Hemiplegia after stroke has become a major cause of the world's high disabilities,and it is vital to enhance our understanding of post-stroke neuroplasticity to develop e±cient rehabilitation programs.This st...Hemiplegia after stroke has become a major cause of the world's high disabilities,and it is vital to enhance our understanding of post-stroke neuroplasticity to develop e±cient rehabilitation programs.This study aimed to explore the brain activation and network reorganization of the motor cortex(MC)with functional near-infrared spectroscopy(fNIRS).The MC hemodynamic signals were gained from 22 stroke patients and 14 healthy subjects during a shoulder-touching task with the right hand.The MC activation pattern and network attributes analyzed with the graph theory were compared between the two groups.The results revealed that healthy controls presented dominant activation in the left MC while stroke patients exhibited dominant activation in the bilateral hemispheres MC.The MC networks for the two groups had small-world properties.Compared with healthy controls,patients had higher transitivity and lower global e±ciency(GE),mean connectivity,and long connections(LCs)in the left MC.In addition,both MC activation and network attributes were correlated with patient's upper limb motor function.The results showed the stronger compensation of the unaffected motor area,the better recovery of the upper limb motor function for patients.Moreover,the MC network possessed high clustering and relatively sparse inter-regional connections during recovery for patients.Our results promote the understanding of MC reorganization during recovery and indicate that MC activation and network could provide clinical assessment signi¯cance in stroke patients.Given the advantages of fNIRS,it shows great application potential in the assessment and rehabilitation of motor function after stroke.展开更多
Reorganization energy(RE)is closely related to the charge transport properties and is one of the important parameters for screening novel organic semiconductors(OSCs).With the rise of data-driven technology,accurate a...Reorganization energy(RE)is closely related to the charge transport properties and is one of the important parameters for screening novel organic semiconductors(OSCs).With the rise of data-driven technology,accurate and efficient machine learning(ML)models for high-throughput screening novel organic molecules play an important role in the boom of material science.Comparing different molecular descriptors and algorithms,we construct a reasonable algorithm framework with molecular graphs to describe the compositional structure,convolutional neural networks to extract material features,and subsequently embedded fully connected neural networks to establish the mapping between features and predicted properties.With our well-designed judicious training pattern about feature-guided stratified random sampling,we have obtained a high-precision and robust reorganization energy prediction model,which can be used as one of the important descriptors for rapid screening potential OSCs.The root-meansquare error(RMSE)and the squared Pearson correlation coefficient(R^(2))of this model are 2.6 me V and0.99,respectively.More importantly,we confirm and emphasize that training pattern plays a crucial role in constructing supreme ML models.We are calling for more attention to designing innovative judicious training patterns in addition to high-quality databases,efficient material feature engineering and algorithm framework construction.展开更多
Reorganization of network information resources for scientific and technical （scitech for abbreviation） documents and development of information infrastructure have brought a great change in China＇s scitech documen...Reorganization of network information resources for scientific and technical （scitech for abbreviation） documents and development of information infrastructure have brought a great change in China＇s scitech documentation information sharing. It integrated not only information resources, but also systems, technologies and funds. High quality services and effective resources sharing are the main goals of the platform construction. The paper describes briefly the principles and aims of the integration of the scitech information resources and networks. The services of the sharing platforms have been also introduced.展开更多
Research on brain function after brachial plexus injury focuses on local cortical functional reorganization,and few studies have focused on brain networks after brachial plexus injury.Changes in brain networks may hel...Research on brain function after brachial plexus injury focuses on local cortical functional reorganization,and few studies have focused on brain networks after brachial plexus injury.Changes in brain networks may help understanding of brain plasticity at the global level.We hypothesized that topology of the global cerebral resting-state functional network changes after unilateral brachial plexus injury.Thus,in this cross-sectional study,we recruited eight male patients with unilateral brachial plexus injury（right handedness,mean age of 27.9±5.4years old）and eight male healthy controls（right handedness,mean age of 28.6±3.2）.After acquiring and preprocessing resting-state magnetic resonance imaging data,the cerebrum was divided into 90 regions and Pearson’s correlation coefficient calculated between regions.These correlation matrices were then converted into a binary matrix with affixed sparsity values of 0.1–0.46.Under sparsity conditions,both groups satisfied this small-world property.The clustering coefficient was markedly lower,while average shortest path remarkably higher in patients compared with healthy controls.These findings confirm that cerebral functional networks in patients still show smallworld characteristics,which are highly effective in information transmission in the brain,as well as normal controls.Alternatively,varied small-worldness suggests that capacity of information transmission and integration in different brain regions in brachial plexus injury patients is damaged.展开更多
基金was supported by the National Key Research and Development Program of China(Nos.2020YFC2004300,2020YFC2004303 and 2020YFC2004302)the National Natural Science Foundation of China(Nos.32000980 and 82171533)+1 种基金the Guangdong Basic and Applied Basic Research Foundation(Nos.2022A1515140142,2019A1515110427 and 2020B1515120014)the Key Laboratory Program of Guangdong Higher Education Institutes(No.2020KSYS001)。
文摘Hemiplegia after stroke has become a major cause of the world's high disabilities,and it is vital to enhance our understanding of post-stroke neuroplasticity to develop e±cient rehabilitation programs.This study aimed to explore the brain activation and network reorganization of the motor cortex(MC)with functional near-infrared spectroscopy(fNIRS).The MC hemodynamic signals were gained from 22 stroke patients and 14 healthy subjects during a shoulder-touching task with the right hand.The MC activation pattern and network attributes analyzed with the graph theory were compared between the two groups.The results revealed that healthy controls presented dominant activation in the left MC while stroke patients exhibited dominant activation in the bilateral hemispheres MC.The MC networks for the two groups had small-world properties.Compared with healthy controls,patients had higher transitivity and lower global e±ciency(GE),mean connectivity,and long connections(LCs)in the left MC.In addition,both MC activation and network attributes were correlated with patient's upper limb motor function.The results showed the stronger compensation of the unaffected motor area,the better recovery of the upper limb motor function for patients.Moreover,the MC network possessed high clustering and relatively sparse inter-regional connections during recovery for patients.Our results promote the understanding of MC reorganization during recovery and indicate that MC activation and network could provide clinical assessment signi¯cance in stroke patients.Given the advantages of fNIRS,it shows great application potential in the assessment and rehabilitation of motor function after stroke.
基金financially supported by the Ministry of Science and Technology of China (2017YFA0204503 and 2018YFA0703200)the National Natural Science Foundation of China (52121002,U21A6002 and 22003046)+1 种基金the Tianjin Natural Science Foundation (20JCJQJC00300)“A Multi-Scale and High-Efficiency Computing Platform for Advanced Functional Materials”program,funded by Haihe Laboratory in Tianjin (22HHXCJC00007)。
文摘Reorganization energy(RE)is closely related to the charge transport properties and is one of the important parameters for screening novel organic semiconductors(OSCs).With the rise of data-driven technology,accurate and efficient machine learning(ML)models for high-throughput screening novel organic molecules play an important role in the boom of material science.Comparing different molecular descriptors and algorithms,we construct a reasonable algorithm framework with molecular graphs to describe the compositional structure,convolutional neural networks to extract material features,and subsequently embedded fully connected neural networks to establish the mapping between features and predicted properties.With our well-designed judicious training pattern about feature-guided stratified random sampling,we have obtained a high-precision and robust reorganization energy prediction model,which can be used as one of the important descriptors for rapid screening potential OSCs.The root-meansquare error(RMSE)and the squared Pearson correlation coefficient(R^(2))of this model are 2.6 me V and0.99,respectively.More importantly,we confirm and emphasize that training pattern plays a crucial role in constructing supreme ML models.We are calling for more attention to designing innovative judicious training patterns in addition to high-quality databases,efficient material feature engineering and algorithm framework construction.
文摘Reorganization of network information resources for scientific and technical （scitech for abbreviation） documents and development of information infrastructure have brought a great change in China＇s scitech documentation information sharing. It integrated not only information resources, but also systems, technologies and funds. High quality services and effective resources sharing are the main goals of the platform construction. The paper describes briefly the principles and aims of the integration of the scitech information resources and networks. The services of the sharing platforms have been also introduced.
文摘目的探讨阿尔茨海默病(Alzheimer's disease,AD)患者大脑灰质体积、灰质皮层厚度及基于皮层厚度的结构协变网络(structural covariance network,SCN)的拓扑属性改变。材料与方法本研究共筛选了250例来自ADNI数据库的被试,包括AD组100人,健康对照(healthy controls,HCs)组150人。首先,利用基于体素的形态学分析方法(voxel-based morphometry,VBM)和基于表面的形态学分析方法(surface-based morphometry,SBM)分别计算每组被试的灰质体积和皮层厚度并比较其组间差异。其次,将有组间差异的脑区定义为感兴趣区(region of interest,ROI),提取每一个ROI的灰质体积和皮层厚度值,与认知量表进行偏相关分析。最后,构建基于皮层厚度的SCN并利用图论分析方法分析该网络的全局属性及局部属性的变化特征。结果第一,相较于HCs组,AD组的灰质体积和皮层厚度显著下降[体素和顶点水平总体误差(family-wise error,FWE)校正后P<0.001]。AD组灰质体积下降的脑区主要包括双侧海马、双侧眶额皮层、左侧岛叶、右侧枕下回、左侧楔前叶、左侧中央前回、左侧中央扣带回。AD组皮层厚度变薄的脑区主要包括双侧颞叶、双侧额叶、双侧顶叶、双侧扣带回、双侧梭状回、双侧岛回、双侧楔前叶等。第二,偏相关分析表明,AD组简易精神状态检查量表(Mini-Mental State Examination,MMSE)得分分别与右侧海马体积[rs=0.35,错误发现率(false discovery rate,FDR)校正后P<0.001]、左侧海马体积(r_(s)=0.38,FDR校正后P<0.001)、右侧梭状回皮层厚度(r_(s)=0.38,FDR校正后P<0.001)呈正相关;临床痴呆评定量表(Clinical Dementia Rating Sum of Boxes,CDR-SB)评分与左侧梭状回皮层厚度(r_(s)=-0.39,FDR校正后P<0.001)呈负相关。第三,脑网络分析表明,AD组SCN的全局效率(P<0.001)、局部效率(P=0.03)及小世界属性(P<0.001)高于HCs组,最短路径低于HCs组(P<0.001)。结论联合VBM、SBM的形态学分析及SCN的图论分析有助于全面理解AD患者脑网络的重组及其意义,进而为AD患者神经影像学改变提供新的见解和证据。
文摘Research on brain function after brachial plexus injury focuses on local cortical functional reorganization,and few studies have focused on brain networks after brachial plexus injury.Changes in brain networks may help understanding of brain plasticity at the global level.We hypothesized that topology of the global cerebral resting-state functional network changes after unilateral brachial plexus injury.Thus,in this cross-sectional study,we recruited eight male patients with unilateral brachial plexus injury（right handedness,mean age of 27.9±5.4years old）and eight male healthy controls（right handedness,mean age of 28.6±3.2）.After acquiring and preprocessing resting-state magnetic resonance imaging data,the cerebrum was divided into 90 regions and Pearson’s correlation coefficient calculated between regions.These correlation matrices were then converted into a binary matrix with affixed sparsity values of 0.1–0.46.Under sparsity conditions,both groups satisfied this small-world property.The clustering coefficient was markedly lower,while average shortest path remarkably higher in patients compared with healthy controls.These findings confirm that cerebral functional networks in patients still show smallworld characteristics,which are highly effective in information transmission in the brain,as well as normal controls.Alternatively,varied small-worldness suggests that capacity of information transmission and integration in different brain regions in brachial plexus injury patients is damaged.
