Improvement of learning and memory abilities and motor function in rats with cerebral infarction by intracerebral transplantation of neuron-like cells derived from bone marrow stromal cells

BACKGROUND： Transplantation of fetal cell suspension or blocks of fetal tissue can ameliorate the nerve function after the injury or disease in the central nervous system, and it has been used to treat neurodegenerative disorders induced by Parkinson disease. OBJECTIVE： To observe the effects of the transplantation of neuron-like cells derived from bone marrow stromal cells （rMSCs） into the brain in restoring the dysfunctions of muscle strength and balance as well as learning and memory in rat models of cerebral infarction. DESIGN ： A randomized controlled experiment.SETTING ： Department of Pathophysiology, Zhongshan Medical College of Sun Yat-sen University.MATERIALS ： Twenty-four male SD rats （3-4 weeks of age, weighing 200-220 g） were used in this study （Certification number：2001A027）. METHODS： The experiments were carried out in Zhongshan Medical College of Sun Yat-sen University between December 2003 and December 2004. ① Twenty-four male SD rats randomized into three groups with 8 rats in each： experimental group, control group and sham-operated group. Rats in the experiment al group and control group were induced into models of middle cerebral artery occlusion （MCAO）. After in vitro cultured, purified and identified with digestion, the Fischer344 rMSCs were induced to differentiate by tanshinone IIA, which was locally injected into the striate cortex （18 area） of rats in the experimental group, and the rats in the control group were injected by L-DMEM basic culture media （without serum） of the same volume to the corresponding brain area. In the sham-operated group, only muscle and vessel of neck were separated. ② At 2 and 8 weeks after the transplantation, the rats were given the screen test, prehensile-traction test, balance beam test and Morris water-maze test. ③ The survival and distribution of the induced cells in corresponding brain area were observed with Nissl stained with toluidine blue and hematoxylin and eosin （HE） staining in the groups.MAIN OUTCOME MEASURES ： ① Results of the behavioral tests （time of the Morris water-maze test screen test, prehensile-traction test, balance beam test）; ② Survival and distribution of the induced cells.RESULTS： All the 24 rats were involved in the analysis of results. ① Two weeks after transplantation, rats with neuron-like cells grafts in the experimental group had significant improvement on their general muscle strength than those in the control group [screen test： （9.4±1.7）, （4.7±1.0） s, P 〈 0.01]; forelimb muscle strength [prehensile-traction test： （7.6±1.4）, （5.2±1.2） s, P 〈 0.01], ability to keep balance [balance beam test： （7.9±0.74）, （6.1±0.91） s, P 〈 0.01] and abilities of learning and memory [latency to find the platform： （35.8±5.9）, （117.5±11.6） s, P 〈 0.01; distance： （623.1±43.4）, （1 902.3±98.6） cm, P 〈 0.01] as compared with those in the control group. The functional performances in the experimental group at 8 weeks were better than those at two weeks, which were still obviously different from those in the sham-operated group （P 〈 0.05）. ② The HE and Nissl stained brain tissue section showed that there was nerve cell proliferation at the infarcted cortex in the experiment group, the density was higher than that in the control group, plenty of aggregative or scattered cells could be observed at the site where needle was inserted for transplantation, the cells migrated directively towards the area around them, the cerebral vascular walls were wrapped by plenty of cells; In the control group, most of the cortices were destroyed, karyopyknosis and necrosis of neurons were observed, normal nervous tissue structure disappeared induced by edema, only some nerve fibers and glial cells remained.CONCLUSION： The rMSCs transplantation can obviously enhance the motor function and the abilities of learning and memory in rat models of cerebral infarction.

Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm

Lung cancer is among the most frequent cancers in the world,with over one million deaths per year.Classification is required for lung cancer diagnosis and therapy to be effective,accurate,and reliable.Gene expression microarrays have made it possible to find genetic biomarkers for cancer diagnosis and prediction in a high-throughput manner.Machine Learning(ML)has been widely used to diagnose and classify lung cancer where the performance of ML methods is evaluated to identify the appropriate technique.Identifying and selecting the gene expression patterns can help in lung cancer diagnoses and classification.Normally,microarrays include several genes and may cause confusion or false prediction.Therefore,the Arithmetic Optimization Algorithm(AOA)is used to identify the optimal gene subset to reduce the number of selected genes.Which can allow the classifiers to yield the best performance for lung cancer classification.In addition,we proposed a modified version of AOA which can work effectively on the high dimensional dataset.In the modified AOA,the features are ranked by their weights and are used to initialize the AOA population.The exploitation process of AOA is then enhanced by developing a local search algorithm based on two neighborhood strategies.Finally,the efficiency of the proposed methods was evaluated on gene expression datasets related to Lung cancer using stratified 4-fold cross-validation.The method’s efficacy in selecting the optimal gene subset is underscored by its ability to maintain feature proportions between 10%to 25%.Moreover,the approach significantly enhances lung cancer prediction accuracy.For instance,Lung_Harvard1 achieved an accuracy of 97.5%,Lung_Harvard2 and Lung_Michigan datasets both achieved 100%,Lung_Adenocarcinoma obtained an accuracy of 88.2%,and Lung_Ontario achieved an accuracy of 87.5%.In conclusion,the results indicate the potential promise of the proposed modified AOA approach in classifying microarray cancer data.

Diagnosis of multiple faults using a double parallel two-hidden-layer extreme learning machine

Multiple faults are easily confused with single faults.In order to identify multiple faults more accurately,a highly efficient learning method is proposed based on a double parallel two-hidden-layer extreme learning machine,called DPTELM.The DPT-ELM method is a variant of an extreme learning machine(ELM).There are some issues with ELM.First,achieving a high accuracy requires too many hidden nodes;second,the direct connection between the input layer and the output layer is ignored.Accordingly,to deal with the above-mentioned problems,DPT-ELM extends the single-hidden-layer ELM to a two-hidden-layer ELM,which can achieve a desired performance with fewer hidden nodes.In addition,a direct connection is built between the input layer and the output layer.Since the input layer weights and the thresholds of the two hidden layers are determined randomly,this simplifies the improved model and shortens the calculation time.Additionally,to improve the signal to noise ratio(SNR),an adaptive waveform decomposition(AWD)algorithm is used to denoise the vibration signal.Then,the denoised signal is used to extract the eigenvalues by the time-domain and frequency-domain methods.Finally,the eigenvalues are input to the DPT-ELM classifier.In this paper,two groups of rolling bearing data at different speeds,which were collected from a real experimental platform,are used to test the method.Each set of data includes three single fault states,two complex fault states and a healthy state.The experimental results demonstrate that the DPT-ELM method achieves fast learning speed and a high accuracy.Moreover,based on 10-fold cross-validation,it proves to be an effective method to improve the accuracy with fewer hidden nodes.

MUTS-Based Cooperative Target Stalking for A Multi-USV System

This paper is concerned with the cooperative target stalking for a multi-unmanned surface vehicle(multi-USV)system.Based on the multi-agent deep deterministic policy gradient(MADDPG)algorithm,a multi-USV target stalking(MUTS)algorithm is proposed.Firstly,a V-type probabilistic data extraction method is proposed for the first time to overcome shortcomings of the MADDPG algorithm.The advantages of the proposed method are twofold:1)it can reduce the amount of data and shorten training time;2)it can filter out more important data in the experience buffer for training.Secondly,in order to avoid the collisions of USVs during the stalking process,an action constraint method called Safe DDPG is introduced.Finally,the MUTS algorithm and some existing algorithms are compared in cooperative target stalking scenarios.In order to demonstrate the effectiveness of the proposed MUTS algorithm in stalking tasks,mission operating scenarios and reward functions are well designed in this paper.The proposed MUTS algorithm can help the multi-USV system avoid internal collisions during the mission execution.Moreover,compared with some existing algorithms,the newly proposed one can provide a higher convergence speed and a narrower convergence domain.

Fault Diagnosis for Wind Turbine Based on Improved Extreme Learning Machine

A fault diagnosis method based on improved extreme learning machine (IELM) is proposed to solve the weakness (weak generalization ability, low diagnostic rate) of traditional fault diagnosis with feedforward neural network algorithm. This method fuses signal feature vectors, extracts six parameters as the principal component analysis (PCA) variables, and calculates correlation coefficient matrix among the variables. The weight values of control parameters in the extreme learning model are dynamically adjusted according to the test samples’ constantly changing. Consequently, the weight fixed drawback in the original model can be remedied. A fault simulation experiment platform for wind turbine drive system is built, eight kinds of fault modes are diagnosed by the improved extreme learning model, and the result is compared with that of other machine learning methods. The experiment indicates that the method can enhance the accuracy and generalization ability of diagnosis, and increase the computing speed. It is convenient for engineering application. © 2017, Shanghai Jiaotong University and Springer-Verlag Berlin Heidelberg.

A novel approach for flip chip inspection based on improved SDELM and vibration signals

This paper proposes a novel nondestructive diagnostic method for flip chips based on an improved semi-supervised deep extreme learning machine(ISDELM)and vibration signals.First,an ultrasonic transducer is used to generate and focus ultrasounds on the surface of the flip chip to excite it,and a laser scanning vibrometer is applied to acquire the chip’s vibration signals.Then,an extreme learning machine-autoencoder(ELM-AE)structure is adopted to extract features from the original vibration signals layer by layer.Finally,the study proposes integrating the ELM with sparsity neighboring reconstruction to diagnose defects based on unlabeled and labeled data.The ISDELM algorithm is applied to experimental vibration data of flip chips and compared with several other algorithms,such as semi-supervised ELM(SS-ELM),deep ELM,stacked autoencoder,convolutional neural network,and ordinary SDELM.The results show that the proposed method is superior to the several currently available algorithms in terms of accuracy and stability.