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Record W2787800101 · doi:10.1139/facets-2017-0101

Nucleobase-containing compounds evoke behavioural, olfactory, and transcriptional responses in model fishes

2018· article· en· W2787800101 on OpenAlexafffundvenue
Angela L. Shamchuk, Brian J. Blunt, Danielle D. Lyons, Mo Qi Wang, Anastasia Gasheva, Carlie R. Lewis, Kirsten Tomlin, E. Starr Hazard, Gary Hardiman, Keith B. Tierney

Bibliographic record

VenueFACETS · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaDSM Nutritional ProductsUniversity of South CarolinaMedical University of South Carolina
KeywordsNucleobaseGenePurineBiologyGene expressionBiochemistryDNAChemistryEnzyme

Abstract

fetched live from OpenAlex

The sensory system of animals detects a massive and unknown array of chemical cues that evoke a diversity of physiological and behavioural responses. One group of nitrogen-containing carbon ring chemicals—nucleobases—are thought to be involved in numerous behaviours yet have received little attention. We took a top-down approach to examine responses evoked by nucleobases at behavioural, tissue, and gene expression levels. Fish generally avoided nucleobases, and this behaviour, when observed, was driven by purines but not pyrimidines. At the tissue level, olfactory neuron generator potential responses tended to be concentration specific and robust at concentrations lower than amino acid detection ranges. In terms of gene expression, more than 2000 genes were significantly upregulated following nucleobase exposure, some of which were expected (e.g., genes involved in purine binding) and some of which were not (e.g., tubulin-related genes). Humanized RNA pathway analysis showed that we had exposed the animal to a nucleobase. Our data indicate that responses to nucleobase-containing compounds may be highly structure based and are evident from changes in behaviour to mRNA expression. Many of these responses were surprising, and all provide numerous routes for further research endeavour.

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 score0.889
Threshold uncertainty score0.603

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 head0.051
GPT teacher head0.260
Teacher spread0.210 · 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

Citations11
Published2018
Admission routes3
Has abstractyes

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