Study of characteristics on metabolism of <i>Penicillium chrysogenum</i> F1 during bioleaching of heavy metals from contaminated soil
Bibliographic record
Abstract
F1 is very efficient in bioleaching heavy metals from the soil and is used for that purpose. We found that F1 can extract 19.8 mg Cd, Cu, Pb, and Zn from 2.5 g soil; the total heavy metals' bioleaching ratio was 60.4%. In this study, the bioleaching mechanism was investigated by means of metabonomics; different metabolite ions were screened (relative standard deviation >30%) and analyzed using clustering, univariate and multivariate analysis. Statistical analyses via Volcano Plot, principal component analysis, and partial least square discriminant analysis models revealed a difference between Ctrl 7 (the controls cultured and sampled on day 7) and Ctrl 15 (the controls cultured and sampled on day 15). Samp 15 (the samples cultured with heavy-metal-contaminated soil) was significantly different from Ctrl 7 and Ctrl 15. Analysis of the different ions demonstrated that the glucose catabolism pathways of glycolysis and the tricarboxylic acid (TCA) cycle were enhanced, and glucose anabolism through the pentose phosphate pathway was inhibited during bioleaching. At the same time, the metabolism of glutathione was also downregulated. Therefore, the catabolism of glucose was unaffected by the addition of heavy-metal-contaminated soil, and increasing glucose is beneficial to catabolism. The extraction of metals is mainly attributed to the metabolites of the TCA cycle.
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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 | 0.001 |
| 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) | 0.001 | 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".