Advances in understanding obligate biotrophy in rust fungi
Bibliographic record
Abstract
Contents Summary 1190 I. Introduction 1190 II. Rust fungi: a diverse and serious threat to agriculture 1191 III. The different facets of rust life cycles and unresolved questions about their evolution 1191 IV. The biology of rust infection 1192 V. Rusts in the genomics era: the ever-expanding list of candidate effector genes 1195 VI. Functional characterization of rust effectors 1197 VII. Putting rusts to sleep: Pucciniales research outlooks 1201 Acknowledgements 1202 References 1202 SUMMARY: Rust fungi (Pucciniales) are the largest group of plant pathogens and represent one of the most devastating threats to agricultural crops worldwide. Despite the economic importance of these highly specialized pathogens, many aspects of their biology remain obscure, largely because rust fungi are obligate biotrophs. The rise of genomics and advances in high-throughput sequencing technology have presented new options for identifying candidate effector genes involved in pathogenicity mechanisms of rust fungi. Transcriptome analysis and integrated bioinformatics tools have led to the identification of key genetic determinants of host susceptibility to infection by rusts. Thousands of genes encoding secreted proteins highly expressed during host infection have been reported for different rust species, which represents significant potential towards understanding rust effector function. Recent high-throughput in planta expression screen approaches (effectoromics) have pushed the field ahead even further towards predicting high-priority effectors and identifying avirulence genes. These new insights into rust effector biology promise to inform future research and spur the development of effective and sustainable strategies for managing rust diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".