Seasonal Shedding of <i>Escherichia coli</i> O157:H7 in Ruminants: A New Hypothesis
Bibliographic record
Abstract
Seasonal shedding of Escherichia coli O157:H7 in ruminants is well documented; however, viable explanations for this phenomenon are lacking. The fecal prevalence of E. coli O157:H7 in ruminants is the highest in the summer months, decreasing to low or undetectable levels in the winter. To determine if day length is correlated to seasonal shedding, nine reports of E. coli O157:H7 prevalence in cattle were selected for similarity of sampling technique, culture methodology, cattle type, and ability to determine approximate sampling month and location, representing three regions in North America (southern Canada, midwestern United States, and the southern United States/Mexico). Correlation and regression analysis revealed a positive correlation existed between day length (r = 0.67; R2 = 0.45; P = 0.0009) and, to a lesser extent, ambient temperature (r = 0.43; R2 = 0.19; P = 0.05) and E. coli O157:H7 prevalence. Based on these results, a study was conducted in a commercial feedlot to examine the effect of artificial lighting on E. coli O157 prevalence. Four pens of cattle received approximately 5 hours of artificial lighting daily (in addition to the natural light) for 60 days and four pens served as controls. Freshly voided fecal samples (representing approximately 30% of the steers/pen) were collected from the pen floor and cultured for E. coli O157 after 25 and 53 days of artificial lighting and 28 and 43 days following cessation of the lighting treatment. Fecal prevalence of E. coli O157 remained constant in the lighted pens whereas prevalence was lower (P < 0.01) in the control treatment after 53 days of lighting. Forty-three days following cessation of the lighting treatment, prevalence decreased in the lighted treatment group to levels comparable (P > 0.20) to control levels. Understanding the mechanisms underlying the seasonality of this important foodborne pathogen will enable the implementation of effective control strategies when it is most prevalent.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".