Molecular Mechanisms of Nitric Oxide (NO) Signaling and Reactive Oxygen Species (ROS) Homeostasis during Abiotic Stresses in Plants
Bibliographic record
Abstract
Abiotic stressors, such as drought, heavy metals, and high salinity, are causing huge crop losses worldwide. These abiotic stressors are expected to become more extreme, less predictable, and more widespread in the near future. With the rapidly growing human population and changing global climate conditions, it is critical to prevent global crop losses to meet the increasing demand for food and other crop products. The reactive gaseous signaling molecule nitric oxide (NO) is involved in numerous plant developmental processes as well as plant responses to various abiotic stresses through its interactions with various molecules. Together, these interactions lead to the homeostasis of reactive oxygen species (ROS), proline and glutathione biosynthesis, post-translational modifications such as S-nitrosylation, and modulation of gene and protein expression. Exogenous application of various NO donors positively mitigates the negative effects of various abiotic stressors. In view of the multidimensional role of this signaling molecule, research over the past decade has investigated its potential in alleviating the deleterious effects of various abiotic stressors, particularly in ROS homeostasis. In this review, we highlight the recent molecular and physiological advances that provide insights into the functional role of NO in mediating various abiotic stress responses in plants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".