Active Nodes Research Articles

Building a Historic Population Platform

ObjectivesWith the increasing digital availability of large population databases of historical census or vital event records, the tasks of storing, cleaning, processing, linking and analysing such data become more challenging. Suitable computing platforms and software systems are required to handle such databases, and facilitate the application of complex record linkage algorithms, for example to reconstruct populations that cover a full country over many decades. We present our efforts to achieve these goals on a database of over 20 million vital event records spanning over 120 years to create a “Historic Population Platform” (HiPP). ApproachWe created a graph database using the Neo4J software, where each birth, death and marriage certificate is represented as a node. We then generated actor nodes from these certificates which represent individuals (such as birth babies and their parents, or marriage brides and grooms). Data cleaning steps included the correction and imputation of invalid, corrupted, and missing age and date values using information from related certificates. ResultsOur initial graph database contains over 100 million nodes and nearly 200 million edges, while our data cleaning methods help to substantially increase the number of valid age and date values. ConclusionThe availability of large historical population databases provides exciting opportunities for social science and health research. However, existing methods have limitations in handling data quality and the sizes of such databases. We presented novel methods to deal with these challenges which we hope will be of use for other projects that aim to build a HiPP.

Dendritic Cell-Hitchhiking In Vivo for Vaccine Delivery to Lymph Nodes.

Therapeutic cancer vaccines are among the first FDA-approved cancer immunotherapies. Among them, it remains a major challenge to achieve robust lymph-node (LN) accumulation. However, delivering cargo into LN is difficult owing to the unique structure of the lymphatics, and clinical responses have been largely disappointing. Herein, inspired by the Migrated-DCs homing from the periphery to the LNs, an injectable hydrogel-based polypeptide vaccine system is described for enhancing immunostimulatory efficacy, which could form a local niche of vaccine "hitchhiking" on DCs. The OVA peptide modified by lipophilic DSPE domains in the hydrogel is spontaneously inserted into the cell membrane to achieve "antigen anchoring" on DCs in vivo. Overall, OVA peptide achieves active access LNs through recruiting and "hitchhiking" subcutaneous Migrated-DCs. Remarkably, it is demonstrated that the composite hydrogel enhances LNs targeting efficacy by approximately six-fold compared to free OVA peptide. Then, OVA peptide can be removed from the cell surface under a typical acidic microenvironment within the LNs, further share them with LN-resident APCs via the "One-to-Many" strategy (One Migrated-DC corresponding to Many LN-resident APCs), thereby activating powerful immune stimulation. Moreover, the hydrogel vaccine exhibits significant tumor growth inhibition in melanoma and inhibits pulmonary metastatic nodule formation.

Modulation of antigen delivery and lymph node activation in non-human primates by saponin adjuvant SMNP.

Saponin-based vaccine adjuvants are potent in preclinical animal models and humans, but their mechanisms of action remain poorly understood. Here, using a stabilized HIV envelope trimer immunogen, we carried out studies in non-human primates (NHPs) comparing the most common clinical adjuvant alum with Saponin/MPLA Nanoparticles (SMNP), a novel ISCOMs-like adjuvant. SMNP elicited substantially stronger humoral immune responses than alum, including 7-fold higher peak antigen-specific germinal center B cell responses, 18-fold higher autologous neutralizing antibody titers, and higher levels of antigen-specific plasma and memory B cells. PET-CT imaging in live NHPs showed that, unlike alum, SMNP promoted rapid antigen accumulation in both proximal and distal lymph nodes (LNs). SMNP also induced strong type I interferon transcriptional signatures, expansion of innate immune cells, and increased antigen presenting cell activation in LNs. These findings indicate that SMNP promotes multiple facets of the early immune response relevant for enhanced immunity to vaccination.

Exploring the evolution trends of port integration policy in China by a text mining approach

Ports are essential and strategic nodes of international trade and economic activities. The integration of port resources helps enhance the optimal division of port functions to further promote economic development. This paper explores the evolutionary trends of port integration policy from 2011 to 2021 in China through a text-mining approach. We first used the Latent Dirichlet Allocation (LDA) topic analysis method to analyze the port integration policies and summarize the evolutionary trends of port policies. In addition, an association analysis was conducted to explore port integration policies' impacts by examining their relationships among the environmental protection, digitalization level, and port development scale. Our findings suggested that China's port integration policy has evolved from specific and simple to abstract and complicated at the managerial level. Port development has enriched from infrastructure construction to the ideology of economic development. Our findings show that China's port integration is gradually improving the port operation. The findings of the study contribute to the extant literature by analyzing the port integration policy evolution in China and can be referred to by other countries.

Contagion dynamics in time-varying metapopulation networks with node's activity and attractiveness.

The metapopulation network model is a mathematical framework used to study the spatial spread of epidemics with individuals' mobility. In this paper, we develop a time-varying network model in which the activity of a population is correlated with its attractiveness in mobility. By studying the spreading dynamics of the SIR (susceptible-infectious-recovered)-type disease in different correlated networks based on the proposed model, we theoretically derive the mobility threshold and numerically observe that increasing the correction between activity and attractiveness results in a reduced mobility threshold but suppresses the fraction of infected subpopulations. It also introduces greater heterogeneity in the spatial distribution of infected individuals. Additionally, we investigate the impact of nonpharmaceutical interventions on the spread of epidemics in different correlation networks. Our results show that the simultaneous implementation of self-isolation and self-protection is more effective in negatively correlated networks than that in positively correlated or non-correlated networks. Both self-isolation and self-protection strategies enhance the mobility threshold and, thus, slow down the spread of the epidemic. However, the effectiveness of each strategy in reducing the fraction of infected subpopulations varies in different correlated networks. Self-protection is more effective in positively correlated networks, whereas self-isolation is more effective in negatively correlated networks. Our study will provide insights into epidemic prevention and control in large-scale time-varying metapopulation networks.

Dual-Responsive Nanomedicine Activates Programmed Antitumor Immunity through Targeting Lymphatic System.

Effective antitumor immunotherapy depends on evoking a cascade of cancer-immune cycles with lymph nodes (LNs) as the initial sites for activating antitumor immunity, making drug administration through the lymphatic system highly attractive. Here, we describe a nanomedicine with dual responsiveness to pH and enzyme for a programmed activation of antitumor immune through the lymphatic system. The proposed nanomedicine can release the STING agonist diABZI-C2-NH2 in the LNs' acidic environment to activate dendritic cells (DCs) and T cells. Then, the remaining nanomedicine hitchhikes on the activated T cells (PD-1+ T cells) through binding to PD-1, resulting in an effective delivery into tumor tissues owing to the tumor-homing capacity of PD-1+ T cells. The enzyme matrix metalloproteinase-2 (MMP-2) being enriched in tumor tissue triggers the release of PD-1 antibody (aPD-1) which exerts immune checkpoint blockade (ICB) therapy. Eventually, the nanomedicine delivers a DNA methylation inhibitor GSK-3484862 (GSK) into tumor cells, and then the latter combines with granzyme B (GZMB) to trigger tumor cell pyroptosis. Consequently, the pyroptotic tumor cells induce robust immunogenic cell death (ICD) enhancing the DCs maturation and initiating the cascading antitumor immune response. Study on a 4T1 breast tumor mouse model demonstrates the prominent antitumor therapeutic outcome of this nanomedicine through creating a positive feedback loop of cancer-immunity cycles including immune activation in LNs, T cell-mediated drug delivery, ICB therapy, and tumor cell pyroptosis-featured ICD.

Topic-Aware Information Coverage Maximization in Social Networks

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Influence maximization</i> (IM) aims to identify a set of nodes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$</tex-math> </inline-formula> to maximize the expected number of nodes influenced during the information propagation starting from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$</tex-math> </inline-formula> . Some works had extended this problem to be <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">topic-aware</i> , where each node is associated with a topic distribution and tends to be activated with different probabilities by different topics. However, whether it is topic-aware or not, IM problem only focuses on the active nodes and overlooks all the inactive ones. Actually, an inactive node may receive the information from their active in-neighbors and become informed. Therefore, this type of nodes should also be considered when measuring the coverage of information propagation. Inspired by this, we formulate a new problem called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">topic-aware information coverage maximization</i> (TAICM), which aims to maximize the sum of the expected number of both active and informed nodes in topic-aware social networks. Then we devise a heuristic method to solve it. Experiments on three real-world datasets demonstrate that our method can achieve similar or higher information coverage in much less or at least acceptable time than some commonly used IM algorithms.

Cities and Urbanization: Balancing the Environmental and Socioeconomic Dimensions of Sustainability

AbstractWith an increasingly urbanized world, there is an urgent need to examine how cities may evolve and achieve sustainability. This paper systematically looks at the Greater Bay Area (GBA) and the Poyang Lake Region (PLR) in China to examine the spatial processes for insights into cities and urbanization, balancing the environmental and socio‐economic dimensions. A total of 226 805 cells are analyzed to unveil the relationship between sustainability changes in 2015–2019 period and urban form indicators, considering sociodemographic variables, geographical features, and city size as control variables. Two tree‐based machine learning models (Random Forest and XGBoost) are developed. This study provides evidence that a monocentric urban form and a high share of small activity clusters are not good for sustainability. For each urban form indicator, there is a non‐linear relationship with sustainability. The results of the machine learning models reconfirm the sustainability benefits of having a strong second activity cluster comparable to the largest one. When planning cities, some forms of land use buffering are desirable. There is also support for developing relatively large activity nodes and promoting compactness in urban form. Beyond urban form characteristics, the levels of urbanization, economic development, and population are still highly relevant.

Designing the architecture of a distributed system for information monitoring of IoT and IIoT infrastructures traffic

The article considers an approach to designing the architecture of a distributed information monitoring and quality management system for communication services provided by the Internet of Things and Industrial Internet of Things infrastructures based on solutions supporting machine-to-machine and human-machine interaction. The choice of a neural network with long Short-Term memory (LSTM) is justified as a theoretical basis for modeling the active node of an information monitoring system. The developed structure of the LSTM network is presented, taking into account the spatial and temporal correlation of the obtained indicators at various observation points of the distributed information monitoring system. The criteria for evaluating the effectiveness of the proposed model for the conditions of information monitoring of non-stationary load in a mode close to real time are substantiated.

"Multi-in-One" Yolk-Shell Structured Nanoplatform Inducing Pyroptosis and Antitumor Immune Response Through Cascade Reactions.

Pyroptosis, a new mode of regulatory cell death,holdsa promising prospect in tumor therapy. The occurrence of pyroptosis can trigger the release of damage-associated molecular patterns (DAMPs) and activate the antitumor immune response. Moreover, enhancing intracellular reactive oxygen species (ROS) generation can effectively induce pyroptosis. Herein, an integrated nanoplatform (hCZAG) based on zeolitic imidazolate framework-8 (ZIF-8) with Cu2+ and Zn2+ as active nodes and glucose oxidase (GOx) loading is constructed to evoke pyroptosis. GOx can effectively elevate intracellular hydrogen peroxide (H2O2) levels to regulate the unfavorable tumor microenvironment (TME). Cu2+ can be reduced to Cu+ by endogenous overexpressed GSH and both Cu2+ and Cu+ can exert Fenton-like activity to promote ROS generation and amplify oxidative stress. In addition, the accumulation of Cu2+ leads to the aggregation of lipoylated dihydrolipoamide S-acetyltransferase (DLAT), thus resulting in cuproptosis. Notably, the outburst of ROS induced by hCZAG activates Caspase-1 proteins, leads to the cleavage of gasdermin D (GSDMD), and induces pyroptosis. Pyroptosis further elicits an adaptive immune response, leading to immunogenic cell death (ICD). This study provides effective strategies for triggering pyroptosis-mediated immunotherapy and achieving improved therapeutic effects.

An energy recovery routing algorithm for opportunistic networks based on connection stability and node encounter type

Routing algorithms have been the focus of research on opportunistic networks to effectively deal with the connectivity problem of intermittent communication in opportunistic networks. Due to the frequent forwarding of messages by active nodes in the opportunistic networks leading to excessive energy consumption, opportunistic networks suffer from communication susceptibility to interruption and unstable network connectivity. In order to solve this problem, this paper proposes an opportunistic network routing algorithm (ST-ProPhet) based on connection stability and energy recovery, which takes into account the node’s historical average connection duration, encounter probability, and the node’s energy consumption. Firstly, a node encounter probability prediction model fusing the average connection duration is developed. Secondly, the base station is introduced and the node wireless energy recovery strategy is designed. Finally, the ST-ProPhet algorithm proposes a message forwarding strategy based on node encounter types on the basis of the encounter probability prediction model and energy recovery model. Simulation experiments show that the ST-ProPhet algorithm has an overall reduction of 5066.27mAh over the ProPhet algorithm in terms of average message energy consumption.

LUSTC: A Novel Approach for Predicting Link States on Dynamic Attributed Networks

Predicting link states on dynamic attributed networks is one of the most fundamental problems for network analysis. To accurately predict the formations and disappearances of links, we need to utilize three types of information, that is, structural information, node content information, and temporal information, simultaneously. To this end, we propose a novel approach called LUSTC for predicting links and unlinks with structural information, temporal information, and node content information on dynamic attributed networks. For each snapshot, LUSTC first randomly chooses some sets of active nodes whose degree changes between adjacent snapshots and collects higher-order structural information using a random walk method based on the active nodes. Then, LUSTC generates two global matrices containing the temporal structural information and temporal node content information, respectively, as well as a sequence of auxiliary matrices for reconstructing the structural information and node content information of snapshots. These generated matrices are optimized using a nonnegative matrix factorization (NMF) method based on the structural information and node content information of snapshots. Finally, LUSTC estimates the similarity matrix for future snapshots to predict the links that are more likely to be formed or broken. The experimental results on various dynamic attributed networks demonstrate the effectiveness of LUSTC on the link prediction and unlink prediction tasks.

Energy-efficient deep Q-network: reinforcement learning for efficient routing protocol in wireless internet of things

&lt;div align="center"&gt;&lt;span&gt;The internet of things (IoT) underscores pivotal real-world applications ranging from security systems to smart infrastructure and traffic management. However, contemporary IoT devices grapple with significant challenges pertaining to battery longevity and energy efficiency, constraining the assurance of prolonged network lifetimes and expansive sensor coverage. Many existing solutions, although promising on paper, are intricate and often impractical for real-world implementations. Addressing this gap, we introduce an energy-efficient routing protocol leveraging reinforcement learning (RL) tailored for wireless sensor networks (WSNs). This protocol harnesses RL to discern the optimal transmission route from the source to the sink node, factoring in the energy profile of each intermediary node. Training of the RL algorithm is facilitated through a reward function that includes energy outflow and data transmission efficacy. The model was compared against two prevalent routing protocols, LEACH and fuzzy C-means (FCM), for a comprehensive assessment. Simulation results highlight our protocol’s superiority with respect to the active node count, energy conservation, network longevity, and data delivery efficiency.&lt;/span&gt;&lt;/div&gt;

Spontaneous Recovery in Directed Dynamical Networks

Complex networked systems, which range from biological systems in the natural world to infrastructure systems in the human-made world, can exhibit spontaneous recovery after a failure; for example, a brain may spontaneously return to normal after a seizure, and traffic flow can become smooth again after a jam. Previous studies on the spontaneous recovery of dynamical networks have been limited to undirected networks. However, most real-world networks are directed. To fill this gap, we build a model in which nodes may alternately fail and recover, and we develop a theoretical tool to analyze the recovery properties of directed dynamical networks. We find that the tool can accurately predict the final fraction of active nodes, and the prediction accuracy decreases as the fraction of bidirectional links in the network increases, which emphasizes the importance of directionality in network dynamics. Due to different initial states, directed dynamical networks may show alternative stable states under the same control parameter, exhibiting hysteresis behavior. In addition, for networks with finite sizes, the fraction of active nodes may jump back and forth between high and low states, mimicking repetitive failure-recovery processes. These findings could help clarify the system recovery mechanism and enable better design of networked systems with high resilience.

AFHO‐based link reliable routing and power‐assisted channel allocation protocol in cognitive radio networks for healthcare applications

AbstractIn the realm of cognitive networks, accommodating the increasing need for radio spectrum to facilitate both secondary and primary users stands as an intricate and demanding task. This study introduces a tailored framework for healthcare applications in Cognitive Radio (CR) networks for link reliable routing and power‐assisted channel allocation. Through the integration of Adaptive Fire Hawk Optimization for routing, the strategy addresses optimization complexities while prioritizing robust link quality via optimal hop selection for effective communication. A new routing optimization challenge is formulated, aiming to establish reliable communication pathways and extend link reliability. Moreover, the framework incorporates power‐assisted channel allocation to strategically enhance resource management and overall network performance. The rigorous evaluation demonstrates significant enhancements across key performance metrics, which showcase percentage improvements of 9.86%, 14.56%, 3.85%, 5.06%, 6.94%, and 17.80% for active nodes, delay, Packet Delivery Ratio (PDR), residual energy, and throughput, respectively. These advancements underscore the proposed approach's efficacy in enhancing CR network performance, particularly for healthcare applications.

Critical branching dynamics in excitable network without refractory periods

Critical state plays an important role in emerged behavior of neural networks. Excitable networks are widely used in simulation studies. However, the simulation of critical state is sophisticated because the state sensitively depends on dynamical and topological features. We study the effect of refractory period of dynamical units and connection symmetry on the excitable network with critical branching ratio. In bidirectional random networks with refractory period, the critical branching ratio is well known. Here, we show that the collective behavior changes from critical state into ceaseless activity, when refractory period vanishes. Instead of mean degree of all nodes, we show that the control parameter is determined by the mean degree of active nodes which is larger than the average degree and induces the transition. When refractory period is present, the number of effective links connected to resting nodes is equal to average degree. The mechanism ensures the critical branching ratio is unity. In directed networks, collective behavior keeps the critical state when refractory period vanished. However, a few reciprocal links make the network without refractory period becoming ceaseless.

Dual-Layer Spectral Detector Computed Tomography Quantitative Parameters: A Potential Tool for Lymph Node Activity Determination in Lymphoma Patients.

Dual-energy CT has shown promising results in determining tumor characteristics and treatment effectiveness through spectral data by assessing normalized iodine concentration (nIC), normalized effective atomic number (nZeff), normalized electron density (nED), and extracellular volume (ECV). This study explores the value of quantitative parameters in contrast-enhanced dual-layer spectral detector CT (SDCT) as a potential tool for detecting lymph node activity in lymphoma patients. A retrospective analysis of 55 lymphoma patients with 289 lymph nodes, assessed through 18FDG-PET/CT and the Deauville five-point scale, revealed significantly higher values of nIC, nZeff, nED, and ECV in active lymph nodes compared to inactive ones (p < 0.001). Generalized linear mixed models showed statistically significant fixed-effect parameters for nIC, nZeff, and ECV (p < 0.05). The area under the receiver operating characteristic curve (AUROC) values of nIC, nZeff, and ECV reached 0.822, 0.845, and 0.811 for diagnosing lymph node activity. In conclusion, the use of g nIC, nZeff, and ECV as alternative imaging biomarkers to PET/CT for identifying lymph node activity in lymphoma holds potential as a reliable diagnostic tool that can guide treatment decisions.

How the Brain Develops from the Epiblast: The Node Is Not an Organizer.

Studies using early-stage avian embryos have substantially impacted developmental biology, through the availability of simple culture methods and easiness in tissue manipulation. However, the regulations underlying brain and head development, a central issue of developmental biology, have not been investigated systematically. Yoshihi et al. (2022a) devised a technique to randomly label the epiblast cells with a green fluorescent protein before their development into the brain tissue. This technique was combined with grafting a node or node-derived anterior mesendoderm labeled with a cherry-colored fluorescent protein. Then cellular events were live-recorded over 18hours during the brain and head development. The live imaging-based analyses identified previously undescribed mechanisms central to brain development: all anterior epiblast cells have a potential to develop into the brain tissues and their gathering onto a proximal anterior mesendoderm forms a brain primordium whereas the remaining cells develop into the covering head ectoderm. The analyses also ruled out the direct participation of the node's activity in the brain development. Yoshihi et al. (2022a) also demonstrate how the enigmatic data from classical models can be reinterpreted in the new model.This chapter was adapted from Yoshihi K, Iida H, Teramoto M, Ishii Y, Kato K, Kondoh H. (2022b). Epiblast cells gather onto the anterior mesendoderm and initiate brain development without the direct involvement of the node in avian embryos: Insights from broad-field live imaging. Front Cell Dev Biol. 10:1019845. doi: 10.3389/fcell.2022.1019845.

Bifunctional porphyrin-based metal-organic polymers for electrochemical water splitting.

Electrochemical water splitting offers the potential for environmentally friendly hydrogen and oxygen gas generation. Here, we present the synthesis, characterization, and electrochemical analyses of four organic polymers where metalloporphyrins are the active center nodes. These materials were obtained from the polymerization reaction of poly(p-phenylene terephtalamide) (PPTA) with the respective amino-functionalized metalloporphyrins, where M = Fe, 1; Co, 2; Ni, 3; Cu, 4. Scanning and transmission electron microscopy images (SEM and TEM) show that these polymers exhibit a layer-type morphology, which is attributed to hydrogen bonding and π-π stacking between the metalloporphyrin nodes. The synthesized materials were characterized by X-ray photoelectron spectroscopy (XPS), X-ray powder diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), UV-Vis spectroscopy, and Fourier-transform infrared spectroscopy (FT-IR). Among the materials studied, the cobalt-based polymer, 2, demonstrates a bifunctional electrocatalytic activity for oxygen (OER) and hydrogen (HER) evolution reactions with overpotentials (η10) of 337 mV and 435 mV, respectively. The Fe, 1, and Ni, 2, polymers are less active for HER with maximum current densities (jmax) of 12.6 and 19.1 mA cm-2 and η10 678 mV, 644 mV. Polymer 2 achieves a jmax of 37.7 mA cm-2 for HER and 133 mA cm-2 for OER. The copper-based material, 4, on the other hand, shows selectivity towards HER with an overpotential (η) of 436 mV and a maximum current density (j) of 45.5 mA cm-2. The bifunctional electrocatalytic performance was tested in the overall water-splitting setup, where polymer 2 requires a cell voltage of 1.64 V at 10 mA cm-2. This work presents a novel approach to heterogenized molecular systems, providing materials with exceptional structural characteristics and enhanced electrocatalytic capabilities.

Energy Efficient Cluster Head Selection Using Hybrid RL-PSO Approach

Wireless Sensor Networks (WSNs) are crucial in several applications, highlighting the need of effective clustering and fault detection systems. This paper introduces a novel approach that uses Reinforcement Learning (RL) and Particle Swarm Optimization (PSO) to optimize cluster head selection and enhance fault detection capabilities within WSNs. The proposed hybrid algorithm operates in two phases, combining the explorative capabilities of RL with the optimization process of PSO to select cluster heads based on residual energy and connectivity considerations. By continuously monitoring the network's residual energy state and the number of active nodes, the proposed method ensures prolonged network lifetime and improved overall performance. Our experimental results demonstrate the superior performance of the hybrid RL-PSO approach compared to traditional clustering algorithms, showcasing significant improvements in optimizer accuracy, residual energy preservation, and fault detection efficiency.