Regulation of low temperature stress in plants by microRNAs
Bibliographic record
Abstract
Low temperature is one of the most common environmental stresses that seriously affect the growth and development of plants. However, plants have the plasticity in their defence mechanisms enabling them to tolerate and, sometimes, even survive adverse environmental conditions. MicroRNAs (miRNAs) are small non-coding RNAs, approximately 18-24 nucleotides in length, and are being increasingly recognized as regulators of gene expression at the post-transcriptional level and have the ability to influence a broad range of biological processes. There is growing evidence in the literature that reprogramming of gene expression mediated through miRNAs is a major defence mechanism in plants enabling them to respond to stresses. To date, numerous studies have established the importance of miRNA-based regulation of gene expression under low temperature stress. Individual miRNAs can modulate the expression of multiple mRNA targets, and, therefore, the manipulation of a single miRNA has the potential to affect multiple biological processes. Numerous functional studies have attempted to identify the miRNA-target interactions and have elaborated the role of several miRNAs in cold-stress regulation. This review summarizes the current understanding of miRNA-mediated modulation of the expression of key genes as well as genetic and regulatory pathways, involved in low temperature stress responses in plants.
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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 | 0.000 | 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".