Distraction spinal cord injury is caused by some degree of distraction or longitudinal tension on the spinal cord and commonly occurs in patients who undergo corrective operation for severe spinal deformity.With the i...Distraction spinal cord injury is caused by some degree of distraction or longitudinal tension on the spinal cord and commonly occurs in patients who undergo corrective operation for severe spinal deformity.With the increased degree and duration of distraction,spinal cord injuries become more serious in terms of their neurophysiology,histology,and behavior.Very few studies have been published on the specific characteristics of distraction spinal cord injury.In this study,we systematically review 22 related studies involving animal models of distraction spinal cord injury,focusing particularly on the neurophysiological,histological,and behavioral characteristics of this disease.In addition,we summarize the mechanisms underlying primary and secondary injuries caused by distraction spinal cord injury and clarify the effects of different degrees and durations of distraction on the primary injuries associated with spinal cord injury.We provide new concepts for the establishment of a model of distraction spinal cord injury and related basic research,and provide reference guidelines for the clinical diagnosis and treatment of this disease.展开更多
At present,the acquisition of seismic data is developing toward high-precision and high-density methods.However,complex natural environments and cultural factors in many exploration areas cause difficulties in achievi...At present,the acquisition of seismic data is developing toward high-precision and high-density methods.However,complex natural environments and cultural factors in many exploration areas cause difficulties in achieving uniform and intensive acquisition,which makes complete seismic data collection impossible.Therefore,data reconstruction is required in the processing link to ensure imaging accuracy.Deep learning,as a new field in rapid development,presents clear advantages in feature extraction and modeling.In this study,the convolutional neural network deep learning algorithm is applied to seismic data reconstruction.Based on the convolutional neural network algorithm and combined with the characteristics of seismic data acquisition,two training strategies of supervised and unsupervised learning are designed to reconstruct sparse acquisition seismic records.First,a supervised learning strategy is proposed for labeled data,wherein the complete seismic data are segmented as the input of the training set and are randomly sampled before each training,thereby increasing the number of samples and the richness of features.Second,an unsupervised learning strategy based on large samples is proposed for unlabeled data,and the rolling segmentation method is used to update(pseudo)labels and training parameters in the training process.Through the reconstruction test of simulated and actual data,the deep learning algorithm based on a convolutional neural network shows better reconstruction quality and higher accuracy than compressed sensing based on Curvelet transform.展开更多
Plant phenotypes are infl uenced by genetic and environmental factors.In this study,the growth traits of 43 one-year-old poplar clones grown at diff erent sites in northeast China(Dongling State-owned Forest Protectio...Plant phenotypes are infl uenced by genetic and environmental factors.In this study,the growth traits of 43 one-year-old poplar clones grown at diff erent sites in northeast China(Dongling State-owned Forest Protection Center,DL;Baicheng State-owned forest farm,BC;and Cuohai Forest farm,CH)were evaluated and analyzed across clones and sites.Results show signifi cant diff erences in height and base diameter among sites and clones.Phenotypic and genetic variation coeffi cients ranged from 49.59%(BC)to 58.39%(DL)and from 49.33%(BC)to 58.06%(DL),respectively.Additive main eff ects and multiplicative interaction(AMMI)analysis showed that the eff ects of genotype,environment,and genotype×environment interaction were signifi cantly diff erent.Genetic variation was the main source of variation,accounting for 48.6%.AMMI biplot showed that clone 30 had high and stable yields at the three sites.From an evaluation of multiple traits and GGE biplot that clone 2,clone 30 and clone 25 had higher yield than the other clones at DL,CH and BC,respectively.These clones will provide material for forest regeneration in northeast China.展开更多
为解决直流换相失败问题并获取更高的传输容量,混合多端高压直流(High Voltage Direct Current,HVDC)输电技术开始在电力系统中应用。整流站大多采用的是基于晶闸管的换流阀(Line Commutated Converter,LCC),逆变站采用的是模块化多电...为解决直流换相失败问题并获取更高的传输容量,混合多端高压直流(High Voltage Direct Current,HVDC)输电技术开始在电力系统中应用。整流站大多采用的是基于晶闸管的换流阀(Line Commutated Converter,LCC),逆变站采用的是模块化多电平换流阀(Modular Multi-level Converter,MMC)。基于整流站与逆变站的不同结构,同时考虑星形连接高压直流输电线路多端拓扑结构,故障后行波的传播过程亟待进一步分析,为行波保护或测距新原理的研究提供理论基础。给出LCC整流站和MMC逆变站处的行波折、反射系数计算方法,并基于典型±400 kV星形连接三端LCC-MMC-HVDC拓扑结构分析行波传播过程。展开更多
基金supported by the National Natural Science Foundation of China,No.81772421(to YH).
文摘Distraction spinal cord injury is caused by some degree of distraction or longitudinal tension on the spinal cord and commonly occurs in patients who undergo corrective operation for severe spinal deformity.With the increased degree and duration of distraction,spinal cord injuries become more serious in terms of their neurophysiology,histology,and behavior.Very few studies have been published on the specific characteristics of distraction spinal cord injury.In this study,we systematically review 22 related studies involving animal models of distraction spinal cord injury,focusing particularly on the neurophysiological,histological,and behavioral characteristics of this disease.In addition,we summarize the mechanisms underlying primary and secondary injuries caused by distraction spinal cord injury and clarify the effects of different degrees and durations of distraction on the primary injuries associated with spinal cord injury.We provide new concepts for the establishment of a model of distraction spinal cord injury and related basic research,and provide reference guidelines for the clinical diagnosis and treatment of this disease.
基金This study was supported by the National Natural Science Foundation of China under the project‘Research on the Dynamic Location of Receiver Points and Wave Field Separation Technology Based on Deep Learning in OBN Seismic Exploration’(No.42074140).
文摘At present,the acquisition of seismic data is developing toward high-precision and high-density methods.However,complex natural environments and cultural factors in many exploration areas cause difficulties in achieving uniform and intensive acquisition,which makes complete seismic data collection impossible.Therefore,data reconstruction is required in the processing link to ensure imaging accuracy.Deep learning,as a new field in rapid development,presents clear advantages in feature extraction and modeling.In this study,the convolutional neural network deep learning algorithm is applied to seismic data reconstruction.Based on the convolutional neural network algorithm and combined with the characteristics of seismic data acquisition,two training strategies of supervised and unsupervised learning are designed to reconstruct sparse acquisition seismic records.First,a supervised learning strategy is proposed for labeled data,wherein the complete seismic data are segmented as the input of the training set and are randomly sampled before each training,thereby increasing the number of samples and the richness of features.Second,an unsupervised learning strategy based on large samples is proposed for unlabeled data,and the rolling segmentation method is used to update(pseudo)labels and training parameters in the training process.Through the reconstruction test of simulated and actual data,the deep learning algorithm based on a convolutional neural network shows better reconstruction quality and higher accuracy than compressed sensing based on Curvelet transform.
基金supported by the National Key Research and Development Program of China(2021YFD2201200)the Fundamental Research Funds for the Central Universities(2572020DR01).
文摘Plant phenotypes are infl uenced by genetic and environmental factors.In this study,the growth traits of 43 one-year-old poplar clones grown at diff erent sites in northeast China(Dongling State-owned Forest Protection Center,DL;Baicheng State-owned forest farm,BC;and Cuohai Forest farm,CH)were evaluated and analyzed across clones and sites.Results show signifi cant diff erences in height and base diameter among sites and clones.Phenotypic and genetic variation coeffi cients ranged from 49.59%(BC)to 58.39%(DL)and from 49.33%(BC)to 58.06%(DL),respectively.Additive main eff ects and multiplicative interaction(AMMI)analysis showed that the eff ects of genotype,environment,and genotype×environment interaction were signifi cantly diff erent.Genetic variation was the main source of variation,accounting for 48.6%.AMMI biplot showed that clone 30 had high and stable yields at the three sites.From an evaluation of multiple traits and GGE biplot that clone 2,clone 30 and clone 25 had higher yield than the other clones at DL,CH and BC,respectively.These clones will provide material for forest regeneration in northeast China.
文摘为解决直流换相失败问题并获取更高的传输容量,混合多端高压直流(High Voltage Direct Current,HVDC)输电技术开始在电力系统中应用。整流站大多采用的是基于晶闸管的换流阀(Line Commutated Converter,LCC),逆变站采用的是模块化多电平换流阀(Modular Multi-level Converter,MMC)。基于整流站与逆变站的不同结构,同时考虑星形连接高压直流输电线路多端拓扑结构,故障后行波的传播过程亟待进一步分析,为行波保护或测距新原理的研究提供理论基础。给出LCC整流站和MMC逆变站处的行波折、反射系数计算方法,并基于典型±400 kV星形连接三端LCC-MMC-HVDC拓扑结构分析行波传播过程。
