G
Gerald T. Ankley
Researcher at United States Environmental Protection Agency
Publications - 374
Citations - 27860
Gerald T. Ankley is an academic researcher from United States Environmental Protection Agency. The author has contributed to research in topics: Vitellogenin & Pimephales promelas. The author has an hindex of 82, co-authored 356 publications receiving 25514 citations. Previous affiliations of Gerald T. Ankley include Michigan State University & Pacific Northwest National Laboratory.
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Risk‐Based Prioritization of Organic Chemicals and Locations of Ecological Concern in Sediment From Great Lakes Tributaries
Austin K. Baldwin,Steven R. Corsi,Owen M Stefaniak,Luke C. Loken,Daniel L. Villeneuve,Gerald T. Ankley,Brett R. Blackwell,Peter L. Lenaker,Michelle A. Nott,Marc A. Mills +9 more
TL;DR: In this paper , the authors examined organic chemical occurrence, individually and as mixtures, in the context of potential biological effects and provided a screening-level prioritization of chemicals with the greatest potential for adverse biological effects.
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Uptake and metabolism of all-trans retinoic acid by three native North American ranids
TL;DR: The results indicate that all-trans RA is rapidly metabolized by these Ranid species to a variety of retinoids derivatives, several of which are known ligands for RA and retinoid receptors, and are capable of activating this signaling transduction pathway.
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Ecotoxicology--a multidisciplinary, problem-driven science.
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Characterization of reproductive toxicity of vinclozolin in the fathead minnow and co-treatment with an androgen to confirm an anti-androgenic mode of action
Dalma Martinović,Lindsey S. Blake,Elizabeth J. Durhan,Katie J. Greene,Michael D. Kahl,Kathleen M. Jensen,Elizabeth A. Makynen,Daniel L. Villeneuve,Gerald T. Ankley +8 more
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Endogenous Lifecycle Models for Chemical Risk Assessment.
TL;DR: In this article, a novel application and interpretation of ecotoxicological models, Endogenous Lifecycle Models (ELM), is proposed that offers some of the benefits sought from population models, at much lower cost of design, parametrization, and verification.