Arsenic is an environmental hazard and the reduction of drinking water arsenic levels is under consideration. People are exposed to arsenic not only through drinking water but also through arsenic-contaminated air and food. Here we report the health effects of arsenic exposure from burning high arsenic-containing coal in Guizhou, China. Coal in this region has undergone mineralization and thus produces high concentrations of arsenic. Coal is burned inside the home in open pits for daily cooking and crop drying, producing a high concentration of arsenic in indoor air. Arsenic in the air coats and permeates food being dried producing high concentrations in food; however, arsenic concentrations in the drinking water are in the normal range. The estimated sources of total arsenic exposure in this area are from arsenic-contaminated food (50-80%), air (10-20%), water (1-5%), and direct contact in coal-mining workers (1%). At least 3,000 patients with arsenic poisoning were found in the Southwest Prefecture of Guizhou, and approximately 200,000 people are at risk for such overexposures. Skin lesions are common, including keratosis of the hands and feet, pigmentation on the trunk, skin ulceration, and skin cancers. Toxicities to internal organs, including lung dysfunction, neuropathy, and nephrotoxicity, are clinically evident. The prevalence of hepatomegaly was 20%, and cirrhosis, ascites, and liver cancer are the most serious outcomes of arsenic poisoning. The Chinese government and international organizations are attempting to improve the house conditions and the coal source, and thereby protect human health in this area.

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ImportanceExisting estimates of the prevalence of vision impairment (VI) in the United States are based on self-reported survey data or measures of visual function that are at least 14 years old. Older adults are at high risk for VI and blindness. There is a need for up-to-date, objectively measured, national epidemiological estimates.ObjectiveTo present updated national epidemiological estimates of VI and blindness in older US adults based on objective visual function testing.Design, Setting, and ParticipantsThis survey study presents a secondary data analysis of the 2021 National Health and Aging Trends Study (NHATS), a population-based, nationally representative panel study of Medicare beneficiaries 65 years and older. NHATS includes community-dwelling older adults or their proxies who complete in-person interviews; annual follow-up interviews are conducted regardless of residential status. Round 11 NHATS data were collected from June to November 2021, and data were analyzed in August 2022.InterventionsIn 2021, NHATS incorporated tablet-based tests of distance and near visual acuity and contrast sensitivity with habitual correction.Main Outcomes and MeasuresNational prevalence of impairment in presenting distance visual acuity (>0.30 logMAR, Snellen equivalent worse than 20/40), presenting near visual acuity (>0.30 logMAR, Snellen equivalent worse than 20/40), and contrast sensitivity (>1 SD below the sample mean). Prevalence estimates stratified by age and socioeconomic and demographic data were calculated.ResultsIn the 2021 round 11 NHATS sample, there were 3817 respondents. After excluding respondents who did not complete the sample person interview (n = 429) and those with missing vision data (n = 362), there were 3026 participants. Of these, 29.5% (95% CI, 27.3%-31.8%) were 71 to 74 years old, and 55.2% (95% CI, 52.8%-57.6%) were female respondents. The prevalence of VI in US adults 71 years and older was 27.8% (95% CI, 25.5%-30.1%). Distance and near visual acuity and contrast sensitivity impairments were prevalent in 10.3% (95% CI, 8.9%-11.7%), 22.3% (95% CI, 20.3%-24.3%), and 10.0% (95% CI, 8.5%-11.4%), respectively. Older age, less education, and lower income were associated with all types of VI. A higher prevalence of near visual acuity and contrast sensitivity impairments was associated with non-White race and Hispanic ethnicity.Conclusions and RelevanceMore than 1 in 4 US adults 71 years and older had VI in 2021, higher than prior estimates. Differences in the prevalence of VI by socioeconomic and demographic factors were observed. These data could inform public health planning.

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A total of 2,402 cases of arsenic-related skin lesions (as of 2002) in a few villages of China's Southwest Guizhou Autonomous Prefecture represent a unique case of endemic arseniasis related with indoor combustion of high arsenic coal. A significant difference of skin lesion prevalence was observed between two clans of different ethnicities (Hmong and Han) in one of the hyperendemic villages in this prefecture. This study was focused on a possible involvement of GST T1 and M1 polymorphisms in risk modulation of skin lesions and in the body burden of As in this unique case of As exposure. GST T1 and M1 polymorphisms were genotyped by an allele-specific PCR-based procedure. Total As contents in hair and urine samples as well as environmental samples of the homes of the two ethnic clans were analyzed. No significant deviations in the population frequencies of GST T1 and M1 0/0 genotypes or their combination were recorded between diagnosed skin lesion patients and asymptomatic individuals in both clans. Significantly higher As contents in hair and urine were observed in GSTM1 0/0 carriers, not in GSTT1 0/0 carriers. After stratified by ethnicity and gender, a statistically significant association of the GSTM1 0/0 genotype and higher As content in hair was only confirmed in the subgroups of ethnic Han clan members and all male villagers, not in ethnic Hmong clan members or in females. GST T1 and M1 homozygous deletions were not associated with an increased susceptibility to skin lesions in long-term exposure to indoor combustion of high As coal. The polymorphic status at the locus of GSTM1 might modulate individual's body burden of total As in some Chinese ethnic groups.

In many real-world applications, different types of misclassification usually suffer from different costs, but the accurate cost is often hard to be determined and usually one can only get an interval-estimation like that one type of mistake is about five to ten times more serious than the other type. On the other hand, there are usually abundant unlabeled data available, leading to great research effort about semi-supervised learning. It is noticeable that cost interval and unlabeled data usually appear simultaneously in practice tasks; however, there is rare study tackling them together. In this paper, we propose the cisLDM approach which is able to handle cost interval and exploit unlabeled data in a principled way. Rather than maximizing the minimum margin like traditional large margin classifiers, cisLDM tries to optimize the margin distribution on both labeled and unlabeled data when minimizing the worst-case totalcost and the mean total-cost simultaneously according to the cost interval. Experiments on a broad range of datasets and cost settings exhibit the impressive performance of cisLDM. In particular, cisLDM is able to reduce 47% more total-cost than standard SVM and 27% more total-cost than cost-sensitive semi-supervised SVM which assumes the true cost value is known in advance.

To maximize cumulative user engagement (e.g. cumulative clicks) in sequential recommendation, it is o en needed to tradeo two potentially con icting objectives, that is, pursuing higher immediate user engagement (e.g., click-through rate) and encouraging user browsing (i.e., more items exposured). Existing works o en study these two tasks separately, thus tend to result in sub-optimal results. In this paper, we study this problem from an online optimization perspective, and propose a exible and practical framework to explicitly tradeo longer user browsing length and high immediate user engagement. Speci cally, by considering items as actions, user's requests as states and user leaving as an absorbing state, we formulate each user's behavior as a personalized Markov decision process (MDP), and the problem of maximizing cumulative user engagement is reduced to a stochastic shortest path (SSP) problem. Meanwhile, with immediate user engagement and quit probability estimation, it is shown that the SSP problem can be e ciently solved via dynamic programming. Experiments on real-world datasets demonstrate the e ectiveness of the proposed approach. Moreover, this approach is deployed at a large E-commerce platform, achieved over 7% improvement of cumulative clicks.