Seed Preconditioning with Natural and Synthetic Antioxidants Induces Drought Tolerance in Tomato Seedlings
Bibliographic record
Abstract
There is strong evidence that Ambiol® (a derivative of 5-hydroxybenzimazole) promotes drought tolerance in many plants; it is often suggested that this is the result of its antioxidant properties. Recent evidence has also shown that several natural antioxidants promote carrot germination under drought stress. Thus, it was hypothesized that seed preconditioning using natural antioxidants might confer drought tolerance. Ambiol®, ascorbic acid, β-carotene, lutein, and lycopene were chosen as antioxidants at concentrations of 0.1 mg·L −1 , 1.0 mg·L −1 , and 10 mg·L −1 . A preconditioning treatment was applied by soaking tomato ( Solanum lycopersicum L.) seeds in an antioxidant solution for 24 h. Of the antioxidants tested, 10 mg·L −1 Ambiol®, 1.0 mg·L −1 β-carotene, 1.0 mg·L −1 ascorbic acid, and 0.1 mg·L −1 lycopene were shown to increase shoot dry mass by 114%, 94%, 56%, and 83%, respectively, in droughted seedlings when compared with a droughted control. Similar benefits were observed in root dry mass, leaf area, photosynthesis, and water use efficiency. Proteins were extracted from the seeds of certain treatments, before and after germination, and separated using isoelectric focusing. Specific proteins were found to be induced through all preconditioning treatments, whereas Ambiol® and β-carotene were found to induce specific proteins, independent of those induced through imbibition, both before and after germination. This result suggests that Ambiol® and β-carotene evoke specific proteins that may confer drought tolerance to the key physiological processes studied. In addition, protein profiles of ascorbic acid, β-carotene, and Ambiol® after germination had fewer visible bands than the controls, suggesting an accelerated mobilization or conversion of proteins within the seed.
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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 | 0.001 |
| 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".