MétaCan
Menu
Back to cohort
Record W7071243769

The role of neuronal activity on radial glia behaviour in «Xenopus laevis» tectum

2009· other· en· W7071243769 on OpenAlexvenueno aff

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2009
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsnot available
Fundersnot available
KeywordsXenopusFilopodiaPopulationPremovement neuronal activityCalcium imagingTectumMotilityZebrafish
DOInot available

Abstract

fetched live from OpenAlex

Glia have been reported to integrate and propagate signals via a neuronal activity-dependent intracellular increase of calcium. In vitro studies have also reported that glia exhibit rapid structural motility. We have performed in vivo two-photon analysis of a morphologically identifiable population of radial glia in the optic tectum of Xenopus laevis tadpoles that spontaneous and neuronal activity-responsive calcium transients much like astrocytes in the mammalian brain. Using live imaging of EGFP-F-expressing individual glial cells in intact Xenopus tadpoles, we found that these radial glia rapidly extend and retract long, fine filopodia within the tectal neuropil. These filopodia interact with retinotectal synapses and their motility is modulated by neuronal NMDAR activation and NO signaling. Finally, we observed that natural visual experience drove both an increase in the rate of calcium transients and a change in filopodial dynamics in these radial glia.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.001
GPT teacher head0.148
Teacher spread0.147 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueLibrary and Archives Canada (Government of Canada)Same topicMachine Learning in BioinformaticsFrench-language works237,207