Data from: High throughput sequencing of small RNAs reveals dynamic microRNAs expression of lipid metabolism during Camellia oleifera and C. meiocarpa seed natural drying
Bibliographic record
Abstract
AbstractBackground: Camellia species are ancient oilseed plants with a history of cultivation over two thousand years. Prior to oil extraction, natural seed drying is often practiced, a process affecting fatty acid quality and quantity. MicroRNAs (miRNA) of lipid metabolism associated with camellia seed natural drying are unexplored. To obtain insight into the function of miRNAs in lipid metabolism during natural drying, Illumina sequencing of C. oleifera and C. meiocarpa small-RNA was conducted. Results: A total of 274 candidate miRNAs were identified and 3733 target unigenes were annotated by performing a BLASTX. Through integrated GO and KEGG function annotation, 23 miRNA regulating 131 target genes were identified as lipid metabolism, regulating fatty acid biosynthesis, accumulation and catabolism. We observed one, two, and four miRNAs of lipid metabolism which were specially expressed in C. Meiocarpa, C. oleifera, and the two species collectively, respectively. At 30% moisture contents, C. meiocarpa and C. oleifer produced nine and eight significant differentially expressed miRNAs, respectively, with high fatty acid synthesis and accumulation activities. Across the two species, 12 significant differentially expressed miRNAs were identified at the 50% moisture content. Conclusions: Sequencing of small-RNA revealed the presence of 23 miRNAs regulating lipid metabolism in camellia seed during natural drying and permitted comparative miRNA profiles between C. Meiocarpa and C. oleifera. Furthermore, this study successfully identified the best drying environment at which the quantity and quality of lipid in camellia seed are at its maximum.
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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 | 0.001 | 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) | 0.002 | 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".