Characterization of arsenic-resistant bacteria from the rhizosphere of arsenic hyperaccumulator<i>Pteris vittata</i>
Bibliographic record
Abstract
Arsenic hyperaccumulator fern Pteris vittata L. produces large amounts of root exudates that are hypothesized to solubilize arsenic and maintain a unique rhizosphere microbial community. Total heterotrophic counts on rich or defined media supplemented with up to 400 mmol/L of arsenate showed a diverse arsenate-resistant microbial community from the rhizosphere of P. vittata growing in arsenic-contaminated sites. Twelve bacterial isolates tolerating 400 mmol/L of arsenate in liquid culture were identified. Selected bacterial isolates belonging to different genera were tested for their resistance to osmotic and oxidative stresses. Results showed that growth was generally better under osmotic stress generated by arsenic than under that generated by NaCl or PEG 6000, demonstrating that arsenic detoxification metabolism also cross-protected bacterial isolates from arsenic-induced osmotic stress. After 32 h of growth, all arsenate at 1 mmol/L was reduced to arsenite by strains Naxibacter sp. AH4, Mesorhizobium sp. AH5, and Pseudomonas sp. AH21, but arsenite at 1 mmol/L remained unchanged. Sensitivity to hydrogen peroxide was similar to that in broad-host pathogen Salmonella enterica sv. Typhimurium wild type, except strain AH4. The results suggested that these arsenic-resistant bacteria are metabolically adapted to arsenic-induced osmotic or oxidative stresses in addition to the specific bacterial system to exclude cellular arsenic. Both these adaptations contribute to the high arsenic resistance in the bacterial isolates.
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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".