Comparative analysis of root anatomy, phytochemicals and gene expression in bolted and unbolted Saposhnikovia divaricata
Bibliographic record
Abstract
Saposhnikovia divaricata (Turcz.) Schischk., known as Fangfeng, is a highly valued traditional Chinese medicinal herb esteemed for its therapeutic properties. Premature bolting in S. divaricata adversely affects root yield and medicinal quality. This study aimed to compare root anatomical structures, active phytochemical contents, and gene expression differences between unbolted (UBF) and bolted (BF) S. divaricata plants, which can provide a theoretical foundation for elucidating potential mechanisms driving premature bolting for future research and practical applications. The result showed that UBF roots exhibited intact secondary xylem and wider secondary phloem, whereas BF roots showed fragmented secondary xylem with lignified parenchyma cells. Chromone concentrations were higher in UBF plants, particularly within the secondary phloem. Transcriptome analysis identified 33 differentially expressed genes (DEGs) associated with bolting and flowering, 22 DEGs involved in plant hormone signal transduction pathways, including ETR, JAR1, EIN3, TCH4, GID2, ABF, BKI1, BSK, BIN, BZR1/2, CYCD3, and 11 DEGs involved in circadian rhythm pathways, including GI, ZTL, FT, PHYA, COP1, SPA, FKF1, were differentially expressed between BF and UBF groups, suggesting their potential role in regulating bolting and flowering in S. divaricata. These findings suggest that plant hormones and circadian rhythms may influence bolting and flowering in S. divaricata. These findings can provide a theoretical basis for analyzing the mechanisms of bolting and flowering in this species and the Apiaceae family. However, premature bolting adversely affects root quality, necessitating further investigation into its regulatory mechanisms to improve cultivation practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".