Stress resistance in naturalised waste water <i>E. coli</i> strains
Bibliographic record
Abstract
The authors recently demonstrated that naturalised strains of Escherichia coli exist in municipal waste water, characterised by (a) biomarker patterns in intergenic regions distinct from human and animal E. coli strains and (b) an insertion element (IS30) located in the uspC–flhDC intergenic region of the genome. Remarkably, these strains are naturally adapted to survival and growth in waste water and differentially survive the treatment process. The authors sought to explore the adaptive mechanisms used by these strains for survival. A serial stress experiment (nutrient deprivation and osmotic stress followed by chlorine treatment) was performed and survival was measured using culture. Waste water strains were shown to be approximately 100 times more resistant to chlorine treatment than a wild-type human faecal strain. Naturalised waste water strains were also more robust at producing biofilms – an adaptive strategy for surviving environmental stressors. Since biofilm formation has been linked to increased motility, the authors examined the expression of the flagellar regulator gene, flhDC, under serial stress conditions. Chlorine was a potent inducer of flhDC expression in waste water strains. The results demonstrate that waste water strains possess adaptive genotypic/phenotypic properties related to their survival in waste water and challenge the understanding of treatment reduction based on E. coli as an indicator of treatment performance.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".