Differential expression of microRNAs in tomato leaves treated with different light qualities
Bibliographic record
Abstract
BACKGROUND: Light is the main source of energy and, as such, is one of the most important environmental factors for plant growth, morphogenesis, and other physiological responses. MicroRNAs (miRNAs) are endogenous non-coding RNAs that contain 21-24 nucleotides (nt) and play important roles in plant growth and development as well as stress responses. However, the role of miRNAs in the light response is less studied. We used tomato seedlings that were cultured in red light then transferred to blue light for 2 min to identify miRNAs related to light response by high-throughput sequencing. RESULTS: A total of 108 known miRNAs and 141 predicted novel miRNAs were identified in leaf samples from tomato leaves treated with the different light qualities. Among them, 15 known and 5 predicted novel miRNAs were differentially expressed after blue light treatment compared with the control (red light treatment). KEGG enrichment analysis showed that significantly enriched pathways included zeatin biosynthesis (ko00908), homologous recombination (ko03440), and plant hormone signal transduction (ko04075). Zeatin biosynthesis and plant hormone signal transduction are related to plant hormones, indicating that plant hormones play important roles in the light response. CONCLUSION: Our results provide a theoretical basis for further understanding the role of miRNAs in the light response of 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) | 0.000 | 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".