Dynamic Network Research Articles

Exploring the interaction between the gut microbiota and cyclic adenosine monophosphate-protein kinase A signaling pathway: a potential therapeutic approach for neurodegenerative diseases

The interaction between the gut microbiota and cyclic adenosine monophosphate (cAMP)- protein kinase A (PKA) signaling pathway in the host's central nervous system plays a crucial role in neurological diseases and enhances communication along the gut–brain axis. The gut microbiota influences the cAMP-PKA signaling pathway through its metabolites, which activates the vagus nerve and modulates the immune and neuroendocrine systems. Conversely, alterations in the cAMP-PKA signaling pathway can affect the composition of the gut microbiota, creating a dynamic network of microbial-host interactions. This reciprocal regulation affects neurodevelopment, neurotransmitter control, and behavioral traits, thus playing a role in the modulation of neurological diseases. The coordinated activity of the gut microbiota and the cAMP-PKA signaling pathway regulates processes such as amyloid-β protein aggregation, mitochondrial dysfunction, abnormal energy metabolism, microglial activation, oxidative stress, and neurotransmitter release, which collectively influence the onset and progression of neurological diseases. This study explores the complex interplay between the gut microbiota and cAMP-PKA signaling pathway, along with its implications for potential therapeutic interventions in neurological diseases. Recent pharmacological research has shown that restoring the balance between gut flora and cAMP-PKA signaling pathway may improve outcomes in neurodegenerative diseases and emotional disorders. This can be achieved through various methods such as dietary modifications, probiotic supplements, Chinese herbal extracts, combinations of Chinese herbs, and innovative dosage forms. These findings suggest that regulating the gut microbiota and cAMP-PKA signaling pathway may provide valuable evidence for developing novel therapeutic approaches for neurodegenerative diseases.

Nonnegative matrix factorization for analyzing state dependent neuronal network dynamics in calcium recordings.

Calcium imaging allows recording from hundreds of neurons in vivo with the ability to resolve single cell activity. Evaluating and analyzing neuronal responses, while also considering all dimensions of the data set to make specific conclusions, is extremely difficult. Often, descriptive statistics are used to analyze these forms of data. These analyses, however, remove variance by averaging the responses of single neurons across recording sessions, or across combinations of neurons, to create single quantitative metrics, losing the temporal dynamics of neuronal activity, and their responses relative to each other. Dimensionally Reduction (DR) methods serve as a good foundation for these analyses because they reduce the dimensions of the data into components, while still maintaining the variance. Nonnegative Matrix Factorization (NMF) is an especially promising DR analysis method for analyzing activity recorded in calcium imaging because of its mathematical constraints, which include positivity and linearity. We adapt NMF for our analyses and compare its performance to alternative dimensionality reduction methods on both artificial and in vivo data. We find that NMF is well-suited for analyzing calcium imaging recordings, accurately capturing the underlying dynamics of the data, and outperforming alternative methods in common use.

Energy Associated With Dynamic Network Changes in Patients With Multiple Sclerosis and Cognitive Impairment.

Patients with multiple sclerosis (MS) often experience cognitive impairment, and this is related to structural disconnection and subsequent functional reorganization. It is unclear how specific patterns of functional reorganization might make it harder for cognitively impaired (CI) patients with MS to dynamically adapt how brain regions communicate, which is crucial for normal cognition. We aimed to identify dynamic functional network patterns that are relevant to cognitive impairment in MS and investigate whether these patterns can be explained by altered energy costs. Resting-state functional and diffusion MRI was acquired in a cross-sectional design, as part of the Amsterdam MS cohort. Patients with clinically definitive MS (relapse-free) were classified as CI (≥2/7 domains Z < -2), mildly CI (MCI) (≥2/7 domains Z < -1.5), or cognitively preserved (CP) based on an expanded Brief Repeatable Battery of Neuropsychological Tests. Functional connectivity states were determined using k-means clustering of moment-to-moment cofluctuations (i.e., edge time series), and the resulting state sequence was used to characterize the frequency of transitions. Control energy of the state transitions was calculated using the structural network with network control theory. Imaging and cognitive data were available for 95 controls and 330 patients (disease duration: 15 years; 179 CP, 65 MCI, and 86 CI). We identified a "visual network state," "sensorimotor network state," "ventral attention network state," and "default mode network state." CI patients transitioned less frequently between connectivity states compared with CP (β = -5.78; p = 0.038). Relative to the time spent in a state, CI patients transitioned less from a "default mode network state" to a "visual network state" (β = -0.02; p = 0.004). The CI patients required more control energy to transition between states (β = 0.32; p = 0.007), particularly for the same transition (β = 0.34; p = 0.049). This study showed that it costs more energy for MS patients with cognitive impairment to dynamically change the functional network, possibly explaining why these transitions occur less frequently. In particular, transitions from a default mode network state to a visual network state were relevant for cognition in these patients. To further study the order of events leading to these network disturbances, future work should include longitudinal data across different disease stages.

MSDG: Multi-Scale Dynamic Graph Neural Network for Industrial Time Series Anomaly Detection

A large number of sensors are typically installed in industrial plants to collect real-time operational data. These sensors monitor data with time series correlation and spatial correlation over time. In previous studies, GNN has built many successful models to deal with time series data, but most of these models have fixed perspectives and struggle to capture the dynamic correlations in time and space simultaneously. Therefore, this paper constructs a multi-scale dynamic graph neural network (MSDG) for anomaly detection in industrial sensor data. First, a multi-scale sliding window mechanism is proposed to input different scale sensor data into the corresponding network. Then, a dynamic graph neural network is constructed to capture the spatial–temporal dependencies of multivariate sensor data. Finally, the model comprehensively considers the extracted features for sequence reconstruction and utilizes the reconstruction errors for anomaly detection. Experiments have been conducted on three real public datasets, and the results show that the proposed method outperforms the mainstream methods.

Dynamic Spatio-Temporal Hypergraph Convolutional Network for Traffic Flow Forecasting

Graph convolutional networks (GCN) are an important research method for intelligent transportation systems (ITS), but they also face the challenge of how to describe the complex spatio-temporal relationships between traffic objects (nodes) more effectively. Although most predictive models are designed based on graph convolutional structures and have achieved effective results, they have certain limitations in describing the high-order relationships between real data. The emergence of hypergraphs breaks this limitation. A dynamic spatio-temporal hypergraph convolutional network (DSTHGCN) model is proposed in this paper. It models the dynamic characteristics of traffic flow graph nodes and the hyperedge features of hypergraphs simultaneously, achieving collaborative convolution between graph convolution and hypergraph convolution (HGCN). On this basis, a hyperedge outlier removal mechanism (HOR) is introduced during the process of node information propagation to hyper-edges, effectively removing outliers and optimizing the hypergraph structure while reducing complexity. Through in-depth experimental analysis on real-world datasets, this method has better performance compared to other methods.

MagNet: Multilevel Dynamic Wavelet Graph Neural Network for Multivariate Time Series Classification

Multivariate time series classification (MTSC) is a fundamental data mining task, which is widely applied in the fields like health care and energy management. However, the existing MTSC methods are mostly adapted from univariate versions and model the static patterns among series in the time domain. We argue they fail to capture the inter-dependencies across variables and rarely consider the unique dynamic features in multilevel frequencies, which are susceptible to signal noise and lack sufficient feature extraction capability to achieve satisfactory classification accuracy. To address these issues, we propose a novel M ultilevel dyn a mic wavelet g raph neural Net work called MagNet, which effectively captures inherent temporal-frequency dependencies in multivariate time series data in a global view, facilitating the information flow among interrelated variables and leveraging learnable graph neural networks (GNNs) to uncover dynamic frequency dependencies. We propose an orthogonal temporal convolution layer that utilizes soft orthogonal losses to constrain features learned at different frequency components to reduce feature redundancy. Additionally, we introduce a hierarchical graph coarsening operator to address the flat learning challenges in traditional GNNs. Our dynamic wavelet graph neural network and hierarchical coarsening enable deep model stacking and end-to-end learning. Extensive experiments on 30 UEA benchmarks demonstrate that our method outperforms the state-of-the-art baselines in the MTSC tasks.

A statistical mechanics investigation of unfolded protein response across organisms.

Living systems rely on coordinated molecular interactions, especially those related to gene expression and protein activity. The Unfolded Protein Response is a crucial mechanism in eukaryotic cells, activated when unfolded proteins exceed a critical threshold. It maintains cell homeostasis by enhancing protein folding, initiating quality control, and activating degradation pathways when damage is irreversible. This response functions as a dynamic signaling network, with proteins as nodes and their interactions as edges. We analyze these protein-protein networks across different organisms to understand their intricate intra-cellular interactions and behaviors. In this work, analyzing twelve organisms, we assess how fundamental measures in network theory can individuate seed proteins and specific pathways across organisms. We employ network robustness to evaluate and compare the strength of the investigated protein-protein interaction networks, and the structural controllability of complex networks to find and compare the sets of driver nodes necessary to control the overall networks. We find that network measures are related to phylogenetics, and advanced network methods can identify main pathways of significance in the complete Unfolded Protein Response mechanism.

Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection

This study investigates the integration of quantum computing, classical methods, and deep learning techniques for enhanced image processing in dynamic 6G networks, while also addressing essential aspects of copyright technology and detection. Our findings indicate that quantum methods excel in rapid edge detection and feature extraction but encounter difficulties in maintaining image quality compared to classical approaches. In contrast, classical methods preserve higher image fidelity but struggle to satisfy the real-time processing requirements of 6G applications. Deep learning techniques, particularly CNNs, demonstrate potential in complex image analysis tasks but demand substantial computational resources. To promote the ethical use of AI-generated images, we introduce copyright detection mechanisms that employ advanced algorithms to identify potential infringements in generated content. This integration improves adherence to intellectual property rights and legal standards, supporting the responsible implementation of image processing technologies. We suggest that the future of image processing in 6G networks resides in hybrid systems that effectively utilize the strengths of each approach while incorporating robust copyright detection capabilities. These insights contribute to the development of efficient, high-performance image processing systems in next-generation networks, highlighting the promise of integrated quantum-classical–classical deep learning architectures within 6G environments.

Ensemble-based community detection for dynamic networks

Ensemble-based community detection for dynamic networks

Physics-inspired machine learning detects “unknown unknowns” in networks: discovering network boundaries from observable dynamics

Abstract Dynamics on networks is often only partially observable in experiment, with many nodes being inaccessible or indeed the existence and properties of a larger unobserved network being unknown. This limits our ability to reconstruct the topology of the network and the strength of the interactions among even the observed nodes. Here, we show how machine learning inspired by physics can be utilized on noisy time series of such partially observed networks to determine which nodes of the observed part of a network form its boundary, i.e. have significant interactions with the unobserved part. This opens a route to reliable network reconstruction. We develop the method for arbitrary network dynamics and topologies and demonstrate it on a broad range of dynamics including non- linear coupled oscillators and chaotic attractors. Beyond these we focus in particular on biochemical reaction networks, where we apply the approach to the dynamics of the Epidermal Growth Factor Receptor (EGFR) network and show that it works even for substantial noise levels.

An electrodiffusive network model with multicompartmental neurons and synaptic connections.

Most computational models of neurons assume constant ion concentrations, disregarding the effects of changing ion concentrations on neuronal activity. Among the models that do incorporate ion concentration dynamics, simplifications are often made that sacrifice biophysical consistency, such as neglecting the effects of ionic diffusion on electrical potentials or the effects of electric drift on ion concentrations. A subset of models with ion concentration dynamics, often referred to as electrodiffusive models, account for ion concentration dynamics in a way that ensures a biophysical consistent relationship between ion concentrations, electric charge, and electrical potentials. These models include compartmental single-cell models, geometrically explicit models, and domain-type models, but none that model neuronal network dynamics. To address this gap, we present an electrodiffusive network model with multicompartmental neurons and synaptic connections, which we believe is the first compartmentalized network model to account for intra- and extracellular ion concentration dynamics in a biophysically consistent way. The model comprises an arbitrary number of "units," each divided into three domains representing a neuron, glia, and extracellular space. Each domain is further subdivided into a somatic and dendritic layer. Unlike conventional models which focus primarily on neuronal spiking patterns, our model predicts intra- and extracellular ion concentrations (Na+, K+, Cl-, and Ca2+), electrical potentials, and volume fractions. A unique feature of the model is that it captures ephaptic effects, both electric and ionic. In this paper, we show how this leads to interesting behavior in the network. First, we demonstrate how changing ion concentrations can affect the synaptic strengths. Then, we show how ionic ephaptic coupling can lead to spontaneous firing in neurons that do not receive any synaptic or external input. Lastly, we explore the effects of having glia in the network and demonstrate how a strongly coupled glial syncytium can prevent neuronal depolarization blocks.

HSF-1 promotes longevity through ubiquilin-1-dependent mitochondrial network remodelling.

Increased activity of the heat shock factor, HSF-1, suppresses proteotoxicity and enhances longevity. However, the precise mechanisms by which HSF-1 promotes lifespan are unclear. Using an RNAi screen, we identify ubiquilin-1 (ubql-1) as an essential mediator of lifespan extension in worms overexpressing hsf-1. We find that hsf-1 overexpression leads to transcriptional downregulation of all components of the CDC-48-UFD-1-NPL-4 complex, which is central to both endoplasmic reticulum and mitochondria associated protein degradation, and that this is complemented by UBQL-1-dependent turnover of NPL-4.1. As a consequence, mitochondrial network dynamics are altered, leading to increased lifespan. Together, our data establish that HSF-1 mediates lifespan extension through mitochondrial network adaptations that occur in response to down-tuning of components associated with organellar protein degradation pathways.

Entropy of Neuronal Spike Patterns

Neuronal spike patterns are the fundamental units of neural communication in the brain, which is still not fully understood. Entropy measures offer a quantitative framework to assess the variability and information content of these spike patterns. By quantifying the uncertainty and informational content of neuronal patterns, entropy measures provide insights into neural coding strategies, synaptic plasticity, network dynamics, and cognitive processes. Here, we review basic entropy metrics and then we provide examples of recent advancements in using entropy as a tool to improve our understanding of neuronal processing. It focuses especially on studies on critical dynamics in neural networks and the relation of entropy to predictive coding and cortical communication. We highlight the necessity of expanding entropy measures from single neurons to encompass multi-neuronal activity patterns, as cortical circuits communicate through coordinated spatiotemporal activity patterns, called neuronal packets. We discuss how the sequential and partially stereotypical nature of neuronal packets influences the entropy of cortical communication. Stereotypy reduces entropy by enhancing reliability and predictability in neural signaling, while variability within packets increases entropy, allowing for greater information capacity. This balance between stereotypy and variability supports both robustness and flexibility in cortical information processing. We also review challenges in applying entropy to analyze such spatiotemporal neuronal spike patterns, notably, the “curse of dimensionality” in estimating entropy for high-dimensional neuronal data. Finally, we discuss strategies to overcome these challenges, including dimensionality reduction techniques, advanced entropy estimators, sparse coding schemes, and the integration of machine learning approaches. Thus, this work summarizes the most recent developments on how entropy measures contribute to our understanding of principles underlying neural coding.

Nanograting-Based Dynamic Structural Colors Using Heterogeneous Materials.

Dynamic structural colors can change in response to different environmental stimuli. This ability remains effective even when the size of the species responsible for the structural color is reduced to a few micrometers, providing a promising sensing mechanism for solving microenvironmental sensing problems in micro-robotics and microfluidics. However, the lack of dynamic structural colors that can encode rapidly, easily integrate, and accurately reflect changes in physical quantities hinders their use in microscale sensing applications. Herein, we present a 2.5-dimensional dynamic structural color based on nanogratings of heterogeneous materials, which were obtained by interweaving a pH-responsive hydrogel with an IP-L photoresist. Transverse gratings printed with pH-responsive hydrogels elongated the period of longitudinal grating in the swollen state, resulting in pH-tuned structural colors at a 45° incidence. Moreover, the patterned encoding and array printing of dynamic structural colors were achieved using grayscale stripe images to accurately encode the periods and heights of the nanogrid structures. Overall, dynamic structural color networks exhibit promising potential for applications in information encryption and in situ sensing for microfluidic chips.

Promoting real-time electricity tariffs for more demand response from German households: a review of four policy options

Abstract Background Demand response is an important option for accommodating growing shares of renewable electricity, and therefore, crucial for the success of the energy transition in Germany and elsewhere. In conjunction with smart meters, real-time (or ‘dynamic’) electricity tariffs can facilitate the flexibilization of power consumption and reduce energy bills. Whilst such tariffs are already quite common in several EU member states, Germany lags behind in this respect. The country makes for an interesting case study because of the sheer volume of additional flexibility that its energy transition necessitates. Main text This paper discusses how German policymakers can make real-time tariffs more attractive for households and thus entice them to better adapt their consumption to current market conditions. Following an analysis of the current impediments to the adoption of such tariffs, we discuss four policy options: (1) a more ambitious legal definition of real-time tariffs that can promote market transparency and leverage potential savings for consumers, (2) a shift in energy taxation that encourages the uptake of renewable power and increases price spreads, (3) a new model of dynamic network charges which combines grid-serving and market-serving incentives, and (4) a subsidy for users of real-time tariffs that helps internalise the benefits they provide to all electricity consumers. Given the similar regulatory framework, our suggestions should generally also apply to other countries in Europe and beyond. Conclusions Overall, we argue that there is considerable scope for policymakers to better exploit market forces to ensure security of electricity supply at lower social cost. Our call for stricter regulation in order to allow the markets to better guide consumer behaviour may seem like a paradox—but it is one well worth embracing.

Resources collection strategy of emergency materials based on evolutionary game model in complex network

PurposeIn recent years, the frequency of emergencies, such as natural disasters and public health crises, has markedly increased globally. These occurrences have introduced new challenges to national public security systems and emergency management capabilities. Post-disaster humanitarian logistic operations involve the collection of emergency relief resources to mitigate the impact of disasters in affected areas. Effective coordination among governments, enterprises and charities is essential to enhance the efficiency of these operations. This study employs evolutionary game theory to explore the strategic interactions and behavioral patterns among these key stakeholders during the collection of emergency materials.Design/methodology/approachA tripartite evolutionary game model involving governments, enterprises and charities is developed. Subsequently, to validate the theoretical findings, a scale-free network is constructed for the purpose of numerical simulations. As this network evolves, both the edges between nodes and the strategy choices of the nodes also change. Numerical simulations are conducted using the network to examine the sensitivity of factors influencing strategic choices among game stakeholders.FindingsAccording to the model simulation results, penalties significantly influence government regulation strength, while enterprise philanthropic behavior is mainly affected by penalties, profit transfer benefits and trust loss. For charities, strategic choices are primarily driven by penalties, tax subsidies, illegal operation benefits and charitable costs. The findings provide a theoretical basis for governments, enterprises and charities to select the sensible strategy.Originality/valueOur study establishes a dynamic network of edges and nodes evolving over time to analyze the strategic evolutionary paths of governments, enterprises and charities from a micro perspective. The results assist governments, enterprises and charities in making more strategic decisions.

Click dechlorination of halogen-containing hazardous plastics towards recyclable vitrimers.

Amid the ongoing Global Plastics Treaty, high-quality circulation of halogen-containing plastics in an environmentally sound manner is a globally pressing issue. Current chemical dechlorination methods are limited by their inability to recycle PVC at the long-chain carbon level and the persistence of eco-toxic organochlorine byproducts. Herein, we propose a click dechlorination strategy for transforming waste PVC into valuable vitrimers via a one-step cascade thiol-ene click reaction and dynamic polymerization. Thermal activation of C-Cl bonds initiates β-elimination dechlorination, while disulfide bonds synchronously undergo homolytic cleavage, generating sulfur-centered radicals that drive precise sulfur-chlorine substitution and the formation of disulfide dynamic networks. This strategy achieves nearly complete chlorine extraction (93.88%) and produces vitrimers with tailorable mechanical and reprocessing properties, spanning from soft elastomers with 784% elongation to rigid plastics with a yield strength of 34 MPa. The significant advantage of this strategy is backbone protective precise dechlorination, enabling ecosystem toxicity reduced by 99.51% compared with widely adopted pyrolysis methods. This work introduces a sustainable pathway for upcycling PVC into valuable materials, marking significant progress in chlorinated plastic recycling.

Approaches to Identifying Emotions and Affections During the Museum Learning Experience in the Context of the Future Internet

The Future Internet aims to revolutionize digital interaction by integrating advanced technologies like AI and IoT, enabling a dynamic and resilient network. It envisions emotionally intelligent systems that can interpret and respond to human feelings, creating immersive, empathy-driven learning experiences. This evolution aspires to form a responsive digital ecosystem that seamlessly connects technology and human emotion. This paper presents a computational model aimed at enhancing the emotional aspect of learning experiences within museum environments. The model is designed to represent and manage affective and emotional feedback, with a focus on how emotions can significantly impact the learning process in a museum context. The proposed model seeks to identify and quantify emotions during a visitor’s engagement with museum exhibits. To achieve this goal, we primarily explored the following: (i) methods and techniques for assessing and recognizing emotional responses in museum visitors, (ii) feedback management strategies based on the detection of visitors’ emotional states. Then, the methodology was tested on 1000 cases via specific questionnaire forms, along with the presentation of images and short videos, and the results of data analysis are reported. The findings contribute toward establishing a comprehensive methodology for the identification and quantification of the emotional state of museum visitors.

Adaptive Random Early Detection Using Deep Reinforcement Learning for Enhanced Congestion Control in Dynamic TCP/IP Networks

AQM is used to meet targets for congestion control and low delay, high throughput in TCP/IP networks. But methods developed for AQM like Random Early Detection (RED) work strictly on the control parameters pre-set on them and thus may not respond very well to changing network conditions. Thus, this paper presents a composite AQM model using DRL based on DQN, and the ability to learn independently regarding the AQM weight parameter of the queue. The fact that DRL is adaptive means the effectiveness of the proposed system is also assured. As it is observed in implementing traditional model-based approach, the stability and performance degrade when tested in different network conditions; however, DRL’s ability to learn independently enables ML to solve network congestion as it happens. Consequently, more stability is achieved, the delay is lesser, more bandwidth is used and the overall packet drop rate is low; the proposed DRL-RED model is ideal for dynamic network environments. The proposed DRL-RED model is compared with the regular RED algorithm for both low density and high-density networks. This proposed model has achieved sustained throughput of up to 49.9 Mbps with 0.949 % reduction on delay and very low PLR of 8.38043%. From the comparative analysis, it is clear that the employment of DRL with RED improves the stock of network throughput, the reduction on packet drop frequency, and the general delay in network situations. There are two main disadvantages of this approach: first, it cannot reach the heavy-traffic, the problem partly solved by model-based approach; second, the values of the congestion-control parameters cannot be reset, an issue eradicated by DRL as a result of its inherent adaptiveness. Consequently, new stability and increased performance can be achieved under various network conditions.

Aberrant dynamic functional network connectivity in patients with diffuse axonal injury

Diffuse axonal injury (DAI) results in aberrant functional connectivity and is significantly linked to cognitive impairment. Nevertheless, the network mechanisms influencing neurocognitive function following DAI remain unclear. This study aimed to examine the characteristics of static and dynamic functional network connectivity (FNC) in patients with DAI. Resting-state functional magnetic resonance imaging data were collected from 26 patients with DAI and 27 healthy controls. Resting-state networks were extracted using independent component analysis. We evaluated the connectivity strength through spatial maps and static FNC, and then further dynamic properties were identified using a sliding time-window approach and k-means clustering, and investigated their associations with clinical variables. Patients with DAI showed stronger intra-network spatial maps in the default mode network and subcortical network than healthy controls, but static inter-network functional connectivity remained stable. Furthermore, three recurring states for dynamic connectivity were identified in all participants, and state 1 occurred most frequently in patients with DAI and exhibited higher fractional time, and as well as longer mean dwell time, which was positively associated with MMSE scores. Meanwhile, patients with DAI exhibited mostly increased functional connectivity strength of dynamic FNC in all states, particularly within the default mode network and visual network. These findings suggest that patients with DAI are characterized by altered dynamic FNC and temporal properties, which provide distinct complementary information different from static functional connectivity, and new insights into the neural pathophysiology of DAI associated with cognitive impairment.