Role of aquaporins in determining transpiration and photosynthesis in water‐stressed plants: crop water‐use efficiency, growth and yield
Bibliographic record
Abstract
The global shortage of fresh water is one of our most severe agricultural problems, leading to dry and saline lands that reduce plant growth and crop yield. Here we review recent work highlighting the molecular mechanisms allowing some plant species and genotypes to maintain productivity under water stress conditions, and suggest molecular modifications to equip plants for greater production in water-limited environments. Aquaporins (AQPs) are thought to be the main transporters of water, small and uncharged solutes, and CO2 through plant cell membranes, thus linking leaf CO2 uptake from the intercellular airspaces to the chloroplast with water loss pathways. AQPs appear to play a role in regulating dynamic changes of root, stem and leaf hydraulic conductivity, especially in response to environmental changes, opening the door to using AQP expression to regulate plant water-use efficiency. We highlight the role of vascular AQPs in regulating leaf hydraulic conductivity and raise questions regarding their role (as well as tonoplast AQPs) in determining the plant isohydric threshold, growth rate, fruit yield production and harvest index. The tissue- or cell-specific expression of AQPs is discussed as a tool to increase yield relative to control plants under both normal and water-stressed conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".