Unsupervised recognition of components from the interaction of BSA with Fe cluster in different conditions utilizing 2D fluorescence spectroscopy
Bibliographic record
Abstract
(Fe) using unsupervised classification methods. Herein, the interaction of bovine serum albumin (BSA) and Fe clusters as an artificial enzyme is studied by extracting the intrinsic excitation-emission (EEM) fluorescence of BSA. The conformation of BSA changes with pH, temperature, and Fe concentration. Three-way fluorescence data were recorded for BSA and BSA/Fe during different days. The obtained results showed that the Fe clusters cause changes in the structure of BSA conformation as a function of pH, temperature, and Fe concentration. Also, the denaturation pathway of the BSA molecule is significantly different in the presence of Fe clusters. Both techniques of PARAFAC and PCA were used in the excitation-emission fluorescence matrices (EEM) of solutions at three different pH (5.0, 7.0, and 9.0) and temperatures (15.0, 25.0, and 35.0 °C) values. Also, we reported the results of the change in concentrations of Fe (4.0, 6.0, and 8.0 mg) using these methods. These three amino acids (tyrosine, tryptophan, and phenylalanine) indicate all datasets and their similarities and differences. The spectral differences were more remarkable in different pH values compared to different temperatures. Also, we could distinguish between the groups of protein samples properly in different concentrations of Fe using low-cost EEM spectral images and PARAFAC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".