Elevated intraocular pressure(IOP)is a major risk factor for the development or progression of glaucoma.Lowering IOP is the only proven therapeutic approach to the management of glaucoma.IOP can be lowered by medicati...Elevated intraocular pressure(IOP)is a major risk factor for the development or progression of glaucoma.Lowering IOP is the only proven therapeutic approach to the management of glaucoma.IOP can be lowered by medication,laser treatment or surgery(1).Generally,instillation of IOP-展开更多
This paper presents a comprehensive framework for analyzing phase transitions in collective models such as theVicsek model under various noise types. The Vicsek model, focusing on understanding the collective behavior...This paper presents a comprehensive framework for analyzing phase transitions in collective models such as theVicsek model under various noise types. The Vicsek model, focusing on understanding the collective behaviors of socialanimals, is known due to its discontinuous phase transitions under vector noise. However, its behavior under scalar noiseremains less conclusive. Renowned for its efficacy in the analysis of complex systems under both equilibrium and nonequilibriumstates, the eigen microstate method is employed here for a quantitative examination of the phase transitions inthe Vicsek model under both vector and scalar noises. The study finds that the Vicsek model exhibits discontinuous phasetransitions regardless of noise type. Furthermore, the dichotomy method is utilized to identify the critical points for thesephase transitions. A significant finding is the observed increase in the critical point for discontinuous phase transitions withescalation of population density.展开更多
The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity.Traditional methods such as Atomic Agglomerative Hierarchical Clustering(AAH...The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity.Traditional methods such as Atomic Agglomerative Hierarchical Clustering(AAHC),K-means clustering,Principal Component Analysis(PCA),and Independent Component Analysis(ICA)are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction.Tackling these limitations,this study introduces a Global Map Dissimilarity(GMD)-driven density canopy K-means clustering algorithm.This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for dynamic modeling of EEG data.Utilizing this advanced algorithm,the study analyzes the Motor Imagery(MI)dataset from the GigaScience database,GigaDB.The findings reveal six distinct microstates during actual right-hand movement and five microstates across other task conditions,with microstate C showing superior performance in all task states.During imagined movement,microstate A was significantly enhanced.Comparison with existing algorithms indicates a significant improvement in clustering performance by the refined method,with an average Calinski-Harabasz Index(CHI)of 35517.29 and a Davis-Bouldin Index(DBI)average of 2.57.Furthermore,an information-theoretical analysis of the microstate sequences suggests that imagined movement exhibits higher complexity and disorder than actual movement.By utilizing the extracted microstate sequence parameters as features,the improved algorithm achieved a classification accuracy of 98.41%in EEG signal categorization for motor imagery.A performance of 78.183%accuracy was achieved in a four-class motor imagery task on the BCI-IV-2a dataset.These results demonstrate the potential of the advanced algorithm in microstate analysis,offering a more effective tool for a deeper understanding of the spatiotemporal features of EEG signals.展开更多
BACKGROUND A growing number of recent studies have explored underlying activity in the brain by measuring electroencephalography(EEG)in people with depression.However,the consistency of findings on EEG microstates in ...BACKGROUND A growing number of recent studies have explored underlying activity in the brain by measuring electroencephalography(EEG)in people with depression.However,the consistency of findings on EEG microstates in patients with depression is poor,and few studies have reported the relationship between EEG microstates,cognitive scales,and depression severity scales.AIM To investigate the EEG microstate characteristics of patients with depression and their association with cognitive functions.METHODS A total of 24 patients diagnosed with depression and 32 healthy controls were included in this study using the Structured Clinical Interview for Disease for The Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition.We collected information relating to demographic and clinical characteristics,as well as data from the Repeatable Battery for the Assessment of Neuropsychological Status(RBANS;Chinese version)and EEG.RESULTS Compared with the controls,the duration,occurrence,and contribution of microstate C were significantly higher[depression(DEP):Duration 84.58±24.35,occurrence 3.72±0.56,contribution 30.39±8.59;CON:Duration 72.77±10.23,occurrence 3.41±0.36,contribution 24.46±4.66;Duration F=6.02,P=0.049;Occurrence F=6.19,P=0.049;Contribution F=10.82,P=0.011]while the duration,occurrence,and contribution of microstate D were significantly lower(DEP:Duration 70.00±15.92,occurrence 3.18±0.71,contribution 22.48±8.12;CON:Duration 85.46±10.23,occurrence 3.54±0.41,contribution 28.25±5.85;Duration F=19.18,P<0.001;Occurrence F=5.79,P=0.050;Contribution F=9.41,P=0.013)in patients with depression.A positive correlation was observed between the visuospatial/constructional scores of the RBANS scale and the transition probability of microstate class C to B(r=0.405,P=0.049).CONCLUSION EEG microstate,especially C and D,is a possible biomarker in depression.Patients with depression had a more frequent transition from microstate C to B,which may relate to more negative rumination and visual processing.展开更多
针对Bentley平台的三维建模基础软件Microstation无法进行桥梁结构参数化建模配筋及钢筋工程量统计问题,利用MICROSTATION SDK软件开发工具、WPF界面、选用主流开发语言C#,辅以C++/CLI,以Vi⁃sual Studio 2019为开发平台,进行二次开发。...针对Bentley平台的三维建模基础软件Microstation无法进行桥梁结构参数化建模配筋及钢筋工程量统计问题,利用MICROSTATION SDK软件开发工具、WPF界面、选用主流开发语言C#,辅以C++/CLI,以Vi⁃sual Studio 2019为开发平台,进行二次开发。开发出了适用于该类桥梁的快速配筋插件以及钢筋工程量统计插件。以某桥梁工程中的30 m长等截面预制箱梁为例,应用箱梁建模配筋及工程量统计插件程序,高效准确地完成了30 m长等截面预制箱梁的三维建模配筋及钢筋工程量统计,验证了该二次开发建模及配筋插件程序的可用性和高效性,大大提高了设计人员的配筋及工作效率。展开更多
文摘Elevated intraocular pressure(IOP)is a major risk factor for the development or progression of glaucoma.Lowering IOP is the only proven therapeutic approach to the management of glaucoma.IOP can be lowered by medication,laser treatment or surgery(1).Generally,instillation of IOP-
基金the National Natural Science Foundation of China(Grant No.62273033).
文摘This paper presents a comprehensive framework for analyzing phase transitions in collective models such as theVicsek model under various noise types. The Vicsek model, focusing on understanding the collective behaviors of socialanimals, is known due to its discontinuous phase transitions under vector noise. However, its behavior under scalar noiseremains less conclusive. Renowned for its efficacy in the analysis of complex systems under both equilibrium and nonequilibriumstates, the eigen microstate method is employed here for a quantitative examination of the phase transitions inthe Vicsek model under both vector and scalar noises. The study finds that the Vicsek model exhibits discontinuous phasetransitions regardless of noise type. Furthermore, the dichotomy method is utilized to identify the critical points for thesephase transitions. A significant finding is the observed increase in the critical point for discontinuous phase transitions withescalation of population density.
基金funded by National Nature Science Foundation of China,Yunnan Funda-Mental Research Projects,Special Project of Guangdong Province in Key Fields of Ordinary Colleges and Universities and Chaozhou Science and Technology Plan Project of Funder Grant Numbers 82060329,202201AT070108,2023ZDZX2038 and 202201GY01.
文摘The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity.Traditional methods such as Atomic Agglomerative Hierarchical Clustering(AAHC),K-means clustering,Principal Component Analysis(PCA),and Independent Component Analysis(ICA)are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction.Tackling these limitations,this study introduces a Global Map Dissimilarity(GMD)-driven density canopy K-means clustering algorithm.This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for dynamic modeling of EEG data.Utilizing this advanced algorithm,the study analyzes the Motor Imagery(MI)dataset from the GigaScience database,GigaDB.The findings reveal six distinct microstates during actual right-hand movement and five microstates across other task conditions,with microstate C showing superior performance in all task states.During imagined movement,microstate A was significantly enhanced.Comparison with existing algorithms indicates a significant improvement in clustering performance by the refined method,with an average Calinski-Harabasz Index(CHI)of 35517.29 and a Davis-Bouldin Index(DBI)average of 2.57.Furthermore,an information-theoretical analysis of the microstate sequences suggests that imagined movement exhibits higher complexity and disorder than actual movement.By utilizing the extracted microstate sequence parameters as features,the improved algorithm achieved a classification accuracy of 98.41%in EEG signal categorization for motor imagery.A performance of 78.183%accuracy was achieved in a four-class motor imagery task on the BCI-IV-2a dataset.These results demonstrate the potential of the advanced algorithm in microstate analysis,offering a more effective tool for a deeper understanding of the spatiotemporal features of EEG signals.
基金Supported by Suzhou Key Technologies Program,No.SKY2021063Suzhou Clinical Medical Center for Mood Disorders,No.Szlcyxzx202109+4 种基金Suzhou Clinical Key Disciplines for Geriatric Psychiatry,No.SZXK202116Jiangsu Province Social Development Project,No.BE2020764the Gusu Health Talents Project,No.GSWS2022091the Science and Technology Program of Suzhou,No.SKYD2022039 and No.SKY2023075the Doctoral Scientific Research Foundation of Suzhou Guangji Hospital,No.2023B01.
文摘BACKGROUND A growing number of recent studies have explored underlying activity in the brain by measuring electroencephalography(EEG)in people with depression.However,the consistency of findings on EEG microstates in patients with depression is poor,and few studies have reported the relationship between EEG microstates,cognitive scales,and depression severity scales.AIM To investigate the EEG microstate characteristics of patients with depression and their association with cognitive functions.METHODS A total of 24 patients diagnosed with depression and 32 healthy controls were included in this study using the Structured Clinical Interview for Disease for The Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition.We collected information relating to demographic and clinical characteristics,as well as data from the Repeatable Battery for the Assessment of Neuropsychological Status(RBANS;Chinese version)and EEG.RESULTS Compared with the controls,the duration,occurrence,and contribution of microstate C were significantly higher[depression(DEP):Duration 84.58±24.35,occurrence 3.72±0.56,contribution 30.39±8.59;CON:Duration 72.77±10.23,occurrence 3.41±0.36,contribution 24.46±4.66;Duration F=6.02,P=0.049;Occurrence F=6.19,P=0.049;Contribution F=10.82,P=0.011]while the duration,occurrence,and contribution of microstate D were significantly lower(DEP:Duration 70.00±15.92,occurrence 3.18±0.71,contribution 22.48±8.12;CON:Duration 85.46±10.23,occurrence 3.54±0.41,contribution 28.25±5.85;Duration F=19.18,P<0.001;Occurrence F=5.79,P=0.050;Contribution F=9.41,P=0.013)in patients with depression.A positive correlation was observed between the visuospatial/constructional scores of the RBANS scale and the transition probability of microstate class C to B(r=0.405,P=0.049).CONCLUSION EEG microstate,especially C and D,is a possible biomarker in depression.Patients with depression had a more frequent transition from microstate C to B,which may relate to more negative rumination and visual processing.
