Biotic elicitors as a means of increasing isoflavone concentration of soybean seeds
Bibliographic record
Abstract
Summary Soybean (Glycine max) seeds contain isoflavones that have positive impacts on human health. Four greenhouse experiments were conducted to determine if isoflavone concentration of mature soybean seeds could be increased using elicitor compounds. The effects on soybean seed isoflavone concentrations following foliar applications of two lipo‐chitooligosaccharides (LCO) [Bj V (C18:1 MeFuc) and Bj V (Ac, C16, MeFuc)], chitosan, actinomycetes spores (Streptomyces melanosporofaciens strain EF‐76) and yeast extract at different concentrations and growth stages were evaluated. Combined chitosan seed treatment and foliar applications were also evaluated. Concentrations of daidzein, genistein, glycitein, and total isoflavones were determined by HPLC. Foliar applications of LCOs, chitosan, and actinomycetes caused a marked increase in individual and total isoflavone concentration (ranging between 21% and 84%) of mature seeds when compared to untreated control plants. There were limited differences between the different concentrations and stages of application tested for chitosan and actinomycetes; however, response to LCOs was greatest at higher concentrations (i.e. 10‐6 M) when applied at the early podding stage. Compared to untreated plants, combined seed treatment and foliar applications of chitosan increased individual and total isoflavone concentration of mature soybean seeds by 16% to 93%. Trends were similar for different cultivars, however, the magnitude of the response varied. Finally, response to foliar applications of yeast extract was highly concentration dependent with increases of up to 56% in total isoflavone observed with 2 mg mL‐1. Results indicate that elicitors hold promise as a way of increasing isoflavone concentration of mature 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 | 0.001 |
| 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".