Proteome profile and functional classification of proteins in Arabidopsis thaliana (Landsberg erecta) mature pollen
Bibliographic record
Abstract
Proteome analysis of mature Arabidopsis thaliana (Landsberg erecta ecotype) pollen was conducted using two-dimensional gel electrophoresis and mass spectrometry. A total of 960 spots were resolved on pH 4–7 IPG strips and 110 distinct proteins were identified from 150 spots analyzed. The identified proteins were categorized based on their functional role in the pollen, which included proteins involved in energy regulation, defense-related mechanisms, calcium-binding and signaling, cytoskeletal formation, pollen allergens, glycine-rich proteins (GRPs), and late embryogenesis abundant (LEA) proteins. These proteins potentially play important roles in pollen function at maturity and during subsequent germination and tube growth. Some of the proteins identified were related to known pollen-specific transcripts, while some were similar to proteins found in the seed. In this study, 66 new proteins were identified which were not reported in two other recent studies on Arabidopsis pollen, 17 proteins were common in all three studies, and 35 or 26 proteins reported here had an overlap with one or the other two studies. These differences may be attributed to the methods of protein extraction, spot selection for analysis, and the ecotype used. Together, the three studies provide a broad spectrum of the Arabidopsis pollen proteome.
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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 | 0.000 |
| 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".