Integration and Persistence of Escherichia Coli O157:H7 86-24 in a Naturally-Occurring Water Well Biofilm
Bibliographic record
Abstract
Studies to determine how a microbe can persist in a foreign environment are essential in understanding water contamination by infectious agents. Annular reactors were designed, constructed, and used as a laboratory-based model to study naturally-occurring biofilms. Untreated groundwater was used a s the bulk liquid, and a foreign microbe with a green fluorescent protein (GFP) marker was introduced. It was demonstrated that under oligotrophic conditions Escherichia coli O157:H7 86-24 did not grow planktonically. The microbe demonstrated an ability to integrate into the existing biofilm. Various environmentally-relevant concentrations of nitrogen and phosphate were also used to detect any effects on its persistence. The results suggest that the persistence of the E. coli O157:H7 86-24 was enhanced when the bulk fluid was amended to contain 100 ppm nitrate, and hindered when phosphate was added. To utilize available molecular tools, polymerase chain reaction (PCR) coupled with denaturing high performance liquid chromatography (DHPLC) were used. It was found that the E. coli O157:H7-specific primers were not as reliable in detecting E.coli O157 within the biofilm when compared to detection using the GFP marker. PCR using 16S rRNA primers were also used to gain insight into the microbial diversity of the biofilm.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".