The role of epigenetic processes in controlling flowering time in plants exposed to stress
Bibliographic record
Abstract
Plants interact with their environment by modifying gene expression patterns. One mechanism for this interaction involves epigenetic modifications that affect a number of aspects of plant growth and development. Thus, the epigenome is highly dynamic in response to environmental cues and developmental changes. Flowering is controlled by a set of genes that are affected by environmental conditions through an alteration in their expression pattern. This ensures the production of flowers even when plants are growing under adverse conditions, and thereby enhances transgenerational seed production. In this review recent findings on the epigenetic changes associated with flowering in Arabidopsis thaliana grown under abiotic stress conditions such as cold, drought, and high salinity are discussed. These epigenetic modifications include DNA methylation, histone modifications, and the production of micro RNAs (miRNAs) that mediate epigenetic modifications. The roles played by the phytohormones abscisic acid (ABA) and auxin in chromatin remodelling are also discussed. It is shown that there is a crucial relationship between the epigenetic modifications associated with floral initiation and development and modifications associated with stress tolerance. This relationship is demonstrated by the common epigenetic pathways through which plants control both flowering and stress tolerance, and can be used to identify new epigenomic players.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".