Prenatal antioxidant treatment suppresses maternal immune activation induced increases in alcohol self-administration in a sex-specific manner - Skylar E. Nicholson; Kelly A. Hewitt; Cara S. Brauen; Angela M. Henricks (Psychopharmacology, 2026)

Blowin’ in the wind: Onshore winds drive the occurrence of bluebottles (Physalia spp.) at east Australian beaches - Daniel E. Hewitt; Amandine Schaeffer; Jasmin C. Lawes; Sean Kelly; Alistair G.B. Poore; Moninya Roughan; Saima Ajaz; Timothy C. Ingleton; Kylie A. Pitt (Ocean & Coastal Management, 2026)

Active surveillance for influenza A virus in swine reveals within-farm reassortment and cocirculation of distinct subtypes and genetic clades - Megan N. Thomas; Garrett M. Janzen; Alexey Markin; Aditi Sharma; Kelly Hewitt; Ganwu Li; Amy L. Baker; Phillip C. Gauger; Tavis K. Anderson (Veterinary Microbiology, 2025)

Remotely Sensed Fire Heterogeneity and Biomass Recovery Predicts Empirical Biodiversity Responses - Rebecca K. Gibson; Don A. Driscoll; Kristina J. Macdonald; Grant J. Williamson; Rachael H. Nolan; Tim S. Doherty; Dale G. Nimmo; Euan G. Ritchie; Mark Tozer; Liz Tasker; Aaron Greenville; Adam Roff; et al. (Global Ecology and Biogeography, 2025)
Aim To compare field‐based evidence of plant and animal responses to fire with remotely sensed signals of fire heterogeneity and post‐fire biomass recovery. Location South‐eastern Australia; New South Wales. Time Period 2019–2022. Major Taxa Studied A total of 982 species of plants and animals, in eight taxonomic groups: amphibians, birds, fish, insects, mammals, molluscs, plants and reptiles. Methods We collated 545,223 plant and animal response records from 47 field surveys of 4613 sites that focussed on areas burnt in 2019–2020. For each site, we calculated remotely sensed signals of fire heterogeneity and post‐fire biomass recovery, including the delayed recovery index. Meta‐regression analyses were conducted separately for species that declined after fire (negative effect sizes) and species that increased after fire (positive effect sizes) for each buffer size (250 m, 500 m, 1 km, 1.5 km, 2 km and 2.5 km radius). Results We found that species exposed to homogenous high‐severity fire (i.e., low fire heterogeneity) were more likely to exhibit decreased abundance/occurrence or inhibited recovery. Areas with delayed recovery of biomass also had significant negative on‐ground responses, with lower abundance or occurrence in areas where biomass recovery was slower. Main Conclusions The fire heterogeneity index and the delayed recovery index are suitable for inclusion in monitoring and reporting systems for tracking relative measures over time, particularly when field survey data is not available at the landscape scales required to support reporting and management decisions. Locations with remotely sensed signals of delayed recovery should be prioritised for protection against further disturbances that may interfere with the recovery process. Research attention must next focus on how cumulative fire heterogeneity patterns of successive fires affect the post‐fire recovery dynamics to further inform the application of remote sensing indicators as management tools for biodiversity conservation.

Climatic, oceanic, freshwater, and local environmental drivers of New Zealand estuarine macroinvertebrates - Orlando Lam-Gordillo; Judi E. Hewitt; Emily J. Douglas; Bruce D. Dudley; Steven J. Holmes; Sarah Hailes; Kelly Carter; Barry Greenfield; Tarn Drylie; Andrew M. Lohrer (Marine Environmental 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.

Development of environmental effects monitoring protocol in Brazil: a fish guide study of three river estuaries - Priscylla da Mata Pavione; Karla Gonçalves da Costa; Clayton Perônico; Mark E. McMaster; Joanne L. Parrott; L. Mark Hewitt; Kelly R. Munkittrick; Francisco Candido Cardoso Barreto; Thiago Hollanda Basilo; Marcelo Paes Gomes; Ricardo Wagner Reis Filho; Tatiana Heid Furley (Environmental Monitoring and Assessment, 2019)

Understanding the Chronic Impacts of Oil Refinery Wastewater Requires Consideration of Sediment Contributions to Toxicity - Jennifer R. Loughery; Tim J. Arciszewski; Karen A. Kidd; Angella Mercer; L. Mark Hewitt; Deborah L. MacLatchy; Kelly R. Munkittrick (Archives of Environmental Contamination and Toxicology, 2014)