A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing values.This method achieves...A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing values.This method achieves precise adjustment of the network structure by constructing a preliminary random network model and introducing small-world network characteristics and combines L1 norm minimization regularization techniques to control model complexity and optimize the inference process of variable dependencies.In the experiment of game network reconstruction,when the success rate of the L1 norm minimization model’s existence connection reconstruction reaches 100%,the minimum data required is about 40%,while the minimum data required for a sparse Bayesian learning network is about 45%.In terms of operational efficiency,the running time for minimizing the L1 normis basically maintained at 1.0 s,while the success rate of connection reconstruction increases significantly with an increase in data volume,reaching a maximum of 13.2 s.Meanwhile,in the case of a signal-to-noise ratio of 10 dB,the L1 model achieves a 100% success rate in the reconstruction of existing connections,while the sparse Bayesian network had the highest success rate of 90% in the reconstruction of non-existent connections.In the analysis of actual cases,the maximum lift and drop track of the research method is 0.08 m.The mean square error is 5.74 cm^(2).The results indicate that this norm minimization-based method has good performance in data efficiency and model stability,effectively reducing the impact of outliers on the reconstruction results to more accurately reflect the actual situation.展开更多
In the concurrent extraction of coal and gas,the quantitative assessment of evolving characteristics in mining-induced fracture networks and mining-enhanced permeability within coal seams serves as the cornerstone for...In the concurrent extraction of coal and gas,the quantitative assessment of evolving characteristics in mining-induced fracture networks and mining-enhanced permeability within coal seams serves as the cornerstone for effective gas extraction.However,representing mining-induced fracture networks from a three-dimensional(3D)sight and developing a comprehensive model to evaluate the anisotropic mining-enhanced permeability characteristics still pose significant challenges.In this investigation,a field experiment was undertaken to systematically monitor the evolution of borehole fractures in the coal mass ahead of the mining face at the Pingdingshan Coal Mining Group in China.Using the testing data of borehole fracture,the mining-induced fracture network at varying distances from the mining face was reconstructed through a statistical reconstruction method.Additionally,utilizing fractal theory,a model for the permeability enhancement rate(PER)induced by mining was established.This model was employed to quantitatively depict the anisotropic evolution patterns of PER as the mining face advanced.The research conclusions are as follows:(1)The progression of the mining-induced fracture network can be classified into the stage of rapid growth,the stage of stable growth,and the stage of weak impact;(2)The PER of mining-induced fracture network exhibited a typical progression that can be characterized with slow growth,rapid growth and significant decline;(3)The anisotropic mining-enhanced permeability of the reconstructed mining-induced fracture networks were significant.The peak PER in the vertical direction of the coal seam is 6.86 times and 4446.38 times greater than the direction perpendicular to the vertical thickness and the direction parallel to the advancement of the mining face,respectively.This investigatione provides a viable approach and methodology for quantitatively assessing the anisotropic PER of fracture networks induced during mining,in the concurrent exploitation of coal and gas.展开更多
Chronic diabetic wounds are the most common complication for diabetic patients.Due to high oxidative stress levels affecting the entire healing process,treating diabetic wounds remains a challenge.Here,we present a st...Chronic diabetic wounds are the most common complication for diabetic patients.Due to high oxidative stress levels affecting the entire healing process,treating diabetic wounds remains a challenge.Here,we present a strategy for continuously regulating oxidative stress microenvironment by the catalyst-like magnesium-gallate metal-organic framework(Mg-GA MOF)and developing sprayable hydrogel dressing with sodium alginate/chitosan quaternary ammonium salts to treat diabetic wounds.Chitosan quaternary ammonium salts with antibacterial properties can prevent bacterial infection.The continuous release of gallic acid(GA)effectively eliminates reactive oxygen species(ROS),reduces oxidative stress,and accelerates the polarization of M1-type macrophages to M2-type,shortening the transition between inflammation and proliferative phase and maintaining redox balance.Besides,magnesium ions adjuvant therapy promotes vascular regeneration and neuronal formation by activating the expression of vascular-associated genes.Sprayable hydrogel dressings with antibacterial,antioxidant,and inflammatory regulation rapidly repair diabetic wounds by promoting neurovascular network reconstruction and accelerating re-epithelialization and collagen deposition.This study confirms the feasibility of catalyst-like MOF-contained sprayable hydrogel to regulate the microenvironment continuously and provides guidance for developing the next generation of non-drug diabetes dressings.展开更多
Many practical systems in natural and social sciences can be described by dynamical networks. Day by day we have measured and accumulated huge amounts of data from these networks, which can be used by us to further ou...Many practical systems in natural and social sciences can be described by dynamical networks. Day by day we have measured and accumulated huge amounts of data from these networks, which can be used by us to further our understanding of the world. The structures of the networks producing these data are often unknown. Consequently, understanding the structures of these networks from available data turns to be one of the central issues in interdisciplinary fields, which is called the network recon- struction problem. In this paper, we considered problems of network reconstructions using partially available data and some situations where data availabilities are not sufficient for conventional network reconstructions. Furthermore, we proposed to infer subnetwork with data of the subnetwork available only and other nodes of the entire network hidden; to depict group-group interactions in networks with averages of groups of node variables available; and to perform network reconstructions with known data of node variables only when networks are driven by both unknown internal fast-varying noises and unknown external slowly-varying signals. All these situations are expected to be common in practical systems and the methods and results may be useful for real world applications.展开更多
Time delays exist widely in real systems, and time-delayed interactions can result in abundant dynamic behaviors and functions in dynamic networks. Inferring the time delays and interactions is challenging due to syst...Time delays exist widely in real systems, and time-delayed interactions can result in abundant dynamic behaviors and functions in dynamic networks. Inferring the time delays and interactions is challenging due to systematic nonlinearity,noises, a lack of information, and so on. Recently, Shi et al. proposed a random state variable resetting method to detect the interactions in a continuous-time dynamic network. By arbitrarily resetting the state variable of a driving node, the equivalent coupling functions of the driving node to any response node in the network can be reconstructed. In this paper,we introduce this method in time-delayed dynamic networks. To infer actual time delays, the nearest neighbor correlation(NNC) function for a given time delay is defined. The significant increments of NNC originate from the delayed effect.Based on the increments, the time delays can be reconstructed and the reconstruction errors depend on the sampling time interval. After time delays are accurately identified, the equivalent coupling functions can also be reconstructed. The numerical results have fully verified the validity of the theoretical analysis.展开更多
The aging behaviors and mechanism of fluoroelastomer(FKM)under lubricating oil(FKM-O)and air(FKM-A,as a comparison)at elevated temperatures were studied from both physical and chemical viewpoints.The obvious changes o...The aging behaviors and mechanism of fluoroelastomer(FKM)under lubricating oil(FKM-O)and air(FKM-A,as a comparison)at elevated temperatures were studied from both physical and chemical viewpoints.The obvious changes of mechanical and swelling performances indicate that the coupling effect of lubricating oil and temperature causes more serious deterioration of FKM-O compared to that of FKM-A.Meanwhile,much stronger temperature dependence of both bulk properties and micro-structures for FKM-O is found.Three-stage physical diffusion process is defined in FKM-O due to the competition between oil diffusion and elastic retraction of network.FTIR results reveal that the dehydrofluorination reaction causes the fracture of C-F bonds and produces a large number of C=C bonds in the backbone.The coupling effect of oil medium and high temperature could accelerate the scission of C=C bonds and generate a series of fragments with different molecular sizes.The TGA results,crosslinking density Ve,and glass transition temperature Tg derived from different measurements coherently demonstrate the network destruction in the initial stage and the simultaneous reconstruction occurring at the final stage.The newly formed local network induced by reconstruction cannot compensate the break of the original rubber network and thus only provides lower tensile strength and thermal stability.展开更多
The problem of network reconstruction, particularly exploring unknown network structures by analyzing measurable output data from networks, has attracted significant interest in many interdisciplinary fields in recent...The problem of network reconstruction, particularly exploring unknown network structures by analyzing measurable output data from networks, has attracted significant interest in many interdisciplinary fields in recent times. In practice, networks may be very large, and data can often be measured for only some of the nodes in a network while data for other variables are bidden. It is thus crucial to be able to infer networks from partial data. In this article, we study the problem of noise-driven nonlinear networks with some hidden nodes. Various difficulties appear jointly： nonlinearity of network dynamics, the impact of strong noise, the complexity of interaction structures between network nodes, and missing data from certain hidden nodes. We propose using high-order correlation to treat nonlinearity and structural complexity, two-time correlation to decorrelate noise, and higher- order derivatives to overcome the difficulties of hidden nodes. A closed form of network reconstruction is derived, and numerical simulations confirm the theoretical predictions.展开更多
The interactions between players of the prisoner's dilemma game are inferred using observed game data.All participants play the game with their counterparts and gain corresponding rewards during each round of the ...The interactions between players of the prisoner's dilemma game are inferred using observed game data.All participants play the game with their counterparts and gain corresponding rewards during each round of the game.The strategies of each player are updated asynchronously during the game.Two inference methods of the interactions between players are derived with naive mean-field(n MF)approximation and maximum log-likelihood estimation(MLE),respectively.Two methods are tested numerically also for fully connected asymmetric Sherrington-Kirkpatrick models,varying the data length,asymmetric degree,payoff,and system noise(coupling strength).We find that the mean square error of reconstruction for the MLE method is inversely proportional to the data length and typically half(benefit from the extra information of update times)of that by n MF.Both methods are robust to the asymmetric degree but work better for large payoffs.Compared with MLE,n MF is more sensitive to the strength of couplings and prefers weak couplings.展开更多
In recent years,defending against adversarial examples has gained significant importance,leading to a growing body of research in this area.Among these studies,pre-processing defense approaches have emerged as a promi...In recent years,defending against adversarial examples has gained significant importance,leading to a growing body of research in this area.Among these studies,pre-processing defense approaches have emerged as a prominent research direction.However,existing adversarial example pre-processing techniques often employ a single pre-processing model to counter different types of adversarial attacks.Such a strategy may miss the nuances between different types of attacks,limiting the comprehensiveness and effectiveness of the defense strategy.To address this issue,we propose a divide-and-conquer reconstruction pre-processing algorithm via multi-classification and multi-network training to more effectively defend against different types of mainstream adversarial attacks.The premise and challenge of the divide-and-conquer reconstruction defense is to distinguish between multiple types of adversarial attacks.Our method designs an adversarial attack classification module that exploits the high-frequency information differences between different types of adversarial examples for their multi-classification,which can hardly be achieved by existing adversarial example detection methods.In addition,we construct a divide-and-conquer reconstruction module that utilizes different trained image reconstruction models for each type of adversarial attack,ensuring optimal defense effectiveness.Extensive experiments show that our proposed divide-and-conquer defense algorithm exhibits superior performance compared to state-of-the-art pre-processing methods.展开更多
The present study aimed to explore the potential of the sodium hyaluronate-CNTF (ciliary neurotrophic factor) scaffold in activating endogenous neurogenesis and facilitating neural network re-formation after the adult...The present study aimed to explore the potential of the sodium hyaluronate-CNTF (ciliary neurotrophic factor) scaffold in activating endogenous neurogenesis and facilitating neural network re-formation after the adult rat spinal cord injury (SCI). After completely cutting and removing a 5-mm adult rat T8 segment, a sodium hyaluronate-CNTF scaffold was implanted into the lesion area. Dil tracing and immunofluorescence staining were used to observe the proliferation, differentiation and integration of neural stem cells (NSCs) after SCI. A planar multielectrode dish system (MED64) was used to test the electrophysiological characteristics of the regenerated neural network in the lesioned area. Electrophysiology and behavior evaluation were used to evaluate functional recovery of paraplegic rat hindlimbs. The Dil tracing and immunofluorescence results suggest that the sodium hyaluronate-CNTF scaffold could activate the NSCs originating from the spinal cord ependymal, and facilitate their migration to the lesion area and differentiation into mature neurons, which were capable of forming synaptic contact and receiving glutamatergic excitatory synaptic input. The MED64 results suggest that functional synapsis could be established among regenerated neurons as well as between regenerated neurons and the host tissue, which has been evidenced to be glutamatergic excitatory synapsis. The electrophysiology and behavior evaluation results indicate that the paraplegic rats’ sensory and motor functions were recovered in some degree. Collectively, this study may shed light on paraplegia treatment in clinics.展开更多
基金supported by the Scientific and Technological Developing Scheme of Jilin Province,China(No.20240101371JC)the National Natural Science Foundation of China(No.62107008).
文摘A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing values.This method achieves precise adjustment of the network structure by constructing a preliminary random network model and introducing small-world network characteristics and combines L1 norm minimization regularization techniques to control model complexity and optimize the inference process of variable dependencies.In the experiment of game network reconstruction,when the success rate of the L1 norm minimization model’s existence connection reconstruction reaches 100%,the minimum data required is about 40%,while the minimum data required for a sparse Bayesian learning network is about 45%.In terms of operational efficiency,the running time for minimizing the L1 normis basically maintained at 1.0 s,while the success rate of connection reconstruction increases significantly with an increase in data volume,reaching a maximum of 13.2 s.Meanwhile,in the case of a signal-to-noise ratio of 10 dB,the L1 model achieves a 100% success rate in the reconstruction of existing connections,while the sparse Bayesian network had the highest success rate of 90% in the reconstruction of non-existent connections.In the analysis of actual cases,the maximum lift and drop track of the research method is 0.08 m.The mean square error is 5.74 cm^(2).The results indicate that this norm minimization-based method has good performance in data efficiency and model stability,effectively reducing the impact of outliers on the reconstruction results to more accurately reflect the actual situation.
基金supported by the National Natural Science Foundation of China (Grant No.42377143)Sichuan Natural Science Foundation (Grant No.2024NSFSC0097)the Open Fund of State Key Laboratory of Coal Mining and Clean Utilization,China (Grant No.2021-CMCU-KFZD001).
文摘In the concurrent extraction of coal and gas,the quantitative assessment of evolving characteristics in mining-induced fracture networks and mining-enhanced permeability within coal seams serves as the cornerstone for effective gas extraction.However,representing mining-induced fracture networks from a three-dimensional(3D)sight and developing a comprehensive model to evaluate the anisotropic mining-enhanced permeability characteristics still pose significant challenges.In this investigation,a field experiment was undertaken to systematically monitor the evolution of borehole fractures in the coal mass ahead of the mining face at the Pingdingshan Coal Mining Group in China.Using the testing data of borehole fracture,the mining-induced fracture network at varying distances from the mining face was reconstructed through a statistical reconstruction method.Additionally,utilizing fractal theory,a model for the permeability enhancement rate(PER)induced by mining was established.This model was employed to quantitatively depict the anisotropic evolution patterns of PER as the mining face advanced.The research conclusions are as follows:(1)The progression of the mining-induced fracture network can be classified into the stage of rapid growth,the stage of stable growth,and the stage of weak impact;(2)The PER of mining-induced fracture network exhibited a typical progression that can be characterized with slow growth,rapid growth and significant decline;(3)The anisotropic mining-enhanced permeability of the reconstructed mining-induced fracture networks were significant.The peak PER in the vertical direction of the coal seam is 6.86 times and 4446.38 times greater than the direction perpendicular to the vertical thickness and the direction parallel to the advancement of the mining face,respectively.This investigatione provides a viable approach and methodology for quantitatively assessing the anisotropic PER of fracture networks induced during mining,in the concurrent exploitation of coal and gas.
基金supported by grants from the National Natural Science Foundation of China(52372272,32201109,32360234)the National Key Research and Development Program of China(2022YFB4601402)+2 种基金the Guangdong Basic and Applied Basic Research Foundation(2022B1515120052,2021A1515110557)the Key Basic Research Program of Shenzhen(JCYJ20200109150218836)the Self-innovation Research Funding Project of Hanjiang Laboratory(HJL202202A002).The authors thank the help of Tong Qiu,Qinghua Hou and Ran Yu of the Wuhan University of Technology,Wuhan 430070,China.
文摘Chronic diabetic wounds are the most common complication for diabetic patients.Due to high oxidative stress levels affecting the entire healing process,treating diabetic wounds remains a challenge.Here,we present a strategy for continuously regulating oxidative stress microenvironment by the catalyst-like magnesium-gallate metal-organic framework(Mg-GA MOF)and developing sprayable hydrogel dressing with sodium alginate/chitosan quaternary ammonium salts to treat diabetic wounds.Chitosan quaternary ammonium salts with antibacterial properties can prevent bacterial infection.The continuous release of gallic acid(GA)effectively eliminates reactive oxygen species(ROS),reduces oxidative stress,and accelerates the polarization of M1-type macrophages to M2-type,shortening the transition between inflammation and proliferative phase and maintaining redox balance.Besides,magnesium ions adjuvant therapy promotes vascular regeneration and neuronal formation by activating the expression of vascular-associated genes.Sprayable hydrogel dressings with antibacterial,antioxidant,and inflammatory regulation rapidly repair diabetic wounds by promoting neurovascular network reconstruction and accelerating re-epithelialization and collagen deposition.This study confirms the feasibility of catalyst-like MOF-contained sprayable hydrogel to regulate the microenvironment continuously and provides guidance for developing the next generation of non-drug diabetes dressings.
文摘Many practical systems in natural and social sciences can be described by dynamical networks. Day by day we have measured and accumulated huge amounts of data from these networks, which can be used by us to further our understanding of the world. The structures of the networks producing these data are often unknown. Consequently, understanding the structures of these networks from available data turns to be one of the central issues in interdisciplinary fields, which is called the network recon- struction problem. In this paper, we considered problems of network reconstructions using partially available data and some situations where data availabilities are not sufficient for conventional network reconstructions. Furthermore, we proposed to infer subnetwork with data of the subnetwork available only and other nodes of the entire network hidden; to depict group-group interactions in networks with averages of groups of node variables available; and to perform network reconstructions with known data of node variables only when networks are driven by both unknown internal fast-varying noises and unknown external slowly-varying signals. All these situations are expected to be common in practical systems and the methods and results may be useful for real world applications.
文摘Time delays exist widely in real systems, and time-delayed interactions can result in abundant dynamic behaviors and functions in dynamic networks. Inferring the time delays and interactions is challenging due to systematic nonlinearity,noises, a lack of information, and so on. Recently, Shi et al. proposed a random state variable resetting method to detect the interactions in a continuous-time dynamic network. By arbitrarily resetting the state variable of a driving node, the equivalent coupling functions of the driving node to any response node in the network can be reconstructed. In this paper,we introduce this method in time-delayed dynamic networks. To infer actual time delays, the nearest neighbor correlation(NNC) function for a given time delay is defined. The significant increments of NNC originate from the delayed effect.Based on the increments, the time delays can be reconstructed and the reconstruction errors depend on the sampling time interval. After time delays are accurately identified, the equivalent coupling functions can also be reconstructed. The numerical results have fully verified the validity of the theoretical analysis.
基金This work was financially supported by the Joint Foundation from Ministry of Education and Advanced Research of Equipment(No.6141A02022201)the National Natural Science Foundation of China(Nos.U19A2096,51721091)Department of Science and Technology of Sichuan Province(No.2019YFH0027).
文摘The aging behaviors and mechanism of fluoroelastomer(FKM)under lubricating oil(FKM-O)and air(FKM-A,as a comparison)at elevated temperatures were studied from both physical and chemical viewpoints.The obvious changes of mechanical and swelling performances indicate that the coupling effect of lubricating oil and temperature causes more serious deterioration of FKM-O compared to that of FKM-A.Meanwhile,much stronger temperature dependence of both bulk properties and micro-structures for FKM-O is found.Three-stage physical diffusion process is defined in FKM-O due to the competition between oil diffusion and elastic retraction of network.FTIR results reveal that the dehydrofluorination reaction causes the fracture of C-F bonds and produces a large number of C=C bonds in the backbone.The coupling effect of oil medium and high temperature could accelerate the scission of C=C bonds and generate a series of fragments with different molecular sizes.The TGA results,crosslinking density Ve,and glass transition temperature Tg derived from different measurements coherently demonstrate the network destruction in the initial stage and the simultaneous reconstruction occurring at the final stage.The newly formed local network induced by reconstruction cannot compensate the break of the original rubber network and thus only provides lower tensile strength and thermal stability.
基金supported by the National Natural Science Foundation of China(Grant No.11135001)China Postdoctoral Science Foundation(Grant No.2015M581905)
文摘The problem of network reconstruction, particularly exploring unknown network structures by analyzing measurable output data from networks, has attracted significant interest in many interdisciplinary fields in recent times. In practice, networks may be very large, and data can often be measured for only some of the nodes in a network while data for other variables are bidden. It is thus crucial to be able to infer networks from partial data. In this article, we study the problem of noise-driven nonlinear networks with some hidden nodes. Various difficulties appear jointly： nonlinearity of network dynamics, the impact of strong noise, the complexity of interaction structures between network nodes, and missing data from certain hidden nodes. We propose using high-order correlation to treat nonlinearity and structural complexity, two-time correlation to decorrelate noise, and higher- order derivatives to overcome the difficulties of hidden nodes. A closed form of network reconstruction is derived, and numerical simulations confirm the theoretical predictions.
基金supported by the National Natural Science Foundation of China(Grant Nos.11705079 and 11705279)the Scientific Research Foundation of Nanjing University of Posts and Telecommunications(Grant Nos.NY221101 and NY222134)the Science and Technology Innovation Training Program(Grant No.STITP 202210293044Z)。
文摘The interactions between players of the prisoner's dilemma game are inferred using observed game data.All participants play the game with their counterparts and gain corresponding rewards during each round of the game.The strategies of each player are updated asynchronously during the game.Two inference methods of the interactions between players are derived with naive mean-field(n MF)approximation and maximum log-likelihood estimation(MLE),respectively.Two methods are tested numerically also for fully connected asymmetric Sherrington-Kirkpatrick models,varying the data length,asymmetric degree,payoff,and system noise(coupling strength).We find that the mean square error of reconstruction for the MLE method is inversely proportional to the data length and typically half(benefit from the extra information of update times)of that by n MF.Both methods are robust to the asymmetric degree but work better for large payoffs.Compared with MLE,n MF is more sensitive to the strength of couplings and prefers weak couplings.
基金supported by the Science and Technology Innovation Program of Hunan Province(No.2022GK5002,2024JK2015,2024JJ5440)the Special Foundation for Distinguished Young Scientists of Changsha(No.kq2209003)+2 种基金the Foreign Expert Project of China(No.G2023041039L)the 111 Project(No.D23006)in part by the High Performance Computing Center of Central South University.
文摘In recent years,defending against adversarial examples has gained significant importance,leading to a growing body of research in this area.Among these studies,pre-processing defense approaches have emerged as a prominent research direction.However,existing adversarial example pre-processing techniques often employ a single pre-processing model to counter different types of adversarial attacks.Such a strategy may miss the nuances between different types of attacks,limiting the comprehensiveness and effectiveness of the defense strategy.To address this issue,we propose a divide-and-conquer reconstruction pre-processing algorithm via multi-classification and multi-network training to more effectively defend against different types of mainstream adversarial attacks.The premise and challenge of the divide-and-conquer reconstruction defense is to distinguish between multiple types of adversarial attacks.Our method designs an adversarial attack classification module that exploits the high-frequency information differences between different types of adversarial examples for their multi-classification,which can hardly be achieved by existing adversarial example detection methods.In addition,we construct a divide-and-conquer reconstruction module that utilizes different trained image reconstruction models for each type of adversarial attack,ensuring optimal defense effectiveness.Extensive experiments show that our proposed divide-and-conquer defense algorithm exhibits superior performance compared to state-of-the-art pre-processing methods.
基金supported by the State Key Program of the National Natural Science Foundation of China (31130022,31320103903, 31271037 & 31670988)the International Cooperation in Science and Technology Project of the Ministry of Science and Technology of China (2014DFA30640)+2 种基金the National Ministry of Education Special Fund for Excellent Doctoral Dissertation (201356)the Special Fund for Excellent Doctoral Dissertation of Beijing (20111000601)the Special Funds for Beijing Base Construction & Talent Cultivation (171100002217066)
文摘The present study aimed to explore the potential of the sodium hyaluronate-CNTF (ciliary neurotrophic factor) scaffold in activating endogenous neurogenesis and facilitating neural network re-formation after the adult rat spinal cord injury (SCI). After completely cutting and removing a 5-mm adult rat T8 segment, a sodium hyaluronate-CNTF scaffold was implanted into the lesion area. Dil tracing and immunofluorescence staining were used to observe the proliferation, differentiation and integration of neural stem cells (NSCs) after SCI. A planar multielectrode dish system (MED64) was used to test the electrophysiological characteristics of the regenerated neural network in the lesioned area. Electrophysiology and behavior evaluation were used to evaluate functional recovery of paraplegic rat hindlimbs. The Dil tracing and immunofluorescence results suggest that the sodium hyaluronate-CNTF scaffold could activate the NSCs originating from the spinal cord ependymal, and facilitate their migration to the lesion area and differentiation into mature neurons, which were capable of forming synaptic contact and receiving glutamatergic excitatory synaptic input. The MED64 results suggest that functional synapsis could be established among regenerated neurons as well as between regenerated neurons and the host tissue, which has been evidenced to be glutamatergic excitatory synapsis. The electrophysiology and behavior evaluation results indicate that the paraplegic rats’ sensory and motor functions were recovered in some degree. Collectively, this study may shed light on paraplegia treatment in clinics.
