The Mexican bean beetle (Epilachna varivestis) regurgitome and insights into beetle-borne virus specificity
Bibliographic record
Abstract
For nearly 400 million years, insects and plants have been embattled in an evolutionary arms race. Insects have developed diverse feeding strategies and behaviors in an effort to circumvent and overcome an extensive collection of plant defense tactics. Sap-sucking insects often inject saliva into hosts plants, which contains a suite of effector proteins and even microbial communities that can alter the plant's defenses. Lacking salivary glands, leaf-feeding beetles represent an interesting group of phytophagous insects. Feeding beetles regurgitate onto leaf surfaces and it is thought that these oral secretions influence insect-plant interactions and even play a role in virus-vector specificity. Since the molecular and biological makeup of the regurgitant is virtually unknown, we carried out RNA sequencing and 16S rDNA analysis on a major soybean pest, Epilachna varivestis, to generate the first ever beetle "regurgitome" and characterize its microbiome. Interestingly, the regurgitant is comprised of a rich molecular assortment of genes encoding putative extracellular proteins involved in digestion, molting, immune defense, and detoxification. By carrying out plant inoculation assays, we reinforced the fundamental role of the regurgitant in beetle-borne virus specificity. Ultimately, these studies begin to characterize the importance of regurgitant in virus transmission and beetle-plant 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 | 0.000 | 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) | 0.000 | 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".