Method for Optimizing Pulsed-Field Gel Electrophoresis Banding Pattern Data
Bibliographic record
Abstract
The genomic DNA of 47 strains of TSST-1 toxin-producing Staphylococcus aureus were cleaved with SmaI restriction endonuclease and resolved in an agarose gel by pulsed-field gel electrophoresis (PFGE). An algorithm was designed to standardize the band weights or brightness (trace quantity) produced to a bounded region between 0 and 1 regardless of DNA fragment size while simultaneously reducing gel-to-gel variability. The algorithm allows for classification of isolates by band intensity as well as DNA mobility without a numerical hierarchy of band intensity that is caused by ranging DNA fragment lengths. On analysis many isolates were classified as separate entities on the basis of DNA co-migration only. Isolates differing by only DNA co-migration were subjected to a second digestion with restriction enzyme SacII. These isolates were characterized similarly to the standardized trace quantity analysis of SmaI PFGE patterns. The standardization method proposed in this article permits characterization of isolates on the basis of band differences, regardless of DNA co-migration, thus increasing the discriminatory power (0.79 to 0.89) of PFGE by increasing band-associated information. An established unbiased approach to the partitioning of data were also explored.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".