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Record W3122775313 · doi:10.1039/d0lc00964d

Electric egg-laying: a new approach for regulating <i>C. elegans</i> egg-laying behaviour in a microchannel using electric field

2021· article· en· W3122775313 on OpenAlexafffund
Khaled Youssef, Daphne Archonta, Terrance J. Kubiseski, Anurag Tandon, Pouya Rezai

Bibliographic record

VenueLab on a Chip · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsOccupational Cancer Research CentreUniversity of TorontoYork UniversityToronto Public Health
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVentral nerve cordBiologyLayingSnailAnatomyEcologyNervous systemNeuroscience

Abstract

fetched live from OpenAlex

In this paper, the novel effect of electric field (EF) on adult C. elegans egg-laying in a microchannel is discovered and correlated with neural and muscular activities. The quantitative effects of worm aging and EF strength, direction, and exposure duration on egg-laying are studied phenotypically using egg-count, body length, head movement, and transient neuronal activity readouts. Electric egg-laying rate increases significantly when worms face the anode and the response is EF-dependent, i.e. stronger (6 V cm-1) and longer EF (40 s) exposure result in a shorter egg laying response duration. Worm aging significantly deteriorates the electric egg-laying behaviour with an 88% decrease in the egg-count from day-1 to day-4 post young-adult stage. Fluorescent imaging of intracellular calcium dynamics in the main parts of the egg-laying neural circuit demonstrates the involvement and sensitivity of the serotonergic hermaphrodite specific neurons (HSNs), vulva muscles, and ventral cord neurons to the EF. HSN mutation also results in a reduced rate of electric egg-laying allowing the use of this technique for cellular screening and mapping of the neural basis of electrosensation in C. elegans. This novel assay can be parallelized and performed in a high-throughput manner for drug and gene screening applications.

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 categoriesMeta-epidemiology (narrow)
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 score0.046
Threshold uncertainty score1.000

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 head0.023
GPT teacher head0.261
Teacher spread0.239 · 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.

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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