Proteomic and microscopic analysis of biofilms formed by<i>Listeria monocytogenes</i>568
Bibliographic record
Abstract
Biofilm formation may be important in the colonization of the food-processing environment by the food-borne pathogen Listeria monocytogenes. Listeria monocytogenes 568 formed adherent multicellular layers on a variety of test surfaces following growth at 37 degrees C with multiple transfers of the test surface into fresh medium. Microscopic examination of these adherent layers suggest that the cells were surrounded by extracellular material. The presence of a carbohydrate containing extracellular polymeric matrix was confirmed by labelling hydrated adherent layers with fluorescein-conjugated concanavalin A, indicating that these adherent layers are biofilms. To gain insight into the physiological state of cells in these biofilms, the proteomes from biofilm- and planktonic-grown cells from the same cultures were compared using 2-dimensional polyacrylamide gel electrophoresis. Nineteen proteins, which exhibited higher levels of expression in biofilm-grown cells, were successfully identified from the 2-D gels using a combination of MALDI-TOF and MS/MS. Proteins that were found to be more highly expressed in biofilm-grown cells were involved in stress response, envelope and protein synthesis, biosynthesis, energy generation, and regulatory functions. In biofilm-grown cells, many proteins in the pH range 4-6 ran as multiple spots arranged horizontally across the 2-D gels.
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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 | 0.000 | 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 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".