What Do Microbes Encounter at the Plant Surface? Chemical Composition of Pea Leaf Cuticular Waxes
Bibliographic record
Abstract
In the cuticular wax mixtures from leaves of pea (Pisum sativum) cv Avanta, cv Lincoln, and cv Maiperle, more than 70 individual compounds were identified. The adaxial wax was characterized by very high amounts of primary alcohols (71%), while the abaxial wax consisted mainly of alkanes (73%). An aqueous adhesive of gum arabic was employed to selectively sample the epicuticular wax layer on pea leaves and hence to analyze the composition of epicuticular crystals exposed at the outermost surface of leaves. The epicuticular layer was found to contain 74% and 83% of the total wax on adaxial and abaxial surfaces, respectively. The platelet-shaped crystals on the adaxial leaf surface consisted of a mixture dominated by hexacosanol, accompanied by substantial amounts of octacosanol and hentriacontane. In contrast, the ribbon-shaped wax crystals on the abaxial surface consisted mainly of hentriacontane (63%), with approximately 5% each of hexacosanol and octacosanol being present. Based on this detailed chemical analysis of the wax exposed at the leaf surface, their importance for early events in the interaction with host-specific pathogenic fungi can now be evaluated. On adaxial surfaces, approximately 80% of Erysiphe pisi spores germinated and 70% differentiated appressoria. In contrast, significantly lower germination efficiencies (57%) and appressoria formation rates (49%) were found for abaxial surfaces. In conclusion, the influence of the physical structure and the chemical composition of the host surface, and especially of epicuticular leaf waxes, on the prepenetration processes of biotrophic fungi is discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".