Soybean Seed Development: Fatty Acid and Phytohormone Metabolism and Their Interactions
Bibliographic record
Abstract
Vegetable oil utilization is determined by its fatty acid composition. In soybean and other grain crops, during the seed development oil accumulation is important trait for value in food or industrial applications. Seed development is relatively short and sensitive to unfavorable abiotic conditions. These stresses can lead to a numerous undesirable qualitative as well as quantitative changes in fatty acid production. Fatty acid manipulation which targets a higher content of a specific single fatty acid for food or industrial application has gained more attention. Despite several successes in modifying the ratio of endogenous fatty acids in most domesticated oilseed crops, numerous obstacles in FA manipulation of seed maturation are yet to be overcome. Remarkably, connections with plant hormones have not been well studied despite their critical roles in the regulation and promotion of a plethora of processes in plant growth and development. While activities of phytohormones during the reproductive phase have been partially clarified in seed physiology, the biological role of plant hormones in oil accumulation during seed development has not been investigated. In this review seed development and numerous effects of abiotic stresses are discussed. After describing fatty acid and phytohormone metabolism and their interactions, we postulate that the endogenous plant hormones play important roles in fatty acid production in soybean seeds.
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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) | 0.000 | 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".