Genome Polyploidization and Adaptive Evolution in Yellow Pitaya: The Impact of Gene Amplification on Stress Resistance
Bibliographic record
Abstract
Due to its excellent environmental adaptability and high economic value, yellow pitaya has gradually become an important tropical cash crop. However, the current research on the polyploidization of yellow pitaya genome and its adaptive evolution mechanism is relatively limited. This study mainly aims to reveal the relationship between gene amplification events and stress resistance during the polyploidization of yellow pitaya genome, and explore its adaptation mechanism under environmental pressures such as drought, high temperature, and salt stress. The content of the article includes the type of polyploidization of yellow pitaya genome, the molecular pathway of gene amplification, and the role in regulating the expression of anti-reverse genes. The study has shown that yellow pitaya has experienced multiple genomic replication events, resulting in a large number of amplifications of stress-resistant related genes, among which gene families such as zinc finger protein and NAC transcription factors are particularly outstanding, and the improvement of these gene amplification events to plant stress-resistant ability is confirmed through multi-level bioinformatic analysis. Combined with the research results of previous generations, we will further use multiplier cultivation of new inverse-resistant varieties of yellow pitaya to provide theoretical basis and technical support.
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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".