Uncovering the Antifungal Potential of Plant-Associated Cultivable Bacteria from the Aral Sea Region against Phytopathogenic Fungi
Bibliographic record
Abstract
Two freshwater rivers, the Amu Darya and Syr Darya, flow into the Aral Sea, but they began to diminish in the early 1960s, and by the 1980s, the lake had nearly ceased to exist due to excessive water consumption for agriculture and the unsustainable management of water resources from rivers, which transformed the Aral Sea into a hypersaline lake. Despite this, the flora and fauna of the region began to evolve in the high-salinity seabed soil, which has received little attention in studies. In this study, we isolated approximately 1400 bacterial strains from the rhizosphere and phyllosphere of plant species of distinct families. Bacterial isolates were examined for antifungal activities against a range of pathogenic fungi such as Rhizoctonia gossypii, Trichothecium ovalisporum, Fusarium annulatum, F. oxysporum, F. culmorum, F. brachygibbosum, F. tricinctum, F. verticillioides, Alternaria alternata, A. terreus, Aspergillus niger, and As. flavus. Eighty-eight bacterial isolates exhibited varying antagonistic ability against pathogenic fungi. Furthermore, DNA barcoding of isolates using the 16S rRNA gene indicated that most antagonistic bacteria belonged to the Bacillus and Pseudomonas genera. The study also explored the activity of hydrolytic and cell-wall-degrading enzymes produced by antagonistic bacteria. The findings revealed that antagonistic bacteria can be utilized to widely protect seabed plants and plants growing in saline areas against pathogenic fungi, as well as agricultural crops.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 teacher head, 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".