Diffractive optics-based nonlinear spectroscopy: application to the study of deterministic protein motion
Bibliographic record
Abstract
Summary form only given. Biological systems constantly transduce various forms of chemical energy into functions in which the inherent response of the system operates at the edge of stability. Excursions from the stability region lead to denaturation; whereas small fluctuations about the stability point lead to highly correlated responses that behave in a deterministic fashion with respect to the function of the system. Exactly how is the bond energy directed in such a complex system and how has the system evolved to minimize entropic losses in conversion efficiency? We have used the oxygen binding heme proteins as model systems for studying the coupling of reaction forces to functionally relevant motions; i.e., structural transitions important to the self regulation of oxygen binding and transport. Since the forces involved become spatially distributed over an enormous number of degrees of freedom, the net relative motions can be exceedingly small (<.1 /spl Aring/). A very sensitive method is needed to detect these motions and the time resolution must be sufficient to follow from the very first events of bond breaking to full relaxation. The use of diffractive optics for the implementation of heterodyne detected grating spectroscopy has recently been demonstrated. The diffractive optic also generates tilted phase fronts to provide true femtosecond time resolution in noncollinear geometries. This approach has sufficient time resolution and sensitivity to follow the mass displacement, as connected through changes in the material index of refraction, to address this issue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".