Genomic Analysis of Yam: Understanding Its Adaptive Evolution and Medicinal Properties
Bibliographic record
Abstract
Yam, commonly known as Shuyu, Tushu, or Shuyao in Chinese, is a traditional Chinese medicinal and edible plant with nutritional and medicinal value.This study aims to elucidate the adaptive evolution and medicinal properties of yam through comprehensive genomic analysis.The study integrates findings from various research efforts, including transcriptome sequencing, chloroplast genome characterization, and metabolomic profiling.Transcriptome analysis has revealed key pathways and hormone activities involved in microtuber formation, highlighting the role of differentially expressed genes in the plant's development and stress responses.Chloroplast genome sequencing has provided insights into the phylogenetic relationships and potential molecular markers for species identification.Metabolomic studies have identified significant metabolites in different parts of the plant, contributing to its medicinal properties.Additionally, the structural characterization of polysaccharides and their bioactivity on gut microbiota underscores the plant's health benefits.The identification of endogenous gibberellins and α-glucosidase inhibitors further supports the plant's therapeutic potential.This study consolidates current genomic and biochemical data, offering a comprehensive understanding of yam's adaptive evolution and medicinal properties, thereby providing references for future research and potential biotechnological applications.
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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 | 0.001 | 0.001 |
| 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".