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Genome-scale metabolic models (GEMs) are extensively used to simulate cell metabolism and predict cell phenotypes. GEMs can also be tailored to generate context-specific GEMs, using omics data integration approaches. To date, many integration approaches have been developed, however, each with specific pros and cons; and none of these algorithms systematically outperforms the others. The key to successful implementation of such integration algorithms lies in the optimal selection of parameters, and thresholding is a crucial component in this process. To improve the predictive accuracy of context-specific models, we introduce a new integration framework that improves the ranking of related genes and homogenizes the expression values of those gene sets using single-sample Gene Set Enrichment Analysis (ssGSEA). In this study, we coupled ssGSEA with GIMME and validated the advantages of the proposed framework to predict the ethanol formation of yeast grown in the glucose-limited chemostats, and to simulate metabolic behaviors of yeast growth in four different carbon sources. This framework enhances the predictive accuracy of GIMME which we demonstrate for predicting the yeast physiology in nutrient-limited cultures. [ABSTRACT FROM AUTHOR]

Abstract A novel method for synchronization of fractional order complex dynamical networks using a new fractional Proportional–Integral (PI) pinning control scheme is presented in this paper. Stability of this network is proved via the Lyapunov stability theorem, and the synchronizability criterion is obtained. Although one can also design a proportional pinning control scheme to synchronize the whole network, our proposed fractional PI control is more efficient. Numerical simulations on Lorenz oscillator's interaction over a sample network have shown that the proposed fractional PI controller can provide a faster synchronization with less overshoot to the reference state compared to conventional proportional controller with the same power consumption. [ABSTRACT FROM AUTHOR]

Abstract Many real-world phenomena can be modelled as spatial networks where nodes have distinct geographical location. Examples include power grids, transportation networks and the Internet. This paper focuses on optimizing the synchronizability of spatial networks. We consider the eigenratio of the Laplacian Matrix of the connection graph as a metric measuring the synchronizability of the network and develop an efficient rewiring mechanism to optimize the topology of the network for synchronizability, i.e., minimizing the eigenratio. The Euclidean distance between two connected nodes is considered as their connection weights, and the sum of all connection weights is defined as the network cost. The proposed optimization algorithm constructs spatial networks with a certain number of nodes and a predefined network cost. We also study the topological properties of the optimized networks. This algorithm can be used to construct spatial networks with optimal synchronization properties. Highlights • A model is proposed to construct random spatial networks. • A rewiring-based method is proposed to improve synchronizability of the networks. • Optimized networks have increased local and global efficiency. [ABSTRACT FROM AUTHOR]

Controlling dynamics of networked systems to a reference state, known as pinning control, has many applications in science and engineering. In this paper, we introduce a method for effective augmentation of networked systems, while also providing high levels of pinning controllability for the final augmented network. The problem is how to connect a sub-network to an already existing network such that the pinning controllability is maximised. We consider the eigenratio of the augmented Laplacian matrix as a pinning controllability metric, and use graph perturbation theory to approximate the influence of edge addition on the eigenratio. The proposed metric can be effectively used to find the inter-network links connecting the disjoint networks. Also, an efficient link rewiring approach is proposed to further optimise the pinning controllability of the augmented network. We provide numerical simulations on synthetic networks and show that the proposed method is more effective than heuristic ones. [ABSTRACT FROM AUTHOR]

Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype–phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers. [ABSTRACT FROM AUTHOR]

Background Acute promyelocytic leukemia (APL) is a curable form of acute myeloid leukemia; in recent years, the use of new treatment strategies, such as combination therapy, have led to improved APL outcomes. Here, outcomes of patients treated with a combination of arsenic trioxide (ATO) and all-trans-retinoic acid (ATRA) are compared against patients treated with single ATO therapy. Patients and methods In total, 67 patients with non-high-risk APL were evaluated. A group of 30 patients received ATO, and another group of 37 patients received ATO plus ATRA. ATO infusion at a dose of 0.15 mg/kg/day was continued till complete remission was achieved or till 60 days of consumption, and after 28 days of rest, second ATO course was initiated for 28 days as consolidation. Four courses separated by 28-day rest were planned. In the second group, 45 mg/m 2 /day ATRA was added to ATO protocol. Results All patients except one in the ATO group and all patients in the ATO plus ATRA group were alive after a median follow-up of 18 and 17 months, respectively; 2.5-year overall survival in the ATO group was 86% ( p -value = .32). Five patients in the ATO group experienced relapse, and 2.5-year leukemia-free survival in this group was 60%. No relapse occurred in the ATO plus ATRA group ( p -value = .01). Differences in the mean of white blood cell ( p -value = .67), platelet ( p -value = .15), liver ( p -value = .37), and renal ( p -value = .95) dysfunctions were not significant. Conclusion Although ATO has been considered a first-line therapy in patients with APL, several studies have reported improved outcomes with a combination of ATO plus ATRA. This study demonstrated a significant decrease in relapse with this combination compared with single ATO therapy and supported the importance of ATRA in APL treatment. [ABSTRACT FROM AUTHOR]

This paper proposed a novel optimization method based on opinion formation in complex network systems. The proposed optimization technique mimics human–human interaction mechanism based on a mathematical model derived from social sciences. Our method encodes a subset of selected features to the opinion of an artificial agent and simulates the opinion formation process among a population of agents to solve the feature selection problem. The agents interact using an underlying interaction network structure and get into consensus in their opinions, while finding better solutions to the problem. A number of mechanisms are employed to avoid getting trapped in local minima. We compare the performance of the proposed method with a number of classical population-based optimization methods and a state-of-the-art opinion formation based method. Our experiments on a number of high dimensional datasets reveal outperformance of the proposed algorithm over others. [ABSTRACT FROM AUTHOR]

Online social networks have significantly changed the way people shape their everyday communications. Signed networks are a class of social networks in which relations can be positive or negative. These networks emerge in areas where there is interplay between opposite attitudes such as trust and distrust. Recent studies have shown that sign of relationships is predictable using data already present in the network. In this work, we study the sign prediction problem in networks with both positive and negative links and investigate the application of network tendency in the prediction task. Accordingly, we develop a simple algorithm that can infer unknown relation types with high performance. We conduct experiments on three real-world signed networks: Epinions, Slashdot and Wikipedia. Experimental results indicate that the proposed approach outperforms the state of the art methods in terms of both overall accuracy and true negative rate. Furthermore, significantly low computational complexity of the proposed algorithm allows applying it to large-scale datasets. [ABSTRACT FROM AUTHOR]

In this paper we study synchronizability of two multiplex cortical networks: whole-cortex of hermaphrodite C. elegans and posterior cortex in male C. elegans . These networks are composed of two connection layers: network of chemical synapses and the one formed by gap junctions. This work studies the contribution of each layer on the phase synchronization of non-identical spiking Hindmarsh–Rose neurons. The network of male C. elegans shows higher phase synchronization than its randomized version, while it is not the case for hermaphrodite type. The random networks in each layer are constructed such that the nodes have the same degree as the original network, thus providing an unbiased comparison. In male C. elegans , although the gap junction network is sparser than the chemical network, it shows higher contribution in the synchronization phenomenon. This is not the case in hermaphrodite type, which is mainly due to significant less density of gap junction layer (0.013) as compared to chemical layer (0.028). Also, the gap junction network in this type has stronger community structure than the chemical network, and this is another driving factor for its weaker synchronizability. [ABSTRACT FROM AUTHOR]

Background: Acute promyelocytic leukemia (APL) is a unique subtype of acute leukemia. APL is a curable disease; however, drug resistance, early mortality, disease relapse and treatment-related complications remain challenges in APL patient management. One issue underlying these challenges is that the molecular mechanisms of the disease are not sufficiently understood. Materials and Methods: In this study, we performed a meta-analysis of gene expression profiles derived from microarray experiments and explored the background of disease by functional and pathway analysis. Results: Our analysis revealed a gene signature with 406 genes that are up or down-regulated in APL. The pathway analysis determined that MAPK pathway and its involved elements such as JUN gene and AP-1 play important roles in APL pathogenesis along with insulin-like growth factor-binding protein-7. Conclusions: The results of this meta-analysis could be useful for developing more effective therapy strategies and new targets for diagnosis and drugs. [ABSTRACT FROM AUTHOR]

As part of invoice processing, businesses are required to manually classify each line item on an invoice to a specific financial account. This can be time-consuming and challenging when there is a large set of account codes to choose from. Failing to select the correct account will lead to errors in financial reporting which can be misleading for stakeholders and have adverse effects during a financial audit. The emergence of cloud-based accounting platforms has introduced potential areas for automated support across invoice processing tasks such as helping bookkeepers to select the correct financial account. Traditionally, account code suggestion is framed as a multi-class classification task, however, we explore the applicability of using a hierarchical single-label classifier. Account codes can be expressed as taxonomic classes, either explicitly through the chart of accounts or implicitly through associations with reconciled bank accounts. Our research provides evidence to support that the exploitation of hierarchical information from induced taxonomies can be highly advantageous for improving classification and recommendation performance. Furthermore, we introduce Top-K Parent Boosting, a novel post-processing strategy. We demonstrate the suitability of Top-K Parent Boosting for DAG structures and highlight its superiority in improving recommendation performance over former strategies. • Bank reconciliation data holds key information for account code classifiers. • Hierarchical classifiers are shown to improve accuracy for suggesting account codes. • Exploiting relations with higher ranked parents can enhance post-processing schemes. • Superior performances are achieved through better representation of financial text. [ABSTRACT FROM AUTHOR]

Various disciplines are trying to solve one of the most noteworthy queries and broadly used concepts in biology, essentiality. Centrality is a primary index and a promising method for identifying essential nodes, particularly in biological networks. The newly created CentiServer is a comprehensive online resource that provides over 110 definitions of different centrality indices, their computational methods, and algorithms in the form of an encyclopedia. In addition, CentiServer allows users to calculate 55 centralities with the help of an interactive web-based application tool and provides a numerical result as a comma separated value (csv) file format or a mapped graphical format as a graph modeling language (GML) file. The standalone version of this application has been developed in the form of an R package. The web-based application (CentiServer) and R package (centiserve) are freely available at [ABSTRACT FROM AUTHOR]

Applying tools available in network science and graph theory to study brain networks has opened a new era in understanding brain mechanisms. Brain functional networks extracted from EEG time series have been frequently studied in health and diseases. In this manuscript, we studied failure resiliency of EEG-based brain functional networks. The network structures were extracted by analysing EEG time series obtained from 30 healthy subjects in resting state eyes-closed conditions. As the network structure was extracted, we measured a number of metrics related to their resiliency. In general, the brain networks showed worse resilient behaviour as compared to corresponding random networks with the same degree sequences. Brain networks had higher vulnerability than the random ones (P < 0.05), indicating that their global efficiency (i.e., communicability between the regions) is more affected by removing the important nodes. Furthermore, the breakdown happened as a result of cascaded failures in brain networks was severer (i.e., less nodes survived) as compared to randomized versions (P < 0.05). These results suggest that real EEG-based networks have not been evolved to possess optimal resiliency against failures. [ABSTRACT FROM AUTHOR]

Control of collective behavior is one of the most desirable goals in many applications related to social networks analysis and mining. In this work we propose a simple yet effective algorithm to control opinion formation in complex networks. We aim at finding the best spreaders whose connection to a reasonable number of informed agents results in the best performance. We consider an extended version of the bounded confidence model in which the uncertainty of each agent is adaptively controlled by the network. A number of informed agents with the desired opinion value is added to the network and create links with other agents such that large portion of the network follows their opinions. We propose to connect the informed agents to nodes with small in-degrees and high out-degree that are connected to high in-degree nodes. Our experimental results on both model and real social networks show superior performance of the proposed method over the state-of-the-art heuristic methods in the facet of opinion formation models. [ABSTRACT FROM AUTHOR]

Controlling networked structures has many applications in science and engineering. In this paper, we consider the problem of pinning control (pinning the dynamics into the reference state), and optimally placing the driver nodes, i.e., the nodes to which the control signal is fed. Considering the local controllability concept, a metric based on the eigenvalues of the Laplacian matrix is taken into account as a measure of controllability. We show that the proposed optimal placement strategy considerably outperforms heuristic methods including choosing hub nodes with high degree or betweenness centrality as drivers. We also study properties of optimal drivers in terms of various centrality measures including degree, betweenness, closeness, and clustering coefficient. The profile of these centrality values depends on the network structure. For homogeneous networks such as random small-world networks, the optimal driver nodes have almost the mean centrality value of the population (much lower than the centrality value of hub nodes), whereas the centrality value of optimal drivers in heterogeneous networks such as scale-free ones is much higher than the average and close to that of hub nodes. However, as the degree of heterogeneity decreases in such networks, the profile of centrality approaches the population mean. [ABSTRACT FROM AUTHOR]

Recommender systems are in the center of network science, and they are becoming increasingly important in individual businesses for providing efficient, personalized services and products to users. Previous research in the field of recommendation systems focused on improving the precision of the system through designing more accurate recommendation lists. Recently, the community has been paying attention to diversity and novelty of recommendation lists as key characteristics of modern recommender systems. In many cases, novelty and precision do not go hand in hand, and the accuracy--novelty dilemma is one of the challenging problems in recommender systems, which needs efforts in making a trade-off between them. In this work, we propose an algorithm for providing novel and accurate recommendation to users. We consider the standard definition of accuracy and an effective self-information--based measure to assess novelty of the recommendation list. The proposed algorithm is based on item popularity, which is defined as the number of votes received in a certain time interval. Wavelet transform is used for analyzing popularity time series and forecasting their trend in future timesteps. We introduce two filtering algorithms based on the information extracted from analyzing popularity time series of the items. The popularity-based filtering algorithm gives a higher chance to items that are predicted to be popular in future timesteps. The other algorithm, denoted as a novelty and population-based filtering algorithm, is to move toward items with low popularity in past timesteps that are predicted to become popular in the future. The introduced filters can be applied as adds-on to any recommendation algorithm. In this article, we use the proposed algorithms to improve the performance of classic recommenders, including item-based collaborative filtering and Markov-based recommender systems. The experiments show that the algorithms could significantly improve both the accuracy and effective novelty of the classic recommenders. [ABSTRACT FROM AUTHOR]

Recommender systems are in the center of network science, and they are becoming increasingly important in individual businesses for providing efficient, personalized services and products to users. Previous research in the field of recommendation systems focused on improving the precision of the system through designing more accurate recommendation lists. Recently, the community has been paying attention to diversity and novelty of recommendation lists as key characteristics of modern recommender systems. In many cases, novelty and precision do not go hand in hand, and the accuracy--novelty dilemma is one of the challenging problems in recommender systems, which needs efforts in making a trade-off between them. In this work, we propose an algorithm for providing novel and accurate recommendation to users. We consider the standard definition of accuracy and an effective self-information--based measure to assess novelty of the recommendation list. The proposed algorithm is based on item popularity, which is defined as the number of votes received in a certain time interval. Wavelet transform is used for analyzing popularity time series and forecasting their trend in future timesteps. We introduce two filtering algorithms based on the information extracted from analyzing popularity time series of the items. The popularity-based filtering algorithm gives a higher chance to items that are predicted to be popular in future timesteps. The other algorithm, denoted as a novelty and population-based filtering algorithm, is to move toward items with low popularity in past timesteps that are predicted to become popular in the future. The introduced filters can be applied as adds-on to any recommendation algorithm. In this article, we use the proposed algorithms to improve the performance of classic recommenders, including item-based collaborative filtering and Markov-based recommender systems. The experiments show that the algorithms could significantly improve both the accuracy and effective novelty of the classic recommenders. [ABSTRACT FROM AUTHOR]

Recommender systems have become an important issue in network science. Collaborative filtering and its variants are the most widely used approaches for building recommender systems, which have received great attention in both academia and industry. In this paper, we studied the relationship between recommender systems and connectivity of users–items bipartite network. This results in a novel recommendation algorithm. In our method recommended items are selected based on the eigenvector corresponding to the algebraic connectivity of the graph – the second smallest eigenvalue of the Laplacian matrix. Since recommending an item to a user equals to adding a new link to the users–items bipartite graph, the intuition behind the proposed approach is that the items should be recommended to the users such that the least increase in the connectedness of the network (i.e., the algebraic connectivity) is obtained. Through experiments on a number of benchmark datasets, we showed that the proposed connectivity-based recommendation method has comparable results to a number of commonly used recommendation methods. These results shed light on the relation between the evolution of users’ behavior and network topology. [ABSTRACT FROM AUTHOR]

In this paper, we study information cascade in networks with positive and negative edges. The cascade depth is correlated with community structure of signed networks where communities are defined such that positive inter-community and negative intra-community links are minimized. The cascade is initialized from a number of nodes that are selected randomly. Finally, the number of nodes that have participated in the cascade is interpreted as cascade depth; the more the number of such nodes, the more the depth of the cascade. We investigate influence of community structure (i.e., percentage of inter-community positive and intra-community negative links) on the cascade depth. We find significant influence of community structure on cascade depth in both model and real networks. Our results show that the more the intra-community negative links (i.e., the worse the community structure), the more the cascade depth. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we investigated the structural properties of the ultra high voltage power transmission network of Iran. We modeled the power grid as a network with 105 nodes and 142 connection links. We found that the Iranian power grid displays a relatively moderate clustering coefficient–much larger than that of corresponding random networks–and small characteristics path length comparable to that of corresponding random networks; i.e. the power grid is a small-world network with exponential degree distribution. Global efficiency was considered as an indicator of grid’s performance and the influence of random and intentional nodal failures on the efficiency was investigated. We also studied the influence of cascaded failures on the largest connected component of the network. The power grid was vulnerable against cascaded failures, which should be considered serious in redesigning the network topology. [Copyright &y& Elsevier]

Social network analysis and mining get ever-increasingly important in recent years, which is mainly due to the availability of large datasets and advances in computing systems. A class of social networks is those with positive and negative links. In such networks, a positive link indicates friendship (or trust), whereas links with a negative sign correspond to enmity (or distrust). Predicting the sign of the links in these networks is an important issue and has many applications, such as friendship recommendation and identifying malicious nodes in the network. In this manuscript, we proposed a new method for sign prediction in networks with positive and negative links. Our algorithm is based first on clustering the network into a number of clusters and then applying a collaborative filtering algorithm. The clusters are such that the number of intra-cluster negative links and inter-cluster positive links are minimal, that is, the clusters are socially balanced as much as possible (a signed graph is socially balanced if it can be divided into clusters with all positive links inside the clusters and all negative links between them). We then used similarity between the clusters (based on the links between them) in a collaborative filtering algorithm. Our experiments on a number of real datasets showed that the proposed method outperformed previous methods, including those based on social balance and status theories and one based on a machine learning framework (logistic regression in this work). [ABSTRACT FROM AUTHOR]

Iran is a country in a dry part of the world and extensively suffers from drought. Drought is a natural, temporary, and iterative phenomenon that is caused by shortage in rainfall, which affects people's health and well-being adversely as well as impacting the society's economy and politics with far-reaching consequences. Information on intensity, duration, and spatial coverage of drought can help decision makers to reduce the vulnerability of the drought-affected areas, and therefore, lessen the risks associated with drought episodes. One of the major challenges of modeling drought (and short-term forecasting) in Iran is unavailability of long-term meteorological data for many parts of the country. Satellite-based remote sensing dataâthat are freely availableâgive information on vegetation conditions and land cover. In this paper, we constructed artificial neural network to model (and forecast) drought conditions based on satellite imagery. To this end, standardized precipitation index (SPI) was used as a measure of drought severity. A number of features including normalized difference vegetation index (NDVI), vegetation condition index (VCI), and temperature condition index (TCI) were extracted from NOAA-AVHRR images. The model received these features as input and outputted the SPI value (or drought condition). Applying the model to the data of stations for which the precipitation data were available, we showed that it could forecast the drought condition with an accuracy of up to 90 percent. Furthermore, TCI was found to be the best marker of drought conditions among satellite-based features. We also found multilayer perceptron better than radial basis function networks and support vector machines forecasting drought conditions. [ABSTRACT FROM AUTHOR]

Social networks are inevitable parts of daily life and there has been an increasing interest in analyzing social phenomena on networked structures. Evolution of opinion formation is one of the topics that has attracted many scholars in the field. In this work we consider the influence of leaders and social power in the evolution of opinion formation. A number of central nodes with specific properties (e.g. nodes with highest degree, betweenness or vulnerability values) are taken as leaders whose opinions are kept unchanged, that is, not influenced by other agents. The leaders try to coordinate the opinions of other agents where the connection structure is considered to be preferential attachment scale-free, Watts–Strogatz small-world or Erdős–Rényi random networks. Numerical simulations show that scale-free networks provide faster consensus compared with other networks. We also study the effects of social power on the consensus time. The social power of a node is considered to be a function of its centrality. Having leaders in the network, we show that the consensus time could be significantly decreased by introducing social power. For scale-free networks, there is an optimal degree of social power in which the consensus time is minimal. These results show the appropriateness of scale-free topology in hierarchal organizations where leaders posit influence on peripheral agents. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we investigate the effects of social power on the evolution of opinions in model networks as well as in a number of real social networks. A continuous opinion formation model is considered and the analysis is performed through numerical simulation. Social power is given to a proportion of agents selected either randomly or based on their degrees. As artificial network structures, we consider scale-free networks constructed through preferential attachment and Watts–Strogatz networks. Numerical simulations show that scale-free networks with degree-based social power on the hub nodes have an optimal case where the largest number of the nodes reaches a consensus. However, given power to a random selection of nodes could not improve consensus properties. Introducing social power in Watts–Strogatz networks could not significantly change the consensus profile. [Copyright &y& Elsevier]

Random geographical networks are realistic models for wireless sensor networks which are used in many applications. Achieving average consensus is very important in sensor networks and the faster the consensus is, the durable the sensors' life, and thus, the better the performance of the network. In this paper we compared the performance of a number of linear consensus algorithms with application to distributed averaging in random geographical networks. Interestingly, the simplest algorithm - where only the degree of receiving nodes is needed for the averaging - had the best performance in terms of the consensus time. Furthermore, we proved that the network has guaranteed convergence with this simple algorithm. [ABSTRACT FROM AUTHOR]

Spike sorting – the process of separating spikes from different neurons – is often the first and most critical step in the neural data analysis pipeline. Spike-sorting techniques isolate a single neuron's activity from background electrical noise based on the shapes of the waveforms obtained from extracellular recordings. Despite several advancements in this area, an important remaining challenge in neuroscience is online spike sorting, which has the potential to significantly advance basic neuroscience research and the clinical setting by providing the means to produce real-time perturbations of neurons via closed-loop control. Current approaches to online spike sorting are not fully automated, are computationally expensive and are often outperformed by offline approaches. In this paper, we present a novel algorithm for fast and robust online classification of single neuron activity. This algorithm is based on a deep contractive autoencoder (CAE) architecture. CAEs are neural networks that can learn a latent state representation of their inputs. The main advantage of CAE-based approaches is that they are less sensitive to noise (i.e., small perturbations in their inputs). We therefore reasoned that they can form the basis for robust online spike sorting algorithms. Overall, our deep CAE-based online spike sorting algorithm achieves over 90% accuracy in sorting unseen spike waveforms, outperforming existing models and maintaining a performance close to the offline case. In the offline scenario, our method substantially outperforms the existing models, providing an average improvement of 40% in accuracy over different datasets. • A novel algorithm for spike sorting based on a Contractive Auto-encoder. • Produce representations of spike waveforms that are robust to additive noise. • Reliably classify spikes for small and large datasets. • Outperform SOTA approaches in various online and offline spike-sorting applications. [ABSTRACT FROM AUTHOR]

Many real-world networks show community structure characterized by dense intra-community connections and sparse inter-community links. In this paper we investigated the synchronization properties of such networks. In this work we constructed such networks in a way that they consist of a number of communities with scale-free or small-world structure. Furthermore, with a probability, the intra-community connections are rewired to inter-community links. Two synchronizability measures were considered as the eigenratio of the Laplacian matrix and the phase order parameter obtained for coupled nonidentical Kuramoto oscillators. We found a power-law relation between the eigenratio and the inter-community rewiring probability in which as the rewiring probability increased, the eigenratio decreased, and hence, the synchronizability enhanced. The phase order parameter also increased by increasing the rewiring probability. Also, small-world networks with community structure showed better synchronization properties as compared to scale-free networks with community structure. [ABSTRACT FROM AUTHOR]

Abstract: Schizophrenia is often considered as a dysconnection syndrome in which, abnormal interactions between large-scale functional brain networks result in cognitive and perceptual deficits. In this article we apply the graph theoretic measures to brain functional networks based on the resting EEGs of fourteen schizophrenic patients in comparison with those of fourteen matched control subjects. The networks were extracted from common-average-referenced EEG time-series through partial and unpartial cross-correlation methods. Unpartial correlation detects functional connectivity based on direct and/or indirect links, while partial correlation allows one to ignore indirect links. We quantified the network properties with the graph metrics, including mall-worldness, vulnerability, modularity, assortativity, and synchronizability. The schizophrenic patients showed method-specific and frequency-specific changes especially pronounced for modularity, assortativity, and synchronizability measures. However, the differences between schizophrenia patients and normal controls in terms of graph theory metrics were stronger for the unpartial correlation method. [Copyright &y& Elsevier]

Abstract: In this paper the robustness of network synchronizability against random deletion of nodes, i.e. errors, in dynamical scale-free networks was studied. To this end, two measures of network synchronizability, namely, the eigenratio of the Laplacian and the order parameter quantifying the degree of phase synchrony were adopted, and the synchronizability robustness on preferential attachment scale-free graphs was investigated. The findings revealed that as the network size decreases, the robustness of its synchronizability against random removal of nodes declines, i.e. the more the number of randomly removed nodes from the network, the worse its synchronizability. We also showed that this dependence of the synchronizability on the network size is different with that in the growing scale-free networks. The profile of a number of network properties such as clustering coefficient, efficiency, assortativity, and eccentricity, as a function of the network size was investigated in these two cases, growing scale-free networks and those with randomly removed nodes. The results showed that these processes are also different in terms of these metrics. [Copyright &y& Elsevier]

Introduction: Severe acquired aplastic anemia (SAA) is a rare disease and matched related hematopoietic stem cell transplantation (HSCT) is the treatment of choice especially in pediatric patients. Immunosuppressive therapy is the alternative treatment in patients who do not have a donor. We retrospectively analyzed patients who received allogeneic HSCT at our institution. Methods: Between 1991 and 2011, 190 patients received allogeneic HSCT from HLA-matched donors (182 siblings and 8 other relatives). Median age was 20.5 years (range 1 to 50 years). The graft source was peripheral blood stem cells in majority of patients. Conditioning regimen consist with a rnyeloablative regimen containing cyclophosphamide with or without annthymocyte globulin. For graft-versus-host disease (GvHD) prophylaxis, we used cyclosporine with or without methotrexate at the standard doses. Results: The median follow-up time was 30 months and 3 year overall survival and disease free survival was 82% and 75% respectively. The median time to neutrophil engraftment and median time to platelet engraftment was 12 day and 15 day respectively. Grade 3 and 4 of aGvHD occurred in 26 (23.7%) patients and chronic GvHD occurred in 46 (291%) of survived patients 100 days after HSCT. At time of report 82.1% of patients were alive with normal hematologic parameters. The engraftment failure rate was about SV The most common cause of death was GvHD Conclusions: However an available treatment in SAA is immunosuppressive therapy. HSCT should be seriously considered as a therapeutic option particularly if a matched sibling donor is available. The outcome of allogeneic HSCT in patients with SAA at our center was consistent with die result of other previous studies. [ABSTRACT FROM AUTHOR]

Neuronal synchronization plays an important role in the various functionality of nervous system such as binding, cognition, information processing, and computation. In this paper, we investigated how random and intentional failures in the nodes of a network influence its phase synchronization properties. We considered both artificially constructed networks using models such as preferential attachment, Watts-Strogatz, and Erdős-Rényi as well as a number of real neuronal networks. The failure strategy was either random or intentional based on properties of the nodes such as degree, clustering coefficient, betweenness centrality, and vulnerability. Hindmarsh-Rose model was considered as the mathematical model for the individual neurons, and the phase synchronization of the spike trains was monitored as a function of the percentage/number of removed nodes. The numerical simulations were supplemented by considering coupled non-identical Kuramoto oscillators. Failures based on the clustering coefficient, i.e., removing the nodes with high values of the clustering coefficient, had the least effect on the spike synchrony in all of the networks. This was followed by errors where the nodes were removed randomly. However, the behavior of the other three attack strategies was not uniform across the networks, and different strategies were the most influential in different network structure. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces the connection-graph-stability method and uses it to establish a new lower bound on the algebraic connectivity of graphs (the second smallest eigenvalue of the Laplacian matrix of the graph) that is sharper than the previously published bounds. The connection-graph-stability score for each edge is defined as the sum of the lengths of the shortest paths making use of that edge. We prove that the algebraic connectivity of the graph is bounded below by the size of the graph divided by the maximum connection-graph-stability score assigned to the edges. [Copyright &y& Elsevier]

We consider electroencephalograms (EEGs) of healthy individuals and compare the properties of the brain functional networks found through two methods: unpartialized and partialized cross-correlations. The networks obtained by partial correlations are fundamentally different from those constructed through unpartial correlations in terms of graph metrics. In particular, they have completely different connection efficiency, clustering coefficient, assortativity, degree variability, and synchronization properties. Unpartial correlations are simple to compute and they can be easily applied to large-scale systems, yet they cannot prevent the prediction of non-direct edges. In contrast, partial correlations, which are often expensive to compute, reduce predicting such edges. We suggest combining these alternative methods in order to have complementary information on brain functional networks. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Psychogenic non-epileptic seizures (PNES) are paroxysmal events that, in contrast to epileptic seizures, are related to psychological causes without the presence of epileptiform EEG changes. Recent models suggest a multifactorial basis for PNES. A potentially paramount, but currently poorly understood factor is the interplay between psychiatric features and a specific vulnerability of the brain leading to a clinical picture that resembles epilepsy. Hypothesising that functional cerebral network abnormalities may predispose to the clinical phenotype, the authors undertook a characterisation of the functional connectivity in PNES patients. METHODS: The authors analysed the whole-head surface topography of multivariate phase synchronisation (MPS) in interictal high-density EEG of 13 PNES patients as compared with 13 age- and sex-matched controls. MPS mapping reduces the wealth of dynamic data obtained from high-density EEG to easily readable synchronisation maps, which provide an unbiased overview of any changes in functional connectivity associated with distributed cortical abnormalities. The authors computed MPS maps for both Laplacian and common-average-reference EEGs. RESULTS: In a between-group comparison, only patchy, non-uniform changes in MPS survived conservative statistical testing. However, against the background of these unimpressive group results, the authors found widespread inverse correlations between individual PNES frequency and MPS within the prefrontal and parietal cortices. INTERPRETATION: PNES appears to be associated with decreased prefrontal and parietal synchronisation, possibly reflecting dysfunction of networks within these regions. [ABSTRACT FROM AUTHOR]

Complex networks serve as generic models for many biological systems that have been shown to share a number of common structural properties such as power-law degree distribution and small-worldness. Real-world networks are composed of building blocks called motifs that are indeed specific subgraphs of (usually) small number of nodes. Network motifs are important in the functionality of complex networks, and the role of some motifs such as feed-forward loop in many biological networks has been heavily studied. On the other hand, many biological networks have shown some degrees of robustness in terms of their efficiency and connectedness against failures in their components. In this paper we investigated how random and systematic failures in the edges of biological networks influenced their motif structure. We considered two biological networks, namely, protein structure network and human brain functional network. Furthermore, we considered random failures as well as systematic failures based on different strategies for choosing candidate edges for removal. Failure in the edges tipping to high degree nodes had the most destructive role in the motif structure of the networks by decreasing their significance level, while removing edges that were connected to nodes with high values of betweenness centrality had the least effect on the significance profiles. In some cases, the latter caused increase in the significance levels of the motifs. [ABSTRACT FROM AUTHOR]

Abstract: Although diffusive electrical connections in neuronal networks are instantaneous, excitatory/inhibitory couplings via chemical synapses encompass a transmission time-delay. In this paper neural networks with instantaneous electrical couplings and time-delayed excitatory/inhibitory chemical connections are considered and scaling of the spike phase synchronization with the unified time-delay in the network is investigated. The findings revealed that in both excitatory and inhibitory chemical connections, the phase synchronization could be enhanced by introducing time-delay. The role of the variability of the neuronal external current in the phase synchronization is also investigated. As individual neuron models, Hindmarsh–Rose model is adopted and the network structure of the electrical and chemical connections is considered to be Watts–Strogatz and directed random networks, respectively. [Copyright &y& Elsevier]

Abstract: Although many complex real-world networks are weighted, unweighted networks are used in many applications such as sensor networks. In this Letter it is shown using properly weighted networks the performance can be greatly enhanced by reducing the time necessary for the average consensus. Random geographical models are adapted as network models and a method based on mutually coupled phase oscillators is used for providing average consensus over the network. The consensus time is calculated by numerically solving the network''s differential equations and monitoring the average error. The simulation results on some sample networks show that the consensus time is dramatically reduced when the proposed weights are used for the links of the underlying network. [Copyright &y& Elsevier]

The interhemispheric asymmetries that originate from connectivity-related structuring of the cortex are compromised in schizophrenia (SZ). Under the assumption that such abnormalities affect functional connectivity, we analyzed its correlate—EEG synchronization—in SZ patients and matched controls. We applied multivariate synchronization measures based on Laplacian EEG and tuned to various spatial scales. Compared to the controls who had rightward asymmetry at a local level (EEG power), rightward anterior and leftward posterior asymmetries at an intraregional level (1st and 2nd order S-estimator), and rightward global asymmetry (hemispheric S-estimator), SZ patients showed generally attenuated asymmetry, the effect being strongest for intraregional synchronization in the alpha and beta bands. The abnormalities of asymmetry increased with the duration of the disease and correlated with the negative symptoms. We discuss the tentative links between these findings and gross anatomical asymmetries, including the cerebral torque and gyrification pattern, in normal subjects and SZ patients. [ABSTRACT FROM AUTHOR]

Abstract: Alzheimer''s disease (AD) is likely to disrupt the synchronization of the bioelectrical processes in the distributed cortical networks underlying cognition. We analyze the surface topography of the multivariate phase synchronization (MPS) of multichannel EEG in 17 patients (Clinical Dementia Rating (CDR) Scale: 0.5–1; Functional Assessment Staging (FAST): 3–4) compared to 17 controls by applying a combination of global and regional MPS measures to the resting EEG. In early AD, whole-head mapping reveals a specific landscape of synchronization characterized by a decrease in MPS over the fronto-temporal region and an increase over the temporo-parieto-occipital region predominantly of the left hemisphere. These features manifest themselves through the EEG delta–beta bands and discriminate patients from controls with an accuracy of up to 94%. Moreover, the abnormal MPS in both anterior and posterior clusters correlates with the Mini Mental State Examination score, binding regional EEG synchronization to cognitive decline in AD patients. The MPS technique reveals that the EEG phenotype of early AD is relevant to the clinical picture and may ultimately become its sensitive and specific biomarker. [Copyright &y& Elsevier]

In this paper, we list four different interpretations of synchronizability, study their profile in different network structures and give evidence that they do not coincide, in general. By changing the network topological properties, their behavior is tracked and compared with each other. It is shown that their trend goes in different directions in heterogeneous networks such as scale-free ones, whereas in homogeneous ones such as random networks they go hand in hand, as networks' structural parameters change. We also consider networks whose synchronization properties are enhanced through proper weighting or rewiring. The weighting procedure considers information on the node-degrees, node and edges betweenness centralities to assign proper weights for the links. In this way, an undirected and unweighted graph is changed to a weighted and directed one and with enhanced synchronizability. The rewiring algorithm uses information on the eigenvectors corresponding to the second smallest and the largest eigenvalues of the Laplacian matrix to perform efficient rewirings to enhance the synchronizability. It is shown that in these networks (weighted or rewired), different synchronizability interpretations have the same trend as the network structural parameter changes. [ABSTRACT FROM AUTHOR]

In this paper, the synchronization behavior of the Hindmarsh–Rose neuron model over Newman–Watts networks is investigated. The uniform synchronizing coupling strength is determined through both numerically solving the network’s differential equations and the master-stability-function method. As the average degree is increased, the gap between the global synchronizing coupling strength, i.e., the one obtained through the numerical analysis, and the strength necessary for the local stability of the synchronization manifold, i.e., the one obtained through the master-stability-function approach, increases. We also find that this gap is independent of network size, at least in a class of networks considered in this work. Limiting the analysis to the master-stability-function formalism for large networks, we find that in those networks with size much larger than the average degree, the synchronizing coupling strength has a power-law relation with the shortcut probability of the Newman–Watts network. The synchronization behavior of the network of nonidentical Hindmarsh–Rose neurons is investigated by numerically solving the equations and tracking the average synchronization error. The synchronization of identical Hindmarsh–Rose neurons coupled over clustered Newman–Watts networks, networks with dense intercluster connections but sparsely in intracluster linkage, is also addressed. It is found that the synchronizing coupling strength is influenced mainly by the probability of intercluster connections with a power-law relation. We also investigate the complementary role of chemical coupling in providing complete synchronization through electrical connections. [ABSTRACT FROM AUTHOR]

This is a comment on a recent paper by A. Hagberg and D. A. Schult [Chaos 18, 037105 (2008)]. By taking the eigenratio of the Laplacian of an undirected and unweighted network as its synchronizability measure, they proposed a greedy rewiring algorithm for enhancing the synchronizability of the network. The algorithm is not capable of avoiding local minima, and as a consequence, for each initial network, different optimized networks with different synchronizabilities are obtained. Here, we show that by employing a simulated annealing based optimization method, it is possible to further enhance the synchronizability of the network. Moreover, using this approach, the optimized network is not biased by the initial network and regardless of the initial networks, the final optimized networks have similar synchronization properties. [ABSTRACT FROM AUTHOR]

In this paper, we present an algorithm for optimizing synchronizability of complex dynamical networks. Starting with an undirected and unweighted network, we end up with an undirected and unweighted network with the same number of nodes and edges having enhanced synchronizability. To this end, based on some network properties, rewirings, i.e., eliminating an edge and creating a new edge elsewhere, are performed iteratively avoiding always self-loops and multiple edges between the same nodes. We show that the method is able to enhance the synchronizability of networks of any size and topological properties in a small number of steps that scales with the network size. For numerical simulations, an optimization algorithm based on simulated annealing is used. Also, the evolution of different topological properties of the network such as distribution of node degree, node and edge betweenness centrality is tracked with the iteration steps. We use networks such as scale-free, Strogatz–Watts and random to start with and we show that regardless of the initial network, the final optimized network becomes homogeneous. In other words, in the network with high synchronizability, parameters, such as, degree, shortest distance, node, and edge betweenness centralities are almost homogeneously distributed. Also, parameters, such as, maximum node and edge betweenness centralities are small for the rewired network. Although we take the eigenratio of the Laplacian as the target function for optimization, we show that it is also possible to choose other appropriate target functions exhibiting almost the same performance. Furthermore, we show that even if the network is optimized taking into account another interpretation of synchronizability, i.e., synchronization cost, the optimal network has the same synchronization properties. Indeed, in networks with optimized synchronizability, different interpretations of synchronizability coincide. The optimized networks are Ramanujan graphs, and thus, this rewiring algorithm could be used to produce Ramanujan graphs of any size and average degree. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an intelligent controller is applied to govern the dynamics of electrically heated micro-heat exchanger plant. First, the dynamics of the micro-heat exchanger, which acts as a nonlinear plant, is identified using a neurofuzzy network. To build the neurofuzzy model, a locally linear learning algorithm, namely, locally linear mode tree (LoLiMoT) is used. Then, an intelligent controller based on brain emotional learning algorithm is applied to the identified model. The intelligent controller is based on a computational model of limbic system in the mammalian brain. The brain emotional learning based intelligent controller (BELBIC) based on PID control is adopted for the micro-heat exchanger plant. The contribution of BELBIC in improving the control system performance is shown by comparison with results obtained from classic PID controller without BELBIC. The results demonstrate excellent improvements of control action, without any considerable increase in control effort for PID+BELBIC. [Copyright &y& Elsevier]

This paper deals with the application of neural networks to design intelligent nonlinear predictive controllers. Predictive controllers are now widely used in many industrial applications. They have been used for linear systems in early applications and then some methods based on predictive control theory were proposed to govern the dynamics of nonlinear systems. In this paper, we will make use of multi-layer perceptron neurofuzzy models with Locally Linear Model Tree (LoLiMoT) learning algorithm as a part of intelligent predictive control system, which has shown excellent performance in identifying of nonlinear systems. The nonlinear dynamics of the system is identified using the neural network based method and then the identified model is used as a part of predictive control algorithm. The proposed method is used to solve the control problems in some benchmark systems. As a first study, the viscosity control in a Continuous Stirred Tank Reactor (CSTR) plant is considered. The mathematical model of the plant is used to generate the input output data set and then the dynamic behavior of the system is identified using a proper multi-layer perceptron neural network, which is used in the predictive control loop. Also, the predictive control based on the locally linear neurofuzzy model is applied to temperature control of an electrically heated micro heat exchanger. The dynamic behavior of the heat exchanger is identified based on some experimental data of the real plant. Comparing the identification results obtained by the neurofuzzy model with those of some linear models such as ARX and BJ, confirms the superior performance for the locally linear neurofuzzy model. Then, the predictive control is applied to the identified model to obtain a satisfactory performance in the output temperature that should track a desired reference signal. As another application, the algorithm is applied to temperature control of a solution polymerization methyl methacrylate in a batch reactor. The results show also somehow satisfactory performance for this highly nonlinear system. All the simulation results reveal the effectiveness of the proposed intelligent control strategy. [ABSTRACT FROM AUTHOR]

Metabolic heterogeneity is a hallmark of cancer and can distinguish a normal phenotype from a cancer phenotype. In the systems biology domain, context-specific models facilitate extracting physiologically relevant information from high-quality data. Here, to utilize the heterogeneity of metabolic patterns to discover biomarkers of all cancers, we benchmarked thousands of context-specific models using well-established algorithms for the integration of omics data into the generic human metabolic model Recon3D. By analyzing the active reactions capable of carrying flux and their magnitude through flux balance analysis, we proved that the metabolic pattern of each cancer is unique and could act as a cancer metabolic fingerprint. Subsequently, we searched for proper feature selection methods to cluster the flux states characterizing each cancer. We employed PCA-based dimensionality reduction and a random forest learning algorithm to reveal reactions containing the most relevant information in order to effectively identify the most influential fluxes. Conclusively, we discovered different pathways that are probably the main sources for metabolic heterogeneity in cancers. We designed the GEMbench website to interactively present the data, methods, and analysis results. [ABSTRACT FROM AUTHOR]