Tri-state logic computation by activating DNA origami chains
Bibliographic record
Abstract
.); however, realizing tri-state logic (high impedance Z) in DNA computation is understudied. Here we actively fold DNA origami chain-like hinged rods to induce conformational changes that return tri-state logic signals. We use rigid six helix-bundle (6HB) DNA origami to self-assemble a linear trimer chain as a circuit platform with functional single-stranded (ss) DNA near each semi-flexible hinge. The presence or absence of ssDNA enable and input strands allows hybridization to take place at the hinges, activating one fold (0) or two folds (1) from the straight linear geometry (defined as High-Z) of the trimer chain. We design two different tri-state logic gate platforms, buffer and inverter, with corresponding enable/input ssDNA to unambiguously return tri-state signals, characterized by Atomic Force Microscopy (AFM) and/or agarose gel electrophoresis (GEL). Our work on tri-state logic significantly enhances DNA computation beyond the current two-state Boolean logic with both research and industrial applications, including cellular treatments and living matter utilizing the biocompatibility of DNA molecules.
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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) | 0.002 | 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".