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Record W3191798860 · doi:10.3390/ijms22168511

Role of DSCAM in the Development of Neural Control of Movement and Locomotion

2021· review· en· W3191798860 on OpenAlexafffund
Maxime Lemieux, Louise Thiry, Olivier D. Laflamme, Frédéric Bretzner

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsUniversité LavalCentre hospitalier universitaire de Québec
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of Canada
KeywordsNeuroscienceBiologyContext (archaeology)Biological neural networkSensory systemSynaptogenesis

Abstract

fetched live from OpenAlex

Locomotion results in an alternance of flexor and extensor muscles between left and right limbs generated by motoneurons that are controlled by the spinal interneuronal circuit. This spinal locomotor circuit is modulated by sensory afferents, which relay proprioceptive and cutaneous inputs that inform the spatial position of limbs in space and potential contacts with our environment respectively, but also by supraspinal descending commands of the brain that allow us to navigate in complex environments, avoid obstacles, chase prey, or flee predators. Although signaling pathways are important in the establishment and maintenance of motor circuits, the role of DSCAM, a cell adherence molecule associated with Down syndrome, has only recently been investigated in the context of motor control and locomotion in the rodent. DSCAM is known to be involved in lamination and delamination, synaptic targeting, axonal guidance, dendritic and cell tiling, axonal fasciculation and branching, programmed cell death, and synaptogenesis, all of which can impact the establishment of motor circuits during development, but also their maintenance through adulthood. We discuss herein how DSCAM is important for proper motor coordination, especially for breathing and locomotion.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.924
Threshold uncertainty score0.218

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.023
GPT teacher head0.361
Teacher spread0.338 · 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 designOther design
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

Citations14
Published2021
Admission routes2
Has abstractyes

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