Phytoglobin expression influences soil flooding response of corn plants
Bibliographic record
Abstract
Background and Aims Excess water is a limiting factor for crop productivity. Under conditions of full submergence or flooding, plants can experience prolonged oxygen depletion which compromises basic physiological and biochemical processes. Severe perturbations of the photosynthetic machinery with a concomitant decline in photosynthetic potential as a result of elevated levels of reactive oxygen species (ROS) are the major consequences of water excess. Phytoglobins (Pgbs) are ubiquitous proteins induced by several types of stress which affect plant response by modulating nitric oxide. Methods Maize plants overexpressing or downregulating two Pgb genes were subjected to soil flooding for 10 d and their performance was estimated by measuring several gas exchange parameters including photosynthetic rate. Above-ground tissue was utilized to localize ROS and to measure the expression and activities of major antioxidant enzymes. Key Results Relative to the wild type, flooded plants overexpressing Pgb genes retained a greater photosynthetic rate and enhanced activity of several antioxidant enzymes. These plants also exhibited high levels of ascorbic acid and reduced ROS staining. This was in contrast to flooded plants downregulating Pgb genes and characterized by the lowest photosynthetic rates and reduced expression and activities of many antioxidant enzymes. Conclusions Induction of Pgb genes alleviates flooding stress by limiting ROS-induced damage and ensuring a sustained photosynthetic rate. This is achieved through improvements of the ascorbate antioxidant status including an enrichment of the ascorbate pool via de novo and recycling mechanisms, and increased activities of several ROS-scavenging enzymes.
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) | 0.000 | 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".