Simultaneous, hybrid single-molecule method by optical tweezers and fluorescence
Bibliographic record
Abstract
As studies on life sciences progress toward the single-molecule level, new experiments have put forward more requirements for simultaneously displaying the mechanical properties and conformational changes of biomolecules. Optical tweezers and fluorescence microscopy have been combined to solve this problem. The combination of instruments forms a new generation of hybrid single-molecule technology that breaks through the limitations of traditional biochemical analysis. Powerful manipulation and fluorescence visualization have been widely used, and these techniques provide new possibilities for studying complex biochemical reactions at the single-molecule level. This paper explains the features of this combined technique, including the application characteristics of single-trap and dual-traps, the anti-bleaching method, and optical tweezers combined with epi-fluorescence, confocal fluorescence, total internal reflection fluorescence, and other fluorescence methods. Using typical experiments, we analyze technical solutions and explain the factors and principles that instrument designers should consider. This review aims to give an introduction to this novel fusion technology process and describe important biological results.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".