Critical Nodes Research Articles

Experience-dependent, sexually dimorphic synaptic connectivity defined by sex-specific cadherin expression.

Early-life experience influences subsequent maturation and function of the adult brain, sometimes even in a sex-specific manner, but underlying molecular mechanisms are poorly understood. We describe here how juvenile experience defines sexually dimorphic synaptic connectivity in the adult Caenorhabditis elegans nervous system. Starvation of juvenile males disrupts serotonin-dependent activation of the CREB transcription factor in a nociceptive sensory neuron, PHB. CREB acts through a cascade of transcription factors to control expression of an atypical cadherin protein, FMI-1/Flamingo/CELSR. During postembryonic development, FMI-1 promotes and maintains synaptic connectivity of PHB to a command interneuron, AVA, in both sexes, but a serotonin-dependent transcriptional regulatory cassette antagonizes FMI-1 expression in males, thereby establishing sexually dimorphic connectivity between PHB and AVA. A critical regulatory node is the CREB-target LIN-29, a Zn finger transcription factor that integrates four layers of information: sexual specificity, past experience, time and cell-type specificity. Our findings provide the mechanistic details of how an early juvenile experience defines sexually dimorphic synaptic connectivity.

Mitochondrial pyruvate transport regulates presynaptic metabolism and neurotransmission.

Glucose has long been considered the primary fuel source for the brain. However, glucose levels fluctuate in the brain during sleep or circuit activity, posing major metabolic stress. Here, we demonstrate that the mammalian brain uses pyruvate as a fuel source, and pyruvate can support neuronal viability in the absence of glucose. Nerve terminals are sites of metabolic vulnerability, and we show that mitochondrial pyruvate uptake is a critical step in oxidative ATP production in hippocampal terminals. We find that the mitochondrial pyruvate carrier is post-translationally modified by lysine acetylation, which, in turn, modulates mitochondrial pyruvate uptake. Our data reveal that the mitochondrial pyruvate carrier regulates distinct steps in neurotransmission, namely, the spatiotemporal pattern of synaptic vesicle release and the efficiency of vesicle retrieval-functions that have profound implications for synaptic plasticity. In summary, we identify pyruvate as a potent neuronal fuel and mitochondrial pyruvate uptake as a critical node for the metabolic control of neurotransmission in hippocampal terminals.

Intelligent Wireless Charging Path Optimization for Critical Nodes in Internet of Things-Integrated Renewable Sensor Networks

Intelligent Wireless Charging Path Optimization for Critical Nodes in Internet of Things-Integrated Renewable Sensor Networks

Fast Identification of Critical Nodes in Complex Network Based on Improved Greedy Algorithm

Abstract Over the past decades, many critical and complex systems, such as power grid, transportation network, and information network, have been effectively modeled using complex network. However, these networks are susceptible to cascading failure, triggered by minor failure, leading to partial or total collapse. Preventing cascading failure necessitates the protection of critical nodes within the network, making the identification of these nodes particularly crucial. In this paper, we introduce an Improved Greedy Algorithm (IGA), inspired by the traditional greedy algorithm and the relationship between the propagation mechanism of cascading failure and N-K failure. This algorithm gets rid of the shortcomings of traditional recognition algorithms for dealing with large-scale networks with long time and low accuracy, and evaluates the critical degree of nodes based on network connectivity and overload rate. The simulation is carried out in Barabsi-Albert (BA) network and IEEE 39-, 118-bus systems, and make comparisons with other different algorithms. The results show that IGA not only has low computational complexity, but also has high accuracy in identifying critical nodes in complex networks.

Integrative analysis of single-cell transcriptomic and multilayer signaling networks in glioma reveal tumor progression stage

IntroductionTumor microenvironments (TMEs) encompass complex ecosystems of cancer cells, infiltrating immune cells, and diverse cell types. Intercellular and intracellular signals within the TME significantly influence cancer progression and therapeutic outcomes. Although computational tools are available to study TME interactions, explicitly modeling tumor progression across different cancer types remains a challenge.MethodsThis study introduces a comprehensive framework utilizing single-cell RNA sequencing (scRNA-seq) data within a multilayer network model, designed to investigate molecular changes across glioma progression stages. The heterogeneous, multilayered network model replicates the hierarchical structure of biological systems, from genetic building blocks to cellular functions and phenotypic manifestations.ResultsApplying this framework to glioma scRNA-seq data allowed complex network analysis of different cancer stages, revealing significant ligand‒receptor interactions and key ligand‒receptor-transcription factor (TF) axes, along with their associated biological pathways. Differential network analysis between grade III and grade IV glioma highlighted the most critical nodes and edges involved in interaction rewiring. Pathway enrichment analysis identified four essential genes—PDGFA (ligand), PDGFRA (receptor), CREB1 (TF), and PLAT (target gene)—involved in the Receptor Tyrosine Kinases (RTK) signaling pathway, which plays a pivotal role in glioma progression from grade III to grade IV.DiscussionThese genes emerged as significant features for machine learning in predicting glioma progression stages, achieving 87% accuracy and 93% AUC in a 3-year survival prediction through Kaplan-Meier analysis. This framework provides deeper insights into the cellular machinery of glioma, revealing key molecular relationships that may inform prognosis and therapeutic strategies.

Arkypallidal neurons in the external globus pallidus can mediate inhibitory control by altering competition in the striatum

Reactive inhibitory control is crucial for survival. Traditionally, this control in mammals was attributed solely to the hyperdirect pathway, with cortical control signals flowing unidirectionally from the subthalamic nucleus (STN) to basal ganglia output regions. Yet recent findings have put this model into question, suggesting that the STN is assisted in stopping actions through ascending control signals to the striatum mediated by the external globus pallidus (GPe). Here, we investigate this suggestion by harnessing a biologically constrained spiking model of the cortico-basal ganglia-thalamic (CBGT) circuit that includes pallidostriatal pathways originating from arkypallidal neurons. Through a series of experiments probing the interaction between three critical inhibitory nodes (the STN, arkypallidal cells, and indirect pathway spiny projection neurons), we find that the GPe acts as a critical mediator of both ascending and descending inhibitory signals in the CBGT circuit. In particular, pallidostriatal pathways regulate this process by weakening the direct pathway dominance of the evidence accumulation process driving decisions, which increases the relative suppressive influence of the indirect pathway on basal ganglia output. These findings delineate how pallidostriatal pathways can facilitate action cancellation by managing the bidirectional flow of information within CBGT circuits.

Altered sphingolipid biosynthetic flux and lipoprotein trafficking contribute to trans-fat-induced atherosclerosis.

Dietary fat drives the pathogenesis of atherosclerotic cardiovascular disease (ASCVD), particularly through circulating cholesterol and triglyceride-rich lipoprotein remnants. Industrially produced trans-unsaturated fatty acids (TFAs) incorporated into food supplies significantly promote ASCVD. However, the molecular trafficking of TFAs responsible for this association is not well understood. Here, we demonstrate that TFAs are preferentially incorporated into sphingolipids by serine palmitoyltransferase (SPT) and secreted from cells invitro. Administering high-fat diets (HFDs) enriched in TFAs to Ldlr-/- mice accelerated hepatic very-low-density lipoprotein (VLDL) and sphingolipid secretion into circulation to promote atherogenesis compared with a cis-unsaturated fatty acid (CFA)-enriched HFD. SPT inhibition mitigated these phenotypes and reduced circulating atherogenic VLDL enriched in TFA-derived polyunsaturated sphingomyelin. Transcriptional analysis of human liver revealed distinct regulation of SPTLC2 versus SPTLC3 subunit expression, consistent with human genetic correlations in ASCVD, further establishing sphingolipid metabolism as a critical node mediating the progression of ASCVD in response to specific dietary fats.

Bioactive compounds regulate appetite through the melanocortin system: a review.

Obesity, a significant health crisis, arises from an imbalance between energy intake and expenditure. Enhancing appetite regulation has garnered substantial attention from researchers as a novel and effective strategy for weight management. The melanocortin system, situated in the hypothalamus, is recognized as a critical node in the regulation of appetite. It integrates long-term and short-term hormone signals from the periphery as well as nutrients, forming a complex network of interacting feedback mechanisms with the gut-brain axis, significantly contributing to the regulation of energy homeostasis. Appetite regulation by bioactive compounds has been a focus of intensive research due to their favorable safety profiles and easy accessibility. These bioactive compounds, derived from a variety of plant and animal sources, modulate the melanocortin system and influence appetite and energy homeostasis through multiple pathways: central nervous system, peripheral hormones, and intestinal microbiota. Here, we review the anatomy, function, and receptors of the melanocortin system, outline the long-term and short-term regulatory hormones that act on the melanocortin system, and discuss the bioactive compounds and their mechanisms of action that exert a regulatory effect on appetite by targeting the melanocortin system. This review contributes to a better understanding of how bioactive compounds regulate appetite via the melanocortin system, thereby providing nutritional references for citizens' dietary preferences.

Multi-modal and multi-layer robustness analysis of the European rail and air networks

The robustness of long-distance transport services is paramount for ensuring network connectivity under disruptions. We conduct a comparative analysis of the European rail and air networks of 124 main metropolitan areas, assessing their ability to withstand successive network degradation. We undertake a multi-modal and multi-layer approach in our analysis of the robustness of long-distance transport networks. In particular, we are interested in the role of individual nodes for both air and rail networks as well as for the integrated multi-modal network. Given the hierarchical nature of long-distance transport services, we adopt a multi-layer perspective by means of clustering nodes based on their criticality in order to identify common performance profiles. Original metrics are formulated to measure the impact of nodes on network fragmentation, both at the individual level and as cluster members. Additionally, a new metric is introduced to assess cities’ reliance on air transportation, when considering the integrated multi-layer air-rail network during disruptions in air connections. Our findings indicate that the air network exhibits significantly greater robustness compared to rail, i.e. 10% versus 71% performance loss in the worst case scenario, respectively. Furthermore, primary sub-graph of nodes whose protection from attacks can greatly enhance network’s overall robustness is identified. We discuss our findings in terms of the relationship between network structure, robustness, and the role of critical nodes as well as propose potential mitigation measures.

13 C-MFA helps to identify metabolic bottlenecks for improving malic acid production in Myceliophthora thermophila

BackgroundMyceliophthora thermophila has been engineered as a significant cell factory for malic acid production, yet strategies to further enhance production remain unclear and lack rational guidance. 13C-MFA (13C metabolic flux analysis) offers a means to analyze cellular metabolic mechanisms and pinpoint critical nodes for improving product synthesis. Here, we employed 13C-MFA to investigate the metabolic flux distribution of a high-malic acid-producing strain of M. thermophila and attempted to decipher the crucial bottlenecks in the metabolic pathways.ResultsCompared with the wild-type strain, the high-Malic acid-producing strain M. thermophila JG207 exhibited greater glucose uptake and carbon dioxide evolution rates but lower oxygen uptake rates and biomass yields. Consistent with these phenotypes, the 13C-MFA results showed that JG207 displayed elevated flux through the EMP pathway and downstream TCA cycle, along with reduced oxidative phosphorylation flux, thereby providing more precursors and NADH for malic acid synthesis. Furthermore, based on the 13C-MFA results, we conducted oxygen-limited culture and nicotinamide nucleotide transhydrogenase (NNT) gene knockout experiments to increase the cytoplasmic NADH level, both of which were shown to be beneficial for malic acid accumulation.ConclusionsThis work elucidates and validates the key node for achieving high malic acid production in M. thermophila. We propose effective fermentation strategies and genetic modifications for enhancing malic acid production. These findings offer valuable guidance for the rational design of future cell factories aimed at improving malic acid yields.

JamBIT: RL-based framework for disrupting adversarial information in battlefields

During battlefield operations, military radios (hereafter nodes) exchange information among various units using a mobile ad-hoc network (MANET) due to its infrastructure-less and self-healing capabilities. Adversarial cyberwarfare plays a crucial role in modern combat by disrupting communication between critical nodes (i.e., nodes mainly responsible for propagating important information) to gain dominance over the opposing side. However, determining critical nodes within a complex network is an NP-hard problem. This paper formulates a mathematical model to identify important links and their connected nodes, and presents JamBIT, a reinforcement learning-based framework with an encoder–decoder architecture, for efficiently detecting and jamming critical nodes. The encoder transforms network structures into embedding vectors, while the decoder assigns a score to the embedding vector with the highest reward. Our framework is trained and tested on custom-built MANET topologies using the Named Data Networking (NDN) protocol. JamBIT has been evaluated across various scales and weighting methods for both connected node and network dismantling problems. Our proposed method outperformed existing RL-based baselines, with a 24% performance gain for smaller topologies (50-100 nodes) and 8% for larger ones (400-500 nodes) in connected node problems, and a 7% gain for smaller topologies and 15% for larger ones in network dismantling problems.

Risk factor analysis of microvascular obstruction after percutaneous coronary intervention for ST-segment elevation myocardial infarction

ObjectiveThis study aimed to explore the risk factors of microvascular obstruction (MVO) after percutaneous coronary intervention (PCI) in patients with ST-segment elevation myocardial infarction (STEMI). Materials and MethodsA retrospective analysis was performed on 165 patients with STEMI who successfully underwent emergency PCI and completed cardiac magnetic resonance (CMR) within 1 week after PCI. Total ischemia time (symptom onset to wire, S2W), first medical contact to wire (FMC2W), and door to wire (D2W) were compared with the recommended critical time nodes for STEMI treatment. Left ventricular function was evaluated by CMR cine, and myocardial infarction characteristics and MVO were evaluated by late-gadolinium enhancement (LGE). Binary logistic regression analysis was used to evaluate the effect of delay in treatment of STEMI on the occurrence of MVO after PCI. ResultsIn this study, 89 (53.9%) patients with STEMI presented with MVO after emergency PCI. The FMC2W time and S2W time in the MVO (+) group were significantly longer than those in the MVO (−) group (P<0.05). Compared with the MVO (−) group, the MVO (+) group had larger myocardial infarction size (IS) and lower left ventricular ejection fraction (LVEF) (P<0.05). Patients with FMC2W time >120min and S2W time >300min had greater myocardial IS and MVO than the FMC2W ≤120min and S2W time ≤300min group, respectively. Logistic regression analysis showed that S2W time >300min (P=0.039, OR=2.756, 95% CI=1.053-7.213) was an independent predictor of MVO after PCI in patients with STEMI. ConclusionShortening the total time of myocardial ischemia and increasing the proportion of early reperfusion therapy can prevent or reduce MVO after PCI.

Decision-making optimization for post-disaster restoration of multimodal transport networks in terms of resilience

IntroductionPublic health emergencies can have a ripple effect on the resilience of multimodal transport network, which will lead to problems such as route disruptions or blockages, route selection change and information transmission delay spreading to the whole network, further hindering the transportation planning and operational efficiency of the network. MethodsThis study constructs a multimodal transport route optimization model under uncertainty with the objective of the sum of transportation cost, transshipment cost, penalty cost and carbon emission cost. To enhance the computational efficiency of the model, a novel invasive weed optimization with memory and encoding value clustering capabilities is proposed. In addition, by fusing the Q-learning algorithm in reinforcement learning with the novel invasive weed algorithm, the action-value function table obtained from the training facilitates the selection of optimal routes. Based on empirical data, explore the sensitivity analysis of node disruptions, time windows, and fuzzy demand on route decision-making under public health emergencies. ResultsThe transport network is affected by public health emergencies, which makes the optimal route deviate from the expected goal, resulting in an increase in the total cost. The proportion of total cost is determined by the position of nodes in the network, with critical nodes suffering more losses than ordinary nodes. Reasonable setting of time windows and fuzzy demand intervals is an effective way to improve the resilience and transportation efficiency of multimodal transport network. ConclusionsThis study provides more applicable decision-making references for enterprises to prevent the risk of supply chain disruptions caused by public health emergencies.

Identifying influential nodes in social networks from the perspective of attack-defense game.

Influence spread analysis, a critical component of social network studies, focuses on the patterns and effects of information dissemination among interconnected entities. The core of influence spread analysis is to identify influential nodes that involve two distinct aspects: influence maximization (IM) and influence blocking maximization (IBM). However, when IM and IBM occur simultaneously, identifying influential nodes becomes an intricate decision-making challenge. This study addresses identifying influential nodes in social networks through an attack-defense game perspective, where an attacker maximizes influence and a defender minimizes it. We first develop a two-player static zero-sum game model considering resource constraints. Based on the equilibrium strategy of this game, we redefine the concept of influential nodes from various viewpoints. Extensive experiments on synthetic and real-world networks show that, in most cases, the defender preferentially defends critical nodes, while the attacker adopts the decentralized strategy. Only when resources are unevenly matched do both players tend to adopt centralized strategies. This study expands the connotation of influential nodes and provides a novel paradigm for the social network analysis with significant potential applications.

Identifying Critical Nodes in Multiplex Complex Networks by using Memetic Algorithms

Identifying Critical Nodes in Multiplex Complex Networks by using Memetic Algorithms

Analysing Sector Safety Performance Based on Potential Conflicts: Complex Network Approach

This paper presents an in-depth analysis of Sector Safety Performance (SeSPe), focusing on potential conflicts within the Spanish Air Traffic Network. SeSPe serves as a comprehensive analysis for evaluating the safety levels of individual airspace sectors, taking into account both the frequency and severity of potential conflicts, along with their geospatial characteristics. Using Complex Network Theory, this study applies a novel methodology to four Madrid ACC sectors, analysing one month of flight track data. Multiple weighted spatial-temporal networks are built using 60 min intervals, allowing for the identification of critical nodes and the examination of interconnections and structural dynamics within the air traffic network. By incorporating temporal variations, the analysis uncovers evolving patterns. The research introduces an innovative approach for calculating potential conflicts by integrating radar data with airspace structure, thus offering a deeper understanding of sector risk profiles. The development of the SeSPe indicator, which combines historical safety data with topological features, enables a more informed and strategic evaluation of Air Traffic Management (ATM) system performance, ultimately contributing to safer and more efficient airspace operations.

DFRDRL: a dynamic fuzzy routing algorithm based on deep reinforcement learning with guaranteed latency and bandwidth for software-defined networks

Traditional routing algorithms have several limitations that become increasingly significant in the context of Software-Defined Networking (SDN). Firstly, these algorithms often have limited support for Quality of Service (QoS) requirements, making them less suitable for handling diverse traffic types efficiently. Moreover, current SDN controllers leverage a default shortest-path routing approach, which does not allow for more dynamic and flexible traffic management based on real-time network policies. To address these issues, this paper introduces a Dynamic Fuzzy Routing algorithm based on Deep Reinforcement Learning (DRL) for SDN that provides guaranteed latency and bandwidth (DFRDRL). DFRDRL provides intelligent and efficient routing that adapts to dynamic traffic by considering path-state criteria and using DRL. Fuzzy logic mainly performs online routing between an ingress-egress pair. To adapt to dynamic traffic changes, DFRDRL can predict the traffic matrix in real time. Meanwhile, DFRDRL reduces the routing reliance on network topology by weighting the network based on critical nodes. Additionally, when the network experiences congestion based on the traffic matrix, a deferral mechanism is applied to prioritize requests with lower resource demands. This mechanism helps ensure efficient resource allocation by temporarily postponing or queuing higher-demand requests, thereby optimizing overall network performance during periods of congestion. The simulation results show that the performance of DFRDRL is better than the equivalent algorithms in terms of latency and throughput. Also, DFRDRL is about 2.5% more efficient than the best existing algorithm in terms of admission rate of routing requests.

Identification of Critical Nodes for Delay Propagation in Susceptible-Exposed-Infected-Recovered (SEIR) and Genetic Algorithm (GA) Route Networks

In response to the challenges associated with forecasting the trajectory of flight delay propagation, pinpointing pivotal nodes within the route network, and the substantial costs involved in enhancing operational efficiency, this study introduces an innovative approach to identifying critical nodes that influence delay propagation across route networks. The methodology commences by establishing a route network model for East China, leveraging the principles of complex network theory. It then incorporates the SEIR (Susceptible-Exposed-Infected-Recovered) model, typically used for analyzing the dynamics of infectious disease spread, to examine the propagation of delays between routes. Subsequently, the approach employs a GA to identify key nodes, which are then compared against those identified by network topology indices. The simulation outcomes demonstrate that the GA’s identification of key nodes offers superior insights into the overall network’s susceptibility to infection, thereby presenting operational managers with novel perspectives for analyzing the spread of flight delays.

AKT and the Hallmarks of Cancer.

Nearly a quarter century ago, Hanahan and Weinberg conceived six unifying principles explaining how normal cells transform into malignant tumors. Their provisional set of biological capabilities acquired during tumor development  cancer hallmarks  would evolve to fourteen tenets as knowledge of cancer genomes, molecular mechanisms, and the tumor microenvironment expanded, most recently adding four emerging enabling characteristics: phenotypic plasticity, epigenetic reprogramming, polymorphic microbiomes, and senescent cells. AKT kinases are critical signaling molecules that regulate cellular physiology upon receptor tyrosine kinases and phosphatidylinositol 3-kinase activation. The complex branching of the AKT signaling network involves several critical downstream nodes that significantly magnify its functional impact, such that nearly every organ system and cell in the body may be affected by AKT activity. Conversely, tumor intrinsic dysregulation of AKT can have numerous adverse cellular and pathological ramifications, particularly in oncogenesis, as multiple tumor suppressors and oncogenic proteins regulate AKT signaling. Herein, we review the mounting evidence implicating the AKT pathway in the aggregate of currently recognized hallmarks of cancer underlying the complexities of human malignant diseases. The challenges, recent successes, and likely areas for exciting future advances in targeting this complex pathway are also discussed.

Development and Evaluation of an Out-of-Control Scale Measuring Global Contextual Stressors Across 20 Countries and Regions

Abstract: Stressful contexts like the COVID-19 pandemic precipitated remarkable socio-economic changes, exacerbating universal uncertainty and a pervasive loss of control. Improving mental health outcomes on the operational definition and measurement of related concepts. This study analyzed cross-sectional data from 20 countries and regions, collected between January 25 and November 10, 2021. Psychometric evaluation occurred in two stages: initially, item review and factor analysis with a 2,749 adult sample from Mainland of China and Hong Kong SAR; subsequently, confirmation of factor structure, measurement invariance testing, and advanced analyses with a 4,000 adult sample from 20 countries and regions. A six-item, single-factor structure was verified, confirming satisfactory factor loading, fit indices and consistency. Measurement invariance across geographies and groupings was also confirmed. Three Out-of-Control profiles representing low, medium, and high stress levels, were identified, with age, depression/anxiety symptoms, education, socio-economic status, and exposure to COVID-19-affected acquaintances significantly influencing affiliation. The item “I think I cannot control things that I previously could” was the most critical node. This newly validated scale represents a substantial advancement in measuring stress within the context of global health crises, thus enhancing comprehension of psychological responses. Continual evaluation of the scale’s effectiveness and accuracy is crucial.