Development and validation of a GC–MS method for soybean organ-specific metabolomics
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
Field mold (FM) can easily deteriorate the preharvest soybean in the field, and Fusarium moniliforme is demonstrated as the dominant pathogenic fungi. Metabolomics is a powerful tool to reveal the resistance mechanism in response to microbial infection. Therefore, in this research, the Design of Experiment (DOE) model was developed to optimize the extraction solvent combinations for metabolomic study of soybean seed and pod based on gas chromatography–mass spectrometry (GC–MS). Combined with the number of extracted peaks and the peak area of common substances, the extraction efficiency of different solvent was analyzed by multivariate statistical analysis. The result showed that isopropanol/water/methanol (1:1:1 and 1:1:4, v/v/v) mixture was highly efficient for metabolites extractions of soybean seed and pod, respectively. Additionally, the potential metabolites and pathways concerned in FM resistance were explored by the optimized extraction solvent system based on metabolomics analysis. Amino acid metabolism in soybean seed was disturbed by F. moniliforme and metabolic pathways related to energy conversion in soybean pod strongly responded to fungal infection. This study constructs a GC–MS-based metabonomic method for soybean metabolites; comparative analysis of organ-specific metabolomics for soybean fruit could be further applied in soybean metabolomics researches.
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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 | 0.001 | 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 it