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Record W1484717174 · doi:10.1109/icra.2015.7139298

An automated robotic system for high-speed microinjection of Caenorhabditis elegans

2015· article· en· W1484717174 on OpenAlexaff
Xianke Dong, Pengfei Song, Xinyu Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsCaenorhabditis elegansMicroinjectionSortingComputer scienceImage stitchingPolydimethylsiloxaneMaterials scienceBiologyComputer visionCell biologyNanotechnologyAlgorithm

Abstract

fetched live from OpenAlex

The tiny nematode worm Caenorhabditis elegans has long been a popular model organism for genetic, developmental, and biochemical studies in which worm microinjection plays a critical role. This paper presents an automated robotic system for high-speed injection of C. elegans with an efficiency more than 10 times faster than that of a proficient injection technician. To facilitate the injection process, a multilayer, hydraulically-controlled polydimethylsiloxane (PDMS) microfluidic device is developed to rapidly load, immobilize, flush, sort and collect individual worms. In addition, a newly proposed contact detection algorithm is adopted to find the optimal injection position along the z axis within the microscope view field. The direction and location of the needle tip are identified online based on an effective image processing algorithm. According to continuous injection of 40 worms, our system is able to perform worm injection at a speed of 6.6 worms per minute with a pre-sorting success rate of 77.5% (post sorting: 100%). The superior performance provided by the system will significantly facilitate large-scale transgenic studies and biomolecule screening on C. elegans.

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: Empirical
Teacher disagreement score0.669
Threshold uncertainty score0.343

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 head0.024
GPT teacher head0.295
Teacher spread0.271 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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