Multiple Signals Can Be Integrated into Pathways of Blue-Light-Mediated Floral Transition: Possible Explanations on Diverse Flowering Responses to Blue Light Manipulation
Bibliographic record
Abstract
Blue light (BL) plays a crucial role in regulating floral transition and can be precisely manipulated in controlled-environment agriculture (CEA). However, previous studies on BL-mediated flowering in CEA have produced conflicting results, likely due to species-specific responses and variations in experimental conditions (such as light spectrum and intensity) as summarized in our recent systematic review. This speculation still lacks a mechanistic explanation at the molecular level. By synthesizing recent advances in our understanding of the signaling mechanisms underlying floral transition, this review highlights how both internal signals (e.g., hormones, carbohydrates, and developmental stage) and external cues (e.g., light spectrum, temperature, nutrients, stress, and magnetic fields) are integrated into the flowering pathway mediated by BL. Key signal integration nodes have been identified, ranging from photoreceptors (e.g., cryptochromes) to downstream components such as transcription factors and central flowering regulator, FLOWERING LOCUS T (FT). This signal integration offers a potential mechanistic explanation for the previously inconsistent findings, which may arise from interspecies differences in photoreceptor composition and variation in the expression of downstream components influenced by hormonal crosstalk, environmental conditions, and developmental stage, depending on the specific context. This review provides novel molecular insights into how BL modulates floral transition through interactions with other signals. By systematically compiling and critically assessing recent research findings, we identify key research gaps and outline future directions, particularly the need for more studies in agriculturally important crops. Furthermore, this review proposes a conceptual framework for optimizing BL-based lighting strategies and exploring underexamined interaction factors in the regulation of flowering.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".