Multi-arm RNA junctions encoding molecular logic unconstrained by input sequence for versatile cell-free diagnostics
Bibliographic record
Abstract
Applications of RNA-based molecular logic have been hampered by sequence constraints imposed on the input and output of the circuits. Here we show that the sequence constraints can be substantially reduced by appropriately encoded multi-arm junctions of single-stranded RNA structures. To conditionally activate RNA translation, we integrated multi-arm junctions, self-assembled upstream of a regulated gene and designed to unfold sequentially in response to different RNA inputs, with motifs of loop-initiated RNA activators that function independently of the sequence of the input RNAs and that reduce interference with the output gene. We used the integrated RNA system and sequence-independent input RNAs to execute two-input and three-input OR and AND logic in Escherichia coli, and designed paper-based cell-free colourimetric assays that accurately identified two human immunodeficiency virus (HIV) subtypes (by executing OR logic) in amplified synthetic HIV RNA as well as severe acute respiratory syndrome coronavirus-2 (via two-input AND logic) in amplified RNA from saliva samples. The sequence-independent molecular logic enabled by the integration of multi-arm junction RNAs with motifs for loop-initiated RNA activators may be broadly applicable in biotechnology.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".