Influence of Drought Stress on Oxidative Damage and Antioxidant Defense Systems in Tolerant and Susceptible Wheat Genotypes
Bibliographic record
Abstract
Drought is one of the major factors limiting crop production in arid and semi-arid regions. Twenty wheat genotypes with wide range of sensitivity to drought, including 18 varieties of bread wheat (Triticum aestivum L.) and two varieties of durum wheat (Triticum turgidum L.) were used in two separate field experiments in 2009-2010 at the Experimental Station of College of Agriculture in Shiraz University. Each experiment was conducted as a randomized completed block design with three replications. The moisture level in one of the experiments was optimum (100% field capacity) while the second experiment was conducted under drought stress (45% field capacity). Several biochemical components including enzymatic (catalase, CAT; peroxidase, POD; superoxide dismutase, SOD and ascorbate peroxidase, APX) and non-enzymatic (proline and carotenoids, Car) antioxidant defense systems and some factors of oxidative damage (hydrogen peroxide, H2O2; lipid peroxidation, LPO and membrane stability index, MSI) were analyzed in the two conditions. Drought stress caused significant increase in enzymatic antioxidant activities, proline content, H2O2 and LPO content at the flowering stage, while Car content and MSI decreased significantly in all genotypes. Drought tolerant genotypes showed the highest enzymatic and non-enzymatic antioxidants, highest MSI and the lowest LPO and H2O2. This trend was reversed in susceptible genotypes. The enzymatic antioxidants had higher correlation than non-enzymatic with oxidative stress factors and yield stability index (YSI). POD showed the highest positive correlation with MSI and the highest negative correlation with LPO. H2O2 and MSI showed the highest correlation with YSI. In present study, Kavir and Alamut varieties were selected respectively as the most tolerant and susceptible genotypes.
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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".