George Nicholson

Multiomic profiling provides a comprehensive physiological overview at the molecular level, but understanding of its spatiotemporal dynamics remains limited in human populations. We profiled longitudinal whole-blood gene expression and metabolite levels in 335 females over 8 years. Levels of 5061 genes and 181 metabolites changed over time, with individual trajectories often diverging from population-level trends. Longitudinally variable genes showed cell type specificity and enrichment for aging-relevant pathways, including cardiometabolic and neurodegenerative disorders. Longitudinal trajectories were further shaped by genetics, circadian rhythm, seasonality, and environmental pollutant exposures. Integrative analyses revealed extensive static and time-variable cross-omic connectivity. Longitudinal profiling offers insight into the temporal evolution of age-related conditions at the molecular level, and understanding individual variation within these longitudinal patterns will be essential for future precision medicine approaches.

In ecology and conservation, a growing number of studies seek to draw causal inference using quasi-experimental designs. Despite the risk of omitted variable bias from such designs, the degree to which results are sensitive to unobserved confounders is seldom assessed. Here, to demonstrate the value of such sensitivity analyses, we use the controversy surrounding whether projects aimed at reducing emissions from deforestation and forest degradation (REDD+) overestimated their effectiveness (resulting in too many credits being sold). Verifiers of REDD+ credits have argued that independent quasi-experimental analyses of REDD+ projects are flawed because they omit site-specific drivers of deforestation. If these drivers also affect where REDD+ projects are established (that is, projects target areas facing threat), omitting them will tend to underestimate the deforestation that projects avoided. We revisit a global sample of 44 REDD+ projects and show that while some projects reduced deforestation, over-crediting was rife. Crucially, we explore the sensitivity of these results to unobserved confounders and demonstrate that unobserved local drivers of both deforestation and REDD+ locations are unlikely to fully account for reported over-crediting. Assessing sensitivity to unobserved confounders remains uncommon in ecology and conservation but should become standard practice where causal conclusions are based on controlling for confounders.Published at: https://www.nature.com/articles/s41559-026-03049-7#MOESM1

In ecology and conservation, a growing number of studies seek to draw causal inference using quasi-experimental designs. Despite the risk of omitted variable bias from such designs, the degree to which results are sensitive to unobserved confounders is seldom assessed. Here, to demonstrate the value of such sensitivity analyses, we use the controversy surrounding whether projects aimed at reducing emissions from deforestation and forest degradation (REDD+) overestimated their effectiveness (resulting in too many credits being sold). Verifiers of REDD+ credits have argued that independent quasi-experimental analyses of REDD+ projects are flawed because they omit site-specific drivers of deforestation. If these drivers also affect where REDD+ projects are established (that is, projects target areas facing threat), omitting them will tend to underestimate the deforestation that projects avoided. We revisit a global sample of 44 REDD+ projects and show that while some projects reduced deforestation, over-crediting was rife. Crucially, we explore the sensitivity of these results to unobserved confounders and demonstrate that unobserved local drivers of both deforestation and REDD+ locations are unlikely to fully account for reported over-crediting. Assessing sensitivity to unobserved confounders remains uncommon in ecology and conservation but should become standard practice where causal conclusions are based on controlling for confounders.

Synthetic data offers a compelling path to scalable pretraining when real-world data is scarce, but models pretrained on synthetic data often fail to transfer reliably to deployment settings. We study this problem in full-body human motion, where large-scale data collection is infeasible but essential for wearable-based Human Activity Recognition (HAR), and where synthetic motion can be generated from motion-capture-derived representations. We pretrain motion time-series models using such synthetic data and evaluate their transfer across diverse downstream HAR tasks. Our results show that synthetic pretraining improves generalisation when mixed with real data or scaled sufficiently. We also demonstrate that large-scale motion-capture pretraining yields only marginal gains due to domain mismatch with wearable signals, clarifying key sim-to-real challenges and the limits and opportunities of synthetic motion data for transferable HAR representations.

Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 24.5 million primary-care health records in over 740,000 individuals in the UK Biobank, Million Veteran Program USA, and Estonian Biobank, to discover and validate the genetic architecture of adiposity trajectories. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI by 14%. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (APOE missense variant). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology of quantitative traits in adulthood.

The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech were collected in the ‘Speak up and help beat coronavirus’ digital survey alongside demographic, symptom and self-reported respiratory condition data. Digital survey submissions were linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,565 of 72,999 participants and 24,105 of 25,706 positive cases. Respiratory symptoms were reported by 45.6% of participants. This dataset has additional potential uses for bioacoustics research, with 11.3% participants self-reporting asthma, and 27.2% with linked influenza PCR test results.

Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 1.5 million primary-care health records in over 177,000 individuals in UK Biobank to study the genetic architecture of weight-change. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (a missense variant in APOE ). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI, and higher in women than in men. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology driving quantitative trait values in adulthood.