Pathogenic Potential, Genetic Diversity, and Population Structure of Escherichia coli Strains Isolated from a Forest-Dominated Watershed (Comox Lake) in British Columbia, Canada
Bibliographic record
Abstract
Escherichia coli isolates (n = 658) obtained from drinking water intakes of Comox Lake (2011 to 2013) were screened for the following virulence genes (VGs): stx1 and stx2 (Shiga toxin-producing E. coli [STEC]), eae and the adherence factor (EAF) gene (enteropathogenic E. coli [EPEC]), heat-stable (ST) enterotoxin (variants STh and STp) and heat-labile enterotoxin (LT) genes (enterotoxigenic E. coli [ETEC]), and ipaH (enteroinvasive E. coli [EIEC]). The only genes detected were eae and stx2, which were carried by 37.69% (n = 248) of the isolates. Only eae was harbored by 26.74% (n = 176) of the isolates, representing potential atypical EPEC strains, while only stx2 was detected in 10.33% (n = 68) of the isolates, indicating potential STEC strains. Moreover, four isolates were positive for both the stx2 and eae genes, representing potential EHEC strains. The prevalence of VGs (eae or stx2) was significantly (P < 0.0001) higher in the fall season, and multiple genes (eae plus stx2) were detected only in fall. Repetitive element palindromic PCR (rep-PCR) fingerprint analysis of 658 E. coli isolates identified 335 unique fingerprints, with an overall Shannon diversity (H') index of 3.653. Diversity varied among seasons over the years, with relatively higher diversity during fall. Multivariate analysis of variance (MANOVA) revealed that the majority of the fingerprints showed a tendency to cluster according to year, season, and month. Taken together, the results indicated that the diversity and population structure of E. coli fluctuate on a temporal scale, reflecting the presence of diverse host sources and their behavior over time in the watershed. Furthermore, the occurrence of potentially pathogenic E. coli strains in the drinking water intakes highlights the risk to human health associated with direct and indirect consumption of untreated surface water.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".