BACKGROUND The prevalence of non-alcoholic fatty liver(NAFLD)has increased recently.Subjects with NAFLD are known to have higher chance for renal function impairment.Many past studies used traditional multiple linear ...BACKGROUND The prevalence of non-alcoholic fatty liver(NAFLD)has increased recently.Subjects with NAFLD are known to have higher chance for renal function impairment.Many past studies used traditional multiple linear regression(MLR)to identify risk factors for decreased estimated glomerular filtration rate(eGFR).However,medical research is increasingly relying on emerging machine learning(Mach-L)methods.The present study enrolled healthy women to identify factors affecting eGFR in subjects with and without NAFLD(NAFLD+,NAFLD-)and to rank their importance.AIM To uses three different Mach-L methods to identify key impact factors for eGFR in healthy women with and without NAFLD.METHODS A total of 65535 healthy female study participants were enrolled from the Taiwan MJ cohort,accounting for 32 independent variables including demographic,biochemistry and lifestyle parameters(independent variables),while eGFR was used as the dependent variable.Aside from MLR,three Mach-L methods were applied,including stochastic gradient boosting,eXtreme gradient boosting and elastic net.Errors of estimation were used to define method accuracy,where smaller degree of error indicated better model performance.RESULTS Income,albumin,eGFR,High density lipoprotein-Cholesterol,phosphorus,forced expiratory volume in one second(FEV1),and sleep time were all lower in the NAFLD+group,while other factors were all significantly higher except for smoking area.Mach-L had lower estimation errors,thus outperforming MLR.In Model 1,age,uric acid(UA),FEV1,plasma calcium level(Ca),plasma albumin level(Alb)and T-bilirubin were the most important factors in the NAFLD+group,as opposed to age,UA,FEV1,Alb,lactic dehydrogenase(LDH)and Ca for the NAFLD-group.Given the importance percentage was much higher than the 2nd important factor,we built Model 2 by removing age.CONCLUSION The eGFR were lower in the NAFLD+group compared to the NAFLD-group,with age being was the most important impact factor in both groups of healthy Chinese women,followed by LDH,UA,FEV1 and Alb.However,for the NAFLD-group,TSH and SBP were the 5th and 6th most important factors,as opposed to Ca and BF in the NAFLD+group.展开更多
BACKGROUND The incidence of chronic kidney disease(CKD)has dramatically increased in recent years,with significant impacts on patient mortality rates.Previous studies have identified multiple risk factors for CKD,but ...BACKGROUND The incidence of chronic kidney disease(CKD)has dramatically increased in recent years,with significant impacts on patient mortality rates.Previous studies have identified multiple risk factors for CKD,but they mostly relied on the use of traditional statistical methods such as logistic regression and only focused on a few risk factors.AIM To determine factors that can be used to identify subjects with a low estimated glomerular filtration rate(L-eGFR<60 mL/min per 1.73 m^(2))in a cohort of 1236 Chinese people aged over 65.METHODS Twenty risk factors were divided into three models.Model 1 consisted of demographic and biochemistry data.Model 2 added lifestyle data to Model 1,and Model 3 added inflammatory markers to Model 2.Five machine learning methods were used:Multivariate adaptive regression splines,eXtreme Gradient Boosting,stochastic gradient boosting,Light Gradient Boosting Machine,and Categorical Features+Gradient Boosting.Evaluation criteria included accuracy,sensitivity,specificity,area under the receiver operating characteristic curve(AUC),F-1 score,and balanced accuracy.RESULTS A trend of increasing AUC of each was observed from Model 1 to Model 3 and reached statistical significance.Model 3 selected uric acid as the most important risk factor,followed by age,hemoglobin(Hb),body mass index(BMI),sport hours,and systolic blood pressure(SBP).CONCLUSION Among all the risk factors including demographic,biochemistry,and lifestyle risk factors,along with inflammation markers,UA is the most important risk factor to identify L-eGFR,followed by age,Hb,BMI,sport hours,and SBP in a cohort of elderly Chinese people.展开更多
Water pollution affects plants and organisms living in these bodies of water; and, in almost all cases the effect is damaging not only to individual species and populations, but also to the natural biological communit...Water pollution affects plants and organisms living in these bodies of water; and, in almost all cases the effect is damaging not only to individual species and populations, but also to the natural biological communities. Genetic algorithm and kernel partial least square （GA-KPLS） and Levenberg- Marquardt artificial neural network （L-M ANN） techniques were used to investigate the correlation between retention time （tR） and descriptors for 150 organic contaminants in natural water and wastewater, which are obtained by gas chromatography coupled to high-resolution time-of-flight mass spectrometry （GC-TOF MS）. The L-M ANN model gave a significantly better performance than the GA-KPLS model. This indicates that L-M ANN can be used as an alternative modeling toot for quantitative structure-retention relationship （QSRR） studies.展开更多
Anews piece from last year about an elderly Chinese man whose life savings were eaten by mice tells us two things; firstly,that the stove,as in 80-yearold Yang Lihong's case,isn't exactly a safe piggy bank.Mor...Anews piece from last year about an elderly Chinese man whose life savings were eaten by mice tells us two things; firstly,that the stove,as in 80-yearold Yang Lihong's case,isn't exactly a safe piggy bank.More importantly,it highlights the norm among Chinese households to stash away cash with the intention of saving for a rainy day.展开更多
Developing an integrated and intelligent approach to securing the ITE(information technology environment)is an emergent and evolving concern for every organization and consumer entity during the last few decades.Major...Developing an integrated and intelligent approach to securing the ITE(information technology environment)is an emergent and evolving concern for every organization and consumer entity during the last few decades.Major topics of concern include“SI”(security intelligence),“D-DA”(data-driven analytics),“PE”(proven expertise),and“R-TD”(real-time defense)capabilities.“DRBTs”(dynamic response behavior types)include“incident response”,“endpoint management”,“threat intelligence”,“network security”,and“fraud protection”.The consumer demand for electricity as essential public access and service is indexed to population growth estimates.Consumer-driven economies continue to add electrical consumption to their grids even though improvements in lower-power consumption and higher design efficiencies are present in new electric-powered products.Dependence on the production of electrical energy has no peer replacement technology and creates a societal vulnerability to targeted public electrical grid interruptions.When access to,or production of,electrical power is interrupted,the result is a“Mass Effect”every consumer feels with equal distribution.Electric grid security falls directly into the levels of authorized,and unauthorized,access via the“IoT”(Internet of Things)concepts,and the“IoM2M”(Internet of Machine-to-Machine)integration.Electrical grid operations that include production and network management augment each other in order to support the demand for electricity every day either in peak or off-peak,thus cybersecurity plays a big role in the protection of such assets at our disposal.With help from AI(artificial intelligence)integrated into the IoT a resilient system can be built to protect the electric grid system nationwide and will be able to detect and preempt Smart Malware attacks.展开更多
When we think of forests we usually think of trees,plants and animals.But forests could not exist without fungi,which lie at the base of the biodiversity webs that support much of life on Earth.Most fungi live as bran...When we think of forests we usually think of trees,plants and animals.But forests could not exist without fungi,which lie at the base of the biodiversity webs that support much of life on Earth.Most fungi live as branching,fusing networks of tubular cells known as mycelium which can make up between a third and a half of the living mass of soils.Globally,the total length of fungal mycelium in the top 10cm of soil is more than 450 quadrillion km:about half the width of our galaxy.These networks comprise an ancient life-support system that easily qualifies as one of the wonders of the living world.Despite that,fungi represent a meagre 0.2%of our global conservation priorities.展开更多
基金Supported by the Kaohsiung Armed Forces General Hospital.
文摘BACKGROUND The prevalence of non-alcoholic fatty liver(NAFLD)has increased recently.Subjects with NAFLD are known to have higher chance for renal function impairment.Many past studies used traditional multiple linear regression(MLR)to identify risk factors for decreased estimated glomerular filtration rate(eGFR).However,medical research is increasingly relying on emerging machine learning(Mach-L)methods.The present study enrolled healthy women to identify factors affecting eGFR in subjects with and without NAFLD(NAFLD+,NAFLD-)and to rank their importance.AIM To uses three different Mach-L methods to identify key impact factors for eGFR in healthy women with and without NAFLD.METHODS A total of 65535 healthy female study participants were enrolled from the Taiwan MJ cohort,accounting for 32 independent variables including demographic,biochemistry and lifestyle parameters(independent variables),while eGFR was used as the dependent variable.Aside from MLR,three Mach-L methods were applied,including stochastic gradient boosting,eXtreme gradient boosting and elastic net.Errors of estimation were used to define method accuracy,where smaller degree of error indicated better model performance.RESULTS Income,albumin,eGFR,High density lipoprotein-Cholesterol,phosphorus,forced expiratory volume in one second(FEV1),and sleep time were all lower in the NAFLD+group,while other factors were all significantly higher except for smoking area.Mach-L had lower estimation errors,thus outperforming MLR.In Model 1,age,uric acid(UA),FEV1,plasma calcium level(Ca),plasma albumin level(Alb)and T-bilirubin were the most important factors in the NAFLD+group,as opposed to age,UA,FEV1,Alb,lactic dehydrogenase(LDH)and Ca for the NAFLD-group.Given the importance percentage was much higher than the 2nd important factor,we built Model 2 by removing age.CONCLUSION The eGFR were lower in the NAFLD+group compared to the NAFLD-group,with age being was the most important impact factor in both groups of healthy Chinese women,followed by LDH,UA,FEV1 and Alb.However,for the NAFLD-group,TSH and SBP were the 5th and 6th most important factors,as opposed to Ca and BF in the NAFLD+group.
基金Supported by the Kaohsiung Armed Forces General HospitalThe study protocol was approved by the Institutional Review Board of the Tri-Service General Hospital,National Defense Medical Center(IRB No.:KAFGHIRB 109-46).
文摘BACKGROUND The incidence of chronic kidney disease(CKD)has dramatically increased in recent years,with significant impacts on patient mortality rates.Previous studies have identified multiple risk factors for CKD,but they mostly relied on the use of traditional statistical methods such as logistic regression and only focused on a few risk factors.AIM To determine factors that can be used to identify subjects with a low estimated glomerular filtration rate(L-eGFR<60 mL/min per 1.73 m^(2))in a cohort of 1236 Chinese people aged over 65.METHODS Twenty risk factors were divided into three models.Model 1 consisted of demographic and biochemistry data.Model 2 added lifestyle data to Model 1,and Model 3 added inflammatory markers to Model 2.Five machine learning methods were used:Multivariate adaptive regression splines,eXtreme Gradient Boosting,stochastic gradient boosting,Light Gradient Boosting Machine,and Categorical Features+Gradient Boosting.Evaluation criteria included accuracy,sensitivity,specificity,area under the receiver operating characteristic curve(AUC),F-1 score,and balanced accuracy.RESULTS A trend of increasing AUC of each was observed from Model 1 to Model 3 and reached statistical significance.Model 3 selected uric acid as the most important risk factor,followed by age,hemoglobin(Hb),body mass index(BMI),sport hours,and systolic blood pressure(SBP).CONCLUSION Among all the risk factors including demographic,biochemistry,and lifestyle risk factors,along with inflammation markers,UA is the most important risk factor to identify L-eGFR,followed by age,Hb,BMI,sport hours,and SBP in a cohort of elderly Chinese people.
文摘Water pollution affects plants and organisms living in these bodies of water; and, in almost all cases the effect is damaging not only to individual species and populations, but also to the natural biological communities. Genetic algorithm and kernel partial least square （GA-KPLS） and Levenberg- Marquardt artificial neural network （L-M ANN） techniques were used to investigate the correlation between retention time （tR） and descriptors for 150 organic contaminants in natural water and wastewater, which are obtained by gas chromatography coupled to high-resolution time-of-flight mass spectrometry （GC-TOF MS）. The L-M ANN model gave a significantly better performance than the GA-KPLS model. This indicates that L-M ANN can be used as an alternative modeling toot for quantitative structure-retention relationship （QSRR） studies.
文摘Anews piece from last year about an elderly Chinese man whose life savings were eaten by mice tells us two things; firstly,that the stove,as in 80-yearold Yang Lihong's case,isn't exactly a safe piggy bank.More importantly,it highlights the norm among Chinese households to stash away cash with the intention of saving for a rainy day.
文摘Developing an integrated and intelligent approach to securing the ITE(information technology environment)is an emergent and evolving concern for every organization and consumer entity during the last few decades.Major topics of concern include“SI”(security intelligence),“D-DA”(data-driven analytics),“PE”(proven expertise),and“R-TD”(real-time defense)capabilities.“DRBTs”(dynamic response behavior types)include“incident response”,“endpoint management”,“threat intelligence”,“network security”,and“fraud protection”.The consumer demand for electricity as essential public access and service is indexed to population growth estimates.Consumer-driven economies continue to add electrical consumption to their grids even though improvements in lower-power consumption and higher design efficiencies are present in new electric-powered products.Dependence on the production of electrical energy has no peer replacement technology and creates a societal vulnerability to targeted public electrical grid interruptions.When access to,or production of,electrical power is interrupted,the result is a“Mass Effect”every consumer feels with equal distribution.Electric grid security falls directly into the levels of authorized,and unauthorized,access via the“IoT”(Internet of Things)concepts,and the“IoM2M”(Internet of Machine-to-Machine)integration.Electrical grid operations that include production and network management augment each other in order to support the demand for electricity every day either in peak or off-peak,thus cybersecurity plays a big role in the protection of such assets at our disposal.With help from AI(artificial intelligence)integrated into the IoT a resilient system can be built to protect the electric grid system nationwide and will be able to detect and preempt Smart Malware attacks.
文摘When we think of forests we usually think of trees,plants and animals.But forests could not exist without fungi,which lie at the base of the biodiversity webs that support much of life on Earth.Most fungi live as branching,fusing networks of tubular cells known as mycelium which can make up between a third and a half of the living mass of soils.Globally,the total length of fungal mycelium in the top 10cm of soil is more than 450 quadrillion km:about half the width of our galaxy.These networks comprise an ancient life-support system that easily qualifies as one of the wonders of the living world.Despite that,fungi represent a meagre 0.2%of our global conservation priorities.
基金MM is supported by a New Investigator award from the New Emerging Teams (NET) of the Canadian Institutes of Health Research (CIHR) PAR is supported by a Career Scientist award from the CIHR DNJ is supported by a fellowship award from the CIHR and fr
