Artificial intelligence transformations in geotechnics: progress, challenges and future enablers - Brian Sheil; Christos Anagnostopoulos; Róisín Buckley; Matteo Oryem Ciantia; Eky Febrianto; Jinlong Fu; Zhiwei Gao; Xueyu Geng; Bin Gong; Kevin Hanley; Pengpeng He; Kostas Kolomvatsos; et al. (Computers and Geotechnics, 2026)

Mind the gap - The need to integrate novel plankton methods alongside ongoing long-term monitoring - Matthew M. Holland; Luis Felipe Artigas; Angus Atkinson; Mike Best; Eileen Bresnan; Michelle Devlin; Dafne Eerkes-Medrano; Marie Johansen; David G. Johns; Margarita Machairopoulou; Sophie Pitois; James Scott; et al. (Ocean & Coastal Management, 2025)

B-SDM: A bounding surface stiffness degradation method for modelling the long-term ratcheting response of offshore wind turbine foundations - Zhiwei Gao; Luoyi Yan; Scott Whyte (Computers and Geotechnics, 2023)

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.

Ensuring climate services serve society: examining tribes’ collaborations with climate scientists using a capability approach - Scott E. Kalafatis; Kyle Powys Whyte; Julie C. Libarkin; Chris Caldwell (Climatic Change, 2019)

High Degree of Genetic Heterogeneity for Hereditary Cerebellar Ataxias in Australia - Ce Kang; Christina Liang; Kate E. Ahmad; Yufan Gu; Sue-Faye Siow; James G. Colebatch; Scott Whyte; Karl Ng; Philip D. Cremer; Alastair J. Corbett; Ryan L. Davis; Tony Roscioli; et al. (The Cerebellum, 2019)

Combined carbohydrate–protein supplementation improves competitive endurance exercise performance in the heat - Andrew J. Cathcart; Scott R. Murgatroyd; Alison McNab; Laura J. Whyte; Chris Easton (European Journal of Applied Physiology, 2011)