Target Node Research Articles

CSIG-16. A CX43-WNK1-C-MYC SIGNALING AXIS DRIVES GLIOBLASTOMA CANCER STEM CELL SURVIVAL

Abstract Tumors represent a highly dynamic system that relies on coordinated signaling to drive growth and adapt to selective pressures, including those induced by anti-cancer therapies. Given the need for tumors to engage in rapid and coordinated cell-cell communication, mechanisms such as gap junction intracellular communication (GJIC) should be essential. However, the connexin proteins that make up gap junctions have traditionally been considered tumor suppressors based on the frequent loss of GJIC and lower connexin expression in tumor cells compared to non-neoplastic tissue. For this reason, connexin 43 (Cx43) has been proposed to suppress the growth of glioblastoma (GBM), the most common primary malignant brain tumor. However, recent data suggest that this tumor-suppressive effect is context dependent and that connexins can also function in a tumor-promoting role. Using next-generation sequencing techniques, we found Cx43 expressed at high levels in a panel of GBM patient-derived xenograft (PDX) CSCs and that these models rely on Cx43 for their survival and self-renewal. Mechanistically, depletion of Cx43 led to a dramatic loss of c-MYC expression through reduced phosphorylation of its upstream mediator WNK lysine-deficient protein kinase 1 (WNK1). Depletion of WNK1 phenocopied knockdown of Cx43 and reduced MYC protein and mRNA as well as tumor growth in vivo. Together, this work defines a novel signaling axis downstream of gap junction protein expression that promotes tumor growth and cancer stem cell phenotypes in GBM. Due to the difficulty in targeting both Cx43 and MYC, the identification of intermediate targetable signaling nodes may lead to improved therapies for patients with GBM.

Selective-Hop Graph Neural Networks for semi-supervised node classification

Message-passing Graph Neural Networks (GNNs) are remarkably successful in graph representation learning. Most of them propagate and aggregate information within fixed-size receptive-fields (RFs) for all nodes. In this paper, our empirical studies discover that different-sized RFs are often desired for distinct graphs and target nodes. To this end, we propose a dynamic selection mechanism by allowing each node to adaptively adjust the size of its RFs according to the graph properties. In particular, we design an architectural unit called Selective-Hop (SH) block, in which information from neighborhoods within different hops are merged using dual attention mechanisms: (i) node-wise attention on each network branch to capture mutual information between the subgraphs and the entire graph; and (ii) channel-wise attention on different network branches to detect the significance of different hops at the node-level granularity. As such, the SH block determines neighbors flexibly without the constraints of hops. By stacking several SH blocks in GNN style, we instantiate two new GNN variants, SHGCN and SHSGC. Empirical results on eight benchmark datasets for semi-supervised node classification reveal that our new models achieve a considerable improvement over the state-of-the-art methods under low-homophily or heterophily and maintain competitive performance under homophily.

Attribute-Aware Graph Convolutional Network Recommendation Method

In recent years, recommendation systems have made significant strides through the application of graph neural networks (GNNs). However, most of the existing methods primarily focus on modeling user–item interactions, often failing to account for the distinct roles of different types of neighboring nodes during the graph convolution process. Moreover, the intricate relationships between user and item attributes are frequently underexplored, which constrains further improvement in the performance of recommendation models. To overcome these challenges, this paper proposes an attribute-aware graph convolutional network recommendation model (AAGCNR). This model accounts for the complex interrelationships between user and item attributes while distinguishing between different types of neighboring nodes during graph convolution. First, a multi-head self-attention mechanism is applied to capture the semantic relationships among attributes across various semantic spaces. Additionally, a bilinear interaction module is employed to facilitate interactions between attributes. Since different neighboring nodes exert different influences on target nodes, the model performs convolutional aggregation by leveraging the interrelationships of these attributes throughout the graph convolution process. Experiments conducted on real-world datasets reveal that AAGCNR outperforms other benchmark algorithms, particularly in terms of RECALL and NDCG metrics.

Speed prediction and nearby road impact analysis using machine learning and ensemble of explainable AI techniques

Traffic speed prediction is an important topic in Intelligent Transport Systems (ITS). Although traffic speed prediction for real-life applications is burgeoning, the study of explaining and interpreting AI-based speed prediction is still in its initial stage. In this paper, we applied multiple advanced regression techniques, such as XGBoost and CatBoost optimized gradient boosting, Random Forest, and LASSO to predict traffic speed more accurately in the subsequent time windows. The experiment with prediction methods was conducted using the traffic speed data of the Seoul metropolitan road network. Each road segment represented as a node in the network is associated with neighboring roads within a configurable range. We picked heavily congested nodes as prediction targets. Then, we evaluated nearby road influences to determine critical contributions to the situation of the target nodes. We interpreted the model’s output and extracted the topmost influential neighboring nodes by using an ensemble of explainable artificial intelligence (XAI) techniques such as feature importance assessment using the GINI entropy function, Recursive Feature Elimination, Shapely Additive Explanation, and a method of measuring the impact of masked nodes. We validated the XAI interpretations through traffic flow simulation by tuning the topmost influential nearby roads’ speed and observing the effect on the roads’ traffic congestion relief correspondingly. We also proved our solution through local explanation techniques such as Local Interpretable Model-Agnostic Explanations. Our methods are applicable to any transport network and open the door to new strategies for controlling the specific nearby roads for effective congestion relief.

Smart Grid Forecasting with MIMO Models: A Comparative Study of Machine Learning Techniques for Day-Ahead Residual Load Prediction

With the fast expansion of intermittent renewable energy sources in the upcoming smart grids, simple and accurate day-ahead systems for residual load forecasts are urgently needed. Machine learning strategies can facilitate towards drastic cost minimizations in terms of operating-reserves avoidance to compensate the mismatches between the actual and forecasted values. In this study, a multi-input/multi-output model is developed based on artificial neural networks to map the relationship between different predictor inputs, including time indices, weather variables, human activity parameters, and energy price indicators, and target outputs such as wind and photovoltaic generation. While the information flows in only one direction (from the predictor nodes through the hidden layers to the target node), benchmark training algorithms are employed and assessed under different case studies. The model is evaluated under both parametric and non-parametric formulations, namely neural networks and Gaussian process regression. Essential improvements are achieved by enhancing the number of embedded predictors, while superior performance is observed by using Bayesian regularization mechanisms. In terms of mean-error indices and determination coefficient, this opens the pathway towards minimization via Bayesian inference-based approaches in the presence of increased and highly stochastic renewable inputs.

Design a novel algorithm for enhancing UWB positioning accuracy in GPS denied environments

Accurate indoor positioning is the key to the development of the Internet of Things and intelligent devices. In view of GPS-denied indoor environments, we propose to build the indoor local positioning system by using ultra-wide band (UWB) system. In order to enhance the localization accuracy of UWB system, we propose a novel algorithm which integrates the Maximum Correntropy Criterion (MCC) and unscented Kalman filter (UKF) method to reconstruct the measurement distance by using the maximum entropy principle to reduce the influence of outliers and unknown process noise on the smooth effect. Subsequently, the least square (LS) method is implemented to attain the target node (TN) initial position coordinates, and the Taylor algorithm is then performed to further optimize the localization results of the LS method. Lastly, the experimental investigation is conducted to assess the effectiveness and applicability of the developed method via the UWB system in indoor scenarios. The experimental outcomes demonstrate that the developed MCCUKF-LS method can achieve the lowest root mean square error (RMSE), and enhance the positioning accuracy of the TN compared with the LS, KF-LS, and UKF-LS methods. The overall average RMSE of MCCUKF-LS method is reduced by 45.7% contracted with the LS algorithm. The average error of x-, y- and z-axis orientation for the LS method is reduced from 0.074 m, 0.067 m, 0.098 m to 0.036 m, 0.034 m, 0.044 m, and the achieved accuracy in the orientation of the three axes is increased by 51.4%, 49.3% and 55.1% respectively, which reveals that the designed fusion technique is capable of enhancing the positioning accuracy of the TN effectively, providing a new positioning methodology and reference for indoor positioning in GPS-denied environments.

An efficient and improved algorithm for generating an equivalent planar architecture of the data vortex switch

The data vortex (DV) switch was originally designed as an all optical interconnection network for high performance computing (HPC) applications. It was highly scalable with low latency and high throughput, compared to traditional switches used for optical packet switching (OPS) and HPC applications. The equivalent planar (chained MIN) model of the DV, proposed in previous literature, is a planar diagrammatic conversion of a 3-D model of the DV network. In this paper, we will focus on exploring a new, efficient and improved generalized algorithm that enables the arrangement of routing pathways to be implemented as an equivalent planar architecture (EPA, chained MIN) of a DV to find all possible routes between each source and target node pair. The original 3-D architecture of any size can be converted to a planar structure more simply by using the new generalized equivalent planar architecture (EPA) algorithm. To verify the proposed EPA algorithm, it was tested with different input traffic loads and network sizes while keeping the active angle ‘A’ constant. Network performance parameters, including injection rate (throughput) and latency (mean hops), obtained from simulation results are also provided to confirm the validity of the proposed EPA algorithm. This new algorithm is versatile, simple and can be applied to networks of various sizes.

Graph node classification algorithm based on similarity random walk aggregation

Aiming at the relatively low accuracy of methods such as MLP and GCN in heterogeneous graph node classification tasks, this paper proposes a graph neural network based on similarity random walk aggregation (SRW-GNN). Most existing node classification methods usually take neighbor nodes as neighborhoods, but the target node and its neighbors in heterogeneous graphs usually belong to different categories. To reduce the impact of heterogeneity on node embedding, SRW-GNN uses the similarity between nodes as probability to perform random walks and takes the sampled paths as neighborhoods to obtain more homogeneous information. The order in which nodes appear in the path is particularly critical for capturing neighborhood information. However, most existing GNN aggregators are insensitive to node order. This paper introduces a path aggregator based on recurrent neural network (RNN) to simultaneously extract the features and order information of nodes in the path. In addition, nodes have different preferences for different paths. In order to adaptively learn the importance of different paths in node encoding, an attention mechanism is used to dynamically adjust the contribution of each path to the final embedding. Experimental results on multiple commonly used heterogeneous graph datasets show that the accuracy of this method is significantly better than that of MLP, GCN, H2GCN, HOG-GCN and other methods, verifying its effectiveness in heterogeneous graph node classification tasks.

KAN-HyperMP: An Enhanced Fault Diagnosis Model for Rolling Bearings in Noisy Environments

Rolling bearings often produce non-stationary signals that are easily obscured by noise, particularly in high-noise environments, making fault detection a challenging task. To address this challenge, a novel fault diagnosis approach based on the Kolmogorov–Arnold Network-based Hypergraph Message Passing (KAN-HyperMP) model is proposed. The KAN-HyperMP model is composed of three key components: a neighbor feature aggregation block, a feature fusion block, and a KANLinear block. Firstly, the neighbor feature aggregation block leverages hypergraph theory to integrate information from more distant neighbors, aiding in the reduction of noise impact, even when nearby neighbors are severely affected. Subsequently, the feature fusion block combines the features of these higher-order neighbors with the target node’s own features, enabling the model to capture the complete structure of the hypergraph. Finally, the smoothness properties of B-spline functions within the Kolmogorov–Arnold Network (KAN) are employed to extract critical diagnostic features from noisy signals. The proposed model is trained and evaluated on the Southeast University (SEU) and Jiangnan University (JNU) Datasets, achieving accuracy rates of 99.70% and 99.10%, respectively, demonstrating its effectiveness in fault diagnosis under both noise-free and noisy conditions.

Modulation effects of repetitive transcranial magnetic stimulation on target and indirect target nodes in patients with major depressive disorder

Clinical studies intensively highlight two critical brain regions, i,e, dorsal lateral prefrontal cortex (DLPFC) (target node) and subgenual anterior cingulate cortex (sgACC) (indirect target node) for the treatment of neuroimaging-guided repetitive transcranial magnetic stimulation (rTMS) in major depressive disorder (MDD). However, it remains unclear whether the clinical rTMS treatment could modulate the activity of the target and indirect target nodes in MDD patients. We aim to identify the rTMS-induced alteration of brain local and functional connectivity (FC) activities in the target and indirect target nodes. 38 patients with MDD were recruited for taking part in the 2-week rTMS treatment. We identified left DLPFC and right sgACC as the target and indirect target nodes for each participant, using the neuroimaging guided method, and further explored the rTMS-induced modulation on the brain functional activity of the two nodes. Ultimately, 28 patients were included in the analysis. We found that subjects had significant improvement in depressive symptoms, and their brain functional activities were reorganized. rTMS reduced the FC activity between the target and indirect target nodes, while the brain local activity in these nodes did not show rTMS-induced changes. The FC reduction was not associated with improvement in depressive symptoms. These results confirmed the clinical significance of the target node (DLPFC) and indirect target node (sgACC) in the rTMS treatment of MDD, and further shed light on the brain functional reorganization underpinning clinical practice of rTMS.

Identifying node importance for networked systems in terms of the cascading model

The TOPSIS method identifies the spreading influences nodes by gathering different methods together with equal weights regardless the physics that different methods are effective for different scenarios. In this paper, by introducing the cascading model to measure the target node's influence, we present a weighted TOPSIS method by taking into 1% nodes cascading influence ability to calculate the weight. Experimental results for nine real-world networks show that, comparing with the traditional TOPSIS method, average speaking, accuracy of the WTOPSIS could be enhanced by 4.471%.

Strong quantum state transfer on graphs via loop edges

We quantify the effect of weighted loops at the source and target nodes of a graph on the strength of quantum state transfer between these vertices. We give lower bounds on loop weights that guarantee strong transfer fidelity that works for any graph where this protocol is feasible. By considering local spectral symmetry, we show that the required weight size depends only on the maximum degree of the graph and, in some less favorable cases, the distance between vertices. Additionally, we explore the duration for which transfer strength remains above a specified threshold.

Optimizing target control in complex networks using edge-addition cost

This paper investigates optimal target control of complex networks through modifications in network topology. A novel edge-addition algorithm is proposed to ensure structural target controllability in directed networks with a single input. An edge-addition cost is introduced to measure the efficiency of achieving target control objectives. The relationships between the target node selection and the average node degree, respectively, and the edge-addition cost are examined by numerical simulations. It concludes that both the choice of target nodes and the average node degree significantly influence the cost-effectiveness of achieving target control. These findings offer valuable insights for the design of optimal networks in practical applications.

Advancing microbial diagnostics: a universal phylogeny guided computational algorithm to find unique sequences for precise microorganism detection.

Sequences derived from organisms sharing common evolutionary origins exhibit similarity, while unique sequences, absent in related organisms, act as good diagnostic marker candidates. However, the approach focused on identifying dissimilar regions among closely-related organisms poses challenges as it requires complex multiple sequence alignments, making computation and parsing difficult. To address this, we have developed a biologically inspired universal NAUniSeq algorithm to find the unique sequences for microorganism diagnosis by traveling through the phylogeny of life. Mapping through a phylogenetic tree ensures a low number of cross-contamination and false positives. We have downloaded complete taxonomy data from Taxadb database and sequence data from National Center for Biotechnology Information Reference Sequence Database (NCBI-Refseq) and, with the help of NetworkX, created a phylogenetic tree. Sequences were assigned over the graph nodes, k-mers were created for target and non-target nodes and search was performed over the graph using the depth first search algorithm. In a memory efficient alternative NoSQL approach, we created a collection of Refseq sequences in MongoDB database using tax-id and path of FASTA files. We queried the MongoDB collection for the target and non-target sequences. In both the approaches, we used an alignment free sliding window k-mer-based procedure that quickly compares k-mers of target and non-target sequences and returns unique sequences that are not present in the non-target. We have validated our algorithm with target nodes Mycobacterium tuberculosis, Neisseria gonorrhoeae, and Monkeypox and generated unique sequences. This universal algorithm is a powerful tool for generating diagnostic sequences, enabling the accurate identification of microbial strains with high phylogenetic precision.

Distance Enhanced Hypergraph Learning for Dynamic Node Classification

Dynamic node classification aims to predict the labels of nodes in the dynamic networks. Existing methods primarily utilize the graph neural networks to acquire the node features and original graph structure features. However, these approaches ignore the high-order relationships between nodes and may lead to the over-smoothing issue. To address these issues, we propose a distance enhanced hypergraph learning (DEHL) method for dynamic node classification. Specifically, we first propose a time-adaptive pre-training component to generate the time-aware representations of each node. Then we utilize a dual-channel convolution module to construct the local and global hypergraphs which contain the corresponding local and global high-order relationships. Moreover, we adopt the K-nearest neighbor algorithm to construct the global hypergraph in the embedding space. After that, we adopt the node convolution and hyperedge convolution to aggregate the features of neighbors on the hypergraphs to the target node. Finally, we combine the temporal representations and the distance enhanced representations of the target node to predict its label. In addition, we conduct extensive experiments on two public dynamic graph datasets, i.e., Wikipedia and Reddit. The experimental results show that DEHL outperforms the state-of-the-art baselines in terms of AUC.

Sign prediction based on node connecting tightness in complex network

Nowadays, there are positive and negative relationships among individuals in social networks, which can be abstracted as a signed network with two edge attributes. These two different attributes between individuals are usually used to infer the underlying attitudes of others and modeled as the edge signs in the complex network. Recently, many studies apply local path information to predict edge signs, ignoring the effect of the clustering coefficient of nodes in the three-hop path when determining signs of links. We believe that clustering coefficients of nodes on paths with different lengths reflect the node connecting tightness and contribute significantly to predicting potential relationships. Therefore, we propose a sign prediction algorithm based on the node Connecting Tightness (CT). First, influences of the connection tightness of first- and second-order common neighbors, i.e. nodes on two- and three-hop paths between target nodes, are calculated. Second, based on these influences, the similarity of target node pairs is modeled, and the link sign is predicted by combining the structural balance theory. Finally, node degrees are used to predict directly when there are no common neighbors between the target nodes. Experiments in real-world and artificial networks demonstrate that CT achieves better accuracy and [Formula: see text] performances in sign prediction.

Dynamic category-sensitive hypergraph inferring and homo-heterogeneous neighbor feature learning for drug-related microbe prediction.

The microbes in human body play a crucial role in influencing the functions of drugs, as they can regulate the activities and toxicities of drugs. Most recent methods for predicting drug-microbe associations are based on graph learning. However, the relationships among multiple drugs and microbes are complex, diverse, and heterogeneous. Existing methods often fail to fully model the relationships. In addition, the attributes of drug-microbe pairs exhibit long-distance spatial correlations, which previous methods have not integrated effectively. We propose a new prediction method named DHDMP which is designed to encode the relationships among multiple drugs and microbes and integrate the attributes of various neighbor nodes along with the pairwise long-distance correlations. First, we construct a hypergraph with dynamic topology, where each hyperedge represents a specific relationship among multiple drug nodes and microbe nodes. Considering the heterogeneity of node attributes across different categories, we developed a node category-sensitive hypergraph convolution network to encode these diverse relationships. Second, we construct homogeneous graphs for drugs and microbes respectively, as well as drug-microbe heterogeneous graph, facilitating the integration of features from both homogeneous and heterogeneous neighbors of each target node. Third, we introduce a graph convolutional network with cross-graph feature propagation ability to transfer node features from homogeneous to heterogeneous graphs for enhanced neighbor feature representation learning. The propagation strategy aids in the deep fusion of features from both types of neighbors. Finally, we design spatial cross-attention to encode the attributes of drug-microbe pairs, revealing long-distance correlations among multiple pairwise attribute patches. The comprehensive comparison experiments showed our method outperformed state-of-the-art methods for drug-microbe association prediction. The ablation studies demonstrated the effectiveness of node category-sensitive hypergraph convolution network, graph convolutional network with cross-graph feature propagation, and spatial cross-attention. Case studies on three drugs further showed DHDMP's potential application in discovering the reliable candidate microbes for the interested drugs. Source codes and supplementary materials are available at https://github.com/pingxuan-hlju/DHDMP.

Hepatocyte MMP14 mediates liver and inter-organ inflammatory responses to diet-induced liver injury.

The matrix metalloproteinase MMP14 is a ubiquitously expressed, membrane-bound, secreted endopeptidase that proteolyzes substrates to regulate development, signaling, and metabolism. However, the spatial and contextual events inciting MMP14 activation and its metabolic sequelae are not fully understood. Here, we introduce an inducible, hepatocyte-specific MMP14-deficient model (MMP14LKO mice) to elucidate cell-intrinsic and systemic MMP14 function. We show that hepatocyte MMP14 mediates diet-induced body weight gain, peripheral adiposity, and impaired glucose homeostasis and drives diet-induced liver triglyceride accumulation and induction of hepatic inflammatory and fibrotic gene expression. Single-nucleus RNA sequencing revealed that hepatocyte MMP14 mediates Kupffer cell and T-cell accumulation and promotes diet-induced hepatocellular subpopulation shifts toward protection against lipid absorption. MMP14 co-immunoprecipitation and proteomic analyses revealed MMP14 substrate binding across both inflammatory and cytokine signaling, as well as metabolic pathways. Strikingly, hepatocyte MMP14 loss-of-function suppressed skeletal muscle and adipose inflammation in vivo, and in a reductionist adipose-hepatocyte co-culture model. Finally, we reveal that trehalose-type glucose transporter inhibitors decrease hepatocyte MMP14 gene expression and nominate these inhibitors as translatable therapeutic metabolic agents. We conclude that hepatocyte MMP14 drives liver and inter-organ inflammatory and metabolic sequelae of obesogenic dietary insult. Modulating MMP14 activation and blockade thus represents a targetable node in the pathogenesis of hepatic inflammation.

Mask-Guided Target Node Feature Learning and Dynamic Detailed Feature Enhancement for lncRNA-Disease Association Prediction.

Identifying new relevant long noncoding RNAs (lncRNAs) for various human diseases can facilitate the exploration of the causes and progression of these diseases. Recently, several graph inference methods have been proposed to predict disease-related lncRNAs by exploiting the topological structure and node attributes within graphs. However, these methods did not prioritize the target lncRNA and disease nodes over auxiliary nodes like miRNA nodes, potentially limiting their ability to fully utilize the features of the target nodes. We propose a new method, mask-guided target node feature learning and dynamic detailed feature enhancement for lncRNA-disease association prediction (MDLD), to enhance node feature learning for improved lncRNA-disease association prediction. First, we designed a heterogeneous graph masked transformer autoencoder to guide feature learning, focusing more on the features of target lncRNA (disease) nodes. The target nodes were increasingly masked as training progressed, which helps develop a more robust prediction model. Second, we developed a graph convolutional network with dynamic residuals (GCNDR) to learn and integrate the heterogeneous topology and features of all lncRNA, disease, and miRNA nodes. GCNDR employs an interlayer residual strategy and a residual evolution strategy to mitigate oversmoothing caused by multilayer graph convolution. The interlayer residual strategy estimates the importance of node features learned in the previous GCN encoding layer for nodes in the current encoding layer. Additionally, since there are dependencies in the importance of features of individual lncRNA (disease, miRNA) nodes across multiple encoding layers, a gated recurrent unit-based strategy is proposed to encode these dependencies. Finally, we designed a perspective-level attention mechanism to obtain more informative features of lncRNA and disease node pairs from the perspectives of mask-enhanced and dynamic-enhanced node features. Cross-validation experimental results demonstrated that MDLD outperformed 10 other state-of-the-art prediction methods. Ablation experiments and case studies on candidate lncRNAs for three diseases further proved the technical contributions of MDLD and its capability to discover disease-related lncRNAs.

Wireless sensor network coverage of improved sea lion algorithm

SummaryThe base station receives the environmental data from a predetermined field that is collected and transferred by the sensors for processing and analysis. However, coverage maximization is the major issue that requires the deployment of varied sensor nodes (SNs), in such a way that optimizes network coverage while enduring practical limitations. This is pointed out to be a significant challenge in constructing WSNs. Since this is considered to be a well‐known NP‐hard issue, metaheuristic methods must be used for solving the realistic problem sizes. Hence, our work considers the problem of finding the best placement to ensure good network coverage in WSN. Accordingly, the solution to the above‐mentioned problem is modeled by covering a new 2‐D distance evaluation based on weighted Minkowski. Further, we deploy the Self Improved Sealion with Opposition Behavior (SI‐SLOB) algorithm for determining the optimal placement of given sensor nodes. In the end, we perform varied evaluations on distance and coverage area to ensure the enhancement of the SI‐SLOB scheme over the other state‐of‐the‐art algorithms. The proposed method achieves minimum distance mean value in target node 25, which is 4.1%, 4.0%, 2.3%, 5.1%, 3.5%, 3.0%, and 4.1% better than the other methods such as SLO, GWO, PSO, BMO, BOA, RHSO, and WOA, respectively. Thus the proposed WSN node coverage models have diverse applications across various domains, contributing to improved efficiency, safety, and resource management.