<scp>TMT</scp>‐labeled quantitative proteomic analysis to identify proteins associated with the stability of peanut milk
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Peanut milk benefits human health mainly due to its high protein content and suitable amino acid composition. To reveal the molecular mechanism affecting the quality of peanut milk, tandem mass tag (TMT)-labeled proteomic analysis was applied to identify the proteome variation between two peanut cultivars that produced peanut milk with the best and worst stability. RESULTS: A total of 478 differentially abundant proteins (fold change >1.2 or <0.83, P < 0.05) were identified. Most of these proteins were located in the cytoplasm and chloroplasts. Correlation analysis showed that RNA recognition motif (RRM) domain-containing protein (17.1 kDa) had a negative relationship with the sedimentation rate of peanut milk and that 22.0 kDa class IV heat shock protein was negatively correlated with the creaming index (P < 0.05). Bioinformatic analysis showed that the molecular function of RRM domain-containing protein (17.1 kDa) was associated with RNA binding and nucleotide binding, and 22.0 kDa class IV heat shock protein was involved in the pathway of protein processing in the endoplasmic reticulum. CONCLUSION: Overall, the differentially abundant proteins in the biological metabolic pathway might offer some potential markers to guide future peanut breeding, especially for the production of peanut milk. © 2021 Society of Chemical Industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it