Effect of Dissolved Oxygen on Two Bacterial Pathogens Examined using ATR-FTIR Spectroscopy, Microelectrophoresis, and Potentiometric Titration
Bibliographic record
Abstract
The effects of dissolved oxygen tension during bacterial growth and acclimation on the cell surface properties and biochemical composition of the bacterial pathogens Escherichia coli O157:H7 and Yersinia enterocolitica are characterized. Three experimental techniques are used in an effort to understand the influence of bacterial growth and acclimation conditions on cell surface charge and the composition of the bacterial cell: (i) electrophoretic mobility measurements; (ii) potentiometric titration; and (iii) ATR-FTIR spectroscopy. Potentiometric titration data analyzed using chemical speciation software are related to measured electrophoretic mobilities at the pH of interest. Titration of bacterial cells is used to identify the major proton-active functional groups and the overall concentration of these cell surface ligands at the cell membrane. Analysis of titration data shows notable differences between strains and conditions, confirming the appropriateness of this tool for an overall charge characterization. ATR-FTIR spectroscopy of whole cells is used to further characterize the bacterial biochemical composition and macromolecular structures that might be involved in the development of the net surficial charge of the organisms examined. The evaluation of the integrated intensities of HPO(2)(-) and carbohydrate absorption bands in the IR spectra reveals clear differences between growth protocols. Taken together, the three techniques seem to indicate that the dissolved oxygen tension during cell growth or acclimation can noticeably influence the expression of cell surface molecules and the measurable cell surface charge, though in a strain-dependent fashion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".