Marine data assimilation in the UK: the past, the present, and the vision for the future - Jozef Skákala; David Ford; Keith Haines; Amos Lawless; Matthew J. Martin; Philip Browne; Marcin Chrust; Stefano Ciavatta; Alison Fowler; Daniel Lea; Matthew Palmer; Andrea Rochner; et al. (Ocean Science, 2025)
In the last 2 decades, UK research institutes have led a wide range of developments in marine data assimilation (MDA), covering areas from operational applications in physics and biogeochemistry to fundamental theory. We highlight the emergence of strong collaboration in the UK MDA community over this period and the increasing unification of its tools. We focus on identifying the MDA stakeholder community and current/future areas of impact, as well as current trends and future opportunities. This includes the rapid growth of machine learning (ML)/artificial intelligence (AI) and digital-twin applications. We articulate a vision for the future, including the need for future types of observational data (whether planned missions or hypothetical) and how the community should respond to increases in computational power and new computer architectures (e.g. exascale computing). We contrast the requirements of different MDA areas, including physics, biogeochemistry, and coupled data assimilation (DA). Although the specifics of the vision depend on each area, common themes emerge. We advocate for balanced redistribution of new computational capability among increased model resolution, model complexity, more sophisticated DA algorithms, and uncertainty representation (e.g. ensembles). We also advocate for integrated approaches, such as strongly coupled DA (ocean–atmosphere, physics–biogeochemistry, and ocean–sea ice) and the use of ML/AI components (e.g. for multivariate increment balancing, bias correction, model emulation, observation re-gridding, or fusion).

How uncertain and observable are marine ecosystem indicators in shelf seas? - Jozef Skákala; David Ford; Alison Fowler; Dan Lea; Matthew J. Martin; Stefano Ciavatta (Progress in Oceanography, 2024)

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.

Assessing Chemical Mechanisms Underlying the Effects of Sunflower Pollen on a Gut Pathogen in Bumble Bees - Lynn S. Adler; Alison E. Fowler; Rosemary L. Malfi; Patrick R. Anderson; Lily M. Coppinger; Pheobe M. Deneen; Stephanie Lopez; Rebecca E. Irwin; Iain W. Farrell; Philip C. Stevenson (Journal of Chemical Ecology, 2020)

An Evaluation of Boundary-Layer Depth, Inversion and Entrainment Parameters by Large-Eddy Simulation - Ian M. Brooks; Alison M. Fowler (Boundary-Layer Meteorology, 2012)