Bibliographic record
Abstract
Rust fungi include many species that are devastating crop pathogens. To develop resistant plants, a better understanding of rust virulence factors, or effector proteins, is needed. Thus far, only six rust effector proteins have been described: AvrP123, AvrP4, AvrL567, AvrM, RTP1, and PGTAUSPE-10-1. Although some are well established model proteins used to investigate mechanisms of immune receptor activation (avirulence activities) or entry into plant cells, how they work inside host tissues to promote fungal growth remains unknown. The genome sequences of four rust fungi (two Melampsoraceae and two Pucciniaceae) have been analyzed so far. Genome-wide analyses of these species, as well as transcriptomics performed on a broader range of rust fungi, revealed hundreds of small secreted proteins considered as rust candidate secreted effector proteins (CSEPs). The rust community now needs high-throughput approaches (effectoromics) to accelerate effector discovery/characterization and to better understand how they function in planta. However, this task is challenging due to the non-amenability of rust pathosystems (obligate biotrophs infecting crop plants) to traditional molecular genetic approaches mainly due to difficulties in culturing these species in vitro. The use of heterologous approaches should be promoted in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".