Genomic and proteomic changes during the development of a tomato‐ <i>Verticillium</i> pathosystem.
Bibliographic record
Abstract
Biology of plant‐microbe interplay is complex and can lead to susceptible, resistant or even tolerant host response states. To better define these at a molecular level we assessed global changes in gene expression at both transcriptional and translational levels in tomato infected by, Verticillium dahliae . DNA microarray (TVR chip) studies of the 3 states reveal complex but distinct patterns of change in plant defense related mRNAs. Many genes that, initially, are strongly induced in the susceptible response are then down regulated at 10 d.p.i. while transcription is less dramatically affected in resistance or tolerance. Similarly, proteomic analyses based on 2D gel comparisons and mass spectrometry showed distinct patterns of protein change. Using Progenesis SameSpots software, 1621 protein spots were mapped. In the three types of interaction, 575 proteins showed significant differences in intensity ≥ 1.5 for 35 pairwise comparisons and 220 spots indicate differences ≥ 2.0. Some proteins were strongly induced but levels of many were reduced in the compatible interaction consistent with "cost of defense". Differences progressed with time. Protein identification by MALD‐TOF indicated most known proteins were plant defense related or had unknown functions. Susceptibility was characterized by the most dramatic increases in defense related proteins but others were reduced relative to a resistant plant.
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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".