Effects of in vitro Drought Stress on Growth, Proline Accumulation and Antioxidant Defense in Sugarcane
Bibliographic record
Abstract
Drought is the most limiting environmental factor to crop productivity and presents a great variability in the degree of tolerance among and within species, among varieties. The aim of this study was to characterize sugarcane accessions regarding tolerance to water stress during in vitro cultivation based on changes in biometric, physiological and biochemical characteristics, within species and among species, to support future breeding programs. Adventitious shoots of five sugarcane accessions: Saccharum robustum, Saccharum spontaneum and Saccharum officinarum species, cultivated in Murashige and Skoog medium supplemented with 2% sucrose and 4 g/l Phytagel were used in five water potentials, 0, -0.3, -0.6, -0.9, -1.2 MPa, induced by mannitol. Survival, length of shoots and roots, number of shoots and roots, biomass, proline content in leaves and activity of antioxidant enzymes were analyzed. There is difference among species, and also, within the same sugarcane species when submitted to in vitro drought stress, and S. officinarum was shown to be the most tolerant. Proline can be used as a biochemical indicator of response to drought in sugarcane accessions and its accumulation was intensified in S. robustum and S. spontaneum accessions. Catalase activity remained unchanged with increased drought in sugarcane accessions evaluated.
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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) | 0.001 | 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".