Utility of the <i>Arabidopsis FAEI</i> and yeast <i>SLCI-I</i> genes for improvements in erucic acid and oil content in rapeseed
Bibliographic record
Abstract
High-erucic acid (HEA) Brassica napus cultivars are regaining interest in industrial contexts. Erucic acid and its derivatives are important renewable raw materials utilized in the manufacture of plastic films, in the synthesis of Nylon 13,13, and in the lubricant and emollient industries. Theoretically, the highest level of erucic acid that can be achieved by means of classical breeding is 66 mol%; however, using new approaches on the basis of genetic engineering, it might be possible to develop a B. napus cultivar containing levels of erucic acid significantly above 66 mol% (>80 mol%). In an attempt to increase the amounts of very-long-chain fatty acids (VLCFAs), and erucic acid in particular, in Canadian HEA B. napus cultivars, we have focused on two targets using a transgenic approach. We examined both the role/function of the Arabidopsis thaliana FAE1 (fatty acid elongase) gene by expressing it under the control of the seed-specific napin promoter in B. napus germplasm with analysis of the changes in VLCFA content in the seed oil of transgenic lines, and the performance of the yeast SLC1-1 (sphingolipid compensation mutant) in B. napus cv. Hero transgenic progeny in the field. Here, we report analyses of the contents of 22:1, total VLCFAand oil in the seed oil, as well as seed yield of the field-grown FAE1 and SLC1-1 B. napus cv. Hero progeny.
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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".