Associations between per- and polyfluoroalkyl substances (PFAS) and female sexual function in a preconception cohort - Samantha Schildroth; Julia C. Bond; Amelia K. Wesselink; Jasmine Abrams; Antonia M. Calafat; Julianne Cook Botelho; Katharine O. White; Ganesa Wegienka; Elizabeth E. Hatch; Lauren A. Wise (Environmental Research, 2025)

The Toxicology Investigators Consortium 2023 Annual Report - Adrienne Hughes; Alexandra Amaducci; Sharan L. Campleman; Shao Li; Mari Costantini; Meghan B. Spyres; Hannah Spungen; Jessica Kent; Alyssa Falise; Rachel Culbreth; Paul M. Wax; Jeffery Brent; et al. (Journal of Medical Toxicology, 2024)

Deep learning of estuary salinity dynamics is physically accurate at a fraction of hydrodynamic model computational cost - Galen Gorski; Salme Cook; Amelia Snyder; Alison P. Appling; Theodore Thompson; Jared D. Smith; John C. Warner; Simon N. Topp (Limnology and Oceanography, 2024)
Salinity dynamics in the Delaware Bay estuary are a critical water quality concern as elevated salinity can damage infrastructure and threaten drinking water supplies. Current state‐of‐the‐art modeling approaches use hydrodynamic models, which can produce accurate results but are limited by significant computational costs. We developed a machine learning (ML) model to predict the 250 mg L −1 Cl − isochlor, also known as the “salt front,” using daily river discharge, meteorological drivers, and tidal water level data. We use the ML model to predict the location of the salt front, measured in river miles (RM) along the Delaware River, during the period 2001–2020, and we compare predictions of the ML model to the hydrodynamic Coupled Ocean–Atmosphere‐Wave‐Sediment Transport (COAWST) model. The ML model predicts the location of the salt front with greater accuracy (root mean squared error [RMSE] = 2.52 RM) than the COAWST model does (RMSE = 5.36); however, the ML model struggles to predict extreme events. Furthermore, we use functional performance and expected gradients, tools from information theory and explainable artificial intelligence, to show that the ML model learns physically realistic relationships between the salt front location and drivers (particularly discharge and tidal water level). These results demonstrate how an ML modeling approach can provide predictive and functional accuracy at a significantly reduced computational cost compared to process‐based models. In addition, these results provide support for using ML models in operational forecasting, scenario testing, management decisions, hindcasting, and resulting opportunities to understand past behavior and develop hypotheses.

Effect of biosecurity practices and diseases on growth and carrageenan properties of Kappaphycus alvarezii and Eucheuma denticulatum cultivated in Zanzibar, Tanzania - Msafiri A. Ndawala; Flower E. Msuya; Jennefe P. Cabarubias; Cicilia S. B. Kambey; Amelia S. Buriyo; Esther F. Mvungi; Elizabeth J. Cottier-Cook (Journal of Applied Phycology, 2022)

Biosecurity policy and legislation of the seaweed aquaculture industry in Tanzania - Sadock B. Rusekwa; Iona Campbell; Flower E. Msuya; Amelia S. Buriyo; Elizabeth J. Cottier-Cook (Journal of Applied Phycology, 2020)
Pest and disease outbreaks have significant impacts on the livelihoods of seaweed farmers each year, particularly in low- to middle-income countries around the world. Commercial seaweed farming of the red carrageenophytes, Eucheuma denticulatum , Kappaphycus alvarezii and Kappaphycus striatus , in Tanzania was established in 1989. The impacts of pests and diseases on the local seaweed industry had serious implications for the environment, society, local culture and human health. The industry was initially characterised by growth, but since 2002 has been severely limited due to pest and disease outbreaks, exacerbated by the effects of climate change. This paper identifies existing biosecurity frameworks in Tanzanian legislation and policies in order to assess their content regarding the management of pests and diseases in the seaweed industry. A total of thirteen frameworks were identified and analysed for their general biosecurity scope and inclusion of specific risks. Of the thirteen, only four were legally binding, and only four were identified to be applicable to the seaweed industry. National frameworks were predominantly designed to support finfish aquaculture; therefore, national authorities lack the expertise, experience and scientific basis on biosecurity issues in seaweed industry compared with other aquatic commercial commodities to identify and mitigate the risks from pest and disease outbreaks in the seaweed farming industry. Recommendations are provided to assist regulators in establishing biosecurity policies, which either explicitly include or are specifically designed, to support the Tanzanian seaweed industry. This analysis reveals where biosecurity capacity can be developed to support a sustainable and economically productive national seaweed farming industry.

Toward the Integrated Marine Debris Observing System - Nikolai Maximenko; Paolo Corradi; Kara Lavender Law; Erik Van Sebille; Shungudzemwoyo P. Garaba; Richard Stephen Lampitt; Francois Galgani; Victor Martinez-Vicente; Lonneke Goddijn-Murphy; Joana Mira Veiga; Richard C. Thompson; Christophe Maes; et al. (Frontiers in Marine Science, 2019)

Mesenchymal Stem Cell-Derived Factors Restore Function to Human Frataxin-Deficient Cells - Kevin Kemp; Rimi Dey; Amelia Cook; Neil Scolding; Alastair Wilkins (The Cerebellum, 2017)