Bark and wood tissues of American elm exhibit distinct responses to Dutch elm disease
Bibliographic record
Abstract
Tolerance to Dutch elm disease (DED) has been linked to the rapid and/or high induction of disease-responsive genes after infection with the fungus Ophiostoma novo-ulmi. Although the fungal infection by O. novo-ulmi primarily takes places in xylem vessels, it is still unclear how xylem contributes to the defense against DED. Taking advantage of the easy separation of wood and bark tissues in young American elm saplings, here we show that most disease-responsive genes exhibited higher expression in wood compared to bark tissues after fungal infection. On the other hand, the stress-related phytohormones were generally more abundant in the bark compared to wood tissues. However, only endogenous levels of jasmonates (JAs), but not salicylic acid (SA) and abscisic acid (ABA) increased in the inoculated tissues. This, along with the upregulation of JA-biosynthesis genes in inoculated bark and core tissues further suggest that phloem and xylem might contribute to the de novo biosynthesis of JA after fungal infection. The comparison between two tolerant elm varieties, 'Valley Forge' and 'Princeton,' also indicated that tolerance against DED might be mediated by different mechanisms in the xylem. The present study sheds some light on the amplitude and kinetics of defense responses produced in the xylem and phloem in response to DED.
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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) | 0.000 | 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) | 0.001 | 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".