Plant Signaling Hormones and Transcription Factors: Key Regulators of Plant Responses to Growth, Development, and Stress
Bibliographic record
Abstract
Plants constantly encounter a wide range of biotic and abiotic stresses that adversely affect their growth, development, and productivity. Phytohormones such as abscisic acid, jasmonic acid, salicylic acid, and ethylene serve as crucial regulators, integrating internal and external signals to mediate stress responses while also coordinating key developmental processes, including seed germination, root and shoot growth, flowering, and senescence. Transcription factors (TFs) such as WRKY, NAC, MYB, and AP2/ERF play complementary roles by orchestrating complex transcriptional reprogramming, modulating stress-responsive genes, and facilitating physiological adaptations. Recent advances have deepened our understanding of hormonal networks and transcription factor families, revealing their intricate crosstalk in shaping plant resilience and development. Additionally, the synthesis, transport, and signaling of these molecules, along with their interactions with stress-responsive pathways, have emerged as critical areas of study. The integration of cutting-edge biotechnological tools, such as CRISPR-mediated gene editing and omics approaches, provides new opportunities to fine-tune these regulatory networks for enhanced crop resilience. By leveraging insights into transcriptional regulation and hormone signaling, these advancements provide a foundation for developing stress-tolerant, high-yielding crop varieties tailored to the challenges of climate change.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".