High-Throughput Genotyping and Its Role in Accelerating Cotton Breeding
Bibliographic record
Abstract
Cotton fibers are important natural textile raw materials, and their maturation process directly affects the length, strength and quality of the fibers. However, the molecular regulatory mechanisms during the fiber maturation period remain unclear, which limits the genetic improvement of high-quality cotton varieties. To deeply reveal the protein regulatory network related to fiber maturation, in this study, Label-Free Quantitative Proteomics technology was adopted to systematically analyze the protein expression profiles in cotton fibers at different developmental stages. Further functional enrichment and protein-protein interaction network analysis indicated that cellulose synthase (CESA), sucrose synthase (SUS), peroxidase, heat shock protein, etc. play a core role in the process of fiber maturation. This study systematically analyzed the developmental biological basis of cotton fiber maturation, the types and expression characteristics of key enzymes, and verified the expression patterns of key genes through case studies. Finally, it explored the application potential of proteomics data in breeding. This research not only enriches the understanding of the maturation mechanism of cotton fibers, but also provides potential functional gene resources for molecular breeding of high-quality cotton.
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 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".