Controlling DNA Tug‐of‐War in a Dual Nanopore Device
Bibliographic record
Abstract
Methods for reducing and directly controlling the speed of DNA through a nanopore are needed to enhance sensing performance for direct strand sequencing and detection/mapping of sequence-specific features. A method is created for reducing and controlling the speed of DNA that uses two independently controllable nanopores operated with an active control logic. The pores are positioned sufficiently close to permit cocapture of a single DNA by both pores. Once cocapture occurs, control logic turns on constant competing voltages at the pores leading to a "tug-of-war" whereby opposing forces are applied to regions of the molecules threading through the pores. These forces exert both conformational and speed control over the cocaptured molecule, removing folds and reducing the translocation rate. When the voltages are tuned so that the electrophoretic force applied to both pores comes into balance, the life time of the tug-of-war state is limited purely by diffusive sliding of the DNA between the pores. A tug-of-war state is produced on 76.8% of molecules that are captured with a maximum two-order of magnitude increase in average pore translocation time relative to the average time for single-pore translocation. Moreover, the translocation slow-down is quantified as a function of voltage tuning and it is shown that the slow-down is well described by a first passage analysis for a 1D subdiffusive process. The ionic current of each nanopore provides an independent sensor that synchronously measures a different region of the same molecule, enabling sequential detection of physical labels, such as monostreptavidin tags. With advances in devices and control logic, future dual-pore applications include genome mapping and enzyme-free sequencing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".