Vastus lateralis surface and single motor unit EMG following submaximal shortening and lengthening contractions
Bibliographic record
Abstract
A single shortening contraction reduces the force capacity of muscle fibers, whereas force capacity is enhanced following lengthening. However, how motor unit recruitment and discharge rate (muscle activation) are adapted to such changes in force capacity during submaximal contractions remains unknown. Additionally, there is limited evidence for force enhancement in larger muscles. We therefore investigated lengthening- and shortening-induced changes in activation of the knee extensors. We hypothesized that when the same submaximal torque had to be generated following shortening, muscle activation had to be increased, whereas a lower activation would suffice to produce the same torque following lengthening. Muscle activation following shortening and lengthening (20 degrees at 10 degrees /s) was determined using rectified surface electromyography (rsEMG) in a 1st session (at 10% and 50% maximal voluntary contraction (MVC)) and additionally with EMG of 42 vastus lateralis motor units recorded in a 2nd session (at 4%-47%MVC). rsEMG and motor unit discharge rates following shortening and lengthening were normalized to isometric reference contractions. As expected, normalized rsEMG (1.15 +/- 0.19) and discharge rate (1.11 +/- 0.09) were higher following shortening (p < 0.05). Following lengthening, normalized rsEMG (0.91 +/- 0.10) was, as expected, lower than 1.0 (p < 0.05), but normalized discharge rate (0.99 +/- 0.08) was not (p > 0.05). Thus, muscle activation was increased to compensate for a reduced force capacity following shortening by increasing the discharge rate of the active motor units (rate coding). In contrast, following lengthening, rsEMG decreased while the discharge rates of active motor units remained similar, suggesting that derecruitment of units might have occurred.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".