The Effects of Far‐red Light on Plant Growth and Flavonoid Accumulation in <i>Brassica napus</i> in the Presence of Ultraviolet B Radiation
Bibliographic record
Abstract
Flavonoid induction is regulated by complex signal transduction pathways involving cryptochrome, phytochrome and UVB photoreceptors. Previously, we identified the UVB-inducible flavonoids in Brassica napus cv. Topas leaves and showed that UVA affected accumulation of the quercetin (Q) and kaempferol (K) glycosides (Wilson et al. [2000] Photochem. Photobiol. 73, 678-684). In this study, we examined the effects of far-red light (FR, 700-780 nm) on UVB-mediated flavonoid accumulation in B. napus. Plants were grown under photosynthetically active radiation (PAR, 400-700 nm, 150 micromol m(-2) s(-1)) plus a moderate level of FR (35 micromol m(-2) s(-1)) for 14 days, and then transferred to five different irradiation regimes (PAR +/- [UVA + UVB] + moderate, intermediate or low fluence FR) for 4 days. Kinetics of flavonoid accumulation were assessed via HPLC. Accumulation of flavonoids, in general, was suppressed by increasing the amount of FR in the spectrum. Furthermore, addition of UVB (290-320 nm) to the spectrum altered the flavonoid composition by causing significant changes in the quantities of individual flavonoids. The relative levels of acylated K glycosides were diminished whereas the relative levels of nonacylated Q glycosides increased dramatically. With UVB exposure there was a five-fold increase in the Q:K ratio. In contrast, increasing the level of FR in the presence of UVB decreased the Q:K ratio by half.
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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) | 0.000 | 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".