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Record W3102647290 · doi:10.1101/2020.11.13.382374

Functional microstructure of Ca <sub>V</sub> -mediated calcium signaling in the axon initial segment

2020· preprint· en· W3102647290 on OpenAlexfundno aff
Anna M. Lipkin, Margaret M Cunniff, Perry W.E. Spratt, Stefan Lemke, Kevin J. Bender

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthNational Science Foundation
KeywordsCalciumBiophysicsChemistryCalcium imagingAxonNeurosciencePotassiumBK channelBiology

Abstract

fetched live from OpenAlex

ABSTRACT The axon initial segment (AIS) is a specialized neuronal compartment in which synaptic input is converted into action potential output. This process is supported by a diverse complement of sodium, potassium, and calcium channels (Ca V ). Different classes of sodium and potassium channels are scaffolded at specific sites within the AIS, conferring unique functions, but how calcium channels are functionally distributed within the AIS is unclear. Here, we utilize conventional 2-photon laser scanning and diffraction-limited, high-speed spot 2-photon imaging to resolve action potential-evoked calcium dynamics in the AIS with high spatiotemporal resolution. In mouse layer 5 prefrontal pyramidal neurons, calcium influx was mediated by a mix of Ca V 2 and Ca V 3 channels that differentially localized to discrete regions. Ca V 3 functionally localized to produce nanodomain hotspots of calcium influx that coupled to ryanodine-dependent stores, whereas Ca V 2 localized to non-hotspot regions. Thus, different pools of Ca V s appear to play distinct roles in AIS function.

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 score0.003
Threshold uncertainty score0.005

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 head0.055
GPT teacher head0.287
Teacher spread0.232 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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