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FoxP2 Regulates Neurogenesis during Embryonic Cortical Development

2013· article· en· W2080531567 on OpenAlexafffund
David Chun Cheong Tsui, John P. Vessey, Hideaki Tomita, David R. Kaplan, Freda D. Miller

Bibliographic record

VenueJournal of Neuroscience · 2013
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of TorontoHospital for Sick Children
FundersCanadian Institutes of Health ResearchHoward Hughes Medical Institute
KeywordsNeurogenesisFOXP2Gene knockdownBiologyNeuroscienceEmbryonic stem cellProgenitor cellCell biologyTranscription factorGeneticsStem cellGene

Abstract

fetched live from OpenAlex

The transcription factor FoxP2 has been associated with the development of human speech but the underlying cellular function of FoxP2 is still unclear. Here we provide evidence that FoxP2 regulates genesis of some intermediate progenitors and neurons in the mammalian cortex, one of the key centers for human speech. Specifically, knockdown of FoxP2 in embryonic cortical precursors inhibits neurogenesis, at least in part by inhibiting the transition from radial glial precursors to neurogenic intermediate progenitors. Moreover, overexpression of human, but not mouse, FoxP2 enhances the genesis of intermediate progenitors and neurons. In contrast, expression of a human FoxP2 mutant that causes vocalization deficits decreases neurogenesis, suggesting that in the murine system human FoxP2 acts as a gain-of-function protein, while a human FoxP2 mutant acts as a dominant-inhibitory protein. These results support the idea that FoxP2 regulates the transition from neural precursors to transit-amplifying progenitors and ultimately neurons, and shed light upon the molecular changes that might contribute to evolution of the mammalian cortex.

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 score0.001
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)0.0010.000

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 head0.021
GPT teacher head0.276
Teacher spread0.255 · 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

Citations156
Published2013
Admission routes2
Has abstractyes

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Same venueJournal of NeuroscienceSame topicLanguage Development and DisordersFrench-language works237,207