Plant Immunity: At the Crossroads of Pathogen Perception and Defense Response
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Plants are challenged by different microbial pathogens that affect their growth and productivity. However, to defend pathogen attack, plants use diverse immune responses, such as pattern-triggered immunity (PTI), effector-triggered immunity (ETI), RNA silencing and autophagy, which are intricate and regulated by diverse signaling cascades. Pattern-recognition receptors (PRRs) and nucleotide-binding leucine-rich repeat (NLR) receptors are the hallmarks of plant innate immunity because they can detect pathogen or related immunogenic signals and trigger series of immune signaling cascades at different cellular compartments. In plants, most commonly, PRRs are receptor-like kinases (RLKs) and receptor-like proteins (RLPs) that function as a first layer of inducible defense. In this review, we provide an update on how plants sense pathogens, microbe-associated molecular patterns (PAMPs or MAMPs), and effectors as a danger signals and activate different immune responses like PTI and ETI. Further, we discuss the role RNA silencing, autophagy, and systemic acquired resistance as a versatile host defense response against pathogens. We also discuss early biochemical signaling events such as calcium (Ca2+), reactive oxygen species (ROS), and hormones that trigger the activation of different plant immune responses. This review also highlights the impact of climate-driven environmental factors on host–pathogen interactions.
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.
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 | 0.000 | 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 it