孤独症谱系障碍作为一种复杂的神经发育性疾病,其临床表现具有高度异质性。泛孤独症表型作为孤独症谱系障碍患者亲属中常见的亚临床症候群,涵盖了一系列与孤独症谱系障碍相似的轻度症状。孤独特质则指一般人群中广泛存在的与孤独症谱系...孤独症谱系障碍作为一种复杂的神经发育性疾病,其临床表现具有高度异质性。泛孤独症表型作为孤独症谱系障碍患者亲属中常见的亚临床症候群,涵盖了一系列与孤独症谱系障碍相似的轻度症状。孤独特质则指一般人群中广泛存在的与孤独症谱系障碍相关的行为特征。在阐述两者的核心概念、功能表现和影响机制的基础上,基于遗传与脑功能的分析,探讨了二者的神经生物学关联,这不仅能帮助揭示孤独症谱系障碍的发病机制,还为临床诊断和干预提供理论依据。Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by high clinical heterogeneity. The Broad Autism Phenotype (BAP) is a common subclinical syndrome among relatives of individuals with ASD, encompassing a range of mild symptoms similar to ASD. Autistic traits refer to the widely prevalent behavioral characteristics associated with ASD in the general population. This study elucidates the core concepts, functional expressions, and mechanisms of influence for both BAP and autistic traits. By analyzing the genetic and brain function basis, the neurobiological associations between the two are explored, which not only helps to reveal the pathogenesis of ASD but also provides a theoretical basis for clinical diagnosis and intervention.展开更多
Detection of Autism Spectrum Disorder(ASD)is a crucial area of research,representing a foundational aspect of psychological studies.The advancement of technology and the widespread adoption of machine learning methodo...Detection of Autism Spectrum Disorder(ASD)is a crucial area of research,representing a foundational aspect of psychological studies.The advancement of technology and the widespread adoption of machine learning methodologies have brought significant attention to this field in recent years.Interdisciplinary efforts have further propelled research into detection methods.Consequently,this study aims to contribute to both the fields of psychology and computer science.Specifically,the goal is to apply machine learning techniques to limited data for the detection of Autism Spectrum Disorder.This study is structured into two distinct phases:data preprocessing and classification.In the data preprocessing phase,four datasets—Toddler,Children,Adolescent,and Adult—were converted into numerical form,adjusted as necessary,and subsequently clustered.Clustering was performed using six different methods:Kmeans,agglomerative,DBSCAN(Density-Based Spatial Clustering of Applications with Noise),mean shift,spectral,and Birch.In the second phase,the clustered ASD data were classified.The model’s accuracy was assessed using 5-fold cross-validation to ensure robust evaluation.In total,ten distinct machine learning algorithms were employed.The findings indicate that all clustering methods demonstrated success with various classifiers.Notably,the K-means algorithm emerged as particularly effective,achieving consistent and significant results across all datasets.This study is expected to serve as a guide for improving ASD detection performance,even with minimal data availability.展开更多
近日,美国国家安全局(NSA)人工智能安全中心(AISC)联合美国网络安全和基础设施安全局(CISA)、澳大利亚信号局网络安全中心(ASD’s ACSC)、新西兰国家网络安全中心(NCSC-NZ)及英国国家网络安全中心(NCSC-UK)发布联合指南《AI数据安全:保...近日,美国国家安全局(NSA)人工智能安全中心(AISC)联合美国网络安全和基础设施安全局(CISA)、澳大利亚信号局网络安全中心(ASD’s ACSC)、新西兰国家网络安全中心(NCSC-NZ)及英国国家网络安全中心(NCSC-UK)发布联合指南《AI数据安全:保障用于训练和运行AI系统的数据的最佳实践》(Al Data Security:Best Practices for Securing Data Used to Train&Operate Al Systems)。展开更多
文摘孤独症谱系障碍作为一种复杂的神经发育性疾病,其临床表现具有高度异质性。泛孤独症表型作为孤独症谱系障碍患者亲属中常见的亚临床症候群,涵盖了一系列与孤独症谱系障碍相似的轻度症状。孤独特质则指一般人群中广泛存在的与孤独症谱系障碍相关的行为特征。在阐述两者的核心概念、功能表现和影响机制的基础上,基于遗传与脑功能的分析,探讨了二者的神经生物学关联,这不仅能帮助揭示孤独症谱系障碍的发病机制,还为临床诊断和干预提供理论依据。Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by high clinical heterogeneity. The Broad Autism Phenotype (BAP) is a common subclinical syndrome among relatives of individuals with ASD, encompassing a range of mild symptoms similar to ASD. Autistic traits refer to the widely prevalent behavioral characteristics associated with ASD in the general population. This study elucidates the core concepts, functional expressions, and mechanisms of influence for both BAP and autistic traits. By analyzing the genetic and brain function basis, the neurobiological associations between the two are explored, which not only helps to reveal the pathogenesis of ASD but also provides a theoretical basis for clinical diagnosis and intervention.
文摘Detection of Autism Spectrum Disorder(ASD)is a crucial area of research,representing a foundational aspect of psychological studies.The advancement of technology and the widespread adoption of machine learning methodologies have brought significant attention to this field in recent years.Interdisciplinary efforts have further propelled research into detection methods.Consequently,this study aims to contribute to both the fields of psychology and computer science.Specifically,the goal is to apply machine learning techniques to limited data for the detection of Autism Spectrum Disorder.This study is structured into two distinct phases:data preprocessing and classification.In the data preprocessing phase,four datasets—Toddler,Children,Adolescent,and Adult—were converted into numerical form,adjusted as necessary,and subsequently clustered.Clustering was performed using six different methods:Kmeans,agglomerative,DBSCAN(Density-Based Spatial Clustering of Applications with Noise),mean shift,spectral,and Birch.In the second phase,the clustered ASD data were classified.The model’s accuracy was assessed using 5-fold cross-validation to ensure robust evaluation.In total,ten distinct machine learning algorithms were employed.The findings indicate that all clustering methods demonstrated success with various classifiers.Notably,the K-means algorithm emerged as particularly effective,achieving consistent and significant results across all datasets.This study is expected to serve as a guide for improving ASD detection performance,even with minimal data availability.
文摘近日,美国国家安全局(NSA)人工智能安全中心(AISC)联合美国网络安全和基础设施安全局(CISA)、澳大利亚信号局网络安全中心(ASD’s ACSC)、新西兰国家网络安全中心(NCSC-NZ)及英国国家网络安全中心(NCSC-UK)发布联合指南《AI数据安全:保障用于训练和运行AI系统的数据的最佳实践》(Al Data Security:Best Practices for Securing Data Used to Train&Operate Al Systems)。
