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Visual Circuit Assembly Requires Fine Tuning of the Novel Ig Transmembrane Protein Borderless

2013· article· en· W2013746272 on OpenAlexafffund
Scott Cameron, Wen-Tzu Chang, Yixu Chen, Yating Zhou, Shaurya Taran, Yong Rao

Bibliographic record

VenueJournal of Neuroscience · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsMcGill University Health Centre
FundersCanadian Institutes of Health ResearchMcGill University Health CentreMcGill University
KeywordsNeuriteNeuroscienceAxonTransmembrane proteinBiologyCell biologyDendrite (mathematics)Axon guidanceRegulatorProtein tyrosine phosphataseSignal transductionReceptorIn vitroGenetics

Abstract

fetched live from OpenAlex

Establishment of synaptic connections in the neuropils of the developing nervous system requires the coordination of specific neurite-neurite interactions (i.e., axon-axon, dendrite-dendrite and axon-dendrite interactions). The molecular mechanisms underlying coordination of neurite-neurite interactions for circuit assembly are incompletely understood. In this report, we identify a novel Ig superfamily transmembrane protein that we named Borderless (Bdl), as a novel regulator of neurite-neurite interactions in Drosophila. Bdl induces homotypic cell-cell adhesion in vitro and mediates neurite-neurite interactions in the developing visual system. Bdl interacts physically and genetically with the Ig transmembrane protein Turtle, a key regulator of axonal tiling. Our results also show that the receptor tyrosine phosphatase leukocyte common antigen-related protein (LAR) negatively regulates Bdl to control synaptic-layer selection. We propose that precise regulation of Bdl action coordinates neurite-neurite interactions for circuit formation in Drosophila.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.003

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)0.0000.000

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 head0.064
GPT teacher head0.319
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 source (direct Gemma or distilled Codex), 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

Citations14
Published2013
Admission routes2
Has abstractyes

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