Protein Profiling in Potato (<i>Solanum tuberosum</i> L.) Leaf Tissues by Differential Centrifugation
Bibliographic record
Abstract
Foliar diseases, such as late blight, result in serious threats to potato production. As such, potato leaf tissue becomes an important substrate to study biological processes, such as plant defense responses to infection. Nonetheless, the potato leaf proteome remains poorly characterized. Here, we report protein profiling of potato leaf tissues using a modified differential centrifugation approach to separate the leaf tissues into cell wall and cytoplasmic fractions. This method helps to increase the number of identified proteins, including targeted putative cell wall proteins. The method allowed for the identification of 1484 nonredundant potato leaf proteins, of which 364 and 447 were reproducibly identified proteins in the cell wall and cytoplasmic fractions, respectively. Reproducibly identified proteins corresponded to over 70% of proteins identified in each replicate. A diverse range of proteins was identified based on their theoretical pI values, molecular masses, functional classification, and biological processes. Such a protein extraction method is effective for the establishment of a highly qualified proteome profile.
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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) | 0.000 | 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 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".