\r\n\tWith this goal in mind, together with the US Prof. John M. Ballato and the InechOpen publishing house since 2011 we have published in 2011, 2013, 2015 and 2017 4 books of our serial “Optoelectronics” and the book “Excitons”, edited in 2018 by Prof. Sergei L. Pyshkin. Publishing the new book “Luminescence” we are pleased to note the growing number of countries participating in this undertaking as well as for a long time fruitfully cooperating scientists from the United States and the Republic of Moldova. \r\n\tSpecialists from all over the world have published in edited by us books their works in the field of research of the luminescent properties of various materials suitable for use in optoelectronic devices, the development of new structures and the results of their application in practice.
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\n
1. Introduction
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Digital era with its opportunity and complexity overwhelms industries and markets that are faced with a huge amount of potential information in each transaction. Being aware of the value of gathered data and benefitting from hidden knowledge create a new paradigm in this era, which redefines the meaning of power for corporation. The power of information leads organizations toward being agile and to hit the goals. Big data analytics (BDA) enforces industries to describe, diagnose, predict, prescribe, and cognate the hidden growth opportunities and leads them toward gaining business value [68]. BDA deploys advanced analytical techniques to create knowledge from exponentially increasing amount of data, which will affect the decision-making process in decreasing complexity of the process [43]. BDA needs novel and sophisticated algorithms that process and analyze real-time data and result in high-accuracy analytics. Machine and deep learning allocate their complex algorithms in this process considering the problem approach [28].
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In this research, a literature review on big data analytics, deep learning and its algorithms, and machine learning and related methods has been considered. As a result, a conceptual model is provided to show the relation of the algorithms that helps researchers and practitioners in deploying BDA on IOT data.
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The process of discussing over DL and ML methods has been shown in Figure 1.
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Figure 1.
The big data analytics methods in this research.
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\n
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2. Big data and big data analytics
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One of the vital consequences of the digital world is creating a collection of bulk of raw data. Managing such valuable capital with different shape and size on the basis of organizations’ needs the manager’s attention. Big data has the power to affect all parts of society from social aspect to education and all in between. As the amount of data increases especially in technology-based companies, the matter of managing raw data becomes much more important. Facing with features of raw data like variety, velocity, and volume of big data entitles advanced tools to overcome the complexity and hidden body of them. So, big data analytics has been proposed for “experimentation,” “simulations,” “data analysis,” and “monitoring.” Machine learning as one of the BDA tools creates a ground to have predictive analysis on the basis of supervised and unsupervised data input. In fact, a reciprocal relation has existed between the power of machine learning analytics and data input; the more exact and accurate data input, the more effective the analytical performance. Also, deep learning as a subfield of machine learning is deployed to extract knowledge from hidden trends of data [28].
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\n
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3. Big data analytics
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In digital era with growing rate of data production, big data has been introduced, which is known by big volume, variety, veracity, velocity, and high value. It brings hardness in analyzing with itself which entitled organization to deploy a new approach and tools in analytical aspects to overcome the complexity and massiveness of different types of data (structured, semistructured, and unstructured). So, a sophisticated technique that aims to cope with complexity of big data by analyzing a huge volume of data is known as big data analytics [50]. Big data analytics for the first time was coined by Chen Chiang (2012) who pointed out the relation between business intelligence and analytics that has strong ties with data mining and statistical analysis [11].
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Big data analytics supports organizations in innovation, productivity, and competition [16]. Big data analytics has been defined as techniques that are deployed to uncover hidden patterns and bring insight into interesting relations in understanding contexts by examining, processing, discovering, and exhibiting the result [69]. Complexity reduction and handling cognitive burden in knowledge-based society create a path toward gaining advantages of big data analytics. Also, the most vital feature that led big data analytics toward success is feature identification. This means that the crucial features that have important affection on results should be defined. It is followed by identifying of corelations between input and a dynamic given point, which may change during times [69].
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As a result of fast evolution of big data analytics, e-business and dense connectivity globally have flourished. Governments, also, take advantages of big data analytics to serve better services to their citizens [69].
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Big data in business context can be managed and analyzed through big data analytics, which is known as a specific application of this field. Also, big data gained from social media can be managed efficiently through big data analytics process. In this way, customer behavior can be understood and five features of big data, which are enumerated as volume, velocity, value, variety, and veracity, can be handled. Big data analytics not only helps business to create a comprehensive view toward consumer behavior but also helps organizations to be more innovative and effective in deploying strategies [14]. Small and medium size company use big data analytics to mine their semistructured big data, which results in better quality of product recommendation systems and improved website design [19]. As Ref. [9] cited, big data analytics gains advantages of deploying technology and techniques on their massive data to improve a firm’s performance.
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According to Ref. [19], the importance of big data analytics has been laid in the fact that decision-making process is supported by insight, which is the result of processing diverse data. This will turn decision-making process into an evidence-based field. Insight extraction from big data has been divided into two main processes, namely data management and data analytics with the former referring to technology support for gathering, storing, and preparing data for analyzing purpose and the latter is about techniques deployed for data analyzing and extracting knowledge from them. Thus, big data analytics has been known as a subprocess of insight extraction. Big data analytics tools are text analytics, audio analytics, video analytics, social media analytics, and predictive analytics. It can be inferred that big data analytics is the main tool for analyzing and interpreting all kinds of digital information [35]. And the processes involved are data storage, data management, data analyzing, and data visualization [9].
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Big data analytics has the potential for creating effective and efficient value in both operational and strategic approach for organization and it plays as a game changer in augmenting productivity [20].
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Industry practitioners believe that big data analytics is the next ‘blue ocean’ that brings opportunities for organizations [33], and it is known as “the fourth paradigm of science” [70].
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Fields of machine learning (ML) and deep learning (DL) were expanded to deal with BDA. Different fields like “medicine,” “Internet of Things (IOT),” and “search engines” deploy ML for exploration of predictive features of big data. In other words, it generalizes learnt patterns to predict future data. Feature construction and data representation are two main elements of ML. Also, useful data extraction from big data is the reason for deploying DL, which is a human-brain inspired technique for processing neural signals as a subfield of ML [28].
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4. Big data analytics and deep learning
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In 1940s, deep learning was been introduced [71], but the birth of deep learning algorithms has been determined in year 2006 when layer-wise-greedy-learning method was introduced by Hinton to overcome the deficiency of neural network (NN) method in finding optimized point by trapping in optima local point that is exacerbated when the size of training data was not enough. The underlying thought of proposed method by Hinton is to use unsupervised learning before layer-by-layer training happens [72].
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Inspiring from hierarchical structure of human brain, deep learning algorithms extract complex hidden features with a high level of abstraction. When massive amounts of unstructured data represent, the layered architecture of deep learning algorithms works effectively. The goal of deep learning is to deploy multiple transformation layers where in every layer output representation is occurred [42]. Big data analytics comprises the whole learnt untapped knowledge gained from deep learning. The main feature of big data analytics, which is extracting underlying features in huge amounts of data, makes it a beneficial tool for big data analytics [42].
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Deep learning as a subfield of machine learning has been introduced when some conditions like rise of chip processing, which results in creating huge amounts of data, decreasing computer hardware costs, and noteworthy development in machine learning algorithms were generated. Four categories of deep learning algorithms are as follows:
CNN inspired from neural network model as a type of deep learning algorithm has a “convolutional layer” and “subsampling layer” architecture. Multi-instance data is deployed as a bag of instances in which each data point is a set of instances [73].
\n
CNN has been known with three features namely “local field,” “subsampling,” and “weight sharing” and comprised of three layers, which are input, hidden that consists of “convolutional layer” and “subsampling layer” and output layer. In hidden layer, each “convolutional layer” comes after “subsampling layer.” CNN training process has been done in two phases of “feed forward” in which the result of previous level entered into next level and “back propagation” pass, which is about modification of errors and deviation through a process of spreading training errors backward and in a hierarchical process [74]. In the first layer, convolution operation is deployed that is to take various filtering phases in each instances, and then, nonlinear transformation function takes place as the result of previous phase transforming into a nonlinear space. After that, the transformed nonlinear space is considered in max-pooling layer, which represents the bag of instances. This step has been done by considering the maximum response of each instance, which was in filtering step. The representation creates a strong pie with the maximum response that can be deployed by predicting instances’ status in each class. This will lead to constructing a classification model [73].
\n
CNN is comprised of feature identifier, which is an automatic learning process from extracted features from data with two components of convolutional and pooling layers. Another element of CNN is multilayer perception, which is about taking features that were learned into classification phase [3].
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4.2 Deep neural network (DNN)
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A deep architecture in supervised data has been introduced with advances in computation algorithm and method, which is called deep neural network (DNN) [3]. It originates from shallow artificial neural networks (SANN) that are related to artificial intelligence (AI) [30].
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As hierarchical architecture of DL can constitute nonlinear information in the set of layers, DNN deploys a layered architecture with complex function to deal with complexity and high number of layers [3].
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DNN is known as one of the most prominent tools for classifying [49] because of its outstanding classification performance in complex classification matters. One of the most challenging issues in DNN is training performance of it, as in optimization problems it tries to minimize an objective function with high amount of parameters in a multidimensional searching space. So, fining and training a proper DNN optimization algorithm requires in high level of attention. DNN is constructed of structure stacked denoising auto encoder (SDAE) [75] and has a number of cascade auto encoder layers and softmax classifier. The first one deploys raw data to generate novel features, and with the help of softmax, the process of feature classification is performed in an accurate way. The cited features are complementary to each other that helps DNN do its main performance, which is classification in an effective way. Gradient descent (GD) algorithm, which is an optimization method, can be deployed in linear problems with no complex objective function especially in DNN training, and the main condition of this procedure is that the amount of optimization parameter is near to optimal solution [6]. According to Ref. [30], DNN with the feature of deep architecture is deployed as a prediction model [30].
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4.3 Recurrent neural network (RNN)
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RNN, a network of nodes that are similar to neurons, was developed in 1980s. Each neuron-like node is interconnected with each other, and it can be divided into categories of input, hidden, and output neurons. The data will receive, transform, and generate results in this triple process. Each neuron has the feature of time-varying real-valued activation and every synapse is real-valued weight justifiable [66]. A classifier for neural networks has outstanding performance in not only learning and approximating [105] but also in dynamic system modeling with nonlinear approach by using present data [29, 52]. RNN with the background of human brain–inspired algorithm has been derived from artificial neural network but they are slightly different from each other. Various fields of “associative memories,” “image processing,” “pattern recognition,” “signal processing,” “robotics,” and “control” have been in the center of focus in research of RNN [67]. RNN with its feedback and feed forward relations can take a comprehensive view from past information and deploy it for adjusting with sudden changes. Also, RNN has the capability of using time-varying data in a recursive way, which simplified the neural network architecture. Its simplicity and dynamic features work effectively in real-time problems [40]. RNN has the ability to process temporal data in hierarchy method and take multilayer of abstract data to show dynamical features, which is another capability of RNN [18]. RNN has the potential to make connection between signals in different levels, which brings significant processing power with huge amounts of memory space [45].
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5. Big data analytics and machine learning
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Machine learning has been defined as predictive algorithms by data interpretation, which is followed by learning algorithm in an unstructured program. Three main categories of ML are supervised, unsupervised, and reinforcement learning [47], which is done during “data preprocessing,” “learning,” and “evaluation phase.” Preprocessing is related to transformation of raw data into right form that can be deployed in learning phase, which comprises of some levels like cleaning the data, extracting, transforming, and combining it. In the evaluation phase, data set will be selected, and evaluation of performance, statistical tests, and estimation of errors or deviation occur. This may lead to modifying selected parameters from learning process [76]. The first one refers to analyzing features that are critical for classification through a given training data. The data deployed in training algorithm will then become trained and then it will be used in testing of unlabeled data. After interpreting unlabeled data, the output will be generated, which can be classified as discrete or regression if it is continuous. On the other hand, ML can be deployed in pattern identification without training process, which is called unsupervised ML. In this category, when pattern of characteristics are used to group the data, cluster analysis is formed, and if the hidden rules of data have been recognized, another form of ML, which is association, will be formed [77]. In the other words, the main process of unsupervised ML or clustering is to find natural grouping from those data, which is unlabeled. In this process, K cluster in a set number of data is much more similar in comparison with other clusters considering similarity measure. Three categories of unsupervised ML are “hierarchical,” “partitioned,” and “overlapping” techniques. “Agglomerative” and “divisive” are two kinds of hierarchical methods. The first one is referred to an element that creates a separate cluster with tendency to get involved with larger cluster; however, the second one is a comprehensive set that is going to divide into some smaller clusters. “Partitioned” methods begin with creating several disjoint clusters from data set without considering any hierarchical structure, and “overlapping” techniques are defined as methods that try to find fuzzy or deffuzy partitioning, which is done by “relaxing the mutually disjoint constraint.” Among all unsupervised learning techniques, K-means grabs attention. “Simplicity” and “effectiveness” are two main characteristics of unsupervised techniques [47].
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5.1 Machine learning and fuzzy logic
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Fuzzy logic proposed by Lotfi Zadeh (1965) has been deployed in many fields from engineering to data analysis and all in between. Machine learning also gains advantage from fuzzy logic as fuzzy takes inductive inference. The changes happened in such grounds like “fuzzy rule induction,” “fuzzy decision trees,” “fuzzy nearest neighbor estimation,” or “fuzzy support vector machines” [27].
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5.2 Machine learning and classification methods
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One of the most critical aspects of ML is classifications [23], which is the initial phase in data analytics [17]. Prior studies found new fields that can deploy this aspect like face recognition or even recognition of hand writing. According to [23], operating algorithm of classification has been divided into two categories: offline and online. In offline approach, static dataset is deployed for training. The training process will be stopped by classifiers after training process is finished and modification of data structured will not be allowed. On the other hand, online category is defined as a “one-pass” type, which is learning from new data. The prominent features of data will be stored in memory and will be kept until the processed training data is erased. Incremental and evolving processes (changing data pattern in unstable environment, which is a result of evolutionary system structure, and continuously updating meta-parameters) are two main approaches for online category [23].
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Support vector machine (SVM) was proposed in 1995 by Cortes and Vapnik to solve problems related to multidimensional classification and regression issues as its outstanding learning performance [64]. In this process, SVM constructs a high-dimensional hyperplane that divides data into binary categories, and finding greatest margin in binary categories considering the hyperplane space is the main objective of this method [10]. “Statistical learning theory,” “Vapnik-Chervonenkis (VC) dimension,” and the “kernel method” are underlying factors of development of SVM [78], which deploys limited number of learning patterns to desirable generalization considering a risk minimization structure [22].
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K-nearest neighbor deploys to classify objects in the nearest training class of features [79], and it is known as one of the most widely used algorithms in classification problems in data mining and knowledge extraction. In this method, an object is assigned to its k-nearest neighbors. The efficiency of this method is on the basis of the level of features’ weighted qualifications. Some drawbacks of this method are as follows:
It is highly dependent on the value of K parameter, which is a gauge for determination of neighborhood space.
The method lacks discrimination ability to differentiate between far and close neighbors.
Overlapping or noise may happen when neighbor are close [80].
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KNN as one of the most important data mining algorithms was first introduced for classification problems, which are expanded to pattern recognition and machine learning research. Expert systems take advantage of KNN classification problems. Three main KNN classifiers that put focus on k-nearest vector neighbor in every class of test sample are as follows:
\n
“Local mean-based k-nearest neighbor classifier (LMKNN)”: despite the fact that existing outlier negative influence can be solved by this method, LMKNN is prone to misclassification because of taking single value of \n\nk\n\n considering neighborhood size per class and applying it in all classes.
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“Local mean-based pseudo nearest neighbor classifier (LMPNN)”: LMKNN and PNN methods create LMPNN, which is known as a good classifier in “multi-local mean vectors of k-nearest neighbors and pseudo nearest neighbor based on the multi-local mean vectors for each class.” Outlier points in addition to \n\nk\n\n sensitivity have been more considered in this technique. However, differentiation of information in nearest sample of classification cannot recognize widely as weight of all classes are the same [81].
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“Multi-local means-based k-harmonic nearest neighbor classifier (MLMKHNN)”: MLMKHNN as an extension to KNN takes harmonic mean distance for classification of decision rule. It deploys multi-local mean vectors of k-nearest neighbors per class of every query sample and harmonic mean distance will be deployed as the result of this phase [82]. These methods are designed in order to find different classification decisions [81].
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In 2006, Huang et al. proposed extreme learning machine (ELM) as a classification method that works by a hidden single layer feedback in neural network [92]. In this layer, the input weight and deviation will be randomly generated and least square method will be deployed to determine output weight analytically [17], which differentiates this method from traditional methods. In this phase, learning happens followed by finding transformation matrix [93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103]. It is deployed to minimize the sum-of-squares error function. The result of minimizing function will then be used in classification or reduction of dimension [48]. Neural networks are divided into two categories of feed forward neural network and feedback neural networks and ELM is on the first category, which has a strong learning ability specially in solving nonlinear functions with high complexity. ELM uses this feature in addition to fast learning methods to solve traditional feed forward neural network problems in a mathematical change without iteration with higher speed in comparison with traditional neural network [13].
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Despite the efficiency of ELM in classification problems, binary classification problems emerge as the deficiency of ELM; as in these problems, a parallel training phase on ELM is needed. In twin extreme learning machine (TELM), the problems will be solved by a simultaneous train and two nonparallel classification hyperplanes, which are deployed for classification. Every hyperplane enters into a minimization function to minimize the distance of it with one class, which is located far away from other classes [60]. ELM is at the center of attention in data stream classification research [83].
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5.3 Machine learning and clustering
\n
Clustering as a supervised learning method aims to create groups of clusters, which members of it are in common with each other in characteristics and dissimilar with other cluster members [84]. The calculated interpoint distance of every observation in a cluster is small in comparison with its distance to a point in other clusters [36]. “Exploratory pattern-analysis,” “grouping,” “decision-making,” and “machine-learning situations” are some main applications of clustering technique. Five groups of clustering are “hierarchical clustering,” “partitioning clustering,” “density-based clustering,” “grid-based clustering,” and “model-based clustering” [84]. Clustering problems are divided into two categories: generative and discriminative approaches. The first one refers to maximizing the probability of sample generation, which is used in learning from generated models, and the other is related to deploying pairwise similarities, which maximize intercluster similarities and minimize similarities of clusters in between [63].
\n
There are important clustering methods like K-means clustering, kernel K means, spectral clustering, and density-based clustering algorithms that are at the center of research topics for several decades. In K-means clustering, data is assigned to the nearest center, which results from being unable to detect nonspherical clusters. Kernel k-means and spectral clustering create a link between the data and feature space and after that k-means clustering is deployed. Obtaining feature space is done by using kernel function and graph model by kernel k-means and spectral clustering, respectively. Also spectral clustering deploys Eigen-decomposition techniques additionally [26]. K-means clustering works effectively in clustering of numerical data, which is multidimensional [85].
\n
Density-based clustering is represented by DBSCAN, and clusters tend to be separate from data set and be as higher density area. This method does not deploy one cluster for clusters recognition in the data a priori. It considers user-defined parameter to create clusters, which has a bit deviation from cited parameter in clustering process [84].
\n
\n
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5.4 Machine learning and evolutionary methods
\n
The main goal of optimization problems is to find an optimal solution among a set of alternatives. Providing the best solution has become difficult if the searching area is large. Heuristic algorithm proposed different techniques to find the optimal solution, but they lack finding the best solution. However, population-based algorithm was generated to overcome the cited deficiency, which is considered to find the best alternative [7].
\n
\n
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5.5 Genetic algorithms (GA)
\n
GA is defined as a randomized search, which tries to find near-optimal solution in complex and high-dimensional environment. In GA, a bunch of genes that are called chromosomes are the main parameters in the technique. These chromosomes are deployed as a search space. A number of chromosomes that seem as a collection are called population. The creation of a random population will be followed by representing the goodness degree of objective and fitness function related to each string. The result of this step that will be a few of selected string with a number of copies will be entered into the mating pool. By deploying cross-over and mutation process, a new generation of string will be created from the string. This process will be continued until a termination condition is found. “Image processing,” “neural network,” and “machine learning” are some examples of application fields for genetic algorithms [38]. GA as nature-inspired algorithm is based on genetic and natural selection algorithms [31].
\n
GA tries to find optimal solution without considering the starting point [104]; also, GA has the potential to find optimal clustering considering clustering metrics [38]. Filter and wrapper search are two main approaches of GA in the field of feature selection. The first one aims to investigate the value of features by deploying heuristic-based data characteristics like correlation, and the second one assesses the goodness of GA solution by using machine learning algorithm [53]. In K-means algorithm, optimized local point is found on the basis of initializing seed values and the generated cluster is on the basis of initial seed values. GA by the aim of finding near-optimal or optimal clustering searches for initial seed values, outperforms K-mean algorithm, and covers the lack of K-mean algorithm [4]. Gaining knowledge from data base is another ground for GA, which plays the role of building “classifier system” and “mining association rules” [58].
\n
Feature selection is a vital problem in big data as it usually contains many features that describe target concepts and chooses proper amount of feature for pre-processing traditionally as a main matter was done by data mining. Feature selection is divided into two groups: independent of learning algorithm, which deploys filter approach, and dependent on learning algorithm, which uses a wrapper approach. However, filter approach is independent of learning algorithm, and the optimal set of feature may be dependent on learning algorithm, which is one of the main drawbacks of filter selection. In contrast, wrapper approach by deploying learning algorithm in evaluation of every feature set works better. A main problem of this approach is complexity in computation field, which is overcome by using GA in feature selection as learning algorithm [56].
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\n
\n
5.6 Ant colony optimization (ACO)
\n
Ant colony optimization method was proposed by Dorigo [17] as a population-based stochastic method [15]. The method has been created biologically from real ant behavior in food-seeking pattern. In other words, this bionic algorithm has been deployed for finding the optimal path [44]. The process is that when ants start to seek food they deposit a chemical material on the ground, which is known as pheromone while they are moving toward food source. As the path between the food source and nest become shorter, the amount of pheromone will become larger. New ants in this system tend to choose the path with greater amount of pheromone. By passing time, all ants follow the positive feedback and choose the shortest path, which is signed by greatest amount of pheromone [86]. The applications of ant colony optimization in recent research have been declared as traveling salesman problem, scheduling, structural and concrete engineering, digital image processing, electrical engineering, clustering, routing optimization algorithm [41], data mining [32], robot path planning [87], and deep learning [39].
\n
Some advantages of ant colony optimization method are as follows:
Less complexity in integration of this method with other algorithms
Gain advantage of distributed parallel computing (e.g., intelligent search)
Work better in optimization in comparison with swarm intelligence
High speed and high accuracy
Robustness in finding a quasi-optimal solution [41]
\n
As it is stated, the emitted material called pheromone causes clustering between species around optimal position. In big data analytics, ant colony clustering is deployed on the grid board to cluster the data objects [21].
\n
All ant solution constructions, improvement of the movement by local search, and update of the emitted material are involved in a single iteration [23]. So, the main steps of ant colony optimization are as follows:
Initializing pheromone trail
Deploying pheromone trail to construct solution
Updating trail pheromone
\n
On the basis of probabilistic state transition rule, which depends on the state of the pheromone, a complete solution is made by each ant. Two steps of evaporation and reinforcement phase are passed in pheromone updating procedure, where evaporation of pheromone fraction happens and emitting of pheromone that shows the level of solution fitness is determined, respectively, which is followed by finalizing condition [46].
\n
Ant colony decision tree (ACDT) is a branch of ant colony decision that aims to develop decision tress that are created in running algorithm, but as a nondeterministic algorithm in every execution, different decision tree is created. A pheromone trail on the edge and heuristics used in classical algorithm is the principle of ACDT algorithm.
\n
The multilayered ant colony algorithm has been proposed after the disability of one layer ant colony optimization has been declared in finding optimal solution. As an item, value with massive amount of quantity takes too long to grow. In this way, through transactions, maximum quantities of an item is determined and a rough set of membership function will be set, which will be improved by refining process at subsequent levels by reduction in search space. As a result, search ranges will be differing considering the levels. Solution derived from every level is an input for next level, which is considered in the cited approach but with a smaller search space that is necessary for modifying membership functions [88]. Tsang and Kwong proposed ant colony clustering in anomaly detection [65].
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5.7 Bee colony optimization (BCO)
\n
BCO algorithm works on inspiration from honey bee’s behavior, which is widely used in optimization problems like “traveling salesman problem,” “internet hosting center,” vehicle routing, and the list goes on. Karaboga in 2005 proposed artificial bee colony (ABC) algorithm. The main features of artificial bee colony (ABC) algorithm are simplicity, easy used and has few elements which need to be controlled in optimization problems. “Face recognition,” “high-dimensional gene expression,” and “speech segment classification” are some examples that ABC and ACO use to select features and optimize them by having a big search space. In ABC algorithms, three types of bees called “employed bees (EBees),” “onlooker bees (OBees),” and “scout bees deployed” are deployed. In this process, food sources are positioned and then EBees, where their numbers are equal to number of food source, pass the nectar information to OBees. They are equal to the number of EBees. The information is taken to exploit the food source till the finishing amount. Scouts in exhausted food source are employed to search for new food source. The nectar amount is a factor that shows solution quality [25, 55].
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This method is comprised of two steps: step forward, which is exploring new information by bees, and step back, which is related to sharing information considering new alternative by bee of hives.
\n
In this method, exploration is started by a bee that tries to discover a full path for its travel. When it leaves the hive, it comes across with random dances of other bees, which are equipped with movement array of other bees that is known as “preferred path.” This will lead in foraging process and it comprises of a full path, which was previously discovered by its partner who guides the bee to the final destination. The process of moving from one node to another will be continued till the final destination is reached. For choosing the node by bees, a heuristic algorithm is used, which involves two factors of arc fitness and the distance heuristic. The shortest distance has the possibility to be selected by bees [7]. In BCO algorithm, two values of alpha and beta will be considered, which are exploitation and exploration processes, respectively [8].
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5.8 Particle swarm optimization (PSO)
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PSO was generated from inspiration from biological organisms, particularly the ability of a grouped animal to work together in order to find the desired location in particular area. The method was introduced by Kennedy and Eberhart in 1995 as a stochastic population-based algorithm, which is known by features like trying to find global optimize point and easy implementation with taking a small amount of parameters in adjusting process. It takes benefit from a very productive searching algorithm, which makes it a best tool to work on different optimization research area and problems [59].
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The searching process is led toward solving a nonlinear optimization problem in a real value search space. In this process, an iterative searching happens to find the destination, which is the optimal point. In other words, each particle has a multidimensional search with a specific space, which is updated by particle experience or the best neighbor’s space and the objective function assesses the fitness value of each particle. The best solution, which is found in each iteration, will be kept in memory. If the optimal solution is found by particle, it is called local best or \n\npbest\n\n and the optimal point among the particle neighbors is called global best or gbest [89]. In this algorithm, every potential solution is considered as a particle, which has several features like the current position and velocity. The balance between global and local search can be adjusted by adopting different inertia weight. One of critical success factors in PSO is a trade-off between global and local search in iteration [59]. Artificial neural network, pattern classification, and fuzzy control are some area for deploying PSO [5]. Social interaction and communication metaphor like “birds flock and fish schooling” developed this algorithm and it works on the basis of improving social information sharing, which is done among swarm particles [12].
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5.9 Firefly algorithm (FA)
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Firefly algorithm was been introduced by Yang [16]. The main idea of FA is that each firefly has been assumed as unisexual, which is attracted toward other firefly regardless of the gender. Brightness is the main attraction for firefly that stimulates the less bright to move toward brighter ones. The attractiveness and brightness are opposed to distance. The brightness of a firefly has been determined by the area of fitness function [90]. As the brightness of firefly increased, the level of goodness of solution increased. A full attraction model has been proposed that shows all fireflies will be attracted to brighter ones and similarity of all fireflies will occur if a great number of fireflies attract to a brighter one, which is measured by fitness value. So, convergence rate during the search method will occur in a slow pace.
\n
FA has been inspired from the lightening feature of fireflies and known as swarm intelligence algorithm. FA better works in comparison with genetic algorithm (GA) and PSO in some cases. “Unit commitment,” “energy conservation,” and “complex networks” are some examples of working area of FA [61]. Fluctuation may occur when huge numbers of fireflies attract to light emission source and the searching process becomes time-consuming. To overcome these issues, neighborhood attraction FA (NaFA) is introduced, which shows that fireflies are just attracted to only some brighter points, which are outlined by previous neighbor [62].
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5.10 Tabu search algorithm (TS)
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Tabu search is a meta-heuristic, which was proposed by y Glover and Laguna (1997) on the basis of edge projection and making it better and it tries to make a progress in local search, which leads to a global optimized solution by taking possibility on consecutive algorithm iterations. Local heuristic search process is taken to find solution that can be deployed to combinatorial optimization paradigm [2]. The searching process in this methodology is flexible as it takes adaptive memory. The process is done during different iterations. In each iteration, a solution is found. The solution has a neighbor point that can be reached via “move.” In every move, a better solution is found, which can be stopped when no better answer is found [37]. In TS, the aspiration criteria are critical factors that lead the searching process by not considering forbidden solutions that are known by TS. In each solution, the constraints of the objective are met. So, the solutions are both feasible and time-consuming. TS process is continued by using a tabu list (TL), which is a short-term history. The short memory just keeps the recent movement, which is done by deleting the old movement when the memory is full to the maximum level [1].
\n
The main idea of TS is to move toward solution space, which remains unexplored, which would be an opportunity to keep away from local solution. So, “tabu” movements that are recent movements are kept forbidden, which prevents from visiting previous solution points. This is proved that the method brings high-quality solutions in its iterations [57].
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6. Big data analytics and Internet of Things (IOT)
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Internet of things (IOT) put focus on creating an intelligent environment in which things socialize with each other by sensing, processing, communicating, and actuating activities. As IOT sensors gathered a huge amount of raw data, which is needed to be processed and analyzed, powerful tools will enforce the analytics process. This will stimulate to deploy BDA and its methods on IOT-based data. Ref. [51] proposed a four-layer model to show how BDA can help IOT-based system to work better. This model comprised of data generation, sensor communication, data processing, and data interpretation [51]. It is cited that beyond 2020 cognitive processing and optimization will be considered on IOT data processing [34]. In IOT-based systems, acquired signals from sensors are gathered and deployed for processing in frame-by-frame or batch mode. Also, gathered data in IOT system will be deployed in feature extraction, which is followed by classification stage. Machine learning algorithms will be used in data classifying [54]. Machine learning classification can be deployed on three types of data, which are supervised, semisupervised, and unsupervised [54]. In decision-making level, which is comprised of pattern recognition, deep learning methods, namely, RNN, DNN, CNN, and ANN can be used for discovering knowledge. Optimization process in IOT can be used to create an optimized cluster in IOT data [91].
\n
In Figure 2, the process of IOT is shown. Data is gathered from sensors. Data enters the filtering process. In this level, denoising and data cleansing happen. Also, in this level, feature extraction is considered for classification phase. After preprocessing, decision making happens on the basis of deep learning methodology (Table 1). Deep learning and machine learning algorithms can be used in analyzing of data generated through IOT device, especially in the classification and decision-making phase. Both supervised and unsupervised techniques can be used in classification phase considering the data type. However, both deep learning and machine learning algorithms are eligible in deploying in decision-making phase.
\n
Figure 2.
IOT process.
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\n\n
\n
Phase
\n
Methods
\n
\n\n\n
\n
Classification
\n
Data type
\n
Supervised
\n
SVM
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\n
\n
Logistic regression
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\n
\n
Naïve Bayes
\n
\n
\n
Linear regression
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\n
\n
k-Nearest neighbors
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\n
\n
Unsupervised
\n
Clustering
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\n
\n
Vector quantization
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\n
\n
Decision-making
\n
Deep learning methods
\n
CNN
\n
\n
\n
RNN
\n
\n
\n
DNN
\n
\n
\n
ANN
\n
\n
\n
Machine learning optimization method
\n
ACO
\n
\n
\n
GA
\n
\n
\n
BCO
\n
\n
\n
FFA
\n
\n
\n
PSO
\n
\n
\n
TS
\n
\n\n
Table 1.
Deep learning and machine learning techniques on IOT phases.
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7. Future research directions
\n
For feature endeavors, it is proposed to work on application of big data analytics methods on IOT fog and edge computing. It is useful to extract patterns from hidden knowledge of data gathered from sensors deploying powerful analytical tools. Fog computing is defined as a technology that is implemented in near distance to end user, which provides local processing and storage to support different devices and sensors. Health care systems gain advantage from IOT for fog computing, which supports mobility and reliability in such systems. Health care data acquisition, processing, and storage of real-time data are done in edge, cloud, and fog layer [47]. In future research, the area that machine learning algorithms can provide techniques for fog computing can be on the focus. IOT data captured from smart houses needs analytical algorithms to overcome the complexity of offline and online data gathered in processing, classification, and also next best action, or even pattern recognition [81]. Hospital information system creates “life sciences data,” “clinical data,” “administrative data,” and “social network data.” These data sources are overwhelmed with illness predictions, medical research, or even management and control of disease [39]. Big data analytics can be a future subject by helping HIS to cover data processing and disease pattern recognition.
\n
Smart house creates ground for real-time data with high complexity, which entitles big data analytics to overcome such sophistication. Classical methods of data analyzing lost their ability in front of evolutionary methods of classification and clustering. So graphic processing unit (GPU) for machine learning and data mining purposes bring advantage for large scale dataset [7], which leads the applications into lower cost of data analytics. Another way to create future research is to work over different frameworks like Spark, which is an in-memory computation, and with the help of big data analytics, optimization problems can be solved [20].
\n
Deployment of natural language processing (NLP) in text classification can be accompanied by different methods like CNN and RNN. These methods can gain the result with higher accuracy and lower time (Li et al., 2018).
\n
Predictive analytics offered by big data analytics works on developing predictive models to analyze large volume data both structured and unstructured with the goal of identifying hidden patterns and relations between variables in near future [76]. Big data analytics can help cognitive computing, and behavior pattern recognition deploys deep learning technique to predict future action as it is used to predict cancer in health care system [59]. It also leads organizations to understand their problems [13].
\n
So, future research can be focused on both the new area for application of different machine learning or deep learning algorithm for censored data gathered and also mixture of techniques that can create globally optimal solution with higher accuracy and lower cost. Researchers can put focus on existing problems of industries through mixed application of machine learning and deep learning techniques, which may results in optimize solution with lower cost and higher speed. They also can take identified algorithms in new area of industries to solve problems, create insight, and identify hidden patterns.
\n
In summary, future research can be done as it is shown in Figure 3.
\n
Figure 3.
Future research on big data analytics (BDA).
\n
\n
\n
8. Conclusion
\n
This chapter has been attempted to give an overview on big data analytics and its subfields, which are machine learning and deep learning techniques. As it is cited before, big data analytics has been generated to overcome the complexity of data managing and also create and bring knowledge into organizations to empower the performances. In this chapter, DNN, RNN, and CNN have been introduced as deep learning methods, and classification, clustering, and evolutionary techniques have been overviewed. Also, a glance at some techniques of every field has been given. Also, the application of machine learning and deep learning in IOT-based data is shown in order to make IOT data analytics much more powerful in phase of classification and decision-making. It has been identified that on the basis of rapid speed of data generation through IOT sensors, big data analytics methods have been widely used for analyzing real-time data, which can solve the problem of complexity of data processing. Hospital information systems (HIS), smart cities, and smart houses take benefits of to-the-point data processing by deploying fog and cloud platforms. The methods are not only deployed to create a clear picture of clusters and classifications of data but also to create insight for future behavior by pattern recognition. A wide variety of future research has been proposed by researchers, from customer pattern recognition to predict illness like cancer and all in between are comprised in area of big data analytics algorithms.
\n
\n
Acknowledgments
\n
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
\n
\n',keywords:"big data analytics, machine learning, deep learning, big data",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/69743.pdf",chapterXML:"https://mts.intechopen.com/source/xml/69743.xml",downloadPdfUrl:"/chapter/pdf-download/69743",previewPdfUrl:"/chapter/pdf-preview/69743",totalDownloads:699,totalViews:0,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,dateSubmitted:"February 18th 2019",dateReviewed:"May 14th 2019",datePrePublished:"October 24th 2019",datePublished:"February 19th 2020",dateFinished:null,readingETA:"0",abstract:"Companies and industries are faced with a huge amount of raw data, which have information and knowledge in their hidden layer. Also, the format, size, variety, and velocity of generated data bring complexity for industries to apply them in an efficient and effective way. So, complexity in data analysis and interpretation incline organizations to deploy advanced tools and techniques to overcome the difficulties of managing raw data. Big data analytics is the advanced method that has the capability for managing data. It deploys machine learning techniques and deep learning methods to benefit from gathered data. In this research, the methods of both ML and DL have been discussed, and an ML/DL deployment model for IOT data has been proposed.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/69743",risUrl:"/chapter/ris/69743",book:{slug:"social-media-and-machine-learning"},signatures:"Iman Raeesi Vanani and Setareh Majidian",authors:[{id:"296037",title:"Mrs.",name:"Setareh",middleName:null,surname:"Majidian",fullName:"Setareh Majidian",slug:"setareh-majidian",email:"setareh_majidian@atu.ac.ir",position:null,institution:{name:"Allameh Tabataba'i University",institutionURL:null,country:{name:"Iran"}}},{id:"296039",title:"Dr.",name:"Iman",middleName:null,surname:"Raeesi Vanaei",fullName:"Iman Raeesi Vanaei",slug:"iman-raeesi-vanaei",email:"Imanrv@gmail.com",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Big data and big data analytics",level:"1"},{id:"sec_3",title:"3. Big data analytics",level:"1"},{id:"sec_4",title:"4. Big data analytics and deep learning",level:"1"},{id:"sec_4_2",title:"4.1 Convolutional neural networks (CNN)",level:"2"},{id:"sec_5_2",title:"4.2 Deep neural network (DNN)",level:"2"},{id:"sec_6_2",title:"4.3 Recurrent neural network (RNN)",level:"2"},{id:"sec_8",title:"5. Big data analytics and machine learning",level:"1"},{id:"sec_8_2",title:"5.1 Machine learning and fuzzy logic",level:"2"},{id:"sec_9_2",title:"5.2 Machine learning and classification methods",level:"2"},{id:"sec_10_2",title:"5.3 Machine learning and clustering",level:"2"},{id:"sec_11_2",title:"5.4 Machine learning and evolutionary methods",level:"2"},{id:"sec_12_2",title:"5.5 Genetic algorithms (GA)",level:"2"},{id:"sec_13_2",title:"5.6 Ant colony optimization (ACO)",level:"2"},{id:"sec_14_2",title:"5.7 Bee colony optimization (BCO)",level:"2"},{id:"sec_15_2",title:"5.8 Particle swarm optimization (PSO)",level:"2"},{id:"sec_16_2",title:"5.9 Firefly algorithm (FA)",level:"2"},{id:"sec_17_2",title:"5.10 Tabu search algorithm (TS)",level:"2"},{id:"sec_19",title:"6. Big data analytics and Internet of Things (IOT)",level:"1"},{id:"sec_20",title:"7. Future research directions",level:"1"},{id:"sec_21",title:"8. 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Information Technology Management, Allameh Tabataba’i University, Iran
Information Technology Management, Allameh Tabataba’i University, Iran
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1. Introduction
Epilepsy is one of the chronic brain disorders characterized by recurrent seizures due to abnormal excessive electrical discharges of cerebral neurons [1]. It is believed that genetic factors play a crucial role in the etiopathogenesis of epilepsy. So far ~1000 genes have been proved to be associated with epilepsy, among which genes encoding VGIC predominate [2].
VGICs are pore-forming membrane proteins. Their functions include establishing APs and maintaining homeostasis by gating the ionic flow traversing the cell membrane, managing the ionic flow across cells and regulating Ca2+ signal transduction, which are essential to the neuroexcitability, so VGICs are potentially involved in epileptogenesis [2]. The association of VGIC genes and epilepsy might provide insights into the etiopathogenesis underlying epilepsy. Pathophysiological studies illuminated that two key defects are (i) a neuronal disinhibition induced by loss-of-function of VGIC gene expressed specifically in inhibitory interneurons (for example, Nav1.1 and P/Q VGCCs) or (ii) dysfunction of axon initial segments, the neuronal structure in which APs are generated and many VGICs (such as Nav1.2 and Kv7) are mainly localized (Figure 1). Moreover, clinically originated studies identified novel genes, defined their neuronal functions, and sometimes established novel physiological principles [2].
Figure 1.
Neuronal localization of some relevant voltage-gated ion channels. A schematic view of an excitatory pyramidal (orange), an inhibitory (green) neuron, and their synaptic connections is shown. Distinctive intracellular compartments are targeted by different populations of VGICs. Examples of which as mentioned in this chapter are shown here: in the somatodendritic compartment, Nav, Cav (L- and T-type), TRP, BK, and Kv channels; at axon initial segments (AIS) and nodes of Ranvier in pyramidal neurons, Nav1.2, Kv7 channels; at AIS of inhibitory neurons, Nav1.1; in the somatodendritic compartment of inhibitory neurons, BK and Nav1.6; in the presynaptic terminals, Cav P/Q type. GOF represents the gain-of-function mutation of VGICs-induced human epilepsy. LOF represents the loss-of-function mutation of VGICs.
In this chapter, we summarize the epilepsy-associated VGIC genes, the mutations, corresponding phenotypes, and functional changes, aiming to provide clues for evaluating the relationship between VGIC genes and epileptogenesis.
2. Voltage-gated sodium channels
VGSCs play a critical role in the generation and propagation of APs in neurons, genetic alterations in VGSC genes are considered to be associated with epileptogenesis. Mammalian VGSC is composed of a large pseudotetrameric pore-forming α subunit with a molecular weight of 260 KDa, and one or more auxiliary β subunits (30–40 KDa) [3, 4, 5] (Figure 2). Nine subtypes of VGSC α subunits have been found in humans, including Nav1.1-Nav1.9, encoded by the genes SCN1A-SCN5A, SCN8A-SCN11A, respectively.
Figure 2.
Structure of voltage-gated sodium channels. Schematic representation of VGSC subunits. The α subunit of the VGSC is illustrated together with β1 and β2 subunits; extracellular domains of the β subunits are shown as immunoglobulin-like folds, interacting with the loops in α subunits. Roman numerals indicate the domains of the α subunit; segments 5 and 6 (shown in green) are the pore-lining segments, and S4 helices (red) make up the voltage sensors. The red circle in the intracellular loop of domains III and IV indicates the inactivation gate IFM motif; Ψ, probable N-linked glycosylation site. The circles in reentrant loops in each domain represent the residues that form the ion selectivity filter.
2.1 Nav1.1
Nav1.1 is mainly distributed in the inhibitory GABAergic neurons of cerebellum and hippocampus. The Nav1.1 gene SCN1A is the clinically most relevant SCN gene for epilepsy. More than 1200 mutants have been identified to be associated with epilepsy; most of them are febrile seizures [6]. M145T mutation, a well-conserved amino acid in the first transmembrane segment of domain I of the Nav1.1 α-subunit, caused a reduction in peak sodium currents and a positive shift in the voltage dependence of activation [7], which provided the first evidence that the mild loss-of-function mutations in Nav1.1 may cause a significant portion of febrile seizures. Complete loss-of-function mutations in Nav1.1 cause severe myoclonic epilepsy of infancy (SMEI or Dravet’s syndrome), which includes severe, intractable epilepsy and comorbidities of ataxia and cognitive impairment. Besides, homozygous null Nav1.1−/− mice developed ataxia and died on half a month of postnatal and did not change the voltage-dependent activity of VGSCs in hippocampal neurons. However, heterozygous Nav1.1+/− mice exhibited spontaneous seizures and sporadic deaths after 3 weeks, and the sodium current density was substantially reduced in inhibitory interneurons, except in excitatory pyramidal neurons [8]. So loss-of-function mutations in Nav1.1 can severely impair sodium currents and AP firing in hippocampal GABAergic inhibitory neurons. The functional downregulation in inhibitory neurons might cause the hyperexcitability of dentate granule or pyramidal neurons, which could lead to epilepsy in patients with SMEI. Experiments in mice have demonstrated that haploinsufficiency of Nav1.1 channels is sufficient to allow induction of seizures by elevated body temperature, supporting that haploinsufficiency of SCN1A is pathogenic in human SMEI which has striking temperature and age dependence of onset and progression of epilepsy [9]. What is more, SCN1A mutations were mostly missense mutations in GEFS+ patients, which are typically well controlled by treatment with antiepileptic drugs and no cognitive impairment is observed. The R1648H channels showed the reduced function in both excitatory and inhibitory neurons although the biophysical mechanisms were different, reducing peak sodium currents and enhancing slow inactivation in inhibitory neurons versus negatively shifted voltage dependence of fast inactivation in excitatory neurons [10]. The similar conclusion had been drawn when the R1648H mutation has been inserted into the mouse genome under the native promoter [11]. In light of these results, GEFS+ and SMEI may be caused by a continuum of mutational effects that selectively impair firing of GABAergic inhibitory neurons, which lead to increase in the excitability of the neural network [12].
2.2 Nav1.2
The mutation of the Nav1.2 gene SCN2A is associated with various epilepsies, such as benign familial neonatal seizures (BFNIS), hereditary epilepsy with febrile seizures plus (GEFS+), Dravet’s syndrome (DS), and other stubborn childhood epilepsy encephalopathy. Nav1.2 subunit is mainly distributed in the axon-initiating segment (AIS) and node of Ranvier. SCN2A mutations cause changes in VGSC function and expression and result in abnormal neuronal discharge. Because Nav1.2 plays an important role in the AIS area during the development, it is more common for infants to show SCN2A mutant-induced epilepsy encephalopathy [13]. BFNIS is the most common phenotype caused by gain-of-function missense mutations in SCN2A [14]. Up to now, at least 10 SCN2A mutations associated with BFNIS have been identified. SCN2A mutations are also found to result in the reduced expression of Nav1.2 on the surface of neurons [15]. Therefore, SCN2A mutants will lead to the decrease of sodium current density at node of Ranvier and AIS, seriously affecting the excitability of neurons [16]. For missense mutation of SCN2A, p.Tyr1589Cys causes a depolarizing shift of steady-state inactivation, increased persistent Na+ current, a slowing of fast inactivation, and an acceleration of its recovery, which contribute to neuronal hyperexcitability and familial epilepsy [17]. Due to the SCN2A mutation, early infantile epileptic encephalopathy (EIEE) patients with burst suppression and tonic-clonic migrating partial seizures showed a specific dose-dependent efficacy of VGSC blockers [18]. It is mainly caused by the dysfunction of VGSC [19]. By replacing neonatal Nav1.2 with adult Nav1.2 in mice, it has been suggested that neonatal Nav1.2 reduced neuronal excitability and had a significant impact on seizure susceptibility and behavior.
2.3 Nav1.3
The SCN3A gene, clustered on human chromosome 2q24, encodes the Nav1.3 subtype [20], which is usually located in the soma of neurons. It is important in the integration of synaptic signals, determination of the depolarization threshold, and AP transmission [21]. In contrast to the rodent gene which is transiently expressed during development, human SCN3A is widely expressed in adult brain [22]. The first epilepsy-associated mutation (K354Q) in SCN3A was found in 2008. K354Q mutation decreased inactivation rate and increased INaP [23]. The mutation is not sensitive to antiepilepsy drug carbamazepine or oxcarbazepine. K354Q mutation causes neuronal abnormal spontaneous discharge and membrane potential paroxysmal depolarization [24]. In 2014, four more missense variants were identified in SCN3A, which are R357Q, D766N, E1111K, and M1323V [25]. Compared to wild-type channels, R357Q caused smaller currents, slower activation, and depolarized voltage dependences of activation and inactivation. The E1111K mutation evoked a significantly greater level of persistent sodium current. All four mutants increase current activation in response to depolarizing voltage ramps. These findings support for a contribution of Nav 1.3 to childhood epilepsy. Recently, a novel SCN3A variant (L247P) was identified by whole exome sequencing of a child with focal epilepsy, developmental delay, and autonomic nervous system dysfunction. Voltage clamp analysis showed no detectable sodium currents in a heterologous expression system. To further test the possible clinical consequences of reduced SCN3A activity, they investigated the effect of a hypomorphic Scn3a allele (Scn3a Hyp) on seizure susceptibility and behavior using a gene trap mouse line. Heterozygous SCN3A mutant mice (SCN3A+/Hyp) neither exhibit spontaneous seizures nor hyperthermia-induced seizures, but they displayed increased susceptibility to electroconvulsive- and chemiconvulsive-induced seizures, which provide evidence that loss-of-function of SCN3A may contribute to increased seizure susceptibility [26].
2.4 Nav1.6
Nav1.6, mainly distributed to the soma and synaptic origin, is important for APs generation and propagation [27]. In the development process, Nav1.2 is gradually replaced by Nav1.6 in the mature node of Ranvier [28]. The first heterozygous missense mutation (p.Asn1768Asp) in the Nav1.6 gene SCN8A was identified in 2012 by whole-genome sequencing (WGS) in a patient with severe epileptic encephalopathy who exhibited early-onset seizures, autistic features, intellectual disability, ataxia, and sudden unexpected death in epilepsy (SUDEP) [29]. Since this initial discovery, more than 100 pathogenic SCN8A variants have been identified in patients with epilepsy [30]. Most of the SCN8A variants have been detected in individuals with EIEE.
Different mutations in the SCN8A gene encoding Nav1.6 have different effects on epilepsy. For the missense mutation V929F, an evolutionarily conserved residue in the pore loop of domain II of Nav1.6, it was found that heterozygous mutations produced well-defined spike-wave discharges and are associated to absence epilepsy in mice [31]. However, missense mutations in Scn8amed−jo were able to improve the epilepsy symptoms of SCN1A+/− heterozygotes. The mechanism might be the decrease in Nav1.6 expression of excitatory neurons compensating for the loss of Nav1.1 in inhibitory neurons [32]. Recently, more and more de novo and inherited SCN8A epilepsy mutations were detected by gene panel analysis [33]. For example, loss-of-function mutants [34], underlying the complex seizure phenotype, were identified using specific mouse line. It was suggested that decreasing Scn8a expression in cortical excitatory neurons could reduce seizures. On the contrary, the decreasing expression of SCN8A in the thalamic reticular nucleus (RT) leads to absence seizures. Loss of Scn8a will impair tonic firing mode behavior and impair desynchronizing recurrent RT-RT synaptic inhibition in the thalamic reticular nucleus, which suggested that Scn8a-mediated hypofunction in cortical circuits, conferring convulsive seizure resistance, while hypofunction in the thalamus is sufficient to generate absence seizures.
2.5 Nav1.7
The SCN9A gene encodes the Nav1.7 subtype, which was initially identified in the peripheral nervous system, sympathetic ganglion, and olfactory sensory neurons [35, 36, 37, 38]. Nav1.7 is also found expressed in the central nervous system such as in the cerebral cortex and hippocampus [39]. A missense mutation of SCN9A (N641Y), at a conserved amino acid residue located at the intracellular loop between domain I and II, is associated with a family of febrile seizures (FS, N641Y). Mice carrying N641Y mutations were more susceptible to electrical stimulation-induced clonic and tonic seizures [40]. However, it is still unclear how SCN9A gene mutation caused epilepsy in the CNS.
3. Potassium channels
K+ channels control the resting membrane potential and enable rapid repolarization of the AP by producing outward K+ currents, thus limiting neuronal excitability. K+ channels are composed of four pore forming a subunits and modulatory b subunits. Kv channels are the largest ion channel group (Kv1–Kv12) that are expressed substantially in the CNS. Dysfunction of Kv channels including Ca2+-activated K+ channels, are associated with epilepsy [2].
3.1 Large conductance calciumactivated potassium channel
Large conductance calcium-activated potassium (BK) channels, consisting of functional α subunit and the tissue-specific regulatory subunits (β1–4 and γ1–4), are widely distributed in the CNS. BK channels are usually considered as vital players in the development of epilepsy (Figure 3), with the evidence including the K+ derangement and regulating AP shape and duration [41, 42].
Figure 3.
Yin and Yang of BK channels in epilepsy. For epilepsy suppression, BK (α) channels act as negative feedback regulators on calcium rise and transmitter release in most synapses. Activation of mitoBK channel subtypes (α or α+β4) may contribute to suppressing seizure as well as conferring neuroprotection via the inhibition of ROS synthesis [54]. For epilepsy promotion, astrocyte and OPCs BK channel subtypes (α+β1 or α+β4) may induce elevate [K+]o, causing membrane depolarization as well as neuronal hyperexcitation. Microglial BK channels (α+β3) may involve in the neuroinflammation during status epilepsy. Mutation D434G of α causes the neurohyperexcitation in hereditary epilepsy. However, ubiquitin ligase CRL4ACRBN could inhibit the overactivation of BK channels.
Gain-of-function mutation of BK, promoting the high-frequency neuron firing, is associated with spontaneous epileptic seizures paradoxically in both humans and rodents [43]. In fact, patients suffering from generalized epilepsy were detected a site mutation D434G at the RCK1 domain of BK α subunit. D434G increased the opening time of BK, through the enhancement of Ca2+ sensitivity [43]. In terms of functionality, the enhanced membrane excitability is associated with the increased BK activity and fAHP consequent [43, 44]. The augment seems to be induced by an increased recovery rate, underlying fast currents of VGSCs with a APs’ reduced refractory period and/or through disinhibiting thalamocortical circuits by blocking brain GABAergic interneurons [43, 45, 46].
The knockout mice of BK channel β4 subunit exhibit temporal lobe epilepsy (TLE) seizure associated with a gain-of-function phenotype of BK, which not only sharpens APs but also induces a higher neuronal firing frequency in hippocampus DG granule cells [47].It is worth mentioned that epileptic seizures themselves also could induce a gain-of-function effect to BK. Picrotoxin and pentylenetetrazol (PTZ) caused generalized tonic-clonic epileptic seizures, with giving rise to a gain-of-function effect on BK channels, presenting increased BK currents and neuron firing in the neocortex [48]. It is of interest that BK-specific inhibitors attenuated generalized tonic-clonic epileptic seizures in picrotoxin or PTZ-induced epilepsy models, which suppressed the increase of neuron firing [48, 49].
Loss-of-function phenotype of BK might also contribute to the pathological process of clinical TLE. It was reported that two siblings suffered from the severe cerebellar atrophy and developmental delay, who adopted the exome analysis that identified a homozygous frameshift duplication in BK gene KCNMA1 (c.2026dupT;p.(Tyr676 Leufs*7)) in children from a consanguineous family with epilepsy [50].
KCNMB3, encoding the auxiliary BK β3, mapping the human chromosome 3 (3q26.3-q27) [51], is duplicated in the dup (3q) syndrome, which is characterized by neurological abnormalities, especially epileptic seizures [51]. Because of the dup (3q) syndrome having early onset during developmental process, the KCNMB3 duplication implies that β3 subunits overexpression might contribute to the etiology of epilepsy. Similarly, site mutations might also contribute to both neurohyperexcitation by a single nucleotide deletion at KCNMB3 exon 4 (delA750), which is associated with the generalized epilepsy, especially in the form of the typical absence epilepsy [52]. BK coexpressed with β3 variant of β3b-V4 (delA750) shows fast inactivation properties [53], which suggest that BK currents were reduced and the repolarization of cell membrane was attenuated during an action potential, eventually leading to neurohyperexcitation.
Kv7 is its seventh member of Kv channel family (Kv1–Kv12). The Kv7.1 mutation mediates type 1 long QT syndrome (long-QT syndrome type 1, LQT1) and is therefore named KCNQ1 (K, potassium; CN, channel; Q, LQT). KCNQ has five subtypes of KCNQ1–KCNQ5, which play crucial roles in physiological functions. Dysfunction of KCNQ is associated with many diseases.
KCNQ1 is mainly distributed in the heart, which mediates cardiac delayed-rectifier K+ current and maintains the normal repolarization process of cardiomyocytes [55]. KCNQ2–KCNQ5 are mainly distributed in central and peripheral neuronal tissues, of which KCNQ2 and KCNQ3 are distributed in brain regions [56]. KCNQ2 and KCNQ3 form functional heterotetramers, which are the main molecular bases for the formation of M currents that can be inhibited by acetylcholine M1 receptor activation [57]. Abundant KCNQ2 and KCNQ3 mutations could induce abnormal M currents, causing similarities in neonatal seizures and other nervous system diseases.
Benign familial neonatal seizure (BFNS) is an autosomal dominant idiopathic epilepsy syndrome that occurs on the 2nd to 8th day after birth and stops spontaneously after a few weeks. Whereas 15% of patients in later life may have recurrence of epilepsy [58]. With the study of pathogenic genes in epilepsy, 60–70% of patients with BFNS were found to be associated with KCNQ2 and KCNQ3 mutations. More than 80 different mutations have been reported on KCNQ2, and multiple mutations on KCNQ3 are associated with BFNS. Soldovieri et al. [58] studied the genes of 17 BFNS clinical patients. Sixteen different heterozygous mutations were found in KCNQ2, including 10 substitutions, 3 insertions/deletions, and 3 large deletions. One substitution was found in KCNQ3. Most of these mutations were novel, except for four KCNQ2 substitutions that were shown to be recurrent. Electrophysiological studies in mammalian cells revealed that homomeric or heteromeric KCNQ2 and/or KCNQ3 channels carrying mutant subunits with newly found substitutions displayed reduced current densities. Borgatti studied a BFNS family with four affected members: two of them exhibit BFNS only, while the other two, in addition to BFNS, present either with a severe epileptic encephalopathy or with focal seizures and mental retardation. All affected members of this family carry a novel missense mutation in the KCNQ2 gene (K526N), disrupting the tridimensional conformation of a C-terminal region of the channel subunit involved in accessory protein binding. When heterologously expressed in CHO cells, potassium channels containing mutant subunits in homomeric or heteromeric configuration with wild-type KCNQ2 and KCNQ3 subunits exhibit an altered voltage-dependence of activation, without changes in intracellular trafficking and plasma membrane expression. The KCNQ2 K526N mutation might affect M-channel function by disrupting the complex biochemical signaling involving KCNQ2 C-terminus [59, 60]. KCNQ2 or KCNQ3 mutations cause M current to be downregulated, and the frequency of neuronal firing increases, leading to epilepsy.
3.3 G protein-coupled Kir channel
Inward-rectifier potassium channels (Kir, IRK) are a specific subset of potassium channels. To date, seven subfamilies have been identified, which are associated with a variety of diseases [61]. The G-protein-coupled Kir (GIRK) channels belong to the subfamily of Kir3 (GIRKs) which are activated by ligand-stimulated G protein-coupled receptors (GPCRs). GPCRs, interacting with GIRK channels, facilitate their activation, resulting in hyperpolarization of the cell membrane [61].
GIRK channels have four identified subunits (GIRK1–4, encoded by KCNJ3, KCNJ6, KCNJ9, and KCNJ5, respectively) in mammals, existing in vivo both as homotetramers and heterotetramers with unique biophysical properties, regulation, and distribution [61, 62]. GIRK 1, 2, 3, and 4 subunits are expressed in the brain, localized in certain axons, postsynaptic, and presynaptic regions [63]. GIRK channels may be involved not only in slow inhibitory postsynaptic potentials but also in the presynaptic modulation of neuronal activity [61].
GIRK in the CNS is a heterotetramer composed of GIRK1 and GIRK2 subunits [63], which is responsible for maintaining the resting membrane potential and excitability of the neuron [64]. GIRK1 and GIRK2 subunits are found in the dendritic areas of neurons highly [63] correlate with the large concentration of GABAB receptors. Once the GABAB receptors are activated by their ligands, they can in turn activate IRK, mediating a significant part of the GABA postsynaptic inhibition [63].
Alterations in GIRK function have been associated with pathophysiology of severe brain disorders, including epilepsy. In this regard, a GIRK2 knockout mouse model resulted to be more susceptible to develop both spontaneous and induced seizures in respect to wild-type mice [65]. In particular, mice carrying a p Gly156Ser mutation displayed an epileptic phenotype [66]. Indeed, this mutation has been found to alter the putative ion-permeable, pore-forming domain of the channel, inducing Ca2+ overload in cells and reducing channel availability, leading thus to neurodegeneration and seizure susceptibility [67].
An increased expression of GIRK was observed in rat brain after an electroconvulsive shock, probably altering the excitability of granule cells and the functions of neurotransmitter receptors which are coupled to these channels [68]. Another evidence in support of a role of GIRK in epilepsy was provided by the demonstration that ML297, a potent and selective activator of GIRK, showed epileptogenic properties in mice [69]. On the other hand, the inhibition of GIRK activity by drugs causes seizures [70]. All these considerations imply that changes in Kir3 channel activity may alter the susceptibility to seizures.
4. Calcium channels
As an important second messenger, Ca2+ plays a vital role in normal brain function and in the pathophysiological process of different neurodegenerative diseases. Ca2+ entry via VGCCs conveys the electric signals to intracellular transduction cascades in a wide variety of cells [71]. VGCCs were first identified by Fatt and Katz [72] and shown to consist of several subunits [73, 74]. VGCCs were divided into low-voltage-activated (LVA) and high-voltage-activated (HVA), based on electrophysiological and pharmacological properties. HVA channels, composed of α1, β, α2δ, γ subunit, are further divided into L, N, P, and Q types, which have an activation threshold at membrane voltage positive to −20 mV [75]. LVA channels, also called T type, consist only of the α1 subunit, activated at a membrane voltage positive to −70 mV. It is composed of transmembrane topology with four homologous transmembrane domains, each containing six transmembrane segments and a pore region between segments S5 and S6.
4.1 L-type Cav
The L-type VGCC family has four members, Cav1.1–1.4, of which α subunits present tissue-specific expression, such as the α1D subunit in the brain. The L-type VGCC family shapes neuronal firing and activates Ca2+-dependent pathways involved in regulation of gene expression [76]. Cav1.2 channels appear to contribute critically to the generation of febrile seizures, which was proved by testing the excitability of hippocampal pyramidal cells in rat brain slices [77]. The Wistar Albino Glaxo/Rij (WAG/Rij) model experiments suggest that L-type calcium channels play a positive role in the frequency and duration of epileptic spikes [78]. Verapamil, an L-type VGCC blocker, could significantly reduce TLE seizure, enhancing the expression of the α subunit of γ-GABAAR [79].
4.2 P/Q-, N-, and R-type Cav
P/Q-, N-, and R-type are corresponding to Cav2.1, Cav2.2, and Cav2.3, respectively, which initiate rapid synaptic transmission, regulated primarily by direct interaction with G proteins and SNARE (soluble N-ethylmaleimide-sensitive factor attachment protein receptor) and secondarily by protein phosphorylation. The loss function of P/Q VGCC could lead to epileptic spikes, paroxysmal dystonia and ataxia. If P/Q VGCCs were blocked, it could disrupt the triggering synaptic neurotransmitter release [80]. Spikes of Cacna1aNtsr−/− mice are increased in layer VI corticothalamic neurons compared with control group, suggesting that Cav P/Q deletion generates absence epilepsy [81]. Cacna1a LOF from parvalbumin (PV)(+) and somatostatin (SST)(+) interneurons results in severe generalized epilepsy. It might be the mechanism for severe generalized epilepsy that the loss of Cav2.1 channel function from cortical PV(+) interneurons inhibits GABA release from these cells, which impairs their ability to constrain cortical pyramidal cell excitability [82]. When knocking out the cerebellar Cav2.1 channel in mice, cortical function is changeable, which caused movement disorders and epilepsy [83]. In two families with idiopathic epilepsy, the loss of function mutation in γ4 subunits, auxiliary subunit of Cav2.1 channels, could also cause seizures, and maybe aggravate seizures [84]. Downregulation of α2δ2 subunits in rats will generate 5–7 Hz epileptic wave accompanied by ataxia [85]. N-type calcium channels are mainly distributed in the nucleus of different neurons and glial cells. In the pilocarpine model, Cav2.2 expression decreased in the granule layer of the dentate gyrus and the pyramidal cells of the CA3 region during the acute phase of seizure. However, the expression of N-type calcium channels increased in the subsequent chronic phase, which demonstrated that the increase of N-type calcium channels might be associated with recurrent status epilepticus [86]. R-type calcium channel, Cav2.3, is mainly distributed in the presynaptic membrane, such as hippocampal mossy fibers, globus pallidus, and neuromuscular junctions. Knocking out R-type calcium channels could increase the susceptibility of seizures, with altering the seizure form [87]. The lack of Cav2.3 resulted in a marked decrease in the sensitivity of the animal to γ-butyrolactone-induced absence epilepsy and change thalamocortical network oscillations [88]. Administration of kainic acid revealed alteration in behavioral seizure architecture, dramatic resistance to limbic seizures and excitotoxic effects in Cav2.3−/− mice compared with controls. It indicated that the Cav2.3 plays a crucial role in both hippocampal ictogenesis and seizure generalization and is of central importance in neuronal degeneration after excitotoxic events [89].
4.3 T-type Cav
T-type channels, widely distributed in the thalamus, are important for the repetitive firing of APs in rhythmically firing cells, which could be activated and inactivated more rapidly at more negative membrane potentials than other VGCCs [90]. Three subtypes of T-type channels have been identified, designated as Cav3.1, Cav3.2, and Cav3.3; they correspond to complexes containing the pore-forming α1 subunits, α1G, α1H, and α1I, respectively [91]. It has long been suggested that generalized absence seizures are accompanied by hyperexcitable oscillatory activities in the thalamocortical network [92]. The evidence that succinimide and related anticonvulsants could block thalamic T-type channels make researchers speculate that T-type Ca2+ channels might be related to the pathogenesis of spike-and-wave discharges (SWDs) in generalized absence seizures [93]. In the kainate epilepsy model, Cav3.1−/− mice display significantly reduced duration of seizures compared to wild type, but the frequency of seizures increased slightly [94]. In the WAG/Rij model, the expression of Cav3.1 may be related to age, and blocking Cav3.1 can reduce the onset of epilepsy [94, 95] which suggested that decrease in Cav3.1 channel expression and Ca2+ current component that they carry in thalamocortical relay neurons serves as a protective measure against early onset of SWD and absence seizures [96]. Notably, Cav3.1−/− mice are resistant to SWD seizures specifically induced by γ-GABABR agonists. Simultaneously, the γ-GABABR agonists induced only very weak and intermittent SWDs in Cav3.1−/− mice [97]. Cav3.2 single nucleotide mutation has been reported in patients with childhood absence epilepsy and other types of idiopathic generalized epilepsies [98, 99]. Gain-of-function mutations (C456S) in Cav3.2 channels increase seizure susceptibility by directly altering neuronal electrical properties and indirectly by changing gene expression [100].
5. Transient receptor potential channels
Transient receptor potential (TRP) channels, which could induce a transient voltage changes to continuous light mutations of Drosophila melanogaster, are expressed in photoreceptors carrying trp gene. The first homologous human gene was reported in 1995. There are 30 trp genes, and more than 100 TRP channels have been identified so far, and TRP channels were divided into 7 subfamilies, including TRPC, TRPV, TRPM, TRPA, TRPP, TRPML, and TRPN. Focus on TRPs, one family of Ca2+ channels, plays a role in neuronal excitability. It is obviously known that Ca2+ is an important second messenger, which is related to the etiology of epilepsy [101]. Therefore, TRP channels are thought to be partially responsible for epileptic seizures, especially for TPRC and TRPV1 channels.
5.1 Canonical transient receptor potential (TRPC)
TRPC channels are the closet homolog to Drosophila TRP channels. Based on the functional comparisons and sequence alignments, four subsets of mammalian TRPCs (TRPC1, TRPC2, TRPC3/6/7, and TRPC4/5) have been generated [101]. These channels form receptor-modulated currents in the mammalian brain and important to SE-induced neuronal cell death. These channels could play a critical role in the generation of spontaneous seizures. TRPC1 and TRPC4 are expressed in CA1 pyramidal neurons. The amplitude of the plateau and the number of spikes were significantly reduced in mice without TRPC1 and TRPC4 [102]. TRPC3 channels are found to be responsible for pilocarpine-induced status epilepticus (SE) in mice. The reduction on SE in TRPC3 KO mice is caused by a selective attenuation of pilocarpine-induced theta wave activity [103]. TRPC7 can be detected in CA3 pyramidal neurons largely. The spontaneous seizures in CA3 pyramidal neurons and the pilocarpine-induced increase in gamma wave activities during the latent period could be significantly reduced by ablating the gene TRPC7 [104].
TRPV1 is one subfamily of TRP channels, expressing in most neurons. The expression of TRPV1 protein in epileptic brain areas was increased [105], but the epileptic activity in hippocampal slices was decreased by iodoresiniferatoxin (IRTX), a selective TRPV1 channel antagonist [106]. It is well known that glutamate could be released when the TRPV1 channel was activated [107], and the glutamate neurotransmitters are related to the etiology of epilepsy. Thus, focusing the TRPV1 channels activity may be important for the modulating neuronal excitability in epilepsy [106]. Recent studies showed that the high expression of TRPV1 channels could induce the temporal lobe epilepsy [105]. Cytosolic calcium elevation through activation of TRPV1 channels plays a physiologically relevant role in the regulation of epileptic seizures [108], decreasing the calcium accumulation by inhibiting the TRPV1 channels, could play a neuronal protective role against epilepsy-induced Ca2+ entry in hippocampal neurons. As mentioned above, the TRPV1 could be activated by hyperthermia; the hyperthermia-induced TRPV1 might be an effective candidate therapeutic target in heat-induced hyperexcitation [109, 110]. The activation of TRPV1 promotes glutamate release by increasing the excitability of neurons and synaptic terminals [111]. Whereas the activities would be reduced in hippocampus slices of rats after given the CPZ and ITRX, which were the TRPV1 channel blockers.
6. Antiepileptic therapy and beyond
At present, the treatment of epilepsy is still dominated by drugs. More than 35% of marketed antiepileptic drugs target VGICs, such as phenytoin, carbamazepine, oxcarbazepine, and ethosuximide. Phenytoin and carbamazepine are broad-spectrum antiepileptic drugs blocking VGSCs as their primary mechanism of action. For example, phenytoin is a more effective inhibitor of SCN8A-I1327V than other drugs [112], which could be used in treating patients with gain-of-function mutations of SCN8A. Different types of VGCCs play different roles in the pathological process of epilepsy. Decreased expression of P/Q type could induce epilepsy, whereas increased expression of N-type and T-type calcium channels could lead to epilepsy. Calcium blockers including ethosuximide have been widely accepted for the treatment of absence epilepsy [71]. Gain-of-function BK channels contribute to epileptogenesis and seizure generation. BK-blocking agents, like paxilline [49], might be used as potential therapeutic drugs.
In the future, novel techniques might contribute to develop reasonable therapies for treating inherited or acquired epileptic syndromes. For instance, induced pluripotent stem cells (IPS) and genetically engineering animal models could be used for accurate treatments of epilepsy. Single-nucleotide polymorphisms (SNPs) of VGIC genes from hereditary epilepsy patients could be detected by de novo genomic sequencing. VGICs of IPS cells could be mutated by CRISPR-Cas9 according to the information of these SNPs [113]. Through inducing IPS cells differentiated into neurons, phenotype of VGIC gene SNPs could be well investigated. It is also a well-detection platform for selecting antiepileptic drugs that would be sensitive to mutated VGICs in vitro [112]. For in vivotests, besides transgenic mice, construction of nematode or zebrafish epileptic models may be creating a shortcut for choosing suitable and personalized antiepileptic drugs [114, 115]. In addition to drug control, optogenetics and ultrasonic control are hopeful to suppress the epileptic seizures induced by VGIC dysfunction [116, 117].
7. Conclusion
We systemically summarized the mutations and phenotype information of 21 epilepsy-associated VGIC genes. The dysfunctional VGICs are like the blasting fuse for neuronal hyperexcitability. We have good reason to believe that epilepsy-associated mutations of VGICs could be considered as a biomarker, which is possible to be one of the molecular bases underlying the classification of epilepsy syndromes identified by modern medicine. VGICs are the important targets for many antiepileptic drugs. Novel VGIC modulators are potentially effective strategy for the development of novel antiepileptic drugs. Individualized precise treatment using matching VGIC drugs will provide novel research directions and antiepileptic strategies.
Acknowledgments
This work was supported by National Science Foundation of China (Nos. 81603410 and 31771191), Innovation Fund of Putuo District Health System (No. 17-PT-10), Shanghai Municipal Commission of Health and Family Planning Fund (Nos. 20184Y0086, 2016JP007, and 2018JQ003), Project within budget of Shanghai University of Traditional Chinese Medicine (No. 18TS086), the Key Speciality Program (No. 2016102A) and Research Project (No. 2016208A) of Putuo Hospital, Shanghai University of Traditional Chinese Medicine.
Conflict of interest
The authors confirm that this article content has no conflict of interest.
\n',keywords:"ion channels, VGSCs, Kv channels, Cav channels, TRPs, mutation, epilepsy",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/65083.pdf",chapterXML:"https://mts.intechopen.com/source/xml/65083.xml",downloadPdfUrl:"/chapter/pdf-download/65083",previewPdfUrl:"/chapter/pdf-preview/65083",totalDownloads:858,totalViews:0,totalCrossrefCites:0,dateSubmitted:"October 4th 2018",dateReviewed:"December 20th 2018",datePrePublished:"January 11th 2019",datePublished:"November 13th 2019",dateFinished:null,readingETA:"0",abstract:"Voltage-gated ion channels (VGICs), extensively distributed in the central nervous system (CNS), are responsible for the generation as well as modulation of neuroexcitability and considered as vital players in the pathogenesis of human epilepsy, with regulating the shape and duration of action potentials (APs). For instance, genetic alterations or abnormal expression of voltage-gated sodium channels (VGSCs), Kv channels, and voltage-gated calcium channels (VGCCs) are proved to be associated with epileptogenesis. This chapter aims to highlight recent discoveries on the mutations in VGIC genes and dysfunction of VGICs in epilepsy, especially focusing on the pathophysiological and pharmacological properties. Understanding the role of epilepsy-associated VGICs might not only contribute to clarify the mechanism of epileptogenesis and genetic modifiers but also provide potential targets for the precise treatment of epilepsy.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/65083",risUrl:"/chapter/ris/65083",signatures:"Shuzhang Zhang, Yudan Zhu, Jiwei Cheng and Jie Tao",book:{id:"7860",title:"Epilepsy",subtitle:"Advances in Diagnosis and Therapy",fullTitle:"Epilepsy - Advances in Diagnosis and Therapy",slug:"epilepsy-advances-in-diagnosis-and-therapy",publishedDate:"November 13th 2019",bookSignature:"Isam Jaber Al-Zwaini and Ban Adbul-Hameed Majeed Albadri",coverURL:"https://cdn.intechopen.com/books/images_new/7860.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"30993",title:"Prof.",name:"Isam Jaber",middleName:null,surname:"Al-Zwaini",slug:"isam-jaber-al-zwaini",fullName:"Isam Jaber Al-Zwaini"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"279620",title:"Associate Prof.",name:"Jie",middleName:null,surname:"Tao",fullName:"Jie Tao",slug:"jie-tao",email:"jietao@shu.edu.cn",position:null,institution:null},{id:"279631",title:"Dr.",name:"Shuzhang",middleName:null,surname:"Zhang",fullName:"Shuzhang Zhang",slug:"shuzhang-zhang",email:"zsz182@shu.edu.com",position:null,institution:null},{id:"279632",title:"Dr.",name:"Yudan",middleName:null,surname:"Zhu",fullName:"Yudan Zhu",slug:"yudan-zhu",email:"yudanzhu_putuo@foxmail.com",position:null,institution:null},{id:"279639",title:"Dr.",name:"Jiwei",middleName:null,surname:"Cheng",fullName:"Jiwei Cheng",slug:"jiwei-cheng",email:"chengjiwei1@126.com",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Voltage-gated sodium channels",level:"1"},{id:"sec_2_2",title:"2.1 Nav1.1",level:"2"},{id:"sec_3_2",title:"2.2 Nav1.2",level:"2"},{id:"sec_4_2",title:"2.3 Nav1.3",level:"2"},{id:"sec_5_2",title:"2.4 Nav1.6",level:"2"},{id:"sec_6_2",title:"2.5 Nav1.7",level:"2"},{id:"sec_8",title:"3. Potassium channels",level:"1"},{id:"sec_8_2",title:"3.1 Large conductance calciumactivated potassium channel",level:"2"},{id:"sec_9_2",title:"3.2 Voltage-gated potassium channel subfamily KQT (KCNQ)",level:"2"},{id:"sec_10_2",title:"3.3 G protein-coupled Kir channel",level:"2"},{id:"sec_12",title:"4. Calcium channels",level:"1"},{id:"sec_12_2",title:"4.1 L-type Cav",level:"2"},{id:"sec_13_2",title:"4.2 P/Q-, N-, and R-type Cav",level:"2"},{id:"sec_14_2",title:"4.3 T-type Cav",level:"2"},{id:"sec_16",title:"5. Transient receptor potential channels",level:"1"},{id:"sec_16_2",title:"5.1 Canonical transient receptor potential (TRPC)",level:"2"},{id:"sec_17_2",title:"5.2 Transient receptor potential vanilloid 1 (TRPV1)",level:"2"},{id:"sec_19",title:"6. Antiepileptic therapy and beyond",level:"1"},{id:"sec_20",title:"7. Conclusion",level:"1"},{id:"sec_21",title:"Acknowledgments",level:"1"},{id:"sec_24",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Chang BS, Lowenstein DH. Epilepsy. The New England Journal of Medicine. 2003;349(13):1257-1266. DOI: 10.1056/NEJMra022308'},{id:"B2",body:'Wei F, Yan LM, Su T, et al. Ion channel genes and epilepsy: Functional alteration, pathogenic potential, and mechanism of epilepsy. Neuroscience Bulletin. 2017;33(4):455-477. DOI: 10.1007/s12264-017-0134-1'},{id:"B3",body:'Catterall WA. From ionic currents to molecular mechanisms: The structure and function of voltage-gated sodium channels. Neuron. 2000;26(1):13-25. 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Department of Central Laboratory and Neurology, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, China
Laboratory of Neuropharmacology and Neurotoxicology, Shanghai University, China
Department of Central Laboratory and Neurology, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, China
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The Open Access model is applied to all of our publications and is designed to eliminate subscriptions and pay-per-view fees. This approach ensures free, immediate access to full text versions of your research.
As a gold Open Access publisher, an Open Access Publishing Fee is payable on acceptance following peer review of the manuscript. In return, we provide high quality publishing services and exclusive benefits for all contributors. IntechOpen is the trusted publishing partner of over 118,000 international scientists and researchers.
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*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
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Services included are:
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English language copyediting and proofreading, including the correction of grammatical, spelling, and other common errors
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XML Typesetting and pagination - web (PDF, HTML) and print files preparation
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Discoverability - electronic citation and linking via DOI
\\n\\t
Permanent and unrestricted online access to your work
What isn't covered by the Open Access Publishing Fee?
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If your manuscript:
\\n\\n
\\n\\t
Exceeds 20 pages (for chapters in Edited Volumes), an additional fee of 40 GBP per page will be required
\\n\\t
If a manuscript requires Heavy Editing or Language Polishing, this will incur additional fees.
\\n
\\n\\n
Your Author Service Manager will inform you of any items not covered by the OAPF and provide exact information regarding those additional costs before proceeding.
\\n\\n
Open Access Funding
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To explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at oapf@intechopen.com for further details or assistance.
\\n\\n
For Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
\\n\\n
Added Value of Publishing with IntechOpen
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Choosing to publish with IntechOpen ensures the following benefits:
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Indexing and listing across major repositories, see details ...
\\n\\t
Long-term archiving
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Visibility on the world's strongest OA platform
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Live Performance Metrics to track readership and the impact of your chapter
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Dissemination and Promotion
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Benefits of Publishing with IntechOpen
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Proven world leader in Open Access book publishing with over 10 years experience
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+4,800 OA books published
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Fully compliant with OA funding requirements
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Optimized processes, enabling publication between 8 and 12 months
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+146,150 citations in Web of Science databases
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Currently strongest OA platform with over 130 million downloads
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OAPF Publishing Options
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1,400 GBP Chapter - Edited Volume
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4,000 GBP Compacts Monograph - Short Form
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*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
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Services included are:
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An online manuscript tracking system to facilitate your work
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Personal contact and support throughout the publishing process from your dedicated Author Service Manager
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Assurance that your manuscript meets the highest publishing standards
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English language copyediting and proofreading, including the correction of grammatical, spelling, and other common errors
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XML Typesetting and pagination - web (PDF, HTML) and print files preparation
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Permanent and unrestricted online access to your work
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\n\t
Exceeds 20 pages (for chapters in Edited Volumes), an additional fee of 40 GBP per page will be required
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If a manuscript requires Heavy Editing or Language Polishing, this will incur additional fees.
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Your Author Service Manager will inform you of any items not covered by the OAPF and provide exact information regarding those additional costs before proceeding.
\n\n
Open Access Funding
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To explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at oapf@intechopen.com for further details or assistance.
\n\n
For Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
\n\n
Added Value of Publishing with IntechOpen
\n\n
Choosing to publish with IntechOpen ensures the following benefits:
\n\n
\n\t
Indexing and listing across major repositories, see details ...
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Long-term archiving
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Visibility on the world's strongest OA platform
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Live Performance Metrics to track readership and the impact of your chapter
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Dissemination and Promotion
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Benefits of Publishing with IntechOpen
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Proven world leader in Open Access book publishing with over 10 years experience
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+4,800 OA books published
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Most competitive prices in the market
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Optimized processes, enabling publication between 8 and 12 months
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Personal support during every step of the publication process
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+146,150 citations in Web of Science databases
\n\t
Currently strongest OA platform with over 130 million downloads
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