Modelling and control of optical manipulation for cell rotation
Bibliographic record
Abstract
Optical tweezers has become a powerful tool in automated cell transportation control and has been used in a variety of biological applications. The use of optical tweezers for cell surgery has great potential for various biomedical applications such as microinjection, organelle extraction and modification, and preimplantation genetic diagnosis (PGD). In these cell surgical manipulation tasks, the cell of interest must be oriented properly such that the desired component, e.g., the polar-body or organelles, can be visualized by optical microscopy; thus cell rotation becomes a necessary procedure. Currently, cell rotational control can be carried out by laser tools that are usually handled by skilled people. The open-loop manual operation cannot be readily used for applications requiring precise and high throughput cell rotational control. This highlights the need of developing an automated controlled robot manipulator to rotate biological cells more accurately and efficiently. In this paper, we propose a cell surgery system that utilizes two optical traps, generated by robotically controlled holographic optical tweezers (HOT), to manipulate the cell for rotation, where the optical tweezers functions as special robot manipulators. Through dynamic modeling using T-matrix approach, the relationship between the applied torques and the spherical coordinates of the optical tweezers is characterized. A rotational controller is further developed to rotate the cell to the pre-desired orientation accurately. Experiments are performed to demonstrate the effectiveness of the proposed approach.
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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".