The concentrations of hexadecane and inorganic nutrients modulate the production of extracellular membrane-bound vesicles, soluble protein, and bioemulsifier by <i>Acinetobacter venetianus</i> RAG-1 and <i>Acinetobacter</i> sp. strain HO1-N
Bibliographic record
Abstract
In the present study, we addressed the possibility that the production of both bioemulsifiers and membrane-bound vesicles may be a common feature of the growth of Acinetobacter spp. on alkanes, and we determined the extent to which the release of extracellular products by these organisms is regulated by the concentrations of the alkane substrate and inorganic nutrients. To accomplish this objective, we grew Acinetobacter venetianus RAG-1 and Acinetobacter sp. strain HO1-N with different concentrations of nutrients and assayed for extracellular products. The results indicated that the release of vesicles, soluble protein, and bioemulsifier was promoted in various degrees by higher concentrations of hexadecane and inorganic nutrients, while the specific activities of the bioemulsifiers were enhanced with lower nutrient concentrations. Based on our findings, we propose that under conditions of nutrient excess, these strains produce membrane-bound vesicles to function in "luxury uptake" of the alkane substrate for delivery and storage in the form of inclusions. Under the same conditions, soluble bioemulsifier and protein may perform auxiliary roles in cell desorption and (or) alkane uptake. With low concentrations of nutrients, the decreased production of vesicles, protein, and bioemulsifier and the increased activity of the emulsifier may represent a mechanism for reducing biosynthetic demands and conserving cellular material.
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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".