Force control for mechanoinduction of impedance variation in cellular organisms
Bibliographic record
Abstract
Constantly exposed to various forms of mechanical forces inherent in their physical environment (such as gravity, stress induced by fluid flow or cell–cell interactions, etc), cellular organisms sense such forces and convert them into biochemical signals through the processes of mechanosensing and mechanotransduction that eventually lead to biological changes. The effect of external forces on the internal structures and activities in a cellular organism may manifest in changes its physical properties, such as impedance. Studying variation in the impedance of a cellular organism induced by the application of an external mechanical force represents a meaningful endeavor (from a biosystems perspective) in exploring the complex mechanosensing and mechanotransduction mechanisms that govern the behavior of a cellular organism under the influence of external mechanical stimuli. In this paper we describe the development of an explicit force-feedback control system for exerting an indentation force on a cellular organism while simultaneously measuring its impedance. To demonstrate the effectiveness of this force-control system, we have conducted experiments using zebrafish embryos as a test model of a cellular organism. We report experimental results demonstrating that the application of a properly controlled external force leads to a significant change in the impedance of a zebrafish embryo. These results offer support for a plausible explanation that activities of pore canals in the chorion are responsible for the observed change in impedance.
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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 | 0.001 | 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".