Exome sequencing of UK birth cohorts.

Birth cohort studies involve repeated surveys of large numbers of individuals from birth and throughout their lives. They collect information useful for a wide range of life course research domains, and biological samples which can be used to derive data from an increasing collection of omic technologies. This rich source of longitudinal data, when combined with genomic data, offers the scientific community valuable insights ranging from population genetics to applications across the social sciences.

Predicting adaptive immune receptor specificities by machine learning is a data generation problem.

Determining the specificity of adaptive immune receptors-B cell receptors (BCRs), their secreted form antibodies, and T cell receptors (TCRs)-is critical for understanding immune responses and advancing immunotherapy and drug discovery. Immune receptors exhibit extensive diversity in their variable domains, enabling them to interact with a plethora of antigens. Despite the significant progress made by AI tools such as AlphaFold in predicting protein structures, challenges remain in accurately modeling the structure and specificity of immune receptors, primarily due to the limited availability of high-quality crystal structures and the complexity of immune receptor-antigen interactions.

The design and evaluation of a bone health teaching module for secondary school students in NSW, Australia.

Issue Addressed: The growing prevalence of osteoporosis requires preventative management starting from an early age as peak bone mass is typically reached by age 30. However, current Australian adolescents are not adequately addressing key osteoprotective factors. Alarmingly, around 17% have insufficient vitamin D levels, 55% consume insufficient dietary calcium, and 79% are insufficiently active.