYWFlhaWmJtbY2NjY12sWBlZUWRIkUYMmQIw4YNY+jQoQwbNgwPDw+6dOnyu0nhy4Z+5c0nOnLkCAsXLqR169aMGTMGd3d3unbtmu+udx5ra2vKlSvHiBEjtOcxcuRI1qxZ84fJgfT2evLkCWPGjKF69ep06NCBsLAw4uPj0el0hIWFGS3EoigKO3bsoGLFikbbcAgh2LFjBzt37mT16tWsWbOGLVu28OGHHzJ69Oh8vYLBwcGcO3eOr776yqj8ZTIzM8nOzjbatsJgMJCcnMzHH3/Mjz/+yKBBg1ixYgWOjo5GQ9nj4uJYuXIl8+fPp0uXLkyZMkWLvUhRFD777DNGjhzJsWPHcHd358qVK7zzzjvY2Njg6OhoeoiR999/n3nz5jFlyhT0ej2bN29mwoQJbNmyhS1btpCdnY23tzcLFy7UPtPW1tbY2tq+sjdW+t+ztLTM18Oo5u4tmfednff7i/J6YF+1J3Ver/qL54miKDRs2JBp06YxdepUli5diqenJ9evX2f37t2kpKQwZ84cgoKCGDJkCOHh4YSHh6PT6YiJiSEpKcnoMdLT01myZAkXL15k+PDhREZGEhwcjMFgIC4u7g+H23bt2hU3NzemT59OiRIlWLZsGWPHjuXUqVN4eHiQmZnJhQsXmDt3LllZWZD72tjZ2b305pIkSf8MmZhKkvSXCSHyXcyQe3FSr149hBBYWlrSrl072rdvT7t27XB1daVEiRIIIRC5W1q8KK/ctCzv36tXr2orndatW5esrCz8/PzybekhhEBRFFq0aEFMTAzlypXTnkebNm0oW7ZsvsU4pH8HVVXZv38/vr6+WFpaMmfOHBYtWsT+/fuJj49nwYIF3L9/X6ufmprK3r176d+/v9F2Rqqq4uXlpQ1HJzcJ+Prrr0lLS9NWqSX3nPT29sbR0ZHmzZtr5a9ibm6OoihGC9mUKFGCQoUK0bFjR63MxsaGVq1a8ejRI8jtXfrtt9/YuXMnw4YNY8aMGb/bs2RpackPP/zAtm3btFEFZcqUoUSJEn96L8f09HRmzZrFu+++S61atVixYgUTJkxg3LhxzJgxg3379hEbGwu5r5mqqq/sjZX+txRFoUyZMvl6/zIyMkhLS6NMmTLY2tpSqFAhoqOjjeqkpKSQlZVlNKrFlIWFRb5VgE01adKENm3acOfOHZ48eYKXlxdOTk6sWbOGBQsW4O7uTmJiIps2bdLWKMgTGhqqLXi0fft23NzcWLBgASkpKezcuZM9e/YY1X+VnJwc3NzcaNSoEW3btmXlypUMGTKE8ePHM3fuXK5du8bDhw8h9zXT6/XyHJWk10gmppIk/WV5K/GaUhSFpk2b0rFjR37++WfWr1/PwYMHWbVqFR4eHtp+ojqdLl9i+6qyvOFeDRs25Pnz5yxbtow9e/awevVqIiIitARXCKFtgaAoCv3798fOzo5Ro0axfft29u/fz4IFC/70BY309lFy55FNmzaNUqVKYW1tTaFChbCxscHMzAxbW1tttVyRu7pnXFwcXbt2NW0Kc3PzfNtWJCUloaqq0Q0WvV7P4cOHad++/St7oF5ka2uLra2tNvxRURSqV69OoUKF8PPz0+oZDAaCg4OpUKECAFu3buXEiRO4u7vzySefaPX+iLOzM1WqVEHkrtD64YcfmlZ5pX379hEXF8fw4cNJSEggMzOTunXrAlChQgWsra211ygzM5Pnz5+/kXvdvgleHC2S93vz5s25ceOG0V66Dx8+JDU1lSZNmmhz+U+ePGl0nI+PDy4uLpQrV04rf5GiKLi4uORLek1lZmYSGxuLk5MTRYsWZfz48TRv3hxFUShUqJA2bNzGxibfKtW2traMGTNG++zZ2dlhZ2eHmZkZ1tbW+YYfv8rp06e5desWY8eOJTExkbS0NG14e9myZXFwcCAuLg5yP6upqal/OGJAkqS/j0xMJUn6SxRFoW3btq/s/bG1teXXX3+lfv367Nmzh+3bt3Pq1CkcHBywsrJCURS6du1KnTp1tGMURaFLly4vLcvrkXr//ff57LPP8Pb2Zu/evVhYWDBx4kTatWsHuauS9u/fH3t7e8hdNXXp0qWYmZmxfft2du7cyY0bN3BxcdEeQ/p3URSFXr16MX78eGbMmMGcOXOYPXs2X3zxBQ4ODsybN4+GDRtCbmJ64sQJSpQokW/1WkVR+PDDDzlx4gSHDx/m2bNn3Lt3j0WLFmFmZqadk+TOD718+fJLFyF6mUKFCuHi4mK0AEu5cuXo3r07c+fOxc/Pj6CgILZt28aDBw/49NNPyc7OZtWqVdStWxeRu4fwlStXuHz5stYDnJycjJeXF4GBgZD7vK5du8azZ8/w9/dn/vz5pKen59vq5VUePXrE2rVr+fHHH7Gzs8Pa2prixYtz5MgRoqKiOHnyJMWKFaNkyZKQuw9lUlIS1atXN21K+h/IyckxWlk673vWzMyMxYsX8+TJE/z9/Vm+fDmtWrWiVq1aKIrCF198wdmzZ/H29ubZs2dcvHiRrVu3MmTIEKNRAi9SFIXatWvz6NEj7WaiyF3h9+rVqzx79owHDx6watUqIiMj6dmzJ1WqVGHChAlMmTKFOXPmMGfOHKZMmYK9vT2jRo3igw8+QAjB8ePHOXr0KOXLl2fcuHFMmjRJ+5zOmzcPGxsbhgwZwsCBA02fVj5hYWEsXbqUYcOGUbJkSaytrSlVqhSHDh0iOjqa06dPo9frqVq1KuTOgc3MzDSa3y1J0j9LEaZj5yRJ+ldTVZWhQ4eye/duAgICtAtLU3k9k78nIiKC9PR0XFxc/nD4bN5X0R+1GRYWhhBCu5v/R89DVVVCQkK0Y0zvzEv/PVVVGTVqFKtXryYoKOiN7A0TQnDkyBFWrFjBzp07tfPUYDAwevRocnJycHd3Nz2M5ORkJkyYQGBgINWrVyctLY0nT54wbNgwPvvsM8ht29fXl6FDh3Lw4MFX9kC9KK/ncvv27Rw7dkwbVnj//n3GjBlD8eLFKVGiBA8fPqRTp05MmDCBpKQkPv74Y1RVpWTJkkbJQu3atZk6dSpPnjyhZ8+ezJkzh48++ojz58+zYsUKnJ2dSU9PJyIigrFjx9KpUyeTZ5SfXq9nyZIlxMTE4ObmBrmv1/r16zl69CiNGjXi0aNHdOnShS+//BKARYsWcerUKQ4fPmzSWsGk1+vp2LEjOp0OHx+fAv29YTAYGDduHDY2NsyaNcsotn79elavXk39+vXR6XTEx8czf/58LRnLyMhg8uTJ3Llzh9q1axMZGUnJkiVxc3PLt3DSi8LDw+nVqxdr1qyhadOmqKrKr7/+iq+vL+XLlyczM5MnT57Qr18/o22WXhQaGsrgwYOZO3cuTZo0wWAw8MUXX5CTk8Pu3btNq5ORkUH37t355ZdfjIa1v0zecPvLly/j6ekJuZ+HnTt3snXrVpo1a0ZQUBB16tThxx9/hNw9Tt3d3Tlz5syf7pH9pxgMBvr06UNISAj+/v5yuLH01pKJqSRJRv5sYipJb0NiSm5PSXBwME2aNNESEFVVuXXrFvb29q/s5VNVlWPHjnHnzh3s7Ozo1KkTtWvXNqoTFRXF06dPad68+Z++2I2OjqZ3797MmTPHaBhxZGQkhw4dIikpiebNm9OhQwfI7S27efOmtohT3o0aIQROTk60adOG9PR0jh07RrNmzShXrhyJiYl4e3sTERFBqVKl6Nq16+/OKXyRTqfj7NmzNGrUiBIlShjFDh8+zJMnT6hbty6dO3cGID4+nr59+/Ljjz/Sr18/o/oF1ZuUmAohePDgAWZmZvlWygW4dOkSly5dwsHBgT59+lCqVCmjuF6v5+DBgzx69IiqVavSt2/f352fTO65//PPP5OWloaHhweKohASEsKpU6eIjo6mSJEitGvXjvr165seqsnIyMDPz49atWpRrFgxhBBcvHgRg8Hw0sRTr9dz5coVatSogZOTk2nYiBCCM2fOUK1atXzn9ZkzZ7hz5w5VqlTR9nBNS0vjk08+oWvXrowaNcqofkEgE1Pp30ImppIkGZGJqfRnvS2JaUG0cuVKTpw4gaen5x9ehBdk2dnZrFixgsuXL7N582ZsbGxMqxRIb1Ji+ro8evSIr7/+mhkzZvypnvaCSq/Xs3XrVjZv3syOHTv+9OJf/ySZmEr/Fi+fQCBJkiRJ0mszaNAg6tatS1RUlGnojaLT6UhJSWHy5MlvTFIq/TnVq1dnxIgRPH361DT0RjEYDERERDBp0qQCmZSakv1J0ttM9phKkpTPd999x44dOwgJCfnDuaHSv9vo0aNZsWIFMTExFCtWzDQsSW+s9u3bo9PpuHz5smlIkv5xffr0ITg4mDt37piGJOmt8UYmpiEhIYSFhf3ugieSJP13hBDMnz+f06dPs2fPHgoXLizv0EovJYRg2bJl7Nu3jwMHDlC0aFF5rvwPvThX9E2mKMob938wGAyMHDkSvV6Pp6enti2VlN+b+P6aMjMzK9DvsaqqTJgwgaioKLZs2ZJvazXpzWdmZkb9+vWxtbU1Df2rvHGJqRACNzc3Vq5cKed8SNLfQFEUYmJiSEtLo3z58nIui/RKiqIQFxdHamoqrq6u8jtZemsIIYiKikIIQenSpV+5dYok/ROEEMTExKDX6ylTpozsmHkLWVtbs2/fvlcutvdv8UYmpjt27ODw4cPygylJfxNra2vMzc3JzMx84++ES38fIQTXr1/nyZMnvP/++//6O73S20NVVU6cOIEQgu7du8vEVHqtVFXl3LlzpKen06tXL3k+voUsLS2ZPn16vlWk/23euMRUkiRJKjjGjBnD8uXLiY2NpWjRoqZhSXpjtW/fHr1ez6VLl0xDkvSP69u3L8+ePZNzTKW3mrzlIkmSJP1XVFVFp9NB7p6EkvS2MBgMqKqKwWCQ8/mk185gMKDX61FVtUDPhZWk/yuZmEqSJEn/tbxBN/JiSXqbvDiYTA4skwoSeT5KbzOZmEqSJEmSJEmSJEmvlUxMJUmSJEmSJEmSpNdKJqaSJEmSJEmSJEnSayUTU0mSJEmSJEmSJOm1kompJEmSJEmSJEmS9FrJxFSSJEmSJEmSJEl6rWRiKkmSJEmSJEmSJL1WMjGVJEmSJEmSJEmSXiuZmEqSJEmSJEmSJEmvlUxMJUmSpH8tIQQ6nc60+I2kqup/9X8xGAwIIUyLX+nPPsZfaVP6e/3Z9+xNkJOTY1r0u1RVRVVV02Lpb/JXzrWcnJw/9T2hqip6vd60+E/7q+eM9Poo4s+cEW85IQR+fn48evQIvV6PoigUL16cBg0aULp0aa3O7du3CQkJoUuXLtjb25s2A7kn/4kTJyhevDitWrVCCMHdu3dJSUmhTZs2ptUlSZLeWKqqMmrUKFavXk1QUBBlypQxrVJgpaamcv36dR4/foxOp6NUqVJ06NCBkiVLmlYFIDs7mxMnTlCtWjWqV69uGgYgISGBs2fPkpWVBbmvT+HChenduzeWlpam1f9n0tPTOX/+PKGhoeTk5ODk5ESHDh20v1+/5969e9y9e5f33nuPQoUKmYaN6PV6Ll68SHh4OGZmZrRs2ZJKlSpp8bi4OO7cuUO7du2wtLQkOTmZq1ev0qZNG+zs7IzaKuj0ej0dO3ZEp9Ph4+ODhYWFaZUCLysri5s3b3L//n2ys7MpVqwY7dq1o3z58qZVIfd8PXnyJE5OTjRq1MgoJoTg3r173Lp1C3Nzc61cURSsrKxwdXWlRYsWRsf8L+W9D0+ePCErK4uiRYvStm1bo/PPlE6n49KlSzx48ACA2rVr07ZtW8zM3rw+GYPBQJ8+fQgJCcHf39/oPSgoAgMD8fHxITU1FUdHRzp27Iizs7NpNYQQPHz4kBs3bpCSkoKiKDRs2JAWLVrke28SExPx8fEhIiICVVWpWLEiPXv2JDMzkyNHjpCenm50jJWVFVZWVnTs2JEiRYrg5+eHn58faWlpWFpa0qxZs3zntlSwyMQ098t4+PDh7N27l3r16qHT6cjMzKRu3brMnDkTZ2dnVFVl5MiRrFmzhg0bNvDJJ5+YNgPA5cuXeeedd+jatSu7d+9GVVXGjRvHw4cP8fb2Nq0uSZL0xnpTE1MhBAcPHsTDwwN7e3ssLCx4+vQpLVq0YO7cuS9Nog4fPswnn3zC5MmTGTdunGkYIQSnT5+mf//+NGrUCCsrK1RVpWTJkqxateoPk77/i8ePHzNmzBgKFy6MEILHjx/TvHlzfv31V4oWLWpaHSEEAQEBXL58mW3bthEdHc2lS5deWjePEIJTp06xePFi6tevT2RkJIqisHTpUuzt7TEYDMydO5fo6GgWLFiAtbU1MTExDB8+nJ49ezJo0CDTJgu0Nz0xFUJw5coVZs2ahY2NDba2tgQGBlK5cmUWLVqEk5OT6SH4+fnRrVs3PvzwQ9zd3Y1iqqqyfft21q9fb/RaqKrKxYsX+eGHH5gzZ47RMf9L8fHxfP/99wghsLS05MmTJ1SoUIFFixa99HtHVVV27tzJ8uXLKV26NHq9nvDwcKZOnUrv3r1Nqxd4BT0xDQ0NZezYscTHx+Ps7MyTJ09o3Lgxbm5uFC5c2Khu3nfF1atXcXR0JDo6msTERGbOnEmPHj20eomJibi5uXH9+nXtfHV0dGTFihU8f/6ckSNHEhsbqyWm5ubmPH36FJ1Ox4ULF3B2dmbcuHE8efKEokWLEh4eTk5ODvPnz/9bb6JI/0dCEgaDQXz55ZeiV69eIjw8XAQEBIgNGzYIe3t7sWjRIq3OkCFDhI2NjejatavIysoybUbo9XoxcOBAYWdnJ/r06aMdN3LkSNG5c2fT6pIkSW80g8Eghg8fLqysrER4eLhpuMBSVVVs3bpV7N69W2RlZYns7Gxx9OhRUaxYMXH06FHT6iI+Pl507dpVFC5cWMycOdM0LERum5s3bxY1atQQz549EzExMSIqKkrExsYKVVVNq79SWlqaOH78uMjMzDQNvVJERITw8fER2dnZIisrSxw8eFDY2toKb29v06pCCCGys7PFV199JZo3by5at24t6tatK5KTk02rGdHpdKJ3797a38S4uDhRp04dce7cOSGEED4+PqJNmzbi3r17RscdOXJENG7cWERERBiVF3Q6nU60adNGNG/eXOh0OtNwgaeqqjh+/LhYu3atSE9PFzk5OeLq1avCxcVFeHh4mFYX6enpYsCAAaJw4cJi8ODBpmEhhBCpqakiKipK+4mOjhYHDhwQ5cuXFzdu3DCt/kqJiYni5MmTf+l1TUpKEmfOnBFZWVkiJydHXLlyRTg6OoolS5aYVhVCCBEcHCyqVasm5s6dK7Kzs0VycrIYOnSoqFOnjkhNTTWtXuDp9XrRs2dPUbt2baHX603Dr1VOTo4YP368aNiwoQgMDBQ5OTni5MmTwtHRUWzdutW0ulBVVZw7d05ERUUJnU4nYmJixLvvvitatWql1TEYDGLhwoWiVq1a4vTp0yInJ0dkZWWJyMhIoaqqMBgMIjY21uh8jIyMFD169BDfffed1sbp06dFYmKi0Ol0IiQkRDRv3lx8+OGHLzwbqaB588Yz/E3MzMxwdHSkTJkyVK9enQEDBlC7dm0eP36s1VEUhSZNmhAaGsrFixeNjge4f/8+Pj4+tGvXzqjc3Nz8jbvbKkmS9LZSFIWPP/6YDz/8EGtra6ysrOjRowfVq1fH39/fqK6qqqxduxZzc3MaN278yrlqQgieP39O2bJlqVChAiVLlsTZ2RknJycURTGt/koJCQlMnz6dlJQU09Arubi40Lp1a6ysrLC2tubdd9+lVKlSREdHm1aF3Odao0YNFi1axPfff4+ZmdkfzvMSQvD06VPat28PQIkSJXB1dSUuLo6UlBQWLVrEZ599Ru3atY2O69GjB6VKlWLHjh1G5dLfS1EUOnfuzFdffYWtrS2WlpY0b96cNm3acPv2bdPq7Nu3j5CQEDp16vTKc6Fw4cI4OztrP46OjuzatYv33nuPxo0bm1Z/KSEE4eHhzJkzh8zMTNPwKxUtWpSOHTtibW2NpaUlLVq0oFatWoSFhZlWRVVVzpw5g06n4+eff8bKyooiRYowZswYoqOj8fX1NT1E+j9ISkpiz549/Pjjj1SqVAlLS0u6dOlCz5492bZtm2l1FEWhffv2ODs7Y2FhQcmSJenbty8RERFanZiYGDw9PZkyZQqdOnXC0tISa2trSpcujaIomJmZ4eTkZHQ+BgYGEhYWxk8//QS51/WdOnXCwcEBCwsLypUrR7du3QgNDX3h2UgFjUxMcymKQnZ2NjqdjpSUFPz9/RFC0K1bN62OXq+nZs2adO3aFXd3d6Mvb1VVWb16NU2bNqVOnTr/p0nakiRJ0t/LdC5TamoqycnJuLq6GpVfuHCBXbt2MXXqVBwcHDAYDEbxF8XFxZGVlcWDBw949OgRaWlpplX+kJWVFRkZGVhZWZmGfpeqqhgMBlJTUzlz5gzOzs40adLEtBoA1tbW/Pzzz7Rs2RL1Ty4MY2ZmRtWqVTl+/DixsbHcvXuXxMREatasqSWd3377relhAPTr1499+/aZFkt/M9Phnjqdjujo6Hzn+L1791i+fDmjR4+mQoUKf/r65dKlS/j4+PDdd9+Zhl5JURTMzc3JyMjA2traNPy7hBAYDAbS09O5ffs2Qgg6depkWg0hBNeuXaNhw4ZGr4GzszOurq75bj5J/z0hBFFRUcTExNC6dWutXFVVmjRpQkhIyCtvQBgMBnJycggPD+fGjRu89957kHvsrVu30Ol0dOvWjevXr3PhwgUCAgJMm9BkZWUxf/58unXrRsWKFY1iBoOB7Oxsnj17xqNHj7THkQommZjmMjc35+HDh6xcuRI3Nze+/fZbqlevbjQXQQiBmZkZ33zzDVeuXOHu3btaLDg4mKNHj/Ltt99qc4skSZKkgi8nJ4c1a9ZgZmZmtEhdWFgYs2bNon///rRs2ZLMzMw/7P2Mj4/Hzc2NWbNmMW/ePB49emRaJZ/k5GSOHDmCt7c3R44cIS0tDW9vb7y9vTl69ChJSUmmhxgRQnDjxg08PT1xc3Nj7ty5fPHFF9SvX9+0qhEhxJ/+W2VmZsawYcO4cOEC69atw9PTk+7du6OqKlu2bGHq1Kmkp6cTGhqaLyFv0KAB8fHxxMfHG5VL/xwhBPv27SMiIsJoHl9CQgIzZ86kWbNm9O7d+5VJhKns7GxWrVpFly5dqFatmmk4n+joaA4dOsShQ4c4deoUKSkp7N+/H29vb06dOkV2drbpIUZE7rzoNWvWsHDhQiZNmkSnTp145513TKtC7me3XLlyRmU2NjbY2dmRmJhoVC7938TExGBpaUnx4sWNyh0dHdHpdPm+D8i9Ebht2zbc3d2ZNGkSMTExjB8/Xos/evQIa2trbS0Ad3d3Jk6cyMGDB43aIffcuHnzJrdv32bo0KFGsfj4eDZu3Mjy5cuZMGEClpaWjBw50qiOVLDIxDSXoijEx8dz5coV7ty5o118+Pj4GNXT6XTUq1ePRo0a4enpqZVv2rSJsmXL0q5dO7Kzs//w4kWSJEl6/aKjo/Hw8GDz5s1MnDhRu5hNTk5m0aJFlCtXThsaZm5u/srVdRVFoVu3bgwfPpyGDRtSsWJFjhw5wpgxY/7wQjghIYEtW7awceNG9u7dS0JCArt27WLjxo1s27aNhIQE00PyCQ4OxsfHh5s3b2JmZoZOp/tTx/1ZiqLQo0cPRo8ejZWVFU2aNOGnn35i+fLldO3aleLFi7NixQrWrl2Lh4cHkZGR2nGOjo4ULlyYkJAQ02alf0BycjKbNm1i4cKFDB06VBt2m5WVhaenJ1lZWcyePRtyb0D80dQjkbvAlo+PD998841pOB8hBKGhoXh5ebFp0ya8vb2JjY1l27ZtbNy4kd27d/+phDg6OhofHx9u3LhBZmYmlpaWhIeHm1ZDCEFWVhY2NjZG5YqiYGFh8bujHqS/RghBRkYGlpaW+UahWFpavvLmV3Z2NteuXePy5csEBgZSvHhxgoKCILfN+Ph4YmNjuXPnDn379mXQoEEUKVKEn3/+madPnxq1ZTAYWLduHa1btza6SSKEIC0tjcuXL3P16lXCw8NxcHAgMDDQ6HipgDGddPpvZDAYxMCBA8VHH30k0tPTRUpKiggKChL/+c9/ROPGjUViYqIwGAziq6++EoMGDRJCCHHo0CFRqVIlER4eLuLj40X16tXFtm3bhBBCjB49Wrzzzjta2z/++KPo3r270WNKkiS96d7UxY/yBAQEiO+//160b99e+/4WQoisrCwxc+ZM4ezsLI4cOSJu3bolfH19RevWrcXQoUPFgwcP/tQCJL6+vsLBwUHs2rXLNGQkOztbhIWFidDQUOHr6yvq1q0r/Pz8RGhoqAgLCxPZ2dmmh+STk5Mj0tPTRVJSkjh27JioVq2amD9/vmk1I6qqio0bN4q6deuKpKQk0/Af2rlzp+jZs6eIiYkRAwYMEN9++61Yvny56Nu3rxg7dqxWLyYmRjRv3lycP3/e6PiC7E1f/ChPaGiomDhxomjbtq1YtmyZVq7X68W6deuEo6Oj8PLyEn5+fsLX11d88MEHok+fPuLOnTsvXeRR5J43U6ZMEW3atDENvVJGRoYIDQ0VISEh4uTJk6JJkybi0aNHIjQ0VERERPypz5NOpxMZGRkiOTlZ+Pj4iCZNmogRI0YIg8FgVE+v14t3331XjBw50qg8OztbtGnT5pULmBVkBXXxI1VVxYkTJ4SDg4NITEzUyg0Gg9iwYcMrv1tUVRWZmZkiNTVVBAcHi+HDh4v69euL8PBwYTAYxLhx44STk5OIjY3VjklKShLly5cXixcvNmorODhYuLq6imPHjhmVi9znkfc4jx49Ep999plo3769eP78uWlVqYCQPaYvsLKywtbWFnt7eypWrMjo0aN5/PgxwcHB+XpAu3TpQunSpdmxYwf79+/HxsaG3r17I16xaIAkSZJUcISEhDBx4kTCwsJYvHix0RZgKSkp7N27FxcXF/bs2cP8+fNZuHAhgYGBXLhwgY0bN/6pDdubNWtGtWrVfnduFLl/e8qWLYurqyvly5dHURQqVKiAq6srZcuW/VPzTS0tLbG1taVo0aJ0796dd999l927d5tW+58JDg7G3d2dn376idTUVC5fvqwtpjRx4kT279+vvUY6nQ5VVf/ynELp/yYuLo5ffvmFK1euMG3aNIYPH67FdDodu3btwtHRkQsXLuDm5saSJUvw8/Pjzp07rF69muTkZKP28uQNw+3fv79p6JUKFSqEq6sr5cqVo0yZMpiZmVGxYkVcXV1xcXHJNx/2ZSwsLChUqBBFihShdevWDB48mEOHDr10qGiFChXy9aylpaWRlJRE5cqVjcql/5vSpUujqqrRYmuKohAWFoaDgwNFihQxqp8Xt7GxoXDhwpQvX55JkyYRGRnJjRs3UBQFZ2dnihcvbrStUdGiRXF1dSUmJkYrU1WVo0ePYmdnR9u2bbXyPGZmZtrjVKtWjQkTJnDnzh2ePHliWlUqIGRi+oIXk8+srCx8fX2xt7fX9nZ7MW5jY8OQIUPw8vJixYoVDB48GDs7O5mYSpIkFXB6vZ7ly5eTkJDAtm3baNCggVHc1taW6dOnM27cONq0aUO3bt3o1q0bTk5OVKtWjdatW2NhYUFOTo6WfAkh8i0a8+jRI6Kjo6latapR+e+xtramadOmfyoZzWN6Ya7X64mPj6dkyZJaWVZW1iuHMJreeP0jOTk5LF++nPr169OxY0fi4uKwtrbW9motXLgw5ubm2hC+pKQkMjMzKV26tElL0t9Fzd131NfXl82bN9O5c2ejuIWFBSNHjmT27Nm0bduWbt260bVrV+1mSIcOHbC1tUWv15OVlaUdJ4TgwYMHxMXF0bFjR6M2/6zChQvTuHHjPxwy/KL09HTTIqKionByctLayVvA0szMjLZt2+Ln52eUxNy4cYP09HS5h+X/kKIouLi4UKFCBaP5nxkZGZw/f562bduiKAoGg0E7j1783swTFhaGlZUVxYoVQ1EUGjVqRGpqqtEc/fDwcKKjo6levbpWptPpOHLkCO3atcPW1lYrJ/c7z/Q7OTQ0lMKFC790r2qpYJCJaS6dTsfDhw/Zu3cv27dvZ/HixXh4eNC/f38qVKigzVl48Qu6d+/e2ip3L945zMnJMZovkZ2d/afmT0iSJEl/v8zMTDZt2kTlypU5e/Ys+/fvZ//+/ezbt49Lly5hZ2dHnz596N+/PwMHDmTAgAF89dVXlCxZkgYNGvDuu+9ibm7O9u3b2bx5MyJ3tdC9e/dy9OhRrl27xokTJ5gzZw6urq75kgJTKSkpnDx5kmPHjnHp0iXq16/PuXPnOHbsGKdOnXplzxW5icKhQ4fYtm0bV65cwcfHh9WrV3Pz5k0GDhwIuf/fJUuWcO7cOdPD0el0pKen/+mbqgaDgaNHj3Lz5k1+/vlnyO0xsbGxYffu3Tx48IA9e/bQqFEjbGxsEEJoC5mULVvWtDnpb6KqKhs2bKB06dL4+/sbneOnTp3CwsKCHj160K9fPwYMGMCAAQMYOHAglStXpkKFCnz44YcULlyY48ePs3LlSnQ6HeSeb2fPnsXGxuZPLXqUJyYmhmPHjnHs2DFu375NrVq1OHHiBMeOHePs2bO/u/iREIJz586xfv16Ll26xJUrV/Dy8uLw4cN8/vnn2NraotPpWLduHfv27UNRFNq1a0e5cuWYPn06Pj4+nDp1ihUrVtC7d+98q7ZK/zfFihXj888/Z9OmTezfv5/Lly/j7u5OYmIiAwYMQAjBiRMnWLp0KQaDgcjISNzd3Tl9+jTXrl3j2LFjLF68mMaNG2sriTdo0ICmTZsybdo0zp8/z4ULF5g3bx6lSpWie/fu2mPHxMTg6+ub72aDEIInT57g7u7OuXPnuHbtGocOHWLlypV07tz5L90slP5Z5tOnT59uWvhv9OTJE54+fcrNmze5fv06YWFhdOnShYkTJ2p3gR8+fEiJEiW0fdzyyuvUqUPPnj2N2rKxsdFWvsv7/VWrx0mSJL2JhBAcO3aMmzdvMmrUqJcO2SqIsrOzOX/+PM+fP+fatWtcv36d69evc+3aNdLS0ujSpYvpIeTk5ODn50flypVp3Lgxer0ed3d3AgMDte0HvLy8OHDgAAEBAVy8eBEhBLNmzaJKlSqmzRmJiIhg8eLFXL9+nbt37xIfH8/169e5ffs29+/fp2nTpvlWvMyTd9G+efNmHj9+zM2bN/Hz82PgwIF8/vnnkLsC5syZM3FwcKBly5ZGx0dHR5OYmEjfvn3/VC9tZmYmGzZsoE+fPlpbRYoUwdzcnEOHDhEVFUVQUBA//fQTzs7OCCGYN28e9erVo2vXrqbNFViqquLl5YWqqnz11Vf5FnYp6IQQnD9/nqSkJO38zjvHw8PD6dOnj+khqKrK/fv3KVasGB06dEBVVXbu3MmZM2f46KOPsLKyQgjBhQsXKFOmjNGuBb8nr5d12bJl+Pn5cf/+feLi4rh27Rq3b98mMDCQDh065FusKI8Qgtu3b7Nu3ToePnzI7du3uXbtGj169GDEiBFYWFiQnZ3NggULSE1NpUePHhQuXJhKlSpx8OBB7t27x7Vr13BycmLGjBlvZG+ZEIJt27aRnJzMsGHDCtT5qCgKtWrVIigoiKNHj/Lo0SMCAgIYPXo0rVq10s6jo0eP8vnnn5Oeno6Hhwe+vr7cu3cPHx8f7OzsmDlzpjaqwtrampo1a3Lq1Clu3LjB7du3iYuLY9q0adSqVQtyX5PY2Fh8fX0ZMmSI0bBfcr/b3N3duX37Nvfu3ePixYu4uroyffp0ihUrZlRXKjgU8Wdvk77lEhMTSU5ORqfToSgK9vb2ODs7G9XJW+HQ0dFRK9PpdOj1ei1JBXj+/Dk6nU4bRmX6uyRJ0ttAVVVGjRrF6tWrCQoKokyZMqZVCiRVVYmIiCA7O9toeKuiKNja2r60Z08IoQ1ZzZve8ezZM1RV1easxcfH8/DhQ54/f46TkxP16tX7UxfBWVlZhISEIITgxT/JiqKgKArlypUz+htjKicnh4CAAMLCwrC2tqZ69epGe1WqqsqDBw9wcnKiVKlSRsemp6eTkpJCqVKl/tTFbnZ2Nk+fPqV27dqmIW7fvk10dDQNGjTQLjBv3rzJ4MGD2bp1q3ZB+SbQ6/V07NgRnU6Hj4/PXxp2WlBERUWRmZmp9Xbmsba2pkKFCkZleUyvc6Kionj+/LnRe/eq7UF+T2pq6ktX0FVy9zWtWLHi777Ger2ep0+f8uzZMxRFoUqVKkY3fPJ6yGxsbIy2iYmMjOTu3bsULlyYZs2avXJV7YLOYDDQp08fQkJC8Pf3/1Nzcv9pqqpy9epVUlNTqVWrlvYdJIQgOjra6DyKjo4mICCA1NRUnJ2dadCgwUvfm+TkZG7evImqqjRo0IASJUoYxbOysoiNjaVMmTIvfU3Cw8N59OgRmZmZlC1bNt+0DangkYmpJEmS9F95UxNT6Z+Rnp7O2LFjKVmyJNOmTTMNF2hvQ2IqvT3ehMRUkv4X/vj2qCRJkiRJ0l+UkZFBjRo1GDVqlGlIkiRJkvKRiakkSZIkSf9zxYsXZ8SIEXI+lyRJkvSnyMRUkiRJkqT/OTncUJIkSforZGIqSZIkSZIkSZIkvVYyMZUkSZIkSZIkSZJeK5mYSpIkSZIkSZIkSa+VTEwlSZKk/4qZmZm296XcTkN6m5iZmaEoCshzWyoA8s7HvH1fJelt9UbuY6rX69Hr9abFkiT9DyiKgk6nw2AwYG1tjaIovIFfE9I/5KeffsLT05MnT55QqlQpea5IbzxFUdDr9XTv3h2dTsf58+fl96D02iiKgsFgoF+/foSGhnLz5k0AeT6+ZRRFwcrKSrsh9m/1xiWmQghOnTqFj4/Pv/7Nk6S/g6Io5OTkoNfrsbGxwczMTP4BlF7K3Nwcb29vbt68yahRoyhatCiqqppWk6Q3iqIoqKrKhg0bMBgMDBo0CAsLC/k9KL0Weefjtm3bSE5O5ttvv8Xc3Fyej28ZCwsLvvnmG0qVKmUa+ld5IxPTX3/9lRUrVmhDyCRJ+t9RFIXIyEj0ej2lS5eWw9ikV1IUhcTERNLT03F2dsbS0lJeLElvBSEEcXFxqKpKqVKl5I1w6bUSQhAfH4/BYJDn41tICIGNjQ0HDx6kZs2apuF/lTcuMQUIDAwkKChIJqaS9DdZtGgR58+fZ9OmTRQpUkQmG9IrrVixgkOHDrFt2zaKFy8uzxXpraCqKmPGjEGv17Ns2TLTsCT9o1RVZerUqcTExODp6Wkalt4CZmZmNG3alMKFC5uG/lXeyMRUkqS/1/Dhw9m+fTvh4eHY2NiYhiVJ89NPP7Fs2TLi4+Oxt7c3DUvSG6tDhw7odDouXbpkGpKkf9x7773Hs2fP8Pf3Nw1J0ltDdjlKkmREVVWys7NRVZXk5GTTsCRp8s4VgJSUFNOwJL2x9Ho9BoNBLrYoFQgGg0FblFCej9LbTCamkiTlkzd/Rc5jkSTp30x+B0oFjTwnpbeZTEwlSZIkSZIkSZKk10omppIkSZIkSZIkSdJrJRNTSZIkSZIkSZIk6bWSiakkSZIkSdK/kNyYQZKkgkRuFyNJkhFVVRk6dCi7d+8mICCAkiVLajEhBJcvX8bBwYFatWoZHWcwGLh06RLly5enfPnyRjHp7aSqKqNGjWL16tUEBQVRpkwZ0yoF3t27d4mKiqJbt25G5Xq9Hl9fXwIDAwGoVq0azZs3N1p4JCkpiatXrxITE4OVlRWNGjWievXqL7QCz5494+bNm6SlpVGsWDFatWpl9Jn6PdeuXaNs2bK4uLgQHR3N7du3iY2NRVEUatasSZMmTfIthBIcHMzt27dJTk7GwsKCli1bUrlyZaM6ABkZGVy9epXIyEhycnIoXbo0LVu2pFixYqZVjeTk5HDlyhXi4+OxtramZcuWODo6avHIyEjCw8Np1qwZALGxsTx9+pRWrVq90ErBp9fr6dixIzqdDh8fHywsLEyrFFjR0dHcvHmT+Ph4FEWhUaNG1KlTx6hOTk4O169fx8LCgubNmxvFfs+dO3ewsbGhatWq3Llzh4CAANLT07G1taVp06ZG51pWVhZXr14lIiKC7OxsSpYsme98eVF2dja3b98mMDCQnJwcrX7x4sVNqxpJT0/nypUrJCcnY29vT8uWLY22rgoMDCQtLY369esjhCAsLIyYmBiaNm1q1E5BZjAY6NOnDyEhIfj7+2Nubm5aRZLeCubTp0+fblooSdK/lxACb29vHjx4wPDhw7Gzs9Niqqoyb948tm7dykcffWR0sXbz5k2+/vpr2rZtKxPTfwkhBMeOHePmzZuMGjWKIkWKmFYpkFRV5dGjRxw6dIg5c+Zw+/Zt/vOf/2hxvV7Ptm3bWLRoESEhIdy/f5+DBw9SsmRJqlWrBrnb48ybN49t27YRFRWFj48P58+fp1GjRpQoUQKAJ0+eMHXqVC5evEhERARHjx4lKCiIli1b/uH+wH5+fkybNo1WrVpRrFgxFi9ezN69ewkLC+P69escOXIEFxcXqlSpoh3z4MEDZs6cydmzZ4mMjOTatWuUKlUqX1IihODx48fMnj2bp0+fEhAQwIEDB4iPj6d58+ZYWloa1c8jhGDv3r2sXr2a7OxsTp8+TVRUFC1atMDc3JycnBxmzZpFZGQk7dq1QwhBeHg4U6ZMoWLFiri6upo2WWCpqoqXlxeqqvLVV19hZvZmDDALCwtjzpw5HD58mPDwcPz9/bGwsNBuFGRnZ+Pn58fOnTuZOXMmAJ07dzZp5eXCwsIYN24cVatWxcXFhYULF3L+/HlCQkI4deoUV65coUmTJhQvXhwhBCEhIfzyyy88fvyYx48fc/DgQSIiImjZsiVWVlZGbQshOH/+PPPnzycgIIDAwEAOHTr0yvp5VFVlw4YN7Nixg6ysLLy9vcnKyqJRo0YoikJqaiqTJk1CURQaN24MQEBAADNmzKBRo0Y4OTmZNlkgCSHYtm0bycnJDBs27I05HyXpLxOSJEkvMBgM4ptvvhEODg4iJibGKKaqqrh06ZIoUqSI2Lt3r1aenZ0tPvnkE9GzZ0+RlZVldIz09jIYDGL48OHCyspKhIeHm4YLrISEBNG/f3/RqlUrUb16ddG9e3ctpqqquHfvnihfvryYP3++SExMFNHR0eLHH38UtWvXFvHx8UJVVeHl5SVcXV3F0aNHRVJSkrh//75o06aN+Oqrr4SqqiI7O1t88803omXLluL+/fvi+fPn4sSJE8LV1VWsX7/e6PmYyszMFJ999pmYPHmyELnPd9KkSeLy5csiKSlJhISEiC+++EI0bdpUpKenCyGESE5OFu+//77o06eP8Pf3F0lJSSIsLOyV78vTp0/FsWPHRGJiooiLixOrV68Wtra24vLly6ZVNcnJyaJdu3Zi48aNQlVVcf36dVGrVi3x9OlTIYQQ3t7eol27diIkJMTouN9++0307t1bZGRkGJUXZDqdTrRp00Y0b95c6HQ603CBlJmZKYYPHy5atWolfHx8xPPnz0VUVJQIDg4WIvfc9vPzE126dBHt2rUTLi4uYvz48abNvJROpxMTJ04Un3/+uTAYDCI1NVUcPnxYhIeHi+fPn4urV6+KsmXLikmTJgmR+1ihoaHC29tbJCQkiPj4eLF582ZhZ2cnjh8/btq8MBgMYuvWrWLVqlUiPj5eJCYmit27dwsHBwdx9OhR0+qa6Oho0aBBA3HixAmhqqo4cuSIqF+/voiPjxdCCLFu3TrxzjvviMTERO0Yg8EgfvzxRzFw4ECh1+tfaK3g0uv1omfPnqJ27dpvzHOWpP+GTEwlSTLye4mpyL3gGDhwoGjdurXIzMwUQghx/vx54ezsLK5cuWJUNyYmRpw7d04cOnRI3LhxQ+Tk5GgxnU4nbt26JQ4fPiyOHTsmHj16ZHSsVPC9qYlpbGys+Pnnn8WlS5fEzJkzRdeuXbWYwWAQixcvFrVr1zY65vnz58LV1VUcOHBAGAwG0a1bNzF06FCjOmfOnBElS5YUcXFxIjo6WlSoUEHs2bPHqM6YMWNEjx49XnlxqaqquHLliqhTp46IjY0VQgiRk5MjVFU1qufv7y/s7e1FeHi4UFVV7N27V1SoUOGln9k/IzMzUzg7O4udO3eahjTBwcGicePGIiIiQitr0KCB8PX1FbGxsaJ79+4vPT4+Pl7Ur19fnDhxwjRUYL1piamqquLGjRvCxcVF+Pv7m4aFeKHOlClTxIMHD8SHH34ofv75Z9NqL/Xs2TNRu3ZtcevWLdOQ5tNPPxX9+/c3LdaoqioqVaokVq1aZRoSQoiX3tRs06aNmDFjhmmxELnt3bp1SzRt2lT7W5SRkSGqVasmQkNDRVBQkGjXrp04f/686aHi2bNnokaNGsLPz880VCDJxFT6t5BjASRJ+ksURWHUqFEEBARw7Ngx9Ho9ixYtokOHDrRo0UKrFxISwuzZs1m+fDlbt25lxowZbN26FSEEer2e3bt3M2vWLLZs2cLatWvZvn270eNI0t/FyckJNzc3WrVqhaqq+RaAiYqKyjcPrkiRIri4uHD79m0URSExMZFSpUoZ1alatSrJyclERESQmZmJTqfLNz+uRo0aBAUFkZaWZlSeRwjB/v37adasmTbM0NLSMt9c0vDwcIoVK4a1tTVCCHbv3k3Pnj2xtbXl5s2b3L59m6SkJKNjXiU7O5tLly5RpkyZfHPHX2RnZ4e1tTWXLl0iNTWV69evU6hQIVxcXPD09MTFxYWPPvrI9DAcHR1p27YtBw4cMA1J/yNCCI4fP07NmjWpVasWt2/f5ubNm8TFxWl18oaz/vLLL9SsWROdTpfv3H8ZVVXx8fGhRIkSNGzY0DSMwWAgODiY+Ph42rVrZxqG3DmtV69epXjx4jRo0MA0DIC1tbXR71lZWaSlpb1yTraiKDg4OGjzwVNSUjh79iylSpWicOHCLFmyhFatWr30OVWoUIHatWtz5MgR05AkSa+RTEwlSfrL6tatywcffMCSJUs4duwY169fZ+zYsVo8MzMTNzc37ty5w9ChQ5k6dSrt2rVj5syZPHjwgISEBMaPH0/NmjWZPHkyP/zww0sveCTp76SqKqqqGpUpikK9evV4+PAh/v7+CCEwGAzcunWL0NBQ0tLSUBSFunXrcubMGWJjYyH3Ivr06dOoqkpiYiLFixfH0dERb29vMjIyAIiPj+fq1aukpqai1+uNHjePXq/n7t27v7sgTXR0NB4eHnTt2hUHBweys7MJDAzE1taW1atXs2rVKubNm8dvv/1GRESE6eGQm8jcuHGDTZs2sWjRIhYvXszgwYOpXbu2aVWNo6Mj77//Ptu2bcPLywtPT0/69etHREQEx44dY9q0aaSnpxMfH2/0ugoh6Ny5M9evX8dgMBi1Kf1v5L2frq6ueHh44OHhwaJFi5g5cyaPHz82rf6X3oe8+Z9t27Y1Ko+JiWHr1q2sXr2aadOmUalSJb744guj4/z9/dm8eTNLlizh119/5ZNPPtHmu/4eIQS7du0iNTWVjh07moY1rq6udO/endWrV7Nx40a2b9/O4MGDuXr1Kvfv32fixImkpKSQkJBgdJyqqnTq1IkLFy4YlUuS9HrJxFSSpL/M3NyckSNHEhgYyLhx4+jTp4+WWAohePr0Kdu2bePrr7+mZs2a2Nra8tFHH2EwGDh48CA6nY709HQqVKhAuXLlaNWqFb179zZ9GEn6xymKQrdu3ahbty7jxo1j/fr1eHl54eHhgbm5ubYw0DfffENMTAzTp09ny5YtrFmzhgMHDlCoUCHMzc0pUqQI33zzDQcOHGDx4sVs27YNd3d3Hjx4gK2tbb4e0Dzp6emkpKRQtWpV0xAADx8+ZO7cuTx//pwxY8Zgbm5ORkYGGRkZXLp0ifT0dHr16kWbNm3Yt28f8+bNe2UScu/ePby9vTlx4gQZGRnY2tq+sieXF0ZL9O7dm+TkZFq2bMlXX33FwoUL+eKLL9DpdHh4eODp6cnmzZtJSUnRjq1UqRJRUVFkZWUZtSn9bwghiIuLw8/Pj/DwcLp27Urnzp25ceMGY8eO/T+97kIIgoODqVixolFZfHw83t7eHD16lPv37+Po6GjUSy+EICAgAG9vb44fP05SUhJFihQhOTlZq/MyycnJbNq0iWXLljFq1Kh8K12/yNzcnMmTJ9O6dWuSk5Pp1asXPXv2ZMmSJYwYMYKgoCBWrVrF6tWr2bVrl/Y6KIpClSpVCAkJMW1SkqTXSCamkiT9V+rUqcM777xDUFAQw4cPN4oFBgaSmprK5cuX+fXXX5k3bx4LFizA1taW9PR0XFxc+Oqrr1i1ahVTp07l8OHDRhexkvQ6lShRgmXLluHi4oK3tzfnz5+nYcOGVK9eXRte27JlS+bPn09MTAwHDhzA39+f999/Hzs7O21V3qFDhzJ8+HCuXbvGwYMHSU5OpmfPnpQsWTLfsMU8WVlZZGdn51vhWAjBmTNnmD59OsHBwSxdulQbdiuEICMjg4oVKzJlyhT69u3Ld999x6RJk9i4cSPx8fFGbZF7Yd6vXz+WLVuGl5cXgwcPZubMmezbt8+0qhFLS0sGDRrEpEmTGDRoENu3b0cIwfvvv8+kSZMICgrCYDCwbt06o7by/r/Z2dkvtCb9L+n1egoVKsScOXPo168fgwYNws3NjVOnTvHgwQPT6n+JTqfD1tZW+11RFKpWrcqSJUvw8PBg8eLF+Pr6MmHCBG00gJmZGb1792bp0qWsX7+eESNGMH/+fLZs2fJCy8ZCQ0OZN28enp6efPnll4wYMcK0Sj52dnba+f7xxx+zYsUKKlWqRNOmTRk/fjxxcXHodDqWLFnC2bNnteNsbGwwGAz5Rk1IkvT6yMRUkqT/ihCCZs2aUaVKFSpUqGAUU1UVCwsLypcvT4UKFahYsSIVKlRg5MiR9O7dGzMzM3755ReGDx9OYGAg06ZNY9WqVeh0OqN2JOl1qVWrFuvWrWPJkiUsWrSIQYMGERYWpm05AdC7d2+2bt2Km5sbK1eupHLlytjZ2Wn7uVpYWDBq1CjWr1/Pb7/9xvz587WRAi9uw/Qic3NzbeuVPEIITp06xaRJkyhVqhTr1q0zGvpua2uLnZ0djRo10soAmjdvTkZGBqmpqUbleQoXLkzJkiVxdXXls88+o3Xr1mzdutW02ivdu3ePLVu2MHnyZG7dukVERATLli1j8uTJ/Oc//2HHjh1ab63BYEAIIbe5+JsoioKTkxP16tUzeo2rVatGiRIlCAsLM6r/V5mZmeX7frayssLJyQkXFxfatGnDxIkT2blzp9GwWVtbW+0c69evH927d2fLli2Il8xtjYmJ4ZdffuHy5ctMnz493w3PP+PixYucPn2aCRMmcPToUczNzXFzc2Pq1Kl06dKFHTt2aHUNBgOKoshzUpIKEPlplCTpvyJy596ZmZnlGypYpkwZ7Ozs6NGjB6NHj9Z+vv32W5o3b47BYMDa2poBAwawf/9++vTpw2+//UZ0dLRRO5L0urm6ulK8eHE2bNhA4cKF882Ftra2pkKFClqPTPfu3SlatKhRHQcHB8qVK0dISAhHjhz53WHrdnZ22NraGs0NjYmJYdKkSbzzzjssXbo038JMNjY2NGzYkGvXrhmV+/v7U7x4cezt7SG3Ry2Pac+lEIKcnByt7h/JyMhg0aJFdO/enXr16hEYGIiLi4t2kV+uXDmysrK03qiEhARsbW2Net2k/x1FUWjdujV+fn5GNzWCgoLIysqiXLlyRvXzvGpI+YvyFhmKiYnRyl58jDypqanY29tr54DpOZZXVqRIEe1x885Jg8HA+vXruXPnDps2bfrTe6u+KDExkYULF/Lll1/i6upKUFAQlStX1uLly5fXhqoLIYiNjc33WZUk6fWSiakkSf+1nJwc0tPTje5+K4pCjRo1aNGiBePHj+fs2bP4+/tz/vx5NmzYQHJyMjExMezatYurV69y69Yt7OzsXrnyoiT9nXJycsjMzDQqy8rK4ty5c1y7do2bN2+yefNm1q1bx+jRo7XELSEhgRMnTnD79m0uX77MkiVLePbsGUOHDoXcC9+HDx9y9uxZ/Pz8OHv2LL/88guVKlWib9++Ro/3okKFClGuXDn8/Pwgt51r165x/fp16tWrx/Hjxzl69Kj2ExkZiZmZGQMGDODWrVusXbuW27dvc/r0aVauXEn//v1xdHQkMTGRpUuX8uDBA4QQHDx4kKNHj+Ln58eNGzdYv349/v7+/Oc//zF9SvnkraodERHByJEjEUJQp04dwsLCuHr1Ko8ePcLb25smTZpoc3J9fX2pXbs2VlZWps1J/wOKotCnTx+Sk5NZvHgxt27d4uLFiyxevJgWLVrkW9RKCKENG/8jiqLQpEkTbt++DbnH3r9/Hy8vL65du4a/vz/Hjh1jxYoVfPLJJ5QoUQIhBEePHuXQoUPaCsGbNm3i0qVL2jkWHh7OkiVLCA8Px2AwsHr1aqpXr87jx4+18/vIkSP4+vqaPKP8srKyWLt2LVZWVgwaNAghBA0bNsTPz487d+5w//59Tp8+TatWrSD3/+Tr62s0AkKSpNdPES8bTyFJ0r+WqqoMHTqU3bt3ExAQ8MqEUeRuUbFt2zY2bdqUb2jirVu3mDx5MuT2oKakpFC0aFEWLFhAdHQ0o0ePpnDhwtjY2BATE8PHH3/MgAEDjNqQCjZVVRk1ahSrV68mKChIG8L6plBVFQ8PD27fvo2np6dWnpKSwtixY0lLS8PKyoro6Gi6dOnC6NGjtTqPHz9m9OjRODs7k5mZSWpqKsOGDeOdd96B3M/Hzp072bx5M6VLlyYhIQEbGxumT5/+yoWNyD1u27ZtrF69mhMnTmBpacm+ffuYPXu20fY0QggUReGnn36iU6dOACxYsIBDhw5RpUoV4uPjKVasGHPmzMHFxYUnT57Qt29ffvnlFz788ENmzZrFuXPnqFixIgaDgYiICHr37v2n5vQlJSUxcuRIvvzyS7p06QK5icH06dOJjY2lbNmyhIaGMnHiRKpVq0ZWVha9e/emX79+fPvtt6bNFUh6vZ6OHTui0+nw8fHBwsLCtEqBtHXrVjw8PKhcuTIZGRno9Xpmz55NjRo1jOoZDAZ+/PFHypcvz5gxY4xipkTuir+DBg3i7NmzODk5cf78eSZPnkz58uWxtbUlOjoaFxcXpk+fjrOzM6qqsmjRIry9valYsSKKohAeHk7nzp0ZM2YMZmZmXLlyhcGDB7N27VqaN2+unUuFChXSHttgMFC/fn3c3NxeeEb5hYeH88MPPzBlyhTq168PuYsojR8/HjMzM4oUKUJCQgIzZsygdOnSpKSk0LlzZ6ZNm8a7775r2lyBYzAY6NOnDyEhIfj7+2Nubm5aRZLeCjIxlf5QdnY2qampODo6/qlhP9Kb7c8mpuQOnYqPj6dy5cov/UOZtz1GUlISpUqVonHjxhQvXhy9Xs/9+/d5/PgxQghq1apFnTp1TA+XCrg3PTEld5hseno6lSpV0spUVeX+/fsEBARA7nxT0x6nrKwsrl27Rnh4OIULF6ZJkya4uLgY1YmPj+fatWskJSVRsmRJWrVq9aeGsiYmJtKrVy/Gjh3L+++/T0JCAmFhYfl6dhVFoXLlytqCTADXr1/nyZMnODo60r59e2xsbCD3+V69epVatWpRsmRJkpOTuXnzJpGRkRQqVIhatWpRs2bNF1p/tZSUFO7fv0/Lli2NynNycjh16hQZGRm0bt2a0qVLA3DixAkmTZrEgQMH8r1GBdWbmpgC3L9/n7t372JnZ0ebNm1wcHAwrYIQgpCQEKytrbX36fdkZGTw+eef06pVK3766Sd0Oh337t3jyZMn6HQ6ypcvT6tWrYzma6ampnLr1i3Cw8OxsrKiRo0a1K1bV4unpKRw8+ZNGjduTJEiRXjw4AHp6elGQ84VRcHe3j7f589UXFwcwcHBNG3a1Kg8LS2NU6dOIYSgQ4cO2muxefNm1qxZw8GDB/MtNFYQFeTEVAiBqqp/6TkZDIZX1s+bj/5nPnM6nU4blWEqbxqBnEP8ZpGJae6Hys/PD4PBQJMmTfLF7t27R3p6Oi1atDCK/RsIIbhw4QJ79uxh5syZcj7Gv8BfSUylf7e3ITEtqDZu3MjevXtZu3Ztvjmlb5KEhARGjBhBixYtGDlypGm4wHqTE9O/y9mzZ5k1axYeHh6/2+tf0EVERDBkyBC+/PJLPv74Y9NwgVQQE1NVVbly5Qr37t1DVVVq1KhBmzZtXpkoCiG4desWN2/exGAwULFiRdq3b6/1kCcmJuLj40NUVBQ6nY5y5crRvn37fNedqqpy/fp17t27R1ZWFkWLFqVnz54UL14ccjtTLl++TEBAAIqiULNmTVq1amX0vDIyMvD19aVt27bys13AyNsIuR+WFStWsGTJEtMQQgi8vLz49ddftbsvr1NoaCgXL140Lf5bJSQk8PTp0z81F0V6u8gecumP5J0j8lz53/r0009p164dsbGxpqE3SlJSEg0bNmTIkCGmoTeGPLf/fx07duSzzz4zWpjrTSNyFz3q1q0b/fv3Nw0XaAXtPDxy5AgTJkzg5MmTnDlzhrFjx7J7927TapD7ul+8eJGxY8dy+PBhzp8/z6RJk1i/fj2qqmqdQKtXr8bHx4fz588zY8YMFi1alG+F8t27dzN58mQOHDjAtWvX2LdvH5GRkZB7Q2nHjh2MGzeO06dPc/LkScaOHcuuXbsgN8HPyMggKSmJuXPnotPpyMzMRPbRFRyyxzT37suAAQPIzs5m586d+WI//PADjx8/5siRI699SMCKFSu0Da3/KUIIdDqdXLTiXyKvx3TPnj0EBQXlu1spSS/68ccfWblyJZGRkW90z54kmWrXrh06nY4rV66YhiTpH9e7d2+Cg4Px8/N77T2msbGxdO/ena5duzJt2jQMBgOzZs1i7969XL58Od9Iq+TkZN577z0qVqzIwoULsbCwwMPDg3nz5uHj40PVqlUJCAggOzubGjVqoNPp2LJlC2PGjOHy5cvUq1cPIQR37tzh008/5fPPP2fYsGHY2tqSnJxMoUKFsLe3JyIigvbt2/Phhx8ydepUDAYDs2fPZtu2bdy9e5fMzEwOHDiAk5MT7u7ujBo1iri4OPr16/enVySX/l4yMc29EB88eDA5OTn5Nn4WQjBmzBgeP37MwYMHCQoKQq/X51tI4OnTpwghqFy5Mv7+/lSuXJmoqCgeP36MXq+nSpUqRnMryJ1/dOfOHRISEnBycqJhw4ZaEpCcnExoaCiurq7cvXsXnU6HnZ0d7u7uXL9+HQ8PD4oVK0adOnVQFIXU1FRu375NTEwMxYoVo379+toXQ0ZGBk+fPqV8+fI8ffqUkJAQbG1tady4sdHcpIiICO7fv09SUhIODg40adIEBwcHEhISiImJoXr16tqXYWJiIvfu3SMmJgZra2sqVqxo9P+Ljo7W5lXl1XNxcaFx48banCepYMpLTLdu3cqGDRtwcHCQdxOll1IUheXLl+Pt7c3mzZtxcnIqECNL3hZ5N0Lf5NfUzMwMRVEwmGwpVdAJIRg9ejR6vZ7Fixe/9kSgoHhT388XmZubI3LnRb4pVFVlypQppKWlcfPmzdd6HaWqKvv27WPMmDHcvn1bm7cbFxdHrVq1WLVqFR988IFWXwjBpUuX6Nu3L35+fri6ukLutWm9evUYNWrUSxddi4uLo2bNmuzYsYPOnTuj0+kYNWoUkZGR7N+/37Q6Qgju3r1L+/btOXPmjLa1171792jYsCGBgYGYmZmxdu1a7t69y+XLl+nWrRt169bl66+/ljfhCwiZmL6QmOp0OjZv3mwaZsyYMQQEBHD48GFWrlzJ8ePH2bVrl9aDmJ2dzTfffEPTpk0ZMmQI77zzDr179yYqKoq4uDiSkpLIyMhg3Lhx2uqJMTEx/PbbbwQGBmJnZ8fz589p3rw5P//8M7a2tly6dInZs2fTtWtXrl69ioODA9bW1pw7d46YmBg6d+5M3bp1GT9+PCkpKSxZsoRr165hb29PWloalStXZsqUKTg6OvL48WO+//57unXrRlhYGElJSTx79owmTZowc+ZMChcuTHh4OLNnzyYuLg5ra2vS0tKYMGECLVq0YP/+/WzYsAEvLy+KFi1KVFQUCxcu5NGjRxQtWpSsrCwyMzMZMmSItj/fli1b2L17N23btiUgIIDk5GSCg4MZNmwYAwcOLHBDUqT/R1VVvvvuO1atWkXFihWxsLCQian0Uubm5kRHR5OSkkL58uWxtLSU54r01ggPDwegbNmypiFJ+sdFRkZStmxZrly5os2nfB0MBgOTJk3i+vXrnD59WitXVZUWLVrQs2dPpk+fblS+dOlSNm3axLVr17SbPAaDgU8++YRChQqxceNGeGEhpbS0NI4fP86iRYvYu3cvpUuXJi4ujs6dOzN16lQaNmxIVFQUdnZ2VK9eXVtULjY2li+++IK2bdsycOBADAYD27dv59SpU+zfvx87OzsSExMZMWIEer2eUqVKsWDBglfOi5X+eTIxzf3QDBkyhLt37zJy5EijO4FmZmZs2rQJa2trDh48iI+PD7169eLUqVPa6m/37t2jS5cu7Nmzh4YNG+Lq6kqlSpUYNWoUrVu3JjExkdmzZxMWFsapU6ewt7dn2rRpnDx5ktmzZ1OzZk0uX77M6NGjWb58OX369OHYsWO899579O3bl0GDBuHo6EhOTg5eXl5cv36dRYsWUbx4cerVq4eHhwfLly9n5syZNGvWjAcPHjB8+HCGDx/OiBEjuHv3Ls2aNaNjx46MHj2aatWqcfz4cX788Uf27NlD9+7dWbhwIW5ubhw6dIjSpUvz4MEDKleuTKVKlVi3bh2//fYbvr6+2NvbM3XqVPbt28esWbNo1qwZ8fHxeHp6cvLkSY4ePUrlypVZsmQJY8eO5fvvv+fLL7/Ezs6O3377jXPnznH16lWjnlqpYMlLTNeuXcvAgQP/1Cqi0r/XuXPnuH//PgMGDMDe3l4mptJbQVVV9u7di6qq9OvX77VP45H+3VRV5dChQxQqVAhfX9/XupKwXq/nm2++IT093Wj6m16vp1+/fpQuXRp3d3et3GAw8PPPP/PkyRMOHjyodUwYDAaGDx9OSEgIR44cQeQuthkQEEBgYCB+fn58++23fPjhh4jcOai9e/dmzJgxBAcHExsbS1paGi1atGDYsGHaa3LgwAFGjBih7Rd98OBB1qxZQ9euXVFVlcOHD3Py5EmmT5/OmDFj+P777/MtfCq9RkISBoNBfPPNN8LJyUn07NlTvPPOO9pPz549Rbly5cR7770nhBAiOztbdOrUSYwdO1Y7fubMmaJjx45CCCGSkpKEi4uLmDBhghYXQohHjx6J4sWLi9OnT4uEhARRunRp4ebmJp49eyYeP34sgoKCRJs2bcR//vMfIYQQp06dEjY2NuLw4cNG7cybN0906tRJ+z05OVnUq1dPfP/99yIkJERr69NPPxVt2rQRqqqK+/fvCwcHB7Ft2zajtho2bChmzJghRO7/oXLlyuL+/ftCr9drdVRVFV5eXqJ+/foiOTlZREZGitKlS4tNmza90JIQmZmZolKlSmLhwoVCCCGWL18uSpYsKSIiIrQ6jx49EoULFxa3bt164UipoDEYDOLrr78WDg4O4vnz56ZhSTIyatQoYWVlJeLi4kxDkvRGa9eunWjRooVpsSS9Fu+++66oU6eO0TXa66DT6cQnn3wivvjii3zl/fv3F4MHDzYq1+v1YujQoeKDDz7IVz5y5EjRtWtXIXKvPdzd3cXHH38sOnbsKHr16iU2bdok0tLShKqq4sqVK6JQoULio48+EqtWrRKnT58Wv/76q3B2dhZr1qwRQgiRmpoqFi5cKHr27Cl+/vlnMXbsWPHOO++IZcuWiYyMDKGqqggODhbh4eFCCCGePn0qoqOjjZ6X9HrJW4C59Ho9rVq1Yu3atUY/q1evpnv37uh0OlRVxcrKis8++4wjR46QmZlJeno6Bw4cYODAgZA7DEEIQfv27Y3aL1myJC4uLoSFhRESEkJ6ejq3bt3Czc2NBQsW4Obmhqqq2p0kVVUpWbKk0b5yQgiys7ON9viKiYkhJiaG0NBQfvvtNxYsWMC8efOIi4ujUKFC2n5QRYsWpVq1akZtFS9enIyMDAD69etHlSpV+Pnnn/ntt9+4efMmqsn8CzMzM23uaN5G2HlsbGxo1qwZ/v7+kPt6VqhQwWi4SdGiRTE3NzdaYU0qmF68oylJr/LiPC15rkhvk7y/neSe55L0Oqm5K9cWlHPR2tqarKws02LS09NfOlfTxsaG7OzsfM8/KysLOzs7yL3GHDhwIKtWrWL79u18/PHHTJkyhcOHD2vXJDqdjnfffZchQ4bQqVMnxo0bR69evfDy8kIIwZkzZ1ixYgVz5szBzc2N3377jenTp7N06VJOnz6NoiiUL19e29qscuXKlCpVyug5Sa+XTExzCSEoUqQIzs7OlC5dWvspU6ZMvg9Zr169yMrKwtfXF39/fzIzM3nnnXeM6pjKycnRPoBCCMzMzKhevTr169enQYMG1KtXj2+//ZZvvvkGcr+ErK2t8y24kHfsi8zMzKhQoYLWVt26dfnss88YM2aMNskfkwU08sryPuw1atTA09OTNm3acPr0aUaNGsXVq1eN5oIqioIQAkVRjJLjPHq9Xpt3qyhKvgUS8o6V80vfHDLZkH7PixdKL/tOkKQ3Vd65LYSQ34PSa1eQklJFUShTpgzR0dFG5UIIwsLCqFKlilG5oihUrFiR8PDwfJ+l8PBwoz1xbWxsKFq0KCVLluSLL76gbt267NmzBwB7e3ucnJyoVauWVl9VVRo0aEBycjI6nY6jR49Su3Zt6tevr9Vp1qwZNWrU4MCBA1qZVHDJxDTXq5ItVVXzlZcsWZKuXbuyY8cOduzYQYcOHfLNmbx+/Xq+31NSUqhTpw4uLi4ULlyYOnXq8O233zJ06FCGDRvGl19+Sbt27Yy+fEy/iIQQRj2OJUqUwMnJCVdXV4YMGaK1NWjQILp3746iKFpCatrWi3Q6Ha6urkyYMIGdO3eSlJSEp6enUR1VVSldujTFixfn8OHDRrHo6Gj8/f1p2bKlVvZ7jydJkiRJkiS9WRRFoXnz5jx79ozAwECt/P79+yQkJNC8eXPIvbGt0+kwMzOjRYsWhIeHc/fuXa1+SEgIDx48oHPnzgBkZmZqMXJ7XzMyMrSRd6VLl6Zs2bJcuHBBq6MoCv7+/pQrVw5LS0vs7e2JjY1Fp9NpdZKTk4mOjtZWD5YKNpmY5srKynrpsARyV9198QNjZmbGJ598go+PD97e3gwYMMCovoWFBYcOHeLAgQNcv36dEydOsGDBAnr06EH16tUpVaoUvXv3Zu7cuVqd8+fPs3btWgIDA7UkOT09PV9yV6ZMGZ4+fcr58+e5d+8exYoV4+OPP2bNmjVs3bqVa9eu4ePjg5eXF7du3YLchDI9PT3f0NzMzEwtyT179ize3t5cv36d+/fv4+DgoCXbOp2OjIwM9Ho9JUuW5Msvv2TlypXs3LmTGzducP78eX777TdKly5Nr169tOTZ9EtGCEF6enq+O2aSJEmSJElSwWdmZkbbtm2pVKkSc+fO5fLly5w/fx43Nzc6depE3bp1UVUVb29v1q9fj8FgoE6dOrRu3ZqZM2fi4+PDxYsXWbBgATVr1tT2C96xYwfe3t7cuHEDHx8f3N3diY2N5ZNPPgGgWLFi9O/fn02bNnHo0CGuX7/O5s2buXz5Mp9//jmKovDee+8RExPD4sWLuXr1KpcuXWLx4sUkJSXx/vvvm/5XpAJIJqa5d1yqVauWb2/SvFjlypWpU6eOUXnLli0xNzenTJky2l5JeRRF0bZZcXd3Z+nSpZQuXZqZM2dqQ1knTZpEzZo1Wbx4MStWrMDd3Z0LFy5oQ3cdHBxo2rSp0V5ViqLQqVMn6tevz/z58zl69CiAthWMu7s7y5Ytw8PDQ4sB2NnZ0axZs3ybB9etW5cKFSpA7j6sixYtYsWKFSxdupTKlSvz3XffAVCqVCkaN26Mubk55ubm/PTTT3To0IFVq1axYsUKVq5cSUREBPPnz9f2Ti1TpgwNGjQwGnZsZWVFy5YtX+tqcpIkSZIkSdJ/z8HBgTlz5hAaGsrSpUvx8PBACMG0adOwtLREVVUuXrzI4cOH0ev12NraMmPGDHJycliyZAkeHh5ER0fz66+/UqhQIQAePnzIokWLWLlyJR4eHpw7d47x48dra7aYmZkxZMgQ2rRpw7Jly1i5ciWbN29mwIABfPTRRwC0atWKqVOncvToUe369OLFi0ydOtVoRJ9UcMntYnLFxMQghMDZ2dk0RFxcHDqdDhcXF60sLS2N9u3bM3ToUG1eKEBSUhK1a9dm8+bNVK5cmVu3blGsWDFatGiRb0Pk7OxsfH19iYuLw9HRkXr16mlDFjIyMoiNjaVs2bJYWFgYHRceHo6/vz+urq7Uq1cPcnsjr127Rnh4OEWLFqVOnTra/yU7O5uoqCicnZ2NnkNERATW1taUKFGClJQU7t69S0xMDA4ODjRv3lzbJiQlJYWkpCTKlCljNOf1xo0bhIaGUqxYMRo3bmw0FzcpKYnU1FTKlCmjJacGg4Hg4GDKlCmT77WQCg5VVRk6dCi7d+8mICBAu9mQRwjBw4cPiYmJoUmTJvlueOj1enx9falWrVq+Ie7S20VVVUaNGsXq1asJCgrSFpSQpDedXq+nY8eO6HQ6fHx88v0dlqR/ksFgoE+fPoSEhODv759v/ZHXJTIyklu3bmFtbU2LFi206wEhBMHBwaSlpVGnTh1tbZH4+HiuXbuGmZkZzZo1M1ogMzk5WbsOzZvu9rK/KaqqcvnyZWJjY6lcubLRfNI8QUFB3L9/H0VRqFOnjtYJIxV8MjH9i1RVJScnh82bNzN//nx8fHwoUaKEFk9KSqJGjRqsW7eOnj17Gh0rSW+CP0pMVVXlxx9/ZP/+/YwfP55hw4YZxVNSUujSpQvTp0+Xn4G33JuamAohiI+PJyMjA/WF1dBLliyZb9/epKQkIiIiMBgMFC1alHLlymn1ARISEoiKisLc3JyKFStqN91ycnKIiooyal8IgYWFBRYWFpQsWfIPLy4TExOxtLREr9eTlJRk9LgAlpaW2Nra4uDggBCC6Oho4uPjIff/8qrVJg0GA5GRkej1eqPnZm5ujqWlJY6OjtpCdi8TExNDbGwshQsXpmLFikYxvV5PXFwcpUuXBiA1NZXs7Gyjv5NvgjctMU1OTiY9Pd3oBjq57+uzZ89IS0vD2tqaihUr5ntvY2NjiY2NRVVVHB0d/9LnOCcnh8TEREqVKqWdS+np6YSGhpKTk4O9vT3lypXL9/rFxcVpHQKOjo75nveLUlJSCA0NBaBs2bIUK1bMtEo+WVlZBAUFAVCxYkWtVy5PdHQ0xYoVw8bGBoPBQHR09F/6f//TCmpiKkn/azIx/QuEEPj5+XH27Fl27drF559/zvfff29UJykpiZo1a7J27Vp5US69kf4oMTUYDHzyySecPn0aR0dHDh48aLSt0fPnz6lcuTIrV67U5oZIb6c3NTHNzs7ml19+ISYmBisrK0TuaufffvutNgqF3MU8tm/fTmRkJCJ3i60ZM2Zo2xsEBASwfv16EhIS0Ol01KtXj8GDB1OsWDEiIiKYN28eWVlZ2gW7mZkZOp0OGxsb5s6dq7XzMs+fP2f58uX079+fR48e4e3trV2MKoqCmZkZKSkp9OjRg88++4yAgADWrl1LcnIyWVlZFCtWjIEDB9KoUSPTpklOTua3334jMTFRe25K7kJ5iqIwffr0Vya1T5480bZmyMzMpG/fvkbbox04cICYmBiGDBmCEIKnT5+yefNmRo4ciaOjo1FbBdmbkphmZWVx//59du3ahbOzMz/88IMWS09PZ+/evVy6dAm9Xk92djZNmjRh4MCB2ginsLAwVqxYQWJiItnZ2djY2PD555/Ttm3bFx7l5YQQbN++Hb1er83xCwoKYtu2bQQFBSFydzsYN26cdqMC4OrVq+zevZvnz58jhKBKlSpMnDjRqO08wcHBrFmzhvDwcBRFwcXFhcGDB1OpUiXTqpq0tDS2bNlCYGAgQgjKly/P4MGDteQ0IiICDw8Phg8fTqlSpcjKymLlypU0aNCATp06mTZXIMjEVPq3kHNM/6KIiAiuXr3KRx99xJAhQ0zDWFlZ8emnn+Lq6moakqS3hrm5Od26daNy5cpMnTqV7Oxso7itrW2+C7mMjAx8fX05fvw4p0+fJiQkxCguSf+UrKwsPDw8yMzMpESJEjg6OubrJQwLC2PcuHHcvXuXBg0a0KpVK5ycnLRF5J4/f87YsWO5d+8eTZs2pXr16ixfvpx169ZpPaPFixenRIkS2k+pUqU4cuQIfn5+v3thKYRg8+bNXL16lZIlS1KoUCGcnJy0dhwdHdHpdGzatEn77OVtxdCsWTOaNm3KgwcPGDFiBAkJCabNY2ZmhoODQ77n5uPjw/nz5/P1qOXJzs5m/vz5REZG0rlzZ4oWLcqsWbNITEwEIDQ0lMWLF2sJqKIoODo6cu/ePdzd3U1ak/6v4uLiWLRoEVOnTmXZsmU8fPhQiwkhiIiIYO/evZQuXZrWrVtTsWJFlixZwubNm7U6cXFxpKSk0KRJE5o3b05kZCQjRowgLCzshUd6uQcPHrB06VKttzQhIYHJkydz7tw5atWqRevWrXFxcTFa8PD27duMGTOG2NhYmjRpQtOmTbG3t+dlfSRZWVn88ssvHDt2jIYNG9KwYUNOnTrF1KlTX7lYpRCCc+fOsWHDBlq0aEHz5s3ZuHEjZ86cgdzFHJcuXUpUVJS23oWFhQWFCxdm1qxZ2ogDSZJeEyH9JdnZ2SIpKcm0WKOqqsjJyREGg8E0JElvBIPBIL755hvh4OAgYmJiTMNCr9eLfv36ie+++07cvHlTODs7i02bNmnxxMREUaZMGbFr1y6tLC4uTsyePVv069dPvP/++6JPnz5iwIAB4vLly1od6c1jMBjE8OHDhZWVlQgPDzcNF1hxcXGiePHi4unTp6YhIYQQOp1OTJgwQbRo0ULEx8ebhoWqqmLHjh2iTJkyRv/v3377TZQrV04kJiYa1c8TGxsrmjZtKk6fPm0aMhIeHi4aNmwozp07ZxrSrFy5UnTv3l1kZmYKIYTIyMgwit+9e1dYW1uLixcvGpW/SkZGhmjfvr3YunWraUgTGRkpGjZsKB4/fixE7vtft25dcf36daHX68WYMWPEyJEjTQ8T169fF3Xq1BGBgYGmoQJLp9OJNm3aiObNmwudTmcafu1UVRU3b94Ubdq0ETNmzBA9e/YU3333nVGdyMhIce/ePaOyKVOmiHr16mm/Z2VlGcVDQ0OFvb292L9/v1G5qZycHDFy5EgxbNgwIXKfz7Jly0StWrXEs2fPTKsLkXuOffzxx+Kjjz4Ser3eKKaqqtHvQggRHBwsChUqJA4fPqyVHTt2TBQrVkwEBAQY1c2j1+vF999/LyZNmqSVjR8/XnueJ06cEG3bthWhoaEvHPX/v989evQQCxcuNCovKPR6vejZs6eoXbt2vtdOkt4mssf0L7KysjJa5MeUoihYWloarUYrSW+j7OxsGjVqxLBhw5g5c+Yr77AbDAZWrFjBxo0b6d27N5MnT2bkyJEkJSUxZswYYmJiTA+RpL9VWloaZmZmWFtbm4Ygd27nrl27mDx5Mo6OjqSmphrFhRAcPHiQTp06GQ1f7t+/P8+fP+fRo0dG9cn9HMyZM4fSpUv/7nBBIQRHjx7FwcGBdu3amYYhd/+/JUuW8N1332lzWk3n0JG7z7Xp4mSvsnz5cvR6PR988IFpSKPX6zEzM9N6adPS0jA3N8fW1pYLFy5w/fp1xo8fb3oYDRs2pGLFiuzdu9c0JP2XFEWhYsWKrF+/nqlTp+Lk5JRvK7bSpUtTu3Zto7KaNWuSkZGh/W76Gcgbsv571znkztE8e/YsgwcPhtxzYePGjYwYMYIKFSqQmppq1AuaN6z78uXLzJ49GzMzM9LS0rS46fxpXujZf/F6ytzcHEdHx98dcWAwGIweOzs7myJFipCUlMTixYsZMmRIvlFt5ubmDBgwgB07dhjtFS9J0j9LZk+SJP2fjBw5kmLFijFnzhxUVcXMzMzooiAmJobVq1czceJEvvzySxo1akTnzp1xd3fn2bNnHD9+3Kg9Sfo75Q1fzMjIYM+ePWzdupUzZ87w/PlzLR4YGEhqaiouLi5s2rQJDw8PPD09tcVUDAYDgYGBVKtWzajtokWL4uTkZLTpfJ6goCAOHDjAjz/+aBoyoqoqZ86coWPHji+9WM8b5luyZEl69eplFIuIiOD+/ftcunSJ7du3895771GrVi2jOi8TFxfHxo0bGTZsWL5E5UVOTk5Ur16ddevWceHCBTw9Palduzb29vYsXLiQ77//3mguYR4zMzO6du3KuXPnTEPS/4GDgwNVqlTBYDD8qWQqJyeHAwcO0KpVK6PyqKgo7t+/z5UrV/Dy8qJbt240btzYqM6LhBDcu3cPS0tLGjVqhBCC0NBQnj9/TsWKFdmxYwerVq3C3d1dG14sctfoKFy4MHq9Hk9PT9zd3dm8efMrb066uLjwzTffsHz5cg4ePMjBgwe1tQtMF93KoygK7du3x8fHh1OnTnH48GEePnxI37598fLywsHBgc8//9z0MBRFoWXLlmRkZBAQEGAaliTpHyITU0mS/k+KFSvGrFmz2LdvHydOnMi3FdDDhw+xsLCgY8eORuWlS5emevXq3Llzx6hckv5uVlZWtGnThuPHj7Nu3TqmT5/OnDlztLmSz549Q1EUtmzZwtmzZwkODmbt2rWMHz+e+Ph4VFUlMzMzX6+SmZkZRYoUITk52agcYPPmzVSoUOGVvaB5MjMziY6Ozrd3dp7Y2Fi2bt3KsGHDjHqNhBCcOHGCxYsXM378eI4fP86AAQOwtLQ0Ot6UEIJ9+/Zhbm5O3759TcNGbGxsGDt2LAkJCezatYsnT54wduxYduzYgaOjI++99x4XLlzg+PHjPHjwwOjYxo0b8/jx41fODZT+XikpKaxfv567d+8ydOhQrVxVVc6dO8fixYuZOnUqu3fvpn///r/b0y6E4P79+1SrVk27eRIbG0tqaipHjx7lyJEjhISEsH37dn788UeePXsGuQsZ6fV61q5di6+vL0+ePGH+/PnMnTvXqBc3T95aBnfu3HSh950AAP/0SURBVGHlypWsWrWKmzdv0rVr11f2mJqZmfHuu+/SoUMHtmzZwsGDB+nXrx+FChViz549TJ48mXv37nH8+HF8fHzIzMzUji1dujS2trY8efLEqE1Jkv45MjGVJOn/rGvXrnz66adMnTqV8PBwo8VTdDrdK4e2v6pckv4uiqJQs2ZNtm3bxqZNm/Dy8mLgwIFs3LiRTZs2oSgK/x979x0VxfX/Dfw9CyxVKaKgqNiwI4oiYjd2jWiisZtiLFixl8RgI6gRwQYIFkBQUUGxd+wFEQGVjvQqvbNt7vPHb5mHWbAk3ySCua9zck68986d3eXO7nzmtvLycuTl5UFfXx8HDhzAwYMHceDAATx8+BCnT59+701xNcWFv6oDublz5360zZeXl6OysrLOPbUJIbhx4wZYlq3VWwoAxsbGMDc3x9dffw1zc3Ps2bOH6+V9n4qKCnh7e+O7776DlpaWYnYtZmZmcHNzw4oVK7Bnzx4IBAJcuHABGzZswLFjx+Du7o5Tp07BwcEBERER3HF6enqorKykgeln8PbtWzg5OcHb2xsbN26ElZUVl8cwDFq1agVzc3OMGjUKgwcPhqurK968ecOroyZCCLKysnjDYSUSCXJzcyEUCrF3714cOHAA7u7uiI+Px+HDhyEQCFBeXo6srCyYm5vDzc0Nrq6u2LZtG7y8vPDw4UPeOSB/QLRz507MmzcPO3fuhL29PWxsbLBr1y4u2K2LlpYWtm7dis2bN8POzg4//vgjHB0dMXnyZFRVVWHnzp04ffo09u/fD19fX26ET/Ww9LoWDKMo6t/x4V9IiqKoT7RhwwZIJBLs3bsXhBDuSXq7du1QVVWFFy9e8MpnZ2cjNTW1zs2xKeqfJBQKoaenx+3ZOHfuXEyePBkBAQGA/MZWVVUVixcvhqamJhiGgYWFBUaMGIGbN29CWVkZampqKCoq4tUrlUpRWFjIG85KCMHdu3dRVVWF0aNH88rXhWVZsCxbK7iF/Ob/xIkTGDt2LLeiaDWGYfDVV19h0aJFWL9+PX777Te8fPkSnp6evHI1EUIQFhaGt2/fYtq0aYrZ76WhoYH27duDEIJt27Zh1qxZUFJSgpubGxYuXIht27ZBTU2NtxIvwzBg5FvSUP+eyMhI2NnZITg4GFu2bKk1jJVhGAwcOBCLFi3C2rVrufUCXFxceOUUSaVSXhtVUVGBlpYW5syZA11dXUA+n/Xbb7/FkydPAHm70dfXx8yZMyEUCqGsrAxra2uYmJjg0aNHXF2QXwfnzp3D27dvsWnTJvTs2RO9evXC+vXr8e7dO/j5+fHK16VNmzYwMjKCl5cXysrKMG/ePOzYsQPt2rWDvb09pk2bhoMHDyIjI4M7RklJibZRivqMaGBKUdSfJhaLIZFIeGkGBgbYsmUL/P39kZGRwQtMx48fDycnJ9y/fx9xcXF4+fIldu/ejRYtWmDUqFG8eijqc6gZ6LVr1w5CoRDl5eW8MpAHtQDQsWPHWsPQ09PTUV5ezpvXybIsLly4AAsLCzRt2pRXvi5qampQVVXl5rzWlJycjFevXuGbb75RzKqlZcuWMDU1/eCwxOphvN27d//gvpDvc+LECUgkEsybNw/3799Hp06dMHjwYLRu3RozZ85EdHQ09z1RUVEBhmE+OIeV+uvqmo+cm5uLTZs2gRACT0/PT/qu1dHRgZWVFRISEhSzOAzDQE9Pj7e1iqGhIXR1dWtdM4QQ7m/eoUMHQD5Hu5pMJquzXRBCkJSUBAMDA94oA4FAgNatW9c5j7susbGx8Pb2xvr16yEWixEfH48FCxagRYsW+Pbbb6Gtrc29V0IIKioqPmnkAEVR/wwamFIU9acwDIN27dqhdevWilmwtrbG1KlT0blzZ+6pubKyMrZs2YKWLVvC0dERzs7OOHDgAOLi4uDg4AADAwPFaijqH5WVlYXMzExUVVWhoqICERERuHnzJoYOHQrIg84OHTrg0KFDKC4uRmVlJcLDwxESEsL1ek6aNAnPnj1DSEgIqqqqkJeXh8OHD6Nfv368a6OoqAh3797FmDFjuLQP0dTUhK6ubq2Vfat7XjU0NGBqasrLk0qliIyMxLt37yASiVBeXo6XL18iJiYGAwcOBOR7QoaFhXHzaCFfSTUoKAiDBg366BBjRVFRUThx4gTWr1/P7QOZn5+P0tJSyGQyREdHo2nTptyw57i4OLRs2bLWHHTq7yGTyXgBH8uyuHHjBu7fvw97e3uoqakhNzcXeXl5yM/Ph1QqBSEEUVFRyMrKgkgkQkVFBd68eYMnT55gyJAhvPoVmZiYcG2UYRgYGxuja9eu8PLyQn5+PqqqqhATE4O7d+9ydVlYWEBFRQW+vr4oLy9HeXk5bt++jdzcXK6dJiQkIC4uDgKBAObm5khISEBISAgqKytRUVGB0NBQvHz5En379uW9nrqUlZVh7969GDx4MKysrCCRSKCqqoq4uDhAviCZWCyGvr4+AKC4uBgVFRV1/rZRFPXvYEj14HqKoij5DY2NjQ38/f0RExODZs2a8fIJIYiNjYWSkhJMTEx4eQBQWFiIiIgImJqaokmTJlx6VVUVbty4gcTEROjr62PkyJF1zqOjGg6WZWFrawsPDw8kJibytk6prwghOHv2LIKCgtCtWzdIJBJuERQPDw9u3pyfnx927tyJiRMnokmTJrh37x6UlZVx5MgRNG7cmBsaWFhYiHHjxiE3Nxd3797Frl27uJtsQgieP3+OUaNG4enTp5+0Qm718NjY2FicPHmSS5fJZFi0aBFSUlJqrWQtFovxyy+/QCqVomPHjqioqMCTJ0+gqqqKAwcOQF9fH0lJSfjhhx+wYcMGjBs3DgAQHx+PQYMGwdvb+5OGGVerqKjAmjVr0KxZM2zZsgWQ9xbPmzcPw4YNg7GxMc6cOYOZM2diypQpIIRgwYIF0NHRwe7duxWrq5ekUimGDRvGtY+6hlbXFzKZDD/88AMaN24MV1dXQH5tbtq0CceOHcO8efO4LX4IIVBXV8eCBQtgZGSELVu2oKCgAJ07d4ZEIsHz589RXl4ONze3917PhBBER0dj8uTJuH37Nlfu5s2bWL9+PUaOHInWrVvj2bNnKCgowOHDh2FkZARCCHbv3o2AgABMnjwZSkpKuH79Orp164Y9e/YAAFatWgWRSIRDhw4hNzcXixYtQkVFBUaNGgVCCG7fvg0VFRUcOXKECyjrQgjBhQsX4OrqilOnTqFJkyaQSqXYtm0bEhISMGXKFAQHB6OkpAQHDhyAsrIygoKCsGbNGty+fRt6enqKVX5WMpkM1tbWSElJQURExEfnuVNUQ6W0pfpXhaIoSv6DfvnyZURFRWHp0qXQ1NTk5TMMA319fV7QWZO6ujratGkDDQ0NXrqysjI6d+4MKysrmJmZ0eFSXwBCCK5fv47Q0FDY2trWmvdYX5WXlyM0NBRZWVkoKCiAiYkJ7OzseIu5dO/eHbq6uggPD0dubi5MTEywefNm7oZVKBRiwIABSEtLQ1JSEiQSCZYsWVKrp6mgoAAGBgYYOXJkreGK76Onpwd3d3eMHDmSd4NcUFCAAQMGoEuXLrzy1XtCVvd+FRUVwdTUFL/99ht3nVZWViIqKgp9+/bleoQqKiqgpKSECRMm/KnrsaSkBHFxcVi2bBm3f2rjxo3Rrl07BAcHIycnB4MHD8bMmTMBedDq6OiI1atXN5jeKJZl4e3tDZZlP2nRqs9NJpOhU6dO6Ny5M5cmkUigo6ODoqIiSCQSSCQSiMViEEJgaWkJHR0dSCQSvHnzBllZWdx2L5s3b35vUAr5b4CWlhYePXqEoqIibvuZ9u3bo1WrVoiIiEBOTg6aNWsGOzs7GBsbc8dZWlpCJpPhzZs3KCwsRL9+/bBx40ZuiHxMTAwaN26MgQMHQlNTE0OGDEF2djYSEhKQl5eHTp06YevWrbUemCoihCA4OBhjx47lVrgWCATo0aMHkpOTkZCQAC0tLaxcuRLa2tpgWRa7d++GsbExJk+erFjdZ0cIwalTp1BcXIxFixbV+/ZIUX8V7TGlKIrnYz2mFFWtIfaY1iQSiSAUCuucn1eT4kIviiQSyUe3ZfkzCCFYvHgx1NTUsHPnzk8OaCEfmfBPD5dl5fsVv0/Nz6uyshI7duxAYmIit+pxQ9CQekz/Dp96LdR05coV7Ny5E15eXmjfvj0vTywW81Znr8vH2lFN1cOUP7WnkNRYgK8uIpGIu64IIXj69CmWL1+O48ePf9LIhn8b7TGl/is+7RuBoiiKor4wqqqqH7x5rfaxoOTvDEoh71las2YNcnJykJ6erpj9Qf90UIpP2Oap5ueVk5ODxMREbNiw4ZM+a+rz+NRroaYxY8agf//+ePDggWLWR4NSfEI7qklJSelPBWMfey81H/ZIpVLcvn0bc+bMqZdBqSLan0R9yT79W4GiqP8EgUAAgUAAhmE+6eaC+u8SCATczeKf6dWjPq59+/b4/fffaw2Jb2i0tLRgZ2fHDadsKJSVlcHIt7j52IOJ/yolJSVs2bLlkxYiqs+UlZUxYcIE2NraKmbVG0pKStzvMm2P1JesQQ7lLS0tRVlZmWIyRVF/k/Xr1+PSpUt49uwZtLW16RNa6r3s7Ozg7e2NsLAwNGnShLaVv5FQKKy12mpDU93TJRaLFbPqLYZhIJVKMWXKFEilUly5cgWEENq261D995VIJA3286l+CCuRSOrtHqYsy+LHH39Eeno67t+/D5ZlG+znTdWNka/f8V9/8NDgAlNCCM6cOYNLly59dKgGRVF/DsMwEAgEePz4MdLS0jBhwgSoq6vTH0Cqluq28uzZM8THx2PSpEnQ0tKqtzd2FPWpGIYBIQQ3btwAIQSjR4+GQCCg34PUZ1HdHu/evYvy8nKMHz+etscvkFAoxJYtW3iL8P0XNcjAdP/+/fD09PxT8xMoivo0DMMgJSUFRUVF6NKly98+f476cjAMg9TUVOTn56Nr16506Df1xSCEID4+HoQQdOzYkT4Ipz4rQgi372rnzp1pe/wCCYVC+Pj41LkN339JgwtMIV8yv7CwkF6YFPUPYBgGv/76K65evYo7d+5AT0+PPpml6sQwDLZu3YqTJ0/i0aNHaNasGe0xpRo8hmEgk8kwY8YMSKVSBAQEcL1WFPU5sCyLBQsWICMjA9euXaPt8QvEMAyMjIz+8w94G2RgSlHUP2vx4sU4ffo0kpOT0ahRI8VsiuKsWrUKLi4uyM7Ohq6urmI2RTVYQ4YMgUQiwZMnTxSzKOpfZ21tjeTkZLx69Uoxi6K+GHQsLEVRPCzLcgtZVFZWKmZTFKe6rQBARUWFYjZFNVhSqRQsy4JlWUilUsVsivpXVS+CRtsj9aWjgSlFUbXQYfIURVEUVf/Q32fqS0YDU4qiKIqiKIqiKOqzooEpRVEURVEURVEU9VnRwJSiKIqiKIqiKIr6rGhgSlHUJyOEoKSkBDKZTDELAFBZWUkXwaG+OO/evUNiYiJycnJqbYeTl5eHxMRE5OXl8dKrVVZWIjk5GcnJyaiqqlLM/qDq662mjIwMpKSkcItOfUj1udPS0t57zdYlJycHycnJdS6yIhKJeIuiFRcX8/Kp+isvL6/OdlNRUYHi4mIUFRVx/9X1t3+fkpIS3tYlWVlZSEpK+qT2LhaLkZKSgpSUFIjFYsXs98rLy0NSUhJEIpFiFiQSCcrLywH5NVRaWkq3VqGoBoJuF0NRFA/LsrCxsYG/vz9iYmLQrFkzXp6Xlxe0tLQwdepU3nFlZWU4dOgQRo4cCTMzM14e9WViWRa2trbw8PBAYmIijIyMFIs0aBUVFbh27RpCQ0NRXl4OTU1NzJ07Fx06dEBFRQVu3ryJsLAwFBUVQU1NDUOGDMGoUaOgrKwMAEhKSkJAQACysrIgFothZGSE7777Du3bt1c8VZ0uXboEFRUVjBkzBoWFhQgMDMTr168hk8nQtm1bfPfdd3V+5oQQRERE4ObNm8jOzgbLsujQoQO+++47GBgYKBbnsCyLR48e4cGDB5BKpWjdujWmTp0KLS0tQB6UHj9+HMOGDUOHDh3AsiwCAwOhra2N4cOHK1bXoEmlUgwbNgwSiQSPHj3i/qYNUWlpKV68eIFbt25hxYoVvO/0qqoqeHp6Ijs7m/ceZ86c+Unt9Pbt2ygrK8PEiRNRUVGBixcvIjQ0lGvvU6ZMqbMeQghiY2Nx7do1ZGRkQCwWo3Xr1vjuu+9gbGysWJznxYsXuHHjBiQSCQwMDDB16lQ0adIEkK9ee/LkSXTv3h29evUCy7IICgpCWVkZJk2apFhVgyGTyWBtbY2UlBRERERASUlJsQhFfRFojylFUX9KcnIy1q5di+zsbF76pUuX4Ozs3KBv4CiqmlQqxZEjR7Bz505UVFTAyMgIIpEIBQUFgDzoPHjwIEpKStCyZUtkZ2dj9erVuH//PiC/8Y6JicHLly/RrFkz6Orq4syZM9i4cSPKysoUzlZbTEwMdu3aBRUVFchkMhw9ehQ7duyAiooKdHR04OXlhe3bt7+3x+jo0aN4/vw5DAwMoKamhkOHDsHR0fGDPaeJiYnYvHkzlJWV0apVK3h5eeHSpUtc/rVr1+Dn5wc1NTUuTSqVYvv27cjMzOTSqPqBEIInT55g9+7dWL16NQ4fPlyrF7O4uBh79uzB06dPkZGRgfT0dKSnp9cqV5eUlBTs2LGD64U9ffo07OzsIJPJ0LRpUwQGBuKXX35BaWmp4qFgWRYnT57EvXv30KRJE2hra+PEiRPYtm3bB0fdZGVlYcuWLaioqICxsTHOnTuH48ePc/mPHz+Gh4cH10arV7DdvXs3oqKiuHIURdVThKIoqgaZTEbmz59PdHV1SU5OjmI2ycjIIG3btiX29vZcWkFBAenTpw/ZuHEjrywhhIjFYlJRUaGYzKmoqCBVVVWKyVQDIJPJyNKlS4lQKCTp6emK2Q3akydPiLGxMTlz5gwvXSqVEpZlSVxcHLl9+zaXXllZSaytrcl3333HpeXl5fHa/u3bt4lQKCQhISFcWl0kEglZsmQJWbx4MSGEkNzcXGJsbEwcHBy4MtevXyfKysokKiqqxpH/p6KiggQGBpKSkhIu7cyZM0RfX58kJSXxylZjWZYcPnyYjB07lktzdXUl48ePJ0R+3Q8fPpwEBgbWOOr/Po+pU6cSOzs7XnpDJ5FIyMCBA4mlpSWRSCSK2Q2CVColc+fOJSNHjiTz5s0jJiYmJCUlhVfm7du3pEuXLuTBgweEyNsBy7K8MnWRSqVk69atZOrUqYQQQsrLy0n37t3JypUruTJPnz4l2tra5NatWzWO/D9SqZRcuHCB5OXlcWl3794lurq65MWLF7yy1ViWJefOnSP9+vXj0vz8/MiQIUMIIYTk5+eTiRMnEg8PjxpH/Z9ly5aRBQsWfNJ7q4+kUikZN24c6datG5FKpYrZFPXFoD2mFEX9KS1atMDixYtx+PBhrpckICAA+fn5WLZsGa/s8+fP4erqir1798LLy4vXq5KdnY0TJ05g//792Lt3L27evMk7lqI+F6lUCk9PT1hZWeG7777j5SkpKYFhGJiYmPCGr6qpqcHKygp5eXncPNQmTZpAXV2dK2NsbAwdHR3u3++TkZGBO3fuYNGiRYD89RBC0KFDB65Mp06dIBQK6+wxVVNTw8SJE9GoUSMuzcrKCurq6u+dC0sIQUFBAZo2bcql6ejocHMNXVxcYGJigokTJ9Y46v8+DxsbG5w7dw6FhYW8POrzYhgGs2fPxvHjx/Hzzz8rZoMQgvLycjAMg+bNm3PHfMo+mfn5+bhw4QJsbGwAeRuVSqW8YbvV7b3mnORqSkpKsLa25obgAoClpSW0tLQ+2EaLi4uhoaHBpTVq1IgbpXP8+HGoqqrip59+qnHU/1mwYAEePnyI5ORkxSyKouoRGphSFPWnff/99xAKhfDy8kJeXh4OHjyI+fPnczc3ABAUFIT169fj4cOHiI6Ohre3N+zt7VFRUYHKykrs3r0bnp6eiImJwYsXL/Do0SPeOSjqcyktLUVoaChGjRqFJ0+e4NixY/Dz80NCQoJiUY5EIkFwcDC6dOkCgeD//7Tm5+cjJSUFUVFROH/+PL766it07dqVd2xNhBA8fvwYhoaGXDk9PT3MmjULZ86cwZMnTxAcHIwDBw5g4sSJvGC1Wl2BxbNnz6CiooLWrVsrZgHyY8zNzREVFYXg4GCEh4fj6tWrmDhxIp48eYIHDx5g06ZNiocBAMzNzaGqqorg4GDFLOozEggEGDZsGAwNDd+7AFBZWRny8vLw6NEj3Lp1CxEREXU+7KiJEILIyEhIJBIMHDgQAKCpqYkffvgB169fx/379/HixQu4u7ujZ8+esLKyUqyiTs+fP4eSktIH22i3bt1QWlqKW7du4dWrV7h06RIGDhyI6OhoBAQEwM7Ors7pJJ06dYK+vj4ePnyomEVRVD1CA1OKov60Zs2aYfHixfD29oaLiwukUinmzZvH5RcUFGDLli3o2bMn/P39cfz4cTg5OeHChQt49OgR8vLy4Obmhnnz5sHT0xNnz57FwoULeeegqM+luLgYOTk5iImJgaenJy5fvowjR45g3bp1iI2NVSwOsViMEydOIDo6GjNmzODSCSG4fv06nJycsHHjRpw5cwYrVqzg9fgoIoTgxYsXMDU15QJcoVCIGTNmIDQ0FA4ODti7dy98fX0xa9YsbmGiD3n16hUOHDiAKVOm8Ba+qYlhGPTv3x+jRo3C4cOH4evrC319fYwbNw6Ojo5YsGABGjduzAWtNVdQ1dTURPv27fHixQtenVT9UFdAWk1VVRXGxsa4fPkyXF1d8csvv8Dd3f2D8zyrA9MOHTpARUUFkPeAzpgxA2/fvsW2bdvg4uKCQ4cO4ZtvvoG+vr5iFbXExsZi9+7d+Oqrr9CxY0fFbEDeRnv16oUpU6bA19cXvr6+AIAffvgBu3fvxsSJE2FiYoLnz58jNDSU9x6UlJTQtWtXvH79ukaNFEXVNzQwpSjqL5k9ezbU1dXh5OQEGxsbbghg9U1LWFgYzMzMcOPGDVy7dg15eXlQU1PD7du3oa6ujg4dOiA4OBgvXrzgFpehqPpAIpEgNzcXMTExmDdvHk6ePIk//vgDCQkJ2LNnD+9GPzMzE66urnB3d4etrS3Xg1RNTU0Nurq6MDIyQrt27RAYGIh3797xytRECMG7d+94N/NFRUU4cuQIhgwZgmnTpmHChAmYOXMm/Pz8kJaWxju+JqlUiuvXr+O3336DkZER1q1bp1iER0NDA/b29pg9ezZGjhyJ3bt349KlS1BXV8eoUaPg6OiIEydOwN3dHcePH+eG+TIMgyZNmiAnJ0exSqoeYxgG7du3x4EDB7BhwwYsWbIEpqamsLe3x5UrVxSLcwghSE1NRdu2bbm0srIyHDlyBGZmZpg9ezZGjx6NuXPn4sqVK4iJieEdXxMhBA8ePMDWrVvBMAzs7Ow+uOKssrIy1q1bBxsbGwwbNgyOjo4ICQlBbm4uZs+eDScnJ/j6+uLYsWM4cuQIbxhxu3btkJGRwauPoqj6hQamFEX9Jfr6+pgyZQpUVVUxbdo0Xt67d+9QVVWFgIAAHDhwAC4uLti3bx+aNWuG5s2bQ19fH3Z2dnj9+jW2bNmC3bt3Iy4ujlcHRX0uAoEAKioqmDx5MiwtLaGmpgZzc3PY2Njg7t273N6iCQkJ2L59OwIDA7Fy5UpuTmg1hmEwefJkbNmyBa6urli7di18fX1x6tQpXjlFEokEQqEQkN+437lzB+fPn4eDgwPmzJmDmTNnwsHBAW/fvsWRI0cUDwcAlJeX48iRI3BwcICJiQnc3Nx48/neh2EYDB06FKNHj0Z0dDROnz6NTZs24dSpUwgNDcXs2bMxbNgw7N+/n7fKqVAorHOPTKp+09bWhqWlJfr06YMRI0Zg586dGDx4MG+l27pUVVVBVVUVkLfRly9f4vDhw7Czs8NPP/2E6dOnw8HBAWVlZXBxcVE8HJCPNDh58iQ2b94MbW1teHh4fHSrmGpWVlYYO3Ys8vPz4eLigvXr1+P27du4du0apk2bhnHjxuHIkSN49uwZd4yqqupHhylTFPV50cCUoqi/hBCCFi1awMjICJqamrw8NTU1aGhoYP369fj999+xfft2/P777/Dy8sLs2bMBAFOmTMGRI0cwYsQIXLlyBb/++usnbaNBUf80LS0ttGzZkrdwEeQ38QKBACzLIj8/H3Z2dsjIyICrq2utfX3r0qdPH/Tu3Zt3s6yIYRioqamhvLwckF9noaGhaN26NW8Ot7q6OszNzfH06dMaR/8fqVSKkydPwt3dHfPnz4ejoyO0tbUVi31QaWkpdu3ahW+//RZdunTBlStXYGNjg759+2Lq1KkwMTHhzs0wDMrLy2t9XlTD1KVLFxQVFSkmcxiGQePGjVFcXAzI22h8fDyUlZXRpUsXXllLS0uEhYVxC4LVdOHCBTg6OuK7776Dm5sbr31/CpFIBEdHR/Tr1w8DBw5EQEAApk+fjgEDBmD8+PHo0aMH71orLi7mLQhGUVT9QwNTiqL+EkIIRCIRxGIxN6QP8puWDh06QFtbG+np6TAzM0OvXr1gZmaGxo0bQ09PD5WVlcjLy0ObNm2wYsUKLF++HNevX6dDAal6QVdXF3379sXZs2e5PRiLi4tx48YNdOnSBdra2jh//jwePXqE/fv3o3PnzqisrOT+I4SAZVmkpqby9oNMSEhAcnIyzMzMapytttatWyM9PR2QX0+dO3dGcnIyIiMjuTKpqal4/vw5unfvDsiH+1Yfk5GRgZ07d2Lu3LmYM2cOqqqquNdW81r9kICAABQVFcHGxgYSiQQCgYD7LEQiEaqqqtC4cWNAHghnZ2d/cm8X9e8jhEAmk9Wab1pUVMR7IJiUlITbt29/dMGi9u3bc4uBMQyDVq1aoaqqivegJCsrCw8fPoSJiQnXflJTU0Hkq+tu3boVEyZMwOLFiyESibg2+qk97zdv3sSbN2+wevVqyGQyqKiocG2UEILKykpeIBobG8sbfkxRVP1DA1OKov4yRr61AKOwCmi7du0wY8YMODg4YP/+/bh48SKOHj2Kffv2oaSkBBkZGdi3bx9OnTqFgIAAPH78GEOGDIGenh6vHor6HIRCIebPn4+YmBg4Ozvj4sWLcHFxwevXr7F06VIwDIOgoCBoa2vj2rVr2L9/P9zd3XHo0CEcO3YMlZWVkMlkOHbsGFxdXXHx4kX4+flhx44d0NfXx/Tp0xVPyWEYBgMGDEBkZCQqKirAMAxGjx6NLl26wM7ODn5+fggICICDgwNkMhnmzp0LQgjOnz+PrVu3QiaTISkpCZmZmSgoKMC+fftw6NAh7vV9bPEXQghiY2Ph7e2NtWvXQl1dHQKBACNHjsTZs2cRFBSEU6dOQSaTYdCgQQCAvLw8JCcno3///orVUfUEwzC81aIh/1s/ffoUTk5OCAwMxOnTp+Hg4ABCSJ1brlRjGAY9evRAamoqCgoKwDAMLCwsMHz4cGzZsgW+vr4IDAyEo6Mj3r17x21Vc+/ePfzyyy8oKSlBWloaEhMTIZFIcODAAbi5uXFt9Pnz54qnrCU1NRWurq5YsmQJmjVrBoZhMHLkSNy7dw+3b9+Gv78/ysrKMGTIEEA+tD06Ohp9+vRRrIqiqHqEBqYURf0lDMOgS5cumDx5MrcyYzUVFRX88ssvmDx5MoKCgnD06FHcv38fzZo1g6amJref4unTp3HixAkoKSnB3t4eurq6vHoo6nMZNGgQtmzZgujoaBw7dgzR0dGws7PDiBEjQAjBgAED0KdPHzx//hzh4eEIDw9HREQEXr9+DYlEAmVlZejr6+Pp06c4evQoAgIC0LhxY+zbtw9t2rRRPB2HYRhYWlpCJpNxW1sYGBjgwIEDaN68OU6fPg1fX19IpVLs378f3bt3B5HvR1k9tLJZs2aYNm0aUlJSuNdW/fpyc3MVzlhbfHw8xowZw93UKykp4ccff0THjh1x6tQpPH/+HKtXr0bLli0BALdv30bjxo3Rs2dPhZqo+sLIyAhTp06ttYqzqqoqwsPD4enpidOnT0NZWRn79++vNSS3JoZh0LFjR7Rq1QoXLlwA5KMMdu/eje7du+P8+fPw9vZGQUEBdu/ejYEDB4JlWVRVVaG0tBQsy0JTUxM//vgjcnJy8PLlS14bzcrKUjwlD5EvvmRhYYHJkycD8nnhM2fOhJWVFfz8/HDnzh3Y2NjA1NQUAPDo0SMoKyujX79+CrVRFFWfMERxXAf1UUVFRZBKpZ+0BDoAvH37FpqamjA0NFTMoqh6h2VZ2NjYwN/fHzExMe/dXgLysizL1rlvXLW8vDyUl5dDW1sbOjo6XDrLssjKygIhBEZGRrV6Xan6j2VZ2NrawsPDA4mJiV/kysplZWXIz8+Hvr4+by61VCqFVCqtNXeOkc8RrW7PRUVFKC4uhqqq6p/6Ddi3bx+CgoLg5eXFe2CTkZFR5zUjlUohkUigrq4OlmUhlUrrHLopFAo/eL1C/pobN25cq4cNAFJSUqCjo8PNWU1PT8f333+PuXPncvPHvwRSqRTDhg2DRCLhgpqGjGVZyGQyKCsr1/quLSkpQWFhIVRUVNCiRQte3of4+PjA29sbJ06cgIGBAZeenZ0NsVgMQ0NDbhEvyF9DZWUlNDU1QQiBRCKps42qqKjUetipqLi4GFpaWnWu4JuWlgYNDQ1usa/c3FwsXLgQlpaWWL9+vWLxBkEmk8Ha2hopKSmIiIio833XFyKRiFsY62Oqv0NrthNFYrEYAoHgf7oGq4eHf6xdUZ8fDUzlT99evHiBqKgoyGQy7sbCwMAA7du3582bIYTA3d0dWVlZ2LJlS60veEUsy2L58uXo3r07bGxsFLMpqt75M4Ep9d/2XwhMP5fCwkLY2Nhgzpw5+PrrrxWz6wWWZeHj44O7d+/Cw8PjgzeXDc2XFpj+EyorK7F48WJ89dVXmDNnjmJ2vUAIwbVr17jtY2o+HG1IGkJgGhsbiwcPHqCsrAx6enoYMWLEe38TRCIRHj58iKioKLAsizZt2mDkyJG8h3+lpaW4d+8eEhISwDAMTE1NMXjw4DqDS5ZlcfXqVejr6/N6xcvLy7k6CCHo0qULhg0b9kV9V31paj8O/Q8ihHCbp589exZ+fn7w8vLCzp078euvv+Lq1au8p+JFRUXIz8+v9ZTvfcLCwpCYmKiY/KcQQhAUFPQ/10NRf0Z9/PGj6g+mxvxi2lb+Xrq6uvj999/r9fB2gUCA1q1bY/v27V/cjV7Nh860bddNXV0d27ZtQ6tWrRSz6g1Gvseuvb19gw1KofBdWx8lJiZi48aN8Pf3R0REBNzc3GBnZ8ctRlWTTCbD6dOnYWdnh8ePHyMkJAS///47PDw8uPtqkUgEDw8PbN26Fc+fP8eDBw+wfv16nD9/XrE6QH6f/cMPP+Do0aNcmlgsxtGjR+Hg4IDnz5/jyZMn2LRpE/z8/HjHUvULDUzlqqqqYGVlhRMnTsDT0xPOzs746aefUFZWhqVLlyIoKAiQfzls2LABBw8erHOYU120tLT+52X0GYaBra0tQkNDFbM4nxooUxRF/V2qb5bq801TQ9WhQwcMGDBAMbleGTZsWL0OTP4XtG1/XKtWrTB06FDF5HrF0tISnTt3VkxuUOrz/Z1YLIarqyvS0tJw9OhReHp64o8//sDFixcRGBjIK0sIQXJyMrZt24Zvv/0WJ06cgLe3NxYuXIg9e/YgJiYGAJCcnAxHR0fMmTMHJ0+ehK+vLwYMGIBff/2Vt9I55L2i+/bt402tIITg9evX+OOPP7Bw4UL4+vrC19cX3377LX7//fePzmOmPh86lFc+BODnn3+GVCqFj48PL6+4uBhTp05FaWkpgoKCoKamhqSkJEgkEnTs2BGQL4keFRWFnJwcMPIFYaoXgWBZFmPHjsWAAQPw/fff4/Xr1ygpKUHr1q1hYWEBNTU17lwlJSUIDQ1FRkYGGjVqBDMzM7Rp0wZ5eXkIDQ3FjBkzsGDBAowaNQqdO3fm5oJkZWXh5cuXKCwshL6+PiwsLLi5FVKpFC9fvkRiYiJYlkX79u3Rp08f+gSYei+WZbFo0SIcP34cO3bsgLa2dr3+UaQ+H0IITp06hfv372Pnzp3Q09OjbYX6IshkMuzevRtSqRQbN26kv5nUZ8WyLPbv3w+pVIoXL15AQ0NDschn8+7dOwwYMABbt27FzJkzufQff/wRubm5uHLlCpfGsizOnj2LlStXIiMjg3voI5PJ0K9fP0ybNg1r1qzB48ePYW1tjZCQELRr1w4AcP/+fYwbNw7p6em8kSQ+Pj7w9PSErq4uGjduDE9PT7AsCycnJ3h7e/NWIi8vL4eZmRl+//13TJs2jUun6g8amNYITEUiEU6ePKmYjbt372LMmDF4+fIlunbtis2bNyMvLw+urq4ghGDv3r0ICgqCjo4Ot4iAk5MT+vTpA5ZlMX78eDRt2hStW7dGZmYmysvLkZqaiu+//x4LFiyAkpISSktL4ezsjCdPnqBp06YoLS1Fo0aNsHPnTuTn58PBwQGXL1+Gqakp2rVrhwULFmDIkCHIyMiAvb09d6FmZWXB3Nwcv/32GzQ1NeHv7w9PT0/o6elBJpNBT08Pu3btqrUyH0VVY1kWixcvhru7OwwNDaGkpESDDaqW6qFlhYWFqKioQLNmzaCsrEzbCtXgVd8sV69grK+vD4ZhaNumPovqtpefnw9jY2M8efKE63z43AghePXqFQYPHoyIiAhuxXGWZeHi4oLDhw8jODiYGzXIsiy8vLxgZ2fH7btcbeTIkWjbti08PDyQmZmJH3/8ESNGjMCUKVMgEolw7NgxpKen48SJE9yIxYiICCxcuBB2dna4desW8vLy4OPjA5lMBnt7ewQGBiIsLIw7ByEEpqammD59OjZt2lTj7FR9QQPTTwhMMzMz0b17d3h5eWHChAlYtGgRsrOzERgYCEIIvLy80Lp1a/Tq1QsFBQWYMWMGunfvzj21+frrrxEVFYWNGzdi9OjREAgEOHbsGNzd3XHjxg306NEDPj4+sLe3h6OjIwYOHIjU1FT8/PPPGDNmDDZu3IioqChMmjQJS5cuxahRo2BsbAxdXV2sX78ez549g7OzM0xMTBAcHIz58+dj3759sLa2RseOHTF+/Hhs2rQJVVVVSE1NRZ8+fXg9tRRVU80e061bt6Jx48b0hoyqk0AggJ+fHx4+fMjN4aJthfoSEELg5OQEmUyG1atX0x5T6rNiWRZubm5gWRYhISG8RYI+J0IIbt++jRkzZuDt27fcat0sy8LPzw/29va4f/8+mjZtypV/+vQpJk6cCFdXV4wbNw4CgQCRkZGYNm0ahgwZgmPHjgEATp06hdWrV+Obb75BaWkp7t+/j8DAQPTq1QuQPzhavHgxWrVqBScnJ9jY2KC8vBw+Pj5gWRYXL17EggULcPr0afTr14/77KZOnYqFCxdi+/btNd4JVV/QwPQTAtOsrCyYmZnB3d0dkyZNwooVK5CdnY3Tp09zZQoKCpCeno7i4mK4urri7du3eP78OViWxZgxY6CsrIyrV69y5SUSCUxNTTF//nysXLkSAwcORJs2bbBt2zZUVVVBIBDAw8MDFy9e5BY8at++PXbv3o1vv/0WkAfM5ubmWL16Naytrbm9837++We0a9cOnp6e6Nq1K8aPHw9bW9sP7p1HUdVY+aq8AQEB3LL7FPU+q1evxsGDB/Hu3TvupoSivgRDhw6FWCzGkydPFLMo6l83ceJEJCYmIiIi4pPXOPmnsSyLy5cvY+HChYiNjUXjxo259LNnz2LLli24d+8ebzuh8vJyrF69Gi9evMB3330HDQ0NREZG4saNGxg3bhxcXFyQn58PZ2dnxMbGwtzcHFVVVYiIiMDo0aMxb948yGQyODo6IiwsDD4+PtDQ0MCSJUtQUVEBT09PQL6y+fz585GTk4NJkyZBIBDgzZs3uHbtGpYtW4aNGzdyr4mqP+pHy67nsrKyUFlZibZt2wIKk9DFYjEuXryIbdu2wc3NDR4eHnjz5g3vS4NhGFhYWHD/hnwvpZYtWyIjIwNSqRQJCQnIz8/HH3/8AScnJzg5OSEhIQGGhoYQi8Vg5ftFVk/6JoQgKSkJVVVVCAkJgaOjI5ycnODo6AiGYbi96uzs7PDw4UNs3LgR7u7uSE1N5b0OinofQgjKy8sVkymKw7Istz8cbSvUl6R6H1hWvv8nRX1OMpkMUqkUhJB6NSqFYRhoaGigqqqq1nVSWVkJFRWVWiP0NDU14eDggAkTJiAiIgKvX7/GoEGDYGpqyq2cHBgYiHPnzsHZ2RkbN27E1q1bsW7dOjg6OuLJkyc4e/YsnJycYG1tjejoaDx58gQ5OTl49+4dwsPDUVVVBV1dXezduxf9+/dHaGgo4uLi8PXXX6NVq1b1Zig0VRsNTD9CKpXi+PHj6Ny5Mzp16lTrC+Hly5dYtWoVAGD06NFYs2YNxo0bB6lUyivHKmzCzrIsysrK0KhRI0C+HH2nTp0wdOhQDB06FIMHD8acOXNgb28PgUDAnbdmwKukpAQlJSX07t0bQ4YM4Y5buXIlt2fq7NmzcfDgQTRr1gyenp7YtGkTiouLuToo6kMU2ztFKapuI7StUF8q2rYp6v1atGgBQggyMzO5NIZhkJqaiiZNmnC9qDXp6elh8+bNOHz4MA4cOIBp06YhPT0d5ubmYFkW169fh6WlJVq2bMkd079/fzRv3hxXr17F9evXYWBggLt372L37t3Yv38/wsLC8ObNG3h4eHD3uS1btsSuXbvg4eGBgwcP4quvvkJRURFMTU1rvBqqPqGBaQ0155AQQpCRkQEvLy9cunQJ69atq3PLl4cPH6KsrAx79+7FpEmT0KNHjzqXoX758iVEIhH37zdv3iAlJQUWFhZQUVFBx44dIRAIMHPmTHz//ff4/vvvMW3aNAwePJjb2Fsmk3G9EwzDwNjYGI0bN4aRkRFmz57NHTd58mT07t0bLMtCLBajX79+2LdvH7Zu3QofHx/ExcVxr4OiKIqiKIqi/iyGYdCiRQu0b9+et8doWVkZ7t27hyFDhoBhGEgkElRUVPCOhbz3VFVVFUePHgXDMBg2bBgIIWjatCm3A0a1zMxM5OTkwNDQEMuWLcMff/yBMWPG4JtvvsGkSZPQrl07tGnTBuPGjas1B1dLSwtEvlhpq1atuJ0zqPpHacuWLVsUE/9rCCEICAhAdHQ0dHV18erVKzx+/BiBgYG4cuUKfvjhB8ybN48re+HCBRQVFWHGjBlISUnB1atX0apVKxQWFiIoKAinTp2CkpISFi1aBEIIPD098ebNGzRq1AhVVVWIjY2Fs7Mz9PT0sHbtWgiFQgiFQri4uEBZWRlisRiJiYm4du0aZDIZt0dcYGAgMjMz0axZM6ioqKBFixbIyMiAt7c3tLW1UVpaipiYGFy9ehX6+vrQ1NSEh4cHSkpKkJmZibdv3+LVq1f4+eefoa+vr/ApUNT/IYTg8uXLiIqKwtKlS2t9wVNUNUIIrl+/jtDQUNja2tb5ZJyiGiKWZeHt7Q2WZTF37tx6M6eP+m8i8q25iouLsWjRonrVHoVCIUQiEby9vdGsWTPk5+cjICAAr1694hbFu3r1KgIDA9G/f38AwNWrV5GamoqMjAzcunULx44dw6JFi9C/f38wDAMtLS34+vpCJBKBEIKEhAR4eHggJycHmzZtQo8ePdCpUyd0796d++/Ro0dQV1fHxo0bIRQKIZFIcPHiRWRnZyM1NRWXL1+Gv78/NmzYgG7duim+DaqeoIGpXGJiIt6+fYvQ0FCEh4cjJSUFjRs3xvz58/H999/zyiYkJKBx48b46quvYGhoiKysLNy4cQMJCQkoKirCkCFDoKGhAWtraxBCEBkZid69eyM5ORnPnj3DkydPoKSkBAcHB7Rs2RIMw6Bz584Qi8W4cOECYmJiEBYWhoSEBAwePBhGRkaA/InP9evX8fbtW+7JUK9evZCQkICbN28iNjYWISEhKCoqwogRI6CpqYl9+/bhyZMnePXqFSIiIrBo0SIMHz6cWw6fohTRwJT6VDQwpb5UNDCl6pP6HJgyDINu3bohLS0NV69eRWxsLBITE7F27VpYWFiAZVmcO3cOt27dwowZM6CkpITDhw/j+vXreP36NcLCwjBlyhQsXLiQq69NmzbQ1tZGYGAgoqOj8fz5cxQUFGDTpk211myBfERhbGwsdHV1MWTIEEA+FW/v3r14+PAhwsPDER0djZ9//hlTpkxRPJyqR+iqvHIlJSUoKyuDVCqFQCCAmprae3sVS0pKwLIsN0lbLBbj5cuXkEgkMDc3h5qaGgoLC7njCwoKoKWlxZVTUVGBmZlZnaudxsfHIyUlBVpaWujUqRNvE2EAiIqKQkFBAXr06MG7CYyIiEBOTg709fXRpUsXbthxXl4e4uPjUVFRgQ4dOsDY2LhGbRRVGytfldff3x8xMTFo1qwZL58QgvDwcGRkZGDgwIHcdVBNKpXi3r176NatG5o3b87Lo74sLMvC1tYWHh4eSExM5B6iUVRDJ5VKMWzYMEgkEjx69IibUkNRn4NMJoO1tTVSUlIQERFRb7cvCg0NRWlpKbp06cJbiTc3NxfFxcXo0KED9++4uDhIJBK0b9+eGxmoKDMzE7GxsRAKhejWrVut+42aFO/NIT8+ISEBANCpUyfea6LqJxqYUhTF87HAlGVZLF68GP7+/li/fj3Wrl3Lyy8uLsaAAQPg4OAAa2trXh71ZWnogWlBQQEiIiKQlZUFVVVVmJmZcTdOhBBERERwc/I7d+6MHj168I4XiUQICwtDcnIyVFVVYWpqyh0P+Y3S69evkZ6eDpZl0a5dO/Ts2ROqqqq8et7n1atXaNq0KZo3bw4i38g+NjYWeM/rqRYdHY3i4mL069dPMYuntLQUoaGhyMzMhLa2Nnr27PnRv6FUKsXLly+RlpYGbW1tWFpacov4Qf4wND09HWZmZmAYBrm5uUhPT+f2HmwoGlpg+vr1a4jFYvTu3Vsxi2unqampYFkW7du35/U65efnIywsDO/evYOuri769OnD7Tv5MVlZWcjNzYWpqSkYhkFkZCRiY2MhFovRoUMHmJub19m7l5eXh4iICPTt25fXfury6tUrxMXFgWVZdOrUCT169PjoqK+kpCS8evUKANC9e3e0b9+ey2NZFi9fvkSnTp3QqFEjSKVShIWFoWPHjvV2y6uGEphS1P+KBqYURfF8LDCVyWSYPn067t+/Dw0NDZw/f55301lYWAgTExO4uLhg2rRpvGOpL0tDDkwzMzPh7OyMuLg4qKmpQSKRYOzYsZg/fz63lsDRo0ehpqYGqVSKqqoqrF27Fl999RUg34v6+PHjOHHiBPT09CAWi6GqqorNmzeje/fuIITg0aNH2L9/P5SVlVFRUYHi4mLMnj37k4aGxsbGws7ODnZ2dujWrRsuXryIw4cP817PqlWrMHLkSEB+XaakpODZs2fw9PSEsbExjhw5olgtp6ysDPv27UNQUBD09PRQUVGB5s2bY/v27R8c6XDlyhX4+PjAwMAAOTk5GDBgABYvXgwlJSXIZDL8/vvvUFdXx9q1a0EIQXx8POzs7LBu3TqYm5srVldvNYTAVCKRICkpCY8fP4anpyf69OkDJycnXpmKigocOXIEt27dgrq6OliWRZs2beDo6AjIe6527dqF+Ph4qKmpoaSkBJ06dcKWLVs+2DsF+XYgdnZ2aN++PRYuXIgHDx7A2dkZKioqYFkWJSUlsLGxweTJk7lj8vPz8ebNG/j7++P+/fu4ePHiB/dYv337NhwdHblRYJWVlVi+fDnGjRunWJSTmZmJbdu2gWEYEEIgkUhgb2/PtetHjx7B1dUV+/btQ9OmTSESibBt2zY0adKE22WhvqGBKfWfQSiKomqQyWRk/vz5RFdXl+Tk5ChmE6lUSqZOnUpmzZpFJk2aRMaPH0/Ky8u5/IKCAmJkZETOnj3LO66oqIjcv3+fXLx4kVy5coXExsby8qmGRyaTkaVLlxKhUEjS09MVs+stsVhMVq1aRfr27UsuXbpEsrKySGRkJAkPDyeEEJKQkEA6d+5M1q1bRxITE0l0dDT5/vvvSd++fUlxcTEhhJCoqChiaGhI7OzsSEpKCgkODiaWlpZkxowZRCKREJZlSUhICPH39ycZGRkkLi6OrFy5khgYGJCkpCSFV8QnFovJ/PnzyfLly4lUKiWJiYmka9euZNWqVSQxMZHExMSQn376ifTp04cUFhYSQghJT08nM2bMIFZWVsTIyIjMmjVLsVoOy7Lkxo0bREdHhxw5coSkp6eTW7dukbZt25LNmzcrFueUlZWRESNGkIMHDxKxWEzu3LlDTE1NSUpKCiGEkPv375MBAwaQN2/ecMdIJBKyceNGMn36dCISiWrUVr9JJBIycOBAYmlpSSQSiWL2Z8eyLImPjyfffPMN6devHzEwMCBLliypVcbX15d07NiRuLm5keTkZJKcnEweP35MiPz63bVrF+nYsSO5fv06ycnJIRcvXiRt27Ylrq6uvLrqcvHiRdK3b1+SnJxMcnJyiJWVFfnpp59IbGwsSUxMJKtWrSJdunThvhtkMhnZvXs3GTx4MOnRowcxMjL64LVQXFxMLCwsyNSpU0l0dDSJjo4ms2fPJr169SJ5eXmKxQmRn+PAgQNk9OjRpKCggLx7944MHz6cHDx4kBBCSH5+Pvnmm2/I3r17ecc9efKEmJmZkdevX/PS6wupVErGjRtHunXrRqRSqWI2RX0xaGBKURTPpwSmU6ZMIYsXLyZv3rwhRkZG5NChQ1x+XYFpZmYm2bhxI5k4cSKxtrYm48aNI9999x25c+cOV4ZqeBpiYMqyLHnx4gVp1aoVCQ4OVswmMpmMuLi4EBMTEyKTybj0nJwc0qJFC3L9+nVCCCGBgYFETU2NsCzLlXF3dydt27YlJSUlXFpNGRkZpFGjRuTBgweKWRyWZUlYWBjp2rUrSU1NJYQQcujQIdK+fXsiFou5crm5ucTIyIhcvnyZEHkw/fPPP5Nbt26RRYsWkenTp3NlFclkMrJjxw7Srl07Xvry5cvJ0KFDeWk1paSkEHNzc5KRkcGl9erViwQHB5PCwkJibW1Njhw5wjuGEEKysrKIqakpefjwoWJWvdUQAtNXr16RefPmkXv37pGZM2cSGxsbXpm8vDzSp08f4uTkxEuvVlVVRTp16lQrSNu5cyfp3bs3L01ReXk5mTBhAnfs5cuXib6+PikqKuLKlJWVkS5duhB3d3dC5J/pb7/9Rg4ePEgCAwNJp06dPhiYvn37lgiFQvLy5UsuLTw8nOjp6ZFXr17xylaTSqVk7ty5ZPfu3Vyavb09+fnnnwkhhOzdu5dMmTKlzr+pjY0NWbRokWJyvUADU+q/4sNjiSiKot5DJBKhW7duWLlyJXbu3MktMKBIKpXC2dkZFy9exA8//AAnJyds27YNKioqWL16NTIyMhQPoah/DCEE586dg6mpKfr06YPXr1/j9evXKC0t5cqkpqaiRYsWvOG2+vr6MDY2xvPnzwEArVu3hpGREe7du4eqqirk5eUhOjoaFhYWdc4hJYQgLCwMbdq0+eDQRUIINzy+VatWYFkWaWlpaN68OVRUVLhyTZo0QZs2bbjX06ZNGxw5cgQjRoyAQCDAh2bpMAyDLl26QFVVFTExMRCJREhPT0dGRgYsLS0Vi3PU1dUhFAoRHh4OyPfjVlFRgYGBAXx8fKChoYEff/xR8TAYGhqiX79+OHfunGIW9RcxDIOuXbvi8OHDGDJkSK2/eXV7y8/Px48//oi4uDiEhYUhJyeHKyORSJCfn8+bFw0A/fr1Q0JCAgoLC3np1QghSEpKQkJCAr777jsQQpCTkwM1NTXeHE1NTU0YGxtz7UVJSQlbtmzBkiVL0KRJE8hkshq11qatrY0ePXrgyZMnKCsrQ2lpKUJCQtCpU6daU0xqMjAwQFRUFEQiEUpKShATE4MePXogNjYWZ8+exaZNm2oNzSaEYObMmXj48OF73zdFUf88GphSFPU/sbGxgbGxMbZt2waJRAIlJSXeDVJmZia8vb2xdetWfPPNN2jfvj169+6N/fv3Izc3F9euXePVR1H/JJZl8ebNGxgaGsLHxwf79+/Hzp074ejoiLS0NAgEAnTu3BmxsbGIj4/njouOjkZqaiqKi4sBAD179sScOXOwYsUKuLm5wcHBAXfu3MG6desgFAoB+c1ucHAw/Pz8cPDgQRw7dgwrV6587wqUkM8le/nyJbffH8Mw6NSpE+Lj4xEdHc2Vi4mJQUpKCvd6quecsSz70Rt+hmEwZswYWFpaYsGCBTh8+DDs7OyQlZWF5cuXKxbnNGnSBKNHj8bx48dx8uRJuLu7Y9y4cSgsLMTZs2exefNmAEBRURHvOJZlMXLkSDx79uyjr436dNV/c5lMVutzJfKt6rS0tPD06VMcOHAATk5O+P333xEaGgoAUFFRQdu2bXH16lXueLFYjMePH4Nl2fcGaES+EJe+vj5atGgByB/UlJWV4enTp1y5lJQUpKSkoLy8HJC3u+oAWiQSceXep0mTJli/fj327duHPXv2YN++fXB2dsaSJUveu7qqQCDAxIkTkZycjGPHjsHHxweVlZUYN24cdu/ejYkTJ8LMzAxFRUWQSCTccdWBvoqKCl6/fs2rk6Kofw8NTCmK+p9oamrC3t4et27dwqVLl6CqqspbMTE2Nhbq6uro27cv77gmTZrAxMSEd7NNUf80sViMkpISRERE4M2bN7CysoK5uTn8/f3h4OAAsViMUaNGoXXr1vj1118REBAAf39/uLm5oaqqiutpKSkpgZqaGtTU1PDmzRukpqbCwMAAFRUV3LkIIXj69ClOnTqFY8eOIS8vD61atYJUKq3xivjKy8tRXFyMjh07AvIb5hEjRqBdu3bYtGkT/P39ERAQADc3N1RWVvJ6Uf+MwsJCaGtrQyaT4dWrV8jOzkbz5s1rBZU1CQQCrF69Gv3790dISAhMTExgY2OD/fv3Y+LEidDS0sKxY8fg7e2NixcvorKyEpC/BxMTE2RkZKCqqkqxWuofkpOTg8LCQly+fBldunTBoEGDEB8fD1tbW2RnZ0MoFGLhwoW4du0anJ2dceHCBRw7dgy3bt2CpqbmBxfYiYuLQ5cuXQD539fc3BwDBgyAnZ0dTp48ifPnz8Pd3R35+fl1jiD42Kq6kPfosiwLPT09xMfHIzY2Ftra2mAY5r3XEMMwsLS0xLp165CQkIDs7Gxs3rwZ4eHhyM7Oxty5cxEYGAhvb294eXkhOTmZO1ZHRwfq6upITU3l1UlR1L+HBqYURf3PBg4ciLlz52Lr1q1ITEzk3SyzLMsrWxMh5KOrk1LU34kQgsrKSgiFQmzZsgVz587F6tWrYW9vj5MnTyIlJQUtWrTAvn37QAiBm5sbfH190aJFC/To0QNNmjQBAJw+fRpnzpzB4cOHcfToUZw8eRLW1tZYsWIF0tPTAXkgN2fOHBw8eBCenp4YP348bG1t8fjxY4VX9f9VVlZCJBLx9rBu3rw59u7dC4FAADc3Nxw/fhyGhobo1asX93r+DJlMhn379iEqKgqnT5+Gh4cHTp8+DWNjY6xZs4br4apLo0aNsHz5cjg7O2P58uW4ffs28vLy8P3338POzg7Pnz9HaWkpnJyceKMhNDQ0PrmnjPrfEUIgFotRUVGB5cuXY/HixViwYAFcXV0RHx+PGzdugGEYzJkzB4sXL8aVK1dw6NAhPHv2DOPGjYOqqir09PQUqwXkdRcUFPDanp6eHv744w8YGhpy14SWlhZ69erF23P9UxFCEBISgq1bt+K3336Dj48PvLy8sHnzZtjb2/N6ZusyZswY7NmzB9u3b4e+vj5cXFywdu1a3LhxA4cOHUJFRQXu3LmDXbt2QSwWA/JzamhofLD9UxT1z6J3hBRF/S1Wr14NdXV17N69GyzLck/EO3TowG25UFNqaipSU1Pr3HePov4pKioq0NTURJ8+faCpqcmlDxw4ECzL4t27d4B8nt3x48fh5uYGFxcX2NraIicnB7169QLLsvD29saoUaO4vUSre58KCwtx9+5drt4mTZqgVatW6NmzJ9atWwddXV2cOHGCy1ekpKRUZ49Q37594ePjg0OHDsHFxQUrVqxAVlbWX9ofVCQSwdvbGz///DNatmwJyAPOpUuXIjQ0FJGRkYqH1CkhIQGHDx/GunXrEB0djcjISDg5OWHTpk2YOHEivL29uWH91UNF6YOofwfDMNDT04OBgQE6d+7Mpbdt2xbt27fn1gQQCoVYs2YNTp48iQMHDsDFxQUGBgYwNDT84P6iysrKtdpo165d4enpicOHD+PgwYNYt24dysvL0bVrV165T8GyLAIDA6Gjo4Px48eDYRgoKSlh9OjRMDY2hr+/v+IhdRKLxfjjjz9gYWGB/v37w83NDTY2Nti4cSMcHBzw6NEjbq9ihmEgk8loG6Woz4hefRRF/WlisZg3PwfyJ+bbt2/H9evXkZaWxgWmbdu2xZQpU+Ds7IwrV64gIiICT548gYODAzp06MDtw0hR/wZlZWX06tULsbGxvLnQSUlJEAqFvJ5KdXV1mJiYwMjICOfOnYOSkhIsLCy4nv6ysjKuLORzK1mWfe8oAZlMBnV19Q8OkdTQ0ICGhgYyMzMVs6CmpgYTExO0bNkSFy5cAABYWFgoFgPDMB8cKln9+qvnp1YrLCzkbs4/RiQSYe/evejXrx8GDhyIyMhItG3blgtmzMzMUFxczAUvBQUF3NBn6u+n+PdmGAY9e/ZEWVkZcnNzufTi4mIUFhbW2nO4efPm6NChA2QyGby8vPDtt9/y8mtiGAYtWrRAWlqaYhaUlZXRoUMHtGnTBnfu3EF+fj639++fxTAMxGIx7zqVSCR/qtf99u3bCA8Px7p165Cfn4/y8nL06dMHkM+L1dPTQ15eHiCvu7y8/L09xRRF/fNoYEpR1J/CMAw6d+5cayVHABg5ciTmzp0Lc3NzNG3aFJD3kGzatAk9e/aEi4sL9u7dCxcXF+Tl5WHXrl1/aSgiRf1VDMNgypQpSEpKwrFjx5CYmIiwsDA4Oztj8ODBaNu2LUQiEUJCQhAbG4uEhAQEBgbi4MGDWLx4MTfH7dtvv8XNmzdx9epVJCUl4c2bN3B2doZQKMSgQYPAsizOnz+PZ8+eISkpCTExMfDy8kJaWhomTpyo+LI46urqaNGiBV69esWlKb6eixcvYv/+/Vi0aFGdN9FisbjWzXtxcTG8vb3x9u1baGho4JtvvsHRo0fx5MkTJCcnIzQ0FHv37kWHDh3QrVs33rGKZDIZrl69iujoaKxatQoA0LFjRyQnJyMyMhLp6em4du0aevTowQ3rDw0NRceOHeucb0j97xQfFjIMgz59+qBNmzZwcHBAdHQ0YmNjcfDgQaioqHAPBDMyMvD8+XMkJSUhPDwcjo6OEIvFmDlzZo3a+RiGQbdu3RAbG8s9xCCE4MWLF4iKisLbt29x+/Zt7N69G5MnT65zFWqZTIaqqipe0EkIwY0bN3Dt2jUoKSlh5MiRyM3Nhbu7O+Lj4xEXFwcvLy/Ex8dj/PjxvPrqkpKSgv3792Px4sXQ19eHmpoamjZtikuXLiErKwt37twBy7Lcb1l2djYqKythYmKiWBVFUf8ShtT8VqAo6j+PZVnY2NjA398fMTExdS7Ln5ycDIFAgNatWytmoby8HNHR0TAxMeFtHUAIwb1795CUlAR9fX0MHTr0L809ouoPlmVha2sLDw8PJCYm1uqFqa8IIXBycsKFCxfQo0cPlJaWIjc3F7///jt69eqF4uJi/PbbbxCJRBAKhUhKSkLPnj1hZ2fHrbhbVFSEX375BW/fvkWnTp1QVlaGhIQELF68GNOnT4dUKuUWmTEyMoJEIkFCQgLGjRuHFStW1OrhqkYIwbFjx+Dn54fr169DSUkJpaWl2LRpE6qqqiAUCpGcnAxTU1Ns2bKFez3VWJbF1q1bUVxcjL1793LpcXFxGD9+PBwcHPDdd98hLS0Nq1evhlgsRps2bZCbm4usrCxs3boVgwYN4tWpqKioCMuXL8ekSZO4nrWKigqsWbMGLMuiZcuWiIyMxIYNG2BmZgaxWIxvv/0Ww4cPx8qVKxWrq5ekUimGDRvGTUNQ3F6kPpHJZFi3bh00NTWxbds2Xt61a9fg4OCAjh07QigUIjY2FvPnz8eMGTNACMH9+/fh6uqK5s2bo7S0FJmZmdi4cSOGDBnCq0dReno6xo8fjyNHjsDCwgIymQwbN25Efn4+NDU1kZaWBiMjI+zcuRNaWlq8YwkhePbsGX777Td4eXlxw8llMhnmzJkDsVgMf39/yGQyODo64vLly+jevTsgXx179OjRWLt27Qf/JizLwtPTE8+fP4e7uzsgP6+fnx9Onz4NCwsLvH37Fr169cKyZcsAAD4+PnB3d0dQUFCt6+pzk8lksLa2RkpKCiIiIj446oKiGjIamFIUxfMpgSlFoQEHptWCgoLw/PlzNGnSBOPHj+e2vmBZFo8ePUJISAgYhoGFhUWdwRrLsrh+/TpevXoFLS0tDB8+nFupFPJ51Hfv3kVGRga0tLQwaNCgT5oTmp2djQkTJmDHjh0YMWIEWJbFkydP8Pz5cxBCYGFhgcGDByseBshvvmNiYiCTybibeQAoKyvD9evX0bdvX+6BUllZGa5cuYK3b9+iadOmGDNmzAe3sqlWWFiIx48f4+uvv+all5aWwt/fH+Xl5RgxYgQ3t/Hx48dYunQpzp07h7Zt2/KOqa8aUmBK5FvDKCkp8dpftcjISNy6dQuEEHz11VcwMzPj8vLz83H58mVkZmbC0NAQo0aN+qTrmGVZrF27FuXl5XBzcwMAhISE4MmTJ5BIJDAzM8PIkSPf+wCmoKAAMTEx6NWrF9TV1QH5+3j48CFYlsXQoUO5so8fP+YWO6oeOv4xhBDcuXMHnTt35gLfardv38abN29gYmLC9byWlpZi+vTpGD169Ae3TPpcaGBK/VfQwJSiKB4amFKfqqEHpvWZq6srbt++DXd3d25YfEOUn58PW1tbdO3aFb/88otidr3VkALTz6W693Xbtm28QLKhkUqlOHHiBE6ePAk/Pz/ePPP6ggam1H8FnWNKURRFUfXMTz/9hO7duyM7O1sxq8Eg8m1FWrVqBVtbW8VsqoHr1KkTli5dyq3w21DJZDJkZmbi119/rZdBqSLan0R9yWhgSlEUj0Ag4LasoCtoUh8iEAi4nqTq4XjU30NdXR3btm2DqampYlaDwTAMTExMsGPHDt7WPA2BsrIyBAIBr41TtU2dOhXz5s1TTG5QVFVVsXHjxvcOj68PlJSUoKSkRNsj9cVrkEN5c3Jy8O7dO7rXFEX9Awgh3LYvN27c+OBedtR/GyEEf/zxB/z8/BAUFNQgehsaEoZhvojekYb4PmQyGebOnQupVIpTp06Bfc8WQFTD/Psqqu/vQSaTwdbWFhkZGbhw4QJtj18YIt/Cq127dv/5DoEGF5gSQuDu7o7jx4/TMfYU9Q9QUlJCbGws8vLy0Lt3bwiFwnr9g019PkpKSoiLi0N2djb69OkDNTU12laoL8arV69ACEGPHj0gEAho26Y+q6ioKFRVVaFXr160PX6BVFVV4e7ujvbt2ytm/ac0yMDU29sbZ8+efe9qbxRF/XUCgQARERHIysrC0KFDoaqqSn8AqToJBAK8fv0aKSkp+Oqrr6ChoUGf5FNfjKdPn4JlWfTv35/eb1CfFSEEISEhqKqqwqBBg2h7/AIJhULs2bOnwaxc/k9pcIEp5EMa6M0PRf0zGIaBjY0Nzp07h8jISDRt2pQGplSdGIbBihUr4OHhgYSEBBgZGdHvZuqLIJPJMGLECEgkEty/f5+O0KI+K5lMhm+//RapqakIDQ2lU9m+UMrKyv/5hw4NMjClKOqftXDhQpw9exYJCQnQ09NTzKYozooVK+Dm5oa0tDS6tRD1xajeS1MikXB7aFLU50IIgbW1NZKTk/H69WvFbIr6YtBHLhRF8bAsy/WQSqVSxWyK4rAsC5lMBgCQSCSK2RTVYFV/DxJC6Pcg9dmxLMu1yervXIr6EtHAlKIoiqIoiqIoivqsaGBKURRFURRFURRFfVY0MKUoiqIoiqIoiqI+KxqYUhRFURRFURRFUZ8VDUwpivpkhBDk5+dDLBYrZgEASkpKUFpaqphMUfUOy7JITk5GZGQkcnJyFLN5cnJyUF5erpgMAKioqEBsbCwSExN5W+VIpVK8e/cOOTk5vP/y8vJQUFDwSdvqlJWVoaysjPt3QUEBoqOj8fbtW95iU3/2XCzLIjc3t1b53NxcFBYWfnQhq+LiYkRHRyMrK0sxC4QQFBQUcP+uqKhASUkJrwz1z8vPz0dUVBTi4+PrbLsZGRmIiopCbm6uYtYHiUQiFBUVAfJ2l5KSgsjISKSnp/PKEUKQl5dXZxsrKCj4YBvLzc1FVFRUne3rffLy8hAdHV3n+5FKpSgsLOT+XVpaWudnQlHU50e3i6EoiodlWdjY2MDf3x8xMTG8LUBkMhkOHToEDQ0N/PTTT7zjioqK4OzsjIkTJ8Lc3JyXR32ZWJaFra0tPDw8kJiYCCMjI8Ui9dK7d+8QGBiI6OholJeXQ0NDA+PHj8eIESN4e8iVlZUhNDQU/v7+WLBgAUxNTXn1JCUl4eTJk8jIyAAAmJubY/r06dDS0kJ2djZcXV15N+AMw4BlWaioqGDDhg3Q1NSsURtfaWkpjh49ijFjxsDExAQPHz7E7du3kZ+fD5Zl0blzZ8yaNQvNmjVDTk4OXF1dIRKJuNdf81zr16+HlpYWr+6DBw+iqKiozv0QV65c+d6tf1JSUuDn54fi4mIIBAJMmjQJffr04fLv3LmDzMxMzJkzB4QQJCYmIjAwEPPmzYO2tjavrvpMKpVi2LBhkEgkePToEZSVlRWL1EssyyIoKAhBQUEoKCiAQCDA2LFjMWHCBACAWCzGlStXcO/ePVRWVqJx48aYNGkSBg4cqFhVLRKJBMePH0enTp1gZWWFkydPIjw8HCUlJRAKhRg6dCisra2hqqqKyspKuLi4IDc3l9fGGIaBRCKBra0tWrZsyasfAJ48eYLAwECUlJRATU0NY8eOxahRoz64t2NMTAz8/f1RXl4OVVVVTJ8+HZ07d+byz507x7VVQggiIiLw4MEDLFiwAGpqary66iuZTAZra2ukpKQgIiKC7qtLfbFq/yJRFEW9B8MwKCoqwsaNG5GamsrLCwgIwJEjR3g3wBRV3xBCEBwcjPPnz0NfXx89e/ZETk4OVqxYgbCwMK7My5cvsWvXLqxfvx7u7u5cL1G10tJS2NnZ4ebNm+jSpQv09fVhb28PPz8/rg6RSISqqiruP5FIhBMnTuDmzZsfvNEGAH9/f1y5cgW6urooLS2Fp6cncnJyYGZmBkNDQ7i5ueHAgQOQSqXcuWqeTyQS4dSpU7h+/Xqtc9VVXiKR4MKFC/D393/vTa9EIsG+ffvw6tUrWFhYQCQSYevWrVyPaHZ2Nv744w9uuymGYdCoUSPcu3cPXl5eCrVR/4SbN29iw4YNyM7OhpmZGdq0acONYiGE4N69e1i9ejVkMhl69uyJ1NRULFu2DNHR0YpV1fLgwQMcPXoUTZo0QVVVFUJCQqCnpwdzc3NIpVKsXr0ad+7cAeTnEovFtdrY9evXcfLkyVoPRAghiImJwapVq5CVlYWePXuipKQEy5cv567LulRWVsLBwQGZmZmwtLRERkYGHBwcuFE9cXFx2Lt3LxeAMgwDbW1tBAQE4Pz58wq1URT12RGKoqgaZDIZmT9/PtHV1SU5OTmK2eTdu3ekU6dO5JdffuHScnNzSY8ePYi9vT2vLCGElJWVkYKCAiKRSBSziFQqJYWFhaS4uJhIpVLFbKqek8lkZOnSpUQoFJL09HTF7HqJZVny4sULEhcXx6WVlZWRfv36kZUrVxIif19bt24lgwcPJhs2bCDt2rUjDx8+5NVx6dIlYmhoSKKjo7n03377jXTq1ImUlJRwaTVlZmYSc3Nzcv78ecUsnnfv3pG+ffuSy5cvE0IIKS4uJrdv3+aV8fT0JAYGBiQ3N5eXXi07O5v07t2bBAQEKGbVqbS0lAwaNIgcOnRIMYuTk5NDevXqRV6/fk0IIaSyspJ0796dvHz5khBCyObNm8nPP/9MZDIZ77i7d+8SMzOzBtNGCCFEIpGQgQMHEktLyzq/u+qj3Nxc0q9fP7J+/XrFLELk37dTp04lo0eP5tIKCwtJ69atyc6dO3llFZWVlZGvv/6aODs7E0IIEYlEpLS0lMuXSCRkyJAhZNasWTWO4quqqiJjxowhu3fvVswiUqmUrFmzhvTv359UVVURIm9fgwcPJj/++KNicULk12FcXBwxNTXlfqsyMjJIx44dSVpaGhGJRMTGxoZs3LhR8VDi5+dHrKysSFFRkWJWvSSVSsm4ceNIt27d6G8l9UWjPaYURf0pTZs2xbJly+Dt7Y2UlBQAwKlTp1BZWYmFCxdy5WQyGe7du4f9+/fDyckJHh4eXHkASE1NxbFjx+Ds7AxHR0dcvXqVy6OofwrDMOjduzdMTEy4NE1NTXTr1g3Z2dlcmUmTJuHkyZNYvnw5VFRUePM0CSE4d+4cBg8ezBsyOGfOHKSnpyM2NpZLq+nQoUPQ19fHxIkTFbM4hBDcuHEDqqqqGDNmDACgcePGGD58OK+chYUF1yNVF3d3d+jq6mLSpEmKWXU6ceIERCIRZs2apZjFqaqqgkAggFAoBOTDRpWVlaGiooLg4GDcuXMHGzZsqNUbNmDAABgYGCAwMJCXTv19CCF48OAB8vLyYG9vD8j/PnVp2rQp9/9qamrQ19fn5SsihODNmzdITEzEDz/8AAAQCoW80THKysrQ19dH48aNaxzJd+7cOWRlZWHu3LmKWRCJRLh8+TJ++uknqKqqAvLXNnv2bNy7d++97byiogIqKipcmxMIBFybvHHjBmJjY7F69WrFwzB+/HhIJBLcu3dPMYuiqM+IBqYURf1pM2bMgI6ODo4cOYLs7Gy4ublhyZIl3A0OIQTXrl3DL7/8gqioKOTl5SEgIAB2dnYoLS1FRUUF7O3t4e/vj/z8fKSlpSE0NFTxNBT1r8jPz0dYWBisrKwAeWDao0cPGBkZobS0tNYNPsuyiI6ORrdu3Xjp+vr6aNq0KeLj43npkD+IOXXqFFasWFFraG1NLMvixo0bGDZs2HuH1ALAtWvX0L59e+jo6ChmIS0tDb6+vlixYkWtILEuBQUFcHd3x7x58z44FL9p06YwNjbG8ePH8fLlSxw/fhzt27eHrq4udu/ejZ9++gkdOnRQPAzKysoYNWoUbt++rZhF/U1YlsXt27dhaWmJmJgYuLu7w83NDXfv3uWCOoFAgJ9//hlJSUnw9fXF06dP4erqiiZNmsDa2lqxSg4hBHfu3EGvXr2gq6vLpVdWViI+Ph6RkZG4evUqRCIRpk+fzju2WnFxMVxcXDBr1izo6ekpZqOgoACZmZno2bMnl0YIQbt27SCVSutcCIlhGLRu3Ro6Ojrw9fXFy5cvcezYMfTr1w9isRgHDx7E2rVr0aRJE8VDoampiUGDBuHatWuKWRRFfUYf/8WiKIpSoKenh+XLl+PEiRNwdHSEqqoq9yQd8hUSt23bhq+++go+Pj5wc3PDoUOHcPv2bdy9exd5eXnw8fHBwoULcfDgQXh6emL58uW8c1DUvyE3Nxd79uyBkpISJk+erJhdJ4lEgsrKylpBoZKSErS1tXkrgFY7deoU9PX1MXLkSMUsnqqqKmRmZqJHjx6KWZzbt2/Dx8cHc+fOrXMBJT8/P+jp6WH06NGKWXW6cuUKqqqqMHXqVMUsHnV1daxatQqJiYnw8fFBSEgIbG1tcfnyZSgpKWHWrFl48eIF7t+/j+TkZN6xlpaWiI6Ofm/PF/W/IYQgPj4eZWVl8PDwwIMHD3Djxg2sX78ep0+fBuSBXP/+/aGnp4cdO3bA3d0de/fuhZmZGbp06aJYJYcQgtDQUPTu3ZuXlpaWhv3798PBwQFr166FmZkZBg8ezDu2uuy9e/eQnZ2N77//XjEbRL6Ss0AgQKNGjXh5GhoaUFZW5q1OXZOuri5Wr16N4OBg+Pj4IDk5Gba2tjh27Bg6dOiAUaNGITg4GA8fPkRmZiZ3HCEE/fr1++D8VYqi/n00MKUo6i+ZOnUqmjRpAjc3Nyxbtoy7SSeEIDIyElFRUTA2NsbFixcRGBiI2NhYqKmp4f79+2jUqBG6d++OGzdu4NGjRygsLKzzKTpF/ZPevHmDnTt34sGDB9i5cydatGihWKROLMuCEFJnzyfDMLV6KYuLi3Hq1Cl8//33H13dtby8HBUVFXW+lpKSEvj4+MDBwQGjRo2qc0hkSUkJTp48+UnngnwIpZeXF7755ptPWjV3wIABcHJygrW1Nezt7WFoaIiTJ09iw4YNOHv2LA4dOoQzZ85g165dvJ5jfX19lJeXo6qqilcf9fcpKyvDmzdvMGbMGPj6+sLX1xcDBw7E5s2b8e7dO8hkMhw9ehQA8Msvv2DmzJnYsGED3rx5g8uXLytWx6kOHGsO+WUYBurq6mjXrh26d++OIUOGIDY2ts7h2lKpFF5eXhg9ejQMDAwUswH5NVXX9VRN8Zqqady4cdi5cycmTpyIXbt2obKyEnfv3sWqVavg5uaGY8eO4dSpU/jjjz94w/WbNm1aa1EziqI+r/df6RRFUR+go6ODyZMnQ1tbu9Y8try8PIhEIly4cAGHDh2Ch4cHXF1dYWRkhDZt2kBXVxdbt25FWloatm3bBgcHB7x584ZXB0X9kx49eoRff/0VSUlJ2LdvH7766ivFIu8lFAqhqqpaa89emUyG4uJi3s03IQSPHj1CUVHRB4dLVpPJZGBZlpvHWS0vLw9OTk44dOgQrK2t8ccff3Bz8aoRQvD06VMUFBR8cB5rNUIIXr9+jcjISMycOVMx+70MDQ0xbNgwrudt3Lhx0NPTg7OzM77++mvY2tqioKAAbm5u3DECgQAMw0AqlfLqov4+Kioq6N27N8aNGweGYdC4cWOsXLmSC1hzcnKwd+9ezJ07F7NmzcKoUaOwePFiDBw4EFu2bIFMJlOsklM9n7imVq1aYeXKldi4cSP279+Ptm3bYu3atbzeTUII3r59i2fPntXZWwp5kNi4cWNIpVJUVFTw8qr3G1UcnaDI2NgYQ4cOBcMw2LFjB+bOnYvi4mJ4enpi+vTpWLx4MSIjI+Hr68sdUz1UntBdEymq3qCBKUVRfwkhBE2bNoWBgUGtveA0NDSgpaWF7du3w83NDQcPHoSrqyvOnDmDH3/8EZA/5T5+/DimT5+Op0+fYu3atSguLubVQ1H/hOjoaPz6669o06YNvLy8eEMUP4WSkhLatWuHqKgoXnp2djZKS0t5wyIJIQgMDETPnj3r7AVVpKamBqFQyOvJEYlEOHDgAG7cuIHt27e/d54qkS/KZGZm9sl7yp4/fx4mJia8RZw+1dmzZ5GTk4MlS5bg7t27aN26NSZNmoSOHTti3rx5CA0N5QLRyspKAKgVTFN/D4ZhYGJiUmteskAggLq6OgghyMjIQFVVVa1h4lZWVoiJiflgb7ZQKOSCxLooKytj5syZSEpKqvU9funSJTRv3pw3f1RRs2bN0KRJE7x+/ZpLYxgGcXFx0NHReW9Pa02EEBw9ehQaGhqYPXs2rly5AktLSwwbNgzdu3fHrFmz8OjRI658eXk5lJSU6ryWKIr6PGhgSlHUX0Lk+9SJxWJeLwjDMOjYsSN0dHTw4sULtGjRAu3atUPr1q1RWVkJNTU1lJeXIyMjA/r6+pg7dy6WLFmCx48fIy8vj3cOivq7yWQyuLi4cPsfKikpobi4GMXFxSgrK6vVe0IIgUwm46UzDANra2s8evQIkZGRgPwm18vLC2ZmZmjXrh1XtqSkBLdu3cLYsWO5tA/R1NSEjo4OEhISAPn5q/di/OWXXzBo0CDu9RYXF0MikXDHlpaWvvdcYrEYsbGx3J6jkL/mW7duYciQIbUCmo95+/Ytjh49ipUrV0JLSwtKSkqoqKjgXk9qaip0dXW5m/63b9/C0NCw1kMs6u8hEAgwfvx4PHv2jHtgIhaLcfXqVaioqKBbt24wMDCAhoYGLl26xM31LSgowNmzZ9GtW7f3PjRgGAZt27ZFYmIil5abm8tbZb2goACBgYHo2rUrb4EkkUiEGzduoF+/frXqr6ysRExMDCorK6GhoYHRo0fDx8cHBQUFAIDMzEwEBARgwoQJn9Q+Q0NDcf78eW5laMUHPOnp6dyKxIQQJCQkwNjYuEYNFEV9bjQwpSjqL1NSUoKysnKtJ85t27bFTz/9hL1798LR0RF+fn5wdXXF/v37UVZWhszMTDg5OeHIkSPw8fHBzZs3MXr06I9uW0BR/yuJRIJXr15BQ0MDbm5u2LdvH1xcXHDw4EGcPn0aYrGYV55hGN52FNVp48aNQ7du3WBnZwdfX18cOnQI9+/fx+rVq7lhuNXDGAsKCrgVfz9GWVkZpqamePr0KZf25s0b7nXv3r0bLi4u3H/VW9MQQpCUlIS8vLw6z5WVlYVVq1YhODiYS8vJyUF6enqd5T+koqICbm5uMDc3x4gRIwAAAwcOhFgsxpEjR3DlyhVcu3YN3377LRdQBAUFoV+/frWGg1J/D4ZhMHz4cPTs2RO//vorfHx84O7ujmPHjmHRokUwNDREy5Yt8dNPP+H48eNwdXWFn58f9u7di8ePH2Pt2rXv/dswDIMhQ4bg2bNnIISAEIJXr15h+/btOHbsGPz8/HDw4EFcu3YNGzduhIaGBndsQUEB4uPjMWDAAF6dRL5Y08qVKxEXFweBQID58+fj3bt32LVrF/z8/ODs7AxCCBYsWMA7ti5FRUU4cOAAJk6ciB49eoAQgpEjRyI1NRW+vr4IDAzEs2fPeAuc3b9/H0OGDOHVQ1HU50UD03pA8Qk9RTUEDMOgW7dumDFjRq35cEpKSli7di3mzZuHsLAw+Pn5ISwsDF26dEGjRo24lRevXbuGCxcuoGnTprC3t/+kxVco6n/1zTffoFu3bkhISEBqair3X3Z2dq2tYXR0dDB79uxaw3B1dHTg7OwMQ0NDBAQE4PXr11i3bl2t3kolJSUsW7YMbdq04aW/D8MwmDhxIsLDw5GRkQGGYWBsbIyff/4Z6enpvNeblpbGG17JMAyWLl1a57kYhoGGhgav50koFGLmzJno1asXr+zHVO8duWbNGi6tbdu2WL9+PUJDQ3H58mUMHjwYM2bMAAC8e/cOjx8/xoQJE2rUQv3dtLW14ejoiDZt2uD8+fN48uQJfvzxR6xatQqQ96quXbsWc+fORUhICPz8/JCeno7NmzdjypQpitVxBAIBBg8ejLy8PLx48QIMw6Bly5ZQVVXFlStXcObMGWRmZmLjxo3c37yakpISvvvuO/Tv35+XDnmb1NLS4h5s9u7dG7t27UJmZibOnj2LyspK7NmzBy1btlQ8lIcQgqKiIrRo0QKLFi0C5HWbm5tj2bJluHv3Lm7cuIEpU6ZwewMnJiYiOjoa48ePV6iNoqjPiSE0KuKwLIuUlBQUFRVBSUkJ+vr6aN68ea3eoL8TIQTPnz9HWVlZrQ3UqyUlJSEzMxN9+/aFioqKYjZF/a1YloWNjQ38/f0RExODZs2aKRb5U8rLyyESiaClpVUrgC0uLgYh5KMLW1D1E8uysLW1hYeHBxITEz95XmN9UN3zU/PfTB0r6n6KwsJCaGho1BqqCPln9GfrlEgk+Pnnn9G6dWts2rSJG/6qGDRDfgNe/Rv1sXPJZDJeYFr9nv8sqVQKmUxW5/utrKxEVVUVN5xTJBLByckJISEhOH36dIP5DZNKpRg2bBgkEgkePXr03t7E+qqwsBBCobDO7YQgf7hQVVWFxo0bf9J7I4Rg69atiI+Ph5ubGxo3bgzIv8NZloW2tnadbe9jbUyxTULe/svKynhDgj9GLBZDIBDU+V6q9yKufvBZVlaG3377DRUVFXB3d1csXi/JZDJYW1sjJSUFERERtT6zf1NOTg4yMjLQqFEjdOjQ4YN/X8gXbktLS4OGhgbat29f628kk8kQHx+PyspKtGrVqtbIqZKSEpSUlHDff4QQNG7cGLq6upBKpXU+UBQIBFBSUkLTpk2585WVlUEqldZ5v1FQUAB1dXWoq6srZlH/MhqYyr179w4BAQF4+fIl9yWmp6eHbdu2/c835h/CsixWrlyJ5ORkXLhwQTEbhBB4eHggICAA/v7+3I8BRf1T/u7AlPpyNeTAtL578+YNNm/ejJ07d8LExEQxu8FITU3FqlWrsH79elhYWChm11sNPTD9J2RnZ2PZsmWwtbXFwIEDFbMbBEII3rx5Azs7O+zevRsdOnRQLFIv1ZfA9NmzZzh16hRKSkogEAgwaNAgzJw5s9ZD52oRERHw9fVFXl4eGIZBnz598MMPP3APTEpKSnDq1Cm8ePECYrEYenp6+Pnnn9G9e3dA/htz/PhxPH78GJqamtwDxWHDhuGbb75BQUEB9uzZg9LSUi5AZhgGMpkMMpkM27Zt4wLdW7duIS8vr1avPgAcOnQIffv2hbm5uWIW9S+r/XjrP0gsFsPZ2Rmurq7Q0dHB4MGD0bt3b1RUVNTaDuCfIBKJPrjpuJmZGaZMmVLn02mK+qcwDENvxqgPEggEXC8JbSt/r+7du+O3335r8E/whUIhNmzY0KCCUtTY3ga0bXMMDQ2xc+fOBr3ndPXw4e3btzeYoBQK7fFzSU5OxurVq5Gbm4vhw4fD0NAQmzZtwo0bNxSLAvIOnw0bNiAuLg7Dhg1Du3btuPnDkAedJ06cwL59+9C5c2cMGTIEL1++xKpVq7hF2mQyGc6fP4/Q0FBoa2tzU4Gq74er/57V6Y0aNULjxo0RFBSEu3fvQiAQIDU1FbGxsXj9+jVCQkKQnJyMpKQkEEIQExOD5ORkPHr0CLGxsYiNjUVOTg7vfVD/LtpjCiAtLQ0dO3bE4cOHMXv2bC6dEAKpVAoVFRXu6YtQKERxcTHEYjGaNGnC3ZQVFhZCIpFAT0+vzh+xkpISVFZWQigU8oansCyL5cuXIzk5mbfBdfVeXjUXEagmEokgFArBMAy3iqni0IdqxcXF3HAddXV1VFVVQVVV9bN/wVH1V80e06ioKOjp6fGGPFIU5DcELMti7dq18PDwQEJCAgwMDGhb+ZswDAOhUAiZTNZg9/5k5EOjlZWVP/jwtb5h5Putjh49GmKxGA8fPqTtWv65qKiogBDCWw26IWEYBkpKSlBSUmowbbK6PU6ZMgWpqakIDw+v8z7zn8SyLHbu3IkzZ87g2bNn3PSCH374AcnJybh//36t8kePHsXOnTvx7NkzbjXk9evX49q1awgODkZlZSX69euHFStWYPHixYB8K6/+/fvD09MTkyZNQlVVFSZNmoTRo0dj5cqVvHO8T1FREb7++mssXLgQc+bMQUBAACIjI7mpen379kXLli0xY8YM7N+/HxKJBLdu3ULXrl2ho6OD4cOHY9CgQYrVUv8SGpjKl7Hv1q0bvLy8MH36dMVsEEIQERGBiIgIdOzYEY8ePUJ2dja6du2KyZMnIyoqCkFBQSgqKkKbNm0wY8YMNGnSBJD3xt67dw/Pnj1DZWUllJSUYGpqinHjxqFRo0Z1BqZRUVG4dOkSrK2t0aVLF4SEhCAuLg7Tpk2DkpISTp06BQMDA8hkMjx58gQlJSXo1asXvv32W254hEQiQVBQEB49egSpVIrmzZujT58+iIiIwOzZs7nFZyhKEcuyWLRoEby9vWFrawstLS3FIhTFuXLlCl68eIGVK1fSqQbUF4NlWRw7dgwsy2LevHl1zp+kqH8Ly7Lw9fWFiooKnj9//q//LkskEgwePBjW1tbYuHEjIL83vnXrFqZPn46oqCgYGhpy5cViMWbNmgVdXV14eHhw5UNCQjBhwgTcu3cPubm5mDZtGt68ecPdMwPA2LFjYWxsjEOHDqGsrAyjRo3CggULuD3QP2bPnj04d+4c7t69C6FQiMTERNy6dQsHDhyAWCzG0qVLMX78eLRv3x7h4eEICAjA8ePHYWhoiHXr1mHgwIGftG8u9c+ggSmAqqoqzJs3D1FRUVi/fj0GDx6M5s2bc/nV8zzt7OwwceJEGBgYIDs7G0FBQZg+fToyMjLQtGlTVFRU4MqVK1iyZAnWrl0LlmVx6tQp7NmzB2ZmZjAwMEB+fj6Cg4Px448/wtbWFgzD8ALTuLg4LF++HKqqqti3bx+MjY3h6OiIM2fO4OHDhxAKhRgyZAhkMhmsrKygoqKCjIwM3LlzB05OTlxgff78eWzZsgWmpqYwMjJCYWEhkpOT8eLFC7x69eqjq9xR/10sy2Lx4sXw8PBA8+bNoaysTHsLqDoJBALk5+ejvLycthXqi1M9pK9Zs2YQCAS0bVOfVW5uLlq3bo3Hjx+/d5TcP6WkpASdO3eGh4cHvv76a6DGfN3Ro0fj3Llz6NevH6/8V199hR9//BFLly7l0jMyMtCnTx+cOnUKMTEx8PLywr1797geWJlMhjVr1iAiIgJBQUEoLCyEpaUlRo8ejQEDBkBHRwfdunVDq1atuDprysrKwogRI7Bp0ybeXNLDhw8jISEBjRo1glAoxLp16wD5CMRNmzbByMgIcXFxGDduHPf+qM+DBqZy6enp+O233/DixQtYWFhg2rRpGDJkCNTU1EAIwbFjx2BjY4NTp05xy6ovW7YMrq6u8PLywpw5cwAA69atw7Vr1xAREYHs7GxuYvj27du5cx08eBD29vZ48OABTExMsGzZMmRkZMDV1RVLlixBeXk5jh49ipYtW4IQAmdnZ5w9e5Z7+jN8+HDk5OTg2rVr3ObQEydOhFgsxrVr11BUVIThw4ejX79+cHFxAeRPrxYtWoSzZ88iOjqaLlJCvVd1j+nJkyfh7e0NbW1tsHWsBkpRAoEABw8exOXLl3HixAk0adKEthWqwatePGXNmjWQyWTYt28fGIahgSn1WVS3x82bN6O0tBShoaFcIPdvSU9Ph4WFBU6fPo3BgwcD8sA0Li4OY8eOhbu7O0aOHMmVz8/Ph5WVFbZu3coLEIuLi9G1a1ccPnwY4eHhuHv3Lq5du8YNTZbJZNi6dStu3LiB4OBglJSUwMbGBiKRCAKBAAUFBWjRogV+++03dOzYkasX8tfj5uYGd3d3PHv2jJufLxKJ4OzsjClTpkBHRwcuLi5Yv3491NTU8O7dO7i6umLTpk0IDg5GeHg4lixZwquX+pcRiiMWi4mPjw8ZNWoUMTU1Jc7OzqSiooKwLEs8PDyIiYkJKS4uJoQQwrIsOX78OGndujUpKCjg0s6cOUM6dOhAysrKyMWLF4mhoSHJz8/nnaeiooK0aNGC+Pr6EpZlybJly0i/fv3IokWLyJgxY0hqaipXlmVZ4uTkRPr3708qKyuJTCYjgwcPJqtWreLV6ezsTCwsLIhEIiGhoaFER0eHJCQk8Mrcv3+fGBgYkLS0NF46RdUkk8nI/PnziZ6eHqmoqFDMpiieVatWEaFQyH03UtSXYsiQIaR///6KyRT1WVhbW5Pu3bsTmUymmPWPS0lJIYaGhuT+/ftcGsuyJCYmhrRt25bcuXOHVz4vL4906NCBnDx5kpdeWFhIWrRoQa5du0bs7e3J8OHDiVQq5fKlUin57bffiJWVFffv1NRUkpqaSt6+fUv8/f1J9+7dyffff08kEkmNmgkpKSkh/fv3Jw4ODrz0urAsq5hE1RN00kQNKioqmD17Nvz8/DB16lRs27YNd+/e5Rb5qGv+VM0FkKopKyuDZVmkpqaiefPmtVZVVFdXR+PGjZGfnw9GvvJpYmIizpw5g6lTp753iEI1It/Dqea/qxdDIoRwy3LX3BCeEAI1NTUoKSnRp77UJyGEcCvjUVRdWJaFWCwG5HsFUtSXonqvVqlU2mAXn6K+HNVtkSjsv/xvqV6XRPF7XiQSQSwW1xparKKiAi0tLRQUFPDSq7d10dbWho6ODkpLS2tdX8XFxdzKz0pKSmjVqhVatWqFdu3aYfLkydi4cSPu3LnDLf4J+f1KeHg4kpOTMXny5Bq11Y2hC4DWWzQwrYOuri42bdqEli1b4urVq1x6XV8GLMu+d+iaoaEhcnNza13I5eXlKC0thaGhIYh85d8uXbpgyZIlOHr0KB4+fMgrXxfFc9Z8bWpqapBKpcjPz+fSGIZBZmYmJBIJvSCpT0bbCkVR/2X0O5Ci/i8wbdmyJcLCwrg0hmGQkJAAFRUVtGvXjldeVVUV7dq1Q3h4OC89ISEBqqqqaN26NTp16oS0tDQUFhbyyrx+/Rp9+/blpdWkr6/PDW+uRgjB5cuX0aFDh1pDfKmGhQam8gnlT58+RVZWFsRiMSorKxEZGYmqqipuLibLsrWe6nwoTSqVom/fvlBWVoanpyeKiooA+dOi48ePo1GjRty+bhKJBNra2ti8eTPatWsHW1tbPHjwgFdnzQtQKpXWCkyry8hkMrRr1w5NmjTBkSNHUFZWBgBISUnBuXPnIBaL6Q8tRVEURVEU9UkEAgEmTZoEf39/pKWlAQCys7Ph4+ODMWPGQEtLC1KpFJGRkcjLy4NQKMTo0aNx9+5dxMbGAvJ5pydOnIC5uTmMjIzQo0cPaGlpwdfXFyKRCCzLIigoCElJSZgwYQIgv2dOSUnhOl9SU1Ph6+uL7t2781byraysxJUrVzB69GgujWqYaGAqv7g2bdqE33//Hfv378f+/fuxfft2NG/eHFOnTgUhBMrKylBTU+MFdUpKSu9NY1kWrVq1wtq1a3Hy5Ek4Ozvj+PHjcHFxwcmTJ2Fra4u2bdtyw3Ahv/APHDiADh06YMOGDXj58iUgHxpcvZkw5D2iKioq3L9rlmFZFkZGRli0aBFOnDgBJycn+Pj44PDhwygtLYWGhkadPb8URVEURVEUpUggEGDWrFnQ0tLCli1b4OXlhf379yM/Px8rVqwAABQWFmLt2rW4c+cOGIbBpEmTYGJiAjs7O3h6euLgwYOIjo7G6tWrAXnP54oVK3D8+HEcOHAAhw8f5naX6NWrFwghSE5Oxo4dO3Do0CF4enrC0dER0dHRWLVqFbcAFCEEmZmZyMnJwYABA3ivm2p4lLZs2bJFMfG/RlVVFRUVFUhLS0NcXBzy8vK44LBTp06AvJdST0+P6wWFfMy/jo4OLC0tuUBRJpOhcePG6NevH4RCISwsLKCjo4OwsDBERERAJBJh+vTpvP2YxGIx2rRpg549e0JNTQ2DBw9GRkYGNDU10bVrV0ilUjRr1gwWFhYQCASoqqpCjx490L59e64OqVSKJk2awMLCAsrKyrCwsIBQKMSLFy+QmJiITp06YejQobhz5w7mz5//r++BRTUc1UNioqKisHTpUm5vXIpSRAjB9evXERoaCltb2zrn4VNUQ8SyLLy9vcGyLObOnVtrLQmK+jcRQnDq1CkUFxdj0aJFn6U96ujooEePHnj16hXCw8MhFAqxZs0amJmZAfJey+DgYPTo0QMdO3aEpqYm+vTpg5iYGLx48QKEECxZsoRb1ZdhGJiZmUFZWRlPnjxBSkoKBg0ahFWrVnHrpkilUjx79gyvXr1CfHw8GjdujOXLl2P48OG811ZUVAQ1NTWMHTuW3rM0cHS7mBoIIaisrISysjLXi/l3YVkWlZWV0NDQ+OShtGKx+H9+HUQ+UV4gEGDHjh24cOECgoKCoKGhoViUogB5W7WxsYG/vz9iYmLQrFkzXj4hBMHBwUhNTcVXX31Va9EDsViM69evo1evXh9dyItq2FiWha2tLTw8PJCYmPif3YaKEPLR73WWZf/SzeSn1K3oz5yr5m/En/Gp5/grr78+kEqlGDZsGCQSCR49esQ9kK7PPuWz/liZT/27Kqqr3g/V9aG896nrHB/zoWM+lFffyGQyWFtbIyUlBREREVBSUlIs8q+qqKj4U/eRFRUVUFdXf+/nLRaLwbLse7fBqaioAIA/dU6qYaKB6ReIZVncvHkT6urqaNasGWQyGeLi4rBp0ybMnz8fK1euVDyEojgfC0xZlsX8+fNx+vRprFmzBoqDLoqKitCnTx84Ojpi0qRJvDzqy9JQA1OZTIbnz59zqzpW36D27dsXBgYGisVRUVGBJ0+eoFu3bmjevDmXXlRUhODgYKSnp0NDQwO9e/eutfBGcnIyQkNDUVBQAAMDA1hZWaFp06a8Mu/z/PlztGrVCsXFxYiNjeXdyDMMA6FQiObNm8PU1BRisRgRERGIj49HeXk5WrRogX79+vHmYdVUVlaGly9fIikpCRKJBK1atYKlpSV0dHQUi/KIRCI8e/YMaWlp0NbWxoABA7gVNCHf4D4tLQ0WFhZgGAbZ2dlITEyElZXVe29K66OGFJjKZDKEhoZCIpG8dyhjeno6wsLCMHToUG6F1ZoyMjIQFhaG7OxsqKqqwtLSslZbrktKSgpyc3Nhbm4OgUAAQgjCwsIQExOD8vJyGBgYYPjw4dDU1IRUKsXr168RGxuLkpIS6Orqom/fvtx+7O8THh6ON2/eQCKRoGvXrujTp89HA7OYmBhu4R0zMzN06dKFy6vuhTM1NYW2tjYkEgmCg4PRpUuX914vn1t9C0wp6p9Ch/J+gViWhaenJ86ePYvIyEg8fPgQQUFBsLKywpo1a3jzVSlKEfnIUF6WZXHy5Enk5eUhPDwclpaWvJ7RyspKODo6YsyYMejWrRvvWOrLQhroUN6Kigr88MMPeP78OeLi4hAWFoZXr17B1NS0zuA6ICAAS5YsgampKXeDW1JSAkdHR5w5cwa5ubl48eIFgoKCYG5uzt3cJiUlYfPmzXj+/DkyMzNx584dxMfHw8rK6qPfwxEREdi+fTuGDh2KFy9e4MSJE4iIiEB4eDjCw8MRGhqKQ4cOccHu/fv3sWfPHiQmJuLt27e4fv06UlNTYWVlVWvkDSEEcXFx2LFjB9LS0hATE4PLly8jNzeXNzVFESEE58+fx9GjRyESifDw4UNkZGTAysoKAoEAEokEDg4OyM3NxYABA0AIQXZ2NrZt24ZWrVqhdevWilXWW2wDGMorEokQGRkJf39/2Nvbo7S0FGPHjuWVyczMxIMHD3DgwAEcO3YMU6ZMqfXwISkpCfb29rh37x6ysrIQGRkJDQ0N9OzZk1dOUVlZGbZs2cIt9kgIQUBAAJycnPD27VukpqYiOjoaffv2ha6uLoqKirBnzx6Eh4cjKSkJDx8+xLNnz9CvX7/3fnfcv38fW7ZsQXJyMuLi4nDu3Dno6+ujc+fOikU5KSkp2Lp1K969e4eUlBRcv34dffv2hba2NgDgzp07cHV1xdixY6GhoQGpVIojR44gPDwcgwcPrpcPUEg9GMpLUf8KxY1NqS9DamoqOXXqFDl48CA5dOgQuX37tmIRiqqTTCYj8+fPJ7q6uiQnJ0cxm0ilUjJ16lTy/fffk1mzZpFhw4aRkpISLr+goIAYGRmRs2fP8o579+4duXXrFjlz5gw5d+4cefXqFS+fanhkMhlZunQpEQqFJD09XTG73iooKCA6OjrkyJEj5MWLFyQkJISEhISQwsJCxaIkJyeHWFlZETU1NeLl5UWIfHN2X19f0rp1axIYGEhKS0tJREQEsbKyIvPmzSMsyxKxWEwWL15MLC0tSVhYGCktLSWXLl0iLVu2JN7e3oqn4amqqiKzZ88mGzZsIIQQkpaWRoKDg7nXGRISQg4ePEiaNm1KQkJCCMuyxNPTk7i4uJD09HRSWFhITp48SZo2bUoCAwMVqyeEEJKYmEguXLhACgoKyLt378j+/fuJlpYWefbsmWJRTmlpKRkyZAjx9PQkhBASHBxMunbtSt6+fUsIIeT69etk8ODB3L+J/LOyt7cnEydOJJWVlVx6fSeRSMjAgQOJpaUlkUgkitmfHcuyJCYmhowZM4YMGDCAtGzZkixdupRXRiqVks2bN5P+/fuTPn36kNatW5OUlBRemfLycjJ//nwyZMgQ8vDhQ1JSUkJSUlJIQkICr1xdzpw5Q/r370+ys7MJIYS8fPmSdO3alWzevJkkJSWRkpISEhkZyV1XhYWF5MKFCyQlJYWUlJSQ27dvk6ZNm5I//vhDoeb/U1hYSAYNGkR++uknkp6eTjIyMsiCBQtI9+7dSW5urmJxQuTfSY6OjmTChAmkqqqKlJWVkbFjx5I9e/YQIv8dGj9+PDly5AjvuLCwMGJmZkZevHjBS68vpFIpGTduHOnWrRuRSqWK2RT1xaCBKUVRPJ8SmE6ZMoXY2NiQ+Ph4YmxsTJycnLj8ugLT1NRUsmzZMjJ+/HhibW1Nxo4dS77++mty+fJlrgzV8DTUwDQrK4vo6uqSjIwMxSwemUxGtm7dSkaNGkV69erFBWRSqZSMGTOG2NjY8MrfvHmTGBoakry8PJKTk0Patm1b6wHNihUryP9j767Do7j2x4+/J0YcAoRAcHdvoVC8lxYoBdpCkXqhQFuKawsULy5BQtHgFtwluGsIIUggIYEQd93s7pzfH9/N/LKbIL23Au15PU+feznnzOxm98zsfI62b9/+mQ+XqqqKS5cuiZo1a4qnT59aZgthKvPFF1+IoUOHamnp6elmZYQQ4p133hEjRoywTM5XZmam8PDwEFu3brXM0jx69Eg0bNjQ7HOrX7++uHjxooiNjRXt27cXGzduNDtGmIKBevXqvVYNpK9DYHr79m0xdOhQcf36dfHZZ5+Jfv36mZUxGAxi6tSpYv369WL//v2iSpUqZoGpqqrizJkzolSpUuLu3btmx75IWlqaaNeunVi6dKkQps+rd+/eolu3bpZFn6tDhw6id+/elslCVVVx8OBBUbJkSbPrICIiQhQtWlTs2bPHrHwOg8Egvv76azFr1iwtbcqUKaJPnz5CCCGmTZsmPv30U6Gqaq6j/s+QIUPyfS+vAhmYSv8WciyAJEn/lezsbCpVqsSoUaOYO3cut2/fhnw2pNfr9UyfPp2zZ88ydOhQli9fzoIFC/D09GT48OGEhYWZlZekP5MQgoSEBFRVJTQ0lLCwMG2/Z0vHjx9n3759jBs3Ls9WW3FxcRQvXtysfI0aNUhKSiIiIoKMjAz0en2e+aS1atUiJCSE9PR0s/QcQgh27dpFo0aNzOaz5nby5ElOnz7N999/r6VZLgqi0+m0OX4vkjPnrlSpUmZz8Sw5OTlhZ2fHxYsXyc7O5urVqxQoUABPT09WrlyJh4cHPXv2tDwMd3d33n77bXbu3GmZJf2XFEWhevXqzJkzR9taw5KVlRU//fQTn376KY6Ojqj57H++f/9+6tevT/ny5bl06RKXL18mISHBrJwlIQQPHjwgLCyMDz/8EEzXw9mzZ+nduzehoaGcP3+eoKCgPK+ZQ5i2AklLS8t3XqwQgosXL1K+fHmz66BEiRKUK1eOmzdvmpXPoSgKpUqVwt/fn5SUFGJjYwkMDKRhw4YEBASwf/9+xo4dm+d3SgjBJ598wqVLl7S555Ik/fVkYCpJ0n8l54f9m2++oXbt2owfP56srCysra3NHpKePHnC1q1bmTJlCm3atKFYsWJUrlyZWbNmkZGRwYEDB3KdVZL+fOnp6Tg7O2v74s2YMYPz58+blQkJCWHy5Ml88803NGrUiKysLC1PURRq166Nn58f8fHxYGqA8fPzw2g0EhcXR+HChSlSpAj79+9Hr9eDabGkixcvkpKSoqVZMhgM3Lx5k7feessyC0wNQkuWLKFdu3ZmW4ZZ2r59O3Fxcc/ccF6YVtdevXo18+fPZ+7cufTp04datWpZFtUUKVKEDz/8kE2bNrFu3TpWrFjBhx9+SGRkJPv372fChAlkZmYSHR1tdpwQgnfeeYcrV65gMBjM8qT/Xs492Gg05hsA5uQLIfKtb0IIrl+/TunSpVm6dCmLFy9m5syZTJkyhZCQEMviGiEEAQEBeHh4UKxYMS3IzM7O5unTpyxevJgFCxYwefJk1qxZg06n0459/Pgxa9asYcmSJUyYMIFatWrRo0cPs/Njeo3IyEizhbUwBdOlSpXi6dOnZuk5FEXhww8/JDY2lhUrVrB27VoUReG9995j5syZ9OrVi2rVqhEdHa2t9JpzXNWqVbGzsyMgIMDsnJIk/XVkYCpJ0v+kQIECTJkyhYsXL7Jt2zZt/7Ec9+/fx8XFRdvrLIerqyuVKlXi/v37ZumS9GdSFAUPDw+GDRtGtWrVKFSoEGfOnGHAgAFcu3YNTBvFz5o1iypVqtC/f39UVUVRFG1RIEVR6NOnD9HR0UyYMIGNGzeyfPlydu7cSYECBbC2tsbV1ZWvv/6anTt3Mnv2bDZs2MBvv/1GYGDgc7cNS09PJyUlhcqVK1tmIYTgzp07XLhwgb59+1pmg2lhp02bNrFgwQL69u373EDz9u3b7N+/n507d5KQkICLi4vZw7olRVEYOHAgHTp0IDg4mEaNGvHNN98wb948evXqhcFgYNmyZfj4+LBp0yaznuiKFSsSGRlpFuBLf7/Y2FiuXLlCdHQ0H374Ie3bt8fPz09b1OhZgoODqVGjhvbvtLQ04uLiOHr0KBUqVKBnz56UK1eOsWPHcvz4cTDV35iYGPbv38+uXbvw9/enWLFiJCUl5Trz/5eVlYWDg4NlMgUKFCA7O9syGUx1tH79+vzyyy8kJCSQnZ3NtGnTOHPmDBkZGfTs2ZP169fj4+PD8uXLCQ4O1o4tWLAgDg4OREREmJ1TkqS/jgxMJUn6nzVo0IAffviBadOmce/ePbNVQK2srDAYDPm22BsMhheuTipJf7QyZcowZMgQhg8fzuTJk9m0aRNWVlZ4e3uTmZnJggUL8PPz49tvvyU0NJT79++j0+mIjo4mKioKRVFo2rQpM2bMIDw8nLVr13L+/Hm6dOmCs7OzNny3X79+9OvXj+PHj7N+/XqePHlC+/btKVas2DP368vKyiI7OzvfVUoVRWHz5s1UqVKF+vXrW2bz9OlT5s+fj7e3N926dWPEiBGWRTSKotCzZ0+WL1/Oxo0b6devHxMmTGD37t2WRc0UKFCAr7/+munTp/PNN9+wc+dO9Ho9Xbt25eeffyY4OBhbW1uWLFlidq6c6/xZAYX098ipa5MnT+bDDz+kd+/eTJs2jT179jyz11QIQVJSkrbKbU5aRkYGH3zwAd9//z1dunTh119/pV69eqxevRpMda5OnTosW7aM9evXM2fOHPz8/Jg8eXK+Pb62trZmva05MjIycHZ2tkw207RpU6ZMmcKYMWOwsrJixYoVjB49mu3bt7Nhwwbs7e25evUqkydP1hpLVFXFwcHhmcPsJUn688nAVJKk/0ru4boAAwYMwMPDg19//RW9Xq/1COXshZfTap7j/v37hIeH8+abb5qlS9JfrUSJEnTs2JGgoCAiIyPx8fHBw8ODLVu2MHv2bObPn09UVBT79u3D19cXo9EIQJcuXdi8eTM+Pj6sXLmSSpUq4ezsjKenJwD29vYMHz4cX19ffHx8WLhwITqdjrJly+bbEwRgbW2NlZVVvgFcSkoKu3fvpnv37pZZxMbGMmXKFA4fPsyoUaMYPny4ZZE8HBwccHNzo2zZsnz++ec0btyYdevWWRZ7pqCgINavX8+YMWO4efMmjx8/Zu7cuQwdOpRPP/2UjRs3agGHqqoIIeQ2F6+YYsWKUb9+fbPvpXbt2jg7O/P48WOzsrnZ2dmZ1VEnJyeKFi1Kw4YNzcq9+eabREZGar2vtra2FCpUSNvfdPDgwaxduzbPPG9FUShRogSxsbFm6UIInj59SoUKFczSnyUzM5Np06bRunVr6tWrx6pVqxg6dCiDBg1ixowZ3Lx5k3v37oHpNQ0Gg9wjVJL+RvIXQpKk302v1+cZ5uXq6sqUKVM4c+YMT5480R50ypQpw2effcaiRYvYtm0bFy9e5MSJE0ybNo3atWvzn//8x+w8kvRns2xU0ev1BAcH4+npiYuLC6NHj6Zr164UKVKEkiVLUqpUKezs7LR5o7mH4To4OFC8eHGEECxatIh33303zz6RBQsWxMPDg8ePH7N//346duz4zKG8Tk5OODo65hlOmDMfMDExkTZt2pjl5exdfeHCBXx8fHj//ffN8i0JIfIEvjnBtouLi1n6s2RkZDBnzhzeeecdGjZsyP3797XPCdPQ3YyMDO28sbGxODo6PjMgl/43z6pPz2NlZUXTpk25fv269j0BPHjwACFEvnv6YnqtUqVKER4erv27XLlyFClShCtXrmjljEYj9+7do0yZMtjY2OQ7aiYrKwtnZ+c8DRaKotCoUSMeP35MaGiolv7gwQNiYmJo3LixWflnOXDgAA8fPmTo0KHEx8ej0+moWrUqAEWLFqVgwYIkJyeD6T6Qnp5O0aJFLc4iSdJfRQamkiT9LoqiULduXWrWrGmZRbNmzRgwYABvv/02xYoV08qPGTOG1q1b4+Pjg5eXF7/99huqqjJr1iyz4WCS9GcTQnDt2jUOHjzI7du3uXnzJitXruTSpUv06NEDd3d3+vfvz6BBg/jpp5/46aefGD9+PO7u7nTt2pWePXtiZWVFfHw8fn5+3L59m+vXrzN//nyCg4Pp16+f9jo580GDgoI4d+4cEyZMoFy5cnTp0sXybWkcHBwoVapUnlVHhRAcPXqUokWLUq5cObO8zMxMfHx8qFmzJrGxsZw4cYITJ05w/Phx/P39wTRvdtGiRdy9exeA3bt3c+TIEW7fvk1AQABr167l+vXr+S5EY8lgMLBz506ePHnCjz/+iBCCGjVq8OjRI65evcqDBw/Yu3cvb775pjYv9/Lly9SoUcNsmL/0x8nOzs438Muhqio6nc6sUUZRFD7++GMeP37M0qVLCQwM5MKFC3h5edGyZUtttIslRVGoVasW9+/f1xooixcvzgcffIC3tzdnz54lKCiITZs2mdWpGzdusHHjRgICAggKCuLw4cMsW7aMTz/9FGdnZ1RVZefOnfj6+qIoCm+//TalSpXSejavXbvGzJkzeeutt/Idyp6bMK0c7O3tzeDBgylYsCCOjo6UKFECX19fHj16xMGDB7GystL+zoiICLKysrTAVZKkv54iLJuOJUn6V1NVlf79++Pr68vdu3e1ADO3yMhIrK2t883Lzs4mNDSUUqVK4eTkZJZ3/fp1wsLCKFy4ME2bNtUeWqXXk6qqDBo0iGXLlhESEvLMHpZXiRCCDRs2sG7dOkqXLo3RaOTJkye0bduWYcOG5TuMLzk5mX79+tGjRw8tqLx//z4jR47E3d0dg8FAdHQ033//PR07dgTT62zdupXt27fj5uZGSkoKQggmTZr0zAd+TMetX7+elStXcvToUe0aMRqNjBo1CoPBwPz5882OSUxM5Msvv0Sn0+Hm5mY2fLZu3bqMGzeO4OBg3n//faZOnUrXrl2ZOHEiV65cwdPTUxsemTO0Mr/PILfExEQGDRpEr169aNeuHZi2pxk7diwJCQl4enoSFhbGzz//TNWqVdHpdHTq1InOnTubbXHzKjMYDLRu3Rq9Xs/Zs2exsbGxLPLKMBqN/Pzzzzg5OTFu3DjLbIRp65WpU6eyfPnyPNsQLV26lM2bN1OpUiXS09PJzs5m5syZz131OTIykvfff5+FCxdq271ERUUxZMgQsrKycHd358mTJ7Ro0YJRo0aBqfdyzpw5lC1bFjs7O2JjYylWrBhTp06lSJEiGI1GvvjiC7Kzs9m2bRuANge1TJkyWFtbk5yczKRJk567qBemur9ixQqCgoLMrpddu3bh4+ND7dq1efLkCc2bN+ebb74BYOXKlaxfv56jR4++ct+30WikU6dOhIWFcfPmzRdeo5L0upKBqSRJZl4mMJUkXtPAFNNczdOnTxMWFoa9vT2NGjWidu3alsU0BoOBO3fuULx4cW1hI51Ox8mTJwkJCcHV1ZWWLVtSqlQps+NiY2M5efIksbGxlCpViv/85z959hvNT1xcHB07dmTs2LFmgW5QUBDOzs6ULVvWrLxerycoKIjExEQMBoM2rFMIQeHChWnQoAGZmZmcOXOGOnXqULx4cVJSUjhz5gyPHj3CwcGBRo0avfBhP0dycjLXr1+ndevWZuk6nY59+/aRlpZGmzZtKF26NJiCi1GjRrFr1648n9Gr6nUKTIUQPHz4EGtra8qXL2+ZDabvLDQ0lOrVq+e74NyVK1e4evUqhQsXpn379vkuvpWbEIKffvqJqKgoVqxYoQVK6enpHD58mKioKOrVq0fTpk21YwwGA1euXOHWrVsYjUaqV69Oy5YtzerrjRs3MBqNZmsPBAcHc/LkSRwdHWnbtu1L/SYJITh//jxVqlTJs5fwpUuXuHXrFlWqVKFFixZgamzp3r073bp149tvvzUr/yqQgan0byEDU0mSzMjAVHpZr2tg+jpYs2YNO3bsYNmyZXh4eFhmvzbi4uL44YcfaNKkCYMHD7bMfmW9ToHp3yU0NJTevXszevRo3n33Xcvs14bBYGDVqlXs27ePDRs2vPQ867+SDEylfws5x1SSJEmSXjGffvopb7/9dp5VSV8nQggSExOpU6fOazOEV3p55cuXZ9iwYTx9+tQy67ViNBpJTU1l/Pjxr2RQKkn/JjIwlSTJjJWVFTY2NiiKIlfQlJ7LyspKmwP5MkNUpZdnY2PDyJEjX3p47atIURQqV67Mzz///NotemRjY4OVlZV2P5Ty9/777/PVV19ZJr9WChQowLBhw3jjjTcss14Z1tbW2lZSsrdU+id7LYfyhoSEEB4e/l8tjy5J0vMJIZg7dy4nT55k06ZNuLi4mK3kKEk5hGmLlN27d+Pr64ubm5usK3+g3HPvXlev699gNBoZMmQIBoMBb2/v1+79/5UURXntP59X/W9QVZWff/6Z6OhofHx8tAXOpH8OKysrGjRokGfRyH+b1y4wFUIwY8YMFi9eLFsxJelPoCgKsbGxZGRkULJkSWxsbF7pH2zp76MoCnFxcaSmplKqVClsbW1lXZH+EYQQREVFgWkrFNkQLv2dhBDExMRgNBopUaKErI//QAUKFGDXrl1Uq1bNMutf5bUMTHfu3Mnhw4flhSlJfwIrKytOnDhBaGgo3bt3x9HRUQYbUr6srKw4ffo0d+7coVevXjg7O8u6Iv0jCCHYvXs3Qgg6d+6MlZWc+ST9fVRV5fDhw6SlpfHxxx/L+vgPZGtry6hRo16blcv/LK9dYCpJ0p/vhx9+YPPmzTx58kTOM5Wea9iwYSxatIiYmBgKFixomS1Jr62WLVui1+s5f/68ZZYk/eU6d+5MaGgoAQEBllmS9I8hm1wkSTKjqip6vR4hBGlpaZbZkqTJqSsAGRkZltmS9NoyGo2oqoqqqhiNRstsSfpLGY1GDAaDVicl6Z9KBqaSJD2THFAhvUhOHZEPS9I/Se57n7wPSq8SWR+lfzIZmEqSJEmSJEmSJEl/KxmYSpIkSZIkSZIkSX8rGZhKkiRJkiRJkiRJfysZmEqSJEmSJEmSJEl/KxmYSpL00nI2nX/WCqxxcXEkJydbJkvSKychIYGAgABu3LhBVFSUZTYZGRk8ffqU8PBw7b+4uDjLYmBa+OnRo0d5Vm8VQhATE8PNmzfx9/d/5vHPkpiYSEpKimUyADExMWRmZlomAxAWFsb169e5c+cO6enpltma7Oxs7t69y7Vr1wgPD7fMfqaYmBj8/f159OiRZRZGo5Ho6Gjt32lpacTHx5uVkf54ycnJPH361DIZgNTUVAICAggMDHxmnYmOjubGjRs8ePDgdy2uo9friY6ORgiBTqfLc83kd23lyKkr2dnZlln5EkIQHR2NTqezzMojOzub27dvExgYmG/5mJgYsrKywPQ+IiMjLYtIkvQ3sJ4wYcIEy0RJkv69hBDs27ePoKAgBgwYgJOTk5anqiorV67k5s2bNG7c2Oy4uLg4pk2bhqenJyVLljTLk/6ZhBAcOnSIa9euMWjQIFxdXS2LvJLCw8Px9vbm+PHjnD59mosXL+Li4kLZsmXB9HcdP36cJUuWcO3aNS5cuMC5c+dITk6mfv36ZucKCwtj+/btbNy4kXbt2mFjY6Od486dOyxdupRTp05x5swZrl69iru7O56enmbnyE9SUhLe3t54enri7u5ulnfv3j2mT5+Oh4cHpUuX1tLT09PZtm0bW7Zs4dy5c1y8eBE3NzcqVKhgdjxAVFQUK1as4NChQ5w9e5YLFy6gqiqVKlXCyurZbdbBwcGsXLmS69evc/nyZRwcHLTPDWD//v3cuHGDBg0aIITg8ePHrF69mpo1a+Lo6Gh2rleZqqqsWbMGVVX55ptvnvuZ/J0MBgP+/v4sX76c0NBQmjRpYpb/6NEjlixZwtGjRzl//jz37t2jevXqZvf18+fPs3TpUs6cOcO5c+dISkqiRo0aWFtbm53LkhACX19fHjx4QK1atfD392fWrFlcv36dCxcucP78ecLCwmjatKnloQCcOXOGhQsXUrt2bdzc3Cyz88g5f+XKlSlWrJhltiY9PZ0NGzbg5+eHv78/YWFh1KpVC1tbWwAiIiL47bffqFu3Lk5OTuj1etasWUNmZibly5e3PN0rQQjBpk2bSE5O5rvvvntl66Mk/a9kzZYk6aUpioKqqkyYMIGHDx+a5W3atIlt27ZRtGhRs3RJepXk9PrHxsby9ttv07p1a8LCwhgwYIDWayiE4NixYxw4cABXV1dcXFxwcXHB3t5e603S6/Vs376dCRMm8Ouvv3Lw4ME8D4uPHz8mPT2dli1b0qxZM65fv87QoUNJTEw0K2dJCMH69es5c+ZMngdwg8HA/PnzWbp0Kbdv39bSVVXFx8eHmTNnUqhQIdq2bUvt2rXz9OJiOv/169c5cuQINWrU4N1338VoNDJs2DCuXbtmWVyTnZ3NnDlzePz4Me3bt8fFxYVJkyaRlJQEwJMnT5g3bx4uLi5gul8ULlyYGzdu8Ntvv1mcTfpfxcbGMmfOHMaNG4eXlxf37t0zy09LS2PKlCn4+fnRpEkT6tWrh4+PD/PmzdPKPHr0iOHDhxMXF8c777xDsWLFGDt2LEeOHDE7V36CgoKYP38+RYsWxcrKiqtXr7J9+3acnZ21ayZ3AJxbamoqU6ZM4bfffntur2qOzMxMZs+ezbJly57buy+E4NSpU6xYsYKmTZvSpEkTVq5cyalTp8B0/SxevJiwsDCtIc3GxgZ7e3umTJlCQkKCxRklSfpLCUmSpFyMRqP49ttvhZubm4iOjrbMFgkJCaJ27dpiyJAhWlpkZKSoXr26mDdvnllZIYSIj48XT58+FRkZGZZZIisrS0RGRoro6GiRlZVlmS294oxGoxgwYICws7MTT548scx+ZVnWxYiICOHs7Cx8fX2FMP1dffv2FV988YVZudySkpLEJ598Ir766isxZswYUaFCBaHT6czKWL7OjRs3RIECBcT58+fN0i09ffpUNGjQQPj5+VlmiePHj4uaNWuKsmXLilWrVmnpd+7cERUqVBDr1683K58fVVVFUFCQCA8P19Kys7NFkyZNxMiRI83K5hYVFSUaNGgg7t27J4QQwmAwiNq1a4srV64Io9EoRo4cKQYMGGB5mLh48aKoVauWCA0Ntcx6Zen1etGsWTPRuHFjodfrLbP/dqqqiqtXr4o33nhDjB07Vrz77rvihx9+MMs/deqUKFasmAgICNDSfXx8RLFixcTjx4+F0WgUU6dOFXXr1jW7/37xxReiVatW2r/zo9frxZAhQ0Tfvn2FMF0zU6dOFe+//75l0XytXLlSVK1aVZQuXfqF14MQQmzfvl00aNBAFCpUSBw6dMgyW2MwGMQPP/wgxowZo6WNHDlSfP/990IIIfz8/ETz5s3Fo0ePch31f/X/3XffFQsXLjRLf1UYDAbRoUMHUbNmTWEwGCyzJekfQ/aYSpL0u7i5uTFo0CA2btzIgwcPAFi3bh2KovDVV19p5bKzszl48CDz5s1jzpw5LFy4kODgYC3/4cOHLF26lLlz5zJr1iz27Nmj5UnSn8nBwcHs34qi4ObmZjYUOTU1Vev5y4+9vT3jx49n9erVtGjRAr1eb1kkz+tYWVlRpEiRZ/YikWt4dMGCBWnVqpVZXlRUFDNnzuSzzz6jbNmyGAwGMPWWbt68mQoVKvDpp5+i0+meO29PURSqV69uNgzY1taW8uXLk5qaalY2N71ej6Io2t+anp6OtbU1jo6OnDlzhosXLzJy5EjLw2jYsCFlypRh165dllnSf0lRFMqXL8/GjRuZPHkyJUqU0OoDpnrk5+dH5cqVqV27tpb+7rvv4uDgwMWLFwHYvXs33bt3p0CBAtpxn376KQEBAcTExGjHWYqJieHYsWP06dMHTMclJCRQqFAhy6J53L59m5UrVzJw4EDc3Nzy7dXPLTQ0FC8vL7777ruXKm80Gs2GIRuNRpycnEhJSWHevHl88803ZsPPMdX/L774gk2bNuV7LUuS9NeQgakkSb9bt27d8PT0xNvbm/DwcJYtW8bgwYO1hxIhBLt27WL8+PHExsaiKAonT55k9OjRJCUlkZaWxvjx4zl8+DCKopCVlcX9+/ctX0aS/jSRkZH4+/tz/vx51qxZQ6tWrXjrrbfAVH9TUlIICQlh06ZN7N69mzt37pgdX6BAAWrWrAmmAO1ZwsPD8ff35+zZs2zZsoWPPvqI6tWrWxbTqKrK0aNHad26tdnQYJ1Ox9y5cylYsCD9+vUzW9DFaDRy+vRpmjZtqs3b8/Ly4uTJk2bByvNERkZy48YN3nzzTcssTdGiRalcuTI+Pj5cuHCBlStXUr16dVxcXJgzZw7ff/+9WbCbw9ramvfeew8/Pz/LLOl/ULhwYSpXrozRaMzTECGE4Pbt29SpU8csvVChQhQpUoSwsDCMRiPh4eFmgSuAp6cndnZ2hIWFmaXnEEIQEBCAtbU1b7zxhpYeFxdHeHg4mzZtYvv27QQEBJgdBxAfH8+kSZNo0aIFH3zwAVlZWSiKYllMk5aWxvTp06lUqRKff/75C4NGRVFo0aIFp0+f5vTp0xw7dozbt2/TpUsX1q5di6urq1kDam7NmjUjJSUlz5BoSZL+OjIwlSTpd3N1dWXo0KH4+vry66+/UrhwYXr16qXlR0dHM2XKFD766COWLl3KrFmz+O2337h06RLHjh0jPj6e7du3069fP2bMmMHChQsZPHiwNn9Pkv5MQgjOnj3L/PnzGT9+POvXr+fzzz/XekiFENSpUwc7Ozv27NnDkiVLGDlyJGfPnrU81XPrrBCCw4cPM3fuXH766ScOHTrE119/rS3Ckp/MzEyioqLyBAvbt2/nxIkTTJ48GWdnZ1RV1R7oMzMzSUpK4s6dO2zcuJE7d+5w9uxZhg4dyr59+8zOk58nT57w66+/UqxYMTp06GCZrbG3t2fEiBHExsayceNG7t27x7Bhw/D19cXNzY0PP/yQ8+fPc+jQoTwP92+88Qb37t3TVkKV/lw5PZhFihQxS7ezs6NAgQJkZGSQkZGBECLPyABbW1vs7e2f2XueE/RWq1ZNq4OKolClShUKFizInj17WLFiBSNGjGDv3r3acXq9nqVLl5KQkMCYMWMQQiCEeGZgKkxzrW/fvs3EiROxsbHBaDQ+szymUQmdO3emVatWrF27lu3bt/Phhx/i7OzM1q1bGT9+PHfu3OHQoUNcuHDBrD6WKFECJyenPHVXkqS/jgxMJUn6r3Tp0oVSpUqxevVqBg8erA1PzHloefjwIa6urmzZsoVNmzZx/vx5ChQowPnz53Fzc+PNN99k+/bt+Pn5ERUVhZOT03MfOCTpj1S2bFmaN29O+/btadasGcuXL+fWrVtgergdNGgQixYtYubMmYwZM0ZbHOhZ27c8S+XKlWnRogUdO3akXr16zJ8//5k9UZh6XzMzMylevDiYelBPnz7NwoULGTNmDJUrV9a2/Mi5XnQ6HRkZGYSHh9O/f39WrlzJunXrqFWrFhMnTiQtLc3sNXK7evUqU6ZM4datW8yYMQMPDw/LImYaNGiAl5cXX3/9NTNmzKBAgQLs3LmTUaNGsWrVKpYtW8aePXuYNm0agYGB2nFFihQhMzNTBqZ/IVVVLZO0hhRra2st/1mNK89alVcIwdOnTylVqpSWZmVlRZ8+fViyZAmzZs1i3LhxuLu7M2LECCIjIzEajezcuZM9e/Ywc+ZMXF1dtV7e/O77RqORI0eOsGrVKiZMmEDJkiW1UQL5lc/N0dGRiRMnMmTIEIYNG0bfvn2ZPXs2H330EXq9nhkzZrBv3z4WLlzIhg0btONyhqXLBZAk6e8jA1NJkv4rzs7OfPTRRxQtWpT27dub5SUmJqLT6Th48CBr1qxh/fr1rF27looVK1K1alVcXV2ZMmUKaWlpTJ06lUmTJnH16lWzc0jSn0VRFBo1akTv3r0ZNmwYU6ZMITw8nAULFoDpIdvDw4NSpUpRunRpWrVqxezZs7V9Hl+Woii0atWKPn36MHLkSMaPH68NHX4WVVVRVVXbdiYuLo5hw4aRmpqKXq9n3bp1bNy4kdjYWM6dO8fx48exsbFBCEGHDh2oW7cuAC4uLgwePJi7d+8SGxtr8Sr/5/Dhw/z888+kpaXh7e2dZwuoZ3F1daVBgwbY2Ngwbdo0unbtSoECBfjtt9/44osvGDt2LADe3t7aMYqioJhW9Zb+fIpp3rTl3rk6nQ6dTkfhwoVxdHREUZQ8DRdZWVlkZ2fnWRE6N4PBoNXRHMWKFaN06dKUKlWKpk2bMn36dJKTk7ly5QqPHj1iyJAhFCpUiJCQENatW8eOHTtISUlh3759XL582excT58+ZciQIWDaOmnt2rWsX7+ezMxMjh49yokTJ8zK56dmzZpUqlSJNWvWkJiYSJ8+fZg6dSqenp6MHz+ezp07s2jRIrP9X62trV84h1WSpD+PDEwlSfqvCCEoVKgQ7u7ueYYm5mwVMGfOHDZs2MDatWtZt24dO3fu1Ob3tGzZkvXr1/P9999z//59hg0bpm07IUl/paJFi9K0aVPu3r1rmaUpVKgQdnZ2+c5xe1EPTo6yZctSq1at5w4VtLe3p0CBAtq1kJ6ejoeHB7Vq1WL79u3s3r2bAwcOkJycjL+/P2fOnMHJyYnSpUvn2xtpbW2dZxsbIQQ3b95k3LhxNGnShNWrV1OtWjWzMi9j48aNZGRk0LdvX06dOkXFihVp06YNnp6efPnllwQGBmqfV0ZGBoqiaIvsSH8syzqoKAq1a9fm5s2bZunR0dEkJiZq+3qWKVOG69evmx0XHByMvb19ngWCciimbYBe1LPo4OCAs7Mzer2elJQUatSoQdGiRdm8eTO7d+/Gz8+P9PR0zp49i7+/v9mxqamplC1blooVK7J161b27t3LgQMHyMrK4sKFC9riTS8SHBzM6tWrGTNmDHq9nvv379O/f3+KFStGt27dcHZ21tY3EEKQkZGBs7Oz5WkkSfqLyMBUkqT/ihCC7OxsdDqdWQuzoihUrVoVd3d39u/fj42NDUWKFMHe3p6wsDAURSE1NZWHDx9ib29P165d6d27N/7+/sTHx5u9hiT90VRV5d69e0RGRoJpyGBgYCAXL16kefPmYOpVevDggRboxcTEsGzZMkqVKkXFihXNzofpnJaLDBkMBm7fvq31WOn1eq5du0ZwcDBvv/22WdncnJyccHNz04LX0qVLs2bNGry8vJg/fz4LFy5k9uzZlCxZkh49ejBw4EBsbW3p2LEjBw8e1Fa+TkhIYO3atTRo0AB3d3eysrLw9/cnMTERIQReXl7Y2toycuRIUlJSiImJISYm5qWvwaCgINatW8eIESOwt7fHwcGBpKQkMjIyALh37x7u7u5aUHzv3j1KliyJvb29xZmkP4LBYMhzH27bti2hoaH4+fmhqiqpqals3LgRDw8PGjZsCMCHH36Ir68vjx49AlNP5dq1a+nYsSOOjo7a+SxVrlzZrIHFYDDw8OFDbSGwxMRENm3ahKqq1KlTh1q1arFp0ybmzJnDggULWLhwIb/88guFCxdm6NCh9OjRA0yrtQcHB1O5cmXWr1/P/PnzmT9/Pl5eXsydOxdHR0e+//57vvvuO+21nyU9PZ158+bRpEkTmjZtSnZ2Nra2ttoe3KGhoej1em3v7Zz6W6ZMGYszSZL0V5GBqSRJ/xVFUbC1tcXBwSFPa33ZsmXp168fK1asYPr06axcuZJFixaxaNEi0tPTefr0KbNnz8bLy4ulS5eyd+9eOnfu/ML5bZL0vxJCsG3bNmbMmMHy5ctZtmwZM2bMoFChQvTr1w+A5ORkbYujlStXMm/ePPbs2cPo0aO1h9jccuam5b4OVFVl9erVzJo1i5UrV7J06VKmTZtGrVq1+OSTT8yOz83W1pb69etz/vx5AK1hp1ixYnh6elKiRAnKli1LoUKF8PT0xM3NDUVR6NmzJ8WKFWPChAmsWLECLy8vzp49y4gRI3B0dCQyMpLBgwdrPU2BgYHY29sza9Ys5s2bh5eXFwsWLGDt2rUW7yiv9PR0vL29admypRZkN2/eHGtra7y9vdm2bRtHjx7lk08+wdraGiEEx44do2nTps+ctyj9bwoUKGDWG60oCm+++SadO3dm8uTJeHt7s2TJEg4ePMjo0aNxcHDAysqKXr164ebmxoQJE7S6npaWxsCBA83On5uiKNStW5fo6GgiIiLANPx36dKlzJ8/n1WrVrFgwQLWrFlDv379qFy5Mra2thQtWpTixYtr9bh06dK4urpq/2s0GvHy8mLevHnY2tpSpEgRPDw8tPJlypTBxcUFT0/PF25LI4Tg+PHjPHjwgGHDhoFpFeN33nmHlStXsmPHDlauXEndunWpVq0aQghtjnmtWrUsziZJ0l9FBqa/U878nz+KEOIPm89gNBqfuYiBJP0Z6tSpw1dffYWdnZ1ZuqIoDBw4kOHDh/P48WMOHjxIWFgYzZs3x9XVVXvguHDhAidPnqRatWpMnTpVDqGS/nRWVla8+eab6HQ6jhw5wtmzZ6latSqLFy+mXLlyYOq1LFmyJNeuXePgwYOkpKQwYcKEZ24zUaVKFfr37282ZNbW1pbGjRsTHx/PwYMHuXz5Mk2aNMHLyyvf4Da3zp07ExgYSGhoqGUWmH6HevXqZTb81sPDg4ULF1KiRAkOHz7M06dPmThxIh988AGYVmOtVKkSLi4uKIrCZ599RsOGDUlKSiI9PZ309HQyMjLyHQ5sKTMzk6JFizJ48GAtrVSpUowbN46QkBBOnTrFBx98QNeuXcHUC3flyhU6deqU6yzSH0VRFD744APatm1rlm5vb8+UKVNo3bo1p06dIjg4mBEjRtC5c2etTJkyZfDy8sLFxYXDhw9jMBiYM2dOviMDciiKQoUKFahWrRpbt24FU/0qU6YMt2/f5sCBA9oc0REjRlgernFwcOCbb77RVg5WFAVPT09KlixpWRRMzzjfffed2aJLzyKEIDY2lmHDhmnXm62tLYMGDaJixYocOXIEa2trxo0bp82V3bFjB2+99RaFCxe2OJskSX8VRchIJo+ceROWN6ecVl9nZ2eaNGlilvffSkhIYM+ePXTt2vV/eig3GAzs3buXqlWrUqNGDcts6QWysrI4d+4clSpVeua8mn8LVVXp378/vr6+3L1797kLYLwMVVXR6/X5zi3T6/UIIfIEttLrQVVVBg0axLJlywgJCXnmA+WrSqfTYW1tnWcRlxx6vR5VVfOtu7/Hi17HksFg4Pvvv8fFxYXJkyc/d0hlfnQ63f/8np/HaDRiZWWVZ6QEpt9JnU6nDdnNyMhgxowZ3Lt3jw0bNrw2PaYGg4HWrVuj1+s5e/bsS393r6qcYaz5fWc5fm+92bt3L7NmzcLHx4cKFSpAriHFv+c8f5b8FmjKkZmZiYODA5juYxcvXmTQoEGsWrUqz1ZNrwKj0UinTp0ICwvj5s2br8x1lJaWxoMHD1BVlfLly+Pm5mZZxExmZibBwcFkZ2dTrly5PI10KSkpPHr0SFuZPL/nsZypFunp6ZQsWTLP705aWhphYWGkpqZSqFAhKlasmGcdDL1eT3x8PMWKFcszB1/6e8lvw0J2djazZ89m9uzZeeYMCSFYtmwZ27ZtM0v/X0RERDBkyJA8K+f9XtnZ2UyfPp3Tp09bZkkWjEYjUVFRZhuSp6SkMG7cOM6dO2dWVvrfWVlZPfMhxdbWVgal0t+mQIECz3xwxVQ/n1V3f48XvY4lGxsbRowYQUREBI8fP7bMfqE/4j0/j7W19TMDHEVRzOaRxsTEcO/ePX7++edX5mH638jOzu6Z31mO31tv2rdvT6NGjTh+/LiWZmNj87vP82d53jWXE5RiClIOHz5Mz549X8mg9FX15MkTZs+ezYIFC5g3bx6//vrrM0d5AMTGxmpDtefPn8/kyZMJCgoC0/P1gwcPmDNnDl5eXnh5eTFp0iT2799vNhIwPj5e28bLy8uLyZMnc+rUKS0/MzOTtWvX4uXlxaJFi5gyZQrLly/Ps/J0zkrk0qtHBqYWgoKCtAqdc8Hkptfr8wSs/4ucBWT+iOHBer3+DxsW/E+WmJjI1KlTzTYPd3Z2pm/fvtpWC9L/PWDKoFF6HisrKy3YeFUeRv8pKleuzLRp017YA/Gqy+n1fd0e+G1sbFBMW9w8L8D5N7OxsWHSpEk0bdrUMuu1Ymdnx0cffcTQoUMts14Z+a2u/XfS6XTMnDmTAwcO0KpVK9q0acPRo0eZNGmSWaN/Dr1ez5IlS9iwYQNNmjShXbt2XLt2jVGjRpGZmYkQglOnTnHnzh2aNWtGly5dMBgMDBs2TNsP2Wg0smbNGhYuXEjdunXp1KkTsbGxDB48mPDwcDCNfnvy5AkNGjSga9eulCtXjokTJ7Jnzx4wdUJcv36dR48ecerUKUJDQ7l586a2R67095NDeXMRQjBu3DjOnz+PlZUVb7zxBtOnT9fyVVXlk08+oXTp0sybN8/s2OTkZJKTk7WFKnI/pGVlZWn7yBUuXBgnJyct79atWzRv3pygoCAKFSpEXFwcDg4OuLu7a2VyCCGIiopCr9fj7OxsNtQ4IyODVq1a8fXXX7/UanWYWq8yMzMpUKAARYsWNWvNTkpKIiUlBRsbGzw8PMzysrOzMRqNODg4EB8fT3p6Om5ubri4uGhlyNUzaTQaKVSoEK6urmBaOMPOzs5saEV2djYGg0EbspaZmYmtrS02NjY8ffoUKysrbcN5o9FIZGQkVlZWeHp6aufAdO6cRUiePn2K0WikWLFi2veRkZHB1atXadOmDTdu3KBUqVIULFjwmTd8vV5PTExMnr8BU33IzMzEyckJnU5HTEwMdnZ2r/0CPjlDebdt28a1a9coWLCgnLss5aGY9qQcN24cq1ev5tatWxQpUkTWlT+IYlpczGg0vrYNjoqiaI0X+T2svqoURcFgMGgPx4cPH/5DGo//aV7X79dSzrVmMBheye85pz5+8cUXRERE4O/v/7c2lgjTdlPt2rXD19eXZs2aAXD8+HE+/vhjTpw4Qb169cyOCQ0NpVmzZixevJguXboAcPPmTVq2bImvry//+c9/uHXrFuXLl9emtWVmZtKkSRO6du3K2LFjtX+/++67zJw5E0wjMurWrcuMGTP44osv8h2+/f777+Pk5MTWrVsJCQnht99+w97engMHDtClSxeMRiMDBw584YJa0l9DBqa5JCYm0qJFC37++WfS09OZNWsWly5domDBgvCMwFQIwblz5/Dz89P2nXv77be1RR8CAwO1hSiMRiMeHh588MEH2qpvt27domXLluzatYvAwEDu37+PEILOnTvTpk0b7b1FRUWxf/9+7t69i16vx9HRkTZt2tC6dWusra1/V2Canp7OoUOHuHz5MjqdDjs7O3r27En9+vVRVZUzZ85w8uRJkpKSUBSFypUr07lzZzw9PRFCcOnSJQIDA6lZsyYnT57k6dOnuLm58eWXX2oLJkRHR7Nnzx7u3r2LwWCgdOnS/Pjjj9jZ2bFkyRIaN27MG2+8AabP8MyZMzx8+JCvv/4aIQTbt29HMe15d+7cOVJSUmjfvj1NmjTh0KFD3Lx5k/T0dJo3b85HH32EnZ0dRqORpUuXUrduXeLj47l48SJpaWmUK1eOHj16ULJkSU6ePMnGjRtZt24d/fv3p0SJEnz77bc4ODiwcuVKWrdurc3RDQkJYf/+/YSFhWEwGHBzc6NDhw68+eabYGqMWLt2LW+88Qb37t3j1q1bZGdn895779GhQ4dnBruvOlVV+e677/Dx8eGTTz753fPbpH8PIQTnz5/n7t27dO/e/X+aJy9JrxJVVdm7dy+qqtK5c+fX9n4u/TOoqsqhQ4dwdnbm0qVLZo3kfzVVVZk/fz4bN27k8uXL2rWh0+moUaMGI0eO1FY4zym/bds2Ro8ezZ07d7Sh/gaDgTZt2tC0aVOzTqDc2rdvT/369Zk2bRqZmZm8//77vPnmm8yYMQOA8PBw3nvvPaZOncpHH31keTgGg4Fu3bpRtmxZ5s+fT0ZGBpcvX2b27NncunWLtm3b8uOPP1KzZs08Aa3095CBaS579uxh0KBBXLlyBZ1OxxtvvIG3t7fWupNfYHrs2DHGjBlD9erVqVixImlpabi7uzNy5EiCg4P58ccfsbe3p27duqiqyo0bN8jMzGTVqlWULVuWwMBAmjZtyieffIK7uzsFChTg4sWLPHr0iKNHj1K6dGnS0tIYM2YMV69epWXLltjb2xMaGsqNGzeYMWMG7du3Jz09ndatW78wMDUYDCxevBgfHx+aNm1KsWLFiI+Pp23btnzwwQccPnyYn376ibp161K2bFnS09M5efIkDRs2ZM6cOTg4ODB//nxmzZrFBx98gKenJzqdjt27d1O/fn1WrlyJjY0NkydPZt++fbRr1w5Mm2VPnjwZZ2dnqlatyqBBgxgwYACYHm4nTZrEoUOHuHDhAqqq0rNnT27evEnHjh1xdXXl7t273Lhxg06dOhEVFUWFChUIDQ3l6NGjbNy4kZYtW6LT6ahbty5lypShWrVqFClShIyMDA4ePKithnnq1CnWrFnDjh07+OyzzyhevDhDhgxBCEGjRo2YNm0a3bt3Jzo6moEDBxIfH89bb72FlZUVd+/eJSwsDG9vbxo0aEB4eDhNmjThzTffpGrVqjg4OBAQEMCNGzfYtWvXazssWFVVvv/+e5YvX061atXkzVp6roiICBISEqhWrVqeBSYk6XUlhCAkJAQhBBUrVnzh/ExJ+jMJIXj06BElSpTgwoULeRbn/CsZjUZ+/PFHnjx5og2RzUl/9913adiwodajmZP+yy+/cPHiRY4cOaIFskajkT59+pCamoqvr69WPsft27fp0qUL8+bNo2PHjggh2LlzJ3PmzOHrr7+mdOnS7Nq1i+TkZLy9vbVOpNTUVO7fv49OpyM0NBRfX1/Gjh2r7d0bEBDA9OnT6dKlC/v27WP27Nn/8yKP0h9HBqYmRqORTz/9lIIFC/Lbb78B8NVXX2kXTM6wtdyBaVpaGv/5z3+oWbMmv/32m/YAn5mZiZ2dHcOHD+fkyZMcOHCAEiVKAPD48WPatm3LRx99xLRp0wgMDKR+/fqMHj2ayZMng2lp/Ro1arBkyRJ69erF7t27+fbbb9m2bRstW7bU3u/XX3/Nw4cP8fPzQwhBy5YtXxiYBgQE0L59e8aPH2/WopWRkYHRaOS9996jevXqLF++XLt5nDhxgi5durBt2zbeffddFi1axMiRI9myZYu2FcHWrVvp06cPN2/epGTJkjRs2JDmzZuzZMkSMAU7QggURaF27doMGDBAe59CCKZNm8aRI0c4deoUqqry+eefc+bMGS5cuEDJkiUxGAy88cYbxMXFcebMGcqXLw9AvXr1eOedd5gzZw7Z2dk0bNgQR0dHdu7cqQ3z3bdvH7169eLo0aM0btyYO3fu8Pbbb3Pnzh1t2G1MTAwtW7Zk8uTJdO3alfnz5zNv3jwOHz6sbcmQlJREp06dKF26NOvXr+fp06c0aNCAdu3asWrVKqytrUlJSaFhw4b06tWLiRMn8jpSTUN5t27dip+fn3azlyRLQgimTp3Kxo0bOX36dJ4VFiXpdWU0Gvnss88wGAxs3bpVBqbS38poNNK/f39iY2O5cePG39oIaDAY+PLLLwHYsGGDWXqPHj1wd3c3W1goZ6hsZGQkO3bsMEsfNGgQDx8+5ODBg1o6wJ07d5g0aRI6nY6NGzdqvazh4eH07NkTo9FI+fLlOXfuHKNGjeKHH34A02/S3bt3mTlzJvHx8QQFBfHZZ5/xyy+/aM/xx44dIyEhgR49euDt7U3jxo1p0KCB2etLfyMhCSGECA0NFUWKFBGLFy8WgYGB4tatW2LOnDnCzc1NhISECCGEMBqN4uOPPxaDBw8WQghx+fJl4e7uLu7cuWNxNiGSkpJEnTp1xIIFCyyzxMSJE0X9+vWFEEIEBgYKR0dHce/ePbMyTZs2FdOnTxeqqoo+ffqIjh07muULIcTp06dFiRIlxP3790V2drZ48803xZIlSyyLaVRVFXPmzBE1a9YUqqpaZovAwEDh4uIibt++bZklmjdvLoYPHy6EEGLBggWidu3aQqfTafnh4eGiYMGC4vr168JoNIoBAwaIxo0bCx8fH7PPx2g0ilq1aglvb28tTVVVMXXqVNGqVSutTM+ePUXPnj21MkII0atXL/HJJ59o/1ZVVXz66afis88+E0IIkZ2dLWrUqCFmz56d6yghsrKyRO3atcX8+fOFEELcuHFDFCpUSISFhWllYmJiRI0aNYSvr69QVVW0bNlSDBs2LNdZ/s+qVatE5cqVRUpKioiIiBBly5YVu3fvNivTrVs38eWXX5qlvU6MRqP49ttvReHChUVaWppltiSZGTp0qLCzsxOJiYmWWZL0WmvRooVo0qSJZbIk/S06deokatasKYxGo2XWX0qv14tevXqJXr165Un/8MMPxY8//miWbjAYxPfffy+6dOmSb3ru51uj0SiOHz8uunfvLjp16qQ9fwvTc3WfPn1E7969xblz54S/v79Yu3ataNmypTh8+LBWLioqSmzcuFH4+PiIX375RXTo0EEsW7ZMy5debXLShMm+fftIT0/Hz8+PGTNmMHPmTM6cOUNmZqbZUIXcnjx5QqFChfJdNTEzM5OsrKw8i/MIIfDw8CA9PV1Ls7GxMVte32AwmC2eFBcXp/W45lawYEFsbW1JTU19qfkvQggiIyMpVqxYntZfIQQpKSmoqprntYQQlCpVivj4eO3frq6uZgtyWJn2tFNVFSsrK0aMGEHjxo1ZtmwZP//8M1u2bEGv12vHW75fKysrcnfeCyHMJqKrqoqNjY1ZmhBCWzkxd5plr43RaMTFxYWMjAytTO7/zU98fDxlypQxSxNCUKxYMfR6PZmZmdqCCbmXnVdVFVtbW9RXcAGF30sIkWeJdUnKTVVVbdGT3Pc0SXrd5SyEYzQaMfyBK/FL0n8jdz183rPLX0FRFAoWLEhycnKe9NjYWG2hytyKFClCYmJinveenJysDaM1GAxs3ryZX375BXd3d5YtW6aNjhNCEBAQwI4dOxg/fjxNmzalbt26fP7559SqVYuFCxdqz6QeHh707NmTL7/8kgkTJtCyZUt++eUXbR0Y6dX24mjmXyAzM5PNmzfTuXNnWrZsSZ06dahTpw6tWrWiY8eObNy4Md+lpN3d3UlOTtZW3M3NwcEBJycnHjx4YJauKAohISHaEFJhGt5qGcjkXLyKouDh4cGjR4/M8jEN+QUoWrRonuPzoygK7u7uREdHa0Fi7jxXV1esrKx4+PChWZ4QgtDQUO0GQa6hubnLYDoPQJkyZViwYAELFiygWLFiDB8+nICAADAF4rkfYnNuZpYsb2BCiDx/p2UZTEFlbsnJyTx+/FhbmClH7rmTlucpVqwYd+/eNUtTFIXQ0FBcXV1xcnJ6ZoCb853+E/xT/g5JkqT/hrwHSpI5RVGoXr06YWFhefaDf/z4cZ4VeRVFoU6dOjx8+NAsmNXpdAQFBdG4cWMwTRubNWsWPXv2ZOHChWY7HAghiImJQVVVSpcuraUDlCtXjqdPn+Z5rs3Rtm1b4uLiZOPpa+JfH5gKIbhw4QIBAQGMHz+egQMHMnz4cIYPH86gQYMYM2YMt2/f5syZMyimJbtzWmVq1qxJqVKl8Pb2JiIiAlVVSUpKIiQkBFdXV9q2bcuWLVsICAhAr9ej0+m4du0ahw4donv37pCrx8EyuMn9Oh07diQoKIgdO3aQmpqKqqqEhoayZs0a3nrrLUqVKoXBYNBaeJ+nVatWJCcns3r1apKSklBVlcjISKKioihdujRvvvkmc+fOJSwsDFVVSUlJYefOnURGRtKhQweEEPm2IAsh0Ov1WvB4584dkpOTeeONNxg6dCipqak8ePBAu6EdPXqU+Ph4jEYjQUFBHD9+3Oy9G/PZIuFZabnfi62tLQcOHODBgweoqkpCQgKrV6/Gzs5OW9LcxsYGvV7P48eP0el02jlzPj9FUfj44485dOgQZ8+eJTMzE4PBwO3bt9m6dSvt2rXDyckJg8GAXq/P85nn9z4lSZIkSZJed4qi0KZNG1JTU9m8eTM6nY7U1FTWrFmDu7s7jRo1AuDhw4fcunULRVF46623KFCgAD4+PqSnp5ORkcGOHTtIT0+nXbt2ZGdnM3v2bMqXL89XX31FZGQkT58+JSIigri4OBRFoVKlSjg4OODj46M9C9+5c4c9e/bQoEED7OzsePr0Kbdu3SIrKwtVVQkPD2ft2rU0bNiQIkWKWP4p0ivIesKECRMsE/9NVFVl0aJFWFlZMWjQILP9OgEKFSrE6dOnSUhIoH379uzbt4/ChQvTtm1bHBwc8PT0ZO3atTx+/JiwsDBt25MmTZpQpUoVTpw4wdmzZ4mNjeXKlSts2rSJkiVLMmbMGAoUKEBcXBxbtmyhb9++2jBVVVXx9fWlSpUqNG3alHLlyhEREcHWrVtJSEggKCiIPXv2EBoaytSpUylRogR6vZ6tW7fSoEEDbeUxS4qi4OnpSXp6Ohs3biQuLo579+5x7NgxChQoQM2aNSldujRbtmwhJCSEx48fc+bMGTZv3swnn3zCJ598AsCNGzd4+PAhn376qTYBPy0tjbVr1/Lll19SqFAhZsyYwY0bNwgKCuLAgQM4ODjQr18/3NzccHJyYv369cTHx/Po0SOOHTtGdHQ0bm5u9OrVCyEEBw4cwNnZmfbt24Mp8D106BD29va8//77Zmm2trZ06tQJVVVZsWIFer2euLg4QkJCOHLkCLt27WLo0KHawlGKouDn58f9+/dJSkqicuXKKIrC1q1bad26NdWqVaNSpUr4+/tz4MABEhISCAgIYOvWrQBMmjQJV1dXUlJS8PX1pUOHDlSoUEF7TwcPHqRAgQJ06tSJ15EQgn379hEUFMSAAQPM9t2VpNxyrsFr164xaNCgv3ULA0n6I6mqypo1a1BVlW+++SbP9BNJ+isJIdi0aRPJycl89913f2t9VBSFYsWKkZiYyKZNm0hLS+PSpUvs2bOHH374gbfeeguj0ciCBQvYs2cPXbp0oWDBglhbW7N69WpSU1O5fv06W7du5auvvqJt27akpKSwePFinJycuHfvHufPn+fixYtcvHiRmJgY6tevT5EiRUhPT2fTpk0kJSURGBjI1q1biY+PZ+LEiZQoUYJLly4xd+5cIiMjCQgI4MCBA1y5coXx48drC1lKrzYZmKoqUVFRtGvXLt9Ka2tri6enJ/b29tSvXx9ra2ttaxiAqlWrUrVqVe7evcu9e/cQQtC8eXMqVKhAoUKFaNGiBTExMQQEBBATE8Nbb73F6NGjtSBUURScnJx4++23zeaZWllZUbt2bUqXLo21tTUtW7bE2tqagIAAQkNDKVGiBCNHjqR27draMdbW1tSvXz/PHNHcFEWhWbNmODk5ERgYSGhoKG5ubrRq1YoiRYpQvnx5ateuzcOHDwkMDESn0/HRRx/Rv39/bUiToiiULFmSunXraoG8YtpztGnTpjg7OxMbG8utW7d4+PAhHh4eDB06lKpVqwJQsWJFPDw8uHnzJhERETRt2pRu3bpRpkwZ7e9RFIVq1apRuXJls/detWpVqlSpkietatWqGI1GvL296dOnDyVKlODMmTNkZWXRu3dvPvvsM+0YJycnypcvz927d0lJSaFVq1Y4OjpiY2NDw4YNKVq0KA4ODrRq1YqMjAwCAgJ48uQJ1atX56effqJUqVLaa9vb29OoUSOzecY57z3n733dvExgmpaWRmpqar57nAohSEpKwtraOk9Dj/TPIgNT6Z9KBqbSq+RVCkxz5ASgV69eJS0tjU8//ZQePXpo+eHh4djb2/P2229rz6cODg5cu3aNhIQEPv74Y3r37o2iKBiNRuzs7HByckI1rVWSs3ZJ0aJFqVevHra2tjRt2hR7e3vt+bJcuXKMGjWKOnXqIITAzs6OiIgIgoKCePToEUWKFOHHH3+kdevWZu9denXJ7WJ+B/EPmjv4Mv6Iv/ePOMfL0ul0NGjQgJEjR2pLmUu/n2raLsbX15e7d+/m2d9LCMG6deu4du0a33zzTZ79WjMzM5k2bRoff/xxnrkm0j+LqqoMGjSIZcuWERISQsmSJS2LvPaMRiOqaVGzZ9HpdNja2j7zYTFnHpadnZ1l1gvlPKS9rP/mtbKzs7G1tf1d92qdTme2SN+z/N73/6owGAy0bt0avV7P2bNnX7v9nP+b7/Rl/VXfqU6nw8bG5qUbOLOysswa+HPL/SzyV73/P5LRaKRTp06EhYVx8+bNl/5M/sle9Hz5onzp1SQDU+kfQ6fTUb16dUaOHEn//v0ts6WX9KLA1Gg00rVrV3bt2kXnzp3ZsGGDWa9qYmIiZcqUYdmyZfTs2dPsWOmf5Z8SmAYEBBAdHU3r1q3NApCrV69y48YNdDod1apVo1mzZmYPvjExMZw9e5aIiAgcHR1p1KiR2SiW2NhYLl68yOPHjzEYDFSoUIHmzZu/1N7AQghtz+bSpUvz8OFD/P39zVbmLFWqFG+//TaKohAXF8elS5cICwvTXqtZs2ZmK5lbioyM5Ny5czx9+hRXV1feeuutfEcO5ZaRkcHp06d58uQJrq6utGzZ0myRkvDwcMLCwmjevDmYFum7c+cOrVq1eq0epl/XwDSnTj558gRnZ2caN25MzZo1LYtpTp48qdXdl3H16lXs7e2pVauWWXp8fDznzp2jSZMmuLu7g+m34ty5c0RFRZH7UbNJkyZ5Vr3P7e7du1y/fp24uDgcHBx45513tOky+UlKSuL06dPExMRQtGhRWrVqZVbvc0ZHNWrUCCEEISEhPH36VKujrwMZmEr/Fq9Xk5EkPYeiKFSuXDnf7XukP5a9vT1Nmzbl5s2brF271jIbV1fXfHuYdDodUVFRJCYmWmZJ0t8iJSWFIUOG8NNPP5GVlaWlnzp1ilGjRnH8+HHOnz/PmDFjWLdunZafmJjItGnTWLJkCf7+/uzdu5dhw4Zx69YtMAWON2/eZMWKFVy7do3z588zYcIEvLy8nrl6ZG6XL19m+vTpZGRkIIRg9+7dDB8+nG3btrF161a2bt3K2bNnwfRaAQEBZq81ceJEvLy8zFbNzC0xMZEpU6awYMECrl27hq+vL8OGDSM4ONiyqEYIwbZt21i6dCmPHj1i+/btLFy4UPt7srKymDVrFpcuXdLKZ2ZmMn/+fE6ePGlxNumPlpKSwq+//srs2bO5du0au3fvZujQoQQGBloWBSA4OJjevXuzdOlSy6x8PXz4kEmTJuW7iv6KFSv4+uuvCQoK0tIyMjKYOHEi8+bNw9fXl61bt7Jt2zbCw8PNjs3t+vXr/PTTT/j6+uLv74+fn592TeXHaDSyatUq1q9fz6NHj1izZg2rV69GNS1KmJyczNSpU7lz5452THJyMr/++is3btzIdSZJkl4JlhubStLrymg0iosXL4qIiAjLLOl3MBqN4ttvvxVubm4iOjraMlsYDAbRtWtX0b9/fzF37lxRvnx5cffuXS0/ISFBlCxZUmzbts3suIsXL4rZs2eL8ePHi4kTJ4pNmzaJxMREszLS68VoNIoBAwYIOzs78eTJE8vs18LKlSuFk5OTqFatmkhJSRFCCJGcnCyaN28uevfuLeLi4kRKSor45ZdfhKenpwgPDxeqqorly5eLMmXKiBMnTgij0ShCQkLEO++8I3r16iWMRqNQVVUEBQWJy5cvi+zsbJGcnCzmzJkjXF1dza6X/GRkZIiPP/5YTJ48WQjT5zxo0CDRokULERwcLO7duyfu3Lmjfeb5vda8efOEs7OzCAoKsjj7/5Xfvn27cHFxEYcPHxbZ2dkiKChI1KpVSwwcONCyuCY5OVm8/fbb2rUdGBgoqlevLu7fvy+EEGLHjh2iTZs2ee7B8+bNE+3atRPp6elm6a8yvV4vmjVrJho3biz0er1l9itHVVVx8OBB4eTkJPbu3St0Op0IDg4Wb7zxhvj6668ti4vs7GwxcOBA4ejoKLp162aZnYderxfDhw8XX331lVBV1Szv7t27okqVKsLJyUkcPnxYS4+OjhY1a9YUXl5eIjg4WNy9e1fcvXtXJCcnmx2fIz4+Xrz33nuiV69eIjg4WBgMBhEdHS0iIyMti2qePn0q6tSpI86cOSOEEOL48eOiVq1aIjY2VgghxJIlS0SnTp3MXlNVVTFq1Cjx6aefvhbfrTD97nbo0EHUrFlTGAwGy2xJ+seQPabSP4aVlRWNGzfG09PTMkv6ExiNRvr160eZMmWYMmXKM3tmMA0XGzZsGBcuXEBRFKKjo5kxY8Zze3Qk6c929+5d1q5dS7du3bTh6Dk9nUFBQUycOJEiRYrg4uLCsGHDsLGx4ejRowBs2bKFLl260KpVK6ysrChfvjzjx4/XVhlXTFtjvfnmm9ja2uLq6kqPHj2wsrLKt8cphxCCa9eucf/+fbMpCWlpaVStWpVKlSpRpUoVqlWrpg2dftZr2djY5PtaQgju37+Ph4cH7777Lra2tlSvXp22bds+t3cqMTGRrKwsmjZtCqYt0+zt7UlKSiIqKoolS5YwcODAPPfgL7/8kpiYGNlr+icSQvDgwQMKFSpEx44dsbOzo1KlSrRv357bt29bFufIkSNcv36d995776XmWz5+/JiDBw8ydOhQs3l7GRkZzJkzh7p161KhQgWtpxLTegNWVla8/fbbVKpUSVuoML9F0oQQHDlyhJCQEJYsWUKlSpWwtramWLFiFC9e3LI4mI6JjIzEzs6OBg0aANC4cWOys7PJysri/v37bNiwgTFjxpi9pqIofPfdd9y4cUPbX12SpFfDi+9GkiRJ+TAajTg6OjJlyhSOHTvG7t27wfSjn3s+UVpaGr/88gvly5fHx8eHCRMmsHjxYkaOHMmCBQu4fv16rrNK0l8jJSWF6dOn06hRIz744AOz/aQvXbpEhQoVzAIsBwcH6taty+XLl8E0HNByrmiFChVITk7myZMnWpqqqhgMBtLS0jh9+jTVqlUzW1nckhCCXbt20aRJE4oWLaqlJSUlkZ6ezr179wgPD893r2TL17JcxTyHoii8+eabODo6cu7cOeLi4rh9+zYPHjzgvffesyyucXFxwcHBgZMnT5KWlsbZs2dxcnKiRIkSLF26lMqVK9O5c2fLw3Bzc6NFixbs2bPHMkv6gyiKQoMGDShcuDAnTpwgLi6Ou3fvEhQURLt27czKhoaGMmvWLPr27UuNGjVe2DioqiqnTp3Cw8PDbA41pgaa+/fvM3r0aLOAVQhBRkYGiYmJPH78mHv37hEXF2d2bG5Go5F9+/bx7rvvotfrOXnyJOfOnSM+Pt6yqEYxrdiqqirnzp0jNTWVo0ePUqpUKRwdHZk3bx5t27blrbfesjyUsmXLUqtWLfbv32+ZJUnS30gGppIk/VdyHkKaNWvG119/zcSJE7XW69yCg4O5c+cOI0eOxNnZWUvv3r07pUuX1nqgJOmvoqoqq1atIiQkhJEjR2I0GrWgVAhBWFgYJUuWNHvQtrKyomjRolpvaMOGDTl8+DBPnz4FU8/Rvn37UFWVpKQk7VzHjx/H29ubadOmsXLlSsaMGZNnQbHc9Ho9QUFBNG7c2Czd3t6eR48eMX/+fKZNm4a3tzcxMTFavhCCEydOsHTpUqZNm8by5csZPXp0vr1NiqLwzjvv0KxZM/r27cuSJUsYPXo0SUlJ9OvXz7K4pnDhwnTv3h1fX1/WrFnDmjVr6N69O6GhoZw8eZLx48eTlJREeHi4tkgTpvfWpk0brly5YpYu/XEURaFp06a8++679O/fn0WLFvHzzz/z5MkTBgwYoJVLSUlh6tSplC9fns8++4ysrCyzep4fIQSnT5/Os1jQhQsXWLx4MaNHj6ZcuXJ5AlxVVbGzs2Pbtm3Mnj2byZMnc/DgwXzrQE69d3FxYcmSJXh7ezNz5kwmT5783DmppUqVonPnzvj4+LB27Vp8fX3p27cvp06dIjQ0lJEjRxIXF8fjx4/NGkxVVaVNmzacPn3a7HySJP29ZGAqSdL/bOjQoTg4ODBz5kyMRqPZ0LCIiAhcXV0pUqSI2TGKaZPu57WiS9Kf4dixY2zbto3p06drPS5WVlbY29ujKAqZmZl5GlgwbcGS01P57bffkpGRwdixY1m+fDlLlizh8OHDODg4aNuoCCG4d+8e586d48yZMyiKQkZGBpmZmRZn/v/S09NJSUnR9srO8emnn/Lll19Sq1YtChQogJeXFzNnzjRbpff+/fucPXtWe63MzEwyMjLMzpMjJCSE7OxsqlatSlZWFkWLFsXd3f2FQxu/++47evbsSUJCAm3btqVnz57MmzeP3r17k5CQgLe3N6tXr8bHx4fk5GTtuHLlyhETE2O2wJT0xwoLCyMjI4Pq1auj0+lwc3OjRIkS3Lx5E0zB37p163j06BHTp09HURQURXnhUF4hBI8fPzZbSTc8PJyZM2fy0Ucf0a5dO+2+n7PoXc79fcyYMbRo0YKqVasSGhrKjz/+mG8waDQaiYuL03rhf/jhB7p3786RI0eYNm2aWVCZm5WVFaNGjeK9994jISGBHj160KJFC5YsWcLQoUO5desWy5YtY/Xq1WzcuFG79hRFoXz58majGyRJ+vs9/24kSZL0EooWLcqkSZPYunUrhw8fNnuoL1y4MOnp6SQkJJgdYzQaSUhIMNtqQpL+TEIIrl+/zpAhQ6hXrx6enp7cvn2bx48fk5mZSWBgIKmpqTg6OuYbQOl0Oq3Xv0GDBixYsIDMzEx2795NUFAQvXr1wtHRUesRtbKyon///lpPTq9evRg9ejRHjhyxOPP/p9Pp0Ov1ZqMLrK2tad++PX379uWHH35gwYIF/Pzzz9rquJheq2/fvtprffbZZ4wePZrDhw/nOvv/MRgMTJs2jbi4ODZu3Mi0adNYvXo1b7zxBiNGjNB6fPNjbW1Nt27dGDduHJ988gnr1q3D0dGRDh06MHr0aOLj4ylevDhr1qxh27Zt2nEFChRAURR0Op3Z+aQ/hsFgYM6cOYSGhrJ+/Xp+/fVXVqxYQatWrRg5ciTx8fEcOHCAqVOn0q1bN1JSUggMDCQuLo7k5GSCg4Of+91kZ2fj6OgIQEJCAuPGjSMsLIzOnTsTFBTE7du3ycrKIjQ0VBtF4O7uTp8+fejbty/Dhw9n8+bNlCtXjsWLF1uc/f/o9XqqVKnCsGHDaNGiBb169eLnn39m165dREdHWxbXFChQgC+++IJx48bx/vvvs2DBAmrVqkXNmjUZM2YMBoOBIkWK4OXlhZ+fn3acvb09qqo+M+iVJOmvJwNTSZJ+N1VVzRa5AGjXrh0fffQR06ZNIzU1VWuFr169OsWLF2fVqlUkJCRgNBrJysri0KFDJCYm0qZNG7PzSNKfRZgWWImJiSEzM5MpU6Ywf/589u7dS2xsLPPmzSMkJIRKlSrx+PFjszouhODJkydUqlRJS2vTpg2bNm1iy5YtrFq1iqJFi+Lq6mo2N9Xa2ho7Ozs8PDz46quvqFy5Mjt27NDyLdnY2GBtbf3cIAHgww8/RAihBQFYvNYXX3xB9erV2b59u9lxmIKM3bt389VXX5nty/r5558TFhb2zO1FLN24cYPt27czZswYLl++THJyMrNnz6Zfv3707t0bX19f7TM0GAwIIV6b/UBfN3q9nl27dtGrVy+zRo1PP/2U6Ohorly5wo4dOyhQoAABAQFMnz6defPmcfnyZe7fv8+iRYueO3rFyspK2xYoNDSUw4cPU7x4cRYvXszcuXNZsmQJ8fHxbNmyhQMHDlgeDoCjoyOdOnUiODg4zxxpGxsbihcvbrY/qhCC6tWrP3MRr/z4+flx4cIFRowYwf79+ylYsCDjx4/nhx9+oFOnTmzZskUrmzPa4EVDmSVJ+uvIwFSSpN8tZwiYZdqYMWNQFIW4uDjtAdTNzY0xY8Zw9OhRfv31V9asWYO3tzdeXl706tUr34UpJOnPoCgKzZs3Z9asWbzxxhvUqVOH2rVrU6FCBRwcHKhXrx7Ozs689dZbhIeHmwVoDx8+JDg4mP/85z9m5wRwcnIiOTkZLy8v3n//fVxdXVFVNU+va1paGllZWXmGtefm5OSEo6Mjjx8/1tKEEHkagq5evYqdnR3u7u4I036hueX3WrnnABYsWDDPnqUPHz5ECIGDg4NZen5SU1OZM2cOnTp1ombNmoSFhZnNnfXw8ECv12sBSExMDM7Ozi91bun3s7KyomDBgjx8+NAsPSQkBIPBgIODAz179uSXX36hevXq1KtXj7p161K8eHEKFSpErVq1zBopclNMiwxFRUWB6budMWMGXbp0oVq1atStW5c6depQoEABKleuTOnSpcE0Kia3rKwsbty4Qfny5bG2toZcddLW1pa33nqLM2fOaHVdURQCAgJwcHB4qZE1MTExzJs3j2+//RZPT08eP36srVwNULx4ce06EUIQFRUl9z2XpFeM9YQJEyZYJkqS9O8lhGDfvn0EBQUxYMAAbRuNHDlz16pWrWrWuo1p1c4KFSpQpEgR/vOf/2gPqjVr1qRkyZJcvXqVsLAwYmJieOeddxg+fPgL5zdJry4hBIcOHeLatWsMGjQo320gXiWKolCmTBnq1avHm2++SePGjWncuDFGo5ELFy6wYcMGChcuTMGCBbl06RJnz56lTJkyPH78mEWLFlGoUCEGDRqk9eD4+fmRmprKo0ePWLt2Lbdu3WLKlCkUKVIEvV7P+vXriYyMJDU1lbCwMLZu3cq1a9cYNWqU9vBuydramjNnzpCcnEzbtm3BtGDN1q1bSU9PJzY2lhs3bjBz5kzeeOMNvvrqKwA2bNhAREQEqamphIeHs3XrVi5fvsyoUaMoU6YMcXFxLFq0iIIFC1KqVCmSkpLYtGkTnp6eZGRkEBQUxJw5cyhdujQ//PDDc3s2c/62gIAAZsyYga2tLQaDgS1btlC1alVSU1NZt24d1apV0wL5zZs3o6oqPXr0sDzdK0lVVdasWYOqqnzzzTev/H3KysqKjIwMNmzYgIeHB5mZmdy/f5+5c+dSqFAhhgwZQo0aNahXrx6NGjXS6n5gYCCKojBjxgxtqG5+wsPDuXr1Kt26dcPV1ZV69erRsGFD7Tx169Zl7dq1/PTTT7zzzjsIIQgICODkyZNkZWXx+PFj9uzZw/bt2xk+fDjVq1cnPDwcb29vypYti5ubGwULFmTFihXY2dmhKAq3bt1iwYIFtGvXLt/VnnPLyspi4cKFZGRkMG7cODA1zuzYsYO6desSGxuLj48PrVq1olGjRgAsX76cUqVK0b59e4uzvXqEEGzatInk5GS+++67V74+StJ/SxFycL0kSbmoqkr//v3x9fXl7t27z11B9L+RnJyMi4uL/GH9B1BVlUGDBrFs2TJCQkLMeideF0IIDh8+zLp161i2bJnWEHPjxg3Gjh2rLWZkMBiYMGEC1atXB9Nq08OGDaNgwYKoqkp6ejqDBw+mVatWYBomOGrUKO7du0fhwoUxGo2kpKTwxRdf0K1bN7P3kJsQAl9fXxYsWMCxY8ewt7cnLi6O7777DiEETk5OpKSkUKhQISZOnEiZMmUwGo2MHj2aO3fuULhwYVRVJTk5mc8++4zu3bsDcP/+fbp06cKkSZPo2rUrSUlJ/PLLLzx48AB3d3dSUlKwtrZm8uTJVKtWzfJtmUlISGDo0KF88803tGjRAkzB6pQpU3j06BEeHh4kJCQwduxYypUrR2ZmJh06dOCzzz6jd+/elqd7JRkMBlq3bo1er+fs2bPPDdRfFampqUycOJGgoCDc3d1JS0tDVVUmTJhA3bp1LYujqipeXl5ER0fz66+/WmZrhBDcuHGDzz//nGPHjlGiRAnLIkRFRTF48GAGDRpEkyZNtEarWbNmaUPb4+Pjad++PQMGDEBRFC5evMjXX3/NqlWraNq0KUIIlixZwu7duylevDhpaWl4eHjw66+/UqhQIcuXNPPkyROGDRtmdo2mp6fz008/kZmZiaurK1lZWdrexElJSbRp04Zp06bl2U7nVWQ0GunUqRNhYWHcvHlT63GWpH8aGZhKkmTmzw5MpX+Of0JgCpCUlERcXBwVKlQwazCJjY3l9OnT2NjY0KpVK7N9Sw0GA1evXiU0NBQXFxfefvvtPMMC09PTuXz5Mk+fPsXJyYnGjRvn+1BvKTExkQ8++IBBgwZpQezDhw/x9/cnMzOTsmXL8vbbb5u91xe9lk6nw9/fn8qVK1O4cGEt/fz584SGhlKkSBFatmz5UkNtU1JSuHv3rtbzlEMIwbFjx0hLS6NFixbaMOJ9+/YxefJkLeB4HbyOgWmOS5cu8eDBA23/2NxzTi1FRkai1+vNVtzNT1ZWFl9++SV169blp59+ssxGr9fz6NEjSpQoob2eTqfTRsnY2NhQv359KleurB2TlpZGYGAgtWvXNhuZc/PmTQIDAylRogStW7fOM20kP7GxsTx58oT69eubpet0Oo4dO4bRaKRNmzbae1u1ahUbNmxg9+7dz/18XhUyMJX+LWRgKkmSGRmYSi/rnxKYvoq2bt3K2rVrWb58+UsFs6+q2NhYvvvuO95991369u1rmf3Kep0D0z/LuXPnGDduHAsXLqRmzZqW2a+NR48e0a9fP77//vsXDhF+VcjAVPq3kGPpJEl6ppdpqZYkZF35w3Xr1o127drl2WbpdZOcnMzbb7/92gzhzY+s2//n7bff5uuvvyYmJsYy67UhhCAhIYHOnTu/NkGpJP2byMBUkiQzVlZW2NjYoCjKM1dplCRy1RVMewJKfxxFURgwYMBr3TMFUKlSJYYMGfLa9fDY2NhgZWWFoiiv3Xv/M33++ee0bt3aMvm1oSgKDRo04Pvvv7fMeqVZW1tjbW2NlZWVrI/SP9prN5RXCEFgYCD37t2zzJIk6Q+gqire3t5cunQJLy+vFy46If17qarK6tWrOXbsGIsXLzabuyhJrzOj0ciECRMwGAxMmTJFBgPS30pVVWbMmEFsbCyzZ8+Wiwf+A1lbW9OmTRuztQz+jV7LwHTChAnMnTtXXpiS9CdQFAW9Xi97CqQXytk6KDs7W1tp+TX7SZGkZ0pPTwfT3rKS9HfLyMjQVuaW/nkcHBzw8/N77UfJ/K9ey8D09OnTnD9/Xj40S9KfQFEUDAYDqqpia2trmS1JZvbu3cuFCxf4+eefcXZ2loGp9I+gqirLli3DaDQyYMAAWa+lv5Vq2lc3KSmJoUOHyvr4D2RjY8Pnn3+Ou7u7Zda/ymsXmEqSJEmvjhEjRuDl5UVSUtJLbTUiSa+L3KvyStLfrUuXLoSGhnLz5k3LLEn6x5BjYSVJkqT/iqqqZGVlAbz2q8dKUm4GgwGDwYBer8dgMFhmS9Jfymg0otfrMRqNsj5K/2gyMJUkSZIkScqH3CpGetXIOin9k8nAVJIkSZIkSZIkSfpbycBUkiRJkiRJkiRJ+lvJwFSSJEmSJEmSJEn6W8nAVJIkSZIkSZIkSfpbycBUkiRJkiRJkiRJ+lvJwFSSJEmSJEmSJEn6W8nAVJIkSZIkSZIkSfpbycBUkiRJkiRJkiRJ+lvJwFSSJEmSJEmSJEn6W8nA1EJWVhZxcXEkJCSg1+sts/9S0dHRXLlyRft3XFwcDx48QAhhVu73iIuL49q1a2RnZ1tmafR6PRcvXiQ5Odky609hMBi4du0acXFxllnSc+h0OsukP50QAqPR+Mw6+Ly8/Oj1elRVtUyWpL9VWlqaZRKY6mtCQsIz8y0ZjUbLpOf6veX/KJZ/j9FoRK/Xm/1nMBgwGAwvfb2qqvq77gXS/yY9Pf2Z9SctLY2EhITn/u4nJyeTkpJimfxM6enpJCUl/W3fcVpamlldFEJgMBjyrbcv+7skhHjp+v1vk5mZSWJi4kt/PllZWSQmJmIwGCyzNGlpaSQlJVkma/R6PYmJiS/1rCOEyPc7ft53/6zrRVXVl/47pT+eIp71jf3LZGZmcurUKa5cuUJycjJCCEqUKEHv3r1xc3OzLP67GQwGbGxsLJOfSQjBtm3bWLBgAefOnUMIwfr16zl8+DArV66kQIECloe8kBCCgwcPMmvWLLZu3Yq7u7tlEQASExNp1aoVv/32G2+99ZZl9h8uKSmJDz/8kGHDhtGxY0fLbCkfV65c4enTp3Tq1AlFUSyz/zRJSUn4+vrSvn17SpYsaZaXkZHB1q1badKkCVWrVjXLe5ZHjx5x9uxZPv74YxwcHCyzpVecqqoMGjSIZcuWERISkqdOvG4SEhI4efIkKSkpfPXVV1q60Wjk5s2bnDp1Smuwq1+/Pu++++4z6+3FixcJCAjgiy++wN7e3jI7j1u3bnH27Fm6d+9O4cKFLbP/FLGxsfj5+QHQo0cPMP1OHDhwgNu3b2Ntba2VtbGxQQhBmzZtqFOnjpaen8jISHbu3Mm7775LpUqVLLNfCwaDgdatW6PX6zl79uzv+v3+K+n1em7cuMGZM2f4+uuvzepOcnIyJ06c4Pbt22RnZ+Pk5ESHDh2oVauWViYuLo6DBw8SGhqKEIKqVavSsWNHnJ2dtTK5GQwGTp48ycWLFzEYDHh6etK5c2c8PDwsi/4pdDodV65c4eLFiwwYMEC7thISEtiwYQM6nU77TVQUBRsbGwoUKMBnn32Gk5OTxdn+P4PBwKZNmyhevDht27a1zP7bGY1GOnXqRFhYGDdv3jS7Nv9Mqqpy7tw5zpw5Q3Z2NsWKFaNz587PvddfvXqVY8eOodPpcHFxoXPnzlSsWFHL1+l0HD58mJs3b2I0GqlQoQIffPCB2bP23bt3OXDgACkpKdjb29OuXTvq1aun5eeWlpbGxo0bqVu3Lo0bNzbL8/X1pXnz5nnqZ2pqKvv27aNbt255ru0TJ07g7u5udp1Ifx3ZY2q64NevX8/YsWO5dOkSiYmJPH36lP379/8hvXiXLl3i8OHDlskvlJycTEhIiPZvR0dHihYt+szWn5eRlpZGaGjoc1uxDAYDDx48ICMjwzLrT2E0GgkNDc3Tai/lLz4+nokTJ/L48eO/NCjF9CA7evRo7t+/b5lFWloaY8eONevlfxGj0cjq1avZuXOnZZYk/WWysrI4cuQIv/76KwMHDmT79u1m+UlJScyfP58zZ86QlJTEnTt3+Omnn9ixY4dZuRwpKSkMGzaM4cOHv9R9LSsri19++YUBAwYQGRlpmf1cRqPxmS3/z5Kens6BAweYNm0agwYN4sCBA1qeEILg4GDOnz+v/Xfx4kV27NjBkCFDePTokdm5LKmqytKlS/nhhx+4fv26Zbb0BxFC4O/vz/z58xkyZAgzZswwq2tCCK5evcqiRYt4+PAhSUlJHDhwgMGDBxMWFgamxsT58+fj7e1NVFQU4eHh/Prrr6xevTrXK/1/QgiOHj3KmDFjuHPnDjExMXh7e/Prr78+tzfWkhDid49IMxqNXLt2jXnz5jF48GDmzJljVu8zMjK4fPmyWb29dOkSM2bMYN68ec995sHUkNSnTx98fHwss/7Vzpw5w4gRI/D39ycuLg4fHx8mTJiQ7/OhEIKAgACGDRvGpUuXiI+PZ9u2bYwaNYrExEStjK+vLxMnTiQkJITIyEhmz57NokWLtF7KJ0+eMHr0aA4fPkxCQgJ+fn4MGjTI7Hk4t71799KvXz/27dsHpntQdHQ0GRkZrFq1ivDwcGJjY8nKyiI7O5uoqCji4uL47bffSE9PJzo6GqPRSFxcHMnJyezZs4fr16+TlJT0l40clHIRkoiOjhbFixcX48aNE9nZ2Vp6WFiYSElJMSv7IpmZmUKv15ul9e/fX3Tt2tUsLbf8jlFVVaxevVpUrFjRLP1Z8juHJVVVxfbt20X16tVFfHy8EEKIrKwsoaqqWbnY2Fjh7u4uTp48aZYuhBDZ2dkiKyvLMtmMqqoiMzPTMjmPnDLp6emievXqYtu2bZZFnutlXsNgMFgmPfc4nU4ndDqdZbIQL/kZv0h+7ycrK0sYjUbL5GdatGiReO+998zqag6j0ZjnPRoMhmf+TTmys7Pz1AOdTpfn/QYHB4vSpUuLU6dOmaULIURMTIyoVKmS2LRpk2WWMBgMz6w3vr6+olGjRiIuLs4yS3rFGY1GMWDAAGFnZyeePHlimf3aCA8PF82aNRPvv/++aN68eZ77dWRkpPDx8RFpaWla2pAhQ0Tt2rXzrdcrVqwQxYsXFyVLlhSxsbGW2Xns2LFDeHp6iuLFi4vbt29bZj+Tqqri6tWrws/PzzLrmVRVFbdu3dL+zvr164tvvvnGsphQVVX7TwghZsyYId555x2RmppqWdSMv7+/qF69unBzcxNbtmyxzH5t6PV60axZM9G4ceM899RXgcFgEP379xeNGjUSn3zyiahcubIIDw/X8lVVFadPnxaHDx/W0sLDw0XFihXFrFmzhBBCnDp1ShQvXlwcOnRIK7NkyRJRrlw5ERYWpqXlSE5OFk2aNBEDBw7U0vbu3StcXFzE+fPnzco+T0xMjNiyZcsLf5dyS01NFd98841o1qyZ6NGjhyhbtqzIyMjQ8nPX15z/kpKSRLNmzYSXl5fZuSwlJyeLbt26iSJFiogvv/zSMvuVYDAYRIcOHUTNmjXz/C7/WdLS0kSHDh3Ep59+qj2jnDhxQri5uYl9+/ZZFhd6vV58/vnnol27dtpzlr+/v3B3dxc+Pj5CCCGioqJEzZo1xZQpU7TjVq5cKYoWLSru3r0rjEajmDp1qqhVq5aIiooSQgjx9OlTUb16dTFy5EjtmBwRERGiRYsWokiRImL8+PFCmN73ggULxMaNG0W7du3E4sWLxZw5c8TDhw9FRESEmD59uli9erVo3bq12Lhxo/Dy8hLJycli06ZNYvny5eKLL74QEyZMEIsWLRInTpywfEnpTyZ7TE2t4VFRUbRs2RJbW1stvUyZMri4uBAcHMyRI0fyjDl/+PAhJ06cANN80J07d7JixQpWrVqFv78/BoOBvXv3cu/ePZ4+fcrOnTu5evWqdnxISAjbtm1j5cqVrFq1ihMnTpCVlaXlC4sx86GhoZw6dUr7N8CDBw/YunUrK1euZPXq1Vy7ds0s35IQAmtra+Lj4zl48CCrVq1i1apV3Lp1y7KomZSUFI4cOcLq1atZsWIF27dvJyIiwqyMqqpcvHiRNWvWsGLFCtauXUtgYKBZGYCIiAi2bt3KihUr2LhxI/fu3cPK6vlVUQjBzZs38ff358GDB2zZsoUVK1awefNmnj59qpXLyMjAz8+PJ0+ecOTIEdasWUN8fDyYvq8NGzawfPlyNm7cqLUa5z5u1apVrFixgsOHD2vfd0xMDDt37tS+p5zPODk5mQMHDph9Z0ajkXPnzhEcHAymIeLHjx/nyZMnHD16FB8fH60XPjExkb1797JixQpWr179Ur0L6enpbNiwgT59+mh19c6dO5w/f5779++zYcMGli1bptUzf39/1q9fz7Jlyzh79qzWwiyE4OLFiwQFBXH16lXWrFnDqlWrCA4ORgjB2bNn8fHxYcWKFWZ1Q1EUFEXJdxiRra1tnu/RaDRy/vx5Vq1axerVqzl58iR3797l7Nmz2ufbsWNHbGxsOHjwoNmxkvRXsbe3p0+fPqxfv55mzZrl6c3x8PDgyy+/NBsK2LVrV548eZJn/lNQUBBr1qyhb9+++V4nlsLCwli0aBGfffYZhQsXzvM78yLHjx9n69atlsnPpCgKTk5OfP/996xdu5Z69erl29uVc60rikJERAQbNmxgwIABzxziiel3Ys6cObRo0YLKlSv/7p5c6eUpikKrVq1YvXo1P/zwQ57RM4qi0Lx5c959910trXTp0jRq1IiHDx8CcPr0aTw9PXnvvfe0Ml999RVWVlacP39eS8P0m3Hnzh3u37/P0KFDtfSOHTtSo0YN9u/fb1b+eZ4+fcq8efPyrXfPYm1tzbvvvouPjw/ffvttnmNz19ec/7Zv305mZqbZsPz8bNiwgdTUVNq1ayfrrIkQgkePHnH9+nWGDBmi/ba3bNmSRo0asWfPHstDiImJwc/PjyFDhmhDrOvWrUu7du3YvXs3mEYQpqWl8d1332nH9ejRg4IFC3Ly5ElUVcXX15dvvvlGG35bokQJvvnmG/bu3WvW863X61myZAkeHh40btxYy1MUBVdXV44fP87169fZuXMnTk5O2NnZYWNjg7OzM9u2bSMwMJCDBw/i5uamHZPzXL97924yMzNxcXHRXk/6azw/GviX8PDwoFmzZkyfPp1Lly6Z3fCEEDx48IB+/fqZDSNQVZXZs2ezYcMGMjMzmT59Ot7e3gQEBHDixAmOHDmCwWBg//79hIWFERERwbZt27hx4wYA9+7dY8yYMaxatYrAwEDOnDnDTz/9xIoVK/IdciJM80NHjRqlpQUFBTFmzBhWr17NrVu3OHnypDaU4VkURSErK4vVq1fj6+vLrVu38PX1ZeDAgQQFBVkWB9NY/Llz5zJ16lQuXbqEv78/CxcuZPz48cTExIDp89i2bRs//fQTR48eJTAwEF9fX4YPH242tDM6Oprx48fj7e3NrVu3OHToEF5eXiQnJ+cJanITQuDj48Po0aNZtmwZR44cwd/fn3nz5jFhwgRtuEVcXBzDhg1j7ty5rF69mmPHjpGUlERISAg///wzmzdv5tatW6xZs4bx48cTHx+PqqqsW7eOSZMmceXKFS5fvsy2bdswGAykpaUxY8YMlixZws2bNzlx4gSHDh0CIDw8nG+//VYLfDHNnZg8eTJ79+4F07yXYcOGMWfOHO39JCQkkJqayuzZs5k3bx7+/v4cPnyYESNGPHcYrBCCoKAgMjIyaNWqlZa2c+dOvvvuOzZt2sTFixc5fPgwP/74I1u3bmXhwoVcuHCBkydP8v3333Pp0iXIVX/HjBnDpk2buHHjBuvXr2fMmDFs3LiRpUuXcuPGDbZv387AgQMJDw/XXg/T36nT6cjMzCQzM5OsrCwyMzPNGlJUVWXPnj2MGDECPz8/AgICWLlyJcOGDWPKlCnaw7+dnR0dOnRg165d2rGS9Fdyd3fnyy+/pFChQmYNTTksH/oxDf0rX7682Xz/xMREpkyZQrNmzejYsWOeh2dLGRkZzJgxg0qVKvHpp5+SnZ1tdg29jJyH8N+jfPny9OjRAwcHhzyBtSVVVVm5ciVubm7PXQNAVVVWr15NVFQUI0aM+N0BtvT7WFlZ0b17d2rUqEF2dvZLfd7p6ek8ePCAKlWqgGnajmUgZmtri42NDaGhoWbpQgiuX79O6dKlKVasmJauqir169fH39/frPzz5NTX31NvHRwc6N69OxUrVszzW5Of+Ph4Fi9ezLfffvvc4OLChQusWrWKMWPGULx48Xyfv/6tchowatasqaWpqkrNmjUJCQkx+w6EENy7dw87OzsqV65sVr5Ro0ba82VgYCAeHh5mc6ELFChA5cqVuXv3LqmpqYSGhtKkSRMtXwhBrVq1SE1N1Z45AQ4cOMCxY8cYP348jo6O2vtxdHSke/fuuLi40KNHDzw8PGjfvj2lSpWiWLFifPjhh7i6uvLFF1/g7OzMhx9+iIuLCx06dKB06dK0bduWWrVqUb9+fRo2bKi9nvTXeHYk8C9SsGBBZsyYQVZWFkOHDmXu3LlaL5GiKDRq1Ag7OzutxQcgKiqKgwcP8tFHHxESEsKCBQsYMmQIy5YtY9GiRXz00UcUKFCAcePG0aRJE+rXr8/MmTP5+OOP0ev1TJ06lfDwcGbOnImXlxcLFizgk08+YfLkydy4cSPfG3ZmZqa2gllWVhbjx48nLi6OmTNnsnDhQu0cz2Ntbc2TJ0+Ij49n3LhxeHl5MXfuXFJTU/n1118hnx+Lffv28dtvv/Hjjz/i5eXFwoULmTBhAseOHWPFihUABAcHM2bMGJo2bcr8+fNZtGgRc+fORVEUxo4di06nQwih9ZpNnTqVRYsWMWvWLDw9PYmJickzAd1SRkYGZ8+epWzZssyaNYtFixbxyy+/sGPHDm2OohCCiIgI/P39te/S3d2dGTNmEB8fz6JFi1i2bBlz5szh7Nmz+Pr6otPpmDFjBk2aNGHZsmUsWbKE4cOHY21tTXBwMAsXLtQWeFm4cKG2SIjBYCAhISHPA0FKSgqZmZlguilHRkZy/fp1Bg8ezLx58yhdujS7d+9my5YtTJgwgeXLl+Pt7U3BggWZPn16ngeF3G7cuIGnpydFixbV0rKysrhz5w41a9Zk9uzZLFy4EJ1Ox8CBA3nrrbeYO3cuixcvxsnJiWXLlmnHJScnc/36dbp27cq8efOYMWMGp0+fZvLkyXz11VcsWLCAOXPmEBgYqLWGK4qC0Whk48aNzJ8/n7lz5zJ37lzmzZvHwoULSUpK0hoYIiIiGDNmDE2aNGHRokXMnz+ffv36ERwcbBbMY2pVjYiIyHfeiiT9VSyv5We5du0aa9eu5csvv9QCU4PBwG+//UZkZCSjRo3C8BIr2G7YsIFr164xbtw4rK2tX1ge0z3uzJkz2iiZK1eucPfuXXx8fFi5cqU2iudlvMzrRUdHs2nTJr7//vvn3qNPnDiBj48PP//8M8WLF8/T6yz9OcRLriSbmZnJypUrycjI4P3330cIQaNGjYiMjGTXrl0kJCQQGxvL/v37iY2NzbdRJTIyEjc3tzz1oHDhwtocwvwIIQgLC9NGaO3cuZO4uDhtpM7mzZu138wXyQk+LJ9TchOmBbyysrK03+v8REREMGnSJN5//31atGhBZmbmc8/7byKEIDo6GicnpzyLt7m7u5Oenm7WqCWEIC4ujgIFCuDo6GhWvmjRotqKz7GxsXkWFBVCUKxYMRITE4mOjkZRlDxlnJycsLGx0Toh7t69y8yZM/n222+pVatWntGGjx49IiMjg+nTp9O4cWOOHz8Opnve5cuXqVq1KjNnzsTOzk4LmpOTkwkICGDs2LF88cUXv+teKv1xZGBq0rRpU9avX0/Lli3ZsmULQ4cOZe/evQghKFKkCD179mTnzp3aj+2hQ4dwc3PTVm8rVqwY9+7d48GDBxQpUoRKlSqhKAolS5akYMGCODs7U6pUKQoXLkxERASHDx/ml19+oXbt2tja2lK4cGEGDx5MyZIl8x0igWlVxJyHoHv37uHn58eMGTO0cxQtWpTq1atbHmZGCIGDgwODBg2iXLly2NjYUL16dYYNG8aRI0dIT083a81UVZWNGzfy3nvv0bVrV+0m1apVK7799ls2bNgAwP79+7Gzs+OXX37B3d0dGxsbKlWqxNixY7l+/ToPHz5EVVXWrl1L//79adq0Kba2tnh4eDBw4ECKFi36wpZKa2trqlatyg8//EDhwoWxt7enQ4cO/Oc//9E+M8U0zPSDDz7gzTffpHjx4qSkpLBt2zbatGlDbGwsFy5cICMjg/Lly7N7926sra0pUaIEUVFR2kqU1apVw9raWnuPOd9t0aJFqVixIkIIFEXB3t4+zw+ZnZ2dNoRPURSsrKz44IMPaNSoEcWLF8fGxoZ169ZRuXJlXF1duXDhAg8ePKBOnTqcO3fuuQtuRUVFUahQIctkKleuTNeuXXFwcKBMmTJ07NgRBwcHvv32WxwdHfHw8OD999/nzp072jHW1ta0bNmSJk2aYGdnR6NGjahZsyY1a9bkP//5D3Z2dtSuXZv69etrix0pioIQgocPH3Ljxg0CAgIICAjA39+foKAgsxURz507R0pKChMmTKBo0aLY2dnRrFkzevXqlad33NPTUwv0JelVpdfrOXjwIGPHjqVhw4Z88803YHrYOXDgAPv372fu3LkULFgQxdSTafmQluPUqVOsXbuWX3/9lVKlSmn3imet8ptDmEZOHDt2DD8/P4KDg4mIiMDPzw8/P78XTsv4PYQQ7N+/H2tr6+f2lt6/f5+5c+fy7bff0rJlS4QQWFlZ/Verx0t/vCdPnrBw4UJtVEyVKlVQFIWWLVvSsWNHpk2bxrx581iyZAk7d+7E0dEx3xVs9Xo91tbWee7f1tbWWsD4LLGxsRw9ehQ/Pz8uXbpEUlISp06dws/Pj7Nnz76w5/73yBkV9tFHH1GwYEHLbDA1IC9YsICiRYvyyy+/gOnveJnh9/8Wer0+TyMEps8pPwaDQXsGyy3n38K06FV+57Qxrfqt1+uxsrLKU8esrKy0e2psbCwzZ86kSZMm9O7dWzs+Z3qToiiUL1+eKVOm4ODgQL9+/fjggw+087Ru3ZoRI0ZgZWXFhAkTtOdmFxcXJk2aRPny5XnnnXcYOHBgrncg/VVkYJpL2bJlmTZtGitWrKBYsWIMGjRIG3rbtWtXwsLCCAoKwmg0sm3bNrp27YqtrS2VKlVi+PDhbNiwgbFjx7J06VKioqLA9MCSe7hMTkuOvb292XCHHBUqVODx48fwjBbBnJv/nTt3cHd3/91L8RuNRooXL262VYwQgtKlS2MwGMyG1OYEphEREbzxxhu5zvJ/x9SpU0fb7/XBgwdUqlTJbI4uQKlSpXBxcSEiIgKdTkdERESe5bwdHBxwc3N7Yauv0WjMsw1JznvPvReWo6OjWbmYmBgSExO5evUq8+fPZ+HChXh5eWEwGChVqhR2dnaMHj2aBw8eMHbsWObMmaMFcNWrV2fw4MFs2bKFsWPH4u3tTWRkpPbd5ASoueX+txACe3t7s/eTnp5OZGQk8fHxzJ07l4ULF7Jw4UKuX79O9erVn9vTkJ2dnaf1UlVVs2Exqqri6OhI8eLF86RZnrts2bLa/zcajbi6uprVKaPRiIuLi3acEAIbGxsmT57M5s2b2bJli/aKahXCAAC9+0lEQVTfqlWrKF68OEbTvmG3b9+mSpUqZnPSVFXF3d09z4OMvb09RqMx31Z6SXoVpKSk8NtvvzFjxgzq1q3L/PnzcXZ2RphWRx08eDDly5dHp9Ph5+fH5cuXyc7O5tixY2bz2TGNMBk4cCAuLi44Ozvj5+enNZidOXNGG0KXHysrK/r06cO6detYvXo1PXr0oE2bNqxZs4a1a9fyww8/WB7yX8vMzGTDhg18+OGHzwywExISGD58OCEhIdSoUYPjx4/j5+dHamoq/v7+3Lx584VBi/TnCQoKYtKkSRw6dIjRo0fz6aefanlOTk7Mnj2bTz75hKioKDIyMujduzfFixc3G66bw9nZmczMzDyjetLS0p4ZAGL6TWzYsCHr1q3Dx8eHyZMnU6lSJX777TfWrFnD/Pnz821w/W8IIbh16xb37t2jZ8+eltlg+l1btWoVS5YsoU2bNpw+fZpjx44RHh5OZGQk586dk6N3cn3fllJSUnBycsrzvOfg4IDBYMjzO56amoqzs7PWUJeenm6WrygKSUlJFCpUCFdXV/R6fZ6GiqysLBRFwc7OjtmzZ7Nnzx6aN2+uTZ2LiYnh4cOHXL58GYPBgL29vTayzM7OjiJFimjnKliwoNbwUqhQIe0ZxcrKSqv3iqLkew1Ifz4ZmOajYcOGrFy5EkVR2Lx5M5jG2NerV4/du3cTGhrKw4cP6d69O5jGxw8fPhwvLy9KlizJwoULmTVrllkwmhOsKIqCg4MDer0+z41PURTS0tKeOx8ih52dnbb09e+hKAoGg8HsQUFRFKKjo7G1tdUetMgVBNvZ2eXZBFlRFJKTk7UeQwcHh3y3RcjKysJgMODi4oIwtaJb/t06nY6MjIw8AV5+LG9WOe8950cx5zVyt8jZ2NhgZ2dH+/bt+fLLL/nyyy/5/PPPmTRpkraIwwcffMDatWtp0qQJBw8eZPjw4URHR2Ntbc2wYcPw8vLC09NT+24NBoMWwOf+LK2trc2GAwnTYlO530/Ov2vWrMlXX32lvZ/Bgwcze/Zss2G6luzt7fN8fvl9bmo+m9vnroeYjrM8VuQzLMyy/ub+39ws02xsbPL8qFlZWZGSkoLRaDQrn5GRIXtYpFeWTqdj+fLlrF69mr59+zJ9+nSz+3RgYKB2bc6cOZMlS5awdetWMjIy8Pb2zrMoXVBQENnZ2djZ2TFjxgwWL17MunXrSEpKYu3atVy8eNGsvKWce0jO6AzF1NtqY2OTp7fivyWE4P79+9y/f58uXbpYZmuioqJ48OABpUuXZvHixSxatIilS5dqw0JzRh5Jf73w8HB++uknEhISWLJkCR999JFlEdzc3Bg+fDjLli1jxowZVKxYkeTkZOrWrWtWTlEUKlWqlGfKhaIoBAcHU7t2bbPylhTTnqK2trbY29sjTI22NjY2+fag/beEEOzevZuKFStSrVo1y2ww/T6ePXuWypUrc+TIEebPn6+tI3H37l1WrFiR55nn30ZRFMqUKUNGRobW0YLpN/zhw4eULVvW7F6Tu3xsbKxZ+aCgIO27KFeuHDExMWaNG0ajkUePHlGlShWKFSuGo6MjDx480PIVRSE8PBwnJyccHR25ePEiVapUYdOmTXh5eWlbIl26dIkNGzbku06A9PqQgamp9ef/sXff0VVU68PHv3PSEyAhlITeQu8d6cVLkyKoIF4BFRWRJhYURECkhRJI6KGHJl06oQqEEloIgQTSe+/9tJn3j9/JvDknoehVKc5nrSxl9p45bc/MfnaboqGmRQoKCrCzszOqPI8ePZozZ86wceNG2rRpI/csFS3+8sYbb7B8+XJGjx7Nxo0b5ZNDMAScRerVq0f58uU5ePCg0fDVBw8eEBoaSo8ePcAk4DHVvHlzdIbFlYozbYkypVKpSExM5ObNm/K23Nxc9u7dS7t27ShXrpwc1BQFVR06dODEiRNGk86zs7P57bff6Nq1K+bm5rzxxhuEhIQYHbdoeFv58uVp0KAB1tbWNGrUiAMHDhj13F28eJHExMRnVqjMzMx48OABsbGx8rbw8HBu3Lhh9J2Zfm/Ozs7Url2bvLw8/vOf/9CvXz/69etHu3btqFevHjrDIkd169ZlxowZLFiwgDNnzhAYGEhhYSFqtVqeqzlmzBi2b99OTk4O5cqVw8zMzGjoXGBgIGFhYUafxfT92NjY0KJFC5KTk+nSpYv8fnr37o2Li0uJHtHiqlatSnp6eoljmv77eZT2XT0v0xZzDMN4io4nCAKtW7cmJCTEqFKelJTE77//LucpEhMTg6WlZYl5JQrFiyZJEo8ePWLZsmXMnz+fDz74wChdEAR69+7Nli1bmDJlCpMnT2bKlCmMGDECW1tbvvzyS9q3bw+Gxdny8vLo2LEjW7du5euvv2bixIlMmTKFDz/8EHt7e8aOHUuXLl2MXuNpXFxcSgQRfwVJkrh48SL29valHj87O5uMjAxq1KjB1q1bmTNnDhMmTGDKlCl88cUXVKxYkUGDBjFs2LASw/IUfz9RFNmyZQuxsbHs2LGj1CBNkiT5Wi4IAjk5OSxZsoQmTZrIC97k5eWRlpaGYFhvQ61Wc+7cOfkYQUFBBAQEMHDgQHnbs5QrV44ePXr8TwHpk+5darWa06dP07dv3xLlTqPRyA3ORYswfv755/J527JlSxo1asT48eP/sh7cV5UgCDRo0ICKFSsaPdc5PDyce/fuySs5q9VquW5Yv359nJycjKajJSYmcv78eXkqQIcOHcjKypLrAQA3b94kIyODrl27YmVlRdeuXfn111/lspmTk8PRo0fp0aMHzs7OuLm5sWzZMj7//HOmTp3K5MmTqVu3Lh07dmTs2LFPrUMpXn7K3cJQKf7pp59YuXIl+/btY8+ePSxYsAC9Xs8777wDxSofgiCwadMmPv74Y3n/8PBwlixZwtGjRzl27Bjh4eF07doVCwsL+eS+f/8+hw4d4u7du1SsWJFJkyaxd+9ePDw8OH36NAcPHmThwoW0bduWAQMGyDeM4i0/Wq1W/reLiwsff/wxq1atYv369Xh7e7N7926jB6WXRq/XU1BQgJeXF/v27ePUqVO4ubnh7+8v9x6KoigP1xEEgXHjxlFYWMjcuXM5evSoPI8qJiaGadOmAdC3b186duzInDlz2Lt3L2fOnMHT05O9e/cyYcIEHB0dUalUTJgwAW9vb1asWIG3tze7du3i6NGjRjfIJzEzMyMjIwMPDw+OHj3KkSNHmDt3Lk5OTnLvtSRJFBYWGh2rYsWKjBs3jtWrV+Pu7s7p06c5cOAAS5cuJTo6mpycHNatWyd/H1evXqVFixZUq1aN0NBQ3N3d5d82LCxMnpNZuXJlOnfuzPLlyzl69Ci//fYbW7duNRrqVNr7MTc35+OPP+bx48fMnDmT48ePc+zYMZYvX8758+flfKaKgr2UlBQSEhLAcPzShr3odLoSrYam29RqdYmhvRqNpkQvfPFtRWXDtFe1SPHP2qVLF1q3bs3MmTM5dOgQx48fZ8uWLcTHx8st5hg+1+3bt6ldu/YThwsqFP8UrVZb4hy4du0aycnJJCUlsXHjRvlv8+bNREdHU716dTp16kT37t3p1asXPXr0oGPHjoiiyH/+8x9q1KhBQUEBrq6uHD16FGdnZzl/79696dmzJ126dEGlUtG1a1dq165t9PrFSYZHPe3cuZM9e/aQlJSEJEns2bOHXbt24ePjY7rLE0mSVOo5T7GGxTZt2pQIIPR6Pdu3b2ft2rWULVuWjh070qVLF/mz/Oc//8HKyooWLVoYreip+HsU1RWKB2uiKHLo0CHKly/PgQMH5DLr6enJ/v37wVDWd+zYwZEjRzh+/DjLly/nxo0bfP/995ibmyOKIr/99huLFy9Go9FQs2ZN/vvf/7Js2TL279/PkSNHWLhwIV26dDFaRbU0MTEx7N69m927d3Px4kWqVavG3r172bVrF4cPHy5xv3qaonqMKUmSCA8PJyAgQG6sLp52+/ZtZs6cSWpqKi1atKBTp0706tVL/itaWLBTp07KvcjQqP/f//6XjRs3snPnTo4dO8bixYtp0KABAwcOlK9FP/74I+np6ZQpU4bx48ezZ88etmzZwokTJ1i6dCkVK1aUR120aNGCvn37snjxYn777TcOHz7MsmXLeOutt2jRogWCIPD5559z584dVq5cyenTp1m1ahVxcXF8+eWXYBjV2LlzZ6PrTfny5alVq1ap1yvFq8Vs7ty5c003/tsUDeu4evUqAQEBBAQEkJOTw3fffUfv3r3lfLa2tly5coW0tDQWLlwoj69PS0tj06ZNBAYGEhAQgCRJzJkzh6pVqyIIAs7OzgQGBnLnzh3Kli1L27Ztad26NQDe3t48evSI+/fvU7lyZebNmycP5czIyECtVjNkyBAwtLYLgsDAgQPl1suMjAzOnj1LcHAwAQEB1KhRgzZt2sjv2VRmZiaWlpa0bduWM2fO4O/vT1hYGFOmTJFfR6/XExISQr9+/XBycsLZ2ZmGDRty8eJF7t+/z4MHD0hJSeG7776ja9euYBjO3KlTJwICArh69SpBQUEEBQUxfPhwvvjiCwTDsNHGjRsjiiKnT58mPDycyMhIhgwZQoUKFejYsSM1a9Y0ecf/R5Ikjh8/jr29PbVq1eLixYvy/N9ffvlFXv5eq9USHR1Nr169qFKlChgCn5YtW5Kbmyt/Vw8ePEClUtGvXz8sLCzYu3cvPj4+BAUFERMTw+TJk+natSspKSl4eXnh7+/PgwcPEASBGTNmUKtWLSwsLKhXrx43btzg/v37hISE0KxZM5o1a0bTpk1p0qSJ/H569uxJ1apV5c9Tu3ZtKleujLe3Nw8fPiQoKIiEhAR69er1xO8Aw+qH+/fvp1y5crRq1QoMLZK2trbyQlwYeiatrKyMnk+XnJyMmZkZAwYMACAyMpKGDRvKx8HwrNz69esblaHIyEjq1KlD+/bt0Wg0REdH8+abb5aYf6HX64mIiKBLly5ykNmqVStu3brF7du3CQkJoUKFCtSvX5/IyEg+/vhjeWjvggUL+Pjjj5VK7CtGkiROnz7NnTt3mDp1KuXKlTPN8kqRJImEhAQcHByMKrYxMTFkZ2fL52rR3+PHj2nTpg3Vq1cvcZyMjAxSUlLo378/VlZWFBQUsGXLFhwcHOjcubNRfgw9kImJifTs2fOpvTVF18JTp04RHBxMTEwMMTExBAYGEhQUhEqlemaQUFxRYG36niRJ4urVqwwcOLDEeSmKIgcPHiQxMZG33nqrRM9UXl4ekZGRtGvX7qlB9stMFEW2b9+OKIp88sknJT7jyyQnJ4fMzEz69+8vz52TDItkZWdnExAQYFRu09LSGDx4MHq9Hk9PT65du8bDhw9JTk7mm2++MXocmbe3N/fv32fYsGFYWVnJI2EuX75MUFCQvGBM8Tl8piTDo0Q2b97M48ePCQsLIykpicDAQB49ekRcXBy9evV67p6u7OxsMjMzGTp0aIkgJDo6mvT0dD788MMSz9y9d+8eR48eZdCgQSVG54iiSHR0NBUrVvxDIxb+KUWNT1lZWUyYMOEfKY+CINCqVStiY2O5cOECwcHBqFQq5s2bR5UqVZAMjxA6ceIEQ4YMwd7enqZNm5KVlYW3tzchISHk5OQwe/ZseU0VMzMz2rZty927d7l27RpBQUFUq1aNOXPmyKMU69Wrh5WVFUePHiUkJITY2Fi+/vrrEteoIjqdjoiICOrVq2dUn1G8mgSpeBPbv1xycrJcya9ZsyaWlpZG6RqNhkGDBtG9e3dmzZpllJaTk0NUVBRmZmbUr1+/xMUyLy+P6OhoqlatarRIQFpaGjExMdjb21OnTh2jffR6PXq9Xn4fpv8uEh8fT3JyMs7OzkYL3pRGFEX0ej0WFhYkJiaSlJREzZo1S1yk1Wo1FhYWRhc/jUYjD3l2cXF54nzAiIgIsrKyqFmzptGiPMXFxcWRlpZGrVq1sLe3R6PRYG5u/sSLrSiKTJgwgfz8fHbs2EFoaCgajYb69esbTcCXDD0Apu+9SHx8PImJiVSsWNEoANRoNERGRlJQUECdOnWMKtg5OTlER0ejUqme+NuGh4djb29PzZo10RsW/zEvtsrckz5bVlaWvBhWnTp1Svy2pfHy8sLLy4u9e/dSoUIFeQht8e9BZ3hURfHjmW7TarUIhnk/RZ617VmfR6PRYGaysmFRpcTGxoZatWoxceJE4uLi+O2339Dr9Xh5ebF9+3aOHTv2XPOrFS8PURTlRymFh4dTrVo10yyvnCedT7pSnvmIYd53adMQRMPCd8XPwdTUVGxsbEpd8bTo3CoaafM0hYWFaLVaTG/fRefps1b2La5o1ITpQiYYXsfKyqrU95OdnY0oiqUG0UWfxfRa8CrR6XT06tULrVaLj49Piev+y0QURbRaLZaWlka/lUajQa/XIxYb4VLUSFxURvLy8oiKikIQBOrWrVvivp6Xl0dhYWGJwDMkJAS9Xl/qEOHS6HS6EusjUOz9mE6deprSzq0ipve54jQaDRkZGTg5OZkmgWFfDKOaXjZ6vZ4hQ4YQFRWFv7//P35ehYWFyXWu4t+PRqMhMzOzREN1VFQUOTk51K9fv0SZoli9wNzwBIfSJCQkkJKSQu3atZ/Z6Fla3UXxalIC0+egVqvJycnh7NmzzJ49m9OnT1OvXj3TbIq/kSiKfP7552RlZcnDkP6t8vPz+fzzz+nXrx+jR482TX5pFBQUEBAQQJUqVeTnnt26dYvJkycza9Ysxo4dS0JCAhMnTmTcuHG89dZbpodQvORex8BUoeAVC0wVr78XHZgqFP+Ukl0eihLCw8NZsWIF69atY/z48UpQ+oJYW1sr8z4MQ8p//vlneaXjl1VeXh5r167F1dUVNzc3li5diqurK7169eK9994DQ4/M6NGjlaBUoVAoFAqF4l9OCUyfg16vJzMzU36kh+KfJwgCAwYMeOpjC/5N6tWrx9tvv/3cQ59ehHLlyjFs2DDs7e2JiYlBr9fzySefsHLlSrmBoU6dOgwbNsx0V4VCoVAoFArFv4wylFehUCgUf4oylFfxulKG8ipeJspQXsW/hdJjqlAoFAqFQqFQKBSKF0oJTBUKhUKhUCgUCoVC8UIpgalCoVAoFAqFQvEKUGbgKV5nSmCqUCgUij9FpVLJc51Ke1adQvGqMjc3l5+xqcwvVbxoZmZmqFQqpTwqXnuv5OJHWVlZ5OTkmG5WKBQKxT9s7ty5eHl5cfv2bSpUqKC05iteeYIgoNPpGDlyJFqtlmPHjiFJklK2FS+MKIqMGzeO2NhYLly4gCiKSnl8zQiCQOXKlbGwsDBN+ld55QJTSZLYvXs3R44cQaVSOnwVCoXiRTEzM+PWrVuEhYXx1ltvYWdnp1SWFK8FSZLkAKBPnz5KfUPxQkmSxJUrV8jPz6dv375KeXwNWVpa8ssvv1CrVi3TpH+VVzIwXbNmDdu2bcPMzEypBCkUCsULUDTMMTIykuTkZFq0aIGVlZVyTVa8NoKCgpAkicaNG7/Uz4xWvN4EQUCSJIKDg9FoNDRt2lQpj68hKysrtmzZQv369U2T/lVeucAUw1De7OxspcVIoVAoXrDZs2ezY8cOfH19cXJyUgJTxWtBp9MxYsQItFqtMkJL8cLp9Xo+/vhj4uLiOHv2rFIeXzOSJKFSqahUqZIylPdVDEwVCoVC8XKYNm0aa9euJSEhAUdHR9NkheKV1aNHDzQaDdevXzdNUij+cUOGDCEyMpL79++bJikUrw2lyUWhUCgUf4ooiuh0OgAKCgpMkxWKV5ZOp5MXmCkq4wrFi6LX69Hr9UbXXIXidaQEpgqFQqFQKBQKxStAmV+qeJ0pgalCoVAoFAqFQqFQKF4oJTBVKBQKhUKhUCgUCsULpQSmCoVCoVAoFAqFQqF4oZTAVKFQKBQKhUKhUCgUL5QSmCoUCoVCoVAoFAqF4oVSAlOFQqFQKBQKhUKhULxQSmCqUCgUCoVCoVAoFIoXSglMFQqFQqFQKBQKhULxQimB6StKrVaTmZmJJEmmSX+aVqslPT39qccURZHMzEw0Go1p0t9CkqR/9PUUCoVCoVAoFArFP0+QnhaF/IvExMSQlJSEVqtFEAQsLS1xcHCgevXqWFpayvkkSeLcuXNkZmby7rvvIgiC0XH+CZIkcfPmTXx8fJg0aRIWFhYcOXIEW1tb+vXrZ5r9uQUHB7N9+3Zmz56NlZWVaTIA2dnZrF27lkGDBtGsWTPT5L+cWq1m+fLlDBw4kFatWpkmK0qRlZVFfHw8DRs2RKX659qetFotjx8/platWpQtW9YoTa/X8/jxY6pUqUL58uWN0p4kPT2dpKQkGjdubJqkeEmIosjUqVPx9PQkPDycatWqmWZ55YiiyIMHD8jOzja6vpuZmWFubk7dunVxdHQEICcnB39/f5KSkqhUqRItW7bE3t6+2NH+P1EUefToUYkGxSZNmjz3OfG/ys/Px8/Pj+bNm1OuXDmjNFEU8ff3JyoqChsbG5o3b07VqlWN8hSn1+sJCAggMjISKysrmjRpQq1atUyzvbJ0Oh29evVCq9Xi4+ODubm5aZaXUlpaGvfv3ycjI4MqVarQsmVLbG1tjfI8fPiQsLAwzM3NadKkCbVr1zZKL01OTg7379+nVatW2NnZmSb/raKiokhOTqZ9+/ZG27Ozs3n48CGJiYlYWVnRqFEj6tata5SnNFqtlrt371KrVi2cnZ1Nk19Ker2eIUOGEBUVhb+/P2ZmZqZZXrisrCz8/f1JTU2lSpUqtGrVChsbG9NsssTERAICAsjLy6NmzZq0atXqiXWW3NxcgoODadiw4RPLX3x8POnp6TRp0kQ+TkpKCuHh4XLdXpIkypcvT9OmTU13V7wklMDUEOgtWrSIkydPUqVKFSRJQhAE+eY8dOhQGjRoAIab9/fff09kZCR79+594kn0vJKSksjLy3uui2kRSZLYuHEjK1eu5MaNG9ja2vLxxx9ToUIFVq5caZr9uUiSxNmzZxk6dCjJycklAosi8fHxdO7cGTc3N4YPH26a/JfLycmhSZMmuLq68sEHH5gmK0zo9Xrc3d2RJIlp06b9z+Xzj0hJSWH06NHMmzePDh06GKXl5OQwZswYJkyYQN++fY3SniQ0NJQFCxbwzTff/CONIIo/7nUMTLVaLXPnzuXRo0dGlb/CwkJiY2PZsGED7du3Jzs7mxUrVuDr64u1tTWFhYW0adOGb775ptRAMzs7m48++oj8/Hw5KJQkiZkzZ9K6dWvT7H+pvLw8Hj16xIkTJ9i7dy8HDhwwavCRJIlDhw7h5eWFubk5arUaZ2dn5s6dS/Xq1Y2OheE6c+jQIbZu3YqlpSV6vR57e3tmzpxJkyZNTLO/kl7FwDQ5ORlXV1ceP36MhYUFhYWF9O7dm4kTJ8rB6blz51i3bh0YyrqdnR1z5syhUaNGJkf7P7m5uQQGBnLkyBGOHTvGiRMnqFGjhmm2v5wkSSQlJeHn58f27dsB+PXXX+X0vLw81q1bx82bN5EkicLCQuzs7Pjhhx+e2IhdWFhIaGgo586dY+vWrbi5udGnTx/TbC+llz0wzcjIYOXKldy7dw8zMzPy8/Pp16+f3HliKiYmhkWLFpGYmIgkSRQUFPDJJ58wYsQI06wAbN68mfXr17NlyxaaN29umoxarWbq1KnEx8ezf/9+rKysEEWRXbt2sWrVKurUqYMkSYiiSMuWLfnpp59MD6F4SfxztdaXmCRJPHz4kLy8PN59913efvttunfvjr29Pbt27WLKlCmEhoYCoFKpmDBhAnPnzv1LKv379u1j48aNppufydzcHGtrayRJwszMjJkzZzJp0iTTbH+ISqXC1tb2qb3AgiBgbW39j10UzczMsLa2fiUqBS+DO3fusGvXLnr27PmXlM8/Qq1Wc/XqVTIyMkyT0Gq1+Pr6kpycbJr0RDVr1qRChQosWbIErVZrmqxQ/C1UKhX9+vXjww8/5L///S///e9/GTNmDDY2NuTm5lKjRg0kSeLkyZOsXr2aESNGsHz5ckaNGsWmTZs4cOCA6SHB0Jtw584dOnfuzOjRo+VjP61X0pQkSQQHB5Oammqa9ERFQeePP/7IsWPHePToUYlrQ3BwMLNmzaJFixYsWbKEqVOncvXqVZYvX45UStt1VFQU06dPp169eixatIjvvvuOx48f88svvyjn6gsiiiK7d+9m9+7djBs3juXLl/PWW2/h6urK77//DkBcXBwzZszA2dmZBQsW8P333xMeHs7PP/+MXq83PSR6vZ7du3cza9YsTp06RVhYWImy8zySkpIICQkx3fxUGRkZzJ8/nwULFnDt2jWysrKM0nNycggLC+Odd95h2bJl/PDDDyQlJTFnzpxSy6BkGO02Y8YM9u3bx8OHDxFF0TSb4k/Q6/Xs27ePnTt3MnbsWJYvX06/fv1YtGgRly9fNs2OWq1mxYoV3Lp1i6+++gpXV1caNWokl0dTMTExrFixAn9/f7Kzs02TAbh8+TJbt24lICBAvmZJkkRAQABarVa+5n744YevTGPEv9Ufv8K8piwtLWnZsiUjR47kww8/ZPLkyaxatYp169bx+PFj5s+fL1/EKlSoYNQzoNfrCQkJ4fr16/j6+pKSklLsyP8nLCyMa9eucfPmTVJSUhBFkZCQELy9vfH19SUwMJDY2FijfYKDg+V9kpKSjNIEQTD6c3JyonLlykZ5iioxxV/3WVQqFebm5kRGRnLt2jX8/PwoKCiQ0wVBKPXGlJiYiK+vL9euXZODeFN6vZ6goCCuXbvGrVu3SE9PN80CQEhICNeuXeP+/ftotdrnCoLT09NJTk6Wh+Fdu3aNx48fG+XJz88nNjZWfh+BgYHybyqKIg8fPuTq1aul3rCSkpK4ceMG169fJyIiwqiyFh4ezrVr14wCL7VaTUxMTIm5sXFxcfINVhRFoqOj0Wg0xMXFcevWLaPvOjo6mmvXrnH79m3y8vKKHaV0Wq0WT09PevfuTdu2bcFwc09JSaGgoAA/Pz98fX3JzMwEQ4vz7du3uXHjhlHZ0Ol0xMbGotFoiImJ4fr160bfZdHnNf2eVCoVZcuWLbURQRAEypQpU6LlNCsri1u3bnH9+nXi4uIoKCiQzwNLS0u+/vpr/Pz8uHXrltF+CsXfxczMjO7duzNs2DCGDh3K0KFD6dmzJ4mJicyaNQtnZ2e5ktuiRQs++ugj6tSpw+jRo+nQoQNnz541PSQYzreyZcvywQcfMHjwYIYOHcrbb7+Nk5OTadYnkiQJDw8Prl+/bpr0RJIkER0dTadOnVi+fHmJ0TCiKLJ3717KlCnDL7/8Qr169fjPf/7D9OnTOXDgQInGpKL7SmxsLIsXL6Zx48Z0796dyZMnc+XKFXJzc43yK/4Zoihy+vRp+vXrx7Bhw6hbty6TJk2iWrVqcnDg7e1Neno6y5cvp1GjRnTp0oVffvmFM2fOEBYWZnpIRFEkJiaGXr16sWjRIrkx/I8QRZGLFy/KvbTPq6CggPT0dCZOnMioUaNMkylfvjyurq6MHDmSWrVq0aVLF+bOncvvv/9eavAiSRLx8fHUq1eP1atXU6FChT/8WRSly83NZdOmTXz66acMHz6cOnXqMG3aNNq1a8eWLVuM8kqSRGxsLAcPHmTevHl0796dBg0aMH/+fMzNzTl06JBRfo1Gw+rVq6lUqRJ16tQxSiuSlJSEh4cHvXv3ply5cka/a1ZWFl27dmXQoEHyNbdz585G+yteLiUjjH8x0yAC4I033mDOnDkcOHCAuLg4JEli//79bNq0CQwn2bFjx1i2bBnu7u4sXLiQX375hfj4eDAEY6dPn8bV1RV3d3eWLFnC8ePHUavV7N69m5CQEKKionB3d+fUqVNguCDv3LmTJUuWsGrVKpYvX87ChQvx8/Mzem9FJEliw4YN7Nu3T96Wl5fH9u3bWbx4MR4eHixbtoxjx44Z7WeqaPy9j48PGzZsYNWqVfzyyy94eHjIwZQkSSUu5teuXZNfp+g72L9/PzqdTs6TlZXFpk2b5M+0bNkyFi1aZBTwFA0PW7BgAe7u7qxatYrdu3fLcwOeRJIkTpw4gbu7O8ePH2fNmjWsXLmS2bNnc+DAAURRRDL0ii9btowjR46wfPlytmzZgk6nQ61Wy9/VypUrWbBgAXv27JHf/4MHD+Tfb8WKFWzatInCwkL0ej1nz541+o6PHDkCQGxsLIsWLTJqUBBFETc3N7n1Oj8/n/nz53PixAnWrl2Lq6urXAm8dOkSCxcuxMPDgyVLlrBixQrS0tLkY5UmOTmZ69evM2bMGDB8L97e3qxYsYL9+/fj6enJ/PnzWbZsGSEhIWzfvp1169axcOFC5s+fT0JCAhh+K1dXV/bu3cv27dtxd3dn1qxZnDt3jps3b7J69Wp52+HDh+XyUPTf4nOyi1hbW5f4DaOioli8eDFLly7Fw8ODtWvXsmXLFnkoMkDVqlXp1KnTE3uhFIp/wqZNm8jLy+O9996TtzVt2pSCggL5vhAbG0teXh4tWrQw2hfDuZGXl0dBQQEZGRlkZGSUuI4+D5VKRUxMzHM1VBVRqVR88803zJ07FxcXlxI9YzqdjmvXrtGrVy+j7W+88QZarbbUhkZnZ2eqVavGnTt30Ol05OTkEBoaSvPmzbG2tjbNrvgHCIJAixYtSE1NJS0tDUmSCAsLw8zMjMaNGyNJEleuXKFjx45Gv1GLFi0oV64c/v7+RsfDMDJr1qxZzJgxg5o1axrd05+XSqUiOzubuLg406SncnJyYufOnYwaNQobG5sSjcVWVlYl5klrtVrKlClTamO2SqVi9OjRrFy5ktatW/+p809RkiRJJCYmEhYWxuDBg+XtoijSq1cvHj58iFqtNspf1CnQtWtXebutrS3t2rXjzp078jaA06dP4+Pjw4wZM+ThucVptVpWr16Nvb09H330UYnXSk1NRRAE0tPTyc/PN9pX8XJSAtPn0KNHD1QqFUFBQQDcvHnTaHiCn58fdevWZdSoUbz99tv89ttv8vDc2NhYvvrqK7mlfODAgdja2mJmZkaTJk2oVKkSjo6OtGvXTh4Df+jQIebOnUvNmjUZOXIkgwcPJiwsjKlTp5KYmCi/bhFJkrh48SI3b94EwwVh165dLFq0iNq1a/P+++/z1ltvlXpxL04QBPLy8vDy8qJatWqMHDmSTp064eHhwc6dO+U8xQUFBTFlyhTS0tJ4++23ef/993F0dOT777/nwoULYAg4t2/fjpubG40bN+b9999nwIAB+Pr68t1338mtmz4+Pvzwww9UrFiRUaNG0aVLF86fP09SUlKpvbRFJEni3r17rF69mvPnz/PGG28watQoKlSoIPe4CYJAfHw8W7Zs4ddff6V58+Z07NgRlUrF4cOHWb58Oa1bt2b06NE0b96cuXPn4uvriyiKzJw5k8DAQN577z3effddqlevjk6nIzExkUmTJmFra8sHH3zAoEGD5En56enpHD58WO6dLHqfx44dIzAwEAwX1IMHD7J27VpsbGzo27cvdnZ2PHz4kO+++w4rKytGjRpFv379+PXXX0u0PBYnSRJBQUFYWlrKc7wkSeL+/fusWrUKf39/+vbtS79+/fDy8uLbb7/Fz8+PgQMHMmjQIPbt2ycfv6CggH379uHp6YmzszMjRozAzMyMKVOmsGzZMmrUqMH777+Pg4MD3377rRx8FzVs3Lx5k0uXLvH777/Lf+fPnycvL0/+HYuC8jNnztCvXz9GjBiBpaUly5cv5/jx40af68033+TmzZt/qlKkUPyv0tLS2Lp1KxMmTJAX8iiq5Nrb2zNt2jS2b9/OzJkzMTMz47PPPjM9BBjmt+Xl5bF7925WrlzJmjVrePTokWm2EkTD6Bp/f3/u3bsnjyp48OCBvPDSsxQFIqVVzNRqNQkJCbi4uBhtr1ixIhiuZcUJgkCzZs0YO3Ys3377LZ6enixfvpyjR4/y3XffPXWxE8Xfx8zMjC+//JKMjAxmzpzJtm3b+Pbbb3FxceGdd95BFEUiIyPl9TKK2NvbY2trW2o5EgQBKysrJMMcQNP7/9MkJSXh7++Pv78/CQkJ5OXl8eDBA+7fv09oaOgzA0Nzc3NUKhWiKKLT6Z752tnZ2WzZsoUePXqUGBVQpKhs5ubmPvP1Fc8vMTERURRLTEuoUqUKhYWFJXqwY2NjsbW1NfqdJEmiZs2a8ohCDPVLV1dXxo8fT7NmzSgsLCx2lP9z8uRJTpw4wY8//oitrW2JhreCggIePHjAqlWrWLlyJadPny71OIqXx5Nr+wqZjY0Ntra28sllbW0tX+AEQeD777/n+++/Z8CAAQwePJiuXbty5swZMJywUVFR8jCCTz75hJEjR2Jpacl7771HixYtaNiwIZ999hlvvvkm2dnZLFmyhPfee4/Zs2fz9ttv8+GHH+Lp6UlISAjHjx8v9QJta2srVz6Sk5NZunQpn3zyiXyMsWPHMmrUqFL3LSIIAhqNhr59+zJp0iTefvttpk+fzpgxY9i4cSMFBQVGLZGSJLF582YAPD09ee+99xg2bBjLli2jbdu2cs9XcnIyK1asYNKkSUyfPp2hQ4fy0UcfsX79eq5cucKVK1cQRREPDw+aN2/OsmXLePvtt/noo4/4/vvvn+sGYmVlhZWVFZMmTWLMmDEMGzYMd3d3nJyc2LFjBxh68goKChg8eDBTp07lvffeo6CggEWLFjFo0CC+/vprBgwYwPTp06lSpYq8382bN+ncuTPDhw9nxIgRTJgwgTJlypCenk5wcDCDBw9myJAhjB07lg8++ABJklCpVCVabgXDcNaiFY8Fw3zdqlWrMmvWLD7//HMqVqyIp6cnKpWKRYsWMXDgQMaOHcuHH37I2rVrn3pBDQoKokGDBkavaWlpia2tLV9//TXDhg1j0qRJDBgwgIsXLzJz5kzeeecdPv/8c4YNGyYPQTQzM8PCwoI33nhDHpqzePFiQkJCqFKlCtOmTWPYsGEsWLCA5ORkOdAWBAFRFDl48CCrVq3C3d0dd3d3PDw8WLduHZmZmXJgGhAQwJEjR1i5ciXjxo1j2LBhzJkzh86dO5focW3SpAkxMTGlVqoVir9bUUXmnXfeMdquVqupWbMmERERXLp0ST4/TCtGGM6NypUr88EHH2Bubk5iYiJeXl5MnTq11CGUxel0Og4fPsz69evZtGkToaGheHt7s3HjRjZs2PA/D3PX6XTk5+dTpkwZo+1WVlaYmZkZ9UAU0Wg0ODk5oVaruXbtGnfv3sXe3l7pLX3B9Ho91apV4+HDh1y+fJmYmBiqVq0qjwbLzc0t8TtbWFjIC179VSRJ4s6dO6xbt45NmzZx4cIFgoOD2bBhA+vXr+fIkSPPdV8v8rR6C4YpMu7u7jx69Iivvvqq1B5Txd9DkiSysrKwsbEpMVXH2toaURSN5vxKkkR2dnaJlaIx1GPVajWCIJCWlsaSJUto2bIlY8aMQa/XIwiC0WsEBASwcuVKvvrqKxo3biyPriuqQ6hUKgYOHEibNm3kaUPfffcdXl5exV5V8bJRAtPnkJOTQ35+fok5nEWioqJYv349S5YsYeXKlcTExMi9Oy4uLvTv3x9XV1fWrl3LvXv35AuyKIpoNBqjk7ZoSMTYsWPlbRiGNHbt2hUfHx+j7UWKX+RDQkJISUlh3LhxRnl4xgVeFEXKlCnDkCFD5G2SJNGnTx95eFBRYCEYescuXLjAe++9V6KVfOjQoQQHB5OXl0dkZCTp6ekl5ok0adKEli1bcu3aNQoKCnj48GGJyl/Lli2fWNkrTqvV0rhxY+rXry9vs7CwoE+fPty/fx8MN20nJyd69Ogh54mKiiI6Opq0tDTmzp3L/PnzmTdvHmq1mujoaFQqFZ999hnHjx9nwYIFXLlyhfz8fARBoFq1agwbNgxXV1fWrFmDn58fkmFF56cp/luJosigQYPkf+v1ei5duoS1tTVubm7Mnz+fX375hcePH5ORkVGi5bG41NRU+TEWRURRpEmTJvKcaFEUqVmzJq1bt5Yf7VC0TaPRyO/NxsaGLl26yMext7enVq1a9OzZU95WtmxZKlasKPeoFH32oUOH8vnnnzN+/Hi++OILxo8fz7hx43BwcJBbQu/cuUPFihXp1q2bfDxJkujdu7f8bwzlzM7ODlEUnxqUKxR/B7VazbZt2xgyZAgODg5G25csWUJycjL79+9n7dq17N27F71ez+zZs0udFuLi4oKbmxtLly5l/fr1eHp6Ehoa+szF71QqFW3atKFnz55069YNJycnmjZtSrdu3ejZs+dzPerjaQRBwMzMrMSCMUXXXNNHh4miyNGjR9m0aROrV69m48aN7Nixg+HDhzNt2jRiYmKM8iv+Gbm5ucyaNQt7e3t5JM6uXbsICQlhxYoVSJKEubl5id9Zp9MhiuJf3qhQdL/o1q0bjRs3xtnZmR49etCjRw9at279zPvk87p37x7z58/nwoULLFq0qMSK8Iq/lyAImJuby+WoOK1Wi7m5uVFjc1FwWdoIKI1Gg42NDQUFBaxYsYLAwEC+/fZb0tLSSE5ORq/Xk5GRQX5+PtHR0cyYMYMaNWrwzjvvkJKSQlZWFnq9npSUFAoLC1GpVEyaNIlly5bh5ubGtm3bGDx4MPPnz//DQ8sV/xwlMH0OZ86cwd7evtRl8CMjI5k2bRpnzpwhIyODChUqUKtWLbmCX/QIl1atWnHo0CF++OEHjh07VuIELqJWq+UA0VSZMmVKrfCYys3NlXvs/ghJkrCwsCi1IvKkobQFBQUlhs1IkoSNjQ2SJKHX6yksLEQwPH7HNJ+dnR1arRa1Wo1OpysxZ6To4vWsm5hg6H00JYqi3HoqSRJly5aVW9wkSSI/Px+VSkVGRgbx8fEkJCSQkJBAx44d5flkM2fO5MMPP5SHGhf1XDo6OuLu7k67du347bff+P777+U5pkXHL/6+VSqVUVAqGXpWi39mSZLQarXywkNF78fa2pqvv/66xG9TnJmZWYlyVfRbFA/sJUnCysrKaJtgWNSq6P2pVCqjm4lOp8Pc3NxoYSNRFDE3N5c/Y9Hn6d69O3379qV///7069ePfv36MWTIEDnAlCSJnJycUj+LXq83+o4oFsg/qwwoFH8lSZJ4/PgxAQEBvP/++0ZpiYmJHDp0iPHjx1O7dm1sbGyoWbMm06ZNY+/evSUWDMKk/AqCQKtWrRg1ahRXrlwxymfK3NycN998k5EjRzJy5EiqVq1K9+7deffddxkxYsT//CglKysrHB0d5XURihQ1RBYN6S0iiiJbt27ljTfeoFu3btjY2GBvb8/EiRPJyMiQRwsp/jmSJBEaGsrFixeZOnUqTk5O2NjY0LBhQz7//HM8PT3RaDRUqVKlxCKLOTk5aLXav/R5noIg0LRpU95//31GjBhBp06dqFGjBu+++y4jR46kd+/ef8n1/NatW8yYMYOMjAzWrFnzPz3HXfHnVa5cGbVabTR1CcPoPTs7uxJ1RCcnJ7Kzs42CU8Ew3apatWo8evQId3d3KleuzO7du3Fzc8PT05P09HR2797N+fPnOXHiBN7e3pQtWxZXV1c8PDw4evSovLjXw4cPjV4TQwP7hAkTEA3PlFa8nEqPNv6lTId/SJKEr68v69at49NPP6VixYolKs2nTp3i5s2bbNq0ieXLlzNt2jSqVq1qVOmvVasWixcvZsOGDVSoUIEZM2bIPV+mgYSjoyNly5YtseqiWq3mwYMHpT6/CZNKj5OTE3q9vtTFDJ5GEAQKCwuNWrwFQcDX15eKFSvi6OiIKIoIht5SQRCoV69eifcqCAJ3796latWq2NnZUblyZXlbcVlZWYSEhNC0aVN5uPSDBw+M8sTExJCSkvLEwLiIIAikpqYafe96vR5fX1+54mZ6IxQEgUqVKmFra8v48ePx9PSU/zw8PPj444/B0Hs4ZcoUdu/ezTvvvMPMmTPli1qNGjVYuHAhnp6eVKtWjWnTpsnPU8vPzzcafpqTk0NWVpbRZyn6LouoVCqcnJzo1KkTGzZskN/PunXrmD17Nvb29nJeU87OziXmIAuG4bWmTLeZlmuekMd0W3FF329pC7NkZ2cbBZhOTk6kpqYazV8TBKFEmZUkiczMTMzNzUsd+qNQ/J28vb1xdnYu8TB2rVaLaJj7Zrpdp9OV6JV6kpSUlBKjHJ6lYsWKpTbq/FmWlpY0a9aMq1evGm2/c+cOZcuWpV69ekbbecJCgYWFhZgZnl+o+OcVjSgxLXtF5VGlUtG2bVv5uZ9FHj16hEajKXXRrr+KjY0NFSpUMN38P0lLS2PmzJnUrVuXXbt2ldpx8DSm9QHFnyMIAtWrV6dChQpG15CiumOzZs2MrleCINCwYUM0Go08mg1DJ0dAQADt27fH1taWCRMm0LhxYxITE8nOziY/Px/JsIhcXl4e1atXlxvrMzIyyMzMpLCwEEmSyM3NLXEeFElOTkYQhBLBsuLl8fTa/r+IXq8nPj6eu3fv4uvry/nz59mwYQOzZs2iefPmTJkyRc5b1KNFsV6jxMREEhISuHz5MidPnpQr8LGxsfj4+BATE0OZMmVo0aKFPHxXEAQcHBwICwsjIiKC5ORkKleuzJAhQ1i5ciW+vr4kJSURHh7Opk2byM3NZejQoUiShM6wmmyR4kOCXVxc6Ny5M4sWLcLPz4+kpCS55b+0AKRI0Um/adMmHj16RGJiIpcuXWLv3r0MHz4cW1tbdDodhYWFcoA6evRoLl26xK+//kp0dDTx8fGcO3eOkydPMnLkSMzNzalduzY9e/Zk8eLF3L17l6SkJIKDg1m1ahW2trb06dMHGxsb+vTpw86dO7lx4wZJSUkEBASwZcsWcnJynhkQmZubExISwt69e4mOjiYmJgYvLy/Cw8Pl4cF6vR61Wm30HVSvXp033ngDV1dX7ty5Q2JiIhEREZw7d46YmBj0ej0XLlwgNDQUnU5Hly5dMDc3l1fX/P3334mJicHW1pa2bduSn5+PVqulatWqlCtXjj179hATE0NkZCR79+4lKiqq2Dv/v8qEac/liBEjOHjwICdOnCA2Npa4uDh8fHxKBPammjRpQnh4uFHFsHhZfdo2nU4nb5MkCbVaXaKXtbRtxd+/KIpyj39piuft1KkTFhYWLF++nIiICOLi4jh16hSXL1826pUF8Pf3p27duqX2iCsUfxetVsupU6do06ZNibJXpUoV2rVrx6ZNmwgICCA5ORl/f3/c3Nzo3Lmz3CNw+PBhuaEvODiYmzdvkpiYSHR0NIcPH+b8+fO8++67Rsc2JRoWrQkKCiIoKIj27dtjaWnJo0ePePTo0R9+pmnR9buISqVi2LBh3L9/n99++43ExET8/f3ZunUrAwYMoHLlyvJ3cf/+fczMzHjrrbe4cOECFy9eJDExkfDwcNavX09ubi7du3c3ek3F308QBOrXr4+Liwtr167l0aNHJCUlcfv2bdatW0e/fv2wtramf//+JCcns23bNuLj4wkKCmL9+vV07tyZ+vXrIxoe7XLjxg3Tl5DLztPqEMWlpqbKZbZcuXK0bt2ax48f8+jRoxL3waeRio0iKr7t+vXr8lSihw8fcvfuXe7evYufn5+86rWfnx/e3t6l3pMKCgpK3a744xwdHRk8eDDr1q3jwYMHJCQkcOzYMfz8/OSnBNy/f1+uGzds2JCWLVuyYsUKQkNDiYmJYfv27eh0OgYMGECjRo1YsmQJS5YsYfXq1axatYr58+djb2/P9OnTef/99xk8eDCurq64ubmxatUqVq1axaeffoq9vT1r166lU6dO5Obm4uPjQ3h4OElJSfj5+eHu7k6DBg3+cEOG4p9jNnfu3LmmG/9tJEPP6P379wkLC+Pq1atcuXKFBw8e0KVLF+bPn0/58uXl/Ddv3kSlUjFo0CDs7e25du2a/HzLW7duUb58eWxsbBg1ahRBQUEsXbqUwMBAbty4wa1bt/jggw/o2bMngmFuz8mTJ4mMjESn09G6dWuaNm2Kj48PPj4+hIaG4uPjw6VLl5g8eTJvvvkmGJ6LGh4eLq9mevHiRZydnenVqxdWVlY0aNCAU6dOcffuXR4/fsyVK1fIzMzkjTfeKPbJjSUmJuLj40PVqlW5ffs2d+/e5bfffqN27drMnj1bHvt/9uxZevfuTb169ahfvz7p6ekcOHCAyMhI7t69y/Hjx2nTpg3ffPMNFhYWWFpa0rhxY7y9vbl9+zbBwcFcvnyZO3fuMHPmTNq1awdAgwYNuHLlCpcuXSIyMpIbN27Iw0v79OlTYjXBIpIkcf78eSIjI7G0tMTPzw8fHx+OHTvG6NGj5QWJYmNj8ff3Z8SIEfIwZzMzMxo1asTJkyfx9fUlODiY69evc/nyZTp27Ej58uWZOXMmfn5+3L17l1OnTtGpUydGjx5NfHw8P/30EyEhIdy4cYOrV68yevRoevbsiZWVFSqVij179hAXF4e/vz+JiYloNBq6detG69at0Wg0HDt2jLfeekueJ1ZUwXj8+DHHjh0jMjKSW7du4e3tTaVKlZ7aY25jY8OuXbto2bKlPJzc39+fvLw8hg8fjmDonQ0ICCA7O5vhw4fL3++DBw9IS0tjxIgR8m/8n//8h7p164IhqPT29qZPnz7y6p0ajYYTJ07w5ptvUr9+ffLz8zl16hSDBw+mZs2aRu9Po9Fw6tQpevbsScOGDalYsSKWlpbs3r2byMhIHj58iJ+fHxUqVCAtLY3PPvsMQRDQ6/W4urrSsWNHo/mtipeDJEmcPn2aO3fuMHXq1BJD8V9lRY10H3zwQYnzztLSEhcXF06fPo2/vz9BQUF4e3sTFxfH/PnzqVevHhEREXz66ac0adJEvv6tW7dOfk7zsWPH6NGjB5MnTy6xaEhxGo0GLy8vzp49y507d8jLyyM4OBhfX19u376NjY2N0dz6p8nJyeHkyZOMGTNG7qkVBIHatWuTlJTEoUOHiI6O5sKFC4iiyC+//ELZsmXJz8/n888/R61W06tXLxo2bCh/5qLFny5cuMAXX3xhtEbBq0wURbZv344oinzyySfPHLXzotna2lKjRg0OHDggN0SfPHkSjUbDwoULqVSpElWqVEFteFRdbGwsV65cISkpiYULF1KxYkV0Oh1fffUVwcHBJX7H9PR0zp07x8cff/zM3ibJ8Ni5HTt2cPfuXaKjo8nMzJSfYZ6UlESHDh2eu9fy3r175OfnM3z4cChWZ7t37x4ZGRlcvXqVGzducOPGDW7evEmdOnWoVq0aHh4e7N69m9GjRxuNiNPpdBw6dIihQ4fKay287CRJYs+ePWRlZTFhwoSXqjyamZnh4uIiX6MePXrEiRMnGDBgAB999BF6vR5PT082b97M6NGjsbOzo0aNGuzfv5/g4GD8/f25ePEiX3zxxRPv81lZWVy4cIEBAwaUWP0Xw/cTERHBgwcP5N87IyODuXPncvfuXfz9/Tl9+jTx8fHMmzevxCrkipeHEpgaVKpUiUaNGlGrVi0aN25M7969GTduHEOHDi3RWl6hQgWaN29OtWrVqFixIp06dSInJwdAXpm2QYMG1KlTh/Lly2NtbU1GRgZWVlaMHDmSDz74AEEQEASBunXrUqtWLTIzM2nUqBEuLi44OjrSt29fBMPw1MqVKzN+/Hj69+8PhopEmTJlaNy4MfXq1cPMzAwnJydatGghzxOpVq0avXr1Qq1Wk5WVRd26dRkyZIhRgF2cYJij2bJlS8aOHUtaWhoZGRn07NmT6dOny0NIzczMqFWrFs2bN6ds2bLy/KcqVaqQkpKCmZkZgwYNYvLkyUZzFJ2dnenTpw9arZaMjAxq1KjBlClTjBbYKV++PH369EFneC5e27ZtmTBhAg0aNKBp06ZPrPRKksSZM2eQJInFixcTGhqKyvA4h48++ggMn8/W1paGDRvSoEEDo145JycnevfujUajIScnBycnJ0aNGkWLFi0wMzOjcuXK8qT67t27M3XqVOzs7ChTpgx2dnZkZWVhYWHBe++9J7cOArRv355q1aqRmpqKs7Mz48ePp2PHjjRp0oQKFSqgUqmoXbs2LVq0MJoPbG1tTb9+/ShbtiwZGRlYW1szYMAA+vfvX2LF2uJsbW2JiYnh8uXLDB06FJVKhYODA82aNaNmzZpyJeBp22rXro25ubn8GxcP4GvWrCn/7hh6WmrWrCk/B6+od7xFixbyY3OKmObF8P00aNCA1NRUypQpw9ixY8nOziYyMlIeRh0QEMCmTZuYNWvWExcfU7w4r3NgKooi1apVo3v37qXO169WrRo9e/YkLy9Pvn5Pnz6dli1bguGccXBwoFOnTjg6OuLo6IhWqyU7O5syZcrw3nvvMXHixKee0xi+4/T0dMqUKYOzszOOjo5UqlQJJycnnJycqF+/PlWqVDHdrVQWFha4uLjQvHlzo9c1MzOjd+/e2NrakpycTNOmTZk+fbp8P1GpVNganjNYo0YNbG1t6devHzY2NiQnJ+Po6Mj48eOf2fv7KnnVAlOAevXq0blzZzIyMsjLy6NNmzb88MMP1KlTBwz3wa5du1KxYkWSkpKoVasW3333nTxcu6iBs1WrVkYVd8Gw0mlRT9PTGlKK5OTkoNfrqVKlijwVyMnJCWdnZ2rWrEmDBg2eOzAtX768fM8qUrZsWZo1a0bVqlWpU6eO0V/Tpk0pX7683EjfsmVLo9cquve2bNmyxL3qZfUyB6YY6sU9evQgMzMTtVrNkCFDGD9+PBjKj5WVFS4uLrRq1Ur+/tu1a0dycjJmZmZ89NFHJRpDijM3N6du3bo0adLkidN6bG1t5XqxSqXC2tqaMmXKkJWVhU6no3nz5nz77bclGhoVLxdBkp5zXIZC8ZISRZEffvgBPz8/+ZEn/1ahoaF88skn/Pjjj3LjxstIr9eTk5NjtNJpWloagwcPpmfPnixcuJDMzEymT5+Og4MDS5YsMdpf8XIQRZGpU6fi6elJeHi4vPqzQvGq0+l09OrVC61Wi4+PT4kpBgrFP0mv1zNkyBCioqLw9/cvsSaKQvG6eLmaXBSKP0mj0VBQUGC6+V/HxcWFL7/8kocPH/IytzkVFhbi6emJl5cX3t7eHD58WH5Mz4cffgiGoWNWVlZ89913prsrFAqFQqFQKF4zSmCqeOUJgkCDBg1o3769adK/0vvvv8/48eNfuqE+xZmZmZGamoqXl5c89yQlJYVly5bJixJUq1aNFStWUKlSJdPdFQqFQqFQKBSvGWUor+K1kJOTg06ne+IcWsXLp2gl7PT0dHkxhL/yMRiKv58ylFfxulKG8ipeJspQXsW/xcvbpaJQ/AFly5ZVgtJXjJmZGTVq1KBly5a4uLgoQalCoVAoFArFv5gSmCoUCoVCoVAoFAqF4oVSAlOFQqFQKBQKhUKhULxQSmCqUCgUij+t6JFEL/NiWwrFH2X63EuF4kUrKpMv62PgFIq/gnK1VSgUCsWfolKp5Eq7sjiM4nViZmYml20lMFW8aGZmZgiCgCAISnlUvNZeuVV5JUkiICCAwMBA5eRUKP4mWq0WvV6PlZWV0jqreCJJkti6dSvnz59n1apVVKhQ4aV+fq5C8bxEUeTnn39Gp9Mxb948ZRVUxQsliiJLliwhJSWFJUuWKOXxNSNJEubm5rz55pvY29ubJv+rvJKB6YIFC3B3d1da6BWKv4EgCKSnp6NWq6lUqZJyA1Q8kSAIZGZmUlhYSMWKFZWyongtFFWLMjIykCSJ8uXLy71VCsU/rag8ZmZmotPpqFixIihDel8rkiRhbW3NyZMn5We5/1u9koHppUuXuH79unJSKhR/A0EQ2L9/PwEBAUyfPp0yZcoovWCKUgmCwNGjR/H19eX777+nXLlySllRvBZEUWTjxo3odDomTJiAmZmZUrYVL4woiuzYsYP09HS++uorpf77GjI3N2fMmDFUrlzZNOlf5ZULTBUKxd9v0qRJ7Nmzh7i4OKytrU2TFQrZt99+y6pVq0hLS6NMmTKmyQrFK6tnz55otVquXr1qmqRQ/OPefvttIiIi8Pf3N01SKF4byiRNhUJhRBRFNBoNkiSRk5NjmqxQyERRRK1WA5CdnW2arFC8snQ6HXq9Xv5TKF4kvV4vr/2glEfF60wJTBUKxRMpAyoUz0spK4rXlVK2FQqF4p+hBKYKhUKhUCgUCoVCoXihlMBUoVAoFAqFQqFQKBQvlBKYKhSKPyQzMxONRmO6GVEUycrKMt0MQE5ODvn5+Wi1Wi5evEhsbKxpFplOp+Pu3bskJCSYJv3P9Ho9ly9fJjw83DTJSFJSEufPn6ewsNA06W+RmprK2bNnKSgoME36y0RFRXHp0iV0Oh0AYWFh+Pj4KPOV/gLJyck8fvyYqKioUs+NIgUFBYSGhhIcHPzC5uQmJyfL84KfRhRFIiMjCQ0N/cfOA8U/Jzc3l5CQEEJDQ59ZFvPz8wkJCSEyMhJRFE2T/zaSJBETE8Pjx49JS0szTS5Bo9EQERFBcHCwsj7CPyAxMZHHjx+TmZlpmlSqmJgYQkJCyM/PN00CQK1WExoa+tRylpmZSXBw8DPrB0+7zuXk5KDVak03o9FoyMvLM90Mhn2K7p3FiaJIdna2Mtz/L2Q2d+7cuaYbFQrFv5ckSRw/fpzAwEAmTZqEnZ2dnCaKIps2bSIpKYnGjRsb7RMUFMT69etp1qyZ0eqshYWFrFixAjs7OypUqMAnn3xCtWrVaNq0qZynuOzsbCZPnoydnR0tWrQAwzG0Wi0WFham2f8QtVrNhAkTsLGxoW3btqbJYPgsV69e5aeffmLYsGF/+0qzkiRx7949pk6dyjvvvEPZsmVNs/zPJEni2LFjrFy5kvfeew8LCwt2797N1q1bee+99/70M6ElSeL06dPcuXOHqVOnUq5cOdMsrzWNRsPJkyfZv38/ly5d4tatWzg4OFCzZk3TrMTHx7Np0ybOnz/P5cuX8fPzo2LFijg7O5tm/Vvk5uZy9epVNmzYQKtWrZ76EPe0tDR27NjBkSNHuHbtGkFBQdSoUQMHBwfTrK8tURTZvn07oijyySefoFK9Pu34d+/excvLiwsXLnD9+nUyMjJo06aNaTYAHj9+zJYtWzh//jzXr18nPj6e+vXrY2VlZZr1L5WcnMyvv/7KyZMnuXz5Mrdu3cLa2ppatWqZZkWSJCIiItixYwfe3t5cuXKFe/fuUbly5dfm0RuSJLFnzx6ysrKYMGHCCy2Per2eU6dOsWfPHq5cucLdu3epVKkSTk5OplnB0LDx66+/cujQIXx8fHj48CG1atUyup7ExsayZcsWvL29uXbtGvHx8TRs2BBLS0s5j5+fH5s3b+b333/H19cXURRxcXExenRO8etcixYtSr1m7dq1i/Lly+Po6Gi0PSgoCB8fH5o1a2a0HWD79u1UqVKlxD0uJyeH7du3065duxf6m7xOlG9RoVD8Ib6+vri6uppu5tChQyxcuJDff/9d3iZJEsHBwSxYsIDs7GxUKhUpKSlP7Rm0tLTkjTfeoEaNGmA4xr59+7h165Zp1j8lLS3tiS22RQoLC0lJSflHehMFQUCj0fytrydJEvn5+aSmpiJJEpIkkZeX91y9EIrSFZXLOXPmkJubS+PGjSlXrlypvQeSJBEbG4ufnx+1a9emXr16XL58ma+++ork5GTT7E+Vl5f31F5ZU5IkcefOHVxdXZkxYwarV69+avnXarV4enqyfPlyHB0dqVGjBvv372f27NlPPW8VrwZ/f3++/fZb7t69S926dalVqxYZGRmm2cAwcmTGjBlcunSJevXqUa5cOZYvX862bdtMsz6VKIrk5uYiPWevkiRJXLt2jQMHDlC2bFkaNmxIaGgoU6ZM4eHDh6bZ5Ya3CxcuULVqVfn8+vbbb0lNTTXNrvgfSJLE77//zg8//IBaraZBgwbcunXridcyURTZs2cPixcvpkyZMtSpU4fDhw8zd+5ceSRGbm4uixcv5uDBg9SqVQt7e3sWL16Ml5cXGF4zKiqKr7/+mkePHtGgQQNycnKYOnUqN2/elPOYXueKX6/0ej3h4eEkJSVx8uRJQkNDiYqKIisri8zMTMLCwggODub06dNkZGQQERGBXq8nLCyMlJQUjh49SmRkJBEREeTm5sqjXxISEjh06BApKSmEhYX9oWuz4gkkhUKhKEav10ufffaZVL58eSkpKckoTRRF6dChQ1L58uWlqKgoeXtBQYE0cOBAyd7eXho/frxR/jVr1ki1a9eW0tLSpOzsbKl58+bSnj17JEmSpJycHCkvL0/Ob0oURSkzM1Nq27attGTJEkmr1Up6vd4oj0ajkbKysiS1Wm20vTT5+flSx44dpTVr1kiSJEm5ublSdna2UR5RFKVjx45JjRs3luLj443SdDqdlJ2dLeXk5BhtN5WTkyNlZ2dLOp3ONElWUFAgZWVlSTqdTrp69apUu3ZtKSYmxjRbqYo+c2nfnVarlbKzs6XCwkJ5myiK0saNG6V27dpJ2dnZkiiK0rJly6Ru3boZ5fuj9Hq9NGnSJMnS0lKKjY01TX6thYaGSg0aNJBWrVpltF0URaN/F8nJyZHy8/Plfz948ECysbGRTp48aZTvaURRlLZu3Sr5+fmZJj2RXq+Xvv/+e6l3797STz/9JJUtW1YKDg42zSZLTk6WatasKS1dulTe9vvvv0tmZmaSv7+/Ud7XmVarlbp27Sp17NhR0mq1psmvpPz8fGnUqFHS8OHDn3kN0+v10vr166XatWsbXetXr14t1atXT0pOTjbK/zQpKSmSh4fHM1+ziCiK0vXr16WgoCB5W3p6utS6dWtp5syZRnklQ/6zZ89KiYmJ8rbg4GDJ2dlZOnDggFHeV5VOp5MGDhwoNW3a9Kn3lf9Fbm6ulJqaarrZSE5OjtS/f39p3Lhx8rawsDCpevXq8n21uPj4eKlJkyaSm5ubvO3333+XypcvL/3++++SJEnShQsXJGdnZ+n27dtynvnz50t169aV0tPTJb1eL82fP19q2bKllJWVJUmSJBUWFkp9+/aVRo4cKUmG76f4da5cuXLS48eP5eMVFBRIbm5u0urVq6U333xTmjNnjrRw4UIpICBAunv3rjR79mxp5syZUr9+/aT169dLa9eulQoKCqQlS5ZIa9eulXr27CnNnz9fWrBggRQcHCzFxMRIc+fOldzc3KQuXbpIq1atklxdXaWMjAz5NRV/jtJjqlAonpsgCLRr1w5bW1suX74sb4+KiiIiIoIRI0Zw8+ZNeX6PKIpcuXKF1q1b4+joiE6nQxAEcnNz8fb2ZtWqVSxduhRvb2+5t1CtVnPo0CHCw8NJTU1lw4YNpKSk4Ovri6enJ48ePZJfNyAggLVr1+Lm5saaNWtKbU03VdRD6ePjw9q1a1m+fDk7dux4Zu9hYGAgGzduZOXKlaxcuZJff/21RGt8YmIie/bswd3dnRUrVuDp6UlwcLBRHkmSuH79Ou7u7ri7u+Pl5UV0dPRzDwO6ffs269atw93dnZUrV+Lr6yunPX78mA0bNuDm5oaHhwd379412vd5eywUTyeKInv37sXJyYlJkyYZpRUfVlZcmTJlsLGxkf9dqVIlKlSo8IeHUf/6668EBgaabn6q4cOHs3PnTiZOnPjMMiCKImZmZlStWlXeVrNmTWxtbf+2Hn3F30+SJAIDA7l69SpLly6lTJkyTy0LkiRx5coVOnToYDQ0ffTo0WRlZfHgwQOj/E+TmZmJp6fnE+f8mRIEgU6dOtGoUSN5W/ny5WnatCnx8fFGeTHkf/PNN42GktavX5+qVauSlJRklFdRkiRJ+Pn5sXLlSrkHsjSSJBEXF4efnx/jx4+Xt9etW5f//Oc/nDp1qkR+Pz8/cnNzGTNmjLy9a9eu1KtXjwsXLiBJEmfOnKFx48ZG02tGjhxJQUEBfn5+SJLEoUOHGD16tDyU1srKio8//pgrV66Ql5eHSqXinXfeYefOnUyaNKnEHFVzc3NatGhBcHAwgYGBXLhwgVq1alGpUiUqVapEtWrV8Pb2JjAwkIiICFq1aoWFhQUtW7bE39+fR48ecfHiRerXr4+joyP29vY0btyYc+fOERYWxs2bN2ndujXW1tZGr6v4456vJqRQKBQGVatWpXnz5ly8eBEMN58bN25gZWXF5MmTiY+Pl4Ox7Oxsbty4Qf/+/eW8AGfPnuXkyZPExMRw7do1Jk6cyO3bt8GwQMzPP/+Mr68varUaPz8/CgoKiImJ4e7du6SkpADw4MEDpk+fzqVLl4iPj+f8+fN88803REdHG95p6QRB4MqVK+zZs4fIyEjCw8NZtmwZK1euLHVBBAxzT7777jtOnTpFXFwcYWFheHh44OrqKg+LTE9PZ8GCBWzcuJGoqChiY2M5cuQI3377rdF7unjxIl9//TW+vr4kJCRw6dIlecjSk4KaIhcuXOC7776TP/PDhw/x8/MDICIigh9++AFvb2/i4uK4evUq06ZNIygoyPQwiv+RXq/n3Llz9OnThzt37uDp6cmmTZtKNASYSk1NJSwsjICAAPbs2UP37t1p3bq1abYSCgsLyc/PJy8vD0tLS0RRlLc9qcwWUalUdOjQgSpVqpCTk/PUYATA0dGRjz76iH379nHx4kV8fHxwc3Nj2LBhNGjQwDS74hVRdJ12cnLCzMwMLy8v1q1bh7e39xMXfLG2tiY9Pd1om06nQ6fTERMTY7TdlF6vJz8/n/z8fNRqNTY2NhQUFMh/zyqHplJTU3nw4AEdOnQwTSpVQEAAGRkZT1zLQPF/QkND2bJlC0uXLuXevXtUqlTJNIuRmJgYtFotDRs2lLfp9XoaNmxYYsEhyTCVp1y5clSoUEHeDtCgQQN5EcKHDx+WuA5WrFgRe3t7oqKiUKvVREVFGeWRJInatWsjGRbIEgSB9u3by9c5U+bm5nTu3BkzMzMmTZqEi4sLLi4uODk5Ub16derVq0eTJk2YOHGifM00MzOT95k2bRrVq1enYcOGVKhQgbJly9KuXTscHByYPn06lpaWdOjQQQlM/wJKYKpQKP4QlUrFgAED8PHxkSsYp06domfPnjRr1oyqVavK80wfPnxIVlYWPXv2BEPgpdVqSUxM5JtvvmHt2rXs2bMHa2trdu3aJb+GKIpotVqqV6/O+vXrqVevHh988AGbNm2ie/fuqNVqXF1dsbS0ZM+ePXh6erJr1y4SExPZunWrfJzS6PV6Hj16xEcffcSaNWvw8vJi2rRpco9r8eBQpVKh1WpZtGgR2dnZrF27lvXr17N161ZmzJjB5s2b5Z7jQ4cOcejQIX7++Wc8PT3x9PTEw8OD0NBQVq1aBYa5NHPmzKF+/frs3r2btWvXsmLFCsqVK0dubu5TA9P09HR++uknatSowebNm1m3bh27du1i2LBh6PV63N3dSU9Px8vLC09PT/bv348gCKxZs+apx1X8cQUFBWRkZBAeHs7WrVu5ePEi+/fvZ9q0aVy6dMk0OxgqUhcvXsTNzY1Zs2axZcsWxo4dS8WKFU2zGtHpdBw9epSNGzeydetWIiMjOXfuHFu2bGHz5s3cu3fPdJcnep5gwMLCguHDh/PgwQOWLFnCqlWr2L9/P++++67RQmiKV4skSURGRqLVatm0aROnT5/mzJkzzJ07F09PzxK94UW9kA8fPuTQoUPExMQQHBzMli1bKCgoeGaDSHx8PJs3b2bz5s38+uuvJCcn4+XlxebNm9mxY0epwcOTpKSk4ObmhoWFBcOGDTNNLiEiIoLFixfTsmVLOnXqZJr8rydJEsnJyZw6dQp3d3eOHTtGt27d2Lp1K+3atTPNLpMkiZSUFGxsbLC1tZW3C4KAg4MDarXaaAVvSZJITU0tdaE1R0dHuaEsLS2tREBsa2uLhYUFeXl5ZGdnIwhCiYWHrKyssLKyMlpVWjKsoWCq6DOrVCqmT5/OoEGD5GunXq/n4cOHvP/++0ydOhW1Wi2PhoqLi8POzo7p06fzn//8R95HFEUCAwNp374906ZNo0aNGjx+/NjoNRV/jhKYKhSKP6xXr16kpqYSGBhIbm4u9+7do1evXgC8+eabeHt7g6GHr0GDBiVWUhw+fLg8PMzR0ZE+ffoQEhIip5uZmcnBlKWlJSqVCjMzMzDcBFNTUzlx4gTNmzfn/PnzHD16lCtXrlClShXOnDkjH6c0oigycOBA2rdvL28bPXo05cuX58aNG0Z5BUEgMzOTU6dOMXPmTKpVqyanDR48mLZt23L8+HFEUeTw4cMMGTKEbt26yXkaNGjAF198weHDhwEIDg7m0aNHzJgxQ25ZLV++PJ999hlmZmal3lAx3FT9/f3lhaSKrzTo5OREXl4eBw8epHnz5ly/fp2jR4/i7e1NtWrVnvl9KP64goIC8vPzefToESNGjGDXrl1s3LiRMmXKMGfOnCcuEmRvb0+NGjVo2LAhLVq04NChQ0RGRppmMyKKovyYhdDQUPLy8khKSiIkJISQkJASPVr/q4yMDFavXk3Pnj359NNPGTVqFB9//DE7d+585mOWFC+3nJwcYmJiqF27Nhs3bmTPnj288847LFq0qMTwcJVKxaBBgxgyZAjLli3D1dWVFStWEBQUhL29fYkgoTjJsNhaaGgoISEhREdHo1ariYiIICQkhLCwsGcGtkUePnyIq6srly5dwtXV9ZmrWN+4cYN58+aRkpLCokWL/vbVg19FmZmZLF26lIkTJ1KuXDk8PT2ZMGHCc61AL4piqdNOBEEotQH0efLr9foS+wqCgEqlQqVSycNyTY9TdAzTfUsjCALVq1fH1dUVMzMzhg8fztixY8FQ35gwYQIDBw7E2tqaxYsXy6s5u7i4sGjRIgD++9//MnLkSDC8l759+/LVV18BMGfOnBK9voo/p2RpUSgUiqcQBIE6depQv359zp07R2BgIDqdTg70evbsSVhYGHFxcfj6+vLGG2/IlQNJkjA3Nzdawl8URaytrUu02BfR6XRIkiTfnCRJIjs7m6ysLK5fv86aNWvYsGEDa9eupaCg4Jk3B5VKRd26dY22mZmZUbZs2RLPYRUEgezsbHnV1eJEUaRhw4bEx8cjiiLJyck0adLEKI8kSbi4uJCdnY1WqyU6OhpHR0ejXjJJkrC2tsbKyuqJgSmG5fRtbGzk1YqLy8vLIyMjA39/f1avXi1/H/Hx8XKPwfPcvBXPx8LCAq1Wy4ABA+jevTsqlYqaNWsyY8YMbt68Wep8ZUEQ6Nu3Lz/88ANLlixh3rx5XL58mQ0bNphmNWJpack333zD6tWrcXNzo3nz5owdO5YVK1bg4eFBv379THf50yRJ4ty5c5w8eZIFCxbwzjvv8PbbbzNv3jySkpKe+V4VLzczMzOcnZ0ZN24cdnZ22NjYMGXKFMqUKYOPj49pdsqUKcOKFSv49NNPsbOzo27dunz77bfY2dkZNdKZEgSBhg0b4u7ujoeHBz/99BO1atVi4cKFuLu74+rqWmJoZ2l8fHyYNWsW4eHhrFy5Um78LI1er+fQoUP89NNPCILAhg0bjOaoKv6/wsJCMjMzqVq1Ks7OzkRERDx3D7aDgwP5+fklVp/NysqSy1Rx9vb2pR47IyMDR0dHBENPqOnK0Pn5+eh0Ouzt7bGzs0MUxRJDzgsLC9HpdCUe+/IkZmZmcgM3hh7XIsUfRWdhYSHfL5+2T/FH2WByDMWfpwSmCoXiD7Ozs6Nr165cuXKF48eP4+LiQpUqVRAEgbZt22Jubs5vv/1GaGhoqRVn0yD0aQEZhvSi1lJBELCyssLGxobx48fj7u6Om5sbK1euZNu2bTzPo5lNb5R5eXlkZmZSpUoVo+1FQaOFhQWxsbFGaSqVivj4eJycnBAEATs7uxJ5BEEgISGBcuXKYWZmho2NDXl5eUaP6xAEgYyMDNRqdYkW4eLs7OxQq9Ul3juGG6S1tTUffPABHh4e8vexdetWli1bJg9vUoLTv4adnR3Ozs4lFi6ys7MzauF/mjp16tCrVy95bvXzKBpJYPq6f5Winvnq1asbLSRTtAjIH3mvipeLIAjUrVu3ROVZpVI9tWHQxsaGTz75BFdXV7777jvi4+OxsbHBxcXFNOsTFb2maUX+SSRJ4sGDB/z444/UqFGDrVu3Go1wMSVJEufPn2fBggX07duXzZs3U6dOHdNsCoMqVarg6enJzz//zIMHD1i+fDkeHh74+PiUen8pIggCNWvWRJIkwsLC5O0qlYpHjx5Ru3Zto99YEATq1atHenq60XBbURQJDQ2VG3IbNGhAQECAnI7hUUX5+fnUr18fOzs7KleubJRHEATCw8OxsbGhevXqRvsqXm1PrgUpFArFU/Tr14+HDx+yZ88eBg0aJG93cnKiSZMmbNmyBb1eX2KOT1EPaHF6vd6oMq/X6+U8giAgiqLcCyWKIpUqVaJZs2bcvHmTqlWr0rBhQxo0aICFhUWJFltTOp2O8+fPk5iYCIbnNu7fvx9RFGnfvr0cxBW9pwoVKtCuXTs2bNhgtArv3bt3uXfvHv3790elUtGrVy+OHz9uNCQ5JiaGffv2yXmKWvAPHTokD2VLTk7myJEjT12xUhAEmjZtirW1NVu3bpWHiqrVatLS0ihXrhzt27fH19eXSpUqyd9HUU8shspbUeWz+GdU/HGWlpb069ePU6dOyauE5ufns3//fpo0aUKFChXIyckhNDRULssRERFGlbPw8HDu3bv31Ap3Ea1Wi0ajobCwkDfeeAMnJyd52x/9DUs7/5KTk4mOjpbLWVRUFHfu3JHTw8LCuHbt2jNHIyheXoIg0KVLFzIyMuSF6zBMt8jLy6NNmzZgGJlRdG3U6XRGo0hCQkJwc3Nj8ODBRqNeSiOKIhqNBo1Gg7m5OX369EGv16PRaNBqtSXKYHF6vZ7Vq1ej1WrltQRyc3PJycmRG/Xy8vIICQlBq9WiVqtZsGABrVq14rvvviM/P1/OX3zOo+L/EwSBPn36sG7dOsaMGUNoaChLly59auOTIAjUqFGDxo0bs23bNrnX9OHDh/j6+jJkyBAwXE+ioqIQBIHWrVtjYWHBvn375ONcunSJ1NRU+vTpA4bpP/7+/nLgqVarOXjwIE5OTjRv3hxBEBgwYAB79uyR6wEpKSn8+uuv9O/fv9Th2qVd5xSvBrO5z9O9oFAo/jUkSeL48eMEBgYyadKkUhc8EQQBR0dHtm7dSlxcHPPnz5d7WCRJIicnB09PT9566y0++OADeb+CggK8vLzo2rUrLVq0kPOfP3+e5ORkRo8eTWFhITt27KBz5860bNkSQRDw9fXF19cXKysrrK2tqVq1Kra2tnh6epKenk5aWhq3bt3i4MGD1K1b94mVJp1Ox65du0hNTSU3N5e0tDQuXbrEpk2beOeddxgxYgQYKmAXLlxgzJgxlC9fHicnJ7y8vEhOTiYzM5ObN2+yceNGGjVqxJQpU7CwsKBWrVqcPXuWmzdvUlhYSFBQEPv37ycuLo4FCxZQoUIFHBwcyMjIwMvLC1EUSUhI4OzZs/LKwx9//PET5245OjpSWFjIzp07KSwsJDExkcuXL5OcnEzz5s2pWLEiGzZsIDk5mYyMDPz8/Ni/f7+86qCfnx93795l7NixWFlZcePGDYKDgxkzZsyf7oGTJInTp09z584dpk6d+sT3/roRBIFatWpx8OBBHj9+TGFhIefPn+fQoUN8++23tGnThosXL7Jo0SL69OmDra0t69at4/r16yQlJXHv3j327NlDRkYGP/3001OHNep0Oo4fP46Pjw/+/v5YWVmRmpqKn58f9+7dQ6VSGT3a5WnS09PZuHEjX3zxhfyaoiiyceNGjh07Rr9+/ahevTq+vr78/vvvaLVagoKC2L59OykpKcybN6/EIiWvK1EU2b59O6Io8sknnzx1NMOrQBAEKlWqRFhYGIcOHQLA398fT09POnbsyKeffookScyZM4dHjx7Ro0cPsrKyWL9+PdHR0dy7d4+dO3eSm5vL/Pnzn3mux8XFsX//fvz8/OSVWQMDA7l37x5BQUHUrVu31IACw31iyZIl2NnZkZuby7Vr17h16xa3bt0iJiaG5s2bc/fuXX788Ue6d++OXq9nwYIFuLi4EBYWhq+vr5w/Nzf3tVhNWpIk9uzZQ1ZWFhMmTPjLyqMgCDRo0IAhQ4ZgYWFBuXLlSqwJUZy1tTWWlpZs27YNnU5HdHQ027Ztw9HRke+//x4zMzM8PT05fPgwgwYNwtHRkYyMDHbv3o0gCAQGBrJ582a6devGmDFjEASBKlWqcOvWLfnxMT4+Phw8eJCvv/5argNUrVqVvXv3kpCQQHZ2NseOHSMwMFC+txaXkZHBxo0bGT9+fIk0xctPCUwVCoWR5wlMMcy1MDMzo3nz5gwdOlQeriUIAuXLl0ej0TBq1Cij+ZyiKJKTk0OHDh2M5ijl5uZSpUoVOnbsiGSYQ1qUx8zMjBo1ahAcHMz9+/dp3rw5tWrVonnz5tjb2+Pr68vdu3eJi4ujZs2a9OjR44nvGcMw3mHDhpGens758+eJjo7mzTff5Ntvv5U/g1arxdzcnK5du2JtbY2Liwu1atXizp073Lx5k+joaFq0aMHMmTPlFQfLly9Phw4dePToETdv3iQoKIhy5coxY8YMo7mnHTp0oKCggKtXr/L48WMqVqzIJ598QtmyZenWrdsTl5sXDM/2KwrU7969S1ZWFm3btqVevXq4uLhQtWpVbt68yd27d4mKiqJy5cr06tULBwcHCgoKKFOmDJ07d8bc3JyCggLKly9Pp06djObQ/BH/1sAUw+9d1Gt/8+ZN0tPTGT16NB988AGSJBESEoKfnx8DBw6kTJkyxMfHc+3aNe7evUtISAgVK1YsUTZKo9PpOHHiBIGBgSQlJZGSkkJiYiIJCQkkJiZSrVq15654i6JIfn4+b775pnyOSIbHiGRkZNCvXz/Kli3LG2+8QUREBDdu3ODBgwc4ODgwc+ZMWrVqZXrI19brFphieGRGx44diY6O5tq1a4SFhdG2bVtmzpwpz+M7c+YMNjY29OjRA61Wy2+//caNGzcIDQ2lWrVqzJo166mBC4YylZCQwG+//UZCQgJJSUmkpqYSHx9PYmIi6enpdO7c2Whl1+J0Op28EmtSUhLp6elyA6RKpaJLly7yI7H69+8vB7BqtZqUlBSj/A4ODnJv8Kvs7wpMiwiG0RLP+m0BmjZtioWFBZcuXSIwMJDq1avLDWyiKHLjxg3S0tIYNGgQKpWKdu3akZGRwZUrV+Qy9/3338sNE9bW1rRt25aHDx9y48YNUlJSeP/99xk1ahQY3puTkxN169bl6tWr3LlzB0mS+Pbbb0uM4hAEAb1eT15eHm+++eZzLeikeLkIktLXrVAoihFFkS+++IIDBw7w+PHjP91DIj1hTmNp24suQ0XbS8uj0WjIycnBwcHBKJAqKCggOzsbGxub5wqMih87NTUVCwuLUpezL+096PV60tLSsLKyKnWfIkXDjZ7WWpuRkSEPFeYJr/ckeXl55OXlUa5cuRKBrFqtJjMzE2tr6xLv8Y+8xvMQRZGvvvqKDRs2EBER8dw9d68TyfAIhTJlypSoaGs0GqM5V4WFhWRnZ2Nubv7cC3ZgaCh50q3a3Nz8D1VSSysDoigiimKJnvO0tDREw9D5fxu9Xk/Pnj3RarVcvXr1TzfevKyKgrzy5csbbdfr9ahUKrmM6PV60tPTMTc3L5H3aSRJeuJwSkEQSsx1NSUZFrwrvr9kWGug6LdQq9WlTlV4Uv5XmV6vZ+jQoURGRnL//v0/dM7/XXJyctBoNCXuc0W/nen3XrTA0dPKUUpKCra2tk9sXNZoNGRmZuLo6FjielVcadc5xatBCUwVCoWRosD04MGDREREPFewp/j3mjZtGmvXriUuLu6Zz+RUKF4l3bt3R6vVcv36ddMkheIfN2TIEDkwVSheV0pgqlAojIiiyIQJE9i5cydLly5VAlPFE0mSxI4dO7h48SJubm44OjqW2kOiULxqRFFkwYIF6PV6Zs+e/VL0UCn+vURRxM3NjbS0NBYuXKj0Br6GzMzM6N+//1N7lP8NlMBUoVAYEUWRiRMnsn79esqUKaNUyBRPVVBQgFarxc7OrsTQLYXiVSVJkrwCrK2trRIIKF4oSZIoKChAFEXs7OyU8viakSQJGxsbzp8/T7NmzUyT/1WUwFShUBgp6jHdsWMHs2fPxs7OTukFUzzRoUOH8PHxYd68eZQtW1YpK4rXgiiKrF69Gr1ez9dff62Ua8ULJYoimzZtIj09nRkzZijl8TUjSRLm5uaMHDnyXz8lRglMFQqFkaI5pocOHSIxMfGpCwwoFNOnT8fd3Z3MzMxnPkNWoXiV9OrVC61Wi4+Pj2mSQvGPGzZsGOHh4fj7+5smKRSvDWWMnkKhKJUoivLqsgpFaURRpKCgAAzPyFQoXhc6nQ6dTodWq0Wn05kmKxT/KL1ej0ajQa/XK+VR8VpTAlOFQvFEyjwWhULxb6ZcAxUvG6VMKl5nSmCqUCheKVqtFq1Wa7r5D9FoNP/zMTD0GKrV6ld2vo9arS7x7D+FQqFQKBSKF0EJTBUKxXOTJImHDx9y48YNfH198fPz4969e/j6+uLr60tAQMDfGuhIkoS3tzfHjh0zTXpukiRx8OBBLl26ZJr0hyUlJbFhwwZSUlJMk156Go0GLy8v7t27Z5qkUCgUCoVC8Y9TFj8ySEtLIz09Hb1ejyAIlCtXjipVqsjpoihy5MgR6tatS8uWLY32/Tvk5ORw5MgR+vTpY/Q+TEmSxLVr18jJyaF///6myS+MJEn8/vvvSJJE7969TZNfmIcPH3L//n1GjRplmqQwKFr86MCBAzx69IjKlSsbpc2cOZOgoCDMzc3JyspCr9dToUIF9Ho9tWrVYtGiRc+9CE5CQgLAU8t4cXq9nrFjx5Kbm8tvv/1mmvxc9Ho9ffr0oVmzZqxevdo0+blJksStW7cYOHAg58+f/5+vCxqNhqioKGrWrImVlZVp8l8uJyeHNm3aMHnyZKZMmWKa/FxEUWTq1Kl4enoSHh5OtWrVTLO8crKyskhPT6dOnTqmSSQkJBAXF4dKpaJ27do4OjqC4XuIjIyksLBQfrySJEmYmZlhYWGBk5MTtra2Jkf7/3Q6HREREdSuXRsLCwvT5L+cXq8nJiaG1NRUJEnC2dmZGjVqGOXJyMggJSVFvidKkkT58uVxdnY2ylcaURSJjo6mUqVK2NnZmSa/EnQ6ndHiRy/zInCiKBIWFlbqtUMURR4/fkxubi52dnY0bNiwxGOd0tPTiYiIQJIkqlev/ly/MUBkZCT29vb/2HMXU1JSiI2NRavVUq5cORo0aPDUx5mlp6dTWFhI1apVTZNKSExMRBTF58r7Iuj1eoYMGUJUVBT+/v4lfsOXgV6vJzg4mNzcXMqXL4+Li4tpFjBcWxISEkr8dhYWFpQtW9aozoHhWhoaGlpq+S6Sm5tLUlIS9erVM9qek5NDZGQkBQUFWFlZ4eLi8spek/4tlMDUUOg9PT05c+YMZcuWRTQ8J6p///4MHDgQCwsL1Go1nTt35t1332XGjBmmh/jLRUZG0qFDB/bv30+PHj1Mk2Wi4ZmTUVFRnDx50jT5hRFFkQ8//BBRFPn1119Nk18ISZLw8PBg+fLlREdHmyYrDJ4WmEqSxPHjx0lOTkYQBDZv3kxiYiJz585Fo9FQvnx5Bg8e/NyV6/nz51O+fHkmTpxomlQqURT58ssvycvLY8eOHabJz0Wv1zNs2DAaN26Mq6urafJzkyQJPz8/Ro4cyeHDh//nZ48lJCQwadIk1q9fT6VKlUyT/3K5ubn06tWLzz77jM8//9w0+bm8ToFpQUEBgYGB7N27F0EQSpSNu3fvsnXrVjIzM9Hr9VSrVo0pU6ZQo0YNCgsLWbBgAVFRUXKFUaVSodPpyM/PZ8GCBTRo0MDoeBjKYlhYGN7e3ly7do01a9bIwe7fRZIkHjx4wNq1a8nPz0etVlOmTBnGjRvHG2+8IefZvn07x44dw8HBAVEUkSSJnj178tFHH5kesoRbt26xfPlyvvrqKzp16mSa/Ep4FQJTvV5PZGQk586d4/z587i7uxs18qnVan799VfOnz8v/7t///58+OGH8jU6PDyctWvXEhsbi5mZGY6OjowfP/6p17Pk5GSuX7/Orl27mDBhAr169TLN8pfLyclh6dKlREdHywtSDRw4kPfff7/UYEWr1fLzzz+j1WpZtGhRiSCoSFZWFv7+/uzYsYO2bdvyxRdfmGZ5KbzsgWlhYSG//vor586dkxuyRo4cyeDBg43ySZLEhQsX2LZtm3xOCYKAIAhkZWXRu3dvvvzySzDcXyIiIrhw4QJnz55lxYoVJe4xhYWFBAUFcfDgQbKzs/Hw8JDTRFFk48aNXL9+Hb1eT2FhIS1atODLL7+kQoUKRsdRvDxKP1P/ZSRJ4vLly9y7d4+GDRtSq1YtEhISmDx5srxMvCAI2NjYPHeF+3+lUqmwtbV94sW0OEtLS6ytrU03v1CCIGBlZVXqDeNFEQQBc3Pzp/ZcKJ5OEAQGDx7MuHHj+OSTT+jUqRONGzdm9OjRjBs3juHDhxudI2lpaQQEBHDv3j0SExPl7TqdjtjYWI4dO8atW7eIi4sjIyMDDOdjcnIy9+/f5/79+6Wu9vq87Wk6nY6wsDDu3r1LaGgoGo0GDPubm5uj0+kICgri/v37ZGVlme4OQGpqKgEBAfj7+5OUlGSaXOp7iYuLw8/Pj9DQUERRNErLz88nMDAQPz8/YmNjkSSJ7OxsAgICOHz4MOHh4cTFxRnNgVWr1Tx69Ih79+6RmppqdLyiXoG8vDwePXpETk6OnJaamsr9+/fx9/cnOTnZaD/F/5eVlYWbmxuzZ8/G09OToKAgo/S4uDimTZtGcnIyAwcOpFevXly6dIn58+ej1+tRqVTUqVOHJk2a0KhRIxo1akSTJk2IjIzk+vXrlClTxuh4GCpNO3fuZNasWbi5uXHlyhX0f2IYfFZW1h9ePTs5OZny5cszcOBA+vfvT2xsLFOnTpXLliRJnDt3joiICBo3bkyjRo1o3Ljxc/Wk5efns2zZMvbu3UtkZKRpsuIvIooi+/bt48cff2TZsmVcvHjRaLVWSZI4efIkCxYsoH79+gwePJj69eszb948fv/9dzBcV3755RcuXLjAm2++Sd++fXn48CHTp08nNze32Kv9/2P6+voyb9485s+fz+HDh5943XwajUZDQkJCiWvj0+Tn58uNaUOGDMHZ2ZkffvjhiY/yuXr1KosWLcLb2/uJrxMVFcXixYuZPXs2O3bsIC4uzjSL4jmIosiZM2dYsGABjRs3ZtCgQdja2vL1118TGBhomh0HBweja2XDhg2pXLkyR44cka+Boihy4MABZs2axdKlS7lw4UKJdSGys7NZu3Ytc+bMYe3atSUeoyNJEomJibRr146hQ4fStm1b1q9fz+bNm43yKV4uSo+p4QQYN24coiiyfft2MBT49u3b07dvX1atWoVGo6FPnz68/fbbfPPNN/K+er2e+Ph4CgoKqFChwhNbYZKTk8nMzMTOzo6qVauWuqpaWloaaWlpODo6otVq6dKlC9u3b6dbt26mWWWiKPL1118TGxvLgQMHSElJITMzk4oVK5Y6vCYxMZHs7Gzs7e1xcnIyTSYhIYGcnBwcHByoVKmS0fvMy8sjISEBa2trqlevbrQfhptcXFwcZmZmVKtWjS+//BKNRsO2bdtMsxrJz8/H0tISQRCIjo7GyspKHk6j0WiIjY3FwsKixFAzDJWylJQUzM3NqVatmlFQVPy4CQkJVKhQgW3btuHh4WFU8czJyUGSJMqVKydvKxrGZvo9aTQadDodtra2JCcno1Kp5IchZ2VlkZycjJWVFU5OTi9VUP5HPK3HtDhRFJk8eTKhoaF4e3ubJnP9+nUOHz5MdnY2Op0OGxsb3n77bfr06UNqairr1q1j8+bNVKxYkT59+tC5c2eGDh3KtWvXOHToEDk5ORQWFuLo6MjkyZOpW7cuoqHHNDc3l507d5q+pJGkpCT27dtHUFAQWq0WMzMzxo4dS8eOHRk2bBjVqlWja9eu3L59m+TkZKpVq8ZXX31l1ONw5coVjhw5Qm5uLnq9HltbW4YPH06PHj2QDD2mI0aM4LfffqNZs2ZIksTZs2c5evQoarUalUpFhw4d+O9//4u1tTXx8fHs2LGDiIgIdDod9vb2zJ49m9u3b7NlyxYOHTrEhx9+SJUqVZg8eTKVKlUiOTkZLy8vQkJC0Ov1lCtXjrFjx9KyZUtEUWTJkiXUqlWLrKwsfH19mTBhAh06dODy5cscPXpUfu92dnYMGzZMHoGh9Jj+fxEREXz66af06tWLsLAwsrOzOXjwIBgqN1u2bGHevHn4+/vj4OAAwOXLl3n33Xe5dOkSjRs3Njni//XYDBkyhKFDh5baC6PVavnkk0+ws7OjZs2a7N69m4sXL/6h3nJJkjhy5AhJSUmMHz/eNPmJtFqt0bUyICCAdu3a4ePjQ/v27dHr9bz77ru0b9+emTNnGu37LPv27WP+/Pnk5OTg6urKiBEjTLO8El72HlO9Xs+ECRPQarU0adKELVu2cObMGfk+qdFo6N27N+3atWPlypXyfp9//jmxsbGcPHmSxMREGjRogJeXF2+//TYAvr6+9OvXjwsXLtCmTRt5Pwzn+4YNGzh9+jQDBgxg9erVLFiwgKFDhxrlexpJkoiMjMTT05OffvrpuRuK9Xp9iV7CVq1a8eabb7Js2TKj7enp6Xz22WcEBwdToUIFzp07V+L3kwzTjRYuXMiQIUP47bff6NatG3PnzjXK97J4mXtMc3Nzefvtt2ncuDGrVq0CQ32wS5cudO/eHTc3N9NdSti9ezcbN27k2LFjlClTBp1Ox6RJkygsLKRZs2Zs2rSJ06dPU7t2bShWjqZOnUrbtm1JSkoiKCiIixcvGh3XtNxMmjSJGzducPv2baN8ipfHs7vj/kWKt6pZWlri4ODw1BbiuLg4NmzYgIeHB2vWrGHp0qWcOHHCqNUyLy+P/fv3s2LFCtasWYObmxtbtmyRe4cwvO65c+dwdXVlzZo1rF27Vm7RLC2ANVU0bOLSpUusX7+eVatWsXDhQq5evSrnKSgowMvLC3d3d1atWsWCBQs4efKk/JkLCgo4cOAAK1asYPXq1SxbtoywsDB5/8DAQNzc3Fi1ahUrVqzAy8tLfn4hhordypUrcXd3Z+3atezevZvs7Oxnvn9Jkjhw4ABHjhzh0KFDbNiwgQULFsjDRbdv38769etZuHAhv/76q9xiJkkSFy9eZPny5axevZolS5awfv16ufVWkiT27NnDmTNn2LNnD8uWLSMwMLBEj3dISAhLly41atXz8fFh6dKl8vdw4sQJ9Ho9kiRx9+5dvLy8uHTpEsuWLWPfvn1gGObn5ubG6tWrcXNze6mGVf+dSvt9JcMCSV999RVRUVF06tSJHj16kJ6ezldffcWdO3cwNzenTJkycm9/uXLlsLGxQZIk7t27R25uLt26daNr166cOnWKpUuX8kfa0AoKCli6dClbt26levXq9OvXjxo1asi9iSqVCh8fH/z9/WnTpg3t2rVj27ZtbNy4EQyf4d69e3z11VckJCTQuXNnunXrRmJiIlOnTiUgIKDUz37z5k1mzJiBIAj07duXmjVrsnjxYk6dOoUkSaxdu1YeMtarVy9sbW3RarVYWVlhb2+PIAg4ODhgb2+PSqVCrVbj5ubGwYMHadGiBb169SI8PJzp06eTlZWFJEns27ePxYsXExoaSoMGDbC1tcXPz4+vv/6ahIQEunTpQteuXUlISJDfu8KYs7Mz27ZtY9asWTg7Oxv1XBad9y1btpSDUoD27dtTqVIlrl27Jm8rbuvWrSQlJfHf//7XNAkAMzMz5s+fz/r162nTps2f6i0VBIFHjx5x8+ZN06SnMr0OFhQU4OzsLDfOiaJIVlaW0ed9HqGhoaxZs4aJEydStWrVP/WZFM9HpVIxa9Ystm7dSufOnUs821Kj0fDo0SOjtSckSeK9997j1q1bFBQUYG5ujpOTk9G9vKCggMqVK5e6ToBKpWLYsGHs3r2bDz/8EAzH/CMEQSAvL49Tp04914iwIqaBWHZ2NjY2NiUarEVRlHvEPvrooxK9bEUEQaB169bs2rWLyZMnY2Njo5TXP0EqNsKpqExgqEMPGjSIq1evGtWtS5OYmMjixYv59NNP5dElZmZmzJw5k23bttGtW7cSv40gCDg7O7Nu3TrmzJlDzZo1S5wDmJSbonpcaesHKF4ez39VeM2pVCqSk5O5ffs2ly5dYuXKlZQvX54PPvjANCsYLoq//PILO3bsoGzZsjRo0IDExESmT5/O6dOnwXCB9PLy4pdffiE/P59GjRphbm4uB39FJ9HVq1f55ptviI+Pp2HDhhQUFLBz506ysrJKXIxLo1KpePz4MTt27EAURVxcXAgICGDKlCkEBweDoQfwyJEjADRq1IiUlBSmTJlCSEgIABcvXmTmzJmYm5vTsGFD8vPz5eFhMTEx/PDDD9y+fRsXFxdsbGxYvHix3KOQmZnJrFmzOHLkCFWrVsXJyYnTp09z8+bNEhUgU5IksXPnTn766Sdu3bpFtWrVyM7O5rvvvmPx4sVcu3aNmjVrotVqmTZtGn5+fvJ+3t7exMTE4OLiQsWKFVm0aFGJXo4FCxbw+++/Y2VlhVarNQomIiMjmT59Oj4+PnKF7M6dO0yfPp24uDj5t/j+++/x9fUFQ+Axd+5cNm/eTGFhIWZmZiQkJPDNN9/w8OFD6tevT7ly5f7Vc1iLAjCVSsWOHTv46KOPGD16NNu3b8fBwYG1a9fi4ODAtGnTaNSoEd27d+fHH3+kb9++CILAyJEjWb9+PR9++CEjRoxgxIgRnDp1CrVabfpSpZIMPZlbt25l8eLFzJw5k3fffVd+DUmSEEURQRCYM2cOH374IV999RUff/wxBw4ckI/h4eFB2bJl2bFjB2PGjGHMmDFs27YNCwsLNmzYYPqyiKKIm5sbTk5OrFy5knfeeYeZM2fSpUsXPDw8EAQBHx8fmjZtymeffcZ///tffvnlFypWrEjXrl355ptvsLa2ZsaMGXzzzTdUqFCBhw8f4unpyaxZs5g4cSKjRo1iyZIlXL9+natXryIYFmozNzfn559/ZsaMGTRr1oyVK1dib2/P9u3bGT16NGPHjmXr1q1YWlqybt0607f+r1dUwRUNj/8xVblyZSIiIuSh4BiGSefl5RkNUS+SmZnJxo0b+fTTTylbtqxpMhiu27Vq1QJDA+YfkZ6eTnR0NNHR0RQWFiKKIvHx8URHRz/3sN6IiAju3LnD2bNn2bVrF2PGjKFu3bpgqMAlJSVx48YNNm3axK+//sqjR49MD2EkOzubBQsW0LRpU0aOHGkU7Cj+eoIgULNmTTD0WJlSqVQ4ODhw//59eZsgCMTFxVFYWEhKSgoVK1Zk0qRJeHp6smvXLvbu3YubmxsjR46kYcOGRscr4uzsjJ2dHZmZmX8oKC0oKCAmJoaYmBiSk5OxtLQkLi6OmJgY4uPjSwQepUlNTcXPz49r166xZcsWateuzfDhw43yXLx4kd27d/Pjjz/i4ODw1OM6ODhQuXJlNBrNU/Mpni4hIQG1Wi1fPzDcQ2vWrElubu4zh3sfOHAAKysro9/yWeUbw3W7WrVq6PX6Uq/bGEbO+fv7c/PmTfbu3Ss3LiteXkpgamBmZsaDBw9Yvnw58+fPZ9myZTRq1OiJLcZnzpzh8OHDrFy5ktmzZzNx4kS2bNlChw4dWLhwIVqtlvj4eBYuXMi4ceNwd3dnwoQJuLq6Mn36dFatWkVYWBh6vZ5ly5bh4uKCl5cXX375JYsWLWLQoEHk5+fLr6fT6cjIyJD/MjMz5VYoQRCIiYlhzJgxzJkzhylTpuDl5UV+fj5eXl4AlCtXDg8PDxYtWsTEiRNZuHAhOTk5XL58GYALFy4gSRILFy5k4sSJrF69mvbt2yMahjeHhYWxefNmJk+ezPz58xk0aBArVqxAp9Ph7e3NxYsX2bhxI99//z1ff/01v/zyC6JhwYxnKZof++OPPzJ58mQ8PT0RRZHjx4+zdOlSJk2axNq1a7G3t5eHjKpUKj799FO2bt3KpEmTmDt3Lp06dTJaaMnGxoaMjAx+/vlnXF1d6dSpE2q1GnNzc5KTk5kzZw6ZmZls3ryZJk2aoNPpWLp0KRUqVGDLli3y91CrVi1Wr16NYJhnnJmZSf/+/fHw8GD8+PFERERw5coVvvvuOyZNmsS8efOYMmXKc33215Fer+fcuXOMGTMGS0tLebu5uTnDhw/nzp075Ofno9fr0Wq1JW4oWq2WQ4cO4eHhwbJly7hz5w56vf65Kw6SYXGFevXq0bdvX9NkMASRPXv2lIeRSZJEq1atyMzMBMP5duHCBT755BOjVv2i4ci3bt1CkiS5oUMQBLRaLVeuXMHe3l7uOXdzc6OgoECeSzps2DAePXrEggUL8PHxkc9xSZLk3tzs7GwwvMeiRRuCgoJYtmwZbm5uHDp0iLJly/L48WP5dXv16iWvNJiens6dO3cYNWqU0fA1W1tb3nnnHXkkRfFGGsWTCYLAgAEDSEtLY/ny5dy9e5cbN27II19K+x7Pnj1Ldnb2cw1jLV6OnodkmP/p7u7O6tWr+f3337l37x4eHh54eHhw6tQp011KEEWRU6dO4e7uzk8//cTNmzcZOnSo3JAoCAJdunQhPT2dw4cPs2bNGr7++mtu3LhheigwnPPbt28nIiKCOXPmYG5ujmRYlVjx93pS+bG2tua9995j69atnDp1ivv373Pu3Dn27dtn1DvYqVMnYmNj2b59O7t27SIgIIAuXbo8szeztNd8EkmSCAsLw8PDg1WrVrF9+3bi4uLkf2/ZsuWZjTOSJBEYGCiP+Fq3bh19+vQx6jENDw9n8eLFjB07ljZt2lBQUIBgWFviaf6t9+q/giRJZGRkYG1tXWL6kp2dHaIoGjXomcrKymL79u2MHTu21F76/+W3kSSJlJQUNmzYwNKlS5k7dy41a9akQ4cOplkVL5GnX3n+RURRpHr16gwcOJAPPviAmTNn4u/vLweZxSugkmFl0i5dutCxY0f5GCqVio8++oioqCgSEhK4desWgiDw8ccfF3slGDFiBBYWFvj5+ZGXl8eVK1eYNGmS0Y1g0KBBVKpUSb55JCQksHHjRnmor6enJ7m5uQiCgF6vp3Xr1nTv3l3ev3LlyvTv318eZlamTBkyMzM5ePAga9asYdu2bZiZmcktWZ07d0alUrFo0SJ8fHzIy8tDpVKh0Wg4c+aM3Au6ceNGNm/eTH5+PgkJCWRkZHDixAl69uxJ06ZN5devV68eXbt2feIwmuKKggR7e3swVP4bNGhA79695fmblpaW1K9f32gBl7Jly3Lq1Ck2bdrE0qVLSUtLM2pZ0+v1dOvWTZ73JkkSKpWK7Oxs5s2bR1RUFBs3bpSHdWRnZ3Pu3DmqVq3Kzp072bhxI1u2bMHa2lpudZYkCScnJwYMGCC/To0aNWjXrh2rVq3i+PHjxMXFIRhWmfs3EkWRvLy8EvOtJUnC3t4erVYrN6qYysjIYM6cOaxatYrw8HCsrKyoUqXKMytJppKTk586DB9DoFZc8d9LZ1hN9UmfQa1WG90wiwLEokWIgoODCQsLIzQ0lIoVK/LRRx8hiiLjx4/n888/x8fHh59++gkPDw9ycnKMXrv4/2dkZGBlZUV4eDhhYWGEhYURFRXFu+++S/PmzeX3UHwlV7VajUajKdGoJhnmURdVEv6t5fOPEgSB9u3bM2PGDA4dOsSyZctYt24dcXFxNGjQoMTCRlqtlq1bt/LWW2/J16+/mq2tLQ4ODpQrVw47Ozt5OLyDg0OJcl0aQRBo3rw5//nPfxg9ejRt2rRh3rx58vQNS0tLVq5cyf79+9m/fz/Lli0jJyeHOXPmlOgJ1ev1HD9+nG3btjF79mycnJzIzc1FkiT0ev0Tz3XF30ulUjFlyhRatWrFkiVLcHd3Z+vWrdSqVUsuP9HR0cydO5exY8eyatUq3Nzc+Pbbb3F1deXhw4emh/yfWFhYYG9vj729PQ4ODlhYWODo6Ii9vT1ly5Z9rmt81apV5bU+RowYwd69ezl8+DAYhoP+/PPPVK1alUmTJqFWq9Ebhm5qtdr/KcBR/DmiKKJSqZ7420qGKWhpaWm89957psn/M0EQKFu2LD179mTgwIF88sknBAcHs2zZsueqmypejNJLy7+QXq+XVxf9+OOP+frrrxk3bhybNm0iOjq6RMtvampqqYsH2dvbY/7/2rvzuKrq/PHjr3PvBQRFwoVyAUHccF8QcckltMVGUqdJy8zJb2aWMy41k6ll2qKR5oKIy1hRkbm2uWQaigtqKm6giIgim+yXHe72+f0x956f94JmM02hfp6Ph3/4+Zxz7jn3fjjnfLb3R6ejrKyM3NxcmjZtatdrhLUl083NjZKSEsrKyqisrKw1mJCt1Rnr+ZWWllJaWqruZ8sTQtQIUCOEwNXVVR0ufODAAf7xj3+wefNmkpOT1Yqn7eU0NDSUmTNnEhsby7x581i8eDH5+fmYTCZKS0upqKggJiaGgwcPcuDAAUpKShg1ahRYKwGOa3/ZPv92Hwa2SinWm5lOp6s1zXa87Oxs5syZw4oVK4iPj6egoMDuerA+mB2/V61Wi16v5/vvvyc4ONhuna3Kykq1wr1//34OHjxIbGwsbm5uanAHi8WCl5eX3RBlb29vwsLCqK6uZtGiRcydO5fjx4+r+Xc7xwqOVqvF29u7xnegKApnz57F29tb7SW3PbhsTp06xfr16wkLC2PZsmXMnTuXQYMGYTKZalQEb8XX15fk5OQaL9E2iqLU+sJsO66zszMtWrSoMXdPURTOnTuHr68vGo0GYe2tEELg4uJCo0aNGDJkCBEREURGRhIZGUlERARz5sxBq9VSr149XnnlFaKjoxkzZgwLFy60C9ZgK+e2z7Kt27ZgwQL1eJGRkSxfvpyhQ4fafb5N/fr1adSoEadPn1bTbMeznTvWz5Juj6Io/O1vf2P16tWEhITw5z//mbfeeguDwWC3DIwQgosXL3Ly5Mmbzi39bymKwp/+9CfefPNN5s6dy2OPPUZQUBCzZ89m7ty5NYY21kZRFB588EHGjx/PK6+8wvz580lOTlZH2CiKgpubG66urri5udGnTx/mzZvH6dOnycrKsjuWbSqDuKFXbP369eTn57Nr1y527Nhx288B6bfVrFkz1q9fz8yZM+nbty8zZszgkUceoX79+nh6erJ9+3YuX77M7Nmzad++PW3atFGX7rIFgvwtKIpC+/btmTt3LnPmzGH69Om0bNmSefPmMWfOHKZNm1ajgceRoii0adOGcePGMWnSJN555x38/f1ZtGgRRqORDRs28Nlnn9GyZUvWrl3L6tWrOXDgANevXyciIuKenl7zv6QoCo0bN1YbZW9UWFiIq6vrTaczmEwmNmzYwIABA2p9n/4tNGrUiKeeeornn3+eWbNmMWbMGJYtWyYjhtdhsmJ6A8ehgr6+viiKYrf8gu0B26JFC1JTU2/Y+t/S09PRaDQ0atSI5s2bc/369RpzfgoLCykvL6dFixbq8IcrV67YbXP9+nVKS0vVl3ZfX1/eeecdPvjgAz744AMWLFiAh4eH+mKal5dnt7+iKFy4cEGdw/T++++j1WpZsmQJy5cvZ/HixTRu3Fi9Zp1Ox+TJk/nkk08YM2YMa9as4YsvvsDV1RUPDw969uzJ6tWriYiIYOXKlaxZs4bly5fTqFGjWudUCmvENMcKfW2Um1QSHH8PrJVNgB9++IFt27bxzjvvEB4eTlhYGA8++GCNye+KQwXGbDbTrFkzFixYoPa22j7HdgMdOXKkWqFYuXIl69at4+2331Z/e1uF5EYDBw5kw4YNzJkzh5SUFN544w27odh3q9qG4mq1WsaPH8/WrVvZu3cvWVlZZGdns2/fPmJiYnjqqafUype7uzvJyclkZmaqy55otVq0Wi05OTmcOnWK6OhoDAaD+lsajcZbDg1SFIVhw4ZRVVXFihUruHLlCrm5uSQmJpKeno6iKBgMhhotpuYb5qloNBrGjRvHhg0b2L9/P9nZ2WRlZbF7924OHTrE2LFjwVq5s83x0+l0jBw5ks8//5yTJ0+Sm5tLVlYWR48e5cqVK1gsFk6ePElKSgpCCIYNG4aHh4f6ou/k5ITZbOb06dNkZ2djNBoZMGAAOp2ONWvWcPXqVfLz80lJSeHQoUPqd1BdXW1X7t3d3XniiSfYunUrMTEx6rnv2bOH2NhYu3Uo5dyqmhzvGTfq1asX//d//0doaCjnz5+noqKCwMBAu222b9+Ot7f3LdeB/C0pivKLc/l/SZMmTQgICCAtLc0xS6XVams0smKdw9WrVy+6detGTEwMcXFxHD9+XB054Lj0jvT7cnNzY8SIEbzwwgsEBgayZcsWhg0bBkBGRga2QGs3at68ORkZGXZptbnV38qtVFVV4ebmVuPZ8Wv16dOHgoIC9Thjx44lPT2dAwcOqPfd0tJSjh49qk7TuJX/9HruZYqi0LJlS+rXr2/XGKrRaDhz5gz+/v43Xc4wNzeXQ4cO8ec//9kxq4bf6rcJCgpCo9HUeC+X6g5ZMbWyWCzo9XoyMjK4cuUKx44dIyIigoCAAPz8/LBYLJhMJsxmM4p1LcfExES2bt1KYWEhZWVlXLp0iaioKPr378/9999PYGAg7u7uREREkJWVpQbKWLNmDc2aNaNXr140bNiQoKAgVq1axbVr1ygrKyMtLY2NGzdSWFh423+Mp0+fZu/evej1eoqLi9m9ezcnTpxg9OjRWKyBMXx9ffHw8KCoqIgtW7aQnJysPpBSU1O5du0anp6ePPvss7Rt25b4+Hi0Wi0hISHExsZy8eJFtVcyMzNTrXgOHTqUQ4cOcfDgQUpKSigsLGTnzp3qUOZfYjQaa7wc/1La9evXqV+/Pq1ataKyspJz586xffv2Gts7VlRt/58wYQLPPfcc7733HlFRUVRXV9OwYUNCQkLYtGkTWVlZag/tpUuX1LU0zWZzjUpRfn4+Fy9epLKykoceeog///nPXLhw4aYT9u8mDRs2tBtGivUB8uyzzzJ48GDee+89VqxYwcqVK1myZAkPPfQQTz/9NFgfXMOHDycpKYklS5Zw6NAhevbsSf/+/Zk1axbh4eHqcjI3LkNiGwp2M4qi0KlTJ3XoZVhYGJGRkSxdulSNvuzp6VmjFdfV1VUdeaDRaJg4cSJ9+vThnXfeUefzrVixgscff5zRo0cjhMDJyQkvLy90Oh2KovDKK6/QuHFjZs+ezbJly1ixYgVLlizh6tWrCCGIjo5m0aJFLF++nAULFtCzZ08eeeQRsAYVGTBgAOHh4axbt46ioiL8/Px4/fXX2bx5M4sWLSI8PJwlS5bw1VdfqeWwcePGdr0NiqIwadIkAgMDef/999V5XEuXLuXxxx9XXwIURaFJkya3NfTzXmIymWrcNwwGA+np6RQXF1NcXEx8fDxhYWE888wzdsN1bVMfAgMDa8y3MpvNxMfH19pSb7FYajSU3EpJSQk5OTnk5OTg4+NDly5dyMvLIycn5xcDjZjNZk6dOkVmZibl5eUUFRURFxdHQkICISEhYF1C68KFC+j1esrKykhJSWH9+vV06tSJZs2aqT3DCQkJtG3blujoaNasWcOnn35KVFQUK1eupFmzZkyZMoXXXnvttp4D0n/OVn4cG0yLi4vV3zk3N5fo6GhOnz7NhAkTEEIQGBhIVlYWe/fupbi4mJKSEo4dO8aRI0cYMGAAWINkxcfH12g8FkJgMBhqpN9MdXW1WmYBQkJC0Ov15OTkkJ+ff8vj2MrbxYsXKSkpobS0lKSkJL755hv69u1LvXr1mDZtGp999hlr164lKiqKTz/9lGeeeYZWrVrx8ccfq38jR48epaqqyvEjav27l25P06ZNGTx4MGvWrCErK4uSkhJ+/vln4uLi1Ebcq1evcvLkSfV3tlgsxMXFUVlZSXBwsMMR7dnKmmP5vpHZbLb7/YR1DdMzZ85QVFREeXk5GRkZfPnll7Ro0QJ/f3+7/aW6Q/t2XV206XckrMEkTpw4QXp6Ovv27WPHjh1kZ2ezYMECNTDOxo0b6dixI8HBwbRq1YqcnBx17dCEhAS+/fZbcnNzeffdd/Hy8sLd3R1PT0+ioqJIT08nNTWVvXv3sm/fPmbMmEFwcDAajQYvLy+io6O5cuUK6enp7N+/n+vXr6PX63nyySdrhEO/kbCuxZWVlUVeXh5Xrlzh559/Jioqir59+zJt2jS0Wi0FBQV899136PV64uPjiY+Pp6ioiD59+hAcHEx0dDRbtmwhJSWFn376iStXrjB58mTatWuHn58f+/fvZ+/evVy/fp0TJ07w/fffg3UdMV9fX44fP86uXbsoKirixIkTHDlyBIt13u6f/vQnx9NWCSHYtm0bDzzwgPpSJITgm2++oUmTJmrLri3N09OTRx99FI1Gw65du7h8+TKpqanExsZSWFiIi4uL+tDdtGkT/v7+duvAnjlzhvj4eKZMmUK/fv0osS7Q3Lp1azp06ECrVq3YuHEjp0+fJiMjgyNHjvDNN9/Qpk0bfHx8OHPmDImJiYwfPx5nZ2eEEJw7d45ly5aRkpLC0aNH2b9/P0OHDmX48OG31WNc1wjrHOrz588zdepUNahObRo0aEDnzp3thkQDuLi4MGzYMHQ6HcXFxeh0OgYPHszMmTPVF3ZFUejQoQMNGjRQo1Z36dKF3r17U15ejsVioW/fvjz33HP4+vrSpUsXNBoN7u7udOzYUR2SWhtFUQgMDMTPz4+ioiLMZjP+/v4MHjwYd3d3GjZsSNeuXdUKr6IouLi40Lp1a7Wny9XVVQ2eVFJSgouLCyEhIUybNk2tiOqs6+d269YNV1dXPD09GTx4MFVVVVRUVFC/fn0GDRpESEgITk5O6vdhNBrx8/NjxowZ6hxnNzc3unTpQkVFBW5ubvTu3RtXV1cCAwPx8fGhqKgIk8lEy5Yteeqpp/D29kaxLi/TvXt3uzm1bm5u6t9OSUkJzs7ODB06VL0f2K65UaNGdOvW7T+eCymE4IcffuDkyZNMmzbNbi3gO5HtflpWVmYXuEiv1xMeHs7p06c5efIkmzZtomHDhsyfP9+uYl9YWMjixYt5/vnn6dKli5qONfLu1KlTqaiooF+/fnZ5ycnJxMXFMWHChF9sKBBCsHPnTrZt28bx48fVZ8WRI0c4evQopaWlt+ytNRqNLFq0iIMHD3LlyhUOHz7Mhg0b6NSpE9OnT8fFxYVr167x7rvvkpKSwtmzZ9m2bRsXL15k3rx5tGvXDpPJxKJFi4iJiSE0NFQd5aDT6dDpdJjNZjZv3syQIUPsYg/cSSzWwH8Wi6VGELS6Ji0tjZ9++onx48erU2CEdZmjyMhIUlNTiYmJ4bvvvmPChAmEhoaiKArNmzcnNTWV7777joKCAk6cOEFUVBTe3t7MmjWLevXqsW7dOj7//HNGjRpl1zNfVlbG5s2bCQkJuWkEXxshBMnJyaxbt47jx4+TkJCAxRrc7dixY5w/f57u3bvXaMyxEULw9ddfExUVxbVr1zh16hRff/01BQUFvPXWW7Ro0QJFUezKoE6n4+zZs1y8eFFdpzkmJoY33niD4cOH2zVuWiwWvv76a7y9vRk8ePANn1x3COsSeMXFxUyZMqVOlUedTkfz5s356quvSE1NJSUlhY0bN9KhQwemT5+OoiisX7+edevWqSOmLBYLn3/+OWazmcmTJ9/yeq5du8bevXt59tlna22UFkJw5MgRMjMzmTBhgpp+9uxZwsLCuHLlComJiezatYvDhw8zY8YMu/gwUt0iK6ZWOp2O++67DyEEbm5udOvWjX/+85920bt0Oh3du3enZcuW6HQ6HnroITQaDampqej1eu6//35mzZpFhw4d1H26du2Kn58fly9fJj8/H51OxwsvvMCIESPUbdq0aUO7du1ISUmhuLiYFi1a8PLLL9OiRQt69uxpN9eyNop1vlCPHj04c+YMJSUl9O/fnzfeeANXV1cURVEXyk5LS0Or1fLMM8/Qo0cPOnXqhLe3NwaDgeTkZAoKChBCMGHCBB5//HGw9lA9+OCD5OTkcP36dSorK/H19WXkyJE0bNgQNzc3Bg4cSEFBAVlZWWi1Wp5++mkGDBhA69at7eZg1Uaj0dClSxe7taU0Gg2dO3e2Cz9uS/P398fHxwcfHx8uX76svug9+eSTtGzZku7du4N16Fm3bt3UkOO2Y7Ro0UIdfjdo0CCwvqx169YNb29vevXqxdWrV8nLy8NoNNKjRw+GDh2Ki4sLinXYSvfu3dFqtWrlJDU1levXr1NeXk5wcDDTp0+vNcLcneB2K6aKouDt7V2jUmrj5OREjx49eOihhxgyZAhdu3at8fBxcnKiV69ePPTQQ+rv37RpUwYNGsRDDz1Ex44d8fDwoHPnzmpvvY+Pzy0rpTdq3bo1gwcPZsiQIQQFBeHu7o6iKPj7+9v1wmLtRXV8oXd2dqZXr16EhIQwePBgOnfujHJD74+bmxudOnWy+609PDzo168fQ4cOZdCgQXTq1EmtyNrOJyQkhH79+uHp6anuh7XXdNCgQfTr18/umG3btmXIkCHqfrb5OIp17lZtgZ5cXFzU77a2c9dqtXTo0OE/rpRyF1ZMsX4v/v7+dOzYUU0T1rV5r127RkVFBZ07d2bu3Lk1RgsYDAZ1JIDjfdtisZCenk779u0JCAiwy9NoNDRt2pTAwMDbGpZ7/vx5srOz1f9bLBa1J8LLy6tGpfhGtoaJK1eukJeXh8FgIDg4mNmzZ6s97xqNhpSUFLKzsykuLqZx48a89tpraoVaURSysrJo3LgxQUFBduUK6/m4uLjQvXv3GvEP7hR3UsVUY50+1Lt3b7vKXVlZmRqDQVEUnn76abu1JuvVq8eQIUOorKwkLS2NkpISunTpwltvvUXTpk0R1qimLi4u9O/fH51DdNt69eoRGBhYI0hcbfLz8+2GelqsUfuFEDg7OxMUFHTTiinWe+21a9fIycmhtLSU5s2bM2vWrJs2fAjrNKeWLVvStWtXFEWhuLiY8vJyBg4cWGNOq06no3Pnzrf9bPm91eWKKdZYGwEBASQlJVFcXEyXLl3Ud1BhXevUycmJgQMHqvegsrIyevfuXeO560ixzmPt3bt3rcOCFWujhK+vL926dVPT6tevT25uLpmZmRQVFeHm5sbkyZNv2Vki/fEUIW7RNy79piwOgV7qItvNXLp3vwuLxcJLL72kDvf+byou0t1NCMG0adNYs2YNaWlptVaQJelOZDabGTx4MEajkSNHjtyTzwKp7rBYLISGhpKWlsbZs2dleZTuWnW7lnSXqeuVUqytTNK/ye/i/5PtV9IvsZWRe6Gs3G3X+EvX80v5d6Mbr/leuv475VrvlPP8rdxp13unna9Ud8geU0mS7FgsFqZMmcJnn33G22+/jYeHhzpMUJJupNFo2LhxI4cOHWL+/Pk0atRIlhXpjqdYI8UvXboUi8XCzJkz0Wq18mVb+kPYymNkZCSFhYXMnj1blse7kE6nY/To0ff8KDVZMZUkyY7FYuHll19mzZo1eHl53RE9/dIfwzZvq6KigqZNm96Rwb4k6WZsS0o0atRIjqCR/nC2QH6NGzeW5fEu5OLiws6dO+3iG9yLZMVUkiQ7N/aYLl68mAYNGsiWWemmvvzyS2JiYvjoo4/UtZUl6U5nsVj44IMPMJvNzJ071zFbkn5XFouFFStWUFBQwLvvvivvs3chrVbLn/70pxpBEe81smIqSZIdW/CjrVu3kpWVdctIiZL02muvER4eTkFBQY1Il5J0JxsyZAgGg4HDhw87ZknS727kyJFcuXKFM2fOOGZJ0l1DjtGTJKlWQgiKi4sdkyVJZbFYqK6uBpBlRbqrmEwmTCYTZrMZk8nkmC1Jvyuz2YzRaJTlUbrryYqpJEmSJEmSJN0B5PxS6W4mK6aSJP3PCCHIycmhrKzMMUuSJEmSJEmSVLJiKknSf0yv1xMXF8emTZv49NNP+eqrrzh48CCFhYUAGAwGIiIiOHLkiOOukiRJkiRJkqSSFVNJkv4jycnJLFy4kPfee48vvviCrVu38vHHH/Pmm2+yb98+sM7T2rZtG4mJiY67S1Kdl52dTUJCAsnJyTed13Xt2jXOnTvH1atXHbNqZTvm5cuXf9c1X9PT0zl37hxJSUlUVlY6ZgOQn5+vXq/RaHTMrlVOTg6JiYkkJiaSl5fnmC39zkwmE5cuXSIhIYGsrCzHbACuX79OQkICFy9exGAwOGbXUFpayvnz5zl//vwfMvqlqqqK9PR0x2SwNo4mJiZy/vx5SktLHbNvKjMz81dtL92eq1evkpCQQGZmpmPWLeXl5VFeXq7+Pzc3l6tXr5KWlqb+u3btGllZWbWWwfT0dBISErhw4QIlJSV2eVVVVSQlJZGYmEhRUZFdnlT3yKi8kiTZsUXl3bJlC0lJSXh5eTluwrVr1/jb3/5GQUEBf/3rXwkKCsLd3Z28vDyOHz+Or68vjz/+OBUVFQwZMoQJEybw8ssvOx5GusNZLBamTZvG2rVrSU1NpUWLFo6b3LFiY2PZunUrlZWVGI1GgoODGT9+PPXr1wfAaDSyY8cOdu7cidlsBmD48OGEhobi5OTkcLR/O3r0KNHR0VRWVmI2mxkyZAhjx47F2dnZcdPfjNFoZPv27cTExFBVVYXBYKBjx45MnDiRpk2bqtudO3eOzz77DL1ej8lkIjAwkAkTJtwy0nJsbCzfffcdJSUlmM1mevfuzZQpUxw3uyOZTCaGDBmC0Wjk0KFD6HQ6x03qHL1ez5dffsmpU6cQQuDs7My4cePo37+/uk1cXBwbN26koqICk8lEjx49eP7553F3d7c7lk1GRgbr168nPT0ds9mMn58fkyZNolmzZo6b/uZslexvv/2W8vJy3nnnHbv8pKQkPv/8c3JycrBYLPj4+PDiiy/SvHlzu+1upNfrOXHiBNHR0bz88sv07t3bcZM6yWw2ExoaSlpaGmfOnKlza0YbjUa+/fZb9uzZg9lsRqvVMmbMGB566CHHTWvIyMhg4cKFjBo1iqFDh2KxWFizZo3ddSqKgkajoaysjIkTJzJgwAAAqqur2b59O/v27aO6uhqz2czIkSMJDQ0Fa2NbVFQU58+fB6Bp06ZMmjQJf3//G85Aqktkj6kkSb+KwWBg2bJlJCcn88knn/DCCy/QtWtX/Pz8CAoK4pVXXuHxxx933A2sC4Q79hSZzWYuXLhg11qKtWdp//79/Pjjjxw7dsyul8dkMnH69Gn27NnD3r17SUlJsdsXICsri3379rF7927i4+PteoCMRiMnTpxg9+7dxMXFUVFRYbevdO8SQnDmzBlmzpxJeXk5QUFBtGzZksWLF7NlyxZ1m6NHj/Lqq6+i1WoJCgrCxcWFf/zjHxw4cMDxkACkpqYyffp0iouLCQoKokmTJsydO5cff/zRcdNbKiwsvGmPZ230ej3R0dG4ubkRHBxM69at+fjjj1m5cqW6FmJOTg7//Oc/uXz5Mr169cLHx4cPP/yQDRs2OB5OFRcXxz/+8Q+Ki4vp2bMn3bt3x8XFRa2kS78vs9nM+vXrWb16NX5+fvTu3ZvCwkKmT59OWloaABcuXGDGjBno9XqCgoLw9fUlIiKCqKgox8OBtafpvffeY/fu3XTt2pVOnTqxceNGPvzww1/1O5eXl//qnqry8nJWr17Nm2++SVhYGMeOHbPLz8/P5/XXX+fUqVP07NmTTp06sX37dt5///1az00IwYkTJ3j//feZP38+n376KXq93nEz6T8ghGDfvn28+eabeHh40Lt3b0pKSpg2bRqXLl1y3NyOEIKPP/6YVatW2S2D4+bmhqenJ/fdd5/679q1a0RHR6PR/LvqIoRg69atvPPOOzg7O9O7d28CAgLU4FAmk4lVq1bx+eefExAQQPfu3YmNjWXOnDlUVVWpnyXVMUKSJOkGZrNZTJo0SXh6eoqcnBzHbHH58mXxwAMPiKioKMesGsrLy0VQUJCIiIgQQgixfft2MXHiRFFRUaFuo9frxWOPPSZOnTqlph0/fly89NJLYuTIkWLkyJFi7Nix4uzZs0IIIUpLS8XKlSvF2LFjxahRo8QTTzwhnn32WfHjjz8KIYSwWCzi/Pnz4pVXXhGjRo0SI0aMEM8//7zIzMwUwnpOq1atEk8++aQIDQ0VI0aMEAsXLhRlZWXq50u3x2w2i6lTpwpnZ2eRkZHhmH1HMpvNYsqUKWLQoEF26e+8847o1q2bKC8vV687MDDQbpv+/fuL559/3i5NWI85f/580b17d2EwGNT0Z599tsbn3IrZbBbLli0TR48edcy6qaKiInH8+HG7tPDwcNGmTRtRXFwsLBaLWLt2rWjdurXIy8tTt5k1a5bo0qVLrX8XpaWl4uGHHxaTJk1yzLprGI1GMWDAANGnTx9hNBods+ucrKws0bp1a7F27Vo1rbi4WHTt2lUsWrRICCHE7NmzRa9eveyuZ+nSpaJDhw6ipKRETRPW++iRI0dE06ZN7crbpk2bhKenp7h06ZLd9jdjsVhETEyMWL16tWPWLaWnp4tBgwaJ6dOni+eee06MGDFCzbNYLGLTpk3i/vvvF+np6Wr6Tz/9JJo2bSoSExPVNBuz2SyWLl0qHnnkEbFmzRrRqFEj8dNPPzluVmeZTCYxfPhw0alTJ2EymRyz/1Dl5eVi+PDhYuLEiWpaSUmJ6Ny5s5g1a5bdto5+/vln0a9fP3H//feL5cuXO2bbmT59upg8ebL6/2vXromOHTvWup/FYhEpKSnC29tbbN68WU2Pi4sTHh4e4sCBA3bbS3WH7DGVJOm2CSFISkqiurqakJAQNd1kMpGWlkZqairXrl1Tgx85ysjIIC4uzq5F22QysXfvXrVFPSMjg5kzZ5KZmcnkyZOZPXs2o0aNUodHfvXVVyxevJh+/foxa9YsZs6cibOzM9OmTSM5ORkhBGFhYfz8889Mnz6d2bNn8/DDD6MoCkIIvv32W8LDw3n00UeZN28eY8aMYe3atXz33XfqOUn3LiEEP//8M8OGDbNLHzduHCkpKWRkZKAoCm5ubri7u6tl2WAw0LBhQxo2bGi3H9Yhz99//z1PP/20Wo6FEDz77LOcPXv2tudmajQadu3aRWpqqmPWTd13330EBgbapbVr1w6NRoPRaMRisbB7925CQkJo0qQJWM9t9OjR5OXlcfnyZbt9bT3KCQkJvP/++1gsFjni4A8mhCA1NRWDwWB3X27YsCGPPfYYsbGxWCwWTp48yZAhQ+yGJY8YMYLy8nJ1qKONEILdu3fTrl07+vTpo6YPHz6cevXqceLECbvtb0ZRFC5cuMCePXscs27J09OTiIgIli5dir+/v92IF4vFwrFjx+jatSstW7ZU0wcMGECzZs04ePCgmmajKAqhoaFER0fz4osvoijK7zrH+26Wl5fHyZMnef7559W0Bg0aEBoaSmxsrDoyw1FhYSELFy7kiSeeoF27drec73zkyBG+++47/v73v4O1DOzevRudTsff//53qqur7XpBbaNaXF1dGT58uJoeFBSEn59frWVEqhtkxVSSpF9Fr9fj5OTEfffdZ5cWERFBWFgY8+bN48svv7Tbx8bJyYn69evXWIfN3d0drVaLEIItW7aQlZXFJ598wqOPPkrv3r156qmn6NChAyUlJYSHh/Pcc8/xt7/9jaCgIAYOHMjy5ctRFIUNGzagKApXr17Fx8eHzp07ExwczNixY2nWrBmVlZUsXbqUwYMH89RTT+Hj48Njjz1G3759Wbt2rd05SfcuDw8Prly5YpdWWFiIwWAgOzsbRVEYN24cJpOJJUuWsGfPHsLDw6msrGTChAl2+wFUVFSQnp5Op06d7NKbNWuGi4sL165ds0u/kRACvV5PXl4eubm56HQ6qqqqKCgoIC8v71dXCoUQ7Nq1C19fXzw9PamuriYtLY3OnTur2yiKos7Tu379+g17/3v/2NhYAgICyMjIYN26dURGRrJlyxY5NPIP5ObmhsFgqFGWSktLyc7OBmtF1TFIV1FRERUVFeTk5NilCyG4dOkSrVq1skt3dXXF29u71ukTN6qoqFDLrMFgQKvVUlRURF5eHsXFxY6b11C/fn06deqExWKhsrLS7pmhKAoeHh5kZ2fbVWZKSkqorq4mIyNDTbNRFIXWrVvTuHFjSktLb1pZkn4dIQTZ2dlUVlbStm1bu3Q/Pz9KSkpqBCPC2iC9Zs0aKioqmDJlSq3Dr22MRiOrV69m4MCBdOzYEawV09jYWIKDgzl69Cj/+te/iIyMZPfu3WqZuHz5Mo0bN8bNzU09lhCC1q1b17i/S3WHrJhKkvSruLm5YTQa7SIaajQaGjdujJeXFydOnLhp72NtLwO2Fw5bj+a+ffsICQmhcePGjpuSkZGBXq+vMYe1QYMGBAcHEx8fj6IoTJ48mZSUFGbNmsWGDRvU6JTFxcVcuHCB3Nxc5s2bx7vvvsuCBQvIysr6VfP2pLuXoiiMHDmSPXv28PXXX3Px4kWOHTvG+vXr0Wq1anTetm3b0r59eyIjI/nkk09YvXq12hjiqLi4GCGEXYAZRVFwcXGhXr16t4wOajKZ2Lx5MytWrCAiIoKUlBS2b99OeHg44eHhHD9+3HGXm6qqquKrr77ixx9/ZMqUKWg0GqqrqykpKVF7S21sQZ4c52LZeucqKipYu3Ytx48fJyEhgQULFrB06dLbjuYr/XYURcHf35/OnTuzfPlyTpw4wcWLF/n+++85fPgwQgg0Gg0jRozg0KFDbN26laSkJI4dO0Z0dHStjRtCCEpLS/Hw8LBLt1gseHp61lrZsBHWUQfLly8nIiKCPXv2cP78eVasWMHKlSvZtm3br+qtdGzIVBSFYcOGkZeXR3h4OImJiZw+fZqoqKgaDSnS/5YQgoKCAurVq4erq6tdXoMGDTCbzTXuIQC7d+9m69atLFiwAHd3d0wmkzp39EZCCC5evMj+/ftrBFBMTU0lNzeX9evXk5iYyPHjx/nnP/+pNlAXFhbWOoLF09Oz1si+Ut1QsxRIkiTdgr+/P4qicPToUTWtUaNGvP766yxYsIBHHnnklhEDLRaL3QPIViG1qaysrPGAszEajQghaj3+jWljx45l4cKFVFdX89FHH/HWW2+pPQIWi4UGDRrg7OyMi4sLLi4uPPjgg0ydOrXWirN0b9FoNIwfP55hw4axePFiPvroIyIjIxFC0LBhQ+677z4sFgsff/wx2dnZLF26lLlz57JkyRKKioqIjIys8dJtK1eOL9g2tZVnGyEERqMRo9FIVVUVQggsFgvV1dUYjcabLmPjKCsri4iICFauXMmkSZMYOXKkmnezcq/RaGp9WaysrCQ5OZmHH36YVatWERkZyauvvsrSpUs5e/as4+bS76Bhw4bMnz+f/Px83n//fZYvX85XX32Fn58fjRo1AmDUqFGMHj2asLAwlixZwqpVqyguLsbb27vWe65jOeaG+/UvRSk2m80YDAaqq6uxWCwIIaiursZgMPzXjReKohAUFMRrr71GdHQ0YWFhREREcO7cOdq2bas2qki/D1s5cbyP2BpEbryHCCGIj48nLCyMKVOmEBQUhNFovOm90WKxEB0dTbt27ejVq5eaLoSgrKyM5ORkXnjhBcLDw1m7di2PP/448+bNo7CwsNZ7F9Zj3uqeK/2xav/VJEmSaqEoCm3atGHgwIEsXbq01iFTJpOp1hcarEMk8/Pzqa6uVtNSUlKoqqpCURQURSEgIIDjx4/X2oPZrFkzXF1dOXTokF16WVkZ586do1u3bmraY489RlRUFGFhYXz22WfExMTQuHFjHnjgAQYPHsyiRYv44IMP+OCDD1iwYAHPPvvsTR+O0r3lvvvuIyIigtdee4327dszatQoJk+ejE6no2XLlhQWFhIeHs6oUaMYOXIkHTt2JDQ0lPHjxxMWFlYjAmnDhg1RFMWul0kIQUVFBQaDwW7ZFkfOzs68/PLLannt0qULEydOZOHChSxcuNBuTuHNXL16lQULFrBjxw6mT5/O9OnT1TwnJyd1qacblZSUoNFoavSYAbi4uNChQwdGjhyJs7Mz9erV45lnnuGBBx6wa7CSfl8DBgzg008/5dFHH6Vjx468/vrrtG/fXp2H2aBBA5YuXcrs2bMJCAjgiSee4JVXXkGr1eLt7W13LEVRcHd3r3XYbX5+/i2Xi1EUhZCQEMLCwli0aBFjx44lMDCQ999/n4ULF/Liiy/etNJwuzQaDa+99hrLli2jR48eDBw4kHnz5qHRaGjTpo3j5tL/iKIoeHp6UlVVVaNnVK/X4+LiYrfkVGlpKW+88QaXLl1Cp9Oxfv16PvnkE3Jzczly5Ajbtm2za2zT6/V8++23jBkzpkaZsTUq9+nTB61WS4MGDXjhhRfUhrMmTZrU6NlXFIX8/Pxal8GT6ob/7s4gSdI9x9XVlTlz5pCfn88bb7zBzp07OX36NGfOnGHv3r3Ex8fbtVhXV1erD5qOHTtSVVXFJ598QnJyMidOnOCzzz6jsrISIQSKojB27Fiys7P56KOPOHfuHMnJyRw4cID09HS8vLz4y1/+wvr169m5cyfJycmcO3eOiIgIKisrGTt2LEIIduzYwfHjx0lJSaFhw4Z4eXkhrOv6jR07lg8//JDdu3eTnJxMQkICmzZt4uLFizdcpXSvc3FxYdSoUcycOZMnnniCn376iYCAAJo2bUpRURElJSW0b9/ebp+OHTuSm5tbY2iuu7s73t7exMfHq2mKopCcnEy9evVqzOO7Fa1W+6t6nEpLS3n33XdJS0sjMjKSv/zlL3b5Li4utG7dmtOnT9ulX7p0qdZzUxSFDh06qL1gNiaTCScnp5uu4Sr9Pvz9/XnxxReZOnUq7du3JyYmhkGDBqn5zs7OPPHEE8ycOZPRo0cTHx+Ph4dHjcqc7XdOSUmx+50LCwvJzs6mS5cudtvfitlsrlGp+K0MHDiQ6dOnM378ePLz88nJybEL1nQzshHyt6EoCt7e3tSrV4/ExEQ1XaPRkJiYiK+vr11vvMFgwMPDg/79+7N9+3a2b9/O7t270ev1XLhwgQMHDqj3N2Fd4qewsJDHHntMPQbWzw0ICKhRGTaZTOh0OpycnGjfvj3Xr1+3ux8bDAYuX75M165d7faT6o7/zZ1CkqS7WmBgIJGRkRQXF7N48WK1N2fZsmU4OTnx1FNPgfXh1K5dO3X+Wtu2bZkyZQobNmxgyZIlrF+/Hg8PD4KCgtTKbN++fZkzZw67du1i8eLFhIeHs27dOnUo7syZM+nTpw8rVqxg2bJlrFixgv379/PWW2/RsWNHtWL64YcfsmTJEj788EOGDx/Oww8/DMDUqVPp2rWrOkwzPDycL7/8Us45kVRVVVXk5uZSXV1NeXk5cXFxfPHFF0yYMAGNRkOjRo1o2bIlmzdvpqCgAKPRSF5eHlFRUbRr1w5PT08KCws5ffq0Okxt1KhRbN68matXr1JdXU1mZiZffPEFTzzxRK3DKG1sQ9aKi4vR6/X0798fLy8vSkpKKC4utht94EhYI1Nu27aNefPm0aJFCwoKCigoKCA/Px+j0YhOp+PRRx/lwIEDJCYmUl1dTW5uLtHR0QQGBuLt7U1FRQWnTp2itLRU7Q3LyMhg586dVFZWUl5ezq5duyguLiY4ONjxNKTfSUFBASUlJRgMBnJzc4mIiEBRFHVOfmVlpTpipby8nOPHj/Ovf/2Lv/71r+h0OoR1Pt+VK1fQaDQ8/PDDZGZmsmPHDqqqqigpKSEqKgpvb+9ffLGvrq5Gr9ej1+vx9vYmKCiI0tJSiouLa6xZfStCCMxmc40h6yaTidzcXCorK6msrCQpKYn33nuP0NBQtfc3PT3drrJ0I9u0EOm/5+XlRb9+/Vi3bh0FBQVUV1dz7tw5Dhw4wJNPPglAZmYmCQkJeHp68vHHH7N27VpWr16trr3r4+PDX/7yF959913q1asH1gaN77//no4dO9booddoNDz++OMcPHiQhIQEqqurKS4u5uuvv8bT05N27drRs2dPtFotGzZsUO9T33zzDVgbNKS6SRHyL1OSpBtYLBZeeukltmzZQlJS0i2HvJSVlXH48GGSk5MB8PHxITg4mPvvvx+sxzpz5gz333+/GuXTbDbzww8/cOHCBdq3b8+jjz7KyZMnCQgIsBs2mJCQwIEDBzCZTPTs2ZO+ffuq80LMZjM//vgjFy5coEGDBoSEhODv76/um5qaSmxsLIWFhfj5+alLHNhUVFTwww8/kJqayn333Uf//v3p0KGDbEX/lSwWC9OmTWPt2rWkpqbSokULx03uSKmpqURGRuLr60tFRQUHDhzA19eXxYsX4+LiAqDObXvkkUfUKI8//PADr776Ks899xzffvsty5YtY8OGDTzwwAOkp6czbtw4WrVqRXBwMJcuXeLs2bOsX78ePz8/x1NQGY1GvvjiC9LT09X5WkII9aV60KBBPPjgg467gfX3WbZsGYsWLbJbysGW99JLL+Hv709BQQHPP/88Wq2WoUOHkp6ezuHDh1m6dCmBgYEkJCQwefJkFi9eTN++fTEajcydO5e4uDhGjx6tvkAOGjSI+fPn3xV/RyaTiSFDhmA0Gjl06NAvzqn8o1ksFjZu3EhycjJeXl6kpaVx+PBhZsyYwejRoxFCcOXKFT7++GMeeOABqqqqiIuLo3Hjxixfvhw3NzfMZjOTJk2iadOmfPDBB5hMJl599VVOnTrF6NGjqaysZMeOHUydOpWxY8c6noJKCMGhQ4eIiYlBq9WiWKdp2KZ4tGrVinHjxt1WL6rFYuHNN9/k1KlT7Ny5U00vLi5m5cqVuLm5odFoiI+Pp7CwkFWrVuHt7Y3FYuHDDz/k559/ZtOmTXZzCktLS/Hz82Pjxo23NRS+LjCbzYSGhpKWlsaZM2fq1BxJYQ1aOGPGDIYNG4avry+HDx9Gp9Oxdu1aXFxcWLFiBTExMWzevFm9h964f//+/RkzZgzTpk1T08vLy3nkkUfo27cvH374od0+WCNKv/TSS1RUVPDII49QVFTE9u3befnll5kwYQJCCD744AO2bNmiDgXesWMHoaGhdtMZpLpF+/bbb7/tmChJ0r1LCMH27ds5f/48U6dOvWUgCWdnZ9q0aUOfPn3o06cPHTp0sJtPoigKzZo1s4tGautF7devH+3bt0er1dKyZUu7iiPWVtjevXvTp08ffHx87F5iNBoNbdu2pW/fvvTq1UsN7mHj6elJjx496NevHwEBATVeKp2cnAgICKBfv3707NmTpk2b3hUv0783IQQ//PADJ0+eZNq0abVGQLwTVVRUsG/fPtLT09Hr9fTr14833njD7oWqa9euNG/enLNnz5KZmYnBYGDSpEmMGTMGYV1C4fr164SEhFC/fn08PDwIDAxUt3d2dmbWrFl06NDB7rMdmc1mDh48qEaOrqqqoqKigvLycioqKvD19a0xDPNGJpMJV1dXSktLqaqqorq6Wg1C06dPH5o0aYKbmxvBwcEkJydz9epVhBBMmzaNvn37IoSgpKSEpKQkBgwYwAMPPIBWq6V///6UlJRw/vx5CgsLCQkJ4dVXX61TL8z/DYvFQlRUFBaLhYkTJ95WJeqPlpqaytGjR8nJyUEIweTJk9XeUkVRqKys5MCBA1y9ehW9Xk+vXr2YM2eO3T3+zJkzeHl50adPHzQaDQMGDKCgoICkpCTKysoYO3YsY8aMueFTa3fhwgXOnDmjzj209VhVVFTg5uZGUFDQbd9zTSYTrVq1onv37mqaxbqWaVJSEgUFBfj4+PDWW2+pvaVCCFJSUjCbzYSEhNT4/cxmMw8++GCNZ0ddJYRgw4YNFBcX89JLL9W4nj+Soij4+fnh4+PDyZMnycvLo3Xr1sydOxcPDw+1UaSqqoqQkJAaz2MhBAaDge7du9tNHTAajVRUVDB06FB8fHzs9sE6rah///5cvXqVy5cvU1payrhx4xg3bhxYzys4OBiDwcC5c+fQ6/U8/PDDvPLKK46HkuoQ2WMqSVINU6ZMYdOmTaSmptYa/ESSbGbMmMGqVavIzMysseTInc7iEEH6Zmzzmm6XsM6nrotu95rvBYMGDcJgMHDkyBHHrDrtl37Dulz+pJsbMWIEaWlpMvJ1LWSZvnvckRXToqIiioqKZCGUpP+RuXPnsmPHDmJjY9UWT0mqzYIFC4iOjiYuLo4mTZrcVWXFcdhsbWzDFGvbzpbuSKPRqMMa65qbXfOtroWbLC1yJzObzTzzzDMYjUa++eabO+b6HIfN1uZmv/Gt/Cf7/B5+qfzdrNzeacxmM5MnTyYjI4Mff/zxptf7R7tV+ftf/ha/VD5/Kb8uUBSF5s2b4+zs7Jh1T7njKqZCCKKiovjuu+9u2SIoSdJ/RqvV8vPPP5ORkcGjjz5KvXr16vTNXPrjaLVaTpw4QWpqKsOHD6d+/fq1vpBI0p1o3759WCwWhgwZIt83pD+UEILDhw+rQ1tlebz76HQ6Fi1ahK+vr2PWPeWOq5harME2Vq5c6ZglSdJv6H/ZuindXWRZkSRJkqT/zsmTJ+nZs6dj8j3ljquYCmtglj179sihvJIkSZIkSZIk3dGcnJyYOXOmuoLBveqOq5hKkiRJkiRJkiRJdxc5SF2SJEmSJEmSJEn6Q8mKqSRJkiRJkiRJkvSHkhVTSZIkSZIkSZIk6Q8lK6aSJEmSJEmSJEnSH0pWTCVJkiRJkiRJkqQ/1P8DUJij22lY8+YAAAAASUVORK5CYII=\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNPs frequently observed in stroke patients were identified through GWAS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e28 Statistically significant SNPs associated with stroke between stroke patient and control were identified through GWAS [30]. All participants were divided into the stroke group (n = 184) and the control group (n = 7,606). Although no significant SNPs were identified at a threshold of 5\u0026times;10\u003csup\u003e-8\u003c/sup\u003e, 28 SNPs were classified at a threshold of 1\u0026times;10\u003csup\u003e-5\u0026nbsp;\u003c/sup\u003e\u003csup\u003e[21]\u003c/sup\u003e (Fig. 2A,B). To confirm the characteristics of identified SNPs, we examined the chromosome, base pair, and associated genes. Significant SNPs were found on chromosome 1 (n = 6), 2 (n = 1) ,5 (n = 1), 7 (n = 3), 9 (n = 4), 10 (n = 3), 12 (n = 8), 13 (n = 1), and 18 (n = 1). Among the 28 SNPs, 1 SNP was located in both FAAP20 (Gene ID: 199990) and LOC124903823 (Gene ID: 124903823), 3 SNPs in SYT14 (Gene ID: 255928), 1 in RYR2 (Gene ID: 6262), 1 in LINC01470 (Gene ID: 101927134), 3 in DGKB (Gene ID: 1607), 3 in OR13C4 (Gene ID: 138804), 3 in LOC107987105 (Gene ID: 107987105), 8 in PPM1H (Gene ID: 57460), 1 in HS6ST3 (Gene ID: 266722), and 1 in ASXL3 (Gene ID: 80816) (Supplementary Table 1).\u003c/p\u003e\n\u003cp\u003eFurthermore, in the genes where the SNPs are located, two of them were previously confirmed to be located in stroke-related genes (PPM1H, RYR2) [31, 32]. Additionally, SNPs were found in genes associated with stroke-related diseases, such as atrial fibrillation (DGKB) [33], Parkinson\u0026rsquo;s disease (ASXL3) [34], and obesity (HS6ST3) [35] (Supplementary Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNPs for PRS calculation were selected through LD pruning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLD pruning can extract only the SNPs with the lowest p-values among those with significant linkage to classify SNPs for PRS calculation (Fig. 2C). As a result, 11 representative SNPs that had a significant impact on stroke were selected and used for PRS calculation (Supplementary Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe PRS levels were different between stroke patients and controls\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PRS levels were significantly higher in stroke patient group compared to the control group (case = 2.970, control 1.869) (Supplementary Fig. 1A). Additionally, to validate the reliability of the PRS, the cohort was expanded by including previously excluded participants (383 cases and 7,715 controls). Consequently, the PRS levels were significantly higher in the stroke patient group compared to the control group as well (case = 2.368, control = 1.870) (Supplementary Fig. 1B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe stroke incidence varies depending on the amount of physical activity across different PRS levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe confirmed the differences in stroke incidence based on the presence or absence of physical activity (Fig. 3). The stroke incidence was lower in the presence of physical activity in the high PRS (Fig. 3A, p \u0026lt; 0.0001, chi-square = 27.77) and middle PRS groups (Fig. 3B, p \u0026lt; 0.0001, chi-square = 16.81). There was no significant difference in the low PRS groups (Fig. 3C, p = 06005, chi-square = 0.2742). Regarding the amount of physical activity, stroke incidence was significantly different based on the amount of physical activity in the high (Supplementary Fig. 2A, p \u0026lt; 0.0001, chi-square = 28.52) and middle PRS groups (Supplementary Fig. 2B, p = 0.0002, chi-square = 16.89). However, there was no significant difference in stroke incidence in the low PRS group (Supplementary Fig. 2C, p = 0.7776, chi-square = 0.5031).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe stroke incidence influenced by physical activity differs across different PRS levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe first examined the effects of various stroke-related risk factors on stroke incidence through regression analysis. For all participants, age and polygenic risk score increased the risk of stroke. While no significant effects were found for sex, hypertension, and smoking. Physical activity significantly reduced the risk of stroke (Fig. 3D) The effect of physical activity on stroke incidence was evaluated as odds ratios for each group (high PRS, middle PRS, and low PRS). At high PRS group, a trend towards a reduction in stroke risk was observed due to physical activity. At middle PRS groups, physical activity significantly reduced stroke risk. The middle PRS group was found to reduce stroke risk more than the high PRS group. No statistical significance was observed at low PRS levels (Fig. 3E).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSNPs classified through GWAS with a disease are associated with the genetic risk of the condition. By calculating PRS based on identified SNPs, an individual\u0026rsquo;s genetic predisposition to disease can be quantified. Identifying a disease-specific PRS in advance provides preventive benefits by enabling individuals to adjust their lifestyle habits and respond to potential health risks more effectively. Physical activity is a key preventive factor for stroke, with its protective effect potentially varying based on an individual\u0026rsquo;s stroke risk factors. However, there is a lack of research confirming the stroke incidence influenced by physical activity according to genetic factors associated with stroke. Therefore, this study aimed to examine the differences in stroke incidence influenced by physical activity based on PRS levels.\u003c/p\u003e \u003cp\u003eFirst, 28 SNPs were classified through GWAS with stroke incidence as a variable. To determine whether the classified SNPs are associated with stroke, the genes where the SNPs are located were identified. With 28 SNPs, 11 genes were identified. Among them, 1 gene was associated with stroke, and 3 genes were associated with other diseases related to stroke. RYR2 is a gene known to regulate cardiac rhythm and calcium signaling. It has been identified as being associated with atrial fibrillation, and has also been found in previous GWAS study related to stroke [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Additionally, DGKB, HS6ST3 and PPM1H have been identified in previous GWAS studies as being linked to diseases associated with stroke, including type 2 diabetes, obesity, and ADHD, respectively. DGKB is a gene that encodes a metabolic enzyme involved in signaling pathways and vascular function. It has been identified in previous GWAS study with type 2 diabetes, a major risk factor for stroke [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. HS6ST3 is a gene involved in heparan sulfate metabolism. It has been found in previous GWAS with obesity and high triglyceride level[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. PPM1H gene is involved in the production of PPM1H, a protein phosphatase associated with cell signaling[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. It has been shown to associated with attention deficit hyperactivity disorder, a neurodegenerative disorder in previous GWAS study.\u003c/p\u003e \u003cp\u003eThis finding is consistent with previous research, which suggests that SNPs classified through GWAS for specific diseases tend to be located in genes associated with those diseases [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. SNPs located in disease-related genes may affect the function of genes and disease incidence. Therefore, additional research is needed to confirm the impact of SNPs to the mechanisms of stroke with those genes. Nevertheless, the significance of this study lies in the fact that stroke-related SNPs were classified through GWAS using KoGES data set from a Korean population, suggesting that these SNPs may have the potential to increase the risk of stroke occurrence [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSecond, the PRS levels of all participants were analyzed using 11 representative SNPs. Consistent with previous study [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], the results showed that the patient group had higher PRS levels compared to the control group, indicating that the PRS based on the SNPs identified in this study may predict stroke. Moreover, a validation was conducted to ensure the reliability of the PRS in predicting stroke. the results showed that patients still had higher PRS levels than the control group. This is consistent with prior research suggesting that higher PRS levels are associated with greater vulnerability to stroke [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These results suggest that the PRS in this study may reflect the genetic risk for stroke, and confirm that the individual\u0026rsquo;s genetic risk of stroke can be calculated with PRS.\u003c/p\u003e \u003cp\u003eThird, the preventive effect of physical activity on stroke is well established. In this study [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], we aimed to investigate whether this effect varies across different PRS levels. A reduction in stroke incidence influenced by physical activity was significant in the middle and high PRS groups. However, no significant effect was observed in the low PRS groups. A previous study confirmed that, in the case of type 2 diabetes, the absolute risk reduction due to lifestyle improvements was significantly greater in individuals with high PRS [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Since physical activity is also a key lifestyle factor, it is possible that the risk reduction due to physical activity in stroke may be relatively smaller in those with low PRS. To understand the cause of this phenomenon, it is necessary to secure a sufficient number of participants with low PRS and further investigate the characteristics of individuals according to PRS levels. Nevertheless, since stroke is a condition where post-onset management is challenging and moreover, the prognosis and treatment outcomes often remain unfavorable, highlighting the importance of preventive approaches is essential [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Therefore, confirming that the preventive effect of physical activity on stroke may vary according to PRS levels holds significance as it can help in developing effective stroke prevention strategies. Next, we examined the differences of stroke incidence based on the amount of physical activity within each PRS level. While no significant difference was observed in the low PRS group, differences were found in the middle and high PRS groups. When examining the stroke incidence with increasing the amount of physical activity, the middle PRS group showed a decrease in stroke incidence as the amount of physical activity increased. On the other hand, in the high PRS group, stroke incidence was higher in the group with more physical activity compared to the group with less physical activity. Some previous studies suggest that excessive endurance exercise may have a negative impact on cardiovascular health [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Furthermore, on study revealed that excessive physical activity duration could be a risk factor for hypertension in middle-aged population [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Physical activity plays a critical role in stroke prevention by regulating key risk factors such as obesity and hypertension [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Although physical activity does not change the genetic sequence itself, it can influence proteins or gene expression [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. This study suggests that the preventive effect of physical activity may vary depending on genetic risk, however, there is insufficient evidence to support this result. Therefore, further research is needed to explore whether excessive physical activity may increase stroke risk in the high PRS levels.\u003c/p\u003e \u003cp\u003eFurthermore, regression analysis was conducted to examine whether the effect of physical activity only on stroke prevention differs based on PRS levels. The lack of significance in the low PRS group may be attributed to the insufficient number of stroke patients, similar to with examining stroke incidence. The reduction in stroke risk due to physical activity was greater in the middle PRS group compared to the high PRS group. Generally, the effect of improving lifestyle tends to be greater in reduce stroke risk with higher PRS levels. However, there is insufficient evidence to explain why the reduction in stroke risk due to physical activity was smaller in the high PRS group compared to the middle PRS group. Nevertheless, the significance of this study lies in identifying the differences in the effects of physical activity on stroke prevention across different PRS levels.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the cohort used in this study consists of individuals residing in rural areas of Korea, which may have influenced the results due to lifestyle factors specific to rural living, thereby limiting their generalizability to the entire Korean population. To address this limitation, further studies incorporating data from non-rural areas and comparing them with data from other countries are necessary to provide a more comprehensive understanding of the characteristics of the Korean population. Moreover, this study has limitations due to the small sample size and the use of a single cohort. Thereby, there were limitations in the process of ensuring the predictive reliability of the PRS through validation using other cohorts, as well as in the significance of the low PRS group due to the insufficient number of patients. Therefore, to overcome these limitations, securing with additional cohorts and conducting further analytical studies are required.\u003c/p\u003e \u003cp\u003eIn conclusion, this study suggests that the stroke incidence influenced by physical activity may differ according to the PRS levels. Additionally, our results suggest that further research is needed to explore the relationships among risk factors for stroke. To the best of our knowledge, this is the first study to examine the differential impact of physical activity on genetic risk in stroke incidence. However, additional research overcoming the limitations of this study and with larger cohorts is required to provide new perspectives on stroke prevention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted with bioresources from National Biobank of Korea, the Center for Disease Control and Prevention, Republic of Korea (NBK-D03-B, NBK-D03-F01, NBK-D03-F02, NBK-D03-F03, NBK-D03-F04).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: [Yun Seo Cho, Hyo Youl Moon]; Methodology: [Yun Seo Cho, Tae Yeon Kim, Kihoon Yuk, Yeon Soo Kim, Kumpei Tanisawa,\u0026nbsp;Hyo Youl Moon]; Formal analysis and investigation: [Yun Seo Cho, Ji Eun Lee, Joo Hee Park, Kumpei Tanisawa,\u0026nbsp;Hyo Youl Moon]; Writing \u0026ndash; original draft preparation: [Yun Seo Cho]; Writing \u0026ndash; review and editing: [Yun Seo Cho, Tae Yeon Kim, Kihoon Yuk, Yeon Soo Kim, Min-Chul Lee,\u0026nbsp;Jin Pyeong Jeon, In cheol Jeong, Hyo Youl Moon]; Supervision: [Hyo Youl Moon].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the financial support by the National Research Foundation (NRF) of Korea (NRF-2020R1C1C1006414 and NRF-2022R1I1A4053049) and was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. RS-2022-NR070859).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are not publicly available due to ethical restrictions regarding data protection issues and the study-specific consent text and procedure, but anonymized data are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional\u0026nbsp;Review Board of Seoul National University in accordance with the standards of the Declaration of Helsinki of the World Medical Association (IRB No. E2411/004-012).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent prior to participation in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eFang, J., Z. 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Folsom, and S.N. Blair, \u003cem\u003ePhysical activity and stroke risk: a meta-analysis.\u003c/em\u003e Stroke, 2003. \u003cstrong\u003e34\u003c/strong\u003e(10): p. 2475-2481.\u003c/li\u003e\n \u003cli\u003eYe, Y., et al., \u003cem\u003eInteractions between enhanced polygenic risk scores and lifestyle for cardiovascular disease, diabetes, and lipid levels.\u003c/em\u003e Circulation: Genomic and Precision Medicine, 2021. \u003cstrong\u003e14\u003c/strong\u003e(1): p. e003128.\u003c/li\u003e\n \u003cli\u003eChambers, B.R., et al., \u003cem\u003ePrognosis of acute stroke.\u003c/em\u003e Neurology, 1987. \u003cstrong\u003e37\u003c/strong\u003e(2): p. 221-221.\u003c/li\u003e\n \u003cli\u003eO\u0026apos;Keefe, J.H., et al. \u003cem\u003ePotential adverse cardiovascular effects from excessive endurance exercise\u003c/em\u003e. in \u003cem\u003eMayo Clinic Proceedings\u003c/em\u003e. 2012. Elsevier.\u003c/li\u003e\n \u003cli\u003eZhu, Z., et al., \u003cem\u003eExcessive physical activity duration may be a risk factor for hypertension in young and middle-aged populations.\u003c/em\u003e Medicine, 2019. \u003cstrong\u003e98\u003c/strong\u003e(18): p. e15378.\u003c/li\u003e\n \u003cli\u003eElkind, M.S. and R.L. Sacco. \u003cem\u003eStroke risk factors and stroke prevention\u003c/em\u003e. in \u003cem\u003eSeminars in neurology\u003c/em\u003e. 1998. \u0026copy; 1998 by Thieme Medical Publishers, Inc.\u003c/li\u003e\n \u003cli\u003eBooth, F.W., M.V. Chakravarthy, and E.E. Spangenburg, \u003cem\u003eExercise and gene expression: physiological regulation of the human genome through physical activity.\u003c/em\u003e The Journal of physiology, 2002. \u003cstrong\u003e543\u003c/strong\u003e(2): p. 399-411.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Stroke, Genome Wide Association Study, Single Nucleotide Polymorphism, Polygenic Risk Score, Physical activity ","lastPublishedDoi":"10.21203/rs.3.rs-6518109/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6518109/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStroke is a disease influenced by various risk factors, including genetic factors determined by single nucleotide polymorphisms (SNPs). Polygenic risk score (PRS) calculated from multiple SNPs can reflect the varying genetic risk of stroke. Physical activity is a critical factor that influences the incidence of stroke by improving other risk factors. Therefore, we investigated the effects of physical activity on the reduction of stroke incidence across different PRS levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized epidemiological and genetic data from Korean Genome and Epidemiology Study. Through genome wide-associated study (GWAS) analysis, SNPs with high frequency in stroke patients were classified. The presence and amount of physical activity was assessed based on the weekly average exercise reported in the epidemiological survey data. All participants were classified into three groups based on PRS levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough GWAS (p \u0026lt; 1×10\u003csup\u003e-5\u003c/sup\u003e), 28 SNPs were identified in the stroke patients for calculating PRS. The physical activity significantly reduced stroke incidence with high (p \u0026lt; 0.0001) and middle PRS levels (p \u0026lt; 0.0001). Notably, the amount of physical activity showed a difference in stroke incidence with high (p \u0026lt; 0.0001) and middle PRS levels (p = 0.0002).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study investigates differences in stroke incidence due to physical activity across PRS levels calculated from stroke-related SNPs in GWAS. The reduction in stroke incidence influenced with the physical activity varied depending on PRS levels. Our findings suggest that the stroke prevention effect of physical activity may differ across PRS levels.\u003c/p\u003e","manuscriptTitle":"Effects of physical activity on the incidence of stroke with polygenic risk score","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-14 12:06:58","doi":"10.21203/rs.3.rs-6518109/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bd0c1c97-c426-46bc-8ca3-f34e9c72cd73","owner":[],"postedDate":"May 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-19T19:08:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-14 12:06:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6518109","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6518109","identity":"rs-6518109","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}