Integrative analysis of green ash phloem transcripts and proteins during an emerald ash borer infestation
Bibliographic record
Abstract
BACKGROUND: Emerald ash borer (Agrilus planipennis; EAB) is an Asian insect species that has been invasive to North America for 20 years. During this time, the emerald ash borer has killed tens of millions of American ash (Fraxinus spp) trees. Understanding the inherent defenses of susceptible American ash trees will provide information to breed new resistant varieties of ash trees. RESULTS: We have performed RNA-seq on naturally infested green ash (F. pennsylvanica) trees at low, medium and high levels of increasing EAB infestation and proteomics on low and high levels of EAB infestation. Most significant transcript changes we detected occurred between the comparison of medium and high levels of EAB infestation, indicating that the tree is not responding to EAB until it is highly infested. Our integrative analysis of the RNA-Seq and proteomics data identified 14 proteins and 4 transcripts that contribute most to the difference between highly infested and low infested trees. CONCLUSIONS: The putative functions of these transcripts and proteins suggests roles of phenylpropanoid biosynthesis and oxidation, chitinase activity, pectinesterase activity, strigolactone signaling, and protein turnover.
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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".