A microelectronic circuit is used to regenerate the neural signals between the proximal end and the distal end of an injured nerve.An experimental scheme is designed and carried out to verify the feasibility of the so...A microelectronic circuit is used to regenerate the neural signals between the proximal end and the distal end of an injured nerve.An experimental scheme is designed and carried out to verify the feasibility of the so-called microelectronic neural bridge（MNB）.The sciatic signals of the source spinal toad which are evoked by chemical stimuli are used as source signals to stimulate the sciatic of the controlled spinal toad.The sciatic nerve signals of the source spinal toad,the regenerated sciatic signals in the controlled spinal toad,and the resulting electromyography（EMG）signals associated with the gastrocnemius muscle movements of the controlled spinal toad are displayed and recorded by an oscilloscope.By analyzing the coherence between the source sciatic nerve signals and the regenerated sciatic nerve signals and the coherence between the regenerated nerve signals and the EMG signals,it is proved that the regenerated sciatic nerve signals have a relationship with the source sciatic nerve signals and control shrinkage of the leg of the controlled toad.展开更多
Objective: To discuss the mechanism of low molecular weight GTP binding protein RAC1 in the injury of neural function based on building the rat model of cerebral ischemia reperfusion. Methods: Middle cerebral artery o...Objective: To discuss the mechanism of low molecular weight GTP binding protein RAC1 in the injury of neural function based on building the rat model of cerebral ischemia reperfusion. Methods: Middle cerebral artery of rats was ligated and the ligature was released to restore the perfusion after 2 h, the rat model of cerebral ischemia reperfusion injury was built, while the middle cerebral artery was ligated. The rats were randomly divided into the sham group, cerebral ischemia reperfusion group(I/R group) and the group with the injection of RAC1 activity inhibitor NSC23766(NSC group). The survival and neurological severity score of rats in each group were observed and recorded. Nissl staining was employed to observe the nerve cells, and Western blot to detect expression of RAC1, superoxide dismutase and malondialdehyde. Results: Number of nerve cells for rats in NSC group was significantly more than that in I/R group, but significantly less than that in sham group, with the statistical difference(P<0.05). The brain water content for rats in NSC group was significantly lower than that in I/R group, but significantly higher than that in sham group, with the statistical difference(P<0.05). The expression of RAC1 and malondialdehyde for rats in NSC group was significantly lower than that in I/R group, but higher than that in sham group; while the expression of superoxide dismutase was lower than that in sham group, but higher than that in I/R group, with the statistical difference(P<0.05). Conclusions: The inhibition of RAC1 activity can reduce the oxidative stress, reduce the neurologic impairment because of cerebral ischemia reperfusion and thus protect the neural function.展开更多
The exchange of information and materials between organelles plays a crucial role in regulating cellular physiological functions and metabolic levels.Mitochondria-associated endoplasmic reticulum membranes serve as ph...The exchange of information and materials between organelles plays a crucial role in regulating cellular physiological functions and metabolic levels.Mitochondria-associated endoplasmic reticulum membranes serve as physical contact channels between the endoplasmic reticulum membrane and the mitochondrial outer membrane,formed by various proteins and protein complexes.This microstructural domain mediates several specialized functions,including calcium(Ca^(2+))signaling,autophagy,mitochondrial morphology,oxidative stress response,and apoptosis.Notably,the dysregulation of Ca^(2+)signaling mediated by mitochondria-associated endoplasmic reticulum membranes is a critical factor in the pathogenesis of neurological diseases.Certain proteins or protein complexes within these membranes directly or indirectly regulate the distance between the endoplasmic reticulum and mitochondria,as well as the transduction of Ca^(2+)signaling.Conversely,Ca^(2+)signaling mediated by mitochondria-associated endoplasmic reticulum membranes influences other mitochondria-associated endoplasmic reticulum membraneassociated functions.These functions can vary significantly across different neurological diseases—such as ischemic stroke,traumatic brain injury,Alzheimer's disease,Parkinson's disease,amyotrophic lateral sclerosis,and Huntington's disease—and their respective stages of progression.Targeted modulation of these disease-related pathways and functional proteins can enhance neurological function and promote the regeneration and repair of damaged neurons.Therefore,mitochondria-associated endoplasmic reticulum membranes-mediated Ca^(2+)signaling plays a pivotal role in the pathological progression of neurological diseases and represents a significant potential therapeutic target.This review focuses on the effects of protein complexes in mitochondria-associated endoplasmic reticulum membranes and the distinct roles of mitochondria-associated endoplasmic reticulum membranes-mediated Ca^(2+)signaling in neurological diseases,specifically highlighting the early protective effects and neuronal damage that can result from prolonged mitochondrial Ca^(2+)overload or deficiency.This article provides a comprehensive analysis of the various mechanisms of Ca^(2+)signaling mediated by mitochondria-associated endoplasmic reticulum membranes in neurological diseases,contributing to the exploration of potential therapeutic targets for promoting neuroprotection and nerve repair.展开更多
Human brain organoids are 3-dimensional brain-like tissues derived from human pluripotent stem cells and hold promising potential for modeling neurological,psychiatric,and developmental disorders.While the molecular a...Human brain organoids are 3-dimensional brain-like tissues derived from human pluripotent stem cells and hold promising potential for modeling neurological,psychiatric,and developmental disorders.While the molecular and cellular aspects of human brain organoids have been intensively studied,their functional properties such as organoid neural networks(ONNs)are largely understudied.Here,we summarize recent research advances in understanding,characterization,and application of functional ONNs in human brain organoids.We first discuss the formation of ONNs and follow up with characterization strategies including microelectrode array(MEA)technology and calcium imaging.Moreover,we highlight recent studies utilizing ONNs to investigate neurological diseases such as Rett syndrome and Alzheimer’s disease.Finally,we provide our perspectives on the future challenges and opportunities for using ONNs in basic research and translational applications.展开更多
The purpose of this study was to address the challenges in predicting and classifying accuracy in modeling Container Dwell Time (CDT) using Artificial Neural Networks (ANN). This objective was driven by the suboptimal...The purpose of this study was to address the challenges in predicting and classifying accuracy in modeling Container Dwell Time (CDT) using Artificial Neural Networks (ANN). This objective was driven by the suboptimal outcomes reported in previous studies and sought to apply an innovative approach to improve these results. To achieve this, the study applied the Fusion of Activation Functions (FAFs) to a substantial dataset. This dataset included 307,594 container records from the Port of Tema from 2014 to 2022, encompassing both import and transit containers. The RandomizedSearchCV algorithm from Python’s Scikit-learn library was utilized in the methodological approach to yield the optimal activation function for prediction accuracy. The results indicated that “ajaLT”, a fusion of the Logistic and Hyperbolic Tangent Activation Functions, provided the best prediction accuracy, reaching a high of 82%. Despite these encouraging findings, it’s crucial to recognize the study’s limitations. While Fusion of Activation Functions is a promising method, further evaluation is necessary across different container types and port operations to ascertain the broader applicability and generalizability of these findings. The original value of this study lies in its innovative application of FAFs to CDT. Unlike previous studies, this research evaluates the method based on prediction accuracy rather than training time. It opens new avenues for machine learning engineers and researchers in applying FAFs to enhance prediction accuracy in CDT modeling, contributing to a previously underexplored area.展开更多
Partial least squares（PLS）,back-propagation neural network（BPNN）and radial basis function neural network（RBFNN）were respectively used for estalishing quantative analysis models with near infrared（NIR）diffuse r...Partial least squares（PLS）,back-propagation neural network（BPNN）and radial basis function neural network（RBFNN）were respectively used for estalishing quantative analysis models with near infrared（NIR）diffuse reflectance spectra for determining the contents of rifampincin（RMP）,isoniazid（INH）and pyrazinamide（PZA）in rifampicin isoniazid and pyrazinamide tablets.Savitzky-Golay smoothing,first derivative,second derivative,fast Fourier transform（FFT）and standard normal variate（SNV）transformation methods were applied to pretreating raw NIR diffuse reflectance spectra.The raw and pretreated spectra were divided into several regions,depending on the average spectrum and RSD spectrum.Principal component analysis（PCA）method was used for analyzing the raw and pretreated spectra in different regions in order to reduce the dimensions of input data.The optimum spectral regions and the models＇ parameters were chosen by comparing the root mean square error of cross-validation（RMSECV）values which were obtained by leave-one-out cross-validation method.The RMSECV values of the RBFNN models for determining the contents of RMP,INH and PZA were 0.00288,0.00226 and 0.00341,respectively.Using these models for predicting the contents of INH,RMP and PZA in prediction set,the RMSEP values were 0.00266,0.00227 and 0.00411,respectively.These results are better than those obtained from PLS models and BPNN models.With additional advantages of fast calculation speed and less dependence on the initial conditions,RBFNN is a suitable tool to model complex systems.展开更多
The Radial Basis Functions Neural Network （RBFNN） is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response syst...The Radial Basis Functions Neural Network （RBFNN） is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response system can be implemented by employing the RBFNN model and state feedback control. In this case, the exact mathematical model, which is the precondition for the conventional method, is unnecessary for implementing synchronization. The effect of the model error is investigated and a corresponding theorem is developed. The effect of the parameter perturbations and the measurement noise is investigated through simulations. The simulation results under different conditions show the effectiveness of the method.展开更多
Near-infrared (NIR) spectroscopy combined with chemometrics techniques was used to classify the pure bayberry juice and the one adulterated with 10% (w/w) and 20% (w/w) water. Principal component analysis (PCA) was ap...Near-infrared (NIR) spectroscopy combined with chemometrics techniques was used to classify the pure bayberry juice and the one adulterated with 10% (w/w) and 20% (w/w) water. Principal component analysis (PCA) was applied to reduce the dimensions of spectral data, give information regarding a potential capability of separation of objects, and provide principal component (PC) scores for radial basis function neural networks (RBFNN). RBFNN was used to detect bayberry juice adulterant. Multiplicative scatter correction (MSC) and standard normal variate (SNV) transformation were used to preprocess spectra. The results demonstrate that PC-RBFNN with optimum parameters can separate pure bayberry juice samples from water-adulterated bayberry at a recognition rate of 97.62%, but cannot clearly detect water levels in the adulterated bayberry juice. We conclude that NIR technology can be successfully applied to detect water-adulterated bayberry juice.展开更多
An adaptive integral dynamic surface control approach based on fully tuned radial basis function neural network （FTRBFNN） is presented for a general class of strict-feedback nonlinear systems,which may possess a wid...An adaptive integral dynamic surface control approach based on fully tuned radial basis function neural network （FTRBFNN） is presented for a general class of strict-feedback nonlinear systems,which may possess a wide class of uncertainties that are not linearly parameterized and do not have any prior knowledge of the bounding functions.FTRBFNN is employed to approximate the uncertainty online,and a systematic framework for adaptive controller design is given by dynamic surface control. The control algorithm has two outstanding features,namely,the neural network regulates the weights,width and center of Gaussian function simultaneously,which ensures the control system has perfect ability of restraining different unknown uncertainties and the integral term of tracking error introduced in the control law can eliminate the static error of the closed loop system effectively. As a result,high control precision can be achieved.All signals in the closed loop system can be guaranteed bounded by Lyapunov approach.Finally,simulation results demonstrate the validity of the control approach.展开更多
Deep excavation during the construction of underground systems can cause movement on the ground,especially in soft clay layers.At high levels,excessive ground movements can lead to severe damage to adjacent structures...Deep excavation during the construction of underground systems can cause movement on the ground,especially in soft clay layers.At high levels,excessive ground movements can lead to severe damage to adjacent structures.In this study,finite element analyses(FEM)and the hardening small strain(HSS)model were performed to investigate the deflection of the diaphragm wall in the soft clay layer induced by braced excavations.Different geometric and mechanical properties of the wall were investigated to study the deflection behavior of the wall in soft clays.Accordingly,1090 hypothetical cases were surveyed and simulated based on the HSS model and FEM to evaluate the wall deflection behavior.The results were then used to develop an intelligent model for predicting wall deflection using the functional linked neural network(FLNN)with different functional expansions and activation functions.Although the FLNN is a novel approach to predict wall deflection;however,in order to improve the accuracy of the FLNN model in predicting wall deflection,three swarm-based optimization algorithms,such as artificial bee colony(ABC),Harris’s hawk’s optimization(HHO),and hunger games search(HGS),were hybridized to the FLNN model to generate three novel intelligent models,namely ABC-FLNN,HHO-FLNN,HGS-FLNN.The results of the hybrid models were then compared with the basic FLNN and MLP models.They revealed that FLNN is a good solution for predicting wall deflection,and the application of different functional expansions and activation functions has a significant effect on the outcome predictions of the wall deflection.It is remarkably interesting that the performance of the FLNN model was better than the MLP model with a mean absolute error(MAE)of 19.971,root-mean-squared error(RMSE)of 24.574,and determination coefficient(R^(2))of 0.878.Meanwhile,the performance of the MLP model only obtained an MAE of 20.321,RMSE of 27.091,and R^(2)of 0.851.Furthermore,the results also indicated that the proposed hybrid models,i.e.,ABC-FLNN,HHO-FLNN,HGS-FLNN,yielded more superior performances than those of the FLNN and MLP models in terms of the prediction of deflection behavior of diaphragm walls with an MAE in the range of 11.877 to 12.239,RMSE in the range of 15.821 to 16.045,and R^(2)in the range of 0.949 to 0.951.They can be used as an alternative tool to simulate diaphragm wall deflections under different conditions with a high degree of accuracy.展开更多
Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, d...Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, decomposing cutting torque components from the original signals by wavelet packet decomposition （WPD）; second, extracting wavelet coefficients of different wear states （i.e., slight, normal, or severe wear） with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network （RBFNN）. The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.展开更多
Spinal cord injury（SCI）is a debilitating condition that affects more than 2.5 million individuals worldwide（Thuret et al.,2006）.In addition to its devastating effects on the individual,this disease is a heavy burd...Spinal cord injury（SCI）is a debilitating condition that affects more than 2.5 million individuals worldwide（Thuret et al.,2006）.In addition to its devastating effects on the individual,this disease is a heavy burden to the society in terms of health care costs, which are estimated in billions of dollars annually in most developed countries （Cadotte and Fehlings, 2011）.展开更多
Human Wharton＇s jelly-derived mesenchymal stem cells（h WJ-MSCs）have excellent proliferative ability,differentiation ability,low immunogenicity,and can be easily obtained.However,there are few studies on their appli...Human Wharton＇s jelly-derived mesenchymal stem cells（h WJ-MSCs）have excellent proliferative ability,differentiation ability,low immunogenicity,and can be easily obtained.However,there are few studies on their application in the treatment of ischemic stroke,therefore their therapeutic effect requires further verification.In this study,h WJ-MSCs were transplanted into an ischemic stroke rat model via the tail vein 48 hours after transient middle cerebral artery occlusion.After 4 weeks,neurological functions of the rats implanted with h WJ-MSCs were significantly recovered.Furthermore,many h WJ-MSCs homed to the ischemic frontal cortex whereby they differentiated into neuron-like cells at this region.These results confirm that h WJ-MSCs transplanted into the ischemic stroke rat can differentiate into neuron-like cells to improve rat neurological function and behavior.展开更多
The air cooler is an important equipment in the petroleum refining industry.Ammonium chloride(NH4 Cl)deposition-induced corrosion is one of its main failure forms.In this study,the ammonium salt crystallization temper...The air cooler is an important equipment in the petroleum refining industry.Ammonium chloride(NH4 Cl)deposition-induced corrosion is one of its main failure forms.In this study,the ammonium salt crystallization temperature is chosen as the key decision variable of NH4 Cl deposition-induced corrosion through in-depth mechanism research and experimental analysis.The functional link neural network(FLNN)is adopted as the basic algorithm for modeling because of its advantages in dealing with non-linear problems and its fast-computational ability.A hybrid FLNN attached to a small norm is built to improve the generalization performance of the model.Then,the trained model is used to predict the NH4 Cl salt crystallization temperature in the air cooler of a sour water stripper plant.Experimental results show the proposed improved FLNN algorithm can achieve better generalization performance than the PLS,the back propagation neural network,and the conventional FLNN models.展开更多
Parkinson’s disease is a neurogenerative disorder and it is difficult to diagnose as no therapies may slow down its progression.This paper contributes a novel analytic system for Parkinson’s Disease Prediction mecha...Parkinson’s disease is a neurogenerative disorder and it is difficult to diagnose as no therapies may slow down its progression.This paper contributes a novel analytic system for Parkinson’s Disease Prediction mechanism using Improved Radial Basis Function Neural Network(IRBFNN).Particle swarm optimization(PSO)with K-means is used to find the hidden neuron’s centers to improve the accuracy of IRBFNN.The performance of RBFNN is seriously affected by the centers of hidden neurons.Conventionally K-means was used to find the centers of hidden neurons.The problem of sensitiveness to the random initial centroid in K-means degrades the performance of RBFNN.Thus,a metaheuristic algorithm called PSO integrated with K-means alleviates initial random centroid and computes optimal centers for hidden neurons in IRBFNN.The IRBFNN uses Particle swarm optimization K-means to find the centers of hidden neurons and the PSO K-means was designed to evaluate the fitness measures such as Intracluster distance and Intercluster distance.Experimentation have been performed on three Parkinson’s datasets obtained from the UCI repository.The proposed IRBFNN is compared with other variations of RBFNN,conventional machine learning algorithms and other Parkinson’s Disease prediction algorithms.The proposed IRBFNN achieves an accuracy of 98.73%,98.47%and 99.03%for three Parkinson’s datasets taken for experimentation.The experimental results show that IRBFNN maximizes the accuracy in predicting Parkinson’s disease with minimum root mean square error.展开更多
The ability of the adult central nervous system to reorganize its circuits over time is the key to understand the functional improvement in subjects with spinal cord injury （SCI）. Adaptive changes within spared neur...The ability of the adult central nervous system to reorganize its circuits over time is the key to understand the functional improvement in subjects with spinal cord injury （SCI）. Adaptive changes within spared neuronal circuits may occur at cortical, brainstem, or spinal cord level, both above and below a spinal lesion （Bareyre et al., 2004）. At each level the reorganization is a very dynamic process, and its degree is highly variable, depending on several factors, including the age of the subject when SCI has occurred and the rehabilitative therapy. The use of electrophysiological techniques to assess these functional changes in neural networks is of great interest, because invasive methodologies as employed in preclinical models can obviously not be used in clinical studies.展开更多
The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial...The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.展开更多
BACKGROUND：Previous studies have demonstrated that acupuncture treatment could ameliorate impaired motor function,and these positive effects might be due to neural plasticity.OBJECTIVE：Myelin basic protein（MBP）,mi...BACKGROUND：Previous studies have demonstrated that acupuncture treatment could ameliorate impaired motor function,and these positive effects might be due to neural plasticity.OBJECTIVE：Myelin basic protein（MBP）,microtubule-associated protein 2（MAP2）,growth-associated protein-43（GAP-43）,and synaptophysin（SYN） were selected as markers of neural remodeling,and expression of these markers was evaluated with regard to altered motor function following brain injury and acupuncture treatment.DESIGN,TIME AND SETTING：A completely randomized experiment was performed at the Central Laboratory of Peking University First Hospital from November 2006 to May 2007.MATERIALS：Twenty-four Sprague Dawley rat pups,aged 7 days,were selected for the present experiment.The left common carotid artery was ligated to establish a rat model of ischemic-hypoxic brain injury.METHODS：All animals were randomly divided into three groups：sham operation,model,and electro-acupuncture treatment,with 8 rats in each group.Rats in the model and electro-acupuncture treatment group underwent establishment of ischemic-hypoxic brain injury.Upon model established,rats underwent hypobaric oxygen intervention for 24 hours.Only the left common carotid artery was exposed in rats of the sham operation group,without model establishment or oxygen intervention.The rats in the electro-acupuncture treatment group were treated with electro-acupuncture.One acupuncture needle electrode was inserted into the subcutaneous layer at the Baihui and Dazhui acupoint.The stimulation condition of the electro-acupuncture simulator was set to an amplitude-modulated wave of 0-100% and alternative frequency of 100 cycles/second,as well as frequency-modulated wave of 2-100 Hz and an alternative frequency of 3 cycles/second.Maximal current through the two electrodes was limited to 3-5 mA.The stimulation lasted for 30 minutes per day for 2 weeks.Rats in the sham operation and model groups were not treated with electro-acupuncture,but only fixed to the table for the same time period.MAIN OUTCOME MEASURES：After 2 weeks stimulation,expression of MBP,MAP2,GAP-43,and SYN were detected in the brain by immunohistochemistry.Motor function was evaluated in the three groups.RESULTS：In the sham operation group,MBP was abundant in the myelinated nerve fibers.In the electro-acupuncture treatment group,however,the corpus callosum exhibited more MBP staining than the model group.MAP2 expression was increased in the model group,and increased further in the electro-acupuncture treatment group compared with the sham operation group.GAP-43 expression in the cerebral cortex was less in model group than in sham operation,but present in abundance in the electro-acupuncture treatment group.SYN expression in the cerebral cortex was less in the model and electro-acupuncture treatment group compared with the sham operation group.There was no significant difference in SYN expression and distribution between the model and electro-acupuncture treatment groups.Motor function of rats in the electro-acupuncture treatment group was significantly better than the model group（P 〈 0.05）,although function remained lower than the sham operation group（P 〈 0.05）.CONCLUSION：Two weeks of electro-acupuncture treatment improved motor function in rats,and protein markers related to neural plasticity also changed,which may be a mechanism for improved motor function in rats with ischemic-hypoxic brain injury.展开更多
In this paper, we discuss some analytic properties of hyperbolic tangent function and estimate some approximation errors of neural network operators with the hyperbolic tan- gent activation function. Firstly, an equat...In this paper, we discuss some analytic properties of hyperbolic tangent function and estimate some approximation errors of neural network operators with the hyperbolic tan- gent activation function. Firstly, an equation of partitions of unity for the hyperbolic tangent function is given. Then, two kinds of quasi-interpolation type neural network operators are con- structed to approximate univariate and bivariate functions, respectively. Also, the errors of the approximation are estimated by means of the modulus of continuity of function. Moreover, for approximated functions with high order derivatives, the approximation errors of the constructed operators are estimated.展开更多
Milling electrical discharge machining(EDM) enables the machining of complex cavities using cylindrical or tubular electrodes.To ensure acceptable machining accuracy the process requires some methods of compensating f...Milling electrical discharge machining(EDM) enables the machining of complex cavities using cylindrical or tubular electrodes.To ensure acceptable machining accuracy the process requires some methods of compensating for electrode wear.Due to the complexity and random nature of the process,existing methods of compensating for such wear usually involve off-line prediction.This paper discusses an innovative model of electrode wear prediction for milling EDM based upon a radial basis function(RBF) network.Data gained from an orthogonal experiment were used to provide training samples for the RBF network.The model established was used to forecast the electrode wear,making it possible to calculate the real-time tool wear in the milling EDM process and,to lay the foundations for dynamic compensation of the electrode wear on-line.This paper demonstrates that by using this model prediction errors can be controlled within 8%.展开更多
基金The National Natural Science Foundation of China(No.90307013,90707005)the Natural Science Foundation of Jiangsu Province(No.BK2008032)+1 种基金Special Foundation and Open Foundation of State Key Laboratory of Bioelectronics of Southeast UniversityNantong Planning Project of Science and Technology(No.K2009037)
文摘A microelectronic circuit is used to regenerate the neural signals between the proximal end and the distal end of an injured nerve.An experimental scheme is designed and carried out to verify the feasibility of the so-called microelectronic neural bridge（MNB）.The sciatic signals of the source spinal toad which are evoked by chemical stimuli are used as source signals to stimulate the sciatic of the controlled spinal toad.The sciatic nerve signals of the source spinal toad,the regenerated sciatic signals in the controlled spinal toad,and the resulting electromyography（EMG）signals associated with the gastrocnemius muscle movements of the controlled spinal toad are displayed and recorded by an oscilloscope.By analyzing the coherence between the source sciatic nerve signals and the regenerated sciatic nerve signals and the coherence between the regenerated nerve signals and the EMG signals,it is proved that the regenerated sciatic nerve signals have a relationship with the source sciatic nerve signals and control shrinkage of the leg of the controlled toad.
基金supported by Natural Science Foundation of Shandong Province(No.:ZR2009CL018)
文摘Objective: To discuss the mechanism of low molecular weight GTP binding protein RAC1 in the injury of neural function based on building the rat model of cerebral ischemia reperfusion. Methods: Middle cerebral artery of rats was ligated and the ligature was released to restore the perfusion after 2 h, the rat model of cerebral ischemia reperfusion injury was built, while the middle cerebral artery was ligated. The rats were randomly divided into the sham group, cerebral ischemia reperfusion group(I/R group) and the group with the injection of RAC1 activity inhibitor NSC23766(NSC group). The survival and neurological severity score of rats in each group were observed and recorded. Nissl staining was employed to observe the nerve cells, and Western blot to detect expression of RAC1, superoxide dismutase and malondialdehyde. Results: Number of nerve cells for rats in NSC group was significantly more than that in I/R group, but significantly less than that in sham group, with the statistical difference(P<0.05). The brain water content for rats in NSC group was significantly lower than that in I/R group, but significantly higher than that in sham group, with the statistical difference(P<0.05). The expression of RAC1 and malondialdehyde for rats in NSC group was significantly lower than that in I/R group, but higher than that in sham group; while the expression of superoxide dismutase was lower than that in sham group, but higher than that in I/R group, with the statistical difference(P<0.05). Conclusions: The inhibition of RAC1 activity can reduce the oxidative stress, reduce the neurologic impairment because of cerebral ischemia reperfusion and thus protect the neural function.
基金supported by Yunnan Province Innovation Team of Prevention and Treatment for Brain Disease with Acupuncture and Tuina,No.202405AS350007Youth Top Talent Project of 10-thousand Talent Plan in Yunnan Province,No.YNWR-QNBJ-2018-345+3 种基金the National Natural Science Foundation of China,No.81960731Joint Special Project of Traditional Chinese Medicine in Science and Technology Department of Yunnan Province,Nos.2019FF002[-008],202001AZ070001-002 and 202001AZ070001-030Yunnan Province University Innovation Team Projects No.2019YGC04Yunnan Province Project Education Fund,Nos.2024Y406,2024Y414(all to PZ)。
文摘The exchange of information and materials between organelles plays a crucial role in regulating cellular physiological functions and metabolic levels.Mitochondria-associated endoplasmic reticulum membranes serve as physical contact channels between the endoplasmic reticulum membrane and the mitochondrial outer membrane,formed by various proteins and protein complexes.This microstructural domain mediates several specialized functions,including calcium(Ca^(2+))signaling,autophagy,mitochondrial morphology,oxidative stress response,and apoptosis.Notably,the dysregulation of Ca^(2+)signaling mediated by mitochondria-associated endoplasmic reticulum membranes is a critical factor in the pathogenesis of neurological diseases.Certain proteins or protein complexes within these membranes directly or indirectly regulate the distance between the endoplasmic reticulum and mitochondria,as well as the transduction of Ca^(2+)signaling.Conversely,Ca^(2+)signaling mediated by mitochondria-associated endoplasmic reticulum membranes influences other mitochondria-associated endoplasmic reticulum membraneassociated functions.These functions can vary significantly across different neurological diseases—such as ischemic stroke,traumatic brain injury,Alzheimer's disease,Parkinson's disease,amyotrophic lateral sclerosis,and Huntington's disease—and their respective stages of progression.Targeted modulation of these disease-related pathways and functional proteins can enhance neurological function and promote the regeneration and repair of damaged neurons.Therefore,mitochondria-associated endoplasmic reticulum membranes-mediated Ca^(2+)signaling plays a pivotal role in the pathological progression of neurological diseases and represents a significant potential therapeutic target.This review focuses on the effects of protein complexes in mitochondria-associated endoplasmic reticulum membranes and the distinct roles of mitochondria-associated endoplasmic reticulum membranes-mediated Ca^(2+)signaling in neurological diseases,specifically highlighting the early protective effects and neuronal damage that can result from prolonged mitochondrial Ca^(2+)overload or deficiency.This article provides a comprehensive analysis of the various mechanisms of Ca^(2+)signaling mediated by mitochondria-associated endoplasmic reticulum membranes in neurological diseases,contributing to the exploration of potential therapeutic targets for promoting neuroprotection and nerve repair.
基金supported by the National Institutes of Health(awards DP2AI160242 and U01DA056242).
文摘Human brain organoids are 3-dimensional brain-like tissues derived from human pluripotent stem cells and hold promising potential for modeling neurological,psychiatric,and developmental disorders.While the molecular and cellular aspects of human brain organoids have been intensively studied,their functional properties such as organoid neural networks(ONNs)are largely understudied.Here,we summarize recent research advances in understanding,characterization,and application of functional ONNs in human brain organoids.We first discuss the formation of ONNs and follow up with characterization strategies including microelectrode array(MEA)technology and calcium imaging.Moreover,we highlight recent studies utilizing ONNs to investigate neurological diseases such as Rett syndrome and Alzheimer’s disease.Finally,we provide our perspectives on the future challenges and opportunities for using ONNs in basic research and translational applications.
文摘The purpose of this study was to address the challenges in predicting and classifying accuracy in modeling Container Dwell Time (CDT) using Artificial Neural Networks (ANN). This objective was driven by the suboptimal outcomes reported in previous studies and sought to apply an innovative approach to improve these results. To achieve this, the study applied the Fusion of Activation Functions (FAFs) to a substantial dataset. This dataset included 307,594 container records from the Port of Tema from 2014 to 2022, encompassing both import and transit containers. The RandomizedSearchCV algorithm from Python’s Scikit-learn library was utilized in the methodological approach to yield the optimal activation function for prediction accuracy. The results indicated that “ajaLT”, a fusion of the Logistic and Hyperbolic Tangent Activation Functions, provided the best prediction accuracy, reaching a high of 82%. Despite these encouraging findings, it’s crucial to recognize the study’s limitations. While Fusion of Activation Functions is a promising method, further evaluation is necessary across different container types and port operations to ascertain the broader applicability and generalizability of these findings. The original value of this study lies in its innovative application of FAFs to CDT. Unlike previous studies, this research evaluates the method based on prediction accuracy rather than training time. It opens new avenues for machine learning engineers and researchers in applying FAFs to enhance prediction accuracy in CDT modeling, contributing to a previously underexplored area.
基金Supported by the Science Technology Development Project of Jilin Province,China(No.20020503-2).
文摘Partial least squares（PLS）,back-propagation neural network（BPNN）and radial basis function neural network（RBFNN）were respectively used for estalishing quantative analysis models with near infrared（NIR）diffuse reflectance spectra for determining the contents of rifampincin（RMP）,isoniazid（INH）and pyrazinamide（PZA）in rifampicin isoniazid and pyrazinamide tablets.Savitzky-Golay smoothing,first derivative,second derivative,fast Fourier transform（FFT）and standard normal variate（SNV）transformation methods were applied to pretreating raw NIR diffuse reflectance spectra.The raw and pretreated spectra were divided into several regions,depending on the average spectrum and RSD spectrum.Principal component analysis（PCA）method was used for analyzing the raw and pretreated spectra in different regions in order to reduce the dimensions of input data.The optimum spectral regions and the models＇ parameters were chosen by comparing the root mean square error of cross-validation（RMSECV）values which were obtained by leave-one-out cross-validation method.The RMSECV values of the RBFNN models for determining the contents of RMP,INH and PZA were 0.00288,0.00226 and 0.00341,respectively.Using these models for predicting the contents of INH,RMP and PZA in prediction set,the RMSEP values were 0.00266,0.00227 and 0.00411,respectively.These results are better than those obtained from PLS models and BPNN models.With additional advantages of fast calculation speed and less dependence on the initial conditions,RBFNN is a suitable tool to model complex systems.
基金This project was supported in part by the Science Foundation of Shanxi Province (2003F028)China Postdoctoral Science Foundation (20060390318).
文摘The Radial Basis Functions Neural Network （RBFNN） is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response system can be implemented by employing the RBFNN model and state feedback control. In this case, the exact mathematical model, which is the precondition for the conventional method, is unnecessary for implementing synchronization. The effect of the model error is investigated and a corresponding theorem is developed. The effect of the parameter perturbations and the measurement noise is investigated through simulations. The simulation results under different conditions show the effectiveness of the method.
基金supported by the National Natural Science Foundation of China (Nos. 60778024 and 30825027)the National Basic Re-search Program (973) of China (No. 2006BAD11A12)
文摘Near-infrared (NIR) spectroscopy combined with chemometrics techniques was used to classify the pure bayberry juice and the one adulterated with 10% (w/w) and 20% (w/w) water. Principal component analysis (PCA) was applied to reduce the dimensions of spectral data, give information regarding a potential capability of separation of objects, and provide principal component (PC) scores for radial basis function neural networks (RBFNN). RBFNN was used to detect bayberry juice adulterant. Multiplicative scatter correction (MSC) and standard normal variate (SNV) transformation were used to preprocess spectra. The results demonstrate that PC-RBFNN with optimum parameters can separate pure bayberry juice samples from water-adulterated bayberry at a recognition rate of 97.62%, but cannot clearly detect water levels in the adulterated bayberry juice. We conclude that NIR technology can be successfully applied to detect water-adulterated bayberry juice.
基金supported by the China Postdoctoral Science Foundation (200904501035 201003548)+3 种基金the National Natural Science Foundation of China (60835001907160289101600460804017)
文摘An adaptive integral dynamic surface control approach based on fully tuned radial basis function neural network （FTRBFNN） is presented for a general class of strict-feedback nonlinear systems,which may possess a wide class of uncertainties that are not linearly parameterized and do not have any prior knowledge of the bounding functions.FTRBFNN is employed to approximate the uncertainty online,and a systematic framework for adaptive controller design is given by dynamic surface control. The control algorithm has two outstanding features,namely,the neural network regulates the weights,width and center of Gaussian function simultaneously,which ensures the control system has perfect ability of restraining different unknown uncertainties and the integral term of tracking error introduced in the control law can eliminate the static error of the closed loop system effectively. As a result,high control precision can be achieved.All signals in the closed loop system can be guaranteed bounded by Lyapunov approach.Finally,simulation results demonstrate the validity of the control approach.
基金financially supported by the Natural Science Foundation of Hunan Province(2021JJ30679)。
文摘Deep excavation during the construction of underground systems can cause movement on the ground,especially in soft clay layers.At high levels,excessive ground movements can lead to severe damage to adjacent structures.In this study,finite element analyses(FEM)and the hardening small strain(HSS)model were performed to investigate the deflection of the diaphragm wall in the soft clay layer induced by braced excavations.Different geometric and mechanical properties of the wall were investigated to study the deflection behavior of the wall in soft clays.Accordingly,1090 hypothetical cases were surveyed and simulated based on the HSS model and FEM to evaluate the wall deflection behavior.The results were then used to develop an intelligent model for predicting wall deflection using the functional linked neural network(FLNN)with different functional expansions and activation functions.Although the FLNN is a novel approach to predict wall deflection;however,in order to improve the accuracy of the FLNN model in predicting wall deflection,three swarm-based optimization algorithms,such as artificial bee colony(ABC),Harris’s hawk’s optimization(HHO),and hunger games search(HGS),were hybridized to the FLNN model to generate three novel intelligent models,namely ABC-FLNN,HHO-FLNN,HGS-FLNN.The results of the hybrid models were then compared with the basic FLNN and MLP models.They revealed that FLNN is a good solution for predicting wall deflection,and the application of different functional expansions and activation functions has a significant effect on the outcome predictions of the wall deflection.It is remarkably interesting that the performance of the FLNN model was better than the MLP model with a mean absolute error(MAE)of 19.971,root-mean-squared error(RMSE)of 24.574,and determination coefficient(R^(2))of 0.878.Meanwhile,the performance of the MLP model only obtained an MAE of 20.321,RMSE of 27.091,and R^(2)of 0.851.Furthermore,the results also indicated that the proposed hybrid models,i.e.,ABC-FLNN,HHO-FLNN,HGS-FLNN,yielded more superior performances than those of the FLNN and MLP models in terms of the prediction of deflection behavior of diaphragm walls with an MAE in the range of 11.877 to 12.239,RMSE in the range of 15.821 to 16.045,and R^(2)in the range of 0.949 to 0.951.They can be used as an alternative tool to simulate diaphragm wall deflections under different conditions with a high degree of accuracy.
文摘Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, decomposing cutting torque components from the original signals by wavelet packet decomposition （WPD）; second, extracting wavelet coefficients of different wear states （i.e., slight, normal, or severe wear） with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network （RBFNN）. The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.
文摘Spinal cord injury（SCI）is a debilitating condition that affects more than 2.5 million individuals worldwide（Thuret et al.,2006）.In addition to its devastating effects on the individual,this disease is a heavy burden to the society in terms of health care costs, which are estimated in billions of dollars annually in most developed countries （Cadotte and Fehlings, 2011）.
基金supported by the National Natural Science Foundation of China,No.31171038the Natural Science Foundation of Jiangsu Province of China,No.BK2011385+3 种基金the "333" Program Funding of Jiangsu Province of China,No.BRA2016450the Training Program of Innovation and Entrepreneurship for Undergraduates of Nantong University of China,No.201510304033Z,201610304053Zthe Training Program of Innovation and Entrepreneurship for Graduates of Nantong University of China,No.YKC14050,YKC15046a grant from Funds for the Priority Academic Program Development of Jiangsu Higher Education Institutions of China
文摘Human Wharton＇s jelly-derived mesenchymal stem cells（h WJ-MSCs）have excellent proliferative ability,differentiation ability,low immunogenicity,and can be easily obtained.However,there are few studies on their application in the treatment of ischemic stroke,therefore their therapeutic effect requires further verification.In this study,h WJ-MSCs were transplanted into an ischemic stroke rat model via the tail vein 48 hours after transient middle cerebral artery occlusion.After 4 weeks,neurological functions of the rats implanted with h WJ-MSCs were significantly recovered.Furthermore,many h WJ-MSCs homed to the ischemic frontal cortex whereby they differentiated into neuron-like cells at this region.These results confirm that h WJ-MSCs transplanted into the ischemic stroke rat can differentiate into neuron-like cells to improve rat neurological function and behavior.
基金supported by the National Natural Science Foundation of China(Grant No.51876194,U1909216)the China Petrochemical Corporation Research Project(318023-2)the Zhejiang Public Welfare Technology Research Project(LGG20F030007)。
文摘The air cooler is an important equipment in the petroleum refining industry.Ammonium chloride(NH4 Cl)deposition-induced corrosion is one of its main failure forms.In this study,the ammonium salt crystallization temperature is chosen as the key decision variable of NH4 Cl deposition-induced corrosion through in-depth mechanism research and experimental analysis.The functional link neural network(FLNN)is adopted as the basic algorithm for modeling because of its advantages in dealing with non-linear problems and its fast-computational ability.A hybrid FLNN attached to a small norm is built to improve the generalization performance of the model.Then,the trained model is used to predict the NH4 Cl salt crystallization temperature in the air cooler of a sour water stripper plant.Experimental results show the proposed improved FLNN algorithm can achieve better generalization performance than the PLS,the back propagation neural network,and the conventional FLNN models.
文摘Parkinson’s disease is a neurogenerative disorder and it is difficult to diagnose as no therapies may slow down its progression.This paper contributes a novel analytic system for Parkinson’s Disease Prediction mechanism using Improved Radial Basis Function Neural Network(IRBFNN).Particle swarm optimization(PSO)with K-means is used to find the hidden neuron’s centers to improve the accuracy of IRBFNN.The performance of RBFNN is seriously affected by the centers of hidden neurons.Conventionally K-means was used to find the centers of hidden neurons.The problem of sensitiveness to the random initial centroid in K-means degrades the performance of RBFNN.Thus,a metaheuristic algorithm called PSO integrated with K-means alleviates initial random centroid and computes optimal centers for hidden neurons in IRBFNN.The IRBFNN uses Particle swarm optimization K-means to find the centers of hidden neurons and the PSO K-means was designed to evaluate the fitness measures such as Intracluster distance and Intercluster distance.Experimentation have been performed on three Parkinson’s datasets obtained from the UCI repository.The proposed IRBFNN is compared with other variations of RBFNN,conventional machine learning algorithms and other Parkinson’s Disease prediction algorithms.The proposed IRBFNN achieves an accuracy of 98.73%,98.47%and 99.03%for three Parkinson’s datasets taken for experimentation.The experimental results show that IRBFNN maximizes the accuracy in predicting Parkinson’s disease with minimum root mean square error.
文摘The ability of the adult central nervous system to reorganize its circuits over time is the key to understand the functional improvement in subjects with spinal cord injury （SCI）. Adaptive changes within spared neuronal circuits may occur at cortical, brainstem, or spinal cord level, both above and below a spinal lesion （Bareyre et al., 2004）. At each level the reorganization is a very dynamic process, and its degree is highly variable, depending on several factors, including the age of the subject when SCI has occurred and the rehabilitative therapy. The use of electrophysiological techniques to assess these functional changes in neural networks is of great interest, because invasive methodologies as employed in preclinical models can obviously not be used in clinical studies.
基金Project supported by the National High Technology Research and Development Program of China (Grant No. 2011AA110303)the Beijing Municipal Science & Technology Project,China (Grant No. Z111100064311001)
文摘The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.
基金International Science and Technology Cooperation Fundation of the Ministry of Science and Technology of China, No.2008DFA31850
文摘BACKGROUND：Previous studies have demonstrated that acupuncture treatment could ameliorate impaired motor function,and these positive effects might be due to neural plasticity.OBJECTIVE：Myelin basic protein（MBP）,microtubule-associated protein 2（MAP2）,growth-associated protein-43（GAP-43）,and synaptophysin（SYN） were selected as markers of neural remodeling,and expression of these markers was evaluated with regard to altered motor function following brain injury and acupuncture treatment.DESIGN,TIME AND SETTING：A completely randomized experiment was performed at the Central Laboratory of Peking University First Hospital from November 2006 to May 2007.MATERIALS：Twenty-four Sprague Dawley rat pups,aged 7 days,were selected for the present experiment.The left common carotid artery was ligated to establish a rat model of ischemic-hypoxic brain injury.METHODS：All animals were randomly divided into three groups：sham operation,model,and electro-acupuncture treatment,with 8 rats in each group.Rats in the model and electro-acupuncture treatment group underwent establishment of ischemic-hypoxic brain injury.Upon model established,rats underwent hypobaric oxygen intervention for 24 hours.Only the left common carotid artery was exposed in rats of the sham operation group,without model establishment or oxygen intervention.The rats in the electro-acupuncture treatment group were treated with electro-acupuncture.One acupuncture needle electrode was inserted into the subcutaneous layer at the Baihui and Dazhui acupoint.The stimulation condition of the electro-acupuncture simulator was set to an amplitude-modulated wave of 0-100% and alternative frequency of 100 cycles/second,as well as frequency-modulated wave of 2-100 Hz and an alternative frequency of 3 cycles/second.Maximal current through the two electrodes was limited to 3-5 mA.The stimulation lasted for 30 minutes per day for 2 weeks.Rats in the sham operation and model groups were not treated with electro-acupuncture,but only fixed to the table for the same time period.MAIN OUTCOME MEASURES：After 2 weeks stimulation,expression of MBP,MAP2,GAP-43,and SYN were detected in the brain by immunohistochemistry.Motor function was evaluated in the three groups.RESULTS：In the sham operation group,MBP was abundant in the myelinated nerve fibers.In the electro-acupuncture treatment group,however,the corpus callosum exhibited more MBP staining than the model group.MAP2 expression was increased in the model group,and increased further in the electro-acupuncture treatment group compared with the sham operation group.GAP-43 expression in the cerebral cortex was less in model group than in sham operation,but present in abundance in the electro-acupuncture treatment group.SYN expression in the cerebral cortex was less in the model and electro-acupuncture treatment group compared with the sham operation group.There was no significant difference in SYN expression and distribution between the model and electro-acupuncture treatment groups.Motor function of rats in the electro-acupuncture treatment group was significantly better than the model group（P 〈 0.05）,although function remained lower than the sham operation group（P 〈 0.05）.CONCLUSION：Two weeks of electro-acupuncture treatment improved motor function in rats,and protein markers related to neural plasticity also changed,which may be a mechanism for improved motor function in rats with ischemic-hypoxic brain injury.
基金Supported by the National Natural Science Foundation of China(61179041,61272023,and 11401388)
文摘In this paper, we discuss some analytic properties of hyperbolic tangent function and estimate some approximation errors of neural network operators with the hyperbolic tan- gent activation function. Firstly, an equation of partitions of unity for the hyperbolic tangent function is given. Then, two kinds of quasi-interpolation type neural network operators are con- structed to approximate univariate and bivariate functions, respectively. Also, the errors of the approximation are estimated by means of the modulus of continuity of function. Moreover, for approximated functions with high order derivatives, the approximation errors of the constructed operators are estimated.
基金the National High Technology Research and Development Program (863) of China(No. 2007AA04Z345)the National Natural Science Foundation of China (No. 50679041)the Foundation of Heilongjiang Science and Technology Committee(No. GA06A501)
文摘Milling electrical discharge machining(EDM) enables the machining of complex cavities using cylindrical or tubular electrodes.To ensure acceptable machining accuracy the process requires some methods of compensating for electrode wear.Due to the complexity and random nature of the process,existing methods of compensating for such wear usually involve off-line prediction.This paper discusses an innovative model of electrode wear prediction for milling EDM based upon a radial basis function(RBF) network.Data gained from an orthogonal experiment were used to provide training samples for the RBF network.The model established was used to forecast the electrode wear,making it possible to calculate the real-time tool wear in the milling EDM process and,to lay the foundations for dynamic compensation of the electrode wear on-line.This paper demonstrates that by using this model prediction errors can be controlled within 8%.
