Automated System for Cell Manipulation and Rotation
Bibliographic record
Abstract
Cell manipulation is an important step for microsurgical operations to extract/inject material from/within the cell. Cell manipulation may include translation and/or rotating cell to a desired position and orientation in preparing it for the next step of microsurgery. Extraction of cellular components is an essential aspect of cell surgery. The extraction must be carried out without adverse effects on the cell. The cellular material must be extracted from the desired location in the cell according to the cell type. Hence, the cell first must be manipulated and rotated precisely. The success of manual cell rotation approaches currently depends on the operator skill, which may vary over the time and from one operator to another one. Additionally, low efficiency, low repeatability and low controllability negatively impact on the process. In this article, an automatic cell rotation and manipulation system is presented, in which we propose the use of conventional tools and techniques that are in use in clinical labs now. The system will replace the manual manipulation procedure, to increase the microsurgical operations efficiency without the introduction of costly new tools and time. The proposed rotation system is efficient, simple and cost-effective. The system uses visual feedback control to rotate and visually track the cell orientation. The system can rotate the cell in the in-plane and out of plane directions. The simulation results show excellent potential for the system since it can reorient the cell with an error margin of less than 5°. The mouse embryo at the blastocyst stage is used for the system validation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".