Comparative transcriptome analysis of different heat stress responses between self-root grafting line and heterogeneous grafting line in rose
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rose is sensitive to high temperature which will make the rose go into a semi-dormancy state. Grafting is an excellent way to enhance rose heat tolerance. Here, heat-tolerant Rosa multiflora ‘Huanong Wuci 1′ (W) and heat-sensitive Rosa chinensis ‘Old Blush’ (X) were selected as experimental materials. The RNA-seq technique was used to investigate the transcriptomes of self-root grafting line (XX0), heterogeneous grafting line (XW0), self-root grafting line under 6 h heat stress (XX6), and heterogeneous grafting line under 6 h heat stress (XW6). Under high temperature stress, multiple signaling pathways were activated, moreover, a large number of transcription factors and functional genes were induced, especially the HSFs and HSPs with remarkably upregulated expression levels. The GO analysis showed that the differences in the expressions of the genes related to fatty acids and carbohydrates were observed between self-root grafting line and heterogeneous grafting line. In addition, 14 P450s were differentially expressed, and one lectin gene was up-regulated in XW0 vs XW6, but down-regulated in XX0 vs XX6. Considering physiological and biochemical traits such as relative electrolyte leakage, SOD activity, proline, and total soluble sugars, DEGs involved in these processes may be key factors to resist high temperature. The present study provides an insight into the complex mechanism underlying grafting in response to heat stress. Our results indicate that grafting is an effective way to improve rose heat resistance.
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.
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) | 0.001 | 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) | 0.000 | 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