Identification of the key proteins associated with different hair types in sheep and goats
Bibliographic record
Abstract
Animal-derived fiber has the characteristics of being light, soft, strong, elastic and a good thermal insulator, and it is widely used in many industries and traditional products, so it plays an important role in the economy of some countries. Variations in phenotypes of wool fibers among different species and breeds are important for industry. We found that the mean fiber diameter of cashmere was significantly smaller than that of sheep wool ( p < 0.01), and sheep wool was significantly smaller than goat wool ( p < 0.01). Compared with traditional proteomics technology, we analyzed cashmere, guard hair, and wool by Laber-free proteomics technology and detected 159, 204, and 70 proteins, respectively. Through the sequential windowed acquisition of all theoretical fragmentations (SWATH), 41 and 54 differentially expressed proteins were successfully detected in the cashmere vs. wool group and guard hair vs. wool group. Protein‒protein interaction network analysis of differentially expressed proteins revealed many strong interactions related to KRT85, KRTAP15-1 and KRTAP3-1. The final analysis showed that the proportion of KRT85, KRTAP15-1 and KRTAP3-1 might be the key to the difference in fiber diameter and could be used as a potential molecular marker for distinguishing different fiber types.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".