Screening of Bacterial Endophytes Able to Promote Plant Growth and Increase Salinity Tolerance
Bibliographic record
Abstract
Bacterial endophytes can colonize plant tissues without harming the plant. Instead, they are often able to increase plant growth and tolerance to environmental stresses. In this work, new strains of bacterial endophytes were isolated from three economically important crop plants (sorghum, cucumber and tomato) grown in three different regions in soils with different management. All bacterial strains were identified by 16S rRNA sequencing and characterized for plant beneficial traits. Based on physiological activities, we selected eight strains that were further tested for their antibiotic resistance profile and for the ability to efficiently colonize the interior of sorghum plants. According to the results of the re-inoculation test, five strains were used to inoculate sorghum seeds. Then, plant growth promotion activity was assessed on sorghum plants exposed to salinity stress. Only two bacterial endophytes increased plant biomass, but three of them delayed or reduced plant salinity stress symptoms. These five strains were then characterized for the ability to produce the enzyme 1-aminocyclopropane-1-carboxylate (ACC) deaminase, which is involved in the increase of stress tolerance. Pseudomonas brassicacearum SVB6R1 was the only strain that was able to produce this enzyme, suggesting that ACC deaminase is not the only physiological trait involved in conferring plant tolerance to salt stress in these bacterial strains.
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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 | 0.000 |
| 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) | 0.001 | 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".