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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]

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]

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]

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]

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]

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]

The operation of liner shipping services follows some typical patterns as Hub and Spoke (H&S), End-to-End (ETE), Round-The-World (RTW), Pendulum (PDM), which reveals the operating mechanism of the world container transport. Different with analytic lens on the technical and economic elements, this paper explored the regional operational patterns of liner shipping services from the perspective of the network, aiming to provide reference for regional cooperations of liner shipping companies (LSC). The concept of motif detection is introduced in verifying the classic patterns and discover the new ones. Through the identification of motifs, the recognition of basic unit in motif study is proved to be applicable in the container shipping network. The (route) connection modes of 3 to 4 ports are discovered which could help to explain the formation of network. Findings show that complying with motifs in biological and technological network, the maritime network motifs have a strong nature of hub configuration in connecting with each other. Since motifs with 3 vertices are the basic structure of the network, we proposed two regional connecting modes as'Branching' and'Circling'. In comparison with the classic modes obtained from empirical analysis, we argue that these two modes are more favored by shipping companies on a regional scale in contributing to the feeder service and local trade development. [ABSTRACT FROM AUTHOR]

Analysis of interactions in biological systems is at the core of almost every biological study. Being a principal tool, graph theory as a mathematical formalism, aims to model relationships and interactions between objects, assisting in the analysis of biological interaction networks at various scales. Over the course of the past few decades, many applications of graph theory to biology have been proposed and developed. In this work, a review of openly available online, graph theory-based tools with biological applications is presented. The selection of tools was based on a structured online search, with a focus on tools that could be utilized to analyze residue interaction and protein–protein interaction networks. After exploring the main features of each tool and with the aim of identifying their use cases in current research, selected papers recent to the extent as possible, referencing the tools, were reviewed with the aim of giving prominence to the effectiveness of graph theory. The latter is of crucial importance upon designing new drug molecules capable of recognizing and modulating the function of proteins (receptors) involved in disease pathophysiology. The knowledge extracted from these tools can be further exploited into cheminformatics approaches and computer-aided new drug discovery techniques. The selected work and specific examples of successful applications were analyzed, in order to highlight how each tool can be integrated in the workflow of different types of studies and to assess to what extent graph theory-based tools can be a valuable ally in the analysis of residue and protein interactions. Moreover, therapeutic development efforts provide further valuable insights and feedback in a plethora of scientific fields like biology, pharmacology and medicine and assist in the further development of graph-theory-based simulation tools for protein structure networks. [ABSTRACT FROM AUTHOR]

With the achievements of deep learning, applications of deep convolutional neural networks for the image denoising problem have been widely studied. However, these methods are typically limited by GPU in terms of network layers and other aspects. This paper proposes a multi-level network that can efficiently utilize GPU memory, named Double Enhanced Residual Network (DERNet), for biological-image denoising. The network consists of two sub-networks, and U-Net inspires the basic structure. For each sub-network, the encoder-decoder hierarchical structure is used for down-scaling and up-scaling feature maps so that GPU can yield large receptive fields. In the encoder process, the convolution layers are used for down-sampling to obtain image information, and residual blocks are superimposed for preliminary feature extraction. In the operation of the decoder, transposed convolution layers have the capability to up-sampling and combine with the Residual Dense Instance Normalization (RDIN) block that we propose, extract deep features and restore image details. Finally, both qualitative experiments and visual effects demonstrate the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Identifying drug-target interactions through computational methods is raised an important and key step in the process of drug discovery and drug-oriented research during the last years. In addition to the advantages of existing computational methods, there are also challenges that affect methods' efficiency and provide obstacles in the direction of developing these computational methods. However, the literature suffers from lacking a comprehensive and comparative analysis concerning drug-target interactions prediction (DTIP) focusing on the analysis of technical and challenging aspects. It seems necessary to provide a comparative perspective and a different analysis on a macro level due to the importance of the DTIP problem. In this paper, we presented the quadruple framework of analytical, named DTIP-TC2A consists of four main components for DTIP. The first component, categorizing DTIP methods based on the technical aspect ahead and investigating the strengths and weaknesses of different DTIP methods. Second, classify DTIP challenges with a major focus on a well-organized and coherent investigation of challenges and presenting a macro view of the DTIP challenges by systematic identification of them. Third, recommending some general criteria to analyze DTIP methods in form of the proposed classifications. Suggesting a suitable set of qualitative criteria along with using quantitative criteria can lead to a more proper choice of DTIP methods. Fourth, performing a two-phase qualitative analysis and comparison between each class of DTIP approaches based on the proposed functional criteria and the identified challenges ahead in order to understand the superiority of each class of DTIP methods over the other class. We believed that the DTIP-TC2A framework can offer a proper context for efficient selection of DTIP methods, improving the efficiency of a DTIP system due to the nature of computational methods, upgrading DTIP methods by removing the barriers, and presenting new directions of research for further studies through systematic identification of DTIP challenges and purposeful evaluation of challenges and methods. [Display omitted] • We have presented an analytical comparative framework for drug-target interactions prediction (DTIP) methods. • We have proposed a technical-oriented classification for DTIP methods. • We have classified challenging aspects of drug-target interactions prediction problem in biological networks. • Our proposed framework can be useful to select, improve, and compare methods efficiently. [ABSTRACT FROM AUTHOR]

Drug repurposing refers to discovery of new medical instructions for existing chemical drugs, which has great pharmaceutical significance. Recently, large-scale biological datasets are increasingly available, and many graph neural network (GNN) based methods for drug repurposing have been developed. These methods often deem drug repurposing as a link prediction problem, which mines features of biological data to identify drug–disease associations (i.e., drug–disease links). Due to heterogeneity of data, we need to deeply explore heterogeneous information of biological network for drug repurposing. In this paper, we propose a Relation-aware Heterogeneous Graph Transformer (RHGT) model to capture heterogeneous information for drug repurposing. We first construct a drug–gene–disease interactive network-based on biological data, and then propose a three-level network embedding model, which learns network embeddings at fine-grained subtype-level, node-level and coarse-grained edge-level, respectively. The output of subtype-level is the input of node-level and edge-level, and the output of node-level is the input of edge level. We get edge embeddings at edge-level, which integrates edge type embeddings and node embeddings. We deem that in this way, characteristics of drug–gene–disease interactive network can be captured more comprehensively. Finally, we identify drug–disease associations (i.e., drug–disease links) based on the relationship between drug–gene edge embeddings and gene–disease edge embeddings. Experimental results show that our model performs better than other state-of-the-art graph neural network methods, which validates effectiveness of the proposed model. • A novel neural model, called RHGT, is proposed for drug repurposing. • RHGT characterizes the heterogeneity of the network at node level and edge level. • A fine-grained method is developed to learn edge type embedding. • RHGT achieves state-of-art performance in CTD and TTD datasets. [ABSTRACT FROM AUTHOR]

In the analysis and control of complex systems, including gene regulatory networks, it is important to reconstruct a mathematical model from a priori information and noisy experimental data. A Boolean network (BN) is well known as a mathematical model of gene regulatory networks. Each state of BNs takes a binary value (0 or 1), and its update rule is given by a set of Boolean functions. In this paper, we consider the optimal reconstruction problem of finding a probabilistic BN consisting of the main dynamics and the noisy dynamics, by giving the main dynamics and the sample mean of the state obtained from noisy experimental data. In the proposed method, the selection probability of the main dynamics is maximized. We show that the optimal Boolean function of the noisy dynamics is a constant (0 or 1) map under no assumption on the structure of noisy dynamics. Finally, as a biological application, the reconstruction of a PBN model of the lac operon networks of Escherichia coli bacterium is addressed using the proposed approach. [ABSTRACT FROM AUTHOR]

• Designing a hierarchical control structure to control the long-term operation of life support systems. • Efficient response to varying operating conditions by using hierarchically distributed control functions. • Coordinating a network of biological compartments with a variety of generation, consumption, and storage capabilities. • Control of O 2 concentration in the crew compartment, as the main consumer of a life support system. Life support systems (LSSs) are autonomous systems integrating various generation, recycling, and consumption subsystems with storage capability to maintain the balance of key system compounds. A novel hierarchical control is proposed in this paper to operate an exemplary controlled ecological LSS. The study focuses on the control of O 2 concentration in the crew compartment, as the main consumer of a LSS, while coordinating a network of biological compartments with a variety of generation, consumption, and storage capabilities. A concentrated gas buffer tank is included in the system to demonstrate its capability to support the system operation flexibility and efficiency. Simulation analyses for changing O 2 demand scenarios are carried to assess the proposed control architecture performance. [Display omitted] [ABSTRACT FROM AUTHOR]