Coaggregation between <i>Rhodococcus</i> and <i>Acinetobacter</i> strains isolated from the food industry
Bibliographic record
Abstract
In this study, coaggregation interactions between Rhodococcus and Acinetobacter strains isolated from food-processing surfaces were characterized. Rhodococcus sp. strain MF3727 formed intrageneric coaggregates with Rhodococcus sp. strain MF3803 and intergeneric coaggregates with 2 strains of Acinetobacter calcoaceticus (MF3293, MF3627). Stronger coaggregation between A. calcoaceticus MF3727 and Rhodococcus sp. MF3293 was observed after growth in batch culture at 30 °C than at 20 °C, after growth in tryptic soy broth than in liquid R2A medium, and between cells in exponential and early stationary phases than cells in late stationary phase. The coaggregation ability of Rhodococcus sp. MF3727 was maintained even after heat and Proteinase K treatment, suggesting its ability to coaggregate was protein independent whereas the coaggregation determinants of the other strains involved proteinaceous cell-surface-associated polymers. Coaggregation was stable at pH 5-9. The mechanisms of coaggregation among Acinetobacter and Rhodococcus strains bare similarity to those displayed by coaggregating bacteria of oral and freshwater origin, with respect to binding between proteinaceous and nonproteinaceous determinants and the effect of environmental factors on coaggregation. Coaggregation may contribute to biofilm formation on industrial food surfaces, protecting bacteria against cleaning and disinfection.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".