Effects of Fruit Shading on Gene and Protein Expression During Starch and Oil Accumulation in Developing Styrax tonkinensis Kernels
Bibliographic record
Abstract
Styrax tonkinensis has great potential as a biofuel feedstock source having industrial oilseeds with excellent fatty acids (FAs) composition and good fuel properties. Photosynthesis in the developing pericarp could affect the carbon distribution in kernel. During kernel development, more carbon sources are allocated to starch rather than lipid, when the pericarp photosynthesis is reduced by fruit shading treatment. After shading the fruits at 50 days after flowering (DAF), samples of shaded fruit (FSK) and controls (CK) were collected at 80 DAF and analyzed using the proteomic method. We identified 3,181 proteins, of which 277 were differentially expressed proteins, all downregulated in the FSK group. There were 56 proteins found involved in carbohydrate metabolism and lipid biosynthesis leading to oil accumulation with their iTRAQ ratios of FSK/CK ranging from 0.7123 to 1.1075. According to the qRT-PCR analyses, the key genes related to FA and triacylglycerol (TAG) biosynthesis were significantly downregulated between 60 and 90 DAF especially at 80 DAF, while the key genes involved in starch biosynthesis and FA desaturase had no significant difference between the two groups at 80 DAF. Fruit shading is a negative treatment for lipid accumulation but not starch accumulation by restraining enzymic protein expression involved in FA and TAG biosynthesis during S. tonkinensis kernel development.
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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".