Evolution of Disease Defense Genes and Their Regulators in Plants
Bibliographic record
Abstract
Biotic stresses do damage to the growth and development of plants, and yield losses for some crops. Confronted with microbial infections, plants have evolved multiple defense mechanisms, which play important roles in the never-ending molecular arms race of plant⁻pathogen interactions. The complicated defense systems include pathogen-associated molecular patterns (PAMP) triggered immunity (PTI), effector triggered immunity (ETI), and the exosome-mediated cross-kingdom RNA interference (CKRI) system. Furthermore, plants have evolved a classical regulation system mediated by miRNAs to regulate these defense genes. Most of the genes/small RNAs or their regulators that involve in the defense pathways can have very rapid evolutionary rates in the longitudinal and horizontal co-evolution with pathogens. According to these internal defense mechanisms, some strategies such as molecular switch for the disease resistance genes, host-induced gene silencing (HIGS), and the new generation of RNA-based fungicides, have been developed to control multiple plant diseases. These broadly applicable new strategies by transgene or spraying ds/sRNA may lead to reduced application of pesticides and improved crop yield.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".