Competition among Escherichia coli Strains for Space and Resources
Bibliographic record
Abstract
Shiga toxin-producing Escherichia coli (STEC) are a subgroup of E. coli causing human diseases. Methods to control STEC in livestock and humans are limited. These and other emerging pathogens are a global concern and novel mitigation strategies are required. Habitats populated by bacteria are subjected to competition pressures due to limited space and resources but they use various strategies to compete in natural environments. Our objective was to evaluate non-pathogenic E. coli strains isolated from cattle feces for their ability to out-compete STEC. Competitive fitness of non-pathogenic E. coli against STEC were assessed in competitions using liquid, agar, and nutrient limiting assays. Winners were determined by enumeration using O-serogroup specific quantitative PCR or a semi-quantitative grading. Initial liquid competitions identified two strong non-pathogenic competitors (O103F and O26E) capable of eliminating various STEC including O157 and O111. The strain O103F was dominant across permeable physical barriers for all tested E. coli and STEC strains indicating the diffusion of antimicrobial molecules. In direct contact and even with temporal disadvantages, O103F out-competed STEC O157E. The results suggest that O103F or the diffusible molecule(s) it produces have a potential to be used as an alternative STEC mitigation strategy, either in medicine or the food industry.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".