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Gene transcription in sea otters (<i>Enhydra lutris</i>); development of a diagnostic tool for sea otter and ecosystem health

2011· article· en· W2113301674 on OpenAlexaff
Lizabeth Bowen, A. Keith Miles, Michael J. Murray, Martin Haulena, JUDY TUTTLE, William Van Bonn, Lance Adams, James L. Bodkin, Brenda E. Ballachey, JAMES ESTES, M. Tim Tinker, Robin A. Keister, Jeffrey L. Stott

Bibliographic record

VenueMolecular Ecology Resources · 2011
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsVancouver Aquarium
FundersU.S. Fish and Wildlife ServiceExxon Valdez Oil Spill Trustee Council
KeywordsBiologyOtterGeneGene expressionImmune systemGeneticsEcology

Abstract

fetched live from OpenAlex

Gene transcription analysis for diagnosing or monitoring wildlife health requires the ability to distinguish pathophysiological change from natural variation. Herein, we describe methodology for the development of quantitative real-time polymerase chain reaction (qPCR) assays to measure differential transcript levels of multiple immune function genes in the sea otter (Enhydra lutris); sea otter-specific qPCR primer sequences for the genes of interest are defined. We establish a 'reference' range of transcripts for each gene in a group of clinically healthy captive and free-ranging sea otters. The 10 genes of interest represent multiple physiological systems that play a role in immuno-modulation, inflammation, cell protection, tumour suppression, cellular stress response, xenobiotic metabolizing enzymes, antioxidant enzymes and cell-cell adhesion. The cycle threshold (C(T)) measures for most genes were normally distributed; the complement cytolysis inhibitor was the exception. The relative enumeration of multiple gene transcripts in simple peripheral blood samples expands the diagnostic capability currently available to assess the health of sea otters in situ and provides a better understanding of the state of their environment.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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

Citations41
Published2011
Admission routes1
Has abstractyes

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