Automatic suspended cell injection under vision and force control biomanipulation
Bibliographic record
Abstract
Biological cell injection is laborious work which requires lengthy training and suffers from a low success rate. Although the development of biomanipulation technology has enabled steps towards automation of adherent cell injection, the automation of suspended cell injection remains a significant challenge. In this paper, a prototype cell injection system for automatic batch injection of suspended cells is proposed. To facilitate the process, these suspended cells are held and fixed by a specially designed cell holding device. A micropipette equipped with a PVDF micro force sensor is integrated in the proposed system. A force sensor is utilized to measure real time injection force applied to the cells during the injection process. With the force feedback provided by the PVDF micro force sensor, the motion of the injecting pipette during insertion, which cannot be directly observed by the microscope, is controlled utilizing the calibrated desired injection force trajectory. A vision and force algorithm is then proposed and applied to the motion control of the injection pipette in three-coordinate directions during an "out-of-plane" cell injection process. Finally, experimental results are given to demonstrate the effectiveness of the proposed approach.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".