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Adhesion Molecules in the Nervous System: Structural Insights into Function and Diversity

2007· review· en· W2110053335 on OpenAlexaff
Lawrence Shapiro, James M. Love, David Colman

Bibliographic record

VenueAnnual Review of Neuroscience · 2007
Typereview
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institute of General Medical Sciences
KeywordsNervous systemImmunoglobulin superfamilyNeuroscienceBiologyCell adhesion moleculeNeural cell adhesion moleculeContext (archaeology)Central nervous systemFunction (biology)Cell adhesionCadherinCell biologyGeneticsCell

Abstract

fetched live from OpenAlex

The unparalleled complexity of intercellular connections in the nervous system presents requirements for high levels of both specificity and diversity for the proteins that mediate cell adhesion. Here we describe recent advances toward understanding the molecular mechanisms that underlie adhesive binding, specificity, and diversity for several well-characterized families of adhesion molecules in the nervous system. Although many families of adhesion proteins, including cadherins and immunoglobulin superfamily members, are utilized in neural and nonneural contexts, nervous system-specific diversification mechanisms, such as precisely regulated alternative splicing, provide an important means to enable their function in the complex context of the nervous system.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.062
GPT teacher head0.376
Teacher spread0.314 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations218
Published2007
Admission routes1
Has abstractyes

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