Fuzzy MUAP recognition in HSR-EMG detection basing on morphological features

The idea of ‘besides the MU properties and depending on the recording techniques, MUAPs can have unique pattern’ was adopted. The aim of this work was to recognise whether a Laplacian-detected MUAP is isolated or overlapped basing on novel morphological features using fuzzy classifier. Training data set was constructed to elaborate and test the ‘if-then’ fuzzy rules using signals provided by three muscles: the abductor pollicis brevis (APB), the first dorsal interosseous (FDI) and the biceps brachii (BB) muscles of 11 healthy subjects. The proposed fuzzy classier recognized automatically the isolated MUAPs with a performance of 95.03% which was improved to 97.8% by adjusting the certainty grades of rules using genetic algorithms (GA). Synthetic signals were used as reference to further evaluate the performance of the elaborated classifier. The recognition of the isolated MUAPs depends largely on noise level and is acceptable down to the signal to noise ratio of 20 dB with a detection probability of 0.96. The recognition of overlapped MUAPs depends slightly on the noise level with a detection probability of about 0.8. The corresponding misrecognition is caused principally by the synchronisation and the small overlapping degree.     

Automatic detection of motor unit innervation zones of the external anal sphincter by multichannel surface EMG

A method to detect automatically the location of innervation zones (IZs) from 16-channel surface EMG (sEMG) recordings from the external anal sphincter (EAS) muscle is presented in order to guide episiotomy during child delivery. The new algorithm (2DCorr) is applied to individual motor unit action potential (MUAP) templates and is based on bidimensional cross correlation between the interpolated image of each MUAP template and two images obtained by flipping upside-down (around a horizontal axis) and left–right (around a vertical axis) the original one. The method was tested on 640 simulated MUAP templates of the sphincter muscle and compared with previously developed algorithms (Radon Transform, RT; Template Match, TM). Experimental signals were detected from the EAS of 150 subjects using an intra-anal probe with 16 equally spaced circumferential electrodes. The results of the three algorithms were compared with the actual IZ location (simulated signal) and with IZ location provided by visual analysis (VA) (experimental signals). For simulated signals, the inter quartile error range (IQR) between the estimated and the actual locations of the IZ was 0.20, 0.23, 0.42, and 2.32 interelectrode distances (IED) for the VA, 2DCorr, RT and TM methods respectively.     

Evaluation of a particle swarm algorithm for biomechanical optimization

Optimization is frequently employed in biomechanics research to solve system identification problems, predict human movement, or estimate muscle or other internal forces that cannot be measured directly. Unfortunately, biomechanical optimization problems often possess multiple local minima, making it difficult to find the best solution. Furthermore, convergence in gradient-based algorithms can be affected by scaling to account for design variables with different length scales or units. In this study we evaluate a recently-developed version of the particle swarm optimization (PSO) algorithm to address these problems. The algorithm's global search capabilities were investigated using a suite of difficult analytical test problems, while its scale-independent nature was proven mathematically and verified using a biomechanical test problem. For comparison, all test problems were also solved with three off-the-shelf optimization algorithms--a global genetic algorithm (GA) and multistart gradient-based sequential quadratic programming (SQP) and quasi-Newton (BFGS) algorithms. For the analytical test problems, only the PSO algorithm was successful on the majority of the problems. When compared to previously published results for the same problems, PSO was more robust than a global simulated annealing algorithm but less robust than a different, more complex genetic algorithm. For the biomechanical test problem, only the PSO algorithm was insensitive to design variable scaling, with the GA algorithm being mildly sensitive and the SQP and BFGS algorithms being highly sensitive. The proposed PSO algorithm provides a new off-the-shelf global optimization option for difficult biomechanical problems, especially those utilizing design variables with different length scales or units.     

Application of particle swarm optimization and proximal support vector machines for fault detection

This paper presents a novel application of particle swarm optimization (PSO) in combination with another computational intelligence (CI) technique, namely, proximal support vector machine (PSVM) for machinery fault detection. Both real-valued and binary PSO algorithms have been considered along with linear and nonlinear versions of PSVM. The time domain vibration signals of a rotating machine with normal and defective bearings are processed for feature extraction. The features extracted from original and preprocessed signals are used as inputs to the classifiers (PSVM) for detection of machine condition. Input features are selected using a PSO algorithm. The classifiers are trained with a subset of experimental data for known machine conditions and are tested using the remaining data. The procedure is illustrated using the experimental vibration data of a rotating machine. The influences of the number of features, PSO algorithms and type of classifiers (linear or nonlinear PSVM) on the detection success are investigated. Results are compared with a genetic algorithm (GA) and principal component analysis (PCA). The PSO based approach gave test classification success above 90% which were comparable with the GA and much better than PCA. The results show the effectiveness of the selected features and classifiers in detection of machine condition.     

Automatic decomposition of multichannel intramuscular EMG signals

We describe an automatic algorithm for decomposing multichannel EMG signals into their component motor unit action potential (MUAP) trains, including signals from widely separated recording sites in which MUAPs exhibit appreciable interchannel offset and jitter. The algorithm has two phases. In the clustering phase, the distinct, recurring MUAPs in each channel are identified, the ones that correspond to the same motor units are determined by their temporal relationships, and multichannel templates are computed. In the identification stage, the MUAP discharges in the signal are identified using matched filtering and superimposition resolution techniques. The algorithm looks for the MUAPs with the largest single channel components first, using matches in one channel to guide the search in other channels, and using information from the other channels to confirm or refute each identification. For validation, the algorithm was used to decompose 10 real 6-to-8-channel EMG signals containing activity from up to 25 motor units. Comparison with expert manual decomposition showed that the algorithm identified more than 75% of the total 176 MUAP trains with an accuracy greater than 95%. The algorithm is fast, robust, and shows promise to be accurate enough to be a useful tool for decomposing multichannel signals. It is freely available at http://emglab.stanford.edu.     

基于粒子群优化算法的脑磁图源定位

脑磁图作为一种新型的脑探测技术,具有较高定位精度和毫秒级时间分辨率的特点。快速准确地利用脑磁图技术对三维空间中的脑神经活动源进行定位,对于脑功能研究和医学临床应用都具有重要的应用价值。可是,目前的脑磁图源定位广泛采用了多信号分类方法,它要求对三维大脑空间进行全局扫描,需要大量的计算,存在速度慢的缺点。针对这一问题,提出了一种基于粒子群优化算法的脑磁图源定位方法。先利用粒子群优化算法全局搜索能力强的特点寻找出目标函数的全局最优值,进行初步的脑磁图源定位;然后,再在小范围内进行小网格的搜索,进一步实现精确的定位。实验结果表明,基于粒子群优化算法的脑磁图源定位能够很好地解决上述问题,具有计算速度快、定位精度高的特点。     

Reverse engineering of regulatory networks: simulation studies on a genetic algorithm approach for ranking hypotheses

Reverse engineering algorithms (REAs) aim at using gene expression data to reconstruct interactions in regulatory genetic networks. This may help to understand the basis of gene regulation, the core task of functional genomics. Collecting data for a number of environmental conditions is necessary to reengineer even the smallest regulatory networks with reasonable confidence. We systematically tested the requirements for the experimental design necessary for ranking alternative hypotheses about the structure of a given regulatory network. A genetic algorithm (GA) was used to explore the parameter space of a multistage discrete genetic network model with fixed connectivity and number of states per node. Our results show that it is not necessary to determine all parameters of the genetic network in order to rank hypotheses. The ranking process is easier the more experimental environmental conditions are used for the data set. During the ranking, the number of fixed parameters increases with the number of environmental conditions, while some errors in the hypothetical network structure may pass undetected, due to a maintained dynamical behaviour.     

Evolutionary and swarm intelligence‐based approaches for optimization of lipase extraction from fermented broth

Evolutionary and swarm intelligence‐based optimization approaches, namely genetic algorithm (GA) and particle swarm optimization (PSO), were utilized to determine the optimal conditions for the lipase extraction process. The input space of the nonlinear response surface model of lipase extraction served as the objective function for both approaches. The optimization results indicate that the lipase activity was significantly improved, more than 20 U/g of dry substrate (U/gds), in both approaches. PSO (133.57 U/gds in the 27th generation) outperforms GA (132.24 U/gds in the 320th generation), slightly in terms of optimized lipase activity and highly in terms of convergence rate. The simple structure associated with the effective memory capability of PSO renders it superior over GA. The proposed GA and PSO approaches, based on a biological phenomenon, are considered as natural and thus may replace the traditional gradient‐based optimization approaches in the field of downstream processing of enzymes.     

Benchmarking the Clustering Performances of Evolutionary Algorithms: A Case Study on Varying Data Size

Background and objectiveClustering is a widely used popular method for data analysis within many clustering algorithms for years. Today it is used in many predictions, collaborative filtering and automatic segmentation systems on different domains. Also, to be broadly used in practice, such clustering algorithms need to give both better performance and robustness when compared to the ones currently used. In recent years, evolutionary algorithms are used in many domains since they are robust and easy to implement. And many clustering problems can be easily solved with such algorithms if the problem is modeled as an optimization problem. In this paper, we present an optimization approach for clustering by using four well-known evolutionary algorithms which are Biogeography-Based Optimization (BBO), Grey Wolf Optimization (GWO), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).Methodthe objective function has been specified to minimize the total distance from cluster centers to the data points. Euclidean distance is used for distance calculation. We have applied this objective function to the given algorithms both to find the most efficient clustering algorithm and to compare the clustering performances of algorithms against different data sizes. In order to benchmark the clustering performances of algorithms in the experiments, we have used a number of datasets with different data sizes such as some small scale, medium and big data. The clustering performances have been compared to K-means as it is a widely used clustering algorithm for years in literature. Rand Index, Adjusted Rand Index, Mirkin's Index and Hubert's Index have been considered as parameters for evaluating the clustering performances.ResultAs a result of the clustering experiments of algorithms over different datasets with varying data sizes according to the specified performance criteria, GA and GWO algorithms show better clustering performances among the others.ConclusionsThe results of the study showed that although the algorithms have shown satisfactory clustering results on small and medium scale datasets, the clustering performances on Big data need to be improved.     

一种新的无标度网络构建方法及其在基因表达谱模拟上的应用

本文提出一种新的基于重连接方法的无标度网络构建算法．根据重连接方法新节点的调控节点会被重选,重连接概率取决于幂率分布模型参数gamma．用本文算法构建的网络通过微分方程模型来模拟基因表达谱数据,所用的优化算法为GA与PSO．候选节点的选择可以根据已有节点的连接数决定．实验的网络可以用log-log图,模拟的基因表达谱也用微分方程模型来验证效果．每个连接的正确性将会通过实验验证,完整的程序可以通过我们的官方网站获得：http://ccst.jlu.edu.cn/CSBG/ourown/.     

A competitive clustering particle swarm optimizer for dynamic optimization problems

Optimization in dynamic optimization problems (DOPs) requires the optimization algorithms not only to locate, but also to continuously track the moving optima. Particle swarm optimization (PSO) is a population-based optimization algorithm, originally developed for static problems. Recently, several researchers have proposed variants of PSO for optimization in DOPs. This paper presents a novel multi-swarm PSO algorithm, namely competitive clustering PSO (CCPSO), designed specially for DOPs. Employing a multi-stage clustering procedure, CCPSO splits the particles of the main swarm over a number of sub-swarms based on the particles positions and on their objective function values. The algorithm automatically adjusts the number of sub-swarms and the corresponding region of each sub-swarm. In addition to the sub-swarms, there is also a group of free particles that explore the environment to locate new emerging optima or exploit the current optima which are not followed by any sub-swarm. The adaptive search strategy adopted by the sub-swarms improves both the exploitation and tracking characteristics of CCPSO. A set of experiments is conducted to study the behavior of the proposed algorithm in different DOPs and to provide guidelines for setting the algorithm’s parameters in different problems. The results of CCPSO on a variety of moving peaks benchmark (MPB) functions are compared with those of several state-of-the-art PSO algorithms, indicating the efficiency of the proposed model.     

Evolutionary algorithms for solving multi-objective travelling salesman problem

This paper studies the application of evolutionary algorithms for bi-objective travelling salesman problem. Two evolutionary algorithms, including estimation of distribution algorithm (EDA) and genetic algorithm (GA), are considered. The solution to this problem is a set of trade-off alternatives. The problem is solved by optimizing the order of the cities so as to simultaneously minimize the two objectives of travelling distance and travelling cost incurred by the travelling salesman. In this paper, binary-representation-based evolutionary algorithms are replaced with an integer-representation. Three existing EDAs are altered to use this integer-representation, namely restricted Boltzmann machine (RBM), univariate marginal distribution algorithm (UMDA), and population-based incremental learning (PBIL). Each city is associated with a representative integer, and the probability of any of this representative integer to be located in any position of the chromosome is constructed through the modeling approach of the EDAs. New sequences of cities are obtained by sampling from the probabilistic model. A refinement operator and a local search operator are proposed in this piece of work. The EDAs are subsequently hybridized with GA in order to complement the limitations of both algorithms. The effect that each of these operators has on the quality of the solutions are investigated. Empirical results show that the hybrid algorithms are capable of finding a set of good trade-off solutions.     

Regulation of phenoloxidase activity by high- and low-molecular-weight inhibitors from the larval hemolymph of Manduca sexta

Insect phenoloxidases (POs) generate quinones and other reactive intermediates to immobilize and kill invading pathogens and parasites. Due to the presumed cytotoxicity of these compounds, PO activity and its proteolytic activation have to be regulated as a local, transient reaction against nonself in order to minimize damage to the host tissues and cells. We identified a Manduca sexta cDNA encoding a polypeptide sequence with its carboxyl-terminal 33 residues similar to the housefly phenoloxidase inhibitor (POI). The recombinant POI, secreted into the Escherichia coli periplasmic space along with its fusion partner DsbC, was released by osmotic shock and isolated by nickel affinity chromatography. Following enterokinase digestion and protein separation, the POI was purified to near homogeneity in a soluble form which inhibited M. sexta PO at a high concentration. We then produced the inhibitor using a modified baculovirus-insect cell system and isolated the glycoprotein from the conditioned medium. Deglycosylation coupled with inhibition assay revealed that O-glycosylation only moderately increased its inhibitory activity. While this led us to speculate the role of Tyr(64) hydroxylation, we were unable to modify the recombinant protein with tyrosine hydroxylase or purify M. sexta POI (Tyr(64)dopa) from the larval plasma. Instead, we isolated a low-M(r), heat-stable compound which strongly inhibited PO. The wavelength of maximum absorbance is 257 nm for the inhibitor. These data suggest that the down-regulation of PO activity in M. sexta is achieved by two mechanisms at least.     

A cooperative particle swarm optimizer with migration of heterogeneous probabilistic models

Particle Swarm Optimization (PSO) is a stochastic optimization approach that originated from simulations of bird flocking, and that has been successfully used in many applications as an optimization tool. Estimation of distribution algorithms (EDAs) are a class of evolutionary algorithms which perform a two-step process: building a probabilistic model from which good solutions may be generated and then using this model to generate new individuals. Two distinct research trends that emerged in the past few years are the hybridization of PSO and EDA algorithms and the parallelization of EDAs to exploit the idea of exchanging the probabilistic model information. In this work, we propose the use of a cooperative PSO/EDA algorithm based on the exchange of heterogeneous probabilistic models. The model is heterogeneous because the cooperating PSO/EDA algorithms use different methods to sample the search space. Three different exchange approaches are tested and compared in this work. In all these approaches, the amount of information exchanged is adapted based on the performance of the two cooperating swarms. The performance of the cooperative model is compared to the existing state-of-the-art PSO cooperative approaches using a suite of well-known benchmark optimization functions.     

A new form of arthropod phenoloxidase is abundant in venom of the parasitoid wasp Pimpla hypochondriaca

We have recently identified phenoloxidase (PO) activity among several biologically active factors in venom from the parasitoid wasp Pimpla hypochondriaca. We have now isolated three genes, designated POI, POII and POIII, from a cDNA library made from venom-producing glands and found that their products are related to pro-phenoloxidases (PPOs), which are expressed as proenzymes in haemocytes and which mediate immune processes in arthropods. This is the first report of PO as a venom constituent. Amino acid sequence comparisons between the three Pimpla POs and PPOs revealed several notable differences, including the absence of sequences which specify the site of proteolytic activation in insect PPOs and the unprecedented occurrence of signal peptide sequences. NH(2)-terminal amino acid analysis of PO purified from venom yielded a peptide sequence matching the predicted mature NH(2) termini of POI and POII, confirming the authenticity of the signal peptide and indicating that proteolytic processing, other than to remove the signal peptide, does not occur in the wasp. Expression of POI, analysed by Northern hybridization, was approximately uniform from the time of adult emergence to day 6 post-emergence, after which it declined. A novel means of host immune suppression, mediated by the unregulated activity of venom PO in the haemocoel, is proposed.     

Benchmarking template selection and model quality assessment for high-resolution comparative modeling

Comparative modeling is presently the most accurate method of protein structure prediction. Previous experiments have shown the selection of the correct template to be of paramount importance to the quality of the final model. We have derived a set of 732 targets for which a choice of ten or more templates exist with 30-80% sequence identity and used this set to compare a number of possible methods for template selection: BLAST, PSI-BLAST, profile-profile alignment, HHpred HMM-HMM comparison, global sequence alignment, and the use of a model quality assessment program (MQAP). In addition, we have investigated the question of whether any structurally defined subset of the sequence could be used to predict template quality better than overall sequence similarity. We find that template selection by BLAST is sufficient in 75% of cases but that there are examples in which improvement (global RMSD 0.5 A or more) could be made. No significant improvement is found for any of the more sophisticated sequence-based methods of template selection at high sequence identities. A subset of 118 targets extending to the lowest levels of sequence similarity was examined and the HHpred and MQAP methods were found to improve ranking when available templates had 35-40% maximum sequence identity. Structurally defined subsets in general are found to be less discriminative than overall sequence similarity, with the coil residue subset performing equivalently to sequence similarity. Finally, we demonstrate that if models are built and model quality is assessed in combination with the sequence-template sequence similarity that a extra 7% of "best" models can be found.     

多源空间数据融合的城市人居环境监测模型与应用研究

人居环境监测作为城市人居环境建设与管理实践提升的基本,是目前人居环境研究落地的重点。传统的城市人居环境监测在数据更新速度、精度等方面存在不足,难以满足精细化管理需求。提出利用遥感数据与互联网的兴趣点POI(Point of interest)数据结合,建立人居环境监测模型。模型主要有两个关键环节,一是构建自动化提取建筑物算法,该算法通过建立地物特征集,以POI点对应样本为种子,利用全局最优和区域生长算法,自动提取城市建筑物,再利用全局最优算法确定其他地类的阈值;二是人居环境指标计算,将建筑物、绿地、水体信息提取结果与POI数据结合,利用密度类与距离类空间分析算法,分别计算自然、社会经济类指标。基于上述模型,利用2018年4月的北京二号遥感影像和POI点数据在北京市回龙观社区进行实验验证,结果显示:信息提取结果中,总体精度超过95%,Kappa系数超过92%,提取效率提高2.3倍,表明信息提取精度高且可信,适合工程化应用。计算回龙观社区人居环境监测指标,分析结果认为,社区内自然类指标差异不大,但缺乏水体生态系统,生物多样性不够丰富,社区内的商业比较繁华,但是学校和医疗不充足,尤其是缺乏大型公立医院。综上,通过人居环境监测模型研究和应用分析,将遥感数据和互联网数据结合应用于人居环境质量监测有效提高了精度和速度,有利于业务化,服务政府管理。     

Effects of Hexaflumuron and Pyriproxyfen on the purified Phenoloxidase of Chilo suppressalis Walker (Lepidoptera: Crambidae)

Effects of two insect growth regulators (IGRs), hexaflumuron and pyriproxyfen, were studied on the purified phenoloxidase (PO) of Chilo suppressalis. Purification procedure revealed two isozymes of PO, namely POI and POII. IC₅₀ concentrations of hexaflumuron and pyriproxyfen on POI were 0.36, 0.23?μg/ml and on POII were 0.105, 0.42?μg/ml, respectively. Determination of optimal pH and temperature revealed pH 5 and temperature 40?°C as the optimal values for the enzymatic activity. Treating POs with IC₅₀ concentrations of two IGRs was pH and temperature dependent. Effects of these IGRs on POI caused significant increase of K_m value versus control suggesting competitive inhibition. Hexaflumuron and pyriproxyfen cause reduction in V_max value of POII versus control suggesting non-competitive inhibition. The current study shows direct effects of two IGRs on purified PO of C. suppressalis for the first time. These findings could be helpful to develop safe compounds with inhibitory mechanism on PO to neutralise insect immune responses against entomopathogenic agents.     

Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm

This paper presents a study of the performance of TRIBES, an adaptive particle swarm optimization algorithm. Particle Swarm Optimization (PSO) is a biologically-inspired optimization method. Recently, researchers have used it effectively in solving various optimization problems. However, like most optimization heuristics, PSO suffers from the drawback of being greatly influenced by the selection of its parameter values. Thus, the common belief is that the performance of a PSO algorithm is directly related to the tuning of such parameters. Usually, such tuning is a lengthy, time consuming and delicate process. A new adaptive PSO algorithm called TRIBES avoids manual tuning by defining adaptation rules which aim at automatically changing the particles’ behaviors as well as the topology of the swarm. In TRIBES, the topology is changed according to the swarm behavior and the strategies of displacement are chosen according to the performances of the particles. A comparative study carried out on a large set of benchmark functions shows that the performance of TRIBES is quite competitive compared to most other similar PSO algorithms that need manual tuning of parameters. The performance evaluation of TRIBES follows the testing procedure introduced during the 2005 IEEE Conference on Evolutionary Computation. The main objective of the present paper is to perform a global study of the behavior of TRIBES under several conditions, in order to determine strengths and drawbacks of this adaptive algorithm.