A Generalized Tip-Membrane Contact Detection Algorithm for Automated Single Cell Electroporation Using Statistical Process Control
Bibliographic record
Abstract
This work presents a fully automated method for detecting the contact between a microcapillary tip and a cell membrane based on a statistical process control (SPC) algorithm known as the double-sided cumulative sum (or "cusum"). By analyzing current measurements obtained through a microcapillary electrode, the proposed goal of this system is to determine when tip-to-membrane (tip-membrane) contact occurs using thin adhered cells (e.g., less than 10 μm) for the purposes of fully automated robotic-assisted, single cell electroporation (SCE) - a powerful method of gene transfection. This SPC algorithm is robust against uncontrollable system parameters such as system noise common in electrode-based systems, nonstationary processes, and variations in the physical parameters of cells. The proposed algorithm was successfully demonstrated on adhered mammal cells as small as 4 μm in thickness and using tip-placement velocities from 1 to 8 μm/s. In addition, a novel method of experimentation is described correlating optical measurements between tip-membrane proximity and changes in icctduring the tip-placement sequence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".