Mitigation of Heat Stress in Solanum lycopersicum L. by ACC-deaminase and Exopolysaccharide Producing Bacillus cereus: Effects on Biochemical Profiling
Bibliographic record
Abstract
Soil microorganisms might be assessed for their capabilities of plant growth promotion in order to identify heat tolerant strategies for crop production. The planned study was conducted to determine the potential of heat tolerant plant growth promoting rhizobacteria (PGPR) in mitigating heat stress effects in tomato. Bacillus cereus was evaluated for plant growth promoting activities and assessed for 1-aminocyclopropane-1-carboxylate (ACC-deaminase) (0.76–C0.9 μM/mg protein/h), and exopolysaccharide (0.66–C0.91 mg/mL) under normal and heat stressed conditions. Plant growth regulators were evaluated through High Performance Liquid Chromatography. Bacterial inoculation effects on important physiological and biochemical parameters were evaluated under normal and heat stressed conditions in growth chamber. The morphological-physiological traits significantly revealed drastic effects on both of un-inoculated tomato varieties under heat stress conditions. Bacterial augmentation significantly promoted shoot, root length, leaf surface area, fresh and dry weight. Heat stress enhanced extracellular polymeric substances (EPS) production and cleavage of ACC into a-ketobutyrate and ammonia due to ACC-deaminase producing bacteria that significantly reduced the adverse effects of heat on tomato growth. In conclusion, the applied plant growth promoting rhizobacteria (PGPR) bacterial strain proved as potential candidate for improving tomato crop growing under heat stressed conditions. However, it is highly suggested to validate the current results by conducting field trials.
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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 | 0.000 | 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".