Isolation and characterization of yeasts associated with plants growing in heavy-metal- and arsenic-contaminated soils
Bibliographic record
Abstract
Yeasts were quantified and isolated from the rhizospheres of 5 plant species grown at 2 sites of a Mexican region contaminated with arsenic, lead, and other heavy metals. Yeast abundance was about 10(2) CFU/g of soil and 31 isolates were obtained. On the basis of the phylogenetic analysis of 26S rRNA and internal transcribed spacer fragment, 6 species were identified within the following 5 genera: Cryptococcus (80.64%), Rhodotorula (6.45%), Exophiala (6.45%), Trichosporon (3.22%), and Cystobasidium (3.22%). Cryptococcus spp. was the predominant group. Pectinases (51.6%), proteases (51.6%), and xylanases (41.9%) were the enzymes most common, while poor production of siderophores (16.1%) and indole acetic acid (9.67%) was detected. Isolates of Rhodotorula mucilaginosa and Cystobasidium sloffiae could promote plant growth and seed germination in a bioassay using Brassica juncea. Resistance of isolates by arsenic and heavy metals was as follows: As(3+) ≥ 100 mmol/L, As(5+) ≥ 30 mmol/L, Zn(2+) ≥ 2 mmol/L, Pb(2+) ≥ 1.2 mmol/L, and Cu(2+) ≥ 0.5 mmol/L. Strains of Cryptococcus albidus were able to reduce arsenate (As(5+)) into arsenite (As(3+)), but no isolate was capable of oxidizing As(3+). This is the first study on the abundance and identification of rhizosphere yeasts in a heavy-metal- and arsenic-contaminated soil, and of the reduction of arsenate by the species C. albidus.
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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 | 0.001 | 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) | 0.000 | 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".