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Record W2737309353 · doi:10.1109/icra.2017.7989780

Three-dimensional robotic control of a 5-micrometer magnetic bead for intra-embryonic navigation and measurement

2017· article· en· W2737309353 on OpenAlexaff
Xian Wang, Mengxi Luo, Han Wu, Zhuoran Zhang, Jun Liu, Zhensong Xu, W. R. Johnson, Yu Sun

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMagnetic beadMicrometerBeadMagnetic fieldMaterials scienceBiomedical engineeringComputer scienceNanotechnologyPhysicsEngineeringMechanical engineeringChemistry

Abstract

fetched live from OpenAlex

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 work is to three-dimensionally navigate a 5-micrometer magnetic bead inside a mouse embryo and 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 robotic magnetic tweezer system that enables, for the first time, intra- embryonic magnetic navigation and force application. A single magnetic bead was introduced into a mouse embryo via robotic microinjection. The robotic magnetic tweezer system accurately controls the position of the magnetic bead via visually servoed magnetic control. The system is also capable of applying forces up to 120 pN with a resolution of 1.78 pN for performing mechanical measurements on the cellular structures inside the mouse embryo, revealing that the middle region is more deformable than the side regions of the inner cell mass.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.241
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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