A Three-Dimensional Magnetic Tweezer System for Intraembryonic Navigation and Measurement
Bibliographic record
Abstract
Magnetic micromanipulation has the advantage of untethered control, high precision, and biocompatibility and has recently undergone great advances. The magnetic micromanipulation task to tackle in this paper is to three dimensionally navigate a 5-μm magnetic bead inside a mouse embryo and accurately apply forces to intraembryonic structures to perform mechanical measurements at multiple locations. Existing technologies are not able to achieve these navigation and measurement goals because of poor magnetic force scaling and/or lacking the capability of applying an accurately controlled force. This paper reports a three-dimensional magnetic tweezer system that enables, for the first time, intraembryonic magnetic navigation and force application. A single magnetic bead was introduced into a mouse embryo via robotic microinjection. The magnetic tweezer system accurately controlled the position of the magnetic bead via visually servoed magnetic control. By moving the magnetic bead with known forces inside the embryo, cytoplasm viscosity was measured, which is eight times the viscosity of water. For performing mechanical measurements on the cellular structures inside the mouse embryo, the system should be capable of applying forces up to 120 pN with a resolution of 4 pN. The results revealed that the middle region is significantly more deformable than the side regions of the inner cell mass.
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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 | 0.000 |
| 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".