Role of Cold Climate and Freeze–Thaw on the Survival, Transport, and Virulence of<i>Yersinia enterocolitica</i>
Bibliographic record
Abstract
Surface and near-surface soils in cold climate regions experience low temperature and freeze-thaw (FT) conditions in the winter. Microorganisms that are of concern to groundwater quality may have the potential to survive low temperature and FT in the soil and aqueous environments. Although there is a body of literature on the survival of pathogenic bacteria at different environmental conditions, little is known about their transport behavior in aquatic environments at low temperatures and after FT. Herein, we studied the survival, transport, and virulence of a Gram-negative bacterial pathogen, Yersinia enterocolitica, when subjected to low temperature and several FT cycles at two solution ionic strengths (10 and 100 mM) in the absence of nutrients. Our findings demonstrate that this bacterium exhibited higher retention on sand after exposure to FT. Increasing the number of FT cycles resulted in higher bacterial cell surface hydrophobicity and impaired the swimming motility and viability of the bacterium. Moreover, the transcription of flhD and fliA, the flagellin-encoding genes, and lpxR, the lipid A 3'-O-deacylase gene, was reduced in low temperature and after FT treatment while the transcription of virulence factors such as ystA, responsible for enterotoxin production, ail, attachment invasion locus gene, and rfbC, O-antigen gene, was increased. Y. enterocolitica tends to persist in soil for long periods and may become more virulent at low temperature in higher ionic strength waters in cold regions.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".