Bibliographic record
Abstract
Rice stands as a pivotal global staple, underpinning the sustenance of billions. However, its production is critically hampered by bacterial diseases, which pose substantial threats to food security. This systematic review delves into the vital role of proteomics in understanding bacterial virulence and its interactions with rice, highlighting the indispensable need for advanced proteomic techniques to address these challenges. Employing methods such as mass spectrometry and two-dimensional electrophoresis, this review consolidates key findings including the identification of specific virulence factors and detailed insights into host-pathogen dynamics. These proteomic methodologies have illuminated the pathogenic processes affecting rice, paving the way for targeted interventions. The implications of these findings are profound, offering potential strategies for the development of disease-resistant rice varieties and thus enhancing agricultural productivity. However, the field faces ongoing challenges such as the complexity of proteomic data and the need for enhanced sensitivity and specificity in detecting virulence factors. Future directions include refining proteomic techniques and integrating multi-omics approaches to foster a holistic understanding of bacterial pathogenesis in rice. This review underscores the transformative potential of proteomics in revolutionizing rice pathology for improved crop resilience and 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".