Phenotypic and Transcriptomic Analysis of Two Pinellia ternata Varieties T2 line and T2Plus line
Bibliographic record
Abstract
Pinellia (Pinellia ternata (Thunb.) Breit.), as important medicinal plant, has been used to treat various ailments for a long time. The sixteen ploid plant (2n = 16 * 13 = 208) Pinellia T2Plus line was obtained from an octoploid (2n = 8 * 13 = 104) T2 line by chromosome-doubling technique. Compared with T2 line, the content of various medicinal components (polysaccharide, guanosine, adenosine and ephedrine) was increased in T2Plus line. In this study, the transcriptome of T2 line and T2Plus line were characterized by RNA sequencing (RNA-seq) technology. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis on differential expressed unigenes (DEGs) revealed that multiple metabolic pathway were enriched significantly, such as 'Starch and sucrose metabolism', 'Purine metabolism', 'Photosynthesis' and six transcription factors (MYB, WRKY, bHLH, lateral organ boundaries domain (LBD), homeodomain-zipper (HD-ZIP) and Ethylene-responsive factor (ERF)) play a key role in difference of transcriptome between T2 line and T2Plus line. These metabolic pathways and transcription factors may play an important role in the difference of medicinal components and epigenetic features between these two Pinellia cultivars. This conclusion provides a robust theoretical basis for the mechanism of the formation of medicinal ingredients in Pinellia cultivars.
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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".