Epilepsy can be defined as a dysfunction of the brain network,and each type of epilepsy involves different brain-network changes that are implicated diffe rently in the control and propagation of interictal or ictal d...Epilepsy can be defined as a dysfunction of the brain network,and each type of epilepsy involves different brain-network changes that are implicated diffe rently in the control and propagation of interictal or ictal discharges.Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice.An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tra ctography,diffusion kurtosis imaging-based fiber tractography,fiber ball imagingbased tra ctography,electroencephalography,functional magnetic resonance imaging,magnetoencephalography,positron emission tomography,molecular imaging,and functional ultrasound imaging have been extensively used to delineate epileptic networks.In this review,we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy,and extensively analyze the imaging mechanisms,advantages,limitations,and clinical application ranges of each technique.A greater focus on emerging advanced technologies,new data analysis software,a combination of multiple techniques,and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.展开更多
Influenza caused by influenza virus,seriously threaten human life and health.Drug treatment is one of the effective measurement.However,there are only two classes of drugs,one class is M2 blockers and another is neura...Influenza caused by influenza virus,seriously threaten human life and health.Drug treatment is one of the effective measurement.However,there are only two classes of drugs,one class is M2 blockers and another is neuraminidase(NA) inhibitors.The recent antiviral surveillance studies reported a global significant increase in M2 blocker resistance among influenza viruses,and the resistant virus strains against NA inhibitor are also reported in clinical treatment.Therefore thediscovery of new medicines with low resistance has become very urgent.As all known,traditional medicines with multi-target features and network mechanism often possess low resistance.Compound Yizhihao,which consists of radix isatidis,folium isatidis,Artemisia rupestris,is one of the famous traditional medicine for influenza treatment in China,however its mechanism of action against influenza is unclear.In this study,the multiple targets related with influenza disease and the known chemical constituents from Compound Yizhihao were collected,and multi-target QSAR(mt-QSAR) classification models were developed by Na?e Bayesian algorithm and verified by various datasets.Then the classification models were applied to predict the effective constituents and their drug targets.Finally,the constituent-target-pathway network was constructed,which revealed the effective constituents and their network mechanism in Compound Yizhihao.This study will lay important basis for the clinical uses for influenza treatment and for the further research and development of the effective constituents.展开更多
针对已有的层流信道模型不能直接应用于存在目标的复杂层流信道的问题,提出一种基于模型驱动的信道建模方法.研究了存在目标的层流扩散信道的系统模型,在无目标平流模型的基础上加入参数,考虑层流和目标对接收分子的影响.结合仿真结果,...针对已有的层流信道模型不能直接应用于存在目标的复杂层流信道的问题,提出一种基于模型驱动的信道建模方法.研究了存在目标的层流扩散信道的系统模型,在无目标平流模型的基础上加入参数,考虑层流和目标对接收分子的影响.结合仿真结果,将有目标的复杂层流信道近似为两个稳定的层流信道,建立有目标的点源-接收机层流扩散信道模型.结合神经网络使用Levenberg-Marquardt算法对信道模型参数进行学习和预测,同时提出基于数据和模型驱动结合(combination of data and model driven, CDMD)的检测方法对目标进行检测.结果表明:通过公式数据与仿真数据对比验证了其信道模型的准确性,所有数据的相关系数为0.999 15,该神经网络模型具有可行性;使用神经网络二分类算法验证提出的目标检测方法,检测准确率达到98.8%时,提出的CDMD检测方法所需数据量约为基于数据检测方法的1/6.展开更多
Aggregation-induced emission(AIE)is a phenomenon where a molecule that is weakly or non-luminescent in a diluted solution becomes highly emissive when aggregated.AIE luminogens(AIEgens)hold promise in diverse applicat...Aggregation-induced emission(AIE)is a phenomenon where a molecule that is weakly or non-luminescent in a diluted solution becomes highly emissive when aggregated.AIE luminogens(AIEgens)hold promise in diverse applications like bioimaging,chemical sensing,and optoelectronics.Investigation in AIE luminescence is also critical for understanding aggregation kinetics as the aggregation process is an essential component of AIE emission.Experimental investigation of AIEgen aggregation is challenging due to the fast timescale of the aggregation and the amorphous aggregate structures.Computer simulations such as molecular dynamics(MD)simulation provide a valuable approach to complement experiments with atomic-level knowledge to study the fast dynamics of aggregation processes.However,individual simulations still struggle to systematically elucidate heterogeneous kinetics of the formation of amorphous AIEgen aggregates.Kinetic network models(KNMs),constructed from an ensemble of MD simulations,hold great potential in addressing this challenge.In these models,dynamic processes are modeled as a series ofMarkovian transitions occurring among metastable conformational states at discrete time intervals.In this perspective article,we first review previous studies to characterize the AIEgen aggregation kinetics and their limitations.We then introduce KNMs as a promising approach to elucidate the complex kinetics of aggregations to address these limitations.More importantly,we discuss our perspective on linking the output of KNMs to experimental observations of time-resolved AIE luminescence.We expect that this approach can validate the computational predictions and provide great insights into the aggregation kinetics for AIEgen aggregates.These insights will facilitate the rational design of improved AIEgens in their applications in biology and materials sciences.展开更多
基金supported by the Natural Science Foundation of Sichuan Province of China,Nos.2022NSFSC1545 (to YG),2022NSFSC1387 (to ZF)the Natural Science Foundation of Chongqing of China,Nos.CSTB2022NSCQ-LZX0038,cstc2021ycjh-bgzxm0035 (both to XT)+3 种基金the National Natural Science Foundation of China,No.82001378 (to XT)the Joint Project of Chongqing Health Commission and Science and Technology Bureau,No.2023QNXM009 (to XT)the Science and Technology Research Program of Chongqing Education Commission of China,No.KJQN202200435 (to XT)the Chongqing Talents:Exceptional Young Talents Project,No.CQYC202005014 (to XT)。
文摘Epilepsy can be defined as a dysfunction of the brain network,and each type of epilepsy involves different brain-network changes that are implicated diffe rently in the control and propagation of interictal or ictal discharges.Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice.An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tra ctography,diffusion kurtosis imaging-based fiber tractography,fiber ball imagingbased tra ctography,electroencephalography,functional magnetic resonance imaging,magnetoencephalography,positron emission tomography,molecular imaging,and functional ultrasound imaging have been extensively used to delineate epileptic networks.In this review,we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy,and extensively analyze the imaging mechanisms,advantages,limitations,and clinical application ranges of each technique.A greater focus on emerging advanced technologies,new data analysis software,a combination of multiple techniques,and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.
基金supported by National Natural Science Foundation of China(81673480) Project of Urumqi science and Technology Bureau of the Xinjiang Uygur Autonomous Region(Y151310010)
文摘Influenza caused by influenza virus,seriously threaten human life and health.Drug treatment is one of the effective measurement.However,there are only two classes of drugs,one class is M2 blockers and another is neuraminidase(NA) inhibitors.The recent antiviral surveillance studies reported a global significant increase in M2 blocker resistance among influenza viruses,and the resistant virus strains against NA inhibitor are also reported in clinical treatment.Therefore thediscovery of new medicines with low resistance has become very urgent.As all known,traditional medicines with multi-target features and network mechanism often possess low resistance.Compound Yizhihao,which consists of radix isatidis,folium isatidis,Artemisia rupestris,is one of the famous traditional medicine for influenza treatment in China,however its mechanism of action against influenza is unclear.In this study,the multiple targets related with influenza disease and the known chemical constituents from Compound Yizhihao were collected,and multi-target QSAR(mt-QSAR) classification models were developed by Na?e Bayesian algorithm and verified by various datasets.Then the classification models were applied to predict the effective constituents and their drug targets.Finally,the constituent-target-pathway network was constructed,which revealed the effective constituents and their network mechanism in Compound Yizhihao.This study will lay important basis for the clinical uses for influenza treatment and for the further research and development of the effective constituents.
文摘针对已有的层流信道模型不能直接应用于存在目标的复杂层流信道的问题,提出一种基于模型驱动的信道建模方法.研究了存在目标的层流扩散信道的系统模型,在无目标平流模型的基础上加入参数,考虑层流和目标对接收分子的影响.结合仿真结果,将有目标的复杂层流信道近似为两个稳定的层流信道,建立有目标的点源-接收机层流扩散信道模型.结合神经网络使用Levenberg-Marquardt算法对信道模型参数进行学习和预测,同时提出基于数据和模型驱动结合(combination of data and model driven, CDMD)的检测方法对目标进行检测.结果表明:通过公式数据与仿真数据对比验证了其信道模型的准确性,所有数据的相关系数为0.999 15,该神经网络模型具有可行性;使用神经网络二分类算法验证提出的目标检测方法,检测准确率达到98.8%时,提出的CDMD检测方法所需数据量约为基于数据检测方法的1/6.
文摘Aggregation-induced emission(AIE)is a phenomenon where a molecule that is weakly or non-luminescent in a diluted solution becomes highly emissive when aggregated.AIE luminogens(AIEgens)hold promise in diverse applications like bioimaging,chemical sensing,and optoelectronics.Investigation in AIE luminescence is also critical for understanding aggregation kinetics as the aggregation process is an essential component of AIE emission.Experimental investigation of AIEgen aggregation is challenging due to the fast timescale of the aggregation and the amorphous aggregate structures.Computer simulations such as molecular dynamics(MD)simulation provide a valuable approach to complement experiments with atomic-level knowledge to study the fast dynamics of aggregation processes.However,individual simulations still struggle to systematically elucidate heterogeneous kinetics of the formation of amorphous AIEgen aggregates.Kinetic network models(KNMs),constructed from an ensemble of MD simulations,hold great potential in addressing this challenge.In these models,dynamic processes are modeled as a series ofMarkovian transitions occurring among metastable conformational states at discrete time intervals.In this perspective article,we first review previous studies to characterize the AIEgen aggregation kinetics and their limitations.We then introduce KNMs as a promising approach to elucidate the complex kinetics of aggregations to address these limitations.More importantly,we discuss our perspective on linking the output of KNMs to experimental observations of time-resolved AIE luminescence.We expect that this approach can validate the computational predictions and provide great insights into the aggregation kinetics for AIEgen aggregates.These insights will facilitate the rational design of improved AIEgens in their applications in biology and materials sciences.
