Fate of<i>Escherichia coli</i>O157:H7 in irrigation water on soils and plants as validated by culture method and real-time PCR
Bibliographic record
Abstract
One of the most common vehicles by which Escherichia coli O157:H7 may be introduced into crops is contaminated irrigation water. Water contamination is becoming more common in rural areas of the United States as a result of large animal operations, and up to 40% of tested drinking-water wells are contaminated with E. coli. In this study, 2 contrasting soil samples were inoculated with E. coli O157:H7 expressing green fluorescent protein through irrigation water. Real-time PCR and culture methods were used to quantify the fate of this pathogen in phyllosphere (leaf surface), rhizosphere (volume of soil tightly held by plant roots), and non-rhizosphere soils. A real-time PCR assay was designed with the eae gene of E. coli O157:H7. The probe was incorporated into real-time PCR containing DNA extracted from the phyllosphere, rhizosphere, and non-rhizosphere soils. The detection limit for E. coli O157:H7 quantification by real-time PCR was 1.2 x 10(3) in the rhizosphere, phyllosphere, and non-rhizosphere samples. E. coli O157:H7 concentrations were higher in the rhizosphere than in the non-rhizosphere soils and leaf surfaces, and persisted longer in clay soil. The persistence of E. coli O157:H7 in phyllosphere, rhizosphere, and non-rhizosphere soils over 45 days may play a significant part in the recontamination cycle of produce in the environment. Therefore, the rapidity of the real-time PCR assay may be a useful tool for quantification and monitoring of E. coli O157:H7 in irrigation water and on contaminated fresh produce.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".