Single-Molecule Fluorescence of Nucleic Acids
Bibliographic record
Abstract
Single-molecule fluorescence studies of nucleic acids are revolutionizing our understanding of fundamental cellular processes related to DNA and RNA processing mechanisms. Detailed molecular insights into DNA repair, replication, transcription, and RNA folding and function are continuously being uncovered by using the full repertoire of single-molecule fluorescence techniques. The fundamental reason behind the stunning growth in the application of single-molecule techniques to study nucleic acid structure and dynamics is the unmatched ability of single-molecule fluorescence, and mostly single-molecule FRET, to resolve heterogeneous static and dynamic populations and identify transient and low-populated states without the need for sample synchronization. New advances in DNA and RNA synthesis, post-synthetic dye-labeling methods, immobilization and passivation strategies, improved dye photophysics, and standardized analysis methods have enabled the implementation of single-molecule techniques beyond specialized laboratories. In this chapter, we introduce the practical aspects of applying single-molecule techniques to investigate nucleic acid structure, dynamics, and function.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".