A Plant biostimulant prepared from <i>Ascophyllum nodosum</i> Induces Flowering by Regulating the <scp><i>MIR156</i></scp>‐mediated Age Pathway in Arabidopsis
Bibliographic record
Abstract
Flowering, the change from vegetative development to the reproductive phase, represents a crucial and intricate stage in the life cycle of plants, which is tightly controlled by both internal and external factors. In this study, we investigated the effect of Ascophyllum nodosum extract (ANE) on the flowering time of Arabidopsis. We found that a 0.1% concentration of ANE induced flowering in Arabidopsis, accompanied by the upregulation of key flowering time genes: FT (FLOWERING LOCUS T), SOC1 (SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1), and LFY (LEAFY). Further investigation showed that ANE specifically promotes flowering through the MIR156-mediated age pathway. ANE treatment resulted in the repression of negative regulator genes, MIR156, while simultaneously enhancing the expression of positive regulator genes, including SPLs and MIR172. This, in turn, led to the downregulation of AP2-like genes, which are known as floral repressors. It is worth noting that ANE did not alleviate the late flowering phenotype of MIR156-overexpressing plants and spl mutants. Furthermore, ANE-derived fucoidan mimics the function of sugars in regulating MIR156, closely mirroring the effects induced by ANE treatments. It suppresses the transcript levels of MIR156 and AP2-like genes while inducing those of SPLs and MIR172, thereby reinforcing the involvement of fucoidan in the control of flowering by ANE. In summary, our results demonstrate that ANE induces flowering by modulating the MIR156-SPL module within the age pathway, and this effect is mediated by fucoidan.
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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".