MétaCan
Menu
Back to cohort
Record W2011733660 · doi:10.1186/1756-6606-4-15

Opposing roles of PlexinA and PlexinB in axonal branch and varicosity formation

2011· article· en· W2011733660 on OpenAlexafffund
Shay Q. Neufeld, Alexa D Hibbert, Brian E. Chen

Bibliographic record

VenueMolecular Brain · 2011
Typearticle
Languageen
FieldNeuroscience
TopicAxon Guidance and Neuronal Signaling
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersMcGill University Health CentreMcGill University
KeywordsAxonNeuroscienceAxon guidanceBiologyGrowth coneSynapseSynaptogenesisReceptorAxon hillockCell biologyGenetics

Abstract

fetched live from OpenAlex

Establishing precise synaptic connectivity during development is crucial for neural circuit function. However, very few molecules have been identified that are involved in determining where and how many synapses form. The Plexin cell-surface molecules are a conserved family of axon guidance receptors that mediate axon fasciculation and repulsion during neural development, and later in development PlexinA receptors are involved in eliminating axonal branches and synapse numbers. Here we investigate the roles of PlexinA and PlexinB receptors in axonal branch and varicosity formation in Drosophila. We knocked down PlexinA or PlexinB expression using RNAi in identified mechanosensory neurons and analyzed axonal branching patterns and varicosity formations. Reducing PlexinA expression increased the axonal arbor complexity by increasing the number of branches and varicosities along the axon. In contrast, knocking down PlexinB expression decreased morphological complexity by decreasing the number of branches and the overall size of the axonal arbor, but did not reduce the number of varicosities. Our results demonstrate opposing roles for PlexinA and PlexinB in local wiring within a target region, where PlexinA functions to suppress excessive axonal branches and synapses and PlexinB facilitates axonal growth.

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 score0.014
Threshold uncertainty score0.358

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 head0.049
GPT teacher head0.250
Teacher spread0.201 · 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

Citations7
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueMolecular BrainSame topicAxon Guidance and Neuronal SignalingFrench-language works237,207