Vision-based cellular force measurement using an elastic microfabricated device
Bibliographic record
Abstract
Manipulation and characterization of individual biological cells require cellular forces to be precisely measured in real time. This paper presents a computer vision-based cellular force measurement platform that allows for the use of a single vision sensor (CCD/CMOS camera) to simultaneously obtain two forms of feedback (i.e., vision and force). A novel silicone elastomer-based cell holding device and a sub-pixel visual tracking algorithm are developed. Deflections of elastic, low-stiffness structures are visually tracked, and material deflections are subsequently transformed into cellular forces. Experimental results demonstrate that the current vision-based force sensing system is capable of performing robust cellular force measurements at a full 30 Hz with a 3.7 µN resolution. Importantly, the vision-based cellular force sensing framework established in this study is not scale- or cell-line-dependent. The device design, visual tracking algorithm, and experimental technique form a powerful framework that would permit visually resolving cellular forces in real time with a sub-nanoNewton (26 pN) resolution for applications in single cell manipulation and characterization.
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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 | 0.000 |
| 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 | 0.001 | 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".