A Rolling Stone Gathers No Moss, but Resistant Plants Must Gather Their MOSes
Bibliographic record
Abstract
The detection of pathogenic microbes by plant resistance (R) proteins and the subsequent activation of R protein-mediated immunity constitute an important layer in the plant innate immune system. Most R genes encode proteins with nucleotide-binding (NB) and leucine-rich repeat (LRR) domains. The autoimmune mutant suppressor of npr1, constitutive 1 (snc1), that constitutively activates resistance signaling, is a unique model used in our laboratory to dissect the details of TIR (Toll/Interleukin1 receptor)-NB-LRR, protein-mediated defense responses. Suppressor screens of snc1 yielded 15 modifier of snc1 (mos) complementation groups containing second-site mutations, and resulted in the identification of 13 novel MOS genes via either positional cloning or T-DNA tagging. Characterizations of the mos mutants have revealed important roles for transcriptional regulation, RNA processing, protein modifications, and nucleocytoplasmic trafficking in R protein-mediated immunity. The MOS genes have taught us a great deal about the complex mechanisms surrounding R protein activation. Future in-depth genetic and biochemical analyses will further enhance our knowledge of how R proteins are deliberately activated and how specific, targeted immunity is achieved in plants.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".