Metabolite profiling coupled with statistical analyses for potential high-throughput screening of quantitative resistance to fusarium head blight in wheat
Bibliographic record
Abstract
Fusarium head blight (FHB) [Fusarium graminearum (teleomorph Gibberella zeae)] causes considerable losses in wheat (Triticum aestivum) yield and grain quality. Because conventional screening for disease resistance based on five separate types of resistance is inefficient, a metabolomics approach to discriminating resistance was investigated. Spikelets of six wheat cultivars/lines varying in level of resistance were inoculated with F. graminearum or water. The spikelet disease severity was quantified, and the metabolic profiles were recorded using gas chromatography - mass spectrometry. A total of 214 metabolites were detected in spikelets and rachis, including 79 with acceptable treatment effects. Univariate analysis of variance identified 41 resistance-related (RR) metabolites and multivariate analysis identified 45 resistance function associated metabolites, including 28 RR metabolites. Highly resistant cultivar 'Wangshuibai' and line AW488 had the maximum numbers of constitutive (22) and induced (14) RR metabolites, respectively. A moderately resistant line, BRS177, had 12 induced RR metabolites. The RR metabolites identified here are potential candidate biomarkers for high-throughput screening of wheat breeding lines against FHB.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".