Enhanced Arsenic Tolerance in Triticum aestivum Inoculated with Arsenic-Resistant and Plant Growth Promoter Microorganisms from a Heavy Metal-Polluted Soil
Bibliographic record
Abstract
In soils multi-contaminated with heavy metal and metalloids, the establishment of plant species is often hampered due to toxicity. This may be overcome through the inoculation of beneficial soil microorganisms. In this study, two arsenic-resistant bacterial isolates, classified as Pseudomonas gessardii and Brevundimonas intermedia, and two arsenic-resistant fungi, classified as Fimetariella rabenhortii and Hormonema viticola, were isolated from contaminated soil from the Puchuncaví valley (Chile). Their ability to produce indoleacetic acid and siderophores and mediate phosphate solubilization as plant growth-promoting properties were evaluated, as well as levels of arsenic resistance. A real time PCR applied to Triticum aestivum that grew in soil inoculated with the bacterial and fungal isolates was performed to observe differences in the relative expression of heavy metal stress defense genes. The minimum inhibitory concentration of the bacterial strains to arsenate was up to 7000 mg·L−1 and that of the fungal strains was up to 2500 mg·L−1. P. gessardi was able to produce siderophores and solubilize phosphate; meanwhile, B. intermedia and both fungi produced indoleacetic acid. Plant dry biomass was increased and the relative expression of plant metallothionein, superoxide dismutase, ascorbate peroxidase and phytochelatin synthase genes were overexpressed when P. gessardii plus B. intermedia were inoculated.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".