Identifying Structure in Short DNA Scaffolds Using Solid-State Nanopores
Bibliographic record
Abstract
The identification of molecular tags along nucleic acid sequences has many potential applications in bionanotechnology, disease biomarker detection, and DNA sequencing. An attractive approach to this end is the use of solid-state nanopores, which can electrically detect molecular substructure and can be integrated into portable lab-on-a-chip sensors. We present here a DNA origami-based approach of molecular assembly in which solid-state nanopores are capable of differentiating 165 bp scaffolds containing zero, one, and two dsDNA protrusions. This highly scalable technique requires minimal sample preparation and is customizable for a wide range of targets and applications. As a proof-of-concept, an aptamer-based DNA displacement reaction is performed in which a dsDNA protrusion is formed along a 255 bp scaffold in the presence of ATP. While ATP is too small to be directly sensed using conventional nanopore methods, our approach allows us to detect ATP by identifying molecular substructure along the DNA scaffold.
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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) | 0.000 | 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 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".