The load dependence and the force-velocity relation in intact myosin filaments from skeletal and smooth muscles
Bibliographic record
Abstract
In the present study we evaluated the load dependence of force produced by isolated muscle myosin filaments interacting with fluorescently labeled actin filaments, using for the first time whole native myosin filaments. We used a newly developed approach that allowed the use of physiological levels of ATP. Single filaments composed of either skeletal or smooth muscle myosin and single filaments of actin were attached between pairs of nano-fabricated cantilevers of known stiffness. The filaments were brought into contact to produce force, which caused sliding of the actin filaments over the myosin filaments. We applied load to the system by either pushing or pulling the filaments during interactions and observed that increasing the load increased the force produced by myosin and decreasing the load decreased the force. We also performed additional experiments in which we clamped the filaments at predetermined levels of force, which caused the filaments to slide to adjust the different loads, allowing us to measure the velocity of length changes to construct a force-velocity relation. Force values were in the range observed previously with myosin filaments and molecules. The force-velocity curves for skeletal and smooth muscle myosins resembled the relations observed for muscle fibers. The technique can be used to investigate many issues of interest and debate in the field of muscle biophysics.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".