Background:Echinococcosis constitutes a major zoonotic parasitic disease with profound public health and socioeconomic implications.Dog deworming remains a cornerstone intervention endorsed by World Health Organizatio...Background:Echinococcosis constitutes a major zoonotic parasitic disease with profound public health and socioeconomic implications.Dog deworming remains a cornerstone intervention endorsed by World Health Organization.This study comparatively assessed automated vs.manual praziquantel(PZQ)bait delivery systems for dog echinococcosis control.Methods:A prospective,randomized,double-blinded field trial employing a One Health framework was conducted in Tianzhu Zangzu Zizhixian,China-a cystic echinococcosis endemic region.Township-level cluster randomization allocated dogs to smart collar deworming group(SCDG)with monthly automated PZQ delivery and manual deworming group(MDG)with conventional bait administration.Intervention assignment remained masked between groups.Laboratory personnel were blinded during fecal antigen analysis using enzyme linked immunosorbent assay(ELISA).Generalized Estimating Equations(GEE)evaluated efficacy via odds ratios(ORs)over 24 months,SPSS software(version 27.0)was used for data processing.Metrics included parasitological outcomes,deworming frequency,collar deployment and recovery rates.Results:From June 2021 to July 2023,1920 dogs(800 from SCDG,1120 from MDG)were enrolled with owner consent.Among the 5119 fecal samples analyzed(2320 from SCDG,2799 from MDG),33 were antigen-positive(8 from SCDG,25 from MDG).Baseline positivity showed no intergroup difference(SCDG:0.8%[6/792]vs.MDG:1.2%[13/1099],P=0.36).At 24 months,SCDG achieved 0%positivity(0/661)while MDG's was 0.6%(5/789).Smart collars conferred significant protection(OR=0.432,95%confidence interval[CI]:0.194-0.959,P<0.0001)-equivalent to 56.8% infection risk reduction.At 12 months,the recycling rate and integrity rate of smart collars were 83.3%(666/800)and 74.3%(495/666),respectively;and the proportion of dogs dewormed 6-12 times per year was 86.7%(577/666).At 24 months,the recycling rate and integrity rate of smart collars were up to 93.6%(749/800,χ^(2)=42.106,P<0.001)and 94.1%(705/749,χ^(2)=107.269,P<0.001),respectively.During this period,91.6%(1202/1312)of dogs received deworming 6-12 times per year.In 2023,a total of 647 smart collars were distributed,with the recycling rate and integrity rate of 99.8%(646/647)and 96.4%(623/646),respectively.The proportion of dogs dewormed 6-12 times per year increased to 96.6%(625/646,χ^(2)=34.969,P<0.001).Conclusions:Field deployment of smart collars proves operationally viable,sustainably enhancing deworming frequency while reducing dog infection rates more effectively than manual methods.The 56.8%protective effect advancement mitigates environmental egg contamination,thereby lowering zoonotic transmission risk.展开更多
With the increasing awareness of privacy protection and the improvement of relevant laws,federal learning has gradually become a new choice for cross-agency and cross-device machine learning.In order to solve the prob...With the increasing awareness of privacy protection and the improvement of relevant laws,federal learning has gradually become a new choice for cross-agency and cross-device machine learning.In order to solve the problems of privacy leakage,high computational overhead and high traffic in some federated learning schemes,this paper proposes amultiplicative double privacymask algorithm which is convenient for homomorphic addition aggregation.The combination of homomorphic encryption and secret sharing ensures that the server cannot compromise user privacy from the private gradient uploaded by the participants.At the same time,the proposed TQRR(Top-Q-Random-R)gradient selection algorithm is used to filter the gradient of encryption and upload efficiently,which reduces the computing overhead of 51.78%and the traffic of 64.87%on the premise of ensuring the accuracy of themodel,whichmakes the framework of privacy protection federated learning lighter to adapt to more miniaturized federated learning terminals.展开更多
基金National Key Research and Development Program of China(grant numbers 2021YFC2300800,2021YFC2300804).
文摘Background:Echinococcosis constitutes a major zoonotic parasitic disease with profound public health and socioeconomic implications.Dog deworming remains a cornerstone intervention endorsed by World Health Organization.This study comparatively assessed automated vs.manual praziquantel(PZQ)bait delivery systems for dog echinococcosis control.Methods:A prospective,randomized,double-blinded field trial employing a One Health framework was conducted in Tianzhu Zangzu Zizhixian,China-a cystic echinococcosis endemic region.Township-level cluster randomization allocated dogs to smart collar deworming group(SCDG)with monthly automated PZQ delivery and manual deworming group(MDG)with conventional bait administration.Intervention assignment remained masked between groups.Laboratory personnel were blinded during fecal antigen analysis using enzyme linked immunosorbent assay(ELISA).Generalized Estimating Equations(GEE)evaluated efficacy via odds ratios(ORs)over 24 months,SPSS software(version 27.0)was used for data processing.Metrics included parasitological outcomes,deworming frequency,collar deployment and recovery rates.Results:From June 2021 to July 2023,1920 dogs(800 from SCDG,1120 from MDG)were enrolled with owner consent.Among the 5119 fecal samples analyzed(2320 from SCDG,2799 from MDG),33 were antigen-positive(8 from SCDG,25 from MDG).Baseline positivity showed no intergroup difference(SCDG:0.8%[6/792]vs.MDG:1.2%[13/1099],P=0.36).At 24 months,SCDG achieved 0%positivity(0/661)while MDG's was 0.6%(5/789).Smart collars conferred significant protection(OR=0.432,95%confidence interval[CI]:0.194-0.959,P<0.0001)-equivalent to 56.8% infection risk reduction.At 12 months,the recycling rate and integrity rate of smart collars were 83.3%(666/800)and 74.3%(495/666),respectively;and the proportion of dogs dewormed 6-12 times per year was 86.7%(577/666).At 24 months,the recycling rate and integrity rate of smart collars were up to 93.6%(749/800,χ^(2)=42.106,P<0.001)and 94.1%(705/749,χ^(2)=107.269,P<0.001),respectively.During this period,91.6%(1202/1312)of dogs received deworming 6-12 times per year.In 2023,a total of 647 smart collars were distributed,with the recycling rate and integrity rate of 99.8%(646/647)and 96.4%(623/646),respectively.The proportion of dogs dewormed 6-12 times per year increased to 96.6%(625/646,χ^(2)=34.969,P<0.001).Conclusions:Field deployment of smart collars proves operationally viable,sustainably enhancing deworming frequency while reducing dog infection rates more effectively than manual methods.The 56.8%protective effect advancement mitigates environmental egg contamination,thereby lowering zoonotic transmission risk.
基金supported by the National Natural Science Foundation of China(Grant Nos.62172436,62102452)the National Key Research and Development Program of China(2023YFB3106100,2021YFB3100100)the Natural Science Foundation of Shaanxi Province(2023-JC-YB-584).
文摘With the increasing awareness of privacy protection and the improvement of relevant laws,federal learning has gradually become a new choice for cross-agency and cross-device machine learning.In order to solve the problems of privacy leakage,high computational overhead and high traffic in some federated learning schemes,this paper proposes amultiplicative double privacymask algorithm which is convenient for homomorphic addition aggregation.The combination of homomorphic encryption and secret sharing ensures that the server cannot compromise user privacy from the private gradient uploaded by the participants.At the same time,the proposed TQRR(Top-Q-Random-R)gradient selection algorithm is used to filter the gradient of encryption and upload efficiently,which reduces the computing overhead of 51.78%and the traffic of 64.87%on the premise of ensuring the accuracy of themodel,whichmakes the framework of privacy protection federated learning lighter to adapt to more miniaturized federated learning terminals.
