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Record W4281689511 · doi:10.1016/j.celrep.2022.110911

Loss of 4E-BP converts cerebellar long-term depression to long-term potentiation

2022· article· en· W4281689511 on OpenAlexafffund
Natasha Saviuk, Yumaine Chong, Peng Wang, Sara Bermudez, Zhe Zhao, Arjun Bhaskaran, Derek Bowie, Nahum Sonenberg, Ellis Cooper, A. Pejmun Haghighi

Bibliographic record

VenueCell Reports · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersResearch Institute for Aging, University of WaterlooCanadian Institutes of Health ResearchBuck Institute for Research on Aging
KeywordsLong-term potentiationSynaptic plasticityLong-term depressionCerebellumNeuroscienceBiologyDeep cerebellar nucleiPurkinje cellCerebellar cortexGlutamate receptorGeneticsReceptor

Abstract

fetched live from OpenAlex

Genetic perturbances in translational regulation result in defects in cerebellar motor learning; however, little is known about the role of translational mechanisms in the regulation of cerebellar plasticity. We show that genetic removal of 4E-BP, a translational suppressor and target of mammalian target of rapamycin complex 1, results in a striking change in cerebellar synaptic plasticity. We find that cerebellar long-term depression (LTD) at parallel fiber-Purkinje cell synapses is converted to long-term potentiation in 4E-BP knockout mice. Biochemical and pharmacological experiments suggest that increased phosphatase activity largely accounts for the defects in LTD. Our results point to a model in which translational regulation through the action of 4E-BP plays a critical role in establishing the appropriate kinase/phosphatase balance required for normal synaptic plasticity in the cerebellum.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.034
GPT teacher head0.320
Teacher spread0.286 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

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