Diffractive optics-based heterodyne detected four-wave mixing studies of protein dynamics: insights into ligand escape and cooperativity in heme proteins
Bibliographic record
Abstract
Summary form only given. The relationship between molecular structure and function is of fundamental importance for understanding biological systems. The heme proteins hemoglobin and myoglobin provide ideal model systems for investigating this relationship because their structure and function are well characterized. In addition, they are amenable to optical probes, allowing their functional processes to be initiated by photodissociation. Previous studies on the femtosecond timescale have characterized the dynamics of myoglobin from femtoseconds to nanoseconds. The current work extends these studies to the millisecond regime to capture the full range of functionally relevant motions. These motions are often small and require a highly sensitive spectroscopy for their study. Diffractive optics-based four-wave mixing provides the sensitivity needed to observe changes in radius of <0.001 /spl Aring/. The use of diffractive optics facilitates the separation of Real and Imaginary parts of the /spl chi//sup 3/ signal by providing the required beam geometry for mixing the signal with a reference beam. In addition it offers passive phase-stabilization. A novel detection method that exploits the symmetry of the four-wave mixing experiment has been implemented to provide automatic isolation of the Real part of the signal. This simplifies the interpretation of the data by obviating the need to identify the Imaginary part of the signal. Further improvement in the signal-to-noise is an added benefit of this method.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".