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Record W2612671643 · doi:10.1111/jsr.12532

Shorter duration of non‐rapid eye movement sleep slow waves in<i>EphA4</i>knockout mice

2017· article· en· W2612671643 on OpenAlexafffund
Marlène Freyburger, Gaétan Poirier, Julie Carrier, Valérie Mongrain

Bibliographic record

VenueJournal of Sleep Research · 2017
Typearticle
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsCanadian Sleep & Circadian NetworkUniversité de MontréalHôpital du Sacré-Cœur de Montréal
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsDuration (music)NeuroscienceEye movementAudiologyMedicinePsychologyOphthalmologyPhysicsAcoustics

Abstract

fetched live from OpenAlex

Slow waves occurring during non-rapid eye movement sleep have been associated with neurobehavioural performance and memory. In addition, the duration of previous wakefulness and sleep impacts characteristics of these slow waves. However, molecular mechanisms regulating the dynamics of slow-wave characteristics remain poorly understood. The EphA4 receptor regulates glutamatergic transmission and synaptic plasticity, which have both been linked to sleep slow waves. To investigate if EphA4 regulates slow-wave characteristics during non-rapid eye movement sleep, we compared individual parameters of slow waves between EphA4 knockout mice and wild-type littermates under baseline conditions and after a 6-h sleep deprivation. We observed that, compared with wild-type mice, knockout mice display a shorter duration of positive and negative phases of slow waves under baseline conditions and after sleep deprivation. However, the mutation did not change slow-wave density, amplitude and slope, and did not affect the sleep deprivation-dependent changes in slow-wave characteristics, suggesting that EphA4 is not involved in the response to elevated sleep pressure. Our present findings suggest a role for EphA4 in shaping cortical oscillations during sleep that is independent from sleep need.

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)0.005
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 score0.102
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.097
GPT teacher head0.401
Teacher spread0.305 · 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

Citations11
Published2017
Admission routes2
Has abstractyes

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