Controlled Aspiration and Positioning of Biological Cells in a Micropipette
Bibliographic record
Abstract
Manipulating single cells with a micropipette is the oldest, yet still a widely used technique. This paper discusses the aspiration of a single cell into a micropipette and positioning the cell accurately to a target position inside the micropipette. Due to the small volume of a single cell (picoliter) and nonlinear dynamics involved, these tasks have high skill requirements and are labor intensive in manual operation that is solely based on trial and error and has high failure rates. We present automated techniques in this paper for achieving these tasks via computer vision microscopy and closed-loop motion control. Computer vision algorithms were developed to detect and track a single cell outside and inside a micropipette for automated single-cell aspiration. A closed-loop robust controller integrating the dynamics of cell motion was designed to accurately and efficiently position the cell to a target position inside the micropipette. The system achieved high success rates of 98% for cell detection and 97% for cell tracking (n = 100). The automated system also demonstrated its capability of aspirating a single cell into a micropipette within 2 s (versus 10 s by highly skilled operators) and accurately positioning the cell inside the micropipette within 8 s (versus 25 s by highly skilled operators).
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".