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Record W3206843041 · doi:10.1021/acs.estlett.1c00722

Insights into the Influence of Natural Retinoic Acids on Imposex Induction in Female Marine Gastropods in the Coastal Environment

2021· article· en· W3206843041 on OpenAlexafffund
Guang‐Jie Zhou, Kevin Ho, Jack Chi‐Ho Ip, Shan Liu, Jianying Hu, John P. Giesy, Kmy Leung

Bibliographic record

VenueEnvironmental Science & Technology Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicMarine Biology and Environmental Chemistry
Canadian institutionsUniversity of Saskatchewan
FundersResearch Grants Council, University Grants CommitteeCanada Research Chairs
KeywordsImposexBiologyEcotoxicologyPseudohermaphroditismZoologySex reversalMolluscaEcologyToxicologyGeneEndocrinologyGenetics

Abstract

fetched live from OpenAlex

Conventionally, development of imposex, i.e., superimposition of male sexual characteristics on females, in marine neogastropods has been solely linked to exposure to synthetic organotin compounds, such as triphenyltin (TPT), in the marine environment. Here, our experimental results show that marine cyanobacteria can produce retinoic acids (RAs) and their oxidative metabolites 4-oxo-RAs, and the most commonly distributed RA, i.e., all-trans-RA, can also trigger expression of genes related to imposex in female whelks Reishia clavigera after chronic exposure of 60 days. Both estimated concentrations of TPT and RAs and 4-oxo-RAs in seawater are positively associated with the Vas Deferens Sequence Index (VDSI) in female whelks collected from various sites along the coast of Hong Kong; the concentration of TPT explains 28% of the total variation of VDSI, while the total concentrations of RAs and 4-oxo-RAs contributed to 14% of the variation based on separate regression analyses. Our discoveries have implications on the cause and magnitude of imposex in marine neogastropods, calling for more research in ecotoxicology of natural RAs and 4-oxo-RAs in the marine environment in the future.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.002
GPT teacher head0.170
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2021
Admission routes2
Has abstractyes

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