Combining Fungicides and Prospective NPR1-Based “Just-in-Time” Immunomodulating Chemistries for Crop Protection
Bibliographic record
Abstract
Each year, crop yield is lost to weeds competing for resources, insect herbivory and diseases caused by pathogens. To thwart these insults and preserve yield security and a high quality of traits, conventional agriculture makes use of improved cultivars combined with fertilizer and agrochemical applications. However, given that regulatory bodies and consumers are demanding environmentally safer agrochemicals, while at the same time resistance to agrochemicals is mounting, it is crucial to adopt a "holistic" approach to agriculture by not excluding any number of management tools at our disposal. One such tool includes chemicals that stimulate plant immunity. The development of this particular type of alternative crop protection strategy has been of great interest to us. We have approached this paradigm by studying plant immunity, specifically systemic acquired resistance (SAR). The deployment of SAR immunity requires the production by the crop plant of an endogenous small molecule metabolite called salicylic acid (SA). Furthermore, immunity can only be deployed if SA can bind to its receptor and activate the genes responsible for the SAR program. The key receptor for SAR is a transcription coactivator called NPR1. Since discovering this NPR1-SA receptor-ligand pair, we have embarked on a journey to develop novel chemistries capable of deploying SAR in the field. The journey begins with the development of a scalable assay to identify these novel chemistries. One such assay, presented here, is based on differential scanning fluorimetry technology and demonstrates that NPR1 is destabilized by binding to SA.
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) | 0.000 | 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 | 0.001 |
| 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".