Transcriptome analysis of maca ( <i>Lepidium meyenii</i> ) root at different developmental stages
Why this work is in the frame
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Bibliographic record
Abstract
PREMISE OF THE STUDY: ; Brassicaceae) has been cultivated by Andeans for thousands of years as a food source and has been used for medicinal purposes. However, little is known about the mechanism underlying material accumulation during plant growth. METHODS: RNA-Seq technology was used to compare the transcriptome of black maca root at three developmental stages. Gene Ontology term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied for the identification of pathways in which differentially expressed genes were significantly enriched. RESULTS: Trinity was used to de novo assemble the reads, and 120,664 unigenes were assembled. Of these, 71.53% of the unigenes were annotated based on BLAST. A total of 18,321 differentially expressed genes were observed. Gene Ontology term enrichment analysis found that the most highly represented pathway among the differentially expressed genes was for genes involved in starch and sucrose metabolism. We also found that genes involved in secondary metabolite biosynthesis, such as glucosinolate biosynthesis, were significantly enriched. DISCUSSION: The genes that were differentially expressed between developmental time points likely reflect both developmental pathways and responses to changes in the environment. As such, the transcriptome data in this study serve as a reference for subsequent mining of genes that are involved in the synthesis of important bioactive components in maca.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it