Effects of <i>Erwinia</i> Infection on the Changes of Metabolisms in <i>Dendrobium officinale</i>
Bibliographic record
Abstract
The current study was aimed to investigate the changes in metabolites and metabolic pathways in Dendrobium officinale stem infected by Erwinia sp., the causal agent of D. officinale . A total of 176 metabolites were obtained by LC-MS in D. officinale before and after infection . Besides, the KEGG in MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/) was used to analyze the pathways of the metabolites, of which only 73 metabolites were obtained Output ID from KEGG. The alanine, aspartate and glutamate metabolism, phenylalanine metabolism, isoquinoline alkaloid biosynthesis, lysine degradation, lysine biosynthesis, citrate cycle (TCA cycle), cutin, suberine, and wax biosynthesis, pyruvate metabolism, starch, and sucrose metabolism, and glyoxylate and dicarboxylate metabolism were the important metabolic pathways found by pathway enrichment analysis on KEGG ID. Furthermore, based on the information above, using VIP>1.0 and P<0.05 as screening criteria, there were only 68 differential metabolites among 73 metabolites. The metabolic pathways and differential metabolites analysis revealed that the contents of amino acid, organic acid, and nucleotides of EI ( D. officinale infected with Erwinia sp.) plant were higher than EF ( D. officinale free from Erwinia sp.) plant. Hence, D. officinale can enhance its structure by increasing the organic acids (citric acid, succinic acid, etc.) and amino acids (proline, arginine, etc.) to resist the pathogens.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".