Biotransformation and partial mineralization of the explosive 2,4,6-trinitrotoluene (TNT) by rhizobia
Bibliographic record
Abstract
Three strains, T10, B5, and M8, each belonging to a different species of the family Rhizobiaceae and isolated from atrazine-contaminated soils, were tested for their ability to transform 2,4,6-trinitrotoluene (TNT) (50 microg x mL(-1)) in liquid cultures using glucose as the C-source. All three strains were able to transform TNT to hydroxylaminodinitrotoluenes (2-HADNT, 4-HADNT), aminodinitrotoluenes (2-ADNT, 4-ADNT), and diaminonitrotoluene (2,4-DANT). The transformation was significantly faster in the presence of glutamate. Furthermore, the major metabolites that accumulated in cultures were 2-ADNT with glucose, and 4-ADNT with glutamate plus glucose. Rhizobium trifolii T10 was also tested for its ability to transform high levels of TNT (approximately 350 microg x mL(-1)) in a soil slurry. Almost 60% of the TNT was transformed within 2 days in bioaugmented soil slurries, and up to 90% when cultures were supplemented with glucose and glutamate. However, mineralization was minimal in all cases, less than 2% in 78 days. This is the first report on the degradation of TNT by rhizobial strains, and our findings suggest that rhizobia have the potential to play an important role in the safe decontamination of soils and sites contaminated with TNT if bioaugmentation with rhizobia is shown to have no ecotoxicological consequence.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".