Formulation of Synthetic Bacteria Consortia for Enzymatic Biodegradation of Polyaromatic Hydrocarbons contaminated soil: Soil Column Study
Bibliographic record
Abstract
Abstract As an efficient method to remove contaminants from highly polluted sites, enzyme biodegradation addresses unresolved issues such as bioremediation inefficiency. In this study, the key enzymes involved in PAHs degradation were brought together from different arctic strains for biodegradation of highly contaminated soil. These enzymes were produced via multi-culture of psychrophilic Pseudomonas, Rhodococcus strains. As a result of biosurfactant production, the removal of pyrene was sufficiently prompted by Alcanivorax borkumensis. The key enzymes obtained via multi-culture were characterized by tandem LC-MS/MS and kinetic studies. To simulate in-situ application of produced enzyme solutions, pyrene and Dilbit contaminated soil was bioremediated in soil columns and flask tests by injecting enzyme cocktails from most promising consortia. Enzyme cocktail contained about 35.2 U/mg protein pyrene dioxygenase, 61.4 U/mg protein naphthalene dioxygenase, 56.5 U/mg protein catechol-2,3-dioxygenase, 6.1 U/mg protein 1-hydroxy-2-naphthoate hydroxylase, and 33.5 U/mg protein protocatechuic acid (P3,4D) 3,4-dioxygenase enzymes. It was found that after 6 weeks, the average pyrene removal values showed that the enzyme solution could be an effective in the soil column system (80–85% degradation of pyrene).
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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".