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Record W22938075 · doi:10.1152/jn.2001.85.3.1332

Enhancement of Short-Term Synaptic Plasticity by Prior Environmental Stress

2001· article· en· W22938075 on OpenAlexaff
Jeff W. Barclay, R. Meldrum Robertson

Bibliographic record

VenueJournal of Neurophysiology · 2001
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsExcitatory postsynaptic potentialNeuroscienceSynapseNeurotransmissionFacilitationPlasticityPostsynaptic potentialLong-term potentiationNeural facilitationSynaptic plasticityNonsynaptic plasticityPost-tetanic potentiationInhibitory postsynaptic potentialBiologyMetaplasticityPhysics

Abstract

fetched live from OpenAlex

All chemical synapses can rapidly up- or downregulate the strength of their connections to reshape the postsynaptic signal, thereby stressing the informational importance of specific neural pathways. It is also true that an organism's environment can exert a powerful influence on all aspects of neural circuitry. We investigated the effect of a prior high-temperature stress on the short-term plasticity of a neuromuscular synapse in the hindleg tibial extensor muscle of Locusta migratoria. We found that the prior stress acted to precondition the synapse by increasing the upper temperature limit for synaptic transmission during a subsequent stressful exposure. As well, preexposure to a stressful high-temperature environment increased short-term facilitation of excitatory junction potentials concurrent with a decrease in excitatory junction potential amplitude and a reduction in its temporal parameters. We conclude that a stressful environment can modify synaptic physiological properties resulting in an enhancement of short-term plasticity of the synapse.

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 score0.005

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.281
Teacher spread0.256 · 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

Citations13
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueJournal of NeurophysiologySame topicNeurobiology and Insect Physiology ResearchFrench-language works237,207