Formatting and ligating biopolymers using adjustable nanoconfinement
Bibliographic record
Abstract
Sensitive visualization and conformational control of long, delicate biopolymers present critical challenges to emerging biotechnologies and biophysical studies. Next-generation nanofluidic manipulation platforms strive to maintain the structural integrity of genomic DNA prior to analysis but can face challenges in device clogging, molecular breakage, and single-label detection. We address these challenges by integrating the Convex Lens-induced Confinement (CLiC) technique with a suite of nanotopographies embedded within thin-glass nanofluidic chambers. We gently load DNA polymers into open-face nanogrooves in linear, concentric circular, and ring array formats and perform imaging with single-fluorophore sensitivity. We use ring-shaped nanogrooves to access and visualize confinement-enhanced self-ligation of long DNA polymers. We use concentric circular nanogrooves to enable hour-long observations of polymers at constant confinement in a geometry which eliminates the confinement gradient which causes drift and can alter molecular conformations and interactions. Taken together, this work opens doors to myriad biophysical studies and biotechnologies which operate on the nanoscale.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".