Growing evidences showed that heavy metals exposure may be associated with metabolic diseases.Nevertheless,themechanism underlying arsenic(As)exposure and metabolic syndrome(MetS)risk has not been fully elucidated.So ...Growing evidences showed that heavy metals exposure may be associated with metabolic diseases.Nevertheless,themechanism underlying arsenic(As)exposure and metabolic syndrome(MetS)risk has not been fully elucidated.So we aimed to prospectively investigate the role of serum uric acid(SUA)on the association between blood As exposure and incident MetS.A sample of 1045 older participants in a community in China was analyzed.We determined As at baseline and SUA concentration at follow-up in the Yiwu Elderly Cohort.MetS events were defined according to the criteria of the International Diabetes Federation(IDF).Generalized linear model with log-binominal regression model was applied to estimate the association of As with incident MetS.To investigate the role of SUA in the association between As andMetS,amediation analysiswas conducted.In the fully adjusted log-binominal model,per interquartile range increment of As,the risk of MetS increased 1.25-fold.Compared with the lowest quartile of As,the adjusted relative risk(RR)of MetS in the highest quartile was 1.42(95%confidence interval,CI:1.03,2.00).Additionally,blood As was positively associated with SUA,while SUA had significant association with MetS risk.Further mediation analysis demonstrated that the association of As and MetS risk was mediated by SUA,with the proportion of 15.7%.Our study found higher As was remarkably associated with the elevated risk of MetS in the Chinese older adults population.Mediation analysis indicated that SUA might be a mediator in the association between As exposure and MetS.展开更多
Unexploded ordnance(UXO)poses a threat to soldiers operating in mission areas,but current UXO detection systems do not necessarily provide the required safety and efficiency to protect soldiers from this hazard.Recent...Unexploded ordnance(UXO)poses a threat to soldiers operating in mission areas,but current UXO detection systems do not necessarily provide the required safety and efficiency to protect soldiers from this hazard.Recent technological advancements in artificial intelligence(AI)and small unmanned aerial systems(sUAS)present an opportunity to explore a novel concept for UXO detection.The new UXO detection system proposed in this study takes advantage of employing an AI-trained multi-spectral(MS)sensor on sUAS.This paper explores feasibility of AI-based UXO detection using sUAS equipped with a single(visible)spectrum(SS)or MS digital electro-optical(EO)sensor.Specifically,it describes the design of the Deep Learning Convolutional Neural Network for UXO detection,the development of an AI-based algorithm for reliable UXO detection,and also provides a comparison of performance of the proposed system based on SS and MS sensor imagery.展开更多
基金supported by the Fundamental Research Funds for the Central Universities(Nos.2021FZZX001-39 and 2020QNA7018)the Key Laboratory of Pollution Exposure and Health Intervention of Zhejiang Province(No.20220204)the Medical Science and Technology Project of Zhejiang Province(No.2023RC037).
文摘Growing evidences showed that heavy metals exposure may be associated with metabolic diseases.Nevertheless,themechanism underlying arsenic(As)exposure and metabolic syndrome(MetS)risk has not been fully elucidated.So we aimed to prospectively investigate the role of serum uric acid(SUA)on the association between blood As exposure and incident MetS.A sample of 1045 older participants in a community in China was analyzed.We determined As at baseline and SUA concentration at follow-up in the Yiwu Elderly Cohort.MetS events were defined according to the criteria of the International Diabetes Federation(IDF).Generalized linear model with log-binominal regression model was applied to estimate the association of As with incident MetS.To investigate the role of SUA in the association between As andMetS,amediation analysiswas conducted.In the fully adjusted log-binominal model,per interquartile range increment of As,the risk of MetS increased 1.25-fold.Compared with the lowest quartile of As,the adjusted relative risk(RR)of MetS in the highest quartile was 1.42(95%confidence interval,CI:1.03,2.00).Additionally,blood As was positively associated with SUA,while SUA had significant association with MetS risk.Further mediation analysis demonstrated that the association of As and MetS risk was mediated by SUA,with the proportion of 15.7%.Our study found higher As was remarkably associated with the elevated risk of MetS in the Chinese older adults population.Mediation analysis indicated that SUA might be a mediator in the association between As exposure and MetS.
基金the Office of Naval Research for supporting this effort through the Consortium for Robotics and Unmanned Systems Education and Research。
文摘Unexploded ordnance(UXO)poses a threat to soldiers operating in mission areas,but current UXO detection systems do not necessarily provide the required safety and efficiency to protect soldiers from this hazard.Recent technological advancements in artificial intelligence(AI)and small unmanned aerial systems(sUAS)present an opportunity to explore a novel concept for UXO detection.The new UXO detection system proposed in this study takes advantage of employing an AI-trained multi-spectral(MS)sensor on sUAS.This paper explores feasibility of AI-based UXO detection using sUAS equipped with a single(visible)spectrum(SS)or MS digital electro-optical(EO)sensor.Specifically,it describes the design of the Deep Learning Convolutional Neural Network for UXO detection,the development of an AI-based algorithm for reliable UXO detection,and also provides a comparison of performance of the proposed system based on SS and MS sensor imagery.
