Incidence of oral cancer in relation to nickel and arsenic concentrations in farm soils of patients' residential areas in Taiwan

<p>Abstract</p> <p>Background</p> <p>To explore if exposures to specific heavy metals in the environment is a new risk factor of oral cancer, one of the fastest growing malignancies in Taiwan, in addition to the two established risk factors, cigarette smoking and betel quid chewing.</p> <p>Methods</p> <p>This is an observational study utilized the age-standardized incidence rates of oral cancer in the 316 townships and precincts of Taiwan, local prevalence rates of cigarette smoking and betel quid chewing, demographic factors, socio-economic conditions, and concentrations in farm soils of the eight kinds of heavy metal. Spatial regression and GIS (Geographic Information System) were used. The registration contained 22,083 patients, who were diagnosed with oral cancer between 1982 and 2002. The concentrations of metal in the soils were retrieved from a nation-wide survey in the 1980s.</p> <p>Results</p> <p>The incidence rate of oral cancer is geographically related to the concentrations of arsenic and nickel in the patients' residential areas, with the prevalence of cigarette smoking and betel quid chewing as controlled variables.</p> <p>Conclusions</p> <p>Beside the two established risk factors, cigarette smoking and betel quid chewing, arsenic and nickel in farm soils may be new risk factors for oral cancer. These two kinds of metal may involve in the development of oral cancer. Further studies are required to understand the pathways via which metal in the farm soils exerts its effects on human health.</p

Awareness in Practice: Tensions in Access to Sensitive Attribute Data for Antidiscrimination

Organizations cannot address demographic disparities that they cannot see. Recent research on machine learning and fairness has emphasized that awareness of sensitive attributes, such as race and sex, is critical to the development of interventions. However, on the ground, the existence of these data cannot be taken for granted. This paper uses the domains of employment, credit, and healthcare in the United States to surface conditions that have shaped the availability of sensitive attribute data. For each domain, we describe how and when private companies collect or infer sensitive attribute data for antidiscrimination purposes. An inconsistent story emerges: Some companies are required by law to collect sensitive attribute data, while others are prohibited from doing so. Still others, in the absence of legal mandates, have determined that collection and imputation of these data are appropriate to address disparities. This story has important implications for fairness research and its future applications. If companies that mediate access to life opportunities are unable or hesitant to collect or infer sensitive attribute data, then proposed techniques to detect and mitigate bias in machine learning models might never be implemented outside the lab. We conclude that today's legal requirements and corporate practices, while highly inconsistent across domains, offer lessons for how to approach the collection and inference of sensitive data in appropriate circumstances. We urge stakeholders, including machine learning practitioners, to actively help chart a path forward that takes both policy goals and technical needs into account

European Communities Studies, 1970-1992, cumulative file

Regular monitoring of social and political attitudes of residents of the member nations of the European Community are cumulated in one file. Satisfaction with democracy / overall life satisfaction / desired social change / first and second most important national political goal / European unification / support of the common market / life expectations / danger of war, nuclear energy, income inequality, terrorism, government management, military defence, environmental pollution / materialist/post-materialist values index / cognitive mobilization index. Background variables: basic characteristics/ residence/ housing situation/ household characteristics/ occupation/employment/ education/ social class/ politics/ religion/ organizational membershi

Additional file 2 of Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Additional file 2: Table S2. Association results for the multi-ancestry index SNPs with the gene prioritization