Post-Transcriptional Regulation by microRNAs during Drought in Rye
Bibliographic record
Abstract
Rye ( Secale cereale L.) is renowned for its strong adaptability in marginal environments and is of great significance for ensuring food security and achieving sustainable agriculture in areas with frequent droughts. This study systematically explored the miRNA expression dynamics of rye under drought stress and its mechanism of action in gene silencing, signal integration and stress adaptation. Through high-throughput sequencing technology, multiple differentially expressed mirnas under drought conditions were identified, and their potential target genes were predicted. The results of functional annotation and network analysis indicated that these mirnas were mainly involved in regulating key pathways such as reactive oxygen species (ROS) clearance, abscisic acid (ABA) signaling, and transcriptional regulation related to stress responses. In the case studies of miR398, miR159 and miR166, the research revealed how specific mirNA-target gene interactions affect the physiological characteristics of rye under drought stress, including ROS clearance efficiency, hormone signal regulation and leaf morphology changes, etc. This study not only deepens the understanding of the molecular response mechanism of rye to drought, but also provides new ideas and technical support for mirNa-based functional genomics research and the improvement of stress-resistant crops in the context 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) | 0.000 | 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) | 0.000 | 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".