A combination of PhP typing and β-<scp>d</scp>-glucuronidase gene sequence variation analysis for differentiation of <i>Escherichia coli</i> from humans and animals
Bibliographic record
Abstract
We investigated the usefulness of the β-d-glucuronidase gene variance in Escherichia coli as a microbial source tracking tool using a novel algorithm for comparison of sequences from a prescreened set of host-specific isolates using a high-resolution PhP typing method. A total of 65 common biochemical phenotypes belonging to 318 E. coli strains isolated from humans and domestic and wild animals were analysed for nucleotide variations at 10 loci along a 518 bp fragment of the 1812 bp β-d-glucuronidase gene. Neighbour-joining analysis of loci variations revealed 86 (76.8%) human isolates and 91.2% of animal isolates were correctly identified. Pairwise hierarchical clustering improved assignment; where 92 (82.1%) human and 204 (99%) animal strains were assigned to their respective cluster. Our data show that initial typing of isolates and selection of common types from different hosts prior to analysis of the β-d-glucuronidase gene sequence improves source identification. We also concluded that numerical profiling of the nucleotide variations can be used as a valuable approach to differentiate human from animal E. coli. This study signifies the usefulness of the β-d-glucuronidase gene as a marker for differentiating human faecal pollution from animal sources.
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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 | 0.003 |
| 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 | 0.000 |
| 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".