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Record W3092872945 · doi:10.1109/tbme.2020.3031043

Automated Embryo Manipulation and Rotation via Robotic nDEP-Tweezers

2020· article· en· W3092872945 on OpenAlexaff
Kaicheng Huang, Ihab Abu Ajamieh, Zhenxi Cui, Jiewen Lai, James K. Mills, Henry K. Chu

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicromanipulatorDielectrophoresisComputer scienceTweezersEmbryoArtificial intelligenceRotation (mathematics)Computer visionEngineeringNanotechnologyMaterials scienceBiologyElectrical engineeringCell biologyMicrofluidics

Abstract

fetched live from OpenAlex

Embryo manipulation is a fundamental task in assisted reproductive technology (ART). Nevertheless, conventional pick-place techniques often require proper alignment to avoid causing damage to the embryo and further, the tools have limited capability to orient the embryo being handled. OBJECTIVE: This paper presents a novel and non-invasive technique that can easily manipulate mouse embryos on a polyvinyl chloride (PVC) Petri dish. METHODS: An inverted microchip with quadrupole electrodes was attached to a micromanipulator to become a robotic dielectrophoresis (DEP) tweezers, and a motorized platform provided additional mobility to the embryos lying on a Petri dish. Vision-based algorithms were developed to evaluate relevant information of the embryos from the image, and to provide feedback signals for precise position and orientation control of the embryo. RESULTS: A series of experiments was conducted to examine the system performance, and the embryo can be successfully manipulated to a specified location with the desired orientation for subsequent processing. CONCLUSION: This system offers a non-contact, low cost, and flexible method for rapid cell handling. SIGNIFICANCE: As the DEP tweezers can grasp the embryo without the need for precise alignment, the overall time required to process a large number of embryos can be shortened.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.935
Threshold uncertainty score0.758

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 head0.013
GPT teacher head0.201
Teacher spread0.188 · 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 designSimulation or modeling
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

Citations15
Published2020
Admission routes1
Has abstractyes

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