Atomic force microscopy and Raman scattering spectroscopy studies on heat‐induced fibrous aggregates of <i>β</i>‐lactoglobulin
Bibliographic record
Abstract
Nanometer‐thick fibrous aggregates of β‐lactoglobulin alone and its mixture with other globular proteins were formed by heating aqueous solutions at pH 2 with maintaining an effective level of electrostatic repulsion among denatured protein molecules. In atomic force microscopy (AFM) images, these fibrous aggregates appeared to be fairly uniform in width and height and composed of strings of globular elements. Fibrous aggregates formed in β‐lactoglobulin individual systems were only slightly thicker than the size of the native β‐lactoglobulin monomer, while those formed in the presence of other globular proteins were more than twice thicker, suggesting that different species of globular proteins were incorporated into each individual fibrous aggregate in the mixed systems. At neutral pH, aggregates were generally composed of ellipsoidal primary particles much larger than the size of the monomer, suggesting that aggregation proceeds in two steps at neutral pH. Molecular structural changes probed by Raman scattering spectroscopy revealed that considerable fractions of β‐sheet structures remained to be folded during the formation of fibrous aggregates but α‐helix structures were partially lost. It was also suggested that a limited extent of hydrophobic interactions among heat‐denatured protein molecules is required for the fibrous aggregation.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".