Exploring the complex relationships between air pollution, life satisfaction and mental health through the lens of daily activity-travel patterns: The case of a suburban residential community in Beijing - Wenbo Guo; Tim Schwanen; Christian Brand; Yanwei Chai (Journal of Transport Geography, 2025)

Nutrient pathways and their susceptibility to past and future change in the Eurasian Arctic Ocean - Robyn E. Tuerena; Claire Mahaffey; Sian F. Henley; Camille de la Vega; Louisa Norman; Tim Brand; Tina Sanders; Margot Debyser; Kirstin Dähnke; Judith Braun; Christian März (Ambio, 2022)
Climate change is altering nutrient cycling within the Arctic Ocean, having knock-on effects to Arctic ecosystems. Primary production in the Arctic is principally nitrogen-limited, particularly in the western Pacific-dominated regions where denitrification exacerbates nitrogen loss. The nutrient status of the eastern Eurasian Arctic remains under debate. In the Barents Sea, primary production has increased by 88% since 1998. To support this rapid increase in productivity, either the standing stock of nutrients has been depleted, or the external nutrient supply has increased. Atlantic water inflow, enhanced mixing, benthic nitrogen cycling, and land–ocean interaction have the potential to alter the nutrient supply through addition, dilution or removal. Here we use new datasets from the Changing Arctic Ocean program alongside historical datasets to assess how nitrate and phosphate concentrations may be changing in response to these processes. We highlight how nutrient dynamics may continue to change, why this is important for regional and international policy-making and suggest relevant research priorities for the future.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Rare Earth Element Distribution in the NE Atlantic: Evidence for Benthic Sources, Longevity of the Seawater Signal, and Biogeochemical Cycling - Kirsty C. Crocket; Emily Hill; Richard E. Abell; Clare Johnson; Stefan F. Gary; Tim Brand; Ed C. Hathorne (Frontiers in Marine Science, 2018)