Bibliographic record
Abstract
In this thesis, we presented our development on optical tweezers, with the focus on axial optical tweezers, for single molecule measurements. We will first start with an introduction to optical tweezers and present the technical details when using optical tweezers. We built and calibrated the dual-tweezers and axial optical tweezers setups. We have investigated four projects with optical tweezers. In the first project, we used holographic optical tweezers to extend the photobleaching lifetime of Alexa 647 near the laser focus, facilitating the combination of optical tweezers with single molecule fluorescence. We show that the photobleaching lifetime can be significantly extended with Laguerre-Gaussian beams, providing a new way to combine optical tweezers with fluorescence microscopy while maintaining a reasonable photobleaching lifetime. In the second project, we put forward a method to extend the force range measured by axial optical tweezers based on the shape of the non-linear signal and the corrected signal, simplifying the application of axial optical tweezers in single molecule research. We show that the force range that can be accessed by axial optical tweezers can be extended by more than 30%. In the third project, we investigate the binding affinity between the adenosine A2A receptor and the G-protein, showing that their binding in apo form under high concentration of GDP is strong, comparable to the binding strength between antigen and antibody. Besides, we measured the binding between them in a single molecular level, proving the feasibility of this measurement. In the fourth project, we show that a small single strand DNA can increase the rupture force between two complementary short DNA oligos, showing a new way to increase the binding stability in single molecule force measurement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".