Characterization of X-ray diffraction intensity function from a biological molecule for single particle imaging
Bibliographic record
Abstract
An attainable structural resolution of single particle imaging is determined by the characteristics of X-ray diffraction intensity, which depend on the incident X-ray intensity density and molecule size. To estimate the attainable structural resolution even for molecules whose coordinates are unknown, this research aimed to clarify how these characteristics of X-ray diffraction intensity are determined from the structure of a molecule. The functional characteristics of X-ray diffraction intensity of a single biomolecule were theoretically and computationally evaluated. The wavenumber dependence of the average diffraction intensity on a sphere of constant wavenumber was observable by small-angle X-ray solution scattering. An excellent approximation was obtained, in which this quantity was expressed by an integral transform of the product of the external molecular shape and a universal function related to its atom packing. A standard model protein was defined by an analytical form of the first factor characterized by molecular volume and length. It estimated the numerically determined wavenumber dependence with a worst-case error of approximately a factor of five. The distribution of the diffraction intensity on a sphere of constant wavenumber was also examined. Finally, the correlation of diffraction intensities in the wavenumber space was assessed. This analysis enabled the estimation of an attainable structural resolution as a function of the incident X-ray intensity density and the volume and length of a target molecule, even in the absence of molecular coordinates.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".