Evaluating Leaf Responses to Microbial Secondary Metabolites Using A High-Throughput Format
Bibliographic record
Abstract
Microbes secrete structurally diverse secondary metabolites during plant infection, some of which are detected by plant cells, which trigger stress responses. In this method, the induction of ion leakage, peroxidase activity, and callose production is measured in the same leaf disk sample. First, Arabidopsis or barley leaf disks are vacuum infiltrated in a 96-well plate. After 4-6 hours, conductivity is measured, followed by peroxidase activity and callose deposition at 24 hours. The flg22 peptide induces all three responses and is an affordable positive control. Surfactin and gramillin cyclic lipopeptides induce peroxidase activity and ion leakage, respectively, while the phytotoxic T-2 trichothecene suppresses peroxidase activity. Overall, this approach enables multiple comparisons across either plant genotypes or metabolite treatments. This approach can be applied to chemical genetics or bioprotection to identify stress-modulating compounds for further study. In plant genetics, this approach can be used to compare responses across plant populations for genetic mapping and to improve our understanding of plant-microbe interactions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.002 |
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".