Conjoint transcriptomic and proteogenomic analysis of quality formation in various <i>Porphyra dentata</i> harvests: Photosynthesis acts as a stressor
Bibliographic record
Abstract
Abstract Porphyra dentata is widely cultivated for its rich nutritional value and superior palatability. However, its quality varies with harvest time and there is a lack of understanding of the molecular mechanism of quality differences. Photosynthesis is a key factor in human‐mediated plant development and quality formation and changes. To explore the quality impact of photosynthesis on P. dentata, we compared transcriptomic and proteogenomic data of the first and fifth harvests. Of the 53,580 genes detected in this study, 7073 were identified as differentally expressed genes by RNA‐seq, and 462 showed differential expression between genes and proteins in proteogenomics. The results show that quality differences between harvest periods were regulated by proteins and genes from the allophycocyanin, Lhca1, chloroplast processing enzyme, and phycocyanin families. Generated cell tissue passivated continuously, the blades gradually became thicker and darker and had an increased degree of lignification, decreased protein levels, increased carbohydrate levels, and decreased quality. Our results demonstrate the complementary power of transcriptomics and proteogenomics and provide a rich database for quality improvement or evolutionary function analysis of P. dentata.
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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 | 0.001 | 0.001 |
| 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".