A5012549475- Zoonotic diseases and public health
- Animal Disease Management and Epidemiology
- Genomics and Phylogenetic Studies
- Probiotics and Fermented Foods
- Machine Learning in Bioinformatics
- Identification and Quantification in Food
- Gut microbiota and health
- Viral gastroenteritis research and epidemiology
- Plant-Microbe Interactions and Immunity
- Vibrio bacteria research studies
- Plant Disease Management Techniques
Nanjing Agricultural University
2023-2024
Bacterial pathogens are one of the major threats to biosafety and environmental health, advanced assessment is a prerequisite combating bacterial pathogens. Currently, 16S rRNA gene sequencing efficient in open-view detection However, taxonomic resolution applicability this method limited by domain-specific pathogen database, profiling method, target variable regions. Here, we present pipeline multiple (MBPD) identify animal, plant, zoonotic MBPD based on large, curated database full-length...
Abstract Pathogen detection from biological and environmental samples is important for global disease control. Despite advances in pathogen using deep learning, current algorithms have limitations processing long genomic sequences. Through the cross-fusion of cross, residual neural networks, we developed DCiPatho accurate based on integrated frequency features 3-to-7 k-mers. Compared with existing state-of-the-art algorithms, can be used to accurately identify distinct pathogenic bacteria...
ABSTRACT A key aspect of “One Health” is to recognize that environmental health and human are inseparable. In this interconnected network, farmland plays a crucial role, as it serves link connecting various ecosystems. However, the threat pathogens in farmlands often overlooked despite growing evidence highlighting its importance pressing public concern via direct indirect pathways. To address issue, we advocate adoption approach, which relies on strengthening development detection tools...
Abstract Understanding how microbiomes resist pathogen invasion remains a key challenge in natural ecosystems. Here, we combined genome-scale metabolic models with synthetic community experiments to unravel the mechanisms driving suppression. We developed curated for each strain, incorporating 48 common resource utilization profiles fully capture their capacities. Trophic interactions inferred from accurately predicted outcomes, achieving an F1 score of 96% across 620 tests involving diverse...