Dynamic Changes and Biological Significance of MicroRNA Expression Profiles in Rice under Cold Stress
Bibliographic record
Abstract
Cold stress is a significant abiotic factor that adversely affects rice ( Oryza sativa ) growth and productivity. This study investigates the dynamic changes and biological significance of microRNA (miRNA) expression profiles in rice under cold stress. Utilizing high-throughput techniques such as microarrays and stem-loop reverse transcription quantitative PCR (ST-RT qPCR), researchers identified several miRNAs that exhibit differential expression in response to cold stress. Notably, miR1320, miR319, and miR156 were found to play crucial roles in enhancing cold tolerance by targeting specific transcription factors and modulating stress-responsive genes. For instance, miR1320 targets the ERF transcription factor OsERF096, which is involved in jasmonic acid (JA)-mediated signaling pathways, while miR319 targets OsPCF6 and OsTCP21, contributing to the regulation of cold stress-responsive genes such as DREB1A/B/C and TPP1/2 . Additionally, miR156 enhances cold tolerance by targeting OsSPL3, which in turn regulates the expression of OsWRKY71 and other stress-related transcription factors. These findings underscore the importance of miRNAs in the complex regulatory networks that govern rice’s response to cold stress, providing valuable insights for developing cold-tolerant rice varieties.
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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) | 0.001 | 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".