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Record W2091640072 · doi:10.1073/pnas.0710316105

Nck adaptor proteins control the organization of neuronal circuits important for walking

2007· article· en· W2091640072 on OpenAlexaff
J. Paul Fawcett, John Georgiou, Julie Ruston, Friedhelm Bladt, Andrew T. Sherman, Neil Warner, Bechara J. Saab, Rizaldy P. Scott, John Roder, Tony Pawson

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2007
Typearticle
Languageen
FieldNeuroscience
TopicAxon Guidance and Neuronal Signaling
Canadian institutionsCanada Research ChairsUniversity of TorontoLunenfeld-Tanenbaum Research InstituteDalhousie UniversityMount Sinai Hospital
Fundersnot available
KeywordsNeuroscienceBiologyAxon guidanceSignal transducing adaptor proteinCorticospinal tractNervous systemReceptor tyrosine kinaseCell biologyAxonSignal transductionMedicine

Abstract

fetched live from OpenAlex

The intracellular signaling targets used by mammalian axon guidance receptors to organize the nervous system in vivo are unclear. The Nck1 and Nck2 SH2/SH3 adaptors (collectively Nck) can couple phosphotyrosine (pTyr) signals to reorganization of the actin cytoskeleton and are therefore candidates for linking guidance cues to the regulatory machinery of the cytoskeleton. We find that selective inactivation of Nck in the murine nervous system causes a hopping gait and a defect in the spinal central pattern generator, which is characterized by synchronous firing of bilateral ventral motor neurons. Nck-deficient mice also show abnormal projections of corticospinal tract axons and defective development of the posterior tract of the anterior commissure. These phenotypes are consistent with a role for Nck in signaling initiated by different classes of guidance receptors, including the EphA4 receptor tyrosine kinase. Our data indicate that Nck adaptors couple pTyr guidance signals to cytoskeletal events required for the ipsilateral projections of spinal cord neurons and thus for normal limb movement.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.044
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.054
GPT teacher head0.300
Teacher spread0.246 · 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

Citations100
Published2007
Admission routes1
Has abstractyes

Explore more

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