Differential expression of leaf proteome of tolerant and susceptible maize (<i>Zea mays</i> L.) genotypes in response to multiple abiotic stresses
Bibliographic record
Abstract
In the present work, tropical maize genotypes were evaluated for multiple stresses (drought × low-N and waterlogging × low-N) applied simultaneously to 30-day-old maize seedlings. Two-dimensional gel electrophoresis was used to examine the protein changes induced by combined stress, in leaves, of tolerant and susceptible genotypes. Moreover, physiological and biochemical parameters were assessed to understand the physiological status of tolerant and susceptible genotypes under combined stress. The results show that up-regulated proteins of the tolerant genotype have a significant role in activating defense response, restoration of plant growth, and to maintain metabolic homeostasis under stressful conditions. Therefore, they contribute to improve and maintain the state of acclimation of the genotype under stress. Alternatively in the susceptible genotype, the up-regulated proteins are representative biomarkers of stress or are involved in the defense against pathogens and efforts to maintain energy metabolism. Thus, protecting the survival of the genotype under multiple stress conditions. We conclude that depending on the given stress treatment, tolerant and susceptible genotypes differed in stress-enduring approaches. Therefore, the study provides insight to comprehend the response of tolerant and susceptible genotypes under combined stress conditions, which could be valuable for further research and will demonstrate that it is advantageous to select combined stress-tolerant 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) | 0.001 | 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".