Socioeconomic and health outcome associations with lifestyle choices uncovered by wastewater information mining

Socioeconomic and health outcome associations with lifestyle choices uncovered by wastewater information mining | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Socioeconomic and health outcome associations with lifestyle choices uncovered by wastewater information mining Barbara Kasprzyk-Hordern, Kishore Kumar Jagadeesan, Natalie Sims, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7658391/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Wastewater-based epidemiology (WBE) represents a powerful tool for population health surveillance, however, the systematic integration of wastewater-derived human behavioural markers (WBECI) with socioeconomic and demographic data remains understudied. The aim of this study was to explore the potential of triangulating wastewater chemical markers (WBECI) with population census data to unravel associations between disease prevalence, lifestyle choices and key risk factors associated with nicotine and caffeinated product use. This was critically evaluated in a multi-city (n = 10), national level WBE study in England. Associations of different wastewater markers revealed that smoking WBECIs (nicotine and cotinine) correlate with multi-disease WBECIs (pharmaceuticals used as proxies for disease: cardiovascular diseases (CVDs, pain, asthma, diabetes, infectious disease). Smoking WBECIs also showed strong positive associations with socioeconomic and demographic indicators (ONSSEDI) such as economic deprivation, poor health and low level of education. Triangulation of WBECI and ONSSEDI indicators revealed that cities with lower education outcomes, poor health and the highest deprivation index cluster with smoking as well as with CVD, cancer, diabetes, asthma, infectious disease and pain WBECIs. Conversely, the least deprived city with the highest level of education and good health clustered with caffeine and oxidative stress, which calls for further work to confirm any causal relationships between lifestyle choices, socioeconomic background and health outcomes. Further exploration of correlations reveals pain management and antibiotics usage, illicit drug use, as well as chronic, often lifestyle driven conditions such as CVDs, diabetes, asthma and cancer cluster closely with disability, deprivation, poor health and low level of education. This is especially notable in cities with high deprivation index and could serve as an early warning for city-focussed interventions. Health sciences/Health care Earth and environmental sciences/Environmental sciences/Environmental chemistry/Environmental monitoring Wastewater-based epidemiology pharmaceuticals public health lifestyle socioeconomic status smoking nicotine caffeine Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Smoking is a significant public health issue and a leading cause of preventable death and disease globally (ONS, 2023a). This is despite significant reductions in smoking prevalence over the past decade (ONS, 2022). According to WHO smoking causes > 8 million deaths each year (WHO, 2022). Smoking prevalence is commonly associated with a variety of socioeconomic factors including education level and household income. Comparison of smoking prevalence and economic activity in the UK indicated that those who were unemployed comprised a higher proportion of current smokers (20.5%), compared with those who were in paid employment (12.7%), and those who were economically inactive (12.7%) (ONS, 2023b). Of those who were classified as being in a routine and manual socio-economic classification, 22.8% were current smokers, compared with 8.3% of managerial and professional occupations. This paper aims to verify smoking as a risk factor in non-communicable diseases (NCDs) via Wastewater-Based Epidemiology (WBE) triangulated with socioeconomic and demographic indices. It also aims to demonstrate that the relationship between those multiple factors can be observed using wastewater information mining (Fig. 1). WBE is an innovative epidemiological approach that enables tracking infectious disease prevalence (Sims and Kasprzyk-Hordern 2020, Wade, Lo Jacomo et al. 2022) and illicit drug use (González-Mariño, Baz-Lomba et al. 2020) in tested communities. WBE has also found applications in estimating usage of other lifestyle chemicals such as alcohol and tobacco (Castiglioni, Senta et al. 2014, Chen, Venkatesan et al. 2019, Mackie, Tscharke et al. 2019, Gracia-Lor, Rousis et al. 2020, Montes, Rodil et al. 2020, Sims and Kasprzyk-Hordern 2020, Zheng, Gartner et al. 2020, Thanh, Vu et al. 2022, Wang, Zheng et al. 2024). WBE can provide a ‘fingerprint’ of community health that can be monitored in near real-time, theoretically at any place and time globally. WBE is relatively comprehensive, timely and unbiased in comparison to other approaches (e.g. clinical surveillance, community-wide participatory surveys), and most importantly, it captures most of the contributing population in communities. When integrated with other epidemiological approaches it can provide in-depth verification of cofounding factors. While WBE finds its applications in pathogen surveillance and illicit drugs use prevalence estimation, its application in the wider public health evaluation remans unexplored. This study explores, for the first time, associations between smoking WBE CI s and pharmaceutical WBE CIs as well as UK National Statistics socioeconomic and demographic indicators (ONS SEDI ) in a national multi-city study focussed on 10 geographically distributed towns and cities in England (Fig. 1) equating to a population of ~7 m people. Detailed discussion on spatial distribution in WBE CI s and inter-CI associations is provided elsewhere (Kasprzyk-Hordern, Sims et al. 2023, Kasprzyk-Hordern, Sims et al. 2025). >60 pharmaceutical and health WBE CI s (Tab. 1) were selected as proxies for disease to test exposure-health outcome associations linked with smoking. >70 ONS SEDI s (Tab. 1) were selected to find associations between smoking demography and socioeconomic status. Correlations between nicotine/cotinine, other WBE markers and socioeconomic/demographic indicators have also been reported in a few studies (Choi, Tscharke et al. 2019, Rousis, Li et al. 2022, Shao, Zhang et al. 2022, Yang, Zheng et al. 2022). For example, Choi et al. found significant negative correlations between IRSAD (a measure of advantage and disadvantage, summarising the economic and social conditions of people and households) and nicotine (Choi, Tscharke et al. 2019). Further, it has been shown that populations presenting disadvantage in income, education, occupation, and marital status were positively correlated with biomarkers and/or proxies of diseases, such as cardiac arrhythmias, cardiovascular disease, anxiety disorder and type 2 diabetes (Rousis, Li et al. 2022). Yang et al. found higher metformin use correlated with socially disadvantaged populations (lower education and lower income levels) (Yang, Zheng et al. 2022). Zhang et al. studied allergy prevalence and environmental factors and found that antihistamine use was significantly associated with smoking, green space and pollen but not significant with air pollution (Zhang, Du et al. 2025). Li et al. (Li et al. 2024) reported associations between the consumption of antimicrobials and socioeconomic status. Table 1. Summary of wastewater-based epidemiology indicators (WBE CIs ) and socioeconomic indicators (ONS SEDI ) used in this study. 2. Results and Discussion 2.1. Inter-WBE CI associations: Smoking-WBE CI s correlate with multi-disease WBE CI s confirming smoking as a multi-disease risk factor Nicotine and cotinine were used as smoking-WBE CI s (Kasprzyk-Hordern et al., 2025). As expected, nicotine and cotinine showed statistically significant strong/moderate positive correlations with each other (r p/s =0.53/0.60). Smoking is a risk factor for several diseases, including Type 2 diabetes, asthma/allergies and cardiovascular diseases, as well as a risk factor of increased pharmaceutical consumption such as pain killers or antibiotics. Indeed, smoking WBE CI s were shown to correlate with wide-ranging groups of pharmaceuticals confirming smoking as a multi-disease risk factor (Fig. 2). Smoking and CVDs: Observed significant correlations of nicotine/cotinine with atenolol (r p/s =0.44-0.57) were expected as smoking is a risk factor for CVDs, including high blood pressure that is treated with atenolol. Moderate positive correlations were also seen between nicotine/cotinine and atorvastatin (r p/s =0.32-0.48) and weaker, but still statistically significant, correlations were seen with bezafibrate. No correlations were seen with irbesartan or lisinopril. Smoking and asthma: Positive correlations were seen with antihistamines (r p/s = 0.29-0.47), which confirms smoking as a risk factor in asthma/allergies. This is supported by previous study reporting a strong positive relationship between cetirizine/fexofenadine and cotinine in wastewater (Thai et al. 2023). Smoking and Type 2 diabetes: Moderate positive correlations between cotinine and metformin (r p/s =0.55/0.47) (Fig. 2) were observed in line with other reports (Shao, Cong et al. 2021) as smoking is a risk factor for Type 2 diabetes. Interestingly, no correlations were observed with sitagliptin. Correlations between cotinine and metformin are due to the higher levels of nicotine from smoking cigarettes making the cells in the body less responsive to insulin, which makes the blood sugar levels higher (Maddatu, Anderson-Baucum et al. 2017). While both drugs aim to improve glycaemic control, the potential for metformin to attenuate smoking-related cardiovascular risks might be a distinguishing factor for the difference (Paul, Klein et al. 2016). Smoking and pain management: Nicotine and its main metabolite cotinine showed strong/moderate positive correlations with several pain management WBE CI s, such as analgesics and NSAiDs (paracetamol, codeine/dihydrocodeine, naproxen/O-desmethylnaproxen, diclofenac, ibuprofen (r p/s =£0.71)) and stimulants (amphetamine (r p/s =£0.46)). Stronger correlations were seen with cotinine having mainly human metabolism origin versus nicotine having more heterogenous sources. Interestingly, weak/no correlations were seen with either chronic pain management pregabalin/N-methylpregabalin, as well as anaesthetic ketamine/norketamine. Nicotine/cotinine showed strong positive correlations with opioids, e.g. cotinine and tramadol (r p/s = 0.65/0.65), codeine (cotinine: r p/s = 0.70/0.71) and dihydrocodeine (cotinine: r p/s = 0.62/0.68). This association was also reported elsewhere in patients in the context of perception of pain and pain management using opioids (Etcheson, Gwam et al. 2018). Smoking and infectious disease: Significant correlations of nicotine/cotinine with several antibiotics (e.g. sulfapyridine/N-acetylsulfapyridine, sulfamethoxazole, trimethoprim): (r p/s =£0.50) were also observed. Indeed, tobacco smoking is a risk factor for increased antibiotic prescription (Steinberg, Akincigil et al. 2016). Smoking and depression: Only two WBE CI s were tested: venlafaxine and desmethylvenlafaxine. No statistically significant correlations were seen. Smoking and other lifestyle chemicals: Interestingly no correlations were found between nicotine/cotinine and caffeine/dimethylxanthine despite being reported elsewhere (Shao, Cong et al. 2021). Weak/no correlations were observed between nicotine and illicit drugs, e.g. cocaine (r p/s =0.45/0.41) and amphetamine (r p/s =0.46/0.41). Smoking and other CIs: Interestingly, no correlations were observed with HNE-MA, an oxidative stress marker, or creatinine, a marker of kidney function. Weak to moderate positive correlations were observed with methotrexate, a pharmaceutical used in cancer and autoimmune conditions (r p/s =0.27-0.30). PCA was used to visually represent key WBE CI correlations. Two principal components were retained (as seen in Fig. 2) as they were explained by 60% of the variance. PCA analysis confirmed strong positive correlations between smoking-WBE CI s and pharma-WBE CI s for CVD, pain and infectious disease with weaker correlations seen with class II diabetes, asthma/allergy. PCA also reinforced lack of correlation (~90 o angle) between smoking-WBE CI s and gastrointestinal: cimetidine, oxidative stress: HNE-MA, caffeine/1,7-dimethylxanthine, anxiety/depression: venlafaxine/desmethylvenlafaxine. It is worth mentioning that PCA is a dimensionality reduction tool. As seen in Fig. 2, nicotine and cotinine vectors are located closer the middle of the plot, indicating that they do not strongly influence PCA1 or 2 (as for example atorvastatin and fexofenadine influence PCA1), which shows that there might be other, relationships or patterns that are not captured by the leading PCs. To manage this, we trialled standardization approaches (by normalization of a value from a distribution by converting it to a z-score) to standardize the range of the continuous initial variables so that each one of them contributes equally to the analysis, but they did not provide any further benefits to PCA outputs. This reinforces complex relationships between WBE CI s and the need for further work. We also used multivariate HCA for visual representation of key WBE CI s associations: smoking WBE CIs and pharma proxies WBE CIs (Fig. 2). As with PCA, strong associations (and two distinct clusters) were seen between smoking- and pharm-WBE CI s proxies for CVD, pain, infectious disease and Type 2 diabetes in cluster 1 vs gastrointestinal: cimetidine, oxidative stress: HNE-MA, caffeine/1,7-dimethylxanthine, anxiety/depression: venlafaxine/desmethylvenlafaxine in cluster 2. 2.2. Smoking-WBE CI and ONS SEDI associations : Smoking-WBE CI s positively correlate with higher deprivation, lower level of education and bad health Strong correlations between smoking-WBE CI and ONS SEDI were observed across several indicators including deprivation, education, health and housing as seen in Fig. 3. Smoking and the level of education: Correlation of smoking-WBE CI s and ONS SEDI ‘highest level of education’ clearly shows strong positive correlations between cotinine and ‘no qualifications’ (r p/s =0.66/0.68), with negative correlations in ‘level 4 qualifications’ and above (r p/s =-0.58/-0.61). Smoking and demographics: No strong correlations were found between nicotine/cotinine and city size, as well as gender. However, significant correlations were seen with different age groups, with >50 correlating positively (nicotine/cotinine, r p/s =>0.50). Breakdown by cities with different age groups shows clear negative correlations in younger population versus the older age brackets. Weak correlations were observed between smoking-WBE CI s and ‘ethnic group’ with negative correlations seen between nicotine/cotinine and ‘Asian’, ‘black’, ‘mixed’ ethnicity (strongest negative correlation seen for cotinine/‘mixed’ r p/s =-0.44/-0.35) and positive weak correlations between cotinine and ‘white’ (r p =0.38/0.31). This confirms conclusions of a recent study conducted by UK government (APS, 2024). Regarding ‘household composition’, positive correlations were seen between smoking-WBE CI s and ONS SEDI ‘one person household’ with older demographics (r p/s =³0.46) and ‘single family household: lone parent family: with dependent children/all households’ (r p/s =³0.32) versus negative correlations seen for ‘single family household: married or civil partnership couple: dependent children’ (r p/s =-0.4-(-0.6)). Regarding ‘accommodation type’, no strong correlations were seen, with semi-detached/terraced houses showing the strongest positive correlations with smoking-WBE CI s. Smoking and socioeconomic status: Positive correlations were seen between smoking-WBE CI s and ONS SEDI ’deprivation status’ (r p/s =0.3-0.6 for cotinine – ‘household deprivation’, one-three dimensions). As expected, negative correlations (r p/s = -0.4-(-0.6)) were found for cotinine and ‘not deprived in any dimension’ which confirms the linkage between smoking prevalence by socio-economic status as also captured by ONS (ONS 2023a). The report (ONS 2023a) states that the proportion of people who smoke in the most deprived areas of England and Wales was more than three times higher than in the least deprived areas in 2021 and that over a quarter of smokers in England and Wales live in the most deprived areas. Similar observations were reported by Choi et al. (Choi, Tscharke et al. 2019) who found significant negative correlations between IRSAD (a general measure of advantage and disadvantage, summarising the economic and social conditions of people and households) and nicotine. Smoking-WBE CI s positively correlate with ‘disabled’ parameters (e.g. r p/s for cotinine-‘disabled under the Equality Act’ = 0.47/0.50), indicating nicotine being a risk factor. It is reported that adults with disabilities are more likely to smoke than those reporting as not disabled (Emerson 2018). Smoking and health: Interestingly, and as expected, smoking-WBE CI s showed strong negative correlations with ONS SEDI ‘very good health’ (cotinine - ‘very good health’ r p/s = -0.60/-0.59) moving to positive correlations from ‘good, ‘fair’, ‘bad health’ with the strongest positive correlation seen for ‘very bad health’ (cotinine/’very bad health’, r p/s =0.60/0.71), which again confirms smoking as a multi-disease risk factor. PCA analysis (with two PCs retaining 83% of the variance) confirmed strong positive correlations between smoking-WBE CI s and ONS SEDI : ‘bad to very bad health’, ‘deprived in more than 1 dimension’, ‘no qualification’ and ‘level 1 qualification’ with the lowest angle seen between nicotine/cotinine and ‘very bad health’ (Fig. 3). Strong negative correlation (~180 o angle) was seen between nicotine/cotinine and ‘fair to very good health’. As with PCA, HCA revealed strong associations (and two distinct clusters) between smoking-WBE CI s and higher level of deprivation, lover qualifications, disability and bad health grouped in cluster 1 vs the lowest level of deprivation, highest level of qualification, no disability and fair and very good health grouped in cluster 2 (Fig. 3). 2.3. Caffeine-WBE CI s show weak correlations with other WBE CI s and ONS SEDI indicators We also assessed another lifestyle chemical – caffeine as a comparator, a common, low-harm (if consumed in moderate quantities) lifestyle marker, to explore further future potential of WBE in risk factor analysis and whether its associations with health and SED indicators differ from those observed for smoking. Relatively few statistically significant correlations were seen between caffeine-WBE CI s and pharma-WBE CIs . Caffeine and 1,7-DMX showed moderate to strong positive correlations with each other (r p/s =0.87-0.88) (Fig. 2). Significant positive correlations were seen between caffeine-WBE CI s and metabolites of tramadol (r p/s =0.50 - 0.83), antibiotics/metabolites (e.g. metronidazole/hydroxymetronidazole, r p/s =<0.40) with stronger correlations seen for 1,7-DMX. The strong correlation between caffeine/1,7-DMX and illicit amphetamines (mephedrone (r p/s =0.41-0.56), MDMA (r p/s =0.38-54)) and ketamine/norketamine (r p/s =0.43-0.59)) was also observed. The stimulating effect of coffee intensifying the effects of amphetamines, e.g. administered as caffeinated energy-containing beverages is indeed widely reported. The positive correlation of caffeine-WBE CIs and venlafaxine/desmethylvenlafaxine (r p/s =0.44-0.64) was also seen. Caffeine has been considered as a risk factor in the increased risk of anxiety and depression (Liu, Wang et al. 2024). There are conflicting results regarding coffee consumption and health outcomes, however, recent results from large studies indicate potential beneficial effects of drinking coffee on risk of death from all causes, especially for circulatory diseases, and digestive diseases, including a more favourable liver function profile and immune response (Gunter, Murphy et al. 2017). Indeed, in this study we found moderate negative associations with atorvastatin (caffeine: r p/s =-0.33 and 1,7-DMX: r p/s =-0.41/-0.40), a statin used to lower cholesterol and prevent heart disease, including heart attacks and strokes (Fig. 2). Interestingly, though, caffeine-WBE CI s showed positive correlations with HNE-MA (r p/s =0.41-0.58). As discussed earlier, there is a lack of clarity in the literature about whether caffeine (known to have antioxidant properties) can decrease oxidative stress. Some literature reports have indicated a positive impact (Vargas-Pozada, Ramos-Tovar et al. 2023), however, this study shows positive correlations and the need for further work, especially in expanding the list of oxidative stress biomarkers tested. A moderate to strong positive association of caffeine-WBE CI s with cimetidine (r p/s =0.35-0.55) was also seen (Fig. 2). While caffeine stimulates gastric acid production (Nehlig 2022), it can also exacerbate gastric irritation in patients with ulcers, the stomach lining and increase acid production in patients with stomach ulcers, potentially explaining the co-occurrence of caffeine consumption and H2-receptor antagonist use in the community. PCA and HCA analysis (Fig. 2) confirmed statistically significant correlation between coffee-WBE CI s and gastrointestinal: cimetidine, oxidative stress: HNE-MA, and weak association with anxiety/depression: venlafaxine/desmethylvenlafaxine. It is worth mentioning that coffee vectors are located closer the middle of the PCA plot, indicating that they do not strongly influence PCA1 or 2, which shows that there might be other, relationships or patterns that are not captured by the leading PCs. This, as in the case of smoking-WBE CI s, reinforces complex relationships between CIs and the need for further work. Interestingly, as opposed to smoking-WBE CI s, caffeine-WBE CI s did not show any strong, statistically significant correlations with tested ONS SEDI with moderate positive correlations seen with good health (Fig. 3). PCA and HCA analysis (Fig. 3) confirmed associations between coffee-WBE CI s and ONS SEDI : ‘not deprived’ and ‘fair to very good health’ and weaker positive associations with higher level of education and no disability. 2.4. WBE CI s and ONS SEDI associations with lifestyle indicators: smoking and caffeine intake PCA analysis of combined WBE CI s and ONS SEDI (Fig. 4, with two PCs retaining 64% of the variance) showed smoking-WBE CI s clustering together with CVD, cancer, illicit drugs, asthma, diabetes and pain WBE CI s as well as with disability, deprivation, bad health and low/no education ONS SEDI s. Furthermore, these indicators clustered with cities with highest deprivation, lowest education and highest index of weak to bad health (HD Cities 3, 6, 7 and MD City 9: ‘deprived in ³1 dimension’: 58, 56, 59, 52%; ‘bad to very bad health’: 8, 6, 8, 6%; and ‘no/level 1 qualification’: 33, 34, 34, 27% for Cities 3, 6, 7, 9 respectively). It is worth mentioning that City 9, despite having MD status due to household deprivation of 52%, performed worse than other MD cities, also due to high percentage of bad health. Interestingly, the least deprived, best educated and healthy city (LD City 5) clusters with ONS SEDI s: ‘not deprived’ (deprived in ³1 dimension: 43%), ‘not disabled, ‘v. good to fair health’ (‘bad to very bad health’: 3%) as well as with oxidative stress and caffeine-WBE CI s. While WBE correlations do not imply causation, the above example confirming smoking as a risk factor in certain diseases and reinforcing the fact that certain socioeconomic and demographic characteristics shape smoking habits, clearly shows a huge potential of WBE as an early warning system for public health risks. New correlations between favourable socioeconomics and demographics of City 5 and oxidative stress and caffeine intake are an exciting opportunity for exploration of WBE’s potential in public health monitoring. Further exploration of correlations reveals pain management and antibiotics usage (infectious disease proxy), high illicit drug use prevalence, as well as chronic, often lifestyle driven conditions such as CVDs, diabetes, asthma and cancer clustering closely with disability, deprivation and low level of education. This is especially notable in HD cities and could serve as an early warning for city focussed interventions, with HD Cities 3, 6, 7, 9 and MD City 8 and 9 in need of immediate attention. Such data could, for instance, guide local public health bodies in allocating resources for smoking cessation programs or managing the burden of chronic, lifestyle-driven diseases in the most vulnerable communities. 3. Conclusions This study explored WBE in risk factor analysis by using two lifestyle choices as disease risk factors: nicotine/smoking known to be a major multi-disease risk factor and caffeine, a common, low-harm lifestyle marker. We used pharmaceutical WBE CI s as proxies for disease, as well as socioeconomic and demographic Census data to unravel associations between community specific disease prevalence, lifestyle choices, demographics and socioeconomic factors. Our results reaffirmed smoking as a multi-disease risk factor. Smoking-WBE CI s showed positive correlations with WBE CI (CVDs, cancer, illicit drugs, asthma, diabetes, pain) and ONS SEDI (disability, high deprivation, bad health and low/no education). Further exploration of correlations revealed pain management and antibiotics usage (infectious disease proxy), high illicit drug use prevalence, as well as chronic, often lifestyle driven conditions such as CVDs, diabetes, asthma and cancer clustering closely with disability, deprivation and low level of education. This is especially notable in HD cities and could serve as an early warning for city focussed interventions, with HD cities 3, 6, 7, 9 in need of immediate attention. In the case of large cities, it also highlights the need for more in-network wastewater sampling within individual city boroughs (e.g. MSOAs 5000-15,000 population; ONS, 2021) to improve the granularity and better target interventions. New correlations between favourable socioeconomics and demographics of City 5 and oxidative stress and caffeine intake provide an important opportunity for exploration of the potential of WBE to monitor public health in a relatively unbiased, non-invasive manner. WBE represents a powerful epidemiological tool when triangulated with other established approaches. WBE output can provide important insights for policy makers and those involved in harm reduction (as well as the wider risk factor analysis), so that support can be tailored for communities who need it the most and where incidence rates are highest. Most strikingly, WBE has the potential to indicate new exposure-health risk associations that provide early-warning for communities at risk from suspected, new and previously disputed or unexplored risks. 3. Experimental 3.1. Wastewater treatment sites selected for the study and sampling approaches utilised Samples were collected in November 2021 at 11 wastewater treatment plants (WWTPs) serving 10 cities on 9 randomly selected days at each site (i.e. 4 - 5 days every week at each site, including weekends) using 24 h composite sampling (with subsamples collected at 15 min or 1 h intervals using refrigerated autosamplers where available). Two of the 11 sites provided additional grab samples: WWTP: 10 / 10 samples and WWTP: 3 / 10 samples. All collected samples were transported frozen and were processed within 24 h after collection. The selection of 11 WWTPs was informed by the following criteria: (i) 24 h composite sampling (primary requirement), (ii) flow measurement on site (primary requirement), (iii) known WWTP population equivalent (primary requirement), (iv) WWTPs covering a wide population range from 10 3 to >10 6 , (v) low industrial input (secondary requirement), (vi) broad geographical distribution (vii) minimum of eight 24 h composite samples per WWTP. 3.2. Analysis of WBE CI s WBE CI s were analysed as described elsewhere (Kasprzyk-Hordern, Sims et al. 2023). 50 mL wastewater samples were pH adjusted to 7.5 ± 0.5, spiked with an IS (Internal Standard) mix (100 ng/compound) and filtered through glass microfibre GF/F (0.7 μm) filters. WBE CI s were extracted with Solid-Phase Extraction (SPE) using HLB (Hydrophilic Lipophilic Balance, Waters) cartridges conditioned with 2 mL MeOH followed by 2 mL H 2 O at ~5 mL min -1 followed by elution with 4 mL of MeOH. The eluent was dried under N 2 at 40 °C and resuspended in 500 μL of 80:20 H 2 O:MeOH. Two full biological replicates were prepared for every sample analysed. Extracted WBE CI s were analysed using an ACQUITY UPLC™ Xevo TQD-ESI triple quadrupole mass spectrometer (Waters, UK) coupled to an ultra-performance liquid chromatography instrument with an electrospray ionisation source (ESI). Two analytical methods were used. NCD (non-communicable WBE CI disease markers) were analysed with a reversed-phase BEH C18 column (150 x 1.0 mm, 1.7 μm particle size) and TQD-ESI set in both ESI+ and ESI- modes using the following mobile phase conditions: ESI+ mobile phase A: H 2 O:MeOH 95:5 + 0.1 % HCOOH and mobile phase B: 100 % MeOH (34 min method time) and ESI– mobile phase A: H 2 O:MeOH 80:20 + 1 mM NH 4 F, mobile phase B: 5:95 H 2 O: MeOH + 1 mM NH 4 F (23 min method time). The mobile phase rate was set at 0.04 mL min -1 with an injection volume of 15 μL. The TQD-ESI was set at: source temperature, 150 °C, desolvation temperature, 400 °C, cone gas flow, 100 L h -1 , desolvation gas flow, 550 L h -1 . Nitrogen was used as the nebulising and desolvation gas, and argon as the collision gas. Antimicrobial agents were analysed with a reversed-phase BEH C18 column (150 x 1.0 mm, 1.7 μm particle size) and TQD-ESI set in ESI+. A 19 min gradient elution using H 2 O:MeOH (95:5, with 0.1% HCOOH) and MeOH (100%) as mobile phases at a mobile phase flow of 0.2 mL min -1 and injection volume were applied (Holton and Kasprzyk-Hordern 2021). The coordinating program used was MassLynx V4.1, and the data processing program was TargetLynx (Waters Lab Informatics, UK). All methods were validated for trace quantification and method performance checked per every sample batch as discussed in (Holton and Kasprzyk-Hordern, 2021) and (Kasprzyk-Hordern, Sims et al. 2023). 3.3. Demographic and socioeconomic indicators (ONS SEDI s) Demographic and socioeconomic indicators (ONS SED Is) for each wastewater treatment plant (WWTP) catchment were estimated using 2021 Census data from the UK Office for National Statistics (ONS)(ONS 2022), provided at the Output Area (OA) level. Variables covered the following domains: Demography: age, sex, household deprivation, household composition, housing Household deprivation (HD) is classified as: (1) Household is not deprived in any dimension, (2) Household is deprived in one dimension, (3) Household is deprived in two dimensions, (4) Household is deprived in three dimensions, (5) Household is deprived in four dimensions, and is based on four selected household characteristics: · Education: A household is classified as deprived in the education dimension if no one has at least level 2 education and no one aged 16 to 18 years is a full-time student. · Employment: A household is classified as deprived in the employment dimension if any member, not a full-time student, is either unemployed or economically inactive due to long-term sickness or disability. · Health: A household is classified as deprived in the health dimension if any person in the household has general health that is bad or very bad or is identified as disabled. People who have assessed their day-to-day activities as limited by long-term physical or mental health conditions or illnesses are considered disabled. This definition of a disabled person meets the harmonised standard for measuring disability and is in line with the Equality Act (2010). · Housing: A household is classified as deprived in the housing dimension if the household's accommodation is either overcrowded, in a shared dwelling, or has no central heating. Household composition (HC) classifies households by the relationships between household members (household composition). One-family households are classified by: (1) the number of dependent children and (2) family type (married, civil partnership or cohabiting couple family, or lone parent family). Other households are classified by: (3) the number of people, (4) the number of dependent children, (5) whether the household consists only of students or only of people aged 66 and over. Housing: Accommodation type has eight categories: (1) Detached, (2) Semi-detached, (3) Terraced, (4) In a purpose-built block of flats or tenement, (5) Part of a converted or shared house, including bedsits, (6) Part of another converted building, for example, former school, church or warehouse, (7) In a commercial building, for example, in an office building, hotel or over a shop, (8) A caravan or other mobile or temporary structure. Education: highest level of qualification Highest level of qualification classifies usual residents aged 16 years and over by their highest level of qualification. There are eight categories: (1) No qualifications, (2) Level 1 and entry level qualifications: 1 to 4 GCSEs grade A* to C, Any GCSEs at other grades, O levels or CSEs (any grades), 1 AS level, NVQ level 1, Foundation GNVQ, Basic or Essential Skills, (3) Level 2 qualifications: 5 or more GCSEs (A* to C or 9 to 4), O levels (passes), CSEs (grade 1), School Certification, 1 A level, 2 to 3 AS levels, VCEs, Intermediate or Higher Diploma, Welsh Baccalaureate Intermediate Diploma, NVQ level 2, Intermediate GNVQ, City and Guilds Craft, BTEC First or General Diploma, RSA Diploma, (4) Apprenticeship, (5) Level 3 qualifications: 2 or more A levels or VCEs, 4 or more AS levels, Higher School Certificate, Progression or Advanced Diploma, Welsh Baccalaureate Advance Diploma, NVQ level 3; Advanced GNVQ, City and Guilds Advanced Craft, ONC, OND, BTEC National, RSA Advanced Diploma, (6) Level 4 qualifications or above: degree (BA, BSc), higher degree (MA, PhD, PGCE), NVQ level 4 to 5, HNC, HND, RSA Higher Diploma, BTEC Higher level, professional qualifications (for example, teaching, nursing, accountancy) (7) Other: vocational or work-related qualifications, other qualifications achieved in England or Wales, qualifications achieved outside England or Wales (equivalent not stated or unknown), (8) Does not apply. Ethnic and cultural identity: ethnic group Health and care: disability and general health Disability: People who assessed their day-to-day activities as limited by long-term physical or mental health conditions or illnesses are considered disabled. This definition of a disabled person meets the harmonised standard for measuring disability and is in line with the Equality Act (2010). Classification: (1) Disabled under the Equality Act: Day-to-day activities limited a lot, (2) Disabled under the Equality Act: Day-to-day activities limited a little, (3) Not disabled under the Equality Act: Has long-term physical or mental health condition but day-to-day activities are not limited, (4) Not disabled under the Equality Act: No long-term physical or mental health conditions. General health: A person's assessment of the general state of their health from very good to very bad. This assessment is not based on a person's health over any specified period of time. Classification: (1) Very good health, (2) Good health, (3) Fair health, (4) Bad health, (5) Very bad health. Catchment boundaries and OA boundaries were processed using R. For each OA, the proportion of its area overlapping with the WWTP catchment (Overlap%) was calculated. If an OA was fully within a catchment, its entire count was included. For partially overlapping OAs, the count for each variable was scaled by Overlap%, assuming a uniform distribution of characteristics within the OA. The resulting adjusted counts were then aggregated to obtain catchment-level estimates. All fractional counts were rounded to the nearest integer prior to aggregation. This spatial weighting method follows the Simple Approach framework, developed and implemented in R. This spatial weighting method, which assumes a uniform distribution of characteristics within an OA, is a robust and widely-used approach for estimating population characteristics in bespoke geographical areas like WWTP catchments where direct census data is unavailable. Full methodological details are available in our previous work (Kasprzyk-Hordern, Jagadeesan et al. 2025). An overview of the implementation is provided in the Supplementary Information. All above data were normalised either to population size or total number of households (housing and household composition) before further analysis. 3.4. Calculations and statistical analysis Population (inhabitants; inh) normalised daily mass loads ( PNDLs ) of CIs (mg day -1 1000inh -1 ) were calculated using Eq. 1: Eq. 1 where: DL is the daily mass load of CI (mg day -1 ) in influent wastewater, PE WW is the water utility PE estimate. Daily mass loads ( DLs ) of CIs (mg day -1 ) were calculated by multiplying total CI concentrations (mg L -1 ) in 24 h composite raw wastewater samples by daily wastewater flows (L day -1 ) using Eq. 2: Eq. 2 where: C is the total concentration of CIs (mg L -1 ) in untreated influent wastewater, and V is the volume of wastewater received by the WWTP per day (L day -1 ). Statistical analysis was undertaken using Origin(Pro), Version 2021 (OriginLab Corp., Northampton, MA, USA). The dataset contained 94 data points (day equivalents) per each CI. This study was designed to examine multivariate relationships between WBE CI and SED ONS . We used descriptive statistics including Pearson/Spearman’s rank correlations to explore the characteristics of the variables. Normal distribution for all BCIs was evaluated using graphical methods (calculated using both skewness and kurtosis, Q-Q (quantile – quantile tests) graphs and histograms) and Kolmogorov-Smirnov normality test to verify if the dataset was normally distributed and Pearson correlation could be applied. CIs that were not normally distributed were evaluated using a non-parametric Spearman rank correlation. All statistical tests were two-sided and a p -value of <0.05 was considered statistically significant. All correlations discussed below were statistically significant with p -values, on average, <0.001. Pearson correlation, as a parametric measure, was considered appropriate for a normally distributed dataset, which applied to most CI markers. Cimetidine, caffeine, creatinine, lisinopril, sitagliptin, dihydrocodeine, ibuprofen and its metabolites, antibiotics, HNE-MA and some illicit drugs were not normally distributed, hence a non-parametric rank Spearman test was also used for these markers. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were also applied to explore similarities among samples and to assess associations between WBE CI and SED ONS indicators. Mean WBE CI values (arithmetic mean was used after testing both arithmetic and geometric mean, the latter is known to be less affected by extreme values in a skewed distribution). Declarations Acknowledgments The work was supported under the C215.3 Wastewater Based Epidemiology Programme within the UK Government Accelerated Capability Environment (ACE), funded by the Environmental Monitoring for Health Protection (EMHP) programme, UK Health Security Agency. We thank Daphne Beniston and Andrew Singer (Accelerated Capability Environment, Homeland Security, UK) for their help organizing the project. We thank Public Health Wales for providing enterovirus primer and probe sequences and the staff at Anglian Water, Northumbrian Water, Southern Water, Thames Water, United Utilities, Yorkshire Water, and Welsh Water/Dŵr Cymru for their support with wastewater sampling. The support of EPSRC Impact Acceleration Account (EP/R51164X/1, ENTRUST IAA) and GCRF EWS-C19 (EP/V028499/1) is greatly appreciated. Credit authorship contribution statement Barbara Kasprzyk-Hordern: Conceptualisation, Methodology (experimental design, data processing), Formal analysis, Data curation, Writing-original draft, Writing – review editing, Supervision, Project administration, Funding acquisition, Resources. Kishore Jagadeesan: Methodology (socioeconomic and demographic data) Natalie Sims: Methodology (sample preparation, LCMS analysis), Writing – review editing. 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Nat Water 2 , 1166–1177 (2024). https://doi.org/10.1038/s44221-024-00349-9 higher socio-economic status communities Additional Declarations There is NO Competing Interest. Supplementary Files ACElifestylenicotineSIKJ.docx Estimation of Socioeconomic and Demographic Indicators (ONSSEDIs) for WWTP Catchments Cite Share Download PDF Status: Under Review 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-7658391","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":526112906,"identity":"8b225926-37be-4d54-a1cb-f5f3b66c2e67","order_by":0,"name":"Barbara 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12:06:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7658391/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7658391/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93519124,"identity":"3c531820-c979-4e9a-ba84-1e02efb036b8","added_by":"auto","created_at":"2025-10-14 17:19:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":266773,"visible":true,"origin":"","legend":"\u003cp\u003eRisk factor analysis combining spatiotemporal wastewater monitoring, WBE\u003csub\u003eCI\u003c/sub\u003e and SED\u003csub\u003eONS\u003c/sub\u003e indicators.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7658391/v1/3779aec2b5595cf11371499d.jpg"},{"id":93519123,"identity":"655b2087-63a8-411f-8ead-5e5a0732455d","added_by":"auto","created_at":"2025-10-14 17:19:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":342300,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Pearson and Spearman correlation (significant correlations in black font), (B) HCA and (C) PCA showing associations between smoking-WBE\u003csub\u003eCI\u003c/sub\u003es\u003csub\u003e \u003c/sub\u003e(nicotine/cotinine) and pharma-WBE\u003csub\u003eCI\u003c/sub\u003es, proxies for disease (city index description: LD-low deprivation, £45%, MD-medium deprivation, 46-54%, HD-high deprivation, ³55%)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7658391/v1/b431acd1be094eb6641524d3.png"},{"id":93519852,"identity":"65de1c6d-90a8-4feb-b817-b7539e84f59e","added_by":"auto","created_at":"2025-10-14 17:27:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":462811,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Pearson and Spearman correlation (significant correlations in black font), (B) HCA and (C) PCA showing associations between smoking-WBE\u003csub\u003eCIs\u003c/sub\u003e (nicotine and cotinine) and ONS\u003csub\u003eSEDI\u003c/sub\u003e (city index description: LD-low deprivation, £45%, MD-medium deprivation, 46-54%, HD-high deprivation, ³55%).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7658391/v1/34147512be5697a5a221054f.png"},{"id":93519853,"identity":"a7fe956b-0104-4d4a-9109-ef0c91d5964b","added_by":"auto","created_at":"2025-10-14 17:27:01","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":118166,"visible":true,"origin":"","legend":"\u003cp\u003ePCA and HCA showing associations between WBE\u003csub\u003eCI\u003c/sub\u003es and ONS\u003csub\u003eSEDI\u003c/sub\u003es.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7658391/v1/e337f64d32d33b5398bed390.jpg"},{"id":93519979,"identity":"2e0e50e6-4fa0-4349-8e3f-e7a69557953c","added_by":"auto","created_at":"2025-10-14 17:35:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2309985,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7658391/v1/52a23383-6ff3-4ea6-9048-678e3f9f8b90.pdf"},{"id":93519122,"identity":"3cff9d90-7d6b-404c-abe9-abcbb0c2910d","added_by":"auto","created_at":"2025-10-14 17:19:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19754,"visible":true,"origin":"","legend":"Estimation of Socioeconomic and Demographic Indicators (ONSSEDIs) for WWTP Catchments","description":"","filename":"ACElifestylenicotineSIKJ.docx","url":"https://assets-eu.researchsquare.com/files/rs-7658391/v1/2682bcee6bc4dc42de056cd2.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Socioeconomic and health outcome associations with lifestyle choices uncovered by wastewater information mining","fulltext":[{"header":"1.\tIntroduction","content":"\u003cp\u003eSmoking is a significant public health issue and a leading cause of preventable death and disease globally (ONS, 2023a).\u0026nbsp;This is despite significant reductions in smoking prevalence over the past decade (ONS, 2022). According to WHO smoking causes \u0026gt; 8 million deaths each year (WHO, 2022). Smoking prevalence is commonly associated with a variety of socioeconomic factors including education level and household income. \u0026nbsp;Comparison of smoking prevalence and economic activity in the UK indicated that those who were unemployed comprised a higher proportion of current smokers (20.5%), compared with those who were in paid employment (12.7%), and those who were economically inactive (12.7%) (ONS, 2023b). Of those who were classified as being in a routine and manual socio-economic classification, 22.8% were current smokers, compared with 8.3% of managerial and professional occupations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis paper aims to verify smoking as a risk factor in non-communicable diseases (NCDs) via Wastewater-Based Epidemiology (WBE) triangulated with socioeconomic and demographic indices. It also aims to demonstrate that the relationship between those multiple factors can be observed using wastewater information mining (Fig. 1). WBE is an innovative epidemiological approach that enables tracking infectious disease prevalence (Sims and Kasprzyk-Hordern 2020, Wade, Lo Jacomo et al. 2022) and illicit drug use (Gonz\u0026aacute;lez-Mari\u0026ntilde;o, Baz-Lomba et al. 2020) in tested communities. WBE has also found applications in estimating usage of other lifestyle chemicals such as alcohol and tobacco (Castiglioni, Senta et al. 2014, Chen, Venkatesan et al. 2019, Mackie, Tscharke et al. 2019, Gracia-Lor, Rousis et al. 2020, Montes, Rodil et al. 2020, Sims and Kasprzyk-Hordern 2020, Zheng, Gartner et al. 2020, Thanh, Vu et al. 2022, Wang, Zheng et al. 2024). WBE can provide a \u0026lsquo;fingerprint\u0026rsquo; of community health that can be monitored in near real-time, theoretically at any place and time globally. WBE is relatively comprehensive, timely and unbiased in comparison to other approaches (e.g. clinical surveillance, community-wide participatory surveys), and most importantly, it captures most of the contributing population in communities. When integrated with other epidemiological approaches it can provide in-depth verification of cofounding factors. While WBE finds its applications in pathogen surveillance and illicit drugs use prevalence estimation, its application in the wider public health evaluation remans unexplored.\u003c/p\u003e\n\u003cp\u003eThis study explores, for the first time, associations between smoking WBE\u003csub\u003eCI\u003c/sub\u003es and pharmaceutical WBE\u003csub\u003eCIs\u003c/sub\u003e as well as UK National Statistics socioeconomic and demographic indicators (ONS\u003csub\u003eSEDI\u003c/sub\u003e) in a national multi-city study focussed on 10 geographically distributed towns and cities in England\u0026nbsp;(Fig. 1)\u0026nbsp;equating to a population of ~7 m people. Detailed discussion on spatial distribution in WBE\u003csub\u003eCI\u003c/sub\u003es and inter-CI associations is provided elsewhere (Kasprzyk-Hordern, Sims et al. 2023, Kasprzyk-Hordern, Sims et al. 2025). \u0026gt;60 pharmaceutical and health WBE\u003csub\u003eCI\u003c/sub\u003es (Tab. 1) were selected as proxies for disease to test exposure-health outcome associations linked with smoking. \u0026gt;70 ONS\u003csub\u003eSEDI\u003c/sub\u003es (Tab. 1) were selected to find associations between smoking demography and socioeconomic status. Correlations between nicotine/cotinine, other WBE markers and socioeconomic/demographic indicators have also been reported in a few studies (Choi, Tscharke et al. 2019, Rousis, Li et al. 2022, Shao, Zhang et al. 2022, Yang, Zheng et al. 2022). For example, Choi et al. found significant negative correlations between IRSAD (a measure of advantage and disadvantage, summarising the economic and social conditions of people and households) and nicotine (Choi, Tscharke et al. 2019). Further, it has been shown that populations presenting disadvantage in income, education, occupation, and marital status were positively correlated with biomarkers and/or proxies of diseases, such as cardiac arrhythmias, cardiovascular disease, anxiety disorder and type 2 diabetes (Rousis, Li et al. 2022). Yang et al. found higher metformin use correlated with socially disadvantaged populations (lower education and lower income levels) (Yang, Zheng et al. 2022). Zhang et al. studied allergy prevalence and environmental factors and found that antihistamine use was significantly associated with smoking, green space and pollen but not significant with air pollution (Zhang, Du et al. 2025). Li et al. (Li et al. 2024) reported associations between the consumption of antimicrobials and socioeconomic status. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Summary of wastewater-based epidemiology indicators (WBE\u003csub\u003eCIs\u003c/sub\u003e) and socioeconomic indicators (ONS\u003csub\u003eSEDI\u003c/sub\u003e) used in this study.\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"640\" height=\"444\" 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VT50kVt2WQWZSEqnlgJoT1OZlSQFag0JkYFEfgH4T95mkmSjkTRCbqmkcRqkQK/0MoHYd9f1Ykv4jcv3kiJlb0wFW0VGd4sYUBxWV02jPsvlUbYVf2BJRFa4izeUUnZnha+ERAfmYP5kVIVnc7DIX5wEkycWWm8Im4HoYZqnm0splyrEX41HfMclUDeDcri0YjUWP1JIdqs4hOSjQSj3WwKnkUGHUZVCXakYgwb/1l7MmFqnVowTdmyadT3XWZ02NJOkdES9KZz7ll7j+JwnJS2zpW9f5XqNdGEuaXp22WQ1mJ32OYEGdp3p+IMnBGbgo5kuZ1zMIXLwGW2nJXLBFHUqhZ24yVJeCJ6BJk3TxGp3FYhKyHUsFnOJ4nk4ElcsZaDxRFyyRn4IN0n+Y1aRZZFj53r/NHgilGsPiF8E5FsT2UWu9D8LZJNINGILFKTHpUU1ZBD1M4CXaFxQlkYDhFhn1ETyxlcxpEz2x2mygn1640pQqljj10lgimsXWF1lhqUJBWVhtk0WpTykYiOvBVN5Wqdw2kD9+aUsBURN1qdAtqfcJ35lxaXUd5au/0KmJLWmFGJMIDg9JimJk9SjtQdF71dry0moEASo87VT/0epqDl/v6dnk6WmQ5WXPZQ+pTZ4ZwWahrp4DbSO2makaNpl4QSqYTojc7Vd73mB5il+2XVTNxg9OFasBmlG2iVZ+dasN7aMYWlCtLZu00qMFmSQIoStPbamOGaQC6estGZ0hASu14R0g6hk2voxG2SudMhJIAePULN5N1atk0VOyepIYFQrdugc9IVjsSmu40SJf2VKXRSw6Yo9AJtJxEWw0GqH3UN9lJhjwNFdiVKs24Qca0quDGs+F4p0+Ho/rVmtftWwHySxvFGwb2lF9OpIXhSw8sY9gIodtqRJ8f/VPrC1fGj6liS7WtqaXS/7rvMKRp/VssUKVULVXAyLbuiKs0jUrclqidC6HFBLWpUndwvnaEG0YJuXoYSRePkqQChLQ1HLWGyYSh2DJjh4NWkzO23rOjqjMhiythqCgx1itxsSNqjYi2x7IX3rIXi7t4F7t3r7t8HjiWwTlHn7tnzLuCUzuIKbNnr7jZILuTISJzsyuU0TuJM7OYkbMnEriJq7uSPSubgTum7rt5D7N5Zbg0XTuK+bupWbLXQbu3N7u64zunCbIXjrubpLuLWLO78LI4qJKUrCLvZyLchLMMyCGRTzvMnbKw0jvfcCHssrMNtxvdobJtc7IN1rL0L/orzUC6yB4Z2LeTHZ+yTNSzAUI77Ty77qsb318r7VG73wS7/ha7998r33i73tCyDuux4BvF0suioGfMAInMAKzCHmu8AO/MAQHMGego+g+In0qDEWrC6YqMEZbI/1+MEYXMEifMEb7MEhTMIdDMIlrMIpfMIcPMIvjMIwbMIr7MI03MLQYbEsPMM7LMM+HMNAfMM8bMM9HMRFLMQ/jMRGTMRMXMNOjMNQPMRPLMVRfMFpK8FYnMVavMV22omKy8VgHMZiPMYTUrwUe77Ge8ZpTL5rjMZq/MZsLMDm28Z0DMduHMd4fMd6XMd5rCQFTMaAHMiCjMUNPMiGfMiIfCq//7rHdszHjNzHjRzJkDzJj6wcFuvImCzJlZzJlMzJm6zJx4uPiTzKpFzKdIK5ppzKqrzKFyURnvzKoBzL1EM1k+aNXUPL8QibrtseMgPLnSzLnyx0VOHLn/zLfQwyt4IypbKIoBuPcMMkoUPM0gzMnWcmrEzG1nNWu2hCeIQ3Z2q5T9SC1xzGhTzOWOyB8bVACmlWs6abJTJ55pzFFJzER1zPTVzFSzzF9HzPVBwu9zZ+TRSfGhZEshZvG7ekFrxf+pzP+KzEDm3PC/3Q/LzPJcyJptHQEI3RE83Q/UzRGh3RsfLPznVmY1TQradxQuXOEk1/Gd3RHO3RLr3SHz3TPv94xfHMxWq2RwvUbFUlY3MGIj9904QMlEKdxWO0XDm1X8sEldb5fSIifkUdwWZszMVc1UHXnzmtTK8koR95jvw7oU1r1dQ81tslzIs8zVSN1uo7eqdYhQR00vVEL0NSPaiU1mQt1pHyx1G9wAe3Z/dWaDbmVFCWQdwUIlC911J9p4jtwD32gA3Ymrr3pb5TIv252Ap81net1pq9JH+nWqDHaykjgdVJfAzyiHh92pt9JZec2axt103yd5VHjhgWZjN0Slmim6mN2q0NwKJs2Qp8cKZ1dXPnT4X4MS20QjLDNF/sZb79wKjc3AjcZPA3g7JF0IdniXi73FQH3Qk81br/7drfXSCd/YW3mZzlRFTklSu9jIrH0dm7ndvwLceKHd7xDd7n8Xd3yc0zdp1XqbV4dz/JbTICtrelWN/0/d7bodfcjSqTl6Wpp4CFCk1B/VrXdD8uCM4LDirlnOGiIn6LdlB6GX/TE3apNqMElH5iG1Mcnirz/NIuLtMx3dIwPeMv3qh8eKMveUkbt07bJG8OlVInmKO6hEIIDdIbDeM0juQ1fuQyvokH4cVJ3uRKPuVSXuVMfuV0GVhPl6jwBlMPtT/VI1atR2+mB5bFha1LbuRqTtNRjuVsri42veKp4oE5ZUhqdHv6JEEFBEFi1YW5dIVyfsA6HOigQudD+z9P/3aBGpc1JHSuT0PXJqNdUgo9kh7UhD7Brozg9q3pANxDk2YyesU5YMPnyU1n1YMrYW5axRZcnG7grq5j8/3qmz7ryUI+/Bo7orikyPw2yixZC5hqWDRBNrvnB07rsm7J1nzppgLPGuJf3HFvR+jnpudgRa7sGj7f1u4pwPfmeytiYSrpcZfi4Z7t1+4Qxt7q6I4euD0psxTkK0M/Xai05z7vxa4pRE3vx57aOytHOep/i/cz1qpoYEeT+Z7uuh3n5P4pw3sisZViZn6VCa/IkBbxCo/hT52kTgRCob14Fk/xcJLp+G7wr/7VRcLMfKtg9V7wIW/WL6LyKY/gJI8gc//4SbD5NpwT8jj/8hWb7B6vys7e86Gy4UAvxvxSwUN/ypsS426u9GvO9Nxu5U3f5lGf5k+/9FJf9VNvjxZdFVjf9U7/9VcP9lQv9lRu9WMf9mh/9kuO8Eff9m5/IoP+9nI/9x6SEbuSG3h/93qf93y/937f94D/94LfGhg1+IYf+Ih/+Iqf+Iy/+I7f+LQBEg0x+RpB+ZJf+Zh/+ZpPEpbP+Znv+Zvf+aL/+aMf+qR/+qaf+qC/+qXP+qjv+qrf+rL/+rMf+7R/+7af+7C/+7W/+xOv+70f/LjP+8Mv/MBf/Mh//MpP/Mtv/Mz//M4f/ckP/dMv/c3/+VKS/dq//dz/3/3e//3gH/7iP/7kX/7mf/7on/7qv/7s3/7u//7wH//yP//0X//2f//4n//6v//83//+//8AAUDgQIIFDR5EmFDhQoYNHT6EGFHiRIoVLV7EmFHjRo4dPX4EGVLkSJIlTZ5EmVLlSpYtXb6EGVPmTJo1bd7EmVPnTp49ff4EGlToUKJFjR5FKhRbK4FLmzIF4DQqVKlVqV59mnWqVqtcsW4F2zXsV7FlyZ71mnasWrNs0a6F2zbuW7l16d51m3euXrt88e4F3Dfw37IMCR/2m3iwYsGNES+G7JjxY8mRKV+enNmy5sqdMc/FFlr0aNKlTZ9GnVr1atatXb+GHVv2/2zatW3fxp1b927evU8z9B1c+HDixY0fR55c+fLirZY+dx4d+nTp1alft54d+3bt3bl/9x4e/Hjx5cmfN58e/Xr17dm/dx8f/nz59b8Dp5/f/n79/fn/9y9AAAcUsEACDzQwweicC43B5xoUzUEJI6QQQgsfxHDCCzXMsMION/SQQxFDJBFEEz9EccQTVUyxxBZXdJFFGWOkEUYbX8Rxxht1zLHGHnf0MTSFeCTSxyKBRPJHJY9c0kgnk2QyyiebhHJKKavEkkotr0SRQgS/VDBMMMcUs0wyzzQzTTTHVEhNN9d8M04455SzTjrvNBNPPe3kc08/+wT0T0Hzwwa4Qf8PDTRRRBdVtFFG7fPSUUkfnbRSSi+1NE38MOU0004/9TRUULObUDoITb3u1A5RtU7VUleF9VVZI53VVVpvtTXXVHdtldfpdO01WBJZHTZWXH0lNlljgf0V2WWdrRXaY4VVNlpqn732VSETynbaZrtltlpvxQ0X22/PJVdaZoEkNssi293yXBDTBVdde+vFF11z6dXX2n6PvXVUUQdGsFyBDyZ40jYRZnhgW7UzOMyHz5u4YYsdvTjjhOMThAUWrmDwChYEebBjQTr2+GOSK964ZY3dK3Qhl2fGcymRR2awlZRTvgKbjnuOTuRWWrmZZ593xrnkj6X7GTuhI/x4KaT/o47OYwmXDg3lna9o+rmnX6Y5zYDDBvvijgNAG+2eBQmABenQFuSKtNNmgWy7yz5v07v3HpDtuUeWmu4A1lb7ORYCIFlwj7GRu+20QTYc7ujYhty6w4H2ue3MHb988sKd89vww9G+XO66qw5gaL5XP5BL162E3d3Y45W99hQ7b8VvQXRGPGvN5Qba754bVP11242nPXnkl5+dxW0RYv745qeXvvoXdR/a9KHhnq7x1AMf+vChVc8ccr9zpls0tk+v8PCVy1+q7V5Hd7tB+ZcaX/LcBx8NbXijVx71Amg95I0NbwfMVObq5xyR9Qx4vnNg73xXP5GRbmgmQ2AGN7Yw/9Z1UD6Xg84VgHY40uyvc9hwHzb81z3NhYZ7mTvbyiinHRCCTnOhG40NP4Y4GwaAOuILjemgIz6iuU2DR2QPEpVYqQe2an3OSWET45e6Jz7wcOxbYhYBFTMOatGLgVuZqVqhNhFCDngpJGLpRGhDkBGthSikogTXR6sr7ox/YxxcGUMWx7rFb4H2+97m7Pi9z3nQkOYx4CEV6Se57Q474gudGx3pHP+NcWm7y17xFrnJQenti1rk3tnSOLfesW134kshKfnnPdI9Z46WXMoMrzM6UtbtbHNj0P3uF8khpq6Hqrwg5j7JSfyNa1/H9Be/lIlMYyaTmRGD5r28prmcOf+ScYijXBDvOMVfAq40+VqmM8XZTHJGE5zPlGY4jfm8g5xznOb8FzzVKU90uhOMUhvd7vSnugeabpSYZND6duhN08Vtha9sHw8veMe2AbSHJ0uhJcW4QgWOD48Omlc945lOjc6To+/86DeJOcw7WZJklORf/Cq4skY2CHhM095IZSqoLs50k0Js0ChLaEoopi1x33PQA/2IUlKSLJuz7J0ON1dCVrYSj14i4jWxCESf2fSTJLWq2BrHtdHljH6iKWgFS8m/OUoRq2dNEBcTktUlTnFkZwOa2k52smsOD4++vNxcjZpS3pkwdybcXBkhd08ITlGv2+PhySpJxjVedIL/xFuhx07KVkMmEq2XpY/fLCidxk2yqYDrqU/dWDnKYhZAniwt63j3ONT9rXye650qfSjEnopQgihVbFEjh7k5avZxOLUZD4F5UQZSE3y8m2RqMwhA5gqwuQR0bnQtlDu65pC6DtLryVA112o+d4Df9W54pasldhpEvNBFL3jH6zyfaZd42aXrXyOkXeoe9oLkq6818YdJ+D5IvvsdH3x3h8n5GjW7oJvkX7E7yeSm97zqdfB4LatcCgMoYhCrcIbrU1PTVtZXGRUWy0Qsrf+Nh2UdPiuKNbxiFSNQrQhhcYxbLOOETXjGN6YxjhmFWh3n2Mc9FhRI7UlPIW90yCEl/3I5kbzkIzfZyE/2qJOjDOWOVtmZ5S2IlK2sZC0Xecpf3nKSxcxkKns5zGQGs5nVzOVm2fjHbwYynAXEYTnH2c51VtCdwRYNPGOWz201VJ8FredBU+zEhSZ0ohE9HR4vWtGPdnQxIzzpFn0hEXwOTTQS8QXw0iJLmtbBF0Jd4hMlQgeJ2DQ2vkC+F9GCSYNYNXRcvd6ceWnWrYg1kL5QBgl5Wku5ntCqRYRr/+qg1qFJxA5IjSMvJKIVvF5vtG+EZYLoKNUNSvWDh/aFWWMD1U56dnRmjWodtUIHo2bStTXNaSLFmnzXZpEXzi3qRGzI0quCNpAGcelbE1tHtPDCDv9CPQgaCbtBnEbSt1tx63ynSOHIRriSvHBsh9ebQYkgOLugvXDnoBrTKbo3g+5N6YtnHENuftkXvPAcPn9hB3+eDsy1I3PohJzlNxePDnaQCFfQQNk4jznQcb1yju+A4NaBOcybTfOZ1xwCrfhztrfD9KmzvAxEB7oOnmPxqksn2RC4tc2dk/TryPwLR6fO2bcziJ9HxxVjnw7a4R6ft9OCBrf2IJ2lA/NkS4cGX6C6eP6sycDfvO/POTWfyY70pfScFuteisrJkwgIYJzyzu76dK6RCBo8xwuAL/t2aNCKzWtd052nusx3MPF9c93reHd75xk/+y9wetPOYbvgobP/elyvPuhzVzvuf174uWMj8a4wPefHE3C/Yz712DE6NogvfeNj3jleKIPihY5zuz8+25IXz9//TAsIvJ36xd87dsz/fOtgvztI9HazQxNqb6Ma1afedc5qX+9o4Hrk2IC1VbuGVkC1XYOA6CPAz+u4AmS3rRu52gsNV3i60AhA0tu0/eu42ts2CLA0Aqy+/VuKAmwFVwBBU9MBaLvAWtM0+cOG0jM1ZKOBUHOFzQNBTStA5/u2FIS4VTu9HfgCV4i/WPsCCDg1AhyafXM/6CgDHgyNJYRAXDvAo0sEBay/C6S+FLyG6stCHaw+CNm0jDO1x8Mfi9NBI7S/IpS+C+Q///tbOm/DNSNshbbroBc7CO5YwhnUwqU4NVWrPSC0vw5ENterPZVztSmMtWggQQg8OEsztkzbv1PLQgLUwEW0OGXjPPrbv+BzwIxrhQlENq0bwNojuD/bP0wzNS48RUAMQv7zv6PbOXMbtU2zwuqAwGighUaMEP/jtM0bQWf7QT5LtgvEQ/+zvquDtmaDwg7EP3ebji+UvlagASDkQ1JExshjxHprwVOkwv4DQWyghUGMvES4xVjcuT40ux/0RhrARnNzDnC0QVmMDoW7RTWUvkTktiyEtVmUvke8tBCcxFAMOWWDxlD7RktTO75TO2+7u5bbAWskw0RYwn40Qgd0uf96U0NMa4XP47ovfMI2OyLKs4DKiwbJCzgvcAW2azYa4DVo3LSdszuM80FsCLhBKIPOG4RzO8nVeztRQ75VuzyXQ7tBCMmYTMJJFEXlQzW20zq227e7Q0mAW7UvUMeKxIa/M8nKY0q7UztYa0rzI0AICEnMK8Kf2zR1HMHKo0qdm7jyWziwA0rl40rKo4Vk04G3ozxXYL7LqzewQ8nLi44hhAAIeA6n/DmUfLsyqEsF/MnoczlLAztcIzi2c8xta0BTA8uVgzWvs0RjYz4JlEkdaDaRVITKC7h60znHfLsdMEvJuzskarRnC0zUUz4C7LzPa0qZ5D2zrEpF+MuJa03/ehs14zs7UVsKgTM1OQy4yQQ1QzzLlhTH3yzCxUQ7y7wATpvE4vs7WCO61eO8S+vEKRS453C50Lw0zlMEqoSAMpBM43M1qXS2xcS6OAzM7ZQ7bEhP7axKZzNOt+PA2tM6NVzNuhy1JcRNbqNLzmPG6NiBwGS3nYs8VzRJdYyGmgTPoOTO9LRFdWTKqlRPyYvOyjvLJfS9vwRL00PMNNS6E4S1FNVJkbTP4hE4RWC+qjTIlUtOv/Q8H6Q8qEvFVZNA56zK9jRL6dw6HbjM4jy62pvH9TxAx6xH2QPAnXvBZEtJ70RLxtRREMWONRszJ5u/wCRCmfyCa4DA0msQHYDK/xEcNQcdH+szthecP2wchOjovNrLwtrDNMDkwJE8QBqoPD3kOXPDxhe0QUA1vo5buT3kQ0ENQU/7PD4MDR8cmpj80sDUujhsx37ctSxUVDwtUwiEtforzvvj0esDQ+ewu0lVUALUOlVryP4D0z+FU0VtxJ5ztk70vzvltT0kU07bQ0o90cRrQjDdzGJbVG/ztv9sVQ9NhDsFvJjs1S4UU0xlszSrVlihtoEIFuQD0+hoxLG81VNLtmz8zx0Awv5b1h8cvdBQx8NjRzg1t25TRAgsQkH1wOqjVktz1jvtR28D0zGtRVxbQgJ8ueL0v08U09BQRFBTWF/10Vhsx1Vz0P9QjVKFbchnBNNGjEGdM7aJxFOXXNetI0tl40lcW8qO0zp9NbcWXNlkxRBNI1ZvJMJzW1daGARPUzlzw0NhdVmX1UD/89iGvLfb87aRZRAJtFSUdQ5xPTVX4Lyha8KVI1p2xUb6w7zb49WOVLWVg9R6jU4+W9N2LIN9nUZk+9eIHUA0dbmFE7U4xEa1s7Q8bdCDdVn6c9U4zDjJg7kS8qAhNFJeI0lNdTeSVcdmI7hNS88MxD6+dLfgQz7EtESim0LPy1jhXDhnTM9mM0lbLD9gvLodxbV6y7/gm9xo5LPP80+ZxDyrnEKv/IIHCMnsuzxYk72rG9UQ7DxhzT0Chcj/E2TL41y5CSW6W7w6B21Nu5vW5JXPkJzBMsDKEVU7KoXHyX02TmPLaVW76+07TfyCkIQAm5TDw/u80UPEOj1G/STD3O1HBQw+223NQ9K7EARLKDVLZftRzQW47DNY6rXGnnPatlO5F3SOwgU9or0+sdRPzf021F297ns2orPd6t09IiQ4KoTI0jzRAUY+6yNZyb3U5FVA1ZxCjOPDz1u5aNw82ZTND4ZCC9hOL3C1Wau9z9O6+q2Onms8FlVgNUXMqLQ9owXABC3OI8XbQpS9U2xXAJ7OWMtMejNcPqw99UVX/LlUAV4K53XhwSTYrxxhG71aZSPQv+Q/29VO4rze/zY84DTmzmarNxP2PTR94CuG4PhcUAs4Ohz+vJGcytI1v9Hlunx8QQktWrxtPLCLxkx9v0My0p/DRISD19FjQYvyxlHbQ0wl1ENdinVd2XlFOHQlwogVDQ38uWJS1DhEOAdtxFTrVIkV3Z+DwCd00GJS0E82vlATtT9VxE4V2gY8NTiu5FdUUOust3mcv6FpxHdt1ckFRgYt39UTtW/1VtEQXYT7WGwMVtJT1BdUVFs00gmEU5ZF03obwNHDVAIdSV99W4DNuGBtVlXTT/gLtOxANTFMVgHGxaFJtdNt2yx01pBD0zhNtj9j2jOFuhasxbO7xYvLSNDrQFemZmpex/8QXEdP1ENI1UOozWQSlNNcg9PJLeXei1i39bYxHWk+y0zo+DZuxkaFbcRQVUZX4LtU3uXQmEtTA8KVlb+WjVc9FMPq+LZx9jVB7ehVq8s4DQ0zNdOPPrzbG9qLXWo5JEBxNmhwDGji6chezpmEDg02lmmRhtSx+1ROC9kRxFN3zteH3mqEi0fXMzoHVcTx2UN0tWJ07UOLhkZiHuRV3pVFCrtEhTaUNDefLL9k+8Gni+PKg0jBXNvIk1qju8VijWOvQ7wObEwxRTWwK825zM/LTs02jsrR21DT5Tyb9b3SxE2e01Jx67jhE87Ts8vRSzaCQ8OvjLV2vcv9s9UJhWP/HwQ443U28ivI62PGQMVN6QBR8BO1b4Rs29XIu4TaivxR3ca7PzY6AiRtk53LncPjWMtjzstJ2TY6yuNNBJS81O6g14Q+LQ21y85f/kzQs1M4QpxR25Y9H0Q+OTxBsL20mszJTUNTJn3PQ47gCMYOvbw8zi3AclVX2S0/pRRP7gY8745tCQ9PlRyaQ55NK84OBJRfpyXOsbxhwSzfoh2EniuDYHzsZ7vUmHzq8pBB2j1tCYzKugTK58i9u/1RKu3EufzwVSM/Z8PhaFAEKO0OI5012xZMYf3KjStOGtfupytLy8bRpY21xPNututd0l45EPXuyCZw7ehe2ctxMy4e/+f1Sst2WhK31a/8X04rzdFk8urQNmmrtPJcQ0Z0t+N0ZFtGUYPcNmmmv5s0yPpbwI9zEFB7Zq5zuT0UtcY0Zj+XyQx053OzuMY9tX2bZCHczxCROp42WZStZLGL5cCGyIgNzoM1NUjvzjfM506nPmxQT9GAvFSPvPUm9PpjyGx2a0r/S5OTEHMTuFGDYZMNOZ905D1n00FU1IlTWIu8Zzp/sGwViCJZbItVNshjEEqlYpULT2I/2tM0vwPltpJDNinUuUrHP2fftjfV1H79EBNEQz0kyAw89VSnWMjD9lRnt0b3uFM0WQ+HuBN5dhW3NF8UR2avub+kdyFc733/Yf9ffMYGNJFxVEsGudF9xz/8Umtkx/gOxYYNruc3HOl4czf/S2Vf/1MHaTaqTNaCZ1sd5bqRLndvNEF0l8VkXfeoNmnMm/hS1vf2dBBTMxV9Z/FGv2l9Fw15szRfFylIc/pI4w9Bvqz4/Y/Rlo4ZvfGGmb6nR7HZnCnwQzzMo8IB4XqoN3vzIFPXsyk6NIi06k4IscVBFLuzp3s4GUlj23rWEbie7vNT7w+Uq/vAL/svQu/BF/zDv6pr5VI0O7MuU/zH79Iyg3zGX3zHj3xrvfzG1xdpBwDLb5FInHzPF33Jz/zKJ33KD/3TH33MR/3IBzElA3zDl33EHymqp/3Zx/3/mcr93b/9rGL7guh93hd+Yor94OeTQxv+5Gc0mTF+5Xd+1YL26Cc56U8e95IQ+tqvA/uv+7og/eL+AlsVa9r+WPp1Ansv+8ov/Sqw7oKO8Z/+94ew+OcSzp9z+K//HKG4Qw/sUollDIE3HwGIL62w0RqILdEXbAoNYmPIMForHTt06EjUUOHFhRi9GGzlaqBDjCE1ksxosqMXkSpLthJ4MVGiaCtPimz5sdXAaAhpspw5kifQn0J9Eu1pNGhRpEcZqgR50SnUp1KjUp1qtSrWq1qzct3qtSvYr1qvBAggSOMVFmUDXMG59q0gtVcuCgrAAltdtk7VBsCpkKzZqWr9//L124psW7plpfJ92yovXJCtGgduZRdkXRZhN4vl7Lkz6M+iQ5MGCeA06tSlV49uzfp16y8Wncp0qqNM1dlSddLAVlvr75aDDroc5DK41kE7EtHysvzr84EIacNOftyrTBofG5bRUfq5ly8yw1+r7vq8+fToDatvv949/PdZsfG9OzCvoLiXBV0hy+KKIIcFplBmDa11Fl5rdbTWXBlZ1qBlZZ01EGIOXpYRYvnl5xYL+QEGUlkdAjZXhfQNGB+K8qW44mjYpPYiaizKqCKNX30BwQ7DtaSDSwN9QVEi10T0RUKt4NbSTtLJtsMXtMiEUEXl7YgbNjrgpAMEAiVSEf+PuX0xXEO9KcSjQlAmAtKPW7ZS2w5ZzpbmQNfQIlB5MB3E42wwERlkQy5ds+WWPQZqETau8KicU0Qql5BCxlX0ZJrR0GLlllm2kogXgda46YydOtWUp6FyOqp7lrFw6oR1QcifYncppNaECd6l32KT2dXXfXapJRVZqbKlV0OqOpUXYycSuJhCEppoH1676icrXgGKOi2p1VYHI4waWbsttastWtCPhu4Akis0lJGIKxAokkiO2EDgCrsVgZQIBIkol5Jx6NJw5g5eGHqmbNg4Z9EgOvhrblSDWADBBQmxaxFCEOfYinatTCSubs4NUlC/EVHZUkoWD1IuvEy6+6X/IhD4lkhvAxXsb5fp2vucucZBUFBD9M6c0J4sc9zkvkhWGfAX/erMLdLdihbSp1ZpS1PTUT3NNNRVU301qFhPnTXXW3vtNNhSX9RrXQjm1WFHxyaGTYYesmXgf2ypOtirffX61K4NDUY3228vZKpDsOLkV2YgDWYrgk8jdrhcT38tNuRSPS552JRHHrXlmWO+udVdV8451Qphi9rlnU8Ouuela/256ayvHvmYINFAi0L7KhRNwJe2MukX1yy3pW8XKadQ0dh8wdGOtDDXUkIV3Vmmd8+TRC8EWQqJI5beQbT8chTTTgsNBkHk3TXFC6nDduxeKbBArnhh5XPYPCeb/0wHiVnu4FfCFH1EifZuvEC6FKiD4OR4hlKSb4hEvCJprnWqS10DXxdBCKLudK3jWtIyqDRq7cpUY2sMgMZml4tQZi2zGiGDbDW4yzxIMn4rUIUElJhcBaBYbzkhW05lFrdUhj/8WWFZEoOqDWqwiEob3WmIqEQjguQ5xkFTyLCxr/EQ6XhSdMUTn8KylXmHBsdDyEcA5UXhYCOLWSQSVdrkxHHlzDtGqki9ptMn3SCqSmVgk25mlync7Us2G5udj3DTI+I00SJeKIPGtjiQiuUsZJhyVxlk469LcQlghnRJeAZySCYukYic/GQnawQ3nMBKJPwJkUFUtRC1/Kc//v+BW4ICU5ZczSUvBrlb3wJUuL7NZUGawZBdXDmht1xIhQY60F+UBcplhhI+LkJiEpkpzRn1T3pVIlT5hqNAAPYvItiQEnFkYhzcgQknwHuelaQHvD+5cSpnWoiYGmKlbX7Jm4WyUnnkCT2F4K885duTwMqwkDOl83wKjNM5KTakMcGEjRbz3wKjEb8CpgSAVULgQSp60GZOk1MY5ChIO2oVwLylLYfpyOH6xqxSHguHwerl4UjKlwl1EBvEnGWCIBRLlbBUMbjaZeNwoh/CjVCkRg0paKCZRKQeVT79Ahf6JGKbmLDMFUVLybvUp8WWAU85I6uXV1kmkB8dxGR1zKT/V9iVKSJ5JzwwqZfFouq8JnqBYxy5jVO21LJWgLVf7tpOS97llJeVSyDdsarJanYjnFGSO94p2iDo5b7fQUCQ+GoXWjfJ1M2uZyhLSYpnlSLa0JIWtKb9LGpHe1q96UqHd2EliAYkrGQmTpWAq8ksI3QqHdbyhUHc7dv8kz9TBWhwvRpcSw1SSvw0RLgLKeYPS5ta6ap2uqulLnavq13rYuSZo8sud6srXvCOd7vifVif0CgSOzEPJhY5EgPLxCg7Ead7AJTNmyxStAIuhFFBYR5FEhINq36JvXtyyUty96OPbYRQBTwolRqFPgfJpkt9qsiZINKzvMbkIO800wLd/0sc/WopT7oJL3lTbF4Vo3i1H20qZ5U4yotIKEK3KqpKSfi2XLkUKoN5ZWwR19wTAY6kIYKMguqjq51SqKgytUusiDWQEMUYxk2FZpWz/B7kZIXLpPEyerwMZqv8ZsxdTnAUo1JmMrfHzFp+82asDOdOxqWXBOpQc3ebuGZBKC2JmwxMZ7hKzfgZLR36z6ChkpbH7PZUbWk0quICaVKuDdCp3K2g9ba2uCBIzp7upHeROOdPk3rUnTFZqVNtaqaoutWrfrWrYz1npQJga7K+Naxzjetde0qCFXQdBR04QQv6Wti/fiCxTXvszPUEc9Z1dnk/u+xpG7vaxb52sLGd7P9tA9uCokMisrst7nCTe9jjNne5qa3tc6s72+7mdrqt/W52yxve6L53u+396xfrut+8/re/A94aLAO84AI/uMETvhWFMxzhDm84rkM9uodTHOIVv/in+W3xjWO84xznFq1t7fGRf6bFWAvL5JpN8pVvisXRfrnLY77imbe45jCn+c1tLnOd43znOf+5z0P77e/2vOg8PzrQjZ50m2uo6Y9p+oY29JT8BEtaDXnM4IqLEQ35hUDFhbrT/6Z1pAdd6WUnu9nT3l2WU9zNrnY72z+eIoLHve5gMbKEbsoWXD5mx0EU4VyCaiIT0tQsSCYms/4u98Wbh/EJv5R7XXOm9YT/cT0wWdNVDIP5+bgHsHafs8Sx9XnHi0UuTs8PKzdUSh7HUrbB9dtM9fNLwaFebvnBaYFETvrdU270/abBRL4QtNU4h4GxaR5jV4MQL1a2KlMEHldaoSPzbJT3qg65760/FVySRJWsdYjfg9hkEr3NlqvcIff5jhgVPj377pfMvOO9bvnHH9/1prdDOjLXmuwOIuTKyaW4Apsg2ORdnUcQytUtj0Ioj1XFhEc0REFsR5mwx9/khPlERIchl9CgkeZJxkBIFQRqYEF04FNcCkY4z/3Rn76loP3NXwsG29BhiwriX/3lGw2uoAva4AzKxdM5xSvVDYAE4QhlRuNUyN2U/0gFmkis9FSEvJL55SAL6uAL4mANRqEVro7GvV8zsUvzSRGfzIsXoY/FWMSj0IvwnQm9+FV4jJEU/YhfXYoXVUy6uOFwaEe8tMk7reEgGQmOGBIb/YZzSMRwlOFtAB8Zdti+cKFLxKFAgE8ZAJ9sGGKfhGGfpJkWWhndaZ8mPgXerQqwCMhNmZTc/NjbNI7gKdqACIi0NJkyncolZt8mphqWVI9FTARCHIkO0A7wRMy4/IuQhI9EkZhMoJEODIeaGArtWNFcTVH3hA87lZF4AJA/UQ8EWElzBFgT+chY6Vc6FU02KUS7EEl5GAdDYWAZ1Yvx7IDxuJHJ7NErwljoZf9LLLpf6gFIrMTQsvhQ4xRIZhjh6+kUcvXU6j3Xjr2j72XhPC4TLUDAwjTM0BxSSjhHpoAgkJSRQykQMAofIv0I+DhJR3pEhQmEQ0XSFckRojwippCVJlVPmzhJG7WLnpBVGYZMuVzTB1JVFMGExoCPb9TkgHXRdjxR9RmkSGFfQo4e903dJ/KNgfTShYAQL/VNDYEEWeDEcQ3ZKmLlUW7l2S2dV3YlWBbFjVRjQThPPnUHTmyHVaHjOVXJcDwHXuFEWYKElWCgkdyVS8qGXebi83Dg7nRE9TTfO5UHMY4Vz5BYnYxLwASjRaUjQfGLdLDRdDiPTngHCoYl2mXmV6r/HVDEIIxg5mZqJmiOJmcGFXvknhK2Ciwd0z8ek2aA4kAMJBNGZXNlWmjeJmmKJmf6HEISpdJsiQB6YV5JpmH5y82MYUPApQACj06kxOxISm8QCRWJTHLWIby4xHQw5yORi7y0IRRtEW9cU2W2kRTd0UIGCfS1i8jwJE5wlWVGI4/gjiVuJVNlom8u3pP9Ut/NEEvt54zFUuDtmI2FCEylouBw4g7FEnvQp+MxaIzdTnVKRdEACcW4AjZ0R1kFmDy9CY/UZTo91JBU0Z10aJB0D/lA34+kSUnkk6FQxERwTIf2DsvsiHOEz0FUo4pGhJW8DJykk5qU1U9OhGTun4NO/1M8vsh9sl1/TBqB9MfYhNB9INph2Bnb4BmniURa/IcPQinbcCmFQOmUFinb9aaYcpJu5BW8hKCaootunKkJSsdLQODGeIR0vBOc0mlDAJb0iYWIkQvE5ClI0MKcyulUpM9H4EybxqnLsIcClmkzGWWSkp7uOROwOSpXViEUZiqmbioVdmrlPFvwhFf9mMSaQajKPQRR5MRuzsSoDoUUvuoVcuoNzmrTQNMM3uoU0mqu4iqsaqqn8mqs/uquDmuvyiqw+irYkGmkLqulMmtn2GezRquzSqtnUOu0Xqu1dsWRqka2Yqu3diurfau4guu1Qiq5nuu43mdu4uaq6qa7sv/ru66rvLYrvNbrvHaXrdLrvdqrvvZrvPorv/6rwAYswVKXsqIrwqar40GrwsYY3NXIwzYsgyaswq2dV6Qcu1Ksw21rjGhsa9jpVYDsZwhJZA3C5i3owoVFhmmRj6QNWKBsXjEWexnggs7sZ0BEZKlqXrUCOHnsaxysxFaZfwSTYXDaXkhpsNyKllIIK+nKZDTtj0GtfTyt3ARtw5mr1YbG0VxFc1RHIN7IOw1fV4jtVrTJRejEA5zJuYAGdCYfVLzhQl7kRErFHtlLadyIG6aETugIImWtfCDrsRorsQarrhbr1NhYYyAIPtrYXoSIgkila9EK4dUHbOnWTCGF4Rb/LuEGrrBmbrx55otwruYC7uBm6qXgTHl0KM4UCn1pIExEYJ1aqM6KjwL94kdEh+4wxOVt6NWRRNdVSZb4ho5O3zsRUJmA7OR9KEYUb/GMqAD2DxoV7wjiCe3Ai4MtL+3mopTIC54YDxpCBX2NYFV07uaWruDu26T67KfZklXi3idmRioeV4ScxSmK0EpVho65ymypr8AxLP8GavUAUnIihI4MyRgtJLhQYlzdCIMp4gf+j1ZNEfikqCYJaR0CTJqVQTWGzPHAJaCM4RZpVYxWMI8c50XowMIIYLo4yUYmBKKYoUQY0swETWGxoY8sTIfVy28oyg4gDMs8InF2qEUi/4zfLlwRy9oxMUQR+o0SjlDdFF6T7Vkq+Y2QdZd/dFqUMsv/mhrHRtMRW8VYBm/54AujhIdCIM+47IT3emFDvMtCzCIE+EiWvE+RBAw5XtSPTodsbFShBGY6UcpyjIsH3xdicpOPKi81siMZXqfxpMR+DY1FZQpDTbI8BeZcZsQW0ZNLOE90/A4wfrHYgDKspZR/zlaBDJGAtBKwsFJaOGmT3e9/tFKzMIiv6KcoX59Spa/V0gsOZ8eZLKQATnBd7Q4gYcrR9MggeQFL+mGmuFWVBCWm/E47fWAPT0WbQEDLtAuWWETQfElyupFGhkemtIwUMdZCVg+VZMp0dKQOKP9CczZSQEVD3y5weDAQDbCkR5iwj6RxyMxz31bWIWlHFm0xVeyrQQPsQZPdbSXTXZTI3ZRN3dwQKzLI1gHL4FF0nlH0YcTKwCZ0wSI0Z4Juang0SYP0QhTMh1kADVBE8CqJGFKKvBCJoTyvGC7g+1gE7iQEYW4j+6BLRUinSBBJ/CRYOrExFoHjIPIsy7CRvPjF8MjTp2gKF5EkALGRJluUbJxP/qyuofTI/q2j9w5jGRDmY14dvETPR3e0SYsW0BI0neVWMvVWgxwZK2mlVJqInfkgjtUvKvZFW99yKPkvYKNJO+3XQbXL7DwK+EBmWWWFhYGMF0qUMebICjdW+Gj/FVao5wnzi0W8S/S6ESIBs07o81W0yXZMKBel9h4dlBWRUVUMTM68izxzMAcnxBSV5B4OtlurWhIT5FlUyOEpS39eRlJOMYT0FGt1Gk7B7GA/aMjttlVcQ7lg03THC0c+VKAUDXOk0wGB028YD/ZQhHTEUYA5z5b0cC3ql0Od7Wb3rAd/gUDZpBv6KCSud5xIhXgLb73s9E5b1ADF1VOtWaK4KApuSS5ukyRHRPA1FnSHa4NrmVz0B5X5p/zKEC8BCJXVI6u8cv76EP1KiIRrxi49+KdhLYm7LWOlKVQoz1O47VYw4Jma819C4JkqKtdiRUHIeJ7GeI1vhovPOJAD/5aKf8WQ/59WoEugknjvki/peq75Mvn5WtuAEm1c25RfH4vdIN5EPy5tXvTjGtkvAVmUN3n5OrmZl3nViDTpPPnojrmb25vKteo3qRa0yfmpKpuwxTmelxa1yTnn+HmoYi6akzmhv3my6rKSLxPWaZ1kFNdjSIWj69KjN/qil2CULfqk4wXWDQtzJ3pICbanh3pzF5Gow+PaNU13hfKpm0Sqp/mqMwWrj/qbdXGtlbqs23rS/DWu3zqvo4eJ9zqw7/qolLRaF3taHzuxI/taJzuzz4San0azG3u0K7u0L7u1Vzu2U3usBzu3C/uMgHq3h7u3t8a4i7u5owetF7HI3f+5tm5slWVluXPeuc97vK/Gr1Mr/KLK0dZy1boQnhmO0vbSbaFy0j6a0iots0iaedRFp3cGw3OG7N0KvLNGVebU0kBZsBc6lG98m3N8ufXEdwu6xgfFdgC6Cy4E7XS8yosuy7P553Jsy5/5yH885NYHwP92iBSGCDGx5SoeTvWnSlFtzgNG4XWaedFWrHsWROfSqlvsFLfWvx89RqwelCJaq2/NiJREqy+ay3e9zMuqrtO7VaBXTijSVCSfM4+GHG3GUmuHLwP5V9htpuQGztx2vcsHuDerxc9yYiiLMl2lraQUscgKh8Qm/u6ncWNG+RXT1U3IpBPOabbvKm5642v/umQIV999naWjLGpy+n1kHeRTOlVe+ddBGeULlWDcyoI++p0Znt/e/Wvk00JUY5zwVUHNeXlI8p2oKoTKxG/IfqvizKj27JwvbxzpTO8Wde8Heln7hUz4E/EUMKoSP+yj3HOnKxJGCCnhiumthKqUyG0ViPZXMYe3lEjAykDm1BObCl80CGDoUOH9nZULHmWkMjKB4ieqTatDRl3fzfg3BkCwaIWNRQBB2AQFuJIwQMMrDQe2gohtIEKFDw8OFFSwoSCJDQMILHiQYsmIJlGeVJmS5UqXLWG+lBmT5syT2HDarLlTZ0+eP30GBTpUqEtXOsoMTLSDRqJo2HR8oUGx/1VUL9i+7IBQZpDWLwO9fLnaitaXo7S8KAqbiFbURNgSJWqlNlFTlUfLvA37NJqrt2a96LCLLawOWmC/IE30ZRC2QXITDSqjw1WrRBB2XDVrWTKNykRBFw09WnRp0qMBpFa9OnXO06Zhv5btMuGVkx2x4X4I0nbJkRJ7bwzJomDEkANxR6ytXOHJ3SFRJmRB8WErhguLM2y12yPG7QZbFWRh+6T4VtZDLjyesDv4kruvEJ8e3qDwK9gwUlwPnj3+ABr5C+mgkSy6rzzwmrPIoN2s++2jjGaLMLYJX6uoJNcwvFBDCznEaUMPO8wwxA9FBNHEElEkUcURWTxxRRdbxP/QFQho/CoRHVqBwLIvbmzlqLi++MqtVrJKxJUgF9OBIh12kEsHHZLkMbDzpkqMx6aewokGGnc4L6pBKqrKSBq+guqrxLA68wuo/rqKybho8PIrMWmBgEc0U4zxxTxh7JPPP/cMVM9B/RS0UAtxYo01QgFl1NBGD40U0hQfMpAiAovLKT6QMoKOoP/0A6khgT4aiMCSpPOwUg05QrRAnH5TCNYFDZrVIwG/uy9TDGvDKb9Sf0u1IoZC2m86/+77FTrr2tO11l5TfbU6D/U7NtNVEfLo0zC1fdRbR8GV9FtxRzSRwnMlTBfddXcaxAII3qVlKctoaLLONI30ossy1rT/TMl5KdqhMQhouQabpgyGwBWDaZD3qzKuikaskpaCV+FoyvrCzoGwZIwiv+DqsknHzmzMi6RGJtLNRBiWq8uA2Y1Z3ZlljlnR1WjOueYIX6UKt+RMWlW68RxS8LxPbTvuUwjR663Am5aLbtnqhK2OwO/QK7Yg24pbCdv+JBJJa6UBnA9VT3vNLzdShyNw2l6Bi6g4snEaGj6kN+zaXJ353hk2v/sOHHDAr8mRRhxbmSqaHdacN7HF4iLsrTThUhInpFp5OfO3Noe5KspBj8ZwGgd66pqj1uSRzacWC1JJJD2GknLZKff4Gh6rWriqwwTvfXDfR8PmZtaA/914mLD1/2++rpW2aDqORJ2v+UpLpY+kgVYt28RoL+x1aeBsVTtWgSgSZEBQoZbVv07/u9r87tX3MNpVlRX7oF4JhNZTZGU9UFTovtabTJ2HacUzIOD2djwFHpCBKrmMwEKmksQR5gsGs0wrIIaNaNwoghRhDC0aNhDKcOwwIJRXlxIxlgxG5IFrgpLp6sKmzgFsXjeKjAf1UjINZvCFUFFE5pSiuQYOcYELHB7OiJjEc0nnCs+pyM86sqlb/UduVNxaE39GPt4Qx3wJappKonYShgjnP1UrY0c4cpBKCcI7ehMjrurXNDZ60T8LauJ5RnI3JppnJMJRo0K6aKD0GAcib6Tipf+sSCsqrjE3tSqiEmnWoZuwxDWTXEklP2RJlGBSkpn0ZCdBmUBRcnKUlDTlJU9pkqNtcCoW6pEraPCYzGBlZIMok1vQVJWIyE5MS6JSmp7yhTI8JSKrrEtcbBgN2UXFR01ZjGeEGRcR7stNTerR7RBnrzhRZJup1OQ3P1lKVI5zk94MJynRac5QppOc4Oxkoo7YznOqU5zllOc66clOe+7TV/8jHyIzAj1ZeQ97zfGnIEElUEDu7yFmq9uyTASfWN1naXUkWkaeI0BQZahVygoAdUBCvmH9b6DiyR5HdqMg4vRva3DjTj+PNVKKFvQ+ZGSBtoRjP326s5483ek879n/U6CWU5SPNCokj5eIpJgELRRpS5CcejIf9YuChnmKF3jnBVdcNauHgUxjOBMT1MUJLFsFoVwSlxe5PO5NbJJmkMpAVg9eRasXpIhckJrXo67riADQ61+F4hHBpuRoGiksVVbiEQJa57CKpYpgj+ZYwyb2sGKMLGIfq0qNqFKy1oGJYyHUWclylrFMO0/cMNvZyaa2sJ41Vf80G53WFpCAmN0rYElzW93idmfELIkFsWEw4f5Wg+XkC0p8i0rfAvclWQouSgzm3Cwl7rS0cy7tgAtc54Zyt93l7U6E11e/epe8waOWOamFz/Py87zlIipM1psh+bqoRC9Jr4rSeV8L/wmrUJjUL7WIFaah6pOU5TUwTYr63QMvWMHqutHjXJKXBjOYwqARr18nnOEKb7hv4dEWu+JDKg2PeF3hmtS4TmxiFK9YxS0m14tTDGMWy9jFMbbxjG9cYxzv2L191fGPaRzkHAuZx0QG8pCRXOQkH1nJTWbyk2WcYA5PmcRVpvKVrUyUvmYZy13m8pe9HGaqgJnMYjZzmdG8wPBu+cxtTvOb3RznmUkZznWW853t/OYLFzjPePZznwHdZXwG9aeD3qdP85loQiv60ENF9KJRed/1umqSkn7vfMP16EYbWtOddvSnOQ1qoYaa1EKF5/AgvekE8u4uhR71qFldXB+5mv/WjPZ0qWsN6cPmZNdU8W2hP9NO3uVa1cSOSKx5bWxRKxvXtl62Ken8Z2l3eCGhfZ8Yr/3YbC9WOTV9n/lMC6G6bTvQ5TYem3WmMaaoTmU7WYwDxxITrsREY1LxmL94UqZixlsmKWRJV+oFAbyKhi2iMQy7OHewYJckLGTh3Ap5shgK2gSWh1m4TOZtE3+/JOE2KYOdyNSYDSqpJ+z+GMlhskF+U4wpWlkqwWPtkxH6bto1Bx4Zi1ZHS30kps9p3qkaWtGX2uqhorqeuZEuszUfMV0Gw4noBH6exznVQsv10MiLi5MgDVe7xe1h6UwEJanrAKyfeTpxn747p3fwuG3/x8own1uRy1QXR7LOOnFPIjGwXh0mFqx7ck30a7zzvXM+AntFeLd1qj/Xt6ZDSViwMeziRhdErSicYc4O9rNDqeuA7wrYmZsy5IZ9Tg/+WNWJG92jIKpHxT0u2IPk+DDp6DxTGr1rmBtcqHIX7yNrfOB5xmebD19Ca7yORSOym+Wlh4znyY90XvtHsr1tVHN0D/Gxz7c9SwiWLxd9w29oS7HflUxvsaXraIF1H1nFqVa5t1PJFBEIDuTz/k7Sy+sC1aUEyTN9aeVTD46CwsKF9M0xhAhLABAu9M3kLAMzVCjkWGIpoMQz1m9icEcBj60wPoP/5OLjZkmrZo5H4qIr/7ZiRzyo/CgirhjQ/iDjSuIirvDKC6qCKZziqcbCBt+vmSaGhUguAYnETlaOLNzCg6hqXlrhKu6PcxSBTMaCcWAQY0LITAIwLKhw4ljuJKLiqhLjMPRNwlgIM0gu//buJJ6pLloBY6JCSSQjIgrwXKBsyYwsDuFwDp0MRqxHU/5oa5xnoJoHb2Jlo56PN4hODx/KQLKNDt+wDhFRDhXRW07tZp5sdNakTipCdBqGmZpETLDJLxBnY9Ak9sSESAiwASfHS7hES0qRuoIkc8Ak9nqkR+rERjzj3UIRTabkMtrk6jhI7pQEc4IEliKim4gpMwYiMSxjZDKPcYgEceyC7P8ShxYqx0OYiYOWIi7EDkoOA0p2z5p2wEyKMXVspEuCpCzqrhhZhoOeJC4wQx3PA4UQpxZHERfTrhkTjhbFDk1syOxO7nBEaE1KAko4aBWzgl685C3ib+6ACCsg43UAkkwqxxXSLgDhokq+YhAMoymwARgzTyyUAkeqkfCIceru0Sy2iYOSLNqSLiV7oqFuyjkUYm6IZjqCjjlsAyOuBm9YcpHO6JB4hW6y7ydnA93QxV3gpZVEb14yyC54x0h8RGHeDWb8TV/ypUk05/1uxGIChqo2Bx15RF7gojLOKiFZ8SgMcBBcQSohKBog7iQ+ryRWESz1BYPoqgOHsIf6jzD/kmIyZFGDTMjwnopIGmPmfIQtPm/3vPIuZ/EryDIsrsHfVK5OIuMyznJkOo5fSKbdJKZfPGYqAEYqJ/MjRdEkwBIpiCkgNWdk7lIltCJeJIbfBAaWzIQtOAcs7cL3xKItQsYsumIxrqIxUY4MhWhe/IIW6u91kuLBTMYpDkY5V/HBxqS4KBAuz2QuYwYorXM2fK43MIJYAml9XCNVxGN/hK4+joN5yIeNmqimxgMlr7M9N2nP0MVgdIBGWIa6suQyyoJzEqPuHswYOQgNQ/NxHsMa049NgEtjBO4pOA8nZqQVbCnysoKXoqJIdkdBgWQiI9NJzvDt1o4Xs+4TgwQf/0VGKeJiq9it7nwTLiInK3LiZULUAr9CiM5vC2mnRTlnZChD/16ngvCF7CJTKSDMLRvndVjGQGUIcSgDK6BkQG+kQL2gSE8nLLqiCIf0ucRO9E4CQW8JSpdzMZ4C4N7iLIOEcaDC8hQSiHIkQzURmE4kcSASJ6gxRClQHN8CmSxv92QnRZFJXspxZF70X2KUXdhTJQnVvqxD+ZDlU2QSOvRwpATCJmHLbWTFpHYSQEbFPTEVNcRL+ECj4GAGOpNiHEUuhhSGYU5I/ixDM76gNAGGFVGCKQPmLbQwVbFCg2ZkOS1ULJuELJ/yglJmB1GiLTFG4GjoliaDJRZURxT05f9sdSquoU7QYlXx0v8gjobEMWJcoTEa5in6TyxUJ/ZWKIMg4LnMYk2GNdgyyJZC01WJxCAd5htZNWXmDyrg7kl50ADlz10BTymCDaquyh0Jgy8Jpoeu9Ea/AIQ+dYNCUy1/8wqLK+pOU4SOdSXsUjkbTjTHlWGy9WXU9WAmVkKaLdVu7dWYjWSdLWSL7WQTaDu6JVWyJzj48Fj6yVmmiCQCEaE64kHORmbr69lE1mdT9mdRdmRLtmjLyceCNmkz0k6sse4wkUeYyS28gPaYdgf+5RoZIyoIA0o+rhRdpF6a9hsz50kFUhubIhaTZEkOFnYYUh1Tx3I0CBddZ1XFpCv/lGSDEvREpu6YBtJcrDZJ/CVKPEhZGbT8pOKEXFBiuqSXLuNu0cQic0hJF0MctbEcDdAazQQVL0dDqyIpqgJq1yQwFkPgng5yrYRiyM4VGGdKs7Ky+O5yADIdHcMwMmNyl1Fw11Eq2nFtGQMffdROsKJh0m4r5tZLuGILGzNvMeSYMHTqKulxIGxJn0SDNEY5jXZQM9U9x6igPip73mNZtsb5CikQyUcPvwYkyuZ+TurDCrV9g0IoaebBxi+slGr8lEqrsIozdu9M8TLe1iJy+i0NB+5I7mp+U+hIKiMyIG4N385/GSNbHWPg4EIvBVNMpKmDXGLjboRZVWIyIDhw/x8DLIRo/ex0ghFngkM3qz4jhPHSR8Bqg1noZDpOcqTphiyDc1b05D6jChEDmUzChsCELfFKmOKtdWpCfgd4LGwohrP1PBQ4fye4GCEm8X6xMS44Iii4sH7kirH0h+8WL2aYB7PVqwASr9qyOt03jXXC53RDfRy1bhSKJICuPGsleV7qO6KHewUse9X4PTcVgdrLRYDL0nBikO9O8+hs0uLOQwx5vviE0vZm7bqOWozYvjQk9ybpuLjuhMaQkV1E8Fai6yD5vjoUup7rkU/5Q4YLk7jOuPaGU13FNQSvke/O0t6pTwZCkkvCuSwol1KpkOHruSgPmPcvXbC3j6+Tjf/GgzzwYyGio4lMYiPuSDlEbDse1ZnZiGnio7CkuSWpwpv5OJxZYvvEecN8eXCAtZzVuEmNJ51BlhETMZ7heZ4X8cb+657pq7/iy780RJHluZ4b8Z8Dmp77eekURaARmqAT+sS2S6Ffd6ABeqEheqIl2lEa+iRhGZk1Wp05WsPgt6NBeqNF+spCuqRH+qQrzKBvBqVZ2qRdOomO+aVluqVpGl3IuaZxeqZ1OpJMVmh7OmmJVmWN1qeHGqiBNqiJWqiVOqmZ2qiH9qih+hEP2qnNRfIiQh9Pa6iS6+KAb9bUKeY+xLdoAat/+th+qqFPAiKzetB67Z5cQa3tqa3Zq0P/kgusOQmsyYLYQFmukbrR0Bqqn3rQFq6o+7rSMnqnETunFft9mQ42+lUrqYLkdrgNaWINydImPCaMWcJeaeIpR0iL2VCzMW5csRiyr1q0/W1+xaqVSoKBZ+LrkLUAVZst8fpM93cmOjaGa4IBGY5ZB0PmuLooQGZnBDPfOvklOHuBjvsfa9t1E9vNXE0nDhu87G4m9nXwnrvOVFpRUg7ZiAspciLWLMjymvT3Okm7dpGrMXlJEg7wsuS2d3mXc6cvmmlDaQe7WOL1sAJ4DQbyqjtNXuJBeceQT8JgWjW4djG+bwK+HW+/S9H3iCszy+l2+qVgnIrA4RTlgolHZdnu/2inwM8Zws/bJLrYvPGOSLJKWnFPlcOpwGOVJrJr8ej6Qiwz73ZC9uyOUwGGmCxcLIkqu+UMrSjGq0Q7JgD4NICkuUXTtClCEZYbyMnspju4Xvol/17HK3Yk41IILRKHKfxiqcJiMMJcglcRNyVDXwxDX8aCMTwQCe3t4chEtW0p/hxWBvWlKfSlBO8t/7QShOYvMchULlOwXhojMTaG4az2WHFHfrOKBpupLvy0TOZ8AWlgMiR4KSiQXvOWYL28Aa3WJKRiMo6z0O2NDSHQlsLcq5Q4DUuoXqR0OU9iL6BTOsVcKiRYru48Bm9klvyla8FiPsHc1n3kSXsR0h8DBf+x+GQqnWN2nTod4ziZMAWtuNRP0GOkimKo/X71TUzK7zIo3WPOnClCxk4aY9I5JrjHLKIdWt3ZnaL9RGPCZGSe9OwyxLlURyFT5KI/JEKD9OygkUiAFJHNUboiz93bvaLXfaKlelE+2XepZK0MA3MUEk+u1F8SWEkaDhjtcUOjEYiOqSo8UnZqs+A+yHEow/Tkj9SnK5ucBK3Eri1YZhx7SYRoREdis+54RGJOfpkKsBo/l8LD0gIXd4TIhBzxbXOORE2IEyPl73A0yJpsXnWmcU4PtiL8k0wz0WnfNoJwcRkpR5fabRppz+SwAQZHUHamhLqM8Ou+deidwmoDF4T/pG5yvBHAYcka50QHZNAiz+RlYPc8SBfuX9Fx4aQdA7Pl0aIbJ8ZxtFJgiASFos5DBkHgjgJKYAkcDwPyXWZNLLIjRchl6D6WoTzMhhsqxP3zMebYBoHV5KVfi7tOD8tIwtgrRfiqIx9WSVEpqK6wlvIrydC5F9vKPppi/GJGzvLluFAz14RgAB42O1Zda7crtIpYY01dEVbWkaK/10pb0y9Nn1Rh4uSqmFX2P9azK+NXwUp1Qi7/2NBiFsYWH2b5t4qCGpPf/JWDpCoxwNLhNpYtFAcbAAKCq0FfWiWikchLIoECo2HTkagVNokQLEDYIRHhtS86HiZKhHHiFy8e/x1mbOXQS5lWX75go1EmES2JrVx9pEGLIE2MCbF52fEl4Y5BIVkmmohU4pcdNJjugICSViKbO6ZC+DgTqQ5Xrapii5aoI8RrLz+GhCgxWhmSXSES9eKKxkRaOKPR/Ro3acuvYR+6ckiDa0i5XeHSDHrwK7ajNW/6XDmxb0aqNPnKvdYXc8erQa8STPklWl6JUxONfJi1Mk3VSVuzfu06NuzZsmvTvm07N+7dunvzrrwYAgSX0VoCxbZxkEdsHL8gnNhUR8GFQGkFbaXDy5fhZJcuZcxXuEvs45FHZK5DR1VX6dHylJ7+aMtoO6TTKMh8hxeIvvv/9g/gfwIGSOCAA/9iA0CCCi6YYGyafUEUS+Vh49NRyyHmEkhgdbTYVgh9NJVI9bVCFmbj6WAQScy5tKJH2JEG0UetYGdhGeVJmFBoX2E3k1coUsifR9cABWJlYQ2nFIsQXfdjhyuSJVGQBLVoUHyLtXRQREVV1RNHRb5I4V5pLSQdcsfReF1gYbok5kT8beQSf9alR2JYpqHYFza0oLiXlzKGRQuQFlYGp2JdHWVaUD+yNCKUXtEgUYeFFneUdEtVFhQ2cYEoE0g0Lfqjp1ol0l1BXh1aoWkmlaFelIk6RGGVdxpU3l5uRtSRSVcmWSp2gxSZZ1+i4ihdV7BRWGCyBirL7LLONtsfBBf/QPCje5VGBCEtENAiWkcIOXRVcePtFa62UuEXIVLCXXSUQKyle+ldNcn1U5Sh9YRQK+aKOiy0z/7rb8AAD1wbgwYDEGtS5pKFk3akRbSWSCS1BJFocumEjU5D8QXiV6claZdcX0CmkEQlZxqYXRAg5xNdiTiEVmSXJUbaYDOeKiaEQmasiGpcSTyRKxe5lPJDZajF1kQRb5jqy/l1pZxReamcU2h5keUFXX+VpVpqXGf8lJaMnVYxUhoZrdYXrhDXl0BkbWtazSn6pKOMp27MmsNmC8YYl8oVl3TKp6kl1nloQfjzYWxtDNZUhGmtctCEiUTcaZAChlJcOv9KVrEQ/yYmWtbbXnOXSiZNvS9+NEnGOkmQYj0Qfi3NKFdxuVqeuGuRxpYwcLz/zlrvSAm/e/DAt0Y8ssYvj/zxvjP/fPPQD+/8RE8Jl+RiGGXqlo6x/igu3RQ6vKVB92mnQ1bMrTsTf7uHHw12XIE692ITr0T/Qi1xFOn0xUsPwOgJkHr+U14ACXjA/w1QgQhcoAEdmLwINu9gClpefRICqYdQjE0bGwt2QuIp8lSpQyODFEgghCJYteIqxuIIcvYSvud4CT0nNN8GkRKWDUaKLqaqlFeWopxLYcdG7jNg51gElqtoJiE/GoQOBpGVsKAvNC3hoWkmBhTnzGg4OZwIC70kHf/breU5yyuTUiAQxCzeLmbBoRhJqkIeO9GQf/prTocqZRwOtoSFwxmeC7/nQ9NA5YR2ghUd1YiSM/HnPiMDzwxzqMVhLQqEIQGSpaCCjbtZ6CmkuQ9IlOOeqtynMKbhSb70xBEnfkEzrXFimHawpsUw8j0e6wh5BiKdg7CpSbt7oMB+SbBgAnOYtLnTz2KmA1Zh61e2nMu2QAM4pbluJqVTnddYUrRvIUVtprOevmp3l5GZbCU2esnWcIIxfQlzncRsJzvfOREKNmg2pskJtx4TNeZExCCRYUxOgpY1GS3GRlFMSO5mlDXlCHQQP2OoQRhjmsiUTKIGyd1HoAgefSX/lJoTvQ5zuGKdj9imFfl8KEDPYzKUDlRtRxnEjGihkHwiZkYZc8VEa8KWhKDUoA6tjdowlZVBRGx1N7LpR7gi0o/cNCH/jEgihqrPccpURkqxKE1NlpWS/UypB00qUyXSU4xi6VJImehTs6avfKp1LiUdiUHBGtSsJJMmRoUoRJca0MjcSKcP61rUBhK0v5r1PAJ9qnxiAliTvgaejHWnYxs7IFh95TjduoqLQoWRM/1wlVGlXysyCCgoKYYswSGsBcwHI296M6KFmt3dZgSqaz52tpCl7TARJM95OshQyBLt+5DiW+D2lnoJo1Bwhacr0Ur2f8hCimSPy1uJiLa4/wgky3JrIzzRkvaAFNKVQySrK+Oy7LrUTViJohsbXTGXt9B1yHmla8Ds+vK87RWuAyVLXej+Ni3ADcuMABW986q3N0GaLnDXtiiWmZe41Psuy3xn4MqE177DhS9///deX/aythy2bYc//LUt9kxnP1GPQp2jn4mscCV7CUxGuKXLU9KzKfeJWoyPOZS86E07YHEdSOsSY5WCeMgeLvKActsgIyuZyExespObHMyErEW2AxNpMXUG5SWvd8vxRSB1ufzlLoPZy2Qes5nFjOYwq7nMaWbzmndYvKv276oqjvNqNEXXSNmkf23e5/D23LuZ1ER9qRH0atIyo5+9uc1nXv+0o90M6UZHmtGUfrSkx0yh3F660pO2NKc37elQd3rUoCb1p08t6lKrGtWmpi55W03mV6861ayeNaQ1/ORcZ3nXuu41r3/ta9rmNtjALjaxj23sZCN72XdWtrOZDe1nSzva1D4QkhE27WxXe9va7ja3e41rb4v72+MuN7nPvaxrJ8/c7Ea3u9sNb24zcN64lmAB7Z3AeleP3viG4L71fe9/97uBBOe3wA8e8ITne+AGV7i/HV5wgC8c4ROHeKbl6b+DRjxWdOb4hCFevNREClaK/rc6K94aV8gW32F24MkfXvGO11kxFDrPxikO8+fJ2d4yh576eHfNmiCcpiE6np7/Gi49WkS44ytXGNJzfvOAh/vdVI+31auO9V0PGzd6+8/l/gmWyQkzbsL6mTj9Ixnb/PM17CFQSX2T9oGBnWatgZRBDpNRJ0OxMhXrCW10svbcbIU2SNobbN5emXxliuuQOQziXbP4uhfFRbUZPLNuRMyr83rdUUe5yx3LeW+HHlqjx03ps55r3G4du3E3lCt6l7gfgtBqrBENfxcMZ+cKevDc4gg1L8xbu7AmolRGSrpeExLbz6WYCX55bUwDpasW/+ijdW6HaHLN39OI7aqhc1o0buFA5SbCZvRY1Kg53azoKl3RV2+PwJNcNylRtbgvIl2TY319qfdn4dtdkJQv/10zwR5/4TUb0UfWcxTxtxMJOBcDRiiC9gVRBD4zh3QO2Dyop3nyJggYOEyC4H0cmIESoW650RI/gRMm4xyQsRSjhBogwUVZwi5JcRD1ITlaFDIzaGPDUSzycxHpA3jqcUoQoR5J0wqKgBCeVCEp5YKRV2JlQBgz2BETlU4iMTRgg4SsoR7woTRN4To0tVT3kT7fxBRUwUXsoQjNtBVeoAj7oXhHYYKy4xyRZz4pxhIOsyfOoVLJNIQ1aHfUkTR9RxJ6qB8zmCFlQBRctCIsSBQwYXfbNIYZUQbb8k3c8lnOwRWRSIdXch8uxj14SBoo5oYj4RwzISYzmFM6iBHakv+Hkeg0FUMXTNEzZkNTQGg4rEKHOcRC1jMcLkEQSKgQgaEdJfZBzXQbtmaMtYaMtFZerXAFLBAALHAFvSQIzsgCLLCBE9GMzhgAz8iM1biNARCN1aiN3DiN4wiNynOOjdYKzngF2DCNAXCN1LWNG3hpfLZmEwGNx1hcggCOqWaPCMSPLJCM/NiOsKaPBpmMCPloF0dBYxZ2N8MmauMim4Uq/HNHGtRdYSEYRFMaUEEeOWFKM+JCwrIVTrKDdicdUpFgcnQpe1EoTmQkh/Me7fMRHLIXr7cTC/g0rSFE0rERtsQnizMoGbQXaAEo/qU9qOIpj2IheBISs/Mj2pGRQCn/E4XnPhnkJjugkhkxHVBhHRGIk5myFyOCHV5QJbCVMZiFglCjkhYSHMJhS9sDlN/UKXbnFd+TIYtkEBjBHrjSUrB0ECI5MaQyJVAjNBDjkZkSK1yCSdyzEoFpEhRJI9GQHvMzLyLplkiJlC5kkU54MygCNW02dSGYed84jjR1Bds4jtGIDdqYje3omtXYjt+YjQKZmuAojjTljvB4ehJBjTPCjvT4QNOIctiQmv9oPAHZgc/YG71TjQnEjB6oYa0AjsyjnKTpbKtHGzxGN9iUKB0xFQThLa6AGbWTGXRXNmBVFFUETvRSNwXxWSuiFCuxnj4GGIkjGaUDGcl0DfG5/zq2NBo2sSmoAp5MsRJ3wTSTsYgsNh6SYRMLIRg1QYwDQSPDIoC1o2MvozOKFzWIgRDaIRfS0VN0FRH5siUK4R2yyDCUATQx4yqstBWAcU84EUK144SJcoRakTjaYhH44S7HgU6q00E7+kwuEhRxYYhR9FD0AhmesqFYAhOzg4DopRFO+BB7qTg6iiojdDYmdBYohaALYRqRuCdOoxAw04pCJhvYaVu/iY/9SJ3wKBH8yJvOGI8UYqfNtY682VzK2VxIUY00IQjSmRSDGil06oEBSaiGmhQz0goeOCOEOhGMCpzVKaiEKqgz0o+NSo+YCqlyhqmT+qhwCqnuqJuPev9Vinqog/pAqRqpNJGa1tg/jLqbBUmprMpxbNphqidPueEk9mM0IMIVWEI+iIIfcZMx3tMmTuIqYNJCLIIdV4IZJGEicUEiK0IffQMe0OcmrVBOLYKs2eNNcdQnH+ESUnFVP2IrNLIYs2ghU2InIHEtihk/uTQ7dYEs/LIDmsEeJOIjJLInOCIjZHEnTtMhMZFjJflZnJIaiwdHuUI3G5FRYhkapzJJaImsuYQVSLml4eE+/GEntNSs9vcjEnulIgKabtWSxBGt/AEUd8cSuSI/7uVcikQQJfp6LaJaWEkU+iQSeCSlOZkfX+IVhTIpWIZdvQmC0EKnxSOrBFkZsdr/mrxZGXlatVSLFNfZGgRpnOMonbe5jaP6jWNbp7T5ppoKjqo5qaa5gWTrjl47p9pIja1Bjaq5gQHpjG/7jV9Ljew4tfS4jdhIm2K7jeFomo3aj6kJtoIAtnIKttZYq8bJnI87p9W5tB02grQhpeI0OInATOYxnkyToGmnN78yLI/CLeGELrTEneTToT42GTRhhHixMt3Ud8QIOoqwMmNREpqDeAgqNF+hLQe1NG1Dnua0IZHnk0azpSsRn2FnPSoRJXyDgIlBOnBBrQthNqTCNVsyNuBheS52N8qhTa7CNNrUEOZEdrmSGSxhLioDfurihTqwMqd7FEXzTefUM37j/zFwsTLl2Z0+sT1x4jRBkS78UneExUlb2iacWDmBGIuDd6Q+Y56XAVNfI70lQXnFKHFQ93Sd58EdDMLDQ50sED2K26fc2LfVuIErbMJ7Ko6Qi7eyGSnHGaeDGrYlPKom/I6J+owtLKd56oxoewWIig3cqMN7Cql2uqdoi8ObGivUyLjkaLd7WsRMPI902o6pCbgCqaiKu8MeiMJcnLWTO4+xGsaaysPMCbWBG5A7LLkC2XIj/MF1LMJ2zHC+xJAHMz2i60LkEUnZ0Ry2A1sR6Er5wXHSsR/yIR10WD7sMRI0KBlIIiz6QYPsiskdAqKkoj9e0Ee3tBON/B5ENB5M0f9AF4QeHAEZvZMeg6zKiwwdzSRLxkESb5EebsJS6mpZ7ArLGtEcaJFLZVJLRCQTPGspvPSxjEwxNYcQr1cckDKWjOE+Iisd82Mf8sFIQdJA2ky/JtPILIKH39UcvbyX6FPIrKIDarGLPwTMI8EfrbwdW+SW5LFNlkUUSwFLkRJJ+sERf5HMPIQlhkInMRuHmaQczKEitmIrvzOalxswdCo9XFwZyim3Pzy1z2jRpvmMW6yaGI0UVHyNjJqob3zEJjwRgdvEOGy4aEshZIyqzRgALR0ANPHDzFidmyq1V0uPQ+yn1Mmaimq5UuvSeWu1rFrCFBKQRSycmhqNZFzSTL3/GmLMxs84xDZtqNxoqroqbL2KG1NBGvgZgQ/TKf20GBGxdoGnFFaWMWq9rdsUESAlWAL4MEmzdxlTaDJhlnQVgV5d1qpRT4zhvnxdLLLhuRQFGxclFXolFTMqZDbFFVsVUX2VMXNRHnWd1/hpGHrVVXst1zhU2JVR16H92Xolg2MtE2CFQzMnULSC2YldTApT2WptECUV2J3NVWI92TJhusZndmqtVXAF2rlT1+bqGl5F13FFEAxFVrjN2qQRRVsVRcXXfQ7dWCc900lRxJLbjofaj3maFN5NE+CN1Fg90YmLtVFM1czZxCctkHFqmrA50zosuUDsjSadp3Ta0UXM/5yROzxDjKfxvalcW6sCzrUurcLXrafkjd+QW7kcDbgxfdRvWt+76abjSNUbRt3txKsU1BvKxWD79WB/amEARLMS1hrgxVuLBnwmLl7w1V0E9+LwRVoxSxuulrTx514fZDwlrscGZIE0m1/CJV8OJjwtnn/N83HCFWHBF+ILtuQWSChHzuMMtF3rNWELNuQ+LuLoNWBzzGXAt0R3AkBVbkTV5atKq9XOwo6wCqcWTcR/S7dY65t8ihRS2zvHyd+Gyo/XiNVhS+HWrWLR2MRHbdUovKeCS9MmPageiNU5DahUy9PqLd9ojNPdzZsQPY+T2o7XeVVgrGL9uOfsrcb/vf/dU63fTc2nIv2Baf5LmZvhwGYaeQdPU8jqtf7qOoV5HHaQu56QvE5ddAqN2niNt3kFt9ne3o3ed2rFxV7EO8zsgk7ehXubNn3F6u3fER6cflvVjQ6PBDnF9m2p2R7E4EjsTvvDUgu1b1rtHsiOxB7H7+jF597m1QnGhv7nBg7g9A6O8B65dvrFZizHCinwvj7wXbbHBkPwCd/rC09rr0fmyshmDs/wBQ/xCl/xE2/xFK/xsobxBo/mr04gU1y4S73g7Q3nOt0aZBu4jZvFWPuOSLynaWvSmQ7Rc662gx648x3z3hgrNw+2P02Njj7nxl65Jm/T8xi3G23thZu3hMv/mmILxKopZ/oOqY3OxC4cue/YjVI/qk8P8u+knV8v9rZO9mP/ZGXPWI4aPGqvPI7aO27fqGov93PP9oemp3JPgWyfq21v927v96f696ea98VV1XLm93XGZ4Gf93Nmj3AP93G/+JAf+HVv+Hxv9sK04Qdz+ZuP9pzv+QTT0J0v+udmtZ9v+uzk6qOv+qfP+qu/WHcM+3ks+zhHx7FP+3h8+7YPcrof0S/M+yE8+7sf/J5X+8MP/Lk/cAfPIMKP/M3P/M9P/POW+Bv3geumccbfeWF2Oth/+HsP/V1zdzTXrl7++8Xv/NFf/rgvdR/v+q3v/u2PnWEP/7r2Mf1xU7bR/3ocpqaOlxSnYv/4ARBeaEXDRsMVwVbYEi5U2JDhQ4cOv3wp6KpVooVfEkWE2JHjR48hQY4UWZLkSZMpUa5cqNIlS5gvZcakOdNmTZw3debkudNnT6A/hQYlyhAbAKRJlSIt2nToU6ccabWaio2gDoytFlZtZVGhVoVVo1mkdQ3bWa0EB1FsiLAVQrIdXZmlSpBuQoUWwUZLRAMjtmteo9HCahYbYa1gWz4Eiy2RF4U6BlnckYhrw2tTD1r9SjfuWatVvWyc2spvR8VcB54tTTcqVNiveYKOXVu2bdy3defmvdt3b+C/Gy4lDgCt8ODJgXr5omOHDoXNv0D4chF6V/8a2JjTqI4N61gaO2go6qrji1++1xs6555w+99EX7zsgPBY/FTC58tgP1/Zu3iM+jpvKo0kMq++hNYKbxDHpIPsi/3KgGAHyGgowznIFnKFu9P+a0+75sQLbULzKNLBFS90OK3A+T6cyDkISjOvQ+VqRC4nvBo6bkcde8zxR9qC5BHIIYX00UgijyxySSWbTPJJJKNkEsoppXTSSiqvrHJLLbvM8kssw+QSzDHF9NJMMs9k8qzikkrzTTTjLHNONet8srmpdjCxOlog0GqHhDRKRAf3KJrIFaysw2YibDa8qLuzmkvIvEVp2KsvjKbTilHmzkoUq0V3iKYVHWR0ZVH/EzfKS4f9TJuKUMcIZbQVQOOLbCNSKRq0pe9IpUVS7wwllFYGWdXq040sPczSRABF9ZpBKduzUVjtlNNaOuHM9tokg7Tx2xvBFTdccsc1t9xy22TqXHbR/Q7VVmb9oliLuuPuC6+wGsRZhgaJr7JBJYIMPO0SQQibtQL9gqCJaMXqCwoRVSjgZjX017x4GVzIP1ovqg/CGLvSKEbmEjKIoJMrSpBfhXZgkK/sRpsUI34ZNSheXCuTL6yYFwaRLwjK0Bjdds0tmmikb4pMB4w/Gsu5Y7t7iGOeGJbaqkGfE+9qhq6JF9WDXWrFLEZJKnCmsVQNOOyk2z5apaPUXddt/7qVQ/WsgAvzbqP4Asa7uUSuERQ6u1BNZNf4XNN711nRKjVWvEtNFKyKHdvB6xMj02hnvQeDFbGKtaoXK8Qz5Ph0tWlYzazFAWU0mnkdPWu0a/wjNDBCO41MUccgaxTiUt+u2zdvhxe+6ETqSwRFqhmab1KundqQaY5csYzWDD+q3KbHsFbPI0tvSjis4403HyK5mTp//diMDTZeijJuWQdF+IIM2uwS9a/v/QFV5PvmWOVC2tlPQ+KToO407HWxyw7BKsccgpTsbNHBlUY2BBjC9GkufaLF+FJ2M5W1rILxylAA36W/+OlJZae7SAP7Aq9F7edsLmNfDZ8CJKN85P84OYTIDo/Ew4j4EIc/JOIQjVi8IwoRiUtUYrO8gJf6nIVhgYPfEwd1LxXpyjnxq04rRtO0WKmQIYP6QlUytQMfEqRRyuoRGQd4uIydJ1N++RvG+sI0+EmnQWipToHwBS0umodvZdAjqaiTqUWtRYWzw6MadfjIHkIyiJIEYiWLyERKXlKJm8zkENmkrkiGcpKitGQSO4lJUmrylJxMJezMg8e9eUpVXvDT0gB3N4iliCK6PM+fBOI4PSlrZ1IMGLQIR7ESEapyFSNMZXKly0WB6lhvIdVBpMOdSr3oWDJE1q0M+BxKkUpPsCobowb1SoyAKkWWsZTg0GlMqzBKOs3/OdUq7ZlKU+ITlaPkZyn36U/QKLF8A9VNsyAAAehsLyIICiBzLJOoDVmPURCwXsQwVs34xK9lB1UPzEbivoM5EVG1gk54DnfI5BFoELSQYPCSl4h9lTFGgcmfRQKGHjw17KUx1Y5/9tUKCcGUhl4ow+FUR1Ck/kZuNmTqTxL1l65oKCHXGOBCYGpGMx6uIf6iilQdAlMDXiaqefHqRTRGC1yhdav4WelY20pWuHZlMhZzTFhwpVVc1XWMYLVqXq/nmKz+9a9z9cqmzOiV5SXkcHlNamNr4ljINkUhB50QzDaTEIRkxZxs8SPAJEXSyIHocPMhyKgoCyuFhQS10oFf/2htNa+zoG5Qi4XA3WwrT0PRrILvysqgOshZLYa2QHvL1eEk01TkxiZuS41sc0ciOLY4qZeuMYtrilRdwBAxoFONyMFcc7DttiUi2JWieDGLGfEWibvrPYkPw2vdHrGtcDw6mHfRyzbn5je8+uUvSyR03Nk6RK3MYQ50Sra3aJTBL4IqCFqdlTDAMTgjE9KoojpShuxppFgFPGDe/gLCUs1ncwHKUPcuAhkImwwstNTYbJ8XMEWND8IlpMjH4lPY5Ob4J+kzjo59LJK/iuSJ/SXyj438LW1hS8lJZjK3nFylxIAGmxjUF3QQhka/wbZsFyHVsWgBOQih5S9izmEA8/8ixWo6Uo0DhFx8YIcraYL2LAyaCNl8BqFRmcd0OZOUK2oZ0QM+a20B053+8KLVbSV6yU9etKKT/Mk2NdnRjJZ0oy2dJkdWWtOU5vSkPX3pToP605setZGWWGRUg6RPExFQVz52HhgmihYLphA2KFq5n876XxV22gtdbbCClAG/lvsiaNFja2DfDHB2PE1Vd+YxsAiSU9WhUNYsTDEV/vTZO4PYyMR6ZHCXhLnhJneqzV1udAPn3OvW4a/k8xf7LZYvd93IqCbS4k1ZpRUM8hqrVwLHyGgsemPUqWMCp1UuT2whGC7go9AaOINHDrM4Y8uv6lUdBiH6cGY54NcORxD/jIC8YQJNN7iXq66Ss1vlKWf5yiPZcphDZdgemTlJUGTekdRcJDqPSHyyt/OYB30nPCa5y4V+dKMn3Xj57Kcq9cnKpjMdoPeM+j+dXnWoT/3pkMSL1K+udYnw2utjtzrZs/51tJud6mBnu3ZPTpytxx3ra0972e1+drW/3NQB3TvU+d6j8PLIvYDnew4DX3jEp+nwh8/R4qPU9rzPXe6QvzvdI2/4oiNd80rn/ObDNe7OE/kKLFAMWlhwekEw5PSnDwALrkASQaxeMaNnQetZkPrRg0UQowdJ7Gvv+tLv3vVXmD3rXf8V4DfE9YLARvIZs/yzCML2tCe987Gx+wBc/4H5CRG+Q2Jf+tCHO/zj9zz5zS9Zop8/sti4QgCyr6P2u9/9t1eI/Fv/fpDU3v6vb4X8f//69mMBhag95psk/dO/AMCL+LO/hfC/+bs+9+s6Auy/BPSh+Hs99rO/+ZO+CmwFBJw/7nM/DMzA7Ss/k8s89TPBFFxBFXy74kDBFhQe42sJEVSIANSK1ivBkJA+4ru+2hvABIQI9xME6SM9kGi/1MtAAYy/1PNA9wPCxBjC/jPChCBAbMhBjgjAAIgIK8RBI4y/xPg9CBxCGwyAAmRByCI1UVtDNWzDUHPDUotDNnxDOpRDOJxDO9w7ublDPqxDPJzDIkTCs6i9EWyFK//gvwhkPNC4wcWYQiKZQjIsEiusv+rDv/ozQ0c8CySkQAkMwghsvP47RDPUkUkswkv8kQjkwNZLCEEcvQL8wz7MQ1lUslOLQc+DQSjxJzS0xcYCPV5sqvZrBVN0QvBriPkbPe0DCQpsvR4cQ2QkPgWswZDYRCcUQDJciE38RApMvVU0xlHsRobIxh6swlH0QnN0iNoTRmbMPkPExAn8xcbaxdDbvUMkQtowxCRciN3Tin2kDe3DvJ/ARZwQSHksORckDnhkqrNIxHS8wnK0v9T7wFHUIUNEQETUwBJkQpKTSE+sQOXDRA18Pw7sRG70yIX4weYzyUmkQAg0wo8kQnb/JEBB7MeCFJ5arMl0W0b54z9nZAiGfMIG3MKW+EegIELbMMqEPD+iS8oa4kDhc8eJdD0yzMFiDImKDMJ0hIjYu7+qLMPTO0S8uEYgjMjsgz5KVL1vpD8aXD5CfMmvWMVMPMkENEUO3MSbZEp2uby6w7vKm7y9tLy+lLyoG0KYnMj4E0C5JEf8y8Toaz3Ca7xJ2hFBXMOUBMXApDy+zEzA1Ey/1Eu9hLTi6MzL/EvR5EzBxMxMykANXELHRItPTERMckXQmMBu3C6ZbM0jmUT4s8SRZEyHiERO5MSAWkD/k8sCHEbdpEv8W8Dta4zNfM7ShM7TJM25u0ucJDLGbD9o/7S9cpxC5kNA5tvKhtjKdIw97UPMQHSIAITGBSS+3Fs9H8REhUjP+jtPTGxPYYRGvEQ6X7xOupHCkmQ++XtKDBTB3bPHr9SRusS+jiRCo1RHI6zBBMVG+WSIrazHGuTAj9C/p4xQZmRLM0y9rQSLLszEuhRFkXTMhBjQdszH/Xwb/3Q52GxLHgxF16xAQoTL2tzKHFRF0gtAMXRCUWRNZnTC+5s//SNH47tE1jPDwyQ+aXxRmDvIpZDSozHFRXw/IxXBoLQ/1hRA9cTIS/TSbWzMBNTOgOrCiOBRZrzRHmrH4hxTBwRTTdTSUoRNNqW/lgQNp0xJnozRtrFOKz2yKf9s0iAMRmJcURwN0ZgUyrfswd7MRCskyp/cPisswu8EyR6kTfpryy5E1EFlt6UEVKNpQu8rQRENPmE0SktFTIdYVR2EVWHkRx20Rw/801m1SqRsCR1c01wdT1l10Fc11V29PvD7VX2M1SYUxlBdOj+MRVh8VmnNFg0cR6psS2JMyUy9QT5tTR4009Wz0yZlxaisQCxFwsPk0Cms00y1zFmEVnid1mgVPCpFinh9V3m91zpRRHpFi9zrlr5LPH90ScW7Fsf7O8jcr4AFyHxtWHx92H0lyGaNR3BsCOLkUu/U1jEEykcNQf4LxAPlRuDrxi5syExEQpDVPpElx0ztVVL/bbn+nFhyM9XYoFmZfVncwNmUg025PEQUxcFFjcaKLUKPdU0w/b5vzdbJbMhzBcmjJb11JUGNZT+1vFlyq9eksNpUk9iX4Fqt/VqPEFSdpdgOfEuX9FOHVNQSlL+IkD8Itdj509G4rcALzNaTfb/D1FEjbMu6XUmwNbJR/duxFVzCLQnPHM3DNU3UjE7GnU5D1E/uS0YJdE9IPdvH/ZHHxb0kjL56xAvzzE/PPUT2G0fRvb5mFD7S/djTFd1+VFzqXFzHRVzZdd3PxNrGhV3cfV3dTVzpzF3evd3dnd3eDV7aFV7g5V2xLdyrba9ToryuhQn1Ul7IitnBrV7pvd6u/7Re7cXer8XauSmf0pIN18gRnssNr1WJ+kpeqCiv8+WM8QWK8u2u7IrfsB2vnKMJw+CMnag5+jUf9eXe7Q3g/Qzc45k1HHuJw5kMf6kwQlqPWgOXA2sfqFKwhgsXRAGcm5iR8zhgnBAImdiQ5/g2lZAZiRgahtAImRAQC9kJrlI194Cq5NJXGXbYea3hGYbYG7ZhGs5haAVNuNvha5kOjSqvKFuSi7AerYC35LmzyggnKyGIL8s00FAzI5FiWqklH/mL/MWhMesRIZ4fAaGTLa7iw4ivs9gQQYkizmASKFaI9gC4JKFi+hKhKSZjYiKd6dgM9tUS+lAV2KkPRzKMYf/KrioRJGur4z2OY1sh3785Y0ae4y/jYYQFYEoW4BilXqRZCxOWjwmCEEFCGPkgCw92DBR5jKsaDO6AjGXiF1fQqDKyjqJalbM5nP1gqWdzCAz7KqzInkTAsGe7kL8g4UIRiJCSo7GorcAIGYSBqmCWDzO6kBXzIoUA5odgqb/4KcC4OetgEWATiF5miJkSMOaIn8U6EaAKHsegZu+oirUwZ4dAlL+glIm5q0TRDvyADGH2EKtAY3J2H6JSLHV2noajIYKgKOtg5kHAMM2In1YGEYCenAlRZa+YiFH2AhSZoLysZI22ZKb0XuNol+Q5qI2Ap3LqI10BmGEBlVGBDxT/3htzsrJY2p1YOWiI0QrlwYpZ05MLdiKHoA+Eyi5DEyHfohWNoJQAeggyWoiHMRSp8Y/JOhXTMJwAmq2+OBYUIiO7mLWDWpiRGrP0gKlhMYiKSZ638KauIpXDUSGmWYtDOmpBCmpW25WzgBbK4ptD+rLo2Jw+gg6jJhwRYpRd3ogIW5hZcZkAW4+tnhnHiY9dXphWaOuEerCEypC3pplE4RSduQ6mOeRz+V+O7l+OdqzwDe2VIOCkSR76KKDCuCBiaRASQqtbVqyDQJQBW5iAGTn5mRloqZDD8bMAmRlaqaAMKaw+sYAJUSx7OYgXerOI8pic6xOD6Z7vgLEQai2Z/3EZB1spgZOp1TgNtVIsn64tqmAO6jAZjJg1YSMjxgqLCQkP6IAYy3ge2ApuEeqg6KgQyvDmB9aOg7KA4YYRjPAzJDbo5qhghlgRjNmZtopqorIs51ZqCLiAkJGj+SCUBlqhjVkewlGE7IAQqmgxAxM2AuqY6GAQ1RkMZc7o4uXLhAM78DolOE67i4BOiuub4xWzUYJhIZrxrlMVHbKe7PpdA1KVUNLx4cULIIevFj81Gz+Sw/E3tllxjiDyHPddTBozzM0rKc+hPYzdLXcIHUAoOHsYUGE1m5kIQqLyiSFlaKmcOoMxwgA2yAGU4hq5iYCYyXDneAkPo5gO6ognv/8YhHAinRPpNlZLkn8JEMKRJzQya1DuGD9D8y7i5E+ZOODpOu/Y6uN4GkJ5DjTHFQuADiXHabBQMDTPc4Ppsjy7uAvBFzq/l4ZBcspyzmMxKggZuZdSEhWS68LIqBR5lFC58y6mpfL+D+ZgEEFHC5dBFLzAY+TmC3IuaorIZWwgJFJpCA9HLdTy8uf07KBIHgPX34/wl/SFCPziq5VgG4ViCNBO5xZpYHOHiWHLZ3DmYOf580IRifGhOZwzCfsRIL+Od4oKjJ+TX/Qd94ErCGDziApXMJaZsFYHIX5HG5bCmtoatqsAtoA3+HQHieZpiAi+DUwGFzhe4gPZDxcrcQz/Wu/E0iq27g/lcZZ15+SECOeNcJ+gToueQuqhoY4ZcRZWIea10auI0DPOYJoD0Wlk9jPVwIrrcJaxaC35OXGEmZjs6JeJLqDMiJETAbmpaHUTbgiKOpiJgAucoeN4aSHAkO48qbcDZqmqOLb06JPsErimgQgayWsNyoyYMZhcBrmIaOHWnjhkVngSJ5ZrTw8QwZ+1N7BuTuuMoIiTmTURJprcCIzlOZhX8gqOg+HFio56vojMRyvrgbiP+4pREX093reFT2IiVyZ4M7jF6O3j8BrLmOuYpgqM8JpBGJuvSLgY/3yraq2VhqqPTzjHoAFYgZ2C76u1KX28eLhGnIw2/8dr0R8bcRLs6oAqxdiU5Vkni7Ap2+d+0Ve4r2Ksct5ngLubqbqI1TjsnY8I3cGfzVh/2dc3reh989e3j/sVybgIgNjRKlEibANbGWzV6ssXbF90fFGoMFErWgQRSiRoUCNGgrQ6UtTh6hq2i9hK6jCoEuPKlixfujwJYCbNmjNjwsyJc6dOnSRJQjxpEIIrhiYH7SBII2TBaAMTlqQYUiA2HUl3pIym8guEQScfDtSx0EurQToG0aKB0BUEqTpokUR4EqFZuVUJJuUIsZWXHdEYYtN6MLBBVxRbYR35MBFEwYG5FnT4hSAEvhETtW0l1uDZhVRZnrzWCkIZgkEZh/81XVIgasE6CiJ0+nphytfY0q4V+4U1jS/RdjTcTTkyQpJasZVpq7TgF7JgATO89pCw5oax72qmCJaxq+zV42IzrtL2SuCeFUKYPF3zIMY7SqpFaroh6oUN9yai0X2z5p48//vnn1ABAsgTLRAg+NFq9hlEC0RWFeRFcwy5QsMOXnSH1Rc0GIRVchO2FQ1j2PRmFYcD0VBGigil11dKiWiYWVXNJUXiQzucmBAEFtCgIGJeLdTbICqqmEg0H1ooYW8ISdhXQ0jpkJyRbG0YEYk+OmQBaQh9UUZfTFr5Eo4qWlliRA6WAWFVGqZoVUqtkEmDIjAuhaKKArHVoH5vwbj/ZX7N5UdmK1RaJaGau/XlRYcpLmkjRAXN1iCCJ64naSuOwflmh/dN6FWTwGHjIldpcuWKiL3VOSpFpFnVUGUIqeWFdukNUiFYJVKVnptpXrgaRJSySpWbO3jVJYHH7mSTsgDMVSCyzwJY2kqTBWpVsRtSVFV3UQ2iSHvWBZYIuIW2FA1bPv4J22KywomRgw9h+ZK4KyVCVoqGlvRQGUxmS+BkGBEJE2oqGarQu+2hpShCiZAKG06u/NovY194ui2G4rL01ksQN7cRbNIO1uS84kb40LY9VZhYR70l4uB4Re1LcL8beTXIugZ1LG6kPZUB5EqCrqbktojWu1FKIls3/91kWhmqoBcf0aKws1ND+5JdVw+YdbNbq9QsVwg29NeT7zlE9r9UoTaviA1dk1RasFUGn2S/WfcabrdlZhtiIS2cUl0OiQVqdkLpgGBXQi11UlBV9S0ZQhABpd3C74E6C6ivRd5f1zsY3so185725mBpnkQ6Q1xOttlkkp0kOGtZjQhYqGQHVePpNdp80t+r4wYjl1Qx9BdsYo2IXErBz706l4ZD2lB5gxyE+nEz+r6gbSOnBPhXX0B8qWaGkT2w0vRFBJjmg8+LXY3os8Z2UqMtrFb1vs+LNdf2d52/XTItOxP+/2tNfwEE4P0GKBTRzCY7JDnJ9LDmCgEWcGtRc/8e/hpowAhCMFRWAgthMEjA/GmFFtNSlAcvqBIRVlAoWnmgBbkWwgy2MINYE2EMP2hDE35wejrEoQmPUyG51DCAOywhEWXIwyLur1lVc9ZotGQkbGSGLabqWGwwQ6qjhalemKEI2tB2mZSoRSv6qd5YrvQ4vEwOTqTBl0hgtS0RGe5kgqNi1CYGp6QBiWIl8dKGSGQxLnYEjCcLFYKoIqFoHKgoEYEYVrCTL9qQqiEuylak9PO4qF2IVANRlIg46aVQ7Wt1+vkc7DTlBS+9zmgbUVi9kuOgbGGMWIXh0GsEgxktXYqKKiFKX+r0SYwAilVKw0q2jBWqbMkKRqd8VIX/AtNMp3CIfvcxUlpogcgcdclJZmzmoN6kxbacDmeZUol+wrnEZ/XvJuekGjuPNbE6tZOdj4rnOt31IOXR01mVqmc++enPfgL0nwINU0ALKlCqHbQnjIGlBXTg0PTcpVlIwcjCSAiWhdRKJJwJT+wasr73pSRzm5FOIiD2QM3MrTB744zDUgMezB2TpWFhXUTVV1KMjZRCatkeYjZmlJMUDT7mU4nYgFob4ijEaIkgKUs7NFOWREcyJDHfa96Euy+QxGZA2df3YDRV590uIj2V3YjIgxo9BrKYfiHMwFgiodBUSFxYTUggfTMv6YTtdJZUyfy6eEbVWeczwFvkbDYz/zDfXZRaIf2e7PpTNEwZlH/pZFZCI+sfx0C2siwBj2b/ExcRWvY/SgxtZ0tL2tOaNrWoBWjWVAtMTe0GRThTVBQ5FM6lmCszfl3QiPZqSVDBqCBRJEpPFzSaj/T2UiRKak9kyaCJzg5OCjMfxfKDM1qeKTNUlOVedWJMNRKUScK6zIm8ZN0jgfGJYVxucB03IunOFmcywq11n4o7PHFIRN58GgSQC4GyPY68OKtNzHJyoJgp0yWkq8iSpBiWClkTMxp8XBm0MpucCaQVJ6ovh7cESgZ/hJuAgVFDeoTI/w6KQ3ViCxcjJJBEytZmqp3sTVbrWhvj+MY6zjGPd+zjHv8D2bVYA01MRjtkCMZ1LhAz0o0M69BaauZ1DHEbfzSK0ljCpkaMsVaHHIpbzVgTpfrFikLWh0/8kUdWioOI6Z6YFK386yETwgbEIGI+rASFNsod7dpWogMS5s+hwHnTQyDyETmrDkhmLgiMHDq8t8jurSWJG4rKLJLcAS51A9mgb5Jn580Iej2F/kKEcdvawHiGBnnOWqEdGjP4OHQiD2K0RZ1XhsA6esAD2YEIydiWhFy4sH2L0mwEu7AvTpk5rjasqsUiGysZ+dRHPrJklwWTaGO7yNq+9rZbkm1ug9vb3SZyuMkt7nIL0NzqTje7py3td3/73PJet7vjTW94j7v/3fhGd73z3W9+73ve+rb3wP2N73f/2F0Cow5qfVTNigQoXgVtKYAkTnGIr0Ti67y4RTJetXhpPFw5wjjJPe4si7uk4x+PeFNa0tKQ9wTkKOuvgWveT5T7eLJB3nnCee7zngP950IPOtFX8uPMEjU8SUe6VjArQKTDBFObcXrUlV71y7LbgtepOtWt7vWt7wSyUO+gSjhLGMeYHexmb3rZr+u5pF9H7AgvV0wsmPYCRXuzDAc7SxzTLLS3ve90h+NS6274u3Od7n8HMjZoTNmhQ77o1D61UATOtconsfKYZ/fmk0jkzosb9JoP4OZH/3nK5+/yo0f9aFsf+ddLPvWx/599SWBve9qzpKS4vz1Adc/7GdM4778fvkBbIQijC4K52Eh+8lnS/JUI4vgIYT5zjW/86Cf1+dNPPvK5T1Hpq4T52O8J9rWfkOiLf/vU//71wU9981///NVHf/utbzXzMx/5xN+/kI9oRCT63w0B4AD+XwEKoAEGYAIS4AEyoAIi4AI6YANC4AQWYBBRoAQ+YAYm0WRhYAdG4AdeIAhmYAAEAPhdQQBcgVCcIAkGAAtgBAuUoFAIQgsaBAyyYApiwwqyYAAoBAkmVQ7GoEHoIApiBAkenwruYAseYde0QhKyQAoixBAaYRMmofQlIQqexAleARXaxQy6IBAGoQ3uIP8LeCESsiAZZqEPCgUMgl8IuqEGiqAHvmHm7V4d8t8d2mEe4uEe1pPO6eEfkhYMyoXxESE22KAgXAEMfiEMQiEQsoAhtmArrODxaWH0XUHzRaJKMGINtuAlkuAjtoIgssQJkmEiGmFLzGAJXsEKQmEl0l8TPiEi+mAolqAsxqAWQmIjbiItYqHxeSIKol8hsuIqKiEYbiEnKh8gKqNBLWMz8uEzOmM0QiOPNV7wSeM1AggtLuEMpmAqDuImsqEMEmE4Lt8lAiEODggJVt4JHp8XDuIphqNccCMSfuE7fuHynSIuYp46puEWZuI5AmQZGoQaNqEpXo0a4iMonmI/gmH/GMYg9k0jNgqI8FXW2PWcRUbk1GCkRGbkEm1kR0Ke41EkSEYjI16iLHZjIUYhDe6iI0IiMA6IFipE88HiJV5iOLbkOdKi8pHiSohi+KkjRrAjPm6h/cGiUJYgFa7hLfaiIBIlQrDhDB5hD9bjPLojUAaAI4qiIHojR3qlS/ybwNUbLTAE92id/syM1zwRwdlPvRjFICqKSdxbVIhlwOXezMhF+hQcuJUltC3EoHgFwWmEwbHluxmG1ZAawBlZWSDXXBJmQvyLXUKQW+bEUwDcYDgmt/1LthRmvNGZmj2mYoobB9ZlZ4ZmaZ5mZqKmaIqmGO5gCurjSbgjORIlJ95g/xqO4UBeYQyS40o+Yjwy5FK24VGqBC5KoUKioCnyIBWu4iYCJCQeXzzC4EA+4lzMYkK6ZDryIDv2pFOyQD1KZliq5nju5WqaJ3n229wJFGaAyBvFBEnA09ZxxN7BHd09zoW4h1dcxC8VyM78x0ceidQ4hlwa3mVt0SnJSFxSBUbqF3165H+Sk6nQi+BBS/UQBYNCKEuwTH1+5EAEC8WR0X/Wl306i0b0F4BmKPWEHWn5IUm6KC2uYoxioUwC5W/2YnYunyAcoiPGqBXG4i9G540S4hb+5Eo24k42SyquhHF24ipi4mtKn2vyIEMOKZIWJFVKH3FWKTc6nzrioklO6f+LimkyBpRGiMavlQ6HiEZkLox2wAbJLMxEbNrJkIzZmURRGVVFMMdlMJrEtCnJlMRAjMRCzAxBtBTGMNBfklUioMU1GEagGipfeQRmlcXbwchxjAZshAWh6qmC9It6YNVEvCm0SYVGEGikmstNHSbDYEReRARJaCqcHOqr0ktBiIZa7I24jMSjOsWbDoRXiIZUbMTSoESYsKlUkJoSMSpopRkFhYdjwZKEDsSaEoRYTcSwKhdFwKplBuqgZIeu0llTPOpBmCm5DmtkyBkw+SmjRlY1tuhXfqVTyiYWCiRDDmU/xl8aPmJsWmdWIiElYuFSpqC8BqduggZxnkQ48qv/wUpEvzKfGuojLM7jS+7gVCJkGXqjOO5rDFLhQo4pSKpnO3GOLMXWSnSHFe3F7EgIcS1UigQoFNEIob1GfIaKllhJysIKGqFsXqBYniQKV5RZgwnT7yiJr6iIqRBKr4hL0U7Uh6iJzeCLuxhOqRFVb1mEh+iWopVUsCiKm2RIlFQGHDXZjbhY1HpIkLgHrEzIibAGq5wGoHTXyAZWQfRG0fZVRbRII0EJRRBJnVhRrSxFXwhXzNLLjmhKbPBnq0bJivQKg+Tt8eCIdjCKkViXzbTX3nRM3fZR9UwUW6CSouwt2vpterTMvUwGlSRKZInkx0pkkU7sJxpEKgLscjIn/xVKHylKYlIaXSZC5UNOYe4uZ1Qan0v2IA12Ke3aIL7uLnhOX1DmIkD27inC6BMqYkLwLpdCogsWL2wGbCouJyKybkfKYddgBtjgFQgNDnZUWUntFPz8i96IhYYtTNxwRuc4BKDRDUohD0jt6UqtFOa8kuIwhTh1jPHcxVf572t0lIUN8OAsD9igxElZj0LsVEeR0owoTnBkz3SgREoBh0hhhG2g6eLUjtZ2lUaNz1dkT/mmh2zsx/CUlJWdSJWFRa+9BQKZymZImoT4L1WcBA0wjwpxx0BcxPn4TUo4iH3AmV+MyAWjhDWVJXMs1bywS41gA0iFy4vQcH5gxE6xBf9CDBbqaE5biWC1Kcv4onEcqvEcOqD0PmfH3qZCSOkX6uCOrqANau91AuERSiHHXuEvjuEg9msLsqBc3DEhF68gG+y8biw6ei8jV570XqdAdqwN4uC9vmTxtiEcsnEnc/InY2DIVg3nQBQPu9xncAeK+VGj8dEd+ZEY0QJSnNIpuVUhMUmBkUi1wpcr8/Dp5kixMRoqv4UqixEsCcT6FE2LpMmmMQhGHUhzLDNFdQ4c0YsgISrGNFMVe0WDRkqk0M90JQ33qDIw48w2l9W2tFEqcTM07wwp0wdV9MgZ/eWCcJO2yNiElfMrbwgfPc2J1PNAPAAE5EhuPdGDfMRu9Zf/jJkPSh3SjLQqo1kAKx3Vy8bzXSBq5+bIGHEaMHnFb0WJhJBafv1zQL1r+EZjIjKXjnKfEH6nOY4iKR5pKLa09OnodyoiVB5jFKLh9CXiEzJXT9t09Nk0TOYEUK9iSn9nC35ndOb0koJiOe7rEzo19yUi+BkfGhpiToei9+XgTBfnTrO0QiSiSUdkZQHqpF2NvcjF7RSHYnnd7WTZTEEWAocHYLhGSNAUVb3X1NXIZvwQT20Gnf2IUQWw45DHyR5NZAjbSQzSphmzXexFhoXUeyXFzaIrVtk1qGyU7CwOuwQ2QzjISNTU6ejNixGPldjMb4hr7ukRneGqD5P2XGhx/wirRgcL8P8Sx36EsLzQKikJsg/RsGSsjqTBSElYVPbcDa889AbxTV1DT/ZcmWDVdhOrzxkpWXGFKD+5azqRtTR6puadXumB9+oV0OSJN3mbXuiNd+aVJwB5Huqtm+q5nrmNJLzuoSj3U3IohOeOE4x0tFQQF6g0raLE88OR7GfERMpCCW8pzC9JSTEPim59zIvIUgL1V8m6rIpcbddmlyvI2C91TJREeGMXbohZBaxcLV1sUCPBSTT1SqSUbGO8FxV90uge9GtdbYkp1289VezAMy6LiXAdBs1V+ItlhoTd82BjuKnUV+Za08PhBGaQaaum0WrkF2Z0Rx+FU3vxsP+G4XPqOi5FOBixOEiGHfl/Jbksfckr58eh9XNCjBxAra5903l323md4zlCheeeG0azfWquyQZwrE6j2Q4gORVKqAl9J0eoybZDPBGxDoxBqJpRJLFgjBqq7UDkqjDOVsnSeEboODqcLdWnE4e4bRmOhIlkG9VJHAhxyNlbyQ7gpGyXiLqoIxAx3UZgP0hQFIW6KASfOA+mjRrWCAaY2Yemy/p6XIijfQ64NFppq0uhZDkwgZuGPRnOJgSvyYY1zYil98bp1I+vCItiFwSLnQS7MISjcYlD7UaxanrxILryrA6sBVaViyfnbfcZn2d78/u9+/uemyZr7vu/B/zAA3z/avb7wQs8eiq8wRc8wz98wkc8Ha5WhxLfe915TGR3j6EV7I20Hl5uQUFX1Xz875V0nqN8xqf8yrPujd1dfTvoihII21GoS2zIb9fnva0didY84uV8ivY84Fwcz8e8f4CHY8DUzEcW0y0I0BcoO9H8f0wMw2Hk4tnFYgRegVQP4NU8TPj8QeX7sqj82LN82ZP98OG32fvH0Kt95MHc2Z+TyhnU29v3nMP93bd93uO9jaWxJ/c9KK8x4Pt94P994RP+4Q9+4gv+4ht+5ZEmAb6Q4dPQB6IQ4jO+A0a+5c8Q2TX+5RdQ5Su+XVgg5x/+5EOg6Xt+6v992u+93rt+68N+/4CcvLM0Non7E92XBMjBBlniGNv/R2JSzSDVio4hl+/zE1oc1MsZfzvZvkuQeMu06bPgvsDgZcUdKrIYf3fwfmcRqD+9/vfHfviDP7SEvbJMzVy8zQK1uXiARwldR2gAhtkp0fooHfLMMwPJW7OIR5vyWdNrxdEDBDaBrbBFw0aw1ZdEBQ9iu9ZwYMNrrnS0Mngt0ReBDA0ihPjRYMiGHglu/KJRYUSVDUUSNBjRJcOEC0tK/EjSJsFEFRue7OhwZNCYMIES1LEw6MuCrSoqjbZjUMGMN4M+xHaS6kOCWjdi1Mj158OfAgft6OjqS6uHYpMyhYCzJFNXGbcWtVuzrv/Smi+n5mV5V+jHwHAHFyZ8uPBPxIsNN2b82HFkyJMlV6Z82XJmzJs1Z1bc2eNnzqNBlyYMAHVq1aiJEk5EC+EOmtjKIqT12nbug3OD4sbmpUwrVyRbzdYxfHdxL0Jvwx7p3LdO563K0ECem2nDRNdh8/Y4e3Yr6Nex4ZbNXHx47a1y3z44vXzv38F7I9/+nbzw8O5xJmJf/7DpaIFgup20yy+w+0a6Div1FExkB9sWOko39uByzwv/DiquN/jE+1BAhLzbsDgdnvvOQ1doKAMCBLejSyjuiNMturk83E094SbDK6jWNsLJxyADa+2klFYiCa0bhQRyqrR49GjJHkf/wipKKCHKiJaBEpmtSqqsBHLIMKNJ6IsMl2wlqie9XFNNnTT88iANu2zzRynBvNPOIo8aKK2bEkKOSI3whJNQNq9609A56yxUTYFWW23QniCAYCFaaKjJwGh2+oIGmnTIMhEaDvpCBx12oEVTHUhdaFNTYytV0POYUlXVgSpiitQ+Ccp1h7ewoaVUVdkLNq5cTc3SC1iHJbVWXNOS7byrUNrB1atgvVSgaLBSldkf0aKWJ1WpXe4og04qSUVxUfKPWwhO6hUpU4XFVQcaBP1iB1IJOg/fUlkVl9Ijv4AgwoMKFojKZE1d6FxpMaK1T14ZZtbEVDnViCKdaBA3qvJM/4QQVojSxTfWkAdp91/ZmMqUqdtKzXdUe4XdiMIdkqVUMfYK9qhfUl3Gpgxa881VJ3AXSrZfnWhNBKMdPDZ1OWsvpnMjH0kj7SeQM7LZMVmTCky08obzmrGfwkbI55hwCvsnLyBYjiAvdJWJbsLOboxtlhKBoAyFcAb7t7nvVrvuv4q7F+7CrEMs76sri8YVSv3ucxD4OvLa7FARZPzxoAYW1LTQ4XpUtcgSsQACC0RtZQeZq4twQFcgh6C8PiEsbvWUrItmQFoaprBMgzW6VFsTE4Lb0vkM+hShvVElNXCjykhVb9pxlzbu4GCG8DVKCbIOuO9pkVt61hcqazbyuf82v6FB+qx5udsNnKkhHegDXiOcI7do76vu1z4qtQmVQ8oQlePc7jcRipzsBhiaRDwAdcGJnLGoVZ6C4Us5cTKR/R5Cg9sU7HbQYx/OYpeQ7/lnQBpyH+s0AqPyYaNTCUmTbJynKdrhi1XPmpgMDbYl44lwVINY4PVIYin41CaBwhEV+WDYp8Wd53bVMdhydkcR7AWPhdigUAkd4zjHTUQhCKmVQHaik4bN5U9HWSFDTrIQTdFkSxp6I4eO4sI3odA/DSvOlsyokfK4iyZt5F0e52bGrXBqT9JySEZ0tSUq7TFNGdHQlh4yCLWQCY6MtMiU3NiKgA3EV71RiIFG1af/MbUxTo5kT0ZkF6eUtGJjKFFISvgYJ/b5R5LaOQmqijMTg+ByIWh55R37SBA0foE+LjHhL80iEBWxqnkpAZZaavcFVzwMKq6c5OHAs0u09UkiaEmEYqhVBquJrjPYIN1qGjORSblLPNx618cK9hR2kdF41LKdPOv4Faxg6yoRSslRHnISrUTIWdTclR/PlUgDue8nBrVgQriVr4wJpGaxuWeTKqqQimXsJX3TYkmaop1ElEUjPMkYU4Ylk0SKNKMGKxpF35WW4xzkOKybmflIaaniNNOF2nmnoOSZK0WO6VLuQxhRT3LNUOmJIvFiV0mayjyeXCMl1NsUolbmkKa6/+sk/Tzo4RZpIqwoREV62glSgloRBCKQJDwpSdCceUCzYAWrbhwlWPVlJDNtqnIeA6m07NnDxiwKnZyhwaTGGbkboXSAWqPoTgqmKrnFT4EebNH3XEE+TiXCC7DzoHCsR61Qwc5dywmt3ExUlluVSXM6PS3PUFcwuWFQeXsDrQL52lrTTnZF7KFdbSy1taiktnUj6ZW7AkqS4GXNZk9lT6dC+xrqZpMGNhtYGeSWuwx1KiMx3Bt3VWY9WFpKdgPb7dGGZqJkZQhne/tCVOyFL9Wm5aH5CtUHj8I3Eb1TdrDUm6ie2N88MsxmUoRQDGEbXPUuLHBhtSB832QpxsZmEP/cTeyG11k6x6RVowyhSwNhqAgYwZWy2yHQh6L3G/+cR1OiyqmsQPue1dGGUwUynoFiSJsIzW9UcBPwFj+kq13eeHEKgal/kKNHr4Uvm+wjiPuEWDOanOcoYWzIW7IXDQ17EHKrE3AMYQMbEOqKIthtBRPNJ+ASBeYkyKNdSAy0UxpWDMgnWSLsinwvIVpPiy8Wz4oDPeVG7hQbm3Wx+cr8ZvEskXKitaKUYYOVaCjCeD1MnhKZQ+i4GXm6yoOhhtTI50rDDYu+g965dsbjccLwP4vh0ZHqRGtbU2Ule5uUqBCoki31UC7X6Gqgy4jThfKkQR8jF9MKLdeK+FQgMZT/686wjOBKf6pTvwKoWxgLNBPlC6saGQRSLsU6Mb75Yba6ZpF2SCaWGlsqqYPAx0wUkXNJkkz41OL7FAJFnXnKUyVapaiQWdAvFHSce0Irv8wZaE2dm5QC9onDJiotZDupoaDK9MAm1UK5TvTH9TZhTvcUbkWGapVvMVK5JlmR4JWnY3+cVL1rXHA0/frWs8b1znVeE0d1uOe1/ki2pWI8npTRkwSha8GxtyeW1vgq5Dp4syfkxzGydL4CyeZSuQ69qNeuJidzhbU8RkYvrEzYt3I2w0pFC6yaKNwq0kkotZVSqWYLZlnE1jQPsjEoYRFmYTz64N8MNJteMzsJW9dO/9xuoGJDiNYkOdjJrVMeXlf2C9py41HkjlNayGZ57AI4tKO89TLeFJb/UWQWE1mRk01Z2fqWGkbjRSZzgXOk5q44TCafqsbTW5HBF/wlPyU32uv+6Rsk90JkFa2dI9aLnVms99wslJ2waC66klUbkcxsdzEzg8ikXwbBPCCZllJFcbox8Oib4AyN/5W/mZRt0zKgNU9vfGWSzaRh6Io1sjnKSiVmQKtvjIh3RstgUmc59CjIgOWfXm2FWoe7ROokvGfUDGJ3YMh33M8/NIw64EakPGtChgMr8i0jgOOGtC+Djuc9WmR2Xs1egMMLss/Q/gt1LCJyRmKAwCeZRmUhFP9NbjRlATkw33joBKWoxz7jnZisYhpkVaLvajoMAEAjynwsbqhlfT7HVSgiV84HC1WLPsLHUqYmgbonV+TMgmhlwIouZtJmDGcju1Qld+ylwLZr0JyDeAbEyOSQgZArfdIi61gnTXzMfmKo+QLpxopGZggp0VAlB8OsDT3orUSrC3OvOtRL2EQlxj4M0IjoEhfHUr6QEEVIvhjMzwgxCwlkwT6rBp8quLSjdZ4wreylONzFVKKiYRDoCywgTWbRifQp08wn/UINSTxtVGwxsxat0BzuqXDGA4OHtbAiCH3xe/5jcbpowzgDG8RJjBZirPJOZT7lV3iC2KgNwVqv2Lb/BSlYLTaGZeyaTWm2IpsUzo92oyLSbpN+SUPerqzU0Sh+CuJort4Czdv2JJEshNqcyxuTbiMiBymsgkrqDCksZOxw5VAMy9ygricYyo+ML6ryKW6kSspCzlaEz4/ejbVk5yCHReNQZSnSAvxo5uBkQ6SuoTvUbh3NyuPqBB27ytyySdighBu16G0GplasIhvTSQpZYzNCRCa4ij2cY5BizZaWAiH+A3L84zuMSSuvI9Z+jSt0JDk2BCmWwjceoiuvcju20a1izS2vkjheIyQ4JDRk5D1IAoXsJtDC0pauYzjekkTI8h0Jh4zexC23oyMAU9aQRC3fcUzuQ6F+7SV6/2kgrmk3OgIqm8dCxvIxqVIzkWMitBLsDFNEKkQgbiMf0WgzsQMuhcIzyXIkLKQ1N1PY/OMsSrMqf6UkUDMxZ7MwoC8pOSNX+GvUahEgDeY40Kc4I4u3UEUR4Cl6qGUusolSMqK0vGADH60hgst5jFA2gEV7rixHGoLNWOTFUOIoUuh61oiF8i/UamN/WHC0jOgwzNM/XtEj0AfE9gtCFGJxvkxDFG20sim0cAc2PBBB/yaGnvELSohFrKVyhGV+1HPV8Mu9vrM6z+ww9gaNqsU6IMSAAPG+hEt2PBBmChSL5stBhwtDueo5xi28Nic4K2Mpp3BGofBGc1TKdNQ0VP9kZ3gUR4MUSLORxITUSIf0aqgmTCJFSfEELbGwQXFKNsJIUGbCX5ZCXnalY0SKKTqmSXZiTyzmXMYQwkyorkplkxSCmWaGJ/gO16bCVjxONs6OhUrFqUAnI2bGTFzsae4FbQhDS+SlY3CiX/qCsgTlUH1wVP40Dfdwj8zl1YrNYyQJUi1mhVqFcl6tTxdVUZlFKzXilMIETGMRbf5lZrgnoBLJSEAmYl5NTQErYkqlB3UOUvlSUW51ST9CCpkUOHGVV33VToKVUXo1V0VkPIF1WJFVToo1UZi1SYW1WX/VWYlVWqsVWlXPWpN1WpE1Wq+VWr21apBUXC+jSC3jR4//dDSqD13HNTg9MDMYcF3jlV3ldTOkcF7vlV7xtTQGR1/z1V/7FWD/NV0DdsMap0d065zsRi74lXPWJnR+qZAAR2C/ZmJ1ajAfpy8Sg2AdtmI3tkdq1GM7VmRDlmRH1mRLFmUdBzhPlmVT1mVbFmZfVmbzFWRn1mZjFmdvVmdzlmexMeeELuh+Vmh5DmiLdmiDlmiP1miTlmmR1mmX9mmVVmqbFmqrdmqjlmqv1mqzlmux1mu39murZlfBtmqzRC/IVmuH9mzV9le6Fm3dViCQMmzhlm7ndm3r9m3nVm/z9m3vdi/4lm2zZG+ndmV31nB7FnEPV3ETF13t1UjJQ0Gi/y86CmMQcVM4QGcxRmQy0CIzIvc3bgRJtOxCzsdFveikghNRPNdxJtc1YkREMNcwXvMxfo0yKodnQFcH6XQweMOajJR255VxF1d4g5d4h9d4IUOdlhKdlAIjbuzr6iIiuCLygEIrSi5Y4YpmmK2WsiVMCKor/mIl9DFvopfrGDVuD1ZmznUjyghOGYJ7x4JuzrYupozZ7nYtQOJrJvMt1gKIuJdwwoJuuNfkiIIt3q0mHMqUejX1zld67UIpjIRWc4Jssgr6gDFiDU7z3HdN5JYhwOJHfGeT/FeDPaKAS6NwixeFj1eFU5iFDbdmHcd2rbB+3sT/oMOG3SQ8argaPf83jjbkxu7jlaYDTW4EiAXF//KjwtLjPcDjPYTINiqHzRZEP4ICAhQBA7lqS4axO8QjTWihcodYJ8ojdTW3iWNtOPwvNitXO6DjR9kMjO8SjqdYRGKYQrhqRK6Rj9yVJrz4NS3JhXQ4PiIXNsxYQjYkhrkYWCD3Ln83noBC0UrTXT+EdhkZjcPYc8etQt5YknN4PDfjWbU1W7t1e/mUTeIPlIUVv5wGLlx0IFbmk7v1OygmhJlVfmtHcO0kIczsWIfkojYEdl+ZRugjSBj5W63kMxBmIqHv14IqSn7FU+8kY7mVR3jHmEy5mGEZm1/559YplPHCLbBlgOK2BIFxV6L/pkFspllyJdsseMrAxeN2xVQ+i+taRVP7NNtapQzezlN/w1/cB65CzqhGhmtspRb3+SrHru6mJH3fDFb0qV+yJGhW5SnkkCdCC19w8eAwNdrC6odoJYSBZVKIDyjK7ioW0SsKAno2RTo9RqLVefCg5vzAVKInalPsxd6wkHnUmfkgZllcblMEwviMRS1axel+RWVkxqa9mQFl2qbni1645UpZh7V0xlT4Ju2YxSgYxqRbJWBU+qayOVxvlq5iYznM5i86xWAZtiDokzwwM9M+Az8Lh2MpdjA4dNwe+cPcKDY1EXDeynIktjxQp33Ul3D0Ujh2LBEDg0Ii42yQQzGe/6swgOOX6PpC3GXcqgUnghCwDTY0ZviXcJd4HTd03GZSMEtmMHGANvH9Cq0E8UsTdzBAdXmiNq30IslEqihxpFp4+u8OCzR6pIh6dqfGdqd3wkeLelASLyhiZ+c6GmiAfEKE1mf/xIzA6IPLsomPauxvNku6OU1TQGcQUGfeGtVe4Cm1B4iJglCEymA7Vif9ZsZ7wNGAzCxWqvS242Wwd+XHXpvANMQQ72VLcseHNCh2bJLL6mc6EayIsis8UnTZ2Ec5C/QZsdMFPSmJfoM9woi/ZVi9jYe9Rad4qeQh6lgzpSUjoiKPdILNbCeOMDPD+004BlVuWGVnyoQ+DBqV9v+oTIpltYVwbsojQyrJaRBmOSypQZWjkcJqqFGJe3YJYs2EpR7O/0ArYnMp7Qa6ZbgCI7bk/dYKM8lnVlKZkaryjRrEkfwojtKkjb6ptVdbIfyZjRqmBIFprcKyb0iqHvcSx/YJtOSEyjPpXLRizWmDkc7HX3rJdzTJZpNXtLVR17pacvLU7qo330wOTG+8qEYS3Qot5NzO4U6OW1wyE1vp/eSJhk7SrNjq4LZlWG4LWrwxYzNSIHsElphtociIJIOcIEfqZOJMoPTqHDWkoagqpdQCpCpxKzyp4w4CrLZGJwka6uZHn2CvSAQqKjSvZW7P3DqPlCFETJ3D2+oKLTL/PVpY/fgQyqyg2qNKVbnuyfNKuNiUiJoKDq1QT+Q+JZ5JRXviEVjyZZNcDtULT9oRTRsTNmSLFCp6h3dox6JZRBFYR7wopbpYe9xWh67sxX1UEaEknvM6elOEK4s7pSyOq0hWJ7rQOlQUwbdr8QY3cXmepY4rgnwYz35saLdA3n3ozW9IkLN24rvmBkImBcWhBcUT+ArhC7Y2K2g6C7+8J3L6hms2q7jHSbfKQrWo5cXXioVUxMyOgsGuK0QTQYoyZBNLBb5ibWBUx2AG8SduUYqsPlSmB5a+xQzdJ3c86sU6xVX0cCEM0esjtmdf2DRCRYXaeHqwKAfJ/ribR1jG/3o7rXE6v8mK4GqYMi3nYRFtKscYJXuisOihZonTPIJ4WuzRvDhiBGd/FEPDDhDHijDKRimxF1sHHl798GsGAc3KMFLVclvrK84gghDIWkT8sGc3CrRcZf/ha5yKzAwhuHQYcezGVpw6ghHWfj84Xkkdyd7xiVGLyONSTgzNBrHHpEzDYuqtAFFQyqz8fji27mzL/O/0RWTikxRw8VZpOa5edCBgzkr0cGpClg8gsGHz8gUbLR07CrbS0Qobw0Q7GraKuNBhIoGJMn5plUgHtmsLW13bcZFgK1deIkbENvHkxVZfvtCi0RDbRoFeIOgs2PFLoi86EoVkGbTVwo4vPf8KNfqQIdGTg1jGXEjrqCsarljSFHgtEQQLEBTSwNaRKdmYiaLFFLj2WsyrSyEY/RKN7EqIankWXAtRYsSOH4G6ghl0rE2eVQHrKGhzB9mGffk6/uKFLWNsOnUyHXzWJ9mXfz2C1HFwo6vFLVmS7DgYqEWBW79EtSmUNNGqAlnmrqlbIO/fu4P7Ft67OPDc2AAoX878uHHizuuSNSx1r9OU2Mp4nFgwKLbTrsoengg60em0Cwc7pfESwsXxK1ML9O7KvbQdUVsO0uFKrUeCAg3imEOLEZUUYapV5ltTGHnkW1l1SWRXQ1+E9ho27M0nFEkTembVVomMNZV3RznU0Ez/HN00VW7SsdQKVjUB1teEgHF3IXvkdVjeazNteFFQ0RxVV18kOsURf0eS5VRMtOxQ2USDHNTfax1xOOOIFyGlG3kVRVOjkpB5N19BM+01CFVdeeSlRxWOmVpLba0V1Ix9AdaVY2Jm+NxwfBYXnISAbilooIQOamihhNZYFX7YDLKRT7RAgFtRsql2kZdjdSVpXZQm2cpBtPwEpW5rNibRWjSpxZ4XiURJQ1UzJVZYGW9uWZ9OiUGgHa0T2sUqq0fSAixKZaRk3lgNwYgUjjfRBBNkN/kFAaMzLiYqsVERxGlaFkHEqk9meSpjRMUOlJaiY3lRBqcJJQIsZS/KGuKj/5Vhepa66a57lbkNAcuSVxBsxyFHKSkFVBki3hRZUD4Nsm9D/KXk07v5eVaURanNiOihHG/sccdbMicyACB/XKjGDq3r0A7WqhYWjvPSwGvBHCKJ4WDzugfxX9MSFFWpMSGkEEv1OUnZWTQkNBDLO2xaLkdjeSlpQwIi9OQXNBws4auijsdSpCx/oauEKWXNJr1SVRxVhUlHxV5dMGcNc2UQWS3VSxZj7aGhKK/I9sBi6x3NVUDdJGDbFsGdFuE4Ln1s2ApVVOFiT/4bFmpLI/Qj1ixbWp1q2zWW9EVsI8wZxOGt5KiEfw/96bSUY3yX6jchhNpMRI/102KvwrT2Dv+jez0V4TrwanLHxydf8vKFrvXRa+dt55RNUQV1zUHvrbWQa0pKtiBT2PvGkWMh5uYdh6yKP1RIgJHlxVUxcgbSmQEOeI1Ap0mEFUbhoaXhfOOLEEOc9xbbkARNFQGUULARpFddbzH84QpGFETAjTiILHa5H51WEhT+uCUqAvqeu9iiILK4IiYKiyBg0jeeIrWiDDzZQV4YqL1/JYIWY0JOUVzYvheVyEXrm9NlbsiQa+TPLXgzTwTTcz/lOZF5IEvOyEQGxeThRiK4aaJRtmQU3AzOKA2J0BbBCEQGDmZKEcLh6a54ujH2JlSwksgWs6MQWqRRIqc7XRnlSKjBsBH/UIMBYx9P8iksemyMeWyIEc2jvj0aSoxO1CMZ+fjGpXARj5gUSKhqgkUjZnKSbqSkIvdIkiySimh4bKIjwXhFR0ZoUKHUpHkKqcdXvlKLohzjFl8pyivixiCWfKKE/lTFYgpzUAjbEqM4ojOH+MQrihgV1GghoIu87WgwgQBn3JOIMkiqTlgRUJTkApP3meRmQdKmtzDTn0g1RigyIwwtgPKSQXllUAPTzglJQhlXRKmcKCFJfTQyFmspQmdKw8qMBLQx8ZxkWoFSF8QWSCnSpOR1QtlXNBQRtYPObCNe0ch/FJaQ05ThJw67SDVf9KhwMoqb3hxP+Yq3LURFyien/1kJN71CzZIWj5mpK0hYMgIB1XzhNBsZWM2atSHIxMeYUD1mQ6ZIxahaVapXzWqhJKrVrmL1q14NK1jHCsVUifWsZE0rWoEzTEMNp62Beutz4Cqo4XSTNyz0ihwr5D8vcLJwWYqWilZCFJLQCib/QshsWEMYwUqkJwpx7GEaJpWWyRVagILJFbWDvsVgTSj0PCpM6taQnGYksM8SlkTed1n8Tc9LrmPgT2j4koxU1jNgehZZjCcm7EGkcLJ1XeGYgsIBtmsvP1Fa9671TJ4sNj97OqEOMLdcWv2kJ48VmlFZ1ooIDXcgNfHrdxYTHtDaEbFxdat608veuq63OFRVTv976UrfudqXrfclJn73q9/+tvayseTvf99bXwETOL8Dni+CD2xgBTfYvQ72L4P/NMcJJxjCGC6whCN8YQ13eMHp7a9aR3wo7YQVItBVHi/RitZlYpPFJN7YihWIrC196WPnRFmMQTbjKj4MxkBOa3xJtuMiB/nIRk4ykpes5CYz+clORlSUYSyYHgtTaVPOsm52V0MtOzk7NFEltKwMy6DtzctajRaU11woKQ6ZzXBGs5zjTOc527nOeMaqiPPM5zv7uc+A/rOgA13MISuntYNONKEVzehFO7rRRXaOpKFD6T5N2tKV3pOmL73pTHP6054ONaZH3WlSg9rUoi61qk//vepUs/rVro41qmfdalrD2tbEEciQb11rTbeI18AODg55Uxcc/prSxpa1boatbFYXu9fBxrW0od3sak872tS+dqaPre1uZxs5kH60uMNN7nGbu9yIerOWZ6kbmaSVmiXzpz0fW0+PLdCJrSrmP4lWOXvX21YlCbBWbavWjEho32SNLaFCNe9/pZhjwYTiXas4T1v1G6oRH2vFm4zuc3u84yD/uMin7Ob4Lvl+dXGL60j0vFueEr/3a9Ao6RqTlC/IjjbpLosEFfOQtMiWxLkf/V7+PEuz3ODIMZSmEiHm3PDlPS2POrEZSPU+oTznErR6hosjHddIRAfQFQjQw6ib/6aL/SnBsTnVZNgb+AWI6UUXlHS+FPOyP4/s35EJqkep4wUeu60pV3OaiqNKbt+dNm8NkVyOvOeQh/zDG0M0iyFfTMk73qqUv3zHDU0yJMsbYnT5Gow+A90bUg03gyD4v2QyOKx8IZq6SX1tK3fU1aFy9a2kmiI09hPc3DD1fDwjIb/2EkWI1kUyudinslTefycLy1s+Ld2AaHyH29jdGATX17RfSKIdn5D5hkz4bQXdcybr3rOFjO9Rr/oUwepVwI89wb8kew8xPEWSFNYJjdd79X8/VEJRFbqUJePHEWcSOpBBFgDofOnnFSfVbtHyE2vEfScEe6SFR4UhSTD2YP8eZmEeyGEfuHUcaF8sIQiCwEkmmIIm2AorWIKCUBwtyIKAYoIuCB00iIKcVIJGoYIp6BsxyEUvyIIpmIM7yIMzeIKWtoK/cYM+yBsuyElCyIO5MYQ+yINIKIQV9oMyGIUqKBEtKFeUF4Yh2IEgWIYiuGHwFV9mSIY2phO24TwG0j2CkSM5EhNe4B2QxRATwTuLQzne4TzTNTlZgTGUARQ6Nx+eFRsQVBsQtB7TdR5foDJTgUIZ4hUxMTCNwiZmMz26ERQAsmWBCCT4cVOTY2ZSQRBI4llhQRSXOFH+8VlkERbA9VudEyNuSAtuoSZskR9hk4pJURKXGFmoSFx6gxP/i6E0XVMhBTMlkQgTMaEnxHWHPOFZeqgDqPgFaTIiPgQbRmE0pLEQjSghCwMUGeJUqDhP07JASiIQ2kFP2VggkMUzJfVWHCKGINh4I7doVxAA/BgALIANgtCPAnkFLBAADbGPARCEAMmPV7CQVyBH/siCAfCQdYWQComQnLSPrVCQAhmRCOmP/yghBSmRAskCCtmR/KgbHMmQEiIIK8kCDckSDKkb+9iQRrGSCSkROBmRGymQJxiQKNkQQGmTC5mQPjiRH+mTQMmPLCBwmjdu6nZkX5GOS5M0SaNNE3E1lWEzvcMhwIdNlBEr9nJOFzVCFUIh8SEb9SFbpTc74jIW/2+DIRtyNUeFLKuBLCESKe00elCTMudHNnTxYuV0irITFRCRJeSilXppREXFUDbRGuZRVIzZK1NhYhwhIToAFhCQLTpglU0DWlqZOHJpIwY3Qi/kEdp0PUW1QUnimI75QjKRmijRiaG3GGqhlfVhR7npmJFBN48SWT4RNeWDR+lSO/ynEIyCFa+0Og/TCkUlXpghLCpSFZUCKLHCcflYbtiwjybpkh7pnSb5kAX5ggjZlALBkQ3JnboBlAsZkr3RCv0Yk0Vpntgwnq1wBQh5BSbInSY4kxLhj9vpj4JAkPwYhAWpn1SIngRqnkOJnzO5lDWxj0HIlAPqnwypgvUpoP8WyYIEip9IyJH/6Bv9WBMB+ZD6WZDdCZQmiKL59ZSDVnJUdWRuoRMORE8ZsR/HIhLwcSA2MV0UVVwZMTsz9B00sRZrESSksS25yCEpZ2JGRBqFU4ijZRkmcj9AUo09ikI+YSR78xMfREMpthIsZBDscT9G6jBLJBpUITT09CYrMTnvMUDYaCntUyOARSoAs3ghclpaoieR8TNWEjY7Y2ZuiiCckiLraBBFdBhKAn054j7OFDRB+nUZ4SDpARFfmjWSGhMa9FRlAUNU+ljPmBaYWCr7YYcbMU9B8ym2UxNiAihGAmT46KJ4dqC+oZ8kOpG+MZIBOp7tqaslCpFNCZ//FNmSIBkANBmfLCGhu4qs56mry3qFAfmC03qrEYkN/OiEAWqQzWoUNZmrwxqfB6mr6imUBckS5wpuvgqQ+qmSRqmTMGmQQtmPIRqsy8qtvZobuAqQV5id5sZ5ludV7sIZNEV1XXkpgGFWfNkRtLJMVaFR+wIkqkEwA6EyGlOd1PRi5mchWzMlEgU4BfEraeF1RFNUE5Vdoql8YuMuTQN7OeIlW9lUvSIqXfYwPwY1oFWpugF/hhMtAVVvjDUQoac4o7JRBaUIcOOySDWYWNQ7uLOxSuQ1U/pCleMooUMDsNeMA/OwUXMztqdbWPGNy8QST/MiW/K0M3Nv2kGdbqNH/zU3nKIXgL1jOd3VZV+DlUiGbXprbd+2t6MGlCYJHDWZG75akyPJAvGanrr6n8gakCHaGyPJrL9qlJILn/hankiIHLw6uDopr/ZJRoO7JSdoub8xko5buEa5rm9luaO7Rej5gm+lutNaoiKKuMAKrRkKu4CbuUWZg97mt33Lt8Cra2oovKnWdSVUIqgBF9hQUmWRHkDDQTJbJ0jiGm0SInOxFd3ypqHHEjqjV3VoQe7mHdpLIjTgFOJxlsgoJpnIqO77GcHJclOxPUiEIlqRFRUxIztAK+jLIIxajaSjFA4CGM2IPT/HIiQ7KvNbPCoXize0Fc45PhRiQV93EpWoO/8UoYchwhnPtCQO4h+s6DU7ZJgWgqS4+F0LXKn9EqtwUhAz5Ip7WiIx4iyBOhT7MZjNuxe8Qo9QB7za6q/otpSIO0b22pPj2p8FSrtFDJ9NWcQuwpMTOa4P6ZE5ia7yeq9MSZSOKxC0664viJINaattxZ4imZMT6Z33qasFWoL6+ZA96ZM3Ccde7K4Nca1AybpFXLlLyZNMTKvkFpVT5i/JspfAs0AQoSu9EzM6hxDxdBpEsx0xY0czYV1hYX6VQhBZo0A94xmH4xlm4yR3Q5gwUWNl4xmHrE8o6y/KV3ZP8zW2Iyc5exaYVZWp+irRgDsvZDahXMqo1SipyjvGYyj/5ocxKxNPdiEzbzMvF1PKySLJtmyJNoMalmh+oFow1SkVZsMrjEwrV4EwXqkwKVR+wDMzLHNjjsxVgjwvBFESoHwqGBw8BZOXo4xNzhnMnzK3SebH4sbGTBmhi5u7AfqCLDq45SqTjQugxtqQd+yQAeqgsEu671mhABqg/kyURtyT+ImfDbGuwwTRNWGfAMqdEmrEBr3GDJnRS4iezfrQPOmrTNyenAvQvCHR3OqUQAxoMDpFbJZ7v0Q0bxVHZkR2WuQbaxRGnHFsnvRItzdMubfUmHg8kGQrC9d9VM1jZ1vVHlPUX6NInDEcUV0oETIlVgRIvKSjNQErb6XV3WcQ//j1RYOCc5N0SXY3R32hgTJGNLw0iNYpY7W0SolEdUYhZvIT1G+tZLN603P2haxbglFsxUeMrvOq0I0tk+EakxLxkRxJngiNolV80duZuRB9rWksn32MrfiarO6ZxnEMu5RtFI29kmG8uCw4kj2ZdEKorym50e8qxExZlPd6oJddxT0oEP6J2P9qaAGrz2jGl8at3CN3s829aCM43WjIhtYNYmdoj89KkQvd0IRLue+K25Kdkz35j44rSEyMuLYr2TG5lCxd0yzZu13s3bO9uLz6niyZxty6oeXdniL6oGucxuFa008MredtxQYKkvEarub5xvK5rtzZuSeo0dp93f/UXeHYbeHVnRu7lt1jmOEgfuEfLuJxjeEk7uFrGOIbfuIabuIr/uJY/eIuPuMjLuMeeNjOPWXgKa73Wqyba5S9Ya8gOqJFSa9ibND+rdudna3j6o/9OOFXvK1OjtCm7eSIy8X0WtxDnq2tzdiLy9smadCc/ZC8/c++SsYIbrnJ2thprLuczZIqGt2MBshyXuc5bud4fudOpOeBRpCI268uSZQESZ5h3pIwOYV+TpEAqd7qTZA5OOguydobWeiT3pJ+Hub1WayL3uiSfumIq+j3+ekKycWNzkmHftmYzq6fbuqMfuibDpMKGerUiunmKp6aTpCCXujLqt5XiJ95zmf/OT0yv87nxD7sxu7cOH7syl7szL7sfAawzt7s0h7t1O5nPmy8v4vtwZvt3L7t3n7t3Q7u367t4l7u5J5qxEtV5h7u587u7j7u777u8D7v8l7v7U7v985pyT7t/F7t/t7vQmZyAP/vR1aaF0dWgkzwA59lC6/wDt/wEF95ABvxFJ9V0pFA6khpzlF3vIFytnXA2lrxIg9V+/7wJj/yKF/n0J7yLN8xwtIqqapS/mRb3Ecb+ZERivA+j6XzJkQ16dcoJ3RvJz/0ycPiKk7jKW70Sm/jSc/0KP70LV7jSA/1Ry/1Vt/0Ux/1CpbuU4T1V0/1S5/1Ve/1ZI/i2wMUbKKJ/5eIitwLXIGINXPBKtZjpNRIFBCkZmH/9VoP9k6/934/9h5e8i2vaGOXj2Q2+ER/VXSO+Ih/Tmc5U9wSly61dkz7UmlRcR8LGm17y1OT+IwPS56P2BzBp4di04TiRkFyKVoVSqYvcTpnMrGEdJ9Pq8FORbMf+u6bv4G5I+QxLyh0Hv9TJXoHQ/FSF9o4saF/+4Kf/ICWNHxYKBL7MfYijvYcVZFiPNeZZg+3McNMsbdPqyvP/I1vsSDlERKrsM5oSNpzzd30H5NKmKPH3N9P9PYe7/l+//ZP1fV41WPEF3rNEQDhqhU2golaJfrSSiAthQMbthpICxtEhwQnQpxosf+VDgiJok3cMRCbK4MEKQq8KJIhxo86DGJ0iNEkxEReMlJcqTCRxIuJXH00qVFoUKIZi4o0mhTp0qFKmzI9+lRqVKpOq0K1mhUpQQBdvX7FGnaq1rFir5ZFe1Yt2bVm2b51G9ditEQ6IH7RgW3QDmxfvozckQikQZet8GJzSbBw4kQh/SJOmAgCxMAg5bZNCzfzZc2YPXcGzVl0U6EVTd9EfVp1atarXbeG/Vp2bNqzbddG7YVjSRo/sX08SKMMjco68O6gQSsaLRpfaJTUgfywF7yJBkVuTnwgQuI2HUo2fpNWRwgMXdGYmCj7z1bN83rZEX0Q5OjoDRuHkFBv/B3/3qU/b6UM4wapCxv3kPvtPOdKuq1B3ByE8EEJI6RwwtS+whAACyvkcEMPOwTxQxFDNE29L6g76D3oUNJBoI2cE6iM+Qyb77rmBPPCI+big44hWnRgaEQhSRyySCKPNDI2JJdMkkknP4QgSvt0cMmvgfC6ki+8GNphvsRcsQtLbODDRr3t8voiJMQSueaLGROLxpUoIbDrpjDz2ggiGs4sM7FW1GzsPpD+Mi7Oyf4ULM/H/vwLviv/OnGixOpsrxXgmsT0SU0zZQ2bDDPkdFNRQyV11NWWi/CajC5VLciKojK1VFljpRXT0mbFtVZdWYPAAgjUbC5HL36qaaLzxvRo/80/XYLvOpvifK+MjxAK0LvH8ssRQJB6heAmOQc1cMwyHApso98kq8m5H5O10s2JvjAIgjK8GO68kuiNZt708mqxJZL6q6myXQfOteBPMQyKYIULXrhhhh92OGKIm2Tq1U4vPi1hpyzOGGPUNK54Y5FDJvnWkkE2OWWT6oKXoMoI+sivaR37y7CdAFzIpWm/iOYxut77C1lEIUKpICplkvRMahcFkyQdYrbLxZ7n63PNllJ8CNGCbIL3o+uiEbiwPRtyRdWTPeb4Y7RHVnntszuGW+2406abbY25OhiAue12u+2935Y78LoBH9xvwfn+2/DCUWa878YTf/xwwhGXXP9xyhd3PHPINa88csw7Tlli0SeuUNvv7CtQZ2UjtbnAAIEe6LpqJ9JXdbtkGy9ILyxAb6/YQzpWofKuYQ6ygXQY112bsYGgbAN/HBex5Gsu8LmPxu7y3URJ5350DvPW0Pvuxxe/fPLPNz/91tRnH307D4RUB/j27CvoxxqLzj/jwiu2ar9UpZek8KIf2dSpJXXCS3QEEyhsXINlOhtgdI4nmDX1aYDX2Eh/dkAoCv5PYJWpS3Rq5r72ScxT4AsfCVVYwhW2kIUvdCFuQhdDGO7KID4ZhEBqQosZxYYkNzlImSbiKh85BCXbOSJttredjCSCQUJ0CA9ZZJAjEjGKOYz/4k5aUcQmQtGLOqFhGB2GQg2J0Yw1PGMa0bhGim3GjZ8ZDRzfGJo5xjErQMnI/+aIR7lIxI5Y4WNTgPIYORaSjob8Yx0VichFggZvB0skIyV5SEpyxiJJueRWGomVTI6lk1X5ZCUvuUnSkHKSbumkJiMpylOakpWpVGMsFfZDNt5Gi7LEZS0vlLdcog8mFxEEMFPTimAm7JcUSQkxj6nMmEBEEBQxzTGx8UxnUiSY0YQmNWEiTYtREzXa9KZqiDnMioQzJUA8TTituc1ucvOc1YTnNJdpTmQ2s5fe02U+ccWqe9IGZfoEaAtPCL5+EmyaAbjCTAIQgIGwIADFxMYV/xwaABYUsxUORcoVEHrQhXY0oYKYKEUb2tGOskAkDrUoR0kqCI0m1CEoDWlHD8oC2EwzpFcQiUZJylCiXLSkOaXoTRYKVJ4SRBAivQlGI7pThmrUojqlaEKDotOTMrWiQTXqTYFqUosoNaAG/WdBxfrVsZaVrBUiY1jPiimN0vSlDPVpMScq0YXiFBsLveZSP1rXK7AUIg69QmCHCtK2siCwGUHpTY5q2MAKwrEIjYlIAdvXvhITsq05alRl2oqWBrayQFyoYSc6kKPaFSJI5WxdM7JY1OAVGw6tKGVfu9HUajaob6UtYUPb2MvqNLCwHYhv3+rOtYpqcse1HHI9d/855iqXc5/r3HObm1zqLte5m8NudLMLXe5O17rVle7kHvmp7Xo3vOAtr3hLe8mhzjaYbRVJZv9aVL0eVKoWWWxOuTqR9ib1oUFhbSb721L3jjLASmmFaycC2AKHksEEaetSaWqS9iYYuEZF7YJ56tRMJnaxFlWwhTOc4KJGmMQWZTBUr5lYZH7Xxed9cXrBO8PimtXGNcZxQQma403B95kg3fB/OYzOu0L2mSm+7WonWxoLn4bFGDYtYv/r0/kes7SrMTGUU/tM1sg3sia98kWQitIhs7YirmVwRRic5ovgdKQsLepp97ve/K52oUvdLEaBTNwbZ4rHfd5UjKVisbD/jpKSRTH0nxVdpIHyctFIUulO31xgYjqWmjH1aKSRCuS65pXEInlrSjP7U/vyl6uY3qtbP0ZgOwcAz5mO74gxmuVP+7TUMzWNUqHqWg4nFpMaLrVD+staVlPYpE6NMEY/DWhZ0ZjZj57QRSvqX5gc1bGwpShFQerWtkYVsdMmbVtNm23YmpQFXA2uuIF4boR6+tnvRlJa4c0p34p2qFSmMn8zvVvDqjaz55aoYtk95Saj5smWBbhh31pfnyY8mGE+DYG/nFqHlzPJAxnsZeV8Wjdv+uJFDuZcDRvyjbI5mpAt+MYnQmeNc7S+fC2qu6FNolXW3JU2b2XOXykW3yoU/6n2XWxJr8rTiRZdw8XcNV45XVL5EqTooRV2aFWrc5zv/OZXp3ojx5uhqncd61bH70Yt0t7EpvnaD803hDe6Xo0o0ySsrrVGfI3hCQsFpjyNq1AOvJU6q52mc+9phfWN8LC7WqchfThWTaLr/2qEw8V+LVcP31EQl3jtip/pUq+50q97PeuTdPbM5+0abGOE01KFuFOHm+8TFzizXKbqSF1a8Ng3fLZuhfjodf89R+8+SbRWNoc/LGauuja4l1f18V09UremXMqzt6xUTRN00ypVsS3PNdptKuTGq6alDvm+ha/5fWwb3cwYQTNtn89R2N+7/KElfqylqtrTbpTVUP91pkt9fyTR99+fCRasynMttmuwZWMxsuO++2qwIau1ubuohGoriyKtc9u/CnyNRjsYCxSSzBMzomu8w5uozaMvAvu3c6soEqOo0aKwOHO6kmq30DJBFJu62zPBvcq2nyMtGKwr2YuNoruw2hq4x6q7Txs14EosuFM7pNNB5NOIChs2rCpC+NO8xbs349PACgm9K9Q91dM4lBKp1NM+EtMwiUKpSTu475ut2RMp58u+TsMwvNNC/xsIeZNDEdk2/5oIjaq2ucqrc8srifqokGIxujq3bRqxyCM3nLpBbwsuZctDcltDSES31SosLtsy2YCqKCMmduuriDKsm3AqICv/PT/0RP1rqE8MtxSEPVR8xM1DN4iTNkYsRSWDKz2sQxC5Lu3Sxe7KRV5Er100r/RyqBYUtmN7Qf3zNQPcKZOKq7RTO5dKxipkwYRZOpdyrF/0RUHLRhjjRhnTRubaOgzxxm4Exl4MxtoAtV+aCVhREo5BGnvCpndiJwQDGW5SRwSrGHcqiswKrK1YDbWax3pqO3dUpUHDx4MsmX8kx20cx4aMriy8RXizsL7ytzvzKTS8RA2br/b7u/9isymktCYTP8XSP5N4PYiMQ3jbsZSMSPXhNJljyZi0jZbUQky7LY+bwWj0wGLKLw/DQQVjQAS0KwnbMiqMCZmsQAz8FKSk/8nyYSamhEqFjMrdU7ZNhKthHCmNUz19G0OKDCoWgy2vVLU0Kzgg8yzV0imWgi82nMoco8O2hMumjEtf6ry6/Dyww0vPM4oD06uC0zdkFDtlJClmTEaoUjgqrLz4EsQ/XEyOAjW7zEvI1MvJ1DqlBAvJxMy7pEzNzMzI5MzP9MzQ3EzRnCOUlMuzGqdysiYgUidPS01l+rHVmidpMid6espoqk0+O81nW8nd9M25/E18Ak5mu5tRIrRf46RbKc7lWs6LKc7gnDfLRJhNMRtcKQp+ug3sLBLtJBLulJDqHB3wFJFLIU/TzAjxJBL0tBD1VCsLQRn1bB/zhM75HE76DP+Rt8ynI1qIJXoQVxk9/xQdAB0SK0oiWypQIxFQCEnQA+WQINmeneBPWyIfc6RQbDxHhixHC63Qb9zQhbzQD+1Qh8TQEQVRDVVO6ewKEd0YCwiapClRo5kgbBgOhOAZw/GJaMiTQDqLSzmKHA2cs6AW1KiLHmIKHu2UYqnOyHEiHXWKIH2xHMGMJM0jNkEIC8oTtQiiMnkWsBOQ3zCvxGgi5AiPSHId/UgThNieqAAOBqEURdpG+azPOLVPOW2N3syU8cieLUqOKAqR5djTITKdB8nT2gDQBE0NgREP3IkJPMmI6OiIDvmC6GmQLrUN1+lPh9AXCnGVRaEd+5AQFEn/DUM1DVHNCFBVUIdQHnBpjAi1ENnJiL2IUFGVEwYlVU2Z01ulU1wNERT1iib5gl9xiP0hlzORoFVRjw2KjKCp0vvYIMtAll91ieuQFB5ZjjQxDsOgE0Ih1jphGRqglOMYodg5GmxgjsdkVp+oE2TpCyr5CwZqJk4lFwUqE7+gkrIBE3YdIQyKDrtoE+pojoOgkxZxFjHpCwr6mjLhj4P4C31FWHntmpuIj+pYnj/ZDv7AkQWCFzrxgo34CQGJD1fok4+ti/xQWJCQj3dJE3mJmYh9lY3IjwaC175g2cdwoLzAHzVRnTj5Ap4A0yvB174oA4J4DFeAD+SpWe351cBw/xf88RNwBRM6MQgzZddHSSCQzSDV2dX21NVc5dqtnUMU0toHkYwLyI/lKI9COZZVHRM1gYjmCZBusZIx4ZngcZPi+ZP5GJvqeQm4BZa7mBF4kROGGI79EIxC6VK13Q5f4Z3BuAnWUVr/SBGUDR7UKIzGtYlAkQyBiBRHYQ4CYg4dQg/4EIyO+Jn0OJRMFZgCQdP6YSAUWVqasVyE/Ys/LY+7rZ/ZsZLDDQn1YIhI6Y3SBVXXvdzKEI62RdjjkVTEUFwcISC90A968Z3VEYx2IVKI3Z40OZ7dfRZ9QRHm4CH9SA61LYzswV7VDYzz8BH0wF7IoAu39d0b8lSa68zRpP/f+bXfs0gb0MSjj/AjyQRZygHN+g0KiWDS0chf0kQ0Xq06yYgSyAUTwohccqWf+jEJuyiQqqGJuoDgCqoMKzEgjHAi8bUURQmaPLlg84WXV5GTKAkJHyXX/ICX52gMuiAUN2EZJzUKQgIiJ0IT+/mL7NFSizARhAgTH66fmPkCVcHcEUKIn6FRIEmMFH4Ov9iTlrmIFlGVFKYUSnGiLoUXB4KUvzgPRDGblXAiMjnhje2X293i4wHcpfjVKJnevICZ6qDXdaFYDabXBz6KMRVaZUUTaQFaHHWiQVgMyzCXnuFhjTDjuhCINiak9qifLE5iDX6JV4LT2zDVd/GO1GD/UNwI0pjoZAgplgZpme1o3lYBkoVh1UodZaHZkGXtz319ohKZEfWo5ViO34KlkRFhHdqp3iX5kVpNjTJIFHUBkZgFklDOFTsNkaL1kdqVDEk21ezJFzwpHle1ko2wDr+wW3f53R3qlopwFlyeWHihVOYhCf1w1eBIZcn4oCXqD5JAieQY32HxCYhg1IqoXExllt3Ni9LtjSuJnval54udILrwjlJGDKJNlsgVXXpOkR8hXFfAokw9XpQ1EN+oXTtOiMegjtKlWE5dDuQhEPU1U2Nm2y793Y1G4kBtjEeVW34G3J3IFzVeWxzq3eptjFeuXGopZe71C/VYZz2lBQaK/9Z3jlbiVQ7v/WntpR3vINr4aCMigRmh7QiCaJOytQhVGYkWuerrXBVGtpgZbqCmqM5A0qOrNlJVuR4KooveSOvzdFat7uqzrgiv5htV4euxDorr6Ovq1GukcOtz8mod/ojCZuuz/mPFRg0++hF4YRkmpY6R0JbBvuu+HojExmukoWCFcGyk4OyB6OuWyOoJEs/FFm26rmPBwE62TQr1xFFubRHC9us70gjfMBvYxiPBzuROSav0VIo62WqANomWZqC6iODDANPxnaArtRKBSQg/mRQKsmBAseC8OFr9MBAKIu2iKVe7BtOW8YvnDVq9cCLYJpcRmojymIjwrdH/of9ZeGXbj/6LngnjP9ZqliHndqVjV6iZ/qiZCd5YKybX5jaIsIngAoHSAvmfjaAFQiKQxhDaHQDjBqpSBNeaPAZkT9ZqmjUak2hXHqmakfgCPg5TwRDsw1hXCoaMCkIU17kdsLaS3niXSrFsyhhWFndwHzaMYG1l9wxbU15lyEgOP2WdG0YM5LghV6WOIh8W40iUkvCLHMETlpkPIhYQ8zCOmslUMPllhUAecukhzJ0RWrjc6kCPQo4e6vAC5VCEhR4ESbUO1IAX6thZ6ugh4xheebmLYkkEASmJQt4ShaCe56jcJ2eIB6JyvCAWbOZzytUP5f6NCEfvK/GI2okPmpD/8jLpZjyhcwIhkBM/dA0+iJ2I8lMG81EGE0l1jvoO5lUv1UJ33sKA5xwZoWH509iR2DHRHS4Rnv54l8q988cgEEw9iOsoZNpxiT2lomxhECUnEvxsktwhl3FekEDdH+cACeEoW8mYF2H/18qYjNJNkydf2xwh3TJ41Ozh82ZdXxRRDwFJ5UN1j3FpD4/dDgiQauGQV5JFjUA1DH837sjFn+FoXjenauRpCY+Qk8jYZ/kRj0cdjhk9nguYcuEoXnT5jvwQobXFDy5xCQmaZpnR3YE4kEFgjnk5jJ6W2/YdIJSvZ5E3Z9jQDeQpDOdYEJRfaY03CHZHiZim93CND4Tf/yKPF3Y3/1WB6I/cQepM1/iEqHjRhQyVr92iL3fXEWp05z8T7YsoAZKpSR0a7ZODCAxTb4X8mI+hLnQF0g2F2JPGIBDjqNlWMOQtIowWdgmWoeAHNoygYY4o0Y/CoIneWF8PhpejSfuWkfLDiHF+CRowHSQZ5hExOXtv1o3pJdkXeZEyyQ+Xlxk7fg7jcXyAnWMninue4WayPx6wB1ndSAiR0Je+4Jobyvk7aVt4wRKX0FcAbw6nkdHnMGQOWvwQOvCobVEaEPwJ0g3FgGuCGA/7llnuOOrFSPDIAJTprohVNXXqLty7uJ3++PuekWGxORMw2SCH5vy3b48Il/LncP8ggE5T6QrHyyTRiimJlfmOS46JEOZinwCIRNiuJfrSqpWraK0SuUrUCttBiBEXDqKFDVsih64eLhSYiFa0hhkvNiQpEOIXhxAvruRIK2NKhtc6PryG8SE2hRlxuhqE8yfHljsFLrwYEWPGly2Rniwq0ajBgzix0WhqdNDFgk2L/kxpdGTXVimjvXSosqPSaENvZnV1cdDZhQcT2dxZEiVDiGf3+pwalGfMkS8NGuWa1atejh1T9mXZMZEOqFLJ7pzoUCpRy3A5JvL5UedczgyjSeS4Eekgt0v9LjXamuXq2H9lU7UAwYLAqmJpfIlWxgvVjK4gONzxMJFxjMnFfqH/6somBFqtjH9prjYyw1a0qpJG/lBHb2yDjCcC7iWlqy/Ld9yGAJE9fAjJaUg3mIjGQFruvSvnuANrQTQ8dx9G9ulQBlY6JEKaggz2tANDXpAXmVr4EWShfoM9NIiB2EDwXHBz9ZdTGZFNh5VvXkCGjYQRrtceSNhUJx9WX5TxkBdl+NbcdBtupF9D62UUGYfX5USggAw69EVF7l0znIzoSehfe+9hRZxYWOXohXQqVTcdUQ6V2NAX+OmQkHNi5SYdRMNp9FCSHm7klY0bdhnZgaSdlyU2CoKJ0hdQQifdhCQCVxVDNNxI22yNtgQApJFKyiiljs5GmqW0XZOho5jS/+ZpUKCu5KmoD5Va6aexnRoqpaeuiqqpqjLqCniZvpopq8yN2iqsveLqq6yx/morpeMJu5Srvd4K7LLDrubsrwRBAAFwUWKkYJStgKfVQTpw5C1E9F2053rd0npSQTOd9wW22p5U3UCt0KAru1oRJeO0ZVzk5520XMOktlFialzABFHY50kQ6DCwromUYVA08EYDIV73aVVjeNWRVp1NYK54bbbgyghgdexaBJlF8rayMZPNlVFVTP7iCwFhsbK7UHP/NmcjROC2sq63K5LlrbZ8clxg0TnB62e59ca00hfTfmFTuxC4i7OX1e0Q6Lw27Vvdw8UJpDOt7wWFkRc6eP+xEcVg0gJuQXqBp8MO1oL8ZXIiq4gcwwHDG3JKT0NLGzaSGh4psIMrvtpZiTu+OOSPSx455UhP3qtDzV5eOeebw2pY56F77qxjo5f0XeNiHQpiamIdRxh/qlMl3XaEtvLbkZFx1+ecwOGoY3/lsTh1Yiu1buVK/HGoYoU5uYKf8utpWe/vRC/lZ3AM4ozpRisuZKLQ+olHmMYo08egIvzxZ/XAAkWsYmQaQ6RIUJeJlxxp0Z3HYG+4F/TQh0hDn9hR7EAUwt33qLIyhGVpOxLBz3k61rjsAEovNNrVlLLVNvp9ATghyc0CC8I8B5qGKCVyjgBtN7yQOOl5CGOZdhT/ZCKv3M4g+CHLAAu1kNXVhU2dO9zhXCO6IY6uiEQ8ohGTiMQlKrGJTHyiE5XIGtdQcSVVnAppjAIeK/apOZAZEkMO1C02VacgOpCOz7y1ne/BpVZb201GiFMZBUFmZ956Xkp2UC0uoi6LrbDavubVs5ToYF7yskgXzdifos2EZlP8238uEsltsetaAvmbtgyyRQLdrTdOI45+YCIguu1ERnS02he1VQZFYvEi24kJ3TAiR7XJiGBnzKRYrPbHwEVGls0Z3lsi87IhNcdPcFMjbxR5xRdeJGpWwQiidhA/CEWtIXTkjSlxogNAZjJ5xlxQvbwASPC8BDv/gcwOIlaV/0FgK2LsKxppIJTJ8ljteenJ2NS++EU+lm6ZVbwIECM1RX76k6AGHShC+3lQhSa0oA1dqEMZKtGIUhSiFn0oRid6UY1mtKId3ahHOSrSkJIUpCb9KEpHChsmqugvwltIMiXip8ad54x6YhOX5DI+L4ilQGdaG4fmJh1adXAlLX0bvCy1SqHU5GXJY5crfEfTonZELzzaWmyiIqPTBApHVB0eQnznU5WUsjI8KlFBEnKfVYppbiqpKVdvRIvziNVRNd0LzN5qltetjUsi/OqJrNomqa5zLgLx3VwhglQeyQYseGEcorJ2ptdhT3bSMh4Nx1dZqHIpJAIqZXV4WsyHff9VPZwRjU+j2iWc6QUr32OSEQMKqShCsba0va1tc4vb3eq2t7z9rWKiKKoqdiwnAznu2YzrqCp6ymtcLG4WdxVdm7yquH75YnGdO1CbUHdX31PNbFxT3ItElzSk4e5StKvQ5irUuc5lL3NL5yj0Erd06K0JSwx23I5V8qHvTe9zkRsb68qXi9FtJX6v6K7svobBFc3i31qCVfweS73k9e5xhXi5wsl2tr79leZER+DBaXhxJfbcfYu4zMWFmFmXe9WJWYzhAwNrxLddVos/TLgYA7fHud1O6iZ3n9PouMhR/J9QBGTkJ77Sti1tSX993KsOz1bKjrpXbomcOC1rx7X/sPKh5LCcPC6HWSJEaQgtvAyt0xkRzEckM+TY9JHjvGQivTpeb91sZSkWmDXJDW9sTizoQBMa0IZ+zaH/jOhFx2rQid6ooiPNT0n7edKWrjSm+6xpRzOan+MtdKczzWlKbxrUjTb1pUudUlKPOtWtBqhsHy1qVM+6fjSzKI2X6SnXyCQ9CyTp835iOU5T7ZeyZmi7SKKDZbcN0nzEllcSQTMkczojG1uQSh9qpENH9EutiZqwI/rnoqnJlBnZY0NHIiP3GbekH40YY6+Y1EWHlNW0LrWml7xnffN73/7uN8D/ve8O79vNL6tZ8lajZ5eIWTvHmXBLlFI/MM+5PwtX/2yjfBhlhmNc4feK00O87W3FpUWBQRGjwifOEZPNpuFulvhS7mWs1cC5JUR+qc3lk/FfVfxP/qmrYrHsQyDvPOXJWziZG86iRdVvJfz6y9AzrvTeBrzqArc61q+u9axzPdBUBoCVo7YcWv0MXeDZQVWuxS5o1+tfdJvs1tjjSYUV0yNv/1a9BpmnTC4bQMYpEbuWcy0sARPKb09PGWzCIYPdzOfenfeF4u6QM/UMJHX01r8uSchtGedt0iSfNMmjMC7BC53tmtsWxXJ3875NYcehT3GRQ0qUYYPs01n2zow9kHOdyVPsnNtCksMf9dAthiGHi3wI+R668SjwZ4xarf+88vu2bW2bRJkbwmWvgxrFXZC0UhvEr5N3dgXeP8y3G3LUriAbQzHfW39/1+MP//nLv/6S+zqPnyht9iyqfCZShC4RiHoclkGUQXQk0O1EBpYMR324FpMwYAJxB5DkSe1MR3MYiwNlUK2gBIy8j1CYyG8QCHPUTvNM1lKMxwEqFpb8Eds8HJyoFZ4kRFE1iAvtCQMlR46kCETgDg3Qzx8RygWSxw09jFFNiwUAR+y8h0dASCRxCJ94DNAxTf1QjLHIi15AT94siUD4CWx9CVWlhBXqCvYwjUAYy3B0DwQRhiFVkHdIW30AR60Q3UoIyIV0EerIDphASYpUoAj6WEr/uRtHhdtBgRkgFuJJlU7KZBsiHmK9MWJpOOIfQuIiZpRLjJuiNWIkcpskGiJC2RknKiJsdFgmgiIXSZvUNNNVvQ7DxI1CxM8v4R469YjZvNDb6EqbaMu6QEgd+UTteR/c6Yp+fce0jJPuqUd11IoanQn5aY3jFUYCsgRk+MdHgIs91R5c4A0BWot3tIucwQRw2Mi1QYZbaEtFUKMhSVPjyNK00IX3ZAXNVMe8cExK6AfYrFN4vEVzkB15CQ2tWNNx9FJGIMcXvI0MbqHdwZNQENPINNOQ5MTbTN7J3FEyncZXOMy8LF7IjM28WWNBGARsGUywoSI2QMjblJONpM0l/26iQbmfbknItMlG2bAQ04UZTgUZEiGZs4zHvTwZ58DcXyCIr8TNQujS5RDF2+wWT1bKfcCHbhgVwjkLBSUl5AAWsHAIDUiTnElT2YhOjvAbwRmZhCCWfPAGcWxb7exP8HiQyVScklUgDcFSzjUEkdECPM6id9Qg8MwcQpxfxJgISvjVaSjIIOgIyrCllmEZxbDQHM6Jf8RQZ5zQvOggDSrgAvFVVOkIAj0MlMCJ2Mzi9zTb7UDAouilcjDEnNEKCf3HS3RJXemRWOiRU64cQtyQC4nQuZRbg8CWE4KmxYgQAyGMDZ6IA3kW1JSJxnmLsXiP8HzI77iEYK5Ig4yPLP8aiY0UyX2s5tThlm9118YYR3qAC6mMyAa6hnkdV3m9RsQQRWLuSncpWnuuxKf5EeW1G7Lkl7TdIwLV5zK9J8ucFxdhi4HJioqEy1Eol3F1F3k+2z8ei38eWnkGxad5zbzR2KkdBDtJRbuxCYLSZythA+54KHAwWEL9GZqcp4bRWB0dRJmEnDkl1INyKEuwHn5R6G1x2Ff2GIM1nupdy4LS3ooczdH8Txqhzr4Myeu4C0qUGwOdyUy0aCWZknGJSoTZDYeo3UEiTbNNzC9JG5ogqbuEpADWjcBQDLyUHnX8UuDkTJmiqSZd3+Q9h7akGZuKV4yES34lTT/e3tQ85Lj/2KJC1JTc/YQg3Ue3hNwO6MxALM3xIQzIAKgM6QujwpaaqAyByqJ3wNZIWocVdpJISsy8sVMz1U1RyQgcnsTpwVY0JmmPsWQTrctfGldFOMxx3MiKpI1u1JHvkNZnGeeGlIlZyAcc/gZsfofJtOIOkgzKAaoDKopH1MzBYZnF4AjavdVMcsSzXiFVIZU5iebIrRIRUlb/ecmSNAcNLNtGHByAlAdWhqVTEk3UMAmQbUub8EauNp9X+QlRbaBV6tXrKEr9QJWphOG60ZFZ2AiiDBO4HoR8PJ+w+iU86hVdWVVKKEJzwA+EPA9oIpV61lUH7R/8KNbcJMRWAmWAMF2Z/0ifmBisTUYR/v0bxOUhzZwf8VWTBxIr3VwJmgSQtC2JloReEM6NZlWS+ngnBH0ec8rGu4JHMQWea7GHUYWeeTQbcpBl48yskiVt59FMTbEIhOAODeLHbhin7N1HbwxHAX4j1Npl5/GGvF4Z2rELrSoKb+AIBLiWvJysdhDeFLpUzBZQ6OHHeCiIovjGKBUHxuAQ3kqbsKLtoSwI2XJHGFqh7L0MkZVJiejQikyt5boEvZZJnxit5cltzUTN09IN0FGdqoVa6m6MEdod9lyH5bUoUSms4SbSfSpSNM7uG/UlGNkhylzNOfqEGM0FILmmacXpCF6o0gzjgn4Ioh5Ydf/QpYlEU93t6JQAWS+qSfswB7wpTd1BE0PcTBvKETrWEVgRKnJcRvSl09WUnrcUVRsSzUFIkyWpEqJ+EVJp0bT00rW4hTqZBZ6cUkzokjx5IdHo0xY1HnHISDQlCEnWybLlkkGgJMjuL0IAUvKcaTkRK0zNb0PKSxhd0vu+L7mekrDZ27FpGKwFVArfW6v5hWFkBc7oFLz1BFDwWlw8xEaglsMhxXVBq0pgBCJlBnPY8IrpRUbwog+3RIGW0nFwmXCIyk60zUh8hDUexGlkhw4rhmj8hEOwiV4MFWq1RVZYRFlQ2mOMBGjgxGqSF02YhICRsRXJhbescUTshA9dxnH/PIRSYEOaxcWc4URSUGQrKDGGHkTKJMWBVfEtPqJceGKaSYUkK/EaM7EQqZsLZzKgseoRGaBtkNVSIiFhmCHYSiqakaHa3s68BMhvRueZxE7plUkWv8QJcSZxos7/hFIcqcQ23UbuNc+ekK1H7LB7RGeX/UdUFQp13si8bUtN9pH/wE6d0UeRrNDSTQdPjQcczqvzpGF34CD9asdHIAeKhGFREWcOoq8BqqBtjJ17fAfweKEHRVKW7CzyLZ2eEE9XRlIdRqcXuhYR0qGH+EsFvkeVON5Qsghd1OZywK9xkkZFqA4JYcQ1Syo989aNcl0fehWOBUuqEMuvHAj7ebRt/wnQykZOjmFY5yQLErWPstAfr1RORy/Zh22T4GkRRPaM5bRLyWCVOX7LXDHJTqQnSjigQ8TJ3xTSyhGSPm8puJDNGdVRQNbEHymMv1SSlgrPVKvdzVw0+AakwJiNVwyoQxKf5nXq9flLHYkvQAIk13SGcMhi0lxqzqwRRNxagaRE2j2PqRpb0OyMLtqPO9WtN+HE3/woXTBqn6heEjtutRChtXRNf3wkdL6LQbSNzwhe1IimQggS1Lhi8BzMgVxEua4c/BDS2RDh+iHMSBORjcoWTOuUF8O0TsT2Sce21nVHzdkfb9do/jkRkUWSSUcPAsKWSDMQDQnf2e5egmDbGP92puycZG8QiH45oUNGh0m7jvjYxEBynFf5R1OaRnRsCgQBT0DWDAA7XS0nVWJFAwDi8wuZKQCBCDUTRldec2ImxQ1VoAt5G39URfAWNTMZiWIyz4psCmOJs1C8s0L3qq4Elpq4EHfD9323FFb5Bp5g255IKg/SzgEW9BOv3MFgs0IbKwFNT4OckA4GEIvMFTM3bXYiUcv2No3jdo3fuI3neG6N4icaY0F0ntIuW88YpCWR32LPxPkg77ZgU73Q5S9FknoEFY8GBzmn6LTxotPYEjMrCAVPkVO5Y7wNUnnMCx4JZCIJrYKodVnW0sZ2pFoQx7tmk8pgsxklkNt5r7n/GeD8RoXw2RKTbNF5QBNzFITVDIIctc7W7FM0ztMWGRSh4rmRNHraRaoMTTcXqukFXlPIvSO2+HObEmjPJNeBLZsZ1U33dlPa4K4tFfI12dGP68qkTzqPW9QKA9Gs3zop4von7rpK6nqv5zqw87qvD3uw/7qwF7uvc/JtOUxlnctmiAethknQdM9pFY/D8KTwHE9XychvWJ5rRdX4BMpHbAlQfs9ZqNYO3bYTN3jMVQe4D01mnWRsKqFR7UVlfZFWtRRZ/UZPLPGz/3sHyVkXKhaPuLhUoVaOzNFvLAqz78VZSaXCiVVjDIVrgYXF3ztw/LtKLE9glgcNWfxh4ZRR/ynlgTBWYwymV/EIWPTUeaRGxaPcZoA8cGW0jtc8jt+8zec8zkNLv10EdOUXfK4X0FOYgFmYpwVoQfmn0f+8f/62htnYSg29pfHXiLdX0F9YecZXgxb91adahWqUe1k90avXctVnglkUg209fOVEJImXShuo5pC9gaIX3WPY0nd9pjHRaweUzvf9zvs94P99byu74Ac+bbVj4Sc+qvCU4uvbjBt+40O+5Ec+5Y9OranuC6cuCmO+JnN+C3++54d+Sl7+5pe+q3W+6ZP+6YN+6mv+6ov+67d+5s/+a4gi7Ks+7rt+7tM+67/+RVwBCwhCS7DAFfyEILAA8gsCRxx/8f8bBfI/hCAAP/HjxPEjv/ULP/BPBfM7hvRfgfA/BPALf2FYf/IfWvQn/2p0v/i3AvA3P0eEPzZU//Vrf/cDhfyvf/bLP/kz/1SoP/U/P0C0woZNEAtBAwUixCYw4UKHDRk6VBhxosSHFiNCvFhRI8WLHTFu/DhwJEeSGUmeHImS5UqXLWG+lBmT5kybNXHe1JmT506fPYH+FBqU6FCjRZEeVdoKQFOnT5dGTTpV6tArAa4gbBWABUMWAcCCvSLwKguHgsAS/PoV7EG0YNdmbeuwLMKwbLO2uioo4lu2XA+iZDv47F+xAtnyHfg261WuYRVfWQt54GC4C7/qNQw4LWL/uJcJinX4NW9VqqdNp4aJmrVq161hv5Ydm/Zs27Vx3974lLdT3bmBy76a1a5ZbGX5FsQamvhCrnqXH+ca2mzGrwcXL2+1tlWrtweHS9za1XtZlI67b9dOuvt36WAZhs2Klvhb5wH4Qg/gHT7m/diuW0i9/MYj67nuSAttLumwC87B31JT6cEJIaTQwgoxvFDDDDlMqrfeTNpQxAz3YqjArfA7K626BLKPu4zoS8mzwKQzy76EYixRxQAeus66/xBiYSy0uqJrusnMmqyx6LCBL8bRsBqvOa+AVA/HzjI76TkinwMwRRYM6lDMETPSSKEzQ0LTzJBaoWXNN11RUyU1/7tLE6M635zzzjbTW4gWOekE1M438XRJUDPdPCmhPPk8KdE8D9WT0UEl9ROiQtWMk9JID8WUzUE97cgVkCDd1FSGRq2U01NTwuZDp0qNVVVWV5W11lltpdUix8AEczoiF0WRv7Hs2g9FrohV8Ipl81qr1wSfbHFFJp0rcqDwFu1So/BIAnYv0rAKb7ju0AuQpHEdw6pBc71scMsuix1WXPzUIy7Xe3HN99Z9dcVXUAl1G2QHGnbQ4dGLvEhEIC92GESohHFyhQaldBh4BwgU/iKRmxLxoioaXEnkizZ1WEhjnwb5gmCQo0mkZJrK8DiRVBOO5reTgaIFY4ZooeHgiP+iceXl1GZeuAybYcJ5JB0UfokWHUbFc+TfWhl6wlebGlPrEd8LCy7+iqQSuuoUREgyyo792kuvL8NWwOd0lJbHK4mVWyToFLPbx7CkW5JtBOk1srqCCLNSbLuJvKgt+gYMT5C8t47cwuBsRlqHL9z8omSkHYrmiy8WYhobzisPiXNsPh8do58T5fyaizgXqHTUQW9lVKFbib3z3T9/XU6PSLXU5dB5ln0k3V0eZNQvIGhF5DyR/jN12l16XVbd32z9+JVoaN76HVDiPBHwVSq9pd9Vv+YLj6kHuvOnF1WdoZNPx+h2GgTCfSDSH/qZ0oisd5JUPchVWMsamSSHoXH/tedX/YnXk6p1KfQAq08DSk+MEqcis7gtglAqzX1gBJ7pXGmDKdpL36jDrIQkKCHcQghj2gUld1WnP/HZDwY/g50QJRCBtQFYbMYHsjZBwBVIG1XHFoJEiDHvCw5jGA2a5gWVNU1kNDgZxGhHgzII5HIVS8TTaMewoT2xaQIJYqqYZr4dbMwLddJB7rBRMRoownkec54VX4bE52FDilAUiMCsuLCQjaxnbirDF5lGsI3xTJEL2YEdF3LI4d1Rc27qmMY6lggIFIyPMeNkK8rwOSdO0WgM84LQHsnHpg1ii63AoyERmYhGMnKNF7HixNrkM5SIUXNJtKIdZZlKUHqh/wwYE9nOsJEyLTpvkwaTohiTObVWaExpVTyZDujIR1LSImEla6McMyZFCNDRFQnDpcpw9jnmJUKZy2NIGUbpRzB+wZNWc40Bs8ZDfeLmhMURiGgMZKO0XCstYNIhfLbSHBkKTkGLmleDypYdY/UoStmhYZg8k5UA9WleU1oIsIr1uIr2zXD+qdONzAW2YRloLhXc50thEzyTzHQkrrgYBHzmOSsOIiHTkyL1xrexL4CvarQYxBsltrEdgE5g0wSdy0i2scyVzGW00KTClkq77qQRG7S4KQRoob6RJUJh8EOdw7BRy6Xe0RWpc2XGXga+1GnOdvirWsZGxrSqOZKLCv+DAOa8UDI/6QACEHhjz+I3kNTpLJxxrOXnnGqyN7psYzoQHV2Tijq7Fsx5TBue55Tnx17q9a/cFCzJCvvGh8i1dqptyFFtVzFsQHWag3DZqERn2e78lZndWSMtIPu5qW71i/ib7RvfiLS1DnWaQkWqXXXQxlassWrpuVxnF/LXUS3VqKrlrSZHVgaDMe0aRcWsH+X6xqo5TKYlca9HDIgSmsr3vXezL/Du+6n20pe/+d2vfQlamgIpSzkpUo9BHLMkgyhHoAt+HOB0+KQEn61IZWHWWJAliASjiTHLWlCCC2wgHQYYhRKKi18I7JiDsMtcIGWMhucSrcTEsL74tbH/fn+Y4/n6V8c1xvGOb9yQHLeGBhbYJKo+h7GWbW54EOuZn2YmsC8aM1EzI+vEajbbQXrsGq4Aa9AmRk+bEXO6CRsqJItsWC4WTGXLq2zThEg+29WxkwyppcBeZtWQNWySs8UG/p4HxuY+ucu4dCQEjOymWgJtkiEr58iamESPcZXSifCcHdeHjWvIUpU2q/R0BRJmaWauuJqW2GoRDVaeqbphf56tyraY6fJO7HIau9z4TrlIt7a6ubIUmSY7OeYyvE4HR8v0EhfJaTPG0dJaHt8gnhzq7dqZ2jozNQRUh95Fkm982CVzq6PhplMTk9hbnI0BYZpu1nCrO9p6C2RM/2SYuqWrS++WT5MMXCO6/AWj9OYM2yDXF34LmN7kiWFEEsSij6QLTHU6G1yaNVErSbQwd6lPRdu9HICqm+NIyc2mDVu7hmjSqa+ba+24/TmoShZq0zTzG92qctE9LRGv67bINJ06LZJVYQIB+eUUC7r9Vddz21YYyFJy8tG50ox/tbnMI6tpRyosrn0F9WzlfFwlZ9YhChNZl0WZadFRb3qpEyuziV1zrEc2Gou+IllBl+mG6JXbWXcZMhmiWsqCz6pkRfurp4vJMmqOfORldldh7tmmlV3oqXte4Vth8h3sz2QbE5mtsZp3RObPtS3ntmqp1/mwFhXU11T7QKDKeP/ZFBDdPey4agL+OPE8TocNoX1ivSP7F9L+wR8NVn5O8uAQ8j73t99h8c2U+0spZvYMYb58lS8h4y8Gcr3/aOx773waRd87r3d9a4ZsmkcBW7lk3dyxLV3I6Rrd75e2dqE7nelLx/FoAsLy1Mgc6U2z11IO0etqQ03tmmf9koisPEaKzIh8oCjO2GuNTi3cJm2QcslkQmshTi0jHkVl5IfPvOkLxgzSageJPo3ZIsvlRqfJzA2KPM3SJCbSmGu2MqYCu8PQiqd/iGhhdECXHqKXluzvbIusPrAE3W5mJuZ1DrDZyEyVuEi8Ks8Csc0In6aIeK1hou3PfGt+dG0Cbef/y/6Mjmqp29YujnrJBFsG2rDM3LCoNvAJAI7P+9wQgdrw+zAkDmUCyN7wDonCX3TFZhBCcz4H6MCLiWZrZ8hHnZinszQGvRJmB8An9XSAlYzpr0TGqt6ICVFHZpaKrt4E6OZusv7qCyxAqDKRqqDoaT4nqxzPmR5xnVCni/Jqykbmke6quo7LI/jQkWoNED3LioaqZAyvdyhLCEFn7FyGnqDoEgciq9pnYP7EsYZqGKnOjFyLJG5R80iiezyiGMVr8lpwqKgulHJrY7ABYwbish7xcjZNr1LHc0CnZZrHZl6wFYtxmhLvmDJv6sSQ6oqI9FxLc1xmB1wBsbDByxjC/7ha4a9Qr9YGkLVGx630RVcGIr4eUg8pciItkl8qEiMvsl82MiM5UiNB8iNF0iNJsiNNMiRLEk3CDzcyif9YKcqSiKkcQvDMSGRSxQCNKokcBpMcTWOwSGZu8iY7xn8iQnkw8JTqyBWgrSYh6ScPKYkYwu8SxsokbZVoIWiAiawWxk9q5yVaIWFcUmHKyXmOaJFCJiaTKVWM0u+YssrUssoSpdgigpjYK4q40ia6sk0gKSISIZRyUi+fsk00pi6TLdmiMg1xjqf+SC3lDLgGKJOiko3I6ixd0k24qTvKaPEeAjKhsjOdx84WCQa9wDIdRjHTEjhaDw9VUw5ZczVds/81NeRCfEck0KchAkhGqNEhbjM3U6J+1gR7YAfAVAd9Xmc2fU43pc44SSKAqnE55ec5Y0I5j3MgZpM5kdN4jKcicFMhbrOtplE5i3N3csI33Usld4dfAuh1bCY9sa4rm5M6LWI9e+wW2VN3lFNQxseecPM4sdM1WA9rXjNAYVNACXRADdQnVrJAFfRAKURk+I9BVSNm3rCq5HAN6RBCMXRBNTRDObTj/ivIeqy/QNQOP/THfKxEUTREeYxEVXRET7RFTVREY3RFX5RFbdTHmtNFZTRFb3RHYZRHRUIidZRGfbRHifRIhzRJZ1RJgbRIa/RJnbQ/jZQ8yZNJVVROqnT/SbW0SZF0S3/0S2kqQTt0TDe0TMn0TDskNdF0Tc1UKoiyTadigOBUNdi0TufUTvH0ToXiP19FTz8O624NOhMIBl1jj2ICCDtnmnRT/7RJPRV1mgCoJP4xNPNT7XIDHi01iSpmZKznJqVJJsIN9PI0OMTUT011VE81VYPDQlU1NRgG0rJqQLkKNh7wJSSUL+XsArFO1ZKJ725QQEIzKsdJYECHYQZBEfBONsDQzkAmZb7ss5SNY/ayVfPwJEfSWimyO6LmTBIFJLt1JJuRUOZsTr7VpWoTW9GVTvBLXUfSdiSiXMm1IvvkVubVIprmI89yx1AyXa81JSRyXwG2XwXW/1YoC/WaR3Voy7au6wRVjhp5bmFbpmGd53kejVL9cSBsK2XQqiExDyEuLyrfaX7Gznka9teES2TFcXour6qCbn8MESGq7iR4bu7EMT1qaSDFEbgSwrZmS4oWyakwBwZPRp1SZX9wLiHKYGKok64s6wv27PI0ZWWj4TOli3wCNiUHll+F7EIL9GIs5qloxtyKIg1dwihXItJaIogGRk4DEFV4NWkMVP1s4v+IIloXpoyWRmx/os9o4i/5dgentScgawfzckPVlFp/4iALy9KYMCKg6KgSBgKOJqnGpytfVZMm93F/6xNpTmA8hlhJznMwZqj4b1M1qYh87QWFaBwVzf9plUyWpskBJffM2vOqWgYTzUma5I51U8aMCMlgUIKsGlBhc0diKna17KhhhIadaklyRWbSHombMIkG/6yIHMKPum6NrmoLmVdmmkiWtuv/Wg5xqxVVgSZVptauMtBg2DE+/SR2Bgh9HTJETtYCjcfwUKU4j3YlRCcBWyJoYivy7FdAUIUorBcDZedR4rdRGJhNrrL/ECJc9SR6ogdV2hcDl7YdxXOAMZiD5bSrRs612HYlRnhcTaQGOSdhuLZD+fRDyFcomKew4u4LmPPWBGt4IMup+HB4FgJkrqtq8irvRC7wBoIJ6akcT6/soA306C7v6qrn3myrDknvSkZlq3j/hhNSdQwvZRBPGvkqOF3uID8naf3q5Sw2dLRpq8ZKIHrH7DTniHA1I/p3IAjy67DOZgRGrLrjjY2rCc139VYYD8GL2jazmbTpieJyYHqOaTbJto5MDKEJlHSNrAwZlAgG0x50nYwmJqBIPcdtlUYGWR9pZJaXqlTmzqaGacaXbEGp2AJpkgopkdoKnbiI8BymZFLmiWhmYNCQBW8pM28JjRR5q0xGi7A3AZHpckKVlHrVijYmly+miBThZU45BPHIZDYJkTqpYqiZqQZGaYbKmSOJYJiKkAim1tqUVf8YN3SmsK6ycR3CmKamd/0RLGdSd6eLaaayCo9pan61qWKu/yvF9x6xa3WJKFQ/qWAyiQhH59TothEnzdwMFW2nSHM+15z9h54acGNaxme4EAH9BMssrZ7ZCXViZrSa7Y54LZKmVQWzF7tgenh4sWP2OCLo9oV5gkut1EiV1B1leCAlkZ3YeDRna7NmeGqh5qr46BiBTmV8CxpDNXdiDnjDsQ8LixxlyQ/lS6F9crKUFrLU0Wc0ib1EZ+WYJmUueBoHIrBIJhbZOK8wx6yFjh6JZ3t7qamqZth0zuv6+BhxeGTuCofVSxlD56qHmv6Q2k3MMbvg6tXaF6pg1wTBsNve+K5IsNWaCh8/57Enq2TWytdy1Et5WrR9LCKxJkq7VKdJG/+1h3QgecpgjQeymWzyRlYce7C8xqtmr1BAJM+y01GbkLgPa+dk+liKbGrbMFOyDm+2nCflIuu354p9biu4vUydLAuNr06473W5YetmeZhjT+Zmu+Oo4oSLI425Q0cc4yixOG1pcXnyehVjsbir9LGnghVMoTS18Zu1TbRU0XQQjMywHAmPhtbc6GmTtYxhGHrTJqYHs4yeje7vDjqN0TaOCssC2CtknibWGkmOCA/bCLADqe3UugzbbKaTx5F8mkpoUEeRo0FCO5oEidsVbK4VUmZzdKaICql3tcqtOG0qG5NnbAsBF03cJmZ1ybZ7DKuytsjXoC3GbYaI9qh3h2f/wq1u7Vh8BlUp7djxb81IKXuJC/8oFnWtcM33cNdZN161nFCRfKQoaIxJmPjoclrQHZsbc3DJy8iKCRX8IEca0yCJq1Qwaduqli7HWa2KCpUZfJ/tAjmt0TtZlhL9m+RMQkE6yn3LYY5ZKT/iqjy3xR0Zb4/qZVxJKddKyjVQfDWm1MecJaaoL4moxPn6BCcG0glabtOcNnK9Fd9o1jQlg3MOdM5OlGB3sj0wms6uB8fuslpLHIeHq3CbE+sEu7tDsTfGelSLnnCOh3Fnssnn7KoG6dZbY8qrT8gd6wAPeN3OJ4UOfxuxZDZt0pwd5mzNflSOjxPC5sBHdMAdIWgA/+iSnR49ZrPTe69+u3fVOqvSd88SsH2pB8g54t6DWDdvq7hrVkNbGER2PTj0PVBNBvWE7h+78rjmMSHdLiprTVPwrNk0BlN3mKPZUrzoB4j3SGQgthgtb2pq3h/n56keaX+fR2k2rZw7S4Dhboyd9rjADVBT+hptuxhrR7wkqehGB7j8sXLYstM3VYCLvuap6GLbynrHBzg33ij8m0CX1SHePI3rbNGoq8En6dnrDwF37c1W0NNi7b4dFwk/tW3tzwJ5sVf915Wc8IFvUOZove+V+8Wp+f9Yhgd7ae0nkMcRkAljHGJOvlfhUa48pgwJH8WXySXkMjDffumwbcrHqv9k5mjMpHfz66qhRdrqhAusruFprpLTftulR5Oe2Wt3/1idcVpMCL7sawJqyF42XnBMQyn4gQJrnV9r6TjkLlaMW1mxBj4TAS3bBWuoNOsUnUucIk+WWqmLtD80z8SVCuZmV2tjV26acOlpRtlg2l9zZP64krG12MciAivJUvZgLVsbAYLGl1YQdHwZ2EpHq0Q6sGFjiK3VDi9fFF47iK3ilx06XGFzJfBgw0QTK7qCmAjCwYkOI7Zs5fCLl4w7RNJqpVHgQxoOUe64xrCiQloGNyaKyHHhDpxfsEXDaDCjQYMwJVI0GC2oDoUodVxNFO2lWIcwyY51aTYt2rVl2Z7/bQv3bUuHAOravRtXbV63euX2/cs38N7BfgUXJgw4a1PEhgEzfnw4suOWWY9CnoxZqmXJjTtfFozTM+fPpEebztxWbFWXq1uzfu06NuzZsmvTvr3QS6IviSy7mpnIFczgMHm3Eo5tUERaylsxf8i8t8tBiY5X3b3cuMsvyGPT0i070c3pLqlur468eUScyMVXRU6jOuwyXlx9GQ+UJ8xB46mLF0+dbkc11113iXgxHnHJJaKec160Yl1EB9ISzXPH1eecbQr2NtN13L0HIi2+aQeTgC71dlSK7Ql3UkS/yTdcbyK6SGJ7MtqGI2465sjjjqzdBSQAPvZI5JBGFonk/5FKJsnkkk42CeWTUkZJ5ZRWVonllUtmyaWWtjl0zWnYhBkmTGWeZVZbZ54ZlllhhSUbmLet6RROCr0m55lm+pXQUrNF9VIZ8em5llppFganU2slKqdabS6q2mx97ZmYS4/uSeihiV7aUqaKwsSpo16O2iU2QQZJaqpdrqpqq6y+6mqssM4qa620pnarrbrmSutum1VJXW3SrQbersbyemyyyNp2KpCHLqtstNBOK2211F5rrbJxrfZspNu6xa2k4rrWbbnj4nluuOSmC267377rbaforkvvvOgm2ha+8Zpbr7r2+guwu/uyCy+//wpscMAFEzxwvwgzfChdzQLg8P/C9U76cMUNy6Xwxh27mfHBFnMc8scQn0yYyRonXHLLI6sssscuywyZvxi//HC82O6cLc8+9wz0z0JLO7GQQx8ddNLLjod000q72p2tTk/9NNVWV4311ayaWrTRV1ZUk0cQ8TjQnAwdZWRG9w2kL6ihvcrbjmWvVrZLF/2qdlmtYDi3pbOFJRRYEYGNN60PLUZTazV9uuMOhb/GG6FFXpOQR6+B1zeTEHrZdzSVtz3qRw+Czi1RrYTpuQ6JkLm35VHqnHXsWstO++y2Y921kF93tJt+TN82CEi/uxYflI4D7yerNLhC+nL6sfZFg7TQ8DtJECTo5/JVMTd8ebylJFz/RbQYZazqVZXxgOALQVBGkY+3hhGV3Wc4/fxGdoQlLdfjG/Wsx+f4hfadyAIQOFHyrlQa1IgpgaJRoAMbCEEGpqYwc6mgBQuFpgvu5YKR4qAHPThBQ2XwgRJcoAlJeMIIpvAzEmuWBCvSimi4QnVkqU5oqLORpohOIxKpikbQhg0vFAQsQWlOGQYRtqBQSDpcgVHdEhKUsjBkB8qpjFOGxUOy6GBxEhEJ2uw0t+RV5GyPGoQOy0IfjKTuJjSsCk7kc6k7racVyyOLFOdWthzG8HA0zMhuwmcevRxwI3eaYfyCYpndDIRBi7RMFu10NrOIyCCJ9FVLSGI+nLAnJihK/wi3fGU+CVHyIYz8whDfOEayZJFDKRolFFM5vpPshosO+U7Y9pPKjJSlbIxUXeqYZx8Y6iVuZQEJR+qkA1pQ7iBP5AzsbgfNYwmCBVe4wuaqeQWXVFMQraAmTKpJTRZw8zWCuAILtGnOdIqznNz0pkvKyYJuqrOasJlmNa85z3XOUxAuCWc6swkhcI4TJvCkZzQP2rOiPQl9ELje+o6oKCS2ogwQYBAVsaE6Sy4vKx1Bom8qur6TOE5/RzQlI2fihfbxxhUFXEhDhjMRQx6nokhsSkphooMyPCWZG3FORmWCDYH0xqEm8cJSZji4jhj1V/FzyfJIMpyyae81veEPqP9AUkS1mRF/y4EA8/Sntt5QjyTBKd4OiieT72Tuii8dnG54ojqKZGV5EmVpGcSjEuWYL3pEGcgXlreRDkloeUsNKi1JMgiCVMdxF+0hfYL6O6MGr3iCsuVDgwPSoH6PqAg6yERByhDm+EkliaAoUtbXG5bg9K6CCqLjwIeT5lxUINTxqiaDuEjqucahBiRJU6CKjYrupq1IQqhxjTXNAChXufxshXIJqlxsCCK6zl2ucsWJFhYwNyLatW4ArjBda2qXnw65wnen691ssqa7y22nd79r3vRG5L3ZRO9y4yld7+L3mbXrL7S4ptAk6aChFR3TLOPzkIZgJIjVuZNn/ZT/0UQM6rRzhKInE9yTHajNdNc4nm4cwtGeKFiHDJmoDjHS0xaN0U4SOUo0HJcQWjjEfEuhY1n6uMbVkGTGlsHf/9hikrAgcowp4c1f96i2vFFFicSByo0R8jjVnYkiUmkKUCEEIa7c6cItljCKaHBhjK5uTNNDG0aOB+IFL2SGwTxc3g74FBKPBMtgJsmb/ofn6sRnqExxyCBUJyLhJE8hCUlygoez5cWauSFSFvOLzXwfQi9ZBw2isNt0q5AZju8LZdpy2rp13FCTSrvgbYU5AwBd8aJ6vvjdbjkDgF0Iwfq7bkTvNWGNDfOeEybjdS5+scya6eI3vli29Te/GxFd/8s61i5RLkDN695xktq/1B5a7kCNm5QcszUsPYlfOwSV6MVNtxM50LDqd5ycLnV6TgGJixoCHg/7dTUSTc63u/od+egGcCaNhqB8pZypUq/eTGE3cGPrEoq0hqKzHIlUhzc+BrGGjopKqRlBdeXDvTG4ZTCycMqgOrNuhiLFo+oBJzoSD7cvIfRxnLthInCc1OSPB29qiweL0aiVXMgDcbcvH6vbhMunrupeytiCCh/4iJY34HksLkGK7vpBdWz1i4YinhcW3l48ekg5CphLNG/9mfs+3H4eTEvkuIGA3Kn9GxLO3k6zuLMM7nOXO8piVi5ht8W8ANXutPO7a+3q7f/v4RW83p5bXmTrfb6o9rUG87vqOTpk8blGNuMvP5duBmCgWCZ1WcY5TfLS/e4wK/3M6o560p8eXUVbGWwWgkxXhAkbMIa3Dr2gnIPA6E5g7oneeI82Gp9d407GRl8RF5FkYvggYYIqRi7SFOV8BAILOWODe3yc3ou5hzHmMZziZseQrCTLyW+NJWFzJ+iD5Mlm2QHIb4JmCC24+M+JCPXT0nHIfWFTGEEwRpVSFX7iRxDRE+vHY3bURwcRDW0UE9YXYwnhWX7EZTExE4e2ZbyjYcnXY9hHcX5mHzoFHZRjPheYbt4mZH5ycNc3JlCBNgiGZjLxgHIEIy/hadN3Y1v/IRVaFCfiwl+i5oNOomvBBmsE5XduNITYsF3HFngBUHkABXnahGual2qsBl6lNnH3ZU2rIWxQ+Gy0Vl1VOE7xxQLiVBX2BV79FIXVpobTEmBfEx9XkRxLAVRPMRPxoR0CsRB2+GNetVN7Q0XsloeiFEQq1RD+ZlsGtB9LUWY1FYe8UVE7ZR/Z00tBJTh8eGQKR4mQVFgQYnautRpm1RR4uB6VRixUBD7+9oY/hlrFYYrXs1QpoWhIUR8R+In9UxGvQRUwtFQvpz/jQWP1gYf/ZlSClYcHcjzxkSgkoRsg5SdeYAHgVocChHbLuFj1sUWBiFPjVonypxBGhRMdIiiu/+BRwfVVDmV0FRVY3mNGPLERtcU8DCdZhrWM8IdSr3WItahje5g9JUVcR/KD/6glTWgWW8h45zR5USh4LbGFjud4ZHGE+aVefIcN4wV51qU3ZHFqzvZ5Dxlfy2VNjGdd5JVc1/V57PVrH7mGKZkrADYxXhIU9zE4pNR8EDJzMHRhiMQa0igTDAIhArQbMUJKh9NKq+E5TVGUY8IUVRRWw5JGbsNMEpJI52MeigFIPNkTYwYqw2Jgv4UQD3KVRtZxUaNIe+RSazWBu+GVS3kUH+JWZjmD+/FFBtYcOxk8C+FD1+FImIhyvUGWokRMWhlVNGQfMvYdpxOUnkQmuudLh/8zb3bpEL/RPjACIyBXNtRRJopkGdK4clZmZEbpFJs2EM3nWd+nOghRE7n0k/bGNniSf1dFdj0Bk79hc5+mkrVJK0G4kfjFXgDlazCRhJV3TroGT433hEq4HkOYkL7GTdxUG6b2XNDFhMf2Xd2FZbC2nJtTa0X4Eui1a7bpnbBybcYiQ9rTbc3jGuZ5GyeIOACJLKXVj9iCnrkSn7eRU/O5LBBhnzqCjE6SmrxSQigEoCoUoP8poAW6kZE3kYpnnUeIXtwVeZrnXvLFeBupXponkYBXQauRTi1BeBxpebgZXZk3hgr5XGN4eL+2Qik6oCpqoCxKoC10KgRaGidRFJv/s6KT8Y0uqqM32qKOIT4yuqM9KqSXIXE8CqS9ESoqFHEmZENBGkLs+Z1XomvgRZ0Q2oTL5pu0dmrxpJytIAiL16DGKZ2bd5xkuJxC6E2nlptceJyrZp1eujkk+WqqVmp+l2vUFKVQSiVtqKd56qd9Cqh/uiOBSqg70pFHeKHV9aXU9V7DZnkO2lyIJ5AOKan25ZFpcqi0dpD7daEJyk2N6qD3RYTWpZuWV6inihbXhqqrKqis6qpJ04OtKqutYWpfqk0DJV22aoVfWoXYWU7YmavNZasENawR8asayk7g1VwT16utUaxfuqzQmqtgGK3HGmzKGmyviqrhqa2z6q3d/wquxzJ6rkeueKd642qu5Wp6I2NBG/RBIJSh8SpC8xqvE+QwmTdCGESv9rp66tqv6Qqw62p3/oozMBokBDuwARsyfXEmqYewDisXjOI36CqwBtOwhlGx55qwGZsy/wozN7OxHruwIvuk32qy4YqyJ7srfCor9pMl76OyWuOyMeskdQkbM7sqOPudKcuzNNuzP5uqXaMqxISUvLN/dvOAPkI6ZUM5NLAVXmGYoDIq/fk3E2cbYSJHAVh9E2uW/LlWgJI6g6MhfbN8WLmVQCNDCpGfPYINrakj1zAjGNawEIE6drJFj5N8FIIbfSOCWDZ8V6u0VUsuMba2OhKrQIu4Pv+ruKzKraOyUbBxdH/7JEdHViJCUW2HJRTXI1NVG0RLLJmDbl9DjA9xVu2RPZjrIp3oQ5Wmlz2jiqNSLDlycYNTaaEEEypRHJQ2Gwd3G5zLGi83JICpJVTLJUBqpE6KvMervEOavMy7vMbrvNELvdPbvBPUeinEURNBk15UlJsmYjnFOx23GFvEHXH2V2fUEh+2HoUYEy9FOIyZFO3rS2+0FVlpEN4Yh5nEG1txFEKUU23BEOahOvwrtgT4kjXGdEmVRytRgWxROflyEPmXfIIGF2CjHL61FbKHHQlYFDOxv60UFajkf8PRcVEBOBwBaTl0FgFcYslnEDchbjQgfWD/lCIlvBgy95Q0zJg4mBCkpTZxRDcCmGQzZBCyB0X1K0qqdZrzOxADtlfV90gy8cK/V8RFicLG55WmwxRjJIIUwhRZKyaHu7hjnLhl3KosyyQsVR23KFQpcVo+YY01JlxEUR0QcbcsBaw9hIjTsRSPJVGwaCd5Mz0iFWn7p8a9I5R0bBWWJXb5WMgSoVSjtWnOIVx/lRxeAcPKoXCmJFKkiFut0Yup+ZOhm4edfMi3uMm5B2M0oFIg9YAYwT5DxRqw6FuuhVlg8VedDBsTwcj4s3ZCpcY5+sdeVcuUBIv+BhznmFr1aBmFlpVmR0uf2But5Vt9JUO2BVRtbFtHZVss/0UTk7XGTbGYzrFnt7hk22wZx0NaKSEiUlXHqrslZCzPW/MaieIpaiIz9zzP+9wkLNksVqJmWYZ9pmNGbdsQyUe6ItYbMDR/6gEUbRURYEYmJbZntHxGCjFzUXTQhbZgzvdTHVFIEv1jFzET19AickRoRmdlGehJhbYTKEJ94Ndo3NJY06FDbfRSyUPHZCFSGYZhUpaANnXT33ONyTNIJ7Z/aGbHwbfGk6ZF4vjRG5c3FR3TSH1zajNDX7bQDYjQGbYajRYWfdQWf9ZpDaZFN4Fl/0tD0KfFRm1iXN17R7cRdhRmTmbSkmYmVOTMlNYbDJgkYmzGiPsbs/xbd3Ui1f/RV1PRdoR2ItLIYIHNzz7SuEyScWEBZpYdPi71yaSbfVeEjhIXSra7fXvMisrIIMIxNsLDyoOgCPwhyvdGfgZBHXVJTFIVNeaxuoPjdZsWYuSscTTBG7QNbuuZurBhUl6Ag6nJuT82nu3GE1unHNQHdsicVLuhCOKhW8Cr2blBjrjUZwg325vdQ172Bdedc8VxVzUJIQcSEY/FW/FW3saBnxAxu76ttdBDjDchYU610FHlU+Jbh8iBOZ3d2ZiYUnMlYcjR22PizWC3cXo8VT8FIZ/tycUVshRLsheu4RDLsQqL4fiMaHTzW7jbEzyBSHypGg3lOgNGljqgfR++4Rr/y+EZPuMwXuMyY7BA8rA0cZU312IxhhEESEciBVNvssaO9CtwVmhlEjYdUYB/JmIIR0qECxEdPWJkkVgd8UsTiGEfwR1qO0NZxt3ANXWFSIDJgVsHqH9pQRDfg9zkp8dq7h8ouH9LbWhAxRuAUwY1xGWepoAZ0T5rzYJq3ib7/RF1xmkSAkUreB/a51dHaxw0FGdBtUvVVxychp+//RANHOItQUlG3WBLxGi31xRoVhMxiGgnUYEoZpWkrYKT3nvTQ5Noo9cvdSdZkbGAHdlAW1Uu0lLtPRNIVHI/CbxtMz0sMVf/cyBOt+vNziNo7CQhwRICR8HBjMLXuN/ZCIf7/1lyEzW6DKEbW/RSXuaNKdFxLGHaxOwnEkVSB9FS5caMw+dK1c3KPq7OIjJbMmFKcehDIQfbGReAtcu0wVWNGDU3sGgUmrvfclVZ6a5Si4FV8bg/vSiVwN6Ip3hlAD848XG3Bk8RdVgTl4uKU+TtbuVajkhEqbjdcKzfgvV1l1NuAqzvDVFvwzxLM6ESJEdhuOvNEXhTO4kh3mNU8BbyRMXxx6jv+sESFDEIrUUlkA31D0FgQ5lwv6UQ99cV66JFZ2M6KG3pzg72caKqUuLlf3kd+6HBi+FZUakoTwkULqaV8mEpwaR7iqIYlWlkmzGWH8EflDPDCudEkcMcHhFLJv9NIoMDfokVJonlmEiKE3dlQw3iK8OxR9WBL7BnfpvxEBeMEKu5TAd/HRExlykSPbAn90Vp9op+2EKGNmM5k0LWlz60SMB0EFWke6F5RZgoHTIplLAHFBGIpEEplGLG57ure9tT+4/Z963QHL7Cl9NcNoMfEeBhQ3AfHBHmfxXylDxXvojP/GxUWnXz2q/Zz9gW9TybEgQEFgSxAxDQ/uhudM+tiO4vEONeLNezVbFoRpats+c/xpMNENhaCSQ40GBBhAcVJiQYjeFBhwslPsQWEWHERDoqXqQ4caBFjx0pgmwoMuRJkylDWiS5kKVKmC4ZWqShqGXKmygHJvqic6P/zJUzYxJMtGMQrUFfEnEMehCCq5xDTw7ERrAqVatZsW6tqrUr16tfxYYl67UsWLNp0a4dq7Yt27Nv5cal6zZsKx0QICzN2MpVq1ZfvlzLWHFHT40ZEwH+G/iLF8A7BuqQ7EpHq2hfLmf8Ytcz3M9zQ9cFXVq0adKnVadm7bkqANixZaMeXZv2bdGJltpezbs17t+9gfsmPnxtz+LCv+pOHty5V55elCZvVcZ4b7E+tUvlvt17d/DfxYenqHtpNFcQfpbxgm2QZGxFdfR0RWPjy1a0INDKb3DHUgh2mC/A8Qokz0AED1QwQQYLku1BABaUsEEKJ7SwQgwv1DBDDjf0/7DCqDDscMQPSyTxRIrC6osg6eLbz6FEIEhMh8UWk+iyja6pLzD4sKGFht1QFNJEIoc0EBsIISxySSOZdLJJKJ+UMkoqyctuSiyr1PJDvApKpD2eDpIuPxp2oOELGgBDSAf+ClJKMC/b23LOLOs0MMkH77JzTzr75PNPPwMFtDuuDtKzUK8MZejQKxF1tFFIGZV0UUoXmtTSShG6VNNMFcX0U05x8zRU5UY1NVFUH930VFU7TTVSV1sFlVVYZ3111a7whM1WWUkdNa5fb80U2NBoxdU0Y2OtlVRie012oGuE5VW1YJ2V1tdrn2U2W26txYqkY5fVVlFIBTV30HPTRf93XXVP1DVC8drcbimdkPKQ3g3lNanNENntTt8M8eUQYH8F1U0igqlst2CGF3a4YYgfRvJdeGMSEDNsogWqIMm+/OnjgaYT8KfpPhIJG8uo2kkyOKGNDz6TYwb5IrxcEeiyiCDoLFoap1tMoP9ECtPCqmgMK6rFNGaIsM4OUqoVmDdSiqSblFYo6pBxRA/HkgmyGr5ooynsizLIKozRfg+ymruig5x545CY9nppgWhJhJZramapZrVbRmhtmccrN2LCHza8cMQPr5DiCGNKhIaEUyL7JORouEBObNL0DmsWrSuoPvLGBLrpGJErCl8gB0q9I9A5/GIQlQoLSbqWfgT/2G/xZOd49cCW0kERj1oviD/2EBq6z9c1FMyn0wUqXkzkDpoPyuueq7457K/XXjnruc/e++27F//78cMn/3zQXtOVNrF1cB8wzeAT7IsdIKNfzp54wsZobOCsSH+80I9eQQsMDYyGFc1MDy9lcB+9elKGzsTnTMvjCZzkgxzpaCaCBKHFdKriPx0MQgeuIIxGvMATHZwpMNLRgeeuokEvyM99PeldA/9HGa4JpoX+mQ+N8obDnbAwfwbEUUV4BKT49BA5T8MRXnq4FB0OxAsNFNt/3pcV/lHlS0URCN8IWJWMzOdmgkGiZVxBNofQr4fxEUwZ2nex/pHxZ2AUjJmo/5JA0z3GfW26i4A0k4gfwvFLGjRIAvlSxzRVZYqCwd8MX0Y/zX1BRnyJWv92Y5XHiOWCA0mKGqkyRRrRZnCKS1wpSXlKU6bSIO+SiuwOg40xxegvZnJFUWAHOcG0TyDJm4wbM8ejycyyM/Q7yHvuFsr6wVIy+oFljZ4SGBMahY1LGROa7PZFwGjEIOnZZWBgR6PMtGd180lEev4iEHMW0D3IQWZ73kOmvzxuf0pJZzIF+MtWyDOGd9tLdbRJEBpYpxX7od9RgBTOzPFHM4/M3HS8YJ0fueI934weGxdCg1k6cCkHFcjjFNqZfsboZdVx539+lL9+1ieeGiHnQOWVnv/FoMk9Jm2h2J75yqu1556p0ydNrQPBl5WznwsVYH3yJyej6GcxZbBP81T3Uvss5KHFDNp8bAo/nXqOdypBZVdPpqwjDYpRFRprSsrqVbRqaGKs3E5SrpLPsUWQf0zMpzYl06U7Nq2qX4jGf1oBubZVJTM7+0vKUHYZqDkGlhHk4tkepxs04WU3PiSI//wD2ThCzXeAJCABDxhHMLKMRkkZYRFplJFBoLB3hlkMRtvGGdX6D06WvZnKFAva5d3VtIvxrFJEiBgwasQh18BdEqXYGVpchrj4auFbEwu0bOKWsRpxa/+MolrP4qsoMNrBYIMLWtxSprVUOS05NXPbjBj/VTdA4km0eNJXxnaGnJx5blH8MkKNRY220PIbeP+KlAjm8jCJ+C1MRqnKwrVCEAgRhJoa1GBBQfhDgliweBSM4LSiiHFnTYjsGDhIku6yaXDCaFKCOVWChDJziYiGflJmX+dhbqua7SboThiN54FueYtN7d28uUuBdW0yW2wxkKJK4p9xVHO7lFOXyClRhRoEgo+TaD73x+Jfwrh3suzxlR0CTt0BRj0mE91DcSzOkYr4lw7ZSzkPycmKYlQhpaPMmKczwkJirsj8AZ19PabiMHm4xFZWsoPlWdcVrwzR+QRTLdPszYFKlDk3XifUejKIlXZUI6vLLWQw7Zeo/hW6/3WDXCEFluKKqjixsvMYe6HsHW+Jq1uyjnW4bK2sTVUlAAGoMDauEIAr0LpRDRYICwKgpltPK1yCALZcxlWQC2OD2SyYyKVaEQBqCzvZ2Ko1rr2t7G9zW9uuUh+ewL0TvjpWIyCcbDk7tjPYNqRlRhNbmRYzwrY5pH8asegIo4EX/lwGqLkcqf50ucsrB/aGWXnjHc8U2JIFzRVfpF9VRNi/9nRzt2cLzBlh17ua1RYvhbztbAfzlQMetSrFo+uKAxs0bEgzProRru6O18UWmodGyYUKxU9+sCYilk3TyWQJwXvxbkr8i2d7z76/S3A0duXf+JbsyLu50CQmdzf52/+BxYeZ8YXCXOc7ucyOrqK1MGMy4xmruNFlO0zGflxYB85wxIytMmZf4Ttk+TVVdo2SXBs4LdLmdVgXgm2DXFhbVzE228CKYVWy1XF2PWGZ/FmRTK4woUXR6z+Py2YsB+aZj1Uhx1rYz6DZl5kTPal07CM6aD5WsVCTO0cVksLKQuDjmYxhZ1Yn57rpzPVAo1HqzPQ4dtaxPSpOHVMzErTey1TF4Bzoz6RYP5nu2MyZVGGM2JPMJRfloZB7pzIL2TQWRa2a0aTXj74Puxcx0883baHlD93Q+yU6c+fMp33qTXymggoGirEOAxLKgJ3oez0BKYPRM8CO8T95kg+moo//gNonbkIPO8Oo/xuym4ihhzohqKGRvfg/XRK/AIQ1yEsrY1uwqsi7rhCEK9C7r4jBK2BBGFSwGNw1Fmiwu4tBFmyFGqyKGAQMvaMwIGwwH0wIGKxBg2DCIbwKGKSwZlOZInzCq0hCgXBCGWwFYwM2JCQ2HNzCrnjCKLy2bMtCK0RDGfS1IlywJQRDxUvBUlorXWklU3O0xYARjLGRn9GNL3ObrbEZ6+sog+CJQSxE5IgPErIb/uhDQAI5PvSSqVFEPcSIifBDI9KuVriGDqqRQlQbSETEgbAMQ3SZpKAX60vF/NnEafofVEREqNiICsIbKwMMvhjFnmCafLJFhYCg/0KsCkfbCZtpCOb4iFr8mUlED7J5RUIMxmJ8GmZ0iFMkRGr8xGocmpnTQ8AJNFY0RLZ7E6r4t6e5RimrkYwBOXJ0HigCpGqkmTcpmTCxxPgwtET0CbqTw3ZZQQqDwWbDhi5cQV3DNoCcNmzbNYH0xy7ctQX7tWDjwmP7NYO8NoNEvIIgSGwTCB2MSCA0yBU8CIj8R4FgNo4stoL8O0FISClkAZI0yF5zyIHIO4fsook8NowcSYdcwY9EPIlsSXwknA0Dj7SZCcvgHEabmaDsl6DsqqBJSpFgShJxSvKASpSQypJIEIuwKqFoCqYYiW6CCaqkSmRDH/AZy/IhS+xJyP+JDDYXjMmTVMuLFEliA0iHLMK35DWSVDBsgzCFdMgf5EIZvDuJ1Dtj0zudHMyuIDxq40szHIhfa7BdC7ZfU8nFxEiVZEi8VEmZzDaYpEjDZEh/tEsXNLbEHMjRpEmxLEvUPM3zKbckMR/auLm7mCPVdE2z1J5Xq03cTE3a1M3cnM2xYA7eZA0rC07suce6c5jBZIErYIHB3EgaVEhpg8HmZLYf7LuANIjG88yY1M7J1M4uqkHmPLaYJDx+DDby9Mh+bEyRjMHmbDzKBAyKnLbPrDAHI8mQLLzGk0gGe8wcFEzT/Ei9O058lDwBLdCeNFAEPVCuUlBSAsgWbDbRZM7/IVzPyPS1KZzPu6xJvGTBu3NByhRCu2Q8bINI+Mw29UzPCyU882xMiWRO5VwwijTDD8VI/8QKq5jMzUxMivwKHZTQYLs7guBH6DROBt0TOsSTIk1QJU1SJvUqIl3SPgHMB3VLFhxC67w2Km1C0yTNALBQtezQKZxMw3zJi3TJ8aRO+XRO9JRMFnVPIAQMIJ22gYjPixxT5iwIIG3TLnXJYuPBLhVCJBRPXyM2z2TCJmWYn4TSQ1VURl1UdOm2cxO3bXs2SJXUcAvIXtPOCL3IyPxIFR3DEb3JLnTDjDTJfpRRC2VBxFzOv+tOCBXIxzxMkQxPEx3RveRI6LxJx5TM/x491c8syR9FvFmFzvWMVQft1I4c0kil1HFb1lmb1GfllHdx1kpl1sCzFn2bm+uw1nDjVmptVkv91tTw1nAtV3KtFnA912hlC3S9tSd1VCixzvsEw04FDC5kTpikzoNwUYQ8ycK7zx1cTmlTTpmUweVsSIDNwWKTTH291x38V8YENobMVNGsMHwd0SBtU8y8V4jtIgltWHwd2LwMUtFsyL4ziGnbwYFQzkY1HAKVkps4NZ3gv9g5ipOg2T6JnAIBGLt5CJ2NCZxNiJ9VCIAJRqFt2aFliINJiKD1C5m1kKdFGARpWQQri2aZu6zgsJDQWregCurMNVSNVD0ZV2FrV/94XZcjTRKYAJyMoRuZ0ROxAcXpqcq2fYnDyie++piWGLkiGij065lbZDvYYVvugoh1LQlwuo+/8RKSUJokEovJiZpo8SBQcYiqKS5JMojk2kQhY7jCsIjINVylCJutDJzS1Vu6XYhooTTLXVzDfV1N0Z3WNdxowTQf6Vsjwt0wEhAs043JPTn8wBaQqCSOUCS34VobpVrlLSXvPIldO9vl1TCKQd5/CYmtgr1W8ry6kSq+yrzSuSyA+pnf6Y+jtR3NjReJ4CWflbKooojJIRjg64/nOdqUiN9d4p2qOsTtXSyEKTU3Qb+TSNrzlYriIloDTgkTSwlb0jSE2DFOCqH/LnqmhSpK9F3f4YGr+SVf7dhN3yRODv7g3gRh63nT0jhCEe5gFD5hFQ7hw0zb2eiNDBIQm8EL3mUaOHkPypgMPNq30suI7psPJOIM81IhM3IrD/IYskgeGFMKHBkbnpCRnpgfnTGUGJkP+fEjHaaf6bqYwZIk05mhA3IfH9JiAxKz96EKy6AizijjzOqsMwGnOoIiPxojG1KRRKwIE+O3hEuet6KMIO4h+bk3gwBjKPqj/XEfgaq/z4KaKeIoSIKP94AklemKMDJkJ/IhzmNjaHolznufFsOjEaMMpGrkmAoQvdqKrqGFV6oK4hJBAzqjw7AjH4mhHaogNnkhyhix/7pCkyaKZS84IyxTo4qqi3eNXuglHOo1ZmWWipelnKhKJjBTwKVI4DFJpuZRNf5dMcKQ5obKj5tCNNHBM4SQM24KjBCDY99Lsp7QDwdzqeqwjwSeHmtOqlKTp+lhZ0Z+mW8qLAUciBFMux2qjw4KmjMxOjbRPM7rZljSKt14JWL633GOqd37pkFMnfRAiqpyo+KRs4USv4aiJoZe4BjappCCZ/iwKlmCpoTIpIRGLBvbqzMDGkx7pgEc6XnSpRVSqv4DGuaCquGx33diNQluDxBcsYT+iEObDvKD6UwaBOuAk3AOPZNYZqo+5qq+6kXZMJ+Yq8GAq/PSn32zZavb3P/lsorCyBvIYpkAO487w9sferSC6GWasyjoK5vkyYytwxfLEi2xLiKlgK3z4mMmIqErA2waASrQSgqQ0Jp4yjiPyS0yuaOl6KyeOYwueVyq+JFJXhE4IWshK6J4iqCB64nKSJPJZbHeQiHgetzCfRnvEogSC7DBiJ97I2wQNK/TiiC+FQzFKAr36jr/KYz5YGLSHuTJUBmfs5rPEgiCOrm3w4sTIiQ9JrtxHDl1S7jCOKGwVqO34RSr/m6sDm/wduE8UQleEu7X0SfPaSkf66yNGsTJ2Z8ySK3XW28sC6W/1qb0xpyDAL4PZLHkUoSoKp7pE1/gcR5t+hH5Nkcacwz/Aa8lRytwLXud9MinnkWxGwsz/dCX+DazVssoAmS+g6EkTooap54znakzILsykkaIMks3ceIPmEEy0LubN7ModPI/0FG1jtEm2cHDGT8KCLBwkEY3S7Mbfq6IHf8ZE6sfJCcTWiiyFVcdB+vcEaysjpETVmuPNsPDiirE0iO/JUKpxwEMoGIvC9/gdD1cda1WNn9zN49zcBWsNf+WbZFzaMXzboXzOu/zPF9z1oQQcBOjeaovtV44Nkq4ze0f2LFc/WE6jUBs2TqKEaouEcKZ7IC5v2oizfCl0XVvniYK+PAzftMfJEq0GV+t1UKiH+GtO1Is79qvpfzrzoj1L1Dl/yHLNwKSrdq+mWFU9a44kxoxmhyKawdCtPjQKcRQnXazpA9irHtbOEUrOI1wO9kmi1avCuPbOpV+3LlqaHTzrlZerA9ydeiqmfzSoukGoyE/O7h7dnj3nzKIdpPxIHlGNF36a7xx9uBGv0kp5vEWb80tp22KWpVo2nkU+IA/kGbuCC/O4XkaPtrZpdPjrY8Lmvz7vIaSePseMs3SaWyYYobg8TGDpb1gMmVKZ4ewvf3xoyOreKg5k9LzQPDDsoMqnd6zLha6afnmRJEv8TV6cRxjqf+gs1fiqAvaDxjTsot2GvhwiJFmwIGWu/vJvjE/544RvppfGSDG6BJPvFB7ef8GBCR5wmkv0Xqdwr5oGpBNTI/4AbWOeub6W+c3Nqkxax00sZF/wmYpS/FkwkDr+NwBvKClRnH2mI79KD/aYcAPy6chhyYGrM+pWHiF18oCmqGQkerg5Yo5ojSNEbq25YrZpXzStxStTonT+hn0OBh7FRgC68Y2uRsYUf3hlDR7befEE0a0rgppUl2P2Qk1abG7ca9utJHcb4haMv6OGoRRTP2DMA8jMkakZn6/2AngtLJ/UxnmBwndoIWOugp6wQwkzxvq30TJ1w0Swn6jtVdowxdOZEYb8ZLzwP52fn+Su/05s92eXX0vscZ8Yn6AuIatFcGBBVsNxBYt0SCCrhD/JoqIDVuZL60aCsTmalAihK06DgT5MRGtRNiulRSp0qPEL18mEvSYEOG1iyIVXnx48CBDhK5MyqzJkWWrnxN/utLRsZXSaxJHuoo2c6rMiVSvysyKdavWrly/eg0LdqzYsmTPmk2LluoXGgl17OhKK+zcgTu8UJ1Li0ZdmUATklQreO3gwoQPG06MeLFZAI4fQ3YME6xLxpYHJ2rL9ctfxYl3dE4o1fPa0ZdPk06NejVCL2VMqxZskfVU2IzvunIZOjHCyb4T/u4NfLjw4laNBz+uPDlz4sudN0cOfbr06s+tR7+uPfkXCzQ8QuhN8KWXjnd10MiMPpHULzt0WEwE/wE+NqXY5MOPyxSb+y86dPj0n1LYUbddgdkheKCCBDJoYIMJPrigg89NFFlkEYIkIYQTbjjQbNSJx6GG0zXU4YgRoihiiiaueKKKL0b3kYssHidjizfCGBJnJtFo1WSx0QakkEESOaRWg1gAAQRuudTVF3hBsFSUH7nlxWw6lNFKZVoO4goED8mHzSAWlcFXK3fVBxINDxXZppFuwvlmWRZCFqedct6ZJ5576tknn3/6uZVtngFaaKCGIvrVQEpC8FIiOkwlEGdMeQSgXVrC5xKk9qVZ2UAA7vdFGRNNCl9FOgxyqKqJstomNnRGtqqsrdI6q6214nqrrlopl6uvu/8Cm5V8sw0S10wdVbZmNLRAoJdbSiViFJceRgRpK3uVRCZeWr4UJUmBBRvur7nCWqe4546LrrrpsrtukcZVddWPzsXLq7z32pvvVPPCS6+//QLc678CB8wvwQcbnDBwdU0EH0LR+IeNlWd6ZCzFnmaWZsMRGZtIXI9K/FLI2OyQKn+hKaxvvfviy7LKA6fssswr0wxzyzUXfLPNL1dY7ss5/4ywzkDPvHPRRMdrW8wPC20dzk0fPeHTSxs99dBQ1yzVZAIhXbXXXYON9ddih63wwe2i7S6ie73XVlRPYmMmswO54hamX4S30JSVwQWfW8W2UgZeEOPlMXzWqp142rH/lQuA4opvtFa015bFppx9bbVbSWB9hLlHmxu2G2qWc+X5fY8vfmfqq6Pu6n0Sca2TRzuxFO1oT0X1ekygH2S5RAazHnzrZr3auOPCIw9cIunRpFCkwy30Eqe87hUi06Zt7a/WClXGdULen/mXpF+YZpt/fbP3OqlfCGRa+6IpV2xGz2tVvvPXK5SU9c5PtBDiMNlHKPf7HgFlorThqetsCEweAxfowAZCsHiNA94DVUWDLNEtNJO7T1/msiWscAZ0gOng5zwirRG2piOeO6EOLDcXwW3FC/YpypcwNZdEFA5z4CphSCC1Qw6a5Yc7DKEGEzKmz5lkTa0BTV5MmBUR/xZFdBFcHI56NKMrVhGLOcoiF7foRSt2EYxf1KIYy0jGM7aoZ7AKIxrHyEYc+YcGFmnLfx6yA5c0yj1Rug+kSlUpoDAlji/xX1tMAjI+eug/g3yUf0xShsNZ7D30Cd+nDieyj0DAAi85k8hO0sj5fIEW/nHPdzTSt/gcziqDyE94GqZIhHBGKk1hZHpI1TaCHI4+/nuPISF1HwjckYlnapTHePQFjuTSI/5BE6bgw7A2mvGNbhSRAqdYQWti85raRNQEs5m6AdWNe3hhHrM64qX7zDFV6blGOCv2EsAxKyphygxCjggyLSUxKuecWNyQta2Sxe1t1mImNhgVF2zJBP9w91yeB/EyoH4WUzTyeUjE+IklbnmoW4lgJ5Nm4xpO3uegX9IbOq9VQ/nU5YgSU2GVjDUxGGqpPHY75jZv5c2a3jSnON0paSRYLp6qCmKpIggyySfMSH5kU708mUBQYilaAKhYowHNIe+pg/JAJFP+weg1XFK99VESQOObKqPmYqnehMkl4wPrVTXVEEh5LzMCGVM0mOgUSJVnY/eRo0m6iiVN6c+UF9nBVKvFx4rRBGR3FZOj8PKpQGrKPA/VabqqCVTKXjazmI2g8Ryn2T4pJRp1iwgMlYjQvTA2cJvkDGdmIkxUJmRN9BSTxR4FmlbwZYOukaV5TOK/+tgRKDD/RSfzCpq+1sRnWzCF4ZQ2N1uWWAtkxQVcRbi3SVpYaVMWKYkHgQJbhMg2PnYLaD1ng88ypOqjJb1jSXwHl6F+FlFluxrV5hu0+pItv9mxmr7EyN9prmxD/03OfwtsNAKPDb8Kpi+D79vgqM1LjXRyMIUh/GAD29fCvKqMp0TFFKCcFUAg44yk3lMjAEWDKSWx1KMIUsqItTZNlLpUh6W3URnbhz71uaRVtCYftNayxhOxEn9EtiPElXd9/PEljNn34RlTqqpeYEpUqNziw+6HYoedyCGXvAPuwRVkOi7P71yyvQpjWL8XTvCahWPZzcKJgmpZcNC8IueoDebOQ8Lv/54OMhVBxFdt3Qy0oQbUikZZaZytWFazGJ0tTJpzSq4Fikvfw0y8tcUidRvlEuGCF/WCU45yXGK0GgWfbWFFPpbelpdgGw0YZkaOkALcsdyykJnKei6HXmZ627atTCvxqJ1mpolJ6TZlKsu4UvFC20a6F04nZZlSJHSQ4NyqgVyBBSy4AqB/pO3iaBvQCBHEtmeS7W2L59zaRje5180CQVSlFeUeSLitQu5u05vb7l73uM99hXSzACHZ/re5340NchPcIwM/+LwJgm55J5zhgFY3v/V8GIljgwUBwHdCNC5uaqfNp7ACeZwKkuLJxWR2KrfcQc46wtHQQiAREVZRaP/yk5uMxCAqx8nMnbeSmm8lJDc5+VxYXruYk27nMRE6LWDykYe4gstPMUiGSCJzWkil50TRnZ8/8kcuT7kj8+u5aLVOcjy9+exoaUUA2q7xAFxhJoJwe0LmHoCAD2TuBL+C29segIL4XePaxgbf7+73iPM97q2wOws6Dve6333ub/d7Kwo/eY+0HdBv73bl2x73wiec8YS/O+QDcHDPK3zjo+873vGUeMIbPNviznbX1f6rzlrc2upyD6p173sjmhi5v0eeNKM5TeM3B/WLz7xV3o73gfi923qHPtwJUniH/33ppye43cWj8YJcHtuPn4jkY+Jxgtgd3p03Pdv/nnH/ylNf9YVXffsfr3fxtH35zB993DWueBDBS0E43c453emJW+olXNcJoEEwYI0sYPFBUwRCoIRZCARa4PFJIAYWx9QhXwZ24AV+4HZoiXjYiAaaYAii4Iik3fBZht0dx72NW+RtnEeE3/0lnnDUG/PJBN813kToINtxnwx2m/9BXg++3+yNnw+qXv69n+BlnLY9Hg+SnhQ2Hg/ORNudnt+JH7zxHcdphSBYXkGAodvFBNy93d4JHhF63PwFHN/RoNuJm//Rnf5VH+z9HwvuyaDhoe3x4R764WX84ZvwX8J0Idv1oCFa4fR5HFYI3hU4IqCVX+c1nsCp3ujBG+kNIvnd/11vdOFELCInVt/3HaEcOqL8bVziwd30Sd5xbKLeWWEnNuIjUoX/XZ+8wV0tuh0ubtzmvd/i+R+8LSItZl77bVvmSd4Y9t/4BeJqiByd9OEzLmM0PuMKQuNVvN7KbCI2MKEL+h8Qdh68OYffHd7qBd5MaFxCZGO9EWEMTuL7Kd737eDjRd4R1t8gvh7lvSLpxaDpraIxmqI4RhxBjJ8jLl/c8Z8hIoTHTZ87HqElit/y9eD9YaElpp/6HRy3VaNi4F5GcqQ0emR8ZVia0ZmGkaRIAscq1p0gQKLnZRv9bSIxPh4RwoRKYp/69YbklaLBQZ9Bpl9Lfl4SoqQtDiH9Tf9EITKhKHZiUppi3KliErafQE5i3yXjvy1dPP5fUY6fRPZgF76eQ7qhQ/biQmolVv4kHPrISJokmrGZWnYNBV5ISU7HmZ3EWmpHWtalzRwQW4qNXPaP1EAPXBINghmH9yzYAeUlYPbYi9jlYnqNXDJmvzim9qiZg1HjR4pF98HEIs5fObafJqJe+cEE8zGhcFgh9OFdJ66fOFahPo5eO37iDYbmErrfJ4JfFD7e0iUiFlIiP2Li/HFhJfLL/R0cvC3kDdahQlaiGpoiEkKkJv7bORrg7BSeznUkYeihZXAEfGVF0o3FBr3JtCERVXAnnnTGCUWL2V0LeNINeY7nYmj/kDntRnt6Bi1kJ1bIJ1fcJ2n8kFdo53reydRVjloEBhSRhWWeBuiR20sOo0rmH0Ji5W02oujZIrep5EoapFAaYmzaZIPCYeHhWyfS292Voj6K4iL6iFJy3PaF6LuB3va9oer9YoVShTHCI41mX29u3DF6aC8eJD0O4/llYxfeX/l1pYEqRjNayMUBB279x3v0xhdEXd3sz/zgDyLNi1RgD/z0C5VaCvgoh8dUhf5IhVPwyFW8T814qTsNBLNYCn+Uh6MYk8gMilWE1lweZvMwTV/mKdw0j/1kRG7MpQH5nKWMacY0Bf+AzJjSpVPQwKo1nZZwmS/VxgB5T03wxbxQ/2mg3qWiUpKX/ojLCcShjqCmTganOkdTaemaWgSq5iWlzOn2QMxLeMqc5V517uDkYWRpYuUlsl+IJtxmtqM4aqEVUuK/VaILgqLiCSs4JuRtXqRqGgTzfaLj/eQdtqZBTF4druI+TpywEuW44Sq4Rp9pNmsy3p1yQqLgdR7mjWNnWqItmuU62upYbGRhHEQ5sYRbeIwXdNcJXcsg1IUhJYSHlWBRwBe4IGzXGYVUyBbpLEUPAQZFAYWXRITDno533oefnSdgJASzQcQOmAl5eZAS4ZCYmM7ktJbB6pDCdgZ3JRR2YVAUTQW4uAfAbmeqLEtuec4HkUSTZNDPQkTGdv+F79SQh3waQB1RwUaOXwCGprWsza4pDxHE09YF8ySdjbTTfVDUUMlHxTKtIa2sX7hQywatEB2EMOVFlyBEaN3T2p2gB8JtFy0eOBqERc4kJDJr3uEb+tGt3laoTdrt3C6eeNCtR6jk3fZt3T6g4L6guNFkcQwu+i2u3Q5n4BbucBru317u3boZ4+rtTN6k+jFr4R4uTXKuvY1u5pJuiKQgCMatgbglZKTIMFlKxLgWHh1TIClSIjXSktkHIa2HXRwOLIkMQPHeDI0SqFjaqPGHJBmSkSlSKfHHfGTGlx1W2zhM88qQSYzSHf0Sj0gpRbjEJomYUnjaR5zHd2zP+bT/RVY1kl+dj/uiyb6ix6IxhSQ5Sn4UznlMyQ4Q03FYmvIyW5QsVl5Blf4WmX0k70PABaZNxqM0ytryWDHpQAQzUvBqyR0pL1zMTi551/Deh2PBjeFM0igFGSR97O7O2KP4jUlAFfXqmG7cRSGhhKZYipW4B/e2KY4NcPq4x3+QRJtuYAsXWZHx5XRU5rwGTJ492J05sWXUKqDsl5IK4llEsRLPxHWmhao9wGs80lQMSCSlCpiG063tmEx47I69GqRAjFKtrXmMsat9sdVCQEjVk37oKxNFFHixB7NgHXOBxJeAKW2BVCJgUGfwDX2Sz/IYcclSidG61hiDBmq52BnH/02W0FT4MHJMqfElc/KZdEmjgcwge8QXBw6/jhQjHxHcWJR4QWxSKZsfKxMwhQdIfU6zqBStWclvrbFx+QUTuccjm7GHScxr8Kmm2RojX1RBBZddkISZeC8or6lbMCq1eImu1TE/WckgT4q/HlM8mXEaf1TKTsWaaESVsIcju62RYjE7uzMCIWmsYAajyCr5iIb3WkpgJcVGaFVckLOVluyhgmlr1ZVJrBJW1ccdcVqIwfLGyJViiaknGVOqtNCMdRVH5JF//MRdDN3AWkRc6E9rjZiRkfRELJYsJZVLeCxNHVZGRxVJFzSXhZIcRZbHcNmXUdlkHGqkfiwskxhAOf8FXniKDGnKRyPWwDKKSYjqY1WVL4mZQjuMy30KABUZ14jYS3RV/i7zKLmEP1NaR1zqGeNx7ogqUu2Hx4wGgHA13PAeSNjGgLCYPw+SQaDYvuRGV2PUrI5FEr9zX7fzXxtKvZ6FSodEHctELb+Wd8Ey5ciYR8NSlgDUI9fpZDHSJ1dtVKCWz4LySFfMX1Q0Y6mUEqXJEekFdJnXTHyYcm1zI0dXdKEareHTJr+FD3OGMN2Qa5PXSlES5ZSxIvjQaUvz59haONFTstD2R6h2OTsL5ljJUEXMw5xTLs9Gk/DpcvvFbBT3tvixNptXZd9T1abPd5Dyk6DWeA9V1pZHeYj/1nityYDo0PLgHLWg1no39lE/UXpEBDr/lr1OJmKiJV0+Zn8H+H+HJIAbeIEjuIAfuIInOIEzuILH7mNUWHBM0pnoB4pRWYrpwIpl1bT0T6KOycjsmJt+SrS00EDwxSqBlVmL1ShbSyv9GEwI0/0OVTTDtSvclT7XcoZ8SpRk3X9EapERb3vY8zkDBWIbkj8PlI1hA1R9hD2r1ZbQh45BS8eQj4oJB5HJWMd8jHYtWZM3WdYZS5Mw9Kl+r0GsU6Kq+Ze5x5AZtWjsx29dWcTcE5pwSvl+bG7UeBCzB27hEh8vCo/M2C5lVZLDclY3te7QqXn09KNgnVeVEuxYqX/E/2pJWRhfA3am+/WmB4kWIwbe+K95lUH/9rTEWBpF3wR3MyzIYneoTwktgcQpJZF1L08hMWx8IBo5M9u2hAl4tQUz1fqry1pHTBQanxW+CvmzLfZwYdKT0BQpAVTfgGz+iFqWLNT+VnvOctpCWUshyUSjjlo7hVPS7o2pZclwMVuj5hPD3mexyJGkaUmKe9SVtM0z65Cx0Jqs20WjXBCVZNqwofobp9i7xzpIA1Nzv0VkB9+uNapzi9oLiRpFGbaHAKzdtFOZ4M1DjDpUWUxCu0Ye82u/r7Omkzynm3yc4Z5qnJwCCmpW0I5o9AWbcBnLm9zTYSl1VmXNR0tGbM7JYf9FQajVpSzsSdg80WdIVSqE5/QOdAOSCbmPjfSFAB7T0Dd5R4xG7uR8UAgtzr+8cDxs1jOM0aG2yytdflId1uN81FrF19ce07g9y9fFypM919uc7YyQRgxo84idxtixAlod1bUC13Sdru0c0Y0GQWBO1jnE7LATE5f845985BOJYEu+XMDFX4xU4tRp5V/WrpnQeFGW64r+245+62Lgc01EdQXHQZw2czwTjgTY65r+7Mt+7UdIhDtG6ds+6TsIzj0g70eTn9G+7hM/8Nu+7xd/CmI652dT+SRCAryGQUTwVayJQIRHmg7QZ6cGOc8p82uWp3t/+EP++IeF+OMhPbX/BxA3jIk5hW74LrKMSV2f8nQD8Uhg95NAhHBBxJNslKptUmYAxJdW2AYWJHjQYEKECxU2ZPjQYUSIEyVWpHjRYkaMGzUOxAYAZEiRIDmW7GgS5UmVKVmudNkS5kuZMWnOtFkT502dKLH13PkzZ1CgQ4UyTAQBKa1oiXQoooHtS5kvAl3RSDToy6BWEBIlwkZj6hcIBL/ocKWDKoQyXmns8IIN66BENFwl2tFqLja7X47WddsKr45WXgQWJXrYcGLEGkc2BuBTceTFkilPtlwZ82XNmTlX9PgQssHQB0d/Vli6J2jVp1cvRP26tejYpGebdl07NWvd2Lwg5Qp1B1Qd/4KZQhXYamo0HV5bLScouBUNj1aheqW1Y1DPHcfdwi07sC7ZL9icN08EeHm027tls6ft3nb79fPl139P/779+Pn57/efG7/9enIsJPgA1O/A/hL8DzYDGwxwwQgflA+yChWc8D4Ld9NwvmsuxO1BDhEM8cPPPMSQQRBVdHBFCFGUsMURU+uMxs1s7GwrCyCg5Zq8Wuluqh+X86Krr1pRjy4kXRnkLrLego48VwaC4Asiv5ASGyoLYmqu8/jSy61EiGSuxjJvPNMwAklCk00zCaLFC8AYCi8RLB3qCs1BaGGTFjIbotPOms4rSa7s2jzUzaEQXTTRRjPCyxX1kiOtOP/nCAJsB+YyJeiu5lJLb9MfmbNUTsKcm6qnrMjzqipXeqrTQ0ZldZTWj9Rck9ZZiULLKwtPfUs0V8YjKzX1PDyRtlgPOhYbDwtrdtloRb3PWGzU04tMZA26lrae1PuMSdY8JEwvHaw9iFv5mJVtuVjlVEg9L9BC69xv0T3XQmW1jVDXXGni199+Be7Mx4O8uMsVrux6soyB6HJYyirhbBIthwc9uE4IWsEKr6d4PSqRaMraswyzvgg14JQHvuxWklR++aVBdPCiTyzrbEUq3hpe6OArW9lTSlp8FnrPS31GSFgsmYyzFTudHuSsortibk/kgAbLUGHffZOvgqQscsv/rsw1aGqCxIRq54JoGUQRg/giciC5yJxa1YHKVihcgtLu8yDmtFYbarUBi3vQlWFuyTYOFc+Q8cQbB3BxxyWH/PHIKZ/c8swr3xxzxhMZ1qPPXx0uu2iaa7gw5Aoq69nmBFortbKWQ445vmjRIVOwUXWlZJQ17xz4y4X/fXjOiw+e+OSNV97xAdVEfvnooZ/++OqZP7AMCDJFFaq3vkTVNLH4Ko71L8A6WbpyyxpU/YrFF0iwa6KLlPXhBpL35Oxwl9dcpnRomCms80kA6XWUk+nAUDuY17OEc7Lx8KVH4/FIdMrSqf/ZjzxT2ZTsvmOaq7DPWlPp0wSBY7e7EFCC/2CpWADjNBV5kel6MZSe9WYow84BzHA5PJwOebhDH/awFbf64RAr8iW4rQo54/FCGdQDNa98Lm4nfIrIxrYdWtAAaDsayP/itacv0Y5TP9vRNRLWpybZBTnAwg5vEuEhq4zLYG3MkhkHEqSS6W1senviXXJGGIPIRUmEcaPQCjMVu+zRT30rA7DIo0CxFO6KJBsPFqNBix3xRUmHBFMdC0dETxYEiJ8UZShJOUpT3sRWQizlKs9Sx/FcA1VEwkbO7EKv5tDiOcbpiaXGhxYNDsR0+/OIc24HHrqgMYxzCYtZLHUq0OHuStci5HeK0yNzWQqKCJFKWXR5NrJNZTnF+f8RcnxpSHPtslefSYRU5CQcn2isJ7AcD3HK2SSsdNBczhpOJ1c5RByeEqD9FGhACdqvlj2moDvcFJHUk54+pq1vhYHiIsVDFqg95U5ziVjtNNWxg2AxSG9KokXZ+E2MIjGSHPNOGq2liDz+aDxoPFtUDMKrr5zHkfqDaDbBuKUx8YyJnDKUdwbRsOgspGJfqUvqzGaVgZIyRSySqotiVCKqTlVGWLVqVq/aVa5+dathjapXxQojrY4VrGgt64vYeptbndWsZFXrXOOa1rhio5izNIvr0AbLPHqrOZEqDrlWhc5pIRMb2+GUV9JzlvP0xTxwkU5V7IbF9KxKns8ZVUH/nMOUwIyuhDCdIDzzMhVrHkQaaxTV57Ckl/SVzDx40Zh66qInb+UmOo/VQdF2WZ7OCmZTXPLUUnZ7Wri29bhVpStyb/PPpyb0udGF7nQPo0rqquyLO6CSUxOGU0au7pgC8WaV9PaWz7VlqOclr2wfS6WD1VGBGl0KWB5WN1XV0jtoYeTBloPFbKoUd//7bvbC0jSncjOKVjELk9DiVGXutmNV+iJcxPS5qyBkLtrd2lLGEsX9wWUHCl5qQcpA30BJd2Uovq6KWbxiF58klWp6McyCBkw7bQ2UCSnazxqC40sNxF027rG98Pozn+wYIe/yMWCugeSG7MnJG75xQt4F/747YgNLHoqGnXY8OC9vK2jKcqWYcVzjhZjuZ2KecZucu2Y3vwQ1MH7znFuskIPGmc55rvNGXFiWEyemLXsWNPVsSENC1xDRh1a0oS/XCkG8SxCPdnRBIh3pdhLE0hN8NKYrnelNX0rSuLU0oy0niCssutCpTjSpV61qVEfPeQR69axZTWtX1xrXt9Z1q3kdH7yQcNc5znWvh21rYu+6zYLWMzZYEABBEOQKATh1tJ8tiABc+9osePZAsn0QawcAG9/GtrOjDe6BNJsFC2l2AJaMklG3otntVva8FWNdet972fjWt6P23W/QNLvaAWDBuZ2NjWhr+wrr3rbCfSLwcP8LvNME3/a1SWjtgd+QchWitk+2NqPPeDzZ+b5ujAkkcn+f3OQpX0nIUd5igLcC4tAuuLWv4JODczvbA7H4w9PtcYu3ItqnPkjCBT7wK2za1M/Wtqm1DUoWXKHmQM/2qQVx8XAnfNNAP/XTt+1olef5zl9vudjJPvZLJxftcq3rWpW79uWuCOAAP4jcg54bisNb4A6Hebp/PumCNLvcoik3xK/dk6DDPO8UN/i4B75uFjS+4IMXOGDGTfFvb5vtabcrczffds5nnjZvVfvn36550Hfe9GMN9uiJTSHQrx71rE+922lP+tqD3OxzRrfeZR5woeP82dc2tbMR//DKy0n/3FEnDc0JMvnF11z4zJb23iUu/YWz+9uAoTbitU/8x5f9zfYGf4vxjKjyjx/9P8n9vqVP9Gf3hO7SRki8pU95Zzvc4lCH+mfQTUKd6935rO3RCu/hjk7+NK7g6o/5CnDvDK/g/mP9Ru7OItBweE40gG7dhM7bHC/rMA3bjq7h5M8jsk/60m2Cos/wONAn1i3b3s8jbo4gWPAD664nqm7qUgPw5m/gUkPcGO8Anw/vsE3bYrDyTDD9/IXljjCUXi7mpM737G4HY27w+M7hcI/mGuIKqe/5sMH5gm4BDULupE7qao4B9W7jlNDFwo4CX0bhQOkDsU1Oyu3xAK8gDu7x/64t6hjO/tgtCJWP4aRP2rBt4gRu8DpQEOGP6Ipu+KLu2+7QDPHwUpxP5+aw6KCOAPcu+Ahx3QAD3Z7u6daQh4xNFIuNFFfPFBsn/nYw/k7N0XIQ7zzC8SzwUiBj5yyE+bgP7waw56itFhNuIM4w3rTw8Low8jTw2I5xFE8xGXkt1hxjGZ+xFJExGuMj74wQBkuw+YiPCANuB6Xu4oTwFw+RBcMx+gLPAc1N8RxN8XiuChtuB7cQ8bou+sYxG89uC+FP/uqO+xwQ+tjtQOxRGadRIANSeZIQFEUpDA9v48QtFoEvEguvEfPOBa/QNRbQ8SCRGGvuInew3LaODw+u2/92zgmz7yDnTfxK0lGoLQyD0eYe7ee8DQRdkQg5MRAjrej4EPCmLycfTe4OIvoksf1kLhe9jffq7uCWb/pAUuiAsg61sfm0jf640B8ZkCWb8vfQ8FBQcs8Mbghj0OiGsOrmEARt7h3D7em8cg6HcBKVzyCqjgzhbema7vEm8f0S7hNTwy65ktIe7/fuEuuY7Sq1MqFIzjGw0k16wvJEEChnRB/fIyrD8dQmb/desf4mU+7S0QEHcCoL8Ckf7v2e8gXz8QD/L93izvum0iAac/FQMBdtsuCE8PGe7SgF0/zOjzYN8zZzEzPUEDdpRCSbzfjWY/seIvqakifBrdyOkyb/1TEpT3Nr6BAoj5L5RDI4ITMoT9DocJLdmLL3EqL/HlII87AaEzDUdJNNSk/20jP21vP02lP1pNH13nPR4hPX2BM97fP2Zs/29tOtnkc93TM///M+ARQvi64NURDaam42a3AIe1LitHAuh5LuVLHgenIb95EfF48Sp/Ilt/Aoh87h4g8OFYIGQZQthW/U6jHjBDRA8ZM/9VNFDLI3zXNGa1QlTtJGKaMIm/P3ZhMS7U9B72417Y8hgrEqe08W1TEKu/HyEE8IybPwrPPyGJT4ok1OHK8hhtM4DQIz/24zvU3ecvQwaJRMxdRMZ4IwG+NMKaPvqhPdEvH9GlEOVxAP//UQM1WU/sDwAR0vA7PREyFxQWtRC+9RDrHU+kIQNU10IRoTQ0+QEPUPG8t0M2RUUte0UmmUNy9VMbQ0KIcPDzGvFaduaxKx6WbSO7UTx7R0CuWRBaEOSX0SLIHTOidRELfUA7sRITjVCd1QLeevCNUxMC3VMKCRIKWxWInVWJMVWZd1IJW1WZlV1ZqxMaDVWY/1WRvtAtvpy7A1P+xxcP5R2ELj0n7MNLaVXMv1y7RVW8lVXNG1W33NXeHVyzjuWq3VXqsV94RVU/fVUnGUX/9VX2cUYHPPdPCEJgxWx24mYCMwTUdiYR92YNGQUiE2oMQHLBIpI4SFIBThuxBiwv8iNvcylWJBdmRVbkBPtkUJ1PNWFkbTjl45xVwkBTp4rCfqwiMKVimEJVNqDDDqwlUIQkqkpOMg5UVZtGhdlGWNNmntQ1pHomWV9mmRNmpVdmpR9mipFmqzVmq1VmUnlmRDyQt0JFLKyF6ygzCcqmTMRymU6Zi0q5cyCHcwKiuuqMRUq8TU9mvXzF9LNm/7ls78ds+OAin6p0lE4zvKJVWuyW7MBSua6IHGxjlmxzlkBnHdyV4Al6AaViRgIl0O53JfRs1uonMvI3TPzM1Kd1s443NR0mv5FmYsCSnE66QMgqEaaYkqRmgCqBVcymyuSREayksS4YqspSp4wy3SFnP/W0xk68zJXNfsmjd5a4Jap7de8ZV6M0eZFjchUGc8ROYLqqZpVqh/JOoLREZTGAunTCiJwNd6q/d62/dem0dzAUDRhiPEfIc22KdzkAl4XOEuOhcy7IIGbInRMIlygom3SAMgMySvOGfCLER2dGCAYchYW0lxKCt+STGbFkeEvujSSIUseqVyzgZ7qQSweIWp2rXYOg5+zYY+Z8R53Qx3SOwpliheCmOJ8iZIxKlSQuamkOOiLuUpTuZ+mCp6o2tvLYIrfiZ/IEK1LmLLZhcjPnYhNGlhioJ/kYqfKAJlLOKyImIroFcxCvchspiVpFgi9kqcKkptHobPjHgiZsdg/wQiknoMjVnCqczEkv4MjGO4xfQFTuxXMHSpJ/gnU25nOxBsOPxCl6JhUyRXe4EDdzDWj01pfnFFIoircDdldOBrdsRCscgpdZgilLdDgY4ETJJKSEomnVLZbiDgZ/FCUuJLfvhihnW3OkbDf/ysXBRLLCpGZ7HJkcLXf7wimE4Zy4ZjKobqmL0iVgaZnEK5wH6JlL+3luMrVeLLhOYlUg6CdXgkOrwIfVoZZ3BnqPQCnApJjiAoK8YnKlS5grLjOU5ZPWoJjvficEUHnfWiYTIlOuynodI5jMhiknsikDsrpshYlOsIAuSYKaiEfysofBv6PHqne61FaLC5OuJHOP/AwpOfyJYLt5rF45RlhqMPqI1cgUgKujfa5SJa94h96LEQAsnOg2yARtgouWnspm/6JkxjupSW9yF8ozDceEqWhI6dSrW6w480yi70RGHMB50Zq59p5mT8RAcgwALGJnvKoHR0xsDqAi0QWU/AIhooimyw4yw6BYtYKo+B+SkO6Yq8a2IcBjAqRbyW+KOQonAtaW0hLCu6yysgeisiZpLympB+Rqn1ZIYPYhAuACnMq0nUQrYwLK5R5mDKmns5ZZ7WSF6yw6ydeo0c7CkADKKaZolLC4fLtziko2PsRG2fWqnO2r8eGYAuSSCe+Efe4r1+eI4NzElKyCm+pq2fGJj/NQY5XPtN5Bhuwllmypq1Aft7FQZkPsdcQkqpacaPWqUjrHZpsXZrwxu8sVbLiudzRwOAo2d0x9u7qzZl25tr1appRcJAmmNwNQshlsOQAog8QueumUmOmoYpcIl8wml9iqu/r2Ur+Dp0ZAc9egl+QAtxu4Z1Rkp+zOWFxOS65fnX0AhV0OyKBiLEn9q6PSuEhupHfMNOKAbCm4OztuTBx4njtkNsbLlm08daBNeEowNTCqJJOA4wqCOEXqmH0YmQSZjHXTpIOqiODFwwDsgvfLwrXglVlhykuYWXcjt8W8FZ4AeY/vuB5wKXCutLvAUrnsNu7vphzqhiaDayugez/4rli3hFZsQGtD6mYga5OMqFnLriL87JU1z8Q2C6koG60B0liRfiZPSaK75lj2lgLa7CiwyFS/wIZaKYeD0mKyQ9b3rKbLSHTL4lL7iiUPqkkAqDY1BFbshjZ6DIfD4IfV2JSLAoj6U8maxifMpoizpp0QsnYTjFSs6jeCGmksrJC4YdWtSiK/TEj8giIUB5jxT7anasKj7niW14k9AaVZi71fkiVHyElJ9k0wMHUoB7i1R6tU/LLIjLY7u3tfdEpHerw85l0bs5ilbnSbaGMCqprbvkvc5CkbTkTbTIODx91yuJ1C9MpYIkTLomkjAd0yFdbugInSGmuw/d0DPek/8u+TE6wt4tpZEyC1v627V84prKoCfkQsbRiKJcOH1uh5zb6cr4vMzxJJ+qSXQI60T2nHxAp43m/MW5SS+gJn4Cyy4GaFquQbeB1l5MHJvSd7EIOUigWSC4Z2oAfa+S7MvbGpqhubDiXK/uvFdOxZiFvsgLPm2eHHSyA1lM3MX3nJs4JrhA5+unftcLGX56BYBs+WdNhy7QyZFhyJvYGpduyS6aaOyfqVe2vZWhIoF6RbdAi7Dw5IzOqOqHxeZdJdAVeiIIXeM/H+MpQ6iVuEru1oSIBJaTW0rCZIa7K7N/PchhtktwRsG6mCHMZ5G6Y2myh+J72446VkgyTLFzn7f/R3sqeD/4lzhUWBycjHeJ9Doi/HrLuAJjYH+POUxMmDqRLxv1V385GvtOBEP6/Xp1hgSBvDlMGh1kdMACQNuH5QX1Q8b1bbv5dxtkEiJhJIxw/4dK2H2GLQkgErXC1moHjURediTCRuNLq0QQECrEltDLFwgUv0RLRIPWwFZfDupwyFAgwYwDaZRJtMNLIh07BkJwqCjilx2DTlYs07Fgzo8Ud3zBdtNLGYw3B1psRQtCmYSDmhJshbFVKxrYWLqE8DBmVq8jf54ESnYgtpNnzaJdq7btWbZv3aaNS3euXbh35eLdq7dvXb5//eYNTHiwYcCHBSNerLhxYcaPHSeO/0x5smXIlyVj3qz3LIDPoENrbpvoi+lEtMaWIUnwy8LSiVwNctWa5MOPJkG6xu3loSu/Dy0u/PilNzbZaXMSfG2Vru6H2KLpXnjWNC1XS3M/pT7ILOvTJ2HnJnz7LHbbudO7TkS99BePtc0+j22YlsPy5dOazh/ttMu0pbXSXVbt6QCbfEsR5Ip78p3m1kPuhffFIOwRiI19rxlH4CCumWccgxKid1aFAO42YlvQWfUeSLMN2IpxL3aolnu0RJPVb3FhOBCIyC3noYzp4Ubbc9INB510Jh7m3FhMktXkk05GCeWUUlZJ5ZVWZonlllp2yeWXXoYJ5philknmmWamif/mmmq2ySZBocUJwJs2Olnnm1beqaWeJ/Fppp/RXXknoHt+SSidiA50KFAWXSOoThoFmueTgFZ65qJMWmpnn3gmKmmnblYZKqikjmpqqV++xWRmlcGl5WBAzRXrkqzeZeuSsz74pKyonuprr8CKiY2ccgZr7K/IHqtssqda6OVGwy0rLbPTVqsmrtZmS+22U17BAgtjCRLAFUB9K0grAbDwkbjnXhHAQOKykG66grgr773fXnGSIN+exAK59qar7kfo3puuvt/Oe4UgBAVsLrwGy3suNt4eHK7AE7fibnNWJbywvywwDDG43Jas7clSEhsnrya3jPLLLscM88wy10z/c5O65owtyzvv6rOTPAf986pD50o00EW7qnQr8gbA8FnuOv2WxBSPS1AA72KDNcNN48t00wpvTZC8+mIjrr5Yzyu22Vjjey6+aXPddtxsC2wuNl23PbbAbQ9EddVzS41uuvKlC+vSPSOt+NGMG+044kIv/rjOkTeutGcqAyA55ElT3nnilnu+ueihgz656ZyPjjrpp1fe+ueup1567KzL/rrqtK+ue+68q2rz7zebWvC4ZomLdXNUM/0u2XsP5O7TZJ29rtUFf7Sx8lZpfDzb5DIptvbkyou84fEyafxHDxPe8LvKi2xxK/EOlHb35QNvf/B4Zj7n/fzj3////gsg/wAHCCoCGlCAVeNX1gBnOLxJjYHgOgvV3FU2VbEtgs37G7rKtj2oWa18vhPfWcr3t7Nd0IKD695YsKbC8cmtgg58G90KZrZ/HfCGNxuW/vbXKUdhykyOwiG1fohAkxHxdH9a01lsdMQt8cxNTTxgFNdSxCryD2sJJAi7mJc8B7KveVXLV8im1z3npY1kVdMX9sgIP4F5q3sshB/Z2mcVsRnvjSSL2rjUCMGMPY+OJ6Gg1v51vPJFzYqItNkO5wQqWqAGWTgSYiKzFS1kwSdMl2RTJsvkEUeWapOT3BYowzSaVpWyVpwxpSpReUq0mHCDHkSb056XlqY9LYYM1Bu8Gv84FVvWMmvte0v5jJc29oGtb16c3wiL2cF6da14elQXLv8WS+zZi4Yag94qU8nKbbbym97cJuZUBk67QEQH6BxKYsYzGK3IJ5zdnItAbHQYlhgtM63QQaDiiY1rvERC5VzMS+65Fg5NpiAP0adk8mlKr7hFIRshqF7QCRMavPMuRUJMSKjTyoz2ZygBDWlcUvSY/2wGW5JMqbLupcCsmTBeJZyXNLGXwChJ72IzhWXzivdB9QGlbfXKGNmuYC+G1Q9KKVyLAsPHvj/6y2rqi1gopxoz/ZEqQSwRi5QikqXiBM+rWXIFVtxkz05xCDcKvdlGxkolWvQES7TwAnwC9CX/GrjiUFwFE04IcpRPOSkpbYVAJNeUIKNM0WQsqSSUnoImlTp2WsxkYRo9+L2tMQ+XgvTdCYFyVFpebYHK0xcI4yJCC/5NkKNdpgopyC8M2guz6vusDD9yRoI99rbG0qFVzXScdPbTNIEaCUhg8hrTmCadisrnDgyUFQiMZCxfgAlIAxVd4X4FJjsSikGs4pX+LOQmI7lGjHRwlOcOVyjRCYlxgVJdkpTGIktULnPzWd3hvEQoHjFNTOyzXOq0V5+OeslNLFod5BIlEY6SEUyYi41BLNc20A1OSBwaXfSWRjo6oIV4AcwR7VrFC0QpDjr7KeCDKOolZUCnfWHiBdp0/xiw4CWJa75ggYYkdLmtuNNLdNCKa9CCwfm8MY9DrF0ITCS71tWBcxNBT/nuyDQ0oLB0s6Jkh5RYys91D2vyqeIEP3ggxtUBiDlUp/ssuLjGdW5OWGLdgozEQEFM1ROpSuc2Sc8q0mMX+sTGvPPRlIILWxgbcbrnsQw1aiSLV6AFITeRGfqLxqtbUMl1R6JubXAhq1i7Hhi3isExZMs03GtxS2pULXLOVtKBBSBggYVYpE44Yaw9ISIQtw5WuE1REYhxM9Yjo8StAtlrQlrhCq7ypCTRkEqCTNyS/O6asQVhDwTyy1YHL4S8G8nrQMhLbKywZCjDLnZtsDETV9farv+J/bFtii2QFDdYIT8uQ3927VUTizUrJDEKWe6dkIUcZK0ewQlDy2rPXLeirwEKScBzwpWDT/clOQn3WBnLVYjQwsEpEQhwaaAIpgj24GktCAQg0Gv7Trvh7h7JQs4aHmHv16JAMTFHgrKQiETD3VmlSBmUAu9/Q1sh7I65QCASFKb43K4+EfdG9GlvrEDlK8Pp0H6i++7plql2WN8d7LaOO67PzutGo+bgwLevQnbwXtOMLE8xuMsImnAqzLRhqIvZaF5p8HiDK6ZowfavEfJ9YhrUI/E+qxa0g7DriP964m+n+MYz/vGX0y2xFt+akUekucttyVTYk2EHp2W+BBn/MlFA7BpHAYg9B/n8QqST0LO4tSvZXXqIsdFfd+rTRqWnr2sMBN+v0Oaj/cynfdRpFo6w5zcM3fDmBXyNI7eEPUIBrj/TemF8RwO90589Np7Lk1r3x0DHZdJLFgRibDAWzgRx5FBcMeSrhBjWre+9gRD6/s+/xaI32f3uP2901+DIQNcXdQZiElNhefrEEOhkEAuxIO4RgNHxT8V3gK9HfwTDHtF1c+oEXDyWYF9wDcAFfB44FA2Beq7hFSPSftxVIAfmKC7hgPOnctEVIRimYcZ3XFdhFibVdShVZzx4dY4GL81xLpyVPUK4L0Q4FeeShBxjNksIhLY1FvAThU6i/4RFWIRl8YPwg4RRaIVSaD5ZmBZZOIQ/yIRXOBWldoalsltnwhJ5ZRHdsWIS8RBshXQKMm1g5hBgtRwH4WBioW0oQRA4wX7LoVAxUQY5sYfQUVa6AYhfIBu/AVb3lnHwB4F9wn6aV1ZnJVYWCGJI1xSJ8Ibbx2QNhh4K9U//9hUHB2LzpiAstn3+FxX7hhVfsHM6F219MnN0NWsK5VajmIqtAWIWkSBP9hEKURyOKIcnYVc2YhFINyEMwTH3tWvLYWQ/0RIL0knkxSFMJ4ospxS2sVdvtW0TMnP6RhHBtmK1qHPLGGwtFhtWkVbRIG6KwhA4Yk+rMW5LcR+CNRuuJv9vqEhzsuFIRwZtZIKGBqlEPqNZaqFZFkRFKPSQCxl5CtmQ5OGQnfGQExmRGmmRkaczB/mRVyJ5KsMmZuGBIGZ6xfYR6PaNlcRczQduxBeKZ4F+2sce/0QQ6OYVZWURGQZ8BJJPdyWTQomHHUgUJniT/1R9xPERL5gaDNV7+zFfE3FgSHl7/1gao3eC2jd/tsFjNwkSmxQTWBmCQ2mBoZcaSbkDAeaV+jSLLKhx7AEU0+eUdgVcP9l+XpmB+gRnR1aUA9UnHPNjJWmU1dGBHUJl/URiB3gVv+FQVGYj9iR9Gkgd8MVY2jeZ1QF9ZNF+RFlg5jiTwzESIMWZi7hhLib/gY3IYOgnLKjWg64JkrD5mrIpM6fWKW3WGiIhlmz1dGDWEiERHQR5cAfhBTogFmx2EY9oIP92b9GgCGNVbLSoFUexIA5Fa8TmFNjlVsVhENBFXjZHiTxnFN6mk2IZERVBbs3hYOIZcORlY3r4gL3WnncFESn2XCNhFAoVXd1HnB8hboWlEcVWYajBVSHBE17REBaBERgHVvVWHIZ4EoOgYjOREts5FCx2EQLpEibmVVChncFIJXvVn07BkyVBjDoQSTFoYuQWSW71FL+ZINgWoNj1FcBIi4Z1FSkWE5GoE745nkYhWKsIEs/2cUFBouUIEjDXfSrxENiJXmkiUvwE/6XcNKVSWqXwRKVXaqVRmqVcuqVeiqVfqqVvoT9QCh2y8okpwlECQoD4tnpZ0RwAwo/v9BBbFiC4cafL0RYcclckRaep4QomgiRDRxZ05SNrYZM1kiJpeh870hYrcUloWklBshx1amXF5xqQKiMElqdmaqaAmhtpGhv0oSPZwxwnAhJDx2ALgqpmMXz9GZfq0RzuCCFMYZOPQYANIhBWMR4QEhd2mqcjtRv2BSD6MR5xqajJZSL5ER6fSBvLMRuNuiORxKawER1NSCLVSqn5oRk7GJveOpvg+q3iSiZqOESPciqa8imDYijnminAginpuq5JBGa8iiqLchWf+BKgBP+v7DqPyRKvlHIp7ZquYtJE8loq45qw4bqwCtuwq3JqDhuxNBOU9qNlc8qwGHs/3ZqxHCuxHtuxVVSbHzuyIEuyJmstWdc7lJeyYGc7LsuyK6t1ZJEaLesYkKcr9FSzoHMnKjsWNBuzPXuzMut4Lzu0jzNOk0e0MKu0Rlu0Qeu0Ovu0Syu0Utu0Uwu1QBu1Wpu1XMu0QrOxJ1ux0th6WtJiUuJgVgclO6omx6mizHJJK0El2EFYo0RzWAKeJVtn5Zq3Ycu3ftu3JQO4M8MrfymOqmIjjmJ6gCgWSxQd+MVES1IncOpXb8EnjSteFYdOswK5ijsQifsWcda522cWouf/Q+JngidhunPxufy3FnXCYIFiuv46uXGWs4f1t7kFsU60JTmLNLd7KrI7M7fbmr8SZ8BDvPYKssZLuVLSuFKyvDLzuxmJu/djGlqFbFCXERlGp8a3bSahjb9xFMCljQIxtw8RFWPmHtLIk4OVUCZRVjbinjwJH+yrFAImENxGEIoQFhShAxs6izL3E9jxTy6xI7V2n8/avfTojdt7EVIXXbsKUjsHEubohpAiV4ILPCILV1rySKPkEe1LSXVbMyPcViNbwm8LktfbnzPbwb8xwk1oLQtCkl0KpjYsplYKESMXLeZ3gP80i8NFFAdBa0SxZgPIlu/xEqh3jgOnT0fB/3k5cRoDVXyWh2D6WnyyaGVtaWUwJ2aloXIjsXTuexUkQVxHoRu4txJOfIDFkU8WaKED2HATcROJkGIXx1wQzBHctYjA1V8QTBQ7R3U4XMODHKatgrRyIhk3yRcaeFHA5Rq9S8iPEYM3fCHc5Iy1ApSTUVbdZIuGXCtidVGVjFFXAR+fPMqAARJyEWRL9E+RbE4WmmOgi5mo7BiQLE7Im8G9chQ7YHNjMRFDHIjW9k/XcH7lG3WHeFcX94v4poz85o8FLBTUOV00NnIgpohQJ8wKwRG/8cJYEY/fLIgjKFYa4YwsAoio0RMOEYlgEVdAN2zA5RPb5xGJeKDRMcOJFf9it/dPAEiPWSUbvqbLVZU5X3JWKNwa0WJuHawjT/JITeIRcSsl3xYeJeymitUlJTx/m1TRENbCUxFyZ9KJUXLQVnLRPptdEs3B4ncl2MwUI10lG81JHxHTUDLCcwVhBt0lMqITtOjRM13ANT3SdWzSMH111Bs8StZ+J/GCVuEUrjEbppFgZeCBUecaqdeX+EkdNDBkkTkUZNkQM2YWGDZyKukKpjdQMxHWVGZeXM3VMeHHFQJnguljG7gb+kRXAzgU3bceP4ZgoUh/WgZzOhlmHzEThemA0UYVq4Gm0HvUMiOSxLIl01dflzkUWImXLYFe81dkCAaoFWYn2PFlLUf/nGlLFhexVzF2hwMoFCpWXUzZX/5kEUMBmZmnTjvGYx+lXki2V1TWX2cZJe1FZQNWXCMyFNpo2QvGXaYxE3HGZvOlXyhNFFytxThGZN/1ZfSlYg+Y3FlRfgjRGr412UBGFM5V1gVxE75MHFNWJ/xlXRFKXF9RX67tgSMxgg68299FydW1vV5BnMIlfPLxZsxFZrN32QuITgkxFLvB2RfCk47pue58m982EjUyJQuO3v74Krns2MoSl02ixHfdXb/x3UIaiqsIfMroVcDHk0zmfuVYwB1yDTM807D4j/EbbIoy4sR4iDUSifeGfu7ofMMxERzSgq6sxKQXKa3Qcebc/yE94XxGh85SLp+yd4oWBb/1KtDKssFWEqHX3Xqyp28TgWsEGhUQ0HFE92q86CTl6LbC1tNQMhHLJm/TueQo93FepdEd4eXQRW/X6REzR+bcdc3AjOYYQVemTYhmPnSyaKMaARHIZ4fuZmNNEUkWNhETmn52eBGKcHOcOJ972SHluNkKvl8fN3NAnBHfB4hGd1c7OggjZwEh7nFDVxWDuLiQknG2mBRUMeJ4vpdeMaEG1yFnpXDIWOJYwXI5CRQ5h3EQSMB4eG07p4mLWHEYoW9ry9PeSxxRDOdRQnG6aodccrXkbrXmXrW9Y7mhxxW4N2NbfBbwBcGncRFMxlz9Zv+dMRFkP1x+47vNFvXKDxgRFGJd825X997F0a2LnHdtJJF6wDaTbR2ayphPK7FcQnnVDpHXHzdkMMgVLLF0w0d71ZjfX2zKW+u1J0+1KR95ZDo7N/l6dBXVjezJk8nKAwVWD34cD5Z/9/caB+gzQ8bbqD7V1hdiHEhfX8x5dPFtJnETYIiqmSl8uMd5TBkboqcWqZd+ubgDGzHbj6xOvZc9Qp4Wm0rh3ZVeqAF+o3scFGIa3hV64DcS7KeSwzeJXo/cJiJg0J0WlteBpczKgD8VAy+KIh8XTh+PbHkWnjeKl+lfqLca0TXAoPn0OvmJBjL3rXx7PyYbGWjZIMZjbvz/WxaK46wez0r9eXE/XczFEkNj10z5OmDL4fcjjPpcI1hJp60RcePoakEZFvn6E6URVw3W9N8LwSUdXRvFXpHPwC+8a/ahICDFfvHc173aq8W8m/mWG3EFVhXSq7DBfoZoEqqobjbZ/cdHejhitrWv5TWzt8dPjOyxa9+tbx0i7jAeFeHso/HMJEM84wGfYgDhqhW2gQUJEvySCBuNRAMT7WhVxgvBRDooWmyVyAstCIkSuVI46IvBgolojDR5cKCOhth0gISI8IsrGgcZJhqpkuCOQSpdYayIzUuZgV6+RAsarQzGgy8H0hCI7WFBiQtpGRQ5qGHCkbRYarXIMtpC/48gdzqEWPWpqy85jZbUgRLqR5KJIOScajUaLRpXLyYaxNNmy6I5W+34WBNpzYwWvbRkuZItYpBX1X6ZWJAvNpEzGbYyzNkw4qRBvxBNiA1C1MfR2lptRVPqDqOZdXb8mIjW2IwdoZLUGXEi1LG/gx9vBVw5cubLnTeH/lx6dOrTrUO/phMbtrHZB27/zn27eIPbx5o/yPsgeGzex6tfPp78evHdx0/Pzj77efqNW6l3zz3+wiPwPQPLE287AedjTz30roOwOgkjpHBCC6fDBgANN+RQQ+quqYgWgvr6Yoft4sKmjC9cauUawQ5bMbKYpsIpue3MGsi1FbFJyMaEPv8Cr8WM0vOqxdd4tOhInLI7sq3DFOLRI6aK+u4mHRwaKbUn+YIysisJ8pIgG1cqY7sfZ9wBRIt4nKit7PKqEbGdFIrmJ4W8aiimHH9iSSgUSzQzzSYnkmqkl65pxaKKHLoyKCSjPLGhL0Ez6Bq/RhRITz3JilTB1675YpBERWyFhozQHGvJ16KRE7SGoMLGlY0SYnLHHq98kadR8yu1oLl2GMuoQlnEZocVWY1R0oIaHWkgRBX6TklRB/rRlSCTg/agt1rd9Ln7LgS3QnHDJXdcc8tF91x1L6SJsHTfXTdeeOeVd90O7wWAvecC06GMjvaCYAcdBBYKtb749XerQVz/gYAg2UqMq81WZ4sLqoFBo2GprRq+WCVuK3ZFqcxSQgooRQX+oiYnVTJpKbe+GHi4RIqb2a6UiRJKYNp20oEjw2hKGSLZolGEsR1OgqCMkpuC2diF/nu6pIxPGmg1kr5oGCkI/vPZqLAIgznlq9BcSecVD0P6qJbjKoq2nZebiqOraPKrN4le5Bnr5Mo4iaFoSuUupcZcmpksmhdLeavM+GpaIQhoKXGojg5j6qG4Jvd5YMfUXs3yxEFb8XGAOUN5Ii86Uinsvi7mG+aN/SSpKqv5qju6AtnDvb9vc79dd9539z343ocHnvjfjxe+eOWRNz555pd3Pvrmp4d+QOip/38+e+mv51577Lf3vnvwxw9vO3w3xP6qjGyMdVqBxmzJo9g0W19M0JPDbNSBSPVot+TsTM7k/hOU8ACJIrrhTVSSU5AxwU9UsYLSelqhFYN4BErsw6BW2GPB+2xlR9zJCGEaWJIFimmDHrFfrNgHQvmlUF/w+44rprXAujxQKiKSirWk4q4QNrCF2+Hg8HBEGB4CJjl+4Z9uvmNE8IgwSCaEYgah1BIxtbCE8oOh/WR4wYy8D4IUIaGNGlLCBbZFIG3hy3dK8kCQJGqMkVmWGh1WPzpC71v1olce8bhHPfaRj3/0YyABOUhBFtI55/NQheAzr7gk5Cs8RA5tcKIlof8oMI+LvA4mqbPIvZxNJ5pUjyYNAsrjiDI9p8wkuEjJR1MCp5XMeeVyQonKd83SlaeMi1ZuAklMYkYjlJzJHgk5TEMWk5jHNGYykblMZTbzXBlCZCKdGRxaeGQk+6kOTng5TWTqi5vfZGY4wTmvN9XIetLRJnC8+cx1jlOc73RnPOE5T3nWk54DiaaH6hlLe/bznv4E6D8FuklDFghB6jxOOxWaUIYi1KHaaShEH3pQiVaUohfVHUbLR5/cyad5HgUeSBc60YyW1KActahJ76jSkab0pC9dKUxbqtGY1nSmLI0oTW96UvMhkqQyzSlOf2rToAJ1qDsl6lGLmlSXMlX/p0tFalShOlWlVrWpUrXqU7Mq1KtStas1HWhYA3ocQQhiIGUVhE6uYFaVtOIKY8JGWd9qkLImZ61yLSty3ArXY+ZVrH8dazijGVjCAtawhUXsYRVbncU21p8sCEBarxCAK7BHEAEIQPkgm1aCQBazlC0IZN36WdIuZ7KVbae3wBVXyqY2sa91bHzymS+xosc+EXLPc7KT21O6Fj/o4i1g+Vmh4MK2lIV07XCNKy+wLte5fNwsNk7b1gCwgCST5SxmWSCIK0D2rdjArFnLitm7JvSybKWQIFgArunG9rnvNchsfdtMuuTQitWpHdyOCBwlolBc+b0QgP8q4HRZ0r3P/8nWIPnKI4IQ+MAFFd/3JBxhCofPwuSrMIZbEd3pqgSzcuRwddX44Q1ndjyi5V13udtaFV9hrdvdjovPygIag9atNEbveLhLY9Re9sXf3fB31ZvWG2/3OzteK3i+i+ELT7jJGXYyk6X8vZ6eL8rNsx7y+FORFblOIwbSclKWh5OfQM08r3nM8w6aKFfkJ3g50Z1YoFw9Oj+PJW4ukIiyHDzX/GvO6NGy+PZMPDrGJHeDFvR20qivLHky0MdjS6IPLWn6IPrJ8oFvpqEbWem2loEiVgl2R5tjsw4kvAc5NUkgu2pOT3az7YVsp7cr4lZo17uqpqx3O13dzxIEs9s5b/+Ju1vduHq3umldtaaVXa/B8rG+rZhbgg+iRILwbWwbIQmOHCZtkcRG2sqhpEIew19L6mbcwYFINVWibrRge44y0cmzp73ubzfYIQq8Sn61XUEFhkoq8a5bvust7ZNMadrlxje0C2Jge2873shhOLlLgrm+yHt+9gYwAv9dwbolhN3awpm3If6cEvXk3vRj98cdUjuLb3zfDXcIvR48c2eGGLVnpfVBohvdg6K4syZez2WtK2xZ4xy1oo11p1sh9OTk9T4wrnVlmY6Nzdbaup1+K63XWuKCtPa886V52N8FzWaP/ScoS05cdKADk99o7WwziQ6MEjBH8kxgImralc7/w+W1K6QtY3mNiohVKrk7zUslQt1PwqZOxa99RyYR2MacZrk+9ekgEIsJxI4CRBpE3o3teQ3mY6P2JZrNJ23pWMTWTirNJeIajUeRGmlwl2lDZFOWentOIPYl0sNsYFJqz6gqMrCeRG7tUGMU8XfihU3tXiE6gMDhd4A6HulM3K4zU1wi74XjS6X1r1d77JnFnbDp/fJrl5PneDN9OTnfJb5/jONnUyKGMEln1Ir88CePsjvz6AvFrZDmErsBfBebo66rKwjs6rQcM4hUAy9OM4gO+7rJ+g5Yy6zuyrqr8ywWqCySUK/JIrb2ErVfYy2zAsHIMrXqGrYAWLYWlBf5/5qX0yiOTCm+TemXp6gZjPC3brsMsLiI2gsJiCCgpRCKLHk+ouAS0OiJf2GYgtkLxkC3FZkKhrkKu5iT2VCIqRA8/CsMrVEgKuyNIlyL0ygKongbisiMiQmMotgKjBCMz5Cbw0jDttMBC4AA1AGNiYgMbckJGSkM0NBBajHD61PC2cgIxsAMj+mJuJmcg1jDnIG2vgCYhmjCGvE/QGSZx8EYhJAZaLMapzmMnlCZmlEMAiIdakEJq6m9geg2MUxEOTREhhEIKyyRLrQLgRA8GtFEApqYeDGqrtqqX9QqYCTGYTRGrtIpA7wPEjyxViM28BAtroNGTluPDlPA6CrB7f+IRl5DwLgCwfU6smPzsWxcQPC6uvbaMRHzLheTMa86RmFExniEx3l0KnmsR54iO3wJxpiqJpxgiZ9gPTXqk6U5kj5ZFPMrirhYiphYmr+5kiaRQj0UiIF8DT88i9dIFOXYlVEBFDB5Poe4i7YwlYQ4jyOhiQn6v/GAGfBgCdrBCdfolxL5giLRl0kyP2zwCmOxkf7zkhypyO8QFvK7Q9ThCIeoCZaUNo8QCT10PYpoCxP5E2ixSf/bjqBcmpVQlvVYSZw0lcMoCE0BRBWBGTxhDzfhkTJgkqzUEWJ5P69EM4XsmLV0SJjAHTZLkZNAvYwEvzZjM0fJSJM0FZHwDpv/pAWRYMm0u0eiEkAXZMzlCDHrEi+ri8xR8zUbc7UUJDKqg8CDELqfk6zMGghX27UN48B23LDv+DCd47TOBMGfS45o3CzuIjKkg0AX88bMJMDcpJeykxej0I3PmJHQWI1gARYCSsRu68WVVDhHHA3gU5OzdBXVYI2ZQSPGkI2HMApPIgmyERzVSA45CQwlugp/w8qvHJwc6U6/CRXBg0No6cWlgAlu4xsPQgjdaBgyic48TJ3PIJ2+YbvBUA+c8AJZyZJFNCOjIBqL4cTJGByRuTw9eY2+MIi3mBPaicS5qYm3qKbxbLvzVAsV4YimGETuqIoMRculyDeDiIv1S4x4/3uZ5czPVhkJ2ViLPfRK4xzQNexOlji3etHNH4WXECOtWSOtyMLGyxrS2aRGaaQPz/qsysLGWtOuD0PSzzIrWyO2CqyuDWStIR0IJyUvKa2xDpzS1kq2xkRTDJGveukTNxoVMPGLtaRKszTIsNgRloCTnAARU5EKqAiMsUARwfsSUDS8rKSRZmmi9CizWGmUL1kUmsxIOLW8pfE+/3OPRfE+/HPUNSGzK8mPohyR7wARW/maSHkSjySc16s/nfAOY3mfuMiPrVxPi3ANpqzUR6EcKTxK00jJvws+Pu0Vr0yPEKnUSDXWRhWRoOg/VKwVMyGKNpUThsDJnvyyPF0iWP8JCkt0ljd1lC/rk0UrEkxdlG5NkrNZjT4Fic3zvm5Bl8UE0neFDheTLAwszSDjQA4sq6ETExczsoOQV39NMpJwKxkbOvUakw/MV9LEORZcutIMDoS1Tdn8QAbCV4MlCH5Fr4HtV9lM0459DhiUF4jhG5hAiwpKGeU0zhVZQ8ahmiJUPrS4nIbgCJ0xHc6Bib5RGNXYjc3gl7H8AgtIMG5pm5fFmoYwic+xC7Dpm5AxxaEFRZ+hir4ZmwSzGebLFn/kG/w5mx2k2Z1IG+egwrH4m/tECKm1nNYpRLRZvJyBlpNlGyLUz4n7HBrdz89xCZ6gHYCpwr5RhFBkGYz40In/4Je3QZm78MRq2hmvwZuIWNrFCI6UwUWcTVsZbYi76LytbdwhPAnmO0+e0aMr+7MpC13QvTTRvTBMQ93U7ajU7R2RCh7X9ShQmzrURRDYXanVtd3VJV3T5d3d9d3RDd0q00fgPaErwiGDQqEnMqFL6ccKoiL7qSY16iIb8QsvWp8R+kJXGc+Z+JJ3g1Ew6qAcmV7lPaDdeLeaJBC4Al+pkKMqtKQm0jgYrZvkLZISIpU7iopraSsqqiMYHSMGqgtqwyA5giDpXbOP2DIaOt/6iSLvTbgDuo/r9d4sqkv+bV/wxZaQwTj+apQICiHzvThsaSv0kLPS3SiPRWF4HbZV/8PNFHbhv+LNaVIuWZoXfspOkwCMiDsuQprhHSYX+JCTHr6lVHoOIe6nVYKlZKqTrJEQUWKY0PDRF5ZimgMPtILXK1a2fDyfKUaXVJGfc+JiBHOwMJ7iBTMXSFoXdyVjLGbjNXZjPALZN5bjNqZjKTZi66BHrCpGe9Tjd0zMPuZjdwzkfQTkPBZkQybkQ/5jRS7kRU5kBImmR95jRJ5kR67kRsZkRtZkSfbjTObkQb7kTQ7lT6bk2gW7OUblOlblVA6OGF7lV2ZlWJZlmYMOBelicrHlU2YnQgJAgtLlH8aOS7plQPpl4OCtU+7lcbljCSnmgyguQGuPlPKmX9ZifP+J5Wue5Wx2Y3eFHP8KFxn65oTwAjOWOUKioHChhceoN3np0OMYI3opzHLR4Xjx5gixuCGRjnlG53V+l2Bil3Yeo27+gr51RAMrN3JejjjW5oXG5oZWZeOBPIjRoT1DtB9KkOiBvragvfGxtPU4yUfjM+DZuw+i6OIpv9ijNIMKOUPzHUe56PDo6AIpWjCLnoYcMw8WaZoeZ5AOD1Dxs1gJmN+ZSUTx1JzWni37EkQTFTcraX0pgwiKaZXEQ4KAgJqwC8WFFujDoT4rnMPYGu0R3nv5XRMm3t4t67E2a7LOHpxQHmvhj7f2ZrVO67lG67o+67uW6xN2jlZQRViMjUH/YDl83qGkzCHnXedEfD1NHOyGWxiHKItsaewbWjhtLUyEwwpZiZ9yK2x7E4h4XrjIrgvR8T6Te7lpmTbBDiGKoLhqiooEg5yQkyG/yMJyq2cXvUHOhpbSjopu2zd8ZpinbjDQVp/cpp9/A+3NBg7uYwrNcUQbCYyZfBJw/uzAdogxEQizqJOKs6Rzxk4FEmzdEE8BniPp3jYA0xo98ReUYIrwXIi3mVmTS5RXrA5XdmiHxmevIE/JDrjp9sTUuFrOZiB+ZugBDxcaRpG2AhW1gxLfM0ja+JMVQZnOazeUntDHQxPmU1ntQx0uo734XlFytUu++7+cvBj87j7lTr/C/0MdQXXwnvE+14GPdtkg/CE98BOYL4EZ7stCt5mJ0/GSuRsJwfA/tgZE7IvwC6fw9ZCIUQHUG+8X+eOyk+1T32vb3os+ohDxKR8Y1OM9SdFoKGE+q9UOzHiNSfo/D8+SgNGN8JuIBHcdN7/ck/0bvESRimC+jqiV47OLOi88KSy8fuE+U3GRxPC9rVXwRWqLMmcJi/hTpkkU9l70LrGm66jme6lvAreO8msMqqkIYyFXhKiYxmCbVqk8mPlOzRljTK9vAcRIIhnOSoyJbvNvIbQI/vzEHf82rFlIk8uVRCia3YDbUzQcsjjVcYsJv/FOCn0MX/XTzBNCxSDCt3jFaP/dDTAEwk8imZRQ9q1ARdDZCVHcWSZsGDLcjL8bmpTYw1vHwuMgDvKc9rNBjKLU24cgROP8GkB1vRCtxbYkHO0dTlp8UZ0YilD8kXsndi3Zv5VoD2XNiaSpGhqck7DV9pdZy4yA90MkUcVIRBlZxGcPHCiEC9WoCLaQi7+VzkQv0LOADepQ6EuPZStEihVBUEgUDVR0XKRgSkCNit94G4wERZdXdY2sKTkziJV5klZvIpGUoaMg9cpN9O4NDx2fpP5GPWsCvOYEkyX6moysX7l7SnpfD1709rRL1+0wTG8tnEbBSx33aD6ljwP/yw8qCdTzmaoX1ruHiEQZli+LnET/P1U3DUlTzx1M7UjLc/srEdZicc6HcIlGikpFS3QhXBFQOZaKzJOShRmJQHryU1nacxKUSfQym0uH/A9X+ZOeUPz61Ht8//STRb0o4Q3CZGuqnFNR6RmiRhLU2xTAMxGbqdS8wL/Uh77nc70QgZGgCusOIWVL7mRRbv7l9+RRdqmwmSJC+bJbVdbCAQ9uyWri3w68xD7nB+Xnl37yN//xR3+gYnWmiHlThIqZLwjMQNzBQfZfKe5RSkRAHVGFI6CUbTDFBogvX1ol0tFKB7ZWA2lBoNUqITYaiaJBVEhDIDYdZSi22kGQBrZoBbEJVDgo4ZdErho+TBSy4g4vFRMl/yojsxVDWjoSPWy5YxAtLwN/JnRIi4ZRpK1cgTRpk+TGkT0jTozoKpHHolMfRisDYQeNHSwxdnSpUGVTq4MGkhz45SZEHa4e7kSLTSjdjXd5fgHatBVIjT0dVrz7haJMkQglbt1h1nFCj0x5JgrM0yrduVlNMg3ZWXBFsm9pYf0YUmoimTs7+iSIcCrHhJVluywpFaVMGnNdnTQZEeyXr1sLbwVg/Dhy4sqHM1/uvDn059KjU59uvTr269qzc69I60vMu2UefhmPbe1D9G8LS6So2/3cvpF7E+5ufzv++/rz89/vv/9z1OnwhWwQ8LQTNgW1AlhLBiGVIEICXQNZWf86mWXQIK3EJhRHOxC40zWuYEhgWxBic2JHBB7U2w4IHrSgh5G5hs01AiUy0EMJ0tBggmytdU1qGbkk5EGP0TRVQWYlAgFBWYEHJELR2IhQjQgNeA2RBiWk5YsQ6kALlBCm5xGFjlm4ZSsZUhTNZAumKOQ1Oj0kZ5FijmQieOR9MeGFDc5YYomrZaVgQbQk6AVBEJEU5UsSriflSQjKiY2Iae5wokCQRmbkSmYxWVqCHhWKTRkflpEQkzoSdFhbWDqqEpcIgYreZeTtgOWJYQ6y2Re61blmliotyFitymGDHLLHAfgfs8s62yy0z0qbHqsYjVQjgSL5+gVCg2iJEVn/MSYpJKasgjgtutGqmy676953onQ3XsQYSS0S9VGv8dFg00Aj+WpVNID1SlN4PQXXa4sJMdXreCMphs1XA8qE1UVwecFlr6SBVXAZYYGE1YDEzuvKwzPiJZ9v8nrIULECffWvFxub1Wu+FnE73sIX0fXTUbRE0/NDELCKlUwxD3jSv+9VNjDREOXptEcoU5gSQwgTiN55bAl1UW3hGT1beQ95cdnYZx3lWVNLI+WFBVN5YV5PUtH8JUEGnoVSIkdxy62Q7lW1Y0cD2g1YQoS30nG+bApd91v9voaaany1xBZ4YdEEwVyyvQYRXktuLFfQC4Y2SBmbd4WQ3c4lmyyK/+267u7rscPOrE57u0SxYQYLPaDYwLm0a7fAsXRv7TrLfvzsyCufPHM5Kvq8mz7D66aiV+VI/WDYix7wUj975LybmZ/481LZa+XmZYVPtej0omN/4lWGYnQVSJSqhOL1c1LnfkrTK3x/RUTnCvUFcCu7cR5PyqeVTbUPfz1x3/YWRDJafO9EhEFR5paCv9aVxn0ZdB8BN5gg/X3wgSbM4AWvMsAuERB8+VtfBomzIIfoD345oiHd8rdCnBCQIvbb4ANFKLr6KGwr/HNh67BRH+tNxRXiI84AKxIfs/gPXsdanXHAV0XobVGLSeTiF70IxjGKsYxdPGMY0UhGNZoxjf9uXOMb27hG6jnwgi1snwFNCMHxFVGJcPyjHAPJxkHGkZCANKQgC6nIQy5Sjv5jHn6OtLzgEDE7AnnbatLFm+VJB3jSkqR/OgPJ/QwihqN0DqhOaR+acPJncIEOFo3DSVXOspa0vKUtc4nLXeqyXbysTmwg+bP06UeSvYSkbKTXrGD2B4S/dE7rnkkRKj6zmvi5YiwBcMxtWrOb3PymN8MJzlo+cpziPKc504nOdaozndmUZTvZKc940nOe9qzns7S4PmPxczjR5OI+/dnPwvxTnwA9qEETWk6FFnShDm0oRAcaUIJKFKEPrShDMXpRgXKUoh2dKEgtGtGPilSjI/X/KEpDmlGSrvR52WTpRlNaUpieVKUxtWlNZyrTluLUpD6l6U93elOd9hSoRhVqTnlK1KUqtalDdaoV8XnPqUq1qlS9ajyzadWtYrWrXP2qV2cXVrDS859YegiuXmLTkFLkrNZpaHPgStax0lVR79RmXfM6173qta/tdKhfA3s8UCqsN6mxSSUT0ZvlFAsnX6ikc4DCnavwtbJ6vatcBatZy3J2s57dTiIZKdrQkhaRpm3kaUeb2hwtaUgL+opIhCaUnfgQYqny4koQEhcDwau2te0JZL6YVucJZZG/7e1qk4va5aqWuaV1rnJFe6KXRve5zb2udbNb3e1Ct7vY5e53/72rXfGCd7zhPa91AdvZ9cIOPA2hiA4SBh6OtAJz1KLtcsRSlV0pJUGsLJ8rHpsZwtJksTShycTOQzKS1GouQclggBvL3gnPTqufvTCFM4zhC29Yw9KC0khutCsT+ZBv0XCMwwoTsJ0AzjHfEghR4isQ0LWIW07ykLh0ELIyVMkxB3nLtwYUnJ4MiHce7vB/sBnLIzMZyU1+MjvV6+QpO+dkQrHLrty2kKb0bzilKchTdHOj1qxHIbmpin0hg5Ub3WQ9Rh4QYg7jMDdfhl5UvrN0MAvlPeO5z3yWHVSDWlSkCpqpTz10UhFt0hBDYGKi2hyevqcRZlIqVWJxiKgqJv+QM5WoyyiWLbcAMyRwnQQbREkISMpDkZT8+C0UUvRRBy1rQye61oUOdKzhRd1Z45rQue41r2Ht62EH29a/Fnaxb41sWivb2MRm9rGd3VMp/xnPJUmQQ0byNLrwRCwIS0tjbufgtbEFJwrJ1khAp5bNYPokIokct1vzlHNDzJTVvndxluznfeO73/zmzr/7HSaunOdSlZlLKy72WvchaCBrAsyCUWSQyYQpvkuhLYUQ5CLdlOTH2KJRoeiNEcOUqCz+vreSsXjygLN85SjPrMsxfO24yWpjEDAPaMQ2lFduBiLvyVl8BMcWg7hHQzlTBE7CIjEioeQidAsSQdocllf/xvzJem451que9Q2bt+vl/Tp5w47eQZr7fA6E3neqksayixBBmXOFHcEXxbJrECKGel9cn+h1se997HwH+3WnG8u+Ex7whf874v2u+MMv3vCOTzzjI59aam9d6xAhrH3IYvnKM8/Cm/8850Fv1dCT3jnMtA9kS696/qR8daJfPexfv03Kyz72tq89u66O+9vzfve+XDawof3s4BMf+MaXtvCTHe1mM3/5zh/+8ZsP/X8KHovPV/70pY/97Se/+8VH/ve17/3oX3/84Cc/oWnf+/X7nv3U8bz72y//+AN8/van//2hiVn88z//8Ve//wVg/4We7g2gAB4g7Eke5D1e9eMtoAM2IAQqYAQyoARWIANW3+pY4ANq4ARuIAV+oAeGYAeOIAcyHgAaIAoiIL7BnwqmoAvG3AvGYAueXOuxjgze4Azi2QnmIA/ioGAVYA8GoQ96WFQVoREeIRImoRIuIRM2oRM+IRRGoRROIRVWoRVeIRZmoRZuIRd2YRTumvmJX/iV3xhmHxmiH/eVYRqiYRie4fm9IfNNjyy1whzWIV7ZYULgIR3eIR/mYR/uoR8GIiAOoh4W4h8aoiAiIiEeIiMmYiMuoiNGIiROoiJW4iNaoiRiIiVeIidmYiduoieGIiiOoiaW4ieaYiC+kxCu4hBuWEAAADs=\" alt=\"image\"\u003e\u003c/p\u003e"},{"header":"2.\tResults and Discussion","content":"\u003cp\u003e\u003cstrong\u003e2.1.\u0026nbsp;Inter-WBE\u003csub\u003eCI\u003c/sub\u003e associations: Smoking-WBE\u003csub\u003eCI\u003c/sub\u003es\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003ecorrelate with multi-disease WBE\u003csub\u003eCI\u003c/sub\u003es confirming smoking as a multi-disease risk factor\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNicotine and cotinine were used as smoking-WBE\u003csub\u003eCI\u003c/sub\u003es (Kasprzyk-Hordern et al., 2025). As expected, nicotine and cotinine showed statistically significant strong/moderate positive correlations with each other (r\u003csub\u003ep/s\u003c/sub\u003e=0.53/0.60). Smoking is a risk factor for several diseases, including Type 2 diabetes, asthma/allergies and cardiovascular diseases, as well as a risk factor of increased pharmaceutical consumption such as pain killers or antibiotics. Indeed, smoking\u0026nbsp;WBE\u003csub\u003eCI\u003c/sub\u003es\u0026nbsp;were shown to correlate with wide-ranging groups of pharmaceuticals confirming smoking as a multi-disease risk factor (Fig. 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking and CVDs:\u0026nbsp;\u003c/strong\u003eObserved significant correlations of nicotine/cotinine with atenolol (r\u003csub\u003ep/s\u003c/sub\u003e=0.44-0.57) were expected as smoking is a risk factor for CVDs, including high blood pressure that is treated with atenolol. Moderate positive correlations were also seen between nicotine/cotinine and atorvastatin (r\u003csub\u003ep/s\u003c/sub\u003e=0.32-0.48) and weaker, but still statistically significant, correlations were seen with bezafibrate. No correlations were seen with irbesartan or lisinopril.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking and asthma:\u003c/strong\u003e Positive correlations were seen with antihistamines (r\u003csub\u003ep/s\u0026nbsp;\u003c/sub\u003e= 0.29-0.47), which confirms smoking as a risk factor in asthma/allergies. This is\u0026nbsp;supported by previous study reporting a strong positive relationship between cetirizine/fexofenadine and cotinine in wastewater\u0026nbsp;(Thai et al. 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking and Type 2 diabetes:\u0026nbsp;\u003c/strong\u003eModerate positive correlations between cotinine and metformin (r\u003csub\u003ep/s\u003c/sub\u003e=0.55/0.47)\u0026nbsp;(Fig. 2)\u0026nbsp;were observed in line with other reports (Shao, Cong et al. 2021) as smoking is a risk factor for Type 2 diabetes. Interestingly, no correlations were observed with sitagliptin. Correlations between cotinine and metformin are due to the higher levels of nicotine from smoking cigarettes making the cells in the body less responsive to insulin, which makes the blood sugar levels higher (Maddatu, Anderson-Baucum et al. 2017). While both drugs aim to improve glycaemic control, the potential for metformin to attenuate smoking-related cardiovascular risks might be a distinguishing factor for the difference (Paul, Klein et al. 2016).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking and pain management:\u003c/strong\u003e Nicotine and its main metabolite cotinine showed strong/moderate positive correlations with several pain management\u0026nbsp;WBE\u003csub\u003eCI\u003c/sub\u003es, such as analgesics and NSAiDs (paracetamol, codeine/dihydrocodeine, naproxen/O-desmethylnaproxen, diclofenac, ibuprofen (r\u003csub\u003ep/s\u003c/sub\u003e=\u0026pound;0.71)) and stimulants (amphetamine (r\u003csub\u003ep/s\u003c/sub\u003e=\u0026pound;0.46)). Stronger correlations were seen with cotinine having mainly human metabolism origin versus nicotine having more heterogenous sources. Interestingly, weak/no correlations were seen with either chronic pain management pregabalin/N-methylpregabalin, as well as anaesthetic ketamine/norketamine.\u003c/p\u003e\n\u003cp\u003eNicotine/cotinine showed strong positive correlations with opioids, e.g. cotinine and tramadol (r\u003csub\u003ep/s\u0026nbsp;\u003c/sub\u003e=\u0026nbsp;0.65/0.65), codeine (cotinine:\u0026nbsp;r\u003csub\u003ep/s\u0026nbsp;\u003c/sub\u003e=\u0026nbsp;0.70/0.71) and dihydrocodeine (cotinine:\u0026nbsp;r\u003csub\u003ep/s\u0026nbsp;\u003c/sub\u003e=\u0026nbsp;0.62/0.68). This association was also reported elsewhere in patients in the context of perception of pain and pain management using opioids (Etcheson, Gwam et al. 2018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking and infectious disease:\u0026nbsp;\u003c/strong\u003eSignificant correlations of nicotine/cotinine with several antibiotics (e.g. sulfapyridine/N-acetylsulfapyridine, sulfamethoxazole, trimethoprim): (r\u003csub\u003ep/s\u003c/sub\u003e=\u0026pound;0.50) were also observed. Indeed,\u0026nbsp;tobacco smoking is a risk factor for increased antibiotic prescription (Steinberg, Akincigil et al. 2016).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking and depression:\u003c/strong\u003e Only two\u0026nbsp;WBE\u003csub\u003eCI\u003c/sub\u003es\u0026nbsp;were tested: venlafaxine and desmethylvenlafaxine. No statistically significant correlations were seen.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking and other lifestyle chemicals:\u003c/strong\u003e Interestingly no correlations were found between nicotine/cotinine and caffeine/dimethylxanthine\u0026nbsp;despite being reported elsewhere (Shao, Cong et al. 2021). Weak/no correlations were observed between nicotine and illicit drugs, e.g. cocaine (r\u003csub\u003ep/s\u003c/sub\u003e=0.45/0.41) and amphetamine (r\u003csub\u003ep/s\u003c/sub\u003e=0.46/0.41).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking and other CIs:\u0026nbsp;\u003c/strong\u003eInterestingly, no correlations were observed with HNE-MA, an oxidative stress marker, or creatinine, a marker of kidney function. Weak to moderate positive correlations were observed with methotrexate, a pharmaceutical used in cancer and autoimmune conditions (r\u003csub\u003ep/s\u003c/sub\u003e=0.27-0.30).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCA was used to visually represent key\u0026nbsp;WBE\u003csub\u003eCI\u003c/sub\u003e correlations. Two principal components were retained (as seen in\u0026nbsp;Fig. 2) as they were explained by 60% of the variance. PCA analysis confirmed strong positive correlations between smoking-WBE\u003csub\u003eCI\u003c/sub\u003es and pharma-WBE\u003csub\u003eCI\u003c/sub\u003es for CVD, pain and infectious disease with weaker correlations seen with class II diabetes, asthma/allergy. PCA also reinforced lack of correlation (~90\u003csup\u003eo\u003c/sup\u003e angle) between smoking-WBE\u003csub\u003eCI\u003c/sub\u003es\u003csub\u003e\u0026nbsp;\u003c/sub\u003eand gastrointestinal: cimetidine, oxidative stress: HNE-MA, caffeine/1,7-dimethylxanthine, anxiety/depression: venlafaxine/desmethylvenlafaxine. It is worth mentioning that PCA is a dimensionality reduction tool. As seen in\u0026nbsp;Fig. 2, nicotine and cotinine vectors are located closer the middle of the plot, indicating that they do not strongly influence PCA1 or 2 (as for example atorvastatin and fexofenadine influence PCA1), which shows that there might be other, relationships or patterns that are not captured by the leading PCs. To manage this, we trialled standardization approaches (by normalization of a value from a distribution by converting it to a z-score) to standardize the range of the continuous initial variables so that each one of them contributes equally to the analysis, but they did not provide any further benefits to PCA outputs. This reinforces complex relationships between\u0026nbsp;WBE\u003csub\u003eCI\u003c/sub\u003es\u0026nbsp;and the need for further work.\u003c/p\u003e\n\u003cp\u003eWe also used multivariate HCA for visual representation of key\u0026nbsp;WBE\u003csub\u003eCI\u003c/sub\u003es\u0026nbsp;associations: smoking WBE\u003csub\u003eCIs\u003c/sub\u003e and pharma proxies WBE\u003csub\u003eCIs\u003c/sub\u003e (Fig. 2). As with PCA, strong associations (and two distinct clusters) were seen between smoking- and pharm-WBE\u003csub\u003eCI\u003c/sub\u003es proxies for CVD, pain, infectious disease and Type 2 diabetes in cluster 1 vs gastrointestinal: cimetidine, oxidative stress: HNE-MA, caffeine/1,7-dimethylxanthine, anxiety/depression: venlafaxine/desmethylvenlafaxine in cluster 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.\u0026nbsp;Smoking-WBE\u003csub\u003eCI\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003eand ONS\u003csub\u003eSEDI\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;associations\u003c/strong\u003e\u003cstrong\u003e: Smoking-WBE\u003csub\u003eCI\u003c/sub\u003es\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003epositively correlate with higher deprivation, lower level of education and bad health\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStrong correlations between smoking-WBE\u003csub\u003eCI\u003c/sub\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003eand ONS\u003csub\u003eSEDI\u003c/sub\u003e were observed across several indicators including deprivation, education, health and housing as seen in\u0026nbsp;Fig. 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking and the level of education:\u0026nbsp;\u003c/strong\u003eCorrelation of smoking-WBE\u003csub\u003eCI\u003c/sub\u003es\u0026nbsp;and ONS\u003csub\u003eSEDI\u003c/sub\u003e \u0026lsquo;highest level of education\u0026rsquo; clearly shows strong positive correlations between cotinine and \u0026lsquo;no qualifications\u0026rsquo;\u0026nbsp;(r\u003csub\u003ep/s\u003c/sub\u003e=0.66/0.68), with negative correlations in \u0026lsquo;level 4 qualifications\u0026rsquo; and above (r\u003csub\u003ep/s\u003c/sub\u003e=-0.58/-0.61).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking and demographics:\u003c/strong\u003e No strong correlations were found between nicotine/cotinine and city size, as well as gender. However, significant correlations were seen with different age groups, with \u0026gt;50 correlating positively (nicotine/cotinine, r\u003csub\u003ep/s\u003c/sub\u003e=\u0026gt;0.50). Breakdown by cities with different age groups shows clear negative correlations in younger population versus the older age brackets.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWeak correlations were observed between smoking-WBE\u003csub\u003eCI\u003c/sub\u003es\u0026nbsp;and \u0026lsquo;ethnic group\u0026rsquo; with negative correlations seen between nicotine/cotinine and \u0026lsquo;Asian\u0026rsquo;, \u0026lsquo;black\u0026rsquo;, \u0026lsquo;mixed\u0026rsquo; ethnicity (strongest negative correlation seen for cotinine/\u0026lsquo;mixed\u0026rsquo; r\u003csub\u003ep/s\u003c/sub\u003e=-0.44/-0.35) and positive weak correlations between cotinine and \u0026lsquo;white\u0026rsquo; (r\u003csub\u003ep\u003c/sub\u003e=0.38/0.31). This confirms conclusions of a recent study conducted by UK government (APS, 2024).\u003c/p\u003e\n\u003cp\u003eRegarding \u0026lsquo;household composition\u0026rsquo;, positive correlations were seen between smoking-WBE\u003csub\u003eCI\u003c/sub\u003es\u0026nbsp;and ONS\u003csub\u003eSEDI\u003c/sub\u003e \u0026lsquo;one person household\u0026rsquo; with older demographics\u0026nbsp;(r\u003csub\u003ep/s\u003c/sub\u003e=\u0026sup3;0.46) and \u0026lsquo;single family household: lone parent family: with dependent children/all households\u0026rsquo; (r\u003csub\u003ep/s\u003c/sub\u003e=\u0026sup3;0.32) versus negative correlations seen for \u0026lsquo;single family household: married or civil partnership couple: dependent children\u0026rsquo;\u0026nbsp;(r\u003csub\u003ep/s\u003c/sub\u003e=-0.4-(-0.6)).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding \u0026lsquo;accommodation type\u0026rsquo;, no strong correlations were seen, with semi-detached/terraced houses showing the strongest positive correlations with smoking-WBE\u003csub\u003eCI\u003c/sub\u003es.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking and socioeconomic status:\u0026nbsp;\u003c/strong\u003ePositive correlations were seen between smoking-WBE\u003csub\u003eCI\u003c/sub\u003es\u0026nbsp;and ONS\u003csub\u003eSEDI\u0026nbsp;\u003c/sub\u003e\u0026rsquo;deprivation status\u0026rsquo; (r\u003csub\u003ep/s\u003c/sub\u003e=0.3-0.6 for cotinine \u0026ndash; \u0026lsquo;household deprivation\u0026rsquo;, one-three dimensions). As expected, negative correlations (r\u003csub\u003ep/s\u003c/sub\u003e = -0.4-(-0.6)) were found for cotinine and \u0026lsquo;not deprived in any dimension\u0026rsquo; which confirms the linkage between smoking prevalence by socio-economic status as also captured by ONS (ONS 2023a). The report (ONS 2023a) states that the proportion of people who smoke in the most deprived areas of England and Wales was more than three times higher than in the least deprived areas in 2021 and that over a quarter of smokers in England and Wales live in the most deprived areas.\u0026nbsp;Similar observations were reported by Choi et al. (Choi, Tscharke et al. 2019)\u0026nbsp;who found\u0026nbsp;significant negative correlations between IRSAD (a general measure of advantage and disadvantage, summarising the economic and social conditions of people and households) and nicotine.\u003c/p\u003e\n\u003cp\u003eSmoking-WBE\u003csub\u003eCI\u003c/sub\u003es\u0026nbsp;positively correlate with \u0026lsquo;disabled\u0026rsquo; parameters (e.g. r\u003csub\u003ep/s\u0026nbsp;\u003c/sub\u003efor cotinine-\u0026lsquo;disabled under the Equality Act\u0026rsquo; = 0.47/0.50), indicating nicotine being a risk factor. It is reported that adults with disabilities are more likely to smoke than those reporting as not disabled (Emerson 2018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking and health:\u003c/strong\u003e Interestingly, and as expected, smoking-WBE\u003csub\u003eCI\u003c/sub\u003es\u0026nbsp;showed strong negative correlations with ONS\u003csub\u003eSEDI\u003c/sub\u003e \u0026lsquo;very good health\u0026rsquo; (cotinine - \u0026lsquo;very good health\u0026rsquo; r\u003csub\u003ep/s\u003c/sub\u003e = -0.60/-0.59) moving to positive correlations from \u0026lsquo;good, \u0026lsquo;fair\u0026rsquo;, \u0026lsquo;bad health\u0026rsquo; with the strongest positive correlation seen for \u0026lsquo;very bad health\u0026rsquo; (cotinine/\u0026rsquo;very bad health\u0026rsquo;, r\u003csub\u003ep/s\u003c/sub\u003e=0.60/0.71), which again confirms smoking as a multi-disease risk factor.\u003c/p\u003e\n\u003cp\u003ePCA analysis (with two PCs retaining 83% of the variance) confirmed strong positive correlations between smoking-WBE\u003csub\u003eCI\u003c/sub\u003es and\u0026nbsp;ONS\u003csub\u003eSEDI\u003c/sub\u003e: \u0026lsquo;bad to very bad health\u0026rsquo;, \u0026lsquo;deprived in more than 1 dimension\u0026rsquo;, \u0026lsquo;no qualification\u0026rsquo; and \u0026lsquo;level 1 qualification\u0026rsquo; with the lowest angle seen between nicotine/cotinine and \u0026lsquo;very bad health\u0026rsquo;\u0026nbsp;(Fig. 3). Strong negative correlation (~180\u003csup\u003eo\u003c/sup\u003e angle) was seen between nicotine/cotinine and \u0026lsquo;fair to very good health\u0026rsquo;.\u0026nbsp;As with PCA, HCA revealed strong associations (and two distinct clusters) between smoking-WBE\u003csub\u003eCI\u003c/sub\u003es and higher level of deprivation, lover qualifications, disability and bad health grouped in cluster 1 vs the lowest level of deprivation, highest level of qualification, no disability and fair and very good health grouped in cluster 2 (Fig. 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.\u0026nbsp;Caffeine-WBE\u003csub\u003eCI\u003c/sub\u003es show weak correlations with other WBE\u003csub\u003eCI\u003c/sub\u003es\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003eand ONS\u003csub\u003eSEDI\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe also assessed another lifestyle chemical \u0026ndash; caffeine as a comparator, a common, low-harm (if consumed in moderate quantities) lifestyle marker, to explore further future potential of WBE in risk factor analysis and whether its associations with health and SED indicators differ from those observed for smoking.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRelatively few statistically significant correlations were seen between caffeine-WBE\u003csub\u003eCI\u003c/sub\u003es and pharma-WBE\u003csub\u003eCIs\u003c/sub\u003e. Caffeine and 1,7-DMX showed moderate to strong positive correlations with each other (r\u003csub\u003ep/s\u003c/sub\u003e=0.87-0.88) (Fig. 2). Significant positive correlations were seen between caffeine-WBE\u003csub\u003eCI\u003c/sub\u003es and metabolites of tramadol (r\u003csub\u003ep/s\u003c/sub\u003e=0.50 - 0.83), antibiotics/metabolites (e.g. metronidazole/hydroxymetronidazole, r\u003csub\u003ep/s\u003c/sub\u003e=\u0026lt;0.40) with stronger correlations seen for 1,7-DMX. The strong correlation between caffeine/1,7-DMX and illicit amphetamines (mephedrone (r\u003csub\u003ep/s\u003c/sub\u003e=0.41-0.56), MDMA (r\u003csub\u003ep/s\u003c/sub\u003e=0.38-54)) and ketamine/norketamine (r\u003csub\u003ep/s\u003c/sub\u003e=0.43-0.59)) was also observed. The stimulating effect of coffee intensifying the effects of amphetamines, e.g. administered as caffeinated energy-containing beverages is indeed widely reported. The positive correlation of caffeine-WBE\u003csub\u003eCIs\u003c/sub\u003e and venlafaxine/desmethylvenlafaxine (r\u003csub\u003ep/s\u003c/sub\u003e=0.44-0.64) was also seen. Caffeine has been considered as a risk factor in the increased risk of anxiety and depression (Liu, Wang et al. 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere are conflicting results regarding coffee consumption and health outcomes, however, recent results from large studies indicate potential beneficial effects of drinking coffee on risk of death from all causes, especially for circulatory diseases, and digestive diseases, including a more favourable liver function profile and immune response (Gunter, Murphy et al. 2017). Indeed, in this study we found moderate negative associations with atorvastatin (caffeine: r\u003csub\u003ep/s\u003c/sub\u003e=-0.33 and 1,7-DMX: r\u003csub\u003ep/s\u003c/sub\u003e=-0.41/-0.40), a statin used to lower cholesterol and prevent heart disease, including heart attacks and strokes (Fig. 2). Interestingly, though, caffeine-WBE\u003csub\u003eCI\u003c/sub\u003es showed positive correlations with HNE-MA (r\u003csub\u003ep/s\u003c/sub\u003e=0.41-0.58). As discussed earlier, there is a lack of clarity in the literature about whether caffeine (known to have antioxidant properties) can decrease oxidative stress. Some literature reports have indicated a positive impact\u0026nbsp;(Vargas-Pozada, Ramos-Tovar et al. 2023), however, this study shows positive correlations and the need for further work, especially in expanding the list of oxidative stress biomarkers tested.\u003c/p\u003e\n\u003cp\u003eA moderate to strong positive association of caffeine-WBE\u003csub\u003eCI\u003c/sub\u003es with cimetidine (r\u003csub\u003ep/s\u003c/sub\u003e=0.35-0.55) was also seen (Fig. 2). While caffeine stimulates gastric acid production (Nehlig 2022), it can also exacerbate gastric irritation in patients with ulcers, the stomach lining and increase acid production in patients with stomach ulcers,\u0026nbsp;potentially explaining the co-occurrence of caffeine consumption and H2-receptor antagonist use in the community.\u003c/p\u003e\n\u003cp\u003ePCA and HCA analysis (Fig. 2) confirmed statistically significant correlation between coffee-WBE\u003csub\u003eCI\u003c/sub\u003es\u003csub\u003e\u0026nbsp;\u003c/sub\u003eand gastrointestinal: cimetidine, oxidative stress: HNE-MA, and weak association with anxiety/depression: venlafaxine/desmethylvenlafaxine. It is worth mentioning that coffee vectors are located closer the middle of the PCA plot, indicating that they do not strongly influence PCA1 or 2, which shows that there might be other, relationships or patterns that are not captured by the leading PCs.\u0026nbsp;This, as in the case of smoking-WBE\u003csub\u003eCI\u003c/sub\u003es, reinforces complex relationships between CIs and the need for further work.\u003c/p\u003e\n\u003cp\u003eInterestingly, as opposed to smoking-WBE\u003csub\u003eCI\u003c/sub\u003es,\u003csub\u003e\u0026nbsp;\u003c/sub\u003ecaffeine-WBE\u003csub\u003eCI\u003c/sub\u003es did not show any strong, statistically significant correlations with tested\u0026nbsp;ONS\u003csub\u003eSEDI\u003c/sub\u003e with moderate positive correlations seen with good health (Fig. 3).\u0026nbsp;PCA and HCA analysis (Fig. 3) confirmed associations between coffee-WBE\u003csub\u003eCI\u003c/sub\u003es and\u0026nbsp;ONS\u003csub\u003eSEDI\u003c/sub\u003e: \u0026lsquo;not deprived\u0026rsquo; and\u0026nbsp;\u0026lsquo;fair to very good health\u0026rsquo; and weaker positive associations with higher level of education and no disability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u003cstrong\u003eWBE\u003csub\u003eCI\u003c/sub\u003es\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003eand ONS\u003csub\u003eSEDI\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eassociations with lifestyle indicators: smoking and caffeine intake\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePCA analysis of combined WBE\u003csub\u003eCI\u003c/sub\u003es\u003csub\u003e\u0026nbsp;\u003c/sub\u003eand ONS\u003csub\u003eSEDI\u003c/sub\u003e (Fig. 4,\u0026nbsp;with two PCs retaining 64% of the variance) showed smoking-WBE\u003csub\u003eCI\u003c/sub\u003es clustering together with CVD, cancer, illicit drugs, asthma, diabetes and pain WBE\u003csub\u003eCI\u003c/sub\u003es as well as with disability, deprivation, bad health and low/no education\u0026nbsp;ONS\u003csub\u003eSEDI\u003c/sub\u003es. Furthermore, these indicators clustered with cities with highest deprivation, lowest education and highest index of weak to bad health (HD Cities 3, 6, 7 and MD City 9: \u0026lsquo;deprived in\u0026nbsp;\u0026sup3;1 dimension\u0026rsquo;: 58, 56, 59, 52%; \u0026lsquo;bad to very bad health\u0026rsquo;: 8, 6, 8, 6%; and \u0026lsquo;no/level 1 qualification\u0026rsquo;: 33, 34, 34, 27% for Cities 3, 6, 7, 9 respectively). It is worth mentioning that City 9, despite having MD status due to household deprivation of 52%, performed worse than other MD cities, also due to high percentage of bad health. Interestingly, the least deprived, best educated and healthy city (LD City 5) clusters with\u0026nbsp;ONS\u003csub\u003eSEDI\u003c/sub\u003es:\u0026nbsp;\u0026lsquo;not deprived\u0026rsquo; (deprived in\u0026nbsp;\u0026sup3;1 dimension: 43%), \u0026lsquo;not disabled, \u0026lsquo;v. good to fair health\u0026rsquo; (\u0026lsquo;bad to very bad health\u0026rsquo;: 3%)\u0026nbsp;as well as with oxidative stress and caffeine-WBE\u003csub\u003eCI\u003c/sub\u003es. While WBE correlations do not imply causation, the above example confirming smoking as a risk factor in certain diseases and reinforcing the fact that certain socioeconomic and demographic characteristics shape smoking habits, clearly shows a huge potential of WBE as an early warning system for public health risks. New correlations between favourable socioeconomics and demographics of City 5 and oxidative stress and caffeine intake are an exciting opportunity for exploration of WBE\u0026rsquo;s potential in public health monitoring.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurther exploration of correlations reveals pain management and antibiotics usage (infectious disease proxy), high illicit drug use prevalence, as well as chronic, often lifestyle driven conditions such as CVDs, diabetes, asthma and cancer clustering closely with disability, deprivation and low level of education. This is especially notable in HD cities and could serve as an early warning for city focussed interventions, with HD Cities 3, 6, 7, 9 and MD City 8 and 9 in need of immediate attention. Such data could, for instance, guide local public health bodies in allocating resources for smoking cessation programs or managing the burden of chronic, lifestyle-driven diseases in the most vulnerable communities.\u003c/p\u003e"},{"header":"3.\tConclusions ","content":"\u003cp\u003eThis study explored WBE in risk factor analysis by using two lifestyle choices as disease risk factors: nicotine/smoking known to be a major multi-disease risk factor and caffeine,\u0026nbsp;a common, low-harm lifestyle marker. We used pharmaceutical\u0026nbsp;WBE\u003csub\u003eCI\u003c/sub\u003es\u0026nbsp;as proxies for disease, as well as socioeconomic and demographic Census data to unravel associations between community specific disease prevalence, lifestyle choices, demographics and socioeconomic factors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur results reaffirmed smoking as a multi-disease risk factor. Smoking-WBE\u003csub\u003eCI\u003c/sub\u003es showed positive correlations with WBE\u003csub\u003eCI\u003c/sub\u003e (CVDs, cancer, illicit drugs, asthma, diabetes, pain) and\u0026nbsp;ONS\u003csub\u003eSEDI\u003c/sub\u003e (disability, high deprivation, bad health and low/no education). Further exploration of correlations revealed pain management and antibiotics usage (infectious disease proxy), high illicit drug use prevalence, as well as chronic, often lifestyle driven conditions such as CVDs, diabetes, asthma and cancer clustering closely with disability, deprivation and low level of education. This is especially notable in HD cities and could serve as an early warning for city focussed interventions, with HD cities 3, 6, 7, 9 in need of immediate attention. In the case of large cities, it also highlights the need for more in-network wastewater sampling within individual city boroughs (e.g. MSOAs 5000-15,000 population; ONS, 2021) to improve the granularity and better target interventions. New correlations between favourable socioeconomics and demographics of City 5 and oxidative stress and caffeine intake provide an important opportunity for exploration of the potential of WBE to monitor public health in a relatively unbiased, non-invasive manner.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWBE represents a powerful epidemiological tool when triangulated with other established approaches. WBE output can provide important insights for policy makers and those involved in harm reduction (as well as the wider risk factor analysis), so that support can be tailored for communities who need it the most and where incidence rates are highest. Most strikingly, WBE has the potential to indicate new exposure-health risk associations that provide early-warning for communities at risk from suspected, new and previously disputed or unexplored risks. \u0026nbsp;\u003c/p\u003e"},{"header":"3.\tExperimental","content":"\u003cp\u003e\u003cstrong\u003e3.1.\u0026nbsp;Wastewater treatment sites selected for the study and sampling approaches utilised\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSamples were collected in November 2021 at 11 wastewater treatment plants (WWTPs) serving 10 cities on 9 randomly selected days at each site (i.e. 4 - 5 days every week at each site, including weekends) using 24 h composite sampling (with subsamples collected at 15 min or 1 h intervals using refrigerated autosamplers where available). Two of the 11 sites provided additional grab samples: WWTP: 10 / 10 samples and WWTP: 3 / 10 samples. \u0026nbsp;All collected samples were transported frozen and were processed\u0026nbsp;within 24 h after collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe selection of 11 WWTPs was informed by the following criteria: (i) 24 h composite sampling (primary requirement), (ii) flow measurement on site (primary requirement), (iii) known WWTP population equivalent (primary requirement), (iv) WWTPs covering a wide population range from 10\u003csup\u003e3\u003c/sup\u003e to \u0026gt;10\u003csup\u003e6\u003c/sup\u003e, (v) low industrial input (secondary requirement), (vi) broad geographical distribution (vii) minimum of eight 24 h composite samples per WWTP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.\u0026nbsp;Analysis of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eWBE\u003csub\u003eCI\u003c/sub\u003es\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWBE\u003csub\u003eCI\u003c/sub\u003es\u0026nbsp;were analysed as described elsewhere (Kasprzyk-Hordern, Sims et al. 2023).\u003c/p\u003e\n\u003cp\u003e50 mL wastewater samples were pH adjusted to 7.5 \u0026plusmn; 0.5, spiked with an IS (Internal Standard) mix (100 ng/compound) and filtered through glass microfibre GF/F (0.7 \u0026mu;m) filters.\u0026nbsp;WBE\u003csub\u003eCI\u003c/sub\u003es\u0026nbsp;were extracted with Solid-Phase Extraction (SPE) using HLB (Hydrophilic Lipophilic Balance, Waters) cartridges conditioned with 2 mL MeOH followed by 2 mL H\u003csub\u003e2\u003c/sub\u003eO at ~5 mL min\u003csup\u003e-1\u003c/sup\u003e followed by elution with 4 mL of MeOH. The eluent was dried under N\u003csub\u003e2\u003c/sub\u003e at 40 \u0026deg;C and resuspended in 500 \u0026mu;L of 80:20 H\u003csub\u003e2\u003c/sub\u003eO:MeOH. Two full biological replicates were prepared for every sample analysed.\u003c/p\u003e\n\u003cp\u003eExtracted\u0026nbsp;WBE\u003csub\u003eCI\u003c/sub\u003es\u0026nbsp;were analysed using an ACQUITY UPLC\u0026trade; Xevo TQD-ESI triple quadrupole mass spectrometer (Waters, UK) coupled to an ultra-performance liquid chromatography instrument with an electrospray ionisation source (ESI). Two analytical methods were used.\u003c/p\u003e\n\u003cp\u003eNCD (non-communicable\u0026nbsp;WBE\u003csub\u003eCI\u003c/sub\u003e disease markers) were analysed with a reversed-phase BEH C18 column (150 x 1.0 mm, 1.7 \u0026mu;m particle size) and TQD-ESI set in both ESI+ and ESI- modes using the following mobile phase conditions: ESI+ mobile phase A: H\u003csub\u003e2\u003c/sub\u003eO:MeOH 95:5 + 0.1 % HCOOH and mobile phase B: 100 % MeOH (34 min method time) and ESI\u0026ndash; mobile phase A: H\u003csub\u003e2\u003c/sub\u003eO:MeOH 80:20 + 1 mM NH\u003csub\u003e4\u003c/sub\u003eF, mobile phase B: 5:95 H\u003csub\u003e2\u003c/sub\u003eO: MeOH + 1 mM NH\u003csub\u003e4\u003c/sub\u003eF (23 min method time). The mobile phase rate was set at 0.04 mL min\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003ewith an injection volume of 15 \u0026mu;L. The TQD-ESI was set at: source temperature, 150 \u0026deg;C, desolvation temperature, 400 \u0026deg;C, cone gas flow, 100 L h\u003csup\u003e-1\u003c/sup\u003e, desolvation gas flow, 550 L h\u003csup\u003e-1\u003c/sup\u003e. Nitrogen was used as the nebulising and desolvation gas, and argon as the collision gas.\u003c/p\u003e\n\u003cp\u003eAntimicrobial agents were analysed with a reversed-phase BEH C18 column (150 x 1.0 mm, 1.7 \u0026mu;m particle size) and TQD-ESI set in ESI+. A 19 min gradient elution using H\u003csub\u003e2\u003c/sub\u003eO:MeOH (95:5, with 0.1% HCOOH) and MeOH (100%) as mobile phases at a mobile phase flow of 0.2 mL min\u003csup\u003e-1\u003c/sup\u003e and injection volume were applied (Holton and Kasprzyk-Hordern 2021).\u003c/p\u003e\n\u003cp\u003eThe coordinating program used was MassLynx V4.1, and the data processing program was TargetLynx (Waters Lab Informatics, UK). All methods were validated for trace quantification and method performance checked per every sample batch as discussed\u0026nbsp;in (Holton and Kasprzyk-Hordern, 2021) and (Kasprzyk-Hordern, Sims et al. 2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.\u0026nbsp;Demographic and socioeconomic indicators (ONS\u003csub\u003eSEDI\u003c/sub\u003es)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic and socioeconomic indicators (ONS\u003csub\u003eSED\u003c/sub\u003eIs) for each wastewater treatment plant (WWTP) catchment were estimated using 2021 Census data from the UK Office for National Statistics (ONS)(ONS 2022), provided at the Output Area (OA) level. Variables covered the following domains:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemography:\u003c/strong\u003e age, sex, household deprivation, household composition, housing\u003c/p\u003e\n\u003cp\u003eHousehold deprivation (HD) is classified as: (1) Household is not deprived in any dimension, (2) Household is deprived in one dimension, (3) Household is deprived in two dimensions, (4) Household is deprived in three dimensions, (5) Household is deprived in four dimensions, and is based on four selected household characteristics:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Education: A household is classified as deprived in the education dimension if no one has at least level 2 education and no one aged 16 to 18 years is a full-time student.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Employment: A household is classified as deprived in the employment dimension if any member, not a full-time student, is either unemployed or economically inactive due to long-term sickness or disability.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Health: A household is classified as deprived in the health dimension if any person in the household has general health that is bad or very bad or is identified as disabled. People who have assessed their day-to-day activities as limited by long-term physical or mental health conditions or illnesses are considered disabled. This definition of a disabled person meets the harmonised standard for measuring disability and is in line with the Equality Act (2010).\u003c/p\u003e\n\u003cp\u003e\u0026middot; Housing: A household is classified as deprived in the housing dimension if the household\u0026apos;s accommodation is either overcrowded, in a shared dwelling, or has no central heating.\u003c/p\u003e\n\u003cp\u003eHousehold composition (HC) classifies households by the relationships between household members (household composition). One-family households are classified by: (1) the number of dependent children and (2) family type (married, civil partnership or cohabiting couple family, or lone parent family). Other households are classified by: (3) the number of people, (4) the number of dependent children, (5) whether the household consists only of students or only of people aged 66 and over.\u003c/p\u003e\n\u003cp\u003eHousing: Accommodation type has eight categories: (1) Detached, (2) Semi-detached, (3) Terraced, (4) In a purpose-built block of flats or tenement, (5) Part of a converted or shared house, including bedsits, (6) Part of another converted building, for example, former school, church or warehouse, (7)\u0026nbsp; In a commercial building, for example, in an office building, hotel or over a shop, (8) A caravan or other mobile or temporary structure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEducation:\u003c/strong\u003e highest level of qualification\u003c/p\u003e\n\u003cp\u003eHighest level of qualification classifies usual residents aged 16 years and over by their highest level of qualification. \u0026nbsp;There are eight categories:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(1) No qualifications, (2) Level 1 and entry level qualifications: 1 to 4 GCSEs grade A* to C, Any GCSEs at other grades, O levels or CSEs (any grades), 1 AS level, NVQ level 1, Foundation GNVQ, Basic or Essential Skills, (3) Level 2 qualifications: 5 or more GCSEs (A* to C or 9 to 4), O levels (passes), CSEs (grade 1), School Certification, 1 A level, 2 to 3 AS levels, VCEs, Intermediate or Higher Diploma, Welsh Baccalaureate Intermediate Diploma, NVQ level 2, Intermediate GNVQ, City and Guilds Craft, BTEC First or General Diploma, RSA Diploma, (4) Apprenticeship, (5) Level 3 qualifications: 2 or more A levels or VCEs, 4 or more AS levels, Higher School Certificate, Progression or Advanced Diploma, Welsh Baccalaureate Advance Diploma, NVQ level 3; Advanced GNVQ, City and Guilds Advanced Craft, ONC, OND, BTEC National, RSA Advanced Diploma, (6) Level 4 qualifications or above: degree (BA, BSc), higher degree (MA, PhD, PGCE), NVQ level 4 to 5, HNC, HND, RSA Higher Diploma, BTEC Higher level, professional qualifications (for example, teaching, nursing, accountancy) (7) Other: vocational or work-related qualifications, other qualifications achieved in England or Wales, qualifications achieved outside England or Wales (equivalent not stated or unknown), (8) Does not apply.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthnic and cultural identity:\u003c/strong\u003e ethnic group\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHealth and care:\u003c/strong\u003e disability and general health\u003c/p\u003e\n\u003cp\u003eDisability: People who assessed their day-to-day activities as limited by long-term physical or mental health conditions or illnesses are considered disabled. This definition of a disabled person meets the harmonised standard for measuring disability and is in line with the Equality Act (2010). Classification: (1) Disabled under the Equality Act: Day-to-day activities limited a lot, (2) Disabled under the Equality Act: Day-to-day activities limited a little, (3) Not disabled under the Equality Act: Has long-term physical or mental health condition but day-to-day activities are not limited, (4) Not disabled under the Equality Act: No long-term physical or mental health conditions.\u003c/p\u003e\n\u003cp\u003eGeneral health: A person\u0026apos;s assessment of the general state of their health from very good to very bad. This assessment is not based on a person\u0026apos;s health over any specified period of time. Classification: (1) Very good health, (2) Good health, (3) Fair health, (4) Bad health, (5) Very bad health.\u003c/p\u003e\n\u003cp\u003eCatchment boundaries and OA boundaries were processed using R. For each OA, the proportion of its area overlapping with the WWTP catchment (Overlap%) was calculated. If an OA was fully within a catchment, its entire count was included. For partially overlapping OAs, the count for each variable was scaled by Overlap%, assuming a uniform distribution of characteristics within the OA. The resulting adjusted counts were then aggregated to obtain catchment-level estimates. All fractional counts were rounded to the nearest integer prior to aggregation. This spatial weighting method follows the Simple Approach framework, developed and implemented in R. This spatial weighting method, which assumes a uniform distribution of characteristics within an OA, is a robust and widely-used approach for estimating population characteristics in bespoke geographical areas like WWTP catchments where direct census data is unavailable. Full methodological details are available in our previous work (Kasprzyk-Hordern, Jagadeesan et al. 2025). An overview of the implementation is provided in the Supplementary Information.\u003c/p\u003e\n\u003cp\u003eAll above data were normalised either to population size or total number of households (housing and household composition) before further analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.\u0026nbsp;Calculations and statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePopulation (inhabitants; inh) normalised daily mass loads (\u003cem\u003ePNDLs\u003c/em\u003e) of \u003cem\u003eCIs\u003c/em\u003e (mg day\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003e1000inh\u003csup\u003e-1\u003c/sup\u003e) were calculated using Eq. 1:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"143\" height=\"27\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Eq. 1\u003c/p\u003e\n\u003cp\u003ewhere: \u003cem\u003eDL\u003c/em\u003e is the daily mass load of \u003cem\u003eCI\u0026nbsp;\u003c/em\u003e(mg day\u003csup\u003e-1\u003c/sup\u003e) in influent wastewater, \u003cem\u003ePE\u003csub\u003eWW\u003c/sub\u003e\u003c/em\u003e is the water utility PE estimate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDaily mass loads (\u003cem\u003eDLs\u003c/em\u003e) of CIs (mg day\u003csup\u003e-1\u003c/sup\u003e) were calculated by multiplying total CI concentrations (mg L\u003csup\u003e-1\u003c/sup\u003e) in 24 h composite raw wastewater samples by daily wastewater flows (L day\u003csup\u003e-1\u003c/sup\u003e) using Eq. 2:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"71\" height=\"17\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Eq. 2\u003c/p\u003e\n\u003cp\u003ewhere: \u003cem\u003eC\u003c/em\u003e is the total concentration of \u003cem\u003eCIs\u0026nbsp;\u003c/em\u003e(mg L\u003csup\u003e-1\u003c/sup\u003e) in untreated influent wastewater, and \u003cem\u003eV\u003c/em\u003e is the volume of wastewater received by the WWTP per day (L day\u003csup\u003e-1\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003eStatistical analysis was undertaken using Origin(Pro), Version 2021 (OriginLab Corp., Northampton, MA, USA). The dataset contained 94 data points (day equivalents) per each CI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was designed to examine multivariate relationships between WBE\u003csub\u003eCI\u003c/sub\u003e and SED\u003csub\u003eONS\u003c/sub\u003e. We used descriptive statistics including Pearson/Spearman\u0026rsquo;s rank correlations to explore the characteristics of the variables. Normal distribution for all BCIs was evaluated using graphical methods (calculated using both skewness and kurtosis, Q-Q (quantile \u0026ndash; quantile tests) graphs and histograms) and Kolmogorov-Smirnov normality test to verify if the dataset was normally distributed and Pearson correlation could be applied. CIs that were not normally distributed were evaluated using a non-parametric Spearman rank correlation. All statistical tests were two-sided and a \u003cem\u003ep\u003c/em\u003e-value of \u0026lt;0.05 was considered statistically significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll correlations discussed below were statistically significant with \u003cem\u003ep\u003c/em\u003e-values, on average, \u0026lt;0.001. Pearson correlation, as a parametric measure, was considered appropriate for a normally distributed dataset, which applied to most CI markers. Cimetidine, caffeine, creatinine, lisinopril, sitagliptin, dihydrocodeine, ibuprofen and its metabolites, antibiotics, HNE-MA and some illicit drugs were not normally distributed, hence a non-parametric rank Spearman test was also used for these markers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrincipal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were also applied to explore similarities among samples and to assess associations between WBE\u003csub\u003eCI\u003c/sub\u003e and SED\u003csub\u003eONS\u003c/sub\u003e indicators. Mean WBE\u003csub\u003eCI\u003c/sub\u003e values (arithmetic mean was used after testing both arithmetic and geometric mean, the latter is known to be less affected by extreme values in a skewed distribution).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was supported under the C215.3 Wastewater Based Epidemiology Programme within the UK Government Accelerated Capability Environment (ACE), funded by the Environmental Monitoring for Health Protection (EMHP) programme, UK Health Security Agency. We thank Daphne Beniston and Andrew Singer (Accelerated Capability Environment, Homeland Security, UK) for their help organizing the project. We thank Public Health Wales for providing enterovirus primer and probe sequences and the staff at Anglian Water, Northumbrian Water, Southern Water, Thames Water, United Utilities, Yorkshire Water, and Welsh Water/Dŵr Cymru for their support with wastewater sampling. The support of EPSRC Impact Acceleration Account (EP/R51164X/1, ENTRUST IAA) and GCRF EWS-C19 (EP/V028499/1) is greatly appreciated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCredit authorship contribution statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBarbara Kasprzyk-Hordern: Conceptualisation, Methodology (experimental design, data processing), Formal analysis, Data curation, Writing-original draft, Writing \u0026ndash; review editing, Supervision, Project administration, Funding acquisition, Resources. Kishore Jagadeesan: Methodology (socioeconomic and demographic data) Natalie Sims: Methodology (sample preparation, LCMS analysis), Writing \u0026ndash; review editing. Davey Jones: Conceptualisation, Writing \u0026ndash; review editing. Matthew Wade: Conceptualisation, Writing \u0026ndash; review editing.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCastiglioni, S., I. Senta, A. Borsotti, E. Davoli and E. Zuccato (2014). \u0026quot;A novel approach for monitoring tobacco use in local communities by wastewater analysis.\u0026quot; \u003cu\u003eTob Control\u003c/u\u003e.\u003c/li\u003e\n\u003cli\u003eCeolotto, N., P. Dollamore, A. Hold, B. Balne, K. K. Jagadeesan, R. Standerwick, M. Robertson, R. Barden and B. Kasprzyk-Hordern (2024). \u0026quot;A new Wastewater-Based Epidemiology workflow to estimate community wide non-communicable disease prevalence using pharmaceutical proxy data.\u0026quot; \u003cu\u003eJ Hazard Mater\u003c/u\u003e \u003cstrong\u003e461\u003c/strong\u003e: 132645.\u003c/li\u003e\n\u003cli\u003eCeolotto, N., K. Jagadeesan, L. Xu, R. Standerwick, M. Robertson, R. Barden, J. 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The aim of this study was to explore the potential of triangulating wastewater chemical markers (WBECI) with population census data to unravel associations between disease prevalence, lifestyle choices and key risk factors associated with nicotine and caffeinated product use. This was critically evaluated in a multi-city (n = 10), national level WBE study in England. Associations of different wastewater markers revealed that smoking WBECIs (nicotine and cotinine) correlate with multi-disease WBECIs (pharmaceuticals used as proxies for disease: cardiovascular diseases (CVDs, pain, asthma, diabetes, infectious disease). Smoking WBECIs also showed strong positive associations with socioeconomic and demographic indicators (ONSSEDI) such as economic deprivation, poor health and low level of education. Triangulation of WBECI and ONSSEDI indicators revealed that cities with lower education outcomes, poor health and the highest deprivation index cluster with smoking as well as with CVD, cancer, diabetes, asthma, infectious disease and pain WBECIs. Conversely, the least deprived city with the highest level of education and good health clustered with caffeine and oxidative stress, which calls for further work to confirm any causal relationships between lifestyle choices, socioeconomic background and health outcomes. Further exploration of correlations reveals pain management and antibiotics usage, illicit drug use, as well as chronic, often lifestyle driven conditions such as CVDs, diabetes, asthma and cancer cluster closely with disability, deprivation, poor health and low level of education. This is especially notable in cities with high deprivation index and could serve as an early warning for city-focussed interventions.","manuscriptTitle":"Socioeconomic and health outcome associations with lifestyle choices uncovered by wastewater information mining","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 17:18:56","doi":"10.21203/rs.3.rs-7658391/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-water","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natwater","sideBox":"Learn more about [Nature Water](https://www.nature.com/natwater/)","snPcode":"44221","submissionUrl":"https://mts-natwater.nature.com/cgi-bin/main.plex","title":"Nature Water","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fb69643f-f9ec-45e9-bbd1-357e27edf8cd","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":55919015,"name":"Health sciences/Health care"},{"id":55919016,"name":"Earth and environmental sciences/Environmental sciences/Environmental chemistry/Environmental monitoring"}],"tags":[],"updatedAt":"2026-03-15T08:55:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-14 17:18:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7658391","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7658391","identity":"rs-7658391","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}