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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]

[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]

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]

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]

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]

• 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]

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]

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]

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]

• 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 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]

• 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]

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]

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]

• 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]

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]

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]

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]

Abstract Automated methods for reconstructing biological networks are becoming increasingly important in computational systems biology. Public databases containing information on biological processes for hundreds of organisms are assisting in the inference of such networks. This paper proposes a multiobjective genetic algorithm method to reconstruct networks related to metabolism and protein interaction. Such a method utilizes structural properties of scale-free networks and known biological information about individual genes and proteins to reconstruct metabolic networks represented as enzyme graph and protein interaction networks. We test our method on four commonly-used protein networks in yeast. Two are networks related to the metabolism of the yeast: KEGG and BioCyc. The other two datasets are networks from protein-protein interaction: Krogan and BioGrid. Experimental results show that the proposed method is capable of reconstructing biological networks by combining different omics data and structural characteristics of scale-free networks. However, the proposed method to reconstruct the network is time-consuming because several evaluations must be performed. We parallelized this method on GPU to overcome this limitation by parallelizing the objective functions of the presented method. The parallel method shows a significant reduction in the execution time over the GPU card which yields a 492-fold speedup. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Coloured Hybrid Petri Nets are proposed to model complex biological networks that exhibit multiple spatial and temporal scales. • Hybrid simulation is shown to be a powerful tool to analyze biological models with multiple time scales. • Two case studies illustrate the applicability of the approach for addressing the multiscale challenges of biological systems. Abstract Coloured Petri nets are an excellent choice for exploring large biological models, particularly when there are repetitions of components. Such models can be easily adapted by slight modifications of parameter values related to colours. Similarly, multi-scale models could involve multiple spatial scales in addition to multiple time scales. Thus, they require the full interplay between stochastic as well as deterministic processes. In this paper we take these two aspects into account and present a modelling and simulation approach for multi-scale biochemical networks using Coloured Generalised Hybrid Petri Nets (G H P N C). G H P N C are a Petri net class that associates colours to Generalised Hybrid Petri Nets (G H P N), which incorporate discrete and continuous places in addition to stochastic and continuous transitions. Moreover, we present two case studies to illustrate typical applications taking advantage of such a Petri net class. [ABSTRACT FROM AUTHOR]

Emergence of synchronization is a remarkable collective phenomena between apparently independent agents in numerous multilevel and complex systems. The evidence of synchronization ranges from the elementary biological organisms to the most sophisticated human societies. In this paper, the problem of synchronization of nonlinearly coupled dynamical networks of Hindmarsh–Rose neurons with a sigmoidal coupling function is addressed. Sufficient condition for synchrony in terms of network structure is developed. A study on the basis of attraction of the complete synchronization is carried out for different structured networks. Also the phase synchronization of dynamical network of Hindmarsh–Rose neurons are studied. The impact of different structural properties of complex network on the phase synchronization are analyzed. The synchronization of Hindmarsh–Rose neurons are evaluated and compared on different structured network like random, regular, small-world, scale-free and modular networks. Interestingly, it was found that networks with high clustering coefficient and neutral degree mixing pattern promote better synchronization. Some chimera like state are also found in different structural networks. Further the effect of time delay dynamics on the synchronization of nonlinearly coupled network of Hindmarsh–Rose neurons are illustrated. [ABSTRACT FROM AUTHOR]

The explosive development of research on real networks have led to the proposal of many mathematical network models explaining properties shared in the real networked systems. However, considering that different models can similarly reproduce the typical properties of networks, the estimation of the evolutionary process is more essential for appropriate network modeling than is the description of current network structures. In this paper, we propose a new measure "distribution of distances between neighbors (DDN) of each vertex" to investigate network development. We first examine several model networks, including growth models based on the attachment rule and on the local rule and their hybrid models, and show that the DDN profiles of these networks accurately reflect the differences in growth mechanisms. In particular, for the local rule model, the dependence of the mean DDN of vertices on the vertex degree records the evolution of the local network structure. The application of the same analysis to several real networks show that they share the characteristics of DDN profiles with the local rule and hybrid models. These results imply that these model networks appropriately describe the evolutionary processes of real networks. In addition, we examine the characteristics of DDN specific to fractal and non-fractal networks, and show that the presence or absence of fractality of some real networks can be explained by the influence of long-range edges on the local rule. An exception to this is the co-authorships network, which is expected to be generated by connecting several fractal sub-networks. • Distance distribution between neighbors (DDN) is newly proposed to examine networks. • Mean DDN records the evolutionary process of local network structures. • Evolutionary processes of real networks were characterized by DDN profiles. • The origin of fractality of networks was uncovered by the investigation using DDN. [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]

The topology of a reaction network can have a significant influence on the network's dynamical properties. Such influences can include constraints on network flows and concentration changes or more insidiously result in the emergence of feedback loops. These effects are due entirely to mass constraints imposed by the network configuration and are important considerations before any dynamical analysis is made. Most established simulation software tools usually carry out some kind of structural analysis of a network before any attempt is made at dynamic simulation. In this paper, we describe a portable software library, libStructural , that can carry out a variety of popular structural analyses that includes conservation analysis, flux dependency analysis and enumerating elementary modes. The library employs robust algorithms that allow it to be used on large networks with more than a two thousand nodes. The library accepts either a raw or fully labeled stoichiometry matrix or models written in SBML format. The software is written in standard C/C++ and comes with extensive on-line documentation and a test suite. The software is available for Windows, Mac OS X, and can be compiled easily on any Linux operating system. A language binding for Python is also available through the pip package manager making it simple to install on any standard Python distribution. The bulk of the source code is licensed under the open source BSD license with other parts using as either the MIT license or more simply public domain. All source is available on GitHub ( https://github.com/sys-bio/Libstructural ). [ABSTRACT FROM AUTHOR]

Identifying missing interactions in complex networks, known as link prediction, is realized by estimating the likelihood of the existence of a link between two nodes according to the observed links and nodes’ attributes. Similar approaches have also been employed to identify and remove spurious links in networks which is crucial for improving the reliability of network data. In network science, the likelihood for two nodes having a connection strongly depends on their structural similarity. The key to address these two problems thus becomes how to objectively measure the similarity between nodes in networks. In the literature, numerous network similarity metrics have been proposed and their accuracy has been discussed independently in previous works. In this paper, we systematically compare the accuracy of 18 similarity metrics in both link prediction and spurious link elimination when the observed networks are very sparse or consist of inaccurate linking information. Interestingly, some methods have high prediction accuracy, they tend to perform low accuracy in identification spurious interaction. We further find that methods can be classified into several cluster according to their behaviors. This work is useful for guiding future use of these similarity metrics for different purposes. [ABSTRACT FROM AUTHOR]

Betweenness centrality is a prominent centrality measure expressing importance of a node within a network, in terms of the fraction of shortest paths passing through that node. Nodes with high betweenness centrality have significant impacts on the spread of influence and idea in social networks, the user activity in mobile phone networks, the contagion process in biological networks, and the bottlenecks in communication networks. Thus, identifying k -highest betweenness centrality nodes in networks will be of great interest in many applications. In this paper, we introduce CS-HiBet, a new method to efficiently detect top- k betweenness centrality nodes in networks, using compressive sensing. CS-HiBet can perform as a distributed algorithm by using only the local information at each node. Hence, it is applicable to large real-world and unknown networks in which the global approaches are usually unrealizable. The performance of the proposed method is evaluated by extensive simulations on several synthetic and real-world networks. The experimental results demonstrate that CS-HiBet outperforms the best existing methods with notable improvements. [ABSTRACT FROM AUTHOR]

High-throughput methods have provided us with a large amount of data pertaining to protein–protein interaction networks. The alignment of these networks enables us to better understand biological systems. Given the fact that the alignment of networks is computationally intractable, it is important to introduce a more efficient and accurate algorithm which finds as large as possible similar areas among networks. This paper proposes a new algorithm named INDEX for the global alignment of protein–protein interaction networks. INDEX has multiple phases. First, it computes topological and biological scores of proteins and creates the initial alignment based on the proposed matching score strategy. Using networks topologies and aligned proteins, it then selects a set of high scoring proteins in each phase and extends new alignments around them until final alignment is obtained. Proposing a new alignment strategy, detailed consideration of matching scores, and growth of the alignment core has led INDEX to obtain a larger common connected subgraph with a much greater number of edges compared with previous methods. Regarding other measures such as edge correctness, symmetric substructure score, and runtime, the proposed algorithm performed considerably better than existing popular methods. Our results show that INDEX can be a promising method for identifying functionally conserved interactions. Availability The INDEX executable file is available at https://github.com/a-mir/index/ . [ABSTRACT FROM AUTHOR]

Graphs can be used as contact networks in models of epidemic spread. Most research seeks to extract the properties of an extant graph, derived from questionnaires or other sources of contact information. The inverse problem of searching the space of graphs for those that exhibit specific properties has received little attention and that is the focus of this study. This is, in part, because searching the space of contact networks is difficult. This paper extends and tests a representation for searching the space of contact networks with evolutionary computation. The focus of this study is on improvements in the representation used to evolve potential contact networks, adding an operator that permits strictly local adjustments to connectivity of the network, and another that does nothing at all. The benefits of doing nothing at some points during the construction of a network are substantial, because this permits evolution to adjust the number of active commands issued automatically. Adjusting local connectivity was identified as a beneficial feature in earlier research. The network induction method is tested on two tasks; finding a network that sustains an epidemic as long as possible and finding a network that, under simulation, closely matches a specified pattern of rise and fall in the number of infections. [ABSTRACT FROM AUTHOR]

In this paper, we address the overlapping cluster synchronization problem of coupled complex networks via adaptive finite-time control. Cluster synchronization indeed has been extensively addressed by various control strategies. The communities in the cluster-based networks always do not share nodes with each other but exchange messages. Then, according to natural community, we propose a new overlapping community model for the coupled complex network to remove the restriction on common community models. The community in our model contains two kinds of nodes: overlapped and non-overlapped nodes, which is different from the common cluster form explored in the synchronization processing. In addition, a new control strategy is expressly proposed to extract every inner cluster in the overlapped regions. Especially, a novel adaptive finite-time control strategy is also proposed to force every community to a desired trajectory in finite time. On the basis of finite time theory, sufficient cluster synchronization criteria are derived. Lastly, the effectiveness of analytical results is confirmed by a numerical example. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the complex networks of interacting amino acids in protein structures. The cellular networks and their random controls are treated for the four threshold distances between atoms. The numerical simulation and analysis are relevant to the topological properties of the complex networks in the structural classification of proteins, and we mainly estimate the network’s metrics from the resultant network. The cellular network is shown to exhibit a small-world feature regardless of their structural class. The protein structure presents the positive assortative coefficients, when the topological property is described as a tendency for connectivity of high-degree nodes. We particularly show that both the modularity and the small-wordness are significantly followed the increasing function against nodes. [ABSTRACT FROM AUTHOR]

Complex systems as networks always exhibit strong regularities, implying underlying mechanisms governing their evolution. In addition to the degree preference, the similarity has been argued to be another driver for networks. Assuming a network is randomly organised without similarity preference, the present paper studies the expected number of common neighbours between vertices. A symmetrical similarity index is accordingly developed by removing such expected number from the observed common neighbours. The developed index can not only describe the similarities between vertices, but also the dissimilarities. We further apply the proposed index to measure of the influence of similarity on the wring patterns of networks. Fifteen empirical networks as well as artificial networks are examined in terms of similarity intensity and degree heterogeneity. Results on real networks indicate that, social networks are strongly governed by the similarity as well as the degree preference, while the biological networks and infrastructure networks show no apparent similarity governance. Particularly, classical network models, such as the Barabási–Albert model, the Erdös–Rényi model and the Ring Lattice, cannot well describe the social networks in terms of the degree heterogeneity and similarity intensity. The findings may shed some light on the modelling and link prediction of different classes of networks. [ABSTRACT FROM AUTHOR]

This paper is motivated by a class of positive systems with an input that is subject to an unknown state-dependent power law delay as well as saturation. For example, biological networks have non-negative protein concentration states. Mass action kinetics in these systems result in power law behavior, while complex interactions cause signal propagation delays. Incomplete network characterization makes delay state-dependence unknown. Manipulating network activity via modulated protein concentrations to attain desired performance is restricted by upper-bounds on concentration actuator authority. Here, an innovative control law exploits system dynamics to compensate for control domain restrictions. A Lyapunov stability analysis establishes that the reference tracking error of the closed-loop system is uniformly ultimately bounded. Numerical simulations on a human coagulation model show controller efficacy and better performance compared to the relevant literature. This example application steps toward personalized, closed-loop treatments for trauma coagulopathy, which currently has 30% mortality with open-loop clinical approaches. [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]

Highlights • Successfully extends our previously published continuous time recurrent neural network (RNN) approach to modularise large cell signalling networks. • Transforms a large ODE system into a flexible RNN system. • Estimates a large number of parameters through iterative learning in modules and reveals crucial parameters supported by biology. • Easily and efficiently produces accurate continuous systems dynamics comparable to the ODE system. • Modifies the RNN system to receive large, more experimentally realistic, data and still produce fine temporal dynamics as in the original RNN and the ODE system. Abstract In this paper, we show how to extend our previously proposed novel continuous time Recurrent Neural Networks (RNN) approach that retains the advantage of continuous dynamics offered by Ordinary Differential Equations (ODE) while enabling parameter estimation through adaptation, to larger signalling networks using a modular approach. Specifically, the signalling network is decomposed into several sub-models based on important temporal events in the network. Each sub-model is represented by the proposed RNN and trained using data generated from the corresponding ODE model. Trained sub-models are assembled into a whole system RNN which is then subjected to systems dynamics and sensitivity analyses. The concept is illustrated by application to G1/S transition in cell cycle using Iwamoto et al. (2008) ODE model. We decomposed the G1/S network into 3 sub-models: (i) E2F transcription factor release; (ii) E2F and CycE positive feedback loop for elevating cyclin levels; and (iii) E2F and CycA negative feedback to degrade E2F. The trained sub-models accurately represented system dynamics and parameters were in good agreement with the ODE model. The whole system RNN however revealed couple of parameters contributing to compounding errors due to feedback and required refinement to sub-model 2. These related to the reversible reaction between CycE/CDK2 and p27, its inhibitor. The revised whole system RNN model very accurately matched dynamics of the ODE system. Local sensitivity analysis of the whole system model further revealed the most dominant influence of the above two parameters in perturbing G1/S transition, giving support to a recent hypothesis that the release of inhibitor p27 from Cyc/CDK complex triggers cell cycle stage transition. To make the model useful in a practical setting, we modified each RNN sub-model with a time relay switch to facilitate larger interval input data (≈ 20 min) (original model used data for 30 s or less) and retrained them that produced parameters and protein concentrations similar to the original RNN system. Results thus demonstrated the reliability of the proposed RNN method for modelling relatively large networks by modularisation for practical settings. Advantages of the method are its ability to represent accurate continuous system dynamics and ease of: parameter estimation through training with data from a practical setting, model analysis (40% faster than ODE), fine tuning parameters when more data are available, sub-model extension when new elements and/or interactions come to light and model expansion with addition of sub-models. [ABSTRACT FROM AUTHOR]

With the progress of studies and researches on the biological networks, plenty of excellent clustering algorithms have been proposed. Nevertheless, not only different algorithms but also the same algorithms with different characteristics result in different performances on the same biological networks. Therefore, it might be difficult for researchers to choose an appropriate clustering algorithm to use for a specific network. Here we present C-DEVA, a comprehensive platform for Detecting clusters from biological networks and its Evaluation, Visualization and Annotation analysis. Ten clustering methods are provided in C-DEVA, covering different types of clustering algorithms, with a discrepancy in principle of each type. For the identified complexes, there are over ten popular and traditional bio-statistical measurements to assess them. And multi-source biological information has been integrated in C-DEVA, such as biology-functional annotations, and gold standard complex sets, which are collected from latest datasets in major databases or related papers. Furthermore, visualization analyses are available throughout the whole workflow, which endows C-DEVA with good usability and simple manipulation. To assure extensibility, development interfaces are offered in C-DEVA, for integrating new clustering as well as evaluating methods. Additionally, operations to the network as for example network randomization are also supported. C-DEVA provides a complete tool for identifying clusters from biological networks. Multiple options are offered during the analysis process, including detection methods, evaluation metrics and visualization modules. In addition, researchers could customize C-DEVA for the workflow according to the properties of their networks, and find the most ideal results. C-DEVA is released under the GNU General Public License (GPL), and the source code and binaries are freely available at https://github.com/cici333/c-deva . [ABSTRACT FROM AUTHOR]

We study the classic Graph Motif problem. Given a graph G = ( V , E ) with a set of colors for each node, and a multiset M of colors, we seek a subtree T ⊆ G , and a coloring assigning to each node in T a color from its set, such that T carries exactly (also with respect to multiplicity) the colors in M . Graph Motif plays a central role in the study of pattern matching problems, primarily motivated from the analysis of complex biological networks. Previous algorithms for Graph Motif and its variants either rely on techniques for developing randomized algorithms that − if derandomized − render them inefficient, or the algebraic narrow sieves technique for which there is no known derandomization. In this paper, we present fast deterministic parameterized algorithms for Graph Motif and its variants. Specifically, we give such an algorithm for the more general Graph Motif with Deletions problem, followed by faster algorithms for Graph Motif and other well-studied special cases. Our algorithms make non-trivial use of representative families , and a novel tool that we call guiding trees , together enabling the efficient construction of the output tree. [ABSTRACT FROM AUTHOR]

Automated verification of living organism models allows us to gain previously unknown knowledge about underlying biological processes. In this paper we show how parametric time model checking can be applied to define the time behavior of biological oscillatory systems more precisely. In particular, we focus on the resilience properties of such systems. This notion was introduced to understand the behavior of biological systems (e.g. the mammalian circadian rhythm) that are reactive and adaptive enough to endorse major changes in their environment (e.g. jet-lags, day-night alternating work-time). We formalize these properties through parametric TCTL and investigate the influence of environmental conditions changes on the resilience of living organisms under the uncertainty in parameters. In particular, we discuss the influence of various perturbations, e.g. artificial jet-lag or components knock-out on the parameters controlling the oscillatory behavior. This analysis is crucial when it comes to model elicitation for dynamic biological systems. We demonstrate the applicability of this technique using a simplified model of circadian clock and discuss its results with regard to other previous studies based on hybrid modeling. [ABSTRACT FROM AUTHOR]