Identification and characterization of heat-responsive microRNAs at the booting stage in two rice varieties, 9311 and Nagina 22
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small, non-coding, regulatory RNAs that play important roles in abiotic stress responses in plants, but their regulatory roles in the adaptive response to heat stress at the booting stage in two rice varieties, 9311 and Nagina 22, remain largely unknown. In this study, 464 known miRNAs and 123 potential novel miRNAs were identified. Of these miRNAs, a total of 90 differentially expressed miRNAs were obtained with 9311 libraries as the control group, of which 54 were upregulated and 36 were downregulated. To gain insight into functional significance, 2773 potential target genes of these 90 differentially expressed miRNAs were predicted. GO enrichment analysis showed that the predicted target genes of differentially expressed miRNAs included NACs, LACs, CSD, and Hsp40. KEGG pathway analysis showed that the target genes of these differentially expressed miRNAs were significantly enriched in the plant hormone signal transduction pathway. The expression levels of 10 differentially expressed miRNAs and their target genes obtained by qRT-PCR were largely consistent with the sequencing results. This study lays a foundation for the elucidation of the miRNA-mediated regulatory mechanisms in rice at elevated temperatures.
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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 | 0.001 |
| 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".