Comparison of Methods for the Extraction and Concentration Determination of Total Protein in Maize
Bibliographic record
Abstract
The extraction and concentration determination of total protein in maize organs is a key step for proteomic research.In this study.Using maize leaves and silks as materials,6 methods for total protein extraction,i.e.Phosphate Buffer(PB),Tris,Improved Tris,TCA-acetone,Plant Total Protein Extraction Kit and its improved one were compared in respective extraction quantity and quality;simultaneously,3 methods of Bradford,2-D Quant Kit and UV were compared in accuracy of protein concentration determination.The results showed:1.the amount of total protein extracted by the above methods was Plant Total Protein Extraction KitImproved Plant Total Protein Extraction KitImproved TrisTrisTCA-acetone PB;2.The quality of total protein extracted was Plant Total Protein Extraction KitImproved Plant Total Protein Extraction KitTCA-acetone Improved TrisTrisPB;3.The protein concentrations determined by both methods of Bradford and 2-D Quant Kit were accurate and no significant difference,however the one determined by UV method was inaccurate.Taking one consideration with another,using Improved Tris for SDS-PAGE and Improved Plant Total Protein Extraction Kit for 2-DE analyses,and Bradford for concentration measurement would be efficient.It should be a good reference for proteomic research in maize.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".