ACID TOLERANCE OF <i>ESCHERICHIA COLI</i> FOLLOWING COLD SHOCK TREATMENT
Bibliographic record
Abstract
ABSTRACT The effect of an initial cold shock treatment (2 h at 10C), following an abrupt downshift in temperature from 37 to 10C, on the subsequent growth and survival of Escherichia coli strains O157:H7 and MY20 (Biotype 1) in acidified Trypticase soy broth (TSB) and fruit juices (orange, apple) was investigated. Overall, no difference in growth at 37C was observed between each cold shocked and noncold shocked E. coli strain when cultured in TSB adjusted with either acetic acid (pH 6.0)or malic, citric and tartaric acid (each adjusted to: pH 4.5, 5.0, 5.5, 6.0). However, significant (P ± 0.05) differences in survival were observed between cold shocked and noncold shocked populations in TSB acidified with acetic acid (pH 5.0) or citric, malic and tartaric acid (pH 4.0). For strain MY20, survivor levels for cold shocked cells in TSB acidified with acetic acid citric, malic and tartaric acid at 8C were significantly (P ± 0.05) higher than in noncold shocked populations. Also, at 37C survival levels for cold shocked cells were significantly (P ± 0.05) higher than noncold shocked cells in TSB acidified with either malic or tartaric acid (pH 4.0). For the O157:H7 strain, survivor levels were higher (P ± 0.05) for cold shocked cells when maintained in TSB at 37C regardless of acid type. At 8C, cold shock treatment only increased (P ± 0.05) the survival of the O157:H7 strain in TSB adjusted with acetic acid (pH 6.0). Acid cross protection induced by cold shocking, as evidenced by enhanced survival, was not apparent for either E. coil strain in apple (pH 3.5) or orange juice (pH 3.8) maintained at 8C.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".