Sequential growth of long DNA strands with user-defined patterns for nanostructures and scaffolds
Bibliographic record
Abstract
DNA strands of well-defined sequence are valuable in synthetic biology and nanostructure assembly. Drawing inspiration from solid-phase synthesis, here we describe a DNA assembly method that uses time, or order of addition, as a parameter to define structural complexity. DNA building blocks are sequentially added with in-situ ligation, then enzymatic enrichment and isolation. This yields a monodisperse, single-stranded long product (for example, 1,000 bases) with user-defined length and sequence pattern. The building blocks can be repeated with different order of addition, giving different DNA patterns. We organize DNA nanostructures and quantum dots using these backbones. Generally, only a small portion of a DNA structure needs to be addressable, while the rest is purely structural. Scaffolds with specifically placed unique sites in a repeating motif greatly minimize the number of components used, while maintaining addressability. This combination of symmetry and site-specific asymmetry within a DNA strand is easily accomplished with our method. Assembling defined sequences of DNA is important for many applications, but the synthesis becomes more difficult as the target size increases. Here, the authors report a method for assembling DNA by combining smaller strands, with the final structure determined by the order of addition of the fragments.
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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".