Momentum measurements of single-beam traps and quantitative holographic experiments: two sides of the same coin
Bibliographic record
Abstract
After an intense development of optical tweezers as a biophysical tool during the last decades, quantitative experiments in living cells have not found in this technique its best ally, due, in part, to the lack of a reliable method to measure forces in complex environments. The attempts to overcome this problem either require complicated in situ calibrations, which make their use impossible in the study of dynamic processes, or they are inaccurate. Using a different approach, Steven Smith at Carlos Bustamante’s lab at the University of Berkeley developed a method based on the direct measurement of the momentum change of the trapping beam. However, its diffusion has been modest mainly because it requires a counter-propagating optical trapping system, which is difficult to implement and combine with other techniques. Although it has not been used for this purpose yet, it seems a more suitable method for in vivo experiments since the measurement depends only on some properties of the sensor apparatus but not on the experiment itself. On the other hand, the use of holographic optical tweezers in molecular biology experiments involving force and position measurements is still far from established. The existence of different effects associated to the use of spatial light modulators to create the optical traps has restricted their use. In this thesis, I present the work that I carried out in the Optical Trapping Lab – Grup de Biofotònica at the University of Barcelona related to these two subjects. During these years, I have focused on the implementation of the force detection method based on the conservation of the light momentum in single-beam optical traps, and on the analysis of several aspects of holographic tweezers oriented to their use in quantitative experiments.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".