Chasing bugs: characterizing the spatiotemporal patterns of recreational lobster fishing in the Florida Keys - Gabrielle F. Renchen; Casey B. Butler; Emily Hutchinson; Thomas R. Matthews (Ocean & Coastal Management, 2026)

Bioeconomic modeling of on-growing Caribbean spiny lobster in Florida - Taryn Garlock; Frank Asche; Casey B. Butler; Sherry Larkin; Thomas R. Matthews; Erica Ross (Aquaculture, 2025)

Efficacy, non-target impacts, and costs of mechanical control options against a bioturbator in bivalve aquaculture - Jennifer L. Ruesink; C. Haleh Mawson; Brian Allen; Jeffrey Barrett; David Beugli; Steven Booth; Laura Butler; Bill Dewey; Cinde R. Donoghue; Brett R. Dumbauld; Kristine Feldman; Zachary Forster; et al. (Aquaculture, 2025)

Refining spiny lobster (Panulirus argus) escape and mortality estimates in Florida’s lobster trap fishery using long-term video - Casey B. Butler; Jack Butler; William C. Sharp; Thomas R. Matthews (Fisheries Research, 2024)

Reflex impairment as a predictor of mortality in Panulirus argus: Implications for the live export market - Erica P. Ross; Casey B. Butler; Carly M. Hannah; Thomas R. Matthews (Fisheries Research, 2024)

Price variation in the Caribbean spiny lobster fishery: Incentives for ongrowing wild-caught lobsters in Florida - Taryn Garlock; Frank Asche; Casey B. Butler; Thomas R. Matthews; Erica Ross (Fisheries Research, 2024)

Escape gaps in wire lobster traps reduce bycatch of coral reef fish while maintaining catch of harvestable lobsters in Florida’s Caribbean spiny lobster (Panulirus argus), fishery - Gabrielle F. Renchen; Casey B. Butler; Emily Hutchinson; Thomas R. Matthews (Fisheries Research, 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.