Search

The applications of centrality measures in protein-protein interaction (PPI) network analysis are diverse and encompass fundamental biological insights; cancer disease-related discoveries, and practical implications for drug development. This multidimensional approach in PPI network analysis provides a comprehensive understanding of the pivotal elements and their impact on biological systems. Analyzing centrality measures in PPI networks enables the identification of essential proteins, hub and bottleneck proteins that occupy strategic positions within the PPI network structure. Essential proteins in PPI networks are significant elements that indicate their importance in maintaining PPI network integrity and functionality. Studying centrality measures can reveal hidden patterns and relationships within these PPI networks. This paper identifes PPI networks with a high degree of connectivity ("hubs") and proteins with high betweenness centrality (bottlenecks), along with closeness centrality and clustering coefficient. This measure's significance in PPI networks has implications for various felds. The proposed approach successfully identifed and characterized infuential proteins and found the top 20 essential proteins. These proteins likely hold significant functional importance through hubs and bottlenecks and serve as potential targets for further investigation. This approach has the potential to identify essential proteins involved in cancer diseases. Leveraging centrality measures in the analysis of PPI networks ofers a multifaceted approach to understanding cancer biology and its implications for personalized medicine, drug design, and the development of innovative cancer therapies. [ABSTRACT FROM AUTHOR]

Cell localization in medical image analysis is a challenging task due to the significant variation in cell shape, size and color. Existing localization methods continue to tackle these challenges separately, frequently facing complications where these difficulties intersect and adversely impact model performance. In this paper, these challenges are first reframed as issues of feature misalignment between cell images and location maps, which are then collectively addressed. Specifically, we propose a feature alignment model based on a multi-scale hypergraph attention network. The model considers local regions in the feature map as nodes and utilizes a learnable similarity metric to construct hypergraphs at various scales. We then utilize a hypergraph convolutional network to aggregate the features associated with the nodes and achieve feature alignment between the cell images and location maps. Furthermore, we introduce a stepwise adaptive fusion module to fuse features at different levels effectively and adaptively. The comprehensive experimental results demonstrate the effectiveness of our proposed multi-scale hypergraph attention module in addressing the issue of feature misalignment, and our model achieves state-of-the-art performance across various cell localization datasets. • This paper innovatively addresses the challenges stemming from significant variations in cell shape, scale, and color by reframing them as a feature misalignment problem between cell images and location maps, thereby presenting a unified solution to these complexities. • We propose an innovative multi-scale hypergraph attention module that achieves feature alignment through the adaptive aggregation of features across various scale ranges. • The proposed model achieves state-of-the-art performance on multiple cell localization datasets and reveals great potential. [ABSTRACT FROM AUTHOR]

• The improved EEMD proposed in this paper effectively solves the problem of EMD pattern aliasing. • This paper proposes a BFN binarization method based on the relationship between network density and average node degree, ensuring the comparability of BFN between groups. • From the perspective of BFN, we explore the abnormal topology alters of brain, and effectively solve the problem of anxiety recognition. Most anxiety studies are based on the features of isolated electroencephalography (EEG) electrode, ignoring that the essence of EEG is the signals overlap from different neurons. Therefore, it's difficult to find abnormal topological alters of the brain from independent neuron. To this end, this paper proposes an anxitey analysis and detection framework of brain function network (BFN) based on improved Ensemble empirical mode decomposition (EEMD). Additional adaptive white noise is adopted to solve the pattern aliasing problem, and several intrinsic mode functions (IMF) for simulating independent neuron signals are obtained. Binary BFN is constructed on different IMF based on phase lag index (PLI) and proportional threshold strategy. Then complex network method is used to analyze the topology alters and attributes of BFN, and explore the potential biomarkers of anxiety detection. In addition, to evaluate the effectiveness of these potential biomarkers for anxiety detection, support vector machine (SVM) classifier is used as an evaluation tool and highest detection accuracy of 92.38% was obtained. Meanwhile, the analysis results show that the adaptive amplitude white noise can affect all extreme points, and improved EEMD can more effectively decompose EEG signals. Functional synchrony altered significantly in the frontal, temporal and central region of the left brain. Our research findings also show that the BFN of anxiety patients shows a tendency of randomization. [ABSTRACT FROM AUTHOR]

• We present a universal theory on information processing in living systems that is based on first principles in biophysics. • Organisms are modeled as 'hierarchical Bayesian control systems' involved in perception, goal-setting and action control. • Goal hierarchies imply the existence of 'highest goals', which inspire highly integrated normative (moral) behavior. • Severe stress causes a top-down collapse of goal hierarchies, producing behavior that lacks control by higher goals. • A temporary collapse causes episodic disorders (psychopathology); insufficient maturition causes personality disorders. In this paper, we show that organisms can be modeled as hierarchical Bayesian control systems with small world and information bottleneck (bow-tie) network structure. Such systems combine hierarchical perception with hierarchical goal setting and hierarchical action control. We argue that hierarchical Bayesian control systems produce deep hierarchies of goal states, from which it follows that organisms must have some form of 'highest goals'. For all organisms, these involve internal (self) models, external (social) models and overarching (normative) models. We show that goal hierarchies tend to decompose in a top-down manner under severe and prolonged levels of stress. This produces behavior that favors short-term and self-referential goals over long term, social and/or normative goals. The collapse of goal hierarchies is universally accompanied by an increase in entropy (disorder) in control systems that can serve as an early warning sign for tipping points (disease or death of the organism). In humans, learning goal hierarchies corresponds to personality development (maturation). The failure of goal hierarchies to mature properly corresponds to personality deficits. A top-down collapse of such hierarchies under stress is identified as a common factor in all forms of episodic mental disorders (psychopathology). The paper concludes by discussing ways of testing these hypotheses empirically. [ABSTRACT FROM AUTHOR]

[Display omitted] • Convective and diffusive transport into the lymphatic system is modeled by a hybrid discrete-continuum vessel network. • Various physiological conditions on the lymphatic vessel wall affects absorption rate. • Evolution of concentration boundary layer is dominated by diffusion across the explicit vessels. • Injection depth shows negligible effect on drug absorption in the multi-layered tissue. • Binding of drug molecules to the extracellular matrix slows down the drug absorption. Subcutaneous administration is a common approach for the delivery of biotherapeutics, which is achieved mainly through the absorption across lymphatic vessels. In this paper, the drug transport and lymphatic uptake through a three-dimensional hybrid discrete-continuum vessel network in the skin tissue are investigated through high-fidelity numerical simulations. We find that the local lymphatic uptake through the explicit vessels significantly affects macroscopic drug absorption. The diffusion of drug solute through the explicit vessel network affects the lymphatic uptake after the injection. This effect, however, cannot be captured using previously developed continuum models. The lymphatic uptake is dominated by the convection due to lymphatic drainage driven by the pressure difference, which is rarely studied in experiments and simulations. Furthermore, the effects of injection volume and depth on the lymphatic uptake are investigated in a multi-layered domain. We find that the injection volume significantly affects the rate of lymphatic uptake through the heterogeneous vessel network, while the injection depth has little influence, which is consistent with the experimental results. At last, the binding and metabolism of drug molecules are studied to bridge the simulations to the drug clearance experients. We provide a new approach to study the diffusion and convection of drug molecules into the lymphatic system through the hybrid vessel network. [ABSTRACT FROM AUTHOR]

Long non-coding RNAs (lncRNAs) are recent listing in RNA Bioinformatics, which is getting more popular due to their important functional roles. According to the available research, lncRNAs play an essential role in multiple complex diseases. Determining the function of lncRNAs in diseases will help to comprehend many missing links in the disease mechanism. Predicting lncRNAdisease association (LDA) is a crucial stage in this process which is getting at most research interest nowadays. The developments in machine learning and deep learning technologies influenced recent research on LDA models. Most of the methods analyse the interactions of lncRNA with other molecules such as microRNA (miRNA), messenger RNA(mRNA), and proteins. Deep learning models, specifically from autoencoder classes, used extensively in unsupervised learning of features from these associations. This research paper proposes a denoising autoencoder (DAE) based LDA prediction approach. The proposed model uses DAE to learn lncRNA-disease representations from multiple biological networks such as lncRNA-miRNA, miRNA-disease, and disease-lncRNA interactions. The experiments show that the model outperforms other state-of-the-art LDA models concerning the area under the ROC curve (AUC-ROC, 0.94) and the area under precision-recall (AUPR, 0.9592). [ABSTRACT FROM AUTHOR]

With the recent success of computer vision and deep learning, remarkable progress has been achieved on automatic personal recognition using vein biometrics. However, collecting large-scale real-world training data for palm vein recognition has turned out to be challenging, mainly due to the noise and irregular variations included at the time of acquisition. Meanwhile, existing palm vein recognition datasets are usually collected under near-infrared light, lacking detailed annotations on attributes (e.g., pose), so the influences of different attributes on vein recognition have been poorly investigated. Therefore, this paper examines the suitability of synthetic vein images generated to compensate for the urgent lack of publicly available large-scale datasets. Firstly, we present an overview of recent research progress of palm vein recognition, from the basic background knowledge to vein anatomical structure, data acquisition, public database, and quality assessment procedures. Then, we focus on the state-of-the-art methods that have allowed the generation of vascular structures for biometric purposes and the modeling of biological networks with their respective application domains. In addition, we review the existing research on the generation of style transfer and biological nature-based synthetic palm vein images algorithms. Afterward, we formalize a general flowchart for the creation of a synthetic database comparing real palm vein images and generated synthetic samples to obtain some understanding into the development of the realistic vein imaging system. Ultimately, we conclude by discussing the challenges, insights, and future perspectives in generating synthetic palm vein images for further works. • Factors that affect the visualization of vascular structures on palm vein images. • Generation of vascular structures for biometric purposes. • Modeling of biological networks with their respective application domains. • Generation of style transfer and biological nature-based synthetic palm vein images. • Challenges, insights, and future perspectives to generate synthetic palm vein images. [ABSTRACT FROM AUTHOR]

Synchronization pertains to the property of interconnected systems according to which their dynamic behavior is coordinated in an appropriate sense. That is to say, some of their state variables, or functions of the latter for that matter, converge to each other. Synchronization may occur naturally or may be induced, controlled, and it may be present between two systems or among a large number. In the latter case, it is convenient to speak of a network of interconnected systems. Understanding synchronization, and how to control it, is an important paradigm as it is present in a variety of scenarios. These involve, e.g. , networks of technological systems (robots and vehicles of different kinds), social networks (by which people exchange opinions and agree, or not), networks of biological systems (uni or pluricellular), etc. Owing to the context, the mathematical models to describe such networks and to define synchronization formally, varies dramatically. Ordinary continuous-time or discrete-time models for which modern control theory and Lyapunov stability theory are tailored result inappropriate to incorporate hybrid phenomena that intervene in the network. These may stem from sudden topology changes, the use of digital or intermittent control strategies, the presence of impacts in the intrinsic dynamics of the nodes, etc. In this invited paper, we give an overview of synchronization control problems, mostly of cooperative control of networks of autonomous vehicles (based on continuous-time models). For that matter, the first part of the paper focuses on the main contributions of [1]. Then, we give further perspectives on what we consider significant open problems on synchronization of hybrid systems and hybrid networks. [ABSTRACT FROM AUTHOR]

Reproductive traits, such as the number of teats and litter size, are essential for animal breeding programs due to the importance of the production chain, since they influence the maternal ability of the sow and can affect the number of weaned piglets. We aim to identify candidate genes associated with reproductive traits in pigs, using GWAS data from a systematic review combined with sequencing data, to build networks of biological processes and transcription factors (TFs) from the identified genes to highlight the most candidate genes for litter size and the number of teats. In the systematic review, only peer-reviewed articles were used, with descriptors related to the evaluated traits, and selected based on eligibility criteria. Fourteen papers were selected and classified for functional analysis of gene networks with 2077 candidate genes identified. After combining with the list of genes presenting known structural variants in the 5′UTR and/or coding region, 306 genes remained to be used to build the gene networks of biological processes and TFs, highlighting processes associated with litter size (e.g., ionotropic glutamate receptor signaling pathway and blastocyte growth) and the number of teats (e.g., growth hormone receptor, regulation of the BMP - Bone Morphogenetic Proteins signaling pathway and blood vessel proliferation). Two most candidate genes for litter size trait (GRID2 and PALB2) and six most candidate genes for the number of teats (GHR, IFT80, FSTL3, SKOR1, SMURF1, and AKT3) were prioritized. TFs associated with candidate genes were also identified for litter size (PALB2 and GRID2) and the number of teats (RIN, LTBP2, and COL6A6). Thus, it is suggested that the most candidate genes and TFs presented in this study may play an important role in the traits studied, being important for genetic studies and animal breeding. [ABSTRACT FROM AUTHOR]

In order to solve a challenging problem, i.e., fixed time synchronization of bilayer networks with derivative coupling and nonlinear delay coupling, fixed time polices are brought to achieve random synchronization for bilayer multiple weight hybrid coupled networks of neuron nodes. Being different from the synthesis method, which is often used to get theoretical conclusions from known conditions for synchronization of networks in general articles, analysis method is applied to seek parameters in fixed time controllers and sufficient conditions for synchronization from conclusions. After analysis, we obtain a relationship between coefficients of controllers and coefficients of a formula which is related to Lyapunov function. Moreover, we find that fixed settling time for synchronization is affected by the maximum eigenvalue of a matrix associated with network topology, parameters in the designed controllers and the size of networks. Finally, synchronous tests of bilayer networks of Hindmarsh–Rose (HR) neuron nodes are carried out to show the effectiveness of theoretical results. • We construct models of bilayer multi-weighted stochastic networks with neuron nodes, which are composed of multiple derivative and nonlinear delay coupling. • In this paper, fixed time controllers are designed. Parameters of fixed time controllers and sufficient criteria of synchronization are obtained via analysis method, which is to seek parameters in fixed time controllers and sufficient conditions for synchronization from the conclusion. This is different from many previous works. • A relationship between some coefficients of fixed time controllers and the coefficients of a formula which is related to Lyapunov function is given. • We find that fixed settling time is affected by maximum eigenvalue of the matrix which is related to network topologies, parameters in the designed controllers and nodes of networks. [ABSTRACT FROM AUTHOR]

How did cooperation emerge and persist while betrayal was beneficial for individuals? Previous studies suggested that network reciprocity was a promising explanation. However, these studies usually analyzed cooperation performances of different types of networks separately and/or statically, thus failing to fully capture the co-evolution and interaction features in our history. We proposed a mechanism that analyzed the cooperation level of different types of networks under co-evolutionary circumstances of cooperation and heterogeneous networks. The paper adopted the prisoner's dilemma game and analyzed Erdos-Renyi (ER) random networks, Watts Strogatz (WS) small-world networks, and Barabasi-Albert (BA) scale-free networks. In this study, these networks were interconnected and continuously grew after gaming evolutions were stable. And the growth versions of WS and ER respectively were proposed that maintained their topology features. Comprehensive experimental results showed that high-level cooperation emerged and was maintained in scale-free networks. This advantage was strengthened with networks grew from a small scale to a middle scale. Moreover, once most nodes of a scale-free network evolved to be cooperators, the group cooperation they formed were able to survive in betrayer-prevailing and high betrayal temptation environments. These findings may deepen our understanding of the relationship between how cooperation evolved and why many social networks exhibited scale-free properties. [ABSTRACT FROM AUTHOR]

In recent years, with the emergence of big-data and high-throughput biological analyses, massive biological data have been generated and accessed, and many heuristic and metaheuristic algorithms have been proposed for further analysis and extraction of the potential knowledge of those data. Biological network alignment (BNA) aligns proteins between species to maximally conserve biological and topological structures of proteins. The studies of BNAs are essential for uncovering conserved protein interactions of biological networks with functional homology and understanding the evolutionary process across species. In this paper, we give a comprehensive review for the works in BNAs from a novel taxonomy: heuristic and metaheuristic BNAs. Moreover, we give some comparative analyses of the alignment models, real data sets, evaluation metrics and experimental results in these works. Finally, we provide some conclusions and give some possible future directions for BNAs. [ABSTRACT FROM AUTHOR]

Community detection can be used as an important technique for product and personalized service recommendation. A game theory based approach to detect overlapping community structure is introduced in this paper. The process of the community formation is converted into a game, when all agents (nodes) cannot improve their own utility, the game process will be terminated. The utility function is composed of a gain and a loss function and we present a new gain function in this paper. In addition, different from choosing action randomly among join, quit and switch for each agent to get new label, two new strategies for each agent to update its label are designed during the game, and the strategies are also evaluated and compared for each agent in order to find its best result. The overlapping community structure is naturally presented when the stop criterion is satisfied. The experimental results demonstrate that the proposed algorithm outperforms other similar algorithms for detecting overlapping communities in networks. [ABSTRACT FROM AUTHOR]

Link prediction is an axial field in complex network analysis as it aims to infer new connections between nodes in a given network. Many applications of this task include: the reference system, the suggestion of friends in the social network and the prediction of interactions between proteins in biological networks. Several methods have been developed in the link prediction task, especially the similarity-based methods which are widely used due to their low complexity and good performance. In this paper, we propose a novel link prediction approach that combines the PageRank algorithm with local information-based methods to improve performance while retaining the advantage of low complexity of local methods. We conducted a series of experimental studies on eleven data sets where we compared our new combined methods with six well-known local methods. The results obtained show a significant gain in terms of performance in almost all data sets. In addition to this and to confirm the superiority of the proposed methods, another comparative study is performed, formed of nine local and global methods. According to the experimental results, our approach outperforms all other compared methods with linear complexity. • The analysis of networks evolution brings us to the problem of link prediction. • Similarity-based methods are used as a local method to predict new links. • New similarity measures between unconnected nodes that do not have shared neighbors. • Our method offers higher precision than some well-known local and global methods. • Our method offers linear computational complexity. [ABSTRACT FROM AUTHOR]

Artificial neural networks, inspired by the biological networks of the human brain, have become game-changing computing models in modern computer science. Inspired by their wide scope of applications, synthetic biology strives to create their biological counterparts, which we denote synthetic biological neural networks (SYNBIONNs). Their use in the fields of medicine, biosensors, biotechnology, and many more shows great potential and presents exciting possibilities. So far, many different synthetic biological networks have been successfully constructed, however, SYNBIONN implementations have been sparse. The latter are mostly based on neural networks pretrained in silico and being heavily dependent on extensive human input. In this paper, we review current implementations and models of SYNBIONNs. We briefly present the biological platforms that show potential for designing and constructing perceptrons and/or multilayer SYNBIONNs. We explore their future possibilities along with the challenges that must be overcome to successfully implement a scalable in vivo biological neural network capable of online learning. [ABSTRACT FROM AUTHOR]

• Neoteric representation of biological networks by weighted directed hypergraph. • Identification of influential nodes using weak tie of directed hypergraph. • Hybridisation of degree centralities with genetic algorithm for optimizing the weights. • Reduced complexity by means of much lesser hyperedges compared to edges and compared results with graph centrality measures. • Influential COVID-19 proteins are identified from protein interactions which could be used for drug design. Network structures have attracted much interest and have been rigorously studied in the past two decades. Researchers used many mathematical tools to represent these networks, and in recent days, hypergraphs play a vital role in this analysis. This paper presents an efficient technique to find the influential nodes using centrality measure of weighted directed hypergraph. Genetic Algorithm is exploited for tuning the weights of the node in the weighted directed hypergraph through which the characterization of the strength of the nodes, such as strong and weak ties by statistical measurements (mean, standard deviation, and quartiles) is identified effectively. Also, the proposed work is applied to various biological networks for identification of influential nodes and results shows the prominence the work over the existing measures. Furthermore, the technique has been applied to COVID-19 viral protein interactions. The proposed algorithm identified some critical human proteins that belong to the enzymes TMPRSS2, ACE2, and AT-II, which have a considerable role in hosting COVID-19 viral proteins and causes for various types of diseases. Hence these proteins can be targeted in drug design for an effective therapeutic against COVID-19. [ABSTRACT FROM AUTHOR]

Network cluster analysis is of great importance as it is closely related to diverse applications, such as social community detection, biological module identification, and document segmentation. Aiming to effectively uncover clusters in the network data, a number of computational approaches, which utilize network topology, single vector of vertex features, or both the aforementioned, have been proposed. However, most prevalent approaches are incapable of dealing with those contemporary network data whose vertices are characterized by features collected from multiple sources. To address this challenge, in this paper, we propose a novel framework, dubbed Multi-Source Propagation Aware Network Clustering (MSPANC) for uncovering clusters in network data possessing multiple sources of vertex features. Different from most previous approaches, MSPANC is able to infer the cluster preference for each vertex utilizing both network topology and multi-source vertex features. To improve the practical significance of the discovered clusters, the learning of cluster membership is also involved into the modeling of the maximization of intra-cluster propagation regarding multi-source features. We propose a unified objective function for MSPANC to perform the clustering task and derive an alternative manner of learning algorithm for model optimization. Besides, we theoretically prove the convergence of the algorithm for optimizing MSPANC. The proposed model has been tested on five real-world datasets, including social, biological and document networks, and has been compared with several competitive baselines. The remarkable experimental results validate the effectiveness of MSPANC. [ABSTRACT FROM AUTHOR]

The paper discusses the architectural elements of neuromorphic systems on the example of behaviour control of robots. The basis of the work is new spiking neuron model, which allows describing the known biological network relying on macroscopic parameters of neurons - the size, the relative length of dendrites, etc. The model also allows no parametric setting but change synaptic and dendritic structures. This implies the possibility of a structural adjustment neuromorphic system. The paper gives examples of simulation results of the proposed elements neuromorphic systems. [ABSTRACT FROM AUTHOR]

Identification of disease genes, using computational methods, is an important issue in biomedical and bioinformatics research. According to observations that diseases with the same or similar phenotype have the same biological characteristics, researchers have tried to identify genes by using machine learning tools. In recent attempts, some semi-supervised learning methods, called positive-unlabeled learning, is used for disease gene identification. In this paper, we present a Perceptron ensemble of graph-based positive-unlabeled learning (PEGPUL) on three types of biological attributes: gene ontologies, protein domains and protein-protein interaction networks. In our method, a reliable set of positive and negative genes are extracted using co-training schema. Then, the similarity graph of genes is built using metric learning by concentrating on multi-rank-walk method to perform inference from labeled genes. At last, a Perceptron ensemble is learned from three weighted classifiers: multilevel support vector machine, k -nearest neighbor and decision tree. The main contributions of this paper are: (i) incorporating the statistical properties of gene data through choosing proper metrics, (ii) statistical evaluation of biological features, and (iii) noise robustness characteristic of PEGPUL via using multilevel schema. In order to assess PEGPUL, we have applied it on 12950 disease genes with 949 positive genes from six class of diseases and 12001 unlabeled genes. Compared with some popular disease gene identification methods, the experimental results show that PEGPUL has reasonable performance. [ABSTRACT FROM AUTHOR]

• Some diseases or therapies can be represented as sequences of reprogramming actions. • Boolean control networks (BCN) naturally model biological network reprogramming. • Complexity of control sequence inference depends on the number of biomarkers. • Inferring sequential controls is hard in general, but is feasible in practice. Network controllability is a major challenge in network medicine. The problem is to rewire the molecular network for reprogramming the cell fate. The reprogramming action is considered as a control usually performed once. However, in some cases, a therapy has to follow a time-scheduled drug administration protocol. Furthermore, some diseases are induced by a sequence of mutations leading to a sequence of actions on molecules. In this paper, we extend the single control action method by investigating the sequential control of Boolean networks. We present a novel theoretical framework for formal study of control sequences, leading to algorithms resolving the PSPACE-hard problem of inferring minimal parsimonious control sequences under the synchronous dynamics. [ABSTRACT FROM AUTHOR]

Role discovery is an emerging research area in the analysis of social networks, biological networks, and neural networks. The fundamental idea of role discovery is partitioning the vertices based on their structural features in a graph. However, most existing studies construct the role representations by learning the pairwise connectivity structures, thus weak to represent the relations among multiple vertices, namely higher-order structures. To this effect, this paper proposes a novel approach termed Low-Rank Persistent Probability representation (LRPP) for higher-order role discovery. In detail, we define local Dowker filtration to compute the persistent homology of the neighbor subgraph, which models the connection patterns among vertices and their neighbors. Then, we propose the Persistent Probability (PP) based on Dirichlet equivalence to vectorize the obtained features. Since the persistent probability matrix is sparse, we finally construct the low-rank representation to cluster the higher-order roles by embedding PP vectors into a series of linear subspaces. We conduct experiments of role discovery on both labeled and unlabeled datasets. The experimental results show that the proposed LRPP outperforms the baseline methods in role discovery and demonstrate that LRPP can effectively represent the higher-order role in graphs. • Define a local filtration to extract higher-order role features. • Propose a fast and interpretable method to vectorize role features. • Model the subspace structure of role vectors for clustering. [ABSTRACT FROM AUTHOR]

In current Optical Coherence Tomography (OCT)-based fingerprint recognition systems, Presentation Attack Detection (PAD) and subsurface fingerprint reconstruction are treated as two independent branches, resulting in high computation and system building complexity. Therefore, this paper proposes a uniform representation model for simultaneous PAD and subsurface fingerprint reconstruction. A novel semantic segmentation network using attention mechanisms was designed to extract and segment multiple subsurface structures from real finger slices (i.e., B-scans). The latent codes derived from the network are directly used to effectively detect PA because they contain abundant subsurface biological information, which is independent of PA materials and has strong robustness for unknown PAs. Segmented subsurface structures were adopted to reconstruct multiple subsurface 2D fingerprints. Extensive experiments were carried out on an in-house database, which is the largest public OCT-based fingerprint database with 2449 volumes. PAD performance was evaluated by comparing the results of the existing methods with those of other segmentation networks. The proposed uniform representation model can obtain an accuracy (Acc) of 96.63%, which achieves a state-of-the-art performance. The effectiveness of subsurface reconstruction was evaluated from the segmentation and recognition results. In the segmentation experiments, the proposed method achieved the best results with an Intersection of Union (mIOU) of 0.834 and a Pixel Accuracy of 0.937. By comparing the recognition performance on surface 2D fingerprints (e.g., commercial and high-resolution), the lowest results with an Equal Error Rate (EER) of 2.25% by minutiae matching and an EER of 5.42% by pore matching are achieved, which indicates the excellent reconstruction capability of the proposed uniform representation model. • A uniform representation model for OCT-based fingerprint PAD and reconstruction. • A novel semantic segmentation network applying biological structure features. • High security PAD is achieved without using any presentation attacks for training. • Reliable reconstruction result is obtained with subsurface biological information. [ABSTRACT FROM AUTHOR]

Based on network analysis and exponential random graph analysis methods (ERGM), the spatial network pattern of gas consumption and its evolution mechanism are investigated using Chinese inter-provincial panel data from 2008 to 2021. This paper shows that the spatial association strength level of natural gas consumption shows an enhanced spatiotemporal evolution. Network density is enhanced. Hierarchy decreases in a stepped way, the network efficiency increases in a fluctuating way, the redundant relation decreases, and the number of its bidirectional spillover relation increases year by year. The eastern seaboard is an economically developed region which is in the "net benefit" position. The provinces of the Tianjin, Hebei, and Shandong are the "two-way spillover" segments. Giving full play to the advantage of the balance between local markets and foreign markets gas consumption. Fujian, Chongqing, Inner Mongolia, Shanxi and Hubei are in the "broker" segment, which tends to concentrate on natural gas consumption outside the segment by their resource endowments. The block of "net spillover" involves the risk of carbon transfer and it should be more responsible for energy saving and carbon reduction; The convergence of per capita economic development, the size of population, industrial structure and urbanization rate leads to selective preferences for inter-provincial natural gas consumption. The large population sizes of provinces have "economies of scale" and "local market effects", which prefer to deploy natural gas. Carbon emission linkage network is the most important for the formation of spatial network patterns of natural gas consumption, followed by the influence of economic linkage networks and land proximity. Less stringent environmental regulations and an open regional environment are conducive to the formation of a network of spatial correlations in natural gas consumption. Accordingly, new ideas and policy recommendations for inter-provincial emission reduction are finally proposed. • Natural gas consumption spatial correlation network correlation has increased and has a star-shaped structure. • Structural barriers in the spatially linked network of natural gas consumption are gradually being broken down. • The eastern region is at risk of carbon transfer and should take on more energy-saving and emission reduction tasks. • Economic, demographic and industrial convergence leads to selective preferences in natural gas consumption. [ABSTRACT FROM AUTHOR]

Community detection is the one of the most relevant and important topics in the field of graph mining, principally for its applications in number of domains such as social or biological networks analysis to transport systems. Different community detection algorithms have been proposed earlier. This paper discusses and proposes a new technique on particular community detection and its retrieval in social community graph. Community detection constitutes a significant role for analysis of a large community graph by enabling and selecting the desired community's graph. It tries to extract a sub-community graph from a very large community graph for analysis. This paper presents both a theoretical and experimental result in this direction with certain example. We have retrieved the sub-community graph from a compressed community graph in the context of graph mining techniques. Observation concludes that the proposed technique is simpler, easier and efficient in terms of complexities. [ABSTRACT FROM AUTHOR]

• Systems metabolic engineering can benefit from regulatory network models. • Regulatory network models should be more considered during strain development. • Increasing availability of bio big data helps better modeling regulatory networks. • Regulatory networks are modeled using a wide range of machine learning techniques. • Datasets on biological sequence-phenotype relationship need to be more generated. Systems metabolic engineering attempts to engineer a production host's biological network to overproduce valuable chemicals and materials in a sustainable manner. In contrast to genome-scale metabolic models that are well established, regulatory network models have not been sufficiently considered in systems metabolic engineering despite their importance and recent notable advances. In this paper, recent studies on inferring and characterizing regulatory networks at both transcriptional and translational levels are reviewed. The recent studies discussed herein suggest that their corresponding computational methods and models can be effectively applied to optimize a production host's regulatory networks for the enhanced biological production. For the successful application of regulatory network models, datasets on biological sequence-phenotype relationship need to be more generated. [ABSTRACT FROM AUTHOR]

• A new 2D axisymmetric model of heap and column leaching under drip emitters, HeapSim2D, presented. • Heap hydrology and solute transport models discussed. • Capillary head parameter identified as the master parameter governing the spread of solution under a drip emitter. • Gangue acid consumption sensitivity study presented. • Acid dissolution rate constant identified as the key parameter governing the degree of acid distribution in the ore column. Most modern heap leaching processes involve drip irrigation, which induces certain flow and dispersion patterns within the heap. However, this important aspect of heap leaching is overlooked in most mathematical models. This is the first in a planned series of papers presenting a two-dimensional comprehensive model of heap leaching under drip emitters, called HeapSim2D. In this paper, the fundamentals of heap leach modelling are discussed, with particular attention paid to the effect of drip irrigation on the flow of water and the dispersion of solutes within heaps. The mathematical and numerical foundations of a 2D axisymmetric model of heap irrigation under a single point source are developed in detail, and a sensitivity study is presented for the simple case of a first-order gangue acid dissolution reaction in a column under a single drip emitter, using hydrological parameters based on experimental work presented in a previous paper. Simulations show that the capillary head parameter h c , 0 is the master parameter which determines the degree of solution spreading under drip emitters. Ores with high capillarity (such as agglomerated ores with significant fines) will spread the solution effectively, whereas ores with low capillarity (such as pebbles without fines) will confine the flow mostly to a channel below the drip emitter. Also, even ores with high capillarity will have a largely unwetted zone at the top of the heap or column. Simulations also show that solute dispersion has a relatively subtle effect on the rate of chemical reactions within heaps and columns, but that, presuming the solution is spread out, the chemical rate constant is the most important parameter in determining the degree of reaction within heaps and columns. In future papers, models for gas flow, gas species transport, heat transport, and various case studies involving custom networks of chemical and biological reactions with kinetics will be presented. [ABSTRACT FROM AUTHOR]

To address important challenges in bioinformatics, high throughput data technologies are needed to interpret biological data efficiently and reliably. Clustering is widely used as a first step to interpreting high dimensional biological data, such as the gene expression data measured by microarrays. A good clustering algorithm should be efficient, reliable, and effective, as demonstrated by its capability of determining biologically relevant clusters. This paper proposes a new minimum spanning tree based heuristic B-MST, that is guided by an innovative objective function: the tightness and separation index (TSI). The TSI presented here obtains biologically meaningful clusters, making use of co-expression network topology, and this paper develops a local search procedure to minimize the TSI value. The proposed B-MST is tested by comparing results to: (1) adjusted rand index (ARI), for microarray data sets with known object classes, and (2) gene ontology (GO) annotations for data sets without documented object classes. [ABSTRACT FROM AUTHOR]

In this paper, we mainly address three issues: externality of an agent, purpose of an agent, and a kind of “softness” of components in a system. Agents are independent of a system in an ordinary multi-agent model, hence the behavior of a system is not autonomous but influenced by the agents. If a multi-agent model is considered as a completely autonomous one, agents in the model are inevitably deprived of their externality and independence from the model. In order to treat the completely autonomous transition of a system, we introduce an agent which is part of a system, and has a purpose which is independent from the system. The interaction between a system and an agent transforms a random graph corresponding to the system into the graph which represents formal logic adequately. In the emergent graph, there are many complete subgraphs, which can be regarded as conceptualized matters. We modify the definition of a conceptualized matter into a subgraph which is a cycle of arrows, and regard the density of arrows of each conceptualized matter as validness. We define this object with the density as a soft object. A complete graph has a maximum number of arrows, hence is the most reliable soft object. In a similar way, we call an arrow with validness a soft arrow, and treat the relation between soft objects and soft arrows. The argument of this paper is relevant to dynamical formal logic, and at the same time, is intended to serve as a basis for an agent model. [ABSTRACT FROM AUTHOR]

In this paper, we investigate both undirected and directed network evolution using the Euler–Lagrange equation. We use the Euler–Lagrange equation to develop a variational principle based on the von Neumann entropy for time-varying network structure. Commencing from recent work to approximate the von Neumann entropy using simple degree statistics, the changes in entropy between different time epochs are determined by correlations in the degree difference in the edge connections. Our Euler–Lagrange equation minimises the change in entropy and allows to develop a dynamic model to simulate the changes of node degree with time. We first explore the effect of network dynamics on the three widely studied complex network models, namely (a) Erdős-Rényi random graphs, (b) Watts-Strogatz small-world networks, and (c) Barabási-Albert scale-free networks. Our model effectively captures both undirected and directed structural transitions in the dynamic network models. We apply our model to a network time sequence representing the evolution of stock prices on the New York Stock Exchange(NYSE) and sequences of Drosophila gene regulatory networks containing different developmental phases of the organism from embryo to adult. Here we use the model to differentiate between periods of stable and unstable stock price trading and to detect periods of anomalous network evolution. Our experiments show that the presented model not only provides an accurate simulation of the degree statistics in time-varying networks but also captures the topological variations taking place when the structure of a network changes violently. Image, graphical abstract [ABSTRACT FROM AUTHOR]

Graphs are used to depict real-world scenarios as they have a wide variety such as online social networks, data and communication networks, word co-occurrence networks, biological networks, transport networks etc. Rigorous analysis of graphs produces the insight of graphs and yields the more profound knowledge of the social structure, language, and different communication patterns through data analysis of each type and provide the interaction between them. Many applications like node classification, link prediction, clustering can be inferred by using the graph. Several proposals are formulated to perform graph analysis. Recently, the most popular methods used by researchers are the presentation of graph nodes in a vector space by transforming the graph information into a low-dimensional manifold with maximum preservation of graph properties. In this course of the research, researchers apply the embedding technique to classify nodes of a graph based on the intrinsic property of nodes. However, most graph analytic methods require excessive space and computation cost. Therefore, a new method of embedding is needed for graph analysis. In this paper, various existing embedding techniques are simulated and comparatively discussed with different datasets. Based on this embedding, communities are created on the real-world network data of the karate club. At last, some future direction of research is mentioned in terms of application scenarios. [ABSTRACT FROM AUTHOR]

Hydrogen/methane mixtures draw attention due to the idea of the injection of hydrogen into natural gas networks and biological production of biohythane by one- and two-step anaerobic fermentation/digestion methods. It is hard to extract hydrogen from dilute mixtures with methane by traditional separation processes, since hydrogen is the minor component with low partial pressure. Metal hydrides selectively absorb hydrogen and offer an opportunity to overcome the limitations of traditional separation methods. In the present paper, we present experimental results on the separation of a dilute mixture of hydrogen (10%) with methane in a flow-through metal hydride reactor with inlet mixture pressure of 0.95 MPa by the LaNi 4.8 Mn 0.3 Fe 0.1 intermetallic compound. Hydrogen was separated in one step with roundtrip (absorption/desorption) recovery of 74%. An exergetic analysis of the metal hydride separation of a binary mixture containing hydrogen was implemented and equations for hydrogen recovery and exergy efficiency of separation are obtained. Thermodynamic analysis shows that the exergy efficiency of the metal hydride purification has a clear maximum at hydrogen concentrations around 5–20%. The advantage of metal hydride purification is the absorption of the minor fraction from the feed, thus it is preferable for dilute mixtures and could be feasible for practical applications. With the use of low potential or waste heat to drive the reaction, it is possible to increase the efficiency of hydrogen purification by metal hydrides. The maximum exergy efficiency is 61% for 0.8 MPa outlet pressure, taking into account the quality of involved heat flows. • H 2 (10%)/CH 4 mixture was separated by LaNi 4.8 Mn 0.3 Fe 0.1 with 74% H 2 recovery. • Equations for the exergy efficiency of separation and the hydrogen recovery are obtained. • Exergy efficiency has maximum at H 2 concentration 5–20%. • Metal hydride purification has advantage for dilute H 2 mixtures. [ABSTRACT FROM AUTHOR]

Advancements in bio-techniques have accelerated the generation of genomic and proteomic data. High throughput experiments generate huge volume of biological data that lead to cutting edge researches. These data may be stored in hierarchical structures known as ontologies. Ontologies serve as rich knowledge sources for biological information mining. Annotations from Gene Ontology (GO), Human Phenotype Ontology (HPO) and Drug Ontology are extensively used for biological studies including disease causing gene prediction, disease comorbidity analysis and drug designing purposes. Several mechanisms exist for extracting semantic similarity among ontological terms. Graph based approaches are mostly used for analyzing the semantic similarity between ontological terms. In this paper, a novel method has been proposed for predicting disease causing genes and disease comorbidities using GO and HPO data. Weighted association rules obtained from Gene Ontology and Human Phenotype Ontology are used for constructing weighted gene network and weighted phenotype network. These networks are then connected using known gene-phenotypic relationships. Modified random walk restart algorithm is performed on this network for extracting novel disease gene correlations and disease comorbidities. This method outperformed the existing methods that depend on similarity measurements. [ABSTRACT FROM AUTHOR]

• A new integrative analysis system for gene-expression value enhancement is proposed. • A novel biomarker selection method and its algorithm are proposed. • Some therapeutic potential markers and pathways for NSCLC are provided. Few proposed gene biomarkers have been satisfactory in clinical applications. That is mainly due to the small studies sample size. Because of the batch effect, different gene-expression studies cannot be merged directly. Many integrative methods have attempted to integrate various datasets to eliminate the batch effect while keeping biological information intact. However, due to the complexity of the batch effect, it cannot be eliminated, and these methods may even add new systematic errors to the data, further complicating integrated data. Therefore, direct analysis of the merged data may cause some issues. In this paper, we suggest a novel integrative analysis framework for merged gene-expression data. The framework adopts the self-paced learning. This method allows samples to be automatically added into the training period, from simple to intricate, in a purely self-paced way. Moreover, the framework includes a new feature selection method, the SCAD-Net regularization method, a combination of SCAD and network-based penalties to integrates the biological network knowledge. The simulation shows that the proposed method outperforms the benchmark with more accurate marker identification. The analysis of seven large NSCLC gene expression datasets shows that the proposed method not only results in higher accuracies, but also identifies potential therapeutic markers and pathways in NSCLC. In conclusion, we provide a new and efficient integrative analysis system of gene expression, for the search for new reliable diagnosis or targeted therapy biomarker. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with a parabolic–elliptic model for biological fluid transportation networks with the large diffusivity and activation. Our investigations reveal that local existence of the unique classical solution to the correspondinginitial–boundary value problem can always be achieved in the n -dimensional setting with n ≤ 3. Moreover, we show that this local solution can be extended to a global one as long as the source term and the initial data are made small enough in a suitable sense, and that the classical solution converges exponentially to a trivial steady state; meanwhile, we present that the smallness condition on the initial data can be removed in the one-dimensional setting. Our result implies that we cannot expect network formation when the diffusivity is sufficiently large, which is a first step toward a qualitative analysis for the role of diffusion in biological transportation networks. [ABSTRACT FROM AUTHOR]

Network topology has a critical effect on its reliability. Due to the complexity of networks, how to design a more reliable network topology is one of the fascinating and significant challenges. In this paper, inspired by the multi-scale model of biological systems which range from gene to individual cells, and up to the individual organism, we provide a novel method to design a reliable topology of large-scale networks. By means of fractal theory and its application in complex networks, a network topology can be described as consisting of the superposition of basic units, called fractal-cells, which are made up of several components and their connections. Based on this fractal-cell structure, we find that a fractal-cell network has structural similarity with its primitive structure, and then develop a method to build a reliable topology which is generated from a primitive ring structure. Numerical simulation compared with the existing methods and performing on real networks show that our proposed method is effective. • The network topology can be described as consisting of the superposition of basic units, called fractal-cells. • Through the fractal-cell division and differentiation, we find that a fractal-cell network has structural similarity with its primitive structure. • A novel method is proposed to build a reliable topology based on fractal-cell division and differentiation. [ABSTRACT FROM AUTHOR]

Histopathological methods based on 2 μm thin sections are routinely used in pathological anatomical diagnosis. Many medical disciplines already rely on a 3D representation, regarding visualization and imaging techniques. Pathology in particular uses different tissue visualizations to make the final diagnosis. Thereby, a standard 2D histological section only represents a flat snapshot of a three-dimensional complex cell system. Despite that, 3D cell analysis is not yet standardly used in clinical routine. This work used 3D analysis systems to investigate the morphological alterations of the fibroblastic reticular cell network inside human lymph nodes during neoplastic transformation and evaluates the added value of 3D visualizations in tissue interpretation. We investigated the surface and volume quotient, cell cross-linking and percentage cell volume of the fibroblastic reticular cell (FRC) network inside Lymphadenopathy (follicular hyperplasia) (LAD), Follicular Lymphoma Grade 1 (FL1), Nodular Sclerosis classical Hodgkin Lymphoma (NScHL) and Angioimmunoblastic T-Cell Lymphoma (AITL). We found that the average quotient of LAD and FL1 differed from those of NScHL and AITL, indicating that the surface and volume quotient changes in the course of neoplastic transformation. This is probably due to an increased network convolution, while the total cell volume remains the same at about 2%. In conclusion, this paper describes the tumor-related morphological changes of the FRC network, which would have been difficult to achieve without the use of 3D analysis systems. [ABSTRACT FROM AUTHOR]

• A biocompatible approach for synthesis the cell-laden double network hydrogel was presented. • The hydrogel exhibited properties which are suitable for load-bearing soft tissues engineering. • The hydrogel could support the proliferation and spreading of the 3D cultured cell. Repair or regeneration of load-bearing soft tissue is one of the great challenges in tissue engineering and regenerative medicine. The main obstacle is mismatch between the properties of most synthetic biomaterials and the target tissue, such as the biomechanical performance. Currently, development of novel hydrogels with high mechanical strength and biocompatibility is the main goal of load-bearing soft tissue engineering. In the paper, a double-network hydrogel strategy involving silk fibroin and methacrylated hyaluronic acid was utilized to synthesize hydrogels through a combination of sonication and photopolymerization. Furthermore, due to the biocompatibility of the entire fabrication process, preosteoblast cells could be encapsulated within the hydrogel with high viability. The hydrogel characterization results demonstrated that the SF-HAMA hydrogels have many excellent properties, such as high mechanical strength, high water content, a slow degradation rate and biocompatibility. Two-dimensional cell experiments confirmed that the preosteoblasts could quickly attach and subsequently proliferate on the hydrogels, shown by cellular fluorescence staining. In addition, the studies of preosteoblast encapsulation and the following fluorescent staining demonstrated that the DN hydrogel could support proliferation and spreading of encapsulated cells, which suggests promising application of the hydrogel in the soft tissue engineering field. [ABSTRACT FROM AUTHOR]

• We report an automatic analytical framework for the WSIs of bone marrow aspirate smears. • We provide a pyramid segmentation network to automatically locate ROI based on prior pathological knowledge. • We design a sampling algorithm of patches and an evaluating network for patch quality and embed it in the framework. • We evaluate the size of the patch images and improve the cell detection model. Cell differential counting in the scanned whole-slide image (WSI) is essential but still challenging in the intelligent diagnosis of hematological disorders based on bone marrow aspirate smear images. The huge size and the complex content of WSI pose multiple obstacles to training an efficient and accurate analysis model. This paper proposes a novel analysis framework termed ROI-BMC-DNNet. To capture patch images in the cell well-spread areas, we provide a pyramid segmentation network based on prior pathological knowledge to automatically locate regions of interest (ROI) that pathologists usually manually focus on to count nucleated cells. Specifically, we describe a simple but effective alignment method for multiscale ROI, aiming to maintain the accuracy of the semantic and the spatial information of the pyramid network. Furthermore, we design a patch sampling algorithm and a patch quality evaluating network to ensure the effectiveness of the sampled patches. Additionally, we provide a cell detection model to realize the automatic differential counting of the nucleated cells in the sampled patches. Experiments are conducted on the datasets of scanned WSIs from 120 patients. Our method achieves 92.27% retrieval precision for the high-quality patches (patches with nucleated cells with better morphology and without particles). When the missed nucleated cells are evaluated, the detection Recall and Precision of the bone marrow nucleated cells from the WSIs are 87.13% and 87.90% respectively. Notably, the recognition Recall and Precision of the blast cell are 91.37% and 90.47%, respectively. The results confirm that the proposed method is promising to work as an auxiliary diagnostic tool for hematological disorders. [ABSTRACT FROM AUTHOR]

• Recent studies on bio-inspired concrete materials and structures are reviewed. • The biological methods of concrete self-healing and soil improvement are discussed. • Bio-inspired building envelopes and facades and their applications are reviewed. • Potential of 3D printing techniques in producing bio-inspired concretes is analysed. • The future prospects of bio-inspired building materials and structures are suggested. Nature provides examples of resilient materials and structures with optimised morphologies and topologies to achieve excellent mechanical and structural properties and sustainable options for the construction industry. This paper provides an overview of nature-inspired materials applicable to buildings and civil structures. Current research on bio-inspired novel cementitious composites, bacteria-enhanced materials, building envelopes and facade systems, advanced manufacturing processes, and their applications are discussed. Moreover, this paper provides insights for future research on designing and developing bio-inspired building materials and resilient structures. [ABSTRACT FROM AUTHOR]

[Display omitted] • Systematic procedures are followed to obtain the most relevant studies. • Mendeley web application is used in filtering process to extract the relevant studies. • Each relevant study is evaluated based on the performance analysis questions. • Selected studies are compared based on the various important parameters. • The articles are categorized based on the type of framework used for implementations. Community detection in social networks is the process of identifying the cohesive groups of similar nodes. Detection of these groups can be helpful in many applications, such as finding networks of protein interaction in biological networks, finding the users of similar mind for ads and suggestions, finding a shared research field in collaborative networks, analyzing public health, future link prediction in social networks, analyzing criminology, and many more. However, with the increase in the number of profiles and content shared on social media platforms, the analysis is often time-consuming and exhaustive. In order to speed up and optimize the community detection process, parallel processing and Shared/Distributed memory techniques are widely used. Despite community detection has widespread use in social networks, no attempt has ever been made to compile and systematically discuss research efforts on the emerging subject of identifying parallel and distributed methods for community detection in social networks. Most of the surveys described the serial algorithms used for community detection. Our survey work comes under the scope of new design techniques, exciting or novel applications, components or standards, and applications of an educational, transactional, and co-operational nature. This paper accommodates and presents a systematic literature review with state-of-the-art research on the application of parallel processing and Shared/Distributed techniques to determine communities for social network analysis. Advanced search strategy has been performed on several digital libraries for extracting several studies for the review. The systematic search landed in finding 3220 studies, among which 65 relevant studies are selected after conducting various screening phases for further review. The application of parallel computing, shared memory, and distributed memory on the existing community detection methodologies have been discussed thoroughly. More specifically, the central significance of this paper is that a systematic literature review is conducted to gather the relevant studies from different digital libraries and gray literature. Then, different parametric values of each selected study are appropriately compared. Moreover, the need for further research to speed up the process of community formation in parallel and shared approaches has been pinpointed more suitably. A pictorial glance of this paper is depicted as follows: [ABSTRACT FROM AUTHOR]

• A wavelet-based change-point detection model in the network structure of multivariate, possibly high-dimensional fMRI time series. • Allows for change-point detection in the presence of frequent changes of possibly small magnitudes, can assign change-points to one or multiple brain regions. • Particularly suited to task based data, where the subject alternates between task and rest. • Graphical representations of estimated functional connectivity. • Experimental block designs for validation and comprehensive task-based and restingstate fMRI data analysis. [Display omitted] Evidence of the non stationary behavior of functional connectivity (FC) networks has been observed in task based functional magnetic resonance imaging (fMRI) experiments and even prominently in resting state fMRI data. This has led to the development of several new statistical methods for estimating this time-varying connectivity, with the majority of the methods utilizing a sliding window approach. While computationally feasible, the sliding window approach has several limitations. In this paper, we circumvent the sliding window, by introducing a statistical method that finds change-points in FC networks where the number and location of change-points are unknown a priori. The new method, called cross-covariance isolate detect (CCID), detects multiple change-points in the second-order (cross-covariance or network) structure of multivariate, possibly high-dimensional time series. CCID allows for change-point detection in the presence of frequent changes of possibly small magnitudes, can assign change-points to one or multiple brain regions, and is computationally fast. In addition, CCID is particularly suited to task based data, where the subject alternates between task and rest, as it firstly attempts isolation of each of the change-points within subintervals, and secondly their detection therein. Furthermore, we also propose a new information criterion for CCID to identify the change-points. We apply CCID to several simulated data sets and to task based and resting state fMRI data and compare it to recent change-point methods. CCID may also be applicable to electroencephalography (EEG), magentoencephalography (MEG) and electrocorticography (ECoG) data. Similar to other biological networks, understanding the complex network organization and functional dynamics of the brain can lead to profound clinical implications. Finally, the R package ccid implementing the method from the paper is available from CRAN. [ABSTRACT FROM AUTHOR]

The analysis of biological networks is an important task in life sciences. Most of biological interactions can be modeled using graphical networks where arcs represent probabilistic relationships between nodes or variables. Such models help scientists to analyze their complex data sets, test candidate interaction networks and understand the studied relationships. These studies face two major problems: the selection of most probable interaction topologies and the clustering of the associated peculiar data. In this paper, we model biological interactions with a mixture of multivariate Gaussian distributions. We, then, introduce a new algorithm for the parameters estimation and data clustering. This algorithm, called Graphical Expectation Maximization (GEM), extends the EM algorithm by taking into account several decomposable graph structures and using an original initialization technique. Applying this algorithm, we propose a model selection procedure based on the Bayesian Information Criterion. The accuracy of the proposed method is demonstrated on the grounds of a simulation study of a signal transduction network of the epidermal growth factor (EGFR) protein. Moreover, we apply the proposed model selection procedure to choose the most appropriate interaction graphs for microbial community in infant gut using a real data set. [ABSTRACT FROM AUTHOR]

• The MONET approach is proposed for the inference of gene regulatory networks. • MONET is based on MOCell multi-objective algorithm with S-system models. • MSE and regularization terms are optimized following pareto optimality approach. • The proposal is validated on synthetic in silico DREAM3 and IRMA in vivo networks. • MONET is compared with traditional and modern methods showing competitive results. Reverse engineering of biochemical networks remains an important open challenge in computational systems biology. The goal of model inference is to, based on time-series gene expression data, obtain the sparse topological structure and parameters that quantitatively understand and reproduce the dynamics of biological systems. In this paper, we propose a multi-objective approach for the inference of S-System structures for Gene Regulatory Networks (GRNs) based on Pareto dominance and Pareto optimality theoretical concepts instead of the conventional single-objective evaluation of Mean Squared Error (MSE). Our motivation is that, using a multi-objective formulation for the GRN, it is possible to optimize the sparse topology of a given GRN as well as the kinetic order and rate constant parameters in a decoupled S-System, yet avoiding the use of additional penalty weights. A flexible and robust Multi-Objective Cellular Evolutionary Algorithm is adapted to perform the tasks of parameter learning and network topology inference for the proposed approach. The resulting software, called MONET, is evaluated on real-based academic and synthetic time-series of gene expression taken from the DREAM3 challenge and the IRMA in vivo datasets. The ability to reproduce biological behavior and robustness to noise is assessed and compared. The results obtained are competitive and indicate that the proposed approach offers advantages over previously used methods. In addition, MONET is able to provide experts with a set of trade-off solutions involving GRNs with different typologies and MSEs. [ABSTRACT FROM AUTHOR]