An automated robotic system for high-speed microinjection of Caenorhabditis elegans
Bibliographic record
Abstract
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.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".