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Record W2552060310 · doi:10.7554/elife.19021

Pan-neuronal screening in Caenorhabditis elegans reveals asymmetric dynamics of AWC neurons is critical for thermal avoidance behavior

2016· article· en· W2552060310 on OpenAlexafffund
Ippei Kotera, Nhat Anh Tran, Donald Fu, Jimmy HJ Kim, Jarlath Byrne Rodgers, William S. Ryu

Bibliographic record

VenueeLife · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaHuman Frontier Science Program
KeywordsNeuroscienceCaenorhabditis elegansCalcium imagingNociceptionNeuronBiological neural networkAdaptation (eye)NeuroethologyBiologySensory systemChemistryCalcium

Abstract

fetched live from OpenAlex

Understanding neural functions inevitably involves arguments traversing multiple levels of hierarchy in biological systems. However, finding new components or mechanisms of such systems is extremely time-consuming due to the low efficiency of currently available functional screening techniques. To overcome such obstacles, we utilize pan-neuronal calcium imaging to broadly screen the activity of the C. elegans nervous system in response to thermal stimuli. A single pass of the screening procedure can identify much of the previously reported thermosensory circuitry as well as identify several unreported thermosensory neurons. Among the newly discovered neural functions, we investigated in detail the role of the AWCOFF neuron in thermal nociception. Combining functional calcium imaging and behavioral assays, we show that AWCOFF is essential for avoidance behavior following noxious heat stimulation by modifying the forward-to-reversal behavioral transition rate. We also show that the AWCOFF signals adapt to repeated noxious thermal stimuli and quantify the corresponding behavioral adaptation.

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 score0.307
Threshold uncertainty score0.681

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 head0.015
GPT teacher head0.272
Teacher spread0.256 · 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

Citations77
Published2016
Admission routes2
Has abstractyes

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