Effects of Brassinosteroids on Photosynthetic Performance and Nitrogen Metabolism in Pepper Seedlings under Chilling Stress
Bibliographic record
Abstract
To investigate the effects of brassinosteroids on plant growth and nitrogen metabolism in pepper seedlings under chilling stress, pepper seedlings with three true leaves were foliar pretreated with 0.1 μM exogenous 24-epibrassinolide (EBR) before carrying out chilling stress for 7 days. The results showed that perapplication of EBR mitigated the chill-induced decrease in plant growth via maintenance of a high net photosynthetic rate (Anet), maximum quantum efficiency (Fv/Fm), and photochemical quenching coefficient (qP). Exogenous EBR markedly increased the levels of partial free amino acids (proline, arginine, aspartic acid, and glycine) and promoted nitrogen metabolism through increasing the activities of nitrate reductase (NR), glutamine synthase (GS), glutamate synthase (GOGAT), and glutamate dehydrogenase (GDH) in the leaves of pepper seedlings under chilling stress. The effect of exogenous EBR on the content of reactive oxygen species was also investigated. Pretreatment with EBR reduced the accumulation of hydrogen peroxide (H2O2) and superoxide anion (O2−·), and concomitantly alleviated membrane lipid peroxidation of pepper leaves under chilling stress. These results suggest that foliar pretreatment of EBR has a positive effect on improving the chilling tolerance of pepper seedlings via maintaining a high photosynthetic capability and enhancing the nitrogen metabolism.
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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".