The effect of <i>K‐</i> fluorescence reabsorption of selenium on the performance of an imaging detector for protein crystallography
Bibliographic record
Abstract
Abstract Protein crystallography is an important technique in the determination of three dimensional atomic structures of proteins. In order to reconstruct the atomic structure of protein from experimental diffraction data, both the magnitude and phase of the atomic scattering factor should be known. The phase can be calculated using the Multiple Wavelength Anomalous Dispersion (MAD) method. Selenium (Se) is commonly used as an anomalous scatterer in the MAD method since the K ‐edge of Se (12.6 keV) is readily accessible on most synchrotron X‐ray sources. Recently, we proposed a novel direct X‐ray conversion imager for protein crystallography which employs amorphous Se for the X‐ray to charge conversion and an amorphous silicon flat panel thin film transistor array as the charge image readout method. For the protein crystallography X‐ray energy range (6‐20 keV), X‐ray interaction in Se is due to the photoelectric effect. Therefore there is a possibility of generation and reabsorption of K ‐fluorescence at or above the K ‐edge. The reabsorption of a fraction of the generated K ‐fluorescence is a random process which leads to fluctuations in conversion gain and hence addition of image noise. In this paper, the physics of K ‐fluorescence is explained and the probability of K ‐fluorescence reabsorption of Se for the useful X‐ray energy of protein crystallography is calculated. Finally, the possible consequences from K ‐fluorescence reabsorption of Se on the performance of a protein crystallography imager where Se is used both as an anomalous scatterer and a photoconductor is elucidated. (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".