Network Patterns Research Articles

Abnormal resting-state brain network dynamics in toddlers with autism spectrum disorder.

Emerging evidence suggests aberrant functional connectivity (FC) of brain networks in children, adolescents, and adults with autism spectrum disorder (ASD). However, little is known about alterations of dynamic FC in toddlers with ASD. The aim of this study was to investigate the characteristics of brain network dynamics in ASD toddlers. We analyzed resting-state functional magnetic resonance imaging data of 41 ASD and 23 typically-developing (TD) toddlers aged 15-45 months using leading eigenvector dynamics analysis. The between-group differences in the temporal properties of state were evaluated, followed by correlation analysis of these differences with autistic factors in ASD. Compared with TD subjects, ASD toddlers had a lower occurrence rate and mean dwell time in the global state. The mean dwell time of this state was negatively correlated with the body and object use of the autism behavior checklist (ABC) score. The ASD group presented a higher occurrence probability in the state related to the visual network, and the dwell time of this state was positively correlated with the relationship building of the ABC score. Moreover, the occurrence probability of the state related to the dorsal attention network and default mode network was positively correlated with the body and object use of the ABC score. Our findings indicate that there are differences in the developmental patterns of large-scale brain functional networks in ASD toddlers, which may contribute to the early identification of ASD and provide meaningful insights into its potential neural mechanisms.

Research on the Measurement of Digitalization on the Integration of China’s Manufacturing Industry into the RCEP Regional Value Chain

In this paper, the industry classification of digital economy is determined, the comprehensive index framework of digital economy development level in China is constructed, and the input-output analysis method and index method are used to measure the digital investment status of manufacturing industry in China. At the same time, based on the realistic demand that China’s manufacturing industry needs to climb the regional value chain, this paper constructs the forward participation index, backward participation index, regional value chain position index, and regional value chain Balassa, and analyzes the current situation of China’s manufacturing industry in RCEP region. Construct a semi-parametric additive model to explore the impact of digital investment on China’s manufacturing industry’s participation in the RCEP regional value chain. In addition, this paper uses the community analysis method to build the RCEP regional manufacturing digital input trade network, and comprehensively evaluates the trade network pattern of digital input from multiple dimensions. The results show that: (1) the digital investment in China’s manufacturing industry is increasing year by year. (2) The indicators of manufacturing industry’s participation in RCEP regional value chain show a fluctuating situation, which is generally consistent with China’s economic development. (3) Within a certain range, digital investment will promote the participation of China’s manufacturing industry in the RCEP regional value chain. China is gradually becoming an important participant in RCEP regional trade. According to the corresponding measurement results, it provides policy suggestions for the deep integration of China’s manufacturing industry and digital economy.

EEG reveals brain network alterations in chronic aphasia during natural speech listening

Aphasia is a common consequence of a stroke which affects language processing. In search of an objective biomarker for aphasia, we used EEG to investigate how functional network patterns in the cortex are affected in persons with post-stroke chronic aphasia (PWA) compared to healthy controls (HC) while they are listening to a story. EEG was recorded from 22 HC and 27 PWA while they listened to a 25-min-long story. Functional connectivity between scalp regions was measured with the weighted phase lag index. The Network-Based Statistics toolbox was used to detect altered network patterns and to investigate correlations with behavioural tests within the aphasia group. Differences in network geometry were assessed by means of graph theory and a targeted node-attack approach. Group-classification accuracy was obtained with a support vector machine classifier. PWA showed stronger inter-hemispheric connectivity compared to HC in the theta-band (4.5–7 Hz), whilst a weaker subnetwork emerged in the low-gamma band (30.5–49 Hz). Two subnetworks correlated with semantic fluency in PWA respectively in delta- (1–4 Hz) and low-gamma-bands. In the theta-band network, graph alterations in PWA emerged at both local and global level, whilst only local changes were found in the low-gamma-band network. Network metrics discriminated PWA and HC with AUC = 83%. Overall, we demonstrate the potential of EEG-network metrics for the development of informative biomarkers to assess natural speech processing in chronic aphasia. We hypothesize that the detected alterations reflect compensatory mechanisms associated with recovery.

Left Insula and Right Middle Temporal Gyrus Dominate Cortical Network Discriminating Arousal-dependent Emotions.

Emotion processing is an integral part of everyone's life. The basic neural circuits involved in emotion perception are becoming clear, though the emotion's cognitive processing remains under investigation. Utilizing the stereo-electroencephalograph with high temporal-spatial resolution, this study aims to decipher the neural pathway responsible for discriminating low-arousal and high-arousal emotions. This study involves 19 patients with pharmacologically resistant epilepsy who participate in a delayed match/mismatch sample task designed to separately assess their ability to discriminate between low-arousal and high-arousal emotions. Three groups of 11 brain subregions, with dominant lateralization, compose a network, which is identified as responsible for discriminating arousal-dependent emotions. The connection of these subregions, leading by the left insula and right middle temporal gyrus, defines the pathways for discriminating emotions with different arousals. Further, the separated network patterns related to emotional discrimination are face-independent. Overall, the left insula and the right middle temporal gyrus emerge as core components in the network, which plays key roles in the dynamic course for discriminating low- and high-arousal emotions in the human brain.

Microglial modulation of neuronal network function and plasticity.

Microglia are the resident immune cells of the central nervous system (CNS), which have been classically viewed as involved in CNS responses to damage and tissue repair. However, microglia are constantly sensing neuronal network activity and changes in the CNS milieu, establishing complex state-dependent microglia-neuron interactions that impact their functions. By doing so, microglia perform a wide range of physiological roles, including brain homeostasis maintenance, control of neural connectivity, network function modulation, as well as functional and morphological plasticity regulation in health and disease. Here, I review recent evidence of the modulations induced by microglia, a highly heterogeneous cell type, on synaptic and intrinsic neuronal properties, as well as on neuronal network patterns during perinatal development and adulthood. The reviewed evidence clearly indicate that microglia are important, if not essential, for brain function and plasticity in both health and disease.

Research on the Pattern and Evolution Characteristics of Global Dry Bulk Shipping Network Driven by Big Data

The dry bulk shipping network is an important carrier of global bulk commodity flow. To better understand the structural characteristics and future development trends of the global dry bulk shipping network (GDBSN), this study proposes a framework for characteristics analysis and link prediction based on complex network theory. The study integrates large-scale heterogeneous data, including automatic identification system data and port geographic information, to construct the GDBSN. The findings reveal that the network exhibits small-world properties, with the Port of Singapore identified as the most influential node. Link prediction results indicate that many potential new shipping routes exist within regions or between neighboring countries, exhibiting clear regional clustering characteristics. The added links mainly influence the local structure, with minimal impact on the overall network topology. This study provides valuable insights for shipping companies in route planning and for port authorities in developing strategic plans.

Evolution characteristics and invulnerability simulation analysis of global zirconium ore trade network

The burgeoning demand for zirconium, driven by the rapid development of smart devices, low-carbon energy technologies, and other emerging industries, underscores the importance of understanding the dynamics of its global trade network. However, the evolutionary patterns of the international zircon ore trade network and its resilience to disruptions remain unclear. This study constructs the international zircon ore trade network from 2013 to 2022, analyzes its structural evolution at both the network and node levels, and evaluates its robustness in 2022 using five attack strategies: random node removal, random edge removal, edge degradation, targeted removal based on node degree, and targeted removal based on node betweenness centrality. Our findings reveal that: (1) the international zircon ore trade network exhibits a shift in the import market towards Asia, with the export market dominated by Spain, the United States, and Brazil. China plays a crucial role as a bridge connecting various countries, while Japan exerts significant influence within the network; (2) the network is particularly vulnerable to targeted attacks based on node degree and betweenness centrality, highlighting the potential for significant disruption following the removal of key nodes. This study provides valuable insights for ensuring the stable and sustainable supply and consumption of zirconium resources, informing the development of targeted policies for countries and relevant industries.

A multilevel social network approach to studying multiple disease-prevention behaviors

The effective prevention of many infectious and non-infectious diseases relies on people concurrently adopting multiple prevention behaviors. Individual characteristics, opinion leaders, and social networks have been found to explain why people take up specific prevention behaviors. However, it remains challenging to understand how these factors shape multiple interdependent behaviors. We propose a multilevel social network framework that allows us to study the effects of individual and social factors on multiple disease prevention behaviors simultaneously. We apply this approach to examine the factors explaining eight malaria prevention behaviors, using unique interview data collected from 1529 individuals in 10 hard-to-reach, malaria-endemic villages in Meghalaya, India in 2020–2022. Statistical network modelling reveals exposure to similar behaviors in one’s social network as the most important factor explaining prevention behaviors. Further, we find that households indirectly shape behaviors as key contexts for social ties. Together, these two factors are crucial for explaining the observed patterns of behaviors and social networks in the data, outweighing individual characteristics, opinion leaders, and social network size. The results highlight that social network processes may facilitate or hamper disease prevention efforts that rely on a combination of behaviors. Our approach is well suited to study these processes in the context of various diseases.

Typology of Social Participation and Network and Health in Older Adults: Results From the Canadian Longitudinal Study on Aging.

This study aimed to document the typology of social participation and network among older Canadians and examine their associations with health. Using 2011-2015 cross-sectional data from the Canadian Longitudinal Study on Aging, a latent profile analysis was conducted to identify patterns of social participation and network, and multinomial logistic regressions examined associations with self-rated health. Four types of social participation and networks characterized older Canadians: diverse (74.0%), childless (12.1%), restricted (9.7%), and very socially active (4.3%). Compared to the diverse group and excellent/very good health, belonging to the restricted group was associated with higher probabilities of reporting fair or poor health, both general (1.95; p < .001) and mental (2.18; p < .001). Still comparing to the diverse group and excellent/very good health, the very socially active group presented lower likelihood of reporting good general health (0.82; p = .03). These results suggest that the social participation and network are associated with health inequalities in older Canadians. Future studies should look at the role of virtual interactions in the health of older adults.

Brain-inspired wiring economics for artificial neural networks.

Wiring patterns of brain networks embody a trade-off between information transmission, geometric constraints, and metabolic cost, all of which must be balanced to meet functional needs. Geometry and wiring economy are crucial in the development of brains, but their impact on artificial neural networks (ANNs) remains little understood. Here, we adopt a wiring cost-controlled training framework that simultaneously optimizes wiring efficiency and task performance during structural evolution of sparse ANNs whose nodes are located at arbitrary but fixed positions. We show that wiring cost control improves performance across a wide range of tasks, ANN architectures and training methods, and can promote task-specific structural modules. An optimal wiring cost range provides both enhanced predictive performance and high values of topological properties, such as modularity and clustering, which are observed in real brain networks and known to improve robustness, interpretability, and performance of ANNs. In addition, ANNs trained using wiring cost can emulate the connection distance distribution observed in the brains of real organisms (such as Ciona intestinalis and Caenorhabditis elegans), especially when achieving high task performance, offering insights into biological organizing principles. Our results shed light on the relationship between topology and task specialization of ANNs trained within biophysical constraints, and their geometric resemblance to real neuronal-level brain maps.

DLIIoT: A Deep Learning based Intelligent Attack Detection in IoT Networks using Cooja Simulator

Internet of things (IoT) has incredibly transformed the whole domain of communication process. The extensive dependency on these devices leads to various advanced cyber security threats. IoT devices fall easily into the ambit of malicious threats and are susceptible to vast range of attacks due to their limited computation capabilities and memory constraints. Intrusion Detection Systems (IDSs) are dedicated outstanding frameworks to protect these devices from cyber threats. In this study, a comprehensive review of different AI based IDS applied on IoTs is done. It has been observed that machine learning and deep learning has widely influenced the domain of IoT security. The focus of the research carried out is to earmark the techniques that are performing best on a given data set. Features selection, type of attacks, proposed solutions in solving security menaces are taken into consideration. Further, we have presented DLIIoT, a deep learning based intelligent attack detection in IoT networks by generating precise IoT datasets in Cooja Simulator. Four Deep learning algorithms are utilized and analysed under standard performance criteria metrics such as Precision, Recall, Accuracy and F1-score. It was found that deep learning algorithms have remarkable potential in detecting and recognizing malicious data patterns in IoT networks.

Flexible inference in heterogeneous and attributed multilayer networks.

Networked datasets can be enriched by different types of information about individual nodes or edges. However, most existing methods for analyzing such datasets struggle to handle the complexity of heterogeneous data, often requiring substantial model-specific analysis. In this article, we develop a probabilistic generative model to perform inference in multilayer networks with arbitrary types of information. Our approach employs a Bayesian framework combined with the Laplace matching technique to ease interpretation of inferred parameters. Furthermore, the algorithmic implementation relies on automatic differentiation, avoiding the need for explicit derivations. This makes our model scalable and flexible to adapt to any combination of input data. We demonstrate the effectiveness of our method in detecting overlapping community structures and performing various prediction tasks on heterogeneous multilayer data, where nodes and edges have different types of attributes. Additionally, we showcase its ability to unveil a variety of patterns in a social support network among villagers in rural India by effectively utilizing all input information in a meaningful way.

An Improved Freight Transportation Planning System for Less‐Than‐Truckload Operations of a Third‐Party Logistics Carrier

ABSTRACTLess‐than‐truckload (LTL) transportation is a widely used shipping modality within the logistics industry, facilitating the movement of loads insufficient in size to occupy the entirety of a truck's capacity. Drawing on the real‐life problem of a third‐party logistics (3PL) carrier, which navigates an expansive transportation network and unpredictable demand patterns, we propose a centralized planning approach for LTL transportation, taking advantage of consolidation opportunities at cross‐dock hubs to minimize long‐term freight costs while ensuring on‐time delivery. We present an integer linear programming (ILP) formulation to generate a multi‐day plan with known demands and a daily planning problem (DPP) model to be solved daily, emulating the available order information in a real planning scenario. DPP optimizes truck routes and loading plans, incorporating a penalty cost for postponed transportation requests. We devise a novel multi‐stage matheuristic integrating geographical decomposition with tractable integer programming models and column generation‐based heuristics. With this approach, the DPP is solved in fewer than 2 h of run‐time for real problem instances with as high as 1400 transportation orders. We benchmark our results against a baseline heuristic that mirrors the firm's existing planning approach through a multi‐period simulation. Our comparative evaluations reveal a noteworthy 7.8% improvement in long‐term freight costs, concurrently maintaining adherence to the 98% on‐time delivery target.

Revisiting Information Cascades in Online Social Networks

It is widely believed that a user’s activity pattern in Online Social Networks (OSNs) is strongly influenced by their friends or the users they follow. Building on this intuition, numerous models have been proposed over the years to predict information propagation in OSNs. Many of these models drew inspiration from the process of infectious spread within a population. While this approach is definitely plausible, it relies on knowledge of users’ social connections, which can be challenging to obtain due to privacy concerns. Moreover, while a significant body of work has focused on predicting macro-level features, such as the total cascade size, relatively little attention has been given to the prediction of micro-level features, such as the activity of an individual user. In this study we aim to address this gap by proposing a method to predict the activity of individual users in an OSN, relying solely on their interactions rather than prior knowledge of their social network. We evaluated our results on four large datasets, each comprising over 14 million tweets, recorded on X social network across four different topics over several month. Our method achieved a mean F1 score of 0.86, with a best result of 0.983.

Distinct network patterns emerge from Cartesian and XOR epistasis models: a comparative network science analysis

BackgroundEpistasis, the phenomenon where the effect of one gene (or variant) is masked or modified by one or more other genes, significantly contributes to the phenotypic variance of complex traits. Traditionally, epistasis has been modeled using the Cartesian epistatic model, a multiplicative approach based on standard statistical regression. However, a recent study investigating epistasis in obesity-related traits has identified potential limitations of the Cartesian epistatic model, revealing that it likely only detects a fraction of the genetic interactions occurring in natural systems. In contrast, the exclusive-or (XOR) epistatic model has shown promise in detecting a broader range of epistatic interactions and revealing more biologically relevant functions associated with interacting variants. To investigate whether the XOR epistatic model also forms distinct network structures compared to the Cartesian model, we applied network science to examine genetic interactions underlying body mass index (BMI) in rats (Rattus norvegicus).ResultsOur comparative analysis of XOR and Cartesian epistatic models in rats reveals distinct topological characteristics. The XOR model exhibits enhanced sensitivity to epistatic interactions between the network communities found in the Cartesian epistatic network, facilitating the identification of novel trait-related biological functions via community-based enrichment analysis. Additionally, the XOR network features triangle network motifs, indicative of higher-order epistatic interactions. This research also evaluates the impact of linkage disequilibrium (LD)-based edge pruning on network-based epistasis analysis, finding that LD-based edge pruning may lead to increased network fragmentation, which may hinder the effectiveness of network analysis for the investigation of epistasis. We confirmed through network permutation analysis that most XOR and Cartesian epistatic networks derived from the data display distinct structural properties compared to randomly shuffled networks.ConclusionsCollectively, these findings highlight the XOR model’s ability to uncover meaningful biological associations and higher-order epistasis derived from lower-order network topologies. The introduction of community-based enrichment analysis and motif-based epistatic discovery emphasize network science as a critical approach for advancing epistasis research and understanding complex genetic architectures.

Sparse Convolution FPGA Accelerator Based on Multi-Bank Hash Selection.

Reconfigurable processor-based acceleration of deep convolutional neural network (DCNN) algorithms has emerged as a widely adopted technique, with particular attention on sparse neural network acceleration as an active research area. However, many computing devices that claim high computational power still struggle to execute neural network algorithms with optimal efficiency, low latency, and minimal power consumption. Consequently, there remains significant potential for further exploration into improving the efficiency, latency, and power consumption of neural network accelerators across diverse computational scenarios. This paper investigates three key techniques for hardware acceleration of sparse neural networks. The main contributions are as follows: (1) Most neural network inference tasks are typically executed on general-purpose computing devices, which often fail to deliver high energy efficiency and are not well-suited for accelerating sparse convolutional models. In this work, we propose a specialized computational circuit for the convolutional operations of sparse neural networks. This circuit is designed to detect and eliminate the computational effort associated with zero values in the sparse convolutional kernels, thereby enhancing energy efficiency. (2) The data access patterns in convolutional neural networks introduce significant pressure on the high-latency off-chip memory access process. Due to issues such as data discontinuity, the data reading unit often fails to fully exploit the available bandwidth during off-chip read and write operations. In this paper, we analyze bandwidth utilization in the context of convolutional accelerator data handling and propose a strategy to improve off-chip access efficiency. Specifically, we leverage a compiler optimization plugin developed for Vitis HLS, which automatically identifies and optimizes on-chip bandwidth utilization. (3) In coefficient-based accelerators, the synchronous operation of individual computational units can significantly hinder efficiency. Previous approaches have achieved asynchronous convolution by designing separate memory units for each computational unit; however, this method consumes a substantial amount of on-chip memory resources. To address this issue, we propose a shared feature map cache design for asynchronous convolution in the accelerators presented in this paper. This design resolves address access conflicts when multiple computational units concurrently access a set of caches by utilizing a hash-based address indexing algorithm. Moreover, the shared cache architecture reduces data redundancy and conserves on-chip resources. Using the optimized accelerator, we successfully executed ResNet50 inference on an Intel Arria 10 1150GX FPGA, achieving a throughput of 497 GOPS, or an equivalent computational power of 1579 GOPS, with a power consumption of only 22 watts.

WePred: Edge Weight-Guided Contrastive Learning for Bipartite Link Prediction

Bipartite networks are common in real-world applications, where link prediction helps understand network evolution and make recommendations. Traditional methods have two major limitations; they often ignore edge weight information by treating links as binary connections, and they struggle to capture complex interaction patterns in sparse networks. We propose WePred, a weight-guided contrastive learning method for link prediction which addresses both challenges through three key components: (1) a weight-guided edge attention mechanism that incorporates edge weights into neighbor aggregation using dynamic attention scores, enabling the fine-grained capture of interaction strengths; (2) a dual-level contrastive learning approach that combines edge-level and node-level contrasts to capture both local weighted patterns and global structural dependencies, which is particularly effective in sparse regions; and (3) a unified learning framework that integrates classification and contrastive objectives. We evaluate WePred on five real-world datasets. The experimental results show that WePred consistently outperformed state-of-the-art methods, achieving improvements of 7.27% in the AUC on ML-1M and 3.18% in the AUC on the sparsest dataset of Amazon-Book (density: 0.001%). Ablation studies confirm the effectiveness of each component, while parameter analyses provide deployment guidance.

QOMIC: quantum optimization for motif identification.

Network motif identification (MI) problem aims to find topological patterns in biological networks. Identifying disjoint motifs is a computationally challenging problem using classical computers. Quantum computers enable solving high complexity problems which do not scale using classical computers. In this article, we develop the first quantum solution, called QOMIC (Quantum Optimization for Motif IdentifiCation), to the MI problem. QOMIC transforms the MI problem using a integer model, which serves as the foundation to develop our quantum solution. We develop and implement the quantum circuit to find motif locations in the given network using this model. Our experiments demonstrate that QOMIC outperforms the existing solutions developed for the classical computer, in term of motif counts. We also observe that QOMIC can efficiently find motifs in human regulatory networks associated with five neurodegenerative diseases: Alzheimer's, Parkinson's, Huntington's, Amyotrophic Lateral Sclerosis, and Motor Neurone Disease. Our implementation can be found in https://github.com/ngominhhoang/Quantum-Motif-Identification.git.

A new species of Galumnella (Acari, Oribatida, Galumnellidae) from Vietnam

The article describes a new species, Galumnella subiasi sp. nov., from Vietnam, which is distinguished from others by the following combination of characteristics: the notogaster, pteromorphs, prodorsum, and ventral shield have a network pattern; there is an interlamellar ridge on the prodorsum; lamellar and sublamellar lines are parallel; rostral and lamellar setae of medium length; bothridial setae slightly expanded in the middle, unilaterally heavily spinose; notogaster with 10 pairs of short setae; the adanal setae ad3 are located anterior to the anal aperture; postanal porose area absent; all leg tarsi with three claws. An identification key is provided for all Asian species of Galumnella, and it is demonstrated that Galumnella angustifrons and Galumnella nipponica are distinct species.

Casting Skin Dressing Containing Extractions of the Organic Part of Marine Sponges for Wound Healing.

Skin wounds are extremely frequent injuries related to many etiologies. They are a burden on healthcare systems worldwide. Skin dressings are the most popular therapy, and collagen is the most commonly used biomaterial, although new sources of collagen have been studied, especially spongin-like from marine sponges (SPG), as a promising source due to a similar composition to vertebrates and the ability to function as a cell-matrix adhesion framework. Despite evidence showing the positive effects of SPG for tissue healing, the effects of skin dressings manufactured are still limited. In this context, this study aimed at investigating the effects of collagen skin dressings in an experimental model of skin wounds in rats. For this purpose, SEM, FTIR, cell viability, morphological and morphometric aspects, collagen deposition, and immunostaining of TGF-β and FGF were evaluated. The results demonstrated micro- and macropores on the rough surface, peak characteristics of collagen, and no cytotoxicity for the skin dressing. Also, the control group (CG) after 5 and 10 days exhibited an intense inflammatory process and the presence of granulation tissue, while the treated group (TG) exhibited re-epithelialization after 10 days. The evaluation of granulation tissue and neoepithelial length had an intragroup statistical difference (p = 0.0216) and no intergroup difference. Birefringence demonstrated an organized mesh arranged in a network pattern, presenting type I and type III collagen fibers in all groups. Moreover, in the morphometric evaluation, there were no statistical differences in intergroups or time points for the different types of collagen evaluated. In conclusion, these findings may indicate that the dressing has not exacerbated the inflammatory process and may allow faster healing. However, further studies using a critical wound healing injury model should be used, associated with longer experimental periods of evaluation, to further investigate the effects of these promising therapeutic approaches throughout the skin repair process.