Molecular Techniques for Detection of Major Diseases in Sugarcane

It is necessary to rapidly diagnose diseases, identify and monitor the pathogens to achieve scientific and effective control of major diseases in sugarcane. In the present study, the molecular techniques for rapid detection of 13 pathogens that cause 10 important diseases of sugarcane including smut, rust, leaf scald, ratoon stunting, red stripe, mosaic, Fiji, yellow leaf, white leaf and bacilliform virus were established by Sugarcane Research Institute, Yunnan Academy of Agricul- tural Sciences after years of effort. The results will provide scientific basis for effective diagnosis and control of sugarcane diseases, detection of virus-free seedlings and quarantine management of exotic species.

Expert consensus on One Health for establishing an enhanced and integrated surveillance system for key infectious diseases

China has been continuously improving its monitoring methods and strategies to address key infectious diseases(KIDs).After the severe acute respiratory syndrome epidemic in 2003,China established a comprehensive report-ing system for infectious diseases(IDs)and public health emergencies.The relatively lagging warning thresholds,limited warning information,and outdated warning technology are insufficient to meet the needs of comprehensive monitoring for modern KIDs.Strengthening early monitoring and warning capabilities to enhance the public health system has become a top priority,with increasing demand for early warning thresholds,information,and tech-niques,thanks to constant innovation and development in molecular biology,bioinformatics,artificial intelligence,and other identification and analysis technologies.A panel of 31 experts has recommended a fourth-generation comprehensive surveillance system targeting KIDs(41 notifiable diseases and emerging IDs).The aim of this surveil-lance system is to systematically monitor the epidemiology and causal pathogens of KIDs in hosts such as humans,animals,and vectors,along with associated environmental pathogens.By integrating factors influencing epidemic spread and risk assessment,the surveillance system can serve to detect,predict,and provide early warnings for the occurrence,development,variation,and spread of known or novel KIDs.Moreover,we recommend comprehensive ID monitoring based on the fourth-generation surveillance system,along with a data-integrated monitoring and early warning platform and a consortium pathogen detection technology system.This series of considerations is based on systematic and comprehensive monitoring across multiple sectors,dimensions,factors,and pathogens that is sup-ported by data integration and connectivity.This expert consensus will provides an opportunity for collaboration in various fields and relies on interdisciplinary application to enhance comprehensive monitoring,prediction,and early warning capabilities for the next generation of ID surveillance.This expert consensus will serve as a reference for ID prevention and control as well as other related activities.

Gene regulatory network inference based on causal discovery integrating with graph neural network

Gene regulatory network (GRN) inference from gene expression data is asignificant approach to understanding aspects of the biological system.Compared with generalized correlation-based methods, causality-inspiredones seem more rational to infer regulatory relationships. We proposeGRINCD, a novel GRN inference framework empowered by graph representationlearning and causal asymmetric learning, considering both linearand non-linear regulatory relationships. First, high-quality representation ofeach gene is generated using graph neural network. Then, we apply theadditive noise model to predict the causal regulation of each regulator-targetpair. Additionally, we design two channels and finally assemble them forrobust prediction. Through comprehensive comparisons of our frameworkwith state-of-the-art methods based on different principles on numerousdatasets of diverse types and scales, the experimental results show that ourframework achieves superior or comparable performance under variousevaluation metrics. Our work provides a new clue for constructing GRNs,and our proposed framework GRINCD also shows potential in identifyingkey factors affecting cancerdevelopment.