Evolutionary Analysis of Fiber-Related Gene Families in Diploid and Polyploid Cotton
Bibliographic record
Abstract
Cotton is a globally important fiber crop. Both its diploid (varieties with normal chromosomes, such as Asiatic and Raymondia cotton) and polyploid (varieties with doubled chromosomes, such as Upland and Sea Island cotton) varieties provide valuable genetic resources for improving cotton fiber quality. This study, using comparative genomic approaches, conducted a detailed evolutionary analysis of gene families involved in fiber growth across different cotton varieties, primarily including cellulose synthase (CesA) genes responsible for cellulose synthesis, expansins, and the MYB transcription factor family. We also investigated how these gene families evolved through whole-genome duplications, small-scale duplications, and external selective pressures. Furthermore, we analyzed the expression of these genes at different stages of fiber development, their epigenetic regulation (influencing development by modulating gene activity), and their co-expression networks (how genes coordinate their function). Furthermore, we conducted an in-depth case study of the CesA gene in Upland cotton, including its classification, expression patterns during development, and functional validation. These studies can help us more clearly understand the evolution and functional differences of cotton fiber genes, and have practical significance for future molecular breeding, gene editing and protection of cotton genetic resources.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".