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You searched for: Source 2018 v.52 no.13 Remove constraint Source: 2018 v.52 no.13 Subject dynamic models Remove constraint Subject: dynamic models Subject term databases Remove constraint Subject term: databases Text availability Citation in PubAg Remove constraint Text availability: Citation in PubAg
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1. In Silico Computational Transcriptomics Reveals Novel Endocrine Disruptors in Largemouth Bass (Micropterus salmoides)

Author:
Danilo Basili; Ji-Liang Zhang; John Herbert; Kevin Kroll; Nancy D. Denslow; Christopher J. Martyniuk; Francesco Falciani; Philipp Antczak
Source:
Environmental science & technology 2018 v.52 no.13 pp. 7553-7565
ISSN:
1520-5851
Subject:
Micropterus salmoides; animal ovaries; databases; dynamic models; endocrine-disrupting chemicals; environmental science; estradiol; females; fish communities; gene expression; gene expression regulation; genes; gonadosomatic index; liver; males; ovarian development; prediction; quantitative polymerase chain reaction; quercetin; transcription (genetics); transcriptomics; vitellogenin
Abstract:
... In recent years, decreases in fish populations have been attributed, in part, to the effect of environmental chemicals on ovarian development. To understand the underlying molecular events we developed a dynamic model of ovary development linking gene transcription to key physiological end points, such as gonadosomatic index (GSI), plasma levels of estradiol (E2) and vitellogenin (VTG), in largemo ...
DOI:
10.1021/acs.est.8b02805