Dual RNA-Sequencing to Elucidate the Plant-Pathogen Duel
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
RNA-sequencing technology has been widely adopted to investigate host responses during infection with pathogens. Dual RNA-sequencing (RNA-seq) allows the simultaneous capture of pathogen specific transcripts during infection, providing a more complete view of the interaction. In this review, we focus on the design of dual RNA-seq experiments and the application of downstream data analysis to gain biological insight into both sides of the interaction. Recent literature in this area demonstrates the power of the dual RNA-seq approach and shows that it is not limited to model systems where genomic resources are available. A reduction in sequencing cost and single cell transcriptomics coupled with protein and metabolite level dual approaches are set to enhance our understanding of plant-pathogen interactions. Sequencing costs continue to decrease and single cell transcriptomics is becoming more feasible. In combination with proteomics and metabolomics studies, these technological advances are likely to contribute to our understanding of the temporal and spatial aspects of dynamic plant-pathogen interactions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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 it