Association of low-level inorganic arsenic exposure from rice with age-standardized mortality risk of cardiovascular disease (CVD) in England and Wales

Adverse health outcomes, including death from cardiovascular disease (CVD), arising from chronic exposure to inorganic arsenic (iAs) are well documented. Consumption of rice is a major iAs exposure route for over 3 billion people, however, there is still a lack of epidemiological evidence demonstrating the association between iAs exposure from rice intake and CVD risks. We explored this potential association through an ecological study using data at local authority level across England and Wales. Local authority level daily per capita iAs exposure from rice (E-iAsing,rice) was estimated using ethnicity as a proxy for class of rice consumption. A series of linear and non-linear models were applied to estimate the association between E-iAsing,rice and CVD age-standardized mortality rate (ASMR), using Akaike's Information Criterion as the principle model selection criterion. When adjusted for significant confounders, notably smoking prevalence, education level, employment rate, overweight percentage, PM2.5, female percentage and medical and care establishments, the preferred non-linear model indicated that CVD risks increased with iAs exposure from rice at exposures above 0.3 μg/person/day. Also, the best-fitted linear model indicated that CVD ASMR in the highest quartile of iAs exposure (0.375–2.71 μg/person/day) was 1.06 (1.02, 1.11; p-trend <0.001) times higher than that in the lowest quartile (<0.265 μg/person/day). Notwithstanding the well-known limitations of ecological studies, this study further suggests exposure to iAs, including from rice intake, as a potentially important confounder for studies of the factors controlling CVD risks

A semi-parametric mixed models for longitudinally measured fasting blood sugar level of adult diabetic patients

Abstract Background At the diabetic clinic of Jimma University Specialized Hospital, health professionals provide regular follow-up to help people with diabetes live long and relatively healthy lives. Based on patient condition, they also provide interventions in the form of counselling to promote a healthy diet and physical activity and prescribing medicines. The main purpose of this study is to estimate the rate of change of fasting blood sugar (FBS) profile experienced by patients over time. The change may help to assess the effectiveness of interventions taken by the clinic to regulate FBS level, where rates of change close to zero over time may indicate the interventions are good regulating the level. Methods In the analysis of longitudinal data, the mean profile is often estimated by parametric linear mixed effects model. However, the individual and mean profile plots of FBS level for diabetic patients are nonlinear and imposing parametric models may be too restrictive and yield unsatisfactory results. We propose a semi-parametric mixed model, in particular using spline smoothing to efficiently analyze a longitudinal measured fasting blood sugar level of adult diabetic patients accounting for correlation between observations through random effects. Results The semi-parametric mixed models had better fit than the linear mixed models for various variance structures of subject-specific random effects. The study revealed that the rate of change in FBS level in diabetic patients, due to the clinic interventions, does not continue as a steady pace but changes with time and weight of patients. Conclusions The proposed method can help a physician in clinical monitoring of diabetic patients and to assess the effect of intervention packages, such as healthy diet, physical activity and prescribed medicines, because individualized curve may be obtained to follow patient-specific FBS level trends

Dataset for Automated Fact Checking in Czech Language

Naše práce prozkoumává existující datové sady pro úlohu automatického faktického ověřování textového tvrzení a navrhuje dvě metody jejich získávání v Českém jazyce. Nejprve předkládá rozsáhlý dataset FEVER CS se 127K anotovaných tvrzení pomocí strojového překladu datové sady v angličtině. Poté navrhuje sadu anotačních experimentů pro sběr nativního českého datasetu nad znalostní bází archivu ČTK a provádí ji se skupinou 163 studentů FSV UK, se ziskem 3,295 křížově anotovaných tvrzení s čtyřcestnou Fleissovou Kappa-shodou 0.63. Dále demonstruje vhodnost datové sady pro trénování modelů pro klasifikaci inference v přirozeném jazyce natrénováním modelu XLM-RoBERTa dosahujícího 85.5% mikro-F1 přesnosti v úloze klasifikace pravdivosti tvrzení z textového kontextu.Our work examines the existing datasets for the task of automated fact-verification of textual claims and proposes two methods of their acquisition in the low-resource Czech language. It first delivers a large-scale FEVER CS dataset of 127K annotated claims by applying the Machine Translation methods to a dataset available in English. It then designs a set of human-annotation experiments for collecting a novel dataset in Czech, using the ČTK Archive corpus for a knowledge base, and conducts them with a group of 163 students of FSS CUNI, yielding a dataset of 3,295 cross-annotated claims with a 4-way Fleiss' Kappa-agreement of 0.63. It then proceeds to show the eligibility of the dataset for training the Czech Natural Language Inference models, training an XLM-RoBERTa model scoring 85.5% micro-F1 in the task of classifying the claim veracity given textual evidence

Micro-macro multilevel analysis of day-to-day lifestyle and carbon emissions in UK multiple occupancy households

Far-reaching changes in daily life present a pressing need to balance energy consumption with environmental impact. Previous research on household carbon emissions generally described its contributors in disposable income, consumption pattern, and household-related lifestyle, whereas they have not fully explored how carbon emissions relate to residents' day-to-day lifestyles. Given that individual lifestyles within a household may be correlated, there is a need to disentangle the clustering effect of household members' lifestyles and their association with household carbon emissions. This study used micro-macro multilevel modelling to examine the structure of individual lifestyles and their impact on household carbon emissions for 8618 multiple occupancy households of 19,816 respondents in the UK Household Longitudinal Study dataset. The results showed that a factor capturing energy-saving lifestyle behaviours significantly reduced housing fuel use emissions and a second capturing transportation and consumption choices cut motor emissions. Interestingly, the contribution of energy-saving lifestyle in cutting down housing-fuel-using emissions becomes more pronounced when household income and household characteristics (e.g., household size, dwelling, house ownership, number of cars, urbanity, employment) were controlled for. Contrarily, the strength of green transportation and consumption lifestyle contributing to lower motor emissions was weakened after controlling for household characteristics. Findings indicated that day-to-day lifestyle not only reflects individual variability in sustainable living but also systematic household variation in carbon emissions. Knowledge of which living patterns are responsible for disproportionately high levels of carbon emissions can enhance effective targeted policy aimed at stimulating sustainable lifestyles and carbon reduction

The aspects of sex, age and nationality in winter swimming performance

OBJECTIVE Winter swimming is a new sport discipline. Very little is known, however, about the sex differences, origin, participation and performance of the world's best winter swimmers. Therefore, the study aimed to investigate sex differences in performance and age. Furthermore, it should be determined which country has the fastest swimmers, the highest numbers of participants and the most successful age group athletes in winter swimming. SUBJECTS AND METHODS A total of 6,477 results from the 25 m events of the IWSA (International Winter Swimming Association) World Cups from 2016-2020 was collected from the official website of IWSA. Data were analyzed using a generalized linear model (GLM) with a gamma probability distribution and identity link function. The 25 m events were carried out in head-up breaststroke style, freestyle and butterfly. The nationalities were grouped into six groups, the five nationalities with the highest number of participants in the 25 m competitions and one group with the other nationalities. The mean time of 25 m races by sex and country of the total sample was compared. For the top 10 comparisons, the best ten athletes from the six groups were selected. The mean time of each top 10 groups was compared by sex and nationality. RESULTS Men were faster than women for all categories. Swimmers in age group 15-29 years were the fastest, where females were the fastest in age group 15-19 years and males in age group 20-29 years. Women from both Russia and Estonia and men from both Russia and China were the fastest. Both Russian and Chinese males were the fastest in all water categories in the top 10 section in the 25 m events. CONCLUSIONS In summary, males were faster than females in the IWSA World Cups between 2016 and 2020. The age group of 15-29 years old athletes was the most successful while females had their age of peak performance earlier than males. Russian and Estonian males and Russian females were the overall fastest in the 25 m events in all water categories. Future studies should investigate the optimal anthropometric characteristics of male and female winter swimming sprint athletes and whether there are distinct areas in Russia, Estonia and China, where many international winter swimming athletes originate

Football and Stock Market Performance Correlation: Evidence from Italy

The increasing growth of soccer economy is delivering new challenges for prospective investors in terms of stock price volatility. Such challenges are rooted in behavioral finance and efficient market hypotheses. Given this, the aim of our paper is to test the link between sport performance and correspondent stock price for the Italian listed football clubs (Juventus, Lazio, AS Roma). Our results suggest that soccer wins are likely to have a positive impact over stock price. This impact is more pronounced for local stocks and thus the findings have policy implications for emotional investors.JEL Codes - G12; G32; M1

Am J Ind Med

Background:Toluene Diisocyanate (TDI) is a known respiratory sensitizer linked to occupational asthma (OA). To better manage worker risks, an appropriate characterization of the TDI-OA dose-risk relationship is needed.Methods:The literature was reviewed for data suitable for dose-response modeling. Previous study data were fit to models to derive prospective occupational exposure limits (OELs), using benchmark dose (BMD) and low-dose extrapolation approaches.Results:Data on eight TDI-exposed populations were suitable for analysis. There were 118 OA cases in a population contributing 13,590 person-years. The BMD-based OEL was 0.4 ppb. The OEL based on low-dose extrapolation to working lifetime extra risk of 1/1000 was 0.3 ppb.Conclusions:This study synthesized epidemiologic data to characterize the TDI-OA dose-risk relationship. This approach yielded prospective OEL estimates below recent recommendations by the American Conference of Governmental Industrial Hygienists, but given significant study limitations, this should be interpreted with caution. Confirmatory research is needed.CC999999/Intramural CDC HHS/United States2019-04-01T00:00:00Z29389014PMC60926316076vault:3069

Satisfaction with social connectedness as a predictor for positive and negative symptoms of psychosis:A PHAMOUS study

PURPOSE: This study examines satisfaction with social connectedness (SSC) as predictor of positive and negative symptoms in people with a psychotic disorder. METHODS: Data from the Pharmacotherapy Monitoring and Outcome Survey (PHAMOUS) was used from patients assessed between 2014 and 2019, diagnosed with a psychotic disorder (N = 2109). Items about social connectedness of the Manchester short assessment of Quality of Life (ManSA) were used to measure SSC. Linear mixed models were used to estimate the association of SSC with the Positive and Negative Syndrome Scale (PANSS) after one and two years against α = 0.01. Analyses were adjusted for symptoms, time since onset, gender and age. Additionally, fluctuation of positive and negative symptom scores over time was estimated. RESULTS: The mean duration of illness of the sample was 18.8 years (SD 10.7) with >65% showing only small variation in positive and negative symptoms over a two to five-year time period. After adjustment for covariates, SSC showed to be negatively associated with positive symptoms after one year (β = -0.47, p < 0.001, 95% CI = -0.70, -025) and two years (β = -0.59, p < 0.001, 95% CI = -0.88, -0.30), and for negative symptoms after one year (β = -0.52, p < 0.001, 95% CI = -0.77, -0.27). The prediction of negative symptoms was not significant at two years. CONCLUSION: This research indicates that interventions on SSC might positively impact mental health for people with psychosis. SSC is a small and robust predictor of future levels of positive symptoms. Negative symptoms could be predicted by SSC at one year