Objective:To uncover the characteristics of tick-borne viruses in Guizhou Province.Methods:A total of 414 Rhipicephalus microplus were collected from 5 counties in Guizhou Province,China from August 2022 to October 20...Objective:To uncover the characteristics of tick-borne viruses in Guizhou Province.Methods:A total of 414 Rhipicephalus microplus were collected from 5 counties in Guizhou Province,China from August 2022 to October 2023.A group of 12 ticks from each study sites was sequenced by next generation sequencing.Results:8 contigs of Wuhan mivirus(Chuviridae,Mivirus)with the length of 2094 bp to 11580 bp and 4 contigs of Lihan tick virus(Phenuiviridae,Uukuvirus)with the length of 1401 bp to 7080 bp were obtained,respectively.The prevalence rate of Wuhan mivirus and Lihan tick virus was 51.98%and 11.30%,respectively.The identities of gene sequences of both Wuhan mivirus and Lihan tick virus were 94%-100%compared with sequences in the National Center for Biotechnology Information.The phylogenetic analysis indicated that the Wuhan mivirus detected in this study was in the same branch with the Wuhan mivirus of Sichuan isolate TIGMIC-27(NCBI Accession:OP628598)and Zhejiang isolate TIGMIC-45(NCBI Accession:OP628613).In addition,the Lihan tick virus was in the same branch with the Sichuan Lihan tick virus isolate TIGMIC-46(NCBI Accession:ON812358).Conclusions:Both Wuhan mivirus and Lihan tick virus were prevalent in Rhipicephalus microplus in Guizhou Province.More studies are needed to understand the pathogenicity and public health threats of these tick-borne viruses.展开更多
Personal daily mobility trajectories/traces like Google Location Service integrates many valuable information from individuals and could benefit a lot of application scenarios,such as pandemic control and precaution,p...Personal daily mobility trajectories/traces like Google Location Service integrates many valuable information from individuals and could benefit a lot of application scenarios,such as pandemic control and precaution,prod-uct recommendation,customized user profile analysis,traffic management in smart cities,etc.However,utilizing such personal mobility data faces many challenges since users’private information,such as home/work addresses,can be unintentionally leaked.In this work,we build an FL system for transportation mode prediction based on personal mobility data.Utilizing FL-based training scheme,all user’s data are kept in local without uploading to central nodes,providing high privacy preserving capability.At the same time,we could train accurate DNN models that is close to the centralized training performance.The resulted transportation mode prediction system serves as a prototype on user’s traffic mode classification,which could potentially benefit the transportation data analysis and help make wise decisions to manage public transportation resources.展开更多
基金supported by grants from the National Natural Science Foundation of China(Grant No.82160633 and 81760605)the GZPH-NSFC-2021-17,the Guizhou Provincial Basic Research Program(Natural Science MS[2025](No.497)+1 种基金the Foundation of State Key Laboratory of Pathogen and Biosecurity of China(Grant No.SKLPBS2442)the High-level and Innovative Talents of Guizhou Province(QKH-GCC[2022]033-1).
文摘Objective:To uncover the characteristics of tick-borne viruses in Guizhou Province.Methods:A total of 414 Rhipicephalus microplus were collected from 5 counties in Guizhou Province,China from August 2022 to October 2023.A group of 12 ticks from each study sites was sequenced by next generation sequencing.Results:8 contigs of Wuhan mivirus(Chuviridae,Mivirus)with the length of 2094 bp to 11580 bp and 4 contigs of Lihan tick virus(Phenuiviridae,Uukuvirus)with the length of 1401 bp to 7080 bp were obtained,respectively.The prevalence rate of Wuhan mivirus and Lihan tick virus was 51.98%and 11.30%,respectively.The identities of gene sequences of both Wuhan mivirus and Lihan tick virus were 94%-100%compared with sequences in the National Center for Biotechnology Information.The phylogenetic analysis indicated that the Wuhan mivirus detected in this study was in the same branch with the Wuhan mivirus of Sichuan isolate TIGMIC-27(NCBI Accession:OP628598)and Zhejiang isolate TIGMIC-45(NCBI Accession:OP628613).In addition,the Lihan tick virus was in the same branch with the Sichuan Lihan tick virus isolate TIGMIC-46(NCBI Accession:ON812358).Conclusions:Both Wuhan mivirus and Lihan tick virus were prevalent in Rhipicephalus microplus in Guizhou Province.More studies are needed to understand the pathogenicity and public health threats of these tick-borne viruses.
文摘Personal daily mobility trajectories/traces like Google Location Service integrates many valuable information from individuals and could benefit a lot of application scenarios,such as pandemic control and precaution,prod-uct recommendation,customized user profile analysis,traffic management in smart cities,etc.However,utilizing such personal mobility data faces many challenges since users’private information,such as home/work addresses,can be unintentionally leaked.In this work,we build an FL system for transportation mode prediction based on personal mobility data.Utilizing FL-based training scheme,all user’s data are kept in local without uploading to central nodes,providing high privacy preserving capability.At the same time,we could train accurate DNN models that is close to the centralized training performance.The resulted transportation mode prediction system serves as a prototype on user’s traffic mode classification,which could potentially benefit the transportation data analysis and help make wise decisions to manage public transportation resources.
