Several promising plasma biomarker proteins,such as amyloid-β(Aβ),tau,neurofilament light chain,and glial fibrillary acidic protein,are widely used for the diagnosis of neurodegenerative diseases.However,little is k...Several promising plasma biomarker proteins,such as amyloid-β(Aβ),tau,neurofilament light chain,and glial fibrillary acidic protein,are widely used for the diagnosis of neurodegenerative diseases.However,little is known about the long-term stability of these biomarker proteins in plasma samples stored at-80°C.We aimed to explore how storage time would affect the diagnostic accuracy of these biomarkers using a large cohort.Plasma samples from 229 cognitively unimpaired individuals,encompassing healthy controls and those experiencing subjective cognitive decline,as well as 99 patients with cognitive impairment,comprising those with mild cognitive impairment and dementia,were acquired from the Sino Longitudinal Study on Cognitive Decline project.These samples were stored at-80°C for up to 6 years before being used in this study.Our results showed that plasma levels of Aβ42,Aβ40,neurofilament light chain,and glial fibrillary acidic protein were not significantly correlated with sample storage time.However,the level of total tau showed a negative correlation with sample storage time.Notably,in individuals without cognitive impairment,plasma levels of total protein and tau phosphorylated protein threonine 181(p-tau181)also showed a negative correlation with sample storage time.This was not observed in individuals with cognitive impairment.Consequently,we speculate that the diagnostic accuracy of plasma p-tau181 and the p-tau181 to total tau ratio may be influenced by sample storage time.Therefore,caution is advised when using these plasma biomarkers for the identification of neurodegenerative diseases,such as Alzheimer's disease.Furthermore,in cohort studies,it is important to consider the impact of storage time on the overall results.展开更多
Inertial system platforms are a kind of important precision devices,which have the characteristics of difficult acquisition for state data and small sample scale.Focusing on the model optimization for data-driven faul...Inertial system platforms are a kind of important precision devices,which have the characteristics of difficult acquisition for state data and small sample scale.Focusing on the model optimization for data-driven fault state prediction and quantitative degreemeasurement,a fast small-sample supersphere one-class SVMmodelingmethod using support vectors pre-selection is systematically studied in this paper.By theorem-proving the irrelevance between themodel’s learning result and the non-support vectors(NSVs),the distribution characters of the support vectors are analyzed.On this basis,a modeling method with selected samples having specific geometry character fromthe training sets is also proposed.The method can remarkably eliminate theNSVs and improve the algorithm’s efficiency.The experimental results testify that the scale of training samples and the modeling time consumption both give a sharply decrease using the support vectors pre-selection method.The experimental results on inertial devices also show good fault prediction capability and effectiveness of quantitative anomaly measurement.展开更多
To the Editor:Accumulating evidence has shown that the preclinical stage of Alzheimer’s disease(AD)(i.e.,asymptomatic amyloidosis)lasts for decades before the onset of cognitive symptoms,providing a large window for ...To the Editor:Accumulating evidence has shown that the preclinical stage of Alzheimer’s disease(AD)(i.e.,asymptomatic amyloidosis)lasts for decades before the onset of cognitive symptoms,providing a large window for early intervention.Amyloid pathology,the earliest pathological change associated with AD,can be detected in vivo with cerebrospinal fluid(CSF)analysis or positron emission tomography(PET),and its presence is necessary for the diagnosis of preclinical AD(pre-AD).However,both PET scans and CSF analyses are expensive,hampering their use in large-scale screening.Thus,blood-based biomarkers are desirable alternatives,as they are cost-effective and not invasive.展开更多
In some nonlinear dynamic systems, the state variables function usually can be separated from the control variables function, which brings much trouble to the identification of such systems. To well solve this problem...In some nonlinear dynamic systems, the state variables function usually can be separated from the control variables function, which brings much trouble to the identification of such systems. To well solve this problem, an improved least squares support vector regression (LSSVR) model with multiple-kernel is proposed and the model is applied to the nonlinear separable system identification. This method utilizes the excellent nonlinear mapping ability of Morlet wavelet kernel function and combines the state and control variables information into a kernel matrix. Using the composite wavelet kernel, the LSSVR includes two nonlinear functions, whose variables are the state variables and the control ones respectively, in this way, the regression function can gain better nonlinear mapping ability, and it can simulate almost any curve in quadratic continuous integral space. Then, they are used to identify the two functions in the separable nonlinear dynamic system. Simulation results show that the multiple-kernel LSSVR method can greatly improve the identification accuracy than the single kernel method, and the Morlet wavelet kernel is more efficient than the other kernels.展开更多
基金supported by the National Key Research&Development Program of China,Nos.2021YFC2501205(to YC),2022YFC24069004(to JL)the STI2030-Major Project,Nos.2021ZD0201101(to YC),2022ZD0211800(to YH)+2 种基金the National Natural Science Foundation of China(Major International Joint Research Project),No.82020108013(to YH)the Sino-German Center for Research Promotion,No.M-0759(to YH)a grant from Beijing Municipal Science&Technology Commission(Beijing Brain Initiative),No.Z201100005520018(to JL)。
文摘Several promising plasma biomarker proteins,such as amyloid-β(Aβ),tau,neurofilament light chain,and glial fibrillary acidic protein,are widely used for the diagnosis of neurodegenerative diseases.However,little is known about the long-term stability of these biomarker proteins in plasma samples stored at-80°C.We aimed to explore how storage time would affect the diagnostic accuracy of these biomarkers using a large cohort.Plasma samples from 229 cognitively unimpaired individuals,encompassing healthy controls and those experiencing subjective cognitive decline,as well as 99 patients with cognitive impairment,comprising those with mild cognitive impairment and dementia,were acquired from the Sino Longitudinal Study on Cognitive Decline project.These samples were stored at-80°C for up to 6 years before being used in this study.Our results showed that plasma levels of Aβ42,Aβ40,neurofilament light chain,and glial fibrillary acidic protein were not significantly correlated with sample storage time.However,the level of total tau showed a negative correlation with sample storage time.Notably,in individuals without cognitive impairment,plasma levels of total protein and tau phosphorylated protein threonine 181(p-tau181)also showed a negative correlation with sample storage time.This was not observed in individuals with cognitive impairment.Consequently,we speculate that the diagnostic accuracy of plasma p-tau181 and the p-tau181 to total tau ratio may be influenced by sample storage time.Therefore,caution is advised when using these plasma biomarkers for the identification of neurodegenerative diseases,such as Alzheimer's disease.Furthermore,in cohort studies,it is important to consider the impact of storage time on the overall results.
基金the National Natural Science Foundation of China(Grant No.61403397)the Natural Science Basic Research Plan in Shaanxi Province of China(Grant Nos.2020JM-358,2015JM6313).
文摘Inertial system platforms are a kind of important precision devices,which have the characteristics of difficult acquisition for state data and small sample scale.Focusing on the model optimization for data-driven fault state prediction and quantitative degreemeasurement,a fast small-sample supersphere one-class SVMmodelingmethod using support vectors pre-selection is systematically studied in this paper.By theorem-proving the irrelevance between themodel’s learning result and the non-support vectors(NSVs),the distribution characters of the support vectors are analyzed.On this basis,a modeling method with selected samples having specific geometry character fromthe training sets is also proposed.The method can remarkably eliminate theNSVs and improve the algorithm’s efficiency.The experimental results testify that the scale of training samples and the modeling time consumption both give a sharply decrease using the support vectors pre-selection method.The experimental results on inertial devices also show good fault prediction capability and effectiveness of quantitative anomaly measurement.
基金supported by grants from the National Natural Science Foundation of China(Nos.82020108013 and 82001773)National Key Research and Development Program of China(No.2022YFC24069004)+1 种基金Beijing Brain Initiative from Beijing Municipal Science&Technology Commission(No.Z201100005520018)STI2030-Major Projects(No.2022ZD0211800)
文摘To the Editor:Accumulating evidence has shown that the preclinical stage of Alzheimer’s disease(AD)(i.e.,asymptomatic amyloidosis)lasts for decades before the onset of cognitive symptoms,providing a large window for early intervention.Amyloid pathology,the earliest pathological change associated with AD,can be detected in vivo with cerebrospinal fluid(CSF)analysis or positron emission tomography(PET),and its presence is necessary for the diagnosis of preclinical AD(pre-AD).However,both PET scans and CSF analyses are expensive,hampering their use in large-scale screening.Thus,blood-based biomarkers are desirable alternatives,as they are cost-effective and not invasive.
基金supported by the National Natural Science Foundation for Young Scientists of China(Nos.61202332,60904083)the China Postdoctoral Science Foundation(No.2012M521905)
文摘In some nonlinear dynamic systems, the state variables function usually can be separated from the control variables function, which brings much trouble to the identification of such systems. To well solve this problem, an improved least squares support vector regression (LSSVR) model with multiple-kernel is proposed and the model is applied to the nonlinear separable system identification. This method utilizes the excellent nonlinear mapping ability of Morlet wavelet kernel function and combines the state and control variables information into a kernel matrix. Using the composite wavelet kernel, the LSSVR includes two nonlinear functions, whose variables are the state variables and the control ones respectively, in this way, the regression function can gain better nonlinear mapping ability, and it can simulate almost any curve in quadratic continuous integral space. Then, they are used to identify the two functions in the separable nonlinear dynamic system. Simulation results show that the multiple-kernel LSSVR method can greatly improve the identification accuracy than the single kernel method, and the Morlet wavelet kernel is more efficient than the other kernels.