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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".