A Survey of Bayesian Statistical Approaches for Big Data

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a review of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data

C. PRESL) at the transcriptional level.

This paper investigates differences in gene expression among the two Thlaspi caerulescens ecotypes La Calamine (LC) and Lellingen (LE) that have been shown to differ in metal tolerance and metal uptake. LC originates from a metalliferous soil and tolerates higher metal concentrations than LE which originates from a non-metalliferous soil. The two ecotypes were treated with different levels of zinc in solution culture, and differences in gene expression were assessed through application of a cDNA microarray consisting of 1,700 root and 2,700 shoot cDNAs. Hybridisation of root and shoot cDNA from the two ecotypes revealed a total of 257 differentially expressed genes. The regulation of selected genes was verified by quantitative reverse transcriptase polymerase chain reaction. Comparison of the expression profiles of the two ecotypes suggests that LC has a higher capacity to cope with reactive oxygen species and to avoid the formation of peroxynitrite. Furthermore, increased transcripts for the genes encoding for water channel proteins could explain the higher Zn tolerance of LC compared to LE. The higher Zn tolerance of LC was reflected by a lower expression of the genes involved in disease and defence mechanisms. The results of this study provide a valuable set of data that may help to improve our understanding of the mechanisms employed by plants to tolerate toxic concentrations of metal in the soil

General and abdominal adiposity and hypertension in eight world regions: a pooled analysis of 837 population-based studies with 7·5 million participants

Background Adiposity can be measured using BMI (which is based on weight and height) as well as indices of abdominal adiposity. We examined the association between BMI and waist-to-height ratio (WHtR) within and across populations of different world regions and quantified how well these two metrics discriminate between people with and without hypertension. Methods We used data from studies carried out from 1990 to 2023 on BMI, WHtR and hypertension in people aged 20–64 years in representative samples of the general population in eight world regions. We graphically compared the regional distributions of BMI and WHtR, and calculated Pearson’s correlation coefficients between BMI and WHtR within each region. We used mixed-effects linear regression to estimate the extent to which WHtR varies across regions at the same BMI. We graphically examined the prevalence of hypertension and the distribution of people who have hypertension both in relation to BMI and WHtR, and we assessed how closely BMI and WHtR discriminate between participants with and without hypertension using C-statistic and net reclassification improvement (NRI). Findings The correlation between BMI and WHtR ranged from 0·76 to 0·89 within different regions. After adjusting for age and BMI, mean WHtR was highest in south Asia for both sexes, followed by Latin America and the Caribbean and the region of central Asia, Middle East and north Africa. Mean WHtR was lowest in central and eastern Europe for both sexes, in the high-income western region for women, and in Oceania for men. Conversely, to achieve an equivalent WHtR, the BMI of the population of south Asia would need to be, on average, 2·79 kg/m² (95% CI 2·31–3·28) lower for women and 1·28 kg/m² (1·02–1·54) lower for men than in the high-income western region. In every region, hypertension prevalence increased with both BMI and WHtR. Models with either of these two adiposity metrics had virtually identical C-statistics and NRIs for every region and sex, with C-statistics ranging from 0·72 to 0·81 and NRIs ranging from 0·34 to 0·57 in different region and sex combinations. When both BMI and WHtR were used, performance improved only slightly compared with using either adiposity measure alone. Interpretation BMI can distinguish young and middle-aged adults with higher versus lower amounts of abdominal adiposity with moderate-to-high accuracy, and both BMI and WHtR distinguish people with or without hypertension. However, at the same BMI level, people in south Asia, Latin America and the Caribbean, and the region of central Asia, Middle East and north Africa, have higher WHtR than in the other regions