The aim of this study was to investigate the clinical heterogeneity of Parkinson's disease(PD) among a cohort of Chinese patients in early stages.Clinical data on demographics,motor variables,motor phenotypes,dise...The aim of this study was to investigate the clinical heterogeneity of Parkinson's disease(PD) among a cohort of Chinese patients in early stages.Clinical data on demographics,motor variables,motor phenotypes,disease progression,global cognitive function,depression,apathy,sleep quality,constipation,fatigue,and L-dopa complications were collected from 138 Chinese PD subjects in early stages(Hoehn and Yahr stages 1-3).The PD subject subtypes were classified using k-means cluster analysis according to the clinical data from five-to three-cluster consecutively.Kappa statistical analysis was performed to evaluate the consistency among different subtype solutions.The cluster analysis indicated four main subtypes:the non-tremor dominant subtype(NTD,n=28,20.3%),rapid disease progression subtype(RDP,n=7,5.1%),young-onset subtype(YO,n=50,36.2%),and tremor dominant subtype(TD,n=53,38.4%).Overall,78.3%(108/138) of subjects were always classified between the same three groups(52 always in TD,7 in RDP,and 49 in NTD),and 98.6%(136/138) between five-and four-cluster solutions.However,subjects classified as NTD in the four-cluster analysis were dispersed into different subtypes in the three-cluster analysis,with low concordance between four-and three-cluster solutions(kappa value= 0.139,P=0.001).This study defines clinical heterogeneity of PD patients in early stages using a data-driven approach.The subtypes generated by the four-cluster solution appear to exhibit ideal internal cohesion and external isolation.展开更多
Our motivation is to build a systematic method in order to investigate the structure of cluster algebras of geometric type. The method is given through the notion of mixing-type sub-seeds, the theory of seed homomorph...Our motivation is to build a systematic method in order to investigate the structure of cluster algebras of geometric type. The method is given through the notion of mixing-type sub-seeds, the theory of seed homomorphisms and the view-point of gluing of seeds. As an application, for(rooted) cluster algebras, we completely classify rooted cluster subalgebras and characterize rooted cluster quotient algebras in detail. Also,we build the relationship between the categorification of a rooted cluster algebra and that of its rooted cluster subalgebras. Note that cluster algebras of geometric type studied here are of the sign-skew-symmetric case.展开更多
Taxonomy is generated to effectively organize and access large volume of data. A taxonomy is a way of representing concepts that exist in data. It needs to continuously evolve to reflect changes in data. Existing auto...Taxonomy is generated to effectively organize and access large volume of data. A taxonomy is a way of representing concepts that exist in data. It needs to continuously evolve to reflect changes in data. Existing automatic taxonomy generation techniques do not handle the evolution of data; therefore, the generated taxonomies do not truly represent the data. The evolution of data can be handled by either regenerating taxonomy from scratch, or allowing taxonomy to incrementally evolve whenever changes occur in the data. The former approach is not economical in terms of time and resources. A taxonomy incremental evolution（TIE） algorithm, as proposed, is a novel attempt to handle the data that evolve in time. It serves as a layer over an existing clustering-based taxonomy generation technique and allows an existing taxonomy to incrementally evolve. The algorithm was evaluated in research articles selected from the computing domain. It was found that the taxonomy using the algorithm that evolved with data needed considerably shorter time, and had better quality per unit time as compared to the taxonomy regenerated from scratch.展开更多
基金Project (No. 2006AA02A408) supported by the National High-Tech R & D Program (863) of China
文摘The aim of this study was to investigate the clinical heterogeneity of Parkinson's disease(PD) among a cohort of Chinese patients in early stages.Clinical data on demographics,motor variables,motor phenotypes,disease progression,global cognitive function,depression,apathy,sleep quality,constipation,fatigue,and L-dopa complications were collected from 138 Chinese PD subjects in early stages(Hoehn and Yahr stages 1-3).The PD subject subtypes were classified using k-means cluster analysis according to the clinical data from five-to three-cluster consecutively.Kappa statistical analysis was performed to evaluate the consistency among different subtype solutions.The cluster analysis indicated four main subtypes:the non-tremor dominant subtype(NTD,n=28,20.3%),rapid disease progression subtype(RDP,n=7,5.1%),young-onset subtype(YO,n=50,36.2%),and tremor dominant subtype(TD,n=53,38.4%).Overall,78.3%(108/138) of subjects were always classified between the same three groups(52 always in TD,7 in RDP,and 49 in NTD),and 98.6%(136/138) between five-and four-cluster solutions.However,subjects classified as NTD in the four-cluster analysis were dispersed into different subtypes in the three-cluster analysis,with low concordance between four-and three-cluster solutions(kappa value= 0.139,P=0.001).This study defines clinical heterogeneity of PD patients in early stages using a data-driven approach.The subtypes generated by the four-cluster solution appear to exhibit ideal internal cohesion and external isolation.
基金supported by National Natural Science Foundation of China (Grant Nos. 11671350 and 11571173)
文摘Our motivation is to build a systematic method in order to investigate the structure of cluster algebras of geometric type. The method is given through the notion of mixing-type sub-seeds, the theory of seed homomorphisms and the view-point of gluing of seeds. As an application, for(rooted) cluster algebras, we completely classify rooted cluster subalgebras and characterize rooted cluster quotient algebras in detail. Also,we build the relationship between the categorification of a rooted cluster algebra and that of its rooted cluster subalgebras. Note that cluster algebras of geometric type studied here are of the sign-skew-symmetric case.
文摘Taxonomy is generated to effectively organize and access large volume of data. A taxonomy is a way of representing concepts that exist in data. It needs to continuously evolve to reflect changes in data. Existing automatic taxonomy generation techniques do not handle the evolution of data; therefore, the generated taxonomies do not truly represent the data. The evolution of data can be handled by either regenerating taxonomy from scratch, or allowing taxonomy to incrementally evolve whenever changes occur in the data. The former approach is not economical in terms of time and resources. A taxonomy incremental evolution（TIE） algorithm, as proposed, is a novel attempt to handle the data that evolve in time. It serves as a layer over an existing clustering-based taxonomy generation technique and allows an existing taxonomy to incrementally evolve. The algorithm was evaluated in research articles selected from the computing domain. It was found that the taxonomy using the algorithm that evolved with data needed considerably shorter time, and had better quality per unit time as compared to the taxonomy regenerated from scratch.
