Biosynthesis of 2-Acetyl-1-pyrroline in Fragrant Rice: Recent Insights into Agro-management, Environmental Factors, and Functional Genomics
Bibliographic record
Abstract
Rice is a staple food for more than half of the world's population, and rice fragrance is a key quality attribute which is highly desired by consumers and attracts premium prices in the international market. There are around 200 volatile compounds involved in rice fragrance, but 2-acetyl-1-pyrroline (2-AP) has been considered a master regulator of aroma in fragrant rice. Consequently, efforts were made to increase the 2-AP contents in the grain by managing agronomical practices or by using modern functional genomic tools, which successfully converted nonfragrant cultivars to fragrant rice. Furthermore, environmental factors were also reported to influence the 2-AP contents. However, a comprehensive analysis of 2-AP biosynthesis in response to agro-management practices, environmental factors, and the application of functional genomic tools for fragrant rice production was missing. In this Review, we summarize how micro/macronutrients, cultivation practices, amino acid precursors, growth regulators, and environmental factors, such as drought, salinity, light, and temperature, influence the 2-AP biosynthesis to modulate the aroma of fragrant rice. Furthermore, we also summarized the successful conversion of nonfragrant rice cultivars to fragrant rice using modern gene editing tools, such as RNAi, TALENS, and CRISPR-Cas9. Finally, we discussed and highlighted the future perspective and challenges related to the aroma of fragrant rice.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".