Transcriptome profiling provides new insights into florets number difference of inflorescence in L.angustifolia
Bibliographic record
Abstract
Abstract Background Lavender flowers essential oil had been used for a variety of therapeutic and cosmetic purposes, and had been popular for centuries. The previous studies of lavender mainly focused on essential oil composition and extraction methods, ignoring the factors that affected the production of essential oils, such as the floret number. This study aims to get a deep insight into the mechanism of florets number difference. Results 1.Hormone profile showed positive correlation between ABA content and the number of florets, while IAA was negatively correlated. 2. RNA-Seq results showed that 2848 differentially expressed genes (DEG) were screened by comparing different florets samples in one plant. By analyzing dynamic changes of differentially expressed genes, many potentially interesting genes that encoded putative regulators or key components of ABA metabolism and signaling transduction were identified, such as NCED, PYL, PP2C, SnRK2. 3.Common network analysis showed that the key genes of ABA pathway metabolism were negatively related to the DEG of IAA, especial IAA18. 4. Exogenous IAA significantly inhibited the number of lavender florets and affected the expression of ABA pathway metabolism genes, such as NCED, PP2C.Conclusions 1. The different concentrations of ABA lead to florets number difference in the L.angustifolia “JX-2” clusters; 2. ABA may affect the florets number by regulating IAA transport and accumulation.The results will be useful for a better understanding of the molecular mechanism on florets number difference, which will lay the foundation for molecular breeding of muti-flortes varieties.
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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".