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Record W3165880840

ALTERATIONS IN MOTOR UNIT POTENTIATION DURING FATIGUING CONTRACTIONS BY SEDENTARY INDIVIDUALS

2021· article· en· W3165880840 on OpenAlexaboutno aff
Christopher C.J. Miller, Michael A. Trevino, TJ Herda, Adam J. Sterczala, Miller Jm, Mandy E. Parra, Hannah L. Dimmick

Bibliographic record

VenueTopSCHOLAR (Western Kentucky University) · 2021
Typearticle
Languageen
FieldEngineering
TopicMechanical and Thermal Properties Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMotor unitLong-term potentiationPhysical medicine and rehabilitationPsychologyMedicineNeuroscienceInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Colin Miller1, Michael Trevino1, Trent Herda2, Adam Sterczala3, Jonathan Miller2, Mandy Parra4, Hannah Dimmick5 1Oklahoma State University, Stillwater, OK; 2University of Kansas, Lawrence, KS; 3University of Pittsburgh, Pittsburgh, PA; 4Baker University, Baldwin City, KS; 5University of Calgary, Calgary, AB PURPOSE: The purpose of this study was to examine the effects of consecutive long duration contractions on motor unit (MU) derecruitment behavior of the vastus lateralis (VL) for sedentary individuals. METHODS: Thirteen females (20.69 ± 2.75 yrs) and nine males (20.00 ± 1.41 yrs) volunteered for this study. An electromyographic (EMG) sensor was placed over the VL. Each participant completed isometric maximal voluntary contractions (MVC) on an isokinetic dynamometer followed by two consecutive isometric trapezoidal submaximal contractions (40% MVC) of the right knee extensors. For the submaximal contractions, the torque was increased at a rate of 10% MVC/s to the deserved torque level for 45s followed by a decrease of 10% MVC/s to baseline. Ten seconds of rest was given between the submaximal contractions. Decomposition techniques were applied to the EMG signals to extract action potentials and the firing events of single MUs. For each MU, the recruitment (REC; %MVC) and derecruitment (DEREC; %MVC) thresholds were calculated and linear regressions were performed on the DEREC versus REC relationships for each individual to determine the slopes and y-intercepts (y-ints). Separate two-way mixed factorial ANOVAs (sex x repetition) examined the slopes and y-ints. Follow-up analyses included Bonferroni corrections and alpha was 0.05. RESULTS: For the slopes and y-ints, there were no significant (p > 0.05) two-way interactions or main effects for sex. However, there were main effects for repetition (REP). The slopes were greater (p = 0.030) for REP 1 (1.33 ± 0.50) than REP 2 (1.12 ± 0.43), whereas the y-ints were greater (p = 0.024) for REP 2 (1.27 ± 14.57) than REP 1 (-6.50 ± 20.39). CONCLUSION: Caution is warranted when interpreting the findings for the y-ints as half of the subjects exhibited negative values. Indeed, the slopes indicated that males and females derecruited MUs at higher torque values (slopes > 1) than the initial recruited torque levels for both REPS. However, the slopes significantly decreased during the second contraction, likely due to fatigue. Future research should investigate if endurance training can improve fatigue resistance and prevent a decrease in MU potentiation during a second, long duration contraction.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.010
GPT teacher head0.183
Teacher spread0.173 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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