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The heat-related health burden is expected to persist and worsen in the coming years due to an aging global population and climate change. Defining the breadth and depth of our understanding of age-related changes in thermoregulation can identify underlying causes and strategies to protect vulnerable individuals from heat. We conducted the first systematic quantitative literature review to provide context to the historical experimental research of healthy older adults – compared to younger adults or unhealthy age matched cases – during exogenous heat strain, focusing on factors that influence thermoregulatory function (e.g. co-morbidities). We identified 4,455 articles, with 147 meeting eligibility criteria. Most studies were conducted in the US (39%), Canada (29%), or Japan (12%), with 71% of the 3,411 participants being male. About 71% of the studies compared younger and older adults, while 34% compared two groups of older adults with and without factors influencing thermoregulation. Key factors included age combined with another factor (23%), underlying biological mechanisms (18%), age independently (15%), influencing health conditions (15%), adaptation potential (12%), environmental conditions (9%), and therapeutic/pharmacological interventions (7%). Our results suggest that controlled experimental research should focus on the age-related changes in thermoregulation in the very old, females, those with overlooked chronic heat-sensitive health conditions (e.g. pulmonary, renal, mental disorders), the impact of multimorbidity, prolonged and cumulative effects of extreme heat, evidence-based policy of control measures (e.g. personal cooling strategies), pharmaceutical interactions, and interventions stimulating protective physiological adaptation. These controlled studies will inform the directions and use of limited resources in ecologically valid fieldwork studies. [ABSTRACT FROM AUTHOR]

Motivation High-throughput reporter assays dramatically improve our ability to assign function to noncoding genetic variants, by measuring allelic effects on gene expression in the controlled setting of a reporter gene. Unlike genetic association tests, such assays are not confounded by linkage disequilibrium when loci are independently assayed. These methods can thus improve the identification of causal disease mutations. While work continues on improving experimental aspects of these assays, less effort has gone into developing methods for assessing the statistical significance of assay results, particularly in the case of rare variants captured from patient DNA. Results We describe a Bayesian hierarchical model, called Bayesian Inference of Regulatory Differences, which integrates prior information and explicitly accounts for variability between experimental replicates. The model produces substantially more accurate predictions than existing methods when allele frequencies are low, which is of clear advantage in the search for disease-causing variants in DNA captured from patient cohorts. Using the model, we demonstrate a clear tradeoff between variant sequencing coverage and numbers of biological replicates, and we show that the use of additional biological replicates decreases variance in estimates of effect size, due to the properties of the Poisson-binomial distribution. We also provide a power and sample size calculator, which facilitates decision making in experimental design parameters. Availability and implementation The software is freely available from www.geneprediction.org/bird. The experimental design web tool can be accessed at http://67.159.92.22:8080 Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]